diff --git a/.DS_Store b/.DS_Store deleted file mode 100644 index f4e2c114..00000000 Binary files a/.DS_Store and /dev/null differ diff --git a/.github/FUNDING.yml b/.github/FUNDING.yml new file mode 100644 index 00000000..a4f4a0d1 --- /dev/null +++ b/.github/FUNDING.yml @@ -0,0 +1,4 @@ +# These are supported funding model platforms + +github: CamDavidsonPilon + diff --git a/.gitignore b/.gitignore new file mode 100644 index 00000000..1abcc3f6 --- /dev/null +++ b/.gitignore @@ -0,0 +1,5 @@ +.DS_Store +*.pyc +*~ +*.png +**/.ipynb_checkpoints diff --git a/Chapter1_Introduction/Ch1_Introduction_PyMC2.ipynb b/Chapter1_Introduction/Ch1_Introduction_PyMC2.ipynb new file mode 100644 index 00000000..65dbd824 --- /dev/null +++ b/Chapter1_Introduction/Ch1_Introduction_PyMC2.ipynb @@ -0,0 +1,1135 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Probabilistic Programming and Bayesian Methods for Hackers \n", + "========\n", + "\n", + "Welcome to *Bayesian Methods for Hackers*. The full Github repository is available at [github/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers](https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers). The other chapters can be found on the project's [homepage](https://camdavidsonpilon.github.io/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/). We hope you enjoy the book, and we encourage any contributions!\n", + "\n", + "#### Looking for a printed version of Bayesian Methods for Hackers?\n", + "\n", + "_Bayesian Methods for Hackers_ is now a published book by Addison-Wesley, available on [Amazon](http://www.amazon.com/Bayesian-Methods-Hackers-Probabilistic-Addison-Wesley/dp/0133902838)! \n", + "\n", + "![BMH](http://www-fp.pearsonhighered.com/assets/hip/images/bigcovers/0133902838.jpg)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Chapter 1\n", + "======\n", + "***" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The Philosophy of Bayesian Inference\n", + "------\n", + " \n", + "> You are a skilled programmer, but bugs still slip into your code. After a particularly difficult implementation of an algorithm, you decide to test your code on a trivial example. It passes. You test the code on a harder problem. It passes once again. And it passes the next, *even more difficult*, test too! You are starting to believe that there may be no bugs in this code...\n", + "\n", + "If you think this way, then congratulations, you already are thinking Bayesian! Bayesian inference is simply updating your beliefs after considering new evidence. A Bayesian can rarely be certain about a result, but he or she can be very confident. Just like in the example above, we can never be 100% sure that our code is bug-free unless we test it on every possible problem; something rarely possible in practice. Instead, we can test it on a large number of problems, and if it succeeds we can feel more *confident* about our code, but still not certain. Bayesian inference works identically: we update our beliefs about an outcome; rarely can we be absolutely sure unless we rule out all other alternatives. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "### The Bayesian state of mind\n", + "\n", + "\n", + "Bayesian inference differs from more traditional statistical inference by preserving *uncertainty*. At first, this sounds like a bad statistical technique. Isn't statistics all about deriving *certainty* from randomness? To reconcile this, we need to start thinking like Bayesians. \n", + "\n", + "The Bayesian world-view interprets probability as measure of *believability in an event*, that is, how confident we are in an event occurring. In fact, we will see in a moment that this is the natural interpretation of probability. \n", + "\n", + "For this to be clearer, we consider an alternative interpretation of probability: *Frequentist*, known as the more *classical* version of statistics, assumes that probability is the long-run frequency of events (hence the bestowed title). For example, the *probability of plane accidents* under a frequentist philosophy is interpreted as the *long-term frequency of plane accidents*. This makes logical sense for many probabilities of events, but becomes more difficult to understand when events have no long-term frequency of occurrences. Consider: we often assign probabilities to outcomes of presidential elections, but the election itself only happens once! Frequentists get around this by invoking alternative realities and saying across all these realities, the frequency of occurrences defines the probability. \n", + "\n", + "Bayesians, on the other hand, have a more intuitive approach. Bayesians interpret a probability as measure of *belief*, or confidence, of an event occurring. Simply, a probability is a summary of an opinion. An individual who assigns a belief of 0 to an event has no confidence that the event will occur; conversely, assigning a belief of 1 implies that the individual is absolutely certain of an event occurring. Beliefs between 0 and 1 allow for weightings of other outcomes. This definition agrees with the probability of a plane accident example, for having observed the frequency of plane accidents, an individual's belief should be equal to that frequency, excluding any outside information. Similarly, under this definition of probability being equal to beliefs, it is meaningful to speak about probabilities (beliefs) of presidential election outcomes: how confident are you candidate *A* will win?\n", + "\n", + "Notice in the paragraph above, I assigned the belief (probability) measure to an *individual*, not to Nature. This is very interesting, as this definition leaves room for conflicting beliefs between individuals. Again, this is appropriate for what naturally occurs: different individuals have different beliefs of events occurring, because they possess different *information* about the world. The existence of different beliefs does not imply that anyone is wrong. Consider the following examples demonstrating the relationship between individual beliefs and probabilities:\n", + "\n", + "- I flip a coin, and we both guess the result. We would both agree, assuming the coin is fair, that the probability of Heads is 1/2. Assume, then, that I peek at the coin. Now I know for certain what the result is: I assign probability 1.0 to either Heads or Tails (whichever it is). Now what is *your* belief that the coin is Heads? My knowledge of the outcome has not changed the coin's results. Thus we assign different probabilities to the result. \n", + "\n", + "- Your code either has a bug in it or not, but we do not know for certain which is true, though we have a belief about the presence or absence of a bug. \n", + "\n", + "- A medical patient is exhibiting symptoms $x$, $y$ and $z$. There are a number of diseases that could be causing all of them, but only a single disease is present. A doctor has beliefs about which disease, but a second doctor may have slightly different beliefs. \n", + "\n", + "\n", + "This philosophy of treating beliefs as probability is natural to humans. We employ it constantly as we interact with the world and only see partial truths, but gather evidence to form beliefs. Alternatively, you have to be *trained* to think like a frequentist. \n", + "\n", + "To align ourselves with traditional probability notation, we denote our belief about event $A$ as $P(A)$. We call this quantity the *prior probability*.\n", + "\n", + "John Maynard Keynes, a great economist and thinker, said \"When the facts change, I change my mind. What do you do, sir?\" This quote reflects the way a Bayesian updates his or her beliefs after seeing evidence. Even — especially — if the evidence is counter to what was initially believed, the evidence cannot be ignored. We denote our updated belief as $P(A |X )$, interpreted as the probability of $A$ given the evidence $X$. We call the updated belief the *posterior probability* so as to contrast it with the prior probability. For example, consider the posterior probabilities (read: posterior beliefs) of the above examples, after observing some evidence $X$:\n", + "\n", + "1\\. $P(A): \\;\\;$ the coin has a 50 percent chance of being Heads. $P(A | X):\\;\\;$ You look at the coin, observe a Heads has landed, denote this information $X$, and trivially assign probability 1.0 to Heads and 0.0 to Tails.\n", + "\n", + "2\\. $P(A): \\;\\;$ This big, complex code likely has a bug in it. $P(A | X): \\;\\;$ The code passed all $X$ tests; there still might be a bug, but its presence is less likely now.\n", + "\n", + "3\\. $P(A):\\;\\;$ The patient could have any number of diseases. $P(A | X):\\;\\;$ Performing a blood test generated evidence $X$, ruling out some of the possible diseases from consideration.\n", + "\n", + "\n", + "It's clear that in each example we did not completely discard the prior belief after seeing new evidence $X$, but we *re-weighted the prior* to incorporate the new evidence (i.e. we put more weight, or confidence, on some beliefs versus others). \n", + "\n", + "By introducing prior uncertainty about events, we are already admitting that any guess we make is potentially very wrong. After observing data, evidence, or other information, we update our beliefs, and our guess becomes *less wrong*. This is the alternative side of the prediction coin, where typically we try to be *more right*. \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "### Bayesian Inference in Practice\n", + "\n", + " If frequentist and Bayesian inference were programming functions, with inputs being statistical problems, then the two would be different in what they return to the user. The frequentist inference function would return a number, representing an estimate (typically a summary statistic like the sample average etc.), whereas the Bayesian function would return *probabilities*.\n", + "\n", + "For example, in our debugging problem above, calling the frequentist function with the argument \"My code passed all $X$ tests; is my code bug-free?\" would return a *YES*. On the other hand, asking our Bayesian function \"Often my code has bugs. My code passed all $X$ tests; is my code bug-free?\" would return something very different: probabilities of *YES* and *NO*. The function might return:\n", + "\n", + "\n", + "> *YES*, with probability 0.8; *NO*, with probability 0.2\n", + "\n", + "\n", + "\n", + "This is very different from the answer the frequentist function returned. Notice that the Bayesian function accepted an additional argument: *\"Often my code has bugs\"*. This parameter is the *prior*. By including the prior parameter, we are telling the Bayesian function to include our belief about the situation. Technically this parameter in the Bayesian function is optional, but we will see excluding it has its own consequences. \n", + "\n", + "\n", + "#### Incorporating evidence\n", + "\n", + "As we acquire more and more instances of evidence, our prior belief is *washed out* by the new evidence. This is to be expected. For example, if your prior belief is something ridiculous, like \"I expect the sun to explode today\", and each day you are proved wrong, you would hope that any inference would correct you, or at least align your beliefs better. Bayesian inference will correct this belief.\n", + "\n", + "\n", + "Denote $N$ as the number of instances of evidence we possess. As we gather an *infinite* amount of evidence, say as $N \\rightarrow \\infty$, our Bayesian results (often) align with frequentist results. Hence for large $N$, statistical inference is more or less objective. On the other hand, for small $N$, inference is much more *unstable*: frequentist estimates have more variance and larger confidence intervals. This is where Bayesian analysis excels. By introducing a prior, and returning probabilities (instead of a scalar estimate), we *preserve the uncertainty* that reflects the instability of statistical inference of a small $N$ dataset. \n", + "\n", + "One may think that for large $N$, one can be indifferent between the two techniques since they offer similar inference, and might lean towards the computationally-simpler, frequentist methods. An individual in this position should consider the following quote by Andrew Gelman (2005)[1], before making such a decision:\n", + "\n", + "> Sample sizes are never large. If $N$ is too small to get a sufficiently-precise estimate, you need to get more data (or make more assumptions). But once $N$ is \"large enough,\" you can start subdividing the data to learn more (for example, in a public opinion poll, once you have a good estimate for the entire country, you can estimate among men and women, northerners and southerners, different age groups, etc.). $N$ is never enough because if it were \"enough\" you'd already be on to the next problem for which you need more data.\n", + "\n", + "### Are frequentist methods incorrect then? \n", + "\n", + "**No.**\n", + "\n", + "Frequentist methods are still useful or state-of-the-art in many areas. Tools such as least squares linear regression, LASSO regression, and expectation-maximization algorithms are all powerful and fast. Bayesian methods complement these techniques by solving problems that these approaches cannot, or by illuminating the underlying system with more flexible modeling.\n", + "\n", + "\n", + "#### A note on *Big Data*\n", + "Paradoxically, big data's predictive analytic problems are actually solved by relatively simple algorithms [2][4]. Thus we can argue that big data's prediction difficulty does not lie in the algorithm used, but instead on the computational difficulties of storage and execution on big data. (One should also consider Gelman's quote from above and ask \"Do I really have big data?\" )\n", + "\n", + "The much more difficult analytic problems involve *medium data* and, especially troublesome, *really small data*. Using a similar argument as Gelman's above, if big data problems are *big enough* to be readily solved, then we should be more interested in the *not-quite-big enough* datasets. \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Our Bayesian framework\n", + "\n", + "We are interested in beliefs, which can be interpreted as probabilities by thinking Bayesian. We have a *prior* belief in event $A$, beliefs formed by previous information, e.g., our prior belief about bugs being in our code before performing tests.\n", + "\n", + "Secondly, we observe our evidence. To continue our buggy-code example: if our code passes $X$ tests, we want to update our belief to incorporate this. We call this new belief the *posterior* probability. Updating our belief is done via the following equation, known as Bayes' Theorem, after its discoverer Thomas Bayes:\n", + "\n", + "\\begin{align}\n", + " P( A | X ) = & \\frac{ P(X | A) P(A) } {P(X) } \\\\\\\\[5pt]\n", + "& \\propto P(X | A) P(A)\\;\\; (\\propto \\text{is proportional to } )\n", + "\\end{align}\n", + "\n", + "The above formula is not unique to Bayesian inference: it is a mathematical fact with uses outside Bayesian inference. Bayesian inference merely uses it to connect prior probabilities $P(A)$ with an updated posterior probabilities $P(A | X )$." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Example: Mandatory coin-flip example\n", + "\n", + "Every statistics text must contain a coin-flipping example, I'll use it here to get it out of the way. Suppose, naively, that you are unsure about the probability of heads in a coin flip (spoiler alert: it's 50%). You believe there is some true underlying ratio, call it $p$, but have no prior opinion on what $p$ might be. \n", + "\n", + "We begin to flip a coin, and record the observations: either $H$ or $T$. This is our observed data. An interesting question to ask is how our inference changes as we observe more and more data? More specifically, what do our posterior probabilities look like when we have little data, versus when we have lots of data. \n", + "\n", + "Below we plot a sequence of updating posterior probabilities as we observe increasing amounts of data (coin flips)." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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eCYAxhmuvvZYhQ4awYcMGXnnlFf7whz/w3nvvAeDxeJg/fz5btmxh+fLl/Pvf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\n7YPE6CfHe09t27aN+fPn90sDQ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GQORB8bzOM4e8tAiFF5cxsv/1jGNW/t5vF1BWSVtaDT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\nEXNnkxFR5+1quHDqzxeWo396iS9+pyU39D++HqM4fzgzGoh29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EFiYiIjR47klltu6RD/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Xp2eBVpF00xyIGWQoEuUXe9q5+6VjdA8OUxg03rG4nLctno1pkbhpIxJ1/N/rndQd7cEBJaEA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\nMfn8fMmM+7p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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\"\"\"\n", + "The book uses a custom matplotlibrc file, which provides the unique styles for\n", + "matplotlib plots. If executing this book, and you wish to use the book's\n", + "styling, provided are two options:\n", + " 1. Overwrite your own matplotlibrc file with the rc-file provided in the\n", + " book's styles/ dir. See http://matplotlib.org/users/customizing.html\n", + " 2. Also in the styles is bmh_matplotlibrc.json file. This can be used to\n", + " update the styles in only this notebook. Try running the following code:\n", + "\n", + " import json, matplotlib\n", + " s = json.load( open(\"../styles/bmh_matplotlibrc.json\") )\n", + " matplotlib.rcParams.update(s)\n", + "\n", + "\"\"\"\n", + "\n", + "# The code below can be passed over, as it is currently not important, plus it\n", + "# uses advanced topics we have not covered yet. LOOK AT PICTURE, MICHAEL!\n", + "%matplotlib inline\n", + "from IPython.core.pylabtools import figsize\n", + "import numpy as np\n", + "from matplotlib import pyplot as plt\n", + "figsize(11, 9)\n", + "\n", + "import scipy.stats as stats\n", + "\n", + "dist = stats.beta\n", + "n_trials = [0, 1, 2, 3, 4, 5, 8, 15, 50, 500]\n", + "data = stats.bernoulli.rvs(0.5, size=n_trials[-1])\n", + "x = np.linspace(0, 1, 100)\n", + "\n", + "# For the already prepared, I'm using Binomial's conj. prior.\n", + "for k, N in enumerate(n_trials):\n", + " sx = plt.subplot(len(n_trials) / 2, 2, k + 1)\n", + " plt.xlabel(\"$p$, probability of heads\") \\\n", + " if k in [0, len(n_trials) - 1] else None\n", + " plt.setp(sx.get_yticklabels(), visible=False)\n", + " heads = data[:N].sum()\n", + " y = dist.pdf(x, 1 + heads, 1 + N - heads)\n", + " plt.plot(x, y, label=\"observe %d tosses,\\n %d heads\" % (N, heads))\n", + " plt.fill_between(x, 0, y, color=\"#348ABD\", alpha=0.4)\n", + " plt.vlines(0.5, 0, 4, color=\"k\", linestyles=\"--\", lw=1)\n", + "\n", + " leg = plt.legend()\n", + " leg.get_frame().set_alpha(0.4)\n", + " plt.autoscale(tight=True)\n", + "\n", + "\n", + "plt.suptitle(\"Bayesian updating of posterior probabilities\",\n", + " y=1.02,\n", + " fontsize=14)\n", + "\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The posterior probabilities are represented by the curves, and our uncertainty is proportional to the width of the curve. As the plot above shows, as we start to observe data our posterior probabilities start to shift and move around. Eventually, as we observe more and more data (coin-flips), our probabilities will tighten closer and closer around the true value of $p=0.5$ (marked by a dashed line). \n", + "\n", + "Notice that the plots are not always *peaked* at 0.5. There is no reason it should be: recall we assumed we did not have a prior opinion of what $p$ is. In fact, if we observe quite extreme data, say 8 flips and only 1 observed heads, our distribution would look very biased *away* from lumping around 0.5 (with no prior opinion, how confident would you feel betting on a fair coin after observing 8 tails and 1 head). As more data accumulates, we would see more and more probability being assigned at $p=0.5$, though never all of it.\n", + "\n", + "The next example is a simple demonstration of the mathematics of Bayesian inference. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Example: Bug, or just sweet, unintended feature?\n", + "\n", + "\n", + "Let $A$ denote the event that our code has **no bugs** in it. Let $X$ denote the event that the code passes all debugging tests. For now, we will leave the prior probability of no bugs as a variable, i.e. $P(A) = p$. \n", + "\n", + "We are interested in $P(A|X)$, i.e. the probability of no bugs, given our debugging tests $X$ pass. To use the formula above, we need to compute some quantities.\n", + "\n", + "What is $P(X | A)$, i.e., the probability that the code passes $X$ tests *given* there are no bugs? Well, it is equal to 1, for code with no bugs will pass all tests. \n", + "\n", + "$P(X)$ is a little bit trickier: The event $X$ can be divided into two possibilities, event $X$ occurring even though our code *indeed has* bugs (denoted $\\sim A\\;$, spoken *not $A$*), or event $X$ without bugs ($A$). $P(X)$ can be represented as:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\\begin{align}\n", + "P(X ) & = P(X \\text{ and } A) + P(X \\text{ and } \\sim A) \\\\\\\\[5pt]\n", + " & = P(X|A)P(A) + P(X | \\sim A)P(\\sim A)\\\\\\\\[5pt]\n", + "& = P(X|A)p + P(X | \\sim A)(1-p)\n", + "\\end{align}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We have already computed $P(X|A)$ above. On the other hand, $P(X | \\sim A)$ is subjective: our code can pass tests but still have a bug in it, though the probability there is a bug present is reduced. Note this is dependent on the number of tests performed, the degree of complication in the tests, etc. Let's be conservative and assign $P(X|\\sim A) = 0.5$. Then\n", + "\n", + "\\begin{align}\n", + "P(A | X) & = \\frac{1\\cdot p}{ 1\\cdot p +0.5 (1-p) } \\\\\\\\\n", + "& = \\frac{ 2 p}{1+p}\n", + "\\end{align}\n", + "This is the posterior probability. What does it look like as a function of our prior, $p \\in [0,1]$? " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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7uyibiyOF/ZpZBWRr1V5EZMKJqdB394UDz83s79z9X4YeY2Z/k8jAglKPvsRK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\nb6HzgO3HgNeBa23/1vavgauBW4BTgeVDdP8GcGz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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "figsize(12.5, 4)\n", + "p = np.linspace(0, 1, 50)\n", + "plt.plot(p, 2 * p / (1 + p), color=\"#348ABD\", lw=3)\n", + "# plt.fill_between(p, 2*p/(1+p), alpha=.5, facecolor=[\"#A60628\"])\n", + "plt.scatter(0.2, 2 * (0.2) / 1.2, s=140, c=\"#348ABD\")\n", + "plt.xlim(0, 1)\n", + "plt.ylim(0, 1)\n", + "plt.xlabel(\"Prior, $P(A) = p$\")\n", + "plt.ylabel(\"Posterior, $P(A|X)$, with $P(A) = p$\")\n", + "plt.title(\"Is my code bug-free?\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can see the biggest gains if we observe the $X$ tests passed when the prior probability, $p$, is low. Let's settle on a specific value for the prior. I'm a strong programmer (I think), so I'm going to give myself a realistic prior of 0.20, that is, there is a 20% chance that I write code bug-free. To be more realistic, this prior should be a function of how complicated and large the code is, but let's pin it at 0.20. Then my updated belief that my code is bug-free is 0.33. \n", + "\n", + "Recall that the prior is a probability: $p$ is the prior probability that there *are no bugs*, so $1-p$ is the prior probability that there *are bugs*.\n", + "\n", + "Similarly, our posterior is also a probability, with $P(A | X)$ the probability there is no bug *given we saw all tests pass*, hence $1-P(A|X)$ is the probability there is a bug *given all tests passed*. What does our posterior probability look like? Below is a chart of both the prior and the posterior probabilities. \n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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OWt2IdIsWLXjqqaf49a9/Tffu3Vm5ciUnnnhihdvG87gq6zt62RlnnMGLL75I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\nbQe61mZQUr+opl1ERDJBzyY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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "figsize(12.5, 4)\n", + "colours = [\"#348ABD\", \"#A60628\"]\n", + "\n", + "prior = [0.20, 0.80]\n", + "posterior = [1. / 3, 2. / 3]\n", + "plt.bar([0, .7], prior, alpha=0.70, width=0.25,\n", + " color=colours[0], label=\"prior distribution\",\n", + " lw=\"3\", edgecolor=colours[0])\n", + "\n", + "plt.bar([0 + 0.25, .7 + 0.25], posterior, alpha=0.7,\n", + " width=0.25, color=colours[1],\n", + " label=\"posterior distribution\",\n", + " lw=\"3\", edgecolor=colours[1])\n", + "\n", + "plt.ylim(0,1)\n", + "plt.xticks([0.20, .95], [\"Bugs Absent\", \"Bugs Present\"])\n", + "plt.title(\"Prior and Posterior probability of bugs present\")\n", + "plt.ylabel(\"Probability\")\n", + "plt.legend(loc=\"upper left\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notice that after we observed $X$ occur, the probability of bugs being absent increased. By increasing the number of tests, we can approach confidence (probability 1) that there are no bugs present.\n", + "\n", + "This was a very simple example of Bayesian inference and Bayes rule. Unfortunately, the mathematics necessary to perform more complicated Bayesian inference only becomes more difficult, except for artificially constructed cases. We will later see that this type of mathematical analysis is actually unnecessary. First we must broaden our modeling tools. The next section deals with *probability distributions*. If you are already familiar, feel free to skip (or at least skim), but for the less familiar the next section is essential." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "_______\n", + "\n", + "## Probability Distributions\n", + "\n", + "\n", + "**Let's quickly recall what a probability distribution is:** Let $Z$ be some random variable. Then associated with $Z$ is a *probability distribution function* that assigns probabilities to the different outcomes $Z$ can take. Graphically, a probability distribution is a curve where the probability of an outcome is proportional to the height of the curve. You can see examples in the first figure of this chapter. \n", + "\n", + "We can divide random variables into three classifications:\n", + "\n", + "- **$Z$ is discrete**: Discrete random variables may only assume values on a specified list. Things like populations, movie ratings, and number of votes are all discrete random variables. Discrete random variables become more clear when we contrast them with...\n", + "\n", + "- **$Z$ is continuous**: Continuous random variable can take on arbitrarily exact values. For example, temperature, speed, time, color are all modeled as continuous variables because you can progressively make the values more and more precise.\n", + "\n", + "- **$Z$ is mixed**: Mixed random variables assign probabilities to both discrete and continuous random variables, i.e. it is a combination of the above two categories. \n", + "\n", + "#### Expected Value\n", + "Expected value (EV) is one of the most important concepts in probability. The EV for a given probability distribution can be described as \"the mean value in the long run for many repeated samples from that distribution.\" To borrow a metaphor from physics, a distribution's EV acts like its \"center of mass.\" Imagine repeating the same experiment many times over, and taking the average over each outcome. The more you repeat the experiment, the closer this average will become to the distributions EV. (side note: as the number of repeated experiments goes to infinity, the difference between the average outcome and the EV becomes arbitrarily small.)\n", + "\n", + "### Discrete Case\n", + "If $Z$ is discrete, then its distribution is called a *probability mass function*, which measures the probability $Z$ takes on the value $k$, denoted $P(Z=k)$. Note that the probability mass function completely describes the random variable $Z$, that is, if we know the mass function, we know how $Z$ should behave. There are popular probability mass functions that consistently appear: we will introduce them as needed, but let's introduce the first very useful probability mass function. We say $Z$ is *Poisson*-distributed if:\n", + "\n", + "$$P(Z = k) =\\frac{ \\lambda^k e^{-\\lambda} }{k!}, \\; \\; k=0,1,2, \\dots, \\; \\; \\lambda \\in \\mathbb{R}_{>0} $$\n", + "\n", + "$\\lambda$ is called a parameter of the distribution, and it controls the distribution's shape. For the Poisson distribution, $\\lambda$ can be any positive number. By increasing $\\lambda$, we add more probability to larger values, and conversely by decreasing $\\lambda$ we add more probability to smaller values. One can describe $\\lambda$ as the *intensity* of the Poisson distribution. \n", + "\n", + "Unlike $\\lambda$, which can be any positive number, the value $k$ in the above formula must be a non-negative integer, i.e., $k$ must take on values 0,1,2, and so on. This is very important, because if you wanted to model a population you could not make sense of populations with 4.25 or 5.612 members. \n", + "\n", + "If a random variable $Z$ has a Poisson mass distribution, we denote this by writing\n", + "\n", + "$$Z \\sim \\text{Poi}(\\lambda) $$\n", + "\n", + "One useful property of the Poisson distribution is that its expected value is equal to its parameter, i.e.:\n", + "\n", + "$$E\\large[ \\;Z\\; | \\; \\lambda \\;\\large] = \\lambda $$\n", + "\n", + "We will use this property often, so it's useful to remember. Below, we plot the probability mass distribution for different $\\lambda$ values. The first thing to notice is that by increasing $\\lambda$, we add more probability of larger values occurring. Second, notice that although the graph ends at 15, the distributions do not. They assign positive probability to every non-negative integer." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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dua5fy1rOY3lQWXpGU287fj+otzw9jZaj/X6I1qvl5Odhw4ZkYktXVxezZs1i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\nWLDAr1i+J8dMn5LjvWmss7uXAyZPYNp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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "figsize(12.5, 4)\n", + "\n", + "import scipy.stats as stats\n", + "a = np.arange(16)\n", + "poi = stats.poisson\n", + "lambda_ = [1.5, 4.25]\n", + "colours = [\"#348ABD\", \"#A60628\"]\n", + "\n", + "plt.bar(a, poi.pmf(a, lambda_[0]), color=colours[0],\n", + " label=\"$\\lambda = %.1f$\" % lambda_[0], alpha=0.60,\n", + " edgecolor=colours[0], lw=\"3\")\n", + "\n", + "plt.bar(a, poi.pmf(a, lambda_[1]), color=colours[1],\n", + " label=\"$\\lambda = %.1f$\" % lambda_[1], alpha=0.60,\n", + " edgecolor=colours[1], lw=\"3\")\n", + "\n", + "plt.xticks(a + 0.4, a)\n", + "plt.legend()\n", + "plt.ylabel(\"probability of $k$\")\n", + "plt.xlabel(\"$k$\")\n", + "plt.title(\"Probability mass function of a Poisson random variable; differing \\\n", + "$\\lambda$ values\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Continuous Case\n", + "Instead of a probability mass function, a continuous random variable has a *probability density function*. This might seem like unnecessary nomenclature, but the density function and the mass function are very different creatures. An example of continuous random variable is a random variable with *exponential density*. The density function for an exponential random variable looks like this:\n", + "\n", + "$$f_Z(z | \\lambda) = \\lambda e^{-\\lambda z }, \\;\\; z\\ge 0$$\n", + "\n", + "Like a Poisson random variable, an exponential random variable can take on only non-negative values. But unlike a Poisson variable, the exponential can take on *any* non-negative values, including non-integral values such as 4.25 or 5.612401. This property makes it a poor choice for count data, which must be an integer, but a great choice for time data, temperature data (measured in Kelvins, of course), or any other precise *and positive* variable. The graph below shows two probability density functions with different $\\lambda$ values. \n", + "\n", + "When a random variable $Z$ has an exponential distribution with parameter $\\lambda$, we say *$Z$ is exponential* and write\n", + "\n", + "$$Z \\sim \\text{Exp}(\\lambda)$$\n", + "\n", + "Given a specific $\\lambda$, the expected value of an exponential random variable is equal to the inverse of $\\lambda$, that is:\n", + "\n", + "$$E[\\; Z \\;|\\; \\lambda \\;] = \\frac{1}{\\lambda}$$" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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or15PqGcxAK4hzJK7H2bl/3sMF4lkuHUiIiIi0tm0W029mU0B7nfOnR+d/hrg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\nXMeJQGAedxoH/HECEQSpZMAs1befC8JWWxDg3uYynipNtD4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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "a = np.linspace(0, 4, 100)\n", + "expo = stats.expon\n", + "lambda_ = [0.5, 1]\n", + "\n", + "for l, c in zip(lambda_, colours):\n", + " plt.plot(a, expo.pdf(a, scale=1. / l), lw=3,\n", + " color=c, label=\"$\\lambda = %.1f$\" % l)\n", + " plt.fill_between(a, expo.pdf(a, scale=1. / l), color=c, alpha=.33)\n", + "\n", + "plt.legend()\n", + "plt.ylabel(\"PDF at $z$\")\n", + "plt.xlabel(\"$z$\")\n", + "plt.ylim(0, 1.2)\n", + "plt.title(\"Probability density function of an Exponential random variable;\\\n", + " differing $\\lambda$\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "### But what is $\\lambda \\;$?\n", + "\n", + "\n", + "**This question is what motivates statistics**. In the real world, $\\lambda$ is hidden from us. We see only $Z$, and must go backwards to try and determine $\\lambda$. The problem is difficult because there is no one-to-one mapping from $Z$ to $\\lambda$. Many different methods have been created to solve the problem of estimating $\\lambda$, but since $\\lambda$ is never actually observed, no one can say for certain which method is best! \n", + "\n", + "Bayesian inference is concerned with *beliefs* about what $\\lambda$ might be. Rather than try to guess $\\lambda$ exactly, we can only talk about what $\\lambda$ is likely to be by assigning a probability distribution to $\\lambda$.\n", + "\n", + "This might seem odd at first. After all, $\\lambda$ is fixed; it is not (necessarily) random! How can we assign probabilities to values of a non-random variable? Ah, we have fallen for our old, frequentist way of thinking. Recall that under Bayesian philosophy, we *can* assign probabilities if we interpret them as beliefs. And it is entirely acceptable to have *beliefs* about the parameter $\\lambda$. \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "##### Example: Inferring behaviour from text-message data\n", + "\n", + "Let's try to model a more interesting example, one that concerns the rate at which a user sends and receives text messages:\n", + "\n", + "> You are given a series of daily text-message counts from a user of your system. The data, plotted over time, appears in the chart below. You are curious to know if the user's text-messaging habits have changed over time, either gradually or suddenly. How can you model this? (This is in fact my own text-message data. Judge my popularity as you wish.)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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09CjG7DKsbe1Jpd25Q+PsB0vukCZ7K3fu7X7daub+ypx7mTn3dDox+xkzZtDT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\nAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "figsize(12.5, 3.5)\n", + "count_data = np.loadtxt(\"data/txtdata.csv\")\n", + "n_count_data = len(count_data)\n", + "plt.bar(np.arange(n_count_data), count_data, color=\"#348ABD\")\n", + "plt.xlabel(\"Time (days)\")\n", + "plt.ylabel(\"count of text-msgs received\")\n", + "plt.title(\"Did the user's texting habits change over time?\")\n", + "plt.xlim(0, n_count_data);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Before we start modeling, see what you can figure out just by looking at the chart above. Would you say there was a change in behaviour during this time period? \n", + "\n", + "How can we start to model this? Well, as we have conveniently already seen, a Poisson random variable is a very appropriate model for this type of *count* data. Denoting day $i$'s text-message count by $C_i$, \n", + "\n", + "$$ C_i \\sim \\text{Poisson}(\\lambda) $$\n", + "\n", + "We are not sure what the value of the $\\lambda$ parameter really is, however. Looking at the chart above, it appears that the rate might become higher late in the observation period, which is equivalent to saying that $\\lambda$ increases at some point during the observations. (Recall that a higher value of $\\lambda$ assigns more probability to larger outcomes. That is, there is a higher probability of many text messages having been sent on a given day.)\n", + "\n", + "How can we represent this observation mathematically? Let's assume that on some day during the observation period (call it $\\tau$), the parameter $\\lambda$ suddenly jumps to a higher value. So we really have two $\\lambda$ parameters: one for the period before $\\tau$, and one for the rest of the observation period. In the literature, a sudden transition like this would be called a *switchpoint*:\n", + "\n", + "$$\n", + "\\lambda = \n", + "\\begin{cases}\n", + "\\lambda_1 & \\text{if } t \\lt \\tau \\cr\n", + "\\lambda_2 & \\text{if } t \\ge \\tau\n", + "\\end{cases}\n", + "$$\n", + "\n", + "\n", + "If, in reality, no sudden change occurred and indeed $\\lambda_1 = \\lambda_2$, then the $\\lambda$s posterior distributions should look about equal.\n", + "\n", + "We are interested in inferring the unknown $\\lambda$s. To use Bayesian inference, we need to assign prior probabilities to the different possible values of $\\lambda$. What would be good prior probability distributions for $\\lambda_1$ and $\\lambda_2$? Recall that $\\lambda$ can be any positive number. As we saw earlier, the *exponential* distribution provides a continuous density function for positive numbers, so it might be a good choice for modeling $\\lambda_i$. But recall that the exponential distribution takes a parameter of its own, so we'll need to include that parameter in our model. Let's call that parameter $\\alpha$.\n", + "\n", + "\\begin{align}\n", + "&\\lambda_1 \\sim \\text{Exp}( \\alpha ) \\\\\\\n", + "&\\lambda_2 \\sim \\text{Exp}( \\alpha )\n", + "\\end{align}\n", + "\n", + "$\\alpha$ is called a *hyper-parameter* or *parent variable*. In literal terms, it is a parameter that influences other parameters. Our initial guess at $\\alpha$ does not influence the model too strongly, so we have some flexibility in our choice. A good rule of thumb is to set the exponential parameter equal to the inverse of the average of the count data. Since we're modeling $\\lambda$ using an exponential distribution, we can use the expected value identity shown earlier to get:\n", + "\n", + "$$\\frac{1}{N}\\sum_{i=0}^N \\;C_i \\approx E[\\; \\lambda \\; |\\; \\alpha ] = \\frac{1}{\\alpha}$$ \n", + "\n", + "An alternative, and something I encourage the reader to try, would be to have two priors: one for each $\\lambda_i$. Creating two exponential distributions with different $\\alpha$ values reflects our prior belief that the rate changed at some point during the observations.\n", + "\n", + "What about $\\tau$? Because of the noisiness of the data, it's difficult to pick out a priori when $\\tau$ might have occurred. Instead, we can assign a *uniform prior belief* to every possible day. This is equivalent to saying\n", + "\n", + "\\begin{align}\n", + "& \\tau \\sim \\text{DiscreteUniform(1,70) }\\\\\\\\\n", + "& \\Rightarrow P( \\tau = k ) = \\frac{1}{70}\n", + "\\end{align}\n", + "\n", + "So after all this, what does our overall prior distribution for the unknown variables look like? Frankly, *it doesn't matter*. What we should understand is that it's an ugly, complicated mess involving symbols only a mathematician could love. And things will only get uglier the more complicated our models become. Regardless, all we really care about is the posterior distribution.\n", + "\n", + "We next turn to PyMC, a Python library for performing Bayesian analysis that is undaunted by the mathematical monster we have created. \n", + "\n", + "\n", + "Introducing our first hammer: PyMC\n", + "-----\n", + "\n", + "PyMC is a Python library for programming Bayesian analysis [3]. It is a fast, well-maintained library. The only unfortunate part is that its documentation is lacking in certain areas, especially those that bridge the gap between beginner and hacker. One of this book's main goals is to solve that problem, and also to demonstrate why PyMC is so cool.\n", + "\n", + "We will model the problem above using PyMC. This type of programming is called *probabilistic programming*, an unfortunate misnomer that invokes ideas of randomly-generated code and has likely confused and frightened users away from this field. The code is not random; it is probabilistic in the sense that we create probability models using programming variables as the model's components. Model components are first-class primitives within the PyMC framework. \n", + "\n", + "B. Cronin [5] has a very motivating description of probabilistic programming:\n", + "\n", + "> Another way of thinking about this: unlike a traditional program, which only runs in the forward directions, a probabilistic program is run in both the forward and backward direction. It runs forward to compute the consequences of the assumptions it contains about the world (i.e., the model space it represents), but it also runs backward from the data to constrain the possible explanations. In practice, many probabilistic programming systems will cleverly interleave these forward and backward operations to efficiently home in on the best explanations.\n", + "\n", + "Because of the confusion engendered by the term *probabilistic programming*, I'll refrain from using it. Instead, I'll simply say *programming*, since that's what it really is. \n", + "\n", + "PyMC code is easy to read. The only novel thing should be the syntax, and I will interrupt the code to explain individual sections. Simply remember that we are representing the model's components ($\\tau, \\lambda_1, \\lambda_2$ ) as variables:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import pymc as pm\n", + "\n", + "alpha = 1.0 / count_data.mean() # Recall count_data is the\n", + " # variable that holds our txt counts\n", + "lambda_1 = pm.Exponential(\"lambda_1\", alpha)\n", + "lambda_2 = pm.Exponential(\"lambda_2\", alpha)\n", + "\n", + "tau = pm.DiscreteUniform(\"tau\", lower=0, upper=n_count_data)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the code above, we create the PyMC variables corresponding to $\\lambda_1$ and $\\lambda_2$. We assign them to PyMC's *stochastic variables*, so-called because they are treated by the back end as random number generators. We can demonstrate this fact by calling their built-in `random()` methods." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Random output: 64 5 12\n" + ] + } + ], + "source": [ + "print(\"Random output:\", tau.random(), tau.random(), tau.random())" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "@pm.deterministic\n", + "def lambda_(tau=tau, lambda_1=lambda_1, lambda_2=lambda_2):\n", + " out = np.zeros(n_count_data)\n", + " out[:tau] = lambda_1 # lambda before tau is lambda1\n", + " out[tau:] = lambda_2 # lambda after (and including) tau is lambda2\n", + " return out" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This code creates a new function `lambda_`, but really we can think of it as a random variable: the random variable $\\lambda$ from above. Note that because `lambda_1`, `lambda_2` and `tau` are random, `lambda_` will be random. We are **not** fixing any variables yet.\n", + "\n", + "`@pm.deterministic` is a decorator that tells PyMC this is a deterministic function. That is, if the arguments were deterministic (which they are not), the output would be deterministic as well. Deterministic functions will be covered in Chapter 2. " + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "observation = pm.Poisson(\"obs\", lambda_, value=count_data, observed=True)\n", + "\n", + "model = pm.Model([observation, lambda_1, lambda_2, tau])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The variable `observation` combines our data, `count_data`, with our proposed data-generation scheme, given by the variable `lambda_`, through the `value` keyword. We also set `observed = True` to tell PyMC that this should stay fixed in our analysis. Finally, PyMC wants us to collect all the variables of interest and create a `Model` instance out of them. This makes our life easier when we retrieve the results.\n", + "\n", + "The code below will be explained in Chapter 3, but I show it here so you can see where our results come from. One can think of it as a *learning* step. The machinery being employed is called *Markov Chain Monte Carlo* (MCMC), which I also delay explaining until Chapter 3. This technique returns thousands of random variables from the posterior distributions of $\\lambda_1, \\lambda_2$ and $\\tau$. We can plot a histogram of the random variables to see what the posterior distributions look like. Below, we collect the samples (called *traces* in the MCMC literature) into histograms." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " [-----------------100%-----------------] 40000 of 40000 complete in 6.5 sec" + ] + } + ], + "source": [ + "# Mysterious code to be explained in Chapter 3.\n", + "mcmc = pm.MCMC(model)\n", + "mcmc.sample(40000, 10000, 1)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "lambda_1_samples = mcmc.trace('lambda_1')[:]\n", + "lambda_2_samples = mcmc.trace('lambda_2')[:]\n", + "tau_samples = mcmc.trace('tau')[:]" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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xLjdW0wfbNH7EaH1Q/2aLy826qefAjwZXVfo4zuOReTwyj0fm8cg8HWjoUdLe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\nRESkgWlCnzPVouVPfZ4/9Xn+VM+dP/V5/jS25E9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o963x9u9oeaLs3223266JP2LECFpa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\nGmu6QlEEjwAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "figsize(12.5, 10)\n", + "# histogram of the samples:\n", + "\n", + "ax = plt.subplot(311)\n", + "ax.set_autoscaley_on(False)\n", + "\n", + "plt.hist(lambda_1_samples, histtype='stepfilled', bins=30, alpha=0.85,\n", + " label=\"posterior of $\\lambda_1$\", color=\"#A60628\", density=True)\n", + "plt.legend(loc=\"upper left\")\n", + "plt.title(r\"\"\"Posterior distributions of the variables\n", + " $\\lambda_1,\\;\\lambda_2,\\;\\tau$\"\"\")\n", + "plt.xlim([15, 30])\n", + "plt.xlabel(\"$\\lambda_1$ value\")\n", + "\n", + "ax = plt.subplot(312)\n", + "ax.set_autoscaley_on(False)\n", + "plt.hist(lambda_2_samples, histtype='stepfilled', bins=30, alpha=0.85,\n", + " label=\"posterior of $\\lambda_2$\", color=\"#7A68A6\", density=True)\n", + "plt.legend(loc=\"upper left\")\n", + "plt.xlim([15, 30])\n", + "plt.xlabel(\"$\\lambda_2$ value\")\n", + "\n", + "plt.subplot(313)\n", + "w = 1.0 / tau_samples.shape[0] * np.ones_like(tau_samples)\n", + "plt.hist(tau_samples, bins=n_count_data, alpha=1,\n", + " label=r\"posterior of $\\tau$\",\n", + " color=\"#467821\", weights=w, rwidth=2.)\n", + "plt.xticks(np.arange(n_count_data))\n", + "\n", + "plt.legend(loc=\"upper left\")\n", + "plt.ylim([0, .75])\n", + "plt.xlim([35, len(count_data) - 20])\n", + "plt.xlabel(r\"$\\tau$ (in days)\")\n", + "plt.ylabel(\"probability\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Interpretation\n", + "\n", + "Recall that Bayesian methodology returns a *distribution*. Hence we now have distributions to describe the unknown $\\lambda$s and $\\tau$. What have we gained? Immediately, we can see the uncertainty in our estimates: the wider the distribution, the less certain our posterior belief should be. We can also see what the plausible values for the parameters are: $\\lambda_1$ is around 18 and $\\lambda_2$ is around 23. The posterior distributions of the two $\\lambda$s are clearly distinct, indicating that it is indeed likely that there was a change in the user's text-message behaviour.\n", + "\n", + "What other observations can you make? If you look at the original data again, do these results seem reasonable? \n", + "\n", + "Notice also that the posterior distributions for the $\\lambda$s do not look like exponential distributions, even though our priors for these variables were exponential. In fact, the posterior distributions are not really of any form that we recognize from the original model. But that's OK! This is one of the benefits of taking a computational point of view. If we had instead done this analysis using mathematical approaches, we would have been stuck with an analytically intractable (and messy) distribution. Our use of a computational approach makes us indifferent to mathematical tractability.\n", + "\n", + "Our analysis also returned a distribution for $\\tau$. Its posterior distribution looks a little different from the other two because it is a discrete random variable, so it doesn't assign probabilities to intervals. We can see that near day 45, there was a 50% chance that the user's behaviour changed. Had no change occurred, or had the change been gradual over time, the posterior distribution of $\\tau$ would have been more spread out, reflecting that many days were plausible candidates for $\\tau$. By contrast, in the actual results we see that only three or four days make any sense as potential transition points. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Why would I want samples from the posterior, anyways?\n", + "\n", + "\n", + "We will deal with this question for the remainder of the book, and it is an understatement to say that it will lead us to some amazing results. For now, let's end this chapter with one more example.\n", + "\n", + "We'll use the posterior samples to answer the following question: what is the expected number of texts at day $t, \\; 0 \\le t \\le 70$ ? Recall that the expected value of a Poisson variable is equal to its parameter $\\lambda$. Therefore, the question is equivalent to *what is the expected value of $\\lambda$ at time $t$*?\n", + "\n", + "In the code below, let $i$ index samples from the posterior distributions. Given a day $t$, we average over all possible $\\lambda_i$ for that day $t$, using $\\lambda_i = \\lambda_{1,i}$ if $t \\lt \\tau_i$ (that is, if the behaviour change has not yet occurred), else we use $\\lambda_i = \\lambda_{2,i}$. " + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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/MXPmTF544QUmTJhAWlqab/k9e/bw8ssvc+LECRYsWMDGjRvp2rUr0dHRdOrU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\n9fdWjQ+H+vspe3+t6A+NDrU/lP35FPF5UeT+Brd5sPsroj9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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "figsize(12.5, 5)\n", + "# tau_samples, lambda_1_samples, lambda_2_samples contain\n", + "# N samples from the corresponding posterior distribution\n", + "N = tau_samples.shape[0]\n", + "expected_texts_per_day = np.zeros(n_count_data)\n", + "for day in range(0, n_count_data):\n", + " # ix is a bool index of all tau samples corresponding to\n", + " # the switchpoint occurring prior to value of 'day'\n", + " ix = day < tau_samples\n", + " # Each posterior sample corresponds to a value for tau.\n", + " # for each day, that value of tau indicates whether we're \"before\"\n", + " # (in the lambda1 \"regime\") or\n", + " # \"after\" (in the lambda2 \"regime\") the switchpoint.\n", + " # by taking the posterior sample of lambda1/2 accordingly, we can average\n", + " # over all samples to get an expected value for lambda on that day.\n", + " # As explained, the \"message count\" random variable is Poisson distributed,\n", + " # and therefore lambda (the poisson parameter) is the expected value of\n", + " # \"message count\".\n", + " expected_texts_per_day[day] = (lambda_1_samples[ix].sum()\n", + " + lambda_2_samples[~ix].sum()) / N\n", + "\n", + "\n", + "plt.plot(range(n_count_data), expected_texts_per_day, lw=4, color=\"#E24A33\",\n", + " label=\"expected number of text-messages received\")\n", + "plt.xlim(0, n_count_data)\n", + "plt.xlabel(\"Day\")\n", + "plt.ylabel(\"Expected # text-messages\")\n", + "plt.title(\"Expected number of text-messages received\")\n", + "plt.ylim(0, 60)\n", + "plt.bar(np.arange(len(count_data)), count_data, color=\"#348ABD\", alpha=0.65,\n", + " label=\"observed texts per day\")\n", + "\n", + "plt.legend(loc=\"upper left\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Our analysis shows strong support for believing the user's behavior did change ($\\lambda_1$ would have been close in value to $\\lambda_2$ had this not been true), and that the change was sudden rather than gradual (as demonstrated by $\\tau$'s strongly peaked posterior distribution). We can speculate what might have caused this: a cheaper text-message rate, a recent weather-to-text subscription, or perhaps a new relationship. (In fact, the 45th day corresponds to Christmas, and I moved away to Toronto the next month, leaving a girlfriend behind.)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Exercises\n", + "\n", + "1\\. Using `lambda_1_samples` and `lambda_2_samples`, what is the mean of the posterior distributions of $\\lambda_1$ and $\\lambda_2$?" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# type your code here." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "2\\. What is the expected percentage increase in text-message rates? `hint:` compute the mean of `lambda_1_samples/lambda_2_samples`. Note that this quantity is very different from `lambda_1_samples.mean()/lambda_2_samples.mean()`." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# type your code here." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "3\\. What is the mean of $\\lambda_1$ **given** that we know $\\tau$ is less than 45. That is, suppose we have been given new information that the change in behaviour occurred prior to day 45. What is the expected value of $\\lambda_1$ now? (You do not need to redo the PyMC part. Just consider all instances where `tau_samples < 45`.)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# type your code here." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### References\n", + "\n", + "\n", + "- [1] Gelman, Andrew. N.p.. Web. 22 Jan 2013. [N is never large enough](http://andrewgelman.com/2005/07/31/n_is_never_larg/).\n", + "- [2] Norvig, Peter. 2009. [The Unreasonable Effectiveness of Data](http://static.googleusercontent.com/media/research.google.com/en//pubs/archive/35179.pdf).\n", + "- [3] Patil, A., D. Huard and C.J. Fonnesbeck. 2010. \n", + "PyMC: Bayesian Stochastic Modelling in Python. Journal of Statistical \n", + "Software, 35(4), pp. 1-81. \n", + "- [4] Jimmy Lin and Alek Kolcz. Large-Scale Machine Learning at Twitter. Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data (SIGMOD 2012), pages 793-804, May 2012, Scottsdale, Arizona.\n", + "- [5] Cronin, Beau. \"Why Probabilistic Programming Matters.\" 24 Mar 2013. Google, Online Posting to Google . Web. 24 Mar. 2013. ." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.core.display import HTML\n", + "\n", + "\n", + "def css_styling():\n", + " styles = open(\"../styles/custom.css\", \"r\").read()\n", + " return HTML(styles)\n", + "css_styling()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python [conda env:bayes]", + "language": "python", + "name": "conda-env-bayes-py" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.2" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/Chapter1_Introduction/Ch1_Introduction_PyMC3.ipynb b/Chapter1_Introduction/Ch1_Introduction_PyMC3.ipynb new file mode 100644 index 00000000..abcb7243 --- /dev/null +++ b/Chapter1_Introduction/Ch1_Introduction_PyMC3.ipynb @@ -0,0 +1,1068 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Probabilistic Programming\n", + "=====\n", + "and Bayesian Methods for Hackers \n", + "========\n", + "\n", + "##### Version 0.1\n", + "\n", + "`Original content created by Cam Davidson-Pilon`\n", + "\n", + "`Ported to Python 3 and PyMC3 by Max Margenot (@clean_utensils) and Thomas Wiecki (@twiecki) at Quantopian (@quantopian)`\n", + "___\n", + "\n", + "\n", + "Welcome to *Bayesian Methods for Hackers*. The full Github repository is available at [github/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers](https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers). The other chapters can be found on the project's [homepage](https://camdavidsonpilon.github.io/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/). We hope you enjoy the book, and we encourage any contributions!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Chapter 1\n", + "======\n", + "***" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The Philosophy of Bayesian Inference\n", + "------\n", + " \n", + "> You are a skilled programmer, but bugs still slip into your code. After a particularly difficult implementation of an algorithm, you decide to test your code on a trivial example. It passes. You test the code on a harder problem. It passes once again. And it passes the next, *even more difficult*, test too! You are starting to believe that there may be no bugs in this code...\n", + "\n", + "If you think this way, then congratulations, you already are thinking Bayesian! Bayesian inference is simply updating your beliefs after considering new evidence. A Bayesian can rarely be certain about a result, but he or she can be very confident. Just like in the example above, we can never be 100% sure that our code is bug-free unless we test it on every possible problem; something rarely possible in practice. Instead, we can test it on a large number of problems, and if it succeeds we can feel more *confident* about our code, but still not certain. Bayesian inference works identically: we update our beliefs about an outcome; rarely can we be absolutely sure unless we rule out all other alternatives. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "### The Bayesian state of mind\n", + "\n", + "\n", + "Bayesian inference differs from more traditional statistical inference by preserving *uncertainty*. At first, this sounds like a bad statistical technique. Isn't statistics all about deriving *certainty* from randomness? To reconcile this, we need to start thinking like Bayesians. \n", + "\n", + "The Bayesian world-view interprets probability as measure of *believability in an event*, that is, how confident we are in an event occurring. In fact, we will see in a moment that this is the natural interpretation of probability. \n", + "\n", + "For this to be clearer, we consider an alternative interpretation of probability: *Frequentist*, known as the more *classical* version of statistics, assume that probability is the long-run frequency of events (hence the bestowed title). For example, the *probability of plane accidents* under a frequentist philosophy is interpreted as the *long-term frequency of plane accidents*. This makes logical sense for many probabilities of events, but becomes more difficult to understand when events have no long-term frequency of occurrences. Consider: we often assign probabilities to outcomes of presidential elections, but the election itself only happens once! Frequentists get around this by invoking alternative realities and saying across all these realities, the frequency of occurrences defines the probability. \n", + "\n", + "Bayesians, on the other hand, have a more intuitive approach. Bayesians interpret a probability as measure of *belief*, or confidence, of an event occurring. Simply, a probability is a summary of an opinion. An individual who assigns a belief of 0 to an event has no confidence that the event will occur; conversely, assigning a belief of 1 implies that the individual is absolutely certain of an event occurring. Beliefs between 0 and 1 allow for weightings of other outcomes. This definition agrees with the probability of a plane accident example, for having observed the frequency of plane accidents, an individual's belief should be equal to that frequency, excluding any outside information. Similarly, under this definition of probability being equal to beliefs, it is meaningful to speak about probabilities (beliefs) of presidential election outcomes: how confident are you candidate *A* will win?\n", + "\n", + "Notice in the paragraph above, I assigned the belief (probability) measure to an *individual*, not to Nature. This is very interesting, as this definition leaves room for conflicting beliefs between individuals. Again, this is appropriate for what naturally occurs: different individuals have different beliefs of events occurring, because they possess different *information* about the world. The existence of different beliefs does not imply that anyone is wrong. Consider the following examples demonstrating the relationship between individual beliefs and probabilities:\n", + "\n", + "- I flip a coin, and we both guess the result. We would both agree, assuming the coin is fair, that the probability of Heads is 1/2. Assume, then, that I peek at the coin. Now I know for certain what the result is: I assign probability 1.0 to either Heads or Tails (whichever it is). Now what is *your* belief that the coin is Heads? My knowledge of the outcome has not changed the coin's results. Thus we assign different probabilities to the result. \n", + "\n", + "- Your code either has a bug in it or not, but we do not know for certain which is true, though we have a belief about the presence or absence of a bug. \n", + "\n", + "- A medical patient is exhibiting symptoms $x$, $y$ and $z$. There are a number of diseases that could be causing all of them, but only a single disease is present. A doctor has beliefs about which disease, but a second doctor may have slightly different beliefs. \n", + "\n", + "\n", + "This philosophy of treating beliefs as probability is natural to humans. We employ it constantly as we interact with the world and only see partial truths, but gather evidence to form beliefs. Alternatively, you have to be *trained* to think like a frequentist. \n", + "\n", + "To align ourselves with traditional probability notation, we denote our belief about event $A$ as $P(A)$. We call this quantity the *prior probability*.\n", + "\n", + "John Maynard Keynes, a great economist and thinker, said \"When the facts change, I change my mind. What do you do, sir?\" This quote reflects the way a Bayesian updates his or her beliefs after seeing evidence. Even — especially — if the evidence is counter to what was initially believed, the evidence cannot be ignored. We denote our updated belief as $P(A |X )$, interpreted as the probability of $A$ given the evidence $X$. We call the updated belief the *posterior probability* so as to contrast it with the prior probability. For example, consider the posterior probabilities (read: posterior beliefs) of the above examples, after observing some evidence $X$:\n", + "\n", + "1\\. $P(A): \\;\\;$ the coin has a 50 percent chance of being Heads. $P(A | X):\\;\\;$ You look at the coin, observe a Heads has landed, denote this information $X$, and trivially assign probability 1.0 to Heads and 0.0 to Tails.\n", + "\n", + "2\\. $P(A): \\;\\;$ This big, complex code likely has a bug in it. $P(A | X): \\;\\;$ The code passed all $X$ tests; there still might be a bug, but its presence is less likely now.\n", + "\n", + "3\\. $P(A):\\;\\;$ The patient could have any number of diseases. $P(A | X):\\;\\;$ Performing a blood test generated evidence $X$, ruling out some of the possible diseases from consideration.\n", + "\n", + "\n", + "It's clear that in each example we did not completely discard the prior belief after seeing new evidence $X$, but we *re-weighted the prior* to incorporate the new evidence (i.e. we put more weight, or confidence, on some beliefs versus others). \n", + "\n", + "By introducing prior uncertainty about events, we are already admitting that any guess we make is potentially very wrong. After observing data, evidence, or other information, we update our beliefs, and our guess becomes *less wrong*. This is the alternative side of the prediction coin, where typically we try to be *more right*. \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "### Bayesian Inference in Practice\n", + "\n", + " If frequentist and Bayesian inference were programming functions, with inputs being statistical problems, then the two would be different in what they return to the user. The frequentist inference function would return a number, representing an estimate (typically a summary statistic like the sample average etc.), whereas the Bayesian function would return *probabilities*.\n", + "\n", + "For example, in our debugging problem above, calling the frequentist function with the argument \"My code passed all $X$ tests; is my code bug-free?\" would return a *YES*. On the other hand, asking our Bayesian function \"Often my code has bugs. My code passed all $X$ tests; is my code bug-free?\" would return something very different: probabilities of *YES* and *NO*. The function might return:\n", + "\n", + "\n", + "> *YES*, with probability 0.8; *NO*, with probability 0.2\n", + "\n", + "\n", + "\n", + "This is very different from the answer the frequentist function returned. Notice that the Bayesian function accepted an additional argument: *\"Often my code has bugs\"*. This parameter is the *prior*. By including the prior parameter, we are telling the Bayesian function to include our belief about the situation. Technically this parameter in the Bayesian function is optional, but we will see excluding it has its own consequences. \n", + "\n", + "\n", + "#### Incorporating evidence\n", + "\n", + "As we acquire more and more instances of evidence, our prior belief is *washed out* by the new evidence. This is to be expected. For example, if your prior belief is something ridiculous, like \"I expect the sun to explode today\", and each day you are proved wrong, you would hope that any inference would correct you, or at least align your beliefs better. Bayesian inference will correct this belief.\n", + "\n", + "\n", + "Denote $N$ as the number of instances of evidence we possess. As we gather an *infinite* amount of evidence, say as $N \\rightarrow \\infty$, our Bayesian results (often) align with frequentist results. Hence for large $N$, statistical inference is more or less objective. On the other hand, for small $N$, inference is much more *unstable*: frequentist estimates have more variance and larger confidence intervals. This is where Bayesian analysis excels. By introducing a prior, and returning probabilities (instead of a scalar estimate), we *preserve the uncertainty* that reflects the instability of statistical inference of a small $N$ dataset. \n", + "\n", + "One may think that for large $N$, one can be indifferent between the two techniques since they offer similar inference, and might lean towards the computationally-simpler, frequentist methods. An individual in this position should consider the following quote by Andrew Gelman (2005)[1], before making such a decision:\n", + "\n", + "> Sample sizes are never large. If $N$ is too small to get a sufficiently-precise estimate, you need to get more data (or make more assumptions). But once $N$ is \"large enough,\" you can start subdividing the data to learn more (for example, in a public opinion poll, once you have a good estimate for the entire country, you can estimate among men and women, northerners and southerners, different age groups, etc.). $N$ is never enough because if it were \"enough\" you'd already be on to the next problem for which you need more data.\n", + "\n", + "### Are frequentist methods incorrect then? \n", + "\n", + "**No.**\n", + "\n", + "Frequentist methods are still useful or state-of-the-art in many areas. Tools such as least squares linear regression, LASSO regression, and expectation-maximization algorithms are all powerful and fast. Bayesian methods complement these techniques by solving problems that these approaches cannot, or by illuminating the underlying system with more flexible modeling.\n", + "\n", + "\n", + "#### A note on *Big Data*\n", + "Paradoxically, big data's predictive analytic problems are actually solved by relatively simple algorithms [2][4]. Thus we can argue that big data's prediction difficulty does not lie in the algorithm used, but instead on the computational difficulties of storage and execution on big data. (One should also consider Gelman's quote from above and ask \"Do I really have big data?\")\n", + "\n", + "The much more difficult analytic problems involve *medium data* and, especially troublesome, *really small data*. Using a similar argument as Gelman's above, if big data problems are *big enough* to be readily solved, then we should be more interested in the *not-quite-big enough* datasets. \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Our Bayesian framework\n", + "\n", + "We are interested in beliefs, which can be interpreted as probabilities by thinking Bayesian. We have a *prior* belief in event $A$, beliefs formed by previous information, e.g., our prior belief about bugs being in our code before performing tests.\n", + "\n", + "Secondly, we observe our evidence. To continue our buggy-code example: if our code passes $X$ tests, we want to update our belief to incorporate this. We call this new belief the *posterior* probability. Updating our belief is done via the following equation, known as Bayes' Theorem, after its discoverer Thomas Bayes:\n", + "\n", + "\\begin{align}\n", + " P( A | X ) = & \\frac{ P(X | A) P(A) } {P(X) } \\\\\\\\[5pt]\n", + "& \\propto P(X | A) P(A)\\;\\; (\\propto \\text{is proportional to })\n", + "\\end{align}\n", + "\n", + "The above formula is not unique to Bayesian inference: it is a mathematical fact with uses outside Bayesian inference. Bayesian inference merely uses it to connect prior probabilities $P(A)$ with an updated posterior probabilities $P(A | X )$." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Example: Mandatory coin-flip example\n", + "\n", + "Every statistics text must contain a coin-flipping example, I'll use it here to get it out of the way. Suppose, naively, that you are unsure about the probability of heads in a coin flip (spoiler alert: it's 50%). You believe there is some true underlying ratio, call it $p$, but have no prior opinion on what $p$ might be. \n", + "\n", + "We begin to flip a coin, and record the observations: either $H$ or $T$. This is our observed data. An interesting question to ask is how our inference changes as we observe more and more data? More specifically, what do our posterior probabilities look like when we have little data, versus when we have lots of data. \n", + "\n", + "Below we plot a sequence of updating posterior probabilities as we observe increasing amounts of data (coin flips)." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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eCYAxhmuvvZYhQ4awYcMGXnnlFf7whz/w3nvvAeDxeJg/fz5btmxh+fLl/Pvf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7P3cDerPj3EjBSbtzMxMVrWsokhkzUQwyzY8+9UVaWspYeN/Z2GspYe33M6BSb0jzLkxZuJNo+O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\nOhAiIiIiIhKaOhAiIiIiIhLa/wNELXdUsJ1f3wAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\"\"\"\n", + "The book uses a custom matplotlibrc file, which provides the unique styles for\n", + "matplotlib plots. If executing this book, and you wish to use the book's\n", + "styling, provided are two options:\n", + " 1. Overwrite your own matplotlibrc file with the rc-file provided in the\n", + " book's styles/ dir. See http://matplotlib.org/users/customizing.html\n", + " 2. Also in the styles is bmh_matplotlibrc.json file. This can be used to\n", + " update the styles in only this notebook. Try running the following code:\n", + "\n", + " import json\n", + " s = json.load(open(\"../styles/bmh_matplotlibrc.json\"))\n", + " matplotlib.rcParams.update(s)\n", + "\n", + "\"\"\"\n", + "\n", + "# The code below can be passed over, as it is currently not important, plus it\n", + "# uses advanced topics we have not covered yet. LOOK AT PICTURE, MICHAEL!\n", + "%matplotlib inline\n", + "from IPython.core.pylabtools import figsize\n", + "import numpy as np\n", + "from matplotlib import pyplot as plt\n", + "figsize(11, 9)\n", + "\n", + "import scipy.stats as stats\n", + "\n", + "dist = stats.beta\n", + "n_trials = [0, 1, 2, 3, 4, 5, 8, 15, 50, 500]\n", + "data = stats.bernoulli.rvs(0.5, size=n_trials[-1])\n", + "x = np.linspace(0, 1, 100)\n", + "\n", + "# For the already prepared, I'm using Binomial's conj. prior.\n", + "for k, N in enumerate(n_trials):\n", + " sx = plt.subplot(len(n_trials)//2, 2, k+1)\n", + " plt.xlabel(\"$p$, probability of heads\") \\\n", + " if k in [0, len(n_trials)-1] else None\n", + " plt.setp(sx.get_yticklabels(), visible=False)\n", + " heads = data[:N].sum()\n", + " y = dist.pdf(x, 1 + heads, 1 + N - heads)\n", + " plt.plot(x, y, label=\"observe %d tosses,\\n %d heads\" % (N, heads))\n", + " plt.fill_between(x, 0, y, color=\"#348ABD\", alpha=0.4)\n", + " plt.vlines(0.5, 0, 4, color=\"k\", linestyles=\"--\", lw=1)\n", + "\n", + " leg = plt.legend()\n", + " leg.get_frame().set_alpha(0.4)\n", + " plt.autoscale(tight=True)\n", + "\n", + "\n", + "plt.suptitle(\"Bayesian updating of posterior probabilities\",\n", + " y=1.02,\n", + " fontsize=14)\n", + "\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The posterior probabilities are represented by the curves, and our uncertainty is proportional to the width of the curve. As the plot above shows, as we start to observe data our posterior probabilities start to shift and move around. Eventually, as we observe more and more data (coin-flips), our probabilities will tighten closer and closer around the true value of $p=0.5$ (marked by a dashed line). \n", + "\n", + "Notice that the plots are not always *peaked* at 0.5. There is no reason it should be: recall we assumed we did not have a prior opinion of what $p$ is. In fact, if we observe quite extreme data, say 8 flips and only 1 observed heads, our distribution would look very biased *away* from lumping around 0.5 (with no prior opinion, how confident would you feel betting on a fair coin after observing 8 tails and 1 head?). As more data accumulates, we would see more and more probability being assigned at $p=0.5$, though never all of it.\n", + "\n", + "The next example is a simple demonstration of the mathematics of Bayesian inference. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Example: Bug, or just sweet, unintended feature?\n", + "\n", + "\n", + "Let $A$ denote the event that our code has **no bugs** in it. Let $X$ denote the event that the code passes all debugging tests. For now, we will leave the prior probability of no bugs as a variable, i.e. $P(A) = p$. \n", + "\n", + "We are interested in $P(A|X)$, i.e. the probability of no bugs, given our debugging tests $X$. To use the formula above, we need to compute some quantities.\n", + "\n", + "What is $P(X | A)$, i.e., the probability that the code passes $X$ tests *given* there are no bugs? Well, it is equal to 1, for a code with no bugs will pass all tests. \n", + "\n", + "$P(X)$ is a little bit trickier: The event $X$ can be divided into two possibilities, event $X$ occurring even though our code *indeed has* bugs (denoted $\\sim A\\;$, spoken *not $A$*), or event $X$ without bugs ($A$). $P(X)$ can be represented as:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\\begin{align}\n", + "P(X ) & = P(X \\text{ and } A) + P(X \\text{ and } \\sim A) \\\\\\\\[5pt]\n", + " & = P(X|A)P(A) + P(X | \\sim A)P(\\sim A)\\\\\\\\[5pt]\n", + "& = P(X|A)p + P(X | \\sim A)(1-p)\n", + "\\end{align}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We have already computed $P(X|A)$ above. On the other hand, $P(X | \\sim A)$ is subjective: our code can pass tests but still have a bug in it, though the probability there is a bug present is reduced. Note this is dependent on the number of tests performed, the degree of complication in the tests, etc. Let's be conservative and assign $P(X|\\sim A) = 0.5$. Then\n", + "\n", + "\\begin{align}\n", + "P(A | X) & = \\frac{1\\cdot p}{ 1\\cdot p +0.5 (1-p) } \\\\\\\\\n", + "& = \\frac{ 2 p}{1+p}\n", + "\\end{align}\n", + "This is the posterior probability. What does it look like as a function of our prior, $p \\in [0,1]$? " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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0e062s+dEW6S4b+dkW3fM55iSn8nCSFG/sDyPheW5TNfEmwlHPfoiIiIiY1R/P/3uE+3s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\nduCWiHgxw7AtIym/W64Gbi/6ScU5EfFmRiFbhiT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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "figsize(12.5, 4)\n", + "p = np.linspace(0, 1, 50)\n", + "plt.plot(p, 2*p/(1+p), color=\"#348ABD\", lw=3)\n", + "#plt.fill_between(p, 2*p/(1+p), alpha=.5, facecolor=[\"#A60628\"])\n", + "plt.scatter(0.2, 2*(0.2)/1.2, s=140, c=\"#348ABD\")\n", + "plt.xlim(0, 1)\n", + "plt.ylim(0, 1)\n", + "plt.xlabel(\"Prior, $P(A) = p$\")\n", + "plt.ylabel(\"Posterior, $P(A|X)$, with $P(A) = p$\")\n", + "plt.title(\"Are there bugs in my code?\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can see the biggest gains if we observe the $X$ tests passed when the prior probability, $p$, is low. Let's settle on a specific value for the prior. I'm a strong programmer (I think), so I'm going to give myself a realistic prior of 0.20, that is, there is a 20% chance that I write code bug-free. To be more realistic, this prior should be a function of how complicated and large the code is, but let's pin it at 0.20. Then my updated belief that my code is bug-free is 0.33. \n", + "\n", + "Recall that the prior is a probability: $p$ is the prior probability that there *are no bugs*, so $1-p$ is the prior probability that there *are bugs*.\n", + "\n", + "Similarly, our posterior is also a probability, with $P(A | X)$ the probability there is no bug *given we saw all tests pass*, hence $1-P(A|X)$ is the probability there is a bug *given all tests passed*. What does our posterior probability look like? Below is a chart of both the prior and the posterior probabilities. \n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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AviUfHTt2pEuXLixdupSlS5eybNkyVqxYwcSJEyv6VHfCZWwckZijk+Xqtm/X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\nVNMuIiKZQDXtkki1Sdp7AQ+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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "figsize(12.5, 4)\n", + "colours = [\"#348ABD\", \"#A60628\"]\n", + "\n", + "prior = [0.20, 0.80]\n", + "posterior = [1./3, 2./3]\n", + "plt.bar([0, .7], prior, alpha=0.70, width=0.25,\n", + " color=colours[0], label=\"prior distribution\",\n", + " lw=\"3\", edgecolor=colours[0])\n", + "\n", + "plt.bar([0+0.25, .7+0.25], posterior, alpha=0.7,\n", + " width=0.25, color=colours[1],\n", + " label=\"posterior distribution\",\n", + " lw=\"3\", edgecolor=colours[1])\n", + "\n", + "plt.xticks([0.20, .95], [\"Bugs Absent\", \"Bugs Present\"])\n", + "plt.title(\"Prior and Posterior probability of bugs present\")\n", + "plt.ylabel(\"Probability\")\n", + "plt.legend(loc=\"upper left\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notice that after we observed $X$ occur, the probability of bugs being absent increased. By increasing the number of tests, we can approach confidence (probability 1) that there are no bugs present.\n", + "\n", + "This was a very simple example of Bayesian inference and Bayes rule. Unfortunately, the mathematics necessary to perform more complicated Bayesian inference only becomes more difficult, except for artificially constructed cases. We will later see that this type of mathematical analysis is actually unnecessary. First we must broaden our modeling tools. The next section deals with *probability distributions*. If you are already familiar, feel free to skip (or at least skim), but for the less familiar the next section is essential." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "_______\n", + "\n", + "## Probability Distributions\n", + "\n", + "\n", + "**Let's quickly recall what a probability distribution is:** Let $Z$ be some random variable. Then associated with $Z$ is a *probability distribution function* that assigns probabilities to the different outcomes $Z$ can take. Graphically, a probability distribution is a curve where the probability of an outcome is proportional to the height of the curve. You can see examples in the first figure of this chapter. \n", + "\n", + "We can divide random variables into three classifications:\n", + "\n", + "- **$Z$ is discrete**: Discrete random variables may only assume values on a specified list. Things like populations, movie ratings, and number of votes are all discrete random variables. Discrete random variables become more clear when we contrast them with...\n", + "\n", + "- **$Z$ is continuous**: Continuous random variable can take on arbitrarily exact values. For example, temperature, speed, time, color are all modeled as continuous variables because you can progressively make the values more and more precise.\n", + "\n", + "- **$Z$ is mixed**: Mixed random variables assign probabilities to both discrete and continuous random variables, i.e. it is a combination of the above two categories. \n", + "\n", + "### Discrete Case\n", + "If $Z$ is discrete, then its distribution is called a *probability mass function*, which measures the probability $Z$ takes on the value $k$, denoted $P(Z=k)$. Note that the probability mass function completely describes the random variable $Z$, that is, if we know the mass function, we know how $Z$ should behave. There are popular probability mass functions that consistently appear: we will introduce them as needed, but let's introduce the first very useful probability mass function. We say $Z$ is *Poisson*-distributed if:\n", + "\n", + "$$P(Z = k) =\\frac{ \\lambda^k e^{-\\lambda} }{k!}, \\; \\; k=0,1,2, \\dots $$\n", + "\n", + "$\\lambda$ is called a parameter of the distribution, and it controls the distribution's shape. For the Poisson distribution, $\\lambda$ can be any positive number. By increasing $\\lambda$, we add more probability to larger values, and conversely by decreasing $\\lambda$ we add more probability to smaller values. One can describe $\\lambda$ as the *intensity* of the Poisson distribution. \n", + "\n", + "Unlike $\\lambda$, which can be any positive number, the value $k$ in the above formula must be a non-negative integer, i.e., $k$ must take on values 0,1,2, and so on. This is very important, because if you wanted to model a population you could not make sense of populations with 4.25 or 5.612 members. \n", + "\n", + "If a random variable $Z$ has a Poisson mass distribution, we denote this by writing\n", + "\n", + "$$Z \\sim \\text{Poi}(\\lambda) $$\n", + "\n", + "One useful property of the Poisson distribution is that its expected value is equal to its parameter, i.e.:\n", + "\n", + "$$E\\large[ \\;Z\\; | \\; \\lambda \\;\\large] = \\lambda $$\n", + "\n", + "We will use this property often, so it's useful to remember. Below, we plot the probability mass distribution for different $\\lambda$ values. The first thing to notice is that by increasing $\\lambda$, we add more probability of larger values occurring. Second, notice that although the graph ends at 15, the distributions do not. They assign positive probability to every non-negative integer." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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dua5fy1rOY3lQWXpGU287fj+otzw9jZaj/X6I1qvl5Odhw4ZkYktXVxezZs1i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\nWLDAr1i+J8dMn5LjvWmss7uXAyZPYNp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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "figsize(12.5, 4)\n", + "\n", + "import scipy.stats as stats\n", + "a = np.arange(16)\n", + "poi = stats.poisson\n", + "lambda_ = [1.5, 4.25]\n", + "colours = [\"#348ABD\", \"#A60628\"]\n", + "\n", + "plt.bar(a, poi.pmf(a, lambda_[0]), color=colours[0],\n", + " label=\"$\\lambda = %.1f$\" % lambda_[0], alpha=0.60,\n", + " edgecolor=colours[0], lw=\"3\")\n", + "\n", + "plt.bar(a, poi.pmf(a, lambda_[1]), color=colours[1],\n", + " label=\"$\\lambda = %.1f$\" % lambda_[1], alpha=0.60,\n", + " edgecolor=colours[1], lw=\"3\")\n", + "\n", + "plt.xticks(a + 0.4, a)\n", + "plt.legend()\n", + "plt.ylabel(\"probability of $k$\")\n", + "plt.xlabel(\"$k$\")\n", + "plt.title(\"Probability mass function of a Poisson random variable; differing \\\n", + "$\\lambda$ values\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Continuous Case\n", + "Instead of a probability mass function, a continuous random variable has a *probability density function*. This might seem like unnecessary nomenclature, but the density function and the mass function are very different creatures. An example of continuous random variable is a random variable with *exponential density*. The density function for an exponential random variable looks like this:\n", + "\n", + "$$f_Z(z | \\lambda) = \\lambda e^{-\\lambda z }, \\;\\; z\\ge 0$$\n", + "\n", + "Like a Poisson random variable, an exponential random variable can take on only non-negative values. But unlike a Poisson variable, the exponential can take on *any* non-negative values, including non-integral values such as 4.25 or 5.612401. This property makes it a poor choice for count data, which must be an integer, but a great choice for time data, temperature data (measured in Kelvins, of course), or any other precise *and positive* variable. The graph below shows two probability density functions with different $\\lambda$ values. \n", + "\n", + "When a random variable $Z$ has an exponential distribution with parameter $\\lambda$, we say *$Z$ is exponential* and write\n", + "\n", + "$$Z \\sim \\text{Exp}(\\lambda)$$\n", + "\n", + "Given a specific $\\lambda$, the expected value of an exponential random variable is equal to the inverse of $\\lambda$, that is:\n", + "\n", + "$$E[\\; Z \\;|\\; \\lambda \\;] = \\frac{1}{\\lambda}$$" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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or15PqGcxAK4hzJK7H2bl/3sMF4lkuHUiIiIi0tm0W029mU0B7nfOnR+d/hrg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\nXMeJQGAedxoH/HECEQSpZMAs1befC8JWWxDg3uYynipNtD4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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "a = np.linspace(0, 4, 100)\n", + "expo = stats.expon\n", + "lambda_ = [0.5, 1]\n", + "\n", + "for l, c in zip(lambda_, colours):\n", + " plt.plot(a, expo.pdf(a, scale=1./l), lw=3,\n", + " color=c, label=\"$\\lambda = %.1f$\" % l)\n", + " plt.fill_between(a, expo.pdf(a, scale=1./l), color=c, alpha=.33)\n", + "\n", + "plt.legend()\n", + "plt.ylabel(\"PDF at $z$\")\n", + "plt.xlabel(\"$z$\")\n", + "plt.ylim(0,1.2)\n", + "plt.title(\"Probability density function of an Exponential random variable;\\\n", + " differing $\\lambda$\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "### But what is $\\lambda \\;$?\n", + "\n", + "\n", + "**This question is what motivates statistics**. In the real world, $\\lambda$ is hidden from us. We see only $Z$, and must go backwards to try and determine $\\lambda$. The problem is difficult because there is no one-to-one mapping from $Z$ to $\\lambda$. Many different methods have been created to solve the problem of estimating $\\lambda$, but since $\\lambda$ is never actually observed, no one can say for certain which method is best! \n", + "\n", + "Bayesian inference is concerned with *beliefs* about what $\\lambda$ might be. Rather than try to guess $\\lambda$ exactly, we can only talk about what $\\lambda$ is likely to be by assigning a probability distribution to $\\lambda$.\n", + "\n", + "This might seem odd at first. After all, $\\lambda$ is fixed; it is not (necessarily) random! How can we assign probabilities to values of a non-random variable? Ah, we have fallen for our old, frequentist way of thinking. Recall that under Bayesian philosophy, we *can* assign probabilities if we interpret them as beliefs. And it is entirely acceptable to have *beliefs* about the parameter $\\lambda$. \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "##### Example: Inferring behaviour from text-message data\n", + "\n", + "Let's try to model a more interesting example, one that concerns the rate at which a user sends and receives text messages:\n", + "\n", + "> You are given a series of daily text-message counts from a user of your system. The data, plotted over time, appears in the chart below. You are curious to know if the user's text-messaging habits have changed over time, either gradually or suddenly. How can you model this? (This is in fact my own text-message data. Judge my popularity as you wish.)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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09CjG7DKsbe1Jpd25Q+PsB0vukCZ7K3fu7X7daub+ypx7mTn3dDox+xkzZtDT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\n8NnFNPba7wBmzl/W523B0hW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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "figsize(12.5, 3.5)\n", + "count_data = np.loadtxt(\"data/txtdata.csv\")\n", + "n_count_data = len(count_data)\n", + "plt.bar(np.arange(n_count_data), count_data, color=\"#348ABD\")\n", + "plt.xlabel(\"Time (days)\")\n", + "plt.ylabel(\"count of text-msgs received\")\n", + "plt.title(\"Did the user's texting habits change over time?\")\n", + "plt.xlim(0, n_count_data);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Before we start modeling, see what you can figure out just by looking at the chart above. Would you say there was a change in behaviour during this time period? \n", + "\n", + "How can we start to model this? Well, as we have conveniently already seen, a Poisson random variable is a very appropriate model for this type of *count* data. Denoting day $i$'s text-message count by $C_i$, \n", + "\n", + "$$ C_i \\sim \\text{Poisson}(\\lambda) $$\n", + "\n", + "We are not sure what the value of the $\\lambda$ parameter really is, however. Looking at the chart above, it appears that the rate might become higher late in the observation period, which is equivalent to saying that $\\lambda$ increases at some point during the observations. (Recall that a higher value of $\\lambda$ assigns more probability to larger outcomes. That is, there is a higher probability of many text messages having been sent on a given day.)\n", + "\n", + "How can we represent this observation mathematically? Let's assume that on some day during the observation period (call it $\\tau$), the parameter $\\lambda$ suddenly jumps to a higher value. So we really have two $\\lambda$ parameters: one for the period before $\\tau$, and one for the rest of the observation period. In the literature, a sudden transition like this would be called a *switchpoint*:\n", + "\n", + "$$\n", + "\\lambda = \n", + "\\begin{cases}\n", + "\\lambda_1 & \\text{if } t \\lt \\tau \\cr\n", + "\\lambda_2 & \\text{if } t \\ge \\tau\n", + "\\end{cases}\n", + "$$\n", + "\n", + "\n", + "If, in reality, no sudden change occurred and indeed $\\lambda_1 = \\lambda_2$, then the $\\lambda$s posterior distributions should look about equal.\n", + "\n", + "We are interested in inferring the unknown $\\lambda$s. To use Bayesian inference, we need to assign prior probabilities to the different possible values of $\\lambda$. What would be good prior probability distributions for $\\lambda_1$ and $\\lambda_2$? Recall that $\\lambda$ can be any positive number. As we saw earlier, the *exponential* distribution provides a continuous density function for positive numbers, so it might be a good choice for modeling $\\lambda_i$. But recall that the exponential distribution takes a parameter of its own, so we'll need to include that parameter in our model. Let's call that parameter $\\alpha$.\n", + "\n", + "\\begin{align}\n", + "&\\lambda_1 \\sim \\text{Exp}( \\alpha ) \\\\\\\n", + "&\\lambda_2 \\sim \\text{Exp}( \\alpha )\n", + "\\end{align}\n", + "\n", + "$\\alpha$ is called a *hyper-parameter* or *parent variable*. In literal terms, it is a parameter that influences other parameters. Our initial guess at $\\alpha$ does not influence the model too strongly, so we have some flexibility in our choice. A good rule of thumb is to set the exponential parameter equal to the inverse of the average of the count data. Since we're modeling $\\lambda$ using an exponential distribution, we can use the expected value identity shown earlier to get:\n", + "\n", + "$$\\frac{1}{N}\\sum_{i=0}^N \\;C_i \\approx E[\\; \\lambda \\; |\\; \\alpha ] = \\frac{1}{\\alpha}$$ \n", + "\n", + "An alternative, and something I encourage the reader to try, would be to have two priors: one for each $\\lambda_i$. Creating two exponential distributions with different $\\alpha$ values reflects our prior belief that the rate changed at some point during the observations.\n", + "\n", + "What about $\\tau$? Because of the noisiness of the data, it's difficult to pick out a priori when $\\tau$ might have occurred. Instead, we can assign a *uniform prior belief* to every possible day. This is equivalent to saying\n", + "\n", + "\\begin{align}\n", + "& \\tau \\sim \\text{DiscreteUniform(1,70) }\\\\\\\\\n", + "& \\Rightarrow P( \\tau = k ) = \\frac{1}{70}\n", + "\\end{align}\n", + "\n", + "So after all this, what does our overall prior distribution for the unknown variables look like? Frankly, *it doesn't matter*. What we should understand is that it's an ugly, complicated mess involving symbols only a mathematician could love. And things will only get uglier the more complicated our models become. Regardless, all we really care about is the posterior distribution.\n", + "\n", + "We next turn to PyMC3, a Python library for performing Bayesian analysis that is undaunted by the mathematical monster we have created. \n", + "\n", + "\n", + "Introducing our first hammer: PyMC3\n", + "-----\n", + "\n", + "PyMC3 is a Python library for programming Bayesian analysis [3]. It is a fast, well-maintained library. The only unfortunate part is that its documentation is lacking in certain areas, especially those that bridge the gap between beginner and hacker. One of this book's main goals is to solve that problem, and also to demonstrate why PyMC3 is so cool.\n", + "\n", + "We will model the problem above using PyMC3. This type of programming is called *probabilistic programming*, an unfortunate misnomer that invokes ideas of randomly-generated code and has likely confused and frightened users away from this field. The code is not random; it is probabilistic in the sense that we create probability models using programming variables as the model's components. Model components are first-class primitives within the PyMC3 framework. \n", + "\n", + "B. Cronin [5] has a very motivating description of probabilistic programming:\n", + "\n", + "> Another way of thinking about this: unlike a traditional program, which only runs in the forward directions, a probabilistic program is run in both the forward and backward direction. It runs forward to compute the consequences of the assumptions it contains about the world (i.e., the model space it represents), but it also runs backward from the data to constrain the possible explanations. In practice, many probabilistic programming systems will cleverly interleave these forward and backward operations to efficiently home in on the best explanations.\n", + "\n", + "Because of the confusion engendered by the term *probabilistic programming*, I'll refrain from using it. Instead, I'll simply say *programming*, since that's what it really is. \n", + "\n", + "PyMC3 code is easy to read. The only novel thing should be the syntax. Simply remember that we are representing the model's components ($\\tau, \\lambda_1, \\lambda_2$ ) as variables." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Applied log-transform to lambda_1 and added transformed lambda_1_log_ to model.\n", + "Applied log-transform to lambda_2 and added transformed lambda_2_log_ to model.\n" + ] + } + ], + "source": [ + "import pymc3 as pm\n", + "\n", + "with pm.Model() as model:\n", + " alpha = 1.0/count_data.mean() # Recall count_data is the\n", + " # variable that holds our txt counts\n", + " lambda_1 = pm.Exponential(\"lambda_1\", alpha)\n", + " lambda_2 = pm.Exponential(\"lambda_2\", alpha)\n", + " \n", + " tau = pm.DiscreteUniform(\"tau\", lower=0, upper=n_count_data - 1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the code above, we create the PyMC3 variables corresponding to $\\lambda_1$ and $\\lambda_2$. We assign them to PyMC3's *stochastic variables*, so-called because they are treated by the back end as random number generators." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "with model:\n", + " idx = np.arange(n_count_data) # Index\n", + " lambda_ = pm.math.switch(tau > idx, lambda_1, lambda_2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This code creates a new function `lambda_`, but really we can think of it as a random variable: the random variable $\\lambda$ from above. The `switch()` function assigns `lambda_1` or `lambda_2` as the value of `lambda_`, depending on what side of `tau` we are on. The values of `lambda_` up until `tau` are `lambda_1` and the values afterwards are `lambda_2`.\n", + "\n", + "Note that because `lambda_1`, `lambda_2` and `tau` are random, `lambda_` will be random. We are **not** fixing any variables yet." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "with model:\n", + " observation = pm.Poisson(\"obs\", lambda_, observed=count_data)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The variable `observation` combines our data, `count_data`, with our proposed data-generation scheme, given by the variable `lambda_`, through the `observed` keyword. \n", + "\n", + "The code below will be explained in Chapter 3, but I show it here so you can see where our results come from. One can think of it as a *learning* step. The machinery being employed is called *Markov Chain Monte Carlo* (MCMC), which I also delay explaining until Chapter 3. This technique returns thousands of random variables from the posterior distributions of $\\lambda_1, \\lambda_2$ and $\\tau$. We can plot a histogram of the random variables to see what the posterior distributions look like. Below, we collect the samples (called *traces* in the MCMC literature) into histograms." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 10000/10000 [00:02<00:00, 4511.50it/s]\n" + ] + } + ], + "source": [ + "### Mysterious code to be explained in Chapter 3.\n", + "with model:\n", + " step = pm.Metropolis()\n", + " trace = pm.sample(10000, tune=5000, step=step, return_inferencedata=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "lambda_1_samples = trace['lambda_1']\n", + "lambda_2_samples = trace['lambda_2']\n", + "tau_samples = trace['tau']" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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IXTt2aOm3f6ztG14r9FQAAABKCi03wehFi0cPfTyO83hkHo/M45F5PDLPBgp6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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "figsize(12.5, 10)\n", + "#histogram of the samples:\n", + "\n", + "ax = plt.subplot(311)\n", + "ax.set_autoscaley_on(False)\n", + "\n", + "plt.hist(lambda_1_samples, histtype='stepfilled', bins=30, alpha=0.85,\n", + " label=\"posterior of $\\lambda_1$\", color=\"#A60628\", density=True)\n", + "plt.legend(loc=\"upper left\")\n", + "plt.title(r\"\"\"Posterior distributions of the variables\n", + " $\\lambda_1,\\;\\lambda_2,\\;\\tau$\"\"\")\n", + "plt.xlim([15, 30])\n", + "plt.xlabel(\"$\\lambda_1$ value\")\n", + "\n", + "ax = plt.subplot(312)\n", + "ax.set_autoscaley_on(False)\n", + "plt.hist(lambda_2_samples, histtype='stepfilled', bins=30, alpha=0.85,\n", + " label=\"posterior of $\\lambda_2$\", color=\"#7A68A6\", density=True)\n", + "plt.legend(loc=\"upper left\")\n", + "plt.xlim([15, 30])\n", + "plt.xlabel(\"$\\lambda_2$ value\")\n", + "\n", + "plt.subplot(313)\n", + "w = 1.0 / tau_samples.shape[0] * np.ones_like(tau_samples)\n", + "plt.hist(tau_samples, bins=n_count_data, alpha=1,\n", + " label=r\"posterior of $\\tau$\",\n", + " color=\"#467821\", weights=w, rwidth=2.)\n", + "plt.xticks(np.arange(n_count_data))\n", + "\n", + "plt.legend(loc=\"upper left\")\n", + "plt.ylim([0, .75])\n", + "plt.xlim([35, len(count_data)-20])\n", + "plt.xlabel(r\"$\\tau$ (in days)\")\n", + "plt.ylabel(\"probability\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Interpretation\n", + "\n", + "Recall that Bayesian methodology returns a *distribution*. Hence we now have distributions to describe the unknown $\\lambda$s and $\\tau$. What have we gained? Immediately, we can see the uncertainty in our estimates: the wider the distribution, the less certain our posterior belief should be. We can also see what the plausible values for the parameters are: $\\lambda_1$ is around 18 and $\\lambda_2$ is around 23. The posterior distributions of the two $\\lambda$s are clearly distinct, indicating that it is indeed likely that there was a change in the user's text-message behaviour.\n", + "\n", + "What other observations can you make? If you look at the original data again, do these results seem reasonable? \n", + "\n", + "Notice also that the posterior distributions for the $\\lambda$s do not look like exponential distributions, even though our priors for these variables were exponential. In fact, the posterior distributions are not really of any form that we recognize from the original model. But that's OK! This is one of the benefits of taking a computational point of view. If we had instead done this analysis using mathematical approaches, we would have been stuck with an analytically intractable (and messy) distribution. Our use of a computational approach makes us indifferent to mathematical tractability.\n", + "\n", + "Our analysis also returned a distribution for $\\tau$. Its posterior distribution looks a little different from the other two because it is a discrete random variable, so it doesn't assign probabilities to intervals. We can see that near day 45, there was a 50% chance that the user's behaviour changed. Had no change occurred, or had the change been gradual over time, the posterior distribution of $\\tau$ would have been more spread out, reflecting that many days were plausible candidates for $\\tau$. By contrast, in the actual results we see that only three or four days make any sense as potential transition points. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Why would I want samples from the posterior, anyways?\n", + "\n", + "\n", + "We will deal with this question for the remainder of the book, and it is an understatement to say that it will lead us to some amazing results. For now, let's end this chapter with one more example.\n", + "\n", + "We'll use the posterior samples to answer the following question: what is the expected number of texts at day $t, \\; 0 \\le t \\le 70$ ? Recall that the expected value of a Poisson variable is equal to its parameter $\\lambda$. Therefore, the question is equivalent to *what is the expected value of $\\lambda$ at time $t$*?\n", + "\n", + "In the code below, let $i$ index samples from the posterior distributions. Given a day $t$, we average over all possible $\\lambda_i$ for that day $t$, using $\\lambda_i = \\lambda_{1,i}$ if $t \\lt \\tau_i$ (that is, if the behaviour change has not yet occurred), else we use $\\lambda_i = \\lambda_{2,i}$. " + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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bwPmw/u9//8usWbN49tlnmTBhAikpKYHH79mzhxdeeIETJ06wcOFCNm7cSLdu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\nGiafmTHtimuYOM7k0Xais7oxlHuc1LLPe9WXG9z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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "figsize(12.5, 5)\n", + "# tau_samples, lambda_1_samples, lambda_2_samples contain\n", + "# N samples from the corresponding posterior distribution\n", + "N = tau_samples.shape[0]\n", + "expected_texts_per_day = np.zeros(n_count_data)\n", + "for day in range(0, n_count_data):\n", + " # ix is a bool index of all tau samples corresponding to\n", + " # the switchpoint occurring prior to value of 'day'\n", + " ix = day < tau_samples\n", + " # Each posterior sample corresponds to a value for tau.\n", + " # for each day, that value of tau indicates whether we're \"before\"\n", + " # (in the lambda1 \"regime\") or\n", + " # \"after\" (in the lambda2 \"regime\") the switchpoint.\n", + " # by taking the posterior sample of lambda1/2 accordingly, we can average\n", + " # over all samples to get an expected value for lambda on that day.\n", + " # As explained, the \"message count\" random variable is Poisson distributed,\n", + " # and therefore lambda (the poisson parameter) is the expected value of\n", + " # \"message count\".\n", + " expected_texts_per_day[day] = (lambda_1_samples[ix].sum()\n", + " + lambda_2_samples[~ix].sum()) / N\n", + "\n", + "\n", + "plt.plot(range(n_count_data), expected_texts_per_day, lw=4, color=\"#E24A33\",\n", + " label=\"expected number of text-messages received\")\n", + "plt.xlim(0, n_count_data)\n", + "plt.xlabel(\"Day\")\n", + "plt.ylabel(\"Expected # text-messages\")\n", + "plt.title(\"Expected number of text-messages received\")\n", + "plt.ylim(0, 60)\n", + "plt.bar(np.arange(len(count_data)), count_data, color=\"#348ABD\", alpha=0.65,\n", + " label=\"observed texts per day\")\n", + "\n", + "plt.legend(loc=\"upper left\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Our analysis shows strong support for believing the user's behavior did change ($\\lambda_1$ would have been close in value to $\\lambda_2$ had this not been true), and that the change was sudden rather than gradual (as demonstrated by $\\tau$'s strongly peaked posterior distribution). We can speculate what might have caused this: a cheaper text-message rate, a recent weather-to-text subscription, or perhaps a new relationship. (In fact, the 45th day corresponds to Christmas, and I moved away to Toronto the next month, leaving a girlfriend behind.)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Exercises\n", + "\n", + "1\\. Using `lambda_1_samples` and `lambda_2_samples`, what is the mean of the posterior distributions of $\\lambda_1$ and $\\lambda_2$?" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#type your code here." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "2\\. What is the expected percentage increase in text-message rates? `hint:` compute the mean of `lambda_1_samples/lambda_2_samples`. Note that this quantity is very different from `lambda_1_samples.mean()/lambda_2_samples.mean()`." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#type your code here." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "3\\. What is the mean of $\\lambda_1$ **given** that we know $\\tau$ is less than 45. That is, suppose we have been given new information that the change in behaviour occurred prior to day 45. What is the expected value of $\\lambda_1$ now? (You do not need to redo the PyMC3 part. Just consider all instances where `tau_samples < 45`.)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#type your code here." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### References\n", + "\n", + "\n", + "- [1] Gelman, Andrew. N.p.. Web. 22 Jan 2013. [N is never large enough](http://andrewgelman.com/2005/07/31/n_is_never_larg).\n", + "- [2] Norvig, Peter. 2009. [The Unreasonable Effectiveness of Data](http://static.googleusercontent.com/media/research.google.com/en//pubs/archive/35179.pdf).\n", + "- [3] Salvatier, J, Wiecki TV, and Fonnesbeck C. (2016) Probabilistic programming in Python using PyMC3. *PeerJ Computer Science* 2:e55 \n", + "- [4] Jimmy Lin and Alek Kolcz. Large-Scale Machine Learning at Twitter. Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data (SIGMOD 2012), pages 793-804, May 2012, Scottsdale, Arizona.\n", + "- [5] Cronin, Beau. \"Why Probabilistic Programming Matters.\" 24 Mar 2013. Google, Online Posting to Google . Web. 24 Mar. 2013. ." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.core.display import HTML\n", + "def css_styling():\n", + " styles = open(\"../styles/custom.css\", \"r\").read()\n", + " return HTML(styles)\n", + "css_styling()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python [bayes]", + "language": "python", + "name": "Python [bayes]" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.2" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/Chapter1_Introduction/Ch1_Introduction_PyMC_current.ipynb b/Chapter1_Introduction/Ch1_Introduction_PyMC_current.ipynb new file mode 100644 index 00000000..aa28e6f5 --- /dev/null +++ b/Chapter1_Introduction/Ch1_Introduction_PyMC_current.ipynb @@ -0,0 +1,1207 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Probabilistic Programming\n", + "=====\n", + "and Bayesian Methods for Hackers \n", + "========\n", + "\n", + "##### Version 0.1\n", + "\n", + "`Original content created by Cam Davidson-Pilon`\n", + "\n", + "`Ported to Python 3 and PyMC3 by Max Margenot (@clean_utensils) and Thomas Wiecki (@twiecki) at Quantopian (@quantopian)`\n", + "\n", + "`Ported to PyMC last (4) by Kurisu Chan (@miemiekurisu)`\n", + "___\n", + "\n", + "\n", + "Welcome to *Bayesian Methods for Hackers*. The full Github repository is available at [github/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers](https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers). The other chapters can be found on the project's [homepage](https://camdavidsonpilon.github.io/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/). We hope you enjoy the book, and we encourage any contributions!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Chapter 1\n", + "======\n", + "***" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The Philosophy of Bayesian Inference\n", + "------\n", + " \n", + "> You are a skilled programmer, but bugs still slip into your code. After a particularly difficult implementation of an algorithm, you decide to test your code on a trivial example. It passes. You test the code on a harder problem. It passes once again. And it passes the next, *even more difficult*, test too! You are starting to believe that there may be no bugs in this code...\n", + "\n", + "If you think this way, then congratulations, you already are thinking Bayesian! Bayesian inference is simply updating your beliefs after considering new evidence. A Bayesian can rarely be certain about a result, but he or she can be very confident. Just like in the example above, we can never be 100% sure that our code is bug-free unless we test it on every possible problem; something rarely possible in practice. Instead, we can test it on a large number of problems, and if it succeeds we can feel more *confident* about our code, but still not certain. Bayesian inference works identically: we update our beliefs about an outcome; rarely can we be absolutely sure unless we rule out all other alternatives. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "### The Bayesian state of mind\n", + "\n", + "\n", + "Bayesian inference differs from more traditional statistical inference by preserving *uncertainty*. At first, this sounds like a bad statistical technique. Isn't statistics all about deriving *certainty* from randomness? To reconcile this, we need to start thinking like Bayesians. \n", + "\n", + "The Bayesian world-view interprets probability as measure of *believability in an event*, that is, how confident we are in an event occurring. In fact, we will see in a moment that this is the natural interpretation of probability. \n", + "\n", + "For this to be clearer, we consider an alternative interpretation of probability: *Frequentist*, known as the more *classical* version of statistics, assume that probability is the long-run frequency of events (hence the bestowed title). For example, the *probability of plane accidents* under a frequentist philosophy is interpreted as the *long-term frequency of plane accidents*. This makes logical sense for many probabilities of events, but becomes more difficult to understand when events have no long-term frequency of occurrences. Consider: we often assign probabilities to outcomes of presidential elections, but the election itself only happens once! Frequentists get around this by invoking alternative realities and saying across all these realities, the frequency of occurrences defines the probability. \n", + "\n", + "Bayesians, on the other hand, have a more intuitive approach. Bayesians interpret a probability as measure of *belief*, or confidence, of an event occurring. Simply, a probability is a summary of an opinion. An individual who assigns a belief of 0 to an event has no confidence that the event will occur; conversely, assigning a belief of 1 implies that the individual is absolutely certain of an event occurring. Beliefs between 0 and 1 allow for weightings of other outcomes. This definition agrees with the probability of a plane accident example, for having observed the frequency of plane accidents, an individual's belief should be equal to that frequency, excluding any outside information. Similarly, under this definition of probability being equal to beliefs, it is meaningful to speak about probabilities (beliefs) of presidential election outcomes: how confident are you candidate *A* will win?\n", + "\n", + "Notice in the paragraph above, I assigned the belief (probability) measure to an *individual*, not to Nature. This is very interesting, as this definition leaves room for conflicting beliefs between individuals. Again, this is appropriate for what naturally occurs: different individuals have different beliefs of events occurring, because they possess different *information* about the world. The existence of different beliefs does not imply that anyone is wrong. Consider the following examples demonstrating the relationship between individual beliefs and probabilities:\n", + "\n", + "- I flip a coin, and we both guess the result. We would both agree, assuming the coin is fair, that the probability of Heads is 1/2. Assume, then, that I peek at the coin. Now I know for certain what the result is: I assign probability 1.0 to either Heads or Tails (whichever it is). Now what is *your* belief that the coin is Heads? My knowledge of the outcome has not changed the coin's results. Thus we assign different probabilities to the result. \n", + "\n", + "- Your code either has a bug in it or not, but we do not know for certain which is true, though we have a belief about the presence or absence of a bug. \n", + "\n", + "- A medical patient is exhibiting symptoms $x$, $y$ and $z$. There are a number of diseases that could be causing all of them, but only a single disease is present. A doctor has beliefs about which disease, but a second doctor may have slightly different beliefs. \n", + "\n", + "\n", + "This philosophy of treating beliefs as probability is natural to humans. We employ it constantly as we interact with the world and only see partial truths, but gather evidence to form beliefs. Alternatively, you have to be *trained* to think like a frequentist. \n", + "\n", + "To align ourselves with traditional probability notation, we denote our belief about event $A$ as $P(A)$. We call this quantity the *prior probability*.\n", + "\n", + "John Maynard Keynes, a great economist and thinker, said \"When the facts change, I change my mind. What do you do, sir?\" This quote reflects the way a Bayesian updates his or her beliefs after seeing evidence. Even — especially — if the evidence is counter to what was initially believed, the evidence cannot be ignored. We denote our updated belief as $P(A |X )$, interpreted as the probability of $A$ given the evidence $X$. We call the updated belief the *posterior probability* so as to contrast it with the prior probability. For example, consider the posterior probabilities (read: posterior beliefs) of the above examples, after observing some evidence $X$:\n", + "\n", + "1\\. $P(A): \\;\\;$ the coin has a 50 percent chance of being Heads. $P(A | X):\\;\\;$ You look at the coin, observe a Heads has landed, denote this information $X$, and trivially assign probability 1.0 to Heads and 0.0 to Tails.\n", + "\n", + "2\\. $P(A): \\;\\;$ This big, complex code likely has a bug in it. $P(A | X): \\;\\;$ The code passed all $X$ tests; there still might be a bug, but its presence is less likely now.\n", + "\n", + "3\\. $P(A):\\;\\;$ The patient could have any number of diseases. $P(A | X):\\;\\;$ Performing a blood test generated evidence $X$, ruling out some of the possible diseases from consideration.\n", + "\n", + "\n", + "It's clear that in each example we did not completely discard the prior belief after seeing new evidence $X$, but we *re-weighted the prior* to incorporate the new evidence (i.e. we put more weight, or confidence, on some beliefs versus others). \n", + "\n", + "By introducing prior uncertainty about events, we are already admitting that any guess we make is potentially very wrong. After observing data, evidence, or other information, we update our beliefs, and our guess becomes *less wrong*. This is the alternative side of the prediction coin, where typically we try to be *more right*. \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "### Bayesian Inference in Practice\n", + "\n", + " If frequentist and Bayesian inference were programming functions, with inputs being statistical problems, then the two would be different in what they return to the user. The frequentist inference function would return a number, representing an estimate (typically a summary statistic like the sample average etc.), whereas the Bayesian function would return *probabilities*.\n", + "\n", + "For example, in our debugging problem above, calling the frequentist function with the argument \"My code passed all $X$ tests; is my code bug-free?\" would return a *YES*. On the other hand, asking our Bayesian function \"Often my code has bugs. My code passed all $X$ tests; is my code bug-free?\" would return something very different: probabilities of *YES* and *NO*. The function might return:\n", + "\n", + "\n", + "> *YES*, with probability 0.8; *NO*, with probability 0.2\n", + "\n", + "\n", + "\n", + "This is very different from the answer the frequentist function returned. Notice that the Bayesian function accepted an additional argument: *\"Often my code has bugs\"*. This parameter is the *prior*. By including the prior parameter, we are telling the Bayesian function to include our belief about the situation. Technically this parameter in the Bayesian function is optional, but we will see excluding it has its own consequences. \n", + "\n", + "\n", + "#### Incorporating evidence\n", + "\n", + "As we acquire more and more instances of evidence, our prior belief is *washed out* by the new evidence. This is to be expected. For example, if your prior belief is something ridiculous, like \"I expect the sun to explode today\", and each day you are proved wrong, you would hope that any inference would correct you, or at least align your beliefs better. Bayesian inference will correct this belief.\n", + "\n", + "\n", + "Denote $N$ as the number of instances of evidence we possess. As we gather an *infinite* amount of evidence, say as $N \\rightarrow \\infty$, our Bayesian results (often) align with frequentist results. Hence for large $N$, statistical inference is more or less objective. On the other hand, for small $N$, inference is much more *unstable*: frequentist estimates have more variance and larger confidence intervals. This is where Bayesian analysis excels. By introducing a prior, and returning probabilities (instead of a scalar estimate), we *preserve the uncertainty* that reflects the instability of statistical inference of a small $N$ dataset. \n", + "\n", + "One may think that for large $N$, one can be indifferent between the two techniques since they offer similar inference, and might lean towards the computationally-simpler, frequentist methods. An individual in this position should consider the following quote by Andrew Gelman (2005)[1], before making such a decision:\n", + "\n", + "> Sample sizes are never large. If $N$ is too small to get a sufficiently-precise estimate, you need to get more data (or make more assumptions). But once $N$ is \"large enough,\" you can start subdividing the data to learn more (for example, in a public opinion poll, once you have a good estimate for the entire country, you can estimate among men and women, northerners and southerners, different age groups, etc.). $N$ is never enough because if it were \"enough\" you'd already be on to the next problem for which you need more data.\n", + "\n", + "### Are frequentist methods incorrect then? \n", + "\n", + "**No.**\n", + "\n", + "Frequentist methods are still useful or state-of-the-art in many areas. Tools such as least squares linear regression, LASSO regression, and expectation-maximization algorithms are all powerful and fast. Bayesian methods complement these techniques by solving problems that these approaches cannot, or by illuminating the underlying system with more flexible modeling.\n", + "\n", + "\n", + "#### A note on *Big Data*\n", + "Paradoxically, big data's predictive analytic problems are actually solved by relatively simple algorithms [2][4]. Thus we can argue that big data's prediction difficulty does not lie in the algorithm used, but instead on the computational difficulties of storage and execution on big data. (One should also consider Gelman's quote from above and ask \"Do I really have big data?\")\n", + "\n", + "The much more difficult analytic problems involve *medium data* and, especially troublesome, *really small data*. Using a similar argument as Gelman's above, if big data problems are *big enough* to be readily solved, then we should be more interested in the *not-quite-big enough* datasets. \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Our Bayesian framework\n", + "\n", + "We are interested in beliefs, which can be interpreted as probabilities by thinking Bayesian. We have a *prior* belief in event $A$, beliefs formed by previous information, e.g., our prior belief about bugs being in our code before performing tests.\n", + "\n", + "Secondly, we observe our evidence. To continue our buggy-code example: if our code passes $X$ tests, we want to update our belief to incorporate this. We call this new belief the *posterior* probability. Updating our belief is done via the following equation, known as Bayes' Theorem, after its discoverer Thomas Bayes:\n", + "\n", + "\\begin{align}\n", + " P( A | X ) = & \\frac{ P(X | A) P(A) } {P(X) } \\\\\\\\[5pt]\n", + "& \\propto P(X | A) P(A)\\;\\; (\\propto \\text{is proportional to })\n", + "\\end{align}\n", + "\n", + "The above formula is not unique to Bayesian inference: it is a mathematical fact with uses outside Bayesian inference. Bayesian inference merely uses it to connect prior probabilities $P(A)$ with an updated posterior probabilities $P(A | X )$." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Example: Mandatory coin-flip example\n", + "\n", + "Every statistics text must contain a coin-flipping example, I'll use it here to get it out of the way. Suppose, naively, that you are unsure about the probability of heads in a coin flip (spoiler alert: it's 50%). You believe there is some true underlying ratio, call it $p$, but have no prior opinion on what $p$ might be. \n", + "\n", + "We begin to flip a coin, and record the observations: either $H$ or $T$. This is our observed data. An interesting question to ask is how our inference changes as we observe more and more data? More specifically, what do our posterior probabilities look like when we have little data, versus when we have lots of data. \n", + "\n", + "Below we plot a sequence of updating posterior probabilities as we observe increasing amounts of data (coin flips)." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\"\"\"\n", + "The book uses a custom matplotlibrc file, which provides the unique styles for\n", + "matplotlib plots. If executing this book, and you wish to use the book's\n", + "styling, provided are two options:\n", + " 1. Overwrite your own matplotlibrc file with the rc-file provided in the\n", + " book's styles/ dir. See http://matplotlib.org/users/customizing.html\n", + " 2. Also in the styles is bmh_matplotlibrc.json file. This can be used to\n", + " update the styles in only this notebook. Try running the following code:\n", + "\n", + " import json\n", + " s = json.load(open(\"../styles/bmh_matplotlibrc.json\"))\n", + " matplotlib.rcParams.update(s)\n", + "\n", + "\"\"\"\n", + "\n", + "# The code below can be passed over, as it is currently not important, plus it\n", + "# uses advanced topics we have not covered yet. LOOK AT PICTURE, MICHAEL!\n", + "%matplotlib inline\n", + "from IPython.core.pylabtools import figsize\n", + "import numpy as np\n", + "from matplotlib import pyplot as plt\n", + "import matplotlib as mpl\n", + "mpl.style.use(\"ggplot\")\n", + "figsize(11, 9)\n", + "\n", + "import scipy.stats as stats\n", + "\n", + "dist = stats.beta\n", + "n_trials = [0, 1, 2, 3, 4, 5, 8, 15, 50, 500]\n", + "data = stats.bernoulli.rvs(0.5, size=n_trials[-1])\n", + "x = np.linspace(0, 1, 100)\n", + "\n", + "# For the already prepared, I'm using Binomial's conj. prior.\n", + "for k, N in enumerate(n_trials):\n", + " sx = plt.subplot(len(n_trials)//2, 2, k+1)\n", + " plt.xlabel(\"$p$, probability of heads\") \\\n", + " if k in [0, len(n_trials)-1] else None\n", + " plt.setp(sx.get_yticklabels(), visible=False)\n", + " heads = data[:N].sum()\n", + " y = dist.pdf(x, 1 + heads, 1 + N - heads)\n", + " plt.plot(x, y, label=\"observe %d tosses,\\n %d heads\" % (N, heads))\n", + " plt.fill_between(x, 0, y, color=\"#348ABD\", alpha=0.4)\n", + " plt.vlines(0.5, 0, 4, color=\"k\", linestyles=\"--\", lw=1)\n", + "\n", + " leg = plt.legend()\n", + " leg.get_frame().set_alpha(0.4)\n", + " plt.autoscale(tight=True)\n", + "\n", + "\n", + "plt.suptitle(\"Bayesian updating of posterior probabilities\",\n", + " y=1.02,\n", + " fontsize=14)\n", + "\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The posterior probabilities are represented by the curves, and our uncertainty is proportional to the width of the curve. As the plot above shows, as we start to observe data our posterior probabilities start to shift and move around. Eventually, as we observe more and more data (coin-flips), our probabilities will tighten closer and closer around the true value of $p=0.5$ (marked by a dashed line). \n", + "\n", + "Notice that the plots are not always *peaked* at 0.5. There is no reason it should be: recall we assumed we did not have a prior opinion of what $p$ is. In fact, if we observe quite extreme data, say 8 flips and only 1 observed heads, our distribution would look very biased *away* from lumping around 0.5 (with no prior opinion, how confident would you feel betting on a fair coin after observing 8 tails and 1 head?). As more data accumulates, we would see more and more probability being assigned at $p=0.5$, though never all of it.\n", + "\n", + "The next example is a simple demonstration of the mathematics of Bayesian inference. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Example: Bug, or just sweet, unintended feature?\n", + "\n", + "\n", + "Let $A$ denote the event that our code has **no bugs** in it. Let $X$ denote the event that the code passes all debugging tests. For now, we will leave the prior probability of no bugs as a variable, i.e. $P(A) = p$. \n", + "\n", + "We are interested in $P(A|X)$, i.e. the probability of no bugs, given our debugging tests $X$. To use the formula above, we need to compute some quantities.\n", + "\n", + "What is $P(X | A)$, i.e., the probability that the code passes $X$ tests *given* there are no bugs? Well, it is equal to 1, for a code with no bugs will pass all tests. \n", + "\n", + "$P(X)$ is a little bit trickier: The event $X$ can be divided into two possibilities, event $X$ occurring even though our code *indeed has* bugs (denoted $\\sim A\\;$, spoken *not $A$*), or event $X$ without bugs ($A$). $P(X)$ can be represented as:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\\begin{align}\n", + "P(X ) & = P(X \\text{ and } A) + P(X \\text{ and } \\sim A) \\\\\\\\[5pt]\n", + " & = P(X|A)P(A) + P(X | \\sim A)P(\\sim A)\\\\\\\\[5pt]\n", + "& = P(X|A)p + P(X | \\sim A)(1-p)\n", + "\\end{align}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We have already computed $P(X|A)$ above. On the other hand, $P(X | \\sim A)$ is subjective: our code can pass tests but still have a bug in it, though the probability there is a bug present is reduced. Note this is dependent on the number of tests performed, the degree of complication in the tests, etc. Let's be conservative and assign $P(X|\\sim A) = 0.5$. Then\n", + "\n", + "\\begin{align}\n", + "P(A | X) & = \\frac{1\\cdot p}{ 1\\cdot p +0.5 (1-p) } \\\\\\\\\n", + "& = \\frac{ 2 p}{1+p}\n", + "\\end{align}\n", + "This is the posterior probability. What does it look like as a function of our prior, $p \\in [0,1]$? " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "figsize(12.5, 4)\n", + "p = np.linspace(0, 1, 50)\n", + "plt.plot(p, 2*p/(1+p), color=\"#348ABD\", lw=3)\n", + "#plt.fill_between(p, 2*p/(1+p), alpha=.5, facecolor=[\"#A60628\"])\n", + "plt.scatter(0.2, 2*(0.2)/1.2, s=140, c=\"#348ABD\")\n", + "plt.xlim(0, 1)\n", + "plt.ylim(0, 1)\n", + "plt.xlabel(\"Prior, $P(A) = p$\")\n", + "plt.ylabel(\"Posterior, $P(A|X)$, with $P(A) = p$\")\n", + "plt.title(\"Are there bugs in my code?\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can see the biggest gains if we observe the $X$ tests passed when the prior probability, $p$, is low. Let's settle on a specific value for the prior. I'm a strong programmer (I think), so I'm going to give myself a realistic prior of 0.20, that is, there is a 20% chance that I write code bug-free. To be more realistic, this prior should be a function of how complicated and large the code is, but let's pin it at 0.20. Then my updated belief that my code is bug-free is 0.33. \n", + "\n", + "Recall that the prior is a probability: $p$ is the prior probability that there *are no bugs*, so $1-p$ is the prior probability that there *are bugs*.\n", + "\n", + "Similarly, our posterior is also a probability, with $P(A | X)$ the probability there is no bug *given we saw all tests pass*, hence $1-P(A|X)$ is the probability there is a bug *given all tests passed*. What does our posterior probability look like? Below is a chart of both the prior and the posterior probabilities. \n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "figsize(12.5, 4)\n", + "colours = [\"#348ABD\", \"#A60628\"]\n", + "\n", + "prior = [0.20, 0.80]\n", + "posterior = [1./3, 2./3]\n", + "plt.bar([0, .7], prior, alpha=0.70, width=0.25,\n", + " color=colours[0], label=\"prior distribution\",\n", + " lw=\"3\", edgecolor=colours[0])\n", + "\n", + "plt.bar([0+0.25, .7+0.25], posterior, alpha=0.7,\n", + " width=0.25, color=colours[1],\n", + " label=\"posterior distribution\",\n", + " lw=\"3\", edgecolor=colours[1])\n", + "\n", + "plt.xticks([0.20, .95], [\"Bugs Absent\", \"Bugs Present\"])\n", + "plt.title(\"Prior and Posterior probability of bugs present\")\n", + "plt.ylabel(\"Probability\")\n", + "plt.legend(loc=\"upper left\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notice that after we observed $X$ occur, the probability of bugs being absent increased. By increasing the number of tests, we can approach confidence (probability 1) that there are no bugs present.\n", + "\n", + "This was a very simple example of Bayesian inference and Bayes rule. Unfortunately, the mathematics necessary to perform more complicated Bayesian inference only becomes more difficult, except for artificially constructed cases. We will later see that this type of mathematical analysis is actually unnecessary. First we must broaden our modeling tools. The next section deals with *probability distributions*. If you are already familiar, feel free to skip (or at least skim), but for the less familiar the next section is essential." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "_______\n", + "\n", + "## Probability Distributions\n", + "\n", + "\n", + "**Let's quickly recall what a probability distribution is:** Let $Z$ be some random variable. Then associated with $Z$ is a *probability distribution function* that assigns probabilities to the different outcomes $Z$ can take. Graphically, a probability distribution is a curve where the probability of an outcome is proportional to the height of the curve. You can see examples in the first figure of this chapter. \n", + "\n", + "We can divide random variables into three classifications:\n", + "\n", + "- **$Z$ is discrete**: Discrete random variables may only assume values on a specified list. Things like populations, movie ratings, and number of votes are all discrete random variables. Discrete random variables become more clear when we contrast them with...\n", + "\n", + "- **$Z$ is continuous**: Continuous random variable can take on arbitrarily exact values. For example, temperature, speed, time, color are all modeled as continuous variables because you can progressively make the values more and more precise.\n", + "\n", + "- **$Z$ is mixed**: Mixed random variables assign probabilities to both discrete and continuous random variables, i.e. it is a combination of the above two categories. \n", + "\n", + "### Discrete Case\n", + "If $Z$ is discrete, then its distribution is called a *probability mass function*, which measures the probability $Z$ takes on the value $k$, denoted $P(Z=k)$. Note that the probability mass function completely describes the random variable $Z$, that is, if we know the mass function, we know how $Z$ should behave. There are popular probability mass functions that consistently appear: we will introduce them as needed, but let's introduce the first very useful probability mass function. We say $Z$ is *Poisson*-distributed if:\n", + "\n", + "$$P(Z = k) =\\frac{ \\lambda^k e^{-\\lambda} }{k!}, \\; \\; k=0,1,2, \\dots $$\n", + "\n", + "$\\lambda$ is called a parameter of the distribution, and it controls the distribution's shape. For the Poisson distribution, $\\lambda$ can be any positive number. By increasing $\\lambda$, we add more probability to larger values, and conversely by decreasing $\\lambda$ we add more probability to smaller values. One can describe $\\lambda$ as the *intensity* of the Poisson distribution. \n", + "\n", + "Unlike $\\lambda$, which can be any positive number, the value $k$ in the above formula must be a non-negative integer, i.e., $k$ must take on values 0,1,2, and so on. This is very important, because if you wanted to model a population you could not make sense of populations with 4.25 or 5.612 members. \n", + "\n", + "If a random variable $Z$ has a Poisson mass distribution, we denote this by writing\n", + "\n", + "$$Z \\sim \\text{Poi}(\\lambda) $$\n", + "\n", + "One useful property of the Poisson distribution is that its expected value is equal to its parameter, i.e.:\n", + "\n", + "$$E\\large[ \\;Z\\; | \\; \\lambda \\;\\large] = \\lambda $$\n", + "\n", + "We will use this property often, so it's useful to remember. Below, we plot the probability mass distribution for different $\\lambda$ values. The first thing to notice is that by increasing $\\lambda$, we add more probability of larger values occurring. Second, notice that although the graph ends at 15, the distributions do not. They assign positive probability to every non-negative integer." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "figsize(12.5, 4)\n", + "\n", + "import scipy.stats as stats\n", + "a = np.arange(16)\n", + "poi = stats.poisson\n", + "lambda_ = [1.5, 4.25]\n", + "colours = [\"#348ABD\", \"#A60628\"]\n", + "\n", + "plt.bar(a, poi.pmf(a, lambda_[0]), color=colours[0],\n", + " label=\"$\\lambda = %.1f$\" % lambda_[0], alpha=0.60,\n", + " edgecolor=colours[0], lw=\"3\")\n", + "\n", + "plt.bar(a, poi.pmf(a, lambda_[1]), color=colours[1],\n", + " label=\"$\\lambda = %.1f$\" % lambda_[1], alpha=0.60,\n", + " edgecolor=colours[1], lw=\"3\")\n", + "\n", + "plt.xticks(a + 0.4, a)\n", + "plt.legend()\n", + "plt.ylabel(\"probability of $k$\")\n", + "plt.xlabel(\"$k$\")\n", + "plt.title(\"Probability mass function of a Poisson random variable; differing \\\n", + "$\\lambda$ values\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Continuous Case\n", + "Instead of a probability mass function, a continuous random variable has a *probability density function*. This might seem like unnecessary nomenclature, but the density function and the mass function are very different creatures. An example of continuous random variable is a random variable with *exponential density*. The density function for an exponential random variable looks like this:\n", + "\n", + "$$f_Z(z | \\lambda) = \\lambda e^{-\\lambda z }, \\;\\; z\\ge 0$$\n", + "\n", + "Like a Poisson random variable, an exponential random variable can take on only non-negative values. But unlike a Poisson variable, the exponential can take on *any* non-negative values, including non-integral values such as 4.25 or 5.612401. This property makes it a poor choice for count data, which must be an integer, but a great choice for time data, temperature data (measured in Kelvins, of course), or any other precise *and positive* variable. The graph below shows two probability density functions with different $\\lambda$ values. \n", + "\n", + "When a random variable $Z$ has an exponential distribution with parameter $\\lambda$, we say *$Z$ is exponential* and write\n", + "\n", + "$$Z \\sim \\text{Exp}(\\lambda)$$\n", + "\n", + "Given a specific $\\lambda$, the expected value of an exponential random variable is equal to the inverse of $\\lambda$, that is:\n", + "\n", + "$$E[\\; Z \\;|\\; \\lambda \\;] = \\frac{1}{\\lambda}$$" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "a = np.linspace(0, 4, 100)\n", + "expo = stats.expon\n", + "lambda_ = [0.5, 1]\n", + "\n", + "for l, c in zip(lambda_, colours):\n", + " plt.plot(a, expo.pdf(a, scale=1./l), lw=3,\n", + " color=c, label=\"$\\lambda = %.1f$\" % l)\n", + " plt.fill_between(a, expo.pdf(a, scale=1./l), color=c, alpha=.33)\n", + "\n", + "plt.legend()\n", + "plt.ylabel(\"PDF at $z$\")\n", + "plt.xlabel(\"$z$\")\n", + "plt.ylim(0,1.2)\n", + "plt.title(\"Probability density function of an Exponential random variable;\\\n", + " differing $\\lambda$\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "### But what is $\\lambda \\;$?\n", + "\n", + "\n", + "**This question is what motivates statistics**. In the real world, $\\lambda$ is hidden from us. We see only $Z$, and must go backwards to try and determine $\\lambda$. The problem is difficult because there is no one-to-one mapping from $Z$ to $\\lambda$. Many different methods have been created to solve the problem of estimating $\\lambda$, but since $\\lambda$ is never actually observed, no one can say for certain which method is best! \n", + "\n", + "Bayesian inference is concerned with *beliefs* about what $\\lambda$ might be. Rather than try to guess $\\lambda$ exactly, we can only talk about what $\\lambda$ is likely to be by assigning a probability distribution to $\\lambda$.\n", + "\n", + "This might seem odd at first. After all, $\\lambda$ is fixed; it is not (necessarily) random! How can we assign probabilities to values of a non-random variable? Ah, we have fallen for our old, frequentist way of thinking. Recall that under Bayesian philosophy, we *can* assign probabilities if we interpret them as beliefs. And it is entirely acceptable to have *beliefs* about the parameter $\\lambda$. \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "##### Example: Inferring behaviour from text-message data\n", + "\n", + "Let's try to model a more interesting example, one that concerns the rate at which a user sends and receives text messages:\n", + "\n", + "> You are given a series of daily text-message counts from a user of your system. The data, plotted over time, appears in the chart below. You are curious to know if the user's text-messaging habits have changed over time, either gradually or suddenly. How can you model this? (This is in fact my own text-message data. Judge my popularity as you wish.)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "figsize(12.5, 3.5)\n", + "count_data = np.loadtxt(\"data/txtdata.csv\")\n", + "n_count_data = len(count_data)\n", + "plt.bar(np.arange(n_count_data), count_data, color=\"#348ABD\")\n", + "plt.xlabel(\"Time (days)\")\n", + "plt.ylabel(\"count of text-msgs received\")\n", + "plt.title(\"Did the user's texting habits change over time?\")\n", + "plt.xlim(0, n_count_data);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Before we start modeling, see what you can figure out just by looking at the chart above. Would you say there was a change in behaviour during this time period? \n", + "\n", + "How can we start to model this? Well, as we have conveniently already seen, a Poisson random variable is a very appropriate model for this type of *count* data. Denoting day $i$'s text-message count by $C_i$, \n", + "\n", + "$$ C_i \\sim \\text{Poisson}(\\lambda) $$\n", + "\n", + "We are not sure what the value of the $\\lambda$ parameter really is, however. Looking at the chart above, it appears that the rate might become higher late in the observation period, which is equivalent to saying that $\\lambda$ increases at some point during the observations. (Recall that a higher value of $\\lambda$ assigns more probability to larger outcomes. That is, there is a higher probability of many text messages having been sent on a given day.)\n", + "\n", + "How can we represent this observation mathematically? Let's assume that on some day during the observation period (call it $\\tau$), the parameter $\\lambda$ suddenly jumps to a higher value. So we really have two $\\lambda$ parameters: one for the period before $\\tau$, and one for the rest of the observation period. In the literature, a sudden transition like this would be called a *switchpoint*:\n", + "\n", + "$$\n", + "\\lambda = \n", + "\\begin{cases}\n", + "\\lambda_1 & \\text{if } t \\lt \\tau \\cr\n", + "\\lambda_2 & \\text{if } t \\ge \\tau\n", + "\\end{cases}\n", + "$$\n", + "\n", + "\n", + "If, in reality, no sudden change occurred and indeed $\\lambda_1 = \\lambda_2$, then the $\\lambda$s posterior distributions should look about equal.\n", + "\n", + "We are interested in inferring the unknown $\\lambda$s. To use Bayesian inference, we need to assign prior probabilities to the different possible values of $\\lambda$. What would be good prior probability distributions for $\\lambda_1$ and $\\lambda_2$? Recall that $\\lambda$ can be any positive number. As we saw earlier, the *exponential* distribution provides a continuous density function for positive numbers, so it might be a good choice for modeling $\\lambda_i$. But recall that the exponential distribution takes a parameter of its own, so we'll need to include that parameter in our model. Let's call that parameter $\\alpha$.\n", + "\n", + "\\begin{align}\n", + "&\\lambda_1 \\sim \\text{Exp}( \\alpha ) \\\\\\\n", + "&\\lambda_2 \\sim \\text{Exp}( \\alpha )\n", + "\\end{align}\n", + "\n", + "$\\alpha$ is called a *hyper-parameter* or *parent variable*. In literal terms, it is a parameter that influences other parameters. Our initial guess at $\\alpha$ does not influence the model too strongly, so we have some flexibility in our choice. A good rule of thumb is to set the exponential parameter equal to the inverse of the average of the count data. Since we're modeling $\\lambda$ using an exponential distribution, we can use the expected value identity shown earlier to get:\n", + "\n", + "$$\\frac{1}{N}\\sum_{i=0}^N \\;C_i \\approx E[\\; \\lambda \\; |\\; \\alpha ] = \\frac{1}{\\alpha}$$ \n", + "\n", + "An alternative, and something I encourage the reader to try, would be to have two priors: one for each $\\lambda_i$. Creating two exponential distributions with different $\\alpha$ values reflects our prior belief that the rate changed at some point during the observations.\n", + "\n", + "What about $\\tau$? Because of the noisiness of the data, it's difficult to pick out a priori when $\\tau$ might have occurred. Instead, we can assign a *uniform prior belief* to every possible day. This is equivalent to saying\n", + "\n", + "\\begin{align}\n", + "& \\tau \\sim \\text{DiscreteUniform(1,70) }\\\\\\\\\n", + "& \\Rightarrow P( \\tau = k ) = \\frac{1}{70}\n", + "\\end{align}\n", + "\n", + "So after all this, what does our overall prior distribution for the unknown variables look like? Frankly, *it doesn't matter*. What we should understand is that it's an ugly, complicated mess involving symbols only a mathematician could love. And things will only get uglier the more complicated our models become. Regardless, all we really care about is the posterior distribution.\n", + "\n", + "We next turn to PyMC, a Python library for performing Bayesian analysis that is undaunted by the mathematical monster we have created. \n", + "\n", + "\n", + "Introducing our first hammer: PyMC\n", + "-----\n", + "\n", + "PyMC is a Python library for programming Bayesian analysis [3]. It is a fast, well-maintained library. The only unfortunate part is that its documentation is lacking in certain areas, especially those that bridge the gap between beginner and hacker. One of this book's main goals is to solve that problem, and also to demonstrate why PyMC is so cool.\n", + "\n", + "We will model the problem above using PyMC. This type of programming is called *probabilistic programming*, an unfortunate misnomer that invokes ideas of randomly-generated code and has likely confused and frightened users away from this field. The code is not random; it is probabilistic in the sense that we create probability models using programming variables as the model's components. Model components are first-class primitives within the PyMC framework. \n", + "\n", + "B. Cronin [5] has a very motivating description of probabilistic programming:\n", + "\n", + "> Another way of thinking about this: unlike a traditional program, which only runs in the forward directions, a probabilistic program is run in both the forward and backward direction. It runs forward to compute the consequences of the assumptions it contains about the world (i.e., the model space it represents), but it also runs backward from the data to constrain the possible explanations. In practice, many probabilistic programming systems will cleverly interleave these forward and backward operations to efficiently home in on the best explanations.\n", + "\n", + "Because of the confusion engendered by the term *probabilistic programming*, I'll refrain from using it. Instead, I'll simply say *programming*, since that's what it really is. \n", + "\n", + "PyMC code is easy to read. The only novel thing should be the syntax. Simply remember that we are representing the model's components ($\\tau, \\lambda_1, \\lambda_2$ ) as variables." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "import pymc as pm\n", + "\n", + "with pm.Model() as model:\n", + " alpha = 1.0/count_data.mean() # Recall count_data is the\n", + " # variable that holds our txt counts\n", + " lambda_1 = pm.Exponential(\"lambda_1\", alpha)\n", + " lambda_2 = pm.Exponential(\"lambda_2\", alpha)\n", + " \n", + " tau = pm.DiscreteUniform(\"tau\", lower=0, upper=n_count_data - 1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the code above, we create the PyMC variables corresponding to $\\lambda_1$ and $\\lambda_2$. We assign them to PyMC's *stochastic variables*, so-called because they are treated by the back end as random number generators." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "with model:\n", + " idx = np.arange(n_count_data) # Index\n", + " lambda_ = pm.math.switch(tau > idx, lambda_1, lambda_2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This code creates a new function `lambda_`, but really we can think of it as a random variable: the random variable $\\lambda$ from above. The `switch()` function assigns `lambda_1` or `lambda_2` as the value of `lambda_`, depending on what side of `tau` we are on. The values of `lambda_` up until `tau` are `lambda_1` and the values afterwards are `lambda_2`.\n", + "\n", + "Note that because `lambda_1`, `lambda_2` and `tau` are random, `lambda_` will be random. We are **not** fixing any variables yet." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "with model:\n", + " observation = pm.Poisson(\"obs\", lambda_, observed=count_data)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The variable `observation` combines our data, `count_data`, with our proposed data-generation scheme, given by the variable `lambda_`, through the `observed` keyword. \n", + "\n", + "The code below will be explained in Chapter 3, but I show it here so you can see where our results come from. One can think of it as a *learning* step. The machinery being employed is called *Markov Chain Monte Carlo* (MCMC), which I also delay explaining until Chapter 3. This technique returns thousands of random variables from the posterior distributions of $\\lambda_1, \\lambda_2$ and $\\tau$. We can plot a histogram of the random variables to see what the posterior distributions look like. Below, we collect the samples (called *traces* in the MCMC literature) into histograms." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Multiprocess sampling (4 chains in 4 jobs)\n", + "CompoundStep\n", + ">Metropolis: [lambda_1]\n", + ">Metropolis: [lambda_2]\n", + ">Metropolis: [tau]\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + "
\n", + " \n", + " 100.00% [60000/60000 00:05<00:00 Sampling 4 chains, 0 divergences]\n", + "
\n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Sampling 4 chains for 5_000 tune and 10_000 draw iterations (20_000 + 40_000 draws total) took 6 seconds.\n" + ] + } + ], + "source": [ + "### Mysterious code to be explained in Chapter 3.\n", + "with model:\n", + " step = pm.Metropolis()\n", + " trace = pm.sample(10000, tune=5000, step=step, return_inferencedata=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "lambda_1_samples = trace['lambda_1']\n", + "lambda_2_samples = trace['lambda_2']\n", + "tau_samples = trace['tau']" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "figsize(12.5, 10)\n", + "#histogram of the samples:\n", + "\n", + "ax = plt.subplot(311)\n", + "ax.set_autoscaley_on(False)\n", + "\n", + "plt.hist(lambda_1_samples, histtype='stepfilled', bins=30, alpha=0.85,\n", + " label=\"posterior of $\\lambda_1$\", color=\"#A60628\", density=True)\n", + "plt.legend(loc=\"upper left\")\n", + "plt.title(r\"\"\"Posterior distributions of the variables\n", + " $\\lambda_1,\\;\\lambda_2,\\;\\tau$\"\"\")\n", + "plt.xlim([15, 30])\n", + "plt.xlabel(\"$\\lambda_1$ value\")\n", + "\n", + "ax = plt.subplot(312)\n", + "ax.set_autoscaley_on(False)\n", + "plt.hist(lambda_2_samples, histtype='stepfilled', bins=30, alpha=0.85,\n", + " label=\"posterior of $\\lambda_2$\", color=\"#7A68A6\", density=True)\n", + "plt.legend(loc=\"upper left\")\n", + "plt.xlim([15, 30])\n", + "plt.xlabel(\"$\\lambda_2$ value\")\n", + "\n", + "plt.subplot(313)\n", + "w = 1.0 / tau_samples.shape[0] * np.ones_like(tau_samples)\n", + "plt.hist(tau_samples, bins=n_count_data, alpha=1,\n", + " label=r\"posterior of $\\tau$\",\n", + " color=\"#467821\", weights=w, rwidth=2.)\n", + "plt.xticks(np.arange(n_count_data))\n", + "\n", + "plt.legend(loc=\"upper left\")\n", + "plt.ylim([0, .75])\n", + "plt.xlim([35, len(count_data)-20])\n", + "plt.xlabel(r\"$\\tau$ (in days)\")\n", + "plt.ylabel(\"probability\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Interpretation\n", + "\n", + "Recall that Bayesian methodology returns a *distribution*. Hence we now have distributions to describe the unknown $\\lambda$s and $\\tau$. What have we gained? Immediately, we can see the uncertainty in our estimates: the wider the distribution, the less certain our posterior belief should be. We can also see what the plausible values for the parameters are: $\\lambda_1$ is around 18 and $\\lambda_2$ is around 23. The posterior distributions of the two $\\lambda$s are clearly distinct, indicating that it is indeed likely that there was a change in the user's text-message behaviour.\n", + "\n", + "What other observations can you make? If you look at the original data again, do these results seem reasonable? \n", + "\n", + "Notice also that the posterior distributions for the $\\lambda$s do not look like exponential distributions, even though our priors for these variables were exponential. In fact, the posterior distributions are not really of any form that we recognize from the original model. But that's OK! This is one of the benefits of taking a computational point of view. If we had instead done this analysis using mathematical approaches, we would have been stuck with an analytically intractable (and messy) distribution. Our use of a computational approach makes us indifferent to mathematical tractability.\n", + "\n", + "Our analysis also returned a distribution for $\\tau$. Its posterior distribution looks a little different from the other two because it is a discrete random variable, so it doesn't assign probabilities to intervals. We can see that near day 45, there was a 50% chance that the user's behaviour changed. Had no change occurred, or had the change been gradual over time, the posterior distribution of $\\tau$ would have been more spread out, reflecting that many days were plausible candidates for $\\tau$. By contrast, in the actual results we see that only three or four days make any sense as potential transition points. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Why would I want samples from the posterior, anyways?\n", + "\n", + "\n", + "We will deal with this question for the remainder of the book, and it is an understatement to say that it will lead us to some amazing results. For now, let's end this chapter with one more example.\n", + "\n", + "We'll use the posterior samples to answer the following question: what is the expected number of texts at day $t, \\; 0 \\le t \\le 70$ ? Recall that the expected value of a Poisson variable is equal to its parameter $\\lambda$. Therefore, the question is equivalent to *what is the expected value of $\\lambda$ at time $t$*?\n", + "\n", + "In the code below, let $i$ index samples from the posterior distributions. Given a day $t$, we average over all possible $\\lambda_i$ for that day $t$, using $\\lambda_i = \\lambda_{1,i}$ if $t \\lt \\tau_i$ (that is, if the behaviour change has not yet occurred), else we use $\\lambda_i = \\lambda_{2,i}$. " + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "figsize(12.5, 5)\n", + "# tau_samples, lambda_1_samples, lambda_2_samples contain\n", + "# N samples from the corresponding posterior distribution\n", + "N = tau_samples.shape[0]\n", + "expected_texts_per_day = np.zeros(n_count_data)\n", + "for day in range(0, n_count_data):\n", + " # ix is a bool index of all tau samples corresponding to\n", + " # the switchpoint occurring prior to value of 'day'\n", + " ix = day < tau_samples\n", + " # Each posterior sample corresponds to a value for tau.\n", + " # for each day, that value of tau indicates whether we're \"before\"\n", + " # (in the lambda1 \"regime\") or\n", + " # \"after\" (in the lambda2 \"regime\") the switchpoint.\n", + " # by taking the posterior sample of lambda1/2 accordingly, we can average\n", + " # over all samples to get an expected value for lambda on that day.\n", + " # As explained, the \"message count\" random variable is Poisson distributed,\n", + " # and therefore lambda (the poisson parameter) is the expected value of\n", + " # \"message count\".\n", + " expected_texts_per_day[day] = (lambda_1_samples[ix].sum()\n", + " + lambda_2_samples[~ix].sum()) / N\n", + "\n", + "\n", + "plt.plot(range(n_count_data), expected_texts_per_day, lw=4, color=\"#E24A33\",\n", + " label=\"expected number of text-messages received\")\n", + "plt.xlim(0, n_count_data)\n", + "plt.xlabel(\"Day\")\n", + "plt.ylabel(\"Expected # text-messages\")\n", + "plt.title(\"Expected number of text-messages received\")\n", + "plt.ylim(0, 60)\n", + "plt.bar(np.arange(len(count_data)), count_data, color=\"#348ABD\", alpha=0.65,\n", + " label=\"observed texts per day\")\n", + "\n", + "plt.legend(loc=\"upper left\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Our analysis shows strong support for believing the user's behavior did change ($\\lambda_1$ would have been close in value to $\\lambda_2$ had this not been true), and that the change was sudden rather than gradual (as demonstrated by $\\tau$'s strongly peaked posterior distribution). We can speculate what might have caused this: a cheaper text-message rate, a recent weather-to-text subscription, or perhaps a new relationship. (In fact, the 45th day corresponds to Christmas, and I moved away to Toronto the next month, leaving a girlfriend behind.)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Exercises\n", + "\n", + "1\\. Using `lambda_1_samples` and `lambda_2_samples`, what is the mean of the posterior distributions of $\\lambda_1$ and $\\lambda_2$?" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "#type your code here." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "2\\. What is the expected percentage increase in text-message rates? `hint:` compute the mean of `lambda_1_samples/lambda_2_samples`. Note that this quantity is very different from `lambda_1_samples.mean()/lambda_2_samples.mean()`." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "#type your code here." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "3\\. What is the mean of $\\lambda_1$ **given** that we know $\\tau$ is less than 45. That is, suppose we have been given new information that the change in behaviour occurred prior to day 45. What is the expected value of $\\lambda_1$ now? (You do not need to redo the PyMC part. Just consider all instances where `tau_samples < 45`.)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "#type your code here." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### References\n", + "\n", + "\n", + "- [1] Gelman, Andrew. N.p.. Web. 22 Jan 2013. [N is never large enough](http://andrewgelman.com/2005/07/31/n_is_never_larg).\n", + "- [2] Norvig, Peter. 2009. [The Unreasonable Effectiveness of Data](http://static.googleusercontent.com/media/research.google.com/en//pubs/archive/35179.pdf).\n", + "- [3] Salvatier, J, Wiecki TV, and Fonnesbeck C. (2016) Probabilistic programming in Python using PyMC3. *PeerJ Computer Science* 2:e55 \n", + "- [4] Jimmy Lin and Alek Kolcz. Large-Scale Machine Learning at Twitter. Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data (SIGMOD 2012), pages 793-804, May 2012, Scottsdale, Arizona.\n", + "- [5] Cronin, Beau. \"Why Probabilistic Programming Matters.\" 24 Mar 2013. Google, Online Posting to Google . Web. 24 Mar. 2013. ." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.core.display import HTML\n", + "def css_styling():\n", + " styles = open(\"../styles/custom.css\", \"r\").read()\n", + " return HTML(styles)\n", + "css_styling()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "pymc_env", + "language": "python", + "name": "pymc_env" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.4" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/Chapter1_Introduction/Ch1_Introduction_Pyro.ipynb b/Chapter1_Introduction/Ch1_Introduction_Pyro.ipynb new file mode 100644 index 00000000..32366073 --- /dev/null +++ b/Chapter1_Introduction/Ch1_Introduction_Pyro.ipynb @@ -0,0 +1,996 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Probabilistic Programming\n", + "=====\n", + "and Bayesian Methods for Hackers \n", + "========\n", + "\n", + "##### Version 0.1\n", + "\n", + "Original content created by Cam Davidson-Pilon\n", + "\n", + "Ported to [Pyro](http://pyro.ai/) by Carlos Souza (souza@gatech.edu), with the help from [Pyro community](https://forum.pyro.ai/).\n", + "___\n", + "\n", + "\n", + "Welcome to *Bayesian Methods for Hackers*. The full Github repository is available at [github/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers](https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers). The other chapters can be found on the project's [homepage](https://camdavidsonpilon.github.io/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/). We hope you enjoy the book, and we encourage any contributions!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Chapter 1\n", + "======\n", + "***" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The Philosophy of Bayesian Inference\n", + "------\n", + " \n", + "> You are a skilled programmer, but bugs still slip into your code. After a particularly difficult implementation of an algorithm, you decide to test your code on a trivial example. It passes. You test the code on a harder problem. It passes once again. And it passes the next, *even more difficult*, test too! You are starting to believe that there may be no bugs in this code...\n", + "\n", + "If you think this way, then congratulations, you already are thinking Bayesian! Bayesian inference is simply updating your beliefs after considering new evidence. A Bayesian can rarely be certain about a result, but he or she can be very confident. Just like in the example above, we can never be 100% sure that our code is bug-free unless we test it on every possible problem; something rarely possible in practice. Instead, we can test it on a large number of problems, and if it succeeds we can feel more *confident* about our code, but still not certain. Bayesian inference works identically: we update our beliefs about an outcome; rarely can we be absolutely sure unless we rule out all other alternatives. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "### The Bayesian state of mind\n", + "\n", + "\n", + "Bayesian inference differs from more traditional statistical inference by preserving *uncertainty*. At first, this sounds like a bad statistical technique. Isn't statistics all about deriving *certainty* from randomness? To reconcile this, we need to start thinking like Bayesians. \n", + "\n", + "The Bayesian world-view interprets probability as measure of *believability in an event*, that is, how confident we are in an event occurring. In fact, we will see in a moment that this is the natural interpretation of probability. \n", + "\n", + "For this to be clearer, we consider an alternative interpretation of probability: *Frequentist*, known as the more *classical* version of statistics, assume that probability is the long-run frequency of events (hence the bestowed title). For example, the *probability of plane accidents* under a frequentist philosophy is interpreted as the *long-term frequency of plane accidents*. This makes logical sense for many probabilities of events, but becomes more difficult to understand when events have no long-term frequency of occurrences. Consider: we often assign probabilities to outcomes of presidential elections, but the election itself only happens once! Frequentists get around this by invoking alternative realities and saying across all these realities, the frequency of occurrences defines the probability. \n", + "\n", + "Bayesians, on the other hand, have a more intuitive approach. Bayesians interpret a probability as measure of *belief*, or confidence, of an event occurring. Simply, a probability is a summary of an opinion. An individual who assigns a belief of 0 to an event has no confidence that the event will occur; conversely, assigning a belief of 1 implies that the individual is absolutely certain of an event occurring. Beliefs between 0 and 1 allow for weightings of other outcomes. This definition agrees with the probability of a plane accident example, for having observed the frequency of plane accidents, an individual's belief should be equal to that frequency, excluding any outside information. Similarly, under this definition of probability being equal to beliefs, it is meaningful to speak about probabilities (beliefs) of presidential election outcomes: how confident are you candidate *A* will win?\n", + "\n", + "Notice in the paragraph above, I assigned the belief (probability) measure to an *individual*, not to Nature. This is very interesting, as this definition leaves room for conflicting beliefs between individuals. Again, this is appropriate for what naturally occurs: different individuals have different beliefs of events occurring, because they possess different *information* about the world. The existence of different beliefs does not imply that anyone is wrong. Consider the following examples demonstrating the relationship between individual beliefs and probabilities:\n", + "\n", + "- I flip a coin, and we both guess the result. We would both agree, assuming the coin is fair, that the probability of Heads is 1/2. Assume, then, that I peek at the coin. Now I know for certain what the result is: I assign probability 1.0 to either Heads or Tails (whichever it is). Now what is *your* belief that the coin is Heads? My knowledge of the outcome has not changed the coin's results. Thus we assign different probabilities to the result. \n", + "\n", + "- Your code either has a bug in it or not, but we do not know for certain which is true, though we have a belief about the presence or absence of a bug. \n", + "\n", + "- A medical patient is exhibiting symptoms $x$, $y$ and $z$. There are a number of diseases that could be causing all of them, but only a single disease is present. A doctor has beliefs about which disease, but a second doctor may have slightly different beliefs. \n", + "\n", + "\n", + "This philosophy of treating beliefs as probability is natural to humans. We employ it constantly as we interact with the world and only see partial truths, but gather evidence to form beliefs. Alternatively, you have to be *trained* to think like a frequentist. \n", + "\n", + "To align ourselves with traditional probability notation, we denote our belief about event $A$ as $P(A)$. We call this quantity the *prior probability*.\n", + "\n", + "John Maynard Keynes, a great economist and thinker, said \"When the facts change, I change my mind. What do you do, sir?\" This quote reflects the way a Bayesian updates his or her beliefs after seeing evidence. Even — especially — if the evidence is counter to what was initially believed, the evidence cannot be ignored. We denote our updated belief as $P(A |X )$, interpreted as the probability of $A$ given the evidence $X$. We call the updated belief the *posterior probability* so as to contrast it with the prior probability. For example, consider the posterior probabilities (read: posterior beliefs) of the above examples, after observing some evidence $X$:\n", + "\n", + "1\\. $P(A): \\;\\;$ the coin has a 50 percent chance of being Heads. $P(A | X):\\;\\;$ You look at the coin, observe a Heads has landed, denote this information $X$, and trivially assign probability 1.0 to Heads and 0.0 to Tails.\n", + "\n", + "2\\. $P(A): \\;\\;$ This big, complex code likely has a bug in it. $P(A | X): \\;\\;$ The code passed all $X$ tests; there still might be a bug, but its presence is less likely now.\n", + "\n", + "3\\. $P(A):\\;\\;$ The patient could have any number of diseases. $P(A | X):\\;\\;$ Performing a blood test generated evidence $X$, ruling out some of the possible diseases from consideration.\n", + "\n", + "\n", + "It's clear that in each example we did not completely discard the prior belief after seeing new evidence $X$, but we *re-weighted the prior* to incorporate the new evidence (i.e. we put more weight, or confidence, on some beliefs versus others). \n", + "\n", + "By introducing prior uncertainty about events, we are already admitting that any guess we make is potentially very wrong. After observing data, evidence, or other information, we update our beliefs, and our guess becomes *less wrong*. This is the alternative side of the prediction coin, where typically we try to be *more right*. \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Our Bayesian framework\n", + "\n", + "We are interested in beliefs, which can be interpreted as probabilities by thinking Bayesian. We have a *prior* belief in event $A$, beliefs formed by previous information, e.g., our prior belief about bugs being in our code before performing tests.\n", + "\n", + "Secondly, we observe our evidence. To continue our buggy-code example: if our code passes $X$ tests, we want to update our belief to incorporate this. We call this new belief the *posterior* probability. Updating our belief is done via the following equation, known as Bayes' Theorem, after its discoverer Thomas Bayes:\n", + "\n", + "\\begin{align}\n", + " P( A | X ) = & \\frac{ P(X | A) P(A) } {P(X) } \\\\\\\\[5pt]\n", + "& \\propto P(X | A) P(A)\\;\\; (\\propto \\text{is proportional to })\n", + "\\end{align}\n", + "\n", + "The above formula is not unique to Bayesian inference: it is a mathematical fact with uses outside Bayesian inference. Bayesian inference merely uses it to connect prior probabilities $P(A)$ with an updated posterior probabilities $P(A | X )$." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Example: Mandatory coin-flip example\n", + "\n", + "Every statistics text must contain a coin-flipping example, I'll use it here to get it out of the way. Suppose, naively, that you are unsure about the probability of heads in a coin flip (spoiler alert: it's 50%). You believe there is some true underlying ratio, call it $p$, but have no prior opinion on what $p$ might be. \n", + "\n", + "We begin to flip a coin, and record the observations: either $H$ or $T$. This is our observed data. An interesting question to ask is how our inference changes as we observe more and more data? More specifically, what do our posterior probabilities look like when we have little data, versus when we have lots of data. \n", + "\n", + "Below we plot a sequence of updating posterior probabilities as we observe increasing amounts of data (coin flips)." + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "\n", + "import torch\n", + "import pyro\n", + "import pyro.distributions as dist\n", + "from pyro.infer import MCMC, NUTS, HMC\n", + "from IPython.core.pylabtools import figsize\n", + "import numpy as np\n", + "from matplotlib import pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "n_trials = [0, 1, 2, 3, 4, 5, 8, 15, 50, 500]\n", + "x = np.linspace(0, 1, 100)\n", + "data = pyro.sample('coin_toss', pyro.distributions.Bernoulli(0.5), [n_trials[-1]])" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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uO1GnpaW5ZcuWVfv9IvWRmaE7tEtjY2YZzrm0uliXcoM0ZsoR0phUNzeoC5OIiIiIiIRMBYRIAH2zJCIi5VGOEFEBIXKMmTNnhjsEERGpp5QjRDQGQuQYjbV/a1FREbm5uRQWFoY7FKlFkZGRJCYmEhERUWa6xkCI1IzGlCOUF5qOms4NVb4Kk4g0TLm5uURHR3PCCSdgZuEOR2qBc478/Hxyc3Np1apVuMMRkXpOeaFpqI3coC5MIk1EYWEhsbGxShKNmJkRGxurbxNFJCTKC01DbeQGFRAiAd5+++1wh1BrlCQaPx1jkdrV2HKE/mc0DTV9nFVAiATo379/uEMQEZF6SjlCRAWEyDE6deoU7hCalE2bNpGcnBzuMI7x8ssvk5SURFJSEi+//HLQeWbMmMHBgwfrODIRCSfliNpXX/PCeeedxwknnMCFF15Y7jwvvfQSWVlZdRhVeKiAEJFG53j7ee7Zs4f77ruPL774gqVLl3Lfffexd+/eY+ZTASEi0jDURP//O++8k1deeaXCeVRAiIjUsMcee4zk5GSSk5OZMWNG6fTCwkKuvfZaUlJSuOyyy0pPyu+66y569uxJSkoKd9xxBwDZ2dmMHTuW9PR00tPT+eyzzwCYOnUqkydPZuTIkYwfP54BAwawatWq0nUMGzaMjIwMDhw4wPXXX096ejp9+/blX//61zFxfvjhh/z0pz+ldevWtGrVip/+9Kd88MEHZeZ54oknyMrKYvjw4QwfPhyAV199ld69e5OcnMzvfvc7wLtM4oQJE0hOTqZ37948/vjjpe8v2bYrr7wSoNzYVq1axVlnnUVqaiopKSlkZmYe/8EQEakHGkpeABgxYgQJCQnlbssbb7zBsmXLGDduHKmpqeTn5/PRRx/Rt29fevfuzfXXX8+hQ4fK3Y7XX3+d5ORk+vTpw5AhQwAvh9x5552kp6eTkpLCc889B8D27dsZMmQIqampJCcn8+mnn1Zr/1ebc67aj/79+zuRxmbSpEnhDqFW7Ny5M6zrX7ZsmUtOTnZ5eXlu//79rmfPnm758uVu48aNDnCLFy92zjl33XXXuWnTprndu3e77t27u+LiYuecc3v37nXOOXfVVVe5Tz/91Dnn3ObNm12PHj2cc87de++9rl+/fu7gwYPOOecee+wxd8899zjnnMvKynJJSUnOOed+//vfu1deeaV0mUlJSS4vL69MrNOmTXMPPPBA6fP777/fTZs27ZhtOuWUU1x2drZzzrlt27a5Ll26uJ07d7ojR4644cOHu7feesstW7bMnXvuuaXvKdmODh06uIKCgjLTyovtlltucbNnz3bOOXfo0KHSbSxPsGMNLHPH8f++Kg/lBmnMGlOOUF4IPS+UWLBggRs9enS52zR06FD35ZdfOuecy8/Pd507d3Zr1651zjl3zTXXuMcff7zc7UhOTnZbt24tM+25554rzUcFBQWuf//+bsOGDW769OnuwQcfdM45V1hY6HJzcyvd3zWZG9QCIRJAdxmtHYsXL+aSSy4hLi6O+Ph4Lr300tJvTLp06cKgQYMAuPrqq1m8eDGJiYnExMQwceJE3nzzTVq0aAHA/PnzueWWW0hNTWXMmDHk5uayf/9+AMaMGUNsbCwAP//5z3n99dcBeO2117j88ssBmDdvHo888gipqakMGzaMgoICfvjhhzKxuiA3iarsChZffvklw4YNo23btkRGRjJu3DgWLVpEt27d2LBhA7/61a/44IMPSExMBCAlJYVx48Yxe/ZsIiMjK4zt7LPP5k9/+hN//vOf2bx5c+k2ikjdU46oOQ0pL1TH2rVrOfXUU+nevTsA1157LYsWLSp3OwYNGsSECRN4/vnnKSoqKo1t1qxZpKamMmDAAHbv3k1mZibp6em8+OKLTJ06lW+++abClpHaoAJCJICusFE7gp2Ulwg8OTczIiMjWbp0KWPHjmXu3Lmcd955ABQXF7NkyRJWrFjBihUr2LZtW+k/zri4uNJldOrUiRNPPJGVK1cyZ86c0m5Czjn++c9/lr7/hx9+4Mwzzyyz/s6dO7Nly5bS51u3bqVjx47V2r5WrVrx9ddfM2zYMJ5++mkmTpwIwHvvvcfNN99MRkYG/fv3p7CwsNzYfvGLX/D2228TGxvLqFGj+PjjjyuMRURqj3JEzWlIeaEmt6+87Xj22Wd58MEH2bJlC6mpqezevRvnHE8++WRpbBs3bmTkyJEMGTKERYsW0alTJ6655hpmzZp13PFWhQoIkQDLly8PdwiN0pAhQ5g7dy4HDx7kwIEDvPXWW5xzzjkA/PDDDyxZsgTwxhEMHjyYvLw8cnJyuOCCC5gxYwYrVqwAYOTIkTz11FOlyy2ZHsyVV17Jo48+Sk5ODr179wZg1KhRPPnkk6X/2L/66qtj3jdq1CjmzZvH3r172bt3L/PmzWPUqFHHzJeQkFD6LdeAAQP45JNP2LVrF0VFRbz66qsMHTqUXbt2UVxczNixY3nggQdYvnw5xcXFbNmyheHDh/Poo4+yb98+8vLyyo1tw4YNdOvWjVtvvZUxY8awcuVKwOuPu23btiocBRE5XsoRNach5YVQ+eeFHj16sGnTJtatWwfAK6+8wtChQ8vdjvXr1zNgwADuv/9+2rRpw5YtWxg1ahTPPPMMR44cAeD777/nwIEDbN68mXbt2jFp0iRuuOGG0s/l+PHjWbp0abXjD1Vkra9BRATo168fEyZM4KyzzgJg4sSJ9O3bl02bNnHmmWfy8ssvc+ONN5KUlMSUKVPIycnh4osvpqCgAOdcmcHHN998MykpKRQWFjJkyBCeffbZoOu87LLL+PWvf83dd99dOu3uu+/mtttuIyUlBeccXbt25d133y3zvtatW3P33XeTnp4OwD333EPr1q2PWf7kyZM5//zz6dChAwsWLODhhx9m+PDhOOe44IILuPjii/n666+57rrrKC4uBuDhhx+mqKiIq6++mpycHJxz/OY3v+GEE04oN7Y5c+Ywe/ZsoqKiaN++Pffccw/FxcWsW7cuaFwiIg1BQ8oLAOeccw5r1qwhLy+Pzp0787e//e2YL5cmTJjATTfdRGxsLEuWLOHFF1/k8ssvp7CwkPT0dG666Sb27NkTdDvuvPNOMjMzcc4xYsQI+vTpQ0pKCps2baJfv34452jbti1z585l4cKFTJs2jaioKOLj40tbIFauXEmHDh2O/+BUwipqPqpMWlqaW7ZsWQ2GIxJ+HTt2bJSXYMvOzqZt27bhDkNqyLfffssLL7zAY489dsxrwY61mWU459LqIjblBmnMGlOOUF5oXHJzc7nhhhtKx3kEqsncoC5MIgEaS2KQxi05OTlo8SAitUs5QuqrxMTEcouHmqYuTCIBpk6dytSpU8MdRq26751VrM7KrdFl9uyYyL0X9arRZYqI1DeNNUcoL0hVVLkFwswmm9kyM1uWnZ1dGzGJhNV9990X7hBEGhzlBmkqlCNEqtEC4ZybCcwEr59rjUckIrWuPnwjlJGRwYQJE8jPz+eCCy7gL3/5yzGX7ZswYQIXXnghl112WY2u+6WXXmLZsmVlrtohx0e5QaRhqw954Q9/+AOzZs1i79695OXlBZ1n6tSpxMfHl969uaYsXLiQ6dOnBx08LcfSGAgRCYspU6Ywc+ZMMjMzyczM5IMPPgh3SCIiEkYXXXRRnVyCVI6fCgiRALp6TO3bvn07ubm5nH322ZgZ48ePZ+7cuUHnXbRoET/5yU/o1q0bb7zxRun0adOmkZ6eTkpKCvfee2/p9J/97Gf079+fXr16lblj7Isvvkj37t0ZOnQon332Wen0119/neTkZPr06cOQIUNqYWtFpDFRjqg9AwcODOkSpKtXr2bYsGF069aNJ554onT67NmzOeuss0hNTeXGG28svZvzlClTSEtLo1evXmXyxQcffECPHj0YPHgwb775Zun0Tz75hNTUVFJTU+nbt2/pfR3kKA2iFpE6t23bNjp37lz6vHPnzuXeEG379u0sXryYNWvWMGbMGC677DLmzZtHZmYmS5cuxTnHmDFjWLRoEUOGDOGFF16gdevW5Ofnk56eztixYzl8+DD33nsvGRkZtGzZkuHDh9O3b18A7r//fj788EM6derEvn376mT7RUSk+tasWcOCBQvYv38/Z5xxBlOmTGHdunXMmTOHzz77jKioKH75y1/y97//nfHjx/PQQw/RunVrioqKGDFiBCtXrqR79+5MmjSJjz/+mNNPP50rrriidPnTp0/n6aefZtCgQeTl5RETExPGra2f1AIhEiAtrU4uld+kBbv/TOD4hxI/+9nPaNasGT179mTHjh0AzJs3j3nz5tG3b1/69evHmjVryMzMBLwbCvXp04eBAweyZcsWMjMz+eKLLxg2bBht27alefPmZRLFoEGDmDBhAs8//3zpt1UiIuVRjgi/0aNHEx0dTZs2bWjXrh07duzgo48+IiMjg/T0dFJTU/noo4/YsGEDAK+99hr9+vWjb9++rFq1itWrV7NmzRpOPfVUkpKSMDOuvvrq0uUPGjSI22+/nSeeeIJ9+/YRGanv2wNpj4hInevcuTNbt24tfb5161Y6duwYdN7o6OjS30sKD+ccv//977nxxhvLzLtw4ULmz5/PkiVLaNGiBcOGDaOgoAAov0B59tln+eKLL3jvvfdITU1lxYoVnHjiice1fSIiUnv880JERASFhYU457j22mt5+OGHy8y7ceNGpk+fzpdffkmrVq2YMGFCpXnhrrvuYvTo0fz73/9m4MCBzJ8/nx49etTeBjVAaoEQkTrXoUMHEhIS+Pzzz3HOMWvWLC6++OKQ3z9q1CheeOGF0qt0bNu2jZ07d5KTk0OrVq1o0aIFa9as4fPPPwdgwIABLFy4kN27d3PkyJEyN9pZv349AwYM4P7776dNmzZs2bKlZjdWRERq3YgRI3jjjTfYuXMnAHv27GHz5s3k5uYSFxdHy5Yt2bFjB++//z4APXr0YOPGjaxfvx6AV199tXRZ69evp3fv3vzud78jLS2NNWvW1P0G1XNqgRAJ4D/ASmrPM888U3oZ1/PPP5/zzz8/5PeOHDmS7777jrPPPhuA+Ph4Zs+ezXnnncezzz5LSkoKZ5xxBgMHDgS8gmXq1KmcffbZdOjQgX79+pV2V7rzzjvJzMzEOceIESPo06dPzW+siDQayhG157e//S3/+Mc/OHjwIJ07d2bixIkh37SvZ8+ePPjgg4wcOZLi4mKioqJ4+umnGThwIH379qVXr15069aNQYMGARATE8PMmTMZPXo0bdq0YfDgwXz77bcAzJgxgwULFhAREUHPnj2rlJ+aCgvWFzlUaWlpTlcjEGkYsrOzadu2bbjDkDoQ7FibWYZzrk46bys3iDQMygtNS03mBnVhEglQXl98ERER5QgRFRAix9i+fXu4QxARkXpKOUJEBYRIk3I8XRalYdAxFpGq0P+MpqGmj7MKCJEA/fr1C3cItSIyMpL8/Hwli0bMOUd+fr6uWS5SixpTjlBeaBpqIzcoy4gEyMjICHcItSIxMZHc3FwOHDgQ7lCkFkVGRpKYmBjuMEQarcaUI5QXmo6azg0qIEQCTJ48mZkzZ4Y7jBoXERFBq1atwh2GiEiD1phyhPKCVJe6MIkEeP7558MdgoiI1FPKESIqIEREREREpApUQIiIiIiISMg0BkIkwLZt28IdgoiI1FPKEdIYFBU7svcfqvb7q1xAmNlkYDLAySefXO0Vi9RXGRkZutOoSBUpN0hToRwh9V1RsWPn/gK25xTwY04BWfvy+TGngO25BWzfl8/2nAJ25h6iyDloFhFRnXXY8Vz7Ny0tzS1btqza7xepj8xM18SWRsfMMpxzaXWxLuUGacyUIySc/IuD7fsK2J6TX1oobM/JJ2tfAdn7fcWBn6gIo2VsFHExkSRER5IQG0Vs8whm3TRiXeHB3KSqxqEuTCIiIiIiYVZYVEx23qGgxUFWTj7bKykO4mMiaZPQnFPbxZEYE0VCTCTxsZG0josmMSaC5pERREU0I6KZAbD34GFmGVadWFVAiIiIiIjUopLiIGvf0daC7X4/s/blk73/EMUBjVvlFgexkcTHlC0Omkc0o1mzatUDVaYCQi01lk0AACAASURBVCTAc889F+4QRESknlKOkECVFQfltRw0j2hGyxZRxEVH0DYhmm7t4kmMiSQhJuqYloO6LA5CoQJCJMDkyZPDHYKIiNRTyhFNS2FRMTv3HyozziCUlgP/4qBNQrTXchDr61YUxpaDmqICQiSABsiJiEh5lCMaj6PFgd9Yg30F/JjrDUbenlN5cdA2IYbT2sWT4CsOEmIiaRUXTWJMJM0jmzXI4iAUKiBEREREpFHxbznYnpNfveIg8djioHV8NAnRjbs4CIUKCBERERFpMAqLitmx/xA/VqPlILFFFPHRkUGLg1YNvFtRXVIBIRLgwgsvDHcIIiJSTylH1C7/4uDooOSyYw525QUpDiKbefc5iI4s260oOpKEWLUc1DQVECIB3nnnnXCHICIi9ZRyRPUdKelWtK/s/Q1KioRQioOTWsZw+kl+xUGLKFrHNVdxUMdUQIgEuOiii5QgREQkKOWI4EqKg+NpOQhWHJwY15x4FQf1jgoIkQDvvvtuuEMQEZF6qinmiMCWg7JXLfJ+D1YcREc2I1EtB42SCggRERGRJupIUTE7cku6ExWUDkzevu9ooZC9/xCBF649pjhoH09CjFoOmgoVECIiIiKNULDioKR7UZavONgVQnGQ1N5rOYiPjiQx1ms5iI+OJDqqGVHNVBw0RSogRALoBkEiIlKe+pIjKioOtufkk1VJcRAfE0n7kuIgxruUqYoDCZUKCJEAM2fOZPLkyeEOQ0RE6qG6yBGBxUHgVYsqaznwLw4SY7znCbFet6KEmCiaR5qKAzkudjyVdFpamlu2bFkNhiMSfmZWb75hEqkpZpbhnEuri3UpN0hjdrw54nChrzjI9V2lKGBg8vZ93oDkiooDrytRpN99DnwDklUcSBXsPXiYG8/ts77wQO7pVX2vWiBEREREakBJy8H2nCDFwb6C0qsVBRYHMSVjDmIiad8qhu4dgnQrKikOIprRzFQcSHipgBARERGpREnLQXSnnrz9dVbILQcxkc1IbOENSG7fKoakDl63ooSYoy0HibFRREWoOJCG47gKiA3ZB7jiuSU1FYtIvTDioXf0uRYRaUJKioOSgqDMTdACWg7aX/0ot776FQAxUc1IjKm45SBBxYE0QlUuIMxsMjAZILZ9N7Jy8ms8KJFwKnRRHNLnWhqZqITW7Wpz+f654eSTT67NVYlUSWBxUDIYuaQ4yMrJZ1fe4WPeFxN19FKmHVrF0L1jAgnRkTQ7nEeHk9qqOJAmrcoFhHNuJjAT4JQevd1vRp1R40GJhNPY1M78c8XWcIchUqOuejiiVrus+ueGtLQ0XYVA6kR5LQdZ+/JLr1hUbnHgazno2CqWMzomlg5GToyJpHW8NyA5WHGgHCGiMRAiIiJSD5XXclBZcRAbFUFCbKSv5cC/OIgkMSaqwuJAREKjAkJERETq1KHCInbmHvKKgYDLmYZSHMRHH205SIyJJD6m8pYDEak5KiBEApx76S/CHYKISIMVWBx4d0fOL71bsjcgOXhxkOhrOejUKpYeHRNJqIfFgXKEiAoIkWNMuefRcIcgIlIvlVcc+Hczqqw4qLjloBlREVavWw6UI0RUQIgc486rzmfaq++HOwwRkTp1qLCIHTmHjt7XIKfqLQcd/VoOEmKiSIiNLL1DckMoDkKhHCGiAkLkGBu++ybcIYiI1KhgxcHRgcn5ZO0rYPeBKhYHDazloKYoR4iogBAREWnQyisO/LsXVVYcdGody5mdEkvvjpwYG0mrFkeLg+YRhjWB4kBEQqMCQiRAq7YnhTsEERHgaHGQlXP00qWld0neV43iICaKxBYqDo6HcoSICgiRY/z1fzPCHYKINAEFR4pK73PgXxyo5aB+U44QUQEhcow5z/w/rpjyX+EOQ0QaMP/ioOxN0I4+3xOkOGjRPIKEGK846Ny6BWd28l2tyFcctI6LJj46UsVBGClHiKiAEDnGa889ruQgIuUK1nKwfV/ZYiFYceDfctDlxBb0VHHQIClHiKiAEBERKVVSHGTtK+DHXN+g5BCKgxbNI4iP8e6QXFIc+HcrUnEgIo2JCggREWkSCo4UlQ5A/jG35CpF/lctymfvwSPHvK9F8wgSYqOIj46gy4kt6NWpJQkx3rSE2EhOjIsmTsWBiDQhKiBEAjz6j3+HOwQRqSL/4sB/zEHJ7xUWBzGRxMdEcnKbOJJjokqLg5KWg7joSJr77nOg4kCUI0RUQIiISD13XMVBbBRxzSP8ioNIEmIjSYyNonVccxUHIiLVoAJCJMBvf3EB/1yxNdxhiDQJJcVBmfsb+P0eSrei0uIgNpKEmEhaxkZxQlxz4lUcSC1QjhBRASEiIrWkouKg5HKm5RUHibFRtChtOfAGI5cUB63UciAiElYqIEREpEZl7syj7/3zKiwO4vxaDhJjvTEILX3dilqoOBARqdfMOVe1N5hNBiYDWLOI5KjE1ntqI7CGruhQfouI6NiD4Y6jvtN+Co32U+i0r4I7kpeTWHzkUHxtLd8/N2DWJyq+1XbMDLNmhgWvBKqagBqRosP5LSKa63NaGe2nymkfhUb7KQgzO5y7p4UrOtKyym9twv+/a5WZLXPOpYU7jvpO+yk02k+h074KPx2DymkfhUb7qXLaR6HRfqpZzcIdgIiIiIiINBwqIEREREREJGQqIGrPzHAH0EBoP4VG+yl02lfhp2NQOe2j0Gg/VU77KDTaTzVIYyBERERERCRkaoEQEREREZGQqYAQEREREZGQqYA4DmZ2npmtNbN1ZnZXkNdvN7PVZrbSzD4ys1PCEWd9UNm+8pvvMjNzZtYkL7UWyn4ys5/7PlerzOwfdR1jfRDC397JZrbAzL7y/f1dEI44G7MQjkG0mc3xvf6FmXWt+yjDT3kiNMoRlVN+CI3yQx1xzulRjQcQAawHugHNga+BngHzDAda+H6fAswJd9z1dV/55ksAFgGfA2nhjrs+7icgCfgKaOV73i7ccdfT/TQTmOL7vSewKdxxN6ZHiMfgl8Czvt+vbIr//5Qnam4/+eZrsjlC+aFG95PyQw081AJRfWcB65xzG5xzh4H/AS72n8E5t8A5V3LXw8+BznUcY31R6b7yeQB4FCioy+DqkVD20yTgaefcXgDn3M46jrE+CGU/OSDR93tLIKsO42sKQjkGFwMv+35/Axhhwe5G3bgpT4RGOaJyyg+hUX6oIyogqq8TsMXv+VbftPLcALxfqxHVX5XuKzPrC3Rxzr1bl4HVM6F8proD3c3sMzP73MzOq7Po6o9Q9tNU4Goz2wr8G/hV3YTWZIRyDErncc4VAjnAiXUSXf2hPBEa5YjKKT+ERvmhjkSGO4AGLNg3aUGviWtmVwNpwNBajaj+qnBfmVkz4HFgQl0FVE+F8pmKxGumHob3TeWnZpbsnNtXy7HVJ6Hsp6uAl5xz/8/MzgZe8e2n4toPr0kI5RiE/D+yEVOeCI1yROWUH0Kj/FBH1AJRfVuBLn7POxOkGczMzgX+AIxxzh2qo9jqm8r2VQKQDCw0s03AQODtJjhILpTP1FbgX865I865jcBavITRlISyn24AXgNwzi0BYoA2dRJd0xDqZ7ULgJlF4nUV2FMn0dUfyhOhUY6onPJDaJQf6ogKiOr7Ekgys1PNrDneIMG3/WfwNbk+h5cUmmJfxBIV7ivnXI5zro1zrqtzriteP+Axzrll4Qk3bCr9TAFz8QZdYmZt8JqsN9RplOEXyn76ARgBYGZn4iWI7DqNsnEL5Ri8DVzr+/0y4GPnXFNrgVCeCI1yROWUH0Kj/FBHVEBUk69P7y3Ah8B3wGvOuVVmdr+ZjfHNNg2IB143sxVmFvghbhJC3FdNXoj76UNgt5mtBhYAdzrndocn4vAIcT/9FzDJzL4GXgUmNMGT11oT4jH4G3Cima0DbgfKvTRnY6U8ERrliMopP4RG+aHumPaZiIiIiIiESi0QIiIiIiISMhUQIiIiIiISMhUQIiIiIiISMhUQIiIiIiISMhUQIiIiIiISMhUQIiIiIiISMhUQIiIiIiISMhUQ0iCYWVcz+7am5jez//h+5gWZdoKZ/fJ44g1Y161m9p2Z/b0qMR7H+qaa2R01vVwRERERUAEh9Yh56uQz6Zz7SQXTTgBqrIDwLesC59y4GlymiIiISFiogJBqMbP/MbM5ZvaFmW02s9EVzNvVzNaY2ctmttLM3jCzFn6vfWdm/w0sB7qY2e1m9q3vcZvfoiLLWcZcM8sws1VmNrmy+X3vySOA37RHgNPMbIWZTTOzB8zs137zPWRmtwZ5/zFxm9mzQDfgbTP7TZDdE2Fmz/tin2dmsb73XW1mS30xPGdmERVtq5n9wczWmtl84Ay/6XFm9p6Zfe2L64rgR0lEREQkNCogpLr6ABuccwOAccC9lcx/BjDTOZcC5FL2G/4zgFnOub5AG+A6YAAwEJhkZn0rWcb1zrn+QBpwq5mdGMI6K3IXsN45l+qcuxP4G3AtgK+F5EogsDtS/2BxO+duArKA4c65x4OsKwl42jnXC9gHjDWzM4ErgEHOuVSgCG8fB91W37qvBPoClwLpfss/D8hyzvVxziUDH4S4D0RERESCUgEhVeb7lrwNcJ9v0mqgVSVv2+Kc+8z3+2xgsN9rm51zn/t+Hwy85Zw74JzLA94EzqlkGbea2dfA50AXvJPyytYZMufcJmC3r5AZCXzlnNsdMFtFcVdko3Nuhe/3DKArMALoD3xpZit8z7v55gm2ref41n3QOZcLvO23/G+Ac83sz2Z2jnMupyrbLiIiIhIoMtwBSIOUDGQ65wp8z/sBX1fyHlfB8wN+v1tVlmFmw4BzgbOdcwfNbCEQE8I6q+qvwASgPfBCkNcrirsih/x+LwJifct62Tn3+zIrqNq2ehOd+97XQnEB8LCZzXPO3V/NWEVERETUAiHV0gc42cxizCwOryXicQAz+8jMOgV5z8lmdrbv96uAxeUsexHwMzNr4Vv2JcCnFSyjJbDXd0LdA6/7UFXXGWg/kBAw7S287kDpwIdVjLuqPgIuM7N2AGbW2sxOofxtXQRcYmaxZpYAXFSyIDPrCBx0zs0GpuMVeyIiIiLVphYIqY4+eGMAFgKJwJ+cc5/5xgecDuwJ8p7vgGvN7DkgE3gm2IKdc8vN7CVgqW/SX51zX5lZ13KWUQTcZGYrgbV4XXuqtM4gMew2s898l1h93zl3p3PusJktAPY554pCjTuU9QVZ1moz+yMwz7dPjwA3441fOGZbfeueA6wANlO2cOkNTDOzYt9yplQnJhEREZES5tzx9OqQpsjMFgGTnHNrA6Yn4w3yvT1gelfgXd8g3gbJdyK/HLjcOZcZ7nhEREREwkVdmKQ6TsP7Rr8M59y3gcVDY2BmPYF1wEcqHkRERKSpUwuEiIiIiIiETC0QIiIiIiISMhUQIiIiIiISMhUQIiIiIiISMhUQIiIiIiISMhUQIiIiIiISMhUQIiIiIiISMhUQIiIiIiISMhUQIiIiIiISMhUQIiIiIiISMhUQIiIiIiISMhUQIiIiIiISssiqvsHMJgOTAeLi4vr36NGjxoMSqQ+ysrLo2LFjuMMQqREZGRm7nHNta2v5yg3S2CknSGNU3dxgzrlqrzQtLc0tW7as2u8Xqc/MjOP5+xCpT8wswzmXVhfrUm6Qxkg5QRqj6uYGdWESKYcShYiIlFBOEDlKBYRIOWbOnBnuEEREpJ5QThA5SgWESDluvPHGcIcgIiL1hHKCyFFVHkRdmaKiInJzcyksLKzpRUs9EhkZSWJiIhEREeEORURERKpJ521NQ02ft9V4AZGbm0t0dDQnnHACZlbTi5d6wDlHfn4+ubm5tGrVKtzhiIiISDXpvK3xq43zthrvwlRYWEhsbKw+hI2YmREbG9vov614++23wx2CiIjUE401J+i8rfGrjfO2WhkDoQ9h49cUjnH//v3DHYKIiNQTjTknNIWc3tTV9DFuMoOoN23aRHJycrjDKGPFihWcffbZ9OrVi5SUFObMmRN0vpdeeomsrKw6jk46deoU7hBERKSeUE6oW/XxvG3z5s3079+f1NRUevXqxbPPPht0vhkzZnDw4ME6jq5uNZkCojYcb1NQixYtmDVrFqtWreKDDz7gtttuY9++fcfMpwJCRERE5Pgc73lbhw4d+M9//sOKFSv44osveOSRR4Ken6mAaKAee+wxkpOTSU5OZsaMGaXTCwsLufbaa0lJSeGyyy4rPbh33XUXPXv2JCUlhTvuuAOA7Oxsxo4dS3p6Ounp6Xz22WcATJ06lcmTJzNy5EjGjx/PgAEDWLVqVek6hg0bRkZGBgcOHOD6668nPT2dvn378q9//euYOLt3705SUhIAHTt2pF27dmRnZ5eZ54033mDZsmWMGzeO1NRU8vPz+eijj+jbty+9e/fm+uuv59ChQ+Vux+uvv05ycjJ9+vRhyJAhgHfFhTvvvJP09HRSUlJ47rnnANi+fTtDhgwhNTWV5ORkPv300+M/GCIiIiIVaCjnbc2bNyc6OhqAQ4cOUVxcfMw8TzzxBFlZWQwfPpzhw4cD8Oqrr9K7d2+Sk5P53e9+B3jnYhMmTCA5OZnevXvz+OOPl76/ZNuuvPJKgHJjW7VqFWeddRapqamkpKSQmZl5HEehipxz1X7079/fBdq5c+cx0+rSsmXLXHJyssvLy3P79+93PXv2dMuXL3cbN250gFu8eLFzzrnrrrvOTZs2ze3evdt1797dFRcXO+ec27t3r3POuauuusp9+umnzjnnNm/e7Hr06OGcc+7ee+91/fr1cwcPHnTOOffYY4+5e+65xznnXFZWlktKSnLOOff73//evfLKK6XLTEpKcnl5eeXG/cUXX7gePXq4oqKiY14bOnSo+/LLL51zzuXn57vOnTu7tWvXOuecu+aaa9zjjz9e7nYkJye7rVu3lpn23HPPuQceeMA551xBQYHr37+/27Bhg5s+fbp78MEHnXPOFRYWutzc3Ar3dbiPdW2bNGlSuEMQqTHAMncc/++r8giWG0QausaaE8KdyxvaedsPP/zgevfu7WJjY91TTz0VdJtOOeUUl52d7Zxzbtu2ba5Lly5u586d7siRI2748OHurbfecsuWLXPnnntu6XtKtqNDhw6uoKCgzLTyYrvlllvc7NmznXPOHTp0qHQbyxPsWFc3NzS6FojFixdzySWXEBcXR3x8PJdeemnpN+ldunRh0KBBAFx99dUsXryYxMREYmJimDhxIm+++SYtWrQAYP78+dxyyy2kpqYyZswYcnNz2b9/PwBjxowhNjYWgJ///Oe8/vrrALz22mtcfvnlAMybN49HHnmE1NRUhg0bRkFBAT/88EPQmLdv384111zDiy++SLNmFR+StWvXcuqpp9K9e3cArr32WhYtWlTudgwaNIgJEybw/PPPU1RUVBrbrFmzSE1NZcCAAezevZvMzEzS09N58cUXmTp1Kt988w0JCQnVOwiNhO46KiIiJZQTakdDO2/r0qULK1euZN26dbz88svs2LGjwu378ssvGTZsGG3btiUyMpJx48axaNEiunXrxoYNG/jVr37FBx98QGJiIgApKSmMGzeO2bNnExkZWWFsZ599Nn/605/485//zObNm0u3sS40ugLCK6aCCxyBbmZERkaydOlSxo4dy9y5cznvvPMAKC4uZsmSJaxYsYIVK1awbdu20hPquLi40mV06tSJE088kZUrVzJnzpzS5ibnHP/85z9L3//DDz9w5plnHhNTbm4uo0eP5sEHH2TgwIHV3r7ytuPZZ5/lwQcfZMuWLaSmprJ7926cczz55JOlsW3cuJGRI0cyZMgQFi1aRKdOnbjmmmuYNWtWpfE0Zo35ihsiIlI1ygm1o6Gdt5Xo2LEjvXr1qrS7d3nb16pVK77++muGDRvG008/zcSJEwF47733uPnmm8nIyKB///4UFhaWG9svfvEL3n77bWJjYxk1ahQff/xxhbHUpEZXQAwZMoS5c+dy8OBBDhw4wFtvvcU555wDwA8//MCSJUsArz/a4MGDycvLIycnhwsuuIAZM2awYsUKAEaOHMlTTz1VutyS6cFceeWVPProo+Tk5NC7d28ARo0axZNPPln6wfnqq6+Oed/hw4e55JJLGD9+fGkFHExCQkJpFd2jRw82bdrEunXrAHjllVcYOnRouduxfv16BgwYwP3330+bNm3YsmULo0aN4plnnuHIkSMAfP/99xw4cIDNmzfTrl07Jk2axA033MDy5csBGD9+PEuXLq1s1zc6JdsvIiKinFA7GtJ529atW8nPzwdg7969fPbZZ5xxxhnHzOd/3jZgwAA++eQTdu3aRVFREa+++ipDhw5l165dFBcXM3bsWB544AGWL19OcXExW7ZsYfjw4Tz66KPs27ePvLy8cmPbsGED3bp149Zbb2XMmDGsXLkSgBEjRrBt27ZQD0G11PidqMOtX79+TJgwgbPOOguAiRMn0rdvXzZt2sSZZ57Jyy+/zI033khSUhJTpkwhJyeHiy++mIKCApxzZQax3HzzzaSkpFBYWMiQIUPKvVzXZZddxq9//Wvuvvvu0ml33303t912GykpKTjn6Nq1K++++26Z97322mssWrSI3bt389JLLwHeFZdSU1PLzDdhwgRuuukmYmNjWbJkCS+++CKXX345hYWFpKenc9NNN7Fnz56g23HnnXeSmZmJc44RI0bQp08fUlJS2LRpE/369cM5R9u2bZk7dy4LFy5k2rRpREVFER8fX9oCsXLlSjp06HD8B0dERETET0M6b/vuu+/4r//6L8wM5xx33HFHaQHib/LkyZx//vl06NCBBQsW8PDDDzN8+HCcc1xwwQVcfPHFfP3111x33XWlA7EffvhhioqKuPrqq8nJycE5x29+8xtOOOGEcmObM2cOs2fPJioqivbt23PPPfdQXFzMunXraN26dY0cn/JYRU1HlUlLS3PLli0rMy07O5u2bdseb1xST+Tm5nLDDTeU9hf019iPdceOHXX5XGk0zCzDOZdWF+sKlhtEGrrGmhMaey5var799lteeOEFHnvssWNeC3asq5sbGl0XJqlZiYmJQYuHpqAxJgoREake5QRpCJKTk4MWDzWtVrsw3ffOKlZn5dboMnt2TOTei3rV6DJFgpk6dSpTp04NdxgiIlIPNIWcoPM2CVWVWyDMbLKZLTOzZYE3PRNpTO67775whyBSI7bsqf07oio3SGOnnCByVJVbIJxzM4GZ4PVzrWjecFecBw8e5PLLL2f9+vVERERw0UUX8cgjjxwz39SpU4mPjy+9m2FNWbhwIdOnTz9mEI6ISG3buOsA//5mO+9/u51vt+XSLCo6pjbXV5XcICL1k87bdN4WqkZ3FaZAd9xxB8OHD+fw4cOMGDGC999/n/PPPz/cYYmI1CjnHJk783xFw4+s/dG7hGCX1rEM7dGW/4lLrN1LcoiI1ACdtzUMjXoQdYsWLRg+fDgAzZs3p1+/fmzdujXovKtXr2bYsGF069aNJ554onT67NmzOeuss0hNTeXGG28svZvzlClTSEtLo1evXtx7772l83/wwQf06NGDwYMH8+abb5ZO/+STT0hNTSU1NZW+ffuWXh9Y6i9dRUbqO+cc32zNYdqHaxjx2CeMfHwRf5mfScGRIv7Pme2Y8n9O48bhp3Nx307hDlWkwVNOqH06b2s4Gn0LRIl9+/bxzjvv8Otf/zro62vWrGHBggXs37+fM844gylTprBu3TrmzJnDZ599RlRUFL/85S/5+9//zvjx43nooYdo3bo1RUVFjBgxgpUrV9K9e3cmTZrExx9/zOmnn84VV1xRuvzp06fz9NNPM2jQIPLy8oiJqdXeBCLSSBUVO5Zt2sOHq3bwwartZO0roJlB1zZx/DT5JLq3T6TTCTHEREWEO1QRkWrTeVv91iQKiMLCQq666ipuvfVWunXrFnSe0aNHEx0dTXR0NO3atWPHjh189NFHZGRkkJ6eDkB+fj7t2rUDvJvAzZw5k8LCQrZv387q1aspLi7m1FNPJSkpCYCrr76amTNnAjBo0CBuv/12xo0bx6WXXkrnzp3rYMvleKSlpZV7C3qRunSosIj/rNvNh6t+ZN7qHew5cJioCKNrmzjOT2lP9/YJtG8ZQ3SkigaR2qKcUHd03lb/NYkCYvLkySQlJXHbbbeVO090dHTp7xERERQWFuKc49prr+Xhhx8uM+/GjRuZPn06X375Ja1atWLChAkUFBQAYGZBl3/XXXcxevRo/v3vfzNw4EDmz59Pjx49amDrRKQxyi04wsK12Xy46kcWrNnJwcNFxEQ1o1vbeAZ3b0NS+3jaxsfQPLJR90QVkSZI5231X6MvIP74xz+Sk5PDX//61yq/d8SIEVx88cX85je/oV27duzZs4f9+/eTm5tLXFwcLVu2ZMeOHbz//vsMGzaMHj16sHHjRtavX89pp53Gq6++Wrqs9evX07t3b3r37s2SJUtYs2aNPogiUsaPOQX873c7mLfqR5Zs2E1hkSMhJpKk9gkktYvntJPiOTG+OZHNVDSISOOk87aGoVEXEFu3buWhhx6iR48e9OvXD4BbbrmFiRMnhvT+nj178uCDDzJy5EiKi4uJiori6aefZuDAgfTt25devXrRrVs3Bg0aBEBMTAwzZ85k9OjRtGnThsGDB/Ptt98CMGPGDBYsWEBERAQ9e/bUFQUaAP9BViK1wTnHmh/3M3/1Dv73ux2s3JoDQJv45vQ7pRWn+YqGE2KiaNYs+LdkIlI3lBNqn87bGg47nv58aWlpLvCqBNnZ2bRt2/Z445IGQMdapOqOFBXz5cY9zFu9g/nf7WDr3nzAu9xq1zbxJLWP55QTWxAfHVlu03p1XDWs7/ZDe3d2rLEFViBYbhCR+km5vOkIdqzNLMM5l1bVZTXqFgiR49GxY0eysrLCHYY0AjkHuHI6owAAIABJREFUj7Dw+53M/24nC9fuZH9BYekg6JHJ7enePp4OJ8QSqysnidRbygkiR6mAECnH9u3bwx2CNFDOOTbsOsDH3+1k/nc7WLZ5L0XF3niGU9vGcVq7eE4/KZ428dFERWg8g0hDoJwgclStFBDOuRptepf6R5eyEynrcGExSzfu4eM1O/lozQ427z4IQPuWMaSf2prTT4qnW9s4EjWeQUTqGZ23NX41fd5W4wVEZGQk+fn5xMbG6sPYSDnnyM/PJzKycTdglQzgEinPztwCFqzdycdrdvJp5i4OHi4iKsI45UTvpm6nt0ugU6sYYqMi9P9QpIFrrDlB522NX22ct9X4GWBiYiK5ubkcOHCgphct9UhkZCSJiYnhDqNWZWRkhDsEqWeKih0rtuxjoa9oWJWVC8AJsVF0b59At3Ze96QT46J1fwaRRqax5gSdtzUNNX3eVuMFREREBK1atarpxYrUucmTJ5fekVKarl15h1j0fTYL12az6Pts9uUfwQxObt2Cc7q35bST4jjlxBYkxETRTN/eiTRajTUn6LxNqqPGL+Mq0liYmcZ6NEGFRcV8vXUfC9dm88n32aX3ZkiIieSUNnHeIOi28bRLjCamAV01SZdxFTk+ygnSGOkyriIi1fRjTgGLvvcKhk8zs8ktKMQMurRqweDubejWNp6ubVpoALSIiAgqIESkCSo4UsSXm/bwydpsFmVm8/2OPABaxkZxSts4Tm0Tx6lt4zmpgbUyiIiI1AUVECLl2LZtW7hDkBrinOP7HXl8mpnNosxdfLFhN4cKi4lsZnQ5sQXDerSla1vfWIZotTKIyLGUE0SOUgEhUo6MjAw6dqyTLuNSC3bmFrB43S4WZ+5i8bpd7Nx/CICTEqNJ7tySrm3i6NYuTldMEpGQKCeIHFXlAsLMJgOTAU4++eQaD0ikvhgzZowGzDUgBw4VsnTjHj7N3MVn67JZ6+uWFBcdwSknxtHv1Fac2jaeji1jaNFc92WoacoN0tgpJ4gcVeUCwjn3/9m77/i4qzPR/5/v9KZp6sVy77LcMSW4AKFtMEkgGwiEkIQY2LC72cJvk3tvEjbZX5IN2fTNAsmFFJJsIFkIWSCwobnE2FjuXbas3tuMZjR9zv3jOxLG2Ei2JY3K8369xjPWtDNn7O+j53vOc85jwGOgr7Qx4i0SQohhSKTS7G3oZduJTrad6GRPfS/JtMJs1Jjmd7Bufj7T853MyCyxapRpSaNKYoMQQkwdMoVJCDEhpNOKwy1Btp/sYtvJTnae6qY/nkIDSn12Vs70MT3XyYx8J36HRaYlCSGEEKNEEgghzuHRRx/NdhOmNKUU1e0htp/sYvvJLt6s6aI3kgCgIMfK/OIcZuQ5mZ7rJN9txS6rJQkhRtFEiQnptCIcTxKKJQlFk4TjKfpjmet4klgyrV8SKWLJNMmUIplOk0gplFKklUIpUIBBQ98gUwOTQcNkMGA2apiMBqwmA3azEVvm4rQacVpNuDIXj90s00UnMUkghDiHTZs2ZbsJU4pSilOdYbbXvJ0wdIbiAPicZsrzHLwv18H0XAdFHrsEJiHEmMpWTAjFknT0xegMxejMXHeHE3SHY3T3J+gJx+mNxOntTxCIJAhFk5zvHEINMBg0DBpoaAwcWhX6sVkpSCtF+jxf2GjQcNtMeB0W8lwW/E4LuS4r+S4rBW4rBTk2Ct1Wij128lwWOaZPIJJACHEOsuvo6FJKUdMZ5s2aLnbUdPNmTdfgSkkeu5kyv51Vs/xMz3VS6tMTBoMEFyFElox0TFBKEYgkaOyJ0NwboTUYpbk3SmtAv90WjNEWjNIfT531+XazEYdVP/tvNRlw280UuK3YzCYsJg2r2YjFZMBiMmDOjBhYjAYsZg2zwYjFpGE2GjAaNYyagcxAA2c7zA587LTSRyuSaUUiqYin0sSTaRLJNNFkilg8TTylj3DEE2kiiRTRRIpIPEVrMMqpzjD98dRZP5PZqFHotjHNp58omubXLzNznczI02vZxPghCYQQYkyk04oTHSF21HTx5qludtZ00xHSEwa33USZz8HyGT7K/Q5KfTZcsh+DEGKCiyZS1Hf3U9fVT11XmMaeCI09/dR399PYE3nXL9JGg4bbbsZlNeKwmlhY4sZl06cEOaxGHBYTHocJt82C1WTAZNQwGjSMmjYhzt6n04pkWhFNpOiNJOjtjxMIJwhGE/RFk/RFE9R1h9nf1Es49s6+yXVZmJ3nZH6Rm7mFLuYUuFhQ5MbvtGTp00xtkkAIIUZFMpXmcEuQnae62Xmqm7dqu+np12sYBkYYVsz0UZ7roNQrCYMQYmJKpNLUd/dT0xHmVGeIU539meswbcHYOx5rNRnwOizk2PXkwG0347GbyLGb8TkseJ1mrCajXmdgmHwLQRgMGhaDhiUzYlLud5z1cYlUmlA0SVtflPZAjO5wnJ5wnKZAlH2NAWLJ9OBj83OsLCp2s6jEzZJSD0tKPZT57BMioZrIJIEQ4hw+8IEPZLsJE0o0kWJvQy9vnepmZ203VXU9g2fX/E4LZX4Hl82xMy3XSbHHhstqkoRBCDFh3HjTBznQGKC6vY8T7aHBS313P8nTigOcVhN+p5m8HCtzi3LwOcx6DUCOBY/dok8tMk6MEYNsMRsN+JwWfE4LC4reeV88maKjL0ZTjz7VqyMY43BLkC3VHYM1Gh67mcoyDyun+1hR7mPpNC8eu0yBGkmSQAhxDn/4wx+y3YRxrSccZ1ddD7tq9dGF/U0BkimFBhS4bcwvzqHM76Dcb5eiZyHEhBFPpjnZEeJYax9HW/uobuvjeFsfjYvu4aYfbgXAqGnkuix4HWZWzfSTmykQLvBY9SQhU3cgRp7FZKTU56DU9/bohVKKcCxJbVc/Dd39tPZGOdLax9bqThR6bcecAhdrZvlZMzOXS2b6KXTbsvYZJgNJIIQ4h5tuukmSiIyBguequh6qanvYVdfNyY4woM/ZLfHaWTHdR5nPQXmegzyXFbvZIAmDEGLcUkrR0RfjUEuQoy19HG0NcqQlSE1HeHBEwahp5OVY8DotmOu2ceOHPkKuy0qRx4bDasJmkuPceKBpGi6bmYpSDxWlHkD/fnsjCU60hWjo7qexO8Jv3mrgyTfrAZiR6+B9c/O4YnYel83OxeuQWorzoV3MigKrVq1Su3btGsHmCDF+TOVVmCLxFPsbe6mq72F3XQ9VdT2D9QsOi5ESr50Sn50yv51pfgdeh37GTYxft69f3hLraS8Zi/eS2CDGm1RaXyb6UHOAw81BDrcEOdQcpDscH3yMz2Em12UlL0e/5Lutg9MtrSYDty6fxu/2NmbxU4iLFU2kONke4mR7iPrMaEUipTBoUFHqYcP8AtbNz2dpmRfjFJliq2lalVJq1fk+T0YghJjilFI09kTYXd/Dnvpedtf3cLg5OHgGLj/HSpnfwSWz7ZT57JT67LhspklZ4CeEmPiSqTQnOkIcbApysCnAwaYAh1uCgzVZRoNGgdtKqc/O8ule8nNsFHtt+J0WbGbjlPnFcSqymY0sLvWwODNKEU2kON6qT1E71RHm+69U871XqvHazWxYUMD7FxVy5dw8WUL2LCSBEGKKCcWS7G/sZW9DL3vqe9lT3zO4YZvFZKDEa2PVTD+lPhtlfgf5OfouzzJML4QYb1JpRU1HiP2NAQ40Bdjf2MvhliDRhL5Kj9VkoMBtY0GJm0K3lUK3jRKffXBUQY5rU5vNbKRympfKaV4AukMx9jcGONEW4sWDLTyzpwmTQeOy2bl8oLKYaxYWkuuyZrnV44MkEEKcw2SYvpRKK4639bG3oZd9Db3saeiluq1vcKWKPJeFIo+dFTN8FHvtTPPZybGbpfhPCDHuDIyW7mvsZX9jgH0NvRxoCgyOLFhNBgo9NhaXeij22Cj06MmC02LCMgJTLGX60uTnd1lZv6CA9QsKiCfTHM5MedvfGGBLdScG7QCrZvj54LJSbqgowjeF96CQBEKIc3jsscfYtGlTtpsxbKcH130NvexrCHCw+e3g6rAYKfLYuHROLiUeO6W5dvJdMroghBifevvjmZMfAfZlRk0HahZMBo1Cj77aW7HXTlEmWXCNULJwNi//9kmuvfXOUXltMf5YTAaWlftYVu4jnU5zoj3EvvoAR1v7+F/PHOCLvz/I5bNz+fCKUq5bXITDMrV+pZYiaiHOYbwXUbcHo+xv1Ifs92WuBwqdTUaNIo+NQrctE1gdlHitOK1SuzBVSRG1GM/iyTRHW4PsqR+YXtlDbVc/oC/Bme+2UuDWaxWKvXo9Vo7NhNVkHLM23rKsTEYhRCaZCLOnrocjLUGCkSQ2s4HrFxfzkVVlXDYrd0LtcSRF1EJMYu19UQ42BdjfqBcEHmgM0Nan73CqaVDo1usVVs+yU+S1ZYKrWVZGEkKMSy2BCLvr9ERhT0MvB5ve3l3YbTNR5LGxdn4+xV4b0/x2fE6rLJkqxgWDwcC8ohzmFeWQSqc51BRkd10PLx5s4dm9TRR5bHx01TT+cvU0Sr32bDd31EgCIcQ4opSiJRDlUHOQA00BDjXphYHtA8kC6MsL5lipmOah0GOj1OfA6zBLcBVCjEvRRIpDzYHBVd6q6npoC+rHNJNRo9hjZ8k0D6U+OyVeO4VuG06rSVZDEuOe0WAYLMKOxlPsrO1ib10v33ulmu+/Us3ls3P5xOUzuGpBAaZJVlsoCYQQ5/Dcc8+N6uun04pTXWEON+vrkR9q1kcXBqYhaZq+hGq+20pFWSZZ8Nvx2i2ySZsQYtxq7o0MJgq763s51KzvUg/gd1oo8tioKPNQ4tP3kXHbzKNWtzCSPv+9J7LdBDGO2SxG1s4rYO28Alp6I2w70cm+hgCbflFFQY6VO9aUc/uacgpyJscO2OddA6Fp2iZgE0B5efnKurq60WiXEFnX3NxMScnITBmPxFMca+vjcLO+0+nhFv36zHXJ8zI7nBa4rZTIyIIYQaNdAyGxYWqKJVMcatancJw5umAxGijy6nVYpZk9ZArdNuwWI4YJeEzrbm/FX1CU7WaICSSZSvNWbTdv1fRQ2xnGZNC4YUkxn7lyJpVl3mw3D7jwGggpohbiHC6kiFopRXMgytFMgnCkpY8jrUFqO8ODS6fazAYK3TZyXVYKPXphYKmsSy5GmRRRi5HQHoy+Y3ThQFOAeKZ2wecwU+y1U+K1UTzBRheGQ4qoxcVo6unn9aMdHGjU/89Ulnm4f91srl1clNXpelJELcQYC0YTHG/t42hrH8da+zjaGuRoax990eTgY/xOC7kuC5fNyaXAbdM3MvLYcFhMsteCEGJcS6TSHG3po6qum931vVTV9dDUGwH0ZVSLvXaWTvNS4rNR5rdT6LbjmKCjC0KMtlKfgzsum044luSNox28VdvN/b/cTZnPzr1rZ3HrymnYLWO3qtjFkgRCiCFEEylOtIc43tbH8bYQx1qDHGvro7k3OvgYm9lAfo6N2QUuCtx6kXOx156ZgmScUEu6CSGmpq5QjN2nFTrvb+wd3NHZYzdT7LWxobiAUp+dMr8dj90iK70JcZ6cVhM3Li3muopCdpzqZtvxTr74+0N86+XjfPrKmdx9+QzcNnO2mzkkSSCEyIjEU5zsCA0mC6v+7sese/g16rv6GZjIZDRo5OdY8TstzC3KIc9lpcBjpSDHKqMKQogJI5FKc6y1j931PZn6hV7qu/V9F4yaRpFX39F5oHahyGPDaTFN6ZMh13z4Y9lugphEjEYDl8/J47LZuRxqDvLakXa+/fJxHn3jJJ+4bAb3XDkL/zje6VoSCDHlBCIJTrSHONkR4mR7JmFo76OxO/J2oqBp5ObOwGY2cvncPPJyLIMFzk6pVRBCTDB67UIvexp62FPXy/6mt0cX3HYTxR476xfkU+K1My1XX8BhLDdpmwju/9I3s90EMQlpmkZFqYeKUg8n2kP8z8FWfvT6SZ7YVssnLp/OvWtn4xuHiYQkEGJSSqUVTT0RTnaGqOkIU9MRGhxd6AzFBx9nMmjkuiz4nBaumJeH32Uhz2WjyGPl6/fczD/9+kVJFIQQE8rp+y7syezqPDDl0mjQKPboowslPjulXvvgiRHZd+G9PXj7DTz86xez3Qwxic0pcDHnqjnUdYV56UArj7xRw8+21/HJy2dw77rZeOzjZ2qTJBBiwlJK0R2Oc6ozzKnOMDWdYU51hKnpDFHb1T+4MgiAw2Ik12Wh2GtncZmHXJdFXwXJbcNhMZ51RKH26EFJHoQQ41o6rajpDLG3IcDehh72NQQ40hIkmVn2zecwU+S1s6DETbHXzjSfHY+MLlyQmiMHst0EMUVMz3Wyaf1sajv1ROJHr5/k59vruH/9LD51xaxxUWwtCYQY1waShLrufuq6wpzq7Ke2M0xtl540nL7ikVHT8LsseBxmlpZ7yXVa8Lss5OdY8Tks2C1GqVEQQkxYSilag1H2NQTY19jL/sZe9jUECMX046DNbKDIY2PlTB8lXr12ocBtw2GWhRyEmIhm5Dm5d8NsTnWEeH5fCw+/dJzHt9byuffP5fbV5Vnd3VoSCJF1qbSiJRChvrufui79Ut8dHrw9EBxB353Z67DgtZuZU+jC57Tic5rxOy2DGxRZTYYRWUbQl1940a8hhBAXqjMU40BTgP0NAQ406clCR0jfpM2oaRR6bMwpdFHstVHitVOS2U9GTpSMDokJIltm5rt44Jq5HG4O8OL+Vr747CF+svkU/+cDC7lmYWFWZktIAiFG3cAoQkNPhIbufhp6+mnojtDY0099dz9NPZHB4XbQ5+j6HGbcdjPzilx4nVZ8DjM+p5n8HCtOqxmryTDq83V/8j9Vo/r6QggxoKMvxsGmAAeaAhxsCrC/KUBrQK9b0ID8HH0PmaXlHoq8dsp8dtx2syzoMIYkJohsW1TiYWGxm12nevjjwRY+8/MqVk738c8bF1NR6hnTtkgCIS5aOq3oCMVo6o3Q1BOhqVdPDpp6IjRmLpFE6h3PcVqNeB0WXDYTq2b68TrMuB36SEJ+jhWb2YjVaMjqsPtv/uPf+Oj9/5C19xdCTD5KKRp7IhxqDnCoOahfmgK09cUGH5PnslDgtrGwxE2Rx0ap34bXbsVulmQhmyQmiPFA0zRWz/KzfLqXV4+088axDm76wVY+vKKUL9y4kDyXdWzaoZQa+lHnsGrVKrVr164RbI4Yb5RSBCNJmgMRWgIRmnujtAQitPRGaeqN6D/vjb5jBAH0omWP3YzLZsLjMOOx6wmC124mL8dKjk0/c2YyaOM2IN6yrIzf7W3MdjOEGBG3r1/eEutpLxmL95LYoIvEUxxv03epP9LSx+HmIIdbgoPTMjUNCnKs5OZYKXLbKPTYKPXZ8Dos2M3GcXtsnKokJojxKBxL8oe9zew61Y3NbORvr57Lp943c9hTGTVNq1JKrTrf95URiCksmUrTGYrTFozSGozq1wH9dmsgSktAvz5z9MCggdtuJsdmwmUzs3KmXU8QbGZy7CbyXFZy7GYsRgNm4/hNEIQQYiQkU2nquvs53trH0da+TNLQR11XmIFzK1aTgQK3lTmFLgo9NvJzrJRmpiHZZBqSEOICOa0mbltTztp5efyuqomvv3iUX++s5xu3VHLprNxRe19JICahaCJFZyhGR1+M9r7Tr6O0B2O0BaO0BWN0hmKcOf40kBw4rSZcVhOLy9zk2Ey4bRacNhN+pxmfw4LVbMRi0jAZpFhPCDE1xJNp6rrCnMhsQFmd2bX+ZEeIREo/mmpArsuC32nhsjm55OfYKHTr9QsOiwmLSY6ZQoiRV+Jz8NfXzGV3bTfP7W3htsfe5C8qi/nyTYsoyLGN+PtJAjEBKKUIRpN0h+N0hWJ0huJ0hWN0heJ0hvTrjlCMzj49KQietrTpAA09S3XZTDgsRoq8NuYWuXDZTDhtJnJsZnyZqUYWkxGLUcvq8mDjwTd/9UK2myCEGGNKKTr6Yvq+MgN7zGQ2oWzojpA6bdqvz2HG77KwfLqPPJeFvBwrxR47OXYzNvPIrAYnxg+JCWIiWDHDT0Wphz/sa+GPB1t57Wg7n79+AXdeOn1E60olgRhjA8lAoD9BT3+cnv44vQO3w3F6+hN0Z253h+N0hfXbZ9YYDLCbjThtJhxmI3arkblFOTitRhwWfQTBaTPhtpvw2i1YzQZMBplWJISY2mLJFE09kbdXhhtYQjqzfHR//O1pmwO71XscFi6Z5ddHF1xWijw23HYTVpNRdnAWQowrFrORW1aVcdmcXJ7a2cCXnjvE07sb+bePLGVeYc6IvIckEOdJKUU0kaYvmiAYTdIXTdAXTdIXTRKMJghGEgSjCQKRBIFIkmBEv93bH6c3kqAvknzHGazTaYDdYsRhMWIzG7FZjBR7bcwucGK36kmCI5McuG0mcuxm7GYjJqM+lUiC2Mj6/z52oxTMCTHBpNOKrnA8U8cVoTUYpTmz6ENTTz+NPRE6+t45fdNk0PA5LbjtZhaWuPE7LXgdFnJzzOTn2LCbz75bvZhaJCaIiabEa+dv3z+Xrcc7efFACzd8bwv3rp3F566Zd9HTKSd1ApFMpYkm00TiKaKJFJFEikg8RX88RSSRpD9zuz+WpD+RIhxLEo5lruNJQpnboViSUFS/DseS5xwNGGDQ9ETAlgk6FpMRm9XITJdlMDGwm43YTAZsFiNOq4mczGiBxWiQhEAIIU4TTaQGR2q7B0ZnM9M5O0P61M32Pr2+qyv07hFbo0HDk1n4ocCtT9/0OCx47PoUJL9TX/XIMkKbUAohxHihaRpXzs9n6TQvT73VwI9eP8mLB1v53m3LqCzzXvDrXlQCEYomeeN4B6CfmVcAChSKdBrSSpFW+n0ppUilFWmlSKX1s0TJdObnqTTJtCKRUiRTaRJp/TqZVsSTaRIp/aLfVsSSaWLJFPFkWr+dSBHN/Cya0P8eS6aH/EX/TJqmr5RhNRkxmwxYjBpmowGLyYDXaabQY8WaSQoGEgOr2YDFaMBu1UcOnBYTNrMBk1FPAIwGDaMmU4aEEFNHXzTJa8faQek7zSfT+rE7mU4PHrejiRSxRJr+ROYkTuaETnBwVDcxON0znkqf9X00DVxWEw6LCbvFSL7bxqx8V6auS6/58jsteBxmrCYjFqOcmBFCTE1uh5l71s1id10Pz1Y18cF/38an3jfzgl/vvPeB0DRtE7Ap87dKk8vbPNRT0N6++fafZ97M/FV718+0Mx6tZW5ob987+BjttKdog88/7Xna6Y/RP3umBwYzIAWogfvgtIdcoFQs4jBa7f0X8xqTnfTR8Eg/DU366OyS4T5vKh5xjNbrvyM2aNpSk9PbdPrdA4ftgQPzwJNOf4WBQ7T+h6bpTxg4nutnYgaO4fq5KqX0Y3fmuH2Rx+qxIv9GhyZ9NDTpo6FJH52LpmEwmlQqEU8EOr1KKed5v8LFbCSnadquC9l8YqqRfhqa9NHwSD8NTfoo++Q7eG/SP0OTPhqa9NHQpI+GdqF9NLXX6RRCCCGEEEKcF0kghBBCCCGEEMN2sQnEYyPSislP+mlo0kfDI/00NOmj7JPv4L1J/wxN+mho0kdDkz4a2gX10UXVQAghhBBCCCGmFpnCJIQQQgghhBg2SSCEEEIIIYQQwzasBELTtOs1TTumadoJTdM+f5b7rZqm/SZz/w5N02aMdEPHu2H00d9rmnZY07T9mqa9omna9Gy0M9uG6qfTHnerpmlK07Qpt/zacPpI07S/zPx7OqRp2q/Guo3jwTD+z5Vrmvaapml7Mv/vbsxGOycriQtDk7gwNIkJQ5OYMDSJB+9N07THNU1r1zTt4Dnu1zRN+36m//ZrmrZiyBdVmT14znUBjMBJYBZgAfYBi854zF8Bj2Ru3wb8ZqjXnUyXYfbRBsCRuX3/VOuj4fZT5nE5wGbgTWBVtts93voImAvsAXyZvxdku93jtJ8eA+7P3F4E1Ga73ZPlInFhxPpoSscFiQkj9u9oSscEiQfD6qO1wArg4DnuvxF4EX2jzkuBHUO95nBGIC4BTiilapRSceA/gZvPeMzNwM8yt38LXK29Y4fRSW/IPlJKvaaUGtgN8U2gbIzbOB4M598SwFeBbwLRsWzcODGcPvoM8O9KqR4ApVT7GLdxPBhOPynAnbntAZrHsH2TncSFoUlcGJrEhKFJTBiaxIMhKKU2A93v8ZCbgZ8r3ZuAV9O04vd6zeEkEKVAw2l/b8z87KyPUUolgQCQO4zXniyG00en+zR6pjfVDNlPmqYtB6Yppf57LBs2jgzn39I8YJ6mads0TXtT07Trx6x148dw+ukh4E5N0xqBF4C/HpumTQkSF4YmcWFoEhOGJjFhaBIPLt75Hq8wDeNFz3bG6My1X4fzmMls2J9f07Q7gVXAulFt0fj0nv2kaZoB+A5w91g1aBwazr8lE/qQ9Xr0M5ZbNE2rUEr1jnLbxpPh9NPtwE+VUv+madplwC8y/ZQe/eZNehIXhiZxYWgSE4YmMWFoEg8u3nkfr4czAtEITDvt72W8e+hn8DGappnQh4fea6hkshlOH6Fp2jXA/wY2KqViY9S28WSofsoBKoDXNU2rRZ+H99wUK5ob7v+33yulEkqpU8Ax9OAxlQynnz4NPAWglNoO2IC8MWnd5CdxYWgSF4YmMWFoEhOGJvHg4g3reHW64SQQbwFzNU2bqWmaBb0Y7rkzHvMc8InM7VuBV1WmKmOKGLKPMsOwj6IHiak2P3HAe/aTUiqglMpTSs1QSs1AnxO8USm1KzvNzYrh/H97Fr34Ek3T8tCHr2vGtJXZN5x+qgeuBtA0bSF6wOgY01ZOXhIXhiZxYWgSE4YmMWFoEg8u3nPAXZnVmC4FAkqplvd6wpBTmJRSSU3THgBeQq90f1wpdUjTtK8Au5RSzwH/F3046AT6GabbLvaTTCTD7KOHARfwdKaOsF4ptTFrjc6CYfbTlDbMPnrt5fCQAAAgAElEQVQJuFbTtMNACnhQKdWVvVaPvWH20z8AP9Y07e/Qh2LvnmK/wI4aiQtDk7gwNIkJQ5OYMDSJB0PTNO3X6FPc8jJ1IF8GzABKqUfQ60JuBE4A/cAnh3zNKdR/QgghhBBCiIskO1ELIYQQQgghhk0SCCGEEEIIIcSwSQIhhBBCCCGEGDZJIIQQQgghhBDDJgmEEEIIIYQQYtgkgRBCCCGEEEIMmyQQQgghhBBCiGGTBEIIIYQQQggxbJJACCGEEEIIIYZNEgghhBBCCCHEsEkCIYQQQgghhBg2SSCEEEIIIYQQwyYJhBBCCCGEEGLYJIEQQgghhBBCDJskEEIIIYQQQohhkwRCCCGEEEIIMWySQAghhBBCCCGGTRIIIYQQQgghxLBJAiGEEEIIIYQYNkkghBBCCCGEEMMmCYQQQgghhBBi2CSBEEIIIYQQQgybJBBCCCGEEEKIYZMEQgghhBBCCDFskkAIIYQQQgghhk0SCCGEEEIIIcSwSQIhhBBCCCGEGDZJIIQQQgghhBDDJgmEEEIIIYQQYthM5/sETdM2AZsAnE7nygULFox4o4TIpubmZkpKSrLdDCFGVFVVVadSKn+0Xl9ig5jsJDaIyehCY4OmlLrgN121apXatWvXBT9fiPFI0zQu5v+FEOORpmlVSqlVY/FeEhvEZCSxQUxGFxobZAqTEEIIIYQQYtgkgRDiDHKGSQghxJkkNgjxNkkghDjDY489lu0mCCGEGGckNgjxNqmBEOIMk3WeayqVIhgMkkwms90UMYpMJhNutxuj0fiOn0sNhBAXZzLGBokLU8dIx4bzXoVJCDExBYNBrFYrXq8XTdOy3RwxCpRSRCIRgsEgPp8v280RQoxzEhemhtGIDTKFSYgpIplMYrfbJUhMYpqmYbfb5WyiEGJYJC5MDaMRGySBEOIMzz33XLabMGokSEx+8h0LMToma2yQY8bUMNLfsyQQQpxh5cqV2W6CEEKIcUZigxBvkwRCiDOUlpZmuwlTSm1tLRUVFdluxlkFg0FKS0t54IEHznr/d7/7Xfr7+8e4VUKIbJDYMHbGa1wwGo0sW7aMZcuWsXHjxrM+5qc//SnNzc1j3LKxJwmEEGLSGal5nl/84hdZt27dOe+XBEIIISaGkYgLdrudvXv3snfv3nNOaZMEQgghRti3v/1tKioqqKio4Lvf/e7gz5PJJJ/4xCeorKzk1ltvHfyl/POf/zyLFi2isrKSf/zHfwSgo6ODW265hdWrV7N69Wq2bdsGwEMPPcSmTZu49tprueuuu1izZg2HDh0afI/169dTVVVFOBzmU5/6FKtXr2b58uX8/ve/P2tbq6qqaGtr49prrz3r/d///vdpbm5mw4YNbNiwAYBf//rXLFmyhIqKCv7pn/4J0JdJvPvuu6moqGDJkiV85zvfGXz+wGe77bbbAM7ZtkOHDnHJJZewbNkyKisrqa6uvrAvQAghxpmJFBeG8tvf/pZdu3Zxxx13sGzZMiKRCK+88grLly9nyZIlfOpTnyIWi53zczz99NNUVFSwdOlS1q5dC+gx5MEHH2T16tVUVlby6KOPAtDS0sLatWtZtmwZFRUVbNmy5YLafMGUUhd8WblypRJisvnMZz6T7SaMivb29qy+/65du1RFRYUKhUKqr69PLVq0SO3evVudOnVKAWrr1q1KKaU++clPqocfflh1dXWpefPmqXQ6rZRSqqenRyml1O233662bNmilFKqrq5OLViwQCml1Je//GW1YsUK1d/fr5RS6tvf/rb60pe+pJRSqrm5Wc2dO1cppdQXvvAF9Ytf/GLwNefOnatCodA72ppKpdS6detUfX29euKJJ9RnP/vZs36m6dOnq46ODqWUUk1NTWratGmqvb1dJRIJtWHDBvXMM8+oXbt2qWuuuWbwOQOfo7i4WEWj0Xf87Fxte+CBB9STTz6plFIqFosNfsZzOdt3DexSF3G8P5+LxAYxGU3G2CBxYfhxQSmljEajWrlypVqzZo165plnzvqZ1q1bp9566y2llFKRSESVlZWpY8eOKaWU+vjHP66+853vnPNzVFRUqMbGxnf87NFHH1Vf/epXlVJKRaNRtXLlSlVTU6O+9a1vqX/5l39RSimVTCZVMBgcsr9HMjbICIQQZ5DdRkfH1q1b+dCHPoTT6cTlcvHhD3948IzJtGnTuOKKKwC488472bp1K263G5vNxj333MN//dd/4XA4APjTn/7EAw88MDgHNRgM0tfXB8DGjRux2+0A/OVf/iVPP/00AE899RQf+chHAHj55Zf5xje+wbJly1i/fj3RaJT6+vp3tPVHP/oRN954I9OmTRv253vrrbdYv349+fn5mEwm7rjjDjZv3sysWbOoqanhr//6r/njH/+I2+0GoLKykjvuuIMnn3wSk8n0nm277LLL+NrXvsa//uu/UldXN/gZhRBjR2LDyJtIcQGgvr6eXbt28atf/YrPfe5znDx58j0/37Fjx5g5cybz5s0D4BOf+ASbN28+5+e44ooruPvuu/nxj39MKpUabNvPf/5zli1bxpo1a+jq6qK6uprVq1fzxBNP8NBDD3HgwAFycnIu/Iu4ALKRnBBnWLlyJVVVVdluxqSj3mMH1zOXl9M0DZPJxM6dO3nllVf4z//8T374wx/y6quvkk6n2b59+1l/iXY6nYO3S0tLyc3NZf/+/fzmN78ZHPZVSvG73/2O+fPnn7M927dvZ8uWLfzoRz8iFAoRj8dxuVx84xvfOO/P5/P52LdvHy+99BL//u//zlNPPcXjjz/O888/z+bNm3nuuef46le/yqFDh87ZtoULF7JmzRqef/55rrvuOn7yk59w1VVXnbMtQoiRJ7Fh5E2kuABQUlICwKxZs1i/fj179uxh9uzZ5/35zvU5HnnkEXbs2MHzzz/PsmXL2Lt3L0opfvCDH3Dddde963U2b97M888/z8c//nEefPBB7rrrrvds/0iSEQghzrB79+5sN2FSWrt2Lc8++yz9/f2Ew2GeeeYZrrzySkA/q7N9+3ZAryN43/veRygUIhAIcOONN/Ld736XvXv3AnDttdfywx/+cPB1B35+Nrfddhvf/OY3CQQCLFmyBIDrrruOH/zgB4MH9j179rzreb/85S+pr6+ntraWb33rW9x1111nTR5ycnIGz3KtWbOGN954g87OTlKpFL/+9a9Zt24dnZ2dpNNpbrnlFr761a+ye/du0uk0DQ0NbNiwgW9+85v09vYSCoXO2baamhpmzZrF3/zN37Bx40b2798PwNVXX01TU9N5fAtCiAslsWHkTaS40NPTM1i/0NnZybZt21i0aNG7Hnd6XFiwYAG1tbWcOHECgF/84hesW7funJ/j5MmTrFmzhq985Svk5eXR0NDAddddx3/8x3+QSCQAOH78OOFwmLq6OgoKCvjMZz7Dpz/96cF/n3fddRc7d+4cVv9fDBmBEEKMiRUrVnD33XdzySWXAHDPPfewfPlyamtrWbhwIT/72c+49957mTt3Lvfffz+BQICbb76ZaDSKUuodxcef/exnqaysJJlMsnbtWh555JGzvuett97K3/7t3/LFL35x8Gdf/OIX+dznPkdlZSVKKWbMmMF///d/X9Bn2rRpEzfccAPFxcW89tprfP3rX2fDhg0opbjxxhu5+eab2bdvH5/85CdJp9MAfP3rXyeVSnHnnXcSCARQSvF3f/d3eL3ec7btN7/5DU8++SRms5mioiK+9KUvkU6nOXHiBH6//4LaLoQQ2TaR4sKRI0e49957MRgMpNPpwSLoM919993cd9992O12tm/fzhNPPMFHPvIRkskkq1ev5r777qO7u/usn+PBBx+kuroapRRXX301S5cupbKyktraWlasWIFSivz8fJ599llef/11Hn74YcxmMy6Xi5///OcA7N+/n+Li4ov/coagvdfw0VBWrVqldu3aNYLNESL7SkpKJuUSbB0dHeTn52e7GWKEHDx4kMcff5xvf/vb77rvbN+1pmlVSqlVY9E2iQ1iMpqMsUHiwuQSDAb59Kc/PVjncaaRjA0yhUmIM0y2ACEmp4qKirMmD0KI0SGxQYx3brf7nMnDSJMpTEKc4aGHHuKhhx7KdjNG1T//4RCHm4Mj+pqLStx8+abFI/qaQggxXkz22CBxQZyP8x6B0DRtk6ZpuzRN29XR0TEabRIiq/75n/85202YEq6//nqWLl3K4sWLue+++waXrDvd3XffzW9/+9sRf++f/vSnPPDAAyP+ulOZxAYx2UlsGH0SFyaO8x6BUEo9BjwG+jzXEW+REGLUjYczQk899RRutxulFLfeeitPP/304I7MYuKR2CDExCZxQZwPqYEQQmTFwIZqyWSSeDz+rjW/B2zevJnLL7+cWbNmveOs08MPP8zq1auprKzky1/+8uDPP/jBD7Jy5UoWL178jo2fnnjiCebNm8e6devYtm3b4M+ffvppKioqWLp0KWvXrh3pjymEEGKYJC5MHFIDIcQZZPWYsXPdddexc+dObrjhBm699dazPqalpYWtW7dy9OhRNm7cyK233srLL79MdXU1O3fuRCnFxo0b2bx5M2vXruXxxx/H7/cTiURYvXo1t9xyC/F4nC9/+ctUVVXh8XjYsGEDy5cvB+ArX/kKL730EqWlpfT29o7lxxdCTCASG8aGxIWJQUYghBBZ89JLL9HS0kIsFuPVV18962M++MEPYjAYWLRoEW1tbQC8/PLLvPzyyyxfvpwVK1Zw9OhRqqurAX098KVLl3LppZfS0NBAdXU1O3bsYP369eTn52OxWPjoRz86+PpXXHEFd999Nz/+8Y/POt9WCCHE2JG4MDHICIQQZ1i1atU5t58XI89ms7Fx40Z+//vf8/73v/9d91ut1sHbA9+LUoovfOEL3Hvvve947Ouvv86f/vQntm/fjsPhYP369USjUYBzDoU/8sgj7Nixg+eff55ly5axd+9ecnNzR+rjCSEmCYkNY0fiwvgnIxBCiDEXCoVoaWkB9LmuL7zwAgsWLBj286+77joef/xxQqEQAE1NTbS3txMIBPD5fDgcDo4ePcqbb74JwJo1a3j99dfp6uoikUi8Y53skydPsmbNGr7yla+Ql5dHQ0PDCH5SIYQQwyFxYWKREQghxJgLh8Ns3LiRWCxGKpXiqquu4r777hv286+99lqOHDnCZZddBoDL5eLJJ5/k+uuv55FHHqGyspL58+dz6aWXAlBcXMxDDz3EZZddRnFxMStWrBgcln7wwQeprq5GKcXVV1/N0qVLR/4DCyGEeE8SFyYW7WKG41atWqWkqEhMNpN1s6CzbWEvJqezfdeaplUppVaNxftLbBCT0WSMDRIXppaRjA0yhUmIM0y2ACGEEOLiSWwQ4m2SQAhxhpKSkmw3QQghxDgjsUGIt0kCIcQZBoq4hBBCiAESG4R4myQQQkwhsgTh5CffsRDifMgxY2oY6e9ZEgghzrBixYpsN2FUmEwmIpGIBItJTClFJBLBZJIF9oQYaZMxNkhcmBpGIzZIlBHiDFVVVdluwqhwu90Eg0HC4XC2myJGkclkwu12Z7sZQkw6kzE2SFyYOkY6NkgCIcQZNm3axGOPPZbtZow4o9GIz+fLdjOEEGJCmoyxQeKCuFAyhUmIM/z4xz/OdhOEEEKMMxIbhHibjEAIIS5IIpWmMxSjKxQfvO7pjxOIJAhGEgSjSUKxJJF4iv54kkgiRSKlSKTSJFOKtFIYNA1NA00Di9GA1WTEZjZgNxtx2Uy4bWbcdjNeu5lcl5U8l4W8HCvFHhsFOTaMBi3b3SCEEKMmnVZEEiliyTSJ1MDl7XoFDTBoGhaTYfBiNxvl2ChGnSQQQoizisRT1HaFqe/up7EnQlNPhMaefloDUVoCUTpDMc5WdqcBdosRq9mAxWjAZDRgMmiYTQaMBg2zyYQhkzSgQAFppQfKWCpFOJ4cTDRiyRTRRJp4Mv2u9zEaNIrcNqb57czMczE738nsfBdzClyU+exomgRQIcT4Eo4laQlEaO6N0t4XozMUozNz3RtJ0NufIBDRL/3xJNHEu499w2E1GXBZTTitJnwOM36nBZ/TQq7TQqHbNngp9ugXk1EmpIjzIwmEEGdoamrKdhPGjFKK5kCU6rY+TnaEOdEeoqYjxKnOMO19sXc81mIy4LWbcdlMFPlszCt24bKacFhNOCxGcmxmXHb9ttmgJwtGg4ZxcJRh+L/QK6VIK0ilFSmliCVSBCJJgpE4gUiSUDRBMJIkEElQ393PvoYAkURq8Pkuq4kFxTlUlHioLPOwotzH9FyHJBVCiAs2nNiglKIzFOdkR4iajjB13WHqu/qp7+6nobufYDT5rudYjAacViN2ixFLZhS23OHAYjJgNmqYMydijAYNgwZGTUNpoGXO4KTJHCtTaVJpRTyliCfTxFNp4skUgWiS1mCMaCJFOJYkmX7nqZ+BkzHlfgcz853MynMyu8DFnHwXpV47BhnNEGdx3gmEpmmbgE0A5eXlI94gIbKtqqpqUu44GoolOdoS5HBLkCMtfRxrDXKsrY9w7O1fvB0WI7kuCwVuG/NLcvA5LHgcZvJcFjx2M1azEbPRgGGUfxHXNA2jxuAwvN1sxOuwAI6zPj6RStMdjtPcG6EtEKUjGKO5N8r+hgA//bN+Bs/rMLOi3Mfls3O5fHYeC4pyJDCOIIkNYrI7Mzb09sc53BLkWGsfx9v6ONrax4n2EH2nJQlGg4bPoU/FnFXgwmM3k2M3kTMwPdNhxmExYTZqmDInXUbjRIdSimRaH9kNRZN0h+ODox3BzHVdd5g9Db1EE++MCfMKc1hU4mZRsZvKMg/zi3Kwmowj3kYxsWgXs/bvqlWr1K5du0awOUJkn6ZpE35N7EAkwcGmAAcyl4NNAeq6+gfvd1iM5OVYyXVZyM+xkuuyUuC24ndasZkNmCfJcHY0kaKuq5/azjBNPRGaevrpCScA8NjNXDEnl2sWFrJ+fgF+pyXLrR1dmqZVKaVWjcV7SWwQk013OE5p5eX862O/5mBTgIPNAZp7o4P3O61G8lxW/C4Lfqd+bM11WcnLsWA3G7GYRv/Ey0iIJ1N0hxO09EZo64vRGYzS0Rejoy82OJ3KZNCYV5TDquk+VmYupV6ZNjpRXWhskARCiDNMtAQikUpzuDnI3oZe9jX0sqehl1Odb6/p7XfqSUKB20p+jo0ir5WCHCt2i2nSJArDpZSipTfKkdYgdZ1hTnWECcdSGDRYWublhiVF3LikmDLf2Uc6JjJJIIQYnmQqzZGWPqrquqmq14+r9d36CRgNyMuxkpc5pg7UEuS6LDgsxklbSxBPpmgJRKnrDNMSiNLSG6WlNzJY0F2QY+XSWX4un53HZbNzKffLlNGJQhIIIUbIeE8gusNxdtV2U1XXw+76HvY3BohliozdNhNFHhuFHhtFXjslPht5Tit2s1Gm65xFMpWmui3EoaYAJ9pDtAf1uo/KMg8bl5Zw87JS8nOsWW7lyJAEQoiziyZS7KnvZeepbnbWdrG7rnewpspjN1Ps1Y+pv/nSPXzryd/htVuxmw1T/hfkaCLFqY4Qpzr7aczUeAxMiS322Fg7L5918/K5YnYeHoc5y60V53KhsUGKqIU4w6OPPprtJgxSStHYE2HnqW7eqtUvJzv00QWjQaPYY2NJmYdSv51Sr50Ctw2n1SRL+A2TyWhgYYmbhSX67pwN3WF21/ZypCXIvzx/hK+9cIS18/K5bXU5Vy0owGKanGcXhZhKYskUe+t7+fPJLrbXdLGnvodESqEBRV4bC0vclPrtTPPZKfLacWROwNg/cQclnsk3OnmhbGYjC0s8LCzxAPoJmbqufo639lHXGebZPU385q2GwRHeaxcXcfXCAuYWuKZ88jUZyAiEEOOIUorarn7erOliR00Xb57qpjWgz7N1WIyU+PREodRnpzzXgcdhlmK2UVLbGWZHTRcHGwOEYyl8DjMfWzOdj186nSKPLdvNO28yAiGmKqUUR1v72FrdyZYTnew81UU0kUbToCRzPJ3mdzA9z0Guy4rNJKMLIyGWTHGstY9jLX3UtIdoy4zwFnts3FBRxA1LillR7pMTXlkmU5iEGCFjPYWpobufP5/sZHvmbNjAQTbHZqLMZ6fM72CaX7922UyYDHIWfCwlUml21/Ww61Q3J9vDGDR4/6JCNq2dxcrp/mw3b9gkgRBTSW9/nC3VnbxxvIPNxzsGl6UuyLFSlkkWZuQ7yXdZsZmHdxLmlmVl/G5v42g2e1JrDUTZ19BLdWsfdV39pNIKv9PC9YsL+cDSEtbMzJVkIgtkCpMQE0R7X5TtJ7vYdqKTP5/sorEnAmQSBr+DZeVepuU6KPXZcVnNckDNMrPRwJpZuayZlUtzT4Q3jnXw+rEOXjrUxvJyL39z1VzWz8+XM5ZCZJFSiiMtfbx2rJ1Xj7azp76HtNJHbqfnOVk508eMfBclHhsOi1H+v2ZBkcdGkaeI6yqKCEYS7Knv5WhLkKerGvnVzgZynRZuXFLMB5eXsqLcK9/ROCcjEEKcYaRHIMKxJDtOdbG1uottJzo41hYC9MBW5ndQnutgeq6DMr8kDBNFJJ7k9aMdvHmyi75okjkFLv7+/fO4fnHRuC1WlxEIMdlEEym2n+ziT0faeOVo++B0z1Kfnem5DmYVuJiR78RjG5njqoxAjI7+WJJddT0cagxwqiNMMq0o89n50PJSPryijJl5zmw3cVKTEQghRsgHPvCBi3p+Kq040BRgy/EOtpzoZHddD8m0wmzUmOZ3sG5Bvp405Dlxy5SkCcluMXFDZTHvX1zI1upOth7v5K9+uZt5hTl8/ob5bJhfIGfPhBgFPeE4rxxt538Ot7KlupP+eAqrycCMfCfLy73MLsyhKDPKMNJWrr1mxF9TgMNqYu28fNbOy6cvmmBnTTcHGgP88NUT/ODVEywt83D7JeX8RWUxOTZZzWm8kBEIIUZAU29ETxiqO9l6opNARN+srMRrY5rfwYw8J9PznfgdFlnJZxJKpdJsO9HJa0c7CPQnWFLm4aGbFrNyui/bTRskIxBiomrs6eflQ228dKiVt2q7SSvw2s3MzHcyu9DF7AIXuU7LpN2DYapqC0bZcbKLA40BukJxrCYD11cU8bFLyrlkpl9O0owQKaIWYoTcdNNN/OEPf3jPx/THk+yo6dYL9Ko7qMksreqxmynP0xOGWXkOCjx27MMs0BMTXzKV5vVjHWw+2kEoluT6xUX8779YyDR/9pd+lARCTCQn2kP88WALLx1q40BTAIBCt41ZBU7mFLiYVeAkx2Ye092dv/Y3d/O/vv/TMXs/oUun0xxrDbGzppujLUFiyTTTcx187JJyPrJqGn6nJdtNnNAkgRBihJytBkIpxbG2PjYf7+CN4x3sPNVNIqVPSyrPdVCe62RmvpNpfjs5VvO4nQcvxkY0keSF/a28eaILTYNPv28mf3v1POyjMK1iuCSBEOOZUorDLUH+eLCVFw+2cqJdrxWb5rczM9/F/OIcyv2OrBZASw1E9kXiSbaf7GJPXS9NPRFMRo0bFhdx1+UzWDXdJ6MSF0BqIIQYYb39cbae6BxMGgaWVy3y2FhW7mNmvoMZ+frQuVmGzsVpbGYTH15Zxtr5eTy7u5lH3qjh2b3NfO2DFVy1sDDbzRNiXFBKrxd74UArLx5ooa67H02DGXlOrllcyLyiHEp9Moor3ma3mLhqYSFXLSzkVEeIbdWdvHy4jT/sb2F2vpNPXjGTDy0vxWmVX29Hm/SwEBmptGJ/Yy+eK27nwz/axt6G3sFlAMtzHSyf4WNmvotSrw27WZYBFEPLc9m4Z+0sDjUFeKaqiU/9bBfXLCzg///QEgrdE28zOiEullKK/Y0Bnj/QwgsHWmjsiWA0aMzIc3LdkiLmF+lF0MPdm0FMXTPzXczMd9EfS7LtRCdVtT38n2cP8vUXj/CRldO4+/IZzJAVnEaNTGESU1p7MMobmRGGrdWd9EYSaECZX9+ZdGaBi5m5DjwOiyyvKi5KMpXmhf0tbD3eidVs4KGNi7l1RdmYJaIyhUlky7mShpn5TuYUulhQlEOhx45VFpgQF2FgL5Btxzs51hpEKVg7L4/PXDmbK+bkykm/c5ApTEIMQyyZoqq2hzeq9d1Jj7T0AeC2mSjPc3JlvpO2qpe4+aqPybC5GFEmo4GNy0tZPdPPr96s58Gn9/Pc3ma+9ZGlMhohJp2BpOGFAy08f0bScEPlwEiDfUKtSvfyb5/k2lvvzHYzxDlomsaiEjeLStx09sV441g7b9X28MbxHczKc3LPlTP58IoyGd0aITICISY1pRSnOsNsPt7B5upO3qzpoj+ewmjQmOa3Mz1PL36enusYLH6WQjkx2tJpxcuHWnn1cDs2s5Fv3LKED1SWjOp7ygiEGG0DNQ3PH2jh+f3vTBrmFrqYX+SmyGObUEnD6SQ2TDzxZIpt1V3sqOmiPRjDYzfz8UvLuevyGRTkyIkbkBEIIQYFown+fKKLLZlRhoaeCAC5Lgvzi3OYmedkZoGTPJdNhsxFVhgMGtcvKWZpuZdfbKvjgV/t4dWj7fzLBytwWOSwLCYOpRQHm4KZpKGZhkkw0iAmD4vJyIaFBaxfkM/BpiBbjnXww9dO8sgbNdy0tIT71s1mflFOtps5IUmkEhNeMpVmf1OALcc72VLdwZ6GXlJphdVkoDzXwfsXFzIz30mZ34Ezi0sACnGmYo+dv79uHs/ubuK/djfxVm03j965ikUl7mw3TYhzOj1peOFAC/Xd/XrSkPd20iA1DWI80TSNJWUelpR5aOzu57Uj7fxhXzPP7Gni8tm53L9+Nu+bkye/H5wHmcIkJhylFHVd/Ww50cm26k62neykL5pEA0p8evHzjHwns/KdeO3m896d9K03/ofV694/Oo0X4hwONQX4zx31xJNpvnLzYm6/ZEel0eEAACAASURBVPqIvr5MYRIX4/QlV09PGmbkZQqhi/WRhsmcNEhsmFwCkTivHemgqrabcCzF3AIXf7VhNh+oLJlSS7OP2UZymqZtAjYBlJeXr6yrqzvf9xTivHWH4/z5ZCfbTnSypbqTxsy0JJ/DzLRcfefnmflOCty2iy5+7m5vxV9QNBLNFuK8BCNxnthSS11XP7esKOVrH16C1TQyBX+jnUBIbJh8lFLsbejlxYOt71pydaokDaeT2DA5xZMptlZ3sf1EJ12hOPkuK5++ciYfW1OO22bOdvNGnexELSaVcCzJztputp/sYtuJTg41BwGwm41M8zsoz3MwM89Jqc+Oy2bCMILDjlIoJ7IplVY8s7uRP1d3Mb8whyc+uZoSr/2iX1dGIMRwpNKKqroeXjzYwh8PttISiJ5RCD11pydJbJjc0kqxp66HLcc7qe/qx2Excvsl5dxz5UyKPRd/DB6vpIhaTGjRRIo99b1sP9nJ9pou9tT3kkwrTAaNMr+d983LY0aevlqS5wKmJQkxURgNGreumsasPCdPvdXIDd/bwuN3r2LldH+2myYmqXgyzZs1XfzxUCsvH2qlMxTHbNSThhUziplXlEOhe+KuniTEcBg0jZUz/Kyc4ae6rY/XjrTz+NZT/OzPtfxFZTH3r5/NgiKpTxsgCYTIilgyxb6GAG/WdPFmTRdVdT3Ekmk0DUq8dlbO9DHd72RGvhO/0yKBS0w5K2b4KfLY+fHmGj766Jv86y1LuGXltGw3S0wS/fEkm4938MeDrbxytJ2+aBKrycCsfBeXzcljXrGLghzblJoLLsSAuYU5zC3MoTUQ5ZXDbbxwoIXf723m8tm5/NX6ObIxHZJAiDESTaTYXd/DjppudpzSRxhiyTQaUOy1UTHNw/RMLUOey5rVjV6u+fDHsvbeQpyuxKev0vSTN2r4h6f3c6wtxBduWDDlA5e4MB19MV450sb/HG5j64lOYsk0TouRWQUu5hS5mFuYQ57TIiO85yCxYeop8ti447Lp3LSsmFePdFB1qps7/+8O5ha4uH+9XnA9VU9wSg2EGBWB/gRV9d3sPNXDW7Xd7GvsJZlSgwlDsc9Oud/B9Fwn+W4rNpNBfikS4hySqTS/fLOeffW93LCkiO/ftvy8zwxLDcTUo5TiWNv/Y+++w+OqroUP/870ptGod6u525LlbuO4YTCYYkgglEASE1pISELCJQn3XsApNwUSIAE+CCGU4IRieoCAcS+428K9yOrN6mVURlP294eMQrEt2ZY0I2m9z6NH7czMmj1nzpp1djnNrDpYxcqDx8ktbkABkXYT6TGdE6GHxzpw2UzodXL8FaI7HT4/G47WsCWvtmvC9Xe+ksY3pqUSbhuYE65lErUIGqUUJXVt7CyuY0dhPTsK6zh83A10judOdFlIcHUur5oabSPKEdoFwz3XL+Khl/4d7DCE+BylFO/tqWD1gSqmpkXw3E3TcJh73oksBcTQ0O71s/lYLasPVbHmcFXXinVJEVbSou2MiHeQFmUjzGIM2WNwqJLcID6llGLXZyZcW4w6rp6cwi1fSSct2h7s8M6ITKIW/aatw8++8kZ2FdWzq7ienUX11Lg7ALAYdSS5Oic9J0VYSY2yE2E39tpSlP0h/+DeYIcgxJdomsZlExIJtxp5a1cZX31iE/+8dQYxYeZghyaCrLCmhbWHq1h3pJrN+bW0ewOYDDrSom1clBVPZpyDJJf1nJe4HuokN4hPaZ+ZcH2sys26Q9W8tK2Yf2wpYu6oGG6dncF5mYN7noQUEOK0AgFFfo2b3JJGPilpILeknoMVzfgCnT1XUQ4T8eFWJqZFkBxhJSnSRpjZIBPvhOgjs0fGEG41suzjIhY/vpHl351JcoQt2GGJftTc7mVLfh0bjlaz7kg1RbWtAEQ7TIxNCiczxk5GrINIu0mOxUL0scxYB5mxDurcHlYfqmJbQR1rD1eTGWPnltkZXJGTiM00+D5uD75nJM7ap0OR9pQ1sLe0kT2ljewta8Tt8QGdvQvx4RampEeSFGEhKcJGTJgZm0k/qKrsiJi4YIcgxGllp7i4fb6eZ9YVcOUTm1h++0zSYxzBDkv0kQ5fgNySBjbl1fDxsZquZa5NBh3DIm1cMC6OzFg7SRE27IPseBxKJDeI04l0mLl6SgqXT0hkU14N2/LruPeNvfz6vQN8fXIK3z4vjfQBNrzpdGQOxBDlDyjyq90cqGhif3kT+8oa2VfWSFN7Z7Fg0GnEhVuICTOT4LKQ6LKSFGHFLr0LQoSM4poWnlqbj8Wo45XbZjDqNGuUyxyIgcPrD7C37NNlruvYUVhHa4cfjc6VuZIjraTH2EmLthNhk14GIUKRUor9ZU18nFfDkcpmAgpmZkTxrZmpXDA2LmTetzKJWpxSY6uXQ5VNHKxo4lBlMwcrmzlc2US7NwD8p1iIDjMTH24hLtxCcoSVMIthQM1d6C2vPPlHrr3j7mCHIUSPlDe08eTqPDRN4+VbZzA+Kfyk20kBEbraOvzsLq5ne2HnqnW7iutp7fADEOc0kxhhJTXaTtqJVetkLkNwSG4QZ6vW7WH94WpyixtobvcRaTdx7ZQUrp2aEvRJ11JACJrbveRVuTla5ebo8WYOH3dzuKKJ482erm1sJj0xYWZinGbinBbinGbiXUO3WDiZq3KSeT23NNhhCNFjVU3tPLEqD6Xg1dtnMjbxyz0RUkCEBqUUpfVt7CquZ3dxAzuL6jlY0YQv0LnMdXy4hXiXhWFRNoZF2YhzWrAaZVhSKJDcIM6V3x9gZ1E92wvqya9yo4BJw1xcN20Yl2QlnNHKer1FVmEaIgIBRXljGwU1LeRXt3Cs2s2xajd5VW6ON/2nUDDqNWLCzEQ7zYxJchIdZu4ckuTonLMgFwoSYvCIdVq4c8FwHluZx3VPb2b5d89jVHxYsMMSdBZ3e8s655N1LkTRQH2rFwCzQUeCy8qU9EiSI60Miwr9Za6FEGdPr9cxLSOKaRlRVDW3szmvlj0ljfz0tT3c/9Y+Lhgbx9cmJTF7REzIDHE6FSkgQlCHL0B5QxvFda0U1bVSXNtCUW0rhbUtFNa20uELdG1rMeqIcpiJDjMzOtFJlN1EdJiZ2HALdpMek14SkRBDQYzTwvcXZPLYyjyu+ctmXr9jJsNjpYjoLz5/gMLaFg5UNHOoonPI6L6yJqrdnSd2NCDWaSY50sbUDCsJLgspEVbCrMaQ/6AghOh9sWEWrpiYxOUTEjhQ0czOgnpWHqzi3T0VhFuNLBofz6KsBGZmRIXk1a5lCFMQtHb4KG9op6KxjfKGNsrq2yitb6O0oY2SulaON7UT+MzLYtRrRNhNOK1GIu0mIuwmXDYjsU4zUXYTFqMhJHeugerYgT1kjs0OdhhCnJWKhjYeX5WHxaDj7TtnMSyyc3ytDGHqHR6fn+La1hO9vy0cOd7M4cpmjlW78fo7D9x6TSPGaT6xzHXnvLLECCsuqwmrUU7qDFSSG0Rf83j95JY08ElxA/lVLXT4AzjMBi4YE8sFY+OYPTym1694LUOYQkBrh4+a5g6q3R6qm9upbvZQ1eyhsrGd480eKhvbqGxs71rp6FOaBuFWI2EWA5EOE+mxdlxWI06biWiHiUiHCYtBj1m6tYUQ3UhwWbnj/EyeWJnH15/azLs/+AoxYZZghzWgNLV7Ka1ro7S+leK6zq/C2lYKa1oorW/93AmeCFvniZ2c1IjO+WVhZhJdFsIsRjlmCyHOiNmoZ3pGFNMzomjr8JFb0sjB8kY+2F/JW7nl6DTISXFx/uhYZmREkZ3sCtoJZOmBOIlAQOHu8NHY6qWp3Utjm5fGVi8NbV7qWztoaPVS19JBXUsHtW4PtS0d1Lo7aPP6v3RfmgZhFgMOsxGbWU+YxYDTYiTMaiDMYiTcZiTKYcZq1GHS62RuQgiQiXJiMMivcvPUmmOkRNp4585ZOK2mId8D4fH5qWvpoKa5g6rmdqqaPVQ1eahsaqOisZ3yhs7vzV84yWM16YmwmXBaDUTaO3sWXHYTceFmwi1GLEY9ep0UCoOd5AYRLB2+AIcqmjp7O6vcVJ2Y82o26Jg0LIKp6ZFkJ4WTlRxOnPPMThgN+h4IpRQB1bk+ttcfwOdXeP0BOvwBOnyf+e4L0O4N4PH5afcGaPf6afP6aff6ae3o/Grr8NHS4afF48Pt8XV9b27v/Grx+DhdWWXQadjNBqwmPRajnjCrkXiXBbvJgM1swGbS47AYcNmMOK2dZ6GMeh0GnSZno4QQ/SIj1sE3Z6XywsZCvvHXLcEO56z5PnOc9/g+e0wP0OrpPJa3dvzn+N3c7qW53UdjW+dJn4bWDupPnPBp6fjySR4Ah9lAmNWA3WxgRJwDp7Xz5I7TaiQ6zITLaj5xHJdjuBCi/5kMOrJTXGSnuACobvZwqKKpazjllvzars+tUQ4To+LCyIixkxHtIC3aRmyYhdgwM5F2U6+dqD6nAuLI8WYW/HHtSf/3aceG6vpdoU78/bM/B5Tq+u4/8bM/oPAHVOffAgrfid97g16nYdLrMBo+/a7r+h5hNxHvsmAy6LEYdJiNesxGHWaDHqtJh8NswGE2YDHpMeh0GPRaZ1HQw8fufA698zxE3/L5A91vJESIG5Pg5OqpKby6rQSd0dxv45g+mxtOlgsCn8kDnz3WB1Tne+/TY77XH+BMD/06DSzGzpM7ZqMOi1GPzWIg2mnGZjJgN3f+z2Ex4LQacdmMmA16jPoTBcJp7luO4UJygwgFETYjMzOjmJkZhQKa230U1rgprWunqrmdgpoWdhXXd13v61M6DWwmw4nPtrpzWr7/jIcwaZp2G3DbiV8mGOyusrN+dE4cq7VT/MpJjuUnPf2jffaG2sludlrnMo6rG4GONrvOZG3pq/sfDKSNTk/ap3vSRt3zuuujld/XZ0XEOeQG7bM54KTH/88d97uO9z071vfh8f1syf7aPWmj7kkbdW9It9EXPy+rzlP4J46JnadwFMrX0hirAn7bGd/9uRxbNU3b0V9jagcqaaPuSRudnrRP96SNutefbSSvx+lJ+3RP2qh70kbdkzbq3tm2kczYFUIIIYQQQvSYFBBCCCGEEEKIHjvXAuLpXolicJM26p600elJ+3RP2qh7/dlG8nqcnrRP96SNuidt1D1po+6dVRud0xwIIYQQQgghxNAiQ5iEEEIIIYQQPdajAkLTtIs1TTusaVqepmk/P8n/zZqmvXLi/1s1TUvr7UBDXQ/a6Ceaph3QNG2PpmmrNE1LDUacwdJd+3xmu6s1TVOapg25VRN60kaapl1zYj/ar2naP/s7xmDrwftsmKZpazRN233ivXZJMOIMFk3TntU0rUrTtH2n+L+madqfT7TfHk3TJp3DY0le6Ibkhe5Jbuie5IbuSW44vT7JDerEkrCn+gL0wDEgAzABnwBjv7DN94CnTvx8HfBKd/c7mL562EbzAduJn+8YSm3Uk/Y5sV0YsB7YAkwJdtyh1kbACGA3EHHi99hgxx2CbfQ0cMeJn8cChcGOu5/baA4wCdh3iv9fAvybzgsozAC29uFrIXlB8sI5t9GJ7SQ3SG441zaS3NDLuaEnPRDTgDylVL5SqgN4GbjiC9tcAbxw4ufXgAXaSS/4Nmh120ZKqTVKqdYTv24Bkvs5xmDqyT4E8CvgQaC9P4MLET1po1uBJ5RS9QBKqap+jjHYetJGCnCe+DkcKO/H+IJOKbUeqDvNJlcAf1edtgAuTdMSzuKhJC90T/JC9yQ3dE9yQ/ckN3SjL3JDTwqIJKDkM7+XnvjbSbdRSvmARiCqB/c9WPSkjT7rZjorvaGi2/bRNG0ikKKUerc/AwshPdmHRgIjNU3bpGnaFk3TLu636EJDT9poKXCjpmmlwPvAD/ontAHjTI9V53I/khckL3RHckP3JDd0T3LDuTvj3GDowZ2e7IzRF5du6sk2g1mPn7+maTcCU4C5fRpRaDlt+2iapgMeAZb0V0AhqCf7kIHOrup5dJ6p3KBp2nilVEMfxxYqetJG1wPPK6X+qGnaTODFE20U6PvwBoTeOlZLXuie5IXuSW7onuSG7kluOHdnfLzuSQ9EKZDymd+T+XLXT9c2mqYZ6OweOl1XyWDTkzZC07QLgP8BFiulPP0UWyjorn3CgPHAWk3TCukcf/fOEJss19P32dtKKa9SqgA4TGfSGCp60kY3A68CKKU2AxYgul+iGxh6dKzqpfuRvCB5oTuSG7onuaF7khvO3Rnnhp4UENuBEZqmpWuaZqJzMtw7X9jmHeDbJ36+GlitTszKGCK6baMT3bB/oTNJDLXxiadtH6VUo1IqWimVppRKo3Ms8GKl1I7ghBsUPXmfvUXnpEs0TYums9s6v1+jDK6etFExsABA07QxdCaJ6n6NMrS9A3zrxIobM4BGpVTFWdyP5IXuSV7onuSG7klu6J7khnN3xrmh2yFMSimfpml3Ah/SOdP9WaXUfk3TfgnsUEq9A/yNzu6gPDrPMF13rs9kIOlhGz0EOIDlJ+YRFiulFgct6H7Uw/YZ0nrYRh8CCzVNOwD4gXuUUrXBi7p/9bCN7gb+qmnaj+nsfl0ylD60apr2Ep3DGKJPjPV9ADACKKWeonPs7yVAHtAK3HQ2jyN5oXuSF7onuaF7khu6J7mhe32RG+RK1EIIIYQQQogekytRCyGEEEIIIXpMCgghhBBCCCFEj0kBIYQQQgghhOgxKSCEEEIIIYQQPSYFhBBCCCGEEKLHpIAQQgghhBBC9JgUEEIIIYQQQogekwJCCCGEEEII0WNSQAghhBBCCCF6TAoIIYQQQgghRI9JASGEEEIIIYToMSkghBBCCCGEED0mBYQQQgghhBCix6SAEEIIIYQQQvSYFBBCCCGEEEKIHpMCQgghhBBCCNFjUkAIIYQQQgghekwKCCGEEEIIIUSPSQEhhBBCCCGE6DEpIIQQQgghhBA9JgWEEEIIIYQQosekgBBCCCGEEEL0mBQQQgghhBBCiB6TAkIIIYQQQgjRY1JACCGEEEIIIXpMCgghhBBCCCFEj0kBIYQQQgghhOgxKSCEEEIIIYQQPSYFhBBCCCGEEKLHpIAQQgghhBBC9JjhTG+gadptwG0Adrt98ujRo3s9KCGCqby8nMTExGCHIUSv2rlzZ41SKqav7l9ygxjsJDeIwehsc4OmlDrrB50yZYrasWPHWd9eiFCkaRrn8r4QIhRpmrZTKTWlPx5LcoMYjCQ3iMHobHODDGES4gskQQghhPgiyQ1C/IcUEEJ8wdNPPx3sEIQQQoQYyQ1C/IcUEEJ8we233x7sEIQQQoQYyQ1C/McZT6IWQgxMfr+fpqYmfD5fsEMRfchgMOB0OtHr9cEO5XNk/xvaQnW/FEKcHSkghBgimpqaMJvNuFwuNE0LdjiiDyilaGtro6mpiYiIiGCH8zmy/w1dobxfCiHOjgxhEuIL3nnnnWCH0Cd8Ph9Wq1U+vA1imqZhtVpD8iy/7H9DVyjvl2disOYGIc6GFBBCfMHkyZODHUKfkQ9vg18ov8ahHJvoW4PhtR/MuUGIMyUFhBBfkJSUFOwQhpTCwkLGjx8f7DC+5Kc//Snjxo1jzJgx/PCHPzzpEo6PPvoora2tQYhO9JZQ3f8uvvhiXC4Xl1122ef+vmTJEtLT08nJySEnJ4fc3Nwv3TY3N5f333+/v0IdMiQ3CPEfUkAIIQadcx0q8fHHH7Np0yb27NnDvn372L59O+vWrfvSdlJAiJPpjaE699xzDy+++OJJ//fQQw+Rm5tLbm4uOTk5X/q/FBBCiL4mBYQQot88/PDDjB8/nvHjx/Poo492/d3n8/Htb3+b7Oxsrr766q4P5T//+c8ZO3Ys2dnZ/Nd//RcA1dXVXHXVVUydOpWpU6eyadMmAJYuXcptt93GwoUL+da3vsX06dPZv39/12PMmzePnTt30tLSwne+8x2mTp3KxIkTefvtt78Up6ZptLe309HRgcfjwev1EhcX97lt/vznP1NeXs78+fOZP38+AC+99BJZWVmMHz+en/3sZ0Dn6kNLlixh/PjxZGVl8cgjj3Td/tPndt111wGcMrb9+/czbdo0cnJyyM7O5ujRo+f+YgxBA2X/A1iwYAFhYWFn/Bw7Ojq4//77eeWVV8jJyeGVV16hrq6OK6+8kuzsbGbMmMGePXsAWLduXVdPxsSJE2lubqaiooI5c+aQk5PD+PHj2bBhAwArVqxg5syZTJo0ia9//eu43e5TtpEQYghQSp311+TJk5UQg82tt94a7BD6RFVVVVAff8eOHWr8+PHK7Xar5uZmNXbsWLVr1y5VUFCgALVx40allFI33XSTeuihh1Rtba0aOXKkCgQCSiml6uvrlVJKXX/99WrDhg1KKaWKiorU6NGjlVJKPfDAA2rSpEmqtbVVKaXUww8/rO6//36llFLl5eVqxIgRSiml7r33XvXiiy923eeIESOU2+3+Urx33323Cg8PV06nU/33f//3SZ9Tamqqqq6uVkopVVZWplJSUlRVVZXyer1q/vz56s0331Q7duxQF1xwQddtPn0eCQkJqr29/XN/O1Vsd955p1q2bJlSSimPx9P1HE/lZK81sEOdw/H+TL5Olhtk/zuz/U8ppdasWaMuvfTSz/3t29/+tho5cqTKyspSd911V9c+9FnPPfec+v73v9/1+5133qmWLl2qlFJq1apVasKECUoppS677LKu593c3Ky8Xq/6wx/+oH79618rpZTy+XyqqalJVVdXq9mzZ3fF+bvf/U794he/OGUbnUqw94FzNVhzgxjazjY3SA+EEF8gVxvtGxs3buSrX/0qdrsdh8PB1772ta6zmykpKcyaNQuAG2+8kY0bN+J0OrFYLNxyyy288cYb2Gw2AFauXMmdd95JTk4OixcvpqmpiebmZgAWL16M1WoF4JprrmH58uUAvPrqq3z9618HOs+k/u53vyMnJ4d58+bR3t5OcXHx52LNy8vj4MGDlJaWUlZWxurVq1m/fv1pn9/27duZN28eMTExGAwGbrjhBtavX09GRgb5+fn84Ac/4IMPPsDpdAKQnZ3NDTfcwLJlyzAYDKeNbebMmfzmN7/h97//PUVFRV3PUfTcQNr/Tue3v/0thw4dYvv27dTV1fH73/++R8/9m9/8JgDnn38+tbW1NDY2MmvWLH7yk5/w5z//mYaGBgwGA1OnTuW5555j6dKl7N27l7CwMLZs2cKBAweYNWsWOTk5vPDCCxQVFZ2yjQYryQ1C/IdcB0KIL5g8eTI7d+4MdhiDjjrJJORPfXGFFk3TMBgMbNu2jVWrVvHyyy/z+OOPs3r1agKBAJs3bz7ph2i73d71c1JSElFRUezZs4dXXnmFv/zlL11xvP7664waNeqU8bz55pvMmDEDh8MBwKJFi9iyZQtz5sw54+cXERHBJ598wocffsgTTzzBq6++yrPPPst7773H+vXreeedd/jVr37F/v37TxnbmDFjmD59Ou+99x4XXXQRzzzzDOeff/4pYxFfNpD2v9NJSEgAwGw2c9NNN/GHP/yh29uc7LlrmsbPf/5zLr30Ut5//31mzJjBypUrmTNnDuvXr+e9997jm9/8Jvfccw8RERFceOGFvPTSS1+6n5O10WD1xdyglKLa7aGwppXCmhYKa1tobPPS7g3g8fnx+RVhFgORdhMRdhNxTjNjEpxkxjgw6uX8rRjYZA8W4gt27doV7BAGpTlz5vDWW2/R2tpKS0sLb775JrNnzwaguLiYzZs3A53zCL7yla/gdrtpbGzkkksu4dFHH+1abWbhwoU8/vjjXfd7slVoPnXdddfx4IMP0tjYSFZWFgAXXXQRjz32WNeHqt27d3/pdsOGDWPdunX4fD68Xi/r1q1jzJgxX9ouLCys6+zz9OnTWbduHTU1Nfj9fl566SXmzp1LTU0NgUCAq666il/96lfs2rWLQCBASUkJ8+fP58EHH6ShoQG3233K2PLz88nIyOCHP/whixcv7hrDvmDBAsrKys7gVRi6BtL+dzoVFRVA54fXt95666QrSH12v/z0uf/jH/8AYO3atURHR+N0Ojl27BhZWVn87Gc/Y8qUKRw6dIiioiJiY2O59dZbufnmm9m1axczZsxg06ZN5OXlAdDa2sqRI0dO2UZvvvkm99577xk9r4Fg165dFNa08NK2Yn708m5m/HYV0/5vFdf8ZTM/fX0Pf1mXz1u55aw6dJzN+bXsLK7nwwOV/G1jAb/79yF+/MonXPzoBsbe/wGX/GkDS9/Zz4aj1Xh8/mA/NSHOmPRACCH6xaRJk1iyZAnTpk0D4JZbbmHixIkUFhYyZswYXnjhBW6//XZGjBjBHXfcQWNjI1dccQXt7e0opT43+fj73/8+2dnZ+Hw+5syZw1NPPXXSx7z66qv50Y9+xH333df1t/vuu4+77rqL7OxslFKkpaXx7rvvful2q1evJisrC03TuPjii7n88su/dP+33XYbixYtIiEhgTVr1vDb3/6W+fPno5Tikksu4YorruCTTz7hpptuIhAIAJ1DUPx+PzfeeCONjY0opfjxj3+My+U6ZWyvvPIKy5Ytw2g0Eh8fz/33308gECAvL4/IyMheeX0Gu4G0/wHMnj2bQ4cO4Xa7SU5O5m9/+xsXXXQRN9xwA9XV1SilyMnJOeljz58/v2uY1L333svSpUu56aabyM7Oxmaz8cILLwCdq4itWbMGvV7P2LFjWbRoES+//DIPPfQQRqMRh8PB3//+d2JiYnj++ee5/vrr8Xg8APz6178mLCzspG107NixrqF6g0FZQxv/+qSchCV/Zt4f1gLgtBhIirQxPjmcSEdnD0NsmAWLUYdO0058gQK8/gBtHQFq3O2U1bdR1eTheGM7y7YU8fzHhVhNemYPj+bqycmcPzoWg/ROiAFAO123bnemTJmiduzY0YvhCBF8iYmJlJeXbiC/dgAAIABJREFUBzuMXlddXU1MTEywwxC9ZN++fTz77LM8/PDDX/rfyV5rTdN2KqWm9EdsJ8sNsv8NHTfeeCOPPPLIl17vgbQPBAKKVYeqeHZjAZvzawHwVeWxcN55pEfbSY60EmY2otOd/QXyWjw+9pY2cLjCzbEqN26Pj2iHiWunDuP6aSkkRwzuOSUiNJxtbpACQoghYiAlb3FupIAQoWgg7APtXj/Ld5bytw35FNa2Emk3MTbJybgkJymRdmwmfZ88rtcfYGdhPTsK6sivbkGnweKcRO5aMJK0aHv3dyDEWTrb3CBDmIT4gqVLl7J06dJgh9GnfvGv/Rwob+rV+xyb6OSBy8f16n2KwUn2PxFqfP4Ay3eW8ujKIxxv8pAcaeWynAQmpLiItJvQNI1Xnvwj195xd588vlGvY0ZmFDMyozje1MaqA1W8+0kF7+SWc/mERH5y4UhSo6SQEKHjjAfaaZp2m6ZpOzRN21FdXd0XMQkRVL/4xS+CHYIQA47kBjEQKaX4YF8lCx9dz71v7MVs1HPN9BRunZvB+WPiiHKYu1bpevUvj/RLTHFOK9+Ykcq9l45mUloE7+2pYMEf1/Hb9w/S4jn3q5wL0RtkCJMQX6Bp2mmXfByoQnX4wOLFi8nPz2ffvn1f+t+SJUu47LLLuPrqq3v1MZ9//nl27NjxudV0BhMZwtS9l156id/85jdomkZiYiLLli0jOjr6c9vI/te7Qm0fyK92c9/b+9iUV0uc08KM4ZFMHBaB02o86fZX5STzem5pP0cJtW4Pb+0qZ39ZI9EOE/976ViuyEn80vLDQpyNs80NMtVfCBE0b7zxRte1FoToLz6fjx/96EesWbOGPXv2kJ2dPeQ+zA9l7V4/j648wkWPrmdXUQMLxsZxy9x05o6KPWXxEExRDjM3z0nn9nkZ6HUad72Syzee2UpFY1uwQxNDmBQQQnyB9Kr1D7fbzcMPP8z//u//nna79evXc95555GRkcFrr73W9feHHnqIqVOnkp2dzQMPPND19yuvvJLJkyczbty4z1059rnnnmPkyJHMnTuXTZs2df19+fLljB8/ngkTJpz2QnFi8FBKoZSipaUFpRRNTU0kJiaedFvZ/waX3cX1LPrTBh5deZSR8WF8Z046F2fFE+Uwd3vbB//5fj9EeGqjEpzcs2g0F2fHs6OwjvP/uI6XtxUPyh5zEfpkErUQIijuu+8+7r77bmy20y9VWFFRwcaNGzl06BCLFy/m6quvZsWKFRw9epRt27ahlGLx4sWsX7+eOXPm8OyzzxIZGUlbWxtTp07lqquuoqOjgwceeICdO3cSHh7O/PnzmThxIgC//OUv+fDDD0lKSqKhoaE/nroIMqPRyJNPPklWVhZ2u50RI0bwxBNPnHRb2f8Ghw5fgMdWH+WJNXmE24xcNSWZiakRfbaqUl/R6TQWjotnQoqLf2wu4udv7OVfe8r503UTie5BESREb5EeCCG+YMqUfhkmPqTl5uaSl5fHV7/61W63vfLKK9HpdIwdO5bjx48DsGLFClasWMHEiROZNGkShw4d4ujRo0Dnhb4mTJjAjBkzKCkp4ejRo2zdupV58+YRExODyWTi2muv7br/WbNmsWTJEv7617/i98sVYYcCr9fLk08+ye7duykvLyc7O5vf/va3J91W9r+BL6+qma89uYnHVueRlexiyVfSOW941BkXDz/9xiV9FOGZi3NauGvhSC7OimdLfh0XPryOTXk1wQ5LDCHSAyGE6HebN29m586dpKWl4fP5qKqqYt68eaxdu/ZL25rN/zmr9mlXvVKKe++9l9tvv/1z265du5aVK1eyefNmbDYb8+bNo729HeCUEw6feuoptm7dynvvvUdOTg65ublERUX10jMVoSg3NxeAzMxMAK655hp+97vfnXRb2f8GtuU7Srj/7f0Y9BpXTEpkavqZFw6hSqdpLBwfz+iEMP6+qYgbn9nKd+dl8l8LR6E/hwvcCdET0gMhhOh3d9xxB+Xl5RQWFrJx40ZGjhx50uLhVC666CKeffZZ3G43AGVlZVRVVdHY2EhERAQ2m41Dhw6xZcsWAKZPn87atWupra3F6/WyfPnyrvs6duwY06dP55e//CXR0dGUlJT06nMVoScpKYkDBw7w6XKzH330EWPGjOnx7WX/C32tHT5+8mou97y2hwSXhW/NSmP2yJhBUzx81rAoO/csGsX45HCeXHuMrz/1MbVuT7DDEoOc9EAI8QWfnRApQtPChQs5ePAgM2fOBMDhcLBs2TIuvvhinnrqKbKzsxk1ahQzZswAICEhgaVLlzJz5kwSEhKYNGlS13CRe+65h6NHj6KUYsGCBUyYMCFoz0v0j8TERB544AHmzJmD0WgkNTWV559/vse3l/0vtOVVNfPdZbs4VuVm1oho5o+JIdJ+7vMDrrn9x70QXd8wG/XcNDudDUeqeWd3ORf/aQPPLZnC+CRXsEMTg5RcB0KIISLU1mAXfUeuAyFCUX/sAx/ur+Qnr+Si12kszIpnclokZsPQGmxRUO3muY2FdHgD/PZrWVw1OTnYIYkQdra5QXogxJDR2ObleFM7Nc0eqt0e6lo6aPH4aO3w09rhx+MLALBs2YvceOM3Mek1rCYDNpMem0lPlMNEtMNMtMNMbJiZSLtJLuQjhBAhIBBQPLryCH9enUdKpI1LJyQwPM6BrheP0bdcOJlnPtrZa/fXV9JjHPzXxSN5Zn0Bdy//hP3lTfzvpWPQybwI0YukgBCDitcfoLCmhUOVzRyubOZYtZuSulaK61ppaved9DY6DcwGPXqdhqaBSsziX5+U4w8oOvwB/IGT99JZjXqSI62kRdnJiLYzKj6MUfFhDI91YDYMvnG2QggRiprbvdz1ci6rDlUxYZiLi8fHERdu7fXHqa8+3uv32VecVhM/unAkL20p5tlNBRTUuPl/N0zGOgjngIjgkAJCDGhlDW3sLKpnd3E9uSUN7C9vouNET4JOg0i7CafVSGacA5fNSJjFiM2sx2424LQYsZv1GPV69LrOVVI04IapF/Pg9iIAAkrh9SvaOnw0e3w0tXpxt/twe3w0tnlpaPWyu7ie1YequgoNvaYxIs7B5NQIclJcTEqNICPaHhK9FUqpkIhD9J1QvqiU7H9DV1/tlyV1rdz8wnbyqtxcMC6WOSNjcFhC72rSwaDXadwwcxixTjMf7K3kyv+3iRdvnkZsmCXYoYlBQAoIMaDUuD1syqvh47xaPj5WQ0l9GwAmvY54l4UJKS5inWZiwszEuyyEWYyY9boz6rrNGJOFxfj5szThViPxp9heKUVrh5/y+jbKGtqpamqnorGN13aW8o+txQBEO0yclxnFrOHRzBoeTXLE6S+e1hcMBgNtbW1YrVb5EDdIKaVoa2vDYAi9Q7vsf0NXX+2X2wvruP3vO/H4/Vw1NZnJqZGY+nC+Q8aYrD67776inVjqNcZp5qUtxVzypw3845bpjIp3Bjs0McDJJGoR0pRS7CtrYvWhKlYfrmJPSQMKsJk6hw+lRNpIirSRHGHFaTH2afI4Ux6fn5LaVo5Vt1Bc20pxbQstns6VVzJj7FwwJo4FY+KYnBrRL2t2+/1+mpqa8PlOPpRLDA4GgwGn04le//kiONiTqGX/G9pOtV+erdd2lnLvG3uIsJm4bGIiYxOcMsa/G0U1LTyzPh+l4G/fnsLMzOhghyRCwNnmBikgRMgJBBS5pQ28v6eC9/dVUN7QjgYkR1pJjbaTEetgWJQNp8WAQdf7BcOTv/wpd9z/YK/fr98foKC2hYPlzeRXdc7NCCiIsBm5eHw8l2YlMiMjEoM+dIogMXgEu4AQojcopXjko87J0pmxdi7PSSQl0tYvvVp9lRv6U22zhyfXHKOpzcsfr5nAFTlJwQ5JBJkUEGLAy6tq5o1dZbyVW0Z5Qzt6nUZGjJ3hcQ6GxzqId1mxGvt+AthVOcm8nlva54/T2Oblk5IGDpY3kV/lxutXuKxGLp+QyFWTk5mQHC5DPUSvkQJCDHQen5+fvraHt3PLyRnm4pIJCUQ7zv36Dj3VX7mhr7V4fDy15hjl9W38bNFovjs3M9ghiSCSZVzFgNTY6uWt3DJe21nK3rJGdBpkxDpYlJ3A6IQw4pyWkBqW1JvCrUbmjIxhzsgYWj0+dpfUs6+0iX9uK+bFLUWkRtm4ZkoyX5+cQqxTJr0JIYau+pYObn9xJ9sK65gzKoYFY2MJk8nSZ8VuNvCDC0bw7IYCfvfvQ9Q0e/ifS8fICStxRqSAEP1OKcW2gjpe3l7C+3sr8PgCJLoszB8Tw7hkF0kuy5BbBtVmNjBreAyzhsfQ1OZla34te0sbeejDI/xxxRHmjYrlhunDmDcqtl/mSwghRKgoqWvl289to7i2lctyEjhvePSXFroQZ8Zk0HHb3Az+uaWIZzYWUNPSwR+uzpYhtKLHpIAQ/abF4+ON3WW8uLmQI8fdWI16xiWFk5UczvA4B3ZzaOyOf10R3KEXTquRC8fFc+G4eErqWvk4r4atBbWsPlRFvNPCt85L5bqpw4i0m4IapxBC9LU9pQ3c9Px22r2frrQUEbQPucHODb1Np9O4YWYqDouRt3aXUef28PS3pkhxJnokND6xiUGtuLaV5z4uYPmOUtweH0kRVi7OiicrOZxYpyXkzqgfO7iXyNhTLdrav1IibVw7bRhfmxxge0Ed2/LrePCDwzzy0REuy07k1tkZjE2U5fiEEIPPqoPHufOfu7GZ9VwzLYVxieFBXWkplHJDb9E0jSsnJRFmMfDeJxVc+/Rmlt08XYaHiW6d8SRqTdNuA24DGDZs2OSioqK+iEsMcEopdhbV88yGAlYcqESnaYxKCCNnmIuR8WEhfXAK9YlyBTUtbDxSzb7SRrx+xfT0SL47N5O5I2NkGUNxSn09iVpyg+hN/9haxH1v7SMxwsriiUlkxgT/YpyhnhvO1db8WpZvKyEzxsHLt80gqh8nqIvg6bdJ1Eqpp4GnoXOljTO9vRjcAgHFyoPHeXLdMXYXN2A36ZmWEcWkNBfDIu2DdkJ0f0qPtpMebaepvYO1B6vZUVDPTc9vJy3Kxp3nj2DxhERpZ9HvJDeI3qCU4o8rjvD4mjxGxoexeGIiiS5rsMMaEqZnRGE16ln2cRGXP76R5d89jyRpe3EKMoRJ9AqvP8DbueU8te4YeVVuouwmFoyNY2Kqi7gQHKY0GDgtJhZPTGJRVjwf59WyKa+W/1r+CQ9+cIjb5mTwjenDsJnkLS6EGBg6fAF+/sYe3thVxsRUF5dkJ8hZ8H6WneLi1nl6nl1fwOWPbeSV22YwIi4s2GGJECTXgRDnxOPzs3xHKU+uPUZZQxsJLguT0iLJSQkn0m4Kepfz2Vjx2jIWXn1jsMM4Y58OG1t/uJrSujbCrUZumZ3OkvPSQnrImOgfch0IEcqa2r18b9kuNubVMHtkNBeMiwu549ZAzQ1no7S2lafWHkOnabx48zQmDosIdkiij8iF5ES/avf6+efWYv6y/hjHmzykRNqYkh5Bdko44VZZHSjYDpQ3supAFQXVLTjMBm6alcYtszMIt4ZWQhb9RwoIEaoqGtu46bntHK1yc+G4OGaPiumXi4aK06tuaueJ1cfweP088+0pzB4RE+yQRB8429wgA6XFGWn3+nl2YwGzH1zDL989gM1k4JppKdwyN53ZI2MGRfFwVU5ysEM4Z2MTw/nBBSO44/xM4l0WHludx3m/W8UfVxymsc0b7PCEEAKAA+VNXPnEJorqWvnq5CTmj4kN2eJhMOSGMxHjtHDXwhGEWQwseW477+0pD3ZIIoTIAGnRI+1ePy9tK+b/rT1GdbOHjBg7C8bGMS7JGTLXbxBfNiIujBFxYRRUu/lwbyWPrc7j2U0FfGdWOrfOycAZYkMEhBBDx/oj1dzxj52Y9Dq+Pi2FrKRwmS8XYlw2Ez9aOJInV+dx50u7qWvp4Jsz04IdlggB8slPnFaHL8CrO0p4fHUelU3tpEfbOX9sLOOTwqVwGEDSYxx89/zh5H+mkHhuUyG3zc3g5lnp8loKIfrVP7cWc9/b+4hzmlk8MYkRcY4BOWduKLCbDfzgwhH8dW0+9729nxp3B3ddMEJeryFOPjWIk/L5A7yxu4w/rzpKaX0bqVE2rpmWQlby4C8cJs+5INgh9JmMGAd3nD+cvCo3H+yp4OEVR/jbhgK+Pz+Tb81MkyuQCiH6VCCg+P2Hh/jLunxGxjm4bGIiyRG2YIfVI4M5N3THbNBzx/nDeWFjIX9adZRqt4dfXzFerj00hMkkavE5gYDi/X0VPPzREfKrW0iOsDI9M4qs5HCcMgF30Dlc2cwHeyooqm0lymHirgtGcO2UYXIdiUFIJlGLYGvr8HP38lze31vJ5LQILs6Kl2VaB5iAUizfVsLW/DoWjo3jsW9MxGyQE08DmUyiFudEKcXqQ8e57LGN3PnP3bR4/FwxMZGb56Yza0T0kCoefvPDJcEOod+Mig/jRwtH8p3Z6ViMeu57az9zH1rD6ztL8QfkWmBCiN5R2djOtU9v5t97K5k3JoYrJiYOuOJhKOWGU9FpGtdMS+HCcXGsOHCc65/eQlO7LMwxFA3usSiiR7bm1/LQh4fZUVRPtMPEJRPimZQWSaRt4K+odDZ2rl8Z7BD63fjkcMYlOdld3MCKvZXcvfwTHl+Tx88XjWbh2DgZ6yqEOGt7Shu49e87aGzzcsWkJKZnRg7Is9ZDMTecjKZpLMpOwGUz8vqOUq54fCMv3zaTOKcl2KGJfiQFxBC2t7SRh1YcZv2RasKtRi4YF8fU9EiiHQPzAnDi3GiaxqTUCHKGudhyrJZVB6q4/cWdjEt08t+XjGHW8OhghyiEGGDe21PB3ctzsZsMXDt9GONlpaVBY+bwztEJf99UyKV/3sA/bpnOqHhnsMMS/UQKiCEor8rNwx8d5v29ldjNeuaOjmFaRiTxTosUDgKdpnHe8GimpUey/kg16w5Vc8MzW5maFsl/XzJarkgqhOiWP6D4w4rDPLn2GKlRNi6bmEhGtF1yzCAzLimc7y8Yzl/X5XPlEx/z5I2TmDcqNthhiX4gk6iHkNL6Vv608iiv7yrFZNAxKS2C6RlRJLmsspKCOKUOn59VB6vYdKSG1g4/80fF8LNFoxktZ5oGFJlELfpLY6uXH768m3VHqpmY6uLirARiwgbWfAdxZurcHp5am0+d28PSxeP4llwrYsCQSdTilKqa23ng7X3M/8Na3sotY3JaJLfNy+Crk5JJibRJ8fAFK15bFuwQQorJoGdRVgL/ffkY5oyKZtOxWhY9uoHv/3MXhTUtwQ5PCBFCDlc2c8UTG9mUV8OF4+P42uTkQVM8SG44tUiHmZ9cNJK0aDv3v72fB97ZLwtxDHLSAzGI1bd08Jf1+bzwcSEen5/sYS5mZEaRHm3HqJfa8VSuyknm9dzSYIcRsprbvPx7byU7CuoIKMVVk5K568KRJLmswQ5NnIb0QIi+9vrOUv7nrb2YDDouyU5gYmrEoMo1khu65w8oXt5azM7Ces7LjOKpb07GaRk6qzgORGebG2QOxCDU1O7lbxsKeGZjPq0eP+OSw5mRGcXwWIes7y/OWZjVyDXTUrhgXCzvf1LB67tKeXN3GddNS+GHC0YQGyYrcQgxlLR7/fziX/t5aVsJ6TF2FmUnkBkj8x2GIr1O44aZqSS6LLz3SQWX/GkDL948nfRoe7BDE71MCohBxO3x8cLHhTy9Pp/GNi9jEp3MyIxiZLxjQC6ZJ0JbpN3MjeelsXB8O+/vqeAfW4t5dUcpN04fxvfmDyd6gK3xLoQ4c8eq3fzgn7s5UNHEzOFRnD8mdsBd30H0vvlj4khwWfn7piIu/fMGHvvGRBaMjgt2WKIXyRCmQaDF4+PFLUX8Zd0x6lu9jIx3MCMzilEJTqxGKRzO1PZ1HzF17oXBDmPAKW9o4997KjhQ1oTJoONbM9O4Y14mkfaheT2RUCNDmERvUkrx6o4Slr5zAL0OLhgfz7T0SCyDOOdIbjhz1c3tPLOugJpmD7fPzeSei0bJMr4h5mxzwxkXEJqm3QbcBjBs2LDJRUVFZ/qYope4PT7+vrmQv67Pp77Vy4i4zsJhdKIUDueirqqSyNj4YIcxYJXWt/LvPZUcLG/CYtTxzRmp3D43U3okgqyvCwjJDUNHY6uXe9/cw/t7K8mIsXNRdjyZ0Y5BvyCH5Iaz0+EL8M8tRewpaWRaeiRP3ThZTiyFkH4rID5LzjIFR2Oblxc3F/LMhgIa2joLh2kZUYxJDMNmklFp50omyvWOkroWPth7nEPlnT0SN0xP5btzM4iVq5UGhfRAiN6w5nAV976+l2q3h6+MjGbuqBhctqHxYVByw9lTSrH+cDXvflKBy2bkiW9MYkZGVLDDEsgk6iGh1u3h2U0FvPBxEW6Pj5HxYVw+MZFRCU5sJulxEKElJdLOrXMzKK1v5cO9lTy3qYAXtxRy9eRkvjdvOCmRtmCHKITooaZ2L79+9wCv7iglPtzCdTOGkZ0cPqhWWRJ9R9M05o6OJTXaxt83FXH9X7dw6+wM7rlolOxDA5QUEANAaX0rz2wo4OXtxXi8AcYkOpmaEcnI+DAZqiRCXnKEjZvnZFDe0MaKfZW8sr2EV7aXcPmERL43bzij4sOCHaIQ4jRWHzrO/7y5j+NN7cwcHsWcUdHEOWXZZnHm0qId/HTRKF7eVsLT6/PZeLSGJ2+cRGqUrNI00EgBEcL2lzfy9Pp83t1TAcC4pM7CITPGMagnqgXbBV/7RrBDGJQSXVaWfCWd6uZ2Ptp/nPf3VvB2bjmzR0Tz/fnDmZ4eKcs+ChFCKhrb+MU7B/hgfyXx4RaunzGMrGTXkF0OXHJD77CYDCz5Sjpb82t5a2cZCx9Zz08vHsVN56UP+nk0g4nMgQgxgYBi7ZEq/raxgE15tViMOrJTXExOiyA1yj5kD9xi8Gls62DNwWp2FNTR2uFnbIKT2+ZkcElWguznfUDmQIie8voDvPBxIY98dARvQDEjM4rzRkQTO0iuKC1CR53bwz+2FFNQ3cKE5HAevW6iXDOin8kk6gHO7fHx5q5Snt1USEFNCy6bkewUF5PSIkhyWWXZs350z/WLeOilfwc7jCHD0+Fn3ZFqtuXXUdfSQbTDxJJZaXxjWqqs1NGLpIAQ3VFKsepgFf/3/kEKaloYGedg7phYRsQ5MOikqJfc0DeUUmw8UsP7eyoIKMWd84fz3XmZcv2qfiKTqAeovKpmXtxcxGu7Smnx+EmOtHLphESyh4UTbTfJkI4gyD+4N9ghDClmk56F4+NZMDaW3UUNbDpawx8+PMKfVh7lkqwEbpqVTk6KK9hhCjGo7S9v5P/eO8jHx2qJdZr56uQkJg5z4bAYgx1ayJDc0Dc0TWP2qBiyksN5eVsxj6w8yvKdpfz6ivHMGx0b7PDEKUgBEQTtXj8f7q/k5W0lbM6vxaDTGJ3gJCc1nBFxYYTJAVsMQXqdjinpkUxJj6SwpoUNR6r5995K3s4tZ0xCGDfOSGXxhER5fwjRiw5XNvPoyiP8e18ldrOe88fGMT0zUk5giX7nspv47vzhfFJSzzu7K1jy/Hbmjozh/svHkhnjCHZ44gukgOhHB8qbWL6zhDd3ldHQ5iXKYWLOyBgmpLpIjrDKUmYhIiImLtghDHlp0XbSou20eHxsOFLNrqJ6/ufNffzqXwe4JDuB66cNY0pqhHzAEeIsHaps4vHVeby3twKzQcd5I6KYlhFFsssqE1lPQXJD/5iQEsHYBCcf7jvOhiM1XPjwOq6alMxPFo4kIVxW/woVMgeijx1vauft3DLe2FXGocpmDDqNkfFhjEsOZ0xCGOFWo3wIEqIbSikOVzaz5VgtB8ub8PoVSS4rV01O4qpJybIEYA/IHAihlOLjY7U8vT6fdUeqMRt0TEqNYFpmFMkRMtdOhJ7G1g7e21PBrsJ6DDodN8wYxnfnZhInFyTtNTKJOoTUuj38e18l7+4pZ2t+HQpIibQxKiGMcUlOEl3S2xDKXnnyj1x7x93BDkOcQovHy9b8evaWNFBc24oCxiU6uTIniUuyE0hyyRmqk5ECYuhye3y8k1vOsi1FHKhowmkxMGGYi0lpkSRFWGSCdA9Jbgie403tvJtbzoGyJvQ6ja9OSuJ784bLik29QAqIICtvaOOjA8dZcaCSLfl1+AOKmDAzI+IdjE0MJy3ahs0kI8YGgqtyknk9tzTYYYgeON7YzraCWg6UNXO8qR2A7ORwFo1P4MKxcQyPlXGzn5ICYmhRSrGntJGXt5fwTm4ZLR1+EsItTBjmYsKwcGLCLOik9/uMSG4IvoqGNj7af5y9pY0EAop5o2L49nlpzBkRI0PvzpKswtTPfP4An5Q2su5INasPHWdfWRMAsU4zU9IjGJPgJC3aTpjFIEOUhOgjceEWLs9J4vIcKKlrYUdhPUcq3Pz+g0P8/oNDpEbZuHBsHPNHxTIlLUKWBRSDXl6Vm3c+Keed3DIKa1sx6XWMTgwjK7lzkQ6nVRYhEANXgsvKt2alUdfiYdWBKrYW1LHmcDWJLgvfnJHKlROTZJ5EP5ECooeUUuTXtLD5WC2bj9WyMa+GxjYvmgYpETbmjIphRLyD1Cg7dpNeigYh+llKpJ2UyM7u7PKGVj4pbuTo8Wae21TIMxsKsBh1zMiI4ivDo5mZGcWYeKecsRIDnj+g2F1cz8qDVaw6eJyjVW40IC3GzsVZ8YxJDCMh3IpBhs2KQSTSbubrU1O4IieRLQV17Cyo4/cfHObBDw4zOTWCr05KYtH4BLmWUB+SIUyn0OELcKCiiV1F9ewqrmdbQR1VzR4Awq1GhkXbSI+2kxHjINZpxmKUM5uDxbEDe8gcmx3sMEQvaW73sr+skaPH3RRUt9DQ6gUgzGJgenokk1MjmZwaQXZy+KB+H8sQpsFBKcWx6hY2H6uK2r9YAAAgAElEQVRhc37nCa36Vi96TWNYlI30WDtjEpwkRVgH9f4cDJIbQltxbSs7Cus4UNZEXUsHOg0mJLu4YGwcC8bEMiouTE7unoQMYToH7V4/eVVu9pc3sq+siX3ljRwob8LjCwDgshlJdFmZmBZBapSNRJcVh9kgZy+FGADCLEZmZEYzIzMapRQVDW0cqmimuK6VncUNrDxYBYBepzEyzkFWUjhZSeGMSwpnZFwYDrMcJkXw1LV0sL+8kdziBnJLOr9qWzqAztyUHGnjKyPtDI9zEOu0YDZIT4MYmoZF2RgWZePKiQHyqlrYU9LAsSo3D314mIc+PEyk3cT09EhmZEQxNS2SkXEO6Zk7B0OmB0IpRXWzh4KaFopqWymobSGvys2R482U1LUSONEMFqOOOKeFGKeZpAgryRFW4sKt2Ix6KRiGCJkoN3Qopahxezh63E1pXRuVjW0cb/LQ1uHv2iY+3MKouDBGxjk6r08R1XmNininZUAteyk9EKErEFBUNrVTWNNCYW0rBTVuDlU2c7iyuavnGyAmzExcuJmkiM4PSkkuKw6LQSZD9xPJDQNTZWM7e0sbKaptoaS2leZ2HwAmg47R8WFkJ4czJsHJ8BgHmbEOoobYRRSHbA9Ehy9AQ1sH9S1e6ls7qHV3UNXcTnWzh6pmD+UNbZQ1tFHR0E6HP9B1O72mEekwEWE3MWN4NNEOE3HhFhLCLdjNBllmVYghQNM0YsIsxIT9Z01xr99PZWM7RbWtVDd5qHF3cKCiiU15NfgC/znhotdpxDnNJLmsJEfYiHWaiXGYiQnr/IqwmYiwmXDZjDKUZAhSSuH2+Gho9dLY5qWh1UuN23Piq4OqpnbKGzvzU2VjO17/f/Ytg04jxmkmNtzMuGQnMU4LyRFWIu0mrEaZYyfEmYgPtxAf3nmMDyhFeUMbRyrdHG/sfO8t31HaNeIEwGkxkBJpIyXCSlJE56iTmDAz0Q4TsWFmIu1mnBbDkO+9OKcCoq6lg5e3FZ/0f58eCj/t4FCozrP8SqHonPjlDyiUAr9S+PwBfAGFz6/w+gN4fAE6/AE83gDtPj9tHX5aO3y0dvhxt/to9vhobvfS7g2c9PF1WufQhTCLAYfFQE6qi3CrEZfNSKTDREyYBatJj8Wgk4OxEKKLUa//3IRs6Pww2Ob1U9Ps4XiTh1q3h6Y2H01tXioa2zly3I3b48MfOHmPrtmgw2HuPBaFWYzYTXqsJj02kx6LUY/ZoMds0GEy6DDqNQy6zu96nQ697v+3d9/xVZb3/8dfV3KyNxkkEPaGJITtRBAFa+umjrqoWlu1tfVb/em341trtXVjbW21FFdFQVEUR4WqWAEXhD3D3rKTkISsc67fH+ckMkLOSXJGxvv5eAROTu5z359z5eR87s+5rwFhxhAeZggzhjADGIPnPwyG2rewlvJO1lBuCJRjW/7YvHP8999u4LLu36uFutsua3G68Pz/7Ve1y0WN0327ssZFlSc/VdU4qah2cbT62xxVVumkrLKGsqoaTvFyIDzMkBDt8OSnCIYmx5AUG0FKbCQdEqJIj48kJjKcyHDlJxF/CjOG7JRYslNi6+6rrHHyTVEFe4qPcqC0ikOlVRQfrWbRtsN8vG7fccX9seIiw0mMcZ9nxkU5iIt0HPOeHkZURBiR4eFEhBsiwsNwhBscYYawMEO45z3deN7Tw4zxvJ8DnvvMCe/oJ74VhPqdodFdmIwxtwK3er7Jc8Ql727O8b9tgbqb5tgfH3O3+Xaz2n+Mcd9Tl1FN3XbWWne1Yl00p59WMzmrjsaGR8aUh+r4rYHaqGFqH+9C3kbGGIwJc78P1b7NW+s+g/WcndbesMf+zHO71vHvVZ5vjjv7bfJ7WU15SaZ11gRs+dYTcsNgR1zyrkAdy8/MMf+e/O1JefrEzG7Mtw/xpP+618AxecnU/RxnRVlUeFTM0VDnp5Ys5H/TrYDayLtmtZExnr9dE3b8+/ox7+3uN/Xa93fPpxanfE/37KDu5nH/0Yz39+aoKStKty5Xo1fka9YYCGPM4mD1qW2t1EbeqY0apvbxTm3kXTDbSL+Phql9vFMbeac28k5t5F1T26h9d+ASEREREZFGUQEhIiIiIiI+a24B8Q+/RNG2qY28Uxs1TO3jndrIu2C2kX4fDVP7eKc28k5t5J3ayLsmtVGzxkCIiIiIiEj7oi5MIiIiIiLiMxUQIiIiIiLiM58KCGPMBcaY9caYjcaY++r5eZQxZobn518ZY7r7O9CWzoc2+h9jzBpjzApjzMfGmG6hiDNUvLXPMdtNNMZYY0y7m3bNlzYyxlzpeR2tNsa8GuwYQ82Hv7Ouxph5xpilnr+1C0MRZ6gYY543xuwzxqw6xc+NMeZpT/utMMYMbcaxlBe8UF7wTrnBO+UG75QbGhaQ3GBr18Q4xRcQDmwCegKRwHJg4Anb3A4867l9NTDD237b0pePbTQWiPXcvq09tZEv7ePZLgH4DPgSGB7quFtaGwF9gKVAiuf7jFDH3QLb6B/AbZ7bA4GtoY47yG00GhgKrDrFzy8E/o17RbPTgK8C+LtQXlBeaHYbebZTblBuaG4bKTf4OTf4cgViJLDRWrvZWlsFTAcuOWGbS4CXPLdnAuO+XYmzXfDaRtbaedba2tUQvwSygxxjKPnyGgL4A/AoUBHM4FoIX9roR8Az1trDANbafUGOMdR8aSMLJHpuJwG7gxhfyFlrPwMONbDJJcDL1u1LINkYk9WEQykveKe84J1yg3fKDd4pN3gRiNzgSwHRGdhxzPc7PffVu421tgYoBlJ92Hdb4UsbHetm3JVee+G1fYwxQ4Au1tr3ghlYC+LLa6gv0NcYs9AY86Ux5oKgRdcy+NJG9wPXGWN2Ah8APwtOaK1GY9+rmrMf5QXlBW+UG7xTbvBOuaH5Gp0bHD7stL5PjE6c+9WXbdoyn5+/MeY6YDhwTkAjalkabB9jTBgwGZgUrIBaIF9eQw7cl6rH4P6kcr4xJsdaWxTg2FoKX9roGuBFa+0TxpjTgX952sgV+PBaBX+9VysveKe84J1yg3fKDd4pNzRfo9+vfbkCsRPocsz32Zx86aduG2OMA/floYYulbQ1vrQRxpjzgF8DF1trK4MUW0vgrX0SgBzgU2PMVtz972a3s8Fyvv6dvWOtrbbWbgHW404a7YUvbXQz8DqAtfYLIBpIC0p0rYNP71V+2o/ygvKCN8oN3ik3eKfc0HyNzg2+FBCLgD7GmB7GmEjcg+Fmn7DNbOBGz+2JwCfWMyqjnfDaRp7LsM/hThLtrX9ig+1jrS221qZZa7tba7vj7gt8sbV2cWjCDQlf/s7exj3oEmNMGu7L1puDGmVo+dJG24FxAMaYAbiTxP6gRtmyzQZu8My4cRpQbK3d04T9KC94p7zgnXKDd8oN3ik3NF+jc4PXLkzW2hpjzE+BObhHuj9vrV1tjHkAWGytnQ1MxX05aCPuT5iubu4zaU18bKPHgHjgDc84wu3W2otDFnQQ+dg+7ZqPbTQHGG+MWQM4gXustQdDF3Vw+dhGvwSmGGPuwn35dVJ7Omk1xryGuxtDmqev7++ACABr7bO4+/5eCGwEyoEfNuU4ygveKS94p9zgnXKDd8oN3gUiN5h21H4iIiIiItJMWolaRERERER8pgJCRERERER8pgJCRERERER8pgJCRERERER8pgJCRERERER8pgJCRERERER8pgJCRERERER8pgJCRERERER8pgJCRERERER8pgJCRERERER8pgJCRERERER8pgJCRERERER8pgJCRERERER8pgJCRERERER8pgJCRERERER8pgJCRERERER8pgJCRERERER8pgJCRERERER8pgJCRERERER8pgJCRERERER8pgJCRERERER8pgJCRERERER8pgJCRERERER8pgJCRERERER8pgJCRERERER8pgJCRERERER8pgJCRERERER8pgJCRERERER8pgJCRERERER85mjOg9PS0mz37t39FIpI21Djshwuq+JgWRXVThfGc789ZpsoRxgZidEkx0SEIkRphwoKCg5Ya9ODcSzlBmnv9hRXcKC0krT4KLKSokMdjsgpNTU3NKuA6N69O4sXL27OLkTalA9X7eGuGcsx1U5GpseR1yWZQdmJxEY6sNZS7bQs317E/ML9HCytIiYlhslX5TO8e4dQhy5tnDFmW7COpdwg7dnKncVc8swCsixkJETx5f+eS1iYOnxIy9TU3NCsAkJE3Ky1PPvfzTzy4Tq6psYybmAGvTsmEBMRftK2o/ulc1bfNL7efIi5q77hmilf8tcfDGXCoMwQRC4iIv5S43Txv7NWkBAdwdBuKcxbt48Fmw4yuk9QLv6JBI1KYpFmqqpxce+bK3jkw3XkdE7k6tO6kpudXG/xUCvMGE7rlcovxvchNT6K214pYPrX24MYtYiI+NvLX2xj1a4Szu6XxrkD0wkPM7z6ld7bpe1RASHSDDVOFz96eTGvL97JGb3TuHxENpmJvvd3TYyJ5M7z+9ClQyz3vbWSv83bGMBoRUQkUHYXHeWJuevp2zGeET06EBcVwcBOiXxWuJ+j1TWhDk/Er/zehcnpdFJSUkJNjf5Y2iuHw0FiYiLh4af+BL6teOiDtfy3cD/nDerI2AEZDV51OJXoiHDuGNeb5+dv4dE560lPiOL7w7sEIFoREQmUB99fQ7XLcs6ADBKi3RNkjOzZgZU7i3l76W6uGdk14DHoHExOxd/nZn4vIEpKSoiKiiI5ORljjPcHSJtireXo0aOUlJSQkpIS6nAC6vVFO3hh4VZG9EhhTP+mFQ+1HOFh3HR2D57+zwZ+8/YqcrOT6J+Z6MdoRUQkUKpqXPxnzV7yuiTTp2N83f39sxKJiwrnjYKdQSkgdA4m9QnEuZnfuzDV1NQQExOjF247ZYwhJiamzX/6UbDtEL9+eyW9M+IZn5NJbGTzK3pHeBg3je5BWJjh5hcXU17VtttQRKStKNx7hGqnpXNKDI5jZlwKDzMM7ZbC8h1F7Ck6GvA4dA4m9QnEuVlAxkDohdu+tfXf/57io/z4XwUkx0by3fwsUuOj/Lbv5NhIrj+jG7uKjvLTV5dirfX+IBERCamVu4oByKxnzYcRPTrgdFmmBWmijLaeg6Vp/P260CBqkUaw1vLL15dTWlnDd/Oz6Noh1u/H6J+VyPmDOvLJun0899lmv+9fRET8a+WuYmIiw8lKPrmA6JwSQ8fEKGYv2x2CyEQCo90UEFu3biUnJyfUYZwkPDyc/Px88vPzufjii+vu37JlC6NGjaJPnz5cddVVVFVVnfTYTz/9lM8//zyY4bZ70xft4PNNBzm7XzqDOiUF7JOeCbmZ9MqI54m569l2sCwgxxAREf9YtauYjolRxEWePLTUGMPgrslsP1TO7iB0Y2qJ2so5WGVlJVdddRW9e/dm1KhRbN269aR9bt26lVdffTVYTyFk2k0BEQj+6EsWExPDsmXLWLZsGbNnz667/9577+Wuu+5iw4YNpKSkMHXq1JMeqwIiuPYUH+XB99fQMz2O03unEh4WuMvEYcZwzWnuAXe/fGN5wI4jIiLNU1XjYu2eEjISo3GE139alZ3ivlq9bGdRMENr00JxDjZ16lRSUlLYuHEjd911F/fee+9J+1QB0Yo9+eST5OTkkJOTw1NPPVV3f01NDTfeeCN5eXlMnDiR8vJyAO677z4GDhxIXl4ed999NwD79+/niiuuYMSIEYwYMYKFCxcCcP/993Prrbcyfvx4brjhBkaNGsXq1avrjjFmzBgKCgooKyvjpptuYsSIEQwZMoR33nnH5/ittXzyySdMnDgRgBtvvJG33377uG22bt3Ks88+y+TJk8nPz2f+/Pls27aNcePGkZeXx7hx49i+3d3f8o033iAnJ4fBgwczevRoAFavXs3IkSPJz88nLy+PDRs2APDKK6/U3f/jH/8Yp9OJ0+lk0qRJ5OTkkJuby+TJkxv1+2gLrLX86q2V1Dgt5w7MIDkmMuDH7BAXyfk5HVm89TBvLdkZ8OOJiEjj1Q6grm/8Q61Onq5NK9tBAdGWz8HeeecdbrzxRgAmTpzIxx9/fNJYxfvuu4/58+eTn5/P5MmTqaio4Ic//CG5ubkMGTKEefPmAfWfh5WVlfHd736XwYMHk5OTw4wZMwAoKCjgnHPOYdiwYUyYMIE9e/YA8PTTT9e13dVXX+3zc/QHv0/jGmoFBQW88MILfPXVV1hrGTVqFOeccw4pKSmsX7+eqVOncuaZZ3LTTTfxt7/9jZtuuolZs2axbt06jDEUFbn/uH/+859z1113cdZZZ7F9+3YmTJjA2rVr646xYMECYmJimDx5Mq+//jq///3v2bNnD7t372bYsGH86le/4txzz+X555+nqKiIkSNHct555xEXF3dcvBUVFQwfPhyHw8F9993HpZdeysGDB0lOTsbhcP96srOz2bVr13GP6969Oz/5yU+Ij4+v+4O76KKLuOGGG7jxxht5/vnnufPOO3n77bd54IEHmDNnDp07d657fs8++yw///nPufbaa6mqqsLpdLJ27VpmzJjBwoULiYiI4Pbbb2fatGkMGjSIXbt2sWrVKoC6fbQnby/bxbz1+zl3QAZ9MxOCdtyx/TuyeMthHnhvDecP7Fg3t7hIS2OMuRW4FaBr18BPVynSUqxqYAB1rZS4SCIdYazZcyRYYYVEWz8H27VrF126uNdpcjgcJCUlcfDgQdLS0ur2+fDDD/P444/z3nvvAfDEE08AsHLlStatW8f48eMpLCys9zzsgw8+oFOnTrz//vsAFBcXU11dzc9+9jPeeecd0tPTmTFjBr/+9a95/vnnefjhh9myZQtRUVFBPzdrc1cgFixYwGWXXUZcXBzx8fFcfvnlzJ8/H4AuXbpw5plnAnDdddexYMECEhMTiY6O5pZbbuGtt94iNtZ9mfGjjz7ipz/9aV2/uJKSEo4ccf/hX3zxxcTExABw5ZVX8sYbbwDw+uuv8/3vfx+AuXPn8vDDD5Ofn8+YMWOoqKiouyJwrO3bt7N48WJeffVVfvGLX7Bp06Z6Z97xpa/9F198wQ9+8AMArr/+ehYsWADAmWeeyaRJk5gyZQpOpxOA008/nT/+8Y888sgjbNu2jZiYGD7++GMKCgoYMWIE+fn5fPzxx2zevJmePXuyefNmfvazn/Hhhx+SmNi+1ic4WFrJ72evoVtqLGf0TTtuir5ACw8zXDWyK0Xl1fzu3dXeHyASItbaf1hrh1trh6enp4c6HJGgWbmrmJiI+gdQ1zLG0Ck5mi0H2vaYtrZ+DtaU87MFCxZw/fXXA9C/f3+6detGYWFhvedhubm5fPTRR9x7773Mnz+fpKQk1q9fz6pVqzj//PPJz8/nwQcfZOdOd6+EvLw8rr32Wl555ZW6gidY2lwB0dC0lyf+ko0xOBwOvv76a6644grefvttLrjgAgBcLhdffPFFXd+4Xbt2kZDg/uT52Aq2c+fOpKamsmLFCmbMmFF3Cclay5tvvln3+O3btzNgwICTYurUqRMAPXv2ZMyYMSxdupS0tDSKiorq+vft3LmzbrvGqH2+zz77LA8++CA7duwgPz+fgwcP8oMf/IDZs2cTExPDhAkT+OSTT7DWcuONN9bFvH79eu6//35SUlJYvnw5Y8aM4ZlnnuGWW25pdCyt2eNzCzlSWcO5gzLoEBv4rksn6pEex4geHZi1ZBdLth8K+vFFROTUVu0qpmNSFLH1DKA+VqfkGPYWV3C0Da/x09bPwbKzs9mxYwfg7pJVXFxMhw4dmtQm9Z2H9e3bl4KCAnJzc/nf//1fHnjgAay1DBo0qO65rFy5krlz5wLw/vvvc8cdd1BQUMCwYcOCugZXmysgRo8ezdtvv015eTllZWXMmjWLs88+G3BXml988QUAr732GmeddRalpaUUFxdz4YUX8tRTT7Fs2TIAxo8fz1//+te6/dbeX5+rr76aRx99lOLiYnJzcwGYMGECf/nLX+peOEuXLj3pcYcPH6ayshKAAwcOsHDhQgYOHIgxhrFjxzJz5kwAXnrpJS655JKTHp+QkFBXkQOcccYZTJ8+HYBp06Zx1llnAbBp0yZGjRrFAw88QFpaGjt27Ki7snDnnXdy8cUXs2LFCsaNG8fMmTPZt28fAIcOHWLbtm0cOHAAl8vFFVdcwR/+8AeWLFkCwF//+tfj2qgtWrO7hBmLtjOkWzL9OobuysslQzsR7QjnN2+v1toQIiItRLXTxdpvjpCeEE3EKQZQ1+qUHENljYvCfaVBii742vo52MUXX8xLL70EwMyZMzn33HNPKoxOPDcbPXo006ZNA6CwsJDt27fTr1+/es/Ddu/eTWxsLNdddx133303S5YsoV+/fuzfv7+u7aqrq1m9ejUul4sdO3YwduxYHn30UYqKiigtLeXrr7/mhhtu8P7LaqY2V0AMHTqUSZMmMXLkSEaNGsUtt9zCkCFDABgwYAAvvfQSeXl5HDp0iNtuu40jR47wve99j7y8PM4555y6AcJPP/00ixcvJi8vj4EDB/Lss8+e8pgTJ05k+vTpXHnllXX3/fa3v6W6upq8vDxycnL47W9/e9Lj1q5dy/Dhwxk8eDBjx46tG0gE8Mgjj/Dkk0/Su3dvDh48yM0333zS4y+66CJmzZpVN4j66aef5oUXXiAvL49//etf/PnPfwbgnnvuITc3l5ycHEaPHs3gwYOZMWMGOTk55Ofns27dOm644QYGDhzIgw8+yPjx48nLy+P8889nz5497Nq1izFjxpCfn8+kSZP405/+BMC6detITU1t4m+q5bPW8of31hAb6eCsPulEOkL35xIb6WB8TkfW7C7hvZV7QhaHiIh8q3DvEapqXGQ20H2pVlayu9vNip3FgQ4rZNr6OdjNN9/MwYMH6d27N08++SQPP/zwSfvNy8vD4XAwePBgJk+ezO23347T6SQ3N5errrqKF198kaioqHrPw1auXFk3sPqhhx7iN7/5DZGRkcycOZN7772XwYMHk5+fz+eff47T6eS6666rG5x91113kZyczPbt2+u6eAWSac6nmcOHD7eLFy8+7r79+/ej/q/tw/e+9z3eeustIiNP7tbTFl4HH676hp+8UsC4QR35Tk4mYQGcttUXNU4Xf3xvLfFRDj67Z2zI45HWxRhTYK0dHoxj1ZcbRNqiGYu2c++bK/nRmJ4MyGr4KnVFtZNfzVzJ94dn89jEwQGJpy3kXmmee+65h+uvv568vLyTflbf66OpuSGgIy5+/+5q1uwu8es+B3ZK5HcXDfLrPqVpamcYaIsqa5z88YO1ZCZFc1rPDi3iZN0RHsb3BmfxyhfbeeHzLdx8Vs9QhyQi0q6t3FVMdEQYWQ3MwFQrOiKc5NgINuwNThcmnYO1T4899lhQjtPmujCJ+MPzC7ay/VA5Z/VNIzU+KtTh1MnvlkJWcjR/+WRjmx6IJyLSGqzcWUzHpGjionz7PLZzcgw7D5fj0lg2aeUCegUi1FVqeXk53//+99m0aRPh4eFcdNFFdf3Vtm/fzo033khRURFOp5OHH36YCy+88LjHf/rpp8fN5etP8fHxlJa23YFUrdnhsiqembeRvpkJ5HdNDnU4xwkzhkuHdObv8zbx5483cN93Tp5VQkREAq92APXgLsleB1DX6pQSw5o9JRwsrSQ9wftVi+Zoyedg4J529f7778cYw+DBg3n11VeZN28ed911V90269atY/r06Vx66aXH7XvMmDE8/vjjDB/u316Z999//3Hra8mptfkrEHfffTfr1q1j6dKlLFy4kH//+98APPjgg1x55ZUsXbqU6dOnc/vtt4c4Umkpnpm3kfKqGs7sk+Z1Wr5Q6JOZQJ+O8bz4+VYOlVWGOhwRkXapbgC1D92XamUlx2AtrNjVdgdSH+tU52AbNmzgT3/6EwsXLmT16tV1K1aPHTu2brrSTz75hNjYWMaPHx/KpyCn0KYLiNjYWMaOHQtAZGQkQ4cOrVt8wxhDSYm7b2BxcfEp11koLS1l4sSJ9O/fn2uvvbZuSrBTLSs+ZcoURowYweDBg7niiivqlmrfsmULp59+OiNGjDhuNoA9e/YwevRo8vPzycnJqVtwRUJjV9FRXv5yGznZyUFdcbqxLh7SiYpqF0/MLQx1KCIi7VLdCtQ+zMBUq5Nn27Y8E1Oths7BpkyZwh133EFKSgoAGRkZJz1+5syZfOc736lbXO5Eb7zxBiNHjqRv3751505Op5N77rmHESNGkJeXx3PPPQe4z+XGjRvH0KFDyc3N5Z133qnbz0MPPUS/fv0477zzWL9+fd39Tz/9NAMHDiQvL69ufQn5VpsuII5VVFTEu+++y7hx4wD3ZapXXnmF7OxsLrzwQv7yl7/U+7ilS5fy1FNPsWbNGjZv3szChQvrlhWfOXMmBQUF3HTTTfz6178G4PLLL2fRokUsX76cAQMGMHXqVMC9LPttt93GokWLyMzMrNv/q6++yoQJE1i2bBnLly8nPz8/wC0hDXnqP4W4XJYz+6QSFcJpW73pnBLLoM6JvFGwk/1HKkIdjohIu7N6d4nPA6hrpcVH4Qgzfh/c3NKdeA5WWFhIYWEhZ555JqeddhoffvjhSY+ZPn0611xzzSn3WVNTw9dff81TTz3F73//ewCmTp1KUlISixYtYtGiRUyZMoUtW7YQHR3NrFmzWLJkCfPmzeOXv/wl1loKCgqYPn06S5cu5a233mLRokV1+3/44YdZunQpK1asaHAa2faq0WdIxphbjTGLjTGL9+/fH4iY/K6mpoZrrrmGO++8k5493TPXvPbaa0yaNImdO3fywQcfcP311+NyuU567MiRI8nOziYsLIz8/Hy2bt3a4LLiq1at4uyzzyY3N5dp06axevVqABYuXFj3h1C7pDnAiBEjeOGFF7j//vtZuXJl3UqLEmsaeAMAACAASURBVHwb9h7hzSU76ZXopHtanPcHhNiFeVlU1bh4XFchRESCrnDvEdLiva9AfaywMEPHpGg2H2g/YyDrOwerqalhw4YNfPrpp7z22mvccsstFBUV1T1mz549rFy5kgkTJpxyv5dffjkAw4YNY+vWrQDMnTuXl19+mfz8fEaNGsXBgwfZsGED1lp+9atfkZeXx3nnnceuXbvYu3cv8+fP57LLLiM2NpbExEQuvvjiuv3n5eVx7bXX8sorr+BwtLzuzKHW6ALCWvsPa+1wa+3w1jLX8K233kqfPn34xS9+UXff1KlT6xYdOf3006moqODAgQMnPTYq6tsZeMLDw6mpqWlwWfFJkybx17/+lZUrV/K73/2OiopvPx0+cbVCcK9Q+Nlnn9G5c2euv/56Xn75Zb89b2mcx+euJ8oRzkcPXOfzgLhQykqOITc7iVlLdrG3RFchRESCqXBvKSnxkY1eZLRzcgx7iiqoqjn5Q8u2qL5zsOzsbC655BIiIiLo0aMH/fr1Y8OGDXU/f/3117nsssuIiIg45X5rz89qz83AvQDsX/7yl7rzsy1btjB+/HimTZvG/v37KSgoYNmyZXTs2LHu/Ky+czOA999/nzvuuIOCggKGDRtWdwxxa/lnSc30m9/8huLi4roBOrW6du3Kxx9/DLhXI6yoqPB58ZVTLSsOcOTIEbKysqiurq5buhzgzDPPZPr06QDH3b9t2zYyMjL40Y9+xM0338ySJUua/mSlyZZuP8yc1XsZ1iMF19HWc2n5O3lZVDldPDZnvfeNRUTELw6WVnKorIr0Jkzz3SklhvIqJ1vawVWIU52DXXrppcybNw+AAwcOUFhYWHd1Aty9RBrqvnQqEyZM4O9//zvV1dWAu6tUWVkZxcXFZGRkEBERwbx589i2bRvg/hB31qxZHD16lCNHjvDuu+8C4HK52LFjB2PHjuXRRx+lqKhIM2eeoE0XEDt37uShhx5izZo1DB06lPz8fP75z38C8MQTTzBlyhQGDx7MNddcw4svvnjKKvREp1pWHOAPf/gDo0aN4vzzz6d///51j/nzn//MM888w4gRIygu/nbw1Keffkp+fj5DhgzhzTff5Oc//7kfW0B89dic9SREOzitV2qoQ2mUzKRoBndN4p1lu9lTdDTU4YiItAuFnsXgUhMiG/3YLM9A6mU7irxs2bo1dA42YcIEUlNTGThwIGPHjuWxxx4jNdWdf7du3cqOHTs455xzGn3MW265hYEDBzJ06FBycnL48Y9/TE1NDddeey2LFy9m+PDhTJs2re78bOjQoVx11VXk5+dzxRVXcPbZZwPuwdjXXXcdubm5DBkyhLvuuovk5JY1rXuoGduMxUyGDx9uFy9efNx9WkZdoHW9Dj7feIAf/PMrzh2QwYWDs3jj2Se56rZfhjosn+0rqeCR99dx6dBOTL5ySKjDkRbKGFNgrfXvpOmnUF9uEGlLXv5iK//3zmpuH9eb3hnxjXpsaWUN//fWKq4/vRt/uCTHr3G1ptwrwVff66OpuaFNX4EQ8cZay2Nz15MSG8GInimEGdOqigeAjMRo8rsl8+7yPboKISISBIV7jxAdEUZ6E65AxEc5SIh2sO6b1tNdVuREKiCkXftk3T6Wbi9iRM8OZHhWBb3l/GEhjqrxJuRk4nS6iyEREQmswr2lpCU0bgamY2UlR7Pj0FGa0wtEJJRUQEi75XJZHp9bSFp8FMN6dKgbA3N4/94QR9Z4GYnRDO6azLvLd/NNsa5CiIgEirWWwr1H6BAX2eQZ+7KSYjhwpJIjlZrZR1qngBQQqqjbt9by+39/5R7W7ilhZK8OpMU1/jJ0SzMhN5NqXYUQEQmog2VVFJVXk5bQ+BmYamUmRVPjsqzb4/9uTK0lB0tw+ft14fcCwuFwcPSoLsu1V9Zajh492uIXXalxupj8USGZSdEM7Z5y3AxcPQfkhjCypuuYGM3gLknMXrZH60KIiARI4d4jgHtV6abK9MzEtNrPBYTOwaQ+gTg38/tZXmJiIiUlJZSVlfl719JKOBwOEhMTQx1Gg95asovN+8u4ZGgnOsQef/Xhsdf+HaKomu+C3CyW7yjmsbnreXzi4FCHIyLS5mzwTOGakdiMAiLRU0Ds8m8BoXMwORV/n5v5vYAIDw8nJSXF37sV8ZuKaieTPyqkS4dY8ruePK/z3x/4f9z2f4+GILLm65gUTV6XJGYv3c3/m9CvbmC4SKAZY24FbgX3Qp0ibVXh3iPERISTFt/0rq9REeEkx0awcb9/FyfTOZgEiwZRS7sz7avt7Cmu4LReHUiKOTkBfPTWqyGIyn8uyM2kyuni8TmFoQ5F2hFr7T+stcOttcM1D720ZRv2lpKWEElME2dgqpWVHMOuw0dxqbuRtEIqIKRdKa2s4a+fbKBXRjx5XdrmqpKZSTHkdUli1tJdGgshIuJH1lrW7z1CSjNmYKqVlRTNwdJKisqr/BSdSPCogJB25Z/zN3O4vJrTe6cSF9WyB3o3x3dys6h2unh0zrpQhyIi0mbsL62k+Gg16c2YgalWVnI0Lgur9xzxQ2QiwaUCQtqNQ2VVTJm/mQFZCQzodOqBRFPmLg5iVIHRMSma/K7JzF62m91anVpExC9qB1A3ZwrXWplJtQOpi5u9L5FgUwEh7cbf5m3kaJWT0/ukERMRfsrtNq1dGcSoAueC3ExqnJZHdBVCRMQvaqdwzfBDAZGRGI0B1gZgLQiRQFMBIe3CjkPlvPTFVnKzk+nTMaHBbR/++Q+DE1SAZSRGM6RbCu8v38OOw5rST0SkuTbsKyU2MpzUZszAVCsiPIzUhCg2+XkmJpFgUAEh7cKjc9ZjjOGMvqlEOdrPy/6C3Eyc1vLoh1qdWkSkuTbsPUJqfFSzZ2Cq1Skpmt1FFThdmolJWpf2cyYl7dbS7Yd5d/luhnfvQI+0uFCHE1RpCVEM657CByu/YcsBfcolItJU1loK95bSwQ8zMNXKTI7mcFkVB8sq/bI/kWBRASFtmrWWh95fS2KMg9P6dMAR5v0l/+PfPByEyILngtwsAB58f22IIxERab32H3HPwJSW0PzuS7Uyk2KwwCoNpJZWRgWEtGlzVn/D4m2HOa1XKp2SYnx6zPiJ1wU4quDqEBfJGb1T+WTtPpbvLAp1OCIirdK6b9wDqNPimz+AulaWZyamlbs0kFpaFxUQ0mZV1bh4+N/ryEyKZkSPDoQZ49PjrsjPDnBkwTc+J5NIRxi/f3d1qEMREWmVVng+gOncwbcPo3yRlhBFeJjRTEzS6qiAkDbr5S+2svVgOWf0SSXVj58YtUZxUQ7OHZDBkm1FzFu3N9ThiIi0Ost2FJOeEEWHOP91YQoPM6QnRLHlgGbKk9ZFBYS0SftKKpj8USF9OsaT3yU51OG0COf0zyA+ysGDH6zFWs34ISLiK2sty3cUkZEY1eA6Qk2RlRTNN8UV1Lhcft2vSCCpgJA26aEP1lJZ7XKfNEdHNOqxw0afF6CoQivSEcZ38jLZtK+MNwp2hjocEZFW45uSCvaXVpKVFI3xsTusr7KSYyg+Ws03xRV+3a9IIKmAkDbn800HeGfZbkb2TKVPx/hGP/5XT7/o/6BaiJE9U0mNj+TxOeuprHaGOhwRkVZh+Q73+IfMZP+Nf6iV6RlIvUIzMUkrogJC2pSqGhf/985qUuMjObNvapPm6v7jnZP8H1gLER5muGxoZ/YdqeTJjwpDHY6ISKuwfGcx4caQ7ccB1LU6pbj3uXT7Yb/vWyRQGn12ZYy51Riz2BizeP/+/YGISaTJnl+4hY37ShndL53MxOgm7aPgs4/8HFXLMrBzEv2zEnhhwVZ2HCoPdTgiIi3e8h1FdEyKIrGRXWJ9kRIbQWK0g4JtmmZbWo9GFxDW2n9Ya4dba4enp6cHIiaRJtlVdJSnP95A/6wEhnZL8Xs/1bbkiuHZOK3lV7NWhjoUEZEWzeWyrNhZTEZiNFEO/3fcMMbQMz2ejXuPaCC1tBrqwiRtgstl+X8zl+N0Wc7pl05clCPUIbVoqfFRjO2fzvwNB/h4raZ1FRE5lc0HyiitrCEr2f8DqGv1yIijpKKGdXuOBGT/Iv6mAkLahGlfbWPhxoOM7p9B744JzdrXm8vaxwxF5w3KJCkmgt++s4oapz71EhGpT+0A6qwADKCu1TM9DoD5G9Q1XFoHFRDS6m09UMYfP1hHn47xnNm7A+FhzfuEaO7MV/wUWcsW6QjjsmGd2V3kXjNDREROtmJnEVGOsLrBzoGQlRRDpCOML7ccCtgxRPxJBYS0ak6X5e43lmMMnJfTkcSY5q8Q+tyD9/khstYhN9s9oPq5/25mzR5NISgicqJlO4vJTIomIYBdY8PCDN1SY1m7u0QLfUqroAJCWrXnF2xh8bbDnDMgnV5pjV/zob0zxnD1qK44wg0/e3WpujJJk2mGPmmLqmpcrNntHkDdlGnBG6NXRjz7jlSys+hoQI8j4g8qIKTVWrr9MI/OWceArARO65lKWDO7LrVXiTERTByezab9ZTw6Z32ow5FWSjP0SVu07psSqp2WrJSmTQveGD084yAWbDgQ8GOJNJcKCGmVDpRWcvu0JSTFRDA+N5MEP87Nfd+fX/DbvlqLod07kNM5kX/O31I3YFBEpL2rfT/slBT4AqJraizGwBebVUBIy6cCQlqdGqeLn726lIOlVVyQl0XXDrF+3X+vAbl+3V9rceWorkRHhPHT15ZQWeMMdTgiIiG3fGcx8VEOMoNQQEQ5wumcHMOKnSUBP5ZIc6mAkFbnsTnr+WLzQcYNyiCvS5Lf5+X+0fjhft1faxEf5eCqUV3Ycego//P68lCHIyIScss8K1DHRfl/Ber69MqIZ+ehcorKq4JyPJGmUgEhrcp7K3bz3GebGdY9hTP7pOEI00vYn3KzkzmnXzrvr9jDPz7bHOpwRERCZuuBMjbuKyW7QyzhYYYZf38i4MfskR5HjcvyxeaDAT+WSHPo7Etajfkb9nPXjGV0T4tlfE4msZFabToQLsrvRK+MeB759zo+36i+uCLSPs1evhuAQZ0TAXj9uckBP2aPNPdA6s83qoCQlk0FhLQKS7Yf5sf/KiA9IYpLh3YmPSEqYMc67/IfBGzfrUFYmOGHZ3cnIcbBT6YVsKdYUwqKSPtireWdZbvonhZHdop7nF0wckNCTAQd4iJZsuNwwI8l0hwqIKTFW/dNCZOe/5q4KAeXDsuma2pcQI932/89GtD9twaxkQ5uGd2Do1VOrvvnV5RUVIc6JBGRoFmzp4RN+8vomxlPdEQ4ELzc0Csjjs37yjhaVROU44k0hQoIadE27D3C9VO/JizMcNmwzvRKD2zxAHDPNd8J+DFag04psVx3Rne2HCjjque+oLRSyUxE2ofZy3YTHmYY0Cmx7r5g5Yac7CSOVjt5a8muoBxPpClUQEiL9eXmg1zx98+prHFy6dDO9MtM8PuMS/XZvHZlwI/RWuRmJ/GD07ux7psjXPOPLynXJ2Ii0sa5XJbZy3fTMz2OrKSYuvuDlRsGdkoiKSaCF7/YGpTjiTSFCghpkWYv3831U78iJjKcq0d1JTfb/9O1im+Gdkvh6lFdWbWrmB9M+YqjVVojQkTarsXbDrOnuIJ+mQlEOoJ/mhQeZjirbxob9payaOuhoB9fxBcqIKRFcbksf/90E3e+tpTslFiuPq0r/bMSg1o8pKR3DNqxWosRPTpw5cguLNtRxGV/W8jekopQhyQiEhDvLNtFZHgY/Y/pvgTBzQ2n9UrFEWZ47r+bgnZMkcZQASEtxt6SCm584Wse+XAdgzoncuWobLoFeMB0ff75n4KgH7M1GNUrlevO6Mam/aVc8NRnFGzTJ2Mi0rZUO118sHIPvTvGk3HCbH/BzA1xUQ7yuyXz38L97DuiD2yk5VEBIS3CByv3MGHyZ3y15RDnDerI90d0oWNijPcHBkAwFgtqrYZ2S+HO8/rgsnDVc1/yypfbQh2SiIjfLNhwgMPl1fTPSsARfvwpUrBzwzn90ql2WqbM3xLU44r4QgWEhNTWA2XcPq2A26ctISHawfVndGN8TkcSYyJCFlMwFgtqzTp3iOXuC/qS3SGG37y9ihumfsXOw+WhDktEpFmqnS7+8skG4qLC6ZeVeNLPg50bOqfE0i01ljcLdlLjdAX12CLeqICQkDhQWsnv3lnFeU/+l4/X7uOsvmlcd1Z3BnVOwhGml2VLFxcdwR3j+nDeoI58sfkg4574L8/M20i1kpyItFKPz1nPku1FjBmQQWpcZKjDAWB0v3QOlVUxa6mmdJWWxRHqAKR92bD3CK98uY2ZBTs5Wu0kv2sKp/dOpXtaHOFhmmWpNQkPM1yYl8WIHim88fVOHpuznumLtnP7mN5cNqRz3eJLIiIt3X/W7OW5zzYzrHsKp/dKI6yF5KO8LskkRO/isTnrObtPGplJoenaK3IiY61t8oOHDx9uFy9e7MdwpC0qPlrNvHX7eO3r7Xy15RCOMEP/rERG9epAn46hmSavIZvWrKDXwLxQh9HqLN5yiI/W7GVfSSUpsRH88MweXDOyK+knDESU0DDGFFhrhwfjWMoN0prsOFTOd5+eT2JMBNee0fWU4+9ClRs27y/luXmbSIuP4q3bziArWUWE+E9Tc4OuQIjfOV2WDfuOsGDDAT5eu49FWw9R47Kkxkcyul8a+V1T6JwSQ0R4yyocpHmG9+jAsO4prNhRzH/X7+fJ/xQy+T+FDOmazPfyOjEhJ5POSnwi0oKUVFTz09eWUuOyfGdwVsgm72hIz/R4bh3Tiymfbubyv3+uIkJaBF2BkGYpPlrNlgNlbN5fyoZ9pSzbXsTynUWUexYb65gYTfe0WHp3jKdXRjxJMREtfkG4K/KzeXPZzlCH0eptO1jGoi2H2PBNKfuPVAKQlRTNsG4pDOuWQl52Et1T4+gQF9niXxNtga5AiHzrUFkVzy/Ywoufb6WssoaLhnTi7L7pDXalDXVu2LSvlCmfbqZDXCQPXjqIs/umE+VQV1FpHl2BEK9cLovLWlzWfZWgxuXC6bJUOy1VThdVNe6vimon5VVOjlbXUF7lpPhodd3XodIq9h6pZG9JBXtLKigqr67bf7gxZCZH0y8rgU7JMWR3iCU7JYbYyHCdILZD3VLj6JYah7WW7QfLWbGzmN2Hj/Lfwv28t2JP3XYJ0Q66p8bROTmG9IQo0uKjSEuIJDE6gvhoBwlRDmIjHURFhBHlCCPKEU5keBjh4QZHmPsrPMzoNSYix3G6LBXVTiprXBwqq2TbwXK2Hiyn8JsjzF6+m4pqJ/07JTKiRwoDOiW1+HF4vTLi+dGYnjw/fwu3vFxAXGQ44wZ0ZNyADDITo0mNjyI9Poq4qHC9J0rANauAWL27hAG//dBfscgJLF6uDlnqtqj7/5grSi7r/t4es11zhIcZYiPDiY92EBfpoEtqLAM7O0iJjSQ5PpKMhCjiohxEnzCm4Wi10w9HD67yqppQh9CmpCdGMW5gBuB+XX5TXMGOQ+UcLquiqNxdnO7edpTySmezXi9hBowxGMAccxuo+58Gcqpp6IetnHFEBG1aGeUGCYYTc6S14PLkQKfL/WFZfaIcYfTpGE9e1xR6ZcQR5QjD6XJRXuV9FrlQ54as5GjuubAfK3cWs253CXPXfMPs5bvr3TbMgCM8rMH3wLb8nic+Mk2b+rLRXZiMMbcCt3q+GeyIS9bcYg1wVVckhEVEHwlxGOaYf0/+9tibx31kcYqPL6zLZa3123ydLaSNWqx20z7GGGNMWP2vO88blT22bHbfsICrqiI+LDKqFPvt/XK8mvKSTOusiQ7U/pUbfNdu/qabwS9tZDDHnCJ7PpL3vL/4OY+Fgqu6IiE8MqYUE+bpx2Qt1no+M6x7q2zX74f6W/Oupry4o3U6Gz2oplljIIwxi4PVp7a1Uht5pzZqmNrHO7WRd8FsI/0+Gqb28U5t5J3ayDu1kXdNbSNNgyMiIiIiIj5TASEiIiIiIj5rbgHxD79E0bapjbxTGzVM7eOd2si7YLaRfh8NU/t4pzbyTm3kndrIuya1UbPGQIiIiIiISPuiLkwiIiIiIuIznwoIY8wFxpj1xpiNxpj76vl5lDFmhufnXxljuvs70JbOhzb6H2PMGmPMCmPMx8aYbqGIM1S8tc8x2000xlhjTLubNcGXNjLGXOl5Ha02xrwa7BhDzYe/s67GmHnGmKWev7ULQxFnqBhjnjfG7DPGrDrFz40x5mlP+60wxgxtxrGUF7xQXvBOucE75QbvlBsaFpDc4F5o7NRfQDiwCegJRALLgYEnbHM78Kzn9tXADG/7bUtfPrbRWCDWc/u29tRGvrSPZ7sE4DPgS2B4qONuaW0E9AGWAime7zNCHXcLbKN/ALd5bg8EtoY67iC30WhgKLDqFD+/EPg37rVfTgO+CuDvQnlBeaHZbeTZTrlBuaG5baTc4Ofc4MsViJHARmvtZmttFTAduOSEbS4BXvLcngmMM6ZdraHutY2stfOsteWeb78EsoMcYyj58hoC+APwKFARzOBaCF/a6EfAM9bawwDW2n1BjjHUfGkjCyR6bicB9S/R2kZZaz8DDjWwySXAy9btSyDZGJPVhEMpL3invOCdcoN3yg3eKTd4EYjc4EsB0RnYccz3Oz331buNtbYGKAZSfdh3W+FLGx3rZtyVXnvhtX2MMUOALtba94IZWAviy2uoL9DXGLPQGPOlMeaCoEXXMvjSRvcD1xljdgIfAD8LTmitRmPfq5qzH+UF5QVvlBu8U27wTrmh+RqdGxw+7LS+T4xOnLrJl23aMp+fvzHmOmA4cE5AI2pZGmwfY0wYMBmYFKyAWiBfXkMO3Jeqx+D+pHK+MSbHWlsU4NhaCl/a6BrgRWvtE8aY04F/edrIFfjwWgV/vVcrL3invOCdcoN3yg3eKTc0X6Pfr325ArET6HLM99mcfOmnbhtjjAP35aGGLpW0Nb60EcaY84BfAxdbayuDFFtL4K19EoAc4FNjzFbc/e9mt7PBcr7+nb1jra221m4B1uNOGu2FL210M/A6gLX2CyAaSAtKdK2DT+9VftqP8oLygjfKDd4pN3in3NB8jc4NvhQQi4A+xpgexphI3IPhZp+wzWzgRs/ticAn1jMqo53w2kaey7DP4U4S7a1/YoPtY60tttamWWu7W2u74+4LfLG1dnFowg0JX/7O3sY96BJjTBruy9abgxplaPnSRtuBcQDGmAG4k8T+oEbZss0GbvDMuHEaUGyt3dOE/SgveKe84J1yg3fKDd4pNzRfo3OD1y5M1toaY8xPgTm4R7o/b61dbYx5AFhsrZ0NTMV9OWgj7k+Yrm7uM2lNfGyjx4B44A3POMLt1tqLQxZ0EPnYPu2aj200BxhvjFkDOIF7rLUHQxd1cPnYRr8Ephhj7sJ9+XVSezppNca8hrsbQ5qnr+/vgAgAa+2zuPv+XghsBMqBHzblOMoL3ikveKfc4J1yg3fKDd4FIjdoJWoREREREfGZVqIWERERERGfqYAQERERERGfqYAQERERERGfqYAQERERERGfqYAQERERERGfqYAQERERERGfqYAQERERERGfqYAQERGRVskY090Ys8pf2xtjPvf8X1rPfcnGmNubE+8Jx7rTGLPWGDOtMTE243j3G2Pu9vd+pX1SASEiIiItlnELyvmKtfaMBu5LBvxWQHj2daG19lo/7lMkKFRAiIiIiF8YY6YbY2YYY74yxmwzxny3gW27G2PWGWNeMsasMMbMNMbEHvOztcaYvwFLgC7GmP8xxqzyfP3imF05TrGPt40xBcaY1caYW71t73lMKSc45r6HgV7GmGXGmMeMMX8wxvz8mO0eMsbcWc/jT4rbGPMs0BOYbYy5q57mCTfGTPHEPtcYE+N53HXGmK89MTxnjAlv6LkaY35tjFlvjPkI6HfM/XHGmPeNMcs9cV1V/29JpH4qIERERMRfBgObrbWjgGuB33nZvh/wD2ttHlDC8Z/w9wNettYOAdKAHwKjgNOAHxljhnjZx03W2mHAcOBOY0yqD8dsyH3AJmttvrX2HmAqcCOA5wrJ1cCJ3ZGG1Re3tfYnwG5grLV2cj3H6gM8Y60dBBQBVxhjBgBXAWdaa/MBJ+42rve5eo59NTAEuBwYccz+LwB2W2sHW2tzgA99bAMRQAWEiIiI+IHnU/I04Peeu9YAKV4etsNau9Bz+xXgrGN+ts1a+6Xn9lnALGttmbW2FHgLONvLPu40xiwHvgS64D4p93ZMn1lrtwIHPYXMeGCptfbgCZs1FHdDtlhrl3luFwDdgXHAMGCRMWaZ5/uenm3qe65ne45dbq0tAWYfs/+VwHnGmEeMMWdba4sb89xFHKEOQERERNqEHGCDtbbC8/1QYLmXx9gGvi875rZpzD6MMWOA84DTrbXlxphPgWgfjtlY/wQmAZnA8/X8vKG4G1J5zG0nEOPZ10vW2v897gCNe67uO60t9FyhuBD4kzFmrrX2gSbGKu2QrkCIiIiIPwwGuhpjoo0xcbivREwGMMZ8bIzpXM9juhpjTvfcvgZYcIp9fwZcaoyJ9ez7MmB+A/tIAg57Tqj74+4+1NhjnugIkHDCfbNwdwcaAcxpZNyN9TEw0RiTAWCM6WCM6capn+tnwGXGmBhjTAJwUe2OjDGdgHJr7SvA47iLPRGf6QqEiIiI+MNg3GMAPgUSgT9aaxd6xgf0Bg7V85i1wI3GmOeADcDf69uxtXaJMeZF4GvPXf+01i41xnQ/xT6cwE+MMSuA9bi79jTqmPXEcNAYs9Azxeq/rbX3WGurjDHzgCJrrdPXuH05Xj37WmOM+Q0w19Om1cAduMcvnPRcPceeASwDtnF84ZILPGaMcXn2c1tTYpL2y1jbnCt3IiIiImCM+Qz4kbV2/Qn35+Ae5Ps/J9zfHXjPM4i3VfKcyC8Bvm+t3RDqeESCsB47BAAAAHlJREFURV2YRERExB964f5E/zjW2lUnFg9tgTFmILAR+FjFg7Q3ugIhIiIiIiI+0xUIERERERHxmQoIERERERHxmQoIERERERHxmQoIERERERHxmQoIERERERHxmQoIERERERHxmQoIERERERHxmQoIERERERHx2f8Hh4W0+mlqmSQAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "figsize(11, 9)\n", + "\n", + "# For the already prepared, I'm using Binomial's conj. prior.\n", + "for k, N in enumerate(n_trials):\n", + " sx = plt.subplot(len(n_trials)/2, 2, k+1)\n", + " plt.xlabel(\"$p$, probability of heads\") \\\n", + " if k in [0, len(n_trials)-1] else None\n", + " plt.setp(sx.get_yticklabels(), visible=False)\n", + " heads = data[:N].sum()\n", + " d = pyro.distributions.Beta(1 + heads, 1 + N - heads)\n", + " y = torch.exp(d.log_prob(torch.tensor(x)))\n", + " plt.plot(x, y, label=\"observe %d tosses,\\n %d heads\" % (N, heads))\n", + " plt.fill_between(x, 0, y, color=\"#348ABD\", alpha=0.4)\n", + " plt.vlines(0.5, 0, 4, color=\"k\", linestyles=\"--\", lw=1)\n", + "\n", + " leg = plt.legend()\n", + " leg.get_frame().set_alpha(0.4)\n", + " plt.autoscale(tight=True)\n", + "\n", + "\n", + "plt.suptitle(\"Bayesian updating of posterior probabilities\",\n", + " y=1.02,\n", + " fontsize=14)\n", + "\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The posterior probabilities are represented by the curves, and our uncertainty is proportional to the width of the curve. As the plot above shows, as we start to observe data our posterior probabilities start to shift and move around. Eventually, as we observe more and more data (coin-flips), our probabilities will tighten closer and closer around the true value of $p=0.5$ (marked by a dashed line). \n", + "\n", + "Notice that the plots are not always *peaked* at 0.5. There is no reason it should be: recall we assumed we did not have a prior opinion of what $p$ is. In fact, if we observe quite extreme data, say 8 flips and only 1 observed heads, our distribution would look very biased *away* from lumping around 0.5 (with no prior opinion, how confident would you feel betting on a fair coin after observing 8 tails and 1 head?). As more data accumulates, we would see more and more probability being assigned at $p=0.5$, though never all of it.\n", + "\n", + "The next example is a simple demonstration of the mathematics of Bayesian inference. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Example: Bug, or just sweet, unintended feature?\n", + "\n", + "\n", + "Let $A$ denote the event that our code has **no bugs** in it. Let $X$ denote the event that the code passes all debugging tests. For now, we will leave the prior probability of no bugs as a variable, i.e. $P(A) = p$. \n", + "\n", + "We are interested in $P(A|X)$, i.e. the probability of no bugs, given our debugging tests $X$. To use the formula above, we need to compute some quantities.\n", + "\n", + "What is $P(X | A)$, i.e., the probability that the code passes $X$ tests *given* there are no bugs? Well, it is equal to 1, for a code with no bugs will pass all tests. \n", + "\n", + "$P(X)$ is a little bit trickier: The event $X$ can be divided into two possibilities, event $X$ occurring even though our code *indeed has* bugs (denoted $\\sim A\\;$, spoken *not $A$*), or event $X$ without bugs ($A$). $P(X)$ can be represented as:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\\begin{align}\n", + "P(X ) & = P(X \\text{ and } A) + P(X \\text{ and } \\sim A) \\\\\\\\[5pt]\n", + " & = P(X|A)P(A) + P(X | \\sim A)P(\\sim A)\\\\\\\\[5pt]\n", + "& = P(X|A)p + P(X | \\sim A)(1-p)\n", + "\\end{align}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We have already computed $P(X|A)$ above. On the other hand, $P(X | \\sim A)$ is subjective: our code can pass tests but still have a bug in it, though the probability there is a bug present is reduced. Note this is dependent on the number of tests performed, the degree of complication in the tests, etc. Let's be conservative and assign $P(X|\\sim A) = 0.5$. Then\n", + "\n", + "\\begin{align}\n", + "P(A | X) & = \\frac{1\\cdot p}{ 1\\cdot p +0.5 (1-p) } \\\\\\\\\n", + "& = \\frac{ 2 p}{1+p}\n", + "\\end{align}\n", + "This is the posterior probability. What does it look like as a function of our prior, $p \\in [0,1]$?" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "figsize(12.5, 4)\n", + "p = torch.linspace(0, 1, 50)\n", + "plt.plot(p, 2 * p / (1 + p), color=\"#348ABD\", lw=3)\n", + "plt.scatter(0.2, 2*(0.2)/1.2, s=140, c=\"#348ABD\")\n", + "plt.xlim(0, 1)\n", + "plt.ylim(0, 1)\n", + "plt.xlabel(\"Prior, $P(A) = p$\")\n", + "plt.ylabel(\"Posterior, $P(A|X)$, with $P(A) = p$\")\n", + "plt.title(\"Are there bugs in my code?\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can see the biggest gains if we observe the $X$ tests passed when the prior probability, $p$, is low. Let's settle on a specific value for the prior. I'm a strong programmer (I think), so I'm going to give myself a realistic prior of 0.20, that is, there is a 20% chance that I write code bug-free. To be more realistic, this prior should be a function of how complicated and large the code is, but let's pin it at 0.20. Then my updated belief that my code is bug-free is 0.33. \n", + "\n", + "Recall that the prior is a probability: $p$ is the prior probability that there *are no bugs*, so $1-p$ is the prior probability that there *are bugs*.\n", + "\n", + "Similarly, our posterior is also a probability, with $P(A | X)$ the probability there is no bug *given we saw all tests pass*, hence $1-P(A|X)$ is the probability there is a bug *given all tests passed*. What does our posterior probability look like? Below is a chart of both the prior and the posterior probabilities. \n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "figsize(12.5, 4)\n", + "colours = [\"#348ABD\", \"#A60628\"]\n", + "\n", + "prior = [0.20, 0.80]\n", + "posterior = [1./3, 2./3]\n", + "plt.bar([0, .7], prior, alpha=0.70, width=0.25,\n", + " color=colours[0], label=\"prior distribution\",\n", + " lw=\"3\", edgecolor=colours[0])\n", + "\n", + "plt.bar([0+0.25, .7+0.25], posterior, alpha=0.7,\n", + " width=0.25, color=colours[1],\n", + " label=\"posterior distribution\",\n", + " lw=\"3\", edgecolor=colours[1])\n", + "\n", + "plt.xticks([0.20, .95], [\"Bugs Absent\", \"Bugs Present\"])\n", + "plt.title(\"Prior and Posterior probability of bugs present\")\n", + "plt.ylabel(\"Probability\")\n", + "plt.legend(loc=\"upper left\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notice that after we observed $X$ occur, the probability of bugs being absent increased. By increasing the number of tests, we can approach confidence (probability 1) that there are no bugs present.\n", + "\n", + "This was a very simple example of Bayesian inference and Bayes rule. Unfortunately, the mathematics necessary to perform more complicated Bayesian inference only becomes more difficult, except for artificially constructed cases. We will later see that this type of mathematical analysis is actually unnecessary. First we must broaden our modeling tools. The next section deals with *probability distributions*. If you are already familiar, feel free to skip (or at least skim), but for the less familiar the next section is essential." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "_______\n", + "\n", + "## Probability Distributions\n", + "\n", + "\n", + "**Let's quickly recall what a probability distribution is:** Let $Z$ be some random variable. Then associated with $Z$ is a *probability distribution function* that assigns probabilities to the different outcomes $Z$ can take. Graphically, a probability distribution is a curve where the probability of an outcome is proportional to the height of the curve. You can see examples in the first figure of this chapter. \n", + "\n", + "We can divide random variables into three classifications:\n", + "\n", + "- **$Z$ is discrete**: Discrete random variables may only assume values on a specified list. Things like populations, movie ratings, and number of votes are all discrete random variables. Discrete random variables become more clear when we contrast them with...\n", + "\n", + "- **$Z$ is continuous**: Continuous random variable can take on arbitrarily exact values. For example, temperature, speed, time, color are all modeled as continuous variables because you can progressively make the values more and more precise.\n", + "\n", + "- **$Z$ is mixed**: Mixed random variables assign probabilities to both discrete and continuous random variables, i.e. it is a combination of the above two categories. \n", + "\n", + "### Discrete Case\n", + "If $Z$ is discrete, then its distribution is called a *probability mass function*, which measures the probability $Z$ takes on the value $k$, denoted $P(Z=k)$. Note that the probability mass function completely describes the random variable $Z$, that is, if we know the mass function, we know how $Z$ should behave. There are popular probability mass functions that consistently appear: we will introduce them as needed, but let's introduce the first very useful probability mass function. We say $Z$ is *Poisson*-distributed if:\n", + "\n", + "$$P(Z = k) =\\frac{ \\lambda^k e^{-\\lambda} }{k!}, \\; \\; k=0,1,2, \\dots $$\n", + "\n", + "$\\lambda$ is called a parameter of the distribution, and it controls the distribution's shape. For the Poisson distribution, $\\lambda$ can be any positive number. By increasing $\\lambda$, we add more probability to larger values, and conversely by decreasing $\\lambda$ we add more probability to smaller values. One can describe $\\lambda$ as the *intensity* of the Poisson distribution. \n", + "\n", + "Unlike $\\lambda$, which can be any positive number, the value $k$ in the above formula must be a non-negative integer, i.e., $k$ must take on values 0,1,2, and so on. This is very important, because if you wanted to model a population you could not make sense of populations with 4.25 or 5.612 members. \n", + "\n", + "If a random variable $Z$ has a Poisson mass distribution, we denote this by writing\n", + "\n", + "$$Z \\sim \\text{Poi}(\\lambda) $$\n", + "\n", + "One useful property of the Poisson distribution is that its expected value is equal to its parameter, i.e.:\n", + "\n", + "$$E\\large[ \\;Z\\; | \\; \\lambda \\;\\large] = \\lambda $$\n", + "\n", + "We will use this property often, so it's useful to remember. Below, we plot the probability mass distribution for different $\\lambda$ values. The first thing to notice is that by increasing $\\lambda$, we add more probability of larger values occurring. Second, notice that although the graph ends at 15, the distributions do not. They assign positive probability to every non-negative integer." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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g5syZLFiwYLN5hnPP/cqVK9mwYQOTJ09m5cqV/OhHP+Lcc88dlnVb3EuSJKltTJ7YBdtPGt71DeCBBx7ghhtu4OabbwYqZ8258MILt7i9k046iRtvvJGnn36aadOmcf7553PaaadxzDHHcNlllzF16lSefPJJTjjhBAB6e3t597vfzZw5c7a4zWoW95IkSWob27X4Qpf77rsvt9566ybDd9555xav75vf/Gbd8QsXLtx4f++99+buu+/e4jYGYnEvSZKktjBWriI7kjygVpIkSeoQFveSJElSh7C4lyRJkjqExb0kSZLUISzuJUmSpA5hcS9JkiR1CE+FKUmSpLZw2+nnjdi6Z19y/oitu520tLiPiDnARcA44LLM/GzN9A8CZwDrgReBuZl5XzHto8BpxbQPZ+b1rcwuSZKkkbduRQ9rV/QM2/om7DCZ8TtMHnS+e+65h2OPPZZ58+bxoQ99aEhtrl+/nlmzZrH77rtz7bXXbjb90Ucf5ZRTTuGJJ55gq622Yu7cuZx11llDarNPy4r7iBgHXAwcBXQDt0fEgr7ivXB1Zv5zMf9xwBeAORGxP3Ai8CpgKvDjiPijzFzfqvySJEkaeWtX9LDqkceHb4XTpzZU3M+cOZP58+dz9tlnD7m4v+iii9hvv/144YUX6k7v6uriH/7hH3jta19LT08PBx98MEcddRT777//kNqF1u65nw0szcyHACJiPnA8sLG4z8zqLbAtkMX944H5mbkG+G1ELC3W98tWBJckSVJrTTls1pDXsfymRU3Nv+uuu7JkyZIhtdnd3c1//ud/8vGPf5wvfOELdefZbbfd2G233QCYPHky++23H4899tioK+53Bx6tGu4GDqmdKSLOAM4GJgBvrFr2lppld6+z7FxgLsD06dOHJbQkSZLGhnnz5rFmzRqWLVvGnnvuucm0ww8/nJ6ezbsLff7zn+fII4/cOPyRj3yEv/u7v6s7bz0PP/wwixcv5pBDNiuLt0gri/uoMy43G5F5MXBxRLwb+ARwahPLXgpcCjBr1qzNpkuSJEn1XHfddaxcuZK3vOUtLFmyZLPi/he/+MWg67j22mvZddddOfjgg7nxxhsHnf/FF1/k7W9/O1/84hfZfvvttzT6JlpZ3HcDe1QNTwMG6lA1H/jKFi4rSZIkNWT16tWcc845LFiwgCuuuIJ7772XY445ZpN5Gtlzf/PNN7NgwQIWLlzI6tWreeGFFzj55JP5+te/vtly69at4+1vfzvvec97eNvb3jZsj6WVxf3twD4RsRfwGJUDZN9dPUNE7JOZvykG3wL03V8AXB0RX6ByQO0+wG0tSS1JkqSWa7a//FB86lOf4pRTTmHGjBnMnDmTBQsWbDZPI3vuP/OZz/CZz3wGgBtvvJHPf/7zdQv7zOS0005jv/324+yzzx76A6jSsuI+M3sj4kzgeiqnwrw8M5dExAXAosxcAJwZEUcC64DnqHTJoZjv21QOvu0FzvBMOZIkSZ1nwg6TYfrU4V3fAB544AFuuOEGbr75ZqBy1pwLL7xw2Nrvc8wxx3DZZZcxdepUbr75Zq666ipmzpzJgQceCMCFF1642a8FW6Kl57nPzIXAwppx51bd7/cEn5n5aeDTI5dOkiRJZRvf4Hnph8u+++7LrbfeusnwnXfeOeT1vuENb+ANb3jDxuGFC18qgQ877DAyR+bwUK9QK0mSpLYwVq4iO5K2KjuAJEmSpOFhcS9JkqRSjVQXldFuS7aLxb0kSZJKM2nSJJ555hkL/BqZyTPPPMOkSZOaWs4+95IkSSrNtGnT6O7uZvny5WVHaTuTJk1i2rRpTS1jcS9JkqTSjB8/nr322qvsGB3DbjmSJElSh7C4lyRJkjqExb0kSZLUISzuJUmSpA5hcS9JkiR1CIt7SZIkqUNY3EuSJEkdwuJekiRJ6hAW95IkSVKHsLiXJEmSOoTFvSRJktQhLO4lSZKkDmFxL0mSJHUIi3tJkiSpQ1jcS5IkSR3C4l6SJEnqEC0t7iNiTkQ8EBFLI2JenelnR8R9EfGriPhJROxZNW19RNxV3Ba0MrckSZI0GnS1qqGIGAdcDBwFdAO3R8SCzLyvarbFwKzMXBURHwL+DnhXMe33mXlgq/KqPBde/+uyI2ziY0e/suwIkiRJDWlZcQ/MBpZm5kMAETEfOB7YWNxn5s+q5r8FOLmF+dRGXlzTS8+a3lIzTJ7YxXYTW/kWkSRJGppWVi67A49WDXcDhwww/2nAD6uGJ0XEIqAX+Gxm/qB2gYiYC8wFmD59+pADqzw9a3p54oXV5YbYfpLFvSRJGlVaWblEnXFZd8aIk4FZwB9XjZ6emY9HxN7ATyPinsx8cJOVZV4KXAowa9asuuvW6HLQtB1LaXdx9/OltCtJkjQUrTygthvYo2p4GvB47UwRcSTwceC4zFzTNz4zHy/+PgTcCBw0kmElSZKk0aaVxf3twD4RsVdETABOBDY5601EHAT8C5XC/qmq8TtFxMTi/i7A66nqqy9JkiSphd1yMrM3Is4ErgfGAZdn5pKIuABYlJkLgL8HtgO+ExEAj2TmccB+wL9ExAYqX0g+W3OWHUmSJGnMa+nRgpm5EFhYM+7cqvtH9rPcfwMzRzadJEmSNLp5hVpJkiSpQ1jcS5IkSR3C4l6SJEnqEBb3kiRJUoewuJckSZI6RMPFfUR8qs64ccMbR5IkSdKWambP/e4RcVLfQETsCvx4+CNJkiRJ2hLNnOf+A8D1EfEgkMAVwN+MSCpJkiRJTRu0uI+IfwPuBBYDZwBXA73AWzNz6cjGkyRJktSoRrrlfK2Y7/1UCvsZwHPAyRHxjpGLJkmSJKkZg+65z8yfAD/pG46ILmB/4ADgUOCaEUs3Sl14/a/LjrDRx45+ZdkRJEmS1CLN9LkHIDN7gV8Vt6uGPVGHeHFNLz1rektrf/LELrab2PTTK0mSpFHM6m+E9Kzp5YkXVpcXYPtJFveSJEljjNXfCDto2o4tb3Nx9/Mtb1OSJEnlG/SA2oi4qvh71sjHkSRJkrSlGjlbzsERsSfw/ojYKSJeVn0b6YCSJEmSGtNIt5x/Bq4D9gbuAKJqWhbjJUmSJJVs0D33mflPmbkfcHlm7p2Ze1XdLOwlSZKkNtHwAbWZ+aGIOAA4vBj188z81cjEkiRJktSsRvrcAxARHwa+Aexa3L4REX8xUsEkSZIkNaeZU2H+OXBIZq4EiIjPAb8EvjQSwSRJkiQ1p+E991QOpF1fNbyeTQ+ulSRJklSiZor7K4BbI+KTEfFJ4Bbgq800FhFzIuKBiFgaEfPqTD87Iu6LiF9FxE+KU3D2TTs1In5T3E5tpl1JkiRpLGjmgNovRMSNwGFU9ti/LzMXN7p8RIwDLgaOArqB2yNiQWbeVzXbYmBWZq6KiA8Bfwe8qzif/nnALCqn37yjWPa5RtuXJEmSOl0zfe7JzDuBO7ewrdnA0sx8CCAi5gPHAxuL+8z8WdX8twAnF/ePBm7IzGeLZW8A5gDf3MIskiRJUsdpplvOUO0OPFo13F2M689pwA+bWTYi5kbEoohYtHz58iHGlSRJkkaXVhb39Q6+zbozRpxMpQvO3zezbGZempmzMnPWlClTtjioJEmSNBo1c577MyNipyG01Q3sUTU8DXi8TjtHAh8HjsvMNc0sK0mSJI1lzfS5fwWVg2DvBC4Hrs/Munve+3E7sE9E7AU8BpwIvLt6hog4CPgXYE5mPlU16XrgwqovF28CPtpE29KYctvp55UdYaPZl5xfdgRJksaMZs6W84mI+P+pFNbvA74cEd8GvpqZDzawfG9EnEmlUB8HXJ6ZSyLiAmBRZi6g0g1nO+A7EQHwSGYel5nPRsTfUvmCAHBB38G1kupbt6KHtSt6Smt/wg6TGb/D5NLalyRpLGr2bDkZEU8ATwC9wE7ANRFxQ2ae08DyC4GFNePOrbp/5ADLXk7lFwNJDVi7oodVj5TYe236VIt7SZJarOHiPiI+DJwKPA1cBvx1Zq6LiK2A3wCDFveSWm/KYbNa3ubymxa1vE1JktTcnvtdgLdl5rLqkZm5ISKOHd5YkiRJkprVzKkwJ9YW9hHxOYDMvH9YU0mSJElqWjPF/VF1xr15uIJIkiRJGppBu+VExIeA04G9I+JXVZMmAzePVDCpnXmqSUmS1I4a6XN/NfBD4DPAvKrxPZ6OUmOZp5qUJEntZtDiPjNXACuAk0Y+jjR6eKpJSZLUbhrplnNTZh4WET1AAlE1OTNz+xFLJ40CnmpSkiS1i0b23B9W/HUXoSRJktTGGtlz37fHvi733EuSJEntoZE99+6xlyRJkkaBZs5zL0mSJKmNDVrcR8RNxd+eiHih+Nt3e2HkI0qSJElqhAfUSpIkSR2ikYtYARARk6hcqfYwKgfY/gL458xcPULZJEmSJDWh4eIe+DegB/hSMXwScBXwzuEOJamz3Xb6eWVH2Gj2JeeXHUGSpGHTTHG/b2YeUDX8s4i4e7gDSRob1q3oYe2KntLan7DDZK/wK0nqOM0U94sj4tDMvAUgIg4Bbh6ZWJI63doVPax65PHyAkyfanEvSeo4jVzE6h4qfezHA6dExCPF8J7AfSMbT1Knm3LYrJa3ufymRS1vU5KkVmhkz/2xI55CkiRJ0pA1cirMZa0IIkmSJGlomulzT0TsBOwDTOobl5k/H+5QkiRJkpo36BVq+0TEnwM/B64Hzi/+frKZxiJiTkQ8EBFLI2JenelHRMSdEdEbEe+ombY+Iu4qbguaaVeSJEkaCxou7oGzgNcByzLzT4CDgOWNLhwR44CLgTcD+wMnRcT+NbM9ArwXuLrOKn6fmQcWt+OayC1JkiSNCc10y1mdmasjgoiYmJm/joh9m1h+NrA0Mx8CiIj5wPFUnXEnMx8upm1oYr2SJEmSaG7PfXdE7Aj8ALghIv4daOYk1bsDj1avrxjXqEkRsSgibomIt9abISLmFvMsWr684R8VJEmSpI7Q8J77zDyhuPvJiPgZsANwXRNtRb3VNrH89Mx8PCL2Bn4aEfdk5oM1GS8FLgWYNWtWM+uWJEmSRr2Gi/uImAScDhxGpSi/iSb3/AN7VA1Po4k9/5n5ePH3oYi4kUqf/wcHXEiSJEkaQ5opzv8NeBXwJeDLwH7AVU0sfzuwT0TsFRETgBOBhs56ExE7RcTE4v4uwOvx6riSJEnSJpo5oHbfzDygavhnEXF3owtnZm9EnEnlFJrjgMszc0lEXAAsyswFEfE64PvATsCfRcT5mfkqKl8k/qU40HYr4LOZaXEvSZIkVWmmuF8cEYdm5i0AEXEIcHMzjWXmQmBhzbhzq+7fTqW7Tu1y/w3MbKYtSZIkaawZtLiPiHuo9LEfD5wSEY8Uk6Zj1xhJkiSpbTSy5/7YEU8hSZIkacgGLe4zc1nf/Yg4ADi8GPxFZjbc516SJEnSyGr4bDkRcRbwDWDX4vb1iPiLkQomSZIkqTnNHFB7GnBIZq4EiIjPAb+kcmpMSZIkSSVr5jz3AayvGl5P/avOSpIkSSpBM3vurwBujYjvF8NvBb46/JE01u188VfYeu16dlvXy87bTiwlw0Er17D1+C62mTAOjr6olAySJEnNaqi4j4gAvgPcCBxGZY/9+zJz8chF01g2btVKtn7hRbpWNvP9c/hsvbqXcdtvBxO2L6V9SZKkLdFQ5ZSZGRE/yMyDgTtHOJNE16pVTHzuGbq6xpXS/ja961nftRXsWL+4X9z9PF1Pv0jXyrV0dz/f4nQwaeVaep9+kd6tnmd2y1uXJEntqpndordExOuKq8hKLbH6VfuX0/Dd9ww6y/oNCRs2sGrt+kHnHW5dGzZU2pckSarSTHH/J8AHI+JhYCWVrjmZma8ZiWBSu+vdkOSG5Pfrelve9jYbkvUbknJ+15AkSe2qmeL+zSOWQhrFyjroV5IkqVYzxf2TwOlUDqhN4CbgKyMRSpIkSVLzminu/w3o4aWLVp0EXAW8c7hDSZIkSWpeM8X9vpl5QNXwzyLi7uEOJEllu+3088qOsNHsS84vO4IkaRRpprhfHBGHZuYtABFxCHDzyMSSpHKtW9HD2hU9pbU/YYfJjN9hcmntS5JGp2aK+0OAUyLikWJ4OnB/RNyDZ82R1FNHAJEAABC6SURBVGHWruhh1SOPlxdg+lSLe0lS05op7ueMWApJalNTDpvV8jaX37So5W1KkjpDw8V9Zi4bySCSJEmShmarsgNIkiRJGh4W95IkSVKHsLiXJEmSOkRLi/uImBMRD0TE0oiYV2f6ERFxZ0T0RsQ7aqadGhG/KW6nti61JEmSNDq0rLiPiHHAxcCbgf2BkyJi/5rZHgHeC1xds+zLgPOonI5zNnBeROw00pklSZKk0aSVe+5nA0sz86HMXAvMB46vniEzH87MXwEbapY9GrghM5/NzOeAG/DUnJIkSdImWlnc7w48WjXcXYwbtmUjYm5ELIqIRcuXL9/ioJIkSdJo1MriPuqMy+FcNjMvzcxZmTlrypQpTYWTJEmSRrtWFvfdwB5Vw9OARq/tPpRlJUmSpDGhlcX97cA+EbFXREwATgQWNLjs9cCbImKn4kDaNxXjJEmSJBVaVtxnZi9wJpWi/H7g25m5JCIuiIjjACLidRHRDbwT+JeIWFIs+yzwt1S+INwOXFCMkyRJklToamVjmbkQWFgz7tyq+7dT6XJTb9nLgctHNKAkSZI0inmFWkmSJKlDWNxLkiRJHcLiXpIkSeoQFveSJElSh7C4lyRJkjqExb0kSZLUISzuJUmSpA5hcS9JkiR1CIt7SZIkqUNY3EuSJEkdwuJekiRJ6hBdZQeQJDXnttPPKzvCRrMvOb/sCJKkKhb3kjQKrVvRw9oVPaW1P2GHyYzfYXJp7UuS6rO4l6RRaO2KHlY98nh5AaZPtbiXpDZkcS9Jo9iUw2a1vM3lNy1qeZuSpMZ4QK0kSZLUISzuJUmSpA5ht5wxaOeLv8LWa9ez27pedt52YsvbP2jlGrYe38U2E8bB0Re1vH1JkqROZXE/Ro1btZKtX3iRrpWtfwlsvbqXcdtvBxO2b3nbkiRJnczifozqWrWKic89Q1fXuJa3vU3vetZ3bQU7WtxLkiQNJ4v7MW71q/ZvfaN339P6NiVJksYAD6iVJEmSOkRLi/uImBMRD0TE0oiYV2f6xIj4VjH91oiYUYyfERG/j4i7its/tzK3JEmSNBq0rFtORIwDLgaOArqB2yNiQWbeVzXbacBzmfmHEXEi8DngXcW0BzPzwFbllSRJkkabVu65nw0szcyHMnMtMB84vmae44GvFfevAf40IqKFGSVJkqRRq5XF/e7Ao1XD3cW4uvNkZi+wAti5mLZXRCyOiP+KiMPrNRARcyNiUUQsWr58+fCmlyRJktpcK8+WU28PfDY4z++A6Zn5TEQcDPwgIl6VmS9sMmPmpcClALNmzapdtzRmLO5+nq6nX6Rr5Vq6u59vefuTVq6l9+kX6d3qeWa3vHVJksauVhb33cAeVcPTgMf7mac7IrqAHYBnMzOBNQCZeUdEPAj8EbBoxFNLo9T6DQkbNrBq7fqWt921YUOlfUmS1FKtLO5vB/aJiL2Ax4ATgXfXzLMAOBX4JfAO4KeZmRExhUqRvz4i9gb2AR5qXXRp9OndkOSG5Pfrelve9jYbkvUbktZfIk2SpLGtZcV9ZvZGxJnA9cA44PLMXBIRFwCLMnMB8FXgqohYCjxL5QsAwBHABRHRC6wHPpiZz7YquzSa7bztxLIjSJKkFmnpFWozcyGwsGbcuVX3VwPvrLPcd4HvjnhASZIkaRTzCrWSJElSh2jpnntJUue77fTzyo6w0exLzi87giS1lMW9JGnYrVvRw9oVPaW1P2GHyYzfYXJp7UtSWSzuJUnDbu2KHlY9Unu24xaaPtXiXtKYZHEvSRoxUw6b1fI2l9/kJVAkjV0eUCtJkiR1CIt7SZIkqUNY3EuSJEkdwuJekiRJ6hAW95IkSVKHsLiXJEmSOoTFvSRJktQhLO4lSZKkDuFFrCRJY8ptp59XdoSNZl9yftkRJHUYi3tJ0pizbkUPa1f0lNb+hB0mM36HyaW1L6lzWdyPgJ0v/gpbr13Pbut62XnbiS1v/6CVa9h6fBfbTBgHR1/U8valwSzufp6up1+ka+Vaurufb3n7k1aupffpF+nd6nlmt7x1tYO1K3pY9cjj5QWYPtXiXtKIsLgfIeNWrWTrF16ka2XrN/HWq3sZt/12MGH7lrctNWr9hoQNG1i1dn3L2+7asKHSvsa8KYfNanmby29a1PI2JY0dFvcjpGvVKiY+9wxdXeNa3vY2vetZ37UV7Ghxr/bVuyHJDcnv1/W2vO1tNiTrNyStf3dKkjSyLO5H2OpX7d/6Ru++p/VtSluojK5rkiR1Kot7SZLaSDudzQc8o4802ljcS5LUZso+mw94Rh9ptLK4lySpzZR+Nh/wjD7SKNXS4j4i5gAXAeOAyzLzszXTJwL/BhwMPAO8KzMfLqZ9FDgNWA98ODOvb2F0SWOIp+pUuyjjbD4w+Bl92qnrkN2GpE21rLiPiHHAxcBRQDdwe0QsyMz7qmY7DXguM/8wIk4EPge8KyL2B04EXgVMBX4cEX+Uma0/h56kMaGdT9Xplw+1g7K7Dg3UbcgvHxrLWrnnfjawNDMfAoiI+cDxQHVxfzzwyeL+NcCXIyKK8fMzcw3w24hYWqzvly3KvuXa/cw15hu6ds9ovi3Sd6rObe6/b/CZh7ttGPRUnX1fPrruubdVsTbqyzeQ51atpWflWp6+/r9bE6rG5FVr2XaA6eYb3GAZn/zdM/QsK6/r0OQ9pzJtgG5Djz32NM8++WwLE23qZS9/Gbvvvku/06//13/n4f/4aQsTbWrGn72Ro//P8f1ON9/ABstXpshszYVcIuIdwJzM/PNi+H8Dh2TmmVXz3FvM010MPwgcQqXgvyUzv16M/yrww8y8pqaNucDcYnBf4IERfVDtbRfg6bJDDKDd80H7ZzTf0JhvaMw3NO2eD9o/o/mGxnxDU3a+PTNzSr0JrdxzH3XG1X6z6G+eRpYlMy8FLm0+WueJiEWZWU5nzQa0ez5o/4zmGxrzDY35hqbd80H7ZzTf0JhvaNo531YtbKsb2KNqeBpQ+3vexnkiogvYAXi2wWUlSZKkMa2Vxf3twD4RsVdETKBygOyCmnkWAKcW998B/DQr/YYWACdGxMSI2AvYB7itRbklSZKkUaFl3XIyszcizgSup3IqzMszc0lEXAAsyswFwFeBq4oDZp+l8gWAYr5vUzn4thc4wzPlDKrduye1ez5o/4zmGxrzDY35hqbd80H7ZzTf0JhvaNo2X8sOqJUkSZI0slrZLUeSJEnSCLK4lyRJkjqExX0Hiog5EfFARCyNiHll56kWEZdHxFPFNQ3aTkTsERE/i4j7I2JJRJxVdqZqETEpIm6LiLuLfG156cOIGBcRiyPi2rKz1BMRD0fEPRFxV0QsKjtPrYjYMSKuiYhfF6/F/1V2pj4RsW+x3fpuL0TER8rOVS0i/rJ4f9wbEd+MiEllZ6oWEWcV2Za0w7ar97kcES+LiBsi4jfF353aLN87i+23ISJKPx1hPxn/vngP/yoivh8RO7ZZvr8tst0VET+KiKntlK9q2l9FREZE/1cEG2H9bL9PRsRjVZ+Fx5SVr5bFfYeJiHHAxcCbgf2BkyJi/3JTbeJKYE7ZIQbQC/x/mbkfcChwRpttvzXAGzPzAOBAYE5EHFpypnrOAu4vO8Qg/iQzD2zT8xRfBFyXma8EDqCNtmVmPlBstwOBg4FVwPdLjrVRROwOfBiYlZmvpnIChxPLTfWSiHg18H+oXGX9AODYiNin3FR1P5fnAT/JzH2AnxTDZbmSzfPdC7wN+HnL09R3JZtnvAF4dWa+Bvgf4KOtDlXlSjbP9/eZ+ZrivXwtcG7LU73kSurUBhGxB3AU8EirA9W4kvq1yz/2fR5m5sIWZ+qXxX3nmQ0szcyHMnMtMB9om+sjZ+bPqZwJqS1l5u8y887ifg+Vomr3clO9JCteLAbHF7e2Oio+IqYBbwEuKzvLaBQR2wNHUDl7GJm5NjOfLzdVv/4UeDAzl5UdpEYXsHVxvZRtaK/rouxH5YrrqzKzF/gv4IQyA/XzuXw88LXi/teAt7Y0VJV6+TLz/sxsm6vQ95PxR8VzDHALlWv0lKKffC9UDW5Lif9LBqgN/hE4h5L/z7V77VLL4r7z7A48WjXcTRsVp6NJRMwADgJuLTfJpoouL3cBTwE3ZGZb5QO+SOXDeEPZQQaQwI8i4o6ImFt2mBp7A8uBK4quTZdFxLZlh+rHicA3yw5RLTMfAz5PZU/f74AVmfmjclNt4l7giIjYOSK2AY5h04s0touXZ+bvoLLTA9i15Dyj3fuBH5YdolZEfDoiHgXeQ7l77jcTEccBj2Xm3WVnGcCZRdemy8vsulbL4r7zRJ1xbbVndzSIiO2A7wIfqdm7UbrMXF/8jDoNmF38zN8WIuJY4KnMvKPsLIN4fWa+lkr3tTMi4oiyA1XpAl4LfCUzDwJWUm6XiLqKixEeB3yn7CzVin+wxwN7AVOBbSPi5HJTvSQz7wc+R6XLxnXA3VS6A6pDRcTHqTzH3yg7S63M/Hhm7kEl25ll5+lTfPH9OG32haPGV4A/oNJF9nfAP5Qb5yUW952nm033Ak2jvX6SbnsRMZ5KYf+NzPxe2Xn6U3TVuJH2Oobh9cBxEfEwlS5hb4yIr5cbaXOZ+Xjx9ykq/cVnl5toE91Ad9UvMtdQKfbbzZuBOzPzybKD1DgS+G1mLs/MdcD3gP+n5EybyMyvZuZrM/MIKj/1/6bsTHU8GRG7ARR/nyo5z6gUEacCxwLvyfa+sNDVwNvLDlHlD6h8Qb+7+H8yDbgzIl5RaqoqmflksbNtA/CvtNH/EYv7znM7sE9E7FXsWTsRWFByplEjIoJKX+f7M/MLZeepFRFT+s64EBFbUylkfl1uqpdk5kczc1pmzqDy2vtpZrbNXlOAiNg2Iib33QfeRKWrRFvIzCeARyNi32LUn1K5One7OYk265JTeAQ4NCK2Kd7Pf0obHZAMEBG7Fn+nUzkotB234wLg1OL+qcC/l5hlVIqIOcDfAMdl5qqy89SqOZD7ONrrf8k9mblrZs4o/p90A68tPh/bQt+X38IJtNH/ka6yA2h4ZWZvRJwJXE/lLBGXZ+aSkmNtFBHfBN4A7BIR3cB5mfnVclNt4vXA/wbuKfq1A3ysjY6C3w34WnFWpK2Ab2dmW55uso29HPh+pe6jC7g6M68rN9Jm/gL4RvEF/SHgfSXn2UTxk/lRwAfKzlIrM2+NiGuAO6l0hVhM+10m/rsRsTOwDjgjM58rM0y9z2Xgs8C3I+I0Kl+Y3tlm+Z4FvgRMAf4zIu7KzKPbLONHgYnADcXnzS2Z+cE2yndMsRNhA7AMKCVbf/naqTboZ/u9ISIOpNL1+WHa6PMw2vtXIkmSJEmNsluOJEmS1CEs7iVJkqQOYXEvSZIkdQiLe0mSJKlDWNxLkiRJHcLiXpIkSeoQFveSJElSh7C4lyRtsYg4MiKuKjuHJKnC4l6SNBQHULkKrCSpDVjcS5KG4gBgcURMjIgrI+LCiIiyQ0nSWNVVdgBJ0qh2APAUcD1wWWZ+veQ8kjSmRWaWnUGSNApFxHjgaWAZ8IHM/GXJkSRpzLNbjiRpS+0P3A70AutLziJJwuJekrTlDgD+GzgRuCIiXl5yHkka8yzuJUlb6gDg3sz8H+BvgG8XXXUkSSWxz70kSZLUIdxzL0mSJHUIi3tJkiSpQ1jcS5IkSR3C4l6SJEnqEBb3kiRJUoewuJckSZI6hMW9JEmS1CH+L8X320z5HoKdAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "figsize(12.5, 4)\n", + "\n", + "lambda_ = [1.5, 4.25]\n", + "\n", + "d = dist.Poisson(torch.tensor(lambda_))\n", + "x = torch.arange(16).float().view(-1, 1)\n", + "y = torch.exp(d.log_prob(x))\n", + "\n", + "colours = [\"#348ABD\", \"#A60628\"]\n", + "plt.bar(x.flatten(), y[:, 0], color=colours[0],\n", + " label=\"$\\lambda = %.1f$\" % lambda_[0], alpha=0.60,\n", + " edgecolor=colours[0], lw=\"3\")\n", + "\n", + "plt.bar(x.flatten(), y[:, 1], color=colours[1],\n", + " label=\"$\\lambda = %.1f$\" % lambda_[1], alpha=0.60,\n", + " edgecolor=colours[1], lw=\"3\")\n", + "\n", + "plt.xticks(x.flatten().numpy() + 0.4, x.flatten().numpy().astype(int))\n", + "plt.legend()\n", + "plt.ylabel(\"probability of $k$\")\n", + "plt.xlabel(\"$k$\")\n", + "plt.title(\"Probability mass function of a Poisson random variable; differing \\\n", + "$\\lambda$ values\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Continuous Case\n", + "Instead of a probability mass function, a continuous random variable has a *probability density function*. This might seem like unnecessary nomenclature, but the density function and the mass function are very different creatures. An example of continuous random variable is a random variable with *exponential density*. The density function for an exponential random variable looks like this:\n", + "\n", + "$$f_Z(z | \\lambda) = \\lambda e^{-\\lambda z }, \\;\\; z\\ge 0$$\n", + "\n", + "Like a Poisson random variable, an exponential random variable can take on only non-negative values. But unlike a Poisson variable, the exponential can take on *any* non-negative values, including non-integral values such as 4.25 or 5.612401. This property makes it a poor choice for count data, which must be an integer, but a great choice for time data, temperature data (measured in Kelvins, of course), or any other precise *and positive* variable. The graph below shows two probability density functions with different $\\lambda$ values. \n", + "\n", + "When a random variable $Z$ has an exponential distribution with parameter $\\lambda$, we say *$Z$ is exponential* and write\n", + "\n", + "$$Z \\sim \\text{Exp}(\\lambda)$$\n", + "\n", + "Given a specific $\\lambda$, the expected value of an exponential random variable is equal to the inverse of $\\lambda$, that is:\n", + "\n", + "$$E[\\; Z \\;|\\; \\lambda \\;] = \\frac{1}{\\lambda}$$" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "lambda_ = [0.5, 1]\n", + "d = dist.Exponential(torch.tensor(lambda_))\n", + "\n", + "x = torch.linspace(0, 4, 100).view(-1, 1)\n", + "y = torch.exp(d.log_prob(x))" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "for i, c in enumerate(colours):\n", + " plt.plot(x.flatten(), y[:, i], lw=3,\n", + " color=c, label=\"$\\lambda = %.1f$\" % lambda_[i])\n", + " plt.fill_between(x.flatten(), y[:, i], color=c, alpha=.33)\n", + "\n", + "plt.legend()\n", + "plt.ylabel(\"PDF at $z$\")\n", + "plt.xlabel(\"$z$\")\n", + "plt.ylim(0,1.2)\n", + "plt.title(\"Probability density function of an Exponential random variable;\\\n", + " differing $\\lambda$\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "### But what is $\\lambda \\;$?\n", + "\n", + "\n", + "**This question is what motivates statistics**. In the real world, $\\lambda$ is hidden from us. We see only $Z$, and must go backwards to try and determine $\\lambda$. The problem is difficult because there is no one-to-one mapping from $Z$ to $\\lambda$. Many different methods have been created to solve the problem of estimating $\\lambda$, but since $\\lambda$ is never actually observed, no one can say for certain which method is best! \n", + "\n", + "Bayesian inference is concerned with *beliefs* about what $\\lambda$ might be. Rather than try to guess $\\lambda$ exactly, we can only talk about what $\\lambda$ is likely to be by assigning a probability distribution to $\\lambda$.\n", + "\n", + "This might seem odd at first. After all, $\\lambda$ is fixed; it is not (necessarily) random! How can we assign probabilities to values of a non-random variable? Ah, we have fallen for our old, frequentist way of thinking. Recall that under Bayesian philosophy, we *can* assign probabilities if we interpret them as beliefs. And it is entirely acceptable to have *beliefs* about the parameter $\\lambda$. \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "##### Example: Inferring behaviour from text-message data\n", + "\n", + "Let's try to model a more interesting example, one that concerns the rate at which a user sends and receives text messages:\n", + "\n", + "> You are given a series of daily text-message counts from a user of your system. The data, plotted over time, appears in the chart below. You are curious to know if the user's text-messaging habits have changed over time, either gradually or suddenly. How can you model this? (This is in fact my own text-message data. Judge my popularity as you wish.)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "figsize(12.5, 3.5)\n", + "count_data = torch.from_numpy(np.loadtxt(\"data/txtdata.csv\"))\n", + "n_count_data = len(count_data)\n", + "plt.bar(np.arange(n_count_data), count_data, color=\"#348ABD\")\n", + "plt.xlabel(\"Time (days)\")\n", + "plt.ylabel(\"count of text-msgs received\")\n", + "plt.title(\"Did the user's texting habits change over time?\")\n", + "plt.xlim(0, n_count_data);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Before we start modeling, see what you can figure out just by looking at the chart above. Would you say there was a change in behaviour during this time period? \n", + "\n", + "How can we start to model this? Well, as we have conveniently already seen, a Poisson random variable is a very appropriate model for this type of *count* data. Denoting day $i$'s text-message count by $C_i$, \n", + "\n", + "$$ C_i \\sim \\text{Poisson}(\\lambda) $$\n", + "\n", + "We are not sure what the value of the $\\lambda$ parameter really is, however. Looking at the chart above, it appears that the rate might become higher late in the observation period, which is equivalent to saying that $\\lambda$ increases at some point during the observations. (Recall that a higher value of $\\lambda$ assigns more probability to larger outcomes. That is, there is a higher probability of many text messages having been sent on a given day.)\n", + "\n", + "How can we represent this observation mathematically? Let's assume that on some day during the observation period (call it $\\tau$), the parameter $\\lambda$ suddenly jumps to a higher value. So we really have two $\\lambda$ parameters: one for the period before $\\tau$, and one for the rest of the observation period. In the literature, a sudden transition like this would be called a *switchpoint*:\n", + "\n", + "$$\n", + "\\lambda = \n", + "\\begin{cases}\n", + "\\lambda_1 & \\text{if } t \\lt \\tau \\cr\n", + "\\lambda_2 & \\text{if } t \\ge \\tau\n", + "\\end{cases}\n", + "$$\n", + "\n", + "\n", + "If, in reality, no sudden change occurred and indeed $\\lambda_1 = \\lambda_2$, then the $\\lambda$s posterior distributions should look about equal.\n", + "\n", + "We are interested in inferring the unknown $\\lambda$s. To use Bayesian inference, we need to assign prior probabilities to the different possible values of $\\lambda$. What would be good prior probability distributions for $\\lambda_1$ and $\\lambda_2$? Recall that $\\lambda$ can be any positive number. As we saw earlier, the *exponential* distribution provides a continuous density function for positive numbers, so it might be a good choice for modeling $\\lambda_i$. But recall that the exponential distribution takes a parameter of its own, so we'll need to include that parameter in our model. Let's call that parameter $\\alpha$.\n", + "\n", + "\\begin{align}\n", + "&\\lambda_1 \\sim \\text{Exp}( \\alpha ) \\\\\\\n", + "&\\lambda_2 \\sim \\text{Exp}( \\alpha )\n", + "\\end{align}\n", + "\n", + "$\\alpha$ is called a *hyper-parameter* or *parent variable*. In literal terms, it is a parameter that influences other parameters. Our initial guess at $\\alpha$ does not influence the model too strongly, so we have some flexibility in our choice. A good rule of thumb is to set the exponential parameter equal to the inverse of the average of the count data. Since we're modeling $\\lambda$ using an exponential distribution, we can use the expected value identity shown earlier to get:\n", + "\n", + "$$\\frac{1}{N}\\sum_{i=0}^N \\;C_i \\approx E[\\; \\lambda \\; |\\; \\alpha ] = \\frac{1}{\\alpha}$$ \n", + "\n", + "An alternative, and something I encourage the reader to try, would be to have two priors: one for each $\\lambda_i$. Creating two exponential distributions with different $\\alpha$ values reflects our prior belief that the rate changed at some point during the observations.\n", + "\n", + "What about $\\tau$? Because of the noisiness of the data, it's difficult to pick out a priori when $\\tau$ might have occurred. Instead, we can assign a *uniform prior belief* to every possible day. This is equivalent to saying\n", + "\n", + "\\begin{align}\n", + "& \\tau \\sim \\text{DiscreteUniform(1,70) }\\\\\\\\\n", + "& \\Rightarrow P( \\tau = k ) = \\frac{1}{70}\n", + "\\end{align}\n", + "\n", + "So after all this, what does our overall prior distribution for the unknown variables look like? Frankly, *it doesn't matter*. What we should understand is that it's an ugly, complicated mess involving symbols only a mathematician could love. And things will only get uglier the more complicated our models become. Regardless, all we really care about is the posterior distribution.\n", + "\n", + "We next turn to [Pyro](https://pyro.ai/), a Python library for performing Bayesian analysis that is undaunted by the mathematical monster we have created. \n", + "\n", + "\n", + "## Introducing our first hammer: Pyro\n", + "\n", + "Pyro is a Python library for programming Bayesian analysis. It is intended for data scientists, statisticians, machine learning practitioners, and scientists. Since it is built on the PyTorch stack, it brings the runtime benefits of PyTorch to Bayesian analysis. These include write-once run-many (ability to run your development model in production) and speedups via state-of-the-art hardware (GPUs and TPUs). \n", + "\n", + "Since Pyro is relatively new, the Pyro community is actively developing documentation, \n", + "especially docs and examples that bridge the gap between beginner and hacker. One of this book's main goals is to solve that problem, and also to demonstrate why TFP is so cool.\n", + "\n", + "We will model the problem above using Pyro. This type of programming is called *probabilistic programming*, an unfortunate misnomer that invokes ideas of randomly-generated code and has likely confused and frightened users away from this field. The code is not random; it is probabilistic in the sense that we create probability models using programming variables as the model's components. \n", + "\n", + "B. Cronin [[4]](#scrollTo=nDdph0r1ABCn) has a very motivating description of probabilistic programming:\n", + "\n", + "> Another way of thinking about this: unlike a traditional program, which only runs in the forward directions, a probabilistic program is run in both the forward and backward direction. It runs forward to compute the consequences of the assumptions it contains about the world (i.e., the model space it represents), but it also runs backward from the data to constrain the possible explanations. In practice, many probabilistic programming systems will cleverly interleave these forward and backward operations to efficiently home in on the best explanations.\n", + "\n", + "Because of the confusion engendered by the term *probabilistic programming*, I'll refrain from using it. Instead, I'll simply say *programming*, since that's what it really is. \n", + " \n", + "Pyro code is easy to read. The only novel thing should be the syntax. Simply remember that we are representing the model's components ($\\tau, \\lambda_1, \\lambda_2$ ) as variables." + ] + }, + { + "cell_type": "code", + "execution_count": 102, + "metadata": {}, + "outputs": [], + "source": [ + "def model(data):\n", + " alpha = 1.0 / data.mean()\n", + " lambda_1 = pyro.sample(\"lambda_1\", dist.Exponential(alpha))\n", + " lambda_2 = pyro.sample(\"lambda_2\", dist.Exponential(alpha))\n", + " \n", + " tau = pyro.sample(\"tau\", dist.Uniform(0, 1))\n", + " lambda1_size = (tau * data.size(0) + 1).long()\n", + " lambda2_size = data.size(0) - lambda1_size\n", + " lambda_ = torch.cat([lambda_1.expand((lambda1_size,)),\n", + " lambda_2.expand((lambda2_size,))])\n", + "\n", + " with pyro.plate(\"data\", data.size(0)):\n", + " pyro.sample(\"obs\", dist.Poisson(lambda_), obs=data)" + ] + }, + { + "cell_type": "code", + "execution_count": 144, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Sample: 100%|██████████| 5500/5500 [00:43, 126.11it/s, step size=2.45e-01, acc. prob=0.802]\n" + ] + } + ], + "source": [ + "kernel = NUTS(model, jit_compile=True, ignore_jit_warnings=True, max_tree_depth=3)\n", + "posterior = MCMC(kernel, num_samples=5000, warmup_steps=500)\n", + "posterior.run(count_data);" + ] + }, + { + "cell_type": "code", + "execution_count": 145, + "metadata": {}, + "outputs": [], + "source": [ + "hmc_samples = {k: v.detach().cpu().numpy() for k, v in posterior.get_samples().items()}\n", + "lambda_1_samples = hmc_samples['lambda_1']\n", + "lambda_2_samples = hmc_samples['lambda_2']\n", + "tau_samples = (hmc_samples['tau'] * count_data.size(0) + 1).astype(int)" + ] + }, + { + "cell_type": "code", + "execution_count": 146, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "figsize(12.5, 10)\n", + "#histogram of the samples:\n", + "\n", + "ax = plt.subplot(311)\n", + "ax.set_autoscaley_on(False)\n", + "\n", + "plt.hist(lambda_1_samples, histtype='stepfilled', bins=30, alpha=0.85,\n", + " label=\"posterior of $\\lambda_1$\", color=\"#A60628\", density=True)\n", + "plt.legend(loc=\"upper left\")\n", + "plt.title(r\"\"\"Posterior distributions of the variables\n", + " $\\lambda_1,\\;\\lambda_2,\\;\\tau$\"\"\")\n", + "plt.xlim([15, 30])\n", + "plt.xlabel(\"$\\lambda_1$ value\")\n", + "\n", + "ax = plt.subplot(312)\n", + "ax.set_autoscaley_on(False)\n", + "plt.hist(lambda_2_samples, histtype='stepfilled', bins=30, alpha=0.85,\n", + " label=\"posterior of $\\lambda_2$\", color=\"#7A68A6\", density=True)\n", + "plt.legend(loc=\"upper left\")\n", + "plt.xlim([15, 30])\n", + "plt.xlabel(\"$\\lambda_2$ value\")\n", + "\n", + "plt.subplot(313)\n", + "w = 1.0 / tau_samples.shape[0] * np.ones_like(tau_samples)\n", + "plt.hist(tau_samples, bins=n_count_data, alpha=1,\n", + " label=r\"posterior of $\\tau$\",\n", + " color=\"#467821\", weights=w, rwidth=2.)\n", + "plt.xticks(np.arange(n_count_data))\n", + "\n", + "plt.legend(loc=\"upper left\")\n", + "plt.ylim([0, .75])\n", + "plt.xlim([35, len(count_data)-20])\n", + "plt.xlabel(r\"$\\tau$ (in days)\")\n", + "plt.ylabel(\"probability\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Interpretation\n", + "\n", + "Recall that Bayesian methodology returns a *distribution*. Hence we now have distributions to describe the unknown $\\lambda$s and $\\tau$. What have we gained? Immediately, we can see the uncertainty in our estimates: the wider the distribution, the less certain our posterior belief should be. We can also see what the plausible values for the parameters are: $\\lambda_1$ is around 18 and $\\lambda_2$ is around 23. The posterior distributions of the two $\\lambda$s are clearly distinct, indicating that it is indeed likely that there was a change in the user's text-message behaviour.\n", + "\n", + "What other observations can you make? If you look at the original data again, do these results seem reasonable? \n", + "\n", + "Notice also that the posterior distributions for the $\\lambda$s do not look like exponential distributions, even though our priors for these variables were exponential. In fact, the posterior distributions are not really of any form that we recognize from the original model. But that's OK! This is one of the benefits of taking a computational point of view. If we had instead done this analysis using mathematical approaches, we would have been stuck with an analytically intractable (and messy) distribution. Our use of a computational approach makes us indifferent to mathematical tractability.\n", + "\n", + "Our analysis also returned a distribution for $\\tau$. Its posterior distribution looks a little different from the other two because it is a discrete random variable, so it doesn't assign probabilities to intervals. We can see that near day 45, there was a 50% chance that the user's behaviour changed. Had no change occurred, or had the change been gradual over time, the posterior distribution of $\\tau$ would have been more spread out, reflecting that many days were plausible candidates for $\\tau$. By contrast, in the actual results we see that only three or four days make any sense as potential transition points. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Why would I want samples from the posterior, anyways?\n", + "\n", + "\n", + "We will deal with this question for the remainder of the book, and it is an understatement to say that it will lead us to some amazing results. For now, let's end this chapter with one more example.\n", + "\n", + "We'll use the posterior samples to answer the following question: what is the expected number of texts at day $t, \\; 0 \\le t \\le 70$ ? Recall that the expected value of a Poisson variable is equal to its parameter $\\lambda$. Therefore, the question is equivalent to *what is the expected value of $\\lambda$ at time $t$*?\n", + "\n", + "In the code below, let $i$ index samples from the posterior distributions. Given a day $t$, we average over all possible $\\lambda_i$ for that day $t$, using $\\lambda_i = \\lambda_{1,i}$ if $t \\lt \\tau_i$ (that is, if the behaviour change has not yet occurred), else we use $\\lambda_i = \\lambda_{2,i}$. " + ] + }, + { + "cell_type": "code", + "execution_count": 147, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "figsize(12.5, 5)\n", + "# tau_samples, lambda_1_samples, lambda_2_samples contain\n", + "# N samples from the corresponding posterior distribution\n", + "N = tau_samples.shape[0]\n", + "expected_texts_per_day = np.zeros(n_count_data)\n", + "for day in range(0, n_count_data):\n", + " # ix is a bool index of all tau samples corresponding to\n", + " # the switchpoint occurring prior to value of 'day'\n", + " ix = day < tau_samples\n", + " # Each posterior sample corresponds to a value for tau.\n", + " # for each day, that value of tau indicates whether we're \"before\"\n", + " # (in the lambda1 \"regime\") or\n", + " # \"after\" (in the lambda2 \"regime\") the switchpoint.\n", + " # by taking the posterior sample of lambda1/2 accordingly, we can average\n", + " # over all samples to get an expected value for lambda on that day.\n", + " # As explained, the \"message count\" random variable is Poisson distributed,\n", + " # and therefore lambda (the poisson parameter) is the expected value of\n", + " # \"message count\".\n", + " expected_texts_per_day[day] = (lambda_1_samples[ix].sum()\n", + " + lambda_2_samples[~ix].sum()) / N\n", + "\n", + "\n", + "plt.plot(range(n_count_data), expected_texts_per_day, lw=4, color=\"#E24A33\",\n", + " label=\"expected number of text-messages received\")\n", + "plt.xlim(0, n_count_data)\n", + "plt.xlabel(\"Day\")\n", + "plt.ylabel(\"Expected # text-messages\")\n", + "plt.title(\"Expected number of text-messages received\")\n", + "plt.ylim(0, 60)\n", + "plt.bar(np.arange(len(count_data)), count_data, color=\"#348ABD\", alpha=0.65,\n", + " label=\"observed texts per day\")\n", + "\n", + "plt.legend(loc=\"upper left\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Our analysis shows strong support for believing the user's behavior did change ($\\lambda_1$ would have been close in value to $\\lambda_2$ had this not been true), and that the change was sudden rather than gradual (as demonstrated by $\\tau$'s strongly peaked posterior distribution). We can speculate what might have caused this: a cheaper text-message rate, a recent weather-to-text subscription, or perhaps a new relationship. (In fact, the 45th day corresponds to Christmas, and I moved away to Toronto the next month, leaving a girlfriend behind.)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Exercises\n", + "\n", + "1\\. Using `lambda_1_samples` and `lambda_2_samples`, what is the mean of the posterior distributions of $\\lambda_1$ and $\\lambda_2$?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Your code here." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "2\\. What is the expected percentage increase in text-message rates? `hint:` compute the mean of `lambda_1_samples/lambda_2_samples`. Note that this quantity is very different from `lambda_1_samples.mean()/lambda_2_samples.mean()`." + ] + }, + { + "cell_type": "code", + "execution_count": 148, + "metadata": {}, + "outputs": [], + "source": [ + "# Your code here." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "3\\. What is the mean of $\\lambda_1$ **given** that we know $\\tau$ is less than 45. That is, suppose we have been given new information that the change in behaviour occurred prior to day 45. What is the expected value of $\\lambda_1$ now? (You do not need to redo the PyMC3 part. Just consider all instances where `tau_samples < 45`.)" + ] + }, + { + "cell_type": "code", + "execution_count": 149, + "metadata": {}, + "outputs": [], + "source": [ + "# Your code here." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### References\n", + "\n", + "\n", + "- [1] Gelman, Andrew. N.p.. Web. 22 Jan 2013. [N is never large enough](http://andrewgelman.com/2005/07/31/n_is_never_larg).\n", + "- [2] Norvig, Peter. 2009. [The Unreasonable Effectiveness of Data](http://static.googleusercontent.com/media/research.google.com/en//pubs/archive/35179.pdf).\n", + "- [3] Salvatier, J, Wiecki TV, and Fonnesbeck C. (2016) Probabilistic programming in Python using PyMC3. *PeerJ Computer Science* 2:e55 \n", + "- [4] Jimmy Lin and Alek Kolcz. Large-Scale Machine Learning at Twitter. Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data (SIGMOD 2012), pages 793-804, May 2012, Scottsdale, Arizona.\n", + "- [5] Cronin, Beau. \"Why Probabilistic Programming Matters.\" 24 Mar 2013. Google, Online Posting to Google . Web. 24 Mar. 2013. ." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.core.display import HTML\n", + "def css_styling():\n", + " styles = open(\"../styles/custom.css\", \"r\").read()\n", + " return HTML(styles)\n", + "css_styling()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/Chapter1_Introduction/Ch1_Introduction_TFP.ipynb b/Chapter1_Introduction/Ch1_Introduction_TFP.ipynb new file mode 100644 index 00000000..117da5e1 --- /dev/null +++ b/Chapter1_Introduction/Ch1_Introduction_TFP.ipynb @@ -0,0 +1,1438 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "working ch1 in tf2.ipynb", + "provenance": [], + "collapsed_sections": [] + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "6Tmmlr92MZVj" + }, + "source": [ + "# Probabilistic Programming and Bayesian Methods for Hackers Chapter 1\n", + "\n", + "\n", + " \n", + " \n", + "
\n", + " Run in Google Colab\n", + " \n", + " View source on GitHub\n", + "
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\n", + "
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\n", + "\n", + "Original content ([this Jupyter notebook](https://nbviewer.jupyter.org/github/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/blob/master/Chapter1_Introduction/Ch1_Introduction_PyMC2.ipynb)) created by Cam Davidson-Pilon ([`@Cmrn_DP`](https://twitter.com/Cmrn_DP))\n", + "\n", + "Ported to [Tensorflow Probability](https://www.tensorflow.org/probability/) by Matthew McAteer ([`@MatthewMcAteer0`](https://twitter.com/MatthewMcAteer0)) and Bryan Seybold, with help from the TFP team at Google ([`tfprobability@tensorflow.org`](mailto:tfprobability@tensorflow.org)).\n", + "\n", + "Welcome to Bayesian Methods for Hackers. The full Github repository is available at [github/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers](https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers). The other chapters can be found on the project's [homepage](https://camdavidsonpilon.github.io/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/). We hope you enjoy the book, and we encourage any contributions!\n", + "\n", + "---\n", + "### Table of Contents\n", + "- Dependencies & Prerequisites\n", + "- The Philosophy of Bayesian Inference\n", + "- The Bayesian state of mind\n", + "- Bayesian Inference in Practice\n", + "- Are frequentist methods incorrect then?\n", + "- Our Bayesian framework\n", + "- Example: Mandatory coin-flip example\n", + "- Example: Bug, or just sweet, unintended feature?\n", + "- Probability Distributions\n", + " - Discrete Case\n", + "- Continuous Case\n", + "- But what is $\\lambda \\;$?\n", + " - Example: Inferring behaviour from text-message data\n", + "- Introducing our first hammer: Tensorflow Probability\n", + "- specify the joint log-density\n", + "- Specify the posterior sampler\n", + "- Execute the TF graph to sample from the posterior\n", + "- Plot the Results\n", + "- Interpretation\n", + "- Exercises\n", + "- References" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "YcJ8nEDVH30J" + }, + "source": [ + "### Dependencies & Prerequisites\n", + "\n", + "
\n", + " Tensorflow Probability is part of the colab default runtime, so you don't need to install Tensorflow or Tensorflow Probability if you're running this in the colab. \n", + "
\n", + " If you're running this notebook in Jupyter on your own machine (and you have already installed Tensorflow), you can use the following\n", + "
\n", + "
    \n", + "
  • For the most recent nightly installation: pip3 install -q tfp-nightly
    • \n", + "
  • For the most recent stable TFP release: pip3 install -q --upgrade tensorflow-probability
    • \n", + "
  • For the most recent stable GPU-connected version of TFP: pip3 install -q --upgrade tensorflow-probability-gpu
    • \n", + "
  • For the most recent nightly GPU-connected version of TFP: pip3 install -q tfp-nightly-gpu
    • \n", + "
\n", + "Again, if you are running this in a Colab, Tensorflow and TFP are already installed\n", + "
" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "Ln-qTC7S2Pm-", + "colab_type": "code", + "outputId": "b8a88544-23ed-4b56-d97a-553dfada639a", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 52 + } + }, + "source": [ + "#@title Imports and Global Variables (make sure to run this cell) { display-mode: \"form\" }\n", + "\n", + "try:\n", + " # %tensorflow_version only exists in Colab.\n", + " %tensorflow_version 2.x\n", + "except Exception:\n", + " pass\n", + "\n", + "\n", + "from __future__ import absolute_import, division, print_function\n", + "\n", + "\n", + "#@markdown This sets the warning status (default is `ignore`, since this notebook runs correctly)\n", + "warning_status = \"ignore\" #@param [\"ignore\", \"always\", \"module\", \"once\", \"default\", \"error\"]\n", + "import warnings\n", + "warnings.filterwarnings(warning_status)\n", + "with warnings.catch_warnings():\n", + " warnings.filterwarnings(warning_status, category=DeprecationWarning)\n", + " warnings.filterwarnings(warning_status, category=UserWarning)\n", + "\n", + "import numpy as np\n", + "import os\n", + "#@markdown This sets the styles of the plotting (default is styled like plots from [FiveThirtyeight.com](https://fivethirtyeight.com/)\n", + "matplotlib_style = 'fivethirtyeight' #@param ['fivethirtyeight', 'bmh', 'ggplot', 'seaborn', 'default', 'Solarize_Light2', 'classic', 'dark_background', 'seaborn-colorblind', 'seaborn-notebook']\n", + "import matplotlib.pyplot as plt; plt.style.use(matplotlib_style)\n", + "import matplotlib.axes as axes;\n", + "from matplotlib.patches import Ellipse\n", + "#%matplotlib inline\n", + "import seaborn as sns; sns.set_context('notebook')\n", + "from IPython.core.pylabtools import figsize\n", + "#@markdown This sets the resolution of the plot outputs (`retina` is the highest resolution)\n", + "notebook_screen_res = 'retina' #@param ['retina', 'png', 'jpeg', 'svg', 'pdf']\n", + "#%config InlineBackend.figure_format = notebook_screen_res\n", + "\n", + "import tensorflow as tf\n", + "\n", + "import tensorflow_probability as tfp\n", + "tfd = tfp.distributions\n", + "tfb = tfp.bijectors\n", + "\n", + "class _TFColor(object):\n", + " \"\"\"Enum of colors used in TF docs.\"\"\"\n", + " red = '#F15854'\n", + " blue = '#5DA5DA'\n", + " orange = '#FAA43A'\n", + " green = '#60BD68'\n", + " pink = '#F17CB0'\n", + " brown = '#B2912F'\n", + " purple = '#B276B2'\n", + " yellow = '#DECF3F'\n", + " gray = '#4D4D4D'\n", + " def __getitem__(self, i):\n", + " return [\n", + " self.red,\n", + " self.orange,\n", + " self.green,\n", + " self.blue,\n", + " self.pink,\n", + " self.brown,\n", + " self.purple,\n", + " self.yellow,\n", + " self.gray,\n", + " ][i % 9]\n", + "TFColor = _TFColor()\n", + "\n", + "def session_options(enable_gpu_ram_resizing=True, enable_xla=False):\n", + " \"\"\"\n", + " Allowing the notebook to make use of GPUs if they're available.\n", + "\n", + " XLA (Accelerated Linear Algebra) is a domain-specific compiler for linear\n", + " algebra that optimizes TensorFlow computations.\n", + " \"\"\"\n", + " config = tf.config\n", + " gpu_devices = config.experimental.list_physical_devices('GPU')\n", + " if enable_gpu_ram_resizing:\n", + " for device in gpu_devices:\n", + " tf.config.experimental.set_memory_growth(device, True)\n", + " if enable_xla:\n", + " config.optimizer.set_jit(True)\n", + " return config\n", + "\n", + "session_options(enable_gpu_ram_resizing=True, enable_xla=True)" + ], + "execution_count": 1, + "outputs": [ + { + "output_type": "stream", + "text": [ + "TensorFlow 2.x selected.\n" + ], + "name": "stdout" + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 1 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "dXqjzSnXRRr3" + }, + "source": [ + "## The Philosophy of Bayesian Inference\n", + "\n", + ">You are a skilled programmer, but bugs still slip into your code. After a particularly difficult implementation of an algorithm, you decide to test your code on a trivial example. It passes. You test the code on a harder problem. It passes once again. And it passes the next, even more difficult, test too! You are starting to believe that there may be no bugs in this code...\n", + "\n", + "If you think this way, then congratulations, you already are thinking Bayesian! Bayesian inference is simply updating your beliefs after considering new evidence. A Bayesian can rarely be certain about a result, but he or she can be very confident. Just like in the example above, we can never be 100% sure that our code is bug-free unless we test it on every possible problem; something rarely possible in practice. Instead, we can test it on a large number of problems, and if it succeeds we can feel more confident about our code, but still not certain. Bayesian inference works identically: we update our beliefs about an outcome; rarely can we be absolutely sure unless we rule out all other alternatives.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "YO2eSwZQRRqv" + }, + "source": [ + "## The Bayesian state of mind\n", + "Bayesian inference differs from more traditional statistical inference by preserving uncertainty. At first, this sounds like a bad statistical technique. Isn't statistics all about deriving certainty from randomness? To reconcile this, we need to start thinking like Bayesians.\n", + "\n", + "The Bayesian world-view interprets probability as measure of believability in an event, that is, how confident we are in an event occurring. In fact, we will see in a moment that this is the natural interpretation of probability.\n", + "\n", + "For this to be clearer, we consider an alternative interpretation of probability: Frequentist, known as the more classical version of statistics, assume that probability is the long-run frequency of events (hence the bestowed title). For example, the probability of plane accidents under a frequentist philosophy is interpreted as the long-term frequency of plane accidents. This makes logical sense for many probabilities of events, but becomes more difficult to understand when events have no long-term frequency of occurrences. Consider: we often assign probabilities to outcomes of presidential elections, but the election itself only happens once! Frequentists get around this by invoking alternative realities and saying across all these realities, the frequency of occurrences defines the probability.\n", + "\n", + "Bayesians, on the other hand, have a more intuitive approach. Bayesians interpret a probability as measure of belief, or confidence, of an event occurring. Simply, a probability is a summary of an opinion. An individual who assigns a belief of 0 to an event has no confidence that the event will occur; conversely, assigning a belief of 1 implies that the individual is absolutely certain of an event occurring. Beliefs between 0 and 1 allow for weightings of other outcomes. This definition agrees with the probability of a plane accident example, for having observed the frequency of plane accidents, an individual's belief should be equal to that frequency, excluding any outside information. Similarly, under this definition of probability being equal to beliefs, it is meaningful to speak about probabilities (beliefs) of presidential election outcomes: how confident are you candidate A will win?\n", + "\n", + "Notice in the paragraph above, I assigned the belief (probability) measure to an individual, not to Nature. This is very interesting, as this definition leaves room for conflicting beliefs between individuals. Again, this is appropriate for what naturally occurs: different individuals have different beliefs of events occurring, because they possess different information about the world. The existence of different beliefs does not imply that anyone is wrong. Consider the following examples demonstrating the relationship between individual beliefs and probabilities:\n", + "\n", + "* I flip a coin, and we both guess the result. We would both agree, assuming the coin is fair, that the probability of Heads is 1/2. Assume, then, that I peek at the coin. Now I know for certain what the result is: I assign probability 1.0 to either Heads or Tails (whichever it is). Now what is your belief that the coin is Heads? My knowledge of the outcome has not changed the coin's results. Thus we assign different probabilities to the result.\n", + "\n", + "* Your code either has a bug in it or not, but we do not know for certain which is true, though we have a belief about the presence or absence of a bug.\n", + "\n", + "* A medical patient is exhibiting symptoms *x*, *y* and *z*. There are a number of diseases that could be causing all of them, but only a single disease is present. A doctor has beliefs about which disease, but a second doctor may have slightly different beliefs.\n", + "\n", + "This philosophy of treating beliefs as probability is natural to humans. We employ it constantly as we interact with the world and only see partial truths, but gather evidence to form beliefs. Alternatively, you have to be trained to think like a frequentist.\n", + "\n", + "To align ourselves with traditional probability notation, we denote our belief about event $A$ as $P(A)$. We call this quantity the prior probability.\n", + "\n", + "John Maynard Keynes, a great economist and thinker, said \"When the facts change, I change my mind. What do you do, sir?\" This quote reflects the way a Bayesian updates his or her beliefs after seeing evidence. Even — especially — if the evidence is counter to what was initially believed, the evidence cannot be ignored. We denote our updated belief as $P(A|X)$, interpreted as the probability of $A$ given the evidence $X$. We call the updated belief the posterior probability so as to contrast it with the prior probability. For example, consider the posterior probabilities (read: posterior beliefs) of the above examples, after observing some evidence $X$:\n", + "\n", + "\n", + "1. $P(A)$: the coin has a 50 percent chance of being Heads. $P(A|X)$: You look at the coin, observe a Heads has landed, denote this information $X$, and trivially assign probability 1.0 to Heads and 0.0 to Tails.\n", + "\n", + "2. $P(A)$: This big, complex code likely has a bug in it. $P(A|X)$: The code passed all $X$ tests; there still might be a bug, but its presence is less likely now.\n", + "\n", + "3. $P(A)$: The patient could have any number of diseases. $P(A|X)$: Performing a blood test generated evidence $X$, ruling out some of the possible diseases from consideration.\n", + "\n", + "It's clear that in each example we did not completely discard the prior belief after seeing new evidence $X$, but we re-weighted the prior to incorporate the new evidence (i.e. we put more weight, or confidence, on some beliefs versus others).\n", + "\n", + "By introducing prior uncertainty about events, we are already admitting that any guess we make is potentially very wrong. After observing data, evidence, or other information, we update our beliefs, and our guess becomes less wrong. This is the alternative side of the prediction coin, where typically we try to be more right.\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "FXUBMaYsRWvl" + }, + "source": [ + "## Bayesian Inference in Practice\n", + "If frequentist and Bayesian inference were programming functions, with inputs being statistical problems, then the two would be different in what they return to the user. The frequentist inference function would return a number, representing an estimate (typically a summary statistic like the sample average etc.), whereas the Bayesian function would return probabilities.\n", + "\n", + "For example, in our debugging problem above, calling the frequentist function with the argument \"My code passed all $X$ tests; is my code bug-free?\" would return a YES. On the other hand, asking our Bayesian function \"Often my code has bugs. My code passed all $X$ tests; is my code bug-free?\" would return something very different: probabilities of YES and NO. The function might return:\n", + "\n", + ">YES, with probability 0.8; NO, with probability 0.2\n", + "\n", + "This is very different from the answer the frequentist function returned. Notice that the Bayesian function accepted an additional argument: \"Often my code has bugs\". This parameter is the prior. By including the prior parameter, we are telling the Bayesian function to include our belief about the situation. Technically this parameter in the Bayesian function is optional, but we will see excluding it has its own consequences.\n", + "\n", + "### Incorporating evidence\n", + "As we acquire more and more instances of evidence, our prior belief is washed out by the new evidence. This is to be expected. For example, if your prior belief is something ridiculous, like \"I expect the sun to explode today\", and each day you are proved wrong, you would hope that any inference would correct you, or at least align your beliefs better. Bayesian inference will correct this belief.\n", + "\n", + "Denote $N$ as the number of instances of evidence we possess. As we gather an infinite amount of evidence, say as $N→∞,$ our Bayesian results (often) align with frequentist results. Hence for large N, statistical inference is more or less objective. On the other hand, for small $N$, inference is much more unstable: frequentist estimates have more variance and larger confidence intervals. This is where Bayesian analysis excels. By introducing a prior, and returning probabilities (instead of a scalar estimate), we preserve the uncertainty that reflects the instability of statistical inference of a small N dataset.\n", + "\n", + "One may think that for large $N$, one can be indifferent between the two techniques since they offer similar inference, and might lean towards the computationally-simpler, frequentist methods. An individual in this position should consider the following quote by Andrew Gelman (2005)[[1]](#scrollTo=nDdph0r1ABCn), before making such a decision:\n", + "\n", + "Sample sizes are never large. If $N$, is too small to get a sufficiently-precise estimate, you need to get more data (or make more assumptions). But once $N$, is \"large enough,\" you can start subdividing the data to learn more (for example, in a public opinion poll, once you have a good estimate for the entire country, you can estimate among men and women, northerners and southerners, different age groups, etc.). $N$, is never enough because if it were \"enough\" you'd already be on to the next problem for which you need more data.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "MeEvqqZ4Wy3G", + "colab_type": "text" + }, + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "rACyvZBVdqB9" + }, + "source": [ + "## Are frequentist methods incorrect then?\n", + "No.\n", + "\n", + "Frequentist methods are still useful or state-of-the-art in many areas. Tools such as least squares linear regression, LASSO regression, and expectation-maximization algorithms are all powerful and fast. Bayesian methods complement these techniques by solving problems that these approaches cannot, or by illuminating the underlying system with more flexible modeling.\n", + "\n", + "### A note on *Big Data*\n", + "Paradoxically, big data's predictive analytic problems are actually solved by relatively simple algorithms [[2]](#scrollTo=nDdph0r1ABCn)[[3]](#scrollTo=nDdph0r1ABCn). Thus we can argue that big data's prediction difficulty does not lie in the algorithm used, but instead on the computational difficulties of storage and execution on big data. (One should also consider Gelman's quote from above and ask \"Do I really have big data?\")\n", + "\n", + "The much more difficult analytic problems involve medium data and, especially troublesome, really small data. Using a similar argument as Gelman's above, if big data problems are big enough to be readily solved, then we should be more interested in the not-quite-big enough datasets." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "drksp6yJW0fO", + "colab_type": "text" + }, + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "TTUDkI8peKw6" + }, + "source": [ + "## Our Bayesian framework\n", + "We are interested in beliefs, which can be interpreted as probabilities by thinking Bayesian. We have a prior belief in event A, beliefs formed by previous information, e.g., our prior belief about bugs being in our code before performing tests.\n", + "\n", + "Secondly, we observe our evidence. To continue our buggy-code example: if our code passes X tests, we want to update our belief to incorporate this. We call this new belief the posterior probability. Updating our belief is done via the following equation, known as Bayes' Theorem, after its discoverer Thomas Bayes:\n", + "\n", + "$$ P(A|X) = \\frac{P(X | A) P(A) }{P(X) } $$\n", + "\n", + "$$ P(A|X) \\propto{P(X | A) P(A) } $$\n", + "\n", + "NOTE: ($\\propto$ is \"proportional to\")\n", + "\n", + "\n", + "The above formula is not unique to Bayesian inference: it is a mathematical fact with uses outside Bayesian inference. Bayesian inference merely uses it to connect prior probabilities $P(A)$ with an updated posterior probabilities $P(A|X)$." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "uDFx3Y0sXCRm", + "colab_type": "text" + }, + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "DkB3Ou8UjW-F" + }, + "source": [ + "\n", + "## Example: Mandatory coin-flip example\n", + "Every statistics text must contain a coin-flipping example, I'll use it here to get it out of the way. Suppose, naively, that you are unsure about the probability of heads in a coin flip (spoiler alert: it's 50%). You believe there is some true underlying ratio, call it p, but have no prior opinion on what p might be.\n", + "\n", + "We begin to flip a coin, and record the observations: either H or T. This is our observed data. An interesting question to ask is how our inference changes as we observe more and more data? More specifically, what do our posterior probabilities look like when we have little data, versus when we have lots of data.\n", + "\n", + "Below we plot a sequence of updating posterior probabilities as we observe increasing amounts of data (coin flips), while also demonstrating some of the best practices when it comes to evaluating tensors and plotting the data. First, the easy part: We define the values in our Tensorflow graph" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "yFd9GboD7hVV", + "colab": {} + }, + "source": [ + "# Build Graph\n", + "rv_coin_flip_prior = tfp.distributions.Bernoulli(probs=0.5, dtype=tf.int32)\n", + "\n", + "num_trials = tf.constant([0,1, 2, 3, 4, 5, 8, 15, 50, 500, 1000, 2000])\n", + "\n", + "coin_flip_data = rv_coin_flip_prior.sample(num_trials[-1])\n", + "\n", + "# prepend a 0 onto tally of heads and tails, for zeroth flip\n", + "coin_flip_data = tf.pad(coin_flip_data,tf.constant([[1, 0,]]),\"CONSTANT\")\n", + "\n", + "# compute cumulative headcounts from 0 to 2000 flips, and then grab them at each of num_trials intervals\n", + "cumulative_headcounts = tf.gather(tf.cumsum(coin_flip_data), num_trials)\n", + "\n", + "rv_observed_heads = tfp.distributions.Beta(\n", + " concentration1=tf.cast(1 + cumulative_headcounts, tf.float32),\n", + " concentration0=tf.cast(1 + num_trials - cumulative_headcounts, tf.float32))\n", + "\n", + "probs_of_heads = tf.linspace(start=0., stop=1., num=100, name=\"linspace\")\n", + "observed_probs_heads = tf.transpose(rv_observed_heads.prob(probs_of_heads[:, tf.newaxis]))" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "IUAm6LEA8FFW" + }, + "source": [ + "Finally, we move onto plotting our tensors in matplotlib." + ] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "k8c5fkkIk8qV", + "outputId": "cedc52c8-00eb-4b1d-faf8-63bfc524180e", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 679 + } + }, + "source": [ + "# For the already prepared, I'm using Binomial's conj. prior.\n", + "plt.figure(figsize(16, 9))\n", + "for i in range(len(num_trials)):\n", + " sx = plt.subplot(len(num_trials)/2, 2, i+1)\n", + " plt.xlabel(\"$p$, probability of heads\") \\\n", + " if i in [0, len(num_trials)-1] else None\n", + " plt.setp(sx.get_yticklabels(), visible=False)\n", + " plt.plot(probs_of_heads, observed_probs_heads[i], \n", + " label=\"observe %d tosses,\\n %d heads\" % (num_trials[i], cumulative_headcounts[i]))\n", + " plt.fill_between(probs_of_heads, 0, observed_probs_heads[i], \n", + " color=TFColor[3], alpha=0.4)\n", + " plt.vlines(0.5, 0, 4, color=\"k\", linestyles=\"--\", lw=1)\n", + " leg = plt.legend()\n", + " leg.get_frame().set_alpha(0.4)\n", + " plt.autoscale(tight=True)\n", + "\n", + "\n", + "plt.suptitle(\"Bayesian updating of posterior probabilities\", y=1.02,\n", + " fontsize=14)\n", + "plt.tight_layout()" + ], + "execution_count": 3, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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xgtjYWCwWC/PmzeOhhx4C0mur1KpVi/DwcLp3786+ffuIjY3lzz//JDQ0lNde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\nLF5eXvj4+PD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XWu7kS8oHd3J1//33ExISQqdOnYiKiqJ58+YMGzbMk2FXWu7ka/r06Wzb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\niIiIiIiIiMGooSMiIiIiIiIiYjD/Hxyt10CX2trnAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "jTqKXlGRmKuh" + }, + "source": [ + "The posterior probabilities are represented by the curves, and our uncertainty is proportional to the width of the curve. As the plot above shows, as we start to observe data our posterior probabilities start to shift and move around. Eventually, as we observe more and more data (coin-flips), our probabilities will tighten closer and closer around the true value of $p=0.5$ (marked by a dashed line).\n", + "\n", + "Notice that the plots are not always peaked at 0.5. There is no reason it should be: recall we assumed we did not have a prior opinion of what p is. In fact, if we observe quite extreme data, say 8 flips and only 1 observed heads, our distribution would look very biased away from lumping around 0.5 (with no prior opinion, how confident would you feel betting on a fair coin after observing 8 tails and 1 head?). As more data accumulates, we would see more and more probability being assigned at $p=0.5$, though never all of it.\n", + "\n", + "The next example is a simple demonstration of the mathematics of Bayesian inference." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "5UKnxit-mevN" + }, + "source": [ + "## Example: Bug, or just sweet, unintended feature?\n", + "Let $A$ denote the event that our code has no bugs in it. Let $X$ denote the event that the code passes all debugging tests. For now, we will leave the prior probability of no bugs as a variable, i.e. $P(A)=p$.\n", + "\n", + "We are interested in $P(A|X)$, i.e. the probability of no bugs, given our debugging tests $X$. To use the formula above, we need to compute some quantities.\n", + "\n", + "What is $P(X|A)$, i.e., the probability that the code passes $X$ tests given there are no bugs? Well, it is equal to 1, for a code with no bugs will pass all tests.\n", + "\n", + "$P(X)$ is a little bit trickier: The event $X$ can be divided into two possibilities, event X occurring even though our code indeed has bugs (denoted $∼A$, spoken not $A$), or event $X$ without bugs $(A)$. $ P(X)$ can be represented as:" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "7rDu4o6DnjT7" + }, + "source": [ + "$$ \\begin{align*}\n", + "P(A|X) &= \\frac{P(X | A) P(A) }{P(X) } \\\\\n", + " P(X) &= P(X \\text{ and } A) + P(X \\text{ and } \\sim A) \\\\\n", + " &= P(X|A)P(A) + P(X | \\sim A)P(\\sim A) \\\\\n", + " &= P(X|A)p + P(X | \\sim A)(1-p) \\end{align*} $$\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "S48e_3wph3I_" + }, + "source": [ + "We have already computed $P(X|A)$ above. On the other hand, $P(X|\\sim A)$ is subjective: our code can pass tests but still have a bug in it, though the probability there is a bug present is reduced. Note this is dependent on the number of tests performed, the degree of complication in the tests, etc. Let's be conservative and assign $P(X|\\sim A)=0.5$. Then:\n", + "\n", + "$$ \\begin{align*}\n", + "P(A | X) &= \\frac{1\\cdot p}{ 1\\cdot p +0.5 (1-p) } \\\\\n", + "&= \\frac{ 2 p}{1+p} \\end{align*} $$\n", + "\n", + "This is the posterior probability. What does it look like as a function of our prior, $p\\in[0,1]$?" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "MwjluXPenvAy", + "outputId": "a3b6cc69-0327-4988-9f43-d221bebde66c", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 437 + } + }, + "source": [ + "# Defining our range of probabilities\n", + "p = tf.linspace(start=0., stop=1., num=50)\n", + "\n", + "# Visualization.\n", + "plt.figure(figsize=(12.5, 6))\n", + "plt.plot(p, 2*p/(1+p), color=TFColor[3], lw=3)\n", + "#plt.fill_between(p, 2*p/(1+p), alpha=.5, facecolor=[\"#A60628\"])\n", + "plt.scatter(0.2, 2*(0.2)/1.2, s=140, c=TFColor[3])\n", + "plt.xlim(0, 1)\n", + "plt.ylim(0, 1)\n", + "plt.xlabel(r\"Prior, $P(A) = p$\")\n", + "plt.ylabel(r\"Posterior, $P(A|X)$, with $P(A) = p$\")\n", + "plt.title(r\"Are there bugs in my code?\");" + ], + "execution_count": 4, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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tGp0YqphwghSA7qVFoWrTpk267777VFhYqMjISF199dV65JFHlJWVFaz6AABA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\nUYsOhPkGT2POwZOYb/AmhszWoqIiRUdHy2Jp7LpnsVgUFRW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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "dvcD8UWloYxn" + }, + "source": [ + "We can see the biggest gains if we observe the $X$ tests passed when the prior probability, $p$, is low. Let's settle on a specific value for the prior. I'm a strong programmer (I think), so I'm going to give myself a realistic prior of 0.20, that is, there is a 20% chance that I write code bug-free. To be more realistic, this prior should be a function of how complicated and large the code is, but let's pin it at 0.20. Then my updated belief that my code is bug-free is 0.33.\n", + "\n", + "Recall that the prior is a probability: $p$ is the prior probability that there are no bugs, so $1 \\text{-} p$ is the prior probability that there are bugs.\n", + "\n", + "Similarly, our posterior is also a probability, with $P(A|X)$ the probability there is no bug given we saw all tests pass, hence $1 \\text{-} P(A|X)$ is the probability there is a bug given all tests passed. What does our posterior probability look like? Below is a chart of both the prior and the posterior probabilities." + ] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "Aot_QO3n1r4o", + "outputId": "20941da1-2b75-4085-8d34-5fa8761aea6e", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 300 + } + }, + "source": [ + "# Defining our priors and posteriors\n", + "prior = tf.constant([0.20, 0.80])\n", + "posterior = tf.constant([1./3, 2./3])\n", + "\n", + "# Our Simple Visualization\n", + "plt.figure(figsize=(12.5, 4))\n", + "colours = [TFColor[0], TFColor[3]]\n", + "plt.bar([0, .7], prior, alpha=0.70, width=0.25,\n", + " color=colours[0], label=\"prior distribution\",\n", + " lw=\"3\", edgecolor=colours[0])\n", + "plt.bar([0+0.25, .7+0.25], posterior, alpha=0.7,\n", + " width=0.25, color=colours[1],\n", + " label=r\"posterior distribution\",\n", + " lw=\"3\", edgecolor=colours[1])\n", + "\n", + "plt.xticks([0.20, .95], [\"Bugs Absent\", \"Bugs Present\"])\n", + "plt.title(r\"Prior and Posterior probability of bugs present\")\n", + "plt.ylabel(\"Probability\")\n", + "plt.legend(loc=\"upper left\");" + ], + "execution_count": 5, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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q2LBh+vrrr6ts64x169ZpyJAhuuyyy2QwGDRmzBiH7b/llltkMplkMBgUHh6u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\n4FJOjyQ9+eSTeu211zR48GD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" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "Xl6KbBeoCkiM" + }, + "source": [ + "Notice that after we observed $X$ occur, the probability of bugs being absent increased. By increasing the number of tests, we can approach confidence (probability 1) that there are no bugs present.\n", + "\n", + "This was a very simple example of Bayesian inference and Bayes rule. Unfortunately, the mathematics necessary to perform more complicated Bayesian inference only becomes more difficult, except for artificially constructed cases. We will later see that this type of mathematical analysis is actually unnecessary. First we must broaden our modeling tools. The next section deals with probability distributions. If you are already familiar, feel free to skip (or at least skim), but for the less familiar the next section is essential." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "2zNt6157C0Cr" + }, + "source": [ + "## Probability Distributions\n", + "Let's quickly recall what a probability distribution is: Let $Z$ be some random variable. Then associated with $Z$ is a probability distribution function that assigns probabilities to the different outcomes $Z$ can take. Graphically, a probability distribution is a curve where the probability of an outcome is proportional to the height of the curve. You can see examples in the first figure of this chapter.\n", + "\n", + "We can divide random variables into three classifications:\n", + "\n", + "* $Z$ is discrete: Discrete random variables may only assume values on a specified list. Things like populations, movie ratings, and number of votes are all discrete random variables. Discrete random variables become more clear when we contrast them with...\n", + "\n", + "* $Z$ is continuous: Continuous random variable can take on arbitrarily exact values. For example, temperature, speed, time, color are all modeled as continuous variables because you can progressively make the values more and more precise.\n", + "\n", + "* $Z$ is mixed: Mixed random variables assign probabilities to both discrete and continuous random variables, i.e. it is a combination of the above two categories.\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "xG03a_sgDRlc" + }, + "source": [ + "### Discrete Case\n", + "\n", + "If $Z$ is discrete, then its distribution is called a *probability mass function*, which measures the probability $Z$ takes on the value $k$, denoted $P(Z=k)$. Note that the probability mass function completely describes the random variable $Z$, that is, if we know the mass function, we know how $Z$ should behave. There are popular probability mass functions that consistently appear: we will introduce them as needed, but let's introduce the first very useful probability mass function. We say $Z$ is *Poisson*-distributed if:\n", + " \n", + "$$P(Z = k) =\\frac{ \\lambda^k e^{-\\lambda} }{k!}, \\; \\; k=0,1,2, \\dots $$\n", + "\n", + "\n", + "$\\lambda$ is called a parameter of the distribution, and it controls the distribution's shape. For the Poisson distribution, $\\lambda$ can be any positive number. By increasing $\\lambda$, we add more probability to larger values, and conversely by decreasing $\\lambda$ we add more probability to smaller values. One can describe $\\lambda$ as the *intensity* of the Poisson distribution. \n", + "\n", + "Unlike $\\lambda$, which can be any positive number, the value $k$ in the above formula must be a non-negative integer, i.e., $k$ must take on values 0,1,2, and so on. This is very important, because if you wanted to model a population you could not make sense of populations with 4.25 or 5.612 members. \n", + "\n", + "\n", + "If a random variable $Z$ has a Poisson mass distribution, we denote this by writing\n", + " \n", + "$$Z \\sim \\text{Poi}(\\lambda) $$\n", + " \n", + "One useful property of the Poisson distribution is that its expected value is equal to its parameter, i.e.:\n", + "\n", + "$$E\\large[ \\;Z\\; | \\; \\lambda \\;\\large] = \\lambda $$\n", + "\n", + "\n", + "We will use this property often, so it's useful to remember. Below, we plot the probability mass distribution for different $\\lambda$ values. The first thing to notice is that by increasing $\\lambda$, we add more probability of larger values occurring. Second, notice that although the graph ends at 15, the distributions do not. They assign positive probability to every non-negative integer." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "7x8Y_YtNqoPY", + "colab_type": "code", + "outputId": "6b88d9a3-f969-40ca-88ac-6c9dcfcab9f4", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 551 + } + }, + "source": [ + "# Build graph.\n", + "x = tf.range (start=0., limit=16.,dtype=tf.float32)\n", + "lambdas = tf.constant([1.5, 4.25])\n", + "\n", + "poi_pmf = tfd.Poisson(\n", + " rate=lambdas[:, tf.newaxis]).prob(x)\n", + "\n", + "plt.figure(figsize=(12.5, 8))\n", + "\n", + "# Display results in two different histograms, for easier comparison\n", + "colours = [TFColor[0], TFColor[3]]\n", + "for i in [0,1]:\n", + " ax = plt.subplot(2,1,i+1)\n", + " ax.set_autoscaley_on(False)\n", + " plt.title(\"Probability mass function of a Poisson random variable\");\n", + "\n", + " plt.bar(x,\n", + " poi_pmf[i],\n", + " color=colours[i],\n", + " label=r\"$\\lambda = %.1f$\" % lambdas[i], alpha=0.60,\n", + " edgecolor=colours[i], lw=\"3\")\n", + " plt.xticks(x)\n", + " plt.ylim([0, .5])\n", + " plt.legend()\n", + " plt.ylabel(r\"probability of $k$\")\n", + " plt.xlabel(r\"$k$\")" + ], + "execution_count": 6, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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ZWr58uXJycrRs2bICbZycnCiaAAAAANyRii2aGjVqpI8//liS9Pjjj2v16tWl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\na9bUeCgAAAAAcBcOXadpzZo1ysjIUGpqqo4cOaK2bdsqOjp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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "ipS19FlBEmqK" + }, + "source": [ + "### Continuous Case\n", + "\n", + "Instead of a probability mass function, a continuous random variable has a *probability density function*. This might seem like unnecessary nomenclature, but the density function and the mass function are very different creatures. An example of continuous random variable is a random variable with *exponential density*. The density function for an exponential random variable looks like this:\n", + "\n", + "$$f_Z(z | \\lambda) = \\lambda e^{-\\lambda z }, \\;\\; z\\ge 0$$\n", + " \n", + "Like a Poisson random variable, an exponential random variable can take on only non-negative values. But unlike a Poisson variable, the exponential can take on *any* non-negative values, including non-integral values such as 4.25 or 5.612401. This property makes it a poor choice for count data, which must be an integer, but a great choice for time data, temperature data (measured in Kelvins, of course), or any other precise *and positive* variable. The graph below shows two probability density functions with different $\\lambda$ values. \n", + "\n", + "When a random variable $Z$ has an exponential distribution with parameter $\\lambda$, we say *$Z$ is exponential* and write\n", + "\n", + "$$Z \\sim \\text{Exp}(\\lambda)$$\n", + " \n", + "Given a specific $\\lambda$, the expected value of an exponential random variable is equal to the inverse of $\\lambda$, that is:\n", + "\n", + "$$E[\\; Z \\;|\\; \\lambda \\;] = \\frac{1}{\\lambda}$$" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "o1aeMH4VE9xs", + "outputId": "56b50a86-cff3-4ac9-d394-f38bf30e71fa", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 319 + } + }, + "source": [ + "# Defining our Data and assumptions (use tf.linspace for continuous)\n", + "a = tf.range(start=0., limit=4., delta=0.04)\n", + "a = a[..., tf.newaxis]\n", + "lambdas = tf.constant([0.5, 1.])\n", + "\n", + "# Now we use TFP to compute probabilities in a vectorized manner.\n", + "expo_pdf = tfd.Exponential(rate=lambdas).prob(a)\n", + "\n", + "# Visualizing our results\n", + "plt.figure(figsize=(12.5, 4))\n", + "for i in range(lambdas.shape[0]):\n", + " plt.plot(tf.transpose(a)[0], tf.transpose(expo_pdf)[i],\n", + " lw=3, color=TFColor[i], label=r\"$\\lambda = %.1f$\" % lambdas[i])\n", + " plt.fill_between(tf.transpose(a)[0], tf.transpose(expo_pdf)[i],\n", + " color=TFColor[i], alpha=.33)\n", + "plt.legend()\n", + "plt.ylabel(\"PDF at $z$\")\n", + "plt.xlabel(\"$z$\")\n", + "plt.ylim(0,1.2)\n", + "plt.title(r\"Probability density function of an Exponential random variable; differing $\\lambda$\");\n" + ], + "execution_count": 7, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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+++/H7XYHK8R+x5M4Ftfwm7AdYQAYrhoi/vH/MAt101kRERERkdYELUlatGgR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\niYiIiIiIRPx/7HakNsGWQKcAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "_1fhqQhAFLkk" + }, + "source": [ + " \n", + "## But what is $\\lambda \\;$?\n", + "\n", + "**This question is what motivates statistics**. In the real world, $\\lambda$ is hidden from us. We see only $Z$, and must go backwards to try and determine $\\lambda$. The problem is difficult because there is no one-to-one mapping from $Z$ to $\\lambda$. Many different methods have been created to solve the problem of estimating $\\lambda$, but since $\\lambda$ is never actually observed, no one can say for certain which method is best! \n", + "\n", + "Bayesian inference is concerned with *beliefs* about what $\\lambda$ might be. Rather than try to guess $\\lambda$ exactly, we can only talk about what $\\lambda$ is likely to be by assigning a probability distribution to $\\lambda$.\n", + " \n", + "This might seem odd at first. After all, $\\lambda$ is fixed; it is not (necessarily) random! How can we assign probabilities to values of a non-random variable? Ah, we have fallen for our old, frequentist way of thinking. Recall that under Bayesian philosophy, we *can* assign probabilities if we interpret them as beliefs. And it is entirely acceptable to have *beliefs* about the parameter $\\lambda$. \n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "JrRddMMfHHKJ" + }, + "source": [ + " \n", + "#### Example: Inferring behaviour from text-message data\n", + " \n", + "Let's try to model a more interesting example, one that concerns the rate at which a user sends and receives text messages:\n", + "\n", + "> You are given a series of daily text-message counts from a user of your system. The data, plotted over time, appears in the chart below. You are curious to know if the user's text-messaging habits have changed over time, either gradually or suddenly. How can you model this? (This is in fact my own text-message data. Judge my popularity as you wish.)\n", + "\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "cOBOnwa2IIaB", + "outputId": "e3ba822f-4f15-4bb2-9f23-16a59246db0d", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 317 + } + }, + "source": [ + "# Defining our Data and assumptions\n", + "count_data = tf.constant([\n", + " 13, 24, 8, 24, 7, 35, 14, 11, 15, 11, 22, 22, 11, 57, \n", + " 11, 19, 29, 6, 19, 12, 22, 12, 18, 72, 32, 9, 7, 13, \n", + " 19, 23, 27, 20, 6, 17, 13, 10, 14, 6, 16, 15, 7, 2, \n", + " 15, 15, 19, 70, 49, 7, 53, 22, 21, 31, 19, 11, 18, 20, \n", + " 12, 35, 17, 23, 17, 4, 2, 31, 30, 13, 27, 0, 39, 37, \n", + " 5, 14, 13, 22,\n", + "], dtype=tf.float32)\n", + "n_count_data = tf.shape(count_data)\n", + "days = tf.range(n_count_data[0],dtype=tf.int32)\n", + "\n", + "# Visualizing the Results\n", + "plt.figure(figsize=(12.5, 4))\n", + "plt.bar(days.numpy(), count_data, color=\"#5DA5DA\")\n", + "plt.xlabel(\"Time (days)\")\n", + "plt.ylabel(\"count of text-msgs received\")\n", + "plt.title(\"Did the user's texting habits change over time?\")\n", + "plt.xlim(0, n_count_data[0].numpy());" + ], + "execution_count": 8, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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uLs6uzGq1aufOnTp48KAuXLhQ6tjgHKmpqU5qqZKbxOF5nHtt6KfS4tp4Bs/v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\n2NhYLVq0yNExlkpxD7+SSv4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" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "EdhCxPaUWR74", + "colab_type": "text" + }, + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "i-PRmpvsIZKq" + }, + "source": [ + "\n", + "Before we start modeling, see what you can figure out just by looking at the chart above. Would you say there was a change in behaviour during this time period? \n", + " \n", + "How can we start to model this? Well, as we have conveniently already seen, a Poisson random variable is a very appropriate model for this type of *count* data. Denoting day $i$'s text-message count by $C_i$, \n", + " \n", + "$$ C_i \\sim \\text{Poisson}(\\lambda) $$\n", + " \n", + "We are not sure what the value of the $\\lambda$ parameter really is, however. Looking at the chart above, it appears that the rate might become higher late in the observation period, which is equivalent to saying that $\\lambda$ increases at some point during the observations. (Recall that a higher value of $\\lambda$ assigns more probability to larger outcomes. That is, there is a higher probability of many text messages having been sent on a given day.)\n", + " \n", + "How can we represent this observation mathematically? Let's assume that on some day during the observation period (call it $\\tau$), the parameter $\\lambda$ suddenly jumps to a higher value. So we really have two $\\lambda$ parameters: one for the period before $\\tau$, and one for the rest of the observation period. In the literature, a sudden transition like this would be called a *switchpoint*:\n", + " \n", + "$$\\lambda = \n", + "\\begin{cases} \\lambda_1 & \\text{if } t \\lt \\tau \\cr\n", + "\\lambda_2 & \\text{if } t \\ge \\tau\n", + "\\end{cases}\n", + "$$\n", + "\n", + "If, in reality, no sudden change occurred and indeed $\\lambda_1 = \\lambda_2$, then the $\\lambda$s posterior distributions should look about equal.\n", + "\n", + "We are interested in inferring the unknown $\\lambda$s. To use Bayesian inference, we need to assign prior probabilities to the different possible values of $\\lambda$. What would be good prior probability distributions for $\\lambda_1$ and $\\lambda_2$? Recall that $\\lambda$ can be any positive number. As we saw earlier, the *exponential* distribution provides a continuous density function for positive numbers, so it might be a good choice for modeling $\\lambda_i$. But recall that the exponential distribution takes a parameter of its own, so we'll need to include that parameter in our model. Let's call that parameter $\\alpha$.\n", + "$$\n", + "\\begin{align}\n", + "&\\lambda_1 \\sim \\text{Exp}( \\alpha ) \\\\\n", + "&\\lambda_2 \\sim \\text{Exp}( \\alpha )\n", + "\\end{align}\n", + "$$\n", + "$\\alpha$ is called a *hyper-parameter* or *parent variable*. In literal terms, it is a parameter that influences other parameters. Our initial guess at $\\alpha$ does not influence the model too strongly, so we have some flexibility in our choice. A good rule of thumb is to set the exponential parameter equal to the inverse of the average of the count data. Since we're modeling $\\lambda$ using an exponential distribution, we can use the expected value identity shown earlier to get:\n", + "\n", + "$$\\frac{1}{N}\\sum_{i=0}^N \\;C_i \\approx E[\\; \\lambda \\; |\\; \\alpha ] = \\frac{1}{\\alpha}$$ \n", + " \n", + "An alternative, and something I encourage the reader to try, would be to have two priors: one for each $\\lambda_i$. Creating two exponential distributions with different $\\alpha$ values reflects our prior belief that the rate changed at some point during the observations.\n", + " \n", + "What about $\\tau$? Because of the noisiness of the data, it's difficult to pick out a priori when $\\tau$ might have occurred. Instead, we can assign a *uniform prior belief* to every possible day. This is equivalent to saying\n", + "$$\n", + "\\begin{align}\n", + "& \\tau \\sim \\text{DiscreteUniform(1,70) }\\\\\n", + "& \\Rightarrow P( \\tau = k ) = \\frac{1}{70}\n", + "\\end{align}\n", + "$$\n", + "So after all this, what does our overall prior distribution for the unknown variables look like? Frankly, *it doesn't matter*. What we should understand is that it's an ugly, complicated mess involving symbols only a mathematician could love. And things will only get uglier the more complicated our models become. Regardless, all we really care about is the posterior distribution.\n", + "\n", + "We next turn to [TensorFlow Probability](https://tensorflow.org/probability), a Python library for performing Bayesian analysis that is undaunted by the mathematical monster we have created." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "mCz2BozPcYNy" + }, + "source": [ + "## Introducing our first hammer: TensorFlow Probability\n", + "\n", + "TensorFlow Probability (TFP) is a Python library for programming Bayesian analysis. It is intended for data scientists, statisticians, machine learning practitioners, and scientists. Since it is built on the TensorFlow (TF) stack, it brings the runtime benefits of TF to Bayesian analysis. These include write-once run-many (ability to run your development model in production) and speedups via state-of-the-art hardware (GPUs and TPUs). \n", + "\n", + "Since TFP is relatively new, the TFP community is actively developing documentation, \n", + "especially docs and examples that bridge the gap between beginner and hacker. One of this book's main goals is to solve that problem, and also to demonstrate why TFP is so cool.\n", + "\n", + "We will model the problem above using TFP. This type of programming is called *probabilistic programming*, an unfortunate misnomer that invokes ideas of randomly-generated code and has likely confused and frightened users away from this field. The code is not random; it is probabilistic in the sense that we create probability models using programming variables as the model's components. \n", + "\n", + "B. Cronin [[4]](#scrollTo=nDdph0r1ABCn) has a very motivating description of probabilistic programming:\n", + "\n", + "> Another way of thinking about this: unlike a traditional program, which only runs in the forward directions, a probabilistic program is run in both the forward and backward direction. It runs forward to compute the consequences of the assumptions it contains about the world (i.e., the model space it represents), but it also runs backward from the data to constrain the possible explanations. In practice, many probabilistic programming systems will cleverly interleave these forward and backward operations to efficiently home in on the best explanations.\n", + "\n", + "Because of the confusion engendered by the term *probabilistic programming*, I'll refrain from using it. Instead, I'll simply say *programming*, since that's what it really is. \n", + " \n", + "TFP code is easy to read. The only novel thing should be the syntax. Simply remember that we are representing the model's components ($\\tau, \\lambda_1, \\lambda_2$ ) as variables." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "gYVjgZQ3hOw-" + }, + "source": [ + "## Specify the joint log-density\n", + "\n", + "We'll assume the data is a consequence of the following generative model:\n", + "\n", + "$$\\begin{align*}\n", + "\\lambda_{1}^{(0)} &\\sim \\text{Exponential}(\\text{rate}=\\alpha) \\\\\n", + "\\lambda_{2}^{(0)} &\\sim \\text{Exponential}(\\text{rate}=\\alpha) \\\\\n", + "\\tau &\\sim \\text{Uniform}[\\text{low}=0,\\text{high}=1) \\\\\n", + "\\text{for } i &= 1\\ldots N: \\\\\n", + "\\lambda_i &= \\begin{cases} \\lambda_{1}^{(0)}, & \\tau > i/N \\\\ \\lambda_{2}^{(0)}, & \\text{otherwise}\\end{cases}\\\\\n", + " X_i &\\sim \\text{Poisson}(\\text{rate}=\\lambda_i)\n", + "\\end{align*}$$\n", + "\n", + "Happily, this model can be easily implemented using TF and TFP's distributions:\n", + "\n", + "\n", + "This code creates a new function `lambda_`, but really we can think of it as a random variable: the random variable $\\lambda$ from above. The [gather](https://https://www.tensorflow.org/api_docs/python/tf/gather) function assigns `lambda_1` or `lambda_2` as the value of `lambda_`, depending on what side of `tau` we are on. The values of `lambda_` up until `tau` are `lambda_1` and the values afterwards are `lambda_2`.\n", + "\n", + "Note that because `lambda_1`, `lambda_2` and `tau` are random, `lambda_` will be random. We are **not** fixing any variables yet.\n", + "\n", + "TFP performs probabilistic inference by evaluating the model parameters using a joint_log_prob function, which we'll describe more in Chapter 2.\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "YBCXrK9gj8Gx", + "colab": {} + }, + "source": [ + "def joint_log_prob(count_data, lambda_1, lambda_2, tau):\n", + " tfd = tfp.distributions\n", + " \n", + " alpha = (1. / tf.reduce_mean(count_data))\n", + " rv_lambda_1 = tfd.Exponential(rate=alpha)\n", + " rv_lambda_2 = tfd.Exponential(rate=alpha)\n", + " \n", + " rv_tau = tfd.Uniform()\n", + " \n", + " lambda_ = tf.gather(\n", + " [lambda_1, lambda_2],\n", + " indices=tf.cast(tau * tf.cast(tf.size(count_data), dtype=tf.float32) <= tf.cast(tf.range(tf.size(count_data)), dtype=tf.float32), dtype=tf.int32))\n", + " rv_observation = tfd.Poisson(rate=lambda_)\n", + " \n", + " return (\n", + " rv_lambda_1.log_prob(lambda_1)\n", + " + rv_lambda_2.log_prob(lambda_2)\n", + " + rv_tau.log_prob(tau)\n", + " + tf.reduce_sum(rv_observation.log_prob(count_data))\n", + " )\n", + "\n", + "\n", + "# Define a closure over our joint_log_prob.\n", + "def unnormalized_log_posterior(lambda1, lambda2, tau):\n", + " return joint_log_prob(count_data, lambda1, lambda2, tau)\n" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "t7Vvrj68jsr7" + }, + "source": [ + "Notice that the implementation is arguably very close to being a 1:1 translation of the mathematical model. The main difference is merely that once we've specified the probabilistic model, we return the sum of the log_probs." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "KnyDyY8Tjyiy" + }, + "source": [ + "## Specify the posterior sampler" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "zy6DFZgPEXyU", + "colab_type": "code", + "colab": {} + }, + "source": [ + "# wrap the mcmc sampling call in a @tf.function to speed it up\n", + "@tf.function(autograph=False)\n", + "def graph_sample_chain(*args, **kwargs):\n", + " return tfp.mcmc.sample_chain(*args, **kwargs)\n", + "\n", + "num_burnin_steps = 5000\n", + "num_results = 20000\n", + "\n", + "\n", + "# Set the chain's start state.\n", + "initial_chain_state = [\n", + " tf.cast(tf.reduce_mean(count_data), tf.float32) * tf.ones([], dtype=tf.float32, name=\"init_lambda1\"),\n", + " tf.cast(tf.reduce_mean(count_data), tf.float32) * tf.ones([], dtype=tf.float32, name=\"init_lambda2\"),\n", + " 0.5 * tf.ones([], dtype=tf.float32, name=\"init_tau\"),\n", + "]\n", + "\n", + "\n", + "# Since HMC operates over unconstrained space, we need to transform the\n", + "# samples so they live in real-space.\n", + "unconstraining_bijectors = [\n", + " tfp.bijectors.Exp(), # Maps a positive real to R.\n", + " tfp.bijectors.Exp(), # Maps a positive real to R.\n", + " tfp.bijectors.Sigmoid(), # Maps [0,1] to R. \n", + "]\n", + "\n", + "step_size = 0.2\n", + "\n", + "kernel=tfp.mcmc.TransformedTransitionKernel(\n", + " inner_kernel=tfp.mcmc.HamiltonianMonteCarlo(\n", + " target_log_prob_fn=unnormalized_log_posterior,\n", + " num_leapfrog_steps=2,\n", + " step_size=step_size,\n", + " state_gradients_are_stopped=True),\n", + " bijector=unconstraining_bijectors)\n", + "\n", + "kernel = tfp.mcmc.SimpleStepSizeAdaptation(\n", + " inner_kernel=kernel, num_adaptation_steps=int(num_burnin_steps * 0.8))\n", + "\n", + "\n", + "# Sample from the chain.\n", + "[\n", + " lambda_1_samples,\n", + " lambda_2_samples,\n", + " posterior_tau,\n", + "], kernel_results = graph_sample_chain(\n", + " num_results=num_results,\n", + " num_burnin_steps=num_burnin_steps,\n", + " current_state=initial_chain_state,\n", + " kernel = kernel)\n", + " \n", + "tau_samples = tf.floor(posterior_tau * tf.cast(tf.size(count_data),dtype=tf.float32))" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "NpNv545ZkLjb", + "outputId": "99ad4fbd-7e03-4a72-8009-61064bbc3b95", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 52 + } + }, + "source": [ + "print(\"acceptance rate: {}\".format(\n", + " tf.reduce_mean(tf.cast(kernel_results.inner_results.inner_results.is_accepted,dtype=tf.float32))))\n", + "print(\"final step size: {}\".format(\n", + " tf.reduce_mean(kernel_results.inner_results.inner_results.accepted_results.step_size[-100:])))\n" + ], + "execution_count": 11, + "outputs": [ + { + "output_type": "stream", + "text": [ + "acceptance rate: 0.5755000114440918\n", + "final step size: 0.030197443440556526\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "vIxEqx9qkhWr" + }, + "source": [ + "## Plot the Results" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "viLRm6DEkRPM", + "outputId": "840c605d-f0f8-4526-f1c9-5082f80d5efd", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 964 + } + }, + "source": [ + "plt.figure(figsize=(12.5, 15))\n", + "#histogram of the samples:\n", + "\n", + "ax = plt.subplot(311)\n", + "ax.set_autoscaley_on(False)\n", + "\n", + "plt.hist(lambda_1_samples, histtype='stepfilled', bins=30, alpha=0.85,\n", + " label=r\"posterior of $\\lambda_1$\", color=TFColor[0], density=True)\n", + "plt.legend(loc=\"upper left\")\n", + "plt.title(r\"\"\"Posterior distributions of the variables $\\lambda_1,\\;\\lambda_2,\\;\\tau$\"\"\")\n", + "plt.xlim([15, 30])\n", + "plt.xlabel(r\"$\\lambda_1$ value\")\n", + "\n", + "ax = plt.subplot(312)\n", + "ax.set_autoscaley_on(False)\n", + "plt.hist(lambda_2_samples, histtype='stepfilled', bins=30, alpha=0.85,\n", + " label=r\"posterior of $\\lambda_2$\", color=TFColor[6], density=True)\n", + "plt.legend(loc=\"upper left\")\n", + "plt.xlim([15, 30])\n", + "plt.xlabel(r\"$\\lambda_2$ value\")\n", + "\n", + "plt.subplot(313)\n", + "w = 1.0 / tau_samples.shape[0] * np.ones_like(tau_samples)\n", + "plt.hist(tau_samples, bins=n_count_data[0], alpha=1,\n", + " label=r\"posterior of $\\tau$\",\n", + " color=TFColor[2], weights=w, rwidth=2.)\n", + "plt.xticks(np.arange(n_count_data[0]))\n", + "\n", + "plt.legend(loc=\"upper left\")\n", + "plt.ylim([0, .75])\n", + "plt.xlim([35, len(count_data)-20])\n", + "plt.xlabel(r\"$\\tau$ (in days)\")\n", + "plt.ylabel(r\"probability\");" + ], + "execution_count": 18, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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v3urdu7f+8Y9/OMw7Y8YM/eIXv9DMmTPt286ePVtp/gEDBtjnr+q4n6ruuClTpmjr1q16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\n91deXp78/Pzs+02aNEnDhw/X3XffrfP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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "FfiTXgF80sDA" + }, + "source": [ + "## Interpretation\n", + "\n", + "Recall that Bayesian methodology returns a *distribution*. Hence we now have distributions to describe the unknown $\\lambda$s and $\\tau$. What have we gained? Immediately, we can see the uncertainty in our estimates: the wider the distribution, the less certain our posterior belief should be. We can also see what the plausible values for the parameters are: $\\lambda_1$ is around 18 and $\\lambda_2$ is around 23. The posterior distributions of the two $\\lambda$s are clearly distinct, indicating that it is indeed likely that there was a change in the user's text-message behaviour.\n", + "\n", + "What other observations can you make? If you look at the original data again, do these results seem reasonable? \n", + "\n", + "Notice also that the posterior distributions for the $\\lambda$s do not look like exponential distributions, even though our priors for these variables were exponential. In fact, the posterior distributions are not really of any form that we recognize from the original model. But that's OK! This is one of the benefits of taking a computational point of view. If we had instead done this analysis using mathematical approaches, we would have been stuck with an analytically intractable (and messy) distribution. Our use of a computational approach makes us indifferent to mathematical tractability.\n", + " \n", + "Our analysis also returned a distribution for $\\tau$. Its posterior distribution looks a little different from the other two because it is a discrete random variable, so it doesn't assign probabilities to intervals. We can see that near day 45, there was a 50% chance that the user's behaviour changed. Had no change occurred, or had the change been gradual over time, the posterior distribution of $\\tau$ would have been more spread out, reflecting that many days were plausible candidates for $\\tau$. By contrast, in the actual results we see that only three or four days make any sense as potential transition points. \n", + "\n", + "### Why would I want samples from the posterior, anyways?\n", + "\n", + "We will deal with this question for the remainder of the book, and it is an understatement to say that it will lead us to some amazing results. For now, let's end this chapter with one more example.\n", + "\n", + "We'll use the posterior samples to answer the following question: what is the expected number of texts at day $t, \\; 0 \\le t \\le 70$ ? Recall that the expected value of a Poisson variable is equal to its parameter $\\lambda$. Therefore, the question is equivalent to *what is the expected value of $\\lambda$ at time $t$*?\n", + " \n", + "In the code below, let $i$ index samples from the posterior distributions. Given a day $t$, we average over all possible $\\lambda_i$ for that day $t$, using $\\lambda_i = \\lambda_{1,i}$ if $t \\lt \\tau_i$ (that is, if the behaviour change has not yet occurred), else we use $\\lambda_i = \\lambda_{2,i}$. \n" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "DgNkjkmO1h4I", + "outputId": "02caf88d-25f9-4387-a0b5-131cea3798ca", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 608 + } + }, + "source": [ + "# tau_samples, lambda_1_samples, lambda_2_samples contain\n", + "# N samples from the corresponding posterior distribution\n", + "\n", + "N_ = tau_samples.shape[0]\n", + "expected_texts_per_day = tf.zeros(N_,n_count_data.shape[0]) #(10000,74)\n", + "\n", + "plt.figure(figsize=(12.5, 9))\n", + "\n", + "day_range = tf.range(0,n_count_data[0],delta=1,dtype = tf.int32)\n", + "\n", + "# expand from shape of 74 to (10000,74)\n", + "day_range = tf.expand_dims(day_range,0)\n", + "day_range = tf.tile(day_range,tf.constant([N_,1]))\n", + "\n", + "# expand from shape of 10000 to 10000,74\n", + "tau_samples_per_day = tf.expand_dims(tau_samples,0)\n", + "tau_samples_per_day = tf.transpose(tf.tile(tau_samples_per_day,tf.constant([day_range.shape[1],1])))\n", + "\n", + "tau_samples_per_day = tf.cast(tau_samples_per_day,dtype=tf.int32)\n", + "#ix_day is (10000,74) tensor where axis=0 is number of samples, axis=1 is day. each value is true iff sampleXday value is < tau_sample value\n", + "ix_day = day_range < tau_samples_per_day\n", + "\n", + "lambda_1_samples_per_day = tf.expand_dims(lambda_1_samples,0)\n", + "lambda_1_samples_per_day = tf.transpose(tf.tile(lambda_1_samples_per_day,tf.constant([day_range.shape[1],1])))\n", + "lambda_2_samples_per_day = tf.expand_dims(lambda_2_samples,0)\n", + "lambda_2_samples_per_day = tf.transpose(tf.tile(lambda_2_samples_per_day,tf.constant([day_range.shape[1],1])))\n", + "\n", + "expected_texts_per_day = ((tf.reduce_sum(lambda_1_samples_per_day*tf.cast(ix_day,dtype=tf.float32),axis=0) + tf.reduce_sum(lambda_2_samples_per_day*tf.cast(~ix_day,dtype=tf.float32),axis=0))/N_)\n", + "\n", + "plt.plot(range(n_count_data[0]), expected_texts_per_day, lw=4, color=\"#E24A33\",\n", + " label=\"expected number of text-messages received\")\n", + "plt.xlim(0, n_count_data.numpy()[0])\n", + "plt.xlabel(\"Day\")\n", + "plt.ylabel(\"Expected # text-messages\")\n", + "plt.title(\"Expected number of text-messages received\")\n", + "plt.ylim(0, 60)\n", + "plt.bar(np.arange(len(count_data)), count_data, color=\"#5DA5DA\", alpha=0.65,\n", + " label=\"observed texts per day\")\n", + "\n", + "plt.legend(loc=\"upper left\");" + ], + "execution_count": 19, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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j4ODgp9r19/dX9erVtXPnTk2bNk3Lli3T/PnzNW7cOJ04cUJ79+5Vp06d1KtX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\n/fv3V0JCgvbt26cXX3xR8+fP1/DhwxtUAAAACK6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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "cgCrDy8M3IZT" + }, + "source": [ + "Our analysis shows strong support for believing the user's behavior did change ($\\lambda_1$ would have been close in value to $\\lambda_2$ had this not been true), and that the change was sudden rather than gradual (as demonstrated by $\\tau$'s strongly peaked posterior distribution). We can speculate what might have caused this: a cheaper text-message rate, a recent weather-to-text subscription, or perhaps a new relationship. (In fact, the 45th day corresponds to Christmas, and I moved away to Toronto the next month, leaving a girlfriend behind.)\n", + "\n", + "\n", + "## Exercises\n", + " \n", + "1. Using `lambda_1_samples` and `lambda_2_samples`, what is the mean of the posterior distributions of $\\lambda_1$ and $\\lambda_2$?\n", + "\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "ddpQzca9ACJF", + "colab": {} + }, + "source": [ + "#type your code here." + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "p4krLq5J_356" + }, + "source": [ + "2. What is the expected percentage increase in text-message rates? `hint:` compute the mean of `lambda_1_samples/lambda_2_samples`. Note that this quantity is very different from `lambda_1_samples.mean()/lambda_2_samples.mean()`" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "qWoCGbmEAEvb", + "colab": {} + }, + "source": [ + "#type your code here." + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "vGHVkSlp_9zf" + }, + "source": [ + "3. What is the mean of $\\lambda_1$ **given** that we know $\\tau$ is less than 45? That is, suppose we have been given new information that the change in behaviour occurred prior to day 45. What is the expected value of $\\lambda_1$ now? (You do not need to redo the TFP part. Just consider all instances where `tau_samples < 45`.)" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "lKYX1MKHgsm0", + "colab": {} + }, + "source": [ + "#type your code here." + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "nDdph0r1ABCn" + }, + "source": [ + "## References\n", + "\n", + "[1] Gelman, Andrew. N.p.. Web. 22 Jan 2013. [N is never large enough](http://andrewgelman.com/2005/07/31/n_is_never_larg)\n", + " \n", + "[2] Norvig, Peter. 2009. [The Unreasonable Effectiveness of Data](http://static.googleusercontent.com/media/research.google.com/en//pubs/archive/35179.pdf).\n", + "\n", + "[3] Jimmy Lin and Alek Kolcz. Large-Scale Machine Learning at Twitter. Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data (SIGMOD 2012), pages 793-804, May 2012, Scottsdale, Arizona.\n", + "\n", + "[4] Cronin, Beau. \"Why Probabilistic Programming Matters.\" 24 Mar 2013. Google, Online Posting to Google . Web. 24 Mar. 2013. ." + ] + } + ] +} \ No newline at end of file diff --git a/Chapter1_Introduction/Chapter1_Introduction.ipynb b/Chapter1_Introduction/Chapter1_Introduction.ipynb deleted file mode 100644 index 89c57d50..00000000 --- a/Chapter1_Introduction/Chapter1_Introduction.ipynb +++ /dev/null @@ -1,851 +0,0 @@ -{ - "metadata": { - "name": "Chapter1_Introduction" - }, - "nbformat": 3, - "nbformat_minor": 0, - "worksheets": [ - { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Chapter 1\n", - "======\n", - "***" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The Philosophy of Bayesian Inference\n", - "------\n", - "\n", - "Think of what you would do in the following situation: \n", - " \n", - " \n", - "####Example\n", - "> You are a skilled programmer, but bugs still slip into your code. After a particularly difficult implementation of an algorithm, you decide to test your code on a trivial example. It passes. You test the code on a harder problem. It passes once again. And it passes the next, *even more difficult*, test too! You are starting to believe that there are may be no bugs present...\n", - "\n", - "If you think this way, then congratulations, you're already a Bayesian practitioner! Bayesian inference is simply updating your beliefs after considering new evidence. A Bayesian can rarely be certain about a result, but he or she can be very confident. Just like in the example above, we can never be 100% sure that our code is bug-free unless we test it on every possible problem; something rarely possible in practice. Instead, we can test it on a large number of problems, and if it succeeds we can feel more *confident* about our code. Bayesian inference works identically: we can only update our beliefs about an outcome, rarely can we be absolutely sure unless we rule out all other alternatives. We will see that being uncertain can have its advantages. \n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "###The Bayesian state of mind\n", - "\n", - "\n", - "Bayesian inference differs from more traditional statistical analysis by preserving *uncertainty* about our beliefs. At first, this sounds like a bad statistical technique. Isn't statistics all about deriving *certainty* from randomness? The Bayesian method believes that probability is better seen as a measure of *believability in an event*. In fact, we will see in a moment that this is the natural interpretation of probability. \n", - "\n", - "For this to be clearer, we consider an alternative interpretation: *Frequentist* methods assume that probability is the long-run frequency of events (hence the bestowed title). For example, the *probability of plane accidents* under a frequentist philosophy is the *long-term frequency of plane accidents*. This makes logical sense for many probabilities, but becomes more difficult to understand when events have no long-term frequency of occurances. Consider: we often assign probabilities to outcomes of presidential elections, but the election itself only happens once! Frequentists get around this by invoking alternative realities [cite]. \n", - "\n", - "Bayesians, on the other hand, have a more intuitive approach. Bayesians interpret a probability as measure of *belief*, or knowledge, of an event occurring. A belief of 0 is you have no confidence that the event will occur; conversely, a belief of 1 implies you are absolutely certain of an event occurring. Beliefs between 0 and 1 allow for weightings of other outcomes. This definition agrees with the probability of a plane accident example, for having observed the frequency of plane accidents, your belief should be equal to that frequency. Similarly, under this definition of probability being equal to beliefs, it is clear how we can speak about probabilities (read: belief) of presidential election outcomes. \n", - "\n", - "Think about how we can extend this definition of probability to events that are not *really* random. That is, think about how we can extend this to anything that is fixed, but we are unsure about: \n", - "\n", - "- Your code either has a bug in it or not, but we do not know for certain which is true. Though we have a belief about the presence or absence of a bug. \n", - "\n", - "- A medical patient is exhibiting symptoms $x$, $y$ and $z$. There are a number of diseases that could be causing all of them, but only has a single disease is present. A doctor has beliefs about which disease.\n", - "\n", - "- My boss probably makes a lot of money.\n", - "\n", - "This philosophy of treating beliefs as probability is natural to humans. We employ it constantly as we interact with the world and only see partial evidence. Alternatively, you have to be *trained* to think like a frequentist. \n", - "\n", - "To align ourselves with traditional probability notation, we denote our belief about event $A$ as $P(A)$.\n", - "\n", - "John Maynard Keynes, a great economist and thinker, said \n", - "> \"When the facts change, I change my mind. What do you do, sir?\"\n", - "\n", - "This quote reflects the way a Bayesian updates his or her beliefs after seeing evidence. Even -especially- if the evidence is counter to what was initialed believed, it cannot be ignored. We denote our updated belief as $P(A |X )$, interpreted as the probability of $A$ given the evidence $X$. We call it the *posterior probability* so as to contrast the pre-evidence *prior probability*. Consider the posterior probabilities (read: posterior belief) of the above examples, after observing evidence $X$.:\n", - "\n", - "- 1. $P(A): \\;\\;$ This big, complex code likely has a bug in it.\n", - "2. $P(A | X): \\;\\;$ The code passed all $X$ tests; there still might be a bug, but its presence is less likely now.\n", - "- 1. $P(A):\\;\\;$ The patient could have any number of diseases.\n", - "2. $P(A | X):\\;\\;$ Performing a urine test generated evidence $X$, ruling out some of the possible diseases from consideration.\n", - "- 1. $P(A):\\;\\;$ My boss probably makes a lot of money. \n", - "2. $P(A | X): \\;\\;$ I saw by boss *making it rain* at a bar with $X$ dollar bills. He's probably rich and a fool too. \n", - "\n", - "It's clear that in each example we did not completely discard the prior belief after seeing new evidence, but we *re-weighted the prior* to incorporate the new evidence (i.e. we put more weight, or confidence, on some beliefs versus others). \n", - "\n", - "By introducing prior uncertainity about events, we are already admitting that any guess we make is potentially very wrong. After observing data, evidence, or other information, and we update our beliefs, our guess becomes *less wrong*. This is the opposite side of the prediction coin, where typically we try to be *more right*.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "Bayesian Inference in Practice\n", - "------ \n", - " If frequentist and Bayesian inference were computer programming functions, with inputs being statistical problems, then the two would be different in what they return to the user. The frequentist inference function would return a number, whereas the Bayesian function would return a *distribution*.\n", - "\n", - "For example, in our debugging problem above, calling the frequentist function with the argument \"My code passed all $X$ tests; is my code bug-free?\" would return a *YES*. On the other hand, asking our Bayesian function \"Often my code has bugs. My code passed all $X$ tests; is my code bug-free?\" would return something very different: a distribution over *YES* and *NO*. The function might return \n", - "\n", - "\n", - "> *YES*, with probability 0.8; *NO*, with probability 0.2\n", - "\n", - "\n", - "\n", - "This is very different from the answer the frequentist function returned. Notice that the Bayesian function accepted an additional argument: *\"Often my code has bugs\"*. This parameter, the *prior*, is that intuition in your head that says \"wait- something looks different with this situation\", or conversely \"yes, this is what I expected\". In our example, the programmer often sees debugging tests fail, but this time we didn't, which signals an alert in our head. By including the prior parameter, we are telling the Bayesian function to include our personal intuition. Technically this parameter in the Bayesian function is optional, but we will see excluding it has its own consequences. \n", - "\n", - "\n", - "As we aquire more and more instances of evidence, our prior belief is *washed out* by the new evidence. This is to be expected. For example, if your prior belief is something ridiculous, like \"I expect the sun to explode today\", and each day you are proved wrong, you would hope that any inference would correct you, or at least align your beliefs. \n", - "\n", - "\n", - "Denote $N$ as the number of instances of evidence we possess. As we gather an *infinite* amount of evidence, say as $N \\rightarrow \\infty$, our Bayesian results align with frequentist results. Hence for large $N$, statistical inference is more or less objective. On the other hand, for small $N$, inference is much more *unstable*: frequentist estimates have more variance and larger confidence intervals. This is where Bayesian analysis excels. By introducing a prior, and returning a distribution (instead of an scalar estimate), we *preserve the uncertainity* to reflect the instability of stasticial inference of a small $N$. \n", - "\n", - "One may think that for large $N$, one can be indifferent between the two techniques, and might lean towards the computational-simpler frequentist methods. An analysist should consider the following quote by Andrew Gelman (2005)[1], before making such a decision:\n", - "\n", - "> Sample sizes are never large. If $N$ is too small to get a sufficiently-precise estimate, you need to get more data (or make more assumptions). But once $N$ is \"large enough,\" you can start subdividing the data to learn more (for example, in a public opinion poll, once you have a good estimate for the entire country, you can estimate among men and women, northerners and southerners, different age groups, etc etc). $N$ is never enough because if it were \"enough\" you'd already be on to the next problem for which you need more data.\n", - "\n", - "\n", - "#### A note on *Big Data*\n", - "Paradoxically, big data's prediction problems are actually solved by relatively simple models [2]. Thus we can argue that big data's prediction difficulty does not lie in the algorithm used, but instead on the computational difficulties of storage and execution on big data. (One should also consider Gelman's qoute from above and ask \"Do I really have a big data prediction problem?\" )\n", - "\n", - "The much more difficult prediction problems involve *medium data* and, especially troublesome, *really small data*. Using a similar argument as Gelman's above, if big data problems are *big enough* to be easily solved, then we should be more interested in the smaller-sized data. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Our Bayesian framework\n", - "\n", - "We are interested in beliefs, which can be interpreted as probabilities by thinking Bayesian. We have a *prior* belief in event $A$: it is what you believe before looking at any evidence, e.g., our prior belief about bugs being in our code before performing tests.\n", - "\n", - "Secondly, we observe our evidence. To continue our example, if our code passes $X$ tests, we want to update our belief to incorporate this. We call this new belief the *posterior* probability. Updating our belief is done via the following equation, known as Bayes' Theorem, after Thomas Bayes:\n", - "\n", - "\\begin{align}\n", - " P( A | X ) = & \\frac{ P(X | A) P(A) } {P(X) } \\\\\\\\[5pt]\n", - "& \\propto P(X | A) P(A)\\;\\; (\\propto \\text{is proportional to } )\n", - "\\end{align}\n", - "\n", - "The above formula is not unique to Bayesian inference: it is a mathematical fact with uses outside Bayesian inference. Bayesian inference merely uses it. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "###Example\n", - "______\n", - "\n", - "\n", - "Let $A$ denote the event that our code has **no bugs** in it. Let $X$ denote the event that the code passes all debugging tests. For now, we will leave the prior probability as no bugs as a variable, i.e. $P(A) = p$. We are interested in $P(A|X)$, i.e. the probability of no bugs, given our debugging tests $X$. To use the formula above, we need to compute some quantities:\n", - "\n", - "What is $P(X | A)$, i.e., the probability that the code passes $X$ tests *given* there are no bugs? Well, it is equal to 1, for a code with no bugs will pass any tests. \n", - "\n", - "$P(X)$ is a little bit trickier: The event $X$ can be divided into two possibilities, event $X$ with bugs (denoted $\\sim A\\;$, spoken *not $A$*), or event $X$ without bugs ($A$). $P(X)$ can be represented as: [needs to be filled in better]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\\begin{align}\n", - "P(X ) & = P(X \\text{ and } A) + P(X \\text{ and } \\sim A) \\\\\\\\[5pt]\n", - " & = P(X|A)P(A) + P(X | \\sim A)P(\\sim A)\\\\\\\\[5pt]\n", - "& = P(X|A)p + P(X | \\sim A)(1-p)\n", - "\\end{align}" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We have already computed $P(X|A)$ above. On the other hand, $P(X | \\sim A)$ is subjective: our code can pass tests but still have a bug in it, though the probability there is a bug present is less. Note this is dependent on the number of tests performed, the degree of complication in the tests, etc. Let's be conservative and assign $P(X|\\sim A) = 0.5$. Then\n", - "\n", - "\\begin{align}\n", - "P(A | X) & = \\frac{1\\cdot p}{ 1\\cdot p +0.5 (1-p) } \\\\\\\\[5pt]\n", - "& = \\frac{ 2 p}{1+p}\n", - "\\end{align}\n", - "This is the posterior probability distribution. What does it look like as a function of our prior, $p \\in [0,1]$? " - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "figsize(9,3.5)\n", - "\n", - "p = np.linspace( 0,1, 50)\n", - "plt.plot( p, 2*p/(1+p), color = \"#348ABD\" )\n", - "plt.fill_between( p, 2*p/(1+p), alpha = .2, facecolor = [\"#348ABD\"])\n", - "plt.scatter( 0.2, 2*(0.2)/1.2, s = 140, c =\"#348ABD\" )\n", - "plt.xlim( 0, 1)\n", - "plt.ylim( 0, 1)\n", - "plt.xlabel( \"Prior, $P(A)$\")\n", - "plt.ylabel(\"Posterior, $P(A|X)$\")\n", - "plt.title( \"Are there bugs in my code?\")" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 2, - "text": [ - "" - ] - }, - { - "output_type": "display_data", - "png": 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WBERS0yc9NUuXLjUoaADgwQcfxPfff4958+bh+PHjPQqAiKg3tGt1SCvqKGQO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\np3A43PU1AfQeoQbAfyESiejw8PBbYwuFgtxutwK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- } - ], - "prompt_number": 2 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can see the biggest gains if we observe the $X$ tests passed are when the prior probability, $p$, is low. Let's settle on a specific value for the prior. I'm a (I think) strong programmer, so I'm going to give myself a realistic prior of 0.20, that is, there is a 20% chance that I write code bug-free. To be more realistic, this prior should be a function of how complicated is code is and large the code is, but let's pin it at 0.20. Then my updated belief that my code is bug-free is 0.33. \n", - "\n", - "Let's not forget from the idea that the prior is a probability distribution: $p$ is the prior probability that there *are no bugs*, so $1-p$ is the prior probability that there *are bugs*. What does our prior probability distribution look like?\n", - "\n", - "Similarly, our posterior is also a probability distribution, with $P(A | X)$ the probability there is no bug *given we saw all tests pass*, hence $1-P(A|X)$ is the probability there is a bug *given all tests passed*. What does our posterior probability distribution look like? Below is a graph of both the prior and the posterior distributions. \n" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "prior = [0.20, 0.80]\n", - "postierior = [1./3, 2./3]\n", - "plt.bar( [0,.7], prior ,alpha = 0.80, width = 0.25, \\\n", - " color = \"#348ABD\", label = \"prior distribution\" )\n", - "plt.bar( [0+0.25,.7+0.25], postierior ,alpha = 0.7, \\\n", - " width = 0.25, color = \"#A60628\", label = \"posterior distribution\" )\n", - "\n", - "plt.xticks( [0.20,.95], [\"Bugs Absent\", \"Bugs Present\"] )\n", - "plt.title(\"Prior and Posterior probability of bugs present, prior = 0.2\")\n", - "plt.ylabel(\"Probability\")\n", - "plt.legend(loc=\"upper left\")" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 3, - "text": [ - "" - ] - }, - { - "output_type": "display_data", - "png": 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noAoLC3H+/HksX74cBgYG6NevH/z8/Oo9jSKRSJCamooHDx7A2toabm5uAOo+\n7SIIQp1jP923euz+/ftj+PDh2L17t1LbrSHKbJcxY8bAy8sLenp6mDRpEi5dutTkcRlj2oVrUrQb\nJylqIpVKxde2trYoKSkR2w0926WkpERhXgCws7MT5xcEodb7NRUXF6Nt27YwMTFRmL8uZmZm2LJl\nC7Zt24Zu3bohMDAQmZmZSq9XXeoau7S0tMF5lKHMdrG2thbfMzY2Fh+fwBhjTDuoLUmJi4uDm5sb\nXF1dsXr16lrvX79+HSNHjkTPnj3h7u6Obdu2qSs0tSgsLFR43alTJ6Xm69SpE4qKihSOThQUFKBz\n585Kz3/r1i2Ul5crzF/faZYhQ4YgJiYG6enpcHV1xfz58wE8283Iavata+zqdTc1NVV479q1a0qP\n0dTtwhhrHbgmRbupJUmRy+UIDQ1FXFwcUlNTERUVhbS0NIU+YWFh8PLywvnz55GQkICFCxeisrJS\nHeGpHBFhy5YtuHr1Km7evIm1a9di/PjxSs3bq1cvmJiYYMOGDaioqEBiYiJ+++03cf7Grn6xs7ND\nz5498dlnn6GiogK///47Dhw4UGffv/76C/v27cP9+/dhYGAAMzMz6OnpAQCsra1x9epVVFRUKKxX\nXev69PTqsU+dOoWDBw9i3LhxAAAPDw/s3bsXDx48QE5ODiIiIhTms7a2Rm5urkq2C2OMMc2nlkuQ\nk5OT4eLiIt7vIjAwELGxsZDJZGKfzp074+LFiwCePDDQysqqSZfiWpsbIrhvw6cimsLaXPkn2gqC\ngIkTJ2LChAkoKSnB6NGjsXDhQoX365oHAAwNDREZGYn33nsPX3zxBWxsbPD111/DxcVF7NfYUY5N\nmzZh7ty56NKlC3r37o3Jkyfj9u3btcaqqqrC119/jblz50IQBPTo0UO8MZuvry/c3Nzg5uYGPT09\nXL58uc6xn57WsWNHtG3bFt26dYOpqSk+//xzMfY5c+bg3LlzcHNzQ/fu3TFp0iQcO3ZMnHfJkiUI\nCQnBgwcPsG7dOrRv375J24VvTc9Y68M1KdpNIDX8l3Pnzp04cOAANm3aBACIiIhAUlISNm7cKPap\nqqrCkCFDcPnyZdy9exc//fQT/P39FZYTHx+PLVu2wN7eHgDQpk0beHh4oEuXLhp9mL9nz57YsGED\nfH19WzoU1kKKi4vFq5qqd5rVh6G5rb3tG+UVcPTwAQCcTz4FAOjZpx+3Nag9YsggdLIw0ojPC7ef\nOHHihHihpedIAAAgAElEQVQT0aCgIAwdOhT1UUuSEhMTg7i4uAaTlI8//hjXr1/HunXrkJ2djeHD\nh+PChQuwsLAQ+8THx8Pb27vW8ouLizlJYRpN0z+j7PlcLL6r0ptGsqbrK+TjrXEjWjoMVo+UlJQG\nkxS11KRIpVIUFBSI7YKCAtja2ir0OXnyJCZNmgQA6NKlC5ycnJCRkaGO8BhjjDGmgdSSpPj4+CAz\nMxN5eXl4/PgxoqOjERAQoNDHzc0Nhw4dAgCUlpYiIyNDvLOptjt//jwfRWGMsRZQffqHaSe1FM7q\n6+sjLCwMfn5+kMvlCAoKgkwmQ3h4OAAgODgYy5cvx9tvvw1PT09UVVXhP//5D9q1a6eO8BhjjDGm\ngdT2gEF/f/9ahbDBwcHi6/bt2+O///2vusJhjDHWCpxPPoUeXJOitfiOs4wxxhjTSJykMMYY01lc\nk6LdOElhjDHGmEbiJKUV++KLLzBv3jyVLX/s2LH4/vvvAQA///wzJk6c2GzL7t+/P06ePAkAWL16\nNWbPnt1sy1b1dmGMqU/1Td2YdlJb4ay6PSgswcOryj+w7lkZ21jDxFa5hwSqQkhICKRSKZYvX/7c\ny1iwYEEzRlRbzVvTT5o0SbwPTkOUXa/qBKWpEhMTMXv2bFy6dEmcpurtwhhjTDk6m6Q8vHoNORu/\nV9nynd+Z2qJJSlPJ5XLx4YHPqrKysknPVWqKlhybMaZ9uCZFu/HpHjXw9PTEunXr0K9fPzg7O+Od\nd97Bo0ePxPd37NgBHx8fdOnSBW+++SZKSkrE995//3107doVDg4OGDBgANLT07F9+3bs3LkTGzZs\ngL29Pd58800AT269Pm3aNLz44ovw8vLCt99+Ky5n9erVmD59OmbPng0HBwdERkbWOk2yf/9+9OvX\nD05OTggICMDly5cV1mHDhg0YMGAA7O3tUVVVVWs9jxw5gr59+8LR0RFLlixReBJxZGQkRo0aBeDJ\nE4qfZb2eHlsul8PT01PhYYSPHj1CUFAQHBwc8Morr+DPP/8U37OyskJeXp7YDgkJwapVq1BeXo7X\nX38dJSUlsLe3h4ODA0pKSp55u3z55ZcYOHAgHB0dERQUpPC7ZYwx9vw4SVGTnTt3IiYmBikpKcjK\nysLatWsBAMeOHcNHH32ErVu3Ii0tDXZ2dpgxYwaAJ88qOnXqFE6fPo0rV65g69atsLS0xLRp0zBp\n0iTMmzcP+fn5+OGHH1BVVYU33ngDHh4eSE1NxZ49e/DNN9/g8OHDYgz79+/HuHHjcOXKlVqnXrKy\nsjBr1ix89tlnyMrKwvDhw/HGG2+gsrJS7LNr1y789NNPyM3NhUSi+NEpKyvD9OnTsWLFCmRnZ8PR\n0RFJSUl1bovDhw8rvV51ja2np1fricb79u3Dq6++ipycHEycOBFTpkyBXC6vc/zq01Cmpqb4+eef\n0alTJ+Tn5+PKlSvo1Enx6Fhj20UQBMTGxmLnzp04f/48UlNTERUVVee4jDH145oU7cZJihoIgoCZ\nM2fCxsYGbdu2xcKFCxETEwPgSfIyZcoUeHh4wNDQECtXrsTp06dRWFgIQ0ND3Lt3D5cvX0ZVVRVc\nXV3RsWNHcbk1j1SkpKSgrKwMixYtgr6+PhwcHDB16lTs3r1b7NOnTx/xhnrGxsYKMe7evRsjRozA\noEGDoKenh9DQUDx8+BDJycniOsyaNQs2NjYwMjKqtY4HDx6Em5sbxo4dCz09PcyZMwfW1tZ1bg8D\nAwOl10uZsYEnD3GsHnvu3Ll49OgRzpw5U2ffmmM09nzNxrYLAMyaNQsdO3ZE27Zt4efnhz/++KPB\nZTLGGFMOJylqIpVKxde2trbiKZ2SkhLY2dmJ75mZmaFdu3YoLi7GwIEDMWPGDCxevBhdu3bFggUL\ncPfu3TqXX1hYiJKSEjg5OYk/69atw19//SX2sbGxqTe+kpIShYc+CoIAGxsbFBcX17kOdc3/9PLr\n6+/r66v0eikzNqC4bnXF/ryU2S41kzETExPcv3+/yeMyxpoH16RoN05S1KSwsFDhdefOnQFAPNVQ\n7f79+7hx44b4/qxZs8TTI9nZ2QgLCwOAWqc7pFIpHBwckJubK/5cuXIFP/74o9jn6Xlq6ty5s8KT\nqokIV69eFeNobP5OnTqhqOh/j6wnIoX205RdL2XGBqAwVlVVlULspqamePDggfh+SUmJuLzGlqvM\ndmGMMaYanKSoARFhy5YtuHr1Km7evIm1a9fitddeAwBMmDABkZGRuHTpEh49eoSPP/4YPj4+sLW1\nxblz53DmzBlUVFTAxMQERkZGYi1Ihw4dFIpBe/XqBXNzc2zYsAEPHjyAXC5HWloazp07p1SM48aN\nw8GDB3Hs2DFUVFTgyy+/hJGREfr06aPU/CNGjEBGRgb27t2LyspKhIeH49q1ui8Bf5b1UtaFCxfE\nsb/55hsYGRnBx8cHAODu7o6ff/4ZcrlcrPOp1qFDB9y8eRN37typc7lN3S6MsZbFNSnaTWev5TS2\nsYbzO1NVunxlCYKAiRMnYsKECSgpKcHo0aOxcOFCAMCgQYOwfPlyTJs2Dbdu3ULfvn2xefNmAMDd\nu3fx/vvv48qVKzAyMsLQoUPxzjvvAACmTJmCt99+G05OThg4cCB27NiBqKgorFy5Et7e3nj06BFc\nXV3x/vvvK8RRV2wA4Orqim+++QZLlixBcXExevTogcjISKUv923Xrh2+++47LFu2DKGhofjb3/6G\nl156SWGc6rGedb2UMWrUKOzevRshISFwdnbGjh07xEusP/30U8ydOxdbtmzBqFGjMHr0aHG+F198\nERMmTIC3tzeqqqrE+680Zbs0dnSGMcaYcgRqrHKwmcTFxWH+/PmQy+WYMWMGlixZovD+mjVrxKs5\nKisrkZaWhuvXr6Nt27Zin/j4eHh7e9dadnFxsUYffu/Zsyc2bNgAX1/flg6FtRBN/4yy53Ox+C7C\nk+o/rclaXnBfKXp0tmjpMFg9UlJSMHTo0HrfV8vpHrlcjtDQUMTFxYmXaKalpSn0WbRoEc6dO4dz\n587h008/xeDBgxUSFMYYY4y1LmpJUpKTk+Hi4gJHR0cYGBggMDAQsbGx9faPjIzE5MmT1REaY4wx\nHcY1KdpNLTUpRUVFCpfZ2tra1nujr/Lychw4cABfffVVne+HhITA3t4eANCmTRt4eHigS5cuzR90\nMzp//nxLh8A0QGJiIgBgwIAB3NaRdk5ZOYAn+6PS9BQAQEc3b25rUBuy9gA04/PC7SdOnDghXtUa\nFBSEhqilJiUmJgZxcXHYtGkTACAiIgJJSUnYuHFjrb7R0dGIjIys80iLttakMMafUd3ENSmaj2tS\nNJtG1KRIpVKFe00UFBQo3CCrph9//PGZT/WoqfaXsefGn1HGGHt2aklSfHx8kJmZiby8PDx+/BjR\n0dEICAio1e/27ds4duwYxo0b90zL19PTQ3l5eXOFy1izISKUlZXVezt/xphqcU2KdlNLTYq+vj7C\nwsLg5+cHuVyOoKAgyGQyhIeHAwCCg4MBAHv27IGfnx9MTEyeafnW1ta4du0abt26xfeoeAa3b9/G\nCy+80NJh6Kzqoydt2rSBubl5C0fDGGPaR233SWkO9dWkMMZYS+CaFM3HNSmaTSNqUhhjjDHGnpXO\n3hafNS4xMVG8PIwxxnRR+uHfYOfq2tJhsPo0koVwksIYY0xnVd28hZyN37d0GKwewoKGr+bl0z2t\nGB9FYYzpul7uni0dAmsCTlIYY4wxppE4SWnFat6mmDHGdNHZSxdaOgTWBJykMMYYY0wjcZLSinFN\nCmNM13FNinbjJIUxxhhjGomTlFaMa1IYY7qOa1K0GycpjDHGGNNInKS0YlyTwhjTdVyTot04SWGM\nMcaYRlJbkhIXFwc3Nze4urpi9erVdfZJSEiAl5cX3N3dMXjwYHWF1mpxTQpjTNdxTYp2U8uze+Ry\nOUJDQ3Ho0CFIpVL07t0bAQEBkMlkYp9bt24hJCQEBw4cgK2tLa5fv66O0BhjjDGmodRyJCU5ORku\nLi5wdHSEgYEBAgMDERsbq9AnMjISEyZMgK2tLQCgffv26gitVeOaFMaYruOaFO2mliMpRUVFsLOz\nE9u2trZISkpS6JOZmYmKigq88soruHv3LubNm4epU6fWWlZISAjs7e0BAG3atIGHh4f4x7b69AW3\nuc1tbqujnVNWDuDJ/qg0PQUA0NHNm9sa1MZLLwIALpQVAwA8rTpzuwXb1a9LH9wDACxGw09BFoiI\nGuzRDGJiYhAXF4dNmzYBACIiIpCUlISNGzeKfUJDQ5GSkoL4+HiUl5ejX79++PXXX+Hq6ir2iY+P\nh7e3t6rDbTUSExP5aApjTXCx+C7Ck4paOgzWgIGFJ+CalN7SYbB6CAsmY+jQofW+r5YjKVKpFAUF\nBWK7oKBAPK1Tzc7ODu3bt4eJiQlMTEzg6+uLCxcuKCQpjDHGGGs9lKpJKSsra9IgPj4+yMzMRF5e\nHh4/fozo6GgEBAQo9Bk3bhwSExMhl8tRXl6OpKQkdOvWrUnjsobxURTGmK7jmhTtptSRFHt7ewwb\nNgxTp05FQEAADA0Nn20QfX2EhYXBz88PcrkcQUFBkMlkCA8PBwAEBwfDzc0NI0eORI8ePSCRSDBz\n5kxOUhhjjLFWTKmalGvXriEqKgrff/89srOzMWnSJLz11ltq/58416Q0L65JYaxpuCZF83FNimZr\nrCZFqdM91tbWmDdvHs6cOYNTp06hQ4cOmDJlCpydnfHPf/4TV65cabaAGWOMMcaA57hPSklJCUpL\nS3Hnzh04OzujqKgIPXv2xKeffqqK+JgK8VEUxpiu45oU7aZUTcqlS5cQERGBqKgomJiYYNq0abhw\n4YJ475OVK1fCw8MDy5YtU2mwjDHGGGs9lDqSMmjQINy9exc//fQT0tPTsWzZMoWbszk6OmL+/Pkq\nC5KpBj+7hzGm6/jZPdpNqSMpu3fvhq+vb63pycnJ6NOnDwDgo48+at7IGGOMMdaqKXUkZcyYMXVO\n9/Pza9ZgmHpxTQpjTNdxTYp2a/BISlVVFYgIRISqqiqF97Kzs2FgYKDS4BhjjDHWejV4JEVfXx8G\nBga4f/8+9PX1FX5kMhnmzJmjrjiZCnBNCmNM13FNinZr8EhKTk4OAMDX1xfHjx9H9X3fBEFAhw4d\nYGpqqvoIGWOMMdYqNZikODo6AgDy8/PVEQtTM65JYYzpul7unrjDd5zVWvUmKTNnzsSmTZsAAFOn\nTq2zjyAI2LFjh2oiY4wxxlirVm+S4uTkJL7u0qULBEHA04/5EQRBdZExleNn9zDGdN3ZSxfg2tJB\nsOdWb5KyfPly8fUHH3zQ5IHi4uIwf/58yOVyzJgxA0uWLFF4PyEhAePGjYOzszMAYMKECVixYkWT\nx2WMMcaYdqo3STl8+LBSCxgyZEijfeRyOUJDQ3Ho0CFIpVL07t0bAQEBkMlkCv0GDRqEX375Ralx\nWdPxURTGmK7jmhTtVm+S8ve//12p0zm5ubmN9klOToaLi4tYiBsYGIjY2NhaScrTp5MYY4wx1nrV\nm6Tk5eU12yBFRUUKz/qxtbVFUlKSQh9BEHDy5El4enpCKpVizZo16NatW61lhYSEwN7eHgDQpk0b\neHh4iEcEqu/7wW3l2l9//TVvP25zuwntnLJyAE/2R6XpKQCAjm7e3Nag9lnzB3AFcKGsGADgadUZ\n4HaLtatflz64BwBYjMloiEBqOHwRExODuLg48WqhiIgIJCUlYePGjWKfu3fvQk9PD6ampti/fz/m\nzZuHy5cvKywnPj4e3t7eqg631eDCWcaa5mLxXYQnFbV0GKwBAwtPwJVP92gsYcFkDB06tN73673j\nrJubm/jazs6uzp/qIxqNkUqlKCgoENsFBQWwtbVV6GNhYSHeHM7f3x8VFRW4ceOGUstnz4cTFMaY\nruNn92i3ek/3VB/1AIDvv/++SYP4+PggMzMTeXl5sLGxQXR0NKKiohT6lJaWwtraGoIgIDk5GUSE\ndu3aNWlcxhhjjGmvepOUgQMHiq8HDx7ctEH09REWFgY/Pz/I5XIEBQVBJpMhPDwcABAcHIydO3fi\n66+/hr6+PkxNTfHjjz82aUzWOD7dwxjTdXyfFO3W4G3xqz169Agff/wxoqKicPXqVdjY2CAwMBAr\nVqyAsbGxUgP5+/vD399fYVpwcLD4OiQkBCEhIc8QOmOMMcZ0mVJJypw5c3D58mVs3LgR9vb2yM/P\nxyeffIKioiJs3bpV1TEyFeGjKIwxXcf3SdFuSiUpe/bsQXZ2NiwtLQEA3bt3R9++fdGlSxdOUhhj\njDGmEvVe3VNT586dUV5erjDtwYMHsLGxUUlQTD2q7/vAGGO66uylCy0dAmuCeo+kxMfHi3ecnTp1\nKvz9/REaGgo7Ozvk5+fjyy+/xFtvvaW2QBljjDHWutR7MzdHR0eF2+ITUZ1tZW6L31z4Zm6MMU3C\nN3PTfPPb3sOdLdEtHQarR2M3c1PLbfEZY4wxxp6VUjUpTDdxTQpjTNdxTYp2U+rqntu3b+ODDz7A\n0aNHUVZWhqqqKgBPHgqYn5+v0gAZY4wx1jopdSQlJCQEKSkp+Oc//4kbN26I90uZP3++quNjKsT3\nSWGM6Tp+do92U+pIyoEDB5CWlob27dtDIpHg1VdfRe/evTF27Fi8++67qo6RMcYYY62QUkdSiAgv\nvPACgCdPK7516xY6d+6MzMxMlQbHVItrUhhjuo5rUrSbUkdSevTogWPHjmHo0KEYMGAAQkJCYGZm\nhq5du6o6PsYYY4y1UkodSdm0aRMcHR0BAOvXr4exsTFu376NHTt2qDI2pmJck8IY03Vck6LdlEpS\nunTpgi5dugAAOnbsiC1btiA6OhrdunVTeqC4uDi4ubnB1dUVq1evrrff6dOnoa+vj127dim9bMYY\nY4zpHqVrUrZs2YJhw4ahW7duGD58ODZv3ixeitwYuVyO0NBQxMXFITU1FVFRUUhLS6uz35IlSzBy\n5EjUcyNc1oy4JoUxpuu4JkW7KVWTsmTJEsTGxmL+/Pmwt7dHfn4+1q5di4yMDPzf//1fo/MnJyfD\nxcVFPGUUGBiI2NhYyGQyhX4bN27ExIkTcfr06WdfE8YYY4zpFKWSlK1btyIlJQV2dnbitDFjxsDL\ny0upJKWoqEhhXltbWyQlJdXqExsbi8OHD+P06dMKzwmqKSQkBPb29gCANm3awMPDQ6ytqD4ywG3l\n2tXTNCUebnNb29o5ZeUAnuyPStNTAAAd3by5rUHtXi954k5SOi6UFQMAPK06AwC3W6hd/br0wT0A\nwGJMRkPqfcBgTV26dMHZs2fRtm1bcdqtW7fQq1cvZGdnNzY7YmJiEBcXh02bNgEAIiIikJSUhI0b\nN4p9Jk2ahEWLFqFv376YPn06xo4diwkTJigshx8wyBjTJPyAQc3HDxjUbM/9gMGcnBzx9fz58zFh\nwgQsWbIEdnZ2yM/Px5o1a7BgwQKlgpBKpSgoKBDbBQUFsLW1Vehz9uxZBAYGAgCuX7+O/fv3w8DA\nAAEBAUqNwZ5dzaMojDGmi85eugDXlg6CPbd6kxQXF5da044cOaLQjo+PR2hoaKOD+Pj4IDMzE3l5\nebCxsUF0dDSioqIU+tRMit5++22MHTuWExTGGGOsFas3SVH2yh2lBtHXR1hYGPz8/CCXyxEUFASZ\nTIbw8HAAQHBwcLONxZTHR1EYY7qul/uTmhSmnZQqnK2Wn5+PoqIiSKVSsXhVWf7+/vD391eYVl9y\nsnXr1mdaNmOMMcZ0j1L3SSkuLsagQYPg4uKC8ePHw8XFBb6+vrh69aqq42MqxPdJYYzpOr5PinZT\nKkmZPXs2PD09cfPmTRQXF+PmzZvw8vLC7NmzVR0fY4wxxloppU73JCYm4ueff4ahoSEAwMzMDP/5\nz39gY2Oj0uCYanFNCmNM13FNinZT6khKu3btkJqaqjAtPT0dlpaWKgmKMcYYY0ypIymLFy/G8OHD\nERQUBAcHB+Tl5WHr1q346KOPVB0fUyG+TwpjTNfxfVK0m1JJysyZM9GlSxf88MMPuHjxImxsbBAV\nFdXgXeIYY4wxxpqi0SSlsrISXbt2RWpqKoYMGaKOmJia8FEUxpiu45oU7dZoTYq+vj4kEgkePHig\njngYY4wxxgAoebpnwYIF+Nvf/oZly5bBzs5O4QnFzs7OKguOqZYqalIeFJbg4dVrzbpM1nyMbaxh\nYtuppcNgTG24JkW7KZWkVD+f5+DBgwrTBUGAXC5v/qiY1np49RpyNn7f0mGweji/M5WTFMaY1mjw\ndM/9+/exbNkyjB49GitWrEB5eTmqqqrEH05QtBvXpDDGdF0vd8+WDoE1QYNJSmhoKPbu3QuZTIZd\nu3Zh0aJF6oqLMcYYY61cg0nK/v37ceDAAfznP//B/v37sXfvXnXFxdSAn93DGNN1/Owe7dbo6Z7q\nW9/b2dnh9u3bzz1QXFwc3Nzc4OrqitW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- } - ], - "prompt_number": 3 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Notice that after we observed $X$ occur, the probability of no bugs present increased. By increasing the number of tests, we can approach confidence (probability 1) that there are not bugs.\n", - "\n", - "\n", - "This was a very simple example, but the mathematics from here only becomes difficult except for artifically constructed instances. We will see that this math is actually unnecessary. First we must broaden our modeling tools. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "_______\n", - "\n", - "Probability Distributions\n", - "------\n", - "\n", - "**Let's quickly recall what a probability distribution is:** Let $Z$ be some random variable. Then associated with $Z$ is a probability distribution function that assigns probabilities to the different values $Z$ can take. There are three cases:\n", - "\n", - "- **$Z$ is discrete**: Discrete random variables may only assume values on a specified list. Things like populations, movie ratings, and number of votes are all discrete random variables. It's more clear when we contrast it with...\n", - "\n", - "- **$Z$ is continuous**: Continuous random variable can take on arbitrarily exact values. For example, temperature, speed, time, color are all modeled as continuous variables because you can constantly make the values more and more precise.\n", - "\n", - "- **$Z$ is mixed**: Mixed random variables assign probabilities to both discrete and continuous random variables, i.e. is is a combination of the above two categories. \n", - "\n", - "###Discrete Case\n", - "If $Z$ is discrete, then its distribution is called a *probability mass function*, which measures the probability $Z$ takes on the value $k$, denoted $P(Z=k)$. Note that the probability mass function completely describes the random variable $Z$, that is, if we know the mass function, we know how $Z$ should behave. There are popular probability mass functions that consistently appear: we will introduce them as needed, but let's introduce the first very useful probability mass function. We say $Z$ is *Poisson*-distributed if:\n", - "\n", - "$$P(Z = k) =\\frac{ \\lambda^k e^{-\\lambda} }{k!}, \\; \\; k=0,1,2, \\dots $$\n", - "\n", - "What is $\\lambda$? It is called the parameter, and it describes the shape of the distribution. For the Poisson random variable, $\\lambda$ can be any positive number. By increasing $\\lambda$, we add more probability to larger values, and conversely by decreasing $\\lambda$ we add more probability to smaller values. Unlike $\\lambda$, which can be any positive number, $k$ must be a non-negative integer, i.e., $k$ must take on values 0,1,2, and so on. This is very important, because if you wanted to model a population you could not make sense of populations with 4.25 or 5.612 members. \n", - "\n", - "If a random variable $Z$ has a Poisson mass distribution, we denote this by writing\n", - "\n", - "$$Z \\sim \\text{Poi}(\\lambda) $$\n", - "\n", - "One very useful property of the Poisson random variable, given we know $\\lambda$, is that its expected value is equal to the parameter, ie.:\n", - "\n", - "$$E\\large[ \\;Z\\; | \\; \\lambda \\;\\large] = \\lambda $$\n", - "\n", - "We will use this property often, so it's something useful to remember. " - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "import scipy.stats as stats\n", - "a = np.arange( 16 )\n", - "poi = stats.poisson\n", - "lambda_ = [1.5, 4.25 ]\n", - "colours = [\"#348ABD\", \"#A60628\"]\n", - "\n", - "plt.bar( a, poi.pmf( a, lambda_[0]), color=colours[0], \\\n", - " label = \"$\\lambda = %.1f$\"%lambda_[0], alpha = 0.95)\n", - "plt.bar( a, poi.pmf( a, lambda_[1]), color=colours[1], \\\n", - " label = \"$\\lambda = %.1f$\"%lambda_[1], alpha = 0.60)\n", - "\n", - "plt.xticks( a + 0.4, a )\n", - "plt.legend()\n", - "plt.ylabel(\"probability of $k$\")\n", - "plt.xlabel(\"$k$\")\n", - "plt.title(\"Probability mass function of a Poisson random variable; differing \\\n", - "$\\lambda$ values\")\n", - "plt.text(-1., -.106, \"\"\"The probability mass function of an Poisson random \\\n", - "variable with different values of $\\lambda$. \n", - "Although the graph stops at 15, the \\\n", - "distribution actually continues on indefinitely. Notice the larger $\\lambda$ is, \n", - "the more weight is put on larger values.\"\"\")" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 4, - "text": [ - "" - ] - }, - { - "output_type": "display_data", - "png": 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XLXS+mOvOFTWECCGEECJa1EdID6iPECGEEKI7zfI5QtHR0XB2doaTk5Py/STV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\n4UNs3LjRIOXQhU2bNuHkyZM6uyVZXf3VrS9d03U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- } - ], - "prompt_number": 4 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "###Continuous Case\n", - "Instead of a probability mass function, a continuous random variable has a *probability density function*. This might seem like unnecessary nomenclature, but the density function and the mass function are very different creatures. An example of continuous random variable is a random variable with a *exponential density*. The density function for an exponential random variable looks like:\n", - "\n", - "$$f_Z(z | \\lambda) = \\lambda e^{-\\lambda z }, \\;\\; z\\ge 0$$\n", - "\n", - "Like the Poisson random variable, an exponential random variable can only take on non-negative values. But unlike a Poisson random variable, the exponential can take on *any* non-negative values, like 4.25 or 5.612401. This makes it a poor choice for count data, which must be integers, but a great choice for time data, or temperature data (measured in Kelvins, of course), or any other precise variable. Below are two probability density functions with different $\\lambda$ value. \n", - "\n", - "When a random variable $Z$ has an exponential distribution with parameter $\\lambda$, we say *$Z$ is exponential* and write\n", - "\n", - "$$Z \\sim \\text{Exp}(\\lambda)$$\n", - "\n", - "Given a specific $\\lambda$, the expected value of an exponential random variable is equal to the inverse of $\\lambda$, that is:\n", - "\n", - "$$E[\\; Z \\;|\\; \\lambda \\;] = \\frac{1}{\\lambda}$$" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "a = np.linspace(0,4, 100)\n", - "expo = stats.expon\n", - "lambda_ = [0.5, 1]\n", - "colours = [ \"#A60628\", \"#348ABD\"]\n", - "for l,c in zip(lambda_,colours):\n", - " plt.plot( a, expo.pdf( a, scale=1./l), lw=3, color=c, label = \\\n", - " \"$\\lambda = %.1f$\"%l)\n", - " plt.fill_between( a, expo.pdf( a, scale=1./l), color=c, alpha = .33)\n", - " \n", - "plt.legend()\n", - "plt.ylabel(\"PDF at $z$\")\n", - "plt.xlabel(\"$z$\")\n", - "plt.title(\"Probability density function of an Exponential random variable;\\\n", - " differing $\\lambda$\")\n", - "plt.text(-.1, -.34, \"\"\"The probability density function of a Exponential random \\\n", - "variable with different values of $\\lambda$. \n", - "Although the graph stops at 4.0, the \\\n", - "distribution actually continues on indefinitely. Notice the larger $\\lambda$ is, \n", - "the more weight is put on larger values.\"\"\")" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 5, - "text": [ - "" - ] - }, - { - "output_type": "display_data", - "png": 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L+fzzz5vc15vPzSU6xrIvH18/eiWm0H/oSAB2b90IcMHL4/skszrrFOVZO/gk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\nYRiGYcKS/wc3Yh7+SA9yUgAAAABJRU5ErkJggg==\n" - } - ], - "prompt_number": 5 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "But what is $\\lambda \\;\\;$?\n", - "-----\n", - "\n", - "**That question is what motivates statistics**. In the real world, $\\lambda$ is hidden from us. We only see $Z$, and must go backwards to try and determine $\\lambda$. The problem is so difficult because there is not a one-to-one mapping from $Z$ to $\\lambda$. Many different methods have been created to solve the problem of estimating $\\lambda$, but since $\\lambda$ is never actually observed, no one can say for certain which method is better! \n", - "\n", - "Bayesian inference is concerned with *beliefs* about what $\\lambda$ is. Rather than try to guess $\\lambda$ exactly, we can only talk about what $\\lambda$ is likely to be by assigning a probability distribution to $\\lambda$.\n", - "\n", - "This might seem odd at first: after all, $\\lambda$ is fixed, it is not (necessarily) random! How can we assign probabilities to a non-random event. Ah, we have fallen for the frequentist interpretation. Recall, under our Bayesian philosophy, we *can* assign probabilties if we interpret them as beliefs. And it is entirely acceptable to have *beliefs* about the parameter $\\lambda$. \n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "###Example\n", - "_____\n", - "Let's try to model a more interesting example, concerning text-message rates:\n", - "\n", - "> You are given a series of text-message counts from a user of your system. The data, plotted over time, appears in the graph below. You are curious if the user's text-messaging habits changed over time, either gradually or suddenly. How can you model this?\n" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "count_data = np.loadtxt(\"chp1data/textmsgdata.csv\")\n", - "plt.bar( np.arange( len(count_data) ), count_data, color =\"#348ABD\" )\n", - "plt.xlabel( \"Time (days)\")\n", - "plt.ylabel(\"# Text-msg recieved\")\n", - "plt.title(\"Did the user's texting habits change over time?\")\n", - "print\n" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n" - ] - }, - { - "output_type": "display_data", - "png": 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Hma+4JiQklHmcp6cnxowZg+XLl+Orr75CVFQUcnJylH8tnTp1qky7FflKqbW1NXr16oV5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\nli0zV9NERERUA5itU7Jq1SqcO3cOt2/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- } - ], - "prompt_number": 6 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "How can we start to model this? Well, as I conveniently already introduced, a Poisson random variable would be a very appropriate model for this *count* data. Denoting a day $i$'s text-message count $C_i$, \n", - "\n", - "$$ C_i \\sim \\text{Poisson}(\\lambda) $$\n", - "\n", - "We are not sure about what the $\\lambda$ parameter is though. Looking at the chart above, it appears that the rate becomes higher at some later date, which is equivalently saying the parameter $\\lambda$ increases at some later date (recall a higher $\\lambda$ means more probability on larger values).\n", - "\n", - "How can we mathematically represent this? We can think, that at some later date (call it $\\tau$), the parameter $\\lambda$ suddenly jumps to a higher value. So we create two $\\lambda$ parameters, one for before the day $\\tau$, and one for after. In literature, a sudden transition like this would be called a *switchpoint*:\n", - "\n", - "$$\n", - "\\lambda = \n", - "\\cases{\n", - "\\lambda_1 & \\text{if } t \\lt \\tau \\cr\n", - "\\lambda_2 & \\text{if } t \\ge \\tau\n", - "}\n", - "$$\n", - "\n", - "\n", - " If, in reality, no sudden change occurred, the $\\lambda$'s should look about equal. What would be a good prior distribution on $\\lambda_{1}$ and $\\lambda_2$? \n", - "\n", - "Recall that $\\lambda_i, \\; i=1,2,$ can be any positive number. The *exponential* random variable has a density function for any positive number. But again, we need a parameter for this exponential distribution: call it $\\alpha$.\n", - "\n", - "\\begin{align*}\n", - "&\\lambda_1 \\sim \\text{Exp}( \\alpha ) \\\\\\\n", - "&\\lambda_2 \\sim \\text{Exp}( \\alpha )\n", - "\\end{align*}\n", - "\n", - "$\\alpha$ is called a *hyper-parameter*, literally a parameter that influence other parameters. The influence is not too strong, so we can choose $\\alpha$ liberally. A good rule of thumb is to set the exponential parameter equal to the inverse of the average of the count data, since \n", - "\n", - "$$\\frac{1}{N}\\sum_{i=0}^N \\;C_i \\approx E[\\; \\lambda \\; |\\; \\alpha ] = \\frac{1}{\\alpha}$$ \n", - "\n", - "Alternatively, and something I encourage the reader to try, is to have two priors: one for each $\\lambda$; creating two exponential distributions with different $\\alpha$ values reflects our belief was that the rate changed (increased) after some period.\n", - "\n", - "What about $\\tau$? Well, due to the randomness, it is too difficult to pick out when $\\tau$ might have occurred. Instead, we can assign an *uniform prior belief* to every possible day. This is equivalent to saying\n", - "\n", - "$$\\tau \\sim \\text{DiscreteUniform(0,70) }$$\n", - "\n", - "So after all this, what does our prior distribution look like? Frankly, *it doesn't matter*. What we should understand is that it would be an ugly, complicated, mess involving symbols only a mathematician would love. And things would only get uglier the more complicated our model. Next, we turn to PyMC, which is agnostic to the mathematical monster we created. \n", - "____\n", - "\n", - "\n", - "Introducing our first hammer: PyMC\n", - "-----\n", - "\n", - "PyMC is a Python library for programming Bayesian analysis. It is a fast, well-maintained library. The only unfortunate part is that documentation can be lacking in application areas, especially the bridge between problem to solution. This book will aids this problem, and explains why PyMC is so cool.\n", - "\n", - "\n", - "We will model the above problem using the PyMC library. This type of programming is called *probabilistic programming*, an unfortunate misnomer that invokes ideas of randomly-generated code and has likely confused and frightened users from the field. The code is not random. The title is given because we create probability models using variables as the model's components. This will be the last time I use the term *probablistic programming*. Instead, I'll simply use *programming*, as that is what it really is. \n", - "\n", - "\n", - "The PyMC code is easy to follow along: the only novel thing should be the syntax, and I will interrupt the code to explain sections. Simply remember we are representing the model's components ($\\tau, \\lambda_1, \\lambda_2$ ) as variables:" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "import pymc as mc\n", - "\n", - "n = count_data.shape[0]\n", - "\n", - "alpha = 1.0/count_data.mean() #recall count_data is \n", - " # the variable that holds our txt counts\n", - "\n", - "lambda_1 = mc.Exponential( \"lambda_1\", alpha )\n", - "lambda_2 = mc.Exponential( \"lambda_2\", alpha )\n", - "\n", - "tau = mc.DiscreteUniform( \"tau\", lower = 0, upper = n )" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 7 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In the above code, we create the PyMC variables corresponding to $\\lambda_1, \\; \\lambda_2$ in lines `8,9`. We assign them to PyMC's *stochastic variables*, called stochastic variables because they are treated by the backend as random number generators. We can test this by calling their built-in `random()` method." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "print \"Random output:\", tau.random(),tau.random(), tau.random()" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "Random output: 2 15 49\n" - ] - } - ], - "prompt_number": 8 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "@mc.deterministic\n", - "def lambda_( tau = tau, lambda_1 = lambda_1, lambda_2 = lambda_2 ):\n", - " out = np.zeros( n ) \n", - " out[:tau] = lambda_1 #lambda before tau is lambda1\n", - " out[tau:] = lambda_2 #lambda after tau is lambda1\n", - " return out" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 9 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This code is creating a new function `lambda_`, but really we think of it as a random variable: the random variable $\\lambda$ from above. Note that because `lambda_1`, `lambda_2` and `alpha` are random, `lambda_` will be random. We are **not** fixing any variables yet. The `@mc.deterministic` is a decorator to tell PyMC that this is a deterministic function, i.e., if the arguments were deterministic (which they are not), the output would be deterministic as well. " - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "observation = mc.Poisson( \"obs\", lambda_, value = count_data, observed = True)\n", - "\n", - "model = mc.Model( {\"obs\":observation, \"lamba1\":lambda_1, \\\n", - " \"lambda2\":lambda_2, \"tau\":tau} )" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 10 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The variable `observations` combines our data, `count_data`, with our proposed data-generation scheme, given by the variable `lambda_`, through the `value` keyword. We also set `observed = True` to tell PyMC that this should stay fixed in our analysis. Finally, PyMC wants us to collect all the variables of interest and create a `Model` instance out of them. This makes our life easier when we try to retrieve the results. " - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "mcmc = mc.MCMC(model)\n", - "mcmc.sample( 300000, 200000, 3, verbose = 0 )" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - " \r", - "[****************100%******************] 300000 of 300000 complete" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n" - ] - } - ], - "prompt_number": 11 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The above code will be explained in the Chapter 2, but this is where our results come from. The machinery being employed is called *Monte Carlo Markov Chains*. It returns random variables from the posterior distributions of $\\lambda_1, \\lambda_2$ and $\\tau$. Be can plot a histogram of the random variables to see what the posterior distribution looks like. Below, we collect the samples (called *traces* in MCMC literature). " - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "lambda_1_samples = mcmc.trace( 'lambda_1' )[:]\n", - "lambda_2_samples = mcmc.trace( 'lambda_2' )[:]\n", - "tau_samples = mcmc.trace( 'tau' )[:]" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 12 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "figsize(9, 10)\n", - "#histogram of the samples:\n", - "plt.subplot(311)\n", - "plt.xlim( 0, 7)\n", - "plt.hist( lambda_1_samples, histtype='stepfilled', bins = 120, alpha = 0.85, \\\n", - " label = \"positerior of $\\lambda_1$\", color = \"#A60628\",normed = True )\n", - "plt.legend()\n", - "plt.title(r\"Posterior distributions of the variables \\\n", - "$\\lambda_1, \\;\\lambda_2, \\;\\tau$\")\n", - "\n", - "plt.subplot(312)\n", - "plt.xlim( 0, 7)\n", - "plt.hist( lambda_2_samples,histtype='stepfilled', bins = 120, alpha = 0.85, \\\n", - " label = \"positerior of $\\lambda_2$\",color=\"#7A68A6\",normed = True )\n", - "plt.legend()\n", - "\n", - "plt.subplot(313)\n", - "plt.hist( tau_samples, bins = 70, alpha = 0.85, label = r\"positerior of $\\tau$\", \\\n", - " color=\"#467821\", normed = True, histtype='stepfilled' )\n", - "plt.legend()\n", - "print\n", - "\n" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n" - ] - }, - { - "output_type": "display_data", - "png": 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adOtY7eWDg4MRERGB1atXIy8vT3s3TZMmTXDmzBm8/fbbSEtLg6urK9577z0M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- } - ], - "prompt_number": 13 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Interpretation\n", - "\n", - "Recall that the Bayesian methodology returns a *distribution*, hence why we have distributions to describe the unknown $\\lambda$'s and $\\tau$. What have we gained? Immediately we can see the uncertainty in our estimates: the more variance in the distribution, the less certain our posterior belief should be. We can also say what a plausible value for the parameters might be: $\\lambda_1$ probably falls between 2.25 and 2.75, and $\\lambda_2$ probably falls between 3.5 and 4.5. The distributions of the two $\\lambda$s look very different, suggesting likely there was a change in the user's text-message behavior.\n", - "\n", - "Also notice that posteriors' distributions do not look like any Poisson distributions. They are really not anything we recognize. But this is OK. This is one of the benefits of taking a computational point-of-view. If we had instead done this mathematically, we would have been stuck with a very intractable (and messy) distribution. Via computations, we are agnostic to the tractability.\n", - "\n", - "Our analysis also returned a distribution for what $\\tau$ might be. Had no change occurred, or the change been gradual, the posterior distribution of $\\tau$ would have been more spread out. On the contrary, it is very peaked. It appears that near day 50, the individual's text-message behavior suddenly changed. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Why would I want samples from the posterior, anyways?\n", - "-------\n", - "\n", - "We will deal with this question for the remainder of the book, and it is an understatement to say we can perform amazingly useful things. For now, let's finishing with using posterior samples to answer the follow question: what is the expected number of texts at day $t, \\; 0 \\le t \\le70$? Recall that the expected value of a Poisson is equal to its parameter $\\lambda$, then the question is equivalent to *what is the expected value of $\\lambda$ at time $t$*?\n", - "\n", - "In the code below, we are calculating the following: Let $i$ index a particular sample from the posterior distributions. Given a day $t$, we average over all $\\lambda_i$ on that day $t$, using $\\lambda_{1,i}$ if $t \\lt \\tau_i$ else we use $\\lambda_{2,i}$. \n", - "\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "___________________" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "figsize( 9, 3.5)\n", - "N = tau_samples.shape[0]\n", - "expected_texts_per_day = np.zeros(70)\n", - "for day in range(0, 70):\n", - " ix = day < tau_samples\n", - " expected_texts_per_day[day] = ( lambda_1_samples[ix].sum() + \\\n", - " lambda_2_samples[~ix].sum() ) / N\n", - "\n", - "plt.plot( range(0,70), expected_texts_per_day, lw =4, color = \"#E24A33\" )\n", - "plt.ylim( 0, 8)\n", - "plt.xlabel( \"Day\" )\n", - "plt.ylabel( \"Expected # text-messages\" )\n", - "plt.title( \"Expected number of text-messages recieved\")\n", - "\n", - "plt.bar( np.arange( len(count_data) ), count_data, color =\"#348ABD\", alpha = 0.4,\\\n", - "label=\"observed texts per day\")\n", - "plt.legend(loc=\"upper left\")" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 14, - "text": [ - "" - ] - }, - { - "output_type": "display_data", - "png": 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4OTlBJBKB4ziMGjUKs2bN4p+MfPv2bRw4cEAwlujoaBw4cABr1qzB0KFD+fEv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\n27dvo0+fPgbzPDw8UFpa2uIVFhUVIS8vD+Hh4Qb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- } - ], - "prompt_number": 14 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Our analysis shows strong support that the user's behavior suddenly changed, versus no change ($\\lambda_1$ would appear like $\\lambda_2$ had this been true), versus a gradual change (more variation in the posterior of $\\tau$ had this been true). We can speculate what might have caused this: a cheaper text-message rate, a recent weather-2-text subscription, or a new relationship.\n", - "\n", - "The next Chapter will explore PyMC through examples. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Exercises\n", - "\n", - "1\\. Using `lambda_1_samples` and `lambda_2_samples`, what is the mean of the positior distributions of $\\lambda_1$ and $\\lambda_2$?" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "#type your code here." - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 17 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "2\\. What is the expected percent increase text-message rates? `hint:` compute the mean of `lambda_1_samples/lambda_2_samples`. Note that quanitity is very different from `lambda_1_samples.mean()/lambda_2_samples.mean()`." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "#type your code here." - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 16 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "3\\. Looking at the posterior distribution graph of $\\tau$, why do you think there is a small number of posterior $\\tau$ samples near 0? `hint:` Look at the data again." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "4\\. What is the mean of $\\lambda_1$ **given** $\\tau$ is less than 45. " - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "#type your code here." - ], - "language": "python", - "metadata": {}, - "outputs": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### References\n", - "\n", - "\n", - "- [1] Gelman, Andrew. N.p.. Web. 22 Jan 2013. .\n", - "- [2] Norvig, Peter. 2009. [*The Unreasonable Effectivness of Data*](http://www.csee.wvu.edu/~gidoretto/courses/2011-fall-cp/reading/TheUnreasonable EffectivenessofData_IEEE_IS2009.pdf)." - ] - } - ], - "metadata": {} - } - ] -} \ No newline at end of file diff --git a/Chapter1_Introduction/README.md b/Chapter1_Introduction/README.md index 2b7b46d9..770228c7 100644 --- a/Chapter1_Introduction/README.md +++ b/Chapter1_Introduction/README.md @@ -1,25 +1,4 @@ -# Chapter 1 - -Instructions on Usage of the IPython Notebook for Chapter 1. There are 3 options available to the learner. - -### Option 1 ------------- -The lesson was created with IPython Notebook. It allows a user to interact with the contents of the notebook. - -*If you do not have IPython Notebook installed, you can use Option 2 or 3 to review the material. The lesson will not be interactive.* - - -### Option 2 -------------- -PDFs of the chapters are available [online](https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/tree/master/previews). - - -### Option 3 -------------- -Open with your browser: http://nbviewer.ipython.org/urls/raw.github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/master/Chapter1_Introduction/Chapter1_Introduction.ipynb - -#### Screenshot of notebook for Chapter 1 ---------------- -![alt text][logo] -[logo]: https://raw.github.com/bigsnarfdude/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/master/Chapter1_Introduction/Screen%20Shot%202013-02-06%20at%205.52.18%20PM.png "Chapter 1 iPython Notebook Screenshot" +Chapter 1: Introduction to Bayesian Methods +=========== +### [Read it online here](http://nbviewer.ipython.org/urls/raw.github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/master/Chapter1_Introduction/Ch1_Introduction_PyMC2.ipynb) diff --git a/Chapter1_Introduction/README.md~ b/Chapter1_Introduction/README.md~ deleted file mode 100644 index 4f0b4119..00000000 --- a/Chapter1_Introduction/README.md~ +++ /dev/null @@ -1,19 +0,0 @@ -# Chapter 1 - -Instructions on Usage - -### Option 1 --------- -The lesson was created with iPython Notebook. It allows a user to interact with the contents of the notebook. - -If you do not have iPython Notebook installed you can use Option 2 to review the material. The lesson will not be interactive. - - -### Option 2 --------- -Open with your browser: http://nbviewer.ipython.org/urls/raw.github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/master/Chapter1_Introduction/Chapter1_Introduction.ipynb - -#### Screenshot of notebook for Chapter 1 - -![alt text][logo] -[logo]: https://raw.github.com/bigsnarfdude "Chapter 1 iPython Notebook Screenshot" diff --git a/Chapter1_Introduction/Screen Shot 2013-02-06 at 5.52.18 PM.png b/Chapter1_Introduction/Screen Shot 2013-02-06 at 5.52.18 PM.png deleted file mode 100644 index b9108b4f..00000000 Binary files a/Chapter1_Introduction/Screen Shot 2013-02-06 at 5.52.18 PM.png and /dev/null differ diff --git a/Chapter1_Introduction/chp1data/textmsgdata.csv b/Chapter1_Introduction/chp1data/textmsgdata.csv deleted file mode 100644 index 7b8b1977..00000000 --- a/Chapter1_Introduction/chp1data/textmsgdata.csv +++ /dev/null @@ -1,70 +0,0 @@ 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a/Chapter2_MorePyMC/Ch2_MorePyMC_PyMC2.ipynb b/Chapter2_MorePyMC/Ch2_MorePyMC_PyMC2.ipynb new file mode 100644 index 00000000..9c2212bf --- /dev/null +++ b/Chapter2_MorePyMC/Ch2_MorePyMC_PyMC2.ipynb @@ -0,0 +1,2515 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Chapter 2\n", + "======\n", + "______\n", + "\n", + "This chapter introduces more PyMC syntax and design patterns, and ways to think about how to model a system from a Bayesian perspective. It also contains tips and data visualization techniques for assessing goodness-of-fit for your Bayesian model." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## A little more on PyMC\n", + "\n", + "### Parent and Child relationships\n", + "\n", + "To assist with describing Bayesian relationships, and to be consistent with PyMC's documentation, we introduce *parent and child* variables. \n", + "\n", + "* *parent variables* are variables that influence another variable. \n", + "\n", + "* *child variable* are variables that are affected by other variables, i.e. are the subject of parent variables. \n", + "\n", + "A variable can be both a parent and child. For example, consider the PyMC code below." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import pymc as pm\n", + "\n", + "\n", + "parameter = pm.Exponential(\"poisson_param\", 1)\n", + "data_generator = pm.Poisson(\"data_generator\", parameter)\n", + "data_plus_one = data_generator + 1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`parameter` controls the parameter of `data_generator`, hence influences its values. The former is a parent of the latter. By symmetry, `data_generator` is a child of `parameter`.\n", + "\n", + "Likewise, `data_generator` is a parent to the variable `data_plus_one` (hence making `data_generator` both a parent and child variable). Although it does not look like one, `data_plus_one` should be treated as a PyMC variable as it is a *function* of another PyMC variable, hence is a child variable to `data_generator`.\n", + "\n", + "This nomenclature is introduced to help us describe relationships in PyMC modeling. You can access a variable's children and parent variables using the `children` and `parents` attributes attached to variables." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Children of `parameter`: \n", + "{.new_class 'data_generator' at 0x7f812404a8d0>}\n", + "\n", + "Parents of `data_generator`: \n", + "{'mu': .new_class 'poisson_param' at 0x7f812404a898>}\n", + "\n", + "Children of `data_generator`: \n", + "{}\n" + ] + } + ], + "source": [ + "print(\"Children of `parameter`: \")\n", + "print(parameter.children)\n", + "print(\"\\nParents of `data_generator`: \")\n", + "print(data_generator.parents)\n", + "print(\"\\nChildren of `data_generator`: \")\n", + "print(data_generator.children)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Of course a child can have more than one parent, and a parent can have many children." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### PyMC Variables\n", + "\n", + "All PyMC variables also expose a `value` attribute. This method produces the *current* (possibly random) internal value of the variable. If the variable is a child variable, its value changes given the variable's parents' values. Using the same variables from before:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "parameter.value = 0.032177775515776275\n", + "data_generator.value = 0\n", + "data_plus_one.value = 1\n" + ] + } + ], + "source": [ + "print(\"parameter.value =\", parameter.value)\n", + "print(\"data_generator.value =\", data_generator.value)\n", + "print(\"data_plus_one.value =\", data_plus_one.value)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "PyMC is concerned with two types of programming variables: `stochastic` and `deterministic`.\n", + "\n", + "* *stochastic variables* are variables that are not deterministic, i.e., even if you knew all the values of the variables' parents (if it even has any parents), it would still be random. Included in this category are instances of classes `Poisson`, `DiscreteUniform`, and `Exponential`.\n", + "\n", + "* *deterministic variables* are variables that are not random if the variables' parents were known. This might be confusing at first: a quick mental check is *if I knew all of variable `foo`'s parent variables, I could determine what `foo`'s value is.* \n", + "\n", + "We will detail each below.\n", + "\n", + "#### Initializing Stochastic variables\n", + "\n", + "Initializing a stochastic variable requires a `name` argument, plus additional parameters that are class specific. For example:\n", + "\n", + "`some_variable = pm.DiscreteUniform(\"discrete_uni_var\", 0, 4)`\n", + "\n", + "where 0, 4 are the `DiscreteUniform`-specific lower and upper bound on the random variable. The [PyMC docs](http://pymc-devs.github.com/pymc/distributions.html) contain the specific parameters for stochastic variables. (Or use `object??`, for example `pm.DiscreteUniform??` if you are using IPython!)\n", + "\n", + "The `name` attribute is used to retrieve the posterior distribution later in the analysis, so it is best to use a descriptive name. Typically, I use the Python variable's name as the `name`.\n", + "\n", + "For multivariable problems, rather than creating a Python array of stochastic variables, addressing the `size` keyword in the call to a `Stochastic` variable creates multivariate array of (independent) stochastic variables. The array behaves like a Numpy array when used like one, and references to its `value` attribute return Numpy arrays. \n", + "\n", + "The `size` argument also solves the annoying case where you may have many variables $\\beta_i, \\; i = 1,...,N$ you wish to model. Instead of creating arbitrary names and variables for each one, like:\n", + "\n", + " beta_1 = pm.Uniform(\"beta_1\", 0, 1)\n", + " beta_2 = pm.Uniform(\"beta_2\", 0, 1)\n", + " ...\n", + "\n", + "we can instead wrap them into a single variable:\n", + "\n", + " betas = pm.Uniform(\"betas\", 0, 1, size=N)\n", + "\n", + "#### Calling `random()`\n", + "We can also call on a stochastic variable's `random()` method, which (given the parent values) will generate a new, random value. Below we demonstrate this using the texting example from the previous chapter." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "lambda_1.value = 4.970\n", + "lambda_2.value = 1.039\n", + "tau.value = 5.000 \n", + "\n", + "After calling random() on the variables...\n", + "lambda_1.value = 1.411\n", + "lambda_2.value = 1.806\n", + "tau.value = 3.000\n" + ] + } + ], + "source": [ + "lambda_1 = pm.Exponential(\"lambda_1\", 1) # prior on first behaviour\n", + "lambda_2 = pm.Exponential(\"lambda_2\", 1) # prior on second behaviour\n", + "tau = pm.DiscreteUniform(\"tau\", lower=0, upper=10) # prior on behaviour change\n", + "\n", + "print(\"lambda_1.value = %.3f\" % lambda_1.value)\n", + "print(\"lambda_2.value = %.3f\" % lambda_2.value)\n", + "print(\"tau.value = %.3f\" % tau.value, \"\\n\")\n", + "\n", + "lambda_1.random(), lambda_2.random(), tau.random()\n", + "\n", + "print(\"After calling random() on the variables...\")\n", + "print(\"lambda_1.value = %.3f\" % lambda_1.value)\n", + "print(\"lambda_2.value = %.3f\" % lambda_2.value)\n", + "print(\"tau.value = %.3f\" % tau.value)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The call to `random` stores a new value into the variable's `value` attribute. In fact, this new value is stored in the computer's cache for faster recall and efficiency." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### **Warning**: *Don't update stochastic variables' values in-place.*\n", + "\n", + "\n", + "Straight from the PyMC docs, we quote [4]:\n", + "\n", + "> `Stochastic` objects' values should not be updated in-place. This confuses PyMC's caching scheme... The only way a stochastic variable's value should be updated is using statements of the following form:\n", + "\n", + " A.value = new_value\n", + "\n", + "> The following are in-place updates and should **never** be used:\n", + "\n", + " \n", + " A.value += 3\n", + " A.value[2,1] = 5\n", + " A.value.attribute = new_attribute_value\n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Deterministic variables\n", + "\n", + "Since most variables you will be modeling are stochastic, we distinguish deterministic variables with a `pymc.deterministic` wrapper. (If you are unfamiliar with Python wrappers (also called decorators), that's no problem. Just prepend the `pymc.deterministic` decorator before the variable declaration and you're good to go. No need to know more. ) The declaration of a deterministic variable uses a Python function:\n", + "\n", + " @pm.deterministic\n", + " def some_deterministic_var(v1=v1,):\n", + " #jelly goes here.\n", + "\n", + "For all purposes, we can treat the object `some_deterministic_var` as a variable and not a Python function. \n", + "\n", + "Prepending with the wrapper is the easiest way, but not the only way, to create deterministic variables: elementary operations, like addition, exponentials etc. implicitly create deterministic variables. For example, the following returns a deterministic variable:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "pymc.PyMCObjects.Deterministic" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "type(lambda_1 + lambda_2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The use of the `deterministic` wrapper was seen in the previous chapter's text-message example. Recall the model for $\\lambda$ looked like: \n", + "\n", + "$$\n", + "\\lambda = \n", + "\\begin{cases}\n", + "\\lambda_1 & \\text{if } t \\lt \\tau \\cr\n", + "\\lambda_2 & \\text{if } t \\ge \\tau\n", + "\\end{cases}\n", + "$$\n", + "\n", + "And in PyMC code:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "n_data_points = 5 # in CH1 we had ~70 data points\n", + "\n", + "\n", + "@pm.deterministic\n", + "def lambda_(tau=tau, lambda_1=lambda_1, lambda_2=lambda_2):\n", + " out = np.zeros(n_data_points)\n", + " out[:tau] = lambda_1 # lambda before tau is lambda1\n", + " out[tau:] = lambda_2 # lambda after tau is lambda2\n", + " return out" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Clearly, if $\\tau, \\lambda_1$ and $\\lambda_2$ are known, then $\\lambda$ is known completely, hence it is a deterministic variable. \n", + "\n", + "Inside the deterministic decorator, the `Stochastic` variables passed in behave like scalars or Numpy arrays (if multivariable), and *not* like `Stochastic` variables. For example, running the following:\n", + "\n", + " @pm.deterministic\n", + " def some_deterministic(stoch=some_stochastic_var):\n", + " return stoch.value**2\n", + "\n", + "\n", + "will return an `AttributeError` detailing that `stoch` does not have a `value` attribute. It simply needs to be `stoch**2`. During the learning phase, it's the variable's `value` that is repeatedly passed in, not the actual variable. \n", + "\n", + "Notice in the creation of the deterministic function we added defaults to each variable used in the function. This is a necessary step, and all variables *must* have default values. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Including observations in the Model\n", + "\n", + "At this point, it may not look like it, but we have fully specified our priors. For example, we can ask and answer questions like \"What does my prior distribution of $\\lambda_1$ look like?\" " + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib inline\n", + "from IPython.core.pylabtools import figsize\n", + "from matplotlib import pyplot as plt\n", + "figsize(12.5, 4)\n", + "\n", + "\n", + "samples = [lambda_1.random() for i in range(20000)]\n", + "plt.hist(samples, bins=70, density=True, histtype=\"stepfilled\")\n", + "plt.title(\"Prior distribution for $\\lambda_1$\")\n", + "plt.xlim(0, 8);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To frame this in the notation of the first chapter, though this is a slight abuse of notation, we have specified $P(A)$. Our next goal is to include data/evidence/observations $X$ into our model. \n", + "\n", + "PyMC stochastic variables have a keyword argument `observed` which accepts a boolean (`False` by default). The keyword `observed` has a very simple role: fix the variable's current value, i.e. make `value` immutable. We have to specify an initial `value` in the variable's creation, equal to the observations we wish to include, typically an array (and it should be an Numpy array for speed). For example:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "value: [10 5]\n", + "calling .random()\n", + "value: [10 5]\n" + ] + } + ], + "source": [ + "data = np.array([10, 5])\n", + "fixed_variable = pm.Poisson(\"fxd\", 1, value=data, observed=True)\n", + "print(\"value: \", fixed_variable.value)\n", + "print(\"calling .random()\")\n", + "fixed_variable.random()\n", + "print(\"value: \", fixed_variable.value)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is how we include data into our models: initializing a stochastic variable to have a *fixed value*. \n", + "\n", + "To complete our text message example, we fix the PyMC variable `observations` to the observed dataset. " + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[10 25 15 20 35]\n" + ] + } + ], + "source": [ + "# We're using some fake data here\n", + "data = np.array([10, 25, 15, 20, 35])\n", + "obs = pm.Poisson(\"obs\", lambda_, value=data, observed=True)\n", + "print(obs.value)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Finally...\n", + "\n", + "We wrap all the created variables into a `pm.Model` class. With this `Model` class, we can analyze the variables as a single unit. This is an optional step, as the fitting algorithms can be sent an array of the variables rather than a `Model` class. I may or may not use this class in future examples ;)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "model = pm.Model([obs, lambda_, lambda_1, lambda_2, tau])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Modeling approaches\n", + "\n", + "A good starting point in Bayesian modeling is to think about *how your data might have been generated*. Put yourself in an omniscient position, and try to imagine how *you* would recreate the dataset. \n", + "\n", + "In the last chapter we investigated text message data. We begin by asking how our observations may have been generated:\n", + "\n", + "1. We started by thinking \"what is the best random variable to describe this count data?\" A Poisson random variable is a good candidate because it can represent count data. So we model the number of sms's received as sampled from a Poisson distribution.\n", + "\n", + "2. Next, we think, \"Ok, assuming sms's are Poisson-distributed, what do I need for the Poisson distribution?\" Well, the Poisson distribution has a parameter $\\lambda$. \n", + "\n", + "3. Do we know $\\lambda$? No. In fact, we have a suspicion that there are *two* $\\lambda$ values, one for the earlier behaviour and one for the later behaviour. We don't know when the behaviour switches though, but call the switchpoint $\\tau$.\n", + "\n", + "4. What is a good distribution for the two $\\lambda$s? The exponential is good, as it assigns probabilities to positive real numbers. Well the exponential distribution has a parameter too, call it $\\alpha$.\n", + "\n", + "5. Do we know what the parameter $\\alpha$ might be? No. At this point, we could continue and assign a distribution to $\\alpha$, but it's better to stop once we reach a set level of ignorance: whereas we have a prior belief about $\\lambda$, (\"it probably changes over time\", \"it's likely between 10 and 30\", etc.), we don't really have any strong beliefs about $\\alpha$. So it's best to stop here. \n", + "\n", + " What is a good value for $\\alpha$ then? We think that the $\\lambda$s are between 10-30, so if we set $\\alpha$ really low (which corresponds to larger probability on high values) we are not reflecting our prior well. Similar, a too-high alpha misses our prior belief as well. A good idea for $\\alpha$ as to reflect our belief is to set the value so that the mean of $\\lambda$, given $\\alpha$, is equal to our observed mean. This was shown in the last chapter.\n", + "\n", + "6. We have no expert opinion of when $\\tau$ might have occurred. So we will suppose $\\tau$ is from a discrete uniform distribution over the entire timespan.\n", + "\n", + "\n", + "Below we give a graphical visualization of this, where arrows denote `parent-child` relationships. (provided by the [Daft Python library](http://daft-pgm.org/) )\n", + "\n", + "\n", + "\n", + "\n", + "PyMC, and other probabilistic programming languages, have been designed to tell these data-generation *stories*. More generally, B. Cronin writes [5]:\n", + "\n", + "> Probabilistic programming will unlock narrative explanations of data, one of the holy grails of business analytics and the unsung hero of scientific persuasion. People think in terms of stories - thus the unreasonable power of the anecdote to drive decision-making, well-founded or not. But existing analytics largely fails to provide this kind of story; instead, numbers seemingly appear out of thin air, with little of the causal context that humans prefer when weighing their options." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Same story; different ending.\n", + "\n", + "Interestingly, we can create *new datasets* by retelling the story.\n", + "For example, if we reverse the above steps, we can simulate a possible realization of the dataset.\n", + "\n", + "1\\. Specify when the user's behaviour switches by sampling from $\\text{DiscreteUniform}(0, 80)$:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "21\n" + ] + } + ], + "source": [ + "tau = pm.rdiscrete_uniform(0, 80)\n", + "print(tau)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "2\\. Draw $\\lambda_1$ and $\\lambda_2$ from an $\\text{Exp}(\\alpha)$ distribution:" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "20.7789591495 62.1938883352\n" + ] + } + ], + "source": [ + "alpha = 1. / 20.\n", + "lambda_1, lambda_2 = pm.rexponential(alpha, 2)\n", + "print(lambda_1, lambda_2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "3\\. For days before $\\tau$, represent the user's received SMS count by sampling from $\\text{Poi}(\\lambda_1)$, and sample from $\\text{Poi}(\\lambda_2)$ for days after $\\tau$. For example:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "data = np.r_[pm.rpoisson(lambda_1, tau), pm.rpoisson(lambda_2, 80 - tau)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "4\\. Plot the artificial dataset:" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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Pnz6dz3zmM3zyk58EYMiQIZSWljYsP++88xg2bBi9evXi9NNPb3J85513Hn369GHnnXfm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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.bar(np.arange(80), data, color=\"#348ABD\")\n", + "plt.bar(tau - 1, data[tau - 1], color=\"r\", label=\"user behaviour changed\")\n", + "plt.xlabel(\"Time (days)\")\n", + "plt.ylabel(\"count of text-msgs received\")\n", + "plt.title(\"Artificial dataset\")\n", + "plt.xlim(0, 80)\n", + "plt.legend();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It is okay that our fictional dataset does not look like our observed dataset: the probability is incredibly small it indeed would. PyMC's engine is designed to find good parameters, $\\lambda_i, \\tau$, that maximize this probability. \n", + "\n", + "\n", + "The ability to generate artificial datasets is an interesting side effect of our modeling, and we will see that this ability is a very important method of Bayesian inference. We produce a few more datasets below:" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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TO4HZkg6WtAvwQeC6cqplZmZmZtZ/Ople/iVJnwWWkjXivxMR95dWMzMzMzOz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\nXYF3kfWpd7YdiohzIuKgiDiU7DP21oj4MHA9zrY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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def plot_artificial_sms_dataset():\n", + " tau = pm.rdiscrete_uniform(0, 80)\n", + " alpha = 1. / 20.\n", + " lambda_1, lambda_2 = pm.rexponential(alpha, 2)\n", + " data = np.r_[pm.rpoisson(lambda_1, tau), pm.rpoisson(lambda_2, 80 - tau)]\n", + " plt.bar(np.arange(80), data, color=\"#348ABD\")\n", + " plt.bar(tau - 1, data[tau - 1], color=\"r\", label=\"user behaviour changed\")\n", + " plt.xlim(0, 80)\n", + "\n", + "figsize(12.5, 5)\n", + "plt.suptitle(\"More examples of artificial datasets\", fontsize=14)\n", + "for i in range(1, 5):\n", + " plt.subplot(4, 1, i)\n", + " plot_artificial_sms_dataset()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Later we will see how we use this to make predictions and test the appropriateness of our models." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Example: Bayesian A/B testing\n", + "\n", + "A/B testing is a statistical design pattern for determining the difference of effectiveness between two different treatments. For example, a pharmaceutical company is interested in the effectiveness of drug A vs drug B. The company will test drug A on some fraction of their trials, and drug B on the other fraction (this fraction is often 1/2, but we will relax this assumption). After performing enough trials, the in-house statisticians sift through the data to determine which drug yielded better results. \n", + "\n", + "Similarly, front-end web developers are interested in which design of their website yields more sales or some other metric of interest. They will route some fraction of visitors to site A, and the other fraction to site B, and record if the visit yielded a sale or not. The data is recorded (in real-time), and analyzed afterwards. \n", + "\n", + "Often, the post-experiment analysis is done using something called a hypothesis test like *difference of means test* or *difference of proportions test*. This involves often misunderstood quantities like a \"Z-score\" and even more confusing \"p-values\" (please don't ask). If you have taken a statistics course, you have probably been taught this technique (though not necessarily *learned* this technique). And if you were like me, you may have felt uncomfortable with their derivation -- good: the Bayesian approach to this problem is much more natural. \n", + "\n", + "### A Simple Case\n", + "\n", + "As this is a hacker book, we'll continue with the web-dev example. For the moment, we will focus on the analysis of site A only. Assume that there is some true $0 \\lt p_A \\lt 1$ probability that users who, upon shown site A, eventually purchase from the site. This is the true effectiveness of site A. Currently, this quantity is unknown to us. \n", + "\n", + "Suppose site A was shown to $N$ people, and $n$ people purchased from the site. One might conclude hastily that $p_A = \\frac{n}{N}$. Unfortunately, the *observed frequency* $\\frac{n}{N}$ does not necessarily equal $p_A$ -- there is a difference between the *observed frequency* and the *true frequency* of an event. The true frequency can be interpreted as the probability of an event occurring. For example, the true frequency of rolling a 1 on a 6-sided die is $\\frac{1}{6}$. Knowing the true frequency of events like:\n", + "\n", + "- fraction of users who make purchases, \n", + "- frequency of social attributes, \n", + "- percent of internet users with cats etc. \n", + "\n", + "are common requests we ask of Nature. Unfortunately, often Nature hides the true frequency from us and we must *infer* it from observed data.\n", + "\n", + "The *observed frequency* is then the frequency we observe: say rolling the die 100 times you may observe 20 rolls of 1. The observed frequency, 0.2, differs from the true frequency, $\\frac{1}{6}$. We can use Bayesian statistics to infer probable values of the true frequency using an appropriate prior and observed data.\n", + "\n", + "\n", + "With respect to our A/B example, we are interested in using what we know, $N$ (the total trials administered) and $n$ (the number of conversions), to estimate what $p_A$, the true frequency of buyers, might be. \n", + "\n", + "To set up a Bayesian model, we need to assign prior distributions to our unknown quantities. *A priori*, what do we think $p_A$ might be? For this example, we have no strong conviction about $p_A$, so for now, let's assume $p_A$ is uniform over [0,1]:" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import pymc as pm\n", + "\n", + "# The parameters are the bounds of the Uniform.\n", + "p = pm.Uniform('p', lower=0, upper=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Had we had stronger beliefs, we could have expressed them in the prior above.\n", + "\n", + "For this example, consider $p_A = 0.05$, and $N = 1500$ users shown site A, and we will simulate whether the user made a purchase or not. To simulate this from $N$ trials, we will use a *Bernoulli* distribution: if $X\\ \\sim \\text{Ber}(p)$, then $X$ is 1 with probability $p$ and 0 with probability $1 - p$. Of course, in practice we do not know $p_A$, but we will use it here to simulate the data." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[False False False ..., False False False]\n", + "77\n" + ] + } + ], + "source": [ + "# set constants\n", + "p_true = 0.05 # remember, this is unknown.\n", + "N = 1500\n", + "\n", + "# sample N Bernoulli random variables from Ber(0.05).\n", + "# each random variable has a 0.05 chance of being a 1.\n", + "# this is the data-generation step\n", + "occurrences = pm.rbernoulli(p_true, N)\n", + "\n", + "print(occurrences) # Remember: Python treats True == 1, and False == 0\n", + "print(occurrences.sum())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The observed frequency is:" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "What is the observed frequency in Group A? 0.0513\n", + "Does this equal the true frequency? False\n" + ] + } + ], + "source": [ + "# Occurrences.mean is equal to n/N.\n", + "print(\"What is the observed frequency in Group A? %.4f\" % occurrences.mean())\n", + "print(\"Does this equal the true frequency? %s\" % (occurrences.mean() == p_true))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We combine the observations into the PyMC `observed` variable, and run our inference algorithm:" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " [-----------------100%-----------------] 18000 of 18000 complete in 1.5 sec" + ] + } + ], + "source": [ + "# include the observations, which are Bernoulli\n", + "obs = pm.Bernoulli(\"obs\", p, value=occurrences, observed=True)\n", + "\n", + "# To be explained in chapter 3\n", + "mcmc = pm.MCMC([p, obs])\n", + "mcmc.sample(18000, 1000)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We plot the posterior distribution of the unknown $p_A$ below:" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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NPbtGQwZWxVI/AOBwsQ/GyRlHWOTpIYr+mjO+58BBbWrfG0vdQwdVZuYifQui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\npP9y91OS5TWSznb3VjMbL2m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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "figsize(12.5, 4)\n", + "plt.title(\"Posterior distribution of $p_A$, the true effectiveness of site A\")\n", + "plt.vlines(p_true, 0, 90, linestyle=\"--\", label=\"true $p_A$ (unknown)\")\n", + "plt.hist(mcmc.trace(\"p\")[:], bins=25, histtype=\"stepfilled\", density=True)\n", + "plt.legend();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Our posterior distribution puts most weight near the true value of $p_A$, but also some weights in the tails. This is a measure of how uncertain we should be, given our observations. Try changing the number of observations, `N`, and observe how the posterior distribution changes.\n", + "\n", + "### *A* and *B* Together\n", + "\n", + "A similar analysis can be done for site B's response data to determine the analogous $p_B$. But what we are really interested in is the *difference* between $p_A$ and $p_B$. Let's infer $p_A$, $p_B$, *and* $\\text{delta} = p_A - p_B$, all at once. We can do this using PyMC's deterministic variables. (We'll assume for this exercise that $p_B = 0.04$, so $\\text{delta} = 0.01$, $N_B = 750$ (significantly less than $N_A$) and we will simulate site B's data like we did for site A's data )" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Obs from Site A: [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0] ...\n", + "Obs from Site B: [0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0] ...\n" + ] + } + ], + "source": [ + "import pymc as pm\n", + "figsize(12, 4)\n", + "\n", + "# these two quantities are unknown to us.\n", + "true_p_A = 0.05\n", + "true_p_B = 0.04\n", + "\n", + "# notice the unequal sample sizes -- no problem in Bayesian analysis.\n", + "N_A = 1500\n", + "N_B = 750\n", + "\n", + "# generate some observations\n", + "observations_A = pm.rbernoulli(true_p_A, N_A)\n", + "observations_B = pm.rbernoulli(true_p_B, N_B)\n", + "print(\"Obs from Site A: \", observations_A[:30].astype(int), \"...\")\n", + "print(\"Obs from Site B: \", observations_B[:30].astype(int), \"...\")" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.054\n", + "0.0333333333333\n" + ] + } + ], + "source": [ + "print(observations_A.mean())\n", + "print(observations_B.mean())" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " [-----------------100%-----------------] 20000 of 20000 complete in 2.6 sec" + ] + } + ], + "source": [ + "# Set up the pymc model. Again assume Uniform priors for p_A and p_B.\n", + "p_A = pm.Uniform(\"p_A\", 0, 1)\n", + "p_B = pm.Uniform(\"p_B\", 0, 1)\n", + "\n", + "\n", + "# Define the deterministic delta function. This is our unknown of interest.\n", + "@pm.deterministic\n", + "def delta(p_A=p_A, p_B=p_B):\n", + " return p_A - p_B\n", + "\n", + "# Set of observations, in this case we have two observation datasets.\n", + "obs_A = pm.Bernoulli(\"obs_A\", p_A, value=observations_A, observed=True)\n", + "obs_B = pm.Bernoulli(\"obs_B\", p_B, value=observations_B, observed=True)\n", + "\n", + "# To be explained in chapter 3.\n", + "mcmc = pm.MCMC([p_A, p_B, delta, obs_A, obs_B])\n", + "mcmc.sample(20000, 1000)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Below we plot the posterior distributions for the three unknowns: " + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "p_A_samples = mcmc.trace(\"p_A\")[:]\n", + "p_B_samples = mcmc.trace(\"p_B\")[:]\n", + "delta_samples = mcmc.trace(\"delta\")[:]" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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//OUv9OzZk6VLl/LP//zPVY+dcsopLFq0iNzcXDIyMujTpw/Tp0/n4MGDJ+3n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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "figsize(12.5, 10)\n", + "\n", + "# histogram of posteriors\n", + "\n", + "ax = plt.subplot(311)\n", + "\n", + "plt.xlim(0, .1)\n", + "plt.hist(p_A_samples, histtype='stepfilled', bins=25, alpha=0.85,\n", + " label=\"posterior of $p_A$\", color=\"#A60628\", density=True)\n", + "plt.vlines(true_p_A, 0, 80, linestyle=\"--\", label=\"true $p_A$ (unknown)\")\n", + "plt.legend(loc=\"upper right\")\n", + "plt.title(\"Posterior distributions of $p_A$, $p_B$, and delta unknowns\")\n", + "\n", + "ax = plt.subplot(312)\n", + "\n", + "plt.xlim(0, .1)\n", + "plt.hist(p_B_samples, histtype='stepfilled', bins=25, alpha=0.85,\n", + " label=\"posterior of $p_B$\", color=\"#467821\", density=True)\n", + "plt.vlines(true_p_B, 0, 80, linestyle=\"--\", label=\"true $p_B$ (unknown)\")\n", + "plt.legend(loc=\"upper right\")\n", + "\n", + "ax = plt.subplot(313)\n", + "plt.hist(delta_samples, histtype='stepfilled', bins=30, alpha=0.85,\n", + " label=\"posterior of delta\", color=\"#7A68A6\", density=True)\n", + "plt.vlines(true_p_A - true_p_B, 0, 60, linestyle=\"--\",\n", + " label=\"true delta (unknown)\")\n", + "plt.vlines(0, 0, 60, color=\"black\", alpha=0.2)\n", + "plt.legend(loc=\"upper right\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notice that as a result of `N_B < N_A`, i.e. we have less data from site B, our posterior distribution of $p_B$ is fatter, implying we are less certain about the true value of $p_B$ than we are of $p_A$. \n", + "\n", + "With respect to the posterior distribution of $\\text{delta}$, we can see that the majority of the distribution is above $\\text{delta}=0$, implying there site A's response is likely better than site B's response. The probability this inference is incorrect is easily computable:" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Probability site A is WORSE than site B: 0.017\n", + "Probability site A is BETTER than site B: 0.983\n" + ] + } + ], + "source": [ + "# Count the number of samples less than 0, i.e. the area under the curve\n", + "# before 0, represent the probability that site A is worse than site B.\n", + "print(\"Probability site A is WORSE than site B: %.3f\" % \\\n", + " (delta_samples < 0).mean())\n", + "\n", + "print(\"Probability site A is BETTER than site B: %.3f\" % \\\n", + " (delta_samples > 0).mean())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If this probability is too high for comfortable decision-making, we can perform more trials on site B (as site B has less samples to begin with, each additional data point for site B contributes more inferential \"power\" than each additional data point for site A). \n", + "\n", + "Try playing with the parameters `true_p_A`, `true_p_B`, `N_A`, and `N_B`, to see what the posterior of $\\text{delta}$ looks like. Notice in all this, the difference in sample sizes between site A and site B was never mentioned: it naturally fits into Bayesian analysis.\n", + "\n", + "I hope the readers feel this style of A/B testing is more natural than hypothesis testing, which has probably confused more than helped practitioners. Later in this book, we will see two extensions of this model: the first to help dynamically adjust for bad sites, and the second will improve the speed of this computation by reducing the analysis to a single equation. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## An algorithm for human deceit\n", + "\n", + "Social data has an additional layer of interest as people are not always honest with responses, which adds a further complication into inference. For example, simply asking individuals \"Have you ever cheated on a test?\" will surely contain some rate of dishonesty. What you can say for certain is that the true rate is less than your observed rate (assuming individuals lie *only* about *not cheating*; I cannot imagine one who would admit \"Yes\" to cheating when in fact they hadn't cheated). \n", + "\n", + "To present an elegant solution to circumventing this dishonesty problem, and to demonstrate Bayesian modeling, we first need to introduce the binomial distribution.\n", + "\n", + "### The Binomial Distribution\n", + "\n", + "The binomial distribution is one of the most popular distributions, mostly because of its simplicity and usefulness. Unlike the other distributions we have encountered thus far in the book, the binomial distribution has 2 parameters: $N$, a positive integer representing $N$ trials or number of instances of potential events, and $p$, the probability of an event occurring in a single trial. Like the Poisson distribution, it is a discrete distribution, but unlike the Poisson distribution, it only weighs integers from $0$ to $N$. The mass distribution looks like:\n", + "\n", + "$$P( X = k ) = {{N}\\choose{k}} p^k(1-p)^{N-k}$$\n", + "\n", + "If $X$ is a binomial random variable with parameters $p$ and $N$, denoted $X \\sim \\text{Bin}(N,p)$, then $X$ is the number of events that occurred in the $N$ trials (obviously $0 \\le X \\le N$), and $p$ is the probability of a single event. The larger $p$ is (while still remaining between 0 and 1), the more events are likely to occur. The expected value of a binomial is equal to $Np$. Below we plot the mass probability distribution for varying parameters. " + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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Zqf52pJB1vvbaa9TW1nLJJZcA8IEPfIAJEybw8MMP8/Wvf73TfVBdpYQUExJS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\nb8LQoUMZM2ZM6wc1cyON2j2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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "figsize(12.5, 4)\n", + "\n", + "import scipy.stats as stats\n", + "binomial = stats.binom\n", + "\n", + "parameters = [(10, .4), (10, .9)]\n", + "colors = [\"#348ABD\", \"#A60628\"]\n", + "\n", + "for i in range(2):\n", + " N, p = parameters[i]\n", + " _x = np.arange(N + 1)\n", + " plt.bar(_x - 0.5, binomial.pmf(_x, N, p), color=colors[i],\n", + " edgecolor=colors[i],\n", + " alpha=0.6,\n", + " label=\"$N$: %d, $p$: %.1f\" % (N, p),\n", + " linewidth=3)\n", + "\n", + "plt.legend(loc=\"upper left\")\n", + "plt.xlim(0, 10.5)\n", + "plt.xlabel(\"$k$\")\n", + "plt.ylabel(\"$P(X = k)$\")\n", + "plt.title(\"Probability mass distributions of binomial random variables\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The special case when $N = 1$ corresponds to the Bernoulli distribution. There is another connection between Bernoulli and Binomial random variables. If we have $X_1, X_2, ... , X_N$ Bernoulli random variables with the same $p$, then $Z = X_1 + X_2 + ... + X_N \\sim \\text{Binomial}(N, p )$.\n", + "\n", + "The expected value of a Bernoulli random variable is $p$. This can be seen by noting the more general Binomial random variable has expected value $Np$ and setting $N=1$." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Example: Cheating among students\n", + "\n", + "We will use the binomial distribution to determine the frequency of students cheating during an exam. If we let $N$ be the total number of students who took the exam, and assuming each student is interviewed post-exam (answering without consequence), we will receive integer $X$ \"Yes I did cheat\" answers. We then find the posterior distribution of $p$, given $N$, some specified prior on $p$, and observed data $X$. \n", + "\n", + "This is a completely absurd model. No student, even with a free-pass against punishment, would admit to cheating. What we need is a better *algorithm* to ask students if they had cheated. Ideally the algorithm should encourage individuals to be honest while preserving privacy. The following proposed algorithm is a solution I greatly admire for its ingenuity and effectiveness:\n", + "\n", + "> In the interview process for each student, the student flips a coin, hidden from the interviewer. The student agrees to answer honestly if the coin comes up heads. Otherwise, if the coin comes up tails, the student (secretly) flips the coin again, and answers \"Yes, I did cheat\" if the coin flip lands heads, and \"No, I did not cheat\", if the coin flip lands tails. This way, the interviewer does not know if a \"Yes\" was the result of a guilty plea, or a Heads on a second coin toss. Thus privacy is preserved and the researchers receive honest answers. \n", + "\n", + "I call this the Privacy Algorithm. One could of course argue that the interviewers are still receiving false data since some *Yes*'s are not confessions but instead randomness, but an alternative perspective is that the researchers are discarding approximately half of their original dataset since half of the responses will be noise. But they have gained a systematic data generation process that can be modeled. Furthermore, they do not have to incorporate (perhaps somewhat naively) the possibility of deceitful answers. We can use PyMC to dig through this noisy model, and find a posterior distribution for the true frequency of liars. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Suppose 100 students are being surveyed for cheating, and we wish to find $p$, the proportion of cheaters. There are a few ways we can model this in PyMC. I'll demonstrate the most explicit way, and later show a simplified version. Both versions arrive at the same inference. In our data-generation model, we sample $p$, the true proportion of cheaters, from a prior. Since we are quite ignorant about $p$, we will assign it a $\\text{Uniform}(0,1)$ prior." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import pymc as pm\n", + "\n", + "N = 100\n", + "p = pm.Uniform(\"freq_cheating\", 0, 1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Again, thinking of our data-generation model, we assign Bernoulli random variables to the 100 students: 1 implies they cheated and 0 implies they did not. " + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "true_answers = pm.Bernoulli(\"truths\", p, size=N)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If we carry out the algorithm, the next step that occurs is the first coin-flip each student makes. This can be modeled again by sampling 100 Bernoulli random variables with $p=1/2$: denote a 1 as a *Heads* and 0 a *Tails*." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ True False True True True False False True False True True False\n", + " True True False True False True True False True False True True\n", + " True True False True True False True False True False True False\n", + " False True True False True True False False True False False False\n", + " True False True True False False False True True False False False\n", + " True True True False False False False False True False False True\n", + " True False False True False False False False True False True False\n", + " False False True True False False True False True True True False\n", + " False False False True]\n" + ] + } + ], + "source": [ + "first_coin_flips = pm.Bernoulli(\"first_flips\", 0.5, size=N)\n", + "print(first_coin_flips.value)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Although *not everyone* flips a second time, we can still model the possible realization of second coin-flips:" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "second_coin_flips = pm.Bernoulli(\"second_flips\", 0.5, size=N)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Using these variables, we can return a possible realization of the *observed proportion* of \"Yes\" responses. We do this using a PyMC `deterministic` variable:" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "@pm.deterministic\n", + "def observed_proportion(t_a=true_answers,\n", + " fc=first_coin_flips,\n", + " sc=second_coin_flips):\n", + "\n", + " observed = fc * t_a + (1 - fc) * sc\n", + " return observed.sum() / float(N)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The line `fc*t_a + (1-fc)*sc` contains the heart of the Privacy algorithm. Elements in this array are 1 *if and only if* i) the first toss is heads and the student cheated or ii) the first toss is tails, and the second is heads, and are 0 else. Finally, the last line sums this vector and divides by `float(N)`, produces a proportion. " + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.58999999999999997" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "observed_proportion.value" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next we need a dataset. After performing our coin-flipped interviews the researchers received 35 \"Yes\" responses. To put this into a relative perspective, if there truly were no cheaters, we should expect to see on average 1/4 of all responses being a \"Yes\" (half chance of having first coin land Tails, and another half chance of having second coin land Heads), so about 25 responses in a cheat-free world. On the other hand, if *all students cheated*, we should expect to see approximately 3/4 of all responses be \"Yes\". \n", + "\n", + "The researchers observe a Binomial random variable, with `N = 100` and `p = observed_proportion` with `value = 35`: " + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "X = 35\n", + "\n", + "observations = pm.Binomial(\"obs\", N, observed_proportion, observed=True,\n", + " value=X)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Below we add all the variables of interest to a `Model` container and run our black-box algorithm over the model. " + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " [-----------------100%-----------------] 40000 of 40000 complete in 10.4 sec" + ] + } + ], + "source": [ + "model = pm.Model([p, true_answers, first_coin_flips,\n", + " second_coin_flips, observed_proportion, observations])\n", + "\n", + "# To be explained in Chapter 3!\n", + "mcmc = pm.MCMC(model)\n", + "mcmc.sample(40000, 15000)" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "figsize(12.5, 3)\n", + "p_trace = mcmc.trace(\"freq_cheating\")[:]\n", + "plt.hist(p_trace, histtype=\"stepfilled\", density=True, alpha=0.85, bins=30,\n", + " label=\"posterior distribution\", color=\"#348ABD\")\n", + "plt.vlines([.05, .35], [0, 0], [5, 5], alpha=0.3)\n", + "plt.xlim(0, 1)\n", + "plt.legend();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With regards to the above plot, we are still pretty uncertain about what the true frequency of cheaters might be, but we have narrowed it down to a range between 0.05 to 0.35 (marked by the solid lines). This is pretty good, as *a priori* we had no idea how many students might have cheated (hence the uniform distribution for our prior). On the other hand, it is also pretty bad since there is a .3 length window the true value most likely lives in. Have we even gained anything, or are we still too uncertain about the true frequency? \n", + "\n", + "I would argue, yes, we have discovered something. It is implausible, according to our posterior, that there are *no cheaters*, i.e. the posterior assigns low probability to $p=0$. Since we started with a uniform prior, treating all values of $p$ as equally plausible, but the data ruled out $p=0$ as a possibility, we can be confident that there were cheaters. \n", + "\n", + "This kind of algorithm can be used to gather private information from users and be *reasonably* confident that the data, though noisy, is truthful. \n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Alternative PyMC Model\n", + "\n", + "Given a value for $p$ (which from our god-like position we know), we can find the probability the student will answer yes: \n", + "\n", + "\\begin{align}\n", + "P(\\text{\"Yes\"}) &= P( \\text{Heads on first coin} )P( \\text{cheater} ) + P( \\text{Tails on first coin} )P( \\text{Heads on second coin} ) \\\\\\\\\n", + "& = \\frac{1}{2}p + \\frac{1}{2}\\frac{1}{2}\\\\\\\\\n", + "& = \\frac{p}{2} + \\frac{1}{4}\n", + "\\end{align}\n", + "\n", + "Thus, knowing $p$ we know the probability a student will respond \"Yes\". In PyMC, we can create a deterministic function to evaluate the probability of responding \"Yes\", given $p$:" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "p = pm.Uniform(\"freq_cheating\", 0, 1)\n", + "\n", + "\n", + "@pm.deterministic\n", + "def p_skewed(p=p):\n", + " return 0.5 * p + 0.25" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "I could have typed `p_skewed = 0.5*p + 0.25` instead for a one-liner, as the elementary operations of addition and scalar multiplication will implicitly create a `deterministic` variable, but I wanted to make the deterministic boilerplate explicit for clarity's sake. \n", + "\n", + "If we know the probability of respondents saying \"Yes\", which is `p_skewed`, and we have $N=100$ students, the number of \"Yes\" responses is a binomial random variable with parameters `N` and `p_skewed`.\n", + "\n", + "This is where we include our observed 35 \"Yes\" responses. In the declaration of the `pm.Binomial`, we include `value = 35` and `observed = True`." + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "yes_responses = pm.Binomial(\"number_cheaters\", 100, p_skewed,\n", + " value=35, observed=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Below we add all the variables of interest to a `Model` container and run our black-box algorithm over the model. " + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " [-----------------100%-----------------] 25000 of 25000 complete in 1.2 sec" + ] + } + ], + "source": [ + "model = pm.Model([yes_responses, p_skewed, p])\n", + "\n", + "# To Be Explained in Chapter 3!\n", + "mcmc = pm.MCMC(model)\n", + "mcmc.sample(25000, 2500)" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "figsize(12.5, 3)\n", + "p_trace = mcmc.trace(\"freq_cheating\")[:]\n", + "plt.hist(p_trace, histtype=\"stepfilled\", density=True, alpha=0.85, bins=30,\n", + " label=\"posterior distribution\", color=\"#348ABD\")\n", + "plt.vlines([.05, .35], [0, 0], [5, 5], alpha=0.2)\n", + "plt.xlim(0, 1)\n", + "plt.legend();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### More PyMC Tricks\n", + "\n", + "#### Protip: *Lighter* deterministic variables with `Lambda` class\n", + "\n", + "Sometimes writing a deterministic function using the `@pm.deterministic` decorator can seem like a chore, especially for a small function. I have already mentioned that elementary math operations *can* produce deterministic variables implicitly, but what about operations like indexing or slicing? Built-in `Lambda` functions can handle this with the elegance and simplicity required. For example, \n", + "\n", + " beta = pm.Normal(\"coefficients\", 0, size=(N, 1))\n", + " x = np.random.randn((N, 1))\n", + " linear_combination = pm.Lambda(lambda x=x, beta=beta: np.dot(x.T, beta))\n", + "\n", + "\n", + "#### Protip: Arrays of PyMC variables\n", + "There is no reason why we cannot store multiple heterogeneous PyMC variables in a Numpy array. Just remember to set the `dtype` of the array to `object` upon initialization. For example:\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "N = 10\n", + "x = np.empty(N, dtype=object)\n", + "for i in range(0, N):\n", + " x[i] = pm.Exponential('x_%i' % i, (i + 1) ** 2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The remainder of this chapter examines some practical examples of PyMC and PyMC modeling:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "##### Example: Challenger Space Shuttle Disaster \n", + "\n", + "On January 28, 1986, the twenty-fifth flight of the U.S. space shuttle program ended in disaster when one of the rocket boosters of the Shuttle Challenger exploded shortly after lift-off, killing all seven crew members. The presidential commission on the accident concluded that it was caused by the failure of an O-ring in a field joint on the rocket booster, and that this failure was due to a faulty design that made the O-ring unacceptably sensitive to a number of factors including outside temperature. Of the previous 24 flights, data were available on failures of O-rings on 23, (one was lost at sea), and these data were discussed on the evening preceding the Challenger launch, but unfortunately only the data corresponding to the 7 flights on which there was a damage incident were considered important and these were thought to show no obvious trend. The data are shown below (see [1]):\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Temp (F), O-Ring failure?\n", + "[[ 66. 0.]\n", + " [ 70. 1.]\n", + " [ 69. 0.]\n", + " [ 68. 0.]\n", + " [ 67. 0.]\n", + " [ 72. 0.]\n", + " [ 73. 0.]\n", + " [ 70. 0.]\n", + " [ 57. 1.]\n", + " [ 63. 1.]\n", + " [ 70. 1.]\n", + " [ 78. 0.]\n", + " [ 67. 0.]\n", + " [ 53. 1.]\n", + " [ 67. 0.]\n", + " [ 75. 0.]\n", + " [ 70. 0.]\n", + " [ 81. 0.]\n", + " [ 76. 0.]\n", + " [ 79. 0.]\n", + " [ 75. 1.]\n", + " [ 76. 0.]\n", + " [ 58. 1.]]\n" + ] + }, + { + "data": { + "image/png": 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LcCQwgtCbPgr4aNFFuoiIiIiIBLkvz+juj7j75939aHc/w90f7omA1KOeDvW2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\nct+odaF+gLvvCRwF/KeZHUQ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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "figsize(12.5, 3.5)\n", + "np.set_printoptions(precision=3, suppress=True)\n", + "challenger_data = np.genfromtxt(\"data/challenger_data.csv\", skip_header=1,\n", + " usecols=[1, 2], missing_values=\"NA\",\n", + " delimiter=\",\")\n", + "# drop the NA values\n", + "challenger_data = challenger_data[~np.isnan(challenger_data[:, 1])]\n", + "\n", + "# plot it, as a function of temperature (the first column)\n", + "print(\"Temp (F), O-Ring failure?\")\n", + "print(challenger_data)\n", + "\n", + "plt.scatter(challenger_data[:, 0], challenger_data[:, 1], s=75, color=\"k\",\n", + " alpha=0.5)\n", + "plt.yticks([0, 1])\n", + "plt.ylabel(\"Damage Incident?\")\n", + "plt.xlabel(\"Outside temperature (Fahrenheit)\")\n", + "plt.title(\"Defects of the Space Shuttle O-Rings vs temperature\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It looks clear that *the probability* of damage incidents occurring increases as the outside temperature decreases. We are interested in modeling the probability here because it does not look like there is a strict cutoff point between temperature and a damage incident occurring. The best we can do is ask \"At temperature $t$, what is the probability of a damage incident?\". The goal of this example is to answer that question.\n", + "\n", + "We need a function of temperature, call it $p(t)$, that is bounded between 0 and 1 (so as to model a probability) and changes from 1 to 0 as we increase temperature. There are actually many such functions, but the most popular choice is the *logistic function.*\n", + "\n", + "$$p(t) = \\frac{1}{ 1 + e^{ \\;\\beta t } } $$\n", + "\n", + "In this model, $\\beta$ is the variable we are uncertain about. Below is the function plotted for $\\beta = 1, 3, -5$." + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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6gVJqP65Tq/20rbby8vLYlV7gWWStaOoc+1tM+Ftd9wFWEwEW8/HTVjMB7nkCrWYCWt7b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\nMrIYlp7h9JLeovSSfKZPpuZSJzQ1VXt44/lN+9siLq+kOIP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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "figsize(12, 3)\n", + "\n", + "\n", + "def logistic(x, beta):\n", + " return 1.0 / (1.0 + np.exp(beta * x))\n", + "\n", + "x = np.linspace(-4, 4, 100)\n", + "plt.plot(x, logistic(x, 1), label=r\"$\\beta = 1$\")\n", + "plt.plot(x, logistic(x, 3), label=r\"$\\beta = 3$\")\n", + "plt.plot(x, logistic(x, -5), label=r\"$\\beta = -5$\")\n", + "plt.title(\"Logistic functon plotted for several value of $\\\\beta$ parameter\", fontsize=14)\n", + "plt.legend();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "But something is missing. In the plot of the logistic function, the probability changes only near zero, but in our data above the probability changes around 65 to 70. We need to add a *bias* term to our logistic function:\n", + "\n", + "$$p(t) = \\frac{1}{ 1 + e^{ \\;\\beta t + \\alpha } } $$\n", + "\n", + "Some plots are below, with differing $\\alpha$." + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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sZWth7Yn0AhgQYSI9JoT0WDMZMSFkxIYQH2rst9MOhRj1LezalIhgbVGWb3Mq\nObLFQkmdjQERQVw1LpGZQ2N6Uamit2jLEXY5HOgMgXvrEno9CbPPIGH2GZR/t4kD//wv1Vt2s+/+\nJzjy3BIy7ryB5Mt/gs7ov+dotdh59+WNTDkznfFTB/Xb+1Zfwel0sfKjPezYWADAaTMzmDZzKEKF\nRSj6CSpMogdxSc1JzqmwcKSykZyKRo5UWsivstBaR3J4kJ50t2M8NNZMZlwIKZHB/WbQXmewOVzk\nV1swm7SZQZrzfW41tVYHmXFmUqOUDfsKdQeOsPmaO8m851cMuPjcPtFzLKWk9Ku1HHz4hRPzFpvT\nUhjxwK0kzPbfVfxsVoeKD+4DHJ8LOj+nAoNBx5z5YxgxboCvZSkUXsEvwyT6mzPcFnani4JqK9nl\njRyuaCS7vIHs8kZqrC0XHgk26MiIDSEzTnOOM+PMDFIOcod8e7iSNUeqOFTeSGm9nSHRwQyNDeGS\n0QkMilLxb4FMxfdb2f/gM0iHg+F/+S2x0yf7WpJXkC4Xxz5aycF/vUTD4XwAEn96FlkP3UHwgJaD\nUBWK7mK12FnywgbKjtURGh7ERddOULNFKPoUfukMB3rMcE8ipaS8wU52eSOHyhs5WNbAwbIGSutb\nDtoLMeoYFmfGULibn55zFsMTzMSZ+2+IRUc02JwcrtDsempqBEmt9CSX1NnYt2UDM2b4b09cINHT\ncW9SSoo/XsX+h54leuo4sh66vU/MygBaGEjeq+9z8J8v4KxvQB9mpuaKmVzyt3v6RE+4P6DiMsHp\ncPH+4s3kHionJi6Uy248hfDIzncUKFt6F2VP76JihgMMIYR7mWkTU1N/HJFd1Wg/yTneX6o5yNuL\n6qg5XMnmlTkAxJgNjIgPZWRCKCMTQ8mMMxNkUD+coMV6j04KY3RSWJtpHl59hM0bDjO5JomsBDMj\nEkLJSggl1hw4g4L6E0IIki6YSdys0zj81GJfy/EqOoOBIb+4nMS5Z7L33oWUfLGG3BffYf22HEY/\neg/hWRm9rslud2JUcwj3GaSUfLlsF7mHyjGHmbjkukldcoQVir6CCpMIQCoa7OwvbWBfaT37SzUH\nud52coiFQSfIiA054RyPSgwlLtTkI8WBQa3V4bZrA3uL6zlY1sDrV45SfyoUPkNKSfFn37D33sew\nFpchDHrSfnMNQ++8AV1Q71zP5SV1LH11E1fffKqaYquPsHbFQb7/OhuDUc+Vv5xCUoqaEk/RN/HL\nMAnlDPf3TTKOAAAgAElEQVQMLik5Wm1lX2k9e4rr2VtST06FheY1mxRuYnRSGGMSQxmdFEZKZJAK\nrWgHKWWr9qmzOnhvZwmjk8LISghVC64oehx7TR0H//E8ea99AFISMXYE41/8G+bByT1ablV5A++8\n9AOnn5vJ6Ik9W5aid9i5uYAvlu5CCLjoZxPJGJHga0kKRY/hqTPcq11e27Zt683i+jxr1qwBQCcE\ng6KCOTczlt9NT+X5S7J4f8FYHp6bwYKJSUxOCcds1HGs1saKgxU8viafG9/byxVv7uLBFYf5YFcJ\nORWNbS613F84bs/jtPVHweGSSODtbcVc9dYubn5/H8+sK2BTQU0vqAwMmtuyt3HZ7Oz/2zM4aut9\nqsNbbNixjZEP38XUj54nJHUgNTv2se7c6zn26eoeK7Ohzsa7izZy6lnpfc4R9nX79BVHDpbx1Qe7\nAZh1wUivOML91ZY9hbKnb1Axw32UUJOeickRTEyOALS5kHMqGtl5rI5dxfXsOlZHZaODNUeqWXOk\nGoDIYANjB4QxbkAY4weEMyhK9Ry3RlSIkesnDwS0mUEOlTeys6iOIxWNTE6J8LE6BQBS4qht4Pu5\nNzJh0cOEDRvia0VeIfqUMUz76hV23fEPij/7hm033svgX1zG8D//Fp3Je/HtUkqWL93JiDFJjJua\n6rV8Fb6jpKiGj97aisslmTIjjfGqXhWKE6gwiX6KlJLCGis7jtWzo6iW7YV1lDVbbjo6xMD4geFM\nSg5nQnI48SrmuEusyq5kb0k9EwaGM3ZAmAqr6EUKlnzC/r89y6h//R9J55/tazleQ0pJ7kvvalPM\n2R1Ejs9i3AsPYU71zvywVeUNrPh4Dxf/bCJ6L6/0qOh9aqstvPnc99TVWBkxNomfXj5OLaih6Beo\nmGFFpzjuHG8vqtNehbVUNDpOSpMSGcTE5HAmJoczbkC4cuo8JL/KwtrcKrYerWNfaT2Do4KZMDCc\n84bFkhzZcpo3hXep3r6PrTfey4ALZjHsvpsR+r7Tbqu27GHbr/6EpeAYhshwxjxxH4lzZvhalsKP\nkC7JOy//QEFOJSlDorn0hlMwqEHBin6CihnuB3gztkgIQXJkMD8ZEccfzx7CkqtH89L8LH5zWgqn\npUZiNuooqLby0Z4yHvgqh/mv7+D3nxxgybZjHCxr6BPxxj0VqzUoKpgrxyXxyE+G8r9rxnDDKQNB\nQL295SIrfQV/inuLHDeCaV8swhgdDgE6T29b9oyaOJJpX71KwuzpOKpr2XrdPeQ8+xa92ckRiPhT\n++xptm3IoyCnEnOoiQuumeB1R7g/2bI3UPb0DSpmWNEqQghSo4NJjQ7molHxOFyS/aX1bDlay5aj\ntewtqWfXMe31yqYiokMMTEqJ4JSUcCYlRxARrJpWa5gMOsYPDGf8wLYXiHhjSxFDokOYmByOWfW+\newVTbBTpty7wtYwewRQdwYRXHyHnmTc58NCz7H/waSyFxYz46219qhdc0XmqKhr49osDAJxz4UjM\nKtRNoWgVFSah6BL1Nidbj9aysaCGjQU1lDVZKU8nYGRCKFNTI5maGsHgqGA1EM9DpJR8sLuUH/Jr\n2FtSz4h4M6cMimTqoAg1FZ6iQ4qWrWDHbX9D2uwk/vQsxj79F/QhKhSnPyJdkndf3kh+TgXDxyQx\n76rxvpakUPQ6Xo0ZFkLMAf6DFlbxspTykWbfRwBvAKmAHlgopXy1eT7KGe6bSCnJrbKwKV9zjHce\nq8fh+rFdJYaZODU1gqmpkYwdEIZJDcjxiEa7k62FtWzIqyG/ysLC8zOVM+xlXDa7V2dh8AfK125h\n6/X34KipI2rKWCa+9i9M0e3PclJT1cihPSVMnDa4l1Qqeppt6/NY8dEeQkJNXP+76ZjDVK+wov/h\ntZhhIYQOeBqYDYwCrhJCjGiW7BZgt5RyPHA2sFAI0eI5uYoZ9i7+ElskhGBIdAiXjk3kkZ9k8r9r\nx3D/rDTOy4whMthAcZ2ND/eUce/ybC5/Yyd//zqHVdmVLVbN8zX+Ys/jhBj1TBscxR1npPLYvGGt\nOsK1Vgd1VkcrR/sWf7NlW2xe8H/kLV7maxkd0hl7xp4+kakfPU/wwASqftjBhgtuoiGvqM30Lpfk\ns3d3YLf5XzvqKQKlfXaV6soGvlm+H4BzLhjZo45wX7dlb6Ps6Rs8CeycAhyUUuYCCCHeBi4E9jVJ\nI4HjQZDhQLmUslN31rq6Oqqrq1XPVyeIjY2lsLDQ1zJaIKVkQnwkZ6QNxumSHChrYH1eNRvyqjlc\nYeGbw1V8c7gKg04wfmAYp6VGctrgSLVcdBfYcrSWx7/LY3i8mdMGRzFtcCQJqgfIY0Y9fBcbr7gd\ne2U16bct6DP3n/AR6Zz66Ytsuvr31O3NZsP5v2LSm48SMWZ4i7QbVh9G6ASnzEj3gVKFt5FS8sX7\nu7HbnAwbncTwMUm+lqRQ+D0dhkkIIeYDs6WUv3JvXwtMkVLe1iRNGPARMAIIA66QUn7ePK+2wiTK\ny8sBiImJ6TM/Rv0ZKSUVFRWA5rA3pajWyve51aw7Us2u4jqaRFMwMiGU6WlRzEiLUg5dJ2i0O9ly\ntJZ1udofjgERQfxqajJjksJ8LS0gsBSXsenKO4idMZkRf7kVEaAzTrSGvaaOrdffQ8XaLRgiwjjl\nf08SOe7HB3tV5Q28+dz3LLj1dMIjg32oVOEttv+Qz1fLdhNiNnLd7dMJDVMx44r+i9dihj10hucD\n06SUdwohMoCvgLFSyrqmef3617+WVVVVpKZqK99ERkYyZswY0tPTGThwYCdPUeHv7Nmzh4qKCqZP\nnw78+Pjn+PYXX3/DnuJ6quJGsLmghrIDWwGIyBjP8HgzCVUHGJsUxoWzz271eLXdctvpkoRnjGNg\nRBCHtm/0uZ5A2bZX1fDKBT8nKDGOBW8/j9Dr/Upfd7annTKF7b95gFUff4ohzMx1Hy8mYlQma9as\nYe2Kg5x51gxOPSvDb/Sq7a5v19da2bfBhd3mZOBwK6kZsX6lT22r7Z7ePv45Ly8PgMmTJ3PnnXd6\nxRk+FXhASjnHvX0PIJsOohNCfAL8U0q51r29ErhbSrmpaV4LFy6UN9xwQ4syCgsLlTPcB+lMvTba\nnfyQX8N3OVVsyK/B6nCd+G5obAhnZURzZlo0ieE9G/t2/MLqq3ywq4SJyeEMjg7p0XIC0ZbOBguF\nS5eTcu2FfveEqrv2dNkdbPvFvZR8sQZjTBRT3n+akIzBfPXhbs65cBRGY/+agi0Q22dHSCl575VN\n5B4qJ3NUIhdcPb5X2nFftKUvUfb0Lp72DHsSM7wRGCqEGAwUAVcCVzVLkwucA6wVQiQCw4DDnZOs\n6M+EGPWcmR7NmenRWBwuNuXX8N2RKtbnVXOovJFD5Y289EMhIxNCOSsjmhlpUcSY+9YsAD2N3eni\nWJ2NP36eTViQnhnp0ZyVHkWKejwOgN4czKCfXeRrGT2Czmhg/AsPseX6P1L29fdsvOw2pnzwDHMv\nHetraQovsW97EbmHygkOMXLOBSP97g+dQuHPdGZqtSf4cWq1h4UQN6H1EL8ghBgAvAoMcB/yTynl\nkub5tBUzrHqG+ybeqFebw8XGghpWH65kfW41VqfWXgUwdkAYZ2dEc0ZaFOFBnvyvUwC4pGRPcT3f\nHK7iu5xKxiSFcd+sNF/LUvQCTouVLQv+QPm3GwlKimPKB88Smpbia1mKbuJ0uFj0n++ormhk9iWj\nGTNZ1alCAV6eZ9hbKGe4f+Htem20O1mfpznGm/JrsLtH3xl1gqmpEcwcGsOUQRFqHuNO4HRJyhvs\nasBiP8LZYGHTNXdS+f1WgpMTmfL+M5gHq/tvILN1fR4rP9pDTHwo1912Ojp1D1QoAC/OM+xN1DzD\niu4QYtRzdkY0fz03nXeuGc2dM1KZMDAch0uy5kg1D67I4co3d/GfNXnsKKrD1ck/ek0D8PsLep1o\n0xFelV3JtzmV2JyuVr9vj75iS0thCUffbTExTq/jTXvqzcFMeuPfRE0Zi+VoMRsvvZXGgmNeyz8Q\n6CvtE8Bmc7B+VTYA08/N7HVHuC/Z0h9Q9vQN6u+jD9m1axf333+/r2UEJGFBBmYPi+WRnwzlzatG\n8aspA8mIDaHO5uSzfeXc9elBfv7OHhZvLqKoxupruQGJUS/4eE8ZV7+1iyfX5rO3pJ7efJLkD0in\nk4OPvEDBkk98LcUr1NVYqK5sxBBqZvKbC4mcMJLG/CI2X30n9upaX8tTdIGt63Kpr7WSmBxB5qhE\nX8tRKAISFSbRSXbs2EFubi4AR44c4dZbb+1SPs888wwbNmwgIiKCp59+2psS/QZf1GtORSNfZ1ey\n8lAFZfX2E/vHJoVx3rAYzkiLIqSfjZzvLsW1NlYeqmDFIW3u6KcuHE6oqf/YsO5QLhvn30rW324n\n6YKZvpbTLT57dwcR0SFMPzcTAHtVDRsu/DV1+3OIPWMyk95c2OeWp+7LWBrtvPjvb7BaHFx2w2QG\nD43ztSSFwq/wyzCJQGfnzp3U1NQwb9485s2bx4oVK7qc1y233MLcuXO9qE4BkBYTwo2nDOSNK0fx\nyNyhzBoaTZBesONYHY9+m8cVb+7i39/ksqOort/1cnaVxHATV09I4uVLs/jTzLR+5QgDhA0dzKS3\nFrLn3oVUrNvqazldpii/itzscqbM+HGwpDEqgomvP4opPoby7zax++5/q+sigPjh28NYLQ5SM2KV\nI6xQdINeHYK/bds2WusZDhT27dvHZZddBmjnkpWVBWg9xIsXL0YIceKH5PhnIQSTJ09Wjm8voxOC\nCcnhTEgO57fTnHybU8VXB8rZVVzPVwcr+OpgBSmRQcwZFss5mTHEmI1qfscOEEKQHtv6/MRFtVZq\nrU4yY0MQQvQ5W0aMymTssw+w7ab7Of3rxQTFx/Rq+d21p5SS1Z/tY/q5mZiazbxiTh3AxNf+xQ/z\nb+Hokk8wp6WQcduC7kr2a/pC+6yrsbBlnfaU8ozzMn2moy/Y0p9Q9vQNATMf1eLNRbyxteUgj2sn\nJLFg0oAO07eVzlMKCgoYNGgQe/bs4a233uLw4cM89thjAAwZMoQ///nPXc5b0bOEmvTMHR7L3OGx\nHK228uXBcr48UEFBtZWXNhayaFMhp6ZGklxTz2kuiV6n5ufsLEerrTyxJp9Qk47Zw2Ix25y+luR1\n4macwuS3H8cUF+1rKZ3mwK5i7DYnoyYmt/p91MSRjHvmAbbeeC8H//E85tSBDLjonF5WqegM36/K\nxmF3kTkqkQGDonwtR6EIaFTMsIcsW7aMefPmoddrj4gXLVpEZWUld955Z5fzXLJkCWvXrlUxwz7A\n6ZJsKqjh8/3lrM+rxj1LG3FmI3OGxzJneKyabqyTuKRke1Edy/eX80N+DaemRrBg0gAGhAf5Wlq/\nRkrJ4qfWMWPOMNKGxbebNuf5Jex/4Cl0QSZOee8pok8Z00sqFZ2hqryBRY9/h5SS6343ndiEMF9L\nUij8Em+uQKcArFbrCUcY4MCBA6SnpwMnh0k0RYVJ+C96nWBqaiRTUyOpaLDz1cEKPt9fTmGNlTe2\nHuOtbceYMiiC87PimJQcoXqLPUAnBBMGhjNhYDg1FgdfHazAqOzmc4QQXLxgIuEerDQ45KYracgp\nIP+1D9jy87s57bMXMA9RCzj4G2tXHMTlkoyelKwcYYXCC6iYYQ9Zv349V1xxBQDl5eVs3LiR++67\nD+hemIQarOJ7YsxGrhiXyMCaA4RPH8+n+8pYe6Sa9Xk1rM+rISHMyE+GxzF7eCyxagloj9ixaT3z\n24h7axpXr/CM7sYRRkS1HuvdHCEEWX+/g8a8IspWrWfTNXdx6icvYIqO6HLZ/kggx2WWFNWwd3sR\ner1g2qyhvpYT0Lb0R5Q9fYOaTcIDdu7cyZw5c3j33Xf5+OOPeemll3jttdcIDw/vcp4vvvgib7zx\nBmvXruWRRx6htlbN8elrhBCMHxjOfTPTePOqUdx4ykAGhJsoqbPz6uYirl2yi7+vzFEzUXSTXcX1\n/PqD/Xyyt4xGe2DHFtdn51Gxvm8tJqQzGBj/wt8IHzmUhuw8dvzmAaSr8wuvKHqGNV8eBGD8qake\n/8lRKBTto2KGPWDp0qXMnz/f1zICDn+vV09wScmWo7V8tq+Mdbk/xhanRQczb2Q8s4ZGq3mLO4lL\nSrYereWTvWXsOFbHzIxozs+KY3B04P2wl6/dwvab7mfqh88RmpHqazlepbHgGOvOux57RTVD77qR\noXfd6GtJ/Z5jR6t545nvMZr0/PKuMzGrcQ0KRbuoeYa9iE6nzNRf0QnB5JQI/nxOOq9fOYprJiQR\nHWIgp9LCk2vzueqtXTyzroD8KouvpQYMOiGYlBLBX85N57mLRxAWZODuzw/xXU6Vr6V1mtjTJ5L5\nx5vY8vM/4Kir97UcrxKSksS4Zx8AITi0cBGlX6/3taR+z6bvjgAwbsog5QgrFF6kV728bdsC83Hi\nxRdf7GsJil6gozXh40NN/HzSAN64chR/PHswoxJDabC7+HBPKTe+t5d7lx/ih/xqXCqEokNbHich\nTLPp61eMYmpqYMalDrrmAqJPHc+uOx/usfAZT+3ZlM1rj1Bb3b0/aXFnTWXo//0CpGTHLQ/QkFfU\nrfz8ha7Y09fUVDWyf9cxhE4wcdpgX8s5QSDa0p9R9vQNqstToegkRr2OszNieHzeMJ67eDhzh8di\n0gs2FdTypy8O84v39vLh7lIa+uBcuz2FUa/DpG95O3K4JNnlDT5Q1Dmy/nYH9dl55L/6vq+lAFBZ\nXs/6VdkEBXd/jHTG7T8n/pxp2Ctr2PaL+3BarF5QqOgsW9blIl2S4aOTVKywQuFlVMywosfoT/Va\nY3Hw+f5yPtpTSmm9HQCzUcfs4bFcNDKeARFqrt2ukFdp4Z7lhxgQHsTFo+M5LTXSb6e5q88poHL9\nNlKuOt/XUvjyg12Yw4KYfq53ViazV9Ww7tzracwvIuXaCxj96D1eyVfhGVaLg/8+shqb1cG1t5xG\nUnKkryUpFAGBihlWKHqRiGADV4xLZPEVo/jTrCGMTtJCKD7YVcr1/9vDgysOs+uYmoWis6RGB7P4\nilHMy4rj3e3FXP+/Pby/q4R6P+x1D01L8QtHuK7GwoFdxUw8zXuP0o1REYx/+R/ogkwUvPERBUs+\n8Vreio7ZuakAm9VBSlq0coQVih5AxQwrFG68Eaul1wlmpEXz2PnDeOai4ZyTGYNOCNYcqeb3nxzk\nto8OsCq7AoerbzvF3ox7M+gEZ2VE8+SFw/nj2UPYW1LP1qP9ayrCzthz89pcRo4f6PUBVpFjhzPy\nn3cBsOePj1Kzc79X8+9NAiku0+V0sWXdEQAmT0/zrZhWCCRbBgLKnr5B9QwrFD1EZpyZP5w5mNev\nHMXV4xOJCNKzv7SBf67KZcE7u3l3RzF1VoevZQYUWQmh3DczjelpUb6W4pc4nS727yxi0vQhPZJ/\nytXnk3LNPFwWG1tvvA97df/6U+ILDuwupqbKQnScmYzh7S+nrVAouoaKGVb0GKpeT8bicLHiYAUf\n7Cohv1obhGQ26pg7PJaLRyeQoKZK6hb1NidbC2v9Kq7Y5XCgM/TuqvcOuxNDD8597bRY2XDBzdTs\n2E/ShbMY9/yDajXBHkJKyZvPredYQTXnXDiS8VP71lzWCkVPo2KGFQo/I9ig4/ysOF68NIuHZqcz\nfmAYDXYXS3eV8vN3dvPwqiMBMXOCv1LRYOed7cXc+N5ePtpTisXh21XTrKUVrD17Abayyl4ttycd\nYQB9cBDjnn8QvTmEYx+upPDdz3u0vP7M0SOVHCuoJsRsZNSEZF/LUSj6LCpmWKFw01uxWjohmDIo\nkn/9JJOnLxrO2RnRSODr7Ep+/cF+7vn8EJsKagJ6sJ0v4t4GRQXz5AXDuGtGKluO1vKzt3ezeHMR\n1RbfhKIExceQMHs6O257qNt16W9xhKHpg8j6x+8B2HPvY9TnFPhYUefwN3u2xaY1RwAYNzUVo8k/\nV7oMFFsGCsqevkH1DCsUPmRYnJk/nj2EVy8fycWj4wk26NhytJZ7l2dzy7L9rM6uxNnHB9t5EyEE\no5PCeODcdB47P5PyBrtPVwfMvPtX2CuqyFu01GcaeorkK35C0oWzcNY3sOPXf8FlV/Hv3qSyrJ5D\n+0rQ6wUTTlXhEQpFT6Jihn3E8uXLqa2tJScnh9jYWG688UZfS/I6/bFeu0ut1cEne8tYtruUykbN\nuRgQbuLSMQmcNyyWIIP6/xpo1GfnsX7eTUz94FnChvvfbADdwV5dy9qZC7AcLSb9tgUMu/dmX0vq\nM6z4cA/bNuQxelIyc+aP8bUchSIg8TRmWDnDnWTHjh3k5uYCcOTIEW699dZO51FTU8OIESPIycnB\nZDIxdOhQVq9ezaBBg7wt16cEUr36GzaHiy8PVvDezmIKa2wARAUbuGhUPPNGxhEe1LuDsvoa5Q12\n8qosjB8Q1iuDv/Lf/IiCNz/m1E9f6JHy1q/KZviYJKLjQr2ed0dUbtjOhotvASk55X9PEjt9Uq9r\n6Gs0Ntj47yOrcdhdXPe704lLDPe1JIUiIPHqADohxBwhxD4hxAEhxN1tpDlLCLFVCLFLCLGqtTSB\nHjO8c+dOampqmDdvHvPmzWPFihVdyiciIoKVK1cSFBSEEAKn0xnQ8aF9BX+K1TK5B9u9fOlI/jRz\nCJlxIVRZHLy6uYifvb2bl344SkWD3dcy28SfbNkaJXU2nlqbz20fHWDtkSpcPXz9pVw9j3HP/bXL\njnB79qyrsbDxuxyvzyvsKdFTx5Fxx3UgJTtufRBbRbVPdHQGf2+f2zfk47C7GDIszu8dYX+3ZaCh\n7OkbOuxeEkLogKeBWUAhsFEI8aGUcl+TNJHAM8B5UsqjQoi4nhLsS/bt28dll10GaI59VlYWoPUQ\nL168GCHECaf2+GchBJMnT2bu3Lkn5XX82O+//55p06aRmqpiwhQt0esEM9KjOSMtim2Fdby9vZit\nhbW8u6OED3aXMntYLJeNTWBAuFruuTNkJYTy4vws1uVW89a2Y7y6qYjLxyVwdkYMhh6Ylk0IgXlw\nzzwl2bGxgBFjBxAUbOyR/D0h447rKP92I1Ubd7L7/x5h/Et/V9OtdRGn08XW9XkATD59iG/FKBT9\nhA7DJIQQpwJ/kVLOdW/fA0gp5SNN0vwaGCCl/HN7eXUnTOLgv18ie+GiFvsz7ryBzP/7RYfp20rn\nKQUFBRQUFBAREcFbb73F4cOHeeyxx0hKSupynkuXLuWTTz7h/vvvJz09vcv5+CsqTKJn2F9az9vb\nilmbq/XA6QScnRHNFeMSGRId4mN1gYeUki1Ha3l3RzG3TBtEalSwryV5jNPp4sV/f8P86yYTn+Tb\nHsSGvCLWzVqAo7aeUY/ezaBrL/SpnkBl/85jfLxkGzHxoVx/+3T1p0Kh6AZeixkWQswHZkspf+Xe\nvhaYIqW8rUmaxwEjMAoIA56UUr7ePK9AjhletmwZ8+bNQ6/XprdZtGgRlZWV3Hnnnd3Kt7a2lrPO\nOotly5apmGFFp8itbOSdHSV8faiC4xNOTBscydXjkxgWb/atOEWvsH/nMbZ+n8uVv5rqaykAFH7w\nJTt+/QD6kGCmrXyN0PS+dU/rDd59eSN52eXMPH8EE6cN8bUchSKg8dQZ9tYoHAMwEZgJhALfCyG+\nl1IeaproiSeeIDQ09ERIQGRkJGPGjAmIXlGr1XrCEQY4cODACd1NwySa0laYxFdffcXChQtZvnw5\n4eHhxMfH8+GHH/Lb3/62d06ml6iurubw4cNMnz4d+DEWyl+3n3vuOcaMGeM3ejrazt+9mWl6WHD5\nKby3s4S3P13J8mzJutzxTE4JJ8uWQ1pMiE/0NY178xd7dXU7a8JUjHrBjk3rvZr/yvc/JCghtlv2\nXLviIBdeMsd/7BVvZsD88yha+iVvXncbWQ/dwRkzZviPvg7s6evtmmoLedkODEYdVY1HWLOmwK/0\ntbbd3Ka+1hPo28qe3bffmjVryMtzhxpNnsysWbPoCE/DJB6QUs5xb7cWJnE3ECyl/Kt7+yXgcynl\nSZNrLly4UN5www0tygiEHsQ77riDxx9/HIDy8nIuv/xyli1bRnh45x9Nrlixgg0bNnDfffchpWTs\n2LE88cQTzJw509uyfUog1GtT1qxZc+LCCkQqGuws3VnCx3vLTqy+NjYpjKvGJzIxObxXH7cGui2b\n8vn+cl764ShzhsUyf0wCMebux+Y2Hi1m3bnXc9rnL2Ie3PHKYm3Z0+lwIQTo9P4z5Z69qoY1Z/8M\na1Epw+77Nem3/szXklrgr+1z1Wf72LzmSEBNp+avtgxUlD29izfDJPTAfrQBdEXAD8BVUsq9TdKM\nAJ4C5gBBwAbgCinlnqZ5BWqYxM6dOyksLKS6upqQkBD27NnDNddcQ0pKSpfzXLRoEQ6Hg/z8fDIy\nMrjuuuu8J9hP8Pd67avUWBws213Kst2l1NmcAAyPN3PNhCSmDopQMYhdoKTOxv92lPB1dgWzhsZw\n+dgE4kK7N3tDzvNLKFn+LVPefwah8x9n1huUrlrP5qt+jzAZmfbFIsKzMnwtye+x25389+HVWBrt\nXPub00hKifS1JIUi4PHqPMNCiDnAE2hTsb0spXxYCHETWg/xC+40dwHXA07gRSnlU83zCVRneOnS\npcyfP9/XMgIOf6/Xvk69zcnHe0tZurP0xJLEQ2NDuHp8EtOGRKJTTnGnqWiw897OElZnV/LyZVmE\nGLu+RK50Ovnhkt+S+JMzGXLTlV5U6R/s/sO/yV/8AeGjMjnt85fQmXw320UgsGvLUZa/t5PE5Ah+\ndss0X8tRKPoEXp1nWEq5XEo5XEqZKaV82L3vv8cdYff2o1LKUVLKsa05whC48wzr+livjaJ1msYc\n9QVCTXquHJfE4itGcvOpycSYDRwqb+TBlTnc/P6+Hl3qua/Z8jgxZiO/mprMq5eP7JYjDCD0esY8\ncV3/9A4AACAASURBVB/ZTyym7uCRdtMGoj2H/+UWQgYPpHb3QbIff8XXck7CH+25fYMW4zh+amBN\ns+mPtgxklD19g/LyPODiiy/2tQSFosuEGPVcMjqBxZeP4rfTUogLNXKk0sI/Vh3hl0v3suJgRY85\nxX0VUxvLYnfWjuYhKWT+341kP+ZfzqI3MISaGfPEn0AIDj/5OlVb9nR8UD+lpLCGovxqgoINDB/b\n9ek6FQpF11DLMSt6DFWv/onN6eKrgxW8va2Y4jptqeeBEUFcPT6RWUNj0PfAohP9hYdW5qATcPWE\nJI/nfJYuFy6rHX2IZwunFOVXUVNlYfiYwHCa9v31aY489xahQ1OZ9tVrHp9nf+LLD3axY2MBE08b\nzMx5Wb6Wo1D0GbwaJqFQKPoOJr2On46I45XLR3LXjFQGRpgorLHy6Ld53PC/PXy+vxyH6inuEr8/\nI5WhsWbu/uwQf1uZw+Hyxg6PETpdpxzEH77NoaHe1h2ZvUrm3b8kbHga9YfyOPCP53wtx++wWhzs\n3V4EwNgpal5mhcIX9KozHKgxw4r+QX+L1TLoBOcNi+XlS0fyhzMHkxIZRFGtjce/y+P6d/fw6b4y\n7E5Xl/Lub7Y8jtmk5/Jxibx6+UiyEkK594tDPPZtXrfzPW7P2moL+YcrGDUhcJ646IODGPPk/QiD\nntwX36V8zWZfS/Kr9rlnWyF2m5OUtGjiEsN8LafT+JMt+wLKnr5B9QwrFP0cvU5wTmYML87P4p6z\nBjMoMojiOhtPrMnn+v/t4ZO9Zdi66BT3V0KMei4dk8Brl4/ip1mxXst356YCho9NwhRk8FqevUHk\nuBFk3H4dADtv/zuOunrfCvITpJRs/yEwB84pFH0J/QMPPNBrhTU2Nj4wYMCAFvtra2u7tHiFwr8J\ntHo9vjJif0UnBGkxIZyfFcfgqGDyqiwU1tjYkF/DVwcrMOm17z2JKe7vtjyOQSc6PR+xo74RS2Ex\nxqiIE/tSU1NxuSSfv7eTs+aOIDQ88OJuo04ZS+nKddQfzMVeVUfCuaf7TIu/tM/CvCp++CYHc6iJ\n8y4ejS4A4/X9xZZ9BWVP71JUVER6evpfO0qneoYVCsVJ6HWCszKi+e/8Efxp5hAGRwdTWm/nqXUF\nXPfuHj7aU6p6iruJS0qeWVfA/tKWPaRlq9azZcHdOC3Wk/bnZZcTGh5EwsCIFscEAjqjgTFP/Alh\nNJC/+APKv9vka0k+Z5t7OrUxk1PQtzFDiUKh6HlUzLBC4UbFap2MTghmpEfz30tG8KdZQ0iLDqas\n3s7T6wq47h23U+xo3SlWtmwfKWFQVBB/XZHDn77IZl/Jj05x4k/PIjRzMIceffnEvjVr1jB4aCyX\nLJjkC7leIzwrg6F33gDAzjv+4bNwCX9onw31Ng7sPAYCxk7p+mqmvsYfbNmXUPb0DeqvqEKhaBed\nEMxIi+a5S0Zw/6w00mOC/5+9Mw+Pqjob+O/OPlkm+wIhhCwEAiQEDJssgiiLGiil1A1bxc/1U7vo\np221aittta20brV1wbqAigtSl0oVKcpqWEISQggQyJ6QfZJJZr/fH5PEAAGyTGYy4fye5z4z595z\nz3nve++dee973/MealvbjeIN5zeKBd2jVEgsGRfBP384jmmxBn67xWUUH61tRZIkxj35AOXvfkbj\n/kOd+0iShF9A/6aAHgzE37MSQ9pYzGVVHPntC94Wx2vk7SvH4ZCJT44gKMTP2+IIBBc1Is+wYMAQ\n53Vo4pRldp5s4q0DlRTVmwEI91NzXXoUi5LDzjkhheDcWB1ONh+pw6BTcVlCCACVH33Jsadf5dIv\n/olS53sxwuej+fBxdi5chWy1kbHhGcLnTPG2SB5Fdsq8uuYbGutbWXbTZBJTIr0tkkAwJBF5hocg\nO3fuxGw2Y7FY2LVrl7fFEVykKCSJWfHB/G3ZWB694nRP8Y835LPpkPAU9xaNUkHmuIhOQxggeul8\nApLjqf73Ni9KNjB0DZfI+9nvsTdfXNklSorqaKxvJTBIR/yYCG+LIxBc9IiYYR/i7rvvJiYmhokT\nJ9LQ0OBtcYYcIlardygkiVmjTjeK61ptvLCrjMzV60T4RD+RJImJL/6GiCVX8PYnX3pbHLcT/783\nYpg4FnN5NQW/fd6jfXv7Xj/4bSngGjjnixkkuuJtXQ41hD69g28lqxwE5OTkUFxcDMDJkye59957\nPdb3z3/+c+bPn090dDRKpdJj/QoE56PDKL40LoidxU28tb+K7ON2nt9ZxjvZ1SJ8oh8oNGq27zjJ\nG1nl5KmOs3JyNCmR/t4Wyy0oVK7sEjsX3ELZm5uIvmYe4ZdN9bZYA46p2cKx/FNIConUDN8dOCcQ\nDCU8+u+Unp7uye7cTm5uLkajkczMTDIzM/nyS896a9RqNTExMcIQHiBmzZrlbRF8mu88xWP48x3L\nSAjVi4F2/cTY2EbOV8d5/xc3MH2kgSe2nODhz49z+NTQCCsIHJtA0gO3ApD38z94LFzCm/d63r4y\nnE6ZxLERBAbpvCaHuxC/m+5F6NM7CM9wLygoKGDFihWAK+QjJSUFcHmI33jjDSRJomNAYsd3SZLI\nyMhg8eLF/e5///79yLJMfX09iYmJbmlTIHA33XmKi+rbOj3F106MYvEY4SnuCTlZZaRMHIa/Xk3m\nuAgWjglj85E6Vm85wS/njWJCtO9N33sm8XffwKnPttGUfZiCx59lwtO/9LZIA4bslDmYVQbAxKmx\nXpZGIBB04NFsEk8//bS8atWqs9b3JOvAji+Psuur42etn3F5IjOvGH3B+ueq11PKysooKyvDYDCw\nfv16ioqKWLNmDdHR0X1us7fk5OSQlpYGwJw5c/jkk08wGAZvAn5fyyaxfft28VTuJrrq0inLpxnF\nAGF+aq4TRvF5cTqcvPSnbSy/OYMjxw4ya9YsmguK8IuLwaFRo1ZISJJvx5t20HLkBDuuvBnZauOS\n9WuIuHz6gPbnrXv9RGENH/xzH4YQPbfdPwfJx+OFQfxuuhuhT/fS02wSPuMZnnnF6F4Zs72tfyH2\n7t1LZmYmSqWS1atXs3btWtatW8f999/fr3afffZZzGbzaes6PMrXX389sbHfeQ8mTJjQ+T04OJjt\n27dz1VVX9at/gWCg6eop3lXcxJvtRvELu8p456DLU3yVMIrPouhIDYZgPRHRgRw51r7umdfRDYtk\nzKP/2+0+TllG4YMGcsCYeEY/eBuFq/9G3v1/YNZ/30Id5DtTufeUjoFzaVNGDAlDWCAYKnjUGPbl\nmGGLxXJarG5hYSEJCQnA6WESXelJmMR9993Xo/7fe+89vvjiC1566SUATCaTiB12M+Jp3H10p0uF\nJDFzVDAz2o3itw5Ucbyujb/tKuOdg1VcmxbFVWPD0QqjGHAZTmntr9I79JnyxE/ZcfmPiFw8h5Ap\nqWft82FeDfvKjKycHM34KN8KoYi/63qq/72Npn2HKHj0GVKfeWTA+vLGvd5iNHO8oAaFQiL1kqEz\ncE78broXoU/v4DOeYW+ze/durr32WgDq6urIysri4YcfBmDUqFE8+uijA9p/bGwsN998M+AyhOvq\n6pg9e/aA9ikQDAQdRvGlcUHsKnF5io/XtfHi7nLePVjNirQork4JR3eRG8VzFo0hOPT0mck04SGk\n/OF+cn+ymplfvo7S7/QBWEvHhaNXK3hyazExQVpumhTNeB+JK5aUSld2iSt+TPm7nxF19TwiF8z0\ntlhuI3dvGbJTZvSEKPwDh9YkKgKBryPyDPeA3NxcFi1axIYNG/j444955ZVXeP311wkM9NxrvOnT\np1NeXs6LL77I6tWreeWVV/DzE1N4uhOR39F99ESXkiRxaVwwf/veGB6/Mp6kMD31bXb+saecH797\niPdzqmmzOTwg7eAkIjoQtcb19qerPqOvnktQegqFv3/xrH3USgVXjw1n7YoULosP5qltxTz02VHM\nPpLFIyApjuRf3gnAoQeexNpgHJB+PH2vO50yOUN04Jz43XQvQp/eQXiGe0BhYSHLly/vLGdmZnpF\njo5MFgLBUKLDKJ4xMog9pUbe2l9FYW0rL31bwbs5p1iRGknmuHD0ahEW1EHK737OniV3Yq1tQBMe\nctZ2tVLB4rHhXJkcxr4yo0952eP+ZwXVn22jYc9BDj+yhokvPO5tkfrNicIampvMBIf6MTIhzNvi\nCASCM/BoNoktW7bIkydPPmv9YM86sHHjRpYtW+ZtMXyOwX5eBYMTWZbJKjPy5v4qjtS0AmDQKlme\nGsmScRH4a4RRDOC021GohqY/w3SijJ2X/whHm5lJa/9A1FWXeVukfvHhG/soKqhhzqJkps5J8LY4\nAsFFQ0+zSfiOu8CLCENYIPAckiQxNTaIZ5ck8/tFiYyL9MdocfDa3kpueucQb+6vpNli97aYXqe/\nhvCmQzXsLTPiSYdIT/GPH0HyI3cDcOjBP2Kta/SyRH3H2NjGiSM1KJQS4yfHeFscgUDQDSJmWCBo\nR8RquQ936FKSJDJGGPhL5mieWpxEanQALVYHb+6v4qZ3DvHa3gqM5qFlFDc1tFFb3XLW+oG4NkP8\nVPxjdzn3/auQ3SVNg84oHnnL9wm9dDLW2gbyf/m0W9v25L2eu7cMWYbR46LwDxh6A+fE76Z7Efr0\nDsIzLBAIBjWSJDEpJpCnrxnNn69OYtLwAFptTt7Oruamdw/xyrflNLTavC2mW/j26yIK86o80tec\n+BD+sXwsK1Ij+efeCu7+6AjbTw4eD6ykUDDhL79C6aen6l9bqPzoC2+L1GucDie5e9sHzk0bWgPn\nBIKhRI+MYUmSFkmSVCBJUqEkSQ+dp94USZJskiR9v7vtvpxnWDD0Efkd3cdA6TJtWCBPXTWav2SO\nJmNEIG02JxtyTnHTu4f4264yakzWAenXE1jMdo7kVJE25ewctBfSpyzL1G3f2+s+FZLEnIQQ/rZs\nLD+aPIyT7TMEDhb84oYz9jf3AnDooT9jrjjllnY9da8fP1JDi9FCaLg/sfGhHunT04jfTfci9Okd\nLmgMS5KkAJ4HFgLjgeslSRp7jnpPApvdLaRAIBB0ZXxUAL9flMRzS5OZEReE1SHz0aEabn43n2e2\nl1DZbPG2iL3m0IFyRiaGEWDQXbjyGTjbLBx64CmqP/+6T30rJIkZcUGsnDysT/sPJCNWLiXiypnY\nm5rJ/clqZKdvpIkDOLCzGIC0qbFDZupsgWAo0hPP8FTgqCzLxbIs24B3gKXd1LsXeB8456O7iBkW\nDGZErJb78JQux0T485srE/j7srFclhCM3SnzaUEdt2zI50/biilpNF+4kUGALMtk7y5h0vSR3W6/\nkD6VfjpSn3mE/Af/hKWm3u3yZZUasXopV7EkSUxY80s0YcHUfbOX4lfe63ebnrg+a6qaKSmqR61R\nkpoxdAfOid9N9yL06R16YgzHAKVdymXt6zqRJGk48D1Zll8ExOOvQCDwKAlheh6+PJ6Xf5DCFaNd\nr6O/OFrPbe8f5oktJzha2+plCc9PaVE9kiQxIv7snME9JWTaRGKuv5q8n/7OrYPhHE6ZTwpq+dGG\nQ7znpYlQtBGhjH/6FwAU/u5FmguKPC5Dbzmwy+UVHj85Bq1O7WVpBALB+XBXksq/Al1jibs1iI8d\nO8bdd9/NyJEu70dQUBCpqakkJIi8i0ORpqYmioqKOmOgOp54B2u5Y91gkceXy7NmzfJa/w9eNoub\nJkXzx3WfklVq5Bsm8s2JRoYZC5mfFMqPllzpdf2cWQ6LDCB0ZAs7duzolz6dM1JQ/fdbSl77kNLk\nKLfJ95srE9jw2Zds+W8BG3LGsnRcOJFNhfiplR7T19EABTXzJhKx9SA59/wGx8O3oFCrBuX12dZq\n5bNPvsBhl7lluvevL1EW5Yul3PG9pKQEgIyMDObPn8+FuOCkG5IkTQcel2V5UXv5F4Asy/JTXep0\nPKZLQDhgAm6XZflfXdvy1Uk3BH1DnFeBt6kz2fgg7xSfHK7tnJJ4QpQ/16VHMWWEYUjGcZqOl5D/\n8Boy1q9BUrg/YVBZk5l3D1YTqldzyxTP3t/2FhM75v+YtuIK4u9ZyZj2XMSDjW+/LuLrzwsZNTqc\nH9yS4W1xBIKLFndOupEFJEmSFCdJkga4DjjNyJVlOaF9iccVN3z3mYYwiJjhM8nLy+PXv/71Rdv/\nYKPrk6WgfwwWXYb5q7l9WgxvXTeelZOiCdQqyas28cjmIu7aWMDW4/U4nIMrv2539Eaf/okjmfLO\nXwfEEAYYEaTj/jlx3Jzh+cF2qgB/0p5/DBQKTrywjvrdfftPGcjr0+lwcmC3yys1+dK4AetnsDBY\n7vWhgtCnd1BdqIIsyw5Jku4B/oPLeH5VluXDkiTd4dosv3TmLgMg56AhJyeH4mJXLNjJkye59957\n+9TOCy+8wJ49ezAYDO4Uz2f6Fwg8iUGn4keXDOMHqZF8UlDLh3mnKKo384etxby2t5IVqZEsSA5D\nqxKp13vKubzqZU1mRgT1PiNGTwmZkkrCfTdR9NfXybnnt8za+iaqQP8B66+3HDt8iuZGMyFhfsSP\nDve2OAKBoAdcMEzCnfh6mERubi5NTU2dMSpLly5l06ZNfW7v7bffZseOHTz//PPuEnFQ9e8r51Vw\n8WF1ONlytJ4NOacoN7rSsAXrVCybEEFmSjgB2gv6CQTd0NBq4+6PjhAfquOHaVFMHBYwIKEoTpud\n3VffjjGngOE/vIq0Zx9xex995Z2X9lB2soHLr0m5KDzDAsFgpqdhEj7xi7/glQNuaec//zOpX/sX\nFBSwYsUKwBXykZKSArg8xG+88QaSJHWO4u74LkkSGRkZLF68uH/CX4DBIINA4CtolAoWjw1nQXIY\nO4obefdgNUdr23htbyXvHKzmqjFhLJsQSWSAZkDlqCprIipm6MQuh/ipef3acWw51sCzO0rx1yhZ\nkRbJzLhglAr3HaNCrSLthUfZueAWKjZ8RvhlUxi+fKHb2u8rpyqMlJ1sQKNVMn7y0E2nJhAMNTxq\nDGdnZ9OdZ9gXKCsrIzY2lvz8fNavX09RURFr1qwBYNSoUTz66KMD1ndVVRXr1q0jNTWVnTt3cuut\ntxISEkJrayuRkZEekeFioGsmCUH/8BVdKhUSc+JDmD0qmOyKFt45WM2BimY+yKvho0M1zE0MYUVq\nFAlherf3farSyEdv7ef2/7sMSXl+Q7E/+rQ3myh/9zNG3voDjxjdGqWCxWPCWJgcyq7iJjbkVNPQ\namfp+Ai39hMwehQpT/yUQw88xaEH/0RQegr+id3naT6Tgbo+97enU5sweQRanU/4mvqNr9zrvoLQ\np3fwibu1vx5dd7B3714yMzNRKpWsXr2atWvXsm7dOu6///4B7be1tZWVK1eyYcMGQkNDCQ8P55FH\nHmHFihUsXOh9T4hAMBSQJIlJMYFMignkWG0r7+WeYltRA1uOuZZLYgJZkRbJpOGBbjMoD+wqYeLU\nWBTKgY1TllQqSt/8CKWfnhE3XDOgfXVFIUnMHBXMpXFBDNQYxRE3LqFu+z6qPvqS7Nt/zfRPX0Kp\n0w5MZxeg1WTl8MFKkGDSjJ4Z5QKBYHDgUWM4PT3dk925FYvFglKp7CwXFhZ25kfuGqLQFXeEKGzc\nuJH09HRCQ10TCURERJCfn48sy6jV3yVyH0gZLhbE07j78GVdJoX78ct5o7glYxgb82r495E69pU3\ns6+8mcQwPcsnRHJZQjDqfhixLUYzRw9Vs+pns3tUvz/6VOq1TPzHE3z7/XsImpRCYEpin9vqC5Ik\n0Z3j2+GUqW6xMtzQd+NVkiQm/OkhjAcLaD50lILHnmX8U/93wf0G4vrMySrFYXeSMCaCkPDBM6Bv\noPHle30wIvTpHXzCMzwY2L17N9deey0AdXV1ZGVl8fDDDwP9C1E4cwBjUVER8fHxnUatzWY7bVIS\nk8mEQqEgMzPztP36KoMnB1AKBL5EdKCWu2aM4MZJ0XxyuJZN+TUcr2vjj9uKeTWrgqXjw7lqTDiG\nPrwO37ejmHHpw/Eb4JjkDgLHJjD2sXvIvu1hZnz+KqoA7xtr5UYL939ylPFR/qxIjWRclH+fvO6q\nQH/SX3qCXVffTunrGwm9dDLDll44yb47cTicZF9E6dQEgqGG8vHHH/dYZxs3bnx80qSzQx6am5sJ\nDAz0mBy9JTc3l6ioKPbv309RURGbN2/m0UcfJSKi7zFwL7/8Mhs2bODQoUM0NTUxceJEtFotixYt\nIikpifj4eAASEhLYtm0bFouFwsJCLBYLp06doqWlhaSkpNO8w+7o350M9vN6Jtu3b++cHVHQP4aS\nLrUqBanDAlg6LoJhBi2VRguVzVYOVLSwKb+WhlYbMQZdj41ic5uNz9/PZdEPUtHpe3b/ukOfhgmj\nac47StWn/yXq6rleH7QXpFORmRJOm83Jq1kVbD3egF6tZESwDkUvZdNGhaMOMlC7ZRd1274lesnl\nqIPPnTbS3ddnYV4VefvKCY3wZ+5VY72uW08ylO71wYDQp3uprKwkISHhNxeqJzzDPaCwsJDly5d3\nls/0yvaF2267jdtuu+2s9bt27WLHjh2dZYPB0OmB7mDu3LkD1r9AIOgejUrBwuQwFowOZV95Mx/m\nnWJvWTOb8mv5V34t00cG8b0JEaRfIJ2YUqng6mvTCApx/6C8C5Hyu59z8qV3kB0OJJX3f/71aiVL\nxkVw9dhwdpU0sTGvBqcsc3lSaK/bGnnL96nfsY/qT/9L9u2PMv3jv6PQesbzfqB94NykGXEXlSEs\nEAwVRJ7hHrBx40aWLVvmsb4WLVqEXu/5P0p3M9jPq0DQX07Ut/Fh3im+OtaArX2UWHyIju+Nj+Dy\npFAxiUcf6Bjn0BdsTc3svOJm2koribvth6Q88VM3S3c2FSWNrP/7bjRaFXf+Yi4akaNaIBg0uHM6\n5oseTxnCAAsWLBgShrBAcDEQH6rn/jlxvHX9eH50yTBC9SpONJj5y/ZSbnw7j9eyKqg1Wb0tpk/R\nnSFstjvZW2bEeQHnjTookIn/eAJJpaT45Q1U/3vbQInZyc6vjgGQPj1WGMICgY/iUWM4O7tv88hf\nTPj7e39gy8WKmBPefVxsugzRq1k5KZo3rxvPg5fFkRzuh9Hi4O2D1ax85xCrt5wgp7KlzwNWLzZ9\nnkmtycor31bwP+8fZtOhGlqtjnPWDZ48juRH7gYg977VtBSePKuOu/RZWdrIycJa1BolGbPi3dKm\nr3GxX5vuRujTOwjPsEAgELgJtVLBFaNDeW5pMn+5ZjRz4oMB+PpEIw98epQ7Pyzg04Ja2mznNuY8\njdNu97YIF2REkI4Xl43hZ7NHklvVwk3vHuKFnWVUNlu6rT/qjuuIzrwce7OJ/Tc/hK3ROCBy7frq\nOADp00fi5++Z+GSBQOB+RMywYMAQ51UggBqTlU8P1/LxoRqabU4A/DVKFiaHkpkSTkyQzmuyybLM\nnsw7SP7VXYRe6v3JjXpKjcnKJ4drmTQ8kPTh3WessZva2LPkTpoPHSV83jQueevPSF1yxfeXqrIm\n3vrbLlRqJbf/32UeS5MnEAh6jogZFggEgkFAhL+G2QEqrmk28dDcOMZF+mOyOvgwr4Zb3jvMQ58d\n45sTjdgHapq28yBJEkkP3kb2Hb+m9WSZx/vvKxH+Gm7JGH5OQxhA5a9n0mtPog4NpnbrHo6sftGt\nMuzqEissDGGBwLcRMcMCQTsiVst9CF2ezrfbipgxJ575SaH8dUkyz39vDAuTQ9EqJQ5UNPPElhOs\nfCeP1/dVcqrl7AF3A6nP8DlTSLp/Fftu+j9sxpYB68dTNLbZ+MPWk+RUNqOPjWbSK79DUik5+eJ6\nKt7/HOi/PqvLmzheUINKrWDKRRor3IG4192L0Kd3EJ5hgUAgGEDKTtRjaraSPCG6c11yuB/3z4lj\n/Q0TuGt6DLFBWupb7aw7UMWP3j3EY/8pYk9JEw4PeYtH3vx9wmZP4eAdv/aJGOLzoVUpSIn055nt\npfzP+4fZGhhD/GM/ASDv/idpOpDf7z46YoUnThuJf6B7JysSCASeR8QMCwYMcV4FAvjg9X0kpUQy\ncWrsOevIskxuVQsfH65lx8mmzpCJcD81C8eEsSg5jKjAgX0V77Tb2bfyAeJuWU7kwtkD2pcnkGWZ\nvGoTnxXUsrvEyO3ffIT08edoo8OZsXktuqjwPrV7qsLIG8/vRKVScNv/XSaMYYFgENPTmGGRFFEg\nEAgGiJqqZk5VGFl6Q/p560mSRNqwQNKGBdLQamPz0To+P1JPhdHCugNVrD9QxSUjAlk0JowZI4NQ\nK93/Uk+hUnHJm39GoR4afwuSJJEaHUBqdABGsx1zZiInaipp2H2Q7Ft/xdQPnu/TDHXfeYVjhSEs\nEAwRRMywl/j888957733+OMf/8irr77qbXF6zPvvv8/zzz/PqlWr+OCDD7wtjlsRsVruQ+jSRVhk\nAD+8dQoqdc+zGIT4qbluYjRrV6Twx6uSmJcYgunEQfaWNbN6y0luePsQf99dxon6NrfLO1QM4TMx\n6FREhviT/vLv0MVEsfPbPeT+9HfITieHqlt6HI5SU9nM0fxqVCoFU2Zf3LHCHYh73b0IfXqHofnL\nN4Dk5ORQXOyah/7kyZPce++9vW7DaDSyatUqTpw4gUajISkpiQULFhAbe+7XqIOBEydOUF9fzz33\n3ENdXR0ZGRlMmTKFkSNHels0gWBQolBIhEUG9G1fSSK9PXXYZDme1sgYPjtSR3GDmQ/zavgwr4bR\n4XoWJocxNyEEg078nF8IbUQok19/iuyrb6Jy4xcog4N4efpVVJtsXDk6jEXJoedNdbdrqyuDRNqU\nWAIM3kuJJxAI3ItHPcPp6ed/VTjYyc3NxWg0kpmZSWZmJl9++WWf2jEYDGzZsgWtVoskSTgcjj7P\nTOVJCgoKeO655wAICwsjISGBAwcOeFkq9zFr1ixvizBkELp0Lwsvv4xlEyJ56ftjeXZJMteM/YeM\nSAAAIABJREFUDcdfo+RobRvP7yzj+vV5/G7LCbJKjR4bdOerGCYkc9O6F5A0aspee5/7TuzmycVJ\nOJwyP/v4KD//uJBtRQ1n7VdT1UxhXjVKlYKplwmvcAfiXncvQp/ewSdcCZ9HX+qWdhZV7ezX/gUF\nBaxYsQJwhXykpKQALg/xG2+8gSRJnUZtx3dJksjIyGDx4sWntdWx765du7j00kv77V3tiwy95cor\nr+Tdd9/tLFdVVZGQkNCvNgUCQc+RJImxkf6MjfTnjukx7CxuZHNhPQfKm9l2opFtJxoJ0auYlxjC\nFUmhJIbpkaQLjh05J5aaevJ++jvS/vY46qBz5/T1NcJmZTDxhcfIvv3XHH3yJcaHh3D7yqXckjGM\nb0uNtLVPjtKVjljhtIwRwissEAwxPGoMZ2dn0102CV+grKyM2NhY8vPzWb9+PUVFRaxZswaAUaNG\n8eijj/a6zQ8++IBPPvmE1atXn7deVVUV69atIzU1lZ07d3LrrbcSEhJCa2srkZGR/ZKhN6hUKsaN\nGwfA5s2bmTRpEqmpqQPapyfZvn27eCp3E0KX7qU7fWpVCuYlhjIvMZRTLVa+PFrPl8fqKWuydIZR\nxIXouCIplHmJIUT2YWIITXgIfgmx7Fv5ABnv/BWVv95dh+RVtm/fzqzMyxn3ZBP5D/2JQw/+CXVI\nENFXz2XmqOCz6peeqKcwrwqlUsHUy4QDoCviXncvQp/ewSc8w/316LqDvXv3kpmZiVKpZPXq1axd\nu5Z169Zx//3397nN5cuXs2DBAubOnctHH33Ubcxwa2srK1euZMOGDYSGhhIeHs4jjzzCihUrWLhw\nYX8OqZNnn30Ws9l82roOj/L1119/llxGo5G3336bv//9727pXyAYSuz+73GGjQgmLinMY31GBmi4\nYVI016dHcaSmlS3H6vlvUSPFDWZezapgbVYFacMCmJsYwuxRwT2OL5YkibG/uY+8n/+BA7f8gslv\n/BGlbuhkUBj542VY6xo59seXOXjXY6jf/gthM0932DgcTr7c5MpNbIoN5v4vTzAvMYR5iSFEi2wS\nAsGQwKPGsC/HDFssFpRd5rUvLCzsDBHoGqLQlXOFKHzxxRc8/fTTfP755wQGBhIREcGmTZu45557\nzup348aNpKenExoaCkBERAT5+fnIsoxare6s11sZunLffff1ShfPPfcczzzzDAEBAZSWlg76gX89\nRTyNu4+LVZeN9a3s/eYkN/9kplvb7ak+Tw+jGEFWqZEtx+rZVdLEwcoWDla28PyOUjJGGJibGMKl\ncUHoL5DpQlIomPD0L8i+41EO3vUY6S+vRqHyCT/KOemqz8Sf3Yy1pp6S1z5g/48fZNrGFzCkjunc\nvn9nMXWnWggO9eNHN0+msN7MV8cauHdTISOCtFyeGMLiseGoFH0PR/FlLtZ7faAQ+vQOPfpFkyRp\nEfBXXAPuXpVl+akztt8APNRebAbukmU5152Cepvdu3dz7bXXAlBXV0dWVhYPP/ww0PsQBUmSmD3b\nldRelmXKy8sZP348AEVFRcTHx3catTab7bS4XJPJhEKhIDMz87Q2PREmAfDyyy9z9dVXY7FY2L9/\nP2azecgYwwJBf9n27yNcMnPUoIgpVSkkZsQFMSMuCJPVwY6TjWw93sCBimb2lBrZU2pEq5SYPjKI\nyxJCmBJrQKvqfky1pFQy8W+Ps//mX1C3LYuI+TM8fDQDhyRJpPzuZ1jrG6natIW91/+cqRtfIGD0\nKIyNbezc4sogcXlmChqNignRAUyIDuCuGTHsK29mX1kzyovTDhYIhgwXnIFOkiQFUAjMByqALOA6\nWZYLutSZDhyWZbmp3XB+XJbl6We29fTTT8urVq06q4/BPlNZbm4uFRUVNDU1odfryc/P58Ybb2TE\niBF9bnPt2rXY7XZKS0tJTEzk5ptvBmDatGk8+eSTzJs3D3CFJDz33HNMnToVu92OXq9n3bp1zJs3\nj2XLlqHXey6Gb/fu3VxzzTXAdx7nnJycc567wX5ez0TEarmPi1GXpSfq+WxDDqt+Pht1L/IK9wR3\n6rOhzcY3Jxr56lgD+adMnet1KgXTRhqYE+8yjHXdGMay04mk8GgSogGhO306rTb23fQAdduy0IQF\nk7HhGbZmt3D0UDWjx0ex9MZJveqj2WJHIUn4a9x7LQw2LsZ7fSAR+nQv7pyBbipwVJblYgBJkt4B\nlgKdxrAsy7u71N8NxPRO3MFNYWEhy5cv7yyf6ZXtC909FIAru8SOHTs6ywaDodMD3cHcuXP73X9f\nmD59OrW1tV7pWyAYzMhOmf9+VsCchcluN4TdTYhezZJxESwZF0F1s5VtRQ18faKRwtpWthU1sq2o\n0WUYxxqYnRDMlBGGzlCKoWAInwuFRs3k157iwK2/pHbrHr68688UzVqGWqNk3tVje93evrJm/rq9\nhInDA5k9KpjpIw0EaH07vEQgGKr05M6MAUq7lMtwGcjn4n+Af3e3wVdjhhUe/APYtGkTixYt8lh/\ngu8QT+Pu42LTZVurjegRQYydOGxA2h8ofUYFavjhxCh+ODGKymYL35xo5JsTjRypae1M1aZRSlwS\nY2DmqCCmjwwaEpN7nEufSj8dk//5FPvufJwj/hMASE/SYwju/Ru4uYkhZIwIZGdxE1+faOD5naWM\ni/Ln5ozhJIf79Uv+wcTFdq8PNEKf3sGtv2qSJM0DbgG6PZvvv/8+r7zySmdO3aCgIFJTUwd9rtpl\ny5Z5rK8FCxZ4NPRhIGlqaqKoqKjz5u6YZlKURXmolf0CNOjDGtixY8egkKcv5eMHsxgOPLd0FlXN\nFl758D/kVrXQEDaWXSVNbN66DYUEM2fOYuaoYJQVeYTo1UwaNhJdTBS79mYNquPpT9ly/Y85tu5T\n1JWHMb+zk9oRv6dAZetTewtmzWJBchhfbv2agpoK/NWxXj8+URbloVru+F5SUgJARkYG8+fP50L0\nJGZ4Oq4Y4EXt5V8AcjeD6NKAD4BFsiwf764tX40ZFvQNXzuvIlbLfQhduhdv6rPOZGNncSM7ips4\nWNGMo8tfRlKYnoTCPBKP5HH1Mw+gCezb1NOe5nz6bKgz8c9nduCwO5lqLaT1rXeQNGrSX3qCqEVz\nBkymD3JPkTosgNH9nCjF04h73b0IfboXd8YMZwFJkiTFAZXAdcD1XStIkjQSlyF807kMYYFAIBD4\nHmH+ajLHRZA5LoJmi509JUZ2FjeSVdbMsbo2joUlwqWJvPnPA8xMGcbM5AgmDQ88Z2aKwYwsy2z5\n12EcdifjJw9n9vKFFOidFL+8gexbHyb1+V8zfNkCt/drczipa7Xxh69OYnE4mTHSFZIycVgAGh/U\no0Dga1zQMwydqdWe4bvUak9KknQHLg/xS5IkvQx8HygGJMAmy/JZccVbtmyRu5uBztc8iIKeIc6r\nQDB0sdqdZFc2s7vEyO6SJmpNts5tGqVE2rAApowwMDXWQEyQ91PN9YQjuVV8/HY2Wp2KVT+fjX+A\nFlmWOfrkPyh65g0ARj90Gwk/vXlAvLeyLFPaZGFXcRN7SppwyvDXJclu70cguFjoqWe4R8awuxDG\n8MWFOK+CoYrT4WTX1uNMmROPRuPWoRc+iSzLFNW38dn729lbZ6My8vT7frhBy5QRBqbEBpI2LLDb\ntG3eprG+lbde2IW5zcYVS8aRPn3kadtP/G09R554AWSZ6MzLmfDXhwd8emq7U+52Mg+T1YFGKaFW\nDj49CgSDiZ4awx69k7Kzsz3ZnUDQK7oG4Av6x1DX5a6tx6koaUSt8kwatcGuT0mSSAzz4947FvDC\nDyfw7g0T+L/LRjI3IZhArZIKo4VN+TU8srmI5W/m8NBnR3n3YDVHa1txetAh08GZ+rRZHWx66wDm\nNhuJKZFMnHr2RELxd9/A5Df+iCrQn6qPv2LP0jtpK6saUDnPNavdl0frWfFWLo/9p4h/5ddQ3mQZ\nUDnOx2C/Nn0NoU/vIFwaAoFA0AvKTtSTk1XGTf87A+kinYL3fAQkxQFw5egwrhwdhsMpU1BjIqvU\nSFaZkWO1bRyoaOFARQuvZkGQTsWk4QFMjjEwaXggUYEaj8oryzL/2ZhHTVUzIWF+XLUi9ZznNfLK\nmUz/9GX2//hBmvOOsmvhKiat/QMh0yZ6VOal4yO4LCGYAxXN7C1rZn12FVqlgp/OHsmk4YEelUUg\nGAqIMAnBgCHOq2CoYW6z8fpzO7hiyTgSx0Z6WxyfpMls50B5M/vLm9lfYeRUi+207dGBGtKHBZI+\nPICJwwMJ81MPqDz7d57kq08KUGuU3HjXdMKjLmxM2hqNZN/xa+q2ZSGpVYz7w/3Erlw6oHKeD1mW\nOdlgJlinIqQbfTmcMkrx4Ca4CHFnNgmBQCC46OnwICalRApDuJfU7zqAQqslePI4gnQq5iaGMDcx\nBFmWKWuyuAzj8mZyqlqoarbyeXMdnxfWARAbpGXi8EDSogNIjQ4gzN99xnHpiXq2fnYEgEXLU3tk\nCAOogw1csu5pjjzxAsX/eJdDDzxF08ECxj5+34DHEXeHJEnEh56739s/OEyIXk3asADShgWQEunv\nk9k+BIKBQvn44497rLONGzc+PmnS2fO7Nzc3ExgoXu0MNXztvG7fvr1zQhhB/xiKupSdMk2NZmbM\nS0Th4YFLvq7P5sNFHLzj16iCAghK+25qY0mSCNKpGBvpz7zEEFakRjIjLojhBi0KCepb7dS32Sms\nbeWbk418kHeKLccaOF7XSovFgZ9aSYBG2evMDtu3byckKJL3Xs3CZnUwZU48l8wc1as2JIWCiHnT\n0cVEUbN1N8YD+VR9spWg9BR0wwfXw9KVo0OJCFBTabSwubCOl/ZUsL+8mflJoSj6mRXD16/NwYbQ\np3uprKwkISHhNxeqJzzDPsTOnTuZPHkykiSxf/9+ZsyY4W2RBIKLBoVSwbTLBvdsmYOVyAUzmbrp\nRbJX/Yra/37LuD/cjzYi9Kx6SoVEcrgfyeF+/DAtCrtT5kiNiYMVLeRVt3Co2kSF0UKF0cLmwnoA\nwvzUjI/yZ1yUP+Oj/EkM8zvnwLMOHA4n/1p/gFaTlZGJYcy+cnSfj23E9ddgSE0m557f0lJQxO7M\nO0n8yY9I/PkqFOrB8Rfrp1EyNTaIqbFBALTZHBTVt3UbOmF1ODFZHN2GWwgEQxURM+xDpKenU1pa\nSkREBGvWrOGqq67yaP+yLBMfH49CoaDjupk3bx5r167ttr44rwKBoCuONgvH/vwK5Rv+zYQ/P0Tk\nwtm9298pc7yujZyqFnKrWsiraqHZ4jitjlYpMSbCZRynRPozNtKPEP3pht0XHx3i4LelBAbruOnu\nS/EL6P+gPYfZwtGnXubk398GWcaQNoa05x4lYEx8v9v2JMdqW3nws2ME6VSdDxkpkf6MDNaJuGOB\nzyHyDA8QOTk5FBcXA3Dy5Enuvfdej/X9xhtvMH/+fKKjo1EqPZPSqSvFxcVkZWUxdepUFAoFn376\nKXPnzmXMmDHd1vel8yoQCDxH04F8HBYrodPT+9WOU5Ypa7RwqLqF/FMmDlWbKOsmzVhUgIaxEX6M\nifRHW1LPkR0nUaoUXH/HNKJjgvolw5nU7zpA7n2raSutRKHVkPyrO4m77YdICt+J0XXKMsUNZvKq\nWjh8ysThU62kRPrx4NxR3hZNIOgVg3IAXXZ2Nt0Zw75Cbm4uRqORzMxMAJYuXepRY1itVhMTE+Ox\n/s5Eq9Vy9dVXo9fraWpqQq1Wn9MQ9kXEnPDuYyjosra6Gb2fBv9ArbdFGRL67ErQpHFuaUchSYwM\n0TEyRMfiseGAK1tFfrWJ/OoWCmpaOVLTSnWLlepmC+X7ykhoNFFcno9u1hzeLmpitNFGcrgfo0J1\naNwQCx46YxIzv3qDgseepWz9xxQ89iyVH33J2Cd+QkhGar/b9wSK9gF58aF6MsdFAK4JQLrj1Y2b\nCUpMJznCn9HhevRqzztqhhJD7V73FQZHQNMF+POvPndLOw/8flG/9i8oKGDFihWAy7BPSUkBXB7i\nN954A0mSOsMHOr5LkkRGRgaLFy/un/C4nnBkWaa+vp7ExES3tNkb2aOjozv3e+2117jrrrv63b9A\nMBipO9XCe2v3suB740lMGVyDoQTnJ0inYkZcEDPiXB5fh1OmuKGNrZ8cpqHRhAycCPHDKik5eKSO\nfx9xZa1QKSTiQ3WMDvcjKcyPxDCXMdiX2fJUgf5MWPNLIhfO4tCDf6LpQD57rrmDYd9fQPLDd6GP\niXLnIXuEc8Vha5UKqlusfH2ikRMNZqIDNSSH+5GZEs7YSH8PSykQ9A2fCJMYDMZwWVkZZWVlGAwG\n1q9fT1FREWvWrDnNQBxocnJySEtLA2DOnDl88sknGAyGc9avqqpi3bp1pKamsnPnTm699VZCQkJo\nbW0lMrLvf/CNjY2sWbOG3/72t+etJ8IkBL5IQ52Jd1/+ltkLkhk/2XtvYi5Gjq15DUt1LUkP3Nrt\nALu+4HA42fxBHvnZFSiVEpnXpxMzOpzjdW0U1rZytLaVwppWyposnPlvqJBgRJCOxDC9a2n3lobo\nVT3OYGE3tVL03JucfPFtnBYrCr2WhP9dSfzdN6L007nlGAcLNoeTkw1mCmtbGRvhR2KY31l1ShrN\nGLRKgvVigJ5g4BExw27mo48+IjMzszNWd+3atTQ0NHD//ff3q91nn30Ws9l82roOr+z1119PbOx3\n04I6nU4U7XFnS5Ys4c477zznILrW1laWLFnChg0bCA0NZf/+/TzzzDOsWLGChQsXolb3/Yfotdde\nQ61Ws3LlyvPW84XzKhB0pamhjXdf3sO0uYndTskrGFis9U0cf+afVGz4N3H/80NG3Xl9v/L22m0O\nPn7nIMcPn0KtUbLspsmMTAzrtq7J6uB4XSuFtW0U1bVyrK6NkkYz3UUHBOlUxIfqiA/VkxCqJz5E\nz8gQ3Xm9yK0llRQ+8QJVH38FgG54JMmP3M2w713hU/HE/eXZ7aVsLWpAq5JIaNdfQqieqbEGArQ+\n8bJa4EOImGE3Y7FYThu0VlhYSEKCK81S11CDrvQkTOK+++7rUf/vvfceX3zxBS+99BIAJpPpvIPo\nNm7cSHp6OqGhLu9KREQE+fn5yLJ8miHcF9m//vprrrvuuh7J7UuIWC334Yu6bDGaeeelPUyZPWrQ\nGcK+qM++oAkNIuU3PyFu1Q84+uRLfDPzWpIeuJURNy7pdS5hq8XOxjf3U1pUj06vZvnNlzAsNhjo\nXp/+GiVpwwJJG/ZdbnSr3eXpPN5uHJ+ob6Oovo0ms53sihayK1o660q4Zs+LC9ERF6wjLkRPXIiO\n2GCXkew3chjpL6+mftcBCh59BmNuITl3P87xv/yThHtWMuz7CwZNKrbe0Ntr875Zsdw7cwSnWmwU\ntetzZ3ET46MCCOgmPL/WZCXUT93vfMi+wsVyrw82fO/O8xK7d+/m2muvBaCuro6srCwefvhhAEaN\nGsWjjz46oP3HxsZy8803Ay5DuK6ujtmzXWmJioqKiI+PP+3PwmazdRrrHfsoFIrOwX8d9EX2oqIi\ndLqh9XpPIPAP1LL0xklEj3BvdgFB7/GLi2Hii7+hKfswNV/t7rUh3NTQysdvH6SqrAn/QC0/uCWD\niOjeTwCkUSlIjvAjOeK71/2yLFNjchlyJzqWBjNljWYqm61UNlvZXWLsrC8BkQEaYoO1xAbriA2O\nZcTavxL25X+pfO51TEdPkvuT1Rz948vE33UDI27IHHLhE2ciSRJRgRqiAjWdsd3n4lefH6ey2Ups\nkEt/I9uXGXFBF8wnLRD0FBEm0QNyc3OpqKigqakJvV5Pfn4+N954IyNGjPCoHO+99x61tbWUlJSw\nfPlyMjIyAJg2bRpPPvkk8+bN66xrNBp57rnnmDp1Kna7Hb1ez7p165g3bx7Lli1Dr+/7q8dly5bx\n1FNPkZycfN56g/28CgSCoYUsy+R8W8p//30Em9VBUIieFaumENxN7Kq7sTtlypvMFDeYKW5s/2ww\nU9ZkxnGOv9lAJUwp2M/YL/6NrqICAEVIMLG3/oCk21agDvKdGTwHEpPVQWmjmZJGM6WNZsqaLDwy\nP/6svMdOWSavqoWYIB2hvYjrFgxdRMywG/nggw9Yvny5t8U4J06nkx07dnR6igcLg/28CgQC36Vs\n/ScEXzK+c1ILY2Mbmz/Mo/iYKztE8oRorlgyzi0TavQHu1Om0mihtMlMaaOF0kYzpU1mShotmKzt\nE4Y4nSQV5DB123+ILnflsbepNVRlTKVtweUYMlIZHqRjmEHL8EAtoX7C0OsOk9XBw58fp9xoweZw\nMtygJcagJT5Uzw2TPDfYXTB4EDHDbkQxyAc3bNq0iUWL+pc2TiBitdzJYNal7JTJ2n6S0eMjCQnz\njdRPg1mf3sJaV8+3P7gX/zHxWC5fTPYpNVarA72fmvlLxjE2bdg59/WkPlUKqT08Qgdx362XZZnG\nNjvlRotrmTSM0qsu49i3B4j77FNijxUQu2s77NpOQ1gk2ybPIH/yNEyBQWiUEpEBGqIDNUQHaIlu\nDzmICnAtwR70ig6ma9Nfo+SvS1xvLI1ml24rjV0eOs6gzmRjQ061S4+B2vZPjVdzJQ8mfV5MiJjh\nHrBs2TJvi3BeFixY0K+wB4HgYqGkqI5vNheiUEikTDy3sSQY/CTc+yNCr13Kp6/tprJMBhxEOZv4\n/k++NygmSrkQkiQR4qcmxE/NhOiA7zZckYD8y+9TcegkJ9Z/TMum/xBSd4rZX2xi5paPKRsznpy0\nKZwcPY6yJj3QfFbbaqVEpL+GyAA1kQGaziXCX024v+tzqE+OYdCpMOhUpJwn17FSARH+asqNFvaV\nN1NptFDdYmVCdABPLk46q77F7sRidxKoVQrP/BBDhEkIBgxxXgWDharyJrb/p5CG2lZmXjmalLRh\nSGLwjc/S1NBK1jcnydtbht3uRKdXc9nckUTZaom8fIa3xXMrTrud2q17KH/nU05t/gbZ7vJySmo1\nyoyJtE2bQlVaOhVqf6parJxqsdJs6d4T2hV/jZJwf7XLQPbTEOavJsyvffFXE+6nJkinOisud6jj\nlGXabE78NWc/LORUtvD4F0XYnDIR7bqL8NeQOiyAhcndp+wTeJdBGSYhEAgEnsbcZuPjt7PJmBVP\nWsYIlH2YUUwwOKipaubbr4soyKlCbk8AnDwhisuvSSHAoANGd7tf/a4DWGsaCJs7FbUhoNs6gxWF\nSkXklTOJvHImlpp6Kjd+QfVn/6VhTw72XXtR79pLLDBh8ngiF80mfO401Mnx1LY5qW6xcsrkMpBP\ntVipNdmoMdmoNVkxWR2YrA6KG8zn7luCEL2aUD8VoXo1oX5qQvQqQv3UhOpd34P1aoL1KvzUiiHh\nLVVIUreGMEDasAA+/FEabTYHNS02Tpms1Jhs+J/Dy55VamR9dhXhfi7duRYV8SF6ksIHflCnoOd4\n1DP89NNPy6tWrTprvfAgDk187byKWC33Mdh06XTKKHzYwzXY9Olpyosb2LOtiKKCGgCk9jCXqXPi\nCY+6cMaFmq92U/zKezR8e5CgtLEUJ4Sz4MYfEpiajELlmz4hS009NV/spPrzr6n7+lucZmvnNlVQ\nIKHTJxJ66WRCL51E4LgkpC556WVZxmhxUNtuzNWabNS12qjr+Gxfmsz2C8phPJ6NITEdjVIipN0w\nDtapCNarCNJ9t7jWu7zNBp0SnWpoGM/nw2i2c7KhrV2fdupbbdS32kgK9+MHqWfPAnugopnXNv6H\nSVNnuPTVrs8Yg5ZILw8E9VV8zjPcdXY1ge/jdDq9LYLgIsPUYsFqthMSfnaMoC8bwhcrtdUtHD1U\nReGhamoqXXGxKpWC1IwRZMyOJyik5+MkIi6fTsTl07Gb2qjfsY/CN98h92e/Z8xj9xAxb/pAHcKA\noo0IZcQN1zDihmuwm9qo2/YtpzZ/Q/3OA7SVVnJq83ZObd4OuIzjkGkTCUpPIWjiWAxpYwiKCCVI\np+IcE/IBYHU4aWxrN+LabNS3G3QN7d8b2mwcq1CjVEpYHDLVLVaqW6znbrALaqVEkFbVHturJEir\nIlCnIlCrJFCrwtD+6SorCdCqCNQo0fjQmx2DTnXaJC4XItJfw8gQHRqVgnKjhfxqE41mO5eMCOS6\niWdnw8gqNbKjuLFTjx0PGrHtmUcEPWdQxAxbrVaqq6uJiYkRBvEQwOl0Ul5eTlRUFBqNeJoVDBx2\nm4PjBTXkHyin7GQDMy5PImPWKG+LJegDsixTU9lM4aFqCvOqqK8xdW7T6lRMmj6SSZfG4d/dNGVu\n5tjTa9GEBWNITSZgbGK/poT2Bq0llTTsOkD9zv2dxvGZ6IZHYkgbgyFtLIYJyQSMGYV+RPRpHuTe\n0GZz0NDmMpAb2+w0mV1Lo9lO0xllo9mO9VzJly+ARikRoFUSqFERoFUSoFF2fvpp2ssaJf7tZX+N\nEn91R1kxpDzSxQ1t5FaZaGrXaZPZjtFiZ/rIIJaMizir/pdH69l6vMGlv/aHjQCNkgnR/oyJ8I3M\nOr3Fp/IMg8sgrq2t9ZgsgoElPDxcGMKCAaO5ycyX/8qn/GQDkcMNjJs0nOTxUWi0g+Zll+ACyE6Z\nupoWKkoaqShppPREPU31bZ3bdXo1SeMiGT0+irikcFQe9AiWvvkRTdmHMeYepeXoCfQxUQROSCZ1\nza98cna41pJKGrNyaMopwHiwAGPuURym1rPqKXQa/BNG4j86joDRo/BPisM/MRZ97DDUwQa3ymS2\nOzGeYcQ1WxwYLQ6aLXaaza6ya52dFouDFqsDu7N/NotCAj+1yzD2Uys7v+vVSvzUrnX69k+dWoFe\n7dqmVynw07jCO3Rql1GtVyvRKiWfMa6rmi0UN5jb9WqnxerSb/rwAC6NCz6r/lsHqvgw95TrgULj\nOn5/tZIrk0OZEx9yVv2yJjO1Jht+GpcuO3SqVSm8Np32oDSGzxUzLOgbF3scobsR+nRklavVAAAN\n/UlEQVQf7tKl3eZA1c3gFJvVwfGCU8TGh/pEGq3+4uvXptMpY2xso77GRGVpY/vShOWMmFQ/fw2j\nx0cxenwUsQmhKJUDYwD3Rp9Omx3TsWKaDx9n2LIrzzJ8nHY7BY89iz52GPoR0ehjotCNiEYTHjJo\njSTZ6cR0vARjzhGacgpoyT9Oy9GTWKrO7ZBSGQJcxxgbjX7kcPSx0eiGRXKgqpTLFlyJLioMhXZg\nHSCyLGO2O2mxOmhpN5RbrHZM7WWTzYmp3cgzWZ2YrHZabc7OwYKtVgeWPnqkz4UEncZxx6LtYjBr\nz1jfuSg7vktoVQo07eW8vbuZMXMmWqUCjUqBVimhUSpQe8HodjjlTt11LjYHww1aRnUTprS5sI4v\nCutptTlotTlpa/+8IT2K69PPDvP44mgd35YY2x86XA8cOrWCScMDGdtNSrz6VhttNme7Ll16UynO\nrxe3xgxLkrQI+CugAF6VZfmpbuo8CywGTMDNsixnn1nn2LFjPelO0ENyc3N9+g9ysCH06T76osva\n6mZqq1val2bqqltoNpr534fnoz5jdLdaozzvpApDDV+4Nq1WOyajhRajhcaGVhpqTDTUtlJfa6Kx\nzoSjGyMkMEjHsNhgho8MJiYumKiYII/Ed/dGnwq1isCURAJTErvdLtsc+MXF0FZaScOeg5jLqmgr\nr0ap0zJ3/0dn1XdarDRk5aKNDEMbGYrKEIDk4fBASaEgYPQoAkaPYvjyhZ3rbcYWTMdKMB09ScvR\nk5iOnqS1uIK2kkrsxhaaDx2l+dDR09r6t70O6ZG/A6AOMaCNCkcbFYYmLARNWDCa0CDUocHt34NR\nhxhQBxtQGQJQ+ul6ZeBJkuQymtRK+vpW3+Zw0mpz0mp10Gpz0GZzuow363fGW8enuX1bm921rq19\nnbm9bLY7sTpcqdjabO4ZJ1P1zVaiq7oP5Na0G8YaVftnu5Gs6WIwq5UKNO2faoX03fcu69RKCbVC\nQtWljkopoVJIqBWKzu+a9k+VQiJQqyLET41aIaFUSFjtTlRK6TSP78LksG5TzJ3L6ZoU5odaoaDN\n7sTcrt+2dv12x5dH6/nsSC1muxOLXcZid+KUZe6YFsOyCWcPSMyuaCY7O5v58+dfUO8XNIYlSVIA\nzwPzgQogS5KkTbIsF3SpsxhIlGV5tCRJ04C/A2eNSjCZTGeuEvSDpqYmb4swpBD6dB8dunQ6nLS1\n2TC32mgzWTG1WIlPDu82nOGrTwrQ6lSERwWQMnE44VEBBIf5DZh30Jfw5LUpO2VsNgc2qwOrxY65\nzfbd0mrD3GbH3GalzWSjxWimpdllAFst5888EGDQEhLmT2SMgeHtBnBgkHdCDtypT6Vey6jbrz1r\nvcNs6ba+ramZY39+FUtNPdZTdThazaiCAjGMT2LKe8+eXd/YQvVn21AHBaAKDEBlCEAV4Icq0B9t\nRKjbjgNAbQggePI4giePO229LMvY6ptoK62kraSSttJKWksqsFTX4tz3DTp1JJbqOmwNRmwNRloK\ninrUn6RSojIEuo7NEIAq0B+Vvx5lgB8qf3+U/npUAX4o/fUo/fQo9VqUel2XRYtCp3V9arUoNGoU\nOg0KreacWULUSgVBSgVBOveEVDmcLm+1y0h2Gcjm9sk5vlvvKlsc3xlxHdutXdZbHU7aJAsjg3VY\n7E5sDicWh4zV7sTmlLE6ZKwOB/RsjKJHUEigVEidRrKqy6I8Y51S6rqeM8rtiwTbihrYfrIRpfTd\nOkX7/leODuvcVyFJSBJIEnxWUItCcrWrkFx1W20ODh482KPj6MnVMBU4KstyMYAkSe8AS4GCLnWW\nAm8AyLK8R5KkIEmSomRZrj6zsapyYXC4i5Zmi9CnG7no9CnLyLLrFbbTIeOUnTgdMlqdGoXybG9N\nVXkT5lYbDrsTh93pMphsTsZMiELv3+X1qAwtRgv/fGY7tada0GqUaHVqtHoVOr0GtVqBXzeDoOYs\nTD6tbLM6OrMInPMQLnB8veX0XeRumzmt3F7oXCWD3FGSu1aTO/fr8JLI8unfO86H3OUT2TUJQE1V\nM3n7y5GdMrIs43TKyM72zy5lh0NuP59OHE4Zp92J0ynjcLjOrd3uwN5+/uw2J3a7w3UurQ6sVpcB\nbLddeMKG7lCqFAQEagkwaAkM0hMa4U9ouD8h4X6EhPtfdPHcSl334TvayDCmffS3zrLTZsfWaMTR\n2tZtfUdrG/Xb92FvbsFmNGE3tuAwtaKNDmfaRy+eVb/1ZBnZdzzaaSwq9ToUOi1+cTGMfui2s+pb\nG4xUbfoShUaNpFahUKtRaNSogw2EXjoJcHlkNWEu727A2ATaiiuQVEoklZLof6iZfu9PkFSutzeW\n6los1XVY6xux1rkWW30T1vrvPu1GEzZjM842C7b6Rmz1jb3W74WQlEqXcaxVI/1/e/caItdZx3H8\n+zs7u8nmYou0pMVYawhFm2I2L2yqfaEGS2OEUFHwSlARJVRb8G4atIhQqS9ENI1IVVAJCoq92NQ2\nJUGqbUPrZpt7baCYi2x8oTZZuzvOnPn74pzNTjZ7mZ2d7NnZ+X1gmPM8c/bMfx5mz/zPOc95nvwz\nJd0lkp4e1FMiKZVQdwmVSiSlLpSXk+4S6so+29hzcnFdkozVdSWg0XICSUJvkrAkSSBRVq+sXkmW\nuSlRVlYCys7Uj9bviFe5s+sUKonsxawbQAiqIaq1oBpBGlDNH2kNKhH5a1CtQRr5cpo915fTiAvr\nVGpQrWV19fVpLdtOtVajRrZOrTa2rTSFasTF++C6s8QhkQIpE+TuU1wNmHKv3UQ3kUt7Nk+skb3T\nG4BTdeXTZAnyVOucyesuSoYHBwf51Y5nGwzNpvP03n6uDLdnq7g9m3P4hdOX1D29t58rN6wHoFxO\nKZdTyI8zTr3yr7kMb0F44bnDXLvk0Jy9X3dPF909XfT0lPKDmO6xx5LsuXdJN8tet5ileQK8uLd7\n3vaRHe/kyZNFh3BB0l2a8gzv4muu5m0/+mbD21u04mpuvO/LpMMj1EbKpCNlaiNlkkmS89pImfNH\nT1CrVIlKhdr/qtQqFRavuOpCMlxv+PQgBz6zjahUibRG/+BRnn3sIEtWvZH1v9+RfZabxtY/f/QE\nf9mwJU/2EujKksLlb13N+oceoHJuiOq5ISqvDlEd+i9DL73Cie89SH5kmSVHEZSWL+P17+yjNlwm\nHR7JHq+VqZw7z8iZs/nBZEAtsuU0JR1OSYcnn1RkPjpc+QcHnzjSkm115Y+Ff1fF5B7/8NsbWm/a\nG+gkfRC4PSI+m5c/AdwcEXfVrfMocF9EPJOXnwK+GhH99dvaunVr1HeVWLt2LX19fQ0FapcaGBhw\n+7WQ27N13Jat5fZsLbdn67gtW8vtOTsDAwMXdY1YunQpO3funP1oEpJuAe6NiI15+etA1N9EJ+nH\nwL6I+E1ePg68a6JuEmZmZmZm80Ujd6Y8D6yW9CZJPcBHgEfGrfMIsAUuJM//cSJsZmZmZvPdtH2G\nIyKV9HngScaGVjsm6XPZy/GTiNgtaZOkE2RDq33q8oZtZmZmZjZ7czrphpmZmZnZfFLYAJ6SviSp\nJqm1AyV2GEnflvSipAOS/ijp0mlerCGS7pd0TNKApN9Jau38ox1G0ockHZaUSpp4HnabkqSNko5L\n+pukrxUdT7uT9FNJZyUdLDqWdidppaS9ko5IOiTprun/yiYjaZGk/flv+SFJ3yo6pnYnKZHUL2l8\n195LFJIMS1oJ3Ab8vYj3X2Duj4i1EbEOeAzwP1DzngTWREQf8DLwjYLjaXeHgA8Afyo6kHZUN+HR\n7cAa4KOS3lJsVG3v52TtabNXBb4YEWuAdwB3+vvZvIgoA+/Jf8v7gPdJGj+Mrc3M3cDRRlYs6szw\n94GvFPTeC0pEDNUVlwKtmROyA0XEUxEx2n7PASuLjKfdRcRLEfEy0B6Dz84/FyY8iogKMDrhkTUp\nIv4M/LvoOBaCiBiMiIF8eQg4Rja/gDUpIl7LFxeR3dPlfqxNyk+6bgIebGT9OU+GJW0GTkXE3I0g\nv8BJ+o6kk8DHgMZHZ7epfBp4vOggrKNNNOGRkw2bdyRdT3Y2c3+xkbS3/LL+AWAQ2BMRzxcdUxsb\nPena0AHFZZkfU9IeYEV9VR7QdmAbWReJ+tdsClO05z0R8WhEbAe2530KvwDcO/dRtofp2jJf5x6g\nEhG7CgixrTTSnma2cElaBvwWuHvclUqbofzK5Lr8fpWHJN0YEQ1d5rcxkt4PnI2IAUnvpoE887Ik\nwxFx20T1km4CrgdeVDZv50rgr5Jujoh/Xo5YFoLJ2nMCu4DdOBme1HRtKemTZJdWNsxJQG1uBt9N\nm7kzwHV15ZV5ndm8IKlElgj/MiIeLjqehSIizknaB2ykwT6vdpFbgc2SNgG9wHJJv4iILZP9wZx2\nk4iIwxFxTUSsiog3k132W+dEuHmSVtcV7yDrt2VNkLSR7LLK5vxmBmsdXwGauUYmPLKZE/4+tsrP\ngKMR8YOiA2l3kq6SdEW+3Et2Bf14sVG1p4jYFhHXRcQqsv3m3qkSYShwaLVc4J3SbH1X0kFJA8B7\nye6etOb8EFgG7MmHY3mg6IDamaQ7JJ0CbgH+IMl9sGcgIlJgdMKjI8CvI8IHu7MgaRfwDHCDpJOS\nPEFUkyTdCnwc2JAPB9afn1Cw5lwL7Mt/y/cDT0TE7oJj6hiedMPMzMzMOlbRZ4bNzMzMzArjZNjM\nzMzMOpaTYTMzMzPrWE6GzczMzKxjORk2MzMzs47lZNjMzMzMOpaTYTMzMzPrWP8H5Ek0tE/SQYUA\nAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def logistic(x, beta, alpha=0):\n", + " return 1.0 / (1.0 + np.exp(np.dot(beta, x) + alpha))\n", + "\n", + "x = np.linspace(-4, 4, 100)\n", + "\n", + "plt.plot(x, logistic(x, 1), label=r\"$\\beta = 1$\", ls=\"--\", lw=1)\n", + "plt.plot(x, logistic(x, 3), label=r\"$\\beta = 3$\", ls=\"--\", lw=1)\n", + "plt.plot(x, logistic(x, -5), label=r\"$\\beta = -5$\", ls=\"--\", lw=1)\n", + "\n", + "plt.plot(x, logistic(x, 1, 1), label=r\"$\\beta = 1, \\alpha = 1$\",\n", + " color=\"#348ABD\")\n", + "plt.plot(x, logistic(x, 3, -2), label=r\"$\\beta = 3, \\alpha = -2$\",\n", + " color=\"#A60628\")\n", + "plt.plot(x, logistic(x, -5, 7), label=r\"$\\beta = -5, \\alpha = 7$\",\n", + " color=\"#7A68A6\")\n", + "\n", + "plt.title(\"Logistic functon with bias, plotted for several value of $\\\\alpha$ bias parameter\", fontsize=14)\n", + "plt.legend(loc=\"lower left\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Adding a constant term $\\alpha$ amounts to shifting the curve left or right (hence why it is called a *bias*).\n", + "\n", + "Let's start modeling this in PyMC. The $\\beta, \\alpha$ parameters have no reason to be positive, bounded or relatively large, so they are best modeled by a *Normal random variable*, introduced next." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Normal distributions\n", + "\n", + "A Normal random variable, denoted $X \\sim N(\\mu, 1/\\tau)$, has a distribution with two parameters: the mean, $\\mu$, and the *precision*, $\\tau$. Those familiar with the Normal distribution already have probably seen $\\sigma^2$ instead of $\\tau^{-1}$. They are in fact reciprocals of each other. The change was motivated by simpler mathematical analysis and is an artifact of older Bayesian methods. Just remember: the smaller $\\tau$, the larger the spread of the distribution (i.e. we are more uncertain); the larger $\\tau$, the tighter the distribution (i.e. we are more certain). Regardless, $\\tau$ is always positive. \n", + "\n", + "The probability density function of a $N( \\mu, 1/\\tau)$ random variable is:\n", + "\n", + "$$ f(x | \\mu, \\tau) = \\sqrt{\\frac{\\tau}{2\\pi}} \\exp\\left( -\\frac{\\tau}{2} (x-\\mu)^2 \\right) $$\n", + "\n", + "We plot some different density functions below. " + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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A032twBjTBMwBFgMbsWbGN4vIbSJyq11mC/ARsA5YCbxkjNnkWZf6ZDuL+sI5\ni+rTOVSXzqL6dBZ/6rO8tJbCvFJCQ0MYlNm/W9rolxRDVEw4tdWH2Lu7olvaaA3tm0pvIKyT531u\njPmpiKQA3wU6tdrCGLMIOM4jb65H+kngyU7KqSiKoii9DlfYvtSBfYiI7OxXeduICAMG9yVv6wE2\nf1PUbTPmitJb6exMdqiIjDDG7AfWAP4JONgKGifbWTQ+qbOoPp1Ddeksqk9n8Zc+TbNh4xprA5qB\nGc7Exm4Nl2Htb5cR7ZtKb6BTRrYxZj5wyE42ANWOSaQoiqIoSqsUFZZTWVZHdEw4qQP7dGtb/RLd\nXUbKu7UtReltdDpOtjGmwP6/xRjznHMi+Y76ZDuL+sI5i+rTOVSXzqL6dBZ/6XPH5v0ApKT3aXPb\naicQEdIHW7PZm9bu7da23NG+qfQGArkZjaIoiqIoPrLTNrJTBzqzw2N7uFxGtm3QKCOK4gu9wshW\nn2xnUV84Z1F9Oofq0llUn87iD32WHayh9EAN4RGhJKfFd3t7AH0TY4i2XUaKCv3jMqJ9U+kN+LQk\nWUQetKN9eObfb4z5g3NiKYqiKIriyc4t1ix2UmocIQ5to94eIsKAIX3J3XKAzWv3dvtiS8X/fPjh\nh2zZsoXQ0FDS0tK46qqrAiLHhg0bePPNN/nVr37VbtlgkbktfJ3JfqSV/Ie7KkhXUJ9sZ1FfOGdR\nfTqH6tJZVJ/O4g997tx8AICUAf6ZxXbh8sv2l8uI9k3/UVlZyRNPPMH999/PPffcwyuvvEJpaanf\n5XjhhRf4/e9/T1lZWbtlg0Xm9uiQkS0iU0VkKlbovnNdafvvZqCqe8VUFEVRlGOb+rrD7N5VhoiQ\nNsi/MatbXEZq/OcyoviHFStWcPzxx7ekx44dy+eff+53Oe68804uuOCCDpUNFpnbo6PuIq/Y/6OA\nV93yDVAM3OWkUL6iPtnOor5wzqL6dA7VpbOoPp2lu/WZt/UAptmQmBLXbRvQtIZl2CeQt+0gOzbv\n73aXkWDum4vSznCsrhnFXzhWlzv5+fnMmzcPEcEY682D67OIkJWV1WLQFhUVkZDw7SLahIQEcnNz\n/SqDr3SXzE7ToVFqjBkKICLzjDHXda9IiqIoiqJ44grdl5wWF5D2Uwb0IW/bQXK37OecGce1f4LS\nbTQ3NzNz5kwWLFgAwAMPPMDtt9/OyJEjAcjMzOSRR1rz8D2S8vJyIiMjW9Lh4eHU1NR06NzKykoe\neughyspbqlKMAAAgAElEQVTK2LVrFxkZGYSHhzN37lyfZPCVrsjsT3z6KRysBvbatWuZMGFCoMXo\nNWRnZwf1LEJPQ/XpHKpLZ1F9Okt36rOpsZn87QcBGDA4MNubJ6bEERIqlOyvoaaqgdj4yPZP6iTB\n3De7a/bZF1atWsXQoUNb0l988QVPPfVUp+qKi4s7wg+6vr6elJSUDp27bt06nnvuOfbu3Ut2djaz\nZs3qlAy+0hWZ/YnP75tEJBWYBCQBLUubjTGvtnqSoiiKoiidZnd+GQ31jcQnRBHXJyogMoSGhZCU\nEsf+vVXkbT/A2AmDAiKHAkuWLGHKlCkAbNq0iVGjRh1x3N1Vwx1vrhqZmZlHBJAoLS1l/PjxHZLD\n9UPo/fffZ+rUqZ2WwVe6IrM/EZefTIcKi1wKvAZsB8YAG4GxQLYx5txukbADLFmyxOhMtqIoitJb\n+eSDzeR8sYuho5IYOzFwxm3u1gNszNnDiNEpXHpN7/3eLSoqIj09PdBitMp5553HH//4R44//nie\neeYZ4uPjGThwIDNmzPC5rtraWqZPn94S0eXss8/m7bffJjk5mdzcXIYOHdruzqJXXnkl//73vzt1\nLe68/vrrZGdn88ILL7Tk5efnk5GRcYQMbcnsJK31g5ycHKZNm9ZuDE1fQ/j9GvihMeZkoMb+fyvw\ntY/1KIqiKIrSAYwxLbs8pg3yzy6PrZGS3geAgp2lNOvujwGhtLSUwsJCFi5cyOLFi4mMjKSkpISI\niIhO1RcTE8Pdd9/Nk08+yRNPPMFdd93VYqzOnj2bTz/9tM3zq6uriYrq+tuVl19+mddee43ly5fz\n+OOPU1VlBa674YYbWL9+fYdlDiZ8ncmuNMb0sT+XGWP6iUgIUGyMCZgzzFNPPWVuvPHGQDXf6whm\nX7ieiOrTOVSXzqL6dJbu0ufBfVX87dnlREaFcf4lYxA/bULTGkve30Rt9SGuvv1U0od0T5SRQPfN\nYJ7Jfvvtt9m8eTMPP9z9W5Q0NzezfPlyzjrrrG5vKxjx90z2ftsnGyBfRE4HhgOhPtYDgIjMEJEt\nIrJNRH7i5fg5IlIuIjn2X0A3vVEURVEUf5O71dqAJik1LuAGNlhRRuDbjXEU/7Jq1Souuugiv7T1\n3nvvkZWV5Ze2eiO+GtkvA66flk8DS4FvgBd9bdieAf8j8B0s/+7ZInK8l6KfGWMm2H+/9laXxsl2\nFp3ZchbVp3OoLp1F9eks3aXPXTtKAEhM9e8uj63h2m3StcV7d6B9s3Uee+wxv9k906dPJzo62i9t\n9UZ8DeH3uNvneSLyKRBrjNncibYnAduNMbsAROQN4BJgi0e5wP9sVxRFUZQA0Hi4iT27rFBlqbY/\ndKBJTI0nJEQ4uK+a2upDxMR1zhdYCX5iY2MDLUKPxteZ7CMwxhR00sAGGAgUuqV323menC4ia0Vk\ngYiM9laRexgXpeu4VusqzqD6dA7VpbOoPp2lO/RZVFBO4+Fm+vSNIio63PH6O0NYWAiJKdaGOHnb\nu8dlRPum0hvw776svvM1MMQYUysiFwDvAqM8Cy1btozVq1czZMgQwNpec9y4cS2vm1yDVdMdS7tW\n8QaLPD09rfrUtKY13dn0B/MXs2vPXs4ZaS08y1nzFQATTp4U0HTygEwOFFfxwXuLKasZ6fj1uwiU\n/ocNG4aiVFRUtGzXnp2dTUFBAQBZWVlMmzat3fN9ii7iJCJyGvBzY8wMO/1TwLi7pHg5Jw+YaIwp\ndc/XONmKoihKb+Sff1rB3sIKsiZnBmynR29UVdbz6YItREaFMefhaUGxINNJgjm6iOI//B1dxElW\nASNEJENEIoBZwHz3Am6RTBCRSVg/CkpRFEVRlF5Ofd1hindXICFCclpwLHp0ERcfSXRsBA31jRTv\nqQi0OIoSlPhkZItIhIjcKiIvisg89z9fGzbGNAFzgMVYO0e+YYzZLCK3icitdrHvi8gGEVkDPANc\n5a0u9cl2FvWFcxbVp3OoLp1F9eksTuuzMK8UY6Bf/xjCwjsVKbfbEBG3KCPO+2Vr31R6A776ZP8d\nGA+8D+zrauPGmEXAcR55c90+vwC84HmeoiiKovR2CuzQff2SgzPCQ3JaPLt2lJC/7QCTzx8ZaHEU\nJejw1cieAQw1xpR3hzCdReNkO4vGJ3UW1adzqC6dRfXpLE7rc9dOy8hOCZLQfZ4kpsaBwL69VRxq\naCQi0rlYCto3ld6AryOiAIjsDkEURVEURbGoqqin9EANYeEh9E8KzpnsiIgw+vaPobyklt35ZQw7\nLjnQIimdZNGiRVRVVZGXl0diYiI33XST32V46623KC4uJicnhwsvvJDLL7+8zfKLFi2iqKiIhoYG\nBg0axMyZM/0kacfxdeHjPOA9EZktIlPd/7pDuI6iPtnOor5wzqL6dA7VpbOoPp3FSX0W2LPY/RJj\nCQniyB1JqXa87G3O+mVr3/QflZWV3HjjjVx88cX86Ec/4re//S2FhYXtn+ggeXl5lJaWMmfOHJ54\n4gkefPDBlnB53tizZw/bt2/nxhtv5I477uDjjz+mpqbGjxJ3DF+N7DlAKvBb4BW3v784LJeiKIqi\nHLO4XEUSU4JzFttFsr3Ve/72gwGWROksffr0YcmSJURGRiIiNDU14e/wzlu2bOH5558HIDExkWHD\nhrFmzZpWy5eUlLBs2TIOHz4MWDtTRkQE386jPrmLGGOGdpcgXUF9sp1FfeGcRfXpHKpLZ1F9OotT\n+jTGsGtHcPtju+iXHEtIqFB2sNbRLdaDuW8++bNFjtX14G9nOFaXO/n5+cybNw8RaTGYXZ9FhKys\nLC644IKW8ieccAIAK1as4IwzzmjZ3M9fMpx//vm8+eabLecWFxe3uSHQiSeeSHNzM1OnTuX6669n\n6tSphIcHx46o7ji3SkFRFEVRlC5TeqCGmqoGIqPC6NM3OtDitEloqOUzfnBfNbt2HuSE8bqBiz9o\nbm5m5syZLFiwAIAHHniA22+/nZEjrSgvmZmZPPLIIz7V+fbbb/PBBx/w61//usPnVFZW8tBDD1FW\nVsauXbvIyMggPDycuXPn+iRDWFgYo0ePBuCjjz7i5JNPZty4cW2ec++99/LMM8/w6KOP8pvf/KbD\nMvsTn41sERkJzAYGAnuw4ltvc1owX1i7di2646NzZGdnB/UsQk9D9ekcqktnUX06i1P6dM1i90+O\nRSR4/bFdJKfFc3BfNXlbDzhmZAdz3+yu2WdfWLVqFUOHfutc8MUXX/DUU091qc7LL7+c6dOnM2XK\nFN59910GDx7c7jnr1q3jueeeY+/evWRnZzNr1qwuyVBZWcnrr7/On//85zbL7dy5k+XLl/Of//yH\nTz/9lLvuuovRo0czadKkLrXvND4Z2SIyE/gn8AGwCyvG9SoRudYYM7/NkxVFURRFaReXP7ZrUWGw\nk5QaD+ylIFc3ZPYXS5YsYcqUKQBs2rSJUaNGHXHc3VXDHW+uGh9//DFPPfUUixYtIj4+nuTkZN57\n7z3mzJnTrhyuH0Lvv/8+U6ceGQPDFxlcPP/88zz77LPExcVRWFjYqqG/cOFCLrnkEgCmTJnCiy++\nyMqVK3u2kY214PESY8xSV4aITAH+iMeW6P5EfbKdJVhnD3oqqk/nUF06i+rTWZzQZ3NTM4W2sZqS\nntDl+vxBQr9owiNCqa5soLy0lr79Y7pcp/bNtvnkk0+47LLLAFi8eDFnn302ixYtYsYMa5bdF1cN\nEeGss84CLAN4z549jBkzBoDc3FyGDh3a7huVpUuXcscddxyR56vLyssvv8yFF15IQ0MDOTk51NfX\nM3jwYPLz88nIyDhChszMTDZv3tziYlJfX09WVlaH2/IXvkYXGQR87pGXbecriqIoitIFivdUcqih\nkdj4CGJigy9agjckRFpm3XdplJFup7S0lMLCQhYuXMjixYuJjIykpKSk09E1zjvvPAYMGMBLL73E\nI488wgMPPMC5554LwOzZs/n000/bPL+6upqoqKhOte1i5cqVPPTQQ5x33nmccMIJTJ8+nczMTABu\nuOEG1q9ff0T5iy66iAMHDvD0008zd+5cSkpKOOOMM7okQ3fg60z2WuAB4HG3vPvt/IChPtnOEsy+\ncD0R1adzqC6dRfXpLE7os8UfO0g3oGmNpNR49hZWkLvtIONP7XpkCu2brbN06VKuvfZa7rvvPsfq\nvPHGG73mr1ixguXLl7d5blxcHPPmzetS+6eddhoHD3r/gdaakX/77bd3qU1/4OtM9v8AN4tIkYh8\nKSJ7gVuBO9o5T1EURVGUdnBtQpOcFtyh+zxJSrPiZRfmlWKa/Rtj+Vhj1apVXHTRRX5p67333gtK\nN4yegvgacFxEwoDTgHSgCPjSGHO4G2TrMEuWLDE6k60oiqL0ZA4dauSFXy2hqcnwncvHEhHRc6Ls\nGmNYMn8TdbWHuXbOGaQGeXzv9igqKiI9XcMR1tTUEBvbs96qOElr/SAnJ4dp06a1G/qn3ZlsETnb\n7fNU4GwgAjho/z8r0NuqK4qiKEpPZ09+GU1NhoR+0T3KwAZr8VySvfuj+mX3Ho5lA9sJOuIu8qLb\n51da+Qvotupr1wbUJbzXkZ2dHWgRehWqT+dQXTqL6tNZuqrPgp1WVJH+yT3TsHG5jORuO9DlurRv\nKr2Bdo1sY8xYt89DW/lrfe/LNhCRGSKyRUS2ichP2ih3iogcFpHLOtOOoiiKogQ7rvjYyQPiAyxJ\n53BFGNlbUEFjY3OApVGUwOPTwkcRebCV/Pt9bVhEQrDia38HGAPMFpHjWyn3GPBRa3VpnGxn0RXd\nzqL6dA7VpbOoPp2lK/qsrTnE/qJKQkKEpJSeaWRHRYcTnxBFU1MzewvKu1SX9k2lN+BrdJHWooo/\n3Im2JwHbjTG77IWTbwCXeCl3F/AWsL8TbSiKoihK0OPagKZfUgyhYb5+NQcPLpeR/B7ulx0aGkpt\nbW2gxVAChDGGkpISIiMju1RPh1ZWuC1sDBWRcwH3FZXDgKpOtD0QKHRL78YyvN3bTQcuNcacKyKt\n7pWpcbKdReOTOovq0zlUl52nubGR6i25lH+9kYb9JUhoKDkFO5k4bBRhsTH0nTiG+LEjCQnrWQvu\ngomu9M9dOyyjtH9yz9hKvTWSU+PI23qAvG0HOes7o9o/oRUCPdZTUlLYv38/5eVdm5EPFioqKkhI\n6Bk7iAYDxhgSEhKIi+vaeOzo0/QV+38U8Kq7HMA+rNnm7uAZwN1X22u4lGXLlrF69WqGDLEC4Cck\nJDBu3LiWAepaQKHpjqVdOysFizw9Pa361HSg0vV7D/Deb5+mcsN2MneV0lRbx6bmGgBGh8Syu7mG\n3dCSDo2JZtewJOJHj+DiH99F9KC0oLqe3pzetbMJgOKS7dSuKWTCyda8Us6arwB6TLqgaDMFRbmI\njKah/jCrVn/ZKX24COT9SU1NDZr+0dU0wAknnBA08vS0tOtzQUEBAFlZWUybNo328ClOtojMM8Zc\n1+ET2q7rNODnxpgZdvqngDHGPO5WJtf1EUgCaoBbjTHz3evSONmKoijfUvHNFvJfeoPi95ZgGpta\n8iOS+xM9JJ2IxAQw1mwNxtBYVUNtbiGHDpa1lJXQUNIumcbQO2bTZ9xxgbiMY4by0lr+8uRnhEeE\n8p3vjUVC2g2/G9Rkf7ydsoM1XHrtBEackBJocRTFcToaJzvMx3rLReQMY8wXrgwROQO40hhzr491\nrQJGiEgGsBeYBcx2L+AetURE/gq872lgK4qiKBYV32xhy6PPUbbSDmsaIiScPJq+E8cQd8Jwwvu0\n/erzcEUV1dt3Ub5qPRVrN7H3P4vZ+5/FJJ6VxciHbqfvhNF+uIpjD9cuj/2TYnu8gQ2QnBZH2cEa\n8rcdUCNbOabxdXXFbGC1R97XwNW+NmyMaQLmAIuBjcAbxpjNInKbiNzq7ZTW6tI42c7i+bpO6Rqq\nT+dQXXqnqa6Brb96gRUX3EzZyrWEREeROGUSJ/zyHob+z9X0O3W8VwP7q80bjkiHJ8TTL2ssQ++Y\nzejfPkDStNMJiYyg5PPVrLzoVrb+8gWa6hr8dVk9js72z107bCO7h8bH9sS1KU3+9pJO16Fj3VlU\nn4HB15lsw9GGeaiXvI5VZswi4DiPvLmtlL2xM20oiqL0ZkpXrGHDA49Rm1sIIiSeM4kB3zuPsNiY\nLtUbkdiXQbMuJG3mVPZ9uIwDHy8n78V/sv+jzxn77P/SL2ucQ1dwbGOaDQV2ZJHUgb1jYVq/RCtC\nSnlpLdWV9cT1iQq0SIoSEHz1yX4byAN+bIxpdothPdIY871ukrFd1CdbUZRjDdPczPbHXiL3uXkA\nRA5IYdDsC4k/YXi3tFeTt5uCV9+iofggiDDsrmsZ+ZNbkNDQbmnvWGH/3krmPf8FUTHhnHfxaER6\nvrsIwJef7mT/3iq+e8WJjD45PdDiKIqjdNQn29cZ6HuA84C9IvIVUAScT/dFF1EURVE8aKypY+3N\n/2sZ2CEhJH9nMqMevqPbDGyA2KGDOO6RO0m54GwQyH1uHmtu+hmNNXXd1uaxgMtVJDE5ttcY2PBt\nvOw8B7ZYV5Seik9GtjFmNzABuBR4wv4/0c4PGOqT7Szqu+Usqk/nUF1CfdF+vrr0DvZ9uIzQmGgy\n75jFwO/PIDQi3Oe6PH2y2yMkPJz0y6Yz/P4fEhoTxf5Fn/PVpXdQv1cNKehc/3QtekxM7dnxsT1x\n+WXv2lGCL2/MXehYdxbVZ2Dw2ZfaGNNsjFlhjPl/xpiVxpjm7hBMURRFOZKKtZtZccHNVK7fRkRK\nf4bf/0P6nuT/iB/xxw1j5EO3EZHUj8r121hxwU1Urt/qdzl6Ok2NzRTmWWETUwb0CbA0ztKnbxQR\nkWHU1hyi7GBNoMVRlIDgq092BHADcBJwxM9up+Jndwb1yVYUpbdTtno9q2fdR1N1LbEjM8i8bRbh\nCfEBlamxqoa8F/9FzY5dhMZGk/X60/SbdGJAZepJFOaV8ubLXxGfEMWU7x4faHEc5+vl+RQVlDN1\n5glMOD0j0OIoimN0l0/234F7sbZR3+nxpyiKonQD5V9vaDGwE04ezbD7bgi4gQ0QFh9rzaafMo6m\nmjpWz76P8q99c0E5lnG5ivRL6h2h+zxx+WXnbzsYYEkUJTD4amTPAM4wxvzEGPML97/uEK6jqE+2\ns6jvlrOoPp3jWNRlec7GIwzsjFuvJDTcd/9rb/jqk+2NkPAwMm6+gr6TLEN71VX3Up6zyQHpeh6+\n9k/XosekXuaP7SLZvq7CvFKam33zyz4Wx3p3ovoMDL4a2QVAZHcIoiiKohxJec4mVl91L41VNS0G\ndkiYr9sbdD8SEkLGjd8nYeIYmqprWX3VPVR8syXQYgU1DfWN7N1dgQikpPcuf2wXMXGRxMRFcPhQ\nE/v2VARaHEXxO776ZD8AXAE8C+xzP2aM+cRZ0TqO+mQritLbqN6ax8qLb6exooqEk04g47argtLA\ndsc0NpH/0ptUrNlEWEIcp777p24NK9iT2bllP+/My6FvYgxnTR8VaHG6jW++KqRgZwmTzx/Jaedq\nX1B6B93lkz0HSAV+C7zi9vcXnyVUFEVRvFK/7yCrr76fxooq+px4HBm3Br+BDSBhoWTceiV9xh9P\nY0U1q6++n/piDe/njd62lXprJGu8bOUYxqentjFmaHcJ0hXWrl2LzmQ7R3Z2NpMnTw60GL0G1WfH\nqW5oZMO+GgrL69lT2cCeigb2VDbQ0NhMU7OhdPta4oeNJyo8hNS4CFLiIkiNi2BgQiTj0uLI6BdF\nSA/f0KOxppacax6kfs8+YoYOIuOWKwkJ7x4D+6vNG5h0wlhH6wwJCyPz1qvY8dSr1OYW8vU1D3Lq\ne3/q8jbvPQFfxrrLyE5JC/wC1u7EFf977+4KDh9uIjy8YzuE6nPTWVSfgcGnJ7eI/LK1Y8aYR7ou\njqIoxxLNxrD1QC1f765k9e4qthyooa31UYeamjncbDjc0ERVQx07So7cbTAhKowTB8QxfkAcZ2X2\npV+MMwsE/UVzYyNrb/k/Ow52IkPvuJrQqJ63DCYkIpxhc65h2+/mUrVhO2tveZgJ837fI2bj/UFN\nVQMl+6sJDQshMaV3Lnp0ERkZRp9+0VSW1VG0q4yMEUmBFklR/IavPtl/9chKA84B3jHG/MBJwXxB\nfbIVpWdRVnuYRdtKWLi1hOKqQy35IQLpfSJJig2nf3Q4SbFhJMdFEBUWQogIIpaPW32joaK+kYr6\nw5TWNrKv+hCF5Q1UH2pqqStUYNLgBKaP6s+pQxIICwnuGW5jDBt/9Di7X5tvh8a7kehBqYEWq0s0\n7DvItt/NpammjsHXXcrox3/Uq7YO7yyb1hbx4b/XkZwWx2nnjgi0ON3OpjVF7Nyyn6zJmb0yHrhy\n7NFRn2xf3UV+6JknIjOA2b7UoyjKscnG4mre2XiA5fnlNNm/7/tEhjK0XxRDE6MZmRRDZFj7S0Xi\nQiEuMpSBCd/O8hpjKKtrJK+0jq0HaskrrWdFQQUrCipIiApj5glJXDY2mbjI4JxN3fXyv9n92nwk\nIpwht1zZ4w1sgMjUJIbOuYadT71K4bx3iR0+hMzbZgVarIDzrT92757FdpGUFsfOLfvJ367xspVj\nC5+3VffCYuBSB+rpNBon21k0nqazqD5h24FafrZoB/d9sJ3P8sppNjAiMZrLx6Yw58xBzByTzNi0\nuHYN7PWrV7Z6TEToHxPOxEF9uPrkNO6ZPIipI/qSGBNORX0jr60p5po3NvL3r/dSWd/o9CV2iZLs\nr9n6iz8CMOjqmfTxU0QOJ+Jkt0fciAyG3Ph9ALb88o+UZK/u9jYDRUfGujGmZROa5F62lXpr9E+O\nJSREOLivmrraQ+2fgD43nUb1GRh89cke5pEVA1wNFHamcXsW/BksY/8VY8zjHscvBn4FNAOHgfuM\nMcs705aiKP4nv6yOv6/ey/JdVozcyFDhpPR4ThkUT99u9peOiwzjjIy+nD4kgYLyBpblllNQXs8/\n1xTznw37ueLEVK4cl0JEB2bOu5O6wr2svfX/ME1NJE07g8Qze5/rW79TxlFXuJf9Cz9j7S0Pc8bi\nvxI9eECgxQoIpQdqqKqoJyIyjL79owMtjl8ICwulX1IsJfurKcwtZdTYtECLpCh+wVef7GbAAC4/\nlFpgDXCvMeZrnxoWCQG2AdOAImAVMMsYs8WtTIwxptb+PA74tzHmBM+61CdbUYKLusNN/CPHMmab\nDYSHCOPT45icmRBQd42C8nqW7SxnV3k9AAPiI7jzjEFMGpwQEHmaauv58pLbqVy/jbjRwxl+z/VI\nSGCN/u7CNDeT+9w/qNq4nfixIznt/ZcIje55izq7ytfL81m6YAsDhiSQdWZQBuzqFrZtKGbr+mLG\nZQ3iO5c5G9FGUfxNt8TJNsaEGGNC7f8hxpg4Y8xZvhrYNpOA7caYXcaYw8AbwCUe7dW6JeOwZrQV\nRQliVhZUcMvbm3lr/X6MgfED4rj9tHRmHJcYcH/oIX2juHZiGtdMSCUpJpy9VYd4+KNcfv5xLvuq\nOvYa2ymMMWz40WN2JJH+ZNx0Ra81sMHeFfKWK4hI6kfVhu1s/PHj+DLJ01vI22b5JSen9u7QfZ64\n4mXn71C/bOXYod0nuojMcfvs5DLogRzpZrLbzvNs/1IR2Qy8D9zorSL1yXYW9d1ylmNFn2V1h/nl\nf/N4ZHEu+6sPkxoXwTUT0pg5OomEaGdcQ9ryyfaFzH7R3HJqOtNG9CM8VPhiVwW3vr2ZhVtL/Gb4\nFbzyFnvfXkxIVAQZt1xFeB//L4Lzh0+2O2GxMQy98wdIRDhF/28RBa+85df2u5v2xvrhQ00U5pUC\nkDrw2PDHdpHQP4aw8BCqyuupLK9rt/yx8tz0F6rPwNCRaaXfAH+0P+cAfn0yGGPeBd4VkcnAr4Hz\nPcssW7aM1atXM2TIEAASEhIYN25cS+B1V+fSdMfS69evDyp5enr6WNDn1gM1LK5Np6yukbq8bxiT\nGsusc6cREiIthvG4rNMsfQRR+vSMBEL2bGDV7irKE4/n6c8LeHvhEr4/LoUZ06Z0m76qt+Ujv3gJ\ngP3njKO+roxJ9hyDy/B1bRLT29Lrqw5Sdd4EEj/8ki0/f44tYYeIHZERVP25u9KFeaXk7tpAbJ9I\noqJPAiBnzVcATDh5Uq9PJ6bE8dVXK3n37Tquu+l7berLRTDdv56cdhEs8vS0tOtzQUEBAFlZWUyb\nNo32aNcnW0TWAJ8AG4EXgDu9lTPGvNpua0fWexrwc2PMDDv9U6uaIxc/epyzEzjFGFPqnq8+2YoS\nGA41NfPXVUW8vcHaMnlwQiQXjU4isYdtAmOMYUNxDQu3lnCoydAvOowHzh7SLb7ah8oq+eL8G6jf\nXUzi2acw+NpL2j+pF7L7Xx9wcOlKogcP4Iwlfw/ITL6/+eT9zeSs2MWw45IYM2FQoMXxO3nbDrDh\n6z2MHJPKJT84OdDiKEqncdIn+yogASsWdjhwrZe/azoh4ypghIhkiEgEMAuY715ARIa7fZ4ARHga\n2IqiBIY9FfXcM38bb284QIjA5IwErp2Y1uMMbLDC/40bEMetpw5kcEIkZXWNPPxRLq98tYemtrag\n9BFjDBvu/TX1u4uJzhhI+qzvOlZ3TyP9iu8QPXgAdYV72fDA744J/+y87daP0ZSBgVloG2iSbD/0\nwtzSY+J+K0q7RrYxZpsx5mZjzPnAMmPMuV7+pvrasDGmCZiDFWd7I/CGMWaziNwmIrfaxS4XkQ0i\nkgM8D1zprS71yXYW9d1ylt6ozy8LKpjz3jZ2ltTRLzqMq09KY8qIfoR0825+Tvlkt0bf6DCunZjG\nucP7IsCb6/bz0KIdlNcddqT+/D+/zv6PsgmNjSbj5u8TGh7YHyT+9sl2JyQ8nMzbriIkMoJ97y9l\n92vvBUwWp2hrrJeX1lJ2sJbw8FASj5FNaDyJ6xNJVHQ49XWHOVBc1WbZ3vjcDCSqz8Dga3SR9h1Q\nfLVztYsAACAASURBVKtvkTHmOGPMSGPMY3beXGPMS/bn3xtjxhpjJhhjzjTGrHCyfUVRfKPZGF5b\nU8wji3OpOdTEyKRobjxlAJn9owItmmOEiHBmZl9+MCGVmPAQ1hZV8z/vbmXL/pou1Vu2ej3bfvMn\nAAbOvoiotGQnxO3RRKYmtbjLbP7fp6natCPAEnUfrqgiiSnWxizHIiJCygBrNnvnpv0BlkZRup9e\nES/qpJNOCrQIvQqXw7/iDL1FnzWHmvjFf/OY9/VeAM7KTODKE1OIDg/1mwyuxYv+ILNfNDdPSie9\nTwQHaw5z/wfbWbytpFN1HS6v5JvbHsE0NpF47qn0P3W8w9J2DtdixEDS79Tx9J88keZDh1lz8//S\nWNN+5Ilgpa2xnr/NchVJPMZC93mSYkdV2b5pX5vlestzM1hQfQaGXmFkK4rSveyrOsS987exYlcF\nUWEhXD42mXOG90O62T0k0PSJCuP6iQOYODCOxmbDk58V8NdVRTT74E9qjGHDA49Rv2cfMZkDSb9i\nRjdK3DMZNOtCIgckU5tbyKaHngy0OI7T2NhMQa61nCht0LHpj+0iOTWekBBh/94qaqv9G5teUfxN\nrzCy1SfbWdR3y1l6uj63Hqjh7vlb2VVeT1JMONdNSOX41NiAyNLdPtneCA0RLjg+iQuO648Ar3+z\nj998kk9DY8f2xtr9z/nsW/ApIdGRDL7x8oD7YbsTSJ9sd0IiI8i8bRYSHkbRvxey598LAy1Sp2ht\nrO/JL+PwoSbiE6KIiY3ws1TBRVh4KIkplk967rbWXUZ6+nMz2FB9BgafjGwReVpE1DdDUY4RlueX\n8+AH2ymraySjbxTXTUgjJf7Y2wobYOKgPsw6KZWIUOHzvHIeXLCd0tq2F0RWb81j8/89A8DAKy4g\nekCKP0TtkUQPTGXQ7IsA2PiT31O9PT+wAjmIK6pIYuqxueDRE9dGPNs3ql+20rvxdSY7FPjIjvjx\nExEJikCf6pPtLOq75Sw9VZ/vbNjPL/+bR0OTYUxqLLNPSiEm0n/+197wp0+2N4YnRvPDrAEkRIWy\n9UAt98zfxp6Keq9lm+oaWHv7IzTXNdB30okknpXlZ2nbJxh8st3pP3ki/SadSHNdA2tveZimuoZA\ni+QTrY31fHvRY8qAY2uXx9ZISbf0ULCjhKYm72+EeupzM1hRfQYGX6OL3A2kAz8FTgI2i8h/ReQ6\nEdGf6IrSCzDG8Jev9vCnlXswwOTMBC4dk0RYaK/wLusyyXER3HhKOgPiI9hXfYh75m9j64GjI49s\n/eUfqd68k8jUJAb9YGYAJO15iAiDrr2EiJT+VG/JZcujzwZapC5TVVHPwX3VhIaFkKwz2QDExkUS\n1yeKw4eb2JNfFmhxFKXb8Plb0xjTZIz5wBgzGzgNSAb+BhSLyF9EZKDDMraL+mQ7i/puOUtP0mdT\ns+EPnxfw73X7CRG4YFQiU4JogWMgfLK9ERsRyrUT0hjWP4rKhiZ+tGAHq3dXthzft3AZBX99GwkL\nY/AN3yMsJjqA0rZOsPhkuxMaFWn5Z4eFUjjvXYrf/yTQInUYb2N952bLJSIxJZYQ/aHaQmp621FG\netJzsyeg+gwMPo94EekjIjeJyFLgM+BL4CzgBKAa6JkrVhTlGKehsZlf/jePj7aVEh4iXDommYmD\nj+1wY20RERbCVeNTGZcWS31jM//30U6W7Cilbs8+Ntz3WwBSLzqXuBEZAZa05xEzJJ3071tRWDbc\n/zvqCvcGWKLOs2OzZUSmpKmriDsuv+ydmw8EWBJF6T7El61NReQt4DtYxvU84F1jTIPb8RCgwhjj\n12/mJUuWmAkTJvizSUXpVdQcauL/Fu9kQ3EN0eEhXDYmhaGJvWeDme7EGMMnO8pYUVCJNDdzz1t/\nJmTdRuLHjGDYPdcHzVuAnoYxhrwX/knlN1vomzWWSe++SEhYWKDF8on6usO8+JtPaDaG6ZeOITIq\neCLLBJrmZsPi/2zg8OEmbrr/LPolBSZikaJ0hpycHKZNm9buw93XmeyvgJHGmAuNMW+6DGwRuR/A\nGNMMpPosraIoAaOyvpEff7idDcU19IkMZfZJqWpg+4CIMG1kf6aN6MekZR8Rsm4jTX3iGXz999TA\n7gIiwpDrv0dYQjzlqzew86m/Blokn8ndeoDmZkP/pFg1sD0ICRFS0u3dHzdrlBGld+Krkf2wMabY\nW77rgzGmtmsi+Y76ZDuL+m45SzDrs7T2MA8u2M72g3X0iw7jByenkt4neEP0BYtPtjcmle/mjE8W\nYET4z+X/v737jo/jLhM//nlmu7qsZrl3O7Ed24ljHBJSMBdSIAlJACeBQOgttByQgws1B4QS2gF3\n3AG/wHEHBEIKhJCeEAfHdty7bNmWbdnqklW3zfP7Y1eybMu2JK+0Wvl5v17z0s7s7Myj0e7Oo+88\n8/2+m8d8ZQzgQmFajMSa7N68udlMfv9bQWD39/8fDSvWpjukUzr+s16xJVkqYr2K9Km7l5GdfdRl\nj+TvzUxkxzM9+nXtTURe372+iFwB9G6emQa0pjowY8zQqmuP8LnHd3GgJUxRlo9bFpZSELLWtsHQ\nllYiX/42okrDxa/lwLTZ7G8XulS4NacTa9AevNzZ0yi75nJq/vI8Gz/yJS5+7n/wjxn5oyZGo3H2\nJLvuGz+pIM3RjEyl5XkgcGh/C+GuGIFgZpUDGXM6/arJFpE9yYeTgKpeTylwGPimqj6a+vD6x2qy\njRmYQ0fCfPbxXdS0RSjN8XHLgjJy7QQ3KKpK5O57ib+4EplQjuf976DCk8MjWoQrwmWhMHfkduBY\noj1oGo9T8e2f07G7ipIrL+b8B7414ktxdm2r5eFfryW/MMSlV81Odzgj1oqnK2isa+dNyxcw57zy\ndIdjTL+ktCZbVaeq6lTgN92Pk9M0VX1tOhNsY8zAVDV3cdefK6hpi1Ce6+e2RZZgn4nYg48Rf3El\nhIJ4bnozjtfLbOniZqnHqy4vdAb4jyPZxEZ46chIJh4PU97/VpxQgLonV1D1iz+mO6TT2pUsgSgp\ntx56TmXshMRViW3rq9MciTGpN9DBaG4fqkDOhNVkp5bVbqXWSDqelQ2d/POfK6jviDIhP8Bti8rI\n9mdOgj3SarLd7RVEf/RzADzXvgGntKjnuWkS5m1Sj19dVnb5+VFLNpERlmiP9Jrs3vxFhUx611sA\n2P6VH3FkS0WaIzpR92fdjbs9N/ONm1SYzpBGvHETE6U0eysaiIRjPctH0vfmaGDHMz1Om2SLyKW9\nHr/+ZNNgdi4iV4nIdhHZKSKf6+P5W0VkQ3J6SUTmD2Y/xhjYUdfOZx6voLkrxuTCILcuLCXoS+8w\n6ZlM2zsI/+t9EIvhXLgQz6ITv54mSYRbpI6gxlkX9nN/cw7hEZZoZ5KCC+ZRdOliNBJl/QfuIdbe\nme6Q+nRgXxOdHVGycwPkFVhPPacSyvZTWJxNPO5Sud36zDajy2lrskVks6rOSz7ec5LVVFWnDWjH\niT61dwLLgGpgNbBcVbf3WmcpsE1VW0TkKuDLqrr0+G1ZTbYxp7b5cBv/+rfddERdpo8JcfP8Enxe\nG31usFSVyBe/RfzpF5FxZXje906cwMlvGq1VL7/VEjrEw0xfjLsKWsmywz8objjCjn/7KeFDdYy/\n9c3Mv/9f0h3SCZ59bBtr/7GPKbOKmX/BhHSHM+JV7qhjy9qDTJ1VzE3vXpzucIw5rZTVZHcn2MnH\nU08yDSjBTloCVKjqPlWNAr8Frj9u3ytVtSU5uxIY9iHbjcl0aw8e4V+eSCTYs0uyeNuCUkuwz1D8\n0b8Rf/pFCATw3PimUybYAKUS4x1SR67GqIh6ua8pl1Z3ZN+4N1I5AT9TPvB2xOvh4P8+xqGHn053\nSMdQVSqSozyWTxz5vaCMBN3Had/uBsJdsdOsbUzmGNCZVkSuEJGpycdjReQBEfmFiIwdxL7HA/t7\nzR/g1En0+zjJkO1Wk51aVruVWuk8niurWrjnyUrCMZe5ZdncNL8ETwZ3czESarLdXXuJ3P+fAHiu\nvgKnvLRfrxsjMW6TOvI1xp6Yl2805tIcT+/fIpNqsnsLTRjL+LddA8Dmu75Be+X+07xieLz00kvU\nVB+htbmLQMhLUUlOukPKCKEsP2NKsnHjyu7kPyh2HkotO57pMdA7nn5CYlh1gPuTP2PAz4DrUhXU\n8ZJ9c98BXNLX8y+88AJr1qxh0qRJAOTn5zN//nwuuSSxeveby+b7N79p06YRFU+mz6freLrj5vKN\n5/bStGs9M4pC3PD6KxGRnkR1/uJE5ZXN939eO7tY9+nPoF1NzD3/NXguXMTm3dsAmDf9HIBTzhdI\nnMW7X+AZzefAjMV8vSmXK2v+QZ6jLDkncdGwO/G1+VPPX3j5Etp27OHl1a+w6+0f5AN//xOeYCDt\nn/c//v5x9h08xCWXXIKIsHbdKgDOX7QEwOZPMj9u0lQa69p56MG/0tg+h27p/nuOlvluIyWeTJvv\nflxVlejFevHixSxbtozT6Vc/2T0rixxR1TwR8QI1wGQgAlSranG/N0RPvfWXVfWq5PzdJGq77ztu\nvfOAPwJXqeruvrZlNdnGHOupiga++2IVrsLi8bm8cfaYEd+vcCYI3/s94n95GiktxvPB23GCgxsd\ns10dfkcxtfgpduLcXdhGqddNcbSjX7yjix33/oRIXSMT3nk98759wv3zw8p1lf/69gu0tnTxmsun\n2UiPA9DVGeWpR7bgiPCRL7yeoA2MZUawlPaT3csRESkDLgO2qmpbcvlgPg2rgRkiMllE/MBy4Jj+\ntkVkEokE+50nS7CNMcf687Z6vv1CIsG+aFKeJdgpEnv8GeJ/eRp8Pjw3XTvoBBsgW1xupY5ywtS7\nHu5tyqU6ZnXyA+XJCjLlQ8sRr4cDv36E6oeeTGs8+ysbaW3pIivHT8lY6x97IIIhH0UlObiu9vQx\nbkymG+i3+o9IJMe/AX6cXHYxsP2krzgJVY0DHwOeBLYAv1XVbSLyQRH5QHK1e4AxwE9EZJ2IrOpr\nW1aTnVpWu5Vaw3k8/7Cplh+uSNSnXjq1gGUzR1eCna6abHfvfiLf+QkAnjdejjNh3BlvMyjKcuqZ\nSBfNrsO/NeZSFR3eLhUztSa7t6xJ4xi//FoANt/1Tdoq9qYtlgd/+2cAyifkj6rP3XAZlxx+fuv6\najsPpZgdz/QY6GA09wFvAC5W1d8mFx8A3juYnavqE6o6W1Vnquo3k8v+U1V/lnz8flUtUtXzVXWR\nqi4ZzH6MGe1Uld+sO8zPXjkIwLIZhVw6rSDNUY0O2t5B+PNfh84uZN4cnKUXpGzbAVHeRgNT6KRV\nHb7elMPuYU60R4OiSy+kYMl5uJ1drHvP54m1dwx7DJFwjAN7mgCYOL3oNGubvpRPLACB/ZVNhLui\n6Q7HmDM20N5F/MDlwF0i8isR+RWJ1ubPDkFs/bZw4cJ07n7U6S74N6kx1MdTVfnFmkM88OohBHjj\nrDFcNHl0dh3WfTPicFFVIv/2fXRPVaIO+4arU95C6RPlZhqYSQcd6nBfUw7bI8MzCmf3zYSZTkSY\n+M7rCYwtpr1iL5s/+XUGcr9RKuzcUsOEsjkUFmeRm2cD0AxGIOiluDQHVaUkf0a6wxlV7LyeHgMt\nF3kA+CTQCuw+bjLGDLO4q/xgxX5+t6EGR+CaOUVcONFutkqV2G8eIv7cCggG8Lz1OpzQ0CRPXoEb\naOQc2ulSh2835bApnDnD3Y8EnmCAqR+5DSfo5/Bjz7L3p/83rPvfujZxFal8ol1BOhPjJyeGod+4\namR0y2jMmRhokn0V8FpV/ZyqfqX3NBTB9ZfVZKeW1W6l1lAdz0jc5evP7eXx7Q34HOH6c4tZNH50\n32w1nDXZ8dXrif70/wHgue6NOOPKhnR/HoE308R5tBFF+F5zDmu7hraHhdFQk91bsLyESe+5GYAd\n9/6Yhr+vGZb9HmnupGpPI1WHtzFx6phh2edoNW5SAV6vwyurVlJf05rucEYNO6+nx0CT7Cpg8LfU\nG2NSoiMS556/7ebve5oJeh1uml/C3LE28EWquIdrCX/xPnBdnIuX4Fkwd1j26whcTTMX0EoM4Yct\n2awc4kR7tClYdC5l11wGrrL+/f9K5/5DQ77PreurQaFwTBb+gF2BOBNen4fxUxKt2etXWmu2yWwD\nTbJ/BTwiIreIyOt7T0MRXH9ZTXZqWe1WaqX6eLZ0xfjs47tYV91Gjt/D8gVlzCjOSuk+RqrhqMnW\nzi7Cd98LzUeQGVPwXHXFkO+zNxF4Ay0s5Qguwk9bsnm6Y2jaNkZLTfbxxl6/jNy5M4k2H2HtHXcT\n7+gasn2pKluSpSKXX3HpkO3nbDJ5ehGTx5/L1vUHiUbi6Q5nVLDzenoMNMn+GFAGfB34ea/pv1Mc\nlzGmD7VtET712E521ndQGPJy66IyJhTYxaVUUdcl8tXvojt2Q1EhnpuvQ5zh779aBC7jCJfRgiL8\nqjWLh9qCDPO9fBlLHIfJ738r/uJCWjdXsPHjX0XdoRns5/CBFprqOwgEvYydYPXYqZA/JouCMVlE\nwnF2bBr6KxHGDJWBduE39STTtKEKsD+sJju1rHYrtVJ1PKuauvjkYzs50BKmNMfHbQvLKM3xp2Tb\nmWKoa7KjP/s18edfhlAQ7/IbcHKzh3R/pyICF0krV9OIqPJwe4gHWrNwU5hoj7aa7N682VlMu/Od\nOMEANX9+nopv/deQ7Gfzq4lW7LET8lm/YfWQ7ONs1BZLDF+97h9VaY5kdLDzenrYEGPGZIDtte18\n+s87qW+PMiE/wK0LyyjIslrdVIr99VliD/weHAfPjdfijBub7pAAWCAdvEUa8KjybGeAH7dkE7EW\n7X4JjitlygeXgwiV33+Agw/+NaXb7+qMsmVdNQATp9kNj6lUXJaD1+dQU32EusN2A6TJTANOskXk\nn0TkFyLyWHJ+sdVkjy5Wu5VaZ3o8V+8/wmcf38WRcJxpY0LcsrCUnLP05qqhqsmOb9hK5Bs/AMBz\n5eV4zp01JPsZrFnSxduljoC6rA77ua8pl1b3zPvrHq012b3lzZvJ+FuSI0J++hs0rdqYsm1vWLWf\nWDROcVkOhUXZnL/IxktLlQsXL2XClMQ/LutXWmv2mbLzenoMdDCaO4GfAjuB7js8OoF7UxyXMQb4\n87Z67nlyN10xl3NLs3jbeSUEvDYiYCq5VQcJ3/01iMZwLliA53WvSXdIfZokEW6TWnI1RkXUy1cb\nc6mJ2cXI/ii5YinFVyxFozHWvuuztO85cMbbjMdc1v1jHwBTZhaf8fbMiSYlR87cur6aSCSW5miM\nGbiBfkN/EnhDcgj07rtItgOzUxrVAFlNdmpZ7VZqDeZ4uqr87JWD/HDFflyF10zM4y3zSvB6zu6k\nKtU12W5dA+FP/GtPTyLOdW9M6fZTrVRi3C61lBKhJu7hK4257IoM/p+u0VyTfbzxb7860eNI0xHW\nvO0TdNXUn9H2tm86RNuRMDn5QcZOSIywunbdqlSEakgcy/zCEAVFWUQjcXZsPJzukDKandfTY6Bn\n7Fygu+PK7qpAHxBJWUTGnOXCMZd7n9nLHzbV4khimPR/mjUm5cN5n+20rZ3wp7+EHq5FxpfjueVG\nnAy4SpArLrdRx1Q6aVOHbzTl8or1pX1a4vEw5UPLCU0eR+f+Q7x6y6eJHmkb1LZUlVdf2gvAlOn2\n2RxKk2ckWrPXvLQXte51TIYZaJL9InD3ccs+DjyXmnAGx2qyU8tqt1JrIMezrj3CXX+u4KW9iUFm\nbp5fasOk95KqmmwNRwh/9mvorj1QPAbPO27CCWZOV4gBUW6mgQXJ0SF/3JLDH9uCA+555Gyoye7N\nEwww/RPvIlBaROvWXay9/TPEu8ID3s7+ykZqD7USCHqZmCxpAKwmO4W6j+X4SYUEgl4aatuo3F6X\n5qgyl53X02OgSfadwFtEZC+QKyI7gLcBn051YMacbbbUtPGxh3cc0wf2rJKzY5CZ4aSxOJEvfxt3\n3SbIy8X7jptx8jJvOHqPwFU0s4xmRJVH2kP8e0s2XUPTHfSo4c3NZvqn3403P5emlRvY8KEv4sYG\nVu+7JtmKPWHqGLwZcPUjk3m8DtPPKQXgpacqrDXbZJSB9pN9CLiQRGJ9K/AuYImqprVYymqyU8tq\nt1KrP8fziR0NfPYvu2jqjDG5IMjtF4xlXF7mtKwOlzOtydZYnMhXv3u0L+zbbsQpKTr9C0coEbhQ\n2nir1BNQlzVhP19ryqUu3r+v9rOpJrs3f1Eh0z/1bpxQkNon/s6mj9+Lxvs3smBDbRuVO+pwPMK0\nWSXHPGc12anT+1hOnlFMIOil7nArlTusNXsw7LyeHqf9JhaRr/aegK8AbwLmA9cAX04uHzARuUpE\ntovIThH5XB/PzxaRl0WkS0SstdyMOtG4y49f3s/9f68i6iqLxuVwy8JScs/SLvqGksbjRL52P/Gn\nXoBgAM8tN+BMGJfusFJimoS5XWop1Cj7Y16+2JDLhrC9h04lNL6M6R+/HSfg59BDT7Lx41/rV6L9\n6oq9AIybVEDQ+qofFl6vw/Q5idbsFdaabTKInO7NKiK/7DUbBG4CVgP7gEnAEuCPqnrLgHYs4pDo\nCnAZUJ3c5nJV3d5rnWJgMnAD0KSq9/e1rWeeeUbPP//8gezemLSraY1w77N72FHXgUfg9TPG8JpJ\nVn89FHoS7L89DwE/nltvxDNjarrDSrkuFR5jDLsJISjXZ3dxQ3YXjt2Xd1JtFXup/MGvcMMRym+6\nkvN+eA/i6bsEpLmxg19+7+/E48qlV80iv9DKuYZLLBbnmUe3EQnHuPFdFzBtdsnpX2TMEFm7di3L\nli077TfraVuyVfWO7gkQ4BZVvVhVb1XVS4Dlg4xxCVChqvtUNQr8Frj+uH3Xq+qrgHWQaUaVV6pa\n+MjD29lR10F+0MMtC8dagj1ENB4ncu/3jybYt7xlVCbYAMHkDZGX0gIKD7eH+E5zTkoGrhmtcmZO\nYdonki3afzx1i/aLT+wkHlfGTS6wBHuYeb2entpsa802mWKgNz5eDTx83LJHSZSNDNR4jnYHCHAg\nuWzArCY7tax2K7V6H8+Yq/x8dTX3PFlJa3IExzsWlzNlTDCNEWaOgdZkazhC5J77iD/xbCLBXv4W\nPDOnDVF0I4MIvFZaebvUE9I4myM+vtCQx9bIieUjZ2tN9vGOT7Q3fPjLuOFje6Y9uK+JnZsP4/E4\nzJk/ts/tWE126vR1LKfMLMIf8FBTfYR9uxrSEFXmsvN6egy0aG8X8FHgh72WfRjYnbKIBuGFF15g\nzZo1TJo0CYD8/Hzmz5/f02VN95vL5vs3v2nTphEVT6bPdx/PyfMWc9/z+1jzyssIcPWyy7hsWgGb\nX30FONo9XXciafNnNj9vznzCn/0am19dCX4/85e/Hc+saWzevS3x/PRzAEb1/B3U8vPdB6jCx33T\nF3BNVphJ+1fjkaPd93Un2jY/j2mfuJ3H7v8JWx9+lEhDE4t++U1e2bgedZV9mxOnyy4OsGNXV08X\nc93JoM2ndr5b7+e9Xg8Rp5p9Bxv4+5N5TJ5RxIoVK4CR830/Uue7jZR4Mm2++3FVVRUAixcvZtmy\nZZzOaWuyj1lZZBHwJxLJ+UESLc8x4EZVXdvvDSW2tRT4sqpelZy/G1BVva+Pdb8EtFpNtslEqspf\ntjfwnysPEI4r+UEv18wew/Riu9w8VLS+ka5PfxGt2AN5OXhvvQln4ui4yXGgXIWXyWWF5qEiTPHG\n+HB+O+Ve6+uvLx1Vh6j8wQPEjrSRe+4MLvi/+6k8FOHx328kGPJy+TVz8PntptJ0iUXjPPNYojb7\njTfNY/4FE9IdkjkLpawmuzdVXQfMBG4B7ifRjd/MgSbYSauBGSIyWUT8JGq7Hz3F+lZUaDJOQ3uU\nLz5ZyQ9X7CccV84tzeK9S8otwR5C7r4DdH3gnxMJdvEYvHfcetYm2ACOwCXSym1SR57G2Bvzck9D\nHk+0BwY8eM3ZIGtSOTPv/gD+0jG0bt3Fius+zAt/2QrAjHPLLMFOM6/Pw9zzE5/n5/+ynfbWgQ8m\nZMxwGWhNNqoaVdW/q+rvVPXF5E2LA6aqceBjwJPAFuC3qrpNRD4oIh8AEJEyEdkPfAr4gohUiUjO\n8duymuzUstqtM6eqPL69nvf9cRtPPf8iQa/Dm+YUceP8UrJ8NnjFYJ2uJju+YhVd7/0UeqgGGT8W\n73tvxSnN3H6wU2mCRHiP1DCXdiII/9uWxZ2rKzkYG/BpYNQLlIxh1t0fJGvKeA4WTKW9PUaOX5ky\no/iUr7Oa7NQ51bEcP7mQkrG5hLtiPPvnbcMYVeay83p6pPVfclV9Aph93LL/7PW4Bpg43HEZcyYO\ntoT5/ktVbDjUBsD43AC3X1hOofWpO2RUldgDvyP6s/8BVWTODDw3vxknZDeU9hYU5c00MUc7+ZsW\ncDDm4Z6GPG7I6eKarC68dr2whzc3m7I738uajYl2pOJH/4eW8G7yb7kJETtQ6SQinHfhBJ57fDs7\nNh1m7vl11qWfGZEGVJM9UllNthkJwjGXBzfV8tv1h4nElWy/wxXTClkwLsdOykNIOzqJ3Ps94s+t\nABE8l16E84ZLEesc+pS6VHiWfDaSuDhY7olze24HcwPWYyqA6ypPbolR36YUtNcx4Xc/BSDrkqWU\nfOZOnKxQmiM0u7fVsnV9Nbn5Ae745Ovw2yBeZpgMSU22MeZEqspLe5t53x+28atXDxFJ1l6/f8k4\nFo7PtQR7CLnbK+i645OJBDsYwPO26/BceZkl2P0QFOUaaebt1FGoUQ7FPdzXnMu/N2fTELfjt2F/\nnPo2JeCF2fPLyH7XbRAI0PHSSqo/9lnCuyrTHeJZb+rsEvIKQ7S2hFnxdEW6wzHmBKMiybaa7NSy\n2q3+29PYyd1/3cVXn95DTVuE0mwfyxeUceP8UnKSrSoD7dvZnFz3sdR4nOgDv6frfXehVQeQSHQ+\nFAAAFjtJREFU0mK8770Nz3nnpjnCzLJ59zamSpj3Sg2X0YxXXVaF/XyuPp+H2oJ0nqUdkFQ3u2yp\ndhFgZqmD3+vgP2c2eR/7IE5JCdH9B6m+83M0/98fjxm4xmqyU6c/x9JxhAVLEhWla1/ex75d9UMd\nVsay83p6jIok25jhVn0kzDef28uHHtrOuuo2Ql6HZTMKed+SccwotsvIQ8k9VEP4o/9C9D8egHgc\n58KFeD78LpxxZekOLWN5BS6SNt4vNcymgwjCw+0h/rk+nyc7AkQzv6qw3zojysu7EiUzE8YIhTlH\nT5OekmLy7vwggaVLIBan6Re/4dBd9xA9dDhd4Z71CsZkMXNuGarwyG/W0VDblu6QjOlhNdnGDEB9\ne4TfrDvMEzsaiCt4HDhvbA6XTSvoabk2Q0NjMWK/e4Toz/8XOrsgLwfPtVfimTf79C82A7Jf/Tyv\n+RyUAADFTpwbcrp4bTAyqm+OVFWe2RbjcIuSH4L5EzwnLfeK7qyg/cE/oa1tSChI4e3LybvhGsRr\n3wPDTVVZ89JeDh9oIb8wxDs+ehGhLH+6wzKjWH9rsi3JNqYfDrR08eDGWp6uaCTqKgLMLcvm0mkF\njLFeQ4ZcfN1mIt/5CVq5DwA5dxaeN78RJ++EHj1NiqjCLoI8r/k0SOI9XuTEuTY7zKWhMP5Rlmyr\nKqv2xKmocfF5YMEkh5Dv1Bd73fYOOv70KNHNiX60fVMmUvSxDxBaMHc4Qja9xGJxVjy9iyNNnYyf\nUsjb3nMhHq9drDdD46y68dFqslPLareO2lHXztee2cN7H9zGX3c0EHOV2SVZvOfCcm6YV9KvBNtq\nsgfPPVxL+CvfJfyRz6GV+9ia7eC55S34brvJEuwU6B6CvS8iMFO6eK/U8CYaGaNRGlwPv2rN4q76\nfB5rD9Lqjo5MW1VZnUywHYGZZadPsAGc7Cxy3rGcnHffhlNYwIbK7Rz+53uo/eb3idVaffCZGGh9\nu9frYcmlUwkEvRzc28RTj2xhNDQipoqd19PDrmsZc5xwzOWFyiYe21bPjroOIFEWMrc0m6WT8ynN\nscuQQ00bGok+8HtiD/8VojHwevFctBjPlGI8c+akO7yziiMwjw7m0sFOgrysedS4fh5sC/GntiBL\ngxH+KSvMVF/89BsbgboT7J3JBHtOuUNRzsDan3xzZpM3fRr+h34Hmyp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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import scipy.stats as stats\n", + "\n", + "nor = stats.norm\n", + "x = np.linspace(-8, 7, 150)\n", + "mu = (-2, 0, 3)\n", + "tau = (.7, 1, 2.8)\n", + "colors = [\"#348ABD\", \"#A60628\", \"#7A68A6\"]\n", + "parameters = zip(mu, tau, colors)\n", + "\n", + "for _mu, _tau, _color in parameters:\n", + " plt.plot(x, nor.pdf(x, _mu, scale=1. / np.sqrt(_tau)),\n", + " label=\"$\\mu = %d,\\;\\\\tau = %.1f$\" % (_mu, _tau), color=_color)\n", + " plt.fill_between(x, nor.pdf(x, _mu, scale=1. / np.sqrt(_tau)), color=_color,\n", + " alpha=.33)\n", + "\n", + "plt.legend(loc=\"upper right\")\n", + "plt.xlabel(\"$x$\")\n", + "plt.ylabel(\"density function at $x$\")\n", + "plt.title(\"Probability distribution of three different Normal random \\\n", + "variables\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A Normal random variable can be take on any real number, but the variable is very likely to be relatively close to $\\mu$. In fact, the expected value of a Normal is equal to its $\\mu$ parameter:\n", + "\n", + "$$ E[ X | \\mu, \\tau] = \\mu$$\n", + "\n", + "and its variance is equal to the inverse of $\\tau$:\n", + "\n", + "$$Var( X | \\mu, \\tau ) = \\frac{1}{\\tau}$$\n", + "\n", + "\n", + "\n", + "Below we continue our modeling of the Challenger space craft:" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import pymc as pm\n", + "\n", + "temperature = challenger_data[:, 0]\n", + "D = challenger_data[:, 1] # defect or not?\n", + "\n", + "# notice the`value` here. We explain why below.\n", + "beta = pm.Normal(\"beta\", 0, 0.001, value=0)\n", + "alpha = pm.Normal(\"alpha\", 0, 0.001, value=0)\n", + "\n", + "\n", + "@pm.deterministic\n", + "def p(t=temperature, alpha=alpha, beta=beta):\n", + " return 1.0 / (1. + np.exp(beta * t + alpha))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We have our probabilities, but how do we connect them to our observed data? A *Bernoulli* random variable with parameter $p$, denoted $\\text{Ber}(p)$, is a random variable that takes value 1 with probability $p$, and 0 else. Thus, our model can look like:\n", + "\n", + "$$ \\text{Defect Incident, $D_i$} \\sim \\text{Ber}( \\;p(t_i)\\; ), \\;\\; i=1..N$$\n", + "\n", + "where $p(t)$ is our logistic function and $t_i$ are the temperatures we have observations about. Notice in the above code we had to set the values of `beta` and `alpha` to 0. The reason for this is that if `beta` and `alpha` are very large, they make `p` equal to 1 or 0. Unfortunately, `pm.Bernoulli` does not like probabilities of exactly 0 or 1, though they are mathematically well-defined probabilities. So by setting the coefficient values to `0`, we set the variable `p` to be a reasonable starting value. This has no effect on our results, nor does it mean we are including any additional information in our prior. It is simply a computational caveat in PyMC. " + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5,\n", + " 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5,\n", + " 0.5])" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "p.value" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " [-----------------100%-----------------] 120000 of 120000 complete in 9.6 sec" + ] + } + ], + "source": [ + "# connect the probabilities in `p` with our observations through a\n", + "# Bernoulli random variable.\n", + "observed = pm.Bernoulli(\"bernoulli_obs\", p, value=D, observed=True)\n", + "\n", + "model = pm.Model([observed, beta, alpha])\n", + "\n", + "# Mysterious code to be explained in Chapter 3\n", + "map_ = pm.MAP(model)\n", + "map_.fit()\n", + "mcmc = pm.MCMC(model)\n", + "mcmc.sample(120000, 100000, 2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We have trained our model on the observed data, now we can sample values from the posterior. Let's look at the posterior distributions for $\\alpha$ and $\\beta$:" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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lIxzKRfkIZqIuIiIiIiK7BTNR1zrq4dD6q2HROuphUT7CofeqsCgf4VAuyodq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\nIiIiIhIo0UC9HrSPejjtZRpHuYVTZnGUW7ixuLf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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "alpha_samples = mcmc.trace('alpha')[:, None] # best to make them 1d\n", + "beta_samples = mcmc.trace('beta')[:, None]\n", + "\n", + "figsize(12.5, 6)\n", + "\n", + "# histogram of the samples:\n", + "plt.subplot(211)\n", + "plt.title(r\"Posterior distributions of the variables $\\alpha, \\beta$\")\n", + "plt.hist(beta_samples, histtype='stepfilled', bins=35, alpha=0.85,\n", + " label=r\"posterior of $\\beta$\", color=\"#7A68A6\", density=True)\n", + "plt.legend()\n", + "\n", + "plt.subplot(212)\n", + "plt.hist(alpha_samples, histtype='stepfilled', bins=35, alpha=0.85,\n", + " label=r\"posterior of $\\alpha$\", color=\"#A60628\", density=True)\n", + "plt.legend();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "All samples of $\\beta$ are greater than 0. If instead the posterior was centered around 0, we may suspect that $\\beta = 0$, implying that temperature has no effect on the probability of defect. \n", + "\n", + "Similarly, all $\\alpha$ posterior values are negative and far away from 0, implying that it is correct to believe that $\\alpha$ is significantly less than 0. \n", + "\n", + "Regarding the spread of the data, we are very uncertain about what the true parameters might be (though considering the low sample size and the large overlap of defects-to-nondefects this behaviour is perhaps expected). \n", + "\n", + "Next, let's look at the *expected probability* for a specific value of the temperature. That is, we average over all samples from the posterior to get a likely value for $p(t_i)$." + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "t = np.linspace(temperature.min() - 5, temperature.max() + 5, 50)[:, None]\n", + "p_t = logistic(t.T, beta_samples, alpha_samples)\n", + "\n", + "mean_prob_t = p_t.mean(axis=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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Vhxtp9/rhcGOX+ROn5eGJ0DH/9l+2s23nQVI9B0hJi+989NTAV/3XUz57TEoS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\ntLQ0FixYQ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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "figsize(12.5, 4)\n", + "\n", + "plt.plot(t, mean_prob_t, lw=3, label=\"average posterior \\nprobability \\\n", + "of defect\")\n", + "plt.plot(t, p_t[0, :], ls=\"--\", label=\"realization from posterior\")\n", + "plt.plot(t, p_t[-2, :], ls=\"--\", label=\"realization from posterior\")\n", + "plt.scatter(temperature, D, color=\"k\", s=50, alpha=0.5)\n", + "plt.title(\"Posterior expected value of probability of defect; \\\n", + "plus realizations\")\n", + "plt.legend(loc=\"lower left\")\n", + "plt.ylim(-0.1, 1.1)\n", + "plt.xlim(t.min(), t.max())\n", + "plt.ylabel(\"probability\")\n", + "plt.xlabel(\"temperature\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Above we also plotted two possible realizations of what the actual underlying system might be. Both are equally likely as any other draw. The blue line is what occurs when we average all the 20000 possible dotted lines together.\n", + "\n", + "\n", + "An interesting question to ask is for what temperatures are we most uncertain about the defect-probability? Below we plot the expected value line **and** the associated 95% intervals for each temperature. " + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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Ez371g/z83m/w8utP0elrT3RofRrNuU52DqdFa4uPLX/bRW11K6A1qHbTfNtL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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from scipy.stats.mstats import mquantiles\n", + "\n", + "# vectorized bottom and top 2.5% quantiles for \"confidence interval\"\n", + "qs = mquantiles(p_t, [0.025, 0.975], axis=0)\n", + "plt.fill_between(t[:, 0], *qs, alpha=0.7,\n", + " color=\"#7A68A6\")\n", + "\n", + "plt.plot(t[:, 0], qs[0], label=\"95% CI\", color=\"#7A68A6\", alpha=0.7)\n", + "\n", + "plt.plot(t, mean_prob_t, lw=1, ls=\"--\", color=\"k\",\n", + " label=\"average posterior \\nprobability of defect\")\n", + "\n", + "plt.xlim(t.min(), t.max())\n", + "plt.ylim(-0.02, 1.02)\n", + "plt.legend(loc=\"lower left\")\n", + "plt.scatter(temperature, D, color=\"k\", s=50, alpha=0.5)\n", + "plt.xlabel(\"temp, $t$\")\n", + "\n", + "plt.ylabel(\"probability estimate\")\n", + "plt.title(\"Posterior probability estimates given temp. $t$\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The *95% credible interval*, or 95% CI, painted in purple, represents the interval, for each temperature, that contains 95% of the distribution. For example, at 65 degrees, we can be 95% sure that the probability of defect lies between 0.25 and 0.75.\n", + "\n", + "More generally, we can see that as the temperature nears 60 degrees, the CI's spread out over [0,1] quickly. As we pass 70 degrees, the CI's tighten again. This can give us insight about how to proceed next: we should probably test more O-rings around 60-65 temperature to get a better estimate of probabilities in that range. Similarly, when reporting to scientists your estimates, you should be very cautious about simply telling them the expected probability, as we can see this does not reflect how *wide* the posterior distribution is." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### What about the day of the Challenger disaster?\n", + "\n", + "On the day of the Challenger disaster, the outside temperature was 31 degrees Fahrenheit. What is the posterior distribution of a defect occurring, given this temperature? The distribution is plotted below. It looks almost guaranteed that the Challenger was going to be subject to defective O-rings." + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "figsize(12.5, 2.5)\n", + "\n", + "prob_31 = logistic(31, beta_samples, alpha_samples)\n", + "\n", + "plt.xlim(0.995, 1)\n", + "plt.hist(prob_31, bins=1000, density=True, histtype='stepfilled')\n", + "plt.title(\"Posterior distribution of probability of defect, given $t = 31$\")\n", + "plt.xlabel(\"probability of defect occurring in O-ring\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Is our model appropriate?\n", + "\n", + "The skeptical reader will say \"You deliberately chose the logistic function for $p(t)$ and the specific priors. Perhaps other functions or priors will give different results. How do I know I have chosen a good model?\" This is absolutely true. To consider an extreme situation, what if I had chosen the function $p(t) = 1,\\; \\forall t$, which guarantees a defect always occurring: I would have again predicted disaster on January 28th. Yet this is clearly a poorly chosen model. On the other hand, if I did choose the logistic function for $p(t)$, but specified all my priors to be very tight around 0, likely we would have very different posterior distributions. How do we know our model is an expression of the data? This encourages us to measure the model's **goodness of fit**.\n", + "\n", + "We can think: *how can we test whether our model is a bad fit?* An idea is to compare observed data (which if we recall is a *fixed* stochastic variable) with an artificial dataset which we can simulate. The rationale is that if the simulated dataset does not appear similar, statistically, to the observed dataset, then likely our model is not accurately represented the observed data. \n", + "\n", + "Previously in this Chapter, we simulated artificial datasets for the SMS example. To do this, we sampled values from the priors. We saw how varied the resulting datasets looked like, and rarely did they mimic our observed dataset. In the current example, we should sample from the *posterior* distributions to create *very plausible datasets*. Luckily, our Bayesian framework makes this very easy. We only need to create a new `Stochastic` variable, that is exactly the same as our variable that stored the observations, but minus the observations themselves. If you recall, our `Stochastic` variable that stored our observed data was:\n", + "\n", + " observed = pm.Bernoulli( \"bernoulli_obs\", p, value=D, observed=True)\n", + "\n", + "Hence we create:\n", + " \n", + " simulated_data = pm.Bernoulli(\"simulation_data\", p)\n", + "\n", + "Let's simulate 10 000:" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " [-----------------100%-----------------] 10000 of 10000 complete in 1.5 sec" + ] + } + ], + "source": [ + "simulated = pm.Bernoulli(\"bernoulli_sim\", p)\n", + "N = 10000\n", + "\n", + "mcmc = pm.MCMC([simulated, alpha, beta, observed])\n", + "mcmc.sample(N)" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(10000, 23)\n" + ] + }, + { + "data": { + "image/png": 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NpKdlvDVnn5eyPwJzgEHgWnf/fn59mkc9HK2tmn81JMojLMojHMoiLMojHMoi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\n48a66svn8zQ3N9dVX9xCz484hdy2tSCuWV8edPd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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "figsize(12.5, 5)\n", + "\n", + "simulations = mcmc.trace(\"bernoulli_sim\")[:]\n", + "print(simulations.shape)\n", + "\n", + "plt.title(\"Simulated dataset using posterior parameters\")\n", + "figsize(12.5, 6)\n", + "for i in range(4):\n", + " ax = plt.subplot(4, 1, i + 1)\n", + " plt.scatter(temperature, simulations[1000 * i, :], color=\"k\",\n", + " s=50, alpha=0.6)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that the above plots are different (if you can think of a cleaner way to present this, please send a pull request and answer [here](http://stats.stackexchange.com/questions/53078/how-to-visualize-bayesian-goodness-of-fit-for-logistic-regression)!).\n", + "\n", + "We wish to assess how good our model is. \"Good\" is a subjective term of course, so results must be relative to other models. \n", + "\n", + "We will be doing this graphically as well, which may seem like an even less objective method. The alternative is to use *Bayesian p-values*. These are still subjective, as the proper cutoff between good and bad is arbitrary. Gelman emphasises that the graphical tests are more illuminating [7] than p-value tests. We agree.\n", + "\n", + "The following graphical test is a novel data-viz approach to logistic regression. The plots are called *separation plots*[8]. For a suite of models we wish to compare, each model is plotted on an individual separation plot. I leave most of the technical details about separation plots to the very accessible [original paper](https://onlinelibrary.wiley.com/doi/10.1111/j.1540-5907.2011.00525.x), but I'll summarize their use here.\n", + "\n", + "For each model, we calculate the proportion of times the posterior simulation proposed a value of 1 for a particular temperature, i.e. compute $P( \\;\\text{Defect} = 1 | t, \\alpha, \\beta )$ by averaging. This gives us the posterior probability of a defect at each data point in our dataset. For example, for the model we used above:" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "posterior prob of defect | realized defect \n", + "0.41 | 0\n", + "0.24 | 1\n", + "0.28 | 0\n", + "0.32 | 0\n", + "0.36 | 0\n", + "0.18 | 0\n", + "0.16 | 0\n", + "0.25 | 0\n", + "0.77 | 1\n", + "0.54 | 1\n", + "0.24 | 1\n", + "0.07 | 0\n", + "0.37 | 0\n", + "0.86 | 1\n", + "0.36 | 0\n", + "0.11 | 0\n", + "0.24 | 0\n", + "0.05 | 0\n", + "0.10 | 0\n", + "0.06 | 0\n", + "0.12 | 1\n", + "0.10 | 0\n", + "0.75 | 1\n" + ] + } + ], + "source": [ + "posterior_probability = simulations.mean(axis=0)\n", + "print(\"posterior prob of defect | realized defect \")\n", + "for i in range(len(D)):\n", + " print(\"%.2f | %d\" % (posterior_probability[i], D[i]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next we sort each column by the posterior probabilities:" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "probb | defect \n", + "0.05 | 0\n", + "0.06 | 0\n", + "0.07 | 0\n", + "0.10 | 0\n", + "0.10 | 0\n", + "0.11 | 0\n", + "0.12 | 1\n", + "0.16 | 0\n", + "0.18 | 0\n", + "0.24 | 1\n", + "0.24 | 1\n", + "0.24 | 0\n", + "0.25 | 0\n", + "0.28 | 0\n", + "0.32 | 0\n", + "0.36 | 0\n", + "0.36 | 0\n", + "0.37 | 0\n", + "0.41 | 0\n", + "0.54 | 1\n", + "0.75 | 1\n", + "0.77 | 1\n", + "0.86 | 1\n" + ] + } + ], + "source": [ + "ix = np.argsort(posterior_probability)\n", + "print(\"probb | defect \")\n", + "for i in range(len(D)):\n", + " print(\"%.2f | %d\" % (posterior_probability[ix[i]], D[ix[i]]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can present the above data better in a figure: I've wrapped this up into a `separation_plot` function." + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from separation_plot import separation_plot\n", + "\n", + "\n", + "figsize(11., 1.5)\n", + "separation_plot(posterior_probability, D)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The snaking-line is the sorted probabilities, blue bars denote defects, and empty space (or grey bars for the optimistic readers) denote non-defects. As the probability rises, we see more and more defects occur. On the right hand side, the plot suggests that as the posterior probability is large (line close to 1), then more defects are realized. This is good behaviour. Ideally, all the blue bars *should* be close to the right-hand side, and deviations from this reflect missed predictions. \n", + "\n", + "The black vertical line is the expected number of defects we should observe, given this model. This allows the user to see how the total number of events predicted by the model compares to the actual number of events in the data.\n", + "\n", + "It is much more informative to compare this to separation plots for other models. Below we compare our model (top) versus three others:\n", + "\n", + "1. the perfect model, which predicts the posterior probability to be equal to 1 if a defect did occur.\n", + "2. a completely random model, which predicts random probabilities regardless of temperature.\n", + "3. a constant model: where $P(D = 1 \\; | \\; t) = c, \\;\\; \\forall t$. The best choice for $c$ is the observed frequency of defects, in this case 7/23. \n" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "figsize(11., 1.25)\n", + "\n", + "# Our temperature-dependent model\n", + "separation_plot(posterior_probability, D)\n", + "plt.title(\"Temperature-dependent model\")\n", + "\n", + "# Perfect model\n", + "# i.e. the probability of defect is equal to if a defect occurred or not.\n", + "p = D\n", + "separation_plot(p, D)\n", + "plt.title(\"Perfect model\")\n", + "\n", + "# random predictions\n", + "p = np.random.rand(23)\n", + "separation_plot(p, D)\n", + "plt.title(\"Random model\")\n", + "\n", + "# constant model\n", + "constant_prob = 7. / 23 * np.ones(23)\n", + "separation_plot(constant_prob, D)\n", + "plt.title(\"Constant-prediction model\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the random model, we can see that as the probability increases there is no clustering of defects to the right-hand side. Similarly for the constant model.\n", + "\n", + "The perfect model, the probability line is not well shown, as it is stuck to the bottom and top of the figure. Of course the perfect model is only for demonstration, and we cannot infer any scientific inference from it." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Exercises\n", + "\n", + "1\\. Try putting in extreme values for our observations in the cheating example. What happens if we observe 25 affirmative responses? 10? 50? " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "2\\. Try plotting $\\alpha$ samples versus $\\beta$ samples. Why might the resulting plot look like this?" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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6565Qq5aTy8H4uFYXtLIAPpWyxa0SCRgYgO7ugJaWiELBFqiKooj+fiWbhe5u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\nMcYYY0yVs6TeGGOMMcaYKvf/A9xHQXd0r+z6AAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# type your code here.\n", + "figsize(12.5, 4)\n", + "\n", + "plt.scatter(alpha_samples, beta_samples, alpha=0.1)\n", + "plt.title(\"Why does the plot look like this?\")\n", + "plt.xlabel(r\"$\\alpha$\")\n", + "plt.ylabel(r\"$\\beta$\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### References\n", + "\n", + "- [1] Dalal, Fowlkes and Hoadley (1989),JASA, 84, 945-957.\n", + "- [2] German Rodriguez. Datasets. In WWS509. Retrieved 30/01/2013, from .\n", + "- [3] McLeish, Don, and Cyntha Struthers. STATISTICS 450/850 Estimation and Hypothesis Testing. Winter 2012. Waterloo, Ontario: 2012. Print.\n", + "- [4] Fonnesbeck, Christopher. \"Building Models.\" PyMC-Devs. N.p., n.d. Web. 26 Feb 2013. .\n", + "- [5] Cronin, Beau. \"Why Probabilistic Programming Matters.\" 24 Mar 2013. Google, Online Posting to Google . Web. 24 Mar. 2013. .\n", + "- [6] S.P. Brooks, E.A. Catchpole, and B.J.T. Morgan. Bayesian animal survival estimation. Statistical Science, 15: 357–376, 2000\n", + "- [7] Gelman, Andrew. \"Philosophy and the practice of Bayesian statistics.\" British Journal of Mathematical and Statistical Psychology. (2012): n. page. Web. 2 Apr. 2013.\n", + "- [8] Greenhill, Brian, Michael D. Ward, and Audrey Sacks. \"The Separation Plot: A New Visual Method for Evaluating the Fit of Binary Models.\" American Journal of Political Science. 55.No.4 (2011): n. page. Web. 2 Apr. 2013." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "from IPython.core.display import HTML\n", + "\n", + "\n", + "def css_styling():\n", + " styles = open(\"../styles/custom.css\", \"r\").read()\n", + " return HTML(styles)\n", + "css_styling()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python [conda env:bayes]", + "language": "python", + "name": "conda-env-bayes-py" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.2" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/Chapter2_MorePyMC/Ch2_MorePyMC_PyMC3.ipynb b/Chapter2_MorePyMC/Ch2_MorePyMC_PyMC3.ipynb new file mode 100644 index 00000000..431b43de --- /dev/null +++ b/Chapter2_MorePyMC/Ch2_MorePyMC_PyMC3.ipynb @@ -0,0 +1,2668 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Chapter 2\n", + "======\n", + "`Original content created by Cam Davidson-Pilon`\n", + "\n", + "`Ported to Python 3 and PyMC3 by Max Margenot (@clean_utensils) and Thomas Wiecki (@twiecki) at Quantopian (@quantopian)`\n", + "\n", + "___\n", + "\n", + "This chapter introduces more PyMC3 syntax and variables and ways to think about how to model a system from a Bayesian perspective. It also contains tips and data visualization techniques for assessing goodness-of-fit for your Bayesian model." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## A little more on PyMC3\n", + "\n", + "### Model Context\n", + "\n", + "In PyMC3, we typically handle all the variables we want in our model within the context of the `Model` object." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Applied log-transform to poisson_param and added transformed poisson_param_log_ to model.\n" + ] + } + ], + "source": [ + "import pymc3 as pm\n", + "\n", + "with pm.Model() as model:\n", + " parameter = pm.Exponential(\"poisson_param\", 1.0)\n", + " data_generator = pm.Poisson(\"data_generator\", parameter)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is an extra layer of convenience compared to PyMC. Any variables created within a given `Model`'s context will be automatically assigned to that model. If you try to define a variable outside of the context of a model, you will get an error.\n", + "\n", + "We can continue to work within the context of the same model by using `with` with the name of the model object that we have already created." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "with model:\n", + " data_plus_one = data_generator + 1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can examine the same variables outside of the model context once they have been defined, but to define more variables that the model will recognize they have to be within the context." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array(0.693147177890573)" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "parameter.tag.test_value" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Each variable assigned to a model will be defined with its own name, the first string parameter (we will cover this further in the variables section). To create a different model object with the same name as one we have used previously, we need only run the first block of code again." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Applied log-transform to theta and added transformed theta_log_ to model.\n" + ] + } + ], + "source": [ + "with pm.Model() as model:\n", + " theta = pm.Exponential(\"theta\", 2.0)\n", + " data_generator = pm.Poisson(\"data_generator\", theta)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also define an entirely separate model. Note that we are free to name our models whatever we like, so if we do not want to overwrite an old model we need only make another." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Applied interval-transform to P(A) and added transformed P(A)_interval_ to model.\n", + "Applied interval-transform to P(B) and added transformed P(B)_interval_ to model.\n" + ] + } + ], + "source": [ + "with pm.Model() as ab_testing:\n", + " p_A = pm.Uniform(\"P(A)\", 0, 1)\n", + " p_B = pm.Uniform(\"P(B)\", 0, 1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You probably noticed that PyMC3 will often give you notifications about transformations when you add variables to your model. These transformations are done internally by PyMC3 to modify the space that the variable is sampled in (when we get to actually sampling the model). This is an internal feature which helps with the convergence of our samples to the posterior distribution and serves to improve the results." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### PyMC3 Variables\n", + "\n", + "All PyMC3 variables have an initial value (i.e. test value). Using the same variables from before:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "parameter.tag.test_value = 0.693147177890573\n", + "data_generator.tag.test_value = 0\n", + "data_plus_one.tag.test_value = 1\n" + ] + } + ], + "source": [ + "print(\"parameter.tag.test_value =\", parameter.tag.test_value)\n", + "print(\"data_generator.tag.test_value =\", data_generator.tag.test_value)\n", + "print(\"data_plus_one.tag.test_value =\", data_plus_one.tag.test_value)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `test_value` is used only for the model, as the starting point for sampling if no other start is specified. It will not change as a result of sampling. This initial state can be changed at variable creation by specifying a value for the `testval` parameter." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Applied log-transform to poisson_param and added transformed poisson_param_log_ to model.\n", + "\n", + "parameter.tag.test_value = 0.49999999904767284\n" + ] + } + ], + "source": [ + "with pm.Model() as model:\n", + " parameter = pm.Exponential(\"poisson_param\", 1.0, testval=0.5)\n", + "\n", + "print(\"\\nparameter.tag.test_value =\", parameter.tag.test_value)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This can be helpful if you are using a more unstable prior that may require a better starting point.\n", + "\n", + "PyMC3 is concerned with two types of programming variables: stochastic and deterministic.\n", + "\n", + "* *stochastic variables* are variables that are not deterministic, i.e., even if you knew all the values of the variables' parameters and components, it would still be random. Included in this category are instances of classes `Poisson`, `DiscreteUniform`, and `Exponential`.\n", + "\n", + "* *deterministic variables* are variables that are not random if the variables' parameters and components were known. This might be confusing at first: a quick mental check is *if I knew all of variable `foo`'s component variables, I could determine what `foo`'s value is.* \n", + "\n", + "We will detail each below.\n", + "\n", + "#### Initializing Stochastic variables\n", + "\n", + "Initializing a stochastic, or random, variable requires a `name` argument, plus additional parameters that are class specific. For example:\n", + "\n", + "`some_variable = pm.DiscreteUniform(\"discrete_uni_var\", 0, 4)`\n", + "\n", + "where 0, 4 are the `DiscreteUniform`-specific lower and upper bound on the random variable. The [PyMC3 docs](https://docs.pymc.io/en/stable/api.html) contain the specific parameters for stochastic variables. (Or use `??` if you are using IPython!)\n", + "\n", + "The `name` attribute is used to retrieve the posterior distribution later in the analysis, so it is best to use a descriptive name. Typically, I use the Python variable's name as the `name`.\n", + "\n", + "For multivariable problems, rather than creating a Python array of stochastic variables, addressing the `shape` keyword in the call to a stochastic variable creates multivariate array of (independent) stochastic variables. The array behaves like a NumPy array when used like one, and references to its `tag.test_value` attribute return NumPy arrays. \n", + "\n", + "The `shape` argument also solves the annoying case where you may have many variables $\\beta_i, \\; i = 1,...,N$ you wish to model. Instead of creating arbitrary names and variables for each one, like:\n", + "\n", + " beta_1 = pm.Uniform(\"beta_1\", 0, 1)\n", + " beta_2 = pm.Uniform(\"beta_2\", 0, 1)\n", + " ...\n", + "\n", + "we can instead wrap them into a single variable:\n", + "\n", + " betas = pm.Uniform(\"betas\", 0, 1, shape=N)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Deterministic variables\n", + "\n", + "We can create a deterministic variable similarly to how we create a stochastic variable. We simply call up the `Deterministic` class in PyMC3 and pass in the function that we desire\n", + "\n", + " deterministic_variable = pm.Deterministic(\"deterministic variable\", some_function_of_variables)\n", + "\n", + "For all purposes, we can treat the object `some_deterministic_var` as a variable and not a Python function. \n", + "\n", + "Calling `pymc3.Deterministic` is the most obvious way, but not the only way, to create deterministic variables. Elementary operations, like addition, exponentials etc. implicitly create deterministic variables. For example, the following returns a deterministic variable:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Applied log-transform to lambda_1 and added transformed lambda_1_log_ to model.\n", + "Applied log-transform to lambda_2 and added transformed lambda_2_log_ to model.\n" + ] + } + ], + "source": [ + "with pm.Model() as model:\n", + " lambda_1 = pm.Exponential(\"lambda_1\", 1.0)\n", + " lambda_2 = pm.Exponential(\"lambda_2\", 1.0)\n", + " tau = pm.DiscreteUniform(\"tau\", lower=0, upper=10)\n", + "\n", + "new_deterministic_variable = lambda_1 + lambda_2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If we want a `deterministic` variable to actually be tracked by our sampling, however, we need to define it explicitly as a named `deterministic` variable with the constructor.\n", + "\n", + "The use of the `deterministic` variable was seen in the previous chapter's text-message example. Recall the model for $\\lambda$ looked like: \n", + "\n", + "$$\n", + "\\lambda = \n", + "\\begin{cases}\\lambda_1 & \\text{if } t \\lt \\tau \\cr\n", + "\\lambda_2 & \\text{if } t \\ge \\tau\n", + "\\end{cases}\n", + "$$\n", + "\n", + "And in PyMC3 code:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "n_data_points = 5 # in CH1 we had ~70 data points\n", + "idx = np.arange(n_data_points)\n", + "with model:\n", + " lambda_ = pm.math.switch(tau >= idx, lambda_1, lambda_2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Clearly, if $\\tau, \\lambda_1$ and $\\lambda_2$ are known, then $\\lambda$ is known completely, hence it is a deterministic variable. We use the `switch` function here to change from $\\lambda_1$ to $\\lambda_2$ at the appropriate time. This function is directly from the `theano` package, which we will discuss in the next section.\n", + "\n", + "Inside a `deterministic` variable, the stochastic variables passed in behave like scalars or NumPy arrays (if multivariable). We can do whatever we want with them as long as the dimensions match up in our calculations.\n", + "\n", + "For example, running the following:\n", + "\n", + " def subtract(x, y):\n", + " return x - y\n", + " \n", + " stochastic_1 = pm.Uniform(\"U_1\", 0, 1)\n", + " stochastic_2 = pm.Uniform(\"U_2\", 0, 1)\n", + " \n", + " det_1 = pm.Deterministic(\"Delta\", subtract(stochastic_1, stochastic_2))\n", + " \n", + "Is perfectly valid PyMC3 code. Saying that our expressions behave like NumPy arrays is not exactly honest here, however. The main catch is that the expression that we are making *must* be compatible with `theano` tensors, which we will cover in the next section. Feel free to define whatever functions that you need in order to compose your model. However, if you need to do any array-like calculations that would require NumPy functions, make sure you use their equivalents in `theano`." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Theano\n", + "\n", + "The majority of the heavy lifting done by PyMC3 is taken care of with the `theano` package. The notation in `theano` is remarkably similar to NumPy. It also supports many of the familiar computational elements of NumPy. However, while NumPy directly executes computations, e.g. when you run `a + b`, `theano` instead builds up a \"compute graph\" that tracks that you want to perform the `+` operation on the elements `a` and `b`. Only when you `eval()` a `theano` expression does the computation take place (i.e. `theano` is lazy evaluated). Once the compute graph is built, we can perform all kinds of mathematical optimizations (e.g. simplifications), compute gradients via autodiff, compile the entire graph to C to run at machine speed, and also compile it to run on the GPU. PyMC3 is basically a collection of `theano` symbolic expressions for various probability distributions that are combined to one big compute graph making up the whole model log probability, and a collection of inference algorithms that use that graph to compute probabilities and gradients. For practical purposes, what this means is that in order to build certain models we sometimes have to use `theano`.\n", + "\n", + "Let's write some PyMC3 code that involves `theano` calculations." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Applied interval-transform to p and added transformed p_interval_ to model.\n" + ] + } + ], + "source": [ + "import theano.tensor as tt\n", + "\n", + "with pm.Model() as theano_test:\n", + " p1 = pm.Uniform(\"p\", 0, 1)\n", + " p2 = 1 - p1\n", + " p = tt.stack([p1, p2])\n", + " \n", + " assignment = pm.Categorical(\"assignment\", p)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here we use `theano`'s `stack()` function in the same way we would use one of NumPy's stacking functions: to combine our two separate variables, `p1` and `p2`, into a vector with $2$ elements. The stochastic `categorical` variable does not understand what we mean if we pass a NumPy array of `p1` and `p2` to it because they are both `theano` variables. Stacking them like this combines them into one `theano` variable that we can use as the complementary pair of probabilities for our two categories.\n", + "\n", + "Throughout the course of this book we use several `theano` functions to help construct our models. If you have more interest in looking at `theano` itself, be sure to check out the [documentation](http://deeplearning.net/software/theano/library/).\n", + "\n", + "After these technical considerations, we can get back to defining our model!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Including observations in the Model\n", + "\n", + "At this point, it may not look like it, but we have fully specified our priors. For example, we can ask and answer questions like \"What does my prior distribution of $\\lambda_1$ look like?\" " + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib inline\n", + "from IPython.core.pylabtools import figsize\n", + "import matplotlib.pyplot as plt\n", + "import scipy.stats as stats\n", + "figsize(12.5, 4)\n", + "\n", + "\n", + "samples = lambda_1.random(size=20000)\n", + "plt.hist(samples, bins=70, density=True, histtype=\"stepfilled\")\n", + "plt.title(\"Prior distribution for $\\lambda_1$\")\n", + "plt.xlim(0, 8);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To frame this in the notation of the first chapter, though this is a slight abuse of notation, we have specified $P(A)$. Our next goal is to include data/evidence/observations $X$ into our model. \n", + "\n", + "PyMC3 stochastic variables have a keyword argument `observed`. The keyword `observed` has a very simple role: fix the variable's current value to be the given data, typically a NumPy `array` or pandas `DataFrame`. For example:" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "value: [10 5]\n" + ] + } + ], + "source": [ + "data = np.array([10, 5])\n", + "with model:\n", + " fixed_variable = pm.Poisson(\"fxd\", 1, observed=data)\n", + "print(\"value: \", fixed_variable.tag.test_value)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is how we include data into our models: initializing a stochastic variable to have a *fixed value*. \n", + "\n", + "To complete our text message example, we fix the PyMC3 variable `observations` to the observed dataset. " + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[10 25 15 20 35]\n" + ] + } + ], + "source": [ + "# We're using some fake data here\n", + "data = np.array([10, 25, 15, 20, 35])\n", + "with model:\n", + " obs = pm.Poisson(\"obs\", lambda_, observed=data)\n", + "print(obs.tag.test_value)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Modeling approaches\n", + "\n", + "A good starting thought to Bayesian modeling is to think about *how your data might have been generated*. Position yourself in an omniscient position, and try to imagine how *you* would recreate the dataset. \n", + "\n", + "In the last chapter we investigated text message data. We begin by asking how our observations may have been generated:\n", + "\n", + "1. We started by thinking \"what is the best random variable to describe this count data?\" A Poisson random variable is a good candidate because it can represent count data. So we model the number of sms's received as sampled from a Poisson distribution.\n", + "\n", + "2. Next, we think, \"Ok, assuming sms's are Poisson-distributed, what do I need for the Poisson distribution?\" Well, the Poisson distribution has a parameter $\\lambda$. \n", + "\n", + "3. Do we know $\\lambda$? No. In fact, we have a suspicion that there are *two* $\\lambda$ values, one for the earlier behaviour and one for the later behaviour. We don't know when the behaviour switches though, but call the switchpoint $\\tau$.\n", + "\n", + "4. What is a good distribution for the two $\\lambda$s? The exponential is good, as it assigns probabilities to positive real numbers. Well the exponential distribution has a parameter too, call it $\\alpha$.\n", + "\n", + "5. Do we know what the parameter $\\alpha$ might be? No. At this point, we could continue and assign a distribution to $\\alpha$, but it's better to stop once we reach a set level of ignorance: whereas we have a prior belief about $\\lambda$, (\"it probably changes over time\", \"it's likely between 10 and 30\", etc.), we don't really have any strong beliefs about $\\alpha$. So it's best to stop here. \n", + "\n", + " What is a good value for $\\alpha$ then? We think that the $\\lambda$s are between 10-30, so if we set $\\alpha$ really low (which corresponds to larger probability on high values) we are not reflecting our prior well. Similar, a too-high alpha misses our prior belief as well. A good idea for $\\alpha$ as to reflect our belief is to set the value so that the mean of $\\lambda$, given $\\alpha$, is equal to our observed mean. This was shown in the last chapter.\n", + "\n", + "6. We have no expert opinion of when $\\tau$ might have occurred. So we will suppose $\\tau$ is from a discrete uniform distribution over the entire timespan.\n", + "\n", + "\n", + "Below we give a graphical visualization of this, where arrows denote `parent-child` relationships. (provided by the [Daft Python library](http://daft-pgm.org/) )\n", + "\n", + "\n", + "\n", + "\n", + "PyMC3, and other probabilistic programming languages, have been designed to tell these data-generation *stories*. More generally, B. Cronin writes [5]:\n", + "\n", + "> Probabilistic programming will unlock narrative explanations of data, one of the holy grails of business analytics and the unsung hero of scientific persuasion. People think in terms of stories - thus the unreasonable power of the anecdote to drive decision-making, well-founded or not. But existing analytics largely fails to provide this kind of story; instead, numbers seemingly appear out of thin air, with little of the causal context that humans prefer when weighing their options." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Same story; different ending.\n", + "\n", + "Interestingly, we can create *new datasets* by retelling the story.\n", + "For example, if we reverse the above steps, we can simulate a possible realization of the dataset.\n", + "\n", + "1\\. Specify when the user's behaviour switches by sampling from $\\text{DiscreteUniform}(0, 80)$:" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "59\n" + ] + } + ], + "source": [ + "tau = np.random.randint(0, 80)\n", + "print(tau)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "2\\. Draw $\\lambda_1$ and $\\lambda_2$ from an $\\text{Exp}(\\alpha)$ distribution:" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "49.7521280843 10.1226712418\n" + ] + } + ], + "source": [ + "alpha = 1./20.\n", + "lambda_1, lambda_2 = np.random.exponential(scale=1/alpha, size=2)\n", + "print(lambda_1, lambda_2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "3\\. For days before $\\tau$, represent the user's received SMS count by sampling from $\\text{Poi}(\\lambda_1)$, and sample from $\\text{Poi}(\\lambda_2)$ for days after $\\tau$. For example:" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "data = np.r_[stats.poisson.rvs(mu=lambda_1, size=tau), stats.poisson.rvs(mu=lambda_2, size = 80 - tau)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "4\\. Plot the artificial dataset:" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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3/9zjNbZ9VVUVq9ZvZuCwkfXan2nP2/NnsXuXHRvcHqDb4OF5j19bPZe5M95l\n+KljE7ensenG8l+7R+eitTff8sjQVL2ejFLkX4zppO//xvJvbHkxfj+yNffnk5kul/dTKacXvroA\n9hjdovYW6/e5WO+XlrY38/vQ0t/n+fPnt7i9aXo/Jfn5pKm9mm7edEZa2lPO0/Pnz2fNmjUALF68\nmJEjR1JZWUlTmXvePnP9lcz+Adzi7n/MmjceuMzdD2vyUaPtrwHWARcR1cmvMLM+wDPuvn/u+lOn\nTvXGRqeZt2xtg18vAg0uH963W6P7GN63W4NtSNqWJPtJyz6SKlV7S/Wayi3/YihG/tD6v2el/l1N\n03GKJU2/z2n5GUHj7+1SSdP7KU0/Q5FyNHv2bCorK62p2yU9E38l8JiZnQlUE50eqQROTHogM+sM\ndHD3dWbWBRgLfB94BLgAuAE4H5iSuPUiIiIiIu1Q0tFpqoBhwEygCzADONDd/9GEY/UGqsxsDvA8\n8Ki7P0nUeT82Lq2pBK7Pt7Fq4sNRnVs4qisOS/mGo2zD0WduOMo2HGWbPknPxOPui8zsRqILUWua\neiB3fwvYbpxId18NjEmyj3nL1uadrzvChac78rUu5S9Jtcf3Snt8zcXSWHaAshVJqUSdeDPbFfgF\nMB7YDHQxs1OAUe5+dcD21amoqNBwWoEkGftVw5k1T8Wow5lcILemUP75FSvftqRY75Vyyrbcfj/S\nNN52Y9lBw9cBKNv2Q9mmT9IhJm8jGo1mAJD5k/w54MwQjRIRERERkcKSltNUAn3dfbOZOYC7v2tm\necd0DyGqiT+4VIdrV6qqqtrdX9il+vo9qivesyj7am+S/IzaWr5pKgtRtuG0x8/cUlG24Sjb9Ena\niV8D7AHU1cKbWf/saZFyUm5fv7dH7fFn1B5fc6koWxFpa5KW0/wGeMjMjgY6mNnhwN1EZTYlUVGx\n3TWxUiT6yzqczM1hJAzlG46yDUefueEo23CUbfokPRN/A/AB8HNgR+C3wK+AWwK1q81L01e7pdIe\nX3OpKFuRtiXJqDEi0r412ok3s45EN2G6zd1brdPe1mri0/TVbqnq3NL0mkulVHXF7TFbaHt122mi\nbMNJ8pmbZNQY2Z7qtsNRtunTaDmNu28Bbnb3jSVoj4iIiIiINCJpOc2jZnayuz8atDUNqKio4P7Z\nrXX0+tpa6cKQgw7RjbQCSdNY223tfQvpyrecJHkvKNtwdDYzHGUbjrJNn6Sd+J2BB83sOWAJ4JkF\n7n5eiIZEzeKOAAAgAElEQVSlWVsrXWhrr0fy089ZMvReEBEpf0lHp3kZuA54BlgIVGc9SiKqiZcQ\notpXCUHZhqV8w1G24VRVVbV2E9osZRuOsk2fRGfi3f37oRsiUo40goSIpFVbLKETkW2SltO0ujTV\nxLc1qn1tvsbKEpRtWMo3HGUbTqlqi9tj2ZTqtsNRtumTtJxGRERERERSomw68aqJD0e1r+Eo27CU\nbzjKNhzVFoejbMNRtulTNuU0IiIiaac69Ob78PVqWLI0/8J99mbnjw0pbYNEUi5RJ97Mzgbmuvsr\nZrYf8GtgC/AVd381ZAMzVBMfjmpfw1G2YSnfcJRt8ySpQ1dtcQFLlrLXGZ/Nu6jmgT9Bgk68sg1H\n2aZP0nKaHwKr4+f/A8wApgO/CNEoEREREREpLGk5zZ7uvsLMdgaOBMYDm4H3grUsR1QTf3CpDteu\nRLWve7Z2M4omTV9nJ8k2Te0tN43lq2ybT9mGU1VVpbOagSjbcJRt+iTtxL9rZkOBYcBMd99oZp0B\nC9c0keYpt2HVyq295UTZhqNsRURaV9JymmuBF4E7gJvieWOAeSEalU9FRUWpDtXuVIw6vLWb0GYp\n27CUbzjKNhydzQxH2YajbNMn6R1b7zKzP8bPN8SznwfOCtUwEZFyodISEREptaSj03QAPsx6DvCe\nu28N1bBcqokPp63VxKeJsg0rLfm2xdKStGTbFqm2OBxlG46yTZ+k5TQfEV3IWu9hZhvN7C0z+7GZ\ndU2yIzPrYGazzeyReLqnmT1pZq+Z2RNm1qM5L0REREREpL1I2om/FJgGjAX2B44DpgJXAF8BPgX8\nJOG+JgD/ypqeCDzt7vvFx5iUbyPVxIej2tdwlG1YyjccZRuOzmaGo2zDUbbpk3R0mm8BI9x9TTz9\nupnNAl509yFmNp/owtcGmdnewInAj+J9AowDjoqf3w38jahjLyIiIiIieSQ9E98d6JwzrzOQKX1Z\nDuySYD//C1wOeNa83u6+AsDdlwO98m0Y1cRLCFHtq4SgbMNSvuEo23CqqqpauwltlrINR9mmT9Iz\n8b8DnjKzW4AlwN5EZTF3x8vHAq81tAMzOwlY4e5zzWx0A6t6vpnTp0/nzWVP0qlnHwA67tKFzn2H\n0n1IVGZTVVVF9XsbyFyIVVsddfozy7f9h1R4+do9OtNt8PC8y2ur5zJ3xrsMP3VsweWRoXX7q61+\nZ7vl2cdraHmS17N2j84MOegQVq7bVPf6Ml+Bz53xHLvusiMDh40syetJW/6leD1J2ptRLu+ncsu/\nUL7Ffj2Z/7wyXyfnTpcy/5rajTw5bTpQ//cdYOwxRxUt/4WvLoA9Rhdsb6RtfZ421t5M3i19Pf+Y\nOYfq9zZs9/OrGHU4vbruRPVLM9vl5+nIeK9/i/8dnTW9asHLHFN5VN3+oPDvo6bDTGekpT3lPD1/\n/nzWrImKWxYvXszIkSOprKykqcw9b5+5/krRiDRfAs4A+gI1wB+BX7v7lvhOrubuHzSwj+uAc4ku\nkt0F6Ab8CRgJjI7vCNsHeMbd98/dfurUqT5xdv57S9104lCG9+3GvGVrGxwhAmhweZJ9FGudUrWl\nVMdJU1tKdZw0taVUxymXthT7OI0pVbZpyr9Ux0lTW0p1nDS1pVTHGd63Gx9Onc5eZ3w27zo1D/yJ\nnSuPyrtMpNzNnj2bysrKJt9ANVE5jbtvdffb3L3S3fd392Pi6S3x8g8b6sDH61zl7v3dfTDR+PLT\n3P3zwKPABfFq5wNTmvoiRERERETak0SdeDM728z2j59/zMymm9kzZvbxIrTheuBYM3sNqIynt6Oa\n+HBU+xqOsg1L+YajbMNRtuGobjscZZs+SWvif0g0jCTAj4GZwDrgF8AxTT2ou08HpsfPVwNjmroP\nEREREZH2KunoNHvGNes7A0cC3wF+AFQEa1kOjRMfjsaDDkfZhqV8w1G24SjbcDSWeTjKNn2Snol/\n18yGAsOAme6+0cw6A00uwhcRERERkZZJeib+WqKbOd0B3BTPGwPMC9GofFQTH47qM8NRtmEp33CU\nbTjKNhzVbYejbNMn0Zl4d7/LzP4YP98Qz36eaJQZEREREREpoaRn4jOd9x3MrK+Z9SX6AyDx9i2l\nmvhwVJ8ZjrINS/mGo2zDUbbhqG47HGWbPonOxJvZGOB2YGDOIgc6FrlNIiKpUlO7kZXrNuVd1qvr\nTiVujYiISPILW+8gqou/H2jwpk6hRDXxB7fGodu8qD5zz9ZuRpukbMMqVb4r121q9K6WbY3eu+Eo\n23Cqqqp0xjgQZZs+STvxOwN3Zu7QKiIiIiIirSdpTfv/AleYWasNKama+HBUnxmOsg1L+YajbMNR\ntuHoTHE4yjZ9kp6Jfwh4AphkZu9lL3D3wUVvlYiIiIiIFJT0TPyDwLPAOcB/5TxKQuPEh6Mxi8NR\ntmEp33CUbTjKNhyNZR6Osk2fpGfiBwEHu/vWkI0REREREZHGJT0TPwU4JmRDGqOa+HBUnxmOsg1L\n+YajbMNRtuGobjscZZs+Sc/EdwIeMbNngRXZC9z9vKK3SkRERERECkp6Jn4BcAPwT6A651ESqokP\nR/WZ4SjbsJRvOMo2HGUbjuq2w1G26ZPoTLy7fz90Q0REREREJJmkZ+LrmNlfQjSkMaqJD0f1meEo\n27CUbzjKNhxlG47qtsNRtunT5E488Omit0JERERERBJrTie+Ve7aqpr4cFSfGY6yDUv5hqNsw1G2\n4ahuOxxlmz7N6cR/ueitEBERERGRxBJ14s1sSua5u9+bNf/hEI3KRzXx4ag+MxxlG5byDUfZhqNs\nw1HddjjKNn2Snok/usD80UVqh4iIiIiIJNTgEJNm9oP46U5ZzzMGA4uCtCqPqCb+4FIdrl2J6jP3\nbO1mtEnKNizlG46yDUfZhlNVVaUzxoEo2/RpbJz4feJ/O2Q9B3BgCfDfSQ9kZp2AvwM7xcd90N2/\nb2Y9gT8AA4C3gc+5+5qk+xURERERaW8aLKdx9wvd/ULgq5nn8eML7j7J3RcmPZC7bwSOdveDgQrg\nBDMbBUwEnnb3/YBpwKR826smPhzVZ4ajbMNSvuEo23CUbTg6UxyOsk2fpDXxH+TOsEjeDnch7r4h\nftqJ6Gy8A+OAu+P5dwOnNmWfIiIiIiLtTdJO/DVm9oe49AUzGwxUASc25WBm1sHM5gDLgafcfSbQ\n291XALj7cqBXvm01Tnw4GrM4HGUblvINR9mGo2zD0Vjm4Sjb9GmsJj6jAvgJ8JKZ3QVcAvwPcENT\nDubuW4GDzaw78CczO4DobHy91fJtO336dN5c9iSdevYBoOMuXejcdyjdh0RlNlVVVVS/t4HMxUK1\n1VGnP7N824dm4eVr9+hMt8HD8y6vrZ7L3BnvMvzUsQWXR4bW7a+2+p3tlmcfr6HlSV5PkvZmvrYN\n/XrSln8pXk+S9ma0lfdT2vIvlG9b/X0uZf4LX10Ae4wu2N6IPk+b83oWvrqA2jW76vM0p70j473+\nLf53dNb0qgUvc0zlUXX7g23lHZouzXRGWtpTztPz589nzZro8s/FixczcuRIKisraapEnXh3X29m\nVwGHAt8hKnu53t3zdrgT7K/WzP4GHA+sMLPe7r7CzPoAK/NtM2HCBGpmF75Z7JFHHkm3ZWuZ/FhU\npp/5cMjIfPg2tHx4327MW7Y27/LuQyqoGDW03nTu8tz9dX9vYbOXJ3k9LW1vZnluW0K1F0qXf0t/\nPsXMf/JjC9vF+6kp08Vqb2Yd/T4HyH/wcF5Iye9zOeTflNcz/ryL6rJtbnuh7X2efjh1OrD92NWj\ngZoDDqy3v9z9F1rW2Pqa1nRrTOfOmz17Ns2R9GZPJwHzgGeAg4D9gGfNbFDSA5nZHmbWI36+C3As\n8ArwCHBBvNr5wJS8OxARERERESB5TfxtwPnuPsHdXwaOBJ4AZjXhWHsBz5jZXOAF4Al3f4yoJOdY\nM3sNqASuz7exauLDUX1mOMo2LOUbjrINR9mGo7rtcJRt+iStiT/I3f+dmYhr2681s78kPZC7zwdG\n5Jm/GhiTdD8iIiIiIu1dojPx7v5vM9vdzD5vZlcAmFlfCtSvh6Bx4sPRmMXhKNuwlG84yjYcZRuO\nxjIPR9mmT9Ka+KOA14D/BL4bz94X+GWgdomIiIiISAFJa+J/Apzp7scDH8XzXgBGBWlVHqqJD0f1\nmeEo27CUbzjKNhxlG47qtsNRtumTtBM/0N2nxs8zw0puInlNvYiIiIiIFEnSTvy/zOy4nHljgPlF\nbk9BqokPR/WZ4SjbsJRvOMo2HGUbjuq2w1G26ZP0TPq3gf+LR6PZxcx+BZwMjAvWMhERERERySvp\n6DTPE93kaQHwW+AtYJS7zwzYtnpUEx+O6jPDUbZhKd9wlG04yjYc1W2Ho2zTJ9GZeDO7zN3/B7gx\nZ/633P3mIC0TEREREZG8ktbEf6/A/KuL1ZDGqCY+HNVnhqNsw1K+4SjbcJRtOKrbDkfZpk+DZ+LN\n7Jj4aUczOxqwrMWDgbWhGiYiIiIiIvk1dib+jvixM1EtfGb6N8AXgEuDti6LauLDUX1mOMo2LOUb\njrINR9mGo7rtcJRt+jR4Jt7dBwGY2e/c/bzSNElERERERBqSdHSaVu/AqyY+HNVnhqNsw1K+4Sjb\ncJRtOKrbDkfZpk/SC1tFRERERCQlyqYTr5r4cFSfGY6yDUv5hqNsw1G24ahuOxxlmz4FO/FmdkrW\n8x1L0xwREREREWlMQ2fi78l6vip0QxqjmvhwVJ8ZjrINS/mGo2zDUbbhqG47HGWbPg2NTrPczL4G\n/AvYIc848QC4+7RQjRMRERERke01dCb+AuBU4FfATtQfJz57vPiSUE18OKrPDEfZhqV8w1G24Sjb\ncFS3HY6yTZ+CZ+Ld/Z/AGAAzW+juQ0vWKhERERERKSjpOPFDAcysv5kdbmb7hG3W9lQTH47qM8NR\ntmEp33CUbTjKNhzVbYejbNMnUSfezPqY2XRgIfAwUG1mfzezvkFbJyIiIiIi20k6TvxtwDygp7vv\nBfQE5sTzS0I18eGoPjMcZRuW8g1H2YajbMNR3XY4yjZ9GhqdJtuRwF7uvhnA3deb2RXAO0kPZGZ7\nA78DegNbgV+7+0/NrCfwB2AA8DbwOXdfk/wliIiIiIi0L0nPxP8b+ETOvP2A95twrI+Ab7n7AcDh\nwFfN7OPAROBpd98PmAZMyrexauLDUX1mOMo2LOUbjrINR9mGo7rtcJRt+iQ9E38j8LSZ3QEsIjpr\nfiHw3aQHcvflwPL4+TozewXYGxgHHBWvdjfwN6KOvYiIiIiI5JF0dJpfA2cCewAnx/+e4+63N+eg\nZjYQqACeB3q7+4r4OMuBXvm2UU18OKrPDEfZhqV8w1G24SjbcFS3HY6yTZ+kZ+Izd2Zt8d1Zzawr\n8CAwIT4j77mHyrfd9OnTeXPZk3Tq2QeAjrt0oXPfoXQfEpXZVFVVUf3eBmBPAGqro05/Zvm2D83C\ny9fu0Zlug4fnXV5bPZe5M95l+KljCy6PDK3bX231O9stzz5eQ8uTvJ4k7c18bRv69aQt/1K8niTt\nzWgr76e05V8o37b6+1zK/Be+ugD2GF2wvRF9njbn9Sx8dQG1a3bV52lOe0fGe/1b/O/orOlVC17m\nmMqj6vYH28o7NF2a6Yy0tKecp+fPn8+aNdHln4sXL2bkyJFUVlbSVIk78cVgZjsQdeB/7+5T4tkr\nzKy3u68wsz7AynzbTpgwgZrZVnDfRx55JN2WrWXyYwuBbR8OGZkP34aWD+/bjXnL1uZd3n1IBRWj\nhtabzl2eu7/u7y1s9vIkr6el7c0sz21LqPZC6fJv6c+nmPlPfmxhu3g/NWW6WO3NrKPf5wD5Dx7O\nCyn5fS6H/Jvyesafd1Fdts1tL7S9z9MPp04HtnXeM0YDNQccWG9/ufsvtKyx9TWt6daYzp03e/Zs\nmiPpha3F8lvgX+5+S9a8R4AL4ufnA1NyNxIRERERkW1K1ok3syOA/wSOMbM5ZjbbzI4HbgCONbPX\ngErg+nzbqyY+HNVnhqNsw1K+4SjbcJRtOKrbDkfZpk+ichozu8zd/yfP/G+5+81J9uHu/wA6Flg8\nJsk+REREREQk+Zn47xWYf3WxGtIYjRMfjsYsDkfZhqV8w1G24SjbcDSWeTjKNn0aPBNvZsfETzua\n2dFA9pWlg4G1oRomIiIiIiL5NXYm/o74sTPRRamZ6d8AXwAuDdq6LKqJD0f1meEo27CUbzjKNhxl\nG47qtsNRtunT4Jl4dx8EYGa/c/fzStMkERERERFpSNI7ttZ14M2sQ/YjXNPqU018OKrPDEfZhqV8\nw1G24SjbcFS3HY6yTZ+ko9OMAH4OHERUWgNRfbxTeMQZERERkVSpqd3IynWb8i7r1XUn9ureqcQt\nEmmepHdsvRt4lKgOfkO45hQW1cQf3BqHbvOi+sw9W7sZbZKyDUv5hqNsw1G24VRVVTV6xnjluk1c\nnnXH3Gw3nThUnfgCkmQrpZW0Ez8A+I67e8jGiIiIiIhI45LWtP8JGBuyIY1RTXw4qs8MR9mGpXzD\nUbbhKNtw2tqZ4prajcxbtjbvo6Z2Y0nb0taybQuSnonfGfiTmVUBy7MXaNQaERERkeJT6Y80JOmZ\n+H8BNwD/AKpzHiWhceLD0ZjF4SjbsJRvOMo2HGUbjsYyD0fZpk+iM/Hu/v3QDRERERERkWQSnYk3\ns2MKPUI3MEM18eGoPjMcZRuW8g1H2YajbMNR3XY4yjZ9ktbE35EzvSewE7AUGFzUFomIiIiISIOS\n3rF1UPYD6AH8CLg1aOuyqCY+HNVnhqNsw1K+4SjbcJRtOKrbDkfZpk/SC1vrcfctRJ34K4rbHBER\nERERaUyzOvGxY4GtxWpIY1QTH47qM8NRtmEp33CUbTjKNhzVbYejbNMnUU28mS0Bsu/W2plo7PhL\nQjRKRERERKS5amo3snLdprzLenXdib26d0q0TpolvbD13Jzp9cDr7l5b5PYUFNXEH1yqw7UrUX3m\nnq3djDZJ2YalfMNRtuEo23Cqqqp0xjiQcss2yY2yyv1mWknHiZ8OYGYdgN7ACncvWSmNiIiIiIhs\nk3Sc+G5m9jvgA+Ad4AMzu9vMegRtXRbVxIej+sxwlG1YyjccZRuOsg2nnM4Ulxtlmz5Jy2l+BnQB\nhgGLgAFEo9P8FDg/TNNEREREpFwUo8a83OvUSylpJ/54YLC7b4inXzezC4HqMM3anmriw1F9ZjjK\nNizlG46yDUfZhlNuddvlJEm2xagxL/c69VJKOsTkh2z/ibMHsDHpgczsDjNbYWYvZc3raWZPmtlr\nZvZEKctzRERERETKVdJO/G+Ap8zsYjM7wcwuBp4Abm/Cse4EjsuZNxF42t33A6YBkwptrJr4cFSf\nGY6yDUv5hqNsw1G24egsfDjKNn2SltP8CFgGnAP0jZ/fCPw26YHcvcrMBuTMHgccFT+/G/gbUcde\nREREREQKSHQm3iO/dfcx7v6J+N873N0b37pBvdx9RXyM5UCvQitGNfESQlSfKSEo27CUbzjKNhxl\nG05VVVVrN6HNUrbpk/SOrT8F7nf3f2bN+xTwOXf/RhHbU/CPgunTp/Pmsifp1LMPAB136ULnvkPp\nPiQqs6mqqqL6vQ1kSvdrq6NOf2b5tg/NwsvX7tGZboOH511eWz2XuTPeZfipYwsujwyt219t9Tvb\nLc8+XkPLk7yeJO3NfG0b+vWkLf9SvJ4k7c1oK++ntOVfKN+2+vtcyvwXvroA9hhdsL0RfZ425/Us\nfHUBtWt21edpTntHxnv9W/zv6KzpVQte5pjKo+r2B9vKO5o6naS9Ldl/ZnrIQYewct2mup9X5v0z\nd8Zz7LrLjow77uhE+2ss/ylPPMP7H2yut//M8Xp13Ynql2YW5fWsWr+ZecvWNvp6GmtvMX4+q9Zv\nZuCwkdu9XoC3589i9y47Fu39X6z3Q/b0/PnzWbNmDQCLFy9m5MiRVFZW0lRJy2nOBi7Lmfci8Gfg\nG00+6jYrzKy3u68wsz7AykIrTpgwgZrZVnBHRx55JN2WrWVyfEVz5oeRkfnhNrR8eN9uzFu2Nu/y\n7kMqqBg1tN507vLc/XV/b2Gzlyd5PS1tb2Z5bltCtRdKl39Lfz7FzH/yYwvbxfupKdPFam9mHf0+\nB8h/8HBeSMnvcznk35TXM/68i+qybW57oe19nn44dTqwrfOeMRqoOeDAevvL3X+hZfnmJWlvMabn\nLVsbj7IS/fEyue5nvic3nTi00e0LtS93euCwkVz+2MJ6+88c76YTh5b89TTW3saOl+Tns60tbNee\nm04cWdT3f7Hyy57OnTd79myaI+mFrZ5n3Y5N2D7D4kfGI8AF8fPzgSlN3J+IiIiISLuT9Ez8s8AP\nzewKd99qZh2A/47nJ2Jm9xL9Qb27mS0GrgGuBx4wsy8Q3UTqc4W21zjx4WjM4nCUbVjKNxxlG46y\nbZ4kNwEq1TjxxbohUWP7SZNivW/L6TW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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.bar(np.arange(80), data, color=\"#348ABD\")\n", + "plt.bar(tau-1, data[tau - 1], color=\"r\", label=\"user behaviour changed\")\n", + "plt.xlabel(\"Time (days)\")\n", + "plt.ylabel(\"count of text-msgs received\")\n", + "plt.title(\"Artificial dataset\")\n", + "plt.xlim(0, 80)\n", + "plt.legend();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It is okay that our fictional dataset does not look like our observed dataset: the probability is incredibly small it indeed would. PyMC3's engine is designed to find good parameters, $\\lambda_i, \\tau$, that maximize this probability. \n", + "\n", + "\n", + "The ability to generate artificial dataset is an interesting side effect of our modeling, and we will see that this ability is a very important method of Bayesian inference. We produce a few more datasets below:" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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Y9xKdly2ZMD37+vrYvXs3EL286Oyzz6a7u5vxksjn28zuAHa5+5dKpi0DXnb3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\nzMwq8v9VOpInhJptAAAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def plot_artificial_sms_dataset():\n", + " tau = stats.randint.rvs(0, 80)\n", + " alpha = 1./20.\n", + " lambda_1, lambda_2 = stats.expon.rvs(scale=1/alpha, size=2)\n", + " data = np.r_[stats.poisson.rvs(mu=lambda_1, size=tau), stats.poisson.rvs(mu=lambda_2, size=80 - tau)]\n", + " plt.bar(np.arange(80), data, color=\"#348ABD\")\n", + " plt.bar(tau - 1, data[tau-1], color=\"r\", label=\"user behaviour changed\")\n", + " plt.xlim(0, 80);\n", + "\n", + "figsize(12.5, 5)\n", + "plt.title(\"More example of artificial datasets\")\n", + "for i in range(4):\n", + " plt.subplot(4, 1, i+1)\n", + " plot_artificial_sms_dataset()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Later we will see how we use this to make predictions and test the appropriateness of our models." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Example: Bayesian A/B testing\n", + "\n", + "A/B testing is a statistical design pattern for determining the difference of effectiveness between two different treatments. For example, a pharmaceutical company is interested in the effectiveness of drug A vs drug B. The company will test drug A on some fraction of their trials, and drug B on the other fraction (this fraction is often 1/2, but we will relax this assumption). After performing enough trials, the in-house statisticians sift through the data to determine which drug yielded better results. \n", + "\n", + "Similarly, front-end web developers are interested in which design of their website yields more sales or some other metric of interest. They will route some fraction of visitors to site A, and the other fraction to site B, and record if the visit yielded a sale or not. The data is recorded (in real-time), and analyzed afterwards. \n", + "\n", + "Often, the post-experiment analysis is done using something called a hypothesis test like *difference of means test* or *difference of proportions test*. This involves often misunderstood quantities like a \"Z-score\" and even more confusing \"p-values\" (please don't ask). If you have taken a statistics course, you have probably been taught this technique (though not necessarily *learned* this technique). And if you were like me, you may have felt uncomfortable with their derivation -- good: the Bayesian approach to this problem is much more natural. \n", + "\n", + "### A Simple Case\n", + "\n", + "As this is a hacker book, we'll continue with the web-dev example. For the moment, we will focus on the analysis of site A only. Assume that there is some true $0 \\lt p_A \\lt 1$ probability that users who, upon shown site A, eventually purchase from the site. This is the true effectiveness of site A. Currently, this quantity is unknown to us. \n", + "\n", + "Suppose site A was shown to $N$ people, and $n$ people purchased from the site. One might conclude hastily that $p_A = \\frac{n}{N}$. Unfortunately, the *observed frequency* $\\frac{n}{N}$ does not necessarily equal $p_A$ -- there is a difference between the *observed frequency* and the *true frequency* of an event. The true frequency can be interpreted as the probability of an event occurring. For example, the true frequency of rolling a 1 on a 6-sided die is $\\frac{1}{6}$. Knowing the true frequency of events like:\n", + "\n", + "- fraction of users who make purchases, \n", + "- frequency of social attributes, \n", + "- percent of internet users with cats etc. \n", + "\n", + "are common requests we ask of Nature. Unfortunately, often Nature hides the true frequency from us and we must *infer* it from observed data.\n", + "\n", + "The *observed frequency* is then the frequency we observe: say rolling the die 100 times you may observe 20 rolls of 1. The observed frequency, 0.2, differs from the true frequency, $\\frac{1}{6}$. We can use Bayesian statistics to infer probable values of the true frequency using an appropriate prior and observed data.\n", + "\n", + "\n", + "With respect to our A/B example, we are interested in using what we know, $N$ (the total trials administered) and $n$ (the number of conversions), to estimate what $p_A$, the true frequency of buyers, might be. \n", + "\n", + "To setup a Bayesian model, we need to assign prior distributions to our unknown quantities. *A priori*, what do we think $p_A$ might be? For this example, we have no strong conviction about $p_A$, so for now, let's assume $p_A$ is uniform over [0,1]:" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Applied interval-transform to p and added transformed p_interval_ to model.\n" + ] + } + ], + "source": [ + "import pymc3 as pm\n", + "\n", + "# The parameters are the bounds of the Uniform.\n", + "with pm.Model() as model:\n", + " p = pm.Uniform('p', lower=0, upper=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Had we had stronger beliefs, we could have expressed them in the prior above.\n", + "\n", + "For this example, consider $p_A = 0.05$, and $N = 1500$ users shown site A, and we will simulate whether the user made a purchase or not. To simulate this from $N$ trials, we will use a *Bernoulli* distribution: if $X\\ \\sim \\text{Ber}(p)$, then $X$ is 1 with probability $p$ and 0 with probability $1 - p$. Of course, in practice we do not know $p_A$, but we will use it here to simulate the data." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1 0 1 ..., 0 0 0]\n", + "77\n" + ] + } + ], + "source": [ + "#set constants\n", + "p_true = 0.05 # remember, this is unknown.\n", + "N = 1500\n", + "\n", + "# sample N Bernoulli random variables from Ber(0.05).\n", + "# each random variable has a 0.05 chance of being a 1.\n", + "# this is the data-generation step\n", + "occurrences = stats.bernoulli.rvs(p_true, size=N)\n", + "\n", + "print(occurrences) # Remember: Python treats True == 1, and False == 0\n", + "print(np.sum(occurrences))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The observed frequency is:" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "What is the observed frequency in Group A? 0.0513\n", + "Does this equal the true frequency? False\n" + ] + } + ], + "source": [ + "# Occurrences.mean is equal to n/N.\n", + "print(\"What is the observed frequency in Group A? %.4f\" % np.mean(occurrences))\n", + "print(\"Does this equal the true frequency? %s\" % (np.mean(occurrences) == p_true))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We combine the observations into the PyMC3 `observed` variable, and run our inference algorithm:" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " [-------100%-------] 18000 of 18000 in 1.7 sec. | SPS: 10329.7 | ETA: 0.0" + ] + } + ], + "source": [ + "#include the observations, which are Bernoulli\n", + "with model:\n", + " obs = pm.Bernoulli(\"obs\", p, observed=occurrences)\n", + " # To be explained in chapter 3\n", + " step = pm.Metropolis()\n", + " trace = pm.sample(18000, step=step)\n", + " burned_trace = trace[1000:]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We plot the posterior distribution of the unknown $p_A$ below:" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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PP54JEyZwxx13lLTddXV1TJw4cb/bsrVx/Pjx3HrrrZx11lmMHj2az33uc+zatSu27qVL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\nidYp0teUfDCunPGwKN8tLJn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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "figsize(12.5, 4)\n", + "plt.title(\"Posterior distribution of $p_A$, the true effectiveness of site A\")\n", + "plt.vlines(p_true, 0, 90, linestyle=\"--\", label=\"true $p_A$ (unknown)\")\n", + "plt.hist(burned_trace[\"p\"], bins=25, histtype=\"stepfilled\", density=True)\n", + "plt.legend();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Our posterior distribution puts most weight near the true value of $p_A$, but also some weights in the tails. This is a measure of how uncertain we should be, given our observations. Try changing the number of observations, `N`, and observe how the posterior distribution changes.\n", + "\n", + "### *A* and *B* Together\n", + "\n", + "A similar analysis can be done for site B's response data to determine the analogous $p_B$. But what we are really interested in is the *difference* between $p_A$ and $p_B$. Let's infer $p_A$, $p_B$, *and* $\\text{delta} = p_A - p_B$, all at once. We can do this using PyMC3's deterministic variables. (We'll assume for this exercise that $p_B = 0.04$, so $\\text{delta} = 0.01$, $N_B = 750$ (significantly less than $N_A$) and we will simulate site B's data like we did for site A's data )" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Obs from Site A: [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0] ...\n", + "Obs from Site B: [0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0] ...\n" + ] + } + ], + "source": [ + "import pymc3 as pm\n", + "figsize(12, 4)\n", + "\n", + "#these two quantities are unknown to us.\n", + "true_p_A = 0.05\n", + "true_p_B = 0.04\n", + "\n", + "#notice the unequal sample sizes -- no problem in Bayesian analysis.\n", + "N_A = 1500\n", + "N_B = 750\n", + "\n", + "#generate some observations\n", + "observations_A = stats.bernoulli.rvs(true_p_A, size=N_A)\n", + "observations_B = stats.bernoulli.rvs(true_p_B, size=N_B)\n", + "print(\"Obs from Site A: \", observations_A[:30], \"...\")\n", + "print(\"Obs from Site B: \", observations_B[:30], \"...\")" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.042\n", + "0.0346666666667\n" + ] + } + ], + "source": [ + "print(np.mean(observations_A))\n", + "print(np.mean(observations_B))" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Applied interval-transform to p_A and added transformed p_A_interval_ to model.\n", + "Applied interval-transform to p_B and added transformed p_B_interval_ to model.\n", + " [-------100%-------] 20000 of 20000 in 3.2 sec. | SPS: 6201.6 | ETA: 0.0" + ] + } + ], + "source": [ + "# Set up the pymc3 model. Again assume Uniform priors for p_A and p_B.\n", + "with pm.Model() as model:\n", + " p_A = pm.Uniform(\"p_A\", 0, 1)\n", + " p_B = pm.Uniform(\"p_B\", 0, 1)\n", + " \n", + " # Define the deterministic delta function. This is our unknown of interest.\n", + " delta = pm.Deterministic(\"delta\", p_A - p_B)\n", + "\n", + " \n", + " # Set of observations, in this case we have two observation datasets.\n", + " obs_A = pm.Bernoulli(\"obs_A\", p_A, observed=observations_A)\n", + " obs_B = pm.Bernoulli(\"obs_B\", p_B, observed=observations_B)\n", + "\n", + " # To be explained in chapter 3.\n", + " step = pm.Metropolis()\n", + " trace = pm.sample(20000, step=step)\n", + " burned_trace=trace[1000:]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Below we plot the posterior distributions for the three unknowns: " + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "p_A_samples = burned_trace[\"p_A\"]\n", + "p_B_samples = burned_trace[\"p_B\"]\n", + "delta_samples = burned_trace[\"delta\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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C2rVruf/++8PWHzBgAG3btmXWrFkcO3aMzMxMli5dWiXXvHxmvPx9XXjhhaxc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FAAAN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\nERERkUI0GBcRERERKUSDcRERERGRQv4FzpBgPKO6HzUAAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "figsize(12.5, 10)\n", + "\n", + "#histogram of posteriors\n", + "\n", + "ax = plt.subplot(311)\n", + "\n", + "plt.xlim(0, .1)\n", + "plt.hist(p_A_samples, histtype='stepfilled', bins=25, alpha=0.85,\n", + " label=\"posterior of $p_A$\", color=\"#A60628\", density=True)\n", + "plt.vlines(true_p_A, 0, 80, linestyle=\"--\", label=\"true $p_A$ (unknown)\")\n", + "plt.legend(loc=\"upper right\")\n", + "plt.title(\"Posterior distributions of $p_A$, $p_B$, and delta unknowns\")\n", + "\n", + "ax = plt.subplot(312)\n", + "\n", + "plt.xlim(0, .1)\n", + "plt.hist(p_B_samples, histtype='stepfilled', bins=25, alpha=0.85,\n", + " label=\"posterior of $p_B$\", color=\"#467821\", density=True)\n", + "plt.vlines(true_p_B, 0, 80, linestyle=\"--\", label=\"true $p_B$ (unknown)\")\n", + "plt.legend(loc=\"upper right\")\n", + "\n", + "ax = plt.subplot(313)\n", + "plt.hist(delta_samples, histtype='stepfilled', bins=30, alpha=0.85,\n", + " label=\"posterior of delta\", color=\"#7A68A6\", density=True)\n", + "plt.vlines(true_p_A - true_p_B, 0, 60, linestyle=\"--\",\n", + " label=\"true delta (unknown)\")\n", + "plt.vlines(0, 0, 60, color=\"black\", alpha=0.2)\n", + "plt.legend(loc=\"upper right\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notice that as a result of `N_B < N_A`, i.e. we have less data from site B, our posterior distribution of $p_B$ is fatter, implying we are less certain about the true value of $p_B$ than we are of $p_A$. \n", + "\n", + "With respect to the posterior distribution of $\\text{delta}$, we can see that the majority of the distribution is above $\\text{delta}=0$, implying there site A's response is likely better than site B's response. The probability this inference is incorrect is easily computable:" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Probability site A is WORSE than site B: 0.208\n", + "Probability site A is BETTER than site B: 0.792\n" + ] + } + ], + "source": [ + "# Count the number of samples less than 0, i.e. the area under the curve\n", + "# before 0, represent the probability that site A is worse than site B.\n", + "print(\"Probability site A is WORSE than site B: %.3f\" % \\\n", + " np.mean(delta_samples < 0))\n", + "\n", + "print(\"Probability site A is BETTER than site B: %.3f\" % \\\n", + " np.mean(delta_samples > 0))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If this probability is too high for comfortable decision-making, we can perform more trials on site B (as site B has less samples to begin with, each additional data point for site B contributes more inferential \"power\" than each additional data point for site A). \n", + "\n", + "Try playing with the parameters `true_p_A`, `true_p_B`, `N_A`, and `N_B`, to see what the posterior of $\\text{delta}$ looks like. Notice in all this, the difference in sample sizes between site A and site B was never mentioned: it naturally fits into Bayesian analysis.\n", + "\n", + "I hope the readers feel this style of A/B testing is more natural than hypothesis testing, which has probably confused more than helped practitioners. Later in this book, we will see two extensions of this model: the first to help dynamically adjust for bad sites, and the second will improve the speed of this computation by reducing the analysis to a single equation. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## An algorithm for human deceit\n", + "\n", + "Social data has an additional layer of interest as people are not always honest with responses, which adds a further complication into inference. For example, simply asking individuals \"Have you ever cheated on a test?\" will surely contain some rate of dishonesty. What you can say for certain is that the true rate is less than your observed rate (assuming individuals lie *only* about *not cheating*; I cannot imagine one who would admit \"Yes\" to cheating when in fact they hadn't cheated). \n", + "\n", + "To present an elegant solution to circumventing this dishonesty problem, and to demonstrate Bayesian modeling, we first need to introduce the binomial distribution.\n", + "\n", + "### The Binomial Distribution\n", + "\n", + "The binomial distribution is one of the most popular distributions, mostly because of its simplicity and usefulness. Unlike the other distributions we have encountered thus far in the book, the binomial distribution has 2 parameters: $N$, a positive integer representing $N$ trials or number of instances of potential events, and $p$, the probability of an event occurring in a single trial. Like the Poisson distribution, it is a discrete distribution, but unlike the Poisson distribution, it only weighs integers from $0$ to $N$. The mass distribution looks like:\n", + "\n", + "$$P( X = k ) = {{N}\\choose{k}} p^k(1-p)^{N-k}$$\n", + "\n", + "If $X$ is a binomial random variable with parameters $p$ and $N$, denoted $X \\sim \\text{Bin}(N,p)$, then $X$ is the number of events that occurred in the $N$ trials (obviously $0 \\le X \\le N$). The larger $p$ is (while still remaining between 0 and 1), the more events are likely to occur. The expected value of a binomial is equal to $Np$. Below we plot the mass probability distribution for varying parameters. \n" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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Zqf52pJB1vvbaa9TW1nLJJZcA8IEPfIAJEybw8MMP8/Wvf73TfVBdpYQUExJS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\nb8LQoUMZM2ZM6wc1cyON2j2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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "figsize(12.5, 4)\n", + "\n", + "import scipy.stats as stats\n", + "binomial = stats.binom\n", + "\n", + "parameters = [(10, .4), (10, .9)]\n", + "colors = [\"#348ABD\", \"#A60628\"]\n", + "\n", + "for i in range(2):\n", + " N, p = parameters[i]\n", + " _x = np.arange(N + 1)\n", + " plt.bar(_x - 0.5, binomial.pmf(_x, N, p), color=colors[i],\n", + " edgecolor=colors[i],\n", + " alpha=0.6,\n", + " label=\"$N$: %d, $p$: %.1f\" % (N, p),\n", + " linewidth=3)\n", + "\n", + "plt.legend(loc=\"upper left\")\n", + "plt.xlim(0, 10.5)\n", + "plt.xlabel(\"$k$\")\n", + "plt.ylabel(\"$P(X = k)$\")\n", + "plt.title(\"Probability mass distributions of binomial random variables\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The special case when $N = 1$ corresponds to the Bernoulli distribution. There is another connection between Bernoulli and Binomial random variables. If we have $X_1, X_2, ... , X_N$ Bernoulli random variables with the same $p$, then $Z = X_1 + X_2 + ... + X_N \\sim \\text{Binomial}(N, p )$.\n", + "\n", + "The expected value of a Bernoulli random variable is $p$. This can be seen by noting the more general Binomial random variable has expected value $Np$ and setting $N=1$." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Example: Cheating among students\n", + "\n", + "We will use the binomial distribution to determine the frequency of students cheating during an exam. If we let $N$ be the total number of students who took the exam, and assuming each student is interviewed post-exam (answering without consequence), we will receive integer $X$ \"Yes I did cheat\" answers. We then find the posterior distribution of $p$, given $N$, some specified prior on $p$, and observed data $X$. \n", + "\n", + "This is a completely absurd model. No student, even with a free-pass against punishment, would admit to cheating. What we need is a better *algorithm* to ask students if they had cheated. Ideally the algorithm should encourage individuals to be honest while preserving privacy. The following proposed algorithm is a solution I greatly admire for its ingenuity and effectiveness:\n", + "\n", + "> In the interview process for each student, the student flips a coin, hidden from the interviewer. The student agrees to answer honestly if the coin comes up heads. Otherwise, if the coin comes up tails, the student (secretly) flips the coin again, and answers \"Yes, I did cheat\" if the coin flip lands heads, and \"No, I did not cheat\", if the coin flip lands tails. This way, the interviewer does not know if a \"Yes\" was the result of a guilty plea, or a Heads on a second coin toss. Thus privacy is preserved and the researchers receive honest answers. \n", + "\n", + "I call this the Privacy Algorithm. One could of course argue that the interviewers are still receiving false data since some *Yes*'s are not confessions but instead randomness, but an alternative perspective is that the researchers are discarding approximately half of their original dataset since half of the responses will be noise. But they have gained a systematic data generation process that can be modeled. Furthermore, they do not have to incorporate (perhaps somewhat naively) the possibility of deceitful answers. We can use PyMC3 to dig through this noisy model, and find a posterior distribution for the true frequency of liars. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Suppose 100 students are being surveyed for cheating, and we wish to find $p$, the proportion of cheaters. There are a few ways we can model this in PyMC3. I'll demonstrate the most explicit way, and later show a simplified version. Both versions arrive at the same inference. In our data-generation model, we sample $p$, the true proportion of cheaters, from a prior. Since we are quite ignorant about $p$, we will assign it a $\\text{Uniform}(0,1)$ prior." + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Applied interval-transform to freq_cheating and added transformed freq_cheating_interval_ to model.\n" + ] + } + ], + "source": [ + "import pymc3 as pm\n", + "\n", + "N = 100\n", + "with pm.Model() as model:\n", + " p = pm.Uniform(\"freq_cheating\", 0, 1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Again, thinking of our data-generation model, we assign Bernoulli random variables to the 100 students: 1 implies they cheated and 0 implies they did not. " + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "with model:\n", + " true_answers = pm.Bernoulli(\"truths\", p, shape=N, testval=np.random.binomial(1, 0.5, N))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If we carry out the algorithm, the next step that occurs is the first coin-flip each student makes. This can be modeled again by sampling 100 Bernoulli random variables with $p=1/2$: denote a 1 as a *Heads* and 0 a *Tails*." + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0 0 1 0 1 1 0 1 0 1 1 1 0 0 0 1 1 1 0 0 0 0 0 1 0 1 0 1 1 1 0 0 0 1 0 1 1\n", + " 1 1 0 1 0 0 1 1 1 1 0 0 0 0 0 0 1 1 0 0 1 1 0 1 0 0 1 0 1 1 0 0 0 0 0 1 1\n", + " 1 0 1 0 1 1 1 0 1 0 0 0 1 0 0 0 0 1 0 1 0 1 0 0 1 0]\n" + ] + } + ], + "source": [ + "with model:\n", + " first_coin_flips = pm.Bernoulli(\"first_flips\", 0.5, shape=N, testval=np.random.binomial(1, 0.5, N))\n", + "print(first_coin_flips.tag.test_value)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Although *not everyone* flips a second time, we can still model the possible realization of second coin-flips:" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "with model:\n", + " second_coin_flips = pm.Bernoulli(\"second_flips\", 0.5, shape=N, testval=np.random.binomial(1, 0.5, N))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Using these variables, we can return a possible realization of the *observed proportion* of \"Yes\" responses. We do this using a PyMC3 `deterministic` variable:" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import theano.tensor as tt\n", + "with model:\n", + " val = first_coin_flips*true_answers + (1 - first_coin_flips)*second_coin_flips\n", + " observed_proportion = pm.Deterministic(\"observed_proportion\", tt.sum(val)/float(N))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The line `fc*t_a + (1-fc)*sc` contains the heart of the Privacy algorithm. Elements in this array are 1 *if and only if* i) the first toss is heads and the student cheated or ii) the first toss is tails, and the second is heads, and are 0 else. Finally, the last line sums this vector and divides by `float(N)`, produces a proportion. " + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array(0.5600000023841858)" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "observed_proportion.tag.test_value" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next we need a dataset. After performing our coin-flipped interviews the researchers received 35 \"Yes\" responses. To put this into a relative perspective, if there truly were no cheaters, we should expect to see on average 1/4 of all responses being a \"Yes\" (half chance of having first coin land Tails, and another half chance of having second coin land Heads), so about 25 responses in a cheat-free world. On the other hand, if *all students cheated*, we should expected to see approximately 3/4 of all responses be \"Yes\". \n", + "\n", + "The researchers observe a Binomial random variable, with `N = 100` and `p = observed_proportion` with `value = 35`: " + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "X = 35\n", + "\n", + "with model:\n", + " observations = pm.Binomial(\"obs\", N, observed_proportion, observed=X)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Below we add all the variables of interest to a `Model` container and run our black-box algorithm over the model. " + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Assigned BinaryGibbsMetropolis to truths\n", + "Assigned BinaryGibbsMetropolis to first_flips\n", + "Assigned BinaryGibbsMetropolis to second_flips\n", + " [-------100%-------] 40000 of 40000 in 1891.9 sec. | SPS: 21.1 | ETA: -0.0" + ] + } + ], + "source": [ + "# To be explained in Chapter 3!\n", + "with model:\n", + " step = pm.Metropolis(vars=[p])\n", + " trace = pm.sample(40000, step=step)\n", + " burned_trace = trace[15000:]" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "figsize(12.5, 3)\n", + "p_trace = burned_trace[\"freq_cheating\"][15000:]\n", + "plt.hist(p_trace, histtype=\"stepfilled\", density=True, alpha=0.85, bins=30, \n", + " label=\"posterior distribution\", color=\"#348ABD\")\n", + "plt.vlines([.05, .35], [0, 0], [5, 5], alpha=0.3)\n", + "plt.xlim(0, 1)\n", + "plt.legend();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With regards to the above plot, we are still pretty uncertain about what the true frequency of cheaters might be, but we have narrowed it down to a range between 0.05 to 0.35 (marked by the solid lines). This is pretty good, as *a priori* we had no idea how many students might have cheated (hence the uniform distribution for our prior). On the other hand, it is also pretty bad since there is a .3 length window the true value most likely lives in. Have we even gained anything, or are we still too uncertain about the true frequency? \n", + "\n", + "I would argue, yes, we have discovered something. It is implausible, according to our posterior, that there are *no cheaters*, i.e. the posterior assigns low probability to $p=0$. Since we started with an uniform prior, treating all values of $p$ as equally plausible, but the data ruled out $p=0$ as a possibility, we can be confident that there were cheaters. \n", + "\n", + "This kind of algorithm can be used to gather private information from users and be *reasonably* confident that the data, though noisy, is truthful. \n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Alternative PyMC3 Model\n", + "\n", + "Given a value for $p$ (which from our god-like position we know), we can find the probability the student will answer yes: \n", + "\n", + "\\begin{align}\n", + "P(\\text{\"Yes\"}) = & P( \\text{Heads on first coin} )P( \\text{cheater} ) + P( \\text{Tails on first coin} )P( \\text{Heads on second coin} ) \\\\\\\\\n", + "& = \\frac{1}{2}p + \\frac{1}{2}\\frac{1}{2}\\\\\\\\\n", + "& = \\frac{p}{2} + \\frac{1}{4}\n", + "\\end{align}\n", + "\n", + "Thus, knowing $p$ we know the probability a student will respond \"Yes\". In PyMC3, we can create a deterministic function to evaluate the probability of responding \"Yes\", given $p$:" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Applied interval-transform to freq_cheating and added transformed freq_cheating_interval_ to model.\n" + ] + } + ], + "source": [ + "with pm.Model() as model:\n", + " p = pm.Uniform(\"freq_cheating\", 0, 1)\n", + " p_skewed = pm.Deterministic(\"p_skewed\", 0.5*p + 0.25)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "I could have typed `p_skewed = 0.5*p + 0.25` instead for a one-liner, as the elementary operations of addition and scalar multiplication will implicitly create a `deterministic` variable, but I wanted to make the deterministic boilerplate explicit for clarity's sake. \n", + "\n", + "If we know the probability of respondents saying \"Yes\", which is `p_skewed`, and we have $N=100$ students, the number of \"Yes\" responses is a binomial random variable with parameters `N` and `p_skewed`.\n", + "\n", + "This is where we include our observed 35 \"Yes\" responses. In the declaration of the `pm.Binomial`, we include `value = 35` and `observed = True`." + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "with model:\n", + " yes_responses = pm.Binomial(\"number_cheaters\", 100, p_skewed, observed=35)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Below we add all the variables of interest to a `Model` container and run our black-box algorithm over the model. " + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " [-------100%-------] 25000 of 25000 in 2.1 sec. | SPS: 12171.2 | ETA: 0.0" + ] + } + ], + "source": [ + "with model:\n", + " # To Be Explained in Chapter 3!\n", + " step = pm.Metropolis()\n", + " trace = pm.sample(25000, step=step)\n", + " burned_trace = trace[2500:]" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "figsize(12.5, 3)\n", + "p_trace = burned_trace[\"freq_cheating\"]\n", + "plt.hist(p_trace, histtype=\"stepfilled\", density=True, alpha=0.85, bins=30, \n", + " label=\"posterior distribution\", color=\"#348ABD\")\n", + "plt.vlines([.05, .35], [0, 0], [5, 5], alpha=0.2)\n", + "plt.xlim(0, 1)\n", + "plt.legend();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### More PyMC3 Tricks\n", + "\n", + "#### Protip: Arrays of PyMC3 variables\n", + "There is no reason why we cannot store multiple heterogeneous PyMC3 variables in a Numpy array. Just remember to set the `dtype` of the array to `object` upon initialization. For example:\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Applied log-transform to x_0 and added transformed x_0_log_ to model.\n", + "Applied log-transform to x_1 and added transformed x_1_log_ to model.\n", + "Applied log-transform to x_2 and added transformed x_2_log_ to model.\n", + "Applied log-transform to x_3 and added transformed x_3_log_ to model.\n", + "Applied log-transform to x_4 and added transformed x_4_log_ to model.\n", + "Applied log-transform to x_5 and added transformed x_5_log_ to model.\n", + "Applied log-transform to x_6 and added transformed x_6_log_ to model.\n", + "Applied log-transform to x_7 and added transformed x_7_log_ to model.\n", + "Applied log-transform to x_8 and added transformed x_8_log_ to model.\n", + "Applied log-transform to x_9 and added transformed x_9_log_ to model.\n" + ] + } + ], + "source": [ + "N = 10\n", + "x = np.ones(N, dtype=object)\n", + "with pm.Model() as model:\n", + " for i in range(0, N):\n", + " x[i] = pm.Exponential('x_%i' % i, (i+1.0)**2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The remainder of this chapter examines some practical examples of PyMC3 and PyMC3 modeling:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "##### Example: Challenger Space Shuttle Disaster \n", + "\n", + "On January 28, 1986, the twenty-fifth flight of the U.S. space shuttle program ended in disaster when one of the rocket boosters of the Shuttle Challenger exploded shortly after lift-off, killing all seven crew members. The presidential commission on the accident concluded that it was caused by the failure of an O-ring in a field joint on the rocket booster, and that this failure was due to a faulty design that made the O-ring unacceptably sensitive to a number of factors including outside temperature. Of the previous 24 flights, data were available on failures of O-rings on 23, (one was lost at sea), and these data were discussed on the evening preceding the Challenger launch, but unfortunately only the data corresponding to the 7 flights on which there was a damage incident were considered important and these were thought to show no obvious trend. The data are shown below (see [1]):\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Temp (F), O-Ring failure?\n", + "[[ 66. 0.]\n", + " [ 70. 1.]\n", + " [ 69. 0.]\n", + " [ 68. 0.]\n", + " [ 67. 0.]\n", + " [ 72. 0.]\n", + " [ 73. 0.]\n", + " [ 70. 0.]\n", + " [ 57. 1.]\n", + " [ 63. 1.]\n", + " [ 70. 1.]\n", + " [ 78. 0.]\n", + " [ 67. 0.]\n", + " [ 53. 1.]\n", + " [ 67. 0.]\n", + " [ 75. 0.]\n", + " [ 70. 0.]\n", + " [ 81. 0.]\n", + " [ 76. 0.]\n", + " [ 79. 0.]\n", + " [ 75. 1.]\n", + " [ 76. 0.]\n", + " [ 58. 1.]]\n" + ] + }, + { + "data": { + "image/png": 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LcCQwgtCbPgr4aNFFuoiIiIiIBLkvz+juj7j75939aHc/w90f7omA1KOeDvW2\npUX5SIvykQ7lIi3KRzqUi76vIc9EZvaNCqMagWeB2939hcKiEhEREREZ4PL2qF8HfAR4EFgC7Ajs\nC8wE3gzsDnzM3W9/rQGpR11ERERE+puevI56HXC8ux/k7ie6+0HAJ4AWd38P8HngwurCFRERERGR\nSvIW6kcQ/jNp1i3AB+P9q4HRRQSkHvV0qLctLcpHWpSPdCgXaVE+0qFc9H15C/VngDPLhp0RhwNs\nC/y7qKBERERERAa6vD3qewK/AeqB54AdgBbCJRpnm9nBwNvc/fLXGpB61EVERESkv+lOj3quq77E\nYnxnYD/gTcDzwAPu3hTH3wfcV2W8IiIiIiJSQTXXUW9y9/vc/fr4t6knAlKPejrU25YW5SMtykc6\nlIu0KB/pUC76vrzXUd8KOB84hNCP3nbY3t1H9khkIiIiIiIDWN4e9asJ10v/HuEKLycBXwZudPfv\nFRmQetRFREREpL/psR514HDg7e6+ysxa3P1mM3uI8A+PCi3URURERESkun94tCbef8XMhhNOKB1T\ndEDqUU+HetvSonykRflIh3KRFuUjHcpF35f3iPpjhP70e4A/AZcBrwD/7KG4REREREQGtLw96qPj\ntM+Y2XbAVGBL4AJ3n1tkQOpRFxEREZH+pievo74gc3858JkqYxMRERERkSrkvo66mR1kZmeb2aTs\nreiA1KOeDvW2pUX5SIvykQ7lIi3KRzqUi74v73XUfwB8gtCfvj4zquu+GRERERERqVreHvXVwDvc\nfWlPB6QedRERERHpb7rTo5639WUJ0Fh9SCIiIiIi0h15C/VPA5eb2bFmdnD2VnRA6lFPh3rb0qJ8\npEX5SIdykRblIx3KRd+X9zrqewEfBA5m0x71kUUHJSIiIiIy0OXtUV8FHOfud/d0QOpRFxEREZH+\npid71NcB91UfkoiIiIiIdEfeQv1c4BIze6OZ1WVvRQekHvV0qLctLcpHWpSPdCgXaVE+0qFc9H15\ne9SviH9PzwwzQo96faERiYiIiIhI7h71UZXGufuiIgNSj7qIiIiI9Dfd6VHPdUS96GJcREREREQ6\nl7vH3MyOMbOLzWyGmV1VuhUdkHrU06HetrQoH2lRPtKhXKRF+UiHctH35SrUzew84Cdx+mOBVcAR\nwEs9F5qIiIiIyMCVt0d9EXC0uz9hZi+5++vMbF/ga+5+TJEBqUddRERERPqbnryO+uvc/Yl4f4OZ\nDXL3B4GAfpwtAAAUqUlEQVRDqopQRERERERyyVuoP2Nmu8X7TwBnmtkngReLDkg96ulQb1talI+0\nKB/pUC7SonykQ7no+/JeR/1rwDbx/leBa4AtgM/3RFAiIiIiIgNdrh713qQedRERERHpb3rsOupm\ntitwELA1sBr4k7vPrT5EERERERHJo9MedQuuAOYAk4BjgMnA42Y23cyq+laQh3rU06HetrQoH2lR\nPtKhXKRF+UiHctH3dXUy6eeAQ4H3uPsod9/P3UcC+xGOsJ/ew/GJiIiIiAxInfaom9n9wIXufksH\n48YDX3X3A4oMSD3qIiIiItLf9MR11HcF7q0w7t44XkRERERECtZVoV7v7i93NCIOz3sd9tzUo54O\n9balRflIi/KRDuUiLcpHOpSLvq+rq74MMrP3ApUO0+e9DruIiIiIiFShqx71fwGdXmjd3XcqMiD1\nqIuIiIhIf1P4ddTd/S2vKSIREREREemWwnvMXyv1qKdDvW1pUT7SonykQ7lIi/KRDuWi70uuUBcR\nERERkS561GtBPeoiIiIi0t/0xHXURURERESkBpIr1NWjng71tqVF+UiL8pEO5SItykc6lIu+L7lC\nXURERERE1KMuIiIiItLj1KMuIiIiItJPJFeoq0c9HeptS4vykZai8tHa2sr69etpbW0tZHkbNmxg\n6dKlbNiwoZDlFR1f0ctrbm7mtttuo7m5uZDlFS317Ve01tZW7rnnnkLzu3r1auW3m4r83Ej9tddf\ndfqfSYtmZkcClxC+IPzc3b/Tm88vIpKKdevWMXPmTObPn09TUxODBg1izJgxTJgwgWHDhlW9vEWL\nFjFx4kQWLFhAc3MzDQ0NjB49mksvvZRRo0bVPL6il7dixQqmTJnCvHnzWL16NdOmTWPs2LFMnjyZ\nESNGVL28oqW+/YqWjW/hwoU88MADheW3tL7Kb22kHNtA0Gs96mZWB/wTOAxYCvwdON7d52WnU4+6\niPR369atY9q0abS0tNDQsPF4SanAPuuss6r6AFy0aBETJkygpaWF+vr6tuGlxzNnzqyqWC86vqKX\nt2LFCk499dSKy5s+fXpNi7nUt1/RlN/28fWn/KYcW1+Ueo/6vsDT7r7I3ZuA64AP9eLzi4gkYebM\nmZt88AE0NDTQ3NzMzJkzq1rexIkTNynSAerr62lpaWHixIk1ja/o5U2ZMqXT5U2ZMqWq5RUt9e1X\nNOU36I/5TTm2gaI3C/UdgCWZx8/GYe2oRz0d6olOi/KRlu7mo7W1lfnz52/ywVfS0NDA/Pnzc/eB\nbtiwgQULFmxSpJfU19ezYMGC3D3rRcdX9PKam5uZN29eu+WtWbOm3fLmzZtXs57m1Ldf0TqKb/Hi\nxW33i8hvlvJbndfyuZH6a2+g6NUe9TzuvfdeHnroIUaOHAnA8OHD2X333TnwwAOBjS86PdZjPdbj\nvvi4sbGRpqYmGhoa2gqa0vtd6fGIESNobGzk4Ycf7nJ5K1eupLm5mfr6el599VUANttsM4C2x3V1\ndaxcuZIFCxb0enxFL2/t2rVty8sW6LCxYB88eDBr165l7ty5heevr2+/3ljfkqLyO3z4cED57U5+\n58yZ0+31nTVrFgsXLmTnnXduF082vsbGRhobGxk6dGgS76+pPZ4zZ07b63bx4sXsvffeHHbYYVSj\nN3vU3wOc7+5HxsfnAF5+Qql61EWkP2ttbWXq1KkVj1JBOKo4adIk6uq6/tFzw4YNjBs3ruIRdQi9\n6o888giDBw/u9fiKXl5zczPjx4/vcnm33HJLp9P0lNS3X9GU3031l/ymHFtflXqP+t+BMWY2yswG\nA8cDv+vF5xcRqbm6ujrGjBlT8af75uZmxowZk/uDb/DgwYwePZqWlpYOx7e0tDB69OhcRXpPxFf0\n8hoaGhg7dmynyxs7dmxNijhIf/sVTfltrz/lN+XYBpJe27ru3gJMBO4E/gFc5+5Plk+nHvV0lH7G\nkTQoH2l5LfmYMGEC9fX1m3wAlq6kMGHChKqWd+mll7adOJpVOsH00ksvrWl8RS9v8uTJ7ZZX+mm5\ntLzJkydXtbyipb79ilYeX6lFoqj8lii/1Xutnxupv/YGgl79GuTut7v729x9Z3e/sDefW0QkFcOG\nDePss89uO1q1fv36tqNT3bnc2ahRo5g5c2bbkfUNGza0HUmv9tKMPRFf0csbMWIEV155ZduR18bG\nxrYjrbW+dB+kv/2KVh5fKR9F5be0vspv70s5toGi13rU81KPuogMJK2trTQ2NjJkyJBCfkLesGED\nK1euZNttt83d7tKb8RW9vObmZtauXctWW21Vs3aIzqS+/Yqm/Ka1vCKlHFtf0Z0e9fRe9SIiA0hd\nXR1Dhw4tbHmDBw9m++23L2x5RcdX9PIaGhrYeuutC1te0VLffkVTftNaXpFSjq0/S+4rkXrU06Ge\n6LQoH2lRPtKhXKRF+UiHctH3JVeoi4iIiIiIetRFRERERHpc6tdRFxERERGRnJIr1NWjng71tqVF\n+UiL8pEO5SItykc6lIu+L7lCXURERERE1KMuIiIiItLj1KMuIiIiItJPJFeoq0c9HeptS4vykRbl\nIx3KRVqUj3QoF31fcoW6iIiIiIioR11EREREpMepR11EREREpJ9IrlBXj3o61NuWFuUjLcpHOpSL\ntCgf6VAu+r7kCnUREREREVGPuoiIiIhIj1OPuoiIiIhIP5Fcoa4e9XSoty0tykdalI90KBdpUT7S\noVz0fckV6vPnz691CBLNmTOn1iFIhvKRFuUjHcpFWpSPdCgXaenOwejkCvV169bVOgSJ1qxZU+sQ\nJEP5SIvykQ7lIi3KRzqUi7Q89thjVc+TXKEuIiIiIiIJFurLli2rdQgSLV68uNYhSIbykRblIx3K\nRVqUj3QoF31fQ60DKHfEEUcwe/bsWochwN57761cJET5SIvykQ7lIi3KRzqUi7S8613vqnqe5K6j\nLiIiIiIiCba+iIiIiIiICnURERERkSTVtFA3s3+Z2WNm9oiZPRiHvd7M7jSzp8zsDjMbXssYB5IK\n+TjPzJ41s9nxdmSt4xwIzGy4md1gZk+a2T/M7N3aN2qnQj60b9SAme0S36Nmx79rzOxs7R+9r5Nc\naN+oETP7bzN7wsweN7NrzGyw9o3a6CAXQ7qzb9S0R93MFgB7ufuLmWHfAVa5+/+a2f8Ar3f3c2oW\n5ABSIR/nAS+7+3drF9nAY2ZXAve6+3QzawCGAZPQvlETFfLxBbRv1JSZ1QHPAu8GJqL9o2bKcnEa\n2jd6nZltD9wPjHX3DWZ2PXAbsCvaN3pVJ7l4C1XuG7VufbEOYvgQMCPenwF8uFcjGtg6ykdpuPQS\nM9sKOMjdpwO4e7O7r0H7Rk10kg/QvlFr7weecfclaP+otWwuQPtGrdQDw+IBhaHAc2jfqJVsLjYn\n5AKq3DdqXag7cJeZ/d3MPhOHvcHdXwBw92XAdjWLbuDJ5uOzmeETzexRM/uZfjLrFTsBK81sevxp\n7KdmtjnaN2qlUj5A+0atHQdcG+9r/6it44BfZh5r3+hl7r4UuBhYTCgK17j73Wjf6HUd5OKlmAuo\nct+odaF+gLvvCRwF/KeZHUQ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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "figsize(12.5, 3.5)\n", + "np.set_printoptions(precision=3, suppress=True)\n", + "challenger_data = np.genfromtxt(\"data/challenger_data.csv\", skip_header=1,\n", + " usecols=[1, 2], missing_values=\"NA\",\n", + " delimiter=\",\")\n", + "#drop the NA values\n", + "challenger_data = challenger_data[~np.isnan(challenger_data[:, 1])]\n", + "\n", + "#plot it, as a function of tempature (the first column)\n", + "print(\"Temp (F), O-Ring failure?\")\n", + "print(challenger_data)\n", + "\n", + "plt.scatter(challenger_data[:, 0], challenger_data[:, 1], s=75, color=\"k\",\n", + " alpha=0.5)\n", + "plt.yticks([0, 1])\n", + "plt.ylabel(\"Damage Incident?\")\n", + "plt.xlabel(\"Outside temperature (Fahrenheit)\")\n", + "plt.title(\"Defects of the Space Shuttle O-Rings vs temperature\");\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It looks clear that *the probability* of damage incidents occurring increases as the outside temperature decreases. We are interested in modeling the probability here because it does not look like there is a strict cutoff point between temperature and a damage incident occurring. The best we can do is ask \"At temperature $t$, what is the probability of a damage incident?\". The goal of this example is to answer that question.\n", + "\n", + "We need a function of temperature, call it $p(t)$, that is bounded between 0 and 1 (so as to model a probability) and changes from 1 to 0 as we increase temperature. There are actually many such functions, but the most popular choice is the *logistic function.*\n", + "\n", + "$$p(t) = \\frac{1}{ 1 + e^{ \\;\\beta t } } $$\n", + "\n", + "In this model, $\\beta$ is the variable we are uncertain about. Below is the function plotted for $\\beta = 1, 3, -5$." + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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TDXry27i2JJ/OUdExoWbGhJq5ekYM20sa+CavmvUFtaSXNpJe2sgz6/dxwohA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\nB6kodS60TEIppU4jHkvxk4fepPdYmOraAB+7aw2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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "figsize(12, 3)\n", + "\n", + "def logistic(x, beta):\n", + " return 1.0 / (1.0 + np.exp(beta * x))\n", + "\n", + "x = np.linspace(-4, 4, 100)\n", + "plt.plot(x, logistic(x, 1), label=r\"$\\beta = 1$\")\n", + "plt.plot(x, logistic(x, 3), label=r\"$\\beta = 3$\")\n", + "plt.plot(x, logistic(x, -5), label=r\"$\\beta = -5$\")\n", + "plt.legend();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "But something is missing. In the plot of the logistic function, the probability changes only near zero, but in our data above the probability changes around 65 to 70. We need to add a *bias* term to our logistic function:\n", + "\n", + "$$p(t) = \\frac{1}{ 1 + e^{ \\;\\beta t + \\alpha } } $$\n", + "\n", + "Some plots are below, with differing $\\alpha$." + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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i1x3L1wHThBB3HrVOEPAxMAoIAq4UQnxx7LaO1yZRV1cHQHh4+ID5ZzSYCSGo\nr68H3AX70SqarWwsamJDYRM5VS0c1U3B6OhAZiWFMjspVBZ0PdBud7KjrJkNRe4PHEOC/fj19Dgy\nYoPUDs0nWKpq2XbVXUTMnsKov92B4qMzTnTFbm5h5033Ur9+B7rgIKa+9zQh43/6Yq+xro03l25k\n0R2nYwrxVzFSyVN2bylh9ao9BBj13PiHWQQGyZ5xafDyWM9wN4vhhcBMIcRiRVFSgNXAOCFEy9Hb\n+u1vfysaGxtJSHCf+SYkJISMjAySk5MZOnRoD39Fydvt3buX+vp6Zs2aBfz09c/h5a+++Z69Va00\nRo5ie6mZ2oM7AQhOmUBalJHoxoOMiw3ikvPP7vLxcrnzstMlMKWMZ2iwH4d2b1U9Hl9ZtjeaeeXi\nG/CLiWTRO8+jaLVeFV9vlmdOncbu2x7k208+Qxdk5MZPlhM8JpV169axfk0uZ541mxlnpXhNvHL5\n1Jdbm63s3+zCbnMyNM1KQkqEV8Unl+VyXy8fvl5cXAzAlClTWLx4sUeK4RnAg0KIeR3L9wLi6IPo\nFEX5FPiXEGJ9x/Ja4B4hxLajt7VkyRJx8803d9pHeXm5LIYHoJ48r+12J1tKzPxY0MjmEjNWh+vI\nfSMiAjgrJYwzk8KIMfVt79vhP6yBamVONZPiTCSGBfTpfnwxl842C+UffEn8dZd43TdUvc2ny+5g\n1y/vo/qrdejDQ5n24TMEpCSy+qM9nHPJGPT6wTUFmy++Pk9GCMH7r2yj6FAdqWNiuPiaCf3yOh6I\nuVSTzKdndXdkuDs9w1uBEYqiJAIVwFXA1cesUwScA6xXFCUGGAnk9yxkaTAL0Gs5MzmMM5PDsDhc\nbCsx82NhI5uKmzhU186hunZe2lLO6OhAzkoJY3ZSKOHGgTULQF+zO11Uttj48xd5BPlpmZ0cxlnJ\nocTLr8cB0Br9GXb9pWqH0Sc0eh0TXniYHTf9mdpvNrL18juZtvJZ5v9inNqhSR6yf3cFRYfq8A/Q\nc87Fo73uA50kebOeTK32FD9Nrfaooii34h4hfkFRlCHAq8CQjof8Swjx9rHbOV7PsBwZHpg88bza\nHC62lpr5Lr+BTUVNWJ3u16sCjBsSxNkpYZyRFIrJrzuf6yQAlxDsrWrl+/xGfixoICM2iPvnJqkd\nltQPnBYrOxb9iboftuIXG8m0lc8RmBSvdlhSLzkdLpb9vx9pqm/n/MvGkjFFPqeSBB6eZ9hTZDE8\nuHj6eW21moZFAAAgAElEQVS3O9lU7C6Mt5WYsXccfafXKExPCGbOiHCmDQuW8xj3gNMlqGuzywMW\nBxFnm4Vt1y6mYeNO/ONimPbhsxgT5fuvL9u5qZi1H+8lPCqQG+88HY18D5QkwIPzDHuSnGdY6o0A\nvZazU8L4+7nJvHvtWBbPTmDiUBMOl2BdYRMPrSngqjdz+H/rismqaMHVww96RzfgDxZajXLcQvjb\nvAZ+KGjA5nR1ef+JDJRcWsqrKVvRaWKcfufJfGqN/kx+4z+EThuHpayKrb+4g/bSSo9t3xcMlNcn\ngM3mYNO3eQDMOje13wvhgZRLbyDzqQ758VFFOTk5/OUvf1E7DJ8U5Kfj/JERPHbBCN68egy/njaU\nlIgAWmxOPt9fx92f5XLDu3tZvr2CCrNV7XB9kl6r8MneWq55K4en15ewr7qV/vwmyRsIp5Pcx16g\n9O1P1Q7FI1rMFpoa2tEFGpny5hJCJo6mvaSC7dcsxt7UrHZ40inYuaGI1mYrMXHBpI6JUTscSfJJ\nsk2ih7KysigqKgKgsLCQO+6445S28+yzz7J582aCg4N55plnPBmi11DjeS2ob+ebvAbWHqqnttV+\n5PZxsUGcNzKcM5JCCRhkR873VlWzjbWH6llzyD139H8vSSPQMHhy2HKoiK0L7yD9H38g9uI5aofT\nK5+vyCI4LIBZ56YCYG80s/mS39JyoICIM6Yw+c0lA+701AOZpd3Oi//5HqvFweU3TyFxRKTaIUmS\nV/HKNglfl52djdlsJjMzk8zMTNasWXPK27r99tuZP3++B6OTAJLCA7hl6lDeuGoMj80fwdwRYfhp\nFbIqW3j8h2KufDOH/3xfRFZFy6Ab5TxVMSYD10yM5eVfpPPAnKRBVQgDBI1IZPJbS9h73xLqN+xU\nO5xTVlHSSFFeHdNm/3SwpD40mEmvP44hKpy6H7ex557/yL8LH7Llh3ysFgcJKRGyEJakXujXQ/B3\n7dpFVyPDvmL//v1cfvnlgPt3SU9PB9wjxMuXL0dRlCP/SA5fVxSFKVOmyMK3n2kUhYlxJibGmfjd\nTCc/FDSy+mAdOVWtrM6tZ3VuPfEhfswbGcE5qeGEG/VyfseTUBSF5Iiu5yeuaLbSbHWSGhGAoigD\nLpfBY1IZ99yD7Lr1L5z+zXL8osL7df+9zacQgu8+38+sc1MxHDPzijFhCJNe+zdbFt5O2dufYkyK\nJ+XORb0N2asNhNdni9nCjg3ubynPOC9VtTgGQi69icynOnxmPqrl2yt4Y2fngzyumxjLoslDTrr+\n8dbrrtLSUoYNG8bevXt56623yM/P54knngBg+PDh/PWvfz3lbUt9K9CgZX5aBPPTIihrsvJ1bh1f\nH6yntMnKS1vLWbatnBkJIcSZWznNJdBq5PycPVXWZOWpdSUEGjScPzICo82pdkgeFzl7KlPeeRJD\nZJjaofTYwZwq7DYnYybFdXl/6KTRjH/2QXbech+5jzyPMWEoQy49p5+jlHpi47d5OOwuUsfEMGRY\nqNrhSJJPkz3D3bRq1SoyMzPRat1fES9btoyGhgYWL158ytt8++23Wb9+vewZVoHTJdhWauaLA3Vs\nKm6iY5Y2Io165qVFMC8tQk431kMuIdhd0cKXB+rYUmJmRkIwiyYPYYjJT+3QBjUhBMv/u4HZ80aS\nNDLqhOsWPP82Bx78Lxo/A1Pf/y9hUzP6KUqpJxrr2lj25I8IIbjx97OIiA5SOyRJ8kqePAOdBFit\n1iOFMMDBgwdJTk4Gft4mcTTZJuG9tBqF6QkhTE8Iob7Nzurcer44UEe52cobOyt5a1cl04YFc1F6\nJJPjguVocTdoFIWJQ01MHGrCbHGwOrcevcyb6hRFYcGiSZi6cabB4bdeRVtBKSWvrWTHDfdw2ucv\nYBwuT+DgbdavycXlEoydHCcLYUnyANkz3E2bNm3iyiuvBKCuro6tW7dy//33A71rk5AHq6gv3Kjn\nyvExDDUfxDRrAp/tr2V9YRObis1sKjYTHaTngrRIzk+LIEKeArpbsrZtYuFx+t6O7quXuqe3fYTB\noV33eh9LURTS/3kX7cUV1H67iW3X3s2MT1/AEBZ8yvv2Rr7cl1ldYWbf7gq0WoWZc0eoHY5P59Ib\nyXyqQ84m0Q3Z2dnMmzePFStW8Mknn/DSSy/x2muvYTKZTnmbL774Im+88Qbr16/nscceo7lZzvGp\nNkVRmDDUxP1zknjz6jHcMnUoQ0wGqlvsvLq9guvezuGfawvkTBS9lFPVym9XHuDTfbW02327t7g1\nr5j6TQPrZEIanY4JL/wD0+gRtOUVk3XbgwhXz0+8IvWNdV/nAjBhRkK3P+RIknRisme4Gz744AMW\nLlyodhg+x9uf1+5wCcGOsmY+31/LhqKfeouTwvzJHB3F3BFhct7iHnIJwc6yZj7dV0tWZQtzUsK4\nKD2SxDDf+8det34Hu2/9C9M/WkpgSoLa4XhUe2klG867CXt9EyPuvoURd9+idkiDXmVZE288uxG9\nQcuv7j4TozyuQZJOSM4z7EEajUzTYKVRFKbEB/PXc5J5/aoxXDsxlrAAHQUNFp5eX8LVb+Xw7IZS\nShotaofqMzSKwuT4YP52bjJLF4wiyE/HPV8c4seCRrVD67GI0yeR+udb2XHDn3C0tKodjkcFxMcy\n/rkHQVE4tGQZNd9sUjukQW/bj4UAjJ82TBbCkuRB/Vrl7drlm18nLliwQO0QpH5wsnPCRwUauGHy\nEN64agx/PjuRMTGBtNldfLS3hlve38d9Xx5iS0kTLtlCcdJcHhYd5M7p61eOYXqCb/alDrv2YsJm\nTCBn8aN91j7T3Xwebfv6QpqbevchLfKs6Yz4v1+CEGTd/iBtxRW92p63OJV8qs3c2M6BnEoUjcKk\nmYlqh3OEL+bSm8l8qkMOeUpSD+m1Gs5OCefJzJEsXZDG/LQIDFqFbaXNPPBVPr98fx8f7amhbQDO\ntdtX9FoNBm3ntyOHS5BX16ZCRD2T/o+7aM0rpuTVD9UOBYCGulY2fZuHn3/vj5FO+cMNRJ0zE3uD\nmV2/vB+nxeqBCKWe2rGhCOESpI2Nlb3CkuRhsmdY6jOD6Xk1Wxx8caCOj/fWUNNqB8Co13B+WgSX\njo5iSLCca/dUFDdYuPfLQwwx+bFgbBSnJYR47TR3rQWlNGzaRfzVF6kdCl+vzMEY5Mescz1zZjJ7\no5kN595Ee0kF8dddzNjH7/XIdqXusVoc/O+x77BZHVx3+2nExoWoHZIk+QTZMyxJ/SjYX8eV42NY\nfuUYHpg7nLGx7haKlTk13PTeXh5ak09OpZyFoqcSwvxZfuUYMtMjWbG7ipve28uHOdW0euGoe2BS\nvFcUwi1mCwdzqph0mue+SteHBjPh5UfQ+BkofeNjSt/+1GPblk4ue1spNquD+KQwWQhLUh+QPcOS\n1METvVpajcLspDCeuGgkz16axjmp4WgUhXWFTfzx01zu/Pgg3+bV43AN7KLYk31vOo3CWSlhPH1J\nGn8+ezj7qlvZWTa4piLsST63ry9i9IShHj/AKmRcGqP/dTcAe//8OObsAx7dfn/ypb5Ml9PFjg2F\nAEyZlaRuMF3wpVz6AplPdciRYUnqI6mRRv50ZiKvXzWGaybEEOyn5UBNG//6tohF7+5hRVYVLVaH\n2mH6lPToQO6fk8SspFC1Q/FKTqeLA9kVTJ41vE+2H3/NRcRfm4nLYmPnLfdjbxpcH0rUcHBPFeZG\nC2GRRlLSTnw6bUmSTo3sGZb6jHxef87icLEmt56VOdWUNLkPQjLqNcxPi2DB2Gii5VRJvdJqc7Kz\nvNmr+opdDgcaXf+e9d5hd6Lrw7mvnRYrmy/+DeasA8ReMpfxzz8kzybYR4QQvLl0E5WlTZxzyWgm\nTB9Yc1lLUl+TPcOS5GX8dRouSo/kxV+k8/D5yUwYGkSb3cUHOTXc8O4eHv220CdmTvBW9W123t1d\nxS3v7+PjvTVYHOqeNc1aU8/6sxdhq23o1/32ZSEMoPX3Y/zzD6E1BlD50VrKV3zRp/sbzMoKG6gs\nbSLAqGfMxDi1w5GkAUv2DEtSh/7q1dIoCtOGhfDvC1J55tI0zk4JQwDf5DXw25UHuPeLQ2wrNfv0\nwXZq9L0NC/Xn6YtHcvfsBHaUNXP9O3tYvr2CJos6rSh+UeFEnz+LrDsf7vVz6W19hIHJw0h/5I8A\n7L3vCVoLSlWOqGe8LZ/Hs21dIQDjpyegN3jnmS59JZe+QuZTHXJkWJJUNDLSyJ/PHs6rV4xmwdgo\n/HUadpQ1c9+Xedy+6gDf5TXgHOAH23mSoiiMjQ3iwXOTeeKiVOra7KqeHTD1nl9jr2+keNkHqsXQ\nV+KuvIDYS+bibG0j67d/w2WX/e+e1FDbyqH91Wi1ChNnyPYISepLsmdYJV9++SXNzc0UFBQQERHB\nLbfconZIHjcYn9fearY6+HRfLav21NDQ7i4uhpgM/CIjmvNGRuCnk59ffU1rXjGbMm9l+srnCErz\nvtkAesPe1Mz6OYuwlFWRfOciRt73G7VDGjDWfLSXXZuLGTs5jnkLM9QOR5J8Und7hmUx3ENZWVkU\nFRUBUFhYyB133NHjbZjNZkaNGkVBQQEGg4ERI0bw3XffMWzYME+Hqypfel69jc3h4uvcet7PrqLc\nbAMg1F/HpWOiyBwdicmvfw/KGmjq2uwUN1qYMCSoXw7+KnnzY0rf/IQZn73QJ/vb9G0eaRmxhEUG\nenzbJ9OweTebF9wOQjD1vaeJmDW532MYaNrbbPzvse9w2F3c+PvTiYwxqR2SJPkkjx5ApyjKPEVR\n9iuKclBRlHuOs85ZiqLsVBQlR1GUb7tax9d7hrOzszGbzWRmZpKZmcmaNWtOaTvBwcGsXbsWPz8/\nFEXB6XT6dH/oQOFNvVqGjoPtXv7FaB6YM5zUyAAaLQ5e3V7B9e/s4aUtZdS32dUO87i8KZddqW6x\n8d/1Jdz58UHWFzbi6uO/v/hrMhm/9O+nXAifKJ8tZgtbfyzw+LzC3RU2fTwpd90IQpB1x0PY6ptU\niaMnvP31uXtzCQ67i+EjI72+EPb2XPoamU91nHR4SVEUDfAMMBcoB7YqivKREGL/UeuEAM8C5wkh\nyhRFieyrgNW0f/9+Lr/8csBd2KenpwPuEeLly5ejKMqRovbwdUVRmDJlCvPnz//Ztg4/duPGjcyc\nOZOEBNkTJnWm1SjMTg7jjKRQdpW38M7uKnaWN7Miq5qVe2o4f2QEl4+LZohJnu65J9KjA3lxYTob\nipp4a1clr26r4Irx0ZydEo6uD6ZlUxQFY2LffEuStbWUUeOG4Oev75Ptd0fKXTdS98NWGrdms+f/\nHmPCS/+U062dIqfTxc5NxQBMOX24usFI0iBx0jYJRVFmAH8TQszvWL4XEEKIx45a57fAECHEX0+0\nrd60SeT+5yXylizrdHvK4ptJ/b9fnnT9463XXaWlpZSWlhIcHMxbb71Ffn4+TzzxBLGxsae8zQ8+\n+IBPP/2Uv/zlLyQnJ5/ydryVbJPoGwdqWnlnVxXri9wjcBoFzk4J48rxMQwPC1A5Ot8jhGBHWTMr\nsqq4feYwEkL91Q6p25xOFy/+53sW3jiFqFh1RxDbiivYMHcRjuZWxjx+D8Ouu0TVeHzVgexKPnl7\nF+FRgdz0h1nyQ4Uk9YLHeoYVRVkInC+E+HXH8nXANCHEnUet8ySgB8YAQcDTQojXj92WL/cMr1q1\niszMTLRa9/Q2y5Yto6GhgcWLF/dqu83NzZx11lmsWrVK9gxLPVLU0M67WdV8c6iewxNOzEwM4ZoJ\nsYyMMqobnNQvDmRXsnNjEVf9erraoQBQvvJrsn77INoAf2aufY3A5IH1ntYfVry8leK8OuZcNIpJ\nM4erHY4k+bTuFsOeOgpHB0wC5gCBwEZFUTYKIQ4dvdJTTz1FYGDgkZaAkJAQMjIyfGJU1Gq1HimE\nAQ4ePHgk7qPbJI52vDaJ1atXs2TJEr788ktMJhNRUVF89NFH/O53v+ufX6afNDU1kZ+fz6xZs4Cf\neqG8dXnp0qVkZGR4TTwnWy7Zs52ZWlh0xVTez67mnc/W8mWeYEPRBKbEm0i3FZAUHqBKfEf3vXlL\nvk51OX3idPRahaxtmzy6/bUffoRfdESv8rl+TS6XXDbPe/IVZWTIwvOo+OBr3rzxTtIfvoszZs/2\nnvhOkk+1l81NForzHOj0GhrbC1m3rtSr4utq+dicqh2Pry/LfPY+f+vWraO4uKPVaMoU5s6dy8l0\nt03iQSHEvI7lrtok7gH8hRB/71h+CfhCCPGzyTWXLFkibr755k778IURxLvuuosnn3wSgLq6Oq64\n4gpWrVqFydTzrybXrFnD5s2buf/++xFCMG7cOJ566inmzJnj6bBV5QvP69HWrVt35A/LF9W32fkg\nu5pP9tUeOfvauNggrp4Qw6Q4U79+3erruTzaFwfqeGlLGfNGRrAwI5pwY+97c9vLqthw7k2c9sWL\nGBNPfmax4+XT6XChKKDRes+Ue/ZGM+vOvh5rRQ0j7/8tyXdcr3ZInXjr6/Pbz/ezfV2hT02n5q25\n9FUyn57lyTYJLXAA9wF0FcAW4GohxL6j1hkF/BeYB/gBm4ErhRB7j96Wr7ZJZGdnU15eTlNTEwEB\nAezdu5drr72W+Pj4U97msmXLcDgclJSUkJKSwo033ui5gL2Etz+vA5XZ4mDVnhpW7amhxeYEIC3K\nyLUTY5k+LFj2IJ6C6hYb72VV801ePXNHhHPFuGgiA3s3e0PB829T/eUPTPvwWRSN9xSznlDz7Sa2\nX/1HFIOemV8tw5SeonZIXs9ud/K/R7/D0m7nuttOIzY+RO2QJMnneXSeYUVR5gFP4Z6K7WUhxKOK\notyKe4T4hY517gZuApzAi0KI/x67HV8thj/44AMWLlyodhg+x9uf14Gu1ebkk301fJBdc+SUxCMi\nArhmQiwzh4egkUVxj9W32Xk/u5rv8hp4+fJ0AvSnfopc4XSy5bLfEXPBmQy/9SoPRukd9vzpP5Qs\nX4lpTCqnffESGoN6s134gpwdZXz5fjYxccFcf/tMtcORpAHBo/MMCyG+FEKkCSFShRCPdtz2v8OF\ncMfy40KIMUKIcV0VwuC78wxrBtiojdS1o3uOBoJAg5arxsey/MrR/GZGHOFGHYfq2nlobQG/+XB/\nn57qeaDl8rBwo55fT4/j1StG96oQBlC0WjKeup+8p5bTklt4wnV9MZ9pf7udgMShNO/JJe/JV9QO\n52e8MZ+7N7t7HCdM961pNr0xl75M5lMdssrrhgULFqgdgiSdsgC9lsvGRrP8ijH8bmY8kYF6Chss\nPPJtIb/6YB9rcuv7rCgeqAzHOS12T/NoHB5P6v/dQt4T3lUseoIu0EjGUw+AopD/9Os07th78gcN\nUtXlZipKmvDz15E27tSn65Qk6dTI0zFLfUY+r97J5nSxOreed3ZVUdXiPtXz0GA/rpkQw9wR4Wj7\n4KQTg8XDawvQKHDNxNhuz/ksXC5cVjvagO6dOKWipBFzo4W0DN8omvb//RkKl75F4IgEZq5+rdu/\n52Dy9cocsraWMum0ROZkpqsdjiQNGB5tk5AkaeAwaDVcOCqSV64Yzd2zExgabKDcbOXxH4q5+b29\nfHGgDoccKT4lfzwjgRERRu75/BD/WFtAfl37SR+jaDQ9KhC3/FBAW6utN2H2q9R7fkVQWhKth4o5\n+MhStcPxOlaLg327KwAYN03OyyxJaujXYthXe4alwWGw9WrpNArnjYzg5V+M5k9nJhIf4kdFs40n\nfyzmphV7+Wx/LXan65S2PdhyeZjRoOWK8TG8esVo0qMDue+rQzzxQ3Gvt3s4n81NFkry6xkz0Xe+\ncdH6+5Hx9F9QdFqKXlxB3brtaofkVa/PvbvKsducxCeFERkTpHY4PeZNuRwIZD7VIUeGJWmQ02oU\nzkkN58WF6dx7ViLDQvyoarHx1LoSbnpvL5/uq8V2ikXxYBWg1/KLjGheu2IMF6ZHeGy72dtKSRsX\ni8FP57Ft9oeQ8aNI+cONAGT/4Z84WlrVDchLCCHYvcU3D5yTpIFE++CDD/bbztrb2x8cMmRIp9ub\nm5tP6eQVknfztef18JkRByuNopAUHsBF6ZEkhvpT3Gih3Gxjc4mZ1bn1GLTu+7vTUzzYc3mYTqP0\neD5iR2s7lvIq9KHBR25LSEjA5RJ88X42Z80fRaDJ9/puQ6eOo2btBlpzi7A3thB97umqxeItr8/y\n4ka2fF+AMdDAeQvGovHBfn1vyeVAIfPpWRUVFSQnJ//9ZOvJkWFJkn5Gq1E4KyWM/y0cxQNzhpMY\n5k9Nq53/bijlxhV7+XhvjRwp7iWXEDy7oZQDNZ1HSGu/3cSORffgtFh/dntxXh2BJj+ihwZ3eowv\n0Oh1ZDz1AIpeR8nyldT9uE3tkFS3q2M6tYwp8WiPM0OJJEl9T/YMS1IH2av1cxpFYXZyGP+7bBQP\nzB1OUpg/ta12ntlQyo3vdhTFjq6LYpnLExMChoX68fc1BTzwVR77q38qimMuPIvA1EQOPf7ykdvW\nrVtH4ogILls0WY1wPcaUnsKIxTcDkH3XI6q1S3jD67Ot1cbB7EpQYNy0Uz+bqdq8IZcDicynOuRH\nUUmSTkijKMxOCmPpZaP4y9wkksP9qW3rKIpXnLgolrqm1ShcPDqKV68YzfRhwTy01l0U59a2oSgK\nox+9m7J3P6dxx54jj1EUBWNQ704B7Q2SfncdweNGYSmt5MBDz6odjmpytpfhdAqSRkYREmZUOxxJ\nGtTkPMNSn5HP68DkEoINhU28sbOC/HoLAJFGPVdNiGHeyIjjnpBCOj6b08VXB+oI9tdxZnIYABWr\n1nBoycvMXP0qWn/f6xE+keZ9eWw4/2aEzc6UFU8ROXuq2iH1K+ESvPzEjzTWt7Hg+kmkpEerHZIk\nDUhynuEBaMOGDVgsFqxWKxs3blQ7HGmQ0igKs5JCeW7BKP56zs9Him9YsZeP9siR4p4yaDVkjo46\nUggDxF4yl6CRSVR98b2KkfWNo9slcu56BEfz4Jpdoji/jsb6Nkwh/iSlRakdjiQNerJn2Ifcdttt\nxMXFMX78eBoaGtQOZ8CRvVo9o1EUZg3/eVFc12bn2Y2lZD78pmyf6CVFURi/9O9EXXwOb3+6Ru1w\nPC7p9msJHj8KS1kV+x96pl/3rfbf+u4tJYD7wDlfnEHiaGrncqCR+VSHb01W6QWysrIoKioCoLCw\nkDvuuKPf9v3HP/6RuXPnEhsbi1ar7bf9StKJHC6KZyaGsKGoiTd2VLIrz8EzG0p5Z1eVbJ/oBY1B\nz7r1hSzfWkaOLo/rJsWSHh2odlgeodG5Z5fYcN5NlL7+EbEXnU3kmdPUDqvPtTZbObS3GkWjkDHF\ndw+ck6SBpF//O02YMKE/d+dx2dnZmM1mMjMzyczMZM2a/h2t0ev1xMXFyUK4j8yaNUvtEHzaTyPF\naTx+6wKSwwPkgXa9ZG5sJ+ubPN6/9xpmJATzj7UF3P9lHvuqB0ZbgWlUMiPuvgWAnD/+q9/aJdT8\nW8/ZXorLJUgZFYUpxF+1ODxFvm96lsynOuTIcA/s37+fyy+/HHC3fKSnpwPuEeLly5ejKAqHD0g8\nfF1RFKZMmcL8+fN7vf8dO3YghKC+vp6UlBSPbFOSPK2rkeL8+vYjI8VXjo9hfpocKe6OrK2lpI8f\nQmCAnszRUZyfFsFXB+p4eG0Bfz57OGNjfe/0vcdKuu0aqj//nqZd+9j/4NOMXfJntUPqM8Il2L21\nFIDx04apHI0kSYf162wSS5YsETfffHOn27sz68D6Nbls/Cav0+2nzUnh9HNST7r+8dbrrtLSUkpL\nSwkODuatt94iPz+fJ554gtjY2FPeZk9lZWUxbtw4AGbPns2nn35KcLD3TsDva7NJrFu3Tn4q95Cj\nc+kS4mdFMUCEUc9Vsig+IZfTxQv/+Z6FN07hwKHdzJo1i+b9+RgT43Aa9Og1Cori2/2mh7UcKGD9\nuTcibHYmv/UEUXNm9On+1PpbLzhYwwevbic4LIBfLZ6N4uP9wiDfNz1N5tOzujubhM+MDJ9+TmqP\nitmern8y27ZtIzMzE61Wy8MPP8yyZct48803Wbx4ca+2+/TTT2OxWH522+ER5auvvpphw34aPRg7\nduyR66Ghoaxbt44LLrigV/uXpL529EjxxqImXu8oip/dWMo7u90jxRfIoriT/AM1BIcGEBVr4sCh\njtueeg3/IdGk/fX2Lh/jEgKNDxbIQWlJpP7pVxx8+DlyFv+LWd+9gT7Ed07l3l2HD5wbNzV+QBTC\nkjRQ9Gsx7Ms9w1ar9We9ugcPHiQ5ORn4eZvE0brTJnHnnXd2a//vvfceq1ev5oUXXgCgtbVV9g57\nmPw07jld5VKjKJw+PJTTOoriN3ZWklfXznMbS3lndyVXjovhglGR+MmiGHAXTuM6vko/nM/0f/yB\n9XMWET1/NmFTMzo95sOcGraXmrluUixjYnyrhSLpt1dT9cX3NG3fw/6/PkXGUw/02b7U+FtvMVvI\n21+DRqOQMXngHDgn3zc9S+ZTHT4zMqy2TZs2ceWVVwJQV1fH1q1buf/++wEYPnw4f/3rX/t0/8OG\nDePGG28E3IVwXV0dZ5xxRp/uU5L6wuGieGZiCBuL3SPFeXXtLN1Uxru7q7h8XAwXpkfiP8iL4tnz\n0ggN//mZyQyRYaT/azHZv3+Y09e8htb48wOwLhkdSYBew6PfFhEX4sf1E2MZ4yN9xYpW655d4pwb\nKHv3c2IuPJvo805XOyyPyd5WinAJUsfGEGgaWCdRkSRfJ+cZ7obs7GzmzZvHihUr+OSTT3jppZd4\n7bXXMJn672u8GTNmUFZWxtKlS3n44Yd56aWXMBrlKTw9Sc7v6DndyaWiKMxMDOW5S9N48NwkRkQE\nUN/u4H+by7jh3T28n1VFu93ZD9F6p6hYE3qD+9ufo/MZe+FZhExI5+AjSzs9Rq/VcOGoSJZdns6Z\nSZVJnwcAACAASURBVKE89n0R93yei8VHZvEIGpHIyD//BoA9dz+KrcHcJ/vp7791l0uQNUAPnJPv\nm54l86kOOTLcDQcPHmThwoVHljMzM1WJ4/BMFpI0kBwuik9LCGFziZk3dlRysLaNF7aU825WNZdn\nRJM5OpIAvWwLOiz9n39k88W/wVbbgCEyrNP9eq2G+aMiOXdkBNtLzT41yp74y8up+vx7GjbvZt8D\nTzD+2QfVDqnXCg7W0NxkITTcSEJyhNrhSJJ0jH6dTWLt2rVi0qRJnW739lkHVq5cyYIFC9QOw+d4\n+/MqeSchBFtLzby+o5IDNW0ABPtpWZgRzcWjowg0yKIYwOVwoNENzPGM1oJSNsxZhLPdwsRl/yLm\ngjPVDqlXPly+nfz9NcyeN5Jps5PVDkeSBo3uzibhO8MFKpKFsCT1H0VRmDYshKcvHskj81IYHR2I\n2erklW0VXP/OHl7fUUGz1aF2mKrrbSH80Z4atpWa6c8Bke4KTIpn5AO3AbDnT//GVteockSnztzY\nTsGBGjRahTGT4tQOR5KkLsieYUnqIHu1PMcTuVQUhSnxwTyZmcpj80eQERtEi83J6zsquf6dPbyy\nrRyzZWAVxU0N7dRWtXS6vS9em2FGHf/bVMadHx9kU3GT1xXFCTddRvjMSdhqG9j75yUe3XZ//q1n\nbytFCEgdHUNg0MA7cE6+b3qWzKc65MiwJEleTVEUJsaZWHJRKo9fOIKJQ4Nos7t4e1cV17+7h5e2\nlNHQZlc7TI/Y8kM+B3Mq+2Vfs5PC+N/CUVyeEc2r28q5bdUB1hV6zwisotEw9sn70BoDqPx4LRWr\nVqsdUo+5nC6yt3UcODd9YB04J0kDSbeKYUVR5imKsl9RlIOKotxzgvWm/v/27jw+qup8/PjnzD7Z\n9wRIgCyEHQKGTURBK+AClFLq7telau1X7aLVtlq11f6qtlrX2q8Ltrag4oLUFTeKsgpCCCGEAIEs\nZCH7JJNk1vP7Y4YQIIEsk0wmOe/Xa14z986de888M5M8c+a55wghHEKIH7R3fyCPM6wMfGp8R9/p\nrVhOGhLK45eO4q+LRpGZGEqzw83q7GNc99Ze/ralhEqrvVeO2xdsLU72Z5czadrpY9CeLZ5SSqo3\n7ujyMTVCcH5KJH9bOobrpw7hiHeGwP4iaMRQxvz+TgD23vcXWkqP+WS/ffVZP7S/kkaLjaiYYJKS\no/rkmH1N/d30LRVP/zhrMiyE0ADPAwuA8cBVQogxHWz3GLDO141UFEVpa3x8CP9vYRrPLUln1ohw\n7C7J+3srueGtXJ7ZWERZg83fTeyyvbuOMjw1mpAw09k3PoW72cbeex6n4tOvu3VsjRDMGhHOtVOH\ndOvxvSnx2iXEXjwbZ30De372KNIdGMPEAezaXAjApOlJA2bqbEUZiDrTMzwdOCClLJRSOoA3gSXt\nbHcn8A7Q4Vd3VTOs9GeqVst3+iqWo2OD+f3FKfx96RguSInA6ZZ8lFfNjatz+fOGQorqWs6+k35A\nSknW1iKmzBze7v1ni6c2yMTEZx4g994/Y6us8Xn7thdbsPtprGIhBBOe+g2G6Aiqv9lB4Stv93if\nffH+rCxvoKigBr1By8TMgXvinPq76Vsqnv7RmWR4GFDcZrnEu66VEGIo8H0p5YuA+vqrKEqfSok2\nc/+Fybz8w7F8b5Tn5+jPD9Rwyzv7eOTLwxyoavJzC8+suKAGIQSJyaePGdxZkTMmM+yqy8j5+R99\nejKcyy35MK+K61fv5W0/TYRijI1i/JO/BiD/jy/SkFfQ523oql1bPL3C46cOw2jS+7k1iqKcia8G\nqXwaaFtL3G5CfPDgQX76058yfLin9yM8PJyJEyeSkqLGXRyI6uvrKSgoaK2BOv6Nt78uH1/XX9oT\nyMvnnXee345/7wXncd2UBJ5Y+RHbiy18w2S+OVzHEEs+F6VFcf3ii/0en1OXo+NCiBreyKZNm3oU\nT/essej++y1Fr71HcXq8z9r3+4tTWP3xF3z53zxWZ49hybgY4urzCdJr+yxeB0I0VM6bTOz63WTf\n8Xtc99+IRq/rl+/P5iY7H3/4OS6n5MaZ/n9/qWW1PFiWj98uKioCIDMzk4suuoizOeukG0KImcDD\nUsqF3uVfA1JK+XibbY5/TRdADGAFbpVS/qftvgJ10g2le9TrqvhbtdXBuznH+HBfVeuUxBPig7ky\nI55piWEDso7TeqiI3PufInPVUwiN7wcMKqlv4a3dFUSZ9dw4rW8/385GK5su+h+aC0tJvuNaRnvH\nIu5vvv26gK8/zWfkqBh+eGOmv5ujKIOWLyfd2A6kCSFGCCEMwJXASUmulDLFe0nGUzf801MTYVA1\nw6fKycnhd7/73aA9fn/T9pul0jP9JZbRwXpunTGMf185nmunJBBq1JJTYeWBdQXcviaP9YdqcLn7\n1/i67elKPINThzPtzad7JREGSAw3cff5I7ghs+9PttOFBDPp+YdAo+HwCyup2dq9/ym9+f50u9zs\n2urplZp67oheO05/0V8+6wOFiqd/6M62gZTSJYS4A/gMT/L8qpRynxDiNs/d8qVTH9IL7ew3srOz\nKSz01IIdOXKEO++8s1v7eeGFF9i2bRthYWG+bF7AHF9R+lKYScf15wzhhxPj+DCvivdyjlFQ08Kf\n1hfy2o4ylk+MY356NEadGnq9szrqVS+pbyExvOsjYnRW5LSJpNx1HQVP/5PsO/7Aeev/hS40uNeO\n11UH9x2joa6FyOggkkfF+Ls5iqJ0wlnLJHwp0Msk9uzZQ319fWuNypIlS1i7dm239/fGG2+wadMm\nnn/+eV81sV8dP1BeV2XwsbvcfHmghtXZxzhq8QzDFmHSsXRCLIvGxhBiPGs/gdKO2iYHP31/P8lR\nJn40KZ7JQ0J6pRTF7XCy9bJbsWTnMfRHlzLp2Qd8fozuevOlbZQcqeXCy8cOip5hRenPOlsmERB/\n8ee/sssn+/nsx1N69Pi8vDyWL18OeEo+xo4dC3h6iF9//XWEEK1ncR+/LYQgMzOTSy65pGeNP4v+\n0AZFCRQGrYZLxsQwPz2aTYV1vLW7ggNVzby2o4w3d1dw6eholk6IIy7E0KvtKC+pJ37YwKldjgzS\n888rxvHlwVqe3VRMsEHL8klxzB4RgVbju+eo0euY9MKDbJ5/I6WrPybmgmkMXbbAZ/vvrmOlFkqO\n1GIwahk/deAOp6YoA02fJsNZWVm01zMcCEpKSkhKSiI3N5dVq1ZRUFDAU089BcDIkSN58MEHe+3Y\n5eXlrFy5kokTJ7J582ZuvvlmIiMjaWpqIi4urk/aMBi0HUlC6ZlAiaVWIzg/OZI5IyPIKm3kzd0V\n7Cpt4N2cSt7fW8nc1EiWT4wnJdrs82MfK7Pw/r93cuuvLkBoz5wo9iSezgYrR9/6mOE3/7BPkm6D\nVsMlo6NZkB7FlsJ6VmdXUNvkZMn4WJ8eJ2TUSMY+8nP23vM4e+/9M+EZYwlObX+c5lP11vtzp3c4\ntQlTEzGaAqKvqccC5bMeKFQ8/SMgPq097dH1hR07drBo0SK0Wi2PPvooK1asYOXKldx99929etym\npiauvfZaVq9eTVRUFDExMTzwwAMsX76cBQv83xOiKAOBEIIpw0KZMiyUg1VNvL3nGBsKavnyoOdy\nzrBQlk+KY8rQUJ8llLu2FDF5ehIabe/WKQudjuJ/vY82yEzi1Zf36rHa0gjB7JERnDsinN46RzHx\nmsVUb/yO8ve/IOvW3zHzo5fQmoy9c7CzaLLa2be7DARMmdW5pFxRlP6hT5PhjIyMvjycT9lsNrRa\nbetyfn5+6/jIbUsU2vJFicKaNWvIyMggKsozkUBsbCy5ublIKdHrTwzk3pttGCzUt3HfCeRYpsUE\n8Zt5I7kxcwhrcir5ZH813x1t4LujDaRGm1k2IY4LUiLQ9yCJbbS0cGBvBTf9Yk6ntu9JPLVmI5P/\n7xG+/cEdhE8ZS+jY1G7vqzuEELTX8e1ySyoa7QwN637yKoRgwp/vw7I7j4a9B8h76FnGP/6rsz6u\nN96f2duLcTndpIyOJTKm/5zQ19sC+bPeH6l4+kdA9Az3B1u3buWKK64AoLq6mu3bt3P//fcDPStR\nOPUExoKCApKTk1uTWofDcdKkJFarFY1Gw6JFi056XHfb0JcnUCpKIEkINXL7rESumZLAh/uqWJtb\nyaHqZp7YUMir20tZMj6GS0fHENaNn8O/21TIuIyhBPVyTfJxoWNSGPPQHWTdcj+zPn0VXYj/k7Wj\nFht3f3iA8fHBLJ8Yx7j44G71uutCg8l46RG2XHYrxf9cQ9S5Uxmy5OyD7PuSy+UmaxANp6YoA432\n4Ycf7rODrVmz5uEpU04veWhoaCA0NLTP2tFVe/bsIT4+np07d1JQUMC6det48MEHiY3tfg3cyy+/\nzOrVq9m7dy/19fVMnjwZo9HIwoULSUtLIzk5GYCUlBQ2bNiAzWYjPz8fm83GsWPHaGxsJC0t7aTe\nYV8c35f6++t6qo0bN7bOjqj0zECKpVGnYeKQEJaMi2VImJEyi42yBju7ShtZm1tFbZODYWGmTifF\nLc0OPn1nDwt/OBGTuXOfX1/EM2zCKBpyDlD+0X+Jv2yu30/aCzfpWDQ2hmaHm1e3l7L+UC1mvZbE\nCBOaLrbNGB+DPjyMqi+3UL3hWxIWX4g+ouNhI339/szPKSfnu6NExQYz99Ixfo9tXxpIn/X+QMXT\nt8rKykhJSfn92bZTPcOdkJ+fz7Jly1qXT+2V7Y5bbrmFW2655bT1W7ZsYdOmTa3LYWFhrT3Qx82d\nO7fXjq8oSvsMOg0L0qOZPyqK74428F7OMXaUNLA2t4r/5FYxc3g4358QS8ZZhhPTajVcdsUkwiN9\nf1Le2Yz94y858tKbSJcLofP/n3+zXsvicbFcNiaGLUX1rMmpxC0lF6ZFdXlfw2/8ATWbvqPio/+S\ndeuDzPzg72iMfdPzvst74tyUWSMGVSKsKAOFGme4E9asWcPSpUv77FgLFy7EbO77f5S+1t9fV0Xp\nqcM1zbyXc4yvDtbi8J4llhxp4vvjY7kwLUpN4tENx89z6A5HfQObv3cDzcVljLjlR4x95Oc+bt3p\nSovqWPX3rRiMOn7y67kY1BjVitJv+HI65kGvrxJhgPnz5w+IRFhRBoPkKDN3nz+Cf181nuvPGUKU\nWcfh2hb+urGYa97I4bXtpVRZ7f5uZkBpLxFucbrZUWLBfZbOG314KJP/7xGETkvhy6up+GRDbzWz\n1eavDgKQMTNJJcKKEqD6NBnOyurePPKDSXCw/09sGazUnPC+M9hiGWnWc+2UBP515XjuvWAE6TFB\nWGwu3thdwbVv7uXRLw+TXdbY7RNWB1s8T1VltfPKt6X8+J19rN1bSZPd1eG2EVPHkf7ATwHYc9ej\nNOYfOW0bX8WzrLiOI/lV6A1aMs9L9sk+A81gf2/6moqnf6ieYUVRFB/RazV8b1QUzy1J56+Xj+L8\n5AgAvj5cxz0fHeAn7+XxUV4VzY6Ok7m+5nY6/d2Es0oMN/Hi0tH8Ys5w9pQ3ct1be3lhcwllDbZ2\ntx9525UkLLoQZ4OVnTfch6PO0ivt2vLVIQAyZg4nKLhv6pMVRfE9VTOs9Br1uioKVFrtfLSvig/2\nVtLgcAMQbNCyID2KRWNjGBZu8lvbpJRsW3Qb6b+9nahz/T+5UWdVWu18uK+KKUNDyRja/og1Tmsz\n2xb/hIa9B4iZN4Nz/v0XRJux4nuqvKSef/9tCzq9llt/dUGfDZOnKErnqZphRVGUfiA22MCcEB2X\nN1i5b+4IxsUFY7W7eC+nkhvf3sd9Hx/km8N1OHtrmrYzEEKQdu8tZN32O5qOlPT58bsrNtjAjZlD\nO0yEAXTBZqa89hj6qAiq1m9j/6Mv+rQNW9rUCqtEWFECm6oZVhQvVavlOyqWJ/t2QwGzzk/morQo\nnl6czvPfH82C9CiMWsGu0gYe+fIw176Zwz+/K+NY4+kn3PVmPGPOn0ba3Tfx3XW/wmFp7LXj9JW6\nZgd/Wn+E7LIGzEkJTHnljwidliMvrqL0nU+Bnsez4mg9h/Iq0ek1TBuktcLHqc+6b6l4+ofqGVYU\nRelFJYdrsDbYSZ+Q0LouPSaIu88fwaqrJ3D7zGEkhRupaXKyclc517+1l4c+K2BbUT2uPuotHn7D\nD4ieM43dt/0uIGqIz8So0zA2LphnNhbz43f2sT50GMkP/QyAnLsfo35Xbo+PcbxWePKM4QSH+nay\nIkVR+p6qGVZ6jXpdFQXe/ed3pI2NY/L0pA63kVKyp7yRD/ZVselIfWvJREyQngWjo1mYHk18aO/+\nFO92Ovnu2nsYceMy4hbM6dVj9QUpJTkVVj7Oq2JrkYVbv3kf8cGnGBNimLVuBab4mG7t91iphdef\n34xOp+GWX12gkmFF6cc6WzOsBkVUFEXpJZXlDRwrtbDk6owzbieEYNKQUCYNCaW2ycG6A9V8ur+G\nUouNlbvKWbWrnHMSQ1k4OppZw8PRa33/o55Gp+Ocf/0FjX5g/FsQQjAxIYSJCSFYWpy0LErlcGUZ\ntVt3k3Xzb5n+7vPdmqHuRK9wkkqEFWWAUDXDfvLpp5/y9ttv88QTT/Dqq6/6uzmd9s477/D8889z\n00038e677/q7OT6larV8R8XSIzouhB/dPA2dvvOjGEQG6blycgIrlo/liUvTmJcaifXwbnaUNPDo\nl0e4+o29/H1rCYdrmn3e3oGSCJ8qzKQjLjKYjJf/iGlYPJu/3caen/8R6Xazt6Kx0+UolWUNHMit\nQKfTMG3O4K4VPk591n1LxdM/BuZfvl6UnZ1NYaFnHvojR45w5513dnkfFouFm266icOHD2MwGEhL\nS2P+/PkkJXX8M2p/cPjwYWpqarjjjjuorq4mMzOTadOmMXz4cH83TVH6JY1GEB0X0r3HCkGGd+iw\nqTKZprhhfLy/msLaFt7LqeS9nEpGxZhZkB7N3JRIwkzqz/nZGGOjmPrPx8m67DrK1nyONiKcl2de\nSoXVwcWjolmYHnXGoe62rPeMIDFpWhIhYf4bEk9RFN/q057hjIwz/1TY3+3ZsweLxcKiRYtYtGgR\nX3zxRbf2ExYWxpdffonRaEQIgcvl6vbMVH0pLy+P5557DoDo6GhSUlLYtWuXn1vlO+edd56/mzBg\nqFj61oILL2DphDhe+sEYnl2czuVjYgg2aDlQ1czzm0u4alUOf/zyMNuLLX120l2gCpuQznUrX0AY\n9JS89g53Hd7KY5ek4XJLfvHBAX75QT4bCmpPe1xleQP5ORVodRqmX6B6hY9Tn3XfUvH0j4DoSvg0\n4Vyf7Gdh+eYePT4vL4/ly5cDnpKPsWPHAp4e4tdffx0hRGtSe/y2EILMzEwuueSSk/Z1/LFbtmzh\n3HPP7XHvanfa0FUXX3wxb731VutyeXk5KSkpPdqnoiidJ4RgTFwwY+KCuW3mMDYX1rEuv4ZdRxvY\ncLiODYfriDTrmJcayffSokiNNiPEWc8d6ZCtsoacn/+RSX97GH14x2P6Bpro8zKZ/MJDZN36Ow48\n9hLjYyK59dol3Jg5hG+LLTR7J0dp63it8KTMRNUrrCgDTJ8mw1lZWbQ3mkQgKCkpISkpidzcXFat\nWkVBQQFPPfUUACNHjuTBBx/s8j7fffddPvzwQx599NEzbldeXs7KlSuZOHEimzdv5uabbyYyMpKm\npibi4uJ61Iau0Ol0jBs3DoB169YxZcoUJk6c2KvH7EsbN25U38p9RMXSt9qLp1GnYV5qFPNSozjW\naOeLAzV8cbCGknpbaxnFiEgT30uLYl5qJHHdmBjCEBNJUEoS3117D5lvPo0u2Oyrp+RXGzdu5LxF\nFzLusXpy7/sze+/9M/rIcBIum8vskRGnbV98uIb8nHK0Wg3TL1AdAG2pz7pvqXj6R0D0DPe0R9cX\nduzYwaJFi9BqtTz66KOsWLGClStXcvfdd3d7n8uWLWP+/PnMnTuX999/v92a4aamJq699lpWr15N\nVFQUMTExPPDAAyxfvpwFCxb05Cm1evbZZ2lpaTlp3fEe5auuuuq0dlksFt544w3+/ve/++T4ijKQ\nbP3vIYYkRjAiLbrPjhkXYuDqKQlclRHP/somvjxYw38L6iisbeHV7aWs2F7KpCEhzE2NZM7IiE7X\nFwshGPP7u8j55Z/YdeOvmfr6E2hNA2cEheH/sxR7dR0Hn3iZ3bc/hP6NvxI9++QOG5fLzRdrPWMT\nW5MiuPuLw8xLjWReaiQJajQJRRkQ+jQZDuSaYZvNhrbNvPb5+fmtJQJtSxTa6qhE4fPPP+fJJ5/k\n008/JTQ0lNjYWNauXcsdd9xx2nHXrFlDRkYGUVFRAMTGxpKbm4uUEr1e37pdV9vQ1l133dWlWDz3\n3HM888wzhISEUFxc3O9P/Oss9W3cdwZrLOtqmtjxzRFu+Nlsn+63s/E8uYwike3FFr48WMOWonp2\nlzWyu6yR5zcVk5kYxtzUSM4dEY75LCNdCI2GCU/+mqzbHmT37Q+R8fKjaHQB0Y/SobbxTP3FDdgr\nayh67V12/s+9zFjzAmETR7fev3NzIdXHGomICuL6G6aSX9PCVwdruXNtPonhRi5MjeSSMTHoNN0v\nRwlkg/Wz3ltUPP2jU3/RhBALgafxnHD3qpTy8VPuvxq4z7vYANwupdzjy4b629atW7niiisAqK6u\nZvv27dx///1A10sUhBDMmeMZ1F5KydGjRxk/fjwABQUFJCcntya1DofjpLpcq9WKRqNh0aJFJ+2z\nL8okAF5++WUuu+wybDYbO3fupKWlZcAkw4rSUxs+2c85s0f2i5pSnUYwa0Q4s0aEY7W72HSkjvWH\natlV2sC2Ygvbii0YtYKZw8O5ICWSaUlhGHXtn1MttFom/+1hdt7wa6o3bCf2oll9/Gx6jxCCsX/8\nBfaaOsrXfsmOq37J9DUvEDJqJJa6ZjZ/6RlB4sJFYzEYdExICGFCQgi3zxrGd0cb+K6kAe3gzIMV\nZcA46wx0QggNkA9cBJQC24ErpZR5bbaZCeyTUtZ7E+eHpZQzT93Xk08+KW+66abTjtHfZyrbs2cP\npaWl1NfXYzabyc3N5ZprriExMbHb+1yxYgVOp5Pi4mJSU1O54YYbAJgxYwaPPfYY8+bNAzwlCc89\n9xzTp0/H6XRiNptZuXIl8+bNY+nSpZjNfVfDt3XrVi6//HLgRI9zdnZ2h69df39dT6VqtXxnMMay\n+HANH6/O5qZfzkHfhXGFO8OX8axtdvDN4Tq+OlhL7jFr63qTTsOM4WGcn+xJjE3tJMbS7UZo+nQQ\nol7RXjzddgffXXcP1Ru2Y4iOIHP1M6zPauTA3gpGjY9nyTVTunSMBpsTjRAEG3z7XuhvBuNnvTep\nePqWL2egmw4ckFIWAggh3gSWAK3JsJRya5vttwLDutbc/i0/P59ly5a1Lp/aK9sd7X0pAM/oEps2\nbWpdDgsLa+2BPm7u3Lk9Pn53zJw5k6qqKr8cW1H6M+mW/PfjPM5fkO7zRNjXIs16Fo+LZfG4WCoa\n7GwoqOXrw3XkVzWxoaCODQV1nsQ4KYw5KRFMSwxrLaUYCIlwRzQGPVNfe5xdN/+GqvXb+OL2v1Bw\n3lL0Bi3zLhvT5f19V9LA0xuLmDw0lDkjI5g5PIwQY2CXlyjKQNWZT+YwoLjNcgmeBLkjPwY+ae+O\nQK0Z1vThP4C1a9eycOHCPjuecoL6Nu47gy2WzU0OEhLDGTN5SK/sv7fiGR9q4EeT4/nR5HjKGmx8\nc7iObw7Xsb+yqXWoNoNWcM6wMGaPDGfm8PABMblHR/HUBpmY+o/H+e4nD7M/eAIAGWlmwiK6/gvc\n3NRIMhND2VxYz9eHa3l+czHj4oO5IXMo6TFBPWp/fzLYPuu9TcXTP3z6V00IMQ+4EWj31XznnXd4\n5ZVXWsfUDQ8PZ+LEif1+rNqlS5f22bHmz5/fp6UPvam+vp6CgoLWD/fxaSbVsloeaMtBIQbM0bVs\n2rSpX7SnO8uHdm9nKPDckvMob7Dxynufsae8kdroMWwpqmfd+g1oBMyefR6zR0agLc0h0qxnypDh\nmIbFs2XH9n71fHqybLvqfzi48iP0ZftoeXMzVYn/jzydo1v7m3/eecxPj+aL9V+TV1lKsD7J789P\nLavlgbp8/HZRUREAmZmZXHTRRZxNZ2qGZ+KpAV7oXf41INs5iW4S8C6wUEp5qL19BWrNsNI9gfa6\nqlot31Gx9C1/xrPa6mBzYR2bCuvZXdqAq82/jLRoMyn5OaTuz+GyZ+7BENq9qaf72pniWVtt5R/P\nbMLldDPdnk/Tv99EGPRkvPQI8QvP77U2vbvnGBOHhDCqhxOl9DX1WfctFU/f8mXN8HYgTQgxAigD\nrgSuaruBEGI4nkT4uo4SYUVRFCXwRAfrWTQulkXjYmmwOdlWZGFzYR3bSxo4WN3MwehUODeVf/1j\nF7PHDmF2eixThoZ2ODJFfyal5Mv/7MPldDN+6lDmLFtAntlN4curybr5fiY+/zuGLp3v8+M6XG6q\nmxz86asj2FxuZg33lKRMHhKCIQDjqCiB5qw9w9A6tNoznBha7TEhxG14eohfEkK8DPwAKAQE4JBS\nnlZX/OWXX8r2ZqALtB5EpXPU66ooA5fd6SarrIGtRRa2FtVTZXW03mfQCiYNCWFaYhjTk8IYFu7/\noeY6Y/+ecj54IwujScdNv5xDcIgRKSUHHvs/Cp55HYBR991Cys9v6JXeWyklxfU2thTWs62oHreE\npxen+/w4ijJYdLZnuFPJsK+oZHhwUa+rMlC5XW62rD/EtPOTMRh8eupFQJJSUlDTzMfvbGRHtYOy\nuJM/90PDjExLDGNaUiiThoS2O2ybv9XVNPHvF7bQ0uzge4vHkTFz+En3H/7bKvY/8gJIScKiC5nw\n9P29Pj210y3bnczDandh0Ar02v4XR0XpTzqbDPfpJykrK6svD6coXdK2AF/pmYEeyy3rD1FaXuFg\nKwAAIABJREFUVIde1zfDqPX3eAohSI0O4s7b5vPCjybw1tUT+NUFw5mbEkGoUUupxcba3EoeWFfA\nsn9lc9/HB3hrdwUHqppw92GHzHGnxtNhd7H237toaXaQOjaOydNPn0go+adXM/X1J9CFBlP+wVds\nW/ITmkvKe7WdHc1q98WBGpb/ew8PfVbAf3IrOVpv69V2nEl/f28GGhVP/1BdGoqiKF1QcriG7O0l\nXPe/sxCDdAreMwlJGwHAxaOiuXhUNC63JK/SyvZiC9tLLBysamZXaSO7Sht5dTuEm3RMGRrC1GFh\nTBkaSnyooU/bK6XkszU5VJY3EBkdxKXLJ3b4usZdPJuZH73Mzv+5l4acA2xZcBNTVvyJyBmT+7TN\nS8bHckFKBLtKG9hR0sCqrHKMWg0/nzOcKUND+7QtijIQqDIJpdeo11UZaFqaHfzzuU18b/E4UsfE\n+bs5Aam+xcmuow3sPNrAzlILxxodJ92fEGogY0goGUNDmDw0lOggfa+2Z+fmI3z1YR56g5Zrbp9J\nTPzZk0lHnYWs235H9YbtCL2OcX+6m6Rrl/RqO89ESsmR2hYiTDoi24mXyy3Rqi9uyiDky9EkFEVR\nBr3jPYhpY+NUItxFNVt2oTEaiZg6jnCTjrmpkcxNjURKSUm9zZMYH20gu7yR8gY7nzZU82l+NQBJ\n4UYmDw1lUkIIExNCiA72XXJcfLiG9R/vB2DhsomdSoQB9BFhnLPySfY/8gKF//cWe+95nPrdeYx5\n+K5eryNujxCC5KiOj3vru/uINOuZNCSESUNCGBsXHJCjfShKb9E+/PDDfXawNWvWPDxlyunzuzc0\nNBAaqn7aGWgC7XXduHFj64QwSs8MxFhKt6S+roVZ81LR9PGJS4Eez4Z9Bey+7XfowkMIn3RiamMh\nBOEmHWPigpmXGsnyiXHMGhHO0DAjGgE1TU5qmp3kVzXxzZE63s05xpcHazlU3USjzUWQXkuIQdvl\nkR02btxIZHgcb7+6HYfdxbTzkzln9sgu7UNoNMTOm4lpWDyV67di2ZVL+YfrCc8Yi2lo//qydPGo\nKGJD9JRZbKzLr+albaXsPNrARWlRaHo4Kkagvzf7GxVP3yorKyMlJeX3Z9tO9QwHkM2bNzN16lSE\nEOzcuZNZs2b5u0mKMmhotBpmXNC/Z8vsr+Lmz2b62hfJuum3VP33W8b96W6MsVGnbafVCNJjgkiP\nCeJHk+JxuiX7K63sLm0kp6KRvRVWSi02Si021uXXABAdpGd8fDDj4oMZHx9ManRQhyeeHedyufnP\nql00We0MT41mzsWjuv3cEq+6nLCJ6WTf8Qca8wrYuugnpP7selJ/eRMaff/4Fxtk0DI9KZzpSeEA\nNDtcFNQ0t1s6YXe5sdpc7ZZbKMpApWqGA0hGRgbFxcXExsby1FNPcemll/bp8aWUJCcno9FoOP6+\nmTdvHitWrGh3e/W6KorSlqvZxsG/vMLR1Z8w4S/3EbdgTtce75Ycqm4mu7yRPeWN5JQ30mBznbSN\nUSsYHetJjsfGBTMmLohI88mJ3efv72X3t8WERpi47qfnEhTS85P2XC02Djz+Mkf+/gZISdik0Ux6\n7kFCRif3eN996WBVE/d+fJBwk671S8bYuGCGR5hU3bEScNQ4w70kOzubwsJCAI4cOcKdd97ZZ8d+\n/fXXueiii0hISECr7ZshndoqLCxk+/btTJ8+HY1Gw0cffcTcuXMZPXp0u9sH0uuqKErfqd+Vi8tm\nJ2pmRo/245aSkjobeysayT1mZW+FlZJ2hhmLDzEwJjaI0XHBGItq2L/pCFqdhqtum0HCsPAeteFU\nNVt2seeuR2kuLkNjNJD+258w4pYfITSBU6PrlpLC2hZyyhvZd8zKvmNNjI0L4t65I/3dNEXpkn55\nAl1WVhbtJcOBYs+ePVgsFhYtWgTAkiVL+jQZ1uv1DBs2rM+Odyqj0chll12G2Wymvr4evV7fYSIc\niNSc8L4zEGJZVdGAOchAcKjR300ZEPFsK3zKOJ/sRyMEwyNNDI80ccmYGMAzWkVuhZXcikbyKpvY\nX9lERaOdigYbR78rIaXOSuHRXEznnc8bBfWMsjhIjwliZJQJgw9qwaNmTWH2V6+T99CzlKz6gLyH\nnqXs/S8Y88jPiMyc2OP99wWN94S85Cgzi8bFAp4JQNrz6pp1hKdmkB4bzKgYM2Z933fUDCQD7bMe\nKPpHQdNZ/OW3n/pkP/f8v4U9enxeXh7Lly8HPIn92LFjAU8P8euvv44QorV84PhtIQSZmZlccskl\nPWs8nm84UkpqampITU31yT670vaEhITWx7322mvcfvvtPT6+ovRH1ccaeXvFDuZ/fzypY/vXyVDK\nmYWbdMwaEc6sEZ4eX5dbUljbzPoP91FbZ0UChyODsAstu/dX88l+z6gVOo0gOcrEqJgg0qKDSI32\nJIPdmS1PFxrMhKd+Q9yC89h775+p35XLtstvY8gP5pN+/+2Yh8X78in3iY7qsI1aDRWNdr4+XMfh\n2hYSQg2kxwSxaGwMY+KC+7iVitI9AVEm0R+S4ZKSEkpKSggLC2PVqlUUFBTw1FNPnZQg9rbs7Gwm\nTZoEwPnnn8+HH35IWFhYh9uXl5ezcuVKJk6cyObNm7n55puJjIykqamJuLju/4Ovq6vjqaee4g9/\n+MMZt1NlEkogqq228tbL3zJnfjrjp/rvl5jB6OBTr2GrqCLtnpvbPcGuO1wuN+vezSE3qxStVrDo\nqgyGjYrhUHUz+VVNHKhqIr+yiZJ6G6f+N9QISAw3kRpt9ly8vaWRZl2nR7BwWpsoeO5fHHnxDdw2\nOxqzkZT/vZbkn16DNsjkk+fYXzhcbo7UtpBf1cSY2CBSo4NO26aoroUwo5YIszpBT+l9qmbYx95/\n/30WLVrUWqu7YsUKamtrufvuu3u032effZaWlpaT1h3vlb3qqqtISjoxLajb7UbjrTtbvHgxP/nJ\nTzo8ia6pqYnFixezevVqoqKi2LlzJ8888wzLly9nwYIF6PXd/0P02muvodfrufbaa8+4XSC8rorS\nVn1tM2+9vI0Zc1PbnZJX6V32mnoOPfMPSld/wogf/4iRP7mqR+P2Oh0uPnhzN4f2HUNv0LL0uqkM\nT41ud1ur3cWh6ibyq5opqG7iYHUzRXUttFcdEG7SkRxlIjnKTEqUmeRIM8MjTWfsRW4qKiP/kRco\n/+ArAExD40h/4KcM+f73AqqeuKee3VjM+oJajDpBijd+KVFmpieFEWIMiB+rlQCiaoZ9zGaznXTS\nWn5+PikpnmGW2pYatNWZMom77rqrU8d/++23+fzzz3nppZcAsFqtZzyJbs2aNWRkZBAV5eldiY2N\nJTc3FynlSYlwd9r+9ddfc+WVV3aq3YFE1Wr5TiDGstHSwpsvbWPanJH9LhEOxHh2hyEqnLG//xkj\nbvohBx57iW9mX0HaPTeTeM3iLo8lbLc5WfOvnRQX1GAy61l2wzkMSYoA2o9nsEHLpCGhTBpyYmx0\nu9PT03nImxwfrmmmoKaZ+hYnWaWNZJU2tm4r8MyeNyLSxIgIEyMizYyINJEU4UmSg4YPIePlR6nZ\nsou8B5/Bsief7J8+zKG//oOUO65lyA/m95uh2Lqiq+/Nu85L4s7ZiRxrdFDgjefmwnrGx4cQ0k55\nfpXVTlSQvsfjIQeKwfJZ728C75PnJ1u3buWKK64AoLq6mu3bt3P//fcDMHLkSB588MFePX5SUhI3\n3HAD4EmEq6urmTPHMyxRQUEBycnJJ/2zcDgcrcn68cdoNJrWk/+O607bCwoKMJkG1s97ihIcamTJ\nNVNISPTt6AJK1wWNGMbkF39PfdY+Kr/a2uVEuL62iQ/e2E15ST3BoUZ+eGMmsQldnwDIoNOQHhtE\neuyJn/ullFRaPYnc4eOX2hZK6looa7BT1mBna5GldXsBxIUYSIowkhRhIikiicQVTxP9xX8pe+6f\nWA8cYc/PHuXAEy+TfPvVJF69aMCVT5xKCEF8qIH4UENrbXdHfvvpIcoa7CSFe+I33HuZNSL8rONJ\nK0pnqTKJTtizZw+lpaXU19djNpvJzc3lmmuuITExsU/b8fbbb1NVVUVRURHLli0jMzMTgBkzZvDY\nY48xb9681m0tFgvPPfcc06dPx+l0YjabWblyJfPmzWPp0qWYzd3/6XHp0qU8/vjjpKenn3G7/v66\nKooysEgpyf62mP9+sh+H3UV4pJnlN00jop3aVV9zuiVH61sorG2hsM57XdtCSX0Lrg7+zYZqYVre\nTsZ8/gmm0lIANJERJN38Q9JuWY4+PHBm8OxNVruL4roWiupaKK5roaTexgMXJZ827rFbSnLKGxkW\nbiKqC3XdysClaoZ96N1332XZsmX+bkaH3G43mzZtau0p7i/6++uqKErgKln1IRHnjG+d1MJS18y6\n93IoPOgZHSJ9QgLfWzzOJxNq9ITTLSmz2Ciub6G4zkZxXQvF9S0U1dmw2r0ThrjdpOVlM33DZyQc\n9Yxj79AbKM+cTvP8CwnLnMjQcBNDwowMDTUSFaQSvfZY7S7u//QQRy02HC43Q8OMDAszkhxl5uop\nfXeyu9J/qJphH9L085Mb1q5dy8KFPRs2TlG1Wr7Un2Mp3ZLtG48wanwckdGBMfRTf46nv9ira/j2\nh3cSPDoZ24WXkHVMj93uwhyk56LF4xgzaUiHj+3LeOo0wlseYYIRJ9ZLKalrdnLUYvNcpgyh+NIL\nOPjtLkZ8/BFJB/NI2rIRtmykNjqODVNnkTt1BtbQcAxaQVyIgYRQAwkhRhK8JQfxIZ5LRB/2ivan\n92awQcvTiz2/WFpaPLEts7T50nGKaquD1dkVnjiGGr3XBr+Oldyf4jmYqJrhTli6dKm/m3BG8+fP\n71HZg6IMFkUF1XyzLh+NRjB2csfJktL/pdx5PVFXLOGj17ZSViIBF/Huen7ws+/3i4lSzkYIQWSQ\nnsggPRMSQk7c8b0U5G9+QOneIxxe9QGNaz8jsvoYcz5fy+wvP6Bk9HiyJ03jyKhxlNSbgYbT9q3X\nCuKCDcSF6IkLMbReYoP1xAR7rgf65BhhJh1hJh1jzzDWsVYDscF6jlpsfHe0gTKLjYpGOxMSQnjs\nkrTTtrc53dicbkKNWtUzP8CoMgml16jXVekvyo/Ws/GzfGqrmph98SjGThqCUCffBKz62ia2f3OE\nnB0lOJ1uTGY9F8wdTryjirgLZ/m7eT7ldjqpWr+No29+xLF13yCdnl5OodejzZxM84xplE/KoFQf\nTHmjnWONdhps7feEthVs0BITrPckyEEGooP1RAd5L8F6YoL0hJt0p9XlDnRuKWl2uAk2nP5lIbus\nkYc/L8DhlsR6YxcbbGDikBAWpLc/ZJ/iX/2yTEJRFKWvtTQ7+OCNLDLPS2ZSZiLabswopvQPleUN\nfPt1AXnZ5UjvAMDpE+K58PKxhISZgFHtPq5myy7slbVEz52OPiyk3W36K41OR9zFs4m7eDa2yhrK\n1nxOxcf/pXZbNs4tO9Bv2UESMGHqeOIWziFm7gz06clUNbupaLRzzOpJkI812qmyOqi0Oqiy2rHa\nXVjtLgprWzo+toBIs56oIB1RZj1RQXoizTqigvREmT23I8x6Isw6gvSaAdFbqhGi3UQYYNKQEN67\nfhLNDheVjQ6OWe1UWh0Ed9DLvr3YwqqscmKCPLHzXHQkR5pJi+n9kzqVzuvTnuEnn3xS3nTTTaet\nVz2IA1Ogva6qVst3+lss3W6JJoB7uPpbPPva0cJatm0ooCCvEgDhLXOZfn4yMfFnH3Gh8qutFL7y\nNrXf7iZ80hgKU2KYf82PCJ2YjkYXmH1CtsoaKj/fTMWnX1P99be4W+yt9+nCQ4maOZmoc6cSde4U\nQselIdqMSy+lxGJzUeVN5qqsDqqbHFQfv/Ze6lucZ22H5VAWYakZGLSCSG9iHGHSEWHWEW46cfGs\n9/Q2h5m0mHQDI3k+E0uLkyO1zd54OqlpclDT5CAtJogfTjx9FthdpQ28tuYzpkyf5YmXN57DwozE\n+flE0EAVcD3DbWdXUwKf2+32dxOUQcbaaMPe4iQy5vQawUBOhAerqopGDuwtJ39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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def logistic(x, beta, alpha=0):\n", + " return 1.0 / (1.0 + np.exp(np.dot(beta, x) + alpha))\n", + "\n", + "x = np.linspace(-4, 4, 100)\n", + "\n", + "plt.plot(x, logistic(x, 1), label=r\"$\\beta = 1$\", ls=\"--\", lw=1)\n", + "plt.plot(x, logistic(x, 3), label=r\"$\\beta = 3$\", ls=\"--\", lw=1)\n", + "plt.plot(x, logistic(x, -5), label=r\"$\\beta = -5$\", ls=\"--\", lw=1)\n", + "\n", + "plt.plot(x, logistic(x, 1, 1), label=r\"$\\beta = 1, \\alpha = 1$\",\n", + " color=\"#348ABD\")\n", + "plt.plot(x, logistic(x, 3, -2), label=r\"$\\beta = 3, \\alpha = -2$\",\n", + " color=\"#A60628\")\n", + "plt.plot(x, logistic(x, -5, 7), label=r\"$\\beta = -5, \\alpha = 7$\",\n", + " color=\"#7A68A6\")\n", + "\n", + "plt.legend(loc=\"lower left\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Adding a constant term $\\alpha$ amounts to shifting the curve left or right (hence why it is called a *bias*).\n", + "\n", + "Let's start modeling this in PyMC3. The $\\beta, \\alpha$ parameters have no reason to be positive, bounded or relatively large, so they are best modeled by a *Normal random variable*, introduced next." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Normal distributions\n", + "\n", + "A Normal random variable, denoted $X \\sim N(\\mu, 1/\\tau)$, has a distribution with two parameters: the mean, $\\mu$, and the *precision*, $\\tau$. Those familiar with the Normal distribution already have probably seen $\\sigma^2$ instead of $\\tau^{-1}$. They are in fact reciprocals of each other. The change was motivated by simpler mathematical analysis and is an artifact of older Bayesian methods. Just remember: the smaller $\\tau$, the larger the spread of the distribution (i.e. we are more uncertain); the larger $\\tau$, the tighter the distribution (i.e. we are more certain). Regardless, $\\tau$ is always positive. \n", + "\n", + "The probability density function of a $N( \\mu, 1/\\tau)$ random variable is:\n", + "\n", + "$$ f(x | \\mu, \\tau) = \\sqrt{\\frac{\\tau}{2\\pi}} \\exp\\left( -\\frac{\\tau}{2} (x-\\mu)^2 \\right) $$\n", + "\n", + "We plot some different density functions below. " + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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s0BrZMTFC2kCnhvtAcY312EIV6VwqZUNcpDZsjLk4iGVu6o1YlFJKqb5uf1E1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\nFl/UtqPRON/9+iv09sRYdtNUxlUUpSPkrFRf28bG1w8SCPr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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import scipy.stats as stats\n", + "\n", + "nor = stats.norm\n", + "x = np.linspace(-8, 7, 150)\n", + "mu = (-2, 0, 3)\n", + "tau = (.7, 1, 2.8)\n", + "colors = [\"#348ABD\", \"#A60628\", \"#7A68A6\"]\n", + "parameters = zip(mu, tau, colors)\n", + "\n", + "for _mu, _tau, _color in parameters:\n", + " plt.plot(x, nor.pdf(x, _mu, scale=1./_tau),\n", + " label=\"$\\mu = %d,\\;\\\\tau = %.1f$\" % (_mu, _tau), color=_color)\n", + " plt.fill_between(x, nor.pdf(x, _mu, scale=1./_tau), color=_color,\n", + " alpha=.33)\n", + "\n", + "plt.legend(loc=\"upper right\")\n", + "plt.xlabel(\"$x$\")\n", + "plt.ylabel(\"density function at $x$\")\n", + "plt.title(\"Probability distribution of three different Normal random \\\n", + "variables\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A Normal random variable can be take on any real number, but the variable is very likely to be relatively close to $\\mu$. In fact, the expected value of a Normal is equal to its $\\mu$ parameter:\n", + "\n", + "$$ E[ X | \\mu, \\tau] = \\mu$$\n", + "\n", + "and its variance is equal to the inverse of $\\tau$:\n", + "\n", + "$$Var( X | \\mu, \\tau ) = \\frac{1}{\\tau}$$\n", + "\n", + "\n", + "\n", + "Below we continue our modeling of the Challenger space craft:" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import pymc3 as pm\n", + "\n", + "temperature = challenger_data[:, 0]\n", + "D = challenger_data[:, 1] # defect or not?\n", + "\n", + "#notice the`value` here. We explain why below.\n", + "with pm.Model() as model:\n", + " beta = pm.Normal(\"beta\", mu=0, tau=0.001, testval=0)\n", + " alpha = pm.Normal(\"alpha\", mu=0, tau=0.001, testval=0)\n", + " p = pm.Deterministic(\"p\", 1.0/(1. + tt.exp(beta*temperature + alpha)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We have our probabilities, but how do we connect them to our observed data? A *Bernoulli* random variable with parameter $p$, denoted $\\text{Ber}(p)$, is a random variable that takes value 1 with probability $p$, and 0 else. Thus, our model can look like:\n", + "\n", + "$$ \\text{Defect Incident, $D_i$} \\sim \\text{Ber}( \\;p(t_i)\\; ), \\;\\; i=1..N$$\n", + "\n", + "where $p(t)$ is our logistic function and $t_i$ are the temperatures we have observations about. Notice in the above code we had to set the values of `beta` and `alpha` to 0. The reason for this is that if `beta` and `alpha` are very large, they make `p` equal to 1 or 0. Unfortunately, `pm.Bernoulli` does not like probabilities of exactly 0 or 1, though they are mathematically well-defined probabilities. So by setting the coefficient values to `0`, we set the variable `p` to be a reasonable starting value. This has no effect on our results, nor does it mean we are including any additional information in our prior. It is simply a computational caveat in PyMC3. " + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " [-------100%-------] 120000 of 120000 in 16.5 sec. | SPS: 7283.1 | ETA: 0.0" + ] + } + ], + "source": [ + "# connect the probabilities in `p` with our observations through a\n", + "# Bernoulli random variable.\n", + "with model:\n", + " observed = pm.Bernoulli(\"bernoulli_obs\", p, observed=D)\n", + " \n", + " # Mysterious code to be explained in Chapter 3\n", + " start = pm.find_MAP()\n", + " step = pm.Metropolis()\n", + " trace = pm.sample(120000, step=step, start=start)\n", + " burned_trace = trace[100000::2]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We have trained our model on the observed data, now we can sample values from the posterior. Let's look at the posterior distributions for $\\alpha$ and $\\beta$:" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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fzMixhzP80CF5HJ0Ugj5DJSHvibqZnQVcA9SY2RJihcJfd/cn831sERGRvmbX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\nYVE8wlHTtDWSK7/seHM5K+7q9KMyLR/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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "alpha_samples = burned_trace[\"alpha\"][:, None] # best to make them 1d\n", + "beta_samples = burned_trace[\"beta\"][:, None]\n", + "\n", + "figsize(12.5, 6)\n", + "\n", + "#histogram of the samples:\n", + "plt.subplot(211)\n", + "plt.title(r\"Posterior distributions of the variables $\\alpha, \\beta$\")\n", + "plt.hist(beta_samples, histtype='stepfilled', bins=35, alpha=0.85,\n", + " label=r\"posterior of $\\beta$\", color=\"#7A68A6\", density=True)\n", + "plt.legend()\n", + "\n", + "plt.subplot(212)\n", + "plt.hist(alpha_samples, histtype='stepfilled', bins=35, alpha=0.85,\n", + " label=r\"posterior of $\\alpha$\", color=\"#A60628\", density=True)\n", + "plt.legend();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "All samples of $\\beta$ are greater than 0. If instead the posterior was centered around 0, we may suspect that $\\beta = 0$, implying that temperature has no effect on the probability of defect. \n", + "\n", + "Similarly, all $\\alpha$ posterior values are negative and far away from 0, implying that it is correct to believe that $\\alpha$ is significantly less than 0. \n", + "\n", + "Regarding the spread of the data, we are very uncertain about what the true parameters might be (though considering the low sample size and the large overlap of defects-to-nondefects this behaviour is perhaps expected). \n", + "\n", + "Next, let's look at the *expected probability* for a specific value of the temperature. That is, we average over all samples from the posterior to get a likely value for $p(t_i)$." + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "t = np.linspace(temperature.min() - 5, temperature.max()+5, 50)[:, None]\n", + "p_t = logistic(t.T, beta_samples, alpha_samples)\n", + "\n", + "mean_prob_t = p_t.mean(axis=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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JAYkTikieNoGkqeP79dArpZRSSg3WsG60n4hun6G+vYf69p5jel6c20VqnJu0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\nO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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "figsize(12.5, 4)\n", + "\n", + "plt.plot(t, mean_prob_t, lw=3, label=\"average posterior \\nprobability \\\n", + "of defect\")\n", + "plt.plot(t, p_t[0, :], ls=\"--\", label=\"realization from posterior\")\n", + "plt.plot(t, p_t[-2, :], ls=\"--\", label=\"realization from posterior\")\n", + "plt.scatter(temperature, D, color=\"k\", s=50, alpha=0.5)\n", + "plt.title(\"Posterior expected value of probability of defect; \\\n", + "plus realizations\")\n", + "plt.legend(loc=\"lower left\")\n", + "plt.ylim(-0.1, 1.1)\n", + "plt.xlim(t.min(), t.max())\n", + "plt.ylabel(\"probability\")\n", + "plt.xlabel(\"temperature\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Above we also plotted two possible realizations of what the actual underlying system might be. Both are equally likely as any other draw. The blue line is what occurs when we average all the 20000 possible dotted lines together.\n", + "\n", + "\n", + "An interesting question to ask is for what temperatures are we most uncertain about the defect-probability? Below we plot the expected value line **and** the associated 95% intervals for each temperature. " + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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rAdixY8dp7a63azuxbc235ns4tzXf6dHuuF5cXAzA8uXLWbduHV05WdNuA/uI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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from scipy.stats.mstats import mquantiles\n", + "\n", + "# vectorized bottom and top 2.5% quantiles for \"confidence interval\"\n", + "qs = mquantiles(p_t, [0.025, 0.975], axis=0)\n", + "plt.fill_between(t[:, 0], *qs, alpha=0.7,\n", + " color=\"#7A68A6\")\n", + "\n", + "plt.plot(t[:, 0], qs[0], label=\"95% CI\", color=\"#7A68A6\", alpha=0.7)\n", + "\n", + "plt.plot(t, mean_prob_t, lw=1, ls=\"--\", color=\"k\",\n", + " label=\"average posterior \\nprobability of defect\")\n", + "\n", + "plt.xlim(t.min(), t.max())\n", + "plt.ylim(-0.02, 1.02)\n", + "plt.legend(loc=\"lower left\")\n", + "plt.scatter(temperature, D, color=\"k\", s=50, alpha=0.5)\n", + "plt.xlabel(\"temp, $t$\")\n", + "\n", + "plt.ylabel(\"probability estimate\")\n", + "plt.title(\"Posterior probability estimates given temp. $t$\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The *95% credible interval*, or 95% CI, painted in purple, represents the interval, for each temperature, that contains 95% of the distribution. For example, at 65 degrees, we can be 95% sure that the probability of defect lies between 0.25 and 0.75.\n", + "\n", + "More generally, we can see that as the temperature nears 60 degrees, the CI's spread out over [0,1] quickly. As we pass 70 degrees, the CI's tighten again. This can give us insight about how to proceed next: we should probably test more O-rings around 60-65 temperature to get a better estimate of probabilities in that range. Similarly, when reporting to scientists your estimates, you should be very cautious about simply telling them the expected probability, as we can see this does not reflect how *wide* the posterior distribution is." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### What about the day of the Challenger disaster?\n", + "\n", + "On the day of the Challenger disaster, the outside temperature was 31 degrees Fahrenheit. What is the posterior distribution of a defect occurring, given this temperature? The distribution is plotted below. It looks almost guaranteed that the Challenger was going to be subject to defective O-rings." + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "figsize(12.5, 2.5)\n", + "\n", + "prob_31 = logistic(31, beta_samples, alpha_samples)\n", + "\n", + "plt.xlim(0.995, 1)\n", + "plt.hist(prob_31, bins=1000, density=True, histtype='stepfilled')\n", + "plt.title(\"Posterior distribution of probability of defect, given $t = 31$\")\n", + "plt.xlabel(\"probability of defect occurring in O-ring\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Is our model appropriate?\n", + "\n", + "The skeptical reader will say \"You deliberately chose the logistic function for $p(t)$ and the specific priors. Perhaps other functions or priors will give different results. How do I know I have chosen a good model?\" This is absolutely true. To consider an extreme situation, what if I had chosen the function $p(t) = 1,\\; \\forall t$, which guarantees a defect always occurring: I would have again predicted disaster on January 28th. Yet this is clearly a poorly chosen model. On the other hand, if I did choose the logistic function for $p(t)$, but specified all my priors to be very tight around 0, likely we would have very different posterior distributions. How do we know our model is an expression of the data? This encourages us to measure the model's **goodness of fit**.\n", + "\n", + "We can think: *how can we test whether our model is a bad fit?* An idea is to compare observed data (which if we recall is a *fixed* stochastic variable) with artificial dataset which we can simulate. The rationale is that if the simulated dataset does not appear similar, statistically, to the observed dataset, then likely our model is not accurately represented the observed data. \n", + "\n", + "Previously in this Chapter, we simulated artificial dataset for the SMS example. To do this, we sampled values from the priors. We saw how varied the resulting datasets looked like, and rarely did they mimic our observed dataset. In the current example, we should sample from the *posterior* distributions to create *very plausible datasets*. Luckily, our Bayesian framework makes this very easy. We only need to create a new `Stochastic` variable, that is exactly the same as our variable that stored the observations, but minus the observations themselves. If you recall, our `Stochastic` variable that stored our observed data was:\n", + "\n", + " observed = pm.Bernoulli(\"bernoulli_obs\", p, observed=D)\n", + "\n", + "Hence we create:\n", + " \n", + " simulated_data = pm.Bernoulli(\"simulation_data\", p)\n", + "\n", + "Let's simulate 10 000:" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Assigned BinaryGibbsMetropolis to bernoulli_sim\n", + " [-------100%-------] 10000 of 10000 in 27.8 sec. | SPS: 359.1 | ETA: 0.0" + ] + } + ], + "source": [ + "N = 10000\n", + "with pm.Model() as model:\n", + " beta = pm.Normal(\"beta\", mu=0, tau=0.001, testval=0)\n", + " alpha = pm.Normal(\"alpha\", mu=0, tau=0.001, testval=0)\n", + " p = pm.Deterministic(\"p\", 1.0/(1. + tt.exp(beta*temperature + alpha)))\n", + " observed = pm.Bernoulli(\"bernoulli_obs\", p, observed=D)\n", + " \n", + " simulated = pm.Bernoulli(\"bernoulli_sim\", p, shape=p.tag.test_value.shape)\n", + " step = pm.Metropolis(vars=[p])\n", + " trace = pm.sample(N, step=step)" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(10000, 23)\n" + ] + }, + { + "data": { + "image/png": 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E8LfABuAPETyniIiIiEhVK8uCR2Z2NvBxd78XOO7YHM2jHg7dKR4W5REW5REO\nZREW5REOZVH5Sp715SR9Ccgfu170ZF3zqKusssoqq6yyyiqrXOnlUOZRvwj4oruvyJZvJTMt4x15\n+7yU+xE4HRgEbnD37xXWp3nUw9HVpflXQ6I8wqI8wqEswqI8wqEswhLLPOrAFmCBmbWQWfDok8C1\n+Tu4+7tyP5vZw8DjE52ki4iIiIhIRiTzqJvZCuAu3lzw6Pb8BY8K9u0Evu/u352oLs2jLiIiIiLV\nJq4r6jme9wd3vz/3gJmt5M0x6n8EkhE+r4iIiIhI1SnXPOovAR9y90XAWuDBYvVpHvVw5G6MkDAo\nj7Aoj3Aoi7Aoj3Aoi8pXlnnU3f1pdz+QLT5NwfSNIiIiIiJyrChmffk/wOXufkO2/H+BC939s0X2\nvwU4N7d/IY1RFxEREZFqE/cY9RMys0uB6wDNFSQiIiIichxRnKj3A/PyynOz245hZucBDwAr3H1f\nscruuusuGhsbteBRAOX8sW0htKfWy8ojrLLyCKec2xZKe2q9nNsWSntqudzd3c1NN90UTHtqrRzK\ngkfTgBeBZWTmUf8VcK279+TtMw/YBHzK3Z8+Xn1a8CgcXV1aKCEkyiMsyiMcyiIsyiMcyiIsUxn6\nUpZ51M3sQeATQIrM6qSH3f3CierSGHURERERqTaxjVF39x8D7y7Ydn/ez9cD10fxXCIiIiIitSCK\n6RkxsxVm9oKZ/dbMPldkny+bWdLMtpvZ4mJ1aR71cOSPN5T4KY+wKI9wKIuwKI9wKIvKV5YFj8zs\nCiDh7q3AjcB9pT5vpUun0+zYsYN0Oh13U0QkRlF/FoT+2RJ1+zZu3EhnZycbN26MpL6ohZ5H1JLJ\nJD//+c9JJqNZgDyZTPL4449HVl/Uaqn/hty2ahbFzaQXAbe5+xXZ8q1kxqbfkbfPfcBT7r4+W+4B\nLnH3PYX1VfsY9UOHDtHR0UFfXx8jIyNMnz6dRCJBe3s7M2bMiLt5IlImUX8WhP7ZEnX7XnrpJZYt\nW8Ybb7yBu2NmzJo1i02bNvGud73rFLyCyQk9j6jt3buXtrY2UqkUo6Oj1NXV0dLSQmdnJ01NTbHX\nF7Va6r8ht63STGWMehRDX84BXskr72L8yqOF+/RPsE9N6OjoIJVK0dDQwOzZs2loaCCVStHR0RF3\n00SkjKL+LAj9syXq9uVO0qdNm0ZdXR3Tpk3jjTfemPTUZ6dK6HlEra2tjV27dlFfX09jYyP19fXs\n2rWLqc6CJmi2AAAgAElEQVTiFnV9Uaul/hty22pBJGPUo1TNY9TT6TR9fX3U1R17D29dXR19fX3B\nfZ2ksW1hUR5hKSWPqD8LQv9sibp9GzduPHqSDnDkyBGAoyfrcQ+DCT2PqCWTSVKp1NHXe/DgQSDz\nelOp1KSHrRTWlzPV+qJWSf231N8btXYshyiKWV9OZsGjfuCdJ9gHgM2bN7N169aqXPBoz5499Pf3\n09jYyFlnnQXA7t27AZg5cyYDAwP09PQE016VVVb51JSbmpoYGRlh//79AMd8HgwODjIwMEBzc3Ns\n9YX+en/605/i7kdP0HOOHDnCkSNH+NnPfsby5cur5vWGXt63bx+jo6OMjY2R7+DBgwwNDZFMJmlt\nbZ1yfTNnziypvtDzPZXHS3d3d0mv9+WXX2ZkZISGhoaj5yu59vX39/PEE0+wcuXKsr7/lVSupAWP\nPgK0u/tHs2Pav+TuF01UXzWPUU+n06xZs4aGhoZxjw0NDbF27Vqam5tjaJmIlFPUnwWhf7ZE3b6N\nGzdy7bXXHr2inm9sbIxvfvObLF++vKQ2lyL0PKKWTCa5+uqrqa+vH/fY8PAwjzzyCK2trbHVF7Va\n6r8ht60SxTJG3d3HgJuBJ4HngW+5e4+Z3WhmN2T3+SHwOzPrBe4HVpX6vJWoubmZRCLB6OjoMdsP\nHz5MIpHQwS5SI6L+LAj9syXq9i1fvpxZs2aNu4I7NjbGrFmzYj1Jh/DziFpraystLS3jXu/o6Cgt\nLS2TPqmOur6o1VL/DblttSKSMeru/mN3f7e7t7r77dlt97v7A3n73OzuC9x9kbtvK1ZXNY9RB2hv\nb6elpYWhoSFef/11hoaGmD9/Pu3t7XE3bZzc1zgSBuURllLziPqzIPTPlqjbt2nTpqMn64cPHz56\nkr5p06aIWz41oecRtc7OTubOncvw8DB79+5leHiYuXPn0tnZWXJ9g4ODJdcXtUrpv1H83qi1Yzk0\nJQ19MbO3A+uBFuBl4Gp3P1Cwz1xgHXAmcAR40N2/XKzOm266yf/1X/91ym2qFOl0moGBAc4444xg\n/0d67733ctNNN8XdDMlSHmGJKo+oPwtC/2yJun0bN27k7rvv5uabb479SvpEQs8jaslkki9/+ct8\n9rOfjeTKdzKZPDomPe4r6RMJvf9G+Xuj1o7lU6Gzs5N//Md/nNTQl7oSn/NWYKO7/3t2RdLPZ7fl\nGwX+wd23m9ks4Ndm9qS7vzBRhYODgyU2qTI0NzcHf6DnboCQMCiPsESVR9SfBaF/tkTdvuXLl7N1\n69YgT9Ih/Dyi1trayjnnnBPZSXWoJ+g5offfKH9v1NqxfCo8++yzk/43pQ59uRL4WvbnrwEfL9zB\n3Xe7+/bsz28APdToHOoiIiIiIier1BP1d+RWF3X33cA7jrezmc0HFgO/LLZPbvofid/OnTvjboLk\nUR5hUR7hUBZhUR7hUBaV74RDX8zsJ2TGlx/dBDiwZoLdiw54zw572QCszl5Zn1AikWD16tVHy4sW\nLWLx4sUnaqacAueffz7bthW971fKTHmERXmEQ1mERXmEQ1nEa/v27ccMd2lsbJx0HaXeTNoDXOLu\ne8zsLOApd/9fE+xXB3wf+JG73zXlJxQRERERqRGlDn35HvA32Z//GnisyH6dwA6dpIuIiIiInJxS\nr6g3AY8A7wRSZKZn3G9mf0JmGsa/NLMPAD8FuskMjXHgC+7+45JbLyIiIiJSpUo6URcRERERkVMj\nkpVJp8rMXjazZ83sGTP7VXbb283sSTN70cyeMLM5cbaxlhTJ4zYz22Vm27J/VsTdzlpgZnPM7Ntm\n1mNmz5vZ+9U34lMkD/WNGJjZudnPqG3Zvw+Y2WfVP8rvOFmob8TEzP7ezH5jZs+Z2dfNbLr6Rjwm\nyKJ+Kn0j1ivqZvYS8Ofuvi9v2x1AOm8Rpbe7e+EiSnIKFMnjNuCP7v4f8bWs9pjZV4HN7v5w9mbs\nRuALqG/Eokgef4f6RqzM7C3ALuD9wM2of8SmIIs21DfKzszOBrqA97j7iJmtB34IvBf1jbI6Thbz\nmWTfiPWKOpmpHgvbcMJFlOSUmSiP3HYpEzObDXzQ3R8GcPdRdz+A+kYsjpMHqG/EbTnQ5+6voP4R\nt/wsQH0jLtOAxuwFhRlAP+obccnPYiaZLGCSfSOSE3Uz+4qZ7TGz54o8vjI7pOJZM+sys4XZhxz4\niZltMbPPZLedOZlFlCRS+Xlcn7f9ZjPbbmYP6SuzsvhT4DUzezj71dgDZjYT9Y24FMsD1Dfidg3w\njezP6h/xugb4Zl5ZfaPM3P1V4E5gJ5mTwgPuvhH1jbKbIIv92Sxgkn0jqivqDwOXH+fxl4APufsi\nYC3wYHb7B9x9CfARoN3MPsj4RZN0t2v5FOaxFLgHeJe7LwZ2A/oq89SrA5YAHdk8BoFbUd+IS2Ee\nB8nkob4RIzN7K/Ax4NvZTeofMZkgC/WNGJjZ28hcPW8BziZzNfevUN8ouwmymGVmK5lC34jkRN3d\nu4B9x3n86byvip8Gzslu/3327wHgUeBCYI+ZnQlgmUWU/hBFG+XECvL4H+BCdx/wN29keBC4IK72\n1ZBdwCvuvjVb/g6ZE0X1jXgU5rEBeJ/6RuyuAH7t7q9ly+of8cllMQCZ3yHqG7FYDrzk7nvdfYzM\n7/GLUd+IQ2EW3wUunkrfiGOM+meAH5nZTDObBWBmjcBlZOZaP9lFlCRCRfL4TbZT53wC+E0c7asl\n2a8oXzGzc7OblgHPo74RiyJ57FDfiN21HDvUQv0jPsdkob4Rm53ARWbWYGZG9rMK9Y04TJRFz1T6\nRmSzvphZC/C4u593nH0uBe4GlgJvI/O/PSfz1fLX3f32iy++2GfNmsVZZ2VeS2NjIwsWLGDx4sUA\nbN++HUDlMpQ3bNjAggULgmlPrZeVR1hl5RFOube3l6uuuiqY9tR6WXmEU968eTOrV68Opj21Vu7t\n7WVwcBCA3bt3k0gkuO+++7qBI8DLwI25+weKKduJupmdR+Yr/BXu3lesnssuu8zXr18fSZukNKtW\nreKee+6JuxmSpTzCojzCoSzCojzCoSzCsnr1atatW1f+WV+yjCJTzpjZPDIn6Z863kk6cPRKusRv\n3rx5cTdB8iiPsCiPcCiLsCiPcCiLylcXRSVm9g3gEqDZzHYCtwHTAXf3B4B/BpqAe7JjdQ67+4VR\nPLeIiIiISDWK5EQdOERmYvcXJxr64u7Xm9khMneGDwI3FKuosbExoiZJqebM0dS3IVEeYVEe4VAW\nYVEe4VAWYVm0aNGk/01Z5lE3syuAhLu3AjcC9xXbN3dzlsRv4cKFJ94pJul0mh07dpBOp+NuStmE\nnEfUKiHfWsojZOl0mtNOOy3oY6WWJJNJ/vjHP5JMJuNuiqDPqdDkbjSdjLLcTGpm9wFPufv6bLkH\nuGSiO103bdrkS5YsiaRNUn0OHTpER0cHfX19jIyMMH36dBKJBO3t7cyYMSPu5kmJlK+cLB0rYdm7\ndy9tbW2kUilGR0epq6ujpaWFzs5Ompqa4m6eSBC2bdvGsmXLYruZ9HjOAV7JK/dnt4lMSkdHB6lU\nioaGBmbPnk1DQwOpVIqOjo64myYRUL5ysnSshKWtrY1du3ZRX19PY2Mj9fX17Nq1i7a2tribJlLR\n4ljw6Lhy81BK/Lq6uuJuwjHS6TR9fX3U1R17a0VdXR19fX1V/9V3aHlErdLyrfY8QlZ4rOzevRsI\n91ipdslkklQqdTSPgwcPApk8UqmUhsHESJ9TlS+qm0lPpB94Z155bnbbOJs3b2br1q1HpxSaM2cO\nCxcuZOnSpcCbB53KtVfes2cP/f39NDY2Hp3GM/cLeubMmQwMDNDT0xNMe1VWviqfmnJTUxMjIyPs\n37+ffLt372ZwcJCBgQGam5uDaW+1l/ft28fo6ChjY2PkO3jwIENDQySTSVpbW4Npby2Vu7u7g2pP\nrZW7u7s5cOAAADt37uT8889n2bJlTEaUY9TnkxmjPu7OBTP7CNDu7h81s4uAL7n7RRPVozHqUkw6\nnWbNmjU0NDSMe2xoaIi1a9fS3NwcQ8skCspXTpaOlbAkk0muvvpq6uvrxz02PDzMI488Qmtrawwt\nEwlLbGPUs/Oo/xw418x2mtl1Znajmd0A4O4/BH5nZr3A/cCqKJ5XaktzczOJRILR0dFjth8+fJhE\nIqFfzBVO+crJ0rESltbWVlpaWsblMTo6SktLi07SRUoQyYm6u69097Pdvd7d57n7w+5+f3axo9w+\nN7v7Andf5O7bitWlMerhyH2NE5L29nZaWloYGhri9ddfZ2hoiPnz59Pe3h530065EPOIWiXlWwt5\nhCz/WOnr6wv6WKkFnZ2dzJ07l+HhYfbu3cvw8DBz586ls7Mz7qbVNH1OVb66KCoxsxXAl8ic+H/F\n3e8oeHw28N/APDILI93p7l+N4rmltsyYMYNbbrmFdDrNwMAAZ5xxhq6eVRHlKycr/1h54oknuPzy\ny3WsxKipqYlHH32UZDLJY489xpVXXqkr6SIRKHmMupm9BfgtsAx4FdgCfNLdX8jb5/PAbHf/vJmd\nDrwInOnuo4X1aYy6iIiIiFSbuMaoXwgk3T3l7oeBbwFXFuzjwGnZn08D0hOdpIuIiIiISEYUJ+qF\nixntYvxiRncD7zWzV4FngdXFKtMY9XBobFtYlEdYlEc4lEVYlEc4lEXlK9eCR5cDz7j72cD7gA4z\nm1Wm5xYRERERqThR3EzaT+Ym0Zy5jF/M6Drg3wDcvc/Mfge8B9haWFlvby+rVq3SgkcBlJcuXRpU\ne2q9rDzCKisPlVVWuRLKOaG0p5bKQSx4ZGbTyNwcugz4PfAr4Fp378nbpwP4g7v/i5mdSeYEfZG7\n7y2sTzeTioiIiEi1ieVmUncfA24GngSeB77l7j35Cx4Ba4GLzew54CfAP010kg4aox6Swv+NS7yU\nR1iURziURViURziUReWri6ISd/8x8O6Cbffn/fx7MuPURURERETkJJQ89AVOvOBRdp9LgP8E3goM\nuPulE9WloS8iIiIiUm2mMvSl5Cvq2QWP7iZvwSMze6xgwaM5QAdwmbv3Zxc9EhERERGRIsq14NFK\n4Dvu3g/g7q8Vq0xj1MOhsW1hUR5hUR7hUBZhUR7hUBaVr1wLHp0LNJnZU2a2xcw+FcHzioiIiIhU\nrUhuJj3J51kCfBhoBH5hZr9w997CHTWPejhlzRMdVll5hFVWHiqrrHIllHNCaU8tlUOZR/0i4Ivu\nviJbvhXw/BtKzexzQIO7/0u2/BDwI3f/TmF9uplURERERKpNLPOoA1uABWbWYmbTgU8C3yvY5zFg\nqZlNM7OZwPuBHiagMerhKPzfuMRLeYRFeYRDWYRFeYRDWVS+ulIrcPcxM8steJSbnrHHzG7MPOwP\nuPsLZvYE8BwwBjzg7jtKfW4RERERkWpVtnnUs/tdAPwcuMbdvzvRPhr6IiIiIiLVJpahL3nzqF8O\n/BlwrZm9p8h+twNPlPqcIiIiIiLVrlzzqAP8LbAB+MPxKtMY9XBobFtYlEdYlEc4lEVYlEc4lEXl\nK8s86mZ2NvBxd78XmNQlfxERERGRWhTFifrJ+BLwubxy0ZP1xYsXn/rWyEnJzQUqYVAeYVEe4VAW\nYVEe4VAWla/kWV+AfmBeXnludlu+84FvmZkBpwNXmNlhdy+cxpENGzbw0EMPacEjlVVWWWWVVVZZ\nZZUrthzKgkfTgBeBZcDvgV8B17r7hPOkm9nDwOPFZn258847va2traQ2STS6urqOHnASP+URFuUR\nDmURFuURDmURlqnM+lJX6pOezDzqhf+k1OcUEREREal2kcyjHiXNoy4iIiIi1SaWedQhs+CRmb1g\nZr81s89N8PhKM3s2+6fLzBZG8bwiIiIiItWqXAsevQR8yN0XAWuBB4vVp3nUw5G7MULCoDzCojzC\noSzCojzCoSwqX1kWPHL3p939QLb4NAXzrIuIiIiIyLGimPXl/wCXu/sN2fL/BS50988W2f8W4Nzc\n/oU0Rl1EREREqk0ss75MhpldClwHaK4gEREREZHjiOJE/WQWPMLMzgMeAFa4+75ild111100NjZq\nwaMAyvlj20JoT62XlUdYZeURTjm3LZT21Ho5ty2U9tRyubu7m5tuuimY9tRauWIWPDKzecAm4FPu\n/vTx6tOCR+Ho6tJCCSFRHmFRHuFQFmFRHuFQFmGZytCXSOZRN7MVwF28ueDR7fkLHpnZg8AngBRg\nwGF3v3CiujRGXURERESqTWxj1N39x8C7C7bdn/fz9cD1UTyXiIiIiEgtKMuCR9l9vmxmSTPbbmaL\ni9VVK/Oop9NpduzYQTqdjrspReWPN6x2ykMmK6o8oj72tmzZwj333MOWLVsiqS/q9p2K13vLLbdE\n9nqjFvr7dypE+VmVTCZ5/PHHSSaTkdUZpdDzjTKLSjj2qlHJV9TzFjxaBrwKbDGzx9z9hbx9rgAS\n7t5qZu8H7gMuKvW5K9GhQ4fo6Oigr6+PkZERpk+fTiKRoL29nRkzZsTdvJqjPCQuUR97r776Kh/9\n6Ed57bXXGBsbY9q0aZx++un84Ac/4Oyzz469fafy9Y6MjLB+/fqSXm/UQn//Qrd3717a2tpIpVKM\njo5SV1dHS0sLnZ2dNDU1xd28mso35LbVgihuJr0IuM3dr8iWbyUzNv2OvH3uA55y9/XZcg9wibvv\nKayv2seo/7//9/9IpVLU1b35f6TR0VFaWlq45ZZbYmxZbVIeEpeoj733ve99pNNppk2bdnTb2NgY\nzc3NPPPMM7G3L/TXG7XQ37/QffzjH2fXrl3jXu/cuXN59NFHY2xZRi3lG3LbKs1UxqhHMfTlHOCV\nvPIuxq88WrhP/wT7VL10Ok1fX98xBztAXV0dfX19+jqpzJSHxCXqY2/Lli289tprx5y0AkybNo3X\nXntt0sNCom5f6K83aqG/f6FLJpPjTgwh83pTqVTsw2BqKd+Q21YrIrmZNErVPI/6nj176O/vp7Gx\nkbPOOguA3bt3AzBz5kwGBgbo6ekJpr2Fc+LG3Z6oy8pD5bjyaGpqYmRkhP379wMcc/wNDg4yMDBA\nc3PzSdf33HPPMTY2drQ9uV+qo6OjjIyMsG3bNi644ILY2neqX2/uNU/19UZdDv39O9Xl3Lap/vt9\n+/YxOjp6NOOZM2cCcPDgQYaGhkgmk7S2tirfkyiXOo/6yy+/zMjICA0NDUd/P+ba19/fzxNPPMHK\nlSvL+v5XUjmUedQvAr7o7iuy5ZMZ+vIC8BcTDX2p5nnU0+k0a9asoaGhYdxjQ0NDrF27lubm5hha\nNrGuruqef1V5SClKySPqY2/Lli184hOfGHfVCzIn69/97ne54IILYmvfqX69uTHMuZ8n+3qjFvr7\nd6qV+lmVTCa5+uqrqa+vH/fY8PAwjzzyCK2traU0sSSVlG+pWVTasRe6uIa+bAEWmFmLmU0HPgl8\nr2Cf7wGfhqMn9vsnOkkHWLy46IQwFa+5uZlEIsHo6Ogx2w8fPkwikQjuYK/2k0LlIaUoJY+oj70L\nLriA008/fdxV5rGxMU4//fRJn7RG3b5T/XpzJ+lTfb1RC/39O9VK/axqbW2lpaVl3OvNjYuO8yQd\nKivfUrOotGOvGpV8ou7uY8DNwJPA88C33L3HzG40sxuy+/wQ+J2Z9QL3A6tKfd5K1d7eTktLC0ND\nQ7z++usMDQ0xf/582tvb425aTVIeEpeoj70f/OAHNDc3Mzo6yvDwMKOjozQ3N/ODH/wgiPaF/nqj\nFvr7F7rOzk7mzp3L8PAwg4ODDA8PM3fuXDo7O+NuGlBb+YbctlpQ0tAXM3s7sB5oAV4Grnb3AwX7\nzAXWAWcCR4AH3f3Lxeqs5qEv+dLpNAMDA5xxxhnB/o+0loZaKA+ZrKjyiPrY27JlC9u2bWPJkiWR\nXFmOun2n4vVu2LCBq666KvYr6RMJ/f07FaL8rEomk0fHpMd9JX0ioecbZRaVcOyFLo6VSW8FNrr7\nv2cXOvp8dlu+UeAf3H27mc0Cfm1mT+bPs56vt7e3xCZVhubm5uAP9O7u7po5MVQeMllR5RH1sXfB\nBRdEesIadftOxevdunVrkCfpEP77dypE+VkV6gl6Tuj5RplFJRx7odu+ffukbyYtdejLlcDXsj9/\nDfh44Q7uvtvdt2d/fgPo4ThTMw4ODpbYJIlK7k5lCYPyCIvyCIeyCIvyCIeyCMuzzz476X9T6on6\nO3I3hbr7buAdx9vZzOYDi4Fflvi8IiIiIiJV7YRDX8zsJ2TGlx/dBDiwZoLdiw54zw572QCszl5Z\nn1Bunk6J386dO+NuguRRHmFRHuFQFmFRHuFQFpXvhCfq7v6/iz1mZnvM7Ex332NmZwF/KLJfHZmT\n9P/P3R873vMlEglWr159tLxo0aKqnrIxZOeffz7btm2LuxmSpTzCojzCoSzCojzCoSzitX379mOG\nuzQ2Nk66jlJnfbkD2Ovud2RvJn27uxf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lOIK5Sft4jx49yoYNGxgZGWFqaoo9e/bQ1NTE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\nRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "figsize(12.5, 5)\n", + "\n", + "simulations = trace[\"bernoulli_sim\"]\n", + "print(simulations.shape)\n", + "\n", + "plt.title(\"Simulated dataset using posterior parameters\")\n", + "figsize(12.5, 6)\n", + "for i in range(4):\n", + " ax = plt.subplot(4, 1, i+1)\n", + " plt.scatter(temperature, simulations[1000*i, :], color=\"k\",\n", + " s=50, alpha=0.6)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that the above plots are different (if you can think of a cleaner way to present this, please send a pull request and answer [here](http://stats.stackexchange.com/questions/53078/how-to-visualize-bayesian-goodness-of-fit-for-logistic-regression)!).\n", + "\n", + "We wish to assess how good our model is. \"Good\" is a subjective term of course, so results must be relative to other models. \n", + "\n", + "We will be doing this graphically as well, which may seem like an even less objective method. The alternative is to use *Bayesian p-values*. These are still subjective, as the proper cutoff between good and bad is arbitrary. Gelman emphasises that the graphical tests are more illuminating [7] than p-value tests. We agree.\n", + "\n", + "The following graphical test is a novel data-viz approach to logistic regression. The plots are called *separation plots*[8]. For a suite of models we wish to compare, each model is plotted on an individual separation plot. I leave most of the technical details about separation plots to the very accessible [original paper](https://onlinelibrary.wiley.com/doi/10.1111/j.1540-5907.2011.00525.x), but I'll summarize their use here.\n", + "\n", + "For each model, we calculate the proportion of times the posterior simulation proposed a value of 1 for a particular temperature, i.e. compute $P( \\;\\text{Defect} = 1 | t, \\alpha, \\beta )$ by averaging. This gives us the posterior probability of a defect at each data point in our dataset. For example, for the model we used above:" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "posterior prob of defect | realized defect \n", + "0.40 | 0\n", + "0.25 | 1\n", + "0.28 | 0\n", + "0.32 | 0\n", + "0.36 | 0\n", + "0.19 | 0\n", + "0.17 | 0\n", + "0.25 | 0\n", + "0.73 | 1\n", + "0.53 | 1\n", + "0.25 | 1\n", + "0.10 | 0\n", + "0.36 | 0\n", + "0.80 | 1\n", + "0.36 | 0\n", + "0.13 | 0\n", + "0.25 | 0\n", + "0.07 | 0\n", + "0.12 | 0\n", + "0.09 | 0\n", + "0.13 | 1\n", + "0.12 | 0\n", + "0.71 | 1\n" + ] + } + ], + "source": [ + "posterior_probability = simulations.mean(axis=0)\n", + "print(\"posterior prob of defect | realized defect \")\n", + "for i in range(len(D)):\n", + " print(\"%.2f | %d\" % (posterior_probability[i], D[i]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next we sort each column by the posterior probabilities:" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "probb | defect \n", + "0.07 | 0\n", + "0.09 | 0\n", + "0.10 | 0\n", + "0.12 | 0\n", + "0.12 | 0\n", + "0.13 | 1\n", + "0.13 | 0\n", + "0.17 | 0\n", + "0.19 | 0\n", + "0.25 | 1\n", + "0.25 | 0\n", + "0.25 | 1\n", + "0.25 | 0\n", + "0.28 | 0\n", + "0.32 | 0\n", + "0.36 | 0\n", + "0.36 | 0\n", + "0.36 | 0\n", + "0.40 | 0\n", + "0.53 | 1\n", + "0.71 | 1\n", + "0.73 | 1\n", + "0.80 | 1\n" + ] + } + ], + "source": [ + "ix = np.argsort(posterior_probability)\n", + "print(\"probb | defect \")\n", + "for i in range(len(D)):\n", + " print(\"%.2f | %d\" % (posterior_probability[ix[i]], D[ix[i]]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can present the above data better in a figure: I've wrapped this up into a `separation_plot` function." + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from separation_plot import separation_plot\n", + "\n", + "\n", + "figsize(11., 1.5)\n", + "separation_plot(posterior_probability, D)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The snaking-line is the sorted probabilities, blue bars denote defects, and empty space (or grey bars for the optimistic readers) denote non-defects. As the probability rises, we see more and more defects occur. On the right hand side, the plot suggests that as the posterior probability is large (line close to 1), then more defects are realized. This is good behaviour. Ideally, all the blue bars *should* be close to the right-hand side, and deviations from this reflect missed predictions. \n", + "\n", + "The black vertical line is the expected number of defects we should observe, given this model. This allows the user to see how the total number of events predicted by the model compares to the actual number of events in the data.\n", + "\n", + "It is much more informative to compare this to separation plots for other models. Below we compare our model (top) versus three others:\n", + "\n", + "1. the perfect model, which predicts the posterior probability to be equal 1 if a defect did occur.\n", + "2. a completely random model, which predicts random probabilities regardless of temperature.\n", + "3. a constant model: where $P(D = 1 \\; | \\; t) = c, \\;\\; \\forall t$. The best choice for $c$ is the observed frequency of defects, in this case 7/23. \n" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "figsize(11., 1.25)\n", + "\n", + "# Our temperature-dependent model\n", + "separation_plot(posterior_probability, D)\n", + "plt.title(\"Temperature-dependent model\")\n", + "\n", + "# Perfect model\n", + "# i.e. the probability of defect is equal to if a defect occurred or not.\n", + "p = D\n", + "separation_plot(p, D)\n", + "plt.title(\"Perfect model\")\n", + "\n", + "# random predictions\n", + "p = np.random.rand(23)\n", + "separation_plot(p, D)\n", + "plt.title(\"Random model\")\n", + "\n", + "# constant model\n", + "constant_prob = 7./23*np.ones(23)\n", + "separation_plot(constant_prob, D)\n", + "plt.title(\"Constant-prediction model\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the random model, we can see that as the probability increases there is no clustering of defects to the right-hand side. Similarly for the constant model.\n", + "\n", + "The perfect model, the probability line is not well shown, as it is stuck to the bottom and top of the figure. Of course the perfect model is only for demonstration, and we cannot infer any scientific inference from it." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Exercises\n", + "\n", + "1\\. Try putting in extreme values for our observations in the cheating example. What happens if we observe 25 affirmative responses? 10? 50? " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "2\\. Try plotting $\\alpha$ samples versus $\\beta$ samples. Why might the resulting plot look like this?" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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Iw8OOYhF8X/3mIxEtigWV3HQtMItFtc4UUR3/ygo0Go6lJc32Z7Oa8W+11O7S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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#type your code here.\n", + "figsize(12.5, 4)\n", + "\n", + "plt.scatter(alpha_samples, beta_samples, alpha=0.1)\n", + "plt.title(\"Why does the plot look like this?\")\n", + "plt.xlabel(r\"$\\alpha$\")\n", + "plt.ylabel(r\"$\\beta$\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### References\n", + "\n", + "- [1] Dalal, Fowlkes and Hoadley (1989),JASA, 84, 945-957.\n", + "- [2] German Rodriguez. Datasets. In WWS509. Retrieved 30/01/2013, from .\n", + "- [3] McLeish, Don, and Cyntha Struthers. STATISTICS 450/850 Estimation and Hypothesis Testing. Winter 2012. Waterloo, Ontario: 2012. Print.\n", + "- [4] Fonnesbeck, Christopher. \"Building Models.\" PyMC-Devs. N.p., n.d. Web. 26 Feb 2013. .\n", + "- [5] Cronin, Beau. \"Why Probabilistic Programming Matters.\" 24 Mar 2013. Google, Online Posting to Google . Web. 24 Mar. 2013. .\n", + "- [6] S.P. Brooks, E.A. Catchpole, and B.J.T. Morgan. Bayesian animal survival estimation. Statistical Science, 15: 357–376, 2000\n", + "- [7] Gelman, Andrew. \"Philosophy and the practice of Bayesian statistics.\" British Journal of Mathematical and Statistical Psychology. (2012): n. page. Web. 2 Apr. 2013.\n", + "- [8] Greenhill, Brian, Michael D. Ward, and Audrey Sacks. \"The Separation Plot: A New Visual Method for Evaluating the Fit of Binary Models.\" American Journal of Political Science. 55.No.4 (2011): n. page. Web. 2 Apr. 2013." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.core.display import HTML\n", + "\n", + "\n", + "def css_styling():\n", + " styles = open(\"../styles/custom.css\", \"r\").read()\n", + " return HTML(styles)\n", + "css_styling()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.2" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/Chapter2_MorePyMC/Ch2_MorePyMC_PyMC_current.ipynb b/Chapter2_MorePyMC/Ch2_MorePyMC_PyMC_current.ipynb new file mode 100644 index 00000000..05424ddb --- /dev/null +++ b/Chapter2_MorePyMC/Ch2_MorePyMC_PyMC_current.ipynb @@ -0,0 +1,3449 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Chapter 2\n", + "======\n", + "`Original content created by Cam Davidson-Pilon`\n", + "\n", + "`Ported to Python 3 and PyMC3 by Max Margenot (@clean_utensils) and Thomas Wiecki (@twiecki) at Quantopian (@quantopian)`\n", + "\n", + "`Ported to PyMC last by Kurisu Chan (@miemiekurisu)`\n", + "\n", + "___\n", + "\n", + "This chapter introduces more PyMC syntax and variables and ways to think about how to model a system from a Bayesian perspective. It also contains tips and data visualization techniques for assessing goodness-of-fit for your Bayesian model." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## A little more on PyMC\n", + "\n", + "### Model Context\n", + "\n", + "In PyMC, we typically handle all the variables we want in our model within the context of the `Model` object." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "import pymc as pm\n", + "import pytensor\n", + "import numpy as np\n", + "RANDOM_SEED = 8927\n", + "rng = np.random.default_rng(RANDOM_SEED)\n", + "# %config InlineBackend.figure_format = 'retina'\n", + "# the proper way to init the value is to use initval paramter\n", + "# The test_value is just for debug\n", + "# reference is at https://pytensor.readthedocs.io/en/latest/tutorial/debug_faq.html#using-test-values\n", + "# and https://github.com/pymc-devs/pymc/issues/562#issuecomment-932146862\n", + "# You can use pytensor.config.compute_test_value = 'warn' to debug\n", + "with pm.Model() as model:\n", + " parameter = pm.Exponential(\"poisson_param\", 1.0, initval=rng.exponential(1))\n", + " data_generator = pm.Poisson(\"data_generator\", parameter)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is an extra layer of convenience compared to PyMC. Any variables created within a given `Model`'s context will be automatically assigned to that model. If you try to define a variable outside of the context of a model, you will get an error.\n", + "\n", + "We can continue to work within the context of the same model by using `with` with the name of the model object that we have already created." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "with model:\n", + " data_plus_one = data_generator + 1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can examine the same variables outside of the model context once they have been defined, but to define more variables that the model will recognize they have to be within the context." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{poisson_param ~ Exp(f()): array(1.44577053),\n", + " data_generator ~ Pois(poisson_param): None}" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.initial_values" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Each variable assigned to a model will be defined with its own name, the first string parameter (we will cover this further in the variables section). To create a different model object with the same name as one we have used previously, we need only run the first block of code again." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "with pm.Model() as model_exp:\n", + " theta = pm.Exponential(\"theta\", 2.0)\n", + " data_generator = pm.Poisson(\"data_generator\", theta)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also define an entirely separate model. Note that we are free to name our models whatever we like, so if we do not want to overwrite an old model we need only make another." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "with pm.Model() as ab_testing:\n", + " p_A = pm.Uniform(\"P(A)\", 0, 1)\n", + " p_B = pm.Uniform(\"P(B)\", 0, 1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You probably noticed that PyMC will often give you notifications about transformations when you add variables to your model. These transformations are done internally by PyMC to modify the space that the variable is sampled in (when we get to actually sampling the model). This is an internal feature which helps with the convergence of our samples to the posterior distribution and serves to improve the results." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "### PyMC Variables\n", + "\n", + "All PyMC variables have an initial value, if you didn't distinct the paramater `initval`, PyMC will automatically initialize it for you after. \n", + "\n", + "Using the same variables from before:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "#leave it to automatically init\n", + "with pm.Model() as model:\n", + " parameter = pm.Exponential(\"poisson_param\", 1.0)\n", + " data_generator = pm.Poisson(\"data_generator\", parameter)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "poisson_param initval is None\n", + "data_generator initval is None\n" + ] + } + ], + "source": [ + "for k,v in model.initial_values.items():\n", + " print(f\"{k} initval is {v}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "#leave it to automatically init\n", + "with pm.Model() as model:\n", + " parameter = pm.Exponential(\"poisson_param\", 1.0,initval=rng.exponential(1))\n", + " data_generator = pm.Poisson(\"data_generator\", parameter,initval=rng.poisson(10))" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "poisson_param initval is 2.2597012384790296\n", + "data_generator initval is 10\n" + ] + } + ], + "source": [ + "for k,v in model.initial_values.items():\n", + " print(f\"{k} initval is {v}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `initval` parameter is used only for the model, as the starting point for sampling if no other start is specified. It will not change as a result of sampling. " + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{poisson_param ~ Exp(f()): array(0.5)}" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "with pm.Model() as model:\n", + " parameter = pm.Exponential(\"poisson_param\", 1.0, initval=0.5)\n", + "\n", + "#You can use initial_values to see all parameters above\n", + "model.initial_values" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This can be helpful (and be also helpful to debug, as the PyTensor document reference said above) if you are using a more unstable prior that may require a better starting point.\n", + "\n", + "PyMC is concerned with two types of programming variables: stochastic and deterministic.\n", + "\n", + "* *stochastic variables* are variables that are not deterministic, i.e., even if you knew all the values of the variables' parameters and components, it would still be random. Included in this category are instances of classes `Poisson`, `DiscreteUniform`, and `Exponential`.\n", + "\n", + "* *deterministic variables* are variables that are not random if the variables' parameters and components were known. This might be confusing at first: a quick mental check is *if I knew all of variable `foo`'s component variables, I could determine what `foo`'s value is.* \n", + "\n", + "We will detail each below.\n", + "\n", + "#### Initializing Stochastic variables\n", + "\n", + "Initializing a stochastic, or random, variable requires a `name` argument, plus additional parameters that are class specific. For example:\n", + "\n", + "`some_variable = pm.DiscreteUniform(\"discrete_uni_var\", 0, 4)`\n", + "\n", + "where 0, 4 are the `DiscreteUniform`-specific lower and upper bound on the random variable. The [PyMC docs](https://docs.pymc.io/en/stable/api.html) contain the specific parameters for stochastic variables. (Or use `??` if you are using IPython!)\n", + "\n", + "The `name` attribute is used to retrieve the posterior distribution later in the analysis, so it is best to use a descriptive name. Typically, I use the Python variable's name as the `name`.\n", + "\n", + "For multivariable problems, rather than creating a Python array of stochastic variables, addressing the `shape` keyword in the call to a stochastic variable creates multivariate array of (independent) stochastic variables. The array behaves like a NumPy array when used like one, and references to its `initval` attribute return NumPy arrays. \n", + "\n", + "The `shape` argument also solves the annoying case where you may have many variables $\\beta_i, \\; i = 1,...,N$ you wish to model. Instead of creating arbitrary names and variables for each one, like:\n", + "\n", + " beta_1 = pm.Uniform(\"beta_1\", 0, 1)\n", + " beta_2 = pm.Uniform(\"beta_2\", 0, 1)\n", + " ...\n", + "\n", + "we can instead wrap them into a single variable:\n", + "\n", + " betas = pm.Uniform(\"betas\", 0, 1, shape=N)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Deterministic variables\n", + "\n", + "We can create a deterministic variable similarly to how we create a stochastic variable. We simply call up the `Deterministic` class in PyMC and pass in the function that we desire\n", + "\n", + " deterministic_variable = pm.Deterministic(\"deterministic variable\", some_function_of_variables)\n", + "\n", + "For all purposes, we can treat the object `some_deterministic_var` as a variable and not a Python function. \n", + "\n", + "Calling [`pymc.Deterministic`](https://www.pymc.io/projects/docs/en/latest/api/generated/pymc.Deterministic.html?highlight=Deterministic) is the most obvious way, but not the only way, to create deterministic variables. Elementary operations, like addition, exponentials etc. implicitly create deterministic variables. For example, the following returns a deterministic variable:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "with pm.Model() as model:\n", + " lambda_1 = pm.Exponential(\"lambda_1\", 1.0,initval=0.5)\n", + " lambda_2 = pm.Exponential(\"lambda_2\", 1.0,initval=0.5)\n", + " tau = pm.DiscreteUniform(\"tau\", lower=0, upper=10)\n", + "\n", + "new_deterministic_variable = lambda_1 + lambda_2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If we want a `deterministic` variable to actually be tracked by our sampling, however, we need to define it explicitly as a named `deterministic` variable with the constructor.\n", + "\n", + "The use of the `deterministic` variable was seen in the previous chapter's text-message example. Recall the model for $\\lambda$ looked like: \n", + "\n", + "$$\n", + "\\lambda = \n", + "\\begin{cases}\\lambda_1 & \\text{if } t \\lt \\tau \\cr\n", + "\\lambda_2 & \\text{if } t \\ge \\tau\n", + "\\end{cases}\n", + "$$\n", + "\n", + "And in PyMC code:" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "n_data_points = 5 # in CH1 we had ~70 data points\n", + "idx = np.arange(n_data_points)\n", + "with model:\n", + " lambda_ = pm.math.switch(tau >= idx, lambda_1, lambda_2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Clearly, if $\\tau, \\lambda_1$ and $\\lambda_2$ are known, then $\\lambda$ is known completely, hence it is a deterministic variable. We use the `switch` function here to change from $\\lambda_1$ to $\\lambda_2$ at the appropriate time. This function is directly from the `pytensor.tensor.basic` package, which we will discuss in the next section.\n", + "\n", + "Inside a `deterministic` variable, the stochastic variables passed in behave like scalars or NumPy arrays (if multivariable). We can do whatever we want with them as long as the dimensions match up in our calculations.\n", + "\n", + "For example, running the following (off course you NEED to define model session firstly):\n", + "\n", + " def subtract(x, y):\n", + " return x - y\n", + " with model:\n", + " stochastic_1 = pm.Uniform(\"U_1\", 0, 1)\n", + " stochastic_2 = pm.Uniform(\"U_2\", 0, 1)\n", + "\n", + " det_1 = pm.Deterministic(\"Delta\", subtract(stochastic_1, stochastic_2))\n", + " \n", + "Is perfectly valid PyMC code. Saying that our expressions behave like NumPy arrays is not exactly honest here, however. The main catch is that the expression that we are making *must* be compatible with `pytensor` tensors, which we will cover in the next section. Feel free to define whatever functions that you need in order to compose your model. However, if you need to do any array-like calculations that would require NumPy functions, make sure you use their equivalents in `pytensor`." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### PyTensor\n", + "\n", + "The majority of the heavy lifting done by PyMC is taken care of with the `pytensor` package, the next generation of `theano`. The notation in `pytensor` is remarkably similar to NumPy. It also supports many of the familiar computational elements of NumPy. However, while NumPy directly executes computations, e.g. when you run `a + b`, `pytensor` instead builds up a \"compute graph\" that tracks that you want to perform the `+` operation on the elements `a` and `b`. Only when you `eval()` a `pytensor` expression does the computation take place (i.e. `pytensor` is lazy evaluated). Once the compute graph is built, we can perform all kinds of mathematical optimizations (e.g. simplifications), compute gradients via autodiff, compile the entire graph to C to run at machine speed, and also compile it to run on the GPU. PyMC is basically a collection of `pytensor` symbolic expressions for various probability distributions that are combined to one big compute graph making up the whole model log probability, and a collection of inference algorithms that use that graph to compute probabilities and gradients. For practical purposes, what this means is that in order to build certain models we sometimes have to use `pytensor`.\n", + "\n", + "Let's write some PyMC code that involves `pytensor` calculations." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "import pytensor.tensor as pt\n", + "\n", + "with pm.Model() as pytensor_test:\n", + " p1 = pm.Uniform(\"p\", 0, 1)\n", + " p2 = 1 - p1\n", + " p = pt.stack([p1, p2])\n", + " \n", + " assignment = pm.Categorical(\"assignment\", p)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here we use `pytensor`'s `stack()` function in the same way we would use one of NumPy's stacking functions: to combine our two separate variables, `p1` and `p2`, into a vector with $2$ elements. The stochastic `categorical` variable does not understand what we mean if we pass a NumPy array of `p1` and `p2` to it because they are both `pytensor` variables. Stacking them like this combines them into one `pytensor` variable that we can use as the complementary pair of probabilities for our two categories.\n", + "\n", + "Throughout the course of this book we use several `pytensor` functions to help construct our models. If you have more interest in looking at `pytensor` itself, be sure to check out the [documentation](https://pytensor.readthedocs.io/en/latest/).\n", + "\n", + "After these technical considerations, we can get back to defining our model!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Including observations in the Model\n", + "\n", + "At this point, it may not look like it, but we have fully specified our priors. For example, we can ask and answer questions like \"What does my prior distribution of $\\lambda_1$ look like?\" " + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib inline\n", + "from IPython.core.pylabtools import figsize\n", + "import matplotlib.pyplot as plt\n", + "import scipy.stats as stats\n", + "figsize(12.5, 4)\n", + "plt.style.use(\"ggplot\")\n", + "samples = pm.draw(lambda_1, draws=20000)\n", + "plt.hist(samples, bins=70, density=True, histtype=\"stepfilled\")\n", + "plt.title(\"Prior distribution for $\\lambda_1$\")\n", + "plt.xlim(0, 8);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To frame this in the notation of the first chapter, though this is a slight abuse of notation, we have specified $P(A)$. Our next goal is to include data/evidence/observations $X$ into our model. \n", + "\n", + "PyMC stochastic variables have a keyword argument `observed`. The keyword `observed` has a very simple role: fix the variable's current value to be the given data, typically a NumPy `array` or pandas `DataFrame`. For example:" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{lambda_1: array(0.5), lambda_2: array(0.5), tau: None}\n" + ] + } + ], + "source": [ + "data = np.array([10, 5])\n", + "with model:\n", + " fixed_variable = pm.Poisson(\"fxd\", 1, observed=data)\n", + "print(model.initial_values)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is how we include data into our models: initializing a stochastic variable to have a *fixed value*. \n", + "\n", + "To complete our text message example, we fix the PyMC variable `observations` to the observed dataset. " + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{lambda_1: array(0.5), lambda_2: array(0.5), tau: None}\n" + ] + } + ], + "source": [ + "# We're using some fake data here\n", + "data = np.array([10, 25, 15, 20, 35])\n", + "with model:\n", + " obs = pm.Poisson(\"obs\", lambda_, observed=data)\n", + "print(model.initial_values)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Modeling approaches\n", + "\n", + "A good starting thought to Bayesian modeling is to think about *how your data might have been generated*. Position yourself in an omniscient position, and try to imagine how *you* would recreate the dataset. \n", + "\n", + "In the last chapter we investigated text message data. We begin by asking how our observations may have been generated:\n", + "\n", + "1. We started by thinking \"what is the best random variable to describe this count data?\" A Poisson random variable is a good candidate because it can represent count data. So we model the number of sms's received as sampled from a Poisson distribution.\n", + "\n", + "2. Next, we think, \"Ok, assuming sms's are Poisson-distributed, what do I need for the Poisson distribution?\" Well, the Poisson distribution has a parameter $\\lambda$. \n", + "\n", + "3. Do we know $\\lambda$? No. In fact, we have a suspicion that there are *two* $\\lambda$ values, one for the earlier behaviour and one for the later behaviour. We don't know when the behaviour switches though, but call the switchpoint $\\tau$.\n", + "\n", + "4. What is a good distribution for the two $\\lambda$s? The exponential is good, as it assigns probabilities to positive real numbers. Well the exponential distribution has a parameter too, call it $\\alpha$.\n", + "\n", + "5. Do we know what the parameter $\\alpha$ might be? No. At this point, we could continue and assign a distribution to $\\alpha$, but it's better to stop once we reach a set level of ignorance: whereas we have a prior belief about $\\lambda$, (\"it probably changes over time\", \"it's likely between 10 and 30\", etc.), we don't really have any strong beliefs about $\\alpha$. So it's best to stop here. \n", + "\n", + " What is a good value for $\\alpha$ then? We think that the $\\lambda$s are between 10-30, so if we set $\\alpha$ really low (which corresponds to larger probability on high values) we are not reflecting our prior well. Similar, a too-high alpha misses our prior belief as well. A good idea for $\\alpha$ as to reflect our belief is to set the value so that the mean of $\\lambda$, given $\\alpha$, is equal to our observed mean. This was shown in the last chapter.\n", + "\n", + "6. We have no expert opinion of when $\\tau$ might have occurred. So we will suppose $\\tau$ is from a discrete uniform distribution over the entire timespan.\n", + "\n", + "\n", + "Below we give a graphical visualization of this, where arrows denote `parent-child` relationships. (provided by the [Daft Python library](https://docs.daft-pgm.org/en/latest/) )\n", + "\n", + "\n", + "\n", + "\n", + "PyMC, and other probabilistic programming languages, have been designed to tell these data-generation *stories*. More generally, B. Cronin writes [5]:\n", + "\n", + "> Probabilistic programming will unlock narrative explanations of data, one of the holy grails of business analytics and the unsung hero of scientific persuasion. People think in terms of stories - thus the unreasonable power of the anecdote to drive decision-making, well-founded or not. But existing analytics largely fails to provide this kind of story; instead, numbers seemingly appear out of thin air, with little of the causal context that humans prefer when weighing their options." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Same story; different ending.\n", + "\n", + "Interestingly, we can create *new datasets* by retelling the story.\n", + "For example, if we reverse the above steps, we can simulate a possible realization of the dataset.\n", + "\n", + "1\\. Specify when the user's behaviour switches by sampling from $\\text{DiscreteUniform}(0, 80)$:" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "68\n" + ] + } + ], + "source": [ + "tau = np.random.randint(0, 80)\n", + "print(tau)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "2\\. Draw $\\lambda_1$ and $\\lambda_2$ from an $\\text{Exp}(\\alpha)$ distribution:" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "25.90695930270913 4.308298783985128\n" + ] + } + ], + "source": [ + "alpha = 1./20.\n", + "lambda_1, lambda_2 = np.random.exponential(scale=1/alpha, size=2)\n", + "print(lambda_1, lambda_2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "3\\. For days before $\\tau$, represent the user's received SMS count by sampling from $\\text{Poi}(\\lambda_1)$, and sample from $\\text{Poi}(\\lambda_2)$ for days after $\\tau$. For example:" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "data = np.r_[stats.poisson.rvs(mu=lambda_1, size=tau), stats.poisson.rvs(mu=lambda_2, size = 80 - tau)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "4\\. Plot the artificial dataset:" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.bar(np.arange(80), data, color=\"#348ABD\")\n", + "plt.bar(tau-1, data[tau - 1], color=\"r\", label=\"user behaviour changed\")\n", + "plt.xlabel(\"Time (days)\")\n", + "plt.ylabel(\"count of text-msgs received\")\n", + "plt.title(\"Artificial dataset\")\n", + "plt.xlim(0, 80)\n", + "plt.legend();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It is okay that our fictional dataset does not look like our observed dataset: the probability is incredibly small it indeed would. PyMC's engine is designed to find good parameters, $\\lambda_i, \\tau$, that maximize this probability. \n", + "\n", + "\n", + "The ability to generate artificial dataset is an interesting side effect of our modeling, and we will see that this ability is a very important method of Bayesian inference. We produce a few more datasets below:" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def plot_artificial_sms_dataset():\n", + " tau = stats.randint.rvs(0, 80)\n", + " alpha = 1./20.\n", + " lambda_1, lambda_2 = stats.expon.rvs(scale=1/alpha, size=2)\n", + " data = np.r_[stats.poisson.rvs(mu=lambda_1, size=tau), stats.poisson.rvs(mu=lambda_2, size=80 - tau)]\n", + " plt.bar(np.arange(80), data, color=\"#348ABD\")\n", + " plt.bar(tau - 1, data[tau-1], color=\"r\", label=\"user behaviour changed\")\n", + " plt.xlim(0, 80);\n", + "\n", + "figsize(12.5, 5)\n", + "plt.title(\"More example of artificial datasets\")\n", + "for i in range(4):\n", + " plt.subplot(4, 1, i+1)\n", + " plot_artificial_sms_dataset()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Later we will see how we use this to make predictions and test the appropriateness of our models." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Example: Bayesian A/B testing\n", + "\n", + "A/B testing is a statistical design pattern for determining the difference of effectiveness between two different treatments. For example, a pharmaceutical company is interested in the effectiveness of drug A vs drug B. The company will test drug A on some fraction of their trials, and drug B on the other fraction (this fraction is often 1/2, but we will relax this assumption). After performing enough trials, the in-house statisticians sift through the data to determine which drug yielded better results. \n", + "\n", + "Similarly, front-end web developers are interested in which design of their website yields more sales or some other metric of interest. They will route some fraction of visitors to site A, and the other fraction to site B, and record if the visit yielded a sale or not. The data is recorded (in real-time), and analyzed afterwards. \n", + "\n", + "Often, the post-experiment analysis is done using something called a hypothesis test like *difference of means test* or *difference of proportions test*. This involves often misunderstood quantities like a \"Z-score\" and even more confusing \"p-values\" (please don't ask). If you have taken a statistics course, you have probably been taught this technique (though not necessarily *learned* this technique). And if you were like me, you may have felt uncomfortable with their derivation -- good: the Bayesian approach to this problem is much more natural. \n", + "\n", + "### A Simple Case\n", + "\n", + "As this is a hacker book, we'll continue with the web-dev example. For the moment, we will focus on the analysis of site A only. Assume that there is some true $0 \\lt p_A \\lt 1$ probability that users who, upon shown site A, eventually purchase from the site. This is the true effectiveness of site A. Currently, this quantity is unknown to us. \n", + "\n", + "Suppose site A was shown to $N$ people, and $n$ people purchased from the site. One might conclude hastily that $p_A = \\frac{n}{N}$. Unfortunately, the *observed frequency* $\\frac{n}{N}$ does not necessarily equal $p_A$ -- there is a difference between the *observed frequency* and the *true frequency* of an event. The true frequency can be interpreted as the probability of an event occurring. For example, the true frequency of rolling a 1 on a 6-sided die is $\\frac{1}{6}$. Knowing the true frequency of events like:\n", + "\n", + "- fraction of users who make purchases, \n", + "- frequency of social attributes, \n", + "- percent of internet users with cats etc. \n", + "\n", + "are common requests we ask of Nature. Unfortunately, often Nature hides the true frequency from us and we must *infer* it from observed data.\n", + "\n", + "The *observed frequency* is then the frequency we observe: say rolling the die 100 times you may observe 20 rolls of 1. The observed frequency, 0.2, differs from the true frequency, $\\frac{1}{6}$. We can use Bayesian statistics to infer probable values of the true frequency using an appropriate prior and observed data.\n", + "\n", + "\n", + "With respect to our A/B example, we are interested in using what we know, $N$ (the total trials administered) and $n$ (the number of conversions), to estimate what $p_A$, the true frequency of buyers, might be. \n", + "\n", + "To setup a Bayesian model, we need to assign prior distributions to our unknown quantities. *A priori*, what do we think $p_A$ might be? For this example, we have no strong conviction about $p_A$, so for now, let's assume $p_A$ is uniform over [0,1]:" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "import pymc as pm\n", + "# The parameters are the bounds of the Uniform.\n", + "with pm.Model() as model:\n", + " p = pm.Uniform('p')#, lower=0, upper=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Had we had stronger beliefs, we could have expressed them in the prior above.\n", + "\n", + "For this example, consider $p_A = 0.05$, and $N = 1500$ users shown site A, and we will simulate whether the user made a purchase or not. To simulate this from $N$ trials, we will use a *Bernoulli* distribution: if $X\\ \\sim \\text{Ber}(p)$, then $X$ is 1 with probability $p$ and 0 with probability $1 - p$. Of course, in practice we do not know $p_A$, but we will use it here to simulate the data." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0 0 0 ... 0 0 0]\n", + "91\n" + ] + } + ], + "source": [ + "#set constants\n", + "p_true = 0.05 # remember, this is unknown.\n", + "N = 1500\n", + "\n", + "# sample N Bernoulli random variables from Ber(0.05).\n", + "# each random variable has a 0.05 chance of being a 1.\n", + "# this is the data-generation step\n", + "occurrences = stats.bernoulli.rvs(p_true, size=N)\n", + "\n", + "print(occurrences) # Remember: Python treats True == 1, and False == 0\n", + "print(np.sum(occurrences))\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The observed frequency is:" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "What is the observed frequency in Group A? 0.0607\n", + "Does this equal the true frequency? False\n" + ] + } + ], + "source": [ + "# Occurrences.mean is equal to n/N.\n", + "print(\"What is the observed frequency in Group A? %.4f\" % np.mean(occurrences))\n", + "print(\"Does this equal the true frequency? %s\" % (np.mean(occurrences) == p_true))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We combine the observations into the PyMC `observed` variable, and run our inference algorithm:" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "with model:\n", + " observed = pm.Bernoulli(\"obs\", p, observed=occurrences)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Multiprocess sampling (3 chains in 4 jobs)\n", + "Metropolis: [p]\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + "
\n", + " \n", + " 100.00% [57000/57000 00:02<00:00 Sampling 3 chains, 0 divergences]\n", + "
\n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Sampling 3 chains for 1_000 tune and 18_000 draw iterations (3_000 + 54_000 draws total) took 3 seconds.\n" + ] + } + ], + "source": [ + "#include the observations, which are Bernoulli\n", + "with model:\n", + " # To be explained in chapter 3\n", + " step = pm.Metropolis()\n", + " trace = pm.sample(18000,step=step,chains=3) #default value of chains is 2, runs independent chains\n", + " # We have a new data structure to burn in pymc current\n", + " # if you use return_inferencedata=False, the code below will still work, but for little ArviZ, let's use the default True value.\n", + " # burned_trace = trace[1000:] " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We plot the posterior distribution of the unknown $p_A$ below:" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "figsize(12.5,4)\n", + "plt.title(\"Posterior distribution of $p_A$, the true effectiveness of site A\")\n", + "plt.vlines(p_true, 0, 90, linestyle=\"--\", label=\"true $p_A$ (unknown)\",color='black')\n", + "combine_3_chains = np.concatenate(trace.posterior.p.data[:,1000:])\n", + "plt.hist( combine_3_chains, bins=25, histtype=\"stepfilled\", density=True)\n", + "plt.legend();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Our posterior distribution puts most weight near the true value of $p_A$, but also some weights in the tails. This is a measure of how uncertain we should be, given our observations. Try changing the number of observations, `N`, and observe how the posterior distribution changes.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The new PyMC (>=4) provids an amazing visualization tool be called `arviz`, can be used to do the trace analyze:" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 263, + "width": 715 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "import arviz as az\n", + "%config InlineBackend.figure_format = 'retina'\n", + "# az.style.use(\"arviz-darkgrid\")\n", + "\n", + "az.plot_trace(trace, figsize=(12.5, 4));\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can clearly see the three independent chains left above." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "image/png": { + "height": 270, + "width": 711 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.lines as lines\n", + "\n", + "ax=az.plot_posterior(trace, var_names=['p'], kind='hist',bins=25,figsize=(12.5,4))\n", + "ax.set_title(\"Posterior distribution of $p_A$, the true effectiveness of site A\")\n", + "ax.vlines(0.05,0, 1800,colors='green',linestyle=\"--\", label=\"true $p_A$ (unknown)\")\n", + "ax.legend()\n", + "ax.plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "### *A* and *B* Together\n", + "\n", + "A similar analysis can be done for site B's response data to determine the analogous $p_B$. But what we are really interested in is the *difference* between $p_A$ and $p_B$. Let's infer $p_A$, $p_B$, *and* $\\text{delta} = p_A - p_B$, all at once. We can do this using PyMC's deterministic variables. (We'll assume for this exercise that $p_B = 0.04$, so $\\text{delta} = 0.01$, $N_B = 750$ (significantly less than $N_A$) and we will simulate site B's data like we did for site A's data )" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Obs from Site A: [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0] ...\n", + "Obs from Site B: [0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0] ...\n" + ] + } + ], + "source": [ + "import pymc as pm\n", + "figsize(12, 4)\n", + "\n", + "#these two quantities are unknown to us.\n", + "true_p_A = 0.05\n", + "true_p_B = 0.04\n", + "\n", + "#notice the unequal sample sizes -- no problem in Bayesian analysis.\n", + "N_A = 1500\n", + "N_B = 750\n", + "\n", + "#generate some observations\n", + "observations_A = stats.bernoulli.rvs(true_p_A, size=N_A)\n", + "observations_B = stats.bernoulli.rvs(true_p_B, size=N_B)\n", + "print(\"Obs from Site A: \", observations_A[:30], \"...\")\n", + "print(\"Obs from Site B: \", observations_B[:30], \"...\")" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.04733333333333333\n", + "0.04933333333333333\n" + ] + } + ], + "source": [ + "print(np.mean(observations_A))\n", + "print(np.mean(observations_B))" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Multiprocess sampling (2 chains in 4 jobs)\n", + "CompoundStep\n", + ">Metropolis: [p_A]\n", + ">Metropolis: [p_B]\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + "
\n", + " \n", + " 100.00% [42000/42000 00:04<00:00 Sampling 2 chains, 0 divergences]\n", + "
\n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Sampling 2 chains for 1_000 tune and 20_000 draw iterations (2_000 + 40_000 draws total) took 4 seconds.\n" + ] + } + ], + "source": [ + "# Set up the pymc model. Again assume Uniform priors for p_A and p_B.\n", + "with pm.Model() as model:\n", + " p_A = pm.Uniform(\"p_A\", 0, 1)\n", + " p_B = pm.Uniform(\"p_B\", 0, 1)\n", + " \n", + " # Define the deterministic delta function. This is our unknown of interest.\n", + " delta = pm.Deterministic(\"delta\", p_A - p_B)\n", + "\n", + " \n", + " # Set of observations, in this case we have two observation datasets.\n", + " obs_A = pm.Bernoulli(\"obs_A\", p_A, observed=observations_A)\n", + " obs_B = pm.Bernoulli(\"obs_B\", p_B, observed=observations_B)\n", + "\n", + " # To be explained in chapter 3.\n", + " step = pm.Metropolis()\n", + " trace = pm.sample(20000, step=step,chains=2)\n", + " # if you use return_inferencedata=False, the code below will still work, but for little ArviZ, let's use the default True value.\n", + " #burned_trace=trace[1000:]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Below we plot the posterior distributions for the three unknowns: " + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [], + "source": [ + "p_A_samples = np.concatenate(trace.posterior.p_A.data[:,1000:])\n", + "p_B_samples = np.concatenate(trace.posterior.p_B.data[:,1000:])\n", + "delta_samples = np.concatenate(trace.posterior.delta.data[:,1000:])" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 592, + "width": 742 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "figsize(12.5,10)\n", + "\n", + "#histogram of posteriors\n", + "\n", + "ax = plt.subplot(311)\n", + "\n", + "plt.xlim(0, .1)\n", + "plt.hist(p_A_samples, histtype='stepfilled', bins=25, alpha=0.85,\n", + " label=\"posterior of $p_A$\", color=\"#A60628\", density=True)\n", + "plt.vlines(true_p_A, 0, 80, linestyle=\"--\", label=\"true $p_A$ (unknown)\")\n", + "plt.legend(loc=\"upper right\")\n", + "plt.title(\"Posterior distributions of $p_A$, $p_B$, and delta unknowns\")\n", + "\n", + "ax = plt.subplot(312)\n", + "\n", + "plt.xlim(0, .1)\n", + "plt.hist(p_B_samples, histtype='stepfilled', bins=25, alpha=0.85,\n", + " label=\"posterior of $p_B$\", color=\"#467821\", density=True)\n", + "plt.vlines(true_p_B, 0, 80, linestyle=\"--\", label=\"true $p_B$ (unknown)\")\n", + "plt.legend(loc=\"upper right\")\n", + "\n", + "ax = plt.subplot(313)\n", + "plt.hist(delta_samples, histtype='stepfilled', bins=30, alpha=0.85,\n", + " label=\"posterior of delta\", color=\"#7A68A6\", density=True)\n", + "plt.vlines(true_p_A - true_p_B, 0, 60, linestyle=\"--\",\n", + " label=\"true delta (unknown)\")\n", + "plt.vlines(0, 0, 60, color=\"black\", alpha=0.2)\n", + "plt.legend(loc=\"upper right\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notice that as a result of `N_B < N_A`, i.e. we have less data from site B, our posterior distribution of $p_B$ is fatter, implying we are less certain about the true value of $p_B$ than we are of $p_A$. \n", + "\n", + "With respect to the posterior distribution of $\\text{delta}$, we can see that the majority of the distribution is above $\\text{delta}=0$, implying there site A's response is likely better than site B's response. The probability this inference is incorrect is easily computable:" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Probability site A is WORSE than site B: 0.604\n", + "Probability site A is BETTER than site B: 0.396\n" + ] + } + ], + "source": [ + "# Count the number of samples less than 0, i.e. the area under the curve\n", + "# before 0, represent the probability that site A is worse than site B.\n", + "print(\"Probability site A is WORSE than site B: %.3f\" % \\\n", + " np.mean(delta_samples < 0))\n", + "\n", + "print(\"Probability site A is BETTER than site B: %.3f\" % \\\n", + " np.mean(delta_samples > 0))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If this probability is too high for comfortable decision-making, we can perform more trials on site B (as site B has less samples to begin with, each additional data point for site B contributes more inferential \"power\" than each additional data point for site A). \n", + "\n", + "Try playing with the parameters `true_p_A`, `true_p_B`, `N_A`, and `N_B`, to see what the posterior of $\\text{delta}$ looks like. Notice in all this, the difference in sample sizes between site A and site B was never mentioned: it naturally fits into Bayesian analysis.\n", + "\n", + "I hope the readers feel this style of A/B testing is more natural than hypothesis testing, which has probably confused more than helped practitioners. Later in this book, we will see two extensions of this model: the first to help dynamically adjust for bad sites, and the second will improve the speed of this computation by reducing the analysis to a single equation. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## An algorithm for human deceit\n", + "\n", + "Social data has an additional layer of interest as people are not always honest with responses, which adds a further complication into inference. For example, simply asking individuals \"Have you ever cheated on a test?\" will surely contain some rate of dishonesty. What you can say for certain is that the true rate is less than your observed rate (assuming individuals lie *only* about *not cheating*; I cannot imagine one who would admit \"Yes\" to cheating when in fact they hadn't cheated). \n", + "\n", + "To present an elegant solution to circumventing this dishonesty problem, and to demonstrate Bayesian modeling, we first need to introduce the binomial distribution.\n", + "\n", + "### The Binomial Distribution\n", + "\n", + "The binomial distribution is one of the most popular distributions, mostly because of its simplicity and usefulness. Unlike the other distributions we have encountered thus far in the book, the binomial distribution has 2 parameters: $N$, a positive integer representing $N$ trials or number of instances of potential events, and $p$, the probability of an event occurring in a single trial. Like the Poisson distribution, it is a discrete distribution, but unlike the Poisson distribution, it only weighs integers from $0$ to $N$. The mass distribution looks like:\n", + "\n", + "$$P( X = k ) = {{N}\\choose{k}} p^k(1-p)^{N-k}$$\n", + "\n", + "If $X$ is a binomial random variable with parameters $p$ and $N$, denoted $X \\sim \\text{Bin}(N,p)$, then $X$ is the number of events that occurred in the $N$ trials (obviously $0 \\le X \\le N$). The larger $p$ is (while still remaining between 0 and 1), the more events are likely to occur. The expected value of a binomial is equal to $Np$. Below we plot the mass probability distribution for varying parameters. \n" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 281, + "width": 507 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "figsize(8, 4)\n", + "\n", + "import scipy.stats as stats\n", + "binomial = stats.binom\n", + "\n", + "parameters = [(10, .4), (10, .9)]\n", + "colors = [\"#348ABD\", \"#A60628\"]\n", + "\n", + "for i in range(2):\n", + " N, p = parameters[i]\n", + " _x = np.arange(N + 1)\n", + " plt.bar(_x - 0.5, binomial.pmf(_x, N, p), color=colors[i],\n", + " edgecolor=colors[i],\n", + " alpha=0.6,\n", + " label=\"$N$: %d, $p$: %.1f\" % (N, p),\n", + " linewidth=3)\n", + "\n", + "plt.legend(loc=\"upper left\")\n", + "plt.xlim(0, 10.5)\n", + "plt.xlabel(\"$k$\")\n", + "plt.ylabel(\"$P(X = k)$\")\n", + "plt.title(\"Probability mass distributions of binomial random variables\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The special case when $N = 1$ corresponds to the Bernoulli distribution. There is another connection between Bernoulli and Binomial random variables. If we have $X_1, X_2, ... , X_N$ Bernoulli random variables with the same $p$, then $Z = X_1 + X_2 + ... + X_N \\sim \\text{Binomial}(N, p )$.\n", + "\n", + "The expected value of a Bernoulli random variable is $p$. This can be seen by noting the more general Binomial random variable has expected value $Np$ and setting $N=1$." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Example: Cheating among students\n", + "\n", + "We will use the binomial distribution to determine the frequency of students cheating during an exam. If we let $N$ be the total number of students who took the exam, and assuming each student is interviewed post-exam (answering without consequence), we will receive integer $X$ \"Yes I did cheat\" answers. We then find the posterior distribution of $p$, given $N$, some specified prior on $p$, and observed data $X$. \n", + "\n", + "This is a completely absurd model. No student, even with a free-pass against punishment, would admit to cheating. What we need is a better *algorithm* to ask students if they had cheated. Ideally the algorithm should encourage individuals to be honest while preserving privacy. The following proposed algorithm is a solution I greatly admire for its ingenuity and effectiveness:\n", + "\n", + "> In the interview process for each student, the student flips a coin, hidden from the interviewer. The student agrees to answer honestly if the coin comes up heads. Otherwise, if the coin comes up tails, the student (secretly) flips the coin again, and answers \"Yes, I did cheat\" if the coin flip lands heads, and \"No, I did not cheat\", if the coin flip lands tails. This way, the interviewer does not know if a \"Yes\" was the result of a guilty plea, or a Heads on a second coin toss. Thus privacy is preserved and the researchers receive honest answers. \n", + "\n", + "I call this the Privacy Algorithm. One could of course argue that the interviewers are still receiving false data since some *Yes*'s are not confessions but instead randomness, but an alternative perspective is that the researchers are discarding approximately half of their original dataset since half of the responses will be noise. But they have gained a systematic data generation process that can be modeled. Furthermore, they do not have to incorporate (perhaps somewhat naively) the possibility of deceitful answers. We can use PyMC to dig through this noisy model, and find a posterior distribution for the true frequency of liars. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Suppose 100 students are being surveyed for cheating, and we wish to find $p$, the proportion of cheaters. There are a few ways we can model this in PyMC. I'll demonstrate the most explicit way, and later show a simplified version. Both versions arrive at the same inference. In our data-generation model, we sample $p$, the true proportion of cheaters, from a prior. Since we are quite ignorant about $p$, we will assign it a $\\text{Uniform}(0,1)$ prior." + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "import pymc as pm\n", + "N = 100\n", + "with pm.Model() as model:\n", + " p = pm.Uniform(\"freq_cheating\", 0, 1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Again, thinking of our data-generation model, we assign Bernoulli random variables to the 100 students: 1 implies they cheated and 0 implies they did not. " + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "with model:\n", + " true_answers = pm.Bernoulli(\"truths\", p, shape=N, initval=np.random.binomial(1, 0.5, N))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If we carry out the algorithm, the next step that occurs is the first coin-flip each student makes. This can be modeled again by sampling 100 Bernoulli random variables with $p=1/2$: denote a 1 as a *Heads* and 0 a *Tails*." + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0 0 1 1 0 1 1 1 1 0 0 0 1 0 1 0 1 0 0 1 0 0 1 0 1 0 0 1 1 1 1 1 0 1 1 1 1\n", + " 1 0 1 0 0 0 0 0 1 0 1 1 1 1 1 1 0 1 1 0 0 1 1 0 1 0 0 0 0 0 0 1 1 0 1 0 0\n", + " 0 1 1 0 0 1 1 1 0 0 0 0 0 1 0 1 0 1 0 0 0 0 0 0 0 1]\n" + ] + } + ], + "source": [ + "with model:\n", + " first_coin_flips = pm.Bernoulli(\"first_flips\", 0.5, shape=N, initval=np.random.binomial(1, 0.5, N))\n", + " \n", + "print(pm.draw(first_coin_flips))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Although *not everyone* flips a second time, we can still model the possible realization of second coin-flips:" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "with model:\n", + " second_coin_flips = pm.Bernoulli(\"second_flips\", 0.5, shape=N, initval=np.random.binomial(1, 0.5, N))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Using these variables, we can return a possible realization of the *observed proportion* of \"Yes\" responses. We do this using a PyMC `deterministic` variable:" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "import pytensor.tensor as at\n", + "with model:\n", + " val = first_coin_flips*true_answers + (1 - first_coin_flips)*second_coin_flips\n", + " observed_proportion = pm.Deterministic(\"observed_proportion\", at.sum(val)/float(N))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The line `fc*t_a + (1-fc)*sc` contains the heart of the Privacy algorithm. Elements in this array are 1 *if and only if* i) the first toss is heads and the student cheated or ii) the first toss is tails, and the second is heads, and are 0 else. Finally, the last line sums this vector and divides by `float(N)`, produces a proportion. " + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{freq_cheating ~ U(0, 1): None,\n", + " truths ~ Bern(freq_cheating): array([1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1,\n", + " 1, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1,\n", + " 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1,\n", + " 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1,\n", + " 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1]),\n", + " first_flips ~ Bern(0.5): array([0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0,\n", + " 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0,\n", + " 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0,\n", + " 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", + " 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1]),\n", + " second_flips ~ Bern(0.5): array([0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1,\n", + " 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0,\n", + " 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0,\n", + " 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0,\n", + " 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0])}" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.initial_values" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next we need a dataset. After performing our coin-flipped interviews the researchers received 35 \"Yes\" responses. To put this into a relative perspective, if there truly were no cheaters, we should expect to see on average 1/4 of all responses being a \"Yes\" (half chance of having first coin land Tails, and another half chance of having second coin land Heads), so about 25 responses in a cheat-free world. On the other hand, if *all students cheated*, we should expected to see approximately 3/4 of all responses be \"Yes\". \n", + "\n", + "The researchers observe a Binomial random variable, with `N = 100` and `p = observed_proportion` with `value = 35`: " + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "X = 35\n", + "\n", + "with model:\n", + " observations = pm.Binomial(\"obs\", N, observed_proportion, observed=X)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Below we add all the variables of interest to a `Model` container and run our black-box algorithm over the model. " + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Sequential sampling (1 chains in 1 job)\n", + "CompoundStep\n", + ">Metropolis: [freq_cheating]\n", + ">BinaryGibbsMetropolis: [truths, first_flips, second_flips]\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + "
\n", + " \n", + " 100.00% [41000/41000 06:38<00:00 Sampling chain 0, 0 divergences]\n", + "
\n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Sampling 1 chain for 1_000 tune and 40_000 draw iterations (1_000 + 40_000 draws total) took 398 seconds.\n" + ] + } + ], + "source": [ + "# To be explained in Chapter 3!\n", + "with model:\n", + " step = pm.Metropolis(vars=[p])\n", + " trace = pm.sample(40000, step=step,chains=1)\n", + " # burned_trace = trace[15000:]\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "image/png": 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LWuUvV1NT02L7r3SPtKS19+zOQNANAAAAQKfw/PPP51vf+laTbTU1NbnlllvSu3fvjBs3rnH7Kaeckvr6+nzlK19pEjauWbMm3/72t5Mk//7v/964/Te/+U2LgfHq1auTbJmJ3KBfv35JkmXLljU7/rjjjsuQIUNy3XXX5Z577mnxPB566KFWrUudbFl/+aijjsrSpUtz9dVXN9n38MMP59Zbb03fvn2bXJPW9vW3v/0t1157bZN9d9111zYH3c8991wefvjhZts3bNiQf/7zn6msrExVVVXj9n79+rUYLpfCokWLcv311zfZ1nAuQ4YMyWGHHda4vWFN8xtvvLHJ8Y8//niuueaaFtt/pXtka7b3nt3ZWLoEAAAAgE7h8MMPz0033ZRHHnkko0ePzqpVqzJjxozU1dXl61//epNlHc4555z85je/yV133ZWxY8fmmGOOSW1tbW6//fasXr065557bt785jc3Hn/uueemW7duGT16dPbaa6/U19dn3rx5efTRR3PQQQflbW97W+OxRx55ZG6//facffbZOeaYY9K9e/cMHjw4J510UnbZZZf86Ec/ymmnnZbTTz89hx56aEaMGJEePXpk+fLlqampyZIlS/LII480Cc93xNe//vWccMIJufTSSzN79uwcdNBBWb58eWbOnJkuXbrkiiuuaPHhnTviq1/9asaPH59LLrkks2fPzvDhw7N48eLceeedGTt2bO6+++5XbWPFihV573vfm/322y8HHnhgqqurs379+vz617/OM888kw9/+MNN6j3yyCNz22235YwzzshBBx2Url275vDDD8/hhx/e6vM5+uij8+Uvfzn33ntv47nccccd6d69ey6//PImD6o87rjjss8+++TWW2/NihUrMmrUqCxbtiy/+tWv8s53vjMzZsxo1v4r3SNbs7337M5G0A0AAADQiU0ZP7TZtsrKLZFQSzOUi2yvvfbK17/+9Xzta1/LT3/602zcuDEjR47MZz7zmYwZM6bJsVVVVbnpppvywx/+MLfeemuuvfbadO3aNcOHD8+XvvSlnHDCCU2Ov+CCCzJ79uzMnz8/9957b7p3755Bgwbli1/8Yk4//fTssssujceeeuqpefrppzN9+vRMmTIlmzZtyhFHHNEYYg4fPjx33313fvjDH+bXv/51pk6dmi5dumT33XfPiBEjct555+U1r3lNq6/H6173usyaNSvf+c53cu+992bOnDnp2bNnxowZk09+8pONM5FLYejQoZkxY0a+9rWv5Xe/+13mzJmTAw44INdcc02ee+65bQq699prr5x//vl54IEH8sADD2TNmjXp27dvXv/61+cLX/hC3ve+9zU5/stf/nIqKiryu9/9Lvfee2/q6uoyadKkkgTdo0aNyqc//el84xvfyHXXXZf6+vq89a1vzeTJk5tdt+7du2fq1Km59NJLc//996empib7779/rrzyyvTt27fFoPvV7pGWbO89u7OpqK+vry93EduirX4Noajqa+Y1vq44eOf9mxrYUQ0PCGn4FTNg+/gcapmxBWgLxhbg1axevXqbHoL4Up0t6F66dGkOP/zwnHzyyY1LOACltS1jTXV1dTtV05w1ugEAAAAAKDRBNwAAAAAAhSboBgAAAACg0DyMEgAAAIBC22uvvbJs2bJylwGUkRndAAAAAAAUmqAbAAAAAIBCE3QDAAAAFFx9fX25SwA6sSKMMYJuAAAAgAKrqKhIXV1ducsAOrG6urpUVFSUu4xXJOgGAAAAKLCqqqps3Lix3GUAndjGjRtTVVVV7jJekaAbAAAAoMB69OiR2trabNiwIZs3by7EEgNAx1dfX5/Nmzdnw4YNqa2tTY8ePcpd0iuqLHcBAAAAAOy4ysrK9OnTJ7W1tVm7du02Bd1dumyZ+2jJE+CVVFRUpKqqKn369EllZceOkjt2dQAAAAC8qsrKyvTq1Wubj+/fv3+SZPXq1W1VEkC7snQJAAAAAACFJugGAAAAAKDQBN0AAAAAABSaoBsAAAAAgEITdAMAAAAAUGiCbgAAAAAACk3QDQAAAABAoQm6AQAAAAAoNEE3AAAAAACFJugGAAAAAKDQBN0AAAAAABSaoBsAAAAAgEITdAMAAAAAUGiCbgAAAAAACk3QDQAAAABAoQm6AQAAAAAoNEE3AAAAAACFJugGAAAAAKDQBN0AAAAAABSaoBsAAAAAgEITdAMAAAAAUGiCbgAAAAAACk3QDQAAAABAoQm6AQAAAAAoNEE3AAAAAACFJugGAAAAAKDQ2iTovu+++zJhwoRMmDAh99xzT1t0AQAAAAAASdog6F69enV+/OMfp3v37qVuGgAAAAAAmilp0F1fX58pU6akV69eGTt2bCmbBgAAAACAFpU06L7jjjsyf/78TJw4Md26dStl0wAAAAAA0KLKUjX09NNP54Ybbsi4ceMyfPjwzJ8/f7veP3ny5Ba3X3bZZUmS/v37t7rGzmRj796Nr7u5NrDdKiu3DH/GFtgxPodaZmwB2oKxBWgLxhagsynJjO7NmzfnyiuvTP/+/XPqqaeWokkAAAAAANgmJZnRffPNN2fRokW59NJLU1VVtUNtNMzc3prVq1fvULudVf3zzze+rnBtYLs1zFowtsCO8TnUMmML0BaMLUBbMLYAbaG6urpsfbd6RvdTTz2VW265Je9973vzhje8oRQ1AQAAAADANmtV0L158+Z897vfzZ577plTTjmlVDUBAAAAAMA2a9XSJS+88EJWrFiRJDnttNNaPOaqq67KVVddleOPPz5nnnlma7oDAAAAAIBmWhV077LLLjnmmGNa3Ldo0aIsWrQow4YNS3V1tWVNAAAAAABoE60KuquqqnLOOee0uG/atGlZtGhR3v72t+fYY49tTTcAAAAAALBVrX4YJQAAAAAAlJOgGwAAAACAQmvV0iWvZMKECZkwYUJbNQ8AAAAAAEnM6AYAAAAAoOAE3QAAAAAAFJqgGwAAAACAQhN0AwAAAABQaIJuAAAAAAAKTdANAAAAAEChCboBAAAAACg0QTcAAAAAAIUm6AYAAAAAoNAE3QAAAAAAFJqgGwAAAACAQhN0AwAAAABQaIJuAAAAAAAKTdANAAAAAEChCboBAAAAACg0QTcAAAAAAIUm6AYAAAAAoNAE3QAAAAAAFJqgGwAAAACAQhN0AwAAAABQaIJuAAAAAAAKTdANAAAAAEChCboBAAAAACg0QTcAAAAAAIUm6AYAAAAAoNAE3QAAAAAAFJqgGwAAAACAQhN0AwAAAABQaIJuAAAAAAAKTdANAAAAAEChCboBAAAAACg0QTcAAAAAAIUm6AYAAAAAoNAE3QAAAAAAFJqgGwAAAACAQhN0AwAAAABQaIJuAAAAAAAKTdANAAAAAEChCboBAAAAACg0QTcAAAAAAIUm6AYAAAAAoNAE3QAAAAAAFJqgGwAAAACAQhN0AwAAAABQaIJuAAAAAAAKTdANAAAAAEChCboBAAAAACi0ylI08rOf/SwLFy7MihUr8vzzz6eqqioDBgzI6NGj8653vSu9evUqRTcAJJk4fWFZ+58yfmhZ+wcAAAB4uZLM6J45c2Y2btyYAw88MMcff3ze9ra3pWvXrvnFL36R888/P6tXry5FNwAAAAAA0ExJZnRff/31qaqqarb9pptuyi233JJbb701Z599dim6AgAAAACAJkoyo7ulkDtJjjjiiCTJihUrStENAAAAAAA0U5IZ3Vvzxz/+MUnyute97lWPnTx5covbL7vssiRJ//79S1dYJ7Cxd+/G191cG9hulZVbhr8iji1duy4pa/9FvGaUns+hlhV5bAE6LmML0BaMLUBnU9Kge/r06XnhhReyYcOGLFy4MH/5y1/yute9LieccEIpuwEAAAAAgEYlDbpnzJiRdevWNf73G9/4xpx77rnp/ZJZX1vTMHN7azzQsqn6559vfF3h2sB2a5i1UMSxZfPmzWXtv4jXjNLzOdSyIo8tQMdlbAHagrEFaAvV1dVl67ukQfePfvSjJMnatWvzxBNP5IYbbsjkyZMzefLkDB06tJRdAQAAAABAkhI9jPLl+vbtmze/+c258MILs379+nzve99ri24AAAAAAKBtgu4GAwYMyODBg7N06dI8/5JfcQYAAAAAgFIp6dIlLfn73/+eJOnSpU0zdaAdTJy+sNwlZMp4yyABAAAA0FSrg+5ly5Zlt912S9++fZtsr6ury9SpU7Nu3brsv//+6dmzZ2u7gp1eRwiaAQAAAKCjaXXQ/eijj+ZnP/tZDjjggOyxxx7p1atX1q5dm8cffzyrVq1K375989GPfrQUtQIAAAAAQDOtDroPPPDAHHvssfnrX/+aJUuW5J///Ge6deuW6urqvO1tb8vxxx9vNjcAAAAAAG2m1UH33nvvnbPPPrsUtQAAAAAAwHZr84dRApRSqdYp79p1SZJk8+bN2/U+D8MEAAAA6HgE3QDbwQNBAQAAADqeLuUuAAAAAAAAWkPQDQAAAABAoQm6AQAAAAAoNEE3AAAAAACF5mGUALCdyv1Q0injh5a1fwAAAOhozOgGAAAAAKDQBN0AAAAAABSaoBsAAAAAgEITdAMAAAAAUGiCbgAAAAAACk3QDQAAAABAoQm6AQAAAAAoNEE3AAAAAACFJugGAAAAAKDQBN0AAAAAABRaZbkLAIDtNXH6wnKXAAAAAHQggm4AtouQGQAAAOhoLF0CAAAAAEChCboBAAAAACg0QTcAAAAAAIUm6AYAAAAAoNAE3QAAAAAAFJqgGwAAAACAQhN0AwAAAABQaIJuAAAAAAAKTdANAAAAAEChVZa7ACiKidMXlrsEAAAAAKAFgm4KQ9AMAAAAALTE0iUAAAAAABSaoBsAAAAAgEITdAMAAAAAUGiCbgAAAAAACk3QDQAAAABAoQm6AQAAAAAotMpyF0BxTJy+sNwlAAAAAAA0Y0Y3AAAAAACFJugGAAAAAKDQBN0AAAAAABSaoBsAAAAAgEITdAMAAAAAUGiCbgAAAAAACk3QDQAAAABAoQm6AQAAAAAoNEE3AAAAAACFJugGAAAAAKDQKlvbwPr16zNv3rw8/PDD+dvf/pY1a9aksrIye++9d44++uiMGTMmXbrI0wEAAAAAaButDrrnzJmTq6++Ov369cuIESPSv3//rF27NvPmzcsPfvCDPPLII5k0aVIqKipKUS8AAAAAADTR6qC7uro6n/vc53LIIYc0mbl96qmn5oILLsjcuXMzd+7cHH744a3tCgAAAAAAmmn1miIjR47MoYce2mx5kr59+2bs2LFJkgULFrS2GwAAAAAAaFGrZ3S/YuOVW5rfljW6J0+e3OL2yy67LEnSv3//0hXWCWzs3bvxdbd2ujZduy5pl36gPTSsptS1a9fyFgI7oCN8Jpbjc6gIGr77dIT/R0DnYWwB2oKxBehs2izo3rx5c2bPnp0keeMb39hW3QDATuffr/9juUvI9cPLXQEAAAD8nzYLum+44YYsXbo0o0aN2qagu2Hm9tasXr26RJV1DvXPP9/4uqKdrs3mzZvbpR9oDw0zud3XsGOeL8PnUBE0zIjyvQUoJWML0BaMLUBbqK6uLlvfrV6juyWzZs3K7bffnkGDBuUTn/hEW3QBAAAAAABJ2mBG95133pnrrrsugwcPzsUXX5yePXuWugsAAAAAAGhU0qB75syZuf7667PXXnvl4osvTp8+fUrZPAAAAAAANFOyoPvWW2/NjTfemCFDhuTCCy9M7969S9U0AAAAAABsVUmC7ptvvjnTpk3L0KFDc+GFF1quBAAAAACAdtPqoPu3v/1tpk2bli5dumTYsGGZNWtWs2N23333jBkzprVd7dQmTl/Y5L9HrlzZ+Hr+koUvPxwAAAAAYKfR6qD7mWeeSZLU1dW1GHInyfDhwwXdAAAAAAC0iVYH3RMmTMiECRNKUQsAAAAAAGy3LuUuAAAAAAAAWkPQDQAAAABAoQm6AQAAAAAoNEE3AAAAAACFJugGAAAAAKDQBN0AAAAAABSaoBsAAAAAgEITdAMAAAAAUGiCbgAAAAAACk3QDQAAAABAoQm6AQAAAAAoNEE3AAAAAACFJugGAAAAAKDQBN0AAAAAABSaoBsAAAAAgEITdAMAAAAAUGiCbgAAAAAACk3QDQAAAABAoQm6AQAAAAAoNEE3AAAAAACFVlnuAopi4vSF5S4BAAAAAIAWmNENAAAAAEChCboBAAAAACg0S5cAANvt+3NXNr6ev6T9l/eaMn5ou/cJAABAx2VGNwAAAAAAhSboBgAAAACg0ATdAAAAAAAUmqAbAAAAAIBCE3QDAAAAAFBogm4AAAAAAApN0A0AAAAAQKFVlruAbTVx+sJylwAAAAAAQAdkRjcAAAAAAIUm6AYAAAAAoNAE3QAAAAAAFJqgGwAAAACAQivMwygBABp0hIdUTxk/tNwlAAAA8L/M6AYAAAAAoNAE3QAAAAAAFJqgGwAAAACAQhN0AwAAAABQaIJuAAAAAAAKTdANAAAAAEChCboBAAAAACg0QTcAAAAAAIUm6AYAAAAAoNAE3QAAAAAAFFplKRr5wx/+kAULFmTx4sVZsmRJamtrc+SRR+aTn/xkKZoHAAAAAICtKknQ/ctf/jJLlixJ9+7d89rXvjbLli0rRbMAAAAAAPCqShJ0n3HGGXnta1+bgQMHZsGCBfnSl75UimYBAAAAAOBVlSToHjlyZCmaAQAojInTFzbb1rXrkiTJ5s2b27z/KeOHtnkfAAAARVGSoLsUJk+e3OL2yy67LEnStWvX9iynw+vS5f+eI+rawParqNjyb39+YMf4HGpZe44t/fv3b/M+gI6hsnLLj23+3AOlZGwBOpsur34IAAAAAAB0XB1mRnfDzO2taY9fAS6Surq6xteuDWy/htmW/vzAjvE51LL2HFtWr17d5n0AHUPDbEt/7oFSMrYAbaG6urpsfZvRDQAAAABAoQm6AQAAAAAoNEE3AAAAAACFJugGAAAAAKDQBN0AAAAAABRaZSkamTdvXh588MEkydq1a5MkTz75ZL73ve8lSXr16pXTTz+9FF0BAAAAAEATJQm6Fy9enNmzZzfZtmrVqqxatSpJMmDAAEE3AAAAAABtoiRB94QJEzJhwoRSNAUAwDaYOH1huUvIlPFDy10CAABAEmt0AwAAAABQcIJuAAAAAAAKTdANAAAAAEChCboBAAAAACg0QTcAAAAAAIUm6AYAAAAAoNAE3QAAAAAAFJqgGwAAAACAQhN0AwAAAABQaIJuAAAAAAAKTdANAAAAAEChCboBAAAAACg0QTcAAAAAAIUm6AYAAAAAoNAE3QAAAAAAFJqgGwAAAACAQhN0AwAAAABQaJXlLgAAgGKaOH1hWfufMn5oWfsHAAA6DjO6AQAAAAAoNEE3AAAAAACFJugGAAAAAKDQBN0AAAAAABSaoBsAAAAAgEITdAMAAAAAUGiV5S4AAAB2xMTpC8tdQqaMH1ruEgAAgJjRDQAAAABAwQm6AQAAAAAoNEE3AAAAAACFJugGAAAAAKDQBN0AAAAAABSaoBsAAAAAgEITdAMAAAAAUGiCbgAAAAAACq2y3AUAAEBRTZy+sKz9Txk/tKz9AwBAR2FGNwAAAAAAhSboBgAAAACg0ATdAAAAAAAUmjW6AQCgoKwRDgAAW5jRDQAAAABAoQm6AQAAAAAoNEuXAAAAO6TcS6cklk8BAGALM7oBAAAAACg0QTcAAAAAAIUm6AYAAAAAoNAE3QAAAAAAFJqgGwAAAACAQqssdwEAAAA7auL0heUuoc117bokSbJ58+YW908ZP7Q9ywEA6JBKFnQ/99xzmTp1ampqarJ+/fr069cvo0ePzkknnZSePXuWqhsAAAAAAGiiJEH3ypUrc9FFF2XdunU59NBDM2jQoDz11FOZNWtWHn300Vx66aXp1atXKboCAAAAAIAmShJ0X3PNNVm3bl3OOuusjBs3rnH79ddfn5kzZ+amm27KRz7ykVJ0BQAAwEuUe/kWS6cAAB1Bqx9GuWrVqtTU1GTAgAE57rjjmuybMGFCunXrlvvvvz8vvPBCa7sCAAAAAIBmWj2je/78+UmSgw8+OF26NM3Ne/TokWHDhqWmpiZPPvlkDjzwwNZ2BwAAAE2Y1Q4AtDroXr58eZJkzz33bHH/wIEDU1NTkxUrVrxi0D158uQWt1922WVJktvOObKVlXY2rgcA5eRzCICO47ZzqstdAhRWdbU/P0Dn0OqlSzZs2JAk2XXXXVvc37D9n//8Z2u7AgAAAACAZkryMMpXUl9fnySpqKh4xeMaZm6/XMNM763tB9gRxhagLRhbgLZgbAHagrEFaAvlHFtaPaO7YcZ2w8zul6utrW1yHAAAAAAAlFKrg+6GtZxWrFjR4v6VK1cm2foa3gAAAAAA0BqtDrpHjBiRJKmpqUldXV2TfbW1tfnLX/6Sqqqq7Lfffq3tCgAAAAAAmml10D1w4MAcfPDBefbZZ3PXXXc12Tdt2rRs3LgxRx11VLp3797argAAAAAAoJmSPIzywx/+cC666KJce+21+fOf/5zBgwfnySefzGOPPZY999wzH/jAB0rRDQAAAAAANFNRX19fX4qGVq9enWnTpuXRRx/N+vXr069fv4wePTonn3xyevbsWYouAAAAAACgmZIF3QAAAAAAUA6tXqMbAAAAAADKSdANAAAAAEChCboBAAAAACg0QTcAAAAAAIUm6AYAAAAAoNAE3QAAAAAAFJqgGwAAAACAQqssR6fPPfdcpk6dmpqamqxfvz79+vXL6NGjc9JJJ6Vnz57t3g7QObR2TFi/fn3mzZuXhx9+OH/729+yZs2aVFZWZu+9987RRx+dMWPGpEsXfz8IO5u2+L5x33335corr0ySfPSjH82xxx5bypKBAijl2PL4449n5syZeeKJJ/KPf/wjPXv2zN57753jjz8+hxxySBudAdARlWpsefjhhzNr1qw8/fTTje0MHTo073nPe/KGN7yhDc8A6Ej+8Ic/ZMGCBVm8eHGWLFmS2traHHnkkfnkJz+53W21R45bUV9fX1+SlrbRypUrc9FFF2XdunU59NBDM2jQoDz11FN57LHHUl1dnUsvvTS9evVqt3aAzqEUY8KvfvWrXH311enXr19GjBiR/v37Z+3atZk3b142bNiQww47LJMmTUpFRUU7nRVQbm3xfWP16tU5//zzU1dXlxdeeEHQDTuhUo4tv/zlLzN16tT06tUrb3rTm9K3b9+sX78+ixcvzogRI/LBD36wjc8G6ChKNbb87Gc/y/Tp09OrV6+MHj06vXr1ysqVK/PQQw+lrq4uH/vYx3LUUUe1wxkB5fbZz342S5YsSffu3fPa1742y5Yt26Ggu71y3Haf0X3NNddk3bp1OeusszJu3LjG7ddff31mzpyZm266KR/5yEfarR2gcyjFmFBdXZ3Pfe5zOeSQQ5rM3D711FNzwQUXZO7cuZk7d24OP/zwNjsPoGMp9feN+vr6TJkyJb169cqb3/zmzJgxoy3KBjq4Uo0tc+bMydSpU3PggQfm/PPPT48ePZrs37RpU8lrBzquUowta9euzYwZM9KnT59885vfTJ8+fRr3zZ8/P1/+8pczbdo0QTfsJM4444y89rWvzcCBA7NgwYJ86Utf2qF22ivHbdffwV+1alVqamoyYMCAHHfccU32TZgwId26dcv999+fF154oV3aATqHUo0JI0eOzKGHHtpseZK+fftm7NixSZIFCxaUtnigw2qL7xt33HFH5s+fn4kTJ6Zbt26lLhkogFKNLXV1dbnhhhvSrVu3fOpTn2oWcidJZWVZVqoEyqBUY8uzzz6b+vr67Lfffk1C7mTLz0s9evTI888/X/L6gY5p5MiR2XPPPVv1m+3tmeO2a9A9f/78JMnBBx/cLEjq0aNHhg0blo0bN+bJJ59sl3aAzqE9xoSGHxSt0Q07j1KPLU8//XRuuOGGjBs3LsOHDy95vUAxlGpseeKJJ/LMM89k1KhR2W233fLwww/n1ltvzaxZs/LEE0+0Wf1Ax1SqsWXPPfdMZWVlnnrqqWaB9oIFC1JbW5sDDzywtMUDnVp75rjtmtgsX748yZaBsyUDBw5MkqxYsaJd2gE6h7YeEzZv3pzZs2cnSd74xjfuUBtA8ZRybNm8eXOuvPLK9O/fP6eeemrpigQKp1Rjy1NPPZUk6dOnTyZPnpyvf/3rufHGG3PdddflwgsvzCWXXGLWJexESjW29OzZM6eddlrWrVuXSZMm5aqrrsqNN96YK664Il/96ldz0EEHWSYW2C7tmeO26++ybdiwIUmy6667tri/Yfs///nPdmkH6Bzaeky44YYbsnTp0owaNUrQDTuRUo4tN998cxYtWpRLL700VVVVpSsSKJxSjS0NIfbdd9+d3XffPRdddFH222+/PPvss/nJT36SmpqaXHHFFfnP//zP0hUPdFil/N7y7ne/OwMGDMiUKVNyzz33NG4fOHBgxowZ02xJE4BX0p45bodatK2+vj5JWrXuSynbATqH1owJs2bNyu23355BgwblE5/4RKlLAwpsW8eWp556Krfcckve+9735g1veEN7lAYU2LaOLXV1dY3HT5o0KUOGDEmS7LXXXvnsZz+bT33qU1mwYEGeeOIJYw+wXT8T3Xbbbbnpppsybty4vOtd70rfvn2zbNmy3HTTTfnv//7vLF68OB/84AfbumRgJ1HKHLddly5pSOgbkvyXq62tbXJcW7cDdA5tNSbceeedue666zJ48OBccskl6dmzZ+sKBQqlFGPL5s2b893vfjd77rlnTjnllNIXCRROqb637LbbbkmSPfbYozHkblBVVZWDDz44yf8tcQJ0bqUaWx577LHccMMNOfTQQ3PGGWdkjz32SLdu3TJ06NCcf/75ec1rXpMZM2Zk1apVpT0BoNNqzxy3XWd0V1dXJ9n6misrV65MsvU1W0rdDtA5tMWYMHPmzFx//fXZa6+9cvHFF/v1PNgJlWJseeGFFxrff9ppp7V4zFVXXZWrrroqxx9/fM4888xWVAwUQal/JmoIvF+uYfuLL764Q3UCxVKqseWPf/xjkmTEiBHN9nXr1i377rtv5s2bl0WLFmWPPfZoTcnATqI9c9x2DbobBsqamprU1dU1edJmbW1t/vKXv6Sqqir77bdfu7QDdA6lHhNuvfXW3HjjjRkyZEguvPDC9O7du03qBjq2Uowtu+yyS4455pgW9y1atCiLFi3KsGHDUl1dbWkB2EmU6nvLAQcckK5du2bFihXZtGlTKiub/mi3dOnSJMmAAQNKfAZAR1SqsWXTpk1JstWH2TZsf/mYA7A17ZnjtuvSJQMHDszBBx+cZ599NnfddVeTfdOmTcvGjRtz1FFHpXv37km2DLDLli1rTPZ3tB2gcyvV2JJseWDcjTfemKFDh+biiy8WcsNOrBRjS1VVVc4555wW/3nTm96UJHn729+ec845J295y1va7+SAsinV95bevXvniCOOyIYNG3LzzTc32fenP/0pNTU12XXXXT1IG3YSpRpbhg0bliT59a9/nTVr1jTZ98gjj+Svf/1rdtlll+y///5teDZAEXWEHLeivmHF73aycuXKXHTRRVm3bl0OPfTQDB48OE8++WQee+yx7LnnnvnKV76SXr16JUmeeeaZfPzjH8+AAQPyve99b4fbATq/Uowtv/3tb/P9738/Xbp0ybve9a4W14fafffdM2bMmPY6LaDMSvW9pSXTpk3LzTffnI9+9KM59thj2/pUgA6kVGPLunXrctFFF2XlypU54IAD8vrXvz6rV6/OvHnzUlFRkU9+8pM54ogjynGKQBmUYmypq6vLV7/61fz5z39Ojx49Mnr06MaHUT788MOpr6/PmWeemeOPP75cpwm0o3nz5uXBBx9MkqxduzY1NTXZY489Gv9SrFevXjn99NOTdIwct91/12TgwIH5r//6r0ybNi2PPvpoHnnkkfTr1y/jxo3LySefvM0PeytVO0DnUIox4Zlnnkmy5cvdrFmzWjxm+PDhgm7Yifi+AbSFUo0tffr0yde+9rX88pe/zLx58/LEE0+kR48eOeSQQ3LCCSdYEgl2MqUYW7p06ZILLrggd911Vx544IE8+OCD2bhxY3r27JlRo0Zl3LhxjQ+7BTq/xYsXZ/bs2U22rVq1qvGBtAMGDGgMul9Je/1c1e4zugEAAAAAoJTadY1uAAAAAAAoNUE3AAAAAACFJugGAAAAAKDQBN0AAAAAABSaoBsAAAAAgEITdAMAAAAAUGiCbgAAAAAACk3QDQAAAABAoQm6AQAAAAAoNEE3AAAAAACFJugGAAAAAKDQBN0AAAAAABSaoBsAAAAAgEITdAMAAAAAUGiCbgAAAAAACk3QDQAAAABAof1/Jl4ablrRr8cAAAAASUVORK5CYII=", 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" + ] + }, + "metadata": { + "image/png": { + "height": 193, + "width": 733 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "figsize(12.5, 3)\n", + "p_trace = np.concatenate(trace.posterior.freq_cheating.data[:,15000:]) #burned_trace[\"freq_cheating\"][15000:]\n", + "plt.hist(p_trace, histtype=\"stepfilled\", density=True, alpha=0.85, bins=30, \n", + " label=\"posterior distribution\", color=\"#348ABD\")\n", + "plt.vlines([.05, .35], [0, 0], [5, 5], alpha=0.3)\n", + "plt.xlim(0, 1)\n", + "plt.legend();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With regards to the above plot, we are still pretty uncertain about what the true frequency of cheaters might be, but we have narrowed it down to a range between 0.05 to 0.35 (marked by the solid lines). This is pretty good, as *a priori* we had no idea how many students might have cheated (hence the uniform distribution for our prior). On the other hand, it is also pretty bad since there is a .3 length window the true value most likely lives in. Have we even gained anything, or are we still too uncertain about the true frequency? \n", + "\n", + "I would argue, yes, we have discovered something. It is implausible, according to our posterior, that there are *no cheaters*, i.e. the posterior assigns low probability to $p=0$. Since we started with an uniform prior, treating all values of $p$ as equally plausible, but the data ruled out $p=0$ as a possibility, we can be confident that there were cheaters. \n", + "\n", + "This kind of algorithm can be used to gather private information from users and be *reasonably* confident that the data, though noisy, is truthful. \n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Alternative PyMC Model\n", + "\n", + "Given a value for $p$ (which from our god-like position we know), we can find the probability the student will answer yes: \n", + "\n", + "\\begin{align}\n", + "P(\\text{\"Yes\"}) = & P( \\text{Heads on first coin} )P( \\text{cheater} ) + P( \\text{Tails on first coin} )P( \\text{Heads on second coin} ) \\\\\\\\\n", + "& = \\frac{1}{2}p + \\frac{1}{2}\\frac{1}{2}\\\\\\\\\n", + "& = \\frac{p}{2} + \\frac{1}{4}\n", + "\\end{align}\n", + "\n", + "Thus, knowing $p$ we know the probability a student will respond \"Yes\". In PyMC, we can create a deterministic function to evaluate the probability of responding \"Yes\", given $p$:" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "with pm.Model() as model:\n", + " p = pm.Uniform(\"freq_cheating\", 0, 1)\n", + " p_skewed = pm.Deterministic(\"p_skewed\", 0.5*p + 0.25)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "I could have typed `p_skewed = 0.5*p + 0.25` instead for a one-liner, as the elementary operations of addition and scalar multiplication will implicitly create a `deterministic` variable, but I wanted to make the deterministic boilerplate explicit for clarity's sake. \n", + "\n", + "If we know the probability of respondents saying \"Yes\", which is `p_skewed`, and we have $N=100$ students, the number of \"Yes\" responses is a binomial random variable with parameters `N` and `p_skewed`.\n", + "\n", + "This is where we include our observed 35 \"Yes\" responses. In the declaration of the `pm.Binomial`, we include `value = 35` and `observed = True`." + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "with model:\n", + " yes_responses = pm.Binomial(\"number_cheaters\", 100, p_skewed, observed=35)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Below we add all the variables of interest to a `Model` container and run our black-box algorithm over the model. " + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Multiprocess sampling (4 chains in 4 jobs)\n", + "Metropolis: [freq_cheating]\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + "
\n", + " \n", + " 100.00% [104000/104000 00:04<00:00 Sampling 4 chains, 0 divergences]\n", + "
\n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Sampling 4 chains for 1_000 tune and 25_000 draw iterations (4_000 + 100_000 draws total) took 5 seconds.\n" + ] + } + ], + "source": [ + "with model:\n", + " # To Be Explained in Chapter 3!\n", + " step = pm.Metropolis()\n", + " # the new kwarg tune means drop the first 2500 unstable data\n", + " trace = pm.sample(25000, step=step,tune=2500)\n", + " # burned_trace = trace[2500:]" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 193, + "width": 733 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "figsize(12.5, 3)\n", + "p_trace = np.concatenate(trace.posterior.freq_cheating.data[:,:])# burned_trace[\"freq_cheating\"]\n", + "plt.hist(p_trace, histtype=\"stepfilled\", density=True, alpha=0.85, bins=30, \n", + " label=\"posterior distribution\", color=\"#348ABD\")\n", + "plt.vlines([.05, .35], [0, 0], [5, 5], alpha=0.2)\n", + "plt.xlim(0, 1)\n", + "plt.legend();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### More PyMC Tricks\n", + "\n", + "#### Protip: Arrays of PyMC variables\n", + "There is no reason why we cannot store multiple heterogeneous PyMC variables in a Numpy array. Just remember to set the `dtype` of the array to `object` upon initialization. For example:\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "N = 10\n", + "x = np.ones(N, dtype=object)\n", + "with pm.Model() as model:\n", + " for i in range(0, N):\n", + " x[i] = pm.Exponential('x_%i' % i, (i+1.0)**2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The remainder of this chapter examines some practical examples of PyMC and PyMC modeling:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "##### Example: Challenger Space Shuttle Disaster \n", + "\n", + "On January 28, 1986, the twenty-fifth flight of the U.S. space shuttle program ended in disaster when one of the rocket boosters of the Shuttle Challenger exploded shortly after lift-off, killing all seven crew members. The presidential commission on the accident concluded that it was caused by the failure of an O-ring in a field joint on the rocket booster, and that this failure was due to a faulty design that made the O-ring unacceptably sensitive to a number of factors including outside temperature. Of the previous 24 flights, data were available on failures of O-rings on 23, (one was lost at sea), and these data were discussed on the evening preceding the Challenger launch, but unfortunately only the data corresponding to the 7 flights on which there was a damage incident were considered important and these were thought to show no obvious trend. The data are shown below (see [1]):\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Temp (F), O-Ring failure?\n", + "[[66. 0.]\n", + " [70. 1.]\n", + " [69. 0.]\n", + " [68. 0.]\n", + " [67. 0.]\n", + " [72. 0.]\n", + " [73. 0.]\n", + " [70. 0.]\n", + " [57. 1.]\n", + " [63. 1.]\n", + " [70. 1.]\n", + " [78. 0.]\n", + " [67. 0.]\n", + " [53. 1.]\n", + " [67. 0.]\n", + " [75. 0.]\n", + " [70. 0.]\n", + " [81. 0.]\n", + " [76. 0.]\n", + " [79. 0.]\n", + " [75. 1.]\n", + " [76. 0.]\n", + " [58. 1.]]\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 254, + "width": 741 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "figsize(12.5, 3.5)\n", + "np.set_printoptions(precision=3, suppress=True)\n", + "challenger_data = np.genfromtxt(\"data/challenger_data.csv\", skip_header=1,\n", + " usecols=[1, 2], missing_values=\"NA\",\n", + " delimiter=\",\")\n", + "#drop the NA values\n", + "challenger_data = challenger_data[~np.isnan(challenger_data[:, 1])]\n", + "\n", + "#plot it, as a function of tempature (the first column)\n", + "print(\"Temp (F), O-Ring failure?\")\n", + "print(challenger_data)\n", + "\n", + "plt.scatter(challenger_data[:, 0], challenger_data[:, 1], s=75, color=\"k\",\n", + " alpha=0.5)\n", + "plt.yticks([0, 1])\n", + "plt.ylabel(\"Damage Incident?\")\n", + "plt.xlabel(\"Outside temperature (Fahrenheit)\")\n", + "plt.title(\"Defects of the Space Shuttle O-Rings vs temperature\");\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It looks clear that *the probability* of damage incidents occurring increases as the outside temperature decreases. We are interested in modeling the probability here because it does not look like there is a strict cutoff point between temperature and a damage incident occurring. The best we can do is ask \"At temperature $t$, what is the probability of a damage incident?\". The goal of this example is to answer that question.\n", + "\n", + "We need a function of temperature, call it $p(t)$, that is bounded between 0 and 1 (so as to model a probability) and changes from 1 to 0 as we increase temperature. There are actually many such functions, but the most popular choice is the *logistic function.*\n", + "\n", + "$$p(t) = \\frac{1}{ 1 + e^{ \\;\\beta t } } $$\n", + "\n", + "In this model, $\\beta$ is the variable we are uncertain about. Below is the function plotted for $\\beta = 1, 3, -5$." + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 193, + "width": 706 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "figsize(12, 3)\n", + "\n", + "def logistic(x, beta):\n", + " return 1.0 / (1.0 + np.exp(beta * x))\n", + "\n", + "x = np.linspace(-4, 4, 100)\n", + "plt.plot(x, logistic(x, 1), label=r\"$\\beta = 1$\")\n", + "plt.plot(x, logistic(x, 3), label=r\"$\\beta = 3$\")\n", + "plt.plot(x, logistic(x, -5), label=r\"$\\beta = -5$\")\n", + "plt.legend();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "But something is missing. In the plot of the logistic function, the probability changes only near zero, but in our data above the probability changes around 65 to 70. We need to add a *bias* term to our logistic function:\n", + "\n", + "$$p(t) = \\frac{1}{ 1 + e^{ \\;\\beta t + \\alpha } } $$\n", + "\n", + "Some plots are below, with differing $\\alpha$." + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 193, + "width": 706 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "def logistic(x, beta, alpha=0):\n", + " return 1.0 / (1.0 + np.exp(np.dot(beta, x) + alpha))\n", + "\n", + "x = np.linspace(-4, 4, 100)\n", + "\n", + "plt.plot(x, logistic(x, 1), label=r\"$\\beta = 1$\", ls=\"--\", lw=1)\n", + "plt.plot(x, logistic(x, 3), label=r\"$\\beta = 3$\", ls=\"--\", lw=1)\n", + "plt.plot(x, logistic(x, -5), label=r\"$\\beta = -5$\", ls=\"--\", lw=1)\n", + "\n", + "plt.plot(x, logistic(x, 1, 1), label=r\"$\\beta = 1, \\alpha = 1$\",\n", + " color=\"#348ABD\")\n", + "plt.plot(x, logistic(x, 3, -2), label=r\"$\\beta = 3, \\alpha = -2$\",\n", + " color=\"#A60628\")\n", + "plt.plot(x, logistic(x, -5, 7), label=r\"$\\beta = -5, \\alpha = 7$\",\n", + " color=\"#7A68A6\")\n", + "\n", + "plt.legend(loc=\"lower left\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Adding a constant term $\\alpha$ amounts to shifting the curve left or right (hence why it is called a *bias*).\n", + "\n", + "Let's start modeling this in PyMC. The $\\beta, \\alpha$ parameters have no reason to be positive, bounded or relatively large, so they are best modeled by a *Normal random variable*, introduced next." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Normal distributions\n", + "\n", + "A Normal random variable, denoted $X \\sim N(\\mu, 1/\\tau)$, has a distribution with two parameters: the mean, $\\mu$, and the *precision*, $\\tau$. Those familiar with the Normal distribution already have probably seen $\\sigma^2$ instead of $\\tau^{-1}$. They are in fact reciprocals of each other. The change was motivated by simpler mathematical analysis and is an artifact of older Bayesian methods. Just remember: the smaller $\\tau$, the larger the spread of the distribution (i.e. we are more uncertain); the larger $\\tau$, the tighter the distribution (i.e. we are more certain). Regardless, $\\tau$ is always positive. \n", + "\n", + "The probability density function of a $N( \\mu, 1/\\tau)$ random variable is:\n", + "\n", + "$$ f(x | \\mu, \\tau) = \\sqrt{\\frac{\\tau}{2\\pi}} \\exp\\left( -\\frac{\\tau}{2} (x-\\mu)^2 \\right) $$\n", + "\n", + "We plot some different density functions below. " + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "image/png": { + "height": 226, + "width": 724 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "import scipy.stats as stats\n", + "\n", + "nor = stats.norm\n", + "x = np.linspace(-8, 7, 150)\n", + "mu = (-2, 0, 3)\n", + "tau = (.7, 1, 2.8)\n", + "colors = [\"#348ABD\", \"#A60628\", \"#7A68A6\"]\n", + "parameters = zip(mu, tau, colors)\n", + "\n", + "for _mu, _tau, _color in parameters:\n", + " plt.plot(x, nor.pdf(x, _mu, scale=1./_tau),\n", + " label=\"$\\mu = %d,\\;\\\\tau = %.1f$\" % (_mu, _tau), color=_color)\n", + " plt.fill_between(x, nor.pdf(x, _mu, scale=1./_tau), color=_color,\n", + " alpha=.33)\n", + "\n", + "plt.legend(loc=\"upper right\")\n", + "plt.xlabel(\"$x$\")\n", + "plt.ylabel(\"density function at $x$\")\n", + "plt.title(\"Probability distribution of three different Normal random \\\n", + "variables\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A Normal random variable can be take on any real number, but the variable is very likely to be relatively close to $\\mu$. In fact, the expected value of a Normal is equal to its $\\mu$ parameter:\n", + "\n", + "$$ E[ X | \\mu, \\tau] = \\mu$$\n", + "\n", + "and its variance is equal to the inverse of $\\tau$:\n", + "\n", + "$$Var( X | \\mu, \\tau ) = \\frac{1}{\\tau}$$\n", + "\n", + "\n", + "\n", + "Below we continue our modeling of the Challenger space craft:" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "import pymc as pm\n", + "\n", + "temperature = challenger_data[:, 0]\n", + "D = challenger_data[:, 1] # defect or not?\n", + "\n", + "#notice the`value` here. We explain why below.\n", + "with pm.Model() as model:\n", + " beta = pm.Normal(\"beta\", mu=0, tau=0.001, initval=0)\n", + " alpha = pm.Normal(\"alpha\", mu=0, tau=0.001, initval=0)\n", + " p = pm.Deterministic(\"p\", 1.0/(1. + at.exp(beta*temperature + alpha)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We have our probabilities, but how do we connect them to our observed data? A *Bernoulli* random variable with parameter $p$, denoted $\\text{Ber}(p)$, is a random variable that takes value 1 with probability $p$, and 0 else. Thus, our model can look like:\n", + "\n", + "$$ \\text{Defect Incident, $D_i$} \\sim \\text{Ber}( \\;p(t_i)\\; ), \\;\\; i=1..N$$\n", + "\n", + "where $p(t)$ is our logistic function and $t_i$ are the temperatures we have observations about. Notice in the above code we had to set the values of `beta` and `alpha` to 0. The reason for this is that if `beta` and `alpha` are very large, they make `p` equal to 1 or 0. Unfortunately, `pm.Bernoulli` does not like probabilities of exactly 0 or 1, though they are mathematically well-defined probabilities. So by setting the coefficient values to `0`, we set the variable `p` to be a reasonable starting value. This has no effect on our results, nor does it mean we are including any additional information in our prior. It is simply a computational caveat in PyMC. " + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + "
\n", + " \n", + " 100.00% [26/26 00:00<00:00 logp = -19.024, ||grad|| = 9.9071]\n", + "
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\n", + " \n", + " 100.00% [484000/484000 00:32<00:00 Sampling 4 chains, 0 divergences]\n", + "
\n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Sampling 4 chains for 1_000 tune and 120_000 draw iterations (4_000 + 480_000 draws total) took 32 seconds.\n" + ] + } + ], + "source": [ + "# connect the probabilities in `p` with our observations through a\n", + "# Bernoulli random variable.\n", + "with model:\n", + " observed = pm.Bernoulli(\"bernoulli_obs\", p, observed=D)\n", + " \n", + " # Mysterious code to be explained in Chapter 3\n", + " start = pm.find_MAP()\n", + " step = pm.Metropolis()\n", + " trace = pm.sample(120000, step=step, initvals=start)\n", + " #burned_trace = trace[100000::2]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We have trained our model on the observed data, now we can sample values from the posterior. Let's look at the posterior distributions for $\\alpha$ and $\\beta$:" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 375, + "width": 741 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "alpha_samples = np.concatenate(trace.posterior.alpha.data[:,100000::2])[:, None] # best to make them 1d\n", + "beta_samples = np.concatenate(trace.posterior.beta.data[:,100000::2])[:, None]\n", + "\n", + "figsize(12.5, 6)\n", + "\n", + "#histogram of the samples:\n", + "plt.subplot(211)\n", + "plt.title(r\"Posterior distributions of the variables $\\alpha, \\beta$\")\n", + "plt.hist(beta_samples, histtype='stepfilled', bins=35, alpha=0.85,\n", + " label=r\"posterior of $\\beta$\", color=\"#7A68A6\", density=True)\n", + "plt.legend()\n", + "\n", + "plt.subplot(212)\n", + "plt.hist(alpha_samples, histtype='stepfilled', bins=35, alpha=0.85,\n", + " label=r\"posterior of $\\alpha$\", color=\"#A60628\", density=True)\n", + "plt.legend();" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 58, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 404, + "width": 719 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "#Here is the ArviZ version \n", + "figure,ax = plt.subplots(2,1)\n", + "\n", + "\n", + "az.plot_posterior(trace, var_names=['beta'], kind='hist',bins=25,\n", + " figsize=(12.5,6),color=\"#7A68A6\",ax=ax[0])\n", + "az.plot_posterior(trace, var_names=['alpha'], kind='hist',bins=25,\n", + " figsize=(12.5,6),color=\"#A60628\",ax=ax[1])\n", + "plt.suptitle(r\"Posterior distributions of the variables $\\alpha, \\beta$\",fontsize=20)\n", + "ax[0].set_title(r\"posterior of $\\beta$\")\n", + "ax[1].set_title(r\"posterior of $\\alpha$\")\n", + "plt.plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "All samples of $\\beta$ are greater than 0. If instead the posterior was centered around 0, we may suspect that $\\beta = 0$, implying that temperature has no effect on the probability of defect. \n", + "\n", + "Similarly, all $\\alpha$ posterior values are negative and far away from 0, implying that it is correct to believe that $\\alpha$ is significantly less than 0. \n", + "\n", + "Regarding the spread of the data, we are very uncertain about what the true parameters might be (though considering the low sample size and the large overlap of defects-to-nondefects this behaviour is perhaps expected). \n", + "\n", + "Next, let's look at the *expected probability* for a specific value of the temperature. That is, we average over all samples from the posterior to get a likely value for $p(t_i)$." + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "t = np.linspace(temperature.min() - 5, temperature.max()+5, 50)[:, None]\n", + "p_t = logistic(t.T, beta_samples, alpha_samples)\n", + "\n", + "mean_prob_t = p_t.mean(axis=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 281, + "width": 750 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "figsize(12.5, 4)\n", + "\n", + "plt.plot(t, mean_prob_t, lw=3, label=\"average posterior \\nprobability \\\n", + "of defect\")\n", + "plt.plot(t, p_t[0, :], ls=\"--\", label=\"realization from posterior\")\n", + "plt.plot(t, p_t[-2, :], ls=\"--\", label=\"realization from posterior\")\n", + "plt.scatter(temperature, D, color=\"k\", s=50, alpha=0.5)\n", + "plt.title(\"Posterior expected value of probability of defect; \\\n", + "plus realizations\")\n", + "plt.legend(loc=\"lower left\")\n", + "plt.ylim(-0.1, 1.1)\n", + "plt.xlim(t.min(), t.max())\n", + "plt.ylabel(\"probability\")\n", + "plt.xlabel(\"temperature\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Above we also plotted two possible realizations of what the actual underlying system might be. Both are equally likely as any other draw. The blue line is what occurs when we average all the 20000 possible dotted lines together.\n", + "\n", + "\n", + "An interesting question to ask is for what temperatures are we most uncertain about the defect-probability? Below we plot the expected value line **and** the associated 95% intervals for each temperature. " + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 283, + "width": 750 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "from scipy.stats.mstats import mquantiles\n", + "\n", + "# vectorized bottom and top 2.5% quantiles for \"confidence interval\"\n", + "qs = mquantiles(p_t, [0.025, 0.975], axis=0)\n", + "plt.fill_between(t[:, 0], *qs, alpha=0.7,\n", + " color=\"#7A68A6\")\n", + "\n", + "plt.plot(t[:, 0], qs[0], label=\"95% CI\", color=\"#7A68A6\", alpha=0.7)\n", + "\n", + "plt.plot(t, mean_prob_t, lw=1, ls=\"--\", color=\"k\",\n", + " label=\"average posterior \\nprobability of defect\")\n", + "\n", + "plt.xlim(t.min(), t.max())\n", + "plt.ylim(-0.02, 1.02)\n", + "plt.legend(loc=\"lower left\")\n", + "plt.scatter(temperature, D, color=\"k\", s=50, alpha=0.5)\n", + "plt.xlabel(\"temp, $t$\")\n", + "\n", + "plt.ylabel(\"probability estimate\")\n", + "plt.title(\"Posterior probability estimates given temp. $t$\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The *95% credible interval*, or 95% CI, painted in purple, represents the interval, for each temperature, that contains 95% of the distribution. For example, at 65 degrees, we can be 95% sure that the probability of defect lies between 0.25 and 0.75.\n", + "\n", + "More generally, we can see that as the temperature nears 60 degrees, the CI's spread out over [0,1] quickly. As we pass 70 degrees, the CI's tighten again. This can give us insight about how to proceed next: we should probably test more O-rings around 60-65 temperature to get a better estimate of probabilities in that range. Similarly, when reporting to scientists your estimates, you should be very cautious about simply telling them the expected probability, as we can see this does not reflect how *wide* the posterior distribution is." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### What about the day of the Challenger disaster?\n", + "\n", + "On the day of the Challenger disaster, the outside temperature was 31 degrees Fahrenheit. What is the posterior distribution of a defect occurring, given this temperature? The distribution is plotted below. It looks almost guaranteed that the Challenger was going to be subject to defective O-rings." + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "image/png": { + "height": 200, + "width": 758 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "figsize(12.5, 2.5)\n", + "\n", + "prob_31 = logistic(31, beta_samples, alpha_samples)\n", + "\n", + "plt.xlim(0.995, 1)\n", + "plt.hist(prob_31, bins=1000, density=True, histtype='stepfilled')\n", + "plt.title(\"Posterior distribution of probability of defect, given $t = 31$\")\n", + "plt.xlabel(\"probability of defect occurring in O-ring\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Is our model appropriate?\n", + "\n", + "The skeptical reader will say \"You deliberately chose the logistic function for $p(t)$ and the specific priors. Perhaps other functions or priors will give different results. How do I know I have chosen a good model?\" This is absolutely true. To consider an extreme situation, what if I had chosen the function $p(t) = 1,\\; \\forall t$, which guarantees a defect always occurring: I would have again predicted disaster on January 28th. Yet this is clearly a poorly chosen model. On the other hand, if I did choose the logistic function for $p(t)$, but specified all my priors to be very tight around 0, likely we would have very different posterior distributions. How do we know our model is an expression of the data? This encourages us to measure the model's **goodness of fit**.\n", + "\n", + "We can think: *how can we test whether our model is a bad fit?* An idea is to compare observed data (which if we recall is a *fixed* stochastic variable) with artificial dataset which we can simulate. The rationale is that if the simulated dataset does not appear similar, statistically, to the observed dataset, then likely our model is not accurately represented the observed data. \n", + "\n", + "Previously in this Chapter, we simulated artificial dataset for the SMS example. To do this, we sampled values from the priors. We saw how varied the resulting datasets looked like, and rarely did they mimic our observed dataset. In the current example, we should sample from the *posterior* distributions to create *very plausible datasets*. Luckily, our Bayesian framework makes this very easy. We only need to create a new `Stochastic` variable, that is exactly the same as our variable that stored the observations, but minus the observations themselves. If you recall, our `Stochastic` variable that stored our observed data was:\n", + "\n", + " observed = pm.Bernoulli(\"bernoulli_obs\", p, observed=D)\n", + "\n", + "Hence we create:\n", + " \n", + " simulated_data = pm.Bernoulli(\"simulation_data\", p)\n", + "\n", + "Let's simulate 10 000:" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Multiprocess sampling (4 chains in 4 jobs)\n", + "CompoundStep\n", + ">Metropolis: [beta]\n", + ">Metropolis: [alpha]\n", + ">Metropolis: [bernoulli_sim]\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + "
\n", + " \n", + " 100.00% [44000/44000 00:06<00:00 Sampling 4 chains, 0 divergences]\n", + "
\n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Sampling 4 chains for 1_000 tune and 10_000 draw iterations (4_000 + 40_000 draws total) took 7 seconds.\n" + ] + } + ], + "source": [ + "N = 10000\n", + "\n", + "with pm.Model() as model:\n", + " beta = pm.Normal(\"beta\", mu=0, tau=0.001)\n", + " alpha = pm.Normal(\"alpha\", mu=0, tau=0.001)\n", + " p = pm.Deterministic(\"p\", 1.0/(1. + at.exp(beta*temperature + alpha)))\n", + " observed = pm.Bernoulli(\"bernoulli_obs\", p, observed=D)\n", + " \n", + " simulated = pm.Bernoulli(\"bernoulli_sim\", p, shape=p.shape)\n", + " step = pm.Metropolis()\n", + " trace = pm.sample(N, step=step)" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(10000, 23)\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 303, + "width": 725 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "figsize(12.5, 5)\n", + "plt.style.use(\"ggplot\")\n", + "simulations = trace.posterior.bernoulli_sim.data[1]\n", + "print(simulations.shape)\n", + "\n", + "plt.title(\"Simulated dataset using posterior parameters\")\n", + "figsize(12.5, 6)\n", + "for i in range(4):\n", + " ax = plt.subplot(4, 1, i+1)\n", + " plt.scatter(temperature, simulations[1000*i, :], color=\"k\",\n", + " s=50, alpha=0.6)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that the above plots are different (if you can think of a cleaner way to present this, please send a pull request and answer [here](http://stats.stackexchange.com/questions/53078/how-to-visualize-bayesian-goodness-of-fit-for-logistic-regression)!).\n", + "\n", + "We wish to assess how good our model is. \"Good\" is a subjective term of course, so results must be relative to other models. \n", + "\n", + "We will be doing this graphically as well, which may seem like an even less objective method. The alternative is to use *Bayesian p-values*. These are still subjective, as the proper cutoff between good and bad is arbitrary. Gelman emphasises that the graphical tests are more illuminating [7] than p-value tests. We agree.\n", + "\n", + "The following graphical test is a novel data-viz approach to logistic regression. The plots are called *separation plots*[8]. For a suite of models we wish to compare, each model is plotted on an individual separation plot. I leave most of the technical details about separation plots to the very accessible [original paper](https://onlinelibrary.wiley.com/doi/10.1111/j.1540-5907.2011.00525.x), but I'll summarize their use here.\n", + "\n", + "For each model, we calculate the proportion of times the posterior simulation proposed a value of 1 for a particular temperature, i.e. compute $P( \\;\\text{Defect} = 1 | t, \\alpha, \\beta )$ by averaging. This gives us the posterior probability of a defect at each data point in our dataset. For example, for the model we used above:" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "posterior prob of defect | realized defect \n", + "0.43 | 0\n", + "0.35 | 1\n", + "0.35 | 0\n", + "0.36 | 0\n", + "0.41 | 0\n", + "0.27 | 0\n", + "0.25 | 0\n", + "0.33 | 0\n", + "0.66 | 1\n", + "0.51 | 1\n", + "0.32 | 1\n", + "0.19 | 0\n", + "0.42 | 0\n", + "0.73 | 1\n", + "0.43 | 0\n", + "0.23 | 0\n", + "0.30 | 0\n", + "0.14 | 0\n", + "0.21 | 0\n", + "0.15 | 0\n", + "0.21 | 1\n", + "0.20 | 0\n", + "0.63 | 1\n" + ] + } + ], + "source": [ + "posterior_probability = simulations.mean(axis=0)\n", + "print(\"posterior prob of defect | realized defect \")\n", + "for i in range(len(D)):\n", + " print(\"%.2f | %d\" % (posterior_probability[i], D[i]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next we sort each column by the posterior probabilities:" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "probb | defect \n", + "0.14 | 0\n", + "0.15 | 0\n", + "0.19 | 0\n", + "0.20 | 0\n", + "0.21 | 0\n", + "0.21 | 1\n", + "0.23 | 0\n", + "0.25 | 0\n", + "0.27 | 0\n", + "0.30 | 0\n", + "0.32 | 1\n", + "0.33 | 0\n", + "0.35 | 1\n", + "0.35 | 0\n", + "0.36 | 0\n", + "0.41 | 0\n", + "0.42 | 0\n", + "0.43 | 0\n", + "0.43 | 0\n", + "0.51 | 1\n", + "0.63 | 1\n", + "0.66 | 1\n", + "0.73 | 1\n" + ] + } + ], + "source": [ + "ix = np.argsort(posterior_probability)\n", + "print(\"probb | defect \")\n", + "for i in range(len(D)):\n", + " print(\"%.2f | %d\" % (posterior_probability[ix[i]], D[ix[i]]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can present the above data better in a figure: I've wrapped this up into a `separation_plot` function." + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 100, + "width": 784 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "from separation_plot import separation_plot\n", + "# plt.tight_layout()\n", + "figsize(11., 1.5)\n", + "separation_plot(posterior_probability, D)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The snaking-line is the sorted probabilities, blue bars denote defects, and empty space (or grey bars for the optimistic readers) denote non-defects. As the probability rises, we see more and more defects occur. On the right hand side, the plot suggests that as the posterior probability is large (line close to 1), then more defects are realized. This is good behaviour. Ideally, all the blue bars *should* be close to the right-hand side, and deviations from this reflect missed predictions. \n", + "\n", + "The black vertical line is the expected number of defects we should observe, given this model. This allows the user to see how the total number of events predicted by the model compares to the actual number of events in the data.\n", + "\n", + "It is much more informative to compare this to separation plots for other models. Below we compare our model (top) versus three others:\n", + "\n", + "1. the perfect model, which predicts the posterior probability to be equal 1 if a defect did occur.\n", + "2. a completely random model, which predicts random probabilities regardless of temperature.\n", + "3. a constant model: where $P(D = 1 \\; | \\; t) = c, \\;\\; \\forall t$. The best choice for $c$ is the observed frequency of defects, in this case 7/23. \n" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 98, + "width": 775 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "figsize(11., 1.25)\n", + "\n", + "# Our temperature-dependent model\n", + "separation_plot(posterior_probability, D)\n", + "plt.title(\"Temperature-dependent model\")\n", + "\n", + "# Perfect model\n", + "# i.e. the probability of defect is equal to if a defect occurred or not.\n", + "p = D\n", + "separation_plot(p, D)\n", + "plt.title(\"Perfect model\")\n", + "\n", + "# random predictions\n", + "p = np.random.rand(23)\n", + "separation_plot(p, D)\n", + "plt.title(\"Random model\")\n", + "\n", + "# constant model\n", + "constant_prob = 7./23*np.ones(23)\n", + "separation_plot(constant_prob, D)\n", + "plt.title(\"Constant-prediction model\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the random model, we can see that as the probability increases there is no clustering of defects to the right-hand side. Similarly for the constant model.\n", + "\n", + "The perfect model, the probability line is not well shown, as it is stuck to the bottom and top of the figure. Of course the perfect model is only for demonstration, and we cannot infer any scientific inference from it." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Exercises\n", + "\n", + "1\\. Try putting in extreme values for our observations in the cheating example. What happens if we observe 25 affirmative responses? 10? 50? " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "2\\. Try plotting $\\alpha$ samples versus $\\beta$ samples. Why might the resulting plot look like this?" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 281, + "width": 752 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "#type your code here.\n", + "figsize(12.5, 4)\n", + "\n", + "plt.scatter(alpha_samples, beta_samples, alpha=0.1)\n", + "plt.title(\"Why does the plot look like this?\")\n", + "plt.xlabel(r\"$\\alpha$\")\n", + "plt.ylabel(r\"$\\beta$\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### References\n", + "\n", + "- [1] Dalal, Fowlkes and Hoadley (1989),JASA, 84, 945-957.\n", + "- [2] German Rodriguez. Datasets. In WWS509. Retrieved 30/01/2013, from .\n", + "- [3] McLeish, Don, and Cyntha Struthers. STATISTICS 450/850 Estimation and Hypothesis Testing. Winter 2012. Waterloo, Ontario: 2012. Print.\n", + "- [4] Fonnesbeck, Christopher. \"Building Models.\" PyMC-Devs. N.p., n.d. Web. 26 Feb 2013. .\n", + "- [5] Cronin, Beau. \"Why Probabilistic Programming Matters.\" 24 Mar 2013. Google, Online Posting to Google . Web. 24 Mar. 2013. .\n", + "- [6] S.P. Brooks, E.A. Catchpole, and B.J.T. Morgan. Bayesian animal survival estimation. Statistical Science, 15: 357–376, 2000\n", + "- [7] Gelman, Andrew. \"Philosophy and the practice of Bayesian statistics.\" British Journal of Mathematical and Statistical Psychology. (2012): n. page. Web. 2 Apr. 2013.\n", + "- [8] Greenhill, Brian, Michael D. Ward, and Audrey Sacks. \"The Separation Plot: A New Visual Method for Evaluating the Fit of Binary Models.\" American Journal of Political Science. 55.No.4 (2011): n. page. Web. 2 Apr. 2013." + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 70, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.core.display import HTML\n", + "\n", + "\n", + "def css_styling():\n", + " styles = open(\"../styles/custom.css\", \"r\").read()\n", + " return HTML(styles)\n", + "css_styling()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "pymc_env", + "language": "python", + "name": "pymc_env" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.4" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/Chapter2_MorePyMC/Ch2_MorePyMC_TFP.ipynb b/Chapter2_MorePyMC/Ch2_MorePyMC_TFP.ipynb new file mode 100644 index 00000000..1e8752ee --- /dev/null +++ b/Chapter2_MorePyMC/Ch2_MorePyMC_TFP.ipynb @@ -0,0 +1,4345 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "Ch2_MorePyMC_TFP.ipynb", + "version": "0.3.2", + "provenance": [], + "collapsed_sections": [] + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "accelerator": "GPU" + }, + "cells": [ + { + "metadata": { + "colab_type": "text", + "id": "phBEJ8iLIAwF" + }, + "cell_type": "markdown", + "source": [ + "# Probabilistic Programming and Bayesian Methods for Hackers Chapter 2\n", + "\n", + "\n", + " \n", + " \n", + "
\n", + " Run in Google Colab\n", + " \n", + " View source on GitHub\n", + "
\n", + "
\n", + "
\n", + "
\n", + "\n", + "Original content ([this Jupyter notebook](https://nbviewer.jupyter.org/github/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/blob/master/Chapter2_MorePyMC/Ch2_MorePyMC_PyMC2.ipynb)) created by Cam Davidson-Pilon ([`@Cmrn_DP`](https://twitter.com/Cmrn_DP))\n", + "\n", + "Ported to [Tensorflow Probability](https://www.tensorflow.org/probability/) by Matthew McAteer ([`@MatthewMcAteer0`](https://twitter.com/MatthewMcAteer0)), with help from Bryan Seybold, Mike Shwe ([`@mikeshwe`](https://twitter.com/mikeshwe)), Josh Dillon, and the rest of the TFP team at Google ([`tfprobability@tensorflow.org`](mailto:tfprobability@tensorflow.org)).\n", + "\n", + "Welcome to Bayesian Methods for Hackers. The full Github repository is available at [github/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers](https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers). The other chapters can be found on the project's [homepage](https://camdavidsonpilon.github.io/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/). We hope you enjoy the book, and we encourage any contributions!\n", + "___\n", + "\n", + "### Table of Contents\n", + "\n", + "- Dependencies & Prerequisites\n", + "- A little more on TFP\n", + " - TFP Variables\n", + " - Initializing Stochastic Variables\n", + " - Deterministic variables\n", + " - Combining with Tensorflow Core\n", + " - Including observations in the Model\n", + "- Modeling approaches\n", + " - Same story; different ending\n", + " - Example: Bayesian A/B testing\n", + " - A Simple Case\n", + " - Execute the TF graph to sample from the posterior\n", + " - A and B together\n", + " - Execute the TF graph to sample from the posterior\n", + "- An algorithm for human deceit\n", + " - The Binomial Distribution\n", + " - Example: Cheating among students\n", + " - Execute the TF graph to sample from the posterior\n", + " - Alternative TFP Model\n", + " - Execute the TF graph to sample from the posterior\n", + " - More TFP Tricks\n", + " - Example: Challenger Space Shuttle Disaster\n", + " - Normal Distributions\n", + " - Execute the TF graph to sample from the posterior\n", + " - What about the day of the Challenger disaster?\n", + " - Is our model appropriate?\n", + " - Execute the TF graph to sample from the posterior\n", + " - Exercises\n", + " - References\n", + "___\n", + "\n", + "This chapter introduces more TFP syntax and variables and ways to think about how to model a system from a Bayesian perspective. It also contains tips and data visualization techniques for assessing goodness-of-fit for your Bayesian model." + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "AIIO6GhdH89m" + }, + "cell_type": "markdown", + "source": [ + "### Dependencies & Prerequisites\n", + "\n", + "
\n", + " Tensorflow Probability is part of the colab default runtime, so you don't need to install Tensorflow or Tensorflow Probability if you're running this in the colab. \n", + "
\n", + " If you're running this notebook in Jupyter on your own machine (and you have already installed Tensorflow), you can use the following\n", + "
\n", + "
    \n", + "
  • For the most recent nightly installation: pip3 install -q tfp-nightly
    • \n", + "
  • For the most recent stable TFP release: pip3 install -q --upgrade tensorflow-probability
    • \n", + "
  • For the most recent stable GPU-connected version of TFP: pip3 install -q --upgrade tensorflow-probability-gpu
    • \n", + "
  • For the most recent nightly GPU-connected version of TFP: pip3 install -q tfp-nightly-gpu
    • \n", + "
\n", + "Again, if you are running this in a Colab, Tensorflow and TFP are already installed\n", + "
" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "jFKYjxy1IAwG", + "outputId": "91a30576-d9c6-47be-fcfc-a81e4304b46f", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 161 + } + }, + "cell_type": "code", + "source": [ + "#@title Imports and Global Variables (run this cell first) { display-mode: \"form\" }\n", + "\"\"\"\n", + "The book uses a custom matplotlibrc file, which provides the unique styles for\n", + "matplotlib plots. If executing this book, and you wish to use the book's\n", + "styling, provided are two options:\n", + " 1. Overwrite your own matplotlibrc file with the rc-file provided in the\n", + " book's styles/ dir. See http://matplotlib.org/users/customizing.html\n", + " 2. Also in the styles is bmh_matplotlibrc.json file. This can be used to\n", + " update the styles in only this notebook. Try running the following code:\n", + "\n", + " import json\n", + " s = json.load(open(\"../styles/bmh_matplotlibrc.json\"))\n", + " matplotlib.rcParams.update(s)\n", + "\"\"\"\n", + "!pip3 install -q wget\n", + "from __future__ import absolute_import, division, print_function\n", + "#@markdown This sets the warning status (default is `ignore`, since this notebook runs correctly)\n", + "warning_status = \"ignore\" #@param [\"ignore\", \"always\", \"module\", \"once\", \"default\", \"error\"]\n", + "import warnings\n", + "warnings.filterwarnings(warning_status)\n", + "with warnings.catch_warnings():\n", + " warnings.filterwarnings(warning_status, category=DeprecationWarning)\n", + " warnings.filterwarnings(warning_status, category=UserWarning)\n", + "\n", + "import numpy as np\n", + "import os\n", + "#@markdown This sets the styles of the plotting (default is styled like plots from [FiveThirtyeight.com](https://fivethirtyeight.com/))\n", + "matplotlib_style = 'fivethirtyeight' #@param ['fivethirtyeight', 'bmh', 'ggplot', 'seaborn', 'default', 'Solarize_Light2', 'classic', 'dark_background', 'seaborn-colorblind', 'seaborn-notebook']\n", + "import matplotlib.pyplot as plt; plt.style.use(matplotlib_style)\n", + "import matplotlib.axes as axes;\n", + "from matplotlib.patches import Ellipse\n", + "%matplotlib inline\n", + "import seaborn as sns; sns.set_context('notebook')\n", + "from IPython.core.pylabtools import figsize\n", + "#@markdown This sets the resolution of the plot outputs (`retina` is the highest resolution)\n", + "notebook_screen_res = 'retina' #@param ['retina', 'png', 'jpeg', 'svg', 'pdf']\n", + "%config InlineBackend.figure_format = notebook_screen_res\n", + "\n", + "import tensorflow as tf\n", + "tfe = tf.contrib.eager\n", + "\n", + "# Eager Execution\n", + "#@markdown Check the box below if you want to use [Eager Execution](https://www.tensorflow.org/guide/eager)\n", + "#@markdown Eager execution provides An intuitive interface, Easier debugging, and a control flow comparable to Numpy. You can read more about it on the [Google AI Blog](https://ai.googleblog.com/2017/10/eager-execution-imperative-define-by.html)\n", + "use_tf_eager = False #@param {type:\"boolean\"}\n", + "\n", + "# Use try/except so we can easily re-execute the whole notebook.\n", + "if use_tf_eager:\n", + " try:\n", + " tf.enable_eager_execution()\n", + " except:\n", + " pass\n", + "\n", + "import tensorflow_probability as tfp\n", + "tfd = tfp.distributions\n", + "tfb = tfp.bijectors\n", + "\n", + " \n", + "def evaluate(tensors):\n", + " \"\"\"Evaluates Tensor or EagerTensor to Numpy `ndarray`s.\n", + " Args:\n", + " tensors: Object of `Tensor` or EagerTensor`s; can be `list`, `tuple`,\n", + " `namedtuple` or combinations thereof.\n", + "\n", + " Returns:\n", + " ndarrays: Object with same structure as `tensors` except with `Tensor` or\n", + " `EagerTensor`s replaced by Numpy `ndarray`s.\n", + " \"\"\"\n", + " if tf.executing_eagerly():\n", + " return tf.contrib.framework.nest.pack_sequence_as(\n", + " tensors,\n", + " [t.numpy() if tf.contrib.framework.is_tensor(t) else t\n", + " for t in tf.contrib.framework.nest.flatten(tensors)])\n", + " return sess.run(tensors)\n", + "\n", + "class _TFColor(object):\n", + " \"\"\"Enum of colors used in TF docs.\"\"\"\n", + " red = '#F15854'\n", + " blue = '#5DA5DA'\n", + " orange = '#FAA43A'\n", + " green = '#60BD68'\n", + " pink = '#F17CB0'\n", + " brown = '#B2912F'\n", + " purple = '#B276B2'\n", + " yellow = '#DECF3F'\n", + " gray = '#4D4D4D'\n", + " def __getitem__(self, i):\n", + " return [\n", + " self.red,\n", + " self.orange,\n", + " self.green,\n", + " self.blue,\n", + " self.pink,\n", + " self.brown,\n", + " self.purple,\n", + " self.yellow,\n", + " self.gray,\n", + " ][i % 9]\n", + "TFColor = _TFColor()\n", + "\n", + "def session_options(enable_gpu_ram_resizing=True, enable_xla=True):\n", + " \"\"\"\n", + " Allowing the notebook to make use of GPUs if they're available.\n", + " \n", + " XLA (Accelerated Linear Algebra) is a domain-specific compiler for linear \n", + " algebra that optimizes TensorFlow computations.\n", + " \"\"\"\n", + " config = tf.ConfigProto()\n", + " config.log_device_placement = True\n", + " if enable_gpu_ram_resizing:\n", + " # `allow_growth=True` makes it possible to connect multiple colabs to your\n", + " # GPU. Otherwise the colab malloc's all GPU ram.\n", + " config.gpu_options.allow_growth = True\n", + " if enable_xla:\n", + " # Enable on XLA. https://www.tensorflow.org/performance/xla/.\n", + " config.graph_options.optimizer_options.global_jit_level = (\n", + " tf.OptimizerOptions.ON_1)\n", + " return config\n", + "\n", + "\n", + "def reset_sess(config=None):\n", + " \"\"\"\n", + " Convenience function to create the TF graph & session or reset them.\n", + " \"\"\"\n", + " if config is None:\n", + " config = session_options()\n", + " global sess\n", + " tf.reset_default_graph()\n", + " try:\n", + " sess.close()\n", + " except:\n", + " pass\n", + " sess = tf.InteractiveSession(config=config)\n", + "\n", + "reset_sess()" + ], + "execution_count": 1, + "outputs": [ + { + "output_type": "stream", + "text": [ + " Building wheel for wget (setup.py) ... \u001b[?25ldone\n", + "\u001b[?25h\n", + "WARNING: The TensorFlow contrib module will not be included in TensorFlow 2.0.\n", + "For more information, please see:\n", + " * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n", + " * https://github.com/tensorflow/addons\n", + "If you depend on functionality not listed there, please file an issue.\n", + "\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "24368dz9IAwM" + }, + "cell_type": "markdown", + "source": [ + "## A little more on TensorFlow and TensorFlow Probability\n", + "\n", + "To explain TensorFlow Probability, it's worth going into the various methods of working with Tensorflow tensors. Here, we introduce the notion of Tensorflow graphs and how we can use certain coding patterns to make our tensor-processing workflows much faster and more elegant. " + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "tOj8bjBmNuxm" + }, + "cell_type": "markdown", + "source": [ + "### TensorFlow Graph and Eager Modes\n", + "\n", + "TFP accomplishes most of its heavy lifting via the main `tensorflow` library. The `tensorflow` library also contains many of the familiar computational elements of NumPy and uses similar notation. While NumPy directly executes computations (e.g. when you run `a + b`), `tensorflow` in graph mode instead builds up a \"compute graph\" that tracks that you want to perform the `+` operation on the elements `a` and `b`. Only when you evaluate a `tensorflow` expression does the computation take place--`tensorflow` is lazy evaluated. The benefit of using Tensorflow over NumPy is that the graph enables mathematical optimizations (e.g. simplifications), gradient calculations via automatic differentiation, compiling the entire graph to C to run at machine speed, and also compiling it to run on a GPU or TPU. \n", + "\n", + "Fundamentally, TensorFlow uses [graphs](https://www.tensorflow.org/guide/graphs) for computation, wherein the graphs represent computation as dependencies among individual operations. In the programming paradigm for Tensorflow graphs, we first define the dataflow graph, and then create a TensorFlow session to run parts of the graph. A Tensorflow [`tf.Session()`](https://www.tensorflow.org/api_docs/python/tf/Session) object runs the graph to get the variables we want to model. In the example below, we are using a global session object `sess`, which we created above in the \"Imports and Global Variables\" section. \n", + "\n", + "To avoid the sometimes confusing aspects of lazy evaluation, Tensorflow's eager mode does immediate evaluation of results to give an even more similar feel to working with NumPy. With Tensorflow [eager](https://www.tensorflow.org/guide/eager) mode, you can evaluate operations immediately, without explicitly building graphs: operations return concrete values instead of constructing a computational graph to run later. If we're in eager mode, we are presented with tensors that can be converted to NumPy array equivalents immediately. Eager mode makes it easy to get started with TensorFlow and debug models.\n", + "\n", + "\n", + "TFP is essentially:\n", + "\n", + "* a collection of tensorflow symbolic expressions for various probability distributions that are combined into one big compute graph, and\n", + "* a collection of inference algorithms that use that graph to compute probabilities and gradients.\n", + "\n", + "For practical purposes, what this means is that in order to build certain models we sometimes have to use core Tensorflow. This simple example for Poisson sampling is how we might work with both graph and eager modes:" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "CmiGas0kXiEw", + "outputId": "a7788041-8cab-4868-bada-fdd89062bb1a", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 35 + } + }, + "cell_type": "code", + "source": [ + "parameter = tfd.Exponential(rate=1., name=\"poisson_param\").sample()\n", + "rv_data_generator = tfd.Poisson(parameter, name=\"data_generator\")\n", + "data_generator = rv_data_generator.sample()\n", + "\n", + "if tf.executing_eagerly():\n", + " data_generator_ = tf.contrib.framework.nest.pack_sequence_as(\n", + " data_generator,\n", + " [t.numpy() if tf.contrib.framework.is_tensor(t) else t\n", + " for t in tf.contrib.framework.nest.flatten(data_generator)])\n", + "else:\n", + " data_generator_ = sess.run(data_generator)\n", + " \n", + "print(\"Value of sample from data generator random variable:\", data_generator_)" + ], + "execution_count": 2, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Value of sample from data generator random variable: 2.0\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "9kArT4GTIAwT" + }, + "cell_type": "markdown", + "source": [ + "In graph mode, Tensorflow will automatically assign any variables to a graph; they can then be evaluated in a session or made available in eager mode. If you try to define a variable when the session is already closed or in a finalized state, you will get an error. In the \"Imports and Global Variables\" section, we defined a particular type of session, called [`InteractiveSession`](https:///www.tensorflow.org/api_docs/python/tf/InteractiveSession). \n", + "This definition of a global `InteractiveSession` allows us to access our session variables interactively via a shell or notebook." + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "4IEk40NbIAwX" + }, + "cell_type": "markdown", + "source": [ + "Using the pattern of a global session, we can incrementally build a graph and run subsets of it to get the results.\n", + "\n", + "Eager execution further simplifies our code, eliminating the need to call session functions explicitly. In fact, if you try to run graph mode semantics in eager mode, you will get an error message like this:\n", + "\n", + "```\n", + "AttributeError: Tensor.graph is meaningless when eager execution is enabled.\n", + "```\n", + "\n", + "As mentioned in the previous chapter, we have a nifty tool that allows us to create code that's usable in both graph mode and eager mode. The custom `evaluate()` function allows us to evaluate tensors whether we are operating in TF graph or eager mode. A generalization of our data generator example above, the function looks like the following:\n", + "\n", + "```python\n", + "\n", + "def evaluate(tensors):\n", + " if tf.executing_eagerly():\n", + " return tf.contrib.framework.nest.pack_sequence_as(\n", + " tensors,\n", + " [t.numpy() if tf.contrib.framework.is_tensor(t) else t\n", + " for t in tf.contrib.framework.nest.flatten(tensors)])\n", + " with tf.Session() as sess:\n", + " return sess.run(tensors)\n", + "\n", + "```\n", + "\n", + "Each of the tensors corresponds to a NumPy-like output. To distinguish the tensors from their NumPy-like counterparts, we will use the convention of appending an underscore to the version of the tensor that one can use NumPy-like arrays on. In other words, the output of `evaluate()` gets named as `variable` + `_` = `variable_` . Now, we can do our Poisson sampling using both the `evaluate()` function and this new convention for naming Python variables in TFP." + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "Bk-vyPB9IAwX", + "outputId": "44667dae-aa3f-49d0-d25d-c47815c40714", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 53 + } + }, + "cell_type": "code", + "source": [ + "# Defining our Assumptions\n", + "parameter = tfd.Exponential(rate=1., name=\"poisson_param\").sample()\n", + "\n", + "# Converting our TF to Numpy\n", + "[ parameter_ ] = evaluate([ parameter ])\n", + "\n", + "print(\"Sample from exponential distribution before evaluation: \", parameter)\n", + "print(\"Evaluated sample from exponential distribution: \", parameter_)" + ], + "execution_count": 3, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Sample from exponential distribution before evaluation: Tensor(\"poisson_param_1/sample/Reshape:0\", shape=(), dtype=float32)\n", + "Evaluated sample from exponential distribution: 0.011206482\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "ZlGWIiPLIAwo" + }, + "cell_type": "markdown", + "source": [ + "More generally, we can use our `evaluate()` function to convert between the Tensorflow `tensor` data type and one that we can run operations on:" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "1tzQmnsFIAwp", + "outputId": "281c282d-abbd-42ef-8a2f-e8762747741b", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 53 + } + }, + "cell_type": "code", + "source": [ + "[ \n", + " parameter_,\n", + " data_generator_,\n", + "] = evaluate([ \n", + " parameter, \n", + " data_generator,\n", + "])\n", + "\n", + "print(\"'parameter_' evaluated Tensor :\", parameter_)\n", + "print(\"'data_generator_' sample evaluated Tensor :\", data_generator_)\n" + ], + "execution_count": 4, + "outputs": [ + { + "output_type": "stream", + "text": [ + "'parameter_' evaluated Tensor : 1.3005725\n", + "'data_generator_' sample evaluated Tensor : 1.0\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "m0PLxpCIc--r" + }, + "cell_type": "markdown", + "source": [ + "\n", + "A general rule of thumb for programming in TensorFlow is that if you need to do any array-like calculations that would require NumPy functions, you should use their equivalents in TensorFlow. This practice is necessary because NumPy can produce only constant values but TensorFlow tensors are a dynamic part of the computation graph. If you mix and match these the wrong way, you will typically get an error about incompatible types." + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "wqkS8vztNoyh" + }, + "cell_type": "markdown", + "source": [ + "### TFP Distributions\n", + "\n", + "Let's look into how [`tfp.distributions`](https://www.tensorflow.org/probability/api_docs/python/tfp/distributions) work.\n", + "\n", + "TFP uses distribution subclasses to represent *stochastic*, random variables. A variable is stochastic when the following is true: even if you knew all the values of the variable's parameters and components, it would still be random. Included in this category are instances of classes [`Poisson`](https://www.tensorflow.org/probability/api_docs/python/tfp/distributions/Poisson), [`Uniform`](https://www.tensorflow.org/probability/api_docs/python/tfp/distributions/Uniform), and [`Exponential`](https://www.tensorflow.org/probability/api_docs/python/tfp/distributions/Exponential).\n", + "\n", + "You can draw random samples from a stochastic variable. When you draw samples, those samples become [`tensorflow.Tensors`](https://www.tensorflow.org/api_docs/python/tf/Tensor) that behave deterministically from that point on. A quick mental check to determine if something is *deterministic* is: *If I knew all of the inputs for creating the variable `foo`, I could calculate the value of `foo`.* You can add, subtract, and otherwise manipulate the tensors in a variety of ways discussed below. These operations are almost always deterministic.\n" + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "NdKiqWtWIAwy" + }, + "cell_type": "markdown", + "source": [ + "#### Initializing a Distribution\n", + "\n", + "Initializing a stochastic, or random, variable requires a few class-specific parameters that describe the Distribution's shape, such as the location and scale. For example:\n", + "\n", + "```python\n", + "some_distribution = tfd.Uniform(0., 4.)\n", + "```\n", + "\n", + "initializes a stochastic, or random, [`Uniform`](https://www.tensorflow.org/probability/api_docs/python/tfp/distributions/Uniform) distribution with the lower bound at 0 and upper bound at 4. Calling `sample()` on the distribution returns a tensor that will behave deterministically from that point on:\n", + "\n", + "```python\n", + "sampled_tensor = some_distribution.sample()\n", + "```\n", + "\n", + "The next example demonstrates what we mean when we say that distributions are stochastic but tensors are deterministic:\n", + "\n", + "```\n", + "derived_tensor_1 = 1 + sampled_tensor\n", + "derived_tensor_2 = 1 + sampled_tensor # equal to 1\n", + "\n", + "derived_tensor_3 = 1 + some_distribution.sample()\n", + "derived_tensor_4 = 1 + some_distribution.sample() # different from 3\n", + "```\n", + "\n", + "The first two lines produce the same value because they refer to the same sampled tensor. The last two lines likely produce different values because they refer to independent samples drawn from the same distribution.\n", + "\n", + "To define a multiviariate distribution, just pass in arguments with the shape you want the output to be when creating the distribution. For example:\n", + "\n", + "```python\n", + "betas = tfd.Uniform([0., 0.], [1., 1.])\n", + "```\n", + "\n", + "Creates a Distribution with batch_shape (2,). Now, when you call betas.sample(),\n", + "two values will be returned instead of one. You can read more about TFP shape semantics in the [TFP docs](https://github.com/tensorflow/probability/blob/master/tensorflow_probability/examples/jupyter_notebooks/Understanding_TensorFlow_Distributions_Shapes.ipynb), but most uses in this book should be self-explanatory." + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "UPt9k8YrIAwz" + }, + "cell_type": "markdown", + "source": [ + "#### Deterministic variables\n", + "\n", + "We can create a deterministic distribution similarly to how we create a stochastic distribution. We simply call up the [`Deterministic`](https://www.tensorflow.org/probability/api_docs/python/tfp/distributions/Deterministic) class from Tensorflow Distributions and pass in the deterministic value that we desire\n", + "```python\n", + "deterministic_variable = tfd.Deterministic(name=\"deterministic_variable\", loc=some_function_of_variables)\n", + "```\n", + "\n", + "Calling `tfd.Deterministic` is useful for creating distributions that always have the same value. However, the much more common pattern for working with deterministic variables in TFP is to create a tensor or sample from a distribution:" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "feDM_HX6IAw0", + "colab": {} + }, + "cell_type": "code", + "source": [ + "lambda_1 = tfd.Exponential(rate=1., name=\"lambda_1\") #stochastic variable\n", + "lambda_2 = tfd.Exponential(rate=1., name=\"lambda_2\") #stochastic variable\n", + "tau = tfd.Uniform(name=\"tau\", low=0., high=10.) #stochastic variable\n", + "\n", + "# deterministic variable since we are getting results of lambda's after sampling \n", + "new_deterministic_variable = tfd.Deterministic(name=\"deterministic_variable\", \n", + " loc=(lambda_1.sample() + lambda_2.sample()))" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "text", + "id": "cRzLJmAJIAw3" + }, + "cell_type": "markdown", + "source": [ + "The use of the deterministic variable was seen in the previous chapter's text-message example. Recall the model for $\\lambda$ looked like: \n", + "\n", + "$$\n", + "\\lambda = \n", + "\\begin{cases}\\lambda_1 & \\text{if } t \\lt \\tau \\cr\n", + "\\lambda_2 & \\text{if } t \\ge \\tau\n", + "\\end{cases}\n", + "$$\n", + "\n", + "And in TFP code:" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "IXdTQeqrIAw3", + "outputId": "08ee59df-b3fe-4400-8019-c314715bc49e", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 127 + } + }, + "cell_type": "code", + "source": [ + "# Build graph\n", + "\n", + "# days\n", + "n_data_points = 5 # in CH1 we had ~70 data points\n", + "idx = np.arange(n_data_points)\n", + "# for n_data_points samples, select from lambda_2 if sampled tau >= day value, lambda_1 otherwise\n", + "rv_lambda_deterministic = tfd.Deterministic(tf.gather([lambda_1.sample(), lambda_2.sample()],\n", + " indices=tf.to_int32(\n", + " tau.sample() >= idx)))\n", + "lambda_deterministic = rv_lambda_deterministic.sample()\n", + "\n", + "# Execute graph\n", + "[lambda_deterministic_] = evaluate([lambda_deterministic])\n", + "\n", + "# Show results\n", + "\n", + "print(\"{} samples from our deterministic lambda model: \\n\".format(n_data_points), lambda_deterministic_ )" + ], + "execution_count": 6, + "outputs": [ + { + "output_type": "stream", + "text": [ + "WARNING:tensorflow:From :6: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "Use tf.cast instead.\n", + "5 samples from our deterministic lambda model: \n", + " [1.0830135 1.0830135 1.0830135 0.03013135 0.03013135]\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "EFgJwLATIAw8" + }, + "cell_type": "markdown", + "source": [ + "Clearly, if $\\tau, \\lambda_1$ and $\\lambda_2$ are known, then $\\lambda$ is known completely, hence it is a deterministic variable. We use indexing here to switch from $\\lambda_1$ to $\\lambda_2$ at the appropriate time. " + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "IMNdtRTtIAxB" + }, + "cell_type": "markdown", + "source": [ + "### Including observations in the model\n", + "\n", + "At this point, it may not look like it, but we have fully specified our priors. For example, we can ask and answer questions like \"What does my prior distribution of $\\lambda_1$ look like?\" \n", + "\n", + "To do this, we will sample from the distribution. The method `.sample()` has a very simple role: get data points from the given distribution. We can then evaluate the resulting tensor to get a NumPy array-like object. " + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "VNdQVTSFIAxC", + "outputId": "5e44d4b8-9ef5-4f3a-9512-fb29ad4a995e", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 393 + } + }, + "cell_type": "code", + "source": [ + "# Define our observed samples\n", + "rv_lambda_1 = tfd.Exponential(rate=1., name=\"lambda_1\")\n", + "lambda_1 = rv_lambda_1.sample(sample_shape=20000)\n", + " \n", + "# Execute graph, convert TF to NumPy\n", + "[ lambda_1_ ] = evaluate([ lambda_1 ])\n", + "\n", + "# Visualize our stepwise prior distribution\n", + "plt.figure(figsize(12.5, 5))\n", + "plt.hist(lambda_1_, bins=70, density=True, histtype=\"stepfilled\")\n", + "plt.title(r\"Prior distribution for $\\lambda_1$\")\n", + "plt.xlim(0, 8);" + ], + "execution_count": 7, + "outputs": [ + { + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.6/dist-packages/matplotlib/axes/_axes.py:6521: MatplotlibDeprecationWarning: \n", + "The 'normed' kwarg was deprecated in Matplotlib 2.1 and will be removed in 3.1. Use 'density' instead.\n", + " alternative=\"'density'\", removal=\"3.1\")\n" + ], + "name": "stderr" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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" + ] + }, + "metadata": { + "tags": [], + "image/png": { + "width": 828, + "height": 322 + } + } + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "9nOs9Gq3IAxH" + }, + "cell_type": "markdown", + "source": [ + "To frame this in the notation of the first chapter, though this is a slight abuse of notation, we have specified $P(A)$. Our next goal is to include data/evidence/observations $X$ into our model. \n", + "\n", + "Sometimes we may want to match a property of our distribution to a property of observed data. To do so, we get the parameters for our distribution fom the data itself. In this example, the Poisson rate (average number of events) is explicitly set to one over the average of the data:" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "qtHXSR6QIAxH", + "outputId": "e7e49f0e-3868-4b6d-af29-5e489afc9a18", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 143 + } + }, + "cell_type": "code", + "source": [ + "# Build graph\n", + "data = tf.constant([10., 5.], dtype=tf.float32)\n", + "rv_poisson = tfd.Poisson(rate=1./tf.reduce_mean(data))\n", + "poisson = rv_poisson.sample()\n", + "\n", + "# Execute graph\n", + "[ data_, poisson_, ] = evaluate([ data, poisson ])\n", + "\n", + "# Show results\n", + "print(\"two predetermined data points: \", data_)\n", + "print(\"\\n mean of our data: \", np.mean(data_))\n", + "print(\"\\n random sample from poisson distribution \\n with the mean as the poisson's rate: \\n\", poisson_)" + ], + "execution_count": 8, + "outputs": [ + { + "output_type": "stream", + "text": [ + "two predetermined data points: [10. 5.]\n", + "\n", + " mean of our data: 7.5\n", + "\n", + " random sample from poisson distribution \n", + " with the mean as the poisson's rate: \n", + " 0.0\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "8oxo5VcbIAxP" + }, + "cell_type": "markdown", + "source": [ + "## Modeling approaches\n", + "\n", + "A good starting thought to Bayesian modeling is to think about *how your data might have been generated*. Position yourself in an omniscient position, and try to imagine how *you* would recreate the dataset. \n", + "\n", + "In the last chapter we investigated text message data. We begin by asking how our observations may have been generated:\n", + "\n", + "1. We started by thinking \"what is the best random variable to describe this count data?\" A Poisson random variable is a good candidate because it can represent count data. So we model the number of sms's received as sampled from a Poisson distribution.\n", + "\n", + "2. Next, we think, \"Ok, assuming sms's are Poisson-distributed, what do I need for the Poisson distribution?\" Well, the Poisson distribution has a parameter $\\lambda$. \n", + "\n", + "3. Do we know $\\lambda$? No. In fact, we have a suspicion that there are *two* $\\lambda$ values, one for the earlier behaviour and one for the later behaviour. We don't know when the behaviour switches though, but call the switchpoint $\\tau$.\n", + "\n", + "4. What is a good distribution for the two $\\lambda$s? The exponential is good, as it assigns probabilities to positive real numbers. Well the exponential distribution has a parameter too, call it $\\alpha$.\n", + "\n", + "5. Do we know what the parameter $\\alpha$ might be? No. At this point, we could continue and assign a distribution to $\\alpha$, but it's better to stop once we reach a set level of ignorance: whereas we have a prior belief about $\\lambda$, (\"it probably changes over time\", \"it's likely between 10 and 30\", etc.), we don't really have any strong beliefs about $\\alpha$. So it's best to stop here. \n", + "\n", + " What is a good value for $\\alpha$ then? We think that the $\\lambda$s are between 10-30, so if we set $\\alpha$ really low (which corresponds to larger probability on high values) we are not reflecting our prior well. Similar, a too-high alpha misses our prior belief as well. A good idea for $\\alpha$ as to reflect our belief is to set the value so that the mean of $\\lambda$, given $\\alpha$, is equal to our observed mean. This was shown in the last chapter.\n", + "\n", + "6. We have no expert opinion of when $\\tau$ might have occurred. So we will suppose $\\tau$ is from a discrete uniform distribution over the entire timespan.\n", + "\n", + "\n", + "Below we give a graphical visualization of this, where arrows denote `parent-child` relationships. (provided by the [Daft Python library](http://daft-pgm.org/) )\n", + "\n", + "\n", + "\n", + "\n", + "TFP and other probabilistic programming languages have been designed to tell these data-generation *stories*. More generally, B. Cronin writes [2]:\n", + "\n", + "> Probabilistic programming will unlock narrative explanations of data, one of the holy grails of business analytics and the unsung hero of scientific persuasion. People think in terms of stories - thus the unreasonable power of the anecdote to drive decision-making, well-founded or not. But existing analytics largely fails to provide this kind of story; instead, numbers seemingly appear out of thin air, with little of the causal context that humans prefer when weighing their options." + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "3RJEK_yjIAxR" + }, + "cell_type": "markdown", + "source": [ + "### Same story; different ending.\n", + "\n", + "Interestingly, we can create *new datasets* by retelling the story.\n", + "For example, if we reverse the above steps, we can simulate a possible realization of the dataset.\n", + "\n", + "1\\. Specify when the user's behaviour switches by sampling from $\\text{DiscreteUniform}(0, 80)$:" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "Ma56S7r1IAxS", + "outputId": "0f39f444-ceb4-4cb3-dfb9-da0529bb72fc", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 35 + } + }, + "cell_type": "code", + "source": [ + "tau = tf.random_uniform(shape=[1], minval=0, maxval=80, dtype=tf.int32)\n", + "\n", + "[ tau_ ] = evaluate([ tau ])\n", + "\n", + "print(\"Value of Tau (randomly taken from DiscreteUniform(0, 80)):\", tau_)" + ], + "execution_count": 9, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Value of Tau (randomly taken from DiscreteUniform(0, 80)): [71]\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "Xt_6sYG6IAxW" + }, + "cell_type": "markdown", + "source": [ + "2\\. Draw $\\lambda_1$ and $\\lambda_2$ from a $\\text{Gamma}(\\alpha)$ distribution:\n", + "\n", + "Note: A gamma distribution is a generalization of the exponential distribution. A gamma distribution with shape parameter $α = 1$ and scale parameter $β$ is an exponential ($β$) distribution. Here, we use a gamma distribution to have more flexibility than we would have had were we to model with an exponential. Rather than returning values between $0$ and $1$, we can return values much larger than $1$ (i.e., the kinds of numbers one would expect to show up in a daily SMS count)." + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "l2QX3nEbofZr", + "outputId": "9bc5a1b7-36a8-420b-975c-84b1f087e8ec", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 53 + } + }, + "cell_type": "code", + "source": [ + "alpha = 1./8.\n", + "\n", + "lambdas = tfd.Gamma(concentration=1/alpha, rate=0.3).sample(sample_shape=[2]) \n", + "[ lambda_1_, lambda_2_ ] = evaluate( lambdas )\n", + "print(\"Lambda 1 (randomly taken from Gamma(α) distribution): \", lambda_1_)\n", + "print(\"Lambda 2 (randomly taken from Gamma(α) distribution): \", lambda_2_)" + ], + "execution_count": 10, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Lambda 1 (randomly taken from Gamma(α) distribution): 19.056139\n", + "Lambda 2 (randomly taken from Gamma(α) distribution): 21.798222\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "uIoKaO4bIAxb" + }, + "cell_type": "markdown", + "source": [ + "3\\. For days before $\\tau$, represent the user's received SMS count by sampling from $\\text{Poi}(\\lambda_1)$, and sample from $\\text{Poi}(\\lambda_2)$ for days after $\\tau$. For example:" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "6xxOtwxvpk_P", + "outputId": "a3b32970-4b82-4eae-e66b-b61d2d39e0e9", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 125 + } + }, + "cell_type": "code", + "source": [ + "data = tf.concat([tfd.Poisson(rate=lambda_1_).sample(sample_shape=tau_),\n", + " tfd.Poisson(rate=lambda_2_).sample(sample_shape= (80 - tau_))], axis=0)\n", + "days_range = tf.range(80)\n", + "[ data_, days_range_ ] = evaluate([ data, days_range ])\n", + "print(\"Artificial day-by-day user SMS count created by sampling: \\n\", data_)" + ], + "execution_count": 11, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Artificial day-by-day user SMS count created by sampling: \n", + " [19. 24. 24. 19. 16. 22. 22. 24. 24. 18. 23. 17. 13. 14. 13. 18. 16. 14.\n", + " 24. 20. 14. 22. 17. 21. 23. 18. 22. 16. 19. 22. 15. 18. 22. 16. 15. 22.\n", + " 26. 12. 17. 22. 16. 24. 16. 17. 21. 20. 21. 19. 24. 14. 21. 22. 25. 20.\n", + " 18. 22. 25. 20. 18. 17. 19. 22. 20. 26. 29. 23. 17. 17. 20. 14. 9. 14.\n", + " 17. 27. 23. 23. 18. 18. 25. 25.]\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "5PWuas1oIAxg" + }, + "cell_type": "markdown", + "source": [ + "4\\. Plot the artificial dataset:" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "vrGXdyZyIAxh", + "outputId": "9e67ca72-9de8-4457-c3a9-f606a1940b82", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 354 + } + }, + "cell_type": "code", + "source": [ + "plt.bar(days_range_, data_, color=TFColor[3])\n", + "plt.bar(tau_ - 1, data_[tau_ - 1], color=\"r\", label=\"user behaviour changed\")\n", + "plt.xlabel(\"Time (days)\")\n", + "plt.ylabel(\"count of text-msgs received\")\n", + "plt.title(\"Artificial dataset\")\n", + "plt.xlim(0, 80)\n", + "plt.legend();" + ], + "execution_count": 12, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [], + "image/png": { + "width": 843, + "height": 337 + } + } + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "ulb46iQIIAxn" + }, + "cell_type": "markdown", + "source": [ + "It is okay that our fictional dataset does not look like our observed dataset: the probability is incredibly small it indeed would. TFP's engine is designed to find good parameters, $\\lambda_i, \\tau$, that maximize this probability. \n", + "\n", + "\n", + "The ability to generate an artificial dataset is an interesting side effect of our modeling, and we will see that this ability is a very important method of Bayesian inference. We produce a few more datasets below:" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "vrLFYvVzIAxp", + "outputId": "df211715-2f83-4847-beb8-68105006dc34", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 488 + } + }, + "cell_type": "code", + "source": [ + "def plot_artificial_sms_dataset(): \n", + " tau = tf.random_uniform(shape=[1], \n", + " minval=0, \n", + " maxval=80,\n", + " dtype=tf.int32)\n", + " alpha = 1./8.\n", + " lambdas = tfd.Gamma(concentration=1/alpha, rate=0.3).sample(sample_shape=[2]) \n", + " [ lambda_1_, lambda_2_ ] = evaluate( lambdas )\n", + " data = tf.concat([tfd.Poisson(rate=lambda_1_).sample(sample_shape=tau),\n", + " tfd.Poisson(rate=lambda_2_).sample(sample_shape= (80 - tau))], axis=0)\n", + " days_range = tf.range(80)\n", + " \n", + " [ \n", + " tau_,\n", + " data_,\n", + " days_range_,\n", + " ] = evaluate([ \n", + " tau,\n", + " data,\n", + " days_range,\n", + " ])\n", + " \n", + " plt.bar(days_range_, data_, color=TFColor[3])\n", + " plt.bar(tau_ - 1, data_[tau_ - 1], color=\"r\", label=\"user behaviour changed\")\n", + " plt.xlim(0, 80);\n", + "\n", + "plt.figure(figsize(12.5, 8))\n", + "for i in range(4):\n", + " plt.subplot(4, 1, i+1)\n", + " plot_artificial_sms_dataset()\n" + ], + "execution_count": 13, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [], + "image/png": { + "width": 827, + "height": 471 + } + } + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "V8QMiJXOIAxv" + }, + "cell_type": "markdown", + "source": [ + "Later we will see how we use this to make predictions and test the appropriateness of our models." + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "2lU8C4C-IAxw" + }, + "cell_type": "markdown", + "source": [ + "### Example: Bayesian A/B testing\n", + "\n", + "A/B testing is a statistical design pattern for determining the difference of effectiveness between two different treatments. For example, a pharmaceutical company is interested in the effectiveness of drug A vs drug B. The company will test drug A on some fraction of their trials, and drug B on the other fraction (this fraction is often 1/2, but we will relax this assumption). After performing enough trials, the in-house statisticians sift through the data to determine which drug yielded better results. \n", + "\n", + "Similarly, front-end web developers are interested in which design of their website yields more sales or some other metric of interest. They will route some fraction of visitors to site A, and the other fraction to site B, and record if the visit yielded a sale or not. The data is recorded (in real-time), and analyzed afterwards. \n", + "\n", + "Often, the post-experiment analysis is done using something called a hypothesis test like *difference of means test* or *difference of proportions test*. This involves often misunderstood quantities like a \"Z-score\" and even more confusing \"p-values\" (please don't ask). If you have taken a statistics course, you have probably been taught this technique (though not necessarily *learned* this technique). And if you were like me, you may have felt uncomfortable with their derivation -- good: the Bayesian approach to this problem is much more natural. \n" + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "AFcmkQEyDgyK" + }, + "cell_type": "markdown", + "source": [ + "### A Simple Case\n", + "\n", + "As this is a hacker book, we'll continue with the web-dev example. For the moment, we will focus on the analysis of site A only. Assume that there is some true $0 \\lt p_A \\lt 1$ probability that users who, upon shown site A, eventually purchase from the site. This is the true effectiveness of site A. Currently, this quantity is unknown to us. \n", + "\n", + "Suppose site A was shown to $N$ people, and $n$ people purchased from the site. One might conclude hastily that $p_A = \\frac{n}{N}$. Unfortunately, the *observed frequency* $\\frac{n}{N}$ does not necessarily equal $p_A$ -- there is a difference between the *observed frequency* and the *true frequency* of an event. The true frequency can be interpreted as the probability of an event occurring. For example, the true frequency of rolling a 1 on a 6-sided die is $\\frac{1}{6}$. Knowing the true frequency of events like:\n", + "\n", + "- fraction of users who make purchases, \n", + "- frequency of social attributes, \n", + "- percent of internet users with cats etc. \n", + "\n", + "are common requests we ask of Nature. Unfortunately, often Nature hides the true frequency from us and we must *infer* it from observed data.\n", + "\n", + "The *observed frequency* is then the frequency we observe: say rolling the die 100 times you may observe 20 rolls of 1. The observed frequency, 0.2, differs from the true frequency, $\\frac{1}{6}$. We can use Bayesian statistics to infer probable values of the true frequency using an appropriate prior and observed data.\n", + "\n", + "\n", + "With respect to our A/B example, we are interested in using what we know, $N$ (the total trials administered) and $n$ (the number of conversions), to estimate what $p_A$, the true frequency of buyers, might be. \n", + "\n", + "To setup a Bayesian model, we need to assign prior distributions to our unknown quantities. *A priori*, what do we think $p_A$ might be? For this example, we have no strong conviction about $p_A$, so for now, let's assume $p_A$ is uniform over $[0,1]$:" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "blTLKyo2IAxy", + "colab": {} + }, + "cell_type": "code", + "source": [ + "reset_sess()\n", + "\n", + "# The parameters are the bounds of the Uniform.\n", + "rv_p = tfd.Uniform(low=0., high=1., name='p')\n" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "text", + "id": "f0XLF9h3IAx2" + }, + "cell_type": "markdown", + "source": [ + "Had we had stronger beliefs, we could have expressed them in the prior above.\n", + "\n", + "For this example, consider $p_A = 0.05$, and $N = 1500$ users shown site A, and we will simulate whether the user made a purchase or not. To simulate this from $N$ trials, we will use a *Bernoulli* distribution: if $X\\ \\sim \\text{Ber}(p)$, then $X$ is 1 with probability $p$ and 0 with probability $1 - p$. Of course, in practice we do not know $p_A$, but we will use it here to simulate the data. We can assume then that we can use the following generative model:\n", + "\n", + "$$\\begin{align*}\n", + "p &\\sim \\text{Uniform}[\\text{low}=0,\\text{high}=1) \\\\\n", + "X\\ &\\sim \\text{Bernoulli}(\\text{prob}=p) \\\\\n", + "\\text{for } i &= 1\\ldots N:\\text{# Users} \\\\\n", + " X_i\\ &\\sim \\text{Bernoulli}(p_i)\n", + "\\end{align*}$$" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "riLrk5KTIAx4", + "outputId": "f38e999d-2c7a-40e5-a269-ea34a1fb0bee", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 71 + } + }, + "cell_type": "code", + "source": [ + "reset_sess()\n", + "\n", + "#set constants\n", + "prob_true = 0.05 # remember, this is unknown.\n", + "N = 1500\n", + "\n", + "# sample N Bernoulli random variables from Ber(0.05).\n", + "# each random variable has a 0.05 chance of being a 1.\n", + "# this is the data-generation step\n", + "\n", + "occurrences = tfd.Bernoulli(probs=prob_true).sample(sample_shape=N, seed=10)\n", + "occurrences_sum = tf.reduce_sum(occurrences)\n", + "occurrences_mean = tf.reduce_mean(tf.cast(occurrences,tf.float32))\n", + "\n", + "[ \n", + " occurrences_,\n", + " occurrences_sum_,\n", + " occurrences_mean_,\n", + "] = evaluate([ \n", + " occurrences, \n", + " occurrences_sum,\n", + " occurrences_mean,\n", + "])\n", + "\n", + "print(\"Array of {} Occurences:\".format(N), occurrences_) \n", + "print(\"(Remember: Python treats True == 1, and False == 0)\")\n", + "print(\"Sum of (True == 1) Occurences:\", occurrences_sum_)" + ], + "execution_count": 15, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Array of 1500 Occurences: [0 0 0 ... 0 1 0]\n", + "(Remember: Python treats True == 1, and False == 0)\n", + "Sum of (True == 1) Occurences: 76\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "UpJrMifMIAx7" + }, + "cell_type": "markdown", + "source": [ + "The observed frequency is:" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "trjtemdNIAx7", + "outputId": "4ce20790-4eed-4386-e02a-ebd1f797791e", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 53 + } + }, + "cell_type": "code", + "source": [ + "# Occurrences.mean is equal to n/N.\n", + "print(\"What is the observed frequency in Group A? %.4f\" % occurrences_mean_)\n", + "print(\"Does this equal the true frequency? %s\" % (occurrences_mean_ == prob_true))" + ], + "execution_count": 16, + "outputs": [ + { + "output_type": "stream", + "text": [ + "What is the observed frequency in Group A? 0.0507\n", + "Does this equal the true frequency? False\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "Gue-SRTYIAyA" + }, + "cell_type": "markdown", + "source": [ + "We can combine our Bernoulli distribution and our observed occurrences into a log probability function based on the two." + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "Ct9o0w7lGaZb", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def joint_log_prob(occurrences, prob_A):\n", + " \"\"\"\n", + " Joint log probability optimization function.\n", + " \n", + " Args:\n", + " occurrences: An array of binary values (0 & 1), representing \n", + " the observed frequency\n", + " prob_A: scalar estimate of the probability of a 1 appearing \n", + " Returns: \n", + " sum of the joint log probabilities from all of the prior and conditional distributions\n", + " \"\"\" \n", + " rv_prob_A = tfd.Uniform(low=0., high=1.)\n", + " rv_occurrences = tfd.Bernoulli(probs=prob_A)\n", + " return (\n", + " rv_prob_A.log_prob(prob_A)\n", + " + tf.reduce_sum(rv_occurrences.log_prob(occurrences))\n", + " )" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "text", + "id": "UN7Mh5U-uFye" + }, + "cell_type": "markdown", + "source": [ + "The goal of probabilistic inference is to find model parameters that may explain\n", + "data you have observed. TFP performs probabilistic inference by evaluating the\n", + "model parameters using a `joint_log_prob` function. The arguments to `joint_log_prob` are data and model parameters—for the model defined in the `joint_log_prob` function itself. The function returns the log of the joint probability that the model parameterized as such generated the observed data per the input arguments.\n", + "\n", + "All `joint_log_prob` functions have a common structure:\n", + "\n", + "1. The function takes a set of **inputs** to evaluate. Each input is either an\n", + "observed value or a model parameter.\n", + "\n", + "1. The `joint_log_prob` function uses probability distributions to define a **model** for evaluating the inputs. These distributions measure the likelihood of the input values. (By convention, the distribution that measures the likelihood of the variable `foo` will be named `rv_foo` to note that it is a random variable.) We use two types of distributions in `joint_log_prob` functions:\n", + "\n", + " a. **Prior distributions** measure the likelihood of input values.\n", + "A prior distribution never depends on an input value. Each prior distribution measures the\n", + "likelihood of a single input value. Each unknown variable—one that has not been\n", + "observed directly—needs a corresponding prior. Beliefs about which values could\n", + "be reasonable determine the prior distribution. Choosing a prior can be tricky,\n", + "so we will cover it in depth in Chapter 6.\n", + "\n", + " b. **Conditional distributions** measure the likelihood of an input value given\n", + "other input values. Typically, the conditional\n", + "distributions return the likelihood of observed data given the current guess of parameters in the model, p(observed_data | model_parameters).\n", + "\n", + "1. Finally, we calculate and return the **joint log probability** of the inputs.\n", + "The joint log probability is the sum of the log probabilities from all of the\n", + "prior and conditional distributions. (We take the sum of log probabilities\n", + "instead of multiplying the probabilities directly for reasons of numerical\n", + "stability: floating point numbers in computers cannot represent the very small\n", + "values necessary to calculate the joint log probability unless they are in \n", + "log space.) The sum of probabilities is actually an unnormalized density; although the total sum of probabilities over all possible inputs might not sum to one, the sum of probabilities is proportional to the true probability density. This proportional distribution is sufficient to estimate the distribution of likely inputs.\n", + "\n", + "Let's map these terms onto the code above. In this example, the input values\n", + "are the observed values in `occurrences` and the unknown value for `prob_A`. The `joint_log_prob` takes the current guess for `prob_A`\n", + "and answers, how likely is the data if `prob_A` is the probability of\n", + "`occurrences`. The answer depends on two distributions:\n", + "1. The prior distribution, `rv_prob_A`, indicates how likely the current value of `prob_A` is by itself.\n", + "2. The conditional distribution, `rv_occurrences`, indicates the likelihood of `occurrences` if `prob_A` were the probability for the Bernoulli distribution.\n", + "\n", + "The sum of the log of these probabilities is the\n", + "joint log probability. \n", + "\n", + "The `joint_log_prob` is particularly useful in conjunction with the [`tfp.mcmc`](https://www.tensorflow.org/probability/api_docs/python/tfp/mcmc)\n", + "module. Markov chain Monte Carlo (MCMC) algorithms proceed by making educated guesses about the unknown\n", + "input values and\n", + "computing what the likelihood of this set of arguments is. (We’ll talk about how it makes those guesses in Chapter 3.) By repeating this process\n", + "many times, MCMC builds a distribution of likely parameters. Constructing this\n", + "distribution is the goal of probabilistic inference." + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "rzm3amOgDAGg" + }, + "cell_type": "markdown", + "source": [ + "Then we run our inference algorithm:" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "g9XHX0h8IAyB", + "outputId": "918d273a-1ba3-4920-c068-9864d0b00faa", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 145 + } + }, + "cell_type": "code", + "source": [ + "number_of_steps = 48000 #@param {type:\"slider\", min:2000, max:50000, step:100}\n", + "#@markdown (Default is 18000).\n", + "burnin = 25000 #@param {type:\"slider\", min:0, max:30000, step:100}\n", + "#@markdown (Default is 1000).\n", + "leapfrog_steps=2 #@param {type:\"slider\", min:1, max:9, step:1}\n", + "#@markdown (Default is 6).\n", + "\n", + "# Set the chain's start state.\n", + "initial_chain_state = [\n", + " tf.reduce_mean(tf.to_float(occurrences)) \n", + " * tf.ones([], dtype=tf.float32, name=\"init_prob_A\")\n", + "]\n", + "\n", + "# Since HMC operates over unconstrained space, we need to transform the\n", + "# samples so they live in real-space.\n", + "unconstraining_bijectors = [\n", + " tfp.bijectors.Identity() # Maps R to R. \n", + "]\n", + "\n", + "# Define a closure over our joint_log_prob.\n", + "# The closure makes it so the HMC doesn't try to change the `occurrences` but\n", + "# instead determines the distributions of other parameters that might generate\n", + "# the `occurrences` we observed.\n", + "unnormalized_posterior_log_prob = lambda *args: joint_log_prob(occurrences, *args)\n", + "\n", + "# Initialize the step_size. (It will be automatically adapted.)\n", + "with tf.variable_scope(tf.get_variable_scope(), reuse=tf.AUTO_REUSE):\n", + " step_size = tf.get_variable(\n", + " name='step_size',\n", + " initializer=tf.constant(0.5, dtype=tf.float32),\n", + " trainable=False,\n", + " use_resource=True\n", + " )\n", + "\n", + "# Defining the HMC\n", + "hmc = tfp.mcmc.TransformedTransitionKernel(\n", + " inner_kernel=tfp.mcmc.HamiltonianMonteCarlo(\n", + " target_log_prob_fn=unnormalized_posterior_log_prob,\n", + " num_leapfrog_steps=leapfrog_steps,\n", + " step_size=step_size,\n", + " step_size_update_fn=tfp.mcmc.make_simple_step_size_update_policy(num_adaptation_steps=int(burnin * 0.8)),\n", + " state_gradients_are_stopped=True),\n", + " bijector=unconstraining_bijectors)\n", + "\n", + "# Sampling from the chain.\n", + "[\n", + " posterior_prob_A\n", + "], kernel_results = tfp.mcmc.sample_chain(\n", + " num_results=number_of_steps,\n", + " num_burnin_steps=burnin,\n", + " current_state=initial_chain_state,\n", + " kernel=hmc)\n", + "\n", + "# Initialize any created variables.\n", + "init_g = tf.global_variables_initializer()\n", + "init_l = tf.local_variables_initializer()" + ], + "execution_count": 18, + "outputs": [ + { + "output_type": "stream", + "text": [ + "WARNING:tensorflow:From :10: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "Use tf.cast instead.\n", + "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/resource_variable_ops.py:435: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "Colocations handled automatically by placer.\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "yUVnbqhDVfAx" + }, + "cell_type": "markdown", + "source": [ + "#### Execute the TF graph to sample from the posterior" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "Q3By4GWdEtQN", + "outputId": "2b54d312-7002-474b-cbe1-68a83057eadf", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 35 + } + }, + "cell_type": "code", + "source": [ + "evaluate(init_g)\n", + "evaluate(init_l)\n", + "[\n", + " posterior_prob_A_,\n", + " kernel_results_,\n", + "] = evaluate([\n", + " posterior_prob_A,\n", + " kernel_results,\n", + "])\n", + "\n", + " \n", + "print(\"acceptance rate: {}\".format(\n", + " kernel_results_.inner_results.is_accepted.mean()))\n", + "\n", + "burned_prob_A_trace_ = posterior_prob_A_[burnin:]" + ], + "execution_count": 19, + "outputs": [ + { + "output_type": "stream", + "text": [ + "acceptance rate: 0.46827083333333336\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "MQUWTY7-LgGv" + }, + "cell_type": "markdown", + "source": [ + "We plot the posterior distribution of the unknown $p_A$ below:" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "w_P52-CRFJPs", + "outputId": "5d35d8fd-c137-43ef-c4d7-b692541f08d1", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 338 + } + }, + "cell_type": "code", + "source": [ + "plt.figure(figsize(12.5, 4))\n", + "plt.title(\"Posterior distribution of $p_A$, the true effectiveness of site A\")\n", + "plt.vlines(prob_true, 0, 90, linestyle=\"--\", label=\"true $p_A$ (unknown)\")\n", + "plt.hist(burned_prob_A_trace_, bins=25, histtype=\"stepfilled\", density=True)\n", + "plt.legend();" + ], + "execution_count": 20, + "outputs": [ + { + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.6/dist-packages/matplotlib/axes/_axes.py:6521: MatplotlibDeprecationWarning: \n", + "The 'normed' kwarg was deprecated in Matplotlib 2.1 and will be removed in 3.1. Use 'density' instead.\n", + " alternative=\"'density'\", removal=\"3.1\")\n" + ], + "name": "stderr" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [], + "image/png": { + "width": 823, + "height": 267 + } + } + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "LdLJ2iriIAyI" + }, + "cell_type": "markdown", + "source": [ + "Our posterior distribution puts most weight near the true value of $p_A$, but also some weights in the tails. This is a measure of how uncertain we should be, given our observations. Try changing the number of observations, `N`, and observe how the posterior distribution changes.\n", + "\n", + "### *A* and *B* Together\n", + "\n", + "A similar analysis can be done for site B's response data to determine the analogous $p_B$. But what we are really interested in is the *difference* between $p_A$ and $p_B$. Let's infer $p_A$, $p_B$, *and* $\\text{delta} = p_A - p_B$, all at once. We can do this using TFP's deterministic variables. (We'll assume for this exercise that $p_B = 0.04$, so $\\text{delta} = 0.01$, $N_B = 750$ (significantly less than $N_A$) and we will simulate site B's data like we did for site A's data ). Our model now looks like the following:\n", + "\n", + "$$\\begin{align*}\n", + "p_A &\\sim \\text{Uniform}[\\text{low}=0,\\text{high}=1) \\\\\n", + "p_B &\\sim \\text{Uniform}[\\text{low}=0,\\text{high}=1) \\\\\n", + "X\\ &\\sim \\text{Bernoulli}(\\text{prob}=p) \\\\\n", + "\\text{for } i &= 1\\ldots N: \\\\\n", + " X_i\\ &\\sim \\text{Bernoulli}(p_i)\n", + "\\end{align*}$$" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "yPDLHl6RIAyJ", + "outputId": "35f82e1b-56d9-4428-dc6e-938f31cea821", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 89 + } + }, + "cell_type": "code", + "source": [ + "reset_sess()\n", + "\n", + "#these two quantities are unknown to us.\n", + "true_prob_A_ = 0.05\n", + "true_prob_B_ = 0.04\n", + "\n", + "#notice the unequal sample sizes -- no problem in Bayesian analysis.\n", + "N_A_ = 1500\n", + "N_B_ = 750\n", + "\n", + "#generate some observations\n", + "observations_A = tfd.Bernoulli(name=\"obs_A\", \n", + " probs=true_prob_A_).sample(sample_shape=N_A_, seed=6.45)\n", + "observations_B = tfd.Bernoulli(name=\"obs_B\", \n", + " probs=true_prob_B_).sample(sample_shape=N_B_, seed=6.45)\n", + "[ \n", + " observations_A_,\n", + " observations_B_,\n", + "] = evaluate([ \n", + " observations_A, \n", + " observations_B, \n", + "])\n", + "\n", + "print(\"Obs from Site A: \", observations_A_[:30], \"...\")\n", + "print(\"Observed Prob_A: \", np.mean(observations_A_), \"...\")\n", + "print(\"Obs from Site B: \", observations_B_[:30], \"...\")\n", + "print(\"Observed Prob_B: \", np.mean(observations_B_))" + ], + "execution_count": 21, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Obs from Site A: [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0] ...\n", + "Observed Prob_A: 0.050666666666666665 ...\n", + "Obs from Site B: [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0] ...\n", + "Observed Prob_B: 0.04\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "LDzYsDVgMgsz" + }, + "cell_type": "markdown", + "source": [ + "Below we run inference over the new model:" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "7ghHBEdXYtxV", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def delta(prob_A, prob_B):\n", + " \"\"\"\n", + " Defining the deterministic delta function. This is our unknown of interest.\n", + " \n", + " Args:\n", + " prob_A: scalar estimate of the probability of a 1 appearing in \n", + " observation set A\n", + " prob_B: scalar estimate of the probability of a 1 appearing in \n", + " observation set B\n", + " Returns: \n", + " Difference between prob_A and prob_B\n", + " \"\"\"\n", + " return prob_A - prob_B\n", + "\n", + " \n", + "def double_joint_log_prob(observations_A, observations_B, \n", + " prob_A, prob_B):\n", + " \"\"\"\n", + " Joint log probability optimization function.\n", + " \n", + " Args:\n", + " observations_A: An array of binary values representing the set of \n", + " observations for site A\n", + " observations_B: An array of binary values representing the set of \n", + " observations for site B \n", + " prob_A: scalar estimate of the probability of a 1 appearing in \n", + " observation set A\n", + " prob_B: scalar estimate of the probability of a 1 appearing in \n", + " observation set B \n", + " Returns: \n", + " Joint log probability optimization function.\n", + " \"\"\"\n", + " tfd = tfp.distributions\n", + " \n", + " rv_prob_A = tfd.Uniform(low=0., high=1.)\n", + " rv_prob_B = tfd.Uniform(low=0., high=1.)\n", + " \n", + " rv_obs_A = tfd.Bernoulli(probs=prob_A)\n", + " rv_obs_B = tfd.Bernoulli(probs=prob_B)\n", + " \n", + " return (\n", + " rv_prob_A.log_prob(prob_A)\n", + " + rv_prob_B.log_prob(prob_B)\n", + " + tf.reduce_sum(rv_obs_A.log_prob(observations_A))\n", + " + tf.reduce_sum(rv_obs_B.log_prob(observations_B))\n", + " )\n" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "code", + "id": "h0TDeF3IIAyQ", + "colab": {} + }, + "cell_type": "code", + "source": [ + "number_of_steps = 37200 #@param {type:\"slider\", min:2000, max:50000, step:100}\n", + "#@markdown (Default is 18000).\n", + "burnin = 1000 #@param {type:\"slider\", min:0, max:30000, step:100}\n", + "#@markdown (Default is 1000).\n", + "leapfrog_steps=3 #@param {type:\"slider\", min:1, max:9, step:1}\n", + "#@markdown (Default is 6).\n", + "\n", + "\n", + "# Set the chain's start state.\n", + "initial_chain_state = [ \n", + " tf.reduce_mean(tf.to_float(observations_A)) * tf.ones([], dtype=tf.float32, name=\"init_prob_A\"),\n", + " tf.reduce_mean(tf.to_float(observations_B)) * tf.ones([], dtype=tf.float32, name=\"init_prob_B\")\n", + "]\n", + "\n", + "# Since HMC operates over unconstrained space, we need to transform the\n", + "# samples so they live in real-space.\n", + "unconstraining_bijectors = [\n", + " tfp.bijectors.Identity(), # Maps R to R.\n", + " tfp.bijectors.Identity() # Maps R to R.\n", + "]\n", + "\n", + "# Define a closure over our joint_log_prob.\n", + "unnormalized_posterior_log_prob = lambda *args: double_joint_log_prob(observations_A, observations_B, *args)\n", + "\n", + "# Initialize the step_size. (It will be automatically adapted.)\n", + "with tf.variable_scope(tf.get_variable_scope(), reuse=tf.AUTO_REUSE):\n", + " step_size = tf.get_variable(\n", + " name='step_size',\n", + " initializer=tf.constant(0.5, dtype=tf.float32),\n", + " trainable=False,\n", + " use_resource=True\n", + " )\n", + "\n", + "# Defining the HMC\n", + "hmc=tfp.mcmc.TransformedTransitionKernel(\n", + " inner_kernel=tfp.mcmc.HamiltonianMonteCarlo(\n", + " target_log_prob_fn=unnormalized_posterior_log_prob,\n", + " num_leapfrog_steps=3,\n", + " step_size=step_size,\n", + " step_size_update_fn=tfp.mcmc.make_simple_step_size_update_policy(num_adaptation_steps=int(burnin * 0.8)),\n", + " state_gradients_are_stopped=True),\n", + " bijector=unconstraining_bijectors)\n", + "\n", + "# Sample from the chain.\n", + "[\n", + " posterior_prob_A,\n", + " posterior_prob_B\n", + "], kernel_results = tfp.mcmc.sample_chain(\n", + " num_results=number_of_steps,\n", + " num_burnin_steps=burnin,\n", + " current_state=initial_chain_state,\n", + " kernel=hmc)\n", + "\n", + "# Initialize any created variables.\n", + "init_g = tf.global_variables_initializer()\n", + "init_l = tf.local_variables_initializer()" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "text", + "id": "beUUmGMbdrRr" + }, + "cell_type": "markdown", + "source": [ + "#### Execute the TF graph to sample from the posterior" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "HTYITb9fdqIe", + "outputId": "d91c2161-7644-4beb-9d17-66413ee86bf0", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 35 + } + }, + "cell_type": "code", + "source": [ + "evaluate(init_g)\n", + "evaluate(init_l)\n", + "[\n", + " posterior_prob_A_,\n", + " posterior_prob_B_,\n", + " kernel_results_\n", + "] = evaluate([\n", + " posterior_prob_A,\n", + " posterior_prob_B,\n", + " kernel_results\n", + "])\n", + " \n", + "print(\"acceptance rate: {}\".format(\n", + " kernel_results_.inner_results.is_accepted.mean()))\n", + "\n", + "burned_prob_A_trace_ = posterior_prob_A_[burnin:]\n", + "burned_prob_B_trace_ = posterior_prob_B_[burnin:]\n", + "burned_delta_trace_ = (posterior_prob_A_ - posterior_prob_B_)[burnin:]\n" + ], + "execution_count": 24, + "outputs": [ + { + "output_type": "stream", + "text": [ + "acceptance rate: 0.6900537634408602\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "YaD67cOkIAyT" + }, + "cell_type": "markdown", + "source": [ + "Below we plot the posterior distributions for the three unknowns: " + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "PpBXqVKELHRO", + "outputId": "b7cfdf00-990c-416b-9b3f-9138eb77197e", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 908 + } + }, + "cell_type": "code", + "source": [ + "plt.figure(figsize(12.5, 12.5))\n", + "\n", + "#histogram of posteriors\n", + "\n", + "ax = plt.subplot(311)\n", + "\n", + "plt.xlim(0, .1)\n", + "plt.hist(burned_prob_A_trace_, histtype='stepfilled', bins=25, alpha=0.85,\n", + " label=\"posterior of $p_A$\", color=TFColor[0], density=True)\n", + "plt.vlines(true_prob_A_, 0, 80, linestyle=\"--\", label=\"true $p_A$ (unknown)\")\n", + "plt.legend(loc=\"upper right\")\n", + "plt.title(\"Posterior distributions of $p_A$, $p_B$, and delta unknowns\")\n", + "\n", + "ax = plt.subplot(312)\n", + "\n", + "plt.xlim(0, .1)\n", + "plt.hist(burned_prob_B_trace_, histtype='stepfilled', bins=25, alpha=0.85,\n", + " label=\"posterior of $p_B$\", color=TFColor[2], density=True)\n", + "plt.vlines(true_prob_B_, 0, 80, linestyle=\"--\", label=\"true $p_B$ (unknown)\")\n", + "plt.legend(loc=\"upper right\")\n", + "\n", + "ax = plt.subplot(313)\n", + "plt.hist(burned_delta_trace_, histtype='stepfilled', bins=30, alpha=0.85,\n", + " label=\"posterior of delta\", color=TFColor[6], density=True)\n", + "plt.vlines(true_prob_A_ - true_prob_B_, 0, 60, linestyle=\"--\",\n", + " label=\"true delta (unknown)\")\n", + "plt.vlines(0, 0, 60, color=\"black\", alpha=0.2)\n", + "plt.legend(loc=\"upper right\");" + ], + "execution_count": 25, + "outputs": [ + { + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.6/dist-packages/matplotlib/axes/_axes.py:6521: MatplotlibDeprecationWarning: \n", + "The 'normed' kwarg was deprecated in Matplotlib 2.1 and will be removed in 3.1. Use 'density' instead.\n", + " alternative=\"'density'\", removal=\"3.1\")\n", + "/usr/local/lib/python3.6/dist-packages/matplotlib/axes/_axes.py:6521: MatplotlibDeprecationWarning: \n", + "The 'normed' kwarg was deprecated in Matplotlib 2.1 and will be removed in 3.1. Use 'density' instead.\n", + " alternative=\"'density'\", removal=\"3.1\")\n", + "/usr/local/lib/python3.6/dist-packages/matplotlib/axes/_axes.py:6521: MatplotlibDeprecationWarning: \n", + "The 'normed' kwarg was deprecated in Matplotlib 2.1 and will be removed in 3.1. Use 'density' instead.\n", + " alternative=\"'density'\", removal=\"3.1\")\n" + ], + "name": "stderr" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [], + "image/png": { + "width": 833, + "height": 729 + } + } + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "Hn-G0eFwIAyh" + }, + "cell_type": "markdown", + "source": [ + "Notice that as a result of `N_B < N_A`, i.e. we have less data from site B, our posterior distribution of $p_B$ is fatter, implying we are less certain about the true value of $p_B$ than we are of $p_A$. \n", + "\n", + "With respect to the posterior distribution of $\\text{delta}$, we can see that the majority of the distribution is above $\\text{delta}=0$, implying there site A's response is likely better than site B's response. The probability this inference is incorrect is easily computable:" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "nZxDurxyIAyh", + "outputId": "8515d96a-0c65-4843-a326-d39aa5bddea6", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 53 + } + }, + "cell_type": "code", + "source": [ + "# Count the number of samples less than 0, i.e. the area under the curve\n", + "# before 0, represent the probability that site A is worse than site B.\n", + "print(\"Probability site A is WORSE than site B: %.3f\" % \\\n", + " np.mean(burned_delta_trace_ < 0))\n", + "\n", + "print(\"Probability site A is BETTER than site B: %.3f\" % \\\n", + " np.mean(burned_delta_trace_ > 0))" + ], + "execution_count": 26, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Probability site A is WORSE than site B: 0.294\n", + "Probability site A is BETTER than site B: 0.706\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "Q8cAEzbUIAyl" + }, + "cell_type": "markdown", + "source": [ + "If this probability is too high for comfortable decision-making, we can perform more trials on site B (as site B has less samples to begin with, each additional data point for site B contributes more inferential \"power\" than each additional data point for site A). \n", + "\n", + "Try playing with the parameters `true_prob_A`, `true_prob_B`, `N_A`, and `N_B`, to see what the posterior of $\\text{delta}$ looks like. Notice in all this, the difference in sample sizes between site A and site B was never mentioned: it naturally fits into Bayesian analysis.\n", + "\n", + "I hope the readers feel this style of A/B testing is more natural than hypothesis testing, which has probably confused more than helped practitioners. Later in this book, we will see two extensions of this model: the first to help dynamically adjust for bad sites, and the second will improve the speed of this computation by reducing the analysis to a single equation. " + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "f-jxOi70IAyl" + }, + "cell_type": "markdown", + "source": [ + "## An algorithm for human deceit\n", + "\n", + "Social data has an additional layer of interest as people are not always honest with responses, which adds a further complication into inference. For example, simply asking individuals \"Have you ever cheated on a test?\" will surely contain some rate of dishonesty. What you can say for certain is that the true rate is less than your observed rate (assuming individuals lie *only* about *not cheating*; I cannot imagine one who would admit \"Yes\" to cheating when in fact they hadn't cheated). \n", + "\n", + "To present an elegant solution to circumventing this dishonesty problem, and to demonstrate Bayesian modeling, we first need to introduce the binomial distribution.\n" + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "qzCqZqzBDpMa" + }, + "cell_type": "markdown", + "source": [ + "## The Binomial Distribution\n", + "\n", + "The binomial distribution is one of the most popular distributions, mostly because of its simplicity and usefulness. Unlike the other distributions we have encountered thus far in the book, the binomial distribution has 2 parameters: $N$, a positive integer representing $N$ trials or number of instances of potential events, and $p$, the probability of an event occurring in a single trial. Like the Poisson distribution, it is a discrete distribution, but unlike the Poisson distribution, it only weighs integers from $0$ to $N$. The mass distribution looks like:\n", + "\n", + "$$P( X = k ) = {{N}\\choose{k}} p^k(1-p)^{N-k}$$\n", + "\n", + "If $X$ is a binomial random variable with parameters $p$ and $N$, denoted $X \\sim \\text{Bin}(N,p)$, then $X$ is the number of events that occurred in the $N$ trials (obviously $0 \\le X \\le N$). The larger $p$ is (while still remaining between 0 and 1), the more events are likely to occur. The expected value of a binomial is equal to $Np$. Below we plot the mass probability distribution for varying parameters. " + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "9I53Ta3maWgJ", + "outputId": "d1d4ea3c-9696-4e77-c821-9704c217c743", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 299 + } + }, + "cell_type": "code", + "source": [ + "N = 10.\n", + "k_values = tf.range(start=0, limit=(N + 1), dtype=tf.float32)\n", + "rv_probs_1 = tfd.Binomial(total_count=N, probs=.4).prob(k_values)\n", + "rv_probs_2 = tfd.Binomial(total_count=N, probs=.9).prob(k_values)\n", + "\n", + "# Execute graph\n", + "[\n", + " k_values_,\n", + " rv_probs_1_,\n", + " rv_probs_2_,\n", + "] = evaluate([\n", + " k_values,\n", + " rv_probs_1,\n", + " rv_probs_2,\n", + "])\n", + "\n", + "# Display results\n", + "plt.figure(figsize=(12.5, 4))\n", + "colors = [TFColor[3], TFColor[0]] \n", + "\n", + "plt.bar(k_values_ - 0.5, rv_probs_1_, color=colors[0],\n", + " edgecolor=colors[0],\n", + " alpha=0.6,\n", + " label=\"$N$: %d, $p$: %.1f\" % (10., .4),\n", + " linewidth=3)\n", + "plt.bar(k_values_ - 0.5, rv_probs_2_, color=colors[1],\n", + " edgecolor=colors[1],\n", + " alpha=0.6,\n", + " label=\"$N$: %d, $p$: %.1f\" % (10., .9),\n", + " linewidth=3)\n", + "\n", + "plt.legend(loc=\"upper left\")\n", + "plt.xlim(0, 10.5)\n", + "plt.xlabel(\"$k$\")\n", + "plt.ylabel(\"$P(X = k)$\")\n", + "plt.title(\"Probability mass distributions of binomial random variables\");" + ], + "execution_count": 27, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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Poi3I5ti5ETgoD9g3qmz1+Lzs6rG7XI+mlB4E9iGrOx/Py/1qnv8c4JCU0gs9\nVfY6KA5GPZHfh7IafI9eIZsD8gra6uo1eVkmp5Suq2U3ZHMnnkX2mT+E7DxmAIellH5RJW+lcs0G\n9iL7/Luf7PkZQ1YXzCHrmT85pXR3UbYeqZslServooahdSVJkqQORcSPyRpsf5lS+mCjyyNJkiRJ\nkvomg1OSJEnqtnxOnsVkPSqO7KNzbUiSJEmSpD7AYf0kSZLULRExhGyOjlHAfANTkiRJkiSpmkrz\nBUiSJElVRcQ/k01YvjUwkmxuh67MeSRJkiRJkjYj9pySJElSV40CdiL7wdNc4PiU0szGFkmSJEmS\nJPV1zjklSZIkSZIkSZKkXmPPKUmSJEmSJEmSJPUag1OSJEmSJEmSJEnqNQanJEmSJEmSJEmS1GsM\nTkmSJEmSJEmSJKnXGJySJEmSJEmSJElSrxnU6AKovlpaWuYCuwArgYUNLo4kSZIkSZIkSerfdgdG\nAU9tueWWB9RjhwMyOBURHwZOB/YDtgAeBa4FrkgptXZz36cBV+b//iCldGaVtO8BzgLeDAwDngR+\nDnw3pbSuO+WoYhdgy3zZvoeOIUmSJEmSJEmSNi+71GtHA25Yv4j4AXAjWUDoj8BdwB7A5cBNEdHl\nc46InYDvAqmGtF8Efgu8E3gIuB3YFvgaMCsiRnS1HB1Y2UP7laS6WL16NatXr250MSSpIuspSf2B\ndZWkvs56SlJfZz3VJXWLPwyo4FREnACcATwH7JdSOjal9H5gAvB/wPuBT3dx3wFcTXbNbugg7ZuB\nC4HVwOEppX9MKZ0I7ArcAxwKfL0r5aiBQ/lJ6tOWLFnCkiVLGl0MSarIekpSf2BdJamvs56S1NdZ\nT3VJ3eIPAyo4BZybv34ppdRcWJlSep5smD+AL3ex99S/AUfmx1jUQdovAwF8K6X0p6JyrAQ+BrQC\nZ0TEmC6UQ5IkSZIkSZIkqd8aMMGpiNgBeBOwHvh16faU0t3AEuANZD2XOrPvXYBvA/eSDQ9YLe0Q\n4J/yf28sU44ngQeAIcDRnSmHJEmSJEmSJElSfzdgglPAAfnrIymlNRXSzC5J26F8OL9rgEHA1JRS\nR/NN7QmMAF5JKT1Rr3JIkiRJkiRJkiQNBIMaXYA62iV/fbpKmr+VpK3FmcARwJdTSo93ohx/q5Km\nU+WIiFOBU2tJO2vWrEmTJk1i9erVjpcpqU9rbm7uOJEkNZD1lKT+wLpKUl9nPSWpr7Oe6tj222/P\niBEj6rrPgRScGpW/rqqSZmX++rpadhgRuwEXAnOA7zaqHMDOwORaEq5cubLjRJIkSZIkSZIkSQ0y\nkIJTdVU0nN9gsuH8XmtgcRZx03qhAAAgAElEQVQBd9eScNSoUZOALUeMGMGECRN6tFCS1BWFX6NY\nR0nqq6ynJPUH1lWS+jrrKUl9nfVUYw2k4FShy9DIKmkKvZpW1LC/zwBvB85PKc1vYDlIKV0HXFdL\n2paWllnU2MtKkiRJkiRJkiSptw2k4NSi/HWnKmnGl6St5v3567siojTYs3MhTURMBFamlI4t2feO\ndSpHj2ptbWXlypWsXr2aDRs2NLo4Ur83ePBgRowYwahRo2hqamp0cSRJkiRJkiSpzxlIwam5+es/\nRMTwlNKaMmkOKklbi7dU2fbGfGkpWvcosAYYGxG7pZSeKJPv4C6Uo+5aW1t56aWXWLduXSOLIQ0o\nGzZsoKWlhbVr17L11lsboJIkSZIkSZKkEgMmOJVSeiYiHgIOBE4Ebijenvd+2gF4Dnighv0dUWlb\nREwDvgr8IKV0Zkm+9RHxW+B44CTg/JK8u5IFvNYDt3dUjp60cuVK1q1bxxZbbMFWW23F0KFDbUiX\nuqG1tZV169axdOlS1q1bx8qVKxk9enSjiyVJkiRJkiRJfcpAi0R8M3/9VkTsXlgZEdsCP8z/vTCl\n1Fq07cyIeDQiNglmddOFQAK+FBGFXlJExCjgGrLr/sOU0rI6HrPTVq9eDcBWW23F8OHDDUxJ3dTU\n1MTw4cMZM2YM0PYekyRJkiRJkiS1GVDRiJTSTcAVwBuABRFxW0TcDDQD+wC3AJeXZNsa2JPqc0R1\nthyzgS8DI4D7I+LOiPgV8AQwGfgT8B/1Ol5XFeaYGjp0aINLIg0sw4YNA+DVV19tcEkkSZIkSZIk\nqe8ZMMP6FaSUzoiIe4FPkQWCtiCbB+oa4IriXlM9XI5vR8R84Atkc10NA54ELgW+m1LqMxM92WNK\nqq+IACCl1OCSSJIkSZIkSVLfM+CCUwAppZ8BP6sx7TRgWif3X1OelNIdwB2d2bek/q8QnJIkSZIk\nSZIktTcgg1OSJEmSJEmS+pkb6zklvPqFk05pdAkkNYjjuUmSJEmSJEmSJKnX2HNKZf1iwfJGF6Eu\nPrjv6EYXQZIkSZIkSZIkFTE4JUmSJEmSJKlvaX680SVQT5mwR6NLIKkPcFg/SZIkSZIkSZIk9Rp7\nTqlDC1/Z0OgidMruYwfXfZ8//elPOfPMMwHYf//9ufvuu8umu+mmm/j4xz/OwQcfzJ133tmtYzY3\nNzNz5kzmzp3L3LlzWbhwISklrr/+eqZMmdJh/l//+tdcc801PPLII7z22mtMmDCBk046ialTp9LU\nNDDi0j19jueffz7f+973ALjgggv49Kc/3e19SpIkSZIkSdLmzuCUVIO//OUvm/z99NNPs9NOO7VL\nN2/ePCALYHXX1VdfzY9+9KMu5T377LO56qqrGDZsGJMnT2bQoEHcc889nHPOOdx9993ccMMN/T5A\n1dPn+NBDD3HJJZcQEaSU6lhySZIkSZLUKQ4D1/85TKOkEv27dVrqJfPnzwdgzz33BOC2224rm64Q\nxNpvv/26fcx99tmHz3zmM1x77bXMnTuXww8/vKZ8t956K1dddRXjxo3jvvvu45e//CU33ngjf/7z\nn9lzzz2ZMWMGV155ZbfL10g9fY7r1q3j9NNPZ9ttt+Xoo4+uY8klSZIkSZIkSfacUqf0xJB59dCT\nQw+2trby8MMPA3Duuedy6qmnMmPGjI3D/BUrBLHq0XPqlFNO6VK+73//+wBMmzaN3XbbbeP6bbfd\nlosuuohjjz2Wiy++mE9+8pP9tvdUT5/jN77xDR577DF+/vOfM3369LqVW5IkSZIkSZJkzympQ83N\nzaxatYrRo0czZcoUdtxxRx588EGef/75TdItWrSIlpYWhg4dyt57792Qsi5ZsoR58+YxZMgQ3ve+\n97Xb/ta3vpU3vvGNPP/888yePbtux91ll13YaqutePbZZ/nmN7/J4Ycfzvbbb892223H0UcfzQMP\nPFC3Y/X0Oc6ZM4fLL7+cE088kX/6p3+qR5ElSZIkSZIkSUUMTkkdKB6qLyI49thjaW1tZcaMGWXT\n7bPPPgwe3NbD7MYbb2TMmDHsu+++PV7WQs+tvfbai+HDh5dNc8ABB2yStrsWLVrE0qVLGTt2LCec\ncALf/e53GTt2LO985zsZM2YM999/P1OmTNlk3i7o+nXpyXNcu3Ytp59+OltttRUXXnhhp/JKkiRJ\nkiRJkmpjcErqQCGoUhiq77jjjgNoN9xbabpGePrppwEYP358xTQ77LDDJmm7a+7cuQC8/PLLRAR/\n+tOfuO222/jJT37CnDlzOOyww1i/fj0XX3xxXY7Xk+d4wQUX0NzczLe//W1e//rXd72QkiRJkiRJ\nkqSKDE5JHSgNOh1yyCGMGzeO++67j6VLl25MN2/evE3SFYwePZoJEyawyy679HhZV61aBcDIkSMr\nphk1ahQAK1eurMsxC+c9duxYbrnlFnbfffeN20aOHMmXvvQlgHZD7HX1uvTUOf7pT3/iiiuu4Jhj\njuH444/vVJkkSZIkSZIkSbUzOCV1YMGCBUBb0KmpqYmjjz6aV199ldtvv31jusIQcpMmTdok/3HH\nHcfs2bPb9bQaKAo9pz7/+c+z7bbbttu+2267AW1BpYK+dF3WrFnDGWecwete9zouuuiiRhdHkiRJ\nkiRJkgY0g1NSFU899RQtLS2MGDGCCRMmbFxfGNrvtttuA2Dx4sW89NJLDB48mH322achZYW23kSl\ngaBihd5Ehd5F3ZFS2tiz7AMf+EDZNIWybLPNNt0+HvTMOZ5//vk88cQTfP3rX+cNb3hD9wspSZIk\nSZIkSapoUKMLIPVlhcDLxIkTaWpqi+W+7W1vY8yYMcyaNYsVK1ZsTLfnnnsydOjQhpQVYMcddwTg\nmWeeqZhmyZIlm6TtjieffJKWlhZ22GEHxo0bVzbNgw8+CMB+++3X7eNBz5zjjBkzaGpq4uc//zk/\n//nPN9nW3NwMwNVXX80dd9zBrrvuymWXXdaVokuSJEmSJEmSMDglVVU631TB4MGDOeqoo/jlL3/J\nnXfeyWOPPVY2XW8rBIAeffRR1qxZw/Dhw9ulKQzDV49gUWG+qdGjR1dM84tf/AJo623WXT11jq2t\nrdx3330Vty9atIhFixbR0tLSyRJLkiRJkiRJkoo5rJ9URSE4VS7IUQi2TJ8+fWO60vmmetsOO+zA\n/vvvz/r167nlllvabb/33ntZsmQJ48aN4+CDD+728QpBoMWLF/Pqq6+2237HHXdw3333sffee9ct\nONUT57hgwQKWLVtWdvnQhz4EwAUXXMCyZcu4995763IekiRJkiRJkrS5MjilTln4yoY+ufSU+fPn\nA+V7RB155JGMHDmSmTNn8tBDD1VMd9ttt3HQQQfx3ve+t8fKWeyss84CYNq0aTz55JMb17/44ouc\nffbZAHzuc5/bZJhCgBtvvJExY8aw77771nysQnBq+fLlXHLJJZtsu/vuuznttNMYMmQIl156abvj\ndee6dPUczzvvPA466CDOO++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" + ] + }, + "metadata": { + "tags": [], + "image/png": { + "width": 851, + "height": 282 + } + } + } + ] + }, + { + "metadata": { + "id": "8qXPOubvsYht", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "text", + "id": "xT-q-ZI0IAys" + }, + "cell_type": "markdown", + "source": [ + "The special case when $N = 1$ corresponds to the Bernoulli distribution. There is another connection between Bernoulli and Binomial random variables. If we have $X_1, X_2, ... , X_N$ Bernoulli random variables with the same $p$, then $Z = X_1 + X_2 + ... + X_N \\sim \\text{Binomial}(N, p )$.\n", + "\n", + "The expected value of a Bernoulli random variable is $p$. This can be seen by noting the more general Binomial random variable has expected value $Np$ and setting $N=1$." + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "9HmW-50PIAyv" + }, + "cell_type": "markdown", + "source": [ + "## Example: Cheating among students\n", + "\n", + "We will use the binomial distribution to determine the frequency of students cheating during an exam. If we let $N$ be the total number of students who took the exam, and assuming each student is interviewed post-exam (answering without consequence), we will receive integer $X$ \"Yes I did cheat\" answers. We then find the posterior distribution of $p$, given $N$, some specified prior on $p$, and observed data $X$. \n", + "\n", + "This is a completely absurd model. No student, even with a free-pass against punishment, would admit to cheating. What we need is a better *algorithm* to ask students if they had cheated. Ideally the algorithm should encourage individuals to be honest while preserving privacy. The following proposed algorithm is a solution I greatly admire for its ingenuity and effectiveness:\n", + "\n", + "> In the interview process for each student, the student flips a coin, hidden from the interviewer. The student agrees to answer honestly if the coin comes up heads. Otherwise, if the coin comes up tails, the student (secretly) flips the coin again, and answers \"Yes, I did cheat\" if the coin flip lands heads, and \"No, I did not cheat\", if the coin flip lands tails. This way, the interviewer does not know if a \"Yes\" was the result of a guilty plea, or a Heads on a second coin toss. Thus privacy is preserved and the researchers receive honest answers. \n", + "\n", + "I call this the Privacy Algorithm. One could of course argue that the interviewers are still receiving false data since some *Yes*'s are not confessions but instead randomness, but an alternative perspective is that the researchers are discarding approximately half of their original dataset since half of the responses will be noise. But they have gained a systematic data generation process that can be modeled. Furthermore, they do not have to incorporate (perhaps somewhat naively) the possibility of deceitful answers. We can use TFP to dig through this noisy model, and find a posterior distribution for the true frequency of liars. " + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "gPcqaqxMIAyw" + }, + "cell_type": "markdown", + "source": [ + "Suppose 100 students are being surveyed for cheating, and we wish to find $p$, the proportion of cheaters. There are a few ways we can model this in TFP. I'll demonstrate the most explicit way, and later show a simplified version. Both versions arrive at the same inference. In our data-generation model, we sample $p$, the true proportion of cheaters, from a prior. Since we are quite ignorant about $p$, we will assign it a $\\text{Uniform}(0,1)$ prior." + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "LIg-xs2LIAyw", + "colab": {} + }, + "cell_type": "code", + "source": [ + "reset_sess()\n", + "\n", + "N = 100\n", + "rv_p = tfd.Uniform(name=\"freq_cheating\", low=0., high=1.)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "text", + "id": "7L0nMGmrIAy0" + }, + "cell_type": "markdown", + "source": [ + "Again, thinking of our data-generation model, we assign Bernoulli random variables to the 100 students: 1 implies they cheated and 0 implies they did not. " + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "aXxhrJdtIAy0", + "outputId": "4c9363b3-9056-4500-8374-beba11e613ff", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 89 + } + }, + "cell_type": "code", + "source": [ + "N = 100\n", + "reset_sess()\n", + "rv_p = tfd.Uniform(name=\"freq_cheating\", low=0., high=1.)\n", + "true_answers = tfd.Bernoulli(name=\"truths\", \n", + " probs=rv_p.sample()).sample(sample_shape=N, \n", + " seed=5)\n", + "# Execute graph\n", + "[\n", + " true_answers_,\n", + "] = evaluate([\n", + " true_answers,\n", + "])\n", + "\n", + "print(true_answers_)\n", + "print(true_answers_.sum())" + ], + "execution_count": 29, + "outputs": [ + { + "output_type": "stream", + "text": [ + "[0 0 1 0 0 1 0 0 1 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 1 1 1 0 1 0 0 1 1 1 1\n", + " 0 1 1 1 1 0 0 1 0 0 1 0 0 1 1 0 1 0 0 0 0 1 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0\n", + " 0 0 0 0 1 1 1 0 0 1 0 1 1 1 0 0 0 0 0 1 0 1 0 0 1 0]\n", + "37\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "vNB9WGYcIAy4" + }, + "cell_type": "markdown", + "source": [ + "If we carry out the algorithm, the next step that occurs is the first coin-flip each student makes. This can be modeled again by sampling 100 Bernoulli random variables with $p=1/2$: denote a 1 as a *Heads* and 0 a *Tails*." + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "68t8O39EIAy4", + "outputId": "9d61b6b0-0a07-4859-ad85-056fcfe8b25e", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 71 + } + }, + "cell_type": "code", + "source": [ + "N = 100\n", + "first_coin_flips = tfd.Bernoulli(name=\"first_flips\", \n", + " probs=0.5).sample(sample_shape=N, \n", + " seed=5)\n", + "# Execute graph\n", + "[\n", + " first_coin_flips_,\n", + "] = evaluate([\n", + " first_coin_flips,\n", + "])\n", + "\n", + "print(first_coin_flips_)" + ], + "execution_count": 30, + "outputs": [ + { + "output_type": "stream", + "text": [ + "[1 0 1 0 0 1 0 0 1 0 1 1 0 0 0 1 1 1 1 0 0 0 0 1 0 0 1 1 1 0 1 0 0 1 1 1 1\n", + " 0 1 1 1 1 1 0 1 0 0 1 0 0 1 1 0 1 0 0 0 1 1 0 0 0 0 0 1 0 0 1 0 0 0 1 0 0\n", + " 0 1 0 0 1 1 1 0 0 1 0 1 1 1 1 1 0 0 0 1 0 1 0 0 1 1]\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "8-ZnScpWIAzA" + }, + "cell_type": "markdown", + "source": [ + "Although *not everyone* flips a second time, we can still model the possible realization of second coin-flips:" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "acP-4TAfIAzB", + "outputId": "5a6909bc-9780-4185-fcd5-d3c7b0acbb1a", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 71 + } + }, + "cell_type": "code", + "source": [ + "N = 100\n", + "second_coin_flips = tfd.Bernoulli(name=\"second_flips\", \n", + " probs=0.5).sample(sample_shape=N, \n", + " seed=5)\n", + "# Execute graph\n", + "[\n", + " second_coin_flips_,\n", + "] = evaluate([\n", + " second_coin_flips,\n", + "])\n", + "\n", + "print(second_coin_flips_)" + ], + "execution_count": 31, + "outputs": [ + { + "output_type": "stream", + "text": [ + "[1 0 1 0 0 1 0 0 1 0 1 1 0 0 0 1 1 1 1 0 0 0 0 1 0 0 1 1 1 0 1 0 0 1 1 1 1\n", + " 0 1 1 1 1 1 0 1 0 0 1 0 0 1 1 0 1 0 0 0 1 1 0 0 0 0 0 1 0 0 1 0 0 0 1 0 0\n", + " 0 1 0 0 1 1 1 0 0 1 0 1 1 1 1 1 0 0 0 1 0 1 0 0 1 1]\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "uiVbAjoTIAzI" + }, + "cell_type": "markdown", + "source": [ + "Using these variables, we can return a possible realization of the *observed proportion* of \"Yes\" responses. " + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "BJxN0jmBIAzJ", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def observed_proportion_calc(t_a = true_answers, \n", + " fc = first_coin_flips,\n", + " sc = second_coin_flips):\n", + " \"\"\"\n", + " Unnormalized log posterior distribution function\n", + " \n", + " Args:\n", + " t_a: array of binary variables representing the true answers\n", + " fc: array of binary variables representing the simulated first flips \n", + " sc: array of binary variables representing the simulated second flips\n", + " Returns: \n", + " Observed proportion of coin flips\n", + " Closure over: N\n", + " \"\"\"\n", + " observed = fc * t_a + (1 - fc) * sc\n", + " observed_proportion = tf.to_float(tf.reduce_sum(observed)) / tf.to_float(N)\n", + " \n", + " return tf.to_float(observed_proportion)\n" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "text", + "id": "OoIWHbsNIAzL" + }, + "cell_type": "markdown", + "source": [ + "The line `fc*t_a + (1-fc)*sc` contains the heart of the Privacy algorithm. Elements in this array are 1 *if and only if* i) the first toss is heads and the student cheated or ii) the first toss is tails, and the second is heads, and are 0 else. Finally, the last line sums this vector and divides by `float(N)`, producing a proportion. " + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "ma5VwRSNIAzM", + "outputId": "6e54ab97-f18a-4d5b-f373-31bda38b3e58", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 35 + } + }, + "cell_type": "code", + "source": [ + "observed_proportion_val = observed_proportion_calc(t_a=true_answers_,\n", + " fc=first_coin_flips_,\n", + " sc=second_coin_flips_)\n", + "# Execute graph\n", + "[\n", + " observed_proportion_val_,\n", + "] = evaluate([\n", + " observed_proportion_val,\n", + "])\n", + "\n", + "print(observed_proportion_val_)" + ], + "execution_count": 33, + "outputs": [ + { + "output_type": "stream", + "text": [ + "0.37\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "HNoBM39rIAzQ" + }, + "cell_type": "markdown", + "source": [ + "Next we need a dataset. After performing our coin-flipped interviews the researchers received 35 \"Yes\" responses. To put this into a relative perspective, if there truly were no cheaters, we should expect to see on average 1/4 of all responses being a \"Yes\" (half chance of having first coin land Tails, and another half chance of having second coin land Heads), so about 25 responses in a cheat-free world. On the other hand, if *all students cheated*, we should expected to see approximately 3/4 of all responses be \"Yes\". \n", + "\n", + "The researchers observe a Binomial random variable, with `N = 100` and `total_yes = 35`: " + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "SLcH6ZPsIAzR", + "colab": {} + }, + "cell_type": "code", + "source": [ + "total_count = 100\n", + "total_yes = 35" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "code", + "id": "-kWZd1ygofav", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def coin_joint_log_prob(total_yes, total_count, lies_prob):\n", + " \"\"\"\n", + " Joint log probability optimization function.\n", + " \n", + " Args:\n", + " headsflips: Integer for total number of observed heads flips\n", + " N: Integer for number of total observation\n", + " lies_prob: Test probability of a heads flip (1) for a Binomial distribution\n", + " Returns: \n", + " Joint log probability optimization function.\n", + " \"\"\"\n", + " \n", + " rv_lies_prob = tfd.Uniform(name=\"rv_lies_prob\",low=0., high=1.)\n", + "\n", + " cheated = tfd.Bernoulli(probs=tf.to_float(lies_prob)).sample(total_count)\n", + " first_flips = tfd.Bernoulli(probs=0.5).sample(total_count)\n", + " second_flips = tfd.Bernoulli(probs=0.5).sample(total_count)\n", + " observed_probability = tf.reduce_sum(tf.to_float(\n", + " cheated * first_flips + (1 - first_flips) * second_flips)) / total_count\n", + "\n", + " rv_yeses = tfd.Binomial(name=\"rv_yeses\",\n", + " total_count=float(total_count),\n", + " probs=observed_probability)\n", + " \n", + " return (\n", + " rv_lies_prob.log_prob(lies_prob)\n", + " + tf.reduce_sum(rv_yeses.log_prob(tf.to_float(total_yes)))\n", + " )" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "text", + "id": "QZC4TITlIAzV" + }, + "cell_type": "markdown", + "source": [ + "Below we add all the variables of interest to our Metropolis-Hastings sampler and run our black-box algorithm over the model. It's important to note that we're using a Metropolis-Hastings MCMC instead of a Hamiltonian since we're sampling inside." + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "Awl3GmgjIAzV", + "colab": {} + }, + "cell_type": "code", + "source": [ + "burnin = 15000\n", + "num_of_steps = 40000\n", + "total_count=100\n", + "\n", + "# Set the chain's start state.\n", + "initial_chain_state = [\n", + " 0.4 * tf.ones([], dtype=tf.float32, name=\"init_prob\")\n", + "]\n", + "\n", + "# Define a closure over our joint_log_prob.\n", + "unnormalized_posterior_log_prob = lambda *args: coin_joint_log_prob(total_yes, total_count, *args)\n", + "\n", + "# Defining the Metropolis-Hastings\n", + "# We use a Metropolis-Hastings method here instead of Hamiltonian method\n", + "# because the coin flips in the above example are non-differentiable and cannot\n", + "# be used with HMC.\n", + "metropolis=tfp.mcmc.RandomWalkMetropolis(\n", + " target_log_prob_fn=unnormalized_posterior_log_prob,\n", + " seed=54)\n", + "\n", + "# Sample from the chain.\n", + "[\n", + " posterior_p\n", + "], kernel_results = tfp.mcmc.sample_chain(\n", + " num_results=num_of_steps,\n", + " num_burnin_steps=burnin,\n", + " current_state=initial_chain_state,\n", + " kernel=metropolis,\n", + " parallel_iterations=1,\n", + " name='Metropolis-Hastings_coin-flips')" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "text", + "id": "Lq0OtJDCufOu" + }, + "cell_type": "markdown", + "source": [ + "##### Executing the TF graph to sample from the posterior" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "x--bCsBrr91E", + "outputId": "3157cffc-ebd7-4588-fc37-4deeaab499ed", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 35 + } + }, + "cell_type": "code", + "source": [ + "# Content Warning: This cell can take up to 5 minutes in Graph Mode\n", + "[\n", + " posterior_p_,\n", + " kernel_results_\n", + "] = evaluate([\n", + " posterior_p,\n", + " kernel_results,\n", + "])\n", + " \n", + "print(\"acceptance rate: {}\".format(\n", + " kernel_results_.is_accepted.mean()))\n", + "# print(\"prob_p trace: \", posterior_p_)\n", + "# print(\"prob_p burned trace: \", posterior_p_[burnin:])\n", + "burned_cheating_freq_samples_ = posterior_p_[burnin:]" + ], + "execution_count": 37, + "outputs": [ + { + "output_type": "stream", + "text": [ + "acceptance rate: 0.105425\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "QhCgk98ynq5s" + }, + "cell_type": "markdown", + "source": [ + "And finally we can plot the results." + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "JoKNmLpxB1yt", + "outputId": "f24aad88-b416-49bf-a883-53ac08022cbf", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 380 + } + }, + "cell_type": "code", + "source": [ + "plt.figure(figsize(12.5, 6))\n", + "p_trace_ = burned_cheating_freq_samples_\n", + "plt.hist(p_trace_, histtype=\"stepfilled\", density=True, alpha=0.85, bins=30, \n", + " label=\"posterior distribution\", color=TFColor[3])\n", + "plt.vlines([.1, .40], [0, 0], [5, 5], alpha=0.3)\n", + "plt.xlim(0, 1)\n", + "plt.legend();" + ], + "execution_count": 38, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [], + "image/png": { + "width": 822, + "height": 363 + } + } + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "tqDMt8xyIAzd" + }, + "cell_type": "markdown", + "source": [ + "With regards to the above plot, we are still pretty uncertain about what the true frequency of cheaters might be, but we have narrowed it down to a range between 0.1 to 0.4 (marked by the solid lines). This is pretty good, as *a priori* we had no idea how many students might have cheated (hence the uniform distribution for our prior). On the other hand, it is also pretty bad since there is a .3 length window the true value most likely lives in. Have we even gained anything, or are we still too uncertain about the true frequency? \n", + "\n", + "I would argue, yes, we have discovered something. It is implausible, according to our posterior, that there are *no cheaters*, i.e. the posterior assigns low probability to $p=0$. Since we started with an uniform prior, treating all values of $p$ as equally plausible, but the data ruled out $p=0$ as a possibility, we can be confident that there were cheaters. \n", + "\n", + "This kind of algorithm can be used to gather private information from users and be *reasonably* confident that the data, though noisy, is truthful. \n", + "\n" + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "Y0bK5tMAIAze" + }, + "cell_type": "markdown", + "source": [ + "### Alternative TFP Model\n", + "\n", + "Given a value for $p$ (which from our god-like position we know), we can find the probability the student will answer yes: \n", + "$$\n", + "\\begin{align}\n", + "P(\\text{\"Yes\"}) &= P( \\text{Heads on first coin} )P( \\text{cheater} ) + P( \\text{Tails on first coin} )P( \\text{Heads on second coin} ) \\\\\n", + "&= \\frac{1}{2}p + \\frac{1}{2}\\frac{1}{2}\\\\\n", + "&= \\frac{p}{2} + \\frac{1}{4}\n", + "\\end{align}\n", + "$$\n", + "Thus, knowing $p$ we know the probability a student will respond \"Yes\". " + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "m_kgk64TIAzh" + }, + "cell_type": "markdown", + "source": [ + "If we know the probability of respondents saying \"Yes\", which is `p_skewed`, and we have $N=100$ students, the number of \"Yes\" responses is a binomial random variable with parameters `N` and `p_skewed`.\n", + "\n", + "This is where we include our observed 35 \"Yes\" responses out of a total of 100, which are then passed to the `joint_log_prob` in the code section further below, where we define our closure over the`joint_log_prob`." + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "GJ2jFKI7ofa9", + "colab": {} + }, + "cell_type": "code", + "source": [ + "N = 100.\n", + "total_yes = 35.\n", + "\n", + "def alt_joint_log_prob(yes_responses, N, prob_cheating):\n", + " \"\"\"\n", + " Alternative joint log probability optimization function.\n", + " \n", + " Args:\n", + " yes_responses: Integer for total number of affirmative responses\n", + " N: Integer for number of total observation\n", + " prob_cheating: Test probability of a student actually cheating\n", + " Returns: \n", + " Joint log probability optimization function.\n", + " \"\"\"\n", + " tfd = tfp.distributions\n", + " \n", + " rv_prob = tfd.Uniform(name=\"rv_prob\", low=0., high=1.)\n", + " p_skewed = 0.5 * prob_cheating + 0.25\n", + " rv_yes_responses = tfd.Binomial(name=\"rv_yes_responses\",\n", + " total_count=tf.to_float(N), \n", + " probs=p_skewed)\n", + "\n", + " return (\n", + " rv_prob.log_prob(prob_cheating)\n", + " + tf.reduce_sum(rv_yes_responses.log_prob(tf.to_float(yes_responses)))\n", + " )" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "text", + "id": "0clIAcyHIAzj" + }, + "cell_type": "markdown", + "source": [ + "\n", + "Below we add all the variables of interest to our HMC component-defining cell and run our black-box algorithm over the model. " + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "C5QLZ17e5u6t", + "colab": {} + }, + "cell_type": "code", + "source": [ + "number_of_steps = 25000\n", + "burnin = 2500\n", + "\n", + "# Set the chain's start state.\n", + "initial_chain_state = [\n", + " 0.2 * tf.ones([], dtype=tf.float32, name=\"init_skewed_p\")\n", + "]\n", + "\n", + "# Since HMC operates over unconstrained space, we need to transform the\n", + "# samples so they live in real-space.\n", + "unconstraining_bijectors = [\n", + " tfp.bijectors.Sigmoid(), # Maps [0,1] to R.\n", + "]\n", + "\n", + "# Define a closure over our joint_log_prob.\n", + "# unnormalized_posterior_log_prob = lambda *args: alt_joint_log_prob(headsflips, total_yes, N, *args)\n", + "unnormalized_posterior_log_prob = lambda *args: alt_joint_log_prob(total_yes, N, *args)\n", + "\n", + "# Initialize the step_size. (It will be automatically adapted.)\n", + "with tf.variable_scope(tf.get_variable_scope(), reuse=tf.AUTO_REUSE):\n", + " step_size = tf.get_variable(\n", + " name='skewed_step_size',\n", + " initializer=tf.constant(0.5, dtype=tf.float32),\n", + " trainable=False,\n", + " use_resource=True\n", + " ) \n", + "\n", + "# Defining the HMC\n", + "hmc=tfp.mcmc.TransformedTransitionKernel(\n", + " inner_kernel=tfp.mcmc.HamiltonianMonteCarlo(\n", + " target_log_prob_fn=unnormalized_posterior_log_prob,\n", + " num_leapfrog_steps=2,\n", + " step_size=step_size,\n", + " step_size_update_fn=tfp.mcmc.make_simple_step_size_update_policy(num_adaptation_steps=int(burnin * 0.8)),\n", + " state_gradients_are_stopped=True),\n", + " bijector=unconstraining_bijectors)\n", + "\n", + "# Sample from the chain.\n", + "[\n", + " posterior_skewed_p\n", + "], kernel_results = tfp.mcmc.sample_chain(\n", + " num_results=number_of_steps,\n", + " num_burnin_steps=burnin,\n", + " current_state=initial_chain_state,\n", + " kernel=hmc)\n", + "\n", + "# Initialize any created variables.\n", + "# This prevents a FailedPreconditionError\n", + "init_g = tf.global_variables_initializer()\n", + "init_l = tf.local_variables_initializer()" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "text", + "id": "eJYLS8EysHqj" + }, + "cell_type": "markdown", + "source": [ + "#### Execute the TF graph to sample from the posterior" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "ALvEN1yQkTIx", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 35 + }, + "outputId": "b20176e2-3b33-4654-e82a-9cb2c44c7e47" + }, + "cell_type": "code", + "source": [ + "# This cell may take 5 minutes in Graph Mode\n", + "evaluate(init_g)\n", + "evaluate(init_l)\n", + "[\n", + " posterior_skewed_p_,\n", + " kernel_results_\n", + "] = evaluate([\n", + " posterior_skewed_p,\n", + " kernel_results\n", + "])\n", + "\n", + " \n", + "print(\"acceptance rate: {}\".format(\n", + " kernel_results_.inner_results.is_accepted.mean()))\n", + "# print(\"final step size: {}\".format(\n", + "# kernel_results_.inner_results.extra.step_size_assign[-100:].mean()))\n", + "\n", + "# print(\"p_skewed trace: \", posterior_skewed_p_)\n", + "# print(\"p_skewed burned trace: \", posterior_skewed_p_[burnin:])\n", + "freq_cheating_samples_ = posterior_skewed_p_[burnin:]\n" + ], + "execution_count": 42, + "outputs": [ + { + "output_type": "stream", + "text": [ + "acceptance rate: 0.56236\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "Ye0uC_c-xrWf" + }, + "cell_type": "markdown", + "source": [ + "Now we can plot our results" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "_P5Z_uySgi-S", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 434 + }, + "outputId": "d45b8c22-40b1-4477-d5e7-34020ca316ce" + }, + "cell_type": "code", + "source": [ + "plt.figure(figsize(12.5, 6))\n", + "p_trace_ = freq_cheating_samples_\n", + "plt.hist(p_trace_, histtype=\"stepfilled\", density=True, alpha=0.85, bins=30, \n", + " label=\"posterior distribution\", color=TFColor[3])\n", + "plt.vlines([.1, .40], [0, 0], [5, 5], alpha=0.2)\n", + "plt.xlim(0, 1)\n", + "plt.legend();" + ], + "execution_count": 43, + "outputs": [ + { + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.6/dist-packages/matplotlib/axes/_axes.py:6521: MatplotlibDeprecationWarning: \n", + "The 'normed' kwarg was deprecated in Matplotlib 2.1 and will be removed in 3.1. Use 'density' instead.\n", + " alternative=\"'density'\", removal=\"3.1\")\n" + ], + "name": "stderr" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [], + "image/png": { + "width": 822, + "height": 363 + } + } + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "Lxt6fSRvIAzy" + }, + "cell_type": "markdown", + "source": [ + "The remainder of this chapter examines some practical examples of TFP and TFP modeling:" + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "KMoiodMmIAzy" + }, + "cell_type": "markdown", + "source": [ + "## Example: Challenger Space Shuttle Disaster \n", + "\n", + "On January 28, 1986, the twenty-fifth flight of the U.S. space shuttle program ended in disaster when one of the rocket boosters of the Shuttle Challenger exploded shortly after lift-off, killing all seven crew members. The presidential commission on the accident concluded that it was caused by the failure of an O-ring in a field joint on the rocket booster, and that this failure was due to a faulty design that made the O-ring unacceptably sensitive to a number of factors including outside temperature. Of the previous 24 flights, data were available on failures of O-rings on 23, (one was lost at sea), and these data were discussed on the evening preceding the Challenger launch, but unfortunately only the data corresponding to the 7 flights on which there was a damage incident were considered important and these were thought to show no obvious trend. The data are shown below (see [1]):\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "tlPZvBWkg5g-", + "outputId": "98b6c9e2-3959-4245-bc38-787c2b91700e", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 35 + } + }, + "cell_type": "code", + "source": [ + "reset_sess()\n", + "\n", + "import wget\n", + "url = 'https://raw.githubusercontent.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/master/Chapter2_MorePyMC/data/challenger_data.csv'\n", + "filename = wget.download(url)\n", + "filename" + ], + "execution_count": 44, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "'challenger_data.csv'" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 44 + } + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "BNOqG_9zIAzz", + "outputId": "45b8f986-7cd6-4c44-e919-73a7c5bd08fa", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 704 + } + }, + "cell_type": "code", + "source": [ + "plt.figure(figsize(12.5, 3.5))\n", + "np.set_printoptions(precision=3, suppress=True)\n", + "challenger_data_ = np.genfromtxt(\"challenger_data.csv\", skip_header=1,\n", + " usecols=[1, 2], missing_values=\"NA\",\n", + " delimiter=\",\")\n", + "#drop the NA values\n", + "challenger_data_ = challenger_data_[~np.isnan(challenger_data_[:, 1])]\n", + "\n", + "#plot it, as a function of tempature (the first column)\n", + "print(\"Temp (F), O-Ring failure?\")\n", + "print(challenger_data_)\n", + "\n", + "plt.scatter(challenger_data_[:, 0], challenger_data_[:, 1], s=75, color=\"k\",\n", + " alpha=0.5)\n", + "plt.yticks([0, 1])\n", + "plt.ylabel(\"Damage Incident?\")\n", + "plt.xlabel(\"Outside temperature (Fahrenheit)\")\n", + "plt.title(\"Defects of the Space Shuttle O-Rings vs temperature\");\n" + ], + "execution_count": 45, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Temp (F), O-Ring failure?\n", + "[[66. 0.]\n", + " [70. 1.]\n", + " [69. 0.]\n", + " [68. 0.]\n", + " [67. 0.]\n", + " [72. 0.]\n", + " [73. 0.]\n", + " [70. 0.]\n", + " [57. 1.]\n", + " [63. 1.]\n", + " [70. 1.]\n", + " [78. 0.]\n", + " [67. 0.]\n", + " [53. 1.]\n", + " [67. 0.]\n", + " [75. 0.]\n", + " [70. 0.]\n", + " [81. 0.]\n", + " [76. 0.]\n", + " [79. 0.]\n", + " [75. 1.]\n", + " [76. 0.]\n", + " [58. 1.]]\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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qIZNAkiRJkiRJkiRJNWQSSJIkSZIkSZIkqYZM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\ndevVwBmjHYfUqVtuuQWAk046aZQjkXSo2O+liWe89vstW7Z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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [], + "image/png": { + "width": 832, + "height": 255 + } + } + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "El9Z_4ulIAz3" + }, + "cell_type": "markdown", + "source": [ + "It looks clear that *the probability* of damage incidents occurring increases as the outside temperature decreases. We are interested in modeling the probability here because it does not look like there is a strict cutoff point between temperature and a damage incident occurring. The best we can do is ask \"At temperature $t$, what is the probability of a damage incident?\". The goal of this example is to answer that question.\n", + "\n", + "We need a function of temperature, call it $p(t)$, that is bounded between 0 and 1 (so as to model a probability) and changes from 1 to 0 as we increase temperature. There are actually many such functions, but the most popular choice is the *logistic function.*\n", + "\n", + "$$p(t) = \\frac{1}{ 1 + e^{ \\;\\beta t } } $$\n", + "\n", + "In this model, $\\beta$ is the variable we are uncertain about. Below is the function plotted for $\\beta = 1, 3, -5$." + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "U4kW2QIddYEs", + "outputId": "6decde20-489e-4bb0-a52d-0aabaa05a131", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 216 + } + }, + "cell_type": "code", + "source": [ + "def logistic(x, beta):\n", + " \"\"\"\n", + " Logistic Function\n", + " \n", + " Args:\n", + " x: independent variable\n", + " beta: beta term\n", + " Returns: \n", + " Logistic function\n", + " \"\"\"\n", + " return 1.0 / (1.0 + tf.exp(beta * x))\n", + "\n", + "x_vals = tf.linspace(start=-4., stop=4., num=100)\n", + "log_beta_1 = logistic(x_vals, 1.)\n", + "log_beta_3 = logistic(x_vals, 3.)\n", + "log_beta_m5 = logistic(x_vals, -5.)\n", + "\n", + "[\n", + " x_vals_,\n", + " log_beta_1_,\n", + " log_beta_3_,\n", + " log_beta_m5_,\n", + "] = evaluate([\n", + " x_vals,\n", + " log_beta_1,\n", + " log_beta_3,\n", + " log_beta_m5,\n", + "])\n", + "\n", + "plt.figure(figsize(12.5, 3))\n", + "plt.plot(x_vals_, log_beta_1_, label=r\"$\\beta = 1$\", color=TFColor[0])\n", + "plt.plot(x_vals_, log_beta_3_, label=r\"$\\beta = 3$\", color=TFColor[3])\n", + "plt.plot(x_vals_, log_beta_m5_, label=r\"$\\beta = -5$\", color=TFColor[6])\n", + "plt.legend();" + ], + "execution_count": 46, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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oAp+mJlizBtY0wYqVGukjssB4PYb9TSHuWxPkbH+Gn7QnONU7MbXj+YEM5weG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\nsFeaK3cGvTIL1kLOQ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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [], + "image/png": { + "width": 827, + "height": 199 + } + } + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "H_rdEcFIIAz7" + }, + "cell_type": "markdown", + "source": [ + "But something is missing. In the plot of the logistic function, the probability changes only near zero, but in our data above the probability changes around 65 to 70. We need to add a *bias* term to our logistic function:\n", + "\n", + "$$p(t) = \\frac{1}{ 1 + e^{ \\;\\beta t + \\alpha } } $$\n", + "\n", + "Some plots are below, with differing $\\alpha$." + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "T0iZj_eCIAz8", + "outputId": "007ec106-e0af-421b-fa6b-0aaa21a09f9c", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 216 + } + }, + "cell_type": "code", + "source": [ + "def logistic(x, beta, alpha=0):\n", + " \"\"\"\n", + " Logistic Function with offset\n", + " \n", + " Args:\n", + " x: independent variable\n", + " beta: beta term \n", + " alpha: alpha term\n", + " Returns: \n", + " Logistic function\n", + " \"\"\"\n", + " return 1.0 / (1.0 + tf.exp((beta * x) + alpha))\n", + "\n", + "x_vals = tf.linspace(start=-4., stop=4., num=100)\n", + "log_beta_1_alpha_1 = logistic(x_vals, 1, 1)\n", + "log_beta_3_alpha_m2 = logistic(x_vals, 3, -2)\n", + "log_beta_m5_alpha_7 = logistic(x_vals, -5, 7)\n", + "\n", + "[\n", + " x_vals_,\n", + " log_beta_1_alpha_1_,\n", + " log_beta_3_alpha_m2_,\n", + " log_beta_m5_alpha_7_,\n", + "] = evaluate([\n", + " x_vals,\n", + " log_beta_1_alpha_1,\n", + " log_beta_3_alpha_m2,\n", + " log_beta_m5_alpha_7,\n", + "])\n", + "\n", + "plt.figure(figsize(12.5, 3))\n", + "plt.plot(x_vals_, log_beta_1_, label=r\"$\\beta = 1$\", ls=\"--\", lw=1, color=TFColor[0])\n", + "plt.plot(x_vals_, log_beta_3_, label=r\"$\\beta = 3$\", ls=\"--\", lw=1, color=TFColor[3])\n", + "plt.plot(x_vals_, log_beta_m5_, label=r\"$\\beta = -5$\", ls=\"--\", lw=1, color=TFColor[6])\n", + "plt.plot(x_vals_, log_beta_1_alpha_1_, label=r\"$\\beta = 1, \\alpha = 1$\", color=TFColor[0])\n", + "plt.plot(x_vals_, log_beta_3_alpha_m2_, label=r\"$\\beta = 3, \\alpha = -2$\", color=TFColor[3])\n", + "plt.plot(x_vals_, log_beta_m5_alpha_7_, label=r\"$\\beta = -5, \\alpha = 7$\", color=TFColor[6])\n", + "plt.legend(loc=\"lower left\");" + ], + "execution_count": 47, + "outputs": [ + { + "output_type": 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Jt2cE+Pf/qXS31MIok1FhULSsb+83PgC5d59al83a84yaF1vtAEA2CylERUhU\nTpYHVCdfgvjG16oemow2qK7miv78fwCmqcKfiikI9PQAbe2qnGWpUIgfrImIbgi/u7Ki8oNHVGWm\nYUkspk3MJU3MJQ3MJU3sLevq7cqqge+OOhUlPpdAT8Rlhz1u3NbhZbduN5nfraEvqqFvTesqKdW4\nP8XxetRkYMEOfswquU/GkLi0XMClZWdsH00A7SG91MKnL+pCf4ObzzPVrdHvjmL25VmYObO0zB10\no3mkGc0jzaVlnpCHLXKI6kQunsPCuQUsnF1AYjZRs1ywPYhoT7QU6HjD3ppliYiIdiKGO7R5lgVX\nIqFCAK+XFe31olYLI0CNd7SZMY+amoA//n8hczkV/GQyQCbt3O7rd8p2dkEevwtIp51yaXu+NrB6\n5SRENlv1IeVPvQd45O12uZeBP/0sEAyq4CcYrLz96E855zcxrgKjYhl/gN3JERG9Ti5NoCOkWmsA\n6ys82oI6HhkJYMru6iues0phgEAGR36yBcWee5+dyMClAb1RN9qDtbuPoxtDCIGIT0fEp2NPc+U6\nw5KYS5qYjhcwFTcwHTcwFTeQLqxPfCwJzCZMzCZMPD+tWjdoAuiNujDU6C5NER+fY9p5srEsli4s\nofVAaymosUwLZs5EoCWA5r0q0Al3hflrfaI6Y+ZMZKeyeOWfXkFsMla1jObS0DzSjNaDrWjob4DL\nxyoxIiKqb3wno01zpVIY+rPPAgCkywWEwkAo5EzB4u01y4vL3Oyfv65pmjMmz0aO36Wmakyj8v7P\n/R+QqZQKgtIpOwhKA6k00NHplMtkICwLSCTUtIZ893udO3/9BYhLF511QthBTwi48zhw4JBakUwA\nzz5Tee0Gg+paDQYZCBERbUJH2IV3hEOl+7GsicmYCgZi2cqu3v7xQgoxezwYn0ug3RNAd8CEa6WA\nvqgLOvuw3zYuzel+7W57mZQSq1mrFPRM29NC2ly3vSWB8VUD46sGnhjLAABaAjqGmtwYtsOe9pDO\nynLaFlJKrFxewdTzU1gdWwWgKng7b1efNXtO9KD7eDf8Tdf4jEtEO46RNbB4YRELZxewMrZSdQwd\noQk0DTeh9WArmkeaoXv44wMiIto9GO7QpmmZTOm2MAxgdUVNmyS9XiAcAcJhNYXCzu3S/QgQZhi0\na+lr/uUcPrK57e67H/LECRX6pJJAKqXCoFRKtQoqv1Y6OtWYRakkkExBZDNAMgkkk2rMoqLFRYgv\nf6nmQ8rf+r+ccYSeeQq4ckUFQOXXbiikurmLNmzuPIiIdrmoT0fUp+Nwe2UrH0tKvGnAj8mYgYlY\nAcsZC+OGG+NpN36wuIJ37QuO7RxdAAAgAElEQVTirXuCANSYMS5NcFyXbSaEQKNfR6O/8vnMGhZm\n7MBnKm7gykoBs8n1gc9i2sRi2sSPplQL3aBbYLDRXQp8eqNuPsd0Q5kFE3On5jD9/DQyS+p7jObW\n0DTcBH+jE+T4olVatxPRjhafiWPqh1NYurQEWa1fUQE09Deg7VAbmvc2w+1n3QIREe1ODHdo0zTT\nQCEchiubhSgUrr3BGmq8lgVgcWFT5aXP7wQ/kYiawpHK29Gomlfrcox2F93lPPcb+ejPVNyVpqFC\noGQS8HiBFTuQDIYgH36rHQLZgVHxdjqtWu8UvXYO4oXnqz6cHBoG/tUn1R3DAP7w95wQM1I+jwDd\nPSoQIiK6xWhClMIbAFjNmnjmzCSmMzrmjQD2NDnjWDx1JYNvXUphoMGNPc1uDDd5MNjIMV12Cp9L\nw1CTB0Nlz1kqb2FspYBRexpfLcCwKrdLFSROz+dxej4PAHBpwEizBwdbPTjY5kFbkF9LaGud/dJZ\nrIyqz32esAfdx7vRcayDlbxEdSw+Hcf40+Ol1/Za7mY3+u/sR8v+Fo6RRUREtwR+i6JNy3Z2YewX\nPo6RkRHIfK7UGsKZEpX3U2XLEkkIa/2vOjcishkgmwEW5q9ZVno868OfSFTNow1264qoWubiZX9L\n0V32tRBV94vhTmsr8L4PVN/GWlMj9cCDkCN77Ws5oa7r4ryjwymXTEKMjdY8FPnzv6i6hgOA7z8B\nPPX9ygCoeJwNDcD+A6/zhImIdr4Gn459kQL2RQoYGemtWLecMVGwgIvLBVxcLgBIQxdAf4Mbh9s9\neMtwsPpOadsEPRoOt3tLLXwKpsRU3MDl5bwKfJYLSK0Zv8ewgHMLeZxbyONLZ4HWgI6DbR4cavNg\nT5OHrXrox5aYTcDlc5Va5bQfbYeRNdB9Vzda9rdA09nlLlG9ik3GMP7MeKlrxXKhjhBaD7Yi5U9B\nD+joGunahiMkIiLaHqzlptfH4wWavEBT87XLAoCUkJkMkIg746YUK8gr7seBhAqFxNoK9g2IfB5Y\nXFTTtQ4lGAKidkX62uCneLuhEfCuHziabhFrx9sZ2aumawkGIX/zt+xr2r7W43F1Ox4HWlqcsgsL\nENNTwPT63cjGJuB3fs9Z8Kn/AAhhX6dlIVAkAvT3Ay2tr+88iYh2oA8djeBd+0O4vFzApaU8Li0X\nMB03MLpSQIPf+f+cNyVeW8hjf6uHrXp2GLeuumAbbFQtJKSUmE+ZGF1WLXsurxSwkKr80c9C2sT3\nr2Tw/SsZuDVgX4sHB1o9ONTmRXOA4yNQddKSWLq4hKkfTSE+GUfHsQ7sfbv6zNZ6oBWtB1o51hNR\nHYtN2KHOlfWhTuvBVvTd14dgm/rRx8WLF9eVISIi2u0Y7tDNIQQQCKipvePa5S0LMpN2KsfLK8iL\nt2Ox0jJhGJs/lJTdqmhmZsNy0udXLShKU6MKg8qXRaKAzgoHsrndwJ6RzZV95O2Qd99jh0Bl13Ms\nDvjLBvS1LGBqEkJW6UsagHz/TwMPPazunHwJ+OpX7ICywblei/OBwfXBFRHRDhTyaLitw4vbOtQP\nLdIFC6PLBUS8zv+w84t5/OmLsVIQcKTdi8NtHkR8fF/eaYQQaA+50B5y4d4+9R63nDFxdj6Psws5\nnF/MI1+W9RQslLpw+5szSXSEdLv7Ni+Gm9xwaaysv9UZOQNzr8xh+oVpZFfVuE66V4c74HS5xlCH\nqH6tjq9i/OlxxCZilSsE0HaoDX339SHQEtiegyMiItpBGO7QzqRpQDCkpo7OjctKCZnNOMFPRQC0\nqirNi1MiXrOSfC2RzQBXM8DV2doPLYTqCq4Y/jSWTQ2NQFOTmrvZtzetEQptbvwdIYBP/S5kLGZf\n2/a1HI+pIKirrNuBxQWI2Rlgdn1wKTUN+MxnnQX/40+BdMq5bhsagEb7em1u5jhWRLSjBNxaqcuv\nIgGgv8GF8VWjYiyX/gYXjrR58ZY9AWis3N2xmvw63tDvxxv6/SiYEpeXCzi7kMOZ+Tzm17TquZo0\ncTWZwffGMvDqAgfbPLir24cDrR4GPbegq69exeXvXIaZU9eJr8GH7ru60X60HS4vv94S1SspJVbH\nVzHx9ARik+tDnfbD7ei9rxeBZoY6RERERfz0S/VPCMAfUNO1WgVZFmQiYYc9q6qyvBgAxctCoNjq\nploDCSnVdvEYMDFes5wMh8vCnyYGQLR5QqjuDzfTBeIb3gR54KC6hldX1bVdnFtWZaudC69BxGJV\ndyPf8pPAe9+v7kxPAd/+lnO9NpZdx6GQOj4iom1QHOMlljVVuDOnWoCMrxoomBI/OeKMzXN5OY+B\nBjd0BgE7klsX2N/qwf5WD95zEFhIGTi7kMfZ+TwuLuVRKOupN2dKnJzN4eRsDgG3wO2dXhzv9mGo\n0c0w7xYR7gzDKliI9kbRfXc3mkeaIfjaJqpbUkqsXlEtdeJT8cqVAmg/0o6++/rgb/JX3wEREdEt\njOEO3Vo0zRlXB321y0kJmUqqivHVVWB1pbKy3L4vEolNPawojis0OVH7ISMRpxK/qUm1nmi0501N\nQCDIinTamN8P9PSq6Vp++ROQq8vqel5ZUdd0cd5aNobPzAzEcz+sugvpdgOf+l3VPSEAvPAjIJdT\n121TM9DUqMbnIiK6gaI+Hff3+XF/nx95U+L8Yh5WWSPd+ZSB//LDVYQ9Ase7fbinx4+uCD8C72St\nQRceCLrwwEAAeVPi4pIKes7M57CUcZKedEHi2Yksnp3Iosmv4c4uH453+9AV5vO7m+STecydnkPP\niR4IIRBsDeKuj98FX4StjInqXXwqjtHvjiI+XRnqCE2g/Wg7eu/thb+RoQ4REVEt/OZDVI0QQCis\npg0qyqVhOK2AShXky+p2cYqtQlhWzX2UHrLYndyVseqP5fWuCXzsqbkZaGlRFewMf2iz+vrUdC2D\ng5Afe6wy/Cle49mceo0UfefbEONXKjaXoZC6Zu+6G3jrI2phLqe6O2xuAYIMLYlo63h0gSNrum9L\n5Cy0BXXMp0w8MZbBE2MZ9EZcONHjw53dPoQ8HItsJ/PoAofavDjU5sX7ZAizCRMvzGTxwkwWK2VB\nz3LGwncup/Gdy2l0hV24q9uLO7t8aPRzDKZ6JaXE7MlZjD0xBjNnwhfxofWg+gEKgx2i+maZFsaf\nHsfkDyeBsh9kCE2g47YO9N7bC18DX+dERETXwnCH6Hq4XCpcad6gyyzLUuOlrK4Ay8tVAiDVeuJa\nYwGJXA6YmVFTFdLtVmFPS4uqNG9uBlpanfusRKfXo6VVTdXkcpVdvd15HLKzU13ny8vAyjJEMgkk\nk5D7DzjlJicg/uDTAMpCy+YWO6xsAe6/X423RUS0BYabPPjtB5owHjPw3GQWL85kMRk3MHk2iW9c\nSOFTD7fArfP9sR4IIdAVceHRSAjv3BfE6EoBL0xncXI2h3TB+Rw1kzDw968Z+OprKQw3uXG824fb\nO70IuBnk1YvUfAoXv3mx1EVT43AjQl38bEC0G6QX03jtq68heTVZWib0slAnylCHiIhosxjuEN1o\nmuaMUzI4VL2MaULGVoGlJWB5ya4ct+f2MpHPb/gwolAA5q6qqQrp89mV6K1O8NNqTy2tHPOHfnze\nNV2uFVvmFFkWZCKuruOgM/YFTBOyuwdYWoLIZtaFlvLECafsn/8ZMDFRGVa2tjm31x4DEVEVQggM\nNLgx0ODGew6GcGouh+emsgh5tFKwY1gS37iQwp1dXvRE+J6402lCYE+TB3uaPHjfIYlzC3k8P53F\n6blcaYweCeDScgGXlgv42zMJHGrz4o39fuxtdkPwBy87klkwMfHMBKaem4K0JDxBD4bfOoyW/S18\nzojqnJQSMy/MYOyJMViG0/Kyob8Be9+5l6EOERHR68Bwh2gn0HWnm7VqpIRMpZzQZ2kJWLHnS4vA\n4iJEOr3hQ4hsFpieVtPa3QsBNDTYgU+bE/oUK9HLK+aJNkvTgGiDmsrt2w/82/8HACDTaXUNF6/l\npSUgHHHKzs5AzM4As+tbrMk7jwM//4vqTiIBPPFdJ7RsaVGPq/FX2kRUya0L3NHlwx1dPlhlrWbP\nzOfx+OU0Hr+cRk+x27YuH8Je/h/Z6Vya6o7vSLsXmYKFV+dyeGE6i/OLhVJvP4YFvHI1h1eu5tAV\n1vHAQADHu33wsNXWjjLzwozqpglA5x2dGHzzIFw+fmUlqne5RA7nv34eq2OrpWVCFxh8cBDdd3Uz\nvCUiInqd+EmZqB4IAYRCaqoxTorMpFXF+OJiKfApn4tcrvbupXS6ibt4Yf2+A4Gylj5tQFs70GbP\nw2F290avXyAABPqA3hrj//z6b0Iu29f14oKaL8yreUenU252BuIfvl6xqXS5VDdvra3AT39EBT4A\nkEoBPp8KVYnolqaVvX+1h3S8sd+PF2eymIobmDqbxJfPJXGk3YufGAxgqImteeqB363hRI8fJ3r8\niGdNvDirgp6JmFEqM5Mw8VenEvjqa0nc3+fHGwf8aPDxPWG7SClLFbtdx7sQn46j995eRLoj19iS\niOrB/Nl5XPrmJRhZ5/9wsC2I/Y/uR7CNPyIkIiK6Hgx3iHYLfwDoCQA9vevXSQmZStpBz5JdSb4A\nLNjT8tKGY/6IdBqYGFfT2l37/ZVhT/F2e4equCe6HoGAmqpd1+WiDZBvf6cTAC0uQMTjpa4Kpcfj\nlP2LzwGnXlVhT1ubHVja866u2i3oiGhX6wi58IHDYbz7QAhn5lW3bWcX8njlag7T8QL+7ZubK8Ig\n2vkiPh0PDgbw4GAAVxMGnhrP4LmpLPKm+syTKkh8+3Iaj4+mcazTizcPBDDYyBDvZpFSYvbkLGae\nn8Gxf34MLp8LulvHofcd2u5DI6ItUMgUcOnbl7BwZqFiee+9veh/Yz80F1vGEhERXS+GO0S3AiGA\nUFhNA4Pr1xsGZDH0WVgAFued4GdhAaJQe7wfkckA41fUtIYMhdYHPh2d6jbH+KGt1N4OPPpTFYtk\nNqtary0sqBZmRbkshGUC83NqKt/m+N3Az/2CuhOPAd/8R6CjQ13D7R2q+0JW7hLtam5d4FinD8c6\nfYhlTTx1JYPWoF4KdmJZE6fm8ri7h1161ZOOsArv3rkviB9OZvHUlTSWM2rMB0sCL83k8NJMDv0N\nLrx5IIBjnV64ND6/N0ounsO5r5xDfCoOAJg/M4+uO7u2+aiIaKusXFnB+a+dRz7hfI/0Rr3Y/679\niPZFt/HIiIiIdheGO0QEuFyqcry9ff06KSHjMTvosUOf+TlgTlWMb9jdWzIJJJPA6OXKXQqhWk20\nd6iK8/ZONe/oVF3PsfKctoLPB3T3qKncr/8GZD6vwsz5eTUtzKvremDAKTc9DfG9xys2lV6v/Vrp\nAN7zfqCxUa2wLI7vQ7QLRX063rU/VLHs+1cy+M7lNL5xIYkHBgJ4Y78fQQ9f//Ui4Nbw0FAADw76\ncWoujyfH0ri0XCitH1818D9fjuPL5zS8sd+P+/v8HHdpiyXnkjj9xdPIJ/PwBD0YfuswWva3bPdh\nEdEWsAwLY0+OYfpHleO8th9tx/BbhuHysgqKiIhoK/GdlYg2JoQamD7aAOwZqVwnJWQ87rSAsAMf\nVWE+B2EY1XcppdMy6PSpyl0GAirk6ehwWvp0dKgxfzhGCm0Vjwfo6lZTLS2tkO9+r+ra7epVYG4O\nIpUEJiaAiQnID37YKfvZP1HdFhaDn85OoLNLXb/RKANLol1koMGN3qgLkzED37iQwncup3BPjx8P\nDgXQEuD7VL3QhMBtHV7c1uHFVKyA71/J4IWZLAzVmAfxnIVvXEjhW5dSON7lw0PDAXSE+NXpei1f\nXsa5L5+DmTcR6Y3g0HsPwR1ga26i3SA5l8Rrf/8a0ovp0jKX34W9b9vLAJeIiOgG4TcUInr9hFAV\n19EoMLK3cp1lQa6uOIHP3Fxp/BMs1R7jR6TTqqXP2tY+Lpddad6lxkXp7AQ6u4FWhj50g7S2Aj/5\ntopFMplU1/PCvGplVrS4ALG6AqyuAOdfq9zm3vuAf/6z6k46DVy6qK7j5ma29iGqQ0c7vDjS7sHF\npQK+O5rG2YU8nhrP4OnxDB7dH8TDwxwcut70RN34yG1uPLo/hGcn1HMZz6mUx7CAf5rK4rmpLE70\n+PC2kSCaGOK9LumlNE7/9WlAAq2HWrHvHfs45gbRLiClxNRzU7jy5BVIy/mO1zjciL3v2AtvyLuN\nR0dERLS7MdwhohtD09TA9E3NwIGDlevyeciFedUa4uqs3SpiVrWMqNHNmzAMYHpKTWVksUu5zq6y\nyR7XR+e/ONpioZCahoYrl//2v1PjVs2VXdNXZ4HZGaC1zSl3ZQziT/4bAEC63U53hMVWPocPAx5+\nASba6YQQ2Nviwd4WD2YSBr43msYL01kMNDgtEFJ5CwG3gGDLvboR9mp4ZCSIh4cDODmbw5NjaUzE\nVCtkCRXyvDCTxf19frx1TxARdtf2Ywk0B9B7by+EEOh/Uz9fG0S7gJQSl799GTMvzpSWaS4NQw8P\nofP2Tr7OiYiIbjDWfBLRzefxVB8LRUrV2ufqVadyfO4qMDsLEVutuisV+kyrqXxXuq5Cn64eoLtb\nTV09gJTsIou2nqaplj6trcDhI85yKdV4PEW6Drn/gHNNT06qqVj8D/+rE+48/m3ANJ3u45qaeO0S\n7UBdYRc+elsEj+4PIexxXqP/65U4knkLP3UghOEmzzYeIf24XJrAXd0+HO/yYmzFwDcvpXBuQQ0K\nblhq3KUfTmbx4KAfAxLwsiFPTWbBRD6Zh7/RDwAYeGCAlb1Eu4S0JC78wwXMvTpXWhbuDGPfo/sQ\naA5s45ERERHdOhjuENHOIQTQ2KSmNa19ZCqlwp6ZGdUawr4tVleq78o0VdmZGeAFZ/mwx4N8c4sa\nP6i7WwVMXd1AkN3o0A0gRGW3gfv2qwmATKft1j2zah5brbwOn3wCYnGhdFf6fHa3hN0ItHcgPTh0\ns86CiDahvBVH1rAwETMQz1n4Lz9cxdF2Dx7dH0I7x2ypK0IIDDW58fG7G3BxKY+vvZbE2KpqyZM3\nJb51KQ2vFsadTTn0mxIenaFFuXwyjzN/ewb5ZB7HHjsGb8jLYIdol7BMC+e/eh4L55zPqq0HWrHv\n0X3QdLZqJCIiuln4DZOI6kMwCAzvUVMZmUmryvFZO/SZmVGtIlaWq+5Gz+fhL5Yt309Do926pxvo\nsVsVdXayaze6cQIB1b3b2i7eit72DsjpKWBmGpiZhojHgbFRYGwU3je8yQl3LpwHvv5Vdc329Krr\nt6sbcHOAaqLt4nNp+LdvbsJ3R9P43mgar87lcXp+Gff3+fG2kSDC7M6r7ow0e/Av72vEmfk8vnY+\nhZmECnlyloYfLPpx+okl/OSeAO7r88OlMcBIL6Zx6ounkIvl4I16YWZNIHTt7Yho57MMC2f/7iyW\nLznft9qPtmPv2/dC8P8fERHRTcVaSyKqb/7qFeQyk1EBzvQ0MDMFTKlKcpFKVd2NWF0BVleAM6ed\nfbhcqqVEb6+qNO/tUxXnfnYzQDfB/W+ouCsTiVLQky4fl2f8CsSF8yrkKZbVNKC9Q12vj/0sQ0qi\nbeBzaXjH3hDe0OfHNy6k8E+TWTw9nsFLM1n8+59ohpcDydcdIQQOt3txsM2Dl2Zy+MaFFBbTJgAg\nnrPwN2eS+N5oGm/fG8Txbh+0W7SVyuqVVZz9u7MwsgZCnSEcfv9heELsmpBoNzDzJs78zRmsjjtd\nZncd78LwW4bZMo+IiGgbsLaHiHYnv3996CMlLr98Et7FBXRLqNBnehqYnVFj96whDAOYnFBTGdnS\nUhb29Krwp5HjodANFg6XunXLXbzoLD9xL2RHJzA1CUxPqfncHMTsDGQ2Uxns/N7vqDGvii18enqB\njk7AxY8DRDdK1Kfjw0cjeHAwgK+cS6ItqJeCHSklJHDLhgD1ShMCx7t9uL3Ti6++OI7nlnxImeo5\nXcpY+MtXEnj8chrv2BfC0XbPLVXhOXdqDhe+cQHSkmje24z9j+6H7uGgRES7gZE1cPqvTyM+FS8t\n672vl2NpERERbSPW5hDRrUMImKEw0qEwMDLiLDdNyIX5UuseVUE+BbG0WH03i4vA4iLw8snSMhkI\nOGFPXx/Q169aTmj8ZTbdYJEIcOSomoryOciZGaC8pVo2CzF6Wd1+7VxpsXS5VDdu73wUOHrbTTpo\noltPZ9iFX7q7AaYlS8tOzubwzUsp/LP9IRxsvbVCgN1A1wQONxSwP1LAjLsb37mUQqqgnt/ZpIk/\nezGGgQYXPng4jJ7o7u8qMzWfwvmvqVak3Xd1Y+ihIXbRRLRLFNIFnPqrU0jOJUvLBh4YQN/9fdt4\nVERERMRwh4hI11XrhY5OAHeVFstUSgU9k5PA1IRqETEzA2Ga63Yh0mng/GtqKm7v9arAp6/fmToY\n+NBN4PECA4OVy7xeyE99Wl3HU5MqzJyahFiYBybGIcvLfu9x4JmnVVBZvIZ7elWLOCK6LnpZZfez\nExnMJkx89vkY9ja78VMHQui9BUKA3calAQ8NBXBfrw/fG0vjidEMcqb6r3pl1cDvP7uCBwcDeNtI\nEF7X7g07gm1BDDwwAN2ro/t493YfDhFtkVwyh1OfP4X0Yrq0bPgtw+i+i69zIiKi7cZwh4iolmAQ\n2LtPTUWGAXl11u6ubbJUUS7S6XWbi1wOuHRRTTbp9Va27ukbUIGPzi5L6AYTAmhuVtNtx0qLZSaj\nruOeHqfslTEIe3wf/NMPnbJt7cCBg8CHPnIzj5xo1/rFuxrw1HgG37qYwoWlAn7/mRUc7/bhXfuC\naPTzfaHe+N1qnKU39QfwncspPD2egWEBlgS+O5rGydksPnA4jENt3mvvrE4UMgXkE3kE24IAwF/x\nE+0y2VgWr37+VWRXsqVlI28fQeexzm08KiIiIipiuENE9ONwuezxSnqBe+1lUkIuLzvj80yMA+Pj\nEPHYus1FLgdcvqSm4uZujxq3p39AtbYYGATa2jiGD90cfj8wsrdy2Ud/BvLBh9S1PDmp5jPTEPNz\nkG1tTrlcDvjP/1GFlf0DaurrB7y7p+KS6EZy6wIPDQVwT48P37qkwoDnp7N49WoOH787iqEmDkJf\nj8JeDe85GMb9fX588VQCF5cLAIDljIXPPh/D7Z1evPdgCFFffQd4ZsHE6S+eRi6Ww7HHjsEX9W33\nIRHRFsosZ/Dq519FLp5TCwSw/9H9aDvUtvGGREREdNMw3CEiul7lLSKO3V5aLFdXgYkrwPi4Cn3G\nxyFiq+s3L+SB0ctqKm4bCDhBz8AA0D8IRKM34WSIoLp1GxxSU1Gx1ZplOcsmxiHmrgJzV4HnfwQA\nkEIAnV1Afz/w9ncBra03+eCJ6k/Qo8KANw0E8OWzScwkjFtijJbdrj3kwq/e04DnprL48rkk0vZ4\nPCdnc3htIY9H94dwX58PWh3+mENKifNfPY/ETALeKAN9ot0mNZ/Cq3/1KgopFU4LXeDAuw+gZW/L\nNh8ZERERlWO4Q0R0ozQ0AA3HgKNlXWDFYqoVhN26BxPjEKsr6zYV6TRw9oyaits2Namwp9jCp38A\n8PFXsnSTFFutlRsagvw3/zdw5QowfgUYHwOmp0tdusl3/TOn7D98HVhdVWHlwBDHnyKqoiWg4+eP\nR5HKW/DoqsI/U7Dw/HQWb+j312UIcKsTQuCeXj8OtXnxlXNJ/GhadW2UMSS+eDqBH01n8dNHwugK\n19fXsrHvjWHx/CJ0r47DHzjMVjtEu0hiNoFTXzgFI2MAADS3hkPvO4TGwcZtPjIiIiJaq76+RRAR\n1btoFDhyVE02GY+poOfKmD1dgUgl120qlpeB5WXgpRfVdkIAnZ2qonxwCBgaVvdZYU43i+4CevvU\n9MY3qWX5PKQ9FhUam5yyLzyvQp+n1F3p86ugZ2gYOHxEzYkIgGrJU/TV11J4ZkJ11/bhoxF01lkI\nQErYq+FjxyK4u8eHL55KYCFtAgDGVgr49NPLeGgogEdGgqVQbyebeWEGU89NQWgCB997EMHW4HYf\nEhFtkdhkDKe/eBpmXv2P0j06Dn/wMKK97EGAiIhoJ+K3QyKi7RZZE/hICbm0WBH2YHxcdd9WRkgJ\nzMyo6QfPqE19fmBwUFWUDw2r0CcQuMknRLc0j8e5/sp96COQY6Pqmh4bg1hZBl47B7x2DtI0nfIL\n88Dp08DQENDdo1oMEd3CDrV5cHo+hyurBn7vmWU8sieIh4cD0LWdHwLQevtaPPjkm5rwrUspPH45\nDVMClgS+czmNk7NZfPBwBPtbd+5YS0sXl3DpO2rcwL3v2IvGAf6Sn2i3WJ1YxekvnoZVUF3wuvwu\nHPnpIwh3hrf5yIiIiKgW1pgQEe00QgAtrWo6frdaZpqQszPA2JgT+sxMq4CnfNNsBjh3Vk022dFp\nV7bbrXs62LqHtsHIXjXZ5MoKcGUUGB2taMmGM6chvvh5VcbtBvr6nfF/hoaBRlYk0q3lcLsXw01u\nfOVcEj+YzOLrF1J4+WoOHz4aRi/H5alLbl3gnftCuLPLhy+cSmB0RY1psZi28Mc/WsXxLi/eczCM\nsHfnvVdnV7OABPrf2I/2I+3bfThEtEWysSzOfulsKdhxB904+qGjCLaxZR4REdFOxnCHiKge6Loa\n76Sn1+n+KpeDnBgHxkaB0cvA6ChEPLZuU3F1Frg667Tu8fudyvI9I2rOsXvoZmtsBBrvBG6/s3J5\nRyfkPfeq1j1zV4HLl9QEu2XaH/1XJ5ycmwNaWxlW0q7nd2v40NEI7ujy4a9ejWMqbuAPnl3Bv3mg\nCW1BfpyvV51hFz5xbwN+OJnF359LImOoH2y8MJPD2YU8PnA4jDu7dtb7c/dd3Qh3h/lLfqJdxCyY\nOPu3Z0tj7LiDbhz72DH4m/zbfGRERER0Lfw2SERUr7zeytYQxe7cRothz2VgagrCMis2E5kMcPaM\nmgBITVOh0Z4RNQ3vUapJeEYAACAASURBVGMDEW2H/QfUBECmkqpbwtHLKsT0+50gp1AA/uO/U922\nDQ6p63Z4D8NK2tVUl17N+Pr5JFIFyWBnF9CEwP19fhxp9+Lvzibw4kwOAJAuSHzuZBwXFvN476Hw\nto7FY2QNFDIF+BtVRW+kK7Jtx0JEW0tKiYv/eBHJOTXep9AEDr7nIIMdIiKiOsFvhEREu0V5d253\nn1DL8jnI8XEn7Bm9DJFIVG5mWcDEuJq+9zgAQLa2ASMjwPAIsGcP0Nau9k90MwVDwKHDalpreRmI\nNkAsLVZ0RSiFUGHlRz4GDAze5AMmuvG8LoH3HgrDKuuWc3y1gBdnsnjnvtC2hgD0+kW8Gh67PYoT\nPTl84VQCyxnVNdIPJrMYWyngX9wRRWf45n91s0wLZ790Fqn5FA5/8DDCXWyxQ7SbzLwwg/nT86X7\nw28ZRrSXP/IiIiKqFwx3iIh2M0+V1j2Li07Yc+li9bF7FubVwPY/eFZtFg6rVhHF1j29vYDOtxDa\nRu3twKd+F3J11em67fIlYHISYnICMhhyyn7ja8D8HLDHfi20M6yk+qfZ17CUEl84lcBU3MCpuTx+\n9o4Ix+KpYwdavfjXb3TjC6cSeGlWteKZTZr4/WeW8f7DYdzT44O4Sf+/ir/oXx1fhTvohjvA64po\nN1kdX8Xlxy+X7rff1o7OOzq38YiIiIjox8WaOSKiW4kQaoyS1lbgxD1qWToNWawYv3QRuDIGYRiV\nmyUSwMsn1QRAer0q7Nm7T1WW9w+o7rGIbraGBuDO42oCnLGoWlqcMi+9ADE9DTz3TwDssLIYeh44\nBHR0bMOBE20NIQQ+dDSM//1KHDMJE3/0gxW871AY9/XevBCAtpbfreGx2yPY25LFl84kULCAggV8\n/tUELizm8cEjYfhcN36ssYlnJzD36hw0t4bDH/j/2bvvsLiuc9H/3zXDDDCUmUFIVCEkCyEhIYEE\n6sUtbrHj2HFix0kcpzmx4/g45SY55+bek19O7k1ybnJyHCfHJcWJT6pbiu3YjpTYKggVQBJYhSKJ\nogISbegwzKzfH4smWQVLiD3A+3me9Yz27DWbF2mDZva73/UuIsIjS14KMVn0tPVw8I8HYeD+rpik\nGDJuzJD/N4QQQogJRq7ECSHEVOdyQfZiMwD8fnNxvKpyKOGjurrOeInq7T2zb4/TCXOuMsmeeZkm\n2eOQO3yFBQZ7UY30iU+jKyuhsgIqy02ysqQYSorR178H7rrbzGtvh6ZGmJkGdvv4xy7EJUpzO/jK\nmjhePNBOQW0Pvy9r50izn7uzre3VIi6dGujFM9vr4JkSH/Udpn9e0Ylealr7+cQVrtBqeLuBmi01\nACy4fQExSbIcmxCTRbA/yMEXD+Lv8gPgcDlY8IEF2MYhaSyEEEKIsTWmyR2l1L3Ag8BiwA4cAp4B\nntBaB9/lsbzA/wBuA+YMxFoPbAF+oLXeO4ahCyGEGORwDDenBwgG0fUnoaoKDpsL5Kq5+YyXqL4+\nOHTQDEA7nDBnznCyJ322JHuEdVJnmnHNtWZpwoYGk+ipqoCF2cPz9pagfvPfw5VpczOGz1+pTBMh\nzmFX3JMdyxyvWdJr1/EeTnX288XV3qEl3MTEkxwTNpS4K6zrAeB0l6nQun1+NBvSI8f8TvvWmlYq\nXqkATP+NafOmjenxhRDW0VpT+Xol7SdND05lUyy4cwERsVKZJ4QQQkxEY3alQin1E+AhoAf4O+AH\nrgN+DFynlLprtAkepVQasBVIAxqBNweOmwN8FLhHKXWP1vrFsYpfCCHEedhskJxixvoNAKZvT2W5\nuUBeUY5qbDzjJcrfB+WHzAB0WNhwZU/mfJg9Ry6WC2soZZZhS0yEdevP3GezoWckoE41nFmZFh4O\nC7Lgsw9Jrx4R8panRpLqdvDzYh8b0l2S2JkEwsMU9y6OZd40J78va6c3oOkPwosHOqho6uMji2OJ\nco7dHfc9rT1orUnJTyElP2XMjiuEsN7JkpM0lDYMbc+5bg6eNI+FEQkhhBDicozJlTWl1AcwiZ16\nYL3WunLg+QRMYuYO4AvAY6M85HcxiZ2/Ah/UWncNHM8G/G/gX4GnlFJ/0Vr7x+J7EEII8S7Ex5ux\nag0AurkJKsySV1SUo06fPmO66u+HCrOPV/5ilnGbmwHzF5iROtMkkYSw0pp1sGYd2tcKg8u4lR9C\n1Z9E9/QMJ3YC/fCzn8LcuSZZmZwi568IKckxYXx9XRyOEUuyHW3xM9MdRphNkj0TVV5KBLM8YTxT\n0kZdm+mNV9bQx/e2NnN/bixz4pxj8nUSlyTiinfJUmxCTDK+Oh+HNx4e2p6xaAbJeckWRiSEEEKI\nyzVWt03/88Dj1wYTOwBa6wal1IPAW8DXlVKPj7J655qBx28PJnYGjhdUSv0b8FVgGpABHBiLb0AI\nIcRliJsGK1eZAeiWlqGqHirLUQ0NZ0xXfX1nVka4XOYieeYCmD8fEhKlQkJYx+2BvHwzwCR7Okf0\nnaqpQe0phj3FZn9U9HBVWmYmJCbJ+SssNzKxU+fz86MdLcyMDeMTS914I6Wn1EQ1PSqML6728udD\nHWyu7gagpSfIYztaee+8KK6/6tKqtQL+AL1tvbimuQCITYkd07iFENbqbe/l4EsH0UENQHRCNBk3\nZ4z5so5CCCGEGF+XndxRSqUCy4A+4Pmz92utNyuljgMpwEpg+ygO23uR/XrgsfGCs4QQQljD64Xl\nK8wAdGurqeopL4dDB1GNZ1X2dHXBnhIzAO32DFT1DCR84uLG/VsQYojbY8agGQnoj3/CnM/lh1At\nzSbRM5js+ddvQdLAnbB9veAMtyBoIYYFNUQ7bRxt7efftzXz8Rw386ePTZWHGH8Ou+KuhTFkxjv5\n9b42uvyaoIaXyzupbOrjYzluYsNHX02otab8L+W01rSy8K6FuNPcVzB6IcR4C/YHOfjSQfo6+wAI\niwwj664s7A5J9AshhBAT3VhU7uQOPO7XWnefZ85uTHInl9Eld14HPgt8Qyk1clk2BfwvwAX8RWt9\n6rIiF0IIMT48HshfYQYDPXvKD8KhQ1B+ENXWdsZ05WuFnYVmAHpGgul5siDLVEdERo77tyDEkOho\nsyThqjWgNfr0qaFED/UnTeXOoB/8P+jtMefu/CxT4SPnrxhnszwOvrYujl/taeNQYx//tauVm+dF\nceNc6ckzkWUnhA/9ux5pMStVH2r08+9bm/lsvpuZbseojlO/t57G8kbs4XYcrtG9RggxcRzeeJi2\n4wPvtRUsuGMBEe4Ia4MSQgghxJhQWuuLz7rQAZR6BNNL509a6zvOM+cx4BHgB1rrr4zimPHAq8By\nTHXODkw1zxJgFvAH4CGtdfsoY7wfuH80c996662cnJwcd1dXF8ePHx/NS4QQQlwOrXE2NeKqrcVV\nW0PksVrsvecv4NRK0Z2cQtesdLrSZ9OTkCj9TkRIUn4/c576yRnns1aKnqRkumal0zY/C79UpYlx\nFNSwqymcXc3hgCLN5efGpG4i7Zf3eUBYK6hhR1M4RQP/rgBhSnNTUhdzovsv+Nr+zn4a/9aI7td4\nVniITJPksxCTSdeRLnzFvqHtmCUxRM+LtjAiIYQQYupKSUnB5XIBbHa73VePxTHHonJn8J1B5wXm\ndAw8jqorp9a6USl1LfAT4OPArSN2lwObR5vYGZAObBjNxI6OjotPEkIIMXaUoi9+On3x02ldugyC\nQcJPNeCqqcFVW03kiePY+ocvTimtcR0/huv4Mdi+jUBEBF1ps+ialU5n+mz6Y2U5GREatMPB4Qe/\nQET9SaJqqnHVVBNx8gSRJ44TeeI4vdOmDSV3HM3NgMbvjZN+PeKKsSlYGd9LUmSAN05GcrI7jO6A\nkuTOBGdTsDq+l5TIAK+fdNEbVPRrxSsnXKyb3kOOp++cv1a01vh2+9D9mojUCCJmyp38QkwmfU19\n+PYMJ3YiZkYQlRFlYURCCCGEGGtjkdwZc0qp+cBfMMmgjwGbgG5Mb5//B/xUKbVaa/3JUR6yGtg8\nmonR0dE5gNvlcpGRkfFuQ5/UKisrAeTvRUxoch5PEJmZsG7gz34/+nAVHDwAB/ejamvPmGrv6SGm\nopyYinIAdEICLFgIWVkwbz5ETL6LVXIeTzDz5w//ubsLXV4OBw+QdPU1ED1w38uvfoEq3I72xkHW\nQli0yPSdinRZE/M4kPPYOhnAsswA9R39LJieYHU4E1oonccZwKK5/Ty5q5Wm7iCg2Ho6ElxxfCAr\nGrvtzAzP8V3HqT9dj8PlIPeuXFmSbQoLpfNYjI2+jj5KXiuBoNmOmhFFzj05k7rPjpzHYjKQ81hM\ndHIOj7+xSO4Mlrpc6BaQweqei1bbKKXCgBeBucAarXXhiN3/UEq9BzgAfEIp9d9a6zcvdkyt9S+B\nX15sHoDP53uLUVb5CCGEGAcOh7nIPX8B3PEBdFub6dezf79J9vh8Z0xXDQ3Q0ABv/QNts8NVV8Gi\nbDOSU6QqQlgr0gU5uWac9byOiUG1NEPBVijYirbZYM5VsHYdrFxtTbxi0vJG2vFGDl/k23msm6au\nADdnRKHk9+SElRgdxpfXxPHTYh9HB/rwbK3pprErwCdyY4l0mGVM/d1+qrdUAzDvlnmS2BFiEgkG\nghx46QB9HX0AhEWGkXVX1qRO7AghhBBT1Vgkd6oHHmddYM7Ms+ZeyAogCzhyVmIHAK11s1LqNUwP\nneuBiyZ3hBBCTCKxsZC/wgyt0SdOwMH9cGA/VFag/P6hqSoYgMoKM/74ItrrhaxFJtEzf4E0theh\n40P3wF0fQh87Bgfehv1vw+EqVFUlel7m8LymJjhcBQuyIGZUq90KcVG+ngC/L2unPwgt3UHuyY55\nR5WHmDhiwm18YYWHX5e2UXLC9Pw6eLqPHxa28Lk8D3EuO45IB9kfzqa5qplp86ZZHLEQYiwd2XSE\ntmNtZkPBgvcvINIj73mFEEKIyWgskjt7Bh4XKqUitdbd55iTf9bcC0kbePRdYE7rwKN0IRZCiKlM\nKUhJMeP6G8wSblWVJtFz8ADqWN2Z01taRlRF2GHuXJPoWZgNyclS1SOsZbNBWpoZN91ilnA7dMic\nm4NKilAvPo9WCtJmwcJFZqTPBrvckSsujTvCzieXunmmxMeOYz34eoN8cmksEWE2q0MTl8hhV3w8\nJ5YZrk5er+oC4GR7gO9vb+GzeW5meRzEpsQSmxJrcaRCiLHUcrSFE8UnhrZnXz0b72yvhREJIYQQ\n4kq67OSO1rpOKVUCLAU+CDw7cr9SagOQCtQD76jEOYfBdyLzlVIerXXrOeasHHg8emlRCyGEmJQc\nDlPRsCALAO3zmQqI/WUm2dPVNTRVBQNQUW7GSy+YXieLFplEz/wFk7JXj5hgIl2Qu/TM5+Lj0Quy\nTJVaTTXUVMNfX0G7XJC7DD72cSsiFZNAdkI4j6zy8tTuVg6e7uOxwlY+l+/GHSFJw4nKphTvzYwm\nPsrO70rbCWho7w3yn9tb+HhuLDlJ8v+cEJNJwB+g8vXKoe1p86aRujLVwoiEEEIIcaWNReUOwHeA\n54HvKaW2a62rAJRSM4D/GpjzXa11cPAFSqmHgYeBXVrr+0YcqxCT4EkGfq6U+oTWum3gNTbgXzDJ\nnX5Mbx4hhBDi3NxuWL3GjEAAffQIvF0G+99G1dWeMVW1NMPWLbB1C9puh7kZkL0YFi+BGdJwXISI\n3GVm9PaiK8oHlnDbjzrVgO7qHJ7X1wd/32gq01JnSlWaGJV0j4MvrfbyxC4fx9r6+Y/tLTy43ENi\n9Fh9ZBBWWJEaSVyknZ8V++jya/o1/LykjdvnB7hujkt6LAkxSdRuq6WnpQcAe7iduTfNlZ9vIYQQ\nYpIbk09qWusXlFJPAA8CZUqpTYAfuA6IBf4E/Pisl8UDmZiKnpHH6lNK3Q/8GbgT2KCU2g10AznA\nbCAIPKq1PjwW8QshhJgCBhM2czPg/Xeifa2mquftgaqe7uFVRVUgAOWHzHjhOXRCoknyZC+Gq+bK\n8lfCeuHh5nzMXgyAPn0K/P3D+yvKUX/+I/z5j6bX1KJsM3f+AnCGWxS0mAimR4XxxdVeni5qpaEj\ngNZWRyTGQsY0J3c7uni+w05HuBOAPx/q5FRngLsXSY8lISa6jlMd1O0YXo54zrVzCI+W/++FEEKI\nyW7MbsPTWj+klNoGfB7YANiBQ8AvgCdGVu2M4lgblVJLgC8B1wJXAzagAfg98JjWesdYxS6EEGIK\ncntg9VozAv3oI0eGkj3v6NXTUA8b62HjG2b5q4WLTLInaxFERVn0DQgxwvQZZ2673eg1a8353NIy\nXJUWFgaZ8+GzD0qSR5xXTLiNL6z0cqqjn6QYqdqZDNpOtNGxo4ZrbXb2LZ9HTZfJ2hXW9dDcHeCT\nS924HNJjSYiJSAc1la9WwkAyPnZmLIk5idYGJYQQQohxMaaf1rTWvwV+O8q53wS+eYH9lZhKICGE\nEOLKsodBxjwz3n8nuqXF9OkpLTVVPf6+oamqqwt274Ldu9A2m6nkWbzEjAT5IC1CxMw0+Nj9EAyi\nj9VBWSmUlaKqj6IbG89M7PztDbjqKpg9B2xycVcYTrsi1e0Y2t5W040/qLlmtsvCqMSlCPgDlL9c\nDhquykvi6vXx/K6sjd3HewEob/Tzw+0tfDbfQ7xLKlOFmGhOFJ+g/WQ7AMqumHfzPFmOTQghhJgi\n5FY8IYQQ4mxeL6xdb0ZfH7r8EJTtMxfHW1qGpqlgECorzHjxefSMBFi8GBbnyPJtIjTYbJA2y4z3\n3oZu80Fz8/D+pibUS88DoGNiIHsJLMmBBbJ8mxh2urOf5/e3E9TQ0h3g/QuiscmFwwmjenM13U3d\nuKa5SN+Qjs2u+NiSWKZHdfHXCtOrq74jwA8KmnlouYeZI5J6QojQ1uPr4ehbR4e201an4YqXJLwQ\nQggxVUhyRwghhLgQp3O4t4nWpgqidN9QFcRI6lQDbNoImzaio6JNomdJLmRlyYVyERpi3WYMUgp9\n7fVQuhfV2Ajbt8H2bWiHExZkwT0fhrhp1sUrQsL0qDA+ujiW35S28ebRblp7gnxsSSwOuyR4Ql2g\nL0BjeSMoyLwtE1uYqc5TSnFzRhTTXXZ+U9pGfxA6+jSP72jloeUe0r2S4BEi1GmtqXqjiqDfrIDv\nincxc/VMi6MSQgghxHiS5I4QQggxWkqZ5a5mppkqCJ8P3i4zyZ6D+1F9I5Zv6+yAwu1QuN1cKM9a\nCDk5pjIiOtrCb0KIEeLi4EP3wAfvRp84Dvv2wr69qJpq9MH9EDXiXN23FxISIDHJuniFZfJTI4iN\nsPGzYh97TvbS1tvKZ5a5iXLKUn6hzO60s+zTy2itbiUmOeYd+/NSIvBG2nhqt4/ufk13v+bHO1v5\n3HI3c+OcFkQshBitxoONNFcNV+Nm3JyBzS6/k4UQQoipRJI7QgghxKVyu2HNWjP8fnRFOezbA/v2\noXytQ9OUv2/g+T1opWBuBuTkmuWv4qdb+A0IMUApSEk145ZbTd+pY3UQPlBxFuiHX/4c1d2NTkgw\nSw8uyYE5V0mfnikkM97Jo6u8PLm7lcPNfv6zsIUH8z3ESZ+WkBYWHkZ8Zvx5918V5+SRlR5+vLOV\nTr+mN6B5YlcrD+R5yIyXBI8Qocjf7adqY9XQdtLSJNwz3Rd4hRBCCCEmI0nuCCGEEGPB4YCFi8y4\n5yPomurhKoiTJ4amKa2H+/Q8/wd06kxzkTwnF1JnmovsQljN6zVjUHc3LM5Bl+1DNTTAxjdg4xum\nT09OLlx/AyQkWhevGDcpsWF8abWXJ3a10t4XJKC11SGJc2g+0kzL4RbSr07H7rh48i3V7eCfVnl5\nfGcr7b1B+gLw5O5WPr3MzcIZsqyoEKHm6JtH8Xf6AXBGO5l99WyLIxJCCCGEFSS5I4QQQow1mw1m\nzzHj/XeiG+pNomfvHjh6xCR4BqhjdaZC4tWX0dPiITcXcpeZ10pFhAgV0THwiU9BIICuqjRLEe7b\nY/r0bN2Cvvra4bkNDeD1SJ+pScwbaefR1V5auoNMj5KPE6Gmv6efilcr6GvvI9IbSXJe8qhelxQT\nxqMrPTy+s5XWniD9QfhZsY9P5LpZnCg/z0KEitaaVur31g9tz71xLmER8rtYCCGEmIrkHYAQQghx\npSUkwg03meHzoQcujHPoIKq/f2iaamqETRth00a022MqIpYuM8u42WXZIxEC7HbInG/GXR9CH6uD\ngwchOWV4zjM/g+PHYdEiyFkK2YvB5bIuZnFFuBw2XI7hBPTOY93M9jiYES0fL6x2eONh+tr7iEmO\nIWnpu+uRNSM6zFTw7GihudskeH5e4uP+3FhykyKuUMRCiNEK9gepfK1yaHvavGkXXHZRCCGEEJOb\nfPoSQgghxpPbDevWm9HTg97/tqnoebsU1d09NE35WmHzm7D5TXR0NCwZSPRkzrcweCFGUApmppkx\nyO8HpUyfqT0lsKcEbbfD/AUmWZmzFGLe2dRdTGyl9b38el87seE2HlnpIUESPJZprGikoawBW5iN\nzNsyUbZ3v9RnvMs+kOBppbErQFDDMyVt9C+B/FRJ8AhhpdqCWrqbzftFe7iduTfOtTgiIYQQQlhJ\nPnkJIYQQVomIgGV5ZvT3o8sPQUmxWe6qo2NomurogIKtULAV7XKRmD6b9oxMmDULnNLsWoQQhwO+\n9i/o5mZTnbanBCorUPvfhv1voyMiIX+5mRsMytKDk0RmvJOMOAeVzX5+tKNVEjwW8Xf5qfyruaN/\n9tWzcU279Iq5uEg7j67y8PiOVho6A2jgv/e10R/UrEqLHKOIhRDvRuepTuoK64a2Z189m/AYWTJR\nCCGEmMrkU5cQQggRCsLCYOEiM+79KLqywlwY31uC8vmGpqmuLmIP7Cf2wH7066/ComxYmmcew+UD\nvggRcXFwzXVmtLejS/eavlOLsoemJGx6A2djI6xZa87huDgLAxaXIzxM8dl8D08VtVLZZBI8X1jp\nIVESPOOqenM1/i4/7jQ3yfmj67NzIe4IO4+s8vLjnS2cbDcJnt+WteMPatany1KLQownrTWVr1Wi\ng6ZvY2xq7LtedlEIIYQQk4984hJCCCFCzeAyVvMXwN0fRh85PLDEVTGquXlomurtheIiKC5CO5ym\nt8myPMjOlmb2InTExMCadWYM0hrX0aM4OtrhhefghefQs+eY83fpMoibZl284pKEhyk+l+/hyd0m\nwfO4JHjGlQ5q+jr7QEHGTRko9e6XYzsXs9Sel5/sbOVYm+kR9/z+DvqDcO0cSfAIMV5OFp+k7Xgb\nAMqmyLh57H7OhRBCCDFx2b/5zW9aHUNI6e3tvR9ItziMkNQ8cEFx2jS54CImLjmPxYSjlLnQvXAR\nXHs9LF5CS18f9q5O7D09w9OCAdTJE6iSIvjH301De6Vg2jSTLBIilCjFkZSZ9E6fQXR0NDQ1oZoa\nUQf2o/6+CaKiYfZsq6MU75LdpshNiqC6xc+J9gBHWvysTouY1BcgQ+V9hVKKGVkzmLFwxmUtx3Yu\nTrtiaXI4VU19tPYEATjU2IfdBnPjZGnQySBUzmNxbr1tvRx48QA6YKp20takMSNrhsVRhR45j8Vk\nIOexmOjkHB61moiIiF+OxYHkVjohhBBiolAKZqXTuG4DjWvXkxHlMj16SopRJ08MT+vthaJdULQL\nHR4Oi3NMRcTCRaYnihAhQDuddMxfALe9D3p70WWlUFIEZWVw1YgG0cVF0Nxklm6TDwkhz2k3S7T9\ntrSNG+dGYZvEiZ1QFBl3ZfrhuBw2Hlru4cndPo60+AF4pbyT/oDmlnlRkzqBJ4SVtNZU/a2KQF8A\nMD/jaavTLI5KCCGEEKFCkjuXKRgM0tHRQVdXF36/3+pwxkVdXd3FJwkxDux2OxEREURGRhIZKc19\nxRSjFKSkmnHb7egTx81F8KLdqIb64Wm9vbB7J+zeaZrZLxlI9CzIkkSPCB3h4ZCXb0ZvLzhHVAP8\nfSPqyGF48Xl0+mwzZ1k+eL3WxSsuyGlX3J/rPuO5bn+QSIfNoogmr2AgSOVrlSQvSyYmKeaKfq3I\ngQTP00WtVDSZzz2vV3XRH4T3zZcEjxBXQlN5E00VTUPb826Zhy1MfpcKIYQQwpDkzmUIBoM0NjbS\n29trdSjjwumUZRdEaAkEAnR2dtLZ2Ul0dDQej0cuLIipKznFjFvfd2ai51TD0BTV0w07C2FnIToy\nEpbkmgvlCxaAXd4SiBARfla/qOtvQBfvhrJSVPVRqD6KfvF5mJsBN9xkek2JkLa1povXKrv4wgoP\nSTHyu2Ys1e+pp6G0AV+dj/zP5qNsV/Z9UHiYqcz6WbGPg6f7ANh0pIuA1tyxIFrehwkxhvp7+qn6\nW9XQdmJOIu409wVeIYQQQoipRj5dXYaOjg56e3ux2+14vV7Cw8Ox2SbvXTQ9A70dIiIiLI5ECLNE\ngd/vp7u7m7a2Njo6OnA6nURFRVkdmhDWOrui51jdcKKn8fTwtO5u2LEddmxHR0XDsmWQv8IshzWJ\n/y8TE9DSZWb09aLLysySg2WlqMoK9Jp1w/NaW03Fj0uavIeSoNaU1vfR3hvkRztaeGSlVxI8Y6S/\np5/qrdUAzLlmzhVP7Axy2hWfWebmmT0+yhpMgufNo924HDZuypD3YUKMlaNvHqWvw/yMOaOczLl2\njsURCSGEECLUyCery9DV1QWA1+uVJaGEGGdKKZxOJ06nE7vdTktLCx0dHZLcEWIkpWBmmhm334Gu\nq4Xi3VBUhGpqHJ7W2QFbNsOWzWivF/KWQ/5y8zq5C1uECme4WVJwWR50d6P37TXLDA569WUoLICs\nRZCfD9lLQG5IsZxNKT6T5+anRT4ONfbxox0tfGGll2RJ8Fy22oJa+rv7cc90My1zfPtROeyKTy11\n88yeNvbVm1UMXq3oJNKh2JAuCVYhLlfbsTZO7jk5tH3VDVcRFiG/N4UQQghxJnl3cBkGe+yEn718\niBBiXLlcLlpaYUufjwAAIABJREFUWqZM3yshLolSkDbLjPd/AF1TDUW7TUVPa8vwtJYW2PgGbHwD\nnZAwkOhZAYmJ1sUuxNkiI2HlqjOf6+yEQABVuhdK96IdTli82JzDi7Klx5SFnHaT4BlcyutxSfBc\ntu6Wbo4XHQdgzvVzLFkOzW5TfDwnlqeLWjnUaN6DvbC/A1eYjfxUSawKcam01hz5x5Gh7biMOOLn\nx1sYkRBCCCFClXyiGgOTeSk2ISaCwQsaWmuLIxFiglAK0mebcedd6KpK2L0LSopNFc/gtIYGUw3x\n6svotDTIW2F69MTFWRi8EOfxwOfA14ouKYbdu1BHDpslCYuL0DfeBHfcZXWEU9rgUl4/HZHg+aeV\nXhIlwXNJjv7jKDqgmZE9g5ikGMvicNgVn17m5sc7W6lu7Qfg16VtRDgU2QlyA5wQl6K5spm2Y20A\nKJviqvdcJf2shBBCCHFO8mlKCDHhyYcdIS6DzQbzMs2458PoAwdMT5O9e1C9vUPTVG0t1NbCS8+j\n52bA8hWwNA+ioy0MXoizuD1wzXVwzXXoxsaBZQh3m3N10K6dUFNtzuG0WbL04DhyjEjwHG/rp19u\nyrgkve29tBxtwRZmY/bVs60Oh/AwG5/L9/DYjhZOtgcIavhFiY+HlnvImOa0OjwhJhQd1Bx96+jQ\ndtLSJCI9sgS8EEIIIc5NkjtCCCGEMOxhkL3YjMHm9bt3wttlqP7+oWmqqhKqKtF/+J1Z7mr5Sli8\nRJa9EqElPh5uvNmMkd76h6nq+ftGdEKiSfIsXwHTZ1gT5xQzWOnR0RskzmW3OpwJKTwmnLzP5dFx\nooPwmNCojoly2vj8cg//WdhCY1eQ/iA8XeTjCys8pHnk/wYhRquhrIGuRtPb1+60k7YmzeKIhBBC\nCBHKJLkjhBBCiHca2by+qwu9t8Qs3XboIGrgbnsVCMC+vbBvLzoiEpYtM4mejHmmIkiIUPTBu9G7\ndph+Uw318PKf4eU/o2fPgZtugSU5Vkc46Tnt6ozETml9L5nxTsLDpIpqtMKjwwmfFxqJnUHuCDuf\nX+Hlh9tbaOsN0tOv+a9drTy6SpbfE2I0Av4A1Vuqh7ZTV6bijJLqNyGEEEKcn7zLFkIIIcSFuVyw\neq0ZbT50cRHs2ok6OtzsV/V0Q8E2KNiG9noHqiFWQUqKhYELcQ6z55hx193oQwdh1w6zDOHRI+gR\nPadoawOnEyKkMfyVtK2mmz+83U7WdCcP5Lmx2yTBcz4Bf4BT+0+RuDgRFaJ/T/EuOw+vMBU8XX5N\np1/zk12tfHGVVyq1hLiIE8Un6GvvA8AR5SB1earFEQkhhBAi1ElyRwghhBCjF+se7mnS0GAujO/a\ngTp9emiKammBN16HN15Hp8401Tz5y8HrtTBwIc5it8PCRWb09qJL98GiRcP7X30ZCgtgSS6sXAXz\nF5jXiDE1b5qDaKfiwOk+flPaxkeXxGKTPkjndGznMWq21NBa3cqC9y+wOpzzSooJ48F8D4/vbKUv\noGntCfLjna08utpLbLhUdQpxLv5uP3Xb64a2Z62dhd0p/+cIIYQQ4sIkuSOEEEKIS5OQALfdDre+\nD330iGlUX7QL1TFc/aCO1cGxOvQfX4DM+SbRk7sUIqU5sAgh4eEmATlSSwuqr8/0ndq9Ex3rNhVp\nK1dB6kxr4pyEZkSbRMCPdrSy+3gv0c4O7lgQjZIEzxl623upKzQXfpOWJlkczcWlex08kOfmyd2t\n9AfhdFeA/9rVyiMrPbgckuAR4mx1hXX095j+hhHeCBJzEi2OSAghhBATgbyzFkIIIcTlUQrmXAX3\n3Avf+z7684+g85ajHcNNtJXWqEMHUc8+A1/9Mvzip3BgPwSDFgYuxAU89DD6376Dvu129PQZqDYf\natPfUN/+/+C1V62OblJJ8zj4dJ4bu4I3j3az6UiX1SGFnOrN1QT9QeIz4/GkeawOZ1Qy453cn+tm\nME13vK2fp3b76AtoS+MSItT0tPVwfPfxoe3ZV8/GZpdLNUIIIYS4OKncEeNq//79PPbYYxQUFHDq\n1CmcTidz587lYx/7GJ/61Kcsu0uzsrKSTZs2sWfPHvbs2UNVVRVaa371q19x++23WxKTEEJMSPYw\nyF5sRnc3em8J7NwB5YdQ2lzQU/4+U+Wzayfa7YEVK2HlakhOtjh4Ic4yfTq89za45VZTnbajEIp2\nQdbC4Tn734bOTsjJAWdoNbifSObHO/lYTiy/2tPGXw51EhtuY0WqVPgBdNR30FDagLIpZl8z2+pw\n3pUlieHcuziG35S2A3Ckxc/Pin08kOcmLET7Bgkx3mq21qAHkp4xSTHEz4+3OCIhhBBCTBSS3BHj\n5uWXX+aTn/wkfr+frKwsli9fTmNjIwUFBXzlK18hLCyM+++/35LYfv7zn/Pkk09a8rWFEGLSioyE\nVWvMaGlB794FOwtRx48NTVG+Vvjb6/C319Fps2DVarM8VnSMhYELcZbB6rQ5V8EH74awEW+hX3sV\nVVWJjoiApctgxSrImAc2uev63VqWHEFHX5C/H+5ilttx8RdMAVprDm86DEByXjKRcRMv4bVyZiTd\n/ZqXDpglOw+e7uPZvW3cnyv9lYToPN1JQ2nD0Hb6NemyLKUQQgghRk2SO2Jc1NfX89BDD9Hf389T\nTz3F3XffPbTv6aef5qtf/SqbN2+2LLmTlZXFI488Qm5uLjk5OTz88MMUFBRYEosQQkxKXi/ccCPc\ncCO6rtZUQOzagWpvH5qiamugtgb9/HOQnW2qebIXn3khXQirjVhuEK1h+Qp0IIA6egS2F8D2AnRc\nnOkvtWYtTJ9hXawT0IZ0F8tTIoiUviwAtBxtwVfrIywyjLQ1aVaHc8mume2iyx/k9Uqz5N6ek724\nHO3cvShGLmSLKa16czUMrFTonePFm+61NB4hhBBCTCxytUSMi2eeeYb29nbuu+++MxI7ADEx5u7s\n6dOnWxEaAPfdd59lX1sIIaacmWlm3PkB9P79JtFTuhfVbxoJq2AA9u2FfXvRUVGQt9xU9MxKNxUU\nQoQKpWD91bD+anRDvVmCcEchqrkJXv8relq8JHcuwcjETkFtN7M9DpJjp+bHFm+6l4ybM1B2hSNy\nYlcz3ZIRRbdfs7m6G4CC2h4iHTZunx9tcWRCWMNX56Opomloe6ItuyiEEEII603NT0li3G3cuBGA\nD33oQ2c8r7Xm2WefBeD6668f97iEEEJYyB4Gi5eY0dmJLi6CHdtRRw4PTVGdnbD5Tdj8JjoxCVav\nMcteud0WBi7EOSQkwvveD7e+D324yiQt8/KG9//xRWhuNudw5nxZtm0USk708PuydmLDbXxptZdp\nLrvVIY07ZVMk5SZZHcaYUEpxZ1Y0Xf4gu4/3ArDpcBfeCBvr010WRyfE+NJac/TNo0PbMxbOIDpB\nEp1CCCGEeHckuSOuuN7eXsrKynA6neTn5w8939jYyDe+8Q0KCwtZsWIF73nPeyyMUgghhKWiomD9\nBli/YUQFxHZUc/PQFFV/El56Af2nl2DhInORPHuJLNsmQovNZnruZMwbfi4YhO3bzDKEu3eivXGw\ncpVZejAhwbpYQ1x2QjgZcQ4qm/38ZFcrX1zlJSZ8aiTF/N1+gv1BwmPCrQ5lTNmU4iOLY+n2+3j7\nVB8AL+zvIC7SzqKEyfW9CnEhzZXNtB1rA0wSd9aGWRZHJIQQQoiJSK6GXGHqc58+7z79kY/Bug1m\nY+tm1G/++/xzn/zZ8Mb//Raqtvbc89auh48OLDFWU436zrfPf8x//oZZ4gbg18+itm058+uMkbKy\nMvx+P7m5uYSHh/PAAw9QW1tLSUkJfX19rF27ll/+8pcXXW/7wQcf5He/+927/vr79u1j1ix5syyE\nEBPGyAqIygrYsR1KilG95k5vFQxCWSmUlaKjo01vk9VrIHWmxYELcR42G3ztf6J3FkJhAaqxEV57\nFV57FX3VXLjjAzA3w+ooQ47DrvhMnpsf7WjlWFs/T+xu5ZGVHiLCJn+Cp2ZrDfX76pl3yzxmLJxc\nS/vZbYpPLHXzox0t1LT2o4Fn9rTx6CoPM90Te+k5IUZDBzVH3xqu2klelkykJ9LCiIQQQggxUUly\nR1xxJSUlACxbtozq6mqee+65M/YnJSXRP9Bn4UJWrVp1SV8/OlrK24UQYkKy2czyVZnz4e570SXF\n5sJ4ZcXQFNXRAf/YBP/YhE5Lg1VrYflyiJLf/SLExMfDe2+Dm9+LrqqEwgKTtDxchbaPWG6srQ2i\no2XZtgGRDhsP5rv5YWELdb5+flbs47N5Hhz2ydt/q6upi5MlJ9FBjWv65FyuzGlXPJDn4QcFzTR3\nB+kLaJ7a7ePLa7x4I6fe8ntiamkoa6CrsQsAu9POzNVyc4oQQgghLo0kd66wUVfCrNuAHqziuZh/\n+d/o0cyblT76r//R+9CDFT9jbDC5s3TpUtLT06mvr6e+vp4dO3bwwx/+kOeff579+/ezbds2bBe4\nkHHfffdx331XJkYhhBAhLiLCVOesXoM+fQoKt0PhdlTLiGXbamuh9rfoF5+DxTlmftZCuUguQovN\nBvMyzbj7XvTbZZA+oon2z5+G06dg1RpYvRamTbMu1hARG2HnoeUefljYSnmjn5cOdHB3dozVYV0x\nR988ig5qEnMSiZ4xeRPVseE2Ppfv4YfbW+ju1/h6gzy528ejqzxEOuT3tpicAv4A1Vuqh7ZTV6bi\njHJaF5AQQgghJjR51yyuuD179gCmcgcgIiKC9PR07rnnHt544w08Hg8HDhwYSgIJIYQQFzR9hlm2\n7f98F/3IF9F5y9Ej+u6o/n5USRHqx4/BP38V/vQSNDRYGLAQ5xERAXn5MLg0bW8vNDWimptRr74M\n3/g6/OcPYPcu8PutjdVi06PCeCjfzUx3GNfMmbzLF7XXt9NU0YQtzEb6+nSrw7nikmLC+NQyN7aB\nH4ET7f08s6eNQHBUt7IJMeGcKDpBX7vpN+WIcpC6PNXiiIQQQggxkUnljrii2tvbqaysJDY2lnnz\n5r1jv8fjITMzk507d+K/yEWLZ599lsLCwncdw7e//W2myV2vQggx+dhspjInayF0dqKLdsH2AlRN\n9dAU5WuF1/8Kr/8VnTEP1qyDpUvBKY27RQgKD4dv/V90RTkUbIU9JahDB+HQQbTLBQ8+DBnvfD81\nVaS6HfyPNd6L9mmcyGq21gCmB4czemrczZ8Z7+TD2TH8prQdgIOn+3hhfwcfWhQ9qf+txdTj7/ZT\nV1g3tD1r7SzsTlmGUAghhBCXTpI74oras2cPwWCQnJycc3448/v9HDp0CJvNxvz58y94rMLCQn73\nu9+96xi+/vWvS3JHCCEmu6go2HANbLgGffy46WeysxDV3j40RVVWQGUF+g+/hfwVsGYtpM0arpoQ\nIhTYbDB/gRmdnejdO2H7NjhxApJThucdroLEJHPuTyGD7ye11myu7ibNHcacuMmRBGk/2U5zZTO2\nMBupK6fW3fwrZ0bS2BXgjSrTh2RbbTfxUXaumzM5ew6JqamusI7+HtNrNsIbQWJOosURCSGEEGKi\nk+SOuKIGl2Q7V9UOwCuvvILP52PDhg14vd4LHuuJJ57giSeeGPMYhRBCTDIpKXDXh+COO9FlZbC9\nAN4uRQWDAKjubtjyFmx5C50601TzLF8x5S6SiwkgKgquvtaM5qbhczTQD0/+BLq7IWepSVRmzp9S\n/aX21vfy4oEOop2KL6+JI9418e9+V3aFJ91DdEL0lOzB8d55UTR2BSg+0QvAnw92MC3SRk5ShMWR\nCXH5etp6OL77+ND27KtnY7NPnd/ZQgghhLgyJLkjrqjBPjrPPfcc99xzD3l5eUP7ioqK+PKXv4xS\niq9//etWhSiEEGKysodBTq4ZvlZ04XYo2IY6fWpoijpWB3/4LfrF5yB3qUn0zMucUhfJxQQRN6IK\nub0DUmfCoYOool1QtAsdNw1Wr4HVayEuzro4x8nihHCypjs5cLqPJ3e18qU1XlyOif1zGz0jmsX3\nLkZP0X4zSik+sjiWlu5WjrT40cCze9vwRNhJ9zqsDk+Iy1KztQYdMD/bMUkxxM+PtzgiIYQQQkwG\nktwRV9RgcqetrY0bbriBFStWkJSURG1tLcXFxdjtdr7//e+zatUqS+Pcu3cvX/nKV4a2y8vLAfjW\nt77F448/PvT8pk2bxj02IYQQY8DtgZtugRtvRldWQME2KClG+U1TY9Xfb5rW796Fjo83F8hXrQbv\n5L9ILiYgjwf+6UvQ1IQuLDC9ppqb4JW/oF99Gb7xr5AyuZf1stsU9+fG8sPCFk62B/hFiY8H8z3Y\nbRN/mUU1Cb6HS+WwKx7Ic/OD7S2c7gzgD8LTRa18aZJUZ4mpqfN0Jw2lDUPbs6+ZLf2khBBCCDEm\nJLkjrpjGxkbq6uqYMWMGjzzyCM8++yzFxcUopUhMTOTDH/4wDz74INnZ2VaHSnt7O0VFRe94/vDh\nwxZEI4QQ4opRylTmzMuEuz9s+pkUbEPV1gxPaWyEv/wJ/fKfYeEiWLsesheDXS4sihAzbRrc+j64\n5VZ0+SEo2Aonz+rNU7AV5s6DhATr4rxCIh02Ppfn4fsFzZQ3+nl+fzt3L4qZcBdN20+2U7Othlnr\nZhGTGGN1OJaLctr4XL6b/yhoodOvae/TPLW7lS+unvjVWWJqqn6rGgYK8rxzvHjSPZbGI4QQQojJ\nY0yTO0qpe4EHgcWAHTgEPAM8obUOXsLx7MBngHuBhUAUcBrYCzyttX55jEIXV8Bg1U5ubi4PP/ww\nDz/8sMURnd+6detobW21OgwhhBDjyeWCDdfAhmvQx+rMRfCdO1BdpqG30hreLoO3y9BuN6xaA2vX\nQfx0iwMX4iw2GyzIMiMQMElMgKYm+PWzKK3R8zJNojJ3KTgmzxJXcS47D+R5+NGOFgpqe0iOCWN9\nusvqsN6Vmq01NFc145rmkuTOgBlRYXwmz82Pd7bSH4T6jgA/K/bx0HIPYVO4sklMPL46H02VTUPb\ns6+ZbWE0QgghhJhsxiy5o5T6CfAQ0AP8HfAD1wE/Bq5TSt31bhI8SqlpwGtAPtAMFAKdwEzgeqAB\nkOROCBtM7ixbtsziSIQQQoiLSJ0Jd98Ld34QvbfEVPMcOji0W/l88Ppf4fW/ohdkmYvkS3IgTIqg\nRYgZWWGmg7ByFbqoCFVRDhXlaJcLVqwy53BKyvmPM4Gkex18dEksb1R1snBGuNXhvCvtJ9pprmrG\n5rCRumJyL6X3bl0V5+Qji2P51d42ACqb/Py+rJ2PLJ541VliatJac/Sto0PbMxbOIDoh2sKIhBBC\nCDHZjMkVCaXUBzCJnXpgvda6cuD5BOBN4A7gC8BjozyeDfgLJrHzGPB1rXXPiP0xQPpYxC6unD17\n9gCwdOlSiyMRQgghRsnhgPwVkL8CffqU6c2zvQDV5huaog4egIMH0DExsHK1qeZJSLQwaCHOI346\nfPyT8KF70Lt3wbYtqNpaePPv6C1vwb//AKImx4XGpckRLEkMn3A9d2q2mSUhk5cl44xyWhxN6MlL\niaCpK8ArFZ0A7DzWQ7zLzk0ZURZHJsTF+Wp9tNWZ5KSyKWZtmGVxREIIIYSYbMbqdtN/Hnj82mBi\nB0Br3aCUehB4C/i6UurxUVbvfAZYDbyitX707J1a63ag7PLDFlfSYOWOJHeEEEJMSNNnwPvvhNve\nhy4thW1b4MB+s1wboNrbYeMbsPENdMY8WLdh0i15JSaJSBesvxrWX42urYFtWyHQP5zYCQbhjy9C\n/nJIm7gXHwcTO1prNld3k5MUjicidHtlSdXO6Nww10VjV4Adx8y9fq9WdBLvspOXEmFxZEJcWO22\n2qE/JyxOINITaWE0QgghhJiMLju5o5RKBZYBfcDzZ+/XWm9WSh0HUoCVwPZRHHawOct/XG58wjqV\nlZUXnySEEEKEOnuYSdrkLoXmJnTBNti+DdXSMjRFVVZAZQU6Kmpgyat1Zza1FyJUpM2Ce89K4Ox/\nGzWYqEybZRKV+cshYmJePP/7kS7+fKiTncd6eHSVl/Cw0KzmqdlqqnZS8lKkaucClFLckx1Dc3eA\niiY/AL8pbcMbaeOqOPl7E6HJV+ejtWagp6uCmatmWhuQEEIIISYl2xgcI3fgcb/Wuvs8c3afNfe8\nlFJJwCIgABQqpeYppf6XUuoppdR3lFI3KVlkWQghhBBWiJsGt90O/+d76M8/gl6Sg7YNv51SnZ2o\nf2xCfetf4fvfg52F4PdbGLAQo5CYiL72erTLhaqtQf3mWfjal+E3z0JtjdXRvWsrZ0YS77JzrK2f\nX+31ERyotgslPW09tFS3SNXOKNltik8tc5MYbSqx+oPws2IfzV0BiyMT4tzOqNrJTiDSK1U7Qggh\nhBh7Sl/mhx2l1COYvjh/0lrfcZ45jwGPAD/QWn/lIse7AXgDOAV8F/h33llhtB24Q2t9apQx3g/c\nP5q5b731Vk5OTo67q6uL48ePX3S+0+kkISFhNIcWQlxBDQ0N9PX1WR2GEGIKsne04367DHdZKY4R\nvXkGBSIiaFu4iNbsHPzTplkQoRCjo/x+oisrcJfuxXX8GAD+WDdHP/1ZmGD3VjX32Xi+NpreoGKp\nt5e103su/qJxFugK4G/xEyHLi41am1/xh9pougMmqR4fHuCDMztwjMUti0KMkb6mPpr+0TS0Pf2m\n6YTFjNWK+EIIIYSYqFJSUnC5XACb3W731WNxzLF4hzHYhbXzAnM6Bh5jRnG8uBGP/wH8Dvg34BiQ\nB/wE04/neWDDKGNMH+3cjo6Oi08SQgghhBgQiI6heeVqmleswlVTjbtsH9FVlaigaTNo7+nBW1yE\nt7iIrtSZ+Bbn0JExDx0mF3pEaNEOB+1ZC2nPWoizqRF36T78bs9QYsfe2cG07QX4Fi+hNyHR4mgv\nLM4Z5JbkTv58LIqSlnA8jgCLPKFVRWd32bG7QrcnUCiKdWjem9zFS3VRBFE09trZWO/i5qSuiZZ/\nFJNYx8HhawoRaRGS2BFCCCHEFROK7zIG77sKA7Zpre8dse/NgcqeCmC9UuoarfWbozhmNbB5NF88\nOjo6B3C7XC4yMjIuOLeurg6AiAm6Hvm71dNj7nicKt+vmFhsNhsRERHMnHnh9awHe0Fd7OdbiFAm\n53EImzcP3nMD+HzowgLYugXV1Di023WsDtexOnR0NKxaA2vXwxStAJbzOMRlZMDKVQBMH3zu9b+i\nSvfiKd070JtnPeSvCNnePBlAhLeb35W189ZpF1mzPWTGj22Plks5j1urW3HPciMrTV+aDMDhMf+u\nAFUdDo6oZG7KiLI2sAlMfh+Pnfb6dk6ePDm0veimRbjiXRZGNHXIeSwmAzmPxUQn5/D4G4vkzuBt\nKRd6Nz1Y3dM+iuONnPPTs3dqrY8ppV4F7gKuAS6a3NFa/xL45Si+Nj6f7y1GXxEkhBBCCPFObjfc\ndAvccBP64AHYuhlK9w1V86iODhhsYJ853zSwz8kFqeYRoSwnF93WBju2o2pr4Df/jX7hOVixEtZf\nDamh1zB8dVokpzoDVDT2kRBtfZVM27E2Sn9bSkxKDDn35UiC5xKtTovkRHs/m6tNy9dXKzpJiglj\nSWK4xZGJqW5kr53pC6ZLYkcIIYQQV9RYXEGoHnicdYE5g5/0qi8wZ9DR8/z5XHNCez0IIYQQQkxt\nNhssXGRGayt6+zbYtgXV3Dw0RZUfgvJD6JgYWL0G1m6A6dMvcFAhLJKYBB+6B+74ALqkyFSmVVXC\nls3olhb4/CNWR3hO75sfRX8wCqfd+kRKzbYaADyzPJLYuUx3LIjmZHs/FU1mub1n97bxpdVeUmIl\nSS6s0XGqg6aK4V47aWvSLIxGCCGEEFPBWLzz3TPwuFApFam17j7HnPyz5l5IOaZ/TxRwvq7D8QOP\n0iBHCCGEEBODxwO33Ao33YLe/zZs3QJl+1BaA6Da2+GN1+GN19FZC00lRPZisFtfbSDEGRwOWLEK\nVqxCnzhuKtOylwzvr6qEkmJYv8EkhCxmUwrnwI9RUGtKTvSyNDkc2zgnV9qOtdFypAW7007q8tRx\n/dqTkd2m+ORSN98vaKGxK0BfQPN0UStfWRNHTLjt4gcQYoyNrNqJz4wnaoYsFSiEEEKIK+uykzta\n6zqlVAmwFPgg8OzI/UqpDUAqUA8UjuJ4fqXUK8DdwHXAn846ngNYP7BZdLnxCyGEEEKMK5vNJG2y\nF0NLM7pgGxRsRbW0DE1RB/bDgf1ojxfWroM168DrtTBoIc4jOQXuvvfM5978O6q4CP6xCZ0xzyQq\nc3JNUshifyhrZ3tdD/UdLm7NjL74C8ZQzVZTtZOcl4zDZf3fxWQQ5bTxQJ6bHxS00BvQNHcH+UWJ\nj8+v8BBmk8ooMX46T3fSeGi4x55U7QghhBBiPIzVLU3fGXj8nlJq7uCTSqkZwH8NbH5Xax0cse9h\npdQhpdQZyaARxwsCDyilbhzxGjvwPeAq4DjwxzGKXwghhBBi/Hnj4Nb3wbe/i37oYfSibPSIagLV\n2oJ65S/wP78GT/4EDuyHYPACBxQiBNx4C3rdenR4OKqyAvXzp+Ffvgp/fAFOn7Y0tNzkCBTwRlUX\ne072jNvX9R3z0XJUqnauhKSYMD6eG8vgb86qZj8v7pcFHsT4qi0YrtqJy4gjOnF8k8dCCCGEmJrG\nZEFirfULSqkngAeBMqXUJsCPqbyJxVTf/Pisl8UDmZiKnrOPt08p9SjwGPCaUmoXcAzIBeYAPuCD\n51kCTgghhBBiYrHbYXGOGY2N6G1bTDVPezsAKhiEvXtg7x709OmwbgOsWgMxMRYHLsQ5pKXBR+6D\nOz+I3rUDtryFOn7cLDlos8Ptd1gW2vx4J3dkRfPSgQ5+va+N6S47qe4rX0VTu9Vc+JWqnSsjOyGc\nWzOjeLm8E4Bttd0kx9pZN0ua2Ysrr6upi9MHhxPXs9ZcqB2xEEIIIcTYGbNuk1rrh5RS24DPAxsA\nO3AI+AUWZZx3AAAgAElEQVTwxMiqnVEe73GlVBnwFWAlZtm3k8DTwHe01tVjFbsQQgghRMiIj4f3\n3wm3vg+9d4+5MF5RPrRbnT4NL72A/suf4P9n787jorqvxo9/vjPDvgwgKKvivivI6q5JzL41Tc1i\nYm3a+MTE5NclSZMmbfO0edKkSZ+0edomMWttVrMvNVubiIi4sKi44wrKoggOO8ww398fF0ESF1Dg\nspz36zUvvHPv3DkDF5y5555z4hNg9hwYPgJkOLvoaXx8YPZcmDUHvX8frF4FM2a1rs/MgGPHjPuC\ngrotrDmxPhyqdLHhUD0vZDu6fEaL1pqQ4SHUO+qlaqcLzRvuy+EqFzlFDQC8u62acH8bIwd4mhyZ\n6OsK1haAMT6P4GHBBETKhRdCCCGE6B6dltwB0Fq/AbzRzm0fAR45yzargFXnGZYQQgghRO9js0Fi\nEiQmoUuKYXUarFuLqq0FQLlcsHE9bFyPjoyEmXMgNRV85Ep10cMoBcOGG7cTtIbPV6JKS9ErP4XJ\n8UaicvSYLk9UKqW4cUIApdUuDh538XKOg6UpQVi7aEaLUoqo5CgikyJRkoTtMkopFkwK5GhNBYUO\nF24NL2U7uHdGCKG+VrPDE31UXUUdR7YeaVkePENm7QghhBCi+3TdJWpCCCGEEKJzhEfA/Bvh8SfR\nCxehY4e2Wa2KilBvvwEP3Aev/xMOFZoUqBAdcNMt6CkJgELlZqP+/Cd45GH495dQ07UzUzysitsT\n7AR6WWhya+pdukufD5DETjfwbP65nqjEqnFqlmUdp94ls8pE1yhcW9hStRMUG4Q92m5uQEIIIYTo\nVzq1ckcIIYQQQnQhTy+YNgOmzUAXHDSqeTasQzU2AqAaGiA9DdLT0MOGG5UQUxLBQ2Z8iB5GKRgz\n1rgdP47OSIf01ajSUnh3BTo4BBISuzQEu7eVe1KDCPGx4mHtmsTLjg924BvmS1RSFDYv+ejVHYJ9\nrNyeYOeZdRW43FBc1cQ/N1Xy4wQ7FkmwiU5Uf7ye0rzSlmWp2hFCCCFEd5PKHdGttm3bxuLFixk/\nfjxhYWFERUUxe/ZsXnzxRbTu+ismT+f5559n0aJFJCcnM3ToUEJDQxk+fDjXXHMNb7/9tqmxCSGE\nEKc0eAjcshCeeAp9481Ga7aTqH17Ua+8ZFTzvP8uHD16mh0JYbKgILjiKvifx9F33IVOTIK4uNb1\nn/0L1qyGhoZOf+pB/raWxI5ba8pqmzpt345CB0d3HOXQ+kNot7yX7E5Dgz24YULr3JMtpY18trvG\nxIhEX1SYWdjyu22PsRM0uPtmhwkhhBBCgFTuiG70ySefcNttt+F0Ohk3bhzJycmUlZWRkZHBvffe\ni81mY9GiRabE9pe//IWjR48yduxYkpOT8fPzo7CwkNWrV5OWlsZHH33Ea6+9hsUi+VAhhBA9jI8v\nzLkAZs9F78mHtFWQm41qMk5Sq5pq+PJz9FdfwLjxRjXPhEkg/6eJnsZqhbh443ZCTTWs/BTldKLf\newdSp8KsORARedrdnIsGl+bVXAcHHS7umx5MsM/5z2g5mH4QgKjEKDx8pHquu6XG+FBU5eKb/XUA\nfL6nlogAG1MivU2OTPQFDZUNlGwpaVmWqh0hhBBCmEGSO6JblJSUcOedd+JyuXj++ee54YYbWtYt\nW7aM+++/n7S0NNOSOy+99BKTJk3Cz8+vzf07duzgmmuuYeXKlbzxxhvccsstpsQnhBBCnJVSMHKU\ncat0oDPWQHoaqrzcWK01bNsK27aiQwbAzFkwfQYEynwA0YN5esGChejVq1D79sI3X8M3X6NHjzGS\nPHFxYD3/jzQ2CzjdmqoGNy9kO/jp1GA8z6NVm+OQg+MHjmP1shKVHHXe8Ylzc80Yf4qrmthZZrSu\nfG1zJWF+VmLskmwT56dwXSG6yajaCYwKJChWqnaEEEII0f3kkk3RLV555RWqqqq49dZb2yR2AAIC\njJYJYWFhZoQGwNSpU7+T2AEYO3YsP/nJTwBYtWpVN0clhBBCnKNAO1x2BTz6OPrOu9HjJ6BPmjWh\nyo+hPvoAHrwfXlwG+btBWpCKnsjDw6jWuf9B9MO/Rc+chfbyQu3aiXrhOTha1ilPY7UofhRvJ9TX\nQqHDxZtbKs+rLW9BRgEAkQmRUrVjIqtF8aMpgYT5GZVYTje8kOWgqsFtcmSiN2uobqA4t7hlefCM\nwSiZ5ySEEEIIE0hyR3SLr776CoD58+e3uV9rzfLlywG46KKLuj2u9rDZjKtBPT09TY5ECCGE6CCL\nBSZNhrt/Cr97DH3JpWh//5bVqqkJlbUB9ac/wqOPwOpVUF9vVrRCnFl0DCxYCI8/ib7hZvTM2RAe\nbqzTGt57B3buOOdEpZ+nhcWJQXhZFVlFDfxnX+057aeqpIqKvRVYPCxEJ0ef0z5E5/H1sLA40Y63\nzTj5XlHv5uUcB00yB0mco0PrDrVU7QREBBA8LNjkiIQQQgjRX0lyR3S5hoYG8vLy8PT0JCkpqeX+\nsrIylixZQmZmJikpKcybN8/EKE/twIEDvPzyywBcdtllJkcjhBBCnIewMPje9fCHJ9E/+gl62PA2\nq9Xhw6g3XoMH7oU3X4eiwyYFKsRZ+PjC3Atgwa2t9x3Yj/rqC9Sf/wSP/Br+82+o7XhyJiLAxq1x\ngQB8vLOG7UcaOryPo9uPGvuKj8DDV6p2eoJwfxs/jAvkRG3FnnInH+yoNjUm0Ts11jRSnCNVO0II\nIYToGWTmThe7+19HTrvuxokBTB/sA0BGQR1v5VWddtv/u2Jgy7//mF5OYaXrlNtNi/HmpknGB9IC\nh5Mn11Scdp/3zQhmcHO/6Te3VLK2sL7N83SWvLw8nE4n8fHxeHl5sXjxYgoKCsjJyaGxsZEZM2bw\n6quvnvVN8ZIlS3jzzTc7/PybN29myJAh7dr2tddeIyMjA5fLxeHDh9mwYQNut5tf/OIXXHXVVR1+\nbiGEEKLH8fCAlFRISUUfKoS0VbBhHarBOImt6ush7RtI+wY9ajTMnttpc02E6DIDQtFXXQPpq1Gl\nJfDOW+iP3oekFOMYHtz+YeeTw724fJQfK3fXsKagjnEDvToUytC5QwkaEoTfoO+2/BXmmTDI+Ln+\na3cNAGkH6oix20iJ9jE5MtGbHFp/CLfLaOvnN8iPkBEhJkckhBBCiP5MPqWLLpeTkwNAQkICBw4c\nYMWKFW3WR0RE4HKdOll1sqlTp57T8/uf1H7mbNavX98mgWSz2XjooYe46667zum5hRBCiB4tOsao\nfrju++h16yDtG1RJ6xXJavcu2L0LHWiHmbNgxkwIlhNZogcKDIQrroJLL0dv2Wwcyzt3QEY6Ojcb\nnviTkdhsp0tG+GL3spAS7d3hUJRShAyX35Oe6OIRvhQ6XGwpNZLZb+VVEeFvY3CQVFiJs3PWOinK\nLmpZHjJ9iFTtCCGEEMJU6nwGhfZFDodjFTC7PdsWFhYCEBMT04UR9Rz1zT34vb079iH3jjvu4K23\n3uLvf/87N998M/X19ZSUlLBu3Tqefvppdu3axbhx41izZg0WS8/oFFhXV8fBgwd5/fXXee655xg9\nejTvvPMOERERZocmTqO9v4/5+fkAjBw5sstjEqKryHEsuozWsHuXUbmzKRflbjt0XFssMDnOqIQY\nPQbO46SWHMeiy5WUGHOkfHzgqmuM++rr4fOVRqIyNKzdu3I3f2ayfOuYP/k4bqhqoKmxCd8Bvp0S\nvuga9S43f8qooKS6CYAgbwv3zQgh0KtnfA4xg/w9bp/9q/ZTuNb4zOEb5kvCTxIkudODyHEs+gI5\njkVvJ8dwu6XZ7fY5nbGj/vsOVnSb3NxcwKjcASM5FBsby4033sgXX3xBUFAQ27dvb6nw6Ql8fHwY\nM2YMv//97/nNb37D1q1bue+++8wOSwghhOhaShlJm8VL4H+eQF95Ndpub13tdqNyc1rnmnx9bnNN\nhOgW4eEw/8bWxA7AhvWoz1fCr38Ff3sGtubBt5KY31bT6ObZDQ4+y68543YH0w+StSyLopyiM24n\nzOVts3B7oh0fm3FS/ni9m1dyHDS55aJHcXrOOidFWVK1I4QQQoieRdqyiS5VVVVFfn4+gYGBjBo1\n6jvrg4KCGD16NOvXr8fpdJ5xX8uXLyczM7PDMTz66KMMGDCgw487YcGCBfz617/m888/x+l04tGB\nlh5CCCFErxUcDFdeDZddjt60CVavQu3a2bJalZbAirfQH75vzPCZPddo8yZETzZ0KDplKmRvROVt\ngbwt6NAwmDUHpk2HU7TzPVzpYldZIzvLGokO9GBy+Hdn8DRUNlC6pRQ0BA0J6oYXIs7HQD8bP4wP\n5PmNDjSwp9zJ+9ur+cGEALNDEz1UUVYRTY1GtZfPAB9Cx4SaHJEQQgghhCR3RBfLzc3F7XYTFxd3\nyiubnE4nO3fuxGKxMGbMmDPuKzMzs808nPZ64IEHziu5ExQUhM1mw+VyUVFRwcCBA895X0IIIUSv\nY7VBQiIkJKKLiow2V+vWoprbtarGRkhfDemr0cNHwJy5EJ8ANnmbKXqgmMHwox/D9fPRa9dA2ipU\n2VF4/x30lk1w7y+/85BRoZ5cPcaPj3bW8M9NlQycHkxEQNvj+9D6Q2i3JmxsmLRl6yXGD/TiitF+\nfLrLqMhafbCOGLuN1BgfkyMTPY2rwcXhjYdblgdPH4yySNWOEEIIIcwnn7pFlzrRku1UVTsAn376\nKQ6Hg9mzZxMcHHzGfT377LM8++yznR7j2WRkZOByubDb7eeVJBJCCCF6vchIuPFmuPY69Ib1kPY1\n6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Mvy4GlStSOEEEKI3keSO6Jbbdu2jcWLFzN+/HjCwsKIiopi9uzZvPjii/1mAOmSJUsI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\nej1CY8Tw11d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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [], + "image/png": { + "width": 827, + "height": 199 + } + } + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "W_B8n8wuIAz9" + }, + "cell_type": "markdown", + "source": [ + "Adding a constant term $\\alpha$ amounts to shifting the curve left or right (hence why it is called a *bias*).\n", + "\n", + "Let's start modeling this in TFP. The $\\beta, \\alpha$ parameters have no reason to be positive, bounded or relatively large, so they are best modeled by a *Normal random variable*, introduced next." + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "_52Ml-KhIAz9" + }, + "cell_type": "markdown", + "source": [ + "### Normal distributions\n", + "\n", + "A Normal random variable, denoted $X \\sim N(\\mu, 1/\\tau)$, has a distribution with two parameters: the mean, $\\mu$, and the *precision*, $\\tau$. Those familiar with the Normal distribution already have probably seen $\\sigma^2$ instead of $\\tau^{-1}$. They are in fact reciprocals of each other. The change was motivated by simpler mathematical analysis and is an artifact of older Bayesian methods. Just remember: the smaller $\\tau$, the larger the spread of the distribution (i.e. we are more uncertain); the larger $\\tau$, the tighter the distribution (i.e. we are more certain). Regardless, $\\tau$ is always positive. \n", + "\n", + "The probability density function of a $N( \\mu, 1/\\tau)$ random variable is:\n", + "\n", + "$$ f(x | \\mu, \\tau) = \\sqrt{\\frac{\\tau}{2\\pi}} \\exp\\left( -\\frac{\\tau}{2} (x-\\mu)^2 \\right) $$\n", + "\n", + "We plot some different density functions below. " + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "OLw3-8x2hxkm", + "outputId": "1f92bc9f-589c-4764-fa41-695b0e81270c", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 245 + } + }, + "cell_type": "code", + "source": [ + "rand_x_vals = tf.linspace(start=-8., stop=7., num=150)\n", + "\n", + "density_func_1 = tfd.Normal(loc=float(-2.), scale=float(1./.7)).prob(rand_x_vals)\n", + "density_func_2 = tfd.Normal(loc=float(0.), scale=float(1./1)).prob(rand_x_vals)\n", + "density_func_3 = tfd.Normal(loc=float(3.), scale=float(1./2.8)).prob(rand_x_vals)\n", + "\n", + "[\n", + " rand_x_vals_,\n", + " density_func_1_,\n", + " density_func_2_,\n", + " density_func_3_,\n", + "] = evaluate([\n", + " rand_x_vals,\n", + " density_func_1,\n", + " density_func_2,\n", + " density_func_3,\n", + "])\n", + "\n", + "colors = [TFColor[3], TFColor[0], TFColor[6]]\n", + "\n", + "plt.figure(figsize(12.5, 3))\n", + "plt.plot(rand_x_vals_, density_func_1_,\n", + " label=r\"$\\mu = %d, \\tau = %.1f$\" % (-2., .7), color=TFColor[3])\n", + "plt.fill_between(rand_x_vals_, density_func_1_, color=TFColor[3], alpha=.33)\n", + "plt.plot(rand_x_vals_, density_func_2_, \n", + " label=r\"$\\mu = %d, \\tau = %.1f$\" % (0., 1), color=TFColor[0])\n", + "plt.fill_between(rand_x_vals_, density_func_2_, color=TFColor[0], alpha=.33)\n", + "plt.plot(rand_x_vals_, density_func_3_,\n", + " label=r\"$\\mu = %d, \\tau = %.1f$\" % (3., 2.8), color=TFColor[6])\n", + "plt.fill_between(rand_x_vals_, density_func_3_, color=TFColor[6], alpha=.33)\n", + "\n", + "plt.legend(loc=r\"upper right\")\n", + "plt.xlabel(r\"$x$\")\n", + "plt.ylabel(r\"density function at $x$\")\n", + "plt.title(r\"Probability distribution of three different Normal random variables\");" + ], + "execution_count": 48, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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CdprZ3uAz8x7wJL6FRNYEwddHg7eLzKwm6jN+abJ108znL/jrc7I0rcBngC34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\niKcDT19P2+uvv/6KK664gunpaQ4fPryet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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [], + "image/png": { + "width": 845, + "height": 228 + } + } + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "HAxWLKGcIA0A" + }, + "cell_type": "markdown", + "source": [ + "A Normal random variable can be take on any real number, but the variable is very likely to be relatively close to $\\mu$. In fact, the expected value of a Normal is equal to its $\\mu$ parameter:\n", + "\n", + "$$ E[ X | \\mu, \\tau] = \\mu$$\n", + "\n", + "and its variance is equal to the inverse of $\\tau$:\n", + "\n", + "$$\\text{Var}( X | \\mu, \\tau ) = \\frac{1}{\\tau}$$\n", + "\n", + "\n", + "\n", + "Below we continue our modeling of the Challenger space craft:" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "F3DBYxvAIA0B", + "colab": {} + }, + "cell_type": "code", + "source": [ + "reset_sess()\n", + "\n", + "temperature_ = challenger_data_[:, 0]\n", + "temperature = tf.convert_to_tensor(temperature_, dtype=tf.float32)\n", + "D_ = challenger_data_[:, 1] # defect or not?\n", + "D = tf.convert_to_tensor(D_, dtype=tf.float32)\n", + "\n", + "beta = tfd.Normal(name=\"beta\", loc=0.3, scale=1000.).sample()\n", + "alpha = tfd.Normal(name=\"alpha\", loc=-15., scale=1000.).sample()\n", + "p_deterministic = tfd.Deterministic(name=\"p\", loc=1.0/(1. + tf.exp(beta * temperature_ + alpha))).sample()\n", + "\n", + "[\n", + " prior_alpha_,\n", + " prior_beta_,\n", + " p_deterministic_,\n", + " D_,\n", + "] = evaluate([\n", + " alpha,\n", + " beta,\n", + " p_deterministic,\n", + " D,\n", + "])\n" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "text", + "id": "PxOWy25CIA0D" + }, + "cell_type": "markdown", + "source": [ + "We have our probabilities, but how do we connect them to our observed data? A *Bernoulli* random variable with parameter $p$, denoted $\\text{Ber}(p)$, is a random variable that takes value 1 with probability $p$, and 0 else. Thus, our model can look like:\n", + "\n", + "$$ \\text{Defect Incident, }D_i \\sim \\text{Ber}( \\;p(t_i)\\; ), \\;\\; i=1..N$$\n", + "\n", + "where $p(t)$ is our logistic function and $t_i$ are the temperatures we have observations about. Notice in the code below we set the values of `beta` and `alpha` to 0 in `initial_chain_state`. The reason for this is that if `beta` and `alpha` are very large, they make `p` equal to 1 or 0. Unfortunately, `tfd.Bernoulli` does not like probabilities of exactly 0 or 1, though they are mathematically well-defined probabilities. So, by setting the coefficient values to `0`, we set the variable `p` to be a reasonable starting value. This has no effect on our results, nor does it mean we are including any additional information in our prior. It is simply a computational caveat in TFP. " + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "vRqoyxqnofbT", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def challenger_joint_log_prob(D, temperature_, alpha, beta):\n", + " \"\"\"\n", + " Joint log probability optimization function.\n", + " \n", + " Args:\n", + " D: The Data from the challenger disaster representing presence or \n", + " absence of defect\n", + " temperature_: The Data from the challenger disaster, specifically the temperature on \n", + " the days of the observation of the presence or absence of a defect\n", + " alpha: one of the inputs of the HMC\n", + " beta: one of the inputs of the HMC\n", + " Returns: \n", + " Joint log probability optimization function.\n", + " \"\"\"\n", + " rv_alpha = tfd.Normal(loc=0., scale=1000.)\n", + " rv_beta = tfd.Normal(loc=0., scale=1000.)\n", + "\n", + " # make this into a logit\n", + " logistic_p = 1.0/(1. + tf.exp(beta * tf.to_float(temperature_) + alpha))\n", + " rv_observed = tfd.Bernoulli(probs=logistic_p)\n", + " \n", + " return (\n", + " rv_alpha.log_prob(alpha)\n", + " + rv_beta.log_prob(beta)\n", + " + tf.reduce_sum(rv_observed.log_prob(D))\n", + " )" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "code", + "id": "oHU-MbPxs8iL", + "cellView": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "number_of_steps = 10000 #@param {type:\"slider\", min:2500, max:120000, step:100}\n", + "burnin = 2000 #@param {type:\"slider\", min:2000, max:100000, step:100}\n", + "\n", + "# Set the chain's start state.\n", + "initial_chain_state = [\n", + " 0. * tf.ones([], dtype=tf.float32, name=\"init_alpha\"),\n", + " 0. * tf.ones([], dtype=tf.float32, name=\"init_beta\")\n", + "]\n", + "\n", + "# Since HMC operates over unconstrained space, we need to transform the\n", + "# samples so they live in real-space.\n", + "# Alpha is 100x of beta approximately, so apply Affine scalar bijector\n", + "# to multiply the unconstrained alpha by 100 to get back to \n", + "# the Challenger problem space\n", + "unconstraining_bijectors = [\n", + " tfp.bijectors.AffineScalar(100.),\n", + " tfp.bijectors.Identity()\n", + "]\n", + "\n", + "# Define a closure over our joint_log_prob.\n", + "unnormalized_posterior_log_prob = lambda *args: challenger_joint_log_prob(D, temperature_, *args)\n", + "\n", + "# Initialize the step_size. (It will be automatically adapted.)\n", + "with tf.variable_scope(tf.get_variable_scope(), reuse=tf.AUTO_REUSE):\n", + " step_size = tf.get_variable(\n", + " name='step_size',\n", + " initializer=tf.constant(0.01, dtype=tf.float32),\n", + " trainable=False,\n", + " use_resource=True\n", + " )\n", + "\n", + "# Defining the HMC\n", + "hmc=tfp.mcmc.TransformedTransitionKernel(\n", + " inner_kernel=tfp.mcmc.HamiltonianMonteCarlo(\n", + " target_log_prob_fn=unnormalized_posterior_log_prob,\n", + " num_leapfrog_steps=40, #to improve convergence\n", + " step_size=step_size,\n", + " step_size_update_fn=tfp.mcmc.make_simple_step_size_update_policy(\n", + " num_adaptation_steps=int(burnin * 0.8)),\n", + " state_gradients_are_stopped=True),\n", + " bijector=unconstraining_bijectors)\n", + "\n", + "# Sampling from the chain.\n", + "[\n", + " posterior_alpha,\n", + " posterior_beta\n", + "], kernel_results = tfp.mcmc.sample_chain(\n", + " num_results = number_of_steps,\n", + " num_burnin_steps = burnin,\n", + " current_state=initial_chain_state,\n", + " kernel=hmc)\n", + "\n", + "## Initialize any created variables for preconditions\n", + "init_g = tf.global_variables_initializer()" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "text", + "id": "eNkhSXDkthRs" + }, + "cell_type": "markdown", + "source": [ + "#### Execute the TF graph to sample from the posterior" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "XJyZIwoyth2j", + "outputId": "4debdde9-d4eb-41af-f59f-335ba214f5af", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 89 + } + }, + "cell_type": "code", + "source": [ + "%%time\n", + "# In Graph Mode, this cell can take up to 15 Minutes\n", + "evaluate(init_g)\n", + "[\n", + " posterior_alpha_,\n", + " posterior_beta_,\n", + " kernel_results_\n", + "] = evaluate([\n", + " posterior_alpha,\n", + " posterior_beta,\n", + " kernel_results\n", + "])\n", + " \n", + "print(\"acceptance rate: {}\".format(\n", + " kernel_results_.inner_results.is_accepted.mean()))\n", + "print(\"final step size: {}\".format(\n", + " kernel_results_.inner_results.extra.step_size_assign[-100:].mean()))\n" + ], + "execution_count": 52, + "outputs": [ + { + "output_type": "stream", + "text": [ + "acceptance rate: 0.7445\n", + "final step size: 0.01269734837114811\n", + "CPU times: user 19min 6s, sys: 2min 18s, total: 21min 24s\n", + "Wall time: 12min 28s\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "xIGyBkilIA0G" + }, + "cell_type": "markdown", + "source": [ + "We have trained our model on the observed data, so lets look at the posterior distributions for $\\alpha$ and $\\beta$:" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "Pdgjgw9RiluO", + "outputId": "0bc1e51c-d0ad-444e-920f-9cfc293d35df", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 393 + } + }, + "cell_type": "code", + "source": [ + "plt.figure(figsize(12.5, 6))\n", + "\n", + "#histogram of the samples:\n", + "plt.subplot(211)\n", + "plt.title(r\"Posterior distributions of the variables $\\alpha, \\beta$\")\n", + "plt.hist(posterior_beta_, histtype='stepfilled', bins=35, alpha=0.85,\n", + " label=r\"posterior of $\\beta$\", color=TFColor[6], density=True)\n", + "plt.legend()\n", + "\n", + "plt.subplot(212)\n", + "plt.hist(posterior_alpha_, histtype='stepfilled', bins=35, alpha=0.85,\n", + " label=r\"posterior of $\\alpha$\", color=TFColor[0], density=True)\n", + "plt.legend();" + ], + "execution_count": 53, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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5HHHEEey6667sueeefOQjH+HJJ5/s8LznnnuO\nz3zmM7ztbW9j7Nix7LHHHkybNo158+Zt9VpLly7lk5/8JPvvvz8777wz48eP54ADDuDUU0/lRz/6\nEQA33XQTY8aMaV0C8G1ve1trjmPGjOHZZ5/dIuaaNWu44ooreNe73sWECRPYZZddOOyww5g9e3br\nHjqd3fMNN9zAUUcdxYQJExgzZgyvvPIKUFm+rqPrdefea7netnTlmrNnz2bMmDHcdNNNAJx11lmt\nOcycObOm6wEsXryYmTNn8pa3vIVddtmFKVOmcM011wCQmRxxxBHsu+++rcvW9XdFzBQCmA3MA+ZE\nxM8y89cAETEWuLra5vLM3NxyQkScDZwNPJSZH2sX73JgOnBeRPw4Mx+qntMEXEelmHV1Zr7SJl4j\n8HHgxszcopdHxPuAf60+vCozNxZx05IkSZIkSZKkwWPWrFl8/etfZ+rUqRx77LE89thj3H777dx9\n99384Ac/YOrUqa1tH374YU488URWrVrVWpxYuXIlCxcuZOHChcyfP59/+Zd/ad2XB+CJJ57gmGOO\nYfXq1bzpTW/imGOOISJ44YUXuPvuu1m/fj3HH388kyZN4uSTT+bWW29lzZo1HHfccYwaNao1TlNT\nU+ufV6xYwQknnMCSJUvYaaedmDJlCttttx2PPvooc+bM4fbbb+eOO+5oLci09zd/8zd885vf5E/+\n5E94z3vew69//estcu5Id+69J9fryjVbHHDAAZx88sn8/Oc/55lnnuGwww5rnfnU9t+xM9deey0X\nXHABQ4YM4YgjjmDo0KHcfffdzJo1i1133ZUhQ4bw+OOP89WvfnXA7LlUSFEoM2+OiGuAmcDjETEf\n2AgcBewA3AJc2e60nYB9qMwgah/v4Yg4H5gD/Cwi7gZeobJnz1jgQaD9YsoNVApQ/xQRjwDLq8+9\nBXhztc2/A1/o2d1KkiRJkiRJkgajb33rW9x2220cfvjhQGUmyCWXXMJXvvIVTj/9dBYtWsSIESNY\nv349p512GqtWrWLmzJlceumlDB06FIAnn3yS448/nu9973scdthhnHbaaa3xr776alavXs0XvvAF\nzj333C2u3dzc3DojaerUqUydOpWFCxeyZs0a/u7v/o499tjjdflmJqeddhpLlizh9NNP55JLLmHk\nyJEArFu3jk996lN8//vfZ9asWa2zW9r73ve+x1133cXBBx9c099Rd++9u9fr6jVPPvlkAKZNm8a0\nadOYOXMmzzzzDB/96Ec59dTXLU62VfPmzeNzn/scO++8M7fddhtvfnOlzPDd736XT37yk9xxxx08\n8cQTTJo0iY985CM1x623Hi8f1yIzz6Sy3NsjVIo37wF+TWU20AmZuamL8b4EvBe4h8qeRO8HXgYu\nBI7MzLXtTlkLXArcB4yjslzcNCpFqVurOZzgLCFJkiRJkiRJUkf+4i/+orUgBBARXHjhhUycOJHn\nn3+eW2+9FYBbbrmF559/nt13351LLrmktUABsO+++zJr1iwAvva1r20R/3e/+x0ARx999Ouu3dTU\nxKGHHtqlfOfPn89DDz3ElClTmDNnTmtBCGDkyJF85StfYeedd2bevHlbXaLtU5/6VJcKNN299+5e\nr4hrdtW6detaY/7DP/xDa0EIKsUmgNtuu40nn3ySWbNmMWxYUYuy9b7CikIAmfmdzDw8M3fIzFGZ\neXBmXtV22bg2bb+YmZGZ7+wk3o8z888z848yc2Rm7peZl2Xmqx203ZCZn8/M92TmxMxsysyGzByX\nmcdn5r8Xea+SJEmSJEmSpMHlQx/60OueGzp0KCeeeCIACxcuBOCnP/0pACeddBLDhw9/3TmnnHIK\nEcHTTz/Nb37zm9bnDzroIADOPfdc7rnnHl599XW/6u6S//iP/wDguOOOY8iQ1/+6f9SoURx44IG8\n9tprPPLIIx3GeP/739+la3b33rt7va5e84UXXuhy/PZuvfVWXn75ZQ466CA+8IEPbPFaU1MTQ4YM\nYf369ey7776tfWOgKLQoJEmSJEmSJEnSQNXREm0Au+++O0BrkaOl8LC19iNGjGDXXXfdoi3AX/3V\nX3HkkUeyaNEipk+fzu67787RRx/NRRddxBNPPNHlfJ999lkAPv/5zzNmzJgOv1oKRy+//HKHMSZM\nmNCla3b33rt7vSKu2VV33XUXwOsKQi02b67Mg7nwwgtr2g+pPxk4c5okSZIkSZIkSRrAGhsb+dGP\nfsSiRYuYP38+Dz74IA8//DCLFi3iiiuuYNasWZx33nk1x9u0qbJry+GHH95auNqarRVj2i451xf6\n+nrd8eijjwLw9re//XWvrVq1CoD99tuPY489tk/zKoJFIUmSJEmSJEmSgOeee44DDjigw+eB1tko\nLceWmTrtrV+/vnXGSkvbtg455BAOOeQQADZs2MC8efP41Kc+xeWXX84HP/hB9t5775ryHTduHFCZ\n0XL66afXdE5P9fTeB8I1n3/+eQB22WWX1702e/ZsAMaPH9/j69SDRSFJkiRJ6sTE6/4VGl6/bnm/\ncvFl9c5AkiRpUJg3b97rikKbNm3iBz/4AQB/+qd/ClRm5tx4443cfPPNzJo1i2HDtvxV+7/927+R\nmUyaNInddtut02s2NDRw6qmn8u1vf5sHHniAJ554orUo1NDQ0JpDR44++mhuuOEGbrnllj4rChV5\n771xzSKKQi1Lwq1cuXKL4s9jjz3GN77xDYAO93AaCAZm1pIkSZIkSZIkFeyb3/wmDzzwQOvjzGT2\n7Nk888wz7Lbbbhx33HFAZWbO+PHjefbZZ7n44otb95gBWLJkSetsknPOOWeL+N/4xjdYunTp6667\nbNkyFi9eDGy5zFtLgeNXv/pVh/lOmzaNyZMn89Of/pS//uu/ZuXKla9r89JLL/Gtb32rpvuvRXfv\nfSBd881vfjNAawEIKnsV/eVf/mVrMWjZsmVkZiHX60vOFJIkSZIkSZIkCfjYxz7G+973Pt7+9rez\nyy678Nhjj7F06VJGjhzJtdde27ofzogRI7j++us58cQT+drXvsbtt9/OQQcdxMqVK7n//vvZuHEj\nH/7wh5kxY8YW8efOnctnP/tZJk6cyFve8haampp46aWX+PnPf86GDRs44YQTOPjgg1vbT5s2jYUL\nF3LGGWfwrne9i9GjRwNw8cUXs+OOOzJkyBBuuukmTjrpJK6//npuvvlm9t9/f8aNG8f69et56qmn\nWLJkCTvvvDMf//jHC/k76u6999U1X3311R5f78wzz+SMM87gW9/6Fo8//jgTJkzgnnvuobm5mauv\nvprLLruMxYsXc/zxxzNjxgw++MEPFnCXfcOikCRJkiRJkiRJwN///d+z1157cf311/OLX/yC7bbb\njve973387d/+Lfvtt98WbadMmcL999/PV7/6VebPn89tt93GiBEjmDJlCjNmzOCkk05qXYasxYUX\nXshPfvITFi1axEMPPcTq1asZO3Yshx9+OB//+MdbZyK1OOOMM1i9ejXz5s3jJz/5SWvB47Of/Sw7\n7rgjUNlX6O677+bGG2/khz/8IU8++SSLFi1ixx13ZNddd+Xss89m2rRphf49defeB9I1P/ShD7F5\n82auueYaFi9ezOLFi5k0aRIXXXQR7373uxk3bhyf/vSnue+++zjxxBMLuWZfiYE4vak/WrVq1QLg\nyM7atEwLrHWTMGkgs7+rbOzzKhP7u8pk6dKlTLzuXxnunkKD30UX1DuDutu4YSNAz/q7fVEDhN/P\nqCyWL18OwM477wxUZluoY2PGjAHglVdeqXMm6qn169cD/be/t4zLtssE1uDe0aNHv7OI67unkCRJ\nkiRJkiRJUglYFJIkSZIkSZIkSSoBi0KSJEmSJEmSJEklMKzeCUiSJEmSJEmSVE/uJaSycKaQJEmS\nJEmSJElSCVgUkiRJkiRJkiRJKgGLQpIkSZIkSZIkSSXgnkKSJEmSNNBddEG9M9i2iy+rdwaSJElS\n6TlTSJIkSZIkSZIkqZdlZr1TsCgkSZIkSZIkSYNZf/hFtKT/GYsRUbccLApJkiRJkiRJ0iA0bFhl\n95CNGzfWORNJABs2bABg6NChdcvBopAkSZIkSZIkDUIjR44EYN26dc4WkuosM1mzZg3wP2OzHobV\n7cqSJEmSJEmSpF7T2NjI6tWrWbduHVBZsqqhoYGIqOvyVVJZZCaZyYYNG1izZg1r164FYNSoUXXL\nyaKQJEmSJEmSJA1CDQ0N7LTTTqxYsYI1a9a0Ll0lDWabN28GYMiQ/rlQ2k477cTw4cPrdn2LQpIk\nSZIkSZI0SI0cOZLMZNOmTYwcOZJNmza5lJwGtZbi54gRI+qcSUVEMHToUEaOHMmoUaPqWhACi0KS\nJEmSJEmSVAq77rprvVOQet3SpUsBmDBhQp0z6Z/65/wpSZIkSZIkSZIkFcqikCRJkiRJkiRJUglY\nFJIkSZIkSZIkSSoBi0KSJEmSJEmSJEklYFFIkiRJkiRJkiSpBCwKSZIkSZIkSZIklYBFIUmSJEmS\nJEmSpBKwKCRJkiRJkiRJklQCw+qdgCRJkqSSu+iCemewVRM3bKx3CpIkSZJUGGcKSZIkSZIkSZIk\nlYBFIUmSJEmSJEmSpBKwKCRJkiRJkiRJklQC7ikkSZIkSZIk9aV+vJ9eq4svq3cGkqRe4EwhSZIk\nSZIkSZKkEii0KBQRp0TE/RGxKiKaI2JRRJwVEd26TkQcExH/ERG/j4i1EfF/I+KCiNiuCzHeHRFZ\n/bq9O3lIkiRJkiRJkiQNdIUVhSLiKuAm4BDgfuAu4E3AlcDNXS0MRcTngDuBPwMeAe4AxgKXAgsi\norGGGKOBbwDZlWtLkiRJkiRJkiQNNoUUhSLiBOBM4EXgrZk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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [], + "image/png": { + "width": 834, + "height": 376 + } + } + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "gp0QmuZvIA0L" + }, + "cell_type": "markdown", + "source": [ + "All samples of $\\beta$ are greater than 0. If instead the posterior was centered around 0, we may suspect that $\\beta = 0$, implying that temperature has no effect on the probability of defect. \n", + "\n", + "Similarly, all $\\alpha$ posterior values are negative and far away from 0, implying that it is correct to believe that $\\alpha$ is significantly less than 0. \n", + "\n", + "Regarding the spread of the data, we are very uncertain about what the true parameters might be (though considering the low sample size and the large overlap of defects-to-nondefects this behaviour is perhaps expected). \n", + "\n", + "Next, let's look at the *expected probability* for a specific value of the temperature. That is, we average over all samples from the posterior to get a likely value for $p(t_i)$." + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "EIzyJL_3IA0P", + "outputId": "960703b4-a5e8-4af4-a2b3-fbf53a03fe5d", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 53 + } + }, + "cell_type": "code", + "source": [ + "alpha_samples_1d_ = posterior_alpha_[:, None] # best to make them 1d\n", + "beta_samples_1d_ = posterior_beta_[:, None]\n", + "\n", + "beta_mean = tf.reduce_mean(beta_samples_1d_.T[0])\n", + "alpha_mean = tf.reduce_mean(alpha_samples_1d_.T[0])\n", + "[ beta_mean_, alpha_mean_ ] = evaluate([ beta_mean, alpha_mean ])\n", + "\n", + "\n", + "print(\"beta mean:\", beta_mean_)\n", + "print(\"alpha mean:\", alpha_mean_)\n", + "def logistic(x, beta, alpha=0):\n", + " \"\"\"\n", + " Logistic function with alpha and beta.\n", + " \n", + " Args:\n", + " x: independent variable\n", + " beta: beta term \n", + " alpha: alpha term\n", + " Returns: \n", + " Logistic function\n", + " \"\"\"\n", + " return 1.0 / (1.0 + tf.exp((beta * x) + alpha))\n", + "\n", + "t_ = np.linspace(temperature_.min() - 5, temperature_.max() + 5, 2500)[:, None]\n", + "p_t = logistic(t_.T, beta_samples_1d_, alpha_samples_1d_)\n", + "mean_prob_t = logistic(t_.T, beta_mean_, alpha_mean_)\n", + "[ \n", + " p_t_, mean_prob_t_\n", + "] = evaluate([ \n", + " p_t, mean_prob_t\n", + "])" + ], + "execution_count": 54, + "outputs": [ + { + "output_type": "stream", + "text": [ + "beta mean: 0.32857734\n", + "alpha mean: -21.576159\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "Ri4BriJHPJNg", + "outputId": "759de914-4b8f-4b45-b988-702159d3b246", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 299 + } + }, + "cell_type": "code", + "source": [ + "plt.figure(figsize(12.5, 4))\n", + "\n", + "plt.plot(t_, mean_prob_t_.T, lw=3, label=\"average posterior \\nprobability \\\n", + "of defect\")\n", + "plt.plot(t_, p_t_.T[:, 0], ls=\"--\", label=\"realization from posterior\")\n", + "plt.plot(t_, p_t_.T[:, -8], ls=\"--\", label=\"realization from posterior\")\n", + "plt.scatter(temperature_, D_, color=\"k\", s=50, alpha=0.5)\n", + "plt.title(\"Posterior expected value of probability of defect; \\\n", + "plus realizations\")\n", + "plt.legend(loc=\"lower left\")\n", + "plt.ylim(-0.1, 1.1)\n", + "plt.xlim(t_.min(), t_.max())\n", + "plt.ylabel(\"probability\")\n", + "plt.xlabel(\"temperature\");" + ], + "execution_count": 55, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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WWFb1vlFjGx2Uagf9gOaeRcvx38Wi8m0xy+ASvsZe/Dkqvjvhg0Txj5n+omzZ1kTea6rk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BBC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\nra/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gUl2GDx/OkiVL6Ny5Mx988IHbJ5Q6dOjgtU+TJk0YMWIETz/9NPPmzXO7of971AZE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\nBjwfWAzsCLwVuAF4Zdvmaeul99L6t6Sd7kZzgfxk4Hk0/3k8gOauo2XAaV1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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [], + "image/png": { + "width": 843, + "height": 282 + } + } + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "iI7Fosv1IA0T" + }, + "cell_type": "markdown", + "source": [ + "Above we also plotted two possible realizations of what the actual underlying system might be. Both are equally likely as any other draw. The blue line is what occurs when we average all the 20000 possible dotted lines together.\n" + ] + }, + { + "metadata": { + "id": "k_rZvVwz7zHI", + "colab_type": "code", + "outputId": "d8a00a3d-4fbc-4362-a670-999f76c94e14", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 302 + } + }, + "cell_type": "code", + "source": [ + "from scipy.stats.mstats import mquantiles\n", + "\n", + "# vectorized bottom and top 2.5% quantiles for \"confidence interval\"\n", + "qs = mquantiles(p_t_, [0.025, 0.975], axis=0)\n", + "plt.fill_between(t_[:, 0], *qs, alpha=0.7,\n", + " color=\"#7A68A6\")\n", + "\n", + "plt.plot(t_[:, 0], qs[0], label=\"95% CI\", color=\"#7A68A6\", alpha=0.7)\n", + "\n", + "plt.plot(t_[:, 0], mean_prob_t_[0,:], lw=1, ls=\"--\", color=\"k\",\n", + " label=\"average posterior \\nprobability of defect\")\n", + "\n", + "plt.xlim(t_.min(), t_.max())\n", + "plt.ylim(-0.02, 1.02)\n", + "plt.legend(loc=\"lower left\")\n", + "plt.scatter(temperature_, D_, color=\"k\", s=50, alpha=0.5)\n", + "plt.xlabel(\"temp, $t$\")\n", + "\n", + "plt.ylabel(\"probability estimate\")\n", + "plt.title(\"Posterior probability estimates given temp. $t$\");" + ], + "execution_count": 56, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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f8KczVd4YM92A6PM17/OxBH7WGLNl8kpjzC3Ajd7ig/Mod7GOca716/PmV5i8AVXy/C+g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\n3/b76PF49BgS4O8jlvbn48HlcuH3+xkzZsx+60PkQNp+ZdLu/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LmuE22u68Sho7s5cHgH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\nAJRSi4AXgGuUUs9qrf84JjUVYuIYSbtJ+LbWeuMY1EW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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [], + "image/png": { + "width": 843, + "height": 285 + } + } + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "rtcc9pBFIA0X" + }, + "cell_type": "markdown", + "source": [ + "The 95% credible interval, or 95% CI, painted in purple, represents the interval, for each temperature, that contains 95% of the distribution. For example, at 65 degrees, we can be 95% sure that the probability of defect lies between 0.25 and 0.85.\n", + "\n", + "More generally, we can see that as the temperature nears 60 degrees, the CI's spread out over $[0,1]$ quickly. As we pass 70 degrees, the CI's tighten again. This can give us insight about how to proceed next: we should probably test more O-rings around 60-65 temperature to get a better estimate of probabilities in that range. Similarly, when reporting to scientists your estimates, you should be very cautious about simply telling them the expected probability, as we can see this does not reflect how *wide* the posterior distribution is." + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "lesq_3oIIA0Y" + }, + "cell_type": "markdown", + "source": [ + "### What about the day of the Challenger disaster?\n", + "\n", + "On the day of the Challenger disaster, the outside temperature was 31 degrees Fahrenheit. What is the posterior distribution of a defect occurring, given this temperature? The distribution is plotted below. It looks almost guaranteed that the Challenger was going to be subject to defective O-rings." + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "AYbamYmdUdBZ", + "outputId": "42a2542e-677f-4786-e39c-fcae90c30559", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 246 + } + }, + "cell_type": "code", + "source": [ + "plt.figure(figsize(12.5, 3))\n", + "\n", + "prob_31 = logistic(31, posterior_beta_, posterior_alpha_)\n", + "\n", + "[ prob_31_ ] = evaluate([ prob_31 ])\n", + "\n", + "plt.xlim(0.98, 1)\n", + "plt.hist(prob_31_, bins=10, density=True, histtype='stepfilled')\n", + "plt.title(\"Posterior distribution of probability of defect, given $t = 31$\")\n", + "plt.xlabel(\"probability of defect occurring in O-ring\");" + ], + "execution_count": 57, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [], + "image/png": { + "width": 847, + "height": 229 + } + } + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "WjAFZ8W9IA0c" + }, + "cell_type": "markdown", + "source": [ + "### Is our model appropriate?\n", + "\n", + "The skeptical reader will say \"You deliberately chose the logistic function for $p(t)$ and the specific priors. Perhaps other functions or priors will give different results. How do I know I have chosen a good model?\" This is absolutely true. To consider an extreme situation, what if I had chosen the function $p(t) = 1,\\; \\forall t$, which guarantees a defect always occurring: I would have again predicted disaster on January 28th. Yet this is clearly a poorly chosen model. On the other hand, if I did choose the logistic function for $p(t)$, but specified all my priors to be very tight around 0, likely we would have very different posterior distributions. How do we know our model is an expression of the data? This encourages us to measure the model's **goodness of fit**.\n", + "\n", + "We can think: *how can we test whether our model is a bad fit?* An idea is to compare observed data with artificial dataset which we can simulate. The rationale is that if the simulated dataset does not appear similar, statistically, to the observed dataset, then likely our model is not accurately represented the observed data. \n", + "\n", + "Previously in this Chapter, we simulated an artificial dataset for the SMS example. To do this, we sampled values from the priors. We saw how varied the resulting datasets looked like, and rarely did they mimic our observed dataset. In the current example, we should sample from the *posterior* distributions to create *very plausible datasets*. Luckily, our Bayesian framework makes this very easy. We only need to gather samples from the distribution of choice, and specify the number of samples, the shape of the samples (we had 21 observations in our original dataset, so we'll make the shape of each sample 21), and the probability we want to use to determine the ratio of 1 observations to 0 observations.\n", + "\n", + "\n", + "Hence we create the following:\n", + "\n", + "```python\n", + "simulated_data = tfd.Bernoulli(name=\"simulation_data\", probs=p).sample(sample_shape=N)\n", + "```\n", + "Let's simulate 10 000:" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "MvFwyz9hwROg", + "colab": {} + }, + "cell_type": "code", + "source": [ + "alpha = alpha_mean_ # We're basing these values on the outputs of our model above\n", + "beta = beta_mean_\n", + "p_deterministic = tfd.Deterministic(name=\"p\", loc=1.0/(1. + tf.exp(beta * temperature_ + alpha))).sample()#seed=6.45)\n", + "simulated_data = tfd.Bernoulli(name=\"bernoulli_sim\", \n", + " probs=p_deterministic_).sample(sample_shape=10000)\n", + "[ \n", + " bernoulli_sim_samples_,\n", + " p_deterministic_\n", + "] =evaluate([\n", + " simulated_data,\n", + " p_deterministic\n", + "])" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "code", + "id": "gDyVY1wmgjx4", + "outputId": "f033f0d4-7e3b-4371-fe9d-ef2fc8d008d5", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 742 + } + }, + "cell_type": "code", + "source": [ + "simulations_ = bernoulli_sim_samples_\n", + "print(\"Number of simulations: \", simulations_.shape[0])\n", + "print(\"Number data points per simulation: \", simulations_.shape[1])\n", + "\n", + "plt.figure(figsize(12.5, 12))\n", + "plt.title(\"Simulated dataset using posterior parameters\")\n", + "for i in range(4):\n", + " ax = plt.subplot(4, 1, i+1)\n", + " plt.scatter(temperature_, simulations_[1000*i, :], color=\"k\",\n", + " s=50, alpha=0.6)\n", + " " + ], + "execution_count": 67, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Number of simulations: 10000\n", + "Number data points per simulation: 23\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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" + ] + }, + "metadata": { + "tags": [], + "image/png": { + "width": 827, + "height": 689 + } + } + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "RFSaMzNQIA0l" + }, + "cell_type": "markdown", + "source": [ + "Note that the above plots are different (if you can think of a cleaner way to present this, please send a pull request and answer [here](http://stats.stackexchange.com/questions/53078/how-to-visualize-bayesian-goodness-of-fit-for-logistic-regression)!).\n", + "\n", + "We wish to assess how good our model is. \"Good\" is a subjective term of course, so results must be relative to other models. \n", + "\n", + "We will be doing this graphically as well, which may seem like an even less objective method. The alternative is to use *Bayesian p-values*. These are still subjective, as the proper cutoff between good and bad is arbitrary. Gelman emphasises that the graphical tests are more illuminating [3] than p-value tests. We agree.\n", + "\n", + "The following graphical test is a novel data-viz approach to logistic regression. The plots are called *separation plots*[4]. For a suite of models we wish to compare, each model is plotted on an individual separation plot. I leave most of the technical details about separation plots to the very accessible [original paper](https://onlinelibrary.wiley.com/doi/10.1111/j.1540-5907.2011.00525.x), but I'll summarize their use here.\n", + "\n", + "For each model, we calculate the proportion of times the posterior simulation proposed a value of 1 for a particular temperature, i.e. compute $P( \\;\\text{Defect} = 1 | t, \\alpha, \\beta )$ by averaging. This gives us the posterior probability of a defect at each data point in our dataset. For example, for the model we used above:" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "3ho1cPLAIA0l", + "outputId": "643b1bf8-6317-48f8-e9c7-74c990e11742", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 449 + } + }, + "cell_type": "code", + "source": [ + "posterior_probability_ = simulations_.mean(axis=0)\n", + "print(\"posterior prob of defect | realized defect \")\n", + "for i in range(len(D_)):\n", + " print(\"%.2f | %d\" % (posterior_probability_[i], D_[i]))" + ], + "execution_count": 68, + "outputs": [ + { + "output_type": "stream", + "text": [ + "posterior prob of defect | realized defect \n", + "0.47 | 0\n", + "0.20 | 1\n", + "0.25 | 0\n", + "0.31 | 0\n", + "0.39 | 0\n", + "0.11 | 0\n", + "0.08 | 0\n", + "0.19 | 0\n", + "0.94 | 1\n", + "0.71 | 1\n", + "0.19 | 1\n", + "0.02 | 0\n", + "0.39 | 0\n", + "0.99 | 1\n", + "0.39 | 0\n", + "0.05 | 0\n", + "0.19 | 0\n", + "0.01 | 0\n", + "0.03 | 0\n", + "0.01 | 0\n", + "0.04 | 1\n", + "0.03 | 0\n", + "0.92 | 1\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "-q4yysOiIA0n" + }, + "cell_type": "markdown", + "source": [ + "Next we sort each column by the posterior probabilities:" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "i3TkXUhSIA0n", + "outputId": "8b269843-6e8c-4757-b712-de7a56364cb4", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 449 + } + }, + "cell_type": "code", + "source": [ + "ix_ = np.argsort(posterior_probability_)\n", + "print(\"probb | defect \")\n", + "for i in range(len(D_)):\n", + " print(\"%.2f | %d\" % (posterior_probability_[ix_[i]], D_[ix_[i]]))" + ], + "execution_count": 69, + "outputs": [ + { + "output_type": "stream", + "text": [ + "probb | defect \n", + "0.01 | 0\n", + "0.01 | 0\n", + "0.02 | 0\n", + "0.03 | 0\n", + "0.03 | 0\n", + "0.04 | 1\n", + "0.05 | 0\n", + "0.08 | 0\n", + "0.11 | 0\n", + "0.19 | 0\n", + "0.19 | 0\n", + "0.19 | 1\n", + "0.20 | 1\n", + "0.25 | 0\n", + "0.31 | 0\n", + "0.39 | 0\n", + "0.39 | 0\n", + "0.39 | 0\n", + "0.47 | 0\n", + "0.71 | 1\n", + "0.92 | 1\n", + "0.94 | 1\n", + "0.99 | 1\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "ajvopQADIA0p" + }, + "cell_type": "markdown", + "source": [ + "We can present the above data better in a figure: we've created a `separation_plot` function." + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "tFR1_yu8IA0p", + "outputId": "a3d0a1cb-9b41-4f8e-c0d8-ff7b8ef31489", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 236 + } + }, + "cell_type": "code", + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "def separation_plot( p, y, **kwargs ):\n", + " \"\"\"\n", + " This function creates a separation plot for logistic and probit classification. \n", + " See https://onlinelibrary.wiley.com/doi/10.1111/j.1540-5907.2011.00525.x\n", + " \n", + " p: The proportions/probabilities, can be a nxM matrix which represents M models.\n", + " y: the 0-1 response variables.\n", + " \n", + " \"\"\" \n", + " assert p.shape[0] == y.shape[0], \"p.shape[0] != y.shape[0]\"\n", + " n = p.shape[0]\n", + "\n", + " try:\n", + " M = p.shape[1]\n", + " except:\n", + " p = p.reshape( n, 1 )\n", + " M = p.shape[1]\n", + "\n", + " colors_bmh = np.array( [\"#eeeeee\", \"#348ABD\"] )\n", + "\n", + "\n", + " fig = plt.figure( )\n", + " \n", + " for i in range(M):\n", + " ax = fig.add_subplot(M, 1, i+1)\n", + " ix = np.argsort( p[:,i] )\n", + " #plot the different bars\n", + " bars = ax.bar( np.arange(n), np.ones(n), width=1.,\n", + " color = colors_bmh[ y[ix].astype(int) ], \n", + " edgecolor = 'none')\n", + " ax.plot( np.arange(n+1), np.append(p[ix,i], p[ix,i][-1]), \"k\",\n", + " linewidth = 1.,drawstyle=\"steps-post\" )\n", + " #create expected value bar.\n", + " ax.vlines( [(1-p[ix,i]).sum()], [0], [1] )\n", + " plt.xlim( 0, n)\n", + " \n", + " plt.tight_layout()\n", + " \n", + " return\n", + "\n", + "plt.figure(figsize(11., 3))\n", + "separation_plot(posterior_probability_, D_)" + ], + "execution_count": 70, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [], + "image/png": { + "width": 777, + "height": 201 + } + } + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "tBuY2lSaIA0s" + }, + "cell_type": "markdown", + "source": [ + "The snaking-line is the sorted probabilities, blue bars denote defects, and empty space (or grey bars for the optimistic readers) denote non-defects. As the probability rises, we see more and more defects occur. On the right hand side, the plot suggests that as the posterior probability is large (line close to 1), then more defects are realized. This is good behaviour. Ideally, all the blue bars *should* be close to the right-hand side, and deviations from this reflect missed predictions. \n", + "\n", + "The black vertical line is the expected number of defects we should observe, given this model. This allows the user to see how the total number of events predicted by the model compares to the actual number of events in the data.\n", + "\n", + "It is much more informative to compare this to separation plots for other models. Below we compare our model (top) versus three others:\n", + "\n", + "1. the perfect model, which predicts the posterior probability to be equal 1 if a defect did occur.\n", + "2. a completely random model, which predicts random probabilities regardless of temperature.\n", + "3. a constant model: where $P(D = 1 \\; | \\; t) = c, \\;\\; \\forall t$. The best choice for $c$ is the observed frequency of defects, in this case 7/23. \n" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "RbX1nHrBIA0s", + "outputId": "fb96b5c1-3e3d-424e-d9bd-3ba831e7b13b", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 599 + } + }, + "cell_type": "code", + "source": [ + "plt.figure(figsize(11., 2))\n", + "\n", + "# Our temperature-dependent model\n", + "separation_plot(posterior_probability_, D_)\n", + "plt.title(\"Temperature-dependent model\")\n", + "\n", + "# Perfect model\n", + "# i.e. the probability of defect is equal to if a defect occurred or not.\n", + "p_ = D_\n", + "separation_plot(p_, D_)\n", + "plt.title(\"Perfect model\")\n", + "\n", + "# random predictions\n", + "p_ = np.random.rand(23)\n", + "separation_plot(p_, D_)\n", + "plt.title(\"Random model\")\n", + "\n", + "# constant model\n", + "constant_prob_ = 7./23 * np.ones(23)\n", + "separation_plot(constant_prob_, D_)\n", + "plt.title(\"Constant-prediction model\");" + ], + "execution_count": 71, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + }, + { + "output_type": "display_data", + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [], + "image/png": { + "width": 777, + "height": 141 + } + } + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "etHuMg8OIA0u" + }, + "cell_type": "markdown", + "source": [ + "In the random model, we can see that as the probability increases there is no clustering of defects to the right-hand side. Similarly for the constant model.\n", + "\n", + "In the perfect model, the probability line is not well shown, as it is stuck to the bottom and top of the figure. Of course the perfect model is only for demonstration, and we cannot infer any scientific inference from it." + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "_I_3h7lsIA0v" + }, + "cell_type": "markdown", + "source": [ + "## Exercises\n", + "\n", + "1\\. Try putting in extreme values for our observations in the cheating example. What happens if we observe 25 affirmative responses? 10? 50? " + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "_B-K0Neyx7pZ", + "colab": {} + }, + "cell_type": "code", + "source": [ + "#type your code here." + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "colab_type": "text", + "id": "ulECHYwMIA0v" + }, + "cell_type": "markdown", + "source": [ + "2\\. Try plotting $\\alpha$ samples versus $\\beta$ samples. Why might the resulting plot look like this?" + ] + }, + { + "metadata": { + "colab_type": "code", + "id": "21v-EuHZIA0v", + "outputId": "06301247-795c-4077-bdfc-bd5bb9b345a9", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 259 + } + }, + "cell_type": "code", + "source": [ + "#type your code here.\n", + "plt.figure(figsize(12.5, 4))\n", + "\n", + "plt.scatter(alpha_samples_, beta_samples_, alpha=0.1)\n", + "plt.title(\"Why does the plot look like this?\")\n", + "plt.xlabel(r\"$\\alpha$\")\n", + "plt.ylabel(r\"$\\beta$\");" + ], + "execution_count": 73, + "outputs": [ + { + "output_type": "error", + "ename": "NameError", + "evalue": "ignored", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m12.5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m4\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscatter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0malpha_samples_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbeta_samples_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtitle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Why does the plot look like this?\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxlabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mr\"$\\alpha$\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mNameError\u001b[0m: name 'alpha_samples_' is not defined" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "colab_type": "text", + "id": "e6RYS1_jIA0y" + }, + "cell_type": "markdown", + "source": [ + "## References\n", + "\n", + "[1] Dalal, Fowlkes and Hoadley (1989),JASA, 84, 945-957.\n", + "\n", + "[2] Cronin, Beau. \"Why Probabilistic Programming Matters.\" 24 Mar 2013. Google, Online Posting to Google . Web. 24 Mar. 2013. .\n", + "\n", + "[3] Gelman, Andrew. \"Philosophy and the practice of Bayesian statistics.\" British Journal of Mathematical and Statistical Psychology. (2012): n. page. Web. 2 Apr. 2013.\n", + "\n", + "[4] Greenhill, Brian, Michael D. Ward, and Audrey Sacks. \"The Separation Plot: A New Visual Method for Evaluating the Fit of Binary Models.\" American Journal of Political Science. 55.No.4 (2011): n. page. Web. 2 Apr. 2013.\n" + ] + } + ] +} \ No newline at end of file diff --git a/Chapter2_MorePyMC/MorePyMC.ipynb b/Chapter2_MorePyMC/MorePyMC.ipynb deleted file mode 100644 index 89a9582c..00000000 --- a/Chapter2_MorePyMC/MorePyMC.ipynb +++ /dev/null @@ -1,1590 +0,0 @@ -{ - "metadata": { - "name": "MorePyMC" - }, - "nbformat": 3, - "nbformat_minor": 0, - "worksheets": [ - { - "cells": [ - { - "cell_type": "code", - "collapsed": false, - "input": [ - "figsize( 9, 3.5 )\n", - "np.set_printoptions(precision=3, suppress= True)\n", - "import scipy.stats as stats" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 1 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Chapter 2: Modeling with PyMC\n", - "======\n", - "\n", - "This chapter introduces more PyMC syntax and design patterns, and ways to think about Bayesian modeling a system. \n", - "______" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## A little more on PyMC\n", - "\n", - "### Parent and Child relationships\n", - "\n", - "To assist with terminology, and to be consistent with PyMC's documentation, we introduce *parent and children* variables. \n", - "\n", - "* *parent variables* are variables that influence another variable. \n", - "\n", - "* *children variable* are variables that are affected by other variables, i.e. are the subject of parent variables. \n", - "\n", - "Variables can be both parent and children variables. For example, consider the PyMC code below" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "import pymc as mc\n", - "\n", - "poi_parameter = mc.Exponential( \"Poisson_param\", 1 )\n", - "\n", - "data_generator = mc.Poisson(\"data_generator\", poi_parameter )" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 2 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "`poi_parameter` controls the parameter of `data_generator`, hence influences its value. The former is a parent of the latter. By symmetry, `data_generator` is a child of `poi_parameter`. \n", - "\n", - "This nomenclature is introduced to help us describe relationships in PyMC modeling. You can access a variables children and parent variables using the `children` and `parents` methods attached to variables." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "print poi_parameter.children\n", - "print\n", - "print data_generator.parents" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "set([])\n", - "\n", - "{'mu': }\n" - ] - } - ], - "prompt_number": 3 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Of course a child can have more than one parent, and parent can have many children." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### PyMC Variables\n", - "\n", - "All PyMC variables also expose a `value` attribute. This method produces the *current* (possible random) value of the variable, given the variable's parents. To use the same variables from before:" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "print poi_parameter.value\n", - "print data_generator.value" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "0.423418460571\n", - "2\n" - ] - } - ], - "prompt_number": 4 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "PyMC is concerned with two types of programming variables: `stochastic` and `deterministic`.\n", - "\n", - "* *stochastic variables* are variables that are not deterministic, i.e., even if you knew all the values of the variables' parents (if it even had any parents), it would still be random. Included in this catagory are instances of classes `Poisson`, `DiscreteUniform`, and `Exponential`.\n", - "\n", - "* *deterministic variables* are variables that not random if the variables' parents are not random. This might be confusing at first: a quick mental check is *if I knew all of variable `x`'s parent variables, I could determine completely what `x` is.* This is relevant as we are dealing with random variables.\n", - "\n", - "#### Stochastic variables\n", - "\n", - "Initializing a stochastic variable requires a `name` argument, plus additional parameters that a class specific. For example:\n", - "\n", - "`some_variable = mc.DiscreteUniform( \"discrete_uni_var\", 0, 4 )`\n", - "\n", - "where 0,4 are the `DiscreteUniform`-specific bounds on the random variable. The [PyMC docs](http://pymc-devs.github.com/pymc/distributions.html) contain the specific parameters for stochastic variables. (Or use `??` if you are using IPython!)\n", - "\n", - "Rather than creating a Python array of stochastic variables, addressing the `size` keyword in the call to `Stochastic` creates multivariate array of the (independent) stochastic variables. The array behaves like a Numpy array when used like one, and references to its `value` attribute return Numpy arrays. \n", - "\n", - "We can also call on a stochastic variables `random()` method, which (given the parent values) will generate a new (random) value. " - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "lambda_1 = mc.Exponential( \"lambda_1\", 1 )\n", - "lambda_2 = mc.Exponential( \"lambda_2\", 1 )\n", - "tau = mc.DiscreteUniform( \"tau\", lower = 0, upper = 10 )\n", - "print \"lambda_1.value = %.3f\"%lambda_1.value\n", - "print \"lambda_2.value = %.3f\"%lambda_2.value\n", - "print \"tau.value = %.3f\"%tau.value\n", - "print\n", - "\n", - "lambda_1.random(), lambda_2.random(), tau.random()\n", - "print \"After calling random() on the variables...\"\n", - "print \"lambda_1.value = %.3f\"%lambda_1.value\n", - "print \"lambda_2.value = %.3f\"%lambda_2.value\n", - "print \"tau.value = %.3f\"%tau.value\n", - "\n" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "lambda_1.value = 1.313\n", - "lambda_2.value = 0.024\n", - "tau.value = 1.000\n", - "\n", - "After calling random() on the variables...\n", - "lambda_1.value = 0.932\n", - "lambda_2.value = 1.067\n", - "tau.value = 10.000\n" - ] - } - ], - "prompt_number": 5 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The call to `random` stores a new value into the variables `value` attribute. In fact, this new value is stored in the computers cache for faster recall and efficiency." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Determinstic variables\n", - "\n", - "Since most variables you will be modeling are are stochastic, we distinguish deterministic variables with the `pymc.deterministic` wrapper. This is the easist way, but not the only way, to create deterministic variables. This is not completely true: elementary operations, like addition, exponentials etc. implicity create determinsitic variables. " - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "type( lambda_1 + lambda_2 )" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 6, - "text": [ - "pymc.PyMCObjects.Deterministic" - ] - } - ], - "prompt_number": 6 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The use of the `deterministic` wrapper was seen in the previous text-message example. Recall the model for $\\lambda$ looked like: \n", - "\n", - "$$\n", - "\\lambda = \n", - "\\cases{\n", - "\\lambda_1 & \\text{if } t \\lt \\tau \\cr\n", - "\\lambda_2 & \\text{if } t \\ge \\tau\n", - "}\n", - "$$\n", - "\n", - "And in PyMC code:" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "@mc.deterministic\n", - "def lambda_( tau = tau, lambda_1 = lambda_1, lambda_2 = lambda_2 ):\n", - " out = np.zeros( 10 ) \n", - " out[:tau] = lambda_1 #lambda before tau is lambda1\n", - " out[tau:] = lambda_2 #lambda after tau is lambda1\n", - " return out" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 7 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Clearly, if $\\tau, \\lambda_1$ and $\\lambda_2$ are known, then $\\lambda$ is known completely, hence it is a deterministic variable." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Including observations\n", - "\n", - "At this point, it may not look like it, but we have fully specified our priors. For example, we can ask and answer questions like \"What does my prior distribution of $\\lambda_1$ look like?\" " - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "samples = [ lambda_1.random() for i in range( 20000) ]\n", - "hist( samples, bins = 35, normed=True )\n", - "plt.title( \"Prior distribution for $\\lambda_1$\")" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 8, - "text": [ - "" - ] - }, - { - "output_type": "display_data", - "png": 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wuN1uZGVlwW63Y9euXSgqKsLu3bu95jz11FPYsmULtmzZgtdeew0TJkxA//79VS2aiIiI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\n2NhYxMTEICcnp83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- } - ], - "prompt_number": 8 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To frame this in the notation of the first chapter, though this is a slight abuse of notation, we have specified $P(A)$. Our next goal is to include data/evidence/observations $X$ into our model. \n", - "\n", - "PyMC stochastic variables have a keyword argument `observed` which accepts a boolean (`False` by default). `observed` has a very simple role: fix the variables current value, i.e. make `value` immutable. For this to make sense, we have to specify an intial `value` in the class call. For example:" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "fixed_variable = mc.Poisson( \"fxd\", 1, value = 10, observed = True )\n", - "print \"value: \",fixed_variable.value\n", - "print \"calling .random()\"\n", - "fixed_variable.random()\n", - "print \"value: \",fixed_variable.value" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "value: 10\n", - "calling .random()\n", - "value: 10\n" - ] - } - ], - "prompt_number": 9 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This is how we include data into our models: initializing the stochastic variable to have a *fixed value*. \n", - "\n", - "> ...the stochastic variable to have a *fixed value*.\n", - "\n", - "That might seem strange to hear at first. In fact, Bayesian analysis sees observed data as simply fixed parameters. Taking this to its logical conclusion, any predictions made by Bayesian analysis is seen as fitting another parameter in the model." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Modeling approaches\n", - "\n", - "A good starting place to modeling is to think about *how your data might have been generated*. Think from a god-like position, and try to think about how *you* would recreate the data. For example, in the last chapter, we investigated text message data:\n", - "\n", - "1. We started by thinking \"what is the best random variable to describe this data?\" A Poisson random variable is a good candidate because it assigns probabilities to count data. \n", - "\n", - "2. Next, we think, \"Ok, assuming texts are Poisson-distributed, what do I need for the Poisson distribution?\" Well, the Poisson distribution has a parameters $\\lambda$. \n", - "\n", - "3. Do we know $\\lambda$? No. In fact, we have a suspicion that there are *two* $\\lambda$ values, one for the earlier behaviour and one for the latter behaviour. We don't know when the behaviour switches though. \n", - "\n", - "4. What is a good distribution for the two $\\lambda$s? The exponential is good, as it assigns probabilites to positive real numbers. Well the exponential distribution has a parmeter too, call it $\\alpha$.\n", - "\n", - "5. Do we know what the parameter $\\alpha$ might be? No. We could continue and assign a disitribution to $\\alpha$, but it's better to stop once we reach a set level of ignorrance: whereas we have a prior belief about $\\lambda$, (\"it probably changes over time\", \"it's likely between 1 and 8\", etc.), we don't really have any strong beliefs about $\\alpha$. So it's best to stop here. \n", - "\n", - "6. What is a good value for $\\alpha$ then? We think that the $\\lambda$s are between 1-8, so if we set $\\alpha$ really low (which corresponds to larger probabilty on high values) we are not reflecting our prior well. Similar, a too-high alpha misses our prior as well. A good idea for $\\alpha$ as to reflect our belief is to set the value so that the mean of $\\lambda$, given $\\alpha$, is equal to our observed mean. This was shown in the last chapter.\n", - "\n", - "In this way, we are thinking about how the data might have been created. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "_____\n", - "### Example: Poisson Regression\n", - "\n", - "Perhaps the most important result from medical research was the *now obvious* link between *smoking and cancer*. We'll try to establish a link using Bayesian methods. We have a decision here: should we include a prior that biases us towards there existing a significant link between smoking and cancer? I think we should act like scientists at the turn of the century, and assume there's is no *a priori* reason to assume a link. \n", - "\n", - "The dataset we will be using contains 36 cohorts (a unique group with a larger population), each cohort has variables:\n", - "\n", - "- age: in five-year age groups coded 1 to 9 for 40-44, 45-49, 50-54, 55-59, 60-64, 65-69, 70-74, 75-79, 80+.\n", - "- cigar/pipe smoking status: 1 if the cohort only smokes cigars/pipes, 0 else.\n", - "- cigar & cigarette smoking status: 1 if the cohort smoke *both* cigars/pipes and cigarettes, 0 else.\n", - "- cigarette smoking status: 1 if the cohort only smoke cigarettes, 0 else.\n", - "- population: the population, in hundreds of thousands, of the age group *and* smoking status cohort. Denote this $P_i$.\n", - "- deaths: number of lung cancer deaths in the of the specfic cohort over the course of a year . Denote this $D_i$.\n", - "\n", - "\n", - "As $D_i$ is count data, a Poisson random variable would be appropriate to model it. \n", - "\n", - "$$D_i \\sim \\text{Poi}(\\lambda_i )$$\n", - "\n", - "Of course, we don't know $\\lambda_i$, but we can hypothesize that it is a function the age and smoking status. The simplest way to connect $\\lambda_i$ and cohort variables age and smoking statuses (not population), denoted $\\mathbb{x}_i$, is with a link function:\n", - "\n", - "\n", - "$$\\lambda_i = P_i\\exp \\left( \\beta^T \\mathbb{x_i} \\right) $$\n", - "\n", - "where $\\beta$ are coefficients to be determined. We require the $\\exp \\;$ link function because the linear combination of variables may be negative, but we require the number of deaths to be positive.\n", - "\n", - "Why did we put the $P_i$ in front of the exponential? This seperates the *rate of deaths*, given variables $\\mathbb{x}_i$ and represented by the exponential term, and the *number of expected deaths*, represented on the left-hand side. A larger population will naturally have more deaths, independent of the age and smoking statuses, so we need to account for this. Suppose the exponential term is 0.01, and the population is 10,000, the the expected number of deaths is $10,000 \\times 0.001 = 10$. Another way to see this is show the equivalent form:\n", - "\n", - "$$\\frac{\\lambda_i}{P_i} = \\exp \\left( \\beta^T \\mathbb{x_i} \\right) $$\n", - "\n", - "where the left hand side is the expected number of deaths over the cohort's population (which is a rate), and the right hand side is an estimate of the rate (given we know $\\beta_i$'s ). With respect to the original form, we can rewrite this as:\n", - "\n", - "$$\\lambda_i = \\exp \\left( \\beta^T \\mathbb{x_i} + \\log{P_i} \\right) $$\n", - "\n", - "which is what more statisticians do. This is also called *adding an offset term*. \n", - "\n", - "This example is quite different from our last example on text-messaging rates, though the two look similar. We are not trying to estimate a *global* $\\lambda$, that is a single parameter $\\lambda$ that determines the distributions of all the observations, but we are actual trying to model a unique $\\lambda$ for each data point using the observed variables, i.e. $\\lambda_i = f( \\mathbf{x}_i, P_i, \\beta )$. to your model.\n", - "\n", - "\n", - "Our conclusions are determined by the posterior distributions of $\\beta_1, \\beta_2$ and $\\beta_3$. If the distributions are shifted to be positive, then a 1 in a smoking status will shift the $\\lambda_i$ forward, resulting in an increase in the number of deaths. Our task in now to find the posteriors of $\\beta_i$. We first need a prior for the $\\beta$s: the most natural being the Normal distrubition, described below. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Normal distributions\n", - "\n", - "A Normal random variable, denoted $X \\sim N(\\mu, 1/\\tau)$, has a distribution with two parameters: the mean, $\\mu$, and the *precision*, $\\tau$. Those familar with the Normal distribution already have probably seen $\\sigma^2$ instead of $\\tau$. They are infact recipricals of each other. The change was motivated by easier mathematics and is an artifact of Bayesian methods. Just remember: The smaller $\\tau$, the larger the spread of the distribution (i.e. we are more uncertain); the larger $\\tau$, the tighter the distribution (i.e. we are more certain). Regardless, $\\tau$ is always positive.\n", - "\n", - "The probability density function of a $N( \\mu, 1/\\tau)$ random variable is:\n", - "\n", - "$$ f(x | \\mu, \\tau) = \\sqrt{\\frac{\\tau}{2\\pi}} \\exp\\left( -\\frac{\\tau}{2} (x-\\mu)^2 \\right) $$\n", - "\n", - "We plot some different density functions below. " - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "import scipy.stats as stats\n", - "nor = stats.norm\n", - "x = np.linspace( -8, 7, 150 )\n", - "mu = (-2, 0, 3)\n", - "tau = (.7, 1, 2.8 )\n", - "colors = [\"#348ABD\", \"#A60628\", \"#7A68A6\"]\n", - "parameters = zip( mu, tau, colors )\n", - "\n", - "for _mu, _tau, _color in parameters:\n", - " plt.plot( x, nor.pdf( x, _mu , scale = 1./_tau ), \\\n", - " label =\"$\\mu = %d,\\;\\\\tau = %.1f$\"%(_mu, _tau), color = _color )\n", - " plt.fill_between( x, nor.pdf( x, _mu, scale =1./_tau ), color = _color, \\\n", - " alpha = .33)\n", - " \n", - "\n", - "\n", - "plt.legend(loc = \"upper right\")\n", - "plt.xlabel(\"$x$\")\n", - "plt.ylabel(\"density function at $x$\")\n", - "plt.title( \"Probability distribution of three different Normal random variables\" )" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 10, - "text": [ - "" - ] - }, - { - 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BEErcv/0MAGDvYgkbOwtcDE3EjehU2DpYoGsvxwa3563t1zRW1+np3dS6Ti6X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\nSGxon88z1J9qXrTKGtRPP0f9OA9tz/5vHoiOwjh5HIYp4zuvtPSk7qO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- } - ], - "prompt_number": 10 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "A Normal random variable can be any real number, but the variable is very likely to be relatively close to $\\mu$. In fact, the expected value of a Normal is equal to its $\\mu$ parameter:\n", - "\n", - "$$ E[ X | \\mu, \\tau] = \\mu$$\n", - "\n", - "and it's variance is equal to the inverse of $\\tau$:\n", - "\n", - "$$Var( X | \\mu, \\tau ) = \\frac{1}{\\tau}$$\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Smoking and Cancer\n", - "\n", - "We will model the $\\beta_i$ as Normal distrbutions. Let's start the PyMC code:\n" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "import pymc as mc\n", - "\n", - "data = np.genfromtxt( \"chp2data/smoking_death.csv\", skip_header = 1,\n", - " delimiter=\",\", dtype = float )\n", - "\n", - "population = data[:,-2].copy()\n", - "deaths = data[:,-1].copy()\n", - "data[:,-2] = 1 #replace the last column with a constant to represent beta_0\n", - "data = data[:,:-1]\n", - "\n", - "#Instead of creating variable beta_1, beta_2, etc., \n", - "beta = mc.MvNormal( \"beta_coefs\", mu = np.zeros(5) , \\\n", - " tau = 0.0001*np.identity(5), value = np.zeros(5))\n", - "print \"initial beta.value = \", beta.value\n", - "\n", - "#we'll create a deterministic function that represents the exponential \n", - "#of a linear combination\n", - "@mc.deterministic\n", - "def exp_lin_comb( beta = beta ):\n", - " return np.exp( np.dot( data, beta ) + np.log(population) )\n", - "\n", - "observations = mc.Poisson( \"obs\", exp_lin_comb, value = deaths, observed = True )" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "initial beta.value = [ 0. 0. 0. 0. 0.]\n" - ] - } - ], - "prompt_number": 114 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "model = mc.Model( [observations, beta, exp_lin_comb] )\n", - "\n", - "\n", - "\n", - "#mysterious code to be explained in Chapter 3\n", - "map_ = mc.MAP( model )\n", - "map_.fit()\n", - "mcmc = mc.MCMC( model )\n", - "mcmc.sample( 300000, 250000, 2 ) #TODO reduce" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - " \r", - "[****************100%******************] 1000000 of 1000000 complete" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n" - ] - } - ], - "prompt_number": 119 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "figsize(9, 10)\n", - "#histogram of the samples:\n", - "plt.figure()\n", - "\n", - "beta_samples = mcmc.trace(\"beta_coefs\")[:]\n", - "\n", - "plt.subplot(311)\n", - "plt.title( r\"Positerior distributions of $\\beta_1, \\beta_2, \\beta_3$\" )\n", - "plt.hist( beta_samples[:, 1],histtype='stepfilled', bins = 50, alpha = 0.85, \\\n", - " label = r\"positerior of $\\beta_1$\", color = \"#A60628\",normed = True )\n", - "plt.legend()\n", - "\n", - "plt.subplot(312)\n", - "plt.hist( beta_samples[:, 2], histtype='stepfilled', bins = 50, alpha = 0.85, \\\n", - " label = r\"positerior of $\\beta_2$\", color = \"#7A68A6\",normed = True)\n", - "plt.legend()\n", - "\n", - "plt.subplot(313)\n", - "plt.hist( beta_samples[:, 3], bins = 50, alpha = 0.85, \n", - " label = r\"positerior of $\\beta_3$\", \\\n", - " color=\"#467821\", normed = True, histtype='stepfilled' )\n", - "plt.legend()\n", - "print" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n" - ] - }, - { - "output_type": "display_data", - "png": 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z8jNLWpgPQyYLj7lz52LhwoWYPXu27jGFQoEXXngBL7zwgt2DI3J2dTkFyP98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- } - ], - "prompt_number": 122 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "What can we say? It looks like all positerior distributions are strictly positive. This gives strong evidence that smoking increases the number of deaths in a population.\n", - "\n", - "[this needs to be cleaned up a bit]\n", - "Let's perform some prediction. As we modeled the parameter $\\lambda_i$ in a Poisson distribution, we can find the *expected rate* of deaths. We are taking the average over the posterior distributions:\n", - "\n", - "We compute `np.dot( data, beta_samples.T )`. This produces a very large matrix which is each posterior sample multiplied with the each data:\n", - "\n", - "$$\\beta_j^T x_i, \\;\\; \\text{ for all $i$, for all $j$} $$\n", - "\n", - "we then exponentiate this matrix and take the mean over all $j$ (over the posterior samples)." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "figsize(9, 7)\n", - "rates = np.mean( np.exp( np.dot( data, beta_samples.T ) ), axis = 1 )\n", - "\n", - "colors = [\"#348ABD\", \"#A60628\", \"#7A68A6\", \"#467821\"]\n", - "labels = [\"does not smoke\", \n", - " \"only cigars/pipes\",\n", - " \"both cigarettes & cigars/pipes\", \n", - " \"only cigarettes\"]\n", - "\n", - "for i in range(4):\n", - " plt.plot( 40 + 5*data[9*i:9*(i+1),0], rates[9*i:9*(i+1)], marker = 'o', \n", - " color = colors[i], label = labels[i], lw=1)\n", - " \n", - "plt.legend(loc=\"upper left\")\n", - "plt.xlabel( \"age\" )\n", - "plt.ylabel(\"expected death ratio (deaths/population)\" )\n", - "plt.title( \"Expected deaths over population segmented by smoking habits\")\n", - "plt.xlim( 42, 87 )" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 121, - "text": [ - "(42, 87)" - ] - }, - { - "output_type": "display_data", - "png": 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a7/OIj49v+dk9TU31vhN0ldcvp3p7cErbpLntV1BQgPfv30NOTq5Zr0uhUL4O\n2sUGg2/fvqVDQxRKC6CkpMTa8ZXy+dBf4W0bar+moV04KQBdYZNCaQlou6NQKE1Ju3BS6IuSQmk5\naPtrHKKjo1taBcoXQO3XNLQLJ4VCoVAoFEr7gzopLcDs2bOxYcOGllajTeHn54fvv/++pdWgUJoM\nGtPQtqH2axqok9ICcDicNtNFTp0DCoVCobQU7WIKcl2EXbuOI+eCUQEOxEEw+dshGOTQv9lliKIN\nLU9DoVCaAbrORtuG2q9paLc9KWHXruP3w0HIMxmLlyZjkGcyFr8fDkLYtevNKgMA7t+/jwEDBoDH\n42HatGkoKSlh5R85cgRWVlbQ09ODl5cX8vLymLyUlBS4ublBT08PNjY2CAoKYvJCQ0Nha2sLHo8H\nExMT7Nq1S+T1/f39MXToUKxevRq6urqwsLBAeHg4k5+bmwtPT0/o6enBysoKR48eBVC1eN7WrVtx\n7tw5aGlpwcHBQaT87du3w8TEBDweDzY2NoiKigJQ1QszdepUfP/99+DxeOjXrx/S09OxdetW9OjR\nA7169UJERMRH9ahNeXk5pk+fjilTpqC8vBy5ubnw9vZG9+7dYWFhgX379tVnDgqFQqG0Edqtk3Lk\nXDC4dhNZaVy7iTgaFNysMsrKyjBx4kR4eHggIyMDo0aNwsWLF5nhnqioKPj6+uLgwYNISkqCpqYm\npk+fDgAoKiqCm5sbxo0bh9TUVBw4cACLFy9GSkoKAGDu3LnYtm0b+Hw+bt68if796+7hiY+Ph4GB\nAdLT0zF37lzMnTuXyZs+fTo0NDSQlJSEQ4cOwdfXF9evX8fAgQPx008/wc3NDdnZ2bh27ZqQ3Gq9\nwsPDwefzERgYCC0tLSb/ypUrcHd3R0ZGBnr16gU3NzcAQGJiIhYvXowFCxZ8VI+alJSUYOLEiZCR\nkcHBgwchJiYGT09PmJqaIjExEUFBQdizZw+uXr3aYBtRKK0B+iu8bUPt1zS02+GeCoiO+Yh7VgSX\nA3cbJONZbhHUTITTy0nD40liY2NRWVnJxHWMHDkSu3fvZvLPnDmDiRMnwtTUFACwatUq6OrqIicn\nB7GxseDxeMzGVaampnB1dUVQUBCWLFkCCQkJJCcnw8jICAoKCujVq1edemhqamLSpEkAAHd3dyxa\ntAgvXrxAWVkZbt++jYCAAEhKSsLExASTJk3CqVOnGKenvqEpMTExlJWVITk5GUpKSkK7W9vZ2cHR\n0ZG590ujvuPbAAAgAElEQVSXLmH+/PngcDgYPXo05s+fj7dv3+Ldu3cf1ePdu3cYO3YsevXqxQQe\nx8XFoaCgAIsWLQIA8Hg8TJo0CefOnYOTk1MDrUShUCiU1ki7dVLEIfrD2ltNFoenWzRIxuSHZ5An\nIl2C0/B4kry8PKGlyjU1NVn55ubmzLGsrCyUlJSQm5uLnJwcxMXFQUdHh8mvrKyEu7s7AODw4cPY\nvHkz1q5dC2NjY6xevRrW1tYi9VBRUWH+79ChA4CqnpqXL19CUVERsrKyTL6GhgYSEhIadH+6urrY\nsGED/Pz8kJycDCcnJ6xbtw6qqqoAgM6dOzNlpaWloaSkxPQiycjIMHrk5uZ+VI/Y2FhUVFTgwIED\nTNqTJ0+Ql5fHekYCgQC2trYN0p9CaS3QmIa2DbVf09Buh3smfzsEgpvHWGmCG0cxafSQZpXRtWtX\n5ObmstJycnKY/1VVVZGdnc0cFxUVobCwEGpqatDQ0EDfvn2RmZnJ/GVnZ2PTpk0AAAsLCxw7dgyp\nqakYNmwYfHx8GqxXNd26dcOrV6+Y/Y+Aqg//p+yDNGbMGFy+fBn37t0Dh8PB2rVrm0QPR0dHzJ8/\nH99++y1evHgBAFBXVwePx2M9Iz6fj5MnT36yDhQKhUJpXbRbJ2WQQ3/87D0a3R6dQeeHgej26Ax+\nnvLtJ83MaQwZffr0gZiYGPbu3Yvy8nJcvHgRd+/+b7hpzJgx8Pf3x8OHD1FaWop169bBysoKGhoa\ncHZ2RlpaGgICAlBeXo7y8nLEx8cjJSUF5eXlOH36NN6+fQsxMTHIyclBTEzsk54RUPWR79OnD3x9\nfVFaWopHjx7h+PHjGDduHIAqJys7O7vOIZ+0tDRERUWhtLQUUlJSkJaWbhI9qvnxxx8xZswYjB49\nGoWFhbC0tIScnBx27NiBDx8+oLKyEklJSaxnTKG0Beiv8LYNtV/T0G6He4AqJ+NLpwt/qQwJCQkc\nOXIE8+fPx4YNGzBo0CCMGDGCyXdwcMDy5cvh7e2N169fw8bGhhnOkJeXR2BgIFauXImVK1dCIBDA\n1NQU69atAwAEBARg6dKlqKysRPfu3euc1SJqXZaax/v378fChQvRs2dPdOrUCcuWLYO9vT0AYNSo\nUQgICICenh60tbWFAlLLysrg6+uLlJQUiIuLw8bGBlu3bhV5nY8d16dHzbKLFi1CWVkZvv32W1y4\ncAEnTpzAqlWrYGlpidLSUhgYGGDFihUinwWFQqFQ2g4c0oYW7AgPD4elpaVQem5ubrNvUU+hUKqg\n7a9xoDENbRtqv88jPj4eAwcOrDO/3Q73UCgUCoVCadtQJ4VCoVBaAfRXeNuG2q9poE4KhUKhUCiU\nVgl1UigUCqUVEB0d3dIqUL4Aar+mgTopFAqFQqFQWiXtegoyhUKhtBVoTEPbpr3aLzwyFMfPHEQl\nKYcYRwJeY6di4ADnZrs+dVIoFAqFQqEIER4Zii37fSFvVsSkbdnvCwDN5qjQ4R4KhUJpBdCYhrZN\ne7Tf8TMHWQ4KAMibFcE/8FCz6UCdlFaOsrIysrKyGk3ekydPoKWlVe/Oxq2BhQsX4o8//mhpNSgU\nCuWrpZKUi0yvEJQ1mw50uOcrQ0NDg7WhYXOzdetWFBUVYeXKlfWW27x5czNpRKG0DtprTMPXQnu0\nnxhHQmS6OFey2XRo105KVEgoLu0/DE5ZOYikBFxneMPe5dPG0RpDxteCQCAAl1t/51xoaCh++eWX\n5lGIQqFQKJ+N19ipQjEpb+/JYsaMKc2mQ7sd7okKCcWpFRvgdD0Djrdy4HQ9A6dWbEBUSGizygCA\nx48fY8SIEdDR0YGdnR2Cg4OZvNmzZ2Px4sXw8PAAj8eDi4uLyOGd+Ph4GBoasoZpLl68yNqAryYf\nPnzAypUrYWZmBm1tbQwbNgylpaXIzs6GsrIyBAIBAIDP52P48OHg8Xhwc3PD4sWL8f333zNypk6d\nCiMjI2hra8PV1RXJycks3RcuXIjx48dDU1MT0dHRCA0Nha2tLXg8HkxMTLBr1y6m/OvXr5Geng5r\na2tER0fDxMQEW7duhYGBAczNzXHmzBmW7A0bNgDAR8uWlpZi1apV6NWrFwwNDbFw4UKUlJQAAAoK\nCuDh4QEdHR3o6elh+PDhrX6oi/J10h5jGr4m2qP9Bg5wxtjRE5B9sxgSWTqQzTHEwhmrmnV2T7t1\nUi7tP4wROaWstBE5pfjvgSPNKqO8vByenp4YOHAgUlNT4efnh5kzZyItLY0pc+7cOSxduhQZGRnQ\n0dFhdjmuiaWlJRQVFVm7EAcEBMDDw0PkdVevXo0HDx7gypUryMjIwNq1a4V2IAaAGTNmwMrKCunp\n6Vi6dCkCAgJY5ZydnREbG4vU1FSYmZlh5syZrPMDAwOxePFi5OTkoE+fPpg7dy62bdsGPp+Pmzdv\non///+0gffXqVTg4ODDynz9/jsLCQiQmJmL37t346aefkJ6eDkB452ZRZauf4a+//orMzExcv34d\nsbGxyM3NxaZNmwAAu3btgrq6OtLS0pCSkoLVq1eLfA4UCoVCYUMIQdqHGGzcsAVHd5/BX/853qwO\nCtCOnRROmeiAn8KIWwhWtWvQ36vI26KFlzY8aCg2NhbFxcWYP38+xMXF0b9/f7i4uCAwMJAp4+rq\nCgsLC4iJiWHcuHF4+PChSFkeHh4ICAgAALx69QoREREYO3asUDmBQAB/f3/89ttvUFVVBZfLhbW1\nNSQl2eOIT548QUJCApYtWwZxcXHY2Nhg6NChrJ4GT09PyMrKQkJCAkuWLMHDhw/x7t07Jn/48OGw\ntrYGAEhLS0NCQgLJycl4+/YtFBQU0KtXL6ZsSEgInJ3ZFXz58uWQkJCAnZ0dnJ2dce7cOSavdo9H\n7bJBQUEghODIkSNYt24dOnbsCDk5Ofz00084e/YsAEBSUhL5+fnIzs6GmJgYbGxsRD5bCqWlaY8x\nDV8T7dF+/z4OAyEC2BkNbjEd2m1MCpEUHfCj5GiDIQFHGyTj6riJwPUM4QyphgcN5eXlQV1dnZWm\nqamJvLw8AFU9BioqKkyetLQ03r9/L1LW2LFjYWdnh+LiYgQFBcHW1pZ1bjUFBQUoKSmBtrZ2vbrl\n5uZCUVER0tLSTJq6ujqePn0KAKisrMS6detw4cIFvHz5kok3KSwshLy8PDgcDtTU1FgyDx8+jM2b\nN2Pt2rUwNjbG6tWrYW1tDYFAgGvXrjFDOADQqVMnyMjIsJ5Lfn6+SF3rKltQUIDi4mI4OjoyeYQQ\nxsGZM2cO/Pz8MGbMGACAt7c35s2bV+9zoVAolK+dispynIjaiZlDVoLLabn+jHbbk+I6wxsXNaVY\naRc1JDF8+uRmlaGqqoqnT5+yegVycnLQrVu3BsuoRl1dHdbW1rh06RICAgLg7u4uspyysjKkpaWR\nmZn5Ud1evXqFDx8+MGlPnjxhhkPOnDmD4OBgBAUFgc/nIyEhAYBwD0dNLCwscOzYMaSmpmLYsGHw\n8fEBUBVTo6mpCSUlJabs69evUVxczBzn5ORAVVWVOa45LFNXWWVlZcjIyCAmJgaZmZnIzMxEVlYW\n+Hw+AEBOTg6+vr6Ij4+Hv78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dtFsnZYCjPab/4I7s1xHIKohA9usIzPjB/ZNm5jSGDKCq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\npqKqBcqj2/cIGTyeav83DvsqHpy7ncCSoFtMbVVO9QLFGEYVKZMnT+b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KAAAg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\ngqQgt4qGuqari1rdiOnmz/CJ0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- } - ], - "prompt_number": 121 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Clearly** smoking affects how long you live. \n", - "\n", - "We can interpret these rates are probabilities, for example, 0.032 is the rate of a non-smokers dying at age 40, which is equilivant to the probablity of a randomly selected non-smoker aged 40-45 will die. Hence 1- 0.032 = 0.968 is the probability he/she survives to 45. Hence, (1- 0.032)(1 - 0.045) is the probability a non-smoker, aged 40 will survive until 50, etc. Let's plot this." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "figsize( 9, 3.5 )\n", - "prob_survival = 1-rates\n", - "\n", - "colors = [\"#348ABD\", \"#A60628\", \"#7A68A6\", \"#467821\"]\n", - "labels = [\"does not smoke\", \n", - " \"only cigars/pipes\",\n", - " \"both cigarettes & cigars/pipes\", \n", - " \"only cigarettes\"]\n", - "\n", - "for i in range(4):\n", - " _p = prob_survival[ 9*i:9*(i+1) ].cumprod()\n", - " plt.plot( 40 + 5*data[9*i:9*(i+1),0], _p, marker = 'o', \n", - " color = colors[i], label = labels[i], lw=1)\n", - "\n", - "plt.legend(loc=\"lower left\")\n", - "plt.xlabel(\"Age\")\n", - "plt.ylabel(\"Probability of survival\")\n", - "plt.title(\"Prbability of survival, segmented by smoking\")" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 19, - "text": [ - "" - ] - }, - { - "output_type": "display_data", - "png": 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a9rbs+AR/c33GuQTSmJSGfmExJY7OVHp6oR/oi21kIF69gvB3tcHaxKBb7pNE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kVU+rXEpSJBEJ6LuFRUtUTsdSv29FsX2HX0O34/vf2ZywtUR9zZN+6Teuw6qUevoaung9y3dGeS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\n" - } - ], - "prompt_number": 19 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "It appears that smoking cigarettes will almost certainly kill you before age 85, whereas not smoking provides you with 15% chances to live this long. So, for you young folks out there, which path do you want to take?" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "_______\n", - "###Example: Challenger Space Shuttle Disaster\n", - "\n", - "\n", - "On January 28, 1986, the twenty-fifth flight of the U.S. space shuttle pro-gram ended in disaster when one of the rocket boosters of the Shuttle Challenger exploded shortly after lift-off, killing all seven crew members. The presidential commission on the accident concluded that it was caused by the failure of an O-ring in a field joint on the rocket booster, and that this failure was due to a faulty design that made the O-ring unacceptably sensitive to a number of factors including outside temperature. Of the previous 24 flights, data were available on failures of O-rings on 23, (one was lost at sea), and these data were discussed on the evening preceding the Challenger launch, but unfortunately only the data corresponding to the 7 flights on which there was a damage incident were considered important and these were thought to show no obvious trend. The data are shown below (see [1]):\n", - "\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "figsize( 9, 3.5 )\n", - "challenger_data = np.genfromtxt(\"chp2data/challenger_data.csv\", skip_header = 1, \\\n", - " usecols=[1,2], missing_values=\"NA\", delimiter=\",\")\n", - "#drop the NA values\n", - "challenger_data = challenger_data[ ~np.isnan(challenger_data[:,1]) ]\n", - "\n", - "#plot it, as a function of tempature (the first column)\n", - "print \"Temp (F), O-Ring failure?\"\n", - "print challenger_data\n", - "\n", - "plt.scatter( challenger_data[:,0], challenger_data[:,1], s = 75, color=\"k\", \n", - " alpha = 0.5) \n", - "plt.yticks([0,1])\n", - "plt.ylabel(\"Damage Incident?\")\n", - "plt.xlabel(\"Outside temperature (Farhenhit)\" )\n", - "plt.title(\"Defects of the Space Shuttle O-Rings vs temperature\")\n", - "print\n" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "Temp (F), O-Ring failure?\n", - "[[ 66. 0.]\n", - " [ 70. 1.]\n", - " [ 69. 0.]\n", - " [ 68. 0.]\n", - " [ 67. 0.]\n", - " [ 72. 0.]\n", - " [ 73. 0.]\n", - " [ 70. 0.]\n", - " [ 57. 1.]\n", - " [ 63. 1.]\n", - " [ 70. 1.]\n", - " [ 78. 0.]\n", - " [ 67. 0.]\n", - " [ 53. 1.]\n", - " [ 67. 0.]\n", - " [ 75. 0.]\n", - " [ 70. 0.]\n", - " [ 81. 0.]\n", - " [ 76. 0.]\n", - " [ 79. 0.]\n", - " [ 75. 1.]\n", - " [ 76. 0.]\n", - " [ 58. 1.]]\n", - "\n" - ] - }, - { - "output_type": "display_data", - "png": 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0OP/mzZsYPnw4QkNDK7Cq6uvLL7/E2rVr4eTkZPE+Xl5eWLRoUQVWVXXMmzcP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\na83OzoaTkxM+++wz1cMld8LR0RHPPfc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- } - ], - "prompt_number": 124 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "It looks clear that *the probability* of damage incidents occuring increases as the outside temperature decreases. We are interested in modeling the probability here because it does not look like there is a strict cutoff point between temperature and a damage incident ocurring. The best we can do is say \"At temperature $t$, the probability of a damage indicident is probably...\". \n", - "\n", - "We need a function of temperature, call it $p(t)$, that is bounded between 0 and 1 (so as to model a probability) and changes from 1 to 0 as we increase temperature. There are actually many such functions, but the most popular choice is the *logistic function.*\n", - "\n", - "$$p(t) = \\frac{1}{ 1 + e^{ \\;\\beta t } } $$\n", - "\n", - "In this model, $\\beta$ is the variable we are uncertain about. Below is the function plotted for $\\beta = 1, 3, -5$." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "def logistic( x, beta):\n", - " return 1.0/( 1.0 + np.exp( beta*x) )\n", - "x = np.linspace( -4, 4, 100 )\n", - "plt.plot(x, logistic( x, 1), label = r\"$\\beta = 1$\")\n", - "plt.plot(x, logistic( x, 3), label = r\"$\\beta = 3$\")\n", - "plt.plot(x, logistic( x, -5), label = r\"$\\beta = -5$\")\n", - "plt.legend()" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 21, - "text": [ - "" - ] - }, - { - "output_type": "display_data", - "png": 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aQqVi0L9WApC1egMNWXlX2KN3pZwqAjNEJfjh4Ki2dTiCIFiIKDKsrC/PgtdT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\nk0yohqB4FUYu0SdDEMLAoS9r2PZhGWaLjqUrrh2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- } - ], - "prompt_number": 21 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "\n", - "But something is missing. In the plot above, the probability changes quickly only near zero, but in our data above the probability changes around 65 to 70. We need to add a *bias* term to our logistic function:\n", - "\n", - "$$p(t) = \\frac{1}{ 1 + e^{ \\;\\beta t + \\alpha } } $$\n", - "\n", - "Some plots are below, with differing $\\alpha$." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "def logistic( x, beta, alpha):\n", - " return 1.0/( 1.0 + np.exp( np.dot(beta, x) + alpha) )\n", - "\n", - "x = np.linspace( -4, 4, 100 )\n", - "plt.plot(x, logistic( x, 1, 1), label = r\"$\\beta = 1, \\alpha = 1$\")\n", - "plt.plot(x, logistic( x, 3, -2), label = r\"$\\beta = 3, \\alpha = -2$\")\n", - "plt.plot(x, logistic( x, -5, 7), label = r\"$\\beta = -5, \\alpha = 7$\")\n", - "plt.legend(loc=\"lower left\")\n" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 50, - "text": [ - "" - ] - }, - { - "output_type": "display_data", - "png": 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7uuEX4GGTe9pL7ram1LzNIYoMwS64a9u7T+4fGYlWJbHhWDkPf51JSZ3zL94l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\n89RTNhv79u1j9OjRnX6sa9XVz5kgXIksy+wtquP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- } - ], - "prompt_number": 50 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Adding a constant term $\\alpha$ amounts to shifting the curve left or right (hence why it called a *bias*. )\n", - "\n", - "Let's start modeling this in PyMC. The $\\beta, \\alpha$ paramters have no reason to be positive, bounded or relativly large, so there are best modeled by a Normal random variable." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "import pymc as mc\n", - "\n", - "\n", - "temperature = challenger_data[:,0]\n", - "D = challenger_data[:,1] #defect or not?\n", - "\n", - "beta = mc.Normal( \"beta\", 0, 0.001, value = 0 )\n", - "alpha = mc.Normal( \"alpha\", 0, 0.001, value = 0 )\n", - "\n", - "@mc.deterministic\n", - "def p( temp = temperature, alpha = alpha, beta = beta):\n", - " return 1.0/( 1. + np.exp( beta*temperature + alpha) ) \n" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 79 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We have our probabilities, but how do we connect them to our observed data? A *Bernoulli* random variable with parameter $p$ (denoted $\\text{Ber}(p)$, is a random variable that takes value 1 with probability $p$, and 0 else. Thus, our model can look like:\n", - "\n", - "$$ \\text{Defect Incident, $D_i$} \\sim \\text{Ber}( \\;p(t_i)\\; ), \\;\\; i=1..N$$\n", - "\n", - "where $p(t)$ is our logistic function and $t_i$ are the temperatures we have observations about. Notice in the above code we had to set the values of `beta` and `alpha` to 0. The reason for this is that if `beta` and `alpha` are very large, they make `p` equal to 1 or 0. Unfortunatly, `mc.Bernoulli` does not like probabilities of 0 or 1, though they are mathematically well-defined probabilties. So by setting the values to `0`, we set `p` to a resonable starting value. This has no effect on our results, nor does it mean we are including information in our prior. It is simply a computational caveat in PyMC. " - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "p.value" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 24, - "text": [ - "array([ 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5,\n", - " 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5,\n", - " 0.5])" - ] - } - ], - "prompt_number": 24 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "observed = mc.Bernoulli( \"bernoulli_obs\", p, \\\n", - " value = D, observed=True)\n", - "\n", - "model = mc.Model( {\"observed\":observed, \"beta\":beta, \"alpha\":alpha} )\n", - "\n", - "#mysterious code to be explained in Chapter 3\n", - "map_ = mc.MAP(model)\n", - "map_.fit()\n", - "mcmc = mc.MCMC( model )\n", - "mcmc.sample( 260000, 220000, 3 )\n" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - " \r", - "[****************100%******************] 260000 of 260000 complete" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n" - ] - } - ], - "prompt_number": 25 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We have trained our model on the observed data, now we can sample values from the positerior. Let's look at the positerior distributions for $\\alpha$ and $\\beta$:" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "alpha_samples = mcmc.trace( 'alpha' )[:, None] #best to make them 1d\n", - "beta_samples = mcmc.trace( 'beta' )[:, None]\n", - "\n", - "figsize(9, 6)\n", - "\n", - "#histogram of the samples:\n", - "plt.subplot(211)\n", - "plt.title(r\"Posterior distributions of the variables $\\alpha, \\beta$\")\n", - "plt.hist( beta_samples, histtype='stepfilled', bins = 45, alpha = 0.85, \\\n", - " label = r\"positerior of $\\beta$\", color = \"#7A68A6\",normed = True )\n", - "plt.legend()\n", - "\n", - "plt.subplot(212)\n", - "plt.hist( alpha_samples, histtype='stepfilled', bins = 45, alpha = 0.85, \\\n", - " label = r\"positerior of $\\alpha$\", color = \"#A60628\",normed = True )\n", - "plt.legend()\n", - "\n", - "\n" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 60, - "text": [ - "" - ] - 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tjr9bYsyPGPNjGZzUSiQj3+89rf3/mfMluKLysGE0RETG4xkSO8ZxSjHOIRFj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\nhoiILOna2VJcPV1isJ3HHUPhPizQYDuyLoPPspk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- } - ], - "prompt_number": 60 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Regarding the spread of the data, we are very uncertain about what the true parameters might be( though considering the low sample size and the large overlap of defects-to-nondefects this behaviour is perhaps expected). \n", - "\n", - "Next, let's look at the *expected probability* for a specific value of the temperature." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "\n", - "t = np.linspace( temperature.min() - 5, temperature.max()+5, 50 )[:,None]\n", - "#linear_combination= np.dot( beta_samples[:,None], t[None,:]) + alpha_samples[:,None]\n", - "#p = 1.0/(1.0 + np.exp( linear_combination ) )\n", - "\n", - "p = logistic( t.T, beta_samples, alpha_samples )\n", - "\n", - "mean_prob_t = p.mean(axis=0)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 61 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "figsize( 9, 4)\n", - "plt.plot( t, mean_prob_t, lw = 3, label = \"average posterior \\nprobability \\\n", - "of defect\")\n", - "plt.plot( t, p[0, :], ls=\"--\",label=\"realization from posterior\" )\n", - "plt.plot( t, p[-2, :], ls=\"--\", label=\"realization from posterior\" )\n", - "plt.scatter( temperature, D, color = \"k\", s = 50, alpha = 0.5 )\n", - "plt.title(\"Posterior expected value of probability of defect; plus realizations\")\n", - "plt.legend(loc= \"lower left\")\n", - "plt.ylim( -0.1, 1.1 )\n", - "plt.xlim( t.min(), t.max() )\n", - "plt.ylabel(\"probability\")\n", - "plt.xlabel(\"temperature\")" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 62, - "text": [ - "" - ] - }, - { - "output_type": "display_data", - "png": 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/UV99kJWVhdOnT0OtVqNHjx4ICAho03G4X3l5eThx4gRUKhVCQ0MRHBwMgYD+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\n0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Pr6Kn2O5OKoRFPcmjYTOTk59O7d2+H748ePN+lwgOTqpf6j1Vcrl/J8kGvW2JA62JA6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\nIpFIJJIWiXRSJBKJRCKRtEj+D7+JcZuGFjDAAAAAAElFTkSuQmCC\n" - } - ], - "prompt_number": 62 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Above we also plotted two possible realizations of what the actual underlying system might be. Both are equally likely as any other draw. An interesting question is for what temperatures are we most uncertain about the probability being? Below we plot the expected value line **and** and the associated distribution at each temperature." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "\n", - "for i, _t in enumerate(t[:-1]):\n", - " plt.scatter( _t*ones( n ) + 0.12*np.random.randn(n), p[:n,i], \n", - " alpha = 0.007,\n", - " color = \"#7A68A6\") \n", - "\n", - "#what's a better way to only have a for loop and include it in the legend? and\n", - "# have it somewhat show. You can see an artifact at t = 85.\n", - "plt.scatter( t[-1]*ones( n ) + 0.20*np.random.randn(n), p[:n,-1], \n", - " alpha = 0.3,\n", - " color = \"#7A68A6\", \n", - " label = \"samples from posterior at temp $t$\") \n", - "\n", - "plt.plot( t, mean_prob_t, lw = 1, ls= \"--\", color = \"k\",\n", - " label = \"average posterior \\nprobability of defect\")\n", - "\n", - "plt.xlim( t.min(), t.max() )\n", - "plt.legend(loc=\"lower left\")\n", - "plt.scatter( temperature, D, color = \"k\", s = 50, alpha = 0.5 )\n", - "plt.xlabel(\"temp, $t$\")\n", - "\n", - "plt.ylabel(\"probability estimate\" )" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 64, - "text": [ - "" - ] - }, - { - "output_type": "display_data", - "png": 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Asys0LQ30u2SpzC9azfR2fl4CkzAKIRODvl7MTUCz1aSW1IjTGNvUwMiW6Y0N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C13UmNSXYP1bLRxbqwrIsj7tIQrkCIytkRnduRoXnQl+5qDuWDgyAkyvjeg7TkvILAjc3eJjn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\nVn2fEibDlVd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- } - ], - "prompt_number": 64 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can see that as the temperature nears 60 degrees, the distributions spread out over [0,1] quickly. As we pass 70 degrees, the distributions tighten again. This can give us insight about how to proceed next: we should probably test more O-rings around 60-65 temperature to get a better estimate of probabilities in that range. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### What about the day of the Challenger disaster?\n", - "\n", - "On the day of the Challenger disaster, the outide temperature was 31 degrees fahrenheit. What is the positerior distribution of a defect, given this temperature? The distribution it plotted below. It looks almost garunteed that the Challenger was going to be subject to defective O-rings." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "figsize(9, 2.5)\n", - "\n", - "\n", - "t = 31\n", - "prob_31 = logistic( t, beta_samples, alpha_samples )\n", - "\n", - "\n", - "plt.xlim( 0.99, 1)\n", - "plt.hist( prob_31, bins = 300, normed = True, histtype='stepfilled' )\n", - "plt.title( \"Posterior distribution of probability of defect, given $t = 31$\")\n", - "plt.xlabel( \"probability of defect occuring in O-ring\" )\n", - "\n", - "print" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n" - ] - }, - { - "output_type": "display_data", - "png": 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x4XyIT3vISfrdUizcc7Wtw9CLahjB39/foH02+c3Jzz//jF69euHOnTtQKpXw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\nVlbi7t276N69OwCga9euwqPoU1NThfW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- } - ], - "prompt_number": 69 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Is there really a relationship between failure and temperature?\n", - "\n", - "An critism of our above analysis is that *assumed* that the relationship followed a logistic model, this we implictly assumed that the probabilities change over temperature. Let's look at the data again. (Top figure)\n", - "\n", - "Could it be that infact this pattern occured by chance? This might explain the large overlap in temperatures. After all, I can produce similar plots. (Bottom figure)" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "figsize( 9, 6)\n", - "subplot(211)\n", - "plt.scatter( challenger_data[:,0], challenger_data[:,1], s = 75, \\\n", - " color=\"k\", alpha = 0.5) \n", - "plt.yticks([0,1])\n", - "plt.ylabel(\"Damage Incident?\")\n", - "plt.title(\"(Real) Defects of the Space Shuttle O-Rings vs temperature\")\n", - "\n", - "subplot(212)\n", - "n = challenger_data.shape[0]\n", - "plt.scatter( challenger_data[:,0], stats.bernoulli.rvs(0.6, size = n) ,\n", - " s = 75, color=\"k\", alpha = 0.5) \n", - "plt.yticks([0,1])\n", - "plt.ylabel(\"Damage Incident?\")\n", - "plt.xlabel(\"Outside temperature (Farhenhit)\" )\n", - "plt.title(\"(Artificial) Defects of the Space Shuttle O-Rings vs \\\n", - "temperature\")\n" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 31, - "text": [ - "" - ] - }, - { - "output_type": "display_data", - "png": 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VxcTE4NSpUyVylcvlyM/Px5YtWyqxuupHpVLh66+/hpGREUxNTcXhMpkMFhYW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YQ0CNQoIYQQQkiDQI2S/2+3jgUAAAAABvlbT2NHUQQALEgJALAgJQDAgpQAAAtSAgAsBEHs9vP9\na8tsAAAAAElFTkSuQmCC\n" - } - ], - "prompt_number": 31 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Introducing the Bayes factor \n", - "\n", - "The *Bayes factor* is a measure comparing two models. In our example, on the one hand we believe that temperature plays an important role in the probability of O-ring failures. On the other hand, we believe that there is no significant connection, and the pattern appears by coincidence. We can compare these model using the ratio of the *probabilities of observing the data, given the model*:\n", - "\n", - "$$ \\frac{ P( \\text{observations} | \\text{Model}_1 ) }{ P( \\text{observations} | \\text{Model}_2 ) }$$\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Calculating this is not at all obvious. For simplicity, let's select a set of parameters from the posterior distribution (which is tantamount to selecting a particular model)." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "alpha_hat = alpha_samples[0,0] #select the first alpha sample\n", - "beta_hat = beta_samples[0,0] #select the first beta sample\n", - "\n", - "phat = logistic( temperature, beta_hat, alpha_hat)\n", - "print \"estimates of probability at observed temperature, defects: \"\n", - "print np.array(zip(p_hat, temperatures, D) )[:3, :]" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "estimates of probability at observed temperature, defects: \n", - "[[ 0.35 66. 0. ]\n", - " [ 0.166 70. 1. ]\n", - " [ 0.204 69. 0. ]]\n" - ] - } - ], - "prompt_number": 85 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To calculate the numerator in the Bayes factor, we start by **assuming a model**, in our case assume `alpha_hat`, `beta_hat` are true, and caluculate the probability of the defects we observe: equal to the product of:\n", - "\n", - "$$ P(\\; \\text{Ber}(\\; p(t_i \\; | \\; \\hat{\\beta}, \\hat{\\alpha} )\\; ) = D_i \\; ),\\;\\; i=1..N $$\n", - "\n", - "For example, using the output above, the first computation, $i=0$, would look like \n", - "\n", - "\\begin{align}\n", - "& p = p(t_1 \\; | \\; \\hat{\\beta}, \\hat{\\alpha} ) = 0.667 \\\\\\\\\n", - "& d = D_0 = 0 \\\\\\\\\n", - "& \\Rightarrow \\; P( \\; \\text{Ber}(p) = d ) = ?? \n", - "\\end{align}\n", - "\n", - "The probability of this ocurring is $(1-0.667) = 0.333\\; $ (Recall the definition of a Bernoulli random variable $\\text{Ber}(p)$: it is equal to $1$ with probability $p$ and equal to 0 with probability $1-p$). As each observation is independent, we multiply all the observations together. A clever way to do this is for a specific $i$ is, recalling the $D_i$ can only be 1 or 0:\n", - "\n", - "$$\\left( p(t_1 \\; | \\; \\hat{\\beta}, \\hat{\\alpha} )\\right)^{D_i} \\left( 1 - p(t_1 \\; | \\; \\hat{\\beta}, \\hat{\\alpha} ) \\right)^{(1-D_i)}$$\n", - "\n", - "\n", - "(it is possible that the quantity can overflow, so it is recommended to take the $\\log$ of the above::\n", - "\n", - "$$ D_i\\log( p(t_1 \\; | \\; \\hat{\\beta}, \\hat{\\alpha} ) ) + (1-D_i)\\log( 1- p(t_1 \\; | \\; \\hat{\\beta}, \\hat{\\alpha} ) ) $$\n", - "\n", - "and sum the terms instead of multiplying. Be sure use this transform for both models you are comparing.)" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "print p_hat\n", - "_product = p_hat**( D )*(1-p_hat)**(1-D)\n", - "prob_given_model_1 = _product.prod()\n", - "print \"probability of observations, model 1: %.10f\"%prob_given_model_1" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "[ 0.372 0.372 0.372 0.372 0.372 0.372 0.372 0.372 0.372 0.372\n", - " 0.372 0.372 0.372 0.372 0.372 0.372 0.372 0.372 0.372 0.372\n", - " 0.372 0.372 0.372]\n", - "probability of observations, assuming model 1: 0.0000005776\n" - ] - } - ], - "prompt_number": 99 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The second model we are comparing against is that $\\beta=0$, i.e. there is no relationship between probabilites and temperature. We perform the same calculations as above. Notice that `beta=0` here in the below PyMC code:" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "\n", - "beta = 0\n", - "alpha = mc.Normal( \"alpha\", 0, 0.001, value = 0 )\n", - "\n", - "@mc.deterministic\n", - "def p( temp = temperature, alpha = alpha, beta = beta):\n", - " return 1.0/( 1. + np.exp( beta*temperature + alpha) ) \n", - "\n", - "\n", - "observed = mc.Bernoulli( \"bernoulli_obs\", p, \\\n", - " value = D, observed=True)\n", - "\n", - "model = mc.Model( {\"observed\":observed, \"beta\":beta, \"alpha\":alpha} )\n", - "\n", - "#mysterious code to be explained in Chapter 3\n", - "map_ = mc.MAP(model)\n", - "map_.fit()\n", - "mcmc = mc.MCMC( model )\n", - "mcmc.sample( 260000, 220000, 3 )\n", - "######\n", - "\n", - "alpha_samples_model_2 = mcmc.trace(\"alpha\")[:, None]\n", - "alpha_hat = alpha_samples_model_2[0] #use the first 'model'\n", - "beta_hat = 0\n", - "\n", - "p_hat = logistic( temperature, beta_hat, alpha_hat )\n", - "print \"estimates of probability at observed temperature, defects: \"\n", - "print np.array(zip(p_hat, temperature, D ) )[:3, :]\n", - "print\n", - "print \"Notice the probability is constant for all temperatures?\"" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - " \r", - "[****************100%******************] 260000 of 260000 complete" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - " estimates of probability at observed temperature, defects: \n", - "[[ 0.397 66. 0. ]\n", - " [ 0.397 70. 1. ]\n", - " [ 0.397 69. 0. ]]\n", - "Notice the probability is constant for all temperatures?\n" - ] - } - ], - "prompt_number": 105 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "#compute the probability of observations given the model_2\n", - "\n", - "_product = p_hat**( D )*(1-p_hat)**(1-D)\n", - "prob_given_model_2 = _product.prod()\n", - "print \"probability of observations, model 2: %.10f\"%prob_given_model_2" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "probability of observations, model 2: 0.0000004761\n" - ] - } - ], - "prompt_number": 106 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "print \"Bayes factor = %.3f\"%(prob_given_model_1/prob_given_model_2)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "Bayes factor = 40.075\n" - ] - } - ], - "prompt_number": 107 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Is this good? Below is a chart that can be used to compare the computed Bayes factors to a degree of confidence in M1. \n", - "\n", - "- <1:1 Negative (supports M2)\n", - "- 1:1 to 3:1 Barely worth mentioning\n", - "- 3:1 to 10:1 Substantial\n", - "- 10:1 to 30:1 Strong\n", - "- 30:1 to 100:1 Very strong\n", - "- \\> 100:1\t Decisive\n", - "\n", - "We are not done yet. If you recall, we selected only one model, but recall we actually have a distribution of possible models (sequential pairs of ($\\beta, \\alpha$) from the posterior disitributions). So to be correct we need to average over samples from the posterior:" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "\"\"\"\n", - "linear_combination= np.dot( beta_samples[:,None], \n", - " observed_temperatures[None,:]) \\\n", - " + alpha_samples[:,None]\n", - " \n", - "p_model_1 = 1.0/(1.0 + np.exp( linear_combination ) )\n", - "product = p_model_1**( D )*(1-p_model_1)**(1-D)\n", - "model_1_product = product.prod(axis=1).mean()\n", - "print model_1_product\n", - "\"\"\"\n", - "\n", - "p = logistic( temperature[None,:], beta_samples, alpha_samples )\n", - "_product = p**( D )*(1-p)**(1-D)\n", - "prob_given_model_1 = _product.prod(axis=1).mean()\n", - "print \"expected prob. of obs., given model 1: %.10f\"%prob_given_model_1\n", - "\n", - " \n", - "p_model_2 = logistic( temperature[:,None], \n", - " np.zeros_like(temperature), alpha_samples_model_2)\n", - "\n", - "_product = p_model_2**( D )*(1-p_model_2)**(1-D)\n", - "prob_given_model_2 = _product.prod(axis=1).mean()\n", - "print \"expected prob. of obs., given model 2: %.10f\"%prob_given_model_2\n", - "print \n", - "print \"Bayes factor: %.3f\"%(prob_given_model_1/prob_given_model_2)\n" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "expected prob. of obs., given model 1: 0.0000190809\n", - "expected prob. of obs., given model 2: 0.0000005072" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "\n", - "Bayes factor: 37.618\n" - ] - } - ], - "prompt_number": 109 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "It looks we have a pretty good model, and temperature *is* significant. Look at you, you're a rocket scientist now." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Exercises\n", - "\n", - "1\\. How would add a prior belief that smoking causes more deaths in first example?" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "2\\. Try plotting $\\alpha$ samples versus $\\beta$ samples. Why might it look like this?" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "#type your code here.\n", - "\n", - "plt.scatter( alpha_samples, beta_samples, alpha = 0.1 )\n", - "plt.title( \"Why does the plot look like this?\" )\n", - "plt.xlabel( r\"$\\alpha$\")\n", - "plt.ylabel( r\"$\\beta$\")" - ], - "language": "python", - "metadata": {}, - "outputs": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## References\n", - "\n", - "- [1] Dalal, Fowlkes and Hoadley (1989),JASA, 84, 945-957.\n", - "- [2] German Rodriguez. Datasets. In WWS509. Retrieved 30/01/2013, from http://data.princeton.edu/wws509/datasets/#smoking.\n", - "- [3] Reference bayes book by Dr. Cyntha" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [], - "language": "python", - "metadata": {}, - "outputs": [] - } - ], - "metadata": {} - } - ] -} \ No newline at end of file diff --git a/Chapter2_MorePyMC/README.md b/Chapter2_MorePyMC/README.md index e181cbfe..ab086abd 100644 --- a/Chapter2_MorePyMC/README.md +++ b/Chapter2_MorePyMC/README.md @@ -1,26 +1,4 @@ -# Chapter 2 - -Instructions on Usage of the IPython Notebook for Chapter 2. There are 3 options available to the learner. - -### Option 1 ------------- -The lesson was created with IPython Notebook. It allows a user to interact with the contents of the notebook. - -*If you do not have IPython Notebook installed, you can use Option 2 or 3 to review the material. The lesson will not be interactive.* - - -### Option 2 -------------- -PDFs of the chapters are available [online](https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/tree/master/previews). -These pdf are compiled periodically and do not express the most up-to-date content. For that, consider Option 3: -For more - -### Option 3 -------------- -Open with your browser: http://nbviewer.ipython.org/urls/raw.github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/master/Chapter2_MorePyMC/MorePyMC.ipynb - -#### Screenshot of notebook for Chapter 2 ---------------- -![alt text][logo] -[logo]: https://raw.github.com/bigsnarfdude/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/master/Chapter2_MorePyMC/Screen%20Shot%202013-02-08%20at%2011.23.49%20AM.png "Chapter 2 iPython Notebook Screenshot" +Chapter 2: A little more on PyMC +================================= +### [Read it online here](http://nbviewer.ipython.org/urls/raw.github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/master/Chapter2_MorePyMC/Ch2_MorePyMC_PyMC2.ipynb) diff --git a/Chapter2_MorePyMC/daft_plot.py b/Chapter2_MorePyMC/daft_plot.py new file mode 100644 index 00000000..5f68e901 --- /dev/null +++ b/Chapter2_MorePyMC/daft_plot.py @@ -0,0 +1,31 @@ +#daft drawing for SMS example +import matplotlib.pyplot as plt + + + +try: + import daft +except ImportError: + print "python library Daft required." + + +pgm = daft.PGM([9, 4], origin=[.5,.5]) +pgm.add_node(daft.Node("tau", r"$\tau$", 4.0, 3.5)) +pgm.add_node(daft.Node("alpha", r"$\alpha$", 6, 4.0)) +pgm.add_node(daft.Node("lambda1", r"$\lambda_1$", 5.5, 3.2,)) +pgm.add_node(daft.Node("lambda2", r"$\lambda_2$", 6.5, 3.2)) +pgm.add_node(daft.Node("lambda", r"$\lambda$", 5.0, 2.0)) +pgm.add_node(daft.Node("obs", "obs", 5.0, 1.0, 1.2, observed=True)) + + + +pgm.add_edge("tau", "lambda") +pgm.add_edge("alpha", "lambda1") +pgm.add_edge("alpha", "lambda2") +pgm.add_edge("lambda1", "lambda") +pgm.add_edge("lambda2", "lambda") + +pgm.add_edge("lambda", "obs") +pgm.render() +plt.figure( figsize=(12,5) ) +plt.show() \ No newline at end of file diff --git a/Chapter2_MorePyMC/chp2data/challenger_data.csv b/Chapter2_MorePyMC/data/challenger_data.csv similarity index 100% rename from Chapter2_MorePyMC/chp2data/challenger_data.csv rename to Chapter2_MorePyMC/data/challenger_data.csv diff --git a/Chapter2_MorePyMC/separation_plot.py b/Chapter2_MorePyMC/separation_plot.py new file mode 100644 index 00000000..15433149 --- /dev/null +++ b/Chapter2_MorePyMC/separation_plot.py @@ -0,0 +1,52 @@ +# separation plot +# Author: Cameron Davidson-Pilon,2013 +# see https://onlinelibrary.wiley.com/doi/10.1111/j.1540-5907.2011.00525.x + + +import matplotlib.pyplot as plt +import numpy as np + + + +def separation_plot( p, y, **kwargs ): + """ + This function creates a separation plot for logistic and probit classification. + See https://onlinelibrary.wiley.com/doi/10.1111/j.1540-5907.2011.00525.x + + p: The proportions/probabilities, can be a nxM matrix which represents M models. + y: the 0-1 response variables. + + """ + assert p.shape[0] == y.shape[0], "p.shape[0] != y.shape[0]" + n = p.shape[0] + + try: + M = p.shape[1] + except: + p = p.reshape( n, 1 ) + M = p.shape[1] + + colors_bmh = np.array( ["#eeeeee", "#348ABD"] ) + + + fig = plt.figure( ) + + for i in range(M): + ax = fig.add_subplot(M, 1, i+1) + ix = np.argsort( p[:,i] ) + #plot the different bars + bars = ax.bar( np.arange(n), np.ones(n), width=1., + color = colors_bmh[ y[ix].astype(int) ], + edgecolor = 'none') + ax.plot( np.arange(n+1), np.append(p[ix,i], p[ix,i][-1]), "k", + linewidth = 1.,drawstyle="steps-post" ) + #create expected value bar. + ax.vlines( [(1-p[ix,i]).sum()], [0], [1] ) + plt.xlim( 0, n) + + plt.tight_layout() + + return + + + diff --git a/Chapter2_MorePyMC/sms_model.png b/Chapter2_MorePyMC/sms_model.png new file mode 100644 index 00000000..b0abef74 Binary files /dev/null and b/Chapter2_MorePyMC/sms_model.png differ diff --git a/Chapter3_MCMC/Ch3_IntroMCMC_PyMC2.ipynb b/Chapter3_MCMC/Ch3_IntroMCMC_PyMC2.ipynb new file mode 100644 index 00000000..5de9c7ec --- /dev/null +++ b/Chapter3_MCMC/Ch3_IntroMCMC_PyMC2.ipynb @@ -0,0 +1,1413 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Chapter 3\n", + "\n", + "\n", + "_______\n", + "\n", + "## Opening the black box of MCMC" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The previous two chapters hid the inner-mechanics of PyMC, and more generally Markov Chain Monte Carlo (MCMC), from the reader. The reason for including this chapter is three-fold. The first is that any book on Bayesian inference must discuss MCMC. I cannot fight this. Blame the statisticians. Secondly, knowing the process of MCMC gives you insight into whether your algorithm has converged. (Converged to what? We will get to that) Thirdly, we'll understand *why* we are returned thousands of samples from the posterior as a solution, which at first thought can be odd. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### The Bayesian landscape\n", + "\n", + "When we setup a Bayesian inference problem with $N$ unknowns, we are implicitly creating an $N$ dimensional space for the prior distributions to exist in. Associated with the space is an additional dimension, which we can describe as the *surface*, or *curve*, that sits on top of the space, that reflects the *prior probability* of a particular point. The surface on the space is defined by our prior distributions. For example, if we have two unknowns $p_1$ and $p_2$, and priors for both are $\\text{Uniform}(0,5)$, the space created is a square of length 5 and the surface is a flat plane that sits on top of the square (representing that every point is equally likely). " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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1lixZwoUXXsiTTz7JU089FVEsjhkzhnfffZeuXbsGzpXfS2232/n6669p06YN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\nR45k06ZNT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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib inline\n", + "import scipy.stats as stats\n", + "from IPython.core.pylabtools import figsize\n", + "import numpy as np\n", + "figsize(12.5, 4)\n", + "\n", + "import matplotlib.pyplot as plt\n", + "from mpl_toolkits.mplot3d import Axes3D\n", + "\n", + "jet = plt.cm.jet\n", + "fig = plt.figure()\n", + "x = y = np.linspace(0, 5, 100)\n", + "X, Y = np.meshgrid(x, y)\n", + "\n", + "plt.subplot(121)\n", + "uni_x = stats.uniform.pdf(x, loc=0, scale=5)\n", + "uni_y = stats.uniform.pdf(y, loc=0, scale=5)\n", + "M = np.dot(uni_y[:, None], uni_x[None, :])\n", + "im = plt.imshow(M, interpolation='none', origin='lower',\n", + " cmap=jet, vmax=1, vmin=-.15, extent=(0, 5, 0, 5))\n", + "\n", + "plt.xlim(0, 5)\n", + "plt.ylim(0, 5)\n", + "plt.title(\"Landscape formed by Uniform priors.\")\n", + "\n", + "ax = fig.add_subplot(122, projection='3d')\n", + "ax.plot_surface(X, Y, M, cmap=plt.cm.jet, vmax=1, vmin=-.15)\n", + "ax.view_init(azim=390)\n", + "plt.title(\"Uniform prior landscape; alternate view\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Alternatively, if the two priors are $\\text{Exp}(3)$ and $\\text{Exp}(10)$, then the space is all positive numbers on the 2-D plane, and the surface induced by the priors looks like a water fall that starts at the point (0,0) and flows over the positive numbers. \n", + "\n", + "The plots below visualize this. The more dark red the color, the more prior probability is assigned to that location. Conversely, areas with darker blue represent that our priors assign very low probability to that location. " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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75rFq0DnQE6gxfmN+oS/b2woM7682fjWaRuLII49k5MiRXHHFFWywwQZEIhEO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\nJ0/2PaZQKMT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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "figsize(12.5, 5)\n", + "fig = plt.figure()\n", + "plt.subplot(121)\n", + "\n", + "exp_x = stats.expon.pdf(x, scale=3)\n", + "exp_y = stats.expon.pdf(x, scale=10)\n", + "M = np.dot(exp_y[:, None], exp_x[None, :])\n", + "CS = plt.contour(X, Y, M)\n", + "im = plt.imshow(M, interpolation='none', origin='lower',\n", + " cmap=jet, extent=(0, 5, 0, 5))\n", + "#plt.xlabel(\"prior on $p_1$\")\n", + "#plt.ylabel(\"prior on $p_2$\")\n", + "plt.title(\"$Exp(3), Exp(10)$ prior landscape\")\n", + "\n", + "ax = fig.add_subplot(122, projection='3d')\n", + "ax.plot_surface(X, Y, M, cmap=jet)\n", + "ax.view_init(azim=390)\n", + "plt.title(\"$Exp(3), Exp(10)$ prior landscape; \\nalternate view\");\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "These are simple examples in 2D space, where our brains can understand surfaces well. In practice, spaces and surfaces generated by our priors can be much higher dimensional. \n", + "\n", + "If these surfaces describe our *prior distributions* on the unknowns, what happens to our space after we incorporate our observed data $X$? The data $X$ does not change the space, but it changes the surface of the space by *pulling and stretching the fabric of the prior surface* to reflect where the true parameters likely live. More data means more pulling and stretching, and our original shape becomes mangled or insignificant compared to the newly formed shape. Less data, and our original shape is more present. Regardless, the resulting surface describes the *posterior distribution*. \n", + "\n", + "Again I must stress that it is, unfortunately, impossible to visualize this in large dimensions. For two dimensions, the data essentially *pushes up* the original surface to make *tall mountains*. The tendency of the observed data to *push up* the posterior probability in certain areas is checked by the prior probability distribution, so that lower prior probability means more resistance. Thus in the double-exponential prior case above, a mountain (or multiple mountains) that might erupt near the (0,0) corner would be much higher than mountains that erupt closer to (5,5), since there is more resistance (low prior probability) near (5,5). The peak reflects the posterior probability of where the true parameters are likely to be found. Importantly, if the prior has assigned a probability of 0, then no posterior probability will be assigned there. \n", + "\n", + "Suppose the priors mentioned above represent different parameters $\\lambda$ of two Poisson distributions. We observe a few data points and visualize the new landscape: " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "observed (2-dimensional,sample size = 1): [[3 4]]\n" + ] + } + ], + "source": [ + "# create the observed data\n", + "\n", + "# sample size of data we observe, trying varying this (keep it less than 100 ;)\n", + "N = 1\n", + "\n", + "# the true parameters, but of course we do not see these values...\n", + "lambda_1_true = 1\n", + "lambda_2_true = 3\n", + "\n", + "#...we see the data generated, dependent on the above two values.\n", + "data = np.concatenate([\n", + " stats.poisson.rvs(lambda_1_true, size=(N, 1)),\n", + " stats.poisson.rvs(lambda_2_true, size=(N, 1))\n", + "], axis=1)\n", + "print(\"observed (2-dimensional,sample size = %d):\" % N, data)\n", + "\n", + "# plotting details.\n", + "x = y = np.linspace(.01, 5, 100)\n", + "likelihood_x = np.array([stats.poisson.pmf(data[:, 0], _x)\n", + " for _x in x]).prod(axis=1)\n", + "likelihood_y = np.array([stats.poisson.pmf(data[:, 1], _y)\n", + " for _y in y]).prod(axis=1)\n", + "L = np.dot(likelihood_x[:, None], likelihood_y[None, :])\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(0, 5)" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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f0A0Ht/PnGrinrCA+Zv5LI3ubLvVHA/dk2TULuUxeyY5JJ3KoPA6b3GIWCuAi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/Vac668bwK/EeFa3of9B+6nPVO+ll5a5Lo2OMeNWAP0R8AhqrPmR+vyvdXVewE7y/mrwI97+7e5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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "figsize(12.5, 12)\n", + "# matplotlib heavy lifting below, beware!\n", + "plt.subplot(221)\n", + "uni_x = stats.uniform.pdf(x, loc=0, scale=5)\n", + "uni_y = stats.uniform.pdf(x, loc=0, scale=5)\n", + "M = np.dot(uni_y[:, None], uni_x[None, :])\n", + "im = plt.imshow(M, interpolation='none', origin='lower',\n", + " cmap=jet, vmax=1, vmin=-.15, extent=(0, 5, 0, 5))\n", + "plt.scatter(lambda_2_true, lambda_1_true, c=\"k\", s=50, edgecolor=\"none\")\n", + "plt.xlim(0, 5)\n", + "plt.ylim(0, 5)\n", + "plt.title(\"Landscape formed by Uniform priors on $p_1, p_2$.\")\n", + "\n", + "plt.subplot(223)\n", + "plt.contour(x, y, M * L)\n", + "im = plt.imshow(M * L, interpolation='none', origin='lower',\n", + " cmap=jet, extent=(0, 5, 0, 5))\n", + "plt.title(\"Landscape warped by %d data observation;\\n Uniform priors on $p_1, p_2$.\" % N)\n", + "plt.scatter(lambda_2_true, lambda_1_true, c=\"k\", s=50, edgecolor=\"none\")\n", + "plt.xlim(0, 5)\n", + "plt.ylim(0, 5)\n", + "\n", + "plt.subplot(222)\n", + "exp_x = stats.expon.pdf(x, loc=0, scale=3)\n", + "exp_y = stats.expon.pdf(x, loc=0, scale=10)\n", + "M = np.dot(exp_y[:, None], exp_x[None, :])\n", + "\n", + "plt.contour(x, y, M)\n", + "im = plt.imshow(M, interpolation='none', origin='lower',\n", + " cmap=jet, extent=(0, 5, 0, 5))\n", + "plt.scatter(lambda_2_true, lambda_1_true, c=\"k\", s=50, edgecolor=\"none\")\n", + "plt.xlim(0, 5)\n", + "plt.ylim(0, 5)\n", + "plt.title(\"Landscape formed by Exponential priors on $p_1, p_2$.\")\n", + "\n", + "plt.subplot(224)\n", + "# This is the likelihood times prior, that results in the posterior.\n", + "plt.contour(x, y, M * L)\n", + "im = plt.imshow(M * L, interpolation='none', origin='lower',\n", + " cmap=jet, extent=(0, 5, 0, 5))\n", + "\n", + "plt.scatter(lambda_2_true, lambda_1_true, c=\"k\", s=50, edgecolor=\"none\")\n", + "plt.title(\"Landscape warped by %d data observation;\\n Exponential priors on \\\n", + "$p_1, p_2$.\" % N)\n", + "plt.xlim(0, 5)\n", + "plt.ylim(0, 5)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The plot on the left is the deformed landscape with the $\\text{Uniform}(0,5)$ priors, and the plot on the right is the deformed landscape with the exponential priors. Notice that the posterior landscapes look different from one another, though the data observed is identical in both cases. The reason is as follows. Notice the exponential-prior landscape, bottom right figure, puts very little *posterior* weight on values in the upper right corner of the figure: this is because *the prior does not put much weight there*. On the other hand, the uniform-prior landscape is happy to put posterior weight in the upper-right corner, as the prior puts more weight there. \n", + "\n", + "Notice also the highest-point, corresponding to the darkest red, is biased towards (0,0) in the exponential case, which is the result from the exponential prior putting more prior weight in the (0,0) corner.\n", + "\n", + "The black dot represents the true parameters. Even with 1 sample point, the mountains attempts to contain the true parameter. Of course, inference with a sample size of 1 is incredibly naive, and choosing such a small sample size was only illustrative. \n", + "\n", + "It's a great exercise to try changing the sample size to other values (try 2,5,10,100?...) and observing how our \"mountain\" posterior changes. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Exploring the landscape using the MCMC\n", + "\n", + "We should explore the deformed posterior space generated by our prior surface and observed data to find the posterior mountain. However, we cannot naively search the space: any computer scientist will tell you that traversing $N$-dimensional space is exponentially difficult in $N$: the size of the space quickly blows-up as we increase $N$ (see [the curse of dimensionality](http://en.wikipedia.org/wiki/Curse_of_dimensionality)). What hope do we have to find these hidden mountains? The idea behind MCMC is to perform an intelligent search of the space. To say \"search\" implies we are looking for a particular point, which is perhaps not an accurate as we are really looking for a broad mountain. \n", + "\n", + "Recall that MCMC returns *samples* from the posterior distribution, not the distribution itself. Stretching our mountainous analogy to its limit, MCMC performs a task similar to repeatedly asking \"How likely is this pebble I found to be from the mountain I am searching for?\", and completes its task by returning thousands of accepted pebbles in hopes of reconstructing the original mountain. In MCMC and PyMC lingo, the returned sequence of \"pebbles\" are the samples, cumulatively called the *traces*. \n", + "\n", + "When I say MCMC intelligently searches, I really am saying MCMC will *hopefully* converge towards the areas of high posterior probability. MCMC does this by exploring nearby positions and moving into areas with higher probability. Again, perhaps \"converge\" is not an accurate term to describe MCMC's progression. Converging usually implies moving towards a point in space, but MCMC moves towards a *broader area* in the space and randomly walks in that area, picking up samples from that area.\n", + "\n", + "#### Why Thousands of Samples?\n", + "\n", + "At first, returning thousands of samples to the user might sound like being an inefficient way to describe the posterior distributions. I would argue that this is extremely efficient. Consider the alternative possibilities:\n", + "\n", + "1. Returning a mathematical formula for the \"mountain ranges\" would involve describing a N-dimensional surface with arbitrary peaks and valleys.\n", + "2. Returning the \"peak\" of the landscape, while mathematically possible and a sensible thing to do as the highest point corresponds to most probable estimate of the unknowns, ignores the shape of the landscape, which we have previously argued is very important in determining posterior confidence in unknowns. \n", + "\n", + "Besides computational reasons, likely the strongest reason for returning samples is that we can easily use *The Law of Large Numbers* to solve otherwise intractable problems. I postpone this discussion for the next chapter. With the thousands of samples, we can reconstruct the posterior surface by organizing them in a histogram. \n", + "\n", + "\n", + "### Algorithms to perform MCMC\n", + "\n", + "There is a large family of algorithms that perform MCMC. Most of these algorithms can be expressed at a high level as follows: (Mathematical details can be found in the appendix.)\n", + "\n", + "1. Start at current position.\n", + "2. Propose moving to a new position (investigate a pebble near you).\n", + "3. Accept/Reject the new position based on the position's adherence to the data and prior distributions (ask if the pebble likely came from the mountain).\n", + "4. 1. If you accept: Move to the new position. Return to Step 1.\n", + " 2. Else: Do not move to new position. Return to Step 1. \n", + "5. After a large number of iterations, return all accepted positions.\n", + "\n", + "This way we move in the general direction towards the regions where the posterior distributions exist, and collect samples sparingly on the journey. Once we reach the posterior distribution, we can easily collect samples as they likely all belong to the posterior distribution. \n", + "\n", + "If the current position of the MCMC algorithm is in an area of extremely low probability, which is often the case when the algorithm begins (typically at a random location in the space), the algorithm will move in positions *that are likely not from the posterior* but better than everything else nearby. Thus the first moves of the algorithm are not reflective of the posterior.\n", + "\n", + "In the above algorithm's pseudocode, notice that only the current position matters (new positions are investigated only near the current position). We can describe this property as *memorylessness*, i.e. the algorithm does not care *how* it arrived at its current position, only that it is there. \n", + "\n", + "### Other approximation solutions to the posterior\n", + "Besides MCMC, there are other procedures available for determining the posterior distributions. A Laplace approximation is an approximation of the posterior using simple functions. A more advanced method is [Variational Bayes](http://en.wikipedia.org/wiki/Variational_Bayesian_methods). All three methods, Laplace Approximations, Variational Bayes, and classical MCMC have their pros and cons. We will only focus on MCMC in this book. That being said, my friend Imri Sofar likes to classify MCMC algorithms as either \"they suck\", or \"they really suck\". He classifies the particular flavour of MCMC used by PyMC as just *sucks* ;)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Example: Unsupervised Clustering using a Mixture Model\n", + "\n", + "\n", + "Suppose we are given the following dataset:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 115.85679142 152.26153716 178.87449059 162.93500815 107.02820697\n", + " 105.19141146 118.38288501 125.3769803 102.88054011 206.71326136] ...\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "figsize(12.5, 4)\n", + "data = np.loadtxt(\"data/mixture_data.csv\", delimiter=\",\")\n", + "\n", + "plt.hist(data, bins=20, color=\"k\", histtype=\"stepfilled\", alpha=0.8)\n", + "plt.title(\"Histogram of the dataset\")\n", + "plt.ylim([0, None])\n", + "print(data[:10], \"...\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "What does the data suggest? It appears the data has a bimodal form, that is, it appears to have two peaks, one near 120 and the other near 200. Perhaps there are *two clusters* within this dataset. \n", + "\n", + "This dataset is a good example of the data-generation modeling technique from last chapter. We can propose *how* the data might have been created. I suggest the following data generation algorithm: \n", + "\n", + "1. For each data point, choose cluster 1 with probability $p$, else choose cluster 2. \n", + "2. Draw a random variate from a Normal distribution with parameters $\\mu_i$ and $\\sigma_i$ where $i$ was chosen in step 1.\n", + "3. Repeat.\n", + "\n", + "This algorithm would create a similar effect as the observed dataset, so we choose this as our model. Of course, we do not know $p$ or the parameters of the Normal distributions. Hence we must infer, or *learn*, these unknowns.\n", + "\n", + "Denote the Normal distributions $\\text{Nor}_0$ and $\\text{Nor}_1$ (having variables' index start at 0 is just Pythonic). Both currently have unknown mean and standard deviation, denoted $\\mu_i$ and $\\sigma_i, \\; i =0,1$ respectively. A specific data point can be from either $\\text{Nor}_0$ or $\\text{Nor}_1$, and we assume that the data point is assigned to $\\text{Nor}_0$ with probability $p$.\n", + "\n", + "\n", + "An appropriate way to assign data points to clusters is to use a PyMC `Categorical` stochastic variable. Its parameter is a $k$-length array of probabilities that must sum to one and its `value` attribute is a integer between 0 and $k-1$ randomly chosen according to the crafted array of probabilities. (In our case $k=2$) *A priori*, we do not know what the probability of assignment to cluster 1 is, so we create a uniform variable over 0,1 to model this. Call this `p`. Thus the probability array we enter into the `Categorical` variable is `[p, 1-p]`." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "prior assignment, with p = 0.32:\n", + "[0 1 1 0 1 1 1 1 0 1] ...\n" + ] + } + ], + "source": [ + "import pymc as pm\n", + "\n", + "p = pm.Uniform(\"p\", 0, 1)\n", + "\n", + "assignment = pm.Categorical(\"assignment\", [p, 1 - p], size=data.shape[0])\n", + "print(\"prior assignment, with p = %.2f:\" % p.value)\n", + "print(assignment.value[:10], \"...\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Looking at the above dataset, I would guess that the standard deviations of the two Normals are different. To maintain ignorance of what the standard deviations might be, we will initially model them as uniform on 0 to 100. Really we are talking about $\\tau$, the *precision* of the Normal distribution, but it is easier to think in terms of standard deviation. Our PyMC code will need to transform our standard deviation into precision by the relation:\n", + "\n", + "$$ \\tau = \\frac{1}{\\sigma^2} $$\n", + "\n", + "In PyMC, we can do this in one step by writing:\n", + "\n", + " taus = 1.0/pm.Uniform( \"stds\", 0, 100, size= 2)**2 \n", + "\n", + "Notice that we specified `size=2`: we are modeling both $\\tau$s as a single PyMC variable. Note that this does not induce a necessary relationship between the two $\\tau$s, it is simply for succinctness.\n", + "\n", + "We also need to specify priors on the centers of the clusters. The centers are really the $\\mu$ parameters in this Normal distributions. Their priors can be modeled by a Normal distribution. Looking at the data, I have an idea where the two centers might be — I would guess somewhere around 120 and 190 respectively, though I am not very confident in these eyeballed estimates. Hence I will set $\\mu_0 = 120, \\mu_1 = 190$ and $\\sigma_{0,1} = 10$ (recall we enter the $\\tau$ parameter, so enter $1/\\sigma^2 = 0.01$ in the PyMC variable.)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Random assignments: [0 1 1 0] ...\n", + "Assigned center: [ 107.08313959 186.02196393 186.02196393 107.08313959] ...\n", + "Assigned precision: [ 0.02216706 0.0013977 0.0013977 0.02216706] ...\n" + ] + } + ], + "source": [ + "stds = pm.Uniform(\"stds\", 0, 100, size=2)\n", + "taus = 1.0 / stds ** 2\n", + "centers = pm.Normal(\"centers\", [120, 190], [0.01, 0.01], size=2)\n", + "\n", + "\"\"\"\n", + "The below deterministic functions map an assignment, in this case 0 or 1,\n", + "to a set of parameters, located in the (1,2) arrays `taus` and `centers`.\n", + "\"\"\"\n", + "\n", + "@pm.deterministic\n", + "def center_i(assignment=assignment, centers=centers):\n", + " return centers[assignment]\n", + "\n", + "@pm.deterministic\n", + "def tau_i(assignment=assignment, taus=taus):\n", + " return taus[assignment]\n", + "\n", + "print(\"Random assignments: \", assignment.value[:4], \"...\")\n", + "print(\"Assigned center: \", center_i.value[:4], \"...\")\n", + "print(\"Assigned precision: \", tau_i.value[:4], \"...\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# and to combine it with the observations:\n", + "observations = pm.Normal(\"obs\", center_i, tau_i, value=data, observed=True)\n", + "\n", + "# below we create a model class\n", + "model = pm.Model([p, assignment, observations, taus, centers])\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "PyMC has an MCMC class, `MCMC` in the main namespace of PyMC, that implements the MCMC exploring algorithm. We initialize it by passing in a `Model` instance:\n", + "\n", + " mcmc = pm.MCMC( model )\n", + "\n", + "The method for asking the `MCMC` to explore the space is `sample( iterations )`, where `iterations` is the number of steps you wish the algorithm to perform. We try 50000 steps below:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " [-----------------100%-----------------] 50000 of 50000 complete in 23.6 sec" + ] + } + ], + "source": [ + "mcmc = pm.MCMC(model)\n", + "mcmc.sample(50000)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Below I plot the paths, or \"traces\", the unknown parameters (centers, precisions, and $p$) have taken thus far. The traces can be retrieved using the `trace` method in the `MCMC` object created, which accepts the assigned PyMC variable `name`. For example, `mcmc.trace(\"centers\")` will retrieve a `Trace` object that can be indexed (using `[:]` or `.gettrace()` to retrieve all traces, or fancy-indexing like `[1000:]`)." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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KmToykexSA326RaL38OT06TP06tVTzl8CrUYQGRnJtGnTeO211wB51uJwvmtF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\ncD0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JCQxlkqqKql7TNP3xak98cazyTwcfdlWuuKvCZA4AmM05L2CMaf6609i2VWDFGMAA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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "figsize(12.5, 9)\n", + "plt.subplot(311)\n", + "lw = 1\n", + "center_trace = mcmc.trace(\"centers\")[:]\n", + "\n", + "# for pretty colors later in the book.\n", + "colors = [\"#348ABD\", \"#A60628\"] \\\n", + "if center_trace[-1, 0] > center_trace[-1, 1] \\\n", + " else [\"#A60628\", \"#348ABD\"]\n", + "\n", + "plt.plot(center_trace[:, 0], label=\"trace of center 0\", c=colors[0], lw=lw)\n", + "plt.plot(center_trace[:, 1], label=\"trace of center 1\", c=colors[1], lw=lw)\n", + "plt.title(\"Traces of unknown parameters\")\n", + "leg = plt.legend(loc=\"upper right\")\n", + "leg.get_frame().set_alpha(0.7)\n", + "\n", + "plt.subplot(312)\n", + "std_trace = mcmc.trace(\"stds\")[:]\n", + "plt.plot(std_trace[:, 0], label=\"trace of standard deviation of cluster 0\",\n", + " c=colors[0], lw=lw)\n", + "plt.plot(std_trace[:, 1], label=\"trace of standard deviation of cluster 1\",\n", + " c=colors[1], lw=lw)\n", + "plt.legend(loc=\"upper left\")\n", + "\n", + "plt.subplot(313)\n", + "p_trace = mcmc.trace(\"p\")[:]\n", + "plt.plot(p_trace, label=\"$p$: frequency of assignment to cluster 0\",\n", + " color=\"#467821\", lw=lw)\n", + "plt.xlabel(\"Steps\")\n", + "plt.ylim(0, 1)\n", + "plt.legend();\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notice the following characteristics:\n", + "\n", + "1. The traces converges, not to a single point, but to a *distribution* of possible points. This is *convergence* in an MCMC algorithm.\n", + "2. Inference using the first few thousand points is a bad idea, as they are unrelated to the final distribution we are interested in. Thus is it a good idea to discard those samples before using the samples for inference. We call this period before converge the *burn-in period*.\n", + "3. The traces appear as a random \"walk\" around the space, that is, the paths exhibit correlation with previous positions. This is both good and bad. We will always have correlation between current positions and the previous positions, but too much of it means we are not exploring the space well. This will be detailed in the Diagnostics section later in this chapter.\n", + "\n", + "\n", + "To achieve further convergence, we will perform more MCMC steps. Starting the MCMC again after it has already been called does not mean starting the entire algorithm over. In the pseudo-code algorithm of MCMC above, the only position that matters is the current position (new positions are investigated near the current position), implicitly stored in PyMC variables' `value` attribute. Thus it is fine to halt an MCMC algorithm and inspect its progress, with the intention of starting it up again later. The `value` attributes are not overwritten. \n", + "\n", + "We will sample the MCMC one hundred thousand more times and visualize the progress below:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " [-----------------100%-----------------] 100000 of 100000 complete in 66.4 sec" + ] + } + ], + "source": [ + "mcmc.sample(100000)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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v0CWlNGvg82I0u9WKaO6hZmyNDcbvvDltcY2FaRfJ2HvE4bnFWTkUJJW/WnEl\ncjlje2nfUeIWrarG3lxbKBt551FRG/mrAUNRMYXpGWQeOlHbXTFzLY3vv5Vr2ka+KD2jVJ7lCkRB\nchqJv5TYglkKfYaiYuIWrODC9n0YioopunDJuZ0tg/y4JC7uPGCdF59slc48eIKkFRtJ/HUD6X/s\nsjqW9NtGsg6fcno/rwQshTRdbp6VUF8ZpF6P1OtBSgw6HVJK4hasIHH5OnKjS5ybTJMrE/qcPC7u\n2GeVV5xZvT+UslhX5nF9XoEmcFbAPKYs/ug0gjPvfm1OrwvqQ+aB45x8bR5Hn32btI3a0qw+31ow\nPvHSRxyd+g6gTTqAGv/+SCkdjrs0SKRejy47l3VBfdgQeiPZJ7T7eOKVj9k14lGOTJnN2Q//W2oC\nfPChl/mz6yjy45P/VT4rp974nGPPvVvb3VAo7HI5O3vWFPr8ArJPnKUgQXt3/5t+PxTO5Zq0kS80\nCvC2GnZDYRHxC1eSaRRqDYWaJk6Xozm4pK7bRtqWf4hbsAJ9vibU50XHcWnvUVLW/um0/pZH+p+7\nyD0XY533xz/mflsii4spTE6jR6u2VvmGwhInm7yYRAy6soVBR6Ss3UruudgqnVsjWPygS5229XLi\nsnXmlZfyKM7KQRoMxP/0P3Oe7lI28QtLHG96tgmvVJdstfoVoejCJS7+rU3eii5mErdgBXkxidpB\nF2EuY+53Zjbpf2oTuLTNO0leuYkLW/dUul1bor9YxLqgPqQahfa/hz1UqsyOQfebP+vzC4n9bjkJ\ni1cDsL3f3ZVqr7psjdM27WRzu1scTmYOPPQyW68rCSmXd944OTO+WxN+XsPZOfOJ+myh1Qv3wjZt\nTLf2GEPaxrJNiiwpSs8g+/jZSl5F9VLZsT0/fympG7Zz7IU5ZOw65KReVR8x3y4r93dN6vVOWZ1U\nNvLOoyI28gcOHLjizWpyTkdbpbMOn6ylnlhTUR8ExZXLNaeR1+Xmg+mH2uIFrMvNJ9kojBddyCDn\nVDSZB7TwQkm/bTSXM82WCxJLzAakQW/8byBuwQrS/vjnsvupzysgbsEKq3Yqi2W/bUnbbC1kGIxC\nLcCFbbtJWLxaG6tKUnQhg/yEipkt1RT58cnaWKbYbCZiFOr1+QXosnMpTL1Q5ktcGgwkr9xEvI0J\nQcr6vy67j+lbd1ut+EiDoUxH5Lzz8eSe1SZvKWv+ACD7+Bl02bnosrUJXPq23YBmDpK8ajP5cUmk\nrv+L4ksVqLpmAAAgAElEQVRavTlnzl92v03Efverw2N552LJOnpaM7exMGn7I/K2Mus88eonJCz9\nnUNPzqIw7WK19RWgKE2bzB967FVAE+BMz/vhJ2aR+vs2ii3G/8AD2vbr2GjJzrz9H9Y36cvFnQfI\nPhll3UZ6+X0uunCJvJhEjkyZzY4BE6t8PY4oSE4zC9n5CSnmyV91cPKVueyf+DxxP/xmzruStYgn\nXv6IvKj4Msts7XUHR555q9J1G3S6yzLTu1yK0jNYF9SnzDLrgvqYV5YUCksMRUXo87WVWsW1x7Vn\nI+9AUEvd8JfZtrwgPpmM3YfMApE9LtmxkzUJeAUWZi2GomLzy82uMGlB3IIV5EbHU5SRRdKKTYAm\ncJtsbnU5uWQfP2tli1pVQX/PGevZfl5UaS168srKa4qvNAw6nXmM0jZsJ+eUJmwVZ+WQvHJTSUEp\nSV3/F/lxyfaqKacR62fKdmwrQn5sopWZz8W/D5KyuoxwWEL7alo+T0XpGSSt2Ejqum2AZsYTt2CF\nOQ1QmFoSp9hQjglOZUjf8neZx3cOmsTRZ9/mr753lfQl2fq7sP2m+zj46ExzOubrJRx55k2Slm/g\nj04jOP3uV/y1bRvVidRr92598A1sChtYbnlDsf0oQ4efep0dN91nlXd06jtE/2ex3fJaXTq2dLiV\nbb3Gme3wL2zf57B8VTj23LvsGvU4x16Yw9Eps9l9+5PmY2mb/7ZaiasOO+7MA8er3VSsJimITyZj\nz+Fyy0kpSd2wg8RfN6DPL+TY9PfZ2GoA+yZMN0+ULalOG/nCtItW32OgzPeKQaczH889E+Ow3NXK\ntWzDnXMmpkZ883Kj4sk5HU1etLX/ljQYyC4nrKUzqUxUt4LE1AqvrP/buOY08liYyklDifZIFuvt\nFK4gZSihkldtJvvYGXM69/R5LmzfW6pcXqy2Y9zF7XtJWb0FqS8RstI27aDowiWSftvIpX1HwWCg\nIDkNfUFhKc365ZJztuSHXupLO01m7DpEbnT8Fe8cqy8oRErJpd1HzMK7JVZCPJC6QXvR2tqvQ4nA\nW9bErropvqiZxZhWZvLjksg6dob4n7VtwU0WQmkbqi4g6K8w5+2ck1Ekr9yMLjuXXaMfL3U8au4P\nZB6smhNYQUo6WUdK/EBMP/iX9hwhbmHF4hFLKa1Ms6zqdzChPjXrU813wKLf64L6cGbOfDY0K1nq\nN5nk7Bn3NKkbdpAXk8DxFz8k+X+OJ3N55+OtBHF7pG3WJlhxP/xmnrSYnqF99z5H0srN5MUklqtJ\nL76Uxbm535vT6Vt32y0Xv3AVm9vdYmVel3XkVKVX96T+Mn6PK0HcghUcfPgVgEqZNu0YMJH9E6dz\n+IlZbGx5s9lULG3jDo4979wwdH8PfZC/KmGSdv4/P/NnF23169KB4w7vtdTrq7SikrRiY42/D/T5\nhSXmbtcAjgRWfV4euWdjamClq6R+U1+KMrLIOnLK6cEZbDHodGbT5ewTZ9EXFlldf+ahExRdzETq\nDRiKisk8dILMQycoTLtwTU5Uq4Nrz0be4kV8cce+apnBOfpBMdnSW2po8s7HkxcdT9yCFcQtWGF+\nQC9s3WW3DhO2L7a0jTusHHEriz07bmkwkGFn6T3ryCmzvXXO6Wgubt9byrzEkoo4FhVlZFF0MbMS\nPa4cib/8TvzClaV8BxxivA/2BIiEn1dTkJLOpX3HSh2zR2Vt5O1hemZMWvqcMzFk7j+GLC5Gl5tP\n1tHTl93GlcqmNoPJ+Me+zbXb2z9Uevk351Q0f3a5jZ2DH+D8/KUAnHjpQ/PxY9Peq1A9l/YeRdjZ\nkKUi/D30Qc2XwGiGde7D/zosu3/idE69/hmx3y13aLKUF5vEtuvHc27ej5yc9SlRny20Om4oKi71\n/bq4U4v9fHTKbLZ0uFXLlJJtvcaRum5bmXbcaVv+sXJs3j/xebvlTL4jRRklbe8c/ADHnq/YGIPm\n87E++AarvLMffEuSxQqhLieX8/OXsueuKXYnfZbkxyWZJ+ql+vvzGvNkKfrLktUTW1Ou4zM+4Nzc\n75EGA1KvJ6cME5XkVZspSE4j5/R5c1512sgXJKSgy8qhMO0iaZvKV+QUWVzL+S9/IteiXwDxP63m\n4s4DrA/pz/omfTlbxrNpwlJwP/TYayX+OTWELivbvPJzJdlw6wsKMRTrzEpC0/tdGgwYiuxPuosz\nc8g+UfYksrgK+72YhGF7aH20mHxZzBOyT5xF6g3kx2oRxbyFi1Mn1pmHTpj7YijWkX3sjJW/QM7J\nc+htFAH5cYlkHT1VatxMZs4Ka64ZjbzJfjHp1w1W+UXpGVpIxsLqXZKxnsU6Fmz1OXkVmkyYNHbO\nxGTOY0vmwROkrP2z1JK5I8eximhKUlZvMdt2l0XuuVgyD58qc+nYkuKsHDIPXZ6T0IUdpTe7KExO\nN/tHOJO4BSvs2tpatp306/pqaavYiRMpZ7K57RCztsgSQ1Gx3RdOosV4nXxlrt37WxF2jXzULIhX\nlf33v1Chcilrt5bKy4tJJDc6nsK0i2wzOuKe+/C/nP/PYqK/WGQuZ4q0s6X9MIf1mybmpheovQhe\nlgijA3X2iXOcevPzclcCjkyebTWRSFq+wdxuYeoFs2Cdc+a8lW23KVoQQMLS35F6Panr/+LsB99y\n6NGZ7Jv4PNJg4Nj097V7+eduMv45xLqgPiSt3IwuN5+C5DTzqgfAqTe/sJp4HHpylnnjM9PETErJ\nBeMqQ35MIn90GkFBSjox839hXVAfYr//lTPvfs0/Ix8tNcmwx547nmF7/3vM6axjZy47MlPO2Rir\n5/uPTiPYd9801gX1Ie+c9r6xu4Jjo1ixNdU4OvVtdo950qzMODtnPuWxvmk/q/dWec9DWViuBBiK\nNW2sNEjzpMnSf+tKRpebT86pKLKPnyHryEn0hUVkHT6JvqCQrCOnyD5xjqyjp8lPSDHfR31+AXnn\ny9d458fZnyg5+s2TUpZyni1Mu0huVBz6gkJjH08hpcSg05sDP1jUYJ3S6ylISkNfWETmoZMUJKVp\n6fyyZRddbr5dU7NS/TVOfCz9ikwriABSp8NQrEOXV3m/PQW41VbDBw8epLo2hCrKyCJl9Ra7G+/k\nnDlPejU4p9piz0TDHkkrHDukWlLW7Loq7DlzspTmuDxTC9voKrmnz+PZpBGFyWkUX8qy2iBIGgwO\ntZeO4mwbCovI2H2YgBt6mOvI2HMEabRLDho1iIKEFOpGhDnu40r7k5HKkBcVi19kBK7enmZb1MpE\nELA3tpWhqiExK4twc62RdqqT44Zc2rv4siViKIB5symp17Mh9EatkIsLQxM1QfHQk7PMQqSJPWOf\nqnL7pV94zsVasz0JXVYON+4traXXZeZQlJ6Bi3flNjo7+uzbgLZ5V0zLhnY1x1nHzpgFrR03T6hQ\nvXnnYtnSfhgh94w056Vt/pt99z6Hf4+OXNp7lKHJJf4/cQtX0uy+UWzpNBJ346ZpR555k4xdBym+\nVKJASNuwnfVN7Wu3D1n4V4CmtGkz/SErp+6M3YesnwejkBv344pSduc7bp5oNnEzkVnBVTnTEv+6\noD60ePwe1n7+DTcNu4VuP1jvl2C5QZ2UsszVzO397qbNjEfsHjv4iGYedPDhV0pvwGZT5z+3Pkz9\n3l1pM/0hGvSxv6lb1tHT+DQPJvdsDH5d29stI4t14KXtNBq/aBUNn76H4uwc3Or4Vjjcoy43n9yz\n5/Fu1gSPBv7kxSSgz81DuLkhdTpcvb3R5+dr7xLhgtTrcPH0wKO+n7mO7OzsGtHKS70B4Vr6nabL\nzUOfV2B25H9gxnOMHDCYMWi/UZamnVKvpyj9Im6+3rj71yslbJdH0YUM8uOTqdcpHOEiyD5xFnf/\nevg0D0bq9QhXV+0e+PoCmhNrcVYung3rU5yRhT4/n5xTJSuaRekZdvfUsfSfypcGxOkYpF5HYaqm\nUDP9l3o9RRdKlACuPt54BQXiVldrvyA+GX1BAX7+2nd6165dPPXUU6SkpPDFJ/MYNlQbo+xjp/EI\nqG9lYpR1tERRkx+XhDQY8GrSqFLjpdCoNUG+OilrRlgQ73wtqz4v/4q3Ka8KUkorh0zL5dWsw6fI\nPhlFvY5tcPevh1u9OuYdUO1NcjIPHEdfoNk9BtzQA31eARf+2mMW4kEzJ9Ln5ePbpjm67DyKMzLx\nbdUMgIzdh+3awleVpF/XU69zuEOb6GsBF6/S231fbeScjeHkzE+sNVYGA7lRcbh4epQS4q82LE04\nTBpQeyt4Uq9nS8fhVW9ISqI/X0TXps0Rri74NA82H9o58H4aV3HXXcswrfvufQ7QTJRA0xAmLNHs\n9Y9Ne49L+45RfPGSlfAcv+h/VJW86HiKs3KsxtDWjKrYOFGytyGarRBfVc5/+RMAhiJtvwldZjbu\n/vXsRpnpvuhDAgf2RkpJYUo6XkGBVsctzZscsbXXOPpu+RE3Xx8AuyaYGX8fYPeYJ/FpGWK3jt1j\nn0ZnsQI7NHknOwdPouWT99JktLZJnKXdcsw3S6l711DydBfwadEMdz/tt74oIwsXD3fcfL2RUlKU\ndhHPRgHo8vLJtZhgmQQ40x4Y0rjaq8/XNLDa+9NoflFYVCpsbGHaRQoSU6jXqV2Vzd/KQpedS25U\nLH5dIsx5jjZt+u7dD+3mW9WXm19q4lgehekZ5pXZrCMnqddRCyG9c9cuJt/yMjuX/Iq7Xz2KM7Pw\naam9F01RigoSUrDnzFfkIBpY7llrk1RLnz2r8y9Yr+Tp8/IpunDJLMib0OXmo8vK5q2Zr/J/99zL\ng/c/QH5cotVExrYuq/aN8lNhSuXGDGDx4sUsWLCAtWvXVvpcR6SkpDB16lQOHjxIcnIyhw4dIiTE\n/nfpSqBcQV4IEQL8CDRG+6Z9I6WcJ4SoDywBmgPngfFSykzjOS8C/wfogMlSylJv2+q0kb9ox7m0\nJilMSTc7nV0pVIcdd9YRazttS6HbZPaQeeA4AF7BQQQOuN7KkQU0++WM3db20Im/bgAp0dsso5nS\nCYtX4xUcREFCslmQr04h3nwNVYzjWx1jWyNcwaECHdHexfoF4SgO/V997qyJ7tQIuuxcUtZtQxqF\nncrG3q8ogX8cMo/bDX8vpSg9g/o9OwGQsubPam/vj04jrNImh9HqZHPbIWUerynnOO25lST9uoHD\nT76OX2SE3XL77n2OITF/cvGfg+y9cwqhk8bQ8ukJeAc3rnBb+TGJFKVdNAvyZZlN5UXbN4PU2ZhR\nnnrzc7KOnObQY6+ZBXmk5MTMueYymomHC9Kgx1CsIz8mEV2uZibl1yUCQ1ExBUmpuDfwL+XcWZiS\nbtTIVs5ER5dXgCEqHtMbJevIKep1akfWkVN4N2uKRwO/Ms+3h16vx9W1ZLWyMO2i2bQMQF9YhKun\nR6XrtaQioWltsTXtNO1zA9JsvFucaV9pqdfrrK7JXIeDKFyW5jreonITI0NRMcVZObjV8UFfoN2Z\n3LPnAUhISqKFf0O7qwAVoSo28OWtdJWH7fMA4OLiwqBBg3j22WcZalxVuJKpyB3UAVOllB2A3sCT\nQohwYAawSUrZDtgCvAgghGgPjAcigGHAF6Katl0z6HSltFWWNq3VHdqtMhRe5i6aFaXxrTfVSDtg\nLbiXR2FKOrrs3FIva1shHjQTH1sh3hbLH7WainDxb8EjwJ/+u5bVdjcURja1GcyRp9+s0Tb/6j2e\nXSMfJcNOmF1F1Uj/YxeHn3wdoMzoS392G40uUzN/iP3+V2Lm/1LptoozssjYe6TaYttHf27tgwGw\nud0txHyz1JxvWvnIj03U9rTILYnylXnohPl3OvvYabsmatnHz5TKK4/cM6VNU0xKJH1BIQadjtzo\neCIjI5k7dy69uvcgLCyMp59+miLjpGHHjh107NiRefPmERERwdNPP42UkrWrVtH/hhto170rI26/\nnZNR55BS8sFrbzBp0iSrNl//dC5vfKZNau5+9imWrtXec1JKPl3wPf3uGkvPMSOZ9u5b5ORp4/LP\nwQP0GX+7VT033D2Onfs1OeXQyROMeuxBOo8YwnVjb2P2l59ZX/u5WPILCpg4+SlS0tPpeOsgOg0f\nTNrFC7wz63WemPUKz779Bp1H3MLy9b9z6OQJxj71KF1GDuX6O0bx2ryP0Flo2k9HRzFh+hS6jhrG\ndWNv48ufFpiv4cufFnDTvePpPno4T7/xKlk5jkN+LvxlCb369qF1qzAeeWUGaRc1LfpN944nLimJ\nB1+aTsehAyi242OXlJbK46++RI/bh9N99HBmzfvYfGzp2tUMnnQvXUcNY9ILz5GQUiIDtBrQj59W\nreDmCXfRqlUrnn9e84k5ffo006ZNY8+ePYSGhtKqVSsAioqKmDlzJp07dyYiIoJp06ZRaPSTtPc8\n2BIYGMgDDzxA165dr+i9M0yUK8hLKZOllAeNn3OAE0AIMAr4wVjsB8BkoH4b8LOUUielPA+cAa6z\nrbcqceQv7jxQahnRcvnKNkbqtUhFzSWqEuv8cpA6XYX9ASpVr8FAfg2YR1WGmh7bqlKnbUu7+W1e\nfLRU3qAzG2nQp3p8Vi6H44aaCwH6b8Pe2O4aUfpZUFSeyjy3RRcuEbeoJCTq+S9/qrSw8Pewh9g1\n4lHOvP9Npc67HIoulu3UKC208NUZOCBfOjJbldpmf1ma0Lls2TIWvP8Re3b+zdmzZ5k9fYZ5ZSA1\nNZXMzEwOHz7Mxx9/zN5NfzD52WeZ/fSzHFz1O3ePHMXDL7/AhQPHGTlgIJs3bSLPaPZjMBhYu3UL\nowbdUqoHv/y+hl83rOPnuZ+z7adfyM3L49W5H5mPl6XDfOOzuTwwdjyHV29g66KlDL9pgPXVGfR4\ne3nx3bsf0rhhQ46u3cSRNRsJbBAAwOad2xl+0wAOr17PqEFDcHN1ZeaTkzm46neWf/YVf+/fx4IV\n2mZuufl5TJg+hZt79WbXslX8uXAJfbppvmrfLF/Kpp3bWTrvC/5ZthK/unWZ+bF986Gd+/fxwfyv\n+HzWW+xavoqmjRrz1Ovapnt/LlpKk0aN+O87cziyZiPubtYGHwaDgQdfnE5Ik6bsWPIrf/+ygpED\nBgGwYftffLl4IV+9+Q77fltDz06dmfzmLKvzt+zayf+++pZt27axYsUKtmzZQtu2bfnwww/p2bMn\nsbGxREVpK/ezZs0iOjqa7du3s3fvXpKSkpgzp8S8zvZ5uNqp1JqKEKIFEAn8AzSWUqaAJuwDJi+F\nYMBSok4w5l02V1pc7NrAnjPOtcyF7ftqJKJPRfHv3tFsuwjgabRxda9f+SVeZ+Pub99BzNXL08qn\n46ZDq3Cr60vX794BoN/WRXbPUygU1cOFrda/aeub9K1SPefL2JCspolbtIqUtVsv+6/CSMBir5iH\nH3qIxg0DqePhydSpU/nflk3mVQJXV1emT3mW4sRUZEY2CxYt4t6Ro+ncLgIhBGOGDMXD3Z0Dxw4T\n3DiIjhHtWb9d25xux/69+Hh50yW8tLnUqs0beeiOOwkJCsLby4vpDz/Gmj83Y6iAz5y7mzsxiQlk\nZGbi7eVFZIR9p2NHdG3fkUF9NMdwTw8POrRpS2REe4QQBDcO4u4Ro9h9SAs3veXvnTRq0JD/G3cn\nHu7u+HiXXM+S1SuZ9uAjNApoiLubG89MfIDft/1h9xpWbt7A+FtH0L51G9zd3Hj+4cc4cPyolfbc\n0Zz00MnjpF68wIuPPoGnhyce7u5076iZ9i1evYIn7plAq2ahuLi48Pg9Ezh+7gyJqSUmOk/cM5E6\nPr6EhITQr18/jh496nBsFixYwOzZs6lXrx6+vr5MnjyZ5cuXm4+7uroyY8YM3N3d8fS8+n3JKuzs\nKoSoAyxDs3nPEULY3q5KqRScFkf+GsfVjka+bvvWpTY76dkm3GxnfjWTH5NQ212wwrdNCwa0bs7F\nfw6SH5NAvc7tSEtOs+s0G9C/J8LV1SlRky4H9wB/PAPrm9NejRtq+X6a4G+KKlIb2NrIK6oPNbbO\nQ40tBN58vUOb7MuhLBtuc3zyomL8i7TP+bGJNGvWjJQL6eScjibnbAwBAQEUWoSMTkhJ5tcN6/jh\nN83EUErQ6XSkpmvRWkbccBP/27yR2wffwv82b+K2gYPttp9yIZ3gxkHmdHDjIIp1OtIzyreRf2/6\nDD7673wG3X8PzZo05ZmJDzCgd2kHaUc0aWQd4SU6Po63vviUI6dOUlBYiF6vp2PbdgAkpqYQ2tS+\nPjUpJYVHZ76Ei9FPQEpwc3MjPeMijQIaWpVNvZBOp7YlPmI+3t741/MjJd16HOyRmJpKcOMgXOw4\nKyckJ/PGZ3OZ/eWn5j4IBCnpaTRtpPmPNKxf8s7y9vYmJ8f+fiPp6enk5eVx8803m/MMBoPVqldA\nQADu7u5l9vdqokKCvBDCDU2IXyClNK0JpgghGkspU4QQQYBp68MEoJnF6SHGPCuWLVvG/PnzCQ0N\nBcDPz49OnTqZw6OZtry2TKf+/Tc924RTkJzGn2vX4RMajMl4wGTuYHJEvFbTzYCmY4ey+tOv0efm\n0bNNOP7dO/LHmnVInc6qfKOWDWn+Lxsfp4+/u2ZBdtKlkNQzJxl+ixZzes/p4/h1iaBlQqa5fMNg\nP/oPuBnXOj78c2D/ZbUfHeJH+h+7Kn2+5g5XsvQ/8rmnqN+zE/8cPGAO9Qgl37fAwX3xCPA3lzcd\nt5fuuexTGv11jOhPF1SovEpfvemTnnoM+QVXTH9UuubTPliHLIQSITtfGqCo0Dpte7ya0/lp6eaw\nlBKIsdDenth3gEAbITRfGsznBzZqxCP3TWDyvffbrX/AjTfy9n8+IzktjfXbt7H4s/9YnV+EJF8a\naBzQkISUZPP5SSnJuLu54evvj2taKvlGZ9B8aUCv15ORlYlXcGPypYFGTZvyycxZAKzcuoUnZr3M\nwVXr8PL0tOqPEAKDsT1T+8VSYqkvz5cGXvp4Dp3btOOzV98ATw8WLP+FTX9pKxwNAwM5v2WTVXlT\n/U0bNebN6TOI7NCx1Hjblm9kc72yoJBLWZn4NwywOsfe/QoIDCQxJQWDwUChUe9lOt64cWMeue9+\nxg0aYvd8gAILQVyn05l9IIQQ6PV6c5jSgIAAvL292bBhA61btwa0EKa2WIY1NR23Tfv4aG/QnJyc\nMsunpKRw8qT2zt2+fTuxsdrO1z169GDgwIGl2q5uKqqR/y9wXEr5iUXeKmAS8B5wP7DSIn+REOJj\nNJOa1kCp/b5bt27NpAcewMWBDZltvON+/foRF63NlnNORtHBvS71GjYhK0mz27ONJFJWOnBgH3oa\nP3s3DyY/JoHbZz1PUXqG2Xm2MvXVRHrMGzPMn119vBj1wjPELVhRUr5tRCnnVG3MVlwR/b8W0q7G\nGN7bt2+nX79+pBW5mbXYtz72IK7e2mpJ3vl4bh3Sz6zpDhoxgJ45eaXqq2i64YDeNAtuTFx8Sczx\nnu3ag8XSp6Pzj7EKv8gIHv38NQ4+MpM2LzwMaM+GLjTMHJ7V/H0z/n/i0CaOz5hDtx/epyAxFbpZ\n79EwJW4PLp4exCdo3z/TCz9wSD/SNmwvpaF0lPYObUp+bKKVwNDexbfC56t0xdOWdtyVPb9nm3Cy\nj56pcPl/W9o2r7b744y0n4W4YKsld2baUoC1Pa7L0Z5pAfy84jduub4vXp6efPn1V9w2wEKA0umt\n6rhv+G089tpL3NitJ5ER7ZEFhew6dIBeXbri4+1NU/8G9OoSyfT3ZxPapCntm7ewat8DgbdwYeSA\nQXy1ZBE3Xnc99f38+ODbrxlx80B8Xd0Ib9acwqIi/tz1N/169OTrRQsoKi7G1csTb+HCio3r6X9d\nLxr4+RNQpy5CCLNNvWVfG9avT2ZWlrZZkjF+vLsQuFpsROktXMjPy6eOry/eXl6ci41hyaoVBBi1\n2EP79GPOfz7nu+W/cO9tozHoijlz/jyREe25Y8RtzPv2az6Y8QrBjYO4cCmD/ceOMrjvDVb1A9w2\nYDCTZ8/itoGDadUslHfnf0Vk+w60atyk3Pt5XUQHGgUE8N7XXzJl0oO4uLiw7/QpunfsxISRo/no\nv9/QpXUb2rRoSXFuHtv37eHWG0u06l4WsqKbmxseHlpkocDAQFJSUvDy0t7PQggmTpzI22+/zfvv\nv0/Dhg3Jzs7m5MmTDBgwwFzGcm8C230K6tatS2FhIQXGiZhle/bKN27cmA4dOgDWsuv+/VXbnLCy\nlGtwLYToC9wLDBBCHBBC7BdCDEUT4AcLIU4BA4F3AaSUx4GlwHFgLfCErGa33/y4JIBSOz9WFI8A\nf/Pn+j00Gy3h4oJHYAOrcoFDqm/b7YpiaX9ton6v8s2Q6nVoTd32rWk61jpUkm36WiVwUNVsTCuK\ncHenyRjrUHeBA67HxdODZhNGm4V4AJ8WIWYhHsDF3Q3XOj5UFVNousYjSpyhAm7oSdBtA8157v72\nzWF6rfyS3uu+xTcslL6bf7A61v3H9wl/a4rd87yaBJo3t/Fq2ogBx3+n18ov8WkRTOT82bgYw7MF\n33kr/XeX2B52/nRmqbpC/2+cw2uz7VNN4FWJUH+2NJs4msCBvauxN1c+ft060HPJJwSNHlTbXVEo\nHDJq0GAmTp/CTfeNp2VwM566736HZTu1C+ed517gtXkfEXnbUAZMvIvl660Dadw2cAg79+9j1CDr\n331LB9bxt47g9sFDuXPyk9x473i8PT157elnAajr68sbU57jhTnv0PuO2/H19qFp06bmc7fu2cWQ\nB+6j0/DBvPX5PD599Q08PUqHvQwLbc7IAYO48Z47iLxtqDlKjC0vPf4kKzdtoNPwwbz04ftmR1IA\nX28fFsyZy+ad27lu7EgGTLiLfw5q9vMTxt7B4L43MHH6s3QeMYRxTz3GoZP2oy717d6DqQ88zOOv\nvkTvO0YTl5zIpzNftzs2tri4uDD/7fc5nxBP3zvH0PfO21nzp7YJ5ZB+/Xns7vt4+s3X6DziFoY9\nNLrVsekAACAASURBVJGtu0tMUk31CmOYSMt2+vfvT3h4OOHh4bRtq8lPr732Gq1atWLIkCG0aNGC\nsWPHcu5cyX4TFaFp06Y0b94cIQS9evUiOLhaXD2dgqit0DqbN2+WkV27OtTI28NSA305NJswmkv7\nj5N97DQh940i58Q56rZvjZSS+IUl0QQa3XKDVXhL3zYtKL6U5XCTherqm+11htw3yu4XJG7BCup1\nbFtqZz7T+aadbm3rc6tbB112jvGzr3nL9KuZRkP7k7pum9Pqr9epncPY0BWhICmNtE07yixT//pI\nMv6xjubkFxlBvU6anWPxpSyS/7cFn5YhBPTTIg7ocnJJ+m2j1b1uOnYoudFxZO4/RtBtA82rBs5k\nXVAfwp77P9pMf6jURjitptxP1NzSAnvgwN50X/Sh3Y1zqgt3/7pWO4cCRLz1LCdeqVqkgluStHuY\nFx1fbiz7uu1b0/2nD4n9/lei5v5A25cf4/Ts/1Sp3fJoOv5WEpdW34YolgyO2oKrjxe50fH81Xu8\nU9q4kmn+yJ3EfL2ktrtR6zR57Qla9Ole292wyw13j+O96S/Sp9uV2T8An5bNzJsmOtpsSlE2ws2N\neh3a1HY3SpGenk7Dhg1L5e/fv5+BAwc6fdfJKy4EikGnM+9WVt00HjHArLn1aKgtOQkhqNu+tfmz\nT6vQkhOEoOnYoQSNHEDg4L40uD7SYUg/Sxr0LfvHxKdlCMHjb3V43NVmG3ZHs9zgO4fb3V67fq9I\nh1p8j4b1aXhzL4TR0SNwcD/z56sV4e5uV1h1q1d1AdZ2deZycTP+gNvDpK138fAwC+T2cPevh2+b\nFlZhIt3q+NJkTElYtGYTRuPq41XjP3Y3HVxJ2LOTrPJMKxiWu4ha0u7Vp5zdLQacWFcqzyesGYGD\nS1ZwvJs1KVWm5ZP32q3PtPxt2qis/+7ldmPy1wlvReT82XgFBdJ2xqMMTd5Jq6cnVvUyHGIa4+YP\njmPAsbV0+c8b1d6Gq4/2e+TrYJfQax2/zu24fs3XRM6fTbcf59R2dxRXKW4Wq7I+LUKo065VLfbm\n6sQ7pGyH2n8rtSbIO4ojr8/N59LeI04Jwu9Rvx5eTbRwgT6hTeyandTrqAlA/j064dHAD1cfL9z9\n65XaShug4c3X223Ht1Uzgu8aUSq/QZ9uBI0cQEC/Hrh4euAXGYFXk0alyvn37IwwxmAN6F8qBL8Z\nFw/7Anidti04mFqyo1/DAZopgE+LEBoPuxF3v7qE3DWc4LtH4ObrTchdw431act6jYb2x7NRgMN2\nryS8mjQi5K7huHi402T0YHzDNPden1ah1L++S5Xq9O/RiUDjmNmLRmNyDK0Mbr7eDrdLDxqu2QHa\nhov0bBRAnfAwq7wG10eW2qLczdfbYbvCrfRuf87AKygQF4u4wX7dOtDli1kMOP47wXcNZ0j8Nm7c\n+6vVOXXaaZPixiO06294c69qjyNvOwnuuexTGvbvSfcFcxh0TnP+Cn/9GSth3NXbizbPa74ELkaT\nKfcGfqXMo4Ym78QntAk+zUuWy7v+Vwvh2f7t58zCfnUT9tz/mT+71fFhaPJO/LqE4xHgT5MyzF+q\nY2x7rfzysuuwR7cf36f/P0vLL1hNuPnV5Ya/y25vcNQW2sx4hMBBffDv3pGgETfTaIh9Ez61/4Hz\ncBxHvoRq2nOyQng08C+/kA0unh5Wv9vufnXtRqCrDSoyvrVN3Q5t8OsSUSOry1cjtaqRtyur16Cp\nj0nTZJXn641wd6duRJjZHssRJq2+PVzcNaGmyZghBN89Av8enfANC7WyZa7XqR2BgzSzAre6JRpb\nn+ZNqdO2RbltVBSTjbVbXWsHJkvBq/GIATQdV6LZrWfHjMTepKO2sdR0u9X1xa+btkJRNyKslMBb\nUepGhFkJwHXCw/BpUQ32cQ4ebRcPd0LuGWn+kfLv0YmGN19Po1tuMD9HVaHJmFvM27jXJL3XfUvk\nN28B4NHADyEELm5uVtqUnstLdjHs8sUsei7/jLApkwi5ewRDk3cS9uwDlWvz9/mA9oPf/v3n8Qjw\nZ2jyTgB8jAJ13z8XEtCvu/l77eLpQeCQfjS+9UYrYbzNy4/h4ulBwwG96fzZa7R4/B5uPriKAYdX\nUx51IrSJl3Bw34Ym78S/R8fyL6iMZ7e859r2e15VImZPNU9MTNTvZT05dm/gZx7nqtJ/93IaDemH\nT4sQui+yvxENQJOxQxhwdE259QX071lun3xbh+LbMsShv0TX797B1ceLsCmTSvmf2K6YKmqfbT/9\nUmNmNV5NK+5j4x2qvTc8Auy/x52lMLOn7Xf1cazwAW1lW7hW/X3jTFzcrsx+XSnUmiDvKI68WY6/\nTIHe8gtSp21L6nUOL6N0CS5ubmYNtT28mzWhfu+u1OvY1uzw54hmE0bj5uuDi5sbdSPCyizr19Va\ncDa9LMrStpaFbdQf72ZN7JoQmPCoX89q4mJy1rQ0u/Hv2cn8uayVgsri36NT+YUcnmstFLl6edJk\n9GBNgLQRePy6dXBYj+nH2TtUE+gsz63fs5PVy9x2bCuKaVfeOu1a0XjYjVbHLMe+bkRYtSwhVvXZ\nuVz8IiPMk0d7tH3lCQL6lpgHuXi4E9C3G/V7dWH8J7MBCL57hFljbymY2jMlA23lYWjyTvpu/oHQ\niaMZcKzEXvy65Z/Rf9cy6oZbv9xc3Nzo/uP7VnmejRvS4iHNDrzHTx8SNPwmwl97ChcPd7sTfxMD\njq2l37b/Z+/Mw6Mos7Z/V2dPZw8hCQmBJIR9B9mRURBhGBVXBmZERhhRX/V1BZnR13H0G3dccFQU\nBnUYUNQRVEAEVCQS9i0Lgez7Qval0+mtvj+6q1LdXdVbutKd5Pyui4t0VXX106e7q85znvucswPK\n5ETM2PeRTWed1duPgE1472+Ysv11fjVNSGBCLD9xEZOAzT3WpeeesrOry6RYpRUpomZPRtK9tyP2\nt/NsHjd1p/1cg4Tl1quTHCHDkxGc1HVdEiYSh08x/736RYSZBTZCRqaISqei54o7dJbSLwAYcu8d\n8BNEWP0iQjHhg79b/T6F3FD0o9U2Z2zrCNO/2+zW8wHGieui6mMIG2ddUMGbsHQ4bdWR70l8Q5Rg\nFAowPgoEDopF2DijTxEQI+6MBw9JhD/Xk0NixUBYeAOA3eChIwQMHGAW7fcJCuLva5aETxjF2zds\n9DBekWCJPf/FFUJGpiJ8wij4hoaIvm+/yAgohw1xSM7c3/GOX4ib4C7y8bfdiIE3dpVOipw+AeET\nHHPk7aHw80XIsCEInzTarct5gQmxZs5xyKhUDLpzsdvOP+A3060uGvYInzwGCXctNo0vDqygi54i\noPu6eoWpo1roqFSbOQOWxN+2ED4hwUhYtkQ0Osk5ftz7jZ43HQOum4GwMWmIvvYaq+MBowPhGxoC\nZWqS6H53EDF5NOJvuxGR08bDf0AkYubP6ncXqWtPfIGha39v97jgpHjMOfIfLKo+hgV5B5HyiFFf\nPv69v5kdl3TvHYiedw0CE6QnPoHxMWYRd1u4KkXyj47gV9EiJo+xeW3g5H3XfPmO5DFh44YjZsEs\nTN3xBsa+9VcAwLXHdyH+toUYdPuNCJ80GtdlfofUR1dZPTcgJgqMvx/Cxo9AzHUzwEhI8KTwiwzD\ntK/elXwPN1amY27GLl7SI0XIiGQsyDuIsRs3SEYDh61bY7VtUfUxDF55q9kkIWhwPKLnTuUfJ917\nB6bu2NjlKJkIGzdcslJX2vr7+O8R5zgk/88fMOa1dfwx11/63qY8iWPQHcbXmHfma7vHOkvskt8g\ncuo4s1WFuJuut/EMIyP+7yEMXDRXcn/czcZyjJyUzVPYy10KSRvaMwNxEsbHhy88EBATBcbUQMlH\nImDiG25cLQ4cFCtZVUxhUaXG0dUeqci5b4iS94P8oyPhq1RCOSwJ/pHhkpMEy3OFTxgl2OfD/28p\n8wQAZYrj98vwCV1S4pARKfAxBUKVKYMRGD/QSjbjHx0BX2UwrYA5gNdp5Pna2BYRea7Vsi0ipo6D\njzKYj0QGiOja3Y1lUqkr0eW4m66HwtfXzNlgGKZbGjpXdNyWhI1JM3OUfUODecfTEdmKvehC3O+u\n48ttKgL8kXDXbxF/20IkrrjJ5vN8lcEYdOtCyRwBS/wiQvkId/CQBMmLYPzSBWaRcEVAgOhn4Kpt\nGR8fsyh54KCBVlKFvk7wkASbciEp2yb+4WYrnSkADL77Flzz+dvwjwrv9tjCxg03cxblYtym/8P1\nWXsROsqYZJ/84B+sJCXCCWXi75dg3pmvETw0ERPe+xtvv4CYKElbXnd2N6750tglcfLHrwCwr+P2\nMcmwwsaPsHkco1BIJr6aJXUzDHxDlWAYBtdn7YVfVLjVbztOwqkc8+pTfIUPAJh36ivELroWADDn\n6A6M/PsjCBzUJfXjVmpSn7iX386tWigC/RG75DcAgOF/uR+zDn2MiR++wD93oEneyPj6OByc4SKU\n3MpT423SDjSHo7914diE2JIdpf3lfiQ/uAKDBInvHPG3LcSI5x5C8gPLAQApj6wUTdCWg/AJo/iA\nDYezSdN8c6AkD5f/U1h/NzjddtDgQQhMiDNzzLnvUkBMFBSOBggYhs+Ps0VAnHmFFN/QEISNHwll\nahJ/TQhKjINyWBJ/zQxKGoSQkanwizC/Vuqjw6y2hYxIQeioVISNHW4sVmBaibBEuErJFQ2xJDh5\nMIIGG30b/5gohI0faXVf9Y+OQFBSAnxDQxA+YZQxUu+hFeXeiNdG5NsLjO2U9R2dqPzqANquFNt9\nTkBMFAYJan0HD02wGalzB1wUjsMVXbbUbN1TSEXPFL6+iJw+gU+G5SqsSElWpBzymBtm89VVhFIJ\nRYA/fJXBNicAtqq6SB3vZ1ExRmrp3ZK4m65H7BLPRq8II8FDBmFhyc/gEg1GPPcQbij6ya1LvrMO\nfoxxpui3nPgqg+A/IBL+0RGYdehjpG1Yi5j5MzHzwL8kn2NLqiSG/4BI/nvPSZSGrL7T7Jhx75jX\n/F+Q9wOuz9lvpYt3hvHvPsf/PfyvD/B/+wQHYn7Ofox5fT0AYPTLT2LwPbe69BohaUPNNLNzf/0M\nk00SKeH1N3zSaCyqPoaFxT9j0tZ/8NvDxg43K17ASSSdWY0Z+sByPll6xr6PkGBj9XTe6f8iZGQK\nkv50OxL/eLPN845751mz6x9XJcc/OgIx82dKJuimmlYaxJzNCe/9DckPrDCvzjZkEO/Mz/x+KyZt\ne8lMhiVFsEUE1lbeFCfl5BxAhUR1NE4GK3RghdFb7nvsrtwPe/hFhiNsrMhk1sYkzz8qHAEDIqFM\nTULAQOsyhI7gHx2JgNhoq4pj/lERCBwUC5+gICiHDbV6no8yGMHJiXYnoQpfH/gE+MM3TAlFYABv\nd9+QYKsVS5/AAH5S4hcRZqzW5eMDv4hwM5mO8LvK+PpCOWyI2XlCR6fBLyyED7QIm15ZwigY2QoE\n9HU8lkEgpZHnIvGNJ84jZPhQ6NtV0Ks6oLPojOkIIWlDe2SZjqv9PvDGuW5JTnUHruq4pRxl4ew4\nwEKX62uj2VH8rTdAU9+MgNho1Ow/An2bymo5UYyQ4clou1Jk/jpuupDbyhUQImzyJMRV2xL2sWdb\nXhLxwIqeGI7sCBvAhU8YiesufmtV9767BCXEQpk2BDc8+yR+PZIJVWEZZuzbgojJo5Fw12K+jj+j\nUHRrZWPG3g8RMcWYGzD8mQcx8AbrCi+MQoHfXPjGrGGaLSKnT4BBq7N5jHD1wtHftiWjX37Sbs6T\nEEah4BPJIyaPwbUArNX6pjElxmHOz9sBGFf9yrd/w++75stNOHXHw/zj+NtuMHvuwIWzMe/M13yl\nlOChCRiydhlKNnflQVx74gv+b+7aPGPfR6j78bjNsrec86ZMG4rwiaPQdCaL33d99j78OOa3iL/1\nBlR9fZDf7heqRNDgQegoqzTawUb0mBtzwMBo6FUd8I+J5lfcfUOUfFdWnxAlUGvd5ChgYDTUVbUI\njxuI9tZix6Pa3SQgJgqMjwIBAwegs7YOCn9/GDQaOLJWo/D3E41c24Lrbi1cDQ5OSYKhUwNtQxP/\nnba87wYOioW6sgY+QYFOyXz9I8PhH9n1O7fsUioFNwHkzsHhExQIfYcaDMPAVxmM0NFpUPj5wqDR\ndqtYA+E4Xmdly7KT+k4Nt4PfFjomDa3ZeWbH+YV7PqrtHxPVo2WweoqEu34rWYUDMEZlYuYbnQF9\nhxp6dSdvB98QJXxDjA541IxJ0LW2O+QsRE6fYOXIw6FLqWMExMbARxkEVWGp285JyE9QYhxmHdzm\n6WHIRsDAaFkqWcw9uhOAcdUh+6lXEDG5ayVs5g/bUPGZ/Wowtrgu8zve0QgYGI2BN0pPyBx14gHg\nmq82OXzshM0v8KsPzpK06jaXnmeL5P/5A4r++R/J/UPX/h7Rc8xXB8WqcwhXYxiGwajn/xdBg2KR\n+5wxx0LoAEZOn4AF+QfhG6JEhI3kflEE9y5uVTb+1hugV3eidn9Xsz2/yDAwCgasgYVfRCgM2hh0\nVl+VPK1feKhA/2x0xpWpSTBoddCrOvjjgocmgNWZS2jDJ4wCy7IIHiohx2EY3jfwiwiHtqnZ4bcL\nGOWTIWlDoCqp5JskcnZQBBondiEjktFeWOZwXxLfUCV8Wx0POomt4vuFKoFQJQJsBAb9IsKgrqyx\nGUgDgPz8fKxevRrFxcV45pln8Oc//9nhsTlCQFwMVEVl/GPOeXdU+kp0H+/TyAtgDQZ0lBhn/kKN\nvNgXJGCQ/Hp4Wwy6fZFXOfHu0MhziGmTORJX3ASFvx8CBw1E4KCBUKYmIWxMmqheLjA+xkqKZIv4\nW2/gq0fELrkOMQusK3i4ysCFsxE9ezKiZk+RrIQihTttS5jjiG25hDPCOdLT0+GrDMIEi4Th8PEj\nMPofj3fr3MJo4XUXv3XbSqjC19fh0nPxt8x3ueRsd0lPT8cNhebVbOJuWWC36o+rmCWMW/aVCHFu\n5bIr0i1w5IMCsKj6GAYunIPJ2142O55hGGMFIVNlMDGbOyIvUfj5wi88lE8a9VUGi9YJb2trs9rO\n6e59gsxzjpwhOHkwQkemgPHxgTJlMELSTPlfFt83RqFAyLAh8AvvWt0oKytDdHQ0DAbrClQ+QYFO\nFU3wDQ1xKmmUq4aj8PN1qLb6O++8g7lz56KkpETUiW9t7d4KoFjU/ddff8XYsQ6U2XWSI0eOYPr0\n6Rg8eDCWLl2K8vJy+0/qB3ifRl4QedfUN6G9oMS4WTBTF6v0oUz2rLbKVmm6vow7ymVJ4RuihF9U\nOCKnTYB/VLjTNyhHUKYMNpM3EARBuIJPcCCvaV9UfQzh40eI5hssKDiEqNmTMXCxMXmXW71IW+9a\npLQ7AaQbK9O7cgRsnCZtw1pEzZokuo9rZGdMaoxH2PiRfGUmR/AJDnK6y6n/gEj4hoY47bxzCd0h\nI1Otcqd8ggONybkOSHhYlgXDMDYbV+odKNABGLXhzshG/SJCnbrvlpWVYeRI91TtE8MnKNCs0g3Q\nZR9XEbNdQ0MD7rnnHjzzzDMoKCjAhAkTcO+994o8u//h0Try4v2gxH8YquKumRcn8+CWusInj3G6\ntGJfp6/ouBmFwuXlcrnoK7b1Rsi28kG2lQ/OtgMXzrbbjMpXGYxpX72LqBnGPLHJn7zqUhO0ub9+\nhoWlR1wbsAmzaLoNxyv1f++RTLhW+PkZI8NhIfCPinDagbNXoU1Mwx0wIBLKlMHwVQYhZHgy5q64\nA/98/z0sXn0PJi79LR554TlotFr++MMZv+LmR+7HuAXzcNfjD+NyQT4AYMeOHVixoivfZurUqbxz\nqAgIwOxltyE7O9vq9X/3O2NvhOTkZCQlJeH06dPYuXMnFi9ejL/+9a8YNmwYXnnlFRQXF2Pp0qUY\nNmwYhg8fjrVr16KlpYU/T0VFBVauXInhw4cjLS0NTz/9NL9v+/btmDFjBlJTU3HnnXfajD7v378f\ns2bNQkpKCm655Rbk5Rmlx0uXLkV6ejrWrVuHpKQkFBYWWj1Xr9fjoYcewpgxY5CamoqVK1fy+w4c\nOIB58+YhOTkZixcvRk5ODr9v4sSJePfddzF37lwkJydj9erV0Gg0UKlUWLZsGaqrq5GUlISkpCTU\n1NSAZVm89dZbmDJlCtLS0rB69Wo0NxulUNwKx/bt2zF+/HgsXWqdq/ftt99i1KhRuOmmm+Dv74/1\n69cjOzsb+fn5knbpL3h1RF6sKVTM/FlgGAYxC2bzzrtloxeCIAiC6MsoU5PcqkOWahrkTQTExiAw\nzjwK7xMUCIWfH7755lt8+tqbOH/+PC4V5OOrA/sBANl5V7D+tZfw1ltvobCoCPfe92esWLECWq0W\ns2fPxvHjxwEA1dXV0Gq1OHXqFACgvLYaHVoNxoyxzjXYu9eYU1JSUoLS0lJMnWosW3vmzBmkpKTg\nypUreOKJJ8CyLB577DHk5ubi+PHjqKysxCuvGMvBGgwGLF++HEOGDMHFixeRnZ2NW281VnLat28f\n3n77bWzfvh15eXmYOXMm1qyx7rkAGDXw9913H15++WXk5eVh/vz5WL58OXQ6HXbv3o2ZM2fi1Vdf\nRWlpKVJSrH2ltWvXQq1WIyMjA1euXMEDDxirTV28eBGPPPKI0W6FhVi1ahVvN449e/bgq6++wvnz\n55GdnY0dO3YgODgYu3btQlxcHEpLS1FaWorY2Fhs3rwZ+/fvx969e5GTk4OIiAg8+eSTZmPJyMjA\niRMn8OWX1uVRc3NzzeQ6wcHBSE5ORm5urqhd+hMeS3Y9f/48xk+0XqrrrOpKmmm7bJns2KWDC4yP\nQUDcAISMSJZV3tFbSU9PpwicTJBt5YNsKx9kW/noC7b1jwrHrEMfozWnwKXnL9xyzi3j+GGNuV/Q\n2trKR+UD46S192vX3oe4xESEhIdj/szZuFxVjrBxI/DZm6/jDzffhkmTjOddtmwZNm7ciNOnT2Pm\nzJkICQlBZmYm8vLycP311yMrKwv5+fk4efIkZs60nZdlKSGJj4/H6tWrAQABAQFITk5GcrJxRTkq\nKgoPPPAAXnvNKL86ffo0ampq8Pzzz0NhWhmZPn06AODjjz/Go48+imHDjLlmjz76KDZu3Ijy8nIk\nJpon/u7evRsLFy7EtdcapVoPP/wwNm/ejJMnT2LWrFk2x19TU4Mff/wRBQUFCAszFgzh3vOnn36K\nVatWSdoNAO6//34MHGj0yRYtWoSsrCyRVwH/nl577TXExRmTs5966ilMmDABmzcbuxgzDIOnn34a\nQUHi5a/b29sRE2Mu2QoNDUVbW5vN99gf8GjVGqOMxnwZrqOihv9bKKcRg2EYq5JMBEEQBEE4T9jY\n4S7nDFk64D3NwNhYhKQZ65iHDohGY00VGIUCVU0N+O+hA/h0z1cAjH6HTqdDVVUVAGDWrFk4evQo\nioqKMGfOHERERCA9PR2nTp2y6whbkpBg3rTq6tWr2LBhAzIyMtDe3g6DwYCICKOSoLKyEoMHD+ad\neCFlZWXYsGEDnn32WX7MDMOgqqrKypGvrq7G4MFdOYIMwyAhIYF/f7aoqKhAREQE78RbjuHzzz/H\nRx99xI9BaDcAZo51UFAQampqrM7DUV5ejrvvvpt/vyzLws/PD7W1tfwxgwZJrwoplUqrxNyWlhaE\nhEiXWO0veF0debEM+MDEOKjLq+UeUp+it0eHvBmyrXyQbeWDbCsfZFv5cLTOuRAfZRDvSwxOScas\nedfiscceEz121qxZOHDgAEpLS/H4448jLCwMX3zxBU6fPo377rtP9DmSTY0str/wwgtQKBTIyMhA\nWFgY9u3bh/XrjU3REhISUF5eDoPBYOXMJyYm4sknn8Ttt99u973GxcXh0qVLZtsqKipsOsUcCQkJ\naGpqQktLi5Uzn5CQgMcff1zSbrYQs09CQgI2bdqEadOmWe0rKyuTfB7HyJEj8dlnn/GP29vbUVxc\nLGsib2/B6zTyrEg5J6Ar25wgCIIgCMIRVq5ciW3btuHMmTMAjA7gwYMH0d5ubEg1e/ZsHD16FGq1\nGvHx8ZgxYwYOHz6MhoYGjB8/XvSc0dHRUCgUKCqylv8KaWtrg1KpREhICCorK7FpU1dfhClTpiA2\nNhbPP/88VCoVOjs7ceLECQDAqlWrsHHjRl7/3dLSgj179oi+xtKlS3Hw4EEcPXoUOp0OmzZtQmBg\nIK655hq7tomNjcWCBQvw1FNPobm5GTqdDhkZGQ7ZzRYxMTFobGw0S+xdtWoVXnzxRT5pt66uDvv3\n7+f326oABBgTjHNzc/Hdd9+hs7MTr776KsaOHcvLj/oz3ldHXuTD1LeqEDYmDX4Rnm/61FugWufy\nQbaVD7KtfJBt5YNsKx+O1Dm3FcmdOHEi3nrrLaxfvx4pKSmYNm0adu7cye9PTU1FaGgor/sODQ1F\ncnIyZsyYIXneoKAgPP7441i8eDFSUlJ4Z9eSdevW4cKFCxg6dChWrFiBm266id+nUCiwY8cOFBYW\nYvz48Rg3bhx2794NAFiyZAkeffRRrFmzBkOHDsWcOXNw+PBh0dcYNmwYPvjgA6xbtw5paWk4ePAg\nduzYAV9TPXx7VYRee+01+Pr6Yvr06RgxYgQ++OADh+xm67xpaWm47bbbMHnyZKSkpKCmpgb3338/\nFi9ejNtvvx1DhgzBokWLcPbsWYfOBxgnT5988gleeOEFpKam4vz589i6davN5/QXGHuzILl44403\n2LvvWQU/i3bGVXsOQ9di/sP1CQnGoFsX9uTwej19IfnKWyHbygfZVj7ItvLRH2xbV1eHAQMc78rr\nLoTJroT7Ift2H6nfxtmzZzF//nzZO4V6XR15sCLSGs/MNXo1ff2m4knItvJBtpUPsq18kG3lg5xM\neSH79n68SiPP6vXQtbYjbNwIBMYPhG+oKRvZQ6sGBEEQBEEQBOGteJVGXtPQDCgUCBs3HDELjI9X\n6gAAIABJREFUZiF+6QIAgL5D3dPD6/WQZlM+yLbyQbaVD7KtfJBt5cMRjTzhOmTf3o9H68hzqEoq\n0HLxMgw6HQLjB1o3eKKIPEEQBEEQBEGY4dk68ib/XNvUCv+YKISMSIFPUIDZcT7BQQga4v2to70N\n0mzKB9lWPsi28kG2lQ+yrXyQhlteyL69H6+IyAOAT2AA/COty0vGLvkNFH5eM0yCIAiCIAiC8Aq8\nQyPPsoBEDVGfwABrqQ1hF9JsygfZVj7ItvJBtpUPsq18kIZbXsi+vR/vqVpjpxkAQRAEQRAEQRBd\neLSOPA8ls7od0mzKB9lWPsi28kG2lQ+yrXyQhrt75OfnY968eRgyZAg++ugjq/1k396PRyPynPtu\nQ1lDEARBEAThNZSVlSE6OhoGg0gDSy/jnXfewdy5c1FSUoI///nPPfKav/76K8aOHevWc2q1Wqxa\ntQoTJ05EdHQ0jh075tbz92bsOvIMw2xlGKaGYZiLgm0TGIbJYBjmHMMwJxmGmSrYt4FhmDyGYS4x\nDLNQ6rxWdeTJk3crpNmUD7KtfJBt5YNsKx9kW/nwRg03y7JgGAasDTWBXq/vwRFJU1ZWhpEjR0ru\nl8O+nH1cRcp2M2fOxObNmxEXF+fyufsijkTktwG40WLbqwCeY1l2EoDnALwGAAzDjAZwF4BRABYD\neI9x6NMkaQ1BEARBEM4zceJEvPvuu5g7dy6Sk5OxZs0aaDQafv+BAwcwb948JCcnY/HixcjJyQEA\n7NixAytWrOCPmzp1Ku69917+8bhx45CdnW31er/73e8AAMnJyUhKSsLp06exc+dOLF68GH/9618x\nbNgwvPLKKyguLsbSpUsxbNgwDB8+HGvXrkVLSwt/noqKCqxcuRLDhw9HWloann76aX7f9u3bMWPG\nDKSmpuLOO+9EeXm55Pvfv38/Zs2ahZSUFNxyyy3Iy8sDACxduhTp6elYt24dkpKSUFhYaPXc5uZm\nPPTQQxgzZgxSU1OxcuVKu3YTs/nq1auh0WigUqmwbNkyVFdXIykpCUlJSaipqQHLsnjrrbcwZcoU\npKWlYfXq1WhubgbQtcKxfft2jB8/HkuXLrUap5+fH9auXYvp06d3a5LQF7HryLMsmw6g0WKzAUC4\n6e8IABWmv28G8BnLsjqWZYsB5AGYJnbeiRMnmrvv9MG4FdJsygfZVj7ItvJBtpUPsq18OKrh3rNn\nD7766iucP38eWVlZ2LFjBwDg4sWLeOSRR/DWW2+hsLAQq1atwooVK6DVajF79mwcP34cAFBdXQ2t\nVotTp04BAIqLi6FSqTBmzBir19q7dy8AoKSkBKWlpZg61ShKOHPmDFJSUnDlyhU88cQTYFkWjz32\nGHJzc3H8+HFUVlbilVdeAQAYDAYsX74cQ4YMwcWLF5GdnY1bb70VALBv3z68/fbb2L59O/Ly8jBz\n5kysWbNG9H3n5+fjvvvuw8svv4y8vDzMnz8fy5cvh06nw+7duzFz5ky8+uqrKC0tRUpKitXzn3ji\nCajVamRkZODKlSt44IEH7NpNzObZ2dnYsWMHgoODsWvXLsTFxaG0tBSlpaWIjY3F5s2bsX//fuzd\nuxc5OTmIiIjAk08+aTaWjIwMnDhxAl9++aVDnzlhxNUC7Y8BOMAwzBsAGACzTNsTAGQIjqswbbMN\nJbsSBEEQRK/l+7hZ9g9ygEXVrmmf77//fgwcONB4jkWLkJWVBQD49NNPsWrVKkyaNAkAsGzZMmzc\nuBGnT5/GzJkzERISgszMTOTl5eH6669HVlYW8vPzcfLkScycOdPma1pKSOLj47F69WoAQEBAAJKT\nk5GcnAwAiIqKwgMPPIDXXnsNAHD69GnU1NTg+eefh0JhjKlOnz4dAPDxxx/j0UcfxbBhwwAAjz76\nKDZu3Ijy8nIkJiaajWH37t1YuHAhrr32WgDAww8/jM2bN+PkyZOYNcv2Z1JTU4Mff/wRBQUFCAsz\n9vHh3rM9u9myuRgff/wxXnvtNV4W89RTT2HChAnYvHkzAIBhGDz99NMICgqyOWbCGlcd+QcA/C/L\nsrsZhrkDwL8A3ODMCd5++20EBgcjecgQqKtqER4RiWtuuI6PbHCaQ3rs2uP3338f48aN85rx9KXH\nQj2sN4ynLz3mtnnLePrS48zMTD7a5g3j6UuP+8P1NjIyEgMGDADQpavmouWtra2YnXfA7LHlflcf\nCzXcUscbDAYolUr+OB8fHzQ1NQEwyjY+++wzfPjhh7yuXavVoqioCDNnzsSsWbNw6NAhFBcX49pr\nr0VERAQOHz6Ms2fP8o6w5eu1tbVBSGtrK9RqNRISEsyOV6vV2LBhA44dOwaVSgWDwYCIiAi0trai\nsLAQgwcPhkKhsDp/SUkJNmzYgGeffZZ/fwBQVVWFxMREs+Orq6sRGxuL1tZWhIaGgmEYxMXFobCw\nkB+/Wq3m9wvHV1FRgYiICDAMY7W/qKgIn3/+OT766COwLAuWZaHX61FVVYXW1lYYDAbExMTwx/v4\n+KC9vR0AoFKpzPIHWltbUVZWhrvvvhsKhYI/n5+fH2pra3l7Dho0yKHvB8uyUKlUZue3dbzcj2tq\napCbmwvA+FspLS0FYJRqzZ8/H3LD2ErW4A9imCEAvmVZdrzpcRPLshGC/U0sy0YwDPM0AJZl2VdM\n27+HUUt/wvKcb7zxBrti5SoE+irQeDoTvsFBCB09zF3vq9+Tnp5Oy70yQbaVD7KtfJBt5aM/2Lau\nro535HsSoYMpxcSJE/HOO+/wUWlOn/7+++/j8ccfx+DBg/HYY4+JPvfTTz/FgQMHUFpail27diEr\nKwtffPEFTp8+jW3btmHChAlWzykvL8fEiRNRW1vLR9N37tyJ7du387IbAHjkkUegVqvx+uuvIyws\nDPv27cP69euRmZmJU6dO4e6770ZOTg5/Do4777wTv//973H77bfbtc/rr7+OS5cuYevWrfy2MWPG\nYMuWLZg5cyZuvvlm3HXXXfjjH/9o9dyamhqMHTvWLCLPYc9utmx+7NgxrF27FpmZmfzx06dPx6ZN\nmzBtmrXauqysDJMmTTKzpy3Gjh2LDz/80O6KQ08h9ds4e/Ys5s+fL7tu3NHyk4zpH0cFwzDzAIBh\nmPkwauEB4BsAv2cYxp9hmGQAwwCcFDuheR15kEbezfT1m4onIdvKB9lWPsi28kG2lY/u1jlfuXIl\ntm3bhjNnzgAA2tvbcfDgQT56PHv2bBw9ehRqtRrx8fGYMWMGDh8+jIaGBowfP170nNHR0VAoFCgq\nKrL52m1tbVAqlQgJCUFlZSU2bdrE75syZQpiY2Px/PPPQ6VSobOzEydOGGOeq1atwsaNG/kob0tL\nC/bs2SP6GkuXLsXBgwdx9OhR6HQ6bNq0CYGBgbjmmmvs2iY2NhYLFizAU089hebmZuh0OmRkZDhk\nN1vExMSgsbHRLLF31apVePHFF/mk3bq6Ouzfv5/f70hQWaPRQK1WAwA6OzvR2dlp9zn9AUfKT+4A\ncAzAcIZhShmG+ROAPwN4g2GYcwBeBHAfALAsmwNgF4AcAPsAPMja+HQc+eAIgiAIgiCksFXFZOLE\niXjrrbewfv16pKSkYNq0adi5cye/PzU1FaGhobzuOzQ0FMnJyZgxY4bkeYOCgvD4449j8eLFSElJ\n4Z1dS9atW4cLFy5g6NChWLFiBW666SZ+n0KhwI4dO1BYWIjx48dj3Lhx2L17NwBgyZIlePTRR7Fm\nzRoMHToUc+bMweHDh0VfY9iwYfjggw+wbt06pKWl4eDBg9ixYwd8fX3t2gYAPvjgA/j6+mL69OkY\nMWIEPvjgA4fsZuu8aWlpuO222zB58mSkpKSgpqYG999/PxYvXozbb78dQ4YMwaJFi3D27FmHzscx\nbdo0JCYmorq6GnfeeScSEhJsVvPpLzgkrZGDN954g11+9z0I8vNB48mL8A1VInRUqkfG0hfpD0u9\nnoJsKx9kW/kg28pHf7CtN0trCNch+3YfT0trXE12dQvtl4ug0Wpg6NQAYSGeHApBEARBEARB9Co8\nFpE/fPgwG5lTjoCgADA+CoSMTEXoSOsapwRBEARBeBZPReQJwtvxdETe0WRXWWBZwDdUCVZvoE5d\nBEEQBEEQBOEEHnPkz58/D4AFo1CA1ek9NYw+i7AuN+FeyLbyQbaVD7KtfJBt5UNYR55wP2Tf3o9H\nI/IAwPj7waDRAAqKyBMEQRCEN0JV5ghCHE//NjyqkY/IKkXSbTdA39EJv8gwKHw9mntLEARBEIQI\nDQ0NCA4ORmBgoKeHQhBeAcuyaG5uBgBERERY7e8XVWv0BhaKwAD4hijtH0wQBEEQhEeIjIxEY2Mj\nWltbKaeN6PdwQXClUong4GCPjsVjjvz58+cxmw2nC4JM9Ie6xp6CbCsfZFv5INvKR3+wLcMwiIqK\n6vHX7Q+29SRk396PxzXyBEEQBEEQBEE4j0c18sHnijDiT7eCUdB8giAIgiAIgugb9Is68gRBEARB\nEARBuIZH68izAEAaeVmgusbyQbaVD7KtfJBt5YNsKx9kW3kh+/Z+PBuRp7K0BEEQBEEQBOESHtXI\nB50twqg1t3vk9QmCIAiCIAhCDkgjTxAEQRAEQRCEJJ7XyBOyQLo3+SDbygfZVj7ItvJBtpUPsq28\nkH17PxSRJwiCIAiCIIheiEc18opTBUi551aEBvhQh1eCIAiCIAiiT9AvNPKVLZ34taQZjR06Tw6D\nIAiCIAiCIHodHtXIA0B4oA8MHloV6MuQ7k0+yLbyQbaVD7KtfJBt5YNsKy9k396PxzXyDEhSQxAE\nQRAEQRDO4lGNfPXhHEQtuwmp0UGIUfp7ZBwEQRAEQRAE4U76hUYeAMXjCYIgCIIgCMIFPKqRH0BR\neNkg3Zt8kG3lg2wrH2Rb+SDbygfZVl7Ivr0fj0bk40P9QVUnCYIgCIIgCMJ5PKqRT0EgrsQkYGhk\nEAaGUHSeIAiCIAiC6P30C428b5jSky9PEARBEARBEL0Wj9eRJ+SBdG/yQbaVD7KtfJBt5YNsKx9k\nW3kh+/Z+7DryDMNsZRimhmGYixbbH2YY5hLDMJkMw7ws2L6BYZg8076Fds7t+sgJgiAIgiAIoh9j\nVyPPMMwcAG0APmVZdrxp228A/AXAb1mW1TEMM4Bl2TqGYUYB2AHgGgCJAA4BSGNFXuTw4cPsiPAB\nyPGPwJDIQNLIEwRBEARBEH0Cr9HIsyybDqDRYvMDAF5mWVZnOqbOtP0WAJ+xLKtjWbYYQB6AafZf\nw5khEwRBEARBEAThqkZ+OIBrGYY5zjDMTwzDTDFtTwBQJjiuwrTNCtLIywvp3uSDbCsfZFv5INvK\nB9lWPsi28kL27f34duN5kSzLzmAY5hoAXwBIceYER44cwfGfjiBwcArCAn2REBOFcePGYc6cOQC6\nvlz02LXHmZmZXjUeekyPHXnM4S3j6UuPMzMzvWo8fekxXW/pMT2mx9zfpaWlAICpU6di/vz5kBuH\n6sgzDDMEwLcCjfw+AK+wLHvE9DgPwAwAfwYAlmVfNm3/HsBzLMuesDwnp5G/FBCBxPAAxIUGuOs9\nEQRBEARBEITH8BqNvAnG9I9jN4DrAYBhmOEA/FmWrQfwDYBlDMP4MwyTDGAYgJNuHC9BEARBEARB\nEHCs/OQOAMcADGcYppRhmD8B+BeAFIZhMmGsUrMSAFiWzQGwC0AOgH0AHhSrWAOQRl5uLKUKhPsg\n28oH2VY+yLbyQbaVD7KtvJB9ez++9g5gWXaFxK67JY5/CcBLzgyCitYQBEEQBEEQhHM4pJGXA6FG\nPiE8APGkkScIgiAIgiD6AN6mkZcHhjEK7ykkTxAEQRAEQRBO4TFHnjTy8kK6N/kg28oH2VY+yLby\nQbaVD7KtvJB9ez+ejcgTBEEQBEEQBOESntXIR8TgckA44kIDMCiMNPIEQRAEQRBE76d/aOQJgiAI\ngiAIgnAJz2rkGdknKv0W0r3JB9lWPsi28kG2lQ+yrXyQbeWF7Nv78XhEngEDD6l7CIIgCIIgCKLX\n4lmNfORAXPEPx8AQfySEk0aeIAiCIAiC6P30H408qWsIgiAIgiAIwmm8pI48aWvcDene5INsKx9k\nW/kg28oH2VY+yLbyQvbt/Xg+Ik8QBEEQBEEQhNN4XiMfEI4YpR8SwwM9Mg6CIAiCIAiCcCf9RiPP\nAOjUGWCg0jUEQRAEQRAE4TAe18iHBvigoL4DFc2dnhpKn4R0b/JBtpUPsq18kG3lg2wrH2RbeSH7\n9n58PT2A1OhgqLUG6AwUkScIgiAIgiAIR/GoRn5k1EAED03Epdp2BPgokBId5JGxEARBEARBEIS7\n6DcaecCokyeNPEEQBEEQBEE4jsc18gDgq2BQ3KjGr8VNyK9Tobq1Ey1qnaeG1icg3Zt8kG3lg2wr\nH2Rb+SDbygfZVl7Ivr0fr4jID40KwpTEUMSHBqCxQ4czFa24fFXF72/X6HG+shVX2zUeHCVBEARB\nEARBeA9eoZHnqG3ToLixA1fbtRio9Mc1g8MAAHtz6wAA0cF+mJEUzh9vYFkoGNnlRwRBEARBEATh\nMP1KI+8o9Sqt2eP9l+tRUK+SOJogCIIgCIIg+i5eoZHvDq2derecp69Bujf5INvKB9lWPsi28kG2\nlQ+yrbyQfXs/vSoiL0ZFCzWSIgiCIAiCIPofHnPkJ06cCB9lsNk2kru7jzlz5nh6CH0Wsq1tzpS3\noLxZ7dJzybbyQbaVD7KtfJBt5YXs2/vxaEQ+ICbKapu93FudgUWDhVa+v9Ks1qFdQ9IiwrvY8H0B\nXj9S6ulhEARBEESfp9dp5A9cqUdGabObR9M7SS9uws+FjeL7SPcmG2Rb+7i6uka2lQ+yrXyQbeWD\nbCsvZN/ej1dp5BkAXEC+uzIbT5XVJAjC+FsmCIIgCEJe7DryDMNsZRimhmGYiyL7nmAYxsAwTJRg\n2waGYfIYhrnEMMxCqfNOnDjR9VHboK3TKDfZd7me31bfrsXe3Dq0dvafbrGke5MPsq0DuOjJk23l\ng2wrH2Rb+SDbygvZt/fjSER+G4AbLTcyDJMI4AYAJYJtowDcBWAUgMUA3mMYx2PrjMjdX60z2H2e\nzsCirEmNypZOHClqQlZ1m9n+JrVRU/9LUZOjQyEIwgXaTJNlsd8yQRAEQRDuxa4jz7JsOgAxIfab\nAJ6y2HYLgM9YltWxLFsMIA/ANLHzSmnkLRUxF6taRY+7WNXlrOfWtuNidRvOVRqP1Rn6vqym0U7C\nL+ne5INsK82zPxQCADKr23C6vEX0mIVbzqGooUN0H9lWPsi28kG2lQ+yrbyQfXs/LmnkGYa5GUAZ\ny7KZFrsSAJQJHleYtrmMVMOnMkF5u5Imx0vd1bRqujMcr+GYRcJvs1qHX4rEE18JwpJvcq5i4y/S\nlWX259Zh7VeXnD5vdk07//dfvi/AcYnE9Erq/0AQBEEQ3cbX2ScwDBME4C8wympcJj8/Hw8++CCS\nkpIAAOHh4RiSNgqRaZMAABdOZUBdrAQbOxoAcOnsCQDAqMnT7T5uUutw6ewJhNeFY86cOWDZrv3A\ndCwZOYCfhXL6MG97fOinX+Dvy+DauXOt9htY1uz9Vrd24udfjqK8uRPXJi/hbZyenu4176cvPZ4z\nZ45XjcfW44TRU3ClToW2wgt485dSpP9jFb67VId//Ps7AMDj1/5J9Pn//f5HZNa0A7ePcur1ACUA\noKXAuOJ2rDgaE+JDcDLjGHwUDH985unjYMtDPG4f7vH973yBGUnhWLV0oVeMR67HHN4ynr7ymNvm\nLePpS4970/W2Nz4m+7r3+pqeno7SUmOQbOrUqZg/fz7khnGkugvDMEMAfMuy7HiGYcYCOARABWNK\nWyKMkfdpAO4FAJZlXzY973sAz7Ese8LynIcPH2YnT55stq1epcWVqyo0dGgRG+KPAUo/swifs4yL\nC0FSRCBOl7egpq0rEr9k5AC0qHXIKG3GgmFRYBhA4WXdqPbm1mH0QCWSo4Ks9jWqtFYR+WHRQciv\n70ByZBDiQ/0RGezXU0MlepjT5S2YnBDq0Hd24ZZzAIAN1w3FSz8V44c1k/htAPDM/KHQ6FgsSDPv\n6fDcwUJklDTjhzWTHBrTTwUN+OhkJerazSVfNw6PwsG8BlyfGol1vxnKj2lETDA23TLCoXPLjUZn\nwO8+voBFw6Px+LVJnh4OQRAE0Qc4e/Ys5s+fL7tz6ai0hjH9A8uyWSzLxrEsm8KybDKAcgCTWJat\nBfANgGUMw/gzDJMMYBiAk2InFNPIW77b+vbuNX7KNCW9Cp14jpZOHXQGFr8UNUpqeT2NpNZf5GvB\nHVvU2IHSJrVVBI5wnV8KG9Gs1vGPOdvuza1DY0fPNyeradNAp3cuD+Sln4oBAE/tzTPb/uLhYrx6\npMT6CU6mmZyvbLNy4jkMLFDWbC6luXxVJXqsJ763elMwo8WUqPvwnsv4+6EiGPpYCVu6JsgH2VY+\nyLbyQvbt/ThSfnIHgGMAhjMMU8owzJ8sDmHR5eTnANgFIAfAPgAPsk4WdGdNHoRaZ0C1iAPuLrhR\nqbQGtKh7f3fU4sauPIHylk6UNDqeN0CY06HV41Jt10pQq0Yv2U34WEnvak52oarN5v6FW85Bozfw\nv0MpShvVeGTPZbuvx81Fvdkp5lY2LlS1YeGWc7h8VYX04iYs2npeMtmeIAiCILwBR6rWrGBZdhDL\nsgEsyyaxLLvNYn8Ky7INgscvsSw7jGXZUSzL/iB1Xnt15IURUKILlmXNKvZIEZEmT53+/kBNmwaF\nDR2obdPgbIX1as2cOXOwN7fOAyNzDFfGJnTKNTqDVfWoBpUWF6vaoDd55her25ArEVUXwh3vaCEp\noea4p+AWuNo11hN6sdW83oonbNtfINvKB9lWXsi+vR9fTw9ACAPz8pN+CgbabpaSzK62dnrbNXpn\nlQNeQ2Z1G9pEHA7CfZQ1GWUgpwSSK72BRXVrJ/wUCgT5eVVDZDNc7WgsdMrFfnK/35HF//3DmknQ\n6s37O0i97I8FxkpKBfXi5Sa9AVsWa5OomkUQBEEQ3oDHPBKpOvLuRiyiJiaTaO3U4Ycr9VbbvYUW\ntQ77L9dbaY2luHT2BFiWNcokvFjW4I20iHQA1hlYnKloxbnKVmz5+oDZvrw6FVr6wArS+n35AIDN\nJypQ1Gjb8eb08PtNHZTtSXEcxRN6TVsT4/ePV/TgSOSFtLDyQbaVD7KtvJB9ez/eFVq0SOKUq5DM\nRYsovdbAokWt63b0X05yatud1hnvu1yPg3kNDjv/hLSkq9xkw069ARqLRNMrdSqzHAU5KG9WOzRZ\nuFJnjKxfrGrF1XbnZCFcQ7WDeQ2obTM66h1avVmVG44vMmsBAG8ela5F31uQak5FEARBEN6Oxxx5\nKY280EVyR5v3Dp1BfIfghQysu+KJ8lFvp5OrJVx9fQBQayVsQFiRI1HutFMgJRHatqe4UNWG3Ktd\nYyuUcD7zTRKWsuZOnCzrfjWmWz65aLXtUF6D1bYDV6y3uYIn9Jr2Jsi3fWptg94IaWHlg2wrH2Rb\neSH79n68KyLvIL9Jiez2OaRc99ImNbR6AwrquzTDap2h26Uwu4OlHpnoPegNRnmTO7jaruUTMgtM\njnypYCXgXIV5hRW5JFWW5Spza13v9dBTaHQG0ci7SqPHMwcKbT6XclIIgiAIb8WrNPLGGpZdjxUS\nAXmlv0+3Xz9LIvJa1dKJunatWfJfTk0bjpd5rszg8VLnI6tdnWwhWneecB0z29rhTEWLaATbVTIt\nZGGZNV2VZCpbPSOheuSbKw4f2yaSfyBELr3ml5m1WPvfXKvtaqkVuz4IaWHlg2wrH2RbeSH79n68\nLiIv2QRJZjpM8hOGMdYNB3qu9rXOwIq+1pU6FTQ6Azq03YsI6r1Y++9tuCqyKmtWi0bAVRqDW2Vb\nzkisvO1Tb+zQ4vZ/Z3rktaWi6t2x0XsZ5ejswxMBjYdXIgmCIAj7eJVGnmEYM007I1e2qwhckiBg\nrEICCCpyyOwRHbhSb1YbXuh4uyrLkNJxX77ajoxe1sRIThpUWuzNreMlKwYHzC1lW61FEmxenQrt\n3ZyEdQdvK1a07D9ZVo7zD1fqsf1cNf/YnXpNlUbP/5YCfY2XOo3eYJ682w0b7c6+ivLm3tN4zVnb\nfniyAst3Ztk/kCCdsYyQbeWF7Nv78bqIvDAyLXTjwwM8U/K+qKHD7F6vkSkCJ2xG871FGczuVtMx\nsF067ZpWDRo6+m6UrbRJLVlGtKlDa6WT5qrUtGv0KGtSo9mO9MMWlp9TlcxSl1o7zYq8zI8X5eMz\nVfj0TJUs51766UUs/pdRwsfFBCyrC2kcmbnBOLkWTrC51Rd3JOR7K40dXb8FvYHFswcKPDgaguj9\n1LRqUGyntC9BOItXaeQtMQvIe+h+aXnjP5jfIItURS8RPnV1VUKo4y5uVOOgSaftKelST9Go0kpO\nfPLrO5AjkZjZrtEjWyJvwhIpjfzPhY04KcilaBU0E6roRgnQsibxqK+wYZVYtZ3e0D+Ay4PhJvBy\n6zUvWdjp+8uO9Y64Z1e2WZLvx6bJRw8uGnYbZ20rfGtqnQEn3FAFqbfiqdwOom/Zdv3+fNz3Va5o\nF2lP0Zfs21/xqoi8ZRKr2T3SQz5Jd/XpjuKnMH4UcmtuOemSRmfA3ty6Hnt/3kBTh/XNuLLF6GAX\n1HdITqac4aqEpvh8VavodinaOnX86oFl3wMxxBo4OVuytKdZuOUcX69+0Vb3NoiTmsR8bBH9F2sO\nJ0ZtmxaXzRLgvb9ST3fpRXMU2bnt35kooUgq0U24gMUFJ+8H3UVvYEW73BN9A6/SyPsqGIQInXkv\nuJOwsO4OK+ec4idTS3uOX4ubXDqPvVrnXNS6L+vlDSzLO+pAVy14oTyqySSt6XQiF8GebTNKmkUd\nSWeiMIUNasnVA0ukJmN1vTBR0V16za2nKvm/WZbl8wUsPwO9kz/m3dlX8fCey7hgymmM2KD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5dJa+u33Tna+QHaQMrJ5ByKnwoa8ODX5iXcxCKUjvCvO80TgG8ZM8Cl80ghpsW29XlcqjW+D42e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\n9bnUSiQjCaOj+Uj1m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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "figsize(12.5, 4)\n", + "center_trace = mcmc.trace(\"centers\", chain=1)[:]\n", + "prev_center_trace = mcmc.trace(\"centers\", chain=0)[:]\n", + "\n", + "x = np.arange(50000)\n", + "plt.plot(x, prev_center_trace[:, 0], label=\"previous trace of center 0\",\n", + " lw=lw, alpha=0.4, c=colors[1])\n", + "plt.plot(x, prev_center_trace[:, 1], label=\"previous trace of center 1\",\n", + " lw=lw, alpha=0.4, c=colors[0])\n", + "\n", + "x = np.arange(50000, 150000)\n", + "plt.plot(x, center_trace[:, 0], label=\"new trace of center 0\", lw=lw, c=\"#348ABD\")\n", + "plt.plot(x, center_trace[:, 1], label=\"new trace of center 1\", lw=lw, c=\"#A60628\")\n", + "\n", + "plt.title(\"Traces of unknown center parameters\")\n", + "leg = plt.legend(loc=\"upper right\")\n", + "leg.get_frame().set_alpha(0.8)\n", + "plt.xlabel(\"Steps\");\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `trace` method in the `MCMC` instance has a keyword argument `chain`, that indexes which call to `sample` you would like to be returned. (Often we need to call `sample` multiple times, and the ability to retrieve past samples is a useful procedure). The default for `chain` is -1, which will return the samples from the lastest call to `sample`.\n", + "\n", + "#### Cluster Investigation\n", + "\n", + "We have not forgotten our main challenge: identify the clusters. We have determined posterior distributions for our unknowns. We plot the posterior distributions of the center and standard deviation variables below:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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RyY34F/J/AX8WkRP9h4m7cNaucnHKqfPuxy0s/h7wYJ6PJdH8SHKts4CuIvJZfz+2znMN\nq3EL8K8TkbN83l6NswRVbjsTp1g5fiVpIj4PBwHX+jzsLiKHiEh82lOUEcAp/pr2FZGfEunsiUhH\nEfmTiJzk28NDcS/l70TSmIWboririOzo/X4DHCgiuTZnL5/GrSKyV9JrivBb4Afi9gXa35eJg3Fr\nC6uP1mBhg/2S/8hjUSNPnO7A/+GGCz8FngT2iYT/HHjLh32CW4BzdCR8b9zUmeU0NrfXGWcici7u\nZW8uzhTXIT4878KnfP7Akf4cK3HDow8AO0XCSy6WjcTdBddwL2HTSv9DY3Fmkmwh7+dxL7crfd68\nAOwVCe/nw1fgpgGNp/HivBcpvZips78/S2lsyrQ9rgJ5z+fvApwVjhN9eDdKLKKLnfdHNDZlelks\nPFEe09jM4irc15gLCmnCzUV9yZefqbjpEtPYtCD5cz5fF/n0pgI/jp3z9zjTqXFTpv19nq/CfSEb\nTWRBXNL7HCk3j+CsYiz1ZWinJMfab/P7YXVrvuutSN2Ke9l716fxIe7F+MBI+GG4L6ur/PXU4aY1\nD8Z1yJbipmgNoLFpyCbXgpuuuNEcqveLmjIdjXuBjVorata5vL/gpuQs9vf1UZwFoySmTB/2xyzC\nvVA3KYOUqPMi+hfhFrP2KlZGEl5rG+//MY1NmcYX77bBtVm5cvs23gpVJM4Gmi6kb7IwP0/+lCrH\nidtDNpmDXYMbpX4kEtaozfbXlGt/lgB/xFmNmunD2+M69O/5e7IQ9xFt90gap+E6C2tj+XoQzkrV\nx/66puE62tuX+2z6+D/GWa9a7cvIKUmPbelPvICCiMjduB7rIlU92PvdjHv41voMvEj94hgRuQpX\n2Nfj7K4P8/59cC8gWwHPqOoV3r8drsd4GK4wf0NV52AYhmEERb72IBL2I9zXrp3ULyC19sAwDCN7\nJJlWdA9N5z0NAw5S1d643vpVsHFO79k4m75fBG73w4fgerIXq2oPoIeI5NK8GFiiqt1xi25ubsH1\nGIZhGNUjX3uAiOyBG5V7P+J3INYeGIZhZI6SnQNVHYUbPo36jVDVnNWSMbj5xOCmZDysbjvv2biO\nw5Ei0hVn3z1npu1+3FQEcObActvXD6XEXD7DMAwjHfK1B55bcEPgUc7A2gPDMIzMUYkFyf1x86bB\nWSyZGwmb7/12p7EpyHlssm6y8Rh1NnyXSsRahGEYhhEuflHtXFWNW7ix9sAwDCODtGifAxH5GVCv\nqg9VSA+Uv1GHYRiGkQLeysnVuClFVTlFldI1DMMwCtDszoGIXAicjtsgIsd8nB3xHHt4v0L+0WMW\niEhr3Jbg0Q2PNtKvXz9ds2YNXbt2BaBjx47st99+9O7dG4CJEycC1NQ9Y8YMzjrrrNTOn8+d8zM9\nhd1xbWnrARg6dGjq5TnutvKdDT0TJ07k+eefB6Br16507NiRwYMH1+LFel/cLqxv+vUEewDjReRI\nXN1eF4m72bcHSd0h1j9ZKu9bqv4Q6+Mst2+bq/5KtAclrRUBeBut/1LVXt79BZxt2ePVbe+di9cT\nZwLqKNzw8HCgu6qqiIzB7T47Fnga+IOqPiciA4DPqOoAETkHOFNVz8mn44ILLtDbbrutnOurOjfe\neCNXXlnMpG7tCU1TpfR8/J9xjP3GFSXj1V18Fm226VA0zr1vjub6++6kVdtwNgkP7b6BaUpCaHoA\nBg4cyP3331+VzkG8PYiFzQL6qOonW2J7kJQQy0xSsqwdsq3ftKdHlvU3pz0o+WYkIkNwO+LtKCJz\ncHZarwbaAcO98YkxqjpAVSeLyKM4O+n1wADd1Pu4lMam657z/ncDD4jIdJxt2LwNAcDCheVuJll9\n5swJz8peaJoqqqehoWSUOXc+WjLO9E6rKqGmooR238A0JSE0PdUkX3ugqvdEoih+KtCW2B4kJctl\nJsvaIdv6TXt6ZF1/uZTsHKjqeXm878njl4t/A2731rj/G0CTL02quhZn7s4wDMMImALtQTR8n5jb\n2gPDMIyM0fraa69NW0NiFi9efO2hhx6atoxGdOrUibq6utIRa0homiqlZ/WcBSz4x7MVUAQ71e1O\nn2+djbSuhMGuyhDafQPTlITQ9AB88MEHHHPMMb9MW0c1CbE9SEqIZSYpWdYO2dZv2tMjy/qb0x4k\nWnMQCi+88IL26dMnbRlGSnz8n3GM/frlFUlr+8M/w5FP3B7UmgPDqBTjx4+nb9++m7WlH2sPDMMw\nStOc9iCcz6YJiFobCIVRo0alLaEJoWkKTQ/AW8sWpy2hCSHmk2kqTWh6thRCbA+SkuUyk2XtkG39\npj09sq6/XDLVOTAMwzAMwzAMo3rYtCIjM9i0IsNIhk0rMgzDMGALmFZkbJ5sWLOOdZ98WvInbexF\n3jDSRETuFpFFIvJWxO9mEZkiIhNF5DER2S4SdpWITPfhp0b8+4jIWyIyTURujfi3E5GH/TGjRSSb\nKwANwzAyTKY6ByHOMQ1xHlpomkrpWTN/IWNO/3bJ34RvX10xTbbmIBmmqTSh6aky9wCnxfyGAQep\nam9gOnAVbNwU82zgQOCLwO1+F2WAwcDFqtoD6CEiuTQvBpaoanfgVuDmQkJCbA+SkuUyk2XtkG39\npj09sq6/XOxTrBEEq2bNS1uCYRglUNVRItIt5jci4hwDfM3/3w94WFXXA7P9xmZHisj7wLaqOtbH\nux84E3geOAO30SbAUOBP1bkSwzAMoxBJdki+G/gvYJGqHuz9OgOPAN2A2cDZqrrMh10F9AfWAwNV\ndZj370PjHTGv8P7tcI3DYcBHwDdUNe9WdL17927udVaN4447Lm0JTQhNU2h6AA7u1CVtCU0IMZ9M\nU2lC05My/YGH/P+7A6MjYfO933og+jVgnvfPHTMXQFU3iMhSEdlBVZfETxRie5CULJeZamlfOXMu\nS14dXzTO9kf0Ytv99ykapxSW9+mQZe2Qff3lkmTk4B7gj7gX+BxXAiNU9WYR+SluGPnK2DDyHsAI\nEemubtVzbhh5rIg8IyKnqerzRIaRReQbuGHkcyp2hYZhGEbVEZGfAfWq+lDJyGUkW8G0jICpX7ac\nd/73pqJxet95fYs7B4ZhlKbkmgNVHQV8EvM+A7jP/38fbkgYIsPIqjobN//0SBHpSv5h5HhaQ4G+\nhbSEOMc0xHlooWkKTQ/YmoOkmKbShKYnDUTkQuB04LyI93xgz4h7D+9XyL/RMSLSGtgu36gBwG23\n3caAAQO48cYbufHGGxk8eHCjezFq1Khg3bn/Q9FTjjt+DZVKf8zE8UxuWLnRPblhZRP361PeDlZ/\nLdyDBw8OSk857iw9n1nXP2rUKAYMGLCxfmzOu3MiU6Z+jum/ItOKlqjqDpHwJaq6g4j8ERitqkO8\n/13AM8D7wA2qeqr3Pw74iar2E5FJwGmqusCHTQeOytcgDBo0SPv371/2RVaTUaNGBTfcFJqmUnpW\nvjeH/xxb28Gi93Zqzzl/+R0N6+qLxttqt53Z9oB9a6IptPsGpikJoemB6poyFZG9cO1BL+/+AjAI\nOF5VP47E6wk8CByFmy40HOiuqioiY4DLgbHA08AfVPU5ERkAfEZVB4jIOcCZqpq3cgixPUhKiGUm\nKdXSvnTCZMZ88dtF4xx8+7V0PuqQonFatW1D+y47Fgy3vE+HLGuHbOtvTntQqQXJldwsoeAFhDjH\nNMTCEpqm0PQA7PvRWsaedVnJeAf8amDNOgch5pNpKk1oeqqJiAwBTgR2FJE5uMXDVwPtgOHeGNEY\nVR2gqpNF5FFgMlAPDNBNX6MupfEatOe8/93AA/4j0ccUmWIaYnuQlCyXmTS1T7rsOlq1b1s0zkG/\nu5LdvnpqwXDL+3TIsnbIvv5yaW7nYJGI7KKqi/yUodwcjZYMIy8oNYw8dOhQ7rrrLurqnOnrTp06\n0atXr403LTekYu5suQ/d1d3P3BByz1Ydg3GveG8ae0FQ+WVuc8fdo0aNYsiQIQDU1dXRpUsX+vYt\nOEOz2ajqeXm87ykS/wbghjz+bwC98vivxa1bM4wm6IYNbFi1oWicDStXsWbhh0XjSJs2tN+pcyWl\nGcZmRdJpRXvReBj5Jtwi4pv8guTOqnrlljiMHOJQU2iaQpxWNLlh5cZOQDEO+NVA9vrON2qgKLz7\nBqYpCaHpgS1jh+QQ24OkhFhmkpLmtKIkSLu2tNmmQ8Hwd9Z+yhlXX0G3i7/e4nPVGis36ZFl/VWZ\nVlRgGPlG4B8i0h+3nuBsgGoPIxuGYRiGYRRC19VTv2RZwfD1DSvZsHptDRUZRvZINHIQCi+88IL2\n6dMnbRlGhUlj5CAptRw5MIxKsSWMHFh7sHlRqZGDJPT4+QD2+f43a3Iuw0ib5rQHJU2ZGoZhGIZh\nGIaxZZCpzoHtc5CM0DSFpgdoZD87FELMJ9NUmtD0bCmE2B4kJctlJsvaIcy6PylZzvssa4fs6y+X\nTHUODMMwjPQQkbtFZJGIvBXx6ywiw0Rkqog8LyKdImFXich0EZkiIqdG/PuIyFsiMk1Ebo34txOR\nh/0xo0WkrnZXZxiGYUDGOgch2rUOcfV6aJpC0wMkslRUa0LMJ9NUmtD0VJl7gNNiflcCI1R1f+BF\n4CrYuAna2cCBwBeB28VvhAAMBi5W1R5ADxHJpXkxzhJed+BW4OZCQkJsD5KS5TLTHO31y1ey9qMl\nRX+t2lRq26XihFj3J2VLKzchkXX95VKbp9EwDMPIPKo6SkS6xbzPAE7w/98HvIzrMPQDHlbV9cBs\nb5HuSBF5H9hWVcf6Y+4HzgSe92ld4/2HAn+q1rUYtWPF5Bm8eck1ReNsWL2mRmoMwyhFpkYOQpxj\nGuI8tNA0haYHwpx3GmI+mabShKYnBbqo6iIAVV0IdPH+uwNzI/Hme7/dgXkR/3ner9ExqroBWCoi\nO+Q7aYjtQVKyXGaao103bGDNgsVFf/WffFoFtU0Jse5PypZWbkIi6/rLJVOdA8MwDCN4Kmkfe7M2\nx2oYhhEiLZpWJCI/wM0RbQAmARcBHYFHgG7AbOBsVV3m418F9AfWAwNVdZj370PjDdKuyHe+EOeY\nhjgPLTRNpfRI29rPbgtx3mlo9w1MUxJC05MCi0RkF1VdJCJdgcXefz6wZyTeHt6vkH/0mAUi0hrY\nTlWX5DvpjBkzGDBgAHV1bs1yp06d6NWr18b7kfvSF6L7uOOOC0pPLdy5L/a5ujdtd9r50Vx3jlD0\nJHXn/ELRsznrHzVqFEOGDAGgrq6OLl260LdvX8qh2ZugichuwCjgAFVdJyKPAM8APYGPVfVmEfkp\n0FlVr/SL0x4EjsA1BiOA7qqqIvIa8H1VHSsizwC3qerz8XPapjfZon7FKt7/60Os+eDD4vE+XcGi\np16skarysE3QjCxSzU3QRGQv4F+q2su7b8ItIr6pQJ1/FG660HA21fljgMuBscDTwB9U9TkRGQB8\nRlUHiMg5wJmqmneHRGsPssOSV8fz+le/n7aMjdgmaMaWRBqboLUGOopIG2Br3FefM3CL0vB/z/T/\nb1ycpqqzgdzitK7kX5zWhBDnmIY4Dy0YTaos/NdLDLv/Ieb9/amCvzQ6BonnnaqyYW19yV9D/foW\nawrmvkUwTaUJTU81EZEhwKs4C0NzROQi4Ebg8yIyFejr3ajqZOBRYDLuw9EA3fQ16lLgbmAaMF1V\nn/P+dwM7+cXLV+AWNuclxPYgKVkuM1nWDrbmIC2yrB2yr79cmj2fQ1UXiMggYA6wChimqiNyw8s+\nzkIRiS5OGx1JIrc4bT2FF6cZRqq8d8s9fPDPESXj9fh/l7LjMYfWQJFhpIeqnlcg6JQC8W8Absjj\n/wbQK4//Wpz5U8MwDCMlmt05EJHtcaME3YBlwD9E5HyaLkar2OI0W3OQjNA0hTi/P6mm+qXLWTZh\ncsl4DRUwwxfafQPTlITQ9GwphNgeJCXLZSbL2iHM9igpWc77LGuH7Osvl5asBD0FmJlbLCYiTwDH\nUNnFaY0YOnQod911VyYXoG2J7ldGj2bKio/ZG0faC9BsgZu5N1d3JRagGYZhGAa0bEHykbj5oUcA\na3E7Z44F6qjQ4rT4OQcNGqT9+/dvlt5qEV29HgqhaKpfvpLXvvxdXp88KbivNZMbVlZU02EPDmLn\nvke3KI1/FcCZAAAgAElEQVRQ7lsU01Sa0PRAdRckh0KI7UFSQiwzSWmO9pAWJE9uWMmJXz6duv8+\nE21oKBiv3Y6d6XTIATVUVpotrdyERJb1N6c9aMmag9dFZCgwAaj3f+8AtgUeFZH+wPv4+aOqOllE\ncovT6mm6OO1eNpkybdIxMAzDMAzDaCmLnx7J4qdHFo1Td/FZwXUODKNWNHvkIA3MdF22yI0crHh3\nZtpSqk4lRg4Mo1JsCSMH1h5kh5BGDpJSd/FZ9Pz1D9OWYRgtJg1TpoZhGIZhGIZhbCZkqnMQol3r\nEG3fhqYpRLvSIWoK7b6BaUpCaHrSQkR+ICJvi8hbIvKgiLQTkc4iMkxEporI8yLSKRL/KhGZLiJT\nROTUiH8fn8Y0Ebm10PlCbA+SkuUyk2XtEGbdn5Qs532WtUP29ZdLpjoHhmEYRniIyG7AZUAfVT0Y\nt57tXNwmZiNUdX/gReAqH78nbj3agcAXgdtFJDfsPRi4WFV74DZbO62mF2MYhrGFk6nOQYh2rUNc\nvR6aptAsFUGYmkK7b2CakhCanhRpDXQUkTbA1jiT1GcA9/nw+4Az/f/9gIdVdb2qzgamA0d689fb\nqupYH+/+yDGNCLE9SEqWy0yWtUOYdX9Sspz3WdYO2ddfLpnqHBiGYRjhoaoLgEHAHFynYJmqjgB2\nUdVFPs5CoIs/ZHdgbiSJ+d5vd2BexH+e9zMMwzBqRKY6ByHOMQ1xHlpomkKc4xmiptDuG5imJISm\nJw1EZHvcKEE3YDfcCML5QNwcXsXM44XYHiQly2Umy9ohzLo/KVnO+yxrh+zrL5eW7JBsGIZhGACn\nADNVdQmAiDwBHAMsEpFdVHWRnzK02MefD+wZOX4P71fIvwkjR45k3Lhx1NXVAdCpUyd69eoVxI7V\nm7M7R7nHp72DfbxTUCr++PmzWRLZ+CqE/J80aVJQespxT5o0KSg9m7P+UaNGMWTIEADq6uro0qUL\nffv2pRxsnwOjatg+B4aRDrXe50BEjgTuBo4A1gL34Ha8rwOWqOpNIvJToLOqXukXJD8IHIWbNjQc\n6K6qKiJjgMv98U8Df8i3Maa1B9nB9jkwjPSo+T4HItJJRP7hTdG9IyJHVdN0nWEYhhEeqvo6MBSY\nALwJCHAHcBPweRGZCvQFbvTxJwOPApOBZ4ABuulL1aW4jsY0YHq+joFhGIZRPVq65uA24BlVPRA4\nBHiXKpquC3GOaYjz0ELTFOIczxA1hXbfwDQlITQ9aaGqv1TVA1X1YFX9lqrWq+oSVT1FVfdX1VNV\ndWkk/g2qup8/ZljE/w1V7aWq3VV1YKHzhdgeJCXLZSbL2iHMuj8pWc77LGuH7Osvl2avORCR7YDP\nqeqFAKq6HlgmImcAJ/ho9wEv4zoMG03XAbNFJGe67n3ym657vrnaDMMwDMOoPutXrqahvr5oHGlj\nyxsNI0u05IndG/hIRO7BjRqMA64gZrpORKKm60ZHjs+ZrltPQtN1Idq1DtH2bWiaQrQrXWlN0rbl\njV9o9w1MUxJC07OlEGJ7kJQsl5m49mUT3uGdn/y26DHrP11RTUllEWJ7lJTNqdxkjazrL5eWvNG0\nAfoAl6rqOBG5BTdCUDXTdYYRKtN+8xfmP/JMyXj7XnEh23TvVgNFhmEY1adhbT2rZs4tHTFjrH5/\nAUsnTEbr1xeM02qrdnQ6+IAaqjKM2tCSzsE8YK6qjvPux3Cdg6qZrrvtttvo2LFjUKbrJk2axCWX\nXJLa+fO5c37VPN+yt6by0r+eBuCI/XsCMHbq5MbuKe/w/vvvsaFhFT1bdQzGlF1US6XS+3TiFMaM\nH1cy/qJjDuRU3zmI5+/gwYNTL89x95ZavrOmpxKm67LGxIkTyaq1olERE5lZI8vawdXDSUYPPhzx\nKh+OeLVonJ1PPY7D7r+5UtJKkuW8z7J2yL7+cmmRKVMRGQn8j6pOE5FrgA4+qCqm6wYNGqT9+/dv\ntt5qEGKBqYWm6TffyXu/vydR3KSVcS1JS9OxLz3AtgfumzdsSy1L5RKaptD0QO1NmaZBiO1BUkIs\nM0mJa//whdG8cf6PUlRUHpWs+61zkJwsa4ds66+5KVPcC/2DIjIRt+7gN1TRdF2Ic0xDLCyhaQqt\nYwBhagrtvoFpSkJoetKi1qatQ2wPkpLlMpNl7RBm3Z+ULOd9lrVD9vWXS4tWUarqm7hNb+KcUiD+\nDcANefzfAHq1RIthGIaRKjnT1l8XkTZAR+BqnGnrm/1I8lVAbiQ5Z9p6D2CEiHT3H4xypq3Hisgz\nInKaqpr1OsMwjBrR0pGDmhKiXesQbd+GpilEu9IhagrtvoFpSkJoetIgYtr6HnCmrVV1GXAGzqQ1\n/u+Z/v+Npq1VdTaQM23dlfymrZsQYnuQlCyXmSxrhzDr/qRkOe+zrB2yr79cMtU5MAzDMIJko2lr\nERkvIneISAdipq2BqGnrqImbnGnr3Ulo2towDMOoDpnqHIQ4xzTEeWihaQpxjmeImkK7b2CakhCa\nnpTImbb+s6r2AVZSZdPWIbYHSclymcmydgiz7k9KlvM+y9oh+/rLxbYtNAzDMFpKzU1bDx06lLvu\nuiso09Zbont/WgPpmqZOy739Rws4DCqan+Y2d0vdlTBt3SJTprUmRNN1IZq3MlOmpTFTpskwTaUJ\nTQ+kY8rUTFsnJ8QykxQzZboJM2WanCxrh2zrb057YCMHhmEYRiXImbZuC8wELgJaA4+KSH/gfZyF\nIlR1sojkTFvX09S09b3AVjjrR3lNWxuGYRjVIVOdgxDnmIbYkwxNU2ijBhCmptDuG5imJISmJy1q\nbdo6xPYgKVkuM1nWDmHW/UnJct5nWTtkX3+5tHhBsoi08tYpnvLuqm16YxiGYRiGYRhG9aiEtaKB\nuKHhHFfiNr3ZH3gRt+kNsU1vvgjcLiK5OVC5TW96AD1E5LR8JwrRrnWItm9D0xSiXekQNYV238A0\nJSE0PVsKIbYHSclymcmydgiz7k9KlvM+y9oh+/rLpUWdAxHZAzgduCviXbVNbwzDMAzDMAzDqB4t\nHTm4BfgxjW1XV23TmxDnmIY4Dy00TSHO8QxRU2j3DUxTEkLTs6UQYnuQlCyXmSxrhzDr/qRkOe+z\nrB2yr79cmt05EJEvAYtUdSJQzERSdmylGkaVabVV+7QlGIZhGIZhFKQl1oqOBfqJyOnA1sC2IvIA\nsLBam97cdtttdOzYMahNbyZNmsQll1yS2vnzuXN+1T5f0k1jcn4hbFoT11Lr888+9xL67N4NgDc/\nWQTAIZ13AeDxOe+y77adOXL/nhx4/Q94fcrbQLrlaUsu31nSU4lNbyqBiLQCxgHzVLWfiHQGHgG6\nAbOBs1V1mY97FdAfWA8MVNVh3r8PjU2ZXpHvXBMnTqRPnz7VvaAqkWWb6VnWDpXd52D9suWsnDWP\nhrXrCsaRVkKHvfekVduWG4fMct5nWTtkX3+5VGQTNBE5AfiRbwxuBj7eUja9CbHA2CZopQlZ09Z1\nu3L0s3fTbsft05a0xZbvcghND6SzCRqAiPwAOAzYzrcHN+Hag5sLtAdH4D4IjWBTe/Aa8H1VHSsi\nzwC3qerz8XOF2B4kJcQykxTbBK08tjvkAI568nZab7VVi9PanMpN1siy/ua0B5WwVhTnRuDzIjIV\n6OvdqOpkILfpzTM03fTmbmAaML3QpjchzjENsbCEpim0l3AwTUkJrSxBeJpC05MWtTZQEWJ7kJQs\nl5ksa4cw69mkZDnvs6wdsq+/XCqyCZqqjgRG+v+XUKVNbwzDMIxgyRmo6BTxa2SgQkSiBipGR+Ll\nDFSsJ6GBCsMwDKM6VGPkoGqEaNc6RNu3LdG0ZtFHrJrzQdHf6vmL2LBydeI0Q7QrbZqSsbmV72oQ\nmp40SMNARYjtQVKyXGayrB3CrGeTkuW8z7J2yL7+cqnIyIGx+bBswmQmXHhl2jIMw8gWNTdQMXLk\nSMaNGxeUgYrNzb167gd8psMOALz+7jvk+ODjtRvd3VdsANI1MFGOO0etzvdZf75KGYgIqXyU4540\naVJQejZn/ZUwUFGRBcm14oUXXtCsWqfICoue/TcTLrLOQZqEtCDZyCZpLUiG2hmosPag+iz8v5eY\n+O2fpS0j02x3yAEc9cSfoXXxb7Gt2rZGWmVqMoeREZrTHtjIgWEYhlEtbgQeFZH+wPvA2eAMVIhI\nzkBFPU0NVNzLJlOmeQ1UGEYWWP72dF7/2mVF47TvuhMH/e5K2tsHISMQMtVNDXGOaYjz0ELTFOIc\nz5A1bVi9lvpPlrH83feK/lbOnFN1TaGVJQhPU2h60kZVR6pqP///ElU9RVX3V9VTVXVpJN4Nqrqf\nqh6Y2+PA+7+hqr1UtbuqDix0nhDbg6RkucyEWHeWQ63164YNLJswuehv+dvTEqWV5XKTZe2Qff3l\nYiMHhhEY6z5cwn+OO7dkvLr+X6Pnb7JjX9wwDMMwjPDJ1MhBiHatQ7R9G5qmEO1Km6ZkhFaWIDxN\noenZUgixPUhKlstMiPVUOWRZf5bLTZa1Q/b1l0umOgeGYRiGYRiGYVSPZncORGQPEXlRRN4RkUki\ncrn37ywiw0Rkqog8LyKdIsdcJSLTRWSKiJwa8e8jIm+JyDQRubXQOUOcYxriPLTQNIU4R9U0JSO0\nsgThaQpNz5ZCiO1BUrJcZkKsp8ohy/qzXG6yrB2yr79cWrLmYD3wQ1WdKCLbAG+IyDDgImCEqt7s\nTdddBeRM150NHIizXT1CRLp7CxWDgYtVdayIPCMip6nq8y26MsPYzPnk9UkseGI4lDBHvN1nurNN\nj71rpMowDMMoB21QdP0G1n64pGi8hvr6GikytnQqts+BiPwT+JP/nRDZ9OZlVT1ARK4EVFVv8vGf\nBa7Fmbd7UVV7ev9z/PGXxM9hdq2rj+1zsPlxyB3XsWu/8jZAMbJNrfc5EJE9gPuBXYAG4E5V/YOI\ndAYeAboBs4GzVXWZP+YqoD/uQ9PAnMUiEelDY1OmV+Q7p7UH1cf2Oagd7bvuBFL4kW3TsQOHP3ob\nW+/WpYaqjM2B1PY5EJG9gN7AGGAXVV0EoKoLRSRXkncHRkcOm+/91gPzIv7zvL9hGIaRDWwk2TBa\nwNqFHxUNX9+xQ42UGEYFFiT7hmAo7svPCiA+FFGxLZhDnGMa4jy00DSFOMfTNDVlxfTZLHjs+Ua/\nf/7mlsZ+Twxn7eKPU9UZWvkOTU8aqOpCVZ3o/18BTMG99J8B3Oej3Qec6f/vBzysqutVdTYwHTjS\njzZvq6pjfbz7I8c0IsT2IClZLjNp11MtJcv6X339tbQlNJssl3nIvv5yadHIgYi0wXUMHlDVJ733\nIhHZJTKtaLH3nw/sGTl8D+9XyL8JI0eOZNy4cdTV1QHQqVMnevXqtdHEVO7m1dI9adKkVM+fz50j\nGq6qiY5fOm0ybf3xuUo0Z/qtue4clUpvc3XPblhTlfS7z17A0vHvMGbiBAA+2/tQgCbuF5/8P2YP\nHtLo+NkNa9iqzY4b3dK2Ld977f+AsMr3lq5n1KhRDBkyBIC6ujq6dOlC377pTCWzkWTDMIxs06I1\nByJyP/CRqv4w4ncTsERVb/LDyJ1VNTeM/CBwFK6yHw50V1UVkTHA5cBY4GngD6r6XPx8Nse0+Xz6\nznSm/PyWkvHWzFvI6rkLa6DIyCLSri3Hj34UaVV6+uJWu9rc2LSo9ZqDHH4k+WXgOlV9UkSWqOoO\nkfCPVXVHEfkjMFpVh3j/u4BncGvQblDVU73/ccBPcjsuR7H2oGWsmDabpePfKRrnwxGvsuj/XqqR\nIqMYrTt24Pgxj9K207ZF40mb1kir4pNClrw6npl/frBonB5Xfofteu1ftk4jPGq65kBEjgXOByaJ\nyATc9KGrgZuAR0WkP66iPxtAVSeLyKPAZKAeGKCbeiaX0ngBWpOOgdEydMMGPhmd3WF4Iwx0XT2j\njj8faV288el89KEcdt9NNVJlhECtR5KHDh3KXXfdFdRIcpbcIx59jJl/uD+YkVNzF3dPWv4h7/X7\nFr132BWAt5a5R+ngTl0auf/7kb/SZtuOvDLaDcwde/TRAI3c9cuW8+/hI4qe7+NTD6fdnJkcc9Rn\nAXj1tTEAjdyt2rXlhM+fAqRfns1d2ZHkilkrqgWDBg3S/v37py2jEaNGjQpu57x8mpa99S6jT00n\n7yY3rAxuV0rTlIzmatrhuMM4cugfq6AovGcuND2QzshBrUeSQ2wPkhJCmZn/j2eZdNl1ZR8XYj1V\nDlnWn0T71nW70WqrdkXjrPtwCfWffFo0TtsdOtG6w9ZF4xz8p1+ww2eT7VQeQplvCVnWn5q1IsMw\njCifTprGlGv+UDJe1y+fTOfDP1MDRUY1sZFkwwiD1XMWVCSd+iXLqF+yrGgcXb+hIucywiNTnYPe\nvZP1UGtJiD3J0DSF+JXGNCWjuZrWL1vO+399uGS8bbp3K7tzEFr5Dk1PGqjqK0DrAsGnFDjmBuCG\nPP5vAL1KnTPE9iApWS4zIdZT5ZBl/VnWnuUyD9nXXy6Z6hwYhmEYhmEY6bPklTeoX1p8etI2++/N\nNt33qo0go2JkqnMwceJEQrNOEeI8tHyapHWhj3rVJ8Q5nqYpGdXWNPuvj7B8ynsl4+1xzpc2Ws4I\n7ZkLTc+WQojtQVKyXGZCrKfKIcv6Q9P+3i33lozT54Hfsk33vTJd5iHbz2xzyFTnwGjK2g+XMO/B\np9iwavVGv7lzZjLt35MaxVs9f3H8UMNInZXTZ7Ny+uyS8bqcuuVUyobRUla9P58Nq9cWjbN2Ubqb\nGRpbBoueeZn6Tz7lo6mTmb9ged44nXofwDY99q6xMqMYmbJWZHatm7Jm4Ye8cvIFJRcOGUaWOfKJ\nP7PD0YemLSMzpLXPQS2x9qAws/7yEFOvrY61MMOoNL3vuJ6u/U5OW8Zmi1krMgxjs2TKz29l67pd\nS8Y74FcD6bBn6XiGYRiGYeQnmM6BiHwBuBVoBdytqk12UApxjmmI89BCm5cYmh4wTUkJRdPyd6az\n/J3pQHFN0qpVyR1E23fdib2/dy5tttumItpCrAOyTlbbg6RkucyEUic0lyzr32y1txLWR6ZG50Na\nCa232qoKypKR5We2OQTRORCRVsCfgL7AAmCsiDypqu9G482YMSMNeUWZNGlS1QrMktETWLOg+FqB\nhvr1rF+xqpHf7IY1QVUgoekB05SUrGla9PTLJY/v2GMv9vreuRXTU806oLlMnDix7B0xQyHL7UFS\nQiwzSQmxTiiHLOvfXLVPvvJ3tO+6c9Hju//k26muPcvyM9uc9iCIzgFwJDBdVd8HEJGHgTOARo3B\nypUrU5BWnGXLqjfXf869j7PwyRfKPm4VDVVQ03xC0wOmKSmmqTTVrAOay5tvvpm2hJaQ2fYgKS0p\nM8vefLf4RyMRlrw6vtnplyK0569csqx/c9W+7qNPWPfRJ0WPL2UytdqEWM8npTntQSidg92BuRH3\nPFwDsVlSv2x5yZ0FpU1rdEN2FosbRlZYv3Q5K6bOpGHd+qLx2nbejm26d3N7/RZBG7LbYAfKFtUe\nlMuCx4cl2mDQMDYn5tz7BGs++LBonB2O6UPnI0run2gkIJTOQSIWLlyYtoQmzJkzZ+P/qz9YzLrF\nS0oes3Tc28z9+5Ml461ZsJjWHTuUremjFdqs46pFaHrANCVlc9S0fuVqxp3zw5Lx2mzXMZF5vRm6\niDULizdaAK07bE3bCq1zMMJsD5ISbTeirJg+m4Z19QWPkzZtWL9searPZIh1QjlkWf+WrH3F1Fms\nmDqr+DlGvs7u3/hS0TjSuhWt2rYp+tGnVbu2tO64NQ3rN31AmvbaOBa/8OpGd5sOW7PN/vtQyuJn\n207b0qpNevtMNZdQOgfzgbqIew/v14h9992XgQMHbnQfcsgh9O7du/rqinD44YczfnyZQ7iH7kOH\nQ39QMlpzH6N+EyeyY8r5EiU0PWCakmKaSnPsxIlMXjC3dMQqMnHixEZDxx07ZnNesiez7UFSmtVu\nAKwFLvgiO17wxYprSkpoz1+5ZFm/aS9NZXd02vSKfNyXT2de5+iCaIXZpTfwTINKtAdB7HMgIq2B\nqbgFaB8ArwPnquqUVIUZhmEYNcXaA8MwjHQJYuRAVTeIyPeBYWwyXWcNgWEYxhaGtQeGYRjpEsTI\ngWEYhmEYhmEY6dMqbQFRRORuEVkkIm9F/DqLyDARmSoiz4tIp0jYVSIyXUSmiMipaeoRkW4iskpE\nxvvf7ZXWU0TTWSLytohsEJE+sfhVzaNyNaWcTzf7fJgoIo+JyHaRsLTyKa+mWuRTAT2/EpE3RWSC\niDwnIl0jYWnlUV5NaZalSNiPRKRBRHaI+KWST4U01SqfqkmBcnGNiMyLXNcX0tSYDxHZQ0ReFJF3\nRGSSiFzu/Qu2ayGRR/9l3j8Led9eRF7z9cYkEbnG+wef90W0B5/vOUSkldf4lHcHn+9RvP4JEf2Z\nyHsRmR1pL1/3fuXnvaoG8wOOA3oDb0X8bgJ+4v//KXCj/78nMAE3NWovYAZ+JCQlPd2i8WqcR/sD\n3YEXgT4R/wOrnUfN0JRmPp0CtPL/3wjcUKuy1AxNVc+nAnq2ifx/GTA4gDwqpCm1suT99wCeA2YB\nO3i/1J65Ippqkk+1vgfANcAP09ZWQndXoLf/fxvcWooDKNCOhPYroj/4vPeaO/i/rYExOJO4Wcn7\nfNozke9e9w+AvwNPeXcm8r2I/kzkPTAT6BzzKzvvgxo5UNVRQHwnjDOA+/z/9wFn+v/7AQ+r6npV\nnQ1Mp8K2sMvUAyCVPH9STao6VVWn5zn/GVQ5j5qhiQJ+tdA0QlVzRunH4F6koAZlqRmaoMr5VEDP\nioizI2zcuSbNPCqkCVIqS55bgB/H/FJ75opoghrkUzUpcr1BX5eqLlTVif7/FcAU3DNerB0JhgL6\nd/fBQec9gKqu8v+2x3XYlezkfT7tkIF8F5E9gNOBuyLemch3KKgfMpD3OI3xd/uy8z6ozkEBuqjq\nInAVFdDF+8c3ypnPpkorDT0Ae/nhppdEJIR9ttPKo1KEkE/9gWf8/6HkU3/g2Yg7lXwSketFZA5w\nHvAL751qHhXQBOnlUT9grqpOigWllk9FNEEYz1w1+L64KXl3ZWCawl640Y8xwC5F2pEgieh/zXsF\nn/e5qSHAQmC4qo4lI3lfQDtkIN/Z9JEiuqg1E/nuyacfspH3CgwXkbEi8m3vV3beZ6FzECe0FdQ5\nPR8AdaraB/gRMEREbMejpiwg5XwSkZ8B9ar6UC3PW4yIpiHeK7V8UtWfq2od8CBuGk/qFNCUyjMn\nIlsDV+OGmYOggKbcV67Un7kqcTuwj6r2xr1A/T5lPQXx+T0UGOi/wMfbsdDatUbk0Z+JvFfVBlU9\nFDdac6SIHERG8j6P9p5kIN9F5EvAIj/iVOxLe5D5XkR/8HnvOdbX9acDl4rI52hGmc9C52CRiOwC\nIG4hYm6Pi/nAnpF4eTfKqZUeVV2nqp/4/8cD7wE9aqCnGGnlUUFUtT7NfBKRC3EPzXkR71TzKZ+m\ntPPJMwT4qv8/lLI0BPgapPrM7YtbT/CmiMzC5cV4EelCwg28aqTpDRHpEkhZqjiq+qH6SbTAncAR\naeophIi0wb1YP6CqT3rvQu1acOTTn5W8z6GqnwIvA18gQ3kPjbVnJN+PBfqJyEzgIeBkEXkAWJiR\nfM+n//6M5D2q+oH/+yHwT9y01rLLfIidA6Fxb+0p4EL//7eAJyP+54hIOxHZG9gPt1lOKnpEZCcR\naeX/38frmVkFPfk0xcNy1CqPEmtKM5+8dYEfA/1UdW0kXmr5VEhTDfMprme/SNiZwLv+/zTzKK5p\nivdPpSyp6tuq2lVV91HVvYF5wKGquhiXT9+odT4V01TjfKom8XLRNRL2VeDtmitKxt+Ayap6W8Sv\nULsWIk30ZyHvfbnPWRPcGvg8ru4IPu8LaH83C/muqlerap2q7gOcA7yoqv8N/IvA8x0K6r8gC3kv\nIh1yo8Ii0hE4FZhEc8q8BrC6OvfDfRVcgNskfg5wEdAZGIGzkjAM2D4S/yqcNZApwKlp6mFTYRkP\njANOr2EenYmb57waN9Xi2VrlUbmaUs6n6cD7/tzjgdsDyKe8mmqRTwX0DMVVJhNxFciuAeRRXk1p\nlqVY+Ey8ZaA086mQplrlUzV/BcrF/cBbvlz8EzevNnWtMd3HAhu8xgn+HnwB2IEC7VpIvyL6s5D3\nvbzeiV7rz7x/8HlfRHvw+R67jhPYZO0n+HwvoT/4vAf2jjyrk4Arm5v3tgmaYRiGYRiGYRhAmNOK\nDMMwDMMwDMNIAescGIZhGIZhGIYBWOfAMAzDMAzDMAyPdQ4MwzAMwzAMwwCsc2AYhmEYhmEYhsc6\nB4ZhGIZhGIZhANY5MAzDMAzDMAzDY50DwzAMwzAMwzAA6xwYhmEYhmEYhuGxzoFhGIZhGIZhGIB1\nDgzDMAzDMAzD8FjnwGgxInKCiGwQkd1S1PAZEXlNRFaLyMy0dISEiLQWkb+JyEf+/hzfjDS6iUiD\niBxTDY2GYRTG6tbmUe16S0QuFJH6Zhx3j4gMq7CWipUREXlJRO6ohK5KICJfF5EZIlIvIn9rZhpB\nXVNWsM5BYPjKo8H/6kVktogMFpEdKniO4c190ArwCrCrqi6oYJrlcjOwDOgBHJGiDkTkThF5MU0N\nnq8B5wBfAnYFXm1mOloxRYCInC8iDZVMM8852vuO0XgRWSsi06p5PiN8rG5tNi2uW0Vkuoj8oqKq\nSlPReitP2tVMvxzKLiMi8jMRmZUn6CvADyumrAWISCvgbuBhYE9gYLqKNuHrjwuqfI7/EZER/uNe\nzT/QWecgTP4N7AJ0Ay4Dvgrcl6qiAohIG1Vdr6qLW5iO+MqguXQHRqrqXFX9uCVaQkJE2rbg8B7A\nfFV9TVUXq+r65spogYZC6VWkYS2SP62BtcBfcY2LYYDVrc0hq3Vri+qtCuRbTWhmGclbB6vqUlVd\nUc2fLbcAACAASURBVBllLWY3YBvgWVVdqKrL0xZUaUqUsQ7AC8CPSaMjqqr2C+gH3AMMi/ldDdQD\n7b27B/A0sNz/ngL2jcTf1qfzAbAGmAP8LpJ+A7Ah8vd4H9YFuBdYDHwK/Af4XCTdE/wxp/uwVcB3\nI/67ReJ+Fhjp4ywBHgR2joRfA0wHzgamAOuA/QvkSVfcC94nPr2XgMN8WLc81/OLIvl7Cu4FYSWw\n1Ke1dyT8HGACsBqYBQwCOkTCXwLuBH7u8/dj3MtFh8h1xfVc4MM6ArcB8/z53wC+Ekk7dy3n+fu7\nArihyLX8L/Ae7iV4BjAwpjOqY2aRdHb25WKhv+4pwIUxTcfkc0fSmB7Nd+DbwGSf3sfAy7jK/gSa\n5s/fIsdd5s+/GpiKK/utI+GzgOuAPwMfAaMTPFPXANPSfrbtl+4Pq1vz5UlF6lZgd2Ao8KF/dmcA\nP/Jh8bpoA1Dnw+7wcVfh6rJfA+3yXEs/fy0rfHr7xc5/to+3GhgFfJlYPVXGuRrlG+5F+jpgkb93\nDwFXAOtKlLfOwCNe8wc+jXtpWgbz1XmtfNj1wLt50h4M/Nv/f2KeMpLvWtv6sG8Vuq+4evqOSDpt\ngBtxbdZa4B3g3JiWBuAS4H6fP3OBKxM8jwXLcQGNxxdJ61KvbY2/T/+IhL0Uu6ZGbu/3M2BWxN0T\neA73XKzwaZ/vw2Z5PRu1RY47DHgeV3csBh7Dl/Vyn83IMXnb3KrXl7U8mf0S3JD8DdgPfUHsCGwF\nvA8MB3oDhwIv+gLXxsf/A+4F93BgD/8QXuzDtvMP5EO4l8IuvgLYyj8Aj/o09wGuwlVY+/tjcw3V\nZNxUlW5seuHbgK+ccF/mlgEP+IfsGOBN4OXINV2De0F+CTdUvR/QsUCevAaMB44GDsI1ZkuAHXAV\ndxdcI/0b/3+HAumcAqzHvfD3wn0R+xbQ3YdfiHuRPc9f23HAROC+SBov+XMPwr1InOKP+aUP7wj8\nHddA5fK3feTYF/117IV7gV4DnOTDc5XAHOBc7+5W4Fou9fl3MbAv8B1/ry7y4dsDv8U1DDsDOxZI\nZytcJTUOOMmf8yTg6zFN0c7BBop0DnAVZD1wPm44+CCgvy8rbYABPo1c/mzrj7sWV/H28+f5AjA7\nl7c+zixcp+4XvswckOCZss6B/cDq1nx5Uqm69SlgGK5erfO6v+HDOgMzcdOTuvifsOml+3B/zH8B\n84FrYteyAnjG35NeuLpqZCTOobh6/XpcnX6mP9/GeqqMczXJN9x0luXAN73f/+JeGkt1Dp4Apvm8\nONDfs2VEyiAl6jx/PRuAIyLHtMO1OblyFy8jRa8VVx5vwJX1XDnNfdyKv0j/Ftfh+6q/9qv8uU6K\nxGnAdX4uBvbG1e8N0Th58qZoOQbae/0NuOehC/4ZzJPWL3Gdkku8xoOJdE7yXFOhzsHMiPtNXDu+\nP66tPg043YfthGvfvu91dfH+PX05+YW/bwfhOodT8Z1Qyng2I1qsc2C/pg2YL3AzgFe8+2JcZdk5\nEqcLrvf9Te/+J5GvsXnOMTwejnsxnoP/YhHxfwH4vf8/14CdF4sTr5yu82m1icQ52B97nHdfg6vQ\ndy+RH3192vtH/NoBC4CfR/xmAVeXSOvfwJNFwmcB34n5fc7r7uTdLwETYnFuz90f774TeDEW50R/\nj7aN+d8NPO7/z1UCRa/Dx51DbFQB+D0wI+Iu+VLsy9Mq3JzVfOH5OgdFRw5wjfMnwDYF0jyfyNcW\n77c1rtI8Neb/38AnsXs0vMxnyjoH9gOrW+NaK1m3TqT4iO30YuGReFcAUyPua3BfV3eI+J3try/3\nwvUA8J9YOpeS5yNGgnM1yTfcl/Bfxfz+QZHOAe6DTQNwcsSvLe4L/DDvTlrnjQb+GHGf5Y/bLl8Z\nSXitjV6GI/4bX5y9vjXAd2NxHgdGRNwNwC2xOJOBXxfRk6Qcl3wpxk29WQX8oEic5nQOluJH/Auk\nWR8Px9UvQ2J+7f296lesjJV4JlLpHLTBCJGTRGQ5bt50O2AErlcMrkGbrKqf5CKr6mIRmYrrqYJ7\nWX1MRA7Hffl6DnhefUkrwOG4RavLRBpN1WyHe/g2ng4YW0J/T2CMRua4q+pbIrLMaxzlvRep6vwE\naX2sqlMjaa0TkdfYdL1JOQz4ab4AEdkJ9xD+XkQGRYNw17wfbhoQuK8KURYAp5Y49+G4imJBLH/b\n4r4uRSmavyKyLe6r5X9iQSOBy0VkK1VdU0JPjj648vRBwvhJGI57oZgtIsNxZfBxLT5f+SBcY/RY\nLH9aA+1EZMfI8a9XUKuxZWF1a+O0KlW33gr8VUROx01NeVpV4/VTE0Tkf3Cdsr1wX+nb0HStwAJV\nXRJ1s2lUY56/jhGxY0bF00l4rkb55uva3XEv6PH0zyhyaT1x93PjcapaLyJj/bkheZ13H/ArEblC\nVTfgOg9PqeqnhU6e8FpLsR+ufcrXzlwZ88vXJu5SJO2k5bgUB+Ha1eEJ4yfld8DdInIRrjw/paoT\nShxzBLCvr1+itMeNJORI8mymjnUOwmQMcAHua8ACLXMhqaoOE5E9cUNhJ+KGx94Skb5FGrFWuN7+\nmTStRFbF3CvL0VOESqVTCXKLgi7HVQZx5kX+XxcLU0ov7m+F+xpxOE3zN55eSPkSJ2dlKH4NGxcG\nq+pKETkMOBY37ep7wM0icnKRCjaXf2fhvjLGib4chJw/RthY3VoFVPVeEXkWNy3mJOBZEXlcVQta\ndBGRrwN/An6CG9X9FDcqcH0sar76FsowqFLGuWqZb0nrvIdxna8viciruDzuVyjRMq41CUk7FM1p\nE9OigSLtF4CqXi8if8fl9cnA1SJyk6oWs7jVCjeKdUOe9KMfxjLRfoV687Z0VqvqLFWdk6fxegfo\nGTW/JyK74ObGTcr5qbM68IiqXoKbs3cirrcO7kFuHUt3HG4u7HJVnRn7LSxT/zvAZ0VkY+dTRA4B\nOkU1lpHWjiJyQCSt9sBRzUjrDQp84Vdn7WEubg57/Ppnqmq88itGofzdHtg6T9rzmiZRGHVWG+YB\n8X0LTsQtqko6agAuT3qWYSP7Q/93Y3wR6YL7uhbVqKo6SlWvVdXDcHNSz/PB6/xx0Qo0t5hs3wL5\nX+zLrGEkxerWxmlVqm5FVRep6n2qeiHuq/X5IrKND86XL58Dxqvqbao6QVXfw81ZL5fJuDnrUY6j\nsYWXZp3L17XzC6RfShPR47xltagp2ER1nqouBf6F69Sei3vRLLZXQpJrzXc/4szALULO1868XeLY\nUhQrx+WkPdlrLDVyH2UxkfbLc1g8kqrOVtW/qOrZuHUEl0SCCz3nB/v6JX4vl5WhLwisc5A9huCs\ntDwiIv+/vXOPk6uq8v33lxeBACEiSZDQPBMkGIGI4IMZHFtFHG/AGT+Mo3d8hDsz16DExzgSnfvx\n3nkI+LlRcLxkPnNxFLxkMMYZ0UF5JKhjS4LB0BAJ5EXIkyRASDrPTndn3T/O6aS6U9VVXV1dZ53q\n9f18zqd779qn6le7Vu1V++y117k0vUJ7H8kP2wUAkv5e0gckTZE0mWQj1R6SGD9IQj7eJOlcSaem\nX9B70/oHJL1byU1kLpd0s6TCqxSlriQU1n+LZHPedyVdJOlKkkwGvzSzfuXaN7NHSZba50t6m6Q3\npM91HPBP/XkukjjHayR9Q9K0tH8+lvYRJHGHN0n6Uqp7iqTrJPX3ddYDr5c0Ne3fUen7WAz8m6Rr\nJZ0jabqkT0m6oZ/PD8nViU9L+m+Szpf0lyTZTf6hn8/zryQb034sqVnS2ZLeKen6Yo3Ticevgb+W\n9MbU/u4mcXIASJoh6TPp+ztT0gdIwqCeSZusT/9eK+m1ksaY2T6STY9flTQr7fupkv5E0q39fE/d\nOi5MHc7pJMv0F6dHrJgGxYixtcqxVdI/Sromfd8XkdxjZaMdTYu5Hnh7Oh6cml4YWAVMS8eLcyXN\nJsmzX9FLFvz/DeCt6WczOR1veufqH8hrzQVmS/qv6Vj7eZL9GiVJf5D/BPg/kt4haSpwF0m2q+42\n/Rnz7iHZWPzfgXuLXCwp7I9K3ut6YKKkt6Sfx/FF3sMBkg34fyfpg2nffokkE1R//Uxv+rLjX1f6\nJGkfzgX+Z9qHk9MxvnfYUyGLgHel7+k8SV+kYLInaYykb0n6g9QfXkqygvBMwXOsJwlRPF3SqWnd\nV4ELJf0/SW9Oz/0DSbdLOrvS91SgY0Lqv7pD/LrfW1/hWrXD6rjBIY6KNp/02DRXos1k4D9Ilgvb\ngPuBcwse/xvg6fSxV0k24Ly14PFzSEJn9tAz3d44khSRm0h+7G0iScV1cfp40Y1PxeqBy9PX2Eey\nPPo94LUFj1e8SZQkdnF++jzdO/0v7dXmeSrbyPtukh+3+9K+WQycXfD4jPTxvSRhQMvpuTnvUcpv\nZhqXfj676JnK9DiSAWRd2r9bSbJwvCN9/CzKbKLr9bqfp2cq00/3eryiPqZnmsX9JFdjPlpKE0ks\n6s9T+1lFEi6xmqMbkn8v7dft6fOtAr7Q6zW/TpI6tXcq05lpn+8nuUK2hIINcZV+zmnb9RxNOVd4\nNFVyfhyNdRBja7H3W5OxleTH3nPpc7xE8sP4woLH30RyZXV/93eQJKx5HsmEbBdJiNYseqaGPOa9\nkIQr9vge0zOV6RKSH7CF2Yqqeq20XiQhOTvSz3UBSQajSlKZ3pees53kB/UxNkiZMa9A/3aSzazT\n+rKRCt/riLT+FXqmMu29eXcEic/qttvfkWahKmjTxbEb6Y/ZmF+kf8rZccX+kKPpYA+SrFJ/v+Cx\nHj47fU/d/mcn8I8kWaOeTx8/jmRCvy79TLaRXEQ7o+A5riaZLLT36teLSLJUvZK+r9UkE+1Tqvhu\nfoWjaVwLj7Ib+2txKBVREknfJpmxbjezN6Z1XyP58rWnHfgJSzfHSJpDYuydJHnXH07rp5P8ABkN\n/NTMPpPWjyKZMb6JxJj/xMw2EgRBELgi/EEQBEHjU0lY0XdIZkmFPAxcZGaXkMzW5wCkS2fXk+T0\nvQa4M10+hGQme4OZTQGmSOp+zhuAnWY2mWTTzdcG8H6CIAiCwSP8QRAEQYNTdnJgZi0ky6eFdYvM\nrDtryVKSeGJIQjLus+R23i+QOIrLJU0kye/enabtHpJQBEjSgXXfvn4hZWL5giAIgmwIfxAEQdD4\n1GJD8kySuGlIMpZsKnhsS1p3Bj1TQW7maHaTI+dYksN3lwqyRQRBEAS5IfxBEARBzhnQ5EDSl4EO\nM/vXGumB/t+oIwiCIMiY8AdBEASNQdUp/SR9HHgfyQ0iutkCnFlQnpTWlaovPGerpOEktwQvvOHR\nEWbMmGEHDx5k4sSJAIwZM4bzzz+fSy65BIDW1laAKFdQ7v7fi548l7vrvOjJa3nhwoXxfa6y3Nra\nykMPPQTAxIkTGTNmDPPmzavbD+vwB0l57dq1fPCDH8zs9b3r6cbbeOnRH3ocD73Zkzc93WRt37Xw\nB2WzFQGkOVp/YmbT0vJ7SXLL/r4lt/fubjeVJAXUFSTLw48Ak83MJC0lufvsMuAB4Jtm9qCkWcAb\nzGyWpA8B15nZh4rp+OhHP2p33HFHf95fUIJbb72Vm2/uKxVwUCnRl7Uh+rF2zJ49m3vuuWdQJgfh\nD0rjzYa96YHQVCmhqTze9IBPTdX4g7IrB5Lmk9wR71RJG0lyr34JGAU8kiafWGpms8xspaQFJHnS\nO4BZdnT2cSM9U9c9mNZ/G/iepDUkuWGLOgKAbdv6ezPJoBQbN0Z2wFoRfVkboh/9E/6gb7zZsDc9\nEJoqJTSVx5se8KmpGspODszsw0Wqv9NH+1tI7t7au/63wLQi9e0k6e6CIAgCx4Q/CIYKK7fv5aV9\nHX22Oe/U45k0dnSdFAVB/ah6z0EWXH117/TaQbV8+MPFfHxQDdGXtSH6sXZcfPHFWUsYdDz6A282\n7E0P5EfTg6t28uDqV4q0PsoNbz6dM8tMDl47ZhRTTjuhJpqyxpsmb3rAp6Zq/EFFew68sHjxYps+\nfXrWMoIgCFyzfPlympubGzrTT/iDYDD5+n9uLDs5qIQPThvPX1xxRvmGQTBIVOMPanGfg7pRuBs8\nGBgtLS1ZS2gYoi9rQ/Rj0B88+gNvNuxND2SvaV97J9v3tPc4fvLwz3uUX9l3iPauw+WfbBDJup+K\n4U2TNz3gU1M15CqsKAiCIAiCoFp2HujkL374bI+6tnUvcPKmU3rUdeUnqCIIak6EFQVBEDQYEVYU\nBMXZtOsgNyx8tnzDGhFhRUHWVOMPYuUgGLKseHEve9o7y7ZrOmU0k06JjBRBEARBEDQ+sedgiNIo\ncXEDYeGK7fzPRevLHi/v7zudXfRlbYh+DPqDR3/gzYa96QGfmtrWhS1VgjdN3vSAT03VUHZyIOnb\nkrZLerqgbpykhyWtkvSQpLEFj82RtEbSs5LeU1A/XdLTklZLur2gfpSk+9JzlkhqquUbDIIgCGpD\n+IMgCILGp5KVg+8AvRNK3wwsMrMLgEeBOQCSppLcwOZC4BrgTqW3zATmATeY2RRgiqTu57wB2Glm\nk4Hbga+VEnLJJZdU9KaC8lx55ZVZS2gYoi9rQ/RjLgh/0AfebNibHvCp6eTzwpYqwZsmb3rAp6Zq\nKDs5MLMW4NVe1dcCd6f/3w1cl/4/A7jPzDrN7AVgDXC5pInASWa2LG13T8E5hc+1EGiu4n0EwaAx\ncnhD7+sMgooJfxAEQdD4VLvnYLyZbQcws23A+LT+DGBTQbstad0ZwOaC+s1pXY9zzKwL2CXpNcVe\n1GOMaV5plLi4enD7rzYx52drSx4fm/t95vxsLXf9ZkvWUnNN2GRuCX+Q4s2GvekBn5piz0FleNPk\nTQ/41FQNtcpWVMt8qHGZNnDFhl0H2bDrYMnH217ez4tb9rDzQAd/cN442jvLfx1OPWEEE046rpYy\ng8AL4Q+CIAhyTLWTg+2SJpjZ9nSJeEdavwU4s6DdpLSuVH3hOVslDQdONrOdxV507dq1zJo1i6am\nZI/a2LFjmTZt2pEYr+4ZW5TLl6+88kpXerIob37mt7Tt2Hck3rT76lG15aeWLeUjy5ZW1P7r75/M\nmqeWueqPrMvddV705Knc0tLC/PnzAWhqamL8+PE0N9ctIif8QUG5Gy/24U1P1uWz3nAZ0HM8Pvm8\nSwY8/pcqM+09Ventrsu6v7zbkzc9Hsq18AcV3QRN0tnAT8xsWlq+jWTT2G2SvgiMM7Ob0w1o9wJX\nkCwPPwJMNjOTtBS4CVgGPAB808welDQLeIOZzZL0IeA6M/tQMR1x05ugHAc7ujhUwa0thw8Tt/3i\nBZZubKuDqmP5+vsn84aJJ2by2kHjM5g3QQt/EOSZet8EbfKpx/NH08aXbXfx6Sfy2jGj6qAoGGoM\nyk3QJM0H3gGcKmkj8BXgVuAHkmYCG0gyUmBmKyUtAFYCHcAsOzr7uBH4LjAa+KmZPZjWfxv4nqQ1\nwCtAUUcASYxpOIPaUHhFopF44dWD3PqLFypqu23PoZq8Ztu6VpfZLvJGo9pkIxH+oG+82bA3PeBT\n02CO4WteOcBtv9hQtt3d10/tUfbYT940edMDPjVVQ9nJgZl9uMRD7yrR/hbgliL1vwWmFalvJ3Um\nQTBQDhtsbavNj/4gCHoS/iDwzEt7D9HedbjPNl2Ha7klJggak7KTA094zGudVxphZuuFWDWoDWGT\nQX/w6A+82bA3PTC4mlZs28utFVyl743HMXyofXbV4E0P+NRUDdWmMg2CIAiCIAiCoMHI1eTAY17r\nvNJ7p39QPR5zZOeRsMmgP3j0B95s2Jse8KnJ4xjusZ+8afKmB3xqqoZcTQ6CIAiCIAiCIBg8cjU5\n8BhjmlcaJS7OAx7jVfNI2GTQHzz6A2827E0P+NTkcQz32E/eNHnTAz41VUOuJgdBEARBEARBEAwe\nuZoceIwxzSuNEhfnAY/xqnkkbDLoDx79gTcb9qYHfGryOIZ77CdvmrzpAZ+aqiFXk4MgCIIgCIIg\nCAaPAU0OJH1W0u8kPS3pXkmjJI2T9LCkVZIekjS2oP0cSWskPSvpPQX109PnWC3p9lKv5zHGNK80\nSlycBzzGq+aRsMl8E/7Anw170wM+NXkcwz32kzdN3vSAT03VUPXkQNLrgE8D083sjSQ3VPtT4GZg\nkZldADwKzEnbTyW58+WFwDXAnZKUPt084AYzmwJMkXR1tbqCIAiC+hL+IAiCoHEYaFjRcGCMpBHA\n8cAW4Frg7vTxu4Hr0v9nAPeZWaeZvQCsAS6XNBE4ycyWpe3uKTinBx5jTPNK3uLitu1pZ2tb+cPM\n6q6tmnjVfYe6WL/zQNlj466Dg6DYJ3mzyeAYhrw/8GbD3vSAT02x56AyvGnypgd8aqqGEdWeaGZb\nJc0FNgL7gYfNbJGkCWa2PW2zTdL49JQzgCUFT7ElresENhfUb07rg+AI9z65jYdW78xaRs34Hw8/\nX1G7q849hS+/85xBVhMEAyP8QRAEQeMwkLCiU0iuCp0FvI7kitFHgN6Xbmt2KddjjGleaZS4OA94\njFfNI2GT+SX8QYI3G/amB3xq8jiGe+wnb5q86QGfmqqh6pUD4F3A82a2E0DSvwNvA7Z3Xy1Kl4h3\npO23AGcWnD8prStVfwwLFy7krrvuoqmpCYCxY8cybdq0Ix9G93JOlBuz3L302z2QD4Xypv0nQrpy\nkHX/R9lvuaWlhfnz5wPQ1NTE+PHjaW5upo6EP4hy5uXfbWkDTgd8jN/9Kf9myWOcOmakq/6Mcj7L\ntfAHqjZGW9LlwLeBNwPtwHeAZUATsNPMbpP0RWCcmd2cbkC7F7iCZJn4EWCymZmkpcBN6fkPAN80\nswd7v+bcuXNt5syZVekNetLS0nLEqPLA3P/c4DasqG1d66BdeRpKYUV5s0nPLF++nObmZpVvWRvC\nHyR4s2FvemBwNf1i3U6++vMN/T5vMMfwSrn7+qmcfvJxR8pD7bOrBm96wKemavzBiGpfzMx+I2kh\n8CTQkf79Z+AkYIGkmcAGkowUmNlKSQuAlWn7WXZ0ZnIj8F1gNPDTYo4gCIIg8En4g2Aw2dveybyl\nW9ix91Cf7bbsbq+TotqzZMMuTjzu6E+yZza1sX/1Kz3aXHDaCZw17vh6SwuGIFWvHGTB4sWLbfr0\n6VnLCDLA88rBYDKUVg6C2lHvlYMsCH8wdNjb3snsH69mU45//NeCv3nn2fz+ueOylhHkjGr8Qdwh\nOQiCIAiCIAgCIGeTA495rfNK9+aVYOB4zJGdR8Img/7g0R94s2FvesCnJo9juEdN3j47b3rAp6Zq\nyNXkIAiCIAiCIAiCwaPqDclZ4DGvdV7xtps+zwxmlovOw8bugx10Hi6/N2j0iOGMGTV80LQMNmGT\nQX/w6A+82bA3PeBTU9aZiorhUZO3z86bHvCpqRpyNTkIgqHGkg27eWbbvora/t3V53LBaWMGWVEQ\nBEEQBI1MrsKKPMaY5pVGiYvzwGDGhh422HWws6IjR4nHihI2GfQHj/7Amw170wM+NXmM7/eoydtn\n500P+NRUDbmaHARBEARBEARBMHgMaHIgaaykH0h6VtIzkq6QNE7Sw5JWSXpI0tiC9nMkrUnbv6eg\nfrqkpyWtlnR7qdfzGGOaVxolLs4DHmND80jYZL4Jf+DPhr3pAZ+aPI7hHjV5++y86QGfmqphoCsH\nd5DcwfJC4GLgOeBmYJGZXQA8CswBkDSV5O6YFwLXAHdK6r4pwzzgBjObAkyRdPUAdQVBEAT1JfxB\nEARBA1D15EDSycDvmdl3AMys08x2A9cCd6fN7gauS/+fAdyXtnsBWANcLmkicJKZLUvb3VNwTg88\nxpjmlUaJi/OAx9jQPBI2mV/CHyR4s2FvesCnJo9juEdN3j47b3rAp6ZqGEi2onOAlyV9h+Qq0RPA\nZ4AJZrYdwMy2SRqftj8DWFJw/pa0rhPYXFC/Oa0PGpxX93fQ1t5Ztt2IYWJPe1cdFAVBUCXhD4Ig\nCBqEgUwORgDTgRvN7AlJ3yBZQu6dM6VmOVQ8xpjmFQ9xcS/v7+DGH63KWsaA8Rgbmkc82GRQNeEP\n8GfD3vSAT00ex/Bimlq37mX4MBVpfZTjRw7jDRNOZNSI2ueb8fbZedMDPjVVw0AmB5uBTWb2RFr+\nIYkz2C5pgpltT5eId6SPbwHOLDh/UlpXqv4YFi5cyF133UVTUxMAY8eOZdq0aUc+jO7lnCjno/zb\nxx+jbd2mI4Ng9zJqlKsrL398CS+PG+3m841y/cotLS3Mnz8fgKamJsaPH09zczN1JPxBlAe1/NKq\n5bTt63Az3mZRnr8O/qNM+zdd/la+/v7JmX9eUc63P5ANIDm6pF8Cf25mqyV9BTghfWinmd0m6YvA\nODO7Od2Adi9wBcky8SPAZDMzSUuBm4BlwAPAN83swd6vN3fuXJs5c2bVeoOjtLS0ZD7DXfPy/oZY\nOWhb1+riytM3Z0zh9ePzexM0DzbZKCxfvpzm5ua+LzHWmPAH/mzYmx6oTtPe9k5m/3g1m3a3D4om\nL2N4IdVqmnzq8Xz9/ZM5buTwmmvyZk/e9IBPTdX4g4GsHEAygN8raSTwPPAJYDiwQNJMYANJRgrM\nbKWkBcBKoAOYZUdnJjcC3wVGk2S7OMYRBEEQBK4JfxAEQdAADGjloN4sXrzYpk+fnrWMoEY0ysqB\nF/K+chDUjixWDupN+IOhw2CvHDQSg7lyEOSTavxB3CE5CIIgCIIgCAIgZ5MDj3mt80r35pVg4HjM\nR51HwiaD/uDRH3izYW96wKcmj2O4R03ePjtvesCnpmoY6J6DIAiCIAiCqnixrZ0dew/12WbEMLHn\nUNzrJgjqRa4mBx7zWucVb7vp84y3LBd5JWwy6A8e/YE3G/amB47V9NK+Dr7w07UZqUnwOIZ71OTN\nnrzpAZ+aqiFXk4MgCErz6w272PDqwbLtzjzlOKZOOLEOioIgCIIgyBux52CI0ihxcR7wEhv6JIIK\n+wAAFNlJREFU/ad2MPdXG8sei9fuzFpqUcImg/7g0R94s2FvesCnJi9jeCEeNXn77LzpAZ+aqiFX\nk4MgCIIgCIIgCAaPXIUVeYwxzSuDGRe35qX9vHKgo2y7V/aXb5MHPMaG5pFGidUM6oNHf+DNhr3p\nAZ+aPI7hHjV5++y86QGfmqphwJMDScOAJ4DNZjZD0jjg+8BZwAvA9Wa2O207B5gJdAKzzezhtH46\nPe+I+ZmB6gqy4+fPv8rCFTuylhEEQZ0JfxAEQZB/ahFWNBtYWVC+GVhkZhcAjwJzACRNBa4HLgSu\nAe6U1H3HtnnADWY2BZgi6epiL+QxxjSvNEpcnAc8xobmkbDJhmBI+wNvNuxND/jU5HEMH4imToMD\nHV19Hh2dh/v9vN4+O296wKemahjQyoGkScD7gH8APpdWXwtclf5/N/ALEgcxA7jPzDqBFyStAS6X\ntAE4ycyWpefcA1wHPDQQbUEQBEH9CH8QBNmzbucBPv8fa8q2++zvnckFp42pg6Igjww0rOgbwBeA\nsQV1E8xsO4CZbZM0Pq0/A1hS0G5LWtcJbC6o35zWH4PHGNO80ihxcR7wGBuaR8Imc8+Q9wfebNib\nHvCpyeMYXq2mwwbP7zxQtl1X/xcO3H123vSAT03VUHVYkaQ/BLabWSugPppata8RBEEQ+Cf8QRAE\nQeMwkJWDtwMzJL0POB44SdL3gG2SJpjZdkkTge6dqVuAMwvOn5TWlao/hjvuuIMxY8bQ1NQEwNix\nY5k2bdqRmVp3rFeUy5cL4+Jq/fyMPAc4GjPZfQWkUcvddV70lNV7YTPgyx4B5s2bF9/nKsstLS3M\nnz8fgKamJsaPH09zc/I514nwBy0trFixgk9+8pOZvb53Pd0U+qCsx8PCsduLHoBtv1rICa87f9Ce\nf/lvlrBz3Ohc25M3Pd0U2nde/YHMBn4hR9JVwOfT7BRfA14xs9skfREYZ2Y3pxvQ7gWuIFkmfgSY\nbGYmaSlwE7AMeAD4ppk92Pt15s6dazNnzhyw3iAxpMFa/vrnx7cMqWxFbetaXS5Ll+Kqc07hY5ed\nTufh8t/9saNHMO74kXVQNbg2OdRYvnw5zc3NfV3BHzSGsj/wZsPe9MCxmp5+cS9/9UD5GPnBxOMY\nPtiabv8vU5g6oX97DrzZkzc94FNTNf5gMO5zcCuwQNJMYANJRgrMbKW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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "figsize(11.0, 4)\n", + "std_trace = mcmc.trace(\"stds\")[:]\n", + "\n", + "_i = [1, 2, 3, 4]\n", + "for i in range(2):\n", + " plt.subplot(2, 2, _i[2 * i])\n", + " plt.title(\"Posterior of center of cluster %d\" % i)\n", + " plt.hist(center_trace[:, i], color=colors[i], bins=30,\n", + " histtype=\"stepfilled\")\n", + "\n", + " plt.subplot(2, 2, _i[2 * i + 1])\n", + " plt.title(\"Posterior of standard deviation of cluster %d\" % i)\n", + " plt.hist(std_trace[:, i], color=colors[i], bins=30,\n", + " histtype=\"stepfilled\")\n", + " # plt.autoscale(tight=True)\n", + "\n", + "plt.tight_layout()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The MCMC algorithm has proposed that the most likely centers of the two clusters are near 120 and 200 respectively. Similar inference can be applied to the standard deviation. \n", + "\n", + "We are also given the posterior distributions for the labels of the data point, which is present in `mcmc.trace(\"assignment\")`. Below is a visualization of this. The y-axis represents a subsample of the posterior labels for each data point. The x-axis are the sorted values of the data points. A red square is an assignment to cluster 1, and a blue square is an assignment to cluster 0. " + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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tQi8RddWiKRRoWpqjaXkOhCBUW38mJb6k1ZC0Owk0lkjYHZQ0sqIWnr3zyDIs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A7FkWZKF+nzjFaPt6kVb1MbtTub1O2sozusM8/o9Hgb2/A2tRKsqSdYU4t7bxttJn3uID5mc7LS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\nMJKw2Mhpdecupi6LMpdFncsiCeXPV0FV3YbhpcERlgZv3493j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KUqVzA7Pgnj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dW8/ORsNX/TJ4t9fxbq+TtNg5aGnjwNvKVs8AB95W/NMP0KeTJG1O5sfuceD14Qpu49zfRXno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\nhAV3KtPZeY8cneZhyRm9S+21Zkp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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib as mpl\n", + "figsize(12.5, 4.5)\n", + "plt.cmap = mpl.colors.ListedColormap(colors)\n", + "plt.imshow(mcmc.trace(\"assignment\")[::400, np.argsort(data)],\n", + " cmap=plt.cmap, aspect=.4, alpha=.9)\n", + "plt.xticks(np.arange(0, data.shape[0], 40),\n", + " [\"%.2f\" % s for s in np.sort(data)[::40]])\n", + "plt.ylabel(\"posterior sample\")\n", + "plt.xlabel(\"value of $i$th data point\")\n", + "plt.title(\"Posterior labels of data points\");\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Looking at the above plot, it appears that the most uncertainty is between 150 and 170. The above plot slightly misrepresents things, as the x-axis is not a true scale (it displays the value of the $i$th sorted data point.) A more clear diagram is below, where we have estimated the *frequency* of each data point belonging to the labels 0 and 1. " + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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rGkVfL6C+qBRT3wBA4i/GEkj0kAFU2VKNtj77NiknHk1oXLTPwB6gdls+dQVF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\nnt3X3F8fvU95lp1V5Rn8ZkcDT+xtprUnRI7TQobdTH13gL/70xH2Nvb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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "cmap = mpl.colors.LinearSegmentedColormap.from_list(\"BMH\", colors)\n", + "assign_trace = mcmc.trace(\"assignment\")[:]\n", + "plt.scatter(data, 1 - assign_trace.mean(axis=0), cmap=cmap,\n", + " c=assign_trace.mean(axis=0), s=50)\n", + "plt.ylim(-0.05, 1.05)\n", + "plt.xlim(35, 300)\n", + "plt.title(\"Probability of data point belonging to cluster 0\")\n", + "plt.ylabel(\"probability\")\n", + "plt.xlabel(\"value of data point\");\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Even though we modeled the clusters using Normal distributions, we didn't get just a single Normal distribution that *best* fits the data (whatever our definition of best is), but a distribution of values for the Normal's parameters. How can we choose just a single pair of values for the mean and variance and determine a *sorta-best-fit* gaussian? \n", + "\n", + "One quick and dirty way (which has nice theoretical properties we will see in Chapter 5), is to use the *mean* of the posterior distributions. Below we overlay the Normal density functions, using the mean of the posterior distributions as the chosen parameters, with our observed data:" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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NOjk5cf/+/XznvHnbmzRpQmJiIgDXr19n4sSJeHt74+7uTnBwsEU91nkBNm3a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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "norm = stats.norm\n", + "x = np.linspace(20, 300, 500)\n", + "posterior_center_means = center_trace.mean(axis=0)\n", + "posterior_std_means = std_trace.mean(axis=0)\n", + "posterior_p_mean = mcmc.trace(\"p\")[:].mean()\n", + "\n", + "plt.hist(data, bins=20, histtype=\"step\", density=True, color=\"k\",\n", + " lw=2, label=\"histogram of data\")\n", + "y = posterior_p_mean * norm.pdf(x, loc=posterior_center_means[0],\n", + " scale=posterior_std_means[0])\n", + "plt.plot(x, y, label=\"Cluster 0 (using posterior-mean parameters)\", lw=3)\n", + "plt.fill_between(x, y, color=colors[1], alpha=0.3)\n", + "\n", + "y = (1 - posterior_p_mean) * norm.pdf(x, loc=posterior_center_means[1],\n", + " scale=posterior_std_means[1])\n", + "plt.plot(x, y, label=\"Cluster 1 (using posterior-mean parameters)\", lw=3)\n", + "plt.fill_between(x, y, color=colors[0], alpha=0.3)\n", + "\n", + "plt.legend(loc=\"upper left\")\n", + "plt.title(\"Visualizing Clusters using posterior-mean parameters\");\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Important: Don't mix posterior samples\n", + "\n", + "In the above example, a possible (though less likely) scenario is that cluster 0 has a very large standard deviation, and cluster 1 has a small standard deviation. This would still satisfy the evidence, albeit less so than our original inference. Alternatively, it would be incredibly unlikely for *both* distributions to have a small standard deviation, as the data does not support this hypothesis at all. Thus the two standard deviations are *dependent* on each other: if one is small, the other must be large. In fact, *all* the unknowns are related in a similar manner. For example, if a standard deviation is large, the mean has a wider possible space of realizations. Conversely, a small standard deviation restricts the mean to a small area. \n", + "\n", + "During MCMC, we are returned vectors representing samples from the unknown posteriors. Elements of different vectors cannot be used together, as this would break the above logic: perhaps a sample has returned that cluster 1 has a small standard deviation, hence all the other variables in that sample would incorporate that and be adjusted accordingly. It is easy to avoid this problem though, just make sure you are indexing traces correctly. \n", + "\n", + "Another small example to illustrate the point. Suppose two variables, $x$ and $y$, are related by $x+y=10$. We model $x$ as a Normal random variable with mean 4 and explore 500 samples. " + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + " [-----------------100%-----------------] 500 of 500 complete in 0.0 sec" + ] + }, + { + "data": { + "image/png": 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z6vOpc19D5V67FeTeXtj05KvY+OhL+Owbf1V+Afmg/qW3sOwn9wAAjvjTL7HH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oNMneTk47JBrCUGsaX7xmKbYMNoNgDRinHC+Od2kfcF80nQ3+uo9Rzkt98+vPJQ1n5nd4dMox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\nM6ZlnNRLatIwGh2jvPqDtjDg4oZ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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import pymc as pm\n", + "\n", + "x = pm.Normal(\"x\", 4, 10)\n", + "y = pm.Lambda(\"y\", lambda x=x: 10 - x, trace=True)\n", + "\n", + "ex_mcmc = pm.MCMC(pm.Model([x, y]))\n", + "ex_mcmc.sample(500)\n", + "\n", + "plt.plot(ex_mcmc.trace(\"x\")[:])\n", + "plt.plot(ex_mcmc.trace(\"y\")[:])\n", + "plt.title(\"Displaying (extreme) case of dependence between unknowns\");\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As you can see, the two variables are not unrelated, and it would be wrong to add the $i$th sample of $x$ to the $j$th sample of $y$, unless $i = j$." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Returning to Clustering: Prediction\n", + "The above clustering can be generalized to $k$ clusters. Choosing $k=2$ allowed us to visualize the MCMC better, and examine some very interesting plots. \n", + "\n", + "What about prediction? Suppose we observe a new data point, say $x = 175$, and we wish to label it to a cluster. It is foolish to simply assign it to the *closer* cluster center, as this ignores the standard deviation of the clusters, and we have seen from the plots above that this consideration is very important. More formally: we are interested in the *probability* (as we cannot be certain about labels) of assigning $x=175$ to cluster 1. Denote the assignment of $x$ as $L_x$, which is equal to 0 or 1, and we are interested in $P(L_x = 1 \\;|\\; x = 175 )$. \n", + "\n", + "A naive method to compute this is to re-run the above MCMC with the additional data point appended. The disadvantage with this method is that it will be slow to infer for each novel data point. Alternatively, we can try a *less precise*, but much quicker method. \n", + "\n", + "We will use Bayes Theorem for this. If you recall, Bayes Theorem looks like:\n", + "\n", + "$$ P( A | X ) = \\frac{ P( X | A )P(A) }{P(X) }$$\n", + "\n", + "In our case, $A$ represents $L_x = 1$ and $X$ is the evidence we have: we observe that $x = 175$. For a particular sample set of parameters for our posterior distribution, $( \\mu_0, \\sigma_0, \\mu_1, \\sigma_1, p)$, we are interested in asking \"Is the probability that $x$ is in cluster 1 *greater* than the probability it is in cluster 0?\", where the probability is dependent on the chosen parameters.\n", + "\n", + "\\begin{align}\n", + "& P(L_x = 1| x = 175 ) \\gt P(L_x = 0| x = 175 ) \\\\\\\\[5pt]\n", + "& \\frac{ P( x=175 | L_x = 1 )P( L_x = 1 ) }{P(x = 175) } \\gt \\frac{ P( x=175 | L_x = 0 )P( L_x = 0 )}{P(x = 175) }\n", + "\\end{align}\n", + "\n", + "As the denominators are equal, they can be ignored (and good riddance, because computing the quantity $P(x = 175)$ can be difficult). \n", + "\n", + "$$ P( x=175 | L_x = 1 )P( L_x = 1 ) \\gt P( x=175 | L_x = 0 )P( L_x = 0 ) $$\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Probability of belonging to cluster 1: 0.00968\n" + ] + } + ], + "source": [ + "norm_pdf = stats.norm.pdf\n", + "p_trace = mcmc.trace(\"p\")[:]\n", + "x = 175\n", + "\n", + "v = p_trace * norm_pdf(x, loc=center_trace[:, 0], scale=std_trace[:, 0]) > \\\n", + " (1 - p_trace) * norm_pdf(x, loc=center_trace[:, 1], scale=std_trace[:, 1])\n", + "\n", + "print(\"Probability of belonging to cluster 1:\", v.mean())\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Giving us a probability instead of a label is a very useful thing. Instead of the naive \n", + "\n", + " L = 1 if prob > 0.5 else 0\n", + "\n", + "we can optimize our guesses using a *loss function*, which the entire fifth chapter is devoted to. \n", + "\n", + "\n", + "### Using `MAP` to improve convergence\n", + "\n", + "If you ran the above example yourself, you may have noticed that our results were not consistent: perhaps your cluster division was more scattered, or perhaps less scattered. The problem is that our traces are a function of the *starting values* of the MCMC algorithm. \n", + "\n", + "It can be mathematically shown that letting the MCMC run long enough, by performing many steps, the algorithm *should forget its initial position*. In fact, this is what it means to say the MCMC converged (in practice though we can never achieve total convergence). Hence if we observe different posterior analysis, it is likely because our MCMC has not fully converged yet, and we should not use samples from it yet (we should use a larger burn-in period ).\n", + "\n", + "In fact, poor starting values can prevent any convergence, or significantly slow it down. Ideally, we would like to have the chain start at the *peak* of our landscape, as this is exactly where the posterior distributions exist. Hence, if we started at the \"peak\", we could avoid a lengthy burn-in period and incorrect inference. Generally, we call this \"peak\" the *maximum a posterior* or, more simply, the *MAP*.\n", + "\n", + "Of course, we do not know where the MAP is. PyMC provides an object that will approximate, if not find, the MAP location. In the PyMC main namespace is the `MAP` object that accepts a PyMC `Model` instance. Calling `.fit()` from the `MAP` instance sets the variables in the model to their MAP values.\n", + "\n", + " map_ = pm.MAP( model )\n", + " map_.fit()\n", + "\n", + "The `MAP.fit()` methods has the flexibility of allowing the user to choose which optimization algorithm to use (after all, this is a optimization problem: we are looking for the values that maximize our landscape), as not all optimization algorithms are created equal. The default optimization algorithm in the call to `fit` is scipy's `fmin` algorithm (which attempts to minimize the *negative of the landscape*). An alternative algorithm that is available is Powell's Method, a favourite of PyMC blogger [Abraham Flaxman](http://healthyalgorithms.com/) [1], by calling `fit(method='fmin_powell')`. From my experience, I use the default, but if my convergence is slow or not guaranteed, I experiment with Powell's method. \n", + "\n", + "The MAP can also be used as a solution to the inference problem, as mathematically it is the *most likely* value for the unknowns. But as mentioned earlier in this chapter, this location ignores the uncertainty and doesn't return a distribution.\n", + "\n", + "Most often it is a good idea, and rarely a bad idea, to prepend your call to `mcmc` with a call to `MAP(model).fit()`. The intermediate call to `fit` is hardly computationally intensive, and will save you time later due to a shorter burn-in period. \n", + "\n", + "#### Speaking of the burn-in period\n", + "\n", + "It is still a good idea to provide a burn-in period, even if we are using `MAP` prior to calling `MCMC.sample`, just to be safe. We can have PyMC automatically discard the first $n$ samples by specifying the `burn` parameter in the call to `sample`. As one does not know when the chain has fully converged, I like to assign the first *half* of my samples to be discarded, sometimes up to 90% of my samples for longer runs. To continue the clustering example from above, my new code would look something like:\n", + "\n", + " model = pm.Model( [p, assignment, taus, centers ] )\n", + "\n", + " map_ = pm.MAP( model )\n", + " map_.fit() #stores the fitted variables' values in foo.value\n", + "\n", + " mcmc = pm.MCMC( model )\n", + " mcmc.sample( 100000, 50000 )\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Diagnosing Convergence\n", + "\n", + "### Autocorrelation\n", + "\n", + "Autocorrelation is a measure of how related a series of numbers is with itself. A measurement of 1.0 is perfect positive autocorrelation, 0 no autocorrelation, and -1 is perfect negative correlation. If you are familiar with standard *correlation*, then autocorrelation is just how correlated a series, $x_\\tau$, at time $t$ is with the series at time $t-k$:\n", + "\n", + "$$R(k) = Corr( x_t, x_{t-k} ) $$\n", + "\n", + "For example, consider the two series:\n", + "\n", + "$$x_t \\sim \\text{Normal}(0,1), \\;\\; x_0 = 0$$\n", + "$$y_t \\sim \\text{Normal}(y_{t-1}, 1 ), \\;\\; y_0 = 0$$\n", + "\n", + "which have example paths like:" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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CpZCBc463znZg67F2iCsiJoQr8flFSbgiPw4hPk6x+XddE9690AkA2FIUhkdXp0qer+rS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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "figsize(12.5, 4)\n", + "\n", + "import pymc as pm\n", + "x_t = pm.rnormal(0, 1, 200)\n", + "x_t[0] = 0\n", + "y_t = np.zeros(200)\n", + "for i in range(1, 200):\n", + " y_t[i] = pm.rnormal(y_t[i - 1], 1)\n", + "\n", + "plt.plot(y_t, label=\"$y_t$\", lw=3)\n", + "plt.plot(x_t, label=\"$x_t$\", lw=3)\n", + "plt.xlabel(\"time, $t$\")\n", + "plt.legend();\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "One way to think of autocorrelation is \"If I know the position of the series at time $s$, can it help me know where I am at time $t$?\" In the series $x_t$, the answer is No. By construction, $x_t$ are random variables. If I told you that $x_2 = 0.5$, could you give me a better guess about $x_3$? No.\n", + "\n", + "On the other hand, $y_t$ is autocorrelated. By construction, if I knew that $y_2 = 10$, I can be very confident that $y_3$ will not be very far from 10. Similarly, I can even make a (less confident guess) about $y_4$: it will probably not be near 0 or 20, but a value of 5 is not too unlikely. I can make a similar argument about $y_5$, but again, I am less confident. Taking this to its logical conclusion, we must concede that as $k$, the lag between time points, increases the autocorrelation decreases. We can visualize this:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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nmT0T/gPeAP5WyAHNbFDq/9jU/13MbFghaeU4xgQzm2dm883sgiz7HGRmL5nZ\nq2Y2NdM+miciWdpqJdr+rpi+pNJZqgiNWS5JpbIrSaRyK7UqV03EzQR9FvYDbgmtd2AFhQ/5epKZ\n7Q4MM7OngWeBnYHFBaa3BTOrA64BGgjmtJhlZg+4+7zQPv2A/wYOc/dlZrZDMY4tlaWmTiIiIiLl\nkTWIcPfbAMxsRvgCvLPc/YpUuuOApcABQDHbo4wBFrj74tRx7gaOBsLncBJwn7svS+UpY7Mq9YlI\ntlpt6qT2uZJUKruSRCq3UquizFg9L9UEaQywA6ERldz91nwOZmaPA28BjwFPuPsqilQDETKEIDhp\n00yQ97DdgW6pZkx9gKvd/Y4i50NiJH1Up1Ufb6T/NkHxVy2FiIiISH6ijM50DPAHYAHweeA1YG+g\nEcgriAAmAvsTNDU638y6ElzA35VnOp3VFRgFHAL0Bp4zs+fcfWF4p6uuuorevXtTX1/PS/Pf491N\n3ek1eFe2HRHUUHywKOgz0WvPUVmXP+laR/dh+2RcXr2wiXUbN7enl77clp6O1/njrd+4mWeffbb9\neL261fH67JkADNt7NJMens/bc18EYL+xB/Dj8fXt7Vzb7jIlZbltXVzyo2UtR12eM2cOZ599dmzy\no2UtR1lO/+6tdH60rOVs36+tra0ALFmyhNGjR9PQ0EBnmLvn3sHsVeBid7/HzFa5e38zOxX4vLv/\nc6cObnY+0B94w93v7kxaoTTHAhe5+4TU8oWAu/vloX0uAHq6+8Wp5ZuBKe5+XzityZMn+2mnnQbA\npIfnb9E0ple3OtZu2LzVY21L/rb+23RlaL8eQDJrKRobNfGRJJPKriSRyq0k0ezZs2loaOjUfG1d\nI+xT7+73pK27DWgB8goizOwuYDBwJ9BI6kLezL6dTzodmAXsmhrx6W3gm8CJafs8APzOzLoAPYAv\nAf+VnpD6RNSmcF+KJE5upx8zSSqVXUkilVupVVGCiJVmNsjdVwBvmdn+wLsUNk/EPcDzwCnAZcD9\nZvZrgqZSReHum8zsXIJ+F3XALe4+18zOCjb7jal+Hn8FXgE2ATe6++vFyoNUD434JCIiIrK1rPNE\nhNwEtIXZVwBTgZeBaws43vPAnu5+qbsfleqY/QQwu4C0snL3R919D3ffzd0vS627wd1vDO3zn+7+\neXffx91/lykdzRMh6dqCijkta2huXVfp7GQUbp8rkiQqu5JEKrdSqzqsiQj3JXD321NzO/R297n5\nHszdmwk6UVnfAAAgAElEQVRGSwqvK3S+CZGKUq2EiIiI1KoozZm24O41Mw2w+kRILnHtO6H2uZJU\nKruSRCq3UqsyBhFmtpRgZuqc3F23XkVQ3wkRERGpLdlqIoo5WlJiNTU1MWrUqEpnQxKokrUUGm5Q\nkkplV5JI5VZqVcYgwt2ndTZhM7skyn7u/ovOHkskzlRLISIiItWmwz4RZtYD+AXBXAsD3L2fmR0G\n7O7u1+R46i6hxz2B4wjmcFgM1ANjgPsyPC821CdCSqHUtRS6IyZJpbIrSaRyK7UqSsfqK4AhwLeA\nKal1r6XWZw0i3P3UtsdmdjdwYnhGaDP7OnBCAXkWqRqqpRAREZEkijJPxLHASe7+HLAZwN2XEQQW\nUR0B3J+27kFgYh5plJ3miZByK8Y8FBqzXJJKZVeSSOVWalWUmohP0vczsx2B9/I4zkLgHODq0Lqz\ngUV5pCFSU9KbOq36eCP9twk+iqqlEBERkUqKEkTcA9xmZj8GMLOdgSuBu/M4zhnAX8zsp0BbLcZG\n4Ov5Zbe81CdCKim9qVOvbnU0t64HcvelUPtcSSqVXUkilVupVVGCiJ8BlwNzgF7AAuAm4OKoB3H3\nl8xsN2B/YGfgbeA5d9+Qd45FRH0pREREpKJyBhFmVgeMAy509x+nmjG96+4dTkSXLhUwPFNYNitD\n80RIUoSDildemEFz69j2bQoqJCk03r4kkcqt1KqcQYS7bzazB9y9b2r5nUIOYmbdge8CI4E+acc4\nuZA0RSSzDZtctRQiIiJSUlGaMz1jZmPdfUYnjnMbsC/wELCiE+mUlfpESBJtt+tI1m7Y3L5cydmz\nRfKhu7mSRCq3UquiBBGLgSlm9gCwFGhvypTHbNMTgM+6++r8sygixaK+FCIiIlIMUYKIbfh0joeh\nofX59ItYAvTIY/9YUJ8ISaLVC5voPmyfSPuqlkLiRG3LJYlUbqVWRelYfQfwrLuv78RxbgceMLOr\nSGvO5O5PdSJdESkS1VKIiIhIVHl1rO6Ec1P/f51+CGB4J9MuGfWJkCRK7xNRqFy1FJr4TkpBd3Ml\niVRupVaVpWO1u3+20OeKSOUVOvGdiIiIVKdydazGzAYBY4AdAAulcWvk3JaZ+kRIEuXTJ6IYcjWD\nCtdYgAIMyU1tyyWJVG6lVpWlY7WZHQP8gWC2688DrwF7A41AbIMIEclfOKgI11iA+lmIiIhUiw6D\nCHc/tQjH+XfgVHe/x8xWufs/mNmpBAFFbKlPhCRRsfpElIJGg5JcdDdXkkjlVmpVlJoIzGw34ERg\nCLAMuMvdF+RxnHp3vydt3W1AC/DPeaQjIlVCo0GJiIgkV11HO5jZkcCLwJ7A+8AewAtmdlQex1mZ\n6hMB8JaZ7Q+MALrkmd+yampqqnQWRPK2emEyy21bUDGnZQ3NresqnR2pgMbGxkpnQSRvKrdSq6LU\nRPwaONrdp7atMLODgGuAByMe5yZgHHAfcAUwFdgMTM4nsyJSG9TUSUREJN6iBBFDgelp6xrZspN1\nTu5+eejx7Wb2NNDb3edGTaMS1CdCkijOfSKiUlOn2qS25ZJEKrdSq6IEEU3AJODy0LqfpNYXxN2X\nFPpcEak96pAtIiISLx32iQDOBs4ws+VmNtPMlgNnptZXNfWJkCRKap+IqMJ9J+a0rGHGklYmPTyf\nSQ/P54rpuj+RZGpbLkmkciu1KsoQr/PMbC9gLDAYWA7MdPcNpc5cocxsAnAlQZB0S7g5Vdp++wF/\nA77h7n8uYxZFpEhUSyEiIlJ+HQYRZjYSeM/dG0PrdjGz7d395ZLmrgBmVkfQ6buBIOCZZWYPuPu8\nDPtdBvw1W1rqEyFJVA19Igql2bOTTW3LJYlUbqVWRekT8QcgfTjX7sAdwD5Fz1HnjQEWuPtiADO7\nGzgamJe233nAvcB+5c2eiJSLZs8WEREpjShBRL27vxle4e6LzOwzhRzQzAa5+wozG+vuM8xsF6Cu\n7aK/CIYAS0PLzQSBRTgPg4Fj3P1gM9tiW1hTUxOjRo0qUrZEymP1wia6D4tjfB8vagYVP42NjYm4\nq3vF9CXtc5mk13CFl4uxLZ80VG4rIynlVqTYogQRzWY2yt1nt60ws1EETYUKcZKZ7Q4MSw31+iyw\nM1CsICKKK4ELQsuWaadp06bxwgsvUF9fz0vz3+PdTd3pNXhXth0RNHP6YFHQgbXXnqOyLn/Sta79\ngi59efXCJtZt3NyeXvpyW3o6XjKOF5fz6dm1LpGvXyWPt37jZp599tn24zW3ruek/7greD0/sy/9\nt+nK23Nf3Gr5w/Wb2H1kcB/i7bkvsmPv7lz1g+OATztbtl1caLnj5Tlz5hT0/CumL2HWjOD9y+f9\nCi/Pb3qevj26sPNeX9xqedXHG1n3VtB6d+e9vkhz63oWv/oCADvtOYrm1vXt5TG83DOtPIaXP1n8\nyhblMbzcq1sdr8+emXf6fXt0obl1bIfnM7RfT/azJRV/v7WsZS2X9/u1tbUVgCVLljB69GgaGhro\nDHP33DuYfQ/4BfBbYBHBTNP/DFzq7jcWfGCzcQQ1BgcAa9z9gULTSkt3LHCRu09ILV8IeLhztZm1\n1awYsAOwBjjT3beYPO/JJ5/0tpqISQ/P36Ktda9ude3tzsOPta02t8U1X9pW3m39t+nK0H49gC3v\nEtfyHeLwXXso/p16CGqSVn28Eah8GUjCd0u4nEJtl0+RWjV79mwaGhoy3kSPKsroTDeZ2WrgdGAX\nggv/Se5+b74HM7PHgbeAx4An3H0Vxa+BmAXsambDgLeBbwInhndw9+GhPP0eeCg9gBARyVe2Phjp\nzaWqIcCI2qQnfIEPW74u6f1UOrNNotNkjiJSDFGaM+Hu9wD3FOF4E4H9CUZOOt/MugJXu/tdRUgb\nAHffZGbnEgQqbUO8zjWzs4LNW9WeZK2KUZ8ISSL1iYif9Iu2qAFGrovzclzs5QoU0u/+57rgj0pl\ntzLUP6hz1CdCalWkIKJYUnNLPJP6+6WZnQ/sbmbfdPe7i3icR4E90tbdkGXf04p1XBGRfOUKMHJd\nnBcafHSmmZDu/lc/DZMsIlGVNYgws7sIJqy7E2gEerr7xWb27XLmIyrNEyFJVMvzRNSSQoOPODcT\nUtmNn6jDJNdygKFaCKlVZQ0iCJpEPQ+cQjDR2/1m9mtgQZnzISIiIp2geVhEalu5g4jngT3d/dK2\nFWZ2CPBemfMRifpESBKpXbkklcpu9ailfhbqEyG1KmMQYWaXRHmyu/8in4O5ezPB5G/hdU/lk4aI\niIgkh0aDEqlO2Woidgk97gkcRzB06mKgnmAG6PtKm7XKU58ISSK1K5ekUtmtDelBRdKpFkJqVcYg\nwt1PbXtsZncDJ7r7faF1XwdOKH32REREpFpVe1MnkWoWZeiNI4D709Y9SDDnQ1VramqqdBZE8rZ6\nocqtJJPKbu1pq5Vo+5uxpJVJD89n0sPzuWL6kkpnL5LGxsZKZ0GkIqJ0rF4InANcHVp3NrAo6kHM\nrDvwXWAk0Ce8zd1PjpqOiIiIVK9cHbKrYaZ3kWoSJYg4A/iLmf0UWAYMATYCX8/jOLcB+wIPASvy\nzWSlqE+EJJHalUtSqexKWD4zvVcyqFCfCKlVHQYR7v6Sme0GjCWYKO5t4LnU7NNRTQA+6+6rC8um\niIiISEAza4tUXt7zRLj7M2bW28y6u3vU4RWWAD3yPValaZ4ISSKNtS9JpbIrharkxHeaJ0JqVYdB\nhJl9gaAj9XpgKPC/wFcIZp3+RsTj3A48YGZXkdacSfNEiIiISKnU0sR3IuUUpSbiOuAX7n6Hma1K\nrZsG3JTHcc5N/f912noHhueRTlmpT4QkkdqVS1Kp7EqplWLiO9VCSK2KEkR8HvhD6rEDuPsaM9sm\n6kHc/bMF5E1ERESkZFRLIVK4KPNEvAV8MbzCzMYQDP1a1TRPhCSRxtqXpFLZlUpKn7OiuXVdpOdp\nngipVVFqIn4OPGJm1wPdzexfge8D38vnQGb2VeBEYEd3P9LMRgPbqk+EiIiIxE2pO2SLJF2UIV4f\nNrMJBEHDNGAY8HV3fzHqQczsPOCHwM3AcanVHxNMYPflfDNdLuoTIUmkduWSVCq7EidRmzqpT4TU\nqpxBhJl1AW4FznT3H3TiOD8CGtz9LTO7ILVuHrBHJ9IUERERKblSdMgWSbqcQYS7bzKzw4DO3hrq\nCyxtSzb1vxvwSSfTLSnNEyFJpLH2JalUdiUpwkHFKy/MoLl1bPs2BRVSK6L0ibgCuNjMfpnnLNVh\nzwAXApeG1p0PTC0wPREREZGK27DJVUshNSlKEHEesBPwEzN7h09rEnD3qJ+M84CHzOx7QF8zewP4\nEPhanvktK/WJkCRSu3JJKpVdSaL0cpve9EmkWkUJIr7d2YO4+9tmth+wH0HH7KXA8+6uXwsRERGp\nSpp7QqpZlNGZphXpWIcC3wQGufvXzGy0mcV6iFf1iZAkUrtySSqVXUmiXOVWHbKlmnUYRJjZJdm2\nufsvohwkbYjX41OrYz/Eq4iIiEixaIZsqSZRmjPtkra8E/AV4C95HCeRQ7yqT4QkkdqVS1Kp7EoS\nFVpuVUshSRelOdOp6etSk8+dmMdxEjnEq4iIiEg5qJZCkqauwOc9BhyTx/5tQ7yGxX6I16ampkpn\nQSRvqxeq3EoyqexKEpWi3LYFFG1/za3rin4Mkc6K0idieNqqXsBJfFqzEEUih3gVERERqTQ1dZI4\nitInYiFBEyRLLa8FmoBToh4kNMTrGKCeEg/xmmpudSVBTcst7n552vaTgLa+GR8CZ7v7nPR01CdC\nkkjtyiWpVHYlicpRbrM1dVJAIZUUpU9EoU2e2pnZPu7+CjAz9VcyZlYHXAM0AMuBWWb2gLvPC+32\nJnCgu7emAo6bgLFbpyYiIiISH5rMTuIiSk3EFszsYGBznvNHPGxmvYHpwLTU30vu7rmfVpAxwAJ3\nXwxgZncDRxOMBgWAu88I7T8DGJIpIc0TIUmksfYlqVR2JYkqWW7VAVsqKUqfiGnAz9z92dTwrD8B\nNprZf7v7r6McxN3rU30rDiQYHvZcYICZNbp7sftFDGHL/hrNBIFFNmcAU4qcBxEREZGS0jCxUklR\naiL2JrhbD/A94GCCfgTPApGCCAB3f9PMugLdU38TgIF55bbIUrUqpwLjMm1fuHAhP/jBD6ivr+el\n+e/x7qbu9Bq8K9uOCPpKfLAoGJGh156jsi5/0rWu/Q5F+vLqhU2s27i5Pb305bb0dLxkHC8u57PT\nnqNYu2Fz4l6/aj9etZ1PKY7Xs+unrWer8fwKOV61nU81Hm+7XUfSMm92Rc4vU/pzWtbwwaImhm/f\nE1JBRGNjIwDjxo3Tco0uz5kzh9bWVgCWLFnC6NGjaWhooDOsoxZFZrYKGAB8FnjM3Uek1n/o7n0j\nHcTsf4H9CfooPE0w5Ot0d/+w8KxnPdZY4CJ3n5BavhDwDJ2r9wHuAya4+6JMaT355JPe1pxp0sPz\nt4j2e3Wra+9IFX6sbbW5La750rZ4bItrvrQt/tvimi9ti8e2XPv136YrQ/v1aN+mmgkJmz17Ng0N\nDdbxntlF6TTdSNBR+T9JzVJtZiOAd/M4zihgM/By6q+pFAFEyixgVzMbZmbdgW8CD4Z3MLN6ggDi\nO9kCCNA8EZJMGmtfkkplV5IoruVWc01IqUVpzvRdYBLwDvAfqXV7AldFPYi772ZmOxP0iTgQuNDM\ntgGecfcz8spxx8faZGbnEkyI1zbE61wzOyvY7DcCPwe2B641MwM2uHuufhNV6cD7/kDflSsA+HDg\nIB496qQK50hERERKQf0lpNiiDPH6HvCztHWP5Hug1FwRbwCDgaEEfSuOyDediMd6FNgjbd0Nocff\nI+jfkVO1zxOx3TsrGfzWQgCWW6dqtCSHcLAGpQ/YNNa+JJXKriRRUspttrkmQEGFFCbSEK9mNhIY\nD+zAp5PO4e6/iPj8Bwk6L39IMLzrQ8A/u/uCfDMskjThYA0UsNUK1fSJSFxpVCcphg77RJjZmQQj\nMR1CMMvzFwiaN+2ax3EagS+6+zB3P9ndb3b3BWb2k0IyXS7qEyFJFNf2ubVmu3dWsstbC9nlrYVs\n987KSmcnEVR2JYmqodyG+0+o74REFaUm4qcEIxhNN7NV7n6smR1B0GE5qn9z999mWg/8Vx7piIiI\niEiJqKmTRBUliBjo7tNTjzebWZ27TzGzOzt6opkd0nac1JwM4XYcwwmaN8VWtfeJkOqUlPa5IulU\ndiWJqq3cpjd1EskmShDRbGafcfe3gPnA0Wb2LvBJhOfekvrfA7g1tN6BFuC8PPJaUXvd9nv2XLqs\nfVltnEVERKTaqb+EZBMliPgtsBfwFnAJcC/BjNPnd/REd/8sgJnd7u4nF57NymhqaqJtsrleK1rY\nXp1jy6LcoxlVm9ULm9pnPRVJEpVdSaJqL7ca1UmyiTLE6/+EHk8xs/5Ad3f/KOpBkhhASDSluOAv\n1mhGGh1HRESkeNTUScKiDvE6AJgI7OzuvzWzHcxsO3dvjnogM/sqcCKwo7sfaWajgW3d/amCcl4G\n6hPRsTgPX1qr82BUW/tcqR0qu5JEtVxu1dSptkUZ4vUrwBvAtwhmegbYDbgu6kHM7LzU/vMJZqwG\n+Bj493wyKyIiIiLxoKFha1uHQQRwJfANd58AbEytmwmMyeM4PwIOdffLgLZwfR5ps0rHjeaJkCSq\nhjHLpTap7EoSqdwG2mol2v6umL6k0lmSEovSnOkz7v5k6rGn/n8S8blt+gJL09LoRrQRnkREREQk\nxtRfovZECQReN7PD3f2voXWHAnPyOM4zwIXApaF15wNT80ij7OLUJ0KdhKtHv3dWcMLNVwCleS+r\noX2uRuiqTdVQdqX2qNxmpv4S1S9KEDEJeNjMHgG2MbMbgCOBo/M4znnAQ2b2PaCvmb1BMNHc1/LN\ncK2q1U7CcVZoYNd1wyfsovcypzh32BcRkY6pZqL6RRnidYaZ7UvQsfpWgmZJY/IZmcnd3zaz/YD9\ngPpUGrPcPdahe3ieCJF0cQ3sqn3McqleKruSRCq3HdP8EtUpUr8Gd19GMOlcQcysO/BvBEO8DgaW\nA3eb2aXuru78IlVETe9ERCQsvVZCTZ2qQ4dBhJn1I+i/8A9An/A2dz8s4nGuIxiJ6XxgMTAM+Bkw\nBDgtj/yWVZz6REh1CvePgCJN1lfh9rnlrqEpd9CiIKl0Kl12RQqhcps/NXWqDlFqIu4BugB/IZjb\noRDHACPcfXVq+XUzmwksJMZBhEiphftHQLyaRUWV3gm637srP31cgiApXbmDlvDx1ry7khNWlvb8\n4krBlIgUg5o6JVeUIGIssIO7d2Y41hagF7A6tG4b4O1OpFly6hMhSVR/y3/whU8+nQKm1Bd46Z2g\n1/fo2f64GoKkXKr9/HIpRfCmtuWSRCq3naOmTskVJYhoBPYEXsknYTM7JLR4B/Comf0OaAZ2Ac4B\nbs8nTREN/dmxPq2r2GXFqvblWrqwldLJVeMkIlIsauqUHFGCiO8C/5dqfrQivMHdL8nxvFsyrPtZ\n2vJZwOUR8lAR6hNROoUGAxr6s2O79toBWNXhfumSEqDpYrYyctU4Fe0YalsuCaRyWzpq6hRvUYKI\nSwlqDt4Ctg2t94x7t210/2zh2Yq/QicMUzvigIKB8olaVpPynpTjYlaSKynBsIh0TE2d4i1KEPFN\nYHd3j3X/hVLI1Sei0AnDorYj1t3WT4Uvgmv5dcglXF5a3l7I4NA2TW4nSZGrbXm4jPdZ8yEf9e7b\nvi0cKOQKhtO/V8PpKNioHrkCyVIEmeoTUT5q6hQvUYKIN4ENpc6IbCmpd1sLraHJJXwRnJTXodzC\n5eX9LhuAbpXNUEyUu+YvW8BbjlGqSiH8+lU6gA+X8fU9etL/nU8vBMOjZOXKZ6bv1bZ0FGBXRikC\nu1yBZFJqXKVjaupUeVGCiDuAB1OdotP7RDxVklzFhPpE5E93vStvz659YZPmcITyD/+aLeBN6ihO\n6RfuJT9egW3Lk3ijodCL52prElvpwK4YN77UJ6Iy1NSp8qIEEeek/v86bb0Dw4ubHSlEXH9Uknr3\nNazQqu843cEViet3RC0r9OK53IFxKcTp+1E3vqpHOKhIr6VY9fFG+m8TXPIqwCieDoOIau8gnUtS\n5omI649KnO6+5gpocgUKhVZ9l/oObq4+M/M2fsi+WZozVUNgV8sKDQbi+h2RrtralquTd2blruGK\nqtDvx2ort9UgvZaiV7c6mlvXA2oGVUxRaiIkZtK/6KLeyanlztq5ApoktpEttM9MnAI7yV+pB2ao\n5e+IYgm/htu/u5LeH33Qvi2unzfVVAUK/X7cZ9rjfOGTJ9uXa/k1TAI1gyoeBRE5xLVPRPoXXdQL\nyEp31k7KKEtJyWc26hNRPfK5qM918Zrrs17o80qhkm3Li1VLF9e77LkUWlOl4CMwckMXBr+1oH05\n14hgcX2davm9VDOowlVlEGFmE4ArgTr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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def autocorr(x):\n", + " # from http://tinyurl.com/afz57c4\n", + " result = np.correlate(x, x, mode='full')\n", + " result = result / np.max(result)\n", + " return result[result.size // 2:]\n", + "\n", + "colors = [\"#348ABD\", \"#A60628\", \"#7A68A6\"]\n", + "\n", + "x = np.arange(1, 200)\n", + "plt.bar(x, autocorr(y_t)[1:], width=1, label=\"$y_t$\",\n", + " edgecolor=colors[0], color=colors[0])\n", + "plt.bar(x, autocorr(x_t)[1:], width=1, label=\"$x_t$\",\n", + " color=colors[1], edgecolor=colors[1])\n", + "\n", + "plt.legend(title=\"Autocorrelation\")\n", + "plt.ylabel(\"measured correlation \\nbetween $y_t$ and $y_{t-k}$.\")\n", + "plt.xlabel(\"k (lag)\")\n", + "plt.title(\"Autocorrelation plot of $y_t$ and $x_t$ for differing $k$ lags.\");\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notice that as $k$ increases, the autocorrelation of $y_t$ decreases from a very high point. Compare with the autocorrelation of $x_t$ which looks like noise (which it really is), hence we can conclude no autocorrelation exists in this series. \n", + "\n", + "\n", + "#### How does this relate to MCMC convergence?\n", + "\n", + "By the nature of the MCMC algorithm, we will always be returned samples that exhibit autocorrelation (this is because of the step `from your current position, move to a position near you`).\n", + "\n", + "A chain that is not exploring the space well will exhibit very high autocorrelation. Visually, if the trace seems to meander like a river, and not settle down, the chain will have high autocorrelation.\n", + "\n", + "This does not imply that a converged MCMC has low autocorrelation. Hence low autocorrelation is not necessary for convergence, but it is sufficient. PyMC has a built-in autocorrelation plotting function in the `Matplot` module. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Thinning\n", + "\n", + "Another issue can arise if there is high-autocorrelation between posterior samples. Many post-processing algorithms require samples to be *independent* of each other. This can be solved, or at least reduced, by only returning to the user every $n$th sample, thus removing some autocorrelation. Below we perform an autocorrelation plot for $y_t$ with differing levels of thinning:" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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eutuh9S+ZPIHVWa5/bfOS6o5ytuoPfT25qr+seUlO6s92PBdMGs+MwHjOXrqG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\nXr16cfLJJzN//vxayznssMMA6NGjB92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gwQIAVqxYweLFi6ufmxyDqo561V2WVq1aAbBy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\nbNkykpKSKjx/VcnoqFGjmDNnDsePH+fgwYMsXLiw0rY33XQ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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "max_x = 200 // 3 + 1\n", + "x = np.arange(1, max_x)\n", + "\n", + "plt.bar(x, autocorr(y_t)[1:max_x], edgecolor=colors[0],\n", + " label=\"no thinning\", color=colors[0], width=1)\n", + "plt.bar(x, autocorr(y_t[::2])[1:max_x], edgecolor=colors[1],\n", + " label=\"keeping every 2nd sample\", color=colors[1], width=1)\n", + "plt.bar(x, autocorr(y_t[::3])[1:max_x], width=1, edgecolor=colors[2],\n", + " label=\"keeping every 3rd sample\", color=colors[2])\n", + "\n", + "plt.autoscale(tight=True)\n", + "plt.legend(title=\"Autocorrelation plot for $y_t$\", loc=\"lower left\")\n", + "plt.ylabel(\"measured correlation \\nbetween $y_t$ and $y_{t-k}$.\")\n", + "plt.xlabel(\"k (lag)\")\n", + "plt.title(\"Autocorrelation of $y_t$ (no thinning vs. thinning) \\\n", + "at differing $k$ lags.\");\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With more thinning, the autocorrelation drops quicker. There is a tradeoff though: higher thinning requires more MCMC iterations to achieve the same number of returned samples. For example, 10 000 samples unthinned is 100 000 with a thinning of 10 (though the latter has less autocorrelation). \n", + "\n", + "What is a good amount of thinning? The returned samples will always exhibit some autocorrelation, regardless of how much thinning is done. So long as the autocorrelation tends to zero, you are probably ok. Typically thinning of more than 10 is not necessary.\n", + "\n", + "PyMC exposes a `thinning` parameter in the call to `sample`, for example: `sample( 10000, burn = 5000, thinning = 5)`. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### `pymc.Matplot.plot()`\n", + "\n", + "It seems silly to have to manually create histograms, autocorrelation plots and trace plots each time we perform MCMC. The authors of PyMC have included a visualization tool for just this purpose. \n", + "\n", + "As the title suggests, the `pymc.Matplot` module contains a poorly named function `plot`, which I prefer to import as `mcplot` so there is no conflict with other namespaces. `plot`, or `mcplot` as I suggest, accepts an `MCMC` object and will return posterior distributions, traces and auto-correlations for each variable (up to 10 variables). \n", + "\n", + "Below we use the tool to plot the centers of the clusters, after sampling 25 000 more times and `thinning = 10`." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " [-----------------100%-----------------] 25000 of 25000 complete in 11.7 secPlotting centers_0\n", + "Plotting centers_1\n" + ] + }, + { + "data": { + "image/png": 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p+0UkA7A+jW60pPgaATyMMWI/Qil1xnWNGo0ju0/kUOg54sEj4WHQslZVqkXr\ndWehRMBS5YiI12z0s2fPJm1dmnsldztl6KSaMaxb8ysAr91/A+OX7SuhHJ2+eR1ZqSeIS+qMICWU\nsHtFHmDe9hNulal7hO8nrGomi3Lqu9xvLVdv25VmCVXYuH6Ny/325WHtarPkXAOP9ZW1/OPiZQ7l\nYCiJZ6XuIi6pM9N/P0y1Y1s5mJUH1C1TfV/PX2xxoBJs7XsuNYUbrurL0tTT5O/bxCsDm3Pllb1c\ntt+5vl9XryYrda9t/9Htxt/L1AQj+fbbX88nN78I8Hw/bpoRzojOdVmxcpXX+7Fq1VmH9hUpRcdu\n3YmPiWTVqlUopejVq5dLe5cuX0lWaipxSZ35aO1Bwg9tISv1UKn7w4dAUmJywPuXq/Kb1yTz6o5q\npTofDCX3vNPGVHBKWIUruW9XSl0BICIfi0g3jNF225CORUzZY4gDBD8XoTcqSmLFnf6R2SRfgqWD\nNXWda/V7Z9zZkxATwZRhrQNtlk9oHazyI2CpcoCdQDsRCcMY1VrjfHJycjLNm9/stnL7jlctKpwZ\nY28lTCCpZlXGL9tXomNe1uMKluUbq0cKilSJm/LC3dfT8c/jvGtZxm5//qe3tKV+XDRndpxk0cr0\nEvvty4KxMnHPaYtj1qQGyefSyOlwFbM3H3M4fvTwDiz5fLPH+gJVDpRyuLVDu9oftz3WVq7arBMd\nIsL43pKrrzTX+/7P40w9VNNle5amGtOiUU060qFb+xL7nevLyS/k7V/3c1HNVsQlOWr7xCV1tolx\n/phVr8Q+V+XsvEI+WHvQp+8/JawWYfsy6NEknp49e/Lqkj38c+YW/n1dC5p26MbYubvI3HaC69rU\nomfPnuSeL+RMXgG7TuTStfvlxKVWB+C/m48BdYhLquPyeq4exPblvy3YXe79Ky6pM716XQw7/ij1\n+WZScldK2Q8/5wHpQFsRWQ78qpR6jgoIcajM6NgbDeh+4I2ApcpRSn0vIlPxko3eHY1qRHMgM4+u\nDauz/kA2t3e+iBa1PK8EDLOL3SlwM2VyTeuaHMrOc4hvea5vE+rHWVSJfZiREhFGXFyX+jWq0Lp2\nVZolxrBq1SGqNKju4GAZ9QV3NfqPW4/z086TtKxVlaiIMG5qX4c61aLcHl9QpDiclcfa/Vk0iIum\n3UWxLo87V1DExOWOS5/XHcjikoalX4q893Suzcn1hvWWupIPefnnNIa0q83qvRluY9UAyyhb+fDD\n1hP8sPXxrdIUAAAgAElEQVQEU4e3ISe/kJWWKdtP1h9i3+lzZOcVMnn1fnYcP0t4mDB/e3EcYH87\nwVBNcBGR64HXMF4ETwLJlmnBKSJynWWbxxAHqLwxWMHCbPaYLQbLbPaA+e6Z2ezxh4CmylFKfQ58\n7q4eTzFYt3euS9+kBMLECAR2not+omcjJq0ygpV7NKnBtmNnuaxR8R/76m5iriLDwxjdvaGDg9U3\nKdH2OdzOSevSoDoZuQWkncotUU9MZDiDWxWPwPTs2ZPfD5TU2gqyf8XbvxqOy64Ths1zthz3qN10\nz9d/cuyMYyD3ogd6opQi7VQuTRJiiAgTth87a4v3srI8LYNVTtt8YWcpVnNa3apVezNL7Fu9L5PV\n+zJpW6ekU7j+QJbDw+vbLcdKHBNIHpi9zaG85chZh/LCnSVj+zw5hc6Y8UFcmVFK/Qj8KCKTgeuU\nUt9bdn0PdAZ+wEuIAxhhDlOnTqVxY0MEtkaNGnTo0KHCE4JfyOUtB7MAY7S6vKfNrdsCXX9C+26m\n+T4v9PLmzZvJzDT+/qSnp9OtW7cyhziYRmj06d6NGdiipst94JjiZNEDF1OkFGEibDp8hmNn8hnQ\nItHtueBePHJ52mn+ZZnyWnh/Z0TE4Vgw5se/crE8f8PBLJ5dkOqw7Yd7OrF6bwavB0H47p2hrXjk\nux0ltntysJzbBjDp+hZMWrmf9Ixz9E1K4Lm+TXnp5zR+21fSyfH1OvbM236CtyzOsTdmjWjP//Zn\nMWml6xQgAO3rxpZwaDSB57VBSXRrGMe3W47x/hpjtWN8lQgmXteCxbtPMcuygve1QUkk14zhlplb\nStTx975NicvcU6FCoyISZVkJjUVWZjWw0DId+CqwCUOCZjGGHM1NQBOlVIkYrIkTJ6r77rsveMZ7\nQcdgeRYaDWQMliehUV/xFoPlTYqmPNAxWJ7xR2jUNLkIW3qZDmyWGMPz/ZrSsIahxWSdHuxYzz89\nU/tpRndyAa42r1q1iq6XXU6ViDBa16lqW30mwMX1q/tlk6+4cq7Kwv3/+a9DPNRzfZt6da4AFmw/\nwdztJxg3KJm4KsVd6bs/j1M1MoyrWhoO8/lC3534wiLl0bkCx1FHe8yW2NUfKrot39zVwSZ1cWP7\nOiTERDJt/SH+0a8pjeKrcFuni1i1J4OeTePp5mLauElCFf7aoxEd6sbyR0mfPtgMEpEnMQZIdwGH\ngXUikg3sAV5USikR+YgyhjhcaOjYGw3ofuANX2Ow6gFzgTZANQwF+BVAe6CzUirNctx24JDltDFK\nqe0uqnMgISaC1wYl0STBeyqWK5sHPobF05RemBhCm09d6XrJf0xkOHNGdqRIKa6dthEwnLFgTxM6\nY68tdSrnPPsyzpWL02edsp29+Rj3WSQb9p7OteXGszpY2Xm+60fNSjni9ZjD2VpAs7yJjnAMTuyb\nlEDfpOLfX0xkOB/f3Nbt+UmJMX6//AQKpdQPGFOA9pTIaKyUmgnM9FSXjsHyjNnsMdsLl9nsAfPd\nM7PZ4w++ig5ZldzXgC0m6wZgttNxx5RS/Sz/SjhXrh5Od3WpR1LNiktr42lItl9yIgvu6+zyDd3a\nCcLDxGHkK0yE+JhIejeLL3FOsNhy9Cxvr97PuYIiRn71J3+bv5sNB93nZnT+0R8/W7qEwPZ6ZQ9+\nU3zbV6SdJv30OWZs8O40WZm33bs4rHMMmRUzPrzKSkW3xZsgqysG2AXxj768YSDN0Wg0mkqHTw6W\nUipfKZWJnUyDUuo4jrINADUt+QmnWHSyvDLQS+xUedOmTiwPXFqff12dZNt2cwdj6fzw9nV8WhUo\nLj4/eWXjAFpZOsbO3cWP207w9cajNsmCFaUITveWRNiZPw5l8+KiVNJPO+Yr/PyPIzzwzTY3Z2l8\nYVi72hVyXW/q+q54rGdjXhuUxLx7O1GjSlCjD4KGzkVooHMRlg2z2QM6F2F54peSuwuusCx9fhZ4\nEHjHfqd9DNZXd7TnfKEqMRVR3sRElryecy66v1zWgHu61fOoKm4fiGf/t6iipwft+fyP4pGj+dtP\nclkjlyvP/Y73ST2ZS+rJXNakO46S7T0duATRvlDRcUuBxNqWIW1rM+dP31LoVDTREWEuR3s1oUeg\nYm+OZOeR4mcy91V7S7+yWRMYdAyWZwL6mmmXo/A74HFPxybEBHe1hDUxdP8k30bMSpOyxWEEy+Jt\n2Y98zbq9Pc8u2M2+jOA6HM689HNahV5fU3oa1Ijm7q71+Ox335SiNeWLjsHyTGntySso4t8rfVth\nXBbM9sJlNnug8vchM1MWJXfnMRoBEJFIDNmHfOAKINXpOHbv3k3aukVEJ9Rl/ImFQdWQefXqJD76\ndiEdCrOBRn7X17NnT4fyrBHtWfvratvIVlR4GLfVPEZEeBg1YyP5aHgbuj83DSjf1CYXctm6zSz2\n+FOOS+qMHNzCqlVnuaNnT6pFhTPu87mlrq9aVDhhjTrQoW41Lgvbx79X7rftz9u3icd7NmLK/gSH\n88cMHwSU/fdh/ZyebqwG9UdHRqPRaCorPulg2Sm5d8FIkPp34CkMR2oP8Aaw1nJMNnAauFMp5SBW\nZNXBGtAi0W0y3lDGqkF1Q9vafL+15LTP4FY1WbDDe5C35sKgW8PqvDYoGTBSDfmqht/+oli2HDV+\neh/e1Jr0jHP0aBJPRJgw+OM/sKpmfHVHexJiIhk7dxc7j58lz7JjVPcG3Ni+jrvqS40/OjJmQ+tg\nGQRKB2vf6Vz+8o3XxeZlRutgeUfrYHmm3HWw3Ci53+ri0BJLn+1JSUlh3KDhdKhrjuXb/lCWTvB8\n/6b8ujeTvkkJLh2sa9vUqhAHK1Ril0KlHWC0ZVC/obay/WvQ6O4NmGIR/nTFE70ac79FaT4xJpKm\ndhIon9/enn2nc7m4fnXbdPaEa5MpVLByz2mWp2VwbetagW2MJuTQsTca0P3AG0Ff6tP1Ag6CvbJZ\nAlc2S+DPI2dc7m8SX8Xl9lpVIzmR41qaQOOZcIFS6JxWKNe1qcXcbSdsZXvdt/7JCXzxxxH6JScw\npG1tqkdHcDArj5l/lJTAqBpZnDYqymkRSc2qkdR0eksWESLESCHV18cYxQsVHYPlGbPZY7YXLrPZ\nA+a7Z2azxx+CuoTPbA8nf/CnE9SIKenXDm1Xm+iIMN4b2oquDapzQ9tafH93RxY9cDFfjGjvj6le\nMeOPviy4asflTWrw2S0lBTFf7N+MJ3tVnJQGGLkv7bm5Qx1b8m3ntlSPjuCrO9ozqntDwsOMaXar\nnIgz9itZo8NDYmbOL0SknYisFpHlIvKxZdvTIrJSRGaISLhl2wjLcT+ISOUfZtdoNBVKcDUSNAA0\nrFGFf/Rraiu3qVOVMRZhxuRaVRk3OJmHezQixm4koiKFSys79eKimXBtssO2ns3iGdTKfe7L5JrF\n02rREWE2xydQXN0ykWucrl8lIoxJ17dkROeLGOtCR81Zm8rdwFx8TAQ9m9bguja1yqRnFYJsV0pd\noZTqDSAilwK9lVK9gM3AUEuc6SigFzDD8rkEWgfLQOtglQ2z2QNaB6s88cnBEpF6IvK7iOSISJiI\nRIjIryKSJSLN7Y7z+AZotoeTP/jbCXo3T+CRHg0JExjV3bvqtTtl7Acure+XHRCcH339uGhu6eh6\nxGVU9wZec1H6gqd2dKxXnYcvb0jt2Ehm3NrOYz0PXFqfd4e2spUHJCc4OMS9AuDsPnpFIy5v4lqX\n7J5u9Yk95l2g1ZUIbpgYjtiLA5rz6BWN/LYzFFBKFdoV84EkYJmlvBi4HGgBbFJKFQFLLNs0bnj0\n0Ud1/I1G9wMvBCxVjq9vgJpihrStzbx7O9OmjvfREXcDEe6mifylio8CsO7ixpz56KbWPHBpA1v5\nskbFsXgDkhN5x86hseeervW81t29cXFdF1Vzn0Dghna1mXl7ey6q7jnJQBiOo0XhYUKt2CiGd6jD\nqO4N/BbT/O+dHYgMDyuhtRZXSvXzKhFhPN+vKa9eZXvHwYdFwRckInK9iGwG6mDEnlpVcTOBeKCG\n0zaX3q/ZwhzMFq9iNnvMFv5gNnvAfPfMbPb4g6+rCPOBfLH7q6OUOi6O8w+2N0ARWQJ85FyP2R5O\n/hCoTuBLKh5wdHgub1yD39IzmTKsFSLCVS0SSTmc7TZHnzdc/eh/uKcT/1yyhxV7MmhTpyrbjuUA\n0Kp2VRJiImyq7Rc3qO6TgGqYk4f4SI9GPBsdTu75QpeOxazb21MlMozYqHBST+Wy0pLq5+0bWiIi\nPPLdDtuxf+vTlN/2ZXLF3R0pUjBs+ibbvla1/Z/as9r+4GWGg1ikFDWrRvD8wrIJt7pKI/PgZQ0c\n+oKv/csaCN+tYXXWH8jmkkYX7iISTyilfgR+FJHJQCFg/aLigAwcnSrrNo1GoykzgUyVE48Pb4Ca\nshETGc7E61oQHRFWYjrtqd5NUEpx9ceBneobe2Vj+icn0rJ2VW7/YgsAb9/QilkpR2wOlq9B1M5+\nZFyVcGIiw4mNCnd5fM3Y4pVuhXbJpK0OU8+m8bYUGbFR4Qywy2k56/b2ZJw7z9ajZz3GWdkTFS62\nvI3W4cJ7utbjmy3HGO40ShgmwqVu0g6Vlmd6N2FZ2mmua+OfNMKzfZqyNPU0fZMSvB98gSEiUZaX\nRDCeUWFAb2ACMABjZH4X0E5Ewuy2leCtt94iNjaWxo2NGLlgiiW7Kk+ZMqVCrr9hwwYAunTp4pc9\n69f8SlZqermJ9x5ZOZuq9ZNNI2bszp6E9t18+r7Ko7x582ZGjx5dpvOfeOIJACZNmmQKewJ1/czM\nTADS09P9Ekr2SWjUdrDIL8AAS5wCIjINeFUplSYibYGHlVIPi0gC8JFSarj9+UOGDFFmejj5U7aP\nwTKDPUCZleKt2+z3L3rgYof6j2TnsXHdGmKjwtkXm8xnvx8mKzWFmzvUYWFOfa/Xs9aXnVdA18t6\nUKdaVAn7r3nlc07lni9x/aYdujF27i66qn30aBpPz549eXFRKouWrgBgzbh7gbL9sXllcRo07ECj\nGtH8uWGtUc8jw7m4QXVWrVqFUopevXq5PP/7RUs5cTafr07WKdX3bbW3svUvf5Tcx44dW2HR9iIy\nBHgS4wVxl1LqQRF5Brge2Afco5QqEJE7gDHAKWCEUqpEkjwtNOoZLTRaNnsqq9BoeWA2e/wRGi2t\ng7UUw8EqtJSnAf9USqVaYrAWY8Rq3QQ0UUpNsD/fbA8nfzBbJ4BipXhfsSp+u/rRL3rgYrfnnc49\nz60zjRGtBfd1ZvAnjiNndapFlpiu9FSflbyCIr7aeJQO9apxcX1HCQOllENc1N9/2s36A9kOdZfl\nnuw7ncvnG45wT7d6hImQdiqXK5qWLojd+XuvHh1Odl6hy2Pv6lKXu7p4jyszY/8qK6Gk5L5kyRJl\nHbXR+E95O1iBJBAOljsq0sHSeKbcldztUuV0BH4SEftUOcki8oZS6kcRmQqsxPIG6FyPjsEyF9Yp\nsdK+4SXERLLw/s5uJQDevqGVzQErDdERYYx0E9TufK1EF8nCy3JPmiTE8I/+zWzlenHRpa7j+f5N\neffXA/zfQCPYvEFcNBNW7CuOU6tfnT8OGc7ggGTfhDxDoX9pNBrNhYxPS8WUUgVKqYFKqZqW/9cp\npW5VSjVUSvWyBJCilPrcojdzvavhdY25yDnvepTFF+wdnh5OcgPRTqvjbmpfu8zXcccDl9ZnQHIC\nbw1pGfC6S8uVzRL4ckR72tSJpU2dWOKqRDDm8oZcXL86b16TzKjuxasnS6RK11QqzCY1o3WwPGM2\n3Smz2QNaB6s8CWqqnJSUFEJleN2MUzgDWiSyeNcpn4+3xo77G6fwQv9mtmnC3s3jqRoVzpvXJLPr\nRA43dahTLmKX8TGRPNOnqcO2irwnzm2sWz2a169JLnFcmI8elhn7l0ZjRWsfaUD3A29oJfcQ57VB\nSW73PdunSUCuYb9C0Jp+plP96gzveJFWEndCfx2VG7OFOZjNCTebPWbTnTKbPWC+e2Y2e/yhTEru\nlm1PucjltV1EfrH8a+1cj9keTv5gxk7gSvSzS4PqNqfnskZxPN27OAVL6zqx1I+L8vtHb+9EVaRD\nZcZ74oyPsmeVoi0ajUajcU+ZlNxFpDbQxz6Xl+W440qpfpZ/lWNpSAgxrH1tB1XzPs3jCRNhUKua\nfDeyI69c1Zz+yYk817cJn99mpIupHl08S/zfOzvQs2k8z/QOzMiWpiSlVWvXmAsdg2WgY7DKhtns\nAR2DVZ74GuSer5TKtNvUjZK5vAASRWSZiEwRkRL5SMz2cPIHM3aCqPAwbu9c11b+e7/i1XFVo8IR\nEcJE6JuUSB1LSpm/9WlCnYwd/Pu6FtSoEsGLA5o5iHaWloqcczbjPbEyZ2RHZt/Zgahw374hM7fF\nDIjIrdaRc03w0TnoNKD7gTfK+jrtrNpuFQ66QimVISLPAg8C7/hpn6aU+DoFZaVhjSqM6t6Q9nVL\n5OYuFfd0rcf5IkWUjzkMLzTcKdZrykw+8JmIbAM+VEodD8ZFzRbmYLapZLPZY7aYJ7PZA+a7Z2az\nxx/K6mBlAta157a8XUopa/6u74DHnU/avXs3Y8aMCQkld3u1bTPYYy0fP5sPJAT9+iMursuqVatY\ntSq1wtpv3Wam+1HWsln7ly9l62d7Jfeypppwh1JqjiV580TgEhH5Qyn1ckAvotFoNH5QFiX3/kBN\n4BOl1PUi8jSwB8OpClNK5YvI/UCiUupN+/O1CnJw+HLjEepVj6Z3c52XTlPxlIeSu4h8CqQBHyil\njorIE0qpSW6OvRSYhJHkeZ1SaqyIZAAbLIfcaBl5HwE8jBFzOkIpdca5LrNlo6goOQ9r3I3z9JBO\nlVM2eyLDhAcva0CUj7ldXdG6TizNEmNKfZ4/fchdP/AHs0nUBFvJfSHwd2CFiKzEyOU1CUgEFohI\nNnAauNO5Hq2DFRxu61TX+0F2mLktpSFU2gGh1ZZy4kWlVDqAiNRy51xZ2Av0tbz8zRCR9sAmpVQ/\n6wGWZ9wooBdGqq9RGMmgNS7QcTeB5XyR4t3fDvhVxyM9GpbJwfIH3Q8845ODpZQqAAY6bV4H2I9Q\nHQO6BsgujUaj8cSjGOm6sPz/rLsDlVLH7IoFGCNZbUVkOfCrUuo5oAWG01UkIkuAj1zVpWOwPGM2\ne8wW82Q2e8B898xs9vhDUCOSzfZw8odQ6gSh0pZQaQeEVlvKiUQ3n90iIh2BWkqpbUCyUqo3EC8i\n11Fy4U4NN9VoNBqNT+glXxqNpjLypYh8IyL/Bb7xdrCIJACTgfvAYUHO90B7HJ0q28IdZ8wmNaN1\nsDxjNt0ps9kDWgerPPE1BqseMBdoA1SzDKM/BdyAEd9wj1Kq0FuQqI7BMieh0pZQaQeEVlvKA6XU\nIhHZBEQDHlfqWPSyPgeeUkodF5GqwDmlVBFwBbAJ2Am0s2SqGIBFVNmZ5cuXs379etOshN68eXNQ\nr2ctW2Nv/LVn/ZpfyUpNt02dWR2QQJVzDu0OaH3WbWaxx7lclvu5efPmMvcH57/ngehf/tgTqOtn\nZhqyn+np6X6tgvZpFaFFNDQGmIPx8KkJTFNKXScizwCpGG+CvwB9MIJEmyilHIJER48erV577bUy\nGWo2pkyZwujRoyvajIAQKm0JlXZAaLXlk08+YezYsYFeRfgJxotcAaCUUn/3cOxtwFvAn5ZNfwfe\nBbIxVkDfp5RSInIHMAY4hfGCmO1cl14JHVjKexVhIAnEKsLy5JEeDRnStnZFmxFylPsqQqVUPpBv\nl2fOWcl9BLAVL0GiZ8+eLYuNpsTq4YYCodKWUGkHhFZbNm7cWB7VblFK/duXA5VSXwJfOm0usSBH\nKTUTmBkA2zQajabMMViulNxroINENRpNcLhBRN4VkTdE5I1gXVTHYBnoGKyyYTZ7QMdglSeBVHL3\nGiR65MiRMl7OfFhVqkOBUGlLqLQDQqst5cTIijbgQkbrH2lA9wNvlNbBss4RrgNGYwjxWQNCd+El\nSDQpKYnHHnvMVu7UqVOllW7o1q0bGzZs8H5gJSBU2hIq7YDK3ZaUlBSHacHY2NjyuMxQoL1S6i8i\n8gLwanlcxBmzPa/MthDCbPaYTXeqPO35KuUoKYdKhA36QH1+WZwGwJ1d6tE8yGKlzpitD/lDwJTc\nlVIFIvIRsBJLkKhzPVOmTAlooGtFEujcahVJqLQlVNoBlbstQbI9Cdhv+Vw9GBfUaMzM8ZzzHN/r\nX+xmabOAaDzjUwyWUqpAKTVQKVXT8v86pdSbSqleSqk7LUrvKKVmKqWuUEpd72oFjkaj0QQIBcRY\n0t7UD9ZFdQyWgY7BKhtmswf8s0nHYHmmrDFYGo1GU5FMxJBUuAt4roJtueDQsTca0P3AG0FTcheR\nf4vIChHxlJTVVIhIExE5IiK/iMhPlm1Pi8hKS9LYcMu2ESKyWkR+EJFqFWt1MSJST0R+F5EcS2wc\nIvKUL/aLSF8R+VVElohI0EYI3OGmLRmWe/OLiMRbtpm6LSJyqcW+FSIy0bLNpz5lpnZY7HHVlmDd\nk77ANgx5mL6BapM3dAyWZ8xmz4UUg1VWzGaT2fqQPwTFwRKRi4FYpdSVQLSIVKak0IuUUv2UUoNE\npDbQWynVC9gMDLXEp40CegEzLJ/NwkmgH5YFBxb7+3iw/3PgIcu5L2AsVngWI+auonFoi4XNlnvT\nTymVUUnashfoa/kt1BGRK/Hcp8zaDijZlvYYWnjBuCdHLP+yLXVrNBqNqQjWCFZ34GfL58XA5UG6\nbiDoJyLLReRxSgqsXg60wCKwCizBRG1TSuUrpeyjHr3Zvxi4XERigBylVI5Sah3QLohmu8SuLfYL\nJdpY7s04S9n0bVFKHbMI94KhQt6WyntPnNtSCLQNxj1RSi20/PsWSPO/Nb6hY7AMdAxW2TCbPaBj\nsMqTYMVgxWOk0wFDL6ttkK7rL4cw/kDkAT8A1YBjln2VUWDVF4FY6zb7RQpmSgpun9sp2TJKMkVE\nrsMY5aoUbRGRjkAtDL24IsvmSnlPrG1RSm0TkaDcEzGSPCuM726Tl2MvBSZhOIDrlFJjReRpYAil\nyKWqKUbH3mhA9wNvBOshnYkhPgoeMtWbDaXUeaVUruXtey6Gk+jcDq8CqybC1X1wZX+W3XFg/GEy\nHUop63f9PdCeStIWEUkAJgP34WhfpbsnTm0J2j1RSt2slLpFKXWbUspbgtO9lG5a1u1Uv47B8ozZ\n7DFbfJHZ7AHz2WS2PuQPwXKwfgOs4jhuM9WbDaeA9SuA3UBvS9lngVUTYC8Q69V+pVQOUEVEYi1v\n/1uDbbAHBBARqWoNdse4N6nATkzeFksQ++fAU0qp41Tie+LclmDeExH5TUSWWoLpfxORr90dW4Zp\nWVNN9Ws0mspJUBwspdQfQJ6IrAAKlFLrg3HdANBLRNaLyCrggCVeZKUYAqudgO8sGmBWgdWRwAcV\nZ64jIhIhIj9TLBDblGKBWG/2v4YRNzcOGB9k00vg1JafMEZH1onIMqAhMLuStOVmjFi4N0TkF6A5\nlfSeULItHQnePVmslOqrlOoHLFFK3eLtBKdp2TLlUtUxWAY6BqtsmM0e0DFY5UnQdLCUUo8H61qB\nQim1AEPB3n7bG8AbTttmAjODaJpPWP64DXTavA540+m4EvYrpZZgvMmbAjdtKbEa1extUUp9CXzp\ntHktlfOeuGpLsO5JsohYVw8293aw3VTmzcAlGA4gVM6p/gpHx95oQPcDb2ihUY1GUxl5FLgVI9Dd\n41PexVRmmXKpAuzevZsxY8bQuHFjAGrUqEGHDh1scSPWt+9gla3bKur6/tqzfs2vZKWm2+KArKMp\ngSpbt5mlvkDbE6iyld/X/sqx+CoV3p+sVMT1N2/eTGamsfg+PT2dbt26lTn9lyilvB+l0Wg0JkJE\n7gA6K6WeFpGHlVLvejj2NuAt4E/LpueAKzFWEe7DWEVYYKlzDJZcqq7SfS1ZskR16dIlwK25cNl3\nOpe/fLO9os3wifXPGH9ku71hmkHkgPPODa1oWbtqRZthKjZs2ED//v3LlEc5qCNYo0ePVsOHDw/m\nJcuN2bNno9tiLkKlHRBabUlJSWHs2LGBTvR+OcWSKU09HejPtKwzKSkpmMnBsh8tCibWuBvnKaKK\nsscd9qNFZsBs9oB/NrnrB/5gtj7kD0F1sFJTU031cPKHqVOn6raYjFBpB4RWWz777LPyqLYAQERq\nAHXL4wIa9+jYGw3ofuANr6sIReRjETkqIm7F/ERksojsEpEUEXHrCtetGzrPQWsMRigQKm0JlXZA\naLWlnPgUSAbeB/4drItqHSzPmM0es40Wmc0eMJ9NZutD/uDLCNY04G1guqudIjIYSFJKtRCRyzAe\neN0DZ6JGo9EUIyICXKmUGlnRtmg0Go07vI5gKaVWAac9HHIDFudLKbUWqCEiF7k6MDY2tiw2mpIa\nNcycEad0hEpbQqUdEFpt6dSpU0DrU8bKnEtE5HYRuUZErgnoBTygdbAMtA5W2TCbPaB1sMqTQMRg\nNQD225UPWrYddT4wOTk5AJczBx06dKhoEwJGqLQlVNoBodWWQE+ricgQDAX2WkBUQCvX+ISOvdGA\n7gfeCGqQu9k0ZPwp9+zZ01T26DK2bWax50LtX9bP6enpAH7pyLhhkFJqjIi8p5QaE8iKvaFjsDxj\nNnvMFl9kNnvAfDaZrQ/5g086WCLSBPhRKdXRxb73gaVKqa8s5e0YiVRLjGBpDRmN5sLDHx0ZV4jI\nPOBd4GHL/yil5geqfk/oZ1hg0TpY5kLrYJXEn+eXr7kIheKEwc78gJFjDBHpDmS4cq7AfPEL/hBK\n88Sh0pZQaQeEVlvKga+B2nb/1w7Whc32DNMxWJ4xW8yT2ewBR5siw0vnR+gYLM94nSIUkS+APkBN\nEUkHXsKIe1BKqQ+VUvMtgaa7gbPAveVpsEajubBRSpWLsJbGd3TsTWgyefV+4mNKETnU9joAXl6c\nBlSycYAAACAASURBVECjGlUY2aUuEeG+jt2ENl6/SaXUCB+OecSXi5ktfsEfQmmeOFTaEirtgNBq\nS0UjIvWAuUAboJpSqkhEMoANlkNuVEpliMgIjGnHkxipcs4412W2Z5jZ+onZ7DFbfJHZ7AFHm/48\netavutrWKfDXHNP1IX/QbqZGowl1TgL9cEzgvFkp1c/yL0NEIoBRQC9ghuWzRqPRlJmgOlhmi1/w\nh1CaJw6VtoRKOyC02lLRKKXylVKZOMaRthGR5SIyzlJuAWxSShUBSzByHZbAbM8wHYPlGbPFPJnN\nHvDPputyVnJdzsoAWmO+PuQPQZVp0Gg0mgrEfsl0smXkaoqIXIcxypVl2ZcJhI7SazkQqBis8LBA\n5wDXBJO5VXtVtAmmxicHS0QGAf/BGPH6WCn1utP+OOBzoDEQDkxUSn3qXI/Z4hf8IZTmiUOlLaHS\nDgittpgRpVSG5eP3QGeM1dBWpyoOyHB1ntm0/KzbKur6k2bN51BWHs07XgJA2p/H+f7POcXlTesA\n3Ja3rF9D1qFsWxyQdTQlUGXrNrPUF2h7AlW2t82f+o5s+53V8UfofeWVgH9aev6c70958+bNZGZm\nApCenu6Xjp9XHSwRCQN2Av2BQ8A64Dal1Ha7Y54D4pRSz4lILWAHcJFSyiHiTWvIaDQXHoHWwSor\nIrIUGABEA+cswe6vApuAORjq8P2Am4AmSqkJznXoZ5gjryxOY9XezIo2IyhcCDpY/tK2TiwTrk0O\nqVWE5a2DdSmwSym1Tyl1HvgSI/+gPQqobvlcHTjp7FyB+eIX/CGU5olDpS2h0g4IrbZUNCISISI/\nAx2Bn4D2wDoRWQY0BGZbnlcfASsxdP0+cFWX2Z5hZovBMluMkbbHOzoGq/zwZYrQOdfgAQyny553\ngB9E5BBQDbg1MOb5TlZWFr/88gtDhw4NWJ05OTmMGjWKkydPMmjQIP76178GrG6NRhMcLM7TQKfN\nXV0cNxOYGRSjKjnWGKxXLPpHmgsTHYPlmUCN410N/KGUqg9cDLwrItWcDyrPGKzMzEy+++67gNY5\nY8YMrrrqKubNm8eKFSs4cuSIbV95xcj4kroo0IRKvE+otANCqy2hhNniSM3WT8ym86Tt8Y7ZbDJb\nn/YHX0awDmIEr1tpaNlmz73AOAClVKqI7AFaA+vtD5o9ezZTp07lyy+/BOBf//qXQ4DoL7/8wuTJ\nkyksLCQiIoKxY8eye/du5syZQ2FhIa1bt2bo0KGsXr2avXv3kpqaSl5eHosWLeKTTz5h+fLl9O7d\nm48++oj9+/fz0ksvoZRi7Nix3Hjjjdx8881ER0eTm5vL3//+d/76178SFRVF165dmThxYomAt/nz\n53PvvYYwfZ8+fZg+fTo9evSw7X/nnXf4+uuviYqKYsiQIXTp0oX8/HxmzZrFkSNHOHPmDK+++ipK\nKV599VXOnDnD4MGDeeGFF3j44YfJzMzk8OHDTJgwgRdeeIHw8HCaN2/OpEmTAPMk79VlXS5N2fq5\nHJM9azQajenxJcg9HCNovT9wGPgfcLtSapvdMe8Cx5RSL4vIRRiOVSel1Cn7uiZOnKjuu+8+EhMT\nATh1ymE3H374IQUFBYwZM8a2bdiwYXz22WfExcUxYsQIJk+ezMcff0x4eDhPPfUUr7zyCpdddhlt\n27blxRdfZNq0aQAMHjyYH3/8kbCwMK699lrmz5/PI488Qo8ePbjjjjv4/PPPOX/+vM2BcsVNN93E\ntGnTiIuLY8aMGSilGDlyJGD8AenWrRtVqlRBKcXAgQOZN28en332WYk2XHXVVXz99ddUq1aNQYMG\nMW/ePJ588kmbLfv372fYsGGsXbuW8PBwj/ejPLBfhVSZCZV2QGi1xSxB7oHA+gwzCxXVT6zxVxlt\nr3MIcrdfIWcGAmlPIILczfb9gH82WeOvrFOFgQhyN9uzz5/nly+pcgpF5BFgEcUyDdtE5CEs+QiB\nfwKfisgmy2nPODtXvrBz507uuusuh21bt27lrrvuQilFVlYWBw8ag2cdO3YEoH79+mRkOK6oPnHi\nBKmpqdx0000opcjOzubEiRMAXHzxxQAMHTqUN998k1GjRtG3b19uvbVk2Fh8fDzZ2dnExcWRlZVl\nW5ptJSUlhddff52CggL279/P8ePHXbahqKiI+Ph4AJo1a2abarTaAtCuXbsKca40Go2mtOgYLA3o\nGCxv+KSDpZT6CWjltO0Du8+HMeKwPOItfqFVq1asXr2aTp06oZRCRGjfvj2ffvop1atXt2376aef\nECl2KJVSREREUFhYCEDNmjVp2bIl33zzjW271XkJCzM864iICF5++WUAevTo4dLBuvTSS1m+fDkj\nRoxg+fLlvPXWW7Z9PXv2ZMSIEUyaNIkmTZrQp08ft20ICwvj9OnTVKtWjbS0NOrWretgC+DQnmBj\nprcFfwiVdkBotSWU0DFYnjHb6Iy2xztms8lsfdofKlTJ3Xmq8K677uLhhx/m+uuvJyIigjlz5vDi\niy8ycuRIioqKiI6OZsaMGS6dkbp165Kbm8u9997Liy++yNixYxk2bBhhYWHUqlWLjz/+2OG8BQsW\nMHXqVESEAQMGuLTvzjvvZNSoUcycOZOrr76aevXqOey//vrrufPOO2nbti3Vq1d324bnn3+eW265\nhbCwMP7yl78QHR1dog0V6WBpNBqNRqMJLF5jsAKJcwyWlVOnTnncZh+r5S5+K9iYbZ7YH0KlLaHS\nDgittugYrPJDx2B5RsdgeUfHYHmmXGOwzIA3R8vdZ1/qtDJu3DjmzZtnG0lq06YN48eP99kuszh+\nGo1GU97oGCwN6BgsbwQkF6HlmD7AJCASOK6U6ut8TDDjF1w5P1ZcOUEPPfQQzz33nK28cuVKxo8f\n79Nom7trezqnNNtctSmQmOltwR9CpR0QWm0JJXQMlmfMNjqj7fGO2WwyW5/2B68OliUX4TvY5SIU\nke+dchHWAN4FrlJKHbTkI9QECFcOnbcp1dI6b97q9sU+X6Z6S1u3RuMvIlIPmAu0AapZchA+hZHy\nay9wj2W19AjgYeAkMEIpdaaibNZoNJWfQOUiHAF8o5Q6CKCUOuGqIrPl8dIYzpH9v9Ic5+0cf2zw\nZ1soEEr5uEzASYwkzmsARKQ20Ecp1QvYDAwVkQhgFNALmGH5XAKzPcN0LkLPaHu8o3MRlh+BykXY\nEoi0ZKuvBkxWSs0IjIkaTekI1OiejqsLDZRS+UC+3UrdbsAyy+fFGC+IW4FNltGtJRiJnzVu0DFY\nGtAxWN4IVJB7BNAF4y0xFvhNRH5TSu22P8hs8QsajS/445zZb/PnetrJCyjx/D97Zx4fVXX+4edN\nwpqQBMIOEnaUNSAqCqiAoLggbhX3jWK1v4pSl2rVttrWvS6txd3WvRZ3K4KgLFGRNRBkDfsWdhIg\nZH9/f9w7k8lkJjOZ3MwSzvP5oHPOPfec99y5c3PuOe/5vpBvf86z0yleeSm+Toy2Z1i0+atEmz+P\nsScw0WZTtN3TtcGpWITbgX2qWggUisg8YABQaYDlikUYKr6mDr3jnwVzjlPtBXtOXbZXG8J9vY73\n76emAzXPul3HPv/8c8aNGxfw3AMHDviMFeg6199xJ9Kuz1EcizAPa2YeIBk4ROVBlSuvCq5nmCuq\nQ0pKSqV4qtESDzJc6W0/LyY/96j7j7Rruam+pl150WJPtKVzVy/h+9RczjrzTCDy92co6ezsbPLy\nLOmRrVu31ur55VQswhOBvwPnAY2An4ArVXWVZ12h6mAFm+dUPcaG+muXsSH8ds2aNSsqdLBsF4ZR\nQBrwhqpeJCL3AJuAT7GWC0cClwHpqvq0dx1GB8vC6GBF3h6nMDpY1RPxWISqukZEZgArgDLgFe/B\nlcFgMEQC24F9OtAfmAE8AMwTkfnAFuBZVS0VkVeB+cABLL8sgx+MD5YBjA9WIByJRWinnwaqvPF5\nEm3+CwaDof6jqqXAaK/sRcBTXuXeBd6trq5oe4ZF05s+RJ8/j7EnMNFmU7Td07Uh9Hk8g8FgMBgM\nBoNPwjrAijYNGYPBYKgJ0fYMMzpY1WPsCYzRwao7YiIWocFgMBiiB+ODZQDjgxWIoGawROQ8EVkj\nIutE5L5qyp0iIiUicqmv49Hmv2AwGAw1IdqeYdHmrxJt/jzGnsBEm03Rdk/XhoADLI9YhOcCfYCr\nbFkGX+Uex9qlYzAYDAaDwXDc4lQsQoDfANOAPf4qijb/BYPBYKgJ0fYMMz5Y1WPsCYzxwao7HIlF\nKCLtgfGqOkJEvOMUGgwGg6EeYXywDGB8sALh1C7C5wBP3yyfqqfR5r9gMBgMNSHanmHR5q8Sbf48\nxp7ARJtN0XZP14ZgBljBxCIcDHwgIpuAy4EXRWScVxmmTZvG7bffHqqtJtadw5hYhHXfXm2I9e/H\n9VuPtmU1g8FgCAfBDLAWAd1FJF1EGgITgM89C6hqV/tfFyw/rNtV9XPvirp3784///nPkI31NbL1\nzAt03On2gj2nLturDeG+Xub7iWx74f5+XL/1aJv1AbCfZ7ki8q2IfG3n3SMi80XkbTsGaxWibbBo\nfLCqx9gTGOODVXc4EovQ+5Q6sNNgMBicZqaqXg8gIq2As1R1uB0AejzwUUSti2KMD5YBjA9WIByL\nReiR7zfUfDS+yRoMhuOWkSIyF/gEWAvMsfNnYwV7rjLAirZnWLT5q0SbP4+xJzDRZlO03dO1wSi5\nGwyG45GdQA+gCMvlIYkKiZk8IDVCdhkMhnpCWAdYWVlZDBo0KJxNGgwGQxVsTb8SABH5EmtQ1cE+\nnAwc8nXe888/T2JiIp06Wft+UlJS6Nevn/ut2+U/Eq701KlTI9L+0qVLAdimbcnPPeqeBcmdP42m\n7bu70y7/nkilnbbHlRct9jiRLtiZQ9vhl4d0fp+f3wLg5z7XW/1bvYTvU3M568wzgdDur+zsbG67\n7baQz69tOjs7m7y8PAC2bt3K4MGDGTVqFKFgZrAMBsNxh4gkqeoROzkUeAFrWfBp4Bxgga/zzjrr\nLG6+2a8XRJXljbpOew6uanr+1oPHKGrbG4DZOQeszCDTfc7vTRywZMkukhOL3XV6Dh6g6vJTuNNO\n2+OdF2l7Ip12DaxcNOuWQdf+XdiwvwCAdidZEyo1SR8oKHHXF+7f07Bhw6rkuV4mQiGoAZaInIel\ndeVycn/C6/jVVOhgHQZuU9Vs73qizX/BYDActwwXkUeBQmC+qi6ydxDOB7YAz/o6KdqeYbXxV8k9\nUswTc7Y4aE30+fMYewLjpE1bDhVyy7TVtarjoVEDHbIm8gQcYHnEIhyF5bewSEQ+U9U1HsU2Ameq\nap49GHsVGFIXBhsMBkNtUdXpwHSvvCeBJyNjkcFgqG84EotQVReoap6dXECFL0Mlok1DxmAwGGpC\ntD3DIqUZ5E//KNp0now9gYk2HazsxT5X52MSR2IRejERrzdDg8FgMNQfjP6RAcx9EAhHndxFZARw\nE+DTMSDa/BcMBoOhJkTbMyzaNIOizcfI2BOYaLOp3+D6413kVCxCRKQ/8AowTlUP+qrIxCJ0vr3a\nEOux7sz342x9JhahwWAwOIcjsQhFpBOW6vF1qrrBX0UmFqHz7dWGWI91Z74fZ+s7nmIRhkq0DRaN\nD1b1GHsCY3yw6g6nYhE+BLQA/ikiApSoanV+WgaDwWCIUYzvjQHMfRAIR2IRquovgV8Gqqc+vcka\nDIbjj2h7hhkfrOox9gQm2mw63nywDAaDwWAwGAw1IKwDrGjzXzAYDIaaEG3PsNr4YMWLhHyu8cEK\njWizB4wPVl1iYhEaDAaDjYj8DRgMLFHVu7yP5+TkhN8oH+w/WswXq/cx+8tMVjXoElIdOXb8t1Dw\n53tTsDMnqpacjD2BqY1NdeGDtXndKuA8x+sNlaysrLoN9hwoFqFd5gVgLHAUuFFVqwyLo81/wWAw\nGFyIyEAgUVXPFJF/isjJqrrEs8zRo0cjZF1lyhU+/Xkv6zfmsjt7T6TNcVN2LDqujwtjT2CizaZF\nG3bx+sIqSlA14qLerWid1NARe5YvXx7yuY7EIhSRsUA3Ve0hIqcBL2FiERoMhthiCPCN/XkWcDqw\nxH9xg8HgNHuOlPCfFbV7aRh7YkuHrKkdwcxguWMRAoiIKxahZ7Dni4G3AFT1JxFJEZE2qrrbs6Ks\nrCwGDRrkjOUGg8HgLKmAS8cvD+jtXSA3NzesBlVHUqN4yvN2k9QwPuxtn31oDgBzUs+ulB8pe/xR\nF/bUpr5ouz5QO5v83QeRsifacCoWoXeZHXbebgwGgyE2yAOS7c/JwCHvAt26dWPy5Mnu9IABAyLm\n+jClJ2RdOZqMvqURaH2Y/d/KbUfOHt84ac+Ds2bZn0KvL9quD9TWJt/3QeTsscjN+ZlQX4WysrIq\nLQsmJiaGbIeoavUFRC4DzlXVSXb6WuBUVb3Do8wXwGOq+oOdngXcq6pLPeuaPXu2nnNOaM5iBoMh\nNpk1azajRo0KfctamLB9sCap6m0i8iLwpqoujrRdBoMhNglmBiuYWIQ7gBMClGHatGnA20BnOycV\nyADOttNz7P+btEmbdOymXZ83A5CV1S/kXTjhRFWXiUiRiMwDlpnBlcFgqA3BzGDFA2uxnNx3AQuB\nq1R1tUeZ84Ffq+oFIjIEeE5Vqzi5P/PMM3rzzTc7aX/EyMzMjDoV5VCpL32pL/2A+tWXpUuXxsQM\nlsFgMDhJQKFRVS0DXLEIfwY+cMUiFJFJdpmvgE0ikgO8DNzuq65o0ZBxguzs7Eib4Bj1pS/1pR9Q\nv/oSbeKcgRCRdiKyREQK7F3UiMjdIjJfRN62XzoRkatF5HsR+VxEkqLAnjUi8q3978Rw2SMiCSLy\ng4jki0hXj3IRuT7V2BOW6+PHps4iMk9E5ojIO3bM3kheI3/2ROoeSrOvw3ci8qmINLLLRer6+LOn\nRtcnKCV3Vf1aVXupag9VfdzOe9kO9Owq83+q2l1VB3j7XrmIFg0ZJ8jLy4u0CY5RX/pSX/oB9asv\ntdGRiRD7gZHAAgARaQWcrarDgWxgvIgkAL8ChmP5PfwqkvbY5faq6kj73xrfVTlvj6qWYu0kn+Yq\nEMnr48semz1huj5VbMLaMHGBqp6NtXZ+fiSvkS977PyI3EPAQVUdqqojgKXAhRG+PlXssfNrdH1M\nLEKDwWDwQFWLVdVzhDuYCgczlz5WD2CFqpYDs+28SNoD0MKekZgqIs6oLFZvj3jk7fVME5nrU509\nAGnhuD6+bFLVQ6p62D5cApQRwWvkxx6I0D1kXwMX8cB6Int9fNkDNbw+YR1gRZOGTG3ZunVrpE1w\njPrSl/rSD6hffakHpAL59uc8O53ilZcSYXsAhtozEluASWGwozoHXm8bw3F9qncoDv/1AS+bRKQ9\ncA6Wy03Er5GXPRCZa+Sy5RQRWQSMADYRmetTnT1Qw+sT1liE5557LkuX+lw9jDkGDx5s+hJl1Jd+\nQP3qy4ABAyJtQm3Jw9L1gwp9LM8Hvk/NrDDbg6q6bPgUuDOM9vgiktfHJ5G+PvaMx7+AiapaLiIR\nvUbe9kBErpF7wKeqi4BTROQu4GasqArhvj6+7Jli2/N8Ta9PWAdYv/3tb+vNTqJY2HYeLPWlL/Wl\nH2D6EiW4nleLgNuAp7He9hdgLRn0sZ3OXXkRs8f2V4lT1WJgKBWK9HVtj/cz3ZVeR2Suj097RKQB\n1q75cF4fd/s2rwD/UNW1djpS18inPRG6RmI1LQ1UtcTOO4y1uhaxe8jLnnwgLpTfWECZBoPBYDie\nsB+k04FBWA6uD2CJfY3DWhq4UVVLReQarB3TB4CrPXxawm4P0MIucxg4CFyrqnWyq8iPPXdj/dHZ\nBDypql+IJUp9G5G5PpXsAX4iTNfHj02PAl9QEdvyeVX9LILXqIo9wI9E7h76Pdb3VIZ1La5T1cII\n/saq2IM1i1aj62MGWAaDwWAwGAwOEzYndxE5z9aQWCci94Wr3doiIh1tzYufRSRbRO6w85uLyEwR\nWSsiM0QkrA54tcHW+VgqIp/b6Zjsi1hBxf8rIqvt7+e0WOyLiNwlIitFZIWIvCsiDWOlHyLyuojs\nFpEVHnl+bReR+0Vkvf2djYmM1QaDwVD3hGWAZa+h/gM4F+gDXBWMSFeUUApMUdU+WNtEf23b/jtg\nlqr2Ar4F7o+gjTVlMrDKIx2rfXke+EpVTwIGAGuIsb7Yu3h+AwxS1f5YfpFXETv9eBPrd+2JT9tF\npDfwC+AkYCzwTxGpN36ZBoPB4Em4ZrBOBdar6hbbcewDLCG4qEdVc1U1y/58BFiNFWvxYuDfdrF/\nUyH2F9WISEcsUbnXPLJjri8ikgwMV9U3wRIXtHVMYq4vWDoribYfQBOsOJ4x0Q9VzcTyR/DEn+3j\nsCJBlKrqZixH8VPDYafBYDCEm3ANsDoA2zzS26nYZhwziEhnrOjUC4A2qrobrEEY0DpyltWIZ4F7\nqKyHEot96QLsE5E37eXOV0SkKTHWF1XdCTwDbMUaWOWp6ixirB9etPZju/dzYAcx+BwwGAyGYDBK\n7kEiVhykacBkeybLe3dA1O8WEJELgN32jFx1SzNR3xespbRBwIuqOgg4irU0FVPfi4ikYs34pAPt\nsWayriHG+hGAWLbdYDAYQiJcA6wdQCePdEc7Lyawl26mAW+r6md29m4RaWMfbwvsiZR9NWAoME5E\nNgLvAyNF5G0gNwb7sh3YpqqL7fRHWAOuWPtezgE2quoBtQKrfwKcQez1wxN/tu8ATvAoF1PPAYPB\nYKgJAQdYInKqWFGl54nIM3bePVI1kvsTIpIpInNFpJtXNYuA7iKSbqvHTgA+d7ozdcgbwCpVfd4j\n73Ms/RmAG4DPvE+KNlT1AVXtpKpdsb6Db1X1Oiw9lBvtYrHSl93ANhHpaWeNAn4m9r6XrcAQEWls\nO3yPwtqAEEv98BZ49Gf758AEe5dkF6A7sDBcRhoMBkM4CaiDJSKtgUOqWmzPdrwK3KuqF4rIvVhq\npt8C/1XVc0TkDOByVZ3iVc95WLu+4oDXVfXxOuiP44jIUGAeVtR6tf89gPWH4UOsN/ItwC88ZPSj\nHhE5C/itqo4TkRbEYF9EZACWs34DYCNwE5bDeEz1RUT+gDXgLQGWAROBZsRAP0TkPSzRyzRgN/AH\nrDAS/8WH7SJyP3ALVl8nq+pMH9UaDAZDzFMjoVEReRNLETdJVZ8WkUHA1VjbsD8ArsDafj1AVf9a\nB/YaDAaDwWAwRD1BxyIUkf5AS6yAi+V2dh6QqqolIrIZWIs1Q3WGw3YaDAaDwWAwxAxBObmLSHPg\nBayI0vlYMXmw/3/IFt7srqo9sGaxzOyVwWAwGAyG45aAM1i2E/s7wN2quldEfEWWF6yZLbACIyb7\nqmvcuHFaWFhI27ZtAUhMTKR79+5kZGQAkJWVBRATadfnaLGnNmnvPkXanlDT06ZNi9n7yTsdy/cX\nwPLly8nNzQWgW7duTJ061Si2GwyG44pgnNwnYDmn/2xn3Q+cSdXI8i8C/bCcjCd7bJ93c/311+vz\nzz/vne04y3ce5tnMrfz2zHT6tU2qkzYef/xxfve739VJ3eGmvvSlvvQD6ldfJk+ezFtvvWUGWAaD\n4bgi4AyWqn6A5cDuyU/AU17lfu2gXbXinq9yALh/eg5f3pQRYWsMBoPBYDAcbzipg5UhIjNFZLaI\njPVVl2vJoDaUq7L3aHFQZcvK605AeuvWrXVWd7ipL32pL/2A+tWXaEBErhORWSLyrYi0E5G7fTzD\nrrafdZ/bkRsMBoMhZIJxct8MjFDVM4HWInImcJaqDsfShhpvl3sIGKeqo1R1uq+KunXz1h+tOU/M\n2cI17//Mj1vyal1XbejXr19E23eS+tKX+tIPqF99GTBgQETbF5H2WM+sc1R1JFAKnO35DLOjNfwK\nGA68bX82GAyGkAk4wFLVParqmjIqBXoDc+z0LOB0W5W5MfCRiHwsIq181XX55ZfX2uDvNhwE4IvV\ne2tdV2247bbbItq+k9SXvtSXfkB09eXzVXt5dv5WaqKZ54nLCT6CnAvE2zNYLwCn4PUMA3oAK1S1\nHJht5xkMBkPIBB2L0EsHK9/OzgNSgTZYD6jLgFeAB501MzSsyCMGg6E2/OOH7Uxfu581ewsibUqo\ntAEaqOo5WEHBU6j6DPPOSwm3kQaDoX7hiA4W1gNpkaoWYoXNOdFXPZ7buGuLeIQ+219QwtQF29l1\nuMix+jfsL6h2GTIzM9OxtiJNfelLfekHRE9fjpWUuT8Xl5ZXUzKqyQPm2p+/A7rg+xmW4pVnMBgM\nIeOUDtZ6LP+sOGAgsMlXXXPnzmXx4sV06tQJgJSUFPr168ewYcOAij8q1aXzN6wnuVtGpeOfHmrD\nitwjfPnNHO4f0RlIBCAvJ4vMzCM1qt+Vvu2TteRvyOJ3Z6cz/tyRNT4/ltIuosWeUNPZ2dlRZU99\nSN/9v4rf29KFP3K4ZdOg7qfMzEy3o/7gwYMZNWoUEeQHrPiOABlYAbavpOozrI/9DHPlVSHcWn7h\n1nYz7cV2e1lZWeTk5LjdcUx7odV/9OhRwNqYVxsdPyd1sH4B/B9QZudt8a5r9uzZOmjQoFDsdDPm\ntWUAnNIxmb+cZznNX/rWCo4UW2/aMycOdJdpECf87+bQ/D9cdTw+thuDOvjUTTUY6jXlqpz3esWs\n8xNjuzOwQ7Ma17N06VJGjRoV0fV6EXkKGAzsxYqfOgW4iMrPsGuA27HEkq9W1cPe9YRLy89FuPXQ\nTHuVadGiBQAHDhwIS3tOEO3XNNbaq42On5M6WB8CH4ZihFc9lJQrDeOrX73051712HebfdYZqj9W\nucLO/CKmZe9hwoA2tE5qGFI9BkOsUVqHMifhRlXv8cp60v7nWeZd4N2wGWUwGOo1QTu5O0EwPlhP\nz9vKhW8uJ7cG/lSeYyfXLkN3mzsPc8lbK/h+c2guFeWq/G56Dl+u3sdfv93szvflI7PnSDE524f6\n/gAAIABJREFU+2LPETha/H1qS33pB0S2L7PWH+Der9ZzuLCsUv5/s3dHyKLowQktv5pQ13poL7zw\nAi+88ELY2vPGtBf9bXrfI3XdXiBiSSPQMaFRO3+QiJTbfgw+Wbitev2qb9ZbU7Gzcg5WWy5YHpm1\niYKScv40y6dbWEDKyiH3sKVSsfVQYbVlr/3gZ27/dC37C0pCastgiAaenLuFrJ1HuO2TNZXyF28/\nzLp9BRwrKWPFrsNVhHw3HTjGn2dvYkeec5tNog0ntPxqQl3rod1xxx3ccccdYWvPG9Ne9LfpfY/U\ndXuBCHd7tdHxq63Q6AoqhEbBcn5f4q+ijIwMHpyxMajZqfJy5WhxGYu353O4qJTisso7mFyDHoCS\nMj9LGeJ/KTFYlu+qcMOIj6uozOXYu2hbPu8s3VVJI2j34eCU5qMFV19infrSD4iOvhwqLK2St+nA\nMS7+9wru/l8On62qrEV331c5zNt0iEdmbQyXiWHHCS2/mhBuPTTTXmy3F4k263t7tdHxC8YHa49H\n0ltodDaWw+hHItIb2A4EfMU7UFBK22aNANiRV0iLpg1o0iC+UpkyVR6csYGfdx915316fX/3Z8/Z\npMI63D7+8cqKPyJ5haXc9OEqmjWK57bTO5Ke2pjfz9gAQB+PoNJPzNnM6ekp3HpaB6PFZYg6VJV1\n+wpIb96Exgk18xJ4c9FO9+eXFuzg0r6t3WnXgGxXjL1gGAwGQ13glNAowJ3A36urw9sHa+P+Y9z0\n39XcMm11lbLl5VppcAXwoD2YqQlOD2925BexZm8BNz37ITd8uMqdf58dYBqsPzAfr9xbxf5opb74\nLtWXfkDd9eXHLXmc+3oWv/lsHfdPzwl8ghcHjlWe1SoqLa+klQXWC8/MdftrZWe04qSWXzDU9T3t\n7V8T7t+QaS/62wzkg3U8XNNQCTiDBZWERq/ACjPR0T6UDBwSke5AnqoekGqmbObOncvGnTN5ZVNv\n2jZryOajceSXtgIPXSuXzlWZQv4G62Hm0uH54YfvK6UrLrSle+VdPj8ni6KEOOJO6FepfLA6QFXq\n80pv+3lxtcd//OF7DrVOjCpdI19pF9Fij9HB8p2eN28+cXHimK7Vjz98T2bzPQwbNozi0nIWLvgB\nCz+/Jx/pEQ9nkdQ1g+k3Z7iPA9w18980PLqXgR2aMWzIqZHWwTL4oTrfGoMBzD1SG4LRwYoHPgf+\noKqL7TiDb6jqRSJyD5aoaBkwGTgGnAp8pKqTvOuaPXu2/m6p0D2tCb88rQM/7z7KW0t2AdbyX9OG\n8W79qbO7pjJnY/U7/4Z0SqZLiya8n7Wb0mNHyF+/hBb9z3IfbxgvNGkQT569dDFz4kDKVcnZd4yt\nhwoZ0a15Jb8qT8a8tozDm7LZ8vFzlBUcZsBDVRUoDm9YToOUljRu2cFnHef2bMGdwzr5bcNgCERe\nYSkpjRPYdqiQW6atZkyPFtx9Vjo/bDnE5gOFXD2wbY3qc/2+XMycOJCthwqZOG0155+Yxp3DOlUp\nEwynd0rhx62+N7A8PkgjroPlFE5o+Rlih9rqYBlin9ro+AUzg3UFlkDfk/bk1P3APBGZjyXS96yq\nlgKfAIjItwSIRJ+z/1ilJTWAD1fs5sbB7d3pQIMrgAVb81mw1VqtLCs8ysHlcyoNsIrLlKYNKp/z\nh5kb+WmbdU5haTk9WzYlIU7o0qIxa/cWkLP/GKenWxEzmrbrRu87prLmpTt9tn9443KaduxZZYDl\n0t2ase4AJ6Q25hf927D/aAn/zd7NJX1a06aZ0dLyJGvnYRZty+fmU9qbwagHn6zcw9QFO+iQ3Igd\n+dbGkJnrD3D3Wen88RtrV+zgjsn0bNW0Vu38b80+AL5as5+zujQPqQ5/gyuDwWA4XnFMaNSj/Eh/\ndVn+CwN9HjtSXMZTs9ez8b2/UJy/H4mPp9cvn+Lo9nVs/9/LaHk5qX3OoO2ZV7Dzm7co2r+T0oJ8\nyosL6THxcfb++DmHN65g7cu/pdMlkyk+uJtd375LvEDakItpkTGC86+6mQ2HlaJ9O2g/5iZue/EO\n4ho0pHHLjjzy2JO8bjvwvvD9NgDiG/v/w3Vo7SL2LZ5B/Mr5HFwxl5aDzyN33n+R+ARSew+h9Gg+\neasX8ODUItr87TE+y2/H4lXreXbKc5zYqikDBgzgkUce4f333+edd96hvLyc3//+9xHZPZaZmRnR\nXWv32oPt9OaNGdMzLeR6wtGPo8Vl/GvxLkb3aFHrgU11ZGZmMnWNtVTnGlz5Ir+osk9UWbmSs7+A\nbmnWi0MgysqVeI9V/ftC8Ms6nsjKyiKcM1h1fU+7fGtcy0DhfhaY9qK/Te97pK7bC0Sk/17VhGBi\nEZ4KPIu1DLhIVX9rLw2Ow5JwuBFoAnxq15cPXKWqNfLwzjtWyofvvE3Tjr3oembFVujt01+l2/V/\nIqFJEuvffJC0QaMBaNSqI11GXcv26a+Rv24JrU4fR9GBXXS79mEAtkx7hl63/o3kxgksev4Omg84\nm/X7j9GsS3/Sx9/BvkXTSRs0mtanXwTgHlwFS1xCA1oOPpemHXuRetJpHN6wnLKiAk781d8AKC8p\npu1Zv6BN/DGefvoxii5+hO3/e4X2503is4evAODgwYN8/PHH/O9//6OgoIAJEybEzI1TFxw4Fh79\nsP1HS0htkhDSbNk7S3fx2aq9fLZqLzMn+n5ZqGvmbPCvEffOslzeXZbLBSemMXlYp4B1PTFnc1Cz\nxYb6ifGvMQTC3COh45QOVjFwjaqeDXyGNeiqQnV6EvlFZRTu2Uqzrv0r5R/btZENb/2BtS9NoSRv\nL8WHLNWIpu27A9AwpSVlx45UOqfkyCEK925n3Wv3suzFKZQVHqX0qPVHJPGEXgA07382RQd2svGD\nx9i/5JsgLkNlXI6+niR27On+vH/JDNZMvYuFLz/Eui3W4K04b4/bboBNmzaxZs0aLr74YiZMmBCx\ndf5wD+r+MnsTz8yrEqqyWhZuy+PpuVsorkaSI5h+rN17lKveX1llidqTrJ2H+dXHa9iwv6oqf7gk\nCKrry199hINy8enPlqzI/9YEt4vPDK5qRm00cUIh3L9N015stxeJNut7e7XBER0sVf0I2O1RpvK+\n7SBYtvMwTVqnc3jjChI79nT7MTVt351u1/6B+MZN3Xl5qxfgKcCgKBIXj5ZbzSYkptC4dSd6TnwS\nibfyJc7W2bKXQyQunhMuuBWAlc/cQtrJo6u5CL6zJT4eyj266rHUsueHz+h916u0jS9k1uOWS1qj\n1NYU7FjPO8vaMe6kNDp37kzfvn15//33ASgrq/FlizkKisuYu8n6o/7bM9MrHatuv8WDMyzxys7N\nG3N5/zYhtz/XHlCsyD3i83hxabl7yfKP32zi7Ql9Qm4rHBw8VsI/f9zORSe15ITUxhgXNoPBYIgO\nnNTBQkSSgEnAe77qCKQh0/LU8zm6bTVrXprCutfuBaDDebeQ89YfWPvyb1n/xgOUl/qeQWiQnEZ5\nSREb3nmEogO7aDfqGta+eg9rX/4tG9//q8tCd/lDq35gzdQ7WTP1LlJ6neqzzmN7trL21Xso2red\nda/eS8HOCh2u/A1ZNOs2kNx5/2Xr5/+sIhmf1KUfa/45mTXT3ya+UROrL+dPYtuXL/H7SVdz2oTf\n0KJFCy655BIuvPBCxo8fz4MPPljt9akrwqkrUtvwwb7UxV040Y8vbYdvqFsB20AE25en5m7l05/3\nctcX6wCI87gPXfpUG/cfY9OBY84beRxidLCcxbQX/W0aHazQcUQHy6Po68ADqpqPD1w6WI2aW1vL\n45sk0rR9d/dy25Gtq2g15KIquju9Jj3lTh/Zsor2o693pxu36ewu3/bsCQA0TmtP47T2SLzVPdfx\ntJNHU3LkEE3aQIsBZ5OQlFrpuLfOT8nhA7QbeU2l4/kbstzp8tJi2o+5wZ1WLXcfT79ksru+3uda\nihXFh/bQ7pzrKul4tW/fni+//NKd9nTge/HD6by9LJczhw/j9yO7kL14AVBV56ig9Um8vTSXy5rv\noWVig6jWwbL+6CcDMG/+fHtAYDlzr122kMwjLarVJVuvzeHUDu7juYeL2NikGzcObh+UDtaG1ftA\n0v0e/2LRTmjWA4AD65aSmZlX6fj2n3dCUo+Q+6+q7EjuwYB2zdi/bpn7+MGCEv7y1hecnp7CZWNH\nufsL1etQudL5RWVkZmZyKGcj0tHSfRvx8Fu0bdaQgta9AbiixR7yN+wMqr7apMGSMCk6aAVGzoob\nY3SwohTjX2MIhLlHQscRHSxVnSYijwJ7VNWvmrtLBysa2Z35MYdWZrpnopq07UKni/+vTtuszkn6\nhy2H3FvxAYZ1TuXhc7r4LOvSLTojPYU/ju7qrJFeHCspQ0RqHGLFRV5hKVe8Yw2Evro5g4Q4cdt/\n48nt/Oo6ucpc1rcVtw7p6M6/6r2V7C8ooUWTBD64pnIQ0FnrD/DthgM8NKqLOxTTKz/tYFq2tert\n6/p7akA1bRDH42O706NlU7dD/B+/2cgPW/L8nn+woIT3snK5qHcrOqU2rnL8kVmbyNxsvZOM6Nac\n3YeLefrCHjwya6NbcsT7ugTLzIkDmfBeNgcK/M/yRQKjg2UIB8Wl5WTvPkJhiTMzz40S4hjTvzNg\ndLCOZyKugyUi7YB7gB9E5BLgP6r6cigGRYo2wy6lzbBLI2rDwYISEhvGEx8nlQZXAFsOBl7i2Xe0\nbnfhbT1YyMSPrLBGD5/ThWGdUwOcUZXy8ooB/QuZ27h6YIU/VTDLh95l9hdYffYO4QLw5FzLkf6L\nVfv4xYCa+20VlJRzx+fW0puvwdSjszdx0+B2dEypGEg9l7mNH7fmMTvnIB9f37/KOa7BFcB39m7A\nj7P3sGR7RVDxnflFLNnucxK4WmItyLghMO+//z4jR46kTZvQ/Q6r4+DBg9x4440sW7aMq6++mscf\nf9xnuYyMDL777juaN6+sk/b111+zbt06v7McK1euZNeuXYweXY2Pq0OUq/L6wp3k7HdmOfyElEaO\n1GM4fgk4DaGqH6hqG1Udaf/7SVWfUtXhqnqtqpaq6i5VbexRxufgKtz+C3WJ53KIE+w7WsyV763k\nxg9XUe5jVtEVgWh7XiHPzNtC7uGq2kjr9lXd9RYMwa5pf7W2wj/p5QU7QmqrzKNvX6/bz0MzN1Y+\nXl79MKu6CVd//SgoKWPv0WJe+WkHe4+GNgiZt/EgT87ZTKmHffM3HeJhL/u32EHIjxQHv2HhtUU7\nKfGo94Plu3nuP9NrbON1//k56mav6hvhfoa99NJL7Nq1q0bn1GSzzOuvv06PHj145JFHAP+/IX8R\n0M4777xql5Cys7OZNWuW3+Ph9qdx+rkdCOOD5Tz1ygcrGB0sVS0TkauBXwP7sXYW+t6mZXCz6cAx\nFm3L57J+rVm1x5IN21dQ/SzUfV/lsPdoCSt2HeGPo7vSpUWTWtlQVq784/ttrGqwg0mnVQ35c6yk\nDFX4xw/bWO/xZhjqbrUyr9n7LQcL3Z+/zTnAv5fs4k+ju7rV9AFyPAaO3204yBX9W9Mysaoa/tPz\ntvBD2QkM75xKlxYVs0rlain41+bN9s/fbvaZn+s1a+TELr5Z6w9QVFqOeX+OXTZt2sSUKVPYv38/\nCQkJvPnmm6Snp/P3v/+dzz77jOLiYi644ALuu+8+tm3bxhVXXMGQIUNYuHAh7du3591332XGjBnk\n5ORw66230qRJE2bMmMGaNWt48MEHKSgooEWLFrz44ou0bt2acePG0bdvXxYuXMill15Khw4dePLJ\nJ0lISCA5OZkvvvjCp5133303gHsnsz9UlZdffpkZM2ZQWlrKm2++Sffu3Xn//ffJysriiSee4NNP\nP+Wpp55yt/nxxx/z2GOPUVRUxE8//cSdd97J+PHjHb/WhrrF+GCFTjBLhJuxdLCKReRtTx0sEbkX\nGC8in2GFxxkOXGZ/ftq7ooyMDD5Y6pzxkcSXDlZNufXjNQD8Z8VuhqZXLLdVN4ez114G3HW4mFs/\nXsPLl54YdHtr9x4lToQeLSsUyLNzj3Ag7USmZe+pMsCav+kQj87eRIM4qTTDArD7SM1ngo4Wl7F4\nh/+lr2151qzcn2Zt5OtbrCW5hdvy+H5zRRiWQ4Wl3PrxGj66rj/e/oMFrXsza/0BZq2v7C9RrurY\nsoE33rONTjkaOXF/GSxEJB0r+sQqoFhVzwv1JTFYHaxJkyYxZcoUxo4dS3FxMeXl5Xz33Xds3LiR\nWbNmoapcffXVLFiwgA4dOrBp0ybeeOMNnnvuOW6++Wa++OILLr/8cl577TX+/Oc/079/f0pLS7nv\nvvt47733aNGiBZ988gmPPvoof/+75fZaWlrqni0aNmwYH330EW3btiU/P/jl5uo0hlq1asV3333H\nG2+8wT/+8Q+ee+45oGJ26+mnn67UZoMGDbj//vtZvny536XHcGsahft3ZXSwYr+92lBbHaxZwNVY\nD64VqlouIrOBVx22s15zuKiMr9d5CEP6GWH58rF5dWFwS3Vl5cpvPqvqT+RvMFdUWs6jsy0/MO/B\nFVizQp4s3p5Ph+RGtEv2P+9y44er3IG3q6Nc4am5W7hmYFu3/pUnh4usJRCXs3kgZqwLzkH1xyDr\n88fBghL3INGTHXlF/H7GBq4bVLPAzNFEw3ihuKy2IhsRZaaqXg9gb9QJ6SUxGI4cOUJubi5jx44F\noGFDa7b1u+++Y86cOZx99tmoKgUFBWzYsIEOHTqQnp5O797Wbs+MjAy2bt3qrs/1IrF+/XpWr17N\npZdeiqpSXl5O27YV99Qll1zi/jxkyBBuv/12xo8fz0UXXRRKN6pwwQUXADBgwAD3zmdP6qJNgyGW\nCUqmAaroYLkWelw6WClU1sZKqVIB1ccijDU85Rqc5tefrq2St/VQIdf95+cq+Ys9nKOro6wa5yXv\nvpSVKw/O2OC3vIsft+RxenoK6/YV8MDXVvnpN2dUCkGzI6+Q/KIyurRoEtTgysU36w/wzXr/A6MP\nludWGRD5+06CbfcP31QdzAXCNdDMLyzlyvdW+iwzdcF2duYX8cSc4NXr6/L+CpbTTkh2B0Zvn9yI\nsb3SmBqi710UMFJE5mIFpV9LiC+JtYlFqKrceeed3HDDDZXyt23b5h6EAcTFxVFaat2zeXl5lc4/\n6aST+Prrr33W37Rpxcz0008/zdKlS5kxYwYjRoxgzpw5pKZW3ZTi8q1p1aoVUH2ct0aNrJen+Ph4\nn35evtoMRLjjytXkd+V6kQNC2nQCsHzRjww45XR3ultaE1KbNAiprmAxsQijB6d0sDwHVd7aWG4C\n6WDVlS5PrKW31OL8L745xM5mPbi0X2vWLlsIVNaRsrAGuZmZmaz38G9yOQ/uTe3J8l1HArZ310sf\nc+PgdnTpd4r7+Kg/rODm8WO4qHdL5s/P5K/fbSa5WwbPj+vp6PV6Y9GuKscLdubUqD5Xf4cOHcrh\norJa6D4NZOXuqtfr7pc+4YKT0igua1fr/kYifW7iLr7ZsN6dbnVoHZM7l/D85tQA1yPqdLB2Aj2A\nIizJmSTANTNfo5fEYEhKSqJ9+/Z89dVXnH/++RQXF1NWVsbIkSN57LHHuPzyy0lMTGTXrl00aGD9\nsfUnl9OkSRMOH7Zeonr06MH+/ftZtGgRp5xyCqWlpeTk5HDiiVXdBDZv3sygQYMYNGgQs2fPZseO\nHT4HWK4/mu+//75fG4LFV5tJSUlu+2MNT1Hj+78O/MLpi/wNO0neb50rwL+v7O2EaWHF+GCFTjBO\n7vHAO8DdqrpXRBYBt2FNn58DLADWA31EJM4jrwqTJ09mVzU6WN5vFtGc9vUWFA32fXWkCRu27CU7\n9yj/GD/MZ3lXyKFhw4aRuPMwyfusgYlrIPbA15UHKtW19/FBaPTD9krH31mWyzvLcoFUd96KXUfq\nvP9th19e7XHvtHTsy7cbDvLaf1czqEMzx+1ZkdCZFesBjtT4/Gi4v4YNG0a7jckcLS6jc/PGDBs2\njMLScp7/1/Ia2Z+REdmlRVUtAUoARORLrAGUy+GwRi+JOTk53H777XTqZAXSTklJoV+/flXEZV96\n6SXuuusuHnzwQRISEpg2bRojRoxgxowZ1u8uMZGkpCQmTpxIXFyc248pMzOTTZs2uWUZRo4cyW23\n3UZaWhozZszgjjvuYMqUKYgIZWVljBo1ijFjxlQ6H+CVV15h48aNHD16lIyMDPr06VPpuKe9EydO\npLS0lOLiYj755BMeeeQRJkyYUKm8Z/05ORVxPNetW8fOnVas1Ycfftgt9jt27Fj69OnDtm3bWLx4\nMWeffTZ33nknLVu2rNS+q06nxIx3r1lKfn6R35cGV16wLxne1PQlxbu9hT/+QIumNReDjmbx6PrW\nXnZ2tnvmeOvWrQwePDjkF8RghEYnAM8DrvWp+4EzsRxEt2A5iJaKyDXA7cABLAfRKq8t0Sw0Wt+I\nE9yO4mCJ8F1o/2H8+pYMd0iVZTsPuwMfd09rwl/P68ZTc7eyKMQp8eORmRMHVhGGrQ/MnDiQ7XmF\nTF+znysHtCG5cQKl5cr5b2QFPtmDSAuNikiSy2FdRN7Gmo1/2FMsGfgUa7lwJJYPVrqqVvHBMkKj\n0UthSRlTvlzv6IaWxfdaf1gHPzm71nW5ZrDaNjP7g2OJ2giNOqKDZZd7V1WHqupFvgZXELqGzNhe\naT5VsSNJuPVUnMR7TO3qS87+Y7y/fLd3WMWYIZa/E2+ipS8dUxrzy9M6kNzYmuxOiBOePL87V2UE\nFr781ZAO/H5k5zq2MCiGi8hiEckEtqvqImC+LZY8APjUfo69CswHrgeiQsvPxCJ0lnD/riLxOzY6\nWNFDsD5Y7YAvgZOAJNsR9AWgH7AB+KWqqog8AQzF0sy6WVVDW7j24q7hnVi4Lc/njrLacu9Z6W7F\n7/qM+vnszScr94YcBud4ppbuK1HDlOGdaJ3UwKfOmIuM9s3IaN+MM7ukctsnVTdkDO+Synk90zjl\nBCvm5NKlkZ3ZU9XpwHSvvCeBJ73y3gXeDaNpYePbb7/lT3/6k3upT1VJT0/nrbfeirBlhmjH+GCF\nTrC7CPdjTZ1/AiAig4EGqjpCRO4CLrTfDk9W1WEicgaWnswUz0q8dbAGdWjG0h3BOUB2bl47QU1/\n9GzVNHAhL96Z0IdrP6gDY+qQVbuPuj9by8LWg1ao6m9QWOpMLK9wE+ldd04Sib48OqYrp3UK3rfb\nFd/RkxHdmnP/iM7OGRVlBKuD5RRO7ZYaOXIkI0eODFt7wVLfdbAi8Tuu799hrOwghCCWCAFUtVhV\nPffEdwVW2J+XA2dgefLm2Y7uzYF9BOD3Iztz17ATgjK0dZL/N+q64JweLfweC7ctTnDf9ArH1NJy\nrfWOIUMFP++OjaAFPVv6fpl4Z0IfPr9xQI0GV2BJN/yif2t3ukXTBO4+s1OtbDQYDIb6QqhrQWuB\ns+zPI4FUe6fOZvvYC8Dr3id5+y80a5TA2BNbBt3oI2O6cm5P/wOfUPAeaJx2QjID2iUx6dT21Z4X\nLT4yofDPH7cz4b2V3PdVDp+v2hfTffEkUv34xw/b+dv8rYEL1oC66EuTBr5/7q2TGoa8LDzx1A7c\nNbwTrRIb8MwFPWkQX7+Xl40PlrMYHyznMT5Y0UPQQqOeqOpyEVlpC/KtBHaLyIlAd1XtISKDgL8C\nt3ie562DNbVkAf369QMSgYqbccqE8ykqK+fF/7oE9azdcKVbszktDk4Z2ccSopwzD6i6RTat50AE\n2LduGQCv33kFd32x3ueW2oU/HgCsQVuL/Ws498QTquhGeZY/r2ea2x7P4x9e05evv53Lc5nbokbH\nKDMzk89W7SU/7cRKx2dgHd+ycr7n1xNxe2ubrqkOllPpDURH/wOld61eQv7+Yz51vCD0Lc5jhw1j\nbK80S2LArjEzM9OtRl6bbc6GusX41xgCYe6R0Ako01CpsMh3wDmqWuaR9wfgK6wlwt+p6g0i0hl4\nSlWv8DzfW6bBFbJlzGvLKrUzc+JAvly9jxe+31apnDfnv5FFqY8wLo3ihXKgxA7tMXPiwCptuJh6\nSS+3o27fNon87aKe7mO+zvFl87k9W/DbM9PJPVzE9f9Z5bOdcOOSafDXb8Pxhy+fx3vPSq92OdwJ\narPNOdowMg3Ri5FpMNQFdSrTACAiCSLyDdAf+FpEThWR7+y8IlVdpKqrgSMiMg9rJ84Tvuryt0zh\njXcQ3RqVEal+q5wHlaoI8U/AjSdby4ltmzXi4t4tmRhgedFgCJYB7ZJCOu/srqlV8pp6/PZO7tCM\nz27oX+eDK4PBYDheCdbJvVRVR6tqmv3/hao6wv78uEe5X6vqmbYe1mLverKyspgyvO6dYOOCH1/h\nuV8uzmuE9cfRXTine3P+el63Kue5llfev6ovaYkVsaV+fcYJ/KJ/Gz66rh9tYsQZ3vhgRR+uvqQ2\nSeCyvq2CPu80WxphTM+0KsdSGzfgtiEduH9EOo+N7e5zF6CheowPlrMYHyznMT5Y0YPTOlgZWNoy\n8cDTtv5MJVIbB+f25WPlL2iEqs7rLZomcKCgatBfz3LeAptnpKdyRnoqK3b5l5LwJ8rZrFECb0/o\nw+sLd/CfFXt8F6pj9h4tjki7Bme5dUhHEhvG89bS3IBl/zi6K/sLSnzudD2rayoD2jerCxMNMYrx\nrzEEwtwjoRPslh+XDtYCqKyDhRVC50K73EPAOFUd5WtwlZGRQf92Sdxwcjv+cm7VWSGASadZIcKC\n8Q2rroj3od6tE32W8xzIxdVgidDlKBzolJtOaU9iw/DPFKjC777KCVyQ+qMfVV/6ARV9OaWjNSM1\nIaMtj4zp6rPsXR6zwvFx4h5cXdzbmvlKiBP+fWVvM7hygFjVwTLtWRgdLNNeOAlqOklVi4FiqZiu\n8dbBGi0iK4HGwEciUgTcqqp7vesSEa4Z2NZnO2N7pXF5P0tXJ5gJrM7NG7PpYGGV/DiMfkD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CWlH40M65zC4I7NKoW2cfHY2O4UlpTT1CPcSjT3pSZEsh9Ox0OoL98JwPLlyx2vU1U/EZFs4Bng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\n1C/uB04Bl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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from pymc.Matplot import plot as mcplot\n", + "\n", + "mcmc.sample(25000, 0, 10)\n", + "mcplot(mcmc.trace(\"centers\", 2), common_scale=False);\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There are really two figures here, one for each unknown in the `centers` variable. In each figure, the subfigure in the top left corner is the trace of the variable. This is useful for inspecting that possible \"meandering\" property that is a result of non-convergence." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The largest plot on the right-hand side is the histograms of the samples, plus a few extra features. The thickest vertical line represents the posterior mean, which is a good summary of posterior distribution. The interval between the two dashed vertical lines in each the posterior distributions represent the *95% credible interval*, not to be confused with a *95% confidence interval*. I won't get into the latter, but the former can be interpreted as \"there is a 95% chance the parameter of interest lies in this interval\". (Changing default parameters in the call to `mcplot` provides alternatives to 95%.) When communicating your results to others, it is incredibly important to state this interval. One of our purposes for studying Bayesian methods is to have a clear understanding of our uncertainty in unknowns. Combined with the posterior mean, the 95% credible interval provides a reliable interval to communicate the likely location of the unknown (provided by the mean) *and* the uncertainty (represented by the width of the interval)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The plots titled `center_0_acorr` and `center_1_acorr` are the generated autocorrelation plots. They look different than the ones I have displayed above, but the only difference is that 0-lag is centered in the middle of the figure, whereas I have 0 centered to the left. \n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Useful tips for MCMC\n", + "\n", + "Bayesian inference would be the *de facto* method if it weren't for MCMC's computational difficulties. In fact, MCMC is what turns most users off practical Bayesian inference. Below I present some good heuristics to help convergence and speed up the MCMC engine:\n", + "\n", + "### Intelligent starting values\n", + "\n", + "It would be great to start the MCMC algorithm off near the posterior distribution, so that it will take little time to start sampling correctly. We can aid the algorithm by telling where we *think* the posterior distribution will be by specifying the `value` parameter in the `Stochastic` variable creation. In many cases we can produce a reasonable guess for the parameter. For example, if we have data from a Normal distribution, and we wish to estimate the $\\mu$ parameter, then a good starting value would be the *mean* of the data. \n", + "\n", + " mu = pm.Uniform( \"mu\", 0, 100, value = data.mean() )\n", + "\n", + "For most parameters in models, there is a frequentist estimate of it. These estimates are a good starting value for our MCMC algorithms. Of course, this is not always possible for some variables, but including as many appropriate initial values is always a good idea. Even if your guesses are wrong, the MCMC will still converge to the proper distribution, so there is little to lose.\n", + "\n", + "This is what using `MAP` tries to do, by giving good initial values to the MCMC. So why bother specifying user-defined values? Well, even giving `MAP` good values will help it find the maximum a-posterior. \n", + "\n", + "Also important, *bad initial values* are a source of major bugs in PyMC and can hurt convergence.\n", + "\n", + "#### Priors\n", + "\n", + "If the priors are poorly chosen, the MCMC algorithm may not converge, or at least have difficulty converging. Consider what may happen if the prior chosen does not even contain the true parameter: the prior assigns 0 probability to the unknown, hence the posterior will assign 0 probability as well. This can cause pathological results.\n", + "\n", + "For this reason, it is best to carefully choose the priors. Often, lack of convergence or evidence of samples crowding to boundaries implies something is wrong with the chosen priors (see *Folk Theorem of Statistical Computing* below). \n", + "\n", + "#### Covariance matrices and eliminating parameters\n", + "\n", + "### The Folk Theorem of Statistical Computing\n", + "\n", + "> *If you are having computational problems, probably your model is wrong.*\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Conclusion\n", + "\n", + "PyMC provides a very strong backend to performing Bayesian inference, mostly because it has abstracted the inner mechanics of MCMC from the user. Despite this, some care must be applied to ensure your inference is not being biased by the iterative nature of MCMC. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### References\n", + "\n", + "1. Flaxman, Abraham. \"Powell's Methods for Maximization in PyMC.\" Healthy Algorithms. N.p., 9 02 2012. Web. 28 Feb 2013. ." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.core.display import HTML\n", + "\n", + "\n", + "def css_styling():\n", + " styles = open(\"../styles/custom.css\", \"r\").read()\n", + " return HTML(styles)\n", + "css_styling()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python [conda env:bayes]", + "language": "python", + "name": "conda-env-bayes-py" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.2" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/Chapter3_MCMC/Ch3_IntroMCMC_PyMC3.ipynb b/Chapter3_MCMC/Ch3_IntroMCMC_PyMC3.ipynb new file mode 100644 index 00000000..95fa0c1d --- /dev/null +++ b/Chapter3_MCMC/Ch3_IntroMCMC_PyMC3.ipynb @@ -0,0 +1,1414 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Chapter 3\n", + "\n", + "\n", + "`Original content created by Cam Davidson-Pilon`\n", + "\n", + "`Ported to Python 3 and PyMC3 by Max Margenot (@clean_utensils) and Thomas Wiecki (@twiecki) at Quantopian (@quantopian)`\n", + "____\n", + "\n", + "\n", + "## Opening the black box of MCMC" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The previous two chapters hid the inner-mechanics of PyMC3, and more generally Markov Chain Monte Carlo (MCMC), from the reader. The reason for including this chapter is three-fold. The first is that any book on Bayesian inference must discuss MCMC. I cannot fight this. Blame the statisticians. Secondly, knowing the process of MCMC gives you insight into whether your algorithm has converged. (Converged to what? We will get to that) Thirdly, we'll understand *why* we are returned thousands of samples from the posterior as a solution, which at first thought can be odd. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### The Bayesian landscape\n", + "\n", + "When we setup a Bayesian inference problem with $N$ unknowns, we are implicitly creating an $N$ dimensional space for the prior distributions to exist in. Associated with the space is an additional dimension, which we can describe as the *surface*, or *curve*, that sits on top of the space, that reflects the *prior probability* of a particular point. The surface on the space is defined by our prior distributions. For example, if we have two unknowns $p_1$ and $p_2$, and priors for both are $\\text{Uniform}(0,5)$, the space created is a square of length 5 and the surface is a flat plane that sits on top of the square (representing that every point is equally likely). " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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1lixZwoUXXsiTTz7JU089FVEsjhkzhnfffZeuXbsGzpXfS2232/n6669p06YN\n999/P+3bt+fWW29l7dq11K1bN9BHUdUGYo0n/Lo89dRTrF+/ntatWwcqLTRt2pQ5c+aQm5tL//79\n6dy5Mw899BD5+flxKeQ/fvx46taty7XXXku/fv2oVatWoKycnyFDhpCVlUXXrl1p2rQpq1ativlc\nRaJSpUpceumlbNq0KeKEDOHnZdq0aVx11VWMHj2ajh07MnDgQObNmxfylKJGjRo0btyYjIwMzjvv\nPEAf0FexYkUaN25MjRo1SnqKYkbE4xd2cVmwYIG8eGivct+vQnGmc1V3Fw9ev4xevXqp6XYMxtGj\nR8v/P+vTgGgzixkZKWVSz5hV0lnbwnE6nTRu3JjJkydz3XXXxTFChRGoVKlS1JtceV5jxYjeRqPF\nbLR4wZgxKxQKQ6JpWqB2qX8wkNfrjYvNIxlZsmQJbdu2VcJVUQBVbUChUCgUpy3JnKGMFb9o1TQN\nIQRmsxkhBG63G03TsNvtBY4z0YI2Huf98ssv5/LLL49DNIrTDVXnNVaM6G00WsxGixeMGbNCcQYx\na9asRIdQYjRNQ9M0pJRFitFInuSSDnArLXXr1i0wZa1CEU9U5lWhUCgUiiQiXLSWNIsZbYBbuIBN\ndJZWoSguyvMaK0b0NhotZqPFC8aMWaFQJCV+e4DX6w3U+iwL24MQIuRlMpkKrDsd7BaK0xeVeVUo\nFAqFIoFEyrSWt3iMJUvrj09KWWQdVYWiLFGe11gxorfRaDEbLV4wZswKhSIpiJdoLcvH/uHx5OXl\nAQWnJVXWA0V5ojKvCoVCoVCUI1LKEOFaUtGayEf7yTRATHHmoTyvsWJEb6PRYjZavGDMmBUKRULw\n12X1+1rh9Cjl5SeSbzaSn1ahKC0q86pQKBQKRRniz7IGD8I6k0RctKmFVdUDRUlRntdYMaK30Wgx\nGy1eMGbMCoWiXPDbA9xuN0AgC6nQidV64Bf9kbZRnJmozKtCoVAoFHEk2NOqKB6RxGl+fj4ADocj\natZWcWahPK+xYkRvo9FiNlq8YMyYFQpFmRDsaS1L4XqmCrdwL63y0565qMyrQqFQKBSloDwyrf5H\n6lJKXC5XwIoAhFQtONNQVQ/OTJTnNVaM6G00WsxGixeMGbNCoYgL5WUP8O8nfJ0fj8eDx+MBCGQk\nwzOTZxJqWtzTH5V5VSgUCoWiGJSnaPW/grFYLJjNZtxuN5qmhQizSHEFP2b3v/zrzyRUlvb0QXle\nY8WI3kZothQYAAAgAElEQVSjxWy0eMGYMSsUihLhF63l4WkNnsQgHJPJhNlsDohQi8VCSkoKDocD\nm82G1WrFbDaHDG7yer243W7y8/NxOp04nU7y8vJwuVyB4zkTRVu02rTB9owzMXud7KjMq0KhUCgU\nRRBpKtdYCfarFrZdtEyrX6T6BXMkkRksvML79McdXm82lixtogdDJVJQa5oWUt5MWQ+SB+V5jRUj\nehuNFrPR4gVjxqxQKGImkmiNt5ArSrRGe7wdC0IIzGZzxP2Fi1r/eq/XG5gBLDiW4ONOxACxZMh+\nKutBcqAyrwqFQqFQhOGfXMAv0spigoFgn2owZe1JLWmWNhin05l0Wdp44r82sRyLGiBW/ijPa6wY\n0dtotJiNFi8YM2aFQhEVv6fV6/WWqdiINOgrWCQnQgD6s7QWiwWbzYbD4Qjx0losofmucC9tXl6e\n8tIGEclLq2rTxgeVeVUoFArFGU80e0C8hVekslelFTJlKQ7DBzH5S3KlpKQY2kubSAqzHvh/EAAF\n7B6KUyjPa6wY0dtotJiNFi8YM2aFQhGgPDytfsJ9raUVcIkUfoV5acMFbVFe2mSsS1ve2eLgY/ZP\nQGG1WpX1IAoq86pQKBSKM47yEq2RBmKdrlnH4GMKFralqXiQ6HNU3vsP99pGytIGtwvf7kxBeV5j\nxYjeRqPFbLR4wZgxKxRnMMGeVr9oKivRGkmYJYsoK08K89La7fZC69K6XK5AP8Fe2rL2JCc7kfy0\nkWrWnq6ozKtCoVAoTnvKO9NaWC1WReFZ2vAMrfLSxsaZVPWgzMSrEMIErAb2SSn7BL+nPK/lhNFi\nNlq8YMyYFYozCH8WL1Gi1T/Q6XQQDOVBeBkvTdPIy8sDwG63l8hLW5JSZ8UplRVPymK/p2Nt2rLM\nvD4A/AJkluE+FAqFQqEoQHjx/USI1mj+REXx8VsP4pWlTXRZskQT6Zi9Xi8ulwuz2YzNZgOS994t\nE8+rEKIOcAXwVqT3lee1nDBazEaLF4wZs0JxGqNpGi6XK+CNhLL3tAZ/wZtMphD/pqLs8AvQknpp\nI9WlTbSXNlEZ33ASvf+iKKvM63+BR4AKZdS/QqFQKBQBIo1kD59Bqrj4H68Gi5lYpnItL1RmtyDx\n8tL68Xq9Z0yWNlmEcyzEXbwKIa4E/pRSrhdCdAcKnIXt27fDH4PA2kBfYaoIjtan/IP+bFayLftJ\nlnjUcuKX07onVzyRlv96GfLXg7UBW3/0sr5xRXr16oVCcToQbVR/WewHCk6TeiYPEDISkabELawu\nrR9/Jt9Pab20RZFIAWkk8Sri/YtNCPE0cDPgAVKADGCmlPIWf5sFCxbIi4eqL0+Fory5qruLB69f\nRq9evZL/fydFCEePHlXptSAKE63+wTulFRaRZsPyUxzRGtxPSWdNCu7DarVitVrxeDwBj6Ldbi9R\nv8WNwel0ApCamlrm+4NTA7aEEKSkpJTLPqWU5Ofno2layMCxaMTTS+t2u3G73QErRHnit074769E\nDzSsVKlS1BMYd8+rlPIxKWU9KWVDYACwMFi4gvK8lhtGi9lo8YIxY1YoDIzft+jxeMot2xpMMhXQ\nV5QNwcLTYrEEvLQpKSmG9dLGgpEyr6rOq0KhUCiSnvKyB/j3Fe+pXONJsougeJLoklV+gn20ZVnx\nwEgCMpGUqXiVUi4BloSvV3VeywmjxWy0eMGYMSsUBiLRotVPaQd/xZMzSbwmmqJEZCQvLRQcQBhr\nXVr/fZ6Ia2wk4awyrwqFQqFIOhItWpNtJH+woEi0F1FRNKWtS+u3Gqi6tJFJyE9J5XktJ4wWs9Hi\nBWPGrFAkMf4vd7+ntSxFWqRarUbwtGqahtPpxO12B5aN4Kk804lUlzY1NTXESxv+I6UwL63b7Y7r\ndVeZV4VCoVAoikn449WSZpmC67NG2j6WWbGSiWiZVv86/+h4CBXfwZm6ZDyuZKY8hVxwltb/Q8Q/\nKEzNHhaZhIhX5XktJ4wWs9HiBWPGrFAkGZqm4Xa7A2KzrESkUUVrpHjtdjsejyeQfQ0W7IV5KsNF\nrSK58F/r4GsV/n5JvLSxPFFQmVeFQqFQKIog/MsXym4q19LOihUti1tWxFJj1h+/yWTC4XAUe+R7\ncYSNIjkorZfW30f4dY9EMttQEiJedc+rwSYpyFlsvCyb0WI2WrxgzJgVigQTyR5QFgR/qQcTq2hN\n1CxH0WbxKmrwWmGzSAWf8+B/C3v8nAyC1kjZwHhQkuMtacWDaHg8noRMd1wcVOZVoVAoFOVCvDyt\nxd2nn2T2f5ZVbdl4l3IK3j4Zz2O8OB1Ec6xZ2vD7zm9FsdlsSXv8yvMaK0bMrhktZqPFC8aMWaEo\nZ8pTtEayB5yJorUoSvv42el0FsjOJjpLezpQ1o/qo/2Y8Xq9gUF/ZrM56X+cqMyrQqFQKMqE8hat\nkR6rJ6ugSsZZvGKxHXg8nsB7RrAdGJVEnTP/YEAg4ucpWVB1XmPFiPU8jRaz0eIFY8asUJQxfpHj\n9XoDX4Cx+kuLm3kqbDKDZMy2RqotC5RK6JVlti64NqnVag2sD65NajabQ66fvzZpfn4+TqczpDZp\nedTvLS2Jii2ZzkkyxRIJlXlVKBQKRVzwC5fytAdEylwCSSuQSjp4LBKJFObRbAfhg8GCM+/Rqh0k\nc/muRGbByxOjeXyV5zVWjOhtNFrMRosXjBmzQhFnwrOJiRKtyTala2Ek2iJQFvgFbTCFDRCKxXZg\nhGupKH9U5lWhUCgUJSLRohWS19MKyelrLW/iVe3A6/XicrmSOktbWhKZ/TRa5lV5XmPFiN5Go8Vs\ntHjBmDErFKXE7XYHPI3+ATxlPRArkkc02GuZTESLGZLTh5sI/Flaq9WK3W4nJSUlxEdrsVgKCF6P\nx4PL5SIvL69MfbRGE3LxwGjHnBDxqlAoFArj0rdv3zIfiXy6iNZIWUdFZIIFrc1mw+FwYLHoD4j9\ng8aCz6V/YGCwoHU6neTn5+N2uwP+a8Xph/K8xooRvY1Gi9lo8YIxY1YoSkmwIIi3OCjtrFjxRghR\n5ExgRrQ0GAX/+TOZTNhsNuDUPRer7SB8+ttktR0o20DsKM+rQqFQKIqNX9SVBUaaFQviW0Eg1v0m\ncw3OssZ/XqNVO4g0FW5hs4adzj7a0xXleY0VI3objRaz0eIFY8asUJQSm80WmI0nHkSbFSuZi9xH\nGi1f3lk9I1VXKA/CfbQOh4OUlBQcDgc2my1m24HL5Qq0Kc9zqzKvsaMyrwqFQqEoFpmZmRw/fpys\nrKxS9VMes2LFe5pLVUHAWBQ1a1i4Tzn42kopcTqdBcp3qeudeJTnNVaM6G00WsxGixeMGbNCUUpK\nK16jeUT9lFYUlIWoCBY7wftRIsZ4FFW+yz87XPD60912oDKvCoVCoTit8YvX4hLLrFjJRqTBaUq0\nlh/lKar8tgO/WDWZTNjt9og1aYECthGgwMCw4jxFMJqATCTK8xorRvQ2Gi1mo8ULxoxZoSgl4eK1\nKF9gYSWkkt3TGk4iY1Ye1/IlOENrsVgC5bvCfbTBpdv8mVt/HWSjlO8ymnBWmVeFQqFQFIvMzExO\nnDhRZLuS+kPj7VMtDtEqCAAFpj5VnJnEa9awcNuB/54r73s/kphORoEdjPK8xooRvY1Gi9lo8YIx\nY1YoSkmFChUKtQ2UpO5pMmR8CitBlQzx+ZFS4na71eQHSYbfdhBevqs4tgPQp8INFsjlee8l031e\nGCrzqlAoFIpikZmZyc6dOwusN2qx/sIyxIUNLCvpvkpyLsLjCM/kOZ3OiAOIFMUnno/Qi6p2EJ6h\nBV3UBpfrCq92EO9razTLACjPa+wY0dtotJiNFi8YM2aFopSEZ14Lm8o1mUVUeXpxS9NXpIywxWIh\nz2UN8cF6vd5CvZbh10eRGKL5aP0C15+9Lera5uXl4XK58Hg8Z9y1VZlXhUKhUBSLogZsJWoq11gp\njhe3LLKvsRItk/3XMSsrf3Iw6UMHF7Tw0PE8N7WqSqpV1qhUwUulDG9gYNDpXuLpdCJ45jCLRZdn\nkerRBmdqo1U7KM61Dc+8GkEEK89rrBjR22i0mI0WLxgzZoWilGRmZpKbm8sTTzzB2LFjA196yVRC\nKlh0Bn8pG8HWEE1cn8w1sWmHnVET0/hlp/71velXC+/Ocvi3pE51jQvbeOhyvodaVTWqZXmpnKlR\nKdODlLqILazEU7IJWiM+0i4pkY7V76MNb1eYmA2/tmVtO0gEKvOqUCgUiphZu3YtTz75JMuXLweg\nV69edOvWLam/EKNVEEi2mKOJVk0zsWW3jf++l8JXS+yF9CDY96eZT+aY+WTOqXY1qmh0aOmha1s3\nnVu70TTITNPIquiBYgjaM4VkF8zxqHaQiMFg8SQh4lX3vPZKxK5LTs5i42XZjBaz0eIFY8asUJSQ\nt99+m0ceeQQAm83G8OHDadu2bdyydJGypaUlUqY1mTLEfqI9At79h43pc+1M/DAFr7dk8R44bGLP\nfhON62r8b6aDt2baqZQpadvCQ6+ObhrU0qiapZGVqVGlogeTyRs1i+fH4/EYXgAlG6UVzdGqHQQL\n2eC/w+0kmqaRl5cX8OMmM8kdnUKhUCiShksvvZT//Oc/DBo0iBUrVvDggw8mrT8uUlxGEq0Hj1jI\ncVpYusbCsrVWvF4JFD/uSpkaU0blsPeAiZseTSfHqfdx5Jjgu5U2vltpC7TNSJOcf7abnh08NKnn\npVqWRlYFPUNrNYcW1y/P0fCKkhNL+a7giROScZa7SCjPa6wYMbtmtJiNFi8YM2aFooTUrVuXTZs2\nkZaWxlVXXZXocCJipExrJIvAiVwL67fYePS/qez700yLRh66t/VwZ998MtIlNgts221i9nIby9ea\niV40SOP54bnUqAqP/jeVvQeKnmDhRI5gyWobS1afErSpDknLpm5eHOHk8N8mHDYvVSp6qVpZYrd6\ninwsrQRt8hFuO/BXLPDPFpasP0iDUZlXhUKhMBDz589n1KhRaJrGzTffzAMPPFCgzciRI5k/fz6p\nqalMmTKFli1bBt7TNI2ePXtSq1Ytpk2bBsCYMWOYM2cOdrudBg0aMHnyZDIzMyPuPy0trWwOrJRE\nG4wFJJVfM3j0uB8hBG6Pia277TzzVgoLV50Sjxu2Wtmw1RpYtpglzc7y0vUCNzdflU9GmobDBjv3\n6oJ2yWoLN1zqYkBvFxM/SGHRj1ZKw5Vd87n5KhdjpqSycJXel80qadHIQ8/2Hs5rqmdoq1bSyKrg\nIcVufEGbKM9ror22/ixtoqprFAfleY0VI3objRaz0eIFY8asMCyapvHoo4/yxRdfUKNGDXr16kXv\n3r1p2rRpoM28efPYtWsXq1evZvXq1Tz00EPMmzcv8P5rr71Gs2bNQqZ37dGjB2PGjMFkMjF27Fhe\nfvllnnjiiZjjSuQXXWGiFZJn0E1wfKHC1cTO322896WDNz5zIGXh8Xq8gp+3W/h5+6mvb5NJ0rS+\nxs1X5zP2PicHjwg0Cdf0zMdhl8z73oLHUzwB36iuhxcfzmXJaivXPZiBpp2Ky+UWrN9iZf2WU8LY\nbJY0rafRo72bC87xUD0gaL2kp3pCfJbhgjZc1CZblvx0J9GiuSSozKtCoVAYhDVr1tCwYUPq1q0L\nQN++ffn2229DxOu3337LjTfeCEDbtm05fvw4Bw8epFq1avz+++/MmzePESNGMHXq1MA23bt3D/zd\ntm1bvvrqqyJjSUlJITc3l5SUlDgdXfEoqoJAMhVtj5YN3n/Yyncr7Ix9NZXcvJILh/QUyai7czl4\nxMSV92VyIlcghKRRXY0L27h5+V+5VMyUpNglfxwy8d0KK3NXWHG5Cgpam01jyqhc8l0waHQ6x07E\nJnq9XsHmXWY27zplT7BYNN75Ty5ej508F9SsolGtskbFTC+VM0/Voi2sXql/fbJcy7LidD++eBN3\n8SqEsANLAZuv/8+klGOD2yjPazlhtJiNFi8YM2aFYdm/fz+1a9cOLNeqVYu1a9cW2Wb//v1Uq1aN\nUaNGMW7cuJAJBsL58MMP6du3b5Gx+CcqSIR4jSR2wn2tiZxcwE+0rPDxHCu/7rGBhBSHpHs7N/N/\nsEQUk4Wj8dRQJw3raDwxJYUde099pUsp2L7HzPY9Zt6dFVhLg9oanVt5eHGEk0qZGmkOycEjJuZ9\nb6V+TQ+dW2uMey2Fn7aVTh4MuDyPAb1dPP+/FFZsCLYuSOrW0LiwtYcuF+i1aKtWLroWrX+WsPLI\n0CY6E3mm2RVKQtzFq5QyXwjRQ0qZK4QwA9lCiG+llKvivS+FQqFQxMZ3331HtWrVaNmyJcuXL48o\n6iZMmIDFYuH6668vsj//FLHVq1ePW4xFCc5YRGtZEmsJr2iiNc9lYstuB09OSWXVJitCSBrU0rjw\nfHdATKY6JPsPm5ibHT07CnBtj3xuuy6fV6c7eHyyLWKbggh2/25m9+9mps3214GV9OnhYthN+fy6\nx0RuHowZ7OToMcGCVRa+WWLjeE7sovqs2h5e+lcui1bpdoOCNgjB3gNmPp5j5uM5p2KoWUXS4Ty9\nFm3d6hrVszQqZ3qpXMEFnMqyG2VyBUXZUia2ASllru9Pu28fIZ9g5XktJ4wWs9HiBWPGrDAsNWvW\nZN++fYHlP/74g5o1axZo8/vvvxdo8+WXX/Ltt98yb9488vLyOHnyJIMHD+bVV18FYNq0acybN49Z\ns2YRCxkZGYVmcONJcaZzjTfF6T9anCDY9puNt2ak8P7Xdvwlr6QU7PrdzK7fzXwQcGro2dGLWrtD\nsqP7D5uYu8LKjr0mnhrqZNlaK32HZ5S49ivoZbReHZ3Dzn0mrh6agTPf35ekVlVdTI4Z7KRKZY20\nFDh5UrBotYUvF9k4ejxU0FosGpMfy0VKuG10On/HaDfwnSX2HxZ8sdDGFwttgMaT9zlpXM/EJ3PS\nubC1iwa1JdWzNLIqamRlFl6L1igDw4JJZPZTZV59CCFMwBqgETBFSvljWexHoVAoziTOP/98du3a\nxd69e6levTozZ87kzTffDGnTu3dv3nrrLfr27cuPP/5IZmYm1apV4/HHH+fxxx8HIDs7mylTpgSE\n6/z583nllVf45ptvsNsLm8HpFH7bgJ94TiwQ3GeiRGtxiebb3HfQxtdLbDzzVir5rlhiPpUd/eCb\nQO80P8vLq4/nsO9PEzlOwUWt3TStrzF3hYU5y6zkFctyoPH0A07q1tB45KVIZbQEfxwSfL7AxucL\nTmV1q1XWaH+um5F3OKmepQtaZz4cPyloUFtjzJQU1vxSuuoGF7Z28egdebw5w86TU/V78avFp2LI\nTNdo09xDrw4eGtf3Uq2yXou2SkUPFvMpD61RKx0oYqOsMq8a0EYIkQl8IYRoIaX8xf++8ryWE0aL\n2WjxgjFjVhgWs9nMc889R79+/QKlspo1a8b//d//ATBo0CAuueQS5s2bxwUXXEBqaiqTJ08ust+R\nI0ficrkCXte2bdvy4osvFrpNZmZmSMWCeBNpwFUyCo5oVoYjx62s3GBj5H/TOPx3aUp1aTx2Vx7n\nNfUy+Kk0tuzyf22f8o4+OzyXrIoy4F/9boWVOdlWcvMK7tdvN5j8kYN5K2O1G+gcPGLi66V2vl6q\ni8rmZ+kVCX7ebuHYScHQgfmkpebhcsOK9XqGdt+fRdeXBX262teeyGHnPjP9R2REFfrHT5qi1KL1\ncHFHD80aeKlRxUONLEmFDC82S/FLdyUiE5lMmVcjDB4TZR2kEOJxIEdK+ZJ/3eDBg+Vr05xgbaCv\nMFUER+tTQiBnsf6vWlbLarn0y3+9DPnrwdqAJg283DOwIiNGjEguBaAokqNHjybVN8r06dM5ceIE\n//jHP4D4CMtoFQJK0newqAyeXagk+AVPcBzRssLOfDO/7LTjzBcIYOtuE18usvHDxsImFIhM7wvz\nuffGfN75ws4XC2PJiEtqV5dc2MZN51YeKlfQs6OH/xas/dnM5V1cZK+z8d/3HaWyG9hsGq+OzuVk\nrmD0K6mcyAntq0KGxvln63Vg61TXSEuReLzww0YLXyyw8tv+0LzZyDtyadXMy2MTU9j1e+lyapd2\nymfoP/KZ+IGDv/4W9Oqg16KtWkmvdFC5godUhzfqTFLB4tVqtWI2m8sl0+/1esnPz8dkMuFwOMp0\nX8FIKXE6nYBeQcRfqSMZqFSpUtSTHnfxKoSoArillMeEECnAXOBZKeVsf5sJEybIh98aEdf9ljlG\n9DYaLWajxQuGi/mq7i4evH4ZvXr1UuLVYCSbeJ07dy6//PILgwcPBkovXqM9doeSZaPKSrz6+w4X\nrZo0sXW3jVempfgetYvAhAI92rk5t4mXzHQNm1WwZaeJrxZHF7T1a+qDnn782cKL76TgKYXQtFg0\n3v1PDs48wfEcQZVKuqA9ckwwb6WF2ctsnMyNXVTfN8BJz/Zuxr6aysZfYxeaGamS1s099Gjvpn4t\njYxUid2uUauqxsffOpjwbumqVlRM13h9TA4/7zDz9JuRz5nFLGnawEv3dh4uaOGhemWNqpVDa9FG\no6wHhnk8HlwuV0LFa2pqKpA8U8QWJl7LwjZQE3jX53s1AZ8EC1eFQqFQGJ9wz2tJiSRagaT1tUay\nMvy238aMeXZeei9UNEWaUMBiljRv6KVneze3X5dPRrqGzSL4ZaeJ2Ust3H5dPl6vibueTOfIsdLN\nDHbvjXlc0tHNf95wsG5zaLmq2tUkHc/zMG6Ik6xKGukpcPS4YL5P0IZXGGjZ1MN/hjqZOd/K9Q9l\n4B90FisncgXL1lpZttZKqkMXmjt/t/D8/2x0b+fm7bEnSUuVmEywYYuZL5fY2Bhjua6Rd+RyXjMv\nD08ofBpcj1fwyw4Lv+w41a+/Hm7XC9x0OM9D87O8OGwamWkeKleQgR8u5VXpQJXJio2yKJW1ETi/\nsDbK81pOGC1mo8ULxoxZoYgDFSpUKJXnNdpj97L4Mo3XYLLgWE0mE4eOWli6xs5jk1JjLubv8Qo2\n/WphU1DW0mqRvPBQDiPvzOfQEROVKviyiL+a+HKxnbWb/bmg2GjT3M2T9+Xx+QIr/YanU1BoCn4/\nKJgx38aM+X7vqF6uqmMrD08MdlK1si5oj52EWlU1duwxc9Oj6eQ4S3ceH/ink86tPIx+JYVff9PP\nQfa6U8LaYZOc29RD7wvdDLspj/RUDYtJ8POOgueiTXMP44bm8t6Xdp59O7VE8fjr4e47IOjR3s2K\n9RaenJpK9SyNzq09dDlfr0VbrbKXShU0Kmd60LTyFbSKgqgZthQKhUJRbPyZ1+DarCWtgRpcQSB4\nQE1piJdYDc+0CiE46TTz0zY7I/+byq97Svc12vUCFw8PyuODb+w8+LxuNwBd0LZo5KFnezf33uAl\nPVXDahFs2m7iiwV2NmwrKGgrpmtMeTyHfQdMDPxXejFn7dLLVQVXGBhxq5MO53n4v1kO2p7jYero\nk6Q6JCdyBAtXWfl6sY2/T8Ymqls19TBuqJPp39m48eFIglonzyVYvcnK6k2nBK3NKjmnkYfu7fRz\nUSlDo3oVDbtN8O+XU1m4qnS2kEHX5HF1dzejJqUEBsTt2W9mz34zH38buRZtvRq6oK1cQZJVwQNE\nL90VLmYjWWwSlQE14mAtSJB4VXVeywmjxWy0eMGYMSsUcSAzM5Njx47F3D5a4f5krSAQKVaP18TW\n3xwcPmrCZpUMvyWPOdlWvssubqkqfarUSf/OYeOvZq5/KAOXO/QcuD2CDVutbNhaUMRd3MnNkIFe\nMtMlZpPkp20mqlWSVK4oGT0ptdSDntqd62b0PU6mfWNnwghdaJ4ScXrJrA7neRh1z6kM7YlcwaJV\nFr5aHFoD1mHTeO3xXP46LkqcuXW5Beu2WFm3xcqtffK4pqebYc+m4fEKerRzc2PvfDLTJDarZPse\nE7OX2Vi21oKmFX5N6lT38spjOcxbES1DHUx4LVqdrIoa7c7x0LODix7tPeQ4BZUzNbIqejCJ2GvR\nGkU0Jgsq86pQKBSKYpOenk5OTk6R7fxfypGyUUYRrQC7/nDw0WwHUz9xoGmC4FJVzz3k1Ef2OyT7\nDpr4dpmNed9b8HgKiieLRWPiyFxsFhgyPo2DR2IXvcEizs81PfK594Z8fthoweHw8syDTswm2LDV\nzBcLbWzaHvvXfGaaxqtP5LBnv4kbR2SQF6Vc1cEj+oCz4PqrVStpdGjpYeSdTmr4asCmpmhUqagx\n/Lk0stcXryxXOHVrenllZA7zvteFpn/mrmD7hdksaVpPo1s7NzdenkvFDInDLtn9u4k5y20s/NF/\nTTSeedBJ9SzJnWPS+asUpcz++tvEob8FzRtqjH4llbnZNjLT9WoLvTp4aFzPS9XKGlUq6IK2sFq0\noA8OdLvd5VaL1qie1zIvlRWJBQsWyIuHGizzqlCcBqhqA8Yl2aoNAFx11VXMmDEjYBnwj8b3U5IK\nApHKUpWU4vQVLdYDf1mZ/72NJ6emcTK3qHgkZ9XW6NrWTbtzPFTI0Guv7v7DxNdLbTRv4Obijl6e\neSuFH38uXTH/OtW9TBqZw6qNFl58N3SgmN2m1z3t0c5N0/peMtLAbIK1m83MWmjjl50FBe3ou3Np\n0cjLqEmlL1fV/CwPzw138tUSK38cMtHtAjc1quiCNjcPlqy28uUiG4eOxiIaNV56JJeMNPjXS6kF\nZvYqCpNJ0qiORpcL3FzQwkOjel7SHPD3CcGrnziY/4Ml6hS8RWGxaLw2Ope/TwpGT0qNKvYB0lL0\na9Krgz4orHqWlyoV9UoHNqsnaua1rCdX8Fc5MJvN2O32QqdnLm/Ku9qAQqFQKM5gohXuT8bBK9Fi\nPZZjYe0vdv7131T2FTKCPZRT072+O8vfl6T/pfmMusvJzn1mnC4YeaeTHXvz+WqxnWVri1f/1WLR\nmPhoLlYr3D02PeIECPkRfKP+gVB9ergYcauTtFQwCdh/GFo09DLl4xT+80bJBj35sdk0pozKxZkn\nuDXczUoAACAASURBVHlkOid8Yn/20oKP2Ufc6qRmVUmqQ+LMh2VrrHyxMFTQXtopnyED83npPUfI\npATFQdMEv+4xs/eAoFtbN+u3WBg7NZWaVTUubONmwsO5VMrU49h/2My8lbHNWNanRz63X5fPuFdT\nWLu56B8iOU7B9xusfL/hVFu7TbeBXH6Rm2t6uPjrb6haWVKlopcUuzdQ9zjS5Arhorakny2jZl6V\n5zVWjOhtNFrMRosXjBmzQlFGxHM617KYbja8/0ix5rtNbNll56nXUsleX7rsaNVKGpNHneTX3yxc\neV9mIDNnMkma1dfo1s7FTVfmk5kusdtg667CJzS4/TonV3Z182wJMrfhA6EqZWq8MeYkJ3PMLPjB\nzFXdXAzonY8QgrW/mPl8gY1tv8UuEQZdk0efHm7GvZbC+i3Rt/vrbxNzsm3MyT4lRitX0Gh3roeH\nbnFSo6qkYrpGViWNvDwTtz+RFvMsXdG4+eo8+vZyM3pSSiDrvHOfmZ37zLz/lb+VpF5Njc6t9BnL\nKleQ+gQPRwXzv7fw7XK9Hm5mmsZbY3NYt8VC3wczfBaSkpHvEjQ/y8sFLbzcPDKdX/dYArVoe7T3\ncP7ZHqpn+SdX8JKe4gkI2mhPCooraJV4VSgUCsUZRXBpq3iJ1uA+y4JoohUE2/fa+fMvE0LAtT3z\nMZkodmYUwGTSmPBwLhUzYfhz6fxxKHR7TRNs3mVm865Thfn99V97tHNze998MtN81QV+NbFxu4lb\nrnbx1RI7/YYXv8ZqKBrjhzmpW0My/IW0AlnlFLukVTMP11/qomEdJxmpEolgzc9mZi6wsWNvqGxo\nVNfDhEdymZttC/GiFocjx0zMzbYxN9um12xtKrl3XDp1qmkMvjGP2tV0y0G+G5attfLlQhv7Dxd9\nTWpW0Zgy+iQLV1ljiE2cqjAw51SFgVpVJZ1a6fVw253jxisFB4+Y2HNAkJkq+ftkya5FpUyNN5/M\nYfk6C9c/dCq2SLVog60PHc7zULOKPrlC5UwvFTO85V6LNhlQnleF4gxCeV6NSzJ6Xm+66SY6depE\n7969qVOnTmB9aXx5/sxSJA9tcQmfGcvffzAmk4k/DtmYvczGf17XfYsmk6RpfY1ubV20OdvrG8kO\nW3aZ+HyhnTU/R6+7eus1eVzb08WL76SUOnNbuYLG+0+fZN9BXVBnpmmYBPy0zcwXC+3FGowFcNmF\n+dw3IJ9XP3aEZD6LItWhC9qe7d00rOMlPVUiJWSmw7GTgnufSou5zm00WjX18NT9uXzwtZ3pcyNP\nhVsxQ6PtOR66t/NQu7pGeorE5YHsdRa+WGAP+pGgC/Ta1SSPTEiN0VsbHf9gsa+W2Hh7pp0aVSTt\nzvXQ9QI31SrrwvpkrmDpagtfLSnay3v/QCcXne9hxITiWFLCkdSvpXFJRxd39nNx4C8TVSt5qZSp\n16KVMvJUyxAqaD0ePZtrs9mwWCyG8bwq8apQnEEo8Wpckkm8Sin58ssvGTlyJH/++Sf9+/dn4sSJ\ncRlMEk/x6u8rEiaTiaPHLfyw0ca/Xip61L/VIjm7oZdeHdyc3fBUmap1my3MXGDDboWxQ3P5ZomV\nt2Y6SpSBDIqcx+9x0qKRxuhXUkKynf7BWD3be2haXxeSQkjW/mJh5nxbxLqz1bM0Jj+mP+p+/n+O\nUk03CzDg8jwGXOHi/S/ttGjkpUFtLxmpEk0KfvjJwsz5Vn7bH5uwtvlKaR09Lnj8ldRi1qaFChn6\nyP6e7T3Uqa5Rs4qXipmSzTstPD4lpRTiEEBj/P26N/fhCamFznhWpaJGu5Yeul3gpnoVSXqqJC8f\nlq/VB6ftP2yiZhWNVx8/GRDBpcug61aNK7u5efjFFH77w0LwRBPd2rqpU12jepZGZd/kCv5atJHw\nf97MZnOpP3fxIunE64QJE+TDb40o9/2WCiN6G40Ws9HiBcPFrMSrcUkW8XrixAn69evH6tWrATjr\nrLMYPXo0vXv3xmwunTcRyibzGowQgrx8M5t22HliSkrYtKnFw26TXNTGxVP357H7dxNWi8SrCVZt\n1D2jJRm136uDiwduzuOtGXa+XBw5AxmOPzPaq6ObhrU1MtIkXg1++MlMk3oeHA4T/345lT//Kn0G\ncuKjOSxeZeWVjwoK9PRUSevmHnq1d1Ovli6svV7BDxt1gb93f+j94a/ZOmZKCht/LZ2L0WbTeOPx\nXP48Kpjwfym0aOShR3sP9WpopKVIPBq6sI4QRyTaNHczdqiT16fb+WZpbNchnEqZ/kyxm+7tPDjz\n4NhJE4tWWZi1OLY4IlExXeOtcTks/tHC5I8cFCWCq1TU4+jV0U29mrqHNstXukvgCWlrsViwWJLD\nUaqqDSgUCoUiLmRkZJCRkUG1atU499xzue++++jcuXOiwwoh2sxYUgq2/mZnykcpfPrdqdmsSobG\n2PtyqVUNBjySxh5fpjEtRdLmbA+3XuOiXk3dM+pyC5autfLFAltUAVmzisakx3JYv8VMv+EZuD2x\nx5abJ1i5wcrKoJHsA6/I459XuVi/xUzNFMkr/87B7YUV6yx8HvKIvWhMJo3//iuXFDvcOSY9agby\nZK5g+Vory9eeiiMjVT8ft1+XT70auqA1CahdTWPuili8qEUzsHce/S9z88TklICV4uARG4t/PGWN\nyEiTnH+2mzuuy6deDY30VImmoQvroEyxyaTx6ugccpx6rVtnfsljO3rcxO7fTbS8SePl9x18+p09\nNI6auvVBk7Bqo5lZiwp6isO5+/o8Lu7o5oHnUvk9xoFsh8MGyQkhGX+/k2t6msiqoPtkrVar7/iT\nI+taFMo2oFCcQajMq3FJlswrwL59+6hYsSJvv/02jRo14uKLLwbiU5u1NJnXwiYZ2HfQwfGTJvYe\nMDE328rXS/XR4yVhwOV5DOjtYtKHDhauKto7WiFDLw/Vo72b2tV04ZTjhAU/2PhmiZUx9zlJdcBj\nE1OLNWlBJOpU9zLp3zlkr7Py8gcOvEEWgcx0PQPXo72HutX1jGSeC5auLlimys91F+czqE8+z7+T\nQva60nl4/TVb09Pgkzk2ul7goW4N3XLg8ghWrrfwxUJbzNUFqmdpTB2dw9I1FiZ9WHyrRkaqpFVz\nD706uKlfS6Nudd0O8uMmCxPedZSy3q3GCyNyqZAOD09I5Xgh0+imp+qZ8x7t3JxVRxe0CFi32cyX\ni/S6vJUyNd4ed5L5K61M/aTobGs0GtbxMGWUk/NbeLHbBE6nEyklDocjMNOX8rxGQYlXhSIxKPFq\nXILF6/z58xk1ahSapnHzzTfzwAMPFGg/cuRI5s+fT2pqKlOmTKFly5aB9zRNo2fPntSqVYtp06YB\nMGvWLJ577jm2bdvGggULaNWqVZExvfPOOzgcDq677jogPuI1uARQcWwIkUoHAfz1t5XsDXb+/XIa\nR4+bqFFFo3NrN13O91Clkj4r1h+HTXyzJPqsWH5aNPTw9AO5zP/eGjTTVsmoWkljzOBcGtbVOHJM\n4LBJjhwT/8/eecc3Va9//H1Odtq0pUBp2SB7KFsQF0vlonAFEURR9Or1J8plKhXkorgvuBAEN3qv\nE0HEAdKyZIiyBWRbVimjrNLs5JzfH6dp0zRp06Yret6vFy9pmiZP0mCefL6f5/OQ9rOO73/SY3OU\nPuHg9cmKOpr6ujnsrVE14iS6tfNwYzc3dWvLmE0yVpvAlj0ifbq72bBdz6sfR/ZYQclsfWyEk1c+\nMrF2S9EmOC5WonNrD72v9lCvjheLGVweWL9Vy5JVhiLpAs88aqVxXWUgK9KGP9asxF/tPqjhzc+M\ntGvmpVc3N43rSVjMMsiwZY+ysSyYpziQjq3czHjMzpzPjPxYisE4f8xGmXbNledjwHUu7E5wOAU2\n79by3Vo92/eFHhoMhiDITLzPwb0DXTRILoihi9bmVc15DZco8zYC0VdztNUL0VmzSlQjSRKTJ09m\nyZIlJCcn06dPH/r370+LFi3yr5OWlkZGRgZbtmxhy5YtTJgwgbS0tPzvz58/n5YtW3L58uX8y9q0\nacN///tfJkyYEHYt8fHxZGdnl88DKyOhoq+sDg2/HdDz5Osx7D9S8FZ3KltkcbqBxekFcUiN60lc\n39nN65Nt1LDIGI0yh4+JLF1jYP02DbFmeOupXM5e0DByioXL1sgauTZNPbwwzsby9TrGvBiTpxjK\n1EuSuaaDmxfG2qmZIGE2ypw8q6ybXfFz6Mb6rv4OhvV38fL7pkLWgXC4kCPy40Y9P270NVkS856y\n0aWtxMGjWq5s4eHLWblcvCyQ/rO21Ip1QqzE29Ot7D6kYfA4S8hhsZxckdWb9az2O+qPt0h0aeNh\n9F0O6id5iTUrsVEN6niZ96WJ6XONpXqswXhoiIObrnHzxKsFm8U2bBcLqcwxJkUZHdbfRZN6DmLz\nGtqtvysNrS8PVxQl5k6x4XQL3Bmh5cDmEMg4oaHbP+x8tNTAe4sMGPTQ9goPvbu7GT3ciyVGRquB\nPYdFvlurZ/Pu4LFujeoqamuXtl6MBiUWzkdgzmt1aVxLQvW8qqioqEQRW7dupWnTpjRo0ACAwYMH\ns2zZskLN67Jlyxg2bBgAXbp0IScnhzNnzpCUlERmZiZpaWlMnDiRt956K/9nmjdvDpTuzSs+Pp4/\n/vijPB5WqQnVtHolkf0ZemYtMLNsfTiql8CRTA1HMjV8vFS5RKNRorJ6X+1k1iQb5y8JyBIcPAqN\nUryljqjyYTYqx9wXckRGplryN1D56sg8I7BwhYGFK4o21q89oQTnm/Qyf2SKfLtGT+YZgZkT7aza\npOP2sZaIvaP9ezr5v+FO3viviVW/Fm6Ck2tJ9LjKwzOj7dSsIWExKwH+P27QsXyDLqhSPOUhG+2a\neZn0ipnjZZj6v3RZZOUvelb+okevV7yoF3I0zP/CyI3d3Cx47nKe9UFg3VYd36wO7SkOpG5tJQN2\n+Xo9QyfGUtwxvNUusHGHjo1+0We+Ibk7bnJxRQMH9ep4SYiV2XVQwxufGCNqXAHGjrTTra2H/5sR\nm68sO12wba+u0EYvnVamzRUebuji4cHBTix5sW4Hjon88JOezm3c3H+7m0YpRZd+REujGowqaV47\ndOhQFXcbGdGorkVbzdFWL0RnzSpRTVZWFvXq1cv/um7dumzbtq3E62RlZZGUlMTUqVOZMWMGOTk5\nEddisVjK5XZKQ3FbvDJO6vlimYE3PzMV8nqWFq9XoGUjDzf18PDMPCPL1hkw6GXaNfMw4HoX40c6\niDVLyLIySf9VesmT46n/sNGxtYdpb5pLsbmqaGMtijKtG3uZO83KqWwRp0vghq4ekmvbWLIqtPpW\nHLVrKE319n1ahoRQR09li3y9Us/XK30fCGTq1VGU4hfHKg2t2SCTdU7DroMCf7vWzX+/NfLCu5Gt\nnAUYdrODYf3dPP2Wid8OKM9d2qaCDyY14pQNXWPvsVO3tuLltTuFoCtnQbEcNEqReWh68PW64eAb\nktu6V8M7/7axbY+Wlz4w0aqJl9v7uGnawEGcWfGu7tin4ZtV+vztXsXhi9NaskrP3anFN9UAbo/A\nzv06du4vaGi1GpneV7uZP81KYoKM2SiWeDvRtrhAVV5VVFRU/iKsWLGCpKQk2rdvz/r16yNWXuLi\n4iq1eQ21EvPMeR1Ol8jMD00sWaWLyJ95RQMPMyfa2LBNx+Dxlvwm2OkS2Pq7jq2/Fz5O7tTawz9u\nd9KorpfYvKbppy06vl6l59xFkRu7uphwr4OPlhp46X0TkWZ73jfIwW3Xu5nwH3O+AufbztXnajcP\nDXHmZdAqywy+XqkvRimWeGGsEuY/5sUYToWxtaoAgczTAgt/NLAwb6mAQS/xvxettG8mcOSklkG9\nlZWzJ06L/LBOz8pfivcUB1K7hsT8f1tZt01TbCrBhRyRFRv1rNgYZOXsfXZSaikNrSDINEiWeOvz\n8rEcDOzl5IHbXUx7syDq69ddIr/uKniNGPNyeQf2cjP+XgdxMTKiCDv3a/hmjZ5dBwp+NxPvtdGh\ntZcHI2iqQebhoU4evMNJvTogCKFvJ1pXw4LqeQ2faPQ2RlvN0VYvRGfNKlFNSkoKJ06cyP/65MmT\npKSkFLlOZmZmkessXbqUZcuWkZaWhsPhIDc3l0ceeYR58+aVqRZf8+pb6epLCShvgjWtgiBw2aZl\n+149k183IyBwQ1c3b06xkWCRMehg/xGRxSuL34jlw6hX1EerXeCBabFcDGNjlNUusG6bjnV+0VCJ\n8RLd2nt49jErV7WUcDjhaJaIKMiYjXKpQ/h9tGri4aXxyhKEwQErYj1egd0Htew+WHiZQbvmHm69\nwc24kQ5l8AjYvFvDonQ9jetJjB/pYN4XxjDtFcUzapCDgb2KZrYKgkyTespq0zcmK78bo0EmI1Pk\n+5/0rN2iRZKKPtf/fsRKs4YSjz5f2qZawX/lrCgqSxpcbg0ffK3j+s5uPnr+cv5w2potOr5ZpedC\nTnj3E2tWVrv+dkDL4HGxxX5YcrgENu/WsXl34Ya2XQsP/a91M/ZuB7XyclfP5wg8NTumzI1rci2J\n+dOsdGvvxWwq7G0Nh2iyEajKq4qKikoU0alTJzIyMjh+/Dh16tRh8eLFvPvuu4Wu079/f9577z0G\nDx7M5s2biYuLIykpiWnTpjFt2jQANmzYwNy5c4M2ruG+iSUkJBQa+ipvQlkE3B6RfRkGnn/XxNot\nBY1XRqaGBUuUv2s0BRuxHr5DiUASBJkte7R8taLwAoHUf9jo0MrLjHmmsI52i+P8JbiphwuTQeCO\n8RaysgXq1pbp2dHNC2NtJCbIxBhlMs8ozVvaz8GbNx++pjrHGswnGxqnS2DrHh1b9xRWim/o4uKT\nl60cyVQsByNvc9Kkvpev04tO9IeDb3XqihCZrbIs8McJDX+c0PDRN8ploijTrIHEDV3cDL3ZRkKs\njEEvc+iYyO5DGobe7OLDr43MmFe25QD+9O3uYszdDl5418QvvynPxQ/rCl4ztRKUDxup/7CTnKfQ\n5toEVv2q49s1RRvaUYMc3Hajm8dfMfHHibK9VhwugS27dWzZrSP1QRs6LTz6bCzJtb3cdI2bMSMU\nhVargd2HRL5ZZSghXUDmn0Od/N+dTprUC/8DpKq8lhLV81pJRFvN0VYvRGfNKlGNRqPh5ZdfZsiQ\nIflRWS1btmTBggUAjBo1in79+pGWlkbnzp0xm83MmTOnxNv9/vvvmTx5MufPn+euu+6iXbt2LFy4\nsNifMZlM2O328nhY+fi/kfqrrcrlAn+c0LPgGxPvLTYUO6DkDaJG+oZsRt7monFdO/WTvcTFwu6D\nWh59PibseKlQ+PJfX/nIVEiNPXm26CBW0/pK8/bmkzbiLTImg8yBoyLfrDawaafiW330Lju9urp5\nZp454g1UABPvs9GyscSdk2Lz16b6MmgfG6HYB2JMMjYHrN5ckhqpLC6INcP902LDVi0BJEngwFEN\nB45qYJFymUEv8enLuSTEwckzGu682cXdA1zs/UNk6ZrSe3nNRiX+av8RTUgfLygB/j+s0xdqaGvX\nkLj6SjdPPminTk2lobXaoWl9ie9/0jNkfMle1JJokOJlzpNWPl9u4KX3FF9w5hmx0IcNvU5W0gW6\nFU4X2H1QScLYtlckKRHmTbPS/SovMWVQW6MVNedVReUvhJrzGr1UpyUF/tx6660sWrSoXFa6+t6P\ngvlaT55V4pxmzDNHPMndIMXLa0/Y2LpHw/uLjXRqowTEJ9dSFghkXxBYtkHP8nU6HK6SH0+LRh7+\nMyGy/FeNRqZlYy+9u7m56Ro3lhhlE9WKjTq+XhnesE8oenZwMflBB+8vMvDN6pLVzFoJEldfqaw1\nTa4pYTbBhRyBHzdo+f4nPdd2dPPYCCevfmxizeZIFxfAkL4ORg5UBrJ27Ct4nP5e3nbNvMTGyOg0\nsOughiUr9ew8EFyNvP92BwOuc/PkGyYOhj0cF5qx99jp3t7DwjQ9Pa70kJTX0F7KFUnfpOX7tXpy\nrOG/7v/9f1aa1JeZ8B9zqZp+UBraNld46NXVw4gBLvQ6aBgkSSAcPB4PLpcLjUaDwWCoVhmvoOa8\nlg/R6G2MtpqjrV6IzppVVMqR8nqzC+VrzbVp2b5Px9iXLGU61vZHr5d480krsiTw8DMFSuvy9XqW\nry88RX9dRzczJyrxVD6P5rdrFI+mr2HyHenn2gXunRpb7BalkvB6BY5kinRr72b/EQ1Pv2XG40UZ\n9untYsJ9DixmCUmGn3fo+Cqt5BWvcTES86dbyTghMnSCBacrvAYn+6Jiafj+p4LnpG5tmZt7Oln5\n/mVOnBbweGDoTS5iTDJpm7S4wmjyA/ENZG3cEdw7GszLq9cpqQ99r3Ez5m5lO5cowra9GtZt0zDx\nXidpm3TcMSFydbR+HS9zp1pZlK7nrsnK7RXkAyse0+5Xupn+iF1ZeGGSuXhZDJmH2yjFw+wn7fzv\nWz0z5pfNEuFyCxw/paHHVQ6Sa8lYYv46aqs/qudVRUVFRaXKCLXS1eEU2XvEyCffG+jQ0stLE6zE\nmmTOnBP5dm3xof3BGHePnWs7uXn2bTM79xf31qdM0X++3MDny5UGQxCU3NdeXV3c1d9FnEWiTqJE\nvEXmubdNLFkV+eT6pFE2urT1Mm2OuZBa6PNG+rCYZTq3dfN/dzpokKIkHNgcsHaznq/9jvmfuN9G\nx9ZeprxhinDNKYDAvQPtXNnCy50TY/MyW2Ua1VUyaF+dZCMhTsZklDmaqfx+Qg1i+ZjykI02V3h5\n7PmYUn0ocbmFIlmnRr3M3Kdy6djay/kckRu6eLihcy6bd2v4epWew8dL//iffsRKo7oy90+L5fyl\n4PWdyhZZssrAklUFdpCUWjLdr3Lz9Gg7tWtIxJhlLlwS0etlBBnunRrDpTCGAUNxz61Oxt7joFnD\nyIcjo9nzqtoGVFT+Qqi2geilutoGBg0axGeffZZvFwh3pWuoYSxZFjh4TM/bC018+oOBwqqSTMMU\npWHqcZWHhDgZo15mX4YmZKrA9Z1dTLzPwefLDXz2g55IVaoeHVykPuDgs2UGDhwR6dfDTYvGEhaz\nhFcS2LBdy+L0klVRH13buXnqn3Y++d7Alz+Wrb6aCRI9rnRzQxcP7Zp7MJvA5YJ5Xxj5IUzrQyg6\ntvLwzKM2/vutv2c3OIKQN4jV1U2nNh4SYpXA/INHRZas0vPLLg3tW0g8P8bGx0sNfJUW+UBWm6Ye\nXhpv56Olehb53Z5vK1bf7m4a11XsIF4Jft6pZXG6nhOng79OWzVRLCAfLjHw9crI62ve0MMbqTbS\nftbRMEWiZoJErEnm7EVFoQ13FXCNOIm3nrLRs6Pi0y4PXC4XHo8HnU6HTqdTbQMqKioqKn8NYmNj\nuXz5MvHx8WFdP5TSKooix0/r+XaNkofqcgd73xI4lqXhf99p+N93yiW+VIF+3d3831AvcTFKMPyu\n/SKd27rZsU/PnZPCPzIPRc0EiblTczlwRFvo9vwVQItZpktbN48Mc9Ag2Zs/uZ7+izL85H+MHBej\nHJkfyRQZNsmCI4L6zl0UWfWrjmH9Xfx2QMvTb5lJTJC5tqOblyfaqRUvYTLKHD+lWB9W/Vq8KgqK\nxeLtfyvbxYY/bgkr4kv54KHh4LGCQSyNRqZVEy/9eriY/aSV7AsiLrdAm6Ze2rfwFMo5LR0Sc6ba\nkWWZEZNjyQ1IYQi2FctilunUxs2DQ5w0SlEaWpcb1m/TsmSVnicesGPUw92Tw091KI7nx1qpnSAz\n7PHAlcJ5Cx6ucvP8v5RVwDEmmbMXBNJ+1rNsXeGNZXfe7GTS/Q6al4Pa+mehSpTXV155RZ703sRK\nv9+IiEZvY7TVHG31QtTVrCqv0Ut1VV5Hjx7N+PHj89fVFqe8hloycP6Slg079KS+HvnEv1ar7JfX\naeH4aZGGKYovMtcqkBakiSwZiZkTbSQlykyZbSYzhGIXito1JHp2dHNdZ49yjGySMOjAEgsP/tvM\nwWORDzyNvdvONR09PPVm6AElQVASDm7s6qZzGw814mT0OkW1DtzMFSqztazc3tfJfQOdPDvfxNbf\ndfm+1T5Xe2jZRPn9IMCW3RoWryz5mL93NxfjRjp4+QMTG7ZH9vzFWyTuG+hgUC8PJ84ImA0ydies\n3aJnSd6iidLSqomy6OLthQa+WxuueitTv47ENR08XNPBQ2KeQqvTQvvmEglx5f+/bKfTidfrRa/X\no9VqVeVVRUVFReWvQVxcHJcuXcpvXoMRahjL5tCw65CBqW+Y2XM48rejh4faubmnm5feM/Hr7sJN\nja+JnPFowXDNiVOKPzOUEjmkr4ORt7mY/YmRVb+WLcj/7IUCX+T1nV1MGuXgf98ZMBhkxo10khDn\nwKiXOZChYXEp17u2b+HhucdsfLVCz7BJxQ8oybLA4eMaDh/X8P5i5TKfKurbzJVcy0tiPORaYezL\nMew/EtnvpGaCEpq/eY+WweMs+QNZwXyrZqNMx1Ye7vqbi6b1HMSaZdxe2Lhdy9crFRuGUS/x7jNW\nMk5oGDLegtsTWUMnihIzJ9i4bBMYMLpA/fZt53rifiUqK9YMl20CKzdp+W6NnoshB/OUDzqxZhjx\nRGnVW4ETpzV8+aOGL380cHsfJ5NG2WhS1wGAwyEiigV/fCuR/6qonlcVlb8QqvIavVRX5fWFF16g\nR48e9OjRAyisvIbytXolkQNH9WRf1KARlXD6r1cV5JuWFp9v9OuVehZ8E+iTDUWBEtm1nYf4WEXl\n2nNIZNMuDQ8PdfHTFh1vfFK26Ct/aiZIvPVULr8f1vLie0UtEb4msu/Vbto28xIXKyEKsOV3LQt/\n1BcZuNLrJd6eZuVCjsi/55qLHJmXHiWzNcYM0+eYaZDipW93N80aSMTGyHg8sHFH6by8qf+wcWUL\nL4+/YibzTOnUah+WGMWG0bubh+s6u5EkyLEKLF1VeDitLCjqrZNn3zYW2n4ViqREJT7shi5u6gD+\n5AAAIABJREFUaidKxBjhYo7Aip+1/LBOT+O6Ei+OszH3MyPLN5R9Y5klRmb2k1au7+Qk1uxFkqSQ\naqh/M1uWhtbhcCBJEgaDAY1GoyqvKioqKip/DXwrYn343vyCNa2CIHAkS89XKwy89l8TXq+AKMq0\nbCTRt4eLB/7uVDZhIbPpNx0LV4QerAGfD9XKoWMahj9uKWX+a8Hmpw++Vi6JNUn87+VcGteXOXdR\npPuVSmO7YbuWr9IMnD5X2mZJ4rkxdhqmyEyYGRPScuD1Cuw5pGXPoYK3ZJOh8EKFWLOMyyOQa4OU\n2hJTXo8pF7X65p5ORg938upHxvxtZVnZIr/u8vOK5jWRo4c7qFdHOea32gVW/qJj6arCSqRvgOp/\n3+l56X1zRLVdtipRWY/d5WBxup43PzWSGC8rG7EetJNSS8JshEuXBdJCxFMFotdLvP+MlSOZGgaP\niw25vCCQM+cVv/C3awoa05RaEtd0cJP+bg5Z2SJeL/y9jwuzUS7ToNyt1zuZ+rCTVk0kBEGLr0Xz\nnVz4//G/zJ9IG9poQc15DZco8zYC0VdztNUL0Vmziko5Eh8fX6R5DTaMdfa8jjVb9UydbS6UhypJ\nAnszNOzNMOVfZjbKdG7j4aE7nDTMi4O6bBVYvkHHd2v1OFwwKy+DdeLMGDLPROaTBRgzws4NXdz8\ne46Z3/yGiCwxMl3buvnX3Xbq11HsBhdylFq+X6sP2aDc3NPJ6GFO3vrcyI8bS6/E2Z0Cm37TsSlv\npWmLRh5eedzGzv06Ll6WeOIBOzEmmfM5IsvX6UrdLNWIUwbGdh0sfgMVKE3k6l/1rP41cAuVh6n/\nZycpUfFnxsdKgMidk2I4f6lsaqs/Tz5ko01TL6Ofi83/4HDuosCydXqW+W3ESq4l0bODhxmPKrWY\njHDqnMjy9Vp+3KjLz6Ad0tfBPbe5eWq2qVwa/6REiVF/d/LUmzGkb9Lh861e29HDyxPs1IiXiDHK\nZGWLLFunD5mHG2uWee2JXHp1c1OrhobAkwNBENBoNEVONSJtaKM5KktVXlVUVFRUykxcXByZmZl4\nvd78o0cfoihy2aphx349qa+bw87btDkE1m3TFVqxmpyncn035zIuj4AgyOw6qKFJfS+ZZ6AsdgMo\nsBx8+aOBOyZYCGwcLlsFVv2qL+R5TaklcW0nNy+NV/yzprwlBt+sMnDomMjsKTa2/674MsNV9kKh\n1Uq8NdWKwyUwYnKQqfUkmWs7KakCiXFKs5RxUmTp6sILFfyZ8qCNNs28TJxlzl8TW1rOXhD5bq2e\n79bque0GJ/8Y4uTF980kJco8PdpBYrzyvBzLUta7lpT76o8SV6Woty++W7J6eypbZFG6nkXpBUsV\nfJFqsybaSanpJbm28rp8dr6JvRmRftiRmP2kHZAZNsk/iUHxrX6+XJOfEQwyjetJXNuxIA/XbFSe\nlx9+0qPRykx9yE7Tenb0+vA/5JRHQ+u/0c6/mY0GVM+rispfCNXzGr1UV8/r0qVL+fLLL/F6vXz4\n4YeA8sbq9orsyzDw8vsmVv5Sdg+gj6taeHjmMRvL1ul45ytlKUCLRhJ9urvo0NJLXKyMKMhs3q3l\nq7SiPtFAlNxMZfvUs29HtnJWWWLg5a2nrJy/JCIjIwDb9yq1HCjjitL7BtkZ1MvNjPnmQmtTS6ql\neUOJG7u56dTKQ7xF8fL+dlBk90ENowa5yi1jtUacxNv/zmXb71pmfqTYQAJradZA8RV3aqPUYtDB\n3j8Uj3PRXF6JN1Jt6DQw+bWYcomr+sftDm6+1s3kV01oNIJSS2sPCRYZg17mwFGRb9cY2LgjPL91\np9ZunnnUzisflX01riDItGvm5cPnrNRMkDHpnUiSlD/1X56EamiDodfrI1rvXN6onlcVFRUVlXLF\n4XAwb948Zs2ahd1uR6vV8scff3DFFVcoimuOFpsDBvVWVohu2KErU+xQQqzE3GlWMk+L3D3ZgtVe\n8H62/4iG/UcK7AYmg0yn1h7uHVjgE7XaBdI2+UdkSTw/1k6DOhJPvGrO2xYVGQN7ubh/kJMZ882s\n3aI0NAa9zJUtPNxxk4um9e3Exci43AJrt+hYvLL4CKYrGniYNcnGjxt1DBlvQZbDb+JkWeDAUQ0H\njhY8rliTxGf/yaVRisyZ8yJDb3IypJ+LTb9pWfhj+ENY/kwaZaNzGy8TZsWEVG+D5b5q83J5+3Z3\n88ideetdNZB1Blo39TJjfkwhxb2s1K4h8fb0XNJ/1nHnxIIkhkPHCmrVaGRaNlbSFkYNUvzWGhH2\nHBb5Ot3AzgP+zbXEvKdsOFwCQydElsvbu5uHZx61c0UDJbfVoQQKVMjxfSiF1uv14nK58q8jy9GV\nIavmvIZLNHobo63maKsXoq5mVXmNXqqb8pqamso777wDQIsWLXj33Xdp3rx5vqfOH1kWOHtB5OwF\nDaeyRfZlaFm2XsfO/doQywgAJF4aZ6Nuksy0OSaOniyb1uKLyLqhs4erWnnQaWXOXxJ59WNjyGP1\ncGmQ4uX1J6xs3KHj9f8ZiyiPgSRYFJ9on6vdJNeSMBtlsi8IfL9Oz48bdXg88OaTNgRgymwzFyNY\nI+pj1CAHt93oYvpcM7v9BsJ8a2b7XO2hQbKXWLOyUCFtk45vV+vJsQa/7zZNPbw4zsbny/R8tizc\nZIfQ6PUSH87I5fhpDbk2gab1Ff+s2wvrt2pZvLL0g3KTRtno0MrLxJkxpf5Z/wzaFo2VpRdmo0Td\nJIlXPzbzyfdlV6xNBplZk2zc3NNNzYSCZjVw6r8ykGUZu90OgNlsrnZJA6AqryoqKioq5cxjjz3G\n1q1beeyxx1i8eDEtWrTIfwMMbF4FQSYp0UtSope2V0Cfq+HBwSJnLmg4c07k5FmRTb9pWblJT0am\nyNB+Tu6+1cWbnxojthycvSCyfa+Gewc6+WGdnjf+Z6RxXYneV7sY8TcXcbESWg1s3avEUoXjyxVF\n5XjboIN/PhMbtqJ88bLIjxv0/LihwJtZv45yrP7D3MtIEsjAtr0aWjb28ssuKGtz3SjFw+upNlZs\n1HHHhKLq7WWbwJrNetZsLjyEdU0HN9MfsefFQclknVO8mem/aHjtcTuSDPekls8Gqrv6O7jjJhdP\nvWlm7x+Fn/e4WImubT3862479ZIKslbTN2lDNteNUjzMftLGwhUG7kktW9KBfwatKErMn2bj+Gkt\nL76vp/fVbj6YkUusWcbjVVbNLkoLT7m+rpObF8baadtMQhQDP9xV/uBU4H2qntcwUD2vKipVg6q8\nRi/BlNf09HSmTp2KJEncc889jB07tsjPpaamkp6ejtlsZu7cubRv3z7/e5Ik0bt3b+rWrcunn34K\nwMWLF3nggQc4ceIEDRo04MMPPyQuLi5kXU6nk+HDh/P555/nN65l9c1dytVwMUfEYBBY9YuW5Rv0\nbPpNy6UyKpBarcScKVZkWWDqbDPnLwW/HaNepkMrD/16uGlSz4slRsbuFEjfpGPJysKN0t1/c3DH\nzS5e/sDEpp2RH283SPHyxhNW1mzR8eanRkRR8fL26+GiXTPFyysIsHmXhoUr9BzNKqm5Lshsnfyq\nOaIsVJBpVFdi0n02WjSSuXgZtFo4cETZhFWahQr+1EyQePvfVtZv1/LG/4xh2yKSEiV6XOXm+s4e\npbk2KRFWP6zT0v1KFym1BCbMLB/FumcHF5MfdPDCO6b8xAd/LDEyndu46dPdQ4O8FAplM5eu0GYu\ng17mPxNs/O06N7VqBG9Q7XZlza3RaKw0z6kkSTgcDgRBwGQyRZ3yWu7NqyAI9YGPgTqABLwry/Js\n/+uozauKStWgNq/RS2DzKkkSXbt2ZcmSJSQnJ9OnTx/ee+89WrRokX+dtLQ03nvvPb744gu2bNnC\nk08+SVpaWv7333rrLXbu3Mnly5fzm9enn36axMRE/vWvf/HGG29w8eJFpk+fXmxtAwYMYPHixRE3\nrz7yh0xkgfOX9Jy9IHIqW2TXQS3LN+jYfVBb4hT/w0Pt3HSNm+feMbF9b+mbzJoJSqPUq6uH2jUl\nalgkaibI7Dmk5ZHnzHg8kU+svzbZRowJUl8L3VhDgZe33zVuGqV4iTWDzQFpP+tY6qdC+jJbX1lg\n4qetkTfWCbESbz9t5bf9Gl76QBnI8veJtm/uG5SDLb9rWJRWsnL9+P3Kkf6kmTFkZUf6HMr06+Em\n9R92Dh7VEBcrYzTIZJxQEg7WbQs/4cCHKCqNdfZFkelzzcXYWoqSGC/Rrb2HG7u6SaklUTtRJj5W\npnVTuYja6o/NZgPAZDJVmvrq9XpxOp2IoojRaIy65rUibAMeYIIsyzsEQYgFtgqCsEKW5X2+K6g5\nr5VEtNUcbfVCdNas8qdg69atNG3aNH8t6+DBg1m2bFmh5nXZsmUMGzYMgC5dupCTk8OZM2dISkoi\nMzOTtLQ0Jk6cyFtvvVXoZ7799lsAhg8fzsCBA0tsXisKUZCpU9NDnZrQrhn07Q7/N1Tk7EUNp7NF\nMs+KrNuqY81mHSdOi4BAp9Yepj9i4+uVeoaMLxp9FS7nLop8t9bA8g065k61cjlXQ+rrRrpf6ebN\nJ20kWGS0Gti+T8NXK0qXKDCot5MH/u5k5odG1m8v2RZhdwps2KFjw46ChrRmgsQ1HTxMf8ROgxQv\nKbVkRFHmhXdNrN8euW9y4n02urT1MmlW4aE2r1fg98Nafj8csFDBb7WrJUbG6YGfNuv4eqXy4eOK\nBh5efdzGF8sNzPwwsuUFCop1QyPCoH/F5W8ZE8W8tIWubu76m61gc9phkSUrDWzfF5hwUECvbi7G\nj3QwY76JLXtK3/yfvySyfL2eVb/oeHGsjR5XeaidGF2DUNFCuTevsiyfAk7l/T1XEIS9QD1gX7E/\nqKKioqISNllZWdSrVy//67p167Jt27YSr5OVlUVSUhJTp05lxowZhRYMAJw9e5akpCQA6tSpw9mz\nZ0uspTLfnE1GiQZ1vNRPkukkywy8QeBCjoZzF7XYnAINU2DSLN/Uf2R1PXC7g1uvd/Gsn3q755CW\n9xcr3zfoZdq38HBHPxdXNLRjMcs43QKrf9EFXV+aUkvizSlWfvlNw+DxlhIHvIrj3EVl41P75h6S\nawmMmByDRgO9urqZM8WWFwUFuw+KLE43sPNAeG/3LRopSQdfLDdw1xMmwnkO7U6BTTt1hWwUvuG0\niaNs3NjFg90hcOyUiNMtY9RLpd4+5c/V7V089bCDWQtM+ekOPiRJyEuhKGi4dVqZNld46Hu1m0fv\nUhIOBAE279bwVZqe46dF3plu49RZgSHjLbg9Zf+9dG3rZuYkO1e28Hlbi7+tqlI7o3lBAVTwwJYg\nCI2BDsAv/pd36NChIu+2YohGdS3aao62eiE6a1b5y7NixQqSkpJo374969evL/YNNJw3t4p8A/Yf\nAPMdbQbeX2K8RM0Ed/7X70x3c/aChjPnFbvBjn2Kf3b/EU1YDaNvov67tToGF6PeOl0CW3br2LK7\noIGqESfR4yoPTz5op04tZXI+K1sgPkbG4xUY/VwMZ85H7mvs2ErJG/14qYEX/IL8P8zU8OES5e9a\njUy75h76XuNmzN0OLGYJGYGNO7R8taLwoJEoSszOSzq4OzVwGULpuXhZJMcKba+QmPJGDKt+1VK3\ntkzPjm5enmCnZoKyxODoSZFv14a3xECrlZg/TcnSvWOCBWeYcVVuj8DO/Tp27i/4PZmNMh1beXj+\nX3ZqJsg4nGDQCzxwu5Mlq/SlTinQamReGGtjYC8PdWqWTW2tioGtaKXCmtc8y8BXwFhZlnMr6n5U\nVFRU/oqkpKRw4sSJ/K9PnjxJSkpKketkZmYWuc7SpUtZtmwZaWlpOBwOcnNzeeSRR5g3bx61a9fO\ntxacPn2aWrVqlViLXq/H5XKh0+nK5U2xaNRWwSYgf4LFcoHSSKTU8pBSC9o3k+h7tczo4RrOXdRx\n+pzIidMa1mzWsW6rrpDv0qiXmDfNyqVcscwT9RdyRH5Yp+eHvPWlt/R08tgIJ6t+0XJFQ4nXJlvR\na2H3IZGFPxr4/Y/SvQ3r9RJvT1M8mcMf99/uVBSPV2DHPh079hU0bbFmZd3t6OEO6tXxYjHJiCLU\nTfIy9c0YVm6KfKGEXi/xzr+tnDxbuMk8eVZg4QoDC1cocVO+JQa9urm582ZFLdZpFbV40UoDu/zU\n4pt6OHn0Licz5pnYWgYPcyAuj8xDd9g5eFTLvVNMeLwC8RaJbu08jL3HTr0kmVizzMXLAmkbtXz3\nky8nuCgdWimWiKtaSmg0Jaut1YloVV4rJG1AEAQt8B2wTJblNwK/P3DgQPnbtYmga6xcICaAsUOB\nimVdo/y3On3t2AE1x1WfesL52ndZdannz1avf63VpZ5gX597HZw7QNeY5o29PHxXAhMnTozO/2P9\nhQkc2PJ6vXTr1o0lS5ZQp04d+vbty7vvvkvLli3zr+M/sLV582amTJlSaGALYMOGDcydO7fQwFaN\nGjUYO3Zs2ANb9913Hy+88AI1a9YEKJesSq/XCwSP8AnVtAbDN/wVrK7sixqy87JnL+YKtGkqMe5l\nM1t/L5+Q/LlTc9mxX8t/PjAVGjDTaWXaN/dw0zUemjdSjrFdHmVSfXF66AUG9+fZGAIzW8tKXIzE\nO09b2XNYw++HNFzX2ZO/7ta31nX1r6XLwh16k4MRA1z8e46ZXQdLX6NeJ9P2Cg/9eig5qwkWL7UT\nZbxegVFPxXIsK/LX1t+udfLwnU6mzTHzWwl2ijo1lfiw6zopz02MSeZUtoYfftKx8lctTz3sYEhf\nN8m1yqa2Bk79VxYulwuPx4NOp8v/0Fnd1NhKTRsAEAThYyBbluUJwb6vLimoJKKt5mirF6KuZjVt\nIHoJFZU1ZcqU/KiscePGsWDBAgBGjRoFwBNPPMHKlSsxm83MmTOHq666qtBtBDavFy5c4IEHHiAz\nM5P69evz4YcfEh8fX2xtY8aMYfTo0TRp0gQo3+bVH0EQ8v+ES3HNayAuj0D2eS2nzwucytaweY+W\ntI16Dh4TS7HlSuK5MXYaJEukvhb+RH28RaJ7ew+98xYYxJplzpxTjtT3ZwjMetzOjxt0zP/SSHko\ne/+6207PDh4mv27iSMAqXX9FtFMbT/7Q0459Il+lGdiXUbThqxEn8c50K7/s0vLqx0YkKfIah93s\nYFh/FzPmm4iLgX493NRP9hJrUjanpW8qnLZQEnq9xPvPWDl0VMNz7xZdZxseSnzYQ0OcDO7ronYN\nGZ2u7DYQtXkNTWVHZfUEfgJ2oeQty8AUWZaX+66jRmWpqFQNavMavVS3DVv+TJ06lYEDB+Y3xpE0\nr8F8rWVpWv1vL9zmNRiXrZr8qK7jpzSs/EXH+u3BV9326upi3L0O5n1hZPn6SI/fZRrV9fLudBsX\nLgvIMmg1sOdQ6QawAmneUBnIWpSu5+Ol4W/I0utkrmzp4aYeHq5ooGThuj3w01YdiXEermwuM+lV\nM5mnI//gkhAr8c4zVn7ZqeXV/wbPga2Vl7ZwfRc3SYk+RVTk+5/0rPhZWyTKbGAvJeFhyhvmUls1\n/NFoZJ76p43BfezUrqH4rEVRLPSnNK/VqmpenU4nXq8XvV6PVquNuua1ItIGNgCVs99MRUVFRaXK\niY+PL5JaUBb8G00f5ZEbGwmWGC+xZg+NUiSubg939BPIvqgl+6KGU9ka9mVoWfWrlsfucrAvQ8vQ\nCZZSZYOG4uaeLkYPd/LcOwWZrVqNTNtmHm7yDWDFyHg88NM2HYvTlEiq0CgDWTotjJwSS05u6Z5T\nl7vocFqHlm5mTbKxL0ODywOzUxUv7rL1On5Yp8NVhkSB/xtmp3dXDxNnFo7oCiT7omJrWLqmYFtZ\no7oSN3R288ZkGwlxMka9zKFjAq2bSvzym5bB4y0RKcKtmigbvNo3dyEKEpIkFGQSB/FjB/4JRrRP\n/VcVVbIeVs15rSSireZoqxeis2YVlXLGYrEUal6DrYgtjlApAlC1b+rB61JW3abUhnbNPPTqauPB\n25XsWUuMxLxpXn7eqWPlJh0ZmUr2bGmoESfx9nQrO/drGDLOUsgr6/EWnZq3xMhc3d7NxPvspNRW\n7AbZFwW+W6tn2XodHo9I3+4uxoxQoqXWbYvczwsSMyfaiLfAkAkWvw1oMvXqyFzfyc0rk2wkxsuY\n9DKHjysDWJt2ht7IVaemxPxpuSzfoOPOSbGU3hohcPSkho9PavhYiSnmzpsV/+3qzXpaN/Hyycu5\naARl/e6iND0Hj4XXAomizJMP2rjzJicptb156qo2X2H1Na9erze/iQ1saH0fwgIV2qoi2pvmKmle\nVVRUVFT+PMTHx5ObW/pQmeIsAv7fq4o32GAqsI/AxkSr9dIgWaBhiowgeBnUy8WFHJGzFwSyzooc\nzdKwYoOOn0tYdTvlIRvtmnmZMNPMiWJUR38uWwXSN+lJ31SgQNavI3F9Zw9vTbXStpmEJMts2KbD\nahdQFl+WXcnu2s7NtIftzPnUyIqfA60RApmnBT5bZuCzZUqigCjKtGwk0ae7i/sHOYmzSGgEga2/\nKxmrh49rSX3QRpumXv75TGwJ6nF4mI0SHzxrZcc+LYPHFVZbjXqZDq09DLvFRZP6Sjavyy3w09aC\nhQr+NG/o4Y0nrbS7wo5Woyjd+Y82ryEVBAGNRoNGo8l/vfpeI74/sizj9XoLebkD7QVV9VqPRqqk\neVVzXiuJaKs52uqF6KxZRaWciYuLIyMjI+zrhxN9VZXh7cEa6pJq8nq9hfyONeIkasRBi0ZewM3d\nf3Nw5rzImTz/7O6DGpatV1bdtm/uCZrZWjYETpzWkBjvIsECI5+M4WiWhtZNvdzUw83o4Uq6gQxs\n2K7kvYYzVKbVKvFX2RdFhk4MP2NVkgT2ZmjYm1Hg5zTqZTq29jB+pIN2zbxYHQLnLgoMuMHFknQ9\nF0tpafBn+C0Oht7sZvKrJg4FWVXrcBVdqOAblvOp1zEmmQs5IucvCdzS003dJA+yrMn/QON7Hfga\nUgBPXlfrr7BqNJp8P2mohtb/34Ldbi/y82X1epeEqryqqKioqPylKY3nNZiiWdVHqBDauuBrInyN\nSqgG1r+RgcJDZkozAim1JVJqS1zVAm6+Bh6508GZCwJ6ncD2vVrMRpkGyV6Onyq93cBHk3oeXp9s\nY8kqfaHj990Htez2i66ymGW6tHXz2Ag79ZIkLDEyFy6J/LBO8av6b8Aa1NvJ/X93Mn2umZ37I28b\nHC6Zgb1c6PUw8F8WcnJFEuOV5Q5T/2knubaE2Shz6pzI92uDD2AFEmuWeH+Glc27tdwxIbYU6RBw\n6bLIjxv1/LhRUZKb1PPw/gwrf7vOg0EPUNBA+l4LQKFmNrCh9X8tBKqz/g2t2+0udF3/n3e7Ix8I\nC0Xg67i6DWuVhOp5DZdo9DZGW83RVi9EZ80qKuVMXFxcic1reacIlIVQx7KhGmpfo+DvZQSlgfF9\nz18583+M/o/VP7PW/6jZbILGJuU6dWt7GXC9k3MXC+wGGZkalm/QsXmXLoyFCRKvPWHDZIB7p8YW\na08AuGwTWL1Zz+rNBXaDekky13Zy85+JdmrGS5hNErUSZPb+oWXwuNgSN2CFQ/sWHp4fY2Pel0aW\nrSuwHZy/pCQFfP9TwABWF/8BLNj7h5K2sG2viM/+cM9tDv7ey82kV4rGfpUGQZAZP9LBvbc5qZ9c\nNKqt4HpC/n99g1ihXge+y6Do68CnxgL5Ta3v+v7qbDgDYWX9d1TVHxrLiqq8qqioqKhERHHNa1U3\nrcXdR3G1iaKILMv5x8FAoaPcwNv3j+Hyv93AY+bARtm/8RAEgZoJEjUToFUTL71wc+9tDs6c13Dm\nvMCpbJFte7Us36BjX4Ym38vZu5uLcSMdvPqxiTWbyzqQJZB5RuCL5Qa+WG7gsbvsXNfJw+TXjHRp\n62H+NBvxsYrd4OedWhal6TlRqlgsiTlTbXi8lLgZzFfP0ZMaPl6q4eOlyiVajUyrPPvDI8O81IyX\nqJ0okWsTeOiZ2LB9wsFokOzlraesdGztxliGlLOSXgehPtj48G9sRVFEqy06EBZuQ+v/4SoY0aay\nBkP1vIZLNKpr0VZztNUL0Vmziko5k5CQQE5OTqFBK9/fo80i4FPS/C0CviPfcCO7/BtzXzMT2MyG\n8v0G2g00GmV1a90k5bq9u3l4ZJiGcxe1nD6nQa8Dg17gvqkxxUZLhUuDFC9vplr5ZrWeYY8rtoON\nOwoa4hiTTOc2Hh4e6qRBspdYs8ylXIHl63V8/5Mem6Poc9Szg4vUBx28/IGJ9RGkHXi8Qr794YG/\nO+h/vZuHno4lKVHm4TuUemJMMrk2gRU/6/h2TeiVrgXIjBnh4P6/O2mYElptLQvBPqBJklToA5E/\ngYs5gg2E+S73nQb4N7G+vwfz34ZKOKjqf4tlRVVeVVRUVFQiwmKxcPny5fyvAxVHqHyLQCgisQhE\ngn9D6qsjUKH1vxwo4qH1XR5rkoiP9dKsoU/RhRXv5HD2vEDWWQ0HjynDYNt+12J3hr8Z7KVxNmon\nwv3TYrmQE7zps9qVyXxf9ixAci2Jazu6eXGsnZo1FL/q4eMiS1fruP92B6fPaRkyvnzyb2vESbz3\ntJXVm7UMnaA01/syKFRPUqJEz45uZjxqp1aiRIxR5vhpkaWr9azZrM23P9RLUtTWTm3cmAwRl1Ys\nvt+x/+9Uq9XmK/yhlPpIB8LCSTiIRipkPWxJqOthK4loqzna6oWoq1ndsBW9VOcNWwADBgzgq6++\nCqrslKVpjXQzlj/BVs361+ZrIAKHbAItAhVNqCYmGIF2g8A6XW44fU6xG2Rli/y6S0v6z8FX3XZq\n7ebpR+289ZmR5Rsi3QymxGM9OkxRRjNPi1hiZEQBft2l4YsVBo5nle33+dAddvpe7WbCrJhSbfIS\nBJmm9SV65627TbDIxMXKJNeUaFS3fNXWYPjsJ77fZ0mvreIGwoIRzPsaqqENhiAI6PVYZko2AAAg\nAElEQVT6Kv9gGUilbthSUVFRUflrIcsyiYmJ3HLLLXz66afUrFkTqD4WgWD4v9n7NxaltQiUJ/5N\nqE8FDlV/KLuB7zHpdQINkr00SFa+f+v1LibeZ1dW3Z71rbrVcOsNHi5cFhg20VIKlTY0ZqPEe89Y\n2XNYw22PWfDmLVkwG5V4rH/c7qRRimI3CPd4v2aCxLvTc0nbpGPY4xZKm8QgywKHj2s4fFxDnTUS\n86bl0r65G7MxkkcaHoE2gXCU/LIMhAUuRAil0ILyYS7QulDV/05LS5UorytXrpT7PhZlaQMqKn8C\nVOU1eqmuyuu+ffuYMmUKa9asAWD8+PE8/vjjEaul5aG8VpVFIBJ8CnDhJQjaQopaMLtBMEpSZyUJ\nsi+InDkvkpUtsv+IYjfYsU8bdo6rP/cMcHB7Xzepr5nC2l7lO96/obOHWokSJoPMkZMiS1YaWLdN\n2cb12F12enb0MOE/MWHl0YZG5h+DnTwyzE7juqGfs/Ii8PcoCEL+77E876OkQTAf/o2wrwEWRRGD\nwYDdbsdorIROvpSoyquKioqKSrmzZ88ebrzxRrxeL1qtlqeffpp77723ylWc4t7IfQ1gSSkCVUGg\nShcq3SCwzrIOgwkCJNWUSKop0a459OsBDw62c+a8htPnBbLOaNi4U1viqtsacRLvPm1l/bbSZaye\nOS/y9UoDX6805NUn07yhRJ/ubh6+007DZCXd4IefdJhNZd8MVruGxJypuXRpY8dkkHC7C6vUvuem\nvCiL2loWinstBPNS+9ti7HY7r7zyCrm5uWzYsIEFCxbQtm3bcq2vIlFzXsMlyryNQPTVHG31QnTW\nrKJSTrRp04brrruOJk2acPDgQUaMGFHoeLKyKUl9gqL+1+qitkZiXShpGKyk7Fl/hdZoEGiY4qVh\nCsht3PztOi+T7hM5d1HLqXMix7K0rNio4+edWi5eFhkzws41HTyMfclM5plI1XaBA0c19L/ehSAI\n3DEhlpxcgY6tPdw9wEXjeg4sZhm7E1Zs1LF0tZ4ca/HP0X0DHTx6l52Gya4iQ3GBR+2BSnXp6694\ntbUkQiUc+FtQ3G43ffr04ciRI/nXue666+jbty9ffvllpdUaCaryqqKioqJSJgRB4Msvv0Sr1fLA\nAw9w6dKlKjt+LM4iAIT0j/pPY5d0zF5RNfs31OXRTJekyIWaaPf/ed/3AOJiJGrEybRoLCIIHkbk\nrbq9bBUwGmDhCj1JiRKnz4l4vGWvO6WW4kddskrP3ZMLtoP9vFPHz37rXGslSFzTwc300XaSEpV0\ng2OnlDSBtVuUNIHEeIm3nrLS4yo3MSYZX7sTSqUO9jyU5vUQ2CBWhw9FUPC4/IfFLly4QJs2bbjt\nttsARVDcvn079erVq8pSS4Wa8xou0aiuRVvN0VYvRGfNKn860tPTmTp1KpIkcc899zB27Ngi10lN\nTSU9PR2z2czcuXNp3749TqeTAQMG4Ha78Xg8DBw4kMmTJwOKJWDChAnYbDYaNmzI22+/TWxsbJHb\n9W0F8q2IrVOnTrk/Pt/kdKjvBaqtvkbDt8oz8Cg+0PtX0jF7ea3k9Cew2alolS4we7a4AaBgTb7/\n9zUaIW/VrfK9iffZ81bdipzKFjl5VmT9Vi2rN+vCXnX7+P02rmzh5cHpsWRfLF5Nzb4osnSNgaVr\nCuwGVzSQ6NXVzbBbbLRp6sVslGhcr6i3NZhKHep5KMl24SNUBFZVExiTpdVq+e6775g3bx7/+c9/\nuOqqq/K/J0kSVqu1KsosE6ryqqKiohLFSJLE5MmTWbJkCcnJyfTp04f+/fvTokWL/OukpaWRkZHB\nli1b2LJlCxMmTCAtLQ2DwcDSpUsxm814vV5uueUW+vbtS+fOnRk7dizPPfcc3bt359NPP2X27NlM\nmTIlZB1xcXGFsl4jpaQmLpRFoDQpAuFmrha3EassEWDFDWRVFv6NbKi6/Ak20e7/XCirbr00zoue\nGtynYNXtqWyRP05o+HGjjl9/K7zqtn4dL3OmWlm0wsDMD81leiyyLHDomIbsCwI3dnXTINmLJSY8\n60rg86DcXukyeH1UF+90MPuCy+XiiSeeQBRFFi1aVOSDqCiKWCyWqii3TKie13CJRm9jtNUcbfVC\ndNas8qdi69atNG3alAYNGgAwePBgli1bVqh5XbZsGcOGDQOgS5cu5OTkcObMGZKSkjCblYbB6XTi\n9Xrz33gPHTpE9+7dAbjhhht48803i21eLRZLyBWx5U0oi4CvoSrtwEx5DEGFo86GM5BVFQTz3Poa\n6nCP2YOpkv6rbm/s6lt1K3L6vKLQWm0CjetJ3DslljPnI1Mq7+jnZNIoO80aRp7bWpLtIlRusK/B\nr2z7SWANga/93bt3k5qayujRoxk0aFCl1VKRqMqrioqKShSTlZVVyKtWt25dtm3bVuJ1srKySEpK\nQpIkevXqRUZGBg8++CCdOnUCoHXr1ixbtoz+/fuzZMkSTp48WWwdcXFxhZrX4o76y0o4FgG3253/\nvUiaw5KGoEqjzgLVJkvWn2Ce20AVONxj9lDDYP6NvVYrUDdJom5SwXN12Srw3dwcTmWLZJ4RWf2r\njnVbdWHHYsXFSsyZYuXajm7iYituUNA/qszfM+z/2iqN3aAi/m34/y599zV//nxWr17N+++/T/36\n9cv1PqsS1fMaLtGorkVbzdFWL0RnzSoqfoiiyNq1a8nJyWHkyJHs27ePVq1aMXv2bFJTU5k1axa3\n3HILen3xm5fi4+PL1TbgTzgWgWBH8eXZHEaizgbWXB3U1kDPbbh1lXTMHjgMVlJjb4kBS4yXpvWV\npmtIXwdnzwtkX1SyZw8f17J8vZ6tv2uxOQrXNqiXk9QH7TQvB7U1HAI9pBqNpli7QWka+0heD4HK\nuSiKZGdnM27cOK6++moWLlwYce5ydUNVXlVUVFSimJSUFE6cOJH/9cmTJ0lJSSlynczMzGKvExcX\nx7XXXsvKlStp1aoVzZs3Z9GiRQAcPnyYFStWFFtHfHw8WVlZkT6coARrWstqEShPilNnQ613reqj\n5VCe20ga/cBhMN/9lMZ24ft5r9dLjTioEQetmmjoc7Wb+//uKLTqdvNuLZ3beLmhs4t4S8XHsgXz\nkAYbrittykNgYx/YzIbzmgg2lJWens6rr77K888/T9euXcv8uKszVXJmoXheowzrmqquoPREW83R\nVi9EZ80qfyo6depERkYGx48fx+VysXjxYm655ZZC1+nfvz9ffPEFAJs3byYuLo6kpCTOnTuXf9Rv\nt9tZs2ZNvlc2OzsbUJqNV155hfvvv7/YOuLi4rh06VK5Pa5gjZ/vzd2nEHo8nkJKlk6nq1JV0/9o\n2b9+f4XYh0/59Hg8uN3u/MSHUE1veSBJEm63O79hEkURnU5XIfYF3+9Kq9Wi0+nQ6XRotdoidgn/\n427/Jsz/+dLroEGyl85tPNx6vYtnRtsYeKOzUhrXwOfMt2o13NeY/2tWp9Oh1+vzn4vA14SvSQ58\nTfgaZ//XhU9t9X/9S5LE1KlTWbJkCQsXLvzTNq6gKq8qKioqUY1Go+Hll19myJAh+VFZLVu2ZMGC\nBQCMGjWKfv36kZaWRufOnTGbzcyZMweA06dPM3r06Hw18Pbbb6dfv34ALFq0iPfffx9BELj11lsZ\nMWJEsXWUl22gOlgEyko4A1ml8c6WRYkLRjDlsLI9t6FUyVAJB/7pBoEKdWUo1eGqrWWhLD5i/5/1\n/xlQ/h9w8OBBJk6cyH333cfw4cMjrrG6I1TUJ7ziWLlypdz3sShLG1BR+RNw640uxt2xjj59+lSt\n4U6l1Fy4cKFq1laFyfHjx3n22WeZPXs2QJmO7oOlCPjwz2b1v6w6+EeDTeuXpjkMdcQeSFkGf6rS\nVlEcoRpqn4IYqnnzUZ6e0UCqw3MW6kOOP3v37iU1NRW328358+d5/PHHGTBgAAkJCZVWZ0VSo0aN\nkE+4qryqqKioqESMb0mB/7F5uG/2oVIE/L8ubntWVRFsWr8sjU6kE/3B1NnqoLaGoqTmsDSZq+Wd\nwRs4sV9Vz1lg/YEfkHJzcxkxYgSnT5/Ov86YMWP46KOPSvSn/xlQc17DJRrzPKOt5mirF6KzZhWV\nCiA2Npbc3NxS/UxJFgEIvdY11LFyRUURBbv/itqQVdqJ/mDrXQOPlauz2lpccxhJykNpXhfBJvar\ng6oPRZt9rVbL7t27qVevHi+99BJer5etW7eybds2evToUYWVVh6q8qqioqKiEjGiKJZq0CjYUWhg\nikDgAI/v+5EokpFSEdP64RA40R9KnfXV6E+wJr+yKc+j+OJSHkr7uvDVVh3XuwZr9mVZ5tlnn+XY\nsWN8+eWXJCYmAspykoomnDXUlUWV/HbUnNdKItpqjrZ6ITprVlGpQnxNa7DBpOJSBPyns30T34FT\n7OFMbkcyzV+Z0/ol4Wu+fFmjxdVQ3BR7cb7S8sCnaPoaV59CXZ6qpv/rpyyvC//XWnVpXIOlHBw9\nepQhQ4bQtGlTPvjgg/zGtbLqmTx5Ml999RUbN25k0aJFHDhwoNLuPxBVeVVRUVFRqXDKkiIQjjoX\nriIZiUcy0oGsiqS4ZQPl4Z2NtLaqzuD13W/gcxFsAMr3e67IYbCSCOW7/fzzz/n000957bXXaNmy\nZaXV4yOcNdSViep5DZdo9DZGW83RVi9EZ80qKhWE70g/mEexJItASRFTpakBgvtFw/VI+jdY5TGQ\nVRGEY1+IxDsbiY+4Og6L+WoXRTHo44SC10OwDzoVZUPxJ5jv1mq1MmnSJFJSUli8eDFGo7Hc7zcc\nwllDXZmoyquKioqKSrkQGxuLzWYjJiYmqMoHhY+7K0vRLMkjWVzT4n8bVd2A+SjralcovVINpVNn\nq0PMVCjCqS3UB53ybO7DqU2r1fLrr78yffp0Jk+eTN++fct8239GqqR5VT2vlUS01Rxt9UJ01qyi\nUkFYLBYuXbpETEwMEDreqqwWgfKiuAn2UOkG/kfKFa3AhaIihsXKS531fb86qa0+ginBoZIhgn3Q\ngfCb+9LaDYLVBjBz5kx27drFJ598QlJSUoTPQOSEs4a6Mqn6V5WKioqKyp+CuLg49u/fz7lz5wpd\n7lMGfUpfsKGnqo4l8m/A/C8LzJOtquGnqljtWtJKU39/ZmADVp3U1kjXu5ZmSNCnoLrdblwuV8j1\nrqFqy8rKYtiwYSQmJlabxhXCW0Ndmaie13CJRm9jtNUcbfVCdNasolIB5ObmkpmZyahRoxg6dCiz\nZs0q9MYPVNuhp3DtC4HHyZUx/FRd/KPBFElfakRgU+ar2fd8RLo8oCxU5PMWaL3w3V+4vmr/Gv1r\nW7JkCe+99x6zZs2iXbt2EddZnoRaQ11VqJ5XFRUVFZUyI8syS5cuZerUqZw8eTL/jViSpHylriot\nAsURONkNxdfm34j7fr4sw0/hHidX12ExKPoY/RXq0g7GlffjCTb4VNHKfml81f58/vnnpKenc+LE\nCVJSUliwYAHJyckVVmck9O3bt9p4b1XPa7hEo7oWbTVHW70QnTWrqJQzH330ESdPnqR+/fqMHDmS\nMWPGIMsy33//PXXq1KFTp05A4WP4qm7AymNDViTDT8WpkcGU4PLa3hUp4SiaZRmMKw91NljDX1W5\nrcEeg78aDfDf//6XyZMn53/922+/0bZtWz788EMGDhxYabVGI6ryqqKioqJSZgRB4OWXX2bDhg2c\nOXOGhQsXcujQIQ4fPsy2bdto06YNy5cvR6vVFjpOrmj1LRQVMfTkozTDT6GOkwVBqJYqNRSdiC9J\n0SxuMK681dmqUFvDJVjDL4oiubm53Hrrrdx4440cOHCArVu3smvXLlq1alVhtYwZM4YVK1ZQu3Zt\n1q9fX2H3U9GUe/MqCML7wK3AaVmWrwx2HdXzWklEW83RVi9EZ80qf0rCWd2YmppKeno6ZrOZuXPn\n0r59e5xOJwMGDMgfPBo4cGC+GrR7924mTJiA0+lEp9Mxc+ZMOnbsWOR2mzdvTvPmzQHljXnWrFm4\n3W5iYmJo1KgR48aNo2vXrnTt2pVWrVrlr5INpb6VR/RQMMozTzZcSlJnA4+TAwfG/C+rqkasPP2j\npTleD0edrU5qazACFX6NRsOZM2cYN24cPXv2ZMGCBYVqdTqd6PX6Cqvn7rvv5p///CePPPJIhd1H\nZVARyuuHwJvAxxVw2yoqKioqAfhWNy5ZsoTk5GT69OlD//79C22/SUtLIyMjgy1btrBlyxYmTJhA\nWloaBoOBpUuXYjab8Xq93HLLLfTt25fOnTszffp0UlNT6d27N2lpaUyfPp2lS5cWW4tv0nr48OFM\nnz6dpKQkjh07xqZNm/jkk0/Yu3cvZrOZzp0707VrVzp27FgkWqu8B5/CHciqDIIN+gQqwT5CeWcr\nU62u6IY/EnU22G1VJ3tFsKZ6+fLlzJ79/+3de1SU1/3v8fceUSIxKMZ7tKhYb4goAqHBRFE8x8So\nNXXZpNWUnGoTbUxs1dp4qW0q0Wq99FRjrKZN8jOalqhRm6MH9RdcMScMCcZIEi8lEvCGViQihqgw\n+/wxzHTEAQZh5nke+L7Wcgk4w/NhVPy6n+/+7v/NSy+9RGxs7G3PCw4O9muuhIQETp8+7ddrBEKD\nF69a60NKqfCaHiM9rwFitcxWywvWzCwaHV+ObtyzZw8//OEPAYiNjaWkpISLFy/SoUMHQkJCAOeq\nj+uWPjgLlZKSEgBKSkp8mus4a9YskpKSSEhIcH8sPDyc8PBw9/WvXLnCxx9/TGZmJuvXr6esrIzI\nyEj36qzrOvUt3sy+6am6wwbg9nYDz495roBWXclsqK/LyCkHvq7OVpc50JMNvOWo2sJw48YNFixY\nwPXr19m2bRv33HNPwHM1JtLzKoQQFufL0Y3eHnP+/Hk6dOiAw+EgKSmJvLw8pk6d6t5glZqaysSJ\nE1m0aBFaa/bu3VtrlpYtW95SuHrTunVrRo4cyciRzvax8vJycnJysNvtvPjii+7NX56tBq5b7r5u\n9GmIDVn+4kvfrS+rkf4a02VEe0VNPL+O2lZhq+ud9Wc7StXrVz0p69ixY8ydO5epU6cyceJEv127\nKTGkeP3Tn/4E5/4Lmnd3fsDWBu4a9J9VrGsZzp/N9P63R+DeWebJ48v7ro+ZJU9jy+uZ1Sx5vL1f\ntAauH4Hm3TnxUQVHerVxFw1CgLM4OXjwICUlJUyZMoXjx4/Tt29f/vrXv7J06VLGjBnDzp07mTlz\nJjt27Gjw6wcFBTF48GAGDx7MM888g9aa06dPk5mZydatW/niiy9o2bKlu9UgJiam2lO8Kioq3JvD\nPD+/mXog76QwrGk1si5julwf88YsM2WrU9Pxrt6mO/izwK+qutdu06ZN7N27lw0bNhAeXuNNaVEH\nqurMsQb5pM62gd3VbdhauXKlnrNpdoNf16+suDHHapmtlhcsl/nR4TeYNfF9Ro4cafzyk6iT4uLi\nar9Zf/TRR/zhD3/g7bffBmDNmjUopW7ZtPXLX/6SoUOH8thjjwFw//33s3v37ttO8FmxYgUhISH8\n/Oc/p3v37nz11VfuXwsPDyc/P78hvyyfXblyhezsbDIzM8nOzqasrIz+/fvf0mqwZ88efv/737N5\n82YiIiLcz/Ucz9UYNj1V9/nh9jFd3ngr3sy22urpTl+76gr8quq7OuttU9bly5eZNWsW0dHRzJkz\nh6Ag89zoLigo4IknnuCDDz4wOkqNwsLCqv1N8Nd/p1TlD6+k5zVArJbZannBmplFo+PL0Y0PP/ww\nf//73wFnsRsaGkqHDh0oKipy97WWlZWRkZHh7pXt3Lmz+x+4gwcP0qtXrwB+Vbdq3bo1I0aMYP78\n+Wzbto1du3YxefJkvv76a+bPn09kZCRTp04lPz+fTZs23bL6WJcjO/2huiNKG3JF01V8+XqMqecR\nt67XxCUoKMg0LRau3zvPY3F9fe1cxajrmFvXj6p9z5690a4jf2/evFnrnxHP19H1mKCgIA4ePMjk\nyZN57rnn+PWvf22qwnXatGmMHj2aL7/8kqioKN58802jI90Rf4zK2gIMB+5VShUAi7XWf2vo6wgh\nhHCq7ujG1157DYCUlBRGjRrFvn37GDJkCCEhIaxduxaACxcuMGPGDPdRnxMmTGDUqFGAcwX3hRde\noKKiguDgYFavXm3Ul3iboKAgBg0aRLdu3UhNTaW0tJRWrVoxZcoUbty4waRJkwgODiYmJob4+Hhi\nYmJo1aoV4L0vsqFvI4Pxt+F9HdNVVXl5uSFHunryxwisuszhra39wvXrnpuyKioqWLx4MYWFhaSl\npdGmTZs7zuovGzduNDpCg/BL20BtpG0gQKyW2Wp5wXKZpW3AumpqG2jq5s6dy6VLl3jppZdumYhQ\nUlJCdnY2drud7OxsSktL6devn7vV4L777vNalFUtVOpauJl9yoG3DWNVN0RVFcgxXUYeOFCX9gtw\nTujIz8+nvLychQsX8uMf/5gf/ehHpvh9trqa2gbMs5YthBBC3IGlS5d6vTUbGhpKUlISSUlJgHPD\nzueff47dbmfZsmWcOXOGTp06ER8fT2xsLJGRkbdMNXA9B3w/vtTbTFmz3IKH2jeM1ffQgPoww4ED\nNa3Ouu5OuJw7d47x48dz9uxZAHr16kV2dja9e/cmLi4uYJmbIkOKV+l5DRCrZbZaXrBmZiEaGV97\nCps1a8bAgQMZOHAg06ZNA+DMmTPY7Xa2bdvG7373O3ergWuqgWseZ23Hl7oeY5XV1upaGOpzaEB9\n2i/MXPR7W5W22Wzk5uYSGRnJXXfdRX5+Prm5ueTm5pKQkCDFq5/JyqsQQogmq2vXrnTt2pUf/OAH\nAFy9etXdavDqq69y9epV+vbt616d7dq16y2jmRwOB7m5uXTv3t1dRJt9xFRdb8P7emjAnY7pqmkE\nltGqWw3+8MMPWbFiBQsWLGD48OF88803HD16lKysLBITE/2W5+zZs8yYMYOLFy9is9l48sknefrp\np/12PbMypHg9cuQIYLE5kxbrbQSsl9lqecGamYUQ1brnnnsYPnw4w4cPB5xtA1988QV2u53ly5dz\n+vRpOnbsSHx8PAMHDiQjI4O1a9fywgsv8MwzzwD/WUU0w6Ynf2wY83V1trYZq8Att+LNtNoK3ntv\nHQ4Hy5Yt48SJE2zdupV27doBEBISQkJCQq0HdNRXUFAQS5YsISoqitLSUkaMGEFSUtItp+k1BbLy\nKoQQQlSjWbNmREVFERUVxdSpUwHn6teWLVtISUmhuLgYgJycHN5//32io6MJDQ0Fam81CORJT/7e\n9HSnhyh4Pt+zz9Ro3laDT58+zfPPP8/48eNZsGCBIUV2x44d6dixIwCtWrWid+/enD9/XorXQJCe\n1wCxWmar5QVrZhZC1Evnzp3ZsWMHxcXFREREsHTpUpo3b47dbmfjxo3uVgPXVANvrQbgn6NLjR7P\n5VLdmK6qUw5cXKucruf6Y3SZL7y9fkFBQaSlpfH666+zatUq+vXrF7A8NSkoKCAnJ4chQ4YYHSXg\nZOVVCCFEne3fv58FCxa458p6nubV2NlsNlatWkV6ejpz586lZcuWAAwbNgxwFmjHjh0jMzOTFStW\nUFBQQIcOHdx9swMGDHAfYduQR5ea+ZQs1yleVTc9ebYP1DZj1d8r1t7aBMrKyvjVr35FWFgY27dv\nd/9eG620tJSUlBSWLl3qnl/clMicV19ZsbfRapmtlhcsl1nmvFqXmea8OhwO4uLieOedd+jUqRMj\nR45k06ZNT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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib inline\n", + "import scipy.stats as stats\n", + "from IPython.core.pylabtools import figsize\n", + "import numpy as np\n", + "figsize(12.5, 4)\n", + "\n", + "import matplotlib.pyplot as plt\n", + "from mpl_toolkits.mplot3d import Axes3D\n", + "\n", + "jet = plt.cm.jet\n", + "fig = plt.figure()\n", + "x = y = np.linspace(0, 5, 100)\n", + "X, Y = np.meshgrid(x, y)\n", + "\n", + "plt.subplot(121)\n", + "uni_x = stats.uniform.pdf(x, loc=0, scale=5)\n", + "uni_y = stats.uniform.pdf(y, loc=0, scale=5)\n", + "M = np.dot(uni_x[:, None], uni_y[None, :])\n", + "im = plt.imshow(M, interpolation='none', origin='lower',\n", + " cmap=jet, vmax=1, vmin=-.15, extent=(0, 5, 0, 5))\n", + "\n", + "plt.xlim(0, 5)\n", + "plt.ylim(0, 5)\n", + "plt.title(\"Landscape formed by Uniform priors.\")\n", + "\n", + "ax = fig.add_subplot(122, projection='3d')\n", + "ax.plot_surface(X, Y, M, cmap=plt.cm.jet, vmax=1, vmin=-.15)\n", + "ax.view_init(azim=390)\n", + "plt.title(\"Uniform prior landscape; alternate view\");\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Alternatively, if the two priors are $\\text{Exp}(3)$ and $\\text{Exp}(10)$, then the space is all positive numbers on the 2-D plane, and the surface induced by the priors looks like a water fall that starts at the point (0,0) and flows over the positive numbers. \n", + "\n", + "The plots below visualize this. The more dark red the color, the more prior probability is assigned to that location. Conversely, areas with darker blue represent that our priors assign very low probability to that location. " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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5e+21F59++inr169n27ZtAIwePZpIJMJ5553H/Pnz+eGHH1iwYAF33XUXn376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\nUJfu3Wuybdt5li//O+J4lSopyMpqzo03akotpozGb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zzC3z1CQUEOXKixoXGH9C60LkEe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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "figsize(12.5, 5)\n", + "fig = plt.figure()\n", + "plt.subplot(121)\n", + "\n", + "exp_x = stats.expon.pdf(x, scale=3)\n", + "exp_y = stats.expon.pdf(x, scale=10)\n", + "M = np.dot(exp_x[:, None], exp_y[None, :])\n", + "CS = plt.contour(X, Y, M)\n", + "im = plt.imshow(M, interpolation='none', origin='lower',\n", + " cmap=jet, extent=(0, 5, 0, 5))\n", + "#plt.xlabel(\"prior on $p_1$\")\n", + "#plt.ylabel(\"prior on $p_2$\")\n", + "plt.title(\"$Exp(3), Exp(10)$ prior landscape\")\n", + "\n", + "ax = fig.add_subplot(122, projection='3d')\n", + "ax.plot_surface(X, Y, M, cmap=jet)\n", + "ax.view_init(azim=390)\n", + "plt.title(\"$Exp(3), Exp(10)$ prior landscape; \\nalternate view\");\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "These are simple examples in 2D space, where our brains can understand surfaces well. In practice, spaces and surfaces generated by our priors can be much higher dimensional. \n", + "\n", + "If these surfaces describe our *prior distributions* on the unknowns, what happens to our space after we incorporate our observed data $X$? The data $X$ does not change the space, but it changes the surface of the space by *pulling and stretching the fabric of the prior surface* to reflect where the true parameters likely live. More data means more pulling and stretching, and our original shape becomes mangled or insignificant compared to the newly formed shape. Less data, and our original shape is more present. Regardless, the resulting surface describes the *posterior distribution*. \n", + "\n", + "Again I must stress that it is, unfortunately, impossible to visualize this in large dimensions. For two dimensions, the data essentially *pushes up* the original surface to make *tall mountains*. The tendency of the observed data to *push up* the posterior probability in certain areas is checked by the prior probability distribution, so that less prior probability means more resistance. Thus in the double-exponential prior case above, a mountain (or multiple mountains) that might erupt near the (0,0) corner would be much higher than mountains that erupt closer to (5,5), since there is more resistance (low prior probability) near (5,5). The peak reflects the posterior probability of where the true parameters are likely to be found. Importantly, if the prior has assigned a probability of 0, then no posterior probability will be assigned there. \n", + "\n", + "Suppose the priors mentioned above represent different parameters $\\lambda$ of two Poisson distributions. We observe a few data points and visualize the new landscape: " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "observed (2-dimensional,sample size = 1): [[0 2]]\n" + ] + } + ], + "source": [ + "# create the observed data\n", + "\n", + "# sample size of data we observe, trying varying this (keep it less than 100 ;)\n", + "N = 1\n", + "\n", + "# the true parameters, but of course we do not see these values...\n", + "lambda_1_true = 1\n", + "lambda_2_true = 3\n", + "\n", + "#...we see the data generated, dependent on the above two values.\n", + "data = np.concatenate([\n", + " stats.poisson.rvs(lambda_1_true, size=(N, 1)),\n", + " stats.poisson.rvs(lambda_2_true, size=(N, 1))\n", + "], axis=1)\n", + "print(\"observed (2-dimensional,sample size = %d):\" % N, data)\n", + "\n", + "# plotting details.\n", + "x = y = np.linspace(.01, 5, 100)\n", + "likelihood_x = np.array([stats.poisson.pmf(data[:, 0], _x)\n", + " for _x in x]).prod(axis=1)\n", + "likelihood_y = np.array([stats.poisson.pmf(data[:, 1], _y)\n", + " for _y in y]).prod(axis=1)\n", + "L = np.dot(likelihood_x[:, None], likelihood_y[None, :])\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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EEMWT6yOjgT7QsUj4qu9eFgn57AYu+ixSsbKyAq0Uc37RmUzKyJSy5ffkLEVT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NRZU4ksaywLMrb3jGuGROuFX3NHW8D/TBkQa/d7wumRmkoZgCXtHMK0iRJQwr6ZyYKPBw/w79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\nmSRHhiT7zJE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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "figsize(12.5, 12)\n", + "# matplotlib heavy lifting below, beware!\n", + "plt.subplot(221)\n", + "uni_x = stats.uniform.pdf(x, loc=0, scale=5)\n", + "uni_y = stats.uniform.pdf(x, loc=0, scale=5)\n", + "M = np.dot(uni_x[:, None], uni_y[None, :])\n", + "im = plt.imshow(M, interpolation='none', origin='lower',\n", + " cmap=jet, vmax=1, vmin=-.15, extent=(0, 5, 0, 5))\n", + "plt.scatter(lambda_2_true, lambda_1_true, c=\"k\", s=50, edgecolor=\"none\")\n", + "plt.xlim(0, 5)\n", + "plt.ylim(0, 5)\n", + "plt.title(\"Landscape formed by Uniform priors on $p_1, p_2$.\")\n", + "\n", + "plt.subplot(223)\n", + "plt.contour(x, y, M * L)\n", + "im = plt.imshow(M * L, interpolation='none', origin='lower',\n", + " cmap=jet, extent=(0, 5, 0, 5))\n", + "plt.title(\"Landscape warped by %d data observation;\\n Uniform priors on $p_1, p_2$.\" % N)\n", + "plt.scatter(lambda_2_true, lambda_1_true, c=\"k\", s=50, edgecolor=\"none\")\n", + "plt.xlim(0, 5)\n", + "plt.ylim(0, 5)\n", + "\n", + "plt.subplot(222)\n", + "exp_x = stats.expon.pdf(x, loc=0, scale=3)\n", + "exp_y = stats.expon.pdf(x, loc=0, scale=10)\n", + "M = np.dot(exp_x[:, None], exp_y[None, :])\n", + "\n", + "plt.contour(x, y, M)\n", + "im = plt.imshow(M, interpolation='none', origin='lower',\n", + " cmap=jet, extent=(0, 5, 0, 5))\n", + "plt.scatter(lambda_2_true, lambda_1_true, c=\"k\", s=50, edgecolor=\"none\")\n", + "plt.xlim(0, 5)\n", + "plt.ylim(0, 5)\n", + "plt.title(\"Landscape formed by Exponential priors on $p_1, p_2$.\")\n", + "\n", + "plt.subplot(224)\n", + "# This is the likelihood times prior, that results in the posterior.\n", + "plt.contour(x, y, M * L)\n", + "im = plt.imshow(M * L, interpolation='none', origin='lower',\n", + " cmap=jet, extent=(0, 5, 0, 5))\n", + "\n", + "plt.scatter(lambda_2_true, lambda_1_true, c=\"k\", s=50, edgecolor=\"none\")\n", + "plt.title(\"Landscape warped by %d data observation;\\n Exponential priors on \\\n", + "$p_1, p_2$.\" % N)\n", + "plt.xlim(0, 5)\n", + "plt.ylim(0, 5);\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The plot on the left is the deformed landscape with the $\\text{Uniform}(0,5)$ priors, and the plot on the right is the deformed landscape with the exponential priors. Notice that the posterior landscapes look different from one another, though the data observed is identical in both cases. The reason is as follows. Notice the exponential-prior landscape, bottom right figure, puts very little *posterior* weight on values in the upper right corner of the figure: this is because *the prior does not put much weight there*. On the other hand, the uniform-prior landscape is happy to put posterior weight in the upper-right corner, as the prior puts more weight there. \n", + "\n", + "Notice also the highest-point, corresponding the the darkest red, is biased towards (0,0) in the exponential case, which is the result from the exponential prior putting more prior weight in the (0,0) corner.\n", + "\n", + "The black dot represents the true parameters. Even with 1 sample point, the mountains attempts to contain the true parameter. Of course, inference with a sample size of 1 is incredibly naive, and choosing such a small sample size was only illustrative. \n", + "\n", + "It's a great exercise to try changing the sample size to other values (try 2,5,10,100?...) and observing how our \"mountain\" posterior changes. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Exploring the landscape using the MCMC\n", + "\n", + "We should explore the deformed posterior space generated by our prior surface and observed data to find the posterior mountain. However, we cannot naively search the space: any computer scientist will tell you that traversing $N$-dimensional space is exponentially difficult in $N$: the size of the space quickly blows-up as we increase $N$ (see [the curse of dimensionality](http://en.wikipedia.org/wiki/Curse_of_dimensionality)). What hope do we have to find these hidden mountains? The idea behind MCMC is to perform an intelligent search of the space. To say \"search\" implies we are looking for a particular point, which is perhaps not an accurate as we are really looking for a broad mountain. \n", + "\n", + "Recall that MCMC returns *samples* from the posterior distribution, not the distribution itself. Stretching our mountainous analogy to its limit, MCMC performs a task similar to repeatedly asking \"How likely is this pebble I found to be from the mountain I am searching for?\", and completes its task by returning thousands of accepted pebbles in hopes of reconstructing the original mountain. In MCMC and PyMC3 lingo, the returned sequence of \"pebbles\" are the samples, cumulatively called the *traces*. \n", + "\n", + "When I say MCMC intelligently searches, I really am saying MCMC will *hopefully* converge towards the areas of high posterior probability. MCMC does this by exploring nearby positions and moving into areas with higher probability. Again, perhaps \"converge\" is not an accurate term to describe MCMC's progression. Converging usually implies moving towards a point in space, but MCMC moves towards a *broader area* in the space and randomly walks in that area, picking up samples from that area.\n", + "\n", + "#### Why Thousands of Samples?\n", + "\n", + "At first, returning thousands of samples to the user might sound like being an inefficient way to describe the posterior distributions. I would argue that this is extremely efficient. Consider the alternative possibilities:\n", + "\n", + "1. Returning a mathematical formula for the \"mountain ranges\" would involve describing a N-dimensional surface with arbitrary peaks and valleys.\n", + "2. Returning the \"peak\" of the landscape, while mathematically possible and a sensible thing to do as the highest point corresponds to most probable estimate of the unknowns, ignores the shape of the landscape, which we have previously argued is very important in determining posterior confidence in unknowns. \n", + "\n", + "Besides computational reasons, likely the strongest reason for returning samples is that we can easily use *The Law of Large Numbers* to solve otherwise intractable problems. I postpone this discussion for the next chapter. With the thousands of samples, we can reconstruct the posterior surface by organizing them in a histogram. \n", + "\n", + "\n", + "### Algorithms to perform MCMC\n", + "\n", + "There is a large family of algorithms that perform MCMC. Most of these algorithms can be expressed at a high level as follows: (Mathematical details can be found in the appendix.)\n", + "\n", + "1. Start at current position.\n", + "2. Propose moving to a new position (investigate a pebble near you).\n", + "3. Accept/Reject the new position based on the position's adherence to the data and prior distributions (ask if the pebble likely came from the mountain).\n", + "4. 1. If you accept: Move to the new position. Return to Step 1.\n", + " 2. Else: Do not move to new position. Return to Step 1. \n", + "5. After a large number of iterations, return all accepted positions.\n", + "\n", + "This way we move in the general direction towards the regions where the posterior distributions exist, and collect samples sparingly on the journey. Once we reach the posterior distribution, we can easily collect samples as they likely all belong to the posterior distribution. \n", + "\n", + "If the current position of the MCMC algorithm is in an area of extremely low probability, which is often the case when the algorithm begins (typically at a random location in the space), the algorithm will move in positions *that are likely not from the posterior* but better than everything else nearby. Thus the first moves of the algorithm are not reflective of the posterior.\n", + "\n", + "In the above algorithm's pseudocode, notice that only the current position matters (new positions are investigated only near the current position). We can describe this property as *memorylessness*, i.e. the algorithm does not care *how* it arrived at its current position, only that it is there. \n", + "\n", + "### Other approximation solutions to the posterior\n", + "Besides MCMC, there are other procedures available for determining the posterior distributions. A Laplace approximation is an approximation of the posterior using simple functions. A more advanced method is [Variational Bayes](http://en.wikipedia.org/wiki/Variational_Bayesian_methods). All three methods, Laplace Approximations, Variational Bayes, and classical MCMC have their pros and cons. We will only focus on MCMC in this book. That being said, my friend Imri Sofar likes to classify MCMC algorithms as either \"they suck\", or \"they really suck\". He classifies the particular flavour of MCMC used by PyMC3 as just *sucks* ;)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Example: Unsupervised Clustering using a Mixture Model\n", + "\n", + "\n", + "Suppose we are given the following dataset:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 115.85679142 152.26153716 178.87449059 162.93500815 107.02820697\n", + " 105.19141146 118.38288501 125.3769803 102.88054011 206.71326136] ...\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "figsize(12.5, 4)\n", + "data = np.loadtxt(\"data/mixture_data.csv\", delimiter=\",\")\n", + "\n", + "plt.hist(data, bins=20, color=\"k\", histtype=\"stepfilled\", alpha=0.8)\n", + "plt.title(\"Histogram of the dataset\")\n", + "plt.ylim([0, None]);\n", + "print(data[:10], \"...\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "What does the data suggest? It appears the data has a bimodal form, that is, it appears to have two peaks, one near 120 and the other near 200. Perhaps there are *two clusters* within this dataset. \n", + "\n", + "This dataset is a good example of the data-generation modeling technique from last chapter. We can propose *how* the data might have been created. I suggest the following data generation algorithm: \n", + "\n", + "1. For each data point, choose cluster 1 with probability $p$, else choose cluster 2. \n", + "2. Draw a random variate from a Normal distribution with parameters $\\mu_i$ and $\\sigma_i$ where $i$ was chosen in step 1.\n", + "3. Repeat.\n", + "\n", + "This algorithm would create a similar effect as the observed dataset, so we choose this as our model. Of course, we do not know $p$ or the parameters of the Normal distributions. Hence we must infer, or *learn*, these unknowns.\n", + "\n", + "Denote the Normal distributions $\\text{N}_0$ and $\\text{N}_1$ (having variables' index start at 0 is just Pythonic). Both currently have unknown mean and standard deviation, denoted $\\mu_i$ and $\\sigma_i, \\; i =0,1$ respectively. A specific data point can be from either $\\text{N}_0$ or $\\text{N}_1$, and we assume that the data point is assigned to $\\text{N}_0$ with probability $p$.\n", + "\n", + "\n", + "An appropriate way to assign data points to clusters is to use a PyMC3 `Categorical` stochastic variable. Its parameter is a $k$-length array of probabilities that must sum to one and its `value` attribute is a integer between 0 and $k-1$ randomly chosen according to the crafted array of probabilities (In our case $k=2$). *A priori*, we do not know what the probability of assignment to cluster 1 is, so we form a uniform variable on $(0, 1)$. We call call this $p_1$, so the probability of belonging to cluster 2 is therefore $p_2 = 1 - p_1$.\n", + "\n", + "Unfortunately, we can't we just give `[p1, p2]` to our `Categorical` variable. PyMC3 uses Theano under the hood to construct the models so we need to use `theano.tensor.stack()` to combine $p_1$ and $p_2$ into a vector that it can understand. We pass this vector into the `Categorical` variable as well as the `testval` parameter to give our variable an idea of where to start from." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Applied interval-transform to p and added transformed p_interval_ to model.\n", + "prior assignment, with p = 0.50:\n", + "[0 0 0 0 1 1 1 0 0 1]\n" + ] + } + ], + "source": [ + "import pymc3 as pm\n", + "import theano.tensor as T\n", + "\n", + "with pm.Model() as model:\n", + " p1 = pm.Uniform('p', 0, 1)\n", + " p2 = 1 - p1\n", + " p = T.stack([p1, p2])\n", + " assignment = pm.Categorical(\"assignment\", p, \n", + " shape=data.shape[0],\n", + " testval=np.random.randint(0, 2, data.shape[0]))\n", + " \n", + "print(\"prior assignment, with p = %.2f:\" % p1.tag.test_value)\n", + "print(assignment.tag.test_value[:10])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Looking at the above dataset, I would guess that the standard deviations of the two Normals are different. To maintain ignorance of what the standard deviations might be, we will initially model them as uniform on 0 to 100. We will include both standard deviations in our model using a single line of PyMC3 code:\n", + "\n", + " sds = pm.Uniform(\"sds\", 0, 100, shape=2)\n", + "\n", + "Notice that we specified `shape=2`: we are modeling both $\\sigma$s as a single PyMC3 variable. Note that this does not induce a necessary relationship between the two $\\sigma$s, it is simply for succinctness.\n", + "\n", + "We also need to specify priors on the centers of the clusters. The centers are really the $\\mu$ parameters in these Normal distributions. Their priors can be modeled by a Normal distribution. Looking at the data, I have an idea where the two centers might be — I would guess somewhere around 120 and 190 respectively, though I am not very confident in these eyeballed estimates. Hence I will set $\\mu_0 = 120, \\mu_1 = 190$ and $\\sigma_0 = \\sigma_1 = 10$." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Applied interval-transform to sds and added transformed sds_interval_ to model.\n", + "Random assignments: [0 0 0 0] ...\n", + "Assigned center: [ 120. 120. 120. 120.] ...\n", + "Assigned standard deviation: [ 50. 50. 50. 50.]\n" + ] + } + ], + "source": [ + "with model:\n", + " sds = pm.Uniform(\"sds\", 0, 100, shape=2)\n", + " centers = pm.Normal(\"centers\", \n", + " mu=np.array([120, 190]), \n", + " sd=np.array([10, 10]), \n", + " shape=2)\n", + " \n", + " center_i = pm.Deterministic('center_i', centers[assignment])\n", + " sd_i = pm.Deterministic('sd_i', sds[assignment])\n", + " \n", + " # and to combine it with the observations:\n", + " observations = pm.Normal(\"obs\", mu=center_i, sd=sd_i, observed=data)\n", + " \n", + "print(\"Random assignments: \", assignment.tag.test_value[:4], \"...\")\n", + "print(\"Assigned center: \", center_i.tag.test_value[:4], \"...\")\n", + "print(\"Assigned standard deviation: \", sd_i.tag.test_value[:4])\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notice how we continue to build the model within the context of `Model()`. This automatically adds the variables that we create to our model. As long as we work within this context we will be working with the same variables that we have already defined.\n", + "\n", + "Similarly, any sampling that we do within the context of `Model()` will be done only on the model whose context in which we are working. We will tell our model to explore the space that we have so far defined by defining the sampling methods, in this case `Metropolis()` for our continuous variables and `ElemwiseCategorical()` for our categorical variable. We will use these sampling methods together to explore the space by using `sample( iterations, step )`, where `iterations` is the number of steps you wish the algorithm to perform and `step` is the way in which you want to handle those steps. We use our combination of `Metropolis()` and `ElemwiseCategorical()` for the `step` and sample 25000 `iterations` below.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " [-------100%-------] 25000 of 25000 in 130.7 sec. | SPS: 191.3 | ETA: 0.0" + ] + } + ], + "source": [ + "with model:\n", + " step1 = pm.Metropolis(vars=[p, sds, centers])\n", + " step2 = pm.ElemwiseCategorical(vars=[assignment])\n", + " trace = pm.sample(25000, step=[step1, step2])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We have stored the paths of all our variables, or \"traces\", in the `trace` variable. These paths are the routes the unknown parameters (centers, precisions, and $p$) have taken thus far. The individual path of each variable is indexed by the PyMC3 variable `name` that we gave that variable when defining it within our model. For example, `trace[\"sds\"]` will return a `numpy array` object that we can then index and slice as we would any other `numpy array` object. \n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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duJUZH+3lvpXZlNfa3LYpdy4Oj/1w1EWD+sqGY9TZHG79dz/YfoJ6u4N7vz7E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A/2YLRURGkyahBa3HgFhmyaj8aqal5mierQCp3/7z+KfQN+9+ZyZ5eNes99F0lfeINhml01HIAy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\nXWRtcALV72xO2+/mWD1+cs4iDk8sOtbkw9ep98h9Ll3zwobt7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NpCE62AUXMvohxiY42f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\nOpR5pvz4mRGT9axCmUTsJsUDoc/SWi3UPNJrpOOLNk/G9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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "figsize(12.5, 9)\n", + "plt.subplot(311)\n", + "lw = 1\n", + "center_trace = trace[\"centers\"]\n", + "\n", + "# for pretty colors later in the book.\n", + "colors = [\"#348ABD\", \"#A60628\"] if center_trace[-1, 0] > center_trace[-1, 1] \\\n", + " else [\"#A60628\", \"#348ABD\"]\n", + "\n", + "plt.plot(center_trace[:, 0], label=\"trace of center 0\", c=colors[0], lw=lw)\n", + "plt.plot(center_trace[:, 1], label=\"trace of center 1\", c=colors[1], lw=lw)\n", + "plt.title(\"Traces of unknown parameters\")\n", + "leg = plt.legend(loc=\"upper right\")\n", + "leg.get_frame().set_alpha(0.7)\n", + "\n", + "plt.subplot(312)\n", + "std_trace = trace[\"sds\"]\n", + "plt.plot(std_trace[:, 0], label=\"trace of standard deviation of cluster 0\",\n", + " c=colors[0], lw=lw)\n", + "plt.plot(std_trace[:, 1], label=\"trace of standard deviation of cluster 1\",\n", + " c=colors[1], lw=lw)\n", + "plt.legend(loc=\"upper left\")\n", + "\n", + "plt.subplot(313)\n", + "p_trace = trace[\"p\"]\n", + "plt.plot(p_trace, label=\"$p$: frequency of assignment to cluster 0\",\n", + " color=colors[0], lw=lw)\n", + "plt.xlabel(\"Steps\")\n", + "plt.ylim(0, 1)\n", + "plt.legend();\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notice the following characteristics:\n", + "\n", + "1. The traces converges, not to a single point, but to a *distribution* of possible points. This is *convergence* in an MCMC algorithm.\n", + "2. Inference using the first few thousand points is a bad idea, as they are unrelated to the final distribution we are interested in. Thus is it a good idea to discard those samples before using the samples for inference. We call this period before converge the *burn-in period*.\n", + "3. The traces appear as a random \"walk\" around the space, that is, the paths exhibit correlation with previous positions. This is both good and bad. We will always have correlation between current positions and the previous positions, but too much of it means we are not exploring the space well. This will be detailed in the Diagnostics section later in this chapter.\n", + "\n", + "\n", + "To achieve further convergence, we will perform more MCMC steps. In the pseudo-code algorithm of MCMC above, the only position that matters is the current position (new positions are investigated near the current position), implicitly stored as part of the `trace` object. To continue where we left off, we pass the `trace` that we have already stored into the `sample()` function with the same step value. The values that we have already calculated will not be overwritten. This ensures that our sampling continues where it left off in the same way that it left off. \n", + "\n", + "We will sample the MCMC fifty thousand more times and visualize the progress below:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " [-------100%-------] 50000 of 50000 in 215.4 sec. | SPS: 232.2 | ETA: 0.0" + ] + } + ], + "source": [ + "with model:\n", + " trace = pm.sample(50000, step=[step1, step2], trace=trace)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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n+OwPsRVb8JYjz7zlUstnKywyPK/lxNxPiPz0R6/TKxWlaDtGdVuQ6OzF41Lm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\n/p/ZPmTratBeFy/I9GJFKlsyd/VwSxpX2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MMwTA3AFneGYRiGYRiGqQFYcWcYhmEYhmGY\nGoAVd4ZhGIZhGIapAVhxZxiGYRiGYZgagBV3hmEYhmEYhqkBWHFnGIZhGIZhmBrg/wNjwpxpUzh+\nXQAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "figsize(12.5, 4)\n", + "center_trace = trace[\"centers\"][25000:]\n", + "prev_center_trace = trace[\"centers\"][:25000]\n", + "\n", + "x = np.arange(25000)\n", + "plt.plot(x, prev_center_trace[:, 0], label=\"previous trace of center 0\",\n", + " lw=lw, alpha=0.4, c=colors[1])\n", + "plt.plot(x, prev_center_trace[:, 1], label=\"previous trace of center 1\",\n", + " lw=lw, alpha=0.4, c=colors[0])\n", + "\n", + "x = np.arange(25000, 75000)\n", + "plt.plot(x, center_trace[:, 0], label=\"new trace of center 0\", lw=lw, c=\"#348ABD\")\n", + "plt.plot(x, center_trace[:, 1], label=\"new trace of center 1\", lw=lw, c=\"#A60628\")\n", + "\n", + "plt.title(\"Traces of unknown center parameters\")\n", + "leg = plt.legend(loc=\"upper right\")\n", + "leg.get_frame().set_alpha(0.8)\n", + "plt.xlabel(\"Steps\");\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "#### Cluster Investigation\n", + "\n", + "We have not forgotten our main challenge: identify the clusters. We have determined posterior distributions for our unknowns. We plot the posterior distributions of the center and standard deviation variables below:" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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p6RbCbhatwNnW3gI5B7gW2Aa4I5czcJxa8cJ3rikp3SG//35DNg4cp0FwX+A4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\ndV375zcmLSEnVu3dqq5S6a0NyfYyqTllw4mmFnYea8z7OnTKsyM7Tgz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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "figsize(11.0, 4)\n", + "std_trace = trace[\"sds\"][25000:]\n", + "prev_std_trace = trace[\"sds\"][:25000]\n", + "\n", + "_i = [1, 2, 3, 4]\n", + "for i in range(2):\n", + " plt.subplot(2, 2, _i[2 * i])\n", + " plt.title(\"Posterior of center of cluster %d\" % i)\n", + " plt.hist(center_trace[:, i], color=colors[i], bins=30,\n", + " histtype=\"stepfilled\")\n", + "\n", + " plt.subplot(2, 2, _i[2 * i + 1])\n", + " plt.title(\"Posterior of standard deviation of cluster %d\" % i)\n", + " plt.hist(std_trace[:, i], color=colors[i], bins=30,\n", + " histtype=\"stepfilled\")\n", + " # plt.autoscale(tight=True)\n", + "\n", + "plt.tight_layout()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The MCMC algorithm has proposed that the most likely centers of the two clusters are near 120 and 200 respectively. Similar inference can be applied to the standard deviation. \n", + "\n", + "We are also given the posterior distributions for the labels of the data point, which is present in `trace[\"assignment\"]`. Below is a visualization of this. The y-axis represents a subsample of the posterior labels for each data point. The x-axis are the sorted values of the data points. A red square is an assignment to cluster 1, and a blue square is an assignment to cluster 0. " + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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Vwpg5lm92EnW5ROD2fWZv3pE1szMp6rc3aFpfQVsssN3iJ9ziI2O2sr65gLP/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QwQ72eITT8SlCIxPIWy1M2RQVrZ6ky0NNo8V3uI33eJ/T8WlOx6eQtVqYMynkrQY5q4PEoId8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\n9Vu8eaakktvWytr9u1l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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib as mpl\n", + "figsize(12.5, 4.5)\n", + "plt.cmap = mpl.colors.ListedColormap(colors)\n", + "plt.imshow(trace[\"assignment\"][::400, np.argsort(data)],\n", + " cmap=plt.cmap, aspect=.4, alpha=.9)\n", + "plt.xticks(np.arange(0, data.shape[0], 40),\n", + " [\"%.2f\" % s for s in np.sort(data)[::40]])\n", + "plt.ylabel(\"posterior sample\")\n", + "plt.xlabel(\"value of $i$th data point\")\n", + "plt.title(\"Posterior labels of data points\");\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Looking at the above plot, it appears that the most uncertainty is between 150 and 170. The above plot slightly misrepresents things, as the x-axis is not a true scale (it displays the value of the $i$th sorted data point.) A more clear diagram is below, where we have estimated the *frequency* of each data point belonging to the labels 0 and 1. " + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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EQf1IHjmEmKTEQHsutbtLiMtMp6GujujYWBrrGzDnSB4zjMRAx7S+soryVRup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\nW5XNe6+p4F+eqeNg2vuxMD/EvbfWjvmezndVuaEx519VkUV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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "cmap = mpl.colors.LinearSegmentedColormap.from_list(\"BMH\", colors)\n", + "assign_trace = trace[\"assignment\"]\n", + "plt.scatter(data, 1 - assign_trace.mean(axis=0), cmap=cmap,\n", + " c=assign_trace.mean(axis=0), s=50)\n", + "plt.ylim(-0.05, 1.05)\n", + "plt.xlim(35, 300)\n", + "plt.title(\"Probability of data point belonging to cluster 0\")\n", + "plt.ylabel(\"probability\")\n", + "plt.xlabel(\"value of data point\");\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Even though we modeled the clusters using Normal distributions, we didn't get just a single Normal distribution that *best* fits the data (whatever our definition of best is), but a distribution of values for the Normal's parameters. How can we choose just a single pair of values for the mean and variance and determine a *sorta-best-fit* gaussian? \n", + "\n", + "One quick and dirty way (which has nice theoretical properties we will see in Chapter 5), is to use the *mean* of the posterior distributions. Below we overlay the Normal density functions, using the mean of the posterior distributions as the chosen parameters, with our observed data:" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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6OTmRmpqa55o373vDhg1JTEwE4Pr160ycOBEfHx88PDwICQmxaMe6LMDGjRvp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\nip54OdBOv+hslBrQiuuDasuuHXqH0O0birKX3sB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26T/vvY9hf3uRGOV/SHL8L24lbvJY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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "norm = stats.norm\n", + "x = np.linspace(20, 300, 500)\n", + "posterior_center_means = center_trace.mean(axis=0)\n", + "posterior_std_means = std_trace.mean(axis=0)\n", + "posterior_p_mean = trace[\"p\"].mean()\n", + "\n", + "plt.hist(data, bins=20, histtype=\"step\", density=True, color=\"k\",\n", + " lw=2, label=\"histogram of data\")\n", + "y = posterior_p_mean * norm.pdf(x, loc=posterior_center_means[0],\n", + " scale=posterior_std_means[0])\n", + "plt.plot(x, y, label=\"Cluster 0 (using posterior-mean parameters)\", lw=3)\n", + "plt.fill_between(x, y, color=colors[1], alpha=0.3)\n", + "\n", + "y = (1 - posterior_p_mean) * norm.pdf(x, loc=posterior_center_means[1],\n", + " scale=posterior_std_means[1])\n", + "plt.plot(x, y, label=\"Cluster 1 (using posterior-mean parameters)\", lw=3)\n", + "plt.fill_between(x, y, color=colors[0], alpha=0.3)\n", + "\n", + "plt.legend(loc=\"upper left\")\n", + "plt.title(\"Visualizing Clusters using posterior-mean parameters\");\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Important: Don't mix posterior samples\n", + "\n", + "In the above example, a possible (though less likely) scenario is that cluster 0 has a very large standard deviation, and cluster 1 has a small standard deviation. This would still satisfy the evidence, albeit less so than our original inference. Alternatively, it would be incredibly unlikely for *both* distributions to have a small standard deviation, as the data does not support this hypothesis at all. Thus the two standard deviations are *dependent* on each other: if one is small, the other must be large. In fact, *all* the unknowns are related in a similar manner. For example, if a standard deviation is large, the mean has a wider possible space of realizations. Conversely, a small standard deviation restricts the mean to a small area. \n", + "\n", + "During MCMC, we are returned vectors representing samples from the unknown posteriors. Elements of different vectors cannot be used together, as this would break the above logic: perhaps a sample has returned that cluster 1 has a small standard deviation, hence all the other variables in that sample would incorporate that and be adjusted accordingly. It is easy to avoid this problem though, just make sure you are indexing traces correctly. \n", + "\n", + "Another small example to illustrate the point. Suppose two variables, $x$ and $y$, are related by $x+y=10$. We model $x$ as a Normal random variable with mean 4 and explore 500 samples. " + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " [-------100%-------] 10000 of 10000 in 0.9 sec. | SPS: 11550.9 | ETA: 0.0" + ] + }, + { + "data": { + "image/png": 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rNmPL4+MdRQBw2yK+fewUzKnVCwvbDsSOse9j04ixruS7550vkbpgJTYMH+NK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\nxZaP0xsr/LPQ0Wbi0xbl8WeKtNjte+BF2DBiuD+RKFJEhNs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AwBZM59YKtKhh37+L7Cff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2AAAg\nAElEQVQuJagtaIprmkTj8NIDpfhhd9u9BrpUmNrOeU8hD0JlfbPsLhtZEzIupM0j3nbEDok8a0lt\nk+pClfxVrY2tFkcwnSMuGF5rchxBJXaex+6TNe58OpPvbMWhcnyyrW1LmdxuER70Uuf/P+4uxnsb\nj+JdBiNKq2eG5XAqJ9T5ZW5ZHd5cdwTlMtHVP+8twarDZW5l0Jrz1Okm3PTDPnyjUSZIWicl0J7i\nE8ty8BWFt7/PuQsSnTlYtMpXW7RJ61Qps4vy/Kp8XPB5lmrGP8HgnLPuCF5ek898bRqWULRhhXoq\nocxJs8iTkbNqarHjt9wKHJPxXOnNgE6L2n9uVR4aW3k8u6ItVqG0tsm1y6IVqsE5Cv0fcDcMBY64\n9HuaQS0UU1BR72ZsLHU+KyW+tBq+ZnyXnlh6GK+sFfetRdkl1ElHqz1n9pxwilEuUi5Q0ArI9SGe\n5xXVDlg44mrtFxMWqFoGoI3SoxeCYURbezztXMQD7uPNB5uPYWNBJb746yTzHFZe1yxrNHGcNEDT\n2HvE2nTS436R2QnTGyemNabgeFUD08KVjLugiRt8+ucJXDtvH/Xc8roWFJQTCw6FDivXjlKDmgeY\nYhqU7qy+uRXXfbfPxSnfW1SDdzYcFdldghxqZQMbTfGZFbmuZF1KuIzgsQvONvIxkHkXlh0sw7Xz\n9uGjrccV+5n4uXg39Nd0j7j0vlcfLsejvx7GvyWqESy3rbVpSK+i0rsiiOircctb7bxhqgcrhPre\nveAgVh4ux9t/yGuZzllX6Pai0bavVhwqw4nqJpfqjFZc8pUjaZHcogAgvBdER2bbmtY3ie1SSCC0\nubAKdh7YweC1dPCay7Euv9KwmghtolMbq9/feBQfbJZXUvn0zxN49bcC2d/VuPxyW8+hjFuU18zb\nhwcXHcLRSvVBUloTmldFFEVPKYOcuHbK9B/pbofgERS3vyP24o6fDuCF1fJB0FKeuRK09tSqhhbs\nPFGD3/MqcaikDtmnatHU4lAmob3XtB0yaRPWN7fizXVHcKikznTvTG2Tev/PK6vH7DUFiseYaZbK\nlcVLfmS9ZkVdMzYWsL3rAUT7frz1uGySKE96xMlrfbrtBMrrmsUBcZRzX1idj292Frl4riRofe6t\n9YX4VYaGKXWI87wj0M9qSGsZG0pfJBlRI5HujMvqlOdW4OYf9+P5VY5x5dudRa4YLSWIjHLn/4KK\njVWQdvP6Zjsu+3qPugKIQjNW1IsdXIspQbrBzpeHlXJ8uFRePEBuAVnb1IrfcitE13hzfaHL8fKd\nM2ZBiIuSL7/tb7UxVQ89SAvM54hLGm+Vk3YgpR+QN55bVocFe4th53mDmrrEZE97hrz7cUpYKMdD\nZURBRb0ogcn+4lpc8+1etzTNADBbMqipealYPOJGZR1rm1rx3sajih5rPZ7Z6tws6hM4UlHvog/J\nQS5xBZm1TNo2RyrqsVKiznK6wbwAFdrpP+5R3iqlycTxPI+3/yjEZ3+ewGaV4BC1F3dDAd1bZdfY\nJfJVpOpYHz/p+acNsLSscwIEHXGWyXZRdinW5To8t0oLwkeWHDYsgyflsgsg2+S+hQfx0OJDijJs\nLJj1xW6sPFyO+ySxBFM+2YnZa/JdaiWP/XoYTy/XFrxGex6FlQ14YOFB12Lnnl/klVLU8O3OIvxr\nZS62FFbJtnl1QwuKa5pQUtuEDQWVmP6ZvHa8KFhTUncp57exxY43fi/Ald/uxQur8/Hr/lLRWHnb\n/P34PyJg/F8rc0Xnz99TLPqdvF5VQwtWHiozTT2lrK4Z245WicoTMliSi9uF2SVuu0FKY1hNUyvT\nGCfXnwEnR5woQ0mNggWCBB39Wm1g9pzrrMfq39ZjuWSXSo46KoxRW49Wo7qhBXP/OokPtxzHkYp6\nXPfdXuo5gHgnT89co6gjLnkXFu4rwYzPs0S0JQHNrTz2FinvctKmBzvP4/1NR0U7YtUNLdSdgZSo\nYIZasx0i11R1zXa8+luBWwzXX84xVppsSsB7G4+KaIfkO6Vmx1jtMGfbg9MA1n5G3tfdCxyTixB4\nqLUsAWLJHLazlQZ8tcQjNAgvRmFFA+746QCCAzgsuXkweJ53ybSVUowwqWFWUNGA2WvysflIFdLj\nQinXEYPWT/Qk26GB7PDunnj56yuB9ngEWbQxGfJZI+VuiaQZCWUXnW7ElzuKqBz0Jjv9hdQDnoeo\nAfLK6jUl/RFQUivPl5NC7dHK3ZPRe3WvCO0a7t+R+ve0Gki9LVLwoHOZeV48bS7QIDP1R0ElZvRO\nVD3urfWFmD010+372+bvx8/XD0BkiGQYpTwcxV0iGlVHoT7S0tfnV+KawQ3oEBWsuGMkB1rs52u/\nFSCnrB5PLM3B4psGye7wCN2pudWO12R2cOY6d+S2FFYjNToEc6/oK/r9YEktM9dY2n3Jjx9tOYac\nfSXoPaQJiREOg2DfqRqszmkzILJP1YrKkBqfWwqr0TU+TPQduRglr/fC6jzYeSDrZA0eH5/BVH85\nHCqpcy2yRqZHu/0ubf5sida3Wn+RGpf3LzyE/13aGxlxjnvdWFCp6PyRcsSV+llhRQPW5Vfg8oEp\nCJXh25OqSVZgwd5iXNw/WfGYirpmPLsyF9GZUaLv5YbI1OgQ132TjoN3NxxFcY28A3E9QeH5vz8K\nMfKI+/PVCiEPCYm/jp8WBTvSoBpI7/w968RpLN5figfGdMbh0joskgSwv7w2HxHB7uZjmPN5myFh\nqrUIlhj2j7a22Zjk4Woe73YTrClwxN0GStokw/OoZlC+0PoghOM///MEnlmR6/47pVwlLxvNkFXb\nchHK3u9MPSsoHEg7Mg1S2bn1+ZVotvOuCHoS0heKtgqkdZ7tx6p1p6WvrG/GNd+KV/56jH01XWi1\npCJqECad1YfLqUY4D7FnWOgCm45U4vdc7TxYqfE8Z72+SYaVqws4YiKUttnlZJvUeK1uA6hJdjs5\n0GhNShWdORj1za3UZFl2Xn2cWHW4DG+tP6I7Pfq2o9VYn1fhks8kQdvBW0hdDMhfm/zleFUDXliV\nJxs7AADf73bfbbHz+kPApc98yic7RTS/9zepx9KsyamQ9USROFHd6PLyClhxkD1Ym4f4eQsxFMU1\nTfhpbwl2BXTBNQT3Vjq+hweL1UNkL0KAnKRFEn7Ov7cySpudbmzB/N2nqEF6KwkFJprKhHSaknZl\nJcOH4+jvCMnxfkUlgPnZFbmKVDoSt/20H1/tKMLXO+SdEUrvotyM4kgAxuYckzr1aNh1skZTjgJy\nnCfpmmobdaRxXNXQghUMEn2Aw8N78nSj27q+udWOm3/IxjXf7hVJaLLIaTa02LGlsEo2jkq4l8eX\n5uCP/Ep89ucJ1FECex30QfcbF+wBM6YNgU1hxHlUKpHUJcUjPEEtY4WpHvHfciuwVsGQuWfBATxz\nblcsO1hqKUdKLmLZDJDJbJQgHUxYaC56NNUFUINsJJWoqG92BfisvG2I5mvsPVWLconnUq/TXS+l\nj0X2sM1LRz+W58UeolY7j6LTjXh+lYMeNCI9GmFBDBO2E+9tPIqxXWIQ60ziQFs4kZAzoOUCM+Vw\noLgWAztGUX/7emcR7DyPIxUNmNk3yfW9WrvbeTFHVq21OXBufYA2cNqIvT8aNUttUNSampvEnHXy\n8RZykN7DbBlDpb7ZDp7nRQtSWpZBpdsjL/XSmgJFIxwAVS7ODvUJ6z+bjuG2s1Pdz1VdyMgbDi12\nHo8uOSxLa5BSwgDgim/24r7RaZjWK8HRLhrGkNqmVvxM4eWycoRtHMNuEnt1ZNFq50UBZIAjoHDB\nvhLsOlmD+8d0xtIDpRidEYueSeGq5WlRgJDieFUj9XeyzZpV2u/k6SamhRaJ/HJxn/iBsoCkQVqT\nDzYfQ3JkMPokhVMpF3phROubnFq0Ko2x4HBZnWuX94XJ3US/1TfbXY7ME9XanomQe2BURoxbuYD7\nOFzZ0CL7fioHQmqqFhUfbT2OGX0SdQsTAMrP8rtdpzCtVwI6Rofov4BJMJUjTgsuIxsip6weN/+Y\nbZkRziocb+VCyEpddBJkR98owweW9l+lup2sblRMU7zyUBleXJ0ve43IEHbD1cERFz+FAsa02SyB\npz/sLsahkjrFgC/SI/7WH4WiRZAeneZ6xhTPhRUNuODzXdTflAJ0aVCr57dZp7DxSJUo4UVjix2b\nj1S5Aluk3klpiXoGVLM9DdW5WbJSX6ZTbZx4eDEbVeKBRYfwikJQrQDWarJIqtLL51WvsTC7hOq9\nVWtDJRttc2EVdhfVyCoZvUlJ2AUA/9t2AvcsOIhbf9yPgyXaDJnfabr6zv+FeAJhrJfWnSXoSsno\npf1yurFVxH2/ff5+TPssy8VZFUCKBHy89Ti+zTrlCvyTVYlx/q+2zlB6hB9uOY56yi6U1Q5B6RhP\nqmso4QnC2XWiuhG/7CvBx1uPY59Bg/fLv06KFvQc2vqLVpiVPVYOBeXyfH0zrrz5SBVVTU2abdIG\n+XdGqR5mjcsz5+5Cg8YdVC2Y44x/oy3KrMycK4WlKu+bjlS6Evd4AqqyaZL/rcD/GAcboxBzFd0N\nZECZNlJBbK3lldXjxh+yFbNyyk2owg5IoJFlK4AnGbOjsSx08srrcd/Cg7ILC4cEWlsLSj20dh3u\nelYt5i8Utmu14unluZoHi2NVjXhuVR7e3VCIL/46iXsWiIP/aLJXWkE7R7pFKMVhFbqUXNIQXuZ6\nRrG/mJ2+RUu4JIXSLglZf70yWtuPnRZJfMmhljKpGZER16tB3mrncaSyAadqmhSVE/SCpjYiQK2F\n5QLh88rqqdv0AHDLj9mY8slOvLg6D0ecuwNPLc+VqAW1/S1ItpU6x2GjfVht7qMFb68+XI5F2SWG\nVaP01kkOpIeZ9NQbmWGOVzXi651FTM4OlnpbLb2vNAyYZeTSnFp7TtaIZGs9UQ81HK1SV+4SoLUv\n0+iOAkg6sdXZ203niJN4flW+oQFG6TnTou89EIfGhIYWO1PggFU4VFKHyvpm5EiMG7J9lhKeAUEM\nv0jj1qPj3NPYX1yrun3E8zyKncZYdOZgt2dlRXY2OSNVyjOVQs8gy7o1rtfjKYeTGrcmBfyeV4lv\ndha5DAEBz0iMh4bmVnyw+Zhs4DLtsdO8RWpSZ0rbu9GZg+WTrPDWe6fMgPL2dVv9bTpHZGaJUhVp\nSU/Bqklc4PwKiZfcdkkZBnVpzYpON+HCubtw14IDeGARPQOuQBXYUCBe1JOJVchblipaqT0Do9lq\naSipbcb7m45hi8GYHDmYYaySj4vm1VfCexuPunb8GmgqLRw9Xkma0IsGq41Q8r6lxrDs7okJVXpz\nfaFLVEKoidwro5Qx08yFysOLD6sfBODV3wpkd5uVcKSinpo9fbtBpSstMF01xUzsLqpxMyYF3PSD\nO59aalTIwuJ5Z+Zc7Z3BTNy38CC6xYcij9jekg4cpJFqtDmKa5oQoRAE9eVfJ7H0QKmIX+5t20lp\nINWTqEPwDM5X4EHyPI8Ak3WQhPugcXH1YNfJGpF04rdZp3Cqpgm/7CtBt3h39R56ZlVTqiKC3OSo\nNmVKM67JIU+F168GI7KrZHt5I7GEN5wGpnsUJc322fYTOCst2u06ZSo7MwB9bBD6n1ZO7uLsUlwx\nMAWAeJwl+dZf7zgpq9/t2sW18BnJxT8YhZEM0gJIxS7WZFoCluwvxZL9pfjk0j7UeCE5E7NcRjee\nxCaLFi8s8OTrWtPUoig1KUVbf/V954iA1TkV1Bg+PQpUemG+jrjJuOcXugeiRmaLkAVyfEYBgrfd\ny8mWDCFPwjG76pu9InoA6cE1+spwgGJE/dc7i0RGuF5enlbIjgW8svExd/sJXPSFtsXUNzuL8Pyq\nPHysQE3iobwVpgfCouGz7eZRooSgVUCs5CLtU3IwewhW6i9S+UIpnlzmrp5Ew10L9OtkA8D6PP1Z\nJ1/7/Qje+L0AxTVNqhQeo6C1lZFJc5vFW7ZaIfQVF91FcmubC6sUY2EA6FJOksOpmibsEgIMZZr5\nS4askGpOJl+0e9Q0q1mgV+GLxG0/7cfH2+gqKrSxpb7Zjs+3n1CkXZ1mUH0zC1IzxJNGbtaJGk3B\n7mV1zVh5qIwqR9vUYse9RD6CJZSEQN7A9xaKe7DCpz3iVuDU6SZVnnGjRZw5b6KyoUW01fLz3hJM\n7ZmAtJgQxcyZLCira9as+OEJfpncFT758wS4P+XPI3WHWSHdkqbhBcLANQvCXFGnkh0xJjTQkkDi\n5lYelcSg+9OeYnSMDlY4w1zwVpHEPYzVORW6+p1W0ChUao6J9gA5n4k0+AxQl8+UKkMZxWNLc7Dy\ntiG6uykLD98XZywj2S7NBrkoOHm6ER2jQhRZSvOyTmH5wTK8OaMHOse67wRaDTIfghs1xcN10QI5\nyWAA2FJYZUk8yJkA0wzxwYMH47sdZpWmH0opXI9VNeL67/fJ/i5g+cEyXD4wBTUmrnqtCojRgo1H\nxAE7d/58AMPSorD9mDFJKK3jbXTmYI8MJlrVD6yGWrZMPWi188gprWPiNVoBKfeZTJhgFpS0fnlY\nH0jDgnlZ3veqsKCOEjfxFYNHtr2A7Cv/XJEr0kP3JhZll+iiavC8urSg40AdlfIQ9hfXYv4ea1O6\na8HukzXoGBUiyxEXUFHfglvn78ej49IxpWeCB2uoDF/c/VDD/uJalFi829eeccZ5xOUURLTgqx1F\nmJd1ytQVvVLmLU+BVgejRjjg03PAGY8Vh8qYEkS0Y5aVKlj1iaUQJrRtR40vkNqLV7m9LBi0gsb3\n9YUFmoD3N7Elw6GBhYpgBh/bKoiD/7yPt9YXIqe0DoNT6TkYpFh+sMyrhvjPe9u84zmldYiSZvJt\nB/C1PuBr8HmOuFboUf6gwZe21XweGpfo1blZ7XJV74tgzdLWnptbiSNu53ndEnqAg6r27Io83ee3\nN2hVnjACT8mbkfBU/ImnsDi7hGnH0T9dacPC7FK8sDqfqb8kRXqOakcDGbC66UgVlW7lR/uGpTri\nfvghB094cPzDVRvUAtTaKxxZUvVj/p4z00PsC5i73TzNfDW058B6JeRXNDAtaITEJH6Yj7Ag3zKT\nXpFRudEap+WH78BSHXE/HGhPUj56oPXuaDriVmCNB4Lf2gu8xSE3A0o8zsZWu27Pq53nsTDbNyL3\nz0R85yE1gvrmVmxxxl8o9ZX2Cpbu7atG2MtrzQ9QNxMs/WXVoXLs82BiQiWU1jbjYAk9Do4mwedH\n+0D7Ixu1Q2Sd8I2X2Cro0T72B274YQaunacefC2HM3x9/LfBrC92e7sKlsKIVK+3wZJ11tfRbOfx\n8BK2pDJWo8U/aJ2ROOM44r6Ig6Xmakf7GqJD5ZP50FCdm2WJlJ4fZyas4v3+R0H73o/2iTONIw7Q\nk9f5YQ7aW3+x+4MBzkgwG+Icx9k4jtvBcdwiKyt0JqKx5cx+efxjgx9++EHiyWU53q6CH36ccdCT\n9dkP34cWj/iDAGSX5n6OuDx+9ZEMUlYhW2O2yDORx+mHdfD3l/YHUunBk/D3FT+0oL31lzOB6uOH\nO5gMcY7j0gBMB/CJtdU5M5EYEeTtKliKZQfLvF0FP/zwww8//PDDj3YHVo/42wAeg4JAhp8jLo8A\n2xmqraUT7Y2X54d34e8vfrDC31f80AJ/f/HDF6CqmsJx3AwAp3iez+I4bgJkkvStW7cOeSdWIiSu\nAwAgICwC4andXVs/Qof/O37mfKw+/s/+z/7P/s9n4mcBvlIf/2ff/izAV+rj/+w7n+tO5KC13kG7\nbawoQpZtCiZNmgQrwKlpXHMc9wqA6wC0AAgDEAXgZ57nbyCPW7NmDf/kDr/nl4a+yRHILjZXOSUz\nIQy5ZfWmlumHH374caYjJNDWrnX1/fDDD8/jtaE8Jk2aZImRq0pN4Xn+aZ7n03me7wbgKgBrpUa4\nH8qwIoukf8njhx9++KEdvZPCvV0FP/zwww8X/DriHoAV8n7tmXcu3Rb0ww8l+PuLH6xg6St+BTg/\nBPjHFj98AZoMcZ7n1/E8P9OqypypkEtJawQBOrJZasXdIztZfg0//NCCgPa7/vTDR2C3YIfSSsSH\n+RNg++GHEtr7vGCaR5ymIx7U3lvHZEzuEW9aWTbTnpw8MhPCLClXCIjwwzOY2TfR21UwBKG/vD69\nO0ICPdDx/Wi3YBlbmlvblyFuM2n3MyTQhhGdo/HSlG6mlHcmoL3ORa9P7+7tKvgUumm0VRLCfUtS\n2tJZ7eGx6VYW75O4bECy7G9mejY84RGXMtFHpkd74JqeRbf4UIzo7Jv3FWqC0ZkYEYRZfZNMqI33\nEdSO6Vh++A6s2KG0EuV1zaaUExUSgJemZmJEeowp5flhPWJC3W2GN6Z3R2a8NU6y9or2TjezlCOe\nHhtqVvHtBrGUF0eAzUTj2cyyWNG/Q6Qp5fgSL+/szjEY0NGc+zIbSos6VnSKDkFaTAjuG52GO0d0\nwrmZcSbUzLMQ+svfcTzxQ4zBqcrvqi+NLVL0TY7QdZ5ZMUb1zWxKMRHBAeZcsB3Al/vL69O647PL\n+7h9Hx4cAC9M/z6NVo0vSaSP9XFLPeKe6ixDVAZnKQYRhteQ1ChzK6Nwz2a2R4AHduiV6muGt9YX\nwHEWvwQGEBlifLDgOIDjOMzsm4RLBySbts3tKZDVjQ4NRB2jMeHHmYmZfZNw3ZAO3q6GLPqlyBvb\nXeP1LSSDTaJ4qkkVC7hhqO+2798JQzpFISrE3bHnqRH8gt7th9KoVbzC1yiOlnLEPTXnR4UEYvmt\n7Fyvywe2eRr7JJsrZdWkwD9s7x5xwLG9CQCxBmg2cry8KT3i0d0iXrocOBjnYL59YQ9zKiOBGTw2\ntzszsIeXGh1sqC5aERZkA8+L+0s7W0f4BN6f1QvzrxuADlGefX5K6N9Bn3fYxgFXDkqR/d2bnN8h\nqVH413ldZX+/bIB8vZVg1q57p5gQpuMCvfCSmT0Ps6I9csRtnF++WIruCeGaYtqskJQ2AkuXBUkR\nxgf+O0awKXfYOE5xgFY6j8Qb07vrKkeAUqIIveMbbZDyBEdceoWGZjseHNMZieFBuMsCRZXIkAA8\nMSHD9HKVkBEXanhQ65cSiYEm0XZInG0Kd118d0aGnwndPE9rkdY3LcY79JQHxnRGz8T2qT/dMykc\n0aGBuLhf+48V4DxoggzSSFkb2DEScWH0xfPSWwYjNIh9uu0aR/Rzk2yGVsbNpGgFeqVVePvCntTv\nQwI4XK1jPv7HOWdOfNoNZ3X02rWHpRljDPzjnHQsuWmQSbWRB8cBD47pzHz8dB/z9lvKEdfKNRMN\nPk50imZbxQPArcNTNUcTS+1ZG8cZ8ggobf/p9WL/c5K7l0WtikNSoxAUwFGVa7pQ2pkFjS12jOsW\nh2+v6Y/RGbG6ygDkeXkcgIw4z3jEZ0/thvtGp2F8tzjZ55IUwe6RNmNddG5mnOgdCAsKwPhu+tsZ\nAE43tog+k93zWo1b/Dd6eEIQ6kr2F2/sKPZOCsf03gl4/6JeeGpiF2oAVXvARb5kiEuGyX/Pohti\nUnAq3kAzOb9adxCEei24YaDo+45RwQi0aVtCkOMJD7YkRLT5k0Qc4y7msLRoTcbvo+OMG71Kc2O8\njp1B1vHYVzniZN+TUrFsHAeOcoOPmPAcSPDgEU2hxpCICQ3EOV1j8cUVffHwWHdDeHRGDII9NGhr\nmYPP75mAZ87tYlldtMLSFtJqz350aR/DepADO7R5J8/rTvfgkcaItENznLFkOUoxA3olB2k8MRrH\niVQ1yYgLxY/XDsCSmwa5dbj3ZvViuq70XlosDk2mDS5WoWt8GGb2TXIOau6/d4wK1rSjQ2sardSS\n+PAgvH9RLwxLi8Kdwk6QwSY/3dgq+kwakVo97p58PnIwQsnqplNp4L1ZvVzXnZgZhx+u7e92TP8O\nEZhgcNFkBcIJL6wvPD8Bdl5sXPZKihDV1ZdxvQyHWmheqQPqs8v7Groez/MYlqb+rkoNnuHEOb2S\nwnHTMLaFdJCNw83DU5EcqT5+fXhxL0yUBIDfNjwVYzLoyizzru6PV87PFH2nSEvhOB8jEXgGL+qQ\nmDyboY9oxe0jOmFopyjZvlDV0IJ/TuqKjtEhOL9XAl6a0g0vTPaOPKaWpW6AjcNAxh0vT6hwW8oR\nZx34A20cvr26HwDg1WndMbRT23aI1rkjwMbhzQt6YNktg/H4hC6qx0uL5wCM66I+oU7rlUD9XtEj\nrnNbldYGNC8BuXXOQYiu5kRbrBHBAcyBljuOV4s+q0Ums/K747p7n5dHNinNuHMEObqf1zc5Ai9M\n7obpvR3P/zknJ7Si3l1ijOzHrAgKsOGV87vjUqdiitHQRLukP143tAPGdY3Fq5LJ0JcRnTnY9bz0\nLJJHpkdj/nUDcPvZqabUhzauPXdeN580GF4533y9YbNkWBMlO05mtJ8c5/cmE3dzlOiHNAh9Vtv9\ntfWx6b0Tmc6V0hXJ9v33rF7olcTGyxdiZlj8LpkJ4W7j5xWDUvDc5G5uDqDLBiQjQfLMX5jcza2P\n3slIR1UCB7EQwwV96FQEFo640s6IGYvv+0anuX3XRWFnWHYINNlg5MAhITwIr03rjknd1XOgcByH\nEekxIgOXxX7LMEkNS6utyOrUCfeAwopXXBDSAbhfSgQSnd7HwalRuGZwm8dBLxeadcKWHsYBSGeg\nbtC26bvFh+GygfJbenod7UYDM0nDQUsdGiQTTouKIU6rJ20bX+5+WKqm16vpdi2iDlqa952ZPTEq\nIwYPjumMH68bgDHORdvknu4DFa2tbxmuzSAwugkhfWRRIYF4dlJXnJUW7ZPaq3KKDcIz0kMb48Ah\nOjTQUhWnmNBA09qTNjHrRV8FFQ+9uO1s44aSnefdxmiW9uNc/5iHmxk9xQLkZDRV+5fO/nHTsI5M\niidSGqLe/i6cxlpdueuQX98zKs0V79UvJQIhARyGp0VjVEaM2w6CWXKypMPpfscn504AACAASURB\nVAPv1D/OSZfNNeGNXSZOxverpSYsuTPIgMYZMpxq2nBMc3K9oUAZnj3VHKeQ2v0/Oi4dg1Mj8dj4\ndKbjBdwwtKPlXnFLOeKyF5Uav+7SDqJjv76qn/6KSXBx/yTRAON2bcnnbjKSU9LTzuoUhTemd1ek\nI+hV56CdVd3QIhp4z1LwvnKUv2f0pnv0SUj52nJ8sbSYEMy7pj81EplmvMtyxBmaJ5USM7DopkG4\nbEAy+iZHMAeR2mT+FtVH4XyO40SLjFGUJBnSBUdKZDCuGqRVGkx+OuzPYGQp7WJ4OnKcJfDnuqFt\nRpFQO7K/0HT6jQYUmQUz9J5X3jYEZ3VSnyTPMzFLr1aYcW0eQGpUiNt3RqFnbAkPCkAvGQ427Xs5\n76Da1rjeTNM0aiIN0vFGr+0gTFOsC0vZ6xA/kFNfWFAAfrlxEGZPVacwcJCP1RnXVdkbTY5vHMfh\nf5f2djuGhSM+ODUKL8kYi9IdRz1gcbSRY73swkfDokDrOyxHG6M5PGnVSI6U31WIDjXH46x2+1N6\nJuCN6T0wuUcC0/EChnaKwoIbrQ04Nd0jrvZy0CAdwMgJjeOUH6JW3D1SvDKW0kWYeUaSw0amx6hG\nm+sZGG8Z3pHaYTrFhOC1ad3RKToEc6Z3d+PdyS02BAmr64eqe4GGEwbOkNQozJIJ9kqLCZFdgDRR\nQvXlBgyW9qGdGmTjcMeITnhnZk8EswqsE+VEMk50SqAFmUrHKOGznHY97f6VjDsWzqfSRBFEBC0s\nvcV6upCRRCFC21xDCTBV3TVz/qw0UckleHiIEoAkB1adZjOQFh2CKSYZ495QZeB54KrBKZjVNxHv\nCIoZFrafkuINxwHvXNiTOne9TDHA5HZb1bphdGgg7hudhqcmqitD6XG0Ss/R660VzmNdqMuP5/LX\nD7DRAw4d55FlO+ZWGq1Ma7B5RlwYzu+p7oCSA5XSKWmizoRE5GTG95PlMd01qs1ukaNNannacq+a\nHLc/MiQQD1BUSWjZjmk78Fpe7YfGdsYKDXLUjmuql+l2DmPZgQGc5XlTTOWId4wKxmPjM9AlLhTj\nFQxy6QsaJUlconU4NjJ8uw9ebkfQz4M4aIflZaIN4PHh8kbggA6RuGpQB2oNAm0cBnSIxOdX9MWg\n1CjmQfeZc7sCYOM9kfWdPbWbavAh68sWK8cRZ7gHs3aISC/EaJnBx8xrAEBprYNH/to09q042kJG\nwMCOUVh440DZ3wGgn4KsYo/EMJzXPQ63n52KQBuHFIUFr17dZxJqklFycnHRmYNd/btHYjj+d2lv\nkVKD2k6T8OsAmbZ4fHwGWiWdd8707nj4nHRNMlc+yPSRxQV9EtEzMRxzpnfHJC9kW7XzPMKCAnDv\n6M4u+kwgwyJaTX1EjvOr5nUMsHH4xznpeJiQveN5bVJ+LGPTzL5JmJjJwLeVfGbpW9KFoGE5cIMd\nmmxyvVUJtHEIsHG4XEL5/OiS3opOFyGRmRTSxYXQX1h2K765uh8u6S92RklH5zkz2nJKxIQG4umJ\nXVTLZWmbCDLoWuYMLeokco/2cQX5YBrPnkYVJJuy7b1j60zxYYGY3jtR1Z4ZlhblFucUGypvn9B2\n81m7N22xYTZMNfM5zqHm8fGlffCMRHLv6sF07nT3hDB33W7SI26BbqxINUXlWDletJ0HumqU2qNd\nS0mZI8FppNM6JbWjMhiywu4CS98KEK1s1U+gDWa0s+SuzeQRp32nciIt2x15Ct3DxRm2+qVt1uzs\nS7KeIMrXlfUt7l8SCAuiL6huG56KC/skKlJ1OI7D4xO6uCa5+8e48yg/uqQ3Ft44EG/OUE9alBYT\nQu0Ds/omYlbfJBG3lvQcPTUxA3ecneouJ0W8qGSpGXFhmEJ4ttTmUOHnQBtHzVJ4Xo942CXv+aDU\nKNmAbDmYla2NxfvCw72/DEmNEskAPj5eflLtEheK9y/qhUFmZxZ2YmyXGHxyqXt6biXMntoNqdHB\nbgsmwVHTMSoYg3XWl8VPER4cIHrmWu1QM2eqqgbl956GlEgx1cfo3Cm9f6WFOg1iz7a2uggxWDSv\nd0xoILrGh1FqSF6b0xRfRo7VQztFUWl/USGBuGtkmkhtSrr4iQ8PcslIdk8IY0p8x9Y2bcfIHR4a\naFPkYotBbzu5+UQOSZQ+ERRgw72j0nD3yE6uMVF6NXKsFMdrtf19aX95udVXzu+OsyQqMQkR8vlN\naHM8zbSjmQKecLCYzBF3v4t/ndcVd5ydKmu0fnBxb/SQbBvatVjKBiHNhCm9XGFlA/W8Frt7sJEa\nqHwqTSW0gXZp0phQC2pTM6w5iOvLMlYI3ElSzkp6WlRIAKpy5HXEVaFjS05OFYXET9cPwPzrBrDU\ngBlmLKSbGYjHVw5Kwcj0aFGGzysGpeD+MZ3RMYpdh5+G5MhghAUFyPYXMpPg5QNTsJiSvOHe0Z1x\n7+g00XO6j9jmjAoJxGUDUxArkwylOjdL8SGrTR5kIK20NSc5JU4VEuIy4zZGVZYxGTFIigiSDTyW\nKkvQMKCDe/KYtJgQkTKGWdxLPZjUPZ4a9P74+AxEhQTgobHumsf9UiIx94p+bqoZL0zuhv9e3Btf\nXNlPkdIAyHN+zXZqfXUlJW5JZ1A8DWmMWTBJXNAnUWQkGl0XSnc4jejnS/MZqOGawSmYd3V/XNI/\nWf1gGbCwFIX+IgSJ33BWR7w2rTvOVVAJIduFHJ4FA3z2+Zl4aUo3jO8Wx2TEkX3lhrM6Urnsot0F\nhb6ltlBNjgzCM+d2Ydq9Zol5kVvsz+qXhIsVnt0A5w6rVPuevLc7R6ZhGSNlUjhNjoJGs4eiQgLQ\nNS4UAztE4pNL++Czy/uIbJ5haVEYlR6DRBMyXKvB1MwUTRRZp7FOVYl1eRWu79SMOvL5B5s4gtIC\n6jpKgv+kdQsJtFHlqoIDOAwnBj3ytFl9E7Ewu9T1OTMhDOmxoVTvt1JfV5pwaL+Q3lM1Lpxas3Kc\n+BilwwXPywV9EtE7OQJd40LxW26F23GPj89A35QIXDtnp1xBqriwTxLW5VVK6spGTSAhNSxZA6K0\nQNrGahMZrZ5d4kJxpIK+GBRw63CHAZh9qlZL9dxAGrRvzuiBVjuvyuseS0h9BnDKCzzyF3KOlNti\nFsU5KNRhdEYMVh0up/722eV9xNk4iUKX3TLYNfCaEXSVFBGMC/okYsn+UsXjLu6fhIEdo1BZ34wr\nvtnLXP74rrF4ZHwGyuuakRodgu4JYfg9rwJFp5sQwAG3DBcvBJQ8oqy32zc5Ao+MS8f7m45i54ka\n5rrK4bwe8ZjUPU7xne2ZFI65V/TFTT9kA3Bk3JWTc7tsQDIigwMw96+TAIAL+yTiqhm9sPxgGf46\nfhrHqhoB6ONLSymTJFIoknbkFZ4+twtmrylwozIooVt8GPLK6wFQxgqG5xUWZMPsqZn49M8TKK9r\n1pzHQArpJbUGd5N0Ly2vFwfH85JbkLLGYmhRG7ukfzIGp0YxqXKR7cDDsfuzoaAKU527KUkRRA4K\nViUgJ64elKLqsBPaRw5JEUEoq2umGtJfX+XIgbA2hz5ekmDJM6E16RXgiMeICQ3EN1lFuKRfkkRl\nRXyskZwuz0/uiudX5QOQV2/78JLeovYMC7ShudWRe8MK6Vc5mMoRL61z11IWIO1IrOhjkvzWiM7R\neH5yV7fvpdnGpJ2X53lq1HZsWJBolUW+GPeO7iySqXtzRg88NbGLqfJptBfRTrz1ahKMalXhefFA\nxjKR2TgOPRPDEUQaVsR55/WIR2p0CK6cMYl6fk5pveo1WEX4SegKfILxrV3NspOU428ZZo72NQv6\np0RgWq8EPDi2MwZ2jMQQihLPs5RsZIL3rovKJEbeHjnA0ugiJEgdcRqCAjjMmd6dGhwlMsIlIOtw\nhYLsqCZoMDhiw4Iw7xr35EA0vH1hDzwzqStCA20u5aDw4AB8eWU/LLtlMJbdOkRTMKxaNZ+d1AWL\nbxqE/7uwBzoz6PwO7BDpJktLYnrvBNeODctYQlNHEkCePaZLjKif3n7JVGQmhOPe0Z1FAbhKRrUU\nT0zIwLC0KFzcT5s3lrytcV3j8PnlfXC7jNRjGFWFQqzyoRVCbolbh6fiMQVaEiv05EEgQSo2aVFe\nl7t1wXsq7KCTfViamZUmj0mDwBEPsHHokRjOdg7htOF5Hk9O7IJ/z+pJzVzbOzkcAZy7+o7cVVg0\nwtXmlf9c1AufX6GcRIplmJKLqSHBKo5ACgP0TYlAp5gQPD4+A90TwxEcwGFURgzCgmwY19VYvAp5\nX6QoglyTSbOU3u7cjbubUX3NLHguV7OGcYVc8BrV0BYQEmijDm5SIr6Um0dbfLNEQ8/qm4TtR0+L\ndFKlV//lhoF4clmOalk00F7YGb0TsfRAmRu3lfaysAz0ejSbpaDtJtwwtCN6Jobjtd+PiL7/81i1\n27FmISiAQzPBP/BA/IVuuUoS0h0bK8FxnChYjQba1uec6T1wqqZJUZkCEL/LYkNcfjAf0CESe4pq\nqIsCG+dYOHeJC0NyZDAGpUbJesYFyE1A1w3tgAV7i9FoBkdFA1jfMaVxUI/XiFz8JEQEITEiyBVM\nDAC9kyJEPE61VrlmSAqGdorGlE8cu100/nq/FHP0ocn7bWnlERKofv9d4sJw+9mpSI0OwQur8xWP\nndQ9nimBiRTSWnRSWAT+eN0A/HfzcSw50LZ7osTIZOmVRlSJaHhgTGfqziYryDhzaQyGHjw+IQPL\nD5ZR1bukyYoSwoMQyGg7aH197hjRCWud7cLzjvFLLllSWFAAFt/scCRc+tVu1DU7GoVsDbHCGb0y\nSlW8dkgHkS54bFgQaFIZcvr3UvxwbX/UN9uZApVZx56UqGBcOSiF6tTkOI4pG2daTIhrh8utDGcL\nkf2MHDNZn/HUngkY1ilaUUTDCnhMR5z0LqqtoqzQNyb7N1l+oMQbJwxmr5yfidjQQMymZCBkeahh\nQQF484IergyJ0jp8fnkfQxmbaFXokRiOX24Y6CbVExJow8tTM/HmDG1bLQHOjKeqXjuNA9mObZsV\nOXhyoFGLWMBxwEuSlMEsCxGt5s01koDkTJOSD9Ewtos1Si9qoA3OCRFBTIljokICcH7PBIxMjxa1\nTbDEkHp+cleEBNrw/ORu+OekLpgQcgyPjnP38H15ZT98eHEvTfKmchSU4ACbKYlE2MYuYkFi4YKQ\n1sVvOzsVfZLDMb5bm+cpOMCGuVf0VfQCGWXuGLlNtXPJ+9y6eaPrb2mVLx+YgjFdYjGrb5JIBUjN\n2XMrQflRUlDQ4sUODrApLiBM8T8ZLCQiOECUR0PaB6T8XilE1BRDNXFA2OlQ2uUSEBTAITXGMS6Q\nuyHkPbw3syeiSvZTlU2Umo7Mas1yX4Lyy8dE8HJ8eCAu6J2Iab0SGEUK5I3KG8/qiN7JyuPv7Knd\n8NElbdxzpfc5NizIEgfQrcNTMbMvO1VLCwSTknT7iVV72N+FhIggjydq8qzZ78SNZ3XEi2vyRQMc\nCVoneWRcOn7aU4xXz++Oq+eJeZUzZdLXkpAbbEmP1Pk9E1zR0sPSovH9tf2pD4Ql+I8GsjMI2tVK\n5ykVKddR5Iz74Qx8LxoSFVRdlPDkhAxsLqxCWW0z9hrkLrvKZNDfpeHW4aluHgs9r9l/L3YPoiFx\n07BU7Dh+GgdK6gA4tnavGZyCb7NOMV2TtU5XDkrBVVKloXYAjuPwD0J2cFzXWJTWNqODJKB0dEYs\nFt0Y4+rj47rGURcAyZHBpuYY0DJYJ0cGobjG4UGmZdllBatHyaxp4YqBKVQaTnCADRf3T8aHW44D\n0G54W6FupefaLOPwvc4si/P3FGNDfqVqcpMrBiYjMyEM6/IqFIMHtbaAOwebKMvDhoCAf0nUzpT6\nwVMUmtp1hMoJSU3RorEvF7QthVKRCeFBmNU3CeFBASLVrK5OB0CHqGD0To7Aw+ekY2w3dzrEqPQY\nvIujqhkotbwn5FhlA4cHnA6z5QfL2AuBvr4RZLMZ4lvTcO8o8zIAs0CJwijsPst5xL30OjHDNEN8\n8ODB+G6H/O9kO4zKiMH86wbIbn3Q+vbUngmY6gxADA7gXGonSuWQiJbhCJLcpWuGpMhK6ZCgySIx\nPWfO/U8liSVFI93HO9a53eNxbvd4PPbrYbffxo4dq3iuQDmQQqu0kgBash219qP93i1B3cP98Dnp\nuPPnAwAcRtaI9BiXIW4WxneNNX0bWg+UOMEseFYy6ZMg3z21/kLi7Qt6IL+iAe9tPKq5Plp24v49\nsxeyTp7GmC6xoh0+lhI6x7YtPFg5lp5+37VwqgG4DYAeNcyJS40cNcb1923DU/HY0hzcJuPwuWxA\nMi4boM4D5zgOw9KiMUwil9YnORz7i+tcnxsoNDwlKBriKscaxYjO0dh61J0KOFaS/0PuuhwgCqB1\n0cQIDzo5hrMYrB9e3AtVDS3MQabhCvOBQAuSemBn9ElEK89jmDN7rdzYEh8ehCU3DVLVF9e7e0++\nzyyB4lJNdqPJw/TW+5yusfgjvxKPjU93Zaj0FG47uxM+3nrcXe4acNGQRMkgid89QUU1AlM94nJp\nUAGIjVBOOVGClj7GmnCB1OYly5dSU1hgo9wm2/YS8bfzw9WDO+CZFbmu7y8bkIyVh8pQ3diKMYQi\nRb+UCOSX17s4ZmYKwN8zKg0fbD6m+3yz+/gLk7th9eFy9EoKx6YjVZo86rQgKC31C7RxaLHziAsL\n0mX8kLxaekCWA1cNSsF3u7Qb6M+d1xWZMgsCTw82WnR6PYV+HSKp2rYClMaWeEZPHADEhQdRE7MM\n7BCJpQfoHq7kyCC8OaOHSHaQ3SOuva31PJ3PL++LhpZWt501w6IyBrqK2r3L/TooNQq/3jxIHDxu\nIl6akoldJ2vw0hoH51zuvWRFGKmtbKgkJxQe2otTuqGplceFc3e5vtOiE07OTQDw7syeyCurx+iM\ntu9J1Q2WJUpmgnKMiRQJEUG4f3Qa4iSGe59k+XJCA23MgdksCXL0vhekt7a2qVXTuUbnJUB/ve8d\nlYbzeyZgWJo1+QeU0DspXETvARx20b5TtTjHuYAkFzVi1Tffm6tImGaIZ2VlISp8hOzv5KTtjSZJ\nkKFYiJRPGGtGpblo5BwLZZCUkdjQQNwxohNuPKsjjlU1iKSU3pzRA02tdsz6YrfzcsZbMTMhDCer\nGzG5R7ybIW6WB4b2wm/YsEHRyzkiPQYjnHzw83rEu0m89U4Kd9E/pBjbJRa/d67AgI6R+GTbCQD0\n/ibH9XxyYga+yzqF285OxWd/npCtoxw6RgXjyoHJSIwIdusn5EfaIK/2RGNCA90mQBI9k8IxPC3a\nsEHACrO3OuWg1l+0QKlf33p2KjgOuNAAj3FiZhwiggNwsKQOX+8sAgBM65WAZQfLcNNZqW40HMDd\ns0qFjqZm0SOXopOMfrW03TjJd2qBulZNhBwnbpptmzdh+nkTXJ+tMsIBhxOI5JprToMtadSMuFDX\nuGb1GpfjOBFH/cqBybhehWJFVveRceKg7l5JEW70P5KKYdSDKwfyXR2VHoPNhVW4TUaphgajY4ve\nGFTy+eaXq6uFST3iWiFVbZE6MO8fnYb+DCop8eFBIo68JzDv6v6obW510XlJzJnRAxX1zS65SJFH\n3GijeRCmesSVtsvFinbKrXJWpyh0iw8Vyc+QmNQ9HssOlmF8N3mjRAo5m4Gss5IHk8TJavfIXSaP\nuMpBgqc9JNDm5h0IsHEIs7XV1Qwb6N0Le6Kp1W6Y5qBUFaPDb2xYEN6f1QuRxFb5DWd1xNPLc6nH\nhwTa8NLUTDS02F2GuLSCb1/QQ7YPjusaR5VQYs2yyHEcbiUmAtEuCPF3Ms1IUnmmaluYNo7Dy5Tg\nYrMhbEOnRpvHz/YFxIUF4RFKUKgWcJyDjnSqpsn13QNjOuPSAcmibKIkWGwULa/7uzN74nhVo6z2\nth5It7KfmtgFr/xWAMAxyUvHEK1ePiPw5saMnBKQHohVU7j/b+/eg+MqzzuO/57VzZYsyZbli7Bs\nWbYsX/AN24AdCMHIY4jpmAQIxcMlwWkn09DgKaEh0Bky9B+SzDAJLU1DCiWEIVAimkIZ0hAuM8Ft\nKHSMwWBoDQRkDDZgsAw2F2O//WN35dVqz+452rO7Z4+/nxnP6KyO1u/uPnvOe97zvM/r/Uu/Arwx\nExrrcqZJrepq1Wvvf6zjpzTpo0NHx7WDnjNK1A8f5vq1s3Tw05F3c0pptCkemWuK+KnUlD2IN6Y2\noSnj6n1d/J08vWVEbJ40vUXnLZyk+55/R5K0vLMlb8nQSprYVKeJyt35r03YsPcyjPUgKiHUHPEl\nk71rHgcpQ1hfm9BPz/VeHvnyVZ1aOaM1Z0kzP/9/5kfVVF+jH6zr0ZEjzneaS7566fkdbUPm92L9\ngnY9sP1drZ9fmhnFXuprE75uvxUj14Eq6AhE7yTv0TavSYteHWBpdHcTsivRFKuvp0279n+iEztb\ndOWDyTz6Qq3KtWBWJfz9OXPVv+1t/bnPlSSLFTRe2hrr1FBjOScapxfoCms5ej9qEpa3dFjYp475\nk5s0v0AVhcCyGnn67An6XFern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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import pymc3 as pm\n", + "\n", + "with pm.Model() as model:\n", + " x = pm.Normal(\"x\", mu=4, tau=10)\n", + " y = pm.Deterministic(\"y\", 10 - x)\n", + "\n", + " trace_2 = pm.sample(10000, pm.Metropolis())\n", + "\n", + "plt.plot(trace_2[\"x\"])\n", + "plt.plot(trace_2[\"y\"])\n", + "plt.title(\"Displaying (extreme) case of dependence between unknowns\");\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As you can see, the two variables are not unrelated, and it would be wrong to add the $i$th sample of $x$ to the $j$th sample of $y$, unless $i = j$." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Returning to Clustering: Prediction\n", + "The above clustering can be generalized to $k$ clusters. Choosing $k=2$ allowed us to visualize the MCMC better, and examine some very interesting plots. \n", + "\n", + "What about prediction? Suppose we observe a new data point, say $x = 175$, and we wish to label it to a cluster. It is foolish to simply assign it to the *closer* cluster center, as this ignores the standard deviation of the clusters, and we have seen from the plots above that this consideration is very important. More formally: we are interested in the *probability* (as we cannot be certain about labels) of assigning $x=175$ to cluster 1. Denote the assignment of $x$ as $L_x$, which is equal to 0 or 1, and we are interested in $P(L_x = 1 \\;|\\; x = 175 )$. \n", + "\n", + "A naive method to compute this is to re-run the above MCMC with the additional data point appended. The disadvantage with this method is that it will be slow to infer for each novel data point. Alternatively, we can try a *less precise*, but much quicker method. \n", + "\n", + "We will use Bayes Theorem for this. If you recall, Bayes Theorem looks like:\n", + "\n", + "$$ P( A | X ) = \\frac{ P( X | A )P(A) }{P(X) }$$\n", + "\n", + "In our case, $A$ represents $L_x = 1$ and $X$ is the evidence we have: we observe that $x = 175$. For a particular sample set of parameters for our posterior distribution, $( \\mu_0, \\sigma_0, \\mu_1, \\sigma_1, p)$, we are interested in asking \"Is the probability that $x$ is in cluster 1 *greater* than the probability it is in cluster 0?\", where the probability is dependent on the chosen parameters.\n", + "\n", + "\\begin{align}\n", + "& P(L_x = 1| x = 175 ) \\gt P(L_x = 0| x = 175 ) \\\\\\\\[5pt]\n", + "& \\frac{ P( x=175 | L_x = 1 )P( L_x = 1 ) }{P(x = 175) } \\gt \\frac{ P( x=175 | L_x = 0 )P( L_x = 0 )}{P(x = 175) }\n", + "\\end{align}\n", + "\n", + "As the denominators are equal, they can be ignored (and good riddance, because computing the quantity $P(x = 175)$ can be difficult). \n", + "\n", + "$$ P( x=175 | L_x = 1 )P( L_x = 1 ) \\gt P( x=175 | L_x = 0 )P( L_x = 0 ) $$\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Probability of belonging to cluster 1: 0.01062\n" + ] + } + ], + "source": [ + "norm_pdf = stats.norm.pdf\n", + "p_trace = trace[\"p\"][25000:]\n", + "prev_p_trace = trace[\"p\"][:25000]\n", + "x = 175\n", + "\n", + "v = p_trace * norm_pdf(x, loc=center_trace[:, 0], scale=std_trace[:, 0]) > \\\n", + " (1 - p_trace) * norm_pdf(x, loc=center_trace[:, 1], scale=std_trace[:, 1])\n", + "\n", + "print(\"Probability of belonging to cluster 1:\", v.mean())\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Giving us a probability instead of a label is a very useful thing. Instead of the naive \n", + "\n", + " L = 1 if prob > 0.5 else 0\n", + "\n", + "we can optimize our guesses using a *loss function*, which the entire fifth chapter is devoted to. \n", + "\n", + "\n", + "### Using `MAP` to improve convergence\n", + "\n", + "If you ran the above example yourself, you may have noticed that our results were not consistent: perhaps your cluster division was more scattered, or perhaps less scattered. The problem is that our traces are a function of the *starting values* of the MCMC algorithm. \n", + "\n", + "It can be mathematically shown that letting the MCMC run long enough, by performing many steps, the algorithm *should forget its initial position*. In fact, this is what it means to say the MCMC converged (in practice though we can never achieve total convergence). Hence if we observe different posterior analysis, it is likely because our MCMC has not fully converged yet, and we should not use samples from it yet (we should use a larger burn-in period ).\n", + "\n", + "In fact, poor starting values can prevent any convergence, or significantly slow it down. Ideally, we would like to have the chain start at the *peak* of our landscape, as this is exactly where the posterior distributions exist. Hence, if we started at the \"peak\", we could avoid a lengthy burn-in period and incorrect inference. Generally, we call this \"peak\" the *maximum a posterior* or, more simply, the *MAP*.\n", + "\n", + "Of course, we do not know where the MAP is. PyMC3 provides a function that will approximate, if not find, the MAP location. In the PyMC3 main namespace is the `find_MAP` function. If you call this function within the context of `Model()`, it will calculate the MAP which you can then pass to `pm.sample()` as a `start` parameter.\n", + "\n", + " start = pm.find_MAP()\n", + " trace = pm.sample(2000, step=pm.Metropolis, start=start)\n", + "\n", + "The `find_MAP()` function has the flexibility of allowing the user to choose which optimization algorithm to use (after all, this is a optimization problem: we are looking for the values that maximize our landscape), as not all optimization algorithms are created equal. The default optimization algorithm in function call is the Broyden-Fletcher-Goldfarb-Shanno ([BFGS](https://en.wikipedia.org/wiki/Broyden-Fletcher-Goldfarb-Shanno_algorithm)) algorithm to find the maximum of the log-posterior. As an alternative, you can use other optimization algorithms from the `scipy.optimize` module. For example, you can use Powell's Method, a favourite of PyMC blogger [Abraham Flaxman](http://healthyalgorithms.com/) [1], by calling `find_MAP(fmin=scipy.optimize.fmin_powell)`. The default works well enough, but if convergence is slow or not guaranteed, feel free to experiment with Powell's method or the other algorithms available. \n", + "\n", + "The MAP can also be used as a solution to the inference problem, as mathematically it is the *most likely* value for the unknowns. But as mentioned earlier in this chapter, this location ignores the uncertainty and doesn't return a distribution.\n", + "\n", + "#### Speaking of the burn-in period\n", + "\n", + "It is still a good idea to decide on a burn-in period, even if we are using `find_MAP()` prior to sampling, just to be safe. We can no longer automatically discard sample with a `burn` parameter in the `sample()` function as we could in PyMC2, but it is easy enough to simply discard the beginning section of the trace just through array slicing. As one does not know when the chain has fully converged, a good rule of thumb is to discard the first *half* of your samples, sometimes up to 90% of the samples for longer runs. To continue the clustering example from above, the new code would look something like:\n", + "\n", + " with pm.Model() as model:\n", + " start = pm.find_MAP()\n", + " \n", + " step = pm.Metropolis()\n", + " trace = pm.sample(100000, step=step, start=start)\n", + " \n", + " burned_trace = trace[50000:]\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Diagnosing Convergence\n", + "\n", + "### Autocorrelation\n", + "\n", + "Autocorrelation is a measure of how related a series of numbers is with itself. A measurement of 1.0 is perfect positive autocorrelation, 0 no autocorrelation, and -1 is perfect negative correlation. If you are familiar with standard *correlation*, then autocorrelation is just how correlated a series, $x_t$, at time $t$ is with the series at time $t-k$:\n", + "\n", + "$$R(k) = Corr( x_t, x_{t-k} ) $$\n", + "\n", + "For example, consider the two series:\n", + "\n", + "$$x_t \\sim \\text{Normal}(0,1), \\;\\; x_0 = 0$$\n", + "$$y_t \\sim \\text{Normal}(y_{t-1}, 1 ), \\;\\; y_0 = 0$$\n", + "\n", + "which have example paths like:" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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0zDT0eBV9znwIncZd0HjKo55f0quID60ddnrUgyfLE3ntwcIuJ3qu9btZjzpl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Nod+O3n9WgftgiOMVodtPiUIkqg43anQJ+eMM6eviHlL3tjRKfuT/riRiGV4L7TC7l+Bm1DZjz2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\nIiIiMiEW6kREREREJvT/YvXijE994isAAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "figsize(12.5, 4)\n", + "\n", + "import pymc3 as pm\n", + "x_t = np.random.normal(0, 1, 200)\n", + "x_t[0] = 0\n", + "y_t = np.zeros(200)\n", + "for i in range(1, 200):\n", + " y_t[i] = np.random.normal(y_t[i - 1], 1)\n", + "\n", + "plt.plot(y_t, label=\"$y_t$\", lw=3)\n", + "plt.plot(x_t, label=\"$x_t$\", lw=3)\n", + "plt.xlabel(\"time, $t$\")\n", + "plt.legend();\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "One way to think of autocorrelation is \"If I know the position of the series at time $s$, can it help me know where I am at time $t$?\" In the series $x_t$, the answer is No. By construction, $x_t$ are random variables. If I told you that $x_2 = 0.5$, could you give me a better guess about $x_3$? No.\n", + "\n", + "On the other hand, $y_t$ is autocorrelated. By construction, if I knew that $y_2 = 10$, I can be very confident that $y_3$ will not be very far from 10. Similarly, I can even make a (less confident guess) about $y_4$: it will probably not be near 0 or 20, but a value of 5 is not too unlikely. I can make a similar argument about $y_5$, but again, I am less confident. Taking this to it's logical conclusion, we must concede that as $k$, the lag between time points, increases the autocorrelation decreases. We can visualize this:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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2958Vq2Aikh+NPSEiIkkzYMAAVqxYwerVHU65lZROnToxYMCAoq0/W0tEeNi8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\nD0qv2kYmz1cpuo6V61hXXRABjAbmuvt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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def autocorr(x):\n", + " # from http://tinyurl.com/afz57c4\n", + " result = np.correlate(x, x, mode='full')\n", + " result = result / np.max(result)\n", + " return result[result.size // 2:]\n", + "\n", + "colors = [\"#348ABD\", \"#A60628\", \"#7A68A6\"]\n", + "\n", + "x = np.arange(1, 200)\n", + "plt.bar(x, autocorr(y_t)[1:], width=1, label=\"$y_t$\",\n", + " edgecolor=colors[0], color=colors[0])\n", + "plt.bar(x, autocorr(x_t)[1:], width=1, label=\"$x_t$\",\n", + " color=colors[1], edgecolor=colors[1])\n", + "\n", + "plt.legend(title=\"Autocorrelation\")\n", + "plt.ylabel(\"measured correlation \\nbetween $y_t$ and $y_{t-k}$.\")\n", + "plt.xlabel(\"k (lag)\")\n", + "plt.title(\"Autocorrelation plot of $y_t$ and $x_t$ for differing $k$ lags.\");\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notice that as $k$ increases, the autocorrelation of $y_t$ decreases from a very high point. Compare with the autocorrelation of $x_t$ which looks like noise (which it really is), hence we can conclude no autocorrelation exists in this series. \n", + "\n", + "\n", + "#### How does this relate to MCMC convergence?\n", + "\n", + "By the nature of the MCMC algorithm, we will always be returned samples that exhibit autocorrelation (this is because of the step `from your current position, move to a position near you`).\n", + "\n", + "A chain that is not exploring the space well will exhibit very high autocorrelation. Visually, if the trace seems to meander like a river, and not settle down, the chain will have high autocorrelation.\n", + "\n", + "This does not imply that a converged MCMC has low autocorrelation. Hence low autocorrelation is not necessary for convergence, but it is sufficient. PyMC3 has a built-in autocorrelation plotting function in the `plots` module. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Thinning\n", + "\n", + "Another issue can arise if there is high-autocorrelation between posterior samples. Many post-processing algorithms require samples to be *independent* of each other. This can be solved, or at least reduced, by only returning to the user every $n$th sample, thus removing some autocorrelation. Below we perform an autocorrelation plot for $y_t$ with differing levels of thinning:" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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3v2dYj+SYcv7zJ3Rw8Myqd9jjvSkt0KLSkYvxAXHKUy5lilPhK/UYxWVWIuX8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\nWyycfvrpbLzxxnTs2JF99tmH9957D4B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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "max_x = 200 // 3 + 1\n", + "x = np.arange(1, max_x)\n", + "\n", + "plt.bar(x, autocorr(y_t)[1:max_x], edgecolor=colors[0],\n", + " label=\"no thinning\", color=colors[0], width=1)\n", + "plt.bar(x, autocorr(y_t[::2])[1:max_x], edgecolor=colors[1],\n", + " label=\"keeping every 2nd sample\", color=colors[1], width=1)\n", + "plt.bar(x, autocorr(y_t[::3])[1:max_x], width=1, edgecolor=colors[2],\n", + " label=\"keeping every 3rd sample\", color=colors[2])\n", + "\n", + "plt.autoscale(tight=True)\n", + "plt.legend(title=\"Autocorrelation plot for $y_t$\", loc=\"lower left\")\n", + "plt.ylabel(\"measured correlation \\nbetween $y_t$ and $y_{t-k}$.\")\n", + "plt.xlabel(\"k (lag)\")\n", + "plt.title(\"Autocorrelation of $y_t$ (no thinning vs. thinning) \\\n", + "at differing $k$ lags.\");\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With more thinning, the autocorrelation drops quicker. There is a tradeoff though: higher thinning requires more MCMC iterations to achieve the same number of returned samples. For example, 10 000 samples unthinned is 100 000 with a thinning of 10 (though the latter has less autocorrelation). \n", + "\n", + "What is a good amount of thinning? The returned samples will always exhibit some autocorrelation, regardless of how much thinning is done. So long as the autocorrelation tends to zero, you are probably ok. Typically thinning of more than 10 is not necessary." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### `pymc3.plots`\n", + "\n", + "It seems silly to have to manually create histograms, autocorrelation plots and trace plots each time we perform MCMC. The authors of PyMC3 have included a visualization tool for just this purpose. \n", + "\n", + "The `pymc3.plots` module contains a few different plotting functions that you might find useful. For each different plotting function contained therein, you simply pass a `trace` returned from sampling as well as a list, `varnames`, of the variables that you are interested in. This module can provide you with plots of autocorrelation and the posterior distributions of each variable and their traces, among others.\n", + "\n", + "Below we use the tool to plot the centers of the clusters." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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VWEq7ZFgDek+PnYIFs1GfL0wBCDzf7q6e4L0F6Dlyip7jZwadd/6cCrJKCsks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\n13otEVkrIo8Wg8wisltE1jv7+pUikTkiIj9zZNgoIhcVsswiMsfZv2u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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pm.plots.traceplot(trace=trace, varnames=[\"centers\"])\n", + "pm.plots.plot_posterior(trace=trace[\"centers\"][:,0])\n", + "pm.plots.plot_posterior(trace=trace[\"centers\"][:,1])\n", + "pm.plots.autocorrplot(trace=trace, varnames=[\"centers\"]);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The first plotting function gives us the posterior density of each unknown in the `centers` variable as well as the `trace` of each. `trace` plot is useful for inspecting that possible \"meandering\" property that is a result of non-convergence. The density plot gives us an idea of the shape of the distribution of each unknown, but it is better to look at each of them individually." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The second plotting function(s) provides us with a histogram of the samples with a few added features. The text overlay in the center shows us the posterior mean, which is a good summary of posterior distribution. The interval marked by the horizontal black line overlay represents the *95% credible interval*, sometimes called the *highest posterior density interval* and not to be confused with a *95% confidence interval*. We won't get into the latter, but the former can be interpreted as \"there is a 95% chance the parameter of interest lies in this interval\". When communicating your results to others, it is incredibly important to state this interval. One of our purposes for studying Bayesian methods is to have a clear understanding of our uncertainty in unknowns. Combined with the posterior mean, the 95% credible interval provides a reliable interval to communicate the likely location of the unknown (provided by the mean) *and* the uncertainty (represented by the width of the interval)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The last plots, titled `center_0` and `center_1` are the generated autocorrelation plots, similar to the ones displayed above." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Useful tips for MCMC\n", + "\n", + "Bayesian inference would be the *de facto* method if it weren't for MCMC's computational difficulties. In fact, MCMC is what turns most users off practical Bayesian inference. Below I present some good heuristics to help convergence and speed up the MCMC engine:\n", + "\n", + "### Intelligent starting values\n", + "\n", + "It would be great to start the MCMC algorithm off near the posterior distribution, so that it will take little time to start sampling correctly. We can aid the algorithm by telling where we *think* the posterior distribution will be by specifying the `testval` parameter in the `Stochastic` variable creation. In many cases we can produce a reasonable guess for the parameter. For example, if we have data from a Normal distribution, and we wish to estimate the $\\mu$ parameter, then a good starting value would be the *mean* of the data. \n", + "\n", + " mu = pm.Uniform( \"mu\", 0, 100, testval = data.mean() )\n", + "\n", + "For most parameters in models, there is a frequentist estimate of it. These estimates are a good starting value for our MCMC algorithms. Of course, this is not always possible for some variables, but including as many appropriate initial values is always a good idea. Even if your guesses are wrong, the MCMC will still converge to the proper distribution, so there is little to lose.\n", + "\n", + "This is what using `MAP` tries to do, by giving good initial values to the MCMC. So why bother specifying user-defined values? Well, even giving `MAP` good values will help it find the maximum a-posterior. \n", + "\n", + "Also important, *bad initial values* are a source of major bugs in PyMC3 and can hurt convergence.\n", + "\n", + "#### Priors\n", + "\n", + "If the priors are poorly chosen, the MCMC algorithm may not converge, or atleast have difficulty converging. Consider what may happen if the prior chosen does not even contain the true parameter: the prior assigns 0 probability to the unknown, hence the posterior will assign 0 probability as well. This can cause pathological results.\n", + "\n", + "For this reason, it is best to carefully choose the priors. Often, lack of covergence or evidence of samples crowding to boundaries implies something is wrong with the chosen priors (see *Folk Theorem of Statistical Computing* below). \n", + "\n", + "#### Covariance matrices and eliminating parameters\n", + "\n", + "### The Folk Theorem of Statistical Computing\n", + "\n", + "> *If you are having computational problems, probably your model is wrong.*\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Conclusion\n", + "\n", + "PyMC3 provides a very strong backend to performing Bayesian inference, mostly because it has abstracted the inner mechanics of MCMC from the user. Despite this, some care must be applied to ensure your inference is not being biased by the iterative nature of MCMC. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### References\n", + "\n", + "1. Flaxman, Abraham. \"Powell's Methods for Maximization in PyMC.\" Healthy Algorithms. N.p., 9 02 2012. Web. 28 Feb 2013. ." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.core.display import HTML\n", + "\n", + "\n", + "def css_styling():\n", + " styles = open(\"../styles/custom.css\", \"r\").read()\n", + " return HTML(styles)\n", + "css_styling()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.11" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file diff --git a/Chapter3_MCMC/Ch3_IntroMCMC_PyMC_current.ipynb b/Chapter3_MCMC/Ch3_IntroMCMC_PyMC_current.ipynb new file mode 100644 index 00000000..5bfc7d90 --- /dev/null +++ b/Chapter3_MCMC/Ch3_IntroMCMC_PyMC_current.ipynb @@ -0,0 +1,1877 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Chapter 3\n", + "\n", + "\n", + "`Original content created by Cam Davidson-Pilon`\n", + "\n", + "`Ported to Python 3 and PyMC3 by Max Margenot (@clean_utensils) and Thomas Wiecki (@twiecki) at Quantopian (@quantopian)`\n", + "\n", + "`Ported to PyMC last by Kurisu Chan (@miemiekurisu)`\n", + "____\n", + "\n", + "\n", + "## Opening the black box of MCMC" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The previous two chapters hid the inner-mechanics of PyMC, and more generally Markov Chain Monte Carlo (MCMC), from the reader. The reason for including this chapter is three-fold. The first is that any book on Bayesian inference must discuss MCMC. I cannot fight this. Blame the statisticians. Secondly, knowing the process of MCMC gives you insight into whether your algorithm has converged. (Converged to what? We will get to that) Thirdly, we'll understand *why* we are returned thousands of samples from the posterior as a solution, which at first thought can be odd. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### The Bayesian landscape\n", + "\n", + "When we setup a Bayesian inference problem with $N$ unknowns, we are implicitly creating an $N$ dimensional space for the prior distributions to exist in. Associated with the space is an additional dimension, which we can describe as the *surface*, or *curve*, that sits on top of the space, that reflects the *prior probability* of a particular point. The surface on the space is defined by our prior distributions. For example, if we have two unknowns $p_1$ and $p_2$, and priors for both are $\\text{Uniform}(0,5)$, the space created is a square of length 5 and the surface is a flat plane that sits on top of the square (representing that every point is equally likely). " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib inline\n", + "import scipy.stats as stats\n", + "from IPython.core.pylabtools import figsize\n", + "import arviz as az\n", + "import matplotlib as mt\n", + "mt.style.use(\"ggplot\")\n", + "import numpy as np\n", + "figsize(12.5, 4)\n", + "\n", + "import matplotlib.pyplot as plt\n", + "from mpl_toolkits.mplot3d import Axes3D\n", + "\n", + "jet = plt.cm.jet\n", + "fig = plt.figure()\n", + "x = y = np.linspace(0, 5, 100)\n", + "X, Y = np.meshgrid(x, y)\n", + "\n", + "plt.subplot(121)\n", + "uni_x = stats.uniform.pdf(x, loc=0, scale=5)\n", + "uni_y = stats.uniform.pdf(y, loc=0, scale=5)\n", + "M = np.dot(uni_x[:, None], uni_y[None, :])\n", + "im = plt.imshow(M, interpolation='none', origin='lower',\n", + " cmap=jet, vmax=1, vmin=-.15, extent=(0, 5, 0, 5))\n", + "\n", + "plt.xlim(0, 5)\n", + "plt.ylim(0, 5)\n", + "plt.title(\"Landscape formed by Uniform priors.\")\n", + "\n", + "ax = fig.add_subplot(122, projection='3d')\n", + "ax.plot_surface(X, Y, M, cmap=plt.cm.jet, vmax=1, vmin=-.15)\n", + "ax.view_init(azim=390)\n", + "plt.title(\"Uniform prior landscape; alternate view\");\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Alternatively, if the two priors are $\\text{Exp}(3)$ and $\\text{Exp}(10)$, then the space is all positive numbers on the 2-D plane, and the surface induced by the priors looks like a water fall that starts at the point (0,0) and flows over the positive numbers. \n", + "\n", + "The plots below visualize this. The more dark red the color, the more prior probability is assigned to that location. Conversely, areas with darker blue represent that our priors assign very low probability to that location. " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "figsize(12.5, 5)\n", + "fig = plt.figure()\n", + "plt.subplot(121)\n", + "\n", + "exp_x = stats.expon.pdf(x, scale=3)\n", + "exp_y = stats.expon.pdf(x, scale=10)\n", + "M = np.dot(exp_x[:, None], exp_y[None, :])\n", + "CS = plt.contour(X, Y, M)\n", + "im = plt.imshow(M, interpolation='none', origin='lower',\n", + " cmap=jet, extent=(0, 5, 0, 5))\n", + "#plt.xlabel(\"prior on $p_1$\")\n", + "#plt.ylabel(\"prior on $p_2$\")\n", + "plt.title(\"$Exp(3), Exp(10)$ prior landscape\")\n", + "\n", + "ax = fig.add_subplot(122, projection='3d')\n", + "ax.plot_surface(X, Y, M, cmap=jet)\n", + "ax.view_init(azim=390)\n", + "plt.title(\"$Exp(3), Exp(10)$ prior landscape; \\nalternate view\");\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "These are simple examples in 2D space, where our brains can understand surfaces well. In practice, spaces and surfaces generated by our priors can be much higher dimensional. \n", + "\n", + "If these surfaces describe our *prior distributions* on the unknowns, what happens to our space after we incorporate our observed data $X$? The data $X$ does not change the space, but it changes the surface of the space by *pulling and stretching the fabric of the prior surface* to reflect where the true parameters likely live. More data means more pulling and stretching, and our original shape becomes mangled or insignificant compared to the newly formed shape. Less data, and our original shape is more present. Regardless, the resulting surface describes the *posterior distribution*. \n", + "\n", + "Again I must stress that it is, unfortunately, impossible to visualize this in large dimensions. For two dimensions, the data essentially *pushes up* the original surface to make *tall mountains*. The tendency of the observed data to *push up* the posterior probability in certain areas is checked by the prior probability distribution, so that less prior probability means more resistance. Thus in the double-exponential prior case above, a mountain (or multiple mountains) that might erupt near the (0,0) corner would be much higher than mountains that erupt closer to (5,5), since there is more resistance (low prior probability) near (5,5). The peak reflects the posterior probability of where the true parameters are likely to be found. Importantly, if the prior has assigned a probability of 0, then no posterior probability will be assigned there. \n", + "\n", + "Suppose the priors mentioned above represent different parameters $\\lambda$ of two Poisson distributions. We observe a few data points and visualize the new landscape: " + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "observed (2-dimensional,sample size = 1): [[1 5]]\n" + ] + } + ], + "source": [ + "# create the observed data\n", + "\n", + "# sample size of data we observe, trying varying this (keep it less than 100 ;)\n", + "N = 1\n", + "\n", + "# the true parameters, but of course we do not see these values...\n", + "lambda_1_true = 1\n", + "lambda_2_true = 3\n", + "\n", + "#...we see the data generated, dependent on the above two values.\n", + "data = np.concatenate([\n", + " stats.poisson.rvs(lambda_1_true, size=(N, 1)),\n", + " stats.poisson.rvs(lambda_2_true, size=(N, 1))\n", + "], axis=1)\n", + "print(\"observed (2-dimensional,sample size = %d):\" % N, data)\n", + "\n", + "# plotting details.\n", + "x = y = np.linspace(.01, 5, 100)\n", + "likelihood_x = np.array([stats.poisson.pmf(data[:, 0], _x)\n", + " for _x in x]).prod(axis=1)\n", + "likelihood_y = np.array([stats.poisson.pmf(data[:, 1], _y)\n", + " for _y in y]).prod(axis=1)\n", + "L = np.dot(likelihood_x[:, None], likelihood_y[None, :])\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "figsize(12.5, 12)\n", + "# matplotlib heavy lifting below, beware!\n", + "plt.subplot(221)\n", + "uni_x = stats.uniform.pdf(x, loc=0, scale=5)\n", + "uni_y = stats.uniform.pdf(x, loc=0, scale=5)\n", + "M = np.dot(uni_x[:, None], uni_y[None, :])\n", + "im = plt.imshow(M, interpolation='none', origin='lower',\n", + " cmap=jet, vmax=1, vmin=-.15, extent=(0, 5, 0, 5))\n", + "plt.scatter(lambda_2_true, lambda_1_true, c=\"k\", s=50, edgecolor=\"none\")\n", + "plt.xlim(0, 5)\n", + "plt.ylim(0, 5)\n", + "plt.title(\"Landscape formed by Uniform priors on $p_1, p_2$.\")\n", + "\n", + "plt.subplot(223)\n", + "plt.contour(x, y, M * L)\n", + "im = plt.imshow(M * L, interpolation='none', origin='lower',\n", + " cmap=jet, extent=(0, 5, 0, 5))\n", + "plt.title(\"Landscape warped by %d data observation;\\n Uniform priors on $p_1, p_2$.\" % N)\n", + "plt.scatter(lambda_2_true, lambda_1_true, c=\"k\", s=50, edgecolor=\"none\")\n", + "plt.xlim(0, 5)\n", + "plt.ylim(0, 5)\n", + "\n", + "plt.subplot(222)\n", + "exp_x = stats.expon.pdf(x, loc=0, scale=3)\n", + "exp_y = stats.expon.pdf(x, loc=0, scale=10)\n", + "M = np.dot(exp_x[:, None], exp_y[None, :])\n", + "\n", + "plt.contour(x, y, M)\n", + "im = plt.imshow(M, interpolation='none', origin='lower',\n", + " cmap=jet, extent=(0, 5, 0, 5))\n", + "plt.scatter(lambda_2_true, lambda_1_true, c=\"k\", s=50, edgecolor=\"none\")\n", + "plt.xlim(0, 5)\n", + "plt.ylim(0, 5)\n", + "plt.title(\"Landscape formed by Exponential priors on $p_1, p_2$.\")\n", + "\n", + "plt.subplot(224)\n", + "# This is the likelihood times prior, that results in the posterior.\n", + "plt.contour(x, y, M * L)\n", + "im = plt.imshow(M * L, interpolation='none', origin='lower',\n", + " cmap=jet, extent=(0, 5, 0, 5))\n", + "\n", + "plt.scatter(lambda_2_true, lambda_1_true, c=\"k\", s=50, edgecolor=\"none\")\n", + "plt.title(\"Landscape warped by %d data observation;\\n Exponential priors on \\\n", + "$p_1, p_2$.\" % N)\n", + "plt.xlim(0, 5)\n", + "plt.ylim(0, 5);\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The plot on the left is the deformed landscape with the $\\text{Uniform}(0,5)$ priors, and the plot on the right is the deformed landscape with the exponential priors. Notice that the posterior landscapes look different from one another, though the data observed is identical in both cases. The reason is as follows. Notice the exponential-prior landscape, bottom right figure, puts very little *posterior* weight on values in the upper right corner of the figure: this is because *the prior does not put much weight there*. On the other hand, the uniform-prior landscape is happy to put posterior weight in the upper-right corner, as the prior puts more weight there. \n", + "\n", + "Notice also the highest-point, corresponding the the darkest red, is biased towards (0,0) in the exponential case, which is the result from the exponential prior putting more prior weight in the (0,0) corner.\n", + "\n", + "The black dot represents the true parameters. Even with 1 sample point, the mountains attempts to contain the true parameter. Of course, inference with a sample size of 1 is incredibly naive, and choosing such a small sample size was only illustrative. \n", + "\n", + "It's a great exercise to try changing the sample size to other values (try 2,5,10,100?...) and observing how our \"mountain\" posterior changes. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Exploring the landscape using the MCMC\n", + "\n", + "We should explore the deformed posterior space generated by our prior surface and observed data to find the posterior mountain. However, we cannot naively search the space: any computer scientist will tell you that traversing $N$-dimensional space is exponentially difficult in $N$: the size of the space quickly blows-up as we increase $N$ (see [the curse of dimensionality](http://en.wikipedia.org/wiki/Curse_of_dimensionality)). What hope do we have to find these hidden mountains? The idea behind MCMC is to perform an intelligent search of the space. To say \"search\" implies we are looking for a particular point, which is perhaps not an accurate as we are really looking for a broad mountain. \n", + "\n", + "Recall that MCMC returns *samples* from the posterior distribution, not the distribution itself. Stretching our mountainous analogy to its limit, MCMC performs a task similar to repeatedly asking \"How likely is this pebble I found to be from the mountain I am searching for?\", and completes its task by returning thousands of accepted pebbles in hopes of reconstructing the original mountain. In MCMC and PyMC3 lingo, the returned sequence of \"pebbles\" are the samples, cumulatively called the *traces*. \n", + "\n", + "When I say MCMC intelligently searches, I really am saying MCMC will *hopefully* converge towards the areas of high posterior probability. MCMC does this by exploring nearby positions and moving into areas with higher probability. Again, perhaps \"converge\" is not an accurate term to describe MCMC's progression. Converging usually implies moving towards a point in space, but MCMC moves towards a *broader area* in the space and randomly walks in that area, picking up samples from that area.\n", + "\n", + "#### Why Thousands of Samples?\n", + "\n", + "At first, returning thousands of samples to the user might sound like being an inefficient way to describe the posterior distributions. I would argue that this is extremely efficient. Consider the alternative possibilities:\n", + "\n", + "1. Returning a mathematical formula for the \"mountain ranges\" would involve describing a N-dimensional surface with arbitrary peaks and valleys.\n", + "2. Returning the \"peak\" of the landscape, while mathematically possible and a sensible thing to do as the highest point corresponds to most probable estimate of the unknowns, ignores the shape of the landscape, which we have previously argued is very important in determining posterior confidence in unknowns. \n", + "\n", + "Besides computational reasons, likely the strongest reason for returning samples is that we can easily use *The Law of Large Numbers* to solve otherwise intractable problems. I postpone this discussion for the next chapter. With the thousands of samples, we can reconstruct the posterior surface by organizing them in a histogram. \n", + "\n", + "\n", + "### Algorithms to perform MCMC\n", + "\n", + "There is a large family of algorithms that perform MCMC. Most of these algorithms can be expressed at a high level as follows: (Mathematical details can be found in the appendix.)\n", + "\n", + "1. Start at current position.\n", + "2. Propose moving to a new position (investigate a pebble near you).\n", + "3. Accept/Reject the new position based on the position's adherence to the data and prior distributions (ask if the pebble likely came from the mountain).\n", + "4. 1. If you accept: Move to the new position. Return to Step 1.\n", + " 2. Else: Do not move to new position. Return to Step 1. \n", + "5. After a large number of iterations, return all accepted positions.\n", + "\n", + "This way we move in the general direction towards the regions where the posterior distributions exist, and collect samples sparingly on the journey. Once we reach the posterior distribution, we can easily collect samples as they likely all belong to the posterior distribution. \n", + "\n", + "If the current position of the MCMC algorithm is in an area of extremely low probability, which is often the case when the algorithm begins (typically at a random location in the space), the algorithm will move in positions *that are likely not from the posterior* but better than everything else nearby. Thus the first moves of the algorithm are not reflective of the posterior.\n", + "\n", + "In the above algorithm's pseudocode, notice that only the current position matters (new positions are investigated only near the current position). We can describe this property as *memorylessness*, i.e. the algorithm does not care *how* it arrived at its current position, only that it is there. \n", + "\n", + "### Other approximation solutions to the posterior\n", + "Besides MCMC, there are other procedures available for determining the posterior distributions. A Laplace approximation is an approximation of the posterior using simple functions. A more advanced method is [Variational Bayes](http://en.wikipedia.org/wiki/Variational_Bayesian_methods). All three methods, Laplace Approximations, Variational Bayes, and classical MCMC have their pros and cons. We will only focus on MCMC in this book. That being said, my friend Imri Sofar likes to classify MCMC algorithms as either \"they suck\", or \"they really suck\". He classifies the particular flavour of MCMC used by PyMC3 as just *sucks* ;)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "##### Example: Unsupervised Clustering using a Mixture Model\n", + "\n", + "\n", + "Suppose we are given the following dataset:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[115.85679142 152.26153716 178.87449059 162.93500815 107.02820697\n", + " 105.19141146 118.38288501 125.3769803 102.88054011 206.71326136] ...\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "figsize(12.5, 4)\n", + "data = np.loadtxt(\"data/mixture_data.csv\", delimiter=\",\")\n", + "\n", + "plt.hist(data, bins=20, color=\"k\", histtype=\"stepfilled\", alpha=0.8)\n", + "plt.title(\"Histogram of the dataset\")\n", + "plt.ylim([0, None]);\n", + "print(data[:10], \"...\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "What does the data suggest? It appears the data has a bimodal form, that is, it appears to have two peaks, one near 120 and the other near 200. Perhaps there are *two clusters* within this dataset. \n", + "\n", + "This dataset is a good example of the data-generation modeling technique from last chapter. We can propose *how* the data might have been created. I suggest the following data generation algorithm: \n", + "\n", + "1. For each data point, choose cluster 1 with probability $p$, else choose cluster 2. \n", + "2. Draw a random variate from a Normal distribution with parameters $\\mu_i$ and $\\sigma_i$ where $i$ was chosen in step 1.\n", + "3. Repeat.\n", + "\n", + "This algorithm would create a similar effect as the observed dataset, so we choose this as our model. Of course, we do not know $p$ or the parameters of the Normal distributions. Hence we must infer, or *learn*, these unknowns.\n", + "\n", + "Denote the Normal distributions $\\text{N}_0$ and $\\text{N}_1$ (having variables' index start at 0 is just Pythonic). Both currently have unknown mean and standard deviation, denoted $\\mu_i$ and $\\sigma_i, \\; i =0,1$ respectively. A specific data point can be from either $\\text{N}_0$ or $\\text{N}_1$, and we assume that the data point is assigned to $\\text{N}_0$ with probability $p$.\n", + "\n", + "\n", + "An appropriate way to assign data points to clusters is to use a PyMC `Categorical` stochastic variable. Its parameter is a $k$-length array of probabilities that must sum to one and its `value` attribute is a integer between 0 and $k-1$ randomly chosen according to the crafted array of probabilities (In our case $k=2$). *A priori*, we do not know what the probability of assignment to cluster 1 is, so we form a uniform variable on $(0, 1)$. We call call this $p_1$, so the probability of belonging to cluster 2 is therefore $p_2 = 1 - p_1$.\n", + "\n", + "Unfortunately, we can't we just give `[p1, p2]` to our `Categorical` variable. PyMC uses PyTensor under the hood to construct the models so we need to use `pytensor.tensor.stack()` to combine $p_1$ and $p_2$ into a vector that it can understand. We pass this vector into the `Categorical` variable as well as the `testval` parameter to give our variable an idea of where to start from." + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + }, + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "prior assignment, with p = [0.48929802 0.51070198]\n", + "{p: None, assignment: array([0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0, 1,\n", + " 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1,\n", + " 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1,\n", + " 0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 0,\n", + " 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0,\n", + " 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0,\n", + " 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1,\n", + " 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0,\n", + " 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1,\n", + " 0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1,\n", + " 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0,\n", + " 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0,\n", + " 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0])}\n" + ] + } + ], + "source": [ + "import pymc as pm\n", + "import pytensor.tensor as pt\n", + "import pytensor\n", + "\n", + "\n", + "with pm.Model() as model:\n", + " p1 = pm.Uniform('p', 0, 1)\n", + " p2 = 1 - p1\n", + " p = pt.stack([p1, p2])\n", + " assignment = pm.Categorical(\"assignment\", p, \n", + " shape=data.shape[0],\n", + " initval=np.random.randint(0, 2, data.shape[0]))\n", + " \n", + "\n", + "print(f\"prior assignment, with p = {pm.draw(p)}\")\n", + "print(model.initial_values)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Looking at the above dataset, I would guess that the standard deviations of the two Normals are different. To maintain ignorance of what the standard deviations might be, we will initially model them as uniform on 0 to 100. We will include both standard deviations in our model using a single line of PyMC code:\n", + "\n", + " sds = pm.Uniform(\"sds\", 0, 100, shape=2)\n", + "\n", + "Notice that we specified `shape=2`: we are modeling both $\\sigma$s as a single PyMC variable. Note that this does not induce a necessary relationship between the two $\\sigma$s, it is simply for succinctness.\n", + "\n", + "We also need to specify priors on the centers of the clusters. The centers are really the $\\mu$ parameters in these Normal distributions. Their priors can be modeled by a Normal distribution. Looking at the data, I have an idea where the two centers might be — I would guess somewhere around 120 and 190 respectively, though I am not very confident in these eyeballed estimates. Hence I will set $\\mu_0 = 120, \\mu_1 = 190$ and $\\sigma_0 = \\sigma_1 = 10$." + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Random assignments: [1 0 1 1] ...\n", + "Assigned center: [164.88903636 122.24034943 122.24034943 122.24034943] ...\n", + "Assigned standard deviation: [86.92440302 21.05705872 86.92440302 21.05705872] ...\n" + ] + } + ], + "source": [ + "with model:\n", + " sds = pm.Uniform(\"sds\", 0, 100, shape=2)\n", + " # form new API, may use the sigma/tau for paramter tau = 1/(sigma**2), \n", + " # the sd is not supported anymore in Normal\n", + " # https://www.pymc.io/projects/docs/en/stable/api/distributions/generated/pymc.Normal.html\n", + " centers = pm.Normal(\"centers\", \n", + " mu=np.array([120, 190]), \n", + " sigma=np.array([10., 10.]), \n", + " shape=2)\n", + " \n", + " center_i = pm.Deterministic('center_i', centers[assignment])\n", + " sd_i = pm.Deterministic('sd_i', sds[assignment])\n", + " \n", + " # and to combine it with the observations:\n", + " observations = pm.Normal(\"obs\", mu=center_i, sigma=sd_i, observed=data)\n", + " \n", + "print(\"Random assignments: \", pm.draw(assignment)[:4], \"...\")\n", + "print(\"Assigned center: \", pm.draw(center_i)[:4], \"...\")\n", + "print(\"Assigned standard deviation: \", pm.draw(sd_i)[:4],\"...\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notice how we continue to build the model within the context of `Model()`. This automatically adds the variables that we create to our model. As long as we work within this context we will be working with the same variables that we have already defined.\n", + "\n", + "Similarly, any sampling that we do within the context of `Model()` will be done only on the model whose context in which we are working. We will tell our model to explore the space that we have so far defined by defining the sampling methods, in this case `Metropolis()` for our continuous variables and `Categorical()` for our categorical variable. We will use these sampling methods together to explore the space by using `sample( iterations, step )`, where `iterations` is the number of steps you wish the algorithm to perform and `step` is the way in which you want to handle those steps. We use our combination of `Metropolis()` and `Categorical()` for the `step` and sample 25000 `iterations` below.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Please notice that the early version of PyMC use `ElemwiseCategorical()` for categorical variable. But in PyMC, it was deprecated. But new PyMC provieds a new functions to do the categorical sampling, the `CategoricalGibbsMetropolis` optimized for categorical variables." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There are another interesting thing with new PyMC. The PyMC use a powerful new sampling principle be called Hamiltonian Monte Carlo (HMC). We'll not talk too much about it here, since it's a complex physical principle. But we should know that [HMC and NUTS take advantage of gradient information from the likelihood to achieve much faster convergence than traditional sampling methods, especially for larger models. ](https://www.pymc.io/projects/docs/en/stable/learn/core_notebooks/pymc_overview.html#pymc-overview)\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Please also notice the parameter `chains` of `sample()`. According to the API document, `\"Running independent chains is important for some convergence statistics and can also reveal multiple modes in the posterior.\"`. But it's unnecessary here, our model is simple enough and we use `1` here (the default value is 2)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Sequential sampling (1 chains in 1 job)\n", + "CompoundStep\n", + ">CompoundStep\n", + ">>Metropolis: [p]\n", + ">>Metropolis: [sds]\n", + ">>Metropolis: [centers]\n", + ">CategoricalGibbsMetropolis: [assignment]\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + "
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These paths are the routes the unknown parameters (centers, precisions, and $p$) have taken thus far. The individual path of each variable is indexed by the PyMC3 variable `name` that we gave that variable when defining it within our model. For example, `trace[\"sds\"]` will return a `numpy array` object that we can then index and slice as we would any other `numpy array` object. \n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "figsize(12.5, 9)\n", + "plt.subplot(311)\n", + "lw = 1\n", + "center_trace = trace1[\"centers\"]\n", + "\n", + "# for pretty colors later in the book.\n", + "colors = [\"#348ABD\", \"#A60628\"] if center_trace[-1, 0] > center_trace[-1, 1] \\\n", + " else [\"#A60628\", \"#348ABD\"]\n", + "\n", + "plt.plot(center_trace[:, 0], label=\"trace of center 0\", c=colors[0], lw=lw)\n", + "plt.plot(center_trace[:, 1], label=\"trace of center 1\", c=colors[1], lw=lw)\n", + "plt.title(\"Traces of unknown parameters\")\n", + "leg = plt.legend(loc=\"upper right\")\n", + "leg.get_frame().set_alpha(0.7)\n", + "\n", + "plt.subplot(312)\n", + "std_trace = trace1[\"sds\"]\n", + "plt.plot(std_trace[:, 0], label=\"trace of standard deviation of cluster 0\",\n", + " c=colors[0], lw=lw)\n", + "plt.plot(std_trace[:, 1], label=\"trace of standard deviation of cluster 1\",\n", + " c=colors[1], lw=lw)\n", + "plt.legend(loc=\"upper left\")\n", + "\n", + "plt.subplot(313)\n", + "p_trace = trace1[\"p\"]\n", + "plt.plot(p_trace, label=\"$p$: frequency of assignment to cluster 0\",\n", + " color=colors[0], lw=lw)\n", + "plt.xlabel(\"Steps\")\n", + "plt.ylim(0, 1)\n", + "plt.legend();\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The powerful Arviz InferenceData and APIs give us more comfortable view." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "az.plot_trace(trace2.posterior.centers,figsize=(20, 4))\n", + "az.plot_trace(trace2.posterior.sds,figsize=(20, 4))\n", + "az.plot_trace(trace2.posterior.p,figsize=(20, 4))\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notice the following characteristics:\n", + "\n", + "1. The traces converges, not to a single point, but to a *distribution* of possible points. This is *convergence* in an MCMC algorithm.\n", + "2. Inference using the first few thousand points is a bad idea, as they are unrelated to the final distribution we are interested in. Thus is it a good idea to discard those samples before using the samples for inference. We call this period before converge the *burn-in period*. You can use the parameter `tune` and `discard_tuned_samples (True by default)` to do the burn-in period.\n", + "3. The traces appear as a random \"walk\" around the space, that is, the paths exhibit correlation with previous positions. This is both good and bad. We will always have correlation between current positions and the previous positions, but too much of it means we are not exploring the space well. This will be detailed in the Diagnostics section later in this chapter.\n", + "\n", + "\n", + "Please notice that \"Starting from existing MultiTrace objects is no longer supported\", so we should restart the sampling progress, and update draws to 50000 \n", + "\n", + "We will sample the MCMC fifty thousand more times and visualize the progress below ( ) :" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Sequential sampling (1 chains in 1 job)\n", + "CompoundStep\n", + ">CategoricalGibbsMetropolis: [assignment]\n", + ">NUTS: [p, sds, centers]\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + "
\n", + " \n", + " 100.00% [55000/55000 13:01<00:00 Sampling chain 0, 0 divergences]\n", + "
\n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Sampling 1 chain for 5_000 tune and 50_000 draw iterations (5_000 + 50_000 draws total) took 781 seconds.\n" + ] + } + ], + "source": [ + "with model:\n", + " trace3 = pm.sample(50000, step=[step3],tune=5000,chains=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "figsize(12.5, 4)\n", + "sample_data=az.extract_dataset(trace3)\n", + "center_trace_after_25000 = sample_data.centers.data[:,25000:]\n", + "center_trace_before25000 = sample_data.centers.data[:,:25000]\n", + "\n", + "x = np.arange(25000)\n", + "plt.plot(x, center_trace_before25000[0,:], label=\"previous trace of center 0\",\n", + " lw=lw, alpha=0.4, c=colors[1])\n", + "plt.plot(x, center_trace_before25000[1,:], label=\"previous trace of center 1\",\n", + " lw=lw, alpha=0.4, c=colors[0])\n", + "\n", + "x = np.arange(25000, 50000)\n", + "plt.plot(x, center_trace_after_25000[0,: ], label=\"new trace of center 0\", lw=lw, c=\"#348ABD\")\n", + "plt.plot(x, center_trace_after_25000[1,: ], label=\"new trace of center 1\", lw=lw, c=\"#A60628\")\n", + "\n", + "plt.title(\"Traces of unknown center parameters\")\n", + "leg = plt.legend(loc=\"upper right\")\n", + "leg.get_frame().set_alpha(0.8)\n", + "plt.xlabel(\"Steps\");\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "az.plot_trace(trace3.posterior.centers,figsize=(20, 4))\n", + "plt.plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "#### Cluster Investigation\n", + "\n", + "We have not forgotten our main challenge: identify the clusters. We have determined posterior distributions for our unknowns. We plot the posterior distributions of the center and standard deviation variables below:" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "image/png": 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sNEuXLjWSTHZ2tuuYMWPGmJ49e5q1a9eaL7/80tx9992mQ4cObm1d3/c6t942b95sLBaLmTVrltm+fbv54IMPTPfu3Y0k89lnn1VbH8YYs2TJEtOuXTvz5ptvmh07dphnnnnGBAYGGi8vL9cxdY1vTp06ZSIiIszTTz/tdu1Ro0aZsWPHuuX77O/qJUuWmE8++cTs2rXL5OXlmcGDB5thw4YZY0yt/XTu3LmmZ8+e9e4fxpzun4GBgWb69Onmq6++Mh9++KEJDAw0c+bMqbFuKisrzTXXXGOuuuoq89lnn5ktW7aYsWPHmo4dO5oDBw6YkydPmpKSEtOlSxczc+ZMU1JSYr777rtqr/XJJ58Yq9VqHnroIZOfn2+++uor89prr5mvvvqq2jKd+9wYYz777DMjyezevdsYY8yvfvUr079/f/Ovf/3LfP3112b9+vWuMe/BgwfN0KFDzdixY139+fjx4w0aC19++eVm9erVZteuXWbfvn011tMZ3bp1M/Pnz6/zOFyaCPCgVTt34LZ161YTFRVlBg0aZIwxpkuXLubRRx91O2f69OmmR48expgfAwxn/sCf69wvgDO6detmXnrpJbe0nJwcI8k4HA5jzOkvwJiYGHPq1Cm3484euH3//femTZs2Jj093e2YMWPGmBtuuMH1XJL5xS9+UVtVGGOMuf76683Pf/5zt7QlS5YYX19fc/z4cWPM6UHn2YON6mRlZRlJxm63V/v6G2+8YSIiIqoEVG644Qbz0EMPGWN+/FL74IMPXK+fCaR89NFHxhhj9u3bZySZTz/91O06CQkJZubMmW5pX3/9tZFkvvjiC2PM6bYPDAyscQBwxmuvvWb8/PxcQRhjjNm/f7/x9fU1b731liutPoGNyMhI88ADD9T4+vkEePr372/mzp1b4zV79uxZ5fW5c+e6+vgZlZWVJioqyixevNgYc7qNJJnc3Nxay3QuAjwALiWME9w11Thh1apVJiAgwBw6dKja18/9rqvJqVOnTMeOHc0777zjSpNkfvWrX7kdd/nll5tZs2YZY4zZuXOnkWQ+/vhj1+vHjx834eHhVb5z6/Ne59bb3XffbQYPHuyW9sILL9QZ4ImIiDCPPfaYW9rPfvYzt7qsz/hm5syZpk+fPq7Xv/32W+Pt7W3++te/uuW7tu/qM/22uLjYGFNzPz03+FGf/pGQkGD69evndsy0adPMtddeW2N+zowrt27d6ko7duyYCQsLM7///e9dafUJbFx//fVm1KhRNb5+PgGe2267zdxzzz01XnPEiBFVXm/IWHjFihW1lulcBHhaN5ZoodVbt26d/P395efnpyuvvFJRUVH64x//qMOHD6u4uFjDhg1zOz4hIUF79uzRDz/8oP79++vmm2/WlVdeqZ/+9KdaunRplWmx5zpw4IC+/vprpaSkuJb8+Pv765ZbbpEk19IfSbr66qtltdb8MS0sLFRFRUW1edy6datb2jXXXFNnXWzdurXaax07dqzGqavV2bRpk4KCghQfH1/t63a7Xfv371fHjh3d6uCzzz7Tzp073Y6NjY11PQ4LC5OXl5e+/fbbWt/fbrdryZIlbtfu27evJLldv0+fPvL396/1Wlu3blXfvn3d1ph37txZl19+eZU6rk1paan27dunm266qd7n1Mf06dP19NNPa9CgQZo5c6bb1N6a2O12bdq0ya1+OnTooD179lSp/4EDBzZpfgHA0zBO+FFTjRN+8pOfKCoqSj169NCdd96pV199VWVlZXWet3v3bk2cOFHR0dEKCAhQQECADh06pK+//trtuLPHDpIUERHhGjts27ZNkjRkyBDX623atKnyfVff9zq33rZt26brrrvOLe3666+vtVyHDx/Wf//7X7c8VXdefcY399xzj7766ivXJsN/+tOfFBwcrJtvvrnG91+3bp1uvvlmRUZGqkOHDq73Pbesdalv/6itfWq6bnBwsKusktS2bVsNGjSoQWMx6fQYtanHYvfff7/+8pe/6Morr9RDDz2kDz/8UJWVlbWe05CxcH0+m8AZTX/fPcDDDBo0SG+99Za8vb112WWXudatHj58WJKqrB83Z63R9vLy0ocffii73a6srCx98MEHmjVrlv785z9r9OjR1b7fmT/4S5cu1Q033FDl9S5durget2/fvl5lqC6P56Y15lrVpTf0OmerrKxUnz59lJGRUeW1du3auT1v06ZNtefXprKyUjNnznSt4T/b2RvPnW+dSNXX8fleqyZWq7XKngDnbnScnJyskSNH6qOPPtKnn36qW265RT/96U9rvZNVZWWlRowYoRdffLHKa4GBga7HXl5eTbIJIwB4MsYJdV+ruvTa+Pv7a+PGjVq/fr2ysrL08ssv6ze/+Y3Wrl2rq6++usbzRo8erZCQEKWnpysyMlJt2rTR9ddfX2Xj2XPHDhaLxVWv536vNva9zq238xkf1LcO6zO+6dOnj+Lj47VixQoNHDhQK1as0F133VXj7db37t2rW2+9VRMnTtScOXMUEhKi4uJiJSYmntem0fXpH7W1T32ve+baLWEsdvPNN2vv3r1as2aN1q1bpwkTJqhfv35au3atvLy8qr1uQ8bC9f1sAhKbLAPy8/NTdHS0a6PdMwICAtSlSxfl5OS4HZ+bm6sePXq4/vhaLBZdc801euyxx5Sbm6uEhAS98cYbkn78Ajt16pTr/M6dOysyMlI7duxQdHR0lX8N+Q91dHS02rZtW20er7jiioZVhKQrrrii2mud2by3vq6++mo5HA7X5nfnio+P165duxQQEFCl/OHh4fV+n+rq98z1t27dWm391jVj51xXXHGFtm7d6vbL4rfffquCgoIG1XGnTp3UpUuXGjcqrumcb775xi1t8+bNVY677LLLlJycrBUrVuj111/Xu+++6/qPR5s2bWqsn4iIiCr1c6nedQIAzhfjhB811ThBOh38GjZsmJ544glt2rRJl112mWvz5+rqpby8XNu2bdOsWbN08803q2/fvvL19VVpaWmDyyBJeXl5rrSKigq322o35r2uuOIKrV+/3i3t3OfnCgwMVERERJ3n1Xd8M2nSJL333nv697//rc2bN+uee+6p8b3tdruOHj2qJUuW6LrrrtPll19eZTZNTeOtczVl/zj3umVlZa7ZV5J0/PhxbdiwocH9+Oqrr27wWKy0tNSt7NWNxWw2m8aPH69XXnlFf/vb35STk+PKb01jsaYYCwPnIsAD1OK3v/2tXnjhBS1fvlw7d+7UK6+8opdeekmPPfaYpNODg/nz5+vzzz/X3r17tXbtWm3ZssU1hbRbt26yWq36+9//rtLSUh06dEiS9NRTTyktLU1PPvmkvvzyS+3YsUOZmZmaNm1ag/LXrl07/frXv9bjjz+uP//5z9q5c6eefvpprV692pXHhpb3gw8+0MKFC1VQUKCVK1dq3rx5evjhh6udSVOTG2+8UUOHDtW4ceO0evVq7d69W+vXr9drr70mSbr77rvVo0cPjRo1Sh9//LH27Nmjzz//XAsWLFBmZma93yckJET+/v76+OOPtX//ftfdJp544gmtXr1aM2bMUH5+voqKivTRRx9pypQpOnr0aIPq5K677lJoaKjGjRunzZs3a9OmTbrzzjsVERGhcePGNehac+fO1SuvvKL58+frq6++0tatW/Xiiy/WOC09MTFR27dv14svvqiioiItX75cK1eudDvmwQcf1N///ncVFRVp69atWrVqlWuKtXT6jhfr16/X3r17VVZWpsrKSj344IM6deqUxowZo88++0x79uzRP/7xD82ePdttwFtfDodD+fn5ys/Pl3T618D8/Hzt3bu3wdcCAE/COOH8xgmrV6/W4sWLtWnTJu3du1eZmZnat2+fq1569OghSfrf//1fHThwQEeOHFFQUJBCQ0O1fPlyFRQU6J///KfGjx/f4NuwR0dH67bbbtMDDzygTz/9VNu2bdPUqVP13XffuY5pzHvNmDFD//znPzV79mwVFBQoIyNDqampdZ738MMPa+nSpXr77be1c+dOpaamKisry+2Y+o5vxo8fr0OHDmny5Mnq37+/rrrqqhrfNyYmRhaLRampqdq9e7cyMzP1xBNPuB1TUz89V1P1j3PdeOONuuaaa3TXXXdp/fr1+vLLLzVp0iQdO3ZMv/zlLxt0rccff1wffvihpk+fri1btmjHjh168803tWPHjmqPv+GGG/TDDz/o8ccfV1FRkf785z8rPT3d7ZjZs2dr1apV2rFjh3bu3Kl3331X/v7+6tq1q6TT/XnTpk0qKipSWVmZTpw40WRj4TOOHDniGotVVFRo//79ys/Pd1vSiVaiOTb+AVqK6jaxPVtlZaV59tlnTffu3Y23t7fp0aOHaxNaY4z58ssvzS233GI6d+5s2rRpY7p27WoeeeQR10ZyxhjzzDPPmPDwcGO1Wk1CQoIrPSMjw1x77bXGz8/PdOjQwVx11VVuG8WdexeMmtIrKirMzJkzTXh4uPHx8TF9+vQx7777rts5asDGt2+++abp3bu38fHxMeHh4eaxxx5z2wCuPpsnGmPM4cOHzYMPPmjCwsKMj4+P6d69u1mwYIHr9bKyMnPfffe58h0eHm7GjBljNm/ebIz5cWO5c+8W4OXlZd544w3X87feesvVPt26dXOl5+bmmhEjRhh/f3/Trl0707t3b/PQQw+5ylJX259t+/bt5pZbbjHt27c37du3N6NGjTI7d+50O6a+dfzOO++Y/v37mzZt2hibzWZuvfVW113BqsvTk08+acLDw0379u3NnXfe6bqzyBn333+/iYmJMb6+vq7rffnll67X7Xa7GTBggPH19XXbEHDPnj3mrrvuMiEhIa6+e/fdd5tdu3YZY+rfzmeOlVTlX20bDgKAJ2CcUFVTjBNycnLMDTfcYEJCQkzbtm1NdHS0WbBggetOl8YY89BDD5lOnToZi8Xi+j5Zt26d6d+/v2nbtq3p1auX+ctf/lLlZgLVleX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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "figsize(16.0, 8)\n", + "sample_data=az.extract_dataset(trace3)\n", + "std_trace_after25000 = sample_data.sds.data[:,25000:]\n", + "std_trace_before25000 = sample_data.sds.data[:,:25000]\n", + "\n", + "_i = [1, 2, 3, 4]\n", + "for i in range(2):\n", + " plt.subplot(2, 2, _i[2 * i])\n", + " plt.title(\"Posterior of center of cluster %d\" % i)\n", + " plt.hist(center_trace_after_25000[i,:], color=colors[i], bins=30,\n", + " histtype=\"stepfilled\")\n", + "\n", + " plt.subplot(2, 2, _i[2 * i + 1])\n", + " plt.title(\"Posterior of standard deviation of cluster %d\" % i)\n", + " plt.hist(std_trace_after25000[i,:], color=colors[i], bins=30,\n", + " histtype=\"stepfilled\")\n", + " # plt.autoscale(tight=True)\n", + "\n", + "plt.tight_layout()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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KCgrSI488og4dOihnzpwaNGiQRo4cKUl64okn9Mgjj/xr35dffhm3rNp/TZ48Wd98881Vv0Zr7VU/Fldm0mWU90w/+W8+L3/Yq7HDBaw4AgAAAABAsmOnT5K2bJDp0EMmPLfrHFyDhLoOmCNHDk2YMIHrgLhmxguS1+FZ+QOelT/qDXmvDJHJlNV1FgD8I7ZCAOKB/WKitG+XvEeelkmb3nUO/qNFixZxbyqKFy+uDh066JlnnpEkZcmSRZI0YMAA1alTRxERETp58qROnjwp3/d18eJFnTx58pJv2FOlSqUCBQooKChIixcv1oYNG/T444/rt99+08CBAzVlyhRNmTJFb7/9tjZs2PCvfSVKlFCpUqUu+bj11luv6TX++eefca8F189kuVXeM32lqCj5g1+RPcb0NwAAAAAAyYm/8ifZedNlajeUd3tN1zm4Rgl1HfD333/nOiCum0mdRt6TL0tRF+WPHCAbGek6CQD+EYMFwE1m1yyXXfiVTJ27ZUpc+/5aiD9BQUF6/fXX9dtvv2nBggVau3atypYtK0lxe6Ft27ZN33zzjYoVKxb3sX//fn344YcqVqyY/vzzz8uOGx0drT59+ujll19WqlSptGjRIlWpUkURERGKiIhQ1apVtXz58nh/fdHR0Vq6dOl17euGy5nwPPK69ZHOnpY/pJfsqeOukwAAAAAAQAKwe3fKThguRRSVeaC96xxch4S6Drhs2TKuA+KGmOy55HXoIe3ZLvvRu6xEASBRYysE4CayRw7K/3C4lLuATLN2rnPwNzJkyKAMGTJIkiZOnKjy5csrIiJCkjRo0CCdPXv2ksd36dJFlSpVUtu2bZU5c+bLjjdp0iSlT59eTZo0ibvtwoULcf/9/PnzCfIN4dChQ3XgwAG1adMm3p8ruTB5C8p7+lX5w16VP6S3vOcGyKRO6zoLAAAAAADEE3v2jPz3BkipUsvr9LxMcIjrJNwArgMiKTClKso0aSU742Mpdz6Zu5q6TgKAK2KwALhJbHSU/LEDJWvldeopE8KbjsRm9erVWrlypYoVK6YzZ85oxowZWrRokaZPnx73mFKlSl32eaGhocqRI4eqVKly2X0nTpzQkCFD9Mknn8TdVr16db300kv69NNPJUlLlizRiy++eFNfy7Zt25QpUyZFRUVp9+7dmjVrlr7//nv16NFDlStXvqnPldyZgsXkPfGy/Hf6yx/WR173/jKpwlxnAQAAAACAm8z6MfLfHywdOxL7ywUZMrlOwnVKqOuAlSpV0uuvv851QNww07C5tGeH7BcTZcPzsBoygESJwQLgJrFffiTt2Czv8edlbsnuOgdXEBISolmzZmnw4MHyPE8VK1bUjBkzVLRo0es+5uDBg3XnnXeqZMmScbeVLl1aL7/8st566y1JUq9evVS8ePEb7v+r/v37S5JSpkypW265ReXKldOXX36p22+//aY+D2KZYqXlPf68/FFvyB/RT163PjKhKV1nAQAAAACAm8jOmiKtXy3TqrNMgSKuc3ADEuo6YIkSJbgOiJvCGCO16yp7cL/8sYPkvTRIJlu46ywAuISxV7kuz/79+6/6oFmyZNGRI0euOwq4ES7OP7vmZ/kjX5ep3VBey8cT9LmR+PBvYODyV/4kO26QVKSkvKd6yYSkcJ10RZyDcInzD65xDgauHDlyuE4AEsS1XH8B/oqvgUgu4utct78ul//eAJlqd8i0fTL2h3yAQ/y7njzZIwflv95DSpNO3osDZcJSu06KV5znSC6S0rn+T9dfvATsAAKSPXpI/ofDpdwFZJo/6joHQDzyKlSTeeQpaeNa+WPelo2Odp0EAAAAAABukN27Q/4HQ6W8BWVadmKoAIAzJsut8h5/QTr8p/z3B8v6Ma6TACAOgwXADbDRUfLHvC1ZX16nnon2t5cB3DxelboyLR+X1q6QHT+Ub+4BAAAAAEjC7Ilj8t/pL6VKJa/LS1zfA+CcKVxC5sGO0rpVsjM/cZ0DAHGCXQcASZmdPknasTl2qOCW7K5zACQQr3ZD+RcvyE6bIIWkkB5+SsZjVg8AAAAAgKTERkbKH/m6dOa0vOfflMmY2XUSAEiSTK0G0p7tst98Lj9nXnkVqrtOAgAGC4DrZdeukJ03Q6ZWQ5ny1VznAEhg3l1N5UdekP3qUylFqMRSiQAAAAAAJBnW9+WPHyrt2hq7UkHuAq6TACCOMUZq2Un2zz2yE4bL3houkzu/6ywAyRy/XglcB3v0sPzxw6Tc+WUeeNR1DgBHzD0Pydx5n+wP38h+MUHWWtdJAAAAAADgKtgZH0u/LJVp1k6m9O2ucwDgMiY4RF7nF6TU6eSPfF329EnXSQCSOQYLgGtko6Pljxso+TGxWyCw7xqQbBljZJo9IlOrgezc6bGrFwAAAAAAgETNX7JQds40mRp3ydzRxHUOAPwtky6jvC4vSqdPyh/9pmx0tOskAMkYgwXANbJfTJC2/SHT9imZW3K4zgHgmDFG5qFOMlXqyn41Rf7MT1i5AAAAAACARMquWy076V2paKnY9/NsawggkTN5C8q0fVLa/Lvs1Pdd5wBIxoJdBwBJiV29RHbBLJm698irUM11DoBEwnie9PCTkpHs7E8lP0a6tzUXJwAAAAAASETs1o3yR78hheeV1/lFmWAujwNIGrxKteTv3SE7d7r8HLnl1W7oOglAMsR3TsBVsgf2yZ8wQspfWKbZI65zACQyxguS2j4leUGy33wuxURL9z/CcAEAAAAAAImA3btT/jv9pAxZ5HV9VSZVmOskALgmpmlb2T/3yk4ZI5sug0y5Kq6TACQzbIUAXAUbGSl/9JtScLC8Tj1lgkNcJwFIhIznybTuIlOroezc6bKTRsrGxLjOAgAAAAAgWbOH9ssf1kdKkVJe934y6TK4TgKAa2a8IHmP9ZTyF5b//iDZTetcJwFIZhgsAP6FtVZ28nvS/t3y2veQyZTVdRKARMx4nkzLTjINH5D9cZ789wbIRka6zgIAAAAAIFmyB/fLH/iSFBMtr1tfmcy3uE4CgOtmQkPlPfmKlDW7/JGvy+7Z4ToJQDLCYAHwL+yP82SXfS9z94MyJcq6zgGQBBhj5N3XWqbl49K6VfKHvCJ7+pTrLAAAAAAAkhV7YJ/8QS9J0dHyerwmE57bdRIA3DCTJp28bn2klGHyh/eRPXzAdRKAZILBAuAf2F3bZKeMlYqVkbn7Qdc5AJIYr3ZDeY8/L+3eLn9AD9m9O10nAQAAAACQLNgDe+UPelmKiZH37OsyOfO6TgKAm8Zkyiqvax8p6qL8YX1kT590nQQgGWCwAPgb9uwZ+aPflNKml9ehh4wX5DoJQBJkylaR99wAKSpK/ps9ZX9Z5joJAAAAAICAZndslv/WC5If85+VCvK4TgKAm86E55b3VC/p+BH5Q3rLnj3tOglAgGOwALgC6/vyPxwmHT8ir1NPmbTpXCcBSMJM/sLyXhks5cgtf9Qb8mdOlvVjXGcBAAAAABBw7LrVsSsVpAqT98JbDBUACGgmopi8J16WDuyRP/RV2XNnXCcBCGAMFgBXYOdOl9aukGn+qEyBIq5zAAQAkyGzvOcGyFStKzt7qvzBvWSPH3WdBQAAAABAwPCXLpQ/8jUpW7i859+SuSWH6yQAiHemeBl5XV6S9u6M3Rbh/DnXSQACFIMFwP+wm9bJTp8kU76aTJ27XecACCAmJIW8R7rKtOsm7doqv9/Tsr+tdJ0FAAAAAECSZv0Y+dM+lP1wuFSohLxnB8ikz+g6CwASjClZXt7jPaXd2+QPY+UCAPGDwQLgL+zRQ/JHvyXdmkPm4SdljHGdBCAAeVXqyHtliJQhi/x3+sv/7APZ6CjXWQAAAAAAJDn23Bn577wmO3e6TK2G8p5+VSZVmOssAEhwpnQleY/1lHZtkz/4FdnTp1wnAQgwDBYA/2EjI+W/N0CKiZb3xMsyKXkDAiD+mGw55b00UKZ2I9n5M+W/0VN2/27XWQAAAAAAJBl23y75bzwnbVwj06aLvFaPywQHu84CAGdM2crynnhZ+nOv/IEvyp445joJQABhsACQZK2V/ehdac8OeR16yGQLd50EIBkwISnktewUuwfascPy+z8jf/5MWd93nQYAAAAAQKJlrZX/4zz5r/eQzp2V172/vBr1XWcBQKJgSpaT1/XV2OuNb78ge+hP10kAAgSDBYAkO3+G7IpFMk1aydxWwXUOgGTGlKkkr+87UvEysp99IH9IL9mjh11nAQAAAACQ6Pjnz8q+PyT2l4Qiisp7dbhMoRKuswAgUTGFS8p7pp907qz8N3vK7tjiOglAAGCwAMme3bBGdtpEqVwVmYbNXecASKZMuoyx27A8/JS0c6v8vk/JX7pQ1lrXaQAAAAAAJAp241od7dpGduWPMve2ltetr0y6jK6zACBRMgWKyHvhLSk0pfxBL8n+ttJ1EoAkjsECJGv28AH5YwdKOXLJe6SrjDGukwAkY8YYedXukPfqcClnXtkPh8sf9Ybs6ZOu0wAAAAAAcMZeOC9/8mj5Q3rFbiv4/JvyGj0g43F5GwD+icmWU94Lb0vZc8l/93X5383mF5kAXDe+80KyZc+dkf9Of0mS1+UlmZSpHBcBQCyTNZu8Z1+XadZOWrdK/qtPyq752XUWAAAAAAAJzm5aJ7/v07KL5sjc0USZh0yUKVDEdRYAJBkmfUZ5z74u3VZedspY2UkjZaOjXGcBSIIYLECyZGNi5I95Wzr0p7zOL8rckt11EgBcwnhB8u66T97LQ6T0meSPfF3+2IGyp467TgMAAAAAIN7ZUyfkT3xH/qCXJc+T99wb8h5oLxMa6joNAJIckzJV7C9YNmwu++M8+YNf4TojgGsW7DoASGjWWtlPx0ob1sg88rRM4RKukwDgb5mceeW9PEh2zhey33wm+/uvMg88KlOlLtu3AAAAAAACjo2Okv1utuzsqdLFSJk775Vp3FImNKXrNABI0oznydzXRn7OvLIThsvv/4y8js/KFOJnJACuDoMFSHbsd7Nlf5gjc1dTeVXruc4BgH9lgkNk7mkhW76q/I9Gyk4YIfvzInmtO8vcksN1HgAAAAAAN4Vdt0r+1A+kg/ukEuXkPdheJltO11kAEFC8CtVls+WUP/ot+YNekWnSUqZBMxmPRc4B/DMGC5Cs2N9Wyk79QCpdSaZpW9c5AHBNTPZc8p4bILt4ruyXE+W/+pTMXffFfuPPb24AAAAAAJIoe2Cv/M/GS+tWSbeGy3u6t0zJ8q6zACBgmVz55PUaIjvpPdkZH8tuXi+vXVeZDJldpwFIxBgsQLJh9+6QP3aQlCuvvA7dmb4DkCQZz5Op1UC2VEXZaRNkv/5Mdul3Ms3byZSvxvYIAAAAAIAkw547K/v1VNmFX0kpQmPf29a5WyY4xHUaAAQ8kzJM6tBDKlxCdur78vs8LdOqs7wK1VynAUikGCxAsmCPHpI/rK+UKkzek734zV4ASZ7JmFmmYw/ZmvXlfzpWduxA2R++kfdgRylLFtd5AAAAAAD8LevHyC5ZKDt9knTmlEzVejL3tZZJl9F1GgAkK8YYmRr1ZQuVkD9+mOzYt+WvWS7T8nGZ1Glc5wFIZBgsQMCzZ07JH/aqFBUpr+dbMhlZygdA4DCFist7ZYjsj/NlZ0yS/9ozOlmzvmz9+2Uy3+I6DwAAAACAS9itG+RPGSft3iZFFJXX9VWZPBGuswAgWTPZcsp7/i3ZOZ/Lzp4qu/l3eY88LVO8jOs0AIkIgwUIaDYyUv47/aUjh+Q9008mPLfrJAC46YwXJFOzvmz5arJzPteF776WfpovU6uRTMPmMmnTuU4EAAAAACRz9tgR2S8myK5YLGXILNOhh0zFGmzpBwCJhAkKkrm7hWzJ8vI/GCp/2KuxK8o0f5TVCwBIYrAAAczGxMgf+7a0Y4u8x5+XKVTcdRIAxCuTOo1Ms3bK2Kytjk4YKbvwK9kl82XuvE/mjiZsAwMAAAAASHD2YqTsvOmyc76QrJW5+0GZ+vfzHhUAEimTJyJ2hdTZn8rOnS67frW8lo/LlK3sOg2AYwwWICBZa2Unj5J+WynTqjNf8AAkK0FZbpX3yNOyd94rf/rHsjMny37/tUyjB2Sq3ykTksJ1IgAAAAAgwFlrpV+Wyv/8Q+noIZlyVWWaPSKT5VbXaQCAf2FShMo0fVi2XDX5E0fIH/WGVK6KvIc6yaTP6DoPgCMMFiDgWGtlv/xI9sd5Mnc/KK9WA9dJAOCEyZFbQU+8JLt1o/wvJ8pOGSs754vY7RGq3SETEuI6EQAAAAAQgOzeHfI/fV/atE7KmVfes6/LFC7pOgsAcI1MngLyXhosO3+G7Kwp8jf+JvNAe5kqddjKBkiGGCxAwDn7+QTZb7+QqdVApnFL1zkA4JyJKCrvuTekjWvlz/pE9pPRst9Ok2nQPHafNAYMAAAAAAA3gT13RnbmJ7LffyOlTh27kmj1O2WCglynAQCukwkOlmnQTLZMJfkT35WdMFx2xSJ5bZ5gFRogmWGwAAHFnzddZz//UKZyHZmHOjExBwD/YYyRipWWV7SUtHGN/FlTZCePkp3zuUzDB2Sq1pUJZsAAAAAAAHDtrO/LLl0o++VH0pnTMrXqyzRpJZM6res0AMBNYrLllPfcANnF38pOmyj/1Sdl7msjU6eRjMcAGZAcMFiAgOF//7Xs5x8qtGpdRbV9UsbzXCcBQKITO2BQRl7R0tLvv8auYPDxe7Jzpsk0bCZTuS4rGAAAAAAArprduUX+J2OkHZuliKLyunWSyZ3fdRYAIB4Yz5Op1VC2ZAX5k0fJTn1fduWP8h5+SiZHbtd5AOIZgwUICP6CWbJT35dKVVT6bq/q6IkTrpMAIFEzxkglysorXkZa/0vsgMGk92S/+lTmjntlatwlkzKV60wAAAAAQCJlT5+Snf6R7E/zpbTpZR59RqZSLVYQBYBkwGTOKu+pXrIrFst+OlZ+v24yjR6QaXA/q6ICAYzBAiR5/tzpstM+lMpWltfxWZlgTmsAuFrGGKlkOXklykob1sifM0328/Gy33wuU+dumbp3s3QlAAAAACCO9WNkF8+Vnf6xdOGcTL3GMvc8JJMqzHUaACABGWNkbq8pW6y07KfjZGd9Irt6ibw2T8gUKOI6D0A84CewSLKstbLffC4742OZ8tVk2ndnqAAArpMxRipeRkHFy8hu+yN2wOCrKbLzpsvUrC9zRxOZDJldZwIAAAAAHLLb/pD/yWhp93apcEl5D3WSCWfpawBIzkza9DIdn5WtWFP+5FHy3+wZuxpq07b8whIQYPgpLJIk6/uyn38ou2CmzO01Zdp1kwkKcp0FAAHBFCiioCdfkd27U3bOF7LzZ8l+N1umSl2Zu5rK3JLddSIAAAAAIAHZU8dlp02UXfadlCGzzGM9ZcpXZdsDAEAcU6qCvMLFZWdNkV34leyvy2WatZOpXJuvF0CA8FwHIDDt2LFDPXv2VL169ZQrVy41a9bsio+bMGGC2rRpo+LFiys8PFxLly694uM2b96sBx54QAUKFFDZMmU0sFUzRc+fIVP3ntj92xgqAICbYvHixerSpYtuv/125by9qoaekrzXR8tUrSe79Dv5r3TWxtdeUMv77lXZsmWVL18+VahQQc8++6wOHjz4t8cKDw/X4MGDHb0qAAAAAMBfXe21uxNHj+iZFs1U/LbSKtxnsNruOK1d7XvKq1At7odEU6dOVXh4+GUfH330UUK+JACAIzExMRo5cqTuu+8+lShXQSX7DlHrQ9Jam0L2w2HyB70su2eHJOnUqVN65plnVKxYMRUpUkQPP/ywjh079rfH/vbbbxUeHq4GDRok1MsB8A8YLEC82Lx5s7777jvlz59f+fPn/9vHTZs2TSdOnFCtWrX+9jEnTpxQixYtZIzR+FGj9PRtERq7ZKWGeFlkHuwg43EaA8DN8v3332vjxo2qVq2aUqVKJUkyWbPJa91F3hvjZO5solMbf1OuAzv0cumC+rjPS+rRvbt+/PFHtWnTRtHR0f94LAAAAACAe/927c76vvwVi9W5fl0tWrFKfe6oqncHvq0ToWFq0fZhnT59+rLP+eyzzzRr1qy4j4YNGybESwEAOHbhwgWNHDlSpUqV0ogRI/TOO+8oOE1a3T/jO62r2kjat0t+/27yxw/T4x3aa9myZRo4cKCGDh2qVatWqX379n973L59+ypr1qwJ/IoA/B22QkC8uOOOO3TXXXdJkjp27Kjjx49f8XGzZs2S53n6448/NGPGjCs+ZtKkSbpw4YLGvd5XqScOV7VU0Tr7UHMNmTZTT5w5o7Rp2aMHAG6WXr166dVXX5UkzZ0795L7TIZMMs3aqWKjB1VhyXzZBV9JP32lSlmzKVvLpmr19ght3LhRJUuW/NdjAQAAAADc+adrd3bjWvlfTNTqNWu0eP8RTX2rv6q2aidjjMrWqadKlSpp8uTJevzxxy85ZunSpZU6deoEfR0AAPdSpkyppUuXKkOGDHG3VatWTdWrV9fEtX9oyOtjZOd8rlWfT9GiJb/r864dVbl2LZmw1CpSpIiqVaumxYsXq0aNGpccd9SoUcqWLZvy5MmjTZs2JeyLAnBF/Ko34oV3lasIXM3jvv/+e9UsVVKpR/SRzp6R172/7n26hy5cuKBly5bdWCgA4BJX8++ySRUmr14Tea+Pkff481K6DMqwZL4kKfKbz2X37b7qYwEAAAAAEt7/vl+z1spuWq+Yoa/KH9JLOn1SG0pUVnBwsCo/9HDctgdZs2ZV0aJFtWDBAhfZAIBEKCgo6JKhAklKkSKFChUqpCNHjsikTiOvWTv9ULyKsqYJ0+1bfpH/8mPyv/lc5YoVVe7cufX9999f8vn79u3TqFGj1K9fvwR8JQD+DVf8kajZmBht/X298v+5TcqaXd7LQ2QKlVB4eLhSpUqlbdu2uU4EgGTLGqOokhW0o9ljeutimErluFWltq+T3+dJxbzZU/6SBbKRF1xnAgAAAAD+hvV96cQxafd2+YNekvZsl2n+qLzXRulijjwKDg5WUFDQJZ8TGhqqrVu3XnasKlWqKHfu3KpevbomTZqUUC8BAJAIRUZGat26dSpUqFDcbdv2/amIkqXkvTJUyltQdvokHXmsqSLShmnrHxsv+fy+ffvqnnvuiVsZFUDiwFYISLTsoT/lfzBEJ8+eVfqIivJ6vikTGhp3f/r06XXixAl3gQCQzLVp00Y//PCDJOm2227TpDnzFBwaIrvsO9kf58lOGCE79X2ZclWlmJjYC1YAAAAAAOds5AXZFYtl582Q3f6HZD2ZVo/LVKkrkyL2+lvevHl14cIFbdy4UUWLFpUknT9/Xn/88YfOnj0bd6xbb71Vzz33nMqUKaOYmBjNmDFDL7zwgs6fP6/HHnvMyesDALg1YsQInTx5Ug899FDcbSdPnlS6dOlk8hRQUNc+sru2KWThLKVfOl679++VP26wTO0GWnLgmBYtWqQff/zR4SsAcCUMFiDRsdbKLlkg++n7kudJXpC8SrUuGSr4r/8uwwYASHj9+/fXiRMntGPHDg0fPlytW7fWjBkzlPLO+2TvuFfaskH2p3myq36Szp+VnT9Dfo40MhWqSxHFZNgqAQAAAAASlN21TfbHubIrFkvnz0m58snkKyRZT16thpc8tlatWsqdO7eef/55DRkyRGnTptWAAQN0+vRphYSEXPK4WrVqxf25Tp06unjxooYPH64OHTqwTR4AJDMLFizQiBEj1Lt3b0VERFxy319/pmPyFFCGnq/LLlkn88cG2XUrFbX8B/X+eauevqe+sqZLm9DpAP4FgwVIVOzRw/IHvyJtWicVKiHv0WeU/oe6OnXq1GWPPXXqlNKnT++gEgAgSfnz55cklS1bVrfffrsqVaqkGTNmqEWLFrFvEgoVlylUXPZipDS/lEzmW2WXLpT9YY6UIZNMuaoyZSrFDhn8z9KaAAAAAICbw54/F7s6weK50u5tUkiK2Pdj1e+UChaTHntM5vjxyz4vRYoUeu+99/TEE0+oZs2akqSKFSuqWbNmWrJkyT8+Z6NGjfTVV19pz549ypMnT7y8LgBA4rNmzRp17txZrVu3VseOHS+5L3369Dp69Ohln3PqYpTS5S8ob+A4TXmtj05d3Kj7D23W8adaypSqoItHDikmJkYnT55UWFjYJcNtABIWgwVIFOzFyNj/nPiOFJ5Vpk0XmWp3ynieIiIiLtu3bd++fTp37pwKFCjgIhcA8D9y5sypDBkyaNeuXZfdZ1KESsHBMuWrynuii+xvK2VX/ii7aI7swq+ksNQyJcpLpSrIlCgrE5bGwSsAAAAAgMBhrZV2bJZdPFd25Y/SxUgpZ16Zhx6Tub2WTOqre99VpkwZLVmyRNu2bVNwcLDy5s2rtm3bqmzZslf1+aw2CgDJx7Zt29S2bVtVq1ZNr7322mX3R0REaMWKFVf8vLvuuksmNKW2mxT68+x5lVv4W+yd3/z/44sVK6bhfXqrWcdO8fYaAPwzBgvglI2Jid324KNxkiRTpKS8nr1l0mWMe0zt2rU1evRonTlzRmnSxL7p+eqrr5QyZUpVrlzZSTcA4FJbt27V8ePHlTt37n98nEmZSqZiDaliDdkL56QNa2TXrJBdt0pasUg2KCh2BYPSFWVuqyhzS/YEegUAAAAAkPTZs2dkl/8g++Ncad8uKTSlTMUaMjXukvIWvK4f9Btj4pay3r59u3766SdNmDDhHz/nm2++UaZMmZQzZ87reRkAgCTm4MGDatWqlfLkyaP33ntPQVdYnbR27doaNmyYVqxYoYoVK0qSVq9erV27dql27dqSpHbt2ql+/fpxn2OjozXy7Te1Z9dOvVE4uyJ+nKGY/RtkKteWub2mTPqMlz0PgPjDYAHixfnz57Vw4UJJ0oEDB3TmzBnNnj1bklS3bl2lDE0h/bJMv34wUnv37NWfqTNIkn7OlEvHFy9Rrly5VKpUKUlSmzZtNH78eHXo0EFdunTR7t27NXjwYD322GNKm5Y9dgDgZtq7d6/WrFkjSYqKitLmzZs1e/ZshYWFqU6dOpKkfv36KTg4WGXKlFG6dOm0ZcsWjRo1Snnz5lWTJk2u+lgmZZhUtopM2Sqyfoy0fbPs2hWxH1M/kJ36gXRLdplipWWKlpIK33bVv1UDAAAAAMmF9X1p07rYX975ZZkUdVHKExG7ImjFGrHvvf7Hv127S5UqlSRp6NChioiIUKZMmfTHH39o2LBhaty4sWrUqBF3rI4dO6p06dIqWrSoYmJiNGvWLM2aNUv9+/eX53kJ8DcAAHDp/Pnzat26tU6ePKnXXntNGzZsiLsvNDRUJUqUkCSVL19etWrVUteuXdWrVy95nqc333xTFStWjPu6ki9fPuXLl++S43/+5Zc6HmNV9cNPY1dBXfa97OfjZadNkIqUjP1aV7Yyq6ACCcBYa+3VPHD//v1XfdAsWbLoyJEj1x2FpG/Pnj2qVKnSFe9b+t5Q5Vz1g3Rgr7pvPqRpW/dc9pjmzZtr2LBhcX/evHmzXn75Zf3yyy9Kly6dHnroIfXo0eOKU2+cf3CNcxCu3cg5OHXqVHXv3v2y23PmzKmff/5ZkjRz5kyNHz9eW7du1YULFxQeHq66devqqaeeUqZMma7pWH/HHvozdsuEDWukzeulyAuS8aQ8BWSKloodNMhXSCZlqut6nYg//BsI1zgHA1eOHDlcJwAJ4lquvwB/xdfA5McePiC79DvZZd9JRw/FbjNXsYZM9Ttlcv/z9qH/dO1u+fLlypUrlySpd+/e+vrrr3Xs2DHlyJFDLVu2VKdOnRQc/P+/r/bGG2/om2++ifv3q2DBgurQoYOaNWt2k17ppTjXkVxwriOp+KevKf97HfDkyZPq06ePvv32W/m+r0aNGumVV1655Jri/+rWrZs2bdqkOXPmxN1m/9wj+/Mi2RWLpcMHpOBgqUT52K+Dt1WQCQ29eS8QuAmS0r/p/3T9hcECJAgbFSW7dKHst19IRw7G7unWsLlMuSoy3uXDATeC8w+ucQ7CtUA7B210lLRji+zGNbIb10rbN0m+HztokDOPTIEiUoEiMvmLSFmzsYenY4F2/iHp4RwMXAwWILlgsADXi6+ByYONvCC7eonskoWxQ9jGSEVLy1StK1OmkkxICteJ8Y5zHckF5zqSgxs9z6210s4tsisWy678STp5TApNJVPmdpmKNaWipWSCWbwd7iWlf9P/6foL/29CvLKRkbI/zpWdO106cVTKV0hei47SbRX4wQ8A4KqY4BCpYDGZgsWkxi1lz5+Ttm6U3f6H7LY/ZJf9IP0wR1aS0qaP3Tc0V36ZXHmlXPljhw1YfhMAAABAEmWjo6U/fpNd9aPsqqVS5PnYbePubR27x3SmrK4TAQBwwhgTu6ppvkKyzdtJm9bHbpeweons8h+kNOlkyleVqVBDiijKNULgBjFYgHhhz5+T/eEb2fkzpdMnpUIl5LV7OnaCmoECAMANMKnCpJLlZEqWkyRZP0bav1t22yZp2x+yu7fJ/v5L7D6jkhSaMnalnFz5pFz5ZHLmk8LzsiQaAAAAgETLRkdLm9bJrvpJ9tfl0tnTUspUMuWryFSpFzt8zTU2AADiGC8odoWCoqVkH+ok/f5L7EoGSxfK/jBHypRFpnx1mbKVY4cRGDIArhmDBbip7JlTsgu/kv1utnTurFSirLyGD8T+likAAPHAeEFSzv8MDNSsL0myURel/Xtk92yX9uyQ3btD9udF/7+ygfGkW3P8/7BBrnxSrvwy6TM6fS0AAAAAki97/Kjs779I63+R3bgm9tpaaCqZ0hVlyleTipdJFlsdAABwo0xIiFT6dpnSt8teOC+7dkXskMHCWbLzpkvpM8qUul2mTCWpSMnYFVMB/CsGC3BT2CMHZRfMkv1pvhR5QSpTSV7D5jJ5C7pOAwAkQyYkhZSngEyeAnG3WWulIwelvTtld2+PHTbYvkla+WPssIEkpcvwn0GD/P//n7dmjx1eAAAAAICbyJ48HrvN29aNsYME+3bF3pE+k0yZSjK3VZRKlJVJwWprAABcL5MylcztNaXba8qeOyO7brXsr8tkf/5BdvG3UqowmRLlpFIVZYqVlkmb3nUykGgxWIAbYndukZ03Q3bVEskzMhWqy9S/XyY8j+s0AAAuYYyRsmaTsmaLnUb+D3v2jLR3R+zqBrv/s7rB/JlSTHTswEGKFLFbJ+TKF7syQnhuKUdumTTpnL0WAAAAAEmLjbzwnyHnbdL2TbJbN8YOPktSSAqpQBGZZo/IFC8rhedhmwMAAOKBCUvz/0MGURelDWtl1yyXXbvi/3/5KHd+maKlZYqXkSKKsloQ8BcMFuCa2aio2GmuRXOkzb/HTnPd2USmzj0ymbK4zgMA4JqY1GmkwiVlCpeMu81GR0l/7v3/rRT27JBd9ZO0eO6lqxvkyC2T4z+DBtlySllvlTJkZo82AAAAIBmzZ05J+3bJ7tom7d4mu3u7dGCfZP3YB6TPKBUoKlO7kf6vvTuPjbO+8zj+eTyH7yu+EjsxORxDNhfHpqTAQlqxsFtWqAcpiKJWkDSiJC0VCKoWJWkSaCluoaXqRbtKSkqWglTUBqHuKkG0JOWQYFGWuiGQkDhx4vhIfB/jGf/2j2fmmcMzYyc+xh6/X9Kj55nf7zePn4m+eY7f831+j1WzxL6BwRDMAABMKsvjlVaukrVylcxQQDpxVKb+PXva9yeZ//6D5HZLl9TIWrRE1qLLpJrLZBXwKlXMXCQWYNTMmVMyr/+3zBuvSt1dUkm5rLX3yPqXm2Rl56R68wAAGDeW2xN8FcICp8wYI51rlU43yJxucObm4D5poD+ccOB2S6UVUulsWWXBeflsqXS2VFohKys7Jb8JAAAAwPgyvT3Drg90ukHqOB9uVFRiv6btn6+VVb1Iql4kFZcwIgEAAFOIleGSFtTKWlAr3fJFmf4+6cj7Mkfelzl6WObVvTL/85LduKTcPrZXLwoe2xfKKiTZADMDiQVIynScl3n3DZm3/yp9VC+5XNLKq5Vx/c3SkpU8kQkAmDEsy5JKyqSSMlnLr3LKzdCQdK5Faj4t03JWammSaW2SWs7KHD0s9fWEkw4kKb9QKp8jq7Qi/GqGUnuuwmKOrQAAAMAUYwb6pdMnZU6fCCcQNDZI51vDjbyZ9khmS6+051WX2DcaCopStt0AAODiWFnZ0opVslaskmSP5K0TH8kc/Yd0/COZhqP2vbPQFwpn2cf9ufPtVxpVXSLNrmJEIqQdEgsQxRgjNTXKvP+O/U6ZI+9Lxkhz5sn6wldkXfNphnkBACCClZERHKGgQvGeOTI9XXayQUuT/Q7V4LL56B/S269LZih8EeLxhtdVPseel82xX7FQWiHLmzmJvwwAAACYWYxvQGo65SQOOCMQtJ4NN3J7pDlzZV26LOrVaCopJ0kYAIA0ZXk8Us0S+xVGQaa3Rzr1sUzDUenEMZmTx2Tq/1cKBOy+PpdLmj3XTjKorHaSDlRSzshFmLZILJjhjDFSW7PMR/XSh/Uy9e+FL5bmzJN1y+2y/vk6WVXVKd3OC/WjH/1ITz75ZKo3AwCmrAceeEAPPvhgqjdjRrBy86XcfFnzFw+rM/5Bqa0lYpSDJplmOwHBHPm7NNAXPdpB0Sw7waBwlv1e1uBkFQSX8wulnDzJ6+UCBQAAAIjDGGO/qqDplExTY3B+SmpqtEciM8EzcJfbftJwQa107Y12AkHVJVJZhT1cMsaEvjsAmJ7oUwyzcnKl2mWyapc5ZcY/aD+823hCajwuc+pE8OGiv4b7+LKyw6MbVc2XNfcSqfISWfkFKfkdwIUgsWCGMV0d0qnjMqeOSx8fkfmwXmpvsyuzc6XapbJu/rysZVfaQzQDAIAJY7k9UkWlVFE5bLQDY4zU3Rke7aClSWptkmltlmk8LtW/J/X12G1jV+x228f1nDwpJ1fKyZXlLIfLlJ1nXwRl50SXeRimDQAAANOX6e2RzjVLrc0ybS3SuWaZNvuzmk9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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "az.plot_posterior(trace3,var_names=[\"sds\"],figsize=figsize(16.0, 4))\n", + "az.plot_posterior(trace3,var_names=[\"centers\"],figsize=figsize(16.0, 4))\n", + "plt.plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The MCMC algorithm has proposed that the most likely centers of the two clusters are near 120 and 200 respectively. Similar inference can be applied to the standard deviation. \n", + "\n", + "We are also given the posterior distributions for the labels of the data point, which is present in `trace[\"assignment\"]`. Below is a visualization of this. The y-axis represents a subsample of the posterior labels for each data point. The x-axis are the sorted values of the data points. A red square is an assignment to cluster 1, and a blue square is an assignment to cluster 0. " + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib as mpl\n", + "figsize(16, 4)\n", + "plt.cmap = mpl.colors.ListedColormap(colors)\n", + "plt.imshow(sample_data.assignment.data.T[::800, np.argsort(data)],\n", + " cmap=plt.cmap, aspect=.4, alpha=.9)\n", + "plt.xticks(np.arange(0, data.shape[0], 40),\n", + " [\"%.2f\" % s for s in np.sort(data)[::40]])\n", + "plt.ylabel(\"posterior sample\")\n", + "plt.xlabel(\"value of $i$th data point\")\n", + "plt.title(\"Posterior labels of data points\");\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Looking at the above plot, it appears that the most uncertainty is between 150 and 170. The above plot slightly misrepresents things, as the x-axis is not a true scale (it displays the value of the $i$th sorted data point.) A more clear diagram is below, where we have estimated the *frequency* of each data point belonging to the labels 0 and 1. " + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "cmap = mpl.colors.LinearSegmentedColormap.from_list(\"BMH\", colors)\n", + "assign_trace = sample_data.assignment.data.T\n", + "plt.scatter(data, 1 - assign_trace.mean(axis=0), cmap=cmap,\n", + " c=assign_trace.mean(axis=0), s=50)\n", + "plt.ylim(-0.05, 1.05)\n", + "plt.xlim(35, 300)\n", + "plt.title(\"Probability of data point belonging to cluster 0\")\n", + "plt.ylabel(\"probability\")\n", + "plt.xlabel(\"value of data point\");\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Even though we modeled the clusters using Normal distributions, we didn't get just a single Normal distribution that *best* fits the data (whatever our definition of best is), but a distribution of values for the Normal's parameters. How can we choose just a single pair of values for the mean and variance and determine a *sorta-best-fit* gaussian? \n", + "\n", + "One quick and dirty way (which has nice theoretical properties we will see in Chapter 5), is to use the *mean* of the posterior distributions. Below we overlay the Normal density functions, using the mean of the posterior distributions as the chosen parameters, with our observed data:" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "norm = stats.norm\n", + "x = np.linspace(20, 300, 500)\n", + "posterior_center_means = center_trace_after_25000.T.mean(axis=0)\n", + "posterior_std_means = std_trace_after25000.mean(axis=0)\n", + "posterior_p_mean = sample_data.p.data.mean()\n", + "\n", + "plt.hist(data, bins=20, histtype=\"step\", density=True, color=\"k\",\n", + " lw=2, label=\"histogram of data\")\n", + "y = posterior_p_mean * norm.pdf(x, loc=posterior_center_means[0],\n", + " scale=posterior_std_means[0])\n", + "plt.plot(x, y, label=\"Cluster 0 (using posterior-mean parameters)\", lw=3)\n", + "plt.fill_between(x, y, color=colors[1], alpha=0.3)\n", + "\n", + "y = (1 - posterior_p_mean) * norm.pdf(x, loc=posterior_center_means[1],\n", + " scale=posterior_std_means[1])\n", + "plt.plot(x, y, label=\"Cluster 1 (using posterior-mean parameters)\", lw=3)\n", + "plt.fill_between(x, y, color=colors[0], alpha=0.3)\n", + "\n", + "plt.legend(loc=\"upper left\")\n", + "plt.title(\"Visualizing Clusters using posterior-mean parameters\");\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Important: Don't mix posterior samples\n", + "\n", + "In the above example, a possible (though less likely) scenario is that cluster 0 has a very large standard deviation, and cluster 1 has a small standard deviation. This would still satisfy the evidence, albeit less so than our original inference. Alternatively, it would be incredibly unlikely for *both* distributions to have a small standard deviation, as the data does not support this hypothesis at all. Thus the two standard deviations are *dependent* on each other: if one is small, the other must be large. In fact, *all* the unknowns are related in a similar manner. For example, if a standard deviation is large, the mean has a wider possible space of realizations. Conversely, a small standard deviation restricts the mean to a small area. \n", + "\n", + "During MCMC, we are returned vectors representing samples from the unknown posteriors. Elements of different vectors cannot be used together, as this would break the above logic: perhaps a sample has returned that cluster 1 has a small standard deviation, hence all the other variables in that sample would incorporate that and be adjusted accordingly. It is easy to avoid this problem though, just make sure you are indexing traces correctly. \n", + "\n", + "Another small example to illustrate the point. Suppose two variables, $x$ and $y$, are related by $x+y=10$. We model $x$ as a Normal random variable with mean 4 and explore 500 samples. " + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Sequential sampling (1 chains in 1 job)\n", + "Metropolis: [x]\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + "
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\n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Sampling 1 chain for 1_000 tune and 10_000 draw iterations (1_000 + 10_000 draws total) took 1 seconds.\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import pymc as pm\n", + "\n", + "with pm.Model() as model:\n", + " x = pm.Normal(\"x\", mu=4, tau=10)\n", + " y = pm.Deterministic(\"y\", 10 - x)\n", + "\n", + " trace_2 = pm.sample(10000, pm.Metropolis(),chains=1)\n", + "\n", + "plt.plot(trace_2.posterior.x.T)\n", + "plt.plot(trace_2.posterior.y.T)\n", + "plt.title(\"Displaying (extreme) case of dependence between unknowns\");\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As you can see, the two variables are not unrelated, and it would be wrong to add the $i$th sample of $x$ to the $j$th sample of $y$, unless $i = j$." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Returning to Clustering: Prediction\n", + "The above clustering can be generalized to $k$ clusters. Choosing $k=2$ allowed us to visualize the MCMC better, and examine some very interesting plots. \n", + "\n", + "What about prediction? Suppose we observe a new data point, say $x = 175$, and we wish to label it to a cluster. It is foolish to simply assign it to the *closer* cluster center, as this ignores the standard deviation of the clusters, and we have seen from the plots above that this consideration is very important. More formally: we are interested in the *probability* (as we cannot be certain about labels) of assigning $x=175$ to cluster 1. Denote the assignment of $x$ as $L_x$, which is equal to 0 or 1, and we are interested in $P(L_x = 1 \\;|\\; x = 175 )$. \n", + "\n", + "A naive method to compute this is to re-run the above MCMC with the additional data point appended. The disadvantage with this method is that it will be slow to infer for each novel data point. Alternatively, we can try a *less precise*, but much quicker method. \n", + "\n", + "We will use Bayes Theorem for this. If you recall, Bayes Theorem looks like:\n", + "\n", + "$$ P( A | X ) = \\frac{ P( X | A )P(A) }{P(X) }$$\n", + "\n", + "In our case, $A$ represents $L_x = 1$ and $X$ is the evidence we have: we observe that $x = 175$. For a particular sample set of parameters for our posterior distribution, $( \\mu_0, \\sigma_0, \\mu_1, \\sigma_1, p)$, we are interested in asking \"Is the probability that $x$ is in cluster 1 *greater* than the probability it is in cluster 0?\", where the probability is dependent on the chosen parameters.\n", + "\n", + "\\begin{align}\n", + "& P(L_x = 1| x = 175 ) \\gt P(L_x = 0| x = 175 ) \\\\\\\\[5pt]\n", + "& \\frac{ P( x=175 | L_x = 1 )P( L_x = 1 ) }{P(x = 175) } \\gt \\frac{ P( x=175 | L_x = 0 )P( L_x = 0 )}{P(x = 175) }\n", + "\\end{align}\n", + "\n", + "As the denominators are equal, they can be ignored (and good riddance, because computing the quantity $P(x = 175)$ can be difficult). \n", + "\n", + "$$ P( x=175 | L_x = 1 )P( L_x = 1 ) \\gt P( x=175 | L_x = 0 )P( L_x = 0 ) $$\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Probability of belonging to cluster 1: 0.00612\n" + ] + } + ], + "source": [ + "norm_pdf = stats.norm.pdf\n", + "p_trace = trace3.posterior.p.data[:,25000:]\n", + "prev_p_trace = trace3.posterior.p.data[:,:25000]\n", + "#update center_trace and std_trace from trace3\n", + "center_trace = trace3.posterior.centers.data[:, 25000:,:]\n", + "std_trace = trace3.posterior.sds.data[:, 25000:,:]\n", + "x = 175\n", + "\n", + "v = p_trace[0,:] * norm_pdf(x, loc=center_trace[:, :,0], scale=std_trace[:,:,0]) > \\\n", + " (1 - p_trace) [0,:]* norm_pdf(x, loc=center_trace[:, :,1], scale=std_trace[:,:,1])\n", + "\n", + "print(\"Probability of belonging to cluster 1:\", v.mean())\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "Giving us a probability instead of a label is a very useful thing. Instead of the naive \n", + "\n", + " L = 1 if prob > 0.5 else 0\n", + "\n", + "we can optimize our guesses using a *loss function*, which the entire fifth chapter is devoted to. \n", + "\n", + "\n", + "### Using `MAP` to improve convergence\n", + "\n", + "If you ran the above example yourself, you may have noticed that our results were not consistent: perhaps your cluster division was more scattered, or perhaps less scattered. The problem is that our traces are a function of the *starting values* of the MCMC algorithm. \n", + "\n", + "It can be mathematically shown that letting the MCMC run long enough, by performing many steps, the algorithm *should forget its initial position*. In fact, this is what it means to say the MCMC converged (in practice though we can never achieve total convergence). Hence if we observe different posterior analysis, it is likely because our MCMC has not fully converged yet, and we should not use samples from it yet (we should use a larger burn-in period ).\n", + "\n", + "In fact, poor starting values can prevent any convergence, or significantly slow it down. Ideally, we would like to have the chain start at the *peak* of our landscape, as this is exactly where the posterior distributions exist. Hence, if we started at the \"peak\", we could avoid a lengthy burn-in period and incorrect inference. Generally, we call this \"peak\" the *maximum a posterior* or, more simply, the *MAP*.\n", + "\n", + "Of course, we do not know where the MAP is. PyMC provides a function that will approximate, if not find, the MAP location. In the PyMC main namespace is the `find_MAP` function. If you call this function within the context of `Model()`, it will calculate the MAP which you can then pass to `pm.sample()` as a `start` parameter.\n", + "\n", + " start = pm.find_MAP()\n", + " trace = pm.sample(2000, step=pm.Metropolis, start=start)\n", + "\n", + "The `find_MAP()` function has the flexibility of allowing the user to choose which optimization algorithm to use (after all, this is a optimization problem: we are looking for the values that maximize our landscape), as not all optimization algorithms are created equal. The default optimization algorithm in function call is the Broyden-Fletcher-Goldfarb-Shanno ([BFGS](https://en.wikipedia.org/wiki/Broyden-Fletcher-Goldfarb-Shanno_algorithm)) algorithm to find the maximum of the log-posterior. As an alternative, you can use other optimization algorithms from the `scipy.optimize` module. For example, you can use Powell's Method, a favourite of PyMC blogger [Abraham Flaxman](http://healthyalgorithms.com/) [1], by calling `find_MAP(fmin=scipy.optimize.fmin_powell)`. The default works well enough, but if convergence is slow or not guaranteed, feel free to experiment with Powell's method or the other algorithms available. \n", + "\n", + "The MAP can also be used as a solution to the inference problem, as mathematically it is the *most likely* value for the unknowns. But as mentioned earlier in this chapter, this location ignores the uncertainty and doesn't return a distribution.\n", + "\n", + "#### Speaking of the burn-in period\n", + "\n", + "It is still a good idea to decide on a burn-in period, even if we are using `find_MAP()` prior to sampling, just to be safe. We can no longer automatically discard sample with a `burn` parameter in the `sample()` function as we could in PyMC2, in new PyMC (v4), it's change to the new paramter `tune` and `discard_tuned_samples`. It provides number of iterations to tune, defaults to 1000. Samplers adjust the step sizes, scalings or similar during tuning. Tuning samples will be drawn in addition to the number specified in the `draws` argument, and will be discarded unless `discard_tuned_samples` is set to `False`. The old `start` parameter also changed to `initvals`, and can now do more. The new code would look something like:\n", + "\n", + " with pm.Model() as model:\n", + " start = pm.find_MAP()\n", + " \n", + " step = pm.Metropolis()\n", + " trace = pm.sample(100000, step=step, initvals=start, tune=50000)\n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Diagnosing Convergence\n", + "\n", + "### Autocorrelation\n", + "\n", + "Autocorrelation is a measure of how related a series of numbers is with itself. A measurement of 1.0 is perfect positive autocorrelation, 0 no autocorrelation, and -1 is perfect negative correlation. If you are familiar with standard *correlation*, then autocorrelation is just how correlated a series, $x_t$, at time $t$ is with the series at time $t-k$:\n", + "\n", + "$$R(k) = Corr( x_t, x_{t-k} ) $$\n", + "\n", + "For example, consider the two series:\n", + "\n", + "$$x_t \\sim \\text{Normal}(0,1), \\;\\; x_0 = 0$$\n", + "$$y_t \\sim \\text{Normal}(y_{t-1}, 1 ), \\;\\; y_0 = 0$$\n", + "\n", + "which have example paths like:" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "figsize(12.5, 4)\n", + "\n", + "x_t = np.random.normal(0, 1, 200)\n", + "x_t[0] = 0\n", + "y_t = np.zeros(200)\n", + "for i in range(1, 200):\n", + " y_t[i] = np.random.normal(y_t[i - 1], 1)\n", + "\n", + "plt.plot(y_t, label=\"$y_t$\", lw=3)\n", + "plt.plot(x_t, label=\"$x_t$\", lw=3)\n", + "plt.xlabel(\"time, $t$\")\n", + "plt.legend();\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "One way to think of autocorrelation is \"If I know the position of the series at time $s$, can it help me know where I am at time $t$?\" In the series $x_t$, the answer is No. By construction, $x_t$ are random variables. If I told you that $x_2 = 0.5$, could you give me a better guess about $x_3$? No.\n", + "\n", + "On the other hand, $y_t$ is autocorrelated. By construction, if I knew that $y_2 = 10$, I can be very confident that $y_3$ will not be very far from 10. Similarly, I can even make a (less confident guess) about $y_4$: it will probably not be near 0 or 20, but a value of 5 is not too unlikely. I can make a similar argument about $y_5$, but again, I am less confident. Taking this to it's logical conclusion, we must concede that as $k$, the lag between time points, increases the autocorrelation decreases. We can visualize this:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def autocorr(x):\n", + " # from http://tinyurl.com/afz57c4\n", + " result = np.correlate(x, x, mode='full')\n", + " result = result / np.max(result)\n", + " return result[result.size // 2:]\n", + "\n", + "colors = [\"#348ABD\", \"#A60628\", \"#7A68A6\"]\n", + "\n", + "x = np.arange(1, 200)\n", + "plt.bar(x, autocorr(y_t)[1:], width=1, label=\"$y_t$\",\n", + " edgecolor=colors[0], color=colors[0])\n", + "plt.bar(x, autocorr(x_t)[1:], width=1, label=\"$x_t$\",\n", + " color=colors[1], edgecolor=colors[1])\n", + "\n", + "plt.legend(title=\"Autocorrelation\")\n", + "plt.ylabel(\"measured correlation \\nbetween $y_t$ and $y_{t-k}$.\")\n", + "plt.xlabel(\"k (lag)\")\n", + "plt.title(\"Autocorrelation plot of $y_t$ and $x_t$ for differing $k$ lags.\");\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notice that as $k$ increases, the autocorrelation of $y_t$ decreases from a very high point. Compare with the autocorrelation of $x_t$ which looks like noise (which it really is), hence we can conclude no autocorrelation exists in this series. \n", + "\n", + "\n", + "#### How does this relate to MCMC convergence?\n", + "\n", + "By the nature of the MCMC algorithm, we will always be returned samples that exhibit autocorrelation (this is because of the step `from your current position, move to a position near you`).\n", + "\n", + "A chain that is not exploring the space well will exhibit very high autocorrelation. Visually, if the trace seems to meander like a river, and not settle down, the chain will have high autocorrelation.\n", + "\n", + "This does not imply that a converged MCMC has low autocorrelation. Hence low autocorrelation is not necessary for convergence, but it is sufficient. PyMC has a firenew plotting library Arviz. Plots, a general purpose library for “exploratory analysis of Bayesian models”. And we've already seen some arviz plot examples above." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Thinning\n", + "\n", + "Another issue can arise if there is high-autocorrelation between posterior samples. Many post-processing algorithms require samples to be *independent* of each other. This can be solved, or at least reduced, by only returning to the user every $n$th sample, thus removing some autocorrelation. Below we perform an autocorrelation plot for $y_t$ with differing levels of thinning:" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "max_x = 200 // 3 + 1\n", + "x = np.arange(1, max_x)\n", + "\n", + "plt.bar(x, autocorr(y_t)[1:max_x], edgecolor=colors[0],\n", + " label=\"no thinning\", color=colors[0], width=1)\n", + "plt.bar(x, autocorr(y_t[::2])[1:max_x], edgecolor=colors[1],\n", + " label=\"keeping every 2nd sample\", color=colors[1], width=1)\n", + "plt.bar(x, autocorr(y_t[::3])[1:max_x], width=1, edgecolor=colors[2],\n", + " label=\"keeping every 3rd sample\", color=colors[2])\n", + "\n", + "plt.autoscale(tight=True)\n", + "plt.legend(title=\"Autocorrelation plot for $y_t$\", loc=\"lower left\")\n", + "plt.ylabel(\"measured correlation \\nbetween $y_t$ and $y_{t-k}$.\")\n", + "plt.xlabel(\"k (lag)\")\n", + "plt.title(\"Autocorrelation of $y_t$ (no thinning vs. thinning) \\\n", + "at differing $k$ lags.\");\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With more thinning, the autocorrelation drops quicker. There is a tradeoff though: higher thinning requires more MCMC iterations to achieve the same number of returned samples. For example, 10 000 samples unthinned is 100 000 with a thinning of 10 (though the latter has less autocorrelation). \n", + "\n", + "What is a good amount of thinning? The returned samples will always exhibit some autocorrelation, regardless of how much thinning is done. So long as the autocorrelation tends to zero, you are probably ok. Typically thinning of more than 10 is not necessary." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### `Arviz`\n", + "\n", + "It seems silly to have to manually create histograms, autocorrelation plots and trace plots each time we perform MCMC. The authors of PyMC have included a visualization tool for just this purpose. \n", + "\n", + "The `arviz` library contains a lot of different plotting functions that you might find useful. For each different plotting function contained therein, you simply pass a `trace` returned from sampling as well as a list, `varnames`, of the variables that you are interested in. This module can provide you with plots of autocorrelation and the posterior distributions of each variable and their traces, among others.\n", + "\n", + "Below we use the tool to plot the centers of the clusters." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "image/png": 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "az.plot_trace(data=trace3, var_names=[\"centers\"],figsize=(16,4))\n", + "az.plot_posterior(data=trace3,var_names=[\"centers\"],figsize=(16,4))\n", + "# az.plot_posterior(data=trace3[\"centers\"][:,1])\n", + "az.plot_autocorr(data=trace3, var_names=[\"centers\"],figsize=(16,4));" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The first plotting function gives us the posterior density of each unknown in the `centers` variable as well as the `trace` of each. `trace` plot is useful for inspecting that possible \"meandering\" property that is a result of non-convergence. The density plot gives us an idea of the shape of the distribution of each unknown, but it is better to look at each of them individually." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The second plotting function(s) provides us with a histogram of the samples with a few added features. The text overlay in the center shows us the posterior mean, which is a good summary of posterior distribution. The interval marked by the horizontal black line overlay represents the *95% credible interval*, sometimes called the *highest posterior density interval* and not to be confused with a *95% confidence interval*. We won't get into the latter, but the former can be interpreted as \"there is a 95% chance the parameter of interest lies in this interval\". When communicating your results to others, it is incredibly important to state this interval. One of our purposes for studying Bayesian methods is to have a clear understanding of our uncertainty in unknowns. Combined with the posterior mean, the 95% credible interval provides a reliable interval to communicate the likely location of the unknown (provided by the mean) *and* the uncertainty (represented by the width of the interval)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The last plots, titled `center_0` and `center_1` are the generated autocorrelation plots, similar to the ones displayed above." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Useful tips for MCMC\n", + "\n", + "Bayesian inference would be the *de facto* method if it weren't for MCMC's computational difficulties. In fact, MCMC is what turns most users off practical Bayesian inference. Below I present some good heuristics to help convergence and speed up the MCMC engine:\n", + "\n", + "### Intelligent starting values\n", + "\n", + "It would be great to start the MCMC algorithm off near the posterior distribution, so that it will take little time to start sampling correctly. We can aid the algorithm by telling where we *think* the posterior distribution will be by specifying the `testval` parameter in the `Stochastic` variable creation. In many cases we can produce a reasonable guess for the parameter. For example, if we have data from a Normal distribution, and we wish to estimate the $\\mu$ parameter, then a good starting value would be the *mean* of the data. \n", + "\n", + " mu = pm.Uniform( \"mu\", 0, 100, testval = data.mean() )\n", + "\n", + "For most parameters in models, there is a frequentist estimate of it. These estimates are a good starting value for our MCMC algorithms. Of course, this is not always possible for some variables, but including as many appropriate initial values is always a good idea. Even if your guesses are wrong, the MCMC will still converge to the proper distribution, so there is little to lose.\n", + "\n", + "This is what using `MAP` tries to do, by giving good initial values to the MCMC. So why bother specifying user-defined values? Well, even giving `MAP` good values will help it find the maximum a-posterior. \n", + "\n", + "Also important, *bad initial values* are a source of major bugs in PyMC3 and can hurt convergence.\n", + "\n", + "#### Priors\n", + "\n", + "If the priors are poorly chosen, the MCMC algorithm may not converge, or atleast have difficulty converging. Consider what may happen if the prior chosen does not even contain the true parameter: the prior assigns 0 probability to the unknown, hence the posterior will assign 0 probability as well. This can cause pathological results.\n", + "\n", + "For this reason, it is best to carefully choose the priors. Often, lack of covergence or evidence of samples crowding to boundaries implies something is wrong with the chosen priors (see *Folk Theorem of Statistical Computing* below). \n", + "\n", + "#### Covariance matrices and eliminating parameters\n", + "\n", + "### The Folk Theorem of Statistical Computing\n", + "\n", + "> *If you are having computational problems, probably your model is wrong.*\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Conclusion\n", + "\n", + "PyMC3 provides a very strong backend to performing Bayesian inference, mostly because it has abstracted the inner mechanics of MCMC from the user. Despite this, some care must be applied to ensure your inference is not being biased by the iterative nature of MCMC. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### References\n", + "\n", + "1. Flaxman, Abraham. \"Powell's Methods for Maximization in PyMC.\" Healthy Algorithms. N.p., 9 02 2012. Web. 28 Feb 2013. ." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.core.display import HTML\n", + "\n", + "\n", + "def css_styling():\n", + " styles = open(\"../styles/custom.css\", \"r\").read()\n", + " return HTML(styles)\n", + "css_styling()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.8" + }, + "vscode": { + "interpreter": { + "hash": "0a54084e6b208ee8d1ce3989ffc20924477a5f55f5a43e22e699a6741623861e" + } + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/Chapter3_MCMC/Ch3_IntroMCMC_TFP.ipynb b/Chapter3_MCMC/Ch3_IntroMCMC_TFP.ipynb new file mode 100644 index 00000000..8b0b5efa --- /dev/null +++ b/Chapter3_MCMC/Ch3_IntroMCMC_TFP.ipynb @@ -0,0 +1,2154 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "working Ch3_IntroMCMC_TFP.ipynb", + "version": "0.3.2", + "provenance": [], + "collapsed_sections": [] + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "liPOTvZyJJ64" + }, + "source": [ + "# Probabilistic Programming and Bayesian Methods for Hackers Chapter 3\n", + "\n", + "\n", + " \n", + " \n", + "
\n", + " Run in Google Colab\n", + " \n", + " View source on GitHub\n", + "
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\n", + "
\n", + "
\n", + "\n", + "Original content ([this Jupyter notebook](https://nbviewer.jupyter.org/github/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/blob/master/Chapter3_MCMC/Ch3_IntroMCMC_PyMC2.ipynb)) created by Cam Davidson-Pilon ([`@Cmrn_DP`](https://twitter.com/Cmrn_DP))\n", + "\n", + "Ported to [Tensorflow Probability](https://www.tensorflow.org/probability/) by Matthew McAteer ([`@MatthewMcAteer0`](https://twitter.com/MatthewMcAteer0)), with help from Bryan Seybold, Mike Shwe ([`@mikeshwe`](https://twitter.com/mikeshwe)), Josh Dillon, and the rest of the TFP team at Google ([`tfprobability@tensorflow.org`](mailto:tfprobability@tensorflow.org)).\n", + "\n", + "Welcome to Bayesian Methods for Hackers. The full Github repository is available at [github/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers](https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers). The other chapters can be found on the project's [homepage](https://camdavidsonpilon.github.io/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/). We hope you enjoy the book, and we encourage any contributions!\n", + "\n", + "---\n", + "### Table of Contents\n", + "- Dependencies & Prerequisites\n", + "- Opening the black box of MCMC\n", + " - The Bayesian landscape\n", + " - Exploring the landscape using the MCMC\n", + " - Why Thousands of Samples?\n", + " - Algorithms to perform MCMC\n", + " - Other aproximation solutions to the posterior\n", + "- Example: Unsupervised Clustering Using a Mixture Model\n", + " - Cluster Investigation\n", + " - Important: Don't mix posterior samples\n", + " - Returning to Clustering: Prediction\n", + "- Diagnosing Convergence\n", + " - Autocorrelation\n", + " - How does this relate to MCMC convergence?\n", + " - Thinning\n", + " - Additional Plotting Options\n", + "- Useful tips for MCMC\n", + " - Intelligent starting values\n", + " - Priors\n", + " - Covariance matrices and eliminating parameters\n", + " - The Folk Theorem of Statistical Computing\n", + "- Conclusion\n", + " - References\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "v-kF7r_1ICAA" + }, + "source": [ + "### Dependencies & Prerequisites\n", + "\n", + "
\n", + " Tensorflow Probability is part of the colab default runtime, so you don't need to install Tensorflow or Tensorflow Probability if you're running this in the colab. \n", + "
\n", + " If you're running this notebook in Jupyter on your own machine (and you have already installed Tensorflow), you can use the following\n", + "
\n", + "
    \n", + "
  • For the most recent nightly installation: pip3 install -q tfp-nightly
    • \n", + "
  • For the most recent stable TFP release: pip3 install -q --upgrade tensorflow-probability
    • \n", + "
  • For the most recent stable GPU-connected version of TFP: pip3 install -q --upgrade tensorflow-probability-gpu
    • \n", + "
  • For the most recent nightly GPU-connected version of TFP: pip3 install -q tfp-nightly-gpu
    • \n", + "
\n", + "In summary, if you are running this in a Colab, Tensorflow and TFP are already installed\n", + "
" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "iRkcSCrUJJ7A", + "colab": {} + }, + "source": [ + "#@title Imports and Global Variables { display-mode: \"form\" }\n", + "\"\"\"\n", + "The book uses a custom matplotlibrc file, which provides the unique styles for\n", + "matplotlib plots. If executing this book, and you wish to use the book's\n", + "styling, provided are two options:\n", + " 1. Overwrite your own matplotlibrc file with the rc-file provided in the\n", + " book's styles/ dir. See http://matplotlib.org/users/customizing.html\n", + " 2. Also in the styles is bmh_matplotlibrc.json file. This can be used to\n", + " update the styles in only this notebook. Try running the following code:\n", + "\n", + " import json\n", + " s = json.load(open(\"../styles/bmh_matplotlibrc.json\"))\n", + " matplotlib.rcParams.update(s)\n", + "\"\"\"\n", + "!pip3 install -q pandas_datareader\n", + "!pip3 install -q wget\n", + "from __future__ import absolute_import, division, print_function\n", + "\n", + "#@markdown This sets the warning status (default is `ignore`, since this notebook runs correctly)\n", + "warning_status = \"ignore\" #@param [\"ignore\", \"always\", \"module\", \"once\", \"default\", \"error\"]\n", + "import warnings\n", + "warnings.filterwarnings(warning_status)\n", + "with warnings.catch_warnings():\n", + " warnings.filterwarnings(warning_status, category=DeprecationWarning)\n", + " warnings.filterwarnings(warning_status, category=UserWarning)\n", + "\n", + "import numpy as np\n", + "import os\n", + "#@markdown This sets the styles of the plotting (default is styled like plots from [FiveThirtyeight.com](https://fivethirtyeight.com/))\n", + "matplotlib_style = 'fivethirtyeight' #@param ['fivethirtyeight', 'bmh', 'ggplot', 'seaborn', 'default', 'Solarize_Light2', 'classic', 'dark_background', 'seaborn-colorblind', 'seaborn-notebook']\n", + "import matplotlib as mpl\n", + "import matplotlib.pyplot as plt; plt.style.use(matplotlib_style)\n", + "import matplotlib.axes as axes;\n", + "from matplotlib.patches import Ellipse\n", + "from mpl_toolkits.mplot3d import Axes3D\n", + "import pandas_datareader.data as web\n", + "%matplotlib inline\n", + "import seaborn as sns; sns.set_context('notebook')\n", + "from IPython.core.pylabtools import figsize\n", + "#@markdown This sets the resolution of the plot outputs (`retina` is the highest resolution)\n", + "notebook_screen_res = 'retina' #@param ['retina', 'png', 'jpeg', 'svg', 'pdf']\n", + "%config InlineBackend.figure_format = notebook_screen_res\n", + "\n", + "import tensorflow as tf\n", + "tfe = tf.contrib.eager\n", + "\n", + "# Eager Execution\n", + "#@markdown Check the box below if you want to use [Eager Execution](https://www.tensorflow.org/guide/eager)\n", + "#@markdown Eager execution provides An intuitive interface, Easier debugging, and a control flow comparable to Numpy. You can read more about it on the [Google AI Blog](https://ai.googleblog.com/2017/10/eager-execution-imperative-define-by.html)\n", + "use_tf_eager = False #@param {type:\"boolean\"}\n", + "\n", + "# Use try/except so we can easily re-execute the whole notebook.\n", + "if use_tf_eager:\n", + " try:\n", + " tf.enable_eager_execution()\n", + " except:\n", + " pass\n", + "\n", + "import tensorflow_probability as tfp\n", + "tfd = tfp.distributions\n", + "tfb = tfp.bijectors\n", + "\n", + " \n", + "def evaluate(tensors):\n", + " \"\"\"Evaluates Tensor or EagerTensor to Numpy `ndarray`s.\n", + " Args:\n", + " tensors: Object of `Tensor` or EagerTensor`s; can be `list`, `tuple`,\n", + " `namedtuple` or combinations thereof.\n", + " \n", + " Returns:\n", + " ndarrays: Object with same structure as `tensors` except with `Tensor` or\n", + " `EagerTensor`s replaced by Numpy `ndarray`s.\n", + " \"\"\"\n", + " if tf.executing_eagerly():\n", + " return tf.contrib.framework.nest.pack_sequence_as(\n", + " tensors,\n", + " [t.numpy() if tf.contrib.framework.is_tensor(t) else t\n", + " for t in tf.contrib.framework.nest.flatten(tensors)])\n", + " return sess.run(tensors)\n", + "\n", + "class _TFColor(object):\n", + " \"\"\"Enum of colors used in TF docs.\"\"\"\n", + " red = '#F15854'\n", + " blue = '#5DA5DA'\n", + " orange = '#FAA43A'\n", + " green = '#60BD68'\n", + " pink = '#F17CB0'\n", + " brown = '#B2912F'\n", + " purple = '#B276B2'\n", + " yellow = '#DECF3F'\n", + " gray = '#4D4D4D'\n", + " def __getitem__(self, i):\n", + " return [\n", + " self.red,\n", + " self.orange,\n", + " self.green,\n", + " self.blue,\n", + " self.pink,\n", + " self.brown,\n", + " self.purple,\n", + " self.yellow,\n", + " self.gray,\n", + " ][i % 9]\n", + "TFColor = _TFColor()\n", + "\n", + "def session_options(enable_gpu_ram_resizing=True, enable_xla=True):\n", + " \"\"\"\n", + " Allowing the notebook to make use of GPUs if they're available.\n", + " \n", + " XLA (Accelerated Linear Algebra) is a domain-specific compiler for linear \n", + " algebra that optimizes TensorFlow computations.\n", + " \"\"\"\n", + " config = tf.ConfigProto()\n", + " config.log_device_placement = True\n", + " if enable_gpu_ram_resizing:\n", + " # `allow_growth=True` makes it possible to connect multiple colabs to your\n", + " # GPU. Otherwise the colab malloc's all GPU ram.\n", + " config.gpu_options.allow_growth = True\n", + " if enable_xla:\n", + " # Enable on XLA. https://www.tensorflow.org/performance/xla/.\n", + " config.graph_options.optimizer_options.global_jit_level = (\n", + " tf.OptimizerOptions.ON_1)\n", + " return config\n", + "\n", + "\n", + "def reset_sess(config=None):\n", + " \"\"\"\n", + " Convenience function to create the TF graph & session or reset them.\n", + " \"\"\"\n", + " if config is None:\n", + " config = session_options()\n", + " global sess\n", + " tf.reset_default_graph()\n", + " try:\n", + " sess.close()\n", + " except:\n", + " pass\n", + " sess = tf.InteractiveSession(config=config)\n", + "\n", + "reset_sess()" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "NSdVS13jJJ68" + }, + "source": [ + "____\n", + "\n", + "## Opening the black box of MCMC\n", + "\n", + "The previous two chapters hid the inner-mechanics of TFP, and more generally Markov Chain Monte Carlo (MCMC), from the reader. The reason for including this chapter is three-fold. The first is that any book on Bayesian inference must discuss MCMC. I cannot fight this. Blame the statisticians. Secondly, knowing the process of MCMC gives you insight into whether your algorithm has converged(Converged to what? We will get to that). Thirdly, we'll understand *why* we are returned thousands of samples from the posterior as a solution, which at first thought can be odd. " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "ZDFPrmtUJJ6-" + }, + "source": [ + "### The Bayesian landscape\n", + "\n", + "When we setup a Bayesian inference problem with $N$ unknowns, we are implicitly creating an $N$ dimensional space for the prior distributions to exist in. Associated with the space is an additional dimension, which we can describe as the *surface*, or *curve*, that sits on top of the space, that reflects the *prior probability* of a particular point. The surface on the space is defined by our prior distributions. For example, if we have two unknowns $p_1$ and $p_2$, and priors for both are $\\text{Uniform}(0,5)$, the space created is a square of length 5 and the surface is a flat plane that sits on top of the square (representing that every point is equally likely). " + ] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "h8wzyt9sJ9R1", + "outputId": "27385536-5dbb-45e4-8f70-56e9838c1582", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 410 + } + }, + "source": [ + "x_ = y_ = np.linspace(0., 5., 100., dtype=np.float32)\n", + "X_, Y_ = evaluate(tf.meshgrid(x_, y_))\n", + "\n", + "uni_x_ = evaluate(tfd.Uniform(low=0., high=5.).prob(x_))\n", + "m_ = np.median(uni_x_)\n", + "\n", + "uni_y_ = evaluate(tfd.Uniform(low=0., high=5.).prob(y_))\n", + "M_ = evaluate(tf.matmul(tf.expand_dims(uni_x_, 1), tf.expand_dims(uni_y_, 0)))\n", + "\n", + "plt.figure(figsize(12.5, 6))\n", + "jet = plt.cm.jet\n", + "fig = plt.figure()\n", + "plt.subplot(121)\n", + " \n", + "im = plt.imshow(M_, interpolation='none', origin='lower',\n", + " cmap=jet, vmax=1, vmin=-.15, extent=(0, 5, 0, 5))\n", + "\n", + "plt.xlim(0, 5)\n", + "plt.ylim(0, 5)\n", + "plt.title(\"Landscape formed by Uniform priors.\")\n", + " \n", + "ax = fig.add_subplot(122, projection='3d')\n", + "ax.plot_surface(X_, Y_, M_, cmap=plt.cm.jet, vmax=1, vmin=-.15)\n", + "ax.view_init(azim=390)\n", + "plt.title(\"Uniform prior landscape; alternate view\");\n" + ], + "execution_count": 36, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + }, + { + "output_type": "display_data", + "data": { + "image/png": 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R9OvXj7y8PF577TWgZoFt5xyPPfYYI0aMoEePHuy9996MGzeO1atXh467ePFi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\nsDDUxmgS+Y405t7amLEQT7K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riwULFrDbbrs127lFRBojNze3dnpQAtokuyEiIiIiIiIi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7+LBx54AO12G6PRKDavoV2S9KK4CELK5TLOnTuH66+/Htdddx3OnDmDUqkERVHgui4GgwGazaY/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pT3/6De3v+vXrfPGLXyQcDvOd73yHEydOAIot8bM/+7OcP3+e3/iN3+A//af/9Jr7+Tf/5t+w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WeK0iNhyeKqdk9HT1W5ZGNbcOmWul+jIYvOfWliUT/zK4KVDY3O5sIuC6evQ9dUvNblWzoH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\n79pvqtdsHu+ztbmrORb1acX955V9VjMeQpbi1lVJ5lTw0aSuVZpdM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eO/Sbj5n0bRCHzr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\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0NnKG5eWlFk21IlOUPjKhpUQbF16PRV+PTkON4MakRjha+Hrs7jSYX1EZGc+uta3BoiHV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\nJADAm970J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+TYzYqa2rzQ8kEqlQoymcy8CmRzhTFWV1B1OBwIhUKUTTEFqqoiHo+jVCrp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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [], + "image/png": { + "width": 799, + "height": 375 + } + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "qkRmzShuJJ7Y" + }, + "source": [ + "Alternatively, if the two priors are $\\text{Exp}(3)$ and $\\text{Exp}(10)$, then the space is all positive numbers on the 2-D plane, and the surface induced by the priors looks like a water fall that starts at the point `(0,0)` and flows over the positive numbers. \n", + "\n", + "The plots below visualize this. The more dark red the color, the more prior probability is assigned to that location. Conversely, areas with darker blue represent that our priors assign very low probability to that location. " + ] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "o8VlhghcJJ7a", + "outputId": "3065d297-db7c-435d-c724-a7d899c629d6", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 411 + } + }, + "source": [ + "exp_x_ = evaluate(tfd.Exponential(rate=(1./3.)).prob(x_))\n", + "exp_y_ = evaluate(tfd.Exponential(rate=(1./10.)).prob(y_))\n", + "\n", + "M_ = evaluate(tf.matmul(tf.expand_dims(exp_x_, 1), tf.expand_dims(exp_y_, 0)))\n", + "\n", + "plt.figure(figsize(12.5, 6))\n", + "jet = plt.cm.jet\n", + "fig = plt.figure()\n", + "plt.subplot(121)\n", + "CS = plt.contour(X_, Y_, M_)\n", + "im = plt.imshow(M_, interpolation='none', origin='lower',\n", + " cmap=jet, extent=(0, 5, 0, 5))\n", + "plt.xlim(0, 5)\n", + "plt.ylim(0, 5)\n", + "plt.title(r\"$Exp(3), Exp(10)$ prior landscape\")\n", + "\n", + "ax = fig.add_subplot(122, projection='3d')\n", + "ax.plot_surface(X_, Y_, M_, cmap=plt.cm.jet)\n", + "ax.view_init(azim=30)\n", + "plt.title(r\"$Exp(3), Exp(10)$ prior landscape; alternate view\");\n" + ], + "execution_count": 37, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + }, + { + "output_type": "display_data", + "data": { + "image/png": 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UU05ht912a7Ttk08+YcKECZxyyimMGDGCAQMG0KdPH/baay/OOOMMXnvtNc/z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\nv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jpybBjptewpFApIKb+zd4FhGKHkrGVZ25acPShJ3oMkAmywseaDaO+0sfaRkZHu+nur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\noNhnBD0tz8eDnsv1ok3BCJZcydE9yI8XqhYfNx6vFLpVI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y1laHQFtfcbwdWKx2HHcC/s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\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TQhAKhQiFQjQ0NKCUolAokMvlyOVy5PN5isUixWKR2dlZhBCEw2Gi0SjRaJRwOHxbk/J3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\nJpF0uBpSQVsuylVvqZ1nNck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nNFIr8ihSwPSsxpo6j1Efv5BraY2WiMf6Ope4hKevV573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\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S0hKf//znmZsLl7irqqripZdeIiPj0coeHnxC9TAHgzgH/+29thew3jNJ3wtuBwL/P3vv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\nABkd+GtdDHbWMNjlJuh25mw+UeFgosLBwe0d2GNxOkYn6BoO0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meMttoIbbVhasuiKNp9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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [], + "image/png": { + "width": 799, + "height": 376 + } + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "HvTFW8YoJJ7h" + }, + "source": [ + "These are simple examples in 2D space, where our brains can understand surfaces well. In practice, spaces and surfaces generated by our priors can be much higher dimensional. \n", + "\n", + "If these surfaces describe our *prior distributions* on the unknowns, what happens to our space after we incorporate our observed data $X$? The data $X$ does not change the space, but it changes the surface of the space by *pulling and stretching the fabric of the prior surface* to reflect where the true parameters likely live. More data means more pulling and stretching, and our original shape becomes mangled or insignificant compared to the newly formed shape. Less data, and our original shape is more present. Regardless, the resulting surface describes the *posterior distribution*. \n", + "\n", + "Again I must stress that it is, unfortunately, impossible to visualize this in large dimensions. For two dimensions, the data essentially *pushes up* the original surface to make *tall mountains*. The tendency of the observed data to *push up* the posterior probability in certain areas is checked by the prior probability distribution, so that less prior probability means more resistance. Thus in the double-exponential prior case above, a mountain (or multiple mountains) that might erupt near the `(0,0)` corner would be much higher than mountains that erupt closer to `(5,5)`, since there is more resistance (low prior probability) near `(5,5)`. The peak reflects the posterior probability of where the true parameters are likely to be found. Importantly, if the prior has assigned a probability of `0`, then no posterior probability will be assigned there. \n", + "\n", + "Suppose the priors mentioned above represent different parameters $\\lambda$ of two Poisson distributions. We observe a few data points and visualize the new landscape: " + ] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "z6bpeXMD50qV", + "outputId": "5f07803c-f327-4086-9943-ce4d1812fc64", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 53 + } + }, + "source": [ + "\n", + "# creating the observed data\n", + "# sample size of data we observe, trying varying this (keep it less than 100 ;)\n", + "N = 1 #param {type:\"slider\", min:1, max:15, step:1}\n", + "\n", + "# the true parameters, but of course we do not see these values...\n", + "lambda_1_true = float(1.)\n", + "lambda_2_true = float(3.)\n", + "\n", + "#...we see the data generated, dependent on the above two values.\n", + "data = tf.concat([\n", + " tfd.Poisson(rate=lambda_1_true).sample(sample_shape=(N, 1), seed=4),\n", + " tfd.Poisson(rate=lambda_2_true).sample(sample_shape=(N, 1), seed=8)\n", + "], axis=1)\n", + "data_ = evaluate(data)\n", + "print(\"observed (2-dimensional,sample size = %d): \\n\" % N, data_)\n", + "\n", + "# plotting details.\n", + "x_ = y_ = np.linspace(.01, 5, 100)\n", + "\n", + "likelihood_x = tf.math.reduce_prod(tfd.Poisson(rate=x_).prob(data_[:,0][:,tf.newaxis]),axis=0)\n", + "likelihood_y = tf.math.reduce_prod(tfd.Poisson(rate=y_).prob(data_[:,1][:,tf.newaxis]),axis=0)\n", + "\n", + "L_ = evaluate(tf.matmul(likelihood_x[:,tf.newaxis],likelihood_y[tf.newaxis,:]))\n" + ], + "execution_count": 38, + "outputs": [ + { + "output_type": "stream", + "text": [ + "observed (2-dimensional,sample size = 1): \n", + " [[3. 4.]]\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "6TVcoduJddFJ", + "colab_type": "code", + "outputId": "183b6879-dff8-4668-99a0-c465b9e23bbc", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 868 + } + }, + "source": [ + "plt.figure(figsize(12.5, 15.0))\n", + "# matplotlib heavy lifting below, beware!\n", + "\n", + "# SUBPLOT for regular Uniform\n", + "plt.subplot(221)\n", + "\n", + "uni_x_ = evaluate(tfd.Uniform(low=0., high=5.).prob(tf.cast(x_,dtype=tf.float32)))\n", + "m = np.median(uni_x_[uni_x_ > 0])\n", + "uni_x_[uni_x_ == 0] = m\n", + "uni_y_ = evaluate(tfd.Uniform(low=0., high=5.).prob(tf.cast(y_,dtype=tf.float32)))\n", + "m = np.median(uni_y_[uni_y_ > 0])\n", + "uni_y_[uni_y_ == 0] = m\n", + "\n", + "M_ = evaluate(tf.matmul(tf.expand_dims(uni_x_, 1), tf.expand_dims(uni_y_, 0)))\n", + "\n", + "im = plt.imshow(M_, interpolation='none', origin='lower',\n", + " cmap=jet, vmax=1, vmin=-.15, extent=(0, 5, 0, 5))\n", + "plt.scatter(lambda_2_true, lambda_1_true, c=\"k\", s=50, edgecolor=\"none\")\n", + "plt.xlim(0, 5)\n", + "plt.ylim(0, 5)\n", + "plt.title(r\"Landscape formed by Uniform priors on $p_1, p_2$.\")\n", + "\n", + "# SUBPLOT for Uniform + Data point\n", + "plt.subplot(223)\n", + "plt.contour(x_, y_, M_ * L_)\n", + "im = plt.imshow(M_ * L_, interpolation='none', origin='lower',\n", + " cmap=jet, extent=(0, 5, 0, 5))\n", + "plt.title(\"Landscape warped by %d data observation;\\n Uniform priors on $p_1, p_2$.\" % N)\n", + "plt.scatter(lambda_2_true, lambda_1_true, c=\"k\", s=50, edgecolor=\"none\")\n", + "plt.xlim(0, 5)\n", + "plt.ylim(0, 5)\n", + "\n", + "# SUBPLOT for regular Exponential\n", + "plt.subplot(222)\n", + "exp_x_ = evaluate(tfd.Exponential(rate=.3).prob(tf.to_float(x_)))\n", + "exp_x_[np.isnan(exp_x_)] = exp_x_[1]\n", + "exp_y_ = evaluate(tfd.Exponential(rate=.10).prob(tf.to_float(y_)))\n", + "exp_y_[np.isnan(exp_y_)] = exp_y_[1]\n", + "M_ = evaluate(tf.matmul(tf.expand_dims(exp_x_, 1), tf.expand_dims(exp_y_, 0)))\n", + "plt.contour(x_, y_, M_)\n", + "im = plt.imshow(M_, interpolation='none', origin='lower',\n", + " cmap=jet, extent=(0, 5, 0, 5))\n", + "plt.scatter(lambda_2_true, lambda_1_true, c=\"k\", s=50, edgecolor=\"none\")\n", + "plt.xlim(0, 5)\n", + "plt.ylim(0, 5)\n", + "plt.title(\"Landscape formed by Exponential priors on $p_1, p_2$.\")\n", + "\n", + "# SUBPLOT for Exponential + Data point\n", + "plt.subplot(224)\n", + "# This is the likelihood times prior, that results in the posterior.\n", + "plt.contour(x_, y_, M_ * L_)\n", + "im = plt.imshow(M_ * L_, interpolation='none', origin='lower',\n", + " cmap=jet, extent=(0, 5, 0, 5))\n", + "\n", + "plt.scatter(lambda_2_true, lambda_1_true, c=\"k\", s=50, edgecolor=\"none\")\n", + "plt.title(\"Landscape warped by %d data observation;\\n Exponential priors on \\\n", + "$p_1, p_2$.\" % N)\n", + "plt.xlim(0, 5)\n", + "plt.ylim(0, 5);" + ], + "execution_count": 39, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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rnxX0gD/44U/03pxNuHWSEA4OjiexW66mvRp/raxMf65YmPlV8c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oivJ7sIaQVy4+9YSiKJ/9vv87IcT04utqAewE8KdCiNFCxwVr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strIJSKp5hSz021sMpukBRnl/gdY51bYK+3eKys/WDFw1pnuWUA43MeBELbRGBbI6x4BInj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\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WYlS253AOywtwWF6AmF7AHbke3J09ikUYqV5prelD1vPIKuu2HOOdIzDqDDHqDBart1VS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\nRrlKWf5sDkbRbIKkZhmJbh1ClbqVaTr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iGSto6LiwEh0XVkIeV9H6/ACWPNWPBa9GIWrs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XgtecXYqO+P4fM/eCxDjPneZ7biyAoSYwiCB5J+Cf/5vsvw449fg0TQWE3tSyt4/wPb8cEHtkNW\nvZ3wlyCI+YloC/qu5cjz6GZEm/e/JrpXiNFSNo8Yv3vDyYylLK+iEj8/IbyHRSuqQqXC10K+mRJt\nsc61/FSu7I4EQXiJ2v2W0Ny3moSY6UJCDEE4TEKxYsiGPOYRAwBjkRpzOxztd9CS/JCXlZjbvAkx\nsqRj09Iuc3/30UYHrZk7Php4HsGJfEoHhUY8Kax22CJ30dQ9jLu//yjKh42BXzLow72f3orDK/kP\nT0cQXuZMcxW+/I9vxrNXrTT/V9c3gn/83sO4ZvsRuHeqjCAIYnbYPWJ0nd8pClGzCUpu9ohJW8uY\nBJ97r/d4yvKICXMkxAyJ1vixQvWuEKMDiC60hJgKEmIIYl5hF2L6V0dogew0ce9blyDmCYm0JcQE\nRb4m+qfDeMTyiAlz5hEDAPKSTCGGpzwxALB5Rae5vfdYIzQPLKauE0fxbv9Oc/+H4rVQaYoyg8ae\nKL7wrT+hYsguxtyAA2saHLaMIIjJ6ACeuH4NvvoPt6Gr0fKSvXDnCXz12w+i9eygc8YRBEEUAVGw\nOqg6xzli7PkwdRcLMZo9NJmLPWLGbR4xYT8/YTmH5olHTLzKh1SpcZ/7xhSE+vj5jgiCmD1l7QkE\nRgyRPFnmw0hzMEcNAgDc2zsg5hmFSOKerc3Zdu7n1tZ4RmiyGQoxLFPyLZvjdsdClkdMyXD/1PXz\nPJ6qZP8u1RyPN4VxH6j2ssYSICgCCQ3RRBqhfh2+2qnbZh0zCX/WMpVhSwqBvMreOL8lzUMoDScR\nGw9gZDyIQ2frsLJlgGlPKt+yYHZt3xdgKECR7EXIEg76w74X8Jv/n73zDo/jug79b2a2L3ohATaw\n9yqRFNWobjVb1S22I8vdsV/iJNaLe5MdP9uxHTuJ7bjKNYotS7YsWSWKJKqZKhRFUhQ72ACiEB3Y\nvrMz74+Fdhck9i6wwGJnFvf3ffgws2funTP93nvuOad7I0HcNCv1POhZw43anpEbie6rRJ6yiTxX\n/hzySaYxMMin/u0h/t/HrqW/yk/M7eTb//gGPvyLp9i0/XhedToFj5VD8Gp1CMrpgvPmzBEOXDQ3\nUiQTXap851tO9lfs9a5shXCr7OTbwMs3tbBof7nqFMm92V9zwvtKdK86BXUKXqvigxSVE/RJBss9\n/OQ9F7N71dzUb65onL++93kufv4wiofs7w7RPiUSicRGjMgRY+fQZJk5YlTrHoeZEZpMcVlzbq6D\nBKF4uoVQ4YriEDbSC8g4dhtDI6gmP/yqaVAejWTfuFB9+ULUOYp8YNbIsGSjmfTignrzbavnKptv\nmUL0K/Ltc+R7iQvlN5ZvvaI2fiFGACeCSJ98+0dT/YiD+Fo5Be8y0bOare+kAHX7ApzakgyB37Ws\nnMrjGe+8YvRVrHZjjYI1v7oSyTRihEdMCeaIifiqMZTkq8YdGkKLCRqjFkRRFbSFaUtBtNle+qsq\nnLMk7RXzcomEJ6tWw9yubU+tf0+/lLiNQ1kUiobTg3zqOw9T0xcAQHdqfO+9l/H4pcuLrJlEInl1\n5Ww++7mbRhhhmlq6ufNf7mfr84eln59EIpk2ZOaIsW1oMtNETWQYlCydI0Z6xBSKfjWdX7TaCJX0\ngNtAk8wPI5FMd+r3ZeSJWSnzxIyFUv4uSCS24OwcMfYKfZULU9UI+2tT67bME5MRnix6xF6GGIBz\nl6UNMbuONBDTS+PV/25tO5Uk3f1bqOF3xsYia2RNGroG+ey3/kxjR9LQa6oKv/zrC/jN288joVq3\n8y2RlCqDZR5+ePtWvvmxqxmoTA/YXPP4q3zuXx+k8fRgEbWTSCSSqWeER4xNQ5MppoEy3I8zFSU5\nG8qiGJkeMU7rtgVH5Ihx2iNHTF+GIabKKN2wZDAyP4w0xEgk05MZmYaY5aJQJ5LXsW7rQCKZJsQT\nPnQj2ch0qFGcSuk1YoIZeWLK+k8XUZP80BanPyiRwxFMw17GsqaZA9RXBgGIxJzsPlIaSdvLlSjv\n155Lrf+7fin9pldQYvpS2xfkM9/+MwuOp/M0/c9Vq/j2x64i6M0ebk4ikUwehgKDHGlyAAAgAElE\nQVRPXbiET955K385f3Hq98rBEHd8/1H+6v6XcOolkMhLIpFIxomSMRHNtOkQhZKRiNHKYckASKTP\nt5UNMZGMMNQepw3izQBBNd2uLjNyxNi1OaHajGNtK+1jlUgko1PdnDY4Bxpl3OSxYM9WjkRSUiiE\nE9WpNa+jr4i6FIZA+czUsr+vs4ia5Ic620uZOxlewAgYxNvsMSPrdRQFzlvZmlrf/tpcwdb24q+1\nF5hD8pkZwMd/6JcWVyELUx6M8qnvPszGV46nftu7eg53fvZNdMzMNxOJRCIZC6caq/h/H7+On912\nMUF/upOyecdRvvK1P7LmwKkiaieRSCTFRVHSRgzTtK5hQIRiZhyDYu1hFjMjT4CiWfd864m0Icap\nFSk/zDiJKOlwah7TXn3G8RKtzDjWvtI+VolEMjruQR0tmvz+xf0OYj6LT0SwANZuIUgk04QRhhit\n9AwxwQxDTJkNDTGKqrCqIX2NIgft57V03spW1OFO7pFTtbT3lUb8Trei80+OR1Prdxub2G+UhsdP\nIXDHdD760ye48U+vpH7raKjkS595E3tWzy6iZhJJaRJzavz+xnP53Gdv5NCS9LupvmuIj//bo3z0\nx9uoCNgv5KVEIpFMJsqI0Mz2HKLINMRgeUNMhkeMRcPUJgyFxHC+IEUx0BR7RCTINMR4S9wQE6lM\n50FyD9jDY0kikUwuCuDrTufwCtXLaBu5sHYLQSKZJpS8IaZsRmrZN9iDqtuvUbo60xBzyH6GmEp/\nlFUL0mHhnt63qIjaTC5Xqgc4X2kGwEDlTv16DJvOppwKVBNuuf8VPvKfT+KMJTtNIb+bb3/sDdx7\n0zkYijx3EslksHvtHD791Zt54Lp1JBzJ2WFawuD6h3fzz1+6j7WvSS8YiUQiASBjkN2uHjHYyiMm\nwxBjUY+YeCJ9Dp2qgV2ap5mGGHeJG2KimYaY/tI+VolEkh1fVzo0YahOGmJy4ci9iUQyWYhmSTgF\nMtFH3Ub5IASHH45Vw3A+eJ/aO3Jb0WnLd+JJrnL57jOLLIGLcFk13kAfimni6+0mUNM4tjoFE4UN\nPbvbYywq/gAk3Nlff1HOLru60ZOWH4sSjyqo7pGdrNgo5dKy7PEyo0KZqM7x7W/Tqg5ePZqckf30\nvoW8acsBHNrZs8vy1Sfqzi5z+bNfSKcopLBfIBsupwCfcz3EjYGPEEdjlzmX+7T1vNnzSs6yoyIK\nbSrSJ9dzJSo71Whw3r5jzPjuIN/9wJX0VfkxVYU/vWk9hxfP4MM/eoqqwVEMjoJWgyLwQvYJypmC\n8+bI0UrRBWXjApmeZ3SLfLuY+ZTrGf6fr++a6KuabzlHnp7mzhzX0evJLhPtUxHVK3qOC1Eu4xg6\n68v5r1vPY9eaeSM2WXysk/fc8xxzOvrBBanXqWifgnMjLCeRSCRTysSGFRQyjBjYZMT9DDJzxJiT\nYDVwOAoYiiuzau31f/l7NORbVlQubsOwZGABj5gpOlWmAtGK9LFqvXre7evJZDr45djJ5CXSNd++\nykQoxIik6OtXiGMsxj0u6q8rbvBneMQEx2qImeQxxxSi/lEhxlbzwNpTNSSSaUJIL22PGIBAVUZ4\nsn77hSer8rqYU+lLriQg1mw/r5hlTb1UDBtEhsIe9hxvzFHCPizQeniv+7nU+rciV9FnJ0NtkVjQ\n0sOd37ifVRn5KfavmMXnv3gj+5aXzv0hkUwFEbeDe244l09/5pYRRhhfKMp7fvcsn/mPPyeNMBKJ\nRCIZQWZoMtOmQxSKmXEMqrWPwRYeMUb6HLo0Q7CltZguOWJifg1z+N5xBnQ03R6h4yQSyeTj65Kh\nycaDtVsIEsk0YURoMsc0MMTYME8MwOrG9HWKHrCfIUZTTTatbE+tP713QRG1mXw+5H6GWUpykLPf\n9PFN3lBkjexBRSDCHT/4H25+aCeKkexEDVT6+MbHr+Hut24ilq8bhEQyTdA1lccuWcEdX3oLD169\nDt2ZfGYUw2Trcwf5+tfu5dLnD6HKMQqJRCIZFSUz/4dNQ5MpdgpNZtjAEGNbj5j0/PhSNsREKzNC\nsPVPBz8UiUSSjZGGGOmynwtrtxAkkmlCONMjxtEP2GfWz1gJVmd6xHQUUZP8WZNhiInsC2Ga9htV\n27yyLTXrcO+JBjr6yoqs0eThVeJ8xvtwav0+NvAUS4qokX1QTZObHtnF//3+I1QMJI2MpqrwyNVr\n+OLnb+BYU22RNZRIrIehKPzlvIV88iu38Ou3ns9QedoLb9Gx03z+Xx7gff/1HBUBQYxNiUQikcAI\njxhrGgZyMqJfYPFjyOxqWlTVhJkeqlIV+/SNddIGJEcJ9ulfR88I0e0I28dQJpFIJh93IG2MjXvl\nJM5cSEOMRGIBEqabaCI5IK4qCTzaQJE1mnwCVQ2pjpVvoBstHstRwnosnVGJ4km+NhN9Onq7/Y6h\ntjLCigXdqfXHdy0uojaTz+XOg1zj3Jta/zxvYkCYZEGSyapD7Xz5S39kzautqd9Oza7mS599E3e/\ndTMRQW4liWS6YCjw/OYFfPrOm/nhBy+la0ZFSlbTG+ADv3yaz37rQRae7BbUIpFIJBKJZGxY1Fo0\nKvabqJcPI53Y7HR9JBLJZJPpyKoY0+MdOBGkIUYisQghvSa17HP0FlGTwpBwughV1gHJONB29Ipx\nqCqeFekZz5G9oSJqkz8Xr29JLT+3v4lgpBip8grH5z0PUasEADhNBV/l2iJrZC+qBsJ8/Dv/w22/\n+guuaDKkgqmqPHL1aj799VvYtX5ukTWUSIqDocCLG+fz2S/dzA8+dBnts6pSMn8wytvve5Gvf+le\nLnrhiAxDJpFIJNMai38EbDBuPjJvkH3IPLW29e4aC5mDrXJUUSKZ1pgZIS6lISY38pUpkViEkYaY\nniJqUjiGqtPJv8t62wVbWhfPKn9qOfJasIia5M/iOX3Mrk16XcV0B8+8Vlq5YqrVEF/wPpha/xPr\neJxlRdTIfijAFdsO8JUv/pGV+9tSv/fUlfGvd1zFf/ztZfRVebNXIJGUEIYCO85t4nNfuInv/c3l\nnJqdEU40FOPGP73Cv3zhHq59fC8uXYbnkEgkkomg2GrYPQO7jrlb9HTb9XSONMSULiWQ1kkikUwS\nppppiCmiIjZBxhiR5IEo6dxU31IiXYoxSChqbglaKDqEYzUwPMbvU3tBT8vyQlQuV535ls0hG6pq\npIE9AJT3tI/tGEWySPZEYAldHJsy5nZllyFIMLa8CrTTkIB4a4xQn4pWnfQoiZK9zgTZ9YkJyol0\niQpkIXxZZShw2fpj/Prx9QA8sWcRl2w4gTY8hVukT1hQr5vsodrcnuwyp1/wtY5mF+HPLrqKA7wp\nuocHEmsB+AJv4hxfC9XKsBfTRJ6PfMsJ9J1yRI9Hxmt8ZnSIf/rxIzy3cTF337CZQFkyzNtL5y1g\n79rZvOX+HVz27EFU00T02IiuoyJIn+HLFVVOcM5NgUw0Vh4XlZvCXKSvm+NrC5Dv0JHnp9opKOcQ\n3FNKrv2J5KJ7VXR/jPEeF9VpKAovnjOfB9+wjpY5NSM3i8S56pnXuOapvZSFYsk6sz3jua6hSC46\nRpkLUyKRlAxyJFeSHdNWI/1FNr9MUXqGzFnvmYOwEolk+pGR0kt6xIwB6REjkViEUDydDNvnLL3Q\nZJA0xLxOeV/bGUkt7YHq1XAuSo+2xV4bKqI2+bNp2SnKPMnR8d4hH7ubG4qs0eTzaffD1CvJ69ND\nGV+OXldkjeyJAly04whf+/q9XLT9UOr3sNfFL99+AXfe8UaOLKgvnoISySQTd6hsu2Apn/jCrfzg\nvZeNMMK4o3Guf3w33/zn3/Hmh3cmjTASiUQikUhj0uRi09M5XUKTSY8YiUTyOpl5oqQhJjfSECOR\nWIRSzxEDEC6vRXckvS1c0RDu8GCRNcoP16qy1HJ8rz0NMU6HwcVrT6TWH39loR3tYkKqlDB3uv6U\nWn84sZo/xtcVUSN7Ux6M8oFfP8snv/MQDZ0Dqd+Pza/ny3e8ie9+9ApaZ1cJapBIrM1AhYc/XLeB\nf/zy27jrnRdxur4iJXNF41z3xB6++ZV7eOufX6Y8KHLXk0gkEonEwthg4NyuIersmttm3IzIEWOD\nG0oikRQMM8MTT4Ymy40MTSaRWISRHjE9gEHJ2UoVhUBVI1XdSQNARe8punyVRVZq/DjXlMMfOgGI\nN4cwAjpqmf1ep1vXHOexHYvQDY1jHTUcbKlj+bzuYqs1qVzqOMwtiZ3cp58DwJdib2SF1sEyOous\nmX1ZcbiDL3/1jzx49VoeumoN8eF4VTvPaeKV9fPY8kIzN//pFWaetqeRUjK9MIHmRfU8cclyXti8\nEN05MqaHLxTlqm37uGrbPsp1aXyRSCSSQjBiMpBi/+FrxeqzmzJmL2PR2cuZKiYM+wz0axnmF12Z\nojhhRUCLZxynp8TGLCQSybiI+9LvOi0mLTG5sN/IoURSosQNH7GED5cWQlPjeLRBIonSm10+WDs7\nbYjpOUXXnJVF1mj8aJVOHAu86MfCYEBszxCeC6pzF7QYlf4oW1a28Oze+QD8+cWlLJvbbYtZcuPh\n065H2J2YS7NZTwQnfxd5G/fwIyoQJCeRCHHpCW758ytc/Pxh7rlhIy9sXAgkY0RvP38xL2xeyMXP\nHubGB3ZR2xcssrYSydmEPQ62n7eIJy9dzsl5tWfJq/uCvGHba1z2zAG80eHkQDIni0QikRQEM2Py\nmV09IUw1PRClmNYeiMq0D5gJa55vt5ZOzBdL2Meg4TXSIUvDirOImhQWT386V2+02omhgmrt214i\nkRSIocZ0UsuydjlxLRfSECORWIhgvA6XdhIAv6uLSLgUDTFzUssVPS1F1GRiuNdXJA0xQPSVQVsa\nYgCu3niE7fvmkTBUmttqOdRay3lz+4ut1qTiV2J81/Nb3hr+ACHcnDRr+KR6M/9h/DeqTTv7VqG+\nJ8BH7trG9Y/t4b5rz2HX+nkAGJrKU5cs47kLFnPB9iNc98CrNLbbMxShpLQ4Ma+GJ69YzvYti4h4\nzh4gWXT0NFc/+Rrn7jqOw6KzhCUSiaTUGGmIsedorqlkHINh7WNQHOlZV6Yu2LCIuB2J1HIsYZ9h\nK7+ZHoQMKa4ialJYNN3EPRAnWunE1BSi1U68PfHcBSUSSckxNCttiClvl5Ndc2GfL5pEMg0Ixuup\n9gwbYpzd9ISXFFmjyWeoqhFDUVFNA1+gF0c0hI6v2GqNG9e6CoJ/7AQT9GMhEv1xsKHdrLYizJYV\nLTz3WhMAD724lPPmNhdZq8lnkdrNV9338/fRtwLwpLqMH5sX8SHzmSJrVho0tfbyD//+vxxZVM89\nt2zkwPJGAHSnxtNbl/HMRUs5d8cJrn9wDwuPllb4O4n1CfpcvLh5AU9vXcrRRfVnyV1RnS0vHuWy\nbQdY2CHvT4lEIplyMrJ9KzYNMG+qGYYYMyHYsvgoWoYhxqIeMS4twxCja5jmyHBlVsVnpj1iStkQ\nA+DtSRpiAMK10hAjkUxXhhrTYQPK26QhJhfSECORWIhgvC617HeW5mCQ4XASqGqgoq8NSOaJ6Z1t\nP4OTWu7AudhP/HAQTIjtHoRL7Bkf95pNh9m+fy6GoXL4VB37W2ewYs7pYqs16Vzt2Md7En/hLv0C\nAL6rXs5qo40LzdIzPBWLxc1dfPJfHmbfilncd+MGjiyZCSRDlu3YPJ8dm+ez8rU2rntgD6v3tpVa\nFDyJhTAUhddWzeKZi5ew85x5xF1nN3lnnerj8m0HuGB7M/7w8MCJ56zNJBKJRFJgSiE0mWFbjxhr\nnm9VNXGoCXRDw0Qhbqi4NGufVwBfRmiykFrihpjeGP0LkxMqw3UuOBQqskYSiWSqSTgUQvXDhhjD\npKxThibLhTTESKYx2fywc83kKNxMj2AsPVPX7+zKXUDkSj4RN/N86x2jbLB6TsoQU9nVSq8uMMQI\n68w+jBuNiIP5R/3Z5QlGi0OcbPjHSDeotQ3VSUMMENkVIHbJ2TOtU/sTJBeIkr2RLpLF8pQBhDK8\nkLwVsHF5Oy/umw3APS9u4I45o3uKuIiN+vuZdZ5Vzp29nOYPZJV5RN/xXPf4KPJ/9P0ve/tn8VJ8\nPqaicId2K793/ZDZysDY681XH3+e9eaL6AsvCrUtKjeGOhVg1ck2Vv57G4cWzuTPV65h9+p5qc32\nrZrFvlWzmNfSw1VP7eO8547ijmWZOZrrnAruD0VQVhSx2ymaxFqE8B0VlQWoNN/WXyHum0mut21m\nJc+ev5jnzl9Mf9XZD51DT7Bp93Eu+8sBlh7rTBoDXcN/IM4DI9JFVC6XcSffsjJnjUQisQzZPpBj\ny5FhmhmTmUrCI2bix6DrhcuLMppHTGICQ0P5ls1Vzu1IoMeS5yGmO3Bp2fsTVsE/wiOmtD/Uvt70\nscZmuHBYIJVPoQY4RW8yUfdApI9oVCnf47CaT5LVsiTle16Ffcc86xQxkW6VsN48n1FFsNNAgxtz\n+Jvi74qhxcdo3M/3IHOdgELVO4lIQ4xEYiGmg0cMwGDNHDj6IgAVva1F1iZ/HGsq4d5TkDAxTobQ\nu+M46qzW3BgbV2w6zo79jRimyoHWGRw6VcfS2aV3DzoUg29V/J5bez5EF+X04+Pv42/ll86f41Ws\n1nS1Nwqw7Ggny37UScvMah66ag3Pn7sQQ0sOVJycW8tP33Uxd9+8mfOfb2brs4eZf7KnuEpLbIcJ\nnJpVxY4N83l5QxMn59aOul1TSzcXv3CYLS8fpTwqZ2pJJBKJlTAzfGRV2+aISY9wSY+YycGlJQgO\nL8cSFhjlHwPeaRaa7HXCNu0DSySSiRFolPlhxos0xEgkFiKsV2OYGqqSwOMYRFMiJEowTspg9WxM\nFBRM/IOn0aIREm77Hafic6AtKyexL5mEPPhykMqrbZgoBqitjHDuig5e2jcLgHufW80n37LNFrGY\nx0u9FuA7zt/x7vjt6GjsNWfzKf0mvu34Papi3c6onZnb1seHfvE0tzywk0evWMVTFywjNhwqKuR3\n8/gVK3n8ipXMO9nD1mcOcf4LzZQFrT/rUVIcDAWOza/j5Y3z2bGhic6Zo7sNVQyGuWBHMxe+eJh5\nbX1pgWz9SiQSiaXI9Iixa44YlGRQNYXh8GqmAYo1wxbbxRDjdqR9DcJxe3y8y4z0ZI8htcQ9YrrT\nbfVAY2kfq0QiGZ3eRemoKDI/zNiwx9dMIpkmmGgE43WUuzoBKHN1McDcIms1+SScbgKVMykf6EDB\npLKzhd559ssTA+DcWJ02xLwUoOKqShTVntaLKzcdY+eBBhKGSnNHLTubZ3Hu4rZiq1UQzlFb+JTj\nEb6sXw/Ao8YqvpYY4lPaIzJvSQGp7w3wrnte4KaHdvH0liU8cfFyuuorUvKT82r59TvP57/fupm1\ne1vZ/Pwx1u86iTdShJhgEksRdTnYt6KR3WvmsHvdXHprykbdzhnXWbu3lYtfOsyafa04DOsOMEkk\nEokkiWFmeJMo1k50nxVFwdAcaIlkm0VN6BgOa3pEqK50a9eIWfc76XfHYCi5HIha81yeSbWRzpPS\nq/pJoKDZNO9RLqqOp4+1b8lUx2CWSCRWoH1DekJc/d6hImpiH6QhRiKxGIHYjLQhxtlZkoYYgP66\nJsoHOgCoajtuW0OMY1UFik/DDCVI9CWIHongWeottlp5UVsZ4cK1rTy9K5nP497n1rBuQTsOrTQ7\nD+/QXuK4WcuvElsA+FViC/UE+ADPFlmz0qcsGOW6x/dyzRN7OTivgacvXspL585PJVTXnRo7NzSx\nc0MTzpjOut0tnPfCMdbtbsUdk0aZ6ULnjHL2rJ/D7rVzObC8gbhz9GarJxJn3astnPvKCdbubcEb\n1WX+FIlEIrERRkayMBWbGmIAQ3PawxDjTnvqGFHreiCVu9PeJYGoPT7sLhJUGGEGVS+GotLv8FGr\nB3MXtCE1zRmGmEU+DE1BTZRmv1EikZxNwqVwek16UmXjKwOCrSWvIw0xEonFCMRmAq8CJA0y1m0b\nT4iBuibmNr8AQFX7ySJrkz+KQ8VxbjXxZ5L5VIIvBmxriAG4cvMxduxvIBR1cXqgjKf2LuSKdc3F\nVqtgfEJ7lNNmOY8aqwD4duJKarUAtyR2FVmz6YFqwoqDHaw42MG7fvM8L2xewNMXLeXYwvrUNnGX\ngx2bFrBj0wJc0TgbXmlhwysnWfNqK2VRGb6slAh5nRxY3si+lY3sXT2b9lnZQz36AxHO2X2Sja+c\nYOX+Nly6fQfuJBKJZLpjmulhCdWuHjGAoWUcR8K6uQcVmxhiytzpdt6QTQwxAHWJAINqsj/Y7Swr\nWUOMe0jH3xEh2ODBcKkMLPBSfSSUu6BEIikJTq8uJzH8PaloCVN2WvbNx4I0xEgkFmMoPjO1XObq\nhBINszhYNYuE6kAzdLxDfbgDA0TLRo/zb3Wcm2tShpjQqyGqQwlUnz0SSp6Jz6Nz/aYD3PPsWgAe\neGEF5y8/gc9dml4ImmLyDcd99Md9vGAuAODzjhuoNkNcZhwqsnbTC384xuVPHeTypw7SMaOCFzct\n4MWNC2iZV5PaJuZ28sKWhbywZSGKYbDk4GnWv9LCup0tzG7tl2HlbEbMqXF46Qz2rZ7FvpWzOLag\nFlPNHk9/9qk+1u1pYd2eVhY3d+KQOZ0kEomkJMgMTWZvQ0w6YbmasG7bWXVn5IixsCFmpEeMNb2L\nRqPOCHCU5KSibmcZy8KdRdaocNQcCRFsSOZ67V3ql4YYiWQa0b4xPX7XuFN6w4wVaYgpaUSNv+lw\n6bPNQirkALnonDuzizKKBcIzUstlztMQyHN3+coKVe8ZBiUTB4NVc6juPQ5A5cmTnF64Jme5scoS\nuvg6JxLZ5VFttBlX4aSMUToBs1woc3yYrSHQYWBXFN8FVWeUzu4l4yN7gzUmiK8TFcrEnRWRfMva\nNh7fvZjeIR+BiJsHXl7Fmy5IGiVcRLOWcwtkIcHxa/7sN447mv0iK5N0H7tI8O/Gf/PuodvZn2gk\noaj8o+st/Mz5SzaoLTl2Mg6y2RlFr+Ncr2qRXPTsiB6P7JdRXKdooqLoWoxSriE6yA3P7uaGbbtp\nm1nJCxsW8OI5C2hrqE5tY6oqh1Y0cGhFA797xyZqewOs29vC6v2nWHakg7LePGfkiHQtxthQIezT\n+X4G871Xh2VBr4sjC2ZweOEMDi+aSfOC+qzhxgBcMZ2Vh9tYt6+FtQdaqevLmFFalmOfovtxqst5\nBDIQXw9RWftMDpZIJBIhls8RM8Y2paGmPxRaLJ67z5UniTw/5Kly7rSeiWju+vLd30TK6mj43On+\n/EDUgz5cV751moLvuCL6xo9zd3WJdAe+x+3P3n6YYLtqymQCee3RIC0XJSdN9S71s+ihrhFyQTMP\np+BRz1bs9dZ9tpGVcPYqRaMxQP7DHKJTJ/KLE+kjKmenkbx8dRWdG1Gdua5xvvXmexyFqHMixygs\nl4dCbRvTY16NuwbOPqhJfK+WEnZ6hiWSaUE4Xk3CcKKpcdyOAE4lSNwszeR3/TVNKUNMVefx0Q0x\nNkHdXE+i9QQAkRcHzzLE2Amnw+D68w/xq/9ZD8CTryzggtUt1FaImrb2plyN8sPy3/COwffRalQT\nwcnfxN/BL513sVQ9XWz1pjWzOge4+ZFd3PTILlobq9m5dh67V83l6Lx6TDU9o7Onpowntq7gia0r\nUAyTua29LDncydIjnSw93ElNn5yhN5XoqkJbYxVH59dzdFE9zfPrOdVYPeKanYlimMxv6WbF4XZW\nHmpj2ZFOXDbOFSCRSCSSsTHSI8a6niS5yPSIUSztEWOT0GSu9KQaO3nE1BppQ0y3s6yImhSe6ub0\nJJnepaU5ZiGRSM4mWq7Rszz5zCsJk4Y90iNmrEhDjERiOVQCsXoqPW0AlDs66I0vKrJOhaG/uim1\nXNV5EgwDBGFprIy6oYbEn06CbqK3RIm3RnDOyTUN2rqcu6yNp3bN5+TpKvSExr1PreQDb3yZUo79\nVK8G+En5r3jHwPvoxc8AXt4bv41fOH/OIrW72OpNexRgbnsfc9v7uPHR3QyWedizfA67Vs9l74rZ\nhL3pDrqpKpycV8vJebU8fsVKAOq6h1h0tIumEz3MP9FD08keyoIi9x/JWAl7nLQ01XBiXg0n59Ry\nYm4Np2ZVoztzT3Vq7Ohn5aE2Vh5qZ8XhdvzhMzyZZEtVIpFISh6zVEKTZXrE2CVHTMS6hphyT7qd\nZrccMa9z2lkh2NL+1GYYYnqW+0k4FDRdho6VSEqd1gurYXiCXe3hAO6Afb/dU43s3kokFmQo2jAt\nDDGhsnpiLj+uWBBnLEx5bztDdbOLrVZeKD4H7nVlRF8eAiD83ADOt9nXEKMqcMsl+/jOPRcAsPfY\nTPYcnckli/qLrFlhadJ6+ZHz17w7fjtB3PRQxu3x27nL+QsWq125K5BMGRWBCBe9cISLXjiCrioc\nXjSTV1fO4cCSBo7Nq8PQRhp1u+vK6a4r54XNC1O/1XUNMf9kD00nepjX0ktjez/1HQFUU3YgRyPo\nddE+q5K2xqrhv+Ry14yxDTKoCYN5rb0sbe5kydHkX/VAeFq7pkskEokEEmZGbhXFugaMXCQc6Ukh\nmm7dpMWqN8MQE7Lu4FmVNx0Tty+UPcyx1ZidSPeXWlzVgi3tj68rRllrhMAcD7pP4/S6chpfHiy2\nWhKJpMAcvCGd23rec71F1MR+SEOMRGJBhqKzgJ0AlDvai6tMIVEUemsX0tD+KgDVbUdta4gB8F1Y\nmTLERHYOUfbGOlS/fUcYFzb2c8Gqk/zltXkA/H7bSjbPbcbrsm6ohclgldrOj52/5v3xdxHCTTdl\n3Ba/nZ84f8VKOoqtnmQUHIbJisMdrDicvD5ht4Pm2TM4tGQmh5fM5MjCemLusyPmdteX011fzo5z\n56d+c8Z0ZnYO0tg+wKy2fhrbB2hsH6Cuewj/YKyUncIwgUCZm9MzyumqTw/iNt4AACAASURBVP+d\nnlFOe2MV/dW+cdVX1z3EvJZeFp3oYtHx0yw42Y0nWtrvD4lEIpGMn0xDjFYyhhjret1qvnT/JBFK\nYJqmJb3e6/zpsLI9wfG1QYrJPD09KNnqqiaBgkZpTvJRgNnb+zj4lkYATl1YLQ0xEkmJ07fQR9fa\n5EQ8NW6w+FEZyn08SEOMRGJBBqMNqeWKUjbEAH11i1KGmJr2Zk6uvbjIGuWPY54Hxxw3emsUdJPw\nCwP4L68ptloT4k0XHuTVozMZCrsZCHq59/m1vGvrzmKrVXA2qC380PkbPhR/JyHc9OHn9vjt/FD5\nNRvM1mKrJ8mBN6qzel8bq/clPQt1TaFlTg3H59dxfF4tJ5pqaZlTM2rorLjLQevcGlrnnv3sesJx\n6rqHqO0OUNcVoK4nQG13kMqBMBWDYSoGwviD1jTW6JpCoNxDX5WP/lof/VU++qqT//urfPTW+Omq\nLyPiHX8MdjVhMKu9n6aWpGdRU0sP81p78YeGZwPL1qZEIpFIBBiZhhjVzoaYdPgsK3vEKC4FxaFg\n6iYkwIyaYEFH/ipfGAUTE4WBsAc9oeDQrG/Q8JsxahMBerQydFWjzVXF3FhfsdUqGHP+0p82xFxQ\nxcZ/O1FkjSQSSSE5eHOGN8xfevEOyIl240F2jSUSCzIUbcQ0FRTFxK91oRLH4OzZ3KVAf/U8DFVD\nNRL4B7pxBweI+iuLrVZeKIqC98Iqhn7bCUD4LwP4LqlG0aw4LDs2/J44N2/dzy8fXQ/AY7uXcsGy\n4yycWfrupxvVk/zM+Us+GH8Xg3gZwsP7XbfxvfjdbDGOFVs9yThwJEwWnOhhwYme1G+6pnJqVhUn\nmmo5Pq+WU7OraW+oZKAq+4zLiNeZ1UjzOppuUD4YpmIwQvlQBF8ohjccwxuOD//F8AwvO/QEDt1A\nSyT/Usu6QZfZjYnJ0YV1GIqCqSoYw3+6QyPqdhB1O4gN/4+6HURdDiJeF4Eyd+ov6HcTKHfnZWA5\n6zzGEzR0DDCrvZ9Z7cn/je39NHQM4sK6oU0kEolEYm0SRmmEJtOdmYYY63rEKIqC6tdIDA+eJUIJ\nSxpiHKpJlTdCX9iLiUJvyMuM8lDughagSe+hRysD4KS7pqQNMTN3DqJFEiQ8GgPzfQzNclPeZt37\nXyKR5E/Mp9F8dX1qfdmDnUXUxp5IQ4xkkhFZQu1iSMhlzS28tTdhugnFa/C7elAVgzLlNIPxUUJ2\niVTJVzaRshGBLEs5AxcD9XOp7jwOQHXrUToWbZiwLkZEPOgYE8hj/tFk4aSM7IkiY7hRN9SiPNiN\nGUxg9OkE98VwrakgKigXIvvAr4vsjViRzC2QJeXZZ+k5zhhQXbm0nyX7ejncUoNpqvzsiS188m1P\noakjZ6SJ6tQEg7Rn7m+ErDK7rBxxR30yzF/rOMUv9J/zvqHb6DX9hBQXH3K9k+86fsul2uEzlM1R\nWTb7oqhcrjpFclFOU1E50a0jqlNUTvQc51sul9wvLufAoGmwl6ZXe9n6avpaBp0u2mdW0jazKvX/\ndF05PTVlREcJb3YmCYdKf42f/hqRAmPAvCf5/84bJlbPOPFE4tT3DlHfM8SMniHqhpcbuwao78mS\nP8eP+J7KFaEx32dAVK9oQKkQz42oXK7BrXyPwz55iyUSie0pbP9n1NBkhernTHa5DEZ4xMSjuest\nwGlNjHGIR/OpJAaSy0YwQaIm/3DKiTyTvYnKvX4cNf4wfeFkfpiuYDm15WJPo7Ee/7jIoz0+z+hl\nJ00AnPTUcGGoeXz1ik5pIdpNE+hzOBMGjS8PJpN3A6cuqmbFfclwwQ5ROUEfIFeLO1u1okxCuR43\nUc+yEBmKwgKZ6HLYyfcg3xFA0fGL6pxI11lUr0gmujfyrXMixyisV1DYMcb3w9Hr6tCHw1tWHgsx\nc99gdqUK8a4qVL9yCq0j0hAjkViUwWgjfldy9naFo210Q0yJ0NuwKGWIqeloHmmIsRmKU8W1pYbo\n48nE7tFnunGtGVsia6uiKHDTZYf5199sQk+onOyq4sndC7lywygdihJkuaOTX1XcxXsHb6PTrCCG\ng7/V3843uI9rtdeKrZ5kkvGHYyw+3sXi410jfjeBgN9Nd00Z3TVl9Az/763yM1jmZbDcw2C5l/Ak\neJ4UAsUwKAtFqRoIUzUQonogNPw/mFqv6w1QFo1aMrSaRCKRSEqX0glNlm4DOCwcmgwYkccyEbKu\nV2uNP0Rzd9ITuSfkA3rEBSzCvEQ6esBJl71DVY+F2dv70oaY86tShhiJRFI6mMCBm9JpFJbf3yH7\njXkgDTESiUUZijbSWL4XgHJnu3jKhM3pa1gIux8HoLKrBS0eI+G05mDmWHBfWEP0yS4wINEcQm8J\nw9xiazUx6qrCXLH5OI9uXwjA/dtXsG5BO/VV9ggPMFEWat38uuJnvGfg3bRSjY7Gx/U3c9os592O\n54utnmQKUIDyYJTyYJQFLaMMAgyPYcQcGkPlnqRhpsxLxOMk7HES9rqS/z1OIu7kuu5QSWgqCVVN\nLeuaRkJTCc1ZgoqC/8QBVNNEMUxU00Q1kiHM3DEdd0zHFdNxR/Xh9TjuqE5ZMEJZOIo/GKUsFKUs\nGMUbiaGOJay6bBlKJBKJZIrJDE2m2Tg02cgcMdYOzaT50oYYI2hdQ0ytP90J7gkUwi+hMGQaYo67\na4uoydQwZ3sfLwwvt59TSaxMwxWw7n0lkUjGT8uF1QzMT0ZzcYQSLPyf7skJQzLNkN1ticSiDEZn\npZYrnaeKqEnhiforCVbW4x/oQjUSVHUepWfO8mKrlTdqpRPn2kriu5L+/tEnu+A2e3vFAGw9p4U9\nB+to760gpju467FzuePWZ1DVYms2NczR+vm162e8N34bR816TBS+lriGE2YNn3I8ghOj2CpKLIBL\nT1DbF6S2Lzihenb84CUANn5jU34V5B9hRCKRSCSSKSVhpidgaaq1PUlE6K50PElHXBSzufho5emG\ngj5k3QHzGeXp9lTHYHkRNRkf8/UeVNPAUFRaXDUEVRd+w773di7KOmPUHAzQu6wMw63SfHU9K+6V\nXjESSamQcCjs+Jum1PqSh07jCiXE4cAlozJNhs8kEvsxGEmHIit3dqDmyIdhd3pmLUkt1506LNjS\nHrgvr0stx/cMEu+y//VzaCa3X7UTVUkaHJrba3nslcVF1mpqmakM8Wvnz9ignEz9drexmQ/G30Vf\nQaIHSyQSiUQikZQ2upHhSaLYd7Bad6bbgo6Ytb3GtYr0nNzEgHWzTsyqHEottw+WFVGT8eElzoJE\nNwCmonDQM7PIGhWeJQ+cTi0fuKWBsThiSyQSe3DgpgaG5iS/cc6AztpftRZZI/siDTESiUWJGz6C\nsaQbs6okKHeW9oySTENMdcdR1IS9DReO2V4cy4c7CyYMPDEkLmAT5s/s57pNh1Lrf9q+gpYu+3v7\njIdqJczPnL/kGnVv6rfnzYW8TfsAh6kvomYSiUQikUgk9kM3MnKrqNYO6SUi7vKllp0xa8eVdlSm\nDTH6oHUNMY0VGYaYgXJMG43ur4i3p5b3exuLqMnUsOjRLpyB5L002OSlfWNlkTWSSCSTQaTCwZ53\nz0mtr/tFKx4LG/CtjjTESCQWZiCSftlVOkvb4hyqqCNclkzwpyXiVHWeKLJGE8dzRXpQPvByCL2/\nND5W1206yLwZfQDohsZPH91ITJ9enxOPovNtx+/5mPZ46rcWpYa/0t7Pk8rSImomkUgkEolEYi8S\nGR4xdjbE6M50aDItHgHDumFr7eIRU+mN4nUmJ+iF404Gwu4cJazDCj1tiNnnKX1DjDNssPjhrtT6\ngVsbBFtLJBK7sPv2OcTKk9+M8tYwy/9Q2pPEC43MESOxCCLvB9Ft6hTI8t3nRMILiY5DoGuWtu9A\naA6zKnYDWQwxojZzvrJcclG443z3GQFQ6J65lLmBZJq/2pZD9NYsFu9PKBPfG9FI9kZ81C+Q4coq\ni50pW+hCnX8a43gQEtD3dJjKG85O1nhWuRGy7LqE8WWVuRGHdXAJ5BrZ40S7iIEG77z6Nb519/nE\ndI323gp+++x6br/0pbzqFMo0gcwv9jLy6wXo/Ga8jhTgwzzD4mgXnxi8hZDpIqi4+aj2V/yD83He\n73wWJTN5XbZJYaJXXK6vtCgPiKif6hHIRM+VaGwk33Kid0OukOX5vo9EiPZZjHGKmgLUmW/rT3S/\nTeQ+FskLsU/RszHV5UD8POa7T4lEIrERupkRmqyQOWIK/R1XVXSnB0c8gkIyT4wuaK+LSOjZX/J6\nnongRpSrSPc/9EFxgyvf/eUqmxiLTIGGygDHupMT9loHK1nsy69OXXAYzol8b7PUu9JID1Ye9DaQ\ncChomQG78m3j5FtO1N7IldpQdH4yZMse7GD/W5JGp5YLq9k0301Z++gdgbCgHe/I0QfINmIjesQn\nMsojCjRYiODU+b6q7NQ0FI3W5DsCmOv4RWVF11FULt8685XlGgF1CN4PXsE7QMly8vqbvBy8MW1U\n3fiTE2iamX4PFaKvJnpXiWQw5nfVuGSTzPSawiyR2IyBcIZHjOtUETWZGnoa0uHJajqbUQzrJo4c\nK84r0h+t0PZBjKD9jwlgRnWIm7YeSK0/u2ceu49Nz1lPV7oPcHf1T5itJL2ETBS+Hb+Sf4reQtic\niLFYIpFIJBKJpPQZEZpMsa9HDEA8M09M3LrhyVS/BlpyxpAZNUhErRvzKzNPTMdAeRE1GR/15hC1\negCAsOrihOvsCXmlRmVrhFkv9idXVIWDt5Z+bhyJpJTZ8dEmzOFvRcPuAeZu7yuyRvZHGmIkEgsz\nFG3AMJOPqd/RjUOxbmN+MghWzCTiTeYbcehRqrrtH55MXVGB0pjskJkxk8AzA0XWaPLYsuoUaxZ1\nptbvemwjvUPTM2H9Usdpfuf9MRvV46nfHkys5S3hD3IiNlg8xSQSiUQikUgsztmhyaxrFMiF7kq3\nhZ0x0Tz64qIoyojwZLEB64ZRa6gIpJbbBsqKqMn4UIDlkekVngxg+f3pYz58w0xiZfl7VEkkkuJx\n/NIaTl2Q9EbEMNn0n8dRxEUkY0AaYiQSC2OYTobi6QZblauliNpMAYpCT0M6v0Zd2/4iKjM5KIqC\n84r0TKDg0wMkAqXhFaMo8LbL91HpT/qVByJufvDQFuLTLF/M69QoIX7q+RVvc+xI/dZs1vPFzud4\neOgohmy2SCQSiUQikZyFiUbCSHoRK4qJphQwPFmBibvTocicUesaYgAcVRmGmD7rGmJmV6U9Yk72\n2isB/OpIW2r5Ze+8Imoydcx+sZ/yU8n+YazCwd6/nl1kjSQSyXgJzHSx/ROLUutLHj1NzVFrf9Ps\nwvQcLZNIbER/PN1gq3La30MkF12zV6aWazsPo8bt2xF7HW1dNY6GZOfSjJoEnuwvskaTh98b57Zr\n96Cqyc7bsc4afvvM2iJrVTxcSoIvuB7ky6778Q7n4dEx+K/+/XyQd3Ia+8zik0gkEolEIpkq4kaG\nJ4lq3ygAcXe6reeMBgRbFh+tJh1CN9JrXUNMU22673Syt5KEYZ/JTRtD6f77bu9cYkrpe4eoBqy/\n62Rqff/bGgnWZ8+JKpFIrIWhKTz9xaXEypPG+rKOCBt/XPpjkVOFNMRIJBanL5Y2xFS7Sv/lFyyv\nJ1iWjJ+rJXRqW44UWaOJo6gK5VenM26HnhskMViMrN+FYeGsfm648FBqfduri9i+f3rM+BoNRYE3\nO1/hXu8PWaWmZ8E9x2Ju4m94kqWC0hKJRCKRSCTTj7iRzsDr0ARZvC1OLMMQ44pY2xDjqE0Pjkd7\nrGuIqfZFqfQm74mo7uDUoH28YmbpA8yOJXMqRFUnr3qmh3fIgm091B5M3v8Jj8ruD8wtskYSiWSs\nvPzheXStSebjUnSTi792GFeJ5Dq2AhM2xCiK4lQU5QpFUb6lKMoORVEGFUWJKYpySlGU3yuKcukk\n6CmRTFv6Y02p5UrXKRRK/AWoKCO8YuqP7iuiMpOHZ40P55xkZ8eMmwz9b+l4xQBsXX+STUvSofN+\n9eQGWroriqhR8Vmg9vBfnp9yffnCVFCyPvx8hHdwJ9cRxiksL5FIJJLphexXSaYzeqJEPGI8/tSy\ny+IeMY4Mj5iohT1iAOZneMUc6akroibjZ2P4eGp5h68p+4YlhGLCuT9KTyJtvr6evoXTM5eoRGIn\nTl5Yzb6/mpVaP+eHJ5mx39rfMrvhyL1JTi4BHhte7gCeBoLASuBW4FZFUb5smubnJ2FfEsk4yNfj\nID4BeZ77FBSLxioI61V4Hf1oSpxypYPB+OzcuxPJck0yK0S9ItkZdXbNWMH8g88AUNV+Amd/cESn\nZsL7A+KR7O7RsWh2WRhfVpmb7GHUXEoM1zWNxH+SbJCGnh9Cu6QBrdaFS1ROINMERjlRuaQ8Kqg3\n+0XOWk6Bt16xj5PdVXT2lRPTHXzvzxdwx9uew+fRhbrmjTuHvHIoq8jnyN7ZFAY7EH01R5G5SPB2\nbQVrPPX8rPsxTptJ49TdbOZ5dSH/7L2fDZog91Ou6AWicxAUyETPh6jO7LcNjPKIjqncRN5Von3m\n+x4TUQw7eG0B6sw3KsY47/8xI9JHVK9HIMu3TtH9n285kZ4TqTfXO1BiR2S/SlJgLOaRnaFOPJF+\nWTpzNgCmmHG0KWKOjNBk4WDe7ZGEnt/HOiH4ACbO+OCotemB8WivkbXsmeXOJEr2vpOorC7UdaRs\nXs0gu1sbADjQO5MLF43efhbpEvNkn4fsFPQNcrabRN95N2yKn+B+NgDwkn8+Hww8k+xv5CiX1/5E\nbW7RceRqq4jqzVK28cAgs5/v49SWakxN4ZWPNnHFJw6k5F7BPvUs/Zie18tmKydQs1DkGj3Khuip\nEh3HZAziWgHRlETRMeZbDrLfN7lkon3mW2feshzvI9Fz5czRrwjMdPPcZxenfpqzvZdVf2hDMASW\n/3tMJMu3H5dLXqi+7DiZjNBkBnAvsNU0zUbTNN9omubbTNNcA7yd5LDF5xRFuWwS9iWRTEv6oxl5\nYtwnBVuWBjFvBQPVcwBQTJO6kwdylLAHjuVlaAuGv2IJk8gjncVVaJJxuxK8//qduJ3JpmP3gJ+7\nHt5gqzjOhWKVp44/lv2AKx37U78dM+p4Z/C9fM24mrApvWMkEolEIvtVkulLfIRHjH0TAo8ITRaz\n9ixirTrd/owNGBi6WURtxDTVDaSWm7vt5RGzOnoKj5GcJNfuqKLVUV1kjaaOc35wEozkfdV6YTUd\n66d3xASJxKokHApPfW5JKi+MvzPKRV9vRrHuZ8G2TNgQY5rmE6Zpvtk0zWdGkf0W+Pnw6rsmui+J\nZLrSH5tehhiArsZ0eLIZJ0ojPJmiKHivn5laj78ygN5q39ALozGzJsg7r9qTWj/YUs99T6/AlB9w\nqtUw/+b7LXd6/4R/eFqZicIvzPO5wfgIT5lLiqyhRCKRSIqJ7FdJpjN6ieSIibvT7rrOSAArN4IV\nh4paOWyMMSHWZ93wZE01aUPMsb5qW030cmJwTjTdh9/uWVhEbaaWmuYQix7tSq2/9LdNGPl6Zksk\nkoJgAs///UK6V7yeF8bgki8fwj1kMS/aEmEyPGJy8crw/zlTsC+JpCTpy/CIqXYdJ/mqLG26Zy7F\nUJOttLK+Tnz9p4us0eTgWODHsTL5gcOE8B/bMS3cQcuH9Ys7uHrz4dT6M3vm88jOZUXUyDooCrzF\ntZM/lX+fCx1HUr+3Us2HjXfy94m30GmWF1FDiUQikVgY2a+SlCwjPGI0+3rEJBxuElrSuKEZOlrM\n2kYlR106lFeky7q5SKt8Uap8yQlsUd3J8T57eZVsCR9LLW/zLZ0Gvfk063/SghpNGvl6l5Xx6rtm\nF1kjiUTyOibw8keaOHLtjNRv5/7oJPUyL0zBmApDzOtTfNunYF8SSUkyFG8gbiSDKHocQ/gcvUXW\nqPAknB56ZqQ9BBqO7hFsbS+8b2pIvX0Tx0IEd5WWVwzAtecd5pwlban13z23ju0H5wlKTC9mqQP8\n2Pdr/tn7RypJDzY8yiquM/4PPze2EDen4hMtkUgkEhsh+1WSkiWWyEhyb2NDDIpC1JMOv+QODBZR\nmdw4ZqQD9Uc6rT37eenMdB94b0dDETUZP+dHmnEbyWwiJ5x1HHHWF1mjqaOsM8b6u9I5fXa/Zy6n\nV5UJSkgkkqli77tm8do7ZqXWFz98mpX3ymZmISnoKI+iKA3A7cOr9xZyXxJJaaPSH21KrVW7jwm2\nLR065qxNLdef2I+q55sKz1poM9y4L05n4O57YAAjat1QAPmgKvDOq/aweHZP6refPraJ/S3Tp9OR\nC0WBW1y7eEj9D25SdqV+D+Hm6+Y13Gh8hG3qkmk1Y04ikUgkoyP7VZJSZ6QhJkumbpsQ82YYYoID\ngi2Lj3NmpiHGuh4xAMtmpvsVr3XOFGxpPXxmnIsiaW/4//WtKKI2U8+qu9uYsSdplDQdCs98YQnx\nMhmjTCIpJgdvnMnOD6fHGec+28v5327GPoEf7YlSqJA4iqI4gEeAK4DHTdO8cozlbifdyRCybdu2\n9evXr68MhUKcOnUqX1UlElvwzMOHefSe1wBYf/5c3vyBc4usUeExTZOv/vBFuvqSHiPvuH45m9fa\na/ZTNkIxnU89uIPBaNK4dMPqedy8pilHKfsR0ON84dVdtISTMxu9msaXV69nnl/OgjqT/ZEeft73\nKm36yMGHNZ463lG1kjlOGbJMIpGImT17Nj6fD+CpysrKS4usjmSSkP0qyXTg+OEefvzNvwAwb2E1\nH/rERUXWKH9+t+0IT+1OeobffPFCrjzHutEETwwE+MwzyaiHM30evnX5piJrlJ22aJC/a94OgE91\ncNeyS9AU+wwZHk2084voYwB4cXGH9y04lOljjIgpvTSXfx1DSfbtK2PnMjt8G4oc9pVIppw+5/O0\n+f4rte7XlzIv+CFUnEXUynoUom9VSI+Y/yTZWWhhfAkl5wOXjOUvEAhUTqK+Eomlmb807UFx7FB3\nyeUVGQ1FUdiyvjG1vn13m2Bre+FzObh13fzU+sP7W+kOWjuGdD6UOZx8auUaql3J+NPhRIKv7n+V\nnmi0yJpZjxWeWr7ScDFvr1yOV3Gkfn810s1nOp7h5317GUrEiqihRCKRSIqE7FdJSh5/eTpXSTBg\n7/ZOTYUntdwzYO32/awyH+rwOPjpUISIbl2vmEaXj2pH8j4JGTonIkNF1mh8zFcbqFSSnl9hYhxK\ntBZZo6nFZdYwK/y21PqA62UGnC8VUSOJZHrS73yJNu/dqXWv3sTc4PulEWaKcOTeZPwoivJd4H1A\nB3CFaZod4yh+HHhqLBuWlZWtByp37uzhjW98cNx6lgb5PiiiS5+rznzLisp585T5xl3upZeSM/E3\nbcrWyMw167wiT5moXsEskLLXt0hwedPzONQYAz1hLnnDIsKemuzlqgS7E8lyyevyLJdnnU7PCjYq\nP0Q1DY61DrJ1+3zCFXW5dRHJAOqyG7KcVWc36v9y7BAA122ccZbsdaroyyor5+w6zXMqUdp8mK0h\n4gmDzx7fT+W7G0dsU01/1jrLRqlzLOUmUna04xhLnR9cCt/5/RaicSe9sRh3Hn+Mj715Oz63TjnZ\nE8GJ9ieS5ZL7Etljj3sD2UPgOUVhvrNE0dgRSHYwNpJlhuEZNqnz3fB/av38e+Ay7omcg4GKgcnj\ngRO8FDjA37ie4q8cO3ArOoj69qKoHiI7mEgm2p8orLioznzL5Sqb7ziCqM4pDJ2+4/bh++bnBZiZ\nmm/rT1RONKFzIq1Nt0AmqjdfXT0CmajOfPXMVVYkG0XX9tmlM2FBkkT2qyabfPsqhSgH+feB8pXl\n21epHfXXl15K5n3YtGldlnI5jj/DQdqphrhs8TYAOtoTbLp5Y/Zy+fYBRDKR071INkqd1b2VLOEo\nAH/qdvPNzizHItxn9kZX/ezTAnW6s8pq6Rn1d2WHG7qimMBX5+zDN8c5pnJjkY+mz9Yd7wXgwMav\njascwOLwWl46nsxnsLf2ByxYeeAMXbIff10iu54VPYIQ2NlPdxLR6Tkjteu13vP4b99mAI713sVt\nbX8eU7kx708UCU9UZ64IeqJ+hajsKOWe+4dFHLkm2afu5Bds/uDHqTg18n4PZdnfvm3J9vHcTaO3\nj0VdtVxZWUVyUYB0UTlR10FUp0iWb52FohAjkvnuL9vXr/ql5H0TyHLf5NJH9FXNR5+cMkFfxSvq\nqwA+v0DohuNba9j32aXJOOlAzeEAb7jjHtz6f2cvl6POvMqJZKLgKaL9TWSfWWTtH5j8vtWke8Qo\nivIt4O+ALpKdhcPjKW+a5s9N07x0LH/r16/flbtGiaQ0MNHoj6STnVd7jhdPmSkk7vHT27Aotd5w\nck8RtZlcFFVBuyl9TaN7AsQO2zg5qYDZ9UO8/407UdVkLpz2nnL+8/5NhKMFmQ9ge2rVIF+seJD7\nvD9ki3o09fsgXr4eu4Zrwn/Lb+KbiJry/EkkEhg0Pfwufg4tcXvNDpaIkf0qyXQibngwzOTwhFOL\noEzl7IdJJupJO5i5AqKhYWvgmJEe2Yt0Wvu8L2lIT3x7rcNeeWIArozuTy3v8M+nVxNNLC1NNn//\nGBUtSfOF7tN46s4l6O6Cpq+WSCRA85V1PP2ZJZha0ghTdTTEVf+0H3fAup6Qpcikvu0URfkG8I8k\nbfRXmqa5bzLrl0imO72RBanlGs9RwZalRWdTepbdjJN7UXV7hyvIRF1Qjvuc9AzEod+fxowbRdSo\ncCyb28M7r0wb0o53VPMDaYwRskzt5GeeX/I9993MU9LT3zrMSr4Su56rjb/jN8ZmaZCRSKYhuqmy\nTV/CP0TezMWhO/hC7AaCRjHmQkoKgexXSaYfKvFEelDapYqm4FubqCfteeQJ9oPFQ0o7GjIMMe3W\nNsQsnZl263itYybxhL0G8BuNQVbFk3m4DEXl4arVRdZo6nFGDbZ+k2OrdAAAIABJREFU7TDqcJ+3\nd1kZT39hCYa9LqVEYhtMYNd75vDsp5ZgOpIPWuWJEG/4p314Bq39zi9FJu1VpyjK14D/C/QBV5mm\nWTrT1iUSi9ATTnuG1HmPkHyllj799U2E/dUAOPQYM1peK7JGk0vZG+tQPMnXcaI7TvB/s4c4szub\nlrfx5kvS1+94RzXfun8roaiMR5oNRYHLHQd5wPt9PuV6mDolHcqtkwq+Yl7HG4y/41fGeUSkQUYi\nKWlME14zG/macTWXhv6Rv4m+k0cSq4kVJtqwpEjIfpVkuhLV05OT3Gr20LVWJ+H0EHcmA8+oiQSu\nkLW9FR2z0kFyQq3WHpSbWRFkZlnyfEZ0J/s6s4eNtipvjLyaWn6wci1RRRQztTSpPRJk83eOp9Zb\nttaw46NNxVNIIilREg6F5z6ziN3vm5v6repYiDf83314++QErmIwKYYYRVG+AnwC6CfZWXhlMuqV\nSCQjGYzNIpZINpTdjgBlWmeRNZoiFIW2hRtSq7OO7rT8zLLxoFU6KLs+HXs79EQvelvpJrPfuu7E\nCGNMc0cd3/qjNMbkwqUkuM35Av/j/S6fcD1CnZLu1J+mgq+a13KV8TF+bFzEgJkjeKxEIrEVx8xa\nvmdcwvXG/+HNxof4hXk+PWcEUF6ptjHDMf1CnJQasl8lmc5E9fR7za1a23iRi4gvncvTOyhKzlF8\nHLPThphIp44Rs24/S1Hg3LmnUus7WuYUUZv8uDB2hBmJZMi6QYeXJyqWF1mj4rDs/k5W3p3Ov7D/\nbbPY9xZR4iSJRDIeIlUOHvvXlTRfmzZYN+7o59qP7cUnyoslKSgTNsQoinID8Jnh1SPA3yqK8vNR\n/j450X1JJBJ1pFeM60gRdZlauuauRne4APAG+6g6fazIGk0uni2VOOcPD54bMPjbTsyEdTtBE2Xr\nuhO85dK9qfWjnbXSGDNGvEqc253P85j3u3xKeZh60gMV3ZTzbfNKLjf+gf/nuJpTVApqkkgkVqad\nCn5mXMCtiQ9ynfG3/Id5GcfOyAo9Qxnkfc5nud/7fe71/ogaTRph7YzsV0mmO9FEhkeMZv3cKiLC\nvurUsmfI2oYY1aPhrhv2yjAhbPHwZBvntKaWX26dbbv5eRomN0bSabn+UL2B0gxMnZuN3z9B05Pp\n8MsvfWwBR66tL6JGEklp0LvYx4M/XkPnhnSozCV/7uTKTx/AFZQ5YYrJZMQxqMlY3jj8NxpPAV+b\nhP1JJBmIrLhegUxEroanaJ8imaheweDzGcV6gotpLEsOYNc6j3B86KLx7y7XIYrkkTxl+dY5XC6B\ni845a5h9/GUg6RXTv2hhfnUCBJSsorjHlVUWDma/r9z+7F4sbrLntQnjAxWcb20i/u1DoJvorVEG\nngriuTz77GaN7B9Q0f5ylXUIZJPJ+rV9RDnCn7YtBpLGmK//8TI+ctOL+NwT7wAmyO7mn9Cyy/TK\nUFaZz5H9xvK4swhej6xRm0UuCoGerU7Ag85twRd4q/ky90TP5SfhCzltJhtaIdz80nE+v3GcxzXa\na7zX+RwrtY50YdHzIXLGEslElyzfchN5V4lu44nsMx8m8khlu29ykW+Ui3xbhqJyuXQRlc1XJtqn\nqJzgmStIOYAM20mnUc5j8RU8El/Fy4mmUSOQ+pQoV7gOcKN/D1vcR9EUm41ASUTIfpXEpmTrA+WY\nYHPGNzcaywxNNsUeMfm2DbLIIu704+wZ6Bt9O2G92T9ksWj2voruFrR/BR9H1xw30e5kG3jolImj\nKf1hi5J9f7nq1QWymKDeqODDunBGD15nnHDcSVewjOb+OuZWDw7Xmb1cVMsui7uz9+Odom88jPiO\nn0WWsm8w9/FfxnkEVTenXNW8WL2ALeFjOcvl3J+oze0XyCbS5s63Xh0U4KJvHCFU76RrdbIf85dP\nLmJrIEHTk9kNmOXZ9inoV+VqjonkYYFM9JYTjQ6J6hSNZIlOqdV8HETnJt8ux0TqLBfIRPWKrodo\nnyI/da/gGfcKnvGc7yM3nLi4hmc/sRjdO/z+NUzO+elJVj/QhpKt7jzeY4D4+RfJCrG/XPJ8ZZPM\nhD1iTNP8uWmayhj+Lp0EfSWSaU9PaHFqudp1HNVyn9vC0T5/Q2pMqrrrON6BHuH2dkOb6cH1hpmp\n9dijHcRPl/b1PW9tO2+9LO0Zc7Kziu//YTOhiMx3MFY8is5fe17gsarv8lX/H1msnU7JEqj8ObGG\nWyMf5j3h23hcX4ZuykyYEomVaDcq+EV0C+8IvJdLhz7OP0euSxphMnCic6V7P/9a8Tuerfsm36j8\nAxd6mqURpsSQ/SrJdKdUcsQARLwZHjEWD00G4JmdNopEWsUTuoqNQzNZOys9wWhn66wiapMfPuJc\nE0j3ge4r3yDYurRxxAyu/PQBqo8krSimpvDMnUs4taWqyJpJJPbCVGDXbXPY9sVlKSOMM6hz+ecO\nsOa/28g+FVkylcjRGInEZkT0KgKxpLuupuhUu04UWaOpI+qrondGOjRb46HSC5vuunQG6pzhORe6\nSe9vezCN0h5o+//snXeYHFeV6H9V1bl7uifnKI1ylqxkyXKUkY1twGAbswbMA5O9pF129+3Ct2bX\n+9jlGR4LLCwYL8EGDMZgA05ylqyccxhpcu6Z6Z7Oqer90fKMRpqukVsz6umZ+/u+/rq6bt1bp7ur\nbt1zzz3nrF/Uwj03DCetbOnJ5T9/vwaPf6zlHoLzMUkJ3mc+wLPO/+K/HY+zWh4Zvm+HOoPPR+7l\n5tAX+L56HR2aCFsmEGQCTYNTajE/jq/nnugnuMH3Zb4Z3sT+RPWI42RU1pka+LecP/JW4bf4nutJ\nNlmOYZWmtoFeIBBMXyKxDHrEjDMjcsT4BzIoyaVhqcweQwzAsqrh3CL7W7PPEANwh+8gipZ0mz5q\nqeCkqWSMGlMXUyDBxr87hrMl6SeiGmVe/+YcOlYJfUUguBTCuQZe/T9zOPjRqqF9OW0hbn3wCFU7\nPBmUTHAhwhAjEGQhfYFhr5hC86kMSnLl6axdMbRdfPYIxrBeXKfsQ1IkLPdUDfXO0aYIvlezO0b2\npbBuYSsfPM8Y09Hn5Nu/vZpWtxh8v1MkCTaYGviZ9ec8ZflvblUOo5wXebpTc/ED7TpuUr/IpxIf\n4mVtLjHhJSMQTCgRzcAWtZ5/id3KTdEv8p7YZ/lO4iYOaSOTDCuorDWc4Z8tf+LNnEd4NPdx3mc9\nQI6sF2tEIBAIpgbne8RYsjxHTMTsRJWS4ytTyI8Sm9z9uLnYiGRIrpeODyaIeSd3npgl5V1I57xC\nG9wFuP3phiXPHIWJANcGTw99/pVrVQalyTxWT5ybv3oMe1cyhnLCIvPqI3NpvCndOL0CwfSgbW0u\nz/x8CW3rhxcAlO318O7PHyG3WS8IniATiJkXgSALcQdnDW0XWU4yahD5KYq3oAq/sxgAJRGn7MS+\nDEs0/ijlVkwbh1dEeV/0EGmZ3MrbeHD1wlY+fPMBZDlpNPD4rfyfp67nWGtxhiXLXhYonTxi+T0v\nWr/LA8Yt5J0XOFlD4k1m86D6QW5Qv8S31I2c0EqmUW8iEEwsXZqT36nL+Xzsg6yNfpVPxu7jV+oq\nOhgZakNBZZ2hgYesz/Jmzv/lMfsvuce8lwJ5ai00EAgEgrEIx4YX4FhkbwYluXw0WSFsPc8rxuvO\noDRjIykSlqphr5hQ0+TWPXIsURaWdg993tZYrXP05OUu7x4kLTn63mOt5aC5cowaUxt7b5Sb//Y4\ntu7k9acaZbb862yO312aYckEgslHJEdh6z/O5JVvzSNcMNx/z3+qg5v+/jhm3+Q2qE9XhCFGIMhC\n+kIziKvJjtZu6MOuTO6B/bgiSbTNXD30sez0fpSoXubx7MR0YwlK7bn0bir0P+5GDav6laYAK+d2\n8Ok79mA2JkPvhKImvv3MBt46XjNGTYEeFbKXL5te4XXbt/m2+Xes4eyIcjc5PKat433qZ3iP8hl+\nIq2jE2eGpBUIspOoprBNm8G31I3ckfgM16tf5uvaHbyiziV0QUJiB2E2yUf4puFp3nL+B4/aH+du\n0z7y5WCGpBcIBILME03YSKjJPIFGOYwiZfcYP2gvGtq2eXszKMmlYasbDgscapzchhiAdTOHQ3S/\ndbYGLQtXE1XHB7gxcHzo80/z1jH1NT59nB1hbnngCK7G4THR7i/XsefzNWjT/tcRCJK0rMvjmV8u\n5cwtw4tWre4oN/7DcVb+sBlZ3CqTFmGIEQiyEE0z0BccDk9WZDmRQWmuPH2lswnakyvMDLEoZacO\nZFii8UdSJCx/VYNkORcioC/OwB8nf6LP8WButZsv3rUDlz3pRptQZR7dvJpnd83LSgVrMmGSEtxi\nOMr/KL/gRfm7fFLaQiEjY7Cflkr4trKRG5Uv8VH5ozwlLcOLJUMSCwSTFw1opIAnpFV8OvEh1qh/\nx8fVj/CYto7TXBznvVZyc7+yjZ8Zf8Y203/wHeNTvEc5hCvLJxoFAoFg/JBHeMVYs9wrZoQhxjP5\nDTHW2mFDTDALDDFXVbVjNiRXfLd7XTT1Z2dy9w97d2BWk4vQzpiKedU+N8MSZR57T5RNnzpK0aFh\nPeXYfeW02H5CAhFqSTB9CTsNbPlaPa/9+1xChcMLvWpfdnPHRw9SuUvkg5nsGDItgEAwNnrudEad\nsrGS2aYqv5z4snqy6smj8z1SNNkzOJcSxzEAis0naPJec+mijOWhmG5dvTK9eaZ3XCbRVr2a2cef\nB6D8xF46apejGs5bcezXaRPQnVf2py6MhFMnkA9abCnLTErqpJcKidEL8m1Y76wg+Ku2ZPu7AzA3\nH9NSl369Mcouhyt1TmchfOruQzz+7Dw6+pKeGX/YsYgun4u7rzuKomgkUHTb0CvXK4tcsHL9fKL2\n1AN/myXFSvaW5Fssf/RiY+pLCsbSgR06ZXr3QASqGeBLvMLntdfYHp3Bn8KLeTkyj/C5/kiTJHZJ\ndeyijoe4jTXGRjbajnGT4QT50ijfVe8+1rs00q0Hl9fPpXvO8T7fWExEZL50R396t9zljCj16qZ7\nTr37Sq/eGG12qzlsj81gR6yOHbEZdGupPceMxLnK2MwGewPXWk5RZ+x75+fUe1bpfUeBQDDO6I3j\nJ0ql1jvnlc6HMZZeNX6EYy7s5mR/acGLPz5KAvOJ0FUmoCxoKRzatnncFx+r227q6yoRT/1wjJpT\nPxz0xrgJFIwVViSjhBbTiHsThAdUjHlGEmNc41G9sbPOw0p3zH0JbcpGWF7dyfazyeTUb56dweKC\n1pT1gjr3TcpxPGC0j7G0XC+S6CU8xwsJ8L7gfn7jSOaI+UXuWtb3N2DRUlwgevrBRFzjl1KeijTH\n1ZIBLMS5+Z+O8eY/zKJ1bVKR8huP0uj4NvNmW3C2X6xEGHUuVcMY8wNGHVn1elw9s5Dez6bXZrqz\nSpeD3jkn4imnN5Ond7506r19d+vFe9A7Z+pZHrDqjMcNOo3q1ZNS9BvN1+Sz40t1hPOH+0dLf5S1\n/3mW6u0DyR12HWH15g709Aq9NvXqTcT59MrGKtfrj6/gulNhiBEIshS3fzaaJiFJGrmmFoxygJg6\nVq80dXCXzKW6+S0swUGM0RCljYfomHVVpsUad0wrcomd8BHbl1wRGHqqHaXaipKfWjmZKrhyonzh\nAzt47LnlnGxNKrLbj1bTN2jjY7fsx2YRq6HGA6OkssHcwAZzAwHVxMuhufwpspjt8Rmo5xxn4yhs\njdWzlXoeit7GVXIzNxuOsVE5QbHsG+MMAkH20qfZ2ZOoYWeilh2hGTSqhbrH1yh9XGNqYJ2pgVWm\nJmxSTBhMBAKB4B0wIk+MkuUeMbZhjxjroBs0DSQpgxLpIykSlhozoYbkBHeoMYIxT2/aM/NcPaNt\nyBCzs7GCz6yQUOTsc6F/f3AfL1gX4FHs9CkO/pC3jHv7d2darIxjiKhc942T7L+/miP3VAAQUbr5\ny08Wcd3XTlK2dzDDEgoEE08o38iuz9fSdMNIPWTGy72s+lGTyAWTZQhDjECQpUQTDjzRKvLMLUiS\nRpHlFB3BZZkW64qhyQrts1cx88DLAJSf3kPnjKVoytTr1mx3luNrCqL2x9BCKsHHW3F8tm5a9OBW\nc5xP3bGb37y6iF3Hk8krT7UW8siTV/PgbduoKBCD7/HELkd5j/kQ7zEfokd18Fx0Ic9FFnE4UTF0\njIrMLrWOXdE6HuZWFsntXKM0sF5rYBHtKFL2Kb8CASTnx9rUPPYmqtmnVrMvUc0ZrUi3To4UZpWh\niXXmBtabz1ClDFwhaQUCgWBqEhphiMnuECtxk52o2YYpEkRJxDAHPEQceZkWSxdrnWXIEBNuDONc\nrrekOfPMK+3FZQ3jDVkYDFs40FnBioq2TIv1jrFpMT4c2Mn3nDcA8FT+ct7lPUp+QuSOk1VY8VgL\neY1Btv7dPDQpTtRpYPMj81nxw2bm/7YToX4IpiIJo8TJO0o48LEqYo7hyR9rX5S13z1L1U6hd2Qj\n02AaTyCYuvSG5pBnTsY9KrYen1aGGIDumoVUHd+OKRLAHPZT0nSIrpnLMy3WuCNZFWz3VeH//llQ\nIdEcIvRMF3nv13OSnToYFI2/uukQha4gz+2YDYDba+fhJ6/nIzfsY83c1CEIBOlTLPu537KD+y07\n6Ei42Bybx+bwPPap1WgkV3NqSBxSKzmkVvIDrsNFiLXSGa6hgfXSGYol4S0jmLyENQPHtDIOqpUc\n1CrZp1bTS45uHRNxlhtaWGs8yxpjI/OVTgySKkbUAoFAME6EY8N5PqxZbogBCDmLMPUmk8rbvL2T\n3xBTOxyfJXQ2hKZqkzqzsCzDmro2XjyWzJ+6+fSsrDTEAGwMHeNZ62KajYWEZRM/Lt7A33e+kGmx\nJg0zXnPT9vkv0Gr7CXF5EM0gsefBWjpW5bL+4Qas/VcuhKJAMJFoEjTeUMj+j1fhLx8ZM2vmSz2s\n/O8mzP6JCUUvmHiE2igQZDE9oXnMzt0MQKHlNIoUJaFN/ZBVb6MpBtpnr6Tu8OsAVJ3YSU/NwpG5\nYqYIhhoblltLCf+5C4Dotn58FRo5a6ZHODpJgk2rGijL9/HLl5YQjRuIxg08+tIqGjoLuOeaQxgN\nY8RvFqRNueLlo8oOPirtoEd18EpiLi/F57NbrSVxnnbuxcoL2kJeYCFoUE8Pq6VGVtHEVTSTj1jV\nJ8gMmgYt5HNIq+BgLGl4OaGVEh8j15SRBIvkdlYqTay2NLLM0IpFEu7/AoFAMFGEYsOGCpsh+1f7\nBnKLcZ0zxDgGuhiomJ1hifQxlZmQbTJqUCURUIl0RqFi7HqZZH19y5AhZmdrDb0BO0V2vcQtkxMF\njQf8W/mnvPcCsCVnFtcOnmJt4GyGJZs82BI1zPD/Lf1tX8Q9P7l4pmN1Ls/8cgkr/7OJqmfdTN7g\nfwLB2HSsdLHv89X0zxnpjZjTFmLNdxopP5LdITsFwhAjEGQ1gXgx/lgxDmMPihyj0HKa7tCCTIt1\nRemasYTy03swh/2YIgHKzuynfc7qTIs1IZivKyDRGiR2MBmOq+/pAYylBiy10ycBwZL6borztvHT\n55bTM5AcnLx+eCZNPXl85padFDjFRP9EUyz7uVfew73GPXg0K9sTM9iaqGdLvP4ib4IGimnQinmC\n5D05i25W0cQqmlhJM3nCMCOYADSgVcvjqFbOEco5ppVxlHJ8b2dh1AlfYSfCMqWV5XILK5QWFsnt\nWKVzKywnd5h8gUAgmBIEo/lD29YpEO4xkFc6tG0f6MygJJeGJEvYZlnxH0waMoInQ5PeEFOR62de\naS/Hu4pQNZkXTs7lw8v3ZlqstFgWbeWm0DFets4H4L9KrmVRUxsONZphySYPRs3Fps8e5cADVRy5\ntxxkiYjLyNavzaLspiJW/dsZ7F3i9xJkF70LHBx4oIrONbkj9pu9MRb/so05z3SjxDSRe3IKIAwx\nAkGW0x2cj8PVA0CJ9ei0M8SoipHWeWup35/0DKo8tYuuuiUksIxRM/uQJAnbPZX4es6idoYhAb0/\n76PsiyUYXPqruqcSZQV+/uaet/jtK/PZczqZnLOpO59v/OYGPnHzbhbVdmdYwulDrhTiFsNRbjEc\nRVPhFCVs1WayVatnLzXELvA2OE0JpykZMszU0McSYxtL1VaWqm3M0nowIDybBJdOFIVGCjkulXJS\nKuEEpRyTyhhUrZdUv05ys0RqY4ncxhKpjdnGbpHnSCAQCDJIJO4goRpQ5DgmJYhBChHXLq1Pn4z4\nzzfEeHpAU0GaxLG+ANvs8wwxp0Jww+SPNnDD3EaOdyXzur10ejb3LDmAScnO0D2f8G1lr7GGAYOd\nfoODR4vW88XuVzMt1qRCiWus+GEL5Ts9bPv7mUPhmzrX5vKXJ5ey+IctzP5tF7JQKwSTHPdcOwcf\nqKJ93ciwlUo4wfzfdbLwNx2YAtnZlwlGRxhiBIIspzu0gJmu1wEosp5EIo42zW7tnpqFVJzajTXg\nwRCLUHlqF83FGzIt1oQgmWXsH6vG///OoAUTJAZVen/eR+lni5AM08cR22JK8KlNu6gv6+d3WxeR\nUGUCYTPffXY9Ny09zfuvPiKecFcYSYI5dDNH6ubjbCOoGdlPFbu1WnZptRym4qIwUM0U0KwU8Kyy\nBACbFmWh2s5SrY0Fagfz1U4qNI8IMSBAA3rIoUEu4rRUzEm5lBNyKWcoIiZdmiE6jwAL6WCJkjS8\nLJLacUnhkQeJi00gEAgyjEwomo/DklxoZjUM4ItlryEmZs0harFjCgdQEjGsvn5CzsJMi6WLbaY1\nmRdGhWhXlKjXgMk1uY1HSyu7KbAH6QvYGIxY2dJYx431DZkWKy1ytAif6XmDfyu/FYDNrgVc6zvN\nsqDIi3khZfsGueOjB9n/QDXHP1AKskTcprDvK3U0byxk9cNnyD0byrSYAsFF9M2xc/CBStrW54/Y\nLyU06p/vYcnP2rC7hWfXVERMUwkmCXrx1q90LJCxkrxd4djweqeLgy9eSjCWh804gEGOUGA8gzs0\nR79eWKdsrPIrXebXKRuKMqPQMms9cw78GYCyhn10LFhOzOpIXfcS2h2NmD+1Ihg0pP7RTa5IyjJF\n588atazAivLheuI/PgkaRJqjdD0dwH5XKZJ0abOICqlXVaRbltDJtaBXpkdC5zGVkAysXtpNcUmY\nnz+3BG8g+ce9fGAWx9pK+OSmXZTlj/5HR0i9si+i4+8b1SmLKPqrBT2u0a9HsyP1tWH16/dHxtRV\nQS99kN49p9emXr0LLg0bMdZxlnWchTBJw0y8it2xWnbFazkSLyd2wf8blEzsUurYRV1yhwmchJgv\ndzJf6WS+3Mk8uYsaqS/ptTBG/5gW6S44mqhHQ/EEtJnu6E/vNr6cEeV5dVVNolvL4axWSINazBmt\niAa1iIZE8XBosUvAJQVZaOxggaGTBYYOFhg7KJe9SBL6Lv1630OvLN02L6NubLR6ItqfQJBl6D3n\n9ToPvXqZMFqk+RBMUS0YGTbE2JR+fLHySz/dZCo7V+53lZIfPgOA3d1FyFZ4Ce2mHtPH4+mNufXG\n1SPGxhYw11qJnJvAdp+CwpXpjZ31xty642rd8fgobcqwYU4rf9g3B4A/nVjI2pkdnK8a2Ug9IR80\n21KW2Sx6iiNIemPuNMfV67xnWBc6zVvWWQB8r/R6ftDza6xaTL/NibqOJwK9ceVYKX5cw5tGVFb9\nvIm6HW62PTgTT13yv+xbnMMLjy9m0RPtLPx1B/mKvntMSOd3Den8VzYd3UGvp9Yr0/s7xpqtSqfN\nyyFdFUBvlk+vTb16qcreHh7n61xzRp2TWnVUEINOm9Io9frrbRy4v4rWCwwwqBp1r7tZ8kQbrt5z\nF5xzlEbHUof09Aq9unr92ES0me75xkqRrFeuMz14JUO+CUOMQJD1SHQHFlCXuxWAEvvRpCFmmuEu\nm0PF2V04BntQ1DhVR3ZwduVNmRZrwpBnO7HdXkzw2aSSGtnpwVBpwXJ13hg1px51ZV7+5t7t/OaV\nBRxtTM5ad7idPPyb67h7w2GuWdDMJdqnBBOITYqxzniWdcZkwtGopnA0XsbBWBUHE5UciFfSpbku\nqjeIlR3qDHaoM4b2mYgzQ3ZTTw8zpd5zLzdVUj9GScQgmOxoGvRjp0XLo1kroFnNp0ktoFkroEkt\nIKQzaTMaFfIAc5Vu5ihdzDF0M1/ppMLkEfe9QCAQZCnB6PB41mboy6Ak40Mgr4z87qQhxuHpwl29\nMMMSjY1ltmPIEDN4Kkbhyskf9nldfRt/PjiTWMJAY38+p3oLmFOcvdfPpz1vctBchV+20G1w8Zhz\nHZ/zvp5psSYtRSf93PbAIQ7/VQWH76tANcqoRpmD91fRcEsxy37YQu1LbhGuTHDF0YDOFS6Of6CM\ntrUXzNeoGrVv9rHkiTZyW84Zi0UemCmNMMQIBFOA7uCwIabYdpxj0zA8GZJEy+z1zN/zNAAlDYfo\nnLOckDN/jIrZi2VDPvG2MNF9gwAE/tCFnGfENE/P1D81cdhifPy2A2w9VMWzW2cTTyjE4gaeeHUZ\nB8+Ucd+NB8hzjOUKJriSmKQEy4xtLJPbhvZ1qU4OJCo5Ei/nmFrGsXgZXi5eoRjFwAm1lBOUjthv\nJEGFNEC11E+1NkA1/clt+qnAg0kS8XWvBJoGA9jo1Fx0aC7atTzatFzaSL53aLnv2NgC4CBMvdJL\nvdzDHKWbOcZu5ijdOOVR7m1hhBEIBIKsJRgZHr/bDP0ZlGR8COSenyemM4OSXDqWOXa8L/QC4D8b\nIxHRUMyT++HqsMRYV9fC6w3JxTt/OTaXOcVvZViq9MlXg3zK8yaP5N8MwHOORSyOtHEN2Rly7Uqg\nxDWW/ryNmjf62P63M+ldkANAoMTM1n+exeGPVrDk0TZqXu1DpAQUTDRRh0LDu4o4+Z5SBqsv9lat\nedPNksfbyGsW4fOmE9NsplYgmJp4IxWE4i6sBi8mJUShtYGgDyBQAAAgAElEQVRe5mZarCvOQFEd\n3vxKXP1tyJpK7b7XOH7d+zMt1oQhSRKOu8vw9kRJtIVBBd8v2nB+ugZqMi3dlUeS4JolrcysGOAX\nLyymuz9pkDrSXMo3nriBuzccZs3cVrFKfhJTKg+yST7GJuMxALQYdGiupFHm3OuEWkqPNpqfNsRQ\naNIKadLOi71+TsmSUSnGRxleyiQvpQxSPrSd/JxLCBmhlekR0oz0YaeXHHpx4NYcdOOkM+akW3PS\npbnowkn0MoaYLoLUyX3MlHupN/QwS+6lXumhWPKNvH/Ti3goEAgEgklOMDr8HLcb3BmUZHzwu0rQ\nkJDQsA26UWIREsbJveTZkGfEUGIi3h1FS8DgiSh5Sya3zAC3zD01ZIjZ3VJJq8dJVe5ghqVKn+tD\nJ9kemsE2az0A/y/vRqr6+qmNZL+BciLJawqx6cEjNNxazL5PVBPJTQat8tbZePPh2eQ2BFj6kzaq\n3ugXa3cE407/LBsn319K47sKiVsvUFhUjZot/Sz+dRv5jSKm8GRmQJmYUK/CECMQTAlkuvyLqcvd\nAkCZ4yC9selniEGSaJx3PUve+iUSkN/RSF77WQYqZoxZNVuRjDLO/1WJ93tNqANxiGr4Hm0l58FC\njMVXOr/S5KC80M+XP7iDl7bV8uqBmWhIBCMmfrZ5BbtOVvKh6w9S7RKDnmxAkqBC8lIhe9nIiaH9\nXs1Cg1pMQ7yIM1oRZ9QizmqFdHFxaLO3UZHpwkUXLvafb2s5b1tBpQA/hee9ivBTQAAXIXIJnnsP\n4SKEk3BWG26iKPiw4MPMIFY8WBnAxgA2POfeB7DRj50+7LgTDvypAgG/w5/BQZhKyUON1EeN0k+N\n1Eed3Eet3EeedN79KUaqAoFAMO0IhC80xGhks6ujajQTdBVh9/YgoeHob8dbMvn1E9vCHAa7k6G9\nBo5khyGmJt/Disp29rZVoCHxh0ML+OsN2zMtVtpIwBcHXqHJWECHIY+wbOLhmlv5TsNvcagikbce\nsgqz/9xD7et9HPtAGcfuKiNmTw4sPfV2Xv/3OeSdDLDw8XZqXu3nHQ9mBYLziOQoNN5cyJl3F9M3\n/+IIJUZ/nPoXepnzTBeu1rAIPzaJ0YA/OxfzP/lX8wixUeJzXB5CvRUIpggd/iVDhphi2wkUb4TE\nNOzdA64SuqsWU9p6CIC6fa/hKa1BU6bu0mnZZcT5QDXe7zejBRNowQRdP3ZT9mAxBtfU/d56mAwq\nd284wtIZnfz85eW4B5NZ2461lPDQEzdw5+rDbFp2AkUWA+5sxCWFWaG0sEJrGbE/oJlo0fJp1fJo\nTuTTSj4tWj4t5NGFC22MSZwEMj046Rk1M+LFSGg4CWMngl2JYCeKQ4skPxPFTgQzcSxaDBNxzBe8\nFFQUVGS0oW0FDVlTORlOTnzEpTpUJDRAQ0JFIoFMDIUoBmIoQ68oChEMhCQTIYwESb6Hzr0HJdOQ\n4cWHhYhuusvLI4cwJdIgZZKXSgaolDxUSB4qpQEqJA8uQsPeLWI0KhAIBILziMRziKlmjHIEoxzG\nJPuJqjmZFuuy8OVXYvcmczvmZIkhxrowh8FXkuMR35kY8aCKwSZnWKqxuXPxUfa2VQCwvbmK93uP\nUOHyZViq9LFrUf6x7zm+UnQXYdlEhzmPR6o28rXmvzD5/43MY/InWPqzNub8qotjHyrjxN1lxG1J\nHXlgjp0t/zKbvZ+LUP+rTmY83YMxIEIZCy4NTYKOVS4a3l1My3X5qOaL78i8hgBz/9hF3StujCGR\noGiyE5UU/qvwOjbnzAegV/WQr7PYMx2E6isQTBH8sVJ80RJyTN0ocowS0zE6ossyLVZGaJ69nsKu\nkxhiEay+AcpO7aNj3spMizWhKCVmcj5RxeCPmiGqkRhI0P0TN6WfK0KxTt8h+uzKPr72oVd5dsc8\nXj04E02TiMUNPPnWMrafrOHjN+2krngg02IKxgm7FGWe1MU8ui5a1BbVFLpw0kUyb0knyVeX5qQD\nF73kMMg7cz/WkPBixYt1eKHueC3Y7d2RfFc+Ok4Njg8GEkOeQoX4KZT8FOOjVPFSJg1SwiCl0iAO\nKZJpUQUCgUCQtUgE4kXkmpJ55ByGXvqj2W2IGSyopLRxHwA5/W1jHD05MOQZMVZaiJ0Lgew9FqXg\nqhSesZOImYX9LCnv4GBHOZom8/uDC7PaKwagNt7PFz2v8M38WwDY5ZzBk8Urubdnd4Ylyx4sg3GW\n/6iV+b/p5Mh9FZz8QAkJS9IgEyw1c+jLtRx7oJIZf+hh1q86sXULjyPB6PgqLTTeWsjZ24oJll28\n+FmOqFS/3s+8P3VRdMSXxf6c0wu3Yufhkls5ZRnO62aYgFjYwhAjGIXYBLU7EZebnqxjrfSNT8A5\n9cr0zqe3Kl+n276gyc7BxeQUbgagzHCQjsEUhpixxs96ourlO7/SZf7Rd8ex0Tp7LXVHXweg6vB2\neovmE7PYhw/S+w1Ga/ft56s/9XUVNqR2WvQbUq+sMdh1yngHZTVWzB9RiDx2BlSIdcboemyA3E+W\nIxlHGmOUNK//hM6DSK9Mj7hOPb2ysc4XfTsZuAlu3tDGvDlefv/KHDrdSVfhFnc+//zkJq5b2sit\na05jNibO1UvtSRbVSTAeGSP5eB8Fo+63KqnDpAVd+gqAidST3eZI6rqmcOrVOEa9+XO9Mr1LKt16\nY12meuUX3B4mElQzQDWpDW8RzYA7aqdPdeA+9+pVHfSrdjyqFa9mxaPa8KpWPJoVvzb5JyP0MJDA\nIUVwymEcUpg8OUiuFEq+y0HypODQdoEcoMjkwyWFkUfLbno5Y1S94UG6ZXry6NXTcyTVqafplEXG\ncE5NGFIbyyPm1P1KYjSBRNRFgSAD6D2MJluY2HR1lYnSD1MTiA4bYuxyL/3xS/QgSXdcMRFl55X7\nnRVDu+yebuRIDDWsc33o6EDRcOoHS8SV3jg21fjXtDCPWFsnAH2HYziuuthrOJ12AYI6AV9sOg80\nvXomkuPf25Y0cLCjHIDtTdXcsvgs1tzUbeqNqX0u/cUlzrDO/aHnXPEOr6traOBUeB9PW5YD8ETx\nauqlHlaGm3XlyygTMcYDUi5Q1xn/SecuRasWZ+Uvm1n0bDsnbivl5B2lhM/lkInnGDj1kXJO/1UZ\nta/1MeeZLooP+7DZU7cb07k8Yjr/cVynTK+eHvFJ5sxjSFM/MOr8/wadslT1Ws+95+s4Nkhj6AeB\nQhNN1xXQeH0hfXMvDj0GUHDKT/2LPdS97sbsTyT1ilSBFvTUSD3dYSz1U69uuu3qXP+6bY7+M41d\nT+98emWQ9vc45irj4Zxb8MjDB90YPk6pnD/GCd85whAjEEwhOn2LmX3OEFNgOoNJ9mW9G3+6dNYt\no6T5EDZ/P4Z4lJrjW2hYtinTYk04yjwXpntqiP46OSCPnQ0x+EQXzo+UIcnTey1GVYmPB+/Zy5b9\nlby8s5ZYQkHTJF7bP4MDDWW875rjLJnZlc0hyAWXiVmKU6F4qVC8l3R8TJPxaRYCmplAzIRfMye3\nteR2EBNRzUBYMxLBQFQzEMFAREu+4sioyCSQku9aMuxYAhm/sgwJcCX2IqEhoSGjIUnJXDYm4hil\nBEYSmIbe45hIYJWiWJUYVimW3D73bpNi5Ejh5EsOYyU2HB7sUpi+znUCgUAguMIEYsN5YhzG3gxK\nMj7ETVaCjgJs/j5kTcXu6cRXUp1pscbEssBJ4MVO0CDUHCM2mMDonPyhj2cWeVhc0cWh9lI0JJ45\nOJvF1zZmWqzL5v7wNhoo4pClCk2S+FbhzXy360nK4oOZFi3rsHjjLH2ijYW/6+DsjYUcvbOcweqk\nd7ymSDTeVEjjTYW4moPM+lMPdZvd2PquvFFakDmCBUZa1+fTuLGA7sVOGGU+xeyNMeOVXupf6iW/\nUayIyjY04AXHAn7kvJa4lHy2yZrKA8Et3B4+RNcEnFMYYgSCKUQ4nkd/qIZ8azOSpFFmOUxz8OpM\ni5URNFmhceH1LNjxewBKWo/QXb0IX0HFGDWzH8NVBRh9AQJ/dgMQORzA99tucu4umfbGGEXRuO6q\nVq6qb+XJ1xZyqjWp5A/4rDz23HJmlvdx74YD1BZ7MiypIBswSir5UpB8guOe33OP/bMAXBX46/Qa\nECM8gUAgEGQx/ljR0Lbd6M6gJOOHL68Smz+Zc8XZ34qPyW+IkXOM2GpNBBuTniaDh8MUrBtrSfLk\n4L1LT3KoPRliZk9zBQ19BdQX9GVYqstDQePv3S/whdIP0mvIISBbeKjodr7V/RQ5qggLmw6GqMrs\n53uY9UwPbWtyOXZ3OV3Lhl0nvDU29ny+lr2fqaF0n5e6zW5q3ujHJHLJTDlUBdzzHLSvzqN9dS59\n80d36ZCjKhW7Pczc3Evl7gGUuMg7m40MyhZ+kHcdW+2zhvY51RD/4HuexfH2CTuvWNsoEEwxOgeX\nDm1X2PYw7rODWYSnuI6+0plDn+sPvoiUSDckXXZhuy4X67W5Q5/Du334ftuDpk7f6+F8inKDfO69\nu7hv40HslmGl5UxHAf/6mxv56UtXMeDP7rBTAoFAIBAIBNlKYIQhpieDkowfvvzKoW2XuyWDkrwz\nnIuGx8Te/UE0LTv0idoCL8uqOoc+P7prDVkiui4uNcz/dj+HQUsaAlqN+Xy96A6C0mQLhZhdSBpU\nbffwri8d47YHDjH72W4MwWFji6ZIdK7MZdv/rufJZ6/ilX+fy6k7igkViN89mwkWGGl4dxFvPDSL\nJ/+0kud/tIhDH6u8yAgjJTTK9nq4+lsN3POBPdzw9ZPUbOkXRpgsZY+lms+V3jvCCDMz3sN3vU9O\nqBEGxHpJgWDK0elfxJyi5zDIMXKMPbiMrXhjk3+11URxduGN5Pa2oCRi2Pz9VJ7eSevcdZkWa8KR\nJAnHbYVoIZXwrqSrenj3IJqq4byn5PLyOUwRJAlWzWtnYV03z++cxZbDNahqcn3C9hM17G2o4F3L\nT7Fpxamh/DECgUAgEAgEgoknlMglrpowyFHMSgCT7Ceq6gWcn/wMFlSjISGhYfd2oURCJMzWTIs1\nJs4FFnpe8KFGNaLuBKGWGLYa/dyIk4W7VhzjUHsJCVXmeG8JbzbO4NoZZzMt1mUzO9rDl/s28x+F\nydDbp8ylPFR0Ow95n8WiTY+FhxNJwekAa799lqt+2ETT9YWcvamQrqXDoalUs0zbujza1uXBV6Hg\niI+KNweofHMA15mgiHQ9iQm7DHQvc9K93EnXchee+tQ5p6S4RsnBQaq391P7eh/WARGaLtsJSwYe\ny13HX3IWj9i/KXyEBwJbsKSdS/zSEYYYgWCKkVAtdPkWUenaB0ClbQ9e7/Q1xERtTprmX8PMw68C\nUHl6J33lswkWFo1RM/uRZImcu4oBhowxkb0+BhMaOR+yIyliiAhgs8R5/7XHWb+4hWe2zuVIYwkA\n0biBP+2az5tH67ht5QmuWdAoDFgCgUAgEAgEVwQZf6yYXHMbAA5jN/2R7DbExE1WAq5SHN5OJDRc\nXS3018zJtFhjIptlnIssePaGAPDsDWaNIabUGWDj3LO8cKwegJ/vW8nqqhYsxuw3VlwbPE2g38wP\n8q8H4IilgodrbuXrzX/GqKkZlm5qYAypzHquh1nP9hAoMtF4UwGNG4vonz0yPF/fwhz6FuZw6LPV\n2NvDlG33ULrbS/6uQcye7L/Wspmwy8Cg4SABQwN/+uViBmbph1a09USo2OmhfKeH8j1eTP6EfmJ5\nQdZwwlTCIwUb6TDmDe3LTQT5Qt8rrFKarpgcwhAjEExB2gZXDhliSi2HOTF4Kwlt+oZZ6qpdRlHb\nCZwDHciaSv3BlzhUey/IUz8645AxRoHw9nPGmAN+utUYJX+Vi2QQxpi3KckL8Mnb99Lc6uC3WxbT\n6k6GdvMGrDzx+jJe3DebD6w+wPo5jciycEEWCAQCgUAgmEhGGGJM3fRHZo5RY/LjKarF4U2Gy3J1\nNGWFIQYgd4VtyBDjOxomsUlFsWWHLnX74lNsO1vJYNhCX9DOU0cWc9+yfZkWa1y41X+EsGTkp3nr\nAdiXU8M3q27hH1qex4Awxown9t4oC3/dycJfd+IrN9O6Lo/W9fl0L3GinadTByosNHyglIYPJPMT\n5Z4IULzbS/EuL4X7fRhFbpkJQ1XAM9OGe3EO7oU59C104KuxAj9NHjCKEUaOqRQf9FGx00PFTg+5\nZ4VH01QjjsyThVfxm8KVqNLwc2tt8AwP9r+KSw3DFUx9JgwxgiuI3koAvbiaeu5/E3EJj7ViQa9c\nT1a9sjR/mxTVvP5KfNESckzdGOQYZaZDtAVWDR8Q1jndWOV69hy9ehktk2iYdzNLt/8SWUuQM9BJ\n2YH9dM5eMXrd0S6rt1dB+HXOaUj94wQNqQdcik7ZuKy+kEF6fw4GuY34W70ABA5FaEv4cX2kFMkw\nUolK6NxXiTRdQvTqxXXK9GSJjvHjREm9Ui+iU7esKsiDH9zLnhNlvLB9JoOB5LHuQTs/2ryOP+xZ\nzO1rTrK0vvNt73QAgozu1uw89+4hd9TyVPUAzOgn3bQSTF3XHE1ZpphTX3MmnXOaIzptxlMre4pO\nF2fU+4pjdcfp6jF67V7pRWupvsPbfU35lRLkHOk+VvXqXY4nmU67mk5ZXOecUUvqiaOEQaevUtLt\nq/RXDev1gXr93Gj9qmuU4wQCwaWi9wDIJrU5E6uvU+k5YyxcietMd8XBHy6Bc04wOYae4a+W7nM8\n3TI9nWOsn/uCut6cGirZDoCrvQliWjJe7js4pxpO/VyJRnTGv+bUzxS9Z1UIG5SDqcxHtDOKlgD3\nwTjOtcmxbZBQyrp6Y1W9c+qNj6065xt1HGuCO5Y38Pi2hQD88egi1te3UpITGDrETOoxrllJXQZg\ncnlTlln0xqrjNB69k/2EQwaesK4BYIdrBt+ecRNf6duMMl75YvW6wIkoG0sHdqbYr1dPX63Sv88v\nqJsTiDD/pS7mv9RFxKLQvjKP1jV5tK/MJWYf+cU8c+145to59eFypIRGfkOAwhN+io76KDzux9kW\nRrrwb9L5/6dK5Dkp3Wvj3PBXlWGw0kr/TBv9s+y45+bQN8dO3KKveEhxlcLTAUoPeSk5PEjxMR/G\niDqsr+SPUklvDizd6zjdemPdG3qy6hkb9NrVa1Ovnt750pVzLIPJBfK0GPL4Tt5GTplKhvZZtSif\nCr3BTdETSG872l5Bh9tsGlEKBIJLRqItcBXzTH8BoNKxZ6QhZhoSchTSNmM11We2AVBzZCv9FfVE\n7NNj2kqSJIzvqwRFIv5mMuFp9GgA7/904rq/DMmYHSvarhSyDKvmd7J0VjfbDlfy6p5aguGkUbR7\nIIdHn7+KqiIvt605wcLanlH1Z4FAIBAIBAJB+viiwxMnDlN3BiUZPwLOUuIGM4Z4BFM4gNXrJpSb\nHSGTHVc56f+TGwDfHh85a1xIWTIIXjWzk+2nyjjjLiCmKjy+dylfue6tTIs1btwb2U1YMvJ7S3Kh\n4Rv2OZi1OA/2v4rQ8iYWsz/BjNfczHjNTcIg0bMgh64lLjqXOnHPzUE7Lxy4pkj0zXHQN8fByfck\nPWZMvjiFx/0UHvdTcNJPbnMIR1sYWTjOAElzfsRlYLDKSv8MGwOz7fTPtDFQZyMxhtEFkh4vZmkG\n9ng9Sx/+z6ThJSy8xaY6CST+4FjGL51riEvD18mCeDtfCW6mRPVlTDZhiBEIpiidwSXMzn0RRYrj\nMnWQY+zAF7vSS6snF211qynoOok90IcSjzFzz0sc2/CB0VehTUEkScJ4R0XSGPNaUpmNngjiebQD\n18fKkC9hIDPdMBlVrlvewpoF7bx5oJo391URjiUNMq29Ln74p9VUFXnZuKKB1fXNKCJkmUAgEAgE\nAsG44D/fEGPsAVTI9mllScabV0tB70kAXJ1NWWOIsS90MPBSH1pEI94XI9wYxjrDmmmxLglZgvtX\n7uVrz98MwO6WKg62l7KkoivDko0PEvCx8DbCMeNQEuqXHAuQ0Phc/+vj5xkj0EWJa5QdHKTs4CDL\nfgExo0z3Iiedy1x0LXPSP9POiJAKQDTHQMeqXDpWDUdPkGMqOW1hXM0hXC0hXM0hcjoiODrCWHpi\nF3vQZDmqIhEsNBIoMeOrsDBYbUm+VyTfY45Ln7q2d0coOpH0Nio+7iP/TID9T3wfgIp9/zJRX0Ew\niThmKuWHrus4axp+thq0BB8O7+B9kf0Z7w+FIUYgmKLEVBvdwQWU2w8CUO3YwdGBOzMsVWbRZIWG\nBe9i8a5fIQF53c2UNeync9byTIt2xZAkCeO7yzEZYgQ39wMQawjh+X4brk+Uo+TqhQmcvljMCW5e\n3chNi0+xeV89rx+sI3Yu9lFrr4vHXljBs8653LzsFOvmN2M2iiVMAoFAIBAIBJdDTLURjudgMfhQ\n5Dh2o5tArDjTYl023vxhQ0xe+xm65q3MsESXhmyWcSzOwbc7mXdycJsnawwxALOK+tkwo5E3z9YB\n8JMdK/nWHc9jNU6NWE8S8OmBN4hIBl52zAfgRcdCfLKFr/RtxpKRsIXTG2NIpXKXh8pdHgAiDgX3\nHAfueQ565+bgnu8g4rpY/1aNMt46G966i0P0yVEVe3cUe1cEe3ck+d4TxdIfwzIQw+JJvhuCasZz\nncTNMuFcA5FcY/I9z0go30iw2EywxESgxESgyEw43zjCc+hSsfVGyD8TJK8hQMHpAEWnfNj69VIC\nCKYyHtnKz3KvZrN9/oj99dFuvjzwMjXm/gxJNhJhiBEIpjAt/tVDhpgy2yFOeTcRU1PH250O+HPL\naa9dSWXTbgBqDr2Jp7iakKsww5JdOSRJwrGpAMkoEXiuD4B4Z5SB77bi+kQFVGRYwEmMwxrjfeuO\nc8PSs7y4p563jtYMGWTcg3Z+9cYynt05nxuXNnD94jM4LGIgKBAIBAKBQJAug5FyLIak0cJp7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QhcvSDkCu3Iw7Pvudi6JXpqerpFt2CeeMY8NbWEte71kAChqP01G/Nu1zhnWSzUcdqXPQBJXU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CUBl6Jt4Ri6/HBW7Jj9+E1/Bfj63WxYLBafACqvvjrP5z73B32t1P2lX32m9vp267U+6x/v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\nkuBMPEg7rnD9gwUqQzkgR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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [], + "image/png": { + "width": 817, + "height": 851 + } + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "22glS6RNJJ7_" + }, + "source": [ + "The plot on the left is the deformed landscape with the $\\text{Uniform}(0,5)$ priors, and the plot on the right is the deformed landscape with the exponential priors. Notice that the posterior landscapes look different from one another, though the data observed is identical in both cases. The reason is as follows. Notice the exponential-prior landscape, top right figure, puts very little *posterior* weight on values in the upper right corner of the figure: this is because *the prior does not put much weight there*. On the other hand, the uniform-prior landscape is happy to put posterior weight in the upper-right corner, as the prior puts more weight there. \n", + "\n", + "Notice also the highest-point, corresponding the the darkest red, is biased towards `(0,0)` in the exponential case, which is the result from the exponential prior putting more prior weight in the `(0,0)` corner.\n", + "\n", + "The black dot represents the true parameters. Even with 1 sample point, the mountains attempts to contain the true parameter. Of course, inference with a sample size of `1` is incredibly naive, and choosing such a small sample size was only illustrative. \n", + "\n", + "It's a great exercise to try changing the sample size to other values (try `2`, `5`, `10`, `100`?...) and observing how our \"mountain\" posterior changes. " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "tMurBAY5JJ7_" + }, + "source": [ + "### Exploring the landscape using the MCMC\n", + "\n", + "We should explore the deformed posterior space generated by our prior surface and observed data to find the posterior mountain. However, we cannot naively search the space: any computer scientist will tell you that traversing $N$-dimensional space is exponentially difficult in $N$: the size of the space quickly blows-up as we increase $N$ (see [the curse of dimensionality](http://en.wikipedia.org/wiki/Curse_of_dimensionality)). What hope do we have to find these hidden mountains? The idea behind MCMC is to perform an intelligent search of the space. To say \"search\" implies we are looking for a particular point, which is perhaps not an accurate as we are really looking for a broad mountain. \n", + "\n", + "Recall that MCMC returns *samples* from the posterior distribution, not the distribution itself. Stretching our mountainous analogy to its limit, MCMC performs a task similar to repeatedly asking \"How likely is this pebble I found to be from the mountain I am searching for?\", and completes its task by returning thousands of accepted pebbles in hopes of reconstructing the original mountain.\n", + "\n", + "When I say MCMC intelligently searches, I really am saying MCMC will *hopefully* converge towards the areas of high posterior probability. MCMC does this by exploring nearby positions and moving into areas with higher probability. Again, perhaps \"converge\" is not an accurate term to describe MCMC's progression. Converging usually implies moving towards a point in space, but MCMC moves towards a *broader area* in the space and randomly walks in that area, picking up samples from that area.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "RFcv5Inpx_LI" + }, + "source": [ + "### Why Thousands of Samples?\n", + "\n", + "At first, returning thousands of samples to the user might sound like being an inefficient way to describe the posterior distributions. I would argue that this is extremely efficient. Consider the alternative possibilities:\n", + "\n", + "1. Returning a mathematical formula for the \"mountain ranges\" would involve describing a N-dimensional surface with arbitrary peaks and valleys.\n", + "2. Returning the \"peak\" of the landscape, while mathematically possible and a sensible thing to do as the highest point corresponds to most probable estimate of the unknowns, ignores the shape of the landscape, which we have previously argued is very important in determining posterior confidence in unknowns. \n", + "\n", + "Besides computational reasons, likely the strongest reason for returning samples is that we can easily use *The Law of Large Numbers* to solve otherwise intractable problems. I postpone this discussion for the next chapter. With the thousands of samples, we can reconstruct the posterior surface by organizing them in a histogram. \n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "YAwoy1cByBcj" + }, + "source": [ + "### Algorithms to perform MCMC\n", + "\n", + "There is a large family of algorithms that perform MCMC. Most of these algorithms can be expressed at a high level as follows: (Mathematical details can be found in the appendix.)\n", + "\n", + "1. Start at current position.\n", + "2. Propose moving to a new position (investigate a pebble near you).\n", + "3. Accept/Reject the new position based on the position's adherence to the data and prior distributions (ask if the pebble likely came from the mountain).\n", + "4. 1. If you accept: Move to the new position. Return to Step 1.\n", + " 2. Else: Do not move to new position. Return to Step 1. \n", + "5. After a large number of iterations, return all accepted positions.\n", + "\n", + "This way we move in the general direction towards the regions where the posterior distributions exist, and collect samples sparingly on the journey. Once we reach the posterior distribution, we can easily collect samples as they likely all belong to the posterior distribution. \n", + "\n", + "If the current position of the MCMC algorithm is in an area of extremely low probability, which is often the case when the algorithm begins (typically at a random location in the space), the algorithm will move in positions *that are likely not from the posterior* but better than everything else nearby. Thus the first moves of the algorithm are not reflective of the posterior.\n", + "\n", + "In the above algorithm's pseudocode, notice that only the current position matters (new positions are investigated only near the current position). We can describe this property as *memorylessness*, i.e. the algorithm does not care *how* it arrived at its current position, only that it is there. " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "0f8WWuHIyEdL" + }, + "source": [ + "### Other approximation solutions to the posterior\n", + "Besides MCMC, there are other procedures available for determining the posterior distributions. A Laplace approximation is an approximation of the posterior using simple functions. A more advanced method is [Variational Bayes](http://en.wikipedia.org/wiki/Variational_Bayesian_methods). All three methods, Laplace Approximations, Variational Bayes, and classical MCMC have their pros and cons. We will only focus on MCMC in this book. " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "jhSDgJ0oJJ8D" + }, + "source": [ + "## Example: Unsupervised Clustering using a Mixture Model\n", + "\n", + "\n", + "Suppose we are given the following dataset:\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "qkEz8lVojOdJ", + "outputId": "fe79505f-b2e7-407d-ee6d-531d06f75e4c", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 35 + } + }, + "source": [ + "reset_sess()\n", + "\n", + "import wget\n", + "url = 'https://raw.githubusercontent.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/master/Chapter3_MCMC/data/mixture_data.csv'\n", + "filename = wget.download(url)\n", + "filename" + ], + "execution_count": 40, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "'mixture_data (1).csv'" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 40 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "eykbFzU3JJ8E", + "outputId": "90bcaab5-fe4a-4a54-a334-2105f7315890", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 428 + } + }, + "source": [ + "plt.figure(figsize(12.5, 6))\n", + "data_ = np.loadtxt(\"mixture_data.csv\", delimiter=\",\")\n", + "\n", + "plt.hist(data_, bins=20, color=\"k\", histtype=\"stepfilled\", alpha=0.8)\n", + "plt.title(\"Histogram of the dataset\")\n", + "plt.ylim([0, None]);\n", + "print(data_[:10], \"...\")\n" + ], + "execution_count": 41, + "outputs": [ + { + "output_type": "stream", + "text": [ + "[115.85679142 152.26153716 178.87449059 162.93500815 107.02820697\n", + " 105.19141146 118.38288501 125.3769803 102.88054011 206.71326136] ...\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [], + "image/png": { + "width": 823, + "height": 375 + } + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "osH9H2ibJJ8K" + }, + "source": [ + "What does the data suggest? It appears the data has a bimodal form, that is, it appears to have two peaks, one near `120` and the other near `200`. Perhaps there are *two clusters* within this dataset. \n", + "\n", + "This dataset is a good example of the data-generation modeling technique from last chapter. We can propose *how* the data might have been created. I suggest the following data generation algorithm: \n", + "\n", + "1. For each data point, choose cluster 1 with probability $p$, else choose cluster 2. \n", + "2. Draw a random variate from a Normal distribution with parameters $\\mu_i$ and $\\sigma_i$ where $i$ was chosen in step 1.\n", + "3. Repeat.\n", + "\n", + "This algorithm would create a similar effect as the observed dataset, so we choose this as our model. Of course, we do not know $p$ or the parameters of the Normal distributions. Hence we must infer, or *learn*, these unknowns.\n", + "\n", + "Denote the Normal distributions $\\text{N}_0$ and $\\text{N}_1$ (having variables' index start at `0` is just Pythonic). Both currently have unknown mean and standard deviation, denoted $\\mu_i$ and $\\sigma_i, \\; i =0,1$ respectively. A specific data point can be from either $\\text{N}_0$ or $\\text{N}_1$, and we assume that the data point is assigned to $\\text{N}_0$ with probability $p$.\n", + "\n", + "\n", + "An appropriate way to assign data points to clusters is to use a [TF `Categorical` variable](https://www.tensorflow.org/probability/api_docs/python/tfp/distributions/Categorical). Its parameter is a $k$-length array of probabilities that must sum to one and its `value` attribute is a integer between `0` and $k-1$ randomly chosen according to the crafted array of probabilities (In our case $k=2$). *A priori*, we do not know what the probability of assignment to cluster 1 is, so we form a uniform variable on $(0, 1)$. We call call this $p_1$, so the probability of belonging to cluster 2 is therefore $p_2 = 1 - p_1$.\n", + "\n", + "Fortunately, we can we just give `[p1, p2]` to our `Categorical` variable. If needed, we can also to use `tf.stack()` to combine $p_1$ and $p_2$ into a vector that it can understand. We pass this vector into the `Categorical` variableto give an idea of the odds of choosing from our multiple distributions." + ] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "rhgmIUpVJJ8M", + "outputId": "52147ac7-8cb1-475a-ada1-b03c96135ba1", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 53 + } + }, + "source": [ + "p1 = tfd.Uniform(name='p', low=0., high=1.).sample()\n", + "p2 = 1 - p1\n", + "p = tf.stack([p1, p2])\n", + "\n", + "rv_assignment = tfd.Categorical(name=\"assignment\",probs=p) \n", + "assignment = rv_assignment.sample(sample_shape=data_.shape[0])\n", + "\n", + "[\n", + " p_,\n", + " assignment_\n", + "] = evaluate([\n", + " p,\n", + " assignment\n", + "])\n", + "\n", + "print(\"prior assignment, with p = %.2f:\" % p_[0])\n", + "print (assignment_[:10])\n" + ], + "execution_count": 42, + "outputs": [ + { + "output_type": "stream", + "text": [ + "prior assignment, with p = 0.98:\n", + "[0 0 0 0 0 0 0 0 0 0]\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "8dlV_Nu3JJ8R" + }, + "source": [ + "Looking at the above dataset, I would guess that the standard deviations of the two Normals are different. To maintain ignorance of what the standard deviations might be, we will initially model them as uniform on `0` to `100`. We will include both standard deviations in our model using a single line of TFP code:\n", + "\n", + "```python\n", + "rv_sds = tfd.Uniform(name=\"rv_sds\", low=[0., 0.], high=[100., 100.])\n", + "```\n", + "\n", + "Here, we are using a batch shape of 2, creating two independent distributions, that happen to have the same parameters. See the [colab on TFP shapes](https://https://github.com/tensorflow/probability/blob/master/tensorflow_probability/examples/jupyter_notebooks/Understanding_TensorFlow_Distributions_Shapes.ipynb) for more info.\n", + "\n", + "We also need to specify priors on the centers of the clusters. The centers are really the $\\mu$ parameters in these Normal distributions. Their priors can be modeled by a Normal distribution. Looking at the data, I have an idea where the two centers might be — I would guess somewhere around `120` and `190` respectively, though I am not very confident in these eyeballed estimates. Hence I will set $\\mu_0 = 120, \\mu_1 = 190$ and $\\sigma_0 = \\sigma_1 = 10$.\n", + "\n", + "Finally, we use the [MixtureSameFamily](https://https://www.tensorflow.org/probability/api_docs/python/tfp/distributions/MixtureSameFamily) distribution to implement a mixture of our two Normal distributions, employing our [Categorical](https://www.tensorflow.org/api_docs/python/tf/distributions/Categorical) distribution as our selecting function. " + ] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "7np_ug0CkqKI", + "outputId": "a67c331d-3a64-41dc-e70b-5308f5d406fa", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 125 + } + }, + "source": [ + "rv_sds = tfd.Uniform(name=\"rv_sds\", low=[0., 0.], high=[100., 100.])\n", + "print (str(rv_sds))\n", + "\n", + "rv_centers = tfd.Normal(name=\"rv_centers\", loc=[120., 190.], scale=[10., 10.])\n", + " \n", + "sds = rv_sds.sample()\n", + "print (\"shape of sds sample:\",sds.shape)\n", + "centers = rv_centers.sample()\n", + "\n", + "rv_assignments = tfd.Categorical(probs=tf.stack([0.4, 0.6]))\n", + "assignments = rv_assignments.sample(sample_shape=10)\n", + "\n", + "# and to combine it with the observations:\n", + "rv_observations = tfd.MixtureSameFamily(\n", + " mixture_distribution=rv_assignments,\n", + " components_distribution=tfd.Normal(\n", + " loc=centers,\n", + " scale=sds))\n", + "\n", + "observations = rv_observations.sample(sample_shape=10)\n", + "\n", + "[ \n", + " assignments_,\n", + " observations_,\n", + " sds_,\n", + " centers_\n", + "] = evaluate([\n", + " assignments,\n", + " observations,\n", + " sds,\n", + " centers\n", + "])\n", + "\n", + "print(\"simulated data: \", observations_[:4], \"...\")\n", + "print(\"Random assignments: \", assignments_[:4], \"...\")\n", + "print(\"Assigned center: \", centers_[:4], \"...\")\n", + "print(\"Assigned standard deviation: \", sds_[:4],\"...\")\n" + ], + "execution_count": 43, + "outputs": [ + { + "output_type": "stream", + "text": [ + "tfp.distributions.Uniform(\"rv_sds/\", batch_shape=[2], event_shape=[], dtype=float32)\n", + "shape of sds sample: (2,)\n", + "simulated data: [174.13666 186.25067 178.12958 352.41147] ...\n", + "Random assignments: [1 0 1 1] ...\n", + "Assigned center: [112.61994 189.91147] ...\n", + "Assigned standard deviation: [76.09806 57.22387] ...\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "OUWoWyXv2Jyg", + "colab_type": "text" + }, + "source": [ + "Similarly, in the joint `log_prob` function below, we create two clusters each with our priors on the centers and standard deviations. Then, we mix them in proportion to their weights as determined by our [`Categorical`](https://www.tensorflow.org/api_docs/python/tf/distributions/Categorical) variable, creating a mixture of gaussians distribution. Finally, for each data point, we generate a sample from that mixture.\n", + "\n", + "Note that this model is marginalizing out the cluster assignment variable, so that all the remaining random variables are continuous, making it all amenable to simple MCMC--[`HamiltonianMonteCarlo`](https://www.tensorflow.org/probability/api_docs/python/tfp/mcmc/HamiltonianMonteCarlo) in particular." + ] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "6LJmUYOGsms2", + "colab": {} + }, + "source": [ + "def joint_log_prob(data_, sample_prob_1, sample_centers, sample_sds):\n", + " \"\"\"\n", + " Joint log probability optimization function.\n", + " \n", + " Args:\n", + " data: tensor array representation of original data\n", + " sample_prob_1: Scalar representing probability (out of 1.0) of assignment \n", + " being 0\n", + " sample_sds: 2d vector containing standard deviations for both normal dists\n", + " in model\n", + " sample_centers: 2d vector containing centers for both normal dists in model\n", + " Returns: \n", + " Joint log probability optimization function.\n", + " \"\"\" \n", + " ### Create a mixture of two scalar Gaussians:\n", + " rv_prob = tfd.Uniform(name='rv_prob', low=0., high=1.)\n", + " sample_prob_2 = 1. - sample_prob_1\n", + " rv_assignments = tfd.Categorical(probs=tf.stack([sample_prob_1, sample_prob_2]))\n", + " \n", + " rv_sds = tfd.Uniform(name=\"rv_sds\", low=[0., 0.], high=[100., 100.])\n", + " rv_centers = tfd.Normal(name=\"rv_centers\", loc=[120., 190.], scale=[10., 10.])\n", + " \n", + " rv_observations = tfd.MixtureSameFamily(\n", + " mixture_distribution=rv_assignments,\n", + " components_distribution=tfd.Normal(\n", + " loc=sample_centers, # One for each component.\n", + " scale=sample_sds)) # And same here.\n", + " return (\n", + " rv_prob.log_prob(sample_prob_1)\n", + " + rv_prob.log_prob(sample_prob_2)\n", + " + tf.reduce_sum(rv_observations.log_prob(data_)) # Sum over samples.\n", + " + tf.reduce_sum(rv_centers.log_prob(sample_centers)) # Sum over components.\n", + " + tf.reduce_sum(rv_sds.log_prob(sample_sds)) # Sum over components.\n", + " )\n" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "Pr5WXm9CJJ8d" + }, + "source": [ + "\n", + "\n", + "We will use our HMC sampling methods to explore the space by using 25000 sample iterations below.\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "bnH5NLrkJJ8e", + "cellView": "code", + "colab": {} + }, + "source": [ + "number_of_steps=25000 #@param {type:\"slider\", min:0, max:50000, step:1000}\n", + "burnin=1000 #@param {type:\"slider\", min:0, max:2000, step:100}\n", + "num_leapfrog_steps=3\n", + "\n", + "# Set the chain's start state.\n", + "initial_chain_state = [\n", + " tf.constant(0.5, name='init_probs'),\n", + " tf.constant([120., 190.], name='init_centers'),\n", + " tf.constant([10., 10.], name='init_sds')\n", + "]\n", + "\n", + "# Since MCMC operates over unconstrained space, we need to transform the\n", + "# samples so they live in real-space.\n", + "unconstraining_bijectors = [\n", + " tfp.bijectors.Identity(), # Maps R to R.\n", + " tfp.bijectors.Identity(), # Maps R to R.\n", + " tfp.bijectors.Identity(), # Maps R to R.\n", + "]\n", + "\n", + "# Define a closure over our joint_log_prob.\n", + "unnormalized_posterior_log_prob = lambda *args: joint_log_prob(data_, *args)\n", + "\n", + "\n", + "# Initialize the step_size. (It will be automatically adapted.)\n", + "with tf.variable_scope(tf.get_variable_scope(), reuse=tf.AUTO_REUSE):\n", + " step_size = tf.get_variable(\n", + " name='step_size',\n", + " initializer=tf.constant(0.5, dtype=tf.float32),\n", + " trainable=False,\n", + " use_resource=True\n", + " )\n", + "\n", + "# Defining the HMC\n", + "hmc=tfp.mcmc.TransformedTransitionKernel(\n", + " inner_kernel=tfp.mcmc.HamiltonianMonteCarlo(\n", + " target_log_prob_fn=unnormalized_posterior_log_prob,\n", + " num_leapfrog_steps=num_leapfrog_steps,\n", + " step_size=step_size,\n", + " step_size_update_fn=tfp.mcmc.make_simple_step_size_update_policy(num_adaptation_steps=int(burnin * 0.8)),\n", + " state_gradients_are_stopped=True),\n", + " bijector=unconstraining_bijectors)\n", + "\n", + "# Sample from the chain.\n", + "[\n", + " posterior_prob,\n", + " posterior_centers,\n", + " posterior_sds\n", + "], kernel_results = tfp.mcmc.sample_chain(\n", + " num_results=number_of_steps,\n", + " num_burnin_steps=burnin,\n", + " current_state=initial_chain_state,\n", + " kernel=hmc)\n", + "\n", + "# Initialize any created variables.\n", + "init_g = tf.global_variables_initializer()\n", + "init_l = tf.local_variables_initializer()" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "UbTCLFtcCbHO", + "outputId": "132ea433-021d-4767-8c7b-cfed3c52d1de", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 53 + } + }, + "source": [ + "evaluate(init_g)\n", + "evaluate(init_l)\n", + "[\n", + " posterior_prob_,\n", + " posterior_centers_,\n", + " posterior_sds_,\n", + " kernel_results_\n", + "] = evaluate([\n", + " posterior_prob,\n", + " posterior_centers,\n", + " posterior_sds,\n", + " kernel_results\n", + "])\n", + " \n", + "new_step_size_initializer_ = kernel_results_.inner_results.is_accepted.mean()\n", + "print(\"acceptance rate: {}\".format(\n", + " new_step_size_initializer_))\n", + "new_step_size_initializer_\n", + "print(\"final step size: {}\".format(\n", + " kernel_results_.inner_results.extra.step_size_assign[-100:].mean()))\n" + ], + "execution_count": 46, + "outputs": [ + { + "output_type": "stream", + "text": [ + "acceptance rate: 0.80616\n", + "final step size: 0.053824782371520996\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "oMjUhg4GDP9f", + "colab_type": "text" + }, + "source": [ + "Let's examine the traces for our unknown parameters. In other words, the routes the unknown parameters (centers, precisions, and p ) have taken thus far." + ] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "mYnyXN4h5KIW", + "outputId": "8ec52372-b81e-4032-abd1-7e9b50c4b9a4", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 571 + } + }, + "source": [ + "plt.figure(figsize(12.5, 9))\n", + "plt.subplot(311)\n", + "lw = 1\n", + "center_trace = posterior_centers_\n", + "\n", + "# for pretty colors later in the book.\n", + "colors = [TFColor[3], TFColor[0]] if center_trace[-1, 0] > center_trace[-1, 1] \\\n", + " else [TFColor[0], TFColor[3]]\n", + "\n", + "plt.plot(center_trace[:, 0], label=\"trace of center 0\", c=colors[0], lw=lw)\n", + "plt.plot(center_trace[:, 1], label=\"trace of center 1\", c=colors[1], lw=lw)\n", + "plt.title(\"Traces of unknown parameters\")\n", + "leg = plt.legend(loc=\"upper right\")\n", + "leg.get_frame().set_alpha(0.7)\n", + "\n", + "plt.subplot(312)\n", + "std_trace = posterior_sds_\n", + "plt.plot(std_trace[:, 0], label=\"trace of standard deviation of cluster 0\",\n", + " c=colors[0], lw=lw)\n", + "plt.plot(std_trace[:, 1], label=\"trace of standard deviation of cluster 1\",\n", + " c=colors[1], lw=lw)\n", + "plt.legend(loc=\"upper left\")\n", + "\n", + "plt.subplot(313)\n", + "p_trace = posterior_prob_\n", + "plt.plot(p_trace, label=\"$p$: frequency of assignment to cluster 0\",\n", + " color=TFColor[2], lw=lw)\n", + "plt.xlabel(\"Steps\")\n", + "plt.ylim(0, 1)\n", + "plt.legend();" + ], + "execution_count": 47, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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Jy7ve4PXT4DWaO119p+yp8yJCWMvmQFozgA0VzVsvB0yZU8o9EwooynJQ6fKR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3ICP0DF+f232EPihbF8/T7qJum+e2Sf/FfqVsIIhfKoXTssmzSVT709bXo5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VvPTSS9x///0EBATQsGFDunXrxty5czX3iYiI4OWXX6ZBgwYEBATw4IMPMnjwYI4f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u0d+Xagi6GiwmcIfvcmDVTJf15jPSBlbassb2K+aEiwLR63fvoloc5uQD+MPRRsYvltVj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\nlKqutpScaHT1sGyvfD2hdn6fUug4l7v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RERER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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [], + "image/png": { + "width": 831, + "height": 554 + } + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "oG-Z7bx4JJ8n" + }, + "source": [ + "Notice the following characteristics:\n", + "\n", + "1. The traces converge, not to a single point, but to a *distribution* of possible points. This is *convergence* in an MCMC algorithm.\n", + "2. Inference using the first few thousand points is a bad idea, as they are unrelated to the final distribution we are interested in. Thus is it a good idea to discard those samples before using the samples for inference. We call this period before converge the *burn-in period*.\n", + "3. The traces appear as a random \"walk\" around the space, that is, the paths exhibit correlation with previous positions. This is both good and bad. We will always have correlation between current positions and the previous positions, but too much of it means we are not exploring the space well. This will be detailed in the Diagnostics section later in this chapter.\n", + "\n", + "\n", + "To achieve further convergence, we will perform more MCMC steps. In the pseudo-code algorithm of MCMC above, the only position that matters is the current position (new positions are investigated near the current position). To continue where we left off, we pass the current values of the unknown parameters into the `initial_chain_state()` variable. The values that we have already calculated will not be overwritten. This ensures that our sampling continues where it left off in the same way that it left off. \n", + "\n", + "We will sample the MCMC fifty thousand more times and visualize the progress below:" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "zYEHgLTTubLZ", + "cellView": "code", + "outputId": "2fec37a0-47d9-4b24-8207-2dc2b2513949", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 53 + } + }, + "source": [ + "number_of_steps=50000 #@param {type:\"slider\", min:0, max:50000, step:1000}\n", + "burnin=10000 #@param {type:\"slider\", min:0, max:2000, step:100}\n", + "\n", + "# Set the chain's start state.\n", + "initial_chain_state = [\n", + " tf.constant(posterior_prob_[-1], name='init_probs_2'),\n", + " tf.constant(posterior_centers_[-1], name='init_centers_2'),\n", + " tf.constant(posterior_sds_[-1], name='init_sds_2')\n", + "]\n", + "\n", + "\n", + "# Initialize the step_size. (It will be automatically adapted.)\n", + "with tf.variable_scope(tf.get_variable_scope(), reuse=tf.AUTO_REUSE):\n", + " step_size = tf.get_variable(\n", + " name='step_size_2',\n", + " #initializer=tf.constant(new_step_size_initializer_, dtype=tf.float32),\n", + " initializer=tf.constant(0.5, dtype=tf.float32),\n", + " trainable=False,\n", + " use_resource=True\n", + " )\n", + " step_size_update_fn=tfp.mcmc.make_simple_step_size_update_policy(num_adaptation_steps=int(burnin * 0.8))\n", + "\n", + "# Defining the HMC\n", + "hmc=tfp.mcmc.TransformedTransitionKernel(\n", + " inner_kernel=tfp.mcmc.HamiltonianMonteCarlo(\n", + " target_log_prob_fn=unnormalized_posterior_log_prob,\n", + " num_leapfrog_steps=num_leapfrog_steps,\n", + " step_size=step_size,\n", + " step_size_update_fn=step_size_update_fn,\n", + " state_gradients_are_stopped=True),\n", + " bijector=unconstraining_bijectors)\n", + "\n", + "# Sample from the chain.\n", + "[\n", + " posterior_prob_2,\n", + " posterior_centers_2,\n", + " posterior_sds_2\n", + "], kernel_results = tfp.mcmc.sample_chain(\n", + " num_results=number_of_steps,\n", + " num_burnin_steps=burnin,\n", + " current_state=initial_chain_state,\n", + " kernel=hmc)\n", + "\n", + "\n", + "# Initialize any created variables.\n", + "init_g = tf.global_variables_initializer()\n", + "init_l = tf.local_variables_initializer()\n", + "\n", + "evaluate(init_g)\n", + "evaluate(init_l)\n", + "[\n", + " posterior_prob_2_,\n", + " posterior_centers_2_,\n", + " posterior_sds_2_,\n", + " kernel_results_\n", + "] = evaluate([\n", + " posterior_prob_2,\n", + " posterior_centers_2,\n", + " posterior_sds_2,\n", + " kernel_results\n", + "])\n", + "\n", + "print(\"acceptance rate: {}\".format(\n", + " kernel_results_.inner_results.is_accepted.mean()))\n", + "new_step_size_initializer_\n", + "print(\"final step size: {}\".format(\n", + " kernel_results_.inner_results.extra.step_size_assign[-100:].mean()))\n" + ], + "execution_count": 48, + "outputs": [ + { + "output_type": "stream", + "text": [ + "acceptance rate: 0.5365\n", + "final step size: 0.05601028352975845\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "mD5kUVVNJJ8r", + "outputId": "032aa8e2-ce9a-4c10-aca1-8bd8dcab8a7e", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 299 + } + }, + "source": [ + "plt.figure(figsize(12.5, 4))\n", + "center_trace = posterior_centers_2_\n", + "prev_center_trace = posterior_centers_\n", + "\n", + "x = np.arange(25000)\n", + "plt.plot(x, prev_center_trace[:, 0], label=\"previous trace of center 0\",\n", + " lw=lw, alpha=0.4, c=colors[1])\n", + "plt.plot(x, prev_center_trace[:, 1], label=\"previous trace of center 1\",\n", + " lw=lw, alpha=0.4, c=colors[0])\n", + "\n", + "x = np.arange(25000, 75000)\n", + "plt.plot(x, center_trace[:, 0], label=\"new trace of center 0\", lw=lw, c=\"#5DA5DA\")\n", + "plt.plot(x, center_trace[:, 1], label=\"new trace of center 1\", lw=lw, c=\"#F15854\")\n", + "\n", + "plt.title(\"Traces of unknown center parameters\")\n", + "leg = plt.legend(loc=\"upper right\")\n", + "leg.get_frame().set_alpha(0.8)\n", + "plt.xlabel(\"Steps\");\n" + ], + "execution_count": 49, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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OHIgJEyagf//+J01rPKZ6I6KyU3QC2LAO+GW1TjdGQEY6sCwFSF6igyHlqTy/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\nAgICEBUVh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xttNeYrXPWBjr0DYRwd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T39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\nFnXW7yIiIiIiosTCwA8REREREaUspVQZgH/pd0e2c/NgwGjQaex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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [], + "image/png": { + "width": 831, + "height": 282 + } + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "PZdiLZKwJJ8u" + }, + "source": [ + "\n", + "\n", + "### Cluster Investigation\n", + "\n", + "We have not forgotten our main challenge: identify the clusters. We have determined posterior distributions for our unknowns. We plot the posterior distributions of the center and standard deviation variables below:" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "mrG6NEh30kAf", + "outputId": "4ddfd670-3b51-45dc-e87b-2c6153024f01", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 580 + } + }, + "source": [ + "plt.figure(figsize(12.5, 8))\n", + "std_trace = posterior_sds_2_\n", + "prev_std_trace = posterior_sds_\n", + "\n", + "_i = [1, 2, 3, 4]\n", + "for i in range(2):\n", + " plt.subplot(2, 2, _i[2 * i])\n", + " plt.title(\"Posterior of center of cluster %d\" % i)\n", + " plt.hist(center_trace[:, i], color=colors[i], bins=30,\n", + " histtype=\"stepfilled\")\n", + "\n", + " plt.subplot(2, 2, _i[2 * i + 1])\n", + " plt.title(\"Posterior of standard deviation of cluster %d\" % i)\n", + " plt.hist(std_trace[:, i], color=colors[i], bins=30,\n", + " histtype=\"stepfilled\")\n", + " # plt.autoscale(tight=True)\n", + "\n", + "plt.tight_layout()\n" + ], + "execution_count": 50, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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43kPpgB5K6bx1/gD049r+p2d5RZYkSRoB+y5rtHFD7Lv8gtK3+BglUHgLsBXl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\nRKyKiJ9HxNERsdk4+Z4cEV+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POziM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\nO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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [], + "image/png": { + "width": 885, + "height": 563 + } + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "6ilzTlnAJJ8x" + }, + "source": [ + "The MCMC algorithm has proposed that the most likely centers of the two clusters are near 120 and 200 respectively. Similar inference can be applied to the standard deviation. \n", + "\n", + "In the PyMC3 version of this chapter, we depict the posterior distributions for the labels each data point.However, in our TFP version, since our model marginalized out the assignment variable, we don't have traces for that variable from the MCMC. \n", + "\n", + "As a substitute, below we can generate a posterior predictive distribution over the assignments, and then generate some samples from it. \n", + "\n", + "Below is a visualization of this. The y-axis represents our samples from the posterior predictive distribution. The x-axis are the sorted values of the original data points. A red square is an assignment to cluster 0, and a blue square is an assignment to cluster 1. " + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "D_n2opAcvHQL", + "colab_type": "code", + "colab": {} + }, + "source": [ + "# put the data into a tensor\n", + "data = tf.constant(data_,dtype=tf.float32)\n", + "data = data[:,tf.newaxis]\n", + "\n", + "# This produces a cluster per MCMC chain\n", + "rv_clusters_1 = tfd.Normal(posterior_centers_2_[:, 0], posterior_sds_2_[:, 0])\n", + "rv_clusters_2 = tfd.Normal(posterior_centers_2_[:, 1], posterior_sds_2_[:, 1])\n", + "\n", + "# Compute the un-normalized log probabilities for each cluster\n", + "cluster_1_log_prob = rv_clusters_1.log_prob(data) + tf.math.log(posterior_prob_2_)\n", + "cluster_2_log_prob = rv_clusters_2.log_prob(data) + tf.math.log(1. - posterior_prob_2_)\n", + "\n", + "x = tf.stack([cluster_1_log_prob, cluster_2_log_prob],axis=-1)\n", + "y = tf.math.reduce_logsumexp(x,-1)\n", + "\n", + "# Bayes rule to compute the assignment probability: P(cluster = 1 | data) ∝ P(data | cluster = 1) P(cluster = 1)\n", + "log_p_assign_1 = cluster_1_log_prob - tf.math.reduce_logsumexp(tf.stack([cluster_1_log_prob, cluster_2_log_prob], axis=-1), -1)\n", + "\n", + "# Average across the MCMC chain\n", + "log_p_assign_1 = tf.math.reduce_logsumexp(log_p_assign_1, -1) - tf.math.log(tf.cast(log_p_assign_1.shape[-1], tf.float32))\n", + " \n", + "p_assign_1 = tf.exp(log_p_assign_1)\n", + "p_assign = tf.stack([p_assign_1,1-p_assign_1],axis=-1)\n", + "\n", + "# for charting \n", + "probs_assignments = p_assign_1 " + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "OusqBiRafHdm", + "colab_type": "code", + "outputId": "7e39502c-17d5-4ce4-c423-a31401b026e0", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 293 + } + }, + "source": [ + "burned_assignment_trace_ = evaluate(tfd.Categorical(probs=p_assign).sample(sample_shape=200))\n", + "plt.figure(figsize(12.5, 5))\n", + "plt.cmap = mpl.colors.ListedColormap(colors)\n", + "plt.imshow(burned_assignment_trace_[:, np.argsort(data_)],\n", + " cmap=plt.cmap, aspect=.4, alpha=.9)\n", + "plt.xticks(np.arange(0, data_.shape[0], 40),\n", + " [\"%.2f\" % s for s in np.sort(data_)[::40]])\n", + "plt.ylabel(\"posterior sample\")\n", + "plt.xlabel(\"value of $i$th data point\")\n", + "plt.title(\"Posterior labels of data points\");" + ], + "execution_count": 52, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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7G9s4nq7u8O37TuKhXQtjSa9JQ9D3zw9Jm58/UySpHhNPkiRJkiRJkiRJaoSJ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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [], + "image/png": { + "width": 847, + "height": 276 + } + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "N8DzxclkJJ82" + }, + "source": [ + "Looking at the above plot, it appears that the most uncertainty is between 150 and 170. The above plot slightly misrepresents things, as the x-axis is not a true scale (it displays the value of the $i$th sorted data point.) A more clear diagram is below, where we have estimated the *frequency* of each data point belonging to the labels 0 and 1. " + ] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "568aaUzJh9TD", + "outputId": "da455e90-fec5-44da-b48b-0aee7a887303", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 354 + } + }, + "source": [ + "plt.figure(figsize(12.5, 5))\n", + "\n", + "cmap = mpl.colors.LinearSegmentedColormap.from_list(\"BMH\", colors)\n", + "assign_trace = evaluate(probs_assignments)[np.argsort(data_)]\n", + "plt.scatter(data_[np.argsort(data_)], assign_trace, cmap=cmap,\n", + " c=(1 - assign_trace), s=50)\n", + "plt.ylim(-0.05, 1.05)\n", + "plt.xlim(35, 300)\n", + "plt.title(\"Probability of data point belonging to cluster 0\")\n", + "plt.ylabel(\"probability\")\n", + "plt.xlabel(\"value of data point\");" + ], + "execution_count": 53, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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kjQoTU5IkSZIkSZIkSRoVJqYkSZIkSZIkSZI0KkxMSZIkSZIkSZIkaVSYmJIk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\n/1BK+ZEkdyZ5WynlE7XWr46hx81JbhhDXZYsWbI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AAIAm9YxeMn611t1JPniU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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [], + "image/png": { + "width": 851, + "height": 337 + } + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "lmqny45RJJ86" + }, + "source": [ + "Even though we modeled the clusters using Normal distributions, we didn't get just a single Normal distribution that *best* fits the data (whatever our definition of best is), but a distribution of values for the Normal's parameters. How can we choose just a single pair of values for the mean and variance and determine a *sorta-best-fit* gaussian? \n", + "\n", + "One quick and dirty way (which has nice theoretical properties we will see in Chapter 5), is to use the *mean* of the posterior distributions. Below we overlay the Normal density functions, using the mean of the posterior distributions as the chosen parameters, with our observed data:" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "5U94W5ILJJ87", + "outputId": "6f6979a7-9fc9-4bab-abf7-2f691d3043da", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 338 + } + }, + "source": [ + "x_ = np.linspace(20, 300, 500)\n", + "posterior_center_means_ = evaluate(tf.reduce_mean(posterior_centers_2_, axis=0))\n", + "posterior_std_means_ = evaluate(tf.reduce_mean(posterior_sds_2_, axis=0))\n", + "posterior_prob_mean_ = evaluate(tf.reduce_mean(posterior_prob_2_, axis=0))\n", + "\n", + "plt.hist(data_, bins=20, histtype=\"step\", density=True, color=\"k\",\n", + " lw=2, label=\"histogram of data\")\n", + "y_ = posterior_prob_mean_ * evaluate(tfd.Normal(loc=posterior_center_means_[0],\n", + " scale=posterior_std_means_[0]).prob(x_))\n", + "plt.plot(x_, y_, label=\"Cluster 0 (using posterior-mean parameters)\", lw=3)\n", + "plt.fill_between(x_, y_, color=colors[1], alpha=0.3)\n", + "\n", + "y_ = (1 - posterior_prob_mean_) * evaluate(tfd.Normal(loc=posterior_center_means_[1],\n", + " scale=posterior_std_means_[1]).prob(x_))\n", + "plt.plot(x_, y_, label=\"Cluster 1 (using posterior-mean parameters)\", lw=3)\n", + "plt.fill_between(x_, y_, color=colors[0], alpha=0.3)\n", + "\n", + "plt.legend(loc=\"upper left\")\n", + "plt.title(\"Visualizing Clusters using posterior-mean parameters\");\n" + ], + "execution_count": 54, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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BGM8w59x6YETwMbp3Tvh5lHNuXYxtp7FvqM7IOW0O5TqaSF7PR6QvgnQFYXCMMmwB5gcf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UoHAAAAoLOp+P293kaSJDV+9DOdtpFUVmJx/c+NO1U3I/sNMMtklPztv+j4d/63ZFaAcAAAAKfK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C05yjaZxzDOlI6QBkKTI0TPJnuLy8g8vLO6hLOzzVEeKJjhCvxgJ9XmOpS2PG4f7m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\n2j3HaF5NtiB0a1chqbN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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [], + "image/png": { + "width": 841, + "height": 321 + } + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "btb0fNbsJJ8-" + }, + "source": [ + "### Important: Don't mix posterior samples\n", + "\n", + "In the above example, a possible (though less likely) scenario is that cluster 0 has a very large standard deviation, and cluster 1 has a small standard deviation. This would still satisfy the evidence, albeit less so than our original inference. Alternatively, it would be incredibly unlikely for *both* distributions to have a small standard deviation, as the data does not support this hypothesis at all. Thus the two standard deviations are *dependent* on each other: if one is small, the other must be large. In fact, *all* the unknowns are related in a similar manner. For example, if a standard deviation is large, the mean has a wider possible space of realizations. Conversely, a small standard deviation restricts the mean to a small area. \n", + "\n", + "During MCMC, we are returned vectors representing samples from the unknown posteriors. Elements of different vectors cannot be used together, as this would break the above logic: perhaps a sample has returned that cluster 1 has a small standard deviation, hence all the other variables in that sample would incorporate that and be adjusted accordingly. It is easy to avoid this problem though, just make sure you are indexing traces correctly. \n", + "\n", + "Another small example to illustrate the point. Suppose two variables, $x$ and $y$, are related by $x+y=10$. We model $x$ as a Normal random variable with mean 4 and explore 500 samples. " + ] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "BQ5Z84afueVa", + "cellView": "code", + "outputId": "fd716249-f97c-4c72-bdc5-dec4a38761b4", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 412 + } + }, + "source": [ + "number_of_steps = 10000 #@param {type:\"slider\", min:0, max:20000, step:1000}\n", + "burnin = 500 #@param {type:\"slider\", min:0, max:500, step:100}\n", + "\n", + "# Set the chain's start state.\n", + "initial_chain_state = [\n", + " tf.to_float(1.) * tf.ones([], name='init_x', dtype=tf.float32),\n", + "]\n", + "\n", + "# Initialize the step_size. (It will be automatically adapted.)\n", + "with tf.variable_scope(tf.get_variable_scope(), reuse=tf.AUTO_REUSE):\n", + " step_size = tf.get_variable(\n", + " name='step_size',\n", + " initializer=tf.constant(0.5, dtype=tf.float32),\n", + " trainable=False,\n", + " use_resource=True\n", + " )\n", + " step_size_update_fn=tfp.mcmc.make_simple_step_size_update_policy(num_adaptation_steps=int(burnin * 0.8))\n", + "\n", + "# Defining the HMC\n", + "# Since we're only using one distribution for our simplistic example, \n", + "# the use of the bijectors and unnormalized log_prob function is \n", + "# unneccesary\n", + "#\n", + "# While not a good example of what to do if you have dependent \n", + "# priors, this IS a good example of how to set up just one variable \n", + "# with a simple distribution\n", + "hmc=tfp.mcmc.HamiltonianMonteCarlo(\n", + " target_log_prob_fn=tfd.Normal(name=\"rv_x\", loc=tf.to_float(4.), \n", + " scale=tf.to_float(1./np.sqrt(10.))).log_prob,\n", + " num_leapfrog_steps=2,\n", + " step_size=step_size,\n", + " step_size_update_fn=step_size_update_fn,\n", + " state_gradients_are_stopped=True)\n", + "\n", + "# Sampling from the chain.\n", + "[\n", + " x_samples,\n", + "], kernel_results = tfp.mcmc.sample_chain(\n", + " num_results = number_of_steps,\n", + " num_burnin_steps = burnin,\n", + " current_state=initial_chain_state,\n", + " kernel=hmc,\n", + " name='HMC_sampling'\n", + ")\n", + "\n", + "y_samples = 10 - x_samples\n", + "\n", + "# Initialize any created variables for preconditions\n", + "init_g = tf.global_variables_initializer()\n", + "\n", + "#Running\n", + "evaluate(init_g)\n", + "[\n", + " x_samples_,\n", + " y_samples_,\n", + "] = evaluate([\n", + " x_samples,\n", + " y_samples,\n", + "])\n", + "\n", + "plt.figure(figsize=(12,6))\n", + "plt.plot(np.arange(number_of_steps), x_samples_, color=TFColor[3], alpha=0.8)\n", + "plt.plot(np.arange(number_of_steps), y_samples_, color=TFColor[0], alpha=0.8)\n", + "plt.title('Displaying (extreme) case of dependence between unknowns', fontsize=14)" + ], + "execution_count": 55, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Displaying (extreme) case of dependence between unknowns')" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 55 + }, + { + "output_type": "display_data", + "data": { + "image/png": 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O/dsnzqiFxpj/hp4DToXcVyI2IyKiArEygoiohtiWv4/BHff6AWNM3C1px3k+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4ulglCv7ynhuTt0BPr3tyeKaXx8xfXaPwyqs1gZZaJe66wKk852LQEh6Yw9HUYGyGOO1Z9mLdwD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XT2LHkiwWFpwnzNFrU3jaRarGME+EdqmkdDutTNPdfiBjBVido3lWEt4aY85TJ8cxNFnD9Ykq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\n5ZNh/SX9NdGb0gO7S3rv3i7159tjHjo0Z+fa9rvuOR7daPrVE9lPcyG+Kb9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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [], + "image/png": { + "width": 785, + "height": 377 + } + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "HLXOn3ecJJ9F" + }, + "source": [ + "As you can see, the two variables are not unrelated, and it would be wrong to add the $i$th sample of $x$ to the $j$th sample of $y$, unless $i = j$." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "xipPeX_2JJ9F" + }, + "source": [ + "### Returning to Clustering: Prediction\n", + "The above clustering can be generalized to $k$ clusters. Choosing $k=2$ allowed us to visualize the MCMC better, and examine some very interesting plots. \n", + "\n", + "What about prediction? Suppose we observe a new data point, say $x = 175$, and we wish to label it to a cluster. It is foolish to simply assign it to the *closer* cluster center, as this ignores the standard deviation of the clusters, and we have seen from the plots above that this consideration is very important. More formally: we are interested in the *probability* (as we cannot be certain about labels) of assigning $x=175$ to cluster 1. Denote the assignment of $x$ as $L_x$, which is equal to 0 or 1, and we are interested in $P(L_x = 1 \\;|\\; x = 175 )$. \n", + "\n", + "A naive method to compute this is to re-run the above MCMC with the additional data point appended. The disadvantage with this method is that it will be slow to infer for each novel data point. Alternatively, we can try a *less precise*, but much quicker method. \n", + "\n", + "We will use Bayes Theorem for this. If you recall, Bayes Theorem looks like:\n", + "\n", + "$$ P( A | X ) = \\frac{ P( X | A )P(A) }{P(X) }$$\n", + "\n", + "In our case, $A$ represents $L_x = 1$ and $X$ is the evidence we have: we observe that $x = 175$. For a particular sample set of parameters for our posterior distribution, $( \\mu_0, \\sigma_0, \\mu_1, \\sigma_1, p)$, we are interested in asking \"Is the probability that $x$ is in cluster 1 *greater* than the probability it is in cluster 0?\", where the probability is dependent on the chosen parameters.\n", + "$$\n", + "\\begin{align}\n", + "& P(L_x = 1| x = 175 ) \\gt P(L_x = 0| x = 175 ) \\\\[5pt]\n", + "& \\frac{ P( x=175 | L_x = 1 )P( L_x = 1 ) }{P(x = 175) } \\gt \\frac{ P( x=175 | L_x = 0 )P( L_x = 0 )}{P(x = 175) }\n", + "\\end{align}\n", + "$$\n", + "As the denominators are equal, they can be ignored (and good riddance, because computing the quantity $P(x = 175)$ can be difficult). \n", + "\n", + "$$ P( x=175 | L_x = 1 )P( L_x = 1 ) \\gt P( x=175 | L_x = 0 )P( L_x = 0 ) $$\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "SNr3_p57JJ9J", + "outputId": "6d759635-026a-4355-c430-102706c9dea3", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 35 + } + }, + "source": [ + "p_trace = posterior_prob_2_[25000:]\n", + "\n", + "x = 175\n", + "\n", + "v = (1 - p_trace) * evaluate(tfd.Normal(loc=center_trace[25000:, 1], \n", + " scale=std_trace[25000:, 1]).log_prob(x)) > \\\n", + " p_trace * evaluate(tfd.Normal(loc=center_trace[25000:, 0], \\\n", + " scale=std_trace[25000:, 0]).log_prob(x))\n", + " \n", + "\n", + "print(\"Probability of belonging to cluster 1:\", (v.mean()))\n" + ], + "execution_count": 56, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Probability of belonging to cluster 1: 0.02772\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "-y6ThW6NJJ9T" + }, + "source": [ + "Giving us a probability instead of a label is a very useful thing. Instead of the naive \n", + "\n", + " L = 1 if prob > 0.5 else 0\n", + "\n", + "we can optimize our guesses using a *loss function*, which the entire fifth chapter is devoted to. \n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "OExk531OzCvc" + }, + "source": [ + "## Diagnosing Convergence" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "_5NQ-0bUJJ9T" + }, + "source": [ + "### Autocorrelation\n", + "\n", + "Autocorrelation is a measure of how related a series of numbers is with itself. A measurement of 1.0 is perfect positive autocorrelation, 0 no autocorrelation, and -1 is perfect negative correlation. If you are familiar with standard *correlation*, then autocorrelation is just how correlated a series, $x_\\tau$, at time $t$ is with the series at time $t-k$:\n", + "\n", + "$$R(k) = \\text{Corr}( x_t, x_{t-k} ) $$\n", + "\n", + "For example, consider the two series:\n", + "\n", + "$$x_t \\sim \\text{Normal}(0,1), \\;\\; x_0 = 0$$\n", + "$$y_t \\sim \\text{Normal}(y_{t-1}, 1 ), \\;\\; y_0 = 0$$\n", + "\n", + "which have example paths like:" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "ED_OoWXEwRJ6", + "outputId": "ee0ebd09-1dd9-4be8-b17e-0c58db8c8a4e", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 289 + } + }, + "source": [ + "x_t = evaluate(tfd.Normal(loc=0., scale=1.).sample(sample_shape=200))\n", + "x_t[0] = 0\n", + "y_t = evaluate(tf.zeros(200))\n", + "for i in range(1, 200):\n", + " y_t[i] = evaluate(tfd.Normal(loc=y_t[i - 1], scale=1.).sample())\n", + "\n", + "plt.figure(figsize(12.5, 4))\n", + "plt.plot(y_t, label=\"$y_t$\", lw=3)\n", + "plt.plot(x_t, label=\"$x_t$\", lw=3)\n", + "plt.xlabel(\"time, $t$\")\n", + "plt.legend();\n" + ], + "execution_count": 57, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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PBhPvdJyHd/sRMTMfUhGNRNEGQUKInwoh5OE/38r1eoiIiIiIiIiIiIhGKxiV+OWmXuW2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B4sYogh++FgBgf+VJQLMh9L5LELzwSkDlofOpxKTfWRZCnENcvkdRlOsA/BvAZYqifEYIkd2j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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [], + "image/png": { + "width": 833, + "height": 272 + } + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "BaIWQyM1JJ9X" + }, + "source": [ + "One way to think of autocorrelation is \"If I know the position of the series at time $s$, can it help me know where I am at time $t$?\" In the series $x_t$, the answer is No. By construction, $x_t$ are random variables. If I told you that $x_2 = 0.5$, could you give me a better guess about $x_3$? No.\n", + "\n", + "On the other hand, $y_t$ is autocorrelated. By construction, if I knew that $y_2 = 10$, I can be very confident that $y_3$ will not be very far from 10. Similarly, I can even make a (less confident guess) about $y_4$: it will probably not be near 0 or 20, but a value of 5 is not too unlikely. I can make a similar argument about $y_5$, but again, I am less confident. Taking this to it's logical conclusion, we must concede that as $k$, the lag between time points, increases the autocorrelation decreases. We can visualize this:\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "vhLvAgtyJJ9a", + "outputId": "6f24d5af-785d-4221-b38d-dbc46dfa73da", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 300 + } + }, + "source": [ + "def autocorr(x):\n", + " # from http://tinyurl.com/afz57c4\n", + " result = np.correlate(x, x, mode='full')\n", + " result = result / np.max(result)\n", + " return result[result.size // 2:]\n", + "\n", + "colors = [TFColor[3], TFColor[0], TFColor[6]]\n", + "\n", + "x = np.arange(1, 200)\n", + "plt.bar(x, autocorr(y_t)[1:], width=1, label=\"$y_t$\",\n", + " edgecolor=colors[0], color=colors[0])\n", + "plt.bar(x, autocorr(x_t)[1:], width=1, label=\"$x_t$\",\n", + " color=colors[1], edgecolor=colors[1])\n", + "\n", + "plt.legend(title=\"Autocorrelation\")\n", + "plt.ylabel(\"measured correlation \\nbetween $y_t$ and $y_{t-k}$.\")\n", + "plt.xlabel(\"k (lag)\")\n", + "plt.title(\"Autocorrelation plot of $y_t$ and $x_t$ for differing $k$ lags.\");\n" + ], + "execution_count": 58, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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ttqNfv37rvYNtxYoV/PKXv+Szn/0sgwYNYsCAAey0005885vf5J///GeD517b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" + ] + }, + "metadata": { + "tags": [], + "image/png": { + "width": 867, + "height": 283 + } + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "5h6HaBT9JJ9d" + }, + "source": [ + "Notice that as $k$ increases, the autocorrelation of $y_t$ decreases from a very high point. Compare with the autocorrelation of $x_t$ which looks like noise (which it really is), hence we can conclude no autocorrelation exists in this series. \n", + "\n", + "\n", + "### How does this relate to MCMC convergence?\n", + "\n", + "By the nature of the MCMC algorithm, we will always be returned samples that exhibit autocorrelation (this is because of the step `from your current position, move to a position near you`).\n", + "\n", + "A chain that is not exploring the space well will exhibit very high autocorrelation. Visually, if the trace seems to meander like a river, and not settle down, the chain will have high autocorrelation.\n", + "\n", + "This does not imply that a converged MCMC has low autocorrelation. Hence low autocorrelation is not necessary for convergence, but it is sufficient. TFP has a built-in autocorrelation tools as well. " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "yJstgksqJJ9d" + }, + "source": [ + "### Thinning\n", + "\n", + "Another issue can arise if there is high-autocorrelation between posterior samples. Many post-processing algorithms require samples to be *independent* of each other. This can be solved, or at least reduced, by only returning to the user every $n$th sample, thus removing some autocorrelation. Below we perform an autocorrelation plot for $y_t$ with differing levels of thinning:" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "SIeWr23CJJ9e", + "outputId": "5e643e13-6bc7-4443-e7b5-7848ee14c887", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 300 + } + }, + "source": [ + "max_x = 200 // 3 + 1\n", + "x = np.arange(1, max_x)\n", + "\n", + "plt.bar(x, autocorr(y_t)[1:max_x], edgecolor=colors[0],\n", + " label=\"no thinning\", color=colors[0], width=1)\n", + "plt.bar(x, autocorr(y_t[::2])[1:max_x], edgecolor=colors[1],\n", + " label=\"keeping every 2nd sample\", color=colors[1], width=1)\n", + "plt.bar(x, autocorr(y_t[::3])[1:max_x], width=1, edgecolor=colors[2],\n", + " label=\"keeping every 3rd sample\", color=colors[2])\n", + "\n", + "plt.autoscale(tight=True)\n", + "plt.legend(title=\"Autocorrelation plot for $y_t$\", loc=\"upper right\")\n", + "plt.ylabel(\"measured correlation \\nbetween $y_t$ and $y_{t-k}$.\")\n", + "plt.xlabel(\"k (lag)\")\n", + "plt.title(\"Autocorrelation of $y_t$ (no thinning vs. thinning) \\\n", + "at differing $k$ lags.\");\n" + ], + "execution_count": 59, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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hSZMmREZG5nYaAJQpU4bp06fndhr3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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [], + "image/png": { + "width": 858, + "height": 283 + } + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "QyjO-xpVJJ9h" + }, + "source": [ + "With more thinning, the autocorrelation drops quicker. There is a tradeoff though: higher thinning requires more MCMC iterations to achieve the same number of returned samples. For example, 10 000 samples unthinned is 100 000 with a thinning of 10 (though the latter has less autocorrelation). \n", + "\n", + "What is a good amount of thinning? The returned samples will always exhibit some autocorrelation, regardless of how much thinning is done. So long as the autocorrelation tends to zero, you are probably ok. Typically thinning of more than 10 is not necessary." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "_M1NvityzPyu" + }, + "source": [ + "## Useful tips for MCMC\n", + "\n", + "Bayesian inference would be the *de facto* method if it weren't for MCMC's computational difficulties. In fact, MCMC is what turns most users off practical Bayesian inference. Below I present some good heuristics to help convergence and speed up the MCMC engine:" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "12fpbZ02JJ9n" + }, + "source": [ + "### Intelligent starting values\n", + "\n", + "It would be great to start the MCMC algorithm off near the posterior distribution, so that it will take little time to start sampling correctly. We can aid the algorithm by telling where we *think* the posterior distribution will be by specifying the `testval` parameter in the `Stochastic` variable creation. In many cases we can produce a reasonable guess for the parameter. For example, if we have data from a Normal distribution, and we wish to estimate the $\\mu$ parameter, then a good starting value would be the *mean* of the data. \n", + "```python\n", + "mu = tfd.Uniform(name=\"mu\", low=0., high=100.).sample(seed=data.mean())\n", + "```\n", + "For most parameters in models, there is a frequentist estimate of it. These estimates are a good starting value for our MCMC algorithms. Of course, this is not always possible for some variables, but including as many appropriate initial values is always a good idea. Even if your guesses are wrong, the MCMC will still converge to the proper distribution, so there is little to lose." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "QYPtfunzzTcB" + }, + "source": [ + "#### Priors\n", + "\n", + "If the priors are poorly chosen, the MCMC algorithm may not converge, or at least have difficulty converging. Consider what may happen if the prior chosen does not even contain the true parameter: the prior assigns 0 probability to the unknown, hence the posterior will assign 0 probability as well. This can cause pathological results.\n", + "\n", + "For this reason, it is best to carefully choose the priors. Often, lack of covergence or evidence of samples crowding to boundaries implies something is wrong with the chosen priors (see *Folk Theorem of Statistical Computing* below). " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "6Nxc-2H7zZFR" + }, + "source": [ + "#### The Folk Theorem of Statistical Computing\n", + "\n", + "> *If you are having computational problems, probably your model is wrong.*" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "b0DSjmO5JJ9o" + }, + "source": [ + "## Conclusion\n", + "\n", + "TFP provides a very strong backend to performing Bayesian inference, mostly because it allows the user to fine-tune the inner workings of MCMC. " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "eESYGOBtJJ9o" + }, + "source": [ + "### References\n", + "\n", + "[1] Tensorflow Probability API docs. https://www.tensorflow.org/probability/api_docs/python/tfp" + ] + } + ] +} \ No newline at end of file diff --git a/Chapter3_MCMC/IntroMCMC.ipynb b/Chapter3_MCMC/IntroMCMC.ipynb deleted file mode 100644 index ae11a7f3..00000000 --- a/Chapter3_MCMC/IntroMCMC.ipynb +++ /dev/null @@ -1,36 +0,0 @@ -{ - "metadata": { - "name": "IntroMCMC" - }, - "nbformat": 3, - "nbformat_minor": 0, - "worksheets": [ - { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This should be a brief intro to what MCMC is, framed in words like *convergence*, *iterations*, *being fit to the data*, *learning*. The chapter should introduce:\n", - "\n", - "- MCMC convergence + some diagnogstics\n", - "- Matplot.plot\n", - "- MAP (and how to estimate it) (maybe move this to another section)\n", - "- mcmc.sample()\n", - "- mcmc.trace() use\n", - "- map.fit()\n" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [], - "language": "python", - "metadata": {}, - "outputs": [] - } - ], - "metadata": {} - } - ] -} \ No newline at end of file diff --git a/Chapter3_MCMC/README.md b/Chapter3_MCMC/README.md new file mode 100644 index 00000000..a060bad8 --- /dev/null +++ b/Chapter3_MCMC/README.md @@ -0,0 +1,4 @@ +Chapter 3: Opening the black box of MCMC (Markov Chain Monte Carlo) +================================================================== + +### [Read it online here](http://nbviewer.ipython.org/urls/raw.github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/master/Chapter3_MCMC/Ch3_IntroMCMC_PyMC2.ipynb) diff --git 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a/Chapter3_MCMC/github_pull.py b/Chapter3_MCMC/github_pull.py new file mode 100644 index 00000000..1fa1f4ae --- /dev/null +++ b/Chapter3_MCMC/github_pull.py @@ -0,0 +1,83 @@ +#github data scrapper + +""" +variables of interest: + indp. variables + - language, given as a binary variable. Need 4 positions for 5 languages + - #number of days created ago, 1 position + - has wiki? Boolean, 1 position + - followers, 1 position + - following, 1 position + - constant + + dep. variables + -stars/watchers + -forks + +""" +from json import loads +import datetime +import numpy as np +from requests import get + + +MAX = 8000000 +today = datetime.datetime.today() +randint = np.random.randint +N = 120 #sample size. +auth = ("username", "password" ) + +language_mappings = {"Python": 0, "JavaScript": 1, "Ruby": 2, "Java":3, "Shell":4, "PHP":5} + +#define data matrix: +X = np.zeros( (N , 12), dtype = int ) + +for i in xrange(N): + is_fork = True + is_valid_language = False + + while is_fork == True or is_valid_language == False: + is_fork = True + is_valid_language = False + + params = {"since":randint(0, MAX ) } + r = get("https://api.github.com/repositories", params = params, auth=auth ) + results = loads( r.text )[0] + #im only interested in the first one, and if it is not a fork. + is_fork = results["fork"] + + r = get( results["url"], auth = auth) + + #check the language + repo_results = loads( r.text ) + try: + language_mappings[ repo_results["language" ] ] + is_valid_language = True + except: + pass + + #languages + X[ i, language_mappings[ repo_results["language" ] ] ] = 1 + + #delta time + X[ i, 6] = ( today - datetime.datetime.strptime( repo_results["created_at"][:10], "%Y-%m-%d" ) ).days + + #haswiki + X[i, 7] = repo_results["has_wiki"] + + #get user information + r = get( results["owner"]["url"] , auth = auth) + user_results = loads( r.text ) + X[i, 8] = user_results["following"] + X[i, 9] = user_results["followers"] + + #get dep. data + X[i, 10] = repo_results["watchers_count"] + X[i, 11] = repo_results["forks_count"] + print + print " -------------- " + print i, ": ", results["full_name"], repo_results["language" ], repo_results["watchers_count"], repo_results["forks_count"] + print " -------------- " + print + +np.savetxt("data/github_data.csv", X, delimiter=",", fmt="%d" ) diff --git a/Chapter4_TheGreatestTheoremNeverTold/Ch4_LawOfLargeNumbers_PyMC2.ipynb b/Chapter4_TheGreatestTheoremNeverTold/Ch4_LawOfLargeNumbers_PyMC2.ipynb new file mode 100644 index 00000000..7c0a8d9d --- /dev/null +++ b/Chapter4_TheGreatestTheoremNeverTold/Ch4_LawOfLargeNumbers_PyMC2.ipynb @@ -0,0 +1,1174 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Chapter 4\n", + "______\n", + "\n", + "## The greatest theorem never told\n", + "\n", + "\n", + "This chapter focuses on an idea that is always bouncing around our minds, but is rarely made explicit outside books devoted to statistics. In fact, we've been using this simple idea in every example thus far. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### The Law of Large Numbers\n", + "\n", + "Let $Z_i$ be $N$ independent samples from some probability distribution. According to *the Law of Large numbers*, so long as the expected value $E[Z]$ is finite, the following holds,\n", + "\n", + "$$\\frac{1}{N} \\sum_{i=1}^N Z_i \\rightarrow E[ Z ], \\;\\;\\; N \\rightarrow \\infty.$$\n", + "\n", + "In words:\n", + "\n", + "> The average of a sequence of random variables from the same distribution converges to the expected value of that distribution.\n", + "\n", + "This may seem like a boring result, but it will be the most useful tool you use." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Intuition \n", + "\n", + "If the above Law is somewhat surprising, it can be made clearer by examining a simple example. \n", + "\n", + "Consider a random variable $Z$ that can take only two values, $c_1$ and $c_2$. Suppose we have a large number of samples of $Z$, denoting a specific sample $Z_i$. The Law says that we can approximate the expected value of $Z$ by averaging over all samples. Consider the average:\n", + "\n", + "\n", + "$$ \\frac{1}{N} \\sum_{i=1}^N \\;Z_i $$\n", + "\n", + "\n", + "By construction, $Z_i$ can only take on $c_1$ or $c_2$, hence we can partition the sum over these two values:\n", + "\n", + "\\begin{align}\n", + "\\frac{1}{N} \\sum_{i=1}^N \\;Z_i\n", + "& =\\frac{1}{N} \\big( \\sum_{ Z_i = c_1}c_1 + \\sum_{Z_i=c_2}c_2 \\big) \\\\\\\\[5pt]\n", + "& = c_1 \\sum_{ Z_i = c_1}\\frac{1}{N} + c_2 \\sum_{ Z_i = c_2}\\frac{1}{N} \\\\\\\\[5pt]\n", + "& = c_1 \\times \\text{ (approximate frequency of $c_1$) } \\\\\\\\ \n", + "& \\;\\;\\;\\;\\;\\;\\;\\;\\; + c_2 \\times \\text{ (approximate frequency of $c_2$) } \\\\\\\\[5pt]\n", + "& \\approx c_1 \\times P(Z = c_1) + c_2 \\times P(Z = c_2 ) \\\\\\\\[5pt]\n", + "& = E[Z]\n", + "\\end{align}\n", + "\n", + "\n", + "Equality holds in the limit, but we can get closer and closer by using more and more samples in the average. This Law holds for almost *any distribution*, minus some important cases we will encounter later.\n", + "\n", + "##### Example\n", + "____\n", + "\n", + "\n", + "Below is a diagram of the Law of Large numbers in action for three different sequences of Poisson random variables. \n", + "\n", + " We sample `sample_size = 100000` Poisson random variables with parameter $\\lambda = 4.5$. (Recall the expected value of a Poisson random variable is equal to its parameter.) We calculate the average for the first $n$ samples, for $n=1$ to `sample_size`. " + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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UXpRQUVs5u6SlpXkGrQMHDqRVq1asX7+eFi1acMkll3i+gq9cuZKYmBi6dOnC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wcFpW6uff+5BOMp/vJfTwBw+Q1LePjPu7jubY08dv9egsMxZs8rweOzs+GqBezf2c0jf8n8m9/1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\nnimeu6GqFlXVIcBZwEUdz70CfKCqDqmqw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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib inline\n", + "import numpy as np\n", + "from IPython.core.pylabtools import figsize\n", + "import matplotlib.pyplot as plt\n", + "\n", + "figsize(12.5, 5)\n", + "import pymc as pm\n", + "\n", + "sample_size = 100000\n", + "expected_value = lambda_ = 4.5\n", + "poi = pm.rpoisson\n", + "N_samples = range(1, sample_size, 100)\n", + "\n", + "for k in range(3):\n", + "\n", + " samples = poi(lambda_, size=sample_size)\n", + "\n", + " partial_average = [samples[:i].mean() for i in N_samples]\n", + "\n", + " plt.plot(N_samples, partial_average, lw=1.5, label=\"average \\\n", + "of $n$ samples; seq. %d\" % k)\n", + "\n", + "\n", + "plt.plot(N_samples, expected_value * np.ones_like(partial_average),\n", + " ls=\"--\", label=\"true expected value\", c=\"k\")\n", + "\n", + "plt.ylim(4.35, 4.65)\n", + "plt.title(\"Convergence of the average of \\n random variables to its \\\n", + "expected value\")\n", + "plt.ylabel(\"average of $n$ samples\")\n", + "plt.xlabel(\"# of samples, $n$\")\n", + "plt.legend();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Looking at the above plot, it is clear that when the sample size is small, there is greater variation in the average (compare how *jagged and jumpy* the average is initially, then *smooths* out). All three paths *approach* the value 4.5, but just flirt with it as $N$ gets large. Mathematicians and statistician have another name for *flirting*: convergence. \n", + "\n", + "Another very relevant question we can ask is *how quickly am I converging to the expected value?* Let's plot something new. For a specific $N$, let's do the above trials thousands of times and compute how far away we are from the true expected value, on average. But wait — *compute on average*? This is simply the law of large numbers again! For example, we are interested in, for a specific $N$, the quantity:\n", + "\n", + "$$D(N) = \\sqrt{ \\;E\\left[\\;\\; \\left( \\frac{1}{N}\\sum_{i=1}^NZ_i - 4.5 \\;\\right)^2 \\;\\;\\right] \\;\\;}$$\n", + "\n", + "The above formulae is interpretable as a distance away from the true value (on average), for some $N$. (We take the square root so the dimensions of the above quantity and our random variables are the same). As the above is an expected value, it can be approximated using the law of large numbers: instead of averaging $Z_i$, we calculate the following multiple times and average them:\n", + "\n", + "$$ Y_k = \\left( \\;\\frac{1}{N}\\sum_{i=1}^NZ_i - 4.5 \\; \\right)^2 $$\n", + "\n", + "By computing the above many, $N_y$, times (remember, it is random), and averaging them:\n", + "\n", + "$$ \\frac{1}{N_Y} \\sum_{k=1}^{N_Y} Y_k \\rightarrow E[ Y_k ] = E\\;\\left[\\;\\; \\left( \\frac{1}{N}\\sum_{i=1}^NZ_i - 4.5 \\;\\right)^2 \\right]$$\n", + "\n", + "Finally, taking the square root:\n", + "\n", + "$$ \\sqrt{\\frac{1}{N_Y} \\sum_{k=1}^{N_Y} Y_k} \\approx D(N) $$ " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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nc/DgQY4cOeI6lq+vL9deey0fffSR641RmzZt+OSTT1zLBR33vvvu45NPPnGd\nT0ZGBgsWLHC9HVGXYYXgQt0cXtP18+JtB8jS4b+UUkqpS8LLy4vp06ezfv16WrZsSaNGjXj88cc5\nevQoR48eZfjw4bz++uvUqVOHtm3bMmDAAB555BHX/pGRkfzxxx+Eh4fzz3/+k88++4zq1asD8MEH\nH3DmzBnatWtHaGgogwcPdjUH6dmzJ3//+9958MEHXf0WDh06BMATTzzBxIkTCQ0NdXXs/eKLL1yj\n51x77bW89957rnb0U6ZMIT4+nrCwMCZMmEC/fv08nu9f//pXoqOj6dWrF61bt3Z13s3Ltm3buOuu\nuwgKCqJHjx4MGTLEdSOcO8Z7772XwMBAmjdvzg033OD4iT5YlY3Y2FjmzZtHkyZNaNOmjaszb3R0\nND169CAqKooGDRrQvXt3EhISPB6rTp06VK9enWbNmhEdHc2bb75JWFgYYHX+DQ0N5ZZbbiE4OJio\nqChXh+OGDRvSu3dvWrVqRWhoqOt7uuGGG8jKyiIyMtK1nJGRkaNJWX7HjYiI4O2332bUqFGEhobS\npk2bHJ2+dahTkMtt3Ns33njD3H///YXe70xmFvdO28DRU9bkZBNva0iLepWLOjxVimg7X1UYWl6U\nU6W5rKSmpnLVVVeVdBhFavr06XzxxRfMnTu3pENRqkh4+j1NSEigS5cuxVJ7cfyGQER8RKSDiPS1\nlyuJSKXiCKok+Hh70Sm0hmt54eY9JRiNUkoppZRSl4bTYUevAbYA/wL+ba/uBHxcTHFdsAvpQ5Ct\nS3gN6uz6k3snT6Tca+9w+mzew2mpy0NpfYKnSictL8opLStKqbLG6RuCGOBFY0wTIHsWhyXAZfW/\nXrPalahcqzpX7dxO8G9r+WWLviVQSimlSrN+/fppcyGlLpLTCkFzrEnJAAyAMSYDKHVjcyYmJl7w\nviJC2+sbk1o/BJ8zp1k768cijEyVNnFxcSUdgipDtLwop7SsKKXKGqcVgh1ApPsKEWkDJBV1QCXt\n5rAabG5xHQBei37myMmzJRyRUkoppcqa1157jejo6CI/7sMPP8yrr75a5Mctav7+/jmGRFWlm9MK\nwQvAXBH5B1BeRJ4FZgLPF1tkF+hi+hAA1K9ekcybbiBLhAZbN/Lzup1FFJkqbbSdryoMLS/KKS0r\nl4eIiAiWLl16Uce4koezvJLPvSxyVCEwxvwP6A7Uwuo70ADobYyZX4yxlZgbWwaTHNaEjMpV+XXl\n7yUdjlL56M4pAAAgAElEQVRKKaVUmXK5DWt/uXM87KgxZo0xZrgx5jZjTLQxZnVxBnahLqYPQbab\nQmswr+8gPho5huV+dUg/eqoIIlOljbbzVYWh5UU5pWXlwqSnpzNw4EAaNWpEq1atmDJlimtb3759\neeGFF1zLQ4YMYcSIEYA1D0GPHj0YNWoUwcHBtG3bNseT/SNHjjBixAiaNWvG1Vdfzbhx43LcrH72\n2We0bduWoKAg2rdvz/r163nooYdISUmhf//+BAUF8e677wLw66+/0r17d0JCQujUqZNr4i6A5ORk\n7rjjDho0aEBUVBQHDhzweK5t27ZlwYIFruXMzEwaNWrE+vXrARg8eDBNmzYlJCSEO+64g82bN+d5\nnOnTp3PrrbfmWOfeVOf06dO88MILtGjRgqZNm/Lkk09y6lTe9zQ7duygV69ehIeH06hRI4YNG5Zj\nxuCIiAjee+89OnToQEhICA888ACnT592bZ80aRLNmjWjefPmfPnll/qGoIxxOuzoGE+fwmQmIt1F\nZLOIbBGRUR7STBKRrSKSKCIRbuurichMEdkkIr+JyPWFybswavj50LzhVeBlXZ7F2w4WV1ZKKaXU\nFc8YQ//+/WnRogWbNm1i9uzZTJ48mcWLFwPw7rvvMnPmTOLi4pg5cyaJiYmMHz/etf/q1asJDQ1l\n27ZtjBo1ivvuu4/Dhw8DVpv78uXLk5CQwJIlS/jpp5+YOnUqALNnz2bChAlMnjyZ5ORkpk2bRo0a\nNYiJiSEwMJDp06eTnJzMo48+SlpaGv369eOpp55i+/btjBkzhoEDB7pu/IcOHUrLli1JSkriySef\nzDETbm59+vRh1qxZruWFCxfi7+/PNddcA0DXrl1ZvXo1W7ZsoUWLFgwbNszjsXLfeLsvv/zyy2zf\nvp24uDji4+NJS0tjwoQJHr+DJ554gs2bN7Ny5UpSU1N57bXXcqSZM2cOsbGxJCYmsmHDBqZNmwbA\njz/+SExMDN988w3x8fEsWbLEY7yqdHL6hqB+rk9r4EkgzGlGIuIFvAd0wxq1qJ+INMmVpgcQZoxp\nCAwDPnTb/A7wnTGmKXAtsCmvfC62D0G2m8PdJilLOqivvi5D2s5XFYaWF+WUlpXCS0hIYP/+/Ywc\nORJvb2+CgoIYMGAAsbGxANSuXZuJEyfy0EMP8dxzzxETE4Ofn59r/1q1ajFs2DC8vb256667CA8P\nZ/78+ezdu5cff/yRcePGUbFiRfz9/YmOjuabb74B4IsvvmDEiBFce+21AAQHBxMYGOg6rvvf/pkz\nZ3LLLbfQpUsXADp16kRERAQLFiwgJSWFxMREnn32WXx8fGjXrh3du3f3eL5RUVF8//33nDx5EoDY\n2FiioqJc2/v374+fnx8+Pj48/fTTbNiwgaNHjzq6lu4xf/7554wbN46qVatSqVIlHnvsMdc1zS37\nrUe5cuWoWbMmDz30EMuXL8+RJjo6mtq1a1OtWjW6d+/Ohg0bAKui0L9/fxo3boyvry+jRuX5zFeV\nYuWcJDLGDM69TkS6A/0KkVcbYKsx5k97/xlAT8D9PVhPYKqd5y/2W4E6wAmggzFmkL3tLHCEYtS+\nQTUqlvPi5Nkskg+dZNv+E4T/xa/gHZVSSilVKDt37iQtLY3Q0FDAuqnNysqiffv2rjTdunVj1KhR\nhIeH06ZNmxz716tXL8dy/fr1SUtLY+fOnZw5c4amTZu6jmuMcd3079q1i5CQEMcxzp49m3nz5rmO\nlZmZSceOHUlPT6d69er4+p4bjb1+/fqkpqbmeayQkBAaN27MvHnz6NatG99//z3PPvssAFlZWYwd\nO5Zvv/2W/fv3IyKICAcOHKBKlSqOYgXYt28fx48fp3Pnzq51WVlZHh9w7t27l2effZYVK1aQkZFB\nVlYW1atXz5GmVq1arp99fX3ZvXs3YDX3atmyZY5z1wepZYujCoEH84GvCpE+AHAfsicFq5KQX5pd\n9rpMYJ+IfIL1diAeeMwYcyJ3JomJibRq1aoQYeXN18ebG4KrsTDJai60MOmAVgguM3FxcfokTzmm\n5UU5pWWl8AICAggODmbVqlUe04wdO5ZGjRqRnJx83hP1tLS0HGlTUlK49dZbCQgIoGLFimzbti3P\nNu0BAQFs3749z/xypw8ICKBv37689dZb56VNSUnh0KFDnDhxwlUpSElJwcvLc0OM3r17ExsbS2Zm\nJk2aNCE4OBiAWbNmMW/ePObMmUNgYCBHjhwhJCQkzxtsPz8/Tpw4dyuUfYMOVl8CPz8/li9fTt26\ndT3GkW3s2LF4eXmxYsUKqlatynfffef4SX+dOnXYtWuXa3nnzp3ah6CMcdqHIDTX52rgFXLevBen\nckAr4H1jTCvgOPBMXgmXLFnC8OHDGT9+POPHjycmJiZHB6+4uDjHy13Ca3Jm/TLqffMRKV/+l8ws\nU6j9dVmXdVmXdVmXS9Nydrv60iYyMpLKlSszadIkTp48SWZmJps2bWLNmjUALF++nBkzZvDhhx/y\n/vvv88wzz5Cenu7af9++fUyZMoWzZ88ye/Zstm7dSteuXalTpw6dO3dm9OjRHD16FGMMO3bscDWF\nGTBgAO+99x5r164FYPv27aSkpADW03D3cfTvvvtufvjhBxYtWkRWVhYnT55k2bJlpKWlERgYSERE\nBOPHj+fMmTOsXLnS9SbBk969e7N48WI++eQT+vTp41p/7NgxKlSoQLVq1cjIyGDMmDEeb66vvvpq\nNm/ezG+//capU6d4/fXXXWlFhAEDBjB69Gj27dsHQGpqKosWLcrzWMeOHaNSpUpUrlyZ1NRUV0dq\nJ3r16sX06dP5/fffOX78uMd+Csq5uLg4YmJiXPezw4cPL5KBczwRJ690RCQLa4bi7BJ5HFgDPO50\ntCERaQu8bIzpbi8/AxhjzGtuaT4EFhtjvrKXNwOd7M0rjDGh9vobgVHGmDty57Nw4UJTFG8IADKz\nDE++/B+6TXmHA3+pTaPvP+O6+tWK5NhKKaXUpZaamspVV11V0mHkaffu3Tz//PPExcVx+vRpwsPD\nee6552jZsiUdOnTg5ZdfplevXgCMGTOGdevWMWvWLKZPn87nn39OixYtmDFjBnXq1OH111+nUyfr\n9uHo0aP84x//YN68eWRkZBAcHMyIESO46667APj000+JiYkhLS2NoKAgPvzwQ66++mq+//57Ro0a\nxbFjxxg5ciQPP/wwCQkJvPTSS2zcuJFy5crRqlUrJk6cSEBAAH/++SfDhw9n/fr1tG7dmoYNG3L4\n8GFiYmI8nvNdd93FihUrWL9+vas5TkZGBsOGDWPp0qXUrFmT0aNHM3z4cOLj4wkODubhhx8mICCA\n0aNHA/DWW2/xwQcf4Ovry4svvkh0dLQr7enTp3n99df5+uuvOXDgAPXq1eP+++9n6NCh58WyefNm\nhg8fTlJSEqGhodxzzz3ExMS4Rj5q2bIl77zzDh07dgSsidd27NjhOr9JkyYRExODl5cXzz33HCNG\njHDFoQrH0+9pQkICXbp0KZZXL04rBN7GmMyLykjEG/gd6AKkAauAfsaYTW5pbgUeNsbcZlcg3jbG\ntLW3LQGGGmO2iMhLgJ8x5rx3WUVZIQCIWfYnte97kEoZR9n6z3E8OrhzwTsppZRSpVB+FYJbPlpT\npHnNf6BlwYmKwPTp0/niiy+YO3fuJclPqeJWEhWCApsM2Tfyx0SkwsVkZFcoHsHqe/AbMMMYs0lE\nhonIg3aa74DtIpIETAaGux1iBPCliCRi9SPIc97uon6d0qVxLbZcY1UwMr5bxIkzF1UvUqWI+6t0\npQqi5UU5pWVFKVXWFNip2BiTKSJbAH8g7+7yDhlj5gGNc62bnGv5EQ/7rsUa7vSSaujvy4H27WHl\nEsIT41mx/QA3N6pV8I5KKaWUUkqVAU6bDD0N3Is1F0AKVn8CAIwxefdOKSFF3WQIYFpCGl5/e5Dq\nB/ez9rkXGPVojyI9vlJKKXUplOY+BEopS0k0GXI67OhD9r8v51pvgNAii6aU6tywJi/d0Zfjlauw\nz7cOD544Qw1fn5IOSymllFJKqYvmaNhRY0yIh0+pqwwUx5BM9apUoHKHNuy5KogshKV/HCryPNSl\np+18VWFoeVFOaVlRSpU1TuchmONh/ddFG07pdXN4TdfPC5MOlGAkSimllFJKFR1HFQLA01ibNxVR\nHEUmIiKiWI7bMaQ65bysZlub9x5n1+GTxZKPunR0JlFVGFpelFNaVpRTSUlJdOrUiQYNGvCvf/2r\npMNx5OGHH+bVV/Mc6LFUa9++vWtCuoJERESwdOnSQm8ry/LtQyAiY+wfy7v9nC0U+LNYoiqFqlYs\nR+v6VVnxpzXL48Kkg9wXWa+Eo1JKKaVUWTVp0iQ6dOjAkiVLSjqUy57TysCVqqA3BPXtj5fbz/WB\nQGAncHexRncBinNa5y7hNQCovm83q5euw8kITar00na+qjC0vCintKyUXpmZpWsuoZ07d9KkSZN8\n08THx9O3b1+aN2/uin/Pnj088MAD9OvXj1WrVnnct7Sdb0nQa+BMvhUCY8xgY8xgrNmDB7t97jfG\nPGuMSbpEcZYKbetXo8WGeO5/ewyNZ8eyee/xkg5JKaWUumy88847REZGEhQURPv27V2zD0+aNIlB\ngwblSPvMM8/w7LPPApCens7AgQNp1KgRrVq1YsqUKa50ERERrifx9evXJysry2M+2dauXctNN91E\ngwYNGDx4MEOGDHE1k8kvr9y2bNnCnXfeSUhICDfccAPz5s1zbevVqxdxcXE8/fTTBAUF8ccff+R5\njOuuu4527dpRpUoVvv32WwBq165Nt27d+Pjjj2nTpk2O9BdyvhEREbz33nt06NCBkJAQHnjgAU6f\nPg3AunXr6Ny5Mw0aNGDIkCGcOnXK8TlGRETw7rvv0qFDB4KCgnjsscfYu3cv99xzD0FBQfTu3Zsj\nR47ked4Ffef5nVPua5CZmZmjqU9B1wOsIT7btWtHWFgYjzzyiOt65JZfeXjnnXdo3rw5QUFBXH/9\n9fz88895HqM0cDrKUNlo2Ebx9SEAKF/Oi/o3X0+WlxcNtm5k8ZorpsXUZUnb+arC0PKinNKycuFC\nQkL4/vvvSU5O5umnnyY6Opo9e/bQu3dvFi5cSEZGBgBZWVl8++233H333Rhj6N+/Py1atGDTpk3M\nnj2byZMns3jxYtdxv/76a/7zn/+wfft2vLy8POYDcObMGe677z7+9re/8ccffxAVFeW6YXSSV7az\nZ8/Sv39/unTpwtatWxk/fjwPPvgg27ZtA2D27Nm0a9eO119/neTkZEJD8x64MSsri4oVKxIdHc3k\nyefmcs3IyMDX1zfPfQpzvtnmzJlDbGwsiYmJbNiwgWnTpnHmzBkGDBjAvffeyx9//EHPnj3573//\n6/gcAf73v/8xe/ZsVq1axbx58+jbty8vvfQSSUlJZGVl5Tgnd/l95+C5rOR1Dby9vXMc28n1mDVr\nFl9//TUJCQls27aNiRMnnhdjfuUhKSmJjz76iMWLF5OcnExsbCxBQUF5nmtp4LRTsbLddF0IyWGN\n8c7KInXOIs5mabMhpZRSqijceeed1K5dG7CeoIeGhpKQkEBgYCAtWrRw3ZgvWbIEPz8/WrVqxerV\nq9m/fz8jR47E29uboKAgBgwYwNdfnxsIcdiwYdSrV48KFSrkmw9YTXQyMzMZOnQo3t7e3H777WRP\neJqQkFBgXtni4+M5fvw4jz32GOXKlaNDhw5069aN2NjYQl2TtWvX0qpVK9dN+bp16wAQ8Tw/VWHO\nN1t0dDS1a9emWrVqdO/enQ0bNhAfH8/Zs2cZNmwY3t7e3HnnnbRs2bJQ5/jggw/i7+9P3bp1adu2\nLZGRkTRv3pzy5ctz2223sX79+jzPIb/v3Mk55b4G7pxcj6FDh1KvXj2qVavG3//+9zy/4/zKg7e3\nN2fOnGHTpk2cPXuWwMBAGjRokOe5lgaXXYWgOPsQAFxTrzIp110PQIOEX4lPyftVlyr9tJ2vKgwt\nL8opLSsXbsaMGXTq1ImQkBBCQkLYvHkz+/fvByAqKsp1oxkbG0tUVBQAKSkppKWlERoaSmhoKCEh\nIbz11lvs27fPddzcs77ml09aWhr16uUcNCQgIACw2vwXlFe2tLS08/KtX78+aWlphboma9eu5brr\nrqNixYoMHjyYyZMns3XrVho2bOhxn8Kcb7ZatWq5fvb19SUjIyPPa1G/fv1CnWPu47ovV6xYkWPH\njnk8D0/fuZNzym9GbifXw33/+vXrk56eft5x8isPISEhjBs3jtdee43GjRszdOjQPI9RWjidqVjZ\nvEQI7dmZszO/IPDPJJb+spW2QdeVdFhKKaVUmZaSksITTzzBnDlzXO3iO3Xq5BrAo2fPnrz44ouk\npqYyd+5c5s+fD1g368HBwfl2rnV/ml5QPnXr1j3vpn3Xrl2EhIQ4yitbvXr1SE1NPe8cw8PDC9zX\nnTEGLy/r+e2QIUNo06YNTZo0ITo62uM+hTnf/OR1LVJSUggJCQGK7hw98fSdOzknT29QnF6PXbt2\nuX7euXMndevWPe9YBZWHqKgooqKiOHbsGE888QRjxozhgw8+KMQVuHQcvyEQkQYicqeI9Hf/FGdw\nF6I4+xBk69wikLVtOvJLp+78mnacjNPag70s0na+qjC0vCintKxcmIyMDLy8vPD39ycrK4svv/yS\nTZs2ubb7+/vTvn17HnnkEYKDg11PyCMjI6lcuTKTJk3i5MmTZGZmsmnTJo8tBgrKp3Xr1nh7e/PR\nRx+RmZnJd99952pO4imvNWvWnJdPZGQkvr6+TJo0ibNnzxIXF8cPP/xA7969HV+Ts2fP5mjyUrt2\nbW6//Xbi4uLw8fFxdIyCzjc/rVu3ply5ckyZMoWzZ8/y3//+N0fTGk/n6P4k/2J4+s4v5pyc7vvv\nf/+b1NRUDh48yFtvvcVdd911Xpr8ykNSUhI///wzp0+fpnz58lSsWDHfZl4lzelMxc8Cm4AXgYfc\nPp6rp5exkJq+7Py//2P5X2/nsF8Vlu04VNIhKaWUUmVa48aNGT58OLfccgtNmjRh8+bNtG3bNkea\nPn36sHTpUvr06eNa5+XlxfTp01m/fj0tW7akUaNGPP74467Ra3LfhBWUj4+PD1OnTuXzzz8nJCSE\nWbNm0a1bNypUqOAxr6NHj553Pj4+PkybNo0FCxYQHh7O008/zYcffpjj6Xl+N4gJCQncf//9LF26\nNMdT+uHDh593XdwV9nzzi8PHx4fPPvuMadOmERYWxpw5c7jjjjsKPMewsLA8j3shN8R5fecFnVNe\n+WSvc3o9+vTpQ1RUFJGRkYSGhjJy5Mjzjp1feTh9+jT/+Mc/aNiwIc2aNWP//v28+OKLANxzzz28\n/fbbhb4WxUmcvDISkX1AR2PMxuIP6eK88cYb5v777y/2fP6zbjcfrbJek7W8qgqv3Vo0r8fUpRMX\nF6dP8pRjWl6UU6W5rKSmpubbtlrlrWvXrtx///3069evpENRVwBPv6cJCQl06dKlWF4zOG0ytB/Y\nURwBlFWdw2qQ/Y0kph5lf8aZEo1HKaWUUkVj+fLl7Nmzh8zMTKZPn86mTZvo0qVLSYelVLFxWiF4\nHJgiIteJSJD7pziDuxCXog8BQK1K5bn2qsoAGGDxtgOXJF9VdErrEzxVOml5UU5pWSn7tm7dSseO\nHQkJCSEmJoZPP/3UNUylUpcjp6MMlQduAXJ3IjaA9/nJrwxdwmuSmHoMjGHx5j30aVGnpENSSiml\n1EUaOHAgAwcOLOkwlLpknL4h+AAYDVQFfNw+5YsprgtW3PMQuLsxuDph2zYxcNIrXDXjK7YfOHHJ\n8lYXT8cKV4Wh5UU5pWVFKVXWOK0QlAM+McYcM8Zkun+KM7jSrlJ5bxoF18J/bzqN169m0ZbzJyZR\nSimllFKqNHNaIZgIPCOleQBV26XqQ5Dt+m6tOVTjL1Q+epjf5v9CloNRm1TpoO18VWFoeVFOaVlR\nSpU1TisEI4CXgWMikuz+Kb7QyobW9auyvVVrAOr9spIN6Z6n4FZKKaWUUqq0cVoh+D/gr8CtwIBc\nn1LlUvYhAPDx9qLmHX8FoOHGRBZt2nNJ81cXTtv5qsLQ8qKc0rKilCprHI0yZIxZUhSZiUh34G2s\nisi/jTGv5ZFmEtADyAAGG2PW2Ot3AIeBLOCMMaZNUcRUFDp0bkF83QC8jGFN4nZOdwyhfDmndS2l\nlFJKKaVKjqMKgYj4AM9jvRG4CkgFPgfGGWNOOzyGF/Ae0MXe/1cRmWOM2eyWpgcQZoxpKCLXAzFA\n9nzSWcBNxpiD+eVzqfsQADSt7cebjz1FcqYPAKt2HuHGkOqXPA5VONrOVxWGlhfllJYVpVRZ4/Qx\n9utYTYaigWvtf28GznvCn482wFZjzJ/GmDPADKBnrjQ9gakAxphfgGoikj24vxQi3ktKROjQor5r\neWGSTlKmlFJKKaXKBqc32HcDdxpj5htjfjfGzAfuAu4pRF4BwE635RR7XX5pdrmlMcACEflVRIZ6\nyuRS9yHIdnNYDdfPq3Ye4cjJsyUSh3JO2/mqwtDyopzSsqKUKmuczlTsabjRSzkM6Q3GmDQRqYVV\nMdhkjDnvf90lS5YQHx9PUFAQANWqVeOaa65xvcLN/o+6OJYb1/Lj15XLAfh5RyC3NflLseany7qs\ny7qsy6VvOVtpicd92d/fn6uuugqlVOkWFxfH+vXrOXz4MADJyclcd911dOnSpVjyE+Ng3HwReRur\nyc8/gGSgAVafgnhjzOOOMhJpC7xsjOluLz8DGPeOxSLyIbDYGPOVvbwZ6GSM2Z3rWC8BR40xb+bO\nZ+HChaZVq1ZOQipy32zYQ8zKXQBcXbcSb97eqETiUEoppfKSmppaJisE/v7+eJoKyRiDiLBvn04O\nqi4Pnn5PExIS6NKlS7E8jHf6huBprArA+1idindh9QF4pRB5/QqEi0gDIA24F+iXK823wMPAV3YF\n4pAxZreI+AFexphjIlIJuAWrclKq3BRagxnfraFZ/Ar21gskvVMD6lapUNJhKaWUUmXWjh07WLVq\nFWFhYSUdilKXrQL7EIiIN9Y8BK8aY8KNMX7GmIbGmBeMMaecZmSMyQQeAeYDvwEzjDGbRGSYiDxo\np/kO2C4iScBkYLi9ex0gTkTWACuB/9r9GM5TUn0IAGr4+dDu2G6uXzqfiJVLWLwt3wGRVAnTdr6q\nMLS8KKe0rBStrVu3amVAqWJWYIXAvpF/0xhz8mIzM8bMM8Y0tisU4+11k40xU9zSPGJXPK41xiTY\n67YbYyKMMS2NMddk71saXdPnZs6W8yHwz20s+2UrTppkKaWUUup8x48fp1KlSq7lzZs38+qrr5Zg\nREpdnpyOMvRfEbmjWCMpIiUxD4G79k3rsaNZCwCqLl9B0v4TJRqP8kzHCleFoeVFOaVl5eJs2LDB\n9fOKFSto166da7lJkyYkJydz6pTjBgpKKQecVggqArNE5CcR+VxEpmZ/ijO4ssjXx5tyXTsC0GRd\nPIt0TgKllFLKkaNHj/LVV1+5RlbJyso6rzNx165d+e6770oiPKUuW04rBBuAV4HFQBKwze1TqpRk\nH4JskVGdOVnRl9ppKfy6aguZWdpsqDTSdr6qMLS8KKe0rFy4KlWqMGjQIGJjY0lISCAyMvK8NOXL\nl2fBggUlEJ1Sly+PowyJyARjzFP24s/GmEWXKKYyr1WwP1/1H0xytVoc8qtBYupRIgOrlnRYSiml\nVKkXFhbGRx99RP369ck9jPjUqVO5/vrrWbRoEUeOHKFqVf3bqlRRyO8NwYNuP88u7kCKSkn3IQDw\n9hLC7ryJQ/61AVioow2VStrOVxWGlhfllJaVi9ekSRPq1KmTY93s2bMJDAykcePG3H333cTGxpZQ\ndEpdfvKbh2CtiMwCNgIVRGRMXomMMS8WS2RlXJfwmnyzYS8Ay3Yc4kT7QHx9vEs4KqWUUqr0Gzhw\n4HnrevXq5fq5ffv2tG/f/lKGpNRlLb8KQR+stwQNAAHq55Gm1DWOT0xMPO8VY0lo6O9L/WoV2Hn4\nFCfOZLEy+TCdw2qWdFjKTVxcnD7JU45peVFOleWyMq9u3jfZ3dOXO07vKa1SqvTyWCEwxuzBnolY\nRMoZYwZfsqguAyJCl/CafLo6DYCFSQe1QqCUUkp54O/vf96IQkCh5vPZv39/UYak1BUjvzcELmWp\nMlAa+hBk6xxeg0/jU6mzK5lt+ypwsGMQNXx9SjosZSurT/BUydDyopwqy2WlsE/3i/JtgKebeU8V\nhdycpFFK5c1RhUBdmHpVKnDb2p9pPOsr1ke2Z8kd19Grea2SDksppZQqM2bNmkXnzp1LOgylLmtO\n5yEoM0rDPATuwm63Jilr+NsaFm/aXcLRKHc6VrgqDC0vyiktK0Vnx44dBAUFlXQYSl32LrsKQWnT\n8aZr2FsvkIonT3B6RTy7Dp8s6ZCUUkqpMmHr1q2EhYUBsHfvXp544gkmTpwIWA8AhwwZQkpKSkmG\nqNRlwXGFQESaiMgLIvK+23KL4gvtwpSmPgQAVSuW43gnqz1pk7XxLEzSOQlKi7LczlddelpelFNa\nVorG8ePHqVSpkmu5Vq1aREVFsXr1agAaNGjAI488QmBgYEmFqNRlw1GFQETuBpYCAcAAe3Vl4M1i\niuuy0qRvdwDCNq9n6W+phRoxQSmllLpSbNiwwfXzihUraNeunWv5xIkTVKxYkZtuuokFCxawfv16\nWrQodc8llSqTnL4hGAN0NcZEA5n2urXAtcUS1UUobX0IANq1Dmfj9R1Y9tfbST9yis17j5d0SApt\n56sKR8uLckrLyoU5evQoX331FYcPHwYgKysrx8hB69ato0WLFtxzzz3MmDGDs2fP4u2tE34qVRSc\njjJUG1hn/2zc/tVH3Q6UL+cFIx9m9RZrSLWFSQdoWrtSAXsppZRSV44qVaowaNAgYmNjiYiIIDIy\nMrdo+r0AACAASURBVMf2EydOUL58ecqXL4+Pjw8HD2oTXKWKitM3BKs511Qo273AqqIN5+KVtj4E\n2bqE13D9/NO2g5zN0rpUSdN2vqowtLwop7SsXLiwsDC2bt3K/v37qVnz3GSeK1euZPr06ezduxeA\nv/3tb9StW7ekwlTqsuP0DcEIYL6IDAEqicgPQCPglmKL7DJzTb3K/KWSD/syznDkVCbxKUdoG1St\npMNSSimlSpUmTZpQp06dHOvatm1L27ZtXcsdOnS41GEpdVlz9IbAGLMZaAK8DzwPfAJcY4zZWoyx\nXZDS2IcAwEuELmHn3hIsTDpQgtEo0Ha+qnC0vCintKxcnIEDB2pnYaUuMaejDAUAFYwx/zHGTDDG\nzAB8ROSq4g3v8nJzuP36MyuLVUn7yDidmf8OSimllFJKFTOnfQhmA7kH+g0EvinacC5eae1DABBS\n05eO29YydOILXB23iGU7DpV0SFc0beerCkPLi3JKy4pSqqxxWiFoZIxZ777CXm5S9CFd3poF+1Pl\nyCGarIvXZkNKKaWUUqrEOa0Q7BWRcPcV9vL+og/p4pTWPgTZbry7Mycr+lIrfRc712xhf8aZkg7p\niqXtfFVhaHlRTmlZUUqVNU4rBB8DsSJyu4g0E5E7gFnAR4XJTES6i8hmEdkiIqM8pJkkIltFJFFE\nInJt8xKRBBH5tjD5lia1a1bmQJs2ADRat5rF2/QtgVJKKaWUKjlOKwTjgS+AicCvwAR7ebzTjETE\nC3gP6AY0B/qJSJNcaXoAYcaYhsAw4MNch3kM2JhfPqW5D0G2gLu6AtBk3a/abKgEaTtfVRhaXpRT\nWlaUUmWN02FHs+zRhZoYYyrZ/040xmQVIq82wFZjzJ/GmDPADKBnrjQ9gal2nr8A1USkDoCIBAK3\nUsi3EqXRDb1u5FjVamRUqcaunfvYfuBESYeklFLqCuDt7c3x48dLOgyllAfHjx/H29v7kufrdGIy\nRKQxcC1Q2X29MeZjh4cIAHa6LadgVRLyS7PLXrcbeAt4Csh3Nq/E/2/vvuPsrur8j78+t08vSWbS\nKymQBAIkIZBAgICiNMuiiCwq7uq6Yvm5rqK7a1m3qeiqWxTXsooK6FoAKaKUQAKEhBTSey+TNr3d\ndn5/3O9MpobvDbmZG+b9fDzuY+733PO93zOTD8P9zPd8zlm9mosuusjnkAZGcUGErd/8Bs8cagfg\n6e21fLCyYIBHNfgsWbJEf8kT3xQv4lc+x0pVVRWHDx+mrk6r3OWL+vp6ysq0UalkBINBqqqqzvh1\nfSUEZvZ54AvAGqDrnxYcmfqCnDKz64Ea59xqM7sSsP76Ll68mBUrVjB27FgAysrKmDlzZucv545i\nr4E+vnLmTJ45tIOG7av51f4QH5h9OwGzvBmfjnWsYx3r+NSOO+TLeHSc38cA5557bt6MR8f5c7x2\n7Vrq6+sB2LNnD7Nnz2bRokXkgjnnXruT2WHgGufcq6d8IbN5wJecc9d5x3cDzjn31S59vgc845x7\n0DveBCwkUztwO5AECoAS4DfOuTt6Xuepp55y+X6HACCRSvOeX6yjoT2zOdk915/D+SNKBnhUIiIi\nIpKPVq5cyaJFi/r9o/jr4beouBXY9DqvtRw4x8zGmVkEuBXouVrQw8Ad0JlA1Dnnapxzn3fOjXXO\nTfTOe7qvZOBsEg4GWDixovP4qW21AzgaERERERms/CYE/wD8h5mN8Jb+7Hz4vZBzLgXcBTwJrAce\ncM5tNLMPm9mHvD6PATvNbBtwL/DXWX035P8+BF0tOqey8/lzO+uIJ7Op0ZbXq+ftfZGTUbyIX4oV\nyYbiRfJByGe///W+/kWXNiNTQ+C7FNo59wQwtUfbvT2O73qN91gMLPZ7zXx2blUh59UfYvhzi9kx\ndQbL9o7l8gnlAz0sERERERlE/CYEE3I6itPobNiHoIOZMf/YbkqWPUdBcyNPb7tMCcEZ1FG4I+KH\n4kX8UqxINhQvkg/87kOwu79Hrgf4Rjf7fdfjzJi0aR2rttbQ0JYc6CGJiIiIyCDiuwbAzG4ys2+Y\n2U/M7Kcdj1wO7lScTTUEABOmjuH4OVMIJROMW7+G53dpbegzRfM2JRuKF/FLsSLZULxIPvCVEJjZ\nF8kU+QaAW4BjwJsBfXo9DUqvvxqAaa+u4Kltxwd4NCIiIiIymPi9Q3AncK1z7v8Bce/rjcD4XA3s\nVJ1NNQQdLrnjLaQCAcbs2MKWXUc51Ng+0EMaFDRvU7KheBG/FCuSDcWL5AO/RcXlzrl13vO4mYWd\ncy+b2cJcDWwwqRo5lE13fZznC6qIxwp4elstt104fKCHJSIiIiKDgN87BNvNbLr3fB3wETP7cyDv\ndtM622oIOlz4rmtoKSkD4P7Vh3hi8zH87CItp07zNiUbihfxS7Ei2VC8SD7we4fg74Eh3vO7gV8A\nxcBHczGowejScWUMLQxztCVBe8rxzef3sOpAIx+fP4aiiO+tHkREREREsmJvtL9CP/XUU+6iiy4a\n6GGckt21rfzTU7vYXdfW2TayNMrnrx7PlKGFAzgyERERERlIK1euZNGiRZaL9/a7ylCfS9+Y2eHT\nO5zBbVxFAf/xtqlcN2UIo3dsYebyJRyob+OTD2/ht+sOawqRiIiIiJx2fmsIwj0bzCwM5N1clrO1\nhqBDLBTgY7MqedfvfsK1D93Pm37zM2hr57sv7edLf9ypjctOI83blGwoXsQvxYpkQ/Ei+eCkCYGZ\nPW9mzwExM3uu6wPYDLxwRkY5yIRLi5n55Y9hsSgzVr3Erd+/h7JjR3hxTz1/9dtNrDvUNNBDFBER\nEZE3iJPWEJjZ+wADvgv8VZeXHFADPO2cS+R0hFk6m2sIemrcuJ2VH/gcrbv20RYr4PFb3s/OqTMI\nGNxx0QjefUE1wUBOppKJiIiISB7JZQ3BSVcZcs79BMDMXnLObcrFAKR/JedO4rInf8S6T/4zNY8/\nRzSambmVdvC/rxxkzcFGPnPleIYU9prRJSIiIiLii98aggvN7FwAM5tqZovN7Bkzm5bDsZ2Ss72G\noKdwaTGzfvgvXPLI9/j7u9/BjOqiztdWHWjiI7/ZxIp9DQM4wrOX5m1KNhQv4pdiRbKheJF84Dch\n+CegY6Whe4DlwGLgv3MxKOnOzKiYPZOq4ghfv34yt82qpuN+UV1bks8/sZ0fvLyfZFqrEImIiIhI\ndnztQ2BmDc65UjOLAQeB4UACOOqcq8zxGLPyRqohOJlVBxr56jO7CG/bTs3IsWDGuVWFfO6q8Qwv\niQ708ERERETkNBrwfQiAI2Z2DvAWYLlzrh2IAapoHSAXjizh36qauO17X+etv/wx4fY2Nh5u4a9/\nu5klO+sGengiIiIicpbwmxB8BXgF+CHwda/tGmBNLgb1erzRaghOpiAZJ1QYY9raV7jt3q9TceQQ\nTfEU//jUTv5j6V7iyfRADzGvad6mZEPxIn4pViQbihfJB74SAufc/wIjgNHOuT96zS8Bt+ZoXOJD\n9VsWcunjP6Ro8niGHD7E7d/7GpPXrQTgkY1H+fjDW9hb1zbAoxQRERGRfNZvDYGZmfNeNLN+Ewfn\nXF79GXqw1BB0lWxuYd3f/BuHfvcnEqWl3PvxLxCPFQCZnY/vumw0b5oyZIBHKSIiIiKnaqD2IagH\nSr3nSTKbkXVlXlswB+OSLISKCrngu1+mYvZMiqZOgCHj+N6y/SRSjrZkmnue28PqA418bP4YCsL6\n5xIRERGRE042ZWh6l+cTgIk9Hh1teWUw1RB0ZWaM+4tbGHr5bG48bxjfuWkKo8tOrDb0p221fPR3\nm9l+rGUAR5lfNG9TsqF4Eb8UK5INxYvkg5NNBdrb5fnu/h7ZXMzMrjOzTWa2xcw+20+f75jZVjNb\nbWazvLaomS0zs1VmttbMvpjNdQejSUMK+a+3TeXayZWQToNz7Ktv5+MPb+HhDUfws9ysiIiIiLzx\nnayG4D56TxPqxTl3h68LZeoQtgCLgANkNje71Tm3qUuftwB3OeeuN7NLgG875+Z5rxU651rMLAgs\nBT7unHu553UGYw3Ba3ny8//JtmUbeOztt3fWFswfV8anrhhLSfRks8ZEREREJB8M1D4E24Dt3qMe\neBuZeoF93nk3A9kseD8X2OrdWUgAD3jv0dXNwE8BnHPLgDIzq/aOO+a6RMnUPuhP3D7EaxsI/uYR\nJq5fzfu//3WGHtoPwNLd9Xzkt5vYUNM8wCMUERERkYF0silDX+54AFOA651z73XOfd45dztwPTA1\ni2uNAvZ2Od7ntZ2sz/6OPmYWMLNVwCHgj8655X1dZLDWEPQnUlHKvMd/SMl551B8uIbbv38P567O\n3Fg53JTgU7/fwgNrDpEehFOING9TsqF4Eb8UK5INxYvkA7/zReaR2Xegq2XApad3OP3zlje90MxK\ngd+Z2XnOuQ09+y1evJgVK1YwduxYAMrKypg5cyYLFiwATvyHN9iOL/3991l/9z386YFfMe6X/01p\noo1lc66gbttqvrVtNWsOXM5nFo5j/cpleTFeHetYxzo+W4875Mt4dJzfxx3yZTw6zp/jtWvXUl9f\nD8CePXuYPXs2ixYtIhf6rSHo1snsWTJz/r/gnGs1swLgy8A859wVvi5kNg/4knPuOu/4bsA5577a\npc/3gGeccw96x5uAhc65mh7v9Q9As3Pumz2voxqC/jnn2Pfzh9nx7Z8y6YH/5J71jWw4fGLKUGVB\niM9cOY6LRpWe5F1ERERE5EwbqBqCrt4PzAfqzayGTE3BAsBXQbFnOXCOmY0zswiZXY4f7tHn4Y73\n9BKIOudcjZkNNbMyr70AuBbYhGTFzBhz+81cvuR+Rk8awT03TObdF1R3vn68NcnnHt/Oj5cfIJUe\nfFOIRERERAYjXwmBc26Xc+4yYBJwE3COc+4y59wuvxdyzqWAu4AngfXAA865jWb2YTP7kNfnMWCn\nmW0D7gX+2jt9BPCMma0mM1XpD17fXlRD8NoC0QgAoYDxwTkj+ZfrJlEeCwGZSu3719Tw6Ue3crgp\nPoCjzL2et2tFTkbxIn4pViQbihfJB6FsOjvn9prZe51z/3YqF3POPUGPQmTn3L09ju/q47y1gOYB\n5cjs0aV89+bJ/PJvvsPjMy6jrbCI9TXNfOS3m7j5vGFcOamCseWxgR6miIiIiOSArxqCbieYNTjn\n8naSuWoITs32f/8xW7/6P6Sqq3jgnR+gZuTYbq+fM6SAqydVsHBSBcOKIgM0ShEREZHBKR9qCLrK\nyUBkYI38s+sovWAawZrDvPcH3+TSdcu6vb7tWCvff/kAt9+/nr99dCuPbTpKQ1tygEYrIiIiIqfL\nqSQEPzvtoziNVENwagrGjOCSh77LmDveBvEElz7wU/522UPMH1tKOHAiB3TAmoNNfGvJXm79xTq+\n+OQOnt1eS1syPXCDP0WatynZULyIX4oVyYbiRfJBVjUEAM65j+RiIDLwgrEo07/2GcovnsH6z36N\nIYlWvvimSTS1J1myq55nttey5mAjHQsQJdOOF/fU8+KeemKhAPPHl3HVpAouGlVKKKAbSSIiIiJn\ng35rCMzsPjJ/ED4p51w2S4/mnGoITo/GjdtJJ5KUnd99M+pjLQme23qUp3c1sPlIS5/nlkaDXDGx\ngqsnVXBedREBU3IgIiIi8nrksobgZHcItnV5PhR4H/AIsBsYC9wI/CQXg5KBV3LupD7bhxSGmfj9\nexlb10jhO97KK+Om8fTOevbVt3f2aWhP8fuNR/n9xqNUFYe5amIFV02qZEJlDFNyICIiIpJX+k0I\nnHNf7nhuZn8ArnfOPd+lbQHwD7kdXvZWr16N7hDkTqqljSNPLiXZ2AxPvcio4UO5+z03kHrLtTzf\nGuHZ7bUcbUl09j/clODBVw/z4KuHGVcR4+pJFVw5qYIRJdEB/C4ylixZ0rlFuMhrUbyIX4oVyYbi\nRfKB3xqCecBLPdqWAZee3uFIvgsWxrjipV+x/1ePs+9nD9G8bQ87/v1/CX7/l3xw3aP8xdyRrDvU\nxNPba3l+Zx2N7anOc3fXtvHjFQf58YqDnFdVxFWTKrhiYjkVBeEB/I5EREREBjdf+xCY2bPAcuAL\nzrlWMysAvgzMc85dkdshZkc1BGeOc47aF1ez977fESotZvpX/7bb64lUmhX7Gnlm+3Fe3F1Pe6p3\nrAUMLhpVwlWTKrhsXDlFkeCZGr6IiIjIWWOgagi6ej/wC6DezGqBCmAF8N5cDErODmZG5WUXUnnZ\nhfSVWIaDAaYd2sXEeD2fvPUSXtzfzDPba1mxr6FzpaK0gxX7Glmxr5FIcC/zxmZWKpozppRI8FRW\nxRURERGRbPj6xOWc2+WcuwyYBNwEnOOcu8w5tzOnozsF2odgYPRXLLztmz9i1fvvZtmltzD217/m\n76YX8cBtM/jYZaOZMbyoW994yvHczjq+/KedvPvn6/jmc3tYtb+RVDq73bT90trPkg3Fi/ilWJFs\nKF4kH/jeh8DMhgBXAiOcc18zs5FAwDm3L1eDk7Obc45hV19K674aWrbvYfu//5jt3/pfhl09j2u/\ncTc3njeFw01xnt1ey9Pba9lxvLXz3OZ4iie2HOOJLceoLAyxcEIFCyaUc15VEUHtcSAiIiJy2vit\nIVgI/JrMNKH5zrkSr+3TzrkbczzGrKiGIP90rTU49OizhEuKuHLVQwQi3YuJd9W28sz2Wp7ZXsuh\nxnif71UeC3HpuDLmjy9j1sgSTSsSERGRQSGXNQR+E4JVZD78P2Vmtc65CjOLAbudc9W5GNipUkKQ\n3+LH6mjaspPKSy/s9Vo6noCAYcEgm4608PS2WhbvqKWuLdnnexWGA8wdU8r88eXMGV1KoQqSRURE\n5A0qlwmB3z+vjnfOPeU978gg4mQx5ehMUQ1BfosMKe8zGQDY98CjLJ7zTrZ9/YeMjzfy0ctGc/9t\nM/iX6ybx1mlDqCjoHm4tiTTP7qjjn5/exS0/X8s//GE7T2w+Rn0/CURPmrcp2VC8iF+KFcmG4kXy\ngd8P9BvM7M3OuT90absGWJuDMckgdWzxy7QfPML2b/6os9ZgzJ/fzEWLLmX26FI+dplj0+Fmlu6u\nZ8muum7TihIpx7K9DSzb20BgCcyoLmb++DLmjy+nqjgygN+ViIiISH7zO2VoHvB74FHgXcBPgRuB\nm51zy3M6wixpytDZyznH8RdWsfe+31Hz6LO4ROYv/XP+7zsMWTC7V98dx1tZuqueF3bXseN4W7/v\nO3loAfPHlTN/fBljy2P9rogkIiIikq8GvIYAwFtV6HZgHLAX+Fk+rjCkhOCNIX60lv2/fJxjz6/g\n4p/fgwW6z25zzlG3Yh1lF0wjEAlzoKGdpbvqWLqrno2Hm+kvqkeXRZk/vpz548qYMqyQgJIDERER\nOQsMaEJgZkHgKeDNzrn2XAzidPrGN77h7rzzzoEehuRY8859PH/puwgURKmYcz6V8y9iyIKLKT1/\nGnUJxwu7M3cOVh9oItnPPga2bx03vulK5o8rZ+aIYkJazlROYsmSJSxYsGCghyFnAcWKZEPxIn4N\n6E7FzrmUmU3AfwGySM7Fj9ZSPHUCTZt3cuy55Rx7bjlbgYp5F3DJ777LDecO5YZzh9LUnmTZ3gaW\n7qpn+b4G2pPpzveob0/y8IajPLzhKCXRIPPGZpYzvXhUKdGQwl1EREQGB781BHcCVwBfBPZxYqUh\nnHPp/s4bCJoyNLi0HznO8RdWcXzpSo6/8ArDrl3AtC/e1atfy56DtNQ2sKW0ihf2NPDinnoa21N9\nvmc0FGDO6BIuG1fOvLGlFEfzbjEtERERGWQGvIbAzDo+9HftbIBzzuXV4u9KCAY3l0phwd4hueVf\nv8eOb/+UcEUplZdeSPmlF3J02rm8HChj6Z4GjjYn+ny/oMGskSXMH1/OpePKGFIY7rOfiIiISC7l\nwz4EE7zHxC6PjuO8on0IBre+kgGAYGEBsVHVJGobqHlsMZv/4Vs8/87buXH7Sn5+63T+4+Yp3HpB\nNaPLot3OSzl4ZX8j31m6l9t+sY5PPryFB9YcYn1NE/FUXt0ckxzTWuHil2JFsqF4kXzgay6Ec273\n6biYmV0HfItMIvJD59xX++jzHeAtQDPwfufcajMbTWap02ogDfyPc+47p2NMMjhM+sT7mPjxO2jd\nvZ9jS1dyfOlKtj71NJWXXICZMXVYEVOHFXHnnJHsqW1j2cPPszweYzVF4K1E5IANh5vZcLgZgEgw\nc96M6iKmDy/ivKoiTS8SERGRs042y47eBCwEhpKZLgSAc+4On+cHgC3AIuAAsBy41Tm3qUuftwB3\nOeeuN7NLgG875+aZ2XBguJccFAOvkNkDYVPP62jKkPjVEfs99yVwzvHsBTfRfvgYkZHVtM6YzqYx\nk3ixchwNZRX9vp8BEypjTK8uZsbwIqZXF2tTNBERETktBnSVIQAz+yLwV8ADwC3AvcBtwINZXGsu\nsLXjboOZPQDcDHT9UH8zmTsBOOeWmVmZmVU75w4Bh7z2JjPbCIzqca5IVvrboCzV3EL57Bkcf2El\n8QM1BA/UMJ2nmR4MYo/+grX1adbVNHOgofsqvA7YcbyNHcfbeGTjUQCqiyNMry5ixvBiplcXMa4i\npr0PREREJK/4nd9wJ3Ctc26dmX3AOff/zOx+4O+zuNYoMhuaddhHJkk4WZ/9XltNR4OZjQdmAcv6\nusjq1avRHQLxo7+1n0PFRVz4o3/FpdM0btjG8aUrObbkFdKJBHNmjeHNXr9jLQnW1zSxcdN+Cr7+\nbfZUDudI9SiODB9F7bBqUqEwNU1xapriPL29FoDiSJDp3hSjGdXFTBlWSCSoJU7PBlorXPxSrEg2\nFC+SD/wmBOXOuXXe87iZhZ1zL5vZwlwNrC/edKH/Az7hnGvqq8/ixYtZsWIFY8eOBaCsrIyZM2d2\n/sfWUbyjYx2/1rEFArxadximj2bBh2/t9fqQwjCB/esZvX0jwQ3rqWI9G9LNjAOmhUvZOW0m982d\nA0DppFkAHNj4Cgc2wjLvuGXnGsaURbnmqoXMqC6ifvtqCsPBvPj+daxjHZ/acYd8GY+O8/u4Q76M\nR8f5c7x27Vrq6+sB2LNnD7Nnz2bRokXkgt9lR1cCf+6cW29mTwO/A2qBrzjnxvu6kNk84EvOueu8\n47vJLFv61S59vgc845x70DveBCx0ztWYWQj4PfC4c+7b/V1HNQRypsVrG6hdtprGDdtp3LCNxo3b\nadmxl+qbr6HoK59l7aEm1tc0se5QM3VtSYYd2Mv0Vcs4MnwkR4eP4tiwESQjJ2oNxlfEmFFd3HkX\nobpEdQgiIiKD3YDXEJCZGjTEe/454OdAMfDXWVxrOXCOmY0DDgK3Au/p0edh4KPAg14CUeec65gu\n9CNgw8mSAZGBEKkopfq6K6i+7orOtmRzK6nmFqLDCpkyrJB3zqzCOceBhnbW/scqePGZzr5pM+qG\nVLFm7gJWXXY1u2rb2FXbxu83ZeoQhhWFO2sQZlQXM64iRjCgOgQRERE5PXwlBM65x7o8Xwack+2F\nnHMpM7sLeJITy45uNLMPZ15233fOPWZmbzWzbXjLjgKY2XzgvcBaM1tFpn7z8865J3peRzUE4teS\nJbmbtxkqKiBUVNCtzcwYVRaj5O2Xc7QsROOG7dSt30br9t1UHq1hZNixxiDd46ZddO16jj1Tw6+H\nj+Le6pFESjJLnM4YXsS0qiImVhZQFvOb28upymW8yBuLYkWyoXiRfODrU4SZ9bsBmXNuh9+LeR/g\np/Zou7fH8V19nLcUyKsdkUVOVen0yZROn9x5nG6P07R1FwsryvhQ1VA2HWlh/aEm1tU0s/FwM+eu\nWc7MV17o7F9XMZSjw0fxx0uv5McTpwBQWRhiYmUBEyoKmFBZwMTKAsaURwmrYFlEREReg98agjSZ\nv8p3nafgAJxzefVBXTUE8kaSSjvW/OxRDvzxRdq37CC2bz/BVBKAh277ENvPu6DXORe8tJiS+loa\nhw4jOnYklZPGMHryKCYOK2ZCZQGVBaF+l1wVERGR/DTgNQTOuW5/ZvQ2Cvsi8HwuBiUiGcGAcdEd\nN3DRHTcAkIon2L12O1uXbeDcsZMJpSLsOt5Ke+pEYj/t1RWM2tP9xl0qGOS777uLvROnUBYLMaEy\n1nknYXxphPFDi4iGdDdBRERkMDqlicfOuUNm9kkyOw//4vQO6fVRDYH4dTbO2wxGwky8eBoTL57W\nuR9CKu042NjOjuOt7DzeRu3bb2LN5h2EDtVQVnuU8uNHKW6sp6mkDID6tiSrDzSx+kBm5d7bvvs1\n1tXX0lpVhY0aQcG4UQyZPIbJNy5kxIgK3U3wnI3xIgNDsSLZULxIPng9lYhTgcLTNRAROTXBgDG6\nLMboshhXTAAufhcAzfEUu463suN4K1sP1jOiMUlbfTutiXS380vqj1PU1EhRUwPs2AbPQyvw8fpi\nUsOrmVAZy9QneHcUCpe8QNGQMgrHjyI2qppASAXNIiIiZzO/NQTP49UMeAqB6cA/Ouf+NUdjOyWq\nIRDpX9o5apri7Dzeyo7jbew83squo8007DlE6fHM3YSy40coqz3KY7d8ABfsUSLkHHd95VNE4vHM\nYTBIYHgVReNHcclPv0q4x8pKmVOc7jKIiIi8TgNeQwD8oMdxM7DGObf1NI9HRHIoYMaIkigjSqJc\nNu5Ee1vyPHbXnkgSdh5vpfh4K43tqW7nh5IJtky/iPLao5QdP0pJQx1u/0Hqao7wjgc3MaaigPEV\nMcZVFDC2PMa4kjDr595EtGoIseHDiI0cRnT4MGIjqhh75zuVKIiIiOQBv0XFP8n1QE4X1RCIX5q3\neUIsFGDqsCKmDivqbHPOcawl0VmbsMNLFJ76sz+no4Y5lIhTWnuMosZ62tOw7Vgr2461ktnIHIob\n6vhQUwstTS207Njb+d7BshLG3PlOeqYDqZY2Xv3YPxIbWUVs+DCiI4d5iUQ1heNG5vincHKKAMyf\nbwAAIABJREFUF/FLsSLZULxIPvC7D8E/+unnnPvC6xuOiOQLM2NoUYShRRHmjinrbI+n0uyta+uS\nKAxh5/FWaE32eo+m0nL+8+/vobihnuKGWoob6iipr8Oc4zs/eZWx5VHGVRQwvjzGuIoYw+uPUvPo\ns73eJzp8KFetfrhXe7K5hUMPPU10xFBiI6qIjRhGqLRYdx5ERESy4LeG4H7gncByYDcwFpgL/Bpo\n87o559ydORqnb6ohEBkYDW1JdtW2sbu2lT11bd7zNuraeicK/Ym0tTJl+wZGx5upam2grKmeWG0t\nRdWVzLvvawR6fNBvWL+VFxa9r1tbsLCA8jkzmPPgt3u9f7Kpmfo1m4kOrSAyrJJweQkW0HKrIiKS\n//KhhsCA9zjnft3ZYPYO4Bbn3AdyMTARObuUxkKcP6KY80cUd2uvb0uyu7aV3bVt7K7LJAm7atuo\n7yNRiMcKWDf9Ytb18f6xn7zKuIpYpjahIsb4ihjVSWPEn11H+6EjtB86QtuBI6RaWkm3J/ocY+PG\nHSx/54nN0C0UJDKkgsrLLuSC7365V/9kYzPNO/YSGVpBdGgFgWgkux+KiIjIWcBvQvAW4L092h4G\nfnx6h/P6qYZA/NK8zTOjLBbi/BElnD+ipFt7XWui252EjoShr0QBoC2ZZvORFjYfaenWXjDn5kyS\nUB5jXHmUMZE0RS7FtqMthIJGOBAgHDTCQaMt7Si75AISR48TP1pHsr6R9pqjJOqb+rxm3cr1rHj3\nJwHYkG7m/PJqIsMqGbpwLuf9y6d69U82NtN++BiRYZWESoo0dWmQ0u8WyYbiRfKB34RgG/BR4Dtd\n2j4CbD/tIxKRQaG8IEx5QbjPRKEjOTiRLLTS0GPFow6tib4TBTja94Vv/BAAAYOoS1HW2kwYR/sD\n67olD+FAgKrNB5gydiyRhgZcbSvJhiaSDU2sHzaCxS/u8/oGCAUy50RffJnoF72VmCNhrKKcQFkJ\nsQVzGfrJDxINBoiGjGgoQCQYIFBXT3zPfiKVZUQqygiVFWtfBxEROeP81hBcCPyWTAKxHxgNJIB3\nOOdW5nSEWVINgcgbU21HotDlbsLJEoXTLp0m1tZCYVMT6UCAuqFVvbpM2riGhY//hsKmRiLx9s72\nDbPm8sSfva9X/2mrX+at/9d9Ebd4QSH75l7K5vfeTjQUIBoMEAkFiIYCFB+poXjnToKlJQQryghX\nlBKpKCVSUkQsHCTSJeHIJB8BL/k4kYQEA7prISJyNhrwGgLn3CozmwzMA0YCB4EXnXN9T9QVETnN\nKgrCVBSEmTXyxB0F5xx1rUl2ebUJe2rb2FvfRlsyTSLlSKYdiVTmeaLjedqRSL32H0J6CQRoKyym\nrbC43y7bz72A7edeAEAoHqewuZFYSzOJSLTP/vFojIOjxxNrbaagpZloWyuR1hYaW9vZeLjnHQ+Y\nuXwZ1z50f6/2tRfN48l3/Hmv9qr9exi3bRNthUW0FhbRHisgVVhIvLKSdHlZJtEIBoiErDOByLSd\nSCB6JhSZJMNOJCpdzo90JiJKQEREzia+7017H/6fBzCzq4BLgedyNK5TphoC8UvzNs9+ZkZFYZiK\nwjAXjix57RM8zjlSjt7JQsp1SyTiaUcy5Uik06xa9iJTL7ykW9/eCUemb8dr8eQw4ilHezJNVSpN\nezJNPJWmPZlp2zvjAnace/6J7yedJtrWOxHo0FBeyeYZFxFraaagtZlYSzOx1mbaCgr77D9q9zYu\n/+NDvdpXzVvIMze8q1f7pA1rmLHyRdqjMdoLCmmMFdAeK+Dg6PEcGH9OXz9IeI06iVDAeiUUsVCA\nokiAokjwtR/hIMXRzPPQWZJc6HeLZEPxIvnA7z4Ei4HPO+eWmtlngU8BSTP7L+fcv+R0hCIip5mZ\nETIIBYIUhP2dk95bwoLJlad9LKm085KEdGfy0J5M0+61tSdPvN5+2Wja33sN7SlHPJnmWCpNPJkm\nEk9yuTPiyTRtnUlHmvTkSay76k2Em5oINzcRbm0l2tZKfcWQPscy5PBBJm1a26t9+eXX9pkQXLz0\nKS59+jHaYwW0FxTSFisgHitg88yL2ThrLgDJdCY5akmkKa6rheYGGmIFtEcLiEdjpEKh10wqOkSD\n1ithKI4EKexxfOL17klHYTioOxYiIn3wW0NwDKhyzqXMbBtwE9AILHXOjc3xGLOiGgIRkb455zqT\njo47FfEudy5adu2nbctOEl7xdLKhiXRjE23nz6Tpogtp75KwxFNphj/4S8Y+0nvDuFeuvYGXrn4r\n7T2mZs17+jEue/rRbm2pYJAXr3orL195Xa/3mbB5HWN2bCEejdEei5GIZL4erR5F7bDqU/oZFIYD\nnQlEsfcoLwh5U9JClHtfK7y2kmhQq0WJSF4Y8BoCIAA4M5tEJonYAGBmFbkYlIiInH5m1jm/v08j\npsGl03y/n7v6M6S++TES9Y0kG5o6vy6YMIa/mzIe5zLTqNpTaeJJx/7EFo4dPIdUQxOp5lZcUzPB\nZJI548qZNKualniK5niK5niapniKcc/tYsrSp3pdd8k1N/aZQMx99glmLXuOeDSWSSKiMeKxGBtm\nXcL28zK1HS2JNC2JNEebE1QcqaG4sZ5dkSiJSJR4JEoimvnqgkEAgkZnknCyxKG8IERpNKQ7ECJy\nVvKbECwB/hMYQWa1IbzkoJ91/QaOagjEL83blGwoXnqzQIBQSRGhkqK+XzcjEjIioQBEYchH3g0f\neXe3Pun2OM45grHehdfHh95I/SUTSTY1k2hopr2xmfaGJt59wxzes2AyzYmuCUSS0LIUxY310Fjf\n7X3qJ0/hYDhASyLdrX3miiXMXvp0r+s+9+a3seLyawFIOTjWkuBYS4IZK5ZSsHk9DZEIx6Inkohd\nU6ZzaPR4ApbZdyOx+1WmXHgJQ9ubKQ86SsuLKassprK0sDOBKCsInTU1EZJb+t0i+cBvQvB+4G+A\nI8DXvLZpwLdzMCYRERkkTrb7c+W8WVTOm+X7vVLf/jSJf/wwycZmkk0tJJuaSTY2c/m0iRRNGksq\n7WhNZBKI5niKmsZzaWw5Qqq5lVRLC66lDWttZciwMkaWRqlrTXRLIqoO7uOcjWt6XbetsIhDo8eT\ndlDbmqShMU7T/kau+v0vGf3SYgAccDgYZH8kynNvfhvrZs+nNBrsvLtQURBi+IrlFG3dgsWiWEEB\ngYIYgcIYNvM8QhPGevtjnNj7ItjWSiQA4cJCItFQ5/4ZJ/oZoWCAcMB050JETspXDcHZRDUEIiJy\nurQn09S1JqltTXB03TYat++hub6Z1vrMHYt4Uys7pk5n5/CxNPbYE2P+Hx/m3NUvE2lvIxJvJ5DO\nJBdPvu021s2e3+ta1/7258x85YVe7X+86VbWzr28V/uih+7nguVLAEgGQyQjERLhCM+/6WY2eUXd\nHQIG5726guEH9pKORkhHo7hYDGJR6iZPIT5yRGcyEfKSjmh7KyGDYEGMcCREKBjoTDA6+oUC3Tfn\nCweMUJf3CHf26dG3S79IMEDAUK2GyGvIhxoCERGRQScaClBdEqG6JAJXX5B59CORSlPXlqS2NUld\na4LaK+6itjXBwdYkdS0J6hpbaapr5njSMJe5a9DVpvMv5ljVcMLxOOFEnHC8nXA8zvGqEX1ezwUC\nxCNRwok4oVSSUGuSWGtLZ+LRVdrBqC0bmL5qWa/X/vD297IxVNar/U2/uY/pK1/KnG9GMpxJOJ69\n/s/YfP7sXv1nLl/CiL27SEQiJEPhTP9IhJ1TpnOsemSv/iW1xwgn4iTDEVLhMC4WxSIRQpFwl2Qj\ns7JUeSxTw1EWC1EeC1FWEKI8Fva+Ztr7rY0Rkdf0hksIVEMgfmnepmRD8SKvJRwMMKwowuZVL79m\nrKTSjro2L3Hw7kC0XDq6z30uLk47zu+yX0bSe1774Q/ybPpOEsk0qfYEtLbhWltpjhZSFgn12ohv\n0/lzOFo9sjPhCHlJx/Fhw/scYzoQpD0aI5SIE0ynicTbicTbsX5mFozZuZVpr67o1d5cUtZnQjD/\nT49w3prlvdoff+cdbLzwkl7tc599gqKdWzkUDrM/FCYZzjzWXXQZNaPHURAOdCYH5QUhhu/ZQVlT\nI4XFMYpKCikpiVFSWkjlxFEMrarI1LbkgWx/t6S8pXwjQdNdFTltzmhCYGbXAd8is2rRD51zX+2j\nz3eAtwDNwAecc6u89h8CNwA1zrnze54nIiJytggGjCGFYYYU+twI43U4sRHf+d024kt6ycJNXTbU\n69hcL5lyJK78XGef1vY4ydZ2Uq1tzI4WMCsSOfEe3gZ+gbe9hZ1zZuHa26A9Dm3tWHs7RVMnMrEy\n1nntjoQmUVbO8WHVhOJxQslEJkFJJEiG+/6ZDDu0j/HbN/Vq3zthCjWjx9GaSNOaiHOwMQ7A9Q88\nxJB1KwFIA/Xe495bPsDmC2ZTGA50uesQZtqPfkDpypVYNEIgGiFYECVUEGXsp/+CMYvmEQmeSCBS\nacfeXz1Ow6aduHAYFw6TjoRJhcIE51xIeuRwEqnue4wkDxwi0dJGeyBEIhgiHgzRHgixecNBFrfv\nJN7R30sI4x17k3Qce/uOdKzmGzAoDJ/Yc6PQ28ivsMdeHCf6nNjsr9Dbo0N7c0iHM5YQmFmAzEpF\ni4ADwHIze8g5t6lLn7cAk5xzk83sEuC7wDzv5R8D/wH89GTXmTXLfwGaDG76a69kQ/EifuVbrJzK\nRnynZOG47PrfPrPzacdfvROpNJckU6Qwkt6H4Kb2VOZuysg7aTxwmOamNlqbW2lrbqO9pY32iRMJ\nGvTY9oJDo8cRSKcIJRKZRzLzaC0qBjqWoI1zoCGTQAyrqaWioeHEmLzHNx7fzLY9BRSGAzgydSVp\nBzf+4hEmb+hdZP7IrR9k64zeMxVuuP9/mLJ+NT33VN986wdZXFDXq/8Vj/+G0bu2kgxlEo1kKEQq\nFGbFgkUcGjOBtIOmeIqmeKZ2ZdKGNURqj3I8FOZIMEQqFCIZCnFw7ESaSst7vX9hUwOBVIpwQZRo\nLEq0MEphQZiiaKhbYpFJNHps8uclFh0JRyhoBN4gdyt6bhaZSKW77N/iJWbeUsrxVP9t7ck051YV\ncd3UvjeCzDf9JgRmdh+9pzj24py7w+e15gJbnXO7vfd/ALgZ6Jru34z3gd85t8zMysys2jlX45xb\nYmZZ/rYRERGRfBf0VkKKhgIQ7eejyfiL+z3fOUdTPEVda5L6tiR1rUnq5n+g8/mRtgR1bUnqW5M0\ntSUJtCVJ9/iE89gt7yMcz0yl6kgeQokEtUOrAHotW7tx1lxqRo4llEwS9PoHk0nqhgzrc4zNJWUc\nH1rdrW8omSQV6jtLqzhaw/D9e3q1b5w1l3DASPT4BmasfLHPncYfuu1DNJ3XOyG45qH7OWfjq737\nv/dDrDi3d63MFU/8hpG7d3gJSibhSAVDrLj8GmpGjcPw/h0t8/WcdSsprT2OC4chFIRImHQozPEp\nU0lUVBIMkFktyzL/9gW1xwklEwTCYQKRMBbxvkYjBENB772t2zW6tQXAsBMf0r0NFONd7rZ0+wDf\nxx2YjmTvdImn0md/QgBs6/J8KPA+4BFgNzAWuBH4SRbXGgXs7XK8j0yScLI++722Gr8XUQ2B+KU5\n4ZINxYv4pVg588yMkmiIkmiIMT76p53rvPPQkTTUtyW9pCHh1Xdk2mKtSeLt3RMIA/aefxE1wcwq\nSZFgZr+NSNCoCgYY3dHutUWCASLnfZTmLv0iwQDBoDF1zXLePXec9z4n3stmfwZraCSUSBJIJggm\nEgSSCRZecj4FI6pIpl2XzfxSHIlfQ+vmiSTa4iTb4yTb4qTa41xw/jjGjC3vXG63OZGiJZ4iWVhE\nU0kZwWSSYCqToATTadKBYJ8/syGHDzJy785e7Ru8eg8HJNOOJEDKMXHZC0zcsr5X/9/e/lfsnDaz\nV/vb7vshEzev69X+u9v/ih199L/md79g1O7tpIJB4l5ykgqGWHrtjRwaM6FX/5nLl1JxrIZYMEQq\nGCQVDJEOBtl23izqK4f26l+1fw/RthbSPfo3lFeSiMZ6/4Ccgx53SeI9b1vlsX4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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "figsize(12.5, 4)\n", + "\n", + "N_Y = 250 # use this many to approximate D(N)\n", + "N_array = np.arange(1000, 50000, 2500) # use this many samples in the approx. to the variance.\n", + "D_N_results = np.zeros(len(N_array))\n", + "\n", + "lambda_ = 4.5\n", + "expected_value = lambda_ # for X ~ Poi(lambda) , E[ X ] = lambda\n", + "\n", + "\n", + "def D_N(n):\n", + " \"\"\"\n", + " This function approx. D_n, the average variance of using n samples.\n", + " \"\"\"\n", + " Z = poi(lambda_, size=(n, N_Y))\n", + " average_Z = Z.mean(axis=0)\n", + " return np.sqrt(((average_Z - expected_value) ** 2).mean())\n", + "\n", + "\n", + "for i, n in enumerate(N_array):\n", + " D_N_results[i] = D_N(n)\n", + "\n", + "\n", + "plt.xlabel(\"$N$\")\n", + "plt.ylabel(\"expected squared-distance from true value\")\n", + "plt.plot(N_array, D_N_results, lw=3,\n", + " label=\"expected distance between\\n\\\n", + "expected value and \\naverage of $N$ random variables.\")\n", + "plt.plot(N_array, np.sqrt(expected_value) / np.sqrt(N_array), lw=2, ls=\"--\",\n", + " label=r\"$\\frac{\\sqrt{\\lambda}}{\\sqrt{N}}$\")\n", + "plt.legend()\n", + "plt.title(\"How 'fast' is the sample average converging? \");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As expected, the expected distance between our sample average and the actual expected value shrinks as $N$ grows large. But also notice that the *rate* of convergence decreases, that is, we need only 10 000 additional samples to move from 0.020 to 0.015, a difference of 0.005, but *20 000* more samples to again decrease from 0.015 to 0.010, again only a 0.005 decrease.\n", + "\n", + "\n", + "It turns out we can measure this rate of convergence. Above I have plotted a second line, the function $\\sqrt{\\lambda}/\\sqrt{N}$. This was not chosen arbitrarily. In most cases, given a sequence of random variable distributed like $Z$, the rate of converge to $E[Z]$ of the Law of Large Numbers is \n", + "\n", + "$$ \\frac{ \\sqrt{ \\; Var(Z) \\; } }{\\sqrt{N} }$$\n", + "\n", + "This is useful to know: for a given large $N$, we know (on average) how far away we are from the estimate. On the other hand, in a Bayesian setting, this can seem like a useless result: Bayesian analysis is OK with uncertainty so what's the *statistical* point of adding extra precise digits? Though drawing samples can be so computationally cheap that having a *larger* $N$ is fine too. \n", + "\n", + "### How do we compute $Var(Z)$ though?\n", + "\n", + "The variance is simply another expected value that can be approximated! Consider the following, once we have the expected value (by using the Law of Large Numbers to estimate it, denote it $\\mu$), we can estimate the variance:\n", + "\n", + "$$ \\frac{1}{N}\\sum_{i=1}^N \\;(Z_i - \\mu)^2 \\rightarrow E[ \\;( Z - \\mu)^2 \\;] = Var( Z )$$\n", + "\n", + "### Expected values and probabilities \n", + "There is an even less explicit relationship between expected value and estimating probabilities. Define the *indicator function*\n", + "\n", + "$$\\mathbb{1}_A(x) = \n", + "\\begin{cases} 1 & x \\in A \\\\\\\\\n", + " 0 & else\n", + "\\end{cases}\n", + "$$\n", + "Then, by the law of large numbers, if we have many samples $X_i$, we can estimate the probability of an event $A$, denoted $P(A)$, by:\n", + "\n", + "$$ \\frac{1}{N} \\sum_{i=1}^N \\mathbb{1}_A(X_i) \\rightarrow E[\\mathbb{1}_A(X)] = P(A) $$\n", + "\n", + "Again, this is fairly obvious after a moments thought: the indicator function is only 1 if the event occurs, so we are summing only the times the event occurs and dividing by the total number of trials (consider how we usually approximate probabilities using frequencies). For example, suppose we wish to estimate the probability that a $Z \\sim Exp(.5)$ is greater than 10, and we have many samples from a $Exp(.5)$ distribution. \n", + "\n", + "\n", + "$$ P( Z > 10 ) = \\frac{1}{N} \\sum_{i=1}^N \\mathbb{1}_{z > 10 }(Z_i) $$\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.0061\n" + ] + } + ], + "source": [ + "import pymc as pm\n", + "N = 10000\n", + "print(np.mean([pm.rexponential(0.5) > 10 for i in range(N)]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### What does this all have to do with Bayesian statistics? \n", + "\n", + "\n", + "*Point estimates*, to be introduced in the next chapter, in Bayesian inference are computed using expected values. In more analytical Bayesian inference, we would have been required to evaluate complicated expected values represented as multi-dimensional integrals. No longer. If we can sample from the posterior distribution directly, we simply need to evaluate averages. Much easier. If accuracy is a priority, plots like the ones above show how fast you are converging. And if further accuracy is desired, just take more samples from the posterior. \n", + "\n", + "When is enough enough? When can you stop drawing samples from the posterior? That is the practitioners decision, and also dependent on the variance of the samples (recall from above a high variance means the average will converge slower). \n", + "\n", + "We also should understand when the Law of Large Numbers fails. As the name implies, and comparing the graphs above for small $N$, the Law is only true for large sample sizes. Without this, the asymptotic result is not reliable. Knowing in what situations the Law fails can give us *confidence in how unconfident we should be*. The next section deals with this issue." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## The Disorder of Small Numbers\n", + "\n", + "The Law of Large Numbers is only valid as $N$ gets *infinitely* large: never truly attainable. While the law is a powerful tool, it is foolhardy to apply it liberally. Our next example illustrates this.\n", + "\n", + "\n", + "##### Example: Aggregated geographic data\n", + "\n", + "\n", + "Often data comes in aggregated form. For instance, data may be grouped by state, county, or city level. Of course, the population numbers vary per geographic area. If the data is an average of some characteristic of each the geographic areas, we must be conscious of the Law of Large Numbers and how it can *fail* for areas with small populations.\n", + "\n", + "We will observe this on a toy dataset. Suppose there are five thousand counties in our dataset. Furthermore, population number in each state are uniformly distributed between 100 and 1500. The way the population numbers are generated is irrelevant to the discussion, so we do not justify this. We are interested in measuring the average height of individuals per county. Unbeknownst to us, height does **not** vary across county, and each individual, regardless of the county he or she is currently living in, has the same distribution of what their height may be:\n", + "\n", + "$$ \\text{height} \\sim \\text{Normal}(150, 15 ) $$\n", + "\n", + "We aggregate the individuals at the county level, so we only have data for the *average in the county*. What might our dataset look like?" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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fj40bN3LXXXfx7W9/m74+47yNJFfiK0M4HOa8887jnHPOYfv27axcuZKnn346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vS7DpmcjWlprJ0T5nmhkqJdH9LtjPDdkFufWWc8dnl095Az58p0miM8N0RRRyytF3j4sXRKGkd9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\nBsZE5MtAJ9Ba99FtEcEa89tRc343aLcaCbNHdDBbRAu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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "figsize(12.5, 4)\n", + "std_height = 15\n", + "mean_height = 150\n", + "\n", + "n_counties = 5000\n", + "pop_generator = pm.rdiscrete_uniform\n", + "norm = pm.rnormal\n", + "\n", + "# generate some artificial population numbers\n", + "population = pop_generator(100, 1500, size=n_counties)\n", + "\n", + "average_across_county = np.zeros(n_counties)\n", + "for i in range(n_counties):\n", + " # generate some individuals and take the mean\n", + " average_across_county[i] = norm(mean_height, 1. / std_height ** 2,\n", + " size=population[i]).mean()\n", + "\n", + "# located the counties with the apparently most extreme average heights.\n", + "i_min = np.argmin(average_across_county)\n", + "i_max = np.argmax(average_across_county)\n", + "\n", + "# plot population size vs. recorded average\n", + "plt.scatter(population, average_across_county, alpha=0.5, c=\"#7A68A6\")\n", + "plt.scatter([population[i_min], population[i_max]],\n", + " [average_across_county[i_min], average_across_county[i_max]],\n", + " s=60, marker=\"o\", facecolors=\"none\",\n", + " edgecolors=\"#A60628\", linewidths=1.5,\n", + " label=\"extreme heights\")\n", + "\n", + "plt.xlim(100, 1500)\n", + "plt.title(\"Average height vs. County Population\")\n", + "plt.xlabel(\"County Population\")\n", + "plt.ylabel(\"Average height in county\")\n", + "plt.plot([100, 1500], [150, 150], color=\"k\", label=\"true expected \\\n", + "height\", ls=\"--\")\n", + "plt.legend(scatterpoints=1);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "What do we observe? *Without accounting for population sizes* we run the risk of making an enormous inference error: if we ignored population size, we would say that the county with the shortest and tallest individuals have been correctly circled. But this inference is wrong for the following reason. These two counties do *not* necessarily have the most extreme heights. The error results from the calculated average of smaller populations not being a good reflection of the true expected value of the population (which in truth should be $\\mu =150$). The sample size/population size/$N$, whatever you wish to call it, is simply too small to invoke the Law of Large Numbers effectively. \n", + "\n", + "We provide more damning evidence against this inference. Recall the population numbers were uniformly distributed over 100 to 1500. Our intuition should tell us that the counties with the most extreme population heights should also be uniformly spread over 100 to 1500, and certainly independent of the county's population. Not so. Below are the population sizes of the counties with the most extreme heights." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Population sizes of 10 'shortest' counties: \n", + "[100 103 138 182 194 100 118 161 156 186]\n", + "\n", + "Population sizes of 10 'tallest' counties: \n", + "[100 147 132 193 270 130 414 101 150 109]\n" + ] + } + ], + "source": [ + "print(\"Population sizes of 10 'shortest' counties: \")\n", + "print(population[np.argsort(average_across_county)[:10]])\n", + "print(\"\\nPopulation sizes of 10 'tallest' counties: \")\n", + "print(population[np.argsort(-average_across_county)[:10]])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Not at all uniform over 100 to 1500. This is an absolute failure of the Law of Large Numbers. \n", + "\n", + "##### Example: Kaggle's *U.S. Census Return Rate Challenge*\n", + "\n", + "Below is data from the 2010 US census, which partitions populations beyond counties to the level of block groups (which are aggregates of city blocks or equivalents). The dataset is from a Kaggle machine learning competition some colleagues and I participated in. The objective was to predict the census letter mail-back rate of a group block, measured between 0 and 100, using census variables (median income, number of females in the block-group, number of trailer parks, average number of children etc.). Below we plot the census mail-back rate versus block group population:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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Vi+dbDIViPxakET+TN2GqlzubLjBbHPLE9qVimUK+PO7lPtC9c+lzun4iUT8r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O7eyCr6aQqF0gJWXmfzbpfeNG11frnMeOjhyAKzC0UESaQxl6e9P2I88JFkAfLZ9e1sDqnP5tnf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\n4w7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SiIiIhFCOPvFP5P0+BvxSmcZWNSvVlq+HmbOtZwvpiwu0tDbS198OGLMzc8ylF3P3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\n5LGp3PbgUBe79vRWcUQ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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "figsize(12.5, 6.5)\n", + "data = np.genfromtxt(\"./data/census_data.csv\", skip_header=1,\n", + " delimiter=\",\")\n", + "plt.scatter(data[:, 1], data[:, 0], alpha=0.5, c=\"#7A68A6\")\n", + "plt.title(\"Census mail-back rate vs Population\")\n", + "plt.ylabel(\"Mail-back rate\")\n", + "plt.xlabel(\"population of block-group\")\n", + "plt.xlim(-100, 15e3)\n", + "plt.ylim(-5, 105)\n", + "\n", + "i_min = np.argmin(data[:, 0])\n", + "i_max = np.argmax(data[:, 0])\n", + "\n", + "plt.scatter([data[i_min, 1], data[i_max, 1]],\n", + " [data[i_min, 0], data[i_max, 0]],\n", + " s=60, marker=\"o\", facecolors=\"none\",\n", + " edgecolors=\"#A60628\", linewidths=1.5,\n", + " label=\"most extreme points\")\n", + "\n", + "plt.legend(scatterpoints=1);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The above is a classic phenomenon in statistics. I say *classic* referring to the \"shape\" of the scatter plot above. It follows a classic triangular form, that tightens as we increase the sample size (as the Law of Large Numbers becomes more exact). \n", + "\n", + "I am perhaps overstressing the point and maybe I should have titled the book *\"You don't have big data problems!\"*, but here again is an example of the trouble with *small datasets*, not big ones. Simply, small datasets cannot be processed using the Law of Large Numbers. Compare with applying the Law without hassle to big datasets (ex. big data). I mentioned earlier that paradoxically big data prediction problems are solved by relatively simple algorithms. The paradox is partially resolved by understanding that the Law of Large Numbers creates solutions that are *stable*, i.e. adding or subtracting a few data points will not affect the solution much. On the other hand, adding or removing data points to a small dataset can create very different results. \n", + "\n", + "For further reading on the hidden dangers of the Law of Large Numbers, I would highly recommend the excellent manuscript [The Most Dangerous Equation](http://nsm.uh.edu/~dgraur/niv/TheMostDangerousEquation.pdf). " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Example: How to order Reddit submissions\n", + "\n", + "You may have disagreed with the original statement that the Law of Large numbers is known to everyone, but only implicitly in our subconscious decision making. Consider ratings on online products: how often do you trust an average 5-star rating if there is only 1 reviewer? 2 reviewers? 3 reviewers? We implicitly understand that with such few reviewers that the average rating is **not** a good reflection of the true value of the product.\n", + "\n", + "This has created flaws in how we sort items, and more generally, how we compare items. Many people have realized that sorting online search results by their rating, whether the objects be books, videos, or online comments, return poor results. Often the seemingly top videos or comments have perfect ratings only from a few enthusiastic fans, and truly more quality videos or comments are hidden in later pages with *falsely-substandard* ratings of around 4.8. How can we correct this?\n", + "\n", + "Consider the popular site Reddit (I purposefully did not link to the website as you would never come back). The site hosts links to stories or images, and a very popular part of the site are the comments associated with each link. Redditors can vote up or down on each submission (called upvotes and downvotes). Reddit, by default, will sort submissions to a given subreddit by Hot, that is, the submissions that have the most upvotes recently.\n", + "\n", + "\n", + "\n", + "\n", + "How would you determine which submissions are the best? There are a number of ways to achieve this:\n", + "\n", + "1. *Popularity*: A submission is considered good if it has many upvotes. A problem with this model is that a submission with hundreds of upvotes, but thousands of downvotes. While being very popular, the submission is likely more controversial than best.\n", + "2. *Difference*: Using the *difference* of upvotes and downvotes. This solves the above problem, but fails when we consider the temporal nature of submission. Depending on when a submission is posted, the website may be experiencing high or low traffic. The difference method will bias the Top submissions to be the those made during high traffic periods, which have accumulated more upvotes than submissions that were not so graced, but are not necessarily the best.\n", + "3. *Time adjusted*: Consider using Difference divided by the age of the submission. This creates a *rate*, something like *difference per second*, or *per minute*. An immediate counter-example is, if we use per second, a 1 second old submission with 1 upvote would be better than a 100 second old submission with 99 upvotes. One can avoid this by only considering at least t second old submission. But what is a good t value? Does this mean no submission younger than t is good? We end up comparing unstable quantities with stable quantities (young vs. old submissions).\n", + "3. *Ratio*: Rank submissions by the ratio of upvotes to total number of votes (upvotes plus downvotes). This solves the temporal issue, such that new submissions who score well can be considered Top just as likely as older submissions, provided they have many upvotes to total votes. The problem here is that a submission with a single upvote (ratio = 1.0) will beat a submission with 999 upvotes and 1 downvote (ratio = 0.999), but clearly the latter submission is *more likely* to be better.\n", + "\n", + "I used the phrase *more likely* for good reason. It is possible that the former submission, with a single upvote, is in fact a better submission than the latter with 999 upvotes. The hesitation to agree with this is because we have not seen the other 999 potential votes the former submission might get. Perhaps it will achieve an additional 999 upvotes and 0 downvotes and be considered better than the latter, though not likely.\n", + "\n", + "What we really want is an estimate of the *true upvote ratio*. Note that the true upvote ratio is not the same as the observed upvote ratio: the true upvote ratio is hidden, and we only observe upvotes vs. downvotes (one can think of the true upvote ratio as \"what is the underlying probability someone gives this submission a upvote, versus a downvote\"). So the 999 upvote/1 downvote submission probably has a true upvote ratio close to 1, which we can assert with confidence thanks to the Law of Large Numbers, but on the other hand we are much less certain about the true upvote ratio of the submission with only a single upvote. Sounds like a Bayesian problem to me.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "One way to determine a prior on the upvote ratio is to look at the historical distribution of upvote ratios. This can be accomplished by scraping Reddit's submissions and determining a distribution. There are a few problems with this technique though:\n", + "\n", + "1. Skewed data: The vast majority of submissions have very few votes, hence there will be many submissions with ratios near the extremes (see the \"triangular plot\" in the above Kaggle dataset), effectively skewing our distribution to the extremes. One could try to only use submissions with votes greater than some threshold. Again, problems are encountered. There is a tradeoff between number of submissions available to use and a higher threshold with associated ratio precision. \n", + "2. Biased data: Reddit is composed of different subpages, called subreddits. Two examples are *r/aww*, which posts pics of cute animals, and *r/politics*. It is very likely that the user behaviour towards submissions of these two subreddits are very different: visitors are likely to be more friendly and affectionate in the former, and would therefore upvote submissions more, compared to the latter, where submissions are likely to be controversial and disagreed upon. Therefore not all submissions are the same. \n", + "\n", + "\n", + "In light of these, I think it is better to use a `Uniform` prior.\n", + "\n", + "\n", + "With our prior in place, we can find the posterior of the true upvote ratio. The Python script `top_showerthoughts_submissions.py` will scrape the best posts from the `showerthoughts` community on Reddit. This is a text-only community so the title of each post *is* the post. Below is the top post as well as some other sample posts:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Post contents: \n", + "\n", + "Toilet paper should be free and have advertising printed on it.\n" + ] + } + ], + "source": [ + "# adding a number to the end of the %run call will get the ith top post.\n", + "%run top_showerthoughts_submissions.py 2\n", + "\n", + "print(\"Post contents: \\n\")\n", + "print(top_post)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Some Submissions (out of 98 total) \n", + "-----------\n", + "\"You will never feel how long time is until you have allergies and snot slowly dripping out of your nostrils, while sitting in a classroom with no tissues.\"\n", + "upvotes/downvotes: [71 6] \n", + "\n", + "\"What if porn ads weren't fake and all these years I've been missing out on these local mums in my area that want to fuck?\"\n", + "upvotes/downvotes: [43 11] \n", + "\n", + "\"You'll be real lucky to find a Penny in Canada.\"\n", + "upvotes/downvotes: [28 11] \n", + "\n", + "\"\"Smells Like Teen Spirit\" is as old to listeners of today as \"Yellow Submarine\" was to listeners of 1991.\"\n", + "upvotes/downvotes: [92 10] \n", + "\n" + ] + } + ], + "source": [ + "\"\"\"\n", + "contents: an array of the text from the last 100 top submissions to a subreddit\n", + "votes: a 2d numpy array of upvotes, downvotes for each submission.\n", + "\"\"\"\n", + "n_submissions = len(votes)\n", + "submissions = np.random.randint( n_submissions, size=4)\n", + "print(\"Some Submissions (out of %d total) \\n-----------\"%n_submissions)\n", + "for i in submissions:\n", + " print('\"' + contents[i] + '\"')\n", + " print(\"upvotes/downvotes: \",votes[i,:], \"\\n\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " For a given true upvote ratio $p$ and $N$ votes, the number of upvotes will look like a Binomial random variable with parameters $p$ and $N$. (This is because of the equivalence between upvote ratio and probability of upvoting versus downvoting, out of $N$ possible votes/trials). We create a function that performs Bayesian inference on $p$, for a particular comment's upvote/downvote pair." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "import pymc as pm\n", + "\n", + "\n", + "def posterior_upvote_ratio(upvotes, downvotes, samples=20000):\n", + " \"\"\"\n", + " This function accepts the number of upvotes and downvotes a particular submission received, \n", + " and the number of posterior samples to return to the user. Assumes a uniform prior.\n", + " \"\"\"\n", + " N = upvotes + downvotes\n", + " upvote_ratio = pm.Uniform(\"upvote_ratio\", 0, 1)\n", + " observations = pm.Binomial(\"obs\", N, upvote_ratio, value=upvotes, observed=True)\n", + " # do the fitting; first do a MAP as it is cheap and useful.\n", + " map_ = pm.MAP([upvote_ratio, observations]).fit()\n", + " mcmc = pm.MCMC([upvote_ratio, observations])\n", + " mcmc.sample(samples, samples / 4)\n", + " return mcmc.trace(\"upvote_ratio\")[:]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Below are the resulting posterior distributions." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'figsize' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfigsize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m11.\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m8\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mposteriors\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mcolours\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m\"#348ABD\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"#A60628\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"#7A68A6\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"#467821\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"#CF4457\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msubmissions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msubmissions\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mNameError\u001b[0m: name 'figsize' is not defined" + ] + } + ], + "source": [ + "figsize(11., 8)\n", + "posteriors = []\n", + "colours = [\"#348ABD\", \"#A60628\", \"#7A68A6\", \"#467821\", \"#CF4457\"]\n", + "for i in range(len(submissions)):\n", + " j = submissions[i]\n", + " posteriors.append(posterior_upvote_ratio(votes[j, 0], votes[j, 1]))\n", + " plt.hist(posteriors[i], bins=18, density=True, alpha=.9,\n", + " histtype=\"step\", color=colours[i % 5], lw=3,\n", + " label='(%d up:%d down)\\n%s...' % (votes[j, 0], votes[j, 1], contents[j][:50]))\n", + " plt.hist(posteriors[i], bins=18, density=True, alpha=.2,\n", + " histtype=\"stepfilled\", color=colours[i], lw=3, )\n", + "\n", + "plt.legend(loc=\"upper left\")\n", + "plt.xlim(0, 1)\n", + "plt.title(\"Posterior distributions of upvote ratios on different submissions\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Some distributions are very tight, others have very long tails (relatively speaking), expressing our uncertainty with what the true upvote ratio might be.\n", + "\n", + "### Sorting!\n", + "\n", + "We have been ignoring the goal of this exercise: how do we sort the submissions from *best to worst*? Of course, we cannot sort distributions, we must sort scalar numbers. There are many ways to distill a distribution down to a scalar: expressing the distribution through its expected value, or mean, is one way. Choosing the mean is a bad choice though. This is because the mean does not take into account the uncertainty of distributions.\n", + "\n", + "I suggest using the *95% least plausible value*, defined as the value such that there is only a 5% chance the true parameter is lower (think of the lower bound on the 95% credible region). Below are the posterior distributions with the 95% least-plausible value plotted:" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0 1 3 2] [0.95553912986585299, 0.94130501756135543, 0.80681345969724116, 0.88775207639838272]\n" + ] + }, + { + "data": { + "image/png": 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+AMNRfYcu64tgTjQ9GW8AbVB99/4PdYmQYzz+4tivoPpP7tLltRl1hp/J66MOX3mjPmTP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IyLmRgXM7EGsvoxA0lEREREjnAtbJ9i3ZC5GBszMS5mYlzMxBrI6scMJBERERE5whpIn2LdkLkY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\nnr6/qTQQROvGDclf5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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "N = posteriors[0].shape[0]\n", + "lower_limits = []\n", + "\n", + "for i in range(len(submissions)):\n", + " j = submissions[i]\n", + " plt.hist(posteriors[i], bins=20, density=True, alpha=.9,\n", + " histtype=\"step\", color=colours[i], lw=3,\n", + " label='(%d up:%d down)\\n%s...' % (votes[j, 0], votes[j, 1], contents[j][:50]))\n", + " plt.hist(posteriors[i], bins=20, density=True, alpha=.2,\n", + " histtype=\"stepfilled\", color=colours[i], lw=3, )\n", + " v = np.sort(posteriors[i])[int(0.05 * N)]\n", + " # plt.vlines( v, 0, 15 , color = \"k\", alpha = 1, linewidths=3 )\n", + " plt.vlines(v, 0, 10, color=colours[i], linestyles=\"--\", linewidths=3)\n", + " lower_limits.append(v)\n", + " plt.legend(loc=\"upper left\")\n", + "\n", + "plt.legend(loc=\"upper left\")\n", + "plt.title(\"Posterior distributions of upvote ratios on different submissions\");\n", + "order = np.argsort(-np.array(lower_limits))\n", + "print(order, lower_limits)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The best submissions, according to our procedure, are the submissions that are *most-likely* to score a high percentage of upvotes. Visually those are the submissions with the 95% least plausible value close to 1.\n", + "\n", + "Why is sorting based on this quantity a good idea? By ordering by the 95% least plausible value, we are being the most conservative with what we think is best. When using the lower-bound of the 95% credible interval, we believe with high certainty that the 'true upvote ratio' is at the very least equal to this value (or greater), thereby ensuring that the best submissions are still on top. Under this ordering, we impose the following very natural properties:\n", + "\n", + "1. given two submissions with the same observed upvote ratio, we will assign the submission with more votes as better (since we are more confident it has a higher ratio).\n", + "2. given two submissions with the same number of votes, we still assign the submission with more upvotes as *better*.\n", + "\n", + "### But this is too slow for real-time!\n", + "\n", + "I agree, computing the posterior of every submission takes a long time, and by the time you have computed it, likely the data has changed. I delay the mathematics to the appendix, but I suggest using the following formula to compute the lower bound very fast.\n", + "\n", + "$$ \\frac{a}{a + b} - 1.65\\sqrt{ \\frac{ab}{ (a+b)^2(a + b +1 ) } }$$\n", + "\n", + "where \n", + "\\begin{align}\n", + "& a = 1 + u \\\\\\\\\n", + "& b = 1 + d \\\\\\\\\n", + "\\end{align}\n", + "\n", + "$u$ is the number of upvotes, and $d$ is the number of downvotes. The formula is a shortcut in Bayesian inference, which will be further explained in Chapter 6 when we discuss priors in more detail.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Approximate lower bounds:\n", + "[ 0.9335036 0.95310536 0.94166971 0.90854227 0.88683909 0.85564276\n", + " 0.85607414 0.93758888 0.95697574 0.91015237 0.9112593 0.91305389\n", + " 0.91341024 0.83335231 0.87543995 0.87081169 0.92748782 0.90747915\n", + " 0.89063214 0.89804044 0.91295322 0.78329196 0.91901344 0.79950031\n", + " 0.84776174 0.83540757 0.77406294 0.81391583 0.7296015 0.79338766\n", + " 0.82895671 0.85331368 0.81849519 0.72362912 0.83662174 0.81019924\n", + " 0.78564811 0.84570434 0.8400282 0.76944053 0.85827725 0.74417233\n", + " 0.8189683 0.8027221 0.79190256 0.9033107 0.81639188 0.76627386\n", + " 0.8010596 0.63657302 0.62988646 0.75041771 0.85355829 0.84522753\n", + " 0.75627191 0.8458571 0.80877728 0.66764706 0.69623887 0.71480224\n", + " 0.72921035 0.86797314 0.73955911 0.90742546 0.80364062 0.72331349\n", + " 0.79249393 0.72708753 0.81109538 0.66235556 0.80480879 0.72039455\n", + " 0.73945971 0.83846154 0.69 0.70597731 0.68175931 0.59412132\n", + " 0.6011942 0.73158407 0.69121436 0.68134548 0.87746603 0.79809005\n", + " 0.6296728 0.87152685 0.81814153 0.86498277 0.81018384 0.54207776\n", + " 0.6296728 0.74107856 0.53025484 0.71034959 0.80149882 0.85773646\n", + " 0.58343356 0.62971097]\n", + "\n", + "\n", + "Top 40 Sorted according to approximate lower bounds:\n", + "\n", + "\n", + "586 18 Someone should develop an AI specifically for reading Terms & Conditions and flagging dubious parts.\n", + "-------------\n", + "2354 98 Porn is the only industry where it is not only acceptable but standard to separate people based on race, sex and sexual preference.\n", + "-------------\n", + "1924 101 All polls are biased towards people who are willing to take polls\n", + "-------------\n", + "949 50 They should charge less for drinks in the drive-thru because you can't refill them.\n", + "-------------\n", + "3726 238 When I was in elementary school and going through the DARE program, I was positive a gang of older kids was going to corner me and force me to smoke pot. Then I became an adult and realized nobody is giving free drugs to somebody that doesn't want them.\n", + "-------------\n", + "164 7 \"Noted\" is the professional way of saying \"K\".\n", + "-------------\n", + "100 4 The best answer to the interview question \"What is your greatest weakness?\" is \"interviews\".\n", + "-------------\n", + "267 17 At some point every parent has stopped wiping their child's butt and hoped for the best.\n", + "-------------\n", + "291 19 You've been doing weird cameos in your friends' dreams since kindergarten.\n", + "-------------\n", + "121 6 Is it really fair to say a person over 85 has heart failure? Technically, that heart has done exceptionally well.\n", + "-------------\n", + "523 39 I wonder if America's internet is censored in a similar way that North Korea's is, but we have no idea of it happening.\n", + "-------------\n", + "539 41 It's surreal to think that the sun and moon and stars we gaze up at are the same objects that have been observed for millenia, by everyone in the history of humanity from cavemen to Aristotle to Jesus to George Washington.\n", + "-------------\n", + "1509 131 Kenny's family is poor because they're always paying for his funeral.\n", + "-------------\n", + "164 10 Black hair ties are probably the most popular bracelets in the world.\n", + "-------------\n", + "26 0 Now that I am a parent of multiple children I have realized that my parents were lying through their teeth when they said they didn't have a favorite.\n", + "-------------\n", + "41 1 If I was as careful with my whole paycheck as I am with my last $20 I'd be a whole lot better off\n", + "-------------\n", + "125 8 Surfing the internet without ads feels like a summer evening without mosquitoes\n", + "-------------\n", + "157 12 I wonder if Superman ever put a pair of glasses on Lois Lane's dog, and she was like \"what's this Clark? Did you get me a new dog?\"\n", + "-------------\n", + "1411 157 My life is really like Rihanna's song, \"just work work work work work\" and the rest of it I can't really understand.\n", + "-------------\n", + "19 0 Binoculars are like walkie talkies for the deaf.\n", + "-------------\n", + "221 22 I'm honestly slightly concerned how often Reddit commenters make me laugh compared to my real life friends.\n", + "-------------\n", + "18 0 Living on the coast is having the window seat of the land you live on.\n", + "-------------\n", + "188 19 I have not been thankful enough in the last few years that the Black Eyed Peas are no longer ever on the radio\n", + "-------------\n", + "29 1 Rewatching Mr. Bean, I've realised that the character is an eccentric genius and not a blithering idiot.\n", + "-------------\n", + "17 0 Sitting on a cold toilet seat or a warm toilet seat both suck for different reasons.\n", + "-------------\n", + "54 4 You will never feel how long time is until you have allergies and snot slowly dripping out of your nostrils, while sitting in a classroom with no tissues.\n", + "-------------\n", + "16 0 I sneer at people who read tabloids, but every time I look someone up on Wikipedia the first thing I look for is what controversies they've been involved in.\n", + "-------------\n", + "1485 222 Kid's menus at restaurants should be smaller portions of the same adult dishes at lower prices and not the junk food that they usually offer.\n", + "-------------\n", + "1417 212 Eventually once all phones are waterproof we'll be able to push people into pools again\n", + "-------------\n", + "35 2 Childhood and adolescence are thinking that no one has ever felt the way you do and that no one has ever experienced the things that you have. Adulthood is realizing that almost everyone has felt and experienced something similar.\n", + "-------------\n", + "60 5 Myspace is so outdated that jokes about it being outdated has become outdated\n", + "-------------\n", + "87 9 Yahoo!® is the RadioShack® of the Internet.\n", + "-------------\n", + "33 2 People who \"tell it like it is\" rarely do so to say something nice\n", + "-------------\n", + "49 4 The world must have been a spookier place altogether when candles and gas lamps were the only sources of light at night besides the moon and the stars.\n", + "-------------\n", + "41 3 Closing your eyes after turning off your alarm is a very dangerous game.\n", + "-------------\n", + "47 4 As a kid, seeing someone step on a banana peel and not slip was a disappointment.\n", + "-------------\n", + "23 1 The phonebook was the biggest invasion of privacy that everyone was oddly ok with.\n", + "-------------\n", + "53 5 I'm actually the most productive when I procrastinate because I'm doing everything I possibly can to avoid the main task at hand.\n", + "-------------\n", + "86 10 \"Smells Like Teen Spirit\" is as old to listeners of today as \"Yellow Submarine\" was to listeners of 1991.\n", + "-------------\n", + "240 36 if an ocean didnt stop immigrants from coming to America what makes us think a wall will?\n", + "-------------\n" + ] + } + ], + "source": [ + "def intervals(u, d):\n", + " a = 1. + u\n", + " b = 1. + d\n", + " mu = a / (a + b)\n", + " std_err = 1.65 * np.sqrt((a * b) / ((a + b) ** 2 * (a + b + 1.)))\n", + " return (mu, std_err)\n", + "\n", + "print(\"Approximate lower bounds:\")\n", + "posterior_mean, std_err = intervals(votes[:, 0], votes[:, 1])\n", + "lb = posterior_mean - std_err\n", + "print(lb)\n", + "print(\"\\n\")\n", + "print(\"Top 40 Sorted according to approximate lower bounds:\")\n", + "print(\"\\n\")\n", + "order = np.argsort(-lb)\n", + "ordered_contents = []\n", + "for i in order[:40]:\n", + " ordered_contents.append(contents[i])\n", + " print(votes[i, 0], votes[i, 1], contents[i])\n", + " print(\"-------------\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can view the ordering visually by plotting the posterior mean and bounds, and sorting by the lower bound. In the plot below, notice that the left error-bar is sorted (as we suggested this is the best way to determine an ordering), so the means, indicated by dots, do not follow any strong pattern. " + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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Rh/U5yn2NEJ0aQYhA/W+MZBwL/ASYaGb3ZNWZzIZIyaTopzvjvbuAB6IP68eT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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "r_order = order[::-1][-40:]\n", + "plt.errorbar(posterior_mean[r_order], np.arange(len(r_order)),\n", + " xerr=std_err[r_order], capsize=0, fmt=\"o\",\n", + " color=\"#7A68A6\")\n", + "plt.xlim(0.3, 1)\n", + "plt.yticks(np.arange(len(r_order) - 1, -1, -1), map(lambda x: x[:30].replace(\"\\n\", \"\"), ordered_contents));" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the graphic above, you can see why sorting by mean would be sub-optimal." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Extension to Starred rating systems\n", + "\n", + "The above procedure works well for upvote-downvotes schemes, but what about systems that use star ratings, e.g. 5 star rating systems. Similar problems apply with simply taking the average: an item with two perfect ratings would beat an item with thousands of perfect ratings, but a single sub-perfect rating. \n", + "\n", + "\n", + "We can consider the upvote-downvote problem above as binary: 0 is a downvote, 1 if an upvote. A $N$-star rating system can be seen as a more continuous version of above, and we can set $n$ stars rewarded is equivalent to rewarding $\\frac{n}{N}$. For example, in a 5-star system, a 2 star rating corresponds to 0.4. A perfect rating is a 1. We can use the same formula as before, but with $a,b$ defined differently:\n", + "\n", + "\n", + "$$ \\frac{a}{a + b} - 1.65\\sqrt{ \\frac{ab}{ (a+b)^2(a + b +1 ) } }$$\n", + "\n", + "where \n", + "\n", + "\\begin{align}\n", + "& a = 1 + S \\\\\\\\\n", + "& b = 1 + N - S \\\\\\\\\n", + "\\end{align}\n", + "\n", + "where $N$ is the number of users who rated, and $S$ is the sum of all the ratings, under the equivalence scheme mentioned above. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Example: Counting Github stars\n", + "\n", + "What is the average number of stars a Github repository has? How would you calculate this? There are over 6 million repositories, so there is more than enough data to invoke the Law of Large numbers. Let's start pulling some data. TODO" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Conclusion\n", + "\n", + "While the Law of Large Numbers is cool, it is only true so much as its name implies: with large sample sizes only. We have seen how our inference can be affected by not considering *how the data is shaped*. \n", + "\n", + "1. By (cheaply) drawing many samples from the posterior distributions, we can ensure that the Law of Large Number applies as we approximate expected values (which we will do in the next chapter).\n", + "\n", + "2. Bayesian inference understands that with small sample sizes, we can observe wild randomness. Our posterior distribution will reflect this by being more spread rather than tightly concentrated. Thus, our inference should be correctable.\n", + "\n", + "3. There are major implications of not considering the sample size, and trying to sort objects that are unstable leads to pathological orderings. The method provided above solves this problem.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Appendix\n", + "\n", + "##### Derivation of sorting comments formula\n", + "\n", + "Basically what we are doing is using a Beta prior (with parameters $a=1, b=1$, which is a uniform distribution), and using a Binomial likelihood with observations $u, N = u+d$. This means our posterior is a Beta distribution with parameters $a' = 1 + u, b' = 1 + (N - u) = 1+d$. We then need to find the value, $x$, such that 0.05 probability is less than $x$. This is usually done by inverting the CDF ([Cumulative Distribution Function](http://en.wikipedia.org/wiki/Cumulative_Distribution_Function)), but the CDF of the beta, for integer parameters, is known but is a large sum [3]. \n", + "\n", + "We instead use a Normal approximation. The mean of the Beta is $\\mu = a'/(a'+b')$ and the variance is \n", + "\n", + "$$\\sigma^2 = \\frac{a'b'}{ (a' + b')^2(a'+b'+1) }$$\n", + "\n", + "Hence we solve the following equation for $x$ and have an approximate lower bound. \n", + "\n", + "$$ 0.05 = \\Phi\\left( \\frac{(x - \\mu)}{\\sigma}\\right) $$ \n", + "\n", + "$\\Phi$ being the [cumulative distribution for the normal distribution](http://en.wikipedia.org/wiki/Normal_distribution#Cumulative_distribution)\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Exercises\n", + "\n", + "1\\. How would you estimate the quantity $E\\left[ \\cos{X} \\right]$, where $X \\sim \\text{Exp}(4)$? What about $E\\left[ \\cos{X} | X \\lt 1\\right]$, i.e. the expected value *given* we know $X$ is less than 1? Would you need more samples than the original samples size to be equally accurate?" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [], + "source": [ + "# Enter code here\n", + "import scipy.stats as stats\n", + "exp = stats.expon(scale=4)\n", + "N = int(1e5)\n", + "X = exp.rvs(N)\n", + "# ..." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "2\\. The following table was located in the paper \"Going for Three: Predicting the Likelihood of Field Goal Success with Logistic Regression\" [2]. The table ranks football field-goal kickers by their percent of non-misses. What mistake have the researchers made?\n", + "\n", + "-----\n", + "\n", + "#### Kicker Careers Ranked by Make Percentage\n", + "
Rank Kicker Make % Number of Kicks
1 Garrett Hartley 87.7 57
2 Matt Stover 86.8 335
3 Robbie Gould 86.2 224
4 Rob Bironas 86.1 223
5 Shayne Graham 85.4 254
… … …
51 Dave Rayner 72.2 90
52 Nick Novak 71.9 64
53 Tim Seder 71.0 62
54 Jose Cortez 70.7 75
55 Wade Richey 66.1 56
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In August 2013, [a popular post](http://bpodgursky.wordpress.com/2013/08/21/average-income-per-programming-language/) on the average income per programmer of different languages was trending. Here's the summary chart: (reproduced without permission, cause when you lie with stats, you gunna get the hammer). What do you notice about the extremes?\n", + "\n", + "------\n", + "\n", + "#### Average household income by programming language\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
LanguageAverage Household Income ($)Data Points
Puppet87,589.29112
Haskell89,973.82191
PHP94,031.19978
CoffeeScript94,890.80435
VimL94,967.11532
Shell96,930.54979
Lua96,930.69101
Erlang97,306.55168
Clojure97,500.00269
Python97,578.872314
JavaScript97,598.753443
Emacs Lisp97,774.65355
C#97,823.31665
Ruby98,238.743242
C++99,147.93845
CSS99,881.40527
Perl100,295.45990
C100,766.512120
Go101,158.01231
Scala101,460.91243
ColdFusion101,536.70109
Objective-C101,801.60562
Groovy102,650.86116
Java103,179.391402
XSLT106,199.19123
ActionScript108,119.47113
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### References\n", + "\n", + "1. Wainer, Howard. *The Most Dangerous Equation*. American Scientist, Volume 95.\n", + "2. Clarck, Torin K., Aaron W. Johnson, and Alexander J. Stimpson. \"Going for Three: Predicting the Likelihood of Field Goal Success with Logistic Regression.\" (2013): n. page. [Web](http://www.sloansportsconference.com/wp-content/uploads/2013/Going%20for%20Three%20Predicting%20the%20Likelihood%20of%20Field%20Goal%20Success%20with%20Logistic%20Regression.pdf). 20 Feb. 2013.\n", + "3. http://en.wikipedia.org/wiki/Beta_function#Incomplete_beta_function" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.core.display import HTML\n", + "\n", + "\n", + "def css_styling():\n", + " styles = open(\"../styles/custom.css\", \"r\").read()\n", + " return HTML(styles)\n", + "css_styling()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.2" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/Chapter4_TheGreatestTheoremNeverTold/Ch4_LawOfLargeNumbers_PyMC3.ipynb b/Chapter4_TheGreatestTheoremNeverTold/Ch4_LawOfLargeNumbers_PyMC3.ipynb new file mode 100644 index 00000000..ad777ca8 --- /dev/null +++ b/Chapter4_TheGreatestTheoremNeverTold/Ch4_LawOfLargeNumbers_PyMC3.ipynb @@ -0,0 +1,1260 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Chapter 4\n", + "`Original content created by Cam Davidson-Pilon`\n", + "\n", + "`Ported to Python 3 and PyMC3 by Max Margenot (@clean_utensils) and Thomas Wiecki (@twiecki) at Quantopian (@quantopian)`\n", + "\n", + "______\n", + "\n", + "## The greatest theorem never told\n", + "\n", + "\n", + "This chapter focuses on an idea that is always bouncing around our minds, but is rarely made explicit outside books devoted to statistics. In fact, we've been using this simple idea in every example thus far. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### The Law of Large Numbers\n", + "\n", + "Let $Z_i$ be $N$ independent samples from some probability distribution. According to *the Law of Large numbers*, so long as the expected value $E[Z]$ is finite, the following holds,\n", + "\n", + "$$\\frac{1}{N} \\sum_{i=1}^N Z_i \\rightarrow E[ Z ], \\;\\;\\; N \\rightarrow \\infty.$$\n", + "\n", + "In words:\n", + "\n", + "> The average of a sequence of random variables from the same distribution converges to the expected value of that distribution.\n", + "\n", + "This may seem like a boring result, but it will be the most useful tool you use." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Intuition \n", + "\n", + "If the above Law is somewhat surprising, it can be made more clear by examining a simple example. \n", + "\n", + "Consider a random variable $Z$ that can take only two values, $c_1$ and $c_2$. Suppose we have a large number of samples of $Z$, denoting a specific sample $Z_i$. The Law says that we can approximate the expected value of $Z$ by averaging over all samples. Consider the average:\n", + "\n", + "\n", + "$$ \\frac{1}{N} \\sum_{i=1}^N \\;Z_i $$\n", + "\n", + "\n", + "By construction, $Z_i$ can only take on $c_1$ or $c_2$, hence we can partition the sum over these two values:\n", + "\n", + "\\begin{align}\n", + "\\frac{1}{N} \\sum_{i=1}^N \\;Z_i\n", + "& =\\frac{1}{N} \\big( \\sum_{ Z_i = c_1}c_1 + \\sum_{Z_i=c_2}c_2 \\big) \\\\\\\\[5pt]\n", + "& = c_1 \\sum_{ Z_i = c_1}\\frac{1}{N} + c_2 \\sum_{ Z_i = c_2}\\frac{1}{N} \\\\\\\\[5pt]\n", + "& = c_1 \\times \\text{ (approximate frequency of $c_1$) } \\\\\\\\ \n", + "& \\;\\;\\;\\;\\;\\;\\;\\;\\; + c_2 \\times \\text{ (approximate frequency of $c_2$) } \\\\\\\\[5pt]\n", + "& \\approx c_1 \\times P(Z = c_1) + c_2 \\times P(Z = c_2 ) \\\\\\\\[5pt]\n", + "& = E[Z]\n", + "\\end{align}\n", + "\n", + "\n", + "Equality holds in the limit, but we can get closer and closer by using more and more samples in the average. This Law holds for almost *any distribution*, minus some important cases we will encounter later.\n", + "\n", + "##### Example\n", + "____\n", + "\n", + "\n", + "Below is a diagram of the Law of Large numbers in action for three different sequences of Poisson random variables. \n", + "\n", + " We sample `sample_size = 100000` Poisson random variables with parameter $\\lambda = 4.5$. (Recall the expected value of a Poisson random variable is equal to its parameter.) We calculate the average for the first $n$ samples, for $n=1$ to `sample_size`. " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "image/png": 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9ls/befMWqzfe9isuv+TkZFJTU8nLy2PYsGEEBweTmJhIy5Yt/boGp0+f5uqr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\nMC8aNWNfLABk161TLRYkSZKkQY19sdDpd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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib inline\n", + "import numpy as np\n", + "from IPython.core.pylabtools import figsize\n", + "import matplotlib.pyplot as plt\n", + "\n", + "figsize( 12.5, 5 )\n", + "\n", + "sample_size = 100000\n", + "expected_value = lambda_ = 4.5\n", + "poi = np.random.poisson\n", + "N_samples = range(1,sample_size,100)\n", + "\n", + "for k in range(3):\n", + "\n", + " samples = poi( lambda_, sample_size ) \n", + " \n", + " partial_average = [ samples[:i].mean() for i in N_samples ]\n", + " \n", + " plt.plot( N_samples, partial_average, lw=1.5,label=\"average \\\n", + "of $n$ samples; seq. %d\"%k)\n", + " \n", + "\n", + "plt.plot( N_samples, expected_value*np.ones_like( partial_average), \n", + " ls = \"--\", label = \"true expected value\", c = \"k\" )\n", + "\n", + "plt.ylim( 4.35, 4.65) \n", + "plt.title( \"Convergence of the average of \\n random variables to its \\\n", + "expected value\" )\n", + "plt.ylabel( \"average of $n$ samples\" )\n", + "plt.xlabel( \"# of samples, $n$\")\n", + "plt.legend();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Looking at the above plot, it is clear that when the sample size is small, there is greater variation in the average (compare how *jagged and jumpy* the average is initially, then *smooths* out). All three paths *approach* the value 4.5, but just flirt with it as $N$ gets large. Mathematicians and statistician have another name for *flirting*: convergence. \n", + "\n", + "Another very relevant question we can ask is *how quickly am I converging to the expected value?* Let's plot something new. For a specific $N$, let's do the above trials thousands of times and compute how far away we are from the true expected value, on average. But wait — *compute on average*? This is simply the law of large numbers again! For example, we are interested in, for a specific $N$, the quantity:\n", + "\n", + "$$D(N) = \\sqrt{ \\;E\\left[\\;\\; \\left( \\frac{1}{N}\\sum_{i=1}^NZ_i - 4.5 \\;\\right)^2 \\;\\;\\right] \\;\\;}$$\n", + "\n", + "The above formulae is interpretable as a distance away from the true value (on average), for some $N$. (We take the square root so the dimensions of the above quantity and our random variables are the same). As the above is an expected value, it can be approximated using the law of large numbers: instead of averaging $Z_i$, we calculate the following multiple times and average them:\n", + "\n", + "$$ Y_k = \\left( \\;\\frac{1}{N}\\sum_{i=1}^NZ_i - 4.5 \\; \\right)^2 $$\n", + "\n", + "By computing the above many, $N_y$, times (remember, it is random), and averaging them:\n", + "\n", + "$$ \\frac{1}{N_Y} \\sum_{k=1}^{N_Y} Y_k \\rightarrow E[ Y_k ] = E\\;\\left[\\;\\; \\left( \\frac{1}{N}\\sum_{i=1}^NZ_i - 4.5 \\;\\right)^2 \\right]$$\n", + "\n", + "Finally, taking the square root:\n", + "\n", + "$$ \\sqrt{\\frac{1}{N_Y} \\sum_{k=1}^{N_Y} Y_k} \\approx D(N) $$ " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "image/png": 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evZk9ezZ//vknaWlprrbuuVm0aJGr0lK5cmXKlSuHt7d3rjGePHmSChUqUK1a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\nLK+//jpfffUVR44coU6dOtx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POhO42RhzNK98LncfArCaDaVNncSXO61Zi6N3H9MKQSmg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\n/yGNdYzcWUOyuBgtKYGyErS0hEBRIXlBv5s8OElEfsBHKOAjzy+EAj4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27wN+axTf175D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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "figsize( 12.5, 4)\n", + "\n", + "N_Y = 250 #use this many to approximate D(N)\n", + "N_array = np.arange( 1000, 50000, 2500 ) #use this many samples in the approx. to the variance.\n", + "D_N_results = np.zeros( len( N_array ) )\n", + "\n", + "lambda_ = 4.5 \n", + "expected_value = lambda_ #for X ~ Poi(lambda) , E[ X ] = lambda\n", + "\n", + "def D_N( n ):\n", + " \"\"\"\n", + " This function approx. D_n, the average variance of using n samples.\n", + " \"\"\"\n", + " Z = poi( lambda_, (n, N_Y) )\n", + " average_Z = Z.mean(axis=0)\n", + " return np.sqrt( ( (average_Z - expected_value)**2 ).mean() )\n", + " \n", + " \n", + "for i,n in enumerate(N_array):\n", + " D_N_results[i] = D_N(n)\n", + "\n", + "\n", + "plt.xlabel( \"$N$\" )\n", + "plt.ylabel( \"expected squared-distance from true value\" )\n", + "plt.plot(N_array, D_N_results, lw = 3, \n", + " label=\"expected distance between\\n\\\n", + "expected value and \\naverage of $N$ random variables.\")\n", + "plt.plot( N_array, np.sqrt(expected_value)/np.sqrt(N_array), lw = 2, ls = \"--\", \n", + " label = r\"$\\frac{\\sqrt{\\lambda}}{\\sqrt{N}}$\" )\n", + "plt.legend()\n", + "plt.title( \"How 'fast' is the sample average converging? \" );" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As expected, the expected distance between our sample average and the actual expected value shrinks as $N$ grows large. But also notice that the *rate* of convergence decreases, that is, we need only 10 000 additional samples to move from 0.020 to 0.015, a difference of 0.005, but *20 000* more samples to again decrease from 0.015 to 0.010, again only a 0.005 decrease.\n", + "\n", + "\n", + "It turns out we can measure this rate of convergence. Above I have plotted a second line, the function $\\sqrt{\\lambda}/\\sqrt{N}$. This was not chosen arbitrarily. In most cases, given a sequence of random variable distributed like $Z$, the rate of convergence to $E[Z]$ of the Law of Large Numbers is \n", + "\n", + "$$ \\frac{ \\sqrt{ \\; Var(Z) \\; } }{\\sqrt{N} }$$\n", + "\n", + "This is useful to know: for a given large $N$, we know (on average) how far away we are from the estimate. On the other hand, in a Bayesian setting, this can seem like a useless result: Bayesian analysis is OK with uncertainty so what's the *statistical* point of adding extra precise digits? Though drawing samples can be so computationally cheap that having a *larger* $N$ is fine too. \n", + "\n", + "### How do we compute $Var(Z)$ though?\n", + "\n", + "The variance is simply another expected value that can be approximated! Consider the following, once we have the expected value (by using the Law of Large Numbers to estimate it, denote it $\\mu$), we can estimate the variance:\n", + "\n", + "$$ \\frac{1}{N}\\sum_{i=1}^N \\;(Z_i - \\mu)^2 \\rightarrow E[ \\;( Z - \\mu)^2 \\;] = Var( Z )$$\n", + "\n", + "### Expected values and probabilities \n", + "There is an even less explicit relationship between expected value and estimating probabilities. Define the *indicator function*\n", + "\n", + "$$\\mathbb{1}_A(x) = \n", + "\\begin{cases} 1 & x \\in A \\\\\\\\\n", + " 0 & else\n", + "\\end{cases}\n", + "$$\n", + "Then, by the law of large numbers, if we have many samples $X_i$, we can estimate the probability of an event $A$, denoted $P(A)$, by:\n", + "\n", + "$$ \\frac{1}{N} \\sum_{i=1}^N \\mathbb{1}_A(X_i) \\rightarrow E[\\mathbb{1}_A(X)] = P(A) $$\n", + "\n", + "Again, this is fairly obvious after a moments thought: the indicator function is only 1 if the event occurs, so we are summing only the times the event occurs and dividing by the total number of trials (consider how we usually approximate probabilities using frequencies). For example, suppose we wish to estimate the probability that a $Z \\sim Exp(.5)$ is greater than 5, and we have many samples from a $Exp(.5)$ distribution. \n", + "\n", + "\n", + "$$ P( Z > 5 ) = \\frac{1}{N}\\sum_{i=1}^N \\mathbb{1}_{z > 5 }(Z_i) $$\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.0001\n" + ] + } + ], + "source": [ + "N = 10000\n", + "print( np.mean( [ np.random.exponential( 0.5 ) > 5 for i in range(N) ] ) )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### What does this all have to do with Bayesian statistics? \n", + "\n", + "\n", + "*Point estimates*, to be introduced in the next chapter, in Bayesian inference are computed using expected values. In more analytical Bayesian inference, we would have been required to evaluate complicated expected values represented as multi-dimensional integrals. No longer. If we can sample from the posterior distribution directly, we simply need to evaluate averages. Much easier. If accuracy is a priority, plots like the ones above show how fast you are converging. And if further accuracy is desired, just take more samples from the posterior. \n", + "\n", + "When is enough enough? When can you stop drawing samples from the posterior? That is the practitioners decision, and also dependent on the variance of the samples (recall from above a high variance means the average will converge slower). \n", + "\n", + "We also should understand when the Law of Large Numbers fails. As the name implies, and comparing the graphs above for small $N$, the Law is only true for large sample sizes. Without this, the asymptotic result is not reliable. Knowing in what situations the Law fails can give us *confidence in how unconfident we should be*. The next section deals with this issue." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## The Disorder of Small Numbers\n", + "\n", + "The Law of Large Numbers is only valid as $N$ gets *infinitely* large: never truly attainable. While the law is a powerful tool, it is foolhardy to apply it liberally. Our next example illustrates this.\n", + "\n", + "\n", + "##### Example: Aggregated geographic data\n", + "\n", + "\n", + "Often data comes in aggregated form. For instance, data may be grouped by state, county, or city level. Of course, the population numbers vary per geographic area. If the data is an average of some characteristic of each the geographic areas, we must be conscious of the Law of Large Numbers and how it can *fail* for areas with small populations.\n", + "\n", + "We will observe this on a toy dataset. Suppose there are five thousand counties in our dataset. Furthermore, population number in each state are uniformly distributed between 100 and 1500. The way the population numbers are generated is irrelevant to the discussion, so we do not justify this. We are interested in measuring the average height of individuals per county. Unbeknownst to us, height does **not** vary across county, and each individual, regardless of the county he or she is currently living in, has the same distribution of what their height may be:\n", + "\n", + "$$ \\text{height} \\sim \\text{Normal}(150, 15 ) $$\n", + "\n", + "We aggregate the individuals at the county level, so we only have data for the *average in the county*. What might our dataset look like?" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "image/png": 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8cq677jq2bNnCsmXLePvtt5k6dSolJSWA5Y1etmwZS5YsYerUqaxZs4aOjg6+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\noua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cyWHfichtP8eo27mymerPdiZC0ViQannJ0ef3+33OCg/07toqSFdGQRAE4f6mX4f+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\n4CzwlW2M4b7g6aef3us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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "figsize( 12.5, 4) \n", + "std_height = 15\n", + "mean_height = 150\n", + "\n", + "n_counties = 5000\n", + "pop_generator = np.random.randint\n", + "norm = np.random.normal\n", + "\n", + "#generate some artificial population numbers\n", + "population = pop_generator(100, 1500, n_counties )\n", + "\n", + "average_across_county = np.zeros( n_counties )\n", + "for i in range( n_counties ):\n", + " #generate some individuals and take the mean\n", + " average_across_county[i] = norm(mean_height, 1./std_height,\n", + " population[i] ).mean()\n", + " \n", + "#located the counties with the apparently most extreme average heights.\n", + "i_min = np.argmin( average_across_county )\n", + "i_max = np.argmax( average_across_county )\n", + "\n", + "#plot population size vs. recorded average\n", + "plt.scatter( population, average_across_county, alpha = 0.5, c=\"#7A68A6\")\n", + "plt.scatter( [ population[i_min], population[i_max] ], \n", + " [average_across_county[i_min], average_across_county[i_max] ],\n", + " s = 60, marker = \"o\", facecolors = \"none\",\n", + " edgecolors = \"#A60628\", linewidths = 1.5, \n", + " label=\"extreme heights\")\n", + "\n", + "plt.xlim( 100, 1500 )\n", + "plt.title( \"Average height vs. County Population\")\n", + "plt.xlabel(\"County Population\")\n", + "plt.ylabel(\"Average height in county\")\n", + "plt.plot( [100, 1500], [150, 150], color = \"k\", label = \"true expected \\\n", + "height\", ls=\"--\" )\n", + "plt.legend(scatterpoints = 1);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "What do we observe? *Without accounting for population sizes* we run the risk of making an enormous inference error: if we ignored population size, we would say that the county with the shortest and tallest individuals have been correctly circled. But this inference is wrong for the following reason. These two counties do *not* necessarily have the most extreme heights. The error results from the calculated average of smaller populations not being a good reflection of the true expected value of the population (which in truth should be $\\mu =150$). The sample size/population size/$N$, whatever you wish to call it, is simply too small to invoke the Law of Large Numbers effectively. \n", + "\n", + "We provide more damning evidence against this inference. Recall the population numbers were uniformly distributed over 100 to 1500. Our intuition should tell us that the counties with the most extreme population heights should also be uniformly spread over 100 to 1500, and certainly independent of the county's population. Not so. Below are the population sizes of the counties with the most extreme heights." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Population sizes of 10 'shortest' counties: \n", + "[109 135 135 133 109 157 175 120 105 131] \n", + "\n", + "Population sizes of 10 'tallest' counties: \n", + "[122 133 313 109 124 280 106 198 326 216]\n" + ] + } + ], + "source": [ + "print(\"Population sizes of 10 'shortest' counties: \")\n", + "print(population[ np.argsort( average_across_county )[:10] ], '\\n')\n", + "print(\"Population sizes of 10 'tallest' counties: \")\n", + "print(population[ np.argsort( -average_across_county )[:10] ])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Not at all uniform over 100 to 1500. This is an absolute failure of the Law of Large Numbers. \n", + "\n", + "##### Example: Kaggle's *U.S. Census Return Rate Challenge*\n", + "\n", + "Below is data from the 2010 US census, which partitions populations beyond counties to the level of block groups (which are aggregates of city blocks or equivalents). The dataset is from a Kaggle machine learning competition some colleagues and I participated in. The objective was to predict the census letter mail-back rate of a group block, measured between 0 and 100, using census variables (median income, number of females in the block-group, number of trailer parks, average number of children etc.). Below we plot the census mail-back rate versus block group population:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "image/png": 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Vi+dbDIViPxakET+TN2GqlzubLjBbHPLE9qVimUK+PO7lPtC9c+lzun4iUT8r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O7eyCr6aQqF0gJWXmfzbpfeNG11frnMeOjhyAKzC0UESaQxl6e9P2I88JFkAfLZ9e1sDqnP5tnf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\n4w7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SiIiIhFCOPvFP5P0+BvxSmcZWNSvVlq+HmbOtZwvpiwu0tDbS198OGLMzc8ylF3P3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\n5LGp3PbgUBe79vRWcUQ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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "figsize( 12.5, 6.5 )\n", + "data = np.genfromtxt( \"./data/census_data.csv\", skip_header=1, \n", + " delimiter= \",\")\n", + "plt.scatter( data[:,1], data[:,0], alpha = 0.5, c=\"#7A68A6\")\n", + "plt.title(\"Census mail-back rate vs Population\")\n", + "plt.ylabel(\"Mail-back rate\")\n", + "plt.xlabel(\"population of block-group\")\n", + "plt.xlim(-100, 15e3 )\n", + "plt.ylim( -5, 105)\n", + "\n", + "i_min = np.argmin( data[:,0] )\n", + "i_max = np.argmax( data[:,0] )\n", + " \n", + "plt.scatter( [ data[i_min,1], data[i_max, 1] ], \n", + " [ data[i_min,0], data[i_max,0] ],\n", + " s = 60, marker = \"o\", facecolors = \"none\",\n", + " edgecolors = \"#A60628\", linewidths = 1.5, \n", + " label=\"most extreme points\")\n", + "\n", + "plt.legend(scatterpoints = 1);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The above is a classic phenomenon in statistics. I say *classic* referring to the \"shape\" of the scatter plot above. It follows a classic triangular form, that tightens as we increase the sample size (as the Law of Large Numbers becomes more exact). \n", + "\n", + "I am perhaps overstressing the point and maybe I should have titled the book *\"You don't have big data problems!\"*, but here again is an example of the trouble with *small datasets*, not big ones. Simply, small datasets cannot be processed using the Law of Large Numbers. Compare with applying the Law without hassle to big datasets (ex. big data). I mentioned earlier that paradoxically big data prediction problems are solved by relatively simple algorithms. The paradox is partially resolved by understanding that the Law of Large Numbers creates solutions that are *stable*, i.e. adding or subtracting a few data points will not affect the solution much. On the other hand, adding or removing data points to a small dataset can create very different results. \n", + "\n", + "For further reading on the hidden dangers of the Law of Large Numbers, I would highly recommend the excellent manuscript [The Most Dangerous Equation](http://nsm.uh.edu/~dgraur/niv/TheMostDangerousEquation.pdf). " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Example: How to order Reddit submissions\n", + "\n", + "You may have disagreed with the original statement that the Law of Large numbers is known to everyone, but only implicitly in our subconscious decision making. Consider ratings on online products: how often do you trust an average 5-star rating if there is only 1 reviewer? 2 reviewers? 3 reviewers? We implicitly understand that with such few reviewers that the average rating is **not** a good reflection of the true value of the product.\n", + "\n", + "This has created flaws in how we sort items, and more generally, how we compare items. Many people have realized that sorting online search results by their rating, whether the objects be books, videos, or online comments, return poor results. Often the seemingly top videos or comments have perfect ratings only from a few enthusiastic fans, and truly more quality videos or comments are hidden in later pages with *falsely-substandard* ratings of around 4.8. How can we correct this?\n", + "\n", + "Consider the popular site Reddit (I purposefully did not link to the website as you would never come back). The site hosts links to stories or images, called submissions, for people to comment on. Redditors can vote up or down on each submission (called upvotes and downvotes). Reddit, by default, will sort submissions to a given subreddit by Hot, that is, the submissions that have the most upvotes recently.\n", + "\n", + "\n", + "\n", + "\n", + "How would you determine which submissions are the best? There are a number of ways to achieve this:\n", + "\n", + "1. *Popularity*: A submission is considered good if it has many upvotes. A problem with this model is that a submission with hundreds of upvotes, but thousands of downvotes. While being very *popular*, the submission is likely more controversial than best.\n", + "2. *Difference*: Using the *difference* of upvotes and downvotes. This solves the above problem, but fails when we consider the temporal nature of submission. Depending on when a submission is posted, the website may be experiencing high or low traffic. The difference method will bias the *Top* submissions to be the those made during high traffic periods, which have accumulated more upvotes than submissions that were not so graced, but are not necessarily the best.\n", + "3. *Time adjusted*: Consider using Difference divided by the age of the submission. This creates a *rate*, something like *difference per second*, or *per minute*. An immediate counter-example is, if we use per second, a 1 second old submission with 1 upvote would be better than a 100 second old submission with 99 upvotes. One can avoid this by only considering at least t second old submission. But what is a good t value? Does this mean no submission younger than t is good? We end up comparing unstable quantities with stable quantities (young vs. old submissions).\n", + "3. *Ratio*: Rank submissions by the ratio of upvotes to total number of votes (upvotes plus downvotes). This solves the temporal issue, such that new submissions who score well can be considered Top just as likely as older submissions, provided they have many upvotes to total votes. The problem here is that a submission with a single upvote (ratio = 1.0) will beat a submission with 999 upvotes and 1 downvote (ratio = 0.999), but clearly the latter submission is *more likely* to be better.\n", + "\n", + "I used the phrase *more likely* for good reason. It is possible that the former submission, with a single upvote, is in fact a better submission than the later with 999 upvotes. The hesitation to agree with this is because we have not seen the other 999 potential votes the former submission might get. Perhaps it will achieve an additional 999 upvotes and 0 downvotes and be considered better than the latter, though not likely.\n", + "\n", + "What we really want is an estimate of the *true upvote ratio*. Note that the true upvote ratio is not the same as the observed upvote ratio: the true upvote ratio is hidden, and we only observe upvotes vs. downvotes (one can think of the true upvote ratio as \"what is the underlying probability someone gives this submission a upvote, versus a downvote\"). So the 999 upvote/1 downvote submission probably has a true upvote ratio close to 1, which we can assert with confidence thanks to the Law of Large Numbers, but on the other hand we are much less certain about the true upvote ratio of the submission with only a single upvote. Sounds like a Bayesian problem to me.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "One way to determine a prior on the upvote ratio is to look at the historical distribution of upvote ratios. This can be accomplished by scraping Reddit's submissions and determining a distribution. There are a few problems with this technique though:\n", + "\n", + "1. Skewed data: The vast majority of submissions have very few votes, hence there will be many submissions with ratios near the extremes (see the \"triangular plot\" in the above Kaggle dataset), effectively skewing our distribution to the extremes. One could try to only use submissions with votes greater than some threshold. Again, problems are encountered. There is a tradeoff between number of submissions available to use and a higher threshold with associated ratio precision. \n", + "2. Biased data: Reddit is composed of different subpages, called subreddits. Two examples are *r/aww*, which posts pics of cute animals, and *r/politics*. It is very likely that the user behaviour towards submissions of these two subreddits are very different: visitors are likely friendly and affectionate in the former, and would therefore upvote submissions more, compared to the latter, where submissions are likely to be controversial and disagreed upon. Therefore not all submissions are the same. \n", + "\n", + "\n", + "In light of these, I think it is better to use a `Uniform` prior.\n", + "\n", + "\n", + "With our prior in place, we can find the posterior of the true upvote ratio. The Python script `top_showerthoughts_submissions.py` will scrape the best posts from the `showerthoughts` community on Reddit. This is a text-only community so the title of each post *is* the post. Below is the top post as well as some other sample posts:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Post contents: \n", + "\n", + "Toilet paper should be free and have advertising printed on it.\n" + ] + } + ], + "source": [ + "#adding a number to the end of the %run call will get the ith top post.\n", + "%run top_showerthoughts_submissions.py 2\n", + "\n", + "print(\"Post contents: \\n\")\n", + "print(top_post)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + }, + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Some Submissions (out of 98 total) \n", + "-----------\n", + "\"Rappers from the 90's used guns when they had beef rappers today use Twitter.\"\n", + "upvotes/downvotes: [32 3] \n", + "\n", + "\"All polls are biased towards people who are willing to take polls\"\n", + "upvotes/downvotes: [1918 101] \n", + "\n", + "\"Taco Bell should give customers an extra tortilla so they can make a burrito out of all the stuff that spilled out of the other burritos they ate.\"\n", + "upvotes/downvotes: [79 17] \n", + "\n", + "\"There should be an /r/alanismorissette where it's just examples of people using \"ironic\" incorrectly\"\n", + "upvotes/downvotes: [33 6] \n", + "\n" + ] + } + ], + "source": [ + "\"\"\"\n", + "contents: an array of the text from the last 100 top submissions to a subreddit\n", + "votes: a 2d numpy array of upvotes, downvotes for each submission.\n", + "\"\"\"\n", + "n_submissions = len(votes)\n", + "submissions = np.random.randint( n_submissions, size=4)\n", + "print(\"Some Submissions (out of %d total) \\n-----------\"%n_submissions)\n", + "for i in submissions:\n", + " print('\"' + contents[i] + '\"')\n", + " print(\"upvotes/downvotes: \",votes[i,:], \"\\n\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " For a given true upvote ratio $p$ and $N$ votes, the number of upvotes will look like a Binomial random variable with parameters $p$ and $N$. (This is because of the equivalence between upvote ratio and probability of upvoting versus downvoting, out of $N$ possible votes/trials). We create a function that performs Bayesian inference on $p$, for a particular submission's upvote/downvote pair." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "import pymc3 as pm\n", + "\n", + "def posterior_upvote_ratio( upvotes, downvotes, samples = 20000):\n", + " \"\"\"\n", + " This function accepts the number of upvotes and downvotes a particular submission recieved, \n", + " and the number of posterior samples to return to the user. Assumes a uniform prior.\n", + " \"\"\"\n", + " N = upvotes + downvotes\n", + " with pm.Model() as model:\n", + " upvote_ratio = pm.Uniform(\"upvote_ratio\", 0, 1)\n", + " observations = pm.Binomial( \"obs\", N, upvote_ratio, observed=upvotes)\n", + " \n", + " trace = pm.sample(samples, step=pm.Metropolis())\n", + " \n", + " burned_trace = trace[int(samples/4):]\n", + " return burned_trace[\"upvote_ratio\"]\n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Below are the resulting posterior distributions." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Applied interval-transform to upvote_ratio and added transformed upvote_ratio_interval_ to model.\n", + " [-------100%-------] 20000 of 20000 in 1.4 sec. | SPS: 14595.5 | ETA: 0.0Applied interval-transform to upvote_ratio and added transformed upvote_ratio_interval_ to model.\n", + " [-------100%-------] 20000 of 20000 in 1.3 sec. | SPS: 15189.5 | ETA: 0.0Applied interval-transform to upvote_ratio and added transformed upvote_ratio_interval_ to model.\n", + " [-------100%-------] 20000 of 20000 in 1.3 sec. | SPS: 15429.0 | ETA: 0.0Applied interval-transform to upvote_ratio and added transformed upvote_ratio_interval_ to model.\n", + " [-------100%-------] 20000 of 20000 in 1.3 sec. | SPS: 15146.5 | ETA: 0.0" + ] + }, + { + "data": { + "image/png": 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biGr7c0FfBHOi6cl4BXgZ1fbuX9QtO47z4JtR90C1f/xbF9c21BV4JtNHnY6qgdqpnkE1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\n4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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "figsize( 11., 8)\n", + "posteriors = []\n", + "colours = [\"#348ABD\", \"#A60628\", \"#7A68A6\", \"#467821\", \"#CF4457\"]\n", + "for i in range(len(submissions)):\n", + " j = submissions[i]\n", + " posteriors.append( posterior_upvote_ratio( votes[j, 0], votes[j,1] ) )\n", + " plt.hist( posteriors[i], bins = 10, normed = True, alpha = .9, \n", + " histtype=\"step\",color = colours[i%5], lw = 3,\n", + " label = '(%d up:%d down)\\n%s...'%(votes[j, 0], votes[j,1], contents[j][:50]) )\n", + " plt.hist( posteriors[i], bins = 10, normed = True, alpha = .2, \n", + " histtype=\"stepfilled\",color = colours[i], lw = 3, )\n", + " \n", + "plt.legend(loc=\"upper left\")\n", + "plt.xlim( 0, 1)\n", + "plt.title(\"Posterior distributions of upvote ratios on different submissions\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Some distributions are very tight, others have very long tails (relatively speaking), expressing our uncertainty with what the true upvote ratio might be.\n", + "\n", + "### Sorting!\n", + "\n", + "We have been ignoring the goal of this exercise: how do we sort the submissions from *best to worst*? Of course, we cannot sort distributions, we must sort scalar numbers. There are many ways to distill a distribution down to a scalar: expressing the distribution through its expected value, or mean, is one way. Choosing the mean is a bad choice though. This is because the mean does not take into account the uncertainty of distributions.\n", + "\n", + "I suggest using the *95% least plausible value*, defined as the value such that there is only a 5% chance the true parameter is lower (think of the lower bound on the 95% credible region). Below are the posterior distributions with the 95% least-plausible value plotted:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1 0 2 3] [0.80034320917496615, 0.94092009444598201, 0.74660503350561902, 0.72190353389632911]\n" + ] + }, + { + "data": { + "image/png": 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45JNPTMoSHh5O48aNAXBxcaFHjx6AOgT2ww8/4OXlpe10c/ToUdq0aYOLiwtt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\npgJ+dfAf8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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "N = posteriors[0].shape[0]\n", + "lower_limits = []\n", + "\n", + "for i in range(len(submissions)):\n", + " j = submissions[i]\n", + " plt.hist( posteriors[i], bins = 20, normed = True, alpha = .9, \n", + " histtype=\"step\",color = colours[i], lw = 3,\n", + " label = '(%d up:%d down)\\n%s...'%(votes[j, 0], votes[j,1], contents[j][:50]) )\n", + " plt.hist( posteriors[i], bins = 20, normed = True, alpha = .2, \n", + " histtype=\"stepfilled\",color = colours[i], lw = 3, )\n", + " v = np.sort( posteriors[i] )[ int(0.05*N) ]\n", + " #plt.vlines( v, 0, 15 , color = \"k\", alpha = 1, linewidths=3 )\n", + " plt.vlines( v, 0, 10 , color = colours[i], linestyles = \"--\", linewidths=3 )\n", + " lower_limits.append(v)\n", + " plt.legend(loc=\"upper left\")\n", + "\n", + "plt.legend(loc=\"upper left\")\n", + "plt.title(\"Posterior distributions of upvote ratios on different submissions\");\n", + "order = np.argsort( -np.array( lower_limits ) )\n", + "print(order, lower_limits)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The best submissions, according to our procedure, are the submissions that are *most-likely* to score a high percentage of upvotes. Visually those are the submissions with the 95% least plausible value close to 1.\n", + "\n", + "Why is sorting based on this quantity a good idea? By ordering by the 95% least plausible value, we are being the most conservative with what we think is best. When using the lower-bound of the 95% credible interval, we believe with high certainty that the 'true upvote ratio' is at the very least equal to this value (or greater), thereby ensuring that the best submissions are still on top. Under this ordering, we impose the following very natural properties:\n", + "\n", + "1. given two submissions with the same observed upvote ratio, we will assign the submission with more votes as better (since we are more confident it has a higher ratio).\n", + "2. given two submissions with the same number of votes, we still assign the submission with more upvotes as *better*.\n", + "\n", + "### But this is too slow for real-time!\n", + "\n", + "I agree, computing the posterior of every submission takes a long time, and by the time you have computed it, likely the data has changed. I delay the mathematics to the appendix, but I suggest using the following formula to compute the lower bound very fast.\n", + "\n", + "$$ \\frac{a}{a + b} - 1.65\\sqrt{ \\frac{ab}{ (a+b)^2(a + b +1 ) } }$$\n", + "\n", + "where \n", + "\\begin{align}\n", + "& a = 1 + u \\\\\\\\\n", + "& b = 1 + d \\\\\\\\\n", + "\\end{align}\n", + "\n", + "$u$ is the number of upvotes, and $d$ is the number of downvotes. The formula is a shortcut in Bayesian inference, which will be further explained in Chapter 6 when we discuss priors in more detail.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Approximate lower bounds:\n", + "[ 0.93349005 0.9532194 0.94149718 0.90859764 0.88705356 0.8558795\n", + " 0.85644927 0.93752679 0.95767101 0.91131012 0.910073 0.915999\n", + " 0.9140058 0.83276025 0.87593961 0.87436674 0.92830849 0.90642832\n", + " 0.89187973 0.89950891 0.91295322 0.78607629 0.90250203 0.79950031\n", + " 0.85219422 0.83703439 0.7619808 0.81301134 0.7313114 0.79137561\n", + " 0.82701445 0.85542404 0.82309334 0.75211374 0.82934814 0.82674958\n", + " 0.80933194 0.87448152 0.85350205 0.75460106 0.82934814 0.74417233\n", + " 0.79924258 0.8189683 0.75460106 0.90744016 0.83838023 0.78802791\n", + " 0.78400654 0.64638659 0.62047936 0.76137738 0.81365241 0.83838023\n", + " 0.78457533 0.84980627 0.79249393 0.69020315 0.69593922 0.70758151\n", + " 0.70268831 0.91620627 0.73346864 0.86382644 0.80877728 0.72708753\n", + " 0.79822085 0.68333632 0.81699014 0.65100453 0.79809005 0.74702492\n", + " 0.77318569 0.83221179 0.66500492 0.68134548 0.7249286 0.59412132\n", + " 0.58191312 0.73142963 0.73142963 0.66251028 0.87152685 0.74107856\n", + " 0.60935684 0.87152685 0.77484517 0.88783675 0.81814153 0.54569789\n", + " 0.6122496 0.75613569 0.53511973 0.74556767 0.81814153 0.85773646\n", + " 0.6122496 0.64814153]\n", + "\n", + "\n", + "Top 40 Sorted according to approximate lower bounds:\n", + "\n", + "\n", + "596 18 Someone should develop an AI specifically for reading Terms & Conditions and flagging dubious parts.\n", + "-------------\n", + "2360 98 Porn is the only industry where it is not only acceptable but standard to separate people based on race, sex and sexual preference.\n", + "-------------\n", + "1918 101 All polls are biased towards people who are willing to take polls\n", + "-------------\n", + "948 50 They should charge less for drinks in the drive-thru because you can't refill them.\n", + "-------------\n", + "3740 239 When I was in elementary school and going through the DARE program, I was positive a gang of older kids was going to corner me and force me to smoke pot. Then I became an adult and realized nobody is giving free drugs to somebody that doesn't want them.\n", + "-------------\n", + "166 7 \"Noted\" is the professional way of saying \"K\".\n", + "-------------\n", + "29 0 Rewatching Mr. Bean, I've realised that the character is an eccentric genius and not a blithering idiot.\n", + "-------------\n", + "289 18 You've been doing weird cameos in your friends' dreams since kindergarten.\n", + "-------------\n", + "269 17 At some point every parent has stopped wiping their child's butt and hoped for the best.\n", + "-------------\n", + "121 6 Is it really fair to say a person over 85 has heart failure? Technically, that heart has done exceptionally well.\n", + "-------------\n", + "535 40 It's surreal to think that the sun and moon and stars we gaze up at are the same objects that have been observed for millenia, by everyone in the history of humanity from cavemen to Aristotle to Jesus to George Washington.\n", + "-------------\n", + "527 40 I wonder if America's internet is censored in a similar way that North Korea's is, but we have no idea of it happening.\n", + "-------------\n", + "1510 131 Kenny's family is poor because they're always paying for his funeral.\n", + "-------------\n", + "43 1 If I was as careful with my whole paycheck as I am with my last $20 I'd be a whole lot better off\n", + "-------------\n", + "162 10 Black hair ties are probably the most popular bracelets in the world.\n", + "-------------\n", + "107 6 The best answer to the interview question \"What is your greatest weakness?\" is \"interviews\".\n", + "-------------\n", + "127 8 Surfing the internet without ads feels like a summer evening without mosquitoes\n", + "-------------\n", + "159 12 I wonder if Superman ever put a pair of glasses on Lois Lane's dog, and she was like \"what's this Clark? Did you get me a new dog?\"\n", + "-------------\n", + "21 0 Sitting on a cold toilet seat or a warm toilet seat both suck for different reasons.\n", + "-------------\n", + "1414 157 My life is really like Rihanna's song, \"just work work work work work\" and the rest of it I can't really understand.\n", + "-------------\n", + "222 22 I'm honestly slightly concerned how often Reddit commenters make me laugh compared to my real life friends.\n", + "-------------\n", + "52 3 The world must have been a spookier place altogether when candles and gas lamps were the only sources of light at night besides the moon and the stars.\n", + "-------------\n", + "194 19 I have not been thankful enough in the last few years that the Black Eyed Peas are no longer ever on the radio\n", + "-------------\n", + "18 0 Living on the coast is having the window seat of the land you live on.\n", + "-------------\n", + "18 0 Binoculars are like walkie talkies for the deaf.\n", + "-------------\n", + "28 1 Now that I am a parent of multiple children I have realized that my parents were lying through their teeth when they said they didn't have a favorite.\n", + "-------------\n", + "16 0 I sneer at people who read tabloids, but every time I look someone up on Wikipedia the first thing I look for is what controversies they've been involved in.\n", + "-------------\n", + "1559 233 Kid's menus at restaurants should be smaller portions of the same adult dishes at lower prices and not the junk food that they usually offer.\n", + "-------------\n", + "1426 213 Eventually once all phones are waterproof we'll be able to push people into pools again\n", + "-------------\n", + "61 5 Myspace is so outdated that jokes about it being outdated has become outdated\n", + "-------------\n", + "52 4 As a kid, seeing someone step on a banana peel and not slip was a disappointment.\n", + "-------------\n", + "90 9 Yahoo!® is the RadioShack® of the Internet.\n", + "-------------\n", + "34 2 People who \"tell it like it is\" rarely do so to say something nice\n", + "-------------\n", + "39 3 Closing your eyes after turning off your alarm is a very dangerous game.\n", + "-------------\n", + "39 3 Your known 'first word' is the first word your parents heard you speak. In reality, it may have been a completely different word you said when you were alone.\n", + "-------------\n", + "87 10 \"Smells Like Teen Spirit\" is as old to listeners of today as \"Yellow Submarine\" was to listeners of 1991.\n", + "-------------\n", + "239 36 if an ocean didnt stop immigrants from coming to America what makes us think a wall will?\n", + "-------------\n", + "22 1 The phonebook was the biggest invasion of privacy that everyone was oddly ok with.\n", + "-------------\n", + "57 6 I'm actually the most productive when I procrastinate because I'm doing everything I possibly can to avoid the main task at hand.\n", + "-------------\n", + "57 6 You will never feel how long time is until you have allergies and snot slowly dripping out of your nostrils, while sitting in a classroom with no tissues.\n", + "-------------\n" + ] + } + ], + "source": [ + "def intervals(u,d):\n", + " a = 1. + u\n", + " b = 1. + d\n", + " mu = a/(a+b)\n", + " std_err = 1.65*np.sqrt( (a*b)/( (a+b)**2*(a+b+1.) ) )\n", + " return ( mu, std_err )\n", + "\n", + "print(\"Approximate lower bounds:\")\n", + "posterior_mean, std_err = intervals(votes[:,0],votes[:,1])\n", + "lb = posterior_mean - std_err\n", + "print(lb)\n", + "print(\"\\n\")\n", + "print(\"Top 40 Sorted according to approximate lower bounds:\")\n", + "print(\"\\n\")\n", + "order = np.argsort( -lb )\n", + "ordered_contents = []\n", + "for i in order[:40]:\n", + " ordered_contents.append( contents[i] )\n", + " print(votes[i,0], votes[i,1], contents[i])\n", + " print(\"-------------\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can view the ordering visually by plotting the posterior mean and bounds, and sorting by the lower bound. In the plot below, notice that the left error-bar is sorted (as we suggested this is the best way to determine an ordering), so the means, indicated by dots, do not follow any strong pattern. " + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "image/png": 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4D7jNzPrEticRfsU/V9IKQnTiHUlnABea2UVZY3cDbk30PxMYZGa5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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "r_order = order[::-1][-40:]\n", + "plt.errorbar( posterior_mean[r_order], np.arange( len(r_order) ), \n", + " xerr=std_err[r_order], capsize=0, fmt=\"o\",\n", + " color = \"#7A68A6\")\n", + "plt.xlim( 0.3, 1)\n", + "plt.yticks( np.arange( len(r_order)-1,-1,-1 ), map( lambda x: x[:30].replace(\"\\n\",\"\"), ordered_contents) );" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the graphic above, you can see why sorting by mean would be sub-optimal." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Extension to Starred rating systems\n", + "\n", + "The above procedure works well for upvote-downvotes schemes, but what about systems that use star ratings, e.g. 5 star rating systems. Similar problems apply with simply taking the average: an item with two perfect ratings would beat an item with thousands of perfect ratings, but a single sub-perfect rating. \n", + "\n", + "\n", + "We can consider the upvote-downvote problem above as binary: 0 is a downvote, 1 if an upvote. A $N$-star rating system can be seen as a more continuous version of above, and we can set $n$ stars rewarded is equivalent to rewarding $\\frac{n}{N}$. For example, in a 5-star system, a 2 star rating corresponds to 0.4. A perfect rating is a 1. We can use the same formula as before, but with $a,b$ defined differently:\n", + "\n", + "\n", + "$$ \\frac{a}{a + b} - 1.65\\sqrt{ \\frac{ab}{ (a+b)^2(a + b +1 ) } }$$\n", + "\n", + "where \n", + "\n", + "\\begin{align}\n", + "& a = 1 + S \\\\\\\\\n", + "& b = 1 + N - S \\\\\\\\\n", + "\\end{align}\n", + "\n", + "where $N$ is the number of users who rated, and $S$ is the sum of all the ratings, under the equivalence scheme mentioned above. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Example: Counting Github stars\n", + "\n", + "What is the average number of stars a Github repository has? How would you calculate this? There are over 6 million respositories, so there is more than enough data to invoke the Law of Large numbers. Let's start pulling some data. TODO" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Conclusion\n", + "\n", + "While the Law of Large Numbers is cool, it is only true so much as its name implies: with large sample sizes only. We have seen how our inference can be affected by not considering *how the data is shaped*. \n", + "\n", + "1. By (cheaply) drawing many samples from the posterior distributions, we can ensure that the Law of Large Number applies as we approximate expected values (which we will do in the next chapter).\n", + "\n", + "2. Bayesian inference understands that with small sample sizes, we can observe wild randomness. Our posterior distribution will reflect this by being more spread rather than tightly concentrated. Thus, our inference should be correctable.\n", + "\n", + "3. There are major implications of not considering the sample size, and trying to sort objects that are unstable leads to pathological orderings. The method provided above solves this problem.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Appendix\n", + "\n", + "##### Derivation of sorting submissions formula\n", + "\n", + "Basically what we are doing is using a Beta prior (with parameters $a=1, b=1$, which is a uniform distribution), and using a Binomial likelihood with observations $u, N = u+d$. This means our posterior is a Beta distribution with parameters $a' = 1 + u, b' = 1 + (N - u) = 1+d$. We then need to find the value, $x$, such that 0.05 probability is less than $x$. This is usually done by inverting the CDF ([Cumulative Distribution Function](http://en.wikipedia.org/wiki/Cumulative_Distribution_Function)), but the CDF of the beta, for integer parameters, is known but is a large sum [3]. \n", + "\n", + "We instead use a Normal approximation. The mean of the Beta is $\\mu = a'/(a'+b')$ and the variance is \n", + "\n", + "$$\\sigma^2 = \\frac{a'b'}{ (a' + b')^2(a'+b'+1) }$$\n", + "\n", + "Hence we solve the following equation for $x$ and have an approximate lower bound. \n", + "\n", + "$$ 0.05 = \\Phi\\left( \\frac{(x - \\mu)}{\\sigma}\\right) $$ \n", + "\n", + "$\\Phi$ being the [cumulative distribution for the normal distribution](http://en.wikipedia.org/wiki/Normal_distribution#Cumulative_distribution)\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Exercises\n", + "\n", + "1\\. How would you estimate the quantity $E\\left[ \\cos{X} \\right]$, where $X \\sim \\text{Exp}(4)$? What about $E\\left[ \\cos{X} | X \\lt 1\\right]$, i.e. the expected value *given* we know $X$ is less than 1? Would you need more samples than the original samples size to be equally accurate?" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "## Enter code here\n", + "import scipy.stats as stats\n", + "exp = stats.expon( scale=4 )\n", + "N = 1e5\n", + "X = exp.rvs( int(N) )\n", + "## ..." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "2\\. The following table was located in the paper \"Going for Three: Predicting the Likelihood of Field Goal Success with Logistic Regression\" [2]. The table ranks football field-goal kickers by their percent of non-misses. What mistake have the researchers made?\n", + "\n", + "-----\n", + "\n", + "#### Kicker Careers Ranked by Make Percentage\n", + "
Rank Kicker Make % Number of Kicks
1 Garrett Hartley 87.7 57
2 Matt Stover 86.8 335
3 Robbie Gould 86.2 224
4 Rob Bironas 86.1 223
5 Shayne Graham 85.4 254
… … …
51 Dave Rayner 72.2 90
52 Nick Novak 71.9 64
53 Tim Seder 71.0 62
54 Jose Cortez 70.7 75
55 Wade Richey 66.1 56
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In August 2013, [a popular post](http://bpodgursky.wordpress.com/2013/08/21/average-income-per-programming-language/) on the average income per programmer of different languages was trending. Here's the summary chart: (reproduced without permission, cause when you lie with stats, you gunna get the hammer). What do you notice about the extremes?\n", + "\n", + "------\n", + "\n", + "#### Average household income by programming language\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
LanguageAverage Household Income ($)Data Points
Puppet87,589.29112
Haskell89,973.82191
PHP94,031.19978
CoffeeScript94,890.80435
VimL94,967.11532
Shell96,930.54979
Lua96,930.69101
Erlang97,306.55168
Clojure97,500.00269
Python97,578.872314
JavaScript97,598.753443
Emacs Lisp97,774.65355
C#97,823.31665
Ruby98,238.743242
C++99,147.93845
CSS99,881.40527
Perl100,295.45990
C100,766.512120
Go101,158.01231
Scala101,460.91243
ColdFusion101,536.70109
Objective-C101,801.60562
Groovy102,650.86116
Java103,179.391402
XSLT106,199.19123
ActionScript108,119.47113
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### References\n", + "\n", + "1. Wainer, Howard. *The Most Dangerous Equation*. American Scientist, Volume 95.\n", + "2. Clarck, Torin K., Aaron W. Johnson, and Alexander J. Stimpson. \"Going for Three: Predicting the Likelihood of Field Goal Success with Logistic Regression.\" (2013): n. page. [Web](http://www.sloansportsconference.com/wp-content/uploads/2013/Going%20for%20Three%20Predicting%20the%20Likelihood%20of%20Field%20Goal%20Success%20with%20Logistic%20Regression.pdf). 20 Feb. 2013.\n", + "3. http://en.wikipedia.org/wiki/Beta_function#Incomplete_beta_function" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.core.display import HTML\n", + "def css_styling():\n", + " styles = open(\"../styles/custom.css\", \"r\").read()\n", + " return HTML(styles)\n", + "css_styling()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/Chapter4_TheGreatestTheoremNeverTold/Ch4_LawOfLargeNumbers_PyMC_current.ipynb b/Chapter4_TheGreatestTheoremNeverTold/Ch4_LawOfLargeNumbers_PyMC_current.ipynb new file mode 100644 index 00000000..f3cfd16c --- /dev/null +++ b/Chapter4_TheGreatestTheoremNeverTold/Ch4_LawOfLargeNumbers_PyMC_current.ipynb @@ -0,0 +1,1478 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Chapter 4\n", + "`Original content created by Cam Davidson-Pilon`\n", + "\n", + "`Ported to Python 3 and PyMC3 by Max Margenot (@clean_utensils) and Thomas Wiecki (@twiecki) at Quantopian (@quantopian)`\n", + "\n", + "`Ported to PyMC last by Kurisu Chan (@miemiekurisu) `\n", + "______\n", + "\n", + "## The greatest theorem never told\n", + "\n", + "\n", + "This chapter focuses on an idea that is always bouncing around our minds, but is rarely made explicit outside books devoted to statistics. In fact, we've been using this simple idea in every example thus far. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### The Law of Large Numbers\n", + "\n", + "Let $Z_i$ be $N$ independent samples from some probability distribution. According to *the Law of Large numbers*, so long as the expected value $E[Z]$ is finite, the following holds,\n", + "\n", + "$$\\frac{1}{N} \\sum_{i=1}^N Z_i \\rightarrow E[ Z ], \\;\\;\\; N \\rightarrow \\infty.$$\n", + "\n", + "In words:\n", + "\n", + "> The average of a sequence of random variables from the same distribution converges to the expected value of that distribution.\n", + "\n", + "This may seem like a boring result, but it will be the most useful tool you use." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Intuition \n", + "\n", + "If the above Law is somewhat surprising, it can be made more clear by examining a simple example. \n", + "\n", + "Consider a random variable $Z$ that can take only two values, $c_1$ and $c_2$. Suppose we have a large number of samples of $Z$, denoting a specific sample $Z_i$. The Law says that we can approximate the expected value of $Z$ by averaging over all samples. Consider the average:\n", + "\n", + "\n", + "$$ \\frac{1}{N} \\sum_{i=1}^N \\;Z_i $$\n", + "\n", + "\n", + "By construction, $Z_i$ can only take on $c_1$ or $c_2$, hence we can partition the sum over these two values:\n", + "\n", + "\\begin{align}\n", + "\\frac{1}{N} \\sum_{i=1}^N \\;Z_i\n", + "& =\\frac{1}{N} \\big( \\sum_{ Z_i = c_1}c_1 + \\sum_{Z_i=c_2}c_2 \\big) \\\\\\\\[5pt]\n", + "& = c_1 \\sum_{ Z_i = c_1}\\frac{1}{N} + c_2 \\sum_{ Z_i = c_2}\\frac{1}{N} \\\\\\\\[5pt]\n", + "& = c_1 \\times \\text{ (approximate frequency of $c_1$) } \\\\\\\\ \n", + "& \\;\\;\\;\\;\\;\\;\\;\\;\\; + c_2 \\times \\text{ (approximate frequency of $c_2$) } \\\\\\\\[5pt]\n", + "& \\approx c_1 \\times P(Z = c_1) + c_2 \\times P(Z = c_2 ) \\\\\\\\[5pt]\n", + "& = E[Z]\n", + "\\end{align}\n", + "\n", + "\n", + "Equality holds in the limit, but we can get closer and closer by using more and more samples in the average. This Law holds for almost *any distribution*, minus some important cases we will encounter later.\n", + "\n", + "##### Example\n", + "____\n", + "\n", + "\n", + "Below is a diagram of the Law of Large numbers in action for three different sequences of Poisson random variables. \n", + "\n", + " We sample `sample_size = 100000` Poisson random variables with parameter $\\lambda = 4.5$. (Recall the expected value of a Poisson random variable is equal to its parameter.) We calculate the average for the first $n$ samples, for $n=1$ to `sample_size`. " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib inline\n", + "import numpy as np\n", + "from IPython.core.pylabtools import figsize\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib as mtl\n", + "mtl.style.use(\"ggplot\")\n", + "\n", + "\n", + "figsize( 12.5, 5 )\n", + "\n", + "sample_size = 100000\n", + "expected_value = lambda_ = 4.5\n", + "poi = np.random.poisson\n", + "N_samples = range(1,sample_size,100)\n", + "\n", + "for k in range(3):\n", + "\n", + " samples = poi( lambda_, sample_size ) \n", + " \n", + " partial_average = [ samples[:i].mean() for i in N_samples ]\n", + " \n", + " plt.plot( N_samples, partial_average, lw=1.5,label=\"average \\\n", + "of $n$ samples; seq. %d\"%k)\n", + " \n", + "\n", + "plt.plot( N_samples, expected_value*np.ones_like( partial_average), \n", + " ls = \"--\", label = \"true expected value\", c = \"k\" )\n", + "\n", + "plt.ylim( 4.35, 4.65) \n", + "plt.title( \"Convergence of the average of \\n random variables to its \\\n", + "expected value\" )\n", + "plt.ylabel( \"average of $n$ samples\" )\n", + "plt.xlabel( \"# of samples, $n$\")\n", + "plt.legend();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Looking at the above plot, it is clear that when the sample size is small, there is greater variation in the average (compare how *jagged and jumpy* the average is initially, then *smooths* out). All three paths *approach* the value 4.5, but just flirt with it as $N$ gets large. Mathematicians and statistician have another name for *flirting*: convergence. \n", + "\n", + "Another very relevant question we can ask is *how quickly am I converging to the expected value?* Let's plot something new. For a specific $N$, let's do the above trials thousands of times and compute how far away we are from the true expected value, on average. But wait — *compute on average*? This is simply the law of large numbers again! For example, we are interested in, for a specific $N$, the quantity:\n", + "\n", + "$$D(N) = \\sqrt{ \\;E\\left[\\;\\; \\left( \\frac{1}{N}\\sum_{i=1}^NZ_i - 4.5 \\;\\right)^2 \\;\\;\\right] \\;\\;}$$\n", + "\n", + "The above formulae is interpretable as a distance away from the true value (on average), for some $N$. (We take the square root so the dimensions of the above quantity and our random variables are the same). As the above is an expected value, it can be approximated using the law of large numbers: instead of averaging $Z_i$, we calculate the following multiple times and average them:\n", + "\n", + "$$ Y_k = \\left( \\;\\frac{1}{N}\\sum_{i=1}^NZ_i - 4.5 \\; \\right)^2 $$\n", + "\n", + "By computing the above many, $N_y$, times (remember, it is random), and averaging them:\n", + "\n", + "$$ \\frac{1}{N_Y} \\sum_{k=1}^{N_Y} Y_k \\rightarrow E[ Y_k ] = E\\;\\left[\\;\\; \\left( \\frac{1}{N}\\sum_{i=1}^NZ_i - 4.5 \\;\\right)^2 \\right]$$\n", + "\n", + "Finally, taking the square root:\n", + "\n", + "$$ \\sqrt{\\frac{1}{N_Y} \\sum_{k=1}^{N_Y} Y_k} \\approx D(N) $$ " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "figsize( 12.5, 4)\n", + "\n", + "N_Y = 250 #use this many to approximate D(N)\n", + "N_array = np.arange( 1000, 50000, 2500 ) #use this many samples in the approx. to the variance.\n", + "D_N_results = np.zeros( len( N_array ) )\n", + "\n", + "lambda_ = 4.5 \n", + "expected_value = lambda_ #for X ~ Poi(lambda) , E[ X ] = lambda\n", + "\n", + "def D_N( n ):\n", + " \"\"\"\n", + " This function approx. D_n, the average variance of using n samples.\n", + " \"\"\"\n", + " Z = poi( lambda_, (n, N_Y) )\n", + " average_Z = Z.mean(axis=0)\n", + " return np.sqrt( ( (average_Z - expected_value)**2 ).mean() )\n", + " \n", + " \n", + "for i,n in enumerate(N_array):\n", + " D_N_results[i] = D_N(n)\n", + "\n", + "\n", + "plt.xlabel( \"$N$\" )\n", + "plt.ylabel( \"expected squared-distance from true value\" )\n", + "plt.plot(N_array, D_N_results, lw = 3, \n", + " label=\"expected distance between\\n\\\n", + "expected value and \\naverage of $N$ random variables.\")\n", + "plt.plot( N_array, np.sqrt(expected_value)/np.sqrt(N_array), lw = 2, ls = \"--\", \n", + " label = r\"$\\frac{\\sqrt{\\lambda}}{\\sqrt{N}}$\" )\n", + "plt.legend()\n", + "plt.title( \"How 'fast' is the sample average converging? \" );" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As expected, the expected distance between our sample average and the actual expected value shrinks as $N$ grows large. But also notice that the *rate* of convergence decreases, that is, we need only 10 000 additional samples to move from 0.020 to 0.015, a difference of 0.005, but *20 000* more samples to again decrease from 0.015 to 0.010, again only a 0.005 decrease.\n", + "\n", + "\n", + "It turns out we can measure this rate of convergence. Above I have plotted a second line, the function $\\sqrt{\\lambda}/\\sqrt{N}$. This was not chosen arbitrarily. In most cases, given a sequence of random variable distributed like $Z$, the rate of convergence to $E[Z]$ of the Law of Large Numbers is \n", + "\n", + "$$ \\frac{ \\sqrt{ \\; Var(Z) \\; } }{\\sqrt{N} }$$\n", + "\n", + "This is useful to know: for a given large $N$, we know (on average) how far away we are from the estimate. On the other hand, in a Bayesian setting, this can seem like a useless result: Bayesian analysis is OK with uncertainty so what's the *statistical* point of adding extra precise digits? Though drawing samples can be so computationally cheap that having a *larger* $N$ is fine too. \n", + "\n", + "### How do we compute $Var(Z)$ though?\n", + "\n", + "The variance is simply another expected value that can be approximated! Consider the following, once we have the expected value (by using the Law of Large Numbers to estimate it, denote it $\\mu$), we can estimate the variance:\n", + "\n", + "$$ \\frac{1}{N}\\sum_{i=1}^N \\;(Z_i - \\mu)^2 \\rightarrow E[ \\;( Z - \\mu)^2 \\;] = Var( Z )$$\n", + "\n", + "### Expected values and probabilities \n", + "There is an even less explicit relationship between expected value and estimating probabilities. Define the *indicator function*\n", + "\n", + "$$\\mathbb{1}_A(x) = \n", + "\\begin{cases} 1 & x \\in A \\\\\\\\\n", + " 0 & else\n", + "\\end{cases}\n", + "$$\n", + "Then, by the law of large numbers, if we have many samples $X_i$, we can estimate the probability of an event $A$, denoted $P(A)$, by:\n", + "\n", + "$$ \\frac{1}{N} \\sum_{i=1}^N \\mathbb{1}_A(X_i) \\rightarrow E[\\mathbb{1}_A(X)] = P(A) $$\n", + "\n", + "Again, this is fairly obvious after a moments thought: the indicator function is only 1 if the event occurs, so we are summing only the times the event occurs and dividing by the total number of trials (consider how we usually approximate probabilities using frequencies). For example, suppose we wish to estimate the probability that a $Z \\sim Exp(.5)$ is greater than 5, and we have many samples from a $Exp(.5)$ distribution. \n", + "\n", + "\n", + "$$ P( Z > 5 ) = \\frac{1}{N}\\sum_{i=1}^N \\mathbb{1}_{z > 5 }(Z_i) $$\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.0001\n" + ] + } + ], + "source": [ + "N = 10000\n", + "print( np.mean( [ np.random.exponential( 0.5 ) > 5 for i in range(N) ] ) )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### What does this all have to do with Bayesian statistics? \n", + "\n", + "\n", + "*Point estimates*, to be introduced in the next chapter, in Bayesian inference are computed using expected values. In more analytical Bayesian inference, we would have been required to evaluate complicated expected values represented as multi-dimensional integrals. No longer. If we can sample from the posterior distribution directly, we simply need to evaluate averages. Much easier. If accuracy is a priority, plots like the ones above show how fast you are converging. And if further accuracy is desired, just take more samples from the posterior. \n", + "\n", + "When is enough enough? When can you stop drawing samples from the posterior? That is the practitioners decision, and also dependent on the variance of the samples (recall from above a high variance means the average will converge slower). \n", + "\n", + "We also should understand when the Law of Large Numbers fails. As the name implies, and comparing the graphs above for small $N$, the Law is only true for large sample sizes. Without this, the asymptotic result is not reliable. Knowing in what situations the Law fails can give us *confidence in how unconfident we should be*. The next section deals with this issue." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## The Disorder of Small Numbers\n", + "\n", + "The Law of Large Numbers is only valid as $N$ gets *infinitely* large: never truly attainable. While the law is a powerful tool, it is foolhardy to apply it liberally. Our next example illustrates this.\n", + "\n", + "\n", + "##### Example: Aggregated geographic data\n", + "\n", + "\n", + "Often data comes in aggregated form. For instance, data may be grouped by state, county, or city level. Of course, the population numbers vary per geographic area. If the data is an average of some characteristic of each the geographic areas, we must be conscious of the Law of Large Numbers and how it can *fail* for areas with small populations.\n", + "\n", + "We will observe this on a toy dataset. Suppose there are five thousand counties in our dataset. Furthermore, population number in each state are uniformly distributed between 100 and 1500. The way the population numbers are generated is irrelevant to the discussion, so we do not justify this. We are interested in measuring the average height of individuals per county. Unbeknownst to us, height does **not** vary across county, and each individual, regardless of the county he or she is currently living in, has the same distribution of what their height may be:\n", + "\n", + "$$ \\text{height} \\sim \\text{Normal}(150, 15 ) $$\n", + "\n", + "We aggregate the individuals at the county level, so we only have data for the *average in the county*. What might our dataset look like?" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "figsize( 12.5, 4) \n", + "std_height = 15\n", + "mean_height = 150\n", + "\n", + "n_counties = 5000\n", + "pop_generator = np.random.randint\n", + "norm = np.random.normal\n", + "\n", + "#generate some artificial population numbers\n", + "population = pop_generator(100, 1500, n_counties )\n", + "\n", + "average_across_county = np.zeros( n_counties )\n", + "for i in range( n_counties ):\n", + " #generate some individuals and take the mean\n", + " average_across_county[i] = norm(mean_height, 1./std_height,\n", + " population[i] ).mean()\n", + " \n", + "#located the counties with the apparently most extreme average heights.\n", + "i_min = np.argmin( average_across_county )\n", + "i_max = np.argmax( average_across_county )\n", + "\n", + "#plot population size vs. recorded average\n", + "plt.scatter( population, average_across_county, alpha = 0.5, c=\"#7A68A6\")\n", + "plt.scatter( [ population[i_min], population[i_max] ], \n", + " [average_across_county[i_min], average_across_county[i_max] ],\n", + " s = 60, marker = \"o\", facecolors = \"none\",\n", + " edgecolors = \"#A60628\", linewidths = 1.5, \n", + " label=\"extreme heights\")\n", + "\n", + "plt.xlim( 100, 1500 )\n", + "plt.title( \"Average height vs. County Population\")\n", + "plt.xlabel(\"County Population\")\n", + "plt.ylabel(\"Average height in county\")\n", + "plt.plot( [100, 1500], [150, 150], color = \"k\", label = \"true expected \\\n", + "height\", ls=\"--\" )\n", + "plt.legend(scatterpoints = 1);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "What do we observe? *Without accounting for population sizes* we run the risk of making an enormous inference error: if we ignored population size, we would say that the county with the shortest and tallest individuals have been correctly circled. But this inference is wrong for the following reason. These two counties do *not* necessarily have the most extreme heights. The error results from the calculated average of smaller populations not being a good reflection of the true expected value of the population (which in truth should be $\\mu =150$). The sample size/population size/$N$, whatever you wish to call it, is simply too small to invoke the Law of Large Numbers effectively. \n", + "\n", + "We provide more damning evidence against this inference. Recall the population numbers were uniformly distributed over 100 to 1500. Our intuition should tell us that the counties with the most extreme population heights should also be uniformly spread over 100 to 1500, and certainly independent of the county's population. Not so. Below are the population sizes of the counties with the most extreme heights." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Population sizes of 10 'shortest' counties: \n", + "[112 112 292 175 360 166 540 135 101 113] \n", + "\n", + "Population sizes of 10 'tallest' counties: \n", + "[122 148 141 141 134 237 117 281 158 216]\n" + ] + } + ], + "source": [ + "print(\"Population sizes of 10 'shortest' counties: \")\n", + "print(population[ np.argsort( average_across_county )[:10] ], '\\n')\n", + "print(\"Population sizes of 10 'tallest' counties: \")\n", + "print(population[ np.argsort( -average_across_county )[:10] ])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Not at all uniform over 100 to 1500. This is an absolute failure of the Law of Large Numbers. \n", + "\n", + "##### Example: Kaggle's *U.S. Census Return Rate Challenge*\n", + "\n", + "Below is data from the 2010 US census, which partitions populations beyond counties to the level of block groups (which are aggregates of city blocks or equivalents). The dataset is from a Kaggle machine learning competition some colleagues and I participated in. The objective was to predict the census letter mail-back rate of a group block, measured between 0 and 100, using census variables (median income, number of females in the block-group, number of trailer parks, average number of children etc.). Below we plot the census mail-back rate versus block group population:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "figsize( 12.5, 6.5 )\n", + "data = np.genfromtxt( \"./data/census_data.csv\", skip_header=1, \n", + " delimiter= \",\")\n", + "plt.scatter( data[:,1], data[:,0], alpha = 0.5, c=\"#7A68A6\")\n", + "plt.title(\"Census mail-back rate vs Population\")\n", + "plt.ylabel(\"Mail-back rate\")\n", + "plt.xlabel(\"population of block-group\")\n", + "plt.xlim(-100, 15e3 )\n", + "plt.ylim( -5, 105)\n", + "\n", + "i_min = np.argmin( data[:,0] )\n", + "i_max = np.argmax( data[:,0] )\n", + " \n", + "plt.scatter( [ data[i_min,1], data[i_max, 1] ], \n", + " [ data[i_min,0], data[i_max,0] ],\n", + " s = 60, marker = \"o\", facecolors = \"none\",\n", + " edgecolors = \"#A60628\", linewidths = 1.5, \n", + " label=\"most extreme points\")\n", + "\n", + "plt.legend(scatterpoints = 1);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The above is a classic phenomenon in statistics. I say *classic* referring to the \"shape\" of the scatter plot above. It follows a classic triangular form, that tightens as we increase the sample size (as the Law of Large Numbers becomes more exact). \n", + "\n", + "I am perhaps overstressing the point and maybe I should have titled the book *\"You don't have big data problems!\"*, but here again is an example of the trouble with *small datasets*, not big ones. Simply, small datasets cannot be processed using the Law of Large Numbers. Compare with applying the Law without hassle to big datasets (ex. big data). I mentioned earlier that paradoxically big data prediction problems are solved by relatively simple algorithms. The paradox is partially resolved by understanding that the Law of Large Numbers creates solutions that are *stable*, i.e. adding or subtracting a few data points will not affect the solution much. On the other hand, adding or removing data points to a small dataset can create very different results. \n", + "\n", + "For further reading on the hidden dangers of the Law of Large Numbers, I would highly recommend the excellent manuscript [The Most Dangerous Equation](http://nsm.uh.edu/~dgraur/niv/TheMostDangerousEquation.pdf). " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Example: How to order Reddit submissions\n", + "\n", + "You may have disagreed with the original statement that the Law of Large numbers is known to everyone, but only implicitly in our subconscious decision making. Consider ratings on online products: how often do you trust an average 5-star rating if there is only 1 reviewer? 2 reviewers? 3 reviewers? We implicitly understand that with such few reviewers that the average rating is **not** a good reflection of the true value of the product.\n", + "\n", + "This has created flaws in how we sort items, and more generally, how we compare items. Many people have realized that sorting online search results by their rating, whether the objects be books, videos, or online comments, return poor results. Often the seemingly top videos or comments have perfect ratings only from a few enthusiastic fans, and truly more quality videos or comments are hidden in later pages with *falsely-substandard* ratings of around 4.8. How can we correct this?\n", + "\n", + "Consider the popular site Reddit (I purposefully did not link to the website as you would never come back). The site hosts links to stories or images, called submissions, for people to comment on. Redditors can vote up or down on each submission (called upvotes and downvotes). Reddit, by default, will sort submissions to a given subreddit by Hot, that is, the submissions that have the most upvotes recently.\n", + "\n", + "\n", + "\n", + "\n", + "How would you determine which submissions are the best? There are a number of ways to achieve this:\n", + "\n", + "1. *Popularity*: A submission is considered good if it has many upvotes. A problem with this model is that a submission with hundreds of upvotes, but thousands of downvotes. While being very *popular*, the submission is likely more controversial than best.\n", + "2. *Difference*: Using the *difference* of upvotes and downvotes. This solves the above problem, but fails when we consider the temporal nature of submission. Depending on when a submission is posted, the website may be experiencing high or low traffic. The difference method will bias the *Top* submissions to be the those made during high traffic periods, which have accumulated more upvotes than submissions that were not so graced, but are not necessarily the best.\n", + "3. *Time adjusted*: Consider using Difference divided by the age of the submission. This creates a *rate*, something like *difference per second*, or *per minute*. An immediate counter-example is, if we use per second, a 1 second old submission with 1 upvote would be better than a 100 second old submission with 99 upvotes. One can avoid this by only considering at least t second old submission. But what is a good t value? Does this mean no submission younger than t is good? We end up comparing unstable quantities with stable quantities (young vs. old submissions).\n", + "3. *Ratio*: Rank submissions by the ratio of upvotes to total number of votes (upvotes plus downvotes). This solves the temporal issue, such that new submissions who score well can be considered Top just as likely as older submissions, provided they have many upvotes to total votes. The problem here is that a submission with a single upvote (ratio = 1.0) will beat a submission with 999 upvotes and 1 downvote (ratio = 0.999), but clearly the latter submission is *more likely* to be better.\n", + "\n", + "I used the phrase *more likely* for good reason. It is possible that the former submission, with a single upvote, is in fact a better submission than the later with 999 upvotes. The hesitation to agree with this is because we have not seen the other 999 potential votes the former submission might get. Perhaps it will achieve an additional 999 upvotes and 0 downvotes and be considered better than the latter, though not likely.\n", + "\n", + "What we really want is an estimate of the *true upvote ratio*. Note that the true upvote ratio is not the same as the observed upvote ratio: the true upvote ratio is hidden, and we only observe upvotes vs. downvotes (one can think of the true upvote ratio as \"what is the underlying probability someone gives this submission a upvote, versus a downvote\"). So the 999 upvote/1 downvote submission probably has a true upvote ratio close to 1, which we can assert with confidence thanks to the Law of Large Numbers, but on the other hand we are much less certain about the true upvote ratio of the submission with only a single upvote. Sounds like a Bayesian problem to me.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "One way to determine a prior on the upvote ratio is to look at the historical distribution of upvote ratios. This can be accomplished by scraping Reddit's submissions and determining a distribution. There are a few problems with this technique though:\n", + "\n", + "1. Skewed data: The vast majority of submissions have very few votes, hence there will be many submissions with ratios near the extremes (see the \"triangular plot\" in the above Kaggle dataset), effectively skewing our distribution to the extremes. One could try to only use submissions with votes greater than some threshold. Again, problems are encountered. There is a tradeoff between number of submissions available to use and a higher threshold with associated ratio precision. \n", + "2. Biased data: Reddit is composed of different subpages, called subreddits. Two examples are *r/aww*, which posts pics of cute animals, and *r/politics*. It is very likely that the user behaviour towards submissions of these two subreddits are very different: visitors are likely friendly and affectionate in the former, and would therefore upvote submissions more, compared to the latter, where submissions are likely to be controversial and disagreed upon. Therefore not all submissions are the same. \n", + "\n", + "\n", + "In light of these, I think it is better to use a `Uniform` prior.\n", + "\n", + "\n", + "With our prior in place, we can find the posterior of the true upvote ratio. The Python script `top_showerthoughts_submissions.py` will scrape the best posts from the `showerthoughts` community on Reddit. This is a text-only community so the title of each post *is* the post. Below is the top post as well as some other sample posts:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Post contents: \n", + "\n", + "It’s socially acceptable to kiss with our mouth sphincters. But not kiss with our butt sphincters\n" + ] + } + ], + "source": [ + "#adding a number to the end of the %run call will get the ith top post.\n", + "#please notice that the praw lib to get info from reddit changed its authorize functions\n", + "#u can get some info from https://praw.readthedocs.io/en/latest/getting_started/configuration/prawini.html\n", + "%run top_showerthoughts_submissions.py 2\n", + "\n", + "print(\"Post contents: \\n\")\n", + "print(top_post)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + }, + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Some Submissions (out of 98 total) \n", + "-----------\n", + "\"Ironically, meteorologists study nothing about meteors.\"\n", + "upvotes/downvotes: [83 11] \n", + "\n", + "\"When cleaning house you are cleaning yourself off of stuff\"\n", + "upvotes/downvotes: [0 0] \n", + "\n", + "\"Every company shipping an electronic device powered by USB without an adapter just assumes you have a spare one. The irritating thing is that they’re usually not wrong.\"\n", + "upvotes/downvotes: [10 4] \n", + "\n", + "\"Cats don’t have lips and butt cheeks\"\n", + "upvotes/downvotes: [12 5] \n", + "\n" + ] + } + ], + "source": [ + "\"\"\"\n", + "contents: an array of the text from the last 100 top submissions to a subreddit\n", + "votes: a 2d numpy array of upvotes, downvotes for each submission.\n", + "\"\"\"\n", + "n_submissions = len(votes)\n", + "submissions = np.random.randint( n_submissions, size=4)\n", + "print(\"Some Submissions (out of %d total) \\n-----------\"%n_submissions)\n", + "for i in submissions:\n", + " print('\"' + contents[i] + '\"')\n", + " print(\"upvotes/downvotes: \",votes[i,:], \"\\n\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " For a given true upvote ratio $p$ and $N$ votes, the number of upvotes will look like a Binomial random variable with parameters $p$ and $N$. (This is because of the equivalence between upvote ratio and probability of upvoting versus downvoting, out of $N$ possible votes/trials). We create a function that performs Bayesian inference on $p$, for a particular submission's upvote/downvote pair." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "import pymc as pm\n", + "\n", + "def posterior_upvote_ratio( upvotes, downvotes, samples = 15000):\n", + " \"\"\"\n", + " This function accepts the number of upvotes and downvotes a particular submission recieved, \n", + " and the number of posterior samples to return to the user. Assumes a uniform prior.\n", + " \"\"\"\n", + " N = upvotes + downvotes\n", + " with pm.Model() as model:\n", + " upvote_ratio = pm.Uniform(\"upvote_ratio\", 0, 1)\n", + " observations = pm.Binomial( \"obs\", N, upvote_ratio, observed=upvotes)\n", + " \n", + " trace = pm.sample(samples, step=pm.NUTS(),tune=5000,chains=1)\n", + " \n", + " \n", + " return trace.posterior.upvote_ratio.data.T\n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Below are the resulting posterior distributions." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Sequential sampling (1 chains in 1 job)\n", + "NUTS: [upvote_ratio]\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + "
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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "figsize( 12., 8)\n", + "posteriors = []\n", + "colours = [\"#348ABD\", \"#A60628\", \"#7A68A6\", \"#467821\", \"#CF4457\"]\n", + "for i in range(len(submissions)):\n", + " j = submissions[i]\n", + " posteriors.append( posterior_upvote_ratio( votes[j, 0], votes[j,1] ) )\n", + " plt.hist( posteriors[i], bins = 10, density = True, alpha = .9, \n", + " histtype=\"step\",color = colours[i%5], lw = 3,\n", + " label = '(%d up:%d down)\\n%s...'%(votes[j, 0], votes[j,1], contents[j][:50]) )\n", + " plt.hist( posteriors[i], bins = 10, density = True, alpha = .2, \n", + " histtype=\"stepfilled\",color = colours[i], lw = 3, )\n", + " \n", + "plt.legend(loc=\"upper left\")\n", + "plt.xlim( 0, 1)\n", + "plt.title(\"Posterior distributions of upvote ratios on different submissions\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Some distributions are very tight, others have very long tails (relatively speaking), expressing our uncertainty with what the true upvote ratio might be.\n", + "\n", + "### Sorting!\n", + "\n", + "We have been ignoring the goal of this exercise: how do we sort the submissions from *best to worst*? Of course, we cannot sort distributions, we must sort scalar numbers. There are many ways to distill a distribution down to a scalar: expressing the distribution through its expected value, or mean, is one way. Choosing the mean is a bad choice though. This is because the mean does not take into account the uncertainty of distributions.\n", + "\n", + "I suggest using the *95% least plausible value*, defined as the value such that there is only a 5% chance the true parameter is lower (think of the lower bound on the 95% credible region). Below are the posterior distributions with the 95% least-plausible value plotted:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[0]\n", + " [0]\n", + " [0]\n", + " [0]] [array([0.91212337]), array([0.78778606]), array([0.74345616]), array([0.78192709])]\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "figsize( 14., 8)\n", + "N = posteriors[0].shape[0]\n", + "lower_limits = []\n", + "\n", + "for i in range(len(submissions)):\n", + " j = submissions[i]\n", + " plt.hist( posteriors[i], bins = 20, density = True, alpha = .9, \n", + " histtype=\"step\",color = colours[i], lw = 3,\n", + " label = '(%d up:%d down)\\n%s...'%(votes[j, 0], votes[j,1], contents[j][:50]) )\n", + " plt.hist( posteriors[i], bins = 20, density = True, alpha = .2, \n", + " histtype=\"stepfilled\",color = colours[i], lw = 3, )\n", + " v = np.sort( posteriors[i] )[ int(0.05*N) ]\n", + " #plt.vlines( v, 0, 15 , color = \"k\", alpha = 1, linewidths=3 )\n", + " plt.vlines( v, 0, 10 , color = colours[i], linestyles = \"--\", linewidths=3 )\n", + " lower_limits.append(v)\n", + " plt.legend(loc=\"upper left\")\n", + "\n", + "plt.legend(loc=\"upper left\")\n", + "plt.title(\"Posterior distributions of upvote ratios on different submissions\");\n", + "order = np.argsort( -np.array( lower_limits ) )\n", + "print(order, lower_limits)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The best submissions, according to our procedure, are the submissions that are *most-likely* to score a high percentage of upvotes. Visually those are the submissions with the 95% least plausible value close to 1.\n", + "\n", + "Why is sorting based on this quantity a good idea? By ordering by the 95% least plausible value, we are being the most conservative with what we think is best. When using the lower-bound of the 95% credible interval, we believe with high certainty that the 'true upvote ratio' is at the very least equal to this value (or greater), thereby ensuring that the best submissions are still on top. Under this ordering, we impose the following very natural properties:\n", + "\n", + "1. given two submissions with the same observed upvote ratio, we will assign the submission with more votes as better (since we are more confident it has a higher ratio).\n", + "2. given two submissions with the same number of votes, we still assign the submission with more upvotes as *better*.\n", + "\n", + "### But this is too slow for real-time!\n", + "\n", + "I agree, computing the posterior of every submission takes a long time, and by the time you have computed it, likely the data has changed. I delay the mathematics to the appendix, but I suggest using the following formula to compute the lower bound very fast.\n", + "\n", + "$$ \\frac{a}{a + b} - 1.65\\sqrt{ \\frac{ab}{ (a+b)^2(a + b +1 ) } }$$\n", + "\n", + "where \n", + "\\begin{align}\n", + "& a = 1 + u \\\\\\\\\n", + "& b = 1 + d \\\\\\\\\n", + "\\end{align}\n", + "\n", + "$u$ is the number of upvotes, and $d$ is the number of downvotes. The formula is a shortcut in Bayesian inference, which will be further explained in Chapter 6 when we discuss priors in more detail.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Approximate lower bounds:\n", + "[0.60091591 0.535 0.65630332 0.02368603 0.34209586 0.39235808\n", + " 0.3726793 0.63447009 0.28273871 0.02368603 0.93490542 0.44960117\n", + " 0.3605993 0.50200985 0.535 0.80491887 0.69226131 0.34209586\n", + " 0.635 0.02368603 0.02368603 0.35627885 0.7609129 0.80556899\n", + " 0.42069919 0.45268299 0.55267475 0.91892384 0.35627885 0.64814153\n", + " 0.02368603 0.51514069 0.39683271 0.74107856 0.02368603 0.56085485\n", + " 0.02368603 0.40084546 0.32754218 0.02368603 0.42360758 0.83265815\n", + " 0.93950433 0.43590471 0.02368603 0.90975941 0.02368603 0.28273871\n", + " 0.02368603 0.02368603 0.61090389 0.02368603 0.46120533 0.59147534\n", + " 0.02368603 0.92871189 0.27 0.94829855 0.59321297 0.02368603\n", + " 0.4722123 0.77097389 0.82620033 0.02368603 0.82934814 0.60993921\n", + " 0.84138267 0.49569509 0.02368603 0.77681961 0.58991998 0.32754218\n", + " 0.02368603 0.68704735 0.65475736 0.43126186 0.27775794 0.4722123\n", + " 0.55184141 0.42657558 0.45093892 0.73835967 0.59123643 0.02368603\n", + " 0.47086045 0.02368603 0.02368603 0.02368603 0.7249286 0.02368603\n", + " 0.02368603 0.89819827 0.02368603 0.02368603 0.94269956 0.30828372\n", + " 0.83540757 0.02368603]\n", + "\n", + "\n", + "Top 40 Sorted according to approximate lower bounds:\n", + "\n", + "\n", + "951 40 Finland is 1 country away from North Korea.\n", + "-------------\n", + "2493 131 Alcohol is a good solution for organic compound, but not a good solution for problems in life.\n", + "-------------\n", + "297 12 Odds are that many brilliant ideas and inventions went undeveloped, because people were too lazy or pessimistic to follow through\n", + "-------------\n", + "106 3 Finding your groceries in a new or different grocery store is an adulting scavenger hunt.\n", + "-------------\n", + "167 7 Chocolate chip pancakes is a loophole to have cookies for breakfast.\n", + "-------------\n", + "1476 111 Whoever comes up with the recommended serving sizes on food packing must not have much of an appetite.\n", + "-------------\n", + "456 34 Due to the declining population of people who own houses with garages and have kids, drummers are probably going to become very rare in the next couple generations\n", + "-------------\n", + "1625 161 Life is not a gift because you cannot reject it.\n", + "-------------\n", + "907 148 You can kill a fire but you can’t kill a water\n", + "-------------\n", + "86 10 Majority of us can't remember a single card that came with a gift but has spent countless hours looking for cards when giving gifts\n", + "-------------\n", + "30 2 Humans are too unreliable to maintain most hypothetical conspiracies\n", + "-------------\n", + "57 6 Omniscience seems great until you think about all the stuff you wouldn’t want to know\n", + "-------------\n", + "288 47 The average six-month-old dog probably knows more English than the average year-old human\n", + "-------------\n", + "11 0 Nothing sounds more lonely than going through an Escape Room by yourself.\n", + "-------------\n", + "43 5 The Piston Cup trophy is an ornamented internal organ.\n", + "-------------\n", + "136 28 It is possible that, at some point in history, a bullet randomly hit an ant.\n", + "-------------\n", + "40 6 Being read bedtime stories as a kid is probably why a lot of people watch TV to fall asleep.\n", + "-------------\n", + "29 4 Radiation can both cause and eliminate cancer.\n", + "-------------\n", + "22 3 The Roman Empire did it all without coffee.\n", + "-------------\n", + "111 28 It's amazing that in just over 100 years that humans have collectively developed the instinct to hit the brakes when they see a cop.\n", + "-------------\n", + "28 5 Every day, the chance of a bridge collapsing increases\n", + "-------------\n", + "14 2 When you cut corners, it just creates even more corners to deal with.\n", + "-------------\n", + "10 1 An explanation can never equal an experience and yet most of our recent human development has focused on increasing our ability to explain.\n", + "-------------\n", + "12 2 No one ever won an argument with their spouse by winning.\n", + "-------------\n", + "36 11 Slow motion makes any scene look dramatic while fast motion makes it look comical\n", + "-------------\n", + "20 5 A large portion of the world probably doesn’t know the pure rage that medical commercials induce.\n", + "-------------\n", + "11 2 A surprise birthday party is not really a surprise because we both knew it was your birthday.\n", + "-------------\n", + "8 1 Watching too many freakout videos on social media is why people believe the whole world is crazy.\n", + "-------------\n", + "10 2 It's impolite to post a picture of a stranger's license plate. Except when the licence plate is a word.\n", + "-------------\n", + "28 10 Fingerless gloves can’t be worn without fingers\n", + "-------------\n", + "4 0 Movies depicting rushed cross country road trips always show the characters driving down one lane country roads in the middle of nowhere instead of the highway for no fucking reason.\n", + "-------------\n", + "32 13 The universe experiences you just as much as you experience the universe\n", + "-------------\n", + "28 11 People don't downsize because the kids moved out, they downsized so the kids don't move back in.\n", + "-------------\n", + "16 5 Landlines that use the internet instead of a phone line are reverse dial-up\n", + "-------------\n", + "18 6 Amish people live like it's the early 18th century so in the 25th century there might be people who live like it's the 21st century\n", + "-------------\n", + "6 1 Winning a low chance carnival game as a kid really built up gambling addiction as an adult.\n", + "-------------\n", + "12 4 The main difference between humans and robots is that it's harder to replace human parts when they start to fail than it is to replace robot parts when they fail.\n", + "-------------\n", + "8 2 A hummingbird is a vampire of the flower world\n", + "-------------\n", + "13 5 Before the camera was invented, no one had seen themselves with their eyes closed.\n", + "-------------\n", + "13 5 Some birds probably have a fear of heights\n", + "-------------\n" + ] + } + ], + "source": [ + "def intervals(u,d):\n", + " a = 1. + u\n", + " b = 1. + d\n", + " mu = a/(a+b)\n", + " std_err = 1.65*np.sqrt( (a*b)/( (a+b)**2*(a+b+1.) ) )\n", + " return ( mu, std_err )\n", + "\n", + "print(\"Approximate lower bounds:\")\n", + "posterior_mean, std_err = intervals(votes[:,0],votes[:,1])\n", + "lb = posterior_mean - std_err\n", + "print(lb)\n", + "print(\"\\n\")\n", + "print(\"Top 40 Sorted according to approximate lower bounds:\")\n", + "print(\"\\n\")\n", + "order = np.argsort( -lb )\n", + "ordered_contents = []\n", + "for i in order[:40]:\n", + " ordered_contents.append( contents[i] )\n", + " print(votes[i,0], votes[i,1], contents[i])\n", + " print(\"-------------\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can view the ordering visually by plotting the posterior mean and bounds, and sorting by the lower bound. In the plot below, notice that the left error-bar is sorted (as we suggested this is the best way to determine an ordering), so the means, indicated by dots, do not follow any strong pattern. 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "r_order = order[::-1][-40:]\n", + "plt.errorbar( posterior_mean[r_order], np.arange( len(r_order) ), \n", + " xerr=std_err[r_order], capsize=0, fmt=\"o\",\n", + " color = \"#7A68A6\")\n", + "plt.xlim( 0.3, 1)\n", + "plt.yticks( np.arange( len(r_order)-1,-1,-1 ), map( lambda x: x[:30].replace(\"\\n\",\"\"), ordered_contents) );" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the graphic above, you can see why sorting by mean would be sub-optimal." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Extension to Starred rating systems\n", + "\n", + "The above procedure works well for upvote-downvotes schemes, but what about systems that use star ratings, e.g. 5 star rating systems. Similar problems apply with simply taking the average: an item with two perfect ratings would beat an item with thousands of perfect ratings, but a single sub-perfect rating. \n", + "\n", + "\n", + "We can consider the upvote-downvote problem above as binary: 0 is a downvote, 1 if an upvote. A $N$-star rating system can be seen as a more continuous version of above, and we can set $n$ stars rewarded is equivalent to rewarding $\\frac{n}{N}$. For example, in a 5-star system, a 2 star rating corresponds to 0.4. A perfect rating is a 1. We can use the same formula as before, but with $a,b$ defined differently:\n", + "\n", + "\n", + "$$ \\frac{a}{a + b} - 1.65\\sqrt{ \\frac{ab}{ (a+b)^2(a + b +1 ) } }$$\n", + "\n", + "where \n", + "\n", + "\\begin{align}\n", + "& a = 1 + S \\\\\\\\\n", + "& b = 1 + N - S \\\\\\\\\n", + "\\end{align}\n", + "\n", + "where $N$ is the number of users who rated, and $S$ is the sum of all the ratings, under the equivalence scheme mentioned above. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Example: Counting Github stars\n", + "\n", + "What is the average number of stars a Github repository has? How would you calculate this? There are over 6 million respositories, so there is more than enough data to invoke the Law of Large numbers. Let's start pulling some data. TODO" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Conclusion\n", + "\n", + "While the Law of Large Numbers is cool, it is only true so much as its name implies: with large sample sizes only. We have seen how our inference can be affected by not considering *how the data is shaped*. \n", + "\n", + "1. By (cheaply) drawing many samples from the posterior distributions, we can ensure that the Law of Large Number applies as we approximate expected values (which we will do in the next chapter).\n", + "\n", + "2. Bayesian inference understands that with small sample sizes, we can observe wild randomness. Our posterior distribution will reflect this by being more spread rather than tightly concentrated. Thus, our inference should be correctable.\n", + "\n", + "3. There are major implications of not considering the sample size, and trying to sort objects that are unstable leads to pathological orderings. The method provided above solves this problem.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Appendix\n", + "\n", + "##### Derivation of sorting submissions formula\n", + "\n", + "Basically what we are doing is using a Beta prior (with parameters $a=1, b=1$, which is a uniform distribution), and using a Binomial likelihood with observations $u, N = u+d$. This means our posterior is a Beta distribution with parameters $a' = 1 + u, b' = 1 + (N - u) = 1+d$. We then need to find the value, $x$, such that 0.05 probability is less than $x$. This is usually done by inverting the CDF ([Cumulative Distribution Function](http://en.wikipedia.org/wiki/Cumulative_Distribution_Function)), but the CDF of the beta, for integer parameters, is known but is a large sum [3]. \n", + "\n", + "We instead use a Normal approximation. The mean of the Beta is $\\mu = a'/(a'+b')$ and the variance is \n", + "\n", + "$$\\sigma^2 = \\frac{a'b'}{ (a' + b')^2(a'+b'+1) }$$\n", + "\n", + "Hence we solve the following equation for $x$ and have an approximate lower bound. \n", + "\n", + "$$ 0.05 = \\Phi\\left( \\frac{(x - \\mu)}{\\sigma}\\right) $$ \n", + "\n", + "$\\Phi$ being the [cumulative distribution for the normal distribution](http://en.wikipedia.org/wiki/Normal_distribution#Cumulative_distribution)\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Exercises\n", + "\n", + "1\\. How would you estimate the quantity $E\\left[ \\cos{X} \\right]$, where $X \\sim \\text{Exp}(4)$? What about $E\\left[ \\cos{X} | X \\lt 1\\right]$, i.e. the expected value *given* we know $X$ is less than 1? Would you need more samples than the original samples size to be equally accurate?" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "## Enter code here\n", + "import scipy.stats as stats\n", + "exp = stats.expon( scale=4 )\n", + "N = 1e5\n", + "X = exp.rvs( int(N) )\n", + "## ..." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "2\\. The following table was located in the paper \"Going for Three: Predicting the Likelihood of Field Goal Success with Logistic Regression\" [2]. The table ranks football field-goal kickers by their percent of non-misses. What mistake have the researchers made?\n", + "\n", + "-----\n", + "\n", + "#### Kicker Careers Ranked by Make Percentage\n", + "
Rank Kicker Make % Number of Kicks
1 Garrett Hartley 87.7 57
2 Matt Stover 86.8 335
3 Robbie Gould 86.2 224
4 Rob Bironas 86.1 223
5 Shayne Graham 85.4 254
… … …
51 Dave Rayner 72.2 90
52 Nick Novak 71.9 64
53 Tim Seder 71.0 62
54 Jose Cortez 70.7 75
55 Wade Richey 66.1 56
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In August 2013, [a popular post](http://bpodgursky.wordpress.com/2013/08/21/average-income-per-programming-language/) on the average income per programmer of different languages was trending. Here's the summary chart: (reproduced without permission, cause when you lie with stats, you gunna get the hammer). What do you notice about the extremes?\n", + "\n", + "------\n", + "\n", + "#### Average household income by programming language\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
LanguageAverage Household Income ($)Data Points
Puppet87,589.29112
Haskell89,973.82191
PHP94,031.19978
CoffeeScript94,890.80435
VimL94,967.11532
Shell96,930.54979
Lua96,930.69101
Erlang97,306.55168
Clojure97,500.00269
Python97,578.872314
JavaScript97,598.753443
Emacs Lisp97,774.65355
C#97,823.31665
Ruby98,238.743242
C++99,147.93845
CSS99,881.40527
Perl100,295.45990
C100,766.512120
Go101,158.01231
Scala101,460.91243
ColdFusion101,536.70109
Objective-C101,801.60562
Groovy102,650.86116
Java103,179.391402
XSLT106,199.19123
ActionScript108,119.47113
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### References\n", + "\n", + "1. Wainer, Howard. *The Most Dangerous Equation*. American Scientist, Volume 95.\n", + "2. Clarck, Torin K., Aaron W. Johnson, and Alexander J. Stimpson. \"Going for Three: Predicting the Likelihood of Field Goal Success with Logistic Regression.\" (2013): n. page. [Web](http://www.sloansportsconference.com/wp-content/uploads/2013/Going%20for%20Three%20Predicting%20the%20Likelihood%20of%20Field%20Goal%20Success%20with%20Logistic%20Regression.pdf). 20 Feb. 2013.\n", + "3. http://en.wikipedia.org/wiki/Beta_function#Incomplete_beta_function" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.core.display import HTML\n", + "def css_styling():\n", + " styles = open(\"../styles/custom.css\", \"r\").read()\n", + " return HTML(styles)\n", + "css_styling()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "pymc_env", + "language": "python", + "name": "pymc_env" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.4" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/Chapter4_TheGreatestTheoremNeverTold/Ch4_LawOfLargeNumbers_TFP.ipynb b/Chapter4_TheGreatestTheoremNeverTold/Ch4_LawOfLargeNumbers_TFP.ipynb new file mode 100644 index 00000000..a9754bef --- /dev/null +++ b/Chapter4_TheGreatestTheoremNeverTold/Ch4_LawOfLargeNumbers_TFP.ipynb @@ -0,0 +1,2100 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "working Ch4_LawOfLargeNumbers_TFP.ipynb", + "provenance": [], + "collapsed_sections": [] + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "I2VgkczEJZhY" + }, + "source": [ + "# Probabilistic Programming and Bayesian Methods for Hackers Chapter 4\n", + "\n", + "\n", + " \n", + " \n", + "
\n", + " Run in Google Colab\n", + " \n", + " View source on GitHub\n", + "
\n", + "
\n", + "
\n", + "
\n", + "\n", + "Original content ([this Jupyter notebook](https://nbviewer.jupyter.org/github/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/blob/master/Chapter4_TheGreatestTheoremNeverTold/Ch4_LawOfLargeNumbers_PyMC2.ipynb)) created by Cam Davidson-Pilon ([`@Cmrn_DP`](https://twitter.com/Cmrn_DP))\n", + "\n", + "Ported to [Tensorflow Probability](https://www.tensorflow.org/probability/) by Matthew McAteer ([`@MatthewMcAteer0`](https://twitter.com/MatthewMcAteer0)), with help from the TFP team at Google ([`tfprobability@tensorflow.org`](mailto:tfprobability@tensorflow.org)).\n", + "\n", + "Welcome to Bayesian Methods for Hackers. The full Github repository is available at [github/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers](https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers). The other chapters can be found on the project's [homepage](https://camdavidsonpilon.github.io/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/). We hope you enjoy the book, and we encourage any contributions!\n", + "\n", + "---------\n", + "### Table of Contents\n", + "- Dependencies & Prerequisites\n", + "- The greatest theorem never told\n", + " - The Law of Large Numbers\n", + " - Intuition\n", + " - How do we compute $Var(Z)$ though?\n", + " - Expected values and probabilities\n", + " - What does this all have to do with Bayesian statistics?\n", + " - The Disorder of Small Numbers\n", + " - Example: Aggregated geographic data\n", + " - Example: Kaggle's U.S. Census Return Rate Challenge\n", + " - Example: How to order Reddit submissions\n", + " - Setting up the Praw Reddit API\n", + " - Register your Application on Reddit\n", + " - Reddit API Setup\n", + " - Sorting!\n", + " - But this is too slow for real-time!\n", + " - Extension to Starred rating systems\n", + " - Example: Counting Github stars\n", + " - Conclusion\n", + " - Appendix\n", + " - Exercises\n", + " - Kicker Careers Ranked by Make Percentage\n", + " - Average Household Income by Programming Language\n", + " - References\n", + "\n", + "______\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "BVrm_s73RW6K" + }, + "source": [ + "### Dependencies & Prerequisites\n", + "\n", + "
\n", + " Tensorflow Probability is part of the colab default runtime, so you don't need to install Tensorflow or Tensorflow Probability if you're running this in the colab. \n", + "
\n", + " If you're running this notebook in Jupyter on your own machine (and you have already installed Tensorflow), you can use the following\n", + "
\n", + "
    \n", + "
  • For the most recent nightly installation: pip3 install -q tfp-nightly
    • \n", + "
  • For the most recent stable TFP release: pip3 install -q --upgrade tensorflow-probability
    • \n", + "
  • For the most recent stable GPU-connected version of TFP: pip3 install -q --upgrade tensorflow-probability-gpu
    • \n", + "
  • For the most recent nightly GPU-connected version of TFP: pip3 install -q tfp-nightly-gpu
    • \n", + "
\n", + "Again, if you are running this in a Colab, Tensorflow and TFP are already installed\n", + "
" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "4jtWqJhSJZhi", + "outputId": "4f040d7e-c2c7-411f-f349-1bb45688a4d6", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 215 + } + }, + "source": [ + "#@title Imports and Global Variables { display-mode: \"form\" }\n", + "\"\"\"\n", + "The book uses a custom matplotlibrc file, which provides the unique styles for\n", + "matplotlib plots. If executing this book, and you wish to use the book's\n", + "styling, provided are two options:\n", + " 1. Overwrite your own matplotlibrc file with the rc-file provided in the\n", + " book's styles/ dir. See http://matplotlib.org/users/customizing.html\n", + " 2. Also in the styles is bmh_matplotlibrc.json file. This can be used to\n", + " update the styles in only this notebook. Try running the following code:\n", + "\n", + " import json\n", + " s = json.load(open(\"../styles/bmh_matplotlibrc.json\"))\n", + " matplotlib.rcParams.update(s)\n", + "\"\"\"\n", + "!pip3 install -q praw\n", + "!pip3 install -q pandas_datareader\n", + "!pip3 install -q wget\n", + "from __future__ import absolute_import, division, print_function\n", + "\n", + "#@markdown This sets the warning status (default is `ignore`, since this notebook runs correctly)\n", + "warning_status = \"ignore\" #@param [\"ignore\", \"always\", \"module\", \"once\", \"default\", \"error\"]\n", + "import warnings\n", + "warnings.filterwarnings(warning_status)\n", + "with warnings.catch_warnings():\n", + " warnings.filterwarnings(warning_status, category=DeprecationWarning)\n", + " warnings.filterwarnings(warning_status, category=UserWarning)\n", + "\n", + "import numpy as np\n", + "import os\n", + "#@markdown This sets the styles of the plotting (default is styled like plots from [FiveThirtyeight.com](https://fivethirtyeight.com/))\n", + "matplotlib_style = 'fivethirtyeight' #@param ['fivethirtyeight', 'bmh', 'ggplot', 'seaborn', 'default', 'Solarize_Light2', 'classic', 'dark_background', 'seaborn-colorblind', 'seaborn-notebook']\n", + "import matplotlib.pyplot as plt; plt.style.use(matplotlib_style)\n", + "import matplotlib.axes as axes;\n", + "from matplotlib.patches import Ellipse\n", + "from mpl_toolkits.mplot3d import Axes3D\n", + "import pandas_datareader.data as web\n", + "%matplotlib inline\n", + "import seaborn as sns; sns.set_context('notebook')\n", + "from IPython.core.pylabtools import figsize\n", + "#@markdown This sets the resolution of the plot outputs (`retina` is the highest resolution)\n", + "notebook_screen_res = 'retina' #@param ['retina', 'png', 'jpeg', 'svg', 'pdf']\n", + "%config InlineBackend.figure_format = notebook_screen_res\n", + "\n", + "import tensorflow as tf\n", + "tfe = tf.contrib.eager\n", + "\n", + "# Eager Execution\n", + "#@markdown Check the box below if you want to use [Eager Execution](https://www.tensorflow.org/guide/eager)\n", + "#@markdown Eager execution provides An intuitive interface, Easier debugging, and a control flow comparable to Numpy. You can read more about it on the [Google AI Blog](https://ai.googleblog.com/2017/10/eager-execution-imperative-define-by.html)\n", + "use_tf_eager = False #@param {type:\"boolean\"}\n", + "\n", + "# Use try/except so we can easily re-execute the whole notebook.\n", + "if use_tf_eager:\n", + " try:\n", + " tf.enable_eager_execution()\n", + " except:\n", + " pass\n", + "\n", + "import tensorflow_probability as tfp\n", + "tfd = tfp.distributions\n", + "tfb = tfp.bijectors\n", + "\n", + " \n", + "def evaluate(tensors):\n", + " \"\"\"Evaluates Tensor or EagerTensor to Numpy `ndarray`s.\n", + " Args:\n", + " tensors: Object of `Tensor` or EagerTensor`s; can be `list`, `tuple`,\n", + " `namedtuple` or combinations thereof.\n", + " \n", + " Returns:\n", + " ndarrays: Object with same structure as `tensors` except with `Tensor` or\n", + " `EagerTensor`s replaced by Numpy `ndarray`s.\n", + " \"\"\"\n", + " if tf.executing_eagerly():\n", + " return tf.contrib.framework.nest.pack_sequence_as(\n", + " tensors,\n", + " [t.numpy() if tf.contrib.framework.is_tensor(t) else t\n", + " for t in tf.contrib.framework.nest.flatten(tensors)])\n", + " return sess.run(tensors)\n", + "\n", + "class _TFColor(object):\n", + " \"\"\"Enum of colors used in TF docs.\"\"\"\n", + " red = '#F15854'\n", + " blue = '#5DA5DA'\n", + " orange = '#FAA43A'\n", + " green = '#60BD68'\n", + " pink = '#F17CB0'\n", + " brown = '#B2912F'\n", + " purple = '#B276B2'\n", + " yellow = '#DECF3F'\n", + " gray = '#4D4D4D'\n", + " def __getitem__(self, i):\n", + " return [\n", + " self.red,\n", + " self.orange,\n", + " self.green,\n", + " self.blue,\n", + " self.pink,\n", + " self.brown,\n", + " self.purple,\n", + " self.yellow,\n", + " self.gray,\n", + " ][i % 9]\n", + "TFColor = _TFColor()\n", + "\n", + "def session_options(enable_gpu_ram_resizing=True, enable_xla=True):\n", + " \"\"\"\n", + " Allowing the notebook to make use of GPUs if they're available.\n", + " \n", + " XLA (Accelerated Linear Algebra) is a domain-specific compiler for linear \n", + " algebra that optimizes TensorFlow computations.\n", + " \"\"\"\n", + " config = tf.ConfigProto()\n", + " config.log_device_placement = True\n", + " if enable_gpu_ram_resizing:\n", + " # `allow_growth=True` makes it possible to connect multiple colabs to your\n", + " # GPU. Otherwise the colab malloc's all GPU ram.\n", + " config.gpu_options.allow_growth = True\n", + " if enable_xla:\n", + " # Enable on XLA. https://www.tensorflow.org/performance/xla/.\n", + " config.graph_options.optimizer_options.global_jit_level = (\n", + " tf.OptimizerOptions.ON_1)\n", + " return config\n", + "\n", + "\n", + "def reset_sess(config=None):\n", + " \"\"\"\n", + " Convenience function to create the TF graph & session or reset them.\n", + " \"\"\"\n", + " if config is None:\n", + " config = session_options()\n", + " global sess\n", + " tf.reset_default_graph()\n", + " try:\n", + " sess.close()\n", + " except:\n", + " pass\n", + " sess = tf.InteractiveSession(config=config)\n", + "\n", + "reset_sess()" + ], + "execution_count": 1, + "outputs": [ + { + "output_type": "stream", + "text": [ + "\u001b[K |████████████████████████████████| 133kB 5.1MB/s \n", + "\u001b[K |████████████████████████████████| 204kB 48.0MB/s \n", + "\u001b[?25h Building wheel for wget (setup.py) ... \u001b[?25l\u001b[?25hdone\n", + "WARNING:tensorflow:\n", + "The TensorFlow contrib module will not be included in TensorFlow 2.0.\n", + "For more information, please see:\n", + " * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n", + " * https://github.com/tensorflow/addons\n", + " * https://github.com/tensorflow/io (for I/O related ops)\n", + "If you depend on functionality not listed there, please file an issue.\n", + "\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "AADx_YZYRbo1" + }, + "source": [ + "## The greatest theorem never told\n", + "\n", + "\n", + "This chapter focuses on an idea that is always bouncing around our minds, but is rarely made explicit outside books devoted to statistics. In fact, we've been using this simple idea in every example thus far. " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "N8r47e-LJZhf" + }, + "source": [ + "### The Law of Large Numbers\n", + "\n", + "Let $Z_i$ be $N$ independent samples from some probability distribution. According to *the Law of Large numbers*, so long as the expected value $E[Z]$ is finite, the following holds,\n", + "\n", + "$$\\frac{1}{N} \\sum_{i=1}^N Z_i \\rightarrow E[ Z ], \\;\\;\\; N \\rightarrow \\infty.$$\n", + "\n", + "In words:\n", + "\n", + "> The average of a sequence of random variables from the same distribution converges to the expected value of that distribution.\n", + "\n", + "This may seem like a boring result, but it will be the most useful tool you use." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "v0J76mQaJZhg" + }, + "source": [ + "### Intuition \n", + "\n", + "If the above Law is somewhat surprising, it can be made more clear by examining a simple example. \n", + "\n", + "Consider a random variable $Z$ that can take only two values, $c_1$ and $c_2$. Suppose we have a large number of samples of $Z$, denoting a specific sample $Z_i$. The Law says that we can approximate the expected value of $Z$ by averaging over all samples. Consider the average:\n", + "\n", + "\n", + "$$ \\frac{1}{N} \\sum_{i=1}^N \\;Z_i $$\n", + "\n", + "\n", + "By construction, $Z_i$ can only take on $c_1$ or $c_2$, hence we can partition the sum over these two values:\n", + "$$\n", + "\\begin{align}\n", + "\\frac{1}{N} \\sum_{i=1}^N \\;Z_i & =\\frac{1}{N} \\big( \\sum_{ Z_i = c_1}c_1 + \\sum_{Z_i=c_2}c_2 \\big) \\\\\n", + "& = c_1 \\sum_{ Z_i = c_1}\\frac{1}{N} + c_2 \\sum_{ Z_i = c_2}\\frac{1}{N} \\\\\n", + "& = c_1 \\times \\text{ (approximate frequency of $c_1$) } \\\\\n", + "& \\;\\;\\;\\;\\;\\;\\;\\;\\; + c_2 \\times \\text{ (approximate frequency of $c_2$) } \\\\\n", + "& \\approx c_1 \\times P(Z = c_1) + c_2 \\times P(Z = c_2 ) \\\\\n", + "& = E[Z]\n", + "\\end{align}\n", + "$$\n", + "\n", + "Equality holds in the limit, but we can get closer and closer by using more and more samples in the average. This Law holds for almost *any distribution*, minus some important cases we will encounter later." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "subV2qHrrUsj" + }, + "source": [ + "### Example\n", + "____\n", + "\n", + "\n", + "Below is a diagram of the Law of Large numbers in action for three different sequences of Poisson random variables. \n", + "\n", + " We sample `sample_size = 100000` Poisson random variables with parameter $\\lambda = 4.5$. (Recall the expected value of a Poisson random variable is equal to its parameter.) We calculate the average for the first $n$ samples, for $n=1$ to `sample_size`. " + ] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "0xOv_zU34Tci", + "outputId": "1c077114-f599-4503-8529-5ab9d4564758", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 315 + } + }, + "source": [ + "sample_size_ = 100000\n", + "expected_value_ = lambda_val_ = 4.5\n", + "N_samples = tf.range(start=1,\n", + " limit=sample_size_,\n", + " delta=100)\n", + "\n", + "plt.figure(figsize(12.5, 4))\n", + "for k in range(3):\n", + " samples = tfd.Poisson(rate=lambda_val_).sample(sample_shape=sample_size_)\n", + " [ samples_, N_samples_ ] = evaluate([ samples, N_samples ]) \n", + "\n", + " partial_average_ = [ samples_[:i].mean() for i in N_samples_ ] \n", + "\n", + " plt.plot( N_samples_, partial_average_, lw=1.5,label=\"average of $n$ samples; seq. %d\"%k)\n", + "\n", + "plt.plot( N_samples_, expected_value_ * np.ones_like( partial_average_), \n", + " ls = \"--\", label = \"true expected value\", c = \"k\" )\n", + "\n", + "plt.ylim( 4.35, 4.65) \n", + "plt.title( \"Convergence of the average of \\n random variables to its \\\n", + "expected value\" )\n", + "plt.ylabel( \"average of $n$ samples\" )\n", + "plt.xlabel( \"# of samples, $n$\")\n", + "plt.legend();" + ], + "execution_count": 2, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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XuSjJeb2XZ38PskcjfQ7MjH9n3oux3+6YtY+6u05Jf92sQJvtQdVnCmq7f4uIiIhIA1CA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\nychC6WobU7nn5BXj5BWidLQi+nroLptCvGAcuX4vsquDliR0Z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HUZrr20Lm10aeFzUpQk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vlZYWnYNRe5GyfgtPBAlO+6tgdaGVLuwniripFtzubcjEXuzGJ1WWzbBPRlcWR0X1SoSy49h7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\nfCQzr5zivPcCDwfOplifahS4EfgH4F2Z+ZOyg5ckSZIkSZIkSVp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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [], + "image/png": { + "width": 852, + "height": 298 + } + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "xbUnsQXuJZhn" + }, + "source": [ + "Looking at the above plot, it is clear that when the sample size is small, there is greater variation in the average (compare how *jagged and jumpy* the average is initially, then *smooths* out). All three paths *approach* the value 4.5, but just flirt with it as $N$ gets large. Mathematicians and statistician have another name for *flirting*: convergence. \n", + "\n", + "Another very relevant question we can ask is *how quickly am I converging to the expected value?* Let's plot something new. For a specific $N$, let's do the above trials thousands of times and compute how far away we are from the true expected value, on average. But wait — *compute on average*? This is simply the law of large numbers again! For example, we are interested in, for a specific $N$, the quantity:\n", + "\n", + "$$D(N) = \\sqrt{ \\;E\\left[\\;\\; \\left( \\frac{1}{N}\\sum_{i=1}^NZ_i - 4.5 \\;\\right)^2 \\;\\;\\right] \\;\\;}$$\n", + "\n", + "The above formulae is interpretable as a distance away from the true value (on average), for some $N$. (We take the square root so the dimensions of the above quantity and our random variables are the same). As the above is an expected value, it can be approximated using the law of large numbers: instead of averaging $Z_i$, we calculate the following multiple times and average them:\n", + "\n", + "$$ Y_k = \\left( \\;\\frac{1}{N}\\sum_{i=1}^NZ_i - 4.5 \\; \\right)^2 $$\n", + "\n", + "By computing the above many, $N_y$, times (remember, it is random), and averaging them:\n", + "\n", + "$$ \\frac{1}{N_Y} \\sum_{k=1}^{N_Y} Y_k \\rightarrow E[ Y_k ] = E\\;\\left[\\;\\; \\left( \\frac{1}{N}\\sum_{i=1}^NZ_i - 4.5 \\;\\right)^2 \\right]$$\n", + "\n", + "Finally, taking the square root:\n", + "\n", + "$$ \\sqrt{\\frac{1}{N_Y} \\sum_{k=1}^{N_Y} Y_k} \\approx D(N) $$ " + ] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "YbPdvgcKKGxY", + "outputId": "28daa158-0a62-4eba-92a1-e3b1c517e080", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 245 + } + }, + "source": [ + "N_Y = tf.constant(250) # use this many to approximate D(N)\n", + "N_array = tf.range(1000., 50000., 2500) # use this many samples in the approx. to the variance.\n", + "D_N_results = tf.zeros(tf.shape(N_array)[0])\n", + "lambda_val = tf.constant(4.5) \n", + "expected_value = tf.constant(4.5) #for X ~ Poi(lambda) , E[ X ] = lambda\n", + "\n", + "[\n", + " N_Y_, \n", + " N_array_, \n", + " D_N_results_, \n", + " expected_value_, \n", + " lambda_val_,\n", + "] = evaluate([ \n", + " N_Y, \n", + " N_array, \n", + " D_N_results, \n", + " expected_value,\n", + " lambda_val,\n", + "])\n", + "\n", + "def D_N(n):\n", + " \"\"\"\n", + " This function approx. D_n, the average variance of using n samples.\n", + " \"\"\"\n", + " Z = tfd.Poisson(rate=lambda_val_).sample(sample_shape=(int(n), int(N_Y_)))\n", + " average_Z = tf.reduce_mean(Z, axis=0)\n", + " average_Z_ = evaluate(average_Z)\n", + " \n", + " return np.sqrt(((average_Z_ - expected_value_)**2).mean())\n", + "\n", + "for i,n in enumerate(N_array_):\n", + " D_N_results_[i] = D_N(n)\n", + "\n", + "plt.figure(figsize(12.5, 3))\n", + "plt.xlabel( \"$N$\" )\n", + "plt.ylabel( \"expected squared-distance \\nfrom true value\" )\n", + "plt.plot(N_array_, D_N_results_, lw = 3, \n", + " label=\"expected distance between\\n\\\n", + "expected value and \\naverage of $N$ random variables.\")\n", + "plt.plot( N_array_, np.sqrt(expected_value_)/np.sqrt(N_array_), lw = 2, ls = \"--\", \n", + " label = r\"$\\frac{\\sqrt{\\lambda}}{\\sqrt{N}}$\" )\n", + "plt.legend()\n", + "plt.title( \"How 'fast' is the sample average converging? \" );" + ], + "execution_count": 3, + "outputs": [ + { + 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7pCuQiMzMTDIzM0u6GiIicelZJSJHCj2vRORIoeeViBwp9LwSkSNBCTyriiT+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CQJWSdNKnN2WK9tazh13Ycs3hLlteVFDZiZiIiIiIiIiIiIiEhFQRbDXgeyzGxEgGNWycw6Af8F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\nKaWUUie6VoZh/wW8TkSeaoy5fb6OIvJUohWnv9XC+yullFJKK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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [], + "image/png": { + "width": 865, + "height": 228 + } + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "Ps0kCQIpJZhr" + }, + "source": [ + "As expected, the expected distance between our sample average and the actual expected value shrinks as $N$ grows large. But also notice that the *rate* of convergence decreases, that is, we need only 10 000 additional samples to move from 0.020 to 0.015, a difference of 0.005, but *20 000* more samples to again decrease from 0.015 to 0.010, again only a 0.005 decrease.\n", + "\n", + "\n", + "It turns out we can measure this rate of convergence. Above I have plotted a second line, the function $\\sqrt{\\lambda}/\\sqrt{N}$. This was not chosen arbitrarily. In most cases, given a sequence of random variable distributed like $Z$, the rate of convergence to $E[Z]$ of the Law of Large Numbers is \n", + "\n", + "$$ \\frac{ \\sqrt{ \\; Var(Z) \\; } }{\\sqrt{N} }$$\n", + "\n", + "This is useful to know: for a given large $N$, we know (on average) how far away we are from the estimate. On the other hand, in a Bayesian setting, this can seem like a useless result: Bayesian analysis is OK with uncertainty so what's the *statistical* point of adding extra precise digits? Though drawing samples can be so computationally cheap that having a *larger* $N$ is fine too. \n", + "\n", + "### How do we compute $Var(Z)$ though?\n", + "\n", + "The variance is simply another expected value that can be approximated! Consider the following, once we have the expected value (by using the Law of Large Numbers to estimate it, denote it $\\mu$), we can estimate the variance:\n", + "\n", + "$$ \\frac{1}{N}\\sum_{i=1}^N \\;(Z_i - \\mu)^2 \\rightarrow E[ \\;( Z - \\mu)^2 \\;] = Var( Z )$$\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "IQBZaHpGH9R0" + }, + "source": [ + "### Expected values and probabilities \n", + "There is an even less explicit relationship between expected value and estimating probabilities. Define the *indicator function*\n", + "\n", + "$$\\mathbb{1}_A(x) = \n", + "\\begin{cases} 1 & x \\in A \\\\\\\\\n", + " 0 & else\n", + "\\end{cases}\n", + "$$\n", + "Then, by the law of large numbers, if we have many samples $X_i$, we can estimate the probability of an event $A$, denoted $P(A)$, by:\n", + "\n", + "$$ \\frac{1}{N} \\sum_{i=1}^N \\mathbb{1}_A(X_i) \\rightarrow E[\\mathbb{1}_A(X)] = P(A) $$\n", + "\n", + "Again, this is fairly obvious after a moments thought: the indicator function is only 1 if the event occurs, so we are summing only the times the event occurs and dividing by the total number of trials (consider how we usually approximate probabilities using frequencies). For example, suppose we wish to estimate the probability that a $Z \\sim Exp(.5)$ is greater than 5, and we have many samples from a $Exp(.5)$ distribution. \n", + "\n", + "\n", + "$$ P( Z > 5 ) = \\frac{1}{N}\\sum_{i=1}^N \\mathbb{1}_{z > 5 }(Z_i) $$" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "PtR6K5PFJZhr", + "outputId": "85697fa4-1ed7-4747-bf71-94e262225c47", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 35 + } + }, + "source": [ + "N = 10000\n", + "\n", + "print(\"Probability Estimate: \", np.shape(np.where(evaluate(tfd.Exponential(rate=0.5).sample(sample_shape=N)) > 5))[1]/N )" + ], + "execution_count": 4, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Probability Estimate: 0.0823\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "uLQ3PC9VJZhu" + }, + "source": [ + "### What does this all have to do with Bayesian statistics? \n", + "\n", + "\n", + "*Point estimates*, to be introduced in the next chapter, in Bayesian inference are computed using expected values. In more analytical Bayesian inference, we would have been required to evaluate complicated expected values represented as multi-dimensional integrals. No longer. If we can sample from the posterior distribution directly, we simply need to evaluate averages. Much easier. If accuracy is a priority, plots like the ones above show how fast you are converging. And if further accuracy is desired, just take more samples from the posterior. \n", + "\n", + "When is enough enough? When can you stop drawing samples from the posterior? That is the practitioners decision, and also dependent on the variance of the samples (recall from above a high variance means the average will converge slower). \n", + "\n", + "We also should understand when the Law of Large Numbers fails. As the name implies, and comparing the graphs above for small $N$, the Law is only true for large sample sizes. Without this, the asymptotic result is not reliable. Knowing in what situations the Law fails can give us *confidence in how unconfident we should be*. The next section deals with this issue." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "D1VCON0qdlVM" + }, + "source": [ + "### The Disorder of Small Numbers\n", + "\n", + "The Law of Large Numbers is only valid as $N$ gets *infinitely* large: never truly attainable. While the law is a powerful tool, it is foolhardy to apply it liberally. Our next example illustrates this." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "o6dhCexPJZhu" + }, + "source": [ + "\n", + "\n", + "\n", + "### Example: Aggregated geographic data\n", + "\n", + "\n", + "Often data comes in aggregated form. For instance, data may be grouped by state, county, or city level. Of course, the population numbers vary per geographic area. If the data is an average of some characteristic of each the geographic areas, we must be conscious of the Law of Large Numbers and how it can *fail* for areas with small populations.\n", + "\n", + "We will observe this on a toy dataset. Suppose there are five thousand counties in our dataset. Furthermore, population number in each state are uniformly distributed between 100 and 1500. The way the population numbers are generated is irrelevant to the discussion, so we do not justify this. We are interested in measuring the average height of individuals per county. Unbeknownst to us, height does **not** vary across county, and each individual, regardless of the county he or she is currently living in, has the same distribution of what their height may be:\n", + "\n", + "$$ \\text{height} \\sim \\text{Normal}(\\text{mu}=150, \\text{sd}=15 ) $$\n", + "\n", + "We aggregate the individuals at the county level, so we only have data for the *average in the county*. What might our dataset look like?" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "5Zy3phgWaLKm", + "outputId": "7fc46f4d-e27d-4668-d9cb-a3706e0e3264", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 299 + } + }, + "source": [ + "plt.figure(figsize(12.5, 4))\n", + "\n", + "std_height = 15.\n", + "mean_height = 150.\n", + "n_counties = 5000\n", + "smallest_population = 100\n", + "largest_population = 1500\n", + "pop_generator = np.random.randint\n", + "norm = np.random.normal\n", + "\n", + "population_ = pop_generator(smallest_population, largest_population, n_counties)\n", + "\n", + "# Our strategy to vectorize this problem will be to end-to-end concatenate the\n", + "# number of draws we need. Then we'll loop over the pieces.\n", + "d = tfp.distributions.Normal(loc=mean_height, scale= 1. / std_height)\n", + "x = d.sample(np.sum(population_))\n", + "average_across_county_array = []\n", + "seen = 0\n", + "for p in population_:\n", + " average_across_county_array.append(tf.reduce_mean(x[seen:seen+p]))\n", + " seen += p\n", + "average_across_county =tf.stack(average_across_county_array)\n", + "# locate the counties with the apparently most extreme average heights.\n", + "[ \n", + " average_across_county_,\n", + " i_min, \n", + " i_max \n", + "] = evaluate([\n", + " average_across_county,\n", + " tf.argmin(average_across_county), \n", + " tf.argmax(average_across_county)\n", + "])\n", + "\n", + "#plot population size vs. recorded average\n", + "plt.scatter( population_, average_across_county_, alpha = 0.5, c=TFColor[6])\n", + "plt.scatter( [ population_[i_min], population_[i_max] ], \n", + " [average_across_county_[i_min], average_across_county_[i_max] ],\n", + " s = 60, marker = \"o\", facecolors = \"none\",\n", + " edgecolors = TFColor[0], linewidths = 1.5, \n", + " label=\"extreme heights\")\n", + "\n", + "plt.xlim( smallest_population, largest_population )\n", + "plt.title( \"Average height vs. County Population\")\n", + "plt.xlabel(\"County Population\")\n", + "plt.ylabel(\"Average height in county\")\n", + "plt.plot( [smallest_population, largest_population], [mean_height, mean_height], color = \"k\", label = \"true expected \\\n", + "height\", ls=\"--\" )\n", + "plt.legend(scatterpoints = 1);" + ], + "execution_count": 5, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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ERKS4UHBL9rZM/K8R/4P/UlgZeK+I6l7jnPtHEdUVSyZ+AOKnnHNfhiYGg87fgh+H4Wkz\nmx7qskNERET2naALvs7Bx8iH+DjnfjazmcAJ+Ie4QyLmbzOzd/DdGnYhd3ArFER5wzmXFVF19eA9\n0QeLFcgb3IobfDGzpvgf1qSHTd4EbMOP4VImWH/FiEVDGQCriO3XGNND2Twl8N3A5adCAmUixQui\nbA/eS0dMD7WrUvDKTyLtync/Oee2m9m6sLLRFGZ7ispofHDrUjPrFxbcvRSf6fZV0P1auBH4YOG1\n+Iy4q4AsM1uID/Y+55z7rQjb+Cd+3Lb8xi6LpSp+WyDngX40oa42E8naK6i9doydc1+b2TygFX58\nptA9qiM5XTG+E2PxRGwjJ1svK/j7B/x3slcisrPC992e7Ot4y4bm7Y3jlIeZnYw/r0P3SYffB6Hj\nVh5/T4m8j+6p0PbF3BfOuS3B/aUq0ffH7hjZc7D37y0iIiIi+z11SygFZmYjg67/Ds+vrHNui3Pu\nSufcE865Wfhsq2LDOfeyc+7a8MBWMN055x4HPsZ/obgkKQ0UERGRS4Bywd+LonVXjA9sQf5dE55n\nZuUAzKwKcHrE/HCh/0ef7ZyzBF7RxriKm7WFH2MnHf8DodOBFOdcJedcNedcdXKCbzHTqwohtF1/\nJrhdpxbhuhNp140JtmvWPmpXso3Dd3tWh5zsNsg5N6IFfJ1zrht+3KMH8dkjO/GZbPcCPwQBgaIS\nCq41K4K6yuVfpFgK9QJxTdi00N+jXdh4f4Uw2jlXPXgd6pw7yjl3tvNj/cUKnMABsK/NrCzwKj5w\nNRX4O1DeOZca2ifAbaHie6kZxX4/ioiIiOyvFNySvxwzyzCzCWb2u5ntNLNVZjYm+HV0QYWCXrWK\nso0iIiKSsFgBq2haxPj3/kN8FtVBQKdg2oX4H7D84JyL1i1UaNyfwwqw/oSZWT18N1W78AG0D51z\nkT8SipVZtSZ4jzemUqx5vwfvBwfjb+0vQu0qyv2d734Kgp37bbdfzrn1wOTgYxcAMzsSf+448o63\nFb7s1865e5xzJ+G7zTwH+BbfjeYrZhavK8aCCHVvV9PMCtMV3Z/4bYH4xz/0//HIjMhdwXu8IEMi\n2YB70+v47lGbmVkLM0vHZ26B70ViXwnfd4XZ1yHxuvgLzSvMcaocZ1407YL1rQE6O+dmRwkUJpKh\nWhih7Yu5H82sAjn3l/y6URURERGRCApuSXFW0cz6mdkwM3vSzP4ZjC8Qk5kNwXc3dCbwI76Lj1X4\nrlvmmdlZBWxDg+A9Xrc/IiIisheYWQNyslWa4x8SxnpNDMrlCYY553YBY4OPofFnQpkvsYIDc4L3\nMwvZ/PyE/k/zW5wu4mJlTYV+fHNs8PA0mvYxps/HZ5SVADLybeW+szf2d/aPlMysTowybYg/3lZh\nhbq5LIpskVB21kXBOE2hc3dmjIzBPJxzO5xzE8npjaAmfmyiojCOnC4570p0oaAbcILsolD2V4c4\ni4Syzb6ImL4+eI/6PcHMjiJnnKmilPAxds5tBt4IPnbFj7VVCt+t5JcxFyx6P5DTBWPUfR0EPU8M\nPkbu65ATY0wPn1fQ43QQftzBggjVtcQ5tz1GmXjZp3tynYa27ygzqx6jzEnkPJOJtS9FREREJAYF\nt6Q4OwT4P6AHcCPwNPCTmQ0IfRkOZ2bX4wdr/hZo6pxr65y72Dl3LHAe/gvkKDNL6Ne5wS+/O+J/\nSTq+KDZIRERECuSq4H2hc26hc259rBc5wavLY2SkhAIEZ5lZY3K6MozWJSH4LgMBOprZafEamej/\nLSJsCN4PNbOqUepsTuxukT/Aj7NTAegZZdnS+LFD8wj2VWh8nwfjZW+ZWel9mN31Mv7/XE3M7Np4\nBQuwv+eTM3bQrTHK9E2wroIKjYFU0EyUaCbiAxLpwCnE6ZIQwMzKxKkrvJu6skXQNoKMw/uCj+eZ\nWb945YPzahC5u1l8K3jvamZ5Mm2CH6i1DD6+GTH76+C9M9HdEa89e6CgxzjUNeFlQLfg7xeLtEX5\nCMYWDH2vudnMykcpdh1+zMEsco5LpFPM7PjIiWbWEP+9C3LuySGh43RG0J1gpD4UfHyp0H20YbTz\nPjhvYgX6IecYphZwveDvw5vxYyPmub8E/w6Fgr2fOufWRJYRERERkfgU3JLiaAfwPHAa/lelFYCm\nwCD8Q49/48cPyBZ8ebgn+Hixc25J+Hzn3DvAMPwXlyvya0DwIGc0PiA2wjm3cA+2R0RERAoo+CHL\nlcHHtxNYZCKQiX8oGy0j6TNgGf6B/qv4/yd/4Zz7Plplzrn3gHfxv+h/18z6mNkhYe2rambnmdl7\nwMMJbVRu3+Azw0sAbwTdFIYe/F8ITCEnwyKybRuAIcHHgWZ2Q9hYYofj91ftOOu+DVgHNAJmB106\nlw6WNzM70sz6AEvwGXN7nXPua2Bo8HFY8GOmmqH5ZnZQ0M7RxOmKL6LOLKB/8LGXmd1nZpWC+tLN\n7AV8sCjeuESF9V98N2xVzSxW0CUhQUZKKCDxf/jslkzyBg9CPjWzIWbWPnReAJhZE3K6wFsBfBc2\nr1TYOHYJZ1+FtXEoMCrURjN738xODQ9imFltM+uJP69uI3e2zJP4rikrAh+Eujc0s5JmdhE5gbwP\nnHMzIlYf2g/NzexxM6scLFvNzJ7G9+CQ9GPsnPscH+CpAhyF/84zKu5Ce0d//P6oDUwMMmQxs7LB\njwUHB+Wed84ti1HHRmC8mZ0R+tGhmZ2I70KzDH47x0UsMwG/zdWAkWaWFiyXamb34ANBGyiYWcB2\nIA14OZRBZWblzaw7PhD6Z5zlvw3eLwu/VhLhnNsEPBR8vMXM7jCzisH6awXrboMPEhb4mhIRERER\nBbckDjPrZmYjI1/4gXgBHo02P/zBzt7gnFvlnLvOOfeRc+5X59w259w3zrk78ONjANxmZuF9vTfH\nj6fwrXPuuzyVetOD9zbx1h883BkLNAG+wmeDiYiIyL51EhDqSi7yIWkeQUbSJ8HHaF0TOvy4N+DH\nK4LYWVshV+CDZuWBR4E/zGytmW3Ej/HyNjnj5hSIc243/v8YWfgAy49BvZvx/w/ZSozsq8B9wMf4\nTIengI1mtg5YCpwOhGc/5RqDxjn3E777v9+AY/AZCFvMbA3+QfH3wfbWI2cspH2hDz67pST+x0wr\nzGyDma3HP/T+AJ+1VJBuBJ/HBzMB7gXWmtla/LZ3BXrjA30QsZ/2hHNuIzkZRu+Y2XozWxa8zi1E\nlaFzNXTuTnHOrY1RtjL+3JqBP65rzWw7PuBwIrAFuDI4B4vSlfgfoO3En18fAtuC9W8DfgaewZ9X\nM4D/hRZ0zv2Jz/hZj/9//X+C62ELfj9WxnczeRURnHOLyAmM3gKsC66FVfgspO7kdJtYZAp5jMMz\ntd5xzq2LUW6vcc79gM8e24G/9/w32F+bgGfxwamp+Osxlvvx94rJwGYz2wxMA+rig5QXBd3Bhq93\nNf66Bh9w/CNY759Bfffgg/4F2ZY/gTvD6lwV3C824q/978kJcEcTOh6XAhvM7Jfg+L2WYBMewl+b\nJYCBwPrg/vILcD7+/t7TOfdZATZLRERERAIKbkk8f8c//Il8hfrfvyDG/KQNPh6MFfAl/kFOeP/p\n9YL3o8N+dZrrRc6Xz7RY9Zsfx+B14Ax83/8ZUQZ3FxERkb0vFKD6r3Pu27glc4SCYOeYWbRupsKD\nWVnkBLuics5tds6dA5yDz5xZhc8sKYUfu+YN4B/AzQm2L7L+t/D/n/kY/2C5ND677BGgBXHG/HTO\n7cAHEPrisw+y8FkkE/DdcIWyWxxRsiGCLJKGQD/8eFdb8BnuW4EF+Myw9s652YXZtsJwzu1yzvXA\ndxk5CliOz7Qrhw+MvAv8k9jdNUar0+HPpW747doZzPoEONM59xxQKZi2Pm8Ne6Q7vueB7/HbUCd4\nFeb/0h8Bf4R9jheYvQYf/JyG32/l8efHYnyGVBPn3LRCtCEu592DH7P2Afx5tRq/vZn4wMXzwInO\nuRMjx5pzzs0Bjsafez/ggyyZ+OPWB2gTBEii6Q30AhbhgzZZ+GDoSc65V2MsUxQKeozDs1BfilFm\nrwt6tTgGH9xZju8pYyswE3+tnOmc2xqnitX4biKfDP4uDazEH9/mcTJiH8cHqD8P1lcCn33V2Tn3\nf4XclseBi/Dn2zb8/XkJcDfQDv+DgVjLTsX/eHJGsGxN/PGLNYZW5PK7nXOXAxfjr9GN+GP/K/4a\nPc4593xhtktEREREwPz3OZHEBdlbVwN143RFEWvZk4BP8RlUTYq8cX4do/Ffiv7tnBsYTLsU30XN\nSvwXi3iWOOceipwYdG04Gv/l5H/4L96/FmXbRURERPYFM8vAP9z/0TlXP9nt2V+ZHyNoCT4L5aDI\nbBORomRmV+PH8/sZ/10rK7ktKhgzm4UPGF3pnEs0u0lEREREpFBKJbsBIntBaND18F/h/RK8r3LO\n/aOgFQaBrdfwga2lwMkKbImIiEhxFIyBc2vw8cNktqUYuC14n6bAluwD1wfvLxa3wJaIiIiIyL6m\nbgnlgBIMEtw++Dg/bNY8fH/tLcysQL9ONrMSwCv4vtaXAx2cc7/EX0pEREQkecysjJm9aWYZZlY5\nbHoTfPeMp+K74Bsaq46/CjN72czON7OqYdPqmdkw/LhbAI8lp3XyV2Fm1wGt8d3fDUtyc0RERERE\n9nsKbsl+ycxqmtmS4FUzYl73yGnB9Mb4cSTKA3Occ3ND85xzmfgBrEviB3RuFWX5MmZ2jpkdFTat\nBDACP6jyz/jA1vKi2UoRERGRvaYEfpyZD4D1ZrbBzLYAXwPnAbuBfzrnvktiG/cXGfiA3xoz22Rm\nm4AfgR7B/Pudc/l1ay1SYGZWx8yWmdmfwHPB5Iecc78ns10iIiIiIsWBuiWUvc7MngGODT6GBuSu\nZ2Zzw4q94Jx7Iexzafwg5qG/w90ADDOzr/GDSe8CjgCakzNA8MWR7XDODTGzOsAtwOdmtgj/4GIn\nfnDgFvhB4M8M6gA/8PRVwd8/Aff6nnzymBXRfhEREZFk2on/P1MG0ARIx//IZxkwA3jCOfdl0lq3\nf+kDnIP/v2A1oBx+nNY5wNPOuWnJa5oc4EoDdfDB5p+A54GHk9oiEREREZFiQsEt2RcaA8dHTCsf\nMe2DAtQ3FB+AagqcAqQAG4HPgLeB551z26It6Jz7l5m9A/TED3bcEd/1xyrgPXzm18ywRaqE/X1S\nPu1ScEtERET2C8F4Pc8EL4nDOTcKGJXsdshfj3Puf0DUX84VR865vye7DSIiIiLy12HOuWS3QURE\nRERERERERERERCQhGnNLREREREREREREREREig0Ft0RERERERERERERERKTYUHBLRERERERERERE\nREREig0Ft0RERERERERERERERKTYKJXsBsj+ZcOGDV8CdYHNwP+S3BwRERERERERERERESne6gMp\nwNLKlSu3KIoKFdySSHWBysGrZpLbIiIiIiIiIiIiIiIiB4a6RVWRuiWUSJuT3QApvK1bt7J169Zk\nN0PkL0nXn0hy6NoTSR5dfyLJo+tPJDl07Ykkj66/A0aRxR8U3JJI6oqwGFu5ciUrV65MdjNE/pJ0\n/Ykkh649keTR9SeSPLr+RJJD155I8uj6O2AUWfxBwS0REREREREREREREREpNhTcEhERERERERER\nERERkWJDwS0REREREREREREREREpNhTcEhERERERERERERERkWJDwS0REREREREREREREREpNhTc\nEhERERERERERERERkWJDwS0REREREREREREREREpNkoluwEiIiIiIiIiIiIiB7KsrCw2b97M1q1b\nyczMTHZzRIqtX375JdlN+MsrXbo0FSpUICUlhRIlkpc/peCWiIiIiIiIiIiIyF6SlZXFmjVr2LFj\nR7KbIlJslSlTJtlNkEBmZiYbNmxg+/btHHLIIUkLcCm4JSIiIiIiIiIiIrKXbN68mR07dlCyZEkO\nPvhgypYtm9RsB5HiaPv27QCUK1cuyS35a8vKymLHjh2sW7eOHTt2sHnzZipVqpSUtuguKiIiIiIi\nIiIiIrKXbN26FYCDDz6Y8uXLK7AlIsVWiRIlKF++PKmpqUDO/S0pbUnamkVEREREREREREQOcKEx\ntsqWLZvkloiIFI1QBt2uXbuS1gYFt6Iws8vMbKaZbTCzzWa2wMxuMLNC7S8zO8PMpprZWjPbambf\nmNmdZhb1XzQzO8vMXjSzL8zpVKb0AAAgAElEQVTsNzPbaWYbzWy+mf3bzFL2bAtFRERERERERERk\nX1LGlogcKMwMAOdc0tqgO2oEM3saGAUcB8wEPgSOBJ4C3ipogMvMbgMmAycDXwCTgHSgPzDNzCpE\nWewyoCtQEVgIvAXMAxoDA4AvzKx6gTdORERERERERERERERkD4SCW8mk4FYYM7sA+CfwG3CMc66T\nc+48oAGwGDgPuLEA9R0HPARsBdo55051zl0E1ANmAK3xwapIjwLVnXMNnXMZzrnLnHOnArWD5RoA\ngwq7nSIiIiIiIiIiIiIiIsWVglu59Qveb3fO/RCa6Jz7HegZfLyjANlbdwAGDHLOfR5W32bgGiAL\n+KeZpYYv5Jz7KlgnEdPXAncFH09LsA0iIiIiIiIiIiIiIiIHDAW3AmZWC/gbsBMYGznfOTcdWAlU\nx2dc5VdfGeDM4OOoKPX9BMwBygBnFaCpoRHadhRgGRERERERERERERERkQOCgls5WgTv3zrntsUo\nMz+ibDwNgQrAWufcj0VQH2aWAtwTfJyQyDIiIiIiIiIiIiIiIn8VAwcOJDU1lYEDB+7VZfZE06ZN\nSU1NZfny5UVSX8eOHUlNTWXmzJlFUl9xUCrZDdiP1A3e451NP0eUTaS+n+OUiVufmbUBrsMHIdPx\nGWOVgcnA3Qm0IVTPP4B/JFJ22rRpzZs3b87WrVtZuXJloquQ/cwPP/yQfyER2St0/Ykkh649keTR\n9SeSPLr+RJKjMNdemTJl2L59+15ozYHhuOOOY8WKFcybN4/DDjss2c2RIvLII4/w2GOP0adPH/r2\n7VskdSZyHe3atSv7PdHrrjDL7AnnHAA7duwokvVlZWUBsHPnzgLVd9NNN/Hmm2/yxBNPcOmllxZ4\nnTt37kzonlizZk0qVKhQoPrzo+BWjpTgfUucMpuD94P2UX1HAFdHTHsduNk5tzGBNoQcDpyYSMHN\nmzfnX0hERERERERERERERArlrbfeIjMzkxo1aiS7KcWWglv7Mefca8BrZlYKqI0fw+s+4DszO885\nNyPBqpYB0xMpmJKS0hyoXKFCBRo0aFDwRktShaLkOnYi+56uP5Hk0LUnkjy6/kSSR9efSHIU9tr7\n5ZdfAChXrlyRt+lAYWYAlC1bVvvpAFKqVKns9z09rqFspETqKcx6i7KtiTjqqKOKtL4SJfwIVGXK\nlClQ+0uWLAlA6dKlC7zdJUqUoFy5ctSuXbtAyxUVjbmVI5SyVDFOmVA21qZ9WZ9zbpdzbqlz7hng\nbHzXhKPMLKE8PufcSOfcSYm8mjdv/lUidYqIiIiIiIiIiIjsiVGjRpGampodAGzWrBmpqanZr9B4\nRKFyPXv2ZO3atdx2220cc8wxpKWlcdlll+UpE83MmTNJTU2lY8eOUeevWLGC22+/neOOO47q1atT\nu3ZtMjIyGDVqVHYXcgXhnGPcuHGcd9551KtXj/T0dJo0acJNN92UZ5yl7du38/e//53U1FQeffTR\nPHVt3bqV1q1bk5qaytChQ6Nu05YtW7jvvvto1qwZ6enpHH300fTt25e1a9fGbGNhttk5x/jx47nw\nwgupX78+aWlpNGrUiHPOOYdhw4Zll0tNTWXQoEEADBo0KNdxjRzXasuWLQwZMoQOHTpQu3Ztqlev\nTuvWrRk4cGDMnsYyMzMZOnQoxx9/PNWqVePII4+kR48e/PxzvFGCEvPHH39w880307hxY9LT0znm\nmGO477774nb3t2DBArp27Urjxo1JS0vjiCOO4NJLL2XOnDlRy8cbc2v16tX06dOHxo0bU61aNVq0\naMGDDz7Itm3bEhpb66uvvuLSSy+lbt26VKtWjXbt2vHKK6/kKrN8+XJSU1MZM2YMADfccEOuYzRq\n1KhEdlVSKXMrx7LgvU6cMqEQ5LI4ZSLri9dJbEHqA8A597mZLQaaAMcDnya6rIiIiIiIiIiIiMj+\nol69enTp0oUJEyawZcsWzjnnHCpWzMkVSElJyVV+7dq1dOjQgY0bN9KmTRtatGhBlSpV9rgdM2bM\n4IorrmDjxo3Uq1ePU045hS1btrBgwQJuuOEGZsyYkStwk5/MzEy6du3KxIkTKV++PM2bNyc9PZ3F\nixfzyiuvMGHCBMaPH0+LFi0An400cuRIOnTowMCBA2nbti1t27bNrq9Pnz4sWbKEjIwMevXqFXV9\nnTt3ZvHixbRv355mzZoxe/Zshg8fzieffMLkyZNJT0/f423euXMnV199NZMnT6ZkyZK0bNmSWrVq\n8ccff7B48WJmzJjBddddB0CXLl34+uuv+eabb2jSpAlNmzbNrif875UrV3LBBRewZMkSDjnkEFq2\nbEnZsmX58ssvGTRoEO+99x6TJk3KlVWUlZXFFVdcwZQpUyhXrhwnnHACKSkpzJgxg5NOOonTTz89\n4WMVaeXKlZx00kk452jVqhWbNm1i7ty5PPHEEyxZsoTXX389zzJDhw7lnnvuAXyAtmXLlvz6669M\nnTqVqVOnMnjwYK6+OnL0oehWrVpFRkYGP//8M2lpaZxxxhns2LGDYcOGMWvWrHyX//jjj3n66adp\n0KABJ598MitWrODzzz/npptuYsOGDdx4442Av7a6dOnC3LlzWbp0Ka1bt6Zu3brZ9dSrVy+h9iaT\ngls5vgzejzaz8s65bVHKtIwoG88SYBtQxcyOcM79GKVMqwLUF2518J4et5SIiIiIiIiIiIjIfqpN\nmza0adOGWbNmsWXLFh588EHq1ImdezBlyhROPvlkXn75ZQ466KAiacNvv/3GVVddxZYtW3jmmWfo\n0qVLdjeJK1asoEuXLrzxxhuccMIJXH755QnVOWDAACZOnEjbtm0ZPnw4NWvWzJ73/PPPc9ttt9G1\na1fmz5+f3R1e/fr1GTx4MN26daNbt27MmjWLKlWqMHr0aMaMGUPNmjV59tlns9sWbt68edSvX5/5\n8+dz6KGHArBp0yauuOIKpk+fzm233cbIkSP3eJvvueceJk+eTP369Rk9ejRHHnlk9rzdu3czZcqU\n7M/PPvssAwcO5JtvvqFjx47069cvT7udc1xzzTUsWbKE7t2788ADD1C+fHkAtm3bRu/evXnzzTfp\n168fgwcPzl5u+PDhTJkyhUMPPZT33nsvOxCzfft2evToETUAlajXXnuNq666ikcffZQyZcoA8P33\n33PKKafwwQcfMHfuXFq3bp1d/sMPP+Tuu++mRo0avPrqqxx33HHZ8+bOncvFF1/MrbfeSrt27ahf\nv36+6+/Tpw8///wzp512GiNHjswO9v7+++907tyZJUuWxF3+iSeeYOjQoVx55ZXZ09544w2uu+46\nHnnkEa699loqVKhA1apVefbZZ+nZsydLly7lyiuvTPj83l+oW8KAc+4X4AugDHBR5HwzOxGoBfwG\nRM8lzF3fTmBy8DHPWWFm9YA2wE5gUqLtNLNKwN+Cjz8kupyIiIiIiIiIiIjsvwYOHJirW7B4r969\ne+dZvnfv3gkvH9ktHMAll1yS8PLhgZJ9qXTp0gwePLjIAlvggzDr16+nV69eXHbZZbmCR7Vq1eLJ\nJ58EfFAqEevWrWPYsGGkpKTw8ssv5wpsAfTo0YOMjAyWLl3Khx9+mGvehRdeyNVXX82vv/7K9ddf\nz+LFi+nbty+lSpXixRdfjJul1r9//+zAFsBBBx3E4MGDKVmyJBMmTGDFihV7tM2rV6/mpZdeokSJ\nErz66qu5Alvgx24666yzEtpHIR999BHz5s2jZcuWDBo0KDuwBVC+fHkGDx5MWloaY8eOZf369bna\nD3DnnXfmyjAqV64cjz32WK56CqpWrVoMGjQoO7AF0LBhQy655BIApk+fnqv8Qw89BMCTTz6ZK7AF\n0Lp1a/r27UtmZiYjRozId93Lly9n8uTJlCpVikcffTRXFmO1atV48MEH863jnHPOyRXYAn9tN2zY\nkI0bN/LllwXNs9l/KbiVW+iuPsjMssOoZpYOPBN8fMg5lxU2r5eZLTGz3J1WBmUBB9xuZq3ClkkB\nXsLv/2ecc+vD5qWbWc8giJWLmR0OvAlUAhY4574o3GaKiIiIiIiIiIiIFC/NmjWLm9lVGKEA07nn\nnht1fvPmzUlJSeHrr7+OO+ZSyIwZM9i2bRvt2rUjLS0tapl27doBMH/+/DzzBg0axNFHH83UqVPJ\nyMhgy5Yt3HnnnbmyhSJVrlyZM844I8/0evXq0bJlS7Kysvjss8+ypxdmm2fMmMHOnTtp1aoVjRo1\nitmWgpg6dSrgAzIlSuQNVVSsWJEWLVqwa9cuvvrqK8B3G7hs2TJKlCjBRRflyVEhLS2NDh06FLpN\n7du3jxoca9CgAeCz3kL+/PNP/vOf/1CpUiVOPvnkqPXFO9aR5syZg3OOli1bRj3PTzvtNFJTU+PW\nkZGREXV6tPYXd+qWMIxz7i0zexboCXxtZh8BmcAp+IDSO8BTEYsdAjTEZ3RF1jffzO4ABgGfmdkn\nwHrgRHyXgp8Dd0YsVgEfSBtsZl8By/FBsMOAY/HH7H/AJXu8wSIiIiIiIiIiIiLFRO3atYu8zmXL\nlgEkFBBZu3ZtruyoaJYvXw74LhTzC0SsWbMmz7Ry5crx4osv0qZNGzZu3MiJJ57IzTffHLeeww47\nLO68uXPn8uuvv2ZPK8w2//LLL0BOkKQohPbV3Xffzd133x237J9//gmQvR01atTIlV0VLt7+yE+t\nWrWiTg9lC4YHOEPt37hxI1WrVo1bb7RjHWnVqlVA/PO8Vq1aubLYos2PJlr7izsFtyI45/5pZrOA\nG/BBqJL48bNeAp4Nz9pKsL6HzWwR0Ac/Zlc54CfgSeBR59yOiEX+AG4FTgCaAI2DZdYBM4DxwAvO\nuQPnLBQREREREREREfmL69evX9RxiRI1ZMgQhgwZUujl33jjjUIvu6+UK1eu0MtmZUV/rLt7924A\nzj//fMqWLRu3jvzmh9fXoEGDPN3URYo1f+zYsTjnAPjxxx9Zv349Bx98cL7rTlRhtjnaWF9F1Y52\n7drlG5CKFbQpatEyyGIJtb9SpUp07Ngxbtn8gl/h4u3r/NpXkPYXdwpuReGcGw2MTrDsfcB9+ZT5\nAPggwfq2Ao8FLxERERERERERERHJRyiLZ8uWLVHnhzKPItWsWZOffvqJvn37Fkl3e6Extho3bpw9\nNlRBfPrppwwePJjU1FTatWvHpEmT6NmzJ6+//nrMZX7++ed859WoUSNXGwu6zaHg0v/+97+Eyici\ntK/OPfdcunfvHrdsKOMotB2rVq1i586dUbO34u2PohRqf+nSpQt1rCNVr14diH2u5jfvr+avE8YT\nERERERERERERkf1OKEARyoQpjFDQ44cffog6PzTOVKRTTz0VgHfeeafQ6w530kknUbp0aaZNmxa3\n+7hofv/9d3r06EFWVhZPPfUUw4cPp2HDhnzwwQc8/fTTMZfbsGFD9vhV4ZYuXcr8+fMxM9q2bZs9\nvTDbfMIJJ1C6dGk+//xzvv/++4SWye+4FqYdtWrVok6dOmRlZTFu3Lg889esWcO0adMSrm9PHHro\noTRu3Jg///yTmTNn7nF9bdq0wcyYP39+1CDWxx9/zLp16/Z4PeGK4tpLFgW3RERERERERERERCRp\nQoGpRIMm0Rx77LEcdNBBLF68mLfeeivXvBdeeIF333036nI33XQTlSpV4vHHH2f48OHs2rUrT5nF\nixczYcKEhNqRnp5Ot27d2LBhA126dOG///1vnjJbtmxh7Nix/PHHH9nTsrKy6N69O6tXr6ZHjx50\n6tSJChUqMGLECMqXL8/999/PF198EXO9d911F7/99lv2582bN9OnTx92795Np06dco3jVJhtTktL\n45prriErK4urrroqTwbX7t27mTx5cq5p+R3XTp060bx5c2bPns0tt9wSNXDz+++/8/LLL+eadt11\n1wEwYMCA7PHDAHbs2MGtt97K1q1bo65vb7jzzjuz2/TJJ5/kmb97926mT5/O/Pnz863r8MMP5/TT\nTyczMzPPdvzxxx/5jktWGEVx7SWLuiUUERERERERERERkaTp1KkTs2bNokePHnTo0IHKlSsDcP/9\n91OlSpWE6qhQoQK33XYbd999N927d+eFF14gPT2d7777juXLl9O7d++oY5LVqlWL1157jauvvpq+\nffvy2GOPcdRRR5GWlsaGDRv47rvvWLFiBeeffz7nnHNOQm154IEH+O233xg/fjxt2rShadOmHH74\n4ZgZP//8M9988w07duxg3rx5pKenA/Dwww8zY8YMmjVrRv/+/bPraty4MQ899BC9e/fmmmuuYcaM\nGdn7J6RVq1bs3r2b4447jvbt21OmTBlmz57NmjVrqFu3Lo8++miRbPODDz7IsmXLmDp1Kq1bt6Zl\ny5bUrFmT1atX891337F69epc2WqnnHIKFSpUYOLEiZx55pnUrVuXkiVLcuaZZ3LWWWdRokQJRo0a\nxUUXXcSIESN46623aNKkCTVr1mT79u38+OOPLFmyhLS0NC655JLseq+77jo+/fRTPvzwQ1q3bs0J\nJ5xAxYoVmTt3Ltu3b+fSSy+N241jUerYsSP9+/fn3nvv5fzzz6d+/frUr1+flJQUfv/9dxYtWsSG\nDRt4/PHHadmyZb71Pf7442RkZDBlyhSaN29O27Zt2bFjB7NmzeKoo46iVatWzJs3L2p3jIVx1lln\n8fDDD/Pss8+yePFiDj30UMyMK664guOPP75I1rG3KLglIiIiIiIiIiIiIknTo0cPNm3axNixY5ky\nZQo7duwA4NZbb004uAVw4403kpqayrBhw/jyyy8pV64crVq1YtiwYWzdujVqcAt8l3tz587l+eef\nZ8qUKSxYsIDMzEzS09OpU6cO1157Leeee27C7ShdujQjRozg4osv5tVXX+WLL77g22+/JSUlherV\nq3PBBRdw1llnUbduXQBmzZrFI488wkEHHcSIESPyBC6uvvpqZsyYwbhx47jxxht55ZVX8qxv/Pjx\nDBw4kAkTJvDbb79xyCGH0L17d+644w6qVq1aJNtctmxZXn/9dcaOHcuoUaNYtGgRCxYsIC0tjaOP\nPppOnTrlKl+tWjVef/11Hn74YRYtWsTcuXNxznHooYdy1llnAX7cqk8++YRXX32V8ePH891337Fg\nwQKqVKlCjRo16NWrV556S5YsyejRo3n66acZPXo006ZNo1KlSpx44oncfffdjBkzJuFjVRR69erF\niSeeyPPPP8+sWbOYNm0apUqVolq1arRt25YzzzyTs88+O6G6Qvtj4MCBTJ48mffff58aNWrQtWtX\nbr/9dtq1awcQ9ZgWxjHHHMOIESMYOnQo8+bNY/PmzQC0bt16vw9umXMu2W2Q/ciGDRumAScmux1S\nOKE+hRs0aJDkloj89ej6E0kOXXsiyaPrTyR5dP2JJEdhr73Q2Dnh3cKJFIWZM2dy9tln065dOyZN\nmpTs5uxV27dvB6BcuXJJbknyLF++nGOPPZaKFSuybNkySpRI7qhThby3Ta9cufJJRbF+jbklIiIi\nIiIiIiIiIiKSZM45vvrqqzzTV6xYwfXXX8/u3bu55JJLkh7Y2h+oW0IREREREREREREREZEk2717\nNyeddBK1atXiyCOPJDU1lZUrV7Jw4UK2b99O48aNueuuu5LdzP2CglsiIiIiIiIiIiIiIiJJVrJk\nSW699VamTZvGokWL2LBhA2XLlqVhw4acffbZXH/99aSkpCS7mfsFBbdERERERERERERERIqZ9u3b\ns379+mQ3Q4qQmXHXXXcpOysB6phRREREREREREREREREig0Ft0RERERERERERERERKTYUHBLRERE\nREREREREREREig0Ft0RERERERERERERERKTYUHBLREREREREREREREREig0Ft0RERERERERERERE\nRKTYUHBLREREREREREREREREig0Ft0RERERERERERERERKTYUHBLREREREREREREREREig0Ft0RE\nRERERERERERERKTYUHBLRERERERERERE5EDhHCz9CT79BN5/D6Z+AJ/Pha1bk90yKQZGjRpFamoq\nPXv23Cfr69ixI6mpqcycObNI6uvZsyepqamMGjWqSOqT/ZeCWyIiIiIiIiIiIiLF3c4dMHM6DHgA\nG/R/2BujsQnvYG+/hY14AW6/FV59GX75OdktTZp9HbiR4mPgwIGkpqYycODAZDdFElQq2Q0QERER\nERERERERkT2w+g94agj2++8xi1jmTpg9E2bPxJ3dGc7qBGb7sJEieT333HNs27aNWrVqJbspUswo\nuCUiIiIiIiIiIiJSXK3+Ax4eiG3alD3JlS4DLVpAlaqQmQlLFmMrV2TPt4nv4rZvhwsuSkaLRbLV\nrl072U2QYkrdEoqIiIiIiIiIiIgURzt3+IytILDlSpfGnX8RPPQIdO0O554PF10Cd92Lu/V2XIMj\nsxe1D6fA7FnJankeW7ZsYciQIXTo0IHatWtTvXp1WrduzcCBA9m8eXOusj/++CO1a9ematWqzJ49\nO09dS5Ys4dBDD+WQQw5h3rx5ADRt2pQbbrgBgDFjxpCampr9Cu+msGnTpqSmprJ8+XLee+89OnXq\nRJ06dUhNTWXRokXZ5ZxzjBs3jvPOO4969eqRnp5OkyZNuOmmm1i+fHmeNs2cOZPU1FQ6duzI9u3b\n6d+/Py1atKB69eo0a9aMRx55hN27dwOwYsUKevXqRaNGjahWrRpt27bljTfeiLnvMjMzeemllzjz\nzDOpU6cO1apV49hjj+Xf//43a9asKcBRyG3Tpk3cfffdHHPMMaSnp9OoUSP+9a9/sW7dupjLfP/9\n9/Tq1YtjjjmGatWqUadOHTp37sz7778ftXy8Mbc2b97M/fffT7NmzTjssMM49thj6du3L+vWrUto\nbK2ffvqJbt260aBBA9LT02nZsiVPPPEEWVlZucqlpqYyaNAgAAYNGpTr3AjvpvCHH37g+uuvp0mT\nJqSlpVGrVi2aNm3K5Zdfzrvvvht3X0rRU+aWiIiIiIiIiIiISHH0+efZXRG60qXhplsgLICVzQzq\nN4De/8INH4Yt/NJPf28CtGkLJZKbA7Fy5UouuOAClixZwiGHHELLli0pW7YsX375JYMGDeK9995j\n0qRJpKamAnDEEUcwePBgunXrRvfu3Zk5cyZVq1YFYOvWrfzjH/9g69atPPDAA7Rq1QqAzp07s2DB\nAubOnUvdunVp3bp19vrbtGmTp01PPfUUw4cP529/+xunnXYaK1eupESwnzIzM+natSsTJ06kfPny\nNG/enPT0dBYvXswrr7zChAkTGD9+PC1atMhTb2ZmJueddx6LFy/m73//O0cccQSfffYZAwYMYNWq\nVdx4441kZGRQvnx52rRpw6pVq5gzZw7XXXcdZsbFF1+cq76NGzdyySWXMGfOHCpVqkTz5s2pXLky\nCxcu5JlnnmHChAlMmjSJOnXqFOiYbNy4kYyMDFatWkXbtm1p1KgRc+fO5aWXXuI///kPH330EaVL\nl861zLhx4+jZsyc7d+6kUaNGZGRksGbNGubMmcP06dPp27cvd955Z0Lr37RpE506dWLhwoVUqlSJ\nk08+mZIlSzJu3Dg+/vhjjjrqqLjLf/311/Tr148qVarQvn17Vq9ezZw5c7jvvvtYuXIljzzySHbZ\nLl268PXXX/PNN9/QpEkTmjZtmj0v9Pe3337LGWecwaZNmzjyyCM544wzMDNWrVrFJ598wvbt2+nc\nuXOiu1eKgIJbIiIiIiIiIiIiIsWNczDj05zPZ58bPbAVrlQpuLYb7s47sE2bsHVrcV8vgmbN925b\n43DOcc0117BkyRK6d+/OAw88QPny5QHYtm0bvXv35s0336Rfv348++yz2ctdeOGFzJo1i5EjR3L9\n9dfz5ptvYmbceuutLFmyhNNPP50bb7wxu3z//v0ZNWoUc+fOpXXr1rnqimbEiBG88cYbZGRk5Jk3\nYMAAJk6cSNu2bRk+fDg1a9bMnvf8889z22230bVrV+bPn0+pUrkfwc+bN482bdqwcOFCKleuDPhA\nzMknn8zIkSOZPXs2559/PgMGDKBkyZIADB8+nL59+zJw4MA8wa2bb76ZOXPm0LlzZ4YMGZIdANy9\nezcPPPAAQ4YM4Z///CeTJk3K91iEmzRpEqeffjpTp04lJSUFgFWrVnHaaaexcOFCxo8fn6st33zz\nDT179qRMmTKMGjWK0047LXve4sWLueiii3jkkUdo3749J5xwQr7rHzBgAAsXLqRZs2a8/fbbVKxY\nEYCdO3dy2WWXxcwEC3nuuee4/fbbuf3227ODkrNnz+bss8/mxRdfpHfv3tnjfD377LMMHDiQb775\nho4dO9KvX7889T3zzDNs2rSJe+65h3/961+55m3evJnvvvsu322SoqVuCUVERERERERERESKm2VL\nsV9+AYIxttr9PbHlypSFtmFlZ0wr+rYVwEcffcS8efNo2bIlgwYNyg5sAZQvX57BgweTlpbG2LFj\nWb9+fa5lH3roIY4++mg+/PBDnnzyScaMGcPo0aOpWbMmzz33HGZW6HZdfvnlUQNb69atY9iwYaSk\npPDyyy/nCmwB9OjRg4yMDJYuXcqHH36YZ/kSJUrwxBNPZAe2wGcHnXbaaWRlZbFt2zYeeOCB7MAW\nwDXXXMPBBx/M0qVL+SU45uC7X3z77bepXbs2zz33XHZgC6BkyZLce++9NG7cmNmzZ/Ptt98WaPtT\nUlIYOnRodmALoEaNGnTv3h2A6dOn5yr/2GOPsXPnTu6///5cgS2ARo0aMWDAAMAH6vKzdetWXn31\nVcAf41BWHkClSpV49NFH8z22xx57LHfccUd2YAugXbt2nHLKKWRlZUXtBjGe1atXA3DqqafmmZeS\nkpKdISj7joJbIiIiIiIiIiIiIsXN8mU5fzdvAUFmS0LatoteTxJMnToVgHPOOSdXICKkYsWKtGjR\ngl27dvHFF1/kmleuXAqacUwAACAASURBVDlefvllUlJSePDBB+nTpw8lS5bkhRdeoEqVKnvUrrPP\nPjvq9BkzZrBt2zbatWtHWlpa1DLt2vn9O3/+/DzzateuTcOGDfNMr1evHgDt27enTJkyueaVKlUq\nu1vB3377LXt6KHh2xhn/z969x0lW1ffe/6x9qVtX3+ZGw8wgGicOKnG4eUODkcQjREVFfURzFLyd\nRDCJiU+QeDwxxhwxcmJMjBiTAIbEY0QfNEajeXmBoIiAgFx0lGBgYKBhevpW1XXZt/X8sbure/pa\nPb2ra3r4vl+vftV01aq1V1XXbnR/+7d+Lz0kFJzhOA7Pf/7zl1zLcp71rGdxzDHHLLh/165dC9aR\nJAnf+ta3MMYsuTXfcu/JfHfeeSdTU1Ps2LFj0S0jd+/ezTOf+cxl5/i1X/u1RQOwxdbfjlNOOQWA\n3/u93+M73/kOzWZzVc+X7GlbQhEREREREREREZGNplab/fdqg5xNs5Uw1OrZrOcwPfjggwC8//3v\n5/3vf/+yY0dGRhbc99SnPpU//uM/5vd///eJoohLL7100UBktXbu3Lnser/xjW8cUinV7nqPO+64\nRcfObLu30uONRmPBWv72b/92xYqoxdaynJkt++br7e1dsI7R0VEmJyeB9Oex1nU8+uijwNI/g5nH\n7r777iUfX8362/Hbv/3brd5hr3rVq8jn85x00kmcccYZvO51r+MZz3jGquaTtVO4JSIiIiIiIiIi\nIrLReP7sv8Nwdc+dO97v7iXiOI6BtLLn+OOPX3bsYmFHHMd88YtfbH1/++23Y61d05aEkFaFLWZm\nvbt27eK0005bdo7FHl+sOm01jy+2lj179nDiiScuO3b37t1tz3u463Bdd0FPsE7J8n1sR6lU4stf\n/jK33XYb3/zmN/nBD37Arbfeym233cbHP/5xLr30Ui655JJMjynLU7glIiIiIiIiIiIistHMrRr6\n6U/AWmg30Nn7k8Xn6YKZnlWvfOUrW/2cVuPDH/4wN910E6eeeiq1Wo1vfOMbfOITn+Bd73pX1ksF\nZtf79Kc/nSuuuKIjx1jtWl74whfyJ3/yJ11bx+bNmykWi9TrdT760Y8e0qfrcAwNDQEc0l9svn37\n9q3pGIfrtNNOa4WWQRBw7bXX8ju/8ztcdtllvPrVr25teyidp55bIiIiIiIiIiIiIhvNSSdhp3sz\nmf374f7/bP+5N3xn9t+nnp7xwlbnV3/1VwH40pe+tOrn3nDDDfz5n/85/f39XHnllVx55ZWUSiU+\n+MEP8sMf/nDB+JleVjOVRofjRS96Eb7vc/311zM+Pn7Y82Rh5r376le/ShRFXVuH53mceeaZAHz5\ny19e83wnn3wypVKJhx9+mFtuuWXB4z/72c+455571nycuQ7ns5HL5XjjG9/I6aefjrWWe++9N9M1\nyfIUbomIiIiIiIiIiIhsNMUSPOe5s99/+TpoJ+C49x7MT/cCYB0HXvjLHVpge172spexZ88evve9\n7/Hud7+bsbGxBWMee+wxPvOZzyy47+1vfztJkvBXf/VXPOlJT+LEE0/ksssuIwxD3vKWtywIn449\n9lgAfvrTnx72erdt28bb3vY2JiYmOP/88/nZz362YMzU1BTXXnstjz/++GEfpx179uzh13/91/n5\nz3/OBRdcwP79+xeMGR8f56qrrup4+HXJJZfg+z6XXnopX/ziF7HWHvK4tZYf/vCHfPvb315xrlKp\nxBvf+MbWvKOjo63HKpUK73nPe0iSJNP1r/TZ+Lu/+zvuu+++Bfc/8MAD/OQnaSXkcj3CJHvallBE\nRERERERERERkI3rRi7HfvRFjLea+n2E//Sl469shn198/L33wKfnbKW352QY3LQ+a12C4zj80z/9\nE6997Wu56qqr+MIXvsAzn/lMtm/fTqPR4P7772fv3r1s3bqVN7/5zQAkScI73vEOHn/8cd7+9rfz\nile8ojXfm970Jm688UauvfZa3vWud3HNNde0Hjv99NM55phj+NGPfsSLXvQidu/eje/7POc5z+E3\nfuM32l7zBz/4QYaHh7nuuut43vOex0knncQJJ5yAMYZ9+/Zxzz330Gw2ueWWW9i2bVt2b9Yirrji\nCs4//3z+9V//lW9+85s885nP5PjjjyeKIh544AHuvfde4jjm/PPPx/M6FwecfPLJfOpTn+Liiy/m\nrW99Kx/4wAfYvXs3g4ODjIyMcPfdd3PgwAF+93d/lxe/+MUrzvf+97+fm2++mTvuuIM9e/Zwxhln\n4DgON998M319fZx99tn827/9W6viaq3OOussSqUSX/nKVzj77LN58pOfjOu6nH322ZxzzjlcffXV\nvOc97+GEE07gxBNPpFwu89hjj3HzzTcTBAHnnXcep556aiZrkfYo3BIRERERERERERHZiLbvgJef\nC/+Sbuln7roT+4eXwBkvgOe/ADZtgjBMe2z9x/Wtii0Au2kTvO78bq38ENu3b+fb3/4211xzDddd\ndx0//vGPue2229i0aRPHHnssF198MS972cta4y+//HJuuOEGTjrpJD70oQ8tmO9jH/sYt99+O1/5\nylf49Kc/zTve8Q4A8vk8X/jCF/jQhz7ELbfcwl133UWSJERRtKpwy/d9rrrqKl73utdxzTXXcPvt\nt3PvvfdSLpcZGhrivPPO45xzzuHJT37y2t+cFfT19fEv//IvXHvttXz+85/nRz/6EXfeeScDAwMM\nDQ1x4YUXcs4551AoFDq+lvPOO49TTjmFT33qU1x//fV873vfA9Jqt5NOOomXvOQlnHvuuW3N1dfX\nx9e+9jUuv/xyrrvuOr71rW+xefNmzj33XN73vvfxlre8BUj7fWXhmGOO4XOf+xx/9md/xl133cXN\nN9+MtZbjjjuOc845h//5P/8n3/jGN7jtttu45ZZbqFQqbNu2jTPOOIM3v/nNhwSssj7M/PLAw57I\nmB5r7VQmk0nXTExMXA+c2e11yOGZKY1V40KR9afzT6Q7dO6JdI/OP5Hu0fkn0h2He+499NBDQAe3\nLLMWrvsi5t+/3v5TNm2C3343DB3bmTWJZKzRaABQKBSYmJhgz549jI+Pc99997Fly5Yur+6J6TB/\nt93Q39//oiyOn2XPrUeNMX9vjHlBhnOKiIiIiIiIiIiIyFKMgVe/BvumC7Gblq9isY6DPeVUuOQP\nFWzJEe/OO+9c0FtrdHSUiy66iLGxMV7ykpco2HoCy3JbwjJwAXCBMeY/gSuBz1hrhzM8hoiIiIiI\niIiIiIjM9/wz4LnPw95zN9x4AzzwX1Cvg+dB/wCcdjq84IVd77El0q43vOENRFHEiSeeyODgII89\n9hj33nsvk5OTbN++nY9+9KPdXqJ0UZbh1kuAtwLnAruA/w38iTHm66RB11estXGGxxMRERERERER\nERGRGY4Dv/Ss9Etkg7vooov42te+xt69exkbG8N1XU444QTe8pa3cPHFF6tq6wkus3DLWvtN4JvG\nmH7gjcCFwKnAy4BfB0aMMdcAV1prf5zVcUVERERERERERERE5Ohy0UUXcdFFFwGH9twSgWx7bgFg\nrZ2w1n7SWns6cBLwcWAE2Aq8G7jbGHOzMebtxpjerI8vIiIiIiIiIiIiIiIiR6/Mw625rLX3Wmvf\nDWwHXgN8FYiB04FPAY8aYz5jjHlBJ9chIiIiIiIiIiIiIiIiR4eOhlszrLUR8GXgauCH03cboAT8\nd+AGY8wtxpgXrsd6REREREREREREREREZPWstd1eQufDLWPMScaYPwceAa4FnkNavfUl4E3AZ4A6\ncBrwbWPMOZ1ek4iIiIiIiIiIiMh6SpKk20sQEcnETLhljOnaGjoSbhljBowxFxljbgPuBH6HtOfW\n/cB7gR3W2ldba//RWnshsAO4BnCBP+7EmkRERERERERERETWm+/7ADSbzS6vREQkG41GAwDP87q2\nhsyObNKI7iXAhcArgDzp1oMN4IvA31lrb1jsudbacWPM24DzgKdntSYRERERERERERGRbiqVSkxM\nTDA2Noa1lkKhgDGmqxUPIiKrZa3FWkuj0WB8fBxIf791S5ax2j7gONJAC+Au4O+Af7TWjq/0ZGtt\naIwZAXZmuCYRERERERERERGRrimXyzQaDZrNJgcPHuz2ckQ2pJltPR2n452WpE35fJ5yudy142cZ\nbm0HKsDngL+11t52GHP8AdCb4ZpEREREREREREREusZxHLZs2UK1WqVWqxFFUatfjYi0JwgCAAqF\nQpdX8sRmjMHzPEqlEuVyuathY5bh1luBf7bW1g53Amvt5zNcj4iIiIiIiIiIiEjXOY5DX18ffX19\n3V6KyIZ03333AbBzpzZ+k1SWsdo3WEXVlTFmmzHmuAyPLyIiIiIiIiIiIiIiIke5LMOth4HbVzH+\nB6R9uo44xpg3GGNuNMZMGGOqxpjbjDEXGWMO6/0yxrzUGPPvxphRY0zNGHOPMeZ9xpj8EuNPNsb8\noTHmO8aYA8aYcPq53zHGXHi46xAREREREREREREREdnostyWEMB0eHzHGWP+Gngn0AC+BYTAWcAn\ngLOMMa+x1iarmO8PgI8AMXA9MAacCXwIeJkx5qy5WzkaYzxmQ8IqcCvwGLADeCHwIuD1xphzrbWN\nw3+lIiIiIiIiIiIiIiIiG083K4CKQNTF4y9gjDmPNNgaBn7JWvsya+2rgF3AT4BXAe9axXynAZcB\nNeAMa+2vWmtfCzwF+A/gucCfLvLUHwKvA7ZYa19srT3fWvtC4GTgUeAlwKWH+TJFRERERERERERE\nREQ2rK6EW8aYpwBbSSuSjiQzgdEl1tr7Zu601j4G/Nb0t+9dxbaA7yWtTvuItfYHc+arAhcCCfBO\nY8zAnMcia+1p1tprrbXNuZNZa+8G/mD6299YxesSERERERERERERERE5Khz2toTGmJcDL593d58x\n5tPLPQ0YAH55+vvvHO7xs2aM2QGcCgTAtfMft9beYIzZD2wnrbi6aYX5csDZ09/+0yLz/dwY833g\nDOAc4LNtLvWO6dsdbY4XERERERERERERERE5aqyl59YpwNvm3Vda5L6lPAD8rzUcP2snT9/ea62t\nLzHmVtJw62RWCLeAp5G+H6PW2vuXme+M6fnaDbd2Td8+2uZ4ERERERERERERERGRo8Zawq3/4NB+\nUe8DqsDHl3lOAkwC9wLfstaGazh+1p48ffvgMmP2zRvbznz7lhmzmvkwxhhmtyX8YjvPmX7eBcAF\n7Yy9/vrr9+zZs4darcb+/fvbPYQcYe67776VB4lIR+j8E+kOnXsi3aPzT6R7dP6JdIfOPZHu0fm3\nMW3fvp1SqZTpnIcdbllrv8OcbQWNMe8Dqtba92exsC4oT99OLTOmOn3b24X5AP4IeB5pr7IPt/kc\ngBOAM9sZWK1WVx4kIiIiIiIiIiIiIiLSJWup3JpvFxBlOJ/MYYx5E+k2jgFwvrV2ZBVPfwC4oZ2B\n5XJ5D9BfKpXYtWvXiuPlyDLzlwv62YmsP51/It2hc0+ke3T+iXSPzj+R7tC5J9I9Ov9kvszCrWX6\nSm0UMyVLPcuMmanGqqznfMaY1wJXAjHw+umqubZZa68Grm5n7MTExPW0WeUlIiIiIiIiIiIiIiKy\n3rKs3GoxxjjAU4BBwF9urLX2pk6s4TA8MH37pGXG7Jw3tp35jl/LfMaYVwOfnf72v1trr2vj2CIi\nIiIiIiIiIiIiIkelTMMtY8yTgD8FXgkU23iKzXoNa3DH9O0zjDFFa219kTGnzxu7nL1AHdhkjPmF\nJSrbnr3cfMaYVwKfAxzgAmvt59o4roiIiIiIiIiIiIiIyFHLyWoiY8xTgFuA84ESYIBR4JFlvh7N\n6vhrZa19CLgdyAGvnf+4MeZMYAcwDHy/jfkC4N+mv33jIvM9BXgeaQ+try7y+MuBz5OGf2+z1l7T\n7msRERERERERERERERE5WmUWbgF/AmwlDaxeDxSttVuttTuX+8rw+Fn48PTtR4wxT5250xizDfjk\n9LeXWWuTOY9dbIzZa4z5h0Xmu4y0Ou0SY8yz5zynTNpDywE+aa0dn/skY8w5wBdIg613WGuvWvtL\nExERERERERERERER2fiy3BLwLNIg53xr7Y0ZzrturLVfMMZcAfwWcLcx5ptASPra+oAvAZ+Y97Qt\nwNNIK7rmz3erMea9wEeAm4wx3wbGgTOBbcAPgPfNfc50kPb/kVaQPQy8wBjzgiXWe8HhvVIRERER\nEREREREREZGNKctwqx+ob9Rga4a19p3GmO8CF5GGUC5p/6wrgSvmVm21Od+fGWPuAn6ftGdXAfg5\n8JfA5dba5rynlID89L93AG9eZvoLVrMWERERERERERERERGRjS7LcGsfaRiz4VlrPwt8ts2xHwA+\nsMKYrwNfb3O+B0j7lYmIiIiIiIiIiIiIiMg8Wfbc+megYIx5cYZzioiIiIiIiIiIiIiIiLRkGW59\nGLgH+BtjzJMynFdEREREREREREREREQEyHZbwlcBfwN8CLjHGPPPwK1AZbknTW8BKCIiIiIiIiIi\nIiIiIrKiLMOtfwTs9L8NcOH010oUbh3JpqbgB9+HfQ9CrQ6eBwMDcNrp8OSngFF7MBERERERERER\nERERWT9Zhls3MRtuyUYXBPC5z8Ktt2DCYOHj3/4mdudO+JWz4LnPByfLHS5FREREREREREREREQW\nl1m4Za19QVZzyRHgwQcwN3132SHmoYfgH67G3n0XXPg2yOXWaXEiIiIiIiIiIiIiIvJElWXllhxN\n7GwRnt2xk/jU59DMlbFBSO7B+/DvuQMThQCYO27HRp+C37wIXLdbKwYgmApojDdI4gTHdSgMFMj1\nKHQTERERERERERERETlaKNySJdlNm2m+5jcYc7ZQG6sT1SKSJMHZuZ3cCWew7f7v03P3zQCYu+/C\nfu1f4eXndmWt9bE6E/smqI3WZtfpOHglj9KmEv3H91McLHZlbSIiIiIiIiIiIiIikh2FW7I4z6P6\n5os58GhEfXSMqB7hlTwc1yEKIhq1mNqxz+e4yGHwJzelz/nOt+C/vRRy+XVdamW4wsjeEeqj9YXr\nHG/QGGtQH6uzdfdWykPldV2biIiIiIiIiIiIiIhkK7NwyxgTHMbTrLV2fZMQaUuyaQsH7hylOlzF\nL/n0bu/FOKb1uB20NCebPLTtNMr7fow/NY6p1bC33QrPX7/2a/WxOiN7R1ZcZ3W4CoCbd1XBJSIi\nIiIiIiIiIiKygTkZzuUdxpef4fElQ5FboD5axy/5FAYKhwRGAMYxFAYK+D05Dh530uwD3/vuuq5z\nYt9Ee+ss+dRH60w8NLGu6xMRERERERERERERkWxluS3hrhUe7wdOB34H2Aa8Fbgnw+NLhuIoIapH\n9G7vXXZcvi/P6MBTGeLG9I7HhtdhdalgKkh7bLW5zsr+CrWDNYKpgFxPbp1WKSIiIiIiIiIiIiIi\nWcos3LLW3t/GsNuNMVcDXwc+DZya1fElWzaxeCVvQSXUfMYx0D+nj1Wj0eGVzTnUeIOoFrW9Tq/k\nEdXSPlwKt0RERERERERERERENqYstyVsi7W2CbwL2Ar80XofX9pkwXHb+3j4STj7Tb7QoQUtlMQJ\nSZK0vU7HdUiShCROOrwyERERERERERERERHplHUPtwCstfcAFeCl3Ti+tKfdEKj8yE9nv9m6tUOr\nWchxHRzHaXudSZzgOE7bYZiIiIiIiIiIiIiIiBx5unKV3xiTA4rAlm4cX1bmxemWfzaxy46zccLg\ng3fO3vH8Mzq8slmFgUJrq8EV15nY1haGhYH1qy4TEREREREREREREZFsdauE5fWk/b4e7dLxZQV+\ndYy8G9KcbC47rudH36NQHwPAFgrw7Oeux/IAyPXkKG0q4RW9FdfZnGziFT1Km0vqtyUiIiIiIiIi\nIiIisoF5WU1kjDluhSEFYAdwLvCbgAW+kNXxJVsmjnjKHV/gv37xv9FgiHxfHuOY2QFhSPnOG9l6\n302z973gl6GwPlVRwVRAY7yB8QyO77TCrfnrtImlOdkkrIWUh8r07+xfl/WJiIiIiIiIiIiIiEhn\nZBZuAQ+tYqwBbgM+mOHxJWO5iQM87dZ/pLrpeA5u3U3SN4BLQunAAww8fA9e1GiNtbt+Ec59VcfX\nVB+rM7FvgtpojagWkSQJcSMmiRJqIzXqY3XyfXkcN+3FFdUivKJHeajM1t1bKQ4WO75GERERERER\nERERERHpnCzDLbPC4xaYBO4GPg/8jbU2zPD4kiUz++Msj+6jPLpvyaH2abvhN98Jvt/RJVWGK4zs\nHaE+Wieqp/2zHNfBeCb9dM2sJ7bggZdL+2uVNpfo39mvYEtERERERERERERE5CiQZbi1bLJhrY0z\nPJZ02s7jsc86Ge66E2PtokPs5i1w5q/Ai88CL8uP0kL1sToje0eoDlfxSz6923sP2X6wOFikOdmk\nWWni5l02PWUThYEChYGCemyJiIiIiIiIiIiIiBxFMkskFF4dZQoF+K2LYHQU+70b4cEHoFZLq7MG\nBuC0Z8MzngmOsy7Lmdg3QX20jl/yKQws7OtlHNO6PwkTkjihb3vfuqxNRERERERERERERETWT2fL\nbWTj27QJXn5uV5cQTAVpj616RO/23mXH5vvyVPZXqB2sEUwFqtoSERERERERERERETnKdCTcMsaU\ngJcCpwBbp+8+ANwOfN1aW+vEceXo1BhvENXSHltztyJcjHEMXskjqkU0xhsKt0RERERERERERERE\njjKZh1vGmD8ALgWW2hNu0hjzp9bay7M+thydkjghSRIct70tEB3XIUnSrQlFREREREREREREROTo\nkmm4ZYy5EngzYIAAuAN4ePrhHcDJQD/wEWPM0621b8ny+HJ0clwHx3GIgqit8Umc4OW8tsMwERER\nERERERERERHZODK7+m+MeSVwwfS3HwWGrLXPs9a+dvrrecAQMFOx9WZjTHebOcmGUBgotLYatIld\ndqxNbGsLw8JAYZ1WKCIiIiIiIiIiIiIi6yXL0pb/AVjgf1lrL7HWjs8fYK0dt9b+AfAB0uqu/5Hh\n8eUolevJUdpUwit6NCeby45tTjbxih6lzSX12xIREREREREREREROQplGW6dCsTAX7Qx9mPTY0/L\n8PhyFOs/vp/ipiJhLaQx3lhQwWUTS2O8QVgLKW4q0r+zv0srFRERERERERERERGRTsqy51YfULHW\nTq000FpbNcZMTj9HZEXFwSJbdm8BoD5ap7K/gldK+2olcZJuRVj0KA+V2bp7K8XBYibHDaYCGuMN\nkjjBcR0KAwVVhImIiIiIiIiIiIiIdFGW4dYB4DhjzLHW2keXG2iMORYYAB7J8PjSResRAvUO9eLl\nPSYemqB2sEZUi0iSBC+X9tcqbS7Rv7M/k2CrPlZnYt8EtdHZ4ziOg1fyKG0qpZVkGQVoIiIiIiIi\nIiIiIiLSvizDrRuB/we4HHjjCmP/z/Ttf2R4fOmC9Q6BioNFioPFjoZpleEKI3tHqI/WiepRq0Is\nCiIa4w0aYw3qY3W27t5KeaicyTFFRERERERERERERKQ9WYZblwOvA14/XZn1v4EbrbVNAGNMP/Ar\nwCXAswE7/RzZoLoZAuV6ch3ZHrA+Vmdk7wjV4Sp+yad3ey/GMa3H7aClOdmkOlwFwM27quASERER\nEREREREREVlHmYVb1trbjTG/DfwlcOb0V2yMGQPyQO/0UAMkwLustXdkdXxZX0drCDSxb4L6aB2/\n5FMYKCx43DimdX99tM7EQxMb4nWJiIiIiIiIiIiIiBwtnCwns9Z+Engx8F3SEMsDtgJ9098b0q0I\nf8Vae0WWx5b1NT8EmhtswWwI5Jf8Vgh0pAumgnR7xXpEvi+/7Nh8X56oHlE7WCOYCjJfx+T+Scb3\njTO5fzLz+UVERERERERERERENrIstyUEwFr7H8CZxpitwMmk4RbAAeAOa+2BrI8p62tuCNS7vXfZ\nsfm+PJX9lVYI1ImtBLPSGG8Q1dLtFZM4IZqKsInFOAav4OH6bmuscQxeySOqpVswZvG61rt/mYiI\niIiIiIiIiIjIRpR5uDVjOsT6907NL90zNwSaX7E1XydCoE5J4oSwERJUA5qVJkmYYK3FGIPjO/hF\nn0J/Aa+QnjaO65AkCUmcrPnY3exfJiIiIiIiIiIiIiKykXQs3JKjVxInaVWR296ullmGQJ3UGE8D\npKgeYZw00DLGkCQJUSMiqqdfpS0lcuUcSZzg5by234elHK39y0REREREREREREREOiGznlvGmJON\nMf9ujPlIG2M/Nj32pKyOL+vHcR0cx2k7rEridHu9tYZAnVQfq1MdrhI3Y2xiyZVz+EUfr+DhF33y\nvXmMYwiqAbWRGmEtbFWvFQYKazr20di/TERERERERERERESkU7JMG94MnAXc1cbYvcCvTj9HNpjC\nQKG11WDUiGhWmjQmGjQrTeIgPmSsTWxmIVAnTeybIKgG5Mo5vIJH1IiIg5iomd7axOIVPBzfIayH\nVB+r4hU9SptLa9pqcW7/snxfftmx+b48UT1q9S8TEREREREREREREXkiynJbwl+Zvv1aG2OvBa4g\nDcNkg8n1TAdAzYixB8ZwXKfVm8o4Bsdz8Et+a0wWIVAnzQ2YCgMFKo9UCGshGMCA4zitbQpd3yWs\nhSRRQu9xvfTv7F/TsY/W/mUiIiIiIiIiIiIiIp2SZbi1Exi31o6tNNBaO2qMGQd2ZHh8WSeV4QrN\nibRKK6yFOLl0y8E4jEnCdKtC45o0FHINPVt6yPcvX5XUTTMBEwaaE01sYgGwcXobmxgs0KQV4BnH\n4Hpp0OXm3MMOmo7W/mUiIiIiIiIiIiIiIp2SZbiVB+IVRx167CN3n7onuKgZkUTJgtCmPlZnZO8I\njfEGhcECXtEjqAaEtRBr7WwwFFocz8HLeSRxwvh/jePnfcpD5W68nGUlcULYCAmmApIwwc25eEWP\nOEjDOhtbLJYk51quHAAAIABJREFUSkiiBOMYkjCh8lha4eWVPEqbSvQf309xsLiqY8/0L4uCqO21\nejnviO5fJiIiIiIiIiIiIiLSSVmGW/uBXzDG/KK19mfLDTTG/CJQBh7I8PiSoaAaMHzX8ILQZmLf\nBPXROn7JpzBQoDHRSLfwI61qcnIOxhhsYjGOobi5SK6cozpcBcDNu6sOgDrNcR3CWkjUiPAKHl4h\nPS1c300rq6KEJEyIiIht2n9rpnIqCtItAhtjDepjdbbu3rqqAG+mf1ljvIEdtMtuTTjTv6wwUDii\n+5eJiIiIiIiIiIiIiHRSluHW9cBTgT8C3rjC2A+QbvT2nQyPLxmyiaX6SPWQ0CbXm2v1purd3gtA\n1IgwjiFXzuF4h/bemulN5Zd8AOqjdSYemjjiwi03Nx1iBQlun3vIY47rgIWoHmFji+u5RM0I4xqK\nA0X8ko8dtDQnm4cV4OV6cpQ2lWiMNWhONpcNrZqTzSO+f5mIiIiIiIiIiIiISKdlubfZXwAJ8Hpj\nzFXGmG3zBxhjthljrgZeTxpufTzD40uGXN+ld3svjudQHa5yYO8BJh6aIKpFeCUP4xjiMCasTwdY\nRT/dzi/v4eZcHM/B8RySKCFqRuT78kT1iNrBGsFU0O2Xd4g4iNPtAXMOcbBwZ82okW7RaFwDBhzP\nSbcmnK7eMo6hMFDAL/mtAG81+o/vp7ipSFgL0wqu6a0dZ9jE0hhPK+SKm4r07+w//BcrIiIiIiIi\nIiIiIrLBZVa5Za39sTHm/wX+D/Am4A3GmNuBfdNDngScPOeY77XW3pXV8SVbSZyQxEmrkqg+Wsda\nS5IkrX5PUSMiCRMc34FFdtMzjmn14TKOwSt5RLV0G7+VKo+CqYDGeIMkTo9XGCh0rFopidPqsqge\ntbYf9PIeGFrbEtrEplFwklZzub67IITK9+Wp7K+0Arx211scLLJl9xYgfZ8r+yt4pbSvVhInaaBY\n9CgPldm6e+sRV/kmIiIiIiIiIiIiIrKestyWEGvtx4wxw8DlwLHAc6a/5hoG3mOt/WyWx5ZsxUHM\n5P5J/KJPvjdPUAkIvACbWKxNQ52ZfxuzeJ8om9hWlROkoVCSJK2Kp8XUx+pM7JtItz+sRWmY5jh4\nJW9B/6+sOK6DX/CJyzFJlBDWQ5qVJo6fVp7FQYyNLY7jYDzTem1RPSIuxri5dCvD1QZ4c/UO9eLl\nPSYemqB2cPa1ezmPwkCB0uYS/Tuzf+1ZWs9AUkRERERERERERESeuDINtwCstf/XGPNF4NeA5wLH\nTD/0GHAz8O/W2jDr40q2rLUElYCoHhHVo1Y/LRyIKhF2MK3GMsaQJAvDKmstSZTgFby0Coq0CsrL\nea3Kr/kqwxVG9o5QH60T1aNW9VIUpGHR3P5f5aFyZmFKYaCAV/JgHHq29dCsNNPtFsO0agtDuiWh\nA1iIwxhrLY2JBmE9xC/5FPoLeAWvrQBvKcXBIsXB4oYLiboRSMr62WifRxERERERERERETn6ZR5u\nAVhrA+Cr018bjjHmDcBvAb8EuMBe4CrgCmvtqlMLY8xLgd8DTgMKwM+B/wtcbq1tLjJ+C/AK4PTp\n5/wSkAP+2lp78eG8ptVyHId8b56oGRFUAxzPwc27FAeL2MjSnGzi9/g4vkPUiNJXNaeAK27GOJ6D\nX0p7cdnEEtUiCgOF1laHc9XH6ozsHaE6XMUv+fRu721VfAHYwfSY1eEqUSMi358nakSZhCm5nhyl\nTSUaYw3iIKZ8TJk4jIka6WufOjCV9uJKII7iVthlE0tQDdJ11CNKW0orBnjtrmejhAerDSRl41Bo\nKSIiIiIiIiIiIkeqjoRbG5kx5q+BdwIN4FtACJwFfAI4yxjzmtUEXMaYPwA+AsTA9cAYcCbwIeBl\nxpizrLW1eU97AfD3a3wpa2fAK3hERESNiLAWsu0Z2zCOoTpcBcDLe2l1VzPCK3hYa4mbMUmYkCvn\nKPSnQVZzsolX9ChtLi0a3Ezsm6A+Wk+roBYJv4xjKAwUCOsh4w+M4+ZcvLyXWZjSf3w/9bF663Xl\n+/Lke/NgYeqxKZIwSau3TPqac+Vcq5otbsYE1SCtbLNQeNLiAd56iBsx8VTMeH6841U2qwkkgVY4\nKkc+hZYiIiIiIiIiIiJyJFO4NYcx5jzSYGsY+GVr7X3T9x8DfAd4FfAu4ONtzncacBlQA15srf3B\n9P1l0qq2Xwb+FHj3vKc+BlwB/HD66zXA+9by2tbCzbkEkwFJnFDaXGoFN/XRelq1BYS1kKgZYYzB\n8Rxy5RylLSXcnEtjvEFYCykPlenf2b9g/mAqSKtD6hG923uXXEfUmA3ZcKBvRx9eYfYjvJYwpThY\nZMvuLa3XVdlfwSt5NCebrW0IDQYv7+GXfBwvrcwyxrQCwGalSa6UWzLA66SZKpup/5wiCRKGDwx3\nvMqm3UAS0vd04qEJhVsbgEJLEREREREREREROdId/t5pR6dLp28vmQm2AKy1j5FuUwjwXmNMu+/b\ne0k36/vITLA1PV8VuBBIgHcaYwbmPsla+31r7TuttX9vrb0TiA7v5WQjDmKcnIPjOsRBTO9QL0PP\nGmLTUzelocnmIn7JxxgDNg3D/JJP1Iyo7K+QRAnloTJbd29d9CJ4Y7xBVEurQ+ZeRF8wbqJB1Ihm\n1xLGhzw+E6b4Jb8VpqzG3NdVPq6MMYaoGeG4TivQcn0X13cPfaKd7jEWpNVdpc2lVR13rSrDFYZ/\nNMzo/aOEYyE2TCvIoiCi+kiV0ftHGf7RcCuMyMLcQDLfl192bL4vT1SPqB2sEUwFma1BOmN+aDn/\nnFzreSYiIiIiIiIiIiKyVqrcmmaM2QGcCgTAtfMft9beYIzZD2wHngvctMJ8OeDs6W//aZH5fm6M\n+T5wBnAO8Nk1vYAOmLvFoFdIK5aSON2RsThYpDhYJJgK0m3KxhtMjUwRNSNsaFv9eQoDBUqbS/Tv\nXLpyKImTdPwyfariMCashyRR2tPKWotN7KJj8315KvsrrTBlNVVUc1/XwfsOEjUjknJCvpxPK9Dq\nIc1KE8d3MMakoVaYpP3Feny8vMfUgSmSOOn4toCwsMrG3+QfUjHVqSqbdgNJSMMQr+QR1dIt7TZK\nP7EnonarKGFt55mIiIiIiIiIiIjIWijcmnXy9O291tr6EmNuJQ23TmaFcAt4GlACRq219y8z3xnT\n8x1R4VaSJASVoLXFoOM7+AV/QQCV68mR68nRt70PoBV2rSbccVwHx0n7+SwlqASEUyGQBl2u7y4Z\nqmQRpuR6chQ3pRVpWNIKFtfAaLo9YhIn4KRr9woejucQ1dOtCQ/+9GDao6jD2wLCwiqb2uih7ds6\ntTVgO4HkXI7rkCRJKxyVI5NCy5Udzu84ERERERERERERyZbCrVlPnr59cJkx++aNbWe+fcuMWc18\n68oYQ66cwy/55Hvz1A/W8Ureor2V5poJu1ajMFDAK3k0xhvYQXvIRfWoEdGYaLSqpmySbrmX5BOa\nk01c3z2k79aMlcKUdi5Qz4RuzWqTqBmllWNhgrXpGowx6fGLHlE97QVmE5tWcFmPKEgv+jfGGtTH\n6mzdvZXyUHlV781y6+xmlU07geRcSZxW3LUbhkl3bJTQshsB00xfu9pojagWtapT5wbYIiIiIiIi\nIiIisj4Ubs2aSR2mlhkz07Ro+SShM/MdNmPMBcAF7Yy9/vrr9+zZswfrWOKeGOtZaiM1bGyxkeXB\nR5bL/g5fLazRiBo0Hm7gldOPZVyPiSYjkiBJQ6XIpl3KDNjAUhmpMFWZwuv1cIuH9sGKJiOMb4gf\niTnQPDB7fzUiOBAQVdJ5bZKGaU7Owev1yG3NzR6/ETM5PklwIEi70yWk1VuGtM9W00INOEg6Dwan\nxyHMhcRJnAYFJCSjCeOPj3Pw8YP07Oppzb+cdtaZNBNqw+nPJho/NGQaHR1dOGcc0RxuUr+7Tm7z\n2oKAuBEz1ZgiHAup2/qyVT42sYSjIf6gTzKa8FjtsTUdWzonOBhQq9awoaWW1FYcv9R51imrOX+z\nFI6F1B+uE1XSbVeNa9KgPYEkSnDyDv79PsUdRfxBn/vuu2/lSUVk1eJGTDwVt859t8fFLcz+91/n\nnkj36PwT6R6dfyLdoXNPpHt0/m1M27dvp1QqZTqnwq0nhhOAM9sZWK2meZtxDcYxRNWIpJngD/rk\ntrQfiKx0AWq+3NYcUTUiHAuJSC+YR5MRcSPGuAan4JDU0ovZTs7B5A0kaQA2s14nl1ab2MSSBAl+\nj4/bM3vM+ReonZwDDtjQEk6FRNWIqBq1LlDbyBLX0p5jxjE4eQfjmdkgx0I0lc6FAZM36WtMIBwP\nSZoJNp6uNIsSGsMNbGQpn1he9gJ8u+v0erz0AvsSRTZJlGADi7U27Q9GejF+qV5lq+EWXLxej6ga\nEdfiZV9PXIsxvsHr85b9DLRrtZ8taZ/b4+LkHMKpsPX+LmWp86xTVnv+ZiWqRtQfrhOOheACDmmw\nNn1uWyzxVJz+rggSyk9b/vyer5OfZ50rcrToVrAtIiIiIiIiIkcuXQmYNVNF1bPMmJlqrEoX5luL\nB4Ab2hlYLpf3AP2OdfDrPsVSkeKOYttb6i25dVd95d5TlWMrjOwdoT5aZ+rAFCYw+J6P4zkkUULi\nJ1jX4uU9cr1p0BY1ImxiKZgC5U3p+hrjDZJNCZueuomhk4Za6xreP0wcxPQM9pDvyx9y4d4mluZk\nk7AWUqwVGThmgMf3P44TOLiOmwZCTTCJaW2FaIyh0WwQhWnVVC6Xo9xfJqgEaVVXBI7nYByD9Sxh\nNcROWvwRn6FjhxZ9P+eus9hbxPHT5Mo4Bi+f9vaaWafv+2lPMKC0KU29R0dHSYKEgikcso2iMQYb\nWXKlHMdsOoatu7a283FY/me9pc7wj4apDlfxHX/p99QLKe8oM/SsoTX1+2rns+XmXPVDWqPhZJjR\n+0dxPGfZbUgXO886ZbXn79CutX3W5hr+0TAWS66cI27GhM0wrdaaObcTmwbJ1hI8HlAv1Dnllae0\n9ZoO93dlN+cWWW+V4Qoj+0ewVYtppL3+HNchiZO0R6DxqFVrFHcUefqzn97t5Yo84cz81eyuXbu6\nvBKRJx6dfyLdoXNPpHt0/sl8HQm3jDHHAs8ABoFl/4TeWvvZTqzhMDwwffukZcbsnDe2nfmOz2i+\nw2atvRq4up2xExMT1wNnGsdQPq5MaXOJ/p3tXQitDM+GU1E9al2Aarf3VO9QL17e4+B/HqQx2SBq\nRGmI5Bi8gofru0SNqPXl5T3cvEtQCQhrYev+sBZSHirTv3O2B87Evgnqo3X8kr/oBXvjmNb9lUcq\nVB6t0JhoEDdivIJHHKTbDEb1iLgZEzUjjGOImzHGma7mcqAx0SCqRTi+Q643hzFzLsBjIYbK/gqu\n7+Lm3QXv68S+CarDVaJmRFALiIM5VRc5l3xvnkJ/us6oHmFtul3kTK+yma0cLekFd8d3MMYQxzFR\nNQKbHqM4UFx1/6/5ioNFtuzeAkB9tE5lf2XhRceiR3mozNbdW9d0MX2lz9bUY1Mc2HuAXE/6ni/W\nD0kX89vTf3w/9bE61eE0n18uSJp/nnXKas7f+midiYcmMvl5z/S1a042wUA4FS5+bltL3IgJJ0Ka\nw00mH5mk77i+Jedd6+/K5XRybpH1Vh+rM7J3JP0jipJP7/beQ38fDU7/PjoQpuN31TfM7/pu9A4U\nEREREREROZpkGm4ZY04D/gJ43iqedqSEW3dM3z7DGFO01tYXGXP6vLHL2QvUgU3GmF+w1t6/yJhn\nr2K+dZUr59jx7B1tX2hp9wLUzAXzxYIdSAOTvuP60gBoOsyZqVpycy5BNaA2UiOshzQrTRzfSSsm\nqgGTD09SGCgsCFNmLlBH9Yje7Wl7sziMW1Vfc8MzN+dSH63PhiNlj1wpRxIlRM0oDbmitAfYzNaN\nbs7FeIYkTAijEDeXVnbN5zgOuOlrX+wCfDAVML5vnKnHp9KL5WF8yAX0oBoQVNKvnm096Ro8g+On\n1VxewSMcD4nrMU7Bwc27uL6bbi/ZiPB7fLyiR320zoG9B5b8GazGTCA58dAEtYOzVSJezqMwUFhV\nOLqUlT5bTb9J5dFKK3gobSnhF31dzD9M6xlatmOx83cp+b48lf0VagdrBFPBmi8UN8bTsNpaS9yM\ncXxn0XPbGINX9DBThrgRc/C+g0uGW1n9rlzvuUW6od1g25l0iCpRZsF2Jy1ZWak/xhARERERERFZ\nlczCLWPMycD1QBEwwDCwH2hkdYxOstY+ZIy5HTgFeC3wD3MfN8acCewgfV3fb2O+wBjzb8CrgTcC\nH5w331NIQ8AA+GoWryFLXt6DfPvjs6ysSOI0OMqXZ6uUZuTKORzPoTHRaG27F5NWN7kFl+LmIpt3\nbT4kxJi5QO2V0gqs2sEaQTUgiRIAHNfBLbj4Rb+1jR9M9/Fy0m0BHc8h5+WwBZte4G9GrV5cbi7t\nYRM2QoxjWlsmzmetxXEccuUcwWSw4AL8xEMTTD48SdSM0h5eZroizACWtMdYGFMfq2MTS643h+u7\nOL5DY7xB8HCQVmcZ0hAuTAhN2Jqr0JcGf1EjyrS6pThYpDhY7NhfoS/32Zp5LUk4XaU2HTgWBgrE\nQYzjOQTVgPEHxomDmO357bpo2Ib1CC3bNff8Xa4HGExv31nyiGppsLnWz18SJ0RBRBymofZS53aL\nBwTpmpcK1zpZhdatCjeRTlhNsO2WXMLRMLNgu1NUWSkiIiIiIiKSnSwrtz4AlIB7gbdaa2/JcO71\n8mHgWuAjxpibrLX/CWCM2QZ8cnrMZdbaZOYJxpiLgYuBW6y1b5o332XAq4BLjDFfn3lPjDFl4ErA\nAT5prR3v5Itaq5VCi6wrKxzXwXHSiz2L8Qoe5UKZZqVJ/WCdsB62HgurISM/HaF2oNb66+ckTkiS\nhGAqoDpcJayH2NjOXig3YKYMgR+0+ue4hTSwmgm6ZhjX4Lpuq1rKJpY4iMGkY13PPaTaqsVCEiZ4\nBQ+/5KeVYPMuwI/91xjBVLoGN++mQc28rc9sbEnChGYl3SatZ1sPud4ctQPp+0kMOOlFeSyt1zlz\nXK+QVsBlWd0yI9eTy/yC4kqfrZmQ0/EdvLxHs9KkWWmSxAlxM271QkqChNGfjxKHMcc//3hd0G9D\np0PLds2cv47rtDXecR2SJEnPgTVyXIckTCs1Hc9Z/Nyew2Cwbvo7YbFwrZNVaN2scBPphNUG207O\nySzY7gRVVoqIiIiIiIhkK8tw6wWk9SVvsNbeneG868Za+wVjzBXAbwF3G2O+CYTAWUAf8CXgE/Oe\ntgV4GmlF1/z5bjXGvBf4CHCTMebbwDhwJrAN+AHwvsXWYoy5ec63O6ZvXzO99eOMd1prb1/dq2xP\n1IgYe2CM5mSTYCpYduucrCsrCgMFvJJHY7zR6iU1X1ANqB+sE9QCkmaC1+PhFxbfis5xHYJKQO3x\nWlqtZdJKrJmKKJukAVbUjNKqMcfgOelfU8dBDAXSsXNfhzG4vptuHYghCtIeXI63+AX4qBnheA5+\n0U+rreZdgA+mgnQ7wtimFWOLzGOMwXgGbFrB1aw2yfem5XU2sTieQ0K6fsd1MBhMzrTWPtOXzCt4\nmVa3dNJyn604iAlr4WxFjQEMBJWAqJ4Go47npD3RXENYDZl4aIL9t+1n6KQh/VV8mzoRWq7GSmH3\nfEmcVpi1G4YtpzBQwM27aeDtu8sPngmTvfT8XSxc62QVWjcr3EQ6YbXBNg6ZBdudoMpKERERERER\nkWxlGW4VgOpGDbZmWGvfaYz5LnARaQjlkvbPuhK4Ym7VVpvz/Zkx5i7g90l7dhWAnwN/CVxurW0u\n8dTnLHLfMdNfMxZv6pKBxmSDfd/dRxylfZ8KA4Ul+xhlXVmR68lR2lSiMdagOdlcdCu62ki6taC1\nFr/Hp7i5SGlLCVj418/lY8s0xhvEQYzx0v5dh4RVNr2IZkOLTdIvbLoFYnOySdSMFu+z46QBVxKl\nVVKLsrS2MMyVc61tFudfgK88WiFqRLOVIZYFgVr6RFqhXNyIaUw2KFDA7/GxWJJauqVjLpdrhXiO\n6xA1IsJ6SGOiQblQXnV1S6erd5aaf7nPVtSMSKLZipokSlrbMTp+uv3jIZVvWIhJ+7n5rv4qfoNo\nJ+yeYRNLVIsoDBQWvXi8Wrme9Jx1vDRc84pL/yczakZptaVrlgzXOlmF1s0KN5FOWG2wTZL2tswi\n2M6aKitFREREREREspdluHU/sMsY41pr4wznXXfW2s8Cn21z7AdIt2RcbszXga+vcg3L/+l9hyVR\nQmMybZfm5lzCeki+N09hoLAwPBoqZ15Z0X98P/WxeusY+b5866J2Y6JBUEuDLWNM+lfQc3pzzf/r\n58Z4gyhMe1E5rrMwNJoOgWxsYc5LyPflScKEoBoQES0IxWySbhPo5ly8okdUjwirIZa0t5a1trWd\nWa6co7SlhFfwFr0AP7PF4UxFWRInh1RvzWyXOBO8zVSbNStNbGwpbSlhauk2ho7vLAjjZrbsC+th\n2j9omZ/B3KAprIVtVe8drvpYnYl9E+lFv0Xmd/Pukp8tm9j0MzD9uYgaadiFk1a5xUEazDquM9s/\nzU23etJfxW8cK4XdczUnm3hFj9LmUmYXhDfv2sz4g+OtLVD9gr8gHG/14PMNhrQqarF1drIKrZsV\nbiKdsNpgOwmSJc+9blNlpYiIiIiIiEj2sgy3rgYuB14BXJfhvNIFNk7DE+MY4mZMvZk2P+/b0Ueh\nv3BIeOT3+JlXVhQHi2zZvaV1jMr+Cl7Jw1qbBiFTEX6Pj1/yW6HRfPm+PBP7JggbITZKt/tLounq\nBu/Qv+621rYqonBme23NVIOF9ZBmpYnjT1cJJQlRNcLr8Rh40gCDJwwyct8IEw9NpH2v3PQvyL2C\nh19Mw7eZNS56AX668st4phXMJKRVGDaxxGE8G2xZ26rsslHa36c52Uzvjw/tE9YKxabvi5vpVn6L\n/QzmB03NqSbBZNBW9d7MFn+rqfCqDFcY2TtCfTT9bHml9EL73PlzvTkSm/Ynm//ZMk76XiVx0uqx\nZSMLTho20JzeytFJAz9sGtTmyjmCyaD1V/FAx6rSut2v6mixXNgN6ee8OdkkrIWUh8r07+zP7Nh9\n2/sYfPIgUT06pDLQGLMgwE6itHJyqXCtk1Vo3axwE1mtdn43ribYjmsxxl/63Os2VVaKbDz633Ai\nIiIiIke+LMOtvwDOBj5ljHnYWntrhnPLOpsbkBgnDYWCasDkw5M4bnohd2brnKCabpvjFb3DqqxY\n6v889g714uU9Jh6aoHZwOnCpNjHW4PV4FDcXDwmN5jNOevE5qAZpL63pi9GEEEcxiZNeiDbGYBPb\nCkHmVknl+/M4nkNjokFYD0nCpBUieT0e/Tv72X76doqDRUpbS+y/bX+67V0+DVH8kt/q1bPcBfj8\nQB7Hd4iaEbly+r4kUULUiLDWtiq1IA1srGNx3LSHF4a0x5QBa9LXl0RJq5pkpsLJxmlIVnmkQqG/\ncMjPYH7QZDzTWiuk1U5xM16yei9shjTHmwsqsBzfwc25lLaWKA4UWz/b+lidkb0jVIer+CWf3u29\nh4YV0/PXD9bTgMo1Cz5bXt5Lt4trpMeLmul7ZTCz4d/0z3LmfXB8B7/kp5WJ4w3237ofa23mVWkr\nVaStteLtiWapsNtx095WUS3dMrA8VOb/Z+/NliQ5siyxo6q2+RZLLkACKABV1V1NiJDTPTJsynBI\n4QP5AfwACn9h3vkBFL7zHyjzSn4AHygUUkYoM9IyNd1k1XRtQKIyEZmx+WqLmi58uHbVzT3cPTwi\nPZCRCbstJWgEIszNzdTUVM+555zn3zx/52u7Pic9+csnqHPKbGurBZnAjlI6l3JSIhpFW8m1h1Sh\nvW+FW1dd7VN3nRv3JbZd5RCfxgcltg9ZnbLy3asjGrpr8GNVt4brqquuuuqqq6666qqrD6cOSW79\nDwD+LwD/KYB/LYT4PwD8GwCzXX/kvf+fDngOXR2ohBBQiQr/ziRCndeYv5lj4AcAiATLL3L0nvTQ\ne9K7k7Ji381j77QXNvTT11Nc//EaKlZBVbWt9FwTsVU3nc+SACZvPXVDOzp/GREBI2MCym1p4a2H\nXmikoxRRFmGYDWFrUj3puYaNLEZfjPDF334RNri90x4+/SefQsVke6enmjKh9gDgRy9GSEcp6nkN\n5yify1R0vX3tgwIJojlnT6C6SlUg5mxNCiuvPQpdBKWXkPS3zjnA0L2Isojs1YAVokkmEslRguKq\ngNU2KM9MZaDnOoyFKFtaP01fT7F4u6Ax0iiwvPWYj+eoFw05FitkpxmGL4boP+lD55pUf/14Iwjf\ntpas8xpCikC08dhSiULcj6HnmuwgHanvojSCUG3/SMBqUr652hHJ16jd6kUNIcRG1di6Km3f2keR\ndt9j36c+FjBsE9ntHAHA2QmRtcdfPpxNZtwn4JxtOlWsICKyvXSaLAnj0xi9n/V2nsNDqtDep8Kt\nq831sTx/h6j7zI37Etv7PHvvszpl5f2rIxq6a/Bj1mNbw3XVVVddddVVV1111VVXu+uQ5Nb/iKCX\nAAD8NwD+6x2/zyZwHbn1GGtHLlV5XcKUBipRMJUJRFjvlJQ5dV7fqqwwlbnT5jEZJAEQzN/mt3Y/\nm9Igv8jp/KSABxFEKlZAQl3R3vhADEU9Ul/UeQ0niFySUmLy/WSp7mrUT8kgweiz0UaFSBuAn5/N\nUU5KmMpAKon+834AlNf/LhkkOP7qmK5tQdc0yiIiZSwpjgAAnlRZMpLIjrOgTEuGCf2uJ6USOPVO\nAMKJ8LcyluE6Tl9P0X/ex+QlnaupDKDJulDPiZhTqYL3nvLGQPaM5aTEMKMNvUoUymvKZht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GJqsFu3ZF5/n7RB0GSQIOpFB18ffgj1mFQm93l33eYg0FVXP2Y99nfsx1gf0vu9q6666qqrw9d9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Od4vY2DuPckmSQfG+sXu/EzP7y19dihQdx3P8fIUBEBVIu19wu1v2oOEhMmgiPRgxhqQZ69rY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dmZeYHI0z99ivN/dn7vo7W9FaXzXUALgJCj4m6CS0BZE56NMgr5UU5A5XV1sDfIXdZEAJF5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\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SI0duvRS4ELg3Ij4TESc3sGxJkiRJkiRJkiSpocGt84FeYC/g1cB/RMTdEXFuRBzXwHok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\nYXBLkiRJkiRJkiRJLcPgliR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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [], + "image/png": { + "width": 859, + "height": 282 + } + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "a0M9eoo1JZhy" + }, + "source": [ + "What do we observe? *Without accounting for population sizes* we run the risk of making an enormous inference error: if we ignored population size, we would say that the county with the shortest and tallest individuals have been correctly circled. But this inference is wrong for the following reason. These two counties do *not* necessarily have the most extreme heights. The error results from the calculated average of smaller populations not being a good reflection of the true expected value of the population (which in truth should be $\\mu =150$). The sample size/population size/$N$, whatever you wish to call it, is simply too small to invoke the Law of Large Numbers effectively. \n", + "\n", + "We provide more damning evidence against this inference. Recall the population numbers were uniformly distributed over 100 to 1500. Our intuition should tell us that the counties with the most extreme population heights should also be uniformly spread over 100 to 1500, and certainly independent of the county's population. Not so. Below are the population sizes of the counties with the most extreme heights." + ] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "kcop0j8IJZhz", + "outputId": "fed09f95-d615-47f1-f6dc-3ab28246e321", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 107 + } + }, + "source": [ + "print(\"Population sizes of 10 'shortest' counties: \")\n", + "print(population_[ np.argsort( average_across_county_ )[:10] ], '\\n')\n", + "print(\"Population sizes of 10 'tallest' counties: \")\n", + "print(population_[ np.argsort( -average_across_county_ )[:10] ])" + ], + "execution_count": 6, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Population sizes of 10 'shortest' counties: \n", + "[175 134 156 430 219 106 116 221 103 175] \n", + "\n", + "Population sizes of 10 'tallest' counties: \n", + "[106 144 138 162 113 188 160 160 112 115]\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "ObSCVpkXJZh2" + }, + "source": [ + "Not at all uniform over 100 to 1500. This is an absolute failure of the Law of Large Numbers. \n", + "\n", + "### Example: Kaggle's *U.S. Census Return Rate Challenge*\n", + "\n", + "Below is data from the 2010 US census, which partitions populations beyond counties to the level of block groups (which are aggregates of city blocks or equivalents). The dataset is from a Kaggle machine learning competition some colleagues and I participated in. The objective was to predict the census letter mail-back rate of a group block, measured between 0 and 100, using census variables (median income, number of females in the block-group, number of trailer parks, average number of children etc.). Below we plot the census mail-back rate versus block group population:" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "Sae3sK9psnYi", + "outputId": "7ab00483-b3a0-4af8-edb8-73b55734b564", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 35 + } + }, + "source": [ + "reset_sess()\n", + "\n", + "import wget\n", + "url = 'https://raw.githubusercontent.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/master/Chapter4_TheGreatestTheoremNeverTold/data/census_data.csv'\n", + "filename = wget.download(url)\n", + "filename" + ], + "execution_count": 7, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "'census_data.csv'" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 7 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "F0WIEfZ3JZh2", + "outputId": "ccc04b12-418e-441f-e92c-7df82f6a6772", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 435 + } + }, + "source": [ + "plt.figure(figsize(12.5, 6.5))\n", + "data_ = np.genfromtxt( \"census_data.csv\", skip_header=1, \n", + " delimiter= \",\")\n", + "plt.scatter( data_[:,1], data_[:,0], alpha = 0.5, c=TFColor[6])\n", + "plt.title(\"Census mail-back rate vs Population\")\n", + "plt.ylabel(\"Mail-back rate\")\n", + "plt.xlabel(\"population of block-group\")\n", + "plt.xlim(-100, 15e3 )\n", + "plt.ylim( -5, 105)\n", + "\n", + "i_min = tf.argmin( data_[:,0] )\n", + "i_max = tf.argmax( data_[:,0] )\n", + "\n", + "[ i_min_, i_max_ ] = evaluate([ i_min, i_max ])\n", + " \n", + "plt.scatter( [ data_[i_min_,1], data_[i_max_, 1] ], \n", + " [ data_[i_min_,0], data_[i_max_,0] ],\n", + " s = 60, marker = \"o\", facecolors = \"none\",\n", + " edgecolors = TFColor[0], linewidths = 1.5, \n", + " label=\"most extreme points\")\n", + "\n", + "plt.legend(scatterpoints = 1);" + ], + "execution_count": 8, + "outputs": [ + { + "output_type": 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mQSf37b1xv7gSrvstXPwzuOwXcONk3Emn4Hr2AsCcg/vvhY8WdVLDm6qqqqKq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120dH03l2cnrvYTNtNm6xBa9zRkJtHOW+hCIxtkW/ud4rlrJg0d01aiGBcYvxiToFd3olNYDvOd\ncRRxmMulO7ixKWoxuIB8TOf2Q/S97CIyrO6bLS16B72tx+mu2Takt7Vdc0gX4+KCW1lIwZ1PuH1/\n0S6vCQBpEc/wiyGGXw5x+OvDnYSt++Y+4+54aMswzKfMQ/7MwDAMwzAMsw0Wnh4x+TBPv6THLP35\n8Rz1pF5GR1mfBo7VaQXd11C5SnF4sTtAapnihq4qdt9cHZoPc4y+HKGdtxBC0JAKyxXTtqUBn3f+\n4/S6MAzDdET3UnK8rBB8SJ1TekCuptGXI+geCUFSSeTDHHsv9nZ+vPJtSb1NOUWLZr0MwZLQ5EED\nfqEFPOgaKzNyHPnWr8X4bTooTGVw+vvTNExVuYLKFRo0KfrO1pbE/q6TRwj6H4qu20GH9F4Q3VYQ\nJCDpPnVGRfdUdEFJJZexrBsEkMNoU9TbRuwnjGJFsbceJSiVTPcjlEBv//IBSjNtUv9TcVikYYvK\nFeBpuyFJEIkLHyCALM9Sd5S3FDtoSoPytEQ9rVP04V33vewqMsR9031ys3wMYWHbkD7G621zSMcY\nxGKPYnQ/hFD3qfC+/UXXvSY2e+WOfnv0yRzn+4y746EtwzCfMg/5MwPDMAzDMMw2WHj6DIhZ+ud/\nPachW9ejEoWjWAS/2guSj3Nk/QxSdVEunZAk5NXF7ttWh24OUFSu0opplVGEVD2plyvuGYZhPjTd\nvDF1GVkkgURIkcSWrJ+h/6SfRHOAHEHNvMH01RQAdnIYxFX+QpEAbxYUWZf1MwCAtySCCClSVJwH\nif6b3VGriwrifU9+mqwNU1dFkhAC0CCJa8EHyIJi8pwh4UVlCjZQ91Hs3BOKouikpuu5EILeAzoX\nGLJuONt1UUWhDqAeKaHp9pe5kyKxnxAgV9HWfsJCJ1eure1aFGLcp2baoJk1qZuq2FueM2ccIJFu\n461HQNcdBTrmzjjar4AksDnjSLSrSHypTqs773u5qciw/6v9O3vsq9gc0nvnU7ze6usBoNeEtz7F\nIEot79Sp8ilxm/6iTXZ6TVzRK/eQuau4u5tGGAI8tGUY5tPnoX5mYBiGYRiG2QYLT58Bkx8nmB/P\naVW9oT6GfJDTELGLEvLep76R4AIGTwcoxgWccWjmFMmHHq4sdr9sdeiFAcovszRgDT6kFedSS4hM\nAJ6GlgzDMB8MQQK89Xbp2hEAJKBzjeGzIfpP+muiia0t6klNAlIQOPvLGco35YXorG0D0bjKv9gr\noAuNUlBHjrMuCTvOOIrCEwLQNCTd+2bvWjfD5jBVakni/rQl945YcTF1oop3Pj3Wap9UQEiiE4D1\nKD3RLT6w9F5hW5u+LyDgvINQAq1vAU9iVjEqdhowj4/G0IXG5KfJhSjB2KGlexqzV7MrB/HZIEsO\n23bRrkW7IlDX1Zrbt4sWjN+HQOoskorEOT3QsI2FKQ3mb+Y4/dMpgKXgeJsB+CoPUWSI+wSQe618\nV0IXOn2G2IzXc41LXZDxNXOXTpVPidv0F2HLKd3lNfGQe5wu433j7m4bYRjhoS3DMJ8yD/EzA8Mw\nDMMwzGWw8PTIiavs6wmVz2/G40gtacDoaNWta6iPo5k1KMYFVKaQ9TPYysLUZm1F8yZXrQ6NA5TT\nP52intawtYXuUcyKkFRKLhW5C0xjaHU6Vz4xDPOh6OLiACCIZb8RAuA9xdGtik4xHrQtW9iFhR52\ngsZKdNbslxnyQQ7n3IWBKAJgagOVKeSjHFLLFFsWh/kqU/DeQ2WKokn3Cxz++nCnoUEcpk5+msDW\nluIAMwnpJHxL7p5IFP1NaZANMggpkvMpulq9oW33xsPCpgUHq+5XEQR861NXVnABoSUxL+5j73D3\nmKr+Yf9S4S6+pwyfD7cO4nWPBCVTGbRzcnnFuMKsn6X9UpmC7FOvVYweXI3xk5rEpuADHbMuCjY6\nolzrcPqHU5i5ASTts1C0YOKmA/BVHorIsDnUN7WBreg5HnvIpJLJJe29RzDkHCvGxVoM4mVOlcfM\nbfuL3DMH1dvufNp8TcReMpUrmNJA5eqTEvbeJ+7ufSMMAR7aMgzz6fNQPjMwDMMwDMNcBwtPj5z6\nvEYzaVKnUz5YH07EFd3eeopJ0rSavZ21cMZBZQq9/R5sZVGdVRBKIOuvF7vvujq0f9jH3td7mL2c\nQeUKxZhWmbrGoXxXpsEfPC6spmYYhrlTNpM9Jeja4ymGrTqrkPUz5KOcYuu6eNAQArJhhv7TPgbP\nBnRXhwHz13PM/zJPMX3FXrE2EA0gUSQf5DR47WmMeiMS+icNmnkDFxyUUoAAbGMRJgFnfzlDMS6u\nHR7EYer89Rz1GQ2ovfLwrU+dRQCSuCQlXfejQIWGvq0KRYJC4+HgKIpOiOQQQ0DqSFKFSu6nECga\nUGD5s9k4u1VM1WaU4OZ+bg7i6/Ma8+M52qpFO2uX2+N8iskTit7bIGnBRa5z6jO0dIygqM8qOb0c\n7Xe8jxTDJ8iZNj2ewsxNcgEPng7ouXKDAfgu+3YbB9Vt2TbUV5lCNshga3J8eUNCYxSfYryiytWF\n9+1t0buPndv2F7nFduEpkg9zuNa9l9PnoXDbuDvXuhtHGF52PB7z0Pa+rh8Mw3xc7vszA8MwDMMw\nzC6w8PTI8Y5K6uE7d9OWInjd06lfJCAgCFrJbGtLHUw5DRhlJqnIvitcv83q0FhsX4yWK6MbNGnA\nByB1bgQRri2lZxiGuZSup+e6nxFKUM+P6JwZAUlsis6ktmyTCJMNMmSDDM2soRX5nSPENAZCUEfe\n4OkgPUQ4DKjeUTdQ1VToP+kjG1C3k2sc2nkLW1l428WYha6PKQCLNwsc/+74WgGjXbRoFy25krxf\nc6RAYCkKdMIaNO2nax2UJodVvE0UXYQSyR0Fh3RfKlOQUqbouhTj14BEKU/RdkKKDxZTFcWp6qzC\n2V/OUrTZ3jd7mP0yQzunmD3REzCVgVmQSARBTpG46EIIkbqfVuMF40IM17jUBxUjEW1Nbqng6Mnl\nnUc9qdN5v8kA/Kp9+5hc1UvUO+ih/6SP87+eozqrAEfCXHSHxeNmSoP58RyDZwMSq7ZE7z52bttf\ndFlvZuQunD4PidvE3d0mwvCq191jG9q+bwQhwzCfJvfxmYFhGIZhGGZXWHh6xLSLFrNfZhTnVBvo\nQi9jgVaIvQwArbIXXsC1Ds20gWtdEpae/OsnyIYZiVKlhW0tvPHIRjQEePq3T68deEglEVxAW7Zp\n5b2QFG9kKgPRdMX1gUUnhmE+MJ0wFSwJ7qsCjasdKltRbF5tYeeWHD6S+oBmr2Y0ZDYk0nvrU5zb\n4s0C3njk4zxFkw6eDdDMGjSTBvPXcxz++jCJW82MxHeVKxKOrIfua/QP+9A9faWAsTpsnP0yw+J4\nQd0pgXqdUidf12UUu/y86QSpGpBDiXyUU+dUQy4nlSuKZQ0kVIVAMXrBh+XXTEhRXypX5IAxAbqn\nIbHe9/Oh2DaMXnXoACTieetT31V9VkP36eOPzGR6v4nnMO6vd37ZcRWo6yiEkFy5Qi2dY+28xfTn\nKaSiY7nrAHwtQq3rO1SZ+ugD8F2G+lk/QzNtaKCdSxTjgsQnRSKkbSzaeQuAzsFl0buPmdv2F13l\njrpKFARu5vR5KNw07k7l6lYRhu2ivfb59xiGto9NmGQYhmEYhmEY5nHAwtMjxM4t6p9r/P6ff09R\ne9MmOZpsY6ELKmCPq7cBWgEuRDdwk4HK1aXYGjsyfTXF6R9O4SwNyYIPMHODkz+coDwpL11VWZ1V\nmL6cojqv0EwamIVJ/U5mYahzo7LLwR7DMMz7cJ14vfF9IcXSqSBA4vo7nyLFfE0upHbekitzRZQA\nkBwwtrKYN3OoMwXd18j6WRrot7MW7bzF4mQBUxlUZ1WKKzONSX1DcXAfnaHbBIzVYWN9XsOUJolO\ntFndtTQsRf4krDj6XhDd9b5zPMWuKZUpCC2Sm8lWFl76JGBFt5A3JL4FTzF2qq8weDKgfRIC9Xn9\nwYa6l/Xp9PZ7aCYNqvMqOUmkkgggN61tug4sReKYVJLi9FqXhLVVt5xQYt0tLLAUCjtxLwpd89dz\n7Ok96J6+cgC+KhjWZzWaWbMmPBV7Bb33fgSnwi69RLGPLBuRkwm+iyPMRTomuqdhgqEex8bi6W+e\nfjDH20Pltv1Ffnj5h567dvo8FG4Sdzd9Ob1VhOGHvP48FB6KMPlYnGMMwzAMwzAMw9wdLDw9MmbH\nM8z+3xnMiYFwNCxLv4B6WsUfB4X5OF9bje4drbJHAMYvxnj+98+RD/O1Xx5nxzOc/uF0bVUlAlBN\nKrhjh9mrGeav5zj67dHaqsrVAalrHHU7tQ4ADWmddSw2MQxzb8hMJlFeZQrNrEFrqOtOYBnBFt0u\nSXS6hOAD9TR17iJbWxKgRhmCpei95ryBsySArMUCdj1C8+M5bG3R2++hfFuuCRirw8YYdxbjShHW\nnTxBdD1N3dd1T1OXX9VCKOqkcrWDzjUGz6mrKO6DM+R+DYEcYTEKUEiBYq8gUarsruEKUGOF0Zcj\nGkB6v9ymD8BlfTq2sTCVWXZbCcCHZc9V/NlVF5R3fk2ki4sqohDoPcUgCi9IwFoVowQ5h4MnB1g9\nqTHqjS4dgG8Khs446pCyIUX62dqiOW8+ilPhul4iZ1w6nsW4SHGN3nq0sza56lK8o/FQY4WD7w4+\nCQHkLrl1f1HPbf2ZXUTByE2dPg+BXePubhth+CGvPw+F+xYmOeKPYRiGYRiGYZjLYOHpEVGdVTj+\nz8do37YILqA/6kP1FIILqM5I8AFoYGobCwgaVEglYRuKzZNaotgrcPivDvHs3zy7cP+rqyr7T/to\nZpTB7y2teLcTGrBVZxW++e+/weF3hxdud/DdARZvFqinNVxDAzeGYZj7IooxKiMh3hm37Fzx5ByK\nA3mhxG5RoFHvtx6qoAi9dt6So6hQcDVF2gklSNBAJ3ZgGfsWXUzNtIGQAvVZjfq8BgAc/+4Yk58n\nkFrCWYoDTAJAJxJFF9Oqcye6VIqDAs456J7G6PkIzawBAIy+XBc35q/nsBXFhkXxKooy3nqoXCEf\n5ySU1A1Cu+w90rlOg+IPsRp+2zA6Rt7Z2lIPlVgRDLu4PJUpZIOM4gNNoEUQARBaIO/nEJqOk60s\nxetFd1jsvpLiokDTRe956ymy0LgUmbc6AN8mGIY6QBcaak8lR5Y3HpB0nD+0U+G6ob6tu88HmUwd\nWUJT9N5qFKPUnajpA4pxcaOYxcfklrhNf9HsZLb1vq4TBVf5lJ0+18Xd3TbCcFeh6lPlvoVJjvhj\nGIZhGIZhGOYqWHh6REx+nGD2agZ4QOYydVgILZAPczS+SSvAg6eeEgj6hV4oigtShUqDkG33H1dV\nSi2xeLNIq6BjbF/wAbal/pOf/uNPyIoM89dzVO8qGlYpKnqHQIr/QwAQZwPc68QwzIcidjhtLIIP\nIDHeNjaJNbELKf1M9/cgbtA/18XSRbeTzMjJ4p2HyiiGT0gBlSnq+GnI+Rmj94IMqW9PZhKucXj1\nz6+gCoXJXycwpYHua9jaIriQ7i9GA0aBJO5LFJ68IXFEZQr5MEf/aZ8WAGzsVzNrUJ6U1JUkkRYY\nxGMUhSeARBEsAN+QuytGiAEkkn2I1fCbw2hbW8xfz1O3k+qptePhrIMIFKeYD3L0DnuY/JXEO13Q\n+6XKFVShkjPYW09iYPccELqLY+yEqBCWx0MqCallcrjF87o6AF99H7WNJYEsk1A5PWYI3XlTgp43\nA4pp/JARatcN9YNf7mf8t9IKg6cD6EInZ5+QArrQdPyji+waPnW3xDbB7Kb9Rf3DPnCy/f7Z6UPc\nNsLwKsfZXXDfgul9CpMPJeKPYRiGYRiGYZiHCwtPj4R20WL2epYGPiJb/wU0G2SAoNXgsY8jeBpq\nqiH1VBR7xfogZOP+46rK/tM+Fm8WafW+7mu4xqXeECEoRm/6aoo//R9/gioU6rMaUsskVAW3XGUO\ngGP2GIb5sHQxcd5evNgEE5K4cC03EJ0ivvWoJhWyXkaChCdBJLpDYrSatx5CUXcQBCCCgG99ctLY\nysL/lcQeZxx93XSClSDhJcWrhqWAlcS2gHR9RqBBYDEuyJmzITy08xazV/SeElyAlMs4tdhlZSpD\nC2WRluUAACAASURBVBa6riMoEmOqdxV0X0NqiTf/9Q0WbxdwtYPqK2S9DF74O1kNvzmMrid1eg+M\n7i+g67cSJLqpQkHlirY9l/Re6YDhF0PqaaoM2lm7Fh236iATQsB7j2ADsKrTeAAZ6Dw0Fu28hcrV\n2gB89X108HxATuTWQWgSmdYEHgkES51bvYMeyjflBafCXQ29rxvqx+PnPR0Lbz10T5NLK1cXnE3N\nrNnJbfIx3BIfShjYRTA7+vdHO/UXXQU7fYjbRhh+KBHooQim9ylM3nfEH8MwDPNhuO9FFQzDMMzj\ngoWnR0J9XqM5b1Lcz1oRekfWp+4SW1m0JcXxSU0fJkZHoysHIaurKptZQ0O7jAZ7ZrGM2otDTqEE\n4EErHbuBHYBl58guUVUMwzB3RHR1bhOerr9x9+d7XLN87dGalgQu0y0GCIBrHbmQOtEpukcBJBEp\nfh/o3Ca5Ipdotz3OOHjnkwMn7m9ybnXCSeoxEuQG0hkNwfde7KE6q5Lw4FqH8oQEkhgHGLcnHoso\nQJjSQMhuuwWJbKYiJ9b5X8/RTBoIQXGCoSK3VYxqda17r9Xwq8Po6l2VYl+Ts231+DtP29ktlmhn\nLdpZi2C6iLg+HYsoXsUOo/jelmL7OnEo+E6M6gQqeDqX8TxWskI9qaE0iW35MMf05TS9jzrjSEg0\nHrDrImHwgfqeAjkGmvNmzangWnfrofdlw4Srhvq6p5Nbz8FBakldaFui9HZ1m9yVW+Ky/fmQwsBN\nBLOj3x691wDnoTp97oPbRBh+CB5SvNx9CZP3HfHHMAzD3D0PZVEFwzAM87hg4emREFc9bhu4rSK1\nRD7OKcansSjGBZ793TM8+7tnV/4ymO4/IA3ldF/DLAyt2FaCVo9HwSsALlBZeozSE0qkzgtnHJXR\nMwzDfASie+VW4tEdxYFGp5B3Hu2ipX6mxsF6ij2NcW9rCCSHku5rCCVSL16MzxMQS2HEkGNGSokg\nO4Gku9QKIdK1OuuTGJKPcriWuvZMYzD5eQIEoJ7WSczyfkWsW3GpBhto20HCgK/9Mtp10tCwPATo\nHu2Xt10MX23J9fNs8N4xcnEYff7DOYlgWkBauYxJDEj7oXIFXWg6Pp0I6ZyDznRyoI16I7jWpRi8\n8qREM2uSSxhh6abyzifRKQlyjtxzriFBEUOgntSYH8/X3Am2tjC1ITGii9pbe+/uRElvPKrzCv3D\nPrz3mL+Zo5k0Nx56XzdMKA4K9J/0tw71VUbHrZk2MLVB/6CP3v52YWNXt8n7uiWu2h+pJExpYEpz\n58LAbQWz2w7bH5rT5z65VYThHfPQ4uXuS5j8XLrHGIZhPhce0qIKhmEY5nHBwtMjIa56RMBug9Gu\n2ykf5Rh9Obr2F8F4/03dpE6nGK+3uko/DndjnN9alF63OjwN18SO28owDPO+eJC75IFcc2xjkcs8\nOWoggKCpQygRsOa4KUbFsoup68nTOYlRviUBA6DvRadSvFZHl4prHfUJOQ9nHKYvpzj5/gTtok2L\nCkIgMSW5ebYdM0HdWCmytaAOPzVQkJlE87ZBCAG9g976YDaQKNPOWwAUcdfO2luvho/D6HpSL6Nk\nQ0gCUNwPlavUTwggCUfwAOS66Bfj44pxgayfkXNr2qy/jwWKQkwC18ppE4H+0X/ah+7RUPjt928x\nOhold0KMMIyxgNuObzxurnVoZg16sofpz1OYhbnR0HuXYUL/SR+D5wMAlwz1awulFTnuiovxejdx\nm7yvW2J2PMPrf3mNxZsFXOPIvdfT8MJj8XYB21CP2uDp4M6FgfuIF3soTp+HwPhoDF3o944wvC0P\nLV7uvoRJ7h5jGIZ5PDy0RRUMwzDM44KFp0dC76CH4qDA4s1iLW5pK91KagGB3v5uKx/jqkp37NKg\nLQ1E8050cgHO0CrvuLJ/VVwKjgaOqQ+EYRjmY/I+otOuov6uOMBZl6L0EABTG7o+dsJGdOrEFeMy\nIxEAgsSRGMkW3TuxLyo5tAJS/5PU5DT11qdrsCkNyrdlio5bjY0Dlg6tRHfZTr+MdrFwAMhtcqCh\nCkXOIyHIobXZFyTIWWRBfUrNrHnv1fDjozGe/d0zNNMGbUnik60t4Ok4yUxCF3pN4EnupUwmB9Q2\n8lGO4ZdDmNKkY5kiEAO5yWIvUxT7hCB37+DpAMVegfqcogCzYQY90CS6GLt+u833xLCMVfTOwywM\n7YOVNxp6A7jRMOHg1wfoP+lfHOp/26OexgU5id7HbfI+bonFyQIv/++XWJws6DxkgiIcrUfWzyAz\nCTd36fOGa11y3F12jHYdntxXvNhDcPo8JPqHffQP+x+9g+KhxsvdhzDJ3WMMwzCPh4e2qIJhGIZ5\nXLDw9EjIhznGX44x+3kGMzEI5vIJqW3oF8VskGF0dL3bKd7/4MkAs1cz2IlN3SGptN2viE4Iy1gq\nYG1Y650HDEX3MAzDfDK8r0Nzy+297VaMx+8FJBcMAiAgAEWiUXTkRHGpGBUQEOQckp2w1DoE0HU5\n62XJeZqcpp6GkrqnUx8RJKAzEoyEEDRYXVw+TBRyRXQBiTDeeZjaQO6Ru6quanpcI2ClhdTywsBR\nFQrtjFxWxbi4cjX8LgPm/V/tY/bLDNOfp8j6GaozcveoXCHrZ2uOpOADbE3bNTgcQChxpVtAF5oE\noG6Fv5AUWSi8SI4pKenruq+BgLXovDiEbuc0gBZSwNckAMaFGmtON6x3UkUXWnS7FV8Ul56f1ccr\nT0vY2t5omNB/0r+yl6g6q97bbXJbt8Ti7QInvz/B4vWCIhz7GgIiObLMwpCrERRLaSqDelJj1LsY\nCXMbYeA+48Xu2+nzEMmH+UeNbXuo8XL3IUzeJuIvH+YwlcH5j+dcVs8wDPNAeKiLKhiGYZjHAwtP\nj4j9b2nwVs5K+NbT0K2n1nqXTE2dBzKTGH89vtHKx/1v9zF/Padfvlu7VjTvrU+ik4BIq+1jhNTq\nwDW4AC/9cqU/wzDMQ+d9L1WrtxcrXxPUfxedRt5RV010HcXvmcpQpJ710D1Nq9q7rj7bWBq4C+pd\nCjag9S2kklCZSkPQfJhDFxrtok0uJ5WpZQeToyjC5Hzasg/BhfV4uU4w88bDzz1c5aj/yZOLRzXk\nOJJaQveWrqPVniVTG/SLfhIiouhRn9cUndZaBBOuLDmOQ2ghqQOrd9BDq9sUUxedTSEE2Irev4bP\nhzj6xyPMXs2udQuEEFCMCvT2e3DWUb8hqN9Q53rNVWVrm44vsD6EHn81RjEuUJ6W5NjRgkRA+NTX\nFZ1uMToxio1CiRsNvZtJg3bW3nqYsG2gcBduk9u4JQDg7C9nKE/KrRGO6AHNvIFtLMUIaxpym8rA\nGXdhsctthIH7jhe7L6cPQ9z3+b+Kjy1M3iTirzwpYRuL6qyC/73nsnqGYZgHxENdVMEwDMM8Hlh4\nekT0D/s4+scjnL07gzkxaBctRCnSSmpvPQQo5mjvmz189Y9fpV/2dhlk9A/7ePKbJ5j9MoOtLKy1\nEEqkoWNcWS8UrYiPg9BNgg/whoUnhmE+Ie7qUhVT6jrhRQgSG1I8qkfqa5JKUnRY40jMcCFFx1Vn\nFfUEhbCM1nNYXoe7BQcB9P0YPVe+K+EbcjoFF1DsL90z3vrlbbbRLShI3w7LrwcX4Cuf3LYCnRM2\nODjrqFOoppX3uqeTcyj4AFc56K80IIDj3x2jfFeiPC1Rn9VwLbl8smFGzijlL5Qcq0Jh8mM3cK0s\n2rKF0gqyIMFLZhQ/6L1P0YTD50O8+KcXOPzuEMUeHYPL3AIBAdkgQz7IsffNHpxxJICc0r5GwSuS\nhKKVr8UhdDbIcPCvDrB4s4Cp6PwJSYIi2uXxlFJCgM6jLjSyfgaVqRsNvU1lAOCDDBPex21yG7eE\n0CSyxqjGC7cR5KS2kgbutqUFNjF2cZvL+qbCwEOJF/vYTh+GeCjn/zI+tjC5S8Tf/PUc5WlJ1/uK\nuva4rJ5hGObh8JAXVTAMwzCPAxaeHhnjozHG/80Y9c81dKnRTJs0TNQ9jWJc4ODbAzz7+2foH/Yp\nNufHCVmsuxWS21Yirv6czOTS0WQCrLdplXwclnrvl/0V3eAzdl9455fDUoZhmM8R2QkCQsDCrv8C\nJygKNetnkFqimTVo520avJu5QRtaiurrnETeUg+QkAJSde4bJWFqk6LabLUcmAZDblSzMMCgi4aL\nRqZ43V5FYBmtGt080ZUlKRIQDoAClO76p9qQXK/OOwCAMy51/YVADtlsn/bz9A+nqN5VaGYNbG1h\na5veN1zr0Mwb5MMc2SiDqQymL6do5y11D5Xk5s0GGe1rbWHPLWQuIXMJnWsooaAGCsMvhjj6h6M0\n6LzOLZCPcpQnJep3dXKJ5aMc7bxNkXjpuIaQXGkxHhFYH0I/+ZsnmL6cYvLjhPquakuup+45IJWE\nKlQSnExpUBwUqfdrF7yjhSYB4cbDhHbRYvpy+sEG1zdxSzRTcquFEBDM0gW2FUH74FoHb3x6jm11\n7+HmwsBtBLPewW49mpuwq+nh8THP//vwsYTJ6yL+mmlD7lJQnOfoyxGX1TMMwzwwHvqiCoZhGObT\nh4WnR4geaYz+foTvvv4Os19maKYN9UIcFBgfjdMvpLPjGU6+P0H1jrow4i+MmysRh8+GWJwsMD+e\no57UCKDBmzcUBwQXH7jr+7A0iIuDTG89Arrhz8qK+bTCn2EY5qEThZX3JcbrSUHCkpLkArU+LRIQ\nQqRrp21IlBCS+oRsSzFu+ShHPs7Tz7auTW6qEEKK30tDdwkITw4joUicCghwDQkeWT9LQ/pLY/a6\n226N4vOA0AIi0HZKIeGFJ3EqOrJA9xEfM7gAVSgUewXMwqA+r5ENsiTGxL8749AuWrTzFs2kgeop\nKK3gWnIeqUJh7+s9jF+MIaSArS3qSQ1TGorEa8hxtfdiD3sv9rZGTl3nFjj+3TFsZZNQonsUr2dr\nC/SQjr1rHKSWyAYZvQfi4hA6H+bY+3ovCYGDZ4PUmwggHWNdaJjSQGqJ8VdjtPMW81fznYfe2Ti7\nkVhlKgPXOrz9/i2UUh80Eus6t4RtbHKwZYOMYhv7CqEK6XWySYwjhOiENyug9Xa312XCwFWCz00F\nM93XGDwd3EgE2HUxEPPx+Rjn/1PjKtHe9aj3tdgrMPxieOG2XFbPMAxz/3wqiyoYhmGYTxcWnh4x\n+TDH0799uvV71VmF1//yGrOXM6hCId/LaVDWxdHElYiTnyY4/+v5ssMphDQsFFJQV4ilYVmwAUHS\n92RGMUPtrKUBZRSbVoeVrDsxDPMpIFb+vIvrVuii6FyAB11bhaRYVAGKK42unSgaKK1gvAEcYL1d\nihqhE3IsxV5E8R+bs3m/FPuDDXCO+okCAtB0MXuCYv4u3UcP+ODTPsRjElxAEAGiJxBa6mJKjx+P\nmQeJd13kHVo6BiojZ087b8nlNcxQnVfw1qMYF3Cto0hBS4KYMy45r0xl0gKIGFkHkLt31BvBtQ62\nIbEo+IDBswGOfnt05am5zC2wTSjJ+hnFzjYWqlB0HoxHPsrR21/+Qr5tCL16f1LLS7ulTGkwOhrh\n6d8+xeTHyY2G3jcRq5ppg/KkTPG5+Tj/oJFYl7klggtoZg058QQ58WxD7jel6TnvrYezNNSOQq7U\nXTSllkvhSZALO3aYbTtG8ZzsKvjsEi+2et5u0qNpzgyOXx5fuxiIY8nujw95/j9Vton2zji8+/M7\nBEvX3avgsnqGYZj7gxdV3IzV97r2tIUaXoxyZhiGYdZh4ekzpDqr8ON//BGTnyeABxWllwYyk8j6\nGXr7tJq7d9BDdVahPqsBSYNPmdEgBgJAnwZ87ayFKalLwjuPTNHqZNe4FMEHYClAMQzDfEIIJe70\n+hUFH1MaigPrhBOlFYQSNGDvBB0hBAn9XUypsw4Q1NkUY0zj1+P9rD9Y9+fmtgcSoC708F23j92g\nP/6/2k8VWlqYENx6DF+M7YvRb8AyTi4bZKjOKgQbMH4xRrto4Q11XHnnYUpy4QhFx8GblTi6znnr\nncfizQJZP1sTGVSuoHKFfJhj9nKGdt7eerC5TSgRuhPAFgamNNCFRj7KMXg2gO7pK4fQ18VU2ZL6\nsEZHIzz/++fJCXCTofeuYpWtLWavZvDGozfqYf/b/Y8SibXplljr9QLFTRbjAra1cLWDE/Tc98bT\nc6KLhIxirdQyCVDOOEADutBr/U7bzsmu7u8o+NzmvF2HnVtUP1dwrUM2yJJ770OfA+Zm3PZ1+zmw\nKtpPX04RTOCyeoZhmE8AXlRxPdsWKJXzEjKXOPbH7EhnGIa5AhaePjNmxzO8/i+vMfl5Aruw0COd\nVqDb2tLq7cpi8GyQVtS71gGKBjirQ73Y1aR7mgafjUsr0vMiT5FNqq8oMir2i7D4xDDMp4AARCaQ\nFdSzc6H36BbIjDqHYpReikIL5NoILqTeIFUoFEVBHUktRbglYSnQAF7KLsMuRtpdsh/JebT5M13s\nH3z3MxIX3VKgr6eIvVXjaudi9a5zOW1Evq9F/XWPI6RAsVdg78UeFm8XSRSJ9x/7AW1jKbq1ExWA\nbhscvc+k6L/uZ+tJjVHvohPkrgab22KlhBCAXDrGVEHvd/H7Vw2hr+uWGjwdrMUCfiixavbLDKY0\nyIYZxl+NLwyKP2QkVnRLTF9O8eqfX5H7a1yg/6SfPm844+CMQ31aU3/kSiRhjIz0LUVVSkWvIakk\nuaVqiuy77BgBwMn3J5gfz3cWfG563nahfdvCziyGh8OtAiHHkj0cPsT5f2xwWT3DPHzaRYv2lJJJ\npoMpdwl+xvCiiqu5bIFSMAFmYfDuz+/Ykc4wDHMFLDx9RlRnFU6+P8Hs1QzwQDbKkA2y5Q/0qFeh\nnbcAgHyUk+jUDSKFomGMtyRSpfg9dBE+3c9571HsFWjmFG+UDTI0s2Y5zAlIgyOGYZgHiQKV52by\nopPoMhfRZXQ/LyT1z8CDeu/MMjouxoQ5T4KK7q0L/SEstyH1QrUesi9T5NtWokMpbsfmNnfbEkWj\nrf1N3f2krwes/7n6OF2fkxAbTtdAXxM5xesNnw9R7BWoz2tyb0UxoYvRc9aljqq4CCLuQ+wMXDse\nLlBHkXFrDpfIXQ02t8VKmdKgmTVo5+2Nh9DXdUttctdiVTtvYaplj9S2WLrIh4zEKk9KeOMxeDq4\nILyoTNE5jS7A7vOGlPQ6iP/2jjrRpJIYPhviyd88gbPuymN0/DuKtssG2Y0En5uet6toFyQ6BUN9\nOFfBsWQPg7s8/48RLqtnmIfLqnOjPC4RfMDx2TF3CX7m8KKK7cT52bYFSqWn14/Ukh3pDMMwV8DC\n02fE5McJqncVdVFYt3Xlu+5pWFiYilb3xxX58Q029m1465cDn26oKaWEDx7BBizeLADQsNSUVO4O\nLFe8B8GxewzDPFDUMjbIVhZN1dz+eqUoYi5eQ4Xoouk6oUYoAa3JNRqdTgA5TFeJQg48EGS3Md2X\nnHFXb8Oq+LQNv/xzW1RfXHRAyX7h8q6r2OUE2kapqOsvuoFkLpPbqdinAbvQ3YKGThDSPRL72kV7\n4T0mPoYQy+MZF0XITMIbWhSxTXi668Hmti6ozSG0yhVc66izqDRXDqUv65a67P4HTwcYfzWGa917\niVVyn46HytW1oseHisRqFy1Fl1QW4xfj7Y9NimM67zH6N7iwFDcF0nHf/3Yf3/0P310pDOzyuJHL\nBJ9dztt11Oc1icjd6+MqOJbsYXEX5/8xwmX1DPMw2XRuBBcACe4SZADwooptxPnZTRcoMQzDMEtY\nePpMWB2w5Hs5iUeXrPzWhUYza1J0XgjUoRBcoK6Frm9D5nI5DAUNLOVKzlIse09DwjioVLg6Foph\nGOae8caniK8L3MDpJJVcuoVE16uEpbNIQMDBQUoaOkspyT26MadL94MAEToRS4sUaXrlNl32vfg4\n8Xoc4/ACUtydzCSyQUYCUuthjaWeJb/cJ6HERUdUFMiiWNC5VVSmkPWzJA5JJSEzmd5vkrulixJM\nMYCyc335AJlTv5OtqOcq9vrE7zvjyEXVCVcqV+892Nzll/A4hN6WAy+lvPVq4ru4v8uGCe2ixcnv\nT3Z+Tn+ISKzZ8QzlSYkQAtpFC91b72VyLb0OpZYQWtC/u+hJlSuKXWzpeaCKpTsqCkSXDUvq85ri\nY27RQxNvfxdDGe9oIc9mTOVlcCwZ89DhsnqGeXhsc27Yc3IlDp4MuEuQSfCiCuIuFigxDMMwLDx9\nNqwOWLJ+BqklDea64eIagnpI4t/jsDO4cKFvIxGjlLSAzjVUriCEgO7TAGlu57C1hcxWVsAzDMM8\nBKKDJ/7pafWnMyvO0JsK5YKuh7G3Kbqc0jW3610KIUBplQbtUVRYJQQSU6KQFUJIQtTaNl7Flp+J\nYhcy2hYpJXSfev9sTYsMEOgxdU+jdS2EWbqNVt1RycW18njB0DBd6mWnlcwkevs0hIxxgcW4gBAi\niQ+mooUR0SkVu5wAEgCklkn0ShFsgeIC42pdb3w61t55KK2Q9bIb/yJ4U9Hnshz4264mvuv72xwm\nTF9O7y0SKx7bsx/O0rm3DX1OyPoZevs9cmF3XV+qUFC5QjtvUy9adHxF0VRIimmc/jzF5KtJ6nG6\nbF9u2kNjakNCncCdiIrxfoUU9HrZAY4lYz4FuKyeYR4W7NxgmJvxPguUWHhiGIZZwsLTZ8LqgEXl\nCtkgo8Fi47Z2OsTBaBzkBB9SvJ7MLw47vPPpNtk4gwgiiVXZQZZWol/3ps0wDPPRWe1sEkh9dSGE\nq/uRriIsfz46GlSm6HoaAon7HtB9yk63FQk9trYkAgSsXXuFFNTl1Fjq2At0TRcQF+PxdsR7D6UU\nin0SfmxloXuanE5aoplST18UgmxNbifvPbmMhFw6ri5bS+CXvTwyk+jt9dJ7TlzpPv5qjPK0xPmP\n58k1pXJF7zk2wHsPWCShKR6P3l6PYppaC1OaJEgFH9LiCdta+NYDQ6Ce1Jgfz3eOj7mp6HNVDnx0\n7LTzFuc/nMMZhxfFiyuHOlfdH4BLVyffJCLlviKxVo9tfV4nd3V8ntmK/h88Gyyd153oqAoFaeVa\n55PKVRJlXe3QmAYnvz9B/6B/6fm+aQ9NO2/Rli1sbSEg3lsEjPQOepC5hFmYaz8ncSwZ86nAZfUM\n83Bg5wbD3JzbLFBiRzrDMMxFWHj6TNgcsPT2adDZzltYWKhCrTmfvPeAo+FfPsxhKpNKu9ccUqEb\nqjpaka5zjXyQpwGbkALtjP4UahkLJUDCFMMwzL0TRaJOdBJi4/p0U9Fp5X5jB40QdA2USsK1DsEG\n6J5G/7CP0Zcj1Oc1mlkDAHCNW/YqiWX83eq2RNdokCSy+OZ2v+SoXGH0fATb0EIEMzdpcYGQgmLP\nckXik/UIIAFN9zRsa1PEWXwvWBXc0mFwywg+1zrUkxrOOPjWY3Q0wujrEcrTMr0vROErFAE22DU3\nlRC0TbqnkQ0yitZ76xBcgHOOtjnXJGJYD5UpFOMCukfiytvv3+4UH3Mb0WfbamJbW9STmo5f58Ly\nrce7P7+Daxy+/Q/fXrotN12d/Pb7t8gH+Y0i+e4jEmvz2O59s4fZLzO0c4rZEz0B29DnEwDIR3ly\nriFgrTNSF5r60DZ6wIILqM+uPt83Ed1MaVCdVQAokqj/pL+TCLgL+TCHHmvYueVYMuZRwWX1DPMw\nYOcGw9ycmy5QYkc6wzDMdlh4+kzYHLDonsbg2QAAYCqDdtZSf0K3wt7OLfRQY/+7fRR7BY5/d4z6\nrIbzLkVExU6P1YFqPspJ1KotsgFF+lXvqrR637UuCU+3HuYyDMPcJavXoeh0WiFFu8Ueo6uuWyvu\nqeACXTMDqNsOXWxpCKnvKMbOFXsF2lmLYq9ILg4hBepJnW4jQKKKqcz6Q3bX4CTw3GC/vfOYvJqQ\ns8h28XYVfS/28606iUIIkEX3XtE9Xoxe9fDLvqgNHSy4AFtbzI5nWLxd0PD8cIDx12M05w1c6zD8\nYkhReyWJXLpHsX/OurTowXtPwpwUyYFlKpOiXXWPRCcpZBKnYmRbfV7vHB9zU9Hn9E+ntJBjZTVx\nO29RnpRJtJNZt3BDAXZuMfl5gpf/6SWOfnt0wSHTLlrMX89RT2r0n/TRzJoL3UeRYq/A2V/O0Mwa\nZP0M3vgbuXFuEonVf9qHyhXOfzy/dbfRtmO76cLWPQ0LC1MZyEymeGBTm+SKiz+3ShQc81GOYlxc\neb5vIrrNX88RXECxX6TPTqu8b0RR/jyHnZNzD+BYMubxwGX1DHP/sHODYW7OfaUCMAzDPDZYePpM\n2DZgyUfkTKondRr0eUtDPZlLDJ4M8MW//QJ7X+8h+IBX/88rGvR1MUcAkgilc418lGPwbEBdIIsW\nWS/D3q/24K2nXza71d7wYLcTwzAPj+h62vhajL/y1l8vlkfHVIyfi2afTniJDiaVKeR7eRqcxxWm\nCIAcSFqdWlPPlMpUcqV66yGFJJEnuqFAfVJJGLvB/sZIM7oTLB2tARTnZzxsZelxJFK8XjNvECxF\nEdrG0u0c3U4oQZ8uwrJ7Kd5/cAEeHr4lwej0T6fUqdUJNnGBgm0s/RJXk/PFlAamJNGhte3aMCSE\nkPqporspumFUvhRqdo2PuU0kzfyXOYIPaTWxrS3KkxLtvIXMJIpxsfbcEiDhbvZqBpWrNYdMdVbh\n+HfHOP/xHK5xKcp2s/so4loHZxzc3EEVaqs7q3xX4vyHczTTBk//zVPs/2o/7f8ukVhCCYpjbCzO\n/nx2626jy47tNhe2LjSaWZNeAyGE5XMxgJxOG7iGIg2zQYb+k/6153tVdHPWJWEvuv2kkijflcnB\nPfry6gi920YU6ZFG/5s++mWfY8mYRwmX1TPM/cHODYa5OfeRCsAwDPMYYeHpM2Lbqmbd0xj1Rmhm\nDa3MrqnLIxtkkJnEye9PUL4tcfDtAerzGuc/nK+trJdKQvXU2jBsdcVHPs6h8pUYvxW3FMMwqT2+\nogAAIABJREFUzKdAcGGri2cbsetuK93tgw+AJsF+Famot2bvmz2Y0qA+q0lU0RKucWnwDkFCUxQX\nvOlEKEVfS5F3O+3c+t8DKDYVksSReO2WWkJrTQsUAnUvpdt2t0siW+eEXXOOKYr1i46yIMjZ9O6P\n7yCkQP9wGV2mcrUUjPaBuldj+vM0CQ5CCaiCugqFEDClIeGldrDaXhBm0rnZMT7mNpE00YWW9TO6\njy5eT2by0m2J+7HqkIndR9OfpzALQ+ciUPztZvdRPsrTYznjILRAPszXtnk16s+UBs28QT2pMftl\ntiYYXRWJJTMJW5IQ2E7a9+o2uuzYXubCDiGgnbX0nMgUrKehmSzk2usshEAinfHJeb3L+e4f9jH+\neozF6wUWbxbUB6ZIYIWi10B0g+tCwzsP17o1QXPb8+E2EUXZYYaj3xw9mlgydrgwDMM8DNi5wTC3\nY5dUgPq8Zkc6wzDMFbDw9Blx2apm17r0himEWIu/mb+ap6FSMSww+nKEgABd6OQC2Iz/iSs+sl6G\nyV8nMAuD3mEPZkGuKmdodfYuQ1yGYZiPyqoQI5E6i3aNsUsi1SargnsgQWD685Ti0QYZRYs1FvmA\nIsKKvQL1eQ3VUwg2JFdPNsggapEixbzxaduCCOuPsxL7F91RMa4ufd91f9VizS0lpEDWy6AKRaJG\nF7vnWhrux2OT+rBWr+fR6dQdw/gL2mrXU7AByLr4l4bcsP0n/SSmrGJrcrnko5w6sxDQP+xj8GwA\nU1L/oJb0ccZUBvWkxqi3XQDZJT7mNpE0Dg4CJPo541K8XjEutt4meBIU81GOdtqiPC0xfTnF6R9P\nMT+ek2tnmCG4sBSueljrPoqCiCkNdYaN1sWczag/oQWCCWgXLaY/TS8IRtsisUxpMPlxAjM3yAbZ\nxV+4b9htdNWx3erC7txewy+G6D/pY348x/z1HK52MDApHthbn45ndF7Hc3PZ+W4XLd79+R3O/r8z\nEg4DqN+sE0dd6ZKDMT6n07kZXHSerT4fbhtR9BhiyaqzCpMfJzfqGmMYhmE+HOzcYJjbcVUqgJ1a\n+NbDP/HsSGcYhrkCFp4+MzZXNccPoK5xyIY0SOkf9tMwZXWo1DvoIRtktFpKh2s7CAJC6nGIA6Nm\n2gDoYobENV0pDMMwnwoKKWoOwHZn54b41Mya5IpRmYI3HsVBgWyYwTXUeVTsFRS51wk+zlIMnTd+\nGQ24cb/xsWI8WfAkSEktKS4QK2KQXPY0CSlIyPLUQSVUt7CgUGhnFG8XQPclQncNx9XXc6UVvKXb\nSSGhcvq3EOQkiT08rnaYv55jT++tx8htiDimMlBaIR/l5ICRFPPnnUfWz9IxjfFsm+wSH3OrSJqe\nBgRgZoaOs6FOp23utyga6p6mTiZLUWqnfzxN75nZICORqWnhrCPBzQV479N7rcwkir0CzjgEBKhc\npWN3WdSfAW1f77AHBGwVjFYjsY5/d4x23u7cdXVdt9F1xza6sGPcYn1eQ2UKX/zbL/D0b5/i7fdv\nKX5xsXRESX2xz2vt3Gyc7yiMTF9NMX05hSnpOZUNsxR76K1HM2lgK3rt0c4CXnXOs/qi8+yqx7wp\nn2osWXTsVe8q2Mq+lzuOYRiGuTu2OTdW4S5BhtnOZakAIhPIhhme/O2TT8qRzjAM87Fh4ekzZHVF\n7cv/9BJt2SIf5xg+H14Y1K0OlUxpMHhOK4mv6yA4+O4AZz+crfV2xBgde2JpNTyLTgzDPCRiP1N0\n5uxqWJBAPsjRlm1yEK0JQvH/Lf1R3vnUZSQgEN4FvC3fIiAgH5IDxNaWxBfjYVtLDhdHA3dVKHKR\ntNS9l7qoQPF3IXSik6JuphiDF78PIN0uClPRrWQb6tpRmSK3U0OuHl1o6qvySOJT2ufu/oILgKLv\nh0AiVhJixDKaT2YSMpMIlpw4m24lW9sk4gQsBZvY76MLnY5RQIDMSPSxtb3wfrZrfMxtImlGX4+Q\nD3NM7RTtvKV9Fttvt9pDpHKVBvPOurX3TJnR+ajf1SluLz2PAujnOwFEaup/ivt8WdSfkMsIxOsE\no9t0XV3XbdQ76EFkAvXrOj3nNru4AIpblFqiOW8weD7A+Ct6/P1f7WP2ywzTn6YknnX7tO0+tp3v\nVWFk8XYB1zpIJSGUIJFOkwgcfIA3HrqvIXOZXnNZP0MAxfqtOs/SYp3POKKoOqtw8v0J5sdzZINs\na9fYTdxxDMMwzN2xzblhnQUkUIaSuwQZ5gq2OdLdKwc1VDj6h6P73jyGYZgHDQtPnzkh0Cr08dH4\nyuFaHCp56/Hs756hPCmv7CAwpYEtLWQu0S7aFMvXf9qHdx6LN4udo6sYhmE+BtGF4+CWAtIudMKS\n0grOdTf02Co0bbttFAKiOGNrS2J+bdFMm3RtjgJNfDzvPGBou5VWSdSJiwGis0gosdwmIMXuwXed\nTl2UmGvJWRO33TWOIld6OglkKlfIhzkN32u33uPU9UxJJeECiVTBrjuq4j7HLiypZOruca274FYK\nPiQRZ1OwAUigyAYZucIal7qlVmMDI7vGx9w2kia+953/cJ66gtZO9ZYeIgDpXCEgdR+18xau7lxu\nhp5TKdawO/+udihtCQQktw9w0SW2tg1dxF88F1cJRrfpurqq2yg6jeqzrnPqpUnC4Ta30rbztXpu\nEHCjuKBVYUTm1E+ZjpFYnp96Svet+5q2JwC2ogUzpjLLaEzYC9GOn3NE0eTHSXLsXeeOW7xZ4Pi/\nHOPw14efXJQgwzDMp8qmc6M5bhB8+GS7BBnmY7PqSH/bvL3nrWEYhvk0YOHpM+a2QyUE4Oi3R1d2\nEExfTbE4WcA2NDiNg0OpJQ0cYywVwzDMAyEO5QEsBaRdEOQ+ie6iTXeTUGLp8tzUQ8L6z+mehtce\n7aJN8Xqx7ydto5LwkrqdvCFxSRXknHGm244N4SsKR8DS4RT/HaPf1gSbThBzrVuKXN1tpZbo7fXQ\noCHnUycgOecA1f1duOXjKZGOa3wMmcv0NaEERQ06DzM3FH22r9LjAeS+klKuCTaR3n4PtqLuI+/J\nEXVVDOwu8TG7lAlv3mdcTeyMw7s/v4Odk4stutCccak7q9gvoHs6OWSyUZbObYrJW7RL5xw6kXCl\nLyv4ALju+WWX566dtTALA4BEqOjoWY34i46xqwSj23RdXdZttBbBVtulk89Tt+RqdF02yK48X7c5\nN8C6MCKUQH1Wr8UhCkHPQwQ6bkkEE0gdbK5xS4dVF0FpSpPi9z7XiKJd3XHRvVm+LVFPalTvKuhc\nc/8TwzDMR2LVuVH9S4XgA46+O+IFAAzDMAzDfBBYePqMed+h0mUdBLPjGc7+ckZDwC6qRkiRispd\n6xAMu50YhnlgBBqMJofQDW7njU9/h1z2K0GQEyqEANtQPN1lMaOuJYdR7EGKUXbeeLTzNgkY0eHk\nhV92MvkAWcjUBxS3JSAsXVJAinATQiSXTfAhxe6tbVv3d2c7B5hcfk1qiXyY037L5c8HG+Dh035G\nB9SqYBK3QSqZBLlsQMKLMw7l2xLe0nuTbWxy/hRPCgyeDdZcMQCJAoNnA4RAvYIOLkWjbcbA7hof\nc1WZ8FX3GVcTu8Zh8vOEzk+g7YjH2tQGizeL1Fel+7TS2MwNdeF0MXnxfEV3V/AhCYJCitTrJDMJ\neGD6copir0B93t0+gJ4v3fGO523VMQZcLhjdqutqS7fRZgTbwXcHMKVBeVImh5sz1OkUu5t0X6O3\n38PBdwcXztdtzs2mMNLMmq1xiN55cu6BXh9xYY3UEhrkzgo+oJlRx1YIAe28xfTnKXoHvc82omiX\nhUztvF2ec+uSqzLenvufmMfMVYv1GOY+yIc58qf0HNx7sXfPW8MwDMMwzGOFhafPmLsaKq0SB0yx\n+DwE6kSIK4q99/Cl534nhmEeLje5PnUOpygGAICUNNy3jU0/sxqRd9XjOuOS6BS/FkDOluhk8vBL\nl4aiyDzIZWyeUF3H0pbHiveRupmiQ2vbfndCSdrVIGjhQDewDyGQiFFQzF95VpLzphPcorMq9goF\nR/+rXCXHTYzP6x30SHhqHXr7vSSG5IMc4XmAWRgakoy2D+ryUb7s4RlmyAf51hjYmwgCl5UJX3ef\n/cM+vv0P3+KH/+sHTH6ckBPMIbmXorunntSQSuLguwM8/c1TnPz+BOVpmZxmq+4wIbpoxEDH1xva\nDqEEdF/DzAyq84qibcNSoIpiHhpy9PT2exccY5e9t9+m62pbt9G2CLZ8RP1lUWSztYVZGLRtm9xE\n3nic/XCGZtpccMLc9NxsCiNCChJf/brYFqMjg6BzYEpDQrDzUDl1EnnnU+ealyQE95/2cfjrw882\noui6hUzJxTdvITOJfJADEsiGFLH42PufWHT4fIkRo+W75XVKSskuP+ZO4WsMwzAMwzAPFRaePmPu\naqi0ShwwFeMCKlcUq9NYihTqYqFWy9EZhmEeFGI5fL7qZ9aivQJF9MV+qOQwqclxAtE5KS4mkKX7\nW97ZxuOL5TbJIEl06hxWQlHkV3RoCZC7KBuQ2N/Olv16Ad3119MQWOUKKldwjVvvQ5LdY25xZgWQ\nkGFKQ8JQ11U0OhpBKAErLOzCQlhBEXNKUEdR6yAdxb3FTiahKF4t3kcxLlCdVtj7Zg9f/rsvKWbQ\nLQfZp388xfx4jvq8vjRazVuPw399iKe/eQoAdzKA2VYmvHmf274HYBkvGGPwVp43sbsKnlw7Zm4w\neDLA7NUM7aQlYa9zhyVXjkTqzZJaQhWKzq0L6ZylKEbhk0MqioQA4INHM29gG5vcVJe9t9+262r1\nWF8VwaZ7GqPeCNV5hcWbBbmJHO1bMaLepfmr+aVOmHhuFm8XmL6cpm6wvRd7GD4frj3WpjCiezr1\nqaGHZZyhI4dgcAHee9jKwrcezjrkwxyqUBiMB+TQqi3qsxqqUPji332Bp3/z9Lqn06PluoVMUWCU\nmYTuabqGiKUTcrX/qXpXYfLT5FEM41l0+LxZixitbHJm2tayy4+5E/gawzAMwzDMQ4eFp8+Yuxgq\nrbI5YNKtTr0bFtQDEoewAFh4YhjmwRHslgvTplC+Gne2Itpk/Qw61ykyzDufos4uFZ1W7z9eG/3G\n9wSSk0mAHlMIkXrzVK5IxPEUbap7mtxTnZtI9yj6rS2pNwp+6XIJ2BC5uv0TUiS3lRCCxC8f4KyD\nn5L7o9hbj77T+xpBBGijSdjKFEVr1Ya2LaNhv7c+9fzkoxyDZwO41qX3mE3RAEAS13aJVvsQA7zN\naNl20eLt928xezVDM28A38UKdgMfszCoziqKFPRy7fwKISAKOnfeUoziL//5F7z4pxfIRznK03Lp\ndNsQJb3za66xeCwRSEwp9gsgAM2sga8p5k/lJFA569DOWriG+ouEoAhcpRWyXrb1vf22fUqR6yLY\nbG3RnDfwLXVPqVwlUa0YF1c6YS4buFVn1YWB26YwojKFrJ/BVjYtjnGtg2vcMhax+89Zes241qE6\nrSCEIMeWkmjOGwyeDTA+urzX6HPgqoVMzjhyiFlP53RL11ik2CswezlDeUodZ5/yin0WHT5vNiNG\nxy/G69fOR+7yYz48fI1hGIZhGOZT4LMXnoQQ3wD4XwD8jwC+BY15fgLwfwL4X0MIf7nkdv8TgP8Z\nwG8BKADfA/jfAfxvIYSrRowPivcdKq2yOWCKvRsAUpROcMtV3gzDMJ8Eq6JQ1Gn8ijMpkBNl/PUY\nwy+HePMvb1CdVqkzJzpPLsUjiUubbitBhTOpmyc6iFLEXxel5h0JXKqg7h7bkKup/6SP/pM+xbvV\nFtVZhea8oYF645bdT0osO2+6x5NKkvjRCVXBdq6djESpdt5i/nqOoiwglIB5ZyAygae/eYqD7w6g\ncoXpyylOvj9JsXLRcaJyBVUoZMMMtrZwrbvyPea2sXd3TRQ7zn86x/x4Tv1TztO+9DPonqahednC\nlhYypy4slVFcWzzGUsl0zOuzGv8/e+8WI1m2p3d967IvERkRmVmV1Z3d1Zc5M5wzLYzmnGE8wuMR\nDNIIWRYPyAKekJBlCSGPhYSExEUCWcAD8AAYYTNIyMj4wTwYkHkCS2DPCDRgGNnMjMdqT585M326\n69ZVlZlx27d14+G/14odkRGRkdVZWXlZP6mVXZkRO3bsHbkj8v+t7/uKkwLzb+Y4/M4h5i/noSOx\n+7rz7jXvGuOSOrC8GJUNMvQf9iEziXpS03EqdRBIg+sYCKKXbSywR46U2fPZueHUm3ZdeS6KYFt1\nwuhKh4hAYLMT5rIDt3XCSL6fh8UxjaHjbZQJLjLGSUh01oHnJPA2syYcP13pCxfk3Be2LWTyzkae\ncOp1qszarjGAzrfsS+iCzuNtPa5RdIisixjtclddfpHrIV5jIpFIJBKJ3BbutfDEGPtZAH8LwAGA\nrwH8zfZHfxTAvwrgX2KM/Qnn3G+s3O8vAfgVABVIoFIAfhnAXwTwy4yxf+G2iE/fdqjUZd2Aqdvj\nUJ1V1DURiUQitw2vB3UFqM73kr0Eo8cjZKMM2TBDNa7AHLtYdPK0zqZ18XbMkrtJ5hLISPDxHUDW\nWDi1ECScdWiKBlZbJP2EhrsJuY+yYYa9R3sYfz0OfUjVuAI4IFNJopJgEFKASRaiz9RcBdEDnK7r\njDHoUqOe1DCNQf9BH8lhAjmSOP7+cXivGD0eYf+Tfbz47RcUIzdrgovKNCZElfUOexh9ONr6HnNR\n7F0zbzB5MnlrHQde7Jg9n6F4XYRhuswXziPGGEQu4Kbk6nDOIe2ndFyFOLdNf151pTF9NsUnv/AJ\nRo9HOPvDMxhODhznHDinKEeecMhMUoyfw6JzizOIhFxQIhXBiebjCY0y5JgTLLjhZC6RDTPInASZ\nl5+/XDuc+jai37YItlUnDNBGBUp+zh3VdcJMnkxC9OJlBm6rwkh3cUx5Wob4QTBQZ5ql3y+ZySCS\nqEKhmlRQlYJMJYaPh1sX5Nwk3nYHyOpCJtkjF1k9rYNY3o3XXO0a8/h+Ny/c30ai6HC/2RYxuspd\ncvlF3h6r1+/Jk8lbvcbEzqhIJBKJRCJXxb0WngD8JZDo9N8A+HPOOQUAjLEEwH8N4M8A+FUA3/d3\nYIz98yDR6TmAf8o590X7/fcB/G0AfwrAvwbgv7i+p/HtuKqV5JsGTDKX6IkeGGcUQdWuGI5EIpFb\nh3c/dUWitsPp+W89h2lMEHVEJshlosmRsvP2Pdx/ywUHEgDqcAK5mkxtgjNJFQpqpoKA5LRD8bqA\nKhTy/TxE4mUDEsfSQYqX/+AldK2R7CUUC9dZOGC1hSrUwgXCHPWytBF/POMwlUEzb8ip8KlE/yf6\na4WLelKjOqOBvWscnKBtiVyQq8sBk6cTZKPswkiYbuxdM28w/mqM6bMpdVq5hVPlsh0H2wYt3dXF\nutZggiFJk3BMnSMHWTNrwGtOzrW2m0tVCsKK8NrpnkuABkRccpjSwDQGow9H0KVGeVZCC4qp9WJT\nV2DxQgkz1Cm26iBJBykdX8Eg2CJuD4xEgd5hL7wuqrNq63Bql66rdWyLYFt1wmyLYOs6YV5/8fqN\nBm7rHN7pIIVzjqIxlSWHnyMh1sc7Agi/11ppOOVIZBzRZ5rxj8fhGN1ErqsDxC9k0pXG9OkUqlDh\n2ujdlT5CsRvRuYqPAd3kkrvpRNEhclHEaJe74vKLvB3WXb+dcSjPSpja4ODTg633v+w1JnZGRSKR\nSCQSuWrurfDEGMsB/EL7zz/vRScAcM4pxti/CxKefoYx1nfOFe2P/53267/lRaf2Pi8YY38WwK8B\n+LcZY//lbXE9AW8+VOqybsCkKx2idKyyi6LzWPAUiURuK6uXLwvqiqk0xdLZRa+TFwu825MxFuLO\nVrfR7YvyopPvDmKSLdwtjEH2JKyysM4CAkvXV55yJHkCcKCZNrRvpUb/qI90kIbBbu+wh2yUwZ3R\n464OenWlqaNKUPSY77ZSpQpxZM6SWFCelmAFgy0syqNyaTBRnpaYPp3CWYe99/YgEhJHfCQrF/zS\nkTAh8u7HZ5i/mENVio53Rs/diym7dBzsMmjxDgaecqABnHGQ/cVHqOBcAsUGWt2KGAZQc0WOD1B0\nIhMsdG9xyUNnlwM5bLxA0swbOENRis52+p5cKzo2JAgyRm6nVQeJUbQfPtbMWRLBuOAYvDdA78Hi\nGO86nFrturqIbRFszrpFvCMWnWTrItgAhCg9o80bDfU3ObxNTceXCQaZkmMsP8iR9BNyOI0rcupp\nF/rPfA9VM25w0pzc2B6Nm9AB4p2STl38uc9ZB11o5Af51t7Rm0wUHSIXRYyuchdcfpGrZ9P1uyka\n1OMajDPMv5mHz3bruMw15ia8X0QikUgkErl73FvhCYABoHHxMZgDKIHQB/VzABoAf331hs65X2eM\nPQHwGMAfA/Abq7e56Vx2qLR63+6AiUtOxfJtlA5POEQioLneffV/JBKJ3AKssTRA5whiQBCJMklD\ndusuHER6GEhsctqRm2mm6NrpXOj7AagriUsO21jafkLbd9ZRRFieLDlNGWdhsDt6PML02RT1uA7C\nmMgEiWPGknhi6fG7Q/eu6MQ4uW2ssrCFRfGHBb7Ov8YHP/ggDCYuip0CcKlImKXIu1cFLZSQHCyj\n4+Uj73oPenDWbRW0dhm0zF7MYBoSO9JRuuTSWUVkArpu+wz1ogPMcRc6nWxDx9ZqSwJiK+7JjB67\nK5BMn05p3yoNVajQ1+TPQzbMwmtrVazpOoq4JBcWqxiSXnJuSPU2B+CbuiT974kxZqcINv+ahMMb\nD/XXOby9cyzZS5ZcYAAgEup14pyD9ds4S0Pnsvegh2yY3dgejevuAPGPV51VwdHkRdPqrEI9rUk0\nrTSKV0UQX7vUk/rW92ZF0SGyLWJ0Hbfd5Re5erZev9liQUu3d3CTi3SXa0zsjIpEIpFIJPK2uLfC\nU+tq+t8B/AkA/z5jbDVq7z9sb/qX3aLt/Wfbr7/rnCs3bPr/BQlPP4tbKDx9W/yAafzVGLrUMMpA\npBSt4qN0tNJheBSJRCJ3AgtAIog2YPTHvjMOSMm15F0v67qcVr/ve30gAQ6+6HNqhSe08wMvsjiQ\nI8ZqC6cp9k1VCukgRdJLoKGhSoXZixlFXT3sY+/RHkYfjlCdVuSusQ7NtCEhy1hyWJlWeOo4bmxj\nwSQN4f33GGMUQ2Ycxl+NIXMJkQmIVFxp7NRq5J13noSBS+sG8sMYL36tE7R2HbRMn05htFn0EHVc\nOudOIWMkXFm9OJet6MQlD/d3xpEzyliIhMS+bD8LApwXSHoPepg8mYRFHN4B5/uJRo9HaOYNZs9m\nax1F1tDrQdeazhtnWx1Fb2MAvslp5Jwj99KMoh7TQboxgs07YZIBObe+zVB/1eE9ezHDye+fAAAG\n7y+v4q7GFUxjIHsy7JcqFBhjQTy7qV09190ztO7x/DGTmQQc9dM554KTbJDT8XaWftdUoTA4Htya\n3qx1RNEhsi1idJW74PKLXD3brt/+vcc76lW5fD1dZZdrTOyli0QikUgk8ra4t8JTy68A+F8B/CsA\n/iRj7Dfb7/88gEMAfwHAv9m5/Xfar19u2eaPV25767lM/J4fMM1fzFGdVjQ8lZxWgLeRTEIKoI8w\nRItEIpG7gHdyMsZCjBgcQr8J3aj9yrG4/nU7ozrzKdMYiEQsdUV5USsIUG38HRgNBpzrPG5jUE/I\nZeCj56y2GH44DIPd7mIB76Lyj+NcR3Rq981qEi+45SSosMUKXD9cc85h/s0c46/G6D/sX2nslB+O\niJScRdZYpP3O7RhC5J0fxuw92lsStACKw3r1D19h+myKpLd90NLMGzSzBjrRSAcpiYt285uX0W2s\nnmCLY9fdLqO4PW00XEMiSr6fn3N5eIHkwU89QHVG4qAqFJJ+gnQvDfs8/mqM4mWB8qSE0Qb9B32Y\nxqA6raAKchyjROguMoocRqsCzy7DqTeN4111GtWTGqaifjKecfCUY3A82Lhi2zth8oOc3H9XMNT3\nDu/8IEc1rjB7OltyJRplgmO7Kzqu66G6aV09190zdNHjeaEUAJqigZor6q4T5BzUhYbsSQyOB3j0\n2aNbPdCMokNkW8ToKnfB5Re5Wi68nradj7qizyTNrIEqqY/TRxl7drnGmMrEXrpIJBKJRCJvjXst\nPDnnfsQY++MA/iqAPwngo86PfxPA/9HtfgLglxLNt2x21n7d/smthTH2pwH86V1u+2u/9ms/+MEP\nfoCiKPDkyZMLb//FF19ceJtt6JlG87KBnupFjBOn/hA5lEgfpZCD8y8hUxmUtoRhBixlUEZRqCED\nmGTgGaeha/Wtdi8SiURuFg4Ur9aKSEYZQLTCU5dVx5Pr/JthIUo5wBgDW9nFdldv331srNzGkjup\nPGu7iRzgpMMcc3z96mvgFd1U9RXMnqFrfW1D/Jk1lkJp/X6Btumcg7EGRhvqn2qj98BIbGlUA/Va\nof6iRnKSoDqrAAdU7OKLvp5roACaLxtkRbb0M1MZzH84hzpV4D0OPW8XNFRrRCAHmNJAn2hUjISS\n8qsSp6en4WfqTME2FqIvMJ1MIfqCjtPqPlmNpmmgJxo609Ba07adPhe356xbdAaljN7nLLmwmO7c\n2LbnywFKKdS8xqk5xfSL6eaDwwDstfv0jUbzu4v3Z1Ma2NKimBQ4+/FZEAxd0wpfoo1Z1Bqz1zMU\nswJyKCF6Iuy3OlFIDhPYE4sXxYvlY/CGnwfOHUujURUV1JSOvbUkcOqJRvOjBvKQnHJLx7MwsLVF\ncpjAZQ7VqwrqVKF05YVD/W3PqUuhClS6QvV1FZ6HKQzUTC29xmxjw+/feDY+99zq5zXK3ymRPnyz\nYdy3/dzmaV43KJ4XcMZBn10s0n3bfd/18WzexncqB9UoTM4mED0BnnO4oQPbZ3g2fQZs+TW4Dax7\nPa1DzxaxnF8+3bamLbILV/X7cxVoq1GihHqpwCccoi+WHbUr17YLr/+Re8Mu11PlFLRfUM+TAAAg\nAElEQVTV0FNyV5uZgX6hIforkbs7XGPM3OD189fX9n4Ridw1btJ7TyRym4i/OzeTx48fo9/vX+k2\n77Xw1IpO/xOACYB/DotovF8E8J8C+B8ZY3/eOfcfvMXd+AkAv7TLDWez2cU3uiLUqUL5dQk91XDK\n0TCuLYdWcwU909Azjd5HPSSHydJ9zZwmlcl+At7ni8EXA1hK0QBqooDxmgeORCKR244XhWz7X3c2\n7oWlVbGJLf6fydYpY+m2vl/pnGtqFYsgMASnTStAOebCgFfs0WDCVAZmTiJJcpiA5xy2tCTolO2D\neB3G7zPHQjhrxRMHB+ao38rfnklGwkJNDimn1u+01RaucSG+zmpLPVNrBAUzN7CNBU85GNg5h9gS\nrQjmDIkvVlsa8lW0P3DkUGNgcFUbAdvYJTHGw7O2I0lRRB5PqVPLKntOqHJ6cd6YYECG0MnlxTmw\n1vXEGEUyZhzJYbKTcAOsf3/2+2GmhgRPC7CEhd4rJhg9r/Z93JQLVxZPOUxhwBIGOZIQubjw8Xb9\nPLDLfltuaRuFhqkM5ECC5zwIpyxhSA6TsH09occzhdl6zLY9p1XSRyn0TEOdKmjQ8C64B1uHoT//\noifODfcA0DFp+7beNWE/dk1v+5b7vuvjhddqe13LjjOkD1KIPXHhObpNrHs9bRMd0qM4vN1G972K\ncXYrXi9yINH7iJx7eqqhTlR47a+7tu16/Y/cfXa5noq+CItO4Ogz3qIZ4HLXmOt+v4hEIpFIJHK/\nuLefchljBwD+Bmj98B93zv2o8+P/mTH2uwB+G8C/xxj7751zX2DhZtrbsmnvitp12dofAvj1XW44\nGAx+AGC/3+/ju9/97sbbeeV42222xeWUpyWeP3kO0xjsHe6FInBPN4u/V/Rw/N3jpViUs+wMz18+\nBxw22vorUeHk5ARa7xaXE4lEIreW1ag1LEQaxkl8YGDhOssluZOsJXeAZTb8ob+1G8/RdpIsCY4X\nqy2ccchy6nVK+ykOHxzCFG20SqFhrQXnHLIvkR6lKE9KzPmcoluqVshwCDF/IVKvHTowSwsKnKAh\nfZ7n1Nmzl+LoO0eYpBPMns4wPFh0KOlKoxpXFGWmbBiYsIoh7+X4+OOPMXo8ArB4v5qcTWC5hRhQ\n/OCsmsFqi6S3XuzQjBxJvayHYloAFtgb7WFwPEA9rTF7MQMclWSb2sAqi0QlGDw8H/l2Oj9FNa4g\nG4lslKHUJUxtIJigyLX2LbLWNRRXEEJASon0IEV+2Mb1TRuKvQP1wIhcAA5IByke//RjHHxycOFL\n6aL35+mzKWYvZrDGIhtkyEYZVKGgS+rDkpkE+nT8nXXIkUNyCSUVBh8NcPz943M9WN/m88Cu21EF\n9Y81swYCAr1eD0meQPYpgqr/kFZdWWOhPlEYo+2j4MnmfdrwnDYx/WCKV5+/QnlSQpcaktF5tdaC\naQaecCSjBP2jPtLB+SFe4QrIVOL40+Pw2t2VXT63XYZJf4Lnp8+hG43+g4tXrH2bff9Wj/f9N3u8\n28C511OfIh+tsdClRq/fQ++jHh599ih00UWWKU9LjH88Pv9eVUr0H/Sx/8k+eoe9K//9uUrK75Yh\nYnT1/bb/sI/9j/dvdbRk5OrZ9XraDBoUrwpU4wqwQMYyZDy71DXmiy++AOMMBwcH1/Z+EYncFW7y\ne08kcpOJvzv3j3srPAH4ZwE8AvC3VkQnAIBz7oeMsb8D4J9u//sCJBIBwKdbtvtx+/UPt9ym+zh/\nBcBf2eW24/H417CjO2oTG/+I6y/+iOsWjCZ7SSidZ5wKzUUithaM7lKsfFHXRyQSidxV/PWUCRY6\nToIrxrURaXAh1s53NwE4H9O3Ztt+QUFw13AGayyaaQOZU89OM22WhqG6oV4lMKCeUS9U/6iP6qyC\nOTMkYKHT6dTuSzdiD5JWcfOEh/eBdV0XzYyGJb4/hyccjDHqbFIWaq7w+ovXaOa0j/79qp7V1B0I\nh3w/D51T3i117lg4B8459TO1z7V/1A/F3L6riTF2rheqW9KtKw3TGDjjUJ2RWOZ7vFShoCsNnnI4\n7aBrir1hnCEdpEGkyPfz0K3UfT+tJ/WFvUpdthWAG2Wgaw0uOJI8CY8zeH8Qjnc9rcETeixdaszV\nHHvv7W3s1rmqwvGLtpP0Exx+5zDsZ+9BD0ffOwIYULws8Or3Xi19ZrGORFBVKHpdd4f6b9gXtK6H\nSs2pNyMbZbTv+zlkLmEaE7orGWcQibhRXT3X3TMUe43Os/p68q9fmVJXWRQdtjN9vl648+9V1WmF\n8rTEo88evetd3Yrv6nvTfrzI/WPX62k6SME4gyoVRCbo9cRw6WuM2BOQZbx+RyKRSCQSeTvcZ+Hp\nk/brtsC3s/brg/br32u//hHGWM85V665z8+v3PbGsMsfcbNvZtC1Rj2tIXOJ8qwMq9EZa1f89mj4\nsqlgdJcPzKF0PRKJRO4jDBCJIMHG46gLyBpyKYEDnPNFpMm2aDmPBUxjYIVduJQcqKvGODDJIFOJ\npJ9g+HjZgWQag+IVXc/99704Zo0Fs2wpysULYIzR4F0ZBSYYZCYptu6Ihh9JP0F5WmL2fAbTGDTz\nBmquwBOObJjBgXqROOfIHmRI91KcfXmGsy/PIDMJZxy9XyUcjjnomUaNOuyLqc05hxIcYJWl/SoV\nnHPoHfQgUopnkjltT1cayNvzkQk00waqUDCNgUgFmlmD+cs5mhkdE5EKWGNhtFn0NFkEAS0bZHCM\nvrf33h645Kin9UJsymTYh8sOcC4qHNcVCXc8ITeVfy69wx4Gx4NzDjOecIhMYO/9PRz/zHlX0EWP\n12Vb4fgu2zGNIYGw0WimDcrTEvWEXGnrPrPoQod+sWSQgDN+JUP91SHxq3/4CtNnUyQ9cjrpSmP2\nYgZVqCXR0xoLIQVkLm/EMHmd4LuJelJD9sh98ab7ft2Pd1uIosObUZ6WePX5K8yez869VwGAOyRX\n4+w5BVHofX3jo+rSvTSe88hOXOZ6ahqDvUd7GH08wujD0RtdY0Qu4vU7EolEIpHIW+Nmf0p/uzxt\nv/4cYyxxzqnuDxljCYCfa//5BwDgnPuKMfZ3AfzjAP5FAH915T6/BOAjAM8B/F9vcd8vza5/xE2f\nTKEqKtPWlQ7DNDjAaAM7o1Xz9aRGPsrhnEPxqsD0+RQPf+ohgIs/MOtKo3hZhMihSCQSuTd0+pys\nsRBi0VPhHUWOtYqOBSwsYDr33QFn3LlYPmfInWOVRTbK0HvQC+8Bqw4koBVFWvcMExSlZxoTup18\nl5SPCPQdRiIXdJ+ELQYTe8DRZ0cAgNMfnaKe1GCSgYMvXE+SB4cQlxzlSQlVKOSHOQ4+PVjE+xmH\nwhUkzrWLF3RN7lqRieB80jUJE1Zb2Maec9qIRCDpJdClhq41ZC6pd0nQ6uHidQGecNSTGvWEXEJ7\nj/bQO+wFAcc0hkQ+RdsffTTC4XcOUU9qnP7olKL8sOzK4pIH54yu9KUGONVZRW6evly7oMM75nx/\nFJccVlvoWiMbZhjkAzRFg2bSwFoLLTXSvRSH3zlcK9Bc9HhdGGeQfQld0EKW7vPZth1d6RAV5B1E\nzjjUsxqTrycQqcDe0d7GzyyqUJCZxP7H+0j6yZUN9f2QOOknYJxh9nyG6bMpdKUXn40kOfVUo2Ab\nC/SBekzD8JsQnbb/yX4QfAFsjUkcHA+w//H+rXq820QUHS7HZZ2WSqkbLzxFIpfhstfTh//Iw2/l\nnozX70gkEolEIm+L+/wp/X8BUICcT/85Y+zfcM7VAMAYywD8BVBs3imAv9m5338E4K8D+E8YY7/h\nnPthe5/3APxX7W3+Y+fcjVJVdv0jrjqraGU3GLL9jAZzDa2i9hFO3hFVnpYQiYBIBb75+99AzVTI\nW9/0AdYPOMuzkkrYI5FI5CbjE9Cu6ore/h3vB+zeCQMATrsQD2e82mQ79+vO7C+6fK7+vL2/VRb1\nlIb6g+MBuOTkcpo15GjtJ3DOUfE5Y0j6SRCjGGewypL7ybHgXHWGRC6ekeBktT03mBgeD+GsC24R\nXwzPJYfM5VKM2ezFDNbahUCnbXAJ5fs5dKnRzBo6N27hoG2mTRBbvPOHZxyyR71Mq66o7raUIVeU\nrvQi7tC4IGr1H/TRO+xB5hKDfLAUm9fMG3DOMXo8wqPPHlEsXK1RnVJ0ocxliDvUlYYuSZwRqcDB\npwcXDnBCx9XTCerZIipvlW58oP+3fy6hU6vj1PG9Vqc/OkU2zM4NrayxFG23YwwgF+Q6smb5l2XT\ndppZg+nTKepJvViI0p5TZxyUap1nUiDpJ0u9St3BczNtYBrzViK3eoc9HH12BF1pnH15Rk69lIfX\no1EGIhHIhvT6qs4qvPz8JUQm3nmEmt93gIbz0yfTK4kkvCmPF7mbvInTUjsNU5mtt41EbhPx+h2J\nRCKRSOSucG+FJ+fcN4yxXwHwlwH8OQB/qnUzAeR0+gBADeDPOOfGnfv9D4yxXwXwZwH8DmPsfwOg\nAPwygBGAvwHgL17fM7mYy/wR51w7RBSL+1q9WFkeVplbGmD6fo/ydQlnXMhbHxwPzn2AZZKRC2pW\nR7dTJBK52bS9RV4wuNJNCwZnSMhnigG8dSkZB5GSE6e2K9fJbszeBT1P6+CSB2HAWYdqUpFjQ5LD\nhyckAPmYFguKk9OVDp1NvpfIKktOH00xfj7azWoLJtjmwYQDsgEtQkj6ydr4OaMMReMZF7p0ypMS\nsifD4/ePqPy6KRrouQbPOdIsDcIRFxzJIMHweIh0mKJ8Xa51i/ltmcYE8cNaeg6+bwoW1N9k3dL5\nEImASGifs+EiZm7yZILp0ykA6i2y1i56rDgDk4zi/RwgUoHRh6ONA5zVTsbQccXoteKFuu7z6cYH\nOuvAJf27Oq2W3GWMMRhFDrb5N3M8/63n5wrId+lr7GKNXdtXtW47utKYPZ+hGlf0OkgYhBQAQ3CR\n+dd5Na7AOMPoo9E58XBbxN9VMTwe4nT/lM73gJ6Pd5bJXIb4YS88beu6um6uu2co9hpFvi1v4rSs\nqxpmHoWnyN0iXr8jkUgkEoncBe6t8AQAzrn/jjH2OwD+dQD/JIB/pv3RE5Ag9Z855/7Bmvv9CmPs\n/wQJVr8Ekmk+B/DfAvjVm+Z22vWPOKMMDYDa0vR6VgMWi5ildkjFOAsRRj7Oqf+oDzgEh5PIxLkP\nsKc/Og2l9X4l9mWHp5FIJHItsM6AGYy6fK4IzjgsX7hIYRCG2L0HPXBBvUBAK1LpRfTernF7/jmE\nbTgHo81CFHBAPa1DTF42zGjfWoHJOQdbk7DE6oUjK9lLMDgeQFc6iDXZKMPgvQGm1RRyJHH8/fN9\nQcDC+SIzGR5vFd9TBICEktYlJDKK8As9g4c5ZE+iYAWYY0j2EvQe9ABOQtDwg2FwEn39/3yN2dPZ\n2s5BLun5goPi/wwHl9R9ZJih85LJ0H/lHVpLh5kz8JScY0+mT9BMG6TDFNkH2bleJSEFksMEcIDM\nJFS1lPIbWNfJKBIBB+q4KhwtJukf9YMTqBsfqCoVBK9m1kCXJCCmwxSMkfiV9MgBLXO59N7tz92u\nBefA9r6qddupxhV1X7VdU1wuXpfOLGIJGafPGvW0RjWuMMiXI+y2RfxdFc28oVjETGL08QimMQvR\nNJdBgASuRwi7LNfdM3Sbeo1uwz7eN97EaRl6ECORO0a8fkcikUgkErnt3GvhCQCcc38XwL/8Bvf7\nawD+2tXv0dWz6x9xPjaISQanHBWzpyJ0ezhL7qYwfGpXI/sYn8H7NBDqrvb1/73+4Wu8/uI1GGhI\nxCWnlc4xbi8SidxAujFlzrk3chmtxVGXExzAwMI1NYgdtUHddNxODBQ/Zhf3X4rd27RP7f244BCJ\ngNEGTjk4Ri4Y7yzxUXB+e97J5Bw5W72rlYEEKlUolGcl8hEJDNkow+FPHqJ30IM9sRD55oixXRw0\nutJopg31/bSLE7TVwU3FGIMuSTTpH/VpEYSyOPzJQ4w+HK0djmzrHKzGFYwySPfS4GpKhylkRkKJ\nFxd0paFKdU788BF25WlJ4hIcYIB0Pw2upN6D3uL9td0eF3yjQLGtk9EaixIlrLEUNwgsiWE+PrA8\nLRfOOmuDow1uIe6lgzTEBwI459S5TMH5tsLx1e0k/QTNrAkCY/eziTUU8eh/L5hgYIbcWfW0Ru+w\nF9xxnk0Rf1dFd/GOzOi/TVyHEPamXHfP0E3uNVp1E1prwTmH7Ev0H/RDbHTk+nkTpyXj7EJ3VCRy\nm4nX70gkEolEIreVey883Qd2/SMuFJNzGvp4N1OI13OLlfN+2MkE/aGnSgWjzMbVvpOvJyRk5QLZ\nKINpDA12o+UpEoncQJxzcKq9Pl3lZcoBtmkH5AJI8xQiFTCNQTNtzjlBGdooPkffDG5RP2PbkC7k\no928a4SDw2hDw3lBrqvQB9TujtU2dACFob9g9HOOEAnYjBvAAAc/cYAPfvBBiGd7UbzY+tQvctD4\nDkBVqYW7y3dTaQtm6fk444LoIjKBbJBh9OEIo8ejtY+7qXPQx/oZZSBTCWfI+TU4pg6nZtYEAVBm\nEvW0DrcXiVjsb6mgSkXHrBUT1UzBVCa4ktY5vDYJFNs6Gbu9VM45NEWzJIaJVJA7LOFBpHPWQfZl\ncF5xyZEOUvSP+kF02vTe/SaF4+tWSne3owpFcYAM4JwHUdcaiu9lkoE5Rp85WDtQtiSK6lqfE542\nRfxdFVfVdRW5GaxzE3JBn5GrswrVabUUG31buCsOhTdxWvKcQ+yJjbe7LdyVcxiJRCKRSCQSiXii\n8HQP2PWPuFBw3/ZCeGxDwyAHWgHvV9wzTp0MIhWwiuKQsmF2bpjWzGkwZrVF0ksAtH0jktOK56g9\nRSKRm8a2uogrcj8xMHBBMaa61otBtZ9Xc4TIP2MNOZBa52m4Fq/bbhuP2r2Oc0HxfrAUmccyFrbj\nRS1dkbPIwUEkAjxte6E6fX5MLjqZsv3sUoPZbQ4aXWkSceYr0XMMQYxz2sEoEs9EJtAUDVjJsPfe\n3lY3zqbSbF2Tu8ppB5e4JTHGi3a60qHPhyc8vNc541C8KtDMGjDJ6HgldLx9PJ+u9VpX0tI5WREo\nLupk7HZcqUJBzRXFDXo3WFsA/uAnH8Aai+mzKUxNCz3WdRKFw7zBqXOZwvHhh8OtLpLhh/R8Jl9P\ngiAGtH2RrRtMpCI4zExjlrrN1sVpbYv4uyququsq8u7Z5iYEAHdIQuq66Mmbyl1zb72J09INHUR+\ne4Wnu3YOI5FIJBKJRCIRTxSe7gG7/hEncxmcTTzlobdAlxrGmqVYKN8L4rsZfCQVcH6YVp1VMLUJ\ntwMQBkymNjTQjEQikZvGaqRdZwj+RttaudQ546jLiS0i7Za27xa3Cz1T7vx2VnGWBBovWoVdEJ34\nQOvAUw5mqUPHakuijrZ0P8mR9lNaIGAsiQMzB5EJ9I/6UDOFelpfusdmk4OmGldo5s3iObbPn4Et\n/s0A5hgJEgAsLGRfIh1cHAmzrjRb1zoILr3D3pIYI1KBpJ+QAFIbEqPY4vj57iYvNnFJ3VM+qtBH\nGGqsj+gD1gsU1VmFelzDMRc6uLzQ50kH6SKuFg7MUdxgNsiWCsDraR1cQuleuraTqIt/727mDSZP\nJkur7o+/f7y1cFzmEtOn060ukt6DHolPDKgmFeycRE7O6XMETzhkJhevOd1+jvAO6zVxWtsi/q6K\nq+q6irx7trkJAXqN+e+vRk/eRO6qe+uyTku2f3tj9u7qOYxEIpFIJBKJRIAoPN0bdvkjTs0VdTBl\nMkTuJb0kiExwtNrd/9uvAAfrxOHg/DDNGuqXYIIKzcHp9iKlFeLGbLMWRCKRyLshHaYkyLTOiysr\nL++IWUF45wjRYpALIcoaS6LTZXqmvDvIGbB8cW1m7QP7uD5TmdD3VI0rcqAyipVL+snCMdX2AnnR\npZ7UMLXB+MsxnqRPcPz9452Hs+scNDzlKE9LioJrRZvFUyFhIvybUYeSj2tNWLI2xm7TY/vS7Omz\nKcZfjoMbaF13UDfWToPEFiEFnHFLEX1WL/qSitfFkktqXUQfsF6gKE9LvPq9V5h9M4PT1KfFGDmv\nkv6yS0nmEoN8EBxz6zquVKEg00X300WoUsE0Bq8+fwXnqLsRAJJegsHxAA+/+xAPfurBuSgo0xg8\n/63nO7tIHn73IZpZg5MfnsA5h2QvgUjE0n1kJsldVusg+IpUhH6ldRF/b4ur6rqKXB/rIssAbHUT\ndtkUPblp2+/iXN9F95bnMk7LR589wrPps3e8x2/GXT6HkUgkEolEIpG3x035m2QXovB0T9j1j7j9\nT/ahK43JE4rC8SutfQeJF52ccTQESqk3QuYyOKbODdNOSpSvS6hCwRlaiS+kWKzyj0QikZuIowG4\nM44EkVYcwmWqW1jnqx+gZ4L6bFTn+tf+zDkX3KX+sUIXnn/8dfifddxCzjiYxtD1Wxk4RZGp3qXj\nnU1MUBcQOCASERw1AGBqg3pWwzaWIvwEp207EkbGX48BAI8+e7TzIfHuo9c/fI3ps2lwxfo4OB/p\nByC835xzgrX/ZoIh6Sc7P3Y30qiekSNIFQrOunPiDuMMskeRebom5xMG9CFvXURfOkihSrXkkgLD\nUkSfF55WBQq/6n36dApTtW41174/V5r+a/ui0kHnAyXDxo6ryzh16kmN4mUBMOraAgdgAOssYIDJ\n0wnO/uAMxz97jKPvHS3d9/kPn1/KRVK8LrD/0T7mL+ZoioYE15V982Kbd+Ix0GtDFeS0Wx08v+1h\n7Jt0XUWun22RZYwxVGcV/f+W3wVgffTkTYtDu2vurVXWuVRXnZb7H7fHfPqu9/bNuOvnMBKJRCKR\nSCRytdy0v0l2IQpP94hd/4jTtcaXv/4lZi9m9Ed6vlhhbGoTCue55FCVCs4okQi6fY+ij8ZfjfH6\ni9eYPp2intY0pGunoiE+J3ZvRyKRmwJbuIEAQNcawrYiedft1K1u2RZ954UdSXF2znXiSe3iehi2\nAxJSNm533fWSdfbbdlxR7X+2sbDN4o4soT4i765K91LIXML0DbmezCJyz2gDXVCfEU840mG6cJy0\nrlgueRjG630NObj4Y4X/sNTMmoW45jpPthXn4EDHrXXcehcRRHtsGYlgPnrvItZFGolUQFc0XPbi\nTjbKYBQJUlZbio5t3WDe+bYpom/VJSUysRTRt06g6K56lz2JdD+Fmik61q0YaWpzri/qoli3XZ06\nutLUBdUY6qsSAk7TOZdMwloLUxlMn03pPR8MD7/3EMD6Tiof7xeOk+8E67hIjn76CMMPhzj50QkJ\nf84hyZNFn5NbCI4iFciGGbm2GNYPnt8yl3Vg3LQP+/eBiyLLnCOnYjbazSHZjY2+aXFoF3XBddnm\n3rrpdF2qt2VF567cl3MYiUQikUgkErkabtrfJLsShad7xq5/xD3+Y4/x5O88wfzlPAwduaZIH2cc\nOHhYJS4zCdMYzL+ZQ5UKIhWkwL4qlgWndtDpnIuCUyQSuTm0DhuRCMAC1lK8ne+iE0zAWUfiA2gg\nyfhCTAqbEWzJxck4Cx08jDGKKmXt/fQatcqLRvz8jy7CC2PeMbTJTepjUvODHL0HPYpZLRTywxzg\nQH1WBzdAGPz3BLJBthS9Z5VFOkyx92gPaq5QnpRQSq0VnrrvN9VZhdnzGQkzXvzJBficQysdFiU4\n48Alp/i7VpjywpM1lo7tul6sDWyKNEr3UjDGUE9rWE37V40rEgAdiTxOu0XkXZ6QCDcgwW7w/vIH\nOplL9I/6ACi6rpk21KclOHViTZpzAsXz31p2DDlDMYi61qFbal1f1C6xbrs4dabPpiRqtQImF7Ri\naum49oB6WqM6rfDs/3uG/qM+eoc9EuwKOo+mIfHSC3b+fC1FBbYuEjjg+AfHsNpi8vUEutBQhQqv\nMafpfCf9BKPHI7z3R96DSMU7HTxfyoERuVZ2iSybPZ9BlySI9g57QSzehI+NVoXC6Y9Ob1QcWvf3\n7k3cW7eNdO/iHr/bxn07h5FIJBKJRCKRN+c2RzRH4emectEfcYefHkJmEi9++wXmL+dopk2IQXLW\nwRgTir6ttph/Mw+RTVySKBW6GYCw+n5pVXskEom8K8RiyC5SgaSXwDlHA3hNYot39aTDFEmeoJpU\nIXIUWFzPeMKXYkgBBKcKNAlZMqfuPKtscL+cw18jLS7uclqZU3VdNVvv6wCjDHjCl1b+W2VpGJuS\nw0VmEsWrAkArVrDFA+pak5jQOl35iGP6ZArtNMXEtazawFWlUJ6WMLVBOkgxeH+ApJ/AKAPTGBhl\nAIXQKRXeUwR9ZSAxCJpuwxM6dz6+bhubIo26QlEza6Aqct+IVED2qb+JS45smAWhrp7WoRfRu3q6\n+KhCL8JUZxV4zpHupchG2ZJAsW7V+6pryjufRCbQTBuoQqF4VcA05sJYt4ucOs20gSoVHc+UQ2Zy\n/UCeUedZdVqhOCnw+oev8dHPfwRryBFmGoPZdBZ6urhsxdmVqECRCXBJj33w+ACf/OInePX5K5z9\n+IzEPy/uZvT63P9k/0Y5iO6yA+M2s0tkWf+oj2pCw/7ytMTwg80uk66bsJ7UNy4Ozf/e+S7Vi+i6\ntyI3g3gOI5FIJBKJRCK7cpsjmqPwFNmIX9378vOXeP17r2EaA9mj4anvZBCJgIODLmm1sufC4Wck\nEom8Q2QuIVMawPOUh0E8mTNdcGwoTeKCSAWSfkK305aEdYvguPGCU9f15AWJ4HYCg2nMejdSpwNq\np2tn93YOFD9nNtxXUHeT30+rLYpXBdJBinw/DzE+sieRH+YoX5M4BEb3C6KTI9HJKhvu65+n7EvU\nVQ0zJ+FpnQ3cu5ycddCVxvybeegs4pLTcfH7bykmEAwwnHoBeUK38U4amUlkw+zCwd1FkUb+8cdf\njYESYIaeL+ccYkiiZDdOD0AQ5TZF2MlcYpAPULwqwCXH8MMhjr53dE6gWLfqfRf3MIQAACAASURB\nVNU1VU9rEjfbhR/VWQUuOUaPRzuJMtucOuyAwdpFnKGPUlxHcF610XzNvKHfB0VOsW4kY1eo7EYF\nspI+N/hz1jvs4eNf+Bjv/8z7mD6foj6rqbdqlGH4wfDGijl30YFxW9k1skykAvl+jul8impcoX/U\n3yhaezdhOkjDdesmxaFxwcE5xWrsgndv7SpyRN4+8RxGIpFIJBKJRHbhtkc0R+EpspFmTiurdaUB\nBvQe9JDtZ8ElYI2lkvVJA+10FJsikcitwTYW2mmK1KvJqRm6nHy3kKMoN6ZY6OcB2p91xKXQOwQa\nzltmg0AiEkH/r1tnzKZOKLfy9ZIsXX8ZKK6vNR8tuYISEo+88JPv50E4ggUG7w8gEoHJ1xOouSKn\nUcWCW4tLipnrH/WXhBgueDhG62zg1ljUU4rxy0YZTLPoLNK1RjNtYLU9//wdPQ9jyBUFhoXgxPnG\nfqMuu0QaMcGo06h1F3HOke/n6D3snRtOZ6MM5WkJgOLn/PdWI+zqSQ3TGIwej3D8/eO1AtGmVe9L\nrqlSBaccFxw8JyFr0zbXscmp08wbPPt7z6ALDZawC2MLfa+XKQ2qswr5QR76sHxf2Ln7eMHKaTTz\nBkaZc+cs3Uvx8Kce7vRcIpEul4ks8/GQpjKYv5xjeDxc+3vrO9iyYYbim+LGxaHlBxRbWZ1VcIfn\nXZddLuqCi7wb4jmMRCKRSCQSiezCbY9ojsJT5BzdeKR6XGP2zQymMkj3U+hKh5XfuqJYpaZoaCAY\nRadIJHKTaDtrgiOpc42yxi6G7A25nLggVwk4DXpCp5C2oZdONzr0M7GEIeklJES1wo/RZuE8agUT\nMBKrwPDt+u38/nrxqut66sTz8YRcWfQPnBM1RCIoym3eoJk3EImAqalTaKAHePi9hwADVKWC84lz\nDpnLte4ffzx9/Oo6G7iek1PKxxL6zqJm1lCEYfs44FuOUdu5lB/myAYk9GzrN+ru27ZII6MMytcl\nCW1tJxETDLIng+hklIGudIjWS/pJEMGssuci7HShz3U5rWPbqnfvmuo+dj2tke6lOPre0RtZ51ed\nOpMnk3CMGBh0rRfHYM2HWt8R5uCWIp/897bh4JacUJHIVXCZyDKZyxCfZ/XFv7e60TcyDi3dS9F/\n0Ed1Wm10XXp26YJ7F9z3uMq7cA4jkUgkEolEIm+f2x7RHIWnyBKr8UiO0Up9MEDNFBWelxr9oz4N\nLWe0enltX0kkEom8S1rhad1AXKQU9xV657zLCYvBuo/dc8bBWIrIc8YtCUA+Oi5cA9+Cm6n7fHzU\n39K23PJtgvupff6rAgLjDEwwWGUxeTKBSATFoHGGk98/QTWmlTGD9wYoXhfoPeiBSxKe1kVT+dXY\nPOdgkq21gXeFPI/IRHgPYYxBpK3IU3fiCDmWzgcTLIiBF/UbeTaJO7rSwVGk5irExTLGwFOKkOve\nxruOGKPuIplJHHxyAJnLcxF2+UG+1OW0iV1WvYuEHGvOOjSTBtkou9JV776DCQzUYcboNeMdYFzS\nB1znKKaRCYo55IKjOquoD6sn4TRFKHrXWKCNaHTakZiXihuz+gqIA/DbzmUjy0Qq0H/YR+9hL1y7\nNv3eTp5Mbmwc2v4n+yhPy1AgvMl16d1bu1wrr4PV7j9rLS1s6Ev0H/Sx/8n2a+Zd4raew0gkEolE\nIpHI9XHbI5qj8BQJQ5fqrMLJj05QT2pkwwzDx0PUU/qDhzsaQOmaVqhbQwM4XesoOkUikZuJQ+ht\n6sbQMU5OJZGKhRjCgOq0osG7YMEpxSUPMXLa6NDpBAs47eCY272X6U3xLiDXcWptiezzrhRwLOL9\nVvWMdlvNrHU8KUMuHgCzpzPInoRRBjKTYIwhG2Ybd8+vxnZDWqiwzgbuIwmt7ay6affVGUcdWK2o\nxBgjh097bnwMolUWVlmoucLwg+FO/UbAenGnmTUoXhUkKOnFPllL7jYHh+JVgfKkpBVG2oaeJWst\nVEFC1OzFDB/9/Ed48FMP3ki8eJer3qfPp3j9xesgqMEAEAuByWo63kmffldMbejDKwOyfRK/itfU\nYZUf5DC1Od9JtRLRKDIBLvmNWH0VB+B3gzeJLBt8OMDjP/oYALb+3t7kOLTeYQ9Hnx0BoALhN3Vd\nXifruv+4oD+iq7MK1WmF8rTEo88eYXA8eNe7+9a5jecwEolEIpFIJHK93OS/SXYhCk/3mNWhy/zV\nHM2sAU84rWBu5GJY2A47fTySmisa0K0OP9/2ADYSiUR2wb8Xr8632+uYFz28O8M0hobv7fe9w8eL\nMf7nzpG4clGs2FU+D8YYHG/j/VrHkONu+bkJhH9zQddwMOqy6oppjJNbqCvIMcGQyAS9wx4G7w/C\nKutm3sBZh2pcAbh4NTbbZzClWWsDl7kETzg5a3KE+EHfo+WfJ7BwZHHOSXRyJIZwSQ41nnLsf7K/\n82ByVdyRuUTxqgjvd9kwC641VzmwhHoMfX9TupeS8NY+9SCmJBz1pMbLz1/i+PvHGD0e7Xxau7yL\nVe/dHq78IA+dW9ZacgOCBp/+dS+UACzC8fLiV3VWgXN6vfUOe+c7qVYiGpt5Q+f1Ha++igPwu8O3\nFW+3ibg3PQ5teDyEzCTGX43f2HV5Xazr/lu6zh3Sdc5fB0UmbsR+v21u0zmMRCKRSCQSiVw/N/1v\nkouIwtM9ZXXowlMOXWsYRW30/vvZfgYu+dLqc5lJWu3tY6k4W8QiRSKRyE1g3SWJAzKV6D/qw2m3\n5M4wjVlyvjDJgtMDILGB1YxEJ9fZ+Cbn0VU+FeeCmOQsuVG44BT9Zx1EJpCNMnJsKQPZIzHNafq5\nXzgQIuvMYtvWkIMo6Seht4lxFj7MNLOGHCoX9KEcfHqApy+eQk80UJ1/DiIRSHoJdKmhaw2ZSxLx\nvGO2s2jHuw9kTy46tJwjEcQ6pIM0CIK70hV3ytMSqlQUJee7qjgWcX6cgUkGp2jfHNzS/pnagEuO\n3mEPMpMoT0qMvxq/8WDwXax6X+3hYoJh8vUEuqTuRi44HRPnYCrqcPS/D13xq7v6yguXq31YPqLR\nWQf98t2vvooD8LvH2xRvb3ocWu+wh95h78ZHRq7r/uvSfd/5ttfU28ZtOYeRSCQSiUQikXfDTf+b\nZBtReLqHrBu6NPMG9aQOw0dT0+pngAaGXHKY2tCQro2fMsqEId3W6KdIJBJ517T9SOkoJVePcUvu\nDOMMGBgcI2eNj+ILd/dOo+sW2Tt9Ul4Y82ISlxx77+3h6LMjjB6P8PX//TXGT8bQtQbnJFoEZ1Yb\nH7cJZx2q04qO0YAGXdkoQzNtkA5T6vWbNUursdO9FM45qELh+e88x3wyJ3Gh1tTTZB2JM624k+/n\n0CXFtWroIPQA7fFt4/+cceS6zch1G8Q/2zqNxOUdM17cMcrg5PdPoOcaySAJAonVFkIIsB6jfzed\n+D1lSSBjJDpZZZEOUuT7OUQqMH0yRfG6QDNv3nhIeJ2r3pt5c66Hq/+wDwCYPZ/R74ShyEHGGCzo\nnCT9BKPHoyXxa93qK99JtcpNWX0VB+B3j7cp3t6WOLR0L72xIsW6a84mslF2JdfU28hNPoeRSCQS\niUQikXfHbfmbZB1ReLqHrBu6dIvfGWOLSL1SIR2mSHpJGBb6jgbGaEDn4BYr6SORSORd041u4yQW\nMdB1Ld+nwTgSoMd7EKmg+DcG6qzTJDxxuSxs+H8vuZ3eNrx9XLuIdws4+tmjf/QRPv4nPgaA4NJo\n6gaOu0V0Xdu1swoTLMQJOuPCYgMfM8g4g+xLwAKjD0fID/LQBzh/OUd5VqJ4VcBUhnqQmAVLGYSg\nzqj5yzlMbdA/6iMdpJC5RP+IBA5VKqhShUULTjtoq4OzK+kny+fAUdwhA0O+v3DMdFeIm8YArF0s\nsWa1+PB4iOqsCquEQnRfJ1aRS47ydUnxgpbOgVFm6dikAxLivKAm+xK6oIi2bzM0vK5V79VZtbaH\nywtCxWuKIfSuZqcdZC4x+miE4585Pvch9jatvooD8LvL2xRvYxzat2PTNWcd/n3nKq6pkUgkEolE\nIpHIXeG2/k0Shad7xqahy7rid5EJNNMGVtkw5FOlQjNtQnm9hYVTbtn1FIlEIu8KDoqkMy4IT8ZS\ntpzIBLluKk1uJx8Z2olxC86ildkY42zhslntjXpLMMFIHLE0/OeMB1ENjhxc82/mmD2fYXBMLq50\nmMIoA6NMcOlsvC63P2OMQeQiLDaoxhUGOfXacMFhLXVCpXsp6mmN+Ut6zOJ1EbqOZC7RlA2cog6m\nbJihKRqUp+WSuJMOUjDOMHsxC5GBpjFwxoV4PZnJc8KfrjUAinsbHA9gGoPnP3yO4qQgt820XhKe\nslFGH74e9LH/yeLDl19wke6lEJlYxMFlcslZpcr2tWEX8XtJP6H77+eLiL6VY3QVvO1V79bYtT1c\nAHVxjR6PYBpDQqx1UHMFkQkcfudw7YfYq1x99a5Et3XEAfjt422KtzEO7c3Zds1Zx1VfUyORSCQS\niUQikbvAbfybJApP94xNQ5d1xe+MsdDtwSXH4HgQhrXVWUXbaBjdx9OJhYpEIpFrx0e3Seqec4rc\nTuBAklFfUPGqCMIC4wxGU3yaM63w1FhUp1WIHvXij7X22kQnAHDKQRsdOpy44Iuo07aTqnhV4OXn\nL2GtRXFSUIfVwz6JQj6az8cD8vPuJ6NIYEhlGhYbqELBNAYiFbCGVtBwwZdiWnWtwQRDklIkodUW\nTlJ/lCoVZCaRDUh8qk4rOOvQf9gPQkQ2zDD8cIhsmGH81RizZzM468A5BxMdQcABqlLURZhwDD8c\nQuYSz3/rOcqTEtVZtRDaNDlwfS9hfVajOq1QnpZ49NkjDI4HFNPHOSyzyIbZ2uPu3U9ejGScof+w\nj96D3lL8oqd7jG4D/hjoRm+8jUhFeK5WX/z8vu3qq/K0xPjHY1oY096Xcw7Zl+fEw2/zITsOwO8H\nb1O8jXFol2eXa06X23ZNjUQikUgkEolErpPb9DdJFJ7uGZuGLuuK3wFa8esL4GUuMcgHKF4V4HIx\nAHz5uy9RnBTkLmBYlMVHIpHIVbLBvcMk9QN5kQBohfOEwwoLqyySXgLnHCZPJjDK0DWQk5MmOFs6\neGFHFQpMkIhlGnMNT3IFSx1DhhuIwUIMMDCQmYTsSZQnJV5/8Rq60OAph9HUxydyAV3osDhApAKM\nM3rOytIxMxThpxtN/3bk9mlmDfKDHLrQyA8o2u7khycoT0rwlAMNwjZ8T5LTdAyNpWOXH+QYvDfA\n/Js5vfeY9ULEwU8c4OlvPsXkyQTNvAErGJ1TkOjhYxJHH43w4CcfYPJ0gtnzGUW+CgZXOXIsjQQY\nOs+P02vCR8CJTCA/yCH7EtVZBXfo1rpevONKVxrOOaR7KXoPe2t7i5x1S8foNrDLMfBc5vm96eqr\n6fMpXn3+CuVJCV3q4JbSDTmNvHg4+nAEVaqdxKlNxAF4JHL9vK1rTiQSiUQikUgkErnZROHpnrFt\n6LJa/C4zSSvQJScBqu1pMI3B6PEIx99fdD08/c2nYfgYiUQibwV/edngrPTRdEkvAU+oh84oA8MM\nslEGIQU5ZLShqNCO4MQ4CVVccBhlwraNMoDqPsj5x30rsFb4Bwn/utYUidpG/lllIRIBmdIwT9ea\nnFnGBZeqTCU5uFq3hncShWhUAA4OpiahiDEWupJm38xQjSvIjBxUAEJMazpKQ0yhP4aMs3BcrLGA\nAupxjfwgx977exTZOqJBYnaQYXg8DGLE8HiIT37xE7z8/CXO/uAM1ZhcTIyxEG138MkB9j/dx+vf\ne43x12NyfWkDXekQ9efxHYW60iEerzwpMf5qjOOfOUb/QZ/i+Sb10mDTKBPexxinY8E4Q9JL1opO\nAPVqyZ4M/Ui3gXQv3XgMVnmT53eZ1VddF13STzB8PFzuhzqkzx1nX57h7Msz+lxi3EZxyjvbNhEH\n4JHI9fO2rzmRSCQSiUQikUjkZhKFp3uGj06qTisa0PYXA7XV4vdqUlGpeF9CFQr1Wb22p2H0eITp\n0ymqcQVdaahCLXeLtE6EGL8XiUSuhBXhyXfOMUaijEgFRCKoF8gCsifJ0VmTgwUMC9GpjRX1Xxlj\nEImAVeQOhcPybQSJ8D6W720/R855EMd8LB4DiWa60ihtCVUpNEVDohTnQbQCFs+puyiASxKu/HXZ\nwIA7TsKVo++pmaKh+yH1GXVjWk1DAo3V1O8kUkHHtM0hlFJC1+SenT6b0vE0FkwwNEUD/pxj/OPx\nOZdK2k/RO6L/140GYwzpMEX/YR8ODi/+/guMvxxDFQqyJ0kkMg7Z/vnIPJlJ1NMaqlTID3IU3xQo\nXhdo5g32P9lHeVoGJxQYxdCqStF5ZRS3aBUJT0viWotfiKEKhcHxAPsf71/t+X/LrB6DbJS9k+c3\n/vEY5UkZurdWYZzcbqYxUHOF/DDHwacHa8WprrNtk/MpDsAjkXfDTbnmRCKRSCQSiUQikesjCk/3\nhG5/QjWu0BQN9FcaySBB0luUpaeDFFxyVGNaPWyZpe/3U8gjubangQuOJE/CH5Cz5zM4R9FHAEJs\nUxSeIpHIlbBat2IBCBpcOeuCc4VLjnRA3UW61qgnNYkZ/TTEsfGEHJ3e7WMNuYXAabvduFHvOOp2\nJF0an9i1+hxWXVyW3Ej+sbxDS80VnHPBmeUj7rxgpC11L4lEhH4qL56simhg7X6wzuO3z1nkAkLS\nooTp0yn6j/ohprWaVkGg4XJNBBlbiFvNrKGnLRbCFhd8yaXSf9RH8bJYilpLB2lYJFG8LABH+220\nAROM4gdrAzBAzRXQx3L/EgNFLSpycMm+hC7IHTN6PMLRZ0fQlcbZl2eop/VicUR7HPz7Gecc1WkF\nUxlkB9Sz5Xuq1i3EuC30Dns4+uwIAFCelJg+mQYX0XU9v2beBBfd8PFw4+2q8eL1BkaOOsEX55px\nFgQk72zbtr9xAL6Z21RSG7ld3IRrTiQSiUQikUgkErleovB0D1jtTxCpgMzIxWRODVRBxe39oz4N\nadufp3spslGGw588RO+gt3EA0Y2uSUdpKJoPxeTGLgabHMA7qEmJRCJ3G+dccPYwMBpota5OXWuo\nuQpCk0gFTGPAJV8STrjksLAwyiwJNfQA1IfkWPs46Lg6LwMDhBQU4efFJ46FK2lVmGq/+p9ZSy4c\nSATnllGG3E6Sw1oL21iKI+tJiESQAKMXopo1FKsHTuJWEIWMAxMUb9c/6lP8aqVRnpQk5HCOpmjo\nmt4KQUvHqHs+DIlhvvfJi19qrigar41yHX81xtmXZyQe7OdLUWu6IrFQz6h/SvbkUvSr/+q7mDKe\nLQthjrq66mkdvuVjBwHq9/KuqXA8WgHPx8z62wGg11Sfr+2puo0Mj4eQmcT4qzGK14vepOt6fl0X\n3abIO9PQZxRrLEROTkRdaYhEnItGlD255GzbJJjEAfh5uouT3rQ/KxK5iHd9zYlEIpFIJBKJRCLX\nSxSe7jib+hOyYYbiVUGiU61RViV0pZHtZxSv15MYPR5d2JcALEfXWGWXStkZYzTUA4NjMW4vEom8\nJVqHkGUW9bQGL6n3R1UKek7DLWcdRCJIEGkj91ZhIDdNiPEzyxctaywJRW1PkoMDzlfmrdtw2E/v\n1FkStRyJPl6w9zGBS9dMBxK+2j4l29hl8cuSOGQ0CWfNrEHvsAeZyeD8McYsXFRg4OmiH8lqS+8T\nHwyRDSm+TqQC0ydTmNqAJQzNjIQnnrZ9Ut4h1t1N5xZRhh2XlXeV6UpDlzrEH+qS3ntWY898fGvS\nSwDQcfPxh+Ex2vcZXdLx8rF7utZQpQJzDBUqOveZwOmPTuGcw9kfnGH+Yg4m2OI+rQhlrQ3vXY4t\nxxwe/fQR9h7t3RknSO+wh95h7504XayxwUW3CV3r0Fnm3YY+0leVJCb71wBP6DVZnVW0EGbL/scB\n+ILVxUlv2p8ViezCu7zmRCKRSCQSiUQikeslCk93nE39Cd1IPVUoqLmioWRjMPpodOmhy2p0DeMM\naqZoOGkdDWf9QvPVSKlIJBLp0o2BuyTeaWMVOYFEQv1DXgRRpYJk8lzvEUC30bXefm1yAAwJQGAU\nIWfZeXcUAHIvtT13Pn7OR4YFYaPtmfJOLO/8EFaEoXrAdrbPFp19jJHbI+2naOYNjDZw2kEVCmDU\nd+S36+MIPZxxOE2iTDbIgvPVwzi5x6yySPYS8ITTMUwljKL3DItl8cA0Zkmw8y4qL3AhJ0GhntYk\nPjQ23M8Lb8Hpoi3SYQoGhmbeLIQG32EFtogZrDXcmBY6WGuDww0MQUycfzPH7MWM9hEkWMhUopk3\nC8dXmgQ3GRwdP6MMmmmD+cs5Dj49uHMD0nQvvfbnxAUH5yRwbMJZF863cw4wtKDGGRc6xhij8+0X\nvFhtMX85x+jxaOvjxwH45sVJnm5/llEG+2f7SPrJvTtOkavnXVxzIpFIJBKJRCKRyPUShac7zEX9\nCTKXGOQDGvCVCsU3BbL9DO//Y+9j79HepR7LR9foSmP84zHFWhm7GBZ1B8gbopkikUgEjK5NYIAu\nNgykt0XcOQAC5IDSDsYZihDNBUxpKKqr1OCCL/f62IXjZSe8I2pFvOqKTj4KDgwhCo+lJHQwwVCd\nVUFYCvuDxW19fxNA4o3fXyYY9RwZitTz8ahgtKiAMUbxcv55lTrEDjrrSCzS5NxKeglkXy51/a3C\nBUX47T3aQz2tUZ1UMNxA9hZOKdMs3EghTpXRvgopll1R7Tk2is4HOG1D13rJ8RWcLmzRURW6tgQL\n8X2MrGfBDcMYW/Rcte4N1mPoHfbAJcf02ZTiYHOKIdQNPRYTbGk/mWCwDUUUikwAloSrizqEIrvR\njel1h25t3F63f807BZkhZ2A2zJY/S+RANamgS43xV2McfHqw03m6zwPwTYuTPIwzyFyiPC1x8vsn\nmD2fIT/IYwxfJBKJRCKRSCQSiUQuJApPd5hd+hMAWjUfepkEdZ98G7jkYZW50Wax4r9LdDtFIpE1\n8ISuH75Xh74JEq9ZR8zZhlvczsfqJUkSRCbbWFhQTJ1RdL2z2gaR5zIE8ap9XA9jbPGVt8JSK2o5\n1vYqCepeAkhoYYKG7F1hxW+nK94762AbEvZFKsiB0BFMfK8VSxiGx0NwwUOnlUwldK1RnBQQUqD/\nqP//s/dmz21dabbn2sOZMHDQYCvttLMy61aFH6qrHqtv3Oj/vzv6paujoq9vRVZmpdNyyhZFEgRw\npj31w3f2xkCABClSlqjvF5EhiQQODg6ADee39loLuqQuqH14t4og+/KfvkRz3qA5b+AEHTMgAAYQ\nYdhoMLxe8ZxkJq/F8QGAyhTM0iR30frruu50AUgU9G4lDiXnmBTJ5ZbEwCFGUeR0X2888kmO8qSk\nPqzB7eZ76iL0ZohhzDevQXwtggsIKkBmEq5zt3YIMYexHtPbXXU7hQ9dUK9Xv+hTpJ4u9U6BNDrY\nZCmT+MSCyH5u25wEAP2ip1jmhiJLAXrdvPAcw8cwDMMwDMMwDMPcCAtPT5hD+hPWibva18vX17kp\njibGtbSXLUYvRhi9HGH5dol+0dNO+LXOFIZhmH1EQSCJQOvupnC9c2n3QZAECmfcSpjwNDTtXJfc\nM9753eL4NnEZ3XZv7rltitBTQ4SeIneONx7GGfjMJ3EpeDq3KJh4UHye1NS/FF1KG89Pk3NKV/q6\nqDMIMkoqPPvDMzz/h+cba7fKFX7+95+x+GmBfJzfuDEh+ABbW5QnZVrz52/mOPufZ3DWkYCWSaAA\nur4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JU/mEelJ2xYUBwJt/e5McRvvcUaY2mLyaQJcab/7tzb1i8h5zl/5tTqp8kuPizxcw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OL/AS09/tEQwo+2tV9mu5x3K2WjjBZ2PYlQKbYK626E84uQ1y1Lnbw/gW0s2mmbjpGPc0gt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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [], + "image/png": { + "width": 847, + "height": 418 + } + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "TSU2zWC3JZh5" + }, + "source": [ + "The above is a classic phenomenon in statistics. I say *classic* referring to the \"shape\" of the scatter plot above. It follows a classic triangular form, that tightens as we increase the sample size (as the Law of Large Numbers becomes more exact). \n", + "\n", + "I am perhaps overstressing the point and maybe I should have titled the book *\"You don't have big data problems!\"*, but here again is an example of the trouble with *small datasets*, not big ones. Simply, small datasets cannot be processed using the Law of Large Numbers. Compare with applying the Law without hassle to big datasets (ex. big data). I mentioned earlier that paradoxically big data prediction problems are solved by relatively simple algorithms. The paradox is partially resolved by understanding that the Law of Large Numbers creates solutions that are *stable*, i.e. adding or subtracting a few data points will not affect the solution much. On the other hand, adding or removing data points to a small dataset can create very different results. \n", + "\n", + "For further reading on the hidden dangers of the Law of Large Numbers, I would highly recommend the excellent manuscript [The Most Dangerous Equation](http://nsm.uh.edu/~dgraur/niv/TheMostDangerousEquation.pdf). " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "yULJJOOOJZh5" + }, + "source": [ + "### Example: How to order Reddit submissions\n", + "\n", + "You may have disagreed with the original statement that the Law of Large numbers is known to everyone, but only implicitly in our subconscious decision making. Consider ratings on online products: how often do you trust an average 5-star rating if there is only 1 reviewer? 2 reviewers? 3 reviewers? We implicitly understand that with such few reviewers that the average rating is **not** a good reflection of the true value of the product.\n", + "\n", + "This has created flaws in how we sort items, and more generally, how we compare items. Many people have realized that sorting online search results by their rating, whether the objects be books, videos, or online comments, return poor results. Often the seemingly top videos or comments have perfect ratings only from a few enthusiastic fans, and truly more quality videos or comments are hidden in later pages with *falsely-substandard* ratings of around 4.8. How can we correct this?\n", + "\n", + "Consider the popular site Reddit (I purposefully did not link to the website as you would never come back). The site hosts links to stories or images, called submissions, for people to comment on. Redditors can vote up or down on each submission (called upvotes and downvotes). Reddit, by default, will sort submissions to a given subreddit by Hot, that is, the submissions that have the most upvotes recently.\n", + "\n", + "\n", + "\n", + "\n", + "How would you determine which submissions are the best? There are a number of ways to achieve this:\n", + "\n", + "1. *Popularity*: A submission is considered good if it has many upvotes. A problem with this model is that a submission with hundreds of upvotes, but thousands of downvotes. While being very *popular*, the submission is likely more controversial than best.\n", + "2. *Difference*: Using the *difference* of upvotes and downvotes. This solves the above problem, but fails when we consider the temporal nature of submission. Depending on when a submission is posted, the website may be experiencing high or low traffic. The difference method will bias the *Top* submissions to be the those made during high traffic periods, which have accumulated more upvotes than submissions that were not so graced, but are not necessarily the best.\n", + "3. *Time adjusted*: Consider using Difference divided by the age of the submission. This creates a *rate*, something like *difference per second*, or *per minute*. An immediate counter-example is, if we use per second, a 1 second old submission with 1 upvote would be better than a 100 second old submission with 99 upvotes. One can avoid this by only considering at least t second old submission. But what is a good t value? Does this mean no submission younger than t is good? We end up comparing unstable quantities with stable quantities (young vs. old submissions).\n", + "3. *Ratio*: Rank submissions by the ratio of upvotes to total number of votes (upvotes plus downvotes). This solves the temporal issue, such that new submissions who score well can be considered Top just as likely as older submissions, provided they have many upvotes to total votes. The problem here is that a submission with a single upvote (ratio = 1.0) will beat a submission with 999 upvotes and 1 downvote (ratio = 0.999), but clearly the latter submission is *more likely* to be better.\n", + "\n", + "I used the phrase *more likely* for good reason. It is possible that the former submission, with a single upvote, is in fact a better submission than the later with 999 upvotes. The hesitation to agree with this is because we have not seen the other 999 potential votes the former submission might get. Perhaps it will achieve an additional 999 upvotes and 0 downvotes and be considered better than the latter, though not likely.\n", + "\n", + "What we really want is an estimate of the *true upvote ratio*. Note that the true upvote ratio is not the same as the observed upvote ratio: the true upvote ratio is hidden, and we only observe upvotes vs. downvotes (one can think of the true upvote ratio as \"what is the underlying probability someone gives this submission a upvote, versus a downvote\"). So the 999 upvote/1 downvote submission probably has a true upvote ratio close to 1, which we can assert with confidence thanks to the Law of Large Numbers, but on the other hand we are much less certain about the true upvote ratio of the submission with only a single upvote. Sounds like a Bayesian problem to me.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "_Uv4KU6jJZh7" + }, + "source": [ + "One way to determine a prior on the upvote ratio is to look at the historical distribution of upvote ratios. This can be accomplished by scraping Reddit's submissions and determining a distribution. There are a few problems with this technique though:\n", + "\n", + "1. Skewed data: The vast majority of submissions have very few votes, hence there will be many submissions with ratios near the extremes (see the \"triangular plot\" in the above Kaggle dataset), effectively skewing our distribution to the extremes. One could try to only use submissions with votes greater than some threshold. Again, problems are encountered. There is a tradeoff between number of submissions available to use and a higher threshold with associated ratio precision. \n", + "2. Biased data: Reddit is composed of different subpages, called subreddits. Two examples are *r/aww*, which posts pics of cute animals, and *r/politics*. It is very likely that the user behaviour towards submissions of these two subreddits are very different: visitors are likely friendly and affectionate in the former, and would therefore upvote submissions more, compared to the latter, where submissions are likely to be controversial and disagreed upon. Therefore not all submissions are the same. \n", + "\n", + "\n", + "In light of these, I think it is better to use a `Uniform` prior.\n", + "\n", + "\n", + "With our prior in place, we can find the posterior of the true upvote ratio. The Python script below will scrape the best posts from the `showerthoughts` community on Reddit. This is a text-only community so the title of each post *is* the post." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "-KaCH4igIUOV" + }, + "source": [ + "#### Setting up the `Praw` Reddit API\n", + "\n", + "Use of the `praw` package for retrieving data from Reddit does require some private information on your Reddit account. As such, we are not releasing the secret keys and reddit account passwords that we originally used for the code cell below. Fortunately, we've provided detailed information on how to set up the next code cell with your custom information." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "DVKFEZmkJZh7" + }, + "source": [ + "#### Register your Application on Reddit\n", + "\n", + "1. Log into your Reddit account.\n", + "\n", + "2. Click the down arrow to the right of your name, then click the Preferences button.\n", + "\n", + "\n", + "\n", + "3. Click the app tab.\n", + "\n", + "\n", + "\n", + "4. Click the create another app button at the bottom left of your screen.\n", + "\n", + "5. Populate your script with the required fields. Refer to the screen shot below:\n", + "\n", + "\n", + "\n", + "6. Hit the create app button once you have populated all fields. You should now have a script which resembles the following:\n", + "\n", + "\n", + "\n", + "\n", + "NOTE: Certain components of the `reddit = praw.Reddit(\"BasyesianMethodsForHackers\")` code have been intentionally omitted. This is because praw requires a user ID for accessing Reddit. the praw function follows the following format:\n", + "```python\n", + "reddit = praw.Reddit(client_id='PERSONAL_USE_SCRIPT_14_CHARS', \\\n", + " client_secret='SECRET_KEY_27_CHARS ', \\\n", + " user_agent='YOUR_APP_NAME', \\\n", + " username='YOUR_REDDIT_USER_NAME', \\\n", + " password='YOUR_REDDIT_LOGIN_PASSWORD')\n", + "```\n", + "For help with creating a Reddit instance, visit\n", + "https://praw.readthedocs.io/en/latest/code_overview/reddit_instance.html.\n", + "\n", + "For help on configuring PRAW, visit\n", + "https://praw.readthedocs.io/en/latest/getting_started/configuration.html." + ] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "ScHOHPunJZh7", + "outputId": "c3823218-9961-4803-d9b7-7c5b19393e01", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 91 + } + }, + "source": [ + "#@title Reddit API setup\n", + "import sys\n", + "import numpy as np\n", + "from IPython.core.display import Image\n", + "import praw\n", + "\n", + "reset_sess()\n", + "\n", + "enter_client_id = 'ZhGqHeR1zTM9fg' #@param {type:\"string\"}\n", + "enter_client_secret = 'keZdvIa1Ge257NKEm3v-eGEdv8M' #@param {type:\"string\"}\n", + "enter_user_agent = \"bayesian_app\" #@param {type:\"string\"}\n", + "enter_username = \"ThisIsJustADemo\" #@param {type:\"string\"}\n", + "enter_password = \"EnterYourOwnInfoHere\" #@param {type:\"string\"}\n", + "\n", + "subreddit_name = \"showerthoughts\" #@param [\"showerthoughts\", \"todayilearned\", \"worldnews\", \"science\", \"lifeprotips\", \"nottheonion\"] {allow-input: true}\n", + "\n", + "reddit = praw.Reddit(client_id=enter_client_id,\n", + " client_secret=enter_client_secret,\n", + " user_agent=enter_user_agent,\n", + " username=enter_username,\n", + " password=enter_password)\n", + "subreddit = reddit.subreddit(subreddit_name)\n", + "\n", + "# go by timespan - 'hour', 'day', 'week', 'month', 'year', 'all'\n", + "# might need to go longer than an hour to get entries...\n", + "\n", + "timespan = 'day' #@param ['hour', 'day', 'week', 'month', 'year', 'all']\n", + "\n", + "top_submissions = subreddit.top(timespan)\n", + "\n", + "#adding a number to the inside of int() call will get the ith top post.\n", + "ith_top_post = 2 #@param {type:\"number\"}\n", + "n_sub = int(ith_top_post)\n", + "\n", + "i = 0\n", + "while i < n_sub:\n", + " top_submission = next(top_submissions)\n", + " i += 1\n", + "\n", + "top_post = top_submission.title\n", + "\n", + "upvotes = []\n", + "downvotes = []\n", + "contents = []\n", + "\n", + "for sub in top_submissions:\n", + " try:\n", + " ratio = sub.upvote_ratio\n", + " ups = int(round((ratio*sub.score)/(2*ratio - 1))\n", + " if ratio != 0.5 else round(sub.score/2))\n", + " upvotes.append(ups)\n", + " downvotes.append(ups - sub.score)\n", + " contents.append(sub.title)\n", + " except Exception as e:\n", + " continue\n", + "\n", + "votes = np.array( [ upvotes, downvotes] ).T\n", + "\n", + "print(\"Post contents: \\n\")\n", + "print(top_post)" + ], + "execution_count": 9, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Post contents: \n", + "\n", + "If giraffes were stealthy meat-eating predators with a taste for human flesh, they would stalk apartment complexes at night and wait for people to walk by their open windows.\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "9vvhJ7o3JZh-" + }, + "source": [ + "Above is the top post as well as some other sample posts:" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "3v8LKHnX4-z5", + "outputId": "8ebcacf5-fe7e-4a66-cb2f-37393212e7fc", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 269 + } + }, + "source": [ + "\"\"\"\n", + "contents: an array of the text from the last 100 top submissions to a subreddit\n", + "votes: a 2d numpy array of upvotes, downvotes for each submission.\n", + "\"\"\"\n", + "n_submissions_ = len(votes)\n", + "submissions = tfd.Uniform(low=float(0.), high=float(n_submissions_)).sample(sample_shape=(4))\n", + "submissions_ = evaluate(tf.cast(submissions,tf.int32))\n", + "\n", + "print(\"Some Submissions (out of %d total) \\n-----------\"%n_submissions_)\n", + "for i in submissions_:\n", + " print('\"' + contents[i] + '\"')\n", + " print(\"upvotes/downvotes: \",votes[i,:], \"\\n\")" + ], + "execution_count": 10, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Some Submissions (out of 98 total) \n", + "-----------\n", + "\"You are free to commit crime but you just have to face the consequences.\"\n", + "upvotes/downvotes: [45 6] \n", + "\n", + "\"You can burn yourself with water.\"\n", + "upvotes/downvotes: [612 53] \n", + "\n", + "\"Telling someone that she was made for you goes from romantic to selfish the more you think about it.\"\n", + "upvotes/downvotes: [335 10] \n", + "\n", + "\"Just need to flip the Flat Earth over so we can chill on the cool side for awhile\"\n", + "upvotes/downvotes: [162 20] \n", + "\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "IwRZoAdAJZiA" + }, + "source": [ + "For a given true upvote ratio $p$ and $N$ votes, the number of upvotes will look like a Binomial random variable with parameters $p$ and $N$. (This is because of the equivalence between upvote ratio and probability of upvoting versus downvoting, out of $N$ possible votes/trials). We create a function that performs Bayesian inference on $p$, for a particular submission's upvote/downvote pair." + ] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "sVsMkqyz0ULQ", + "colab": {} + }, + "source": [ + "def joint_log_prob(upvotes, N, test_upvote_ratio):\n", + " \"\"\"\n", + " Args:\n", + " upvotes: observed upvotes for a submission\n", + " N : observed upvotes+downvotes for the submission\n", + " test_upvote_ratio: hypothesized value for true value of upvote ratio\n", + " Returns: \n", + " Joint log probability optimization function to compute true upvote ratio.\n", + " \"\"\"\n", + " tfd = tfp.distributions\n", + "\n", + " # use a uniform prior\n", + " rv_upvote_ratio = tfd.Uniform(name=\"upvote_ratio\", low=0., high=1.)\n", + " rv_observations = tfd.Binomial(name=\"obs\",\n", + " total_count=float(N),\n", + " probs=test_upvote_ratio)\n", + " return (\n", + " rv_upvote_ratio.log_prob(test_upvote_ratio)\n", + " + rv_observations.log_prob(float(upvotes))\n", + " )" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "A7Otcm3a31oo" + }, + "source": [ + "in some cases we might want to run someting like an HMC for multiple, or a variable number, of inputs. Loops are common examples of this. Here we define our function for setting up an HMC that can take in different numbers of upvotes and/or downvotes." + ] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "_aUOU-FLJZiB", + "colab": {} + }, + "source": [ + "def posterior_upvote_ratio(upvotes, downvotes):\n", + " reset_sess()\n", + " \n", + " burnin = 5000\n", + " N = float(upvotes) + float(downvotes)\n", + "\n", + " # Initialize the step_size. (It will be automatically adapted.)\n", + " with tf.variable_scope(tf.get_variable_scope(), reuse=tf.AUTO_REUSE):\n", + " step_size = tf.get_variable(\n", + " name='step_size',\n", + " initializer=tf.constant(0.5, dtype=tf.float32),\n", + " trainable=False,\n", + " use_resource=True\n", + " ) \n", + "\n", + " # Set the chain's start state.\n", + " initial_chain_state = [\n", + " 0.5 * tf.ones([], dtype=tf.float32, name=\"init_upvote_ratio\")\n", + " ]\n", + "\n", + " # Since HMC operates over unconstrained space, we need to transform the\n", + " # samples so they live in real-space.\n", + " unconstraining_bijectors = [\n", + " tfp.bijectors.Sigmoid() \n", + " ]\n", + "\n", + " # Define a closure over our joint_log_prob.\n", + " unnormalized_posterior_log_prob = lambda *args: joint_log_prob(upvotes, N, *args)\n", + "\n", + " hmc=tfp.mcmc.TransformedTransitionKernel(\n", + " inner_kernel=tfp.mcmc.HamiltonianMonteCarlo(\n", + " target_log_prob_fn=unnormalized_posterior_log_prob,\n", + " num_leapfrog_steps=2,\n", + " step_size=step_size,\n", + " state_gradients_are_stopped=True),\n", + " bijector=unconstraining_bijectors)\n", + "\n", + " hmc = tfp.mcmc.SimpleStepSizeAdaptation(\n", + " inner_kernel=hmc, num_adaptation_steps=int(burnin * 0.8)\n", + " )\n", + "\n", + " # Sample from the chain.\n", + " [\n", + " posterior_upvote_ratio\n", + " ], kernel_results = tfp.mcmc.sample_chain(\n", + " num_results=20000,\n", + " num_burnin_steps=burnin,\n", + " current_state=initial_chain_state,\n", + " kernel=hmc)\n", + "\n", + " # Initialize any created variables.\n", + " init_g = tf.global_variables_initializer()\n", + " init_l = tf.local_variables_initializer()\n", + " \n", + " evaluate(init_g)\n", + " evaluate(init_l)\n", + " \n", + " return evaluate([\n", + " posterior_upvote_ratio,\n", + " kernel_results,\n", + " ])" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "xLXqEPVj5PoJ", + "outputId": "1e314f4e-5939-4e7d-e9d5-7e2cfa56c915", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 575 + } + }, + "source": [ + "plt.figure(figsize(11., 8))\n", + "posteriors = []\n", + "colours = [\"#5DA5DA\", \"#F15854\", \"#B276B2\", \"#60BD68\", \"#F17CB0\"]\n", + "for i in range(len(submissions_)):\n", + " j = submissions_[i]\n", + " posteriors.append( posterior_upvote_ratio(votes[j, 0], votes[j, 1])[0] )\n", + " plt.hist( posteriors[i], bins = 10, normed = True, alpha = .9, \n", + " histtype=\"step\",color = colours[i], lw = 3,\n", + " label = '(%d up:%d down)\\n%s...'%(votes[j, 0], votes[j,1], contents[j][:50]) )\n", + " plt.hist( posteriors[i], bins = 10, normed = True, alpha = .2, \n", + " histtype=\"stepfilled\",color = colours[i], lw = 3, )\n", + " \n", + "plt.legend(loc=\"upper left\")\n", + "plt.xlim( 0, 1)\n", + "plt.title(\"Posterior distributions of upvote ratios on different submissions\");" + ], + "execution_count": 13, + "outputs": [ + { + "output_type": "stream", + "text": [ + "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_probability/python/distributions/uniform.py:182: add_dispatch_support..wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "Use tf.where in 2.0, which has the same broadcast rule as np.where\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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fAsjkJF80PNyH3B0DD2UK6P3BmzT7681DO/K7qWxJer2NszsXz/mybSfXkPk6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\nTk4AbNq0Se++f/wx+3mYm5ub1nZjY2NcXFx48uQJ3377Lenp6Vozu9U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i7u6u82mD5y1Z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\nbN26lXXr1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VlniKYtqxFtxqIZiVq/fn0WL16cqz5mZmbY2tpy+/ZtLl26pJdUBePGn58Y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\nFk1SP6tR09kllAGuXLlicPm1a9dIT0/H0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GF57733MmDAgAwdOjRTp07NqFGj8vvf/z4n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\nK9cToD24lgDtxfWE5aJTxwXTxYLJvuus037b7bhg+sN2PE/vNL1hurZmQ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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [], + "image/png": { + "width": 735, + "height": 484 + } + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "eyvNr1XUJZiG" + }, + "source": [ + "Some distributions are very tight, others have very long tails (relatively speaking), expressing our uncertainty with what the true upvote ratio might be.\n", + "\n", + "#### Sorting!\n", + "\n", + "We have been ignoring the goal of this exercise: how do we sort the submissions from *best to worst*? Of course, we cannot sort distributions, we must sort scalar numbers. There are many ways to distill a distribution down to a scalar: expressing the distribution through its expected value, or mean, is one way. Choosing the mean is a bad choice though. This is because the mean does not take into account the uncertainty of distributions.\n", + "\n", + "I suggest using the *95% least plausible value*, defined as the value such that there is only a 5% chance the true parameter is lower (think of the lower bound on the 95% credible region). Below are the posterior distributions with the 95% least-plausible value plotted:" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "19LfJYnb2tNk", + "outputId": "e60045fb-be9d-4c14-a0c3-16a94ae13e26", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 519 + } + }, + "source": [ + "N = posteriors[0].shape[0]\n", + "lower_limits = []\n", + "for i in range(len(submissions_)):\n", + " j = submissions_[i]\n", + " plt.hist( posteriors[i], bins = 20, normed = True, alpha = .9, \n", + " histtype=\"step\",color = colours[i], lw = 3,\n", + " label = '(%d up:%d down)\\n%s...'%(votes[j, 0], votes[j,1], contents[j][:50]) )\n", + " plt.hist( posteriors[i], bins = 20, normed = True, alpha = .2, \n", + " histtype=\"stepfilled\",color = colours[i], lw = 3, )\n", + " v = np.sort( posteriors[i] )[ int(0.05*N) ]\n", + " plt.vlines( v, 0, 30 , color = colours[i], linestyles = \"--\", linewidths=3 )\n", + " lower_limits.append(v)\n", + " plt.legend(loc=\"upper left\")\n", + "\n", + "plt.legend(loc=\"upper left\")\n", + "plt.title(\"Posterior distributions of upvote ratios on different submissions\");\n", + "order = np.argsort( -np.array( lower_limits ) )\n", + "print(order, lower_limits)" + ], + "execution_count": 14, + "outputs": [ + { + "output_type": "stream", + "text": [ + "[2 1 3 0] [0.7836006, 0.9008505, 0.9514801, 0.8460546]\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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UF1P082xuN78BkMVJvmikcR9ydwzSKJNf7w+epNlfb2m0I7+Zypak19s4u3Px\ngjfbdnINma+DODjez5RdnrLO5nt6fUAbcslYdwK0nuLR0H7J4ck5W2HKn6LXaXP7mgSgr5N8nWHb\ntt+A9iWsAjAPLt5zmPcFqe/VjPprXt9MX+omPLt/uWojXJX5ut3xiIHtdf8PgFqm5X19D+rXtsfT\n+gsnbYKv+8CJEydOnFIn9tAmInrIKaV2AagGLSh0HFpQIgnaeK7DATRQjg8G+w7AQGi9O3+H9iEr\nD7Sg5yoAzyilBjvZ1pd6vl36NkoBKAMteGpebjeAqtB+1r0N2ht9C7QPr3ug9ZZprpTanL69d+Tj\n8fDn9i9AC2j9B1rAKwu0D7LzoPUW9LZ30E1o43ROg3bc/wGQF9oH6t3QhhCoo5Q6a1eOK9DGk1ym\n5ykC7VyV8WW/PBAD7cuEadDGY80GrYfaXL18vznLpJSaAe1n9jug1Y8gaL3bOiulxqexzZcBfACt\nt2t2pO6fR0M5KKX+AlAbwHikjr8O/f/3oH0AP+7Jujz0O7ThPz6DNs7vdWg98G8AiALwfwAaKy97\njAZgP8zbTs/5M9bxOrRA115ow4fchjZkRDuljS3uLu8JaNeFwWnvbNPyyUqpF6Cdh1+gnQOj7VsE\n4DGl1CwXeTND+3cS2nX4DbQAaRZo+/gtgDCl1C9u8iZC6xH6NbSAfAGkXlPuhj0yr2M1tOFO5kIL\nAuWCtt9R0MbSbau0nvJmU6F9MbISWpst0K7nv6Hdo5oppd6Hl5RSn0Nri7+Ddn7zQLvWNgDoqpTq\no7QhTe6ZDLg/+LNsfwGoC+AVaNffNWhDOiVB+xXIHADtodUtX7fxJYAq0O4TR6EFaPNBu39EQnsg\nbhVf1+9im/+FFlzeDC3gWRJanQ7xcBUjAYyA1oP+JLR7WxZo5+pLAPWUUl872e5yaF8g/Q/alxZZ\noD0LYIBSaoCHZZ8G4Bm97EHQvgTdAaCPUupVD8t/L3SEdi82eqbngRZMPgSt9391pdQh0/I+vQd1\nx8e2Jz38vg9ERA8bUUoFugxERERERB4TkX7QgkGblVLhgS0NERERERHdS+yhTURERERERERERESZ\nAgPaRERERERERERERJQpMKBNRERERERERERERJkCA9pERERERERERERElCnwoZBERERERERERERE\nlCmwhzYRERERERERERERZQoMaBMRERERERERERFRpsCANhERERERERERERFlCgxoExERERERERER\nEVGmwIA2EREREREREREREWUKwYEuQCDcuHFjP4ByAG4D+CvAxSEiIiIiIiIiIiJ6EFUEkAfAqfz5\n89f1xwofyoA2tGB2fn0qGeDg4V24AAAgAElEQVSyEBERERERERERET3IyvlrRQ/rkCO3A10AenjE\nxcUhLi4u0MUgIrLBtomI7kdsm4jofsS2iYjuR5mwbfJbPPZhDWhzmBG6Z86dO4dz584FuhhERDbY\nNhHR/YhtExHdj9g2EdH9KBO2TX6Lxz6sAW0iIiIiIiIiIiIiymQY0CYiIiIiIiIiIiKiTIEBbSIi\nIiIiIiIiIiLKFBjQJiIiIiIiIiIiIqJMgQFtIiIiIiIiIiIiIsoUGNAmIiIiIiIiIiIiokyBAW0i\nIiIiIiIiIiIiyhSCA12AzCAlJQW3b99GXFwcEhMTA10cyqT+/vvvQBeBiMgB2yaizCNr1qzIlSsX\n8uTJg6Ag9kshIiIioocTA9ppSElJwZUrV5CQkBDoolAmlS1btkAXgYjIAdsmoswnMTERN27cQHx8\nPAoXLsygNhERERE9lBjQTsPt27eRkJCALFmyoECBAsiePTs/PJBX4uPjAQA5cuQIcEmIiFKxbSLK\nXFJSUpCQkIBr164hISEBt2/fRr58+QJdLCIiIiKie46R2TTExcUBAAoUKICcOXMymE1ERERE91xQ\nUBBy5swJi8UCIPU9KhERERHRw4bR2TQYY2Znz549wCUhIiIiooed8auKpKSkAJeEiIiIiCgwGND2\nEHtmExEREVGgiQgAQCkV4JIQEREREQUGo7RERERERJmEEdAmIiIiInpYMaBNRERERERERERERJkC\nA9pERERERERERERElCkwoE1EREREREREREREmQID2uQ3u3fvRoECBTB27Ng0l7106RLKlSsHi8WC\nkiVLOl3m9OnTsFgsbqelS5f6eS8C4/r165gwYQIaNWqE0NBQlCxZEvXq1cOgQYNw7Ngxv21ny5Yt\nsFgsaN++vd/WGUg9e/ZEaGgoLl68GOiiEBERERERERHRPRAc6ALQg0Ephbfeegv58uVDREREmstH\nRETg+vXrHq07d+7ceOaZZ5ymlSlTxqty3o8OHDiArl274p9//kGZMmXQokULpKSkIDo6GkuWLEHL\nli1RtWrVQBfzvjRq1Cg0bdoU48ePx6xZswJdHCIiIiIiIiIiymAMaJNf/Pjjj9i3bx+GDx8Oi8Xi\ndtlFixbhp59+wsCBAzF37tw0112wYEHMnj3bX0W9r1y4cAGdO3dGbGwsZs+ejZ49e9qknz9/HsnJ\nyQEq3f2vRo0aePrpp7Fo0SK88sorqFGjRqCLREREREREREREGYhDjpBfzJ49GyKCPn36uF3uwoUL\nGDlyJOrXr49XXnnlHpXu/jV69Ghcu3YN77zzjkMwGwBKlCiBUqVKBaBkmUefPn2glMKcOXMCXRQi\nIiIiIiIiIspgDGhTuu3btw/79u1D48aN0xwCJCIiAnfu3MHMmTMRFHTvq1/79u1hsViwZcsWp+lD\nhgyBxWLBt99+63L+oUOH0KtXL5QvXx4hISFo3rw5vvnmG6/LcunSJaxcuRK5cuVC//79fdofV9as\nWYO2bduiZMmSKFOmDDp16oSoqKg08+3cuRN9+/ZF5cqVUaRIEVSuXBnPP/88du/e7bBsjx49YLFY\nsGHDBpv5169fR8GCBWGxWDBmzBiHfC1atIDFYsGBAwes88zn5cCBA+jRowfKlSuHYsWKoXHjxli4\ncKHLMrdq1QpFixbFjz/+6PEwNkRERERERERElDkxoE3ptnbtWgBAeHi42+W++eYbrF+/Hm+88QYe\neeQRj9cfFxeH//znP4iIiMCIESPwxRdf4Ny5c+kpss/27t2LNm3a4Pfff8cTTzyBxx57DEeOHMGr\nr76KESNGOM0TEhKCkJAQhyD6li1bkJSUhFq1aiF37tyIiorCmDFjEBERgY8++giHDx/2qYzTp09H\nnz59sHPnTtSoUQOtW7fG5cuX8cwzz1jPlTPz5s1Du3btsHr1aoSGhqJjx44IDQ3FqlWr0LZtW3z1\n1Vc2yzdv3hwAEBkZ6bBfKSkpTtOuX7+OgwcPomDBgqhdu7ZDGTZt2oTWrVvjzJkzaNGiBerUqYOj\nR49i6NCh+OSTT5yWO0uWLGjSpAni4uKwefPmtA4PERERERERERFlYhxDm9LN6PkbFhbmcplz587h\n3//+N6pXr45hw4Z5tf6YmBiMHz/eZt7bb7+NoUOHYvTo0RAR7wvto/nz52Pw4MF4//33kSVLFgDA\nnj170LlzZ8yZMwetWrVCmzZtPFrXb7/9BgAoUqQI+vfvj2XLltmkT5w4ES+++CKmTJli3VZaDh48\niPHjxyM4OBhff/012rVrZ02bMWMG3n33Xaf5Dh8+jLfeegsAsGDBAnTq1MmatnTpUgwcOBBvvvkm\nwsLCUK1aNQCpAW37IPKvv/4KAKhWrRoOHz6Mq1evomDBggC0upKcnIymTZs6PW/Tpk3DJ598gr59\n+1rn/fDDDxg8eDA++ugjDBgwALly5XLIFxYWhmXLluHXX39Fx44d0z5QRERERERERESUKbGHNqWb\n0ZO4SpUqLpd57bXXEBsbi08//RRZs2b1aL3Zs2dHv379sGLFCvz++++4cOECtm3bhoiICIgIPv74\nY0ycONEv++CpEiVKYPz48TYB5kcffRRDhgwBAMyaNcshT8WKFVGxYkWHQOy1a9cAAD///DNWrVqF\nd999F0eOHMGJEycwe/Zs5M+fH19++SUmT57scfnmzp2L5ORkdO3a1SaYDQBDhw5FnTp1nOb7/PPP\nkZSUhOeee84mmA3AOi8xMRGfffaZdX61atVQrFgxHD16FFeuXLHO37x5M4oXL46BAwciJSXFGuA2\n0oDUYLi9Z555xiaYDQDdu3dHlSpVcPPmTezfv99pvqpVqwIADh065DSdiIiIiIiIiIgeDAxoU7rE\nxsYiLi4OAKy9cO0tXLgQGzduxP/93/+5DKg6ExISgmnTpiE8PBzFixdHzpw5Ua1aNYwdO9Y6/MX0\n6dNx4cKF9O+Ih5555hlkz57dYX6PHj0AADt27EBSUpJNWlRUFKKiolC/fn2b+cawHImJiYiIiMCw\nYcMQGhqKQoUKoWfPnpgxYwYA4NNPP8Xt27c9Kt/WrVsBaEFgZ7p16+Y2X69evZymGw/7tB+Hu1mz\nZlBKWYPWFy5cwPHjx9GsWTPrEDTmYUeM5VwNT9O2bVun8ytVqgQAuHjxotP0AgUKAAAuX77sNJ2I\niIiIiIiIiB4MDGhTuty8eROA1ps6W7ZsDulnz57F6NGjUalSJYwcOdJv223Xrh1q1aqFxMREh3Ga\nM5Krh16GhoYiKCgI8fHxuHr1qkfrypMnj/X/F154wSG9Y8eOKFSoEGJjY7F3716P1nn+/Hm35Sxd\nurTT+caXAq7ylS1b1mY5Q7NmzQCkBq2NHtjh4eEoV64cSpcubU27ePEi/vjjD4SGhqJ8+fJOtxMa\nGup0ft68eQEA8fHxbtNv3LjhNJ2IiIiIiIiIiB4MHEOb0iV//vwAgISEBNy9e9chqL1582bcvHkT\nBQoUwHPPPWeTlpCQAAC4c+cO2rdvDwAYPXo0GjZs6NG2K1eujEOHDvm1h7bRa/peMILHwcHBLgO5\nZcqUQUxMzD3reezteORGT2sjkG0/pEjz5s3x9ddf4/Tp09ixY4dNmjNBQb59x3br1i0AgMVi8Sk/\nERERERERERFlDgxo+2jUxitpL5QJTGxVOF35c+XKhdy5cyM2NhZXr15FSEiI0+VOnz6N06dPO01L\nSUmxDnkRExPj8baNntC5c+f2OI8RcI+NjXWa/vfff7vNf+bMGafzz549i5SUFOTIkcPl0Cv2ateu\nDQBISkrCjRs3nAZjjePh6T4WL14c0dHROHPmDMqVK+dx+YsXL45Tp04hOjraab7o6GjrcmalSpVC\n+fLlcfLkSURHR+PXX39F5cqVUaJECQBawPvrr79GZGQkdu7caZ3nb0ZdKFKkiN/XTURERERERERE\n9w8OOULpVqtWLQDAH3/84ZDWu3dvXL9+3el08OBBAFqw1pj39NNPe7TNS5cuYfv27QCAevXqeVxW\nIyD7559/OqRdvnw5zYcKrlq1Cnfv3nWYv3jxYgBAgwYNEBzs2fdEYWFh1i8AjJ7NZidPnrQG2OvW\nrevROhs3bmxTHntLlixxm2/RokVO07/99lsAQJMmTRzSjB7XX3zxBc6dO2fTA7tZs2YQEURGRlrH\nzzaGKfGnY8eOAUj9koCIiIiIiIiIiB5MDGhTujVt2hQAsGvXLr+u96uvvrKOCW127Ngx9OzZE3fu\n3MFjjz2GsLAwj9dpBFvnzp1r84DBa9euYciQIWk+fPHcuXMYO3aszdAk+/btw6xZswAAL7/8skOe\nJk2aoEmTJg7jYAcFBeH1118HAIwZMwYnT560pl2/fh1Dhw5FSkoKOnTo4NAz2pWBAwciKCgIP/zw\nA3755RebtE8//RT79+93mm/w4MEIDg7G0qVLsXr1apu0FStWYPny5ciaNSsGDx7skNfocf3FF18A\nsB1SpEiRIqhWrRp++uknnD17Fo888giKFSvm0b54Y/fu3QBS6yIRERERERERET2YOOQIpVv79u3x\n4YcfIjIyEsOHD/fbeufOnYuIiAhUq1YNFSpUQHBwME6dOoXDhw8jKSkJlStXxpdffunVOjt37oxP\nP/0Uhw4dwuOPP44GDRogMTER+/btQ/HixdG+fXusXbvWZf7+/ftj3rx5+Pnnn1G3bl1cuXIFW7du\nRVJSEl566SW0a9fOIc9ff/0FAIiLi3NIGzhwIHbs2IHly5ejSZMmCAsLQ44cObB7925cvXoVVatW\nxbRp0zzevzp16mD06NEYP348unfvjgYNGqBUqVI4evQojh07hsGDB+Pzzz93yFezZk1MmjQJw4cP\nR9++ffHoo4+iXLlyOHnyJPbu3YugoCB89NFHqF69ukPepk2bQkQQHx+PLFmyOPTibt68OY4ePWr9\n39+SkpIQFRWFXLlyZcj6iYiIiIiIiIjo/sEe2pRutWvXRlhYGLZt2+ZynGxfDBo0CB06dEBCQgI2\nb96MVatWITo6Go899hg++OADbN68GSVLlvRqndmyZcPKlSsxYMAA5MyZE//9739x/Phx9OzZE+vX\nr0e+fPnc5q9fvz7Wr1+PSpUqYdOmTdi5cyeqVauGGTNm4KOPPvJ6H4OCgjB//nzMmDED1atXx759\n+7B582aEhIRg5MiR2LhxIwoVKuTVOocNG4aFCxciLCwMhw4dwvr161G4cGEsX77c7ZAuL730En76\n6Sc8/fTTOH36NJYvX44zZ86gQ4cO+Pnnn9GvXz+n+QoWLIiaNWsC0ALq9mOBm8fMzoiA88aNG/HP\nP/+gS5cufCgkEREREREREdEDTpRSgS7DPXfjxo1IAB5F1owxjEuVKmUznw+FtLV06VIMGDAAw4cP\nx6hRo/yyzvvJkCFDsGjRInz66afo3bu3V3nj4+MBADly5MiIoj30+vTpg7Vr12LLli2oUaNGoItD\nlGmwbSLKvFy9P30QGM85qVSpUoBLQkSUim0TEd2PMmHbtDl//vzh/lgRhxzxM38FiP0towPwzz77\nLGbNmoU5c+bgX//6F3vK0j1x5MgRrF27Fj179mQwm4iIiIiIiIjoIcCANvmFiGDy5Mlo3bo1pk2b\nhrFjxwa6SPQQmDhxInLnzo1333030EUhIiIiIiIieqjsmLHDL+t5fOjjflkPPTwY0Ca/efTRR3Ht\n2rVAF4MeIosWLQp0EYiIiIiIiIiI6B5iQJvIA7Nnz8bs2bMDXQwiIiIiIiIiIqKHGgPaRERERERE\nRERE5LOU5BSvlg/KEpRBJaGHAWsPEREREREREREREWUKDGhTunTr1g0WiwX/+te/3C4XFRWFAgUK\nIDQ0FNHR0femcA+olStXonXr1ggNDYXFYoHFYsHx48cDXSzKhMaOHQuLxYKpU6cGuihpmj9/PiwW\nC15//fVAF8Unmb38mcnx48dhsVgQFhYW6KL4pHLlyrBYLLh06VKgi0JERERERHRfYkCb0mX69Omw\nWCz49ttv8csvvzhdJjY2Fq+++iqUUnjvvfdQtmzZe1vIB8iePXvQv39/HDhwAA0aNEDPnj3Rs2dP\n5MuXL9BF80p8fDwsFguKFSsW6KKQExs3boTFYsFzzz0X6KKQj3gOA4dfXnivVatWsFgs2L17d6CL\nQkRERER+ULNHTacTkb9wDG1Kl+LFi+PDDz/EoEGD8Nprr2H79u2wWCw2y4wdOxbR0dFo0aIFXnzx\nxQCV9MGwevVqJCcn46233sKIESMCXRzK5F599VX06tULRYoUCXRR0vTss8+iSZMmDu0Lkb2yZcti\n165dyJYtW6CLQkRERERERBmAAW0/G7XxSqCLcM9169YNq1evxurVq/HWW2/h888/t6ZFRUXhiy++\nQL58+fDJJ58EsJQPhnPnzgEAKlSoEOCS0IOgcOHCKFy4cKCL4RFjeB2itGTLlg2VK1cOdDGIiIiI\niIgog3DIEfKLqVOnonDhwvjhhx+wbt06ALZDjUyaNAklS5a0yZOQkIBZs2bhiSeeQGhoKIoXL47H\nH38cEyZMwPXr1x22kdZP6H0dN3Xjxo0YNmwYGjVqhHLlyqFo0aKoWbMmXnnlFfz1119O8/Tv3x8W\niwVLly7FgQMH0LdvX1SqVAkFCxbE/PnzbZbdtWsXBg4ciKpVq6JIkSKoWLEievfu7dVPq42xjn/8\n8UcAwIABA6wBPuNn7ebjc/v2bYwbNw7169dHsWLF0KpVK5v1XblyBePGjUPDhg1RokQJlCxZEk88\n8QTmzJmDpKQkl+X4+eef0b17d1SsWBFFihRB1apVMWjQIPzxxx9e7UtISAgArQ4Y++FsCJKUlBR8\n++23aNeuHUqXLo1ixYqhbt26GDFiBC5cuODxNs1u3bqFqVOnomXLlihdujRCQkJQp04d9O/fH//9\n738dlv/nn38watQoPProoyhWrBhKly6NNm3a4Msvv0RycrLD8ubhBq5evYo33ngD1atXR0hICB5/\n/HEsXLjQuuzhw4fRp08fVKxYESEhIWjVqhU2b97ssE77IVq++uorNGvWDMWLF0eVKlUQERGBa9eu\nAQDi4uIwYcIE1K1bF8WKFUONGjXwwQcfOC2rszG0W7VqhS5dugAANm3aZHN+vBm+IiUlBUuWLMGz\nzz6LChUqoEiRIqhWrRo6derkcI14cj25GsbBXO/j4uIwbtw41K5d21pXpk2bhpQU7Wnb0dHRGDJk\nCKpWrYpixYqhcePGWL58uct9uHv3LubOnYu2bdta61/9+vXx7rvvWo+3Ly5fvoyhQ4fikUceQbFi\nxVCvXj1MmjQJ8fHxDsumNc65s+OSnnOYnJyMGjVqwGKx4NChQy6XM56f8PXXX9vMv3XrFiZNmoRG\njRpZ25ZmzZph+vTpuHPnjkflN/Nl6BRX94K07hHuhkI6duwYBg0ahBo1aqBIkSIoVaoUatWqhb59\n+2Lt2rXW5SpXroxhw4YBAL788kubY+/LECQbNmxA+/btUapUKZQoUQJt27bFhg0bnC579OhRTJgw\nAa1atUKVKlVQpEgRVKpUCd27d0dkZKTD8iNHjoTFYsG4ceNcbn/lypWwWCx48sknHdJ+++03vPLK\nK6hZsyaKFi2KsmXLonPnzi7L54xxfvfs2QMAaN26tc0xs79Pbt26Fb169UKlSpVQpEgRVKlSBS++\n+CL279/v8TaJiIiIiCjzYw9t8ovChQvj448/xgsvvIDXX38dDRs2xPvvv4/o6Gg8+eST6NWrl83y\ncXFxePbZZ7Fjxw7kyZMHTZo0Qfbs2bFt2zZMmTIFS5cuxapVq1CqVKkML/vQoUNx9epVVKlSBY0b\nN0ZKSgp+++03fPfdd1i1ahVWrlyJ+vXrO827ZcsWvPzyyyhVqhSaNm2KW7duIUeOHNb0jz/+GO+9\n9x5EBHXq1MHjjz+Os2fPYt26dfj5558xc+ZM9OzZM80y1qlTBz179sTWrVtx5swZNGrUCGXKlAEA\nh+BMXFwcnnzySZw+fRqNGjVCrVq1bNIPHjyIrl274vLlywgNDUWzZs2QlJSE3bt3Y8SIEdiwYQMW\nLVqE4ODU5kEphddffx0LFixAtmzZUKdOHZQoUQJ//fUXFi9ejDVr1uC7775DeHi4R/vSrVs3LF68\nGEFBQejevbs1LWvWrNb/U1JS0K9fP6xatQrZs2dHkyZNkD9/fuzatQtz5szBsmXLsGzZMof9c+fk\nyZN47rnncOrUKeTLlw8NGjRA3rx5cfbsWfz000+4ceMGWrRoYV3+jz/+QMeOHXHx4kWUKFECTz31\nFGJjY7Flyxbs2rUL69atw3fffWdTbkNMTAxatGiBu3fvokGDBrhy5Qq2bduGoUOHIjY2FjVr1kS3\nbt1QtmxZNGvWDMePH8eePXvQpUsXrFu3zmXQ7a233sKCBQvQpEkTlC5dGjt37sSCBQtw8OBBrF69\nGh06dEB0dDQaN26M8uXLY+vWrZg8eTKuXbuGDz/8MM1j1KZNG+TJkweRkZEoXry4zTmtVq2aR8c5\nPj4effr0wcaNGxEcHIywsDCULFkSly5dwpEjR7B9+3b079/fIV9a11Na2+zQoQNOnTqFxo0bo2LF\niti6dSvGjh2LS5cu4cUXX0S7du2QL18+NGrUCGfPnsWuXbvw4osvIigoCB07drRZ3/Xr19GlSxfs\n2bMHFosFdevWRd68eXHgwAHMmDEDK1euxLp16xy+qEvLlStX0KJFC9y5cweNGzfG3bt3ERUVhUmT\nJmHz5s1YsWIFsmfP7tU67aXnHGbJkgUDBgzAuHHjMG/ePEyfPt1hmejoaGsg0gicA8ClS5fQoUMH\nHD9+HAUKFEDLli2RnJyMqKgojBkzBitWrMCKFSuQP3/+dO3fvXbgwAE89dRTiIuLQ9WqVVGvXj0A\nwPnz57FhwwYkJyejffv2AIDOnTtj37592L17NypWrGhzHXv7ZevcuXPx8ccf49FHH0Xr1q1x/Phx\n7Ny5E927d7d+0Wc2ffp0LFmyBFWqVEGtWrWQO3dunDp1CuvXr8f69evx0UcfYeDAgdble/Xqhc8+\n+ww//PAD3nnnHQQFOfZx+O6776zL2s9/7bXXkJiYiOrVq6NevXq4fPkyoqKi8L///Q+jR4/Gm2++\nmeY+Fi9eHD179sQvv/yCmJgYtGnTBoUKFbKmm39BMmvWLIwaNQpKKYSFhaFMmTL4888/sXz5cqxa\ntcrj+ykREREREWV+DGiT33Ts2BFdu3bFkiVL0LVrV+zduxcFCxbEjBkzHJYdO3YsduzYgWrVqmH5\n8uXWHnGxsbHo378/1q9fjyFDhmDNmjUZXu7JkyejefPmNg9WVErh888/x8iRIxEREYEtW7Y4zbtg\nwQKMGjUKb775JkTEJm3NmjV47733ULJkScyfPx8NGjSwpm3ZsgU9evTA66+/bhOcdqVTp07o1KkT\n+vfvjzNnzmDAgAEueyxu374d9erVw4EDB2wCA4DWe7J37964fPkyJkyYgCFDhiBLliwAtADs888/\njw0bNuCTTz6x6U04e/ZsLFiwADVr1sSCBQtshjxZtmwZXnrpJQwYMAAHDhxA3rx509yXJ598EosX\nL0bWrFkxe/Zsp8vNmjULq1atQokSJbBq1SpUrFgRAJCYmIjhw4djwYIFePHFF7Fz506b4LsrSUlJ\n6N27N06dOoVOnTph+vTpNoG1Gzdu4MCBA9bXSin0798fFy9eRLdu3fDJJ59YA42nT59Gx44dsWHD\nBnz88ccYOXKkw/ZWrVqFbt26YebMmdaxfFevXo2+ffti0qRJyJ07N8aNG2cTYBo+fDjmzp2Ljz76\nCIsXL3ZYZ0JCAlatWoVt27ZZz0FMTAxatmyJ/fv3W4NBhw4dsp6HvXv3onXr1pg3bx6GDRtm7R3v\nyogRI7Bx40ZERkaiWrVqLs+PO2+//TY2btyIRx55BN9++y3Kly9vTUtKSnLZg9Pd9ZSWrVu3olmz\nZjh48KB13/fv349WrVph7ty52LRpE/r06YMxY8ZYA3effPIJ3nnnHXzwwQcOAe1XXnkFe/bsQdeu\nXTFlyhRrXUlKSsLo0aPx2WefYejQoVi6dKlX5Vy9ejWaNm2K7777zlrOCxcuoGPHjti+fTumTJmC\nUaNGebVOe+k9h88//zwmT56MH3/8EePHj3cIQM+fPx8pKSno06cPcubMaZ3/2muv4fjx42jevDm+\n/vpra5saExODrl27Yt++fXj77bcxa9asdO3fvTZz5kzExcVh4sSJ+Ne//mWTdvPmTZtfqEyePBnz\n58/H7t270bRpU5c96z3d7ooVK9C8eXPrvAkTJmDKlCl47733HALavXv3xjvvvOPwRfD27dvRpUsX\njB49Gp06dbKOmV+rVi3UrFkThw8fRmRkpM2XeYD265RNmzYhV65c6NSpk3X+/v37MXToUOTKlQtL\nliyx+cLkyJEj6NKlCyZOnIimTZva3PecqV69OmbPno1WrVohJiYGw4cPdxr437dvH0aPHo0sWbLg\nq6++sn6BAGjB9VdeeQUREREICwuz3iuIiIiIiOjBxSFHyK8+/PBDhISEYM+ePVBKYcqUKShatKjN\nMrdu3bIOuzBlyhSbn3fnzp0b06dPR44cORAVFWUTYMwoHTp0sAlmA4CI4OWXX0bt2rVx+PBhnDp1\nymne6tWruwy+ffDBBwC0XnO1a9e2SWvatCkiIiIQHx9vMwSFv/znP/9xCGYDwMKFC3H27Fn06NED\nr776qjWYDQCFChXCZ599hqCgIMydO9c6/+7du/j4448RFBSEr776ymH87meffRa9e/dGTEwMli1b\n5rd9MIJe7777rk2AImvWrPjggw9QrFgxnDhxwubn/u6sXLkSv//+OypUqIA5c+Y4BOny589vEziK\njIzE0aNHYbFYMGXKFJtes2XKlMGECRMAAJ999pnTYVosFgs+/PBDmwfTdejQAZUqVcKNGzdQoUIF\nm2A2ALzxxhsAtOCsUrcDVgoAACAASURBVMrpfrz77rs256BQoUJ44YUXAGhDKsyYMcPmS4X69esj\nPDwcycnJ2L59u/uD5Afnzp3DwoULERwc7BDMBoDg4GCHQJzB3fWUluDgYEyfPt1m3+vWrYvw8HAk\nJSUhOTnZoRfq4MGDkTdvXhw7dgz//POPdf7Bgwexbt06VKhQATNnzrSpK8HBwZgwYQIqVaqETZs2\n4cSJE16VMygoCFOnTrUpZ/HixTFx4kQAWq/cxMREr/ffnwoVKoTnnnsOsbGxWLRokU1afHw8vvnm\nG4gIBgwYYJ1/4sQJ/Pzzz9bzYG5TCxUqZA3sLl68GJcvX743O+InRnlbt27tkJYvXz6ve1576tVX\nX7VpkwCtjciVKxd+++03mzoLAM2bN3f6q6aGDRuiX79+SEhIwE8//WSTZvS8Nnpim/3www9ISkrC\n008/bXM+P/zwQyQlJeH99993+FVOjRo1MG7cOCil8MUXX3i1v+7Mnj0bKSkp6NGjh00w29iHp556\nCgkJCTbP8CAiIiIiogcXA9rkVwUKFLCOH1qnTh08++yzDsvs3bsX8fHxKFu2LBo1auSQbowlDMBl\nz2h/O3PmDObNm4eRI0fi1VdfxZAhQzBkyBBcvXoVAFyOpf300087Db6dP38eR48eRaFChdC4cWOn\neY35u3bt8tNeaEqVKoU6deo4TTN6xpp729nnLV26NM6fP4+///4bgNYzLiYmBrVq1XIIThr8vS8n\nT57E+fPnkS1bNnTt2tUhPWfOnNYe6lFRUR6tc+PGjQCAHj162ASZXdm6dSsAOARzDO3bt0eBAgVw\n/fp1HDlyxCE9LCzM6UMMjWNoP645oNX93LlzIzY2Fjdu3HBaLmf5jHVWqFAB5cqVc0g3AuC+jjvu\njcjISCQnJ1uHPPGGq+vJE6723ShDeHi4Q0/+bNmyITQ0FIDtsTGuk3bt2jkd/iM4ONja89TbOl+v\nXj2nPUhbt26NggUL4vr16zh69KhX68wIgwYNAgCH8c6XLVuGq1evomXLljbH27heGjVqhLJlyzqs\nr3bt2qhVqxaSkpLuyRcr/mQMOTV06FBs3rwZd+/evSfbbdu2rcO8nDlzWoPWzq7nGzduYMmSJRgz\nZgyGDh1qvZft3LkTABy+gOnWrRuyZs2KtWvXOrQ5xpcZ5uFGEhMTERkZiSxZsqBDhw5Oy50R9zaj\nftkPfWLo06ePzXJERERERPRg45AjPprYqnDaCz2kcufObfPX3vnz5wHA7TAbRkDkXgTgxo0bhxkz\nZjh9aJ7h1q1bTue7GuM7OjoagPZT++LFi7vdfkxMjGcF9ZC7cceNcvXo0SPN9cTExKBUqVLWPAcO\nHHAaoDW7cuWKx+V0x6gjJUuWtOlFbuZtHTEC9JUrV/aqDK7qqYigTJkyuHbtGs6fP+/wJUKJEiWc\n5jOuC3fpsbGxSEhIcEjLli2bdbgAb9cJwOk6/c3b42yWnjHz03O8AdtjY9T5mTNnYubMmW63622d\nd9fulS5dGlevXsW5c+dcfil1r9SuXRsNGzbE9u3bsXnzZmtP4Xnz5gEAXnrpJZvlPW3XDx06dE/a\ndX8aNmwYdu7ciW3btqFjx47Inj07ateujSZNmqBbt26oWrVqhmzX+LLFntG73/56XrFiBV577TWX\nX4YBjveyQoUKoW3btlizZg1WrFhh/bXHoUOHcPToUetzFgyXLl2yPtwzraGy/HVvU0rh4sWLbrdp\n3A+MekhERERERA82BrQpYHztielKSkqK13kWL16MqVOnIn/+/Hj//ffRuHFjhISEWB9E16dPH6xZ\ns8bl8A/m8WPNjOB4gQIFrD9TdxWYTWtMY2+5KpO5XO3atUszOG0Ms2DkCQ0NRdOmTd3m8fShgZ7y\nZx3xdV2+5nP2gDVv0n0piy/r9Lf0nDN3dTct/jzeRp2vX79+moF5XwL3/uRLu+epQYMGYfv27Zg3\nbx6aN2+OAwcOYO/evShdujTatGnjNM/90K77e1t58+bFunXrsHPnTmzatAk7d+7Enj17sGvXLkyd\nOhVjxoxBRESE38vjTZ2Njo7GoEGDkJiYiBEjRqBz584oVaoUcufODRHBZ599hpEjRzq9l/Xq1cv6\nYF8joG30zu7Ro4dNOYxrI1u2bC6f42Bw9rDc9PJ3/SIiIiIiosyJAW2654yekkYvSGeMNHPvZuPD\ncWxsrNM8Rs9Qb6xYsQIA8N5776F3794O6SdPnvR6nUBqz7pcuXJZH4ppBMkDKTQ0FH///Tdefvll\nh7FZ3eUBtN6zvjwg0BdGHTl79iySk5OdfhngrI64Y+zHn3/+6VUZ3NXT06dP2yxL3h/n+1HJkiUB\nAE888QRGjx7t13WfOXPGZZrRhpnrU0a0e57q0KEDSpQogXXr1uHChQvWsfX79+/vEGy9n9p1V4yh\nhnzdVoMGDaxDzSQkJOD777/HsGHDMH78eHTu3DnNHssZad26dbh79y66deuGf//73w7p7u5lbdq0\nQZEiRbBz506cOHECZcqUwY8//gjAcYiPYsWKIWvWrEhOTsa0adOcDsnjbyKCkJAQnD9/HtHR0U7b\nW6NusS0mIiIiIno4BL47Hz106tWrhxw5ciA6Oho7duxwSL906RI2bdoEADY9go0PqidOnHA6PMgv\nv/zidVmuXbsGIDWAZXbo0CEcO3bM63UCQLly5VChQgWcO3cOe/fu9WkdGcEYf9kI5HviscceQ758\n+bBnzx6/BZeMIJarYV7Kly+PEiVK4O7du1i6dKlDenx8vPUBlE2aNPFomy1btgQAfP/99x49dM8Y\nB3bt2rVOh5xZu3Ytrl27BovFgho1anhUhszCOD/OHnaZlvDwcGTJkgVRUVEuH6Z6vzN+VbFmzRq3\nQxH5Yt++fU4fJLlp0ybExMQgf/78NvXJaPeOHz/ukCclJcXaVtpLzzk0BAcH48UXX0RSUhKmTZuG\nZcuWIXv27Ojbt6/DssbzELZt22b9osfs0KFDOHz4MIKDg9GwYUPrfHf7B/jWrrtStGhRZMmSBRcv\nXnQ6LIc328qePTteeOEF1KpVCykpKfjtt9+saf449t5ydy+Li4vDunXrXOYNDg5Gt27dAGg9s3/5\n5Rf8888/ePzxxx3Gwc+RIweaNGmC5ORkrFmzxm/lT+uYGe3x999/7zT922+/tVmOiIjo/9m787ia\n8++B469bqRS5lhQia9myRCqhkLFkLMNYxpKv3RjG1jD2MQyGGcNYx65fYxtDyL60oBJZwjC2LGMp\nk0olqe7vD3PvuO6NIpI5z8ejR7f38vmcz+12x5zP+563EEKID5sktMU7Z2FhoUmI+Pj4EBsbq+lL\nSUlh5MiRPH78mEaNGmnVka1cuTI2NjbExsaydOlSrWNu3bqV1atX5zgWdbmANWvWaP2P9L179xg6\ndOgbfdx9woQJAAwaNEjvxoXp6ekcPnyYU6dOvfY5cqpfv35YW1uzZs0a5s6dS2pqqs6Y69evs3nz\nZs3PBQsWZNSoUTx9+pTu3btz+vRpnTlPnjxhx44d2V7RbmhoiJWVFenp6XqTewBDhgwBYNq0aVrH\nTU9PZ8KECdy7d4+KFSvi5eWVrXN26NABe3t7rly5wuDBg3WS1AkJCQQHB2t+9vDwoEaNGjx8+JCv\nvvpKayO4mzdvMmnSJAAGDx6ss9lgfvf8zaOc/g2UKVOGnj17kp6eTo8ePXRW7Kanp7Nnz57cCvWt\ncHZ2xtPTk4sXL9K/f39N/d7nPXz4ULNiOScyMjIYNWoUSUlJmrb79+9r3i/69++vVapBXb949+7d\nREZGatqfPn3KpEmT9G5ICm/2O3xenz59MDExYdmyZTx+/JgOHTpQvHhxnXGVK1emZcuWpKenM3Lk\nSK2/r7i4OEaNGoVKpaJLly6ULFlS0+fk5ISZmRmnT5/WSpCqVCoWLVrE3r17Xzv2F5mZmeHk5ERm\nZiazZs3SKr8RHBzMnDlz9M5btmyZ3ve2K1euaDYMfr7++6uS9G+D+r9lW7du1apd/eTJE0aPHs3t\n27dfOl+9EnvDhg2a5HBWGzCOGzcOQ0NDRo8ejb+/v06/SqXi+PHjBAUFZTv+Vz1ngwcPxsDAgPXr\n1+u8JjZu3MiuXbswMTHRbGaaHb///jtOTk56Nx5+3T4hhBBCCCHEu/FhZWFEvjF16lSioqIICwvD\n0dGRRo0aYWJiwrFjx4iJiaF8+fI65S0UCgWTJk1i0KBBTJgwgd9++41y5cpx+fJl/vjjD0aNGsUP\nP/yQoziGDh3Kli1b2LFjB3Xr1qVevXqkpKRw5MgRKlSoQMuWLV87ofLJJ58QHR3N9OnT6dy5M3Z2\ndlSqVAlzc3Pu3bvH2bNnSUxMZNGiRdStW/e1zpFTSqWSDRs20L17d6ZPn86SJUuoXr06pUqVIiEh\ngUuXLhEdHY2bm5vW/6x/+eWX3Lp1i5UrV9K0aVNq1KhBhQoVKFCgAH/99RdRUVGkpKSwY8cOnRV9\nWWnbti0rV66kTZs2uLm5YW5uToECBfjxxx+BZ7+b48ePs2PHDlxdXWncuDFFihTh+PHj3Lp1i+LF\ni7NmzZpsJ5ONjIzw8/Pjk08+YcuWLezfvx9XV1cKFSrE7du3OXv2LA0bNtQkEBUKBatWraJ9+/as\nX7+e4OBgnJ2dSU5OJjg4mMePH9OiRQtGjx6dw9/C+69KlSpUrVqVixcv4ubmRq1atTA2NqZatWp8\n/vnnr5w/c+ZMbty4QWBgIE5OTjRo0IDSpUtz//59Lly4wKNHj7h///47uJLXt2LFCrp27crWrVvZ\ns2cPNWvWpFy5cjx9+pRr167xxx9/kJGRwYABA3J03I8//phTp05pNhVMS0sjJCSEpKQkXFxc8PHx\n0RpfqVIlevXqha+vL61atcLV1RVzc3PN+8eAAQP0Jtbf9HeoZmlpSceOHTUrY192vQsWLODjjz/m\n0KFDmuvLzMwkODiYxMRE6tSpw8yZM7XmWFhYMHLkSGbMmEGvXr1wcXGhRIkSnD9/ntu3bzN8+HBN\n2abcMGHCBDp27MiSJUsIDAzEzs6Omzdvcvr0aUaPHs3cuXN15qxYsYKxY8dSsWJFqlatqnkPDwsL\n4+nTp3z22Wdaq+pdXV0pXrw44eHhNGvWDHt7e4yMjGjUqBFdu3bNtWt5Xrt27Zg3bx4XLlygbt26\nuLm5YWxsTGhoKKmpqVm+TtRq1KhB7dq1OXPmDLdv38bMzIwOHTroHevs7MzChQsZMWIE3t7e2Nra\nYm9vT5EiRYiNjeXcuXM8ePCAsWPHZru0Vdu2bdmyZQtjx45l7969mpsmo0ePpnz58tSrV49vv/2W\niRMn0rVrVxo0aICtrS2XL1/m9OnTGBoaMm/ePKpUqZLt5yw+Pj7L0kiv2yeEEEIIIYR4N2SFtsgT\n5ubm+Pv7M2PGDCpVqkRwcDB79uxBqVQyevRoAgMDtVa8qXXt2pW1a9dSr149Ll68yOHDhylRogTb\ntm17rUSBnZ0dQUFBdOjQgYyMDHbv3s2VK1cYMGAAe/fuxdzc/I2uc9SoUezevZuuXbvy5MkTDh06\nxN69e7l79y6NGzdm4cKF2V5hnFvq1KlDaGgoEyZMwNbWljNnzrBt2zbOnTuHlZUV48aN00nqKBQK\nfvjhB7Zv306HDh14+PAhe/fu5eDBg8THx9OmTRtWrVpF/fr1sx3HtGnTGDRoECYmJuzYsQNfX1/N\nykB4tiHa2rVrWbhwIY6OjoSHh7Njxw4MDQ0ZMGAAISEh1KpVK0fXXrlyZUJCQhg/fjy2trYcOXKE\nXbt2cf/+fby8vBg+fLjWeHt7e4KDgxk6dCimpqYEBARw9OhRatasybx589iwYcNb2fjsfbB+/Xra\ntWvHgwcP2Lx5M76+vlmWt3iRmZkZW7ZsYdGiRTg7O3P+/Hn8/f25evUqtWrV4vvvv3/L0b85pVJJ\nQECA5hquXbuGv78/oaGhKBQK+vbtm6PSPWolSpTg4MGDtGrVSrPJoKWlJV999RVbt27VW2v/p59+\nYtKkSZQtW5bQ0FCOHz+Os7MzwcHBVKtWLctzvcnv8HlNmzYFnr13vOxv3MrKigMHDjB27Fisra3Z\nv38/hw4dwtbWlqlTp7J7927NZrPP8/HxYd68eVSrVo2TJ08SEhJClSpV2Ldvn+YG0+vQt4Fg48aN\n2bp1K40aNeLmzZscOHAAQ0NDVq1axZgxY/QeZ8qUKXh7e1OwYEHCwsLw9/fn+vXruLu74+vry6JF\ni7TGq1//np6eXL9+nY0bN+Lr66u3xFZuMTExYffu3QwdOhRLS0sOHTpEeHg4Hh4eBAUFvfR1ovb8\nPhJt27bFwsIiy7Hdu3fn6NGjDBgwAGNjY0JCQggICCA6Opo6deowZ84c+vbtm+34O3bsyKxZs6hY\nsSKHDh3C19cXX19frU9wDR06lB07dtC6dWuuXbvG1q1buXPnDh06dGD//v1ZrigXQgghhBBCfHgU\n+na8/9AlJCQEAtlaNqSuGawvuSpEdqjLerwPm0IKIYRadt+bOnfuzIEDB1i4cCE9e/Z8F6G9kdOn\nT+Ph4UHt2rVzVPZCiPzkQ/73qXoFfE5W3AshxNsm701C6Be24N9FG5kZ/5Y6dOjmoHd81IYozWMD\nw3/X2LoMd3kL0X348uF7U1CRIkU8cuNAskJbCCGEEHqFh4dz4MABrKys8k3N4BMnTgD56h91Qggh\nhBBCCCFyQGpoCyGEEEJDvbFjUlIS+/btA2DixImYmJjkcWQvt3LlSvbs2cPhw4cB7RIaQgghhBBC\nCCE+HJLQFkIIIYRGeno6vr6+GBoaUrZsWSZOnEivXr3yOqxXOnr0KEFBQdjZ2TF8+HBN7W8hhBBC\nCCGEEB8WSWgLIYQQQsPU1JT4+Pi8DiPHVq1aldchCCGEEEIIIYR4B6SGthBCCCGEEEIIIYQQQoh8\nQRLaQgghhBBCCCGEEEIIIfIFSWgLIYQQQgghhBBCCCGEyBckoS2EEEIIIYQQQgghhBAiX5CEthBC\nCCGEEEIIIYQQQoh8QRLaQgghhBBCCCGEEEIIIfIFSWgLIYQQQgghhBBCCCGEyBeM8joAIYQQQggh\nhBBCCCHEf1PYgrAcjXcZ7vKWIhH5hazQFkIIIYQQQgghhBBCCJEvSEJbCCGEEEIIIYQQQgghRL4g\nCW2RayIiIihatChTp07NcsyJEycYMGAANWrUoGTJklSsWJFmzZoxZcoUnbHJycls2rSJcePG0bJl\nS0qXLo1SqaRr164vjePy5cssWrSITp06YW9vT4kSJShXrhwtWrRg8eLFPHny5E0vNc95eXmhVCqz\n/OrUqZPOnMTERKZPn07nzp2pXbs2ZcuWxdLSkho1avC///2P0NDQXI/Tz88PpVLJkCFDcv3Y75pK\npaJRo0bUrFmTx48f53U4QgghhBBCCCFEvpWZkZmjLyGeJzW0Ra5QqVSMHTsWCwsLRowYoXfM7Nmz\nmTVrFgYGBtSvXx8XFxfi4uK4dOkSCxcu5JtvvtEaf/XqVQYOHJjjWNq3b8+dO3cwNTWlbt26NGrU\niJiYGCIiIoiIiGDDhg34+/tTtGjR17rW90nz5s0pWbKkTnv16tV12h48eMDcuXMpXLgw1apVo1at\nWqhUKv7880+2bt3K1q1b+fbbbxk2bNi7CD3fUSgUTJw4kW7dujF//nzGjRuX1yEJIYQQQgghhBBC\n/OdIQlvkit9++43IyEh8fHxQKpU6/atWrWLmzJlUr16ddevWUblyZU2fSqXixIkTOnMKFy5Mz549\nqVu3LnXq1OHs2bOMHDnylbFUrlyZr7/+mo4dO1KoUCFN+40bN+jWrRtnz57l66+/ZunSpa95te+P\nESNG0Lhx42yNLVmyJAcOHKBu3boYGhpq9f3+++8MGDCAqVOn4uXlRcWKFd9GuPleq1atqF27NgsW\nLKBfv35YWlrmdUhCCCGEEEIIIYQQ/ymS0Ba5YsmSJSgUCnr27KnTFxcXx+TJkzEzM2Pjxo2ULVtW\nq1+hUODk5KQzr0KFCixcuFDz88WLF7MVy/bt2/W229ra8uOPP9K6dWu2bdvGggULMDY2ztYxPwSF\nChWifv36evs++eQT1qxZQ3BwMMHBwZLQfomePXvi4+PD2rVrGTNmTF6HI4QQQgghhBBC5AsO3Rxy\nND5qQ9RbikTkd1JDW7yxyMhIIiMjcXNzw9bWVqffz8+PpKQk2rVrp5PMftdq1aoFQGpqKnFxcdme\np65ZHRISord/yJAhKJVK/Pz8dNqtra3ZsGEDZ8+e5bPPPqNixYpYW1vj7u7O//3f/73+xeQyI6Nn\n97dymuRXqVSsW7eOJk2aYG1tTcWKFfnss884d+7cK+fu3buXzp07U7FiRU0978GDB3Pp0iWdsQ0b\nNkSpVOr0nTt3TlM7fOXKlTqx2dnZUbRoUa3ft4ODA0qlkhs3bnD48GHatWtHuXLlKFWqFJ6enuza\ntSvLmDt37kyBAgVYs2YNmZlSx0sIIYQQQgghhBDiXZKEtnhjAQEBAHh4eOjtP3z4MPAsIZmUlMS6\ndevw8fHBx8eHNWvWEB8f/65C5erVq8CzpO27rKEdGRnJRx99xB9//EHTpk1p0KAB586d44svvuCr\nr77SO0edpM0qiQ6wc+dOxo4dy8iRI5k9ezbHjh17rfj2799PSEgIZmZmWf4eszJmzBiGDx/O+fPn\nadCgAU2bNuXChQt4enoSGRmZ5bxvvvmGrl27cujQIapWrUr79u2xsLBgw4YNuLu7s3fvXq3x7u7u\nAAQGBmq1BwUFaR6/2HfhwgViYmJwcHCgWLFiOjH4+vryySefkJycTIsWLahSpQonTpygR48e+Pv7\n6427aNGi1K5dm9u3b3PmzJmXPTVCCCGEEEIIIYQQIpdJyRHxxo4cOQKgt2wIPEsqAiQkJODi4sLt\n27e1+qdMmcIvv/xCy5Yt326gwE8//QRAy5YtMTExeevnU1u3bh2DBg3iu+++09SvPnHiBB07duSX\nX37B09OTjz76KMfHXbZsmdbPM2fOxMXFhRUrVmBjY5PlvClTphATE8Pjx4+5cuUK586do3Dhwixe\nvJjSpUtn+/y7d+9m5cqVWFhYsHXrVurVqwdARkYGX3/9Nb/88oveefv27WPevHmYm5uzadMm3Nzc\nNH0LFixg8uTJDBgwgJMnT2rqVLu7u7NkyRKCgoIYNGiQZnxwcDBGRkZUqlSJkJAQMjMzMTB4dq9O\nnexWJ8NftGDBAjZv3oynp6embc6cOcyYMYNvvvmG9u3b653n5OTEiRMnCA4Opm7dutl9uoQQQggh\nhBBCCCHEG5IV2uKNRUU9q2lkb2+vt//hw4cATJs2DUNDQ7Zs2cLNmzc5ceIE3t7eJCQk4O3tne0a\n2a/Lz8+P33//HTMzMyZPnvxWz/WiUqVKaa5frX79+gwZMgSAxYsX68ypUqUKVapUwczMTKfP1dWV\nn3/+mZMnT3L37l2ioqJYuXIltra2hIWF0aFDB5KTk7OMZ/v27axfv55t27Zx7tw5ihcvzsKFC/n4\n449zdF1LliwBnpVWUSezAQwNDfn2228pVaqU3nnq2uiDBw/WSmYDDB8+HCcnJxITE1m7dq2m3c3N\nDSMjI44cOUJGRgYA6enpHDt2DEdHR9q0aUN8fDynT5/WzHlVQnvgwIFayWyAL7/8EgsLC65du8at\nW7f0zqtatSoAZ8+e1dsvhBBCCCGEEEIIId4OSWiLN5KcnExKSgqA3pIOgKbOsEql4rfffqN58+ZY\nWFhQuXJl5s+fT8uWLUlNTdWsnn4bgoKCGDlyJAqFgnnz5lGlSpW3di592rZtq3dFeLdu3QAICwsj\nPT1dqy8iIoKIiAitRLHaxIkT6dWrF5UqVaJgwYKULVuWTp06ERwcTPny5bly5QqrVq3KMp5Tp04R\nHx9PdHQ0+/btw8XFBW9vb/r166dJFr9Keno64eHhAHTt2lWn38TERO8K5+fnffbZZ3qP3aNHD+Df\n1f8AhQsXpl69eiQmJnLq1CkATp48yaNHj3B3d9eUSlGXHVEnu42NjWnYsKHe8+j7VICxsTHly5cH\n4N69e3rnKZVKAGJiYvT2CyGEEEIIIYQQQoi3QxLa4o0kJiYCz5KXWW0mWKhQIeDZqmJ9ieS+ffsC\n2snL3BQaGspnn31GWloas2bN0pt8fdvKlSunt93GxgYDA4Mcb1KZlSJFijB48GDgWVmPV1EqlTRo\n0AA/Pz9atWrFli1bWL58ebbO9ffff/PkyRMMDAyy3OxT33XHxcW9cp46oXz37l2t9iZNmgD/Jq3V\nK7A9PDxwdnbG1NRU0xcZGcmjR4+oX7++3lXuQJbnL1y4MPBs81B9LCwsgGdldIQQQgghhBBCCCHE\nuyMJbfFGihQpAsCTJ09IS0vTO8bW1lbre1b99+/fz/X4wsPD6dKlC8nJyUybNk2r9nJuUq9Cfx/Y\n2dkBusngV+nevTvwrBzJu6JQKHI0Xr0KW53IDgoKwtzcHCcnJ0xNTXF2dub48eOkpqa+stzI65xf\nTX0jR71SWwghhBBCCCGEEEK8G29tU0iFQvEd8PU/P/qoVKq5WYz7DBgC1AIMgYvAamCJSqV6f7KE\nLxo7Oq8jyB2zf3ij6WZmZpibm5OcnExcXBzW1tY6Y2rXrs3p06ezXIH8999/A2Bubv5GsbwoIiKC\nzp078+jRIyZOnMjw4cNf+1jq1edZ1aXOqtbyq/pv375NZmYmpqamWZZsySn185zT57NEiRIAPHjw\nIFvjixcvjomJCU+ePOH27dtUqFBBZ8zNmzd12ooVK6aZd/PmTSpVqqQzJjo6GkCnBreTkxNmZmYc\nP36cuLg4IiIiaNKkieb34+HhQVBQEKGhoVqrt3Obui68esNKIYQQQgghhBBCCPFuvJUV2gqFwgn4\nClC9YtwiwA+oD4QA+wE7YCHwm0KhkBXk+UCtWrUAuHTpkt5+9UaDERERmnrbz1OXiKhbt26uxXTy\n5Ek6derEo0ePFI/dHAAAIABJREFUGDduHGPGjHmj46kTq5cvX9bpi4mJeeXmgDt37tS7gn3Tpk0A\nODs7Y2SUO/eXtm7dCoCjo2OO5gUHBwNQsWLFbI03MjKiQYMGwL/X8by0tDS9q72NjIxwdnYGYP36\n9XqP/euvvwLQqFEjrXZjY2NcXV158uQJP/74I2lpaVorsNWPd+/eTUREhKbudm5Tb2Bau3btXD+2\nEEIIIYQQQgghhMharieMFQqFCbAWuA/4v2RcJ+Bz4B5QS6VStVWpVB2BKsAfQEdgWG7HJ3Jf48aN\nATh+/Lje/ubNm1O3bl1iY2MZN24cT58+1fQdO3aMJUuWADBw4MBciefUqVN07NiRxMREfHx8GDdu\n3BsfU50oXb58udZGgQ8fPmTIkCEkJSW9dP6dO3eYOnWqVmmSyMhIFi9eDKCpe/08JycnnJycOHny\npFZ7SEgIR44cQaXSvl+UkpLC5MmTCQgIwMjISOf53Lx5M6dPn9Y5T0ZGBuvXr2f+/PkA9OnT56XX\n8jx1CZdFixZpNmqEZyVYpkyZwp07d/TOGzp0KABLly4lLCxMq2/hwoUcP34cCwsLevfurTNXveJ6\nxYoVgHZJkTp16qBUKlm3bh1PnjyhYcOGuXaj4HkRERHAv699IYQQQgghhBBCCPFuvI2SI9OAakA7\noNNLxqnLkYxVqVSaZa8qleq+QqEYAgQC4xQKxc/vdekRgZeXF99//z2BgYH4+Pjo9CsUClauXEnr\n1q1Zt24dhw4dok6dOsTGxnLixAkyMjIYPnw4rVu31pnbo0cPTW1tdSmMsLAwPD09NWN8fHxo2bKl\n5md1MrtIkSLcvn2bIUOG6I17+vTpFC9ePFvX2LFjRxYtWsTZs2dxcXHB2dmZp0+fEhkZSalSpfDy\n8iIgICDL+b1792blypXs2bOHunXr8uDBA44ePUp6ejr9+/fXe+3q1eAvrmqPiopi/PjxWFtbU7Nm\nTYoWLUpMTAxRUVHExcVhYmLCzz//TLVq1bTmHTp0iAEDBmBjY0ONGjUoUqQIDx484OLFi9y5cwcD\nAwPGjRtHq1atsvWcALRt25Y+ffqwZs0aWrRogZubG5aWlpw8eZK7d+/Sr18/Vq5cqTOvZcuWjBgx\ngp9++ok2bdrg6upKqVKluHDhAhcuXMDU1JRffvmFkiVL6sxVbwyZmppK8eLFcXBw0PQZGBjQqFEj\ndu7cCby8fvbrevjwIWfPnsXGxkZWaAshhBBCCCGEEEK8Y7ma0FYoFM7AaOBXlUq1459V2PrG2QD1\ngDRg84v9KpUqSKFQ/AWUAVyAY7kZp8hdtWvXxsnJiWPHjnHjxg29mz9WrFiRo0ePMnfuXHbv3s2+\nffsoWLAgTZo0YcCAAbRp00bvsc+ePatTfzohIYETJ05ofn6x5nN8fLxmXFYlLQDGjRuX7YS2sbEx\n/v7+TJ8+nV27dnHo0CGsrKzo3r07X3/9NWPHjn3pfEdHR/r27cvMmTM5ePAgqampVK9enf79+9Or\nV69sxaDm5uZG3759OXXqFGfPnuXhw4cUKFCAcuXK0alTJwYNGkTlypV15nl7e1O4cGEiIiI4deoU\nDx8+xNjYGBsbG7y9vfnf//5HnTp1chQLwLx586hbty4rVqwgLCyMggUL4uzszNq1a4mKitKb0AaY\nOnUqLi4uLF++nMjISI4fP46lpSVdu3Zl5MiRVK1aVe+8WrVqUaxYMeLi4mjSpInOxo4eHh5vNaG9\nefNmnj59Sp8+fTAwkKpIQgghhBBCCCGEEO+S4sWyBa99IIXCFDgNFAOqq1SqBwqFYg3gzQubQioU\nio+B7cAplUqlt9CvQqHYCnQAvlCpVItyJch/JCQkBALZynSpk6lly5bV7pBNIbVs2bKFfv364ePj\nw4QJE3LlmB+CIUOGsH79en766acclfIQ7y93d3cuX77MmTNnZFNIka+lpqYCYGpqmseRCCFyKst/\nn34A1J9Qq1KlSh5HIoQQ/5L3JiH0C1vwbwnRzIx/iys4dHPQNzzHojZEaR4bGP67oMxluEuuHD+/\ny4fvTUFFihTxyI0D5eYK7RmAPdBNpVI9eMXYCv98v/GSMTdfGPtSCoWiD9AnO2MDAwPr1KlTh5SU\nFP76669Xjjc2Ntb8j7+aaRY3AlKnfZedEN4508nj9ba/eF2vq02bNtStW5dffvmFfv36oVQqc+W4\n+V1GRobmcW491yLv7Nu3jzNnzjBmzBgKFy4sv1PxQZDXsRD5T2ZmJmlpaXo3q/5QfMjXJoTIv+S9\nSQht6enpmseqzH/zZC9+0v51PZ9TyXyuGrH8LWp735+PMmXKYGZmlqvHzJWEtkKhaAiMALapVKqN\n2ZhS6J/vyS8Zo95lr3A2wyhPNlddv2oDP5FzCoWCGTNm4OXlxcKFC5k4cWJehyRErlKpVHz//feU\nLl2azz//PK/DEUIIIYQQQgghhPhPeuOEtkKhKAisARKBvMzyRANB2RlYqFChOkARMzOzVy7LV99V\n0vlI9gt1e9Xe249uv4N4GzZsyMOHD3PteB8CQ0NDzeP39rUhsu3IkSN5HYIQuUZKjgiRfxkYGGBq\naiolR4QQ4h2R9yYh9Pvb6G/N4+dLjuTWv1HiDeM1j58vOSJ/i8/8l9+bcmOF9ndAFaCvSqW6m805\n6iXS5i8Zo17F/Sg7B1SpVGt4llh/pZzU0BbiTSxZsoR58+bldRhCCCGEEEIIIYQQQnwQciOh3RHI\nBLwVCoX3C31V//k+RKFQtAWuqFSq/jxbTQ1g+5Ljqm/nRL9kjBBCCCGEEEIIIYQQQoj/iNzaFNKA\nl694rvjPl3qnwFP/fK+hUCgKqlSqx3rmOL0wVgghhBBCCCGEEEIIIcR/mMGrh7ycSqUqr1KpFPq+\ngLX/DPP5p63OP3NuAZGAMfDpi8dUKBTugA1wDwh90xiFEEIIIYQQQgghhBBC5H9vnNB+AzP/+T5b\noVBUVjcqFIqSwOJ/fpylUqkydWaK90aXLl1QKpUMHTr0peOOHDlC0aJFsbGxITo6+t0E94GbOnUq\nSqVSanQLjb59+6JUKtmyZYtO399//83w4cOpUaMGJUqUQKlU0rdv3/citlexs7NDqVRy//79txCZ\nEEIIIYQQQggh8pM8S2irVKrfgCWANRClUCh2KBSK34HLQHVgG7Awr+IT2TN//nyUSiV+fn7s27dP\n75jk5GS++OILVCoV3377LeXLl3+3QQoh+Pzzz1m3bh3Gxsa0b9+e7t274+bmltdhsWrVKpRKJSNH\njszrUN6ZAwcOoFQq6dSpU16HIoQQQgghhBBC5Du5VUP7tahUqs8VCsURYCjPanAbAheBVcCSfLk6\ne+zovI7gnSpVqhTff/89AwcO5MsvvyQ0NBSlUqk1ZurUqURHR9OsWTP+97//5VGkQvx3paSksG/f\nPgoXLsyRI0cwNzd/5zHMmDGDcePGUapUqXd+biGEEEIIIYQQrxa2ICyvQxAiW97qCm2VStXnn9rZ\nc18y5leVSuWmUqksVCqVuUqlqqdSqRbly2T2f1SXLl34+OOPuXv3LmPHjtXqO3LkCCtWrMDCwoKf\nf/45jyIU4r/tzp07qFQqrK2t8ySZDc9uftnZ2VG4cOE8Ob8QQgghhBBCCCE+DHlZQ1t8QObNm0eJ\nEiXYuHEju3btArRLjcyaNYsyZcpozXny5AmLFy+madOm2NjYUKpUKVxcXJg+fTrx8fE653jVx/T/\n/PNPlEolTk5OOY7/wYMHfPvtt7i5uWFjY0Pp0qWpV68eQ4cO5eTJk1pjw8PDGT9+PO7u7lSuXBlL\nS0uqVatGnz59OHXqlN7jT58+XVPv+u7duwwbNoyqVatSsmRJateuzfTp00lLS8tx3GrXrl2jX79+\nVKlSBWtraxo2bMiSJUvIyMjQGfuqWsZZ1eZ+vj06OppBgwZRrVo1ihcvztSpUwHt8hEJCQmMHz8e\nBwcHSpYsSfXq1fHx8SEhISHb1/Xtt9+iVCoZN25clmO2bduGUqmkdevWOn0BAQF88sknVKhQgZIl\nS1KzZk2GDh3KlStXdMampqaiVCqxsrLK8lxZ1XJ+vn3btm20bt2acuXKoVQq+fPPPwF4+PAhkydP\npkGDBlhbW2NtbU316tX5+OOPs7zZc/PmTXx8fHB0dMTa2ppy5crRunVrNm7cmGWM+q6pfv36AFy+\nfBmlUqn5elVN6o4dO6JUKgkODtZqj42NpWjRoiiVSr777judeQ0bNkSpVHLx4kVNm77XnZ2dHaNG\njQJg9erVWrFlVYJk//79eHl5UbZsWUqXLk3Lli3Zv39/tp4PtXr16qFUKnXq+Z88eVJz/v/7v//T\n6svMzMTW1pYSJUqQmJioaT9//jzTp0/H09MTe3t7LC0tqVKlCl27diUwMFDn3J6ennTu3BmAgwcP\nal3zi+9tmZmZbNy4kfbt21OhQgUsLS1xcHBg5MiR3L59W+fYz79HJiUl8c0331CvXj2srKzw9PTM\n0XMkhBBCCCGEEEK8r/K05Ij4cJQoUYIffvgBb29vRo4ciaurK9999x3R0dG0atWKzz77TGt8SkoK\nn3zyCWFhYRQqVIhGjRphYmLCsWPHmDt3Llu2bGH79u2ULVv2rcd+8uRJunXrRmxsLMWLF6dx48aY\nmJhw8+ZNNm3ahLGxMfXq1dOMnzJlCidOnKBq1arUr18fY2Nj/vzzT7Zt20ZAQABr1qzBy8tL77mi\no6Nxd3enQIECuLi4kJCQQFhYGHPnzuXy5cusXbs2x/FfuXIFDw8PChcuTOPGjUlISCAkJISvv/6a\niIgIVq5ciUKheO3n50UXL16kSZMmFC5cGGdnZ9LS0rCwsNAa8/DhQ1q0aMHff/+Nq6sraWlphIWF\nsXz5ck6fPs2ePXswNDR85bn69u3L/PnzWb9+PZMnT8bMzExnzPLlywEYMGCAVvvXX3/NkiVLMDQ0\nxMXFBWtra6KiovDz82Pr1q34+fnRtGnTN3gmdM2dO5fly5fj5OTERx99xK1btzAwMODRo0d4enpy\n9epVrKysaNq0KQULFuTevXucP3+eM2fOMGzYMK1jHTp0iN69e5OUlETlypXx9PQkMTGREydOMGjQ\nII4dO8b8+fNfGo+hoSHdu3cnMTGRgIAALCwstF6bpqamL53v7u7O4cOHCQoKokmTJpr2oKAgVCqV\n5vH48eM1fbGxsfzxxx9YW1tTtWrVlx6/Y8eOREZGEhERQeXKlbVuRum7MbV8+XJ++OEH6tevT4sW\nLfjzzz8JDw+na9eu+Pn56b2pkdV1Xb16laCgIK2a/kFBQVqPe/bsqfn59OnTJCQk0KBBA63X+/z5\n89m8eTP29vbUqlULc3Nzrl+/zt69e9m7dy9z5szRem1+9NFHFCpUiMDAQEqVKoWHh4emr3r16prH\naWlp9OrVi71792Jubk7t2rWxtLTk/PnzrF69mu3bt+Pv70/NmjV1ri8lJYVWrVpx48YNGjZsSK1a\ntbL1vAghhBBCCCGEWmaGFE4Q7y9JaItc0759ez799FM2b97Mp59+ysmTJylWrBgLFizQGTt16lTC\nwsKoXr06W7du1ayKTU5Opm/fvuzdu5chQ4awc+fOtxpzfHw83bt3JzY2lkGDBvHNN99oJfliYmK4\nfv261pxRo0bh6OhIiRIltNr9/f3p27cvI0eOxNPTExMTE53zrV27lv79+zNr1iyMjJ79+Z0/f57m\nzZvj7+/P6dOnqVOnTo6uwc/Pj08//ZRFixZhbGwMwKVLl/j444/5/fffadq0Kb169crRMV9m48aN\n9OnThzlz5lCgQAG9Y7Zt24aXlxeBgYGaJPStW7do0aIFERER7Ny5k/bt27/yXGXKlMHLywt/f39+\n++03evfurdV/8eJFjh49ipWVFR9//LGmffv27SxZsoTChQuzZcsWGjRoAIBKpWLOnDl899139O3b\nl8jISIoWLfq6T4WOdevWsXXrVp1E+Zo1a7h69Spt27Zl7dq1Wsn89PR0jh49qjX+1q1b9OnThydP\nnrBixQrNil6AGzdu0K1bN9auXUuTJk1eurFggQIFWLJkCX/++ScBAQFYWVmxZMmSbF+Pu7s78Cy5\nO2nSJE27OvFbvXp1Tp48yaNHjzSlRIKDg1GpVFoJ8KzMnj2bVatWERERQePGjXU+FfCihQsXsm3b\nNk1c8OzTD3PnzuXbb7/NUUJ71apVBAUF4e3trXVdJiYmlC1bViu5/fw1v3hdPXr0YNKkSTo330JD\nQ+ncuTMTJ06kQ4cOWFpaAvDVV19x4MABAgMDqV69epa/j8mTJ7N37148PDxYunQp1tbWwLPX8MKF\nC5k0aRL9+vUjNDQUAwPtD1uFhobi6OjI6dOnKV68eLaeEyGEEEIIIYQQIr+QkiMiV33//fdYW1tz\n4sQJVCoVc+fOpWTJklpjHj16xLp164BnK1qfL/Fgbm7O/PnzMTU15ciRI5w+ffqtxrt69WpiYmJo\n1KgRs2fP1lmxWrJkSZydnbXaPvroI51kNjxL6Ldp04aYmBjCwvRvpFC+fHm+++47TTIboEaNGpqk\n5ItJtOwoXLgwc+bM0SSzAezt7TX1zHOSwMyOkiVL8t1332WZzAY0NdOfX1FdtmxZzaagL5aweJmB\nAwcCsHLlSp2+FStWANC7d2+teBYtWgTAsGHDNMlsAIVCgY+PDw4ODjx8+FCnrMSb8vb21rvqOyYm\nBoBmzZrprEw3MjLSStDCs8RtYmIiI0eO1EpmA9ja2vLjjz8C8Msvv+Rm+Dpq166NUqnk1KlTWqVi\ngoKCsLW1pVevXqSnp3PkyBGtPkDnmnLDF198oXPc0aNHY2ZmxoULF4iNjc3WcZo0aYKBgYEm+Q7P\nyrOEh4fToEEDWrRoQUxMDOfPn9fMyeq63N3d9X6SxNXVVXNTYvfu3Tm6zpiYGE35nlWrVmmS2fDs\nNTxs2DDc3d25dOlSlu8ZP/74oySzhRBCCCGEEEJ8kGSF9uua/UNeR/BeKlq0KKNGjeKrr76iTp06\nfPLJJzpjTp48SWpqKuXLl6dhw4Y6/dbW1nh6erJz505CQkJyvGI5Jw4cOACQ4xXMsbGx7Nmzh0uX\nLpGQkEB6ejqApjbzlStX9Cb0mjZtqpV4VrOzswPg7t27OYoDoEWLFiiVSp32rl27Mnr0aC5cuEBc\nXBzFihXL8bH1ad68ud7SH89zcnLSe77XuU43Nzdq1qzJmTNniIiI0JSiSEpKYuPGjRgZGWkS5QCP\nHz/W1D1/sdQNPEsI9ujRg3HjxnHkyBGdUh9v4vlV4s9Tl6yZO3cuFhYWtGjRgiJFimR5HHVN6A4d\nOujtb9CgAcbGxkRGRpKRkZGt8i2vw8DAgMaNG7Njxw6OHDmCl5cX0dHR3Lx5E29vb025jMDAQM3q\naHWC9flSGrmlZcuWOm0FCxakbNmyXLp0ibt372pWQr9M0aJFcXBw4MyZM5w7dw4HBwfCw8NJTU3F\nw8ODmjVrsmTJEgIDA6lRowZPnjwhPDwcMzMznRtcAAkJCezbt49z587x8OFDnj59CjyrWQ5w9erV\nHF1nYGAgaWlptGrVKsu/Wzc3N4KCgjh+/LjOTZSyZcu+1fdNIYQQQgiRT40dnTvHkXzIf45DN4e8\nDkEILZLQFrnO3Nxc6/uL7ty5AzxbaZoVdV3b10nw5sStW7eAfxOt2bFs2TKmTJlCampqlmMePXqk\nt93GxkZvu7pcw5MnT7Idh1pWz2OhQoUoVqwYcXFx/PXXX7mW0M5OXfPcvs6BAwcyfPhwVqxYoUlo\nb9y4kUePHtGuXTtKly6tGRsbG0t6ejrGxsZa7c97W6+vrJ6b5s2bM3jwYJYtW0b//v0xMDDAzs4O\nV1dX2rdvr5P8vXnzJoDeGz4vSkhIyLXfrT7u7u7s2LGDwMBAvLy8tBLW1apVw9raWtMWHR3NjRs3\nqFy5ss4msLkhN19X7u7unDlzhsDAQBwcHDSfGvDw8MDe3p4CBQoQFBTE0KFDCQ8P5/HjxzRr1kzn\nhtS2bdv48ssvX7rZaVbvB1lRb1a5fft2vTernvfgwQOdtnex94AQQgghhBBCCJFXJKEt8kxublQI\nkJmZ8w0LchpDaGgoY8eOxcTEhJkzZ9KiRQtKly5NwYIFUSgUjB8/nsWLF2vKGLzoxVq376NXPY8F\nCxZ85TFy+zo//fRTpkyZgr+/PzNnzqRYsWKaEiT9+vXLct67fo297LmZNWsWAwcOZNeuXYSFhREe\nHs7q1atZvXo1rVq14tdff8XAwIDMzEzNeT799FOt8jT6vK3V2WrqZLs64RsUFIRCodDUkm7SpAmb\nNm3i3r17mjFvo9wI5O7rysPDgwULFhAcHMywYcMICgqiSJEi1KlTB0NDQ+rVq8exY8d4+vRpltcV\nHR3NwIEDefr0KV999RUdO3akbNmymJubo1AoWLp0KePGjcvy/SArGRkZAFStWpW6deu+dKy+/uz8\njQohhBBCCCGEEPmVJLTFO6deNatehaiPuq9UqVKaNnWN5OTkZL1z1Kutc8LGxoYbN25w+fLlVyaO\n4NnGj/CsNvOQIUN0+q9du5bjGN6UejXvi5KSkoiLiwPQWqn8Np7Ht61gwYL07t2b+fPn4+vrS/36\n9blw4QL29vY6SUZLS0uMjIxIS0vjr7/+0ruqV9/rS73yNi0tjbS0NJ2VuCkpKfz9999vdB0VK1bk\niy++4IsvvkClUnHkyBH69+/Pnj172LhxI927d8fAwIBSpUrx119/MX78eCpUqPBG53xT6tXWly5d\n4s6dOwQHB1OzZk1NfWZ3d3c2bdpEYGDgW62fndtcXFwwNjbm2LFj/P3335w6dYpWrVppbhC4u7sT\nFhZGRERElte1a9cu0tLS6NKlC+PHj9c5x+u+H6hXt9eqVSvXa+ALIYQQQggBwD+LKLLtLS+kEUKI\nnHj/l4uKD46joyOmpqZER0fr3Tzx/v37HDx4EIDGjRtr2tVJ2atXr2pWMD5v3759OY6lefPmAPj6\n+mZr/MOHDwH0llO4d++e1uZ478r+/fuJj4/Xad+8eTMA1apV09ocTv08/vnnnzpzkpKSCA0NfUuR\nvpl+/fphaGjI6tWrWb58uabtRQULFtTUrN6wYYPeY/36668ANGrUSNNmYGCAlZUVKpVKUwv9efv3\n78/xStuXUSgUNG7cmC5dugBw7tw5TZ+npyfwrJzF+0C9Gnvx4sU8ePBAK7Grfnz48GGCg4MxMDDQ\njM8O9Q0WdR36d8XMzAwnJyeSk5OZN28eGRkZeq9rx44dREZGUrRoUWrVqqV1jJe9H6SkpLBr1y69\n537VNas3Dz148GCOy5UIIYQQQgghhBAfOkloi3fOwsJCswmjj48PsbGxmr6UlBRGjhzJ48ePadSo\nkdbGZpUrV8bGxobY2FiWLl2qdcytW7eyevXqHMfSt29fLC0tCQkJYcKECTo1eGNiYggPD9f8rK61\n/euvv5KSkqJpT0hI4PPPPycpKSnHMbypxMREvvrqK9LS0jRtly9fZvbs2QAMHjxYa7w6Uefn58f1\n69c17cnJyYwYMYJ79+69g6hzrly5crRq1Yro6Gi2bdtGoUKF6Natm96xn3/+OQALFizQbBAJoFKp\n+OGHHzh79ixFixalZ8+eWvPUz83s2bM1G/sBREVFMWHChNeO3d/fn7CwMJ2EeHJyMiEhIYB23eMR\nI0Zgbm7OrFmzWLNmjd4bOOfOnSMgIOC1Y8oJddmRFStWaP0Mzz7lULlyZfz9/YmNjaVWrVqvrPv8\nvJfdYHnbXnZdTk5OmJubs3r1ajIyMmjcuLFOyRP1+8HWrVu1Vu8/efKE0aNHc/v2bb3nff7mnL4y\nNjY2Nnh7e/P333/To0cPvSu9k5KS2LBhg+ZTGDnRo0cPnJyc2LRpU5Z969evz7U+IYQQQgghhBAi\nN0nJEZEnpk6dSlRUFGFhYTg6OtKoUSNMTEw4duwYMTExlC9fXuej9gqFgkmTJjFo0CAmTJjAb7/9\nRrly5bh8+TJ//PEHo0aN4ocfcrbbslKpxM/Pj27durFo0SI2btxIgwYNMDY25tatW5w9e5aePXvi\n7OwMgLe3N8uXLyciIoI6derg7OxMRkYGR48e1SRYs1oV/Lb06NGDHTt24OjoSIMGDUhMTCQkJIQn\nT57Qvn17evfurTXe3d2dpk2bcvjwYRo3boyrqysKhYJTp05hYmKSJ9eQXYMGDdIkcbt06YKFhYXe\nce3bt2fw4MEsXbqUjz76iIYNG2JlZUVUVBSXLl3CzMyMlStXUrRoUa15Y8aMYdeuXfj7+3PmzBlq\n167NvXv3iIyMpHv37uzZs4eYmJgcxx0YGMjq1auxtLSkVq1aFC9enISEBMLCwkhISKB69epayfUK\nFSrg6+tLnz59GDFiBLNnz6Zq1apYWlry8OFDzp8/z507d+jevTteXl45jien1CuuU1NTMTY2xtXV\nVavf3d1dU9P8xQ0uX8XV1ZXixYsTHh5Os2bNsLe3x8jIiEaNGtG1a9dciT8r7u7uzJgxg9TUVEqX\nLq21OWyBAgVo2LAh+/fvB/RfV7t27Zg3bx4XLlygbt26uLm5YWxsTGhoKKmpqQwYMEDzaYLnValS\nhapVq3Lx4kXc3NyoVasWxsbGVKtWTXMzZubMmdy/f5+AgAAaNGiAg4ODZgPYmzdvcu7cOdLS0jhz\n5kyONwVVl1nS98kOdZ969Xlu9AkhhBBCiPfcsBH623/+6d3GIYQQ2SQrtEWeMDc3x9/fnxkzZlCp\nUiWCg4PZs2cPSqWS0aNHExgYqLViVa1r166sXbuWevXqcfHiRQ4fPkyJEiXYtm3baye/GjRoQGho\nKMOHD6d48eIcPnyYAwcOkJCQQLdu3bQSwiVKlCAwMJCePXtiamrKvn37OHv2LJ06deLw4cNYW1u/\n9nPyuipXrsyhQ4eoV68eQUFBhISEUKFCBWbMmMGqVat0NkZUKBT4+fkxbNgwlEolgYGBREVF0aZN\nmzy7huyAM5TXAAAgAElEQVRydXXVbHjXv3//l46dNWsWvr6+NGnShKioKPz9/UlKSuKzzz4jKCiI\nZs2a6cyxs7Nj9+7dtGzZkri4OPbu3UtSUhKzZs1i/vz5rx23t7c3w4cPp0KFCpw/f55t27Zx6tQp\n7OzsmD17Nvv376dQoUJac5o1a0Z4eDgjRoygWLFiHD9+nO3bt3Px4kUqVarEtGnTGDt27GvHlBOl\nSpXC3t4egPr162Nubq7Vr69UR3aZmZmxZcsWPD09uX79Ohs3bsTX11dvOaLc5ujoqLkpoq9Myquu\ny8TEhN27dzN06FAsLS05dOgQ4eHheHh4EBQURLVq1bI89/r162nXrh0PHjxg8+bN+Pr6akotqY/t\n5+eHr68vnp6e/PXXXwQEBBAcHExqaipdunTh119/1VvuRAghhBBCCCGE+JApcrMmbH6RkJAQCGQr\n66LeIE9fclWI7EhNTQXA1NQ0jyPJ/37//Xf69u1Lw4YNs6xPLITIHnlvEiL/+pD/fXr58mXg2adZ\nhBDiffHBvDeNHf3v4+fLGmZnhfbzm0LOztkno0X+Ebbg34VFmRn/lkd06OaQF+EQtSFK89jA8N81\nuS7DXfIinPdOPnxvCipSpIhHbhxIVmgLIfKFtLQ05s6dC8DQoUPzOBohhBBCCCGEEEIIkRekhrYQ\n4r22Zs0awsPDOXHiBJcvX6Zhw4bvpG60EEIIIYQQQgghhHj/yAptIcR7LTg4mPXr1/PgwQM6d+7M\n2rVr8zokIYQQQgghhBBCCJFHZIW2EOK9tmrVKlatWpXXYQghhBBCCCGEEEKI94Cs0BZCCCGEEEII\nIYQQQgiRL0hCWwghhBBCCCGEEEIIIUS+IAltIYQQQgghhBBCCCGEEPmCJLSFEEIIIYQQQgghhBBC\n5AuS0BZCCCGEEEIIIYQQQgiRL0hCWwghhBBCCCGEEEIIIUS+IAltIYQQQgghhBBCCCGEEPmCJLSF\nEEIIIYQQQgghhBBC5AuS0BZCCCGEEEIIIYQQQgiRL0hCWwghhBBCCCGEEEIIIUS+IAltkWsiIiIo\nWrQoU6dO1Wq/ePEi48aNo3Xr1tSoUQNra2tKlSqFk5MTPj4+3LhxQ+/xwsPDGTVqFM2bN6dq1aqU\nLFmSMmXK0LBhQ6ZOncqDBw/0zvPz80OpVL706/79+7l9+e/U3r17mT59Op06daJixYoolUrKlCmT\nrbkJCQlMnjwZR0dHrKysqFy5Mj169ODkyZO5HmdISAhKpRIvL69cP3Ze6N69OzY2Nty7dy+vQxFC\nCCGEEEIIIYT4TzLK6wDEh0GlUjF27FgsLCwYMWKEVt/x48dZunQp1tbWVKpUCWdnZ5KSkjhz5gzL\nly/n119/ZdOmTbi5uWnN279/P6tWraJcuXLY29tTokQJHj58SGRkJD/99BN+fn7s3LkTe3t7vTFV\nqFABFxcXvX2mpqa5c+F5ZMCAASQmJuZ43v3792nZsiXR0dGULVuWNm3acPfuXQICAtizZw8rV66k\nQ4cObyHiD8OECRNo3Lgx06ZNY/HixXkdjhBCCCGEEEIIIcR/jiS0Ra747bffiIyMxMfHB6VSqdXn\n7u5OREQEVapU0Wp/+vQpU6ZMYfHixQwZMoQzZ86gUCg0/V26dMHb25uyZctqzUtOTmbYsGH8/vvv\njBw5kl27dumNycXFhSVLluTSFb5f2rVrR5UqVahTpw5FixalSZMm2Zo3fPhwoqOj6dSpE8uWLcPI\n6NlbQEBAAL169eLzzz/H2dmZUqVKvc3w862aNWvStm1b1q9fz+eff07NmjXzOiQhhBBCCCGEEEKI\n/xQpOSJyxZIlS1AoFPTs2VOnz9bWVieZDVCgQAG++eYbTE1NuXnzJlevXtXqt7Oz00lmA5ibmzNt\n2jQAQkNDefLkSS5dRf6xcOFCvvzyS9zd3SlSpEi25ly4cIG9e/diYWHBTz/9pElmA3h5edGtWzdS\nUlI+2JsAuaVnz56oVCp++eWXvA5FCCGEEEIIIYQQ4j9HEtrijUVGRhIZGYmbmxu2trY5mmtgYICB\nwbOXobGxcbbnqZOxRkZGGBoa5uicr0NdezsrDg4OKJVKnXrgDg4OWFtbc/PmTfz9/fnoo4+wsbGh\nXLlydOzYkdDQ0LcdukZAQAAArVq1onDhwjr9Xbp00RqXEzt37qRly5aUKVMGW1tbOnTowJEjR145\nLzw8nF69emFnZ4elpSV2dnb07t2biIgInbHdunVDqVSyf/9+rfb4+HiKFSuGUqlkypQpOvOaNWuG\nUqnk9OnTmjYvLy+USiUhISGcPn2abt26UaFCBaysrHBzc2PdunVZxuzp6UnJkiX57bffiI+Pf+U1\nCiGEEEIIIYQQQojcIwlt8cbUCVAPD48czcvMzOT7778nJSWFmjVr6l2NrU9aWhozZswAniUXn19p\n/Lzr168zffp0vvzySyZOnMjmzZtJSkrKUYy5ZcWKFXh7e5OZmUmrVq2wtbXl8OHDtG3blm3btumM\nV2+m+LIkek6dPXsWAEdHR739devWBeDatWs5ep7mz59Pz549CQ8Pp2bNmrRo0YKYmBjatWv30uT4\nypUrad26NTt27MDGxob27dtjY2PD9u3badmyJWvXrtUa7+7uDkBgYKBWe0hICJmZmXr74uPjOXPm\nDMWKFaN27do6MRw8eJAWLVpw8+ZNmjVrRp06dTh//jzDhw/n559/1hu3oaEhjRo1IiUlhaCgoFc9\nPUIIIYQQQgghhBAiF0kNbfHG1CtxnZycXjouPj6er7/+WvM4KiqK27dvU6lSJVauXKlVP/t5V69e\nZe7cuQDExcURGRlJbGwsjo6O/Pjjj1meLywsjLCwMK02pVLJ/Pnzad++fbavLzesWLGC1atX07Fj\nR03bypUrGT16NMOGDcPV1RUrK6u3GoN69XhWNw6KFCmChYUFiYmJ3Lx5k+rVq7/ymGfOnGHatGkY\nGRnh6+tL69atNX0LFixg8uTJeudFRUUxduxYANasWaO1EeWWLVsYMGAAY8aMwcnJSROHOqH9YhI5\nODgYgOrVqxMVFUVcXBzFihUDnr02MzIyaNy4sd7X108//cTPP/9Mr169NG0bN25k0KBBzJkzh379\n+mFmZqYzz8nJid9//53g4OB3/loSQgghhBBCCCGE+C+TFdrijUVFRQFgb2//0nHJycmsX7+e9evX\ns3v3bm7fvo2DgwNr1qx56dyYmBjNvL179xIbG4u7uzurVq3Su3mhtbU1Y8aM4dChQ1y7do0bN26w\nf/9+2rZtS3x8PP/73/84ePDgm110DrVu3VormQ3Qr18/GjZsyKNHj/D19dXqMzMzo0qVKnprj7+u\n5ORk4FkN8qyo+7K7Qnv58uVkZGTw6aefaiWz4dkGlHXq1NE7b9myZaSnp9OpUyetZDagaXv69ClL\nly7VtFevXh0rKyvOnz/PgwcPNO1BQUGUKlWKAQMGkJmZqUlwq/vg32T4i9q1a6eVzAbo2rUr9vb2\nJCYmcurUKb3zqlatCvy76l0IIYQQQgghhBBCvBuS0BZvJDk5mZSUFADNqtislClThvj4eOLj47l4\n8SJ+fn5kZmbi4eGhlbh8kaurK/Hx8cTFxXHu3DmWLVtGdHQ0rq6u+Pv764xv3rw5EydOxNHRkWLF\nilGkSBH+n737Dovi6gI4/BuKIqCuDRsioMbexYYUNYkxlqixt9iNGrsmauzGGGsUS2wYjTVWikTs\nKMVCsaKxxq6xVwQp+/1BdmXdXQRcJOY77/P4uMydO3tmdnYWztw918XFhTVr1jBgwACSkpIYO3bs\nu+14OrVu3drg8vbt2wPo1ZuuXr064eHhBmtJ/5uEhoYCyUlgQzR1uY3169ixo8F2zeSibx4Xd3d3\n1Gq1Nml9+/Ztzp8/j7u7u7bkTcqyI5r1jJXDadSokcHlmhsJd+7cMdieJ08eIPlmixBCCCGEEEII\nIYR4fyShLd7J06dPAciePXu6JnUsVKgQTZo0ISAggMKFCzNmzBhOnDiRah8zMzPs7e1p164dvr6+\nWFpaMmDAAG7fvp3m5x05ciTm5uacPXuW69evp7nfu3JwcEh1+a1btzI9Bs3oa81IbUM0bba2tmna\npiZuY5OBGttvzWtmrJ+jo6POehru7u7A66S1ZgS2p6cnTk5OODg4aNvu3LnDuXPnsLe3x9nZ2eDz\n2NvbG1yumTQzNjY21fYnT54YbBdCCCGEEEIIIYQQmUNqaGfQYa/Db1/pA1B7UO136p87d24A4uLi\nePXqVbqS2pBc07px48YsW7aMgIAAgxP3GeLo6EidOnXYuXMne/fu1Y7oTcvzFShQgDt37nD79u00\nT0T5Nmq12iTbyUwODg6cPHnSaCL/6dOn2hsUpjoub2OsbroxmpHWmkT2myVFPDw8WL16NVevXtXW\nTzdWbgSSb5JkxLNnzwBMOmmnEEIIIYQQQgghhHg7GaEt3om1tbV25O/Dhw8ztI38+fMD6NRFzqx+\niYmJ2qRtarWk32RpaQkYri0dHx9vtDSFhrEk8rVr1wAM1gI3Nc3NgqioKIPtmuXOzs7aEchvo4lb\nsx9vMrZc0+/KlSsG2zXL3zwuxYoVw9nZmatXr3LlyhUOHjzIRx99RJEiRQB0yo6kHL1tappzvUCB\nAibfthBCCCGEEEIIIYQwThLa4p1VqlQJgHPnzmWov6bOsbGyEIYkJCQQFhaW7n6BgYHExMSQM2dO\nPvroozT30yRWL1y4oNe2b98+EhISUu2/ZcsWg8s3btwIQL169dIcS0Z9/vnnQPIx0IwwNhRL06ZN\n07xNV1dXnb5v2rRpU6r91q9fb7B97dq1gOHjohlxvXz5cm7evKkzAtvd3R1FUQgKCtKeV5oyJab0\n559/AqT5GwVCCCGEEEIIIYQQwjQkoS3emZubGwBHjx412L5o0SJu3Liht/zp06eMHz+e0NBQcubM\nyZdffqnT/vPPP/PgwQO9fvfu3WPAgAH89ddf2Nvb8/HHH2vbYmJi8Pb2NjiSeufOnQwePBiAXr16\naUddp4UmaTp9+nRevXqlXX727Fm+/fbbt/YPCAjQm8By5cqVhISEYGtrS5cuXXTaIiMjcXFxwcXF\nJc0xvk358uVp1KgRT58+ZciQITpJ+ICAADZs2IC1tTX9+vVL8zZ79+6NmZkZv//+O7t27dJpW7hw\nIceOHTPYr2/fvlhYWLBlyxb8/f112nx8fNi2bRuWlpb07dtXr69mxPXy5csB3ZIiBQoUoFy5cuzY\nsYMbN25QtmxZChYsmOb9SSvNZJ2ac18IIYQQQgghhBBCvB9SQ1u8syZNmjBjxgyCgoIYOXKkXvsv\nv/zC999/T5kyZShZsiTZs2fn1q1bnD59mqdPn5IzZ068vb31yktMmjSJH374gfLly+Pk5IS5uTm3\nbt3ixIkTvHz5Ejs7O1avXo21tbW2z6tXrxg+fDjff/89lStXpmjRorx69Yrz589z/vx5AJo1a8aY\nMWPStY/Dhg3D19eXwMBAatSoQZUqVbh79y5RUVG0aNGCpKSkVCeZ7NWrF1999RUuLi4UL16c8+fP\nc/LkSczNzZk3bx6FChXSWT8mJsbgaHCNGTNmaBPIcXFxALx8+VInuf/pp5/qJdu9vLxo1KgRW7Zs\n4ejRo7i4uHD79m0OHz6MmZkZCxcuTFf5kypVqjB27FgmT55Mu3btqFWrFsWKFSM6Opo///yTvn37\nsmTJEr1+FStW5KeffmLkyJF06dKFGjVq4OTkxOXLl4mMjMTMzIyZM2dSvnx5vb5ubm4oikJsbCzm\n5uZ6o7g9PDyIjo7WPja1hIQEQkJCsLa2zpTtCyGEEEIIIYQQQgjjJKEt3lnlypVxcXEhLCyMq1ev\nUrx4cZ328ePHs3fvXo4fP05ISAhPnz7F1taWkiVL0qBBA3r27GkwiTpz5kzCwsI4deoU+/fvJyYm\nhly5clG5cmUaNWpE9+7d9Sbls7a2ZsSIEURFRXHhwgVOnz7Nq1evyJ8/P40bN6ZDhw40b9483fvo\n5OREYGAgU6ZMISwsjF27duHs7MzkyZPp27evtuyKMb169aJ27dosWrSIHTt2YGZmhqenJyNHjtSW\n30iPv/76i4iICJ1lSUlJOstKlSql169gwYIEBQUxe/Zstm/fzvbt28mZMyeNGzdm+PDhVK9ePd2x\nDBs2jJIlS7JgwQJOnjzJmTNnqFKlCtu2bcPMzMxgQhuSj0mFChVYsGABR44c4fjx4+TJk4dmzZox\ncOBAatasabBf3rx5qVixIidPnqRKlSp654CnpyeLFi0CMiehvWfPHu7du0fXrl1lUkghhBBCCCGE\nEEKI90xRq9VZHcN79+TJkyAgTZkuzajbYsWK6Sw/7HXY4Pq1B9V+p9gyS2bHu2XLFnr27MnIkSP5\n/vvvTbLN/4KKFSty/fp1jh49mq6a3eLfq3PnzgQEBBAcHEyFChWyOhwhMiw2NhYAKyurLI5ECJFe\nxn4//S/QfEPN0I15IYTIKv+Za9N3w18/Tkx8/XjgEMPrz5/7+rG5+evH02ebNi7xr5Eyd5SUmKR9\nXLF9xawIh1MbTmkfm5m/rpr8b829vW8f4LXpQO7cuT1NsSGpoS1MolWrVlSvXp2lS5fy+PHjrA5H\niExx+vRpAgIC6NChgySzhRBCCCGEEEIIIbKAJLSFSSiKwvTp03n69Clz5859ewchPkBTp07FxsaG\n8ePHZ3UoQgghhBBCCCGEEP+XpIa2MJkaNWrw6NGjrA5DiEyzfv36rA5BCCGEEEIIIYQQ4v+aJLSF\nyESnTp3S1qkVQgghhBBCCCGEEEK8Gyk5IoQQQgghhBBCCCGEEOKDIAltIYQQQgghhBBCCCGEEB8E\nSWiLDFOpVOn+169fv3d6Tk9PT1QqFceOHdNZPnbsWFQqFfPnz9dZHhgYiEqlol27du/0vEKkR9eu\nXVGpVPj6+mZ1KCb1/PlzVCoVRYsWzepQRBZ43+f10qVLqVevHoULF9Z+hiQkJLyX5xYfvuPHj9O2\nbVucnZ3JmzcvKpWK3377LavDEkIIIf4bvhue9n9CCJEJpIa2yLAOHTroLbt79y579+7FxsaG5s2b\n67XXqVPnfYQmRKY5c+YMdevWpWzZshw6dCirw+H58+fY29tjY2PDzZs3szocIUxiy5YtfPvtt1hb\nW1O/fn1y584NgJmZ3IcXb/f48WPatWvH33//jYuLC87OzpiZmVGiRImsDk0IIYQQQghhApLQFhn2\nyy+/6C0LDg5m79695M2b12D7++bm5sbRo0exsbHJ6lCEEEKkkY+PDwBeXl60bt06i6MRH5pDhw7x\n999/06BBA7Zu3ZrV4QghhBBCCCFMTBLaJnbY63BWhyBSsLGx4aOPPsrqMIQQQqSD5tsGMqJWZISc\nP0IIIUQmSExM+7rm5pkXhxBCIDW0RRY7ffo0X3/9NRUqVMDOzg5HR0datWrF3r17TbJ9YzW0z5w5\ng0qlok6dOiQlJbFo0SLq1q1LoUKFcHJyokuXLly8eNHodvfu3UuTJk2wt7fHwcGBJk2asGfPHp3t\npsehQ4fo1KkTpUuXJn/+/Dg4OFCtWjX69OljsKxFXFwcCxYswNPTE3t7ewoXLkydOnX44YcfePLk\nid76KeNKSEjg559/pmbNmhQqVIgKFSowadIk4uLiALh//z4jRoygfPny2NnZ4eLiwvLly43GnpSU\nxPr162nevDlOTk7Y2dlRqVIlhg8fzq1bt4z2Cw4OpkOHDpQsWZICBQpQpkwZevTowfHjxw2un7J+\n+pEjR2jTpg2Ojo4UKlQId3d3Nm7caPIY39S1a1fq1q0LwNmzZ3Xqwxt7zc+dO8dXX31FiRIlsLOz\no1atWixatAi1Wq237p07d5g/fz4tWrSgQoUKFCxYEAcHBxo1asSqVav0+owdOxZ7e3sAXrx4oRNP\nWutcJyQksHjxYho0aECxYsUoUKAAH330EfXr12fChAk8fvzYaN/169dTv359ihQpQrFixWjVqhVR\nUVFG13/27BmzZs3C3d1de97WrVuX2bNn8/LlyzTFCzB9+nRUKhUzZszQa6tevToqlcpgyaPhw4ej\nUqlYuXKldtnjx4/x9vamXbt2VKlShUKFCmFvb4+npydeXl68evXKYAxnzpyhd+/elC9fngIFClCs\nWDEqV65M165d2bFjR5r3JWX9/ytXrtC7d29KlSpFkSJFqF+/vs62Dh48SIsWLXB0dKRo0aK0bNmS\nU6dOGdzu7t27GTp0KHXr1sXR0RE7OzsqVqzIN998w6VLl4zG8/TpU8aNG0fFihWxs7OjQoUKjBo1\nyuB15U1//PEHbdu21b6ny5YtS58+fTh//nyaj4emTrfmPKpfv772nH5zjgQ/Pz/t8dDE+s0333D5\n8mWD23Z2dkalUvHgwQO2bt1K48aNcXBwQKVSGe0Dye+RcuXKoVKpOHPmjNH1WrVqhUqlYv369TrL\nnz59yo8//kjdunUpUqQI9vb2eHh4MH/+fGJjY/W2s3TpUlQqFSNHjjT4PO8yN8TGjRtp2LAhRYoU\nwdHRkbZt2xIREfHWbab3eg0Zu+7u2LGDVq1aUaJECfLnz4+TkxO1atVi0KBBqR57Dc1+jBgxAoBl\ny5bpXaNTzgWgVqvx9vbWfpa+Waf977//ZsyYMVSvXl3vepyYnj/qhRBCCCGEECYlI7RFllm9ejVD\nhw4lISGB8uXLU716de7evUtwcDD79u1j4sSJDBkyJFNjSEpKolu3buzatQtXV1dKlixJZGQk/v7+\nhIaGEhISQpEiRXT6/PrrrwwdOhRITp45OTlx6dIl2rRpw4ABA9IdQ0BAAF27diUxMZGqVatSt25d\nYmNjuXXrFlu2bMHOzk4nWfr8+XNatmxJeHg4OXPmxM3NjWzZshEaGsqsWbPYunUr/v7+BhOaSUlJ\ndOrUibCwMFxdXXFycuLQoUP8/PPPXL58mRkzZvDxxx+TmJhIzZo1uX//PmFhYYwYMYKEhAS+/vpr\nne3FxsbSpUsXdu/ejY2NDVWqVCFfvnxER0fj7e2Nr68vfn5+lCtXTqff/PnzGTduHAA1a9bEwcGB\n8+fPs3XrVvz8/Fi0aBFt27Y1eLz8/f2ZN28e5cqVo2HDhly9epWIiAj69OnDixcv6N69u0liNMTN\nzY2EhAT++OMPcufOzeeff65tM3S8w8PD6devH0WKFMHT05M7d+5w6NAhxowZw71795gwYYLO+n/8\n8Qfjxo2jWLFiODs7U6tWLe7cucPRo0c5cuQIISEhLFu2TLt+tWrVaNu2LRs3bsTCwoI2bdpo26ys\nrN66PwA9evTAz88PGxsbateuTZ48ebh//z6XLl1i3rx5tGvXDpVKpddvzJgxLF26lNq1a/Ppp59y\n6tQp9u3bR1hYGLt27aJSpUo661+5coVWrVpx+fJlChYsSO3atbGwsCAyMpIpU6YQEBCAn58ftra2\nb43Zw8ODadOmERQUxLfffqtdfuPGDW2y9ujRo8TGxuochwMHDgDJN0c0IiIiGD58OAULFqREiRJU\nr16d+/fvExERwfjx49m5cyc+Pj5YWlpq+0RFRdGkSRNevnxJ2bJlqV69Omq1mtu3b7Nz504AGjdu\nnIaj/9qFCxfw9PQkX758uLm5cf36dcLDw+nUqRNr167l2bNn9OvXj2rVqlG/fn1OnjzJ/v37adKk\nCaGhoRQrVkxnewMGDODp06eUKVNGe96eOXOGNWvW4Ovri7+/P2XKlNHp8/jxY5o0aUJ0dDS5c+fm\n008/Ra1Ws27dOoKCgozeJFGr1QwaNIjVq1eTLVs2qlatSuHChblw4QIbN24kICCA9evX4+7u/tbj\n4Obmho2NDTt37uThw4c0atSIvHnzAlC2bFnteiNHjmTZsmWYm5tTp04dChYsyMmTJ1mzZg3btm1j\n3bp1eHh4GHyOn376ieXLl+Pi4sKnn37KtWvXUBTFaEwWFhZ0796dqVOn4u3tzezZs/XWuXTpEvv3\n7ydfvny0atVKu/z27ds0a9aMixcvkjdvXj7++GMSEhIICQlh3Lhx+Pj44OPjQ86cOd96bN7V5MmT\nmTNnDmZmZtSuXZvChQsTHR1N48aN6dmzp9F+GbleZ+S6u3TpUr799lvMzc1xcXGhaNGiPH/+nOvX\nr7N69WoqVKjw1ut00aJF6dChAxcuXCAiIoKPPvqI6tWra9veNGDAADZu3Ejt2rX57LPPOHfunPZc\nOHv2LC1atODvv/+maNGifP755zx//pzg4GCOHDnCjh07WLNmDRYW8qu0EEIIIYQQ75v8Fi6yRHh4\nOEOGDMHW1pbVq1frJDpOnDhBmzZtmDx5Mm5ubto/RjPDuXPnSEpKIiIiQjvSNSYmhnbt2hEcHMz8\n+fOZNm2adv2//vqLUaNGoSgK3t7eOomLjRs30rdv33THMHv2bBITE1m/fr1eEuzevXvcvn1bZ9mE\nCRMIDw+nQoUKbN26FTs7OyA50d29e3d2797NgAEDtDVo39xfCwsLoqKiKFCggHaf3N3d8fX15cKF\nC7i6uuLl5UW2bNkA2Lp1Kz169GDmzJn07NlTJ7E3btw4du/eTYMGDVi8eLE2FrVazbx585g4cSK9\nevUiNDRUmyQIDw9nwoQJWFpasnr1aj777DPt9lavXs3AgQMZNGgQLi4uODk56e3D3LlzWb58uc6x\nX7FiBcOGDWPq1Kl07tz5nWM0pnfv3ri6uvLHH39QpEiRt9aJX7BgAZMnT2bgwIHabe/evZs2bdqw\ncOFCBgwYQP78+bXr16xZk6CgIKpUqaKznRs3btCqVSs2bdpE69atadSoEZA8IvTTTz9l48aNZM+e\nPd116//880/8/PxwdnZm79695MmTR6f92LFjejd0IHk0+ObNmzlw4ADly5cHIDExka+//ppNmzYx\nffp01q5dq10/MTGRzp07c/nyZQYPHsyYMWPInj07kHze9u/fHz8/PyZNmsTMmTPfGneNGjWwtbUl\nIrv4uAIAACAASURBVCKCmJgYrK2tgdcJ63LlynHmzBkOHTpE/fr1geQSBBcvXsTBwQFHR0fttkqW\nLElAQAB169bVef0fPnxIly5dCA0NZeXKlfTu3Vvb5uXlxcuXL5k+fbree/7JkydcuHDhrfvwpt9+\n+43hw4czduxYbRxeXl6MHz+e7777jsePH7Nu3Trta5+QkEDnzp0JDAxkwYIFTJ8+XWd7s2bNwtPT\nk1y5cmmXab6NMnbsWIYOHao3knzixIlER0dTrVo1tmzZoj0fHjx4kOo3ZxYsWMDq1aupXLkyK1eu\n1Hnfbtq0iT59+tCzZ0+OHz/+1vkMNMfZ09OThw8fMmrUKKpWraqzzrZt21i2bBm5cuVi27Zt2s8I\ntVrNTz/9xPTp0+nRoweRkZEGb8asXr0aHx8fowlvQ7p168bMmTPZuHEjEydO1EtAe3t7o1ar6dKl\ni/bcBhg0aBAXL16kQYMGrFq1Stvv/v37fPnll0RGRvL999/j5eWV5lgyIiwsjDlz5pAjRw42bdpE\nvXr1tG1z5sxh8uTJBvtl9HqdkevujBkzsLCwYO/evVSuXFknjmvXrmm/RZSaihUr8ssvv7B06VIi\nIiLw8PAwek158eIFO3fuZP/+/VSsWFGnLSkpiR49evD333/ToUMH5s2bp/1MvHLlCs2bNycwMJC5\nc+dqR4MLIYQQ/0kD0zHAbP7czItDCCHeICVHRJaYOXMmiYmJTJ8+XW/UXuXKlZkwYQJJSUk6o1Ez\ny5w5c7TJbABra2vtH6iaBJnGr7/+SlxcHI0bN9ZJqAK0bduWTz75JN3Pf/fuXczMzGjQoIFeW4EC\nBXRGuj5+/Jg1a9Zo49YkCQBsbW2ZN28e2bNnJygoyGgpgjlz5miT2QBOTk60aNECSC55MWPGDO0f\n7pCcNHVycuLBgwdER0drl9++fZuVK1eSN29eVqxYoROLoigMGTIEV1dXzpw5Q3BwsLZt0aJF2pHi\nKZMjAF26dKFRo0bExsaydOlSg/G3a9dO79h3794de3t77t+/b5IYTaVevXoMGjRIJ1H6ySefUKdO\nHV69ekVYWJjO+hUqVNBLZgPY29trR0j6+vqaLL67d+8CyQniN5PZAFWrVjWYEITkGyuaZDaAubk5\n33//PYDesfT39+f06dO4u7szadIknYSfra0tXl5e5MqVi9WrV6ep9IiFhQWurq56x1DzftUcq6Cg\nIL22N5OYjo6OuLq66t3MyJs3Lz/++COgf8w1x83Q+z137tzUqFHjrfvwpo8++ogxY8boxNG3b1+s\nra25du0aLVu21CazIfkYDBo0CNA/3gDNmzfXSWYDmJmZ8c0331C+fHmOHTvG9evXtW2PHz/WlsqY\nPXu2zvmQL18+o0nBuLg45syZg7m5OatWrdK7CdWmTRvat2/PvXv32LZtW1oPR6oWLFgAwJAhQ3Ru\neCqKwqhRoyhXrhwPHjzQK/2h0aNHj3QlsyH5WtyyZUuePXvG77//rtP28uVL1q5di5mZmc43RM6d\nO8fu3buxtLRk3rx5Oknw/PnzM3du8h9969ev58GDB+mKJ70019OvvvpKJ5kNMHToUKMjnzNyvc7I\ndTchIYGHDx9iZ2enl8wGcHBwoFSpUhnb+VSMHDlSL5kNyWXFzp49S968eZk5c6bOZ6KjoyNTpkwB\nko+PlB4RQgghhBDi/ZOEtnjv4uLiOHDgABYWFjRt2tTgOq6urkDy6LDMZGtri5ubm95yzUSSd+7c\n0VkeGhoKoFPaIaXWrVunO4bq1atrR4NFRESQlJRkdN2IiAji4uIoWbIkNWvW1GvX1N0FCAkJ0WvP\nmTMntWrV0lvu7OwMJI8QfjMJlrI95fHYv38/8fHx2trWhhh6HTXHsGPHjgb7dOrUyWj8gF5SBZKT\nJJpkhyliNJWUCciUjJ1fAPHx8ezevZtp06YxdOhQ+vfvT79+/Vi3bh1AqvWP06t8+fLkyJEDHx8f\n5s+fr51ILS0MvQ7Fixcne/bsPH36lOfPn2uX79q1C0B74+RNKpWKChUqEBsby8mTJ9P0/JobYSlv\nOgUHB1O6dGltmQpDCe2U5UY0kpKSCA4OZubMmQwfPlx7zDWJ0zePuSaJOmDAAA4ePEh8fHyaYk6N\np6cn5m9MnpM9e3ZtmYSGDRvq9dFMeGfoPAK4evUqy5cvZ9SoUXzzzTf069ePfv36aethp6wbrbm2\nlCpVSm9ENCRfGwx9YyI8PJxHjx5RtWpVnZHvKZnyPfbixQuOHTsGGL6GKIqiXW7sGtKsWbMMPbdm\nNP6KFSt0lm/atIknT57wySefULx4ce1yzbXOzc1NryQMJN8wKleuHPHx8Rw+nLkTSqf22aUoit5N\nwjf7ped6nZHrroWFBZUrV+bWrVsMHjyY6Ohog/MMmJqxc0Gz382bNzdYBklzw+jhw4dpqu0thBBC\nCCGEMC0pOZJBtQfVzuoQPli3b9/WfnU45choQ+7fv5+psRirCasZSffmV5w15T8MJSdSW56aH374\ngfPnzxMQEEBAQAC2trZUqVIFDw8P2rdvr7NNzURaKZMmb9Ikld4sVQIYLB8BaMsAGDsemvaUx+PK\nlStAckmSrVu3Go0HXr+OCQkJ2tGtxvYhtfjB+Dlj6DXLSIymlJ5YAaKjo+nSpUuqE9Q9e/bMZPHl\ny5ePn3/+mWHDhjFu3DjGjRuHvb09NWvW5LPPPqNFixY6IxM1cuTIQb58+fSWK4qCjY0NcXFxxMXF\naRNBmtdh2LBhDBs2LNWY0vo6aEbYapLW586d09YrNjMzw83NDX9/fx4+fEjevHk5ePAgiqLofSPk\n5s2bdOrUKdXJ7d485iNGjCA8PJxDhw7RvHlzrKysqFSpEm5ubrRt25bSpUunaR9Sett7z1C7ofcl\nJJd1mDBhAgsWLEj1BlnK/UrLtcXBwYG//vpLZ5nmtY2IiDCavNQwxXvs7t27JCUlYWVlRcGCBQ2u\n87ZrSEau05Bcs97FxYXw8HBCQ0O1SVlvb28AnbI0kPbr9ZkzZ4zGagoJCQncu3cPSN9nV0av1xm9\n7s6bN4+OHTuyatUqVq1ahUqlokaNGnh6etKhQweD15x3YWlpSeHChQ22ve21UxQFBwcHTp8+ze3b\ntw2O8hZCCCGEEEJkHkloi/dOk2DJnj270VFhGmmd2C6jzMwy9iUFY7WWM7K9YsWKERISwoEDBwgK\nCuLIkSMcPXqUkJAQZs6cyS+//MKXX36Zpud/m7fFl574Na9j2bJlDZbJSMlQe2btQ0rvGuO7Sk+s\nmjrTf/31Fy1btqR///6UKlWKnDlzYm5uzvHjx/H09DT5qMX27dvz6aefEhAQQFhYGEeOHNEmoqZP\nn86OHTt0Sgakd7/g9evg4eFh9KaKxtvaNcqXL0+BAgU4ffo0Dx480Csp4uHhga+vLwcPHqRcuXLc\nvn2bcuXK6ZTbgeRRt8ePH8fd3Z2RI0dSrlw5cuXKhaWlJY8ePcLJyUnvmOfOnZvAwEAOHz7M3r17\nOXLkCBERERw9elRbj3jgwIFp2g8NU743169fj5eXFyqVih9//BFXV1cKFiyovZ62b9+ewMBAk5xL\nmte2WLFieqUs3mTKpJ+iKBm+huTIkSPDz9unTx/Cw8Px9vbG1dWV8PBwTpw4gZOTk8FR9JpYTSm1\nmxRvk9HPrvTsQ0avu5UqVSIyMpLdu3drJ17cv38/e/bsYcaMGaxbt+6t51h6ZMuWzaT7LYQQQggh\nhHh/JKEt3ruCBQtiYWGBWq3Gy8tLZwK/f7tChQpx69Ytrl+/bnCyymvXrmVou+bm5jRo0EBbR/vZ\ns2faid4GDx5Ms2bNyJYtmzbZpxkBZ4imzdjIM1PRjBitWrUqixYtSlMfCwsL7OzsuHv3LleuXNFL\nlIJp489IjFnl5MmT/PXXXxQvXhxvb2+9RMubI2NNKW/evHTp0oUuXboAcOHCBQYMGMDRo0eZOnUq\n8+bNe6fta16Hdu3aGS1dkF6a0dZbtmzh4MGDHDhwAHNzc23CS1NaJCgoSDs69c1yI/fv3yckJAQr\nKyt+//13vURnaiPlAWrXrk3t2snf1omLi2Pt2rWMGDGCiRMn0rJly7d+AyWzaGp+T5061eDxNnQu\nad5vqV3DDLVpXtvixYune1LSjLCzs8PMzIyXL19y584dChUqpLdOZl4DW7RowdixY/H39+fu3bss\nX74cSK7L/WbyM6PXa823IlKW7UkpZe3ztLCwsCB//vzcv3+f69ev693UAcOvbUav1+9y3c2ePTtN\nmzbVliN78OABU6ZMYeXKlQwePJjIyMh0bS+j3vbaqdVq7THL7M9aIYQQQgghhD6poS3eOxsbG+2k\neH/88UdWh5MudevWBWDz5s0G240tT6+cOXMyevRocufOzfPnz7UJqBo1apA9e3YuXrxIRESEXr/b\nt2+zf/9+AJOOZDOkQYMGmJmZsXv3bl68eJHmfpqv6W/YsMFg+9q1awHTxJ/RGFOjSTYlJCSYZHsa\njx49ApITKYZGDW7cuPG9xVOqVCkGDx4MwOnTp995ex9//DFg2gkt4fVo7H379hESEkLVqlXJnTs3\nkFz33d7enqCgIKMTQj5+/BhILr1iaNSusWNuSPbs2enRowfly5cnMTGRs2fPZmifTEFzLhlKqB8/\nfpwLFy7oLXdxcSFbtmycP3/eYPmViIgIg4nw2rVrY2trS3h4uLZMQ2aysbHR1vg2dA1Rq9XaySAz\n4xpoaWlJt27diI+PZ86cOfj4+JAjRw46d+6st67mWhcSEsKNGzf02o8fP86ZM2ewtLTU3hiB1wlS\nQ68TwJ49e9Idd2qfXWq12mhpkIxcr0153c2XLx+TJk0CkmvZp2XSWFPQ7Le/v7/BffDz8+Pp06fk\nzZvX6ISaQgghhBBCiMwjCW2RJUaNGoWZmRlDhw7F399fr12tVnP48GEOHjyYBdEZ1717d7Jly0ZA\nQIBecm7Lli3aye/SY+7cuQbrp4aFhfHkyRMsLS21tWJVKpV2Eq7hw4fr1B998eIFQ4YMIS4uDk9P\nz0yv6Vm8eHE6d+7MvXv36NSpk8Fk17Nnz1i3bp02cQjQr18/FEVhzZo1eomZdevWsXPnTqysrPTq\n0b7PGFNTsGBBFEXhxo0bJkuSA5QsWRKAqKgooqKidNqWLl3Kjh07DPbLli0b+fLlIy4uLtWRoIaE\nh4fj5+dnsAbzzp07gYzXG06pdevWlClThp07dzJ69GjtpIQp3bp1i9WrV6dru5oEtWZSvjdHYHt6\nenLlyhX27NmDhYWFNqmnYW9vj7W1NTdv3tS7uebv7683+Z/G4sWLDZ5L58+f147qNsVxyyjNBKkr\nV67UudFx69YtBgwYYLBkhUqlol27dgCMHDlS5/3w8OFDvv32W4PPZWNjo73udOjQweCknrGxsfj6\n+qb7/DRmwIABQPK1M2XyXa1WM3PmTKKjo8mXLx8dOnQwyfO9qXv37lhaWrJ48WLi4uJo1aoVefLk\n0VuvdOnSfPzxx7x69YqhQ4fqjLh+8OCBtp78m/Wha9WqhZWVFRERETrve823mnbv3p3umPv06QMk\nnxNhYWE6bV5eXkRHRxvsl5HrdUauu48ePWLp0qXamzEpBQYGAlCgQIF3KheTHg0bNqRs2bI8ePCA\n7777Tmfi1ytXrjB+/HgA+vfvrzOZa0xMDC4uLri4uOjd1MpomxBCCCGEEEKflBwRWcLV1RUvLy+G\nDRtGly5dcHR05KOPPiJ37tzcu3ePU6dO8eDBA77//nu9SdyyUokSJfjxxx8ZMWIEX331FTVq1MDJ\nyYnLly8TGRlJ//79WbRoUbrKqEyZMoVJkyZRtmxZSpYsSbZs2bh27Rrh4eEAfPfddzqTrU2ePJlT\np04RHh5O1apVcXNzw9LSkrCwMO7du4ezszMLFy40+b4bMmPGDO7evUtgYCA1a9akYsWKFC9eHLVa\nzdWrVzl9+jTx8fFER0dr96FmzZpMnjyZcePG0bp1a2rVqoWDgwPnz5/nxIkTWFhY4OXlhbOzc5bF\nmJqcOXPi6enJ/v37cXV1pWbNmmTPnp3ChQszZsyYDMfp4OBAx44dWbduHZ9++in16tUjX758nDp1\nigsXLjB06FB+/vlng32bNm3KqlWr+Oyzz3B1dcXa2horKytmzpyZ6nNevnyZvn37YmtrS6VKlShS\npAhxcXEcP36c69evkzt3bqOJzPSwtLRkw4YNtGnThl9++YW1a9dSvnx5ihYtSkxMDBcvXuT8+fM4\nOTlpy56kRfHixXF0dNQmSt+8Vnh4eLBmzRpiY2OpXbu2djJODSsrKwYNGsRPP/1Ep06dqF27NkWL\nFuXChQucOHGC4cOHM3v2bL3nXbJkCaNGjaJEiRKULl0aGxsbbt++zZEjR4iPj6dr166UKVMm/QfK\nRAYOHIiPjw/btm0jMjKSatWq8eLFC0JCQihVqhSffPKJwaTolClTiIiIIDw8nCpVquDm5oZarSY4\nOJhChQrRsGFD9u7dq9dv+PDh3Lhxg5UrV+Lh4UGFChVwdHTE0tKSmzdvcurUKWJiYtixY4d2EsF3\n0apVKw4dOsSyZcto2LAhrq6u2NnZcfLkSc6fP4+NjQ0rVqxI0/s5IwoVKkSLFi3YtGkToD8ZZErz\n58+nWbNm7N69mypVquDq6kpCQgLBwcE8ffqU6tWrM3XqVJ0+KpWKwYMHM336dDp16kStWrXInz8/\np0+f5tatWwwcOJD58+enK+Z69eoxePBg5s2bR9OmTalTpw6FCxcmOjqaCxcu0KdPH5YuXar32ZXR\n63V6r7sxMTF8++23jBkzhvLly+Pk5ISZmRkXL17k5MmTmJmZMWXKlHTt87swMzNjxYoVfPHFF6xZ\ns4agoCBq1qzJ8+fPOXjwILGxsTRq1IghQ4bo9EtKStKOrI+NjTVJmxBCCCGEEEKfjNAWWaZz586E\nhobSs2dPLCwsCA4OJiAggKtXr1KtWjVmzZrFV199ldVh6unVqxebNm2ibt26nD17lsDAQLJly8aG\nDRu0NbBTjrZ7m3nz5tGmTRsSExM5cOAA27dv5+7duzRr1gx/f39GjBihs76trS3+/v5MmTIFZ2dn\nDhw4wM6dO8mbNy8jRoxg37592hqmmc3Kyor169ezatUqGjRowI0bN9i+fTsHDx4kLi6O9u3bs2HD\nBr06twMHDsTPz4/PPvuMixcv4uPjw507d2jZsiV79uyhbdu2WR5japYsWUL79u2Ji4tj69atrF69\n2uA3DdJr/vz5zJgxg9KlS3P06FH27dtHsWLF8PHxoU2bNkb7/fDDD/Tp0wdLS0v8/PxYvXo169at\ne+vz1atXj7Fjx1KzZk2uX7/O9u3bCQ4OJleuXAwdOpRDhw6Z7Ov0jo6OHDhwgB9//JFy5cpx5swZ\nfH19OXbsGLa2tgwZMgRvb+90b1czSjtHjhzUqlVLr01T19jYjbFRo0axbNkyqlatyunTp9m1axfW\n1tasWrWKoUOHGuwzadIkunbtipWVFYcPH8bX15erV6/i6enJ2rVr37nm+LsqU6YMQUFBNG/enPj4\neAIDA7U3L3bs2GF0lKtKpSIwMJABAwZga2vLzp07OXbsGG3btmXnzp3Y2NgY7KcoCnPnzsXX15cv\nvviCBw8esHPnTvbu3cvjx49p0qQJv/76q7ZUiCnMnDmTVatWUa9ePU6cOIGvry8xMTF06tSJgwcP\n6pWXMTXNtwFq1KiR6sSHhQsXZu/evXz77bcUKFCAXbt2sX//fhwdHZk8eTIBAQF6N1oARo8ezaxZ\nsyhdujSRkZGEhIRQpkwZdu/erS2HkV6TJk1i8eLFVK5cmaioKHbv3k2RIkUICAjQ3oAx9NmVket1\neq+7+fPnZ+bMmTRp0oQXL16wd+9eAgMDiYmJoUOHDuzbt4/27dtnaL8zqmzZsgQHB9OvXz+yZcvG\n9u3bOXToEJUqVWLu3LmsW7cOCwsZFyKEEEIIIURWUNRqdVbH8N49efIkCEjTX7uayZey8uvj4sMx\nYcIE5s2bx9ChQ5kwYQLwerSVlZVVVoYmhBA65NqUcS1atCAoKIjFixe/90RrZujevTvbtm3j559/\npnv37lkdjkiD//Lvp5rR6prySUII8W/wn7k2fTf89ePExNePBw7RXze95s99/ThFSS6m63/jUfx7\nHfY6rH2clPi6VGHF9plb0tSYUxtOaR+bmb8ek1t7UG1Dq//f+QCvTQdy587taYoNyQhtIdLpypUr\nPHjwQG+5r68vixYtwszMTFuLVgghxH9LaGgoQUFBFC5cmFatWmV1OGl2/vx5nj17prMsKSmJZcuW\nsW3bNmxsbPjiiy+yKDohhBBCCCGESDv5rqQQ6fTHH38wbtw4KlWqhL29PQkJCVy4cIGLFy8CMHHi\nxCytnyuEEMK04uLiGDFiBM+ePdPWHx8/fjzZsmXL4sjSbsWKFfz6669UrlyZIkWKEBMTw9mzZ7l+\n/Trm5ub8/PPP5M2bN6vDFEIIIYQQQoi3koS2EOlUr149WrduTXh4OJcuXSImJoa8efPSuHFj+vTp\nQ/369bM6RCGEECYUHx/P6tWrMTc3x8HBgX79+tGhQ4esDitdPv/8c+7cuUNkZCTR0dG8evUKOzs7\nvvzySwYMGEC1atWyOkQhhBBCCCGESBNJaAuRTpUqVWLJkiVZHYYQQoj3xNbWlsePH2d1GO/E3d3d\n6OSoQgghhBBCCPEhkRraQgghhBBCCCGEEEIIIT4IktAWQgghhBBCCCGEEEII8UGQhLYQQgghhBBC\nCCGEEEKID4IktIUQQgghhBBCCCGEEEJ8ECShLYQQQgghhBBCCCGEEOKDIAltIYQQQgghhBBCCCGE\nEB8ESWgLIYQQQgghhBBCCCGE+CBIQlsIIYQQQgghhBBCCCHEB0ES2kIIIYQQQgghhBBCCCE+CCZJ\naCuKMlBRlI2KopxVFOWBoijxiqLcUxRlj6IonRVFUQz0CVIURZ3Kv0BTxCaEEEIIIYQQQgghhBDi\nv8FUI7S/A1oAL4EwYAtwEWgArAa2KYpi7Ll2AqsM/NttotjEexIeHk6ePHmYOHGizvLExER8fHyY\nMGECzZo1w8HBAZVKRZ06ddK87YiICHr37k358uWxs7PD2dmZBg0aMGHCBL11L1y4wMKFC/nyyy8p\nXbo0+fPnx8HBgU8++YRFixYRFxf3rruapWJiYtixYwfDhw+nbt26FC1aFDs7OypWrEjfvn05ceJE\nqv2TkpJYtmwZnp6eFC1aFAcHBxo3bszmzZszJV6VSoVKpcqUbb9vCxcuRKVSsWPHjqwORQghhBBC\nCCGEEOL/koWJttMeOKZWq1+kXKgoSnlgL/AF8BXwq4G+P6nV6iATxSGyiFqt5rvvviNXrlwMGTJE\np+3Zs2d069Ytw9uePn06P/30E2ZmZtSoUYPatWvz8OFDzp07x4IFC5g0aZLO+l988QW3bt3CysqK\nqlWrUq9ePe7evUt4eDjh4eFs2LABX19f8uTJk+GYstLmzZsZNGgQAMWKFcPDwwMLCwtOnz7N77//\nzubNm5kzZw5fffWVXt/ExEQ6d+7Mjh07yJUrF/Xr1+fVq1ccOHCAQ4cOER4ezvTp09/3Ln0wevbs\nyaJFixg3bhwff/wxlpaWWR2SEEIIIYQQQgghxP8VkyS01Wp1iJHl0YqiLAQmA59gOKEt/gM2b95M\nVFQUI0eO1BuNa2lpSdu2balSpQpVq1bl6dOntGvXLk3bXbFiBdOmTaNcuXL89ttvlCxZUtumVquJ\niIjQ61OyZElGjx5Ny5YtsbW11S6/evUq7du35+TJk4wePZrFixdncG+zloWFBZ07d6Z3795UrlxZ\nu1ytVrNw4ULGjh3LiBEjcHV11TleAIsWLWLHjh2UKVMGPz8/7OzsALh06RKNGzdmyZIluLu706RJ\nk/e6Tx8KKysrBg0axHfffcevv/5Knz59sjokIYQQQgghhBBCiP8r72NSyIR//v+w6zyIVP3yyy8o\nikLnzp312mxsbFi6dCn9+/enTp06WFtbp2mbDx8+ZPz48VhbW/P777/rJWcVRcHFxUWvn5+fH126\ndNFJZgMUL16cOXPmAODj48OrV6/Sunv/Kh07dmTBggU6yWxIPh7ffPMNHh4exMfHs3XrVp32xMRE\nvLy8AJg9e7Y2mQ1QokQJbamY2bNnZ+4OfODatm1L9uzZWbJkCWq1OqvDEUIIIYQQQgghhPi/kqkJ\nbUVRnICv//nRz8hqLRVFmacoymJFUcYriuKWmTEJ04uKiiIqKgpXV1eKFy9usu2uXbuW58+f07x5\nc4oVK2aSbVaqVAmA2NhYHj58mOZ+TZo0QaVSERwcbLC9X79+qFQq1q5dq7e8UKFCbNiwgZMnT9Kx\nY0ecnZ0pVKgQHh4erFmzJuM7Y4RmH2/duqWz/OjRo9y7d4+iRYvi6uqq169FixZYWloSFRWl1/dt\noqOj6dSpE46OjhQpUgR3d3d+++23t/a7du0aw4cPp3LlytjZ2VG8eHGaNm3Kpk2b9NZdsGABKpXK\nYN10d3d3VCoVDRs21GsbP348KpWKBQsWaJdNmzYNlUrFtGnTuHv3LkOGDKFcuXLY2dlRqVIlJk6c\nSGxsrMGY8+TJQ6NGjbh06RJBQUFv3UchhBBCCCGEEEIIYTqmqqENgKIo3QEPwBKwB+qSnDT/Ua1W\nbzPSbdAbP09SFCUU6KBWq6+n47m7Ad3Ssm5QUFCVKlWqEBMTw82bN9+6frZs2YwmtwT4+voCUK9e\nvTQdJ83I6KSkpFTX37t3LwAuLi48ePAAX19fTp06haIolC1blmbNmqV7ssGzZ88Cya9pjhw50vy6\nJiUlaWM31CcxMRGA+Ph4nXbN8qioKEaNGkWhQoVwd3fn/v37HDp0iG+++YZjx44xdepUvW0WKlQI\ngC1bthhMQBtz4cIFAPLly6cTS2RkJACVK1c2uA9mZmaULl2a06dPExkZSd68edP0fGFhYXTq7ZMd\nigAAIABJREFU1ImXL19SsmRJKlSowN9//82QIUOIjo7Wrvfmc0ZGRtKxY0eePHminZjy8ePHhIWF\nERISws6dO5k/fz6KogBoJxHdv38/o0eP1m7n4cOHnDp1CoDjx4/z999/kzt3bm27Julct25dbQwJ\nCclfHLl27RoeHh6o1Wpq1KjB8+fPOXLkCHPnzuXMmTNGk/Kurq74+fnh5+eXrslNhfg3ks83IT48\nSUlJvHr1SvuZ/1/0X943IcSH60O/NjknJL7+IfH145vX05x6Mapoym2n+CLr5Q/8mP2/0fytDKBO\nev1CXjfBOZIRiSnO0yR1kvbxh/5eNLV/+/EoWrRomqs1pJVJE9qAK8mTP2okAOOAOQbWDQZ+++f/\nG0ABkhPgP/6znT2KolR7c6LJVDiSnEx/q+fPn6dxkyItwsLCAKhRo4ZJt6tJPj958gR3d3e9mw8/\n/PADCxYs4JNPPknzNufPnw/AJ598Qvbs2U0X7Fv89ttv9OrVi0mTJmFubg4kJ7nbtm2Lt7c39evX\n5+OPP37n5zl79ix79uxBURQ+//xznTbNB5C9vb3R/kWLFuX06dNcu3YtTc/38uVLBgwYwMuXLxk0\naBCjR4/WJqA1iW5DYmNj6dOnD0+ePKFPnz5MmDBBe1zOnj1LmzZt2Lx5MzVr1qRr164AlCtXjnz5\n8nH69GkePXqkndQzNDQUtVpN2bJlOXv2LKGhodp9f/ToEadPnyZfvnyULVtWL47169fTqVMnpk2b\nRrZs2QA4f/48jRs3ZteuXRw9epSaNWvq9dOc6yEhBqcPEEIIIYQQQgghhBCZxKQJbbVa3QvopShK\nDsAJ6A5MBNoqivK5Wq2+lWLdcW90vwZcUxRlBxAFfAT0A2al8emvAAfSsqKtrW0VILe1tTWlSpVK\ndV1NEtDKyiqNYfz/0YzCrVChQpqOkyZxaGZmlur6jx8/BpLLQxQpUoQtW7bg4uLC3bt3mT9/PqtW\nraJ3794EBQVRpkyZtz7v2rVr8fX1xdramokTJ6brNTUzM9PGbqifJhlraWmp065ZXrhwYaZOnaqT\nRK9bty79+/dnxowZLF++nKZNm+psU3NuqlSqNMX6/PlzBgwYQEJCAp07d6Z69eo67ZpRmLly5TK6\nvVy5cgEQFxeXpuf08fHh9u3bODk56SSlARo0aECPHj1YuHAhoPse8vHx4ebNmzg4ODB16lQsLS21\nbVWrVmXMmDEMGzaMxYsX60y86OHhwdatWzl69ChffPEFAIcOHQKSS4t06NCBsLAwWrVqBSSXWUlK\nSsLDw4McOXJot2NhkXzps7e3Z9asWTptlSpVon379nh7e3Po0CHc3d319rtixYrA67ugcn0QHyLN\nNUHOXyE+PJrfoUxVku3fRPPZ+rbf0YUQ4n36z1ybLF7/vYby+qGDKT5PUm47xd+FH/wx+z/zwOKB\n9nFS4usR0Vn1O8dj88fax2bmr6smy3mV7D9zbcqATKmhrVarX6rV6jNqtXokMBqoDCx4SzdN3yfA\nvH9+/Dy1dd/ot1KtVnum5V+VKlWOp3unhEEvXrwgJiYGIM0lKtJKU+ZDrVazefNmGjZsSK5cuShZ\nsiTz5s2jUaNGxMbGMnfu3Ldu68CBAwwdOhRFUfj555/f+5u9adOmBkeEt2/fHoDDhw/rfLUHIDw8\nnPDwcL3EtCHx8fF069aNM2fOULFiRaZPn26awN8iNDQUgC+//FInma3Rrl27VPu1adNGJ5mt0bFj\nRxRF4fLlyzr1vD08kr+EceDA63tXBw4cwN7ensaNG2Nvb69T1/rgwYMAeHp6GozDzc1NJ5mtoTk/\n7ty5Y7BftmzZtJOO3rt3z+A6QgghhBBCCCGEEML0MnVSyH+s/Of/Zoqi6GeuDPvzn/+Lmj4cYUpP\nnz4FIHv27NqR16aiSRjWqVPHYAK6R48ewNvLPhw6dIiOHTvy6tUrfvrpJ6NJ1szk4OBgcLm9vT1m\nZmbpnqQypYSEBHr06MGePXsoXbo0W7duxcbGRm89zbIXL4xX8dGU49Ec+7fRJJuN7Z+x5bdv3wYw\nOomolZUVhQsX1lkXXie0NUnrGzducOnSJe1yd3d3Ll68yI0bN4DXiW9Do6zBePmVnDlzAqnXFtas\n8+TJE6PrCCGEEEIIIYQQQgjTMnUNbUMekVxL2wLIC/ydhj75/vn/X1vsevxJ/Un8PkSTK33/Tv01\nk+/FxcXx6tUrkya1ixcvzqNHj4wmPTXL//7b+Cl15MgR2rZty4sXL5g8eTJ9+/Y1WXwpaUaTv2+J\niYn07t0bf39/nJ2d8fHxoUCBAgbX1SSXU5vMQVOn3FgiOqs5OjpSvHhxLl++zPXr17UjsDUJbQ8P\nD9atW0dQUBD169fn4sWLFC9eHEdHR4Pb0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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [], + "image/png": { + "width": 730, + "height": 484 + } + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "rGPproRzJZiI" + }, + "source": [ + "The best submissions, according to our procedure, are the submissions that are *most-likely* to score a high percentage of upvotes. Visually those are the submissions with the 95% least plausible value close to 1.\n", + "\n", + "Why is sorting based on this quantity a good idea? By ordering by the 95% least plausible value, we are being the most conservative with what we think is best. When using the lower-bound of the 95% credible interval, we believe with high certainty that the 'true upvote ratio' is at the very least equal to this value (or greater), thereby ensuring that the best submissions are still on top. Under this ordering, we impose the following very natural properties:\n", + "\n", + "1. given two submissions with the same observed upvote ratio, we will assign the submission with more votes as better (since we are more confident it has a higher ratio).\n", + "2. given two submissions with the same number of votes, we still assign the submission with more upvotes as *better*.\n", + "\n", + "#### But this is too slow for real-time!\n", + "\n", + "I agree, computing the posterior of every submission takes a long time, and by the time you have computed it, likely the data has changed. I delay the mathematics to the appendix, but I suggest using the following formula to compute the lower bound very fast.\n", + "\n", + "$$ \\frac{a}{a + b} - 1.65\\sqrt{ \\frac{ab}{ (a+b)^2(a + b +1 ) } }$$\n", + "\n", + "where \n", + "$$\n", + "\\begin{align}\n", + "& a = 1 + u \\\\\n", + "& b = 1 + d \\\\\n", + "\\end{align}\n", + "$$\n", + "$u$ is the number of upvotes, and $d$ is the number of downvotes. The formula is a shortcut in Bayesian inference, which will be further explained in Chapter 6 when we discuss priors in more detail." + ] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "VhBY5JmNJZiI", + "outputId": "2e13489d-b0ee-406d-98ea-69e5554ecd97", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + } + }, + "source": [ + "def intervals(u, d):\n", + " a = tf.add(1., u)\n", + " b = tf.add(1., d)\n", + " mu = tf.divide(x=a, y=tf.add(1., u))\n", + " std_err = 1.65 * tf.sqrt((a * b) / ((a + b) ** 2 * (a + b + 1.)))\n", + " \n", + " return (mu, std_err)\n", + " \n", + "print(\"Approximate lower bounds:\")\n", + "posterior_mean, std_err = evaluate(intervals(votes[:,0],votes[:,1]))\n", + "lb = posterior_mean - std_err\n", + "print(lb)\n", + "print(\"\\n\")\n", + "print(\"Top 40 Sorted according to approximate lower bounds:\")\n", + "print(\"\\n\")\n", + "[ order ] = evaluate([tf.nn.top_k(lb, k=lb.shape[0], sorted=True)])\n", + "ordered_contents = []\n", + "for i, N in enumerate(order.values[:40]):\n", + " ordered_contents.append( contents[i] )\n", + " print(votes[i,0], votes[i,1], contents[i])\n", + " print(\"-------------\")" + ], + "execution_count": 15, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Approximate lower bounds:\n", + "[0.99269927 0.99397486 0.9954533 0.99409133 0.99016213 0.9921388\n", + " 0.9932303 0.9950698 0.9924482 0.9905308 0.99219704 0.9883038\n", + " 0.98586273 0.98920876 0.98701847 0.9825861 0.9784089 0.98187244\n", + " 0.9896374 0.98088 0.9845036 0.9757879 0.98391163 0.97651654\n", + " 0.9760173 0.96944773 0.97180194 0.98139995 0.984723 0.98333776\n", + " 0.97501504 0.98007494 0.9776715 0.9744575 0.97023726 0.9856403\n", + " 0.97080845 0.97544485 0.9791857 0.961427 0.9745684 0.9577916\n", + " 0.9674608 0.97471726 0.9576553 0.96101296 0.9536104 0.9696114\n", + " 0.9635106 0.9534545 0.95889676 0.9733387 0.9485553 0.95074564\n", + " 0.9599505 0.9509203 0.9458036 0.95018905 0.9485553 0.9536757\n", + " 0.94073695 0.95903665 0.95053595 0.9481962 0.96678776 0.9522802\n", + " 0.96910393 0.95128834 0.94423944 0.9413886 0.9619237 0.9421574\n", + " 0.94563764 0.9444311 0.9643911 0.94299465 0.9511917 0.9458798\n", + " 0.95053595 0.9304495 0.93613225 0.93222207 0.94465697 0.93661344\n", + " 0.9331166 0.94067496 0.9303111 0.9245648 0.9280292 0.938496\n", + " 0.92397803 0.93731874 0.9207582 0.9331846 0.9306861 0.9498649\n", + " 0.9209626 0.9306861 ]\n", + "\n", + "\n", + "Top 40 Sorted according to approximate lower bounds:\n", + "\n", + "\n", + "4750 648 Dragons would probably fear us as we can create water in our mouth\n", + "-------------\n", + "3983 254 Having a car the same age as you is lame. Having a car the same age as your parents is awesome.\n", + "-------------\n", + "3747 116 All pebbles seem small and insignificant until you get one get inside your shoes\n", + "-------------\n", + "2898 121 If you listen to slightly older music, you are out of the loop. But if you listen to really old music, you are listening to the classics.\n", + "-------------\n", + "2102 208 If everyone smelt bad no one would smell bad\n", + "-------------\n", + "2007 106 At some point in your life, you've probably made eye contact with a murderer, without even knowing it.\n", + "-------------\n", + "2208 92 The reason ghosts always make lights flash is because they are amazed by modern technology so just keep pressing the switch like a child.\n", + "-------------\n", + "2199 45 Most kids today will never have to face the awkwardness of calling a girl at home and having her dad answer the phone.\n", + "-------------\n", + "1777 74 If we could view life in the third person, we would probably spend a lot less time on our phones and watching TV because we would actually see ourselves mindlessly watching and/or scrolling.\n", + "-------------\n", + "893 28 The gasses found in Jupiter's atmosphere (ammonia, methane, etc) can cause brain damage, so those who go to Jupiter do, indeed, get more stupider.\n", + "-------------\n", + "914 19 Humans are probably the most consistently noisy animal to most other animals.\n", + "-------------\n", + "748 31 You'll never be in the same place in the universe ever again\n", + "-------------\n", + "727 46 If public libraries didn’t already exist, they’d be thought of now as the most impractical and unrealistic idea ever.\n", + "-------------\n", + "697 22 We tend to think that the knowledge we have today will be preserved forever. But actually the way we store information today is way less secure than ancient stone carvings.\n", + "-------------\n", + "618 26 A minor disadvantage to being older is that you have to scroll down further to select your birth year.\n", + "-------------\n", + "612 53 You can burn yourself with water.\n", + "-------------\n", + "544 74 We just walk around pretending it's not weird that one of our hands is better at stuff than the other.\n", + "-------------\n", + "513 39 \"Pull Up\" still reads as pull up backwards.\n", + "-------------\n", + "510 10 If you’re a short person, people are always looking down on you at a flattering selfie angle.\n", + "-------------\n", + "354 19 \"Space\" button on keyboard takes a lot of space for just a \" \".\n", + "-------------\n", + "335 10 Telling someone that she was made for you goes from romantic to selfish the more you think about it.\n", + "-------------\n", + "321 28 A male mermaid would probably be a merbutler not a merman\n", + "-------------\n", + "322 10 If hydras were real, we’d probably farm them by hacking off their heads for an infinite food source\n", + "-------------\n", + "306 23 \"Nice guys finish last\" takes on a whole new meaning in a bedroom setting.\n", + "-------------\n", + "387 43 “You a real one” and “You are alone” is the same sentence, just with differently placed spaces\n", + "-------------\n", + "330 63 With self-driving Cars on the horizon, we’ll see some people suddenly die while in the car for any particular reason, and just arrive at their destination dead.\n", + "-------------\n", + "261 26 For adults, Halloween is a candy tax day in which children come around like irs agents to collect what you owe.\n", + "-------------\n", + "251 8 The dinosaur game is the only browser game where when your internet is better it gets laggier.\n", + "-------------\n", + "254 5 Empathy is a great emotion to have, but it can make you feel horrible\n", + "-------------\n", + "232 5 Some of the most beautiful singing voices will never be heard because the singers are too self conscious.\n", + "-------------\n", + "209 11 Songs that you like immediately usually don't last long, but songs that take a while to like usually last a long time.\n", + "-------------\n", + "207 6 We are all side characters in other people’s lives.\n", + "-------------\n", + "217 9 The reason a ghost will try to kill you is because it needs a friend.\n", + "-------------\n", + "204 11 We'll soon see gamers passing away of old age before being able to play the next installment of their favorite game franchise.\n", + "-------------\n", + "211 18 Once the internet disappears and all the books printed on acid-based paper the past 100 years decay, nothing will be left of our civilization except the highway system, skyscrapers, and billions of discarded water bottles.\n", + "-------------\n", + "193 2 Every house has an odor and it is kind of like the house’s personality.\n", + "-------------\n", + "162 9 People who are sensitive to smells could just say that they’re scentsitive\n", + "-------------\n", + "154 5 Every time you recall a memory you spend a portion of your current life reliving the life you already lived.\n", + "-------------\n", + "151 3 The worse you are the easier it is to improve.\n", + "-------------\n", + "162 20 Just need to flip the Flat Earth over so we can chill on the cool side for awhile\n", + "-------------\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "U1__TyMJJZiK" + }, + "source": [ + "We can view the ordering visually by plotting the posterior mean and bounds, and sorting by the lower bound. In the plot below, notice that the left error-bar is sorted (as we suggested this is the best way to determine an ordering), so the means, indicated by dots, do not follow any strong pattern. " + ] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "dHtWEE7_EbJL", + "outputId": "c9ca2fa5-95cb-4554-afc5-b3594018bff2", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 488 + } + }, + "source": [ + "r_order = order.indices[::-1][-40:]\n", + "ratio_range_ = evaluate(tf.range( len(r_order)-1,-1,-1 )) \n", + "r_order_vals = order.values[::-1][-40:]\n", + "plt.errorbar( r_order_vals, \n", + " np.arange( len(r_order) ), \n", + " xerr=std_err[r_order], capsize=0, fmt=\"o\",\n", + " color = TFColor[0])\n", + "plt.xlim( 0.3, 1)\n", + "plt.yticks( ratio_range_ , map( lambda x: x[:30].replace(\"\\n\",\"\"), ordered_contents) );" + ], + "execution_count": 16, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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AX+DYHH/kY5uT+kArvSV+UvL5I8DhEfGrshj7k5LV7YqIByTdRno3dwKuK2tr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\n/ViL9JzuRLrOZuCDwA+AK8v0YQDpuTuNdA9uJn3n2+R6yobnZmZmZmZmZ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hYAACAASURB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BK4l/QHALc2fGPt27+bNFr0uNz+hsDupGT2c8DPgO/OyTXMzMzMzMzMzMzMeqUBK8KWW8MO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\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SMmLEiBG0tbXR2ur/cM1KJk+e3NchmPUr7hNmHblPmHXkPmHWkfuEWTv3h/5hyK23MOS2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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [], + "image/png": { + "width": 905, + "height": 471 + } + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "G77bofA0JZiM" + }, + "source": [ + "In the graphic above, you can see why sorting by mean would be sub-optimal." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "Jx19EikOJZiM" + }, + "source": [ + "### Extension to Starred rating systems\n", + "\n", + "The above procedure works well for upvote-downvotes schemes, but what about systems that use star ratings, e.g. 5 star rating systems. Similar problems apply with simply taking the average: an item with two perfect ratings would beat an item with thousands of perfect ratings, but a single sub-perfect rating. \n", + "\n", + "\n", + "We can consider the upvote-downvote problem above as binary: 0 is a downvote, 1 if an upvote. A $N$-star rating system can be seen as a more continuous version of above, and we can set $n$ stars rewarded is equivalent to rewarding $\\frac{n}{N}$. For example, in a 5-star system, a 2 star rating corresponds to 0.4. A perfect rating is a 1. We can use the same formula as before, but with $a,b$ defined differently:\n", + "\n", + "\n", + "$$ \\frac{a}{a + b} - 1.65\\sqrt{ \\frac{ab}{ (a+b)^2(a + b +1 ) } }$$\n", + "\n", + "where \n", + "$$\n", + "\\begin{align}\n", + "& a = 1 + S \\\\\n", + "& b = 1 + N - S \\\\\n", + "\\end{align}\n", + "$$\n", + "where $N$ is the number of users who rated, and $S$ is the sum of all the ratings, under the equivalence scheme mentioned above. " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "M9KpvDCzJZiN" + }, + "source": [ + "### Example: Counting Github stars\n", + "\n", + "What is the average number of stars a Github repository has? How would you calculate this? There are over 6 million respositories, so there is more than enough data to invoke the Law of Large numbers. Let's start pulling some data." + ] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "0AbPT5Ajju3t", + "outputId": "563e7960-7981-419d-81df-86090910d438", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 35 + } + }, + "source": [ + "reset_sess()\n", + "\n", + "import wget\n", + "url = 'https://raw.githubusercontent.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/master/Chapter3_MCMC/data/github_data.csv'\n", + "filename = wget.download(url)\n", + "filename" + ], + "execution_count": 17, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "'github_data.csv'" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 17 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "K8qsQwdgTwg0", + "outputId": "dd8328f0-da0f-4a91-e4d4-ce0867b2e8b5", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + } + }, + "source": [ + "# Github data scrapper\n", + "# See documentation_url: https://developer.github.com/v3/\n", + "\n", + "from json import loads\n", + "import datetime\n", + "import numpy as np\n", + "from requests import get\n", + "\n", + "\"\"\"\n", + "variables of interest:\n", + " indp. variables\n", + " - language, given as a binary variable. Need 4 positions for 5 langagues\n", + " - #number of days created ago, 1 position\n", + " - has wiki? Boolean, 1 position\n", + " - followers, 1 position\n", + " - following, 1 position\n", + " - constant\n", + " \n", + " dep. variables\n", + " -stars/watchers\n", + " -forks\n", + "\"\"\"\n", + "\n", + "\n", + "MAX = 8000000\n", + "today = datetime.datetime.today()\n", + "randint = np.random.randint\n", + "N = 20 #sample size. \n", + "auth = (\"mikeshwe\", \"kick#Ass1\" )\n", + "\n", + "language_mappings = {\"Python\": 0, \"JavaScript\": 1, \"Ruby\": 2, \"Java\":3, \"Shell\":4, \"PHP\":5}\n", + "\n", + "#define data matrix: \n", + "X = np.zeros( (N , 12), dtype = int )\n", + "\n", + "for i in range(N):\n", + " is_fork = True\n", + " is_valid_language = False\n", + " \n", + " while is_fork == True or is_valid_language == False:\n", + " is_fork = True\n", + " is_valid_language = False\n", + " \n", + " params = {\"since\":randint(0, MAX ) }\n", + " r = get(\"https://api.github.com/repositories\", params = params, auth=auth )\n", + " results = loads( r.text )[0]\n", + " #im only interested in the first one, and if it is not a fork.\n", + " #print(results)\n", + " is_fork = results[\"fork\"]\n", + " \n", + " r = get( results[\"url\"], auth = auth)\n", + " \n", + " #check the language\n", + " repo_results = loads( r.text )\n", + " try: \n", + " language_mappings[ repo_results[\"language\" ] ]\n", + " is_valid_language = True\n", + " except:\n", + " pass\n", + "\n", + " #languages \n", + " X[ i, language_mappings[ repo_results[\"language\" ] ] ] = 1\n", + " \n", + " #delta time\n", + " X[ i, 6] = ( today - datetime.datetime.strptime( repo_results[\"created_at\"][:10], \"%Y-%m-%d\" ) ).days\n", + " \n", + " #haswiki\n", + " X[i, 7] = repo_results[\"has_wiki\"]\n", + " \n", + " #get user information\n", + " r = get( results[\"owner\"][\"url\"] , auth = auth)\n", + " user_results = loads( r.text )\n", + " X[i, 8] = user_results[\"following\"]\n", + " X[i, 9] = user_results[\"followers\"]\n", + " \n", + " #get dep. data\n", + " X[i, 10] = repo_results[\"watchers_count\"]\n", + " X[i, 11] = repo_results[\"forks_count\"]\n", + " print()\n", + " print(\" -------------- \")\n", + " print(i, \": \", results[\"full_name\"], repo_results[\"language\" ], repo_results[\"watchers_count\"], repo_results[\"forks_count\"]) \n", + " print(\" -------------- \") \n", + " print() \n", + " \n", + "np.savetxt(\"github_data.csv\", X, delimiter=\",\", fmt=\"%d\" )" + ], + "execution_count": 18, + "outputs": [ + { + "output_type": "stream", + "text": [ + "\n", + " -------------- \n", + "0 : josephj/f2e-class PHP 14 1\n", + " -------------- \n", + "\n", + "\n", + " -------------- \n", + "1 : bellini666/gnome-shell-notifications-alert JavaScript 38 12\n", + " -------------- \n", + "\n", + "\n", + " -------------- \n", + "2 : PanKleszcz/ET Java 1 0\n", + " -------------- \n", + "\n", + "\n", + " -------------- \n", + "3 : Yorda/SolnRss Java 0 0\n", + " -------------- \n", + "\n", + "\n", + " -------------- \n", + "4 : Jefersonandrade/Vendas Java 1 0\n", + " -------------- \n", + "\n", + "\n", + " -------------- \n", + "5 : raduwen/HackathonGameDevelopWikiSamples Ruby 0 0\n", + " -------------- \n", + "\n", + "\n", + " -------------- \n", + "6 : kdmny/redmine-heroku Ruby 47 7\n", + " -------------- \n", + "\n", + "\n", + " -------------- \n", + "7 : stewartduffy/contact_form PHP 1 0\n", + " -------------- \n", + "\n", + "\n", + " -------------- \n", + "8 : bluedynamics/bda.bfg.tile Python 1 1\n", + " -------------- \n", + "\n", + "\n", + " -------------- \n", + "9 : eldog/fmobile Java 6 2\n", + " -------------- \n", + "\n", + "\n", + " -------------- \n", + "10 : KimiyukiYamauchi/Memopad Java 0 0\n", + " -------------- \n", + "\n", + "\n", + " -------------- \n", + "11 : sheldonh/life Ruby 1 0\n", + " -------------- \n", + "\n", + "\n", + " -------------- \n", + "12 : Curacion/demo_app Ruby 1 0\n", + " -------------- \n", + "\n", + "\n", + " -------------- \n", + "13 : sunaku/inochi Ruby 10 2\n", + " -------------- \n", + "\n", + "\n", + " -------------- \n", + "14 : gokzz/twitter Ruby 1 0\n", + " -------------- \n", + "\n", + "\n", + " -------------- \n", + "15 : ryanj/twitGrep JavaScript 3 0\n", + " -------------- \n", + "\n", + "\n", + " -------------- \n", + "16 : xiaomen/selfstudy JavaScript 2 1\n", + " -------------- \n", + "\n", + "\n", + " -------------- \n", + "17 : artem-bochkarev/PPMA_HOME_TASK Java 0 0\n", + " -------------- \n", + "\n", + "\n", + " -------------- \n", + "18 : tc/tc.github.com JavaScript 0 0\n", + " -------------- \n", + "\n", + "\n", + " -------------- \n", + "19 : olivopaolo/boing Python 3 0\n", + " -------------- \n", + "\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "i0rE6kLWJZiN" + }, + "source": [ + "### Conclusion\n", + "\n", + "While the Law of Large Numbers is cool, it is only true so much as its name implies: with large sample sizes only. We have seen how our inference can be affected by not considering *how the data is shaped*. \n", + "\n", + "1. By (cheaply) drawing many samples from the posterior distributions, we can ensure that the Law of Large Number applies as we approximate expected values (which we will do in the next chapter).\n", + "\n", + "2. Bayesian inference understands that with small sample sizes, we can observe wild randomness. Our posterior distribution will reflect this by being more spread rather than tightly concentrated. Thus, our inference should be correctable.\n", + "\n", + "3. There are major implications of not considering the sample size, and trying to sort objects that are unstable leads to pathological orderings. The method provided above solves this problem.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "_YGvCpGyJZiN" + }, + "source": [ + "### Appendix\n", + "\n", + "##### Derivation of sorting submissions formula\n", + "\n", + "Basically what we are doing is using a Beta prior (with parameters $a=1, b=1$, which is a uniform distribution), and using a Binomial likelihood with observations $u, N = u+d$. This means our posterior is a Beta distribution with parameters $a' = 1 + u, b' = 1 + (N - u) = 1+d$. We then need to find the value, $x$, such that 0.05 probability is less than $x$. This is usually done by inverting the CDF ([Cumulative Distribution Function](http://en.wikipedia.org/wiki/Cumulative_Distribution_Function)), but the CDF of the beta, for integer parameters, is known but is a large sum [3]. \n", + "\n", + "We instead use a Normal approximation. The mean of the Beta is $\\mu = a'/(a'+b')$ and the variance is \n", + "\n", + "$$\\sigma^2 = \\frac{a'b'}{ (a' + b')^2(a'+b'+1) }$$\n", + "\n", + "Hence we solve the following equation for $x$ and have an approximate lower bound. \n", + "\n", + "$$ 0.05 = \\Phi\\left( \\frac{(x - \\mu)}{\\sigma}\\right) $$ \n", + "\n", + "$\\Phi$ being the [cumulative distribution for the normal distribution](http://en.wikipedia.org/wiki/Normal_distribution#Cumulative_distribution)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "KDZNt3PrJZiO" + }, + "source": [ + "##### Exercises\n", + "\n", + "1\\. How would you estimate the quantity $E\\left[ \\cos{X} \\right]$, where $X \\sim \\text{Exp}(4)$? What about $E\\left[ \\cos{X} | X \\lt 1\\right]$, i.e. the expected value *given* we know $X$ is less than 1? Would you need more samples than the original samples size to be equally accurate?" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "yKqgiSdAJZiO", + "outputId": "9561bf88-d139-4813-a782-d5b8895a64ea", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 253 + } + }, + "source": [ + "## Enter code here\n", + "%%time\n", + "import tensorflow as tf\n", + "import tensorflow_probability as tfp\n", + "tfd = tf.distributions\n", + "\n", + "exp = tfd.Exponential(rate=4.)\n", + "N = 10000\n", + "X = exp.sample(sample_shape=int(N))\n", + "with tf.Session() as exercise_1:\n", + " print(X.eval())\n", + " \n", + "## ..." + ], + "execution_count": 19, + "outputs": [ + { + "output_type": "stream", + "text": [ + "WARNING:tensorflow:From :5: Exponential.__init__ (from tensorflow.python.ops.distributions.exponential) is deprecated and will be removed after 2019-01-01.\n", + "Instructions for updating:\n", + "The TensorFlow Distributions library has moved to TensorFlow Probability (https://github.com/tensorflow/probability). You should update all references to use `tfp.distributions` instead of `tf.distributions`.\n", + "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/distributions/exponential.py:114: Gamma.__init__ (from tensorflow.python.ops.distributions.gamma) is deprecated and will be removed after 2019-01-01.\n", + "Instructions for updating:\n", + "The TensorFlow Distributions library has moved to TensorFlow Probability (https://github.com/tensorflow/probability). You should update all references to use `tfp.distributions` instead of `tf.distributions`.\n", + "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/distributions/gamma.py:181: Distribution.__init__ (from tensorflow.python.ops.distributions.distribution) is deprecated and will be removed after 2019-01-01.\n", + "Instructions for updating:\n", + "The TensorFlow Distributions library has moved to TensorFlow Probability (https://github.com/tensorflow/probability). You should update all references to use `tfp.distributions` instead of `tf.distributions`.\n", + "[0.4458776 0.30991256 0.13016596 ... 0.21326569 0.25109488 0.18059786]\n", + "CPU times: user 37.8 ms, sys: 3.66 ms, total: 41.5 ms\n", + "Wall time: 65.3 ms\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "uhwWG-sdJZiQ" + }, + "source": [ + "2\\. The following table was located in the paper \"Going for Three: Predicting the Likelihood of Field Goal Success with Logistic Regression\" [2]. The table ranks football field-goal kickers by their percent of non-misses. What mistake have the researchers made?\n", + "\n", + "-----\n", + "\n", + "#### Kicker Careers Ranked by Make Percentage\n", + "
Rank Kicker Make % Number of Kicks
1 Garrett Hartley 87.7 57
2 Matt Stover 86.8 335
3 Robbie Gould 86.2 224
4 Rob Bironas 86.1 223
5 Shayne Graham 85.4 254
… … …
51 Dave Rayner 72.2 90
52 Nick Novak 71.9 64
53 Tim Seder 71.0 62
54 Jose Cortez 70.7 75
55 Wade Richey 66.1 56
" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "3iQ9mmGkJZiQ" + }, + "source": [ + "In August 2013, [a popular post](http://bpodgursky.wordpress.com/2013/08/21/average-income-per-programming-language/) on the average income per programmer of different languages was trending. Here's the summary chart: (reproduced without permission, cause when you lie with stats, you gunna get the hammer). What do you notice about the extremes?\n", + "\n", + "------\n", + "\n", + "#### Average household income by programming language\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
LanguageAverage Household Income ($)Data Points
Puppet87,589.29112
Haskell89,973.82191
PHP94,031.19978
CoffeeScript94,890.80435
VimL94,967.11532
Shell96,930.54979
Lua96,930.69101
Erlang97,306.55168
Clojure97,500.00269
Python97,578.872314
JavaScript97,598.753443
Emacs Lisp97,774.65355
C#97,823.31665
Ruby98,238.743242
C++99,147.93845
CSS99,881.40527
Perl100,295.45990
C100,766.512120
Go101,158.01231
Scala101,460.91243
ColdFusion101,536.70109
Objective-C101,801.60562
Groovy102,650.86116
Java103,179.391402
XSLT106,199.19123
ActionScript108,119.47113
" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "09kE1C-vJZiQ" + }, + "source": [ + "### References\n", + "\n", + "1. Wainer, Howard. *The Most Dangerous Equation*. American Scientist, Volume 95.\n", + "2. Clarck, Torin K., Aaron W. Johnson, and Alexander J. Stimpson. \"Going for Three: Predicting the Likelihood of Field Goal Success with Logistic Regression.\" (2013): n. page. [Web](http://www.sloansportsconference.com/wp-content/uploads/2013/Going%20for%20Three%20Predicting%20the%20Likelihood%20of%20Field%20Goal%20Success%20with%20Logistic%20Regression.pdf). 20 Feb. 2013.\n", + "3. http://en.wikipedia.org/wiki/Beta_function#Incomplete_beta_function" + ] + } + ] +} diff --git a/Chapter4_TheGreatestTheoremNeverTold/LawOfLargeNumbers.ipynb b/Chapter4_TheGreatestTheoremNeverTold/LawOfLargeNumbers.ipynb deleted file mode 100644 index 8933c6be..00000000 --- a/Chapter4_TheGreatestTheoremNeverTold/LawOfLargeNumbers.ipynb +++ /dev/null @@ -1,453 +0,0 @@ -{ - "metadata": { - "name": "LawOfLargeNumbers" - }, - "nbformat": 3, - "nbformat_minor": 0, - "worksheets": [ - { - "cells": [ - { - "cell_type": "code", - "collapsed": false, - "input": [ - "import scipy.stats as stats\n", - "figsize( 10, 3.5)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 1 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#Chapter 3\n", - "\n", - "##The greatest theorem never told\n", - "\n", - "\n", - "\n", - "> This relatively short chapter focuses on an idea that is always bouncing around our heads, but is rarely made explicit outside books devoted to statistics or Monte Carlo. In fact, we've been used this idea in every example so far. \n", - "\n", - "______" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The your other hammer\n", - "------" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "###The Law of Large Numbers\n", - "\n", - "Let $Z_i$ be samples from some probability distribution. According to *the Law of Large numbers*, so long as $E[Z]$ is finite, the following holds,\n", - "\n", - "$$\\frac{1}{N} \\sum_{i=0}^N Z_i \\rightarrow E[ Z ], \\;\\;\\; N \\rightarrow \\infty$$\n", - "\n", - "In words:\n", - "\n", - "> The average of a sequence of random variables from the same distribution converges to the expected value of that distribution.\n", - "\n", - "This may seem like a boring result, but it will be the most useful tool you use." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Intution \n", - "\n", - "If the above Law is somewhat surprising, it can be made more clear be examining a simple example. \n", - "\n", - "Consider a random variable $Z$ that can take only two values, $c_1$ and $c_2$. Suppose we have a large number of samples of $Z$, denoting a specific sample $Z_i$. The Law says that we can approximate the expected value of $Z$ by averaging over all samples. Consider the average:\n", - "\n", - "\n", - "$$ \\frac{1}{N} \\sum_{i=0}^N \\;Z_i $$\n", - "\n", - "\n", - "By construction, $Z_i$ can only take on $c_1$ or $c_2$, hence we can partition the sum over these two values:\n", - "\n", - "\\begin{align*}\n", - "\\frac{1}{N} \\sum_{i=0}^N \\;Z_i\n", - "& =\\frac{1}{N} \\big( \\sum_{ Z_i = c_1}c_1 + \\sum_{Z_i=c_2}c_2 \\big) \\\\\\\\[5pt]\n", - "& = c_1 \\sum_{ Z_i = c_1}\\frac{1}{N} + c_2 \\sum_{ Z_i = c_2}\\frac{1}{N} \\\\\\\\[5pt]\n", - "& = c_1 \\times \\text{ (approximate frequency of $c_1$) } + c_2 \\times \\text{ (approximate frequency of $c_2$) } \\\\\\\\[5pt]\n", - "& \\approx c_1 \\times P(Z = c_1) + c_2 \\times P(Z = c_2 ) \\\\\\\\[5pt]\n", - "& = E[Z]\n", - "\\end{align*}\n", - "\n", - "\n", - "Equality holds in the limit, but we can get closer and closer by using more and more samples in the average. This Law holds for *any distribution*, minus some pathological examples that only mathematicians have fun with. \n", - "\n", - "____\n", - "### Example\n", - "\n", - "Below is a diagram of the Law of Large numbers in action for three different sequences of Poisson random variables. \n", - "\n", - " We sample `sample_size= 100000` poisson random variables with parameter $\\lambda = 4.5$. (Recall the expected value of a Poisson random variable is equal to it's parameter.) We calculate the average for the first $n$ samples, for $n=1$ to `sample_size`. " - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "figsize( 10, 5 )\n", - "sample_size = 100000\n", - "expected_value = lambda_ = 4.5\n", - "poi = stats.poisson\n", - "N_samples = range(0,sample_size,100)\n", - "\n", - "samples = poi.rvs( lambda_, size = sample_size ) \n", - "partial_average = [ samples[:i].mean() for i in N_samples ]\n", - "plt.plot( N_samples, partial_average, lw=1.5,label=\"average \\\n", - " of $n$ samples; seq. 1\")\n", - "\n", - "samples = poi.rvs( lambda_, size = sample_size ) \n", - "partial_average = [ samples[:i].mean() for i in N_samples ]\n", - "plt.plot( N_samples, partial_average, lw=1.5, label=\"average \\\n", - " of $n$ samples; seq. 2\")\n", - "\n", - "samples = poi.rvs( lambda_, size = sample_size ) \n", - "partial_average = [ samples[:i].mean() for i in N_samples ]\n", - "plt.plot( N_samples, partial_average, lw=1.5, label=\"average \\\n", - " of $n$ samples; seq. 3\")\n", - "\n", - "plt.plot( N_samples, expected_value*np.ones_like( partial_average), \\\n", - " ls = \"--\", label = \"true expected value\", c = \"k\" )\n", - "\n", - "plt.ylim( 4.35, 4.65) \n", - "plt.title( \"Convergence of the average of \\n random variables to its \\\n", - " expected value\" )\n", - "plt.ylabel( \"average of $n$ samples\" )\n", - "plt.xlabel( \"# of samples, $n$\")\n", - "plt.legend()" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stderr", - "text": [ - "-c:8: RuntimeWarning: invalid value encountered in double_scalars\n", - "-c:13: RuntimeWarning: invalid value encountered in double_scalars\n" - ] - }, - { - "output_type": "stream", - "stream": "stderr", - "text": [ - "-c:18: RuntimeWarning: invalid value encountered in double_scalars\n" - ] - }, - { - "output_type": "pyout", - "prompt_number": 2, - "text": [ - "" - ] - }, - { - "output_type": "display_data", - "png": 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JJBJxUkUpmUxGkyZNIgMDA9LT0yMfHx9avXo1SSQSscz8+fMVJnYQEZ05c4Yk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r0Ji/QjKMQHk9mIQQOrj5mouEBCI/yoqqjHL8BKfb0d5VQopSjIrSRtQCRh/l78v/FJNDhc1Nmc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\nxcVRUFDQhS0SADk5OezZs4eRI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- } - ], - "prompt_number": 2 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Looking at the above plot, it is clear that when the sample size is small, there is greater variation in the average (compare how *jagged and jumpy* the average is initially, then *smooths* out). All three paths *approach* the value 4.5, but just flirt with it as $N$ gets large. Mathematicians and statistician have another name for *flirting*: convergence. \n", - "\n", - "Another very relevant question we can ask is *how quickly am I converging to the expected value?* Let's plot something new. For a specific $N$, let's do the above trials thousands of times and compute how far away we are from the true expected value, on average. But wait-- *compute on average*? This simply the law of large numbers again! For example, we are interested in, for a specific $N$, the quantity:\n", - "\n", - "$$\\sqrt{ \\;E\\left[\\;\\; \\left( \\frac{1}{N}\\sum_{i=0}^NZ_i - 4.5 \\;\\right)^2 \\;\\;\\right] \\;\\;}$$\n", - "\n", - "(We take the square root so the dimensions of the above quantity and our random variables are the same). As the above is an expected value, this can be approximated using the law of large numbers: instead of averaging $Z_i$, we caluculate the folloing thousands of times and average them:\n", - "\n", - "$$ \\left( \\;\\frac{1}{N}\\sum_{i=0}^NZ_i - 4.5 \\; \\right)^2 $$" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "figsize( 10, 3.5)\n", - "n_samples = 250\n", - "expected_value_n = [] \n", - "x = np.arange( 0, 50000, 2000 )\n", - "for N in x:\n", - " #for a specific N, we need to calulate the above expected value.\n", - " samples = poi.rvs( lambda_, size = (N, n_samples) )\n", - " averages = samples.mean(axis=0)\n", - " expected_value_n.append( ((averages - expected_value)**2).mean() )\n", - "\n", - "plt.xlabel( \"$N$\" )\n", - "plt.ylabel( \"expected squared-distance from true value\" )\n", - "plt.plot(x, np.sqrt(expected_value_n), lw = 3, label=\"expected squared distance\" )\n", - "plt.plot( x, np.sqrt(expected_value)/np.sqrt(x), ls = \"--\", label = r\"$\\frac{\\sqrt{\\lambda}}{\\sqrt{N}}$\" )\n", - "plt.legend()" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stderr", - "text": [ - "-c:8: RuntimeWarning: invalid value encountered in divide\n", - "-c:14: RuntimeWarning: divide by zero encountered in divide\n" - ] - }, - { - "output_type": "pyout", - "prompt_number": 3, - "text": [ - "" - ] - }, - { - "output_type": "display_data", - "png": 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tSsOGDenWrZu2q74icejWjqp1HWjsZE2n+2bofn0kCbVGlmkRQojy9Nxzzz0w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\nie6cNuFzu924XC7v+1N/FRQUoNfrz0slxcVp3759vV0F8QNI/PyXxM6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- } - ], - "prompt_number": 3 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As expected, the distance between our average and the actual value shrinks as $N$ grows large. Also notice that the *rate* of convergence decreases, that is, we need only 10 000 additional samples to move from 0.020 to 0.015, a difference of 0.005, but *20 000* more samples to again decrease from 0.015 to 0.010, again only a 0.005 decrease.\n", - "\n", - "\n", - "It turns out we can measure this rate of convergence. Above I have plotted a second line, the function $\\sqrt{\\lambda}/\\sqrt{N}$. This was not choosen arbitrarily. In most cases, given a sequence of random variable distributed like $Z$, the rate of converge to $E[Z]$ of the Law of Large Numbers is \n", - "\n", - "$$ \\frac{ \\sqrt{ \\; Var(Z) \\; } }{\\sqrt{N} }$$\n", - "\n", - "This is useful to know: for a given large $N$, we know (on average) how far away we are from the estimate. On the other hand, in a Bayesian setting, this can seem like a useless result: Bayesian analysis is OK with uncertainity so what's the *statistical* point of adding extra precise digits? Though drawing samples can be so computationally cheap that having a *larger* $N$ is fine too. \n", - "\n", - "### How do we compute $Var(Z)$ though?\n", - "\n", - "The variance is simply another expected value that can be approximated! Consider the following, once we have the expected value (by using the Law of Large Numbers to estimate it, denote it $\\mu$), we can estimate the variance:\n", - "\n", - "$$ \\frac{1}{N}\\sum_{i=0}^N \\;(Z_i - \\mu)^2 \\rightarrow E[ \\;( Z - \\mu)^2 \\;] = Var( Z )$$\n", - "\n", - "### Expected values and probablities \n", - "There is an even less explicit relationship between expected value and estimating probabilities. Define \n", - "\n", - "$$\\mathbb{1}_A(x) = \n", - "\\begin{cases} 1 & x \\in A \\\\\\\\\n", - " 0 & else\n", - "\\end{cases}\n", - "$$\n", - "Then, by the law of large numbers:\n", - "\n", - "$$ \\frac{1}{N} \\sum_{i=1}^N \\mathbb{1}_A(X_i)$$\n", - "\n", - "Again, this is fairly obvious after a moments thought. (Consider how we usually approximate probablities using frequencies). \n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### What does this all have to do with Bayesian statistics? \n", - "\n", - "\n", - "*Point estimates*, to be introduced in the next chapter, in Bayesian inference are computed using expected values. In more analytical Bayesian inference, we would of been required to evaluate complicated expected values represented as multi-dimensional integrals. No longer. If we can sample from the posterior distibution directly, we simply need to evaluate averages. Much easier. If accuracy is a priority, plots like the ones above show how fast you are converging. And if further accuracy is desired, just take more samples from the posterior. \n", - "\n", - "In the next chapter, we introduce *loss functions* which takes great advantage of the Law of Large Numbers. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Confidence should be proportional to sample size\n", - "\n", - "The Law of Large Numbers is only valid as $N$ gets *infinitely* large: the law is a pot of gold at the end of an infinite rainbow. While the law is a powerful tool, it is foolhardy to apply it liberally. \n", - "\n", - "\n", - "Often data comes in aggregated form. For instance, data may be grouped by state, county, or city level. Of course, the population numbers vary per geographic area. If included in the data is an average of some characteristic of each the geographic area, we must be concious of the Law of Large Numbers and how it can *fail* for areas with small populations.\n", - "\n", - "--------\n", - "Suppose there are five thousand counties in our dataset. Suppose populations in each state are uniformly distributed between 100 and 4000. The way the population numbers is generated is irrelevant here. Furthermore, we are interested in measuring the average height of individuals per county. Unbeknowst to the us, height does not vary across county, and each individual has the same distribution of what their height may be:\n", - "\n", - "$$ \\text{height} \\sim \\text{Normal}(150, 15 ) $$\n", - "\n", - "We aggregate the individuals are the county level, so we only see the *average across the county*. What might our dataset look like?" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "n_counties = 5000\n", - "_lambda = 2000\n", - "pop_generator = stats.randint( 100, 4000 )\n", - "norm = stats.norm( 150, scale = 15 )\n", - "\n", - "#generate some artifial population numbers\n", - "population = pop_generator.rvs( n_counties )\n", - "average_across_county = np.zeros( n_counties )\n", - "\n", - "for i in range( n_counties ):\n", - " #generate some individuals and take the mean\n", - " average_across_county[i] = norm.rvs( population[i] ).mean()\n", - " \n", - "\n", - "#plot population vs. average\n", - "i_min = np.argmin( average_across_county )\n", - "i_max = np.argmax( average_across_county )\n", - "\n", - "plt.scatter( population, average_across_county, alpha = 0.5 )\n", - "plt.scatter( population[i_min], average_across_county[i_min], \n", - " s = 60, marker = \"o\", facecolors = \"none\",\n", - " edgecolors = \"r\")\n", - "plt.scatter( population[i_max], average_across_county[i_max], \n", - " s = 60, marker = \"o\", facecolors = \"none\",\n", - " edgecolors = \"r\")\n", - "plt.xlim( 100, 4000 )\n", - "plt.title( \"Average height vs. County Population\")\n", - "plt.xlabel(\"County Population\")\n", - "plt.ylabel(\"Average height in county\")\n", - "plt.plot( [100, 4000], [150, 150], color = \"k\", label = \"true expected \\\n", - "height\", ls=\"--\" )\n", - "plt.legend()" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 28, - "text": [ - "" - ] - }, - { - "output_type": "display_data", - "png": 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168eQIUMYP348ACNGjOD5559nxowZREZG0qdPH3744QcAvLy8WL58Od9//z1t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\nkpIYOXIk4KJzRxENKjo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- } - ], - "prompt_number": 28 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "What do we see? *Without accounting for population* we run the risk of making an enourmous inference error. If we ignored population size, we would say that the county with the shortest and tallest individuals are circled. But this is wrong. These two counties do *not* necessarily have the most extreme heights. The problem is that the calculated average of the small population was not a good reflection of the true expected value of the popuation (which would have been 150). The sample size,population size,$N$, whatever you want to call it, is simply too small to invoke the Law of Large Numbers effectively. \n", - "\n", - "We provide more damning statistics. Recall the population numbers were uniformly distributed over 100 to 4000. Our intuition should tell us that the most extreme population heights should also be uniformly spread over 100 to 4000. Not so." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "print \"Population sizes of 10 'shortest' counties: \"\n", - "print population[ np.argsort( average_across_county )[:10] ]\n", - "print\n", - "print \"Population sizes of 10 'tallest' counties: \"\n", - "print population[ np.argsort( -average_across_county )[:10] ]" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "Population sizes of 10 'shortest' counties: \n", - "[104 127 111 164 124 285 233 302 200 271]\n", - "\n", - "Population sizes of 10 'tallest' counties: \n", - "[242 200 163 155 237 174 102 158 233 568]\n" - ] - } - ], - "prompt_number": 38 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Not at all uniform over 100 to 4000. This is an absolute failure of the Law of Large Numbers.\n", - "\n", - "Below is data from the 2010 US census, which partitions populations beyond counties to the level of block groups (which are aggregates of city blocks or equivilants). The dataset is from a machine learning competition some collegues and I participated in. The objective was to predict the census form mail-back rate of a group block, measured between 0 and 1, using census variables (median income, number of females, number of trailer parks, average number of children etc. per block group). Below we plot the, now classic, mail-back rate versus block group population:" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "data = np.genfromtxt( \"data/census_data.csv\", skip_header=1)\n", - "plt.scatter( data[:,0], data[:,1], alpha = 0.5)\n", - "plt.title(\"Census mail-back rate vs Population\")\n", - "plt.ylabel(\"Mail-back rate\")\n", - "plt.xlabel(\"population of block-group\")\n" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "ename": "IndexError", - "evalue": "invalid index", - "output_type": "pyerr", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mIndexError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgenfromtxt\u001b[0m\u001b[1;33m(\u001b[0m \u001b[1;34m\"data/census_data.csv\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mskip_header\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mscatter\u001b[0m\u001b[1;33m(\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0malpha\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m0.5\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 3\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtitle\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Census mail-back rate vs Population\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mylabel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Mail-back rate\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mxlabel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"population of block-group\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;31mIndexError\u001b[0m: invalid index" - ] - } - ], - "prompt_number": 40 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "data.shape" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 41, - "text": [ - "(129605,)" - ] - } - ], - "prompt_number": 41 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "When I say *classic*, I am describing the \"shape\" of the scatter plot above. It follows a classic triangular form, that tightens as we increase the sample size (and hence the Law of Large Numbers becomes more exact). " - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [], - "language": "python", - "metadata": {}, - "outputs": [] - } - ], - "metadata": {} - } - ] -} \ No newline at end of file diff --git a/Chapter4_TheGreatestTheoremNeverTold/README.md b/Chapter4_TheGreatestTheoremNeverTold/README.md new file mode 100644 index 00000000..3d789004 --- /dev/null +++ b/Chapter4_TheGreatestTheoremNeverTold/README.md @@ -0,0 +1,3 @@ +Chapter 4: The Greatest Theorem Never Told +==== +### [Read it online here](http://nbviewer.ipython.org/urls/raw.github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/master/Chapter4_TheGreatestTheoremNeverTold/Ch4_LawOfLargeNumbers_PyMC2.ipynb) diff --git a/Chapter4_TheGreatestTheoremNeverTold/data/census_data.csv b/Chapter4_TheGreatestTheoremNeverTold/data/census_data.csv new file mode 100644 index 00000000..0f5e9a2e --- /dev/null +++ b/Chapter4_TheGreatestTheoremNeverTold/data/census_data.csv @@ -0,0 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+77.099999999999994,987 +88.200000000000003,954 +89.599999999999994,1165 +78.700000000000003,93 +77.900000000000006,1726 +81.099999999999994,821 +83.400000000000006,1141 +81.400000000000006,569 diff --git a/Chapter4_TheGreatestTheoremNeverTold/reddit_comments.png b/Chapter4_TheGreatestTheoremNeverTold/reddit_comments.png new file mode 100644 index 00000000..be17be66 Binary files /dev/null and b/Chapter4_TheGreatestTheoremNeverTold/reddit_comments.png differ diff --git a/Chapter4_TheGreatestTheoremNeverTold/top_showerthoughts_submissions.py b/Chapter4_TheGreatestTheoremNeverTold/top_showerthoughts_submissions.py new file mode 100644 index 00000000..1c3bf30e --- /dev/null +++ b/Chapter4_TheGreatestTheoremNeverTold/top_showerthoughts_submissions.py @@ -0,0 +1,41 @@ +import sys + +import numpy as np +from IPython.core.display import Image + +import praw + + + +#subreddit = reddit.get_subreddit("showerthoughts") + +#top_submissions = subreddit.get_top(limit=100) + +#update old praw usage to current version (7.6.0) +#please notice that new Reddit Object usage, especially the praw.ini file +reddit = praw.Reddit("BayesianMethodsForHackers",user_agent="BMFH") +top_submissions = reddit.subreddit("showerthoughts").new(limit=100) + + +n_sub = int( sys.argv[1] ) if sys.argv[1] else 1 + +i = 0 +while i < n_sub: + top_submission = next(top_submissions) + i+=1 +top_post = top_submission.title + +upvotes = [] +downvotes = [] +contents = [] + +for sub in top_submissions: + try: + ratio = sub.upvote_ratio + ups = int(round((ratio*sub.score)/(2*ratio - 1)) if ratio != 0.5 else round(sub.score/2)) + upvotes.append(ups) + downvotes.append(ups - sub.score) + contents.append(sub.title) + except Exception as e: + continue +votes = np.array( [ upvotes, downvotes] ).T \ No newline at end of file diff --git a/Chapter5_/LossFunctions.ipynb b/Chapter5_/LossFunctions.ipynb deleted file mode 100644 index 090ea4d1..00000000 --- a/Chapter5_/LossFunctions.ipynb +++ /dev/null @@ -1,1298 +0,0 @@ -{ - "metadata": { - "name": "LossFunctions" - }, - "nbformat": 3, - "nbformat_minor": 0, - "worksheets": [ - { - "cells": [ - { - "cell_type": "code", - "collapsed": false, - "input": [ - "figsize( 9, 3.5)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 1 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Chapter 4: \n", - "## Would you rather lose an arm or a leg?\n", - "____" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Statisticians can be a sour bunch. Instead of considering their winnings, they only worry about how much they are losing. In fact, they consider their wins to be negative loses! \n", - "\n", - "The author Nassim Taleb of *The Black Swan* and *Antifragility* stresses the importance of *payoffs* of decisions, *not the accuracy*. For example, consider the following vignette:\n", - "\n", - "> A meteorologist is predicting the probability of a possible hurricane striking his city. He estimates, with 95% confidence, that the probability of it *not* striking is between 99% - 100%. He is very happy with his precision and advises the city that a major evacuation is unecessary. Unfortunately, the hurricane does strike and the city is flooded. \n", - "\n", - "The stylized example shows the flaw in using a pure accuracy metric to measure how good your estimate is, while an appealing and *objective* thing to do, misses the point: decision making. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Loss Functions\n", - "\n", - "We introduce what statisticians and decisions theorist call *loss functions*. A loss function is a function of the true parameter, and an estimate of that parameter\n", - "\n", - "$$ L( \\theta, \\hat{\\theta} ) = f( \\theta, \\hat{\\theta} )$$\n", - "\n", - "The important point of loss functions is that it measures how *bad* our current estimate is: the large the loss, the worse the estimate according to the loss function. An example of a loss function is the *squared-error loss*. The squared-error loss is a very common loss function, defined:\n", - "\n", - "$$ L( \\theta, \\hat{\\theta} ) = ( \\theta - \\hat{\\theta} )^2$$\n", - "\n", - "The squared-error loss function is used in estimators like linear regression, UMVUEs and many areas of machine learning. We can also consider an asymmetric loss function, something like:\n", - "\n", - "$$ L( \\theta, \\hat{\\theta} ) = \\begin{cases} ( \\theta - \\hat{\\theta} )^2 & \\hat{\\theta} \\le \\theta \\\\\\\\ c( \\theta - \\hat{\\theta} )^2 & \\hat{\\theta} \\gt \\theta, \\;\\; 0\\lt c \\lt 1 \\end{cases}$$\n", - "\n", - "\n", - "which means estimating smaller than the true estimate is preferable to estimating above. A situation where this might be useful is in estimating a factory's next month sales, where a more pessemistic outlook is preferred to avoid an overallocation of resources. \n", - "\n", - "A negative property about the squared-error loss or any loss that contains a squared-error loss is that they put more emphasis on large outliers because the loss increases quadraticly an the estimate drifts away. The more *robust* loss function that increases linearly with the difference is the *absolute-loss*\n", - "\n", - "$$ L( \\theta, \\hat{\\theta} ) = | \\theta - \\hat{\\theta} | $$\n", - "\n", - "Other popular loss function include:\n", - "\n", - "- $ L( \\theta, \\hat{\\theta} ) = \\mathbb{1}_{ \\hat{\\theta} \\neq \\theta } $ is the zero-one loss often used in machine learning classification algorithms.\n", - "- $ L( \\theta, \\hat{\\theta} ) = -\\hat{\\theta}\\log( \\theta ) - (1-\\hat{ \\theta})\\log( 1 - \\theta ), \\; \\; \\hat{\\theta} \\in {0,1}, \\; \\theta \\in [0,1]$$, called the *log-loss*, also used in machine learning. \n", - "\n", - "Historically, loss functions have been motivated from 1) mathematical convience, and 2) they are robust to application, i.e., the are objective measures of loss. The first reason has really held back the full breadth of loss functions in application. With computers being agnogstic to mathematical convience, we are free to design our own loss functions, which we will see later.\n", - "\n", - "The above loss functions are indeed objective, in that they are most often a function of the difference between estimate and true parameter, independent of signage or payoff of choosing that estimate. This last point, its independence of payoff, causes quite pathological results. Consider our hurricane example above: the statistician equivilantly predicted that the probability of the hurricane striking was between 0% to 1%. But if he had ignored being precise and instead focused on outcomes (99% change of no flood, 1% chance of floor), he might have advised differently. \n", - "\n", - "By shifting our focus from trying to be incredibly precise about parameters to focusing on the outcomes of our inference, we can customize our estimates to be optimized for our application. This requires us to create new loss functions that reflect our goals and outcomes. Some examples of more interesting loss functions:\n", - "\n", - "\n", - "- $ L( \\theta, \\hat{\\theta} ) = \\frac{ | \\theta - \\hat{\\theta} | }{ \\theta(1-\\theta) }, \\; \\; \\hat{\\theta}, \\theta \\in [0,1] $ emphasizes an estimate closer to 0 or 1 since if the true value $\\theta$ is near 0 or 1, the loss will be *very* large unless $\\hat{\\theta}$ is similarly close to 0 or 1. \n", - "This loss function might be used by a political pundit who's job requires him or her to give confident \"Yes/No\" answers. This loss reflects that if the true parameter is close to 1 (for example, if a political outcome is very likely to occcur), he or she would want to strongly agree as to not look like a fool. \n", - "\n", - "- $L( \\theta, \\hat{\\theta} ) = 1 - \\exp \\left( (\\theta - \\hat{\\theta} )^2 \\right) $ is bounded between 0 and 1 and reflect that the user is indifferent to suffiently missed estimates. It is similar to the zero-one loss above, but not quite as penalizing to estimates that are close to the true parameter. \n", - "- Complicated non-linear loss functions can programmed: \n", - "\n", - " def loss(true_value, estimate):\n", - " if estimate*true_value > 0:\n", - " return abs( estimate - true_value)\n", - " else:\n", - " return abs(estimate)*(estimate - true_value)**2\n", - " \n", - "\n", - "\n", - "- Another example from the book *The Signal and The Noise* is that weather forcasters have a interesting loss function for their predictions. **cite!**\n", - "\n", - "\n", - "> *People notice one type of mistake--the failure to predict rain--more than other, false alarms. If it rains when it isn't supposed to, they curse the weatherman for ruining their picnic, whereas an unexpectedly sunny day is taken as a serendipitous bonus.*\n", - "\n", - "> *[The Weather Channel's bias] is limited to slightly exaggerating the probability of rain when it is unlikely to occur--saying there is a 20 percent change when they know it is really a 5 or 10 percent chance--covering their butts in the case of an unexpected sprinkle.*\n", - "\n", - "\n", - "As you can see, loss functions can be used for good and evil: with great power, comes great-- well you know.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Loss functions in the real world\n", - "\n", - "So far we have been under the unrealistic assumption that we know the true parameter, then even bothering to create an estimate is a redundant task. Hence a loss function is only practial if it is wrapped by an expected value, where the true parameter is random variable. Random variable? Parameter? Bayesian.\n", - "\n", - "First it will be useful to explain a *Bayesian point esimate*. Our world and machinery in our world are not built to accept a posterior distribution as input. We need to distill our posterior distribution down to a single number (or vector in the multivariate case). If done correctly, we can avoid the flaw of frequentist methodologies that mask the uncertainity and provide a more informative number. \n", - "\n", - "As Bayesian analysis returns a posterior distribution of the parameters, we can examine the expected value of loss function of the (random) parameters. For example, suppose $P(\\theta | X)$ is the posterior distribution of $\\theta$ after observing data $X$, then the following function is understandable:\n", - "\n", - "$$ l(\\hat{\\theta} ) = E_{\\theta}\\left[ \\; L(\\theta, \\hat{\\theta}) \\; \\right] $$\n", - "\n", - "which measures the *expected loss* of using estimate $\\hat{\\theta}$, also known as the *risk* of estimate $\\hat{\\theta}$. The subscript $\\theta$ under the expectation symbol is to denote that $\\theta$ is the random variable in the expectation, something that at first can be challening to think about. \n", - "\n", - "We spent all of last chapter discussing how to approximate expected values. Given $N$ samples $\\theta_i,\\; i=1,...,N$ from the posterior distribution, and a loss $L$, we can approximate the expected loss of using estimate $\\hat{\\theta}$ by the Law of Large Numbers:\n", - "\n", - "$$\\frac{1}{N} \\sum_{i=1}^N \\;L(\\theta_i, \\hat{\\theta} ) \\approx E_{\\theta}\\left[ \\; L(\\theta, \\hat{\\theta}) \\; \\right] = l(\\hat{\\theta} ) $$\n", - "\n", - "Notice that measuring your loss via an *expected value* uses more information from the distribution than the MAP estimate which, if you recall, will only find the maximum value and ignore the shape of the distribution. Ignoring this information can overexpose yourself to tail risks and leaves yourself ignorant to how ignorant you really are about the parameter (which is important!).\n", - "\n", - "Similarly, compare this with frequentist methods, that traditionally only aim to be minimize the error, and not the more-broad loss of that assuming that error. Compounded this with the fact that frequentist methods are almost garunteed to have an error. Loss functions fix this by planning ahead: your estimate is going to be wrong, you might as well err on the right side of wrong." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "______________________________________\n", - "### Example: Optimizing for the *Showcase* on *The Price is Right*\n", - "\n", - "Bless you if you are ever choosen as a contestant on the Price is Right, for here we will show you how to optimize your final price on the *Showcase*. For those who forget the rules:\n", - "\n", - "\n", - "1. Two contestants compete in *The Showcase*. \n", - "2. Each contestant is shown a unique suite of prizes (we'll assume two prizes per suite for brevity, but this can be extended to any number).\n", - "3. After the viewing, the contestants are asked to bid on the price for their unique suite of prizes.\n", - "4. If a bid price is over the actual price, the bid's owner is disqualified from winning.\n", - "6. Else the winner is the owner of the closer bid to the true prices.\n", - "\n", - "The difficulty in the game is balancing your uncertainty in the prices and keeping your bid low enough so as to not bid over.\n", - "\n", - "\n", - "What might a contestant's loss function look like? I would think it would look something like:\n", - "\n", - " def showcase_loss( guess, true_price, pain = 80000):\n", - " if true_price < guess:\n", - " return pain\n", - " else:\n", - " return np.abs( true_price - guess )\n", - "\n", - "where `pain` is a level of how bad it is if your guess is over the true price. A lower `pain` means that you are more comfortable with the idea of going over (remember going over does not gauruntee you lose--your competitor might go over as well). If we do bid under, we want to be as close as possible, hence the `else` loss is a increasing function of the distance between the guess and true price.\n", - "\n", - "Suppose we have recorded the *Showcases* from previous *The Price is Right* episodes and have *prior* beliefs about what distribution the true price follows. For simplicty, suppose it follows a Normal:\n", - "\n", - "$$\\text{True Price} \\sim \\text{Normal}(\\mu_p, \\sigma_p )$$\n", - "\n", - "We need a model of how we should think playing the *Showcase*. For each prize, we have an idea of what it might cost, but this could be significantly off of the true price. (Couple this with increased pressure being onstage and you can see why some bids are so wildly off). Let's be rational and suppose your beliefs about the prices (we assume two prizes in the suite, but this can be extended) prices of prizes also follow Normal distributions:\n", - "\n", - "$$ \\text{Prize}_i \\sim \\text{Normal}(\\mu_i, \\sigma_i ),\\;\\; i=1,2$$\n", - "\n", - "This is really why Bayesian analysis is great: we can specify what we think a fair price is through the $\\mu_i$ parameter, and express uncertainty of our guess in the $\\sigma_i$ parameter. The true price of the prize suite is given by $\\text{Prize}_1 + \\text{Prize}_2 + \\epsilon$, where $\\epsilon$ is some error term.\n", - "\n", - "\n", - "We are interested in the updated $\\text{True Price}$ given we have observed both prizes and have belief distributions about them. We can perform this using PyMC. Below, I assumed the following parameters for our prior beliefs:\n", - "\n", - "\\begin{align*}\n", - "& \\text{Prize}_1 \\sim \\text{Normal}(15000, 5000 )\\\\\\\\\n", - "& \\text{Prize}_2 \\sim \\text{Normal}(20000, 5000 )\\\\\\\\\n", - "&\\text{True Price} \\sim \\text{Normal}(50000, 10000 )\n", - "\\end{align*}\n" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "import pymc as mc\n", - "\n", - "data_mu = [ 1.5e4, 2.0e4]\n", - "\n", - "data_std = [ 5e3, 5e3 ] \n", - "\n", - "mu_prior = 5e4\n", - "std_prior = 1e4 \n", - "\n", - "true_price = mc.Normal( \"true_price\", mu_prior, 1.0/std_prior**2 )\n", - "\n", - "\n", - "prize_1 = mc.Normal( \"first_prize\", data_mu[0], 1.0/data_std[0]**2 )\n", - "prize_2 = mc.Normal( \"second_prize\", data_mu[1], 1.0/data_std[1]**2 )\n", - "price_estimate = prize_1 + prize_2\n", - "\n", - "@mc.potential\n", - "def error( true_price = true_price, price_estimate = price_estimate ):\n", - " return mc.normal_like( true_price, price_estimate, 1e-8)\n", - "\n", - "\n", - "model = mc.Model([true_price, prize_1, prize_2, price_estimate, error ])\n", - "mcmc = mc.MCMC(model)\n", - "mcmc.sample( 200000, 180000) \n", - "\n", - "price_trace = mcmc.trace( \"true_price\" )[:]" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - " \r", - "[****************100%******************] 200000 of 200000 complete" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n" - ] - } - ], - "prompt_number": 3 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "_hist = plt.hist( price_trace, bins = 50, normed= True, histtype= \"stepfilled\")\n", - "plt.title( \"Posterior of the true price estimate\" )\n", - "plt.vlines( mu_prior, 0, 1.1*np.max(_hist[0] ), label = \"prior's mean\" )\n", - "plt.vlines( price_trace.mean(), 0, 1.1*np.max(_hist[0] ), \\\n", - " label = \"posterior's mean\", linestyles = \"--\")\n", - "plt.legend()" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 4, - "text": [ - "" - ] - }, - { - "output_type": "display_data", - "png": 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KeyEtpdFoYGBg0KK6NTU1kMlkejd/V2tq8jlAkydPxpUrV5CVlYXVq1cDqOv4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\n3I4UQEIIIYRwO1IACSGEEMLt/A/W9fsbOtC3MQAAAABJRU5ErkJggg==\n" - } - ], - "prompt_number": 4 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Notice that because of our two observational prizes, we shifted our previous price estimate down about $6000 dollars.\n", - "\n", - "A frequentist, seeing the two prizes and having the same beliefs about their prices, would bid $\\mu_1 + \\mu_2 = 35000$, regardless of any uncertainty. Meanwhile, the *naive Bayesian* would simply pick the mean of the posterior distribution. But we have more information about our eventual outcomes; we should incorporate this into our bid. We will use the loss function above to find the **best** bid (*best* according to our loss).\n", - "\n", - "For every possibe bid, we calculate the *expected loss* associated with that bid. We vary the pain parameter to see how it affects our loss:" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "figsize( 8.5, 7 )\n", - "#numpy friendly showdown_loss\n", - "def showdown_loss( guess, true_price, pain = 80000):\n", - " loss = np.zeros_like( true_price )\n", - " ix = true_price < guess\n", - " loss[ix] = pain\n", - " loss[~ix] = np.abs( guess - true_price[~ix] )\n", - " return loss\n", - "\n", - "\n", - "guesses = np.linspace( 5000, 50000, 25) \n", - "pains = np.linspace( 30000, 150000, 6 )\n", - "expected_loss = lambda guess, pain: \\\n", - " showdown_loss( guess, price_trace, pain ).mean()\n", - " \n", - "for _p in pains:\n", - " results = [ expected_loss( _g, _p ) for _g in guesses ]\n", - " plt.plot( guesses, results, label = \"%d\"%_p )\n", - " \n", - "plt.title(\"Expected loss of different guesses, \\nvarious pain-levels of \\\n", - "overestimating\")\n", - "plt.legend( loc=\"upper left\", title=\"Pain parameter\")\n", - "plt.xlabel(\"price bid\")\n", - "plt.ylabel(\"expected loss\")\n", - "plt.xlim( 15000, 50000 )\n", - "\n" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 5, - "text": [ - "(15000, 50000)" - ] - }, - { - "output_type": "display_data", - "png": 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1rvVVDBw4EK6urhg5ciRWr14NR0dHZGRk4MyZM9DU1MSUKVMAAHK5HGPHjsWG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WNSzB3cPeYx1JbdU/ZfMAkk7T7PoT3nQQSfAEemzRmMg1PPfxEyaprI++Q7\nct7/CmO9+R7nPmU0oc8+iF1kSA+PTqItSEKmm7lehcwvKS2uYPPWlWh1R/C2LSZcXUaQqvKK/bL1\nrmQ1e6NTDWR4wkL8/Ie23HCluG5rJHu0pq/ZQxRFhNLNGE49Z15eADmKoMUoI55HprL/3fYfr1vH\nj01acmvrAJgVHsZTCfE4WnUswTS7NJ0NyV+QfG43oigAMCx0HDPj7yfYq2vz4S6noqSBnetTKc6v\nBRmMGBfMiPHByBVtD1N3x7lh0uooWLWe7OUr0Veaqx+7jB5K6HNLcIzt2vWiupq+dq10FilHRqJN\neHq78cADT7W812i0rFv/HWVVu3G1KSDUpoxwy1L6WVTSz6ISOAUnvqbwqC1ntd5UCyE0mQaQMGI4\n8i5ej0VCordhrgnzLELZDgBkjjGoBr+N3PH3Fw08VlLCR8dS2HXgIBZB/ejn6Mjfx41hqHfblia4\nHFEUOZN3mPXJX3Am7zAACrmS0QOnMz1uPj4uQe3us03HFUSyz1ZwLCmX/PPmAp629pbcMmcwfv16\nNlFWMBopWrOF8+983lLMzjF2IKF/XiLVgrlOqNcaOVXSyImSBk4UN/Jov+47luSRoe94ZH4Po9HE\n7p0/k5q5HnvrbAJtyxhgWYKjornVfk2CirM6L0r0fjh5jyF+xAKsbaQEOom+gbkmzPsYz70FpmZz\nTZiIF1AE3febNWFEUeRgYREfHjvOkeISANRKJYuHxHB/zOB2J/MKgonkc7vZkPwFOWXmwm1WKjUT\no2cxbei9ONu5d/xD/gZ6nZHU40UcS8qjtspc8kFloSBqqC8jxgf3aG0YURAo3bCLzDc+oSm7AAC7\nyBBCn1uC26SRvWrKt0RrmvQmzpQ1cqK4kRPFDZyvauZycfHaEFEKLXUnN4qQ+SWiKJJy+Az7k79F\nqUzDz86cZ+Onqrli3zy9Czk6L4zqQUQPuxcf3+HIZNLsKInrC6EyyZzMe7EmjM8sVAP/gczq15NZ\nRVHk59w8PjyWwunycgDsLS2YFxXFvEED2x1G0ht17D2zkU2HV1FWay4w56B2ZmrsXCbF3IGt1e+H\ntDpCXU0zKQfzOH20EJ3WCIC9kzVDRgQQNdQHS6ue88KKokjFzgNkvvYRDWlZAKj7+RH6zGI8Z0yQ\nitn1QvRGgbRyDSeKzR6XjAoNwmVqQqWQEeluQ7S3HdHetuiKzklCpju5UYXM1fjuu/VUVubQbDiK\nu10poepywixLsZIZW+3XIFhyTutFFSEEhE0jcsDtqCwdemjU3YcU527N9WoPUVeJMfXvmAq+BkBm\n0w/loDdRuP/67B+TILDjfDYfHU/hbJU59OJkZcWi6EHMHTgAWwuLdtlDo21g54m1bD36NXVN5v7c\nHX2ZHjefsQNu7ZaKvKIoUpxfy7EDuWSmlnHxbu8b6MSQkQGEhLu3Kw/mt+jouVGVeJRzr3xI3fFU\nAKx8PAj50/14z7kZeSfWn+pprtdr5dcwCiLnKprMwqWkgdQyDQbTJfkgl0G4mw3R3rZEe9sR4W6D\npfLSuSXlyEhcM7y8XLjzztta3tfWNrJ903Zyy3Zjb1tAoF05EZZleCrriFXnArlQ8BOG/D+RaXCl\nwOCHlXs8g6PvxsFlgOQKluhRRFHAlP8VxtQXL9SEsUAZ+iSK0Cd+tSaMwWRiU2YWHx9PaUnidbdR\nc3/0YO6IjECtap/norqhnC1Hv2bXyR9o1msACPQI57bhixjef3y3FLMzGQXOnSnl2IFcSovqAZDL\nZUQM9mLIyAA8fXr+S0ft8VQyX/2Qqv1HAbBwdaLfEwvxm38bCitpaZaeRhBFcqqbOVHcSEpxA6dL\nG2k2CK32CXaxJtrL7HEZ6GmLTQ/VGZI8Mkgemfag0xtJ+vkoh09uRmGVibdDOf2tywmzKEMlM7Xa\nt8ak5rzOm2b1AEIiZhIQNAG5yraHRi5xoyHUpWI4+cc214TRGY38mHGWT1JOUNxgnuLra2/H4pho\nZoaHtTsHpqgqh42HV7E/dTMmwezRHBgQx4zhC4kKGN4tIr+5Sc/JwwWcOJRPY70OAGu1isFxfkTH\n+2Nr3/Pl+hvSssh8/WPKt+8HQGlvS9Bj9xKw+E6UNuoeHt2NiyiKFNTpOFHcwMlic5Jug671Pd3P\nwZJobzsGe9sy2MsOB6u2+0Kk6dfdjCRkOo5JEEk7mc3u3VtpEFJwcSyln20FAyyvrGljFOXk6N0p\nIwhn//GEh8/A2iFE8tpIdBmirhKh/GdMZTsQitddqgkz8J/IfW6/6rnWZDDwbWo6n584SUWTOfm1\nn6MjS2JjmBYagrKd+RmZxafZkPwFRzP3IiIiQ8bwsAlMH76QYM/ILvmcv6SyvJHjB3JJSynGaDR/\na3bxsCU2IYCIaG9Uqp6vyFt/+hw57/+PkvW7QBRRWFsRsOQugh6Zi8qxe/KCJH6bOq2Ro4X1HCmo\n50RJA9VNrVMI3G1VxHjbmcWLly2uNh1PBJdCSxLXjPbGdRVyGVExwUTF/AG4oOoLq/h5y27OVx7C\nxiEfP4dKwqzKCVWVE2pZSiilUHYQyv5JhcmKXIM3OpsIvILGExA4EaXat9eIm74W5+4svc0eomBE\nrD2OqWwXQvlPiLUnoGWuhAxF0IMoI/5y1ZowDTodX59J5cuTp6nRagEId3FhSWwMk/oFoWiDgLlo\nD1EUOZmTxIbklaQVHANApbBg7MDp3Bo3H0+nrq86KwoiuVmVHDuQS25mVcv2oDA3YhMCCAhxuabX\n0dXODVEQqNx9iJz/fkP1AbNdZBYq/BfeTr+lC7B069lp3t1Jb7tWwHx/zq5uJjm/nsMF9Vck6Dpa\nKVuSc6O97fCys+g19+LfQhIyEl2KTCbD38+V+Q/NAeYAUF3XxMHtyWw6mYhok4WHUznB6krCLctw\nVTQySJENpmzI2owpCypNNhQJvoiO0fgGTcTDeyRy695RIl2i5xG1pZjKdyGU7UKo2GPOfbmI3BK5\ny0jkHuORe0xFbntl8Yqa5ma+PHWar0+n0qDXAzDYw52HY4cwJsC/XTduQTSRmLaVDckrya/IBEBt\nacvkmDuZOuRuHG1dO/VZr4ZBbyI1pYjjSXlUV5hzbpQqBQOHeBOTEICLW8+Hb01aHcXfbyf3w9Vo\nzuUCoLBR4zdvBgEPzsHaV7qerxXNBhMpxQ0cviBeKpsuLWujlMuI9rJlmJ89sT52BDhZXRfC5ZdI\noSWk0NK1pklv5PiBNI4nJVJqOIuNcxme9tX0s6omwqIUR0XTFW0qTXaUy4OwcB2Kr/8EHDzikFm6\n9MDoJa41omBArD58Qbz8hFh/ptXfZTb9kLtPQO4xEblLAjKlzVX7qdBo+PzEKdakptFsNLvQh/t4\n81DsEIb7eLfrBl5UlcOB9G3sP7OZinpzTRknG1emDbuXCYNnobbsejHRUKcl5VA+pw4XoG02P4zs\nHKyIifcnaphvj9Z/uYi+qpb8lT+S/9nalkq8ll5uBC6eg++8Gagcumd5BYnWFNfrOFxQR3J+PadK\nGjFc5nZxViuJ83NguJ89Md52qK9Rgq4UWpLoU6gtlIy6aRCjbhoEmPNszmWXceqnw+zNPonGthgH\n1yp8bKsJsawm3KIEV0UDrpyCqlNQ9Rk6oNzkRI1lKDZu8Xj5jUHtOrRNZeUlej9icxGmsp8Qynch\nVOwFY8OlPyqskbuONosX9wlX9bpcTlF9A5+mnOCHjLPoTebkxTEB/jw0JIYYr7Z7BirrS0hK30FS\n+nZyy8+2bPd2DmR63HxGRU5DpexaMSEIIkV5NZxMLuDsmVLECw8kLz8HYkcGEjrAA0UXTZ/uDJrs\nAnI/Wk3Rmi0IzeYkY/uo/gQ+PBfPGROQq6RHTXdiMAmklmkuhIzqKKjTtfxNBkS4q4nzcyDOz54Q\nF+vr0uvyW0geGSSPzOX0hriuKIqU1jZzKvEMWcnHydfkYnSrwtGlhgB1NaGW1YSrSq5Y+RugVHBD\no47A3iMBD++RqJyiOiVueoM9ehPdZQ/RpEOoPmgOF5X/hNhwttXfZbZh5nCR+0TkLiN+der05eTW\n1vLx8RNsPJeJURCQAZP6BbEkNoZIN7c2jau+qYZDZ38iKX0bGYUnWrarLW2J6z8BtcaLebPu79Ip\n1Aa9kdysKrLSysnOKKf5QihAJpfRf6AHsQmBePt3fEXtrkIURWoPnyLnv19Tvj2RNJOGSLkatwkJ\nBD5yN84jY/vcA7M9dPe9o7rJwOECs3A5XtRA02VTo20tFMT62jHcz4GhvnY4Wvf8kjN91iNTUFDA\nggULKC8vRyaTsWTJEpYuXUp1dTV33XUXeXl5BAYG8u233+LoaL5wX331VT777DMUCgXvvvsukydP\nBuDYsWMsWrQIrVbLtGnTWL58OQA6nY4FCxZw/PhxXFxcWLNmDQEBAT32mSV+H5lMhpeTGq/pcUyZ\nHgdAg87ImdO5ZP58kr156ay2qkbmWYerQw2B1rX0t6gizKIET3kFaCsgbx9CHugAjaimUeGGYOWH\nhV0ANg79sLXvh1zth0ztBxauN/QNt6cQNLkIF70ulfvBdFlIUWmL3HUMco+JKNzHI1P7/3Zfokhu\nbS2pFZWklldwpqKCE6VlCKKIXCZjev9QHhwSQ4iz0++Oq1mv4UjmHg6kbeN0bjKCaPbiWCitiA0Z\nzciIqQwOSkClNBfE6woRo2nQcT6jnPPp5eRlVbXMPAJwdFbTP8qT6OF+2Dv2/ErUgtFI2Za95P73\nG+pS0gBzAq/bTSMY9eIz2IZ1z9pQNzqCaC5Id7ignuSCOjIrWy8tE+BkxXA/e+L8HBjgYYNCfuPc\n03rUI1NaWkppaSnR0dE0NjYSGxvLunXr+Pzzz3F1deWZZ57h9ddfp6amhtdee420tDTuuecejhw5\nQlFRERMnTiQzMxOZTEZcXBzvvfcecXFxTJs2jaVLlzJ16lRWrFjBmTNnWLFiBWvWrOHHH39k9erV\nrcYheWSuPwwmgXNFtaTvO0HxsQyqm/Ipc9Gi9KjHy7aaIKs6wi0qCFKVX1GV+Iq+sECr8gRrHyxs\nA1HbB5lFjvUFoWPlhUwuucY7g2hsRKxPR6hPQ6w/g1C+B1FzvtU+MvsByN0noPCYiMw5Dpn86mEa\nQRQpqKvnTEUFqeUVpFZUkFZRicbQ2kOnksuZGR7GAzGD8Xf47QJweqOOE9lJHEjfxvHz+zEYza55\nhVzJoMB4EiKmMDR0LNYWV8+/aS+iKFJdoeF8ejlZ6eUUF9Ry+cI0Xn4OBEe4ExLhjou7ba8Q2sZG\nDYXfbCbvozU0F5jzglRO9vgvmoX/fbOxdJdy1rqaBp2RlKIGkgvMU6RrtZfuZRYKGdHedsT52RPn\nZ4+nXe8uIthnPTKenp54eppj1La2tkRERFBUVMSGDRvYu3cvAAsXLmTcuHG89tprrF+/nrlz56JS\nqQgMDCQkJITk5GQCAgJoaGggLs787X3BggWsW7eOqVOnsmHDBl566SUAZs+ezR/+8Iee+bASXYpK\nIWeAvzMD5o2HeeMRRZHCOi1njmaSn3iawsJcUi2rKHUWMTrosLZuxN6iGReVATelDk9lA16KGryU\ntTjIm1EZ8sGQD/UHMRXD5WWgROToVe7I1H5Y2gYgV/sjU/shU/sis/Y1Cx6lVMgLQBRNiI3ZiPWp\nCPXpiPWpiPVpiE25V+6stEfufpNZvLiPR2btfZX+RArq6y94WSpJq6ggtaKSxguzjS7H09aGSDc3\nBrq5EenmyiAP999cB8kkGEnNP0pS+naSz+6mWX+p7lGE3xASIqYwvP8E7NW/78VpC4IgUpxfYxYv\naeXUVF3yQCmUcvyDXQiJcCc43K1XFK67iLakgrxPv6Pgy3UY6802UvfzI3DJXfjMmYZC3XvGej0j\niCL5tVrSyzSklWtIL28iv1bbah93WxXDL+S6DPa2w0rZ8/lRvYFe8zUzNzeXlJQUhg8fTllZGR4e\n5tWWPTw8KCszL+NeXFxMfHx8SxtfX1+KiopQqVT4+vq2bPfx8aGoqAiAoqIi/PzMNRyUSiUODg5U\nV1fj7Ny6fsFjjz2Gv7/ZfW1vb09UVFRLfDMxMRHghnh/8ffeMp72vvebOIhEq3oggIGxw0nPrWLb\n199Sf7wYd9EeOfUc1uZTZ++EMmwodSo91YVF2FgIhPZ3wVVloDGnGBd5AzdFGCk6X4mDrAmZrJSE\n8FLEuiPsNy9UTEK4+TUpAwSlA6PiwlHY9CPprAyZlTejxk1BZtOPA4dTe419Ovv+8vNj5ND+CA1p\nJO7ejNiUy4igSsSGsySlaa+wDzIFI4eFI7MfwMFMNXL7cEZNvR+ZXGnusyCbkSO9KGpo4Nut28it\nraXRy5u0igoqM8wGtwgyJ/Xqc7JxtLJk+IgEBri5QX4eAY4O3HLh2565v3wcA/yvGL8oiqxZv4oz\neYepUJylrqma6nyziz52WAwJEVOR1zrgoHZmVHTnrxeD3sj3322lKLcGK9GH5iYDecXmcExY8GCC\nw92o0+Xi6ePAuJtie/z/e/n7QU4e5H7wDbvWrkM0CUTK1TgNH0z5mChkwwbiP2ZMq/1/aZOeHn9P\nv/89e8TEjSCjXMP6nT+TX6OlzjUCjd5Ew3lzLpZdcDQqhQyHygwi3G2YP30iAU5WHDhwAEM+WPn3\nrs97tc9/4MAB8vPzAXjggQfoLnpFsm9jYyNjx47lhRdeYObMmTg5OVFTc2kFZmdnZ6qrq3n88ceJ\nj4/n3nvvBWDx4sXcfPPNBAYG8txzz7Fz504A9u/fzxtvvMHGjRuJiopi+/bteHubv+2FhIRw+PDh\nVkJGCi1doi8ntwqiSEGtlvTCGvKOZlB76izygjLstDqwEKl1V1LnJFCn1lCraqRBaaCsoBabfgHY\nq+S4qATclVq8lLV4KmrxUtTipazBU1F3xfIMlyMqHZDb9kNmE4jMxvwqVwchswkCK89eETb4LURj\nE2LDWbNo2bOThOA6hIY00FVcvYG1L3L7SHOo6MKrzDYYmfxSwqEoipQ0NnKmvKIlryW1ooI6ne6K\n7lytrRng7sZAdzcGuLkxwM0VN5v2hXjyKzJJSt/OgfTtVNQVt2z3cgpgZOQUEiKm4O0c2K4+4erX\ny8V8l6z0cvKvku8SEulOcIQ7Pv6OXbZgY1chiiKVPyeT+8E3VO0zL+2AXI7nLeMIfGQujkMG/Grb\nvnzv6AiX20MQRQrrdKSXa0gv05BariG/RssvH75uNioi3G2I9LAh0t2GQbYWhgAAIABJREFUfi7W\nWPSyc6Sj9NnQEoDBYGD27NnMnz+fmTNnAmYvTGlpKZ6enpSUlODu7g6YPS0FBQUtbQsLC/H19cXH\nx4fCwsIrtl9sk5+fj7e3N0ajkbq6uiu8MRKX6Ms3IrlMRoCTNQFO1hDlDYxHazCRWdVMRlEtmhOZ\nWJzKwD+jkOhGOVaiDK1TCDVGFXVOAs0W1dTIG8hRQYPCHp3CG73MHoPcDgeVAk+VFj9lNb7KKnyV\n1fgpqvBVVmFjrEOsTUGsTblyUAprZOpAZDZBV/5Y+3Z7bo4oCiDoL/wYEA11iA3prUNDjdmA+WEc\n7wBC5YXGSjtk9pEXxEokcvsByOwjkKnM+SgNOh2ljRpKazWUFmZR2thIaaOGksZGzlZWtVTTvRxn\naysGuF0uWtxwt1F3SOyV1xZxIH07SenbKajMunQMW3cSIiaTEDGVII/wTgnJi16e6goNWRdCRiWF\nV+a7hESYxUtvyXf5JYa6Bsq27iX3g9U0ZmQDoFBb43vvdAIenIPa/8qw3y/py/eO9tKkN2ETNJiv\nUkpJK9OQUaG5Yt0ipVxGqKu1Wbi42xDhYYNbJ5YAuJHpUSEjiiIPPPAAkZGRLFu2rGX7jBkzWLly\nJc8++ywrV65sETgzZszgnnvu4Y9//CNFRUVkZmYSFxeHTCbD3t6e5ORk4uLiWLVqFUuXLm3VV3x8\nPGvXru02RShxfWKlUhDlaUuUpy3E+gI3UaUxkFGhIaOknuJTWWjOnMMpNZ+Amibs9CqM9sE0OblS\n525Jg72BZmUlWkUxjfJKzqh0HFXYoZOFoZM7oJM7YKNU4a2qbxE2vspq/JRV+CmqcUJjFg4N6VcO\nTqZEpvZv8eSgcmgRHBfFhygafrHN/CoKehB/ue1Su5a/ib/uSbo0DgUy27DLxEokWutQSk1OlGo0\nZrFS2UhprobSxsQWwfLL5Ntf4mRlxQA3V7O3xc2NSDc3PG1t2vyg1+qbqawvufBTSmV9CRV1F38v\npaqhtGVfWysH4sMmkhAxhXC/GOSyzn3LbazXUlJQR1GeOefll/kuAcEuhES60y+sd+W7XERfVUtN\n8kmqD6ZQc+gE9Wcy4YJz3tLTlYDFd+I37zZpDaQ2IIoixfU6Uss0Zo9LuYbcGm2r0v8ALmrVBcGi\nJsLdhlAXNRZSjkuX0KOhpcTERMaMGcOgQYNabl6vvvoqcXFxzJkzh/z8/CumX7/yyit89tlnKJVK\nli9fzpQpU4BL06+bm5uZNm0a7777LmCefj1//nxSUlJwcXFh9erVBAYGthqHFFq6hOQebk1iYiIj\nEkaSX6slo1xDRmkjhanZGM9m4VZUgFtZMQ5NzQg2jmhdPGl2dqPOzQqNVQ1aeTnNijKaFGU0KJrQ\nKuzQyR3RyRzNr3IHLBUqfJVV+Cmr8FWYBY7vhd89lXXX5kPKLcw/MhUo1Yg2oWisQqlUBFAg+pJj\ndKNIY6C0sZGM48fRevu0lPb/LayVSjxtbfG0tWn16mFjQ4izE162v+6dEEWRhubaXwiUy0VLKQ3N\ntVdtexFLlTXDQseREDGFQYHxKBUdq6Vh0JsoK66npKD2wk8dDXVmb1JecRoB3pFYWasIDncjONKd\nwBBXLCx73NndCl15FdWHTlCTlEL1wRM0ns1u9XeZSonjkAH43jsDr5kTkVu031Y3yr3DKJinQZ8q\naSC1XENGeRN1l80mAlDIwK4yg3FjRpvFi7sN7raqXumNu1ZIq193M5KQucSNcjNqK79mjya9icyq\nJjLKm0gvbaAoIw+r89l4FOXjXpyPc309gq0jzc6eaJ09aHJ2o9HBRLOiDK2inGZ5Gc2Kcprkdeha\nBI4DOrkjWrkjerkDKhl4X/De+CqqsZbrMYgKjKICA0oMosL8g/nViAKDqGy9reX3S9uNl70iV6KU\nK1DK5ShkMgRR/E2Ros/JxiKoH5YKBR62NnjZ2l4SKTaXixZb7C1/fcE5k2CkprGCirrSq3pVqhpK\n0RmuDD1djlKhwtXeE1d7L1ztvXCz98LVwbNlm4udR7vFiyiIVFdpKCmooyS/lv9v706D2zrLPYD/\nz9EuS7YlL7Js2XHiOM3m2OaWJAVyGUpaYCBtITBZpmmHpsxQpp3CMJ0SYCAMbdK0kw8lhGGZMJQA\nDdCZktJp0nBpcxua3uQ2JGlvk9IsdrxvklftOue9HyQd+8RyStN4kfT/zWiOdBb76JnX0uP3fc57\nujuG0d8zqs2om2a2GFHhK8LA6CWsu/P2OVfvEu7sxeAbpxH4nzMIvHEGoUttuu2y1Yzi/2iA+5Ym\nuG5pQvFHlsNg+3CX7+bqZ4eiClwYCOFs9xjOdo/i/3qCiEyoewIAl82oDREt9RSgvtSO//2f4zkZ\nj+vFRGaaMZGhD0sIgf5gHO+mLps83zuK/n9dQUn7FS25KR3og+pwIez2IOL2IOKuQNjlQtg0hLCh\nV5fkRORBxGV7qucmmeQokgUCMoQkQ0CGJBkhyyZIshGSZEzW00iG5APJfVRIEJCgCgmKAFQBKEIg\noaqTCg3TTLKsJScehwNeRwE8BQUosZpQbJFRZJJgluKIxiOIxkOIxEKIxiOIpJ5H4uGrliFEY+Pr\nwrEghoMBbaK5qRRYnCgprEglKN5UwpJMVMoKvSgscH/oIaJQMIaeVC9Ld0dyGb3qv2tJAkornPD6\nilBZU4yK6mKUlBZAmiMTjgkhEL7SmexxeeMMAsdPa/O8pBnsNhSvbID7lma4VzehqGkJZAvrMTJR\nVIFLgTDOdo3ibPcY/q9nTDdrLgBUF1vQ6HViuSeZuHgc2XGX6NmU08W+RLlAkiSUO8wod5jxnwuS\n847ElUVoCUS0cfPjvWMIX25HeVcbKjrb4Dnzd9T0dEEtKETE7UklOAsRKalAzGpExNCv9dxEDL2I\nySNQpSgUOYoEwsCUqci/RwCwmBywWZywmp2wWpwwGy2AEkwmKH1hDHeE0BsPvW/vyPVwFZRqCYqW\nrDgrUr0q3ht+48VEQkV/90gyaUklL0OByTcodRRa4K0uhre6CN7qYngqC+fUUJEQAsGLbRN6XE4j\n2q2/gsxY6IBrVSPcq5vg+lgzCpcv4v2OpqAKgZZAGGe6kj0ub/cEEYzpk+yqQgsavQ40VjqwwutE\niX32p/yncWzZpJOr3cPX68PEw2SQsajMjkVldty5LHlvn+HI4mStTX8I5/uCeLFnFNauLpR3tSeT\nm3Ovo6qnA5LJOt5rU1KNiPtmxJ0uiNR0+AICKmJQpMiERxQJKQxFikA2xyFZEpBMMQhjNJkASRHE\nRRgxJYRwPJmsxOJjiMXHMIzua70VAMmak5FOBTWLymE122E12WE127SlxWRPrbddtUxtT62zmGwo\nsrtv+A0WJ1IVFSPDES1h6W4fQl/XCBRFn/wZTTIqqop0iYuz6N8vzp2Jvxehqhj7VwsCb5xOFue+\ncUa7s3SayV2UTFpSPS7OpXWQDDNzV+O0bPnsUIXAlcEIznaP4kzXGN7uGZt0RVGF04xGrwNNlU6s\n8Dqu62qibIlHLmAiQzSDiqxGrKopwqqa5CXKqhDoGFqC8/3JXpuTfUFcGQjC1dejDUlVXDiFyp5O\nGBJxKBYbEnYn4nZHaulEwu6E6i6FUliGuLUAUckEEZOAsWufi4AKs12FxSlgsqsw2uIwWFRYTMmk\nxGK0wmJMJiMWkxUGgwFn3z6F//jIShhkCbJBhixLkA0SZFlOLZPrDfL4c219eptBQjwqEBoNIxFX\nEI8ryWVMRSL9PK4gEVeTy9iEfTKuU5CIqbrXqpK5t6qkrAAVqaSlsroYpR7HnKltiQ+NIHi5HcFL\n7QheuoLQpXYEL7cjdLkdSljfI2YucyeHiW5phutjTXDU10KS58b7mGtEasbcs91jONs1hrd6xiYV\n55Y7TGj0OpO9Ll4nPE4Ou2UT1siANTI0t4TjCt4bCOF8XyhVcxPE8FgUhUMBFPv7UOzvh8vfD1eg\nH6VDAyjwD0BSxv+jFAAUqx1xmxMJuwOorAIqvFDdJUgUOBEzWhFRDQiFFeTqn78kAbYCMyp8qd4W\nXxEqfEWwzvJdgNVoDKHWTgQvtSF4uQ3Bi23JZOVSO2L+wSmPs1Z5koW5q5vgvqUZ9gXVrMmYgkhN\nPpcuzn2rewyDYX3iUmo3obHSoSUvFU7WuEw31sgQ5RGbyZD6gHUCSH4w943FcdEfwuVAGJcDYbzr\nD6N7NHl1kawocA4F4PL3ozjQD++IH57hARQO9MPW0wp0Xsr4e4QkwVDtg7FuAVDlgygrhygshmSz\nQbJaIFksgMkEVQioioCqCqiKClUVUNLP0+vV5HNFERCqmto+vr+qCija/ioMBhkmswFGowFGswEm\nkwyjyQCTKf3aAKNJvup1cmkyyZPWGc2y7rVskGbti0moKiJdfanelTaELiWTleDFNoQ7egBVzXic\nwWaFva4aBXXzUFBXjYIFyef2BT6Yipwz/C6yRySV+J/rC+F8ai6Xoat6XFw2Y/JvqtKBJq8DlYUW\nJi45hIkM6XBcV28uxEOSJHicZnicZny8tlhbH4wpaA2EcSkQxuVAOS775+H8YARnJlwaKikKCocH\nUTbUjwXhIXhHkr06lp5eKJ3dUNvaEWtrz/Rrk8cbDDC5i2ArKYa51IVzSggrly6HuaQYllIXTKXJ\npTm13VjkzPkvCCEElLEQYv5B/Pd/vYIVhWVaD0voUjuCLe1Qw5NvtQAk42mb70PBgppU0lKTTFrq\n5sFSUZrVsZuReiEh0DMaS91UMTnd/6VAeNLkc0VWY2qYyIHGSieqi2Y+cZkLnx35gokMUZYqMBuw\nrMKBZRXjV/coqkD3aBSX/cmem0uBMC77rbjoLsXFq46XVBXz42O4KTYE3+gg3IF+2Ab6IQ+PIOYf\nRMw/hMTwKGL9AcT6AwCAgBpC2xsZZiFO/0yjAeaS8cRm8tKVTHicdhjsNhjs1uTSapm1Gg8hBBIj\nY4j5hxAbSL7v9PsffwxO2DYEEUvOWnxeDUGSJ9/53FzmRsHCGhQsqEn2rCysgX1BDezzKq9rsrl8\nFUmoeC9VGH++L4hzvZN7W2QJqCuxaRPPLSkvQGUhh4ryCWtkwBoZyn0jkQRaBsNagnPZH0brUATx\nDEWxJlmCt9ACX5EFPrsMH2IoS4RREg3CNDKCeGB4/Et9YFD3JZ8YDV73Oco2Cwx2G4x2Kwy2dJIz\n8XlqabPqX+vWj68DoCUk8SkSkph/CLHAEEQ88T5np2ewWWEuLYa5xAXbvKpU0lKt9bSYCm/speP5\nQAiB3rEYzqWGh871BXHZH8bVTbTIasTicruWuNxUZofNNLNXaNEHxxoZIvpQCq1GXd0NkOy9aR+O\njCc3gTBaAxEMhOJoG4qgbejquWNsKDA74PPMR1V9KtEpsqKqyIKqQgvsZgPUaAwx/xCiA4OIp5aT\nkoeBQShjISjhMBKhCJRQGGo4qj3i/pmNDQAYCuzjPUfaY8JrbVtyabDPvfsnZZtoQk0WtfcGtaGi\nq4tyJ/e22FnfQpMwkSEdjuvq5XI8DLKEWpcNtS4bbp2wPhxX0DUSRcdw+hHRnvecP4VgXRP+1T95\nIrkSuwm+IguqiizwFRXDt6ACvo9YUO20wPg+s+AKVYUSiUIJhaGkkhslFIESjuhf69ZPvW8iFAFU\n9ZoJiWlC0mKwXt/0/LncPj6oa8UipqjoGI6iJRBO3tajL4hL/tCk3pZCiwFLPAU50dvCtjFzmMgQ\nkY7NZEBdiR11JfraDyEEXn4lAN+yenQMR9E5HEFHKuHpGo7CH4rDH4rjbLd+AhtZArxOy4Qkxwpv\noRkehxnlBWaYjTIkWYbRboMxNSRE2UlRBXpGo2gdjKB1MIKWQBitgxF0DE++G7QsAQvcNiz1FKTu\nU8TeFro+rJEBa2SIPixFFegPxrSem87hCNqHo+gcjqJvLHbNmym47UZ4HGZ4HJbk1VmO1MOZvOWD\n1ciJ3uYaIQT8oThaAhG0DoZTiUsYbYMRRDPUXckSUFloQa3LioUldiz1FGBRqR12c3b2ttAHxxoZ\nIprTDLKECqcFFU4Lbvbpt0UTKrpH9MNUPWMx9I7G0B+MIRBKIBBK4Hzf5OEqACi2GscTnKsSHY/D\nnLVDD9liJJLQEpUrE3pZxmKZb/pZVmBCrcuKWrctNXRpRU2xFRYmpDRNmMiQDsd19RgPveuJh8Uo\nJ7/U3JOHjRRVYCAYR+9YLPkYjU54HkN/MI6hSAJDkUTGuhwgWVfhcZpR4bCgPJXgVDjMcBeYUGCS\nYTMZYDfJsBjlGz5skUvtIxJX0DYURctgsug73dPiD8Uz7u+0GFDrsmG+24palw2DF07ji5/5FBxz\n6AabsymX2sZcxxZHRLPGII9P9peJogoEwnH0jo4nNxMTnb6xGEaiCkaiYVwYCF/zd8kSYDXKsJsM\nsJlTS9PkZTrxSS+tV71O72syzG4thxACkYSKUFxFOK5oy3BMRSiuIByfeqnbP578GdFE5hmHLQYJ\n8yYkLOneFrfNqEsM/+G3MYmhWcEaGbBGhihbqUJgMJzQkpqeVK9O31gM/lBC94Udm+JGktfLIAF2\nswGyJEGWkvd3MkgSJAmQkF43vk1GctvE9bIkQUJqOWHb1eviisiYgNzId2SQAF+xNZmoTEhcKpxm\nyCzApQ+JNTJERBnIkoQSuwkldhOWegquuW9CFRN6ICYsY/qeinBCRSimZt53wjKhCoxGM9eJzBSL\nQUr2FJn1PUmZepp0S7MMm1G/3mqSmbBQVmIiQzoc19VjPPSyOR5GWYLTYoTz+qaMmSSmqDj638ew\n8paPQRHJoR5VAEIAAunn4+tUiORSJHuSRHqJia+TxwqhX2eQkXHoy/A+8/PMpGxuG9OB8Zg5TGSI\niK6D2SDDbjag2MZ7JxHNJtbIgDUyRERE02k6a2R4YT8RERFlLSYypPOPf/xjtk9hTmE89BgPPcZj\nHGOhx3jMHCYyRERElLVYIwPWyBAREU0n1sgQERERZcBEhnQ4rqvHeOgxHnqMxzjGQo/xmDlMZIiI\niChrsUYGrJEhIiKaTqyRISIiIsqAiQzpcFxXj/HQYzz0GI9xjIUe4zFzmMgQERFR1mKNDFgjQ0RE\nNJ1YI0NERESUARMZ0uG4rh7jocd46DEe4xgLPcZj5jCRISIioqzFGhmwRoaIiGg6sUaGiIiIKAMm\nMqTDcV09xkOP8dBjPMYxFnqMx8xhIkNERERZizUyYI0MERHRdGKNDBEREVEGTGRIh+O6eoyHHuOh\nx3iMYyz0GI+Zw0SGiIiIshZrZMAaGSIiounEGhkiIiKiDJjIkA7HdfUYDz3GQ4/xGMdY6DEeM4eJ\nDBEREWUt1siANTJERETTiTUyRERERBkwkSEdjuvqM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- } - ], - "prompt_number": 5 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "### Minimizing our losses\n", - "\n", - "We would like to minimize our expected loss by finding the solution to\n", - "\n", - "$$ \\text{arg} \\min_{\\hat{\\theta}} \\;\\;E_{\\theta}\\left[ \\; L(\\theta, \\hat{\\theta}) \\; \\right] $$\n", - "\n", - "The minimum of the expected loss is called the *Bayes action*. We can solve for the Bayes action using Scipy's optimization routines. The function in `fmin` in `scipy.optimize` module uses an intelligent search to find a minimum (not necessarily a *global* minimum) of any uni- or multivariate function. For most purposes, `fmin` will provide you with a good answer. \n", - "\n", - "We'll compute the minimum loss for the *Showcase Showdown* example above:\n" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "import scipy.optimize as sop\n", - "\n", - "ax = subplot(111)\n", - "\n", - "\n", - "for _p in pains:\n", - " _color = ax._get_lines.color_cycle.next()\n", - " _min_results = sop.fmin( expected_loss, 30000, args=(_p,), disp = False)\n", - " _results = [ expected_loss( _g, _p ) for _g in guesses ]\n", - " plt.plot( guesses, _results , color = _color )\n", - " plt.scatter( _min_results, 0, s = 60, \\\n", - " color= _color, label = \"%d\"%_p)\n", - " plt.vlines( _min_results, 0, 120000, color = _color, linestyles=\"--\" )\n", - " print \"minimum at pain %d: %.2f\"%( _p, _min_results )\n", - " \n", - "plt.title(\"Expected loss & Bayes actions of different guesses, \\n \\\n", - "various pain-levels of overestimating\")\n", - "plt.legend( loc=\"upper left\", scatterpoints = 1, title = \"Bayes action at pain:\")\n", - "plt.xlabel(\"price guess\")\n", - "plt.ylabel(\"expected loss\")\n", - "plt.xlim( 25000, 45000 )\n", - "plt.ylim( -1000, 120000)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "minimum at pain 30000: 37096.36\n", - "minimum at pain 54000: 32861.80\n", - "minimum at pain 78000: 30419.10" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "minimum at pain 102000: 29558.16\n", - "minimum at pain 126000: 28532.31" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "minimum at pain 150000: 27939.33\n" - ] - }, - { - "output_type": "pyout", - "prompt_number": 11, - "text": [ - "(-1000, 120000)" - ] - }, - { - "output_type": "display_data", - "png": 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RuoZf/MnJycTj8ejMmTNiaYYOHUrz588notdryPzxxx+kpKT0yiULGrK2tiZd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\neYUN8Sk/+ok+8VO+emxIGqKlQBnQKpDkDVwKf2HAI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- } - ], - "prompt_number": 11 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As intution suggests, as we decrease the pain theshold, we increase our bid, willing to edge closer to the true price. It is interesting how far away our optimized loss is from the posterior mean, which was about 43000. \n", - "\n", - "Suffice to say, in higher dimensions being able to eyeball the minimum expected loss is impossible. Hence why we require use of Scipy's `fmin` function.\n", - "\n", - "\n", - "### Can we do better?\n", - "\n", - "Let's try to pick a rational `pain` value. I would assume that bidding over the true price should be equivilant to guessing \\$1: essentially you are handing the keys to winning to your opponent. Hence we should modify our loss to reflect this. Hypothetically, if you bid \\$1, we measure your loss equal `true_price`. So your rational pain should be equal to this. To encourage an even closer estimate, we will use some square-error losses in this new loss function." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "def showdown_lossII( guess, true_price):\n", - " loss = np.zeros_like( true_price )\n", - " ix = true_price < guess\n", - " loss[ix] = true_price**2 #notice we square here\n", - " loss[~ix] = np.abs( guess - true_price[~ix] )**2 #squared here.\n", - " return loss\n", - "\n", - " \n", - "guesses = np.linspace( 5000, 50000, 25) \n", - "expected_lossII = lambda guess: \\\n", - " showdown_lossII( guess, price_trace ).mean()\n", - " \n", - "results = [expected_lossII( _g ) for _g in guesses ]\n", - "plt.plot( guesses, results )\n", - " \n", - "_min_results = sop.fmin( expected_lossII, 30000 )\n", - "plt.scatter( _min_results, expected_lossII(_min_results), s = 60,\\\n", - " color=\"k\") \n", - "print \"Bayes action at %.2f\"%_min_results\n", - "plt.title(\"Expected loss of guess; more rational loss\")\n", - "plt.xlabel(\"price guess\")\n", - "plt.ylabel(\"expected loss\")\n" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "Optimization terminated successfully.\n", - " Current function value: 318227797.901998\n", - " Iterations: 54\n", - " Function evaluations: 111\n", - "Bayes action at 31263.42" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n" - ] - }, - { - "output_type": "pyout", - "prompt_number": 12, - "text": [ - "" - ] - }, - { - "output_type": "display_data", - "png": 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cvlwjdDmEEGIS9uX8eXaDFvwSJ2o4ephcxmFKaNtZju3nKwSu5k80HswfZccf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\n1dqF/3phDdqFFs5DA+HsEwSYTOiqK0N3fQVU7fV4ZcUTuHZUnEM3FUR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- } - ], - "prompt_number": 12 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "______\n", - "\n", - "### Shortcuts\n", - "\n", - "For some loss functions, the Bayes action is known in closed form. We list some of them below:\n", - "\n", - "- If using the mean-squared loss, the Bayes action is the mean the positerior distribution, i.e. the value \n", - "$$ E_{\\theta}\\left[ \\theta \\right] $$\n", - "\n", - "> minimizes $E_{\\theta}\\left[ \\; (\\theta - \\hat{\\theta})^2 \\; \\right]$. Computationally this requires us the caluculate the average of the posterior samples [See chapter 4 on The Law of Large Numbers]\n", - "\n", - "- Whereas the *median* of the posterior distribution minimizes the expected absolute-loss. The sample median of the posterior samples is an appropriate and very acccurate approximiation to the true median.\n", - "\n", - "- Infact, it is possible to show that the MAP estimate is the solution to using a loss function that shrinks to the zero-one loss.\n", - "\n", - "\n", - "Maybe it is clear now why the first-introduced loss functions are used most often in the mathematics of Bayesian inference: no complicated optimizations are necessary. Luckily, we have machines to do the complications for us. \n", - "\n", - "### Machine Learning via Bayesian Methods\n", - "\n", - "Whereas frequentist methods strive to achieve the best precision amount all possible parameters, machine learning cares to acheive the best *prediction* among all possible parameters. Of course, one way to achieve accurate predictions is to aim for accurate predictions, but often your prediction measure and what frequentist methods are optimizing for are very different. \n", - "\n", - "For example, least-squares linear regression is the most simple active machine learning algorithm. I say active as it engages in some learning, whereas predicting the sample mean is technically *simplier*, but is learning very little if anything. The loss that determines the coefficients of the regressors is a squared-error loss. On the other hand, if your prediction loss function (or score function, which is the negative loss) is not a squared-error, like AUC, ROC, precision, etc., your least-squares line will not be optimal for the prediction loss function. This can lead to prediction results that are suboptimal. \n", - "\n", - "Finding Bayes actions is equilivant to finding parameters that optimize *not parameter accuracy* but an arbitrary performance measure, however we wish to define performance (loss functions, AUC, ROC, precision/recall etc.).\n", - "\n", - "The next two examples demonstrate these ideas. The first example is a linear model where we can choose to predict using the least-squares loss or a novel, outcome-sensitive loss. \n", - "\n", - "The second example is adapted from a Kaggle data science project. The loss function associated with our predictions is incredibly complicated. \n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "____\n", - "### Example: Financial prediction\n", - "\n", - "Suppose the return of a stock price is very small, say 0.01. We would still much rather have our prediction on the *right side of the return*, that is, to get the sign of the return correct. A squared-error loss is agnogstic to the signage, and would penalize a prediction of -0.01 equally as a prediction of 0.03, but this difference could result in loss or profit respectively. \n", - "\n", - "Let's define the following loss:" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "figsize(9, 3.5)\n", - "def stock_loss( true_return, yhat, alpha = 100. ):\n", - " if true_return*yhat < 0:\n", - " #opposite signs, not good\n", - " return alpha*yhat**2 - sign( true_return )*yhat \\\n", - " + abs( true_return ) \n", - " else:\n", - " return abs( true_return - yhat )\n", - " \n", - " \n", - "true_value = .05\n", - "pred = np.linspace( -.04, .12, 75 )\n", - "\n", - "plt.plot( pred, [stock_loss( true_value, _p) for _p in pred], \\\n", - " label = \"Loss associated with\\n prediction if true value = 0.05\") \n", - "plt.vlines( 0, 0, .25, linestyles=\"--\" )\n", - "\n", - "plt.xlabel( \"prediction\")\n", - "plt.ylabel( \"loss\" )\n", - "plt.xlim( -0.04, .12 )\n", - "plt.ylim( 0, 0.25)\n", - "\n", - "true_value = -.02\n", - "plt.plot( pred, [stock_loss( true_value, _p) for _p in pred], alpha = 0.6, \\\n", - " label = \"Loss associated with\\n prediction if true value = -0.02\") \n", - "plt.legend()\n", - "plt.title(\"Stock returns loss if true value = 0.05, -0.02\" )" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 27, - "text": [ - "" - ] - }, - { - "output_type": "display_data", - "png": 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4uODq6sqlS5eeqb5M1VAvDZvm+lp0d2iKAILP36lpcaqchhprAbLuDZEive9H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- } - ], - "prompt_number": 27 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Note the change in the shape of the loss as the prediction crosses zero. This loss reflects that the user really does not want to guess the wrong sign, especially be wrong *and* a large magnitude. \n", - "\n", - "Why would the user care about the magnitude? Why is the loss not 0 for predicting the correct sign? Surely, if the return is 0.01 and we bet millions we will still be (very) happy.\n", - "\n", - "Financial institutions treat downside risk, as in predicting a lot on the wrong side, and upside risk, as in predicting a lot on the right side, similarly. Both are seen as risky behaviour and discouraged. Hence why we have an increasing loss as we move further away from the true price. (With less extreme loss in the direction of the correct sign.)\n", - "\n", - "We will perform a regression on a trading signal that we believe predicts future returns well. Our dataset is artificial, as most financial data is not even close to linear. Below, we plot the data along with the least-squares line." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "## Code to create artificial data\n", - "N = 100\n", - "X = 0.025*np.random.randn(N )\n", - "Y = 0.5*X + 0.01*np.random.randn( N) \n", - "\n", - "ls_coef_ = np.cov( X, Y )[0,1]/np.var(X)\n", - "ls_intercept = Y.mean() - ls_coef_*X.mean()\n", - "\n", - "plt.scatter( X, Y)\n", - "plt.xlabel(\"trading signal\")\n", - "plt.ylabel(\"returns\")\n", - "plt.title( \"Empirical returns vs trading signal\" )\n", - "plt.plot( X, ls_coef_*X + ls_intercept, label = \"Least-squares line\")\n", - "plt.xlim( X.min(), X.max())\n", - "plt.ylim( Y.min(), Y.max() )\n", - "plt.legend( loc=\"upper left\" )" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 14, - "text": [ - "" - ] - }, - { - "output_type": "display_data", - "png": 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9tbz7br6ls1RlZGRI9OnjyoULumaOsWNzeeedAiNHCabWpImWJk0KjabLyYEN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\nxGmHmZxxavaNlh/MYfXh2jdxTEzoiYer3B5paiYTHDzowt6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- } - ], - "prompt_number": 14 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We perform a simple Bayesian linear regression on this dataset. We look for a model like:\n", - "\n", - "$$ R = \\alpha + \\beta x + \\epsilon$$\n", - "\n", - "where $\\alpha, \\beta$ are our unknown parameters and $\\epsilon \\sim \\text{Normal}(0, 1/\\tau)$. The most common priors on $\\beta$ and $\\alpha$ are Normal priors. We will also assign a prior on $\\tau$, so that $\\sigma = 1/\\sqrt{\\tau}$ is uniform over 0 to 100 (equivilantly then $\\tau = 1/\\text{Uniform}(0, 100)^2$)." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "import pymc as mc\n", - "from pymc.Matplot import plot as mcplot\n", - "\n", - "std = mc.Uniform( \"std\", 0, 100, trace = False ) #this needs to be explained.\n", - "\n", - "@mc.deterministic\n", - "def prec( U = std ):\n", - " return 1.0/( U )**2\n", - "\n", - "beta = mc.Normal( \"beta\", 0, 0.0001 )\n", - "alpha = mc.Normal( \"alpha\", 0, 0.0001 )\n", - "\n", - "@mc.deterministic\n", - "def mean( X = X, alpha = alpha, beta = beta ):\n", - " return alpha + beta*X\n", - " \n", - "obs = mc.Normal( \"obs\", mean, prec, value = Y, observed = True)\n", - "model = mc.Model( {\"obs\":obs, \"beta_0\":beta, \"alpha_0\":alpha, \"prec\":prec} )\n", - "mcmc = mc.MCMC( model )\n", - "\n", - "mcmc.sample( 100000, 80000)\n", - "mcplot( mcmc )\n", - "\n" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - " \r", - "[****************100%******************] 100000 of 100000 complete" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "Plotting alpha\n", - "Plotting" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - " beta\n", - "Plotting" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - " prec\n", - "\n" - ] - }, - { - "output_type": "display_data", - "png": 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nSdBe1t7/t4cPN4vaO+uVr29Sojh1lMrrtZr6Pxz5jsrsMtWyUqm3f006jchY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GnCnRT8NzgiEFg/KlmdyDg1fuMDoQ7Tmk/nlWjLqmVqw9chNX/9Tf+WJizS83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L14y9lO//UMY53dmjpipasOTycrJtVWbjcS2c1FnDNbvoKtcbXJdk4o2sYmZ+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\nefhENW2EEELMRjVVwqLtL250pY1xYuqrN0Q1bYRYHmvHFms1ZKzlYW1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- } - ], - "prompt_number": 15 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "It appears the MCMC has converged so we may continue.\n", - "\n", - "For a specific trading signal, call it $x$, the distribution of possible returns has the form:\n", - "\n", - "$$R_i(x) = \\alpha_i + \\beta_ix + \\epsilon $$\n", - "\n", - "where $\\epsilon \\sim \\text{Normal}(0, 1/\\tau_i) $ and $i$ indexes our posterior samples. We wish to find the solution to \n", - "\n", - "$$ \\arg \\min_{r} \\;\\;E_{R(x)}\\left[ \\; L(R(x), r) \\; \\right] $$\n", - "\n", - "according to the loss given above. This $r$ is our Bayes action for trading signal $x$. Below we plot the Bayes action over different trading signals. What do you notice?\n" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "from scipy.optimize import fmin\n", - "\n", - "\n", - "def stock_loss( price, pred, coef = 500):\n", - " \"\"\"vectorized for numpy\"\"\"\n", - " sol = np.zeros_like(price)\n", - " ix = price*pred < 0 \n", - " sol[ix] = coef*pred**2 - sign(price[ix])*pred + abs(price[ix])\n", - " sol[ ~ix ] = abs( price[~ix] - pred )\n", - " return sol\n", - "\n", - "\n", - "\n", - "tau_samples = mcmc.trace( \"prec\" )[:]\n", - "alpha_samples = mcmc.trace( \"alpha\" )[:]\n", - "beta_samples = mcmc.trace( \"beta\" )[:]\n", - "\n", - "N = tau_samples.shape[0]\n", - "\n", - "noise = 1./np.sqrt(tau_samples)*np.random.randn(N) \n", - "\n", - "possible_outcomes = lambda signal: alpha_samples + beta_samples*signal \\\n", - " + noise\n", - "\n", - " \n", - "opt_predictions = np.zeros(50)\n", - "trading_signals = np.linspace( X.min(), X.max(), 50 )\n", - "for i, _signal in enumerate( trading_signals ):\n", - " _possible_outcomes = possible_outcomes( _signal )\n", - " tomin = lambda pred: stock_loss( _possible_outcomes, pred).mean()\n", - " opt_predictions[i] = fmin( tomin, 0, disp = False )\n", - " \n", - " \n", - "figsize( 9, 6 )\n", - "plt.xlabel(\"trading signal\")\n", - "plt.ylabel(\"prediction\")\n", - "plt.title( \"Least-squares prediction vs. Bayes action prediction\" )\n", - "plt.plot( X, ls_coef_*X + ls_intercept, label =\"Least-squares prediction\")\n", - "plt.xlim( X.min(), X.max())\n", - "plot( trading_signals, opt_predictions, label =\"Bayes action prediction\")\n", - "plt.legend( loc=\"upper left\" )" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 16, - "text": [ - "" - ] - }, - { - "output_type": "display_data", - "png": 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Ki4vrvR9CCJEF79B4scLmuH/HehU2APXcvFVD9rY0JBMTE2zcuBEeHh5i74WH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- } - ], - "prompt_number": 16 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "What is interesting about the above graph is that when the signal is near 0, and many of the possible returns outcomes are possibly both positive and negative, our best (with respect to our loss) prediction is to predict close to 0, hence *take on no position*. Only when we are very confident do we enter into a position. I call this style of model a *sparse prediction*, where we feel uncomfortable with our uncertainity so choose not to act. (Compare with the least-squares prediction which will rarely, if ever, predict zero). \n", - "\n", - "A good sanity check that our model is still reasonable: as the signal becomes more and more extreme, and we feel more and more confident about the positive/negativeness of returns, our position converges with that of the least-squares line. \n", - "\n", - "The sparse-prediction model is not trying to *fit* the data the best (according to a *squared-error loss* definition of *fit*). That honour would go to the least-squares model. The sparse-prediction model is trying to find the best prediction *with respect to our `stock_loss`-defined loss*. We can turn this reasoning around: the least-squares model is not try to *predict* the best (according to a *`stock-loss`* definition of *predict*). That honour would go the *sparse prediction* model. The least-squares model is trying to find the best fit of the data *with respect to the squared-error loss*.\n", - "\n", - "\n", - "-------\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Example: Kaggle contest on *Observing Dark World*\n", - "\n", - "A personal motivation for learning Bayesian methods was trying to piece together the winning solution to Kaggle's [*Observing Dark Worlds*](http://www.kaggle.com/c/DarkWorlds) contest. From the contest's website:\n", - "\n", - "\n", - "\n", - ">There is more to the Universe than meets the eye. Out in the cosmos exists a form of matter that outnumbers the stuff we can see by almost 7 to 1, and we don\u2019t know what it is. What we do know is that it does not emit or absorb light, so we call it Dark Matter. Such a vast amount of aggregated matter does not go unnoticed. In fact we observe that this stuff aggregates and forms massive structures called Dark Matter Halos. Although dark, it warps and bends spacetime such that any light from a background galaxy which passes close to the Dark Matter will have its path altered and changed. This bending causes the galaxy to appear as an ellipse in the sky.\n", - "\n", - "\n", - "\n", - "\n", - "The contest required predictions about where dark matter was likely to be. The winner, [Tim Salimans](http://timsalimans.com/), used Bayesian inference to find the best locations for the halos (interestingly, the second-place winner also used Bayesian inference). With Tim's permisson, we provided his solution [1] here:\n", - "\n", - "1. Construct a prior distribution for the halo positions $p(x)$, i.e. formulate our expectations about the halo positions before looking at the data.\n", - "2. Construct a probabilistic model for the data (observed ellipticities of the galaxies) given the positions of the dark matter halos: $p(e | x)$.\n", - "3. Use Bayes\u2019 rule to get the posterior distribution of the halo positions, i.e. use to the data to guess where the dark matter halos might be.\n", - "4. Minimize the expected loss with respect to the posterior distribution over the predictions for the halo positions: $ \\hat{x} = \\arg \\min_{\\text{prediction} } E_{p(x|e)}[ L( \\text{prediction}, x) ]$ , i.e. tune our predictions to be as good as possible for the given error metric.\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The loss function in this problem is very complicated. For the very determined, the loss function is contained in the file DarkWorldsMetric.py in parent folder. Though I suggest not reading it all, suffice to say the loss function is about 160 lines of code -- not something that can be written down in a single mathematical line. The loss function attemps to measure the accuracy of prediction, in a Euclidean distance sense, and that no shift-bias is present. More details can be found on the metric's [main page](http://www.kaggle.com/c/DarkWorlds/details/evaluation). \n", - "\n", - "We will attempt to implement Tim's winning solution using PyMC and our knowledge of loss functions." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### The Data\n", - "\n", - "The dataset is actually 300 seperate files, each representing a sky. In each file, or sky, are between 300 and 720 galaxies. Each galaxy has an $x$ and $y$ position associated with it, ranging from 0 to 4200, and measures of ellipticity: $e_1$ and $e_2$. Information about what these measures mean can be found [here](https://www.kaggle.com/c/DarkWorlds/details/an-introduction-to-ellipticity), but for our purposes it does not matter besides for visualization purposes. Thus a typical sky might look like the following:" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "from draw_sky2 import draw_sky\n", - "\n", - "n_sky = 6 #choose a file/sky to examine.\n", - "data = np.genfromtxt( \"data/Train_Skies/Train_Skies/\\\n", - "Training_Sky%d.csv\"%(n_sky),\n", - " dtype = None,\n", - " skip_header = 1,\n", - " delimiter = \",\",\n", - " usecols = [1,2,3,4])\n", - "print \"Data on galaxies in sky %d.\"%n_sky\n", - "print \"position_x, position_y, e_1, e_2 \"\n", - "print data[:3]\n", - "\n", - "fig = draw_sky( data )\n", - "plt.title(\"Galaxy positions and ellipcities of sky %d.\"%n_sky)\n", - "plt.xlabel( \"x-position\")\n", - "plt.ylabel( \"y-position\" )\n" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "Data on galaxies in sky 6.\n", - "position_x, position_y, e_1, e_2 " - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "[[ 1.04090000e+02 2.17845000e+03 -1.30533000e-01 3.34760000e-02]\n", - " [ 1.07497000e+03 1.39099000e+03 -2.07479000e-01 -2.01728000e-01]\n", - " [ 1.93625000e+03 3.46154000e+03 -7.21930000e-02 2.18193000e-01]]" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n" - ] - }, - { - "output_type": "pyout", - "prompt_number": 166, - "text": [ - "" - ] - }, - { - "output_type": "display_data", - "png": 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CmDFqBAVlok6dsplUJBJm0vLyyl+IARjNy6BBWhw7lomgIAUCAgr21YmIiCh2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elUry5hNvv/ulSyhGkUhKpVZZYutbN3r9KTIzZWpmJFiXr1SofXJT+aN/fy3HMOpk83YTbLvPlm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4vLPlPiJz+LDI7t3qbfjnP+N45RWoWdNNSkqIL6wYyHmagp57Ljsksy7KO06nGkq3WKLDqYhR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\nZ46Z334zcPKkNtLgp07lrM0q7U11Ue164oTa8TdokJW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- } - ], - "prompt_number": 166 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Priors\n", - "\n", - "Each sky has one, two or three dark matter halos in it. Tim's solution details that his prior distribution of halo positions was uniform, i.e.\n", - "\n", - "\\begin{align*}\n", - "& x_i \\sim \\text{Uniform}( 0, 4200)\\\\\\\\\n", - "& y_i \\sim \\text{Uniform}( 0, 4200), \\;\\; i=1,2,3\\\\\\\\\n", - "\\end{align*}\n", - "\n", - "Tim and other competitors noted that most skies had one large halo and other halos, if present, were much smaller. Larger halos, having more mass, will influence the surrounding galaxies more. He decided that the large halos would have a mass distributed as a log-uniform random variable between 40 and 180 i.e.\n", - "\n", - "$$ \\exp( m_{\\text{large} } )= \\text{Uniform}( 40, 180 ) $$\n", - "\n", - "and in PyMC, \n", - "\n", - " exp_mass_large = mc.Uniform( \"exp_mass_large\", 40, 180)\n", - " @mc.deterministic\n", - " def mass_large(u = exp_mass_large):\n", - " return np.log( u )\n", - "\n", - "(This is what we mean when we say *log*-uniform.) For smaller galaxies, Tim set the mass to be 20. \n", - "\n", - "Tim logically assumed that the ellipicity of each galaxy is dependent on the position of the halos, the distance between the galaxy and halo, and the mass of the halos. Thus the vector of ellipticy of each galaxy, $\\mathbf{e}_i$, are *children* variables of the vector of halo positions $(\\mathbf{x},\\mathbf{y})$, distance (which we will formalize), and halo masses.\n", - "\n", - "Tim conceived a relationship to connect positions and ellipicity by reading literature and forum posts. He supposed the following was a reasonable relationship:\n", - "\n", - "$$ e_i | ( \\mathbf{x}, \\mathbf{y} ) \\sim \\text{Normal}( \\sum_{j = \\text{halo positions} }d_{i,j} m_j f( r_{i,j} ), \\sigma^2 ) $$\n", - "\n", - "where $d_{i,j}$ is the *tangential direction* (the direction in which halo $j$ bends the light of galaxy $i$), $m_j$ is the mass of halo $j$, $f(r_{i,j})$ is a *decreasing function* of the Euclidean distance between halo $j$ and galaxy $i$. \n", - "\n", - "Tim's function $f$ was defined:\n", - "\n", - "$$ f( r_{i,j} ) = \\frac{1}{\\min( r_{i,j}, 240 ) } $$\n", - "\n", - "for large halos, and for small halos\n", - "\n", - "$$ f( r_{i,j} ) = \\frac{1}{\\min( r_{i,j}, 70 ) } $$\n", - "\n", - "This fully bridges our observations and unknown. This model is incredibly simple, and Tim mentions this simplicty was purposefully designed: it prevents the model from overfitting. \n", - "\n", - "\n", - "### Training & PyMC implementation\n", - "\n", - "For each sky, we run our Bayesian model to find the posteriors for the halo positions -- we ignore the (known) halo position. This is slightly different than perhaps traditional approaches to Kaggle competitions, where this model uses no data from other skies nor the known halo location. That does not mean other data are not necessary -- infact, the model was created by comparing different skies. \n" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "def euclidean_distance( x, y):\n", - " return np.sqrt( ( ( x - y )**2).sum(axis=1) ) \n", - "\n", - "def f_distance( gxy_pos, halo_pos, c):\n", - " # foo_position should be a 2-d numpy array\n", - " return np.minimum(euclidean_distance( gxy_pos, halo_pos), c)[:,None]\n", - "\n", - "def tangential_distance(glxy_position, halo_position):\n", - " # foo_position should be a 2-d numpy array\n", - " delta = glxy_position - halo_position\n", - " t = (2*np.arctan( delta[:,1]/delta[:,0] ))[:,None]\n", - " return np.concatenate( [-np.cos( t), -np.sin(t) ], axis=1)\n", - "\n", - "import pymc as mc\n", - "\n", - "#set the size of the halo's mass\n", - "exp_mass_large = mc.Uniform( \"exp_mass_large\", 40, 180, trace = False)\n", - "@mc.deterministic\n", - "def mass_large(u = exp_mass_large):\n", - " return np.log( u )\n", - "\n", - "\n", - "#set the initial prior position of the halos, it's a 2-d Uniform dist.\n", - "halo_position = mc.Uniform( \"halo_position\", 0, 4200, size = (1,2) )\n", - "\n", - "\n", - "@mc.deterministic\n", - "def mean(mass=exp_mass_large, h_pos = halo_position, glx_pos=data[:,:2]):\n", - " return mass/f_distance( glx_pos, h_pos, 240)*\\\n", - " tangential_distance( glx_pos, h_pos )\n", - " \n", - "\n" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 168 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "ellpty = mc.Normal( \"ellipcity\", mean, 1./0.05, observed = True,\n", - " value = data[:,2:] )\n", - "model = mc.Model( [ellpty, mean, halo_position, exp_mass_large,\n", - " mass_large] )\n", - "mcmc = mc.MCMC( model )\n", - "map_ = mc.MAP( model )\n", - "map_.fit()\n", - "mcmc.sample(100000, 40000, 3)\n" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - " \r", - "[****************100%******************] 100000 of 100000 complete" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n" - ] - } - ], - "prompt_number": 169 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Below we plot a \"heatmap\" of the posterior distribution. (Which is just a scatter plot of the posterior, but we can visualize it as a heatmap.)" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "t = mcmc.trace(\"halo_position\")[:].reshape( 20000,2)\n", - "\n", - "\n", - "fig = draw_sky( data )\n", - "plt.title(\"Galaxy positions and ellipcities of sky %d.\"%n_sky)\n", - "plt.xlabel( \"x-position\")\n", - "plt.ylabel( \"y-position\" )\n", - "scatter( t[:,0], t[:,1], alpha = 0.002, c = \"r\")\n", - "plt.xlim( 0, 4200 )\n", - "plt.ylim(0, 4200 )" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 173, - "text": [ - "(0, 4200)" - ] - }, - { - "output_type": "display_data", - "png": 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hQx2++y4VDRvqYGNTio0rBp48oXHnDg+JiTTq1NGjXDkCFxcGFSpw40FGLl/m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- } - ], - "prompt_number": 173 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The most probable position reveals itself like a lethal wound.\n", - "\n", - "Associated with each sky is another data point, located in `./data/Training_halos.csv` that holds the locations of up to three dark matter halos contained in the sky. For example, the night sky we trained on has halo locations:" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "halo_data = np.genfromtxt( \"data/Training_halos.csv\", \n", - " dtype = None,\n", - " skip_header = 1,\n", - " delimiter = \",\")\n", - "print halo_data[n_sky-1]" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "('Sky6', 1, 4129.17, 3097.58, 4129.17, 3097.58, 0.0, 0.0, 0.0, 0.0)\n" - ] - } - ], - "prompt_number": 182 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The third and fouth column represent the true $x$ and $y$ position of the halo. It appears that the Bayesian method has located the halo within a tight vicinity. " - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "fig = draw_sky( data )\n", - "plt.title(\"Galaxy positions and ellipcities of sky %d.\"%n_sky)\n", - "plt.xlabel( \"x-position\")\n", - "plt.ylabel( \"y-position\" )\n", - "plt.scatter( t[:,0], t[:,1], alpha = 0.002, c = \"r\")\n", - "plt.scatter( halo_data[n_sky-1][2], halo_data[n_sky-1][3], \n", - " label = \"True halo position\",\n", - " c = \"k\", s = 70)\n", - "plt.legend(scatterpoints = 1, loc = \"lower left\")\n", - "plt.xlim( 0, 4200 )\n", - "plt.ylim(0, 4200 )" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 183, - "text": [ - "(0, 4200)" - ] - }, - { - "output_type": "display_data", - "png": 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hQx2++y4VDRvqYGNTio0rBp48oXHnDg+JiTTq1NGjXDkCFxcGFSpw40FGLl/m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QGgzHeSe/fKTJgQS+4w0+rV/UK5dWuF5cuzmDPHxvff23j55ZxSVVkEk6q0P1clW6Fi7D1wQIRX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\ntWvdluXLjbRvX/h88kWJy5cF2rXLqIsk31MtWgTvqW3b5NpIGS4NtVri0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GTcKa8kQWrq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\nRa+Lh4eH5ZrYgpAQmc8+y+Hdd3PRaMwv8StXFPj4mB9OTz2lpVs3HcuX53D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- } - ], - "prompt_number": 183 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Our next step is to use the loss function to optimize our location. A naive strategy would be to simply choose the mean:" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "mean_posterior = t.mean(axis=0).reshape(1,2)\n", - "print mean_posterior" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "[[ 4117.08204365 3010.6052508 ]]\n" - ] - } - ], - "prompt_number": 180 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "from DarkWorldsMetric import main_score\n", - "\n", - "_halo_data = halo_data[n_sky-1]\n", - "\n", - "nhalo_all = np.array( [_halo_data[1]] )\n", - "x_true_all = np.array( [[_halo_data[2]]])\n", - "y_true_all= np.array( [[_halo_data[3] ]])\n", - "x_ref_all= np.array( [_halo_data[4] ])\n", - "y_ref_all= np.array( [_halo_data[5] ] )\n", - "sky_prediction = mean_posterior\n", - "\n", - "print \"Using the mean:\"\n", - "main_score( nhalo_all, x_true_all, y_true_all, \\\n", - " x_ref_all, y_ref_all, sky_prediction)\n", - "\n", - "#what's a bad score?\n", - "print\n", - "random_guess = np.random.randint( 0, 4200, size=(1,2) )\n", - "print \"Using a random location:\", random_guess\n", - "main_score( nhalo_all, x_true_all, y_true_all, \\\n", - " x_ref_all, y_ref_all, random_guess )\n", - "print" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "Using the mean:\n", - "Your score for the training data is" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - " 1.08781073788\n", - "\n" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "Using a random location: [[2427 3957]]\n", - "Your score for the training data is" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - " 2.90682601338\n", - "\n" - ] - } - ], - "prompt_number": 181 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This is a good guess, it is not very far from the true location, but it ignores the loss function that was provided to us. " - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [], - "language": "python", - "metadata": {}, - "outputs": [] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [], - "language": "python", - "metadata": {}, - "outputs": [] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [], - "language": "python", - "metadata": {}, - "outputs": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## References\n", - "\n", - "1. [Tim Saliman's solution to the Dark World's Contest](http://www.timsalimans.com/observing-dark-worlds)" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [], - "language": "python", - "metadata": {}, - "outputs": [] - } - ], - "metadata": {} - } - ] -} \ No newline at end of file diff --git a/Chapter5_LossFunctions/Ch5_LossFunctions_PyMC2.ipynb b/Chapter5_LossFunctions/Ch5_LossFunctions_PyMC2.ipynb new file mode 100644 index 00000000..1a8e87d3 --- /dev/null +++ b/Chapter5_LossFunctions/Ch5_LossFunctions_PyMC2.ipynb @@ -0,0 +1,1481 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Chapter 5\n", + "____\n", + "### Would you rather lose an arm or a leg?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Statisticians can be a sour bunch. Instead of considering their winnings, they only measure how much they have lost. In fact, they consider their wins as *negative losses*. But what's interesting is *how they measure their losses.*\n", + "\n", + "For example, consider the following example:\n", + "\n", + "> A meteorologist is predicting the probability of a possible hurricane striking his city. He estimates, with 95% confidence, that the probability of it *not* striking is between 99% - 100%. He is very happy with his precision and advises the city that a major evacuation is unnecessary. Unfortunately, the hurricane does strike and the city is flooded. \n", + "\n", + "This stylized example shows the flaw in using a pure accuracy metric to measure outcomes. Using a measure that emphasizes estimation accuracy, while an appealing and *objective* thing to do, misses the point of why you are even performing the statistical inference in the first place: results of inference. The author Nassim Taleb of *The Black Swan* and *Antifragility* stresses the importance of the *payoffs* of decisions, *not the accuracy*. Taleb distills this quite succinctly: \"I would rather be vaguely right than very wrong.\" " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Loss Functions\n", + "\n", + "We introduce what statisticians and decision theorists call *loss functions*. A loss function is a function of the true parameter, and an estimate of that parameter\n", + "\n", + "$$L( \\theta, \\hat{\\theta} ) = f( \\theta, \\hat{\\theta} )$$\n", + "\n", + "The important point of loss functions is that it measures how *bad* our current estimate is: the larger the loss, the worse the estimate is according to the loss function. A simple, and very common, example of a loss function is the *squared-error loss*:\n", + "\n", + "$$L( \\theta, \\hat{\\theta} ) = ( \\theta - \\hat{\\theta} )^2$$\n", + "\n", + "The squared-error loss function is used in estimators like linear regression, UMVUEs and many areas of machine learning. We can also consider an asymmetric squared-error loss function, something like:\n", + "\n", + "$$L( \\theta, \\hat{\\theta} ) = \\begin{cases} ( \\theta - \\hat{\\theta} )^2 & \\hat{\\theta} \\lt \\theta \\\\\\\\ c( \\theta - \\hat{\\theta} )^2 & \\hat{\\theta} \\ge \\theta, \\;\\; 0\\lt c \\lt 1 \\end{cases}$$\n", + "\n", + "\n", + "which represents that estimating a value larger than the true estimate is preferable to estimating a value below. A situation where this might be useful is in estimating web traffic for the next month, where an over-estimated outlook is preferred so as to avoid an underallocation of server resources. \n", + "\n", + "A negative property about the squared-error loss is that it puts a disproportionate emphasis on large outliers. This is because the loss increases quadratically, and not linearly, as the estimate moves away. That is, the penalty of being three units away is much less than being five units away, but the penalty is not much greater than being one unit away, though in both cases the magnitude of difference is the same:\n", + "\n", + "$$\\frac{1^2}{3^2} \\lt \\frac{3^2}{5^2}, \\;\\; \\text{although} \\;\\; 3-1 = 5-3$$\n", + "\n", + "This loss function imposes that large errors are *very* bad. A more *robust* loss function that increases linearly with the difference is the *absolute-loss*\n", + "\n", + "$$L( \\theta, \\hat{\\theta} ) = | \\theta - \\hat{\\theta} |$$\n", + "\n", + "Other popular loss functions include:\n", + "\n", + "- $L( \\theta, \\hat{\\theta} ) = \\mathbb{1}_{ \\hat{\\theta} \\neq \\theta }$ is the zero-one loss often used in machine learning classification algorithms.\n", + "- $L( \\theta, \\hat{\\theta} ) = -\\theta\\log( \\hat{\\theta} ) - (1-\\theta)\\log( 1 - \\hat{\\theta} ), \\; \\; \\theta \\in {0,1}, \\; \\hat{\\theta} \\in [0,1]$, called the *log-loss*, also used in machine learning. \n", + "\n", + "Historically, loss functions have been motivated from 1) mathematical convenience, and 2) they are robust to application, i.e., they are objective measures of loss. The first reason has really held back the full breadth of loss functions. With computers being agnostic to mathematical convenience, we are free to design our own loss functions, which we take full advantage of later in this Chapter.\n", + "\n", + "With respect to the second point, the above loss functions are indeed objective, in that they are most often a function of the difference between estimate and true parameter, independent of signage or payoff of choosing that estimate. This last point, its independence of payoff, causes quite pathological results though. Consider our hurricane example above: the statistician equivalently predicted that the probability of the hurricane striking was between 0% to 1%. But if he had ignored being precise and instead focused on outcomes (99% chance of no flood, 1% chance of flood), he might have advised differently. \n", + "\n", + "By shifting our focus from trying to be incredibly precise about parameter estimation to focusing on the outcomes of our parameter estimation, we can customize our estimates to be optimized for our application. This requires us to design new loss functions that reflect our goals and outcomes. Some examples of more interesting loss functions:\n", + "\n", + "\n", + "- $L( \\theta, \\hat{\\theta} ) = \\frac{ | \\theta - \\hat{\\theta} | }{ \\theta(1-\\theta) }, \\; \\; \\hat{\\theta}, \\theta \\in [0,1]$ emphasizes an estimate closer to 0 or 1 since if the true value $\\theta$ is near 0 or 1, the loss will be *very* large unless $\\hat{\\theta}$ is similarly close to 0 or 1. \n", + "This loss function might be used by a political pundit whose job requires him or her to give confident \"Yes/No\" answers. This loss reflects that if the true parameter is close to 1 (for example, if a political outcome is very likely to occur), he or she would want to strongly agree as to not look like a skeptic. \n", + "\n", + "- $L( \\theta, \\hat{\\theta} ) = 1 - \\exp \\left( -(\\theta - \\hat{\\theta} )^2 \\right)$ is bounded between 0 and 1 and reflects that the user is indifferent to sufficiently-far-away estimates. It is similar to the zero-one loss above, but not quite as penalizing to estimates that are close to the true parameter. \n", + "- Complicated non-linear loss functions can programmed: \n", + "\n", + " def loss(true_value, estimate):\n", + " if estimate*true_value > 0:\n", + " return abs(estimate - true_value)\n", + " else:\n", + " return abs(estimate)*(estimate - true_value)**2\n", + " \n", + "\n", + "\n", + "- Another example is from the book *The Signal and The Noise*. Weather forecasters have a interesting loss function for their predictions. [2]\n", + "\n", + "\n", + "> People notice one type of mistake — the failure to predict rain — more than other, false alarms. If it rains when it isn't supposed to, they curse the weatherman for ruining their picnic, whereas an unexpectedly sunny day is taken as a serendipitous bonus.\n", + "\n", + "> [The Weather Channel's bias] is limited to slightly exaggerating the probability of rain when it is unlikely to occur — saying there is a 20 percent change when they know it is really a 5 or 10 percent chance — covering their butts in the case of an unexpected sprinkle.\n", + "\n", + "\n", + "As you can see, loss functions can be used for good and evil: with great power, comes great — well you know.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Loss functions in the real world\n", + "\n", + "So far we have been under the unrealistic assumption that we know the true parameter. Of course if we knew the true parameter, bothering to guess an estimate is pointless. Hence a loss function is really only practical when the true parameter is unknown. \n", + "\n", + "In Bayesian inference, we have a mindset that the unknown parameters are really random variables with prior and posterior distributions. Concerning the posterior distribution, a value drawn from it is a *possible* realization of what the true parameter could be. Given that realization, we can compute a loss associated with an estimate. As we have a whole distribution of what the unknown parameter could be (the posterior), we should be more interested in computing the *expected loss* given an estimate. This expected loss is a better estimate of the true loss than comparing the given loss from only a single sample from the posterior.\n", + "\n", + "First it will be useful to explain a *Bayesian point estimate*. The systems and machinery present in the modern world are not built to accept posterior distributions as input. It is also rude to hand someone over a distribution when all they asked for was an estimate. In the course of an individual's day, when faced with uncertainty we still act by distilling our uncertainty down to a single action. Similarly, we need to distill our posterior distribution down to a single value (or vector in the multivariate case). If the value is chosen intelligently, we can avoid the flaw of frequentist methodologies that mask the uncertainty and provide a more informative result.The value chosen, if from a Bayesian posterior, is a Bayesian point estimate. \n", + "\n", + "Suppose $P(\\theta | X)$ is the posterior distribution of $\\theta$ after observing data $X$, then the following function is understandable as the *expected loss of choosing estimate $\\hat{\\theta}$ to estimate $\\theta$*:\n", + "\n", + "$$ l(\\hat{\\theta} ) = E_{\\theta}\\left[ \\; L(\\theta, \\hat{\\theta}) \\; \\right] $$\n", + "\n", + "This is also known as the *risk* of estimate $\\hat{\\theta}$. The subscript $\\theta$ under the expectation symbol is used to denote that $\\theta$ is the unknown (random) variable in the expectation, something that at first can be difficult to consider.\n", + "\n", + "We spent all of last chapter discussing how to approximate expected values. Given $N$ samples $\\theta_i,\\; i=1,...,N$ from the posterior distribution, and a loss function $L$, we can approximate the expected loss of using estimate $\\hat{\\theta}$ by the Law of Large Numbers:\n", + "\n", + "$$\\frac{1}{N} \\sum_{i=1}^N \\;L(\\theta_i, \\hat{\\theta} ) \\approx E_{\\theta}\\left[ \\; L(\\theta, \\hat{\\theta}) \\; \\right] = l(\\hat{\\theta} ) $$\n", + "\n", + "Notice that measuring your loss via an *expected value* uses more information from the distribution than the MAP estimate which, if you recall, will only find the maximum value of the distribution and ignore the shape of the distribution. Ignoring information can over-expose yourself to tail risks, like the unlikely hurricane, and leaves your estimate ignorant of how ignorant you really are about the parameter.\n", + "\n", + "Similarly, compare this with frequentist methods, that traditionally only aim to minimize the error, and do not consider the *loss associated with the result of that error*. Compound this with the fact that frequentist methods are almost guaranteed to never be absolutely accurate. Bayesian point estimates fix this by planning ahead: your estimate is going to be wrong, you might as well err on the right side of wrong." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Example: Optimizing for the *Showcase* on *The Price is Right*\n", + "\n", + "Bless you if you are ever chosen as a contestant on the Price is Right, for here we will show you how to optimize your final price on the *Showcase*. For those who forget the rules:\n", + "\n", + "\n", + "1. Two contestants compete in *The Showcase*. \n", + "2. Each contestant is shown a unique suite of prizes.\n", + "3. After the viewing, the contestants are asked to bid on the price for their unique suite of prizes.\n", + "4. If a bid price is over the actual price, the bid's owner is disqualified from winning.\n", + "5. If a bid price is under the true price by less than $250, the winner is awarded both prizes.\n", + "\n", + "The difficulty in the game is balancing your uncertainty in the prices, keeping your bid low enough so as to not bid over, and trying to bid close to the price.\n", + "\n", + "Suppose we have recorded the *Showcases* from previous *The Price is Right* episodes and have *prior* beliefs about what distribution the true price follows. For simplicity, suppose it follows a Normal:\n", + "\n", + "\n", + "$$\\text{True Price} \\sim \\text{Normal}(\\mu_p, \\sigma_p )$$\n", + "\n", + "\n", + "In a later chapter, we will actually use *real Price is Right Showcase data* to form the historical prior, but this requires some advanced PyMC use so we will not use it here. For now, we will assume $\\mu_p = 35 000$ and $\\sigma_p = 7500$.\n", + "\n", + "We need a model of how we should be playing the *Showcase*. For each prize in the prize suite, we have an idea of what it might cost, but this guess could differ significantly from the true price. (Couple this with increased pressure being onstage and you can see why some bids are so wildly off). Let's suppose your beliefs about the prices of prizes also follow Normal distributions:\n", + "\n", + "$$ \\text{Prize}_i \\sim \\text{Normal}(\\mu_i, \\sigma_i ),\\;\\; i=1,2$$\n", + "\n", + "This is really why Bayesian analysis is great: we can specify what we think a fair price is through the $\\mu_i$ parameter, and express uncertainty of our guess in the $\\sigma_i$ parameter. \n", + "\n", + "We'll assume two prizes per suite for brevity, but this can be extended to any number. \n", + "The true price of the prize suite is then given by $\\text{Prize}_1 + \\text{Prize}_2 + \\epsilon$, \n", + "where $\\epsilon$ is some error term.\n", + "\n", + "We are interested in the updated $\\text{True Price}$ given we have observed both prizes and have belief distributions about them. We can perform this using PyMC. \n", + "\n", + "Lets make some values concrete. Suppose there are two prizes in the observed prize suite: \n", + "\n", + "1. A trip to wonderful Toronto, Canada! \n", + "2. A lovely new snowblower!\n", + "\n", + "We have some guesses about the true prices of these objects, but we are also pretty uncertain about them. I can express this uncertainty through the parameters of the Normals:\n", + "\n", + "\n", + "\\begin{align}\n", + "& \\text{snowblower} \\sim \\text{Normal}(3 000, 500 )\\\\\\\\\n", + "& \\text{Toronto} \\sim \\text{Normal}(12 000, 3000 )\\\\\\\\\n", + "\\end{align}\n", + "\n", + "For example, I believe that the true price of the trip to Toronto is 12 000 dollars, and that there is a 68.2% chance the price falls 1 standard deviation away from this, i.e. my confidence is that there is a 68.2% chance the trip is in [9 000, 15 000].\n", + "\n", + "We can create some PyMC code to perform inference on the true price of the suite." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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lkz5MeyjNX/68jqZqL7n8uVw4v47Ino1A+X9ftaxlu5Y3b95MOBwGoK2tjbPP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XcDnwD2icgecpffvsTZbYr3WeBfABe5FUd+P53PRRXNt9G+ok7vb4F/ExE7cIzchpxWtL+oMYwx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\nffd1+zRMnyoX65w+Mcf46UU816dWdSkt1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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib inline\n", + "import scipy.stats as stats\n", + "from IPython.core.pylabtools import figsize\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "figsize(12.5, 9)\n", + "\n", + "norm_pdf = stats.norm.pdf\n", + "\n", + "plt.subplot(311)\n", + "x = np.linspace(0, 60000, 200)\n", + "sp1 = plt.fill_between(x, 0, norm_pdf(x, 35000, 7500),\n", + " color=\"#348ABD\", lw=3, alpha=0.6,\n", + " label=\"historical total prices\")\n", + "p1 = plt.Rectangle((0, 0), 1, 1, fc=sp1.get_facecolor()[0])\n", + "plt.legend([p1], [sp1.get_label()])\n", + "\n", + "plt.subplot(312)\n", + "x = np.linspace(0, 10000, 200)\n", + "sp2 = plt.fill_between(x, 0, norm_pdf(x, 3000, 500),\n", + " color=\"#A60628\", lw=3, alpha=0.6,\n", + " label=\"snowblower price guess\")\n", + "\n", + "p2 = plt.Rectangle((0, 0), 1, 1, fc=sp2.get_facecolor()[0])\n", + "plt.legend([p2], [sp2.get_label()])\n", + "\n", + "plt.subplot(313)\n", + "x = np.linspace(0, 25000, 200)\n", + "sp3 = plt.fill_between(x, 0, norm_pdf(x, 12000, 3000),\n", + " color=\"#7A68A6\", lw=3, alpha=0.6,\n", + " label=\"Trip price guess\")\n", + "plt.autoscale(tight=True)\n", + "p3 = plt.Rectangle((0, 0), 1, 1, fc=sp3.get_facecolor()[0])\n", + "plt.legend([p3], [sp3.get_label()]);" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " [-----------------100%-----------------] 50000 of 50000 complete in 8.0 sec" + ] + } + ], + "source": [ + "import pymc as pm\n", + "\n", + "data_mu = [3e3, 12e3]\n", + "\n", + "data_std = [5e2, 3e3]\n", + "\n", + "mu_prior = 35e3\n", + "std_prior = 75e2\n", + "\n", + "true_price = pm.Normal(\"true_price\", mu_prior, 1.0 / std_prior ** 2)\n", + "\n", + "\n", + "prize_1 = pm.Normal(\"first_prize\", data_mu[0], 1.0 / data_std[0] ** 2)\n", + "prize_2 = pm.Normal(\"second_prize\", data_mu[1], 1.0 / data_std[1] ** 2)\n", + "price_estimate = prize_1 + prize_2\n", + "\n", + "\n", + "@pm.potential\n", + "def error(true_price=true_price, price_estimate=price_estimate):\n", + " return pm.normal_like(true_price, price_estimate, 1 / (3e3) ** 2)\n", + "\n", + "\n", + "mcmc = pm.MCMC([true_price, prize_1, prize_2, price_estimate, error])\n", + "mcmc.sample(50000, 10000)\n", + "\n", + "price_trace = mcmc.trace(\"true_price\")[:]" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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X+7dd4ZzbE7StrXPuLOdcY+dcvHMu3Tn3nXOujnOus3Ouu3Ouh3Pu71VzGrzh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\n5WRQYIwxfmbBgLkdLS0tvP766zQ0NPDuu++iqgA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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "figsize(12.5, 4)\n", + "\n", + "import scipy.stats as stats\n", + "\n", + "x = np.linspace(5000, 40000)\n", + "plt.plot(x, stats.norm.pdf(x, 35000, 7500), c=\"k\", lw=2,\n", + " label=\"prior dist. of suite price\")\n", + "\n", + "_hist = plt.hist(price_trace, bins=35, density=True, histtype=\"stepfilled\")\n", + "plt.title(\"Posterior of the true price estimate\")\n", + "plt.vlines(mu_prior, 0, 1.1 * np.max(_hist[0]), label=\"prior's mean\",\n", + " linestyles=\"--\")\n", + "plt.vlines(price_trace.mean(), 0, 1.1 * np.max(_hist[0]),\n", + " label=\"posterior's mean\", linestyles=\"-.\")\n", + "plt.legend(loc=\"upper left\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notice that because of our two observed prizes and subsequent guesses (including uncertainty about those guesses), we shifted our mean price estimate down about $15 000 dollars from the previous mean price.\n", + "\n", + "A frequentist, seeing the two prizes and having the same beliefs about their prices, would bid $\\mu_1 + \\mu_2 = 35000$, regardless of any uncertainty. Meanwhile, the *naive Bayesian* would simply pick the mean of the posterior distribution. But we have more information about our eventual outcomes; we should incorporate this into our bid. We will use the loss function above to find the *best* bid (*best* according to our loss).\n", + "\n", + "What might a contestant's loss function look like? I would think it would look something like:\n", + "\n", + " def showcase_loss(guess, true_price, risk=80000):\n", + " if true_price < guess:\n", + " return risk\n", + " elif abs(true_price - guess) <= 250:\n", + " return -2*np.abs(true_price)\n", + " else:\n", + " return np.abs(true_price - guess - 250)\n", + "\n", + "where `risk` is a parameter that defines of how bad it is if your guess is over the true price. A lower `risk` means that you are more comfortable with the idea of going over. If we do bid under and the difference is less than $250, we receive both prizes (modeled here as receiving twice the original prize). Otherwise, when we bid under the `true_price` we want to be as close as possible, hence the `else` loss is a increasing function of the distance between the guess and true price." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For every possible bid, we calculate the *expected loss* associated with that bid. We vary the `risk` parameter to see how it affects our loss:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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zj3/8g1GjRpXY5rbbbqNnz5589tlnDB06lL/85S/Ex8cTExNDXFwc33zzTakB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XOIvOGFgEyGdz5l1UX87sMHKK7Mo3tyBj//wd8JDgpttuM1VVlxDZ8t2kvu8XIAouNCGDGhB70y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\nIEKsdRGJpa0kli4J18FSc/fqBZU0bYwhVfDoTxfpyxTpT5foz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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "figsize(12.5, 7)\n", + "# numpy friendly showdown_loss\n", + "\n", + "\n", + "def showdown_loss(guess, true_price, risk=80000):\n", + " loss = np.zeros_like(true_price)\n", + " ix = true_price < guess\n", + " loss[~ix] = np.abs(guess - true_price[~ix])\n", + " close_mask = [abs(true_price - guess) <= 250]\n", + " loss[close_mask] = -2 * true_price[close_mask]\n", + " loss[ix] = risk\n", + " return loss\n", + "\n", + "\n", + "guesses = np.linspace(5000, 50000, 70)\n", + "risks = np.linspace(30000, 150000, 6)\n", + "expected_loss = lambda guess, risk: \\\n", + " showdown_loss(guess, price_trace, risk).mean()\n", + "\n", + "for _p in risks:\n", + " results = [expected_loss(_g, _p) for _g in guesses]\n", + " plt.plot(guesses, results, label=\"%d\" % _p)\n", + "\n", + "plt.title(\"Expected loss of different guesses, \\nvarious risk-levels of \\\n", + "overestimating\")\n", + "plt.legend(loc=\"upper left\", title=\"Risk parameter\")\n", + "plt.xlabel(\"price bid\")\n", + "plt.ylabel(\"expected loss\")\n", + "plt.xlim(5000, 30000);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Minimizing our losses\n", + "\n", + "It would be wise to choose the estimate that minimizes our expected loss. This corresponds to the minimum point on each of the curves above. More formally, we would like to minimize our expected loss by finding the solution to\n", + "\n", + "$$ \\text{arg} \\min_{\\hat{\\theta}} \\;\\;E_{\\theta}\\left[ \\; L(\\theta, \\hat{\\theta}) \\; \\right] $$\n", + "\n", + "The minimum of the expected loss is called the *Bayes action*. We can solve for the Bayes action using Scipy's optimization routines. The function in `fmin` in `scipy.optimize` module uses an intelligent search to find a minimum (not necessarily a *global* minimum) of any uni- or multivariate function. For most purposes, `fmin` will provide you with a good answer. \n", + "\n", + "We'll compute the minimum loss for the *Showcase* example above:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "minimum at risk 30000: 14068.38\n", + "minimum at risk 54000: 12967.09\n", + "minimum at risk 78000: 12031.03\n", + "minimum at risk 102000: 11991.21\n", + "minimum at risk 126000: 11803.17\n", + "minimum at risk 150000: 11803.17\n" + ] + }, + { + "data": { + "image/png": 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4qWxlwUlNbV8G31NS20rfngxARLuGxabxBA7baTLXzwEg/PIyb9NRZVm57VuW\nbJpLgH8gj9406bxVGMB8uwzVk0rdEVop9QJnNndxcopidv5USk0EJhYRvgHoWER4DpbSYTAYDAaD\np8lPyyL7wCnE34+I1vW9WlbmLzMgL4ug1lcQ2KCNV8vyNvuP7uTDxXoWdmT/J2hxYQcfS2QwGMqL\n2RHa4FWGDRvmaxEMNRjTvgzeori2lbFDb+4c1qIefsGBXitf2fOwrdJbGoT3e8Br5VQGGdlp/GvB\n4+Tl59Cv42D6d77Z1yL5HPPtMniTzp07eyVfozQYvEqfPn18LYKhBmPal8FbFNW2lFKkb9dKg7dN\nk7I3L8SRepiA+q0IblN9txxyKAf//eafHEs5RNP6bRh11ROc2af1/MV8uwzexLlQ2tNUqnlSdeDk\nyZPk5OT4WowaQ2pqKrVq1fJqGcHBwdSpU8erZRiqJqtXrzY/vgavUFTbyj2WTt6JDPxCAwlrWtdr\nZSulsK14D4CwvmOrdSf7y5+nsmnvGiJCavHoTZMJCgzxtUhVAvPtMlRHjNLgQkZGBgANG3p3BOl8\nojLu5cmTJ8nIyCAiIsLrZRkMhvOXDOcsQ5sGiL/3Jurz9q8j78BGJCyasEuq7zK9TXvXMG/NBwjC\n3wa+xAW1zG+rwVCdMUqDC6mpqUZhqIbExMSQnJxcqUqDcblaGF+5XDUjdQZv4d62lEMVrGfwtmmS\nbbk1y9D7HiQozKtleYujKQf5zzf/RKG4rc8DdG7ay9ciVSnMt8tQHTFrGlwQkWo9DXy+Yp6bwWDw\nNlkHTmK35RBQO4zgC71ncpl/8gDZf3wDfgGE9x7ltXK8SW5eNm9+9QS27DQubn4ZN11aPethMBgK\nY5QGg8FQbVm9erWvRTDUUNzbVsY2PcsQ2e5Crw5SZK7+EJSDkItuxr929Zv5Vkox9YeJ7D+2k/q1\nY3nohgn4ielquGO+XYZzwWF3kJuTjy09h5RTmZw4msGRg6kk7TvFvj+Pe61cY55UBurWrUuHDh1w\nOBwEBATw2muvcckll/harGJJS0tj3rx5jBqlR3eOHDnCP/7xDz766COvlTl79myuvPJK6tcv3W/5\n9OnTCQsL47bbirbVfe2114iIiOChhx7ytJgGg8FQbhy5+dh2HQW8a5rkyE4n85eZupxq6mZ16eYv\nWLn1G4ICgnnsptcJD4n0tUgGg0+w2x1kZuRiy8jBlp5DdmYe+Xl28vMd+sizk5/nID/f/a8zTqe1\n5znIc8bG4Eb3AAAgAElEQVTl2bHnO3A4VIllXznkAq/UySgNZSAsLIzly5cD8NNPP/Hiiy/y9ddf\n+1aoEkhJSWHatGkFSkODBg28qjCAVhratm1bqtJgt9sZOXKkV2UxnD8Yu2CDt3BtW7bdx1B5doIb\n1iYw2ntrDLJ+/RSVnU5gs54ENvaOy0RvsvPgZqb/OBmA+675J/EXtPSxRFUX8+2qniilyM7Kw5ae\nS6alDGil4Ixy4DyyMvO8J4hAQIA/gUH+BAb6ERDoT2CgPwGB/gQEem9mzygN5SQtLY3o6GgAbDYb\nd911F6mpqeTl5fHMM89w7bXXMnHiRKKjoxk7diwAL7/8MvXq1WPMmDG88847LFiwgNzcXG644Qae\nfPJJMjMzGTVqFIcPH8ZutzN+/HhuuummQuXOnDmTmTNnkpeXR9OmTXn//fcJCQnh+PHjjBs3jv37\n9yMivP7660yZMoX9+/fTr18/+vXrx7333ssdd9zBmjVryMnJYdy4cWzevJnAwEAmTJhAnz59mD17\nNosWLSIrK4vExESuv/56nn/++bPqP3nyZBYvXkx2djbdu3fnX//6FwsXLmTz5s3cf//9hIaGsnjx\nYoKDgwuuGTRoEB06dGDdunX85S9/IT09vWAmYcqUKUyfPp3AwEBat27Nhx9+WKi8GTNm8N133zFz\n5sxCeRoMBkNl4fSaFNnuQq+VoRx2bKs+ACD88uo3y3Aq/ThvLngCuyOf67oO5bL21/taJIOhXNjt\nDtJTs0k7nUXq6SzSUrIspSC3QDnITM/Bbi95lN+JCIRFBBMeEURYZDChYYG6Yx/gj3+gHwEBurNf\n8DfQj4AA979Fx/n5l7yWc+PGjZ66LYUwSkMZyMrKol+/fmRlZXHs2DEWLFgAQGhoKB9//DERERGc\nOnWKAQMGcO2113LXXXcxfPhwxo4di1KK+fPn8+OPP7Js2TL27t3L0qVLUUoxbNgw1q5dy/Hjx7nw\nwguZM2cOAOnp6WfJMGjQIIYPHw5oJWTWrFmMHj2ap556it69ezNz5kyUUmRkZPDcc8+RkJBQMDuS\nlJRU0LimTp2Kn58fq1evZteuXdxyyy389ttvAGzbto0VK1YQGBhI9+7dGTNmzFnepMaMGcPjjz8O\nwAMPPMCSJUsYNGgQU6dO5aWXXqJTp05F3sP8/HyWLl0KaPMjJ2+//XaBApOWllYQrpRi6tSprFix\ngk8++YSAgACmT58OUCVmKly9JjmZ88QGH0jiW69JTirba5IT4+vc4C2cbSs/I4es/SfATwhv08Br\n5eVsW4z9xD78Y+II6Vi9Otz59jzeXPAEKbaTtG3clTv7Pexrkao85ttV+Tgcioy0bFKdSsHpLFJP\nZxacZ6Rmo8qgDwQFBxAeGUR4ZDDhEcH6b6RWDlzDQsOD8POrWU5ajNJQBkJDQws64OvXr2fs2LH8\n/PPPOBwOJkyYwM8//4yfnx9Hjhzh+PHjNG7cmJiYGLZu3crRo0fp1KkTtWvXZtmyZSxfvpx+/fqh\nlCIzM5M9e/bQs2dPnn32WV588UUGDBhAz549z5Jh+/btvPzyy6SmppKZmcmVV14JwKpVq3j//fcB\n7UUoMjKSlJSUYuvy66+/MmbMGABatmxJXFwcu3fvBqBv374Fbktbt25NUlLSWUrDihUreOedd8jK\nyiIlJYW2bdsyYMAAQHf0i+Pmm28uMrx9+/bcd9993HDDDVx//Zkfys8++4zY2FhmzZqFv78/UDWU\nBYPBcH5hSzgMCsKa18M/NMh75azQ3/GwvmMQP3+vleMNpv84mV3JfxATWZ9HBr1KgH+gr0UynIco\nh8KWkVOgBKSe0rMFqacySU3JIj0lu+S1AAKRtUKIqh1KrZhQomqHEuFUCCKDrFmDYAKDqtf76UmM\n0lBOLrnkEk6dOsXJkydZsmQJJ0+eZMWKFfj5+dGlS5eC3aTvvvtuPvnkE44dO8add94J6E71I488\nwogRI87Kd/ny5fzwww+8/PLLXH755YwfP75Q/EMPPcQnn3xCu3btmD17NmvWrAE4Zy8erh19V/Mf\nf39/7HZ7obQ5OTk88cQTLFu2jAsvvJDXXnuN7OzsMpUTFla0HfBnn33Gzz//zKJFi3jjjTf4+Wc9\nat6+fXu2bNnCoUOHiIuLK2+1DOcJZqTO4C2cbSvduaGbF02T8g5uIXf3aiQ4grCed3mtHG/w0+9f\nsnTzFwT6BzHupsnUCo/xtUjVAvPtOneys/JIPpBCcuJpDiae5sjBNPLz7CVeEx4ZTK3o0ALFoFa0\nPqKiQ4msFUpAgPH0VRJGaSgnf/75Jw6Hg5iYGNLS0qhbty5+fn6sWrWKpKSkgnQ33HADEydOJD8/\nn6lTpwJw5ZVXMnHiRIYMGUJ4eDiHDx8mMDCQ/Px8oqOjGTJkCFFRUcyaNeuscm02G/Xr1ycvL4/P\nP/+8YAagb9++TJs2jbFjx+JwOAp2Rnbubu1Oz549+fzzz+nTpw+7d+/m0KFDtGzZkt9//73Uuufk\n5CAixMTEkJGRwcKFCxk8eDAAERERRZpVlcbBgwfp3bs33bt358svvyyQu2PHjtxzzz0MGzaMefPm\n0aCB98wCDAaDoShyT2SQezQNv+AAwprX81o5thV6M7fQHnfiFxLltXI8za7kLfxvqTY3vXfAP2h+\nYXsfS2SoqSilSD2dxaHE0yQnpnAo8TQnjmWA28RBaHhQIUWglssRWTuUwMDzd5bAExiloQxkZ2cX\nmBQBvPvuu4gIt956K0OHDuWyyy6jS5cutGrVquCawMBA+vTpQ+3atQtmA6644gp27drFNddoe/iI\niAimTJnCnj17eO655/Dz8yMwMJA33njjLBmefvpprrrqKurWrUvXrl0LOtevvPIKjz76KLNmzSIg\nIIDXX3+dbt260b17d/r06cNVV13FvffeW5DPvffey7hx4+jTpw+BgYG8++67BAaePZVc1AxGVFQU\nd999N7169aJ+/fpcfPHFBXFDhw5l3LhxRS6ELm42JD8/n/vvv5/09HSUUtx///1ERZ35wezRowcv\nvvgiQ4cOZf78+QVrSYyZksGJsQs2eIvVq1fTzqHdFoa3ro9fgHc6G/a0o2Rt/AJECO97v1fK8AYp\nGSd486snyLfnMeCi2+jXcZCvRapWmG9XydjtDo4lp3HIUhCSD6RgS88plMbfX2gQW4uG8dE0io+m\nYVxtwsK9Z0JoACnJDr2m8uOPPyrXDq+T5OTks2z4K4rD4eCKK65g+vTpNG3a1CN5GorHk8/OUH0w\nP7wGb7Fq1SritzvIT8vmwjsuIbSxd8xu0r97hYwlrxPc6UZiRs30ShmeJt+ex4TPxrLz4GZax3bh\n/25/36xjKCfm21UYV1OjQ4kpHD6YQn6eo1Ca0LBAS0GoTaP4aOo3jCLAzBwUycaNG+nfv7/HV2Gb\nmQYvsHPnToYOHcrAgQONwlBD2Tt5MVDYi9Idk7oC0C3lZQDGv3JtpcjyfYNegG+9KA2YugmofC9K\n5kfX4C26NWnP4bXrCYgKISQ22itlqLxsMtfoPXTCLx/rlTK8wcfL3mTnwc1ER9Tj0UGvGYWhApzv\n36701GyS9p3i0P7TxZoaRdcNo5E1i9AovjbRdcO9uhu7oXSM0uAFWrdu7TUfuQaDwWDwPhnbkwG9\nA7S3OipZGz7HYTtJQGxngppd6pUyPM3yLQtZvPEzAvwDeeymydSOqOtrkQzVAFt6Dkl7T3Fg70mS\n9p7i9MnMQvH+/kL9RrUKFISGcdGERRhTo6qGURoMBkO1xUzxG7yBI9/Oiu9/4uKGrb3mNUkphW25\nXgAd3u+BajGCuufwdqYtmQjAqKuepGXDjj6WqPpS079dmbZcDu47xYG9p0jae4qTxwo7ZwkM8ie2\naQyxTaKJbWJMjaoLRmkwGAwGg8GFzD3HceTZCaofRVCdCK+UkfvnCvKPJOAX1YDQLjd5pQxPkmo7\nxb++Gk+ePZerOt/ClZ2L3nvHcH6SnZVXSEk4fqSwN8WAQD8axUcT1yyGuOZ1qN8wCj9/4960umGU\nBoPBUG2pySN1Bt+RvvUQ3eLbEenFvRmcblbD+tyLBFRtM4x8ex5vLXyKk+lHadmwEyP6jy/9IkOJ\nVPdvV25OPgf3n9bmRntOcfRwWqE1Cf4BfjSMq01csxgaN6vDhbG18Dd7IFR7jNJgMBgMBoNF7ikb\nWXtPIAF+RLTzjke2/KN/krP9BwgMIbzXSK+U4Uk+Wf42O5I2UDu8Do8OnkRgFVdyDJ7Hnu/gUOJp\n9u8+QdLeUxw5lIZy2V3Zz1+4MLY2jZvFENcshoZxtY25UQ3EKA0GQwVw9ZrkZM4TG3wgiW+9Jjmp\nbK9JTmq6XbCh8knbkAjANo7SNMw7nWPbyg8ACO12G34RdbxShqdYte07Fm34FH+/AB4dPImYSO9t\ncnc+UR2+Xbb0HPb9eZw9CcdJ3H2C3Jwzuy2Ln3Bh41rENatD42YxNIyvTVCQ6VLWdMwTNhgMBoMB\nsGflkr5Ne00Kb1XfK2U4bKfJWj9Hl1HF3azuO5rAB4tfAmBk/8dpHdvFxxIZvIlyKI4eTmNvwnH2\n7jzOkYOpheLrXBBB01Z1iWteh9gm0QQFmy7k+YYxMKuhjB07lrZt29KkSRN69OjBxx9/XBC3YsUK\nevToQePGjbnppps4ePBgoWuff/55WrRoQcuWLXnhhRcKxSUlJTF48GBiY2Pp2bMnK1asKBQ/b948\nOnfuTFxcHMOHDyc1tfBHx2DwJFV9pM5QvUj/4xAqz05okzpcccPVXikj85eZqNxMglpfQWCDNl4p\nwxOkZZ7mjS/Hk5efwxUdB3NVl1t8LVKNoqp8u3Jz8tm17SiL52/l/deWM+u/v/Dzj7s5cjAV/wA/\nmraqS/+Bbbnv8b7c80gf+l3fhmat6xmF4TzFPHUfkGt3sPOYDQW0qRdOkBcWBz3yyCO89dZbhISE\nsHv3bgYOHEjnzp2JjY1lxIgRvPPOO1xzzTW8/PLLjBo1iiVLlgAwffp0Fi1axOrVqwG4+eabiY+P\nZ+TIkQCMHj2aHj16MHfuXJYsWcLIkSPZsGEDMTEx7Nixg8cee4y5c+fSqVMnHnnkEcaNG8fUqVM9\nXj+DwWDwJMruIHXTAQBqdY33Uhl52FZp06Twfg94pQxPYHfk8/bXT3Mi7TDNL2zPPVc/WS1cwhrK\nxumTtoLZhKR9p3DYz6xNiIgKpnmbC2jWuh6Nm8cYkyNDIUxrqGRmbz7C0l2nSE7LQQGNooK5onk0\nd13sWS8dbdqcGcFSSiEi7Nu3j02bNtG2bVsGDhwIwJNPPknLli3ZvXs3LVq0YM6cOTz00EM0aNAA\ngL/+9a/MnDmTkSNHsnv3brZs2cL8+fMJDg5m4MCBTJkyhYULFzJy5Ei++OILrrvuOnr27AnA008/\nTc+ePbHZbISHh3u0fgYDVA+7YEP1wLbrKPb0bAJjwgltWtcrbSt780IcqYcJqN+K4Db9PZq3J5m9\n4j9sTVxHrbAYHrtpMkEBwb4WqcZRmd8u5yLmPTuPszfhGKdPuGysJtAwrjbN2tSjWet61GsQaRRE\nQ7EYpaES+WrrcT77/SiZeY6CsKTUHD7fcozgAD9u7eRZG9rHH3+c2bNnk5WVRefOnbn66quZMGEC\nHTp0KEgTFhZG06ZNSUhIoEWLFiQkJBSK79ChAwkJCQDs3LmT+Pj4QgqAa3xCQgLdu3cviGvSpAlB\nQUHs2bOHTp06ebRuBoPB4ElSf9MLoGt1jfdKp0kpdcbNat+xVbZj9vOOxXyz/mP8/fx5ZPBr1In0\nztoOg3dRSrF353G2bTzE/l2FFzEHhwTQpGVdmre5gCat6hIWbrxhGcqGURoqCaUUS3adLKQwOMnK\nc/Dj7tPc0vEC/Dz4QzJ58mQmTZrEunXrWLNmDUFBQdhsNurVK+z9IjIykowMvVujzWYjKiqqUJzN\nZisyzhl/+PDhEuOdedck9k5eDBT2onTHpK4AdEt5GYDxr1xbKbJ836AX4FsvSgOmbgIq34uSmWUw\neILs5BRyDqfiFxJQsAO0p9tW3v515B3YiIRFE3bJbR7N21MkHtvF+4v0Ora7rxxH28YX+1iimou3\nvl0Ou4OELUdYt3IvJ46c+e2tc0EEzVrXo1mbejSKq202VjNUCKM0VBIp2fmczMwrNv5kZi4nbHlc\nEOFZjV9ECtYg/O9//yM8PJz09MI7NaalpRERoXc9dY9PS0srmFko77UA6enpBfEGg8FQFUm13KxG\ndW6Mn5dsuG0r3gcgrPc9SFCYV8o4FzKyUnnjq3Hk5ufQt8ONXHNR1VRsDEWTl2tn64aDrF+9n7TT\nWYBen3Bxr3hadWhA7Ziq1+YM1Q+jalYSQf5++PsVP4vg7ycEe3G3xPz8fPbv30/btm3ZsmVLQbjN\nZisIB70WYuvWrQXxW7ZsKVgf0aZNGxITEwtmHgC2bt1aKH7btm0Fcfv27SMvL4/mzZt7rV6G8xvn\ngn2DoaLkp2Vh23kURIi6KK4g3JNtK/9UEtm/fw1+AYT3HuWxfD2Fw2HnnW+e4VjKIZrVb8voq/9R\nZc2nagqeal/ZWXmsXbaHDyav4Mevd5B2OovoumFc85cOjB5/Od37NjMKg8FjGKWhkggP8qdhVPGL\nyRpGBlMrxDMjXCdOnGD+/PnYbDYcDgc//vgjX375Jf369eOGG24gISGBb775hpycHCZNmkSHDh0K\nOvZ33HEH7777LocPHyY5OZl3332XYcOGAdC8eXM6dOjApEmTyMnJ4euvv2bHjh0MGjQIgCFDhvD9\n99+zdu1abDYbEydOZODAgWYRtMFgqLKkbkoCpQhvXZ+AyBCvlJG56gNQDkIuuhn/2t7ZZfpc+Gz1\ne/y+7xciQ2vz2M2TCQr0zn0weI6MtGyWL0pgymvLWf3DLrJsudRvFMWgYV2455HL6NgtlgAvDkQa\nzk+MeVIlMvqShrz00z6OZRQ2U6oXHsioSzz3QyIifPTRR4wfPx6Hw0Hjxo155ZVXGDBgAAAzZszg\n8ccfZ+zYsXTt2pVp06YVXDty5EgSExPp06cPIsLw4cMZMWJEQfy0adN48MEHadasGbGxscyYMYOY\nmBhAzzS88cYbjBkzhpSUFPr168c777zjsXoZDO6YNQ2Gc8GRm0/6H0kA1OpW2M2qp9qWIyeDzF/0\nPjlVcTO3jXtWsWDtR/iJP48MepW6UZ715Gcomoq2r1MnbKxfuY/tmw5ht1ylxreoQ/e+zYhrHmNm\niAxexSgNlUibC8J5cUBzpq9P5pDlcrVhVDDDuzagVV3PjcbXqVOHr7/+utj4vn378uuvvxYb/9xz\nz/Hcc88VGRcbG8vChQuLvfaWW27hllvMJkAGg6Hqk7EtGUd2PsENaxNyYW2vlJG1/jNUdhqBTXsQ\nFFe5jgJKw5adzoeLteOGoX3/Svv4S3wskaE4jhxMZd3Kvfy57SgoQKBVhwZ0v7wpDRrV8rV4hvME\nozRUMs1iQnnxGmPjX91x9ZrkZM4TG3wgiW+9JjmpbK9JTsw+DYaKopQidWPxm7l5om0phwPbSmsz\nt8vvP6e8vMHHy97kdMZxWjbsxA2X3Olrcc4rytK+lFIc2HOKdSv3krj7JAD+/kL7ixvR7bKmxHhw\nsNFgKAtGaTAYDAbDeUfWvhPknbLhHxlCeKsLvFJGzs6fsB/bhV/thoR0vMErZVSU3/f9zPItCwj0\nD2Lsdc/i5+fva5EMFg6HYvf2o/y6Yi9HD6UBEBjkT5cecXTtHU9ElFlzYvANRmkwGAzVFjPLYKgo\nBZu5XRyH+J29YNQTbSvTOcvQZzTiH3jO+XmKzJwMPvj+JQCG9LmfRnWa+lii84+i2pfd7mD7pmTW\nrdxbsGtzaHgQXXvH06VHHCGhVacNGc5PjNJgMBgMhvOK3BMZZCWeRAL9iewU65Uy8o/uImfHUggM\nIezS4V4po6J8uuJtTqYfpVmDdtx4yV2+Fue8RynFvj9PsPzbBE6d0C7No6JDueSypnTo2ojAQDML\nZKgaGKXBYDBUW8yaBkNFSN2wH4DIDg3xDyl69PZc25Zt1YcAhHa9Fb/wmArn42m2Ja5n6eYv8PcL\n4IHrnsPfz3QDfIGzfZ04ms7y73ayf9cJAKLrhNGrfwtad2xgdm02VDnM18JgMBgM5w32zFwyth0G\nIOrisxdAewJHVhpZ62YDEN636iyAzs7NYsriCQD8pddoGtdr4WOJzl+ys/NYunA7v69LQjkUwSEB\nXHplCy7qGYe/2V/BUEWpNKVBRFoBn1HgLIxmwP8BH1vh8cB+4DalVKp1zT+AUUA+8LBSaokVfjEw\nHQgBvlNKPWKFBwEzga7ACeB2pdSByqmh4Xxi7+TFQGEvSndM6gpAtxTtwnD8K9dWiizfN+gF+NaL\n0oCpm4DK96JkZhkM5SXt9ySU3UFYs3oExRTvfeZc2lbmr7NQuTaCWvYlsGG7Cufjaeas+g/HUg4R\nf0ErBvcY6Wtxzkvs+Q42/3qAHT/byck+gAh06RFHr6taEBYe5GvxDIYSqTSlQSn1J3ARgIj4AQeB\nL4GngKVKqUki8iTwD+ApEWkH3Aa0BWKBpSLSUimlgPeAe5VS60XkOxG5Rim1GLgXOKWUaikitwOT\ngDsqq44Gg8FgqLoou4O0TUVv5uaxMhx2Mi3TpPC+Y7xSRkVIOLiJxRs+w0/8GXvdcwRUoYXZ5wNK\nKfbuPM7y7xIKFjnHt6hDv+vbUK9BpI+lMxjKhq/mwK4C9iilkoDBwAwrfAZwk/X/IGCOUipfKbUf\n2AV0F5EGQKRSar2VbqbLNa55zQP6e7UWBoPBp6xevdrXIhiqERkJR7DbcgisG0FIXMnrDCratnK2\nL8F+MhH/OvEEtz97PxdfkJuXzZRFE1AoBvccSdP6bXwt0nnFiaPpzPvoN76cuZHTJzKJrhtGXIc8\nhtzTzSgMhmqFr5SG24FPrf/rK6WOAiiljgBOh9mNgCSXaw5ZYY3QsxRODlphha5RStmBFBGpOivQ\nKpGBAwfSsGFD4uLiiIuLo0ePHmelmTRpEnXq1GHlypWFwp9//nlatGhBy5YteeGFFwrFJSUlMXjw\nYGJjY+nZsycrVqwoFD9v3jw6d+5MXFwcw4cPJzU11fOVMxgMhnKilCJ1g+VmtWs8IuKVcmwrpgAQ\ndtl9SBXZ++DzNVM4fDqR2LrN+culo30tznlDpi2XpQu2M+PtNSTuPklwSABX3NCGkX/vQ8O4aK+1\nQYPBW1T6QmgRCUTPIjxpBSm3JO7n51RcUYHz5s1j6tSpxMXFAVCrVi06duxIs2bNPFh08ThycknZ\ntB2UotZF7fAPCfZ4GSLC5MmTufPOonf53L9/PwsXLqRBgwaFwqdPn86iRYsKRtluvvlm4uPjGTly\nJACjR4+mR48ezJ07lyVLljBy5Eg2bNhATEwMO3bs4LHHHmPu3Ll06tSJRx55hHHjxjF16lSP18+d\n1NRUGjZsCJwZIXTaJHvjPDlxO93i2xWKd5J4aLv137WVIs92h82ltMqpv/t52p5dRDXvUunl9+nT\nxyf1NefV77xrk/bkHk1j45GdXHA6lL7Eery8vOTtrF61EgkI4cYed1WJ+s/5aiYzl35ITHwoY697\nll/XrvOpPOfDucPuIMw/jl9+2s2fe/5A/IRBN19Dr/4t2LhpHb+sPVil5DXn1f/c+f+BA3oZb7du\n3ejf3/PGNqKXCFQeIjIIeFApda11vgPop5Q6apkeLVNKtRWRpwCllHrNSvc98ByQ6Exjhd8BXK6U\nesCZRin1q4j4A4eVUmdt9fnjjz+qiy+++CzZkpOTCzqe3mLPv2eQ/Pn32PYfBKUIb9qYC2++mhbj\nRnm0nEGDBnHbbbdx1113FRl/6623cv/99zN+/Hjefvtt+vbtC8C1117LsGHDGD5c+xX/5JNPmDlz\nJosXL2b37t307duXXbt2ER6uFxDeeOONDBkyhJEjR/LSSy+RlJTElCl6pG3//v307NmTPXv2FKT3\nFpXx7FwxC6EL46uF0AZDWTny1SYydx2j9qXNiOnT0itlpMx5mKy1HxPWZzS1hkzyShnlIS8/l3/M\nuJODJ/cysPtw7uz3sK9FqtEopdibYK1bOKnXLTRpqdct1K1vzJAMlcfGjRvp37+/x6eyAjydYRkY\nCsx2OV8IjAReA0YAC1zCPxGRN9FmRy2AdUopJSKpItIdWA8MB952uWYE8CtwK/CTd6tSPhKnzmXv\nO7OwZ9gKwmy7E9n33qf4hwbT9MGiZwUqyoQJE3jxxRdp0aIFzzzzDL179wbgq6++IiQkhKuuuuqs\naxISEujQoUPBeYcOHUhISABg586dxMfHF1IAXOMTEhLo3r17QVyTJk0ICgpiz549dOrUyaN18zWu\nyoKTOU9s8IEkvlUWnPhKWVi92uzTYCidvJRMMncfAz8hqktcma4pb9ty2E6RteFzAMIvu69Ccnqa\n+b9M5eDJvVwYHc+tvauO69eayPEj6Sz/LoHE3ScBiK4bRr/r29Csdb0izZDMt8tQHalUpUFEwtCL\noF1dSrwGzBWRUehZhNsAlFLbRWQusB3IQ89OOKdFHqKwy9XvrfBpwMcisgs4SRXynKSU4tBniwop\nDE7sGZkkf7GEJmOHIn6eWWby/PPP07p1a4KCgvjiiy8YOnQoq1atok6dOrz88st8+eWXRV5ns9mI\niooqOI+MjMRmsxUZ54w/fPhwifEZGRkeqZPBYDBUhLSNB0BBRLsLCYjwvDkoQOYvMyEvm+C2VxFQ\n3zszGeVh35EdLFg7HUEYe92zBAWG+FqkGklmRi5rlu7ij/VJKAXBIQH06t+CLj3j8DebsxlqGJWq\nNCilMoF6bmGn0IpEUeknAhOLCN8AdCwiPAdL6ahq5J44TfaRE8XG5xw5Tvbh44Q2qu+R8lzNr+64\n4w7mz5/PkiVLOHDgALfffjuxsbFFXhceHk56enrBeVpaWsHMgnucMz4iIqLY+PT09IJ4g8HTmJE6\nQ3MAk8wAACAASURBVGk4cvJJ23IQ0Augy0p52pay52FbPRWAsCrgZjXfnsd7i17Aoexc13UorWO7\n+FqkGodyKH5fn8SqxX+Sk52P+AkX9WhMr6taEBpW+n4L5ttlqI4YNbiS8AsOwi+geE8aEhDglQXR\n7qxatYoPPviAtm3b0rZtWw4dOsSoUaN4+21t4dWmTRu2bt1akH7Lli20adOmIC4xMbFg5gFg69at\nheK3bdtWELdv3z7y8vJo3ry51+tlMBgMRZG+9RAq105IbDTB9aNKv6ACZG/5FkdKMv4XtCS49ZVe\nKaM8LFj7EQeO7+KC2o24/bKHfC1OjeP4kXQ+nbKWpQu2k5OdT5OWdRnxt970H9SuTAqDwVBdMUpD\nJREYFUFo00bFxoc1aURQndoeKSstLY2ffvqJnJwc7HY7n3/+OWvXrqV///589dVXrFmzhpUrV7Jy\n5UoaNGjAm2++yejRowE9K/Huu+9y+PBhkpOTeffddxk2bBgAzZs3p0OHDkyaNImcnBy+/vprduzY\nwaBBgwAYMmQI33//PWvXrsVmszFx4kQGDhzo9UXQhvMXV88RBoM7yuHiZrWcm7mVp2053ayG9x3j\nMRPTinLg+C7m/zINgPuvfZaQoFCfylOTyMu1s3LxTj7+z88cTkolPDKYgUO7cMvIrtStX74ZdfPt\nMlRHfLEQ+ryl9T8fZPOY/yP74JFC4SGN6tPq6Qc8Vk5eXh6vvPIKu3btwt/fn5YtWzJr1qwiXcoG\nBARQq1YtwsLCABg5ciSJiYn06dMHEWH48OGMGDGiIP20adN48MEHadasGbGxscyYMYOYGL0VRps2\nbXjjjTcYM2YMKSkp9OvXj3feecdj9TIYDIbykLnnOPmpWQTUCiWs+VmO9DxCXtJm8vb9ioREEXrJ\n7V4po6zYHfm8/90L2B35XN1lCO3juvlUnprEvj+Ps3TBdlJPZ4FAlx5xXHZNS4JDzM7ahvOHSne5\nWhXwpcvVtO272fXqFDL3HgQUYU1iafHEaGp1Mjt0ngvG5apxuWowuJM8Zx3ZSaepc0VranVr4pUy\nUj55kKz1cwjv9yBRN73klTLKyoK1HzF75X+oG9WAyffMJTTYzPKeK7b0HJZ9m0DCH9rhR90GEQy4\nqQMN4zxjGWAweIOa5HL1vCaqXQu6zpzsazEMBoOhRpNzNI3spNNIkD+RHYt2/HCu2NOPkbVxPogQ\n5mM3q4dO7mPemg8AuO+afxqF4RxRDsUfvx1k5fc7ycnOJyDQj179W9C1dxPjFclw3mJavsFgqLYY\nu2BDcTjXMkR2jMUvuPzjY2VpW5lrPgJ7LsHtryOgTvnWTHgSh8PO+4teIM+eS7+Og+nc9FKfyVIT\nOHE0nTkf/soPX23TC51b1WXkw33o3reZxxQG8+0yVEfMTIPBYDAYahT5GTlkJGhzkloXl20zt/Ki\n8nO10gCEX+7bjdMWbZjDruQtREfU4+4rHvWpLNWZvDw7a5ftYf3Kff/P3p3HRXXdj/9/3YEZlmGG\nzQUUQcUFBcVd4hKNmETjmmiiSVNDE+OnTW3TT0xjYn/9JmmzNCb209bUJqm2MatrFpdETTSimLhv\noGAUBUFEZZ8ZYBhmzu+PgQlEjCizMHiej4cPmHvuPecMXg7zvmfDZhMEBmkYN7kPvftFNLlBmyTd\namTQIEmS15JrnUtNqTiaB1ZBYM8OqEMCbyqP691bVUc/w2a4jG9kXzQ9PHcfFpbmsXr3PwGYe9ci\ntP46j9XFm+WcLuLrz09SVlIJQOKwLoy+uxf+Aa6Z6CzbLskbyaBBkiRJajNstVYqjuUBN7aZ240Q\nQlDZcJlVDz2Ftgkbb3/5J2pqzYzqew+De9zukXp4M5PRzM4vssg8au+ZCu8QxF33xtM5JtTDNZOk\n1kcGDZJ0ExqumlRv1TOHPFATz66aVM9TqyalpaXJJ3ZSI6bMQmyVNWg66PCPuvkPfj91b1lyDmDJ\nO4KiDSNg8P03XUZLfXVkHZn5hwnWhpOS/LTH6uGNhE2QcfgCqV+eorrKgq+vituSezBkZFd8fF0/\n3VO2XZI3kkGDJEmS1CYIISg/mANA8JCuLusBMO2y9zIE3vYIioc2T7tcdoGPUv8BwGN3PktQQLBH\n6uGNii8b+eqzE+TnlALQtWc446fGExJ+c0PZJOlWIYMGSZK8lnxSJzVUfb6EmiIjPloNQb0jWpTX\nte4ta9kFqo9tAJUP2lGPtqiMmyWE4J2tL2G2VJHU+06G9RrnkXp4G5tNsPebbPbuzMZmFQRqNdwx\nKY64xEi3DzGTbZfkjWTQIEmSJLUJ9cus6gdEo7hoiIkp7T9gs+I/YDo+IZ1dUsb17Dj+KRm5+9EF\nhPDonQs9UgdvU1VZw+bVx8k5XQRA/6FRjL67FwGBGg/XTJK8h9ynoY2Kjo5u9K99+/Y8++yzjvRP\nP/2UpKQkYmJiGDFiBF988UWj61944QV69OhBz549efHFFxul5eXlMW3aNKKiokhKSiI1NbVR+rp1\n60hMTCQ6Opo5c+ZQXl7uujcq3dLkWudSPUupicrsKyg+KvQDurQ4v6buLVFTReV3KwHPLbNaZirm\ng2/+BsAvxj+DPlBO2L2eK4UGPlj2HTmniwgIVHP/o0O4694EjwYMsu2SvJEMGjygttZG3rkS8s6V\nUGuxuqSM8+fPO/5lZmYSEBDA9OnTAbh48SK/+tWveOWVV8jNzeXFF19k3rx5FBcXA/Duu+/y5Zdf\nkpaWxu7du9myZQvvvvuuI++5c+eSmJhIdnY2f/jDH0hJSaGkpASAzMxMnnrqKd5++22ysrLw9/dn\nwYIFLnmPkiRJ9coPnQcgqG8kPi76MFh1eB3CVIK6y0DUXYe5pIzrWb/nHapqTAzsPorb4u7ySB28\nyan0Qj56ay/lJVV06KTn4V+PIKZHO09XS5K8khye5GZ7d2Zz8kgBZcUmBBAapiUuMZIRyT1cVuaG\nDRto3749SUlJABQUFBASEsK4cfZxsHfeeSeBgYGcO3eO8PBwVq1axa9//WsiIuxjgufPn897771H\nSkoKZ86cIT09nU8++QQ/Pz+mTJnC22+/zYYNG0hJSWH9+vVMnDjRUdaiRYtISkrCZDKh1Wpd9h7d\n7ezrW4HGqyjNXjwYgCFlLwPw9CsT3FKXLREjAM+uonTX8iOA+1dRkuOCJQBrtQVDxgXAecus/vje\nEkJgqltmNdBDy6wWFOew/dinKIqKh+/4ndxw7CfYbIK0r75nf+o5APoMiOSu6QmoNT4erpmdbLsk\nbyR7Gtzo0Le57E89S8kVEzYbCBuUFJk4sPscB3adc1m5q1evZtasWY7XAwcOpFevXmzduhWbzcbm\nzZvx8/MjPj4egKysLBISEhznJyQkkJWVBcCpU6eIiYlpFAA0TM/KynLkA9C1a1c0Gg3Z2dkue3+S\nJN3aDOkXEBYrATHhaNq7ZnOzmjN7qL14EpWuAwEDp7ukjOv5eNeb2ISVcf2n0zm8m0fq4A2qKmv4\nZOUh9qeeQ1Ep3DEpjnvu799qAgZJ8lYyaHATIQQnDl+gxnz1cCRLjZWTRwsQNuH0cvPy8vj22295\n8MEHHcdUKhUPPPAAjz/+OBEREfzyl7/kr3/9KwEB9qUDTSYTer3ecb5Op8NkMjWZVp9uNBqblS5J\nziTHBUtCCCqO2Icm6QdFOy3fH99bjmVWR/4CxdfPaeU016n8oxw4/Q1+an9mjpzn9vK9xVXzF34x\nhMEjXbf87s2SbZfkjWTQ4CaVphqMFdXXTDdWmDH8RPrNWr16NUlJSXTp8sPEwJ07d/LCCy+wadMm\nLl++zIYNG3jyySc5ceIEAFqtFoPB4Di/oqLC0bPw47T69KCgoGumGwwGR7okSZIzVZ0rora8Ct/g\nAAK7t3dJGbXFuZgzvgAfNYEjUlxSxk8RQvDBzr8DMGnIw4QGueZ9erus4xf58F+N5y9Ex4Z7ulqS\n1GbIoMFNfH1VqHyu/eNW+Sj4qp3fdbpmzZpGvQwAGRkZjBgxgv79+wP24UqDBw9m586dAMTFxZGR\nkeE4Pz09nbi4OEdabm6uo+ehPr+G6fXBB8C5c+ewWCzExsY6/b1JkhwXLJUfrutlGNAFReW8p8kN\n763KtOUgBAED78NH39FpZTTXgdPfcLrgOPrAUKYMm+P28ls7m02wa8spNq06Rq3FSt8BnXjwf4YT\nHOqZjfeaQ7ZdkjeSQYOb+PmrCQ27dgMWEh5IoNa5K37s27ePwsJCpk6d2uj4oEGD2LdvnyMwOH78\nON99951jHsPs2bNZtmwZFy9epKCggGXLlvHQQw8BEBsbS0JCAosXL8ZsNrNx40YyMzMdZcycOZMt\nW7awd+9eTCYTr776KlOmTGlTk6AlSWodLKUmqs4Vofiq0PVzzZ4JNrORyu/eBzyzzGqt1cLHqW8C\nMGPEPAL8ZFvakGP+wq4f5i9MvL8fahc8hJOkW51cPcmNbp/Qm40fH6WirPEwJF2wP6Pv6uX08lav\nXt3kB/YRI0bwzDPPkJKSwpUrV2jXrh0LFixgzJgxAKSkpJCbm8uoUaNQFIU5c+bwyCOPOK5fsWIF\nTzzxBN27dycqKoqVK1cSFhYG2HsalixZwrx58ygrK2Ps2LEsXbrU6e/N0xqumlRv1TOHPFATz66a\nVM/dqybVS0tLk0/sbmEVR/MA0MZF4hPg3Icu9fdW1YHViOoK1N2Goe4ywKllNMeO459xsTSXiNBo\nkhPvdXv5rdmVQgOffXCY8pIqAgLVTHlwgNcMR5Jtl+SNZNDgRpFdQpj+80Hs+eo0pUWVAASHBzJy\nfA8iOgc7vby//vWv10x77LHHeOyxx66Z/vzzz/P88883mRYVFcWGDRuuee2MGTOYMWNG8ysqSZJ0\ng2w1tRjS65ZZdeIE6IaEzYZp1zsAaG93fy9DldnE+j328h+8fT6+Pmq316G1yjp+kS3rM6i1WOnQ\nSc+0nw1s1cORJKktkEGDm3WI1HPvnMGeroYktQnySd2ty5h5EZu5Fr9OIfh11F//ghs0atQozJnb\nsV4+jSqkE/79Jzu9jOvZdOADyitL6NmpH8N6jXN7+a2RzSbYve17xzLlfQd04s57471uOJJsuyRv\nJIMGSZIkyas0WmZ1YJfrnH3z6pdZ1Y6ai+Lmp/ylxitsOmCfS/GzsU+2uiVDPaGqsobNq4+Rc7oY\nRaUwdmJvBo2IkT8bSXITORFakiSvJdc6vzWZL5RRc8WIT6CGoF4RLikjdeNqzJlfg9qfwNvcv2LR\n+j3/xmypYkiPMcRFeWbOUGty5WL9/gvFrXr/heaSbZfkjWRPgyRJkuRVyut6GXSJUSi+rnn2VZ2+\nCYCAwfej0oa5pIxruVB8jh3HP0Ol+PDgmN+4tezWqOH8hY6d9EyV8xckySNk0CBJN+Hs61uBxqso\nzV5sn6sypOxlAJ5+ZYJb6rIlYgTg2VWU7lp+BHD/KkpyXPCtp9ZoxvT9JVAU9ImuGZpkq6ogsTwV\ngWcmQK/a9SY2YWV84gw6h3dze/mthRCCPV+fYe832YD3zl9oimy7JG8kgwZJkiTJaxiO5YFNoO3V\nEV+dv0vKqNz3AcJsRNNzNOpOfV1SxrVk5R/hwOmd+Kn9mTHycbeW3ZoIIdj55SkOpeXI+QuS1ErI\nOQ2SJHktOS741iKsNiqO2fdm0A900TKrNZWYdrzJ/kL39zIIIfhw598BmDz054QGtXdr+a2FsAm2\nb8zkUFoOKh+FqQ8O8Or5C02RbZfkjWRPgyRJkuQVTKcvYTXVoG4XhH+XUNeUsXs5topCfDvE4hfv\nniGG9fZ/v4PTBekEB4YxeejP3Vp2ayFsgm2fnSD9YD4+PgpTfzaQ2LgOnq6WJEnIoEGSJC8mxwXf\nWiqO2HsZggd0cclTZ1tlOcbtfwNg/BOvoajc1xlfa7Xw8a43AZgx8nEC/LRuK7u1sNkEWz9J58Th\nAnx9VUz/+SC69mzn6Wq5hGy7JG8khye1UcuXLyc5OZnIyEjmz5/fKC01NZXhw4fTpUsXpk+fTn5+\nviNt6dKljBw5kujoaAYNGsTSpUsbXZuXl8e0adOIiooiKSmJ1NTURunr1q0jMTGR6Oho5syZQ3l5\nuSOtpqaG+fPnExMTQ9++fVm2bJkL3rkkSW2R+XIF1fmlKBpfguI7uaQM0zdvIirL0PQYhab3HS4p\n41p2HP+UwtLzRIRGM67/vW4tuzWwWW18sea4PWBQ+3DfI4PbbMAgSd5K9jR4gKW2hjMXTwCC2Mh4\nNL5+Ti8jMjKSp59+mh07dlBVVeU4XlJSwiOPPMLSpUu5++67efnll3n00UfZtm2b45y33nqL+Ph4\nzp49y4wZM4iKiuLee+1/xObOncvw4cNZs2YN27ZtIyUlhUOHDhEWFkZmZiZPPfUUa9asoX///vzu\nd79jwYIFLF++HIC//OUv5OTkkJ6eTmFhIdOmTSMuLo5x47xvp9OGqybVW/XMIQ/UxLOrJtVz96pJ\n9dLS0uQTu1tEfS+DLqETKo3z/3RZDZcxpf7LXsbkP7Jnzx633VtVZhPr9rwDwENjfoOvmzeS8zRr\nrY1Nq49x+sQlNH4+3PfIEKK6umb4WWsh2y7JG8mgwc0+/e4/7D6xmcKyPBCCiNBoRva5mxkj5zm1\nnEmTJgFw+PDhRkHDxo0b6dOnD1OmTAFg4cKF9OzZkzNnztCjRw9+85sf1gTv0aMHEydOZN++fdx7\n772cOXOG9PR0PvnkE/z8/JgyZQpvv/02GzZsICUlhfXr1zNx4kSSkpIAWLRoEUlJSZhMJrRaLatX\nr2bZsmXo9Xr0ej1z5szh448/9sqgQZIk97FWWzBmXgRAP8A1y6waty1B1FTilzARTdehkO++iaob\n979HRWUpPTv1Z2hP9/ZweFptrY2NHx0hO+sKfv6+zPzFECK7hHi6WpIkNUEOT3KjLw+tYsO+dyko\nycFms2ITNgpKcti4/3027HvPLXXIysoiISHB8TowMJBu3bqRlZXV5Pl79+6lT58+AJw6dYqYmBi0\n2h/G2iYkJDiuzcrKIj4+3pHWtWtXNBoN2dnZlJeXU1hY2Ci94bWSdDPkk7pbgyHjAsJiJSAmHE14\nkNPzry3OpfLbd0FR0E36A+C+e6vUeIXNBz8A4OGxT7apFYKux2Kx8tn7h8nOuoJ/gJr7Hxt6ywQM\nsu2SvJEMGtxECMGujI1U1ZiuSqu2VLIn80tswubyephMJvR6faNjOp0Oo9F41bmvvvoqQggeeuih\nZl37U+lGoxFFURqlX6tcSZKkekIIKup2gHbVMqvGLa+B1ULA4PtRR7p3X4Z1e97BbKlmaM+x9I4a\n4NayPammppZPVx4i53QRAVoNs+YOI6JzsKerJUnST5BBg5tUVJZSarhyzfQSw2VKDJddXg+tVovB\nYGh0rKKigqCgxk/v/v3vf7N27VpWr16NWq1u1rVNpRsMBoKCghznNExvqlxJuhFyrfO2ryqniNqy\nKnz1/gTGOn/fAsvFTKoOrgaVL0ETnnUcd8e9daH4HDuOf4ZK8WH27fOvf0EbUWOu5ZN3D3H+bAla\nnR+z5g6jfaTO09VyK9l2Sd5IBg1uovZVo1JdewqJj8rXJROifywuLo709HTHa5PJRE5ODnFxcY5j\nH3zwAf/4xz/4/PPPiYiIaHRtbm4uJtMPvSUZGRmOa+Pi4jhx4oQj7dy5c1gsFmJjYwkODqZjx45k\nZGQ0ea0kSVJTKg7XbeY2oAuKyvlDdwxfvAJCEDjiEXzbdXV6/j/lo9SlCGFjXOJ0Ood3c2vZnmKu\ntrDuvwfJzyklSO/HrMeH0a6jfHgkSd7ArUGDoijBiqKsVRQlU1GUE4qiDFcUJVRRlG2KopxSFGWr\noijBDc5/TlGU03Xn39Xg+CBFUY4rivK9oih/a3BcoyjKqrprvlMUxTV92Tch0E9HRGjUNdMjQqPQ\nBzpvtQir1Up1dTU2mw2r1YrZbMZqtTJ58mSysrLYtGkTZrOZxYsXk5CQQI8ePQBYu3YtL7/8Mp98\n8gldujSecBgbG0tCQgKLFy/GbDazceNGMjMzmTp1KgAzZ85ky5Yt7N27F5PJxKuvvsqUKVMccyBm\nzZrFkiVLKC8v59SpU7z//vuOoU/e5uzrWzn7+tZGx2YvHszsxYN5Y9EW3li0xW112RIxgi0RI9xW\nXlPuWn6Eu5YfcXu5clxw22Ypq6Ty7BUUHxW6ftduP29WTc5BzOmbQR1A0J0LGqW5+t7KzDvCoTOp\n+KkDmDnCuQthtFZVlTWsWXGAgvNl6EP8mT1vOGHtbr39KEC2XZJ3cndPw9+BL4QQfYBEIAt4Fvha\nCNEb2AE8B6AoSl/gAaAPMBFYpvwwQ+xfwGNCiF5AL0VR6te/fAwoEUL0BP4GLHbP22qeh8b+lnb6\niKuOh+s6Or1r+o033qBz5878/e9/Z+3atXTu3JklS5YQHh7OypUr+fOf/0xsbCxHjx5lxYoVjute\neeUVSktLSU5OJjo6mujoaJ5++mlH+ooVKzhy5Ajdu3fnpZdeYuXKlYSFhQH2noYlS5Ywb948+vTp\nQ3V1Na+//rrj2meffZaYmBj69+/P9OnTefLJJ7njjltrpRBJkpqv4qi9l0EbF4FPoMbp+Rs2v2TP\nf8wv8Qm+um12FSEEH+60P++aPPTnhAS1/f0IKo01rF1xgEsXKggOC2DW48MJCQv0dLUkSboBblty\nVVEUPTBaCJECIISoBcoVRZkGjKk7bSWwE3sgMRVYVXdejqIop4FhiqLkAjohxIG6a94DpgNbgWnA\n83XH1wFvuvp93YgekQn8/r7/Y/Xuf1FYeh6BICKkC/eP+iXdI/o4tayFCxeycOHCJtNuv/129u3b\n12TakSM//bQ4KiqKDRs2XDN9xowZzJgxo8k0jUbD0qVLr9owTpJullzrvO2yWawY0u0bTwYPcn6n\nsfnUTmpO70IJCCZo3G+uSnflvbXv++2cuZhBsDacyUMfdkkZrYnJYGbNigMUXzYS2i6QBx4bhi7Y\n39PV8ijZdkneyJ37NHQDihRF+S/2XoaDwO+AjkKISwBCiEJFUTrUnd8Z+K7B9RfqjtUC+Q2O59cd\nr78mry4vq6IoZYqihAkhSlz0nm5YTIdePDPj/zxdDUmSpFbNmHkRW3UtfpHB+EU4d1UdIYSjlyEo\n+UlUge5b5rPWamFVqv151swR8wjwa9vDcwzl1axdcYCSIhPhHYJ44LGhaHWun78nSZLzuXN4ki8w\nCPinEGIQYMLeoyB+dN6PX7fErbPgtSTdguSTurbJ1cusVh/fhOX8YVT6jmhvb3o+gavure3HPqWw\nLI/I0Bju6D/NJWW0FhVlVaz+935Kiky0j9Axa+4wGTDUkW2X5I3c2dOQD+QJIQ7WvV6PPWi4pChK\nRyHEJUVRIoD6dUcvAA1n4kbVHbvW8YbXFCiK4gPom+plWLduHcuXLyc62v7HKDg4mH79+tG9e3dn\nvE/JA8rLy+nUqRPww1J29Y2yK14X5J5kSEzfRun1ci+crPtuglvqc9JmalCae97/j19XZJ9GHzvA\nY+XL123rdU2Rga6XVagCNRwpOoMq7azT8t+9K5WyjxYxxB+C7nqaPfsPu+39VZqN/OvDJVRWV/HU\n9Pn4+qhbxc/bFa8T+g5izYoDpGccIrR9IL+e+ygBgZpWUz/5Wr5uS6/rvz9/3v6wZciQISQnJ+Ns\nihDOfLB/ncIUJRV4XAjxvaIozwP1s6BKhBCvKYqyEAgVQjxbNxH6Q2A49mFHXwE9hRBCUZS9wG+B\nA8Bm4B9CiC2KojwBJAghnlAUZTYwXQgx+8f12L59uxg0aNBV9SsoKHB88JS8i/y/uzWlpclxwW3R\npU3HMGUWEpLUnbDRPZ2ad+W+Dyn/+Df4hMfQ/rl9KL5NT7B2xb21evcyPv1uBb06J/LiQyva7O7P\npUUm1qw4gKG8msguwcxIGYJ/gNrT1WpVZNsludLhw4dJTk52egPj6+wMr+O3wIeKoqiBs8AvAB9g\njaIojwK52FdMQghxUlGUNcBJwAI8IX6IcH4NvAv4Y1+NqX59yxXA+3WTpouBqwIGSZIkqfWqNZox\nnboECugTo5yat6g123d/BoImPnfNgMEVSgxX2HzgAwB+NvbJNhswFF82smbFAUwGM51jQpmRMhiN\nn7s/akiS5Apu/U0WQhwDhjaRNP4a578KvNrE8UNAvyaOm6kLOiRJavvkk7q2x3A8H2yCwJ4d8NUH\nODXvyj3/xVqaj29kHwIGNb3KWz1n31vr9rxFTa2ZoT3voHfnRKfm3VpcKqhg3X8OUFVpoUv3MO6d\nMwiNRgYMTZFtl+SN5G+zJEmS1CoIq42KY/a9GYKdPAHaVm3A+NVfAdBN+v9QVD5Ozf+n5Bed5Zv0\nDagUHx508p48rUXB+VLWv3sIc3Ut3Xq1Y+rPBqJWu+9nLEmS67l7czdJkiSnaTgJTPJ+pjOXsRrN\nqMO1+EeHOTfv1LewGYtQdx2KX/yE657vzHvr49SlCGEjOfFeOoV3dVq+rcX57GLW/ucg5upaesZ3\nZPrDg2TAcB2y7ZK8kexpkCRJkloFxzKrA6KdOubfZirB9I19bwTdpD+6dT5BZt4RDmXvwk8dwIwR\nj7utXHfJzrrMho+OYq210XdgJybcl4DKRz6PlKS2SP5mt1HLly8nOTmZyMhI5s//oTv84MGD3Hff\nfcTGxtK7d28effRRLl261OjaY8eOMXnyZKKjo+nTpw/vvPOOIy0vL49p06YRFRVFUlISqampja5d\nt24diYmJREdHM2fOHMrLyx1pNTU1zJ8/n5iYGPr27cuyZctc9O5d7+zrWzn7+tZGx2YvHszsxYN5\nY9EW3li05RpXOt+WiBFsiRjhtvKactfyI9y1/Kd3E3cFOS647ai5YqA6rxRF44MuwbkroRm//hui\n2oBf3Dj8ejbvnnHGvSWE4KPUfwAweejDhAS1a3Gercmp9EI+/+AI1lobicO7MHFGPxkwNJNsKX4w\nNwAAIABJREFUuyRvJH+7PUDUmjFnf4f5zLcIS7VLyoiMjOTpp5/m4YcfbnS8rKyMlJQUjh07xrFj\nx9BqtY2CipKSEh544AF+8YtfcPbsWQ4ePMgdd9zhSJ87dy6JiYlkZ2fzhz/8gZSUFEpK7FthZGZm\n8tRTT/H222+TlZWFv78/CxYscFz7l7/8hZycHNLT0/nss89YunQpO3bscMn7lyTJu5TX9TLo4juh\ncuLkWWvZBUxpy+15T/qj0/JtjoNndnK64Dj6wFAmDX34+hd4kfRD+WxadRSbTTB0dDfGT+2Lomqb\nK0JJkmQngwY3M3z1V668fjsl/5xGybJpXHl9DIYti51ezqRJk5g4cSIhISGNjo8fP56pU6cSFBSE\nv78/jz/+OPv373ekL1u2jOTkZGbMmIGvry9arZaePe3rpGdnZ5Oens7ChQvx8/NjypQpxMfHs2HD\nBgDWr1/PxIkTSUpKIjAwkEWLFrFp0yZMJvvmY6tXr+b3v/89er2eXr16MWfOHD7++GOnv3fp1iHH\nBbcN1moLxpMXAefvAG3Y+jpYqvEfMA11l+avWtTSe8tqq2XVrn8CcN9tcwn0C2pRfq3J4e9y2bo+\nAyFg5Pge3D6hV5tdQtZVZNsleSMZNLiRcdc7mL7+O9ZLp8FWCzYr1sunMX7zJsYdSz1Spz179hAX\nF+d4ffDgQYKDg5kwYQK9e/fmZz/7Gfn5+QBkZWURExODVqt1nJ+QkEBWVpYjPT4+3pHWtWtXNBoN\n2dnZlJeXU1hY2Ci94bWSJN26jCcKEBYrAdFhaMKd9+G69vIZqvZ9CCofdBMXOS3f5kjN2MSF4nN0\nCO7M+AE/vbyrN9mXepYdGzMBGHtPb24b10MGDJJ0i5BBg5sIIaja/xHCbLg60Wyk6uBahM3m1jqd\nOHGCN954gz/96U+OYwUFBaxevZrXXnuN9PR0unTpwuOP2yfvmUwm9Hp9ozx0Oh1Go/G66UajEUVR\nGqU3vFaSboYcF+z9hBA/TIB2di/Dl6+CzUrAsAfx7XhjO0u35N6qsVSzLu1tAB4Y/St8fbx/N2Qh\nBLu3fc/urd+DAndOj2fIqG6erpbXkm2X5I1k0OAmNmMRtvLCa6ZbKy5iKy9wW33Onj3LAw88wGuv\nvcbw4cMdx/39/Zk0aRKJiYloNBoWLlzI/v37MRgMaLVaDIbGQU9FRQVBQfYng02lGwwGgoKCHOc0\nTG94rSRJt6aqnGIspZX46PwJ7NHeafla8o9TfeRT8PVDd/czTsu3ObYcXk2J8TJdO/RmRJ+73Vq2\nKwib4JtNWezbeRZFpXDP/f1JHNbF09WSJMnN5JKrbqL4+oHPtX/cisoXRe3c3U+vJS8vj/vuu49n\nnnmGmTNnNkqLj4+/qqu5/nVcXBy5ubmYTCbHEKWMjAzuv/9+R/qJEycc1507dw6LxUJsbCxarZaO\nHTuSkZHBmDFjHNc2HBrlTbr//uoPAqueOeSBmsCEwm89Um5D2+YO9Ei5aWlp8omdl/thmdUuKCrn\nPccybH4JAO3IR/EJjbrh62/23jJWlfP53v8C8OCY36BSvPvZnM0m2PZpBhmHLuDjozB59gB6xnf0\ndLW8nmy7JG/k3a2ZF1EF6PFpd+2uXJ923VAFhTutPKvVSnV1NTabDavVitlsxmq1cvHiRaZPn87j\njz/OI488ctV1Dz30EJs3b+bEiRNYLBZef/11kpKS0Ol0xMbGkpCQwOLFizGbzWzcuJHMzEymTp0K\nwMyZM9myZQt79+7FZDLx6quvMmXKFEeAMWvWLJYsWUJ5eTmnTp3i/fff56GHHnLae5YkybtYyiqp\nzL4CPgr6/jf+wf5aarK/w5z5NYpfENo7/9dp+TbH5/vexWQ2kBAzjP5dk9xatrNZrTY2rz5GxqEL\n+KpV3DtnsAwYJOkWJnsa3Eg/5QVK330UW2leo+OqkM7oJ/8/p5b1xhtvsHjxYkcvwdq1a3nmGXsX\nfW5uLq+99hqvvfaa4/zz5+1P+0aPHs0f//hHHnjgAaqrq0lKSmq0T8OKFSt44okn6N69O1FRUaxc\nuZKwMPvOrXFxcSxZsoR58+ZRVlbG2LFjWbr0hwnezz77LAsWLKB///4EBgby5JNPNlrOVZJulHxS\n590qjtnbwqDekfgEapySpxCCik32eVrasU/gc5N7I9zMvVVUUciWQ6sAePD233j1BOFai5WNHx8l\nO+sKGj8f7pszmKhuzt2l+1Ym2y7JGylCCE/Xwe22b98uBg0adNXxgoICOnVy7qZCP2YpOIFh88vU\nXskGwLddN4ImPofmBpYClK7mjv87SZKcx2axcv6tVGzVFjo9PBz/yJDrX9QM1Se2Ufrv2SjaMDr8\n8TAqf/31L3KSt778EzvTPyep9538btpf3Faus9WYa/nsgyOczy7GP0DNjF8MITIq2NPVkiSpmQ4f\nPkxycrLTn1rIngY3U3eKJ+zxjzxdDUlqE+S4YO9lyirEVm3BL0LvtIBB2GyOuQxB4/+3RQHDjd5b\neUXZpGZsxEflw6zRT9x0uZ5WXWXhk5WHKDhfRmCQhvsfHUr7CJ2nq9XmyLZL8kYyaJAkSZLcSgjh\n2AHamcusVh/5lNqCDFQhndCOesxp+TbHql3/RAgbyQPuJzLMuUvHukulqYZ1/z3I5YIKdMH+3P/Y\nUMLaaa9/oSRJtwQZNEjSTTj7+lag8SpKsxcPBmBI2csAPP3KBLfUZUvECMCzqyjdtfwI4P5VlOST\nOu9kvlBGzaUKVAFqtHERTslTWC0YvnwFAN2EhShq/xbldyP3Vlb+EQ6dScVP7c99t81tUbmeYqyo\nZu1/DlJ82UhIWCD3PzaU4FD3rOh3K5Jtl+SNZNAgSZIkuVXZgXMA6BO7oPL1cUqelXs/wFp0Dp8O\nPQkY+qBT8mwOIQQfp9oXfJg05GFCbnLitSeVl1aydsVBykoqCe8QxP2PDiFI37KgS5KktkcuuSpJ\nktdKS0vzdBWkG1RTbKTyzBUUHxXBg5wzjEfUVGLc+joAunueQ/mJPXGaq7n31qEzuzh14Ri6gBAm\nD/t5i8t1t7KSSla9s5+ykko6dtIz6/FhMmBwA9l2Sd5I9jRIkiRJblO2PweAoITO+Gj9nJKnKW0F\ntopCfKMS8e8/1Sl5NofNZmXVrjcBuG/EXAL9vGuH+/pJz4byajpFhzAjZTB+/mpPV0uSpFZK9jRI\nkuS15Lhg71JrqMZ4sgAUCBka45Q8bWYjpu3/AEB3zyKn7SrdnHtr14nN5BefpX1wJ8YnznBKue5i\nrbWx4cMjlFwxEd4hSAYMbibbLskbyaBBkiRJcovyQ7lgE2h7dUQd6pxVeSp3L8dmKkYdMwS/PuOd\nkmdz1FiqWZP2FgCzRv0Kta9zNqdzByEEX31+gvNnSwgM0nDfIzJgkCTp+uTwJEm6CQ1XTaq36plD\nHqiJZ1dNqufuVZPqybXOvYe12uLYATpkWDen5GmrrsC4wz4JWXfPc07dgfl699bWw2soMVwipkMv\nRvR1z0ppzrI/9SwZhy7gq1Zx75zBcpUkD5Btl+SNZE9DG7V8+XKSk5OJjIxk/vz5juN5eXmEh4cT\nHR3t+LdkyZJG177wwgv06NGDnj178uKLLzZKy8vLY9q0aURFRZGUlERqamqj9HXr1pGYmEh0dDRz\n5syhvLzckVZTU8P8+fOJiYmhb9++LFu2zAXvXJKk1shwLA9RY8U/Ogy/COfsLmza9W9EZSnqbsPR\n9BrrlDybw1hdwWd7/wPAQ2N+g0rxnj+lWccvsnvbaVBg0gOJcqdnSZKaTfY0eICotVF9sQwAv8hg\npy052FBkZCRPP/00O3bsoKqqqlGaoijk5uY2+VTu3Xff5csvv3Ss7HDvvfcSExNDSkoKAHPnzmX4\n8OGsWbOGbdu2kZKSwqFDhwgLCyMzM5OnnnqKNWvW0L9/f373u9+xYMECli9fDsBf/vIXcnJySE9P\np7CwkGnTphEXF8e4ceOc/v6lW4N8UucdbLVWyg/mAk7sZaiqwLTznwDoJjq3lwF++t7asO9dTGYD\n8dFD6N/1NqeW60oXckv5cl06AGMn9qZnfEcP1+jWJdsuyRt5z+ORNqJ0bzb5K7/l4poDXFx9gPyV\n31Ly7RmnlzNp0iQmTpxISEjIVWlCCGw2W5PXrVq1il//+tdEREQQERHB/Pnz+fjjjwE4c+YM6enp\nLFy4ED8/P6ZMmUJ8fDwbNmwAYP369UycOJGkpCQCAwNZtGgRmzZtwmQyAbB69Wp+//vfo9fr6dWr\nF3PmzHHkLUlS22U8UYC1sgZNBx0BXcOdkqcp9S1EZRma2BFoeo52Sp7NUWy4xJeHVgHw0JjfOj1Y\ncZWy4ko+e/8w1lobicO7MHhkV09XSZIkJxFCYLPUUmuqpKa0wmXlyJ4GNyo7lEvZvnOIGqvjWG1J\nJeX7c1D5+jjtCdz1KIpCYmIiiqIwZswY/vSnPxEWFgZAVlYWCQkJjnMTEhLIysoC4NSpU8TExKDV\naptMz8rKYtiwYY60rl27otFoyM7OJiYmhsLCQuLj4xtd+8UXX7j0vUptmxwX3PoJm6D8QA5g72Vw\nxodsW2U5pp324Y1BLuhlgGvfW+vS3sZSayap93hiI+ObuLL1qaqs4ZOVh6iqtNC1VzuSJ/fxmmCn\nrZJtV9skhMBWZcZSbsBSVtHgn4FaowlRU4vNYsFW91XUWLBZfvR9jQXR8BxLLTZzTeO0+mM1Fmw1\nNdjMFhDCUY8OX7zpkvcngwY3EUJgzLjQKGBwpFmsGE5eJHhoV5c35GFhYWzfvp1+/fpRUlLC008/\nzbx581i3bh0AJpMJvV7vOF+n0zl6Cn6cVp9+8eLFn0w3Go0YjUYURbkqb6PR6JL3KUlS61B55jKW\n0kp8gwPQ9nbOcBhT6jJEdQWanrfj12OkU/JsjgvF59iZsRGV4sOs0b92W7ktYa218fmHRygpMtE+\nQseU2QNQ+chBBpL0U4TNhqW07gN/ucH+ff3XHx8rq6C2zOA4bjPXeKbSKhUqPzUqjetWcpNBg5vY\nKmuoNZqvmW41VmM1VOOrd+0qFlqtlsTERADatWvH4sWL6dOnDyaTCa1Wi1arxWAwOM6vqKhw9Cz8\nOK0+PSgo6JrpBoOBoKAgxzkGg4Hw8PCrrvU2Z1/fCjReRWn24sEADCl7GYCnX3HPiipbIkYAnl1F\n6a7lRwD3r6Ikn9S1bkIIyvafAyB4SIxT9lCwmUoxpdqXOtVNfLbF+V1LU/fWql1vIoSN5AEziAxz\nzm7WriSEYOunGeSfK0Wr8+PeOYPw85d/9lsD2Xa5n7DZqCkuo6aoFPOVEmqulGC+XEzNlbrXRSWY\nL9uP1xSXIaxXP+RtDpWfBnWIHnWwDnWoHnWIDt9gPb66QFQaDSqNLyq1GkWjRqX+0fcadd1r+3GV\nRo3iOO6LUndMpfa1X6PR2F/7qVH5/vC7ffjwYWf92BqRrYebKL4qFNW1exEUlYLiggnRzaEoimOO\nQ1xcHBkZGQwcaP/wl56eTlxcnCMtNzfXEWAAZGRkcP/99zvST5w44cj33LlzWCwWYmNj0Wq1dOzY\nkYyMDMaMGeO4tj5vSZLanuq8UswXy1EFqNH1i3JKnqadyxDVBjS9x6LpnuSUPJvj1IVjHDi9Ez+1\nPzNGPO62clti7zfZnDxSgK/ah/vmDEIfIpdWldoeIQTmy8WYTudgLiyqCwjsgYD5ij0ouJlAwDdY\nhyZUj2/9h/9gnT0YaPh9SP3XH/75BDhnp/vWSAYNbqLyU6MODcR6jd4GdWggPoHO61KyWq1YLBZs\nNhtWqxWz2Yyvry9Hjx4lODiY2NhYSktLee655xg9ejQ6nQ6A2bNns2zZMsaPH48QgmXLlvHLX/4S\ngNjYWBISEli8eDGLFi1i27ZtZGZmMnXqVABmzpzJhAkT2Lt3L/369ePVV19lypQpjgBj1qxZLFmy\nhAEDBlBYWMj7778vl12VWkSOC27dHL0MA6NRqVv+UMRmLMa0620AdBNc18sAje8tIQQfpdp3nb5n\nyM8IDWrv0rKd4eTRAvZ8fQYUmDI7kY6d5dKqrYlsu26cEALzxSsYvz+H8dQ5+9fvczB+n0NtueH6\nGQDqUD2admH4tQ9D0z7U/rVDOH7tfnjt1yEcTbtQVBq54eGPyaDBjcLG9OLShmNYK6obHffR+RM6\nupdTy3rjjTdYvHixY47E2rVreeaZZ4iNjeWll16iuLgYnU7H2LFjeeeddxzXpaSkkJuby6hRo1AU\nhTlz5vDII4840lesWMETTzxB9+7diYqKYuXKlY5J1HFxcSxZsoR58+ZRVlbG2LFjWbp0qePaZ599\nlgULFtC/f38CAwN58sknueOOO5z6viVJah3Mlw1UnStCUfugH+ScoTzGb/6JMBvxi0tG023Y9S9w\nksPZuzmVfxRdQDBThs1xW7k3K/9cCVvX25dWHTcpjtg+HTxcI0lqPmGzUX3hUl1AUBcY1AUJVmNl\nk9eoQ3Roe3XDv1OHuoDAHhg0/F4GAi2niAazrW8V27dvF4MGDbrqeEFBAZ06dXJp2ebLFZSkncFS\nap9crA4JJGxkT/wi9Ne5Uvop7vi/a0jOaWjMU3MapNbr8ubjGE9eRD8omnbJfVqcn9VYxJU/DUTU\nmAj/36/QxAx2Qi2vz2az8sy7D5JflM2ccQu4Z8hDbin3ZpUWmfjwX3uprrIwMCma5Kl9PV0lSWqS\nsNmoyits1Gtgqvtqraxq8hp1WAhBvbsR1KsrQb26EdTb/lXTPkyuCNbA4cOHSU5OdvoPRPY0uJlf\nBz2R910dsEiSJLUVlvIqjJmFoCgED+nqlDxNO5Yiakz49b3LbQEDwK4Tm8kvyqadPpI7B8x0W7k3\no6qyhvUrD1FdZaF77/bcMUnOGZNah5rSCoyZ2RhOnsGQlY3hZDbGzGysVdVNnq9pH2YPDHp3/yFA\n6NUVTbtQN9dcakgGDZJ0Exr2MNRb9cwhD9TEsz0M9TzVwyDHBbdO5YdyQAiC+kSiDm755Fur4TKm\n3fad5YMmLGxxfs2RlpbGsKShrE2zr9Q0a/QTqH1dt5RhS9XW2vjs/SOUFVfSIVLH5NmJcmnVVqyt\ntl02Sy2m7PMYMs/YA4OTZzBkZlNdcLnJ8/06tnP0HGh7dUPXuxvanl3RhMk5OK2RDBokSZIkp7FW\n1WA4fgGA4GFdnZKnafs/wFKFX8JENNHuC1C3HV5DseESMR16MbKve4Yb3gwhBFvXp3Mht5QgvR/3\nzhmMxk/+eZdcy3ylxN5zcNIeIBgyz2D8PgdRY7nqXFWAH7q4WHR9Y9H16YGubw+C+sSiCZVDs72J\nbFUkSfJabfFJnberOHIeYbES0K0dfh1a/oHAWl6Iac9/ANC5qZcBYOCQRH77zh8BePD236BSWu9T\n+2+3nyHz2EXUGh/umzMYXbC/p6skXYc3tV3CZsOUfZ7yo5kYMk5jqBtmVFNU2uT5ATGd0PXtURcc\nxKLr24PAmE4oPp5ZVl5yHhk0SJIkSU5hs1gpP3wegBAn9TIYt/8dLNX49Z+MOqq/U/Jsjg373sVU\nXUHfLoNJ7Hab28q9UScOX+C7HdkoCkyenUiHTvLJrXTzhBBU5xdSfjTT8a/i+ClqDaarzvXVaeuC\ng1iC+tYFCHHd8Q3SeqDmkjvIoKEBIQRCCDkD38vU/79Jt562Oi7YWxnSL2CrsuAXoce/S1iL87OW\nFVD57buAe3sZSgyXeX/9cvRRvjw09ret9m/C+bPFbP00A4Bxk/sQGyeXVvUWraXtMl8p+SE4qPta\nU1x21Xl+ke0JHtCH4P690cX3RNcnFv+oiFb7uyG5hgwaGggODqakpITw8HBPV0W6ASUlJQQHy0lT\nkuRJwmaj/GAOAMHDujnlw4Tx679BrRn/xKmoO8W3OL/mWrX7n9RaLQzvfTc9IhPcVu6NKL5s5PMP\njmCzCgaPjGHgbTGerpLUylkqjFQcz6L8yA+9CNUXLl11njpUbw8Q6v7pB/TBv2M7D9RYam3cGjQo\nipIDlAM2wCKEGKYoSiiwGogBcoAHhBDldec/BzwK1AJPCiG21R0fBLwL+ANfCCF+V3dcA7wHDAaK\ngFlCiPPNrV9QUBBms5mCgoKWv1nJbfz8/AgKCnJrmXKfhsY8tU9Da3hSJ9mZTl2itrwKdWgg2p4d\nW5yftTSfyu/eA0UhaMIzTqhh8xw9+y27MjbRsXsws0fPd1u5N6LSWMMn7x3CXF1LbJ8OjJkol1b1\nNu5ou4yncyhK3e/oQTCdufrjkI82EH3/3gQnxtmDhIF9CIjuJHsQpCa5u6fBBowVQjScPfMs8LUQ\nYrGiKAuB54BnFUXpCzwA9AGigK8VRekp7ONQ/gU8JoQ4oCjKF4qi3C2E2Ao8BpQIIXoqijILWAzM\nvpEKyl4GSZKkGyOEoGz/OQCCh3ZFUTmhl+Gr/wNrDf4D70Ud6Z4NyirNBt7Z+hIA94/6JZFhztnJ\n2pmstTY+++Aw5SVVdOykZ9Ks/qic8POW2gZD1lkubfqGwo07MJ461yhN0ajRx/ds1Iug7REtJyhL\nzebuoEEBfrwExTRgTN33K4Gd2AOJqcAqIUQtkKMoymlgmKIouYBOCHGg7pr3gOnA1rq8nq87vg54\n00XvQ2qm1jJuU2qb5P3VOlTlFlNz2YBPoIag+JbvzF5bkkflvg/svQx3u6+X4YNv/k6J4RKxkfGE\nmLu6rdwbkfbVaQrOl6EL9ufeOYPQaOQoY2/krLZLCIEx6yyFG+2Bgul0jiPNN1hHhztHEjK0H8ED\n+qDrE4tKo25xmdKty92tjQC+UhTFCrwthFgOdBRCXAIQQhQqilI/k6sz8F2Day/UHasF8hscz687\nXn9NXl1eVkVRyhRFCRNClLjsHUmSJN3iyvfZn2jqB8eg8m35U0vjV0vAasF/8EzUEb1bnF9zHM/Z\ny47jn+Lro+ZXE18gJ6v1DVPNPVPEgd3nUFQKUx5MJEgvl1a9FQkhMGZmU7hxhz1QaDDsSB2qp8OE\n24mYMo7wUYNlkCA5lbuDhpFCiIuKorQHtimKcgp7INGQM5fBkX22HiafAkuuJO8vzzMXllN1vgRF\n7YN+QJcW51dbnEvVvo9AUaG76/dOqOH1VZlNvLPlzwDMHDmPqHbdiRrV3S1lN1elsYYv1qYDMGJc\nLJ2iQz1cI6klbrTtEkJgOHG6LlD4hsqzeY40dVgwHSfaA4WwkYNRqWXvk+Qabr2zhBAX675eURTl\nM2AYcElRlI5CiEuKokQA9XuNXwAa/gWKqjt2reMNrylQFMUH0DfVy7Bu3TqWL19OdLR9vGpwcDD9\n+vVz/BKnpaUByNfy9TVfF+SeZEhM30bp9XIvnKz7boJb6nPSZmpQmmd+HhXZp9HHDvBY+fK1515v\nW/kZVXklJM+4Bx9/dYvz+/rN32MuqGXM5Fn4duzplvez+cAHFFkK6d6xD2E1sY2Gjnj655uWloYQ\ngqJzgZgMZiqteVh8tUCPVlM/+do1r4UQbHtvFSXfHabzsVwqz+U72vvEdp3pOGkMuV3DCYzvScLY\nMR6vr3ztudf1358/b+91GjJkCMnJyTib4q717RVFCQRUQgijoihaYBvwIpCMffLya3UToUOFEPUT\noT8EhmMfdvQV0FMIIRRF2Qv8FjgAbAb+IYTYoijKE0CCEOIJRVFmA9OFEFdNhN6+fbsYNGiQG961\n1PCPryQ5m7y/PMtSWkneit2gKETPux1fXcuGy9QWnePKK8MAaP/cXnzbxzqjmj8pI3c/L63+FT4q\nX/7yyId0af/Dh/HWcm8d2Xue7RtO4ufvyyO/HYk+JMDTVZJa6Fr3lxCCimNZ9h6FTd9QlfvDMDlN\nu1A63jOWiCl3EHrbAFS+vu6ssuRFDh8+THJystNH27jzjusIfKooiqgr90MhxDZFUQ4CaxRFeRTI\nxb5iEkKIk4qirAFOAhbgCfFDhPNrGi+5uqXu+Arg/bpJ08Xc4MpJkiRJUvOVH8wBAUHxkS0OGACM\nW98Am5WAYQ+6JWCorqnk7bphSTNGPO4IGFqTK4UGUr/IAuDO6fEyYGijqgsuk//xJi6s2kxV3kXH\ncU37MCImjaXj5DsIu22AXOlI8ii3BQ1CiHPAgCaOlwDjr3HNq8CrTRw/BPRr4riZuqBDah1ay5M6\nqW2S95fnWE1mDBn2kaEhQ7u1OL/aK9lUHVwNKh+C3DSX4aPUpVwpL6Brh95MHf5Io7TWcG9ZLFY2\nrz5Gba2NhMGdiesf6ekqSU4yatQohNXKlR17yf/gcy5/9S3YbAD4dWxHx0ljiZh8B6HD+8tAQWo1\nZN+WJEmSdMPKj5xH1NoIjG2Ppl3LN1c0bn0dhI2A4T/Ht13XllfwOk6eP8S2I2vwUfnwq3tewNen\n9a0ys2vLKYouGQkND2Tc5D6ero7kJPW9CvkfbXTsyKz4+tBx0jiifj6N8FGDUVQ/Xp1ekjxPBg2S\nS7WmccFS2yPvL8+w1dRSccQ+4S5kmBN6GS59T9WhdaDyJejOBS3O73qqa6p4a8uLAExPeoyYDr2u\nOsfT91Z21mWOfHcelY/CpNmJaPzkn2tvJqxWir7ZR977n3H5q285WWugr0pLQEwnujw8jc6zJ+HX\nPszT1ZSknyRbIUmSJOmGGI7nY6uuxa9TCP5RLV/601DXyxB42xx8w12/C/Pq3f/kctkFotv35N7b\nHnV5eTfKWFHNlnX25VVH39WLiM7BHq6RdLOqL16x9yp8uKFRr0LYiEEMeeoJ2asgeRUZNEgu1Vaf\nAp99fSsA3X9/t+PY7MWDARhS9jIAT78y4eoLXWBLxAgAJhR+65bymnLX8iMAbJs70K3lttX7qzUT\nVhtlB3MBCBne8l4Gy8VMqo98Aj5qgu58qsX5XU9W/hG2HFqFSvHhVxOfv+awJE/dW8Im+HJdOlWV\nFmJ6hDNkZFeP1EO6eY5ehQ8+58pX3yKsVgDZqyB5PRk0SJIkSc1mzLyI1VCNOlxLYGybFuIVAAAg\nAElEQVT7lue39XUQgsDb5uATGuWEGl6b2VLFW1/+CYFgelIK3SJa3zyBg3tyyD1TTECgmokz+6Go\n5B6l3qK68Ar5H13dq9Bx8h10mTNd9ipIXk8GDZJLeXpcsNS2yfvLvYQQlB3IAewrJilKyz7QWgpO\nUn30M/DREDT+d06o4U9bs/tfFJaeJ6pdLPfdNvcnz/XEvXXpQjm7t30PwIQZ/QjSt3wZW8m1hNVK\n0c795L3/WRO9ClPpPHtyk70Ksu2SvNFNBQ2KotwB2IQQqU6ujyRJktRKVZ0twlJkxCfIj6A+LV/+\n07j1NQACR6TgE9K5xfn9lFMXjvHFwY/qhiW9gNpX49LyblRNTS2bVh/DZhUMSIomtk8HT1dJ+gk1\npRXkf7iB8+9+QnV+IdCgV+Hn0wgfPUT2KkhtTrOCBkVRUoFFQog9dbs2PwXUKoryTyHEKy6toeTV\n5JMUyZXk/eVeZfvPARA8OAbFt2UfiCz56VQf2whqf4LGP+mM6l1TjaWat798EYFg6rCfExvZ97rX\nuPve+mZTFqVFlYR3CGLMxN5uLVtqPsPJM+SuWEvBJ9uwVZmBBr0Ksybh1yG8WfnItkvyRs3taUgA\n9tZ9/zhwB2AA9gAyaJAkSWrjqgvKqM4vReXniz6xS4vzM2xdDIB2RAo+wa7dtGztnrcpKMmlc3g3\nZoyc59Kybsap9ELSD+bj46ti8uxE1Gq5mVdrYqut5cq2PeQuX0vJt4cdx9vdMZyYx+6n3bgk2asg\n3RKaGzSoAKEoSiygCCFOAiiK0vK19qQ2ra2O22y4alK9Vc8c8kBNPLtqUj13r5pUr63eX61R2T57\nL4N+QBdULdwzoOb8Yczpm0EdgDbZtb0MpwvS2XTgAxRFxS8nPo/G169Z17nr3qooq2LbpxkAjJnY\nm/YROpeXKTWPYwjSf9c7Jjb7aAPpPOseoh+dQVCPmJvOW7ZdkjdqbsufBrwJRAKfAtQFEEUuqpck\nSZLUSpgvVVB55jL4KOgH3fwHJbBPpjZ8/v8A0I5+HB99R2dUsUk1tWbe+vJFhLAxZdgcenbq57Ky\nbobNJvhizXHM1bV0792egUmu36NCur6mhiAFdosi+rGZRM2ahK9O6+EaSpJnNDdoSAEWAFeAxXXH\n4oC/u6BOUhsin6RIriTvL9cTQlC88xQAwQOj8Q1q3pP6azGnf0FN9rco2jCCxv+vM6p4Teu//TcX\nis8RGRrD/SP/54audce9tW/nWfJzStHq/Jgwo1+LV6OSbp67hyDJtkvyRs0KGoQQxcCiHx3b7JIa\nSZIkSa1GVU4R1edLUPn5EpLUvUV5CauFio0vAKCbsBBVoOt2Os6+eJKN+95DQeGX9zyPRt26li8t\nOF/KtzvOADBxZj8Cg1rXak63ClcOQZKktqZZYbOiKE8pijKg7vskRVHOK4pyTlGU21xbPcnbpaWl\neboKUhsm7y/XEjZByU77vgEhSd3xCWjZB9vKPf/FeiUbn/Y9CByR4oQaNs1SW8NbX76ATVi5Z8hD\n9O6ceMN5uPLeMldb2LT6OMImGDKqK117tnNZWVLTDCfPkLHgVXYOmsb3Ly2j+sIlArtFEffnJxl7\n5DP6vvKUSwMG2XZJ3qi5w5P+F1hR9/2rwF+xr570N2C4C+olSZIkeZjxZAE1RUZ89f7oB7VsvL2t\nstyxYpJ+6gsoPmpnVLFJn3y3nLyibCJCuvDA6F+5rJyb9fWGk1SUVtGxk57Rd/XydHVuGUIIrnz1\nLTlvfSxXQZKkm9DcoCFYCFGuKIoOSATGCyGsiqIscWHdpDagrY7bPPv6VqDxKkqzFw8GYEjZywA8\n/coEt9RlS8QIwLOrKN21/Ajg/lWU2ur91RrYLFZKdp8GIHRUT1S+LVsG1Pj1XxGmEjSxI/BLmOiM\nKjbpXGEmn+99FwWF/5n4PH7qgJvKx1X31skjBWQevYiv2odJs/rj08L9LqTmKT+aSdaLb1L6nb2t\n8vQQJNl2Sd6ouUFDnqIoI4B4YFddwKAHrK6rmiRJkuQp5YdysRrNaDroCOrbsn0UaotzMaW+DYBu\n2p9dNuG31mrhX1++iE1YmTB4Nn26eGYp4GspK67k6w0nAEie0oew9kEerlHbV5VfyPevvsXF9dsA\nUIcF0/23c4h6aApqvfz5S9KNaO4jjt8D64A/AH+uOzYZ2O+KSklthxy3KbmSvL9cw1pZQ9m+s/8/\ne/cdHlXRNnD4N9vSO4TQQXrvFkAUKaKogBULNuy9oB/Yu4L9FfVV0VcUGzYQRUARxdCkt9AJoQdI\n79vm+2MTCIiyyZ7NZpfnvq5c2T3ZM+cBJsOZMzPPAJB0dhufb/ILfnwWXHbCe1yGrYn/buS/X/QR\nOw9uITm+ISPPvMunsoyuWy6Xm5+mrsZe5qJ1x3p07NHQ0PLF0ZwFRWx+4b/82Xck+76dg7JZaX7H\n1fRbNJXmt10Z8A6DtF0iGHmbPWkm0OCYw1+XfwkhhAghOYu2oe0uIprXIaJpkk9l2Xcso3Tld2AJ\nI/aCxw2K8O8yDmxm2mLP0rtbhzxBuK1605L8ZdHcrezblUdMXDiDR3SU9Kp+4nY62T3lB7a+PAl7\nVi4AKcMH0vqR24ls4t+dx4UIdV5v66mUagVcCTQE9gBfaK23+CswERpk3qbwJ6lfxnPkFJG/ahcA\nif18W6SrtSZ/uqejEHXW7ZgTGvkc3/E4XQ7enfkULreLwd0up0OTnj6XaWTd2rU9m8V/bAcF51/e\nmfAI/y0CP1lVLHLe9OxEirZkABB/amfaPnU38d07BDi6v5O2SwQjrzoNSqkLgc+AH4EMoA2wTCk1\nSmv9gx/jE0IIUYOy/9wCbk10xwaEJcf4VFbpmhk40pdgiq5D9KD7DIrw72b89Sk7DmyiblwDrjrr\nbr9dpzpKiu3M/HoNaDi9fwsaN08MdEghJ3/tJjY+PZHs1OUARDZrSOvH7qDe0LNlREcIA3k70vAC\nMExrPa/igFLqbGAiIJ0G8Y9SU1ND8olK5axJFb58eHkAIgls1qQKNZ01qUKo1q9AKd2bS9GmTJTF\nRGLfVj6VpZ12CmY8DUD0kLGYwmONCPFv9mZn8N3CDwC45dzHCLdFGlKuEXVLa80v09ZTkFdK/cZx\nnHFOC0NiEx6lew+w+aX32fv1z6A11vgYWtx/A01uuASTrXaP5kjbJYKRt52GRsCfxxxLLT8uhBAi\nyGmtyf7Ds5FbXI+mWGJ820G5OPVDXIfSMSe3IvKMa40I8W+01kya/TwOl51+HS+gU7PatW3QuhV7\n2LwuE6vNzNDLu2A2S3pVIzgLi0h/+zPS//sF7pIylNVCkxsvocV9N2BL8E/nVAjhfadhFfAgML7S\nsQfKjwvxj+RJivAnqV/GKd52kNLdOZgirMSf1tynstzFuRTMeRmA2GHPoMxeL5+rkt/X/kDaruXE\nRMRzzdnGTn/ytW7lZBXx24wNAAy8qD3xScaMgJzM3E4ne778iS3jP8B+MBuAehf0p81jtxPZLLie\nYUrbJYKRty357cAMpdS9wC6gMVAMXOivwIQQQtQM7XYfHmVIOKMFpjDfpnYUznkFXZyLrdWZhLUf\nbESIf5NblMWU398A4LpzHiQ2MsEv16kOl8vNT1+twWF30aZTCu27HZt8UFTVwd8Ws+nptyjclA5A\nXPcOtH3qbhJO7RzgyIQ4eXg1Vqq13gi0A64AXgUuB9pprTf4MTYRAiQXtfAnqV/GKFizB0d2EZb4\nCGK7NvapLOehdIr+/ACUItaPG7l9MvdVikrz6dL8DPq0N36HaV/q1qK5W9m/25NeddDwDrIY1wcF\nG7ezdOR9LL/qAQo3pRPRuD5d/vsMp//0flB3GKTtEsHI6zFjrbWTv69rEEIIEcTcdic5C7YCnhSr\nysd59wU/PgMuBxG9RmJt5J+bupXbUlm4cTY2SxijB42rVTflu9IlvaoR3E4n6ROnsPXVj9AOJ5bY\naFrcdz1NbrwEc3hYoMMT4qT0j50GpdQuQJ+oAK11E0MjEiElVOdtbn95NnB0FqWRE3oA0DP3eQDG\nvDCkRmKZldIbCGwWpcGTVgI1n0UpVOtXTcpbugNXsZ2w+nFEta7nU1n29CWUrpoO1ghihj5qUIRH\nK7UX8+EvLwJwWd/bSI73z87K1albpSWOw+lVTzv7FEmvWk0FG7ax9t7nyV+zEYBG11xE63G3YUuK\nD3BkxpG2SwSjfxtpuKbGohBCCFHjnIVl5C7dAUDiWa19emKvtSZ/mmcjt+j+d2D208381NT/cih/\nP82S23B+z6v8co3q0Frz6/T1FOSWktIojt4DWgY6pKDjdjpJf/szz+iC3UF4w3p0fP0R6vTrFejQ\nhBD8S6dBa/1HTQYiQpPkohb+JPXLNzkLt6IdLiJb1iWisW9PxUtXTcORsQxTTDJR59xjUIRH27Zv\nPT8v/wKlTNwy5HHMJv9kZYKq1620VXvZuGZ/eXrVzpJetYoKNm5n7b3Pkb+6fHRh1DDaPnEXlpio\nAEfmH9J2iWDkvxZXCCFErWXPKqRgzR5QisR+rX0qSzvLPGsZgJjzxmIK920n6eNxuhy8P/s5tHYz\ntNc1nJLSzvBrVFdudjFzf0gD4JwL2pFQJzRvdP3B7XSS/s7nbH3lwyOjC6+No85ZpwY6NCHEMaTT\nIPxKnqQIf5L6VX3Z87eA1sR0aYQtKdqnsor+/ABXVgaWlDZEnOafma0zl31OxoHN1I1rwGV9bvPL\nNSrztm65XW5mTl2DvcxFqw716NjDP9OyQtHJNrpQmbRdIhhJp0EIIU4yJbtzKN56AGU1k9Dbt7n3\n7qJsCue8CkDMRf7ZyG1/zi6+WfAeADcNHke4LcLwa1TXonnb2Lszl+jYMAaPkPSq3pDRBSGCk3Qa\nhF+F6rzNylmTKnz58PIARBLYrEkVajprUoVQrV/+pLUm+/dNAMT1aoYl2rf0lQWzX0aX5GFrfRZh\n7QYaEeJRtNZ8OOdF7M4y+rY/jy7Next+jePxpm7tychh8bxtnvSql3UmItJWI7EFs8JN6ay99zny\nVnm2eWp0zUW0ffLuk2J0oTJpu0Qw+reUq5/iXcrVaw2NSAghhN8Ubc6kbF8e5kgb8b2a+VSW8+A2\nilM/9OtGbn+mzWRtxhKiw+MY1f8Bw8uvrrJSJz9NXYPW0Ktfc5q0SAp0SLWa2+lkx7ufs+XlSqML\nr46lztmnBTo0IYSX/m2kYWul13WA64AZQAbQBLgQmOy/0EQokCcpwp+kflWNdrnJnr8ZgIQ+LTHZ\nfBtsLpjxNLidRJx2NdaGHY0I8Sj5xTl8+ptn6tOoc+4nLqrm9j04Ud2a+0Ma+Tkl1GsQS9+BrWoo\nquAkowt/J22XCEb/lnL16YrXSqnZwFCt9Z+VjvUFHvdveEIIIYySv3oXztwSrIlRxHT2bcGufdsi\nStf8iLJFEnP+IwZFeLRP571OQUkeHZueSr8OF/jlGtWxYdVe0lbtxWI1MfSKzpgtkl71eDyjC1+w\n5eVJMrogRAjwtqU7HVh8zLElwBnGhiNCTWpqaqBDECFM6pf33GUOchZuAyCxX2uUqfo3utrtJn+6\n55lRVP+7MMfVNyTGylanL+LP9T9htYRx0+BHanyB8T/VrbycYn6Z7kmv2n9oOxLr+pZ5KlQVbkpn\nyYW3sfn5d9F2B42uvpA+8z6VDkM5abtEMPJ2bHol8IJS6gmtdYlSKgJ4Gljlv9CEEEIYJXdJOu4S\nB+GNEohsWdenskpXfo9j5wpMsfWIOucugyI8osxRwodzXgTg0t43k5LQ2PBrVIcnvepa7GVOWrZP\npnOvRoEOqdapGF3Y+sqHuMvshDdIpsOrY6nb//RAhyaE8JG3nYbrgc+BPKVUDpAALAOuruoFlVKm\n8nN3a60vUkolAF8BTYEdwOVa67zyz44DbgScwL1a6znlx7sDHwPhwEyt9X3lx23AJ0AP4BBwhdZ6\nZ1VjFMYJ1Xmb21+eDRydRWnkhB4A9Mx9HoAxLwypkVhmpXiyyQQyi9LgSSuBms+iFKr1y2jOglLy\nlmcAkHhWa5+e2mtH6ZGN3M5/BFOY8U/av1nwPgfy9tCkbiuG9vLPvg8ncry6teSPdPZk5BAVE8bg\nER0lveoxCtK2svb+F47su3D1hbR58m6ssTIacyxpu0Qw8mp8Wmu9Q2vdG2gBXAS01Fr31lqnV+Oa\n9wJpld6PBX7VWrcBfgPGASil2gOXA+2A84B31JEW+l1gtNa6NdBaKVVx5zYayNZatwLeACZUIz4h\nhAgp2alb0U43UW3qEd4g3qeyiua/jytnF5b67Yk49SqDIjwiPXMjPy39DIXiliGPYTFbDb9Gdezb\nlcvC3zz5Qc67tBORUZJetYK7zM6W8e+zcPAN5K/eSHjDevT44jU6vjpOOgxChBCvJ7UqpZKAs4Gz\ntNY7lVINlFJVGpst//z5wKRKh4dxJAvTZGB4+euLgC+11k6t9Q5gC3CqUioFiNFaLy3/3CeVzqlc\n1jfAgKrEJ4wn8zaFP0n9OjH7wQIK1+0BkyLxTN+y/LgKD1H4iyebUeywZ1AmsxEhHinf7eSDWc/h\n1i6G9BhJy/rGZ2TyVuW6ZS9z8uNXq9FuTc++zWjWqk7A4qptcpauZcHA69n2+sdop4smN1xC3z+m\nyHSkE5C2SwQjr6YnKaXOAr7FM62oD54n+K2AMXhSr3rrdeAhIK7SsXpa60wArfV+pVRy+fGGwKJK\nn9tTfswJ7K50fHf58YpzdpWX5VJK5SqlErXW2VWIUQghQkbWH54Uq7FdG2NN8C3FZeHsl9GlBYS1\nHUBY23OMCO8os5Z/xfbMDSTF1OPyvrcbXn51zZ2xgbzsEurWj6Hv4NaBDqdWcBYVs/mF/7Lzo29B\na6JaNqHjq+NIOK1LoEMTQviJt2sa3sCzPmBu+ZoG8GRP8nrPd6XUUCBTa71KKXX2v3z0hBvKVYFM\nOA0wmbcp/Enq178r3pFFSfohlM1CwhktfCrLmbmF4gX/A2UiZtjTJz6hig7k7WVq6jsAjB48joiw\nwObwr6hbG9fsY/2KPVgsJoZe3gWLpFfl4LzFrH9oAqW796PMZprfdTUt7r8Bc7hvu4ufTKTtEsHI\n205DM6313PLXFTf19iqcD54RiouUUucDEUBM+a7T+5VS9bTWmeVTjw6Uf34PUDllRqPyY/90vPI5\ne5VSZiD2eKMM33zzDZMmTaJJkyYAxMXF0alTp8O/xBXDhvJe3v/T+70ZafRs2v6on1fI2FOxZGdI\njcST5i6qdLXA/H3kb9tCbIuuAbu+vP/7+z59+pD9xyaWZaQR07khzSNtPpXXfuO74HayOmkw0duy\n6VueZdWIeLXWpO6fSpmjlBTdgeJ9yrOCLsB/n/m5Jbz/1lQcdhejb7+UOvWia82/byDe27Pz+OK2\nh8j6fQntTVHEdm5DwajBHGjemNblHYbaFK+8l/cny/uK1zt3enL/9OzZkwEDjJ+hr7Q+8YN9pdQC\n4Bmt9WylVLbWOlEpNRh4RGt9dpUv6pnu9GB59qQJQJbWerxS6v+ABK312PKF0J8Bp+GZdvQL0Epr\nrZVSi4F7gKXAT8B/tNazlFJ3AB211ncopUYCw7XWI4+9/ty5c3X37t2rGraohtTU1MOVWwijSf36\nZwVpezn401rM0WE0vulMTNbqrz8oXfMjOR9di7JFUffRpZjjUgyMFBakzeKtHx8lKiyGV0d/Q3x0\n4NcMzJ//J3s32didnkOLtnUZPqr7SZstSWtN5ox5pD3yKvZDOZjCbbR66Gaa3noFJosl0OEFJWm7\nhD+tWLGCAQMGGN5gefvb/iDwo1LqJyBCKfUenrUMwwyI4SVgqlLqRiADT8YktNZpSqmpeDItOYA7\n9JEezp0cnXJ1VvnxD4FPlVJbgCzgbx0GIYQIdW67k+z5WwBI7NvKpw6DuyibvK/HABBz4ROGdxgK\nS/KY/NsrAFx99r21osMAsGntPvL2xBIZbePcizudtB2G0v0HSRv7Cgdm/QlAwhnd6PjqWKJOqR17\nZwghao5XnQat9WKlVGfgGuAjPIuNT9Va767ORbXWfwB/lL/OBgb+w+deBF48zvHlQKfjHC+jvNMh\nagd5kiL8SerX8eUu3o6roBRbvViiOzTwqay878bhLjiArUVvIvuMNijCI6b8/ib5xTm0a9yD/p2H\nn/iEGrB/dx4F++IA7UmvGn3ypVfVWrP78xlsenoizvxCLDFRtH78Thpfc5FPu4kLD2m7RDDyqtOg\nlBqjtX6FY/Y9UEo9oLV+zS+RCSGEqDJ7dhG5S3cAUGdgO5Sp+k/IS9f9TOnyr8EaQdzI/xh+s7g+\nYym/r52O1Wzj5sGP1Iqn+fm5JUybsgK3W9O9d1Oat/Zt9+xgVJS+m/VjXiJ7wQoA6g7qQ4fxDxHe\nIPkEZwohQpm3/wM88Q/HHzMqEBGaKi/SEcJoUr+OprUm69cN4NbEdG7o00Zu7uJc8qY+CEDs0Mew\n1D3FqDABsDtK+WC2Z/f0EWeMpkFSM0PLr47SEgfffrycwvwyil276HfuyZVe1e10kv7u5yw4ZxTZ\nC1ZgS4qny3+fpvsnE6TDYDBpu0Qw+teRBqVURSJus1KqP0enMD0FKPBXYEIIIaqmaHMmJRlZmMIt\nJJ7p2w1v/veP4s7fj7X5aUT2u8WgCI/4btGH7M/dRaM6LbjotOsML7+qnE4306asIOtAIUnJ0XTv\n1AqLD2tBgk1B2lbW3v8C+as3AtDg0nNp+/S92JJ820FcCBE6TjQ96cPy7+F41jJU0EAmcLc/ghKh\nI1TnbW5/eTYApzx07uFjIyf0AKBnrufp6ZgXhvz9RD+YldIbgCH7F9bI9Y5n8KSVAMy5qVuNXjdU\n61d1uO1OsuZtAiDxzNaYI6s/D790/RxKln4B1nDir3zL8J2fd2RuYsZfk1Eobjn3MSxmq6HlV5V2\na37+eg2703OIjg3jkut7EBsfEdCYaorb4WTba/9j+1ufoJ0uwhvWo8OEh6k74IxAhxbSpO0Swehf\nOw1a6+YASqlPtNbX1kxIQgghqqry4ueYzo2qXY67OI+8qfcDEHP+I1iSWxoVIgB2ZxkTf3wMl9vF\nud2voHXDzoaWXx2//7yRTWv3YwuzcMl1PU+aDkPZwWxW3fwYOYtXAdDkhkto/ehtWKIDu7GeEKJ2\n8nZNw2tKqaPyqymlGiulZL948a9k3qbwJ6lfHvaswiOLnwf5tvg5f/pjuPP2YW3ak6izbjcowiO+\n+GMiu7O20yCxKVedFfjB6mWpO1i+IAOTWTH8mm7UrR8DhH7dyl+7iUVDRpOzeBVhKXU4ddo7tH/x\nQekw1JBQr18iNHnbaZgCHDt+bAM+NTYcIYQQVaG1JmvuxiOLn+tXfw562Ya5lCz5DCxhxPlhWtLa\nHUv4efnnmE1m7hz6HGHWwD7R37hmH7/P9MzhP++STjRpkRTQeGrK3u/nsPii2yjdk0lcjw6cMfsj\nEk/vGuiwhBC1nLebuzXRWm+vfEBrvU0p1czwiERIkXmbwp+kfhm3+Nldmk/uV/cCEHPeOKwpbYwK\nEYDC0nzenfkUABf3vpkW9dsbWn5V7dyexc9frwGg35A2tOt69H4WoVi3tMvF5hf+S/rbnwHQ6KoL\naf/ig5jCTr59KAItFOuXCH3edhp2K6W6a61XVBxQSnUH9vonLCGEECdi5OLngulP4s7di7VJd6LO\nvsOoEA/7aM5LZBceoFWDTgw//QbDy6+Kg/sLmD5lJS6XptsZTeh1ZrOAxlMTHLn5rL79KQ7NW4yy\nmGn7zH00ueHiWrE3hhAiOHjbaXgdmK6UmgBsA1oAY4Dn/RWYCA2pqakh+USlctakCl8+vDwAkQQ2\na1KFms6aVCFU65e3chYZs/i5bNPvFC+aDGabZ1qS2dv/GryTmvYzCzfOJswawZ1Dn8VsMrb8qijI\nK+Xbj5dRVuqkVYd69B/a7rg3zqFUtwo3pbPihrEUb9+FNTGerh88R1Kf7oEO66QWSvVLnDy8arm1\n1h8opXKB0UBjYBfwoNb6G38GJ4QQ4vjsWYXkLdsB+Lb42V1aQN6X5dOShjyMtX47o0IE4FD+Pj76\n5SUArjvnQVISGp/gDP/xbN62jML8Mho2TWDo5Z0x+bBoPBgcmP0nq+98GldhMTEdW9H9fy8R0bh+\noMMSQgQhrx/3aK2/Br72YywiBMmTFOFPJ2v9OnrxcyOfFj8XzHgaV84urI27EnXOPQZGCW7t5t2Z\nT1FcVkiPlmfRv/NwQ8uviorN2w5lFpJYN4rho7r96+ZtwV63tNvNtjcms3XCBwCkDBtAp9cfxRwZ\nHuDIBAR//RInJ686DcozdnsTMBKoq7XurJTqB6Roraf6M0AhhBBHO7L42Urima2qXU7Z5vkUL/gI\nzFbirpxo+LSkn5d9zvqdy4iLTOSWcx8L2Pz5ypu3RcWEcekNPYnwYf1HbecsKmbtPc+R+dPvoBSt\nH7mN5nddI+sXhBA+8Tbl6jN4piZ9ADQpP7Yb+D9/BCVCh+SiFv50Mtavoxc/t6r24md3WSF55dmS\nogePwdrA2GxGOw9u4Yv5EwG4ZcjjxEUlGlp+Vfwxa1P55m1mr3d7Dta6VZyxh8VDbyHzp9+xxEbT\n49OXOeXuUdJhqGWCtX6Jk5u3j5WuB7pprQ8ppd4tP5YOnOKXqIQQQhxXxeLnsBTfFj8X/PgsrqwM\nLA07ET3wPgMjBIfTzsQfH8PpcjCgy8X0aNnP0PKrYvmCHSxL3YHJrBh2dXeS68cGLBZ/OzR/Katv\nfRxHTj5RrZrS/ePxRLVocuIThRDCC952GsxAYflrXf49utIxIY4rVOdtbn95NnB0FqWRE3oA0DPX\nk1RszAtDaiSWWSm9gcBmURo8aSVQ81mUQrV+/ZPKi5+TBlZ/8XPZ1gUU//kBmCzEXzkRZT52707f\nTE19l50Ht5IS35hR/e83tOyq2LhmH/PKN28bckknmrb0fvO2YKpbWmsy3v+KjfZS4uUAACAASURB\nVE9PBLebuoP60PntJ7HGRgc6NPEPgql+CVHB207DTOA1pdT9cHiNw7PADH8FJoQQ4gijFj9rezF5\nX3oWPEcPegBro05GhknazuX8+NenmJSZOy94lnBbpKHle2vX9mzP5m0a+g1pTftjNm8LFa6SMtY/\nNJ6938wC4JT7rqPVwzejTN7OPhZCCO9426o8ANQH8oA4PCMMTZE1DeIEZN6m8KeTqX4Ztfi54Kfn\ncB1Kx9KgA9GDHjAwQiguK+CdmU+g0Yw440ZaNTC2Q+Ktg/sLmDZlhWfzttOb0OvM5lUuIxjqVune\nAywZfjt7v5mFOSKcrh88R+uxt0qHIQgEQ/0S4lje7tOQD4xQSiXj6Szs0lrv92tkQgghgPLFz795\nptn4svjZvn0xRfPfA5PZMy3JYmwGoY9+mcCh/P20SOnAiDNGG1q2twrySvlu8vIjm7ddcPzN24Jd\nzl9rWDn6EewHs4loXJ/uk8cT075loMMSQoQwr/PrKaXigUFAA2CvUmqm1jrHb5GJkCDzNoU/nSz1\nK2fRdlyFZT4tftb2EnK/uBu0JnrgfVgbdzE0xkUb55CaNhObJYw7L3gWi8HrJLxRsXlbQV4pDZvG\nc74Pm7fV5rq1a8p00sa9inY4Sezbg67vPYstqfp7dYiaV5vrlxD/xNt9Gs4BvgM2ARl40q6+rZS6\nRGs914/xCSHESe3oxc/tq734ueDnF3Ad3IYlpS3R544xMELILjjApDkvAnBN//tpkNjU0PK94XS6\nmT5lZaXN27pj/ZfN24KR1prtb3zMlvGeDdua3nw5bZ68C5PF2P01hBDieLxtaSYCt1TeyE0pdRnw\nNtDWH4GJ0JCamhqST1QqZ02q8OXDywMQSWCzJlWo6axJFUK1flX4++LnuGqVY0//i6Lf3wVlIu6q\niShLmGExurWbd39+iqLSfLqd0odBXS81rGxvabdm1jdr2JWeTVRMGJdc7/vmbbWtbmmt2fzcO6S/\n/RkoRYeXH6bxNcMCHZaoptpWv4TwhrerpRoA3x5z7HsgxdhwhBBCVDBi8bN2lJZPS3ITdc492Jp0\nNzTG2Su+Yu2OJcRExHPrkCcCsn7gzzmb2bimfPO263oQl3DizduCiXa7SRv7Culvf4aymOny7tPS\nYRBC1DhvOw2fAncec+x24BNjwxGhRp6kCH8K5fp11OLnftVf/FwwazyuA1uw1GtNzJCHjQyR3Ye2\n8/kfbwFw87mPEh9dx9DyvbF22W7+mp+OyaS46KpuJDcwZvO22lK33E4na+95jl2Tv8cUZqPbRy9R\nf/jAQIclfFRb6pcQVeHt9KRuwG1KqYeBPUBDIBlYopSaX/EhrXXgtv0UQogQctTi507VW/xsz1hO\n0W9veaYlXfkWyhpuWHxOl4OJPz6Gw1nG2Z0u4tTW5xhWtrd2bsvil2nrARg4rD3NWtV8p8Wf3GV2\nVt/xFJk//Y45MoLun4wnqW/PQIclhDhJedtp+KD8S4gqkXmbwp9CtX4ZsfhZO8vI++Iuz7Sk/ndh\na9bL0Bi/XvAeOw5sIjmuIdedY+zCam9kHShk+mcrcbs1vc5sTudejQ0tP9B1y1VcysrRj3Bo3mIs\nsdH0+PxVEnoGZt8LYbxA1y8hqsPbfRom+zsQIYQQ5Yuff93g8+LnglkTcO7fhLluS2LOG2dojBt3\nr+SHJZNRysSdQ58hIizK0PJPpLjIznefePZiaNk+mX7ntq7R6/ubs6CI5aMeImfxKmxJ8fT86g1i\nO4bWn1EIEXy8Tbk6CbhHa11c6Vh94H9a6yH+Ck4Ev1B9krL95dnA0VmURk7oAUDP3OcBGPNCzfxq\nzErpDQQ2i9LgSSuBms+iFIr1q2hzJiU7sz2Ln/tVb/Fz2ZZUiua+AUoRf9VbKJtxC4OLywp5+6cn\n0NrN8NNvpE2jroaV7Q1PatUV5GWXUK9hLOdf3rnaaWj/TaDqlj0nn+VX3k/eqg2EpdSh19f/IbpV\ns4DEIvwnFNsuEfq8XQgdDaxRSp0BoJQaCawBVvorMCGEONn8bfFzRNUXP7sLs8idcqtnE7dBD2Jr\nfpqhMX7y26sczNtL83ptubTPLYaWfSJaa2Z/t5Y9GbnExIUzYlR3bLbQ2aOg7EAWf424g7xVG4ho\n0oDTpr8rHQYhRK3hVadBaz0SeBKYrpT6E3gOGKG1NnbMW4Sc1NTUQIcgQlio1a+cRdt8WvystSb3\ny3tw5+3D2vw0os81NlvSX5t/4/e1P2C1hHHXBc/V+K7Pi37bxoZV+7DazIy4tjvRscYt7D5WTdet\nkt37WTL8Dgo3bieqVVNOm/4ukU0b1mgMouaEWtslTg7ejjSAJ2tSKXAKkA5s9UtEQghxEvIsfs4A\nqr/4uTj1Q8rW/YyKiCN+1Psos3FP4XMKD/LB7OcAuPqse2iY1Nywsr2xYdVeFs7dilJwwcguJNc3\nJrVqbVCUvpslw26nePsuYjq24rTv3yG8ft1AhyWEEEfxqtOglHoF+BK4F2gGrMIzXeky/4UmQoHM\n2xT+FCr1S7s1B2et92nxs2PPOvKnPw5A3BVvYEk0LpuQW7v578/PUFCSR+dmpzO4++WGle2NPRk5\nzPp2LQD9h7alRdtkv1+zpupWwYZt/DXsdkr3ZBLfsyOnfjsRW52EGrm2CJxQabvEycXbx1DtgC5a\n68zy9w8ppWYAk4Gv/RKZEEKcJPKW7qBsby7m6LBqLX52lxWRM3k0OMuIPOM6Iroau1vwV/PfZnX6\nQqLD47jtvCcxqaoMUvsmN7uYaZ+uwOXSdD29Cd3OaFpj1/a3vFUbWHbl/Thy8kns24Puk8djiYoM\ndFhCCHFc3qZcHXqcY/OVUp2ND0mEklDNRV05a1KFLx9eHoBIAps1qUJNZ02qEAr1y36wgOwFWwCo\nO6RDtRY/50971LPrc0obYkc8b2h8f6ybwfQlH2NSZu4d9hKJMf5/yl+htMTBd5OXU1LsoFnrOpwz\ntC1KGZ8p6Xj8XbeyF69i+TVjcBUWU3dQH7p+8Bzm8DC/XU/ULqHQdomTj9ePi5RSg5RSH5WPMKCU\n6gkYu1uQEEKcRLTLzYGZa8HlmZYU2bzq89hLVk2jZNEnYAkj/toPUTbjnlRv3L2S92d51jHcMPBh\nOjU91bCyT8TlcvPD56vIPlhEnXrRXDiyKyZzzY1w+NOh35ew7Mr7cRUWkzJsAN0+elE6DEKIWs/b\nNQ13A+8Cm4F+5YdL8GRREuIfyZMU4U/BXr9yFm3DfqAAS1wESf3bVPl8Z9ZO8r68D4DY4c9hbdDe\nsNgO5O3l1e/H4HI7GdJjJIO6XWpY2SeitebX6Wns3JZFZLSNEdf2ICy8ZlOr+qtuZf78B8uvfRh3\nSRkNr7yALu88hckaOmljhXeCve0SJydvH9vcBwzUWr8EuMuPbQSq/r+cEEIISvflkbs4HYC653XE\nVMX9BrTLSe6nN6NL8wnrNJTIPjcaFltxWSETvr2PgpJcujQ/g1H97zesbG8sS93B2mW7sVhMjBjV\nnbgE4zanC6S9385m1U2Poe0Omt50GR1fHYsymwMdlhBCeMXbTkMMsKv8tS7/bgXshkckQorkohb+\nFKz1y+1wcXDmWtCauJ5NiWicWOUyCmePx7FjKab4BsSP/I9hc/3dbhdvzXiE3Ye20TCpOfde9CJm\nU809Cd+yPpM/Zm0C4LzLOlO/cXyNXbsyo+vWzk+mseauZ9AuF6fcdx1tn70PZQqN6Vai6oK17RIn\nN29brPnA2GOO3QPMMzYcIYQIfTl/bsGRXYQ1KYqEM6ueLalsy58U/vIaKBPx17yHKcq4FJ2f/f4m\nK7cvICYijocufp3IsBjDyj6R/Xvy+GnqatBw5rmtadMppcau7U/p735O2sMTQGtaP3o7rcfeWmML\nuoUQwijePj66G5ihlLoZiFFKbQIKgAv8FpkICaE6b3P7y7OBo7MojZzQA4CeuZ7sNWNeGFIjscxK\n6Q0ENovS4EkrgZrPohSM9atkVzZ5yzNAKZLP74TJUrXpKe7CLHKn3AZaE33uGMJa9jEstt9Wf89P\nyz7DbLLwwPBXSEkwbq+HE8nPLeH7T1bgdLjp2KMhp/ar2c3jjmVE3dJas/WVD9n26kcAtH/xQZrc\ncInP5YrgF4xtlxBejTRorffhyZR0OXAVcB1wqtZ6v7cXUkqFKaWWKKVWKqXWKqWeLD+eoJSao5Ta\npJSarZSKq3TOOKXUFqXUBqXU4ErHuyul1iilNiul3qh03KaU+rL8nEVKqSbexieEEP7mtjs5+PM6\nAOJPP4WwlKpt4qa1JveLu3Hn7cN6yulED37IsNjW71zGh7+8CMDoweNo17i7YWWfiL3MyfefrqCo\noIzGzRMZNKxD0D+J11qz6am3PB0Gk4lObz4mHQYhRFDzekKl9vhLa/211nqx1tp94rOOOr8M6K+1\n7gZ0Bc5TSp2KZ9rTr1rrNsBvwDgApVR7PJ2UdsB5wDvqyP8i7wKjtdatgdZKqYrHvaOBbK11K+AN\nYEJVYhTGk3mbwp+CrX5lzduIM68EW71YEs44pcrnF6dOomz9LFREHAmj3keZjVlrsD9nF69PexiX\n28XQXtdwTufhhpTrDbdb8+OXqzm4r4CEpEguurorZkvg5/r7Ure0y8X6hyew470vUVYLXd97hoZX\nnG9gdCLYBVvbJQRUodNgBK11cfnLMDxTozQwDM/O0pR/r/jf6iLgS621U2u9A9gCnKqUSgFitNZL\nyz/3SaVzKpf1DTDAT38UIYSokuLtBylYswfMnmlJqop7Djj2rCN/+hMAxI18E3NCI0PiKiotYMK3\n91FYmkf3Fmdy9Vn3GFKut36fuZHtmw4SHmHl4ut6EBFZ9c3tahO3w8mau59l96fTMYXb6P6/l0i5\n8JxAhyWEED6r0U6DUsqklFoJ7Ad+Kb/xr6e1zgQon+5Usd1oQ45kbALYU36sIbC70vHd5ceOOkdr\n7QJylVJVT0siDCPzNoU/BUv9cpXYOThrPQCJfVthqxNdpfPdZUXkTB4NzjIie19PRJeLjInL7eSN\nH/6Pvdk7aFynBXdf8DwmU82lAF25KIMVCzMwmRXDrulGQp2oGrv2iVSnbrnL7Ky6+VH2fTcHc1Qk\nPT57jboDe/shOhHsgqXtEqKyGt1RpnxKUzelVCzwvVKqA0dSuB7+mIGXPO6k2G+++YZJkybRpIln\nyUNcXBydOnU6/EtcMWwo7+X9P73fm5FGz6btj/p5hYw9aeWvhtRIPGnuokpXC8zfR/62LcS26Bqw\n69f29zkLt9FBpRDeMJ61pbtRqXuqdH7hb2/RJXsLlpS2rKk7FJWaakh8n/z2Gn/88TuR4TE8dOsb\nRIRF1djfT8PkNvz24wYy9qRx2lmn0Lh5YsD+fYx4f3q3Hqy8cSx/zvsdc1Qk1339JvHdO9Sa+OS9\nvJf3xr7XWjNv/p/YnW66nnoGpU43C1MXYHe5adv9NEodblb8tQi7y02zTj2xOzWbVy3BraF5p164\ntWbL6qVorWnasRcurUlfsxQX0Kh9D9xak7FuGW63pkH7Hrg17Fq3DLfWpLTrgcut2ZO2HJfWuN2Q\nuXEFhYf2ooH7Lh3IgAHGT7ZRWht5j16FCyv1OFAM3AScrbXOLJ96NE9r3U4pNRbPUorx5Z+fBTwJ\nZFR8pvz4SOAsrfXtFZ/RWi9RSpmBfVrr5GOvPXfuXN29e80t8juZpVa6uRHCaMFQvwo37ufAjNUo\nq5lG1/XGmhBZpfNLVnxH7ic3gTWcOg/8irW+Mbs+z1k5lY9+GY/FbOXxke/RpmEXQ8r1RubefL76\nYAn2Mhen929B30FVTzvrb1WpW478QlaMeoicJaux1Umg19Q3iWnf0s8RimAWDG1XqHFrTanDTbHD\nRbHdTZHDRbHdRfHhYy6KHO7yY573pU6358vh/tvrMqfb0KfcRnqpu2bAgAGGZ5OwGF3gP1FK1QEc\nWus8pVQEMAh4CfgBuB4Yjycr0/TyU34APlNKvY5n2lFL4C+ttVZK5ZUvol4KXAv8p9I51wFLgMvw\nLKwWQoiAcBaWcehXz8hT0tltqtxhcGZlkDfVsxtz7LDnDOswrNmxmI9/fQWAW4Y8XqMdhuyDhXzz\nv2XYy1y07ZxCnwHBfXNtz8pl2ZUPkL9mI+ENkuk59U2iWzYNdFhChCytNUV2F9nFTrJKHGQXO8gq\ndpBT7KCwohNw+Mb/SOegxGH8Tb7NrAi3mAi3mgi3mD2vD78/+rXNbMJkUpgVmJXCpMBsUpj+5bVZ\nUX5O5eNgUgqLSWEu/5nZRKXXiv1b1xv8J/WosU4DUB+YrJQy4VlL8ZXWeqZSajEwVSl1I55RhMsB\ntNZpSqmpQBrgAO7QR4ZF7gQ+BsKBmVrrWeXHPwQ+VUptAbKAkTXzRxP/RJ6kCH+qzfVLa82hOetx\nlziIaJZETJdGVTvf5SD3k5vRpQWEdb6AyD43GBLXnqx03pj+f7i1i2Gn30C/DkMNKdcb+bklfP3R\nMkqK7DRrXYfzLu2MMtXO1Kre1K3SzEMsu+xeCjenE9msIT2n/ofIJvVrIDoR7Gpz2xUoWmsKylxk\nFXs6AtkljvLXzsMdg+zyrzJX9W7/I6wmIq1mIq0mIm1mIq1momzlx2xHH4+0Vr75N/+tMxBmMWGu\npe2X1/shVFGNdRq01muBv80J0lpnAwP/4ZwXgRePc3w50Ok4x8so73QIIUQgFazbQ/G2g5jCLNQd\n0rHK+w4UzpqAI2MZpvgGxF/xpiH7FhSU5PLyt/dTXFZIr1b9ueLMO3wu01tFBWV8/eFSCvJKadg0\nnmFXdasVqVWrq2TXPpZedg/FO/YQ3bo5Pae+QXhK3UCHJUStVGx3cajYQVaRg0PFdg4VHRkhONwx\nKHHg8LIzEG4xkRRpJTHSSlKkhcTy1zE285Gb/vLOQFR5ZyDCaq61N/nBoiZHGsRJSOZtCn+qrfXL\nkVdC1m8bAUga0A5LTHiVzi/bPJ/CX18DZSJ+1PuYohJ8jsnpcvD6tIfZn7uLZsltuHPos5hUzdy0\nl5Y4+ObjZeRkFZNcP4YR1/bAaqu5LE3V8W91q2jbTpZedg+lew8Q27kNPb94HVtSfA1HKIJZbW27\nqsrl1uSUODhU5KjUKXCQVWTnULHjcOeg2OHd1l6RVlN5R8B6zHfL4deJEVYia3n7Eaqk0yCEEAbS\nWnNw1jq03UVkq2Si21dtuoq7MIvcKbeB1kSf+xBhLXxP2am15qNfxpO2aznxUUk8dMnrhNsifC7X\nG3a7k+8mL/ds3lYnkktu6El4hLVGru0PBWlbWXr5vdgP5RB/amd6THkFa2zVUugKEQxcbk1WsYOD\nhXYyC+0cKLJzsNBx1IhBbokTtxeDAzazok6UlaRIW/l3z1edqCMdgcRICxFW6QzUZtJpEH4VCk9S\njmf7y7MBOOWhcw8fGzmhBwA9c58HYMwLQ/5+oh/MSvHcVA7Zv7BGrnc8gyetBGDOTd1q9Lq1sX7l\nr9hJ6c5sTJE26g7uUKVpRVprcr+4C3f+fmynnEH04DGGxPTz8i/4bc33WC1hjLn4NZJi6hlS7ok4\nnW5++Gwle3fmEhMXzmU39iIqOqxGru2r49Wt3BVpLL/qfhy5BST160W3/72EJapmOl8itNSGtqvU\n6eZAof3wV2ahvbyD4OBAoZ1DRXZONFtIAQkRlsMdgDqRNhKjrNQpf19xPNpmNmSKpQgs6TQIIYRB\n7NlFZM/fDEDdwe0xV3F34+L571O2fjYqMp74Ue+hzL430Su3pfLpvNcBuP28p2hZv6PPZXrD7dbM\nnLqaHVuyiIiycdnoXsTGB+8NdvbClSwf9RCuomKSz+1Ll/eexRweHB0gcXIqcbjYlVvmGSU4tnNQ\n5CCv1HnCMhIjLCRH2w5/1Y2yUifqyGhBYqQVi6wTOGlIp0H4VajM2xS1U22qX9rt5uDMtWinm+j2\nDYhqVbWn+Y7da8n/4UkA4q54E3NC1bItHc+uQ9v4z4xH0NrNJb1voXe7wT6X6Q2tNXO+X8fmdZmE\nhVu49IaeJNai3Z69UbluHZy7iJWjx+EutVN/xCA6/edxTFb571NUn9FtV36pk61ZxWzNKmHrIc/3\nPXll/5pi1GpS1I22kRxtpV60jbpRNurF2EiOKu8gRFuxmYM3WYEwnrR6QghhgNy/dlC2Lw9zTDhJ\nA9pW6VxXwUFyPrwGXHYie99ARJcLfY4nvziHl7+9nxJ7EWe0HcylfW7xuUxvaK35/edNrFu+B4vV\nxMXX9aBeg9gaubY/7P9xHqtvfxLtcNLomovoMP4hlFnmXYvA0FpzqNjB1kMlbMsqZkuW5/uBQsff\nPmtW0CQ+nJSYMJKjrUeNGNSLthEfYcEkU4ZEFUinQfhVbXkKLEJTbalfZQfyyVmwFYC653bAHO79\nQl/tLCPno2tx5ezC2rQHsSOe9zmeUnsJL3/3AAfy9tAipQO3n/dkjc0nXjxvO8tTd2AyK4Zd3Y2G\nTX3P/BQIffv2Zc9XM1l7/wvgdtP01ito+9Q9Mi9bGMKbtsutNfvyyzyjB5VGEI43rSjMYqJFYgQt\nkiJoWSeSlkkRNE0Il5ECYSjpNAghhA+00zMtCbcmtmtjIpvX8f5crcmb+gCO9CWY4huQMHoKylq1\n9KzHcrocvD79YbbsXUNSTD0eHPEqNh/L9NaKhRks+HULSsHQy7vQvHXw7luw83/fkjbuVQBaPHAj\nLR8aLR0G4Tdaa/bkl7HhQBFbD5WwJauY7Vklx01VGhNm9nQOkjydg5ZJkTSMC5M9CITfSadB+FVt\nmnNupMpZkyp8+fDyAEQS2KxJFWo6a1KF2lC/chZuxX6wEEt8BIlnta7SuUXzJlLy1xcoWySJN32O\nOda3rEZu7eadn55kdfpCYiLiefTyd0iMqZkb9/Ur9vDbjxsAGDyiI206pdTIdY2mXS62vvIRM16d\nSHtTFG2euIvmd1wV6LBEiJk3fz7JbbqzPrOI9ZlFpGUWHXcEISnSSsuko0cQ6kXbpAMrAkI6DUII\nUU2le3LJ/SsdgOTzOmGyed+klq6fQ8GMpwCIu/odrI06+xSL1pqPf53Awo2zibBFMe6yiTRIauZT\nmd7ampbJrO/WAXDWeW3o1NP3RdyBULr/IGvueJrshStAKdqPf4gm140IdFgiBOSVOknLLGJ9ZiHr\nM4tYung7kZtjjvpMQoSF9slRtK4beXgUISEyePc0EaFHOg3CrwL9FFiEtkDWL7fDxcGf14KGuF7N\nCG/k/dx9x74N5H5yk2cDt/PGEdHlIp/j+WbBe8xZ+TVWs40xF7/GKSntfC7TGxlbs5jxxSq0W3N6\n/xb0OrN5jVzXaAfnLWbtXc9gz8rFVjeRa99+kzr9egU6LBGEtNbsyisrH0HwdBJ255Ud9ZnI5l1o\nmhBOh3pR5V/R1I+REQRRu0mnQQghqkhrzaE5aThyirHWiSahb0uvz3UXZpEz6Sp0WSHh3S42ZAO3\nn5d/wbcLP0ApE/dc9AIdmvT0uUxv7NuVy7QpK3C5NN1Ob0Kfgd7/PdQWboeTLePfJ33iFACS+vWi\n88QnCEtOCnBkIljYnW42Hyoun2pUSFpmEfllrqM+E2ZWtKlb3kFIiaJdchQxYXILJoKL1FjhV7Vh\nzrkIXYGqX/krd1KYthdlNVPvgs6YLN6l4NROOzn/uw5XVgbWJt2Jv/Itn58s/rl+JpPnvgLArUMe\np1er/j6V562D+wv49uPlOOwu2ndtwDkXtAu6p6Qlu/ax+vYnyV22DkwmWv3fzZxy9yiUySRtl/hH\nuSWOo9YibDlUjMN99I4IiREW2teLPjyS0LJO5FGboEn9EsFIOg1CCFEFpbtzyJq3CfCkV7XVjTnB\nGR5aa/K+GYN920JMcfVJGP0pyubbDsnLt87n3ZlPAXDN2fdxdiffpzl5IzermG/+t4zSEgct2iVz\n7iUdUUGWuSVz1nzW3fc8jtwCwurXpcu7T5N4etdAhyVqmcNTjfYXHu4o7Mk/eqqRApodnmrk6Sik\nyFQjEYKk0yD8KlSfpGx/eTZwdBalkRN6ANAz15Nnf8wLQ2okllkpvYHAZlEaPGklUPNZlGq6fjkL\nS8n8YRW4NXE9mxLdrr7X5xb98S4li6eANYKE0VMwx3l/7vFs2LWSN34Yi1u7GHb6DVxw6iifyvNW\nQV4pX3+0lKKCMhqfksiFI7tgDqJc8O4yO5uefZuMSV8DUHdQHzq98Si2pPijPheqbZf4d95ONWqb\nHEX78lGE9slRRFdxqpHULxGMpNMghBBe0C43mdNX4yqyE944oUrpVUvTfqFg+hMAxF81EVsT3zpX\nOzI3MeHbe3E4yxjQ5WJGnnmnT+V5q6TYzjf/W0ZeTgkpjeIYMao7Fmvw7I5clL6b1bc+Tv6aTSir\nhTaP3UHTW66QJ8InMa+mGkVaDo8gdKgXRYuko6caCXGykE6D8CuZtyn8qSbrV9a8jZTtzcUcE069\nC7ugTN49XXfs31SeKclN9LkPE9HNtxSe+7J38uLXd1FiL+K0NgMYPWhsjdz0lpU6+eZ/y8g6UEhS\ncjSXXN8DWxAt5Nw37RfWjRmPq7CYiCYN6PLfZ4jv3v4fPy9tV2jal1/Gqn2F/5jV6G9TjVKiSPHD\nvghSv0QwCp4WXwghAqRg3R7yV+4Cs6LesC6Yo8K8Os9dlO3JlFRaQHjXYUSf+7BPcWQXHOCFr+8k\nrzibTs1O466hz2Ey+f9Jf1mpg+8/WUHmnnziEiO47MaeRETa/H5dI7iKS9nwxBvsnvIDAPUu6E/H\n18ZhjY0OcGSiJmit2ZpVwsKMPBbsyGVHTulRPzdiqpEQJwv5zRB+JU9ShD/VRP0qy8zn0C9pANQZ\n2J7w+vEnOMPDkynpelyH0rE06kL8VW97PTpxPIUlebzw9V0czNtLi/odeHD4K1gt/r9xL8gr5dvJ\nyzi0v5ComDAuu7EX0bHhfr+uEQo3pbPq1scp3LgdU5iNts/cS+Nrh3v1c6/EUAAAIABJREFU1Fja\nruDlcmvW7S9kYUYeCzPyyCy0H/5ZpNVE94Yxh6cbHZvVqKZI/RLBSDoNQgjxD1wldjKnrUQ73cR0\nbkRsZ+92OtZak//t/2HfmoopNoXEm6agbJHVjqPUXsL4b+9l96FtNEo6hbGX/odwH8rz1sF9BXw7\neRmF+WUk1oni4ut7EJ/o/+v6SmvNnq9msmHcq7hKSols0YSu7z9LbIdWgQ5N+EmZ082KPQUs2JHL\n4p15Ry1eToywcEbTOPo0i6dL/WisQbRwX4jaRDoNwq9Cdd5m5axJFb58eHkAIgls1qQKNZ01qYI/\n65d2aw7MWIMzv5Sw+nHUGeD9DsvF89+neNFksIaTMPpTzPENqx2H0+XgtWlj2LJ3LXVi6/PI5W8T\nE+HdaIcvMrZmMf2zldjLnDRsmsDwUd2CYkqSs7CItLGvsPcbT4azBpcOof34MViiqtbZCdW2K5QU\nlDlZsjOfhRm5LN1dQJnTffhnDWLD6FPeUWibHImpli12l/olgpF0GoQQ4jhyUrdQkpGFKdJGvYu6\noCzePZ0s2/gb+dMeBSB+5FvYmvaodgxut4uJPz7Omh2LiY1M4JHL3yYxJrna5Xlr/Yo9zP5uHW63\npk2nFM67tFNQZEnKX7eZVbc+QfG2nZgjwmn/0hgaXnF+oMMSBjpUZC9fn5DHmn0FuColOmpVJ4I+\nTePp3SyOpvHhkhVLCINJp0H4lTxJEf7kr/pVtDmT3CXpoBT1LuyMJda7TdicmZvJ+fhGT6akQQ8S\n0eOSasegteajX8azeNMvRNiiGHfZRBokNq12ed5ec/G87Sz4dQsAPfs246whbWr9xm1aa3ZN/p6N\nT/4Hd5md6HYt6Pres0S3blbtMqXtqj125payMCOXBTvy2HSw+PBxk4Iu9aPp0yye3k3jSI6u/SNh\nFaR+iWAknQYhhKjEnlXIgZlrAUg8qzURTZK8Os9dlEP2B1ehS/MJ63wB0eeN8ymOqanv8uvqb7Fa\nwnjo4tdpXq+tT+WdiNvl5pfpaaxdthsUnDO0Hd17+7eTYgRnUTHrHniR/dPnAtD42uG0ffpezBHe\nZbgStdPBIjvztuYwd2s26ZUyHoWZFT0axdK7aRynN4kjNlxuY4SoKfLbJvxK5m0KfzK6frnLnGRO\nW4V2uIhqm0JcT+9umrXLQc7HN+A6tB1Lw07EX/2uT5mSZi77nO8XfYhJmbn3whdp36T6U5y8YS9z\nMuOLVaRvPoTFYmLoFV1o1aGeX69phMLNO1g5+hGKtuzAHBVJx1fHUn/4QEPKlrar5hXbXaTuyGXu\n1mxW7S2kYuZRTJiZ05rE0adpHD0axRLu5VTB2kzqlwhG0mkQQgg8U1wO/LwWR3YR1jrR1D23g9dz\novO/G4d9y3xMMckk3vQZprCoascxf92PfPLbqwDcdt4T9Gx1VrXL8kZRQRnfTV5O5t58IiKtjLi2\nOw2aJPj1mkbYN+1X1j3wIq7iEqJbN6frRy8Q3bL2j4yIo7ncmuV78pm7NYeFO3IpK1+kYDUpTm8a\nx4CWCfRqFCsZj4SoBaTTIPwqVJ+kbH/Zk5mlchalkRM8T4N75j4PwJgXhtRILLNSegOBzaI0eNJK\noOazKBlZv/L+Sqd4ywFMYRZShnfFZPOueSxK/ZDiBR+BJcyTKSnBu7Ssx7Nsyx/89+dnALj2nAfp\n1/GCapfljawDhXz78TLyc0uJT4zkkut7kFCn+h2emuC2O9j07NtkfDAVgPojBtHhlf+rcnakEwnV\ntqs20FqzJauEuVuymbcth9xS5+GfdUyJYmDLRPo1jw/pTdakfolgFLq/kUII4aXi9ENk/+lZ/Ft3\naCesCd7dOJdt+p3878YCEDfyTWzNelU7hnUZf/HmD2NxaxcjzhjN+T2vqnZZ3tidns20KSspLXGQ\n0iiOEdd2Jyq6dq8DKN13kFW3PEbu0rUoq4W2T99Lkxsuliw5QSKzwM5v27KZuzWHnblH1ik0igtj\nYMtEzmmZQEpM7a6DQpzMpNMg/ErmbQp/MqJ+OfJKOPDjGtAQ37sFUS28S2nq2L+JnI9vALeLqAH3\nEdnz8mrHsHzrfN6Y/n84XHYGdr2Ey/veXu2yvLFxzT5+/noNLpemZbtkhl7RBautdqdUzUpdxupb\nn8CelUt4g2S6fvAc8T06+u160nYZo7DMyZ/puczdmsOa/YWHj8eFW+jfIoGBLRNpVSfipOv4Sf0S\nwUg6DUKIk5bb4SJz2krcpQ4iTqlDQu8WXp3nPLid7HdGoEvyCOt4PjFDH6t2DAvSZvHOzCdwuV0M\n7HoJNw4a67cbKK01y1J38MfPmwDoenoTzrmgHaZanFJVu92kvz2FzS++D243Sf160eWdp7DVqf3r\nLk5WDpebZbsLmLs1m0U783CUr1OwmRW9m8YxoGUiPRrFYqnF9U4I8XfSaRB+JU9ShD/5Ur+01hz6\nJQ37gQIs8REkD+3s1c26K2c32e8Mx52/H1vLviRc+361MyX9uuo7PpzzAhrNRaddx5X97vZbh8Ht\n1sz7cQMrF+8EoN+QNvQ6s1mtfsLryCtg7T3PcmB2KgAt7r+elmNGo8z+HxWRtqtq3Fqzbn8hv2/P\n5c/0XPLK1ykoPHspDGyVSN9m8UTV8hGtmiL1SwQj6TQIIU5K+at2Ubh+L8pqpt6wbpjDrSc8x5W3\nj6y3h+HK2Y21WS8SbvoMZaveAtwZSz7hsz/eBODKfncx7PQbqlWONxx2Fz9NXc3WtAOYzYrzLu1M\n2y71/XY9I+Sv28zK0Y9QkrEXS1wMnSc+QfKgPoEOS1SitWbjwWJ+357D/O25ZBU7Dv+saUI4A1sm\n0r9FQlBtuiaE+GdKa33iT4WYuXPn6u7duwc6jJOCzNsU/lTd+lW6J4e9Xy4Ftyb5gs5EtzvxDbSr\n8BDZb12AM3MzlkZd+H/2zjs+iuvc+9/Z2d531YWQQPQiOhh3G2yMHeMeG8eOY6fe68ROc/p7k9wU\n53Wc8l7H6c6NS5y44N4wGAwONjZgQIARQiDUe1lptX1nzvvHLgJMFUhCgvP9fOYzM2fKOSOdPTO/\nc87zPBl3v4jB7ulz3kIInln3R15Y/zcAPnv5d1g08+TtIY5HuCfOC098SGNtFxarketun8XIYv+A\n5dcf1D31Gju/+yB6NI67ZDwzHrkfe1H+oJZBtl1HRghBZUeENXs7WVMZoLkn3nssx2nmkjE+Lin2\nUuw/++wU+oKsX5KBZPPmzSxcuLDff4BypEEikZxVJHtiNL9UCrrAM7vohASDHg7Q8YcbUoIhdyIZ\n/7HspASDLnQeX/Urlm9+GoOi8p9X/ZgLp1x1Mo9xQnS2h3ju0Q8JtIdxea3c+Jk5ZOY4Byy/U0WL\nxij7P7+l7h8vA1Bw2xIm/fwbqFbpUed0U9MZZU1lJ2sqO6nrivWmZ9hNXFzs5ZJiHxOy7FIoSCRn\nMFI0SAYU2ZMiGUj6Wr+EptP88la0UAzrSB/+i8cf9xo92k3Hn24i2bADNWsM/rtfwODM6HNZNT3J\nn5f/lHd2vIpRNfG1a/4vc8Zd0uf7nCgNNZ288PhmIuEE2flubrhjFk63dcDyO1XC1Q1s/cIP6N5W\njsFqZvL991HwqYGNU3EsZNsFjd0x1lR2srayk8qOAy5SPVYjF45OCYWpuQ4MUij0GVm/JMMRKRok\nEslZQ/vb5cTqA6hOC9lLpqMcJ8qsiIfp/MutJGo2o/oLybj7BVR3Tp/zTSTjPPTK99lY8TYWk437\nbvgNJUXzTvYxjsu2jbWsenknmiYYNT6Ta26dgXkIB8pqfes9tn3lv0kEgtiK8pn5yM9xl0w43cU6\nK2kNxXmnMsCayk7KW8O96U6zyvmjPFxS7GNGvgtVej6SSM46hu5bRHJGIOdtSgaSE61fQgg63tlN\n95YaUBVyrp2B0XHsKS8iEaXjb7cTr1yPwZOH/+4XTyraczQe4dcvfpPtVR/gsLj4zk0PMX7EtD7f\n50RIJnVWvbyT7ZvqgJRL1Us/MRH1OOLodCE0jT2/+l/2/vbvAGQtuoBpD/0fTF73aS7Z2dV2dUYS\n/HtfSijsaAr1pluNBs4r8nBxsY/ZBS7MQ7QeDUfOpvolOXOQokEikZzRCF3QtvIjgtvqwaCQfVUJ\n1nzvsa9Jxul89C7i5WswOLPIuPtFjJmj+px3KBrkgee+yu76Ujx2P9+/+fcUZR9/StTJ0B2I8PI/\nt9JU14XRaOCy66YwddaIAcmrP4i3Byi9+0e0r90IBgPjvvtFir9y+0m7r5X0nT1tYZZtb2FtZSfp\nUAqYVYV5Iz1cMsbLvJEerEb5/5BIJCmkaJAMKGdqT0rlg28CUPytK3rTlv5yNgBzAj8H4L77Fw9K\nWZbnngfA4qb3BiW/I7HokS0ArPj8zEHN93j1SyR1Wl7bRmh3M4rRQM61M7AXZx37Gi1J4B9fIvbR\nmyh2H/67X8CYM67PZesKdfCLZ79CVUs5Ga4cfnDLH8n3F/X5PidCTWU7r/yrlEgojttr5drbZpIz\nou+G2oNFx3tb2PaV/yba0II5w8v0P/2EjAvnnO5iHcKZ2nYJIfiwPsiz25rZ0pCK0GxQ4JyRbi4u\n9nFukUfGUhgEztT6JTmzGTTRoChKAfA4kAPowF+FEA8piuIDngaKgCrgZiFEV/qa7wGfBZLAV4UQ\nK9Lps4BHASvwuhDia+l0czqP2UAbcIsQomawnlEikQwd9HiS5he3EqluRzEbyb1xFraCY0cRFrpO\n11P3Et36EorVhf8/lmHKn9znvNu6m7j/mbtp6Kgmz1fED275PZnu/o+LIITgw3erWbu8HKELisZm\n8IlbpmN3DE2/+Ho8QcWDj7Dv4X+AEHjnTGXGX36GNT/7dBftjCeh6ayp7GTZthb2daaMmm0mA1dO\nyOD6KdnkuIZmnZFIJEOHwRx3TALfEEJMAc4FvqwoykTgu8BbQogJwGrgewCKokwGbgYmAVcCf1AO\n+HL7I/A5IcR4YLyiKPu7ez8HdAghxgH/D/jl4Dya5GisW7fudBdBcgZztPqlReI0PrOJSHU7qt1M\n/tK5xxcMQtC97D4iG59CMTvwf+kZzIV9Hzlp7Kjhx//8HA0d1RRlj+dHn/rrgAiGeDzJa09vY83r\nuxC6YN5Fo7nxzjlDVjD0VFTx/tVfZN/vngBFYczX72LeC38YsoLhTGm7QnGNZ0qbuePpnTy4toZ9\nnVH8diOfm5vPP5ZO4T/mF0jBcBo4U+qX5Oxi0EYahBBNQFN6u0dRlDKgALgWuDh92mPAGlJC4hrg\nKSFEEqhSFKUCmKcoSjXgEkJsTF/zOHAd8Gb6Xj9Kpy8DHh7o55JIJEOLZE+Mxmc3kWjrwei2knfz\nHEw+xzGvEUIQfPEHhN97FExWfJ9/EvPoc/qcd3VLBfc/+2W6Qu2My5/Gd276H5zW/jfqDbSHefHJ\nzbQ19WAyq1x5Uwnjp+b2ez79gRCC2kefZ9dPHkaPxLAV5jPt4R/imzcwxuCSFC09cV7Y0cIb5e2E\nEzqQitL8yZJsLhnjk0bNEomkz5wWmwZFUUYBM4D3gRwhRDOkhIWiKPu7nUYA6w+6rD6dlgTqDkqv\nS6fvv6Y2fS9NUZSAoih+IUTHAD2K5DjIeZuSgeTj9SvRGabx2U0kuyKYMhzkfXIORtfxYxP0vH4/\nobV/AtWE767HsIy/qM9lqWjYzv999h5CsSAlRefwzet/hdVs7/N9jkdleSuvPV1KLJrEl2nn2ttm\nDdmAbbHWDnZ87ee0rko15fk3X8Xkn38do+vYIm4oMFzbrr3tKePmNXsPGDfPyHdyU0k2cwvcMvja\nEGG41i/J2c2giwZFUZykRgG+mh5xEB875eP7p5RdP95LIpEMYeKtQRqf3YQWimPJdZN742xU+/Gn\nXfSs/A09K38NBhXfZ/6GdfLlfc57R/UGHnz+G8QSEeaOu4R7ltyP2di/UYyFLnh/zV7eXbUHBIyZ\nlM1VnyzBYjX1az79RcuKdez4+v3E2wOYvC6m/PI75F6z4HQX64zkgHFzC1sagkDKuPnSMT5uLMlm\nfGb/i1eJRHL2MaiiQVEUIynB8IQQ4qV0crOiKDlCiGZFUXKBlnR6PTDyoMsL0mlHSz/4mgZFUVTA\nfaRRhmXLlvHII49QWFgIgMfjoaSkpFf5759rKPdPff/geZtDoTz9tn+u47DjT337w0P29zPQ5XEu\nO9R053T8PX448fTWr3hbD8U1JvRYkm3RWvwF4xiRFgzHuj605o+s+tvPQIFF9/0J67Sr+1yevz31\ne5577y+4C4xcOOUTlLgWsuH9jf36vIl4kkC9m71lLVQ37GTqrAKuu+0KFIMyNH4PB+2vfWsVtY8+\nT+ZbKY9atVMKGHPPp3sFw+ku34nu708bKuU50n5C0/nDs8tZW9lJT3bKYD9atY1zCt1889aryHVZ\nWLduHS1DpLxyf3jVL7k/fPb3b9fUpHz/zJkzh4ULF9LfKEL0Z8f+cTJTlMeBNiHENw5Ke4CU8fID\niqJ8B/AJIb6bNoR+EjiH1LSjlcA4IYRQFOV94F5gI/Aa8JAQYrmiKHcDU4UQdyuKshS4Tgix9OPl\nWLVqlZg1a9ZAP66EVCXeX7klkv5m3bp1zBoxkeaXtiISGvax2WQvmYbBeHyXkaF3H6X72VRT5Fn6\nEPb5t/c9/51v8IfXfoQuNBbNvJk7L/sWBqV/54q3NQd56R9b6GwPY7Ea+cQt0ymecGy3saeLrq1l\nlH75vwnvrUExmxj/vS8x6ktLh2XshaHcdoXiGq/tauPFHa20hRMA+O1GrpuSxScmZuIawtG/JSmG\ncv2SDH82b97MwoUL+322zaC1LIqinA/cBmxXFGULqWlI3wceAJ5RFOWzQDUpj0kIIXYqivIMsBNI\nAHeLAwrnyxzqcnV5Ov1vwBNpo+l24DDBIBlcZKMoGUhmZI2l6fnNoAucU/LJWjzlhD5QwxufpnvZ\nNwFw3/hAnwWDEIKXNzzGU2sfRiC4bv5nueXCu/t9vnj59iaWP7edRFwjK9fFtbfNxJsx9KaaCE2j\n8uF/sOfBRxBJDeeE0Uz7w49xT+l7fIuhwlBsu0Jxjed3tPD8jlZCcQ2AIq+Vm6Zlc6k0bh5WDMX6\nJZEcj0ETDUKId4Gjdf9ddpRrfgH84gjpHwIlR0iPkRYdEonkzKa7tJa2lTtBgHt2ERmXTjihj/bI\n1hfp+ueXQQhc1/wYx4Vf6FO+sUSEPy//Ke+VpQL83XbxV1lyzh0n9QxHQ9d0/r2ygo3v7ANg4rQ8\nFt0wBbN56PUgh2sa2X7vT+h8vxSAoi/czPjv/yeqrX9tOs5mwnGNFz9qZdn2FnrSYmF6npNPTpPG\nzRKJZPAYem8gyRmFHIKVDASBD/bR8c5uNlXv5PLbrsE7v/iEPpyiH71J4PEvgtBxLv4OzgX39inf\n9mAzv37+m1Q2l2E12fnK1T9lzrhLTvIpjkw4FOfVp0qp2duOYlC45MoJzDqvaMh9GAohaHzuTXZ+\n79ckgyEs2RlM/Z8fkHXp/NNdtH5hKLRdkYTGKzvbeGZbM92xlFiYluvkjtl5TMsbmh6zJCfGUKhf\nEklfkaJBIpEMG4QQdLxTQdeGVA+8Z1YhvnPHnNB14X//le6X/gv0JI4F9+C84tt9yru8vpTfvPgt\nukLtZHtH8K0bfsvIzOPn3Rea6rt46cktBANR7A4zS26dwchif7/m0R8kAt189N1f0fTiWwBkX3kR\nU3/1XcwZ3tNcsjODaFLn1bI2ni5tpiuaBGBKjoPPzM5jRr7rNJdOIpGcrUjRIBlQztSelMoHU1NT\nir91RW/a0l/OBmBO4OcA3Hf/4kEpy/Lc8wBY3PTeoOR3JBY9kvKUs+LzfY+gfKIIXdC2cifBbXVg\nUMi+cirFk/OPe50e6abrqXuIlr4CgOPSr+Ba8uM+9dyv3vYif1vxCzQ9ydSieXz1ml/gsvXfB7IQ\ngq0f1LLm9V1oSZ28kR6u+dRMXJ7jx5gYbNrf3cz2e39KtL4Z1W5j0s++xohbrx5yIyGnyulou+JJ\nndd2pcRCRyQlFiZm2fnM7DxmjXCdcX/js5kz9d0oObORokEikQx5hKbT8to2QuXNKEYDOdfMwD7m\n+B6EErWldD56F1p7FYrVhWfpQ9hmXHvC+Sa1BP94+7cs3/w0AFfOvpXbL/0aqqH/ms7uQIQ3n99B\n9Z52AKbNLWDBkskYjUPLqFWPxal44K/s++M/QQg8Mycz7fc/wlE88vgXS45JXNNZXt7OU1ube70h\njc+0c8fsXGmzIJFIhgxSNEgGFDlvU3Kq6PEkzS9tJVLVjmI2knvDTGwjU1N2jla/hBCE3/1ful/4\nAWhxjAXT8H3mfzFmFZ9wvsFIgP/30nf5qGYjqsHI5xd9n0unnbjgOB5CCD7aXM/qV3cRjyWx2U0s\nvGYyE6fl9Vse/UX7vzdR9sP/oadsLxgMjPn6XYz5+p0YTGfuK2Qw2q6EprOiooN/bmmiNZQSC8V+\nG5+Zncf8QikWzmTku1EyHDlzW3yJRDLs0aIJmp7bTKwhgMFuJu+m2Vhy3Me8Ro920/X014lueQEA\n+/mfxX3dz1BMJz7Vp7Z1Dw++8A1aAvV4HBl847oHmTBi+ik9y8GEgjFWvLCDvbtagVR050XXTcHh\nGloeh3r2VFP+k9/TuiIVQMhWlM+0h3+Eb+5hzuskfUDTBSsrOnhySxPNPXEARvms3DErj/NGeTBI\nsSCRSIYgUjRIBhTZkyI5WRKdYZpe2EyiPYTRbSX3k3Mw+x2HnPPx+pWo35GajtS6F8XixHPLb7HN\nurFP+W6seJvfv/pDookwxTmT+OYNvybDlXPKz7OfXdsaeeulnUQjCSxWIwuunsTkmflDqlc53tHF\nnl//jdrHXkAkNVSHneJ7P82oLy49a1ypDkTbpemCt/d28o8tjTR0p8RCodfKp2flcuForxQLZxHy\n3SgZjkjRIJFIhhzhqjZaXilFjyYxZTjIu2k2RrftqOcLIYisf4yu578HyRjG/Cn47vw7xuyxJ5yn\nEILn1z/Cs+v+BMD5kxbzpcX/hbkPIxTHIhyK89ZLO9m9owmAUeMyuOKGkiFl7KzHE1T/7zL2/vZR\nkl1BMBgouP0axn37C1iyM0538YYtmi54Z18nT2xuoq4rBkCBx8LtM3O5uNiHapBiQSKRDH2UA0GW\nzx5WrVolZs2adbqLcVYg521K+oIQgq4Pq+lYUw4C7GOyyP7ENAyWI/dvrFu3jvPmzqDrmW8Q/XAZ\nALZz78Bz/S9QzEcXGR8nGo/wxzd+xAflq1BQuPXie1gy745+6/3fU9bCiud3EA7FMZlVLrlyAtPm\njRwyowtCCFreeIfynzxMuKoegIyL5jLxx/fgmnziwutMoj/arv1i4cktzdQEogDkuczcPiuXBWP8\nUiycxch3o2Qg2bx5MwsXLuz3BkaONEgkkiGBntBoW7GTnp0NAHjPLcZ3/thjflgn26po+/U30Voq\nUMwOPDf/BtucT/Yp35auBn71/Deoaa3AZnZw75L7mTmmf17m0UiCt18r46PNqWcqGOVj8U0leP32\nfrl/f9BVuotdP3qIzve3AuAYV8TEH91D5sJzh4yoGW7sn4b0z60HRhZynGZum5nLZeP8GKVYkEgk\nwxApGiQDiuxJkZwIyWCU5he3EGvqRjGpZF05FeeE3KOeL4Qg8sGTTHj3O2iJCMbcifjuehRjzvg+\n5buz5kN++9K3CUYC5PmKuO+GXzMiY/SpPg4AVRVtvPn8DoJdUYxGAxdeMZ5Z5xahDJEPxmhjK7vv\n/xMNz74BgMnvYex9n2fkp689o70inSgn03ZpumD13g7+uaWZ+u6UWMh1mbl1Ri6XjfVhUoeWG13J\n6UO+GyXDEflmkEgkp5VofSfNL21FC8UxemzkXDcTS/bRo97qsRDdy75FZONTANjmfQrPTb9EMfet\n937llmU8uuqXaLrG9NHnce+S+3FYTz3abjyWZO0b5ZRuqAUgb6SHK28qwZ/lPOV79wfJUIR9f3iS\nfX94Ej0SQzEZKfr8zYz52mcweWS04ZMhqQtW7engX1ubeg2c890psbBwrBxZkEgkZwZSNEgGFDlv\nU3IsurfV0bZyJ+gCa6GfnCXTUe3mo56faNpF4NG7SDaVg8nGR+O/yGWf+lGf8kxqCR5960HeKn0O\ngCXz7uDWi76CwaCe0rMA1O7rYPmy7XR1RjCoCucvHMvcC0djGAI9zELXqX/mDSp+8WdizW0A5Hzi\nEib8193YRxWc5tINPU6k7UpoOm9VdPCv0maagimxMMJt4VMzc6TNguSYyHejZDgiRYNEIhl0hKbT\n/nY53VtqAHDPKiTjkgkox/i4Dm94iu5l9yHiYYw54/He+Xese9v7lG9XqIPfvvRtdtVtwaSa+eLi\n/+LCKVed0rMAJBIa61bs5sP3qkFAdp6LK2+aRlbe0Oi5b393M+U/foju7bsBcE+fyMT/vhf//Bmn\nuWTDk4Sm8+buDp4ube6Ns1DgsfCpGblcOkZ6Q5JIJGcmUjRIBpQztSel8sE3ASj+1hW9aUt/ORuA\nOYGfA3Df/YsHpSzLc88DYHHTe4OS35FY9MgWAFZ8fuZxz9XCcZpf3kq0thNUhazLp+AqGXHU80U8\nTNdz3yHywZMA2ObcgvuTD2KwOLngBIMnCyH4oPwtHn/7t3QEm/E5s/jm9b9ibN7UE7vBMWisDfDG\ns9vpaAuhGBTOuaSYcy8dg2o8/aMLocpayn/6e1reeAcAa34247//H+TdsAjFcPrLN5Q5UtsV13Te\nLG/nqdLm3gjOhV4rt83M4aLRUixITpwz9d0oObORokEikQwaseZuml/cQrI7iuowk3PdTKz53qOe\nn2zeTeejd5FsLAOTFc+ND2A75/Y+efWpai7nsVW/oqxuMwDj8kv4xnUP4nNmndKzJBMa69/ey4a1\nlQgB/iwHV31yGrkFnlO6b38QbWyl8qHHqX3ixVRwNruN4ntuZ9T5HM+GAAAgAElEQVSXbkW1D524\nEMOFeFLnjfJ2ni5tpi2cEgtFPiu3zUgFZZNiQSKRnA1I0SAZUOS8Tcl+esqbaH1jByKhYcnzkHPt\nDIyuI3/ACl0jtPaPBF+/HxJR1Kyx+O76O6b8KYecd6z61R3u5Ol//4HVpS8gELhsXm658MssmHbt\nKdkvCCHYu6uVt18to6szAgrMuXAUF1w2DqPp1O0iToVoQwuVv3uC2idfRsQToCiMuPVqxn3nC1hz\nT00knW2sW7eOufPP4/VdbTyzrYX2tFgY7bNy26xcLhglIzhLTh75bpQMR6RokEgkA4oQgs51ewi8\nXwmAc0o+mYsmYzAe+QM70bSLrn/dQ6L6QwBsc5fivvEBDCfo2SipJXhz8zM8995fCMd6UA0qV8xa\nyo3nfeGUvSN1tIVY/WoZVbtThsSZOU4uu3YKBaN8p3TfU+UwsQDkLlnAmG/chWvSmNNatuFIJKHx\nTmUnD1d/REckCUCx38btM3M5b5RHigWJRHJWIkWDZECRPSlnN3osQctr2wnvbQVFIePSCbhnFR5x\nepHQkoRW/47g8gdAi2Pw5OG5+bdYpyw66v0/Xr+2VL7LE6t/TUNHNQDTR5/HHQu+ccqxF+KxJOvf\n3suH71ahawKL1cj5l41lxjmFp9UzUqS+mX2/e4Laf77SO7KQe81Cxnz9TikW+khSF3xY183qvZ28\nVxUgphUAScZm2Lh9Vi7nFnpksDtJvyHfjZLhiBQNEolkQIh3hGh+YQuJjhAGq4mca6ZjK8o44rmJ\nho8I/PMrJOtKAbDNvx33tT/DYHOfUF4N7VU88fZv2FL5LgC5vkLuWPANZhZfcEofekIIykobeWd5\nOT3pYF0lcwq4YNE4HE7LSd/3VJFioX8QQlDWEmb13g7WVgboiiZ7j03NcfDJaTnML3RLsSCRSCSA\nIoQ43WUYdFatWiVmzZp1uotxViDnbZ6dhCtbaXl1G3osiSnTSe71MzF5Dw++JpJxelb+hp6VvwE9\nieorwLP0f7BMuPSE8nnr7RU0ih0s3/wUmq5hMzu48bwvsHj2Uoyq6ZSeoaWxm9WvlFFX1QlAboGH\nhUsmkTfy6IbbA02kvpnKhx6n7l+vHiQWFjDm63fhmlh82so13KgNRFm9t5PVezpoTMdXgJQnpIVj\nfVw6xsee0o2y7ZIMGPLdKBlINm/ezMKFC/u9t0OONEgkkn5DCEHXhio63knFA7CPyyb7qhIM5sOb\nmkTt1tToQuPO1LkXfA7X1T88IdsFXdd4e/tLPPzqA1hzkigoLJh2PbdceDceh/+UniESjvPuyj2U\nbqhBCLA5zFx0xXimzhqBcpq85ETqmqh86Anq/vUKIpFMiYXrLmPs1+/COeHUpl6dLXSEE6yp7GT1\nnk52t4V70zPsJi4d42PBGB9jMmy9owp7TldBJRKJZIgiRYNkQJE9KWcHQggi1R0E3t+bir8A+M4f\ni/fc4sOmdohElOCbDxJa/RDoGmrGKDxLH8Iy7sTqSlntFh5b9SBVLeVYc2BCwQzuXHAfo3MnndIz\n6Lpg+8Za1q2sIBJOoBgUZs0v5LzLxmK1ndqoxckixcKpEY5rvFsdYPWeTrY0BNHTA+t2k4ELR3tZ\nMMbPtDznEV2myrZLMpDI+iUZjkjRIJFIThohBOE9LQTeryTW1A2AwWIk68qpOMblHHZ+vGojXf+6\nh2TzblAUHBf/B86rfoDB4jhuXm3djTy55iHW71oBQIYrh9su+RrnTrz8lOecN9R0surlMpobUs8w\ncrSfBUsmkZV7eiI6R2obqfzdE6lpSGmxkHf95Yz52p1SLByHpC7YVNfN6j0drK/uIqallILRoHBO\noZuFY3ycU+jBMgSC70kkEslwQooGyYAi522emQhdp6esicAHlSTaQwAY7GY8s4vwzByJwXJoz7yI\nhwm+8QtCa/4IQkfNHof31ocwjz7nuHnFEhFe/uBxXt7wGIlkDLPRwjXn3MmSeZ9m4wcfnpJgCAVj\nrF1ezs4tDQC4PFYuvnICE0pyT4vx6xHFwg2LUmJh/KhBL89wQQjBzpYQq/d08s6+jxk05zpYMMbP\nRaO9uK0n/sqTbZdkIJH1SzIckaJBIpGcMHpSo2dHA4EN+0h2RQBQXVa880bhKinAcITgZvG96wn8\n6x60tkpQDDgW3Itr8XdQzLZj56VrvFf2Jv9852E6gs0AnDfxCj51yT1kuvNO6Tk0TWfze9WsX72H\neExDVRXmXjiaeZcUYz6C/cVAIoQg8OEOav7+HE0vr5ZioQ80B+O8taeDlRUdNKS9WwEUea0sGOtj\nwRg/OS7zaSyhRCKRnDlI70kSyUlQ+eCbABR/64retKW/nA3AnMDPAbjv/sWDUpbluecBsLjpvQHL\nQ48n6S6to2tjFVoo9XFm8tnxnDMa1+R8rvh7ylXqis/PPHBNrIfgqz8l/O+/AmDMnYjnUw9jLjz2\nb08XOu/vWslz7/2V+vZ9AIzKnsBnFn6LSSNnHvPaE6Gqoo3Vr5bR0ZoaISmemMWln5iIL+P4U6T6\nk2QoQuOLK6n5+3MEd1SkEg0G8q6/LCUWxo0a1PIMFyIJjXVVAVbs7qC0sac33W83smCMn4VjfRT7\nbdJNqkQiOWuR3pMkEsmgo0XidG+uoWtzDXo0FWnYnOXCO78Yx/ico3oTiu1eS9dTX0XrqAGDEedl\nX8O56JsoxqPHNtCFzobyVSx776/Ute0FIMuTz43nfYGLpnwCg+HIEaRPBKELaio72PxeFXt3tQLg\nzbCz4OpJFE/IOun7ngyhvTXUPPYC9U+9RrI79dFr8nspuG0JIz99HfbCUxtFORPRhWB7Yw8rKjr4\n974A0aQOgFlVOH+Ul8vH+ZmZ7zqiQbNEIpFI+oezVjRs/dkDJMwCW24W2VNL8I8vQXVYZe9UPyPn\nbQ5Pkj0xujZV0b21FpHQALDke/GdW4xtdOZRfyd6qCM1urD+MQCMI0rw3vowpoKSo+alC51NFWtY\n9u6fqWlNObrMdOdy/bmf4+KpS44Zb+F49SscivPR5nq2bailsz3lZtNkVpl/6Rhmnz8K4yAZw+rJ\nJK0r36Xm0edpX7uxN90zewpFd91IztWXolpPX7C4oUpDd4yVFR28VdFBc8+BeApTchxcPs7PxcU+\nHOaTF5PHQrZdkoFE1i/JcOSsFQ1uy4zURjP0NHfSs+odhB4mqbUTE93oFoEtL5OMCZPwjJmI0euS\ngkJyxpMIhAlsqKJnRz1CS/Xm2kZl4J1fjLXAd8TfgNASzOx+j/MDK2j+4QegxUE14bzi2zgX3oty\nlI9+IQQf7lnLsnf/QlVLOQB+Vw7Xz/8sl0679qSDswkhqNvXSemGWio+akJLe89xui1MmzuSaXML\ncLqtJ3XvvhJr7aDuyZepfeIlovUpuwyDzUL+9YsYeecNeKZNGJRyDCdCcY13KjtZWdHBjuZQb3q2\n08RlY/1cPs7PCM/g/P8kEolEcoCzVjRUdb+OU7XhUN1Y1AwMag6KwY7JYMcEIIAG6G7opPvt9QgR\nR9M6SNCNbhXYcv34isfiGj0eU4YXRZXu+46E7EkZHsTbegh8UElPWROk7Zzs47LxzS/Gkus57Hwh\nBMm6UsIbnyL64XN8LdSeOqAYsExciOvan2DKO3LcBCEEm/f+m2Xv/pl9zbsA8DmzuG7+Z1kw7TpM\nxhM3XD24fkXCcT7a3MC2jbW99gooMHpCFtPnjaR4fCaGQfidCiEIbNyeMmx+9e2UYTNgH11A4Z03\nMOKWqzB53QNejuGEpgu2NARZWdHBu1UB4mmhZzGm4ilcPs7P9DwnhkHsuJFtl2QgkfVLMhw5a0XD\ngp/+DwCxhEZleR1lGzYSqvk3tlgnblXgNqYEhdWQgapmo6gejMZcjORCEqiDrrouut7ZiBAaut5B\ngiCKHay5fryjirGPGofZ70aR/sAlQxA9oRGpaiO4o4HwnpZUoqLgnJKPd95ozJnOw67RuhqJbHqW\nyManSDbt6k2vtxSxzruIe/7zq6je/CPmJ4Rg6773WLbuz+xt+ggAryODa+ffxcLpN2A+hr3D0RBC\n0FAToHRDLeXbm9DSc90dLgslcwoomVOAx3dsL039RTIUpvH5FdT8/XmCO9PxhA0GshdfSOGdN5Bx\n0VwUg2wLDqYmEGVlRQerKjpoCyd606fnObl8nJ8LRnmxD9D0I4lEIpH0Dek96SgIIWjrilCxpYK9\n27YSbfoIpx7AYxR4jDacqgt7WlCgZoBy5I8BIXR0vRNN6UFxKthyMnAXjcZWNBqT34XBeGa/EOW8\nzaGFFo4TrmwlVNFCpKoNkf7IVlQDrpIReOaNxuQ59CNbxMNEt79OeMO/iO9eCyJ1jcGRgXX2jdjn\nLsVYMP2o0/eEEGyrWs+z6/7MnsYdAHjsfq45504un3EjZlPfp5pEIwl2bmng+Wdfx2s7EOxs1LgM\nps8rpHhiFuogjf71VFRR+9gL1D/9OslgaoTDnOGl4PZrGHn7tdhGSsPmg2kLxXlnX4C393ZS3hru\nTc9zmbl8nJ/LxvnJdZ1++w7ZdkkGElm/JAOJ9J40yCiKQpbXTtal0znv0umHHIsldapr2ti7uZya\n8p0kOzbiVrrxGRU8Jitu1YOtd4QiC1XNQCUDwpDcBx37WoCWlKAQXeiGHgxOA7ZsP84RhVhHFGDy\nezDYzdKOQnLKJAJhwntaCO1pIVrXmZp6l8aS58E+NhvX1BEYnQc+1ISuE69cT2TjU0S3voSIpV1b\nqiasU67CNu9WLBMXohxjKpEQgh3VG3j23T+zuz7lktVt93HNvM9w+cybsJj6NgIghKCprovSDbXs\n2tZIMqHT1REhf7yZqXNGMG3uSLx+e5/uebIkQxFa3lhL3VOv0bHuw95079wSCu+8gdyrL8VgkfEB\n9tMZSbBuX4A1lQF2NPX0VkG7ycBFo30sGu9nSo5DtncSiUQyhJEjDf2IEILWYJSqnTVUl1bQVFOO\nCO3BZ+rBb1bxGG24VDd2QwZGNRuhZoNy9JEGIeLodIMlicltwZ6VhTU/D3NuHiavA4PNJF+yksMQ\nQhBvCRLa00K4ooV4a/DAQYOCrdCPY1wO9rFZGJ2H9vInWyuJbHyKyKZnUu5S05iKZmObuxTbzOsx\nOPzHLcNHNZt4dt2f2FW3BQCXzcOSeZ9h0cybsR4nqNvHiUWTlG1toHRjLa2NB56lcEwG0+eNZOyk\nbNRBmAIoNI32dzfT8Oxyml9bgxZOB7ezWcm7cRGFd96Ae+r4AS/HcKE7muTd6i7WVnaytSGInn7V\nmFSFc0a6ubjYxzmFHqxy+qZEIpH0K3KkYRigKArZbhvZ8ycwb/4E4OreY5GERk1jgOrSPTTsrKS9\neQ9GbR0+cwS/2YDXZMeluLAZfJgMmQhjJorBiUomxEFvg562JD1ltUAtkBIVQg1hsOpYvE6sWZmY\nc3MwZWVi8tikqDiLELpOtC5AaE8z4YoWkt3R3mOKWcVenIVjbDb24kwMlkO9EunhLqJbXyC88SkS\n+zb0phu8I7DNvQX7nJsx5hz/YzjQ08aOmo2sLn2BnbWp3neH1c2SeZ/mipm3YLOcePA0XRfUVnZQ\nVtpA+fYmEvGU21eb3cSU2SOYPnckvszBCcYWLNtLw7LlNDy/glhja2+6d85U8m9aTN71l2PyuAal\nLEOdUFxjfVoofFgfJJlWCkaDwtwCFxcX+zi3yDNgblIlEolEMnBI0TBI2EwqEwozmFCYAUvO6U3X\nhaA5GKOqvI492ytp3VdLV6AWVWzCZe7GaxF4TCY8BhtOxY3N4Ec1ZPWKCkU3QxhiYYg1BKE0CKSM\nMAVxFDWKaleweJ2YMnyYsrMxZ3gxuq2oTutRg3P1F3Le5sCx35A5VNFCeG9rb/A1ANVhxj42G8e4\nbGwjMw4xxtcj3cT3fUC88n3iletJVG9OuUkFFLMD6/Ql2OYuxTz2gmMa7oZjQXbWfMiO6g3sqN5I\nXXtl7zGHxcUn5t7O4tlLsVsON6g+EkIImuu7KSttYNe2JkLBWO+xgtE+ps8bybgpuYfEVhio+hVr\n7aDx+RU0LFtO9/bdvem2wnzyb1pM/k1X4Cge2e/5DkeiSZ0PalJC4YPabhJpz0cGBWbmu7hkjI/z\nizy4rcPrdSPbLslAIuuXZDgyvFrxMxCDopDntpI3dyzMHXvIsXBco7Y9RG1ZFXvLauiqaiDU3UyS\nj7Ba2nDYwvjMCj6DCQ92nIoPqyEDRc1CGLNQDA7QzGhBCAeB2gAQ6L2/QENRYxjtCiaPHZPfizk7\nC6PPjdFtxei2nvGG2sMJkdSJt/cQa+oiXNl2iCEzgMlnxz4uB8e4bCx5nt5RJq2riXjl+pRI2Lue\nZONHvW5VAVAUzOMvxjbnFqzTr8ZwlI/8eDJGeX1pWiRsoLKpDCEO5G8xWZlYMItpo+Zz6bRrsFtO\nrPe9oy1E2dYGdpU29gZgA/D67UycnsfkGXn4s05MeJwKWiRGy5vvUP/MctrXbkBoqdENo8dF7jUL\nGHHTYrzzpsnROyCu6Wyq62ZtZYD11V29EZoVoCTXycXFXi4c5cVnP7lYGxKJRCIZekibhmGILgRt\noQQ1TQHqy6ppK68mVNNCLNhOQunCYOvCbOvEaY7gV8GnmPAoLpyKD4uSAcZMhJoJqvf4makxjDYF\nk9uK0evG5PehepwYHRZUpwXVYcFgMZ51H1KVD74JQPG3ruhNW/rL2QDMCfwcgPvuX3zS99fCcWIt\n3cRbgsRbg8RagiQ6QvRODE9jyfNQ98xz9OzZxcKdryCEQGvdmxIJe1MjCVp71aE3V02YRs7AXHwu\n5uL5mEefg8HhO7wMepLKpjJ2VG9kR/UGdteXktAOROVVDSpj80uYWjiPv2/3kjQWs+ILc0/o+Xq6\no+za1kRZaQPN9d296XanmYkleUyakUdugWfA65XQdTrWb6Vh2XKaXlmN1pMSLYpRJWvhueTftJis\ny8+X0ZqBpC7YUh9kbWUn71Z3EUpPGQOYmGXn4mIfFxV7yXJIA3CJRCI5nQx7mwZFUf5GapJ/sxBi\nWjrNBzwNFAFVwM1CiK70se8BnyUVFeGrQogV6fRZwKOAFXhdCPG1dLoZeByYDbQBtwghDlhynkEY\nFIVsp5nssdnMGZsNSw58qEUSGvVdMWrq2mneVUVrRS119W0ku7sRhh7i9g4URw0mWydOtYsMBH4s\neBUPLsWHxeAHNSUqhJoBmoVkDyR7dGg4dKSiF0VHtQhUm4rqsmH0ejB6XBjTomL/WtpYHI7QBYnO\nEPHWIPGWlDiItwTRQrEjnm/yOzBnubCO9OEYm41qN7Ln53fjyQrT+b93EK/8AL2n9ZBrFIsT06i5\nmMecmxIKhTNRzId7GRJCUNde2TvdaGfNJiLx0CHnjMqewJSiuUwtmsekgplY0/f5664tx33WWDTB\n7h3NlJU2UlvZ3jvYYbaojJuSy6TpeRQW+wclAFtPRVXKTmHZm72RmgE8Myen7BSuXYg583AhdTaR\n1AUVbWG2N/ZQ2tjDR809hBMHRpbGZNi4JC0U8oaAi1SJRCKRDCyDOT3p78DvSH3Y7+e7wFtCiF8q\nivId4HvAdxVFmQzcDEwCCoC3FEUZJ1LDIn8EPieE2KgoyuuKolwhhHgT+BzQIYQYpyjKLcAvgaWD\n93hDA5tJZWymnbGZdphxYM61EIKOcJKaQIT62jZad1XRvaeGmoY26oNhDEqUuD1K2N0DjgZspnYc\noo0MoZOp2PHhxaF4sCpuFIMXofoQqhdh8IHBihYFLQp0RqAmAjQBsKl6J3OKJqcKoQhUS2q+veqy\no7ocqDYzqtWEIb2otvQ6va+Y1DNGaOjxZEoYpAVCvDW1HDzFaD+KScWc5cKS7cKc7cKc5cLktyFC\nTWht+4hXraDr/fUkqjYx/oqUO9TotlcBMDizMI+Znx5JOBdj/hQU9fCfejQepr59H9WtFXxUvZGP\najYS2B/ZOU2udyRTi+YxtWgukwvn4Lb37UM6mdCoLG+lrLSRyvLW3uBrqqpQPCGbidPzKJ6Yhcl0\nctPgTnResJ5IEtyxm473t9L00iq6tpb1HrOOyCH/k4vJv/EKnONGnVQ5zgQSms7utjDbGnvY3tTD\njqZQ77Sj/RR6rVxS7OXiYh8jvX2PrzGckHPOJQOJrF+S4cigiQYhxDpFUYo+lnwtcHF6+zFgDSkh\ncQ3wlBAiCVQpilIBzFMUpRpwCSE2pq95HLgOeDN9rx+l05cBDw/UswxHFEUhw2Eiw2Fi5gg3zC/u\nPZbQdBq749QEIjTsa6J9dzWhylpCje0kQhE6RJKELU7IoxByaGBrxCJ2YEu04RCdeLQEGTjx4sSJ\nC4fiQTV4EKoPQ7wLJeFGqD4wONLiIgHtXUDX8QtuAINFRbWaMdgtvWJCtZowHCIwjCmjXYOSEhkG\nJWXkbVBS6QrpfUNqrRx8/GP7ioIQAqHpiKSOSGiIpIae0BAJHZE8MC0juL0+lZ7UWMACTJgYYQOj\nAs2vlqbOT2gkusIkA5EjPqLqsqbEQZYLo1PDaOyAaD1a51a0tmoSu2uItlejBepB1w67PhY0EWq1\nM/r7P8U85lzUzOJDhFYwEqC+fR/17fuoa9tHQ8c+6toqaQ82H3YvnyOTKWmRMLVoLpnuvgcmS3k+\naqestJHdO5qJx5KpAwoUFvuZNCOfcVNysNoGbr57vKOLwKYddG7cRmDjdrpKy9AjB0ZvVKed3CUL\nGPHJK/HNn35WRmqOazq7W1MiobSxh50tIWIfEwkFHgsluU6m5zmZluckU049kkgkkrOW020InS2E\naAYQQjQpipKdTh8BrD/ovPp0WhKoOyi9Lp2+/5ra9L00RVECiqL4hRAdA/kAZwIm1UChz0qhzwqj\nfbBgUu+x7miSuq4YtZ1hmvY10bm3lsi+OvTGVoyhMGpCIyag0q0S9BoIuSFsi4HagZWt2PM7qI+u\nwiUi+DQDfuHEgxMnbiyKHWFwgsGJMDgO2k7tozgBC3pEQ49EUqMYg4HCIQHQjkXr8h2925cpl6U2\n0h2wobKmQ082KJj9NowugdEcxCAaUaJ7EYG9JHdVE1tfSywR5agoCgZPHmpGEaYR01KjCaPns3LC\nDQgEeVOuYG/HPuo3b0wJhPZ9NLTvoyt85J+AUTWR7y9iREYxEwpmUFI0j3z/qD6N7MSiCTpaQ+QF\nIzgSGi89uYWGmsAhno9yRriZND2fidNycbr7t3f6ggsuQOg6oT01BDZtp3PDNgKbthPac/jMRMfY\nQrxzSsi8ZB7Ziy5EtZ/ZPeUfJ57U2dUaZltjkG1NPexsDhHXDq3oIz0Wpue5KEmLhIyz2JBZ9gJL\nBhJZvyTDkdMtGj5Of1plnxlzWk4zbquRyVYjk3McMDELKAFSxtjt4QR1XTHqA1Ea69pgTy1KdT32\nykbcXQacMTdWTcdgtBD2ONjrUyl1Q9ieIK52oykBTKIDC/uwim5sehhnUsOp7190nJoBFxYc2DAr\njoNEhRMMDoSyX2jYATUdLM+ASK/B0JuWWqupY+ntA8fS56IeVAs1FEUDJYmChqIke7e1cAhFJFFd\nKgpJFCVJU2clQsSxJD0gEmTlWoEkiDjEmhBtW6C2AwEkODKKw4/RX4SaUYjqL0LNKMLoL0TNKELx\njSCSjNMdCVDbWUtdeyX17/2Fj5bGCfgFj/3xyIbXFpONERmjGZExmoL0ekRGMdnefFTD8ZsAXRd0\nd0boaAvR0dpDR2sovR0i3JMyjC5Jn1sRSNlAeDPsTJqex6Tp/e/5KBmK0LW1jMCm7QQ2bCPw4Q4S\ngeAh5xisZjwzJuOdW4Jv7jS8s6dgzjgBw/8ziGhSZ1dLiG2NPWxr7KGsNdTrDnU/RV4r09ICoSTX\nif8sFgkSiUQiOTanWzQ0K4qSI4RoVhQlF2hJp9cDBztBL0inHS394GsaFEVRAffRRhmWLVvGI488\nQmFhIQAej4eSkpJe5b9u3ToAuX8C+1kOM6HKUrx2uODuRQCsfeffdEaS5E+exVtr3qGtM0ywvpUM\n1YPS0ERPZTn2aJQZFj8mkUt50knMZie7YCIht8qW7ioSZo3skX7ihm7q6ipJGsJkFaqYRSeBmips\nus7EESpOXaexKoZV15iWr2ISgt31CYwCZuUpmIVgR4OGKgTn5oJZCDamBwDm5abWGw7aFwftn3OE\n44fttx39eGbo8PMVs51NPRngymLOjEnEHX7e3RcgZLYxatYkurUEWzZtI9LWQ5azk559VVS8Uk00\n3oMtD4TQ6ahOjbj4i1KRlTsSEWiGkRNyKMgYTbTZRJY7j8sWLKIgczQ7SyswKIZD/n97m2vIu6Dw\nkP/nnNnn0NkWYtXKNXR3RRiRPZGO1hBbt21E1wRFI1K2KdX1OwEoGjEZo8lAe7ASl8fK+eefjz/T\nwd6aHXh8CudfOK5f6tuqF1+hZ1cl43t0Ojds54NtWxG6xmSDg5166o9s8nu48MKL8M4rodyYwDqq\ngHMuveTA/cp2DInfy0DtJ3Wd/Mlz2N0aZsXqtdR2RYnkTEYX0L13KwDuMTMY7bPiai2jOMPOp65e\niNdmSt2vAfzFQ+d5hsL+/rShUh65f2bt708bKuWR+8N7f/92TU1qlH3OnDksXLiQ/mZQXa4qijIK\neEUIUZLef4CU8fIDaUNonxBivyH0k8A5pKYdrQTGCSGEoijvA/cCG4HXgIeEEMsVRbkbmCqEuFtR\nlKXAdUKIIxpCD3eXq8OJdesONfaKJnUau2PUdcVo6I5R3xmhraaZUE0DNDbj6WzD09mOuyuAPRbH\nJBQ0h4uEw0PC4SbhcBNzOAi7VWLmGAlDkIQSJGEIoinRAwupdfKgNBQBQqACZqFjEgKzEJiEwCT0\n3m2zEKjp34UARHrMSqQHrwQHBiMEoKpGDAYjRtWEwWBEVU0YVSOqwZTeNhFWFBqFRmsiTDDajaYn\nT+rvabc4cVo9ZHnyKcgsZkTGKEZkFDMiYzQeu/+wqUVCCMtfbCYAABePSURBVOIxjXAoRrgnnl5i\nhEPp7VCcUDBGR9uBUYMj4XRb8Gc58Wc68GelFl+mA7enfwMERpvb6C4tp3vbLrq2p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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import scipy.optimize as sop\n", + "\n", + "ax = plt.subplot(111)\n", + "\n", + "\n", + "for _p in risks:\n", + " _color = next(ax._get_lines.prop_cycler)\n", + " _min_results = sop.fmin(expected_loss, 15000, args=(_p,),disp = False)\n", + " _results = [expected_loss(_g, _p) for _g in guesses]\n", + " plt.plot(guesses, _results , color = _color['color'])\n", + " plt.scatter(_min_results, 0, s = 60, \\\n", + " color= _color['color'], label = \"%d\"%_p)\n", + " plt.vlines(_min_results, 0, 120000, color = _color['color'], linestyles=\"--\")\n", + " print(\"minimum at risk %d: %.2f\" % (_p, _min_results))\n", + "\n", + "plt.title(\"Expected loss & Bayes actions of different guesses, \\n \\\n", + "various risk-levels of overestimating\")\n", + "plt.legend(loc=\"upper left\", scatterpoints=1, title=\"Bayes action at risk:\")\n", + "plt.xlabel(\"price guess\")\n", + "plt.ylabel(\"expected loss\")\n", + "plt.xlim(7000, 30000)\n", + "plt.ylim(-1000, 80000);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As intuition suggests, as we decrease the risk threshold (care about overbidding less), we increase our bid, willing to edge closer to the true price. It is interesting how far away our optimized loss is from the posterior mean, which was about 20 000. \n", + "\n", + "Suffice to say, in higher dimensions being able to eyeball the minimum expected loss is impossible. Hence why we require use of Scipy's `fmin` function.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "______\n", + "\n", + "### Shortcuts\n", + "\n", + "For some loss functions, the Bayes action is known in closed form. We list some of them below:\n", + "\n", + "- If using the mean-squared loss, the Bayes action is the mean of the posterior distribution, i.e. the value \n", + "$$ E_{\\theta}\\left[ \\theta \\right] $$\n", + "\n", + "> minimizes $E_{\\theta}\\left[ \\; (\\theta - \\hat{\\theta})^2 \\; \\right]$. Computationally this requires us to calculate the average of the posterior samples [See chapter 4 on The Law of Large Numbers]\n", + "\n", + "- Whereas the *median* of the posterior distribution minimizes the expected absolute-loss. The sample median of the posterior samples is an appropriate and very accurate approximation to the true median.\n", + "\n", + "- In fact, it is possible to show that the MAP estimate is the solution to using a loss function that shrinks to the zero-one loss.\n", + "\n", + "\n", + "Maybe it is clear now why the first-introduced loss functions are used most often in the mathematics of Bayesian inference: no complicated optimizations are necessary. Luckily, we have machines to do the complications for us. \n", + "\n", + "## Machine Learning via Bayesian Methods\n", + "\n", + "Whereas frequentist methods strive to achieve the best precision about all possible parameters, machine learning cares to achieve the best *prediction* among all possible parameters. Of course, one way to achieve accurate predictions is to aim for accurate predictions, but often your prediction measure and what frequentist methods are optimizing for are very different. \n", + "\n", + "For example, least-squares linear regression is the most simple active machine learning algorithm. I say active as it engages in some learning, whereas predicting the sample mean is technically *simpler*, but is learning very little if anything. The loss that determines the coefficients of the regressors is a squared-error loss. On the other hand, if your prediction loss function (or score function, which is the negative loss) is not a squared-error, like AUC, ROC, precision, etc., your least-squares line will not be optimal for the prediction loss function. This can lead to prediction results that are suboptimal. \n", + "\n", + "Finding Bayes actions is equivalent to finding parameters that optimize *not parameter accuracy* but an arbitrary performance measure, however we wish to define performance (loss functions, AUC, ROC, precision/recall etc.).\n", + "\n", + "The next two examples demonstrate these ideas. The first example is a linear model where we can choose to predict using the least-squares loss or a novel, outcome-sensitive loss. \n", + "\n", + "The second example is adapted from a Kaggle data science project. The loss function associated with our predictions is incredibly complicated. \n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "##### Example: Financial prediction\n", + "\n", + "\n", + "Suppose the future return of a stock price is very small, say 0.01 (or 1%). We have a model that predicts the stock's future price, and our profit and loss is directly tied to us acting on the prediction. How should we measure the loss associated with the model's predictions, and subsequent future predictions? A squared-error loss is agnostic to the signage and would penalize a prediction of -0.01 equally as bad a prediction of 0.03:\n", + "\n", + "$$ (0.01 - (-0.01))^2 = (0.01 - 0.03)^2 = 0.004$$\n", + "\n", + "If you had made a bet based on your model's prediction, you would have earned money with a prediction of 0.03, and lost money with a prediction of -0.01, yet our loss did not capture this. We need a better loss that takes into account the *sign* of the prediction and true value. We design a new loss that is better for financial applications below:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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qjP+a0sXLmd4+zfX0B3vTKLOytyAKhUJhKWRZGb/tP6qnXXp1ofF9zvUokUKh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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "figsize(12.5, 4)\n", + "\n", + "\n", + "def stock_loss(true_return, yhat, alpha=100.):\n", + " if true_return * yhat < 0:\n", + " # opposite signs, not good\n", + " return alpha * yhat ** 2 - np.sign(true_return) * yhat \\\n", + " + abs(true_return)\n", + " else:\n", + " return abs(true_return - yhat)\n", + "\n", + "\n", + "true_value = .05\n", + "pred = np.linspace(-.04, .12, 75)\n", + "\n", + "plt.plot(pred, [stock_loss(true_value, _p) for _p in pred],\n", + " label=\"Loss associated with\\n prediction if true value = 0.05\", lw=3)\n", + "plt.vlines(0, 0, .25, linestyles=\"--\")\n", + "\n", + "plt.xlabel(\"prediction\")\n", + "plt.ylabel(\"loss\")\n", + "plt.xlim(-0.04, .12)\n", + "plt.ylim(0, 0.25)\n", + "\n", + "true_value = -.02\n", + "plt.plot(pred, [stock_loss(true_value, _p) for _p in pred], alpha=0.6,\n", + " label=\"Loss associated with\\n prediction if true value = -0.02\", lw=3)\n", + "plt.legend()\n", + "plt.title(\"Stock returns loss if true value = 0.05, -0.02\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note the change in the shape of the loss as the prediction crosses zero. This loss reflects that the user really does not want to guess the wrong sign, especially be wrong *and* a large magnitude. \n", + "\n", + "Why would the user care about the magnitude? Why is the loss not 0 for predicting the correct sign? Surely, if the return is 0.01 and we bet millions we will still be (very) happy.\n", + "\n", + "Financial institutions treat downside risk, as in predicting a lot on the wrong side, and upside risk, as in predicting a lot on the right side, similarly. Both are seen as risky behaviour and discouraged. Hence why we have an increasing loss as we move further away from the true price. (With less extreme loss in the direction of the correct sign.)\n", + "\n", + "We will perform a regression on a trading signal that we believe predicts future returns well. Our dataset is artificial, as most financial data is not even close to linear. Below, we plot the data along with the least-squares line." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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rxGEScD7iw905LsvJtgDPXlCDmZbMLBdR/wxRwi8iIiJtkk25TrolQOPuxhfn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\nbmHfXv3cEzPrDQwKn88md/8H8JcUz/fn7r7R3d8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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Code to create artificial data\n", + "N = 100\n", + "X = 0.025 * np.random.randn(N)\n", + "Y = 0.5 * X + 0.01 * np.random.randn(N)\n", + "\n", + "ls_coef_ = np.cov(X, Y)[0, 1] / np.var(X)\n", + "ls_intercept = Y.mean() - ls_coef_ * X.mean()\n", + "\n", + "plt.scatter(X, Y, c=\"k\")\n", + "plt.xlabel(\"trading signal\")\n", + "plt.ylabel(\"returns\")\n", + "plt.title(\"Empirical returns vs trading signal\")\n", + "plt.plot(X, ls_coef_ * X + ls_intercept, label=\"Least-squares line\")\n", + "plt.xlim(X.min(), X.max())\n", + "plt.ylim(Y.min(), Y.max())\n", + "plt.legend(loc=\"upper left\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We perform a simple Bayesian linear regression on this dataset. We look for a model like:\n", + "\n", + "$$ R = \\alpha + \\beta x + \\epsilon$$\n", + "\n", + "where $\\alpha, \\beta$ are our unknown parameters and $\\epsilon \\sim \\text{Normal}(0, 1/\\tau)$. The most common priors on $\\beta$ and $\\alpha$ are Normal priors. We will also assign a prior on $\\tau$, so that $\\sigma = 1/\\sqrt{\\tau}$ is uniform over 0 to 100 (equivalently then $\\tau = 1/\\text{Uniform}(0, 100)^2$)." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " [-----------------100%-----------------] 100000 of 100000 complete in 17.2 secPlotting prec\n", + "Plotting beta\n", + "Plotting alpha\n" + ] + }, + { + "data": { + "image/png": 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cg7VGrmHaKfexa5henArgXOM/8zbyuDch4CcntOPKY9ryxer4F/TetPsAG3bu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\nEVkuIitE5NIizw8Xkfki0i4iHSLy9WLvY6mGpJSQ5u9DySW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BRLaKSL7/35igOveIyBoRKRSRC4L254jIUr+m9Qkv/DEMo+7iZDhyGxA8ZbGr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\nSsqENR9lwvKJ5kxY6ExFUVK4Rl2mWuKTSh2pCd1/KRPWmu23cu0Q/rN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OA/PXMtlieTBjYP3eQ7y3chfTv9xiUboux41hS2V4p8zguTfum70m6NDu3NW7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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import pymc as pm\n", + "from pymc.Matplot import plot as mcplot\n", + "\n", + "std = pm.Uniform(\"std\", 0, 100, trace=False) # this needs to be explained.\n", + "\n", + "\n", + "@pm.deterministic\n", + "def prec(U=std):\n", + " return 1.0 / (U) ** 2\n", + "\n", + "beta = pm.Normal(\"beta\", 0, 0.0001)\n", + "alpha = pm.Normal(\"alpha\", 0, 0.0001)\n", + "\n", + "\n", + "@pm.deterministic\n", + "def mean(X=X, alpha=alpha, beta=beta):\n", + " return alpha + beta * X\n", + "\n", + "obs = pm.Normal(\"obs\", mean, prec, value=Y, observed=True)\n", + "mcmc = pm.MCMC([obs, beta, alpha, std, prec])\n", + "\n", + "mcmc.sample(100000, 80000)\n", + "mcplot(mcmc)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It appears the MCMC has converged so we may continue.\n", + "\n", + "For a specific trading signal, call it $x$, the distribution of possible returns has the form:\n", + "\n", + "$$R_i(x) = \\alpha_i + \\beta_ix + \\epsilon $$\n", + "\n", + "where $\\epsilon \\sim \\text{Normal}(0, 1/\\tau_i) $ and $i$ indexes our posterior samples. We wish to find the solution to \n", + "\n", + "$$ \\arg \\min_{r} \\;\\;E_{R(x)}\\left[ \\; L(R(x), r) \\; \\right] $$\n", + "\n", + "according to the loss given above. This $r$ is our Bayes action for trading signal $x$. Below we plot the Bayes action over different trading signals. What do you notice?\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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IpNxFCCGEENmXlPSYpKRB5GQS/0IIIUTWJiU9QgghhBBC5FCS8AshcgR3r68U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\n4xhDms0XERHpmtTDL50qUFvHjpfeZNND/6T0w6VN+3tNPpy8C87kkBl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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "figsize(12.5, 6)\n", + "from scipy.optimize import fmin\n", + "\n", + "\n", + "def stock_loss(price, pred, coef=500):\n", + " \"\"\"vectorized for numpy\"\"\"\n", + " sol = np.zeros_like(price)\n", + " ix = price * pred < 0\n", + " sol[ix] = coef * pred ** 2 - np.sign(price[ix]) * pred + abs(price[ix])\n", + " sol[~ix] = abs(price[~ix] - pred)\n", + " return sol\n", + "\n", + "tau_samples = mcmc.trace(\"prec\")[:]\n", + "alpha_samples = mcmc.trace(\"alpha\")[:]\n", + "beta_samples = mcmc.trace(\"beta\")[:]\n", + "\n", + "N = tau_samples.shape[0]\n", + "\n", + "noise = 1. / np.sqrt(tau_samples) * np.random.randn(N)\n", + "\n", + "possible_outcomes = lambda signal: alpha_samples + beta_samples * signal \\\n", + " + noise\n", + "\n", + "\n", + "opt_predictions = np.zeros(50)\n", + "trading_signals = np.linspace(X.min(), X.max(), 50)\n", + "for i, _signal in enumerate(trading_signals):\n", + " _possible_outcomes = possible_outcomes(_signal)\n", + " tomin = lambda pred: stock_loss(_possible_outcomes, pred).mean()\n", + " opt_predictions[i] = fmin(tomin, 0, disp=False)\n", + "\n", + "\n", + "plt.xlabel(\"trading signal\")\n", + "plt.ylabel(\"prediction\")\n", + "plt.title(\"Least-squares prediction vs. Bayes action prediction\")\n", + "plt.plot(X, ls_coef_ * X + ls_intercept, label=\"Least-squares prediction\")\n", + "plt.xlim(X.min(), X.max())\n", + "plt.plot(trading_signals, opt_predictions, label=\"Bayes action prediction\")\n", + "plt.legend(loc=\"upper left\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "What is interesting about the above graph is that when the signal is near 0, and many of the possible returns outcomes are possibly both positive and negative, our best (with respect to our loss) prediction is to predict close to 0, hence *take on no position*. Only when we are very confident do we enter into a position. I call this style of model a *sparse prediction*, where we feel uncomfortable with our uncertainty so choose not to act. (Compare with the least-squares prediction which will rarely, if ever, predict zero). \n", + "\n", + "A good sanity check that our model is still reasonable: as the signal becomes more and more extreme, and we feel more and more confident about the positive/negativeness of returns, our position converges with that of the least-squares line. \n", + "\n", + "The sparse-prediction model is not trying to *fit* the data the best (according to a *squared-error loss* definition of *fit*). That honor would go to the least-squares model. The sparse-prediction model is trying to find the best prediction *with respect to our `stock_loss`-defined loss*. We can turn this reasoning around: the least-squares model is not trying to *predict* the best (according to a *`stock-loss`* definition of *predict*). That honor would go the *sparse prediction* model. The least-squares model is trying to find the best fit of the data *with respect to the squared-error loss*.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Example: Kaggle contest on *Observing Dark World*\n", + "\n", + "\n", + "A personal motivation for learning Bayesian methods was trying to piece together the winning solution to Kaggle's [*Observing Dark Worlds*](http://www.kaggle.com/c/DarkWorlds) contest. From the contest's website:\n", + "\n", + "\n", + "\n", + ">There is more to the Universe than meets the eye. Out in the cosmos exists a form of matter that outnumbers the stuff we can see by almost 7 to 1, and we don’t know what it is. What we do know is that it does not emit or absorb light, so we call it Dark Matter. Such a vast amount of aggregated matter does not go unnoticed. In fact we observe that this stuff aggregates and forms massive structures called Dark Matter Halos. Although dark, it warps and bends spacetime such that any light from a background galaxy which passes close to the Dark Matter will have its path altered and changed. This bending causes the galaxy to appear as an ellipse in the sky.\n", + "\n", + "\n", + "\n", + "\n", + "The contest required predictions about where dark matter was likely to be. The winner, [Tim Salimans](http://timsalimans.com/), used Bayesian inference to find the best locations for the halos (interestingly, the second-place winner also used Bayesian inference). With Tim's permission, we provided his solution [1] here:\n", + "\n", + "1. Construct a prior distribution for the halo positions $p(x)$, i.e. formulate our expectations about the halo positions before looking at the data.\n", + "2. Construct a probabilistic model for the data (observed ellipticities of the galaxies) given the positions of the dark matter halos: $p(e | x)$.\n", + "3. Use Bayes’ rule to get the posterior distribution of the halo positions, i.e. use to the data to guess where the dark matter halos might be.\n", + "4. Minimize the expected loss with respect to the posterior distribution over the predictions for the halo positions: $\\hat{x} = \\arg \\min_{\\text{prediction} } E_{p(x|e)}[ L( \\text{prediction}, x) ]$ , i.e. tune our predictions to be as good as possible for the given error metric.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The loss function in this problem is very complicated. For the very determined, the loss function is contained in the file DarkWorldsMetric.py in the parent folder. Though I suggest not reading it all, suffice to say the loss function is about 160 lines of code — not something that can be written down in a single mathematical line. The loss function attempts to measure the accuracy of prediction, in a Euclidean distance sense, such that no shift-bias is present. More details can be found on the metric's [main page](http://www.kaggle.com/c/DarkWorlds/details/evaluation). \n", + "\n", + "We will attempt to implement Tim's winning solution using PyMC and our knowledge of loss functions." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### The Data\n", + "\n", + "The dataset is actually 300 separate files, each representing a sky. In each file, or sky, are between 300 and 720 galaxies. Each galaxy has an $x$ and $y$ position associated with it, ranging from 0 to 4200, and measures of ellipticity: $e_1$ and $e_2$. Information about what these measures mean can be found [here](https://www.kaggle.com/c/DarkWorlds/details/an-introduction-to-ellipticity), but for our purposes it does not matter besides for visualization purposes. Thus a typical sky might look like the following:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Data on galaxies in sky 3.\n", + "position_x, position_y, e_1, e_2 \n", + "[[ 1.62690000e+02 1.60006000e+03 1.14664000e-01 -1.90326000e-01]\n", + " [ 2.27228000e+03 5.40040000e+02 6.23555000e-01 2.14979000e-01]\n", + " [ 3.55364000e+03 2.69771000e+03 2.83527000e-01 -3.01870000e-01]]\n" + ] + }, + { + "data": { + "image/png": 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RJB59NInRo1O5+eZktm+vvD5Ro193EWkJjADeDFp9LvBe4P/vAecF/j8K+Fgp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\n3nKoevMrTVwugZMnCzfV9HQxZD7q1YhWW1Aj+cdngbJpvfuugzp11Gu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80LFjUoFayVyq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\nYuvs9OluHnussMaj1yMiSatORpyfD4cPlyUZ4VpUMyNDuPXWRDZ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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from draw_sky2 import draw_sky\n", + "\n", + "n_sky = 3 # choose a file/sky to examine.\n", + "data = np.genfromtxt(\"data/Train_Skies/Train_Skies/\\\n", + "Training_Sky%d.csv\" % (n_sky),\n", + " dtype=None,\n", + " skip_header=1,\n", + " delimiter=\",\",\n", + " usecols=[1, 2, 3, 4])\n", + "print(\"Data on galaxies in sky %d.\" % n_sky)\n", + "print(\"position_x, position_y, e_1, e_2 \")\n", + "print(data[:3])\n", + "\n", + "fig = draw_sky(data)\n", + "plt.title(\"Galaxy positions and ellipcities of sky %d.\" % n_sky)\n", + "plt.xlabel(\"x-position\")\n", + "plt.ylabel(\"y-position\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Priors\n", + "\n", + "Each sky has one, two or three dark matter halos in it. Tim's solution details that his prior distribution of halo positions was uniform, i.e.\n", + "\n", + "\\begin{align}\n", + "& x_i \\sim \\text{Uniform}( 0, 4200)\\\\\\\\\n", + "& y_i \\sim \\text{Uniform}( 0, 4200), \\;\\; i=1,2,3\\\\\\\\\n", + "\\end{align}\n", + "\n", + "Tim and other competitors noted that most skies had one large halo and other halos, if present, were much smaller. Larger halos, having more mass, will influence the surrounding galaxies more. He decided that the large halos would have a mass distributed as a *log*-uniform random variable between 40 and 180 i.e.\n", + "\n", + "$$ m_{\\text{large} } = \\log \\text{Uniform}( 40, 180 ) $$\n", + "\n", + "and in PyMC, \n", + "\n", + " exp_mass_large = pm.Uniform(\"exp_mass_large\", 40, 180)\n", + " @pm.deterministic\n", + " def mass_large(u = exp_mass_large):\n", + " return np.log(u)\n", + "\n", + "(This is what we mean when we say *log*-uniform.) For smaller galaxies, Tim set the mass to be the logarithm of 20. Why did Tim not create a prior for the smaller mass, nor treat it as a unknown? I believe this decision was made to speed up convergence of the algorithm. This is not too restrictive, as by construction the smaller halos have less influence on the galaxies.\n", + "\n", + "Tim logically assumed that the ellipticity of each galaxy is dependent on the position of the halos, the distance between the galaxy and halo, and the mass of the halos. Thus the vector of ellipticity of each galaxy, $\\mathbf{e}_i$, are *children* variables of the vector of halo positions $(\\mathbf{x},\\mathbf{y})$, distance (which we will formalize), and halo masses.\n", + "\n", + "Tim conceived a relationship to connect positions and ellipticity by reading literature and forum posts. He supposed the following was a reasonable relationship:\n", + "\n", + "$$ e_i | ( \\mathbf{x}, \\mathbf{y} ) \\sim \\text{Normal}( \\sum_{j = \\text{halo positions} }d_{i,j} m_j f( r_{i,j} ), \\sigma^2 ) $$\n", + "\n", + "where $d_{i,j}$ is the *tangential direction* (the direction in which halo $j$ bends the light of galaxy $i$), $m_j$ is the mass of halo $j$, $f(r_{i,j})$ is a *decreasing function* of the Euclidean distance between halo $j$ and galaxy $i$. \n", + "\n", + "Tim's function $f$ was defined:\n", + "\n", + "$$ f( r_{i,j} ) = \\frac{1}{\\min( r_{i,j}, 240 ) } $$\n", + "\n", + "for large halos, and for small halos\n", + "\n", + "$$ f( r_{i,j} ) = \\frac{1}{\\min( r_{i,j}, 70 ) } $$\n", + "\n", + "This fully bridges our observations and unknown. This model is incredibly simple, and Tim mentions this simplicity was purposefully designed: it prevents the model from overfitting. \n", + "\n", + "\n", + "### Training & PyMC implementation\n", + "\n", + "For each sky, we run our Bayesian model to find the posteriors for the halo positions — we ignore the (known) halo position. This is slightly different than perhaps traditional approaches to Kaggle competitions, where this model uses no data from other skies nor the known halo location. That does not mean other data are not necessary — in fact, the model was created by comparing different skies. \n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "def euclidean_distance(x, y):\n", + " return np.sqrt(((x - y) ** 2).sum(axis=1))\n", + "\n", + "\n", + "def f_distance(gxy_pos, halo_pos, c):\n", + " # foo_position should be a 2-d numpy array\n", + " return np.maximum(euclidean_distance(gxy_pos, halo_pos), c)[:, None]\n", + "\n", + "\n", + "def tangential_distance(glxy_position, halo_position):\n", + " # foo_position should be a 2-d numpy array\n", + " delta = glxy_position - halo_position\n", + " t = (2 * np.arctan(delta[:, 1] / delta[:, 0]))[:, None]\n", + " return np.concatenate([-np.cos(t), -np.sin(t)], axis=1)\n", + "\n", + "import pymc as pm\n", + "\n", + "# set the size of the halo's mass\n", + "mass_large = pm.Uniform(\"mass_large\", 40, 180, trace=False)\n", + "\n", + "# set the initial prior position of the halos, it's a 2-d Uniform dist.\n", + "halo_position = pm.Uniform(\"halo_position\", 0, 4200, size=(1, 2))\n", + "\n", + "\n", + "@pm.deterministic\n", + "def mean(mass=mass_large, h_pos=halo_position, glx_pos=data[:, :2]):\n", + " return mass / f_distance(glx_pos, h_pos, 240) *\\\n", + " tangential_distance(glx_pos, h_pos)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " [-----------------100%-----------------] 200000 of 200000 complete in 141.5 sec" + ] + } + ], + "source": [ + "ellpty = pm.Normal(\"ellipcity\", mean, 1. / 0.05, observed=True,\n", + " value=data[:, 2:])\n", + "mcmc = pm.MCMC([ellpty, mean, halo_position, mass_large])\n", + "map_ = pm.MAP([ellpty, mean, halo_position, mass_large])\n", + "map_.fit()\n", + "mcmc.sample(200000, 140000, 3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Below we plot a \"heatmap\" of the posterior distribution. (Which is just a scatter plot of the posterior, but we can visualize it as a heatmap.)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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0BgbP2dnC++87ymxda9JEcc892fzrX9lAYKdDh7RVqKx06WLy9tsZ1K9vfUCb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EsvWdm7N3/Tee3aCrfd5qNjxwD16ytlOsFm+3aZTp2SAAmDQTBypI977vEVuGPLn3/KPPxwAocO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\npDh11o4DjaWUWXmuWYH9UspaZTS+UlHS3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GAZgBCiBpB9wWdFIBYL53V1mUzQrZuPBQts\npKRk8vrrDrp189CqlY+OHb088ICL2NjyH29lQ0r4809NMenXL57XX4/GVWkiKwvi80FKiomrr45j\n8WJTAVe9TtlitWq9CdesyWT5chtDhnhy6zru2aNw992x3H13LMuXGzl1Sru+b5/C6NFWMjL8S/SN\nN7rDJr5Gp3B69/Zy112BaeROp2D27GiGDo3nwQdj2bu3cpq/Dx8O/Nz5Oy1UZorjBu0MTAU8wDgp\n5b5zyttgKWVI9gcNthu0JDidWisik6nwKtE6wcPj0ZoU33KLNVcpSUrysXRpZqVtB5WaauTaa614\nvYLrr3cxY0aW7goNEb7/3siYMf/P3nmHR1GtDfx3ZnsqSCeQ0DsoKEW6yAVEVOACXvQqRYEPFRU7\nKla8iB0VLNgAUUGwISiKiIgogkrvLaGE0MnuZrP1fH9Mks2SkLqbbDbzex4fs8PszOTNOWfe81Z/\nPNuIEU4eecTB889bWLTIlHM8JkaybJnaSkqj4mO1wq+/Gnjooag8CgpAzZpq+ZFIbw92IR99ZMxK\nxlF54QU7t99eMXp2g2o1/eknA9WqSbp185SoLMrF3KBF9pBLKTcAXS84tgBYUOynqUSYzZqSVlZI\nCb/8ouemm2IC4gZHjHAVW1GTkpCX4Ni3T0GvD22dowMHBKNHR+cUgRw82KUpauXImTOq9cBkgtq1\nfXkKti5aZMJqJaB/r6JI3n/fpilqEURsLAwc6KZ9+3S2b9fx6adGli834nSq8/TECYV580w8/3xo\nHFc2G6SlCU6eVHC71U444WC1NZsDnyE+vvyfqaicPQt33hnNli2qWpWU5GHBAnvQFO5iLdtCiN5C\niA+EECuy/n9VUJ4iRAQ7Zq2iEg7+9rJg926FUaMCFbX4eB/XXeffmRUkCzWBRM+NN0bz5JOWkNbB\nSksTjBkTzY03RpOcHBrtKTNTLeJ85ox6fYtF5lQqryxjojDKUg6HDimMHRtDz57xdOkSxy23xBAV\nJenTJzB+7bvvTNSsKYmNlRiNko8+stO7d9442GCjjQmVspRDnTqSvn09vPNOBmvXpvPtt+ksXGhl\n8WIrEycGP3bj6FHBihV6Ro6MoXPneAYOjOOGG+L48cf8a0uV9ZioWzdQOatXLzwsi0WRg80m2LHD\nH2OXnKznrruiOXkyOO+RIlvWhBC3A/8D3gPWA4nAp0KIqVLKOUF5Gg2NUrB2rR6Hwz8xzGbJp5/a\nipQ9l5GhKjaPPmoBBD/+CNdc46J69dBYM5KTFbZvV6ffr7/qSUoKvql/+3Ydl1wi6dbNzW+/GXjk\nEcdF28pohJ6dOxXWrFFfilIK1qwxMGhQDF98YefkSWjf3kft2j62b9exbJmB6dPttGvnpWVLH7rw\niLPWCBF6vVr/q3Hj0Fw/LU3wxx96Hn88iqNHAzeHSUkeevUK/WagKLRo4aVFCw+7dunp2dNNixYV\nx5pcrZqka1dPzhwH2LRJz969CjVqlP73KM6W/iHgX1LKR6WU70gpHwP6ZR0vFCFEPSHEKiHEdiHE\nViHE3VnHqwohfhBC7M6y2MXn+s4UIcReIcROIUS/XMc7CCG2CCH2CCFeu9g9Q9EbtCISDn3NyoKN\nG/17j1q1fCxdaqVLl8BJcjFZbNyoz1HUsgllG5Lcu62XXjIH3Yq3b5/CbbdF8dRTFgYOdBMf7+Oa\na9w5rt385HDunOpG3rmz8vhJy3Ju5OfSOX9ex8KFBl580cHatTpeeslMaqrCjTe66NbNQ5s2Zaeo\nVZZ1ojAiTQ7qWhDNmDExeRS1Hj3cLF5sD5veoDVrqmVt5syxMXNmRtjEGRdFDlFRcN99mVzYlfPE\nieCsp8W5SjVgxwXHdgNFLQHqAe6TUrYGrgTuFEK0AB4BVkopmwOrgCkAQohWwAigJXANMFuInCii\nt4DbpJTNgGZCiP7F+D00IpTx49Xg7HnzbKxYkc7llxdtN2OzwYwZZnIravHxvpyyLKEgd0ZmSoou\naKZyUHuWfvaZkZQUtaHzpk063nzz4gsyqIkZn3xiYsiQWB58MAqrNWiPo5FF69Ze/u//8sYgnTql\ncPCgwoEDenw+wd9/63nyySimTbNw/LiWuatRcnbtUhg8OIZ16wLdnDVq+HjnHRtvv22ncePwsrY3\naeLj3/92k5QUXs9VFDp29PDmm3Z0OlVhE0JSv37Zx6ytBV4RQkSpDyGigReBdUX5spTyuJRyU9bP\nNmAnUA+4AZibddpcYHDWz9cDn0kpPVLKQ8BeoJMQojYQm5XwADAv13cC0GLWVCpLLEqHDl4eeiiT\nQYPcFw2WzU8Wx44p/P57YETAY485qF8/dMGtFwbSnjkTvJdyaqpgzhx/VsuhQzo6dw50c1woh82b\n1VInABs26Dl3rnIoCWU5N+Lj4f77M3n7bRvNm3vR6SQdOriZPDkz68UUOCaWLDHx4Ycm3KXoFHTy\npGDXLoXTpwv/e1aWdaIwIkUOHg+8+aaZY8f8ptlWrTx88IGNH36wMny4mzp1Cl7jyloWJ04Ifv5Z\nz6xZJt5808SKFXpSU8t/LSqqHCwWGDHCzcqVVj74wBbUDO7i1Ev+P2AhcF4IcQbVorYOGFncmwoh\nGgCXAX8AtaSUaaAqdEKImlmnJQC/5/ra0axjHuBIruNHso5raJQIKdWixtluzxtvdHLddaHtpRcX\nF7hI2u3BW5BSUhSsVv/16tb1Ub36xc+XEhYvNuYkZrjdlEpB0Lg41aqpi3n//m7On1eyGkmD3Q5P\nPeXgqacCCzHOnGnm5ptdJbLybt6sY8KEKPbs0XPNNS7efNMeNm4ljdCj18Nttznp29dNdLSkVi0f\n9er5wrYd3v79gvHjY/jnn0C15NprXbz2mp1q1crpwYqJXg+XXurl0kuDG29XnNIdqUBPIUQ9oC5w\nTEp5pJCv5UEIEYPaV/QeKaVNCHGhah80c4YWs6YSaTEYpSE/WSQl+Xj6aQfffGPkv/910r+/O6B0\nQiioVi3w+sFUjk6eDDSYX2hVg0A5HD0q+PRTf02vunUlsbF5vhKRlNfciI9XXe3ZREfDqFFOEhJ8\nPPRQFGfPqn/DmjV9GI2Fj0WXC6xWQZUqEp0OduxQuP762Byl/bvvjBw4kFlgaIC2TqhEkhzat/eW\nqsVhWcrim2+MeRQ1gGXLDDz6qEK1auXnFg2HMVGsTmRCiCpAL7KUNSHEMillkZtjCCH0qIrafCnl\n11mH04QQtaSUaVkuzhNZx48C9XN9vV7WsYsdz8PixYt57733SExMBCA+Pp62bdvmCD7btKl91j5P\nnOikVaufMBigZs3Q369OHR+NG//E/v06oDdxcTJo1/d4sivqrAagZcvLCzzfbO6V9VJXz+/d+0qq\nVw/e82ifi/Z569a11KoFv/zSg4MHFf76ay0JCT5q1+5W6Pc//tjICy/8waWXepk06Uo2bVKwWn9B\npTcA27atweGQYfP7ap+1z7k/p6b+AljIHq/Z69HQoVdSv76v3J8vVJ+zf05JSQHgiiuu4Oqrr+ZC\nitPBoA/wBWpSQTJq6Y4WwL+llD8V8RrzgFNSyvtyHZsBnJFSzhDejj0cAAAgAElEQVRCPAxUlVI+\nkpVgsADojOrm/BFoKqWUQog/gLuBDcAy4HUp5fcX3u/ll1+WY8eOLdLvF8msXbs2LHYG4UA4yWLZ\nMgO33BKDoqgNups1C87Occ0aPYMHq6axpk09fPWVLU9sSm45qHWX/Ka0Tz+10r+/JyjPEu6E03go\nDU89Zeb11y05n4cPd1KnjuT1102AoEEDDytW2PIU4c1NpMiitGhy8FOWskhNFXz8sYl580ycOCFo\n0MDHpEmZXH114bF1oaYs5VDqDgbAm8B4KeWi7ANCiOHALFSlrUCEEN2Am4GtQoh/UN2djwIzgEVC\niLGoSuAIACnlDiHEItQMVDdwh/RrlncCHwFmYHl+ipqGRrjTqZOHceMyqVMnuJmnDRp4SUjwcuqU\nwsyZGYUudPpcq0CtWqHNgg1HXC5Yt07Pjh062rb10qGDJ6d3Z0XhhhvczJplxutV1/jPPzfRrp2H\nJ55wMG2amRdecBSoqGmUPd4s76RWQ0+lTh3JAw9kMnq0k8xMiI6WYRtfVx4Ux7J2DqgmpfTmOqZH\ntZRVCdHzlYpw6A2qoVEQdrva1ioqqvBzi8Pu3QpeL7Ro4Su0vdSmTTquvlq1rP3vfw4OHhQ88URm\n0J8pXNmxQ6FXr7gsRUfy6KOZjBuXSXx8oV8NG9xueP99I48+GqhltmnjZto0B506eSO+7V12D+Zw\nxW6HXbt0rF+vZ+NGPcePC8xmyQ03uOnc2VOk4t0akU8wLGvzUS1ar+c6NhG1dIaGhkYJCJUFp3nz\noi/8LVp4ef31DKxWwccfG9m5U8e4cU4aN64clpizZ0WORQoE//ufhebNvSHPCA4mBgPcdJMLIQRT\npviLO2/bZuDIERc9e1acSvDF5fhxwXffGVi0yESbNh5uvNFF+/besLJYnTsHL79sYdYs1S2dm9Wr\njVSv7mP5cqvWYaQCkZYmMBjgkkvKZp0sTp219sDLQogjQoj1QogjwMtAeyHEmuz/QvOYJUOrs6aS\nO5CxsqPJQiW3HM6dE7zyipmpUy1s364WZk1PrxxdDNauXUvt2j5MpsAF97nnzEGtfQdqb9A//9Rx\n9GjxrnvihGDVKj1ffmlg+XI9mzcrpKfnPS8uDm691cnixTaqVvW/9N95x4TdXvh9KurcWLTIyP33\nR7N+vZ733zczcGAsGzaUXFMLhRz27dMxa1Zg4e3cCEGhFvDyoKKOiWBzoRwOHVLo2zeO/v1j+fln\nPRkZoX+G4ljW5mT9p6GhEUEcPapw8KD/5aYokri4yrPDT0yU3HlnJq+84g/Q37NHdVMFY9d85Ihg\nxQoDzz5rIT1dYe5cKz6fl/h4SVxcwd91ueDJJy0sXGjKdVRy+eUepkzJpEsXT4C72mKBPn08rFxp\nZft2HT//rKdLF09YuwdLg8sFy5cbA455PIJnn7Xw+ee2sHHlN2ni5bnn7Dz/fFRADUS9XjJ6tJPR\no500alR55lxF59gxkdO669//jmH2bDvDh7tDas0tsrImpZxb+FnhhVZnTUXLbPKjyUIltxwutCB1\n6OAJeZ25cCFbDrfe6uSff/T8/LOq1URHSyyWgr5ZNHbvVpg4MZpNm7KXWkl6usIVV8TQvLmXadMc\ndO3qCUjyyI1erxY1DkTw118Ghg3T8+yzDkaPduZxpzds6KNhQx+DBhXdlVsR54bRCF27uvnzz0AB\nnjmjlLh2YSjkUKUKTJjg4tpr3aSmKvh8aheTqlUhIcEXtsp0RRwToeBCOQT2+RXcc080zZtbS1XT\nrjBKZHgVQmwN9oNoaGiUD1FRgYrZxInOSlMUN5vERMmbb9qZNcvOzTc7+fBDGw0alM7SsX27wg03\nxOZS1ODmm13Mm2fC7RZs26Zn2LAYtm+/+HZcUWD0aCfDhjnz+VfB1KkW9u8PQ/9ZGXLLLU5atMhd\naka1lIZbgoiiqOOsc2cvV17ppX17Hw0ahK+ipnFx6tTxcfnl/t2A2y147jkL54pcdbb4lHSWJwX1\nKUKEFrOmosUd+AlnWZw7p9ZIe+45M/fea2HbttC9hHPLoX59H7VqqYrJ4MFOevasHDXWIFAOdepI\nRo508cYbGfTt60GUImRt926FESNiOHHC/zds0sRD48ZeNmzwK28ej2DjxoJ9J/XrS6ZPz2DJEitD\nhjiJiclWriVXX+3Jo5Rs2aIwY4aZ777TFys+LpznRkE0bChZtMjGJ59YmTnTztKlVoYMcZX4ehVV\nDqFAk4XKhXK45BJ4+mkHuRsurVpl4NCh0K3ZxYlZy035d1bV0IggDh4UPP+8hc8/98cmtWrlpU2b\nkr90ikpiomTxYivHjyu0bu3N0wpLo3icPw/PPGMhNdWvhDVo4GHuXDtPPpnXt1qUjOBq1eCqqzz0\n6OEhNdXBuXMCs1ltR3Whsnb6tMKMGep96tTx8cILGfTq5SYmplS/VlhTr56kXr3Ks8nQKH86dPAy\nZUom06f757S6OfNb5M+ehYwMQc2astQW1OLUWXsVmCul3CSE6C6lDHuVW6uzplER2LdPYcyYaLZv\nD9w7ffmllV69tBdQRSN3BwlQY6pmzsygcWMfW7YoDBkSm9P7s149L198YQtqyYa0NMGNN8awZYt/\nPE2c6ODOO53Urasp4hoaweLECcHChUaeftqCzwfffWelc2cv58/D2rUGpk83k5KiY+5cG1ddVbS1\nPBh11nTACiHESWC+EOJQSRq5a2ho+LHZYPp0cx5FrV8/F5deqilquTlxQrB3r8Lu3Tpq15Z07+4u\nNJuyPNizR1XE4uJ8TJ3qYMAANwkJqpLUrp2PH36w5pzTunXwO0bUqiWZOdPOgAFxOJ3qmv/WWxZ2\n79bzyisZla5DhYbGhezerbBwoZFmzXx07OihceOSzYmaNSUTJjjp3dtNRoagTRsvZ8/C22+befFF\nv8Xtt9/0RVbWLkaRHaxSyrtRG7g/AlwG7BRCrBRC3CqECEsDuxazpqLFHfgJN1ns2aPjyy8DSw/0\n7+/ixRczqBLCviDhJofC2LJF4aabornuujgeeCCa//43htTU0seHhEIOPXt6+OqrdH7+2cptt7ly\nFLVsGjf2cc01Hq65xhMyxaltWx+ffWbDYgmMqRk3Lopjx/KPYqloYyJUaHLwE6myOHtW8NprFu64\nI5o+feJYsMDI+fMXP78gORiN6nzr3NmL0QhffWUMUNRALTxeWoq12kkpvVLKb6WUI4EuQA3UHp3H\nhRDvCSESSv1EGhqVCE+uzVZUlOT55zOYOTOD+vU1dxWo/RNXr9YzcGAcf//tD/po3twTtrF1zZr5\n6NnTS8OG5WfBUhTo1cvDV19ZqV7d/xwbNhiYN8+EM7/kUg2NSkKjRj5atVIXX6tVMGlSNNOmWUhN\nLV04/t69Cg89FFjcLyHBx5kzAputVJcueswagBAiDhgO/BdoBywB5gIpwP1AHyllu9I9UvDQYtY0\nwh27HbZv1+F0qll/9ev7wqpNTnnz1186rrkmFo8n9yIq+eYbK927R24LpeJw+rQgLU1w7pxAr1eV\n/ipVJDVqSEwm9QUyZUoUq1apyq4Qkp9+snLZZZr8NCov69frGDgwFin9a8uwYU6eecZB7dol2wh+\n/bWBMWP8jsaYGMkTTzh4+mkzv/xiLVLh41LHrAkhFgP9gTXA28BXUkpnrn+/DyjAkFjxkRIOH1YL\nGpa2BpOGBqiZgJ06aS/N/Dh/Hp56yhKgqOl0kvfes3PFFZrMANau1XPPPVEBHShALeo7YICL225z\n0ratl3fesbN1q46ZM838+aees2e1hH6Nyk379l7mzLEzblx0jsK2eLGJxEQfDz6YiclUyAXywZtr\nWapb18e992YyY4YZu13h1ClBo0Ylf97iuEH/AJpKKa+VUi7MragBSCl9QK2SP0rwCWbMmscDS5ca\n6NEjjl694krVe66sCWXcQWqqYO1aHd9/r2fbNoViGGrLhQtlkZYm2LZN4cABhczMcnqocqAixKKc\nOiX47Te/6zMpycO331q59lo3ZnNw7lER5HAxPB74+GNjHkUNwG4XLFliYuDAWBYuNFKliqR3bw8L\nFthYv/48HTvmDXauyLIIJpoc/ESyLIxGGDTIzbx5doxG/4vr1VfNbN0aOKeKKofOnT3MnWtj4UIr\nr79u55lnLJw+rapZdnvpNkjFaTf1UhHOKYN2puXDrl0Kt98enbPLf/hhC199ZQvLbLSyYtMmHaNH\nR5GSog4jk0myeLGNbt0qRhbjnj1q0PqBA3qMRknv3m7uvTeT9u29JdpVaQSX6tUlc+bY2L1bR6dO\nHpo392qxfLnQ6+HhhzOpUcPHnDlmXK78XgaClBRdziYqKipvxwoNjcqK0QjXXOPm22+tTJgQxcGD\neqQUfP+9oUTW+4QESUKC2tngt9902Gz+OVmaIttQzJi1ikYwY9beftvEo4/6AwdNJsmff56vtC+P\n/fsV+veP5cyZQOPs2LGZvPSSo5yeqnisW6dj0KBAbVsIycsvZzB8uKtIxUo1NMobj0et1ZeSonDy\npEJamoLVCk2b+khK8tK6tTekmcUa5cvJkwKdTnLJJeX9JBWbtDTB5s06fvjBQJ8+HgYOLGFz2SxO\nnRIMHx7D5s16QLJ2bTqtWpVBzFplZ/PmQLOo2SwrdSD4wYNKHkUNoE2bihNL1KiRj3btPAHFQ6UU\n3HdfNImJPvr0qRgWQo2Lc+4cJCcreDyCGjV8JCZG3uZKr4cWLXy0aFHx4mjdbnUtcbnU+M369X0X\nbWofLmRkqApyOHhVjh4V3HBDDIoiePppB1de6dYU8xJSq5akXz8P/foFZ92vXl3ywgsZDB0aS79+\nLurVK938jOgOwMGMWbtQ0CNGuKhZs2Is/KGIO6hSRZK7LxrAVVe5ufrq0u1GQk1uWdSurQart2+f\nd3IuWxbZ3ZUjORYlm7Q0wf33R3HVVfH8619x9OgRz/vvGzl92n9OZZBDUSkPWXzxhZFu3eLo2TOe\nrl3jePBBC3//rQtp/KjbrWbIrlqlZ+VKPevW6di3T00cg8LlsGOHjuuui+Xrrw2cPRu65ywKVqvg\nwAE9+/bpuPnmGGbMMAf1mbT5oVJSOXTs6GXlynSeecZRauU+zPcw4UPfvm5ee82M1yuoWtXH6NHO\nsN8BhpI2bbx89ZWN994zYTZL+vd3c+WVngrXzqZJEx/z59tYu1bPnDkm/vlHT7VqksGDw1vp1Cic\nlBSFL7/0Bx9arYIHH4wmJUXh4YcziYoq4Msa+ZKZCUeOKNjtahN6vV4SEyOpV0+WKM7z6FGB16t6\nfJxOwdy5ZubNM/HII5mMG5cZdCuR3Q4LFhh54omogBg/s1ny8MMORowovBfvJZdI9u/XMWZMDMOG\nOZkyxUHDhuq653CopVRiY2Wenq2hoGZNHy1aeNi1S30ZvfOOhXr1JOPGOTEaC/myRpnQvPnFLWo+\nH2zfroYu1Knjo2XLi5+rxawVEY9HrfmUkqLQtq23QrocQoGUpQ+cDBfsdrUJttEoS1xnRyN8OHBA\noU+fWNLTL3QgSH77Lb3AhVEjELdbrUv1xhtmVq0y5ChYAHq9pG9fN3fdlUmnTt5ibWL37lUYOjSG\no0fzxpTMmGHn9ttdQV1fduxQ6N49Dsj/oi+8oN6zIKSE2bNNTJ2qavtJSR4+/dROjRo+XnjBwscf\nm2ja1MuLL2Zw+eVelBD7r774wsDtt/trewkh+fZbK1deWXFCUior//yj1pF0uQRGo+TNN+00arQ+\n35i1iHaDBhO9Hjp39jJ8uDviFLVt23RMnWpm8mQLO3YUb0hEiqIGasxMYqJPU9QihEaNfMyfbycm\nJvDvaTAQ8hdopLFtm47Bg2P58UdjgKIGqoXt+++N3HBDLNu2FS+Qt2lTH4sW2fINn3jpJQsnTgR3\ngalf38e4cRdv33DhWMkPIeD6613Ur68qQ8nJev7znxj27tXx4YcmHA7Bli16rr8+li1bQh/Y3KuX\nh379/AqmlILnnrNgt4f81hql5K+/dDkWXpdLMGHCxbPaInrJ0nqDqhTkb9+zR+H662OYNcvC3Llm\nRoyI5ciRCNLALkCLwVCJdDn4fKo1qEcPDytWpDNtWgb9+7sYMsTF119badJE3XBFuhyKQ0GyqFtX\nVXKEyF+ZEUIydqyTOnWKv5Ft2dLHe+/Z+PZbK7fdlkmrVh5atPDyzDMZxMcHd+MUGwtTpjj46isr\nY8Zk0r69h5YtvVx3nYuFC60MGOAu0pioX18ye7YdnU59vpQUHZMmRTFlSibZsbxOp+Cjj4whrz1Z\nrZpk+vQMmjXzx96uW6cnOTk8e+dWREIlh5gLuqrn7qZwIZU46koDYOVKA+fO+Sf1sWMKhw8r1Kun\nmdArO263mn5evbrEUEHyLY4cEfz1l54lS4ycOSOYNCmTPn08tGzpZOJE1aJSVtZgq1UthOl2q8qj\nlGCxqFliFS2TvFYttW3OLbc4OXBAx/nzAqtVEBMjiY+XNG7spUEDX4njAOPjoWtXD126eMjIUOUV\nqmzLKlWgZ08PPXt6cDjUEBezmWKP8U6dvLzxhp077lDfuPv36/nhB8n48U7efVet2rxmjYGzZx0h\nL6vRsKFk/nw706aZWbrUBKjjrqyJpLCYsqBtWw8mk8TpLFxoWsxaJWfs2Gi++iowEnXp0nS6ddOU\ntcrM8eOCd981MXeuiTlz7BWijMnWrQqjR0dz8KB/D1qW9RAzMtT6g3v36li2zMiuXTpOnFB7dma3\noalZU9KmjYfrr3fTq5eHpKTICqmobNhs8OabZl54wZJzbMQIJ6mpCr/+amDIECezZ2eUWZHt8+fV\nbFWnU9Chg6dMy4tICZ9/bkSvlwwa5NYSHIqAzwcrVugZOzYmR2FbufInrc5aYWQnEDRp4qVVq8rR\nULtFi0ClrEYNH/XrR8YLxOVSd81a1l/xOHcOnn3Wwqefqm+Y994zhb2ytnu3wrBhsZw8Gej6adnS\nU6Q4pNKSliaYOdPE22+buVjwOsCJE4JVq4ysWmVk+vQMJky4ePyURvgTEwPjxmVit8OsWarCtmiR\nkZdfziA2tuQ9JktKfDx5EgtOnRKsWaPn/HlBz54eGjcOzfp+9KjgwQejsNvhxx+ttG+vbfgLQ1Fg\nwAAPK1ZY2bVLIS7u4muVFrOWRUqKYMSIGG67LYa+feP44Qd9uZiRQ0FB/vZBg1zUrKlOXrNZMmeO\nPSIKh+7ZozB2bDTXXRfDN98YcoJttRgMlYLksGWLLkdRqyisXGnIo6jFxEheeslB1aoX/16wxoPP\np7rRCnsxx8ZKhg1zsmiRlRtvDC9FTZsbKsWVQ7VqcM89TiZOzO7cIpg928TzzzvCIhlt8WIjt98e\nw/33R3PzzdHFikkujizOnVNd4z6fYMkSQ0BT89Jy+LBg/XpducVTh3JuCAHt2nkZMcLNgAEX3xRr\nlrUszp8XnD2rLvZut+CWW2L47jsrHTtG9u6gVSsfy5alk5Kio04dX4E1YSoK58/D/fdH5TQBHz1a\nzxdf2OjdO7ytQ+FARga8/npgl/Qrrwx/uWVkBC7izZp5mDUrgw4dymb+1qkjeeyxTEaPdnHypODM\nGTVmSFHUTPKoKEl0NFSv7iMhoeLFrEUCNhs4HIIaNYK/Ga1eXfLQQ2pf4UmTotm/X8+OHTrq1Svf\nuXPqlOCtt/w7iD179Pzzj5569YJvicg9Bz/4wMxtt7lo2LD075OzZ2Hy5GhWrTJQr56XBQtstG1b\n8d9TxUWLWcvi6FFBr15xAS2UevVys2CBTXOjVTD27xd07BhPbndU165uPv/chsVy8e9pwM6dah0q\nf1aSZOVKa5kpPfmRmir48EMTGzfqGD7cxYAB7jzWsuRkNbHAahU0bOijWTOvVoJFI4f9+xXuuCOK\nY8d03Hqrk2HDnDmFbIOJlLB1q4733jPSsaOXW24pvMhuKDl8WHD55fF4PP61cNy4TGbMCH7/5r/+\n0vGvf/mD5JYvT6dLl9KvG2ptPH+F4cREL19/bYvYeM+L9QaNaDdoNlKqBU8L0ksTEmSe+JE1a/Sk\npFQKEUUUQpDHcpGcrMNm09KUCiMjQwSkj990k4uWLcvXurx2rZ6XXrKwerWRO++M4fXXzdhsgeck\nJUmGDnUzapSLnj09mqKmEcChQwobNhg4elRh+nQLgwbFsm1b8Nf2bJfWq686GDy4fBU1UEMBGjQI\nVGoMhtDMjdjYwOueOBEc+apZuv5rp6ToWLu28jkFI1oT2bRpE8nJgv/9z8w118QyZYqFf/7R5fSA\nu5Dhw100aeI3W0spsNsr/gu+ssWi1Kkj+fe/AxfK1q09xMXJSieLi3ExOVgsEkVRF8bmzT3ce29m\nuVsj//47cGGeOdPM9u3B8SNGwng4f17NKPvgAyOrV+s5ebJka1YkyOJiVK8e2Ms4NVXtpZmSkldW\nhcnh+HHBL7/oeestE3PnGtm/P+81dDq1plt5U7UqTJ4c2Gi1c+eib76KMybi4iTVqvlfrsePB+fd\nWaOGj1atAp95yRJDmcaUh8PciGhlDeDrr428/LKFbdv0vPuuqrStX5//Qt+ggY9PPrHTs6c6Clq0\n8ERMZmRlwmKB++7LpGVLVfGuWtXHI4+UbVZWRaVxYx+zZ9uZPt3OggW2nOKx5UnTphe+XAQ7d2pB\nX9ns3Klj5MhYHnggmqFDYxkyJIbNmyN+aS8WTZt6GT8+0HNy+LCOlSuLV1xt0yYdQ4bEMGRILI89\nFsXkydG88Ya58C+WI1df7ebOOzMxGiX//a+TK64ITRxdjRqSf/3LxSOPOHj0UUfQPBlVqsBjjwUq\nnCkpOqzWwr+7c6fCqFHRbNlS8edDxMesvfdedz77LPAtnZTkZcUKKzVr5v+7nzunFoeNjZVlUp9J\nIzSkpQmOHFGoWlXSqFH5Kx0aJWPJEgP33x8V0OPzxRft3HZb+buZwoG//9bRt29gQa2YGMm336bT\nrp027rNJTlYYPz6KDRv8ClqXLm6+/tpWpIK4mzcrXH99HFZroBJy//2OPMpEuOF2Q2qqwiWX+PJU\nzQ8mP/6oZ8yYGDIy4I47MnnggUyqVCn9da1W1aL+yiuqmf/22zOZPt1RYKKOzweTJkXx6acmatXy\nsWKFlcTE8J8PlTZmLb+4geRktQL3xahSRc2S1BS1ik2tWpLLL/dqiloFxudTG1U//nhmjoslJkbS\npUv4Z6iWFU2aeBk+PNBqZLMJ7rgjmlOnKn4YR7BISvIxZ46dKVMcOXFb3bt7iqSoWa3w2GNReRS1\n6tV9eUIuwhGDQe17HEpFDeDHHw1ZWaGC2bMt/PprcFqfxMbCpEmZfPmllbfftvF//+csNKP61CnB\n6tXq/dPSlAof5xbRytqmTZvo3NnDffc5yB2v0LGjO8C3HumEg789XNBkoVJR5OByQVqajmeftXDL\nLU6eeiqD5cvTad06OPO3osihIOLi4NFHM+nYMTCIZ8cOPYcPF32JjwRZFEZiouTeezNZt+48P/98\nngkT8lrE8pODzSbYsSNQO2jc2MOSJdZyqaXmcqkZ0J4Q71mKMybOn4c2bbwMGeJXXp9+2sLp08HZ\nMMTHq03rR4xwF2kDnpkpAuI3lywx4CqhXh0OcyOilTVQ/8CTJ2fy3XdWZs608/77Nt59NyPkvdo0\nNCKB33/X8eWXBrZs0eEIfrZ/oZjN0KWLB6tV8NprFnw+aNOm8my0ikpSko8PPlBjDePjVflccokv\nT4ZeJCIl/POPjl9+0ZOaWrhiYDBA48aSSy/1Ua1a0e5Rs6bkgw/s9OnjYuhQJ/Pm2fjqq/Kr97Vn\nj0LXrvG8+aaJM2fK5REC8Hhg3jwT994bjV4v6dVL3TgcOKDj7Nnyse6aTDIrsURl40ZDmVma9+5V\nWLTIwHvvGfnxRz0nTpT+vhEfs6b1Bi0eTqca23H8uMDpFAihdjZISJAkJlaOFlwaKlLCyJHR/PCD\nESEkw4e7uP/+TJo2LdsX1E8/6Rk+PJa4OB8//GClWTNNWSuIlBTB6dMK1ar5IqIbSWHs2aNw1VVx\nOByCtm09fPCBPWQtlcKlUfm6dToGDVLjFB9/3MH48Zkhd3EWxL59Cj16xGX1t5Q8/bSDJ5+MAiTr\n16eX+ZoBqvVx5Mhofv7Z36T0zz/Phzxpav9+weDBsRw96n9Z9ujhZubMjDxlVPKj0sasaRSdAwcU\nJk2Konv3OAYPjuPGG2MZMSKW66+Po0ePOJ56ypJvqrtGZCIEjByp+g2kFCxaZGLQoFj+/FNXYM3C\nYNOpk4f5860sXaopakUhMVHSvr23UihqoCYSORzqurR1q54JE6KKZGErCeGgqAFccom/FMm0aRb+\n+KN847GSk5WcRuQgsNkEOp1k6FAXdeqUz5w1GmHAgNyhAfKiZbuCycGDugBFDeDXXw0sW1a6+L2I\nVtaK0xs0kimqv/2zz4wsXmwKqHadTUaGYNYsMytWBCdgtLwIh9iDcKCocujWzUP37v4F7+RJhcGD\nY1mzRl8mCx+owcXXXusJictJGw9+Kqos4uMDldK//zbw558lV14qghxq1pQBrZwefNDCsWPB1ySL\nKgvnBW1uPR749FMbTzzhKFeLX9euHoxGdXwkJMgsJbf4FGdM1KrlQ6fLe58L60UWl4hW1jSKx/Dh\nLvr2dZE7GcOPGodw1VVaFl5lonp1ySuvZNC8uf/vnpkpuPHGGLZt03zioeDcObV1z7Jlet5918jj\nj5t58kkz06aZmT3bxOefG/juOz2bNytatidQr56PNm0C16WPPzbmUSAiiUsukdx/vz85IjlZz/r1\n5WdduzA8RlGgb19PuVt3W7TwMWNGBkJI7rnHERDDFkzcbjh4UOB0qvd8/307JpP/XgaD5LbbSlfe\nRYtZ0wjAZlNbsxw5opCZKZBSDdSsV89HgwY+4uIKv4ZG5HHggMLdd0exbp3fstqjh5uPPrLl6dOp\nUXJOnhTce28U331nLPxkoH59L3femcm117pJSIjctbww1syySwMAACAASURBVKzRM3hwDNn9gGvU\n8PHLL+kR3XZs/36FPn38dd8aNfKwfLntovVDQ8kff+gYOND/cpgxw864ceFR0iQzUw34r1u36Akl\nxcHlgoULjTzwQBQLF9ro3duDlLBrl8L+/Qper9qvuE0bL0oRzGMXi1mr2IVHNIJOTIyabadl3Gnk\nplEjtUbVRx+ZePVVMx6P4NdfDRw6pKNq1fLtHRpJxMdL7rork7NnBevX6wP6tObH4cM6HnssisaN\nbSQkVF6rd6dOHl5/PYN77olCSkHr1t6Iz4Rt3NjHc89lcPfd0QAcOKDnwAGFmjVLPx/Pn4dTpxSM\nRjW5rDAlo3FjH02aeNi3T1UpLr00fNYEs5mQZu1u365j8uQofD7B66+bslyv0LKlj5Ytg3ffiFbW\nNm3ahGZZU/3t3bt3x25XA0FTUxVq1JC0bu2tdNmd2bKo7JREDnXqSB54IJPrr3fxxx96jhxRiIur\n2Ep9uI0HoxGuvNLLokU2jh5VOHxYIS1NnbOHDwtsNgW3Wy0/0aGDh6QkH0lJ3qDU+go3WRQHsxmG\nDXPRvLmXQ4cU2rb1Eh1dsmvllsOpU/DHHwZiYiSXXeYJSjX+YNKnj5vWrT1s366+yo8fV4CSK0pe\nL6xdq+fRRy3s3KnDbF7N+PFdGDvWWaBLs0YNycyZGYwYEcutt2bSokX4KGvBoKC5MW+eEZ9P3VRt\n3qzn9GlBnTrB3yhEtLKm4WfvXoVXXzXz2WdGQGAwSNauLZ+Uao2Ki8EArVv7aN06PFwckUpMDDRv\n7qN5c21+FsTx44LDhxWaNvVSpQp07OilY8fgKQq7dum49VY1Qv7mm508/LCDevXCx2JXt67k/fft\nDBkSQ2qqLqAIbEnYuVPhxhtjcLnU62RmCl5/3YLbLXjqKUeB3R6uvNLL2rXpVKlS8cNlnE44cUKg\nKBToVk5LE3z/fWDIQqgyhiM6weCyyy4r70cICyyWXlx7bWxWj1R1JOn16n+hZu9ehSNHwicIuqJa\nDoKNJgcVTQ5+KqIs0tIE/fvH8dBDURw+HJx1JrccMjP911ywwMScOSYyMoJym6DRrJmPJUts3HJL\nJm3alE5RTUtTchQ1ld4ALF1qKLBFYzZJST7i40v1COXO4cOCBx6IomPHeLp0iefxxy0kJPTI99yz\nZwVpaX41qn59H3FxoVHmI1pZ01ALRg4dGsupU4F/6qlTHSQlhXbXLqXafHfo0Bj27tWGmoZKaqrg\nzz91Ws0+jVJTpYraK3bxYhN33RVNcnJwx1SNGj5yZ8e/8YaZLVvCL3akRQsfL7/s4MorS6esNWvm\npUGDvLGPEyc6S1z2oqKxaJGJBQtMuFwCu10wZ46ZMWNiOH4879iy2QKP9e/vJioqNM8V0W9Qrc4a\nLF5sxGr9JeDYzTc7ueEGV5EyU0qDzwf79+vYt0/PY49FBa1HXGmoCDWUyoLykIPPp2aNXXddDAMG\nxPHqq+YyLa6bH9p48FMRZVG/vi+nJMKvvxq4777oUrf2yS2Hxo19FxRWFbz2mjnsrGsQHE9J/fqS\nRYvsTJuWQceOHjp1+pH5822MGOEM+fsiXMivoPKWLWs5cCCvACyWwAWsa9fQJflUEvGXjqNHBT/9\npGflSj27diklbgZb1khJQO0do1Hy0kt2nnrKEZIAyAvR6aBVK3XwrlxpYOVKLUSyMrNhg47Bg2M5\ncEAdB3v26HC7C/mShkYBKAoMGuQm2/r1888GFi0yBm2NjomBBx/MxGDwr5erVxvyeCoiiSZNfNxx\nh5OlS61MmaKWhQlFyYtwZcgQF0Jc+H6UmM15z61eXVK3ruqh6tvXxaWXhk5Z0+qsFcLZs3DzzTH8\n8YcaWakokokTMxk3ruDsmHBhyxYdGzfqqF5d0qSJmjVWljukBQuMTJqkpmXVru3jxx/TK3U9qPLA\n5QKHg3KNJTl4UHDNNXGcOOEffNOmZXDHHRFcuVSjTLDb4cknLXzwgfo2FUKyfLmVzp2Dk2jg9cKi\nRQbuvDMaNeZX8vvv6VryR4TidMIff+h55BELu3frqFJF8uKLGQwc6MZiyXv+unU6vv7ayPjxmTRu\nXPp3m1ZnrYQ4nYK9e/0xCj6fYNYsC1u36pk1yx72ike7dl7atSu/NOrmzf33Pn5cYcsWXaWuB1XW\nbN+uMHWqhePHdUyYkMmAAW5q1Sr7Mbt4sSlAUTOZJD16aONAo/RER8OECU6++MLIuXMKUqqFhZcs\nsVG3bunHuk4HQ4a4qV3bxlNPWWja1Evt2pqiFqmYTNCrl4fly62cPy8wGilwHHXt6qVrV0fInyty\nbbkEJ2atVi3JPffkbROxZo2hXNt7FIfyjEVp1EgtlpjNe++VbzZVRYzLKSkZGfD00xZWrzaya5eO\nyZOjmT7dQnq6Xw42m5pRF8o+nydPCubNMwUcmzEjg9aty78WU2UaD4VRkWXRtKmPN9/MINsdunu3\nnuXLS9bHOD85mM1w1VUevv3WyquvZlT4jMeiUpHHRGmpWhUaNJDUrSvDQg4RrawFAyHgP/9xcffd\nDi7smXnsmCa+wrjkEsm99/pdXb/8YuDQIU1uZYHdLti5M3BDMW+eiR07VEtxcrJgzJhorroqjuef\nNwc0gk5Ph82bFXbvVvCWUqeyWgm49vjxamHdyhKwrFE29OrlZuJE/8b6+ectQc84jo1V/9PQKGt0\nTz31VHk/Q8hwOBxP1alTp9TXiYqCK67wcPXVbqpW9eFyCQYMcDFypItq1cLbDQqQmJhYrvePivLx\nyScm3G6BlIKrrnLTrFn5uBHKWxZlicUCKSkKf/8dqLB16+Zm4MB6rFlj4NVXLdhsgnXrDOzapaN3\nbzfnzgnuuy+axx6LZv58EwkJPlq0KHm3C70e0tMFiiJ58UUHI0c6w6YSfGUaD4VR0WVhNKphF1u2\n6Dh8WIfDIejZ002TJsVbayq6HIKJJguVspRDamoqjRo1evrC4xXDj1cKkpPVBq4FVV4uCjEx2b5p\nLw5HJmZz6CoVRxqNGkkefdTBY4+pBWg0i2TZoCgwapSThQuNpKf7ZZ5dL+nCjLlVqwxs3KjDZhMs\nW2bMOkdw991RXHaZh9atS6Zgx8bCs8868PkIWQ0iDQ2AevUks2fbmTw5mlWrDCxfbmDAAC02UqPi\nE9FvzU2bNtGlSxwvvmgOao0vi6ViKWr5+dvT0gQ7dyrs3Klw7lxo7y+Emg59+eVqnYZ9+8qvqGQ4\nxB6UJa1a+Vi+3MqQIS6SkrxMmpTJpZd6Wbt2LQ0a5FW+/vxTz7ffGomJkdx3n4POnT1IKUhOLt1S\nYTaHp6JW2cZDQUSKLOrXl7zxhlorLCam+N+PFDkEA00WKuEgh4i3rDmdgpdestCunTerHk/l5vRp\ntebZtGlRHD2qAJK+fd288IIj35d3sKhdW/LGGxnccks0TZqUf2B5ZaJVKx9vv23HahVUqSJRFNi7\nV3UZ3XyzkwUL/MH/KSk6GjXy0qGDh9mzzfznPy68Xkr00tPQKC/q1JHccYcTq7W8n0RDIzhEfJ21\nvn2vBmDKFAcPPpg3q7MyYbfD88+bmTUrb7GYjz6ycf31oVdmjx4VSElYNUOuzBw9Knj5ZTMffaQq\nbB9/bKNOHR9Llxp57TULQvhrDNWurf3NNDQ0NEJJJa+zJrnySs2qlpysMGtWPmWYkWVWN6gkdel2\n7lTYvVuHxSJp2tRHo0ZajaNgkZAgefZZB2PGOBGCnGDsZ55RXdVSCk6eVDRFTUOjkuLzwalTAodD\n1R9q1fLlW81fI7REfMxa7do+PvrITvv2ldf1lu1vNxrzpp0bDGpAbnkWzi2If/7R0a9fHGPHxjBy\nZCz9+sXy99+Fx7ydOCE4dChva7BwiD0IB3LLIToa2rb10aaNugi7XJCa6pfxqlX6CtNirbiE+3jw\neODcOThzhlKXUCmMcJdFWaHJQcXhgDlz1jF+fDS9e8fRoUMcnTrFcdNN0WzYEH7N7ENJOIyJMrOs\nCSHeBwYBaVLKdlnHngTGASeyTntUSvl91r9NAcYCHuAeKeUPWcc7AB8BZmC5lPLegu67alV6uVsF\nPB51ZyIlxMfLcgu0btLEx7ffWvnuOwOnTgmaNfPSqZOXNm28YVvzauNGHXa73yJ85ozCqFExrFiR\nftGq0ikpav2wHTv0PPSQg9GjnVStWlZPXPExGAIbFB88qOPMGVHu8yiSkVJN+jl+XHD8uMKZMwo7\ndihs2aInLU2tdTd8uIs778zU4gc1Qo7PB4sXG3n44SjAmHPc7YbVq438+aeB335LJykpOF6OkycF\nGRmCGjV8YZmIFA6UpRv0Q+ANYN4Fx1+RUr6S+4AQoiUwAmgJ1ANWCiGaSjXA7i3gNinlBiHEciFE\nfynlivxueNlll5XrC8Zuhw0b9Lz/vok//9TjdkPLll5uvdVJjx6eoLRCyb7Pjh06Tp4UNGrko0WL\nwAnUvXv3nJ/btvXStm14WtHyIy4u77GjRxVOnBAXld+WLXr++Uet1fLss1EkJfkYOlR1g+eWRWWm\nIDlYLJCU5GPzZvWzzSbwRGj1g/IcD8ePC44cUTh0SGHlSgM//2zg5Mm8u6Zq1Xw8/riDAQPcIVXU\ntLmhoslBVZ6ee84CXJXvvw8Z4qJ69eAoahs36hg/PoqjR3Vcd52Lxx7LpGHD8Ap1CYcxUWbKmpRy\nrRAiKZ9/yq8Ixg3AZ1JKD3BICLEX6CSESAZipZQbss6bBwwG8lXWyps//9Tz73/HkPtX/P13hd9/\nN/Dvfzt5+eWMfJWR4nDsmOCtt0xZsWiChAS1WXqkWEG6dPHQqpWHHTv8QzU+3ldgu5cLM8AefzyK\nrl0jRyZlQceOHr75Rt1RS6n+p1E63G44eFDhwAGF1asNfPmlMV/lDNRm5Fdf7ea225y0bOklMVH7\nA2iUHTVqSObMsXPPPVEcOpTt8pQ0auTjoYcc9OrlITq69PdJSRHcdFMMp06p8+CLL0xUqSJ57jkH\nJlMhX65khIPz6y4hxCYhxHtCiOxXcAJwONc5R7OOJQBHch0/knUsX4LRG7Q0nD8vyF8XhZ9+MmCz\nla5YW0YGWYqaJec+R48qeWrKhYO/vaQkJfmYN8/G1KkZXHaZh/79XXz5pa3AndeF1ofjxxWOH1dl\nUpFlEUwKk8MVV/hNaZde6qF69chUFspiPBw/LlizRs9990XRo0ccN90Uy7vvmvMoagaDpHdvF2++\naWf16nQ++shO//6eMlPUtLmhoslBLajdo4eHadO+Y82a86xadZ7169P54Yd0RoxwU6tWcMZkWpqS\no6hl8/HHJo4fDwfVxE84jInyzgadDTwjpZRCiGnAy8Dt5fxMQaNrVw9Tpjh49VUzmZl+BSohwce7\n79pK7Qbds0fH7NmBaTkJCb6Ie7E2aiSZPNnJhAlOjEa1fVFBNG3qxWiUuFx+mef+WaNwWrTw0qeP\nm1WrDNx8swtL3movGoWQmipYvdrA//5nyappGIiiSFq39nD99R7at/eQlOQlIUFqmXYViPR0NUGn\npK3Ywp24OEmbNqFzSaqxsZLcRg2TCfT6yHqHBYNyVdaklCdzfZwDLM36+ShQP9e/1cs6drHj+bJv\n3z7uuOOOnL5e8fHxtG3bNsf/nK0th+rznj2/0qkTrFvXk2PHBH//vZboaMnAgd2oVUuW+vrLlv2G\nlBagd9ZvvJqRIx3UqtWlTH6/sv78999FO79r1+5Mn57B/fer3vLo6F5Ury7z7I7K+/cpz8/du3cv\n8N/j4+E//1lBhw46+vW7styfN5Sfswnm9fftU/jPfzZy4IAO6EXVqj6qVfuZRo28DBjQjYYNfaSm\n/sIll0j69fN/PzW1/OSRfay8/x4V5fNXX/3GjBlmunbtzl13ZXL06K9h9XwFfU5NFcyd+zuHDyuM\nHn0lHTt6y3R+ZH/OzIQ77ujL7NkWYDUAd9/dOSjvx7JcL0vzOfvnlJQUAK644gquvvpqLqRMi+IK\nIRoAS6WUbbM+15ZSHs/6eTLQUUp5kxCiFbAA6Izq5vwRaJplgfsDuBvYACwDXs/OIL2Qn376SXbo\n0CHEv1X58f33em66KbsWh+S++zK5885MLfMRtdxBdlzQhAlOunaN0Ah5jbAkPR1On1ZwOMBkUjPA\nq1SRmoUygvjjDx0DB6pBx23aePjwQxuNG4e/RejAAYWJE6PYsEFNwqpd21euVRPS0gTr1un5/Xc9\n3bp56N7dTbVq5fIoYcHFiuKWmWNYCPEJsA5oJoRIEUKMAV4QQmwRQmwCegGTAaSUO4BFwA5gOXCH\n9GuVdwLvA3uAvRdT1KD8Y9ZCzWWXeXn5ZTt33ungm2+sTJ6cv6IWDv72sqZKFRg82M3cufYARa0y\nyiI/NDmohEoOcXHQsKGPVq18NG4sqVMn/BU1bUyoFFUOZrNfudm2Tc/bb5vJyAjVUwWHs2fh4Yf9\nihqoNSmdzvzDRMpiTNSqJRkyRG15eMMN4amohcPc0JfVjaSUN+Vz+MMCzp8OTM/n+F9A2yA+WoWl\ndm3JmDERWq1UQ0NDI4ypVk0SH+/j/HnV5vH++yZuvNHFFVcUrTSS263WNCxLdu3S8dNPgTft188d\ntDIcGqEj4nuDRrIbVENDQ0Oj/HjjDRNPPumv4jpqVCYzZjgwGi/+HZcLfvxRzzvvmGnZ0suIES7a\ntPEWWqoiOVmwZYseo1HSsKGPBg18Bd4nPz75xMhdd/lrbiiKZPlyK506VZzamxdy8KBCRgY0aOAL\nSjmR8qbc3aAaGhoaZU1KiuC77/Rs3Bih6XplgM8Hv/+u45df9Jw+Xd5PE14MGOAmLs5vlVqyxMTJ\nkwVnnqelCcaNi2HtWgNz5pjp1y+Wjz82YrMVfK8dO3SMGqW23evePY4XXzSTnFy8V3jVqv5nNZkk\nH35YsVsxbt+uY8CAWHr0iOOll8ycPVveTxQ6IlpZi/SYtaISDv72cEGThUplkMPWrQrDhsVw882x\njB8fnaf+IFQOORSVi8nixAnBLbfEMGRILHffHc2BAxH92ijWmGja1Merr2aglp9Qu31YrQUrawaD\nWhIjGykFDz4Yzbp1BUclJSb6MJnU73k8gpdftnDddTH8/beuyEWrr7jCy4cf2njtNTs//GBl0CB3\nga7YcJ8f8+dnF5YWzJxpYdOm0ER2hYMcInvWaWhoVEr271cYMSKWffvUxdvpFCFvhB6p6HTkJEd8\n952R22+P5uhRrW5hNv36uXnzTTs6naRKFR/R0QVrTrVrS554wkHt2j4aNfIPygceiCrQKteqlY/X\nXrMHHDtyRMd118UW2XJco4bkhhvc3Hqri7ZtvYgK/GfMzFTbOeZm3jwTvggNv9Ni1jQ0NCIKhwMe\neSSK+fP9QUBDh7p49107SiXYnu7cqfD333qioyWJiT7q1/dRo0bp1vmpUy1ZLe1U7r7bwSOPZGoF\nfLNwu2HPHgW3W3DZZYXvCnbuVFi40IjVquBywYIFJkCyceN5GjW6+N8qPR2++srIffdF4fP5Na3E\nRC/ffGOtVG3JPB4YMSKa1av9gXstW3r5/vt0YmML+GKYo8WsaWhoBI1z52DDBh3794ffErJtm475\n83NHXkvGjs2sFIoaqJawxx+3MHZsDH37xnH11bHMnm1i1y6lxNbFoUNdKIpfEXjjDTNbt2pxgNkY\nDNC6ta9IilpyssKtt0bz+usWPvzQRMuWXkwmycSJzkK7z8TFwciRLr791krTpv6SRCkpOnbsqFx/\nD70eRoxwBxyrW9cbEUkG+RHRy1c4xqy5XPDbbzrWri27iRUO/vZwQZOFSmnkkJIieOCBKPr3j2P9\n+jKr/lNkNm3Skbt9zciRqssnPyJxPDRr5mPxYhu1aqn+oCNHdDz+eBRXXRXH9Olmdu5U8o1xKkgW\nbdp4efDBzJzPUgreeceEJwJrTYdyTLjd8OGHRvbv98+bKlV8rF9/jqlTHcTFFX4NgwG6dPHyxRc2\nPv3UyujRmQwa5KJu3eD7/8J9fnTr5qZ16+xBKBk3zhmSTVk4yCH8VtoIZ/16PYMHx1CrluTnn9OD\n1hBXQ6MsOHBAMGpUDNu3q0tHYfE55cHGjf5lLTHRw/33Oyq0W6QkXH65l6VLrTzySBSrVqkR5E6n\n4JVXLLz1lpnHHnNw7bVukpKK9oI3GKBfPxfff69n82b1eitWGElNdVC/fviNgXDl4EGFt98O9B1f\ncokkqyNisUhIkCQkeOjf34OUVOj4s5JSv75k/nw7W7boqFJF0rFjaHYP6ekixzJdo4akZs2yH/Na\nzFoZcuiQwrXXxpKaqiCE5J9/0klMjNBoSI2I4/RpwX33WVi6VI0FUxTJmjXptGoVXmN4wQIDd98d\nzbBhLh5+2FFgDFCkc/Kk2srnkUeiSEsLNDkkJXl4//0M2rcvWqD5woV6jh3Ts3Klnt9/NyCE5I8/\nztO0aeWVb3FZs0bH4MF+85leL/nll3RatgyvOaShkpys8NtvembONLF3r7oJbNLEw6efhq612MVi\n1jTLWhmyfr2e1FR1wTQaCYgB0bg4e/cqbNmi48wZwRVXeLn0Um9ExB8dPSpyAsH79Alff9KuXQpb\nt+qw2USOogYweLCLBg3C7yUzaJCbLl3OU7u2jNj4laKSnf3Xvr2Vn37S89xzFs6cUSdPcrKea6+N\nZc4cO//6l7vQoqwNG0omTjRz000u+vXLwGCAEycUmjbV0myLSkZG4Dv47rszadIk/OaQhlpbcNSo\nGE6dCnzZHDigw+0WZJdrKS0nTwq2b1fjf+vX91G9ev7nRcAr7+KEU8za+fPw1lv+1bBJE29ArZ1Q\nEg7+9pKyfr2Ovn3jGDcuhocfjmbgwFh27Cj5sA0HWfh8alzVsGExjBoVUy51q4oiB69Xja+85ppY\n0tIUpk71V2o3myWTJ2cSFVXABcqJ+Hho3Lhoilo4jIeyIDHRx5gxLlatsvLuuzbatPEAEqdTMGpU\nNH/9pStUFs2be+nTx8Mnn5h4+ukonnzSwogRsWzeHFmvkVCOibp1fej16rrfpYub0aOdZd5yqjhU\nlvlxIZs26Rg+PDaXorY66/+SV17JoHHj4CjYyckKEyZEM3RoLA8+GM1//nPxeA3NslZGpKQobNni\nF/ewYa4iBZNWZpKTFcaMiQkoMul0Co4dU2jTpmLuRt1utdXMbbfF4HQKdDpJx47haZn45x8dQ4fG\nUqeOj4MHdQFWgZdeyggb9+fp04K0NEHz5j50lSshrtgkJvpITPTRr5+blBSF5GSF06cVzGYKbUIe\nHw/Tp2cweHAMqak6vF6BwwGvvmrmrbcywr5RfTjQqpWPL76wYrMJ2rXzUreu5l0JR1auNOSxgtao\n4eOllzLo06fgQsJFxeWC994zsnp10S6mxayVEcuW6bnlFr/WvHRpOt26hedLOlxYtUrPsGGBOw1F\nkaxaZaVdu4onO69XVdT++9+YnBpJ99/v4KGHMsNud52SIhgyJIaDB/XceWcmn31m5PRpdZd5881O\nnn02gypVyvkhUV3kkyZFkZKi45df0ktdT0yjcLZtU7jxRlVhAxBC8uuv4Re7qKFRUrZuVfjf/yyk\npirUq+dj5Ei1f2tRE3KKQnKyQufOcbhcgUrhypU/aTFr5cmRI/4tf0KCT4tTKAIWS94X79SpDlq2\nrHiKGsDatXpuvdWvqNWu7WPkyPBzg3i98PXXRg4eVJeHuDiZo6iNGuXkgQccYaGo7dun8N//RrN3\nr57ExMiIY6wItGnj44svbMyaZebjj41IidYdQiOiaNvWx7x5djweMJkIydpiMEiio2WAsqaGJ+RP\nRC9v4RSzltuk+vTTGWVasqOixh20aOFl2jQ7der4aNfOzdy5NkaNKp1yU16y2LpV4dZbY/B41HFg\nNkvmz7eFLFMxPV21gBw7ln+aX0Fy2LtX4bnncvu0JH37unj/fRtTp2aQkFD+1qsTJwT33BOVk6E1\nbJiLatWK/1wFyeHUKcGSJQb27o3oZTKH4syN5s19zJiRwZo16fz0k5WmTSNn81lR18tQUJllYTCo\nbdYUJTRyqFtX8tFHdlq18pCY6OWJJzKYP99+0fM1y1oZUaOGupj17++id293IWdrAFStCnfc4WLY\nMDdms6ywMX6HDwvGjo3Oib1TFMmHH9ro0CE05oj9+xWefdbMN9+YGD8+k+nTHcWqwbR3ry5gt9eo\nkZfJk53ow2S1kBK+/97A77/7tfaePYOfTbtihYFJk6IZNMjFO+/YtZisC7BYqLCxoxoa4UCPHh6+\n/daK1ytyNpunT+d/rhazVkbs+X/2zjs+qjL7/+97pyWZSQVCCaGDSAcRFRBQFMQGlhVX0bWj6GJX\ncNe+X7vu4trLD3EVsbCgiLoKioiKoFTpAqGXkEAymUy/z++Ph2EyaaRN5s7kvl+vvDJzM3Nz58xz\nn/u555znnM0qCxdaOPdcH+3bJ67No4EQ8icew1xCwOuv23jgAblsUlEEb77p4oILGiZJtTw7dqhc\ncomdbduksjr55ADvvuukZcua7+Phh5P497+lMrnnnlJuu82rK6G8YYPKyJFpeDxSUA4a5Of99111\n8qxVxcGDCmefncquXSbMZllPrCnXazMwMGgcjDprMaZbN41u3byxPoy4Y9cuhffekyVPbrvNE3eV\n6DdtUnnsMSl8LBbBK6+4OPfc+gm1rVtVtm5V6dw5GFGYsbQUXnrJdkyoAfToEWTrVhMtW9bci9e1\na5AbbvAwYoSfwkKVRx9NJj9fpX17jSuu8Ma0gKfPBzNnWo8JNRA89JC7QYUayNXbu3bJPNNAQGH/\nfpVOnYzELL2wcaPKiy8mceaZfoYNC8SkoryBQWMSh76KmqOnnLVYEq95B4WF8PjjyTz7rPzZtKn+\ndRka2xYbNpjweBQ6dQrwxRdOxo3zk5R0/PdVvT+V889P5fLLU7nxRgf5+eEbsN9/N/H222Urmwq6\ndQsye7a1wn6qssOePQpWq1wMMWFCKpMn25k+PYnPn+N15AAAIABJREFUP7fy8stJ5OXFtjbGli0q\nr74aNuC113rp27fuIqoqOxQVRd7YJmIPzPLE0zyRl6cya5aNm25ycMMNdrZubbhLWTzZIdoYtpDo\nwQ6GZ81At6xYYeaTT8Lio2y9tXihXTuNd98toV+/AG3b1u/uv6gIHnss+VjboFWrzOzapdKihRQr\nc+ZYKdvA/Jxz/Hz7rYVDh2Q9rOPlXK1Zo3LrrfZjfT8jEdx3n4dBg2Kbb7lxo+nYatpmzTQmTfJG\npUuByxU51oz6bfqibImWJUssTJ6cwhtvuHSx+MXAIBoktGetX79+sT4EXTB06NBYH0Kt8XrhnXci\n+9+EKn/Xh8a2xUknBTn/fH+9hRrINif/+1+kl0w7GpEsLiaiuGJ2tsYppwRYuNDCnj1qBU9ReTts\n2KBywQVplQq1nj0DzJlTwuTJHpo1q/fHqDOaBp99Jj9/aqo42p+vfiHZqsaDVm63emxY39DE0zzR\nsWOQ7t3D7s6ff7YwfboNj6f++44nO0QbwxYSPdghocWaQfyyd6/KggVh8WEyCVq3TvwLZnXs21fe\n2yNIT5c2MZshI0MqjB49ArzwgovnnpOuNK9XqSA+yuN0KmXEsKBDhyB//7ubL74o5rPPnAwfHoh5\naykhoLBQoXlzjdmznQwcGL0cMqtVRDxOT4/avzKoA1lZ8OyzpZTtz/jPfyaxZo3hAjVITBJarBk5\naxI9xNtrS36+ElE+YtQoPzk59U9sj0dbhAgGI8XauHE+cnOlTVJS5MXrk0+cfPRRCcXFSkQor/yi\n7/J2GDQoyOLFxfzySxErVhSzYIGTu+7ycOqpQTIzI9/r98tE/8bGZJLtjhYsaDihVtV4KDvWLrrI\nR9u2OilREQhI40chiS7ezo2TTgrywANhV5oQCrNnW497Y3I84s0O0cSwhUQPdjBy1gx0SWSOkOCm\nm7xNvs5VmzYaiiIQQiE1VXDHHZ6IxQqy5pW8UpUND/bsGajRakmZ71P969atU3nkkWSKixUGDw4w\ncGCQ7t2DdOqk1aqWW11prLpeubkaJ53kZ+VKc70LMTcYgUBYpIUUiV6K38WApCTZUWPzZvVYbut/\n/2vlzjs9tGrVtL3wBomHUWfNQJds364yYkQaTqfCnXe6uesuT1QSyeMJrxcWLDDz229mxo3zV9sf\n9fBhuPRSBytXWnj6aRc33tgwrrBt2xRGjkyjqCjslLfbBbfc4mHcOF9CNVPfvFmlsFBhwIAg1ooL\nahsfny8ymU5V0ceBxZZDhxTmz7fwt7+l0KaNxldfOcnKStzrmkFiU1WdNUOsGeiWZctMHD6sMHBg\noNaJ7cXFsHmzCadToWNHjQ4ddBLGakR+/13l2WeTefTRUjp0aLjzfMUKE1dc4eDgwcgsCqtVcO+9\nHv78Zy9t2iTuvBIzQiHQUCNOm0161oQARdFl1eidO1U2bFBRVWjbVvZEjoaXUgj5v4SgSZ7rBolD\nVWJNf2d3A2LkrEn0EG+vC4MGBRk9uvZCbedOhb/+NYVRo1K55JJURo1KZeNGOdTj1RZ1oVcvjenT\nXZUKtfrYYcCAIPPmOTn3XB9lw6Y+n8L//V8yN97YsHWvoklcjIeQSAt51ULiLBCQ7la/X/7UM4+t\noW2xf7/CpZfa+fOfUxk/PpVhw9J48smkKvvV1gdFgfbtG+amLC7GRCNh2EKiBzvEx4xqYFBDvF54\n7bUk5s2zEao5duiQyvr1CRKbqyXRcrZ07arx2msu5s93ctZZkaLt558tXH+9nYMH468unu7weGRr\nCp9PDm6fT4o1l0tWjS4ultu9Xvlar7di3ZEY4XbLcjMhgkGFf/0rmSlTkiOKORsYGBwfIwxqkFBs\n3apwyinpxwqnhpgxo4QLLohtQddEpbRUhpyXLTPz9ddm1q83k5kpeOutkpi2poprNE16y9xu+djl\nkj9ut/SgeTxgsciVODabzF1LS4P0dLndbI55WNTrhYceSubNNyu27Pj4YycjRzaBthAGBrXE6A1q\n0CRQFAWzObK0RHa2Vm0yvkH9SEmBfv2C9OsX5LrrvBQWKthsRm2yOhMIyLw0txsOHYL8fNm+4vBh\nKdKOHJEiLSNDijSbDbKzpbgDuV0HOWw2G9x+u4eiIoWPPooscO12G541A4PakNBhUCNnTVJdvL20\nVF4DEoW2bTUefrgUVZUe4759A3z0kZP27aWHRw+5B3ogWnYwmyE7O36Emu7Gg6aFhdrBg7BvH2zd\nCitWwMqVsHw5LFsmf//6K2zYIP++dasMix4+LE9qv7/W4dBo2KJNG8FTT5UyZ46TW291M3iwn8cf\nL2XAAP161XQ3JhoRTYPffjMxbZqN774z8913TdcWZanNmCgoUHjssSQmTUph0SIzBQUNcwyGZ62J\n4nbLk/KJJ5LJz1e57z43WVmCE08MxvVKPqsVrrnGx4gRATwemXSclRXrozIwqCFCyPjhoUOQlydF\n2P79UFAAe/bIHDWPR3rWCgulWzM7G0pK5PaUFOlWTkqS+7FaY+5ly8iA4cMDDB8eQNNi7vAzqIbl\ny01ceGEqfr+CogieekrljDNifVTxRWGhzM0EmDXLxtixXh57zE1ubv2uq0bOWhNE02DuXAs33GAn\nlISfmiq49lovBw4oPPdcaZOvaWZgEBN8PulR27lTes4OHJAibd8+GQrdt49jiqdtW3A4ZJ5aRga0\nbg1Dh0L79uFQqN0uBZuqGirJoFoKChTOP9/Bpk1hH87LL5fw5z8bub61obBQ4dxzHWzeHLbjGWf4\neeklV41aJjbJ0h0GlfPHHyq33RYWaiA9bUlJgg8/tLJ7tzEsDAxigqZJD9muXVKo7d0rc9SKi6Un\nraREhjmdTvn37duluDtwQIZAf/897IETQp7YHk/FfmMGUeHIkVgfQd3ZuVONEGpg6Pu6kJUl+Nvf\nPBHbvvvOwksvJeHxVPGmGpDQX4WRsyYpH2/ftMmExxMp3M85x8+iRRZAobS0EQ+ukWnK+ShlMewg\n0Z0dhJBiLC9PetH27ZPia8cOGRotK7oKC+W233+XXrfiYinmiovlj6rK1/v9NRJrurNFjKiLHYJB\n+OorM+edl8b69fF5Wa1s3j98+Pvjvq+oSAq9KLSr1Q21HROnn+7nppsildnrr9vYsqXuYyM+R5VB\nvSg/b2dmagweHGDZMhOqKnA4YnNcBgZNHp9PetXy86W3rKhI5q15vTKsGWrAGnqsKNIbl58v89rc\nblniIxQqVRRZyqMxGrc2YdatM3H11Q42bDDx+efx2QIsPV2gKOGLwznn+GjXrvpFKjt2qEyaZOfk\nk9N49VUbTme0jzI+yMiAO+/0cN11YcGmaQp//FH3ep8JLdb69esX60PQBUOHDo143qtXkJNP9mOx\nCEaP9nHffR6efDIZULj4Yh85OYlbG6u8LZoqhh0kurGDpskw5vbtYS/ali2weXNFoSVE+CdEKERa\nXCwFXqhOW3KyXGxQA7GmG1vEmNraQdNgxgwrgYC08aJF5ojSQfFC584aTz9disMhGDvWxz/+Ucq5\n51Zvi/nzLXz5pRW/X+Hhh1NYuTIx1yzW5dxo2VIwdaqbd98t4YQTAlitgmbN6p6OkJiWNaiWTp00\nPvywBKdTQdMEF1/swOlUaNZM4/bbPaSkxPoI9c3OnQrz5llp21bj7LP9hr0M6kcgIAXW/v1SrO3a\nJR8fOiT/rihhYVaZ6FIU6ZErKJC5bB6P9KqFuh0YiwuiyoEDCp9+GvamFRcr+HxyXUc8kZwMf/mL\njzFj/GRlCZKTq399SQl8+GHkh/zySwvDhiVwPLSWNGsG55/vZ/BgPy6XQsuWdRdrCX0GGzlrksri\n7RkZkJsraN8ePvrIxQcfOPnySyc9eyauVw3qn5dTXAz/93/JPPhgCtdea+f33+OzjZWRnySJuR00\nTYY48/Nlftr69XIl6N69ka8rGwItT8jLFmo/VVQE27bJ37XIaI65LXRCbe2Qn69QWBi+lHbqpMVt\nKonFAjk5YaFWnS18PigpiRyTmzfHp6TYsUNh5kwLq1aZKk3vrO+5kZUlr7f1EfDxaVmDBqVzZ8Ho\n0QG6dElsodYQrF1r5uOPQ9XYFbZujU+xZqADNE161dxuuYwwL0+KtIKCsFctRNnQZ9l8tbJXFrdb\netaOHJErREN9Q91umQGvk56hiUZxcaRgGTasaZS6SE+HIUMivWinnhqfnWLWrDFz220OxoxJ5fvv\nzbo8VRJarBk5axIjFyVMfW2xenWkOIvXhFpjTEhiZgdNC4snp1OKtG3bZBj0wIGKCwrKU164hbBa\n5bK+0lIp0FRVCkIhIv9nJRhjQlJbO5T3xHTrpsMrfR2pzhYmE0yY4MVkkgZQVcEZZ8SnUPV6Q78V\nLr/cwbp14Xn+0CGFgQNjf24ktFgzMGhofvopMs2zPjkIBk2Y0BXe55OeL7dbirTiYpmzVvY1lVFW\noIVEmxAy9GkyhUWa3y9jW+X/r0GD0by5AKRdBwzw06NH/HiXNm1S+fBDC598YqlTCLN//yCffeZk\n8mQ3n37qpG/f+PnsZcnMDJ8XPp/CtGk29u+Ht96ycuaZqdxzTwo//mhizZrYlShJaLFm5KxJjFyU\nMPWxhc8n77LKEq8rZ40xIYmZHUJiy+eT4gqkJywYrCioyoc+y++j7PPSUlmXzemE1FS575BXrbL3\nlMEYE5La2iEnR+OSS3xkZWk895z7qHjTP2vWqJx7biq33OLgppscXHBBKn/8ESkJjmcLiwVOOy3I\nI494GDIkGHFfEE907qyRnh6ey7/4wsKSJRbuu8/O7t0mZs78mVmzbNx8s53ffotN6ktCizUDg4bE\naoUzzwzfVg0d6qdTp4p3kpoGq1aZmDfPEtcVzQ2iSNkVmh6P9Kj5fNITFiLkLSvvRStbtqP8393u\n8KIFn09eTcu+3lgV2uCkpcGjj7r55hsn/frFh2dJCHjttSQOHw6Ph/x8lV9+aZoFIjp00LjrrvBi\nnOHDA7z1li3iNQsWWBg8OFAhFaaxSOgz18hZkxi5KGHqa4vRo/04HII2bTSeeqq0QpN4vx+++srC\n6NGp/OUvdgoL9VmMtLHGhM8Hixebef99K3v26M8WMT83QgVrDx+WXrWSkoqLCWpDSoq8qwjlrgGY\nzdLDdpx9xdwWOqEudmjTRtCxY/x42V0uWLu2oujwl0s5i/aYKC2FrVsV1qxROXAgtvPDBRf4ad5c\nfofNmgn27y8rj0bg8cjTKicnNp7ThBZrBgYNTd++QRYsKOZ//yumR4+Kk/PChWauvtqO36+QkSFI\nSorBQeqI3383cdFFDv76VzuvvJJ0LJHXgLAgO3JEhi2Li2UJjxAhcVUb8ZaUBC1ayNo8aWlSDJrN\nkfszaPI4HDB2bKQyU1VB796N4xnUNPjtNxPXXGPn1FPTGTEinbFjHfVqx1RfOnTQmDGjhJQUwcaN\nJgYMiExOGzEiwN69SoXtjUVCizUjZ01i5KKEaQhbdOumVXp3tXGjysSJDjRNXhTHjvXRurU+81ca\na0z88IMZIaQ9Xn/dxqZN0Z9y8vJU8vJqJkxiem6Ecsn27w+v4KwJ5dtOld1fSooUbFlZ8iclRb6m\nBoVxjXlC0lTsMH68l6uu8pKcLMjJ0Zg1q6SCWIuWLb7/3sx556WyYIGVYFCO4c2bzezYEVtJctpp\nQebNc9KpU4Brr/WSmirn77S0bxk92s/f/uapMKevW6fy4IPJXH21nWnTbCxbZopKf+2mGaA2MGhg\nnE545pkknM7wxfPii/1N3pmxdm14itE0hb17Vfr0iV64qKQEnngiCYdD8MQTbn17NkODI5RfFopB\n1WTQVNbRwGqVHrXMTCnU/H4pBuOtlL5Bo5CbK3j66VLuvdeNzQYtWjTOjeW2bSrXXmvH54sc53a7\noG3b2IeS+/cP8sorbsxm+OyzYvLyVPLz3YwZ4yM9PfK1BQUKV1/tYPt2GVKWfWEFd9/t4cYbvWRn\nN5xNE1qs1TZnraBAQVEgK0uf3pC6YuSihImWLdauNTF3bviiePbZPvr00W/blcYaEykpkZNvWTEb\nDXbsUPnkEytmM0ya5D1uoeeYnhuhJuxJSeHWUCHKhjwrK9NRGdnZYLPJGJfdLh/7/VKs1WBhgTFP\nSI5nh8OH5ddQPl81HklKgrZtq77eRWNMFBYqFBdHjsfkZMF775XQvXvsxRqEMwf69tXo21cDBlf6\nOqtVkJmpHRNrEoXnn0+meXONiRMbrklsQodBa0pJCcycaeGMM1IZNSqVX34xqtLriVB+w7x5Flau\nNFFUFOsjisTthuefTwLkRdRiEUyd6qlwF9YU6dMnMqwSbU/j7t0qoBAIKOzbp1O3pqbJxQShx0lJ\nshy8qcy8Uz5frfz2ygrmNm8u95ORERZtodIdBvUmGITvvjNz7rmpnHtuKkuWmA3T1oH27TUmT3bj\ncAhat9a4+WYP//ufk+HD9XtzWxWpqfDUU+5j4dKyzJpla9BwaEKLtZrmrC1ebOG22xzs3m1i2zYT\nEyY42L1bpxN9HYj3HIxdu1TOOy+Vv/zFwciRqdx7b0qNc5LKEw1b7NqlsnhxuMDQ//1faaMl6taV\nxhoT/fsHUZTwRNa+fXTvnDdvDguegwePP701+rkR6iIghPSmBQJyEYCmUW2RqrKlOkK/yz622+X7\nHQ4p2jIz5XOrtcYKuTFt4ffDnj0KhYXR+x+lpbLoa21XGVZlh/XrVcaPd7Bpk5nNm81cdpmjQg7m\njh0KCxeaY5oo35BEY0y0aCF44AEPP/9cxHffFfPEE2569Yrf+XLgwCBffFHMJZeEuzlYrYLbb/eQ\nktJwx5AYI6oeFBXJHJeyFBSoFBQkjliLNaWlsH+/wsGDSp3uRBVFYDtW8kbhk09s/PnPDrZu1cfw\n3bNHPZYke9NNHsaO9Uc4SZoyPXoEmTLFAwguushLly7RnZTLJihHO+RaJ8qeAIGAdOu73fJxqOl6\nTU+Ssl621FQp0EL5atnZMpZTg4UFjYnbDb/+auLuu1M47bR0Xn45ekmFX39t4bTT0jjnHEeDFDJd\nu9ZMIBAeUx6PUmEOWrnSzJ/+lMpZZ6Uxf74Fl6ve/zYhCZXAaMicrljSs6fGSy+V8ssvxXz7bRE/\n/ljEhRc2bOst/ZzFUaAmOWulpUq5eipgMokGVcSxJla5KHl5Ku++a+XCCx0MG5bGiBFpPPNMUq3D\nU7m5gjvvdEds27TJzN13p9Q6JBoNW9hsguRkwcMPy2TdxkrUrQ+NNSaSk+GWWzx8+62TJ590k5kZ\n3f9XWBg+lz2e44+zRj83ynq5vF5ZruPwYblCpXwfm+qatpcNk5pMUpxlZ0uxZrWGvWq1EGrRtsXB\ngwovv5zEqFGpvPeejZISJWrnihAwY4YVUNixw8zFF6eydm3NbFGVHTyeittCK79D2Gzy8zidCldd\n5WD+fEuDtScKBGDlShPTptn49tvGSTc38hglNbGDzQadOmn066fRubNo8HukhF5gUBMyMgQDBwb4\n+utwcvgtt3ho104fiY7xyq+/mrjySgf5+ZEj9umnkzn55ACtW9d8BlMUuPhiH99+K1uAhFi82ML6\n9SZOOy22LvS+fYP89FMxOTnascTURMXng2XLTHzwgQ2bTTBmjJ9BgwLV5uc5HDRaZfey4r18gU9d\nEJrBhZBJUKH+nYFA5U3Wy676rCqcmZsLrVrJumqtW9csrNrI7N6tMGVKCl98EZ5nMzM1Ro6Mzpek\nKESUWHA6Ff7+9xRmzCghI6Nu++zePXIMm82Czp0jt3XqpGE2i2MeuFtvtdO+vZNTTqnf+C8thY8+\nsnLvvSkEgwpduwb53/+K6/xZDOKPhPas1SRnLTlZtgrp399PRobGpElubr7ZWybsFv80dl7OgQMK\n115bUagBZGRodRLC7doJXnrJxfjxkVVVa9shIBq2sNtlLlY8CbW62mH5chNjx6bywQc23nknifHj\nU3n7bRtu9/Hf2xiULdWRlnZ8r01M8jlVNbKjQCAQ7joAtV+FYbfLempZWdCypVxgEOoxWpkArIJo\n2WLPHoXJk+0RQk1VBa+/7qJr1+jdFI8ZEykEf/jBUm7VXuVUZYfevYM8/ngpZrMgLU3j7bdddOsW\nefwdO2rccUfYBRcMKtx6awo7d9bsO92/X2HTJpW8PDWigPR331m4666UY+kWzZppjXKNivd854ZC\nD3aIo8tL9DjhBI3Zs0twuxWaNxd6uiGNS7xeKq1UP2iQn2eeKT1uOYWqaNdO8MQTpVx+uY/Fi83Y\nbFTaRcAgemzYYDpW5DbEP/6RzNln++ndO/bfhd0eFmjJyToOR2uazCnzemV8LRgMt56qrIZaZSFQ\nVYUOHaRXLTtb/g6FQFVVFz1B/X65Km7RovCkajIJZsxwRX31X79+Adq00di7N/zZd+9W6d+/bl6u\n1FSYONHLOef4sFjkfFQeqxUmTPAxe7aF7dvl5XXbNjO//mqmXbuqvYg+n2wefv/9KeTnq5jNgiuu\n8HLLLXIivflmO6HV5gDjx/tITq7TxzCIUxJarNWmzlpGhgyJVsW+fQobN5rYulXF61U45ZQAffsG\n40LY1Sfv4I8/VObMsbJtm0qHDhrDhvnp3j1Ybe5Ru3aCzz5z8vPPZvbvV2nZUqNbtyAnnqjRrFn9\nLqCZmbLJbl0neiMHQ1JXO1TuFVVwu/WRzF/WU5OefvyxFrPxEAzKK3RpqfSsheqiZWXBoUPyNdV5\n2Ewm6NJFhj3bt4euXeV7k5Lkfsom3dZwwUI0bLFhg8qTT4bdnc2ayZY+gwYFo+6Jzs0VvPNOCePG\npVJaGi6rczyqs4PZDJ07V7+Pdu00pk93cf75aZSUyP/7/vtWzjvPX6U3bO1aE9dfbz92IxQIKLz7\nbhI7dsgi0i5XeCy0aqUxYkTjlLkw5kuJHuzQaGJNUZS3gfOBA0KIPke3ZQIfAu2BPOAyIUTR0b9N\nBa4DAsDtQoivj24fALwDJAFfCCHuiOZxCyGTOm+8MeXYnRLIu8NFi4rp2TP23oRo8sknVp55JnwL\n9/TTyZx+up/HHy+tthJ99+4a3bs3XEFAA31w0kkBJk1y88or4TExfLg/6iU5asqJJ4YvYm3a6OOY\nKhAq3eF2S49X8+bSw+Z0yr+npcH27VW/PyUFOneGnBxo107+5OTI3LUWLeTfK6vZFgP27lXRNAWb\nTTBpkofLLvNxwgmN970MHBjk88+d/PvfSWga9OzZOLmTffpozJ3rPHbd2LHDhMtFlWJNOkErfk+d\nOglmzw6Hj00mwRtvlOjmfDNoPBrTNz4dGF1u2xRggRDiBOBbYCqAoig9gMuAE4ExwCuKcmzGeRW4\nXgjRDeimKEr5fR6jIXqDrllj4oILUiOEWoh4yVGqT7z95JMr3sH98IOFiy5KZf36+Et51EPugR6o\nqx2aNYP77vMwf34xb71VwgcfOHn5ZRctW+oj5NimjTyOdu2CNSoLEJPxIIQUaaH2UK1bS8Pm5IS9\nY5V1Mwj1+GzWTK7ayMqSj3v2lOIt1MHAZpOTUw17goaIhi369Any5ZfF/PBDMQ884GlUoRaiX78g\nb73l4o03XOTmNt6YGDAgyJw5Jbz7bgn//GdptdGIbt2C3H67GxDltgeO5eXabII333TVe7FCbTDm\nS4ke7NBockMIsURRlPblNo8Fhh99PANYhBRwFwKzhBABIE9RlC3AIEVRdgCpQojlR9/zLjAO+F+0\njvuzzyyVhHgETz1VSqdOsb+7WbdOJT1dVNsypD6cckqAhx4q5bHHkimbM3H4sMrrr9uYNk0nmeUG\njUZaGkdX4B7/orFtm8rOnSo5OVpUk8lDtG4dZMwYHwMGBMjPV3QjIiMI5aU5HFJs2WxSuIV++/0y\nTOrxwI4d4bZRGRnyPdnZ0KaN/N29u8xbczgihZpOaNNG0KZN7AueKkpsFse2ayeqzVULkZYGd93l\nYcwYP7/9ZqaoSKFHjyD9+/vJyhLs2aMycmSAnj2DeiqbZ9CIxPqszhZCHAAQQuxXFCX76PYc4Ocy\nr9tzdFsA2F1m++6j2yultr1BKyM3V0Pe7Uih0qqVxvPPlzJ8uD/m+WqaBs8/n0xensqMGSVV3jXW\nJ97ucMANN3gZNCjAc88ls2iRmZAtWrQQaJquam4eFz3kHuiBxrBDfr7C9dfbWb3aTGqqYOZMJ0OG\nRPfCnZoKPXsGWLhQ3mT16OGpdnzGZDyEDigpSSZhpqWFQ5dOpwxlBoPyJykp3JoqI0MqjpYtpRfu\nxBNlCNRul22mQp60OmKcG5JY2SE1FQYNCjJoUOQ5kpsbuxo0xpiQ6MEOsRZr5dHdbfCll/ro3j1I\nYaFCZqagfXvtWKgl1gghl3qvWmVm5kwbd93liYqAdDhg8OAg//lPCdu3qxQVKdhs0nWvB6G2fr3K\njz+aadNGcNpp/oRosJwI7NunsHq1nGKcToXLL0/lq6+im+dpt8Nvv5lZutTC6tVmxo/3NYpHr9ao\nqvSAJSeDyyWFms0mhVtmpsxhc7mkB81qlR/M4ZBiLSsLOnWSQq1164o5agYGcYzXC1u2qLRpoxlz\neRliLdYOKIrSUghxQFGUVsDBo9v3ALllXtf26LaqtlfKtGnTsNvttGvXDoD09HR69+59TCWH4tC1\neb5tG7RpU/f3N+Tzn39eQnq6DRjFc88lkZ39LV27ahVeH3pPff/fsmVL2LNH4dRTT6dLFy3mn3/J\nkiXs2qXw4IPnHq1cv4ibb3bzxBOnVvn6tWvXcsstt0TteDweGDBgKFlZsR8f1T0vPzai8f82bPgB\nRbEjxBkAuFzf889/ennrrUFR/XydOp3Nd9+B2/09n35ayj33nFbl66M9Ho77PBBgaI8eIARL1q2D\nI0cY2rIlqCpLtm0Di4WhXbpAejpLDh6EjAyGnnEGWK3y70VFDB05skGO59VXX633/JgIz0Pb9HI8\nsXwei/PjwIEzuOkmO2PHfsMVV3g566zY2yPEYeWxAAAgAElEQVSa82Xo8c6dOwEYOHAgI4+e02VR\nRF2aNdYRRVE6APOEEL2PPn8aKBRCPK0oyv1AphBiytEFBu8DpyDDnN8AXYUQQlGUpcBkYDkwH3hR\nCPFVZf/v+eefF9ddd120P1ZMeecdK3fdZQfgyiu9vPBCaQXv2pIlS+rtxhUC5s+3cO21djp0CDJ3\nbgk5ObH3MP7znzYefzxcpqBduyALFzqrLBHSELaoim3bVB59NIktW0y8/rpLF3XHqiKadghRUgJX\nXunghx/CA/J4309D8PHHFiZOdADynJg2rbRKD3Bj2KFKQh0MSkpk+PPgQdl+6tAh2LdPrha12WRu\nWo8eMiRqMsnfVquMm9ntDZaHEFNb6AjDDmEa2xbbt6uccUYqxcUqIPj++2JdzKONaYcVK1YwcuTI\nCkuDGy2IpSjKTOAn5ArOnYqiXAs8BZytKMomYOTR5wgh1gMfAeuBL4BJIqwqbwXeBjYDW6oSatAw\nOWt6p0OH8ED+6CNZD608DTHIfv9dZeJEO8GgwtatZnbujH380+sloio6wP79KqWlVb8nWidcIACv\nvmpj3jwbGzeaue++2vctbUwaY+JxOOCBB9yYTGFhVlSk4ItyRZecnPA5MW+ehb17qy5dEdOLcmiF\nZ2hxQXq6DHG2aSPrpp1wglw80LatNGaoSXtKivxp4JwHQ6BIDDuEaWxb/PGHelSoAShs26aP8L4e\nxkSjhUGFEFdU8aezqnj9k8CTlWz/DejdgIcW13TsqJGaKnA6Ffx+mb92wgkNfzVctChyVazTGfsi\nqGYzZGVF3nX17BmoUTHUhmbHDpX337fRtm2Qs88OkJamkZ+vkp4e+7vCWHLSSUFmzCjhxhsduN0K\n11zjpXnz6H4/rVsLrFaBz6dQXKyyd69K27axX5FYgVDeWmhlaMhbJoTMVyvbzcBsDuemhUp/BALy\nNYnUG8+gSVNcHHld8XiqeGETJPbukSjSEHXW9E5ursYll4R7O82YYcXlinxN2dh4XThyBN57L/KC\nUJO+i9HGZIJLLokUpvff7yEtrer31NcWVVFQADfe6OWCC/x89ZWFN95I4uabU1i1Sh93huWJlh3K\nYzbDmDEBvvuumC++KOaWW7xRX0XdurXGkCHhFXR79lQ9zTWWHapEVaXYstvlT2qq/MnOlosN0tPl\nKtDMTOlJM5kiw54NmMYSc1voBMMOYRrbFv5yC199PoXFi80x7z2shzGR0GKtKaCqcNFF4RH+yy8N\nH6IsKFD444/wPpOThW7qV515ZoAnn3QxZIifd98tYciQQEyOw2yGJUvMvPpqEvv2qbjdCitWWJg8\nOZn9+xWcTukIaYooCnTrpnHqqTUrVFtfkpLgT38KnxM//thoAYS6EyqSm5wsPWihvDSTKRwmDdVh\nU9VwmQ4d1VQzMKgvSUmRzw8cUBk3zsGvvxrjPKEt0BRy1gBOOCFIbm6QXbtkk+0dO1ROPDEcfqtv\nvD0YVCJaoVx0ke9o/bnY07y5YOJEH9de68NqPf7ro5V7cOiQysqVkadTr14BLr/cz/jxDgIBhT59\nAlx6qY9evYIxF7t6yMGIJt26hcOeP/9swel0k5pa8XW6skPIYyaEFGmpqVLha5p8XlaYhQocNqBY\n05UtYohhhzCNbQuZgy3rmtrt4miJQYX//c/M6afH7m5XD2PC8KzpEJdL9iP98UdTtcnRIbKzBQ89\nFPYTHzrUsF9rWpqgRQspzlJSBDff7NHdDX1IqG3cqDJnjoVZsyysWGE6Vk802sg8uUgBduqpAV56\nKYm1a81s2GDiww9t/OlPqVx4oYNly/QZHk0UOncOcvLJ0rtWUKAca+Ste1RVCrNQ3TSzWQ7usrHj\n0Da9nYQGcc+6dSpffmlm167YnC+dOwcZP94HyF6ys2bJid3hiMnh6IqEFmvxmLNWVATTpiUxcmQq\nF1yQxhVXONix4/hf07BhAU46SV6ctmyJfH194+2tWgmefbaUPn38fPhhCb166cOrVp5ly0yMGpXG\n9dc7mDTJwZgxqSxdGnlBi1buQd++Qd5/v4Q+ffy0bx9k3DgfV1zh5Z57KiZbbNli5qKLUlm+PHaC\nTQ85GNEkPV227wEoLKx6Bapu7RDq6VnL/p71Qbe2aGSaqh02b1Y5//xUrrwylZtusrNvn9LotkhN\nheuv9/LQQ25+/NHMjh1yjuzbN7Y5JHoYE8atmc749Vczzz2XfOz5mjVmli830b599QKpRQvBs8+6\nOfdcc1QSuM8/38+IEf6I5P19+xR+/dXMr7+aaNdO49RTA1GtTl8d+fkKN9+cQklJ+I7Q71eYPt3a\nKHlsSUkykf7000vw+WRuuNkMnTr58PvhoYdS8PvDx+Z2Kzz7bBLvveeqUfjWQLJ5s8rWrSpduhy/\n12ifPkE6dAiQl2dC0+f9RfXooT1IDCkpkfPhkiVm/vQnX0yawDcl1q0zUVQkx9wvv1hYu9ZESspx\n3hQFbDYR0Yu6Z88AffrocDV3I5PQYi0ec9Z++qniV1JZmYyNG1V++cXM999b8PvhvPN8jBwZYP58\nZ4XoSEPE21WVCKF2+DDceWcKX38dVhopKYJ585z07x8+sTZsUNmxQyUjQ9CjR7DalZr1weWCvLyK\nnqq2bSMn+GjnHpR316elwfXX+xg6NMA331h4880k9u1TSE6GsWP9MYtkxTIHQ9Nki7DNm0107hyk\nb9+aXYTz8xVuvNHO2rVmmjfXmDOnhJ49q57EW7cWvPxyKffcY69y9bIeclH0gp5sceiQwquv2vjn\nP+WNa6dOWlRKElWGnuzQmPzxR+T8+fnnFl58sfFt0bGjxuTJHl58MZlevQK8/ror5gXY9TAmElqs\nRZONG1W++spCr15BBg4MkJHRMPutrGRS2cK3QsDChWauv94RIeLmz7fy7LMurr++cSa0zZtNEUIN\noLRU4b33rPTvL0N/a9aonH9+2jFv1733urn11upLa9SV5s0FY8f6+PTTsAFbttSO5j/EFrMZevbU\n6NnTy5//7MPjkeI3J0c0OeeJ1wuffmph8mQ7Pp9C9+7yBiMz8/jv3b9fYe1aOWUdOqRy773J/Oc/\nrmq7IZxySpB33y2p0f4N9EEwKLulhIQayBXoBtElJSXSxlu2mAkEGj810uGQKQxXXukjK0tEtdtJ\nPJHQl4q65qxt2KAybZqNt9+28uOP5gqV6IWAf/87icceS+Gyy1L517+SOHKkAQ4YGD3aj90eHpx/\n+YuXPn3CYbw//lC56ipHrYrSRiPebrcLVLXiSVTWbf7uu7aIsOSzzyazZk10znyHA/7xDzcvvOBi\nwgQPTzxRyqefOiNWxULscw9athS0by/IzY2tUIuVHZYuNXPLLVKoARw+rB57fDzK9ypfutTCxo3V\nG1FVoXPnqj13sR4PekIvttiwQeW++yLjb2W7UkQbvdihsenePdJL3bFjkKVLY2OLtDTo2lXTjVDT\nw5gwPGuVMHu2lRdeCN/VnXOOj4cectO9u5ww/P5Il7F01wa59FJ/hX3Vlr59g8yf7+SPP2TosE+f\nIM2ahf/udCp4vRUvbn36BDjzzMZLwuzSRePpp0u5//4UNE0eT7t2ASZMCBfolc3VI9m1K3oKJSdH\ncM01Pq65Jmr/wqAe7NqlMGmSPaIMzMknB2rc0SAzU9C8uRax2nnHDhNDhhj5LImC3w/Tp9si8jtH\njfJVEBLH48gR6X3NzNQi5k+DqunePUjbtkF275bXttGj6389M2g4EtqzVtectbI5VwBffWXlvPNS\n+e03OYitVjjrrMiBPHVqCrt3N8xy5z59glx8sZ8zz6x4ITvhhCBvvllCp05BmjfX6NPHz0svufjg\ngxI6dqz87jMa8fakJJgwwceiRcW8956T2bOdfP55SUQS8KhRFU/2sl7DWKCH3AM9EAs7rF1rYt++\nslOO4NZbPRU8ZlXRurXg9tsj+88UFNTvnDPGQxg92GL/foWPPw6nMlitgilTap464fHAkiUmxo93\nMGhQOjNn1r4Vlx7sEAtycgQzZ5Zw9tk+7rzTzWmnBZqsLcqjBzsYnrVKOPXUACNH+lm4MLys8vBh\nlWuusTNvXgkdOmgMG+bnySeTCK1YKShQycuLfg9Cux0uucTPmWf68XoVHA4Rsxo0Nhv06qVVWcpj\n+HA/48d7+fBDOWGefLKf/v2baBl/A7ZsiVRld93loXfv2p0vF17o4z//sbJ5s5y6unQxVggmEkeO\nKGVSJwT/+perxisBXS6YM8fK5MkphObl/fsT2h/R4PTqpTFjhguz2SjjpzcSeiTXNWetWTNxNPfJ\nG7F9zx4Ta9fKC06vXkFuuCHy74cPN14hwcxMWf+sJkItVvH21q0FTzxRypdfFvPZZ8VMn+4iNze2\nnjU95B7ogVjYoUWL0HcvmDjRw/XXe2tdGiA3V979P/hgKVOnuhk4sH7i3xgPYfRgi4wMGerOytL4\n4IMSxo711yi3U9Pg668tEUIN6hbK04MdokEwCF98Yebuu5P54gsL+/dXfr1KSgoLtUS1RW3Rgx0M\n7VwFubmCxx8v5cILfTz6aDLr15tQVUhNlRccux3uvNPDgQMK8+bZAEHr1vpIhtQTmZlyRZ6BwfDh\nft5/30lWlqB372Cdazh16iS4807v8V9oEHfk5gq++caJxSJo06bm8+maNSZuvtlOWaE2YoSfE080\n5p4Qhw4p3H23nQMHVKZPl5GO115z0bGjcd2KBxQhEveLWrhwoRgwYEC991NUBAcPytu73Fwtotls\nYSFs2GBCURT69w+QnFzFTgwMDAwMGhxNg0ceSeKll8KTb25ukP/+t6TalcBNDacTxo1zsHJlOL1n\n0CA/06e7DEeDjlixYgUjR46s4PZM6DBoQ5GeLpcRd+0aKdQAsrJgyJAggwcbQs3AwMCgsSksVJgz\nJ7yQoEuXAB9/XD+hJgQcPKiwfbvCrl0K3gRw5KamwsSJkR9k2TILH31ktFCJBxJarMVjb9BooId4\nu14wbCEx7CAx7BAmXm2RkiIYN85Hr14Bpk1zMXt2Cd261V2offDBjzz3XBIjRqRx0knpDBqUzqRJ\nKWzbFv+XyxEjApx5ZmQe37//ncSePZXnr8XrmGho9GAHI2fNwMDAwCDq+HxytWdWlmjQlYYpKfD3\nv7uZMkXmEteHdetUpkxJwekMh0m8Xpgzx0abNoLHH3fX82hjS3a24LnnXEycaGf5chkOLSxUOXJE\niXlLJ4PqMXLWDAwMDAyiysaNKs89l8TSpRaGDfNz991uOnfW37Xn1Vdt/O1vla98mT5drk5NBHbt\nUvj+ewuvv26je/cgTzzhLrNa2yCWVJWzZnjWDAwMDAyiRl6eyp/+5GDPHln2aNYsGxYLPPNMaaW9\nkGPJKacEsNsFLlf4WpmRofHYY26GD08MoQZy1e2ECT7GjvVhtVbek9pAX8R/EL4ajJw1iR7i7XrB\nsIXEsIPEsEOYaNli40b1mFAL8fnnFgoLG68uZU0ZMCDIM8/M58MPncyY4WTOHCfffutkwgQfGRmx\nPrqGJzW1eqHWkGNi2zaVRYvMLF9uwuVqsN02CnqYJwzPmoGBgYFB1PB4KoqyE08Mkp6uz7Bbbq5g\n6FCj00pDkZ+vMG+ehUcfTcHpVADBp5+WcPrpho1rg5GzZmBgYGAQNTZtUjn77LRjbaQsFsGcOU4G\nDzYK1iY6hw4pPPhg8rGWgyE++MDJ6NGGWKsMI2fNwMDAwKDROeEEjXnznHzzjQW/H8aM8de6J6xB\nfPL555YKQi07W+OEE4xixbXFyFlrAugh3q4XDFtIDDtIPvjgxyp7JDY1ojkm+vYNcs89HqZO9dCv\nXxCT6fjviRXGuRGmPrbYs0fhscciK8VbrYLp00vo0CG+xJoexoThWTMwMGiS5OcrPPJIMk8/7eDd\nd1306VP5BeTIESguVhFCoGmyur3FAlYrJCUJHA7qJT6EkMficsn8Lp9PNtNOSQGbTZCZKbBYjr8f\nAwM9oWkKbnf4RqhDhwCvvlrKyScbXtW6YOSsGRgYNEn27VMYOjSNw4dVMjM15sxxVirYdu1SWLfO\nxKefWvn8cysul4LVKsjIkD8tWmh07BikY0dBq1YaDodGSgrY7QKHQ5CWBg6HRmYmKOWceHv3Krz7\nro0ZM2wcOKBQthG52Sxo0ULQuXOQgQMD9O4dpHPnIO3aaQm5MtEgsdA0+PVXE1u2mGjVSqNnzyCt\nWiWu3mgoqspZM8SagUEc4XLB0qVmFi2ycO21Xjp1iq9wgp4IBODaa+3Mny97I3bvHmD27JIqm1oH\ng7B7t8rOnQq7dplYsMDCjz+ayc+vPpvEYhG0bavRpUuQfv2k4AoJveRkwcGDKjNn2li3zkRenorP\nV11YVtC9e5A77/QwYkTAKGRaQ5xOOHJEJSNDIzU11kdjYFA1TXKBwapVq2hosZafr3D4sILZDG3b\naljjoAfukiVLGDp0aKwPQxfEuy1++MHMFVc4AIXiYnjhBXedQnDxboeGwGyGE09cyPz5YwDYuNHM\n/PkWbrjBV+nrTSZo316jfXuAIJdf7uPgQYWCAoWDB1X27VNZssTMzz+b2bFDJeQl8/sVtm83sX27\niW++qbjftDSNjh01TjnFz5//rCGEbM2kKAoHDihs3Wrijz9M7N2rIITCxo1mJk60M21aKVddVfmx\n1oVEHBNCwOrVJh58MJmlS81cc42XRx5xV9uWKhHtUFcMW0j0YIeEFmsNicsF339vYerUZHbtMmE2\nCx55xM3VV3txOGJ9dHXD55M/8Xr8TY29exXuvddOSAQsWGCloMBDdrZ+vSsul6xgn50tdOkFOuGE\nIM2aaRQUSO/YY4+lMHx4gK5dj++xVFVo1UrQqpWgZ0/5+iuu8FFYGBJwCvn5Ktu3qyxfbmbDBhO7\ndqloWuRNc3GxyurVKqtXl5+Opc26dg1ywQU+unQJkpkpyMrSyMgQdO5seFWPx88/m7jkklS8Xmnz\n6dNt3HKLh06d9DcWDQyqwwiD1gAh4D//sXLHHSmUzSkBwQ8/FB+bqOMFtxuWLTMzbZqN/HyV88/3\nc9llPjp21Pfn2L9fYdcuFZMJunYN6i6csX69ysqVZhQFmjeXy9Pbt284my5ZYuLCC9OOPW/eXGPx\n4mJd54F89pmFa66x07dvkLfeculSYMyaZWHSpPAdyzPPuKr0rtUVIaCwUKG4WHrmCwvlz4YNJn77\nzczWrSb275eesxrsjTZtBIMGBRg2zE/bthrZ2RotWgiys4WuV1o2Jnl5KqNGpXLoUDhM3ayZxg8/\n6PucMWjaNMkwaEOxe7fCAw+UF2pyxVZSUmyOqT78/LOZSy+VoTSAdevMrFlj4pVXXKSnx/bYqmL1\napVrrrGzY4ccsldc4eXhh6tvPux2w6FD6rHk7mgSCMCDDybz3XfhuHhamsaUKR7GjPE3iGg7eDAy\nN6pDhyBpafq96BQVwZNPJgMKq1ebefHFJJ5+ulR358yZZwY46SQ/v/0ml1y+/76Vyy/3NajHWVGg\nWTNBs2aCjh3L/sWP3w+HD8vVoEVFCkeOqBw5orB3r8ratSbWrTOxY4fpaPV3AIW9exXmzrUyd254\nvGVmagweHODss/3HFiLk5AjUhC7QVDWrV5sihBrAvfd6DKFmEJck9GncUHXWhFAqrOJSFMG0aS7d\ne6MgskaMpsHbb9soLzy//NLCgQP6HA67dytcdZXjmFADmDnTxoYN1bsQPvzQyoABaVxyiYPffpOv\njVa9HLNZhsDKUlys8sADKYwd62DTpvrbtrwT/KqrfKSk1G1fjVE3qLRUIT8/PM7ee8/K5s36GmNL\nliwhO1vwz3+WkpUlz+W1a80cPNh4tdcsFsjOFnTsKOjXT2PEiADjxvmZNMnLq6+W8uWXTn7+uYhf\nfini22+LmDevmFmznPy//1fCv/7l4p573Fx8sY9u3TS2bjXx1FPJXHmlg3HjUnnyySR++81Eaenx\nj0MPtaQaktWrI+eHvn0DjBlzfI9potmhPhi2kOjBDoZnrQbk5mq8/34JU6emUFCg0LdvgL/+1cPJ\nJwfj7q5VVeXnKU/79hqZmfq849y5U2X37orCzOut+j0HDyo891wywaDCqlUWLrzQzNy5zigeJZx1\nlp8HHnDzxBORhSB37jRx5ZV25s4toW3butu4rBexWTONIUP03a7FZgOHQ1BYKJ8LIRPtq6pnFkt6\n9dL473+dXHmlg4MHVVRV9jDUA3a7LANyvOMJBKC0VNZqCwSkN09V5cKIuor6eCZcz0tw6aU+pk51\nk5urj+/UwKC2GDlrtaCoSE6EGRkCm+34r9crmzerXH21nc2bpVbPydF4550STjpJn8UK165VGTEi\nLSKfJycnyOefl1QZXiwqgnPOSWPTprDI69gxwPz5JVENg5SUwKpVJh55JJkVKyIrmc6fX8xpp9Xd\nxgUFMqy4erWJp592M2CAPr+vskyZkswbb4Tjnq+8UsLll/tjeETVs3u3woEDKr17B+NipbdB1ZSU\nwMaNJsxm6NQpSFra8d9jYBBrjDprBhHs36+wY4dKICC9avXx+EQbjwfmzrVw1112PB44+eQAzz1X\nSu/e1Xto3n7benT1ZJg5c5wMHx59j9SRI7Bli4mtW1Xy81U6dNA49dT618UqLQW/H93mFpbnt99M\nnH12KqGw+1tvlXDxxfoVawYGBgaxpCqxFmdBvNph9AaVVBZvb9VKcMopQYYMCepaqIFcxDF+vJ+f\nfipm6dJiPvyw5LhCDWDkyAA5OZHep8WLGyf3ICNDhmEuv9zPX//q5YIL/A1SuiIlpWGEWmPlYPTo\nEeShh9yADOWdcIK+vIF6yEXRC4YtJIYdwhi2kOjBDkbOmkFcoCjUuvlvhw4aH35YwoQJdvLy5FDP\nyNC3ME00kpPh+uu9nHZaAJuNuCtzYxAbPB658MIoQ2JgIDHCoAYJz86dCuvXm7BaYcCAgNFX0aBS\nCgpkeZSUFGjdOj66kyQau3Yp/PSThXfesdKiheDRR91xseLewKChMOqsGTRZ2rUTtGun75WTBrHn\n44+tPPCAnaQkwdChfm6+2Uv//oGo1+gzkGzerHLzzXZWrQpflm64wVOuLp2BQdPEyFlrAugh3q4X\nDFtIDDtIytqhe3fpwfF4FBYssHLppalcc42DtWvVCjXuEpFYjolt2xSuvjpSqFkssWlRZpwbYQxb\nSPRgh4QWawYGVRHUV567gQ4YODDAffe5I7b98IOFUaPS+PRTC76G7UBlcBRNg/fesx0rJRTi5ps9\ndOlihEANDMDIWWtyHDqksGePQmqqLNnRlBJ4vV5Z1fzbby0sXWoiJQVGjfJz8skBevTQKnSpMGh6\nFBTAf/5j4x//SI5ouK4ogvffL+Gcc4xwekOzY4fKkCFplJaG7T16tI8XXiildevEvT4ZxAf5+QpC\nyC4jjYGRs2bAmjUqkyfbWbPGTFKS4NVXXYwd23RqXv36q4kLL0yNKK771VdWkpMFs2c7OfVUw93W\n1GnWDG691cvgwQEeeCBc2FgIhRtvdLBoUbEum9HHM4oivWsAqiqYNMnDLbd4DaFmEHP271cYN86B\n06ly//1uzjrLT5s2sRmXCR0GNXLWJEuWLGHnToXLLktlzRqpzz0ehTvuSGHXrqbjTtq61YQQ31fY\n7nYrvPBC0rELRlNADzkYeqAyO1gsMGhQkFmzXHz0kZMJEzy0aqXRvLmGxxODg2wkYjUmcnM1Pv/c\nyfvvO/n++2IeeMATU6FmnBthmrotSktlcfN9+xZzxx12Jk2yx+yaaXjWmggbN5o4eDBSmxcVKXi9\n+umBGG2GDQvQp0+ANWsit5tMguuu88Zdn1eD6NK8ueCsswKMHBkgP9+D2QxZWU3jXGlMFIW4aJ1m\n0PRo2VJw+ukBFi+WzxcvtnDffSm8+GJpoy9+MXLWmghff23m8stTI7aNGePjjTdc2O1VvCkBKShQ\n2LxZZetWE4cPK7Rtq9GlS5Du3TUsluO/P9HYt09h9WoTSUkcK1zb1Cgthfx8FRBYrZCSIuKmnZeB\ngUF0WbTIzMUXOwi1zAN48kkXN93ki0qes5Gz1sTp3j1I164BtmyRX3m3bgEeftjdpIQaQLNmgtNO\nC9aroXqisHmzym23pfDrrxZMJsGPPxbTrVsTigUDLhdMmZLCrFlWNA3S0gQtWwpGjPAzdGiAdu2C\ntGunGYWUa0FREWzbZmLDBhMuF5x1VsAobGsQtwwaFODuuz08/3zysW1PPJHC6NGBWnfVqQ8JHfgx\nctYkS5YsoV07wccfl/Dxx05mz3Yyd25Jk7swg5GDEWLevB+5/XYp1ACCQQV/01lrcoyVK5dw000e\n2rcPIoRCUZHK5s0m3ngjiauvdjBiRBrnnpvKG29YWb3ahNcbfm9BgcK2bQqrVqksXWrixx9N/P67\nyt69SlzWZavvueH3w4oVJq66ysHIkancdpudKVNS4i7Pz5gjwhi2kP2Y+/RZyIQJ4ZPf6VTYvr1x\n5ZPhWWtCGJX8DUKsWGHil1/Ccd/0dI3MzDhUGA1A794an35awtKlZh5+OIW9e8tOwgobN5qZMsWM\nqgqmTnUzaFCQpUvNvP++ld271YgSHwBZWRp//7ubSy/14XA07meJFQcOKMyaZeUf/0gmGAzb4+9/\nd9OpU+LcFBYWQl6eCadTplAYK4ObBpmZgr/9zc2ppwb4+9+TOXJEISXFyFlrMIycNQODiuzcqXDW\nWWkcOhQWJf/3f6Xccou3mnc1DfbsUdiyxcSiRWZmzrRF2OikkwKcdlqAl16yUTZ/pTLGjPHx4osu\nmjWL8gHrgK1bVe6+O4XFiyOTPi+91MsTT7hp3jwxrjF//KFyxx0p/PST/JyZmRqffFJC//51T6lw\nuWDnTpVDhxSysgTdujXN3Nl4YtcuBZdLoUMHjaSkht+/kbNmYGAAwL59aoQIadMmyDnnNMEYaCXk\n5AhycgKMGBFg4kQve/eq7N6tsnSpmRAivXUAACAASURBVLQ0jR07TFQn1Pr18zN5spdTTgk0CaG2\na5fCTTfZWbmy7KVEcN99Hv7yF2/CCLWCAoXbbkth2bKwkjp8WGXePEudxdq6dSr/+lcSs2dbAQVV\nFSxY4KRfPyOfVs/k5gpiUUEhocXaqlWrMDxrMu9g6NChsT4MXWDYgqOV4hcBI0hKErz5pqvJJoBX\nNx5atxa0bh3kpJOCx4pHHzoEN93koaBA5fBhBbMZbDZBerogK0uQk6PF7UrS2p4bbje8+GJShFBr\n1UrjpZdcnHpqgJSUaBxl9KnMDjt3qhFCLUSzZrW/aAeDsHChmeuvd+ByhYW/psmiwHrCmC8lerBD\nQos1g/jC7ZYTVlNbodrYtG+v0bZtkE6d/DzyiJu+fY07+ZrSvDk0b64BTVPclmXvXpV33pG1Xtq0\n0bjtNg9jxvhp3z7xbJOcLDCbBYFAWFzl5AQZNar2Huk1a0xMmOCI2BfALbcYvVANqsbIWTPQDa+9\nZmPWLCv33uvh9NP9pKXF+ogSl8JChaQkEbfej8akuBgOHJBhY6tVLiBITT3Om+qJ1wuHDysEgxxb\nWWq1QosWQjc9bJ1OWL/ehKJAu3YarVol7rUkEJD1tqZOTaG0VGH0aB8TJ3o54YTaiatgEK6/3s5n\nn1kjto8f72XqVDft2iWuDQ1qhpGzZqBrAgH47DMLa9aYueoqBzfc4OH++z11CjMYHB+jEn/NKCxU\nmDgxhYULLYCCogh69Qpw2WV+BgwI0KWLVq9K5sEgHDyocOiQwoEDKvv3q6xda2LVKhPbt5soLZWC\nDeSKtEsv9fHXv3oavXp6ZaSmwimnNA2vrNks68UNGFBMIKDQvLmoU8eTQICI8i/Z2RpPP13KsGF+\nMjMb7ngNEg+jzloTIB5q5ZjNRIQU3noriZkzrQ1eoykebNEYGHaQHM8O6emCcePC41IIhbVrLTz4\nYArnnZfG2Wen8sEHVg4cqLm7SwjIy1NZuNDMXXclc/rpaQwfns5ll6UyebKdN99MYvlyC4cOqZSW\nypZwXi906xbk4ou9UbuBMcaEpDo7ZGVBdnbdhBqAzQbPPlvK3LnFfPVVMQsXFjN2rH6FWizHxLp1\nsmahHtDDuWF41gx0w+DBAeQqG3mCPvJIMoMGBZrM3buB/jCZ4OKLfeTmatxxRzJ5eZFT5s6dJm69\n1U6fPgFeftlFz57Vh8V27FCZNcvKyy8nUVJyvAuRoHfvIDfc4KVPnyCdOwebTN22RKZtW0Hbtsac\nVh2//64yZkwap5wS4LXXXAmzqrg+6CJnTVGUPKAImbXrF0IMUhQlE/gQaA/kAZcJIYqOvn4qcB0Q\nAG4XQnxd2X6NnLX4wu2Gf/wjiVdfDbf16NUrwNy5JUbYziDm7Nmj8PvvJt5918bXX1siir8C5OYG\n+eILJzk5lY/VQABmz7awdasJi0UupgFQFIHFIpPYW7QQNGum4XDI/qTZ2fG7utTAoK689pqNBx6Q\nCbVz5jgZPrzpFHPXe86aBowQQhwus20KsEAI8YyiKPcDU4EpiqL0AC4DTgTaAgsURekq9KA6mwBC\nhIsCtmypkZXVcPtOToYbb/SxcKGFzZvl0Pz9dzN5eSpZWcadqEFsCdVgGzYswO7dKrt2qeTlqeTn\nq/h80L9/sMqq5sXFsGyZmdmzrfz0k+Vo+ZTKEOTkCC65xMsVV/gMoWbQ5Cgthf/+N1wmZe5cS5MS\na1Whl5w1hYrHMhaYcfTxDGDc0ccXArOEEAEhRB6wBRhU2U6NnDVJQ8Xbi4rg7betDBuWxpAh6Zx3\nXirLlpkaZN8hOnTQePttFzk5YXG2b1/D5S3oIfdADxh2kNTFDsnJ0LWrxplnBrjuOh/33+/hwQc9\nnH9+1blHeXkqf/mLgwULrNUINZm0P3iwn5QUWTF/506Zt9MY/TWNMSFJRDvs36+waZPK1q0KJSVy\nkcOWLSrLl5vYs6fq8RgLW/h8cORIWA6sWGGmtLTRDyMCPYwJvXjWBPCNoihB4HUhxFtASyHEAQAh\nxH5FUbKPvjYH+LnMe/cc3WYQZVauNHPffeEiaJs2mbniCgcLFzobtLZSz54an33mZMYMG/PnW2nT\nxnCaGsQ3vXtrfPNNMatXm1mwwMIvv5gpKpIiTNNks2i7XfDoo6X8+99JfPyxrGqvKAKHA7p3l10V\nevcOkpOj0bp1YpfKMGgYduxQmDnTxvTpsnWaqgr69g1www0+XnrJxoYNZtq00fjvf51066aPGm+q\nKnNFQxw4oOJ0Nn4vTr2hF7E2RAixT1GUFsDXiqJsomI/h1p/U3/88QeTJk2iXbt2AKSnp9O7d+9j\nlYhDatl4XrPnn3/+I5AEjDhq4UUUFsL+/QNo375h/1/HjoIzz1zAoEHQv3/Dfp4QsbZnLJ8PHTpU\nV8cTy+chovn/FAUKCxeTmwtvvTWUggKFJUuWoGkwcODpmEyCFSuWkJwsePzxEdx2m519+75HCHA6\nR7B8uYXly388eqQjaNlSY+DABQwZ4ueyy4aQlSXqfbyhbbH+PoznDfP822+X8PrrNr75ZhSSRWga\nrFw5gr/+1cw11/yPDRuS2Lt3BHv3qhw8uLjS/YVorOMfPHgoOTlBNm36AYBgcBhCJO58GXq8c+dO\nAAYOHMjIkSMpjy4WGPz/9s48volqe+Dfm61p07RlkVXKLjsiAoqAijwRHzsiiMsDFXfwqc8FV9w3\nXHB5iILyBH34QBHlhyCKIDsIsm+ySaFsUqBt0qbNcn9/TCANbaFbmklyv59PP00mM8mdM2funHvu\nuecURAgxFnAAI9Hi2I4KIWoBi6SULYQQYwAppXzDv/98YKyUcvXZ36UWGFQsS5aYGDAgOBtotWo+\nFi7MUskcFYoK5PRihg8+sLJihYlz1SNt3tzDc8+56NzZrdsYN4cDnE4tEbPdTplTXyhKjtMJN9+c\nyNKlRVWGl7z4Yi7PPZeAxSJZvDiL5s314VkDePNNK6+/ri00a9jQy08/ZVVofLSeKW6BQdhvGSFE\nghAi0f/aBvQENgPfAyP8uw0HvvO//h64SQhhEUI0BJoAa4r6bhWzplFR8+2XXOJh/HgnVatqN3Wb\nNh6mT3dElKGmh9gDPaDkoKFXOdStK7nuOu3+Wro0i//+N5sHH8yleXMvRmPw/bZjhxaO8J//xFGe\nsXdFyuLwYcGSJSamTrUwcqSN66+3c+WVSVx3XRIjRtj45RcTubkV9nMVil51orTYbPD66zl06uSm\n4MSU3S554gkX8+aZAcnEic5ip0DDJYurrgrkNuzfPz/shpoedMIU7gYANYFvhRASrT1fSikXCCHW\nAjOEEHcA+9FWgCKl3CaEmAFsA9zA/bG0EtTt1gJDd+40cuqUoHZtH5de6q2UjOZOp/Z7r73mxOnU\nMqtv2WIgK0tQr56PRo18QbEGisjG44GtW40cOiSoVUvSurUXc1GDdEXIsNu1GM5WrXz06uXhkUdc\nHD9u4K+/BNnZArdbu07x8drinHCXojp+XPDrryaefz6B9PTCvoC//oI//jCyZ4+Bb791EB8fM113\nWGjRwsdXXzk4eNDAiRNalYytW4188UUccXGSmTMdXHGFR3eezubNvQwZksd331no27f09VejEd1N\ng1Yk0TYNunevYOrUOCZMsAYVAZ45M5sePUK/tHnWLDMjRxadldNikfzrXy4GD86jYcPo1alYYsUK\nI/372/F6BQaD5NVXc7jllnxstvMfq4g9vF4YN87Km2/GF7uPwSC56aZ8HnzQpZuA9ljC4YC9ew34\nfFCvntR1Ob9jxwRHjwpatw7/IKQy0XueNcV52LXLwKBBiaSnB7uujEZZadmd27f30rmzm5UrC7tX\n8vMFr70Wz9dfm/n6awf16um3E1CcHylhwgTrmcSvPp9gzJgE2rTx0rmzynmnKIzRCJ06eWjZ0sOe\nPUby8gRms6ROHR8dOni45hoPrVp5adbMS1xcuFsbmyQmQtu2kWMk5+QI5s0z4XIJGjTw0a6dV3de\nwMoiqk87WmLWnE6t9NLZhhrA++87adny3A/Pippvb9DAx+efO5k+PZtu3dzExRU2yKSEvDz9DoP0\nEHsQSqSEEye0XEXnoiRyKPxAFaxeHV3ju2jXh9JQEbK45hoPP/yQzZo1maxenclvv2n1Lz/5JIdh\nw/Jp21b/hprSiQDnksXevQbmzDHzwAMJ3HKLjfffj2P//vL3/Xv3Gpg61ULPnnauvz6JW2+1M3Jk\nInfcYSMjIzzPFj3oRHT1vBGK1wtCFL9C6sQJweLFwd6sevW8jB+fQ6dOnkqNI6peXQt8vuoqB4cP\nGzh+XJCZqd1AdrskNdVH7drKqxYOPB748MM4pk6NIzXVx3XXubn8cg/NmnlJSCjddwkBw4bl8e23\nlqDtVmsFNlgRlSQlQVKSpAzZlhQRwKlTMGeOhWefjScrK/DQmjfPQsOGPurXL1uMmdMJCxaYeeSR\nBDIzz34YSl54IbdSYrP1iopZCyMeD6xZY2TiRCs1a/p48EFXkdOHHg+sXm1k4UIzVapIWrb00rKl\nVxlFZSQnBzZtMtK0qe9MzIbXCxkZgsREWWrDRk98+GEczz1X8AQkAwbk8+CDebRqVboFApmZ8MUX\ncYwdG4/PJ6hRw8fs2dm6WuKvUCgqD6dTC4947bXCcYk2m2T+/CxatSp9/+BwwOTJcbz4Yjxnp6mJ\ni5N89pmDq6/2EF98OGTUUFzMmjLWwsi6dUauv95+ZrHAY4/l8uSTlVBXJsY5ckRw5ZVJdOjgYdy4\nHJKSJFOmxPHJJ1aaNvXywgs5ERXXUZD0dC22bO7cYI+Y0SgZM8bFP/6RV6rRqdsNO3cayMwU1K0r\nadAgMuWiUCjKz8aNBrp3T+Jsgyo5WVt1etllZYtnXbnSSO/eSUHbrFbJqFEuBg7Mp3nz2FlkoNs8\na6FEzzFrOTnw6qvBqzrnzzeHpAaaHubb9cKyZcuw2yX16nmZP9/CK6/E89tvWqqBQ4cM/PqrmQED\n7OzcGZm3Rt26kjffzOH++11o2XA0vF7BK6/E8+KL8Rw9KkqsE2YztG7to0sXb1QaaureCKBkoaHk\nEOBsWdhs0LRpwCBLTfUybpyTn3/OKrOhBlC1qmT4cBfdu+fzwAO5fP65g6VLM3niCRctWoTfUNOD\nTqiYtTCRkWEolFk6MVFiUlck5NhscMst+axfb+arr+K49NLgtCenThlYvNhMs2Z5ldquXbsMfPJJ\nHEOG5NOhg7fMHVTt2pJnnsmlf/98XnklniVLAnr25ZdxdOzooVGjCmq0QqGIGZo08fH99w7++ktg\nMECNGhWTjaBZMx/vvlvyLMm5udoCqFhaGRrVp9quXbtwN6FU3HRTPhbL+fcrLQVr/8U6p2XRrl1g\nFHjqVGGraM2ayreap0+38OmnVvr1s7NpU/myC1ut0LGjl2nTHMybl8UTT+TSrp2bmjV9ZGQIunRR\nOgHq3iiIkoWGkkOAomRRs6akdWsfLVv6Ki1t1Gny8mDmTDO9e9t5+ul49uypHBOma9eupKUJVq82\nsn27gRMnyvY9OTla/rjzrdYviqg21vTMBRf4uOGGwBVr29ZDjx4qU3Nl0bixl27dNHkfOWKgdetg\n71r79qFPMlyQ/HxYtkzzgOXlCZ5/Ph6Ho/zfa7fDZZd5eeIJF3PmOFi8OIvRo/PCPq2gqFy8KjWe\nIgrYssXIvffa2LDBxMcfW7njDhtpaefvzI4dExw5UvZOT0p48cV4rr8+iS5dtLJpM2eaOXiwZN95\n6JDgyy8t9Otnp3v3JB54IIHdu0tnfkW1sabnmDWrFZ58Mpc333QyYYKDzz93UqdOaEYpephvDwVO\npzZ16C6FjXtaFsnJMGaM5nafMiWOgQPd9O+fT1KSj+uuy6/0EiduN7gKrC1ZssREWlrF3p42mzYq\nNpnOrxMuF2zaZGD5ciPr1hk5fjw6rbtovTdA80KsWWPkqafi6dPHzmOPxbNqlbHYUX00y6I0KDkE\n0Jssjh4VSBnoizZvNrFqVfGzIIcOCaZPt9CjRxK9etnLbLAtX76MgQNPPxMEe/YYueeeRP7+dzu/\n/mrC6Sz+2LQ0wb332hg92sbvv5s4fNjAN9/E8fnnpUs4GNXGmp5Yv97I3Lkmtm83nCm2nJoqGTky\nn5tuclO/fvQFb4cSpxPef99K585J/PBD2RLNtW7tpVevfHw+wUsvWTEYfPz0UzaTJjlJTa3c62Gz\nQefOAW+elIL9+8N3ey5ebKJ79yT69k3i2muTuPZaO9OnW0o8klSEnwULzFx/vZ2JE62sXm3i00+t\n9OljZ8MGVcA30pBSSxa7YoWRffti97GdnFzYoXF27Pdpdu82cPPNiTzwgI30dANeryhX7epu3dw8\n/nhwXN3Bg0YGDkxkwgQrmZlFHzdvnuXMrElBqlUr3TMmqq+6XmLWDh4UDB6cyG232bnmGs24yC15\nLGW5icYYjG3bjIwbZ8XnEzz8cAIHDpTMiCgoC7sdHn/chckkAcG331rZutVIYtHlT0POVVcFe/MO\nHAjdQ/V8OuHzBY9g9+838sADNgYMSOSPP6Kn24jGewO0nIFjx8YHXUPQrusffxStV9Eqi9KiNzkc\nOCB46y0rV12VRJ8+SXz5ZQgCm4tBb7Jo2tRHy5bBISoJCYUNuF27DAwdamPTpoDX7ZFHyp5Ut2vX\nriQlwd13u3jvPSdWa8Hv0UotzphhKXKWZ/PmwvdbvXpeevcu3exN9PS6OiYnR3DypCbqvDzBbbfZ\nWLas8gPYd+824KncUKyQsXKlidO5fk6dMvDnn2VT5datvUG57T74II6srIpoYelp2tQX1PEEdwiV\nS4cOHoYOLbwadu9eEyNG2Dh8uGTGsZSa3v3yi4l580ysXFkxU6pSqjiscxEXJ2nevLCA4uMlbdoo\nwZWHrCzYts1Qolip8rJzp4HbbkvktdficTq132vYMHZnYWrUkEye7KRRI+1BFh8vuemm4Hn9tDTB\nPffY2Lcv8IytV8/LNdeU/+FXtSrcems+CxZkMWJEcHqkp55KKNLreffdLlJTA+296y4X33zjoGlT\n5Vk7g15i1i64wHdWALvgvvtKFhhZESxbtozMTBgxwsby5dGRG2T79uDRistVMlmeHYNhMsENN+TR\nqpV2fdavN5XZ8CsvTZv6ePfdQPBDnTqh65TPF4tSo4Zk7FgtptJuDzYad+wwceTI+WV06hRMnmyh\ne/ckBg+2c8stdnr3TuKOO2zlmk5du9bITTfZuPlmG8uXG0sVs3g2eovJqSgSE+Gll3K5/XYXNWr4\nSE72MXhwHv/3f9m0bVu0sRatsigtxcnB54ONG43cfbeNrl2TeP/90NZe27nTwODBiUHeoQsu8NGl\nS+WNuPWoE82b+5gzx8H8+Vrd2YIr+71e+N//4tiwISAzu10ybZqjXKFGBeUghJZ78rXXcvnll2wm\nT3Zw990unnkml/j4wgPstm19/Pijg5UrM1m1KpNXXsmlSZPStyU6ntw6p0oVePJJF7fcEphfO3HC\nwPbtxjMWd6jJyjKwY4e2kmbBgqwiy1pFEmcbZ+WJRUhNlXzyiZO+fe2cOGFg925jsRUM/vzTwPz5\nJnr08JR6ZFQSevVy89lnDnbuNHLJJeH1gNSqpcVUdu/uYft2I2vWGMnNFXTt6qFBg/O3beVKE088\nYSu0fdkyMzt3GrnwwtLr/u7dBm68MfFM7cCFC818/bVWikYRTOPGPt54I5fHH3fh9UK1alL3RdT1\nis+nLfq56aZE8vO1viclJXR96LFjmncoPT3QsZnNkkmTnFGZnNrn02pg5+ZqibhTUmRQHWKnExIS\nOLOKvXZtSe3ahfugXbsMvPNO4MCUFB8zZjhCUpEmLg4uvtjLxRd7GTTo3CPGmjUlNWuWT1+i2ljT\nS8waQOfObm6/3cWUKQFFSk+vvBwxBw9KLBY4etTAunUm6tWL7DQhZ3udStpxFheD0aKFdlMPGZLI\nsWNFXxeHA8aOtTJnThyDBuXx0Uc5paq1WRLsdhgwwA2E9vqUJhalcWMfjRv76NOndG06eLBoOVap\n4ivzAyc93RBU5Pn04pBLL3Vgt5f++/QWk1PRmEyU+CFRUBZOp7byLjlZUq1aqFqnT4rSiVWrjEGG\nGkh69QrdPfrbb6Ygj1p8vOSLLxx07Vq5g5LKuD/27BFMnGhlwQIzhw8bSEqSNGrkpWdPD+3be6he\n3cfYsfE884yLSy899yDx4EEDeXnaNbr8cjdvvJFDmzblN9T00E9E9TSonkhJ0bxrL7yQg8UiEULS\nrFnleU7i4jhTtPzVV60cOxbZq/r+9rdAR9mxo5tGjcovy/btvfz4YxZXXll0J7x1q5E5c7Tg3p9+\nsnD0aOlleDreZc8eUa7pu0jguus89OuXD2h6ZzBIhgzJ4/vvs2ncuGwd6NlTsgC7dpnIyopsfdYT\nW7YYeeCBBDp0SOadd+KpqPLRDgf8/LOJRYtMEZUKZscOLW4sYKhpcUtFxQRWFD/9FDDUmjb1MHdu\nNt27e6IyY/++fUY+/dTKgQNGPB7BiRMG1q418+qr8QwebOeuu2xcf72nRHGy9ev7eOcdJ7NmZfPF\nF44KMdT0QhRe+gB6iVk7TfXqkvvvz2P58iyWL8+iU6fKMdaWLVtGYqKkRg1NcXfvNpU6IZ/eaNPG\ny0035VG9uo/XX88lJaVkx50vBqNxY0nLlkXf4L//HljUkJ0tSm0gaJ65eLp2TeKKK5J5+ul4du0K\nz3WojFiU1FQf//63kzVrsli0KJPVqzMZPz6HVq3K3oE2bqylWylIhw6eMk9J6TEmJ1wsW7aMTZsM\n9OmTyPffxwGCRYvMZGdXzPcfPGhgyBA7N9xgZ/hwG1u36rMPKqgTeXnw0UfWMwvEAFq18vDoo7kh\nXTV+8835vPxyDt98k82cOY6guKzKpDLuj/btPbz2mrPIeC/QBmPPPRdPfPz5v6tpUx8jRuRz9dUe\nqlatuDbqoZ+I6mlQPWI0UmavQnmIj9dSQ6xfr13yzZtNXHFF5K4Kq15d8uqrOTz9tKBu3dDH33m9\nWu6xgsTFle53T50SzJihPQTdbpg82cqcORa+/Tab5s2jZwRYEJuNMgXTFkdyMrzxRg4NGviYNctC\nkyZeXn01B1vh0DhFKTl6VDB6tI2srIBh0r27m6Skivn+uDhthbPLJVi50swNN9iZPVvfur9njyEo\nVUbTph4++8xJampo+5xOnbyVNpgPN1Wrwl135XPVVR7WrTMxZ46ZtWtNnDghAEFCguS669xhXR2v\nB4SsKB+3Dlm4cKFs3759uJuhG374wcStt2qBPa1aefi//8smOTnMjYoQMjOhV68kdu7UAn5TUnws\nX55F7dolv39ycmD4cBsLFwbnSerWzc1//uOgSpWyte3AAYHdLkvsXYwGPB4tl5jNJsOWFy/aeOed\nOF5+OeHMeyEkP/yQzWWXVYzR4PHAo4/GM3VqIG63dWsP06c7KmXAVRZ+/dXIwIFJgGTo0Hz+9S9X\nhQ4+FIVxu+HwYcHixSaOHDGSkyP4/Xcj48fn0KhR9Mv+999/p0ePHoWmbfTph1aEhLp1A4q+dauR\nw4eLv/wHDwqWLDGxY4dSEYD8fIHDEbh/LrnEU+oixgkJ8NxzuYWSOC5dai5zaan9+w0MGpTIW29Z\nK2y6KhI4HTivDLWK4ehRwaefBqeiGDPGVaHTbyYTjByZH6T/W7aYmDHDUmFxcRVNw4Y+pkxxMHdu\nNuPG5ShDrRIwm8HhEDz8sI3XX4/n/fetNGrkrfSqMnojqp/EeotZCxen59tr15YFDDZR7GrU3bsN\nDByYyIABdnr2TGLdusovT3PwoGD3bkOFJ6gta+xBSorkoosCK7GGDcsv00rQNm18zJ6dTf36ge8y\nm4OXqZcGbbGCiQkTrGzbVvLrVBo5pKUJ5s838cUXFubMMYctD10o0EMsih5wOASHD/965v3gwXkM\nH55X4ak+Wrf28tFHTk4vOgF47z1riSuQVAYFdSI1VdK/v5vOnb0xOTAI1/3xxx/GoOobw4blYwpj\n0JYe+gkVsxZD1KghGTYsj7fe0iI1d+0y0qNH8FJwnw+++srCnj2aajgcgvfeszJlirNcucxKitMJ\nP/1k5uGHE8jMFPTvn89rr+VSq1Z4h95mMwwc6GbRIgv16nm5/PKyL6Hv0MHLvHkOtm0zkpEhaNTI\nV+acbdqiBwDB//2fhcsuq9g6Zhs3aivhDh4MXPwWLTz8978O6tfXqTtEUYi8PFi+3MTs2WaOHjVg\nMGgr5zp08FC7to9atSQ9e7o5ftzNqFF5dO1aes9xSfnb39yMG5fDY48lAIKsLAMHDhhITY2NGC3F\n+TkdbgJamEgoV95GCipmLcZYtMjEDTdocWu9e+czbZoz6PO//hJcc01SkNctNdXLwoXZZ1J/hJKf\nfzYxZEgip1ddAnz1VTY9e4Y/6enhw4IffjDTpYtHN0HRd9xhY/ZsLQaueXMv8+ZlVVgcYmYm9O+f\nyKZNhV2Is2dnc+WV4b8mipLhdMIrr1iZOLHoJXUJCZL77nNx7bX5NG7sC3lutZwc2LDByLPPxrN3\nr5E5c7Jp3Vof95Qi/NxzTwIzZ8aRmCj54YesmNINFbOmAKBZM++ZhLKHDhlwuQrv4zvrvqhRw1dk\nsdyKxumEd9+1UtBQAy1Nhh6oXVty5535ujHUAOz2QFv27TNUaL6x7GzB7t2Fne82m6RWLf3IQHF+\nbDZ46KE8Jk50nEnhU5CcHMHbb8fTq1cy/frZWbXKGNI4soQEuOIKL99842D58qxypXNRRB+tW3tJ\nSJB8/rkjpgy1cxHVxpqKWdMoON9ep47kqae0qbKMDEFOTvDDvWpVSc+ewXmsRo/OK1GOm/LidAr2\n7Tt7rlVW6AogPcQeVCQXXhh4oublCfLzz7FzAUoih1q1JG+84QwqVly3rpeZM7O56KLo6ECjTR/O\nRY0akiFD3Pz0UxbTpjkYMiTvETZbfwAAIABJREFUrCTDiwHYvt3EgAF2Nm8OfdxDSorWJwl9jMeA\n2NKJ8xEuWfTrl88vv2TppoycHnRCxazFIJ07e6ha1YfXKwp50YxGuP/+PPbuNbJ+vYmHH87liisq\nJ9V+tWqSm2/O4513TluGkpdeyqVFCxWvUBxnV27wegUFg7fLg1bk3k3btlkcO2YgLk7SsKFPt2kW\nFCWjXj1JvXpuevVyc/hwLkeOGDh2TLBiRS4NGjgxm7WExrVrR4dBrog8tHhY1c8URMWsxSiLF2ur\n+yZOzClylc2pU9rigjp1ZKWWODl0SLBypYk//zRy+eVuLrnES0LC+Y+LVdauNdKzpx0QpKT4WLYs\nizp1oveeVijKgpRazOnx4wKTSStbVrOmVi9ZodATxcWsKc9ajNK1q4emTb3FLodOSSl5cfSKpE4d\nyQ03hL6QebTQtKmXzp09rFxp5uqrPdSooQw1haIgJ07A9OlxvPWWlcxMbeQZHy/p0MHD/fe76Nix\nYksTKRShQMWsxQBFzbebTMTkdJYeYg8qkuRkeP31HFq08DJ6tKvEuYgiRQ4+n5b4d8sWQ0hycUWK\nHCqDSJSFy6Xpx8GDotgFEQcPGnj22fgzhhpAbq5g6VIzw4bZee65BE6dCuwfiXIIFUoWGnqQQ1Qb\nawpFLNCmjY85c7LCVuw5VGRkwKRJFrp2TeLKK5O56qokli5VkwEKLfXH8uVGbropkcsuS6Jr1yTW\nri16QUTjxj7GjHFRXAzUf/9rKXMFEYWislAxawqFQpd88omFMWOCK7RXr+5jyZKssCdJVoSPY8cE\nn30Wx5tvBqf5+fLLbK6/vujVgzk5sGWLkVmzLPz8s5mDBw14vdCihZfHHnNxzTVubLYiD1UoKhUV\ns6ZQKCKGEydg4sTCNbg8HnRbR1IRenJyYPx4ayHduOAC3znzHyYkQKdOXjp1yuXkyVwcDoHXK6he\n3ReTZaQUkUdU+35VzJqGHubb9YKShYZe5OByaQ/gszGboWbNwg/fMWMqtvSYXuSgByJBFps2GZk4\nMbhgqdUqmTzZScOGJUs1UqWKlr6kQYOiDbVIkENloWShURo5nDoFR49WfHxtVBtrCoVCn2RkwP/+\nZ+aGGxL5+9/tTJpkIS0t0MHZ7fDuuzl07uzGYpGkpnqZONHB0KH5ukqgqqhc/vzTQMGpzyZNPMyZ\nk03XrvpInqqoWHJz4fffjaxebSyUE1SP5OfDM88k0L17Eh9/HEd6esV1VipmTaFQVDr/+Y+FRx4J\nDhLq2TOfiROdpKQEtjkccPKkICGBSqlNq9A327cbmDBBmwL929/cdOjgiclV7bHAoUOCTz+N4913\nrVx8sZe5c7N1n3PzxAno2TOJvXu1xS7t2nmYMsXhT/JbMlTMmkKhEzx+J0BJ02xEGy4XfPFFXKHt\nCxZYSEvLJSUlMIROTITERPUwVmi0aOHjgw+KmDdXRBVpaQbuvjuBNWvMgFZ1pzJKHpaXqlW1Ulnj\nx2uN3bDBxCOP2JgwwUnNmuXrx6J6GlTFrGmouIMA4ZbF5s1GBg5M5L77Eli61ITDEZ52hFMOViv0\n7l24iGlioqz0kXO49UFPKFlonEsODkfRMZbRSjh04uhRwdNPx58x1EAyYEDx4Q/5+eANcdai0sih\nd283RmPAMFu0yMw331jODNLLSlQbawpFReFwaJ1IeTuFHTsMLF9u5ptv4ujf386ECVZOnKiYNkYS\nN96Yz+23uzAYtE4tJcXHlCkOmjSJgMAURUyyYoWRPn0S6dcvkZkzzRw+XDnBkzk5sHWrgQ0bjGRl\nVcpPhg2XC6ZNi2Pu3EAdsL5982ne3Mtffwk2bQrEruXnw5IlJoYOtfHee3E4nWFq9Fm0bevlpZdy\ng7a9/HI8+/eXT19UzJpCcR7WrTPy5JPxHDhgpGfPfEaPziuzUbFkiYkBA+xB255/Poe77sqLCDd/\nRZKXB/v2GcjNFVxwgY8LL4zevkgR+QwfbmPOnIAR0aGDm48+yqFx49ANMDIz4cMPrbz9tpZT7qGH\ncnnoIRdJSSH7ybCydKmRAQPsSKkZNlWr+pg3L5vUVB+vv25lwgQrc+dm06GD96x9JQsWaNv1QEaG\n4KWX4pk6NRDu8dVX2fTseX73WnExa8qzplCcgz17DAwaZGftWjNHjxqYNs3K/fcnlHlpdrNmXpo3\nD75hn38+ng0bis6+Hs3ExUHz5j4uucSrDDWFLsnJ4Ywn5+KLgw2BtWvNjBmTwPHjofOw/fabibff\njuf0Ctjx4+PZuTM6+4qMDMGYMQlnDDWjUTJlipOmTX3s22fggw+suN2C6dMtHDkC99yTeGZfEDgc\n+lkmXq2aZMyYXF54IQeTSevbyjsrE9XGWmli1k6cQDdu1IpGxaIEKK0sjhwRZGcHdwJr15rZu7ds\nt07NmpJJk5wkJxccjQu++85S7DGhoCJ0wumENWuMzJhhZsYMM7//biQ39/zH6Ql1bwRQsoDjxwWf\nfLKCDz6I4x//sPH3v9t55BFtCqtPn3yqVAn2oi1caGbTptAZT7NmFe4XMjMrzyipTJ3Yvt3I9u2n\nV11JPv7YSefO2sB2924jPp923rNnW9i/38iRI8F9cMGFSRVNWeRQq5bk/vvzWLQoi2+/zeaSS8pn\nrcXoerRgjh/XAhp79XIzcKA73M1R6IiUFInJJPF4gjtIs7mYA0pAq1Y+vvsum1tuSSQ9Xevod+/W\nyt8YI2jQPGeOmfvvtxHIeyX5179cPPCAKyj9hkKhdw4cEKxda+KVV6zs3WsDAitdMjPh8cehfn0f\nX33lYOjQRE6dChgKofSsFeWNqVo1Or3Qp43exETJpEkOunXznFkxf+hQQMYnTxoKDaAvushDvXr6\nk4vRqPX3UH5DMqqNtXbt2pVovy1bjMycGce6dSauuspN1aohblgl07Vr15D/Rnq64LffTPz2m4mh\nQ/No21afgeKllUWzZj7efjuHf/4zgdNGyaBBeTRqVL5RUtu2PubOzWbjRhNbtxq59lp3pRpq5dWJ\njAzBG28Epmc0BG+/Hc/VV7vp0kUfsSPnozLujUghFmXh8cDatUZGjkzk0KHTBtjVZz5v1szD5MlO\n6tTRDIGOHb3Mm5fN999b+OorCzVr+mjdOnS63q+fm5kzA3FPI0a4aNq08u6tytSJli29PPdcDj17\numnZMvj5cexYsBct2IiVvPZabkjzMOrh3ohqY60k+Hzw3Xeam2TvXgMZGQaqVtWnoaFXNm40Mnx4\nAmlpmjq1aePRrbFWWkwmbeViixZe0tIMJCdLLrnEUyEGfWqqJDXVTd++kefNTU6WXHmlm2nTCluY\nWVn6iR1RKM7Fjz+aGDEiEa83WGctFsno0S7+8Y+8Qh6bZs18PPaYizvvdGG1EtJ0M1de6WbyZAez\nZ1vo3t1Nr15u7PbzHxeJXH21h6uvLjoAP3ggK7ngAh9Go0RKGDcuh8sv128Fi5Mn4cQJgckkqFat\n7LVoYz5m7fBhwaxZp0cughMnIvtB43RqWb63bTOcicELZdzBhg1GBgxIPGOoAboOFi+LLKxW6NDB\ny6BBbnr0qBhDLdyUVydMJnjoIRd9++YBget93XX5tGsXGV41UHFaBYk1Wezfb+D++4MNtXr1vNx1\n1zwWL85izBjXOafWqlYNraEGWtm1QYPcTJ3q5Pbb86ldu3L7Vr3oREpK4LyrVJHUrCn55Zcsli7N\n4tZb80O+kr4scsjKgrlzzfTta6djx2Q6dkzi5psT2bKlbGZXzHvWMjODA8gjOZPJnj0GXnwxnjlz\nzAgBd92Vx5gxoYv43r3bwG232cjMDChfy5YeWrSInId1pHDqFKxda2LDBhPZ2dC+vZemTb00buwj\nrnAxgEqhYUPJhx/m8NBDeZw8KUhKkjRu7KVKlfC0R6EoDdWq+fjvfx0cPSqw2SRVq0rq1/fxxx8e\nmjevnJkBl0vLF1aZqThOniTi7tGLLgo8U/r2zadWLan7MmM//2xm5MiAG83jgWXLzIwYYWPePAcX\nXFC69ke1sVaSmLWzAxUjJcA7I0Nw+LCgdm0f1appQa733ZfA2rXalK6U8MknVoYOzQ/JfLvHA1Om\nxJ0JkAew2ST//KeLnTsFbdvKMrt7Q4keYg/KwvLlZm67LVigRqNk5Mg8Ro1ylbrjqig52O2Ue5VT\nOIlUfQgFsSaLxETo0qXw9FmNGpUjhw0bjLz+upUDB4y8+GIOPXqEdirv1Cn45hsLkyZZmTjRWSIP\nuF50onFjH0lJPrKyDAwaVLnxvVB6OXi9Wv3j4iiuGsO5iOpp0JLgcgW/j4/Xt7UOmgdt+HAbV16Z\nzOzZmkJs3Wo8Y6gVxBeiAeKffxr47LOAS8dqlTz7bC5PP51Anz5JPPpoAunpkT2lrCeSkyUFpxsB\nvF7Bxx9bGTMmnszM8LRLoVCUng0bjPTrZ2fBAgvbtxu5445E9u8P3eP4dGWAxx6z8ccfRhYtiiw/\nTYMGPv73PweTJzto316/8WmnMRph1Ki8oLJToCX5nTAhh+rVS29nRLWxVpKYtYIpGYxGqfvgzYMH\nBcOG2VixQjPMvv3Wgs+nudLPpmtXN40aeUMSd5CVJcjL02TXuLGHl17KZfx4K8ePGwDBjBlxPPpo\n2ZPHhgq9xGCUlksu8fDhh84z5ZkKMneupcBKtpIRqXKoaJQcAihZaIRaDk4nvPRSfFAS1+xsEdKa\nozt2GHn++UBg1+m++3zoSScuu0yLGw7HjE1Z5NC9u4eff85m8mQH77zjZPr0bBYuzKZTp7LNRESW\neR0CCrpTU1N92O36XsW4YIGZ3bsDl61GDYnBAE2b+mjQwMOff2qfXXqpm7feyglZbEL9+l6+/NKB\n2Sxp1MjLhg3GQgbjjz9a2Lw5j5o19T8S0js2G9x4o5tWrbJZutTEjBkW9u0zUquWzz8Nqm+9Veib\n7OzIjteNJI4cESxZEvzorVvXF7LUE1Jq05+BbP9Qr57qL0KN2axVvTi78kVZifnaoBs3GujePRmA\nZ5/N4eGH8yqjaWXiwAHBNdckkZER8KJ8/LGDG290n/l8zx4jcXGSiy4K3c1fHGlpBjZ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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "t = mcmc.trace(\"halo_position\")[:].reshape(20000, 2)\n", + "\n", + "fig = draw_sky(data)\n", + "plt.title(\"Galaxy positions and ellipcities of sky %d.\" % n_sky)\n", + "plt.xlabel(\"x-position\")\n", + "plt.ylabel(\"y-position\")\n", + "plt.scatter(t[:, 0], t[:, 1], alpha=0.015, c=\"r\")\n", + "plt.xlim(0, 4200)\n", + "plt.ylim(0, 4200);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The most probable position reveals itself like a lethal wound.\n", + "\n", + "Associated with each sky is another data point, located in `./data/Training_halos.csv` that holds the locations of up to three dark matter halos contained in the sky. For example, the night sky we trained on has halo locations:" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 1.00000000e+00 1.40861000e+03 1.68586000e+03 1.40861000e+03\n", + " 1.68586000e+03 0.00000000e+00 0.00000000e+00 0.00000000e+00\n", + " 0.00000000e+00]\n" + ] + } + ], + "source": [ + "halo_data = np.genfromtxt(\"data/Training_halos.csv\",\n", + " delimiter=\",\",\n", + " usecols=[1, 2, 3, 4, 5, 6, 7, 8, 9],\n", + " skip_header=1)\n", + "print(halo_data[n_sky])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The third and fourth column represent the true $x$ and $y$ position of the halo. It appears that the Bayesian method has located the halo within a tight vicinity. " + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "True halo location: 1408.61 1685.86\n" + ] + }, + { + "data": { + "image/png": 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0BgbP2dnC++87ymxda9JEcc892fzrX9lAYKdDh7RVqKx06WLy9tsZ1K9vfUCb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EsvWdm7N3/Tee3aCrfd5qNjxwD16ytlOsFm+3aZTp2SAAmDQTBypI977vEVuGPLn3/KPPxwAocO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\npDh11o4DjaWUWXmuWYH9UspaZTS+UlHS3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GAZgBCiBpB9wWdFIBYL53V1mUzQrZuPBQts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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = draw_sky(data)\n", + "plt.title(\"Galaxy positions and ellipcities of sky %d.\" % n_sky)\n", + "plt.xlabel(\"x-position\")\n", + "plt.ylabel(\"y-position\")\n", + "plt.scatter(t[:, 0], t[:, 1], alpha=0.015, c=\"r\")\n", + "plt.scatter(halo_data[n_sky - 1][3], halo_data[n_sky - 1][4],\n", + " label=\"True halo position\",\n", + " c=\"k\", s=70)\n", + "plt.legend(scatterpoints=1, loc=\"lower left\")\n", + "plt.xlim(0, 4200)\n", + "plt.ylim(0, 4200);\n", + "\n", + "print(\"True halo location:\", halo_data[n_sky][3], halo_data[n_sky][4])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Perfect. Our next step is to use the loss function to optimize our location. A naive strategy would be to simply choose the mean:" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ 2324.2292868 1123.41563759]]\n" + ] + } + ], + "source": [ + "mean_posterior = t.mean(axis=0).reshape(1, 2)\n", + "print(mean_posterior)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Using the mean:\n", + "Your average distance in pixels you are away from the true halo is 42.3177214405\n", + "Your average angular vector is 1.0\n", + "Your score for the training data is 1.04231772144\n", + "\n", + "\n", + "Using a random location: [[2712 364]]\n", + "Your average distance in pixels you are away from the true halo is 820.025908676\n", + "Your average angular vector is 1.0\n", + "Your score for the training data is 1.82002590868\n" + ] + }, + { + "data": { + "text/plain": [ + "1.8200259086760613" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from DarkWorldsMetric import main_score\n", + "\n", + "_halo_data = halo_data[n_sky - 1]\n", + "\n", + "nhalo_all = _halo_data[0].reshape(1, 1)\n", + "x_true_all = _halo_data[3].reshape(1, 1)\n", + "y_true_all = _halo_data[4].reshape(1, 1)\n", + "x_ref_all = _halo_data[1].reshape(1, 1)\n", + "y_ref_all = _halo_data[2].reshape(1, 1)\n", + "sky_prediction = mean_posterior\n", + "\n", + "print(\"Using the mean:\")\n", + "main_score(nhalo_all, x_true_all, y_true_all,\n", + " x_ref_all, y_ref_all, sky_prediction)\n", + "\n", + "print(\"\\n\")\n", + "# what's a bad score?\n", + "random_guess = np.random.randint(0, 4200, size=(1, 2))\n", + "print(\"Using a random location:\", random_guess)\n", + "main_score(nhalo_all, x_true_all, y_true_all,\n", + " x_ref_all, y_ref_all, random_guess)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is a good guess, it is not very far from the true location, but it ignores the loss function that was provided to us. We also need to extend our code to allow for up to two additional, *smaller* halos: Let's create a function for automatizing our PyMC. " + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "from pymc.Matplot import plot as mcplot\n", + "\n", + "\n", + "def halo_posteriors(n_halos_in_sky, galaxy_data,\n", + " samples=5e5, burn_in=34e4, thin=4):\n", + " # set the size of the halo's mass\n", + " \"\"\"\n", + " exp_mass_large = pm.Uniform(\"exp_mass_large\", 40, 180)\n", + " @pm.deterministic\n", + " def mass_large(exp_mass_large = exp_mass_large):\n", + " return np.log(exp_mass_large)\n", + " \"\"\"\n", + "\n", + " mass_large = pm.Uniform(\"mass_large\", 40, 180)\n", + "\n", + " mass_small_1 = 20\n", + " mass_small_2 = 20\n", + "\n", + " masses = np.array([mass_large, mass_small_1, mass_small_2], dtype=object)\n", + "\n", + " # set the initial prior positions of the halos, it's a 2-d Uniform dist.\n", + " halo_positions = pm.Uniform(\"halo_positions\", 0, 4200,\n", + " size=(n_halos_in_sky, 2)) # notice this size\n", + "\n", + " fdist_constants = np.array([240, 70, 70])\n", + "\n", + " @pm.deterministic\n", + " def mean(mass=masses, h_pos=halo_positions, glx_pos=data[:, :2],\n", + " n_halos_in_sky=n_halos_in_sky):\n", + "\n", + " _sum = 0\n", + " for i in range(n_halos_in_sky):\n", + " _sum += mass[i] / f_distance(glx_pos, h_pos[i, :], fdist_constants[i]) *\\\n", + " tangential_distance(glx_pos, h_pos[i, :])\n", + "\n", + " return _sum\n", + "\n", + " ellpty = pm.Normal(\"ellipcity\", mean, 1. / 0.05, observed=True,\n", + " value=data[:, 2:])\n", + "\n", + " map_ = pm.MAP([ellpty, mean, halo_positions, mass_large])\n", + " map_.fit(method=\"fmin_powell\")\n", + "\n", + " mcmc = pm.MCMC([ellpty, mean, halo_positions, mass_large])\n", + " mcmc.sample(samples, burn_in, thin)\n", + " return mcmc.trace(\"halo_positions\")[:]" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "n_sky = 215\n", + "data = np.genfromtxt(\"data/Train_Skies/Train_Skies/\\\n", + "Training_Sky%d.csv\" % (n_sky),\n", + " dtype=None,\n", + " skip_header=1,\n", + " delimiter=\",\",\n", + " usecols=[1, 2, 3, 4])" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/maxwell/anaconda3/envs/bayes/lib/python3.5/site-packages/pymc/Node.py:403: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n", + " self.__name__ = input['__name__']\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " [-----------------100%-----------------] 1050000 of 1050000 complete in 948.7 sec" + ] + } + ], + "source": [ + "# there are 3 halos in this file.\n", + "samples = 10.5e5\n", + "traces = halo_posteriors(3, data, samples=samples,\n", + " burn_in=9.5e5,\n", + " thin=10)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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m3HST4bTTlrBu3cwH/AEHON773i5PfGJgzRpht92U/feffW2aJCFpNklazWmG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GAAAg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\n8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d54wypYAvjqK40vv6y8S2WmYD2ebY1VIy9e+l5MA6eguGPVyW6tXUuf6Ywc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Ij8+/qeGrzduxJVKNWu5PWVCz/e/q90YhyNUXI0aYnHZajEcfDfP883XccksjRxyht5pXVlUl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ev3yF+PrEbbyoucep8udssy4LbbBD71qQa2bOF43etSnHtuioMP3rn5f8/pgTnY7uRZC0Pg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\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VOEcfsx6lFDd/+ya0Vjx3y+VLHh/vHNt3hOPfvGlT/7UPrwG2bduGVIqnPW0L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\n/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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "fig = draw_sky(data)\n", + "plt.title(\"Galaxy positions and ellipcities of sky %d.\" % n_sky)\n", + "plt.xlabel(\"x-position\")\n", + "plt.ylabel(\"y-position\")\n", + "\n", + "colors = [\"#467821\", \"#A60628\", \"#7A68A6\"]\n", + "\n", + "for i in range(traces.shape[1]):\n", + " plt.scatter(traces[:, i, 0], traces[:, i, 1], c=colors[i], alpha=0.02)\n", + "\n", + "\n", + "for i in range(traces.shape[1]):\n", + " plt.scatter(halo_data[n_sky - 1][3 + 2 * i], halo_data[n_sky - 1][4 + 2 * i],\n", + " label=\"True halo position\",\n", + " c=\"k\", s=90)\n", + "\n", + "# plt.legend(scatterpoints = 1)\n", + "plt.xlim(0, 4200)\n", + "plt.ylim(0, 4200);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This looks pretty good, though it took a long time for the system to (sort of) converge. Our optimization step would look something like this:" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(10000, 3, 2)\n", + "[[ 3819.11446472 3962.91150552 2494.95860815 1335.19792536\n", + " 933.11887832 367.979512 ]]\n", + "Using the mean:\n", + "Your average distance in pixels you are away from the true halo is 124.438987331\n", + "Your average angular vector is 1.0\n", + "Your score for the training data is 1.12443898733\n", + "\n", + "\n", + "Using a random location: [[2984 1669]]\n", + "Your average distance in pixels you are away from the true halo is 2382.06551852\n", + "Your average angular vector is 1.0\n", + "Your score for the training data is 3.38206551852\n" + ] + }, + { + "data": { + "text/plain": [ + "3.3820655185153914" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "_halo_data = halo_data[n_sky - 1]\n", + "print(traces.shape)\n", + "\n", + "mean_posterior = traces.mean(axis=0).reshape(1, 6)\n", + "print(mean_posterior)\n", + "\n", + "\n", + "nhalo_all = _halo_data[0].reshape(1, 1)\n", + "x_true_all = _halo_data[3].reshape(1, 1)\n", + "y_true_all = _halo_data[4].reshape(1, 1)\n", + "x_ref_all = _halo_data[1].reshape(1, 1)\n", + "y_ref_all = _halo_data[2].reshape(1, 1)\n", + "sky_prediction = mean_posterior\n", + "\n", + "\n", + "print(\"Using the mean:\")\n", + "main_score([1], x_true_all, y_true_all,\n", + " x_ref_all, y_ref_all, sky_prediction)\n", + "\n", + "# what's a bad score?\n", + "print(\"\\n\")\n", + "random_guess = np.random.randint(0, 4200, size=(1, 2))\n", + "print(\"Using a random location:\", random_guess)\n", + "main_score([1], x_true_all, y_true_all,\n", + " x_ref_all, y_ref_all, random_guess)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## References\n", + "1. Antifragile: Things That Gain from Disorder. New York: Random House. 2012. ISBN 978-1-4000-6782-4.\n", + "1. [Tim Saliman's solution to the Dark World's Contest](http://www.timsalimans.com/observing-dark-worlds)\n", + "2. Silver, Nate. The Signal and the Noise: Why So Many Predictions Fail — but Some Don't. 1. Penguin Press HC, The, 2012. Print." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "from IPython.core.display import HTML\n", + "\n", + "\n", + "def css_styling():\n", + " styles = open(\"../styles/custom.css\", \"r\").read()\n", + " return HTML(styles)\n", + "css_styling()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python [conda env:bayes]", + "language": "python", + "name": "conda-env-bayes-py" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.2" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/Chapter5_LossFunctions/Ch5_LossFunctions_PyMC3.ipynb b/Chapter5_LossFunctions/Ch5_LossFunctions_PyMC3.ipynb new file mode 100644 index 00000000..bf249e3b --- /dev/null +++ b/Chapter5_LossFunctions/Ch5_LossFunctions_PyMC3.ipynb @@ -0,0 +1,1607 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Chapter 5\n", + "\n", + "`Original content created by Cam Davidson-Pilon`\n", + "\n", + "`Ported to Python 3 and PyMC3 by Max Margenot (@clean_utensils) and Thomas Wiecki (@twiecki) at Quantopian (@quantopian)`\n", + "____\n", + "\n", + "\n", + "### Would you rather lose an arm or a leg?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Statisticians can be a sour bunch. Instead of considering their winnings, they only measure how much they have lost. In fact, they consider their wins as *negative losses*. But what's interesting is *how they measure their losses.*\n", + "\n", + "For example, consider the following example:\n", + "\n", + "> A meteorologist is predicting the probability of a possible hurricane striking his city. He estimates, with 95% confidence, that the probability of it *not* striking is between 99% - 100%. He is very happy with his precision and advises the city that a major evacuation is unnecessary. Unfortunately, the hurricane does strike and the city is flooded. \n", + "\n", + "This stylized example shows the flaw in using a pure accuracy metric to measure outcomes. Using a measure that emphasizes estimation accuracy, while an appealing and *objective* thing to do, misses the point of why you are even performing the statistical inference in the first place: results of inference. The author Nassim Taleb of *The Black Swan* and *Antifragility* stresses the importance of the *payoffs* of decisions, *not the accuracy*. Taleb distills this quite succinctly: \"I would rather be vaguely right than very wrong.\" " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Loss Functions\n", + "\n", + "We introduce what statisticians and decision theorists call *loss functions*. A loss function is a function of the true parameter, and an estimate of that parameter\n", + "\n", + "$$ L( \\theta, \\hat{\\theta} ) = f( \\theta, \\hat{\\theta} )$$\n", + "\n", + "The important point of loss functions is that it measures how *bad* our current estimate is: the larger the loss, the worse the estimate is according to the loss function. A simple, and very common, example of a loss function is the *squared-error loss*:\n", + "\n", + "$$ L( \\theta, \\hat{\\theta} ) = ( \\theta - \\hat{\\theta} )^2$$\n", + "\n", + "The squared-error loss function is used in estimators like linear regression, UMVUEs and many areas of machine learning. We can also consider an asymmetric squared-error loss function, something like:\n", + "\n", + "$$ L( \\theta, \\hat{\\theta} ) = \\begin{cases} ( \\theta - \\hat{\\theta} )^2 & \\hat{\\theta} \\lt \\theta \\\\\\\\ c( \\theta - \\hat{\\theta} )^2 & \\hat{\\theta} \\ge \\theta, \\;\\; 0\\lt c \\lt 1 \\end{cases}$$\n", + "\n", + "\n", + "which represents that estimating a value larger than the true estimate is preferable to estimating a value below. A situation where this might be useful is in estimating web traffic for the next month, where an over-estimated outlook is preferred so as to avoid an underallocation of server resources. \n", + "\n", + "A negative property about the squared-error loss is that it puts a disproportionate emphasis on large outliers. This is because the loss increases quadratically, and not linearly, as the estimate moves away. That is, the penalty of being three units away is much less than being five units away, but the penalty is not much greater than being one unit away, though in both cases the magnitude of difference is the same:\n", + "\n", + "$$ \\frac{1^2}{3^2} \\lt \\frac{3^2}{5^2}, \\;\\; \\text{although} \\;\\; 3-1 = 5-3 $$\n", + "\n", + "This loss function imposes that large errors are *very* bad. A more *robust* loss function that increases linearly with the difference is the *absolute-loss*\n", + "\n", + "$$ L( \\theta, \\hat{\\theta} ) = | \\theta - \\hat{\\theta} | $$\n", + "\n", + "Other popular loss functions include:\n", + "\n", + "- $L( \\theta, \\hat{\\theta} ) = \\mathbb{1}_{ \\hat{\\theta} \\neq \\theta }$ is the zero-one loss often used in machine learning classification algorithms.\n", + "- $L( \\theta, \\hat{\\theta} ) = -\\theta\\log( \\hat{\\theta} ) - (1- \\theta)\\log( 1 - \\hat{\\theta} ), \\; \\; \\theta \\in {0,1}, \\; \\hat{\\theta} \\in [0,1]$, called the *log-loss*, also used in machine learning. \n", + "\n", + "Historically, loss functions have been motivated from 1) mathematical convenience, and 2) they are robust to application, i.e., they are objective measures of loss. The first reason has really held back the full breadth of loss functions. With computers being agnostic to mathematical convenience, we are free to design our own loss functions, which we take full advantage of later in this Chapter.\n", + "\n", + "With respect to the second point, the above loss functions are indeed objective, in that they are most often a function of the difference between estimate and true parameter, independent of signage or payoff of choosing that estimate. This last point, its independence of payoff, causes quite pathological results though. Consider our hurricane example above: the statistician equivalently predicted that the probability of the hurricane striking was between 0% to 1%. But if he had ignored being precise and instead focused on outcomes (99% chance of no flood, 1% chance of flood), he might have advised differently. \n", + "\n", + "By shifting our focus from trying to be incredibly precise about parameter estimation to focusing on the outcomes of our parameter estimation, we can customize our estimates to be optimized for our application. This requires us to design new loss functions that reflect our goals and outcomes. Some examples of more interesting loss functions:\n", + "\n", + "\n", + "- $L( \\theta, \\hat{\\theta} ) = \\frac{ | \\theta - \\hat{\\theta} | }{ \\theta(1-\\theta) }, \\; \\; \\hat{\\theta}, \\theta \\in [0,1]$ emphasizes an estimate closer to 0 or 1 since if the true value $\\theta$ is near 0 or 1, the loss will be *very* large unless $\\hat{\\theta}$ is similarly close to 0 or 1. \n", + "This loss function might be used by a political pundit whose job requires him or her to give confident \"Yes/No\" answers. This loss reflects that if the true parameter is close to 1 (for example, if a political outcome is very likely to occur), he or she would want to strongly agree as to not look like a skeptic. \n", + "\n", + "- $L( \\theta, \\hat{\\theta} ) = 1 - \\exp \\left( -(\\theta - \\hat{\\theta} )^2 \\right)$ is bounded between 0 and 1 and reflects that the user is indifferent to sufficiently-far-away estimates. It is similar to the zero-one loss above, but not quite as penalizing to estimates that are close to the true parameter. \n", + "- Complicated non-linear loss functions can programmed: \n", + "\n", + " def loss(true_value, estimate):\n", + " if estimate*true_value > 0:\n", + " return abs(estimate - true_value)\n", + " else:\n", + " return abs(estimate)*(estimate - true_value)**2\n", + " \n", + "\n", + "\n", + "- Another example is from the book *The Signal and The Noise*. Weather forecasters have an interesting loss function for their predictions.\n", + "\n", + "\n", + "> People notice one type of mistake — the failure to predict rain — more than other, false alarms. If it rains when it isn't supposed to, they curse the weatherman for ruining their picnic, whereas an unexpectedly sunny day is taken as a serendipitous bonus.\n", + "\n", + "> [The Weather Channel's bias] is limited to slightly exaggerating the probability of rain when it is unlikely to occur — saying there is a 20 percent change when they know it is really a 5 or 10 percent chance — covering their butts in the case of an unexpected sprinkle.\n", + "\n", + "\n", + "As you can see, loss functions can be used for good and evil: with great power, comes great — well you know.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Loss functions in the real world\n", + "\n", + "So far we have been under the unrealistic assumption that we know the true parameter. Of course if we knew the true parameter, bothering to guess an estimate is pointless. Hence a loss function is really only practical when the true parameter is unknown. \n", + "\n", + "In Bayesian inference, we have a mindset that the unknown parameters are really random variables with prior and posterior distributions. Concerning the posterior distribution, a value drawn from it is a *possible* realization of what the true parameter could be. Given that realization, we can compute a loss associated with an estimate. As we have a whole distribution of what the unknown parameter could be (the posterior), we should be more interested in computing the *expected loss* given an estimate. This expected loss is a better estimate of the true loss than comparing the given loss from only a single sample from the posterior.\n", + "\n", + "First it will be useful to explain a *Bayesian point estimate*. The systems and machinery present in the modern world are not built to accept posterior distributions as input. It is also rude to hand someone over a distribution when all they asked for was an estimate. In the course of an individual's day, when faced with uncertainty we still act by distilling our uncertainty down to a single action. Similarly, we need to distill our posterior distribution down to a single value (or vector in the multivariate case). If the value is chosen intelligently, we can avoid the flaw of frequentist methodologies that mask the uncertainty and provide a more informative result.The value chosen, if from a Bayesian posterior, is a Bayesian point estimate. \n", + "\n", + "Suppose $P(\\theta | X)$ is the posterior distribution of $\\theta$ after observing data $X$, then the following function is understandable as the *expected loss of choosing estimate $\\hat{\\theta}$ to estimate $\\theta$*:\n", + "\n", + "$$ l(\\hat{\\theta} ) = E_{\\theta}\\left[ \\; L(\\theta, \\hat{\\theta}) \\; \\right] $$\n", + "\n", + "This is also known as the *risk* of estimate $\\hat{\\theta}$. The subscript $\\theta$ under the expectation symbol is used to denote that $\\theta$ is the unknown (random) variable in the expectation, something that at first can be difficult to consider.\n", + "\n", + "We spent all of last chapter discussing how to approximate expected values. Given $N$ samples $\\theta_i,\\; i=1,...,N$ from the posterior distribution, and a loss function $L$, we can approximate the expected loss of using estimate $\\hat{\\theta}$ by the Law of Large Numbers:\n", + "\n", + "$$\\frac{1}{N} \\sum_{i=1}^N \\;L(\\theta_i, \\hat{\\theta} ) \\approx E_{\\theta}\\left[ \\; L(\\theta, \\hat{\\theta}) \\; \\right] = l(\\hat{\\theta} ) $$\n", + "\n", + "Notice that measuring your loss via an *expected value* uses more information from the distribution than the MAP estimate which, if you recall, will only find the maximum value of the distribution and ignore the shape of the distribution. Ignoring information can over-expose yourself to tail risks, like the unlikely hurricane, and leaves your estimate ignorant of how ignorant you really are about the parameter.\n", + "\n", + "Similarly, compare this with frequentist methods, that traditionally only aim to minimize the error, and do not consider the *loss associated with the result of that error*. Compound this with the fact that frequentist methods are almost guaranteed to never be absolutely accurate. Bayesian point estimates fix this by planning ahead: your estimate is going to be wrong, you might as well err on the right side of wrong." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Example: Optimizing for the *Showcase* on *The Price is Right*\n", + "\n", + "Bless you if you are ever chosen as a contestant on the Price is Right, for here we will show you how to optimize your final price on the *Showcase*. For those who forget the rules:\n", + "\n", + "\n", + "1. Two contestants compete in *The Showcase*. \n", + "2. Each contestant is shown a unique suite of prizes.\n", + "3. After the viewing, the contestants are asked to bid on the price for their unique suite of prizes.\n", + "4. If a bid price is over the actual price, the bid's owner is disqualified from winning.\n", + "5. If a bid price is under the true price by less than $250, the winner is awarded both prizes.\n", + "\n", + "The difficulty in the game is balancing your uncertainty in the prices, keeping your bid low enough so as to not bid over, and trying to bid close to the price.\n", + "\n", + "Suppose we have recorded the *Showcases* from previous *The Price is Right* episodes and have *prior* beliefs about what distribution the true price follows. For simplicity, suppose it follows a Normal:\n", + "\n", + "\n", + "$$\\text{True Price} \\sim \\text{Normal}(\\mu_p, \\sigma_p )$$\n", + "\n", + "\n", + "In a later chapter, we will actually use *real Price is Right Showcase data* to form the historical prior, but this requires some advanced PyMC3 use so we will not use it here. For now, we will assume $\\mu_p = 35 000$ and $\\sigma_p = 7500$.\n", + "\n", + "We need a model of how we should be playing the *Showcase*. For each prize in the prize suite, we have an idea of what it might cost, but this guess could differ significantly from the true price. (Couple this with increased pressure being onstage and you can see why some bids are so wildly off). Let's suppose your beliefs about the prices of prizes also follow Normal distributions:\n", + "\n", + "$$ \\text{Prize}_i \\sim \\text{Normal}(\\mu_i, \\sigma_i ),\\;\\; i=1,2$$\n", + "\n", + "This is really why Bayesian analysis is great: we can specify what we think a fair price is through the $\\mu_i$ parameter, and express uncertainty of our guess in the $\\sigma_i$ parameter. \n", + "\n", + "We'll assume two prizes per suite for brevity, but this can be extended to any number. \n", + "The true price of the prize suite is then given by $\\text{Prize}_1 + \\text{Prize}_2 + \\epsilon$, \n", + "where $\\epsilon$ is some error term.\n", + "\n", + "We are interested in the updated $\\text{True Price}$ given we have observed both prizes and have belief distributions about them. We can perform this using PyMC3. \n", + "\n", + "Lets make some values concrete. Suppose there are two prizes in the observed prize suite: \n", + "\n", + "1. A trip to wonderful Toronto, Canada! \n", + "2. A lovely new snowblower!\n", + "\n", + "We have some guesses about the true prices of these objects, but we are also pretty uncertain about them. I can express this uncertainty through the parameters of the Normals:\n", + "\n", + "\n", + "\\begin{align}\n", + "& \\text{snowblower} \\sim \\text{Normal}(3 000, 500 )\\\\\\\\\n", + "& \\text{Toronto} \\sim \\text{Normal}(12 000, 3000 )\\\\\\\\\n", + "\\end{align}\n", + "\n", + "For example, I believe that the true price of the trip to Toronto is 12 000 dollars, and that there is a 68.2% chance the price falls 1 standard deviation away from this, i.e. my confidence is that there is a 68.2% chance the trip is in [9 000, 15 000].\n", + "\n", + "We can create some PyMC3 code to perform inference on the true price of the suite." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "image/png": 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lkz5MeyjNX/68jqZqL7n8uVw4v47Ino1A+X9ftaxlu5Y3b95MOBwGoK2tjbPP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XcDnwD2icgecpffvsTZbYr3WeBfABe5FUd+P53PRRXNt9G+ok7vb4F/ExE7cIzchpxWtL+oMYwx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\nffd1+zRMnyoX65w+Mcf46UU816dWdSkt1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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib inline\n", + "import scipy.stats as stats\n", + "from IPython.core.pylabtools import figsize\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "figsize(12.5, 9)\n", + "\n", + "norm_pdf = stats.norm.pdf\n", + "\n", + "plt.subplot(311)\n", + "x = np.linspace(0, 60000, 200)\n", + "sp1 = plt.fill_between(x , 0, norm_pdf(x, 35000, 7500), \n", + " color = \"#348ABD\", lw = 3, alpha = 0.6,\n", + " label = \"historical total prices\")\n", + "p1 = plt.Rectangle((0, 0), 1, 1, fc=sp1.get_facecolor()[0])\n", + "plt.legend([p1], [sp1.get_label()])\n", + "\n", + "plt.subplot(312)\n", + "x = np.linspace(0, 10000, 200)\n", + "sp2 = plt.fill_between(x , 0, norm_pdf(x, 3000, 500), \n", + " color = \"#A60628\", lw = 3, alpha = 0.6,\n", + " label=\"snowblower price guess\")\n", + "\n", + "p2 = plt.Rectangle((0, 0), 1, 1, fc=sp2.get_facecolor()[0])\n", + "plt.legend([p2], [sp2.get_label()])\n", + "\n", + "plt.subplot(313)\n", + "x = np.linspace(0, 25000, 200)\n", + "sp3 = plt.fill_between(x , 0, norm_pdf(x, 12000, 3000), \n", + " color = \"#7A68A6\", lw = 3, alpha = 0.6,\n", + " label = \"Trip price guess\")\n", + "plt.autoscale(tight=True)\n", + "p3 = plt.Rectangle((0, 0), 1, 1, fc=sp3.get_facecolor()[0])\n", + "plt.legend([p3], [sp3.get_label()]);" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " [-------100%-------] 50000 of 50000 in 8.2 sec. | SPS: 6119.0 | ETA: 0.0" + ] + } + ], + "source": [ + "import pymc3 as pm\n", + "\n", + "data_mu = [3e3, 12e3]\n", + "\n", + "data_std = [5e2, 3e3] \n", + "\n", + "mu_prior = 35e3\n", + "std_prior = 75e2\n", + "with pm.Model() as model:\n", + " true_price = pm.Normal(\"true_price\", mu=mu_prior, sd=std_prior)\n", + " \n", + " prize_1 = pm.Normal(\"first_prize\", mu=data_mu[0], sd=data_std[0])\n", + " prize_2 = pm.Normal(\"second_prize\", mu=data_mu[1], sd=data_std[1])\n", + " price_estimate = prize_1 + prize_2\n", + " \n", + " logp = pm.Normal.dist(mu=price_estimate, sd=(3e3)).logp(true_price)\n", + " error = pm.Potential(\"error\", logp)\n", + " \n", + "\n", + " trace = pm.sample(50000, step=pm.Metropolis())\n", + " burned_trace = trace[10000:]\n", + "\n", + "price_trace = burned_trace[\"true_price\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "image/png": 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9cHAwBw8eJC0tjebNmxcbwCwiPPXUU1SrVo3rr7+emjVrMnz4cFeFqGfPnmzd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\nadUg0L5DZOrpSoGIiMg009raytNPP82GDRtG3EF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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "figsize(12.5, 4)\n", + "\n", + "import scipy.stats as stats\n", + "\n", + "x = np.linspace(5000, 40000)\n", + "plt.plot(x, stats.norm.pdf(x, 35000, 7500), c = \"k\", lw = 2, \n", + " label = \"prior dist. of suite price\")\n", + "\n", + "_hist = plt.hist(price_trace, bins = 35, normed= True, histtype= \"stepfilled\")\n", + "plt.title(\"Posterior of the true price estimate\")\n", + "plt.vlines(mu_prior, 0, 1.1*np.max(_hist[0]), label = \"prior's mean\",\n", + " linestyles=\"--\")\n", + "plt.vlines(price_trace.mean(), 0, 1.1*np.max(_hist[0]), \\\n", + " label = \"posterior's mean\", linestyles=\"-.\")\n", + "plt.legend(loc = \"upper left\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notice that because of our two observed prizes and subsequent guesses (including uncertainty about those guesses), we shifted our mean price estimate down about \\$15,000 dollars from the previous mean price.\n", + "\n", + "A frequentist, seeing the two prizes and having the same beliefs about their prices, would bid $\\mu_1 + \\mu_2 = 35000$, regardless of any uncertainty. Meanwhile, the *naive Bayesian* would simply pick the mean of the posterior distribution. But we have more information about our eventual outcomes; we should incorporate this into our bid. We will use the loss function above to find the *best* bid (*best* according to our loss).\n", + "\n", + "What might a contestant's loss function look like? I would think it would look something like:\n", + "\n", + " def showcase_loss(guess, true_price, risk = 80000):\n", + " if true_price < guess:\n", + " return risk\n", + " elif abs(true_price - guess) <= 250:\n", + " return -2*np.abs(true_price)\n", + " else:\n", + " return np.abs(true_price - guess - 250)\n", + "\n", + "where `risk` is a parameter that defines of how bad it is if your guess is over the true price. A lower `risk` means that you are more comfortable with the idea of going over. If we do bid under and the difference is less than $250, we receive both prizes (modeled here as receiving twice the original prize). Otherwise, when we bid under the `true_price` we want to be as close as possible, hence the `else` loss is a increasing function of the distance between the guess and true price." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For every possible bid, we calculate the *expected loss* associated with that bid. We vary the `risk` parameter to see how it affects our loss:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "image/png": 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8vDyGDh3KP//5T0aNGlVqmyFDhtCzZ08+/fRTbrvtNv7617+SkJBA/fr1iY+P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+HMKsYQYSZ1esOTsAPBxNM4t8xDs0SR+OBcTKmZDW7n9/WXA6d28Ju5D1JaVUjP9EH8+vb/tIhg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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "figsize(12.5, 7)\n", + "#numpy friendly showdown_loss\n", + "\n", + "\n", + "def showdown_loss(guess, true_price, risk = 80000):\n", + " loss = np.zeros_like(true_price)\n", + " ix = true_price < guess\n", + " loss[~ix] = np.abs(guess - true_price[~ix])\n", + " close_mask = [abs(true_price - guess) <= 250]\n", + " loss[close_mask] = -2*true_price[close_mask]\n", + " loss[ix] = risk\n", + " return loss\n", + "\n", + "\n", + "guesses = np.linspace(5000, 50000, 70) \n", + "risks = np.linspace(30000, 150000, 6)\n", + "expected_loss = lambda guess, risk: \\\n", + " showdown_loss(guess, price_trace, risk).mean()\n", + " \n", + "for _p in risks:\n", + " results = [expected_loss(_g, _p) for _g in guesses]\n", + " plt.plot(guesses, results, label = \"%d\"%_p)\n", + " \n", + "plt.title(\"Expected loss of different guesses, \\nvarious risk-levels of \\\n", + "overestimating\")\n", + "plt.legend(loc=\"upper left\", title=\"Risk parameter\")\n", + "plt.xlabel(\"price bid\")\n", + "plt.ylabel(\"expected loss\")\n", + "plt.xlim(5000, 30000);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Minimizing our losses\n", + "\n", + "It would be wise to choose the estimate that minimizes our expected loss. This corresponds to the minimum point on each of the curves above. More formally, we would like to minimize our expected loss by finding the solution to\n", + "\n", + "$$ \\text{arg} \\min_{\\hat{\\theta}} \\;\\;E_{\\theta}\\left[ \\; L(\\theta, \\hat{\\theta}) \\; \\right] $$\n", + "\n", + "The minimum of the expected loss is called the *Bayes action*. We can solve for the Bayes action using Scipy's optimization routines. The function in `fmin` in `scipy.optimize` module uses an intelligent search to find a minimum (not necessarily a *global* minimum) of any uni- or multivariate function. For most purposes, `fmin` will provide you with a good answer. \n", + "\n", + "We'll compute the minimum loss for the *Showcase* example above:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "minimum at risk 30000: 14723.45\n", + "minimum at risk 54000: 13500.92\n", + "minimum at risk 78000: 11900.78\n", + "minimum at risk 102000: 11649.08\n", + "minimum at risk 126000: 11649.08\n", + "minimum at risk 150000: 11329.30\n" + ] + }, + { + "data": { + "image/png": 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UBScFKQtOyquy4MSflAUnRSkLTvy5fRnKN65tK2WrXkYX3rxWmSkMALafJ6Oy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GVRGj0rEzh9FqVg7l/BrGLHIeyHy9BHm4jp27pObZWnvwRSI3LItRgSU30T4PHnsJTy9o/PAnDp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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import scipy.optimize as sop\n", + "\n", + "ax = plt.subplot(111)\n", + "\n", + "\n", + "for _p in risks:\n", + " _color = next(ax._get_lines.prop_cycler)\n", + " _min_results = sop.fmin(expected_loss, 15000, args=(_p,),disp = False)\n", + " _results = [expected_loss(_g, _p) for _g in guesses]\n", + " plt.plot(guesses, _results , color = _color['color'])\n", + " plt.scatter(_min_results, 0, s = 60, \\\n", + " color= _color['color'], label = \"%d\"%_p)\n", + " plt.vlines(_min_results, 0, 120000, color = _color['color'], linestyles=\"--\")\n", + " print(\"minimum at risk %d: %.2f\" % (_p, _min_results))\n", + " \n", + "plt.title(\"Expected loss & Bayes actions of different guesses, \\n \\\n", + "various risk-levels of overestimating\")\n", + "plt.legend(loc=\"upper left\", scatterpoints = 1, title = \"Bayes action at risk:\")\n", + "plt.xlabel(\"price guess\")\n", + "plt.ylabel(\"expected loss\")\n", + "plt.xlim(7000, 30000)\n", + "plt.ylim(-1000, 80000);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As intuition suggests, as we decrease the risk threshold (care about overbidding less), we increase our bid, willing to edge closer to the true price. It is interesting how far away our optimized loss is from the posterior mean, which was about 20 000. \n", + "\n", + "Suffice to say, in higher dimensions being able to eyeball the minimum expected loss is impossible. Hence why we require use of Scipy's `fmin` function.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "______\n", + "\n", + "### Shortcuts\n", + "\n", + "For some loss functions, the Bayes action is known in closed form. We list some of them below:\n", + "\n", + "- If using the mean-squared loss, the Bayes action is the mean of the posterior distribution, i.e. the value $$ E_{\\theta}\\left[ \\theta \\right] $$ minimizes $E_{\\theta}\\left[ \\; (\\theta - \\hat{\\theta})^2 \\; \\right]$. Computationally this requires us to calculate the average of the posterior samples [See chapter 4 on The Law of Large Numbers]\n", + "\n", + "- Whereas the *median* of the posterior distribution minimizes the expected absolute-loss. The sample median of the posterior samples is an appropriate and very accurate approximation to the true median.\n", + "\n", + "- In fact, it is possible to show that the MAP estimate is the solution to using a loss function that shrinks to the zero-one loss.\n", + "\n", + "\n", + "Maybe it is clear now why the first-introduced loss functions are used most often in the mathematics of Bayesian inference: no complicated optimizations are necessary. Luckily, we have machines to do the complications for us. \n", + "\n", + "## Machine Learning via Bayesian Methods\n", + "\n", + "Whereas frequentist methods strive to achieve the best precision about all possible parameters, machine learning cares to achieve the best *prediction* among all possible parameters. Of course, one way to achieve accurate predictions is to aim for accurate predictions, but often your prediction measure and what frequentist methods are optimizing for are very different. \n", + "\n", + "For example, least-squares linear regression is the most simple active machine learning algorithm. I say active as it engages in some learning, whereas predicting the sample mean is technically *simpler*, but is learning very little if anything. The loss that determines the coefficients of the regressors is a squared-error loss. On the other hand, if your prediction loss function (or score function, which is the negative loss) is not a squared-error, like AUC, ROC, precision, etc., your least-squares line will not be optimal for the prediction loss function. This can lead to prediction results that are suboptimal. \n", + "\n", + "Finding Bayes actions is equivalent to finding parameters that optimize *not parameter accuracy* but an arbitrary performance measure, however we wish to define performance (loss functions, AUC, ROC, precision/recall etc.).\n", + "\n", + "The next two examples demonstrate these ideas. The first example is a linear model where we can choose to predict using the least-squares loss or a novel, outcome-sensitive loss. \n", + "\n", + "The second example is adapted from a Kaggle data science project. The loss function associated with our predictions is incredibly complicated. \n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "##### Example: Financial prediction\n", + "\n", + "\n", + "Suppose the future return of a stock price is very small, say 0.01 (or 1%). We have a model that predicts the stock's future price, and our profit and loss is directly tied to us acting on the prediction. How should we measure the loss associated with the model's predictions, and subsequent future predictions? A squared-error loss is agnostic to the signage and would penalize a prediction of -0.01 equally as bad a prediction of 0.03:\n", + "\n", + "$$ (0.01 - (-0.01))^2 = (0.01 - 0.03)^2 = 0.004$$\n", + "\n", + "If you had made a bet based on your model's prediction, you would have earned money with a prediction of 0.03, and lost money with a prediction of -0.01, yet our loss did not capture this. We need a better loss that takes into account the *sign* of the prediction and true value. We design a new loss that is better for financial applications below:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "image/png": 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qjP+a0sXLmd4+zfX0B3vTKLOytyAKhUJhKWRZGb/tP6qnXXp1ofF9zvUokUKh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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "figsize(12.5, 4)\n", + "\n", + "\n", + "def stock_loss(true_return, yhat, alpha = 100.):\n", + " if true_return * yhat < 0:\n", + " #opposite signs, not good\n", + " return alpha*yhat**2 - np.sign(true_return)*yhat \\\n", + " + abs(true_return) \n", + " else:\n", + " return abs(true_return - yhat)\n", + " \n", + " \n", + "true_value = .05\n", + "pred = np.linspace(-.04, .12, 75)\n", + "\n", + "plt.plot(pred, [stock_loss(true_value, _p) for _p in pred], \\\n", + " label = \"Loss associated with\\n prediction if true value = 0.05\", lw =3) \n", + "plt.vlines(0, 0, .25, linestyles=\"--\")\n", + "\n", + "plt.xlabel(\"prediction\")\n", + "plt.ylabel(\"loss\")\n", + "plt.xlim(-0.04, .12)\n", + "plt.ylim(0, 0.25)\n", + "\n", + "true_value = -.02\n", + "plt.plot(pred, [stock_loss(true_value, _p) for _p in pred], alpha = 0.6, \\\n", + " label = \"Loss associated with\\n prediction if true value = -0.02\", lw =3) \n", + "plt.legend()\n", + "plt.title(\"Stock returns loss if true value = 0.05, -0.02\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note the change in the shape of the loss as the prediction crosses zero. This loss reflects that the user really does not want to guess the wrong sign, especially be wrong *and* a large magnitude. \n", + "\n", + "Why would the user care about the magnitude? Why is the loss not 0 for predicting the correct sign? Surely, if the return is 0.01 and we bet millions we will still be (very) happy.\n", + "\n", + "Financial institutions treat downside risk, as in predicting a lot on the wrong side, and upside risk, as in predicting a lot on the right side, similarly. Both are seen as risky behaviour and discouraged. Hence why we have an increasing loss as we move further away from the true price. (With less extreme loss in the direction of the correct sign.)\n", + "\n", + "We will perform a regression on a trading signal that we believe predicts future returns well. Our dataset is artificial, as most financial data is not even close to linear. Below, we plot the data along with the least-squares line." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "image/png": 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4+EOx8Ivi4ZfYeMSe+d24cWPT39lMpUnUyG8pxact6VOp5L5vDztOfqg26X0f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\n0uWTnZqzELTmLLkvcW1JsTeMRUSkMJl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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "## Code to create artificial data\n", + "N = 100\n", + "X = 0.025*np.random.randn(N)\n", + "Y = 0.5*X + 0.01*np.random.randn(N) \n", + "\n", + "ls_coef_ = np.cov(X, Y)[0,1]/np.var(X)\n", + "ls_intercept = Y.mean() - ls_coef_*X.mean()\n", + "\n", + "plt.scatter(X, Y, c=\"k\")\n", + "plt.xlabel(\"trading signal\")\n", + "plt.ylabel(\"returns\")\n", + "plt.title(\"Empirical returns vs trading signal\")\n", + "plt.plot(X, ls_coef_*X + ls_intercept, label = \"Least-squares line\")\n", + "plt.xlim(X.min(), X.max())\n", + "plt.ylim(Y.min(), Y.max())\n", + "plt.legend(loc=\"upper left\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We perform a simple Bayesian linear regression on this dataset. We look for a model like:\n", + "\n", + "$$ R = \\alpha + \\beta x + \\epsilon$$\n", + "\n", + "where $\\alpha, \\beta$ are our unknown parameters and $\\epsilon \\sim \\text{Normal}(0, \\sigma)$. The most common priors on $\\beta$ and $\\alpha$ are Normal priors. We will also assign a prior on $\\sigma$, so that $\\sigma$ is uniform over 0 to 100." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Applied interval-transform to std and added transformed std_interval_ to model.\n", + " [-------100%-------] 100000 of 100000 in 26.5 sec. | SPS: 3769.6 | ETA: 0.0" + ] + } + ], + "source": [ + "import pymc3 as pm\n", + "\n", + "with pm.Model() as model:\n", + " std = pm.Uniform(\"std\", 0, 100)\n", + " \n", + " beta = pm.Normal(\"beta\", mu=0, sd=100)\n", + " alpha = pm.Normal(\"alpha\", mu=0, sd=100)\n", + " \n", + " mean = pm.Deterministic(\"mean\", alpha + beta*X)\n", + " \n", + " obs = pm.Normal(\"obs\", mu=mean, sd=std, observed=Y)\n", + " \n", + " trace = pm.sample(100000, step=pm.Metropolis())\n", + " burned_trace = trace[20000:] " + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "image/png": 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JQeXcXNXcu4uQDDOtLVUttHl9g/ZfUVFB7txzae4avG40apq72d/QwXkzCnjdDbRx8Zzi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l2bNnW1U8cXFxxKdm8/O+DLKP7aK7UzIOdroqHa+mr8dDu7Zyt0caDnYKfxw8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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pm.plots.traceplot(trace=burned_trace, varnames=[\"std\", \"beta\", \"alpha\"])\n", + "pm.plot_posterior(trace=burned_trace, varnames=[\"std\", \"beta\", \"alpha\"], kde_plot=True);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It appears the MCMC has converged so we may continue.\n", + "\n", + "For a specific trading signal, call it $x$, the distribution of possible returns has the form:\n", + "\n", + "$$R_i(x) = \\alpha_i + \\beta_ix + \\epsilon $$\n", + "\n", + "where $\\epsilon \\sim \\text{Normal}(0, \\sigma_i)$ and $i$ indexes our posterior samples. We wish to find the solution to \n", + "\n", + "$$ \\arg \\min_{r} \\;\\;E_{R(x)}\\left[ \\; L(R(x), r) \\; \\right] $$\n", + "\n", + "according to the loss given above. This $r$ is our Bayes action for trading signal $x$. Below we plot the Bayes action over different trading signals. What do you notice?\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + }, + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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44Rix11MAqB4RTuttGwjYvxt3m43qP82ma9euubrbJ30AhMB1LxUTxZPk1FqL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\nrkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "figsize(12.5, 6)\n", + "from scipy.optimize import fmin\n", + "\n", + "\n", + "def stock_loss(price, pred, coef = 500):\n", + " \"\"\"vectorized for numpy\"\"\"\n", + " sol = np.zeros_like(price)\n", + " ix = price*pred < 0 \n", + " sol[ix] = coef*pred**2 - np.sign(price[ix])*pred + abs(price[ix])\n", + " sol[~ix] = abs(price[~ix] - pred)\n", + " return sol\n", + "\n", + "std_samples = burned_trace[\"std\"]\n", + "alpha_samples = burned_trace[\"alpha\"]\n", + "beta_samples = burned_trace[\"beta\"]\n", + "\n", + "N = std_samples.shape[0]\n", + "\n", + "noise = std_samples*np.random.randn(N) \n", + "\n", + "possible_outcomes = lambda signal: alpha_samples + beta_samples*signal + noise\n", + "\n", + "\n", + "opt_predictions = np.zeros(50)\n", + "trading_signals = np.linspace(X.min(), X.max(), 50)\n", + "for i, _signal in enumerate(trading_signals):\n", + " _possible_outcomes = possible_outcomes(_signal)\n", + " tomin = lambda pred: stock_loss(_possible_outcomes, pred).mean()\n", + " opt_predictions[i] = fmin(tomin, 0, disp = False)\n", + " \n", + " \n", + "plt.xlabel(\"trading signal\")\n", + "plt.ylabel(\"prediction\")\n", + "plt.title(\"Least-squares prediction vs. Bayes action prediction\")\n", + "plt.plot(X, ls_coef_*X + ls_intercept, label =\"Least-squares prediction\")\n", + "plt.xlim(X.min(), X.max())\n", + "plt.plot(trading_signals, opt_predictions, label =\"Bayes action prediction\")\n", + "plt.legend(loc=\"upper left\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "What is interesting about the above graph is that when the signal is near 0, and many of the possible returns outcomes are possibly both positive and negative, our best (with respect to our loss) prediction is to predict close to 0, hence *take on no position*. Only when we are very confident do we enter into a position. I call this style of model a *sparse prediction*, where we feel uncomfortable with our uncertainty so choose not to act. (Compare with the least-squares prediction which will rarely, if ever, predict zero). \n", + "\n", + "A good sanity check that our model is still reasonable: as the signal becomes more and more extreme, and we feel more and more confident about the positive/negativeness of returns, our position converges with that of the least-squares line. \n", + "\n", + "The sparse-prediction model is not trying to *fit* the data the best (according to a *squared-error loss* definition of *fit*). That honor would go to the least-squares model. The sparse-prediction model is trying to find the best prediction *with respect to our `stock_loss`-defined loss*. We can turn this reasoning around: the least-squares model is not trying to *predict* the best (according to a *`stock-loss`* definition of *predict*). That honor would go the *sparse prediction* model. The least-squares model is trying to find the best fit of the data *with respect to the squared-error loss*.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Example: Kaggle contest on *Observing Dark World*\n", + "\n", + "\n", + "A personal motivation for learning Bayesian methods was trying to piece together the winning solution to Kaggle's [*Observing Dark Worlds*](http://www.kaggle.com/c/DarkWorlds) contest. From the contest's website:\n", + "\n", + "\n", + "\n", + ">There is more to the Universe than meets the eye. Out in the cosmos exists a form of matter that outnumbers the stuff we can see by almost 7 to 1, and we don’t know what it is. What we do know is that it does not emit or absorb light, so we call it Dark Matter. Such a vast amount of aggregated matter does not go unnoticed. In fact we observe that this stuff aggregates and forms massive structures called Dark Matter Halos. Although dark, it warps and bends spacetime such that any light from a background galaxy which passes close to the Dark Matter will have its path altered and changed. This bending causes the galaxy to appear as an ellipse in the sky.\n", + "\n", + "\n", + "\n", + "\n", + "The contest required predictions about where dark matter was likely to be. The winner, [Tim Salimans](http://timsalimans.com/), used Bayesian inference to find the best locations for the halos (interestingly, the second-place winner also used Bayesian inference). With Tim's permission, we provided his solution [1] here:\n", + "\n", + "1. Construct a prior distribution for the halo positions $p(x)$, i.e. formulate our expectations about the halo positions before looking at the data.\n", + "2. Construct a probabilistic model for the data (observed ellipticities of the galaxies) given the positions of the dark matter halos: $p(e | x)$.\n", + "3. Use Bayes’ rule to get the posterior distribution of the halo positions, i.e. use to the data to guess where the dark matter halos might be.\n", + "4. Minimize the expected loss with respect to the posterior distribution over the predictions for the halo positions: $\\hat{x} = \\arg \\min_{\\text{prediction} } E_{p(x|e)}[ L( \\text{prediction}, x) ]$ , i.e. tune our predictions to be as good as possible for the given error metric.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The loss function in this problem is very complicated. For the very determined, the loss function is contained in the file DarkWorldsMetric.py in the parent folder. Though I suggest not reading it all, suffice to say the loss function is about 160 lines of code — not something that can be written down in a single mathematical line. The loss function attempts to measure the accuracy of prediction, in a Euclidean distance sense, such that no shift-bias is present. More details can be found on the metric's [main page](http://www.kaggle.com/c/DarkWorlds/details/evaluation). \n", + "\n", + "We will attempt to implement Tim's winning solution using PyMC3 and our knowledge of loss functions." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### The Data\n", + "\n", + "The dataset is actually 300 separate files, each representing a sky. In each file, or sky, are between 300 and 720 galaxies. Each galaxy has an $x$ and $y$ position associated with it, ranging from 0 to 4200, and measures of ellipticity: $e_1$ and $e_2$. Information about what these measures mean can be found [here](https://www.kaggle.com/c/DarkWorlds/details/an-introduction-to-ellipticity), but for our purposes it does not matter besides for visualization purposes. Thus a typical sky might look like the following:" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Data on galaxies in sky 3.\n", + "position_x, position_y, e_1, e_2 \n", + "[[ 1.62690000e+02 1.60006000e+03 1.14664000e-01 -1.90326000e-01]\n", + " [ 2.27228000e+03 5.40040000e+02 6.23555000e-01 2.14979000e-01]\n", + " [ 3.55364000e+03 2.69771000e+03 2.83527000e-01 -3.01870000e-01]]\n" + ] + }, + { + "data": { + "image/png": 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RJB59NInRo1O5+eZktm+vvD5Ro193EWkJjADeDFp9LvBe4P/vAecF/j8K+Fgp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\n3nKoevMrTVwugZMnCzfV9HQxZD7q1YhWW1Aj+cdngbJpvfuugzp11Gu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80LFjUoFayVyq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\nYuvs9OluHnussMaj1yMiSatORpyfD4cPlyUZ4VpUMyNDuPXWRDZ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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from draw_sky2 import draw_sky\n", + "\n", + "n_sky = 3 #choose a file/sky to examine.\n", + "data = np.genfromtxt(\"data/Train_Skies/Train_Skies/\\\n", + "Training_Sky%d.csv\" % (n_sky),\n", + " dtype = None,\n", + " skip_header = 1,\n", + " delimiter = \",\",\n", + " usecols = [1,2,3,4])\n", + "print(\"Data on galaxies in sky %d.\"%n_sky)\n", + "print(\"position_x, position_y, e_1, e_2 \")\n", + "print(data[:3])\n", + "\n", + "fig = draw_sky(data)\n", + "plt.title(\"Galaxy positions and ellipcities of sky %d.\" % n_sky)\n", + "plt.xlabel(\"x-position\")\n", + "plt.ylabel(\"y-position\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Priors\n", + "\n", + "Each sky has one, two or three dark matter halos in it. Tim's solution details that his prior distribution of halo positions was uniform, i.e.\n", + "\n", + "\\begin{align}\n", + "& x_i \\sim \\text{Uniform}( 0, 4200)\\\\\\\\\n", + "& y_i \\sim \\text{Uniform}( 0, 4200), \\;\\; i=1,2,3\\\\\\\\\n", + "\\end{align}\n", + "\n", + "Tim and other competitors noted that most skies had one large halo and other halos, if present, were much smaller. Larger halos, having more mass, will influence the surrounding galaxies more. He decided that the large halos would have a mass distributed as a *log*-uniform random variable between 40 and 180 i.e.\n", + "\n", + "$$ m_{\\text{large} } = \\log \\text{Uniform}( 40, 180 ) $$\n", + "\n", + "and in PyMC3, \n", + "\n", + " exp_mass_large = pm.Uniform(\"exp_mass_large\", 40, 180)\n", + " mass_large = pm.Deterministic(\"mass_large\", np.log(exp_max_large))\n", + "\n", + "(This is what we mean when we say *log*-uniform.) For smaller galaxies, Tim set the mass to be the logarithm of 20. Why did Tim not create a prior for the smaller mass, nor treat it as a unknown? I believe this decision was made to speed up convergence of the algorithm. This is not too restrictive, as by construction the smaller halos have less influence on the galaxies.\n", + "\n", + "Tim logically assumed that the ellipticity of each galaxy is dependent on the position of the halos, the distance between the galaxy and halo, and the mass of the halos. Thus the vector of ellipticity of each galaxy, $\\mathbf{e}_i$, are *children* variables of the vector of halo positions $(\\mathbf{x},\\mathbf{y})$, distance (which we will formalize), and halo masses.\n", + "\n", + "Tim conceived a relationship to connect positions and ellipticity by reading literature and forum posts. He supposed the following was a reasonable relationship:\n", + "\n", + "$$ e_i | ( \\mathbf{x}, \\mathbf{y} ) \\sim \\text{Normal}( \\sum_{j = \\text{halo positions} }d_{i,j} m_j f( r_{i,j} ), \\sigma^2 ) $$\n", + "\n", + "where $d_{i,j}$ is the *tangential direction* (the direction in which halo $j$ bends the light of galaxy $i$), $m_j$ is the mass of halo $j$, $f(r_{i,j})$ is a *decreasing function* of the Euclidean distance between halo $j$ and galaxy $i$. \n", + "\n", + "Tim's function $f$ was defined:\n", + "\n", + "$$ f( r_{i,j} ) = \\frac{1}{\\min( r_{i,j}, 240 ) } $$\n", + "\n", + "for large halos, and for small halos\n", + "\n", + "$$ f( r_{i,j} ) = \\frac{1}{\\min( r_{i,j}, 70 ) } $$\n", + "\n", + "This fully bridges our observations and unknown. This model is incredibly simple, and Tim mentions this simplicity was purposefully designed: it prevents the model from overfitting. \n", + "\n", + "\n", + "### Training & PyMC3 implementation\n", + "\n", + "For each sky, we run our Bayesian model to find the posteriors for the halo positions — we ignore the (known) halo position. This is slightly different than perhaps traditional approaches to Kaggle competitions, where this model uses no data from other skies nor the known halo location. That does not mean other data are not necessary — in fact, the model was created by comparing different skies. \n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Applied interval-transform to mass_large and added transformed mass_large_interval_ to model.\n", + "Applied interval-transform to halo_position and added transformed halo_position_interval_ to model.\n" + ] + } + ], + "source": [ + "import pymc3 as pm\n", + "import theano.tensor as T\n", + "\n", + "def euclidean_distance(x, y):\n", + " return np.sqrt(((x - y)**2)).sum(axis=1)\n", + "\n", + "def f_distance(gxy_pos, halo_pos, c):\n", + " # foo_position should be a 2-d numpy array\n", + " # T.maximum() provides our element-wise maximum as in NumPy, but instead for theano tensors\n", + " return T.maximum(euclidean_distance(gxy_pos, halo_pos), c)[:, None]\n", + "\n", + "def tangential_distance(glxy_position, halo_position):\n", + " # foo_position should be a 2-d numpy array\n", + " delta = glxy_position - halo_position\n", + " t = (2*T.arctan(delta[:,1]/delta[:,0]))\n", + " return T.stack([-T.cos(t), -T.sin(t)], axis=1)\n", + "\n", + "\n", + "with pm.Model() as model:\n", + " #set the size of the halo's mass\n", + " mass_large = pm.Uniform(\"mass_large\", 40, 180)\n", + " \n", + " #set the initial prior position of the halos, it's a 2-d Uniform dist.\n", + " halo_position = pm.Uniform(\"halo_position\", 0, 4200, shape=(1,2))\n", + " \n", + " mean = pm.Deterministic(\"mean\", mass_large /\\\n", + " f_distance(T.as_tensor(data[:,:2]), halo_position, 240)*\\\n", + " tangential_distance(T.as_tensor(data[:,:2]), halo_position))\n", + " \n", + " ellpty = pm.Normal(\"ellipcity\", mu=mean, tau=1./0.05, observed=data[:,2:])" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Iteration 0 [0%]: ELBO = 58.1\n", + "Iteration 5000 [10%]: Average ELBO = 51.72\n", + "Iteration 10000 [20%]: Average ELBO = 69.89\n", + "Iteration 15000 [30%]: Average ELBO = 82.23\n", + "Iteration 20000 [40%]: Average ELBO = 102.96\n", + "Iteration 25000 [50%]: Average ELBO = 130.65\n", + "Iteration 30000 [60%]: Average ELBO = 145.73\n", + "Iteration 35000 [70%]: Average ELBO = 149.08\n", + "Iteration 40000 [80%]: Average ELBO = 149.4\n", + "Iteration 45000 [90%]: Average ELBO = 149.36\n", + "Finished [100%]: Average ELBO = 149.36\n", + " [-------100%-------] 5000 of 5000 in 32.4 sec. | SPS: 154.4 | ETA: 0.0" + ] + } + ], + "source": [ + "with model:\n", + " mu, sds, elbo = pm.variational.advi(n=50000)\n", + " step = pm.NUTS(scaling=model.dict_to_array(sds), is_cov=True)\n", + " trace = pm.sample(5000, step=step, start=mu)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We use ADVI here to find a good starting point and scaling for our NUTS sampler. NUTS is a \"smarter\" MCMC sampling method than Metropolis, so as a result we need fewer total samples for our chains to converge. ADVI follows a completely different methodology to fit a model that we will not get into here. We may cover ADVI, as well as NUTS, in-depth in a later chapter.\n", + "\n", + "Below we plot a \"heatmap\" of the posterior distribution. (Which is just a scatter plot of the posterior, but we can visualize it as a heatmap.)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "image/png": 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0BgbP2dnC++87ymxda9JEcc892fzrX9lAYKdDh7RVqKx06WLy9tsZ1K9vfUCb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EsvWdm7N3/Tee3aCrfd5qNjxwD16ytlOsFm+3aZTp2SAAmDQTBypI977vEVuGPLn3/KPPxwAocO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\npDh11o4DjaWUWXmuWYH9UspaZTS+UlHS3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GAZgBCiBpB9wWdFIBYL53V1mUzQrZuPBQts\npKRk8vrrDrp189CqlY+OHb088ICL2NjyH29lQ0r4809NMenXL57XX4/GVWkiKwvi80FKiomrr45j\n8WJTAVe9TtlitWq9CdesyWT5chtDhnhy6zru2aNw992x3H13LMuXGzl1Sru+b5/C6NFWMjL8S/SN\nN7rDJr5Gp3B69/Zy112BaeROp2D27GiGDo3nwQdj2bu3cpq/Dx8O/Nz5Oy1UZorjBu0MTAU8wDgp\n5b5zyttgKWVI9gcNthu0JDidWisik6nwKtE6wcPj0ZoU33KLNVcpSUrysXRpZqVtB5WaauTaa614\nvYLrr3cxY0aW7goNEb7/3siYMf/P3nmHR1GtDfx3ZnsqSCeQ0DsoKEW6yAVEVOACXvQqRYEPFRU7\nKla8iB0VLNgAUUGwISiKiIgogkrvLaGE0MnuZrP1fH9Mks2SkLqbbDbzex4fs8PszOTNOWfe81Z/\nPNuIEU4eecTB889bWLTIlHM8JkaybJnaSkqj4mO1wq+/Gnjooag8CgpAzZpq+ZFIbw92IR99ZMxK\nxlF54QU7t99eMXp2g2o1/eknA9WqSbp185SoLMrF3KBF9pBLKTcAXS84tgBYUOynqUSYzZqSVlZI\nCb/8ouemm2IC4gZHjHAVW1GTkpCX4Ni3T0GvD22dowMHBKNHR+cUgRw82KUpauXImTOq9cBkgtq1\nfXkKti5aZMJqJaB/r6JI3n/fpilqEURsLAwc6KZ9+3S2b9fx6adGli834nSq8/TECYV580w8/3xo\nHFc2G6SlCU6eVHC71U444WC1NZsDnyE+vvyfqaicPQt33hnNli2qWpWU5GHBAnvQFO5iLdtCiN5C\niA+EECuy/n9VUJ4iRAQ7Zq2iEg7+9rJg926FUaMCFbX4eB/XXeffmRUkCzWBRM+NN0bz5JOWkNbB\nSksTjBkTzY03RpOcHBrtKTNTLeJ85ox6fYtF5lQqryxjojDKUg6HDimMHRtDz57xdOkSxy23xBAV\nJenTJzB+7bvvTNSsKYmNlRiNko8+stO7d9442GCjjQmVspRDnTqSvn09vPNOBmvXpvPtt+ksXGhl\n8WIrEycGP3bj6FHBihV6Ro6MoXPneAYOjOOGG+L48cf8a0uV9ZioWzdQOatXLzwsi0WRg80m2LHD\nH2OXnKznrruiOXkyOO+RIlvWhBC3A/8D3gPWA4nAp0KIqVLKOUF5Gg2NUrB2rR6Hwz8xzGbJp5/a\nipQ9l5GhKjaPPmoBBD/+CNdc46J69dBYM5KTFbZvV6ffr7/qSUoKvql/+3Ydl1wi6dbNzW+/GXjk\nEcdF28pohJ6dOxXWrFFfilIK1qwxMGhQDF98YefkSWjf3kft2j62b9exbJmB6dPttGvnpWVLH7rw\niLPWCBF6vVr/q3Hj0Fw/LU3wxx96Hn88iqNHAzeHSUkeevUK/WagKLRo4aVFCw+7dunp2dNNixYV\nx5pcrZqka1dPzhwH2LRJz969CjVqlP73KM6W/iHgX1LKR6WU70gpHwP6ZR0vFCFEPSHEKiHEdiHE\nViHE3VnHqwohfhBC7M6y2MXn+s4UIcReIcROIUS/XMc7CCG2CCH2CCFeu9g9Q9EbtCISDn3NyoKN\nG/17j1q1fCxdaqVLl8BJcjFZbNyoz1HUsgllG5Lcu62XXjIH3Yq3b5/CbbdF8dRTFgYOdBMf7+Oa\na9w5rt385HDunOpG3rmz8vhJy3Ju5OfSOX9ex8KFBl580cHatTpeeslMaqrCjTe66NbNQ5s2Zaeo\nVZZ1ojAiTQ7qWhDNmDExeRS1Hj3cLF5sD5veoDVrqmVt5syxMXNmRtjEGRdFDlFRcN99mVzYlfPE\nieCsp8W5SjVgxwXHdgNFLQHqAe6TUrYGrgTuFEK0AB4BVkopmwOrgCkAQohWwAigJXANMFuInCii\nt4DbpJTNgGZCiP7F+D00IpTx49Xg7HnzbKxYkc7llxdtN2OzwYwZZnIravHxvpyyLKEgd0ZmSoou\naKZyUHuWfvaZkZQUtaHzpk063nzz4gsyqIkZn3xiYsiQWB58MAqrNWiPo5FF69Ze/u//8sYgnTql\ncPCgwoEDenw+wd9/63nyySimTbNw/LiWuatRcnbtUhg8OIZ16wLdnDVq+HjnHRtvv22ncePwsrY3\naeLj3/92k5QUXs9VFDp29PDmm3Z0OlVhE0JSv37Zx6ytBV4RQkSpDyGigReBdUX5spTyuJRyU9bP\nNmAnUA+4AZibddpcYHDWz9cDn0kpPVLKQ8BeoJMQojYQm5XwADAv13cC0GLWVCpLLEqHDl4eeiiT\nQYPcFw2WzU8Wx44p/P57YETAY485qF8/dMGtFwbSnjkTvJdyaqpgzhx/VsuhQzo6dw50c1woh82b\n1VInABs26Dl3rnIoCWU5N+Lj4f77M3n7bRvNm3vR6SQdOriZPDkz68UUOCaWLDHx4Ycm3KXoFHTy\npGDXLoXTpwv/e1aWdaIwIkUOHg+8+aaZY8f8ptlWrTx88IGNH36wMny4mzp1Cl7jyloWJ04Ifv5Z\nz6xZJt5808SKFXpSU8t/LSqqHCwWGDHCzcqVVj74wBbUDO7i1Ev+P2AhcF4IcQbVorYOGFncmwoh\nGgCXAX8AtaSUaaAqdEKImlmnJQC/5/ra0axjHuBIruNHso5raJQIKdWixtluzxtvdHLddaHtpRcX\nF7hI2u3BW5BSUhSsVv/16tb1Ub36xc+XEhYvNuYkZrjdlEpB0Lg41aqpi3n//m7On1eyGkmD3Q5P\nPeXgqacCCzHOnGnm5ptdJbLybt6sY8KEKPbs0XPNNS7efNMeNm4ljdCj18Nttznp29dNdLSkVi0f\n9er5wrYd3v79gvHjY/jnn0C15NprXbz2mp1q1crpwYqJXg+XXurl0kuDG29XnNIdqUBPIUQ9oC5w\nTEp5pJCv5UEIEYPaV/QeKaVNCHGhah80c4YWs6YSaTEYpSE/WSQl+Xj6aQfffGPkv/910r+/O6B0\nQiioVi3w+sFUjk6eDDSYX2hVg0A5HD0q+PRTf02vunUlsbF5vhKRlNfciI9XXe3ZREfDqFFOEhJ8\nPPRQFGfPqn/DmjV9GI2Fj0WXC6xWQZUqEp0OduxQuP762Byl/bvvjBw4kFlgaIC2TqhEkhzat/eW\nqsVhWcrim2+MeRQ1gGXLDDz6qEK1auXnFg2HMVGsTmRCiCpAL7KUNSHEMillkZtjCCH0qIrafCnl\n11mH04QQtaSUaVkuzhNZx48C9XN9vV7WsYsdz8PixYt57733SExMBCA+Pp62bdvmCD7btKl91j5P\nnOikVaufMBigZs3Q369OHR+NG//E/v06oDdxcTJo1/d4sivqrAagZcvLCzzfbO6V9VJXz+/d+0qq\nVw/e82ifi/Z569a11KoFv/zSg4MHFf76ay0JCT5q1+5W6Pc//tjICy/8waWXepk06Uo2bVKwWn9B\npTcA27atweGQYfP7ap+1z7k/p6b+AljIHq/Z69HQoVdSv76v3J8vVJ+zf05JSQHgiiuu4Oqrr+ZC\nitPBoA/wBWpSQTJq6Y4WwL+llD8V8RrzgFNSyvtyHZsBnJFSzhDejj0cAAAgAElEQVRCPAxUlVI+\nkpVgsADojOrm/BFoKqWUQog/gLuBDcAy4HUp5fcX3u/ll1+WY8eOLdLvF8msXbs2LHYG4UA4yWLZ\nMgO33BKDoqgNups1C87Occ0aPYMHq6axpk09fPWVLU9sSm45qHWX/Ka0Tz+10r+/JyjPEu6E03go\nDU89Zeb11y05n4cPd1KnjuT1102AoEEDDytW2PIU4c1NpMiitGhy8FOWskhNFXz8sYl580ycOCFo\n0MDHpEmZXH114bF1oaYs5VDqDgbAm8B4KeWi7ANCiOHALFSlrUCEEN2Am4GtQoh/UN2djwIzgEVC\niLGoSuAIACnlDiHEItQMVDdwh/RrlncCHwFmYHl+ipqGRrjTqZOHceMyqVMnuJmnDRp4SUjwcuqU\nwsyZGYUudPpcq0CtWqHNgg1HXC5Yt07Pjh062rb10qGDJ6d3Z0XhhhvczJplxutV1/jPPzfRrp2H\nJ55wMG2amRdecBSoqGmUPd4s76RWQ0+lTh3JAw9kMnq0k8xMiI6WYRtfVx4Ux7J2DqgmpfTmOqZH\ntZRVCdHzlYpw6A2qoVEQdrva1ioqqvBzi8Pu3QpeL7Ro4Su0vdSmTTquvlq1rP3vfw4OHhQ88URm\n0J8pXNmxQ6FXr7gsRUfy6KOZjBuXSXx8oV8NG9xueP99I48+GqhltmnjZto0B506eSO+7V12D+Zw\nxW6HXbt0rF+vZ+NGPcePC8xmyQ03uOnc2VOk4t0akU8wLGvzUS1ar+c6NhG1dIaGhkYJCJUFp3nz\noi/8LVp4ef31DKxWwccfG9m5U8e4cU4aN64clpizZ0WORQoE//ufhebNvSHPCA4mBgPcdJMLIQRT\npviLO2/bZuDIERc9e1acSvDF5fhxwXffGVi0yESbNh5uvNFF+/besLJYnTsHL79sYdYs1S2dm9Wr\njVSv7mP5cqvWYaQCkZYmMBjgkkvKZp0sTp219sDLQogjQoj1QogjwMtAeyHEmuz/QvOYJUOrs6aS\nO5CxsqPJQiW3HM6dE7zyipmpUy1s364WZk1PrxxdDNauXUvt2j5MpsAF97nnzEGtfQdqb9A//9Rx\n9GjxrnvihGDVKj1ffmlg+XI9mzcrpKfnPS8uDm691cnixTaqVvW/9N95x4TdXvh9KurcWLTIyP33\nR7N+vZ733zczcGAsGzaUXFMLhRz27dMxa1Zg4e3cCEGhFvDyoKKOiWBzoRwOHVLo2zeO/v1j+fln\nPRkZoX+G4ljW5mT9p6GhEUEcPapw8KD/5aYokri4yrPDT0yU3HlnJq+84g/Q37NHdVMFY9d85Ihg\nxQoDzz5rIT1dYe5cKz6fl/h4SVxcwd91ueDJJy0sXGjKdVRy+eUepkzJpEsXT4C72mKBPn08rFxp\nZft2HT//rKdLF09YuwdLg8sFy5cbA455PIJnn7Xw+ee2sHHlN2ni5bnn7Dz/fFRADUS9XjJ6tJPR\no500alR55lxF59gxkdO669//jmH2bDvDh7tDas0tsrImpZxb+FnhhVZnTUXLbPKjyUIltxwutCB1\n6OAJeZ25cCFbDrfe6uSff/T8/LOq1URHSyyWgr5ZNHbvVpg4MZpNm7KXWkl6usIVV8TQvLmXadMc\ndO3qCUjyyI1erxY1DkTw118Ghg3T8+yzDkaPduZxpzds6KNhQx+DBhXdlVsR54bRCF27uvnzz0AB\nnjmjlLh2YSjkUKUKTJjg4tpr3aSmKvh8aheTqlUhIcEXtsp0RRwToeBCOQT2+RXcc080zZtbS1XT\nrjBKZHgVQmwN9oNoaGiUD1FRgYrZxInOSlMUN5vERMmbb9qZNcvOzTc7+fBDGw0alM7SsX27wg03\nxOZS1ODmm13Mm2fC7RZs26Zn2LAYtm+/+HZcUWD0aCfDhjnz+VfB1KkW9u8PQ/9ZGXLLLU5atMhd\naka1lIZbgoiiqOOsc2cvV17ppX17Hw0ahK+ipnFx6tTxcfnl/t2A2y147jkL54pcdbb4lHSWJwX1\nKUKEFrOmosUd+AlnWZw7p9ZIe+45M/fea2HbttC9hHPLoX59H7VqqYrJ4MFOevasHDXWIFAOdepI\nRo508cYbGfTt60GUImRt926FESNiOHHC/zds0sRD48ZeNmzwK28ej2DjxoJ9J/XrS6ZPz2DJEitD\nhjiJiclWriVXX+3Jo5Rs2aIwY4aZ777TFys+LpznRkE0bChZtMjGJ59YmTnTztKlVoYMcZX4ehVV\nDqFAk4XKhXK45BJ4+mkHuRsurVpl4NCh0K3ZxYlZy035d1bV0IggDh4UPP+8hc8/98cmtWrlpU2b\nkr90ikpiomTxYivHjyu0bu3N0wpLo3icPw/PPGMhNdWvhDVo4GHuXDtPPpnXt1qUjOBq1eCqqzz0\n6OEhNdXBuXMCs1ltR3Whsnb6tMKMGep96tTx8cILGfTq5SYmplS/VlhTr56kXr3Ks8nQKH86dPAy\nZUom06f757S6OfNb5M+ehYwMQc2astQW1OLUWXsVmCul3CSE6C6lDHuVW6uzplER2LdPYcyYaLZv\nD9w7ffmllV69tBdQRSN3BwlQY6pmzsygcWMfW7YoDBkSm9P7s149L198YQtqyYa0NMGNN8awZYt/\nPE2c6ODOO53Urasp4hoaweLECcHChUaeftqCzwfffWelc2cv58/D2rUGpk83k5KiY+5cG1ddVbS1\nPBh11nTACiHESWC+EOJQSRq5a2ho+LHZYPp0cx5FrV8/F5deqilquTlxQrB3r8Lu3Tpq15Z07+4u\nNJuyPNizR1XE4uJ8TJ3qYMAANwkJqpLUrp2PH36w5pzTunXwO0bUqiWZOdPOgAFxOJ3qmv/WWxZ2\n79bzyisZla5DhYbGhezerbBwoZFmzXx07OihceOSzYmaNSUTJjjp3dtNRoagTRsvZ8/C22+befFF\nv8Xtt9/0RVbWLkaRHaxSyrtRG7g/AlwG7BRCrBRC3CqECEsDuxazpqLFHfgJN1ns2aPjyy8DSw/0\n7+/ixRczqBLCviDhJofC2LJF4aabornuujgeeCCa//43htTU0seHhEIOPXt6+OqrdH7+2cptt7ly\nFLVsGjf2cc01Hq65xhMyxaltWx+ffWbDYgmMqRk3Lopjx/KPYqloYyJUaHLwE6myOHtW8NprFu64\nI5o+feJYsMDI+fMXP78gORiN6nzr3NmL0QhffWUMUNRALTxeWoq12kkpvVLKb6WUI4EuQA3UHp3H\nhRDvCSESSv1EGhqVCE+uzVZUlOT55zOYOTOD+vU1dxWo/RNXr9YzcGAcf//tD/po3twTtrF1zZr5\n6NnTS8OG5WfBUhTo1cvDV19ZqV7d/xwbNhiYN8+EM7/kUg2NSkKjRj5atVIXX6tVMGlSNNOmWUhN\nLV04/t69Cg89FFjcLyHBx5kzAputVJcueswagBAiDhgO/BdoBywB5gIpwP1AHyllu9I9UvDQYtY0\nwh27HbZv1+F0qll/9ev7wqpNTnnz1186rrkmFo8n9yIq+eYbK927R24LpeJw+rQgLU1w7pxAr1eV\n/ipVJDVqSEwm9QUyZUoUq1apyq4Qkp9+snLZZZr8NCov69frGDgwFin9a8uwYU6eecZB7dol2wh+\n/bWBMWP8jsaYGMkTTzh4+mkzv/xiLVLh41LHrAkhFgP9gTXA28BXUkpnrn+/DyjAkFjxkRIOH1YL\nGpa2BpOGBqiZgJ06aS/N/Dh/Hp56yhKgqOl0kvfes3PFFZrMANau1XPPPVEBHShALeo7YICL225z\n0ratl3fesbN1q46ZM838+aees2e1hH6Nyk379l7mzLEzblx0jsK2eLGJxEQfDz6YiclUyAXywZtr\nWapb18e992YyY4YZu13h1ClBo0Ylf97iuEH/AJpKKa+VUi7MragBSCl9QK2SP0rwCWbMmscDS5ca\n6NEjjl694krVe66sCWXcQWqqYO1aHd9/r2fbNoViGGrLhQtlkZYm2LZN4cABhczMcnqocqAixKKc\nOiX47Te/6zMpycO331q59lo3ZnNw7lER5HAxPB74+GNjHkUNwG4XLFliYuDAWBYuNFKliqR3bw8L\nFthYv/48HTvmDXauyLIIJpoc/ESyLIxGGDTIzbx5doxG/4vr1VfNbN0aOKeKKofOnT3MnWtj4UIr\nr79u55lnLJw+rapZdnvpNkjFaTf1UhHOKYN2puXDrl0Kt98enbPLf/hhC199ZQvLbLSyYtMmHaNH\nR5GSog4jk0myeLGNbt0qRhbjnj1q0PqBA3qMRknv3m7uvTeT9u29JdpVaQSX6tUlc+bY2L1bR6dO\nHpo392qxfLnQ6+HhhzOpUcPHnDlmXK78XgaClBRdziYqKipvxwoNjcqK0QjXXOPm22+tTJgQxcGD\neqQUfP+9oUTW+4QESUKC2tngt9902Gz+OVmaIttQzJi1ikYwY9beftvEo4/6AwdNJsmff56vtC+P\n/fsV+veP5cyZQOPs2LGZvPSSo5yeqnisW6dj0KBAbVsIycsvZzB8uKtIxUo1NMobj0et1ZeSonDy\npEJamoLVCk2b+khK8tK6tTekmcUa5cvJkwKdTnLJJeX9JBWbtDTB5s06fvjBQJ8+HgYOLGFz2SxO\nnRIMHx7D5s16QLJ2bTqtWpVBzFplZ/PmQLOo2SwrdSD4wYNKHkUNoE2bihNL1KiRj3btPAHFQ6UU\n3HdfNImJPvr0qRgWQo2Lc+4cJCcreDyCGjV8JCZG3uZKr4cWLXy0aFHx4mjdbnUtcbnU+M369X0X\nbWofLmRkqApyOHhVjh4V3HBDDIoiePppB1de6dYU8xJSq5akXz8P/foFZ92vXl3ywgsZDB0aS79+\nLurVK938jOgOwMGMWbtQ0CNGuKhZs2Is/KGIO6hSRZK7LxrAVVe5ufrq0u1GQk1uWdSurQart2+f\nd3IuWxbZ3ZUjORYlm7Q0wf33R3HVVfH8619x9OgRz/vvGzl92n9OZZBDUSkPWXzxhZFu3eLo2TOe\nrl3jePBBC3//rQtp/KjbrWbIrlqlZ+VKPevW6di3T00cg8LlsGOHjuuui+Xrrw2cPRu65ywKVqvg\nwAE9+/bpuPnmGGbMMAf1mbT5oVJSOXTs6GXlynSeecZRauU+zPcw4UPfvm5ee82M1yuoWtXH6NHO\nsN8BhpI2bbx89ZWN994zYTZL+vd3c+WVngrXzqZJEx/z59tYu1bPnDkm/vlHT7VqksGDw1vp1Cic\nlBSFL7/0Bx9arYIHH4wmJUXh4YcziYoq4Msa+ZKZCUeOKNjtahN6vV4SEyOpV0+WKM7z6FGB16t6\nfJxOwdy5ZubNM/HII5mMG5cZdCuR3Q4LFhh54omogBg/s1ny8MMORowovBfvJZdI9u/XMWZMDMOG\nOZkyxUHDhuq653CopVRiY2Wenq2hoGZNHy1aeNi1S30ZvfOOhXr1JOPGOTEaC/myRpnQvPnFLWo+\nH2zfroYu1Knjo2XLi5+rxawVEY9HrfmUkqLQtq23QrocQoGUpQ+cDBfsdrUJttEoS1xnRyN8OHBA\noU+fWNLTL3QgSH77Lb3AhVEjELdbrUv1xhtmVq0y5ChYAHq9pG9fN3fdlUmnTt5ibWL37lUYOjSG\no0fzxpTMmGHn9ttdQV1fduxQ6N49Dsj/oi+8oN6zIKSE2bNNTJ2qavtJSR4+/dROjRo+XnjBwscf\nm2ja1MuLL2Zw+eVelBD7r774wsDtt/trewkh+fZbK1deWXFCUior//yj1pF0uQRGo+TNN+00arQ+\n35i1iHaDBhO9Hjp39jJ8uDviFLVt23RMnWpm8mQLO3YUb0hEiqIGasxMYqJPU9QihEaNfMyfbycm\nJvDvaTAQ8hdopLFtm47Bg2P58UdjgKIGqoXt+++N3HBDLNu2FS+Qt2lTH4sW2fINn3jpJQsnTgR3\ngalf38e4cRdv33DhWMkPIeD6613Ur68qQ8nJev7znxj27tXx4YcmHA7Bli16rr8+li1bQh/Y3KuX\nh379/AqmlILnnrNgt4f81hql5K+/dDkWXpdLMGHCxbPaInrJ0nqDqhTkb9+zR+H662OYNcvC3Llm\nRoyI5ciRCNLALkCLwVCJdDn4fKo1qEcPDytWpDNtWgb9+7sYMsTF119badJE3XBFuhyKQ0GyqFtX\nVXKEyF+ZEUIydqyTOnWKv5Ft2dLHe+/Z+PZbK7fdlkmrVh5atPDyzDMZxMcHd+MUGwtTpjj46isr\nY8Zk0r69h5YtvVx3nYuFC60MGOAu0pioX18ye7YdnU59vpQUHZMmRTFlSibZsbxOp+Cjj4whrz1Z\nrZpk+vQMmjXzx96uW6cnOTk8e+dWREIlh5gLuqrn7qZwIZU46koDYOVKA+fO+Sf1sWMKhw8r1Kun\nmdArO263mn5evbrEUEHyLY4cEfz1l54lS4ycOSOYNCmTPn08tGzpZOJE1aJSVtZgq1UthOl2q8qj\nlGCxqFliFS2TvFYttW3OLbc4OXBAx/nzAqtVEBMjiY+XNG7spUEDX4njAOPjoWtXD126eMjIUOUV\nqmzLKlWgZ08PPXt6cDjUEBezmWKP8U6dvLzxhp077lDfuPv36/nhB8n48U7efVet2rxmjYGzZx0h\nL6vRsKFk/nw706aZWbrUBKjjrqyJpLCYsqBtWw8mk8TpLFxoWsxaJWfs2Gi++iowEnXp0nS6ddOU\ntcrM8eOCd981MXeuiTlz7BWijMnWrQqjR0dz8KB/D1qW9RAzMtT6g3v36li2zMiuXTpOnFB7dma3\noalZU9KmjYfrr3fTq5eHpKTICqmobNhs8OabZl54wZJzbMQIJ6mpCr/+amDIECezZ2eUWZHt8+fV\nbFWnU9Chg6dMy4tICZ9/bkSvlwwa5NYSHIqAzwcrVugZOzYmR2FbufInrc5aYWQnEDRp4qVVq8rR\nULtFi0ClrEYNH/XrR8YLxOVSd81a1l/xOHcOnn3Wwqefqm+Y994zhb2ytnu3wrBhsZw8Gej6adnS\nU6Q4pNKSliaYOdPE22+buVjwOsCJE4JVq4ysWmVk+vQMJky4ePyURvgTEwPjxmVit8OsWarCtmiR\nkZdfziA2tuQ9JktKfDx5EgtOnRKsWaPn/HlBz54eGjcOzfp+9KjgwQejsNvhxx+ttG+vbfgLQ1Fg\nwAAPK1ZY2bVLIS7u4muVFrOWRUqKYMSIGG67LYa+feP44Qd9uZiRQ0FB/vZBg1zUrKlOXrNZMmeO\nPSIKh+7ZozB2bDTXXRfDN98YcoJttRgMlYLksGWLLkdRqyisXGnIo6jFxEheeslB1aoX/16wxoPP\np7rRCnsxx8ZKhg1zsmiRlRtvDC9FTZsbKsWVQ7VqcM89TiZOzO7cIpg928TzzzvCIhlt8WIjt98e\nw/33R3PzzdHFikkujizOnVNd4z6fYMkSQ0BT89Jy+LBg/XpducVTh3JuCAHt2nkZMcLNgAEX3xRr\nlrUszp8XnD2rLvZut+CWW2L47jsrHTtG9u6gVSsfy5alk5Kio04dX4E1YSoK58/D/fdH5TQBHz1a\nzxdf2OjdO7ytQ+FARga8/npgl/Qrrwx/uWVkBC7izZp5mDUrgw4dymb+1qkjeeyxTEaPdnHypODM\nGTVmSFHUTPKoKEl0NFSv7iMhoeLFrEUCNhs4HIIaNYK/Ga1eXfLQQ2pf4UmTotm/X8+OHTrq1Svf\nuXPqlOCtt/w7iD179Pzzj5569YJvicg9Bz/4wMxtt7lo2LD075OzZ2Hy5GhWrTJQr56XBQtstG1b\n8d9TxUWLWcvi6FFBr15xAS2UevVys2CBTXOjVTD27xd07BhPbndU165uPv/chsVy8e9pwM6dah0q\nf1aSZOVKa5kpPfmRmir48EMTGzfqGD7cxYAB7jzWsuRkNbHAahU0bOijWTOvVoJFI4f9+xXuuCOK\nY8d03Hqrk2HDnDmFbIOJlLB1q4733jPSsaOXW24pvMhuKDl8WHD55fF4PP61cNy4TGbMCH7/5r/+\n0vGvf/mD5JYvT6dLl9KvG2ptPH+F4cREL19/bYvYeM+L9QaNaDdoNlKqBU8L0ksTEmSe+JE1a/Sk\npFQKEUUUQpDHcpGcrMNm09KUCiMjQwSkj990k4uWLcvXurx2rZ6XXrKwerWRO++M4fXXzdhsgeck\nJUmGDnUzapSLnj09mqKmEcChQwobNhg4elRh+nQLgwbFsm1b8Nf2bJfWq686GDy4fBU1UEMBGjQI\nVGoMhtDMjdjYwOueOBEc+apZuv5rp6ToWLu28jkFI1oT2bRpE8nJgv/9z8w118QyZYqFf/7R5fSA\nu5Dhw100aeI3W0spsNsr/gu+ssWi1Kkj+fe/AxfK1q09xMXJSieLi3ExOVgsEkVRF8bmzT3ce29m\nuVsj//47cGGeOdPM9u3B8SNGwng4f17NKPvgAyOrV+s5ebJka1YkyOJiVK8e2Ms4NVXtpZmSkldW\nhcnh+HHBL7/oeestE3PnGtm/P+81dDq1plt5U7UqTJ4c2Gi1c+eib76KMybi4iTVqvlfrsePB+fd\nWaOGj1atAp95yRJDmcaUh8PciGhlDeDrr428/LKFbdv0vPuuqrStX5//Qt+ggY9PPrHTs6c6Clq0\n8ERMZmRlwmKB++7LpGVLVfGuWtXHI4+UbVZWRaVxYx+zZ9uZPt3OggW2nOKx5UnTphe+XAQ7d2pB\nX9ns3Klj5MhYHnggmqFDYxkyJIbNmyN+aS8WTZt6GT8+0HNy+LCOlSuLV1xt0yYdQ4bEMGRILI89\nFsXkydG88Ya58C+WI1df7ebOOzMxGiX//a+TK64ITRxdjRqSf/3LxSOPOHj0UUfQPBlVqsBjjwUq\nnCkpOqzWwr+7c6fCqFHRbNlS8edDxMesvfdedz77LPAtnZTkZcUKKzVr5v+7nzunFoeNjZVlUp9J\nIzSkpQmOHFGoWlXSqFH5Kx0aJWPJEgP33x8V0OPzxRft3HZb+buZwoG//9bRt29gQa2YGMm336bT\nrp027rNJTlYYPz6KDRv8ClqXLm6+/tpWpIK4mzcrXH99HFZroBJy//2OPMpEuOF2Q2qqwiWX+PJU\nzQ8mP/6oZ8yYGDIy4I47MnnggUyqVCn9da1W1aL+yiuqmf/22zOZPt1RYKKOzweTJkXx6acmatXy\nsWKFlcTE8J8PlTZmLb+4geRktQL3xahSRc2S1BS1ik2tWpLLL/dqiloFxudTG1U//nhmjoslJkbS\npUv4Z6iWFU2aeBk+PNBqZLMJ7rgjmlOnKn4YR7BISvIxZ46dKVMcOXFb3bt7iqSoWa3w2GNReRS1\n6tV9eUIuwhGDQe17HEpFDeDHHw1ZWaGC2bMt/PprcFqfxMbCpEmZfPmllbfftvF//+csNKP61CnB\n6tXq/dPSlAof5xbRytqmTZvo3NnDffc5yB2v0LGjO8C3HumEg789XNBkoVJR5OByQVqajmeftXDL\nLU6eeiqD5cvTad06OPO3osihIOLi4NFHM+nYMTCIZ8cOPYcPF32JjwRZFEZiouTeezNZt+48P/98\nngkT8lrE8pODzSbYsSNQO2jc2MOSJdZyqaXmcqkZ0J4Q71mKMybOn4c2bbwMGeJXXp9+2sLp08HZ\nMMTHq03rR4xwF2kDnpkpAuI3lywx4CqhXh0OcyOilTVQ/8CTJ2fy3XdWZs608/77Nt59NyPkvdo0\nNCKB33/X8eWXBrZs0eEIfrZ/oZjN0KWLB6tV8NprFnw+aNOm8my0ikpSko8PPlBjDePjVflccokv\nT4ZeJCIl/POPjl9+0ZOaWrhiYDBA48aSSy/1Ua1a0e5Rs6bkgw/s9OnjYuhQJ/Pm2fjqq/Kr97Vn\nj0LXrvG8+aaJM2fK5REC8Hhg3jwT994bjV4v6dVL3TgcOKDj7Nnyse6aTDIrsURl40ZDmVma9+5V\nWLTIwHvvGfnxRz0nTpT+vhEfs6b1Bi0eTqca23H8uMDpFAihdjZISJAkJlaOFlwaKlLCyJHR/PCD\nESEkw4e7uP/+TJo2LdsX1E8/6Rk+PJa4OB8//GClWTNNWSuIlBTB6dMK1ar5IqIbSWHs2aNw1VVx\nOByCtm09fPCBPWQtlcKlUfm6dToGDVLjFB9/3MH48Zkhd3EWxL59Cj16xGX1t5Q8/bSDJ5+MAiTr\n16eX+ZoBqvVx5Mhofv7Z36T0zz/Phzxpav9+weDBsRw96n9Z9ujhZubMjDxlVPKj0sasaRSdAwcU\nJk2Konv3OAYPjuPGG2MZMSKW66+Po0ePOJ56ypJvqrtGZCIEjByp+g2kFCxaZGLQoFj+/FNXYM3C\nYNOpk4f5860sXaopakUhMVHSvr23UihqoCYSORzqurR1q54JE6KKZGErCeGgqAFccom/FMm0aRb+\n+KN847GSk5WcRuQgsNkEOp1k6FAXdeqUz5w1GmHAgNyhAfKiZbuCycGDugBFDeDXXw0sW1a6+L2I\nVtaK0xs0kimqv/2zz4wsXmwKqHadTUaGYNYsMytWBCdgtLwIh9iDcKCocujWzUP37v4F7+RJhcGD\nY1mzRl8mCx+owcXXXusJictJGw9+Kqos4uMDldK//zbw558lV14qghxq1pQBrZwefNDCsWPB1ySL\nKgvnBW1uPR749FMbTzzhKFeLX9euHoxGdXwkJMgsJbf4FGdM1KrlQ6fLe58L60UWl4hW1jSKx/Dh\nLvr2dZE7GcOPGodw1VVaFl5lonp1ySuvZNC8uf/vnpkpuPHGGLZt03zioeDcObV1z7Jlet5918jj\nj5t58kkz06aZmT3bxOefG/juOz2bNytatidQr56PNm0C16WPPzbmUSAiiUsukdx/vz85IjlZz/r1\n5WdduzA8RlGgb19PuVt3W7TwMWNGBkJI7rnHERDDFkzcbjh4UOB0qvd8/307JpP/XgaD5LbbSlfe\nRYtZ0wjAZlNbsxw5opCZKZBSDdSsV89HgwY+4uIKv4ZG5HHggMLdd0exbp3fstqjh5uPPrLl6dOp\nUXJOnhTce28U331nLPxkoH59L3femcm117pJSIjctbww1syySwMAACAASURBVKzRM3hwDNn9gGvU\n8PHLL+kR3XZs/36FPn38dd8aNfKwfLntovVDQ8kff+gYOND/cpgxw864ceFR0iQzUw34r1u36Akl\nxcHlgoULjTzwQBQLF9ro3duDlLBrl8L+/Qper9qvuE0bL0oRzGMXi1mr2IVHNIJOTIyabadl3Gnk\nplEjtUbVRx+ZePVVMx6P4NdfDRw6pKNq1fLtHRpJxMdL7rork7NnBevX6wP6tObH4cM6HnssisaN\nbSQkVF6rd6dOHl5/PYN77olCSkHr1t6Iz4Rt3NjHc89lcPfd0QAcOKDnwAGFmjVLPx/Pn4dTpxSM\nRjW5rDAlo3FjH02aeNi3T1UpLr00fNYEs5mQZu1u365j8uQofD7B66+bslyv0LKlj5Ytg3ffiFbW\nNm3ahGZZU/3t3bt3x25XA0FTUxVq1JC0bu2tdNmd2bKo7JREDnXqSB54IJPrr3fxxx96jhxRiIur\n2Ep9uI0HoxGuvNLLokU2jh5VOHxYIS1NnbOHDwtsNgW3Wy0/0aGDh6QkH0lJ3qDU+go3WRQHsxmG\nDXPRvLmXQ4cU2rb1Eh1dsmvllsOpU/DHHwZiYiSXXeYJSjX+YNKnj5vWrT1s366+yo8fV4CSK0pe\nL6xdq+fRRy3s3KnDbF7N+PFdGDvWWaBLs0YNycyZGYwYEcutt2bSokX4KGvBoKC5MW+eEZ9P3VRt\n3qzn9GlBnTrB3yhEtLKm4WfvXoVXXzXz2WdGQGAwSNauLZ+Uao2Ki8EArVv7aN06PFwckUpMDDRv\n7qN5c21+FsTx44LDhxWaNvVSpQp07OilY8fgKQq7dum49VY1Qv7mm508/LCDevXCx2JXt67k/fft\nDBkSQ2qqLqAIbEnYuVPhxhtjcLnU62RmCl5/3YLbLXjqKUeB3R6uvNLL2rXpVKlS8cNlnE44cUKg\nKBToVk5LE3z/fWDIQqgyhiM6weCyyy4r70cICyyWXlx7bWxWj1R1JOn16n+hZu9ehSNHwicIuqJa\nDoKNJgcVTQ5+KqIs0tIE/fvH8dBDURw+HJx1JrccMjP911ywwMScOSYyMoJym6DRrJmPJUts3HJL\nJm3alE5RTUtTchQ1ld4ALF1qKLBFYzZJST7i40v1COXO4cOCBx6IomPHeLp0iefxxy0kJPTI99yz\nZwVpaX41qn59H3FxoVHmI1pZ01ALRg4dGsupU4F/6qlTHSQlhXbXLqXafHfo0Bj27tWGmoZKaqrg\nzz91Ws0+jVJTpYraK3bxYhN33RVNcnJwx1SNGj5yZ8e/8YaZLVvCL3akRQsfL7/s4MorS6esNWvm\npUGDvLGPEyc6S1z2oqKxaJGJBQtMuFwCu10wZ46ZMWNiOH4879iy2QKP9e/vJioqNM8V0W9Qrc4a\nLF5sxGr9JeDYzTc7ueEGV5EyU0qDzwf79+vYt0/PY49FBa1HXGmoCDWUyoLykIPPp2aNXXddDAMG\nxPHqq+YyLa6bH9p48FMRZVG/vi+nJMKvvxq4777oUrf2yS2Hxo19FxRWFbz2mjnsrGsQHE9J/fqS\nRYvsTJuWQceOHjp1+pH5822MGOEM+fsiXMivoPKWLWs5cCCvACyWwAWsa9fQJflUEvGXjqNHBT/9\npGflSj27diklbgZb1khJQO0do1Hy0kt2nnrKEZIAyAvR6aBVK3XwrlxpYOVKLUSyMrNhg47Bg2M5\ncEAdB3v26HC7C/mShkYBKAoMGuQm2/r1888GFi0yBm2NjomBBx/MxGDwr5erVxvyeCoiiSZNfNxx\nh5OlS61MmaKWhQlFyYtwZcgQF0Jc+H6UmM15z61eXVK3ruqh6tvXxaWXhk5Z0+qsFcLZs3DzzTH8\n8YcaWakokokTMxk3ruDsmHBhyxYdGzfqqF5d0qSJmjVWljukBQuMTJqkpmXVru3jxx/TK3U9qPLA\n5QKHg3KNJTl4UHDNNXGcOOEffNOmZXDHHRFcuVSjTLDb4cknLXzwgfo2FUKyfLmVzp2Dk2jg9cKi\nRQbuvDMaNeZX8vvv6VryR4TidMIff+h55BELu3frqFJF8uKLGQwc6MZiyXv+unU6vv7ayPjxmTRu\nXPp3m1ZnrYQ4nYK9e/0xCj6fYNYsC1u36pk1yx72ike7dl7atSu/NOrmzf33Pn5cYcsWXaWuB1XW\nbN+uMHWqhePHdUyYkMmAAW5q1Sr7Mbt4sSlAUTOZJD16aONAo/RER8OECU6++MLIuXMKUqqFhZcs\nsVG3bunHuk4HQ4a4qV3bxlNPWWja1Evt2pqiFqmYTNCrl4fly62cPy8wGilwHHXt6qVrV0fInyty\nbbkEJ2atVi3JPffkbROxZo2hXNt7FIfyjEVp1EgtlpjNe++VbzZVRYzLKSkZGfD00xZWrzaya5eO\nyZOjmT7dQnq6Xw42m5pRF8o+nydPCubNMwUcmzEjg9aty78WU2UaD4VRkWXRtKmPN9/MINsdunu3\nnuXLS9bHOD85mM1w1VUevv3WyquvZlT4jMeiUpHHRGmpWhUaNJDUrSvDQg4RrawFAyHgP/9xcffd\nDi7smXnsmCa+wrjkEsm99/pdXb/8YuDQIU1uZYHdLti5M3BDMW+eiR07VEtxcrJgzJhorroqjuef\nNwc0gk5Ph82bFXbvVvCWUqeyWgm49vjxamHdyhKwrFE29OrlZuJE/8b6+ectQc84jo1V/9PQKGt0\nTz31VHk/Q8hwOBxP1alTp9TXiYqCK67wcPXVbqpW9eFyCQYMcDFypItq1cLbDQqQmJhYrvePivLx\nyScm3G6BlIKrrnLTrFn5uBHKWxZlicUCKSkKf/8dqLB16+Zm4MB6rFlj4NVXLdhsgnXrDOzapaN3\nbzfnzgnuuy+axx6LZv58EwkJPlq0KHm3C70e0tMFiiJ58UUHI0c6w6YSfGUaD4VR0WVhNKphF1u2\n6Dh8WIfDIejZ002TJsVbayq6HIKJJguVspRDamoqjRo1evrC4xXDj1cKkpPVBq4FVV4uCjEx2b5p\nLw5HJmZz6CoVRxqNGkkefdTBY4+pBWg0i2TZoCgwapSThQuNpKf7ZZ5dL+nCjLlVqwxs3KjDZhMs\nW2bMOkdw991RXHaZh9atS6Zgx8bCs8868PkIWQ0iDQ2AevUks2fbmTw5mlWrDCxfbmDAAC02UqPi\nE9FvzU2bNtGlSxwvvmgOao0vi6ViKWr5+dvT0gQ7dyrs3Klw7lxo7y+Emg59+eVqnYZ9+8qvqGQ4\nxB6UJa1a+Vi+3MqQIS6SkrxMmpTJpZd6Wbt2LQ0a5FW+/vxTz7ffGomJkdx3n4POnT1IKUhOLt1S\nYTaHp6JW2cZDQUSKLOrXl7zxhlorLCam+N+PFDkEA00WKuEgh4i3rDmdgpdestCunTerHk/l5vRp\ntebZtGlRHD2qAJK+fd288IIj35d3sKhdW/LGGxnccks0TZqUf2B5ZaJVKx9vv23HahVUqSJRFNi7\nV3UZ3XyzkwUL/MH/KSk6GjXy0qGDh9mzzfznPy68Xkr00tPQKC/q1JHccYcTq7W8n0RDIzhEfJ21\nvn2vBmDKFAcPPpg3q7MyYbfD88+bmTUrb7GYjz6ycf31oVdmjx4VSElYNUOuzBw9Knj5ZTMffaQq\nbB9/bKNOHR9Llxp57TULQvhrDNWurf3NNDQ0NEJJJa+zJrnySs2qlpysMGtWPmWYkWVWN6gkdel2\n7lTYvVuHxSJp2tRHo0ZajaNgkZAgefZZB2PGOBGCnGDsZ55RXdVSCk6eVDRFTUOjkuLzwalTAodD\n1R9q1fLlW81fI7REfMxa7do+PvrITvv2ldf1lu1vNxrzpp0bDGpAbnkWzi2If/7R0a9fHGPHxjBy\nZCz9+sXy99+Fx7ydOCE4dChva7BwiD0IB3LLIToa2rb10aaNugi7XJCa6pfxqlX6CtNirbiE+3jw\neODcOThzhlKXUCmMcJdFWaHJQcXhgDlz1jF+fDS9e8fRoUMcnTrFcdNN0WzYEH7N7ENJOIyJMrOs\nCSHeBwYBaVLKdlnHngTGASeyTntUSvl91r9NAcYCHuAeKeUPWcc7AB8BZmC5lPLegu67alV6uVsF\nPB51ZyIlxMfLcgu0btLEx7ffWvnuOwOnTgmaNfPSqZOXNm28YVvzauNGHXa73yJ85ozCqFExrFiR\nftGq0ikpav2wHTv0PPSQg9GjnVStWlZPXPExGAIbFB88qOPMGVHu8yiSkVJN+jl+XHD8uMKZMwo7\ndihs2aInLU2tdTd8uIs778zU4gc1Qo7PB4sXG3n44SjAmHPc7YbVq438+aeB335LJykpOF6OkycF\nGRmCGjV8YZmIFA6UpRv0Q+ANYN4Fx1+RUr6S+4AQoiUwAmgJ1ANWCiGaSjXA7i3gNinlBiHEciFE\nfynlivxueNlll5XrC8Zuhw0b9Lz/vok//9TjdkPLll5uvdVJjx6eoLRCyb7Pjh06Tp4UNGrko0WL\nwAnUvXv3nJ/btvXStm14WtHyIy4u77GjRxVOnBAXld+WLXr++Uet1fLss1EkJfkYOlR1g+eWRWWm\nIDlYLJCU5GPzZvWzzSbwRGj1g/IcD8ePC44cUTh0SGHlSgM//2zg5Mm8u6Zq1Xw8/riDAQPcIVXU\ntLmhoslBVZ6ee84CXJXvvw8Z4qJ69eAoahs36hg/PoqjR3Vcd52Lxx7LpGHD8Ap1CYcxUWbKmpRy\nrRAiKZ9/yq8Ixg3AZ1JKD3BICLEX6CSESAZipZQbss6bBwwG8lXWyps//9Tz73/HkPtX/P13hd9/\nN/Dvfzt5+eWMfJWR4nDsmOCtt0xZsWiChAS1WXqkWEG6dPHQqpWHHTv8QzU+3ldgu5cLM8AefzyK\nrl0jRyZlQceOHr75Rt1RS6n+p1E63G44eFDhwAGF1asNfPmlMV/lDNRm5Fdf7ea225y0bOklMVH7\nA2iUHTVqSObMsXPPPVEcOpTt8pQ0auTjoYcc9OrlITq69PdJSRHcdFMMp06p8+CLL0xUqSJ57jkH\nJlMhX65khIPz6y4hxCYhxHtCiOxXcAJwONc5R7OOJQBHch0/knUsX4LRG7Q0nD8vyF8XhZ9+MmCz\nla5YW0YGWYqaJec+R48qeWrKhYO/vaQkJfmYN8/G1KkZXHaZh/79XXz5pa3AndeF1ofjxxWOH1dl\nUpFlEUwKk8MVV/hNaZde6qF69chUFspiPBw/LlizRs9990XRo0ccN90Uy7vvmvMoagaDpHdvF2++\naWf16nQ++shO//6eMlPUtLmhoslBLajdo4eHadO+Y82a86xadZ7169P54Yd0RoxwU6tWcMZkWpqS\no6hl8/HHJo4fDwfVxE84jInyzgadDTwjpZRCiGnAy8Dt5fxMQaNrVw9Tpjh49VUzmZl+BSohwce7\n79pK7Qbds0fH7NmBaTkJCb6Ie7E2aiSZPNnJhAlOjEa1fVFBNG3qxWiUuFx+mef+WaNwWrTw0qeP\nm1WrDNx8swtL3movGoWQmipYvdrA//5nyappGIiiSFq39nD99R7at/eQlOQlIUFqmXYViPR0NUGn\npK3Ywp24OEmbNqFzSaqxsZLcRg2TCfT6yHqHBYNyVdaklCdzfZwDLM36+ShQP9e/1cs6drHj+bJv\n3z7uuOOOnL5e8fHxtG3bNsf/nK0th+rznj2/0qkTrFvXk2PHBH//vZboaMnAgd2oVUuW+vrLlv2G\nlBagd9ZvvJqRIx3UqtWlTH6/sv78999FO79r1+5Mn57B/fer3vLo6F5Ury7z7I7K+/cpz8/du3cv\n8N/j4+E//1lBhw46+vW7styfN5Sfswnm9fftU/jPfzZy4IAO6EXVqj6qVfuZRo28DBjQjYYNfaSm\n/sIll0j69fN/PzW1/OSRfay8/x4V5fNXX/3GjBlmunbtzl13ZXL06K9h9XwFfU5NFcyd+zuHDyuM\nHn0lHTt6y3R+ZH/OzIQ77ujL7NkWYDUAd9/dOSjvx7JcL0vzOfvnlJQUAK644gquvvpqLqRMi+IK\nIRoAS6WUbbM+15ZSHs/6eTLQUUp5kxCiFbAA6Izq5vwRaJplgfsDuBvYACwDXs/OIL2Qn376SXbo\n0CHEv1X58f33em66KbsWh+S++zK5885MLfMRtdxBdlzQhAlOunaN0Ah5jbAkPR1On1ZwOMBkUjPA\nq1SRmoUygvjjDx0DB6pBx23aePjwQxuNG4e/RejAAYWJE6PYsEFNwqpd21euVRPS0gTr1un5/Xc9\n3bp56N7dTbVq5fIoYcHFiuKWmWNYCPEJsA5oJoRIEUKMAV4QQmwRQmwCegGTAaSUO4BFwA5gOXCH\n9GuVdwLvA3uAvRdT1KD8Y9ZCzWWXeXn5ZTt33ungm2+sTJ6cv6IWDv72sqZKFRg82M3cufYARa0y\nyiI/NDmohEoOcXHQsKGPVq18NG4sqVMn/BU1bUyoFFUOZrNfudm2Tc/bb5vJyAjVUwWHs2fh4Yf9\nihqoNSmdzvzDRMpiTNSqJRkyRG15eMMN4amohcPc0JfVjaSUN+Vz+MMCzp8OTM/n+F9A2yA+WoWl\ndm3JmDERWq1UQ0NDI4ypVk0SH+/j/HnV5vH++yZuvNHFFVcUrTSS263WNCxLdu3S8dNPgTft188d\ntDIcGqEj4nuDRrIbVENDQ0Oj/HjjDRNPPumv4jpqVCYzZjgwGi/+HZcLfvxRzzvvmGnZ0suIES7a\ntPEWWqoiOVmwZYseo1HSsKGPBg18Bd4nPz75xMhdd/lrbiiKZPlyK506VZzamxdy8KBCRgY0aOAL\nSjmR8qbc3aAaGhoaZU1KiuC77/Rs3Bih6XplgM8Hv/+u45df9Jw+Xd5PE14MGOAmLs5vlVqyxMTJ\nkwVnnqelCcaNi2HtWgNz5pjp1y+Wjz82YrMVfK8dO3SMGqW23evePY4XXzSTnFy8V3jVqv5nNZkk\nH35YsVsxbt+uY8CAWHr0iOOll8ycPVveTxQ6IlpZi/SYtaISDv72cEGThUplkMPWrQrDhsVw882x\njB8fnaf+IFQOORSVi8nixAnBLbfEMGRILHffHc2BAxH92ijWmGja1Merr2aglp9Qu31YrQUrawaD\nWhIjGykFDz4Yzbp1BUclJSb6MJnU73k8gpdftnDddTH8/beuyEWrr7jCy4cf2njtNTs//GBl0CB3\nga7YcJ8f8+dnF5YWzJxpYdOm0ER2hYMcInvWaWhoVEr271cYMSKWffvUxdvpFCFvhB6p6HTkJEd8\n952R22+P5uhRrW5hNv36uXnzTTs6naRKFR/R0QVrTrVrS554wkHt2j4aNfIPygceiCrQKteqlY/X\nXrMHHDtyRMd118UW2XJco4bkhhvc3Hqri7ZtvYgK/GfMzFTbOeZm3jwTvggNv9Ni1jQ0NCIKhwMe\neSSK+fP9QUBDh7p49107SiXYnu7cqfD333qioyWJiT7q1/dRo0bp1vmpUy1ZLe1U7r7bwSOPZGoF\nfLNwu2HPHgW3W3DZZYXvCnbuVFi40IjVquBywYIFJkCyceN5GjW6+N8qPR2++srIffdF4fP5Na3E\nRC/ffGOtVG3JPB4YMSKa1av9gXstW3r5/vt0YmML+GKYo8WsaWhoBI1z52DDBh3794ffErJtm475\n83NHXkvGjs2sFIoaqJawxx+3MHZsDH37xnH11bHMnm1i1y6lxNbFoUNdKIpfEXjjDTNbt2pxgNkY\nDNC6ta9IilpyssKtt0bz+usWPvzQRMuWXkwmycSJzkK7z8TFwciRLr791krTpv6SRCkpOnbsqFx/\nD70eRoxwBxyrW9cbEUkG+RHRy1c4xqy5XPDbbzrWri27iRUO/vZwQZOFSmnkkJIieOCBKPr3j2P9\n+jKr/lNkNm3Skbt9zciRqssnPyJxPDRr5mPxYhu1aqn+oCNHdDz+eBRXXRXH9Olmdu5U8o1xKkgW\nbdp4efDBzJzPUgreeceEJwJrTYdyTLjd8OGHRvbv98+bKlV8rF9/jqlTHcTFFX4NgwG6dPHyxRc2\nPv3UyujRmQwa5KJu3eD7/8J9fnTr5qZ16+xBKBk3zhmSTVk4yCH8VtoIZ/16PYMHx1CrluTnn9OD\n1hBXQ6MsOHBAMGpUDNu3q0tHYfE55cHGjf5lLTHRw/33Oyq0W6QkXH65l6VLrTzySBSrVqkR5E6n\n4JVXLLz1lpnHHnNw7bVukpKK9oI3GKBfPxfff69n82b1eitWGElNdVC/fviNgXDl4EGFt98O9B1f\ncokkqyNisUhIkCQkeOjf34OUVOj4s5JSv75k/nw7W7boqFJF0rFjaHYP6ekixzJdo4akZs2yH/Na\nzFoZcuiQwrXXxpKaqiCE5J9/0klMjNBoSI2I4/RpwX33WVi6VI0FUxTJmjXptGoVXmN4wQIDd98d\nzbBhLh5+2FFgDFCkc/Kk2srnkUeiSEsLNDkkJXl4//0M2rcvWqD5woV6jh3Ts3Klnt9/NyCE5I8/\nztO0aeWVb3FZs0bH4MF+85leL/nll3RatgyvOaShkpys8NtvembONLF3r7oJbNLEw6efhq612MVi\n1jTLWhmyfr2e1FR1wTQaCYgB0bg4e/cqbNmi48wZwRVXeLn0Um9ExB8dPSpyAsH79Alff9KuXQpb\nt+qw2USOogYweLCLBg3C7yUzaJCbLl3OU7u2jNj4laKSnf3Xvr2Vn37S89xzFs6cUSdPcrKea6+N\nZc4cO//6l7vQoqwNG0omTjRz000u+vXLwGCAEycUmjbV0myLSkZG4Dv47rszadIk/OaQhlpbcNSo\nGE6dCnzZHDigw+0WZJdrKS0nTwq2b1fjf+vX91G9ev7nRcAr7+KEU8za+fPw1lv+1bBJE29ArZ1Q\nEg7+9pKyfr2Ovn3jGDcuhocfjmbgwFh27Cj5sA0HWfh8alzVsGExjBoVUy51q4oiB69Xja+85ppY\n0tIUpk71V2o3myWTJ2cSFVXABcqJ+Hho3Lhoilo4jIeyIDHRx5gxLlatsvLuuzbatPEAEqdTMGpU\nNH/9pStUFs2be+nTx8Mnn5h4+ukonnzSwogRsWzeHFmvkVCOibp1fej16rrfpYub0aOdZd5yqjhU\nlvlxIZs26Rg+PDaXorY66/+SV17JoHHj4CjYyckKEyZEM3RoLA8+GM1//nPxeA3NslZGpKQobNni\nF/ewYa4iBZNWZpKTFcaMiQkoMul0Co4dU2jTpmLuRt1utdXMbbfF4HQKdDpJx47haZn45x8dQ4fG\nUqeOj4MHdQFWgZdeyggb9+fp04K0NEHz5j50lSshrtgkJvpITPTRr5+blBSF5GSF06cVzGYKbUIe\nHw/Tp2cweHAMqak6vF6BwwGvvmrmrbcywr5RfTjQqpWPL76wYrMJ2rXzUreu5l0JR1auNOSxgtao\n4eOllzLo06fgQsJFxeWC994zsnp10S6mxayVEcuW6bnlFr/WvHRpOt26hedLOlxYtUrPsGGBOw1F\nkaxaZaVdu4onO69XVdT++9+YnBpJ99/v4KGHMsNud52SIhgyJIaDB/XceWcmn31m5PRpdZd5881O\nnn02gypVyvkhUV3kkyZFkZKi45df0ktdT0yjcLZtU7jxRlVhAxBC8uuv4Re7qKFRUrZuVfjf/yyk\npirUq+dj5Ei1f2tRE3KKQnKyQufOcbhcgUrhypU/aTFr5cmRI/4tf0KCT4tTKAIWS94X79SpDlq2\nrHiKGsDatXpuvdWvqNWu7WPkyPBzg3i98PXXRg4eVJeHuDiZo6iNGuXkgQccYaGo7dun8N//RrN3\nr57ExMiIY6wItGnj44svbMyaZebjj41IidYdQiOiaNvWx7x5djweMJkIydpiMEiio2WAsqaGJ+RP\nRC9v4RSzltuk+vTTGWVasqOixh20aOFl2jQ7der4aNfOzdy5NkaNKp1yU16y2LpV4dZbY/B41HFg\nNkvmz7eFLFMxPV21gBw7ln+aX0Fy2LtX4bnncvu0JH37unj/fRtTp2aQkFD+1qsTJwT33BOVk6E1\nbJiLatWK/1wFyeHUKcGSJQb27o3oZTKH4syN5s19zJiRwZo16fz0k5WmTSNn81lR18tQUJllYTCo\nbdYUJTRyqFtX8tFHdlq18pCY6OWJJzKYP99+0fM1y1oZUaOGupj17++id293IWdrAFStCnfc4WLY\nMDdms6ywMX6HDwvGjo3Oib1TFMmHH9ro0CE05oj9+xWefdbMN9+YGD8+k+nTHcWqwbR3ry5gt9eo\nkZfJk53ow2S1kBK+/97A77/7tfaePYOfTbtihYFJk6IZNMjFO+/YtZisC7BYqLCxoxoa4UCPHh6+\n/daK1ytyNpunT+d/rhazVkbs+X/2zjs+ijr//8+ZrcluEpLQQ0ITROmIWEBAURAbWE4s6NlR9Kyn\ngnd2v/ZyePbyQzxF1ONAESsoIiqCUqULJoQeEkg2yfb5/P74sNlsGiHJZmc383w88kh2szuZvPcz\nM695180qCxdaOOssH507J67No4EQ8isew1xCwOuv27jvPlk2qSiCN98s49xzmyZJtSp5eSoXXuhg\n2zaprI4/PsC777po167+23jwQTv//rdUJn//ezm33OLVlVDesEFl1KhUPB4pKIcM8fP++2UN8qzV\nxr59CmeckUJ+vgmzWfYTa8n92gwMDJoHo89ajOnZU6NnT2+sdyPuyM9XeO892fLklls8cdeJftMm\nlUcekcLHYhG88koZZ53VOKG2davK1q0q3bsHIxozlpfDSy/ZKoQawLHHBtm61US7dvX34vXoEeS6\n6zyMHOmnqEjl4YeTKChQ6dxZ47LLvDFt4OnzwcyZ1gqhBoIHHnA3qVADWb2dny/zTAMBhT17VLp1\nMxKz9MLGjSovvmjntNP8DB8eiElHeQOD5iQOfRX1R085a7EkXvMOiorg0UeTeOYZ+bVpU+P7MjS3\nLTZsMOHxKHTrFuDzz12MH+/Hbj/8+2rfnso556RwySUpXH+9k4KC8A3Y77+bePvtyp1NBT17Bpk9\n21ptO7XZYedOBatVFkNMnJjCrbc6mD7dzmefWXn5tK78tgAAIABJREFUZTu5ubHtjbFli8qrr4YN\nePXVXvr3b7iIqs0OxcWRN7aJOAOzKvF0nsjNVZk1y8YNNzi57joHW7c23aUsnuwQbQxbSPRgB8Oz\nZqBbVqww89//hsVH5X5r8UJOjsa775YyYECATp0ad/dfXAyPPJJUMTZo1Soz+fkqbdpIsTJnjpXK\nA8zPPNPPt99a2L9f9sM6XM7VmjUqN9/sqJj7GYngnns8DBkS23zLjRtNFdW0mZkakyd7ozKloKws\ncq0Z/dv0ReUWLUuWWLj11mTeeKNMF8UvBgbRIKE9awMGDIj1LuiCYcOGxXoXjhivF955J3L+Tajz\nd2Noblscd1yQc87xN1qogRxz8tVXkV4y7VBEsqSEiOaKbdtqnHBCgIULLezcqVbzFFW1w4YNKuee\nm1qjUOvdO8CcOaXcequHzMxG/xsNRtPg00/l/5+SIg7N52tcSLa29aBV2aweB9Y3NfF0nujaNUiv\nXmF3588/W5g+3YbH0/htx5Mdoo1hC4ke7JDQYs0gftm1S2XBgrD4MJkEHTok/gWzLnbvrurtEaSl\nSZuYzdCqlVQYxx4b4Pnny3j2WelK83qVauKjKi6XUkkMC7p0CfLPf7r5/PMSPv3UxYgRgZiPlhIC\niooUWrfWmD3bxeDB0cshs1pFxM9paVH7UwYNICMDnnmmnMrzGV94wc6aNYYL1CAxSWixZuSsSfQQ\nbz9SCgqUiPYRo0f7ycpqfGJ7PNoiRDAYKdbGj/eRnS1tkpwsL17//a+Ljz4qpaREiQjlVS36rmqH\nIUOCLF5cwi+/FLNiRQkLFri4804PJ54YJD098r1+v0z0b25MJjnuaMGCphNqta2Hymvt/PN9dOqU\n+C0q4u3YOO64IPfdF3alCaEwe7b1sDcmhyPe7BBNDFtI9GAHI2fNQJdE5ggJbrjB2+L7XHXsqKEo\nAiEUUlIEt9/uiShWkD2v5JWqcniwd+9AvaolZb5P3a9bt07loYeSKClROPnkAIMHB+nVK0i3btoR\n9XJrKM3V1ys7W+O44/ysXGludCNmg+hgt8uJGps3qxW5rf/7n5U77vDQvn3L9sIbJB5GnzUDXfLn\nnyojR6bicinccYebO+/0RCWRPJ7wemHBAjO//WZm/Hh/nfNRDxyAiy5ysnKlhaeeKuP665vGFbZt\nm8KoUakUF4ed8g6H4KabPIwf70uoYeqbN6sUFSkMGhTEWr2g1kAn7N+vMH++hX/8I5mOHTW+/NJF\nRkbiXtcMEpva+qwZYs1AtyxbZuLAAYXBgwNHnNheUgKbN5twuRS6dtXo0iXxw1hV+f13lWeeSeLh\nh8vp0qXpjvMVK0xcdpmTffsisyisVsHdd3u49FIvHTsm7nml2dA0Gb9WlPjsCI3sV7dhg4qqQqdO\nciZyNLyUQsi/JQQt8lg3SBxqE2vxeQaoJ0bOmkQP8faGMGRIkDFjjlyobd+u8Le/JTN6dAoXXpjC\n6NEpbNwol3q82qIh9OmjMX16WY1CrTF2GDQoyLx5Ls46y0flsKnPp/B//5fE9dc3bd+raKK79aBp\ncip6IBAWa4GATBQMBOTvQklZodc2NknrEE1tiz17FC66yMGll6YwYUIKw4en8sQT9lrn1TYGRYHO\nnZvmpkx3ayKGGLaQ6MEO8XFGNTCoJ14vvPaanXnzbIR6ju3fr7J+fYLE5o6QaDlkevTQeO21MubP\nd3H66ZGi7eefLVx7rYN9++KvL15M0bSwQPP7wwJN0+R3nw9KS2XDveJicLvlc4FAdSGnA9xu2W4m\nRDCo8K9/JTFlSlJEM2cDA4PDY4RBDRKKrVsVTjghraJxaogZM0o599zYNnRNVMrLZch52TIzX39t\nZv16M+npgrfeKo3paKq4IiTIQoTEVzAYDoN6vVQ0EtM0mWEfqjBRVbBa5ffQV4zDqF4vPPBAEm++\nWX1kx8cfuxg1qgWMhTAwOEKM2aAGLQJFUTCbI1tLtG2r1ZmMb9A4kpNhwIAgAwYEueYaL0VFCjab\n0Zus3oQ8aqGfhZAL2OuV34WQX8FgOCwa8rLZ7WCzyQ9BUWQZtcUSuc3QDXkzCzabDW67zUNxscJH\nH0U2uHa7Dc+agcGRkNBhUCNnTVJXvL28XFYOJgqdOmk8+GA5qiovUP37B/joIxedO8sLlx5yD/RA\ntOxgNkPbtvEj1HSxHkJiStPkAVlUBGVlskomPx927oQ9e+T3/HzYvRt27YJ9++TX/v1SuPn9YUFX\nNWJSjwhKNGzRsaPgySfLmTPHxc03uzn5ZD+PPlrOoEH69arpYk3ECE2D334zMW2aje++M/Pddy3X\nFpU5kjVRWKjwyCN2Jk9OZtEiM4WFTbMPhmetheJ2y4Py8ceTKChQueceNxkZgmOOCcZ1JZ/VCldd\n5WPkyAAej0w6zsiI9V4ZGNSBokihVVoaDnWWl0shduCAfD4YlK9RFOlVM5nA4YC2bcPPBQLyAFAU\n+VVZoDVHE7xaaNUKRowIMGJEAE2L28LWFsHy5SbOOy8Fv19BUQRPPqly6qmx3qv4oqhI5mYCzJpl\nY9w4L4884iY7u3HXVSNnrQWiaTB3roXrrnMQSsJPSRFcfbWXvXsVnn22vMX3NDMwaDY0TXrSiovl\n98JC6S3bv1/+fPCgFHGaJkWXpkkXZnIytG8POTmQlQUdOkBKihRsZnPMc9YM4ovCQoVzznGyaVPY\nh/Pyy6VceqmR63skFBUpnHWWk82bw3Y89VQ/L71UVq+RiS2ydYdBzfzxh8ott4SFGkhPm90u+PBD\nKzt2GMvCwKBZCOWWBQJSlO3eLb+vXw8bNsiw565dkJcHf/4JmzfLn/Pzpddt+3bYulW+ZscO6ZEL\nbdeg2Tl4MNZ70HC2b1cjhBoYGr8hZGQI/vEPT8Rz331n4aWX7BX1QQ0hoT8KI2dNUjXevmmTCY8n\nUrifeaafRYssgFJxvk9EWnI+SmUMO0hiaofKLTlCX3v3wpo10su2fz/k5kohduAAFBTIr5C3bfdu\n+Xj3bingcnPlY58v3MZDiMhigzow1oSkIXYIBuHLL82cfXYq69fH52W1pvP+gQPfH/Z9xcVS6AX0\nm4bYaI50TZxyip8bbohUZq+/bmPLloavjfhcVQaNomrkOz1d4+STAyxbZkJVBU5nbPbLwKDFULnR\nrdstw5/l5fLKV1mIFRRIARd6vGdP+HFxsRRx+/ZJQVdQIAWe11v97yVwuoseWLfOxJVXOtmwwcRn\nn8XnbLK0NIGihNfJmWf6yMmpW+Tn5alMnuzg+ONTefVVGy5XtPcyPmjVCu64w8M114QFm6Yp/PFH\nw/t9JrRYGzBgQKx3QRcMGzYs4nGfPkGOP96PxSIYM8bHPfd4eOKJJEDhggt8ZGUlbgilqi1aKoYd\nJDGxg6ZJoebxhNtzuFxSaIXEWmGhrAbds0eGPPfskb93uaQo27FDCrXy8rCnzeWS7w8EqhcU1KPA\nwFgTkiO1g6bBjBlWAgFp40WLzBGtg+KF7t01nnqqHKdTMG6cj8ceK+ess+q2xfz5Fr74worfr/Dg\ng8msXJmYNYsNOTbatRNMnerm3XdLOfroAFarIDOz4TdNiWlZgzrp1k3jww9LcbkUNE1wwQVOXC6F\nzEyN227zkJwc6z3UN9u3K8ybZ6VTJ40zzvAb9jKoP5oW2UMtEJAiy+WSoivUtsPjCRcdhKhc4ako\n8rUWC7RuLb1zLpf8Hpp6oKrhylAj+Shq7N2r8MknYW9aSYmCzyfrPOKJpCT46199jB3rJyNDkJRU\n9+tLS+HDDyP/yS++sDB8eALHQ4+QzEw45xw/J5/sp6xMoV27hou1hD6CjZw1SU3x9latIDtb0Lkz\nfPRRGR984OKLL1z07p24XjVofF5OSQn83/8lcf/9yVx9tYPff4/PMVZGfpKkWe0Qyh0LTSbQNCnK\n3G555fP5pMctlL9WUFB9GyHxBdKr5nbLRXnggNxWIBDuuRZqkltPoWasCcmR2qGgQKGoKGzjbt20\nuE0lsVggKyss1OqyhezLHOmx3bw5PiVFXp7CzJkWVq0y1Zgx0NhjIyNDXm8bI+Dj07IGTUr37oIx\nYwIcdVRiC7WmYO1aMx9/HOrGrrB1a3yKNYMYUHmSgMkkv8xmKbJCbTv8fvm9tFR+r2kboe34fDJX\nrbg4XFQQCIS3U8/CAoPGUVISKViGD28ZrS7S0mDo0Egv2oknxuekmDVrzNxyi5OxY1P4/nuzLg+b\nhBZrRs6axMhFCdNYW6xeHSnO4jWh1lgTkmaxQ8ibFhJZJpOMkVmt0pUR6p1WVCRFWigPDcKetKpN\nbkOEvGxCyO2FCheOsBIUjDUR4kjtUPVj6dlTh1f6BlKXLUwmmDjRi8kkDaCqglNPjU+hGqrJ8XoV\nLrnEybp14fP8/v0KgwfH/tgwctYMDI6An36KPGQak4Ng0AKoSSyFQpNmsxRxJlO4QGD3bllIUFIi\nX1tXYYAQUpjZbNLNERrm7vdLwRfqbB0SbUaD3KjQurUABKAwaJCfY4+NH+/Spk0qq1aZMJmgX7/g\nEQvNgQODfPqpi6++snDGGX7694+f/70y6enh87jPpzBtmo3HHivns8+svPiineHDA1x6qZeUFMGx\nx2qYY6CcEvqoNXLWJEYuSpjG2MLnk3dZlYnXylljTUiiboeqbpfKnrJQsUGo9UZJiRRvtbXeqM3L\nZrFIgWYySeHm98scNpdLetoqi7U6PG3GmpAcqR2ysjQuvNBHRobGs8+6D4k3/bNmjcpZZ6Vw001O\nbrjBybnnpvDHH5GS4HC2sFjgpJOCPPSQh6FDg1gs0dzj6NG9u0ZaWvi4+PxzC0uWWLjnHgc7dpiY\nOfNnZs2yceONDn77LTapLwkt1gwMmhKrFU47LZyjMWyYn27dqt9JahqsWmVi3jxLXHc0N2gCqnrG\nhJACyuORIc+dO6U3rXLeWk2x9ZBAC31V3m6oVYeiyG0UF4eLF0LFCpUFmtFzrUlJTYWHH3bzzTcu\nBgyID8+SEPDaa3YOHAhLgIIClV9+aZnBti5dNO68M9wTbcSIAG+9ZYt4zYIFFk4+OVAtFaa5SGix\nZuSsSYxclDCNtcWYMX6cTkHHjhpPPllebUi83w9ffmlhzJgU/vpXB0VFsRugXRfNtSZ8Pli82Mz7\n71vZuVN/toi6HVQ13EJDVcNiLSTYQsPaDx4MN8f1eiOrPmtDUWSo02KRIdXQ36mcB1e50CAk2GrZ\nrnGekDTEDh07Crp2jR8ve1kZrF1bXXT4q6ScRXtNlJfD1q0Ka9ao7N0b2/PDuef6ad1afoaZmYI9\neyrLo5F4PPKGPSsrNjc7CS3WDAyamv79gyxYUMJXX5Vw7LHVT84LF5q58koHfr9Cq1YCuz0GO6kj\nfv/dxPnnO/nb3xy88oq9xghfwhOq/oRw2w4hwtWbBw6EG9weOBBZ8VmZmkRWRgbY7TL8aTJBerr8\nqiwOQ0IutC9GzlqLx+mEceMilZmqCvr2bR7PoKbBb7+ZuOoqByeemMbIkWmMG+ds1DimxtKli8aM\nGaUkJws2bjQxaFBkpevIkQF27VKqPd9cJPRRa+SsSYxclDBNYYuePbUa7642blSZNMmJpsmL6rhx\nPjp00GfIqbnWxA8/mBFC2uP1121s2hT9U05urkpubv3u0pvt2Ah5tkLeL02TIsrplD/bbPL5UGFB\niMqhT4j0uFks0rNms8nt2GyyQW5GBqSkyN/b7WGxVlm01YBxnpC0FDtMmODliiu8JCUJsrI0Zs0q\nrSbWomWL7783c/bZKSxYYCUYlOt582YzeXmxlSQnnRRk3jwX3boFuPpqWVAAkJr6LWPG+PnHPzzV\nzunr1qncf38SV17pYNo0G8uWmaIyX7tlBqgNDJoYlwueftqOyxUWCRdc4K/PlJ+EZu3a8ClG0xR2\n7VLp1y964aLSUnj8cTtOp+Dxx9368WxW7rFmtUqvGsi4U6tWsm1HyDsWrMO7UdnjlpwsxVpamhRo\nrVuH+7c5HFKwWa1GFahBjWRnC556qpy773Zjs0GbNs1zY7ltm8rVVzvw+SJPjg6HoFOn2IeSBw4M\n8sorbsxm+PTTEnJzVQoK3Iwd6yMtLfK1hYUKV17p5M8/pedczoUV3HWXh+uv99K2bdPZNKHF2pHm\nrBUWKigKZGTo0xvSUIxclDDRssXatSbmzg23pz7jDB/9+ul37EpzrYnk5MiTb2UxGw3y8lT++18r\nZjNMnuw9bKPnZjs2aqrgdLvDY6dCkwtCr60PrVrJGUGh75mZ8rvNJj1qIXFmql9CtHGekBzODqFI\nddV81XjEbodOnWq/3kVjTRQVKZSURN44JCUJ3nuvlF69Yi/WgIrWHP37a/TvrwEn1/g6q1WQnq5V\niDWJwnPPJdG6tcakSU03JNa41ULejc+caeHUU1MYPTqFX34xutLriVB+w7x5FlauNEWMS9QDbjc8\n95wdkBdZi0Uwdaqn2l1YS6Rfv0gvUbQ9jTt2qIBCIKCwe7eO3JqVCw0UJTypIBAI55aBFF6Vqdyu\no+rzDge0bSvf06qV9LTZbFIIhjx0oTmhBo0mGITvvjNz1lkpnHVWCkuWmI3C2gbQubPGrbe6cToF\nHTpo3Hijh6++cjFihH5vbmsjJQWefNJdES6tzKxZtiYNhya0WKtvztrixRZuucXJjh0mtm0zMXGi\nkx07dHSibyTxnoORn69y9tkp/PWvTkaNSuHuu5PrnZNUlWjYIj9fZfHicIOh//u/8mZL1G0ozbUm\nBg4MoijhE1nnztEVDps3h2+09u07/OmtWY+NUKGBooTHQ4Xme4b6oFks1fPUoHrOWrt28kphs0lv\nWkaG/F5Z2IXeU88pBs1pC78fdu5UKCqK3t8oL5dNX4+0yrA2O6xfrzJhgpNNm8xs3mzm4oud1XIw\n8/IUFi40xzRRvimJxppo00Zw330efv65mO++K+Hxx9306RO/58vBg4N8/nkJF14YnuZgtQpuu81D\ncnLT7UNirKhGUFwsc1wqU1ioUliYOGIt1pSXw549Cvv2KQ26E1UUga2i5Y3Cf/9r49JLnWzdqo/l\nu3OnWpEke8MNHsaN89c38pTwHHtskClTPIDg/PO9HHVUdE/KlROUox1ybTCVKzVBCji7XXrGQpWd\nlancVy0k4kIFCWazzEsLvS/UygMiCwp04gJyu+HXX03cdVcyJ52UxssvRy+p8OuvLZx0Uipnnuls\nkkama9eaCQTCa8rjUaqdg1auNPOXv6Rw+umpzJ9vqXG8q0G4BUZT5nTFkt69NV56qZxffinh22+L\n+fHHYs47r2lHb+njahcl6pOzVl6uVOmnAiaTaFJFHGtilYuSm6vy7rtWzjvPyfDhqYwcmcrTT9uP\nODyVnS244w53xHObNpm5667kIw6JRsMWNpsgKUnw4IMyWbe5EnUbQ3OtiaQkuOkmD99+6+KJJ9yk\np0f37xUVhY9lj+fw6ywmx4aiSIFmscjigPR0KbSs1vBw96pUFluZmfJ9drvcRkaGfE+rVpFh0MoF\nBfWIP0fbFvv2Kbz8sp3Ro1N47z0bpaVK1I4VIWDGDCugkJdn5oILUli7tn6Xu9rs4PFUfy5U+R3C\nZpP/j8ulcMUVTubPtxBoouheIAArV5qYNs3Gt982T7q5kccoqY8dbDbo1k1jwACN7t1Fk9fzJHSB\nQX1o1UoweHCAr78OJ4ffdJOHnBwjz6Mx/Pqricsvd1JQELlin3oqieOPD9ChQ/3PYIoCF1zg49tv\n5QiQEIsXW1i/3sRJJ8XWhd6/f5CffiohKys2M+OaE58Pli0z8cEHNmw2wdixfoYMCdSZn+d00myd\n3SuL96oNPmNOKNQphBRToeKA9u3lJAOHQ36lpsrCg1BVaMiD5nRK75vTKUOgqany58xMuQ2nUwq+\nkEct5L3TQSXojh0KU6Yk8/nn4fNserrGqFHR+ZAUhYgWCy6Xwj//mcyMGaXV0gLrS69ekWvYbBZ0\n7x75XLduGmazqPDA3Xyzg86dXZxwQuPWf3k5fPSRlbvvTiYYVOjRI8hXX5U0+H8xiD8S2rNWn5y1\npCQ5KmTgQD+tWmlMnuzmxhu91SIR8Uxz56zt3atw9dXVhRpAq1Zag4RwTo7gpZfKmDAhsqvqkU4I\niIYtHA6ZixVPQq2hdli+3MS4cSl88IGNd96xM2FCCm+/bcPtPvx7m4PKrTpSUw/vtWn2PmuhZrgm\nU7j1RjAoRZbFIp/LyJA5aU6nFFkh71nr1rKgoG1b6NJFfh1zDHTvDjk58r2hEGjIs1bVw1YH0bLF\nzp0Kt97qiBBqqip4/fUyevSI3k3x2LGRQvCHHyxVqvZqpjY79O0b5NFHyzGbBampGm+/XVZt8HnX\nrhq33x52wQWDCjffnMz27fU7T+3Zo7Bpk0purhrRQPq77yzceWdyRbpFZqbWLNeoeM93bir0YIc4\nurxEj6OP1pg9uxS3W6F1axG3w2j1gtdb8yzqIUP8PP10+WHbKdRGTo7g8cfLueQSH4sXm7HZqHGK\ngEH02LDBVNHkNsRjjyVxxhl++vaN/WfhcIQFWlKSjsLRVYsF/P7q+WYhr1hycriVRygPzWqVP9ts\nYW/cgAHQsSO0aRMOoYYKGHSC3y+r4hYtCp9UTSbBjBllUa/+GzAgQMeOGrt2hcXqjh0qAwc2zMuV\nkgKTJnk580wfFos8H1XFaoWJE33Mnm3hzz/l5XXbNjO//momJ6d2L6LPJ4eH33tvMgUFKmaz4LLL\nvNx0kzyR3nijg1C1OcCECT6Skhr0bxjEKQkt1o6kz5qsfq/95L57t8LGjSa2blXxehVOOCFA//7B\nuBB2jck7+OMPlTlzrGzbptKli8bw4X569QrWmXuUkyP49FMXP/9sZs8elXbtNHr2DHLMMRqZmY27\ngKanyyG7DT3RGzkYkobaoWavqILbrQ+BUNlTk5Z2+LXW7H3WQlWZqhoeOxUIyO9JSeHwaOg5oOKq\nHJoB2qEDdO4sPW9paZGzQUOVLQ0oKIiGLTZsUHniibC7MzNTjvQZMiQYdU90drbgnXdKGT8+hfLy\ncFudw1GXHcxm6N697m3k5GhMn17GOeekUloq/+7771s5+2x/rd6wtWtNXHuto+JGKBBQePddO3l5\nsol0WVn4+GrfXmPkyOZpc2GcLyV6sEOziTVFUd4GzgH2CiH6HXouHfgQ6AzkAhcLIYoP/W4qcA0Q\nAG4TQnx96PlBwDuAHfhcCHF7NPdbCJnUef31yRV3SiDvDhctKqF379h7E6LJf/9r5emnw7dwTz2V\nxCmn+Hn00fI6O9H36qXRq1fTNQQ00AfHHRdg8mQ3r7wSXhMjRvij3pKjvhxzTPgi1rGjPvYJCIci\nQyOnKt/l2e3SbXPggAx1FheH889UVbqnQp63zEzpaQuFSlNTpRAMBCKrSHXiXdu1S0XTFGw2weTJ\nHi6+2MfRRzff5zJ4cJDPPnPx73/b0TTo3bt5cif79dOYO9dVcd3IyzNRVla90DeETGWs/pl16yaY\nPTscPjaZBG+8Uaqb482g+WjOnLXpwJgqz00BFgghjga+BaYCKIpyLHAxcAwwFnhFUSrOPq8C1woh\negI9FUWpus0KmmI26Jo1Js49NyVCqIWIlxylxsTbjz+++h3cDz9YOP/8FNavj7+URz3kHuiBhtoh\nMxPuucfD/PklvPVWKR984OLll8to104fIceOHeV+5OQE69UWoNn7rIU8YIoixVdIgGVlye+tWkkh\nVtlrFnocKi7o0SMcGjWbw+FPszlcTNCAgoJo2KJfvyBffFHCDz+UcN99nmYVaiEGDAjy1ltlvPFG\nGdnZzbcmBg0KMmdOKe++W8oLL5TXGY3o2TPIbbe5AVHl+UBFXq7NJnjzzbJGFyscCcb5UqIHOzSb\n3BBCLFEUpXOVp8cBIw79PANYhBRw5wGzhBABIFdRlC3AEEVR8oAUIcTyQ+95FxgPfBWt/f70U0sN\nIR7Bk0+W061b7O9u1q1TSUsTdY4MaQwnnBDggQfKeeSRJCrnTBw4oPL66zamTdNJZrlBs5GayqEK\n3MNfNLZtU9m+XSUrS4tqMnmIDh2CjB3rY9CgAAUFim5EZAWV+56FctMCATnNoHNnyMuTv7fZ5PM+\nnxRsqanycahNR8jzZrGEhZ8OZ3927Cjo2DH2DU8VhZikrOTkiDpz1UKkpsKdd3oYO9bPb7+ZKS5W\nOPbYIAMH+snIEOzcqTJqVIDevYN6/JgNmoFY+4baCiH2Aggh9iiK0vbQ81nAz5Vet/PQcwFgR6Xn\ndxx6vkaOdDZoTWRna8i7HSlU2rfXeO65ckaM8Mc8X03T4LnnksjNVZkxo7TWu8bGxNudTrjuOi9D\nhgR49tkkFi0yE7JFmzaiIqoTL+gh90APNIcdCgoUrr3WwerVZlJSBDNnuhg6NLoX7pQU6N07wMKF\n8ibr2GM9da7PmKyHyqOnUlLClaIpKfL3e/aAyxX2koX6sjkcMmkzVOFZuUFuE7j5jWNDEis7pKTA\nkCFBhgyJPEays2PXg8ZYExI92CHWYq0qOrsNhosu8tGrV5CiIoX0dEHnzlpFqCXWCCFLvVetMjNz\npo077/RERUA6nXDyyUH+859S/vxTpbhYwWaTrns9CLX161V+/NFMx46Ck07yJ8SA5URg926F1avl\nKcblUrjkkhS+/DK6eZ4OB/z2m5mlSy2sXm1mwgRfs3j0jphQoYHdHq4KtdnkoGKTSX63WMLtPIJB\neSBCuPIzdLDr4SA0MGgivF7YskWlY0fNOJdXItZiba+iKO2EEHsVRWkP7Dv0/E4gu9LrOh16rrbn\na2TatGk4HA5ycnIASEtLo2/fvhUqORSHPpLH27ZBx44Nf39TPv755yWkpdmA0Tz7rJ22bb+lRw+t\n2utD72ns31u2bAk7dyqceOIpHHWUFvP/f8mSJeTnK9x//1mHOtcv4sYb3Tz++Im1vn7t2rXcdNNN\nUdsfjwcGDRpGRkbs10ddj6uujWj8vQ0bfkCu/eZiAAAgAElEQVRRHAhxKgBlZd/zwgte3nprSFT/\nv27dzuC778Dt/p5PPinn738/qdbXR3s91PpYCJb8+KN8fNJJ4HSyZPlycLsZ1qsXFBWxZOVKSE1l\nWO/e8v2rVoEQDBs0CJKSWLJsGdhsDDv1VDCbWfLTT43av1dffbXR58dEeBx6Ti/7E8vHsTg+9u49\nlRtucDBu3DdcdpmX00+PvT2ieb4M/bx9+3YABg8ezKhRo6iKIppxZpyiKF2AeUKIvocePwUUCSGe\nUhTlXiBdCDHlUIHB+8AJyDDnN0APIYRQFGUpcCuwHJgPvCiE+LKmv/fcc8+Ja665Jtr/Vkx55x0r\nd97pAODyy708/3x5Ne/akiVLGu3GFQLmz7dw9dUOunQJMnduKVlZsfcwvvCCjUcfDc8Gy8kJsnCh\nq9YWIU1hi9rYtk3l4YftbNli4vXXy3TRd6w2ommHEKWlcPnlTn74IbwgD/f5NAUff2xh0iTphbr8\nci/TppXX6nxqDjvUSOXh6sGgDHVqmmxVX1wsh2i63dLrlpISbvURas8RKk6wWKRHLhRabYSXLWa2\n0BmGHcI0ty3+/FPl1FNTKClRAcH335fo4jzanHZYsWIFo0aNqlYa3Gz+c0VRZgI/ISs4tyuKcjXw\nJHCGoiibgFGHHiOEWA98BKwHPgcmi7CqvBl4G9gMbKlNqEHT5KzpnS5dwgv5o49kP7SqNMUi+/13\nlUmTHASDClu3mtm+PfahF6+XiK7oAHv2qJSX1/6eaB1wgQC8+qqNefNsbNxo5p57jnxuaXPSHCce\npxPuu8+NyRQWZsXFCr4od3TJygofE/PmWdi1q/Y2FjG7KFfOWwvloIUqO83mcPgzOTk86D00wSBU\nHRp6XRMNazcEisSwQ5jmtsUff6iHhBqAwrZth5840RzoYU00WxhUCHFZLb86vZbXPwE8UcPzvwF9\nm3DX4pquXTVSUgQul4LfL/PXjj666a+GixZFVsW6XLHv42Q2Q0ZG5F1X796BejVDbWry8lTef99G\np05BzjgjQGqqRkGBSlpa7O8KY8lxxwWZMaOU66934nYrXHWVl9ato/v5dOggsFoFPp9CSYnKrl0q\nnTrFviKxGpW9YKFRVKGmuKoqPWuBQOTvLJawh02IIx7WbmCgZ0pKItewx1PLC1sgsXePRJGm6LOm\nd7KzNS68MDzbacYMK2Vlka+pHBtvCAcPwnvvRXZzrM/cxWhjMsGFF0YK03vv9ZCaWvt7GmuL2igs\nhOuv93LuuX6+/NLCG2/YufHGZFat0sedYVWiZYeqmM0wdmyA774r4fPPS7jpJm/Uq6g7dNAYOjRc\nQbdzZ+2nueayw2EJedkqf4dw7zSQIi00V7RyX7VG9FarjG5sEWMMO4Rpblv4qxS++nwKixebYz57\nWA9rIqHFWktAVeH888Mr/Jdfmj5EWVio8Mcf4W0mJQnd9K867bQATzxRxtChft59t5ShQwMx2Q+z\nGZYsMfPqq3Z271ZxuxVWrLBw661J7Nmj4HJJJ0lLRFGgZ0+NE0+sX6PaxmK3w1/+Ej4mfvyx2QII\nDSeUwxYKbZrNYQ9b5SHwECnKQsLNqAg1SADs9sjHe/eqjB/v5Ndf4+AYjjIJbYGWkLMGcPTRQbKz\ng+TnyyHbeXkqxxwTDr81Nt4eDCoRo1DOP993qP9c7GndWjBpko+rr/ZhtR7+9dHKPdi/X2XlysjD\nqU+fAJdc4mfCBCeBgEK/fgEuushHnz7BmItdPeRgRJOePcNhz59/tuByuSvamFVGN3aoLMTs9rC3\nrHIhQuUJCEJEeuCaAN3YIsYYdgjT3LaQOdiyr6nDIQgGARS++srMKafE7m5XD2vCuB3TIWVlch7p\njz+a6kyODtG2reCBB8J+4v37m/ZjTU0VtGkjLxjJyYIbb/TobtRWSKht3KgyZ46FWbMsrFhhOnSw\nRx+ZJxcpwE48McBLL9lZu9bMhg0mPvzQxl/+ksJ55zlZtkyf4dFEoXv3IMcfL71rhYVKxSBv3VI5\n30xVpVfNZpMFBna7/ApVglb+bmDQhKxbp/LFF2by82NzvHTvHmTCBB8gZ8nOmiVP7KEWgy2ZhD7a\n4zFnrbgYpk2zM2pUCueem8pllznJyzv8xzR8eIDjjpMXpy1bIl/f2Hh7+/aCZ54pp18/Px9+WEqf\nPvrwqlVl2TITo0encu21TiZPdjJ2bApLl0aqymjlHvTvH+T990vp189P585Bxo/3cdllXv7+9+rJ\nFlu2mDn//BSWL4+dYNNDDkY0SUuT43sAiopqr0DVjR0qV4eGPGhWqxRpDof8HuWRUrqxRYxpqXbY\nvFnlnHNSuPzyFG64wcHu3Uqz2yIlBa691ssDD7j58UczeXnyHNm/f2xzSPSwJnTmHzH49Vczzz6b\nVPF4zRozy5eb6Ny5boHUpo3gmWfcnHWWOSoJ3Oec42fkSH9E8v7u3Qq//mrm119N5ORonHhiIKrd\n6euioEDhxhuTKS0N3xH6/QrTp1ubJY/NbpeJ9KecUorPJ2f9mc3QrZsPvx8eeCAZvz+8b263wjPP\n2HnvvbJ6hW8NJJs3q2zdqnLUUYefNdqvX5AuXQLk5poqIom6pqoQC4mz0Ew3vbmzo0xpqTwfLlli\n5i9/8cVkCHxLYt06E8XFcg3+8ouFtWtNJCcf5k1RwGYTEbOoe/cO0K+fDqu5m5mEPvrjMWftp5+q\nfyQ1tcnYuFHll1/MfP+9Bb8fzj7bx6hRAebPd1U7pzdFvF1ViRBqBw7AHXck8/XXYaWRnCyYN8/F\nwIHhA2vDBpW8PJVWrQTHHhuss1KzMZSVQW5udU9Vp06RJ/ho5x5UddenpsK11/oYNizAN99YePNN\nO7t3KyQlwbhx/phdf2OZg6FpckTY5s0muncP0r9//S7CBQUK11/vYO1aM61ba8yZU0rv3rWfxDt0\nELz8cjl//7uj1uplPeSi1EkzLhA92WL/foVXX7XxwgvyxrVbNy0qLYlqQk92aE7++CPy/PnZZxZe\nfLH5bdG1q8att3p48cUk+vQJ8PrrZTFvwK6HNZHQYi2abNyo8uWXFvr0CTJ4cIBWrZpmuzZb9ecq\nN74VAhYuNHPttc4IETd/vpVnninj2mub54S2ebMpQqgBlJcrvPeelYEDZehvzRqVc85JrfB23X23\nm5tvrru1RkNp3VowbpyPTz4JG7BdO+1Q/kNsMZuhd2+N3r29XHqpD49Hit+sLNHi0o68XvjkEwu3\n3urA51Po1UveYKSnH/69e/YorF0rT1n796vcfXcS//lPWZ3TEE44Ici775bWa/sG+iAYlNNSQkIN\nZAW6QXRJTo608ZYtZgKB5nfoOp0yheHyy31kZIioTjuJJxL6UtHQnLUNG1SmTbPx9ttWfvzRXK0T\nvRDw73/beeSRZC6+OIV//cvOwYNNsMPAmDF+HI7w4vzrX7306xcO4/3xh8oVVziPqCltNOLtDodA\nVasfRJXd5u++a4sISz7zTBJr1kTnyHc64bHH3Dz/fBkTJ3p4/PFyPvnEFVEVC7HPPWjXTtC5syA7\nO7ZCLVZ2WLrUzE03SaEGcOCAWvHz4TBVcZwuXWph48a6jaiq0L177Z67WK8HPaEXW2zYoHLPPZHx\nt8pTKaKNXuzQ3PTqFeml7to1yNKlsbFFair06KHpRqjpYU0YnrUamD3byvPPh+/qzjzTxwMPuOnV\nS54w/P5Il7F01wa56CJ/tW0dKf37B5k/38Uff8jQYb9+QTIzw793uRS83uoXt379Apx2WvMlYR51\nlMZTT5Vz773JaJrcn5ycABMnhhv0yuHqkeTnR0+hZGUJrrrKx1VXRe1PGDSC/HyFyZMdEW1gjj8+\nUO+JBunpgtattYhq57w8E0OHGvksiYLfD9On2yLyO0eP9lUTEofj4EHpfU1P1yLOnwa106tXkE6d\nguzYIa9tY8Y0/npm0HQktGetoTlrlXOuAL780srZZ6fw229yEVutcPrpkQt56tRkduxomnLnfv2C\nXHCBn9NOq34hO/roIG++WUq3bkFat9bo18/PSy+V8cEHpXTtWvPdZzTi7XY7TJzoY9GiEt57z8Xs\n2S4++6w0Igl49OjqB3tlr2Es0EPugR6IhR3WrjWxe3flU47g5ps91TxmtdGhg+C22yLnzxQWNu6Y\nM9ZDGD3YYs8ehY8/DqcyWK2CKVPqnzrh8cCSJSYmTHAyZEgaM2fWkFdyGPRgh1iQlSWYObOUM87w\ncccdbk46KdBibVEVPdjB8KzVwIknBhg1ys/CheGyygMHVK66ysG8eaV06aIxfLifJ56wE6pYKSxU\nyc2N/gxChwMuvNDPaaf58XoVnE4Rsx40Nhv06aPV2spjxAg/EyZ4+fBDecI8/ng/Awe20Db+BmzZ\nEqnK7rzTQ9++R3a8nHeej//8x8rmzfLUddRRRoVgInHwoFIpdULwr3+V1bsSsKwM5syxcuutyYTO\ny3v2JLQ/osnp00djxowyzOYWV3ysexJ6JTc0Zy0zUxzKffJGPL9zp4m1a+UFp0+fINddF/n7Awea\nr5Fgerrsf1YfoRareHuHDoLHHy/niy9K+PTTEqZPLyM7O7aeNT3kHuiBWNihTZvQZy+YNMnDtdd6\nj7g1QHa2vPu///5ypk51M3hw48S/sR7C6MEWrVrJUHdGhsYHH5Qybpy/XrmdmgZff22JEGrQsFCe\nHuwQDYJB+PxzM3fdlcTnn1vYs6fm65XdHhZqiWqLI0UPdjC0cy1kZwsefbSc887z8fDDSaxfb0JV\nISVFXnAcDrjjDg979yrMm2cDBB066CMZUk+kp8uKPAODESP8vP++i4wMQd++wQb3cOrWTXDHHd7D\nv9Ag7sjOFnzzjQuLRdCxY/3Pp2vWmLjxRgeVhdrIkX6OOcY494TYv1/hrrsc7N2rMn26jHS89loZ\nXbsa1614QBEicT+ohQsXikGDBjV6O8XFsG+fvL3LztYihs0WFcGGDSYURWHgwABJSbVsxMDAwMCg\nydE0eOghOy+9FD75ZmcH+d//SuusBG5puFwwfryTlSvD6T1DhviZPr3McDToiBUrVjBq1Khqbs+E\nDoM2FWlpsoy4R49IoQaQkQFDhwY5+WRDqBkYGBg0N0VFCnPmhAsJjjoqwMcfN06oCQH79in8+adC\nfr6CNwEcuSkpMGlS5D+ybJmFjz4yRqjEAwkt1uJxNmg00EO8XS8YtpAYdpAYdggTr7ZIThaMH++j\nT58A06aVMXt2KT17NlyoffDBjzz7rJ2RI1M57rg0hgxJY/LkZLZti//L5ciRAU47LTKP79//trNz\nZ835a/G6JpoaPdjByFkzMDAwMIg6Pp+s9szIEE1aaZicDP/8p5spU2QucWNYt05lypRkXK5wmMTr\nhTlzbHTsKHj0UXcj9za2tG0rePbZMiZNcrB8uQyHFhWpHDyoxHykk0HdGDlrBgYGBgZRZeNGlWef\ntbN0qYXhw/3cdZeb7t31d+159VUb//hHzZUv06fL6tREID9f4fvvLbz+uo1evYI8/ri7UrW2QSyp\nLWfN8KwZGBgYGESN3FyVv/zFyc6dsu3RrFk2LBZ4+unyGmchx5ITTgjgcAjKysLXylatNB55xM2I\nEYkh1EBW3U6c6GPcOB9Wa80zqQ30RfwH4evAyFmT6CHerhcMW0gMO0gMO4SJli02blQrhFqIzz6z\nUFTUfH0p68ugQUGefno+H37oYsYMF3PmuPj2WxcTJ/po1SrWe9f0pKTULdSack1s26ayaJGZ5ctN\nlJU12WabBT2cJwzPmoGBgYFB1PB4qouyY44Jkpamz7BbdrZg2DBj0kpTUVCgMG+ehYcfTsblUgDB\nJ5+Ucsopho2PBCNnzcDAwMAgamzapHLGGakVY6QsFsGcOS5OPtloWJvo7N+vcP/9SRUjB0N88IGL\nMWMMsVYTRs6agYGBgUGzc/TRGvPmufjmGwt+P4wd6z/imbAG8clnn1mqCbW2bTWOPtpoVnykGDlr\nLQA9xNv1gmELiWEHyQcf/FjrjMSWRjTXRP/+Qf7+dw9Tp3oYMCCIyXT498QK49gI0xhb7Nyp8Mgj\nkZ3irVbB9OmldOkSX2JND2vC8KwZGBi0SAoKFB56KImnnnLy7rtl9OtX8wXk4EEoKVERQqBpsru9\nxQJWK9jtAqeTRokPIeS+lJXJ/C6fTw7TTk4Gm02Qni6wWA6/HQMDPaFpCm53+EaoS5cAr75azvHH\nG17VhmDkrBkYGLRIdu9WGDYslQMHVNLTNebMcdUo2PLzFdatM/HJJ1Y++8xKWZmC1Spo1Up+tWmj\n0bVrkK5dBe3bazidGsnJ4HAInE5Baio4nRrp6aBUceLt2qXw7rs2ZsywsXevQuVB5GazoE0bQffu\nQQYPDtC3b5Du3YPk5GgJWZlokFhoGvz6q4ktW0y0b6/Ru3eQ9u0TV280FbXlrBlizcAgjigrg6VL\nzSxaZOHqq7106xZf4QQ9EQjA1Vc7mD9fzkbs1SvA7NmltQ61DgZhxw6V7dsV8vNNLFhg4ccfzRQU\n1J1NYrEIOnXSOOqoIAMGSMEVEnpJSYJ9+1RmzrSxbp2J3FwVn6+usKygV68gd9zhYeTIgNHItJ64\nXHDwoEqrVhopKbHeGwOD2mmRBQarVq2iqcVaQYHCgQMKZjN06qRhjYMZuEuWLGHYsGGx3g1dEO+2\n+OEHM5dd5gQUSkrg+efdDQrBxbsdmgKzGY45ZiHz548FYONGM/PnW7juOl+NrzeZoHNnjc6dAYJc\ncomPffsUCgsV9u1T2b1bZckSMz//bCYvTyXkJfP7Ff7808Sff5r45pvq201N1ejaVeOEE/xceqmG\nEHI0k6Io7N2rsHWriT/+MLFrl4IQChs3mpk0ycG0aeVccUXN+9oQEnFNCAGrV5u4//4kli41c9VV\nXh56yF3nWKpEtENDMWwh0YMdElqsNSVlZfD99xamTk0iP9+E2Sx46CE3V17pxemM9d41DJ9PfsXr\n/rc0du1SuPtuByERsGCBlcJCD23b6te7UlYmO9i3bSt06QU6+uggmZkahYXSO/bII8mMGBGgR4/D\neyxVFdq3F7RvL+jdW77+sst8FBWFBJxCQYHKn3+qLF9uZsMGE/n5KpoWedNcUqKyerXK6tVVT8fS\nZj16BDn3XB9HHRUkPV2QkaHRqpWge3fDq3o4fv7ZxIUXpuD1SptPn27jpps8dOumv7VoYFAXRhi0\nHggB//mPldtvT6ZyTgkIfvihpOJEHS+43bBsmZlp02wUFKicc46fiy/20bWrvv+PPXsU8vNVTCbo\n0SOou3DG+vUqK1eaURRo3VqWp3fu3HQ2XbLExHnnpVY8bt1aY/HiEl3ngXz6qYWrrnLQv3+Qt94q\n06XAmDXLwuTJ4TuWp58uq9W71lCEgKIihZIS6ZkvKpJfGzaY+O03M1u3mtizR3rO6rE1OnYUDBkS\nYPhwP506abRtq9GmjaBtW6HrSsvmJDdXZfToFPbvD4epMzM1fvhB38eMQcumRYZBm4odOxTuu6+q\nUJMVW3Z7bPapMfz8s5mLLpKhNIB168ysWWPilVfKSEuL7b7VxurVKldd5SAvTy7Zyy7z8uCDdQ8f\ndrth/361Irk7mgQCcP/9SXz3XTgunpqqMWWKh7Fj/U0i2vbti8yN6tIlSGqqfi86xcXwxBNJgMLq\n1WZefNHOU0+V6+6YOe20AMcd5+e332TJ5fvvW7nkEl+TepwVBTIzBZmZgq5dK//Gj98PBw7IatDi\nYoWDB1UOHlTYtUtl7VoT69aZyMszHer+DqCwa5fC3LlW5s4Nr7f0dI2TTw5wxhn+ikKErCyBmtAN\nmmpn9WpThFADuPtujyHUDOKShD6Mm6rPmhBKtSouRRFMm1ame28URPaI0TR4+20bVYXnF19Y2LtX\nn8thxw6FK65wVgg1gJkzbWzYULcL4cMPrQwalMqFFzr57Tf52mj1yzGbZQisMiUlKvfdl8y4cU42\nbWq8bas6wa+4wkdycsO21Rx9g8rLFQoKwuvsvfesbN6srzW2ZMkS2rYVvPBCORkZ8lheu9bMvn3N\n13vNYoG2bQVduwoGDNAYOTLA+PF+Jk/28uqr5XzxhYuffy7ml1+K+fbbYubNK2HWLBf/7/+V8q9/\nlfH3v7u54AIfPXtqbN1q4sknk7j8cifjx6fwxBN2fvvNRHn54fdDD72kmpLVqyPPD/37Bxg79vAe\n00SzQ2MwbCHRgx0Mz1o9yM7WeP/9UqZOTaawUKF//wB/+5uH448Pxt1dq6rK/6cqnTtrpKfr845z\n+3aVHTuqCzOvt/b37Nun8OyzSQSDCqtWWTjvPDNz57qiuJdw+ul+7rvPzeOPRzaC3L7dxOWXO5g7\nt5ROnRpu48pexMxMjaFD9T2uxWYDp1NQVCQfCyET7WvrZxZL+vTR+N//XFx+uZN9+1RUVc4w1AMO\nh2wDcrj9CQSgvFz2agsEpDdPVWVhRENFfTwT7ucluOgiH1OnusnO1sdnamBwpBg5a0dAcbE8EbZq\nJbDZDv96vbJ5s8qVVzrYvFlq9awsjXfeKeW44/TZrHDtWpWRI1Mj8nmysoJ89llpreHF4mI488xU\nNm0Ki7yuXQPMn18a1TBIaSmsWmXioYeSWLEispPp/PklnHRSw21cWCjDiqtXm3jqKTeDBunz86rM\nlClJvPFGOO75yiulXHKJP4Z7VDc7dijs3avSt28wLiq9DWqntBQ2bjRhNkO3bkFSUw//HgODWGP0\nWTOIYM8ehbw8lUBAetUa4/GJNh4PzJ1r4c47HXg8cPzxAZ59tpy+fev20Lz9tvVQ9WSYOXNcjBgR\nfY/UwYOwZYuJrVtVCgpUunTROPHExvfFKi8Hvx/d5hZW5bffTJxxRgqhsPtbb5VywQX6FWsGBgYG\nsaQ2sRZnQbwjw5gNKqkp3t6+veCEE4IMHRrUtVADWcQxYYKfn34qYenSEj78sPSwQg1g1KgAWVmR\n3qfFi5sn96BVKxmGueQSP3/7m5dzz/U3SeuK5OSmEWrNlYNx7LFBHnjADchQ3tFH68sbqIdcFL1g\n2EJi2CGMYQuJHuxg5KwZxAWKwhEP/+3SRePDD0uZONFBbq5c6q1a6VuYJhpJSXDttV5OOimAzUbc\ntbkxiA0ejyy8MNqQGBhIjDCoQcKzfbvC+vUmrFYYNChgzFU0qJHCQtkeJTkZOnSIj+kkiUZ+vsJP\nP1l45x0rbdoIHn7YHRcV9wYGTYXRZ82gxZKTI8jJ0XflpEHs+fhjK/fd58BuFwwb5ufGG70MHBiI\neo8+A8nmzSo33uhg1arwZem66zxV+tIZGLRMjJy1FoAe4u16wbCFxLCDpLIdevWSHhyPR2HBAisX\nXZTCVVc5WbtWrdbjLhGJ5ZrYtk3hyisjhZrFEpsRZcaxEcawhUQPdkhosWZgUBtBfeW5G+iAwYMD\n3HOPO+K5H36wMHp0Kp98YsHXtBOoDA6hafDee7aKVkIhbrzRw1FHGSFQAwMwctZaHPv3K+zcqZCS\nIlt2tKQEXq9XdjX/9lsLS5eaSE6G0aP9HH98gGOP1apNqTBoeRQWwn/+Y+Oxx5IiBq4riuD990s5\n80wjnN7U5OWpDB2aSnl52N5jxvh4/vlyOnRI3OuTQXxQUKAghJwy0hwYOWsGrFmjcuutDtasMWO3\nC159tYxx41pOz6tffzVx3nkpEc11v/zSSlKSYPZsFyeeaLjbWjqZmXDzzV5OPjnAffeFGxsLoXD9\n9U4WLSrR5TD6eEZRpHcNQFUFkyd7uOkmryHUDGLOnj0K48c7cblU7r3Xzemn++nYMTbrMqHDoEbO\nmmTJkiVs365w8cUprFkj9bnHo3D77cnk57ccd9LWrSaE+L7a8263wvPP2ysuGC0BPeRg6IGa7GCx\nwJAhQWbNKuOjj1xMnOihfXuN1q01PJ4Y7GQzEas1kZ2t8dlnLt5/38X335dw332emAo149gI09Jt\nUV4um5vv3r2Y2293MHmyI2bXTMOz1kLYuNHEvn2R2ry4WMHr1c8MxGgzfHiAfv0CrFkT+bzJJLjm\nGm/czXk1iC6tWwtOPz3AqFEBCgo8mM2QkdEyjpXmRFGIi9FpBi2Pdu0Ep5wSYPFi+XjxYgv33JPM\niy+WN3vxi5Gz1kL4+mszl1ySEvHc2LE+3nijDIejljclIIWFCps3q2zdauLAAYVOnTSOOipIr14a\nFsvh359o7N6tsHq1Cbudisa1LY3ycigoUAGB1QrJySJuxnkZGBhEl0WLzFxwgZPQyDyAJ54o44Yb\nfFHJczZy1lo4vXoF6dEjwJYt8iPv2TPAgw+6W5RQA8jMFJx0UrBRA9UThc2bVW65JZlff7VgMgl+\n/LGEnj1bUCwYKCuDKVOSmTXLiqZBaqqgXTvByJF+hg0LkJMTJCdHMxopHwHFxbBtm4kNG0yUlcHp\npweMxrYGccuQIQHuusvDc88lVTz3+OPJjBkTOOKpOo0hoQM/Rs6aZMmSJeTkCD7+uJSPP3Yxe7aL\nuXNLW9yFGYwcjBDz5v3IbbdJoQYQDCr4W06tSQUrVy7hhhs8dO4cRAiF4mKVzZtNvPGGnSuvdDJy\nZCpnnZXCG29YWb3ahNcbfm9hocK2bQqrVqksXWrixx9N/P67yq5dSlz2ZWvsseH3w4oVJq64wsmo\nUSnccouDKVOS4y7PzzhHhDFsIecx9+u3kIkTwwe/y6Xw55/NK58Mz1oLwujkbxBixQoTv/wSjvum\npWmkp8ehwmgC+vbV+OSTUpYuNfPgg8ns2lX5JKywcaOZKVPMqKpg6lQ3Q4YEWbrUzPvvW9mxQ41o\n8QGQkaHxz3+6uegiH05n8/4vsWLvXoVZs6w89lgSwWDYHv/8p5tu3RLnprCoCHJzTbhcMoXCqAxu\nGaSnC/7xDzcnnhjgn/9M4uBBheRkI2etyTBy1gwMqrN9u8Lpp6eyf39YlPzf/5Vz003eOt7VMti5\nU2HLFhOLFpmZOdMWYaPjjgtw0kkBXm5a5FkAACAASURBVHrJRuX8lZoYO9bHiy+WkZkZ5R3WAVu3\nqtx1VzKLF0cmfV50kZfHH3fTunViXGP++EPl9tuT+ekn+X+mp2v897+lDBzY8JSKsjLYvl1l/36F\njAxBz54tM3c2nsjPVygrU+jSRcNub/rtGzlrBgYGAOzerUaIkI4dg5x5ZguMgdZAVpYgKyvAyJEB\nJk3ysmuXyo4dKkuXmklN1cjLM1GXUBswwM+tt3o54YRAixBq+fkKN9zgYOXKypcSwT33ePjrX70J\nI9QKCxVuuSWZZcvCSurAAZV58ywNFmvr1qn86192Zs+2AgqqKliwwMWAAUY+rZ7JzhbEooNCQou1\nVatWYXjWZN7BsGHDYr0busCwBYc6xS8CRmK3C958s6zFJoDXtR46dBB06BDkuOOCFc2j9++HG27w\nUFiocuCAgtkMNpsgLU2QkSHIytLitpL0SI8NtxtefNEeIdTat9d46aUyTjwxQHJyNPYy+tRkh+3b\n1QihFiIz88gv2sEgLFxo5tprnZSVhYW/psmmwHrCOF9K9GCHhBZrBvGF2y1PWC2tQrW56dxZo1On\nIN26+XnoITf9+xt38vWldWto3VoDWqa4rcyuXSrvvCN7vXTsqHHLLR7GjvXTuXPi2SYpSWA2CwKB\nsLjKygoyevSRe6TXrDExcaIzYlsAN91kzEI1qB0jZ81AN7z2mo1Zs6zcfbeHU07xk5oa6z1KXIqK\nFOx2Ebfej+akpAT27pVhY6tVFhCkpBzmTY3E64UDBxSCQSoqS61WaNNG6GaGrcsF69ebUBTIydFo\n3z5xryWBgOy3NXVqMuXlCmPG+Jg0ycvRRx+ZuAoG4dprHXz6qTXi+QkTvEyd6iYnJ3FtaFA/jJw1\nA10TCMCnn1pYs8bMFVc4ue46D/fe62lQmMHg8Bid+OtHUZHCpEnJLFxoARQURdCnT4CLL/YzaFCA\no47SGtXJPBiEffsU9u9X2LtXZc8elbVrTaxaZeLPP02Ul0vBBrIi7aKLfPztb55m755eEykpcMIJ\nLcMrazbLfnGDBpUQCCi0bi0aNPEkECCi/UvbthpPPVXO8OF+0tObbn8NEg+jz1oLIB565ZjNRIQU\n3nrLzsyZ1ibv0RQPtmgODDtIDmeHtDTB+PHhdSmEwtq1Fu6/P5mzz07ljDNS+OADK3v31t/dJQTk\n5qosXGjmzjuTOOWUVEaMSOPii1O49VYHb75pZ/lyC/v3q5SXy5FwXi/07Bnkggu8UbuBMdaEpC47\nZGRA27YNE2oANhs880w5c+eW8OWXJSxcWMK4cfoVarFcE+vWyZ6FekAPx4bhWTPQDSefHEBW2cgD\n9KGHkhgyJNBi7t4N9IfJBBdc4CM7W+P225PIzY08ZW7fbuLmmx306xfg5ZfL6N277rBYXp7KrFlW\nXn7ZTmnp4S5Egr59g1x3nZd+/YJ07x5sMX3bEplOnQSdOhnntLr4/XeVsWNTOeGEAK+9VpYwVcWN\nQRc5a4qi5ALFyKxdvxBiiKIo6cCHQGcgF7hYCFF86PVTgWuAAHCbEOLrmrZr5KzFF243PPaYnVdf\nDY/16NMnwNy5pUbYziDm7Nyp8PvvJt5918bXX1simr8CZGcH+fxzF1lZNa/VQABmz7awdasJi0UW\n0wAoisBikUnsbdoIMjM1nE45n7Rt2/itLjUwaCivvWbjvvtkQu2cOS5GjGg5zdz1nrOmASOFEAcq\nPTcFWCCEeFpRlHuBqcAURVGOBS4GjgE6AQsURekh9KA6WwBChJsCtmunkZHRdNtOSoLrr/excKGF\nzZvl0vz9dzO5uSoZGcadqEFsCfVgGz48wI4dKvn5Krm5KgUFKj4fDBwYrLWreUkJLFtmZvZsKz/9\nZDnUPqUmBFlZggsv9HLZZT5DqBm0OMrL4X//C7dJmTvX0qLEWm3oJWdNofq+jANmHPp5BjD+0M/n\nAbOEEAEhRC6wBRhS00aNnDVJU8Xbi4vh7betDB+eytChaZx9dgrLlpmaZNshunTRePvtMrKywuJs\n9+6my1vQQ+6BHjDsIGmIHZKSoEcPjdNOC3DNNT7uvdfD/fd7OOec2nOPcnNV/vpXJwsWWOsQajJp\n/+ST/SQny47527fLvJ3mmK9prAlJItphzx6FTZtUtm5VKC2VRQ5btqgsX25i587a12MsbOHzwcGD\nYTmwYoWZ8vJm340I9LAm9OJZE8A3iqIEgdeFEG8B7YQQewGEEHsURWl76LVZwM+V3rvz0HMGUWbl\nSjP33BNugrZpk5nLLnOycKGrSXsr9e6t8emnLmbMsDF/vpWOHQ2nqUF807evxjfflLB6tZkFCyz8\n8ouZ4mIpwjRNDot2OAQPP1zOv/9t5+OPZVd7RRE4ndCrl5yq0LdvkKwsjQ4dErtVhkHTkJenMHOm\njenT5eg0VRX07x/guut8vPSSjQ0bzHTsqPG//7no2VMfPd5UVeaKhti7V8Xlav5ZnHpDL2JtqBBi\nt6IobYCvFUXZRPV5Dkf8Sf3xxx9MnjyZnJwcANLS0ujbt29FJ+KQWjYe1+/xZ5/9CNiBkYcsvIii\nItizZxCdOzft3+vaVXDaaQsYMgQGDmza/ydErO0Zy8fDhg3T1f7E8nGIaP49RYGiosVkZ8Nbbw2j\nsFBhyZIlaBoMHnwKJpNgxYolJCUJHn10JLfc4mD37u8RAlyukSxfbmH58h8P7elI2rXTGDx4AUOH\n+rn44qFkZIhG72/ouVh/Hsbjpnn87bdLeP11G998MxrJIjQNVq4cyd/+Zuaqq77i/7d33vFRVNsD\n/95t2WSzSShSJXTpiAgoUgR5Ij46IojlgYodfOqzYMXesGB5iILyBH34QBHlhyCKIB0E6U2ahI4E\nSLKbbLLl/v6YDZtNgbTNTnbv9/PJJ7uzM7t3zpy5c+65556zc6eVo0d7cPSogZMnlxX6fblUVPuv\nuqordet62b17OQBeb3ekjNz+Mvd1SkoKAB06dKBXr17kRxcLDPIihBgPOIDRaHFsJ4QQtYAlUsoW\nQohxgJRSvuHffyEwXkq5Nv93qQUG5cuyZSYGDQrOBlqtmo/Fi9NVMkeFohzJXczwwQdWVq0ycb56\npM2be3juORedO7t1G+PmcIDTqSVittspdeoLRfFxOuHmm+NZvrywyvCSF1/M4rnn4rBYJEuXptO8\nuT48awBvvmnl9de1hWYNG3r56af0co2P1jNFLTAI+y0jhIgTQsT7X9uA3sBW4HtglH+3kcB3/tff\nAzcJISxCiIZAE2BdYd+tYtY0ymu+/bLLPEyc6KRqVe2mbtPGw8yZjkplqOkh9kAPKDlo6FUOdetK\nrrtOu7+WL0/nv//N4MEHs2je3IvRGHy/7dqlhSP85z8xlGXsXZ6yOHZMsGyZienTLYwebeP66+10\n757AddclMGqUjV9+MZGVVW4/V67oVSdKis0Gr7+eSadObvJOTNntkieecLFggRmQTJ7sLHIKNFyy\nuPrqQG7DgQNzwm6o6UEnTOFuAFAT+FYIIdHa86WUcpEQYj0wSwhxB3AQbQUoUsodQohZwA7ADdwf\nTStB3W4tMHT3biNnzwpq1/Zx+eXeCslo7nRqv/faa06cTi2z+rZtBtLTBfXq+WjUyBcUa6Co3Hg8\nsH27kaNHBbVqSVq39mIubJCuCBl2uxbD2aqVjz59PDzyiItTpwz89ZcgI0PgdmvXKTZWW5wT7lJU\np04Jfv3VxPPPx3HkSEFfwF9/wR9/GNm3z8C33zqIjY2arjsstGjh46uvHBw+bOD0aa1KxvbtRr74\nIoaYGMns2Q6uusqjO09n8+Zehg3L5rvvLPTvX/L6q5GI7qZBy5NImwbdv18wfXoMkyZZg4oAz56d\nQa9eoV/aPGeOmdGjC8/KabFI/vUvF0OHZtOwYeTqVDSxapWRgQPteL0Cg0Hy6quZ3HJLDjbbhY9V\nRB9eL0yYYOXNN2OL3MdgkNx0Uw4PPujSTUB7NOFwwP79Bnw+qFdP6rqc38mTghMnBK1bh38QUpHo\nPc+a4gLs2WNgyJB4jhwJdl0ZjbLCsju3b++lc2c3q1cXdK/k5Aheey2Wr7828/XXDurV028noLgw\nUsKkSdZziV99PsG4cXG0aeOlc2eV805REKMROnXy0LKlh337jGRnC8xmSZ06Pjp08HDNNR5atfLS\nrJmXmJhwtzY6iY+Htm0rj5GcmSlYsMCEyyVo0MBHu3Ze3XkBK4qIPu1IiVlzOrXSS/kNNYD333fS\nsuX5H57lNd/eoIGPzz93MnNmBt26uYmJKWiQSQnZ2fodBukh9iCUSAmnT2u5is5HceRQ8IEqWLs2\nssZ3ka4PJaE8ZHHNNR5++CGDdevSWLs2jd9+0+pffvJJJiNG5NC2rf4NNaUTAc4ni/37DcybZ+aB\nB+K45RYb778fw8GDZe/79+83MH26hd697Vx/fQK33mpn9Oh47rjDRmpqeJ4tetCJyOp5KyleLwhR\n9Aqp06cFS5cGe7Pq1fMycWImnTp5KjSOqHp1LfD56qsdHDtm4NQpQVqadgPZ7ZLkZB+1ayuvWjjw\neODDD2OYPj2G5GQf113n5sorPTRr5iUurmTfJQSMGJHNt99agrZbreXYYEVEkpAACQmSUmRbUlQC\nzp6FefMsPPtsLOnpgYfWggUWGjb0Ub9+6WLMnE5YtMjMI4/EkZaW/2EoeeGFrAqJzdYrKmYtjHg8\nsG6dkcmTrdSs6ePBB12FTh96PLB2rZHFi81UqSJp2dJLy5ZeZRSVksxM2LLFSNOmvnMxG14vpKYK\n4uNliQ0bPfHhhzE891zeE5AMGpTDgw9m06pVyRYIpKXBF1/EMH58LD6foEYNH3PnZuhqib9Coag4\nnE4tPOK11wrGJdpskoUL02nVquT9g8MBU6fG8OKLseRPUxMTI/nsMwc9eniILTocMmIoKmZNGWth\nZMMGI9dfbz+3WOCxx7J48skKqCsT5Rw/LujePYEOHTxMmJBJQoJk2rQYPvnEStOmXl54IbNSxXXk\n5cgRLbZs/vxgj5jRKBk3zsU//pFdotGp2w27dxtISxPUrStp0KByykWhUJSdzZsN9OyZQH6DKjFR\nW3V6xRWli2ddvdpI374JQdusVsmYMS4GD86hefPoWWSg2zxroUTPMWuZmfDqq8GrOhcuNIekBpoe\n5tv1wooVK7DbJfXqeVm40MIrr8Ty229aqoGjRw38+quZQYPs7N5dOW+NunUlb76Zyf33u9Cy4Wh4\nvYJXXonlxRdjOXFCFFsnzGZo3dpHly7eiDTU1L0RQMlCQ8khQH5Z2GzQtGnAIEtO9jJhgpOff04v\ntaEGULWqZORIFz175vDAA1l8/rmD5cvTeOIJFy1ahN9Q04NOqJi1MJGaaiiQWTo+XmJSVyTk2Gxw\nyy05bNxo5quvYrj88uC0J2fPGli61EyzZtkV2q49ewx88kkMw4bl0KGDt9QdVO3akmeeyWLgwBxe\neSWWZcsCevbllzF07OihUaNyarRCoYgamjTx8f33Dv76S2AwQI0a5ZONoFkzH+++W/wsyVlZ2gKo\naFoZGtGn2q5du3A3oUTcdFMOFsuF9yspeWv/RTu5smjXLjAKPHu2oFW0bl3FW80zZ1r49FMrAwbY\n2bKlbNmFrVbo2NHLjBkOFixI54knsmjXzk3Nmj5SUwVduiidAHVv5EXJQkPJIUBhsqhZU9K6tY+W\nLX0VljYql+xsmD3bTN++dp5+OpZ9+yrGhOnatSspKYK1a43s3Gng9OnSfU9mppY/7kKr9Qsjoo01\nPXPRRT5uuCFwxdq29dCrl8rUXFE0buylWzdN3sePG2jdOti71r596JMM5yUnB1as0Dxg2dmC55+P\nxeEo+/fa7XDFFV6eeMLFvHkOli5NZ+zY7LBPKygqFq9KjaeIALZtM3LvvTY2bTLx8cdW7rjDRkrK\nhTuzkycFx4+XvtOTEl58MZbrr0+gSxetbNrs2WYOHy7edx49KvjySwsDBtjp2TOBBx6IY+/ekplf\nEW2s6TlmzWqFJ5/M4s03nUya5ODzz53UqROaUYoe5ttDgdOpTR26S2Dj5soiMRHGjdPc7tOmxTB4\nsJuBA3NISPBx3XU5FV7ixO0GV561JcuWmUhJKd/b02bTRsUm04V1wuWCLVsMrFxpZMMGI6dORaZ1\nF6n3BmheiHXrjDz1VCz9+tl57LFY1qwxFjmqj2RZlAQlhwB6k8WJEwIpA33R1q0m1qwpehbk6FHB\nzJkWevVKoE8fe6kNtpUrVzB4cO4zQbBvn5F77onn73+38+uvJpzOoo9NSRHce6+NsWNt/P67iWPH\nDHzzTQyff16yhIMRbazpiY0bjcyfb2LnTsO5YsvJyZLRo3O46SY39etHXvB2KHE64f33rXTunMAP\nP5Qu0Vzr1l769MnB5xO89JIVg8HHTz9lMGWKk+Tkir0eNht07hzw5kkpOHgwfLfn0qUmevZMoH//\nBK69NoFrr7Uzc6al2CNJRfhZtMjM9dfbmTzZytq1Jj791Eq/fnY2bVIFfCsbUmrJYletMnLgQPQ+\nthMTCzo08sd+57J3r4Gbb47ngQdsHDliwOsVZapd3a2bm8cfD46rO3zYyODB8UyaZCUtrfDjFiyw\nnJs1yUu1aiV7xkT0VddLzNrhw4KhQ+O57TY711yjGRdZxY+lLDORGIOxY4eRCROs+HyChx+O49Ch\n4hkReWVht8Pjj7swmSQg+PZbK9u3G4kvvPxpyLn66mBv3qFDoXuoXkgnfL7gEezBg0YeeMDGoEHx\n/PFH5HQbkXhvgJYzcPz42KBrCNp1/eOPwvUqUmVRUvQmh0OHBG+9ZeXqqxPo1y+BL78MQWBzEehN\nFk2b+mjZMjhEJS6uoAG3Z4+B4cNtbNkS8Lo98kjpk+p27dqVhAS4+24X773nxGrN+z1aqcVZsyyF\nzvJs3VrwfqtXz0vfviWbvYmcXlfHZGYKzpzRRJ2dLbjtNhsrVlR8APvevQY8FRuKFTJWrzaRm+vn\n7FkDf/5ZOlVu3doblNvugw9iSE8vjxaWnKZNfUEdT3CHULF06OBh+PCCq2H37zcxapSNY8eKZxxL\nqendL7+YWLDAxOrV5TOlKqWKwzofMTGS5s0LCig2VtKmjRJcWUhPhx07DMWKlSoru3cbuO22eF57\nLRanU/u9hg2jdxamRg3J1KlOGjXSHmSxsZKbbgqe109JEdxzj40DBwLP2Hr1vFxzTdkfflWrwq23\n5rBoUTqjRgWnR3rqqbhCvZ533+0iOTnQ3rvucvHNNw6aNlWetXPoJWbtoot8+QLYBffdV7zAyPJg\nxYoVpKXBqFE2Vq6MjNwgO3cGj1ZcruLJMn8MhskEN9yQTatW2vXZuNFUasOvrDRt6uPddwPBD3Xq\nhK5TvlAsSo0akvHjtZhKuz3YaNy1y8Tx4xeW0dmzMHWqhZ49Exg61M4tt9jp2zeBO+6wlWk6df16\nIzfdZOPmm22sXGksUcxifvQWk1NexMfDSy9lcfvtLmrU8JGY6GPo0Gz+7/8yaNu2cGMtUmVRUoqS\ng88HmzcbuftuG127JvD++6GtvbZ7t4GhQ+ODvEMXXeSjS5eKG3HrUSeaN/cxb56DhQu1urN5V/Z7\nvfC//8WwaVNAZna7ZMYMR5lCjfLKQQgt9+Rrr2Xxyy8ZTJ3q4O67XTzzTBaxsQUH2G3b+vjxRwer\nV6exZk0ar7ySRZMmJW9LZDy5dU6VKvDkky5uuSUwv3b6tIGdO43nLO5Qk55uYNcubSXNokXphZa1\nqkzkN87KEouQnCz55BMn/fvbOX3awN69xiIrGPz5p4GFC0306uUp8cioOPTp4+azzxzs3m3kssvC\n6wGpVUuLqezZ08POnUbWrTOSlSXo2tVDgwYXbtvq1SaeeMJWYPuKFWZ27zZy8cUl1/29ew3ceGP8\nudqBixeb+fprrRSNIpjGjX288UYWjz/uwuuFatWk7ouo6xWfT1v0c9NN8eTkaH1PUlLo+tCTJzXv\n0JEjgY7NbJZMmeKMyOTUPp9WAzsrS0vEnZQkg+oQO50QF8e5Vey1a0tq1y7YB+3ZY+CddwIHJiX5\nmDXLEZKKNDExcOmlXi691MuQIecfMdasKalZs2z6EtHGml5i1gA6d3Zz++0upk0LKNKRIxWXI+bw\nYYnFAidOGNiwwUS9epU7TUh+r1NxO86iYjBatNBu6mHD4jl5svDr4nDA+PFW5s2LYciQbD76KLNE\ntTaLg90Ogwa5gdBen5LEojRu7KNxYx/9+pWsTYcPFy7HKlV8pX7gHDliCCrynLs45PLLHdjtJf8+\nvcXklDcmE8V+SOSVhdOprbxLTJRUqxaq1umTwnRizRpjkKEGkj59QneP/vabKcijFhsr+eILB127\nVuygpCLuj337BJMnW1m0yMyxYwYSEiSNGnnp3dtD+/Yeqlf3MX58LM884+Lyy88/SDx82EB2tnaN\nrrzSzRtvZNKmTdkNNT30ExE9DaonkpI079oLL2RisUiEkDRrVnGek5gYzhUtf/VVKydPVu5VfX/7\nW6Cj7NjRTaNGZZdl+/Zefvwxne7dC++Et283Mm+eFtz7008WTpwouQxz41327RNlmr6rDFx3nYcB\nA3IATe8MBsmwYdl8/30GjRuXrgPNPyULsGePifT0yq3PemLbNiMPPBBHhw6JvPNOLOVVPtrhgJ9/\nNrFkialSpYLZtUuLGwsYalrcUmExgeXFTz8FDLWmTT3Mn59Bz56eiMzYf+CAkU8/tXLokBGPR3D6\ntIH16828+mosQ4fauesuG9df7ylWnGz9+j7eecfJnDkZfPGFo1wMNb0QgZc+gF5i1nKpXl1y//3Z\nrFyZzsqV6XTqVDHG2ooVK4iPl9SooSnu3r2mEifk0xtt2ni56aZsqlf38frrWSQlFe+4C8VgNG4s\nadmy8Bv8998DixoyMkSJDQTNMxdL164JXHVVIk8/HcuePeG5DhURi5Kc7OPf/3aybl06S5aksXZt\nGhMnZtKqVek70MaNtXQreenQwVPqKSk9xuSEixUrVrBli4F+/eL5/vsYQLBkiZmMjPL5/sOHDQwb\nZueGG+yMHGlj+3Z99kF5dSI7Gz76yHpugRhAq1YeHn00K6Srxm++OYeXX87km28ymDfPERSXVZFU\nxP3Rvr2H115zFhrvBdpg7LnnYomNvfB3NW3qY9SoHHr08FC1avm1UQ/9RERPg+oRo5FSexXKQmys\nlhpi40btkm/dauKqqyrvqrDq1SWvvprJ008L6tYNffyd16vlHstLTEzJfvfsWcGsWdpD0O2GqVOt\nzJtn4dtvM2jePHJGgHmx2ShVMG1RJCbCG29k0qCBjzlzLDRp4uXVVzOxFQyNU5SQEycEY8faSE8P\nGCY9e7pJSCif74+J0VY4u1yC1avN3HCDnblz9a37+/YZglJlNG3q4bPPnCQnh7bP6dTJW2GD+XBT\ntSrcdVcOV1/tYcMGE/PmmVm/3sTp0wIQxMVJrrvOHdbV8XpAyPLyceuQxYsXy/bt24e7Gbrhhx9M\n3HqrFtjTqpWH//u/DBITw9yoSkJaGvTpk8Du3VrAb1KSj5Ur06ldu/j3T2YmjBxpY/Hi4DxJ3bq5\n+c9/HFSpUrq2HToksNtlsb2LkYDHo+USs9lk2PLiRRrvvBPDyy/HnXsvhOSHHzK44oryMRo8Hnj0\n0VimTw/E7bZu7WHmTEeFDLhKw6+/Ghk8OAGQDB+ew7/+5SrXwYeiIG43HDsmWLrUxPHjRjIzBb//\nbmTixEwaNYp82f/+++/06tWrwLSNPv3QipBQt25A0bdvN3LsWNGX//BhwbJlJnbtUioCkJMjcDgC\n989ll3lKXMQ4Lg6eey6rQBLH5cvNpS4tdfCggSFD4nnrLWu5TVdVBnID55WhVj6cOCH49NPgVBTj\nxrnKdfrNZILRo3OC9H/bNhOzZlnKLS6uvGnY0Me0aQ7mz89gwoRMZahVAGYzOByChx+28frrsbz/\nvpVGjbwVXlVGb0T0k1hvMWvhIne+vXZtmcdgE0WuRt2718DgwfEMGmSnd+8ENmyo+PI0hw8L9u41\nlHuC2tLGHiQlSS65JLASa8SInFKtBG3TxsfcuRnUrx/4LrM5eJl6SdAWK5iYNMnKjh3Fv04lkUNK\nimDhQhNffGFh3jxz2PLQhQI9xKLoAYdDcOzYr+feDx2azciR2eWe6qN1ay8ffeQkd9EJwHvvWYtd\ngaQiyKsTycmSgQPddO7sjcqBQbjujz/+MAZV3xgxIgdTGIO29NBPqJi1KKJGDcmIEdm89ZYWqbln\nj5FevYKXgvt88NVXFvbt01TD4RC8956VadOcZcplVlycTvjpJzMPPxxHWppg4MAcXnsti1q1wjv0\nNpth8GA3S5ZYqFfPy5VXln4JfYcOXhYscLBjh5HUVEGjRr5S52zTFj0ACP7v/yxccUX51jHbvFlb\nCXf4cODit2jh4b//dVC/vk7dIYoCZGfDypUm5s41c+KEAYNBWznXoYOH2rV91Kol6d3bzalTbsaM\nyaZr15J7jovL3/7mZsKETB57LA4QpKcbOHTIQHJydMRoKS5MbrgJaGEioVx5W1lQMWtRxpIlJm64\nQYtb69s3hxkznEGf//WX4JprEoK8bsnJXhYvzjiX+iOU/PyziWHD4slddQnw1VcZ9O4d/qSnx44J\nfvjBTJcuHt0ERd9xh425c7UYuObNvSxYkF5ucYhpaTBwYDxbthR0Ic6dm0H37uG/Jori4XTCK69Y\nmTy58CV1cXGS++5zce21OTRu7At5brXMTNi0ycizz8ayf7+RefMyaN1aH/eUIvzcc08cs2fHEB8v\n+eGH9KjSDRWzpgCgWTPvuYSyR48acLkK7uPLd1/UqOErtFhueeN0wrvvWslrqIGWJkMP1K4tufPO\nHN0YagB2e6AtBw4YyjXfWEaGYO/egs53m01Sq5Z+ZKC4MDYbPPRQNpMnO86l8MlLZqbg7bdj6dMn\nkQED7KxZYwxpHFlcHFx1lZdv96VsNAAAIABJREFUvnGwcmV6mdK5KCKP1q29xMVJPv/cEVWG2vmI\naGNNxaxp5J1vr1NH8tRT2lRZaqogMzP44V61qqR37+A8VmPHZhcrx01ZcToFBw7kn2uV5boCSA+x\nB+XJxRcHnqjZ2YKcnPPsnIfiyKFWLckbbziDihXXretl9uwMLrkkMjrQSNOH81GjhmTYMDc//ZTO\njBkOhg3LzpdkeCkAO3eaGDTIztatoY97SErS+iShj/EYEF06cSHCJYsBA3L45Zd03ZSR04NOqJi1\nKKRzZw9Vq/rwekUBL5rRCPffn83+/UY2bjTx8MNZXHVVxaTar1ZNcvPN2bzzTq5lKHnppSxatFDx\nCkWRv3KD1yvIG7xdFrQi927atk3n5EkDMTGShg19uk2zoCge9epJ6tVz06ePm2PHsjh+3MDJk4JV\nq7Jo0MCJ2awlNK5dOzIMckXlQ4uHVf1MXlTMWpSydKm2um/y5MxCV9mcPastLqhTR1ZoiZOjRwWr\nV5v4808jV17p5rLLvMTFXfi4aGX9eiO9e9sBQVKSjxUr0qlTJ3LvaYWiNEipxZyeOiUwmbSyZTVr\navWSFQo9UVTMmvKsRSldu3po2tRb5HLopKTiF0cvT+rUkdxwQ+gLmUcKTZt66dzZw+rVZnr08FCj\nhjLUFIq8nD4NM2fG8NZbVtLStJFnbKykQwcP99/vomPH8i1NpFCEAhWzFgUUNt9uMhGV01l6iD0o\nTxIT4fXXM2nRwsvYsa5i5yKqLHLw+bTEv9u2GUKSi6uyyKEiqIyycLk0/Th8WBS5IOLwYQPPPht7\nzlADyMoSLF9uZsQIO889F8fZs4H9K6McQoWShYYe5BDRxppCEQ20aeNj3rz0sBV7DhWpqTBlioWu\nXRPo3j2Rq69OYPlyNRmg0FJ/rFxp5Kab4rniigS6dk1g/frCF0Q0buxj3DgXRcVA/fe/llJXEFEo\nKgoVs6ZQKHTJJ59YGDcuuEJ79eo+li1LD3uSZEX4OHlS8NlnMbz5ZnCany+/zOD66wtfPZiZCdu2\nGZkzx8LPP5s5fNiA1wstWnh57DEX11zjxmYr9FCFokJRMWsKhaLScPo0TJ5csAaXx4Nu60gqQk9m\nJkycaC2gGxdd5Dtv/sO4OOjUyUunTlmcOZOFwyHwegXVq/uisoyUovIR0b5fFbOmoYf5dr2gZKGh\nFzm4XNoDOD9mM9SsWfDhO25c+ZYe04sc9EBlkMWWLUYmTw4uWGq1SqZOddKwYfFSjVSpoqUvadCg\ncEOtMsiholCy0CiJHM6ehRMnyj++NqKNNYVCoU9SU+F//zNzww3x/P3vdqZMsZCSEujg7HZ4991M\nOnd2Y7FIkpO9TJ7sYPjwHF0lUFVULH/+aSDv1GeTJh7mzcuga1d9JE9VlC9ZWfD770bWrjUWyAmq\nR3Jy4Jln4ujZM4GPP47hyJHy66xUzJpCoahw/vMfC488Ehwk1Lt3DpMnO0lKCmxzOODMGUFcHBVS\nm1ahb3buNDBpkjYF+re/uenQwROVq9qjgaNHBZ9+GsO771q59FIv8+dn6D7n5unT0Lt3Avv3a4td\n2rXzMG2aw5/kt3iomDWFQid4/E6A4qbZiDRcLvjii5gC2xctspCSkkVSUmAIHR8P8fHqYazQaNHC\nxwcfFDJvrogoUlIM3H13HOvWmQGt6k5FlDwsK1WraqWyJk7UGrtpk4lHHrExaZKTmjXL1o9F9DSo\nilnTUHEHAcIti61bjQweHM9998WxfLkJhyM87QinHKxW6Nu3YBHT+HhZ4SPncOuDnlCy0DifHByO\nwmMsI5Vw6MSJE4Knn449Z6iBZNCgosMfcnLAG+KsRSWRQ9++bozGgGG2ZImZb76xnBukl5aINtYU\nivLC4dA6kbJ2Crt2GVi50sw338QwcKCdSZOsnD5dPm2sTNx4Yw633+7CYNA6taQkH9OmOWjSpBIE\npiiiklWrjPTrF8+AAfHMnm3m2LGKCZ7MzITt2w1s2mQkPb1CfjJsuFwwY0YM8+cH6oD1759D8+Ze\n/vpLsGVLIHYtJweWLTMxfLiN996LwekMU6Pz0batl5deygra9vLLsRw8WDZ9UTFrCsUF2LDByJNP\nxnLokJHevXMYOza71EbFsmUmBg2yB217/vlM7roru1K4+cuT7Gw4cMBAVpbgoot8XHxx5PZFisrP\nyJE25s0LGBEdOrj56KNMGjcO3QAjLQ0+/NDK229rOeUeeiiLhx5ykZAQsp8MK8uXGxk0yI6UmmFT\ntaqPBQsySE728frrViZNsjJ/fgYdOnjz7StZtEjbrgdSUwUvvRTL9OmBcI+vvsqgd+8Lu9eKillT\nnjWF4jzs22dgyBA769ebOXHCwIwZVu6/P67US7ObNfPSvHnwDfv887Fs2lR49vVIJiYGmjf3cdll\nXmWoKXRJZibnPDmXXhpsCKxfb2bcuDhOnQqdh+2330y8/XYsuStgJ06MZffuyOwrUlMF48bFnTPU\njEbJtGlOmjb1ceCAgQ8+sOJ2C2bOtHD8ONxzT/y5fUHgcOhnmXi1apJx47J44YVMTCatbyvrrExE\nG2sliVk7fRrduFHLGxWLEqCksjh+XJCREdwJrF9vZv/+0t06NWtKpkxxkpiYdzQu+O47S5HHhILy\n0AmnE9atMzJrlplZs8z8/ruRrKwLH6cn1L0RQMkCTp0SfPLJKj74IIZ//MPG3/9u55FHtCmsfv1y\nqFIl2Iu2eLGZLVtCZzzNmVOwX0hLqzijpCJ1YudOIzt35q66knz8sZPOnbWB7d69Rnw+7bznzrVw\n8KCR48eD++C8C5PKm9LIoVYtyf33Z7NkSTrffpvBZZeVzVqL0vVowZw6pQU09unjZvBgd7ibo9AR\nSUkSk0ni8QR3kGZzEQcUg1atfHz3XQa33BLPkSNaR793r1b+xliJBs3z5pm5/34bgbxXkn/9y8UD\nD7iC0m8oFHrn0CHB+vUmXnnFyv79NiCw0iUtDR5/HOrX9/HVVw6GD4/n7NmAoRBKz1ph3piqVSPT\nC51r9MbHS6ZMcdCtm+fcivmjRwMyPnPGUGAAfcklHurV059cjEatv4eyG5IRbay1a9euWPtt22Zk\n9uwYNmwwcfXVbqpWDXHDKpiuXbuG/DeOHBH89puJ334zMXx4Nm3b6jNQvKSyaNbMx9tvZ/LPf8aR\na5QMGZJNo0ZlGyW1betj/vwMNm82sX27kWuvdVeooVZWnUhNFbzxRmB6RkPw9tux9OjhpksXfcSO\nXIiKuDcqC9EoC48H1q83Mnp0PEeP5hpgPc593qyZh6lTndSpoxkCHTt6WbAgg++/t/DVVxZq1vTR\nunXodH3AADezZwfinkaNctG0acXdWxWpEy1bennuuUx693bTsmXw8+PkyWAvWrARK3nttayQ5mHU\nw70R0cZacfD54LvvNDfJ/v0GUlMNVK2qT0NDr2zebGTkyDhSUjR1atPGo1tjraSYTNrKxRYtvKSk\nGEhMlFx2madcDPrkZElyspv+/SufNzcxUdK9u5sZMwpamOnp+okdUSjOx48/mhg1Kh6vN1hnLRbJ\n2LEu/vGP7AIem2bNfDz2mIs773RhtRLSdDPdu7uZOtXB3LkWevZ006ePG7v9wsdVRnr08NCjR+EB\n+MEDWclFF/kwGiVSwoQJmVx5pX4rWJw5A6dPC0wmQbVqpa9FG/Uxa8eOCebMyR25CE6frtwPGqdT\ny/K9Y4fhXAxeKOMONm0yMmhQ/DlDDdB1sHhpZGG1QocOXoYMcdOrV/kYauGmrDphMsFDD7no3z8b\nCFzv667LoV27yuFVAxWnlZdok8XBgwbuvz/YUKtXz8tddy1g6dJ0xo1znXdqrWrV0BpqoJVdGzLE\nzfTpTm6/PYfatSu2b9WLTiQlBc67ShVJzZqSX35JZ/nydG69NSfkK+lLI4f0dJg/30z//nY6dkyk\nY8cEbr45nm3bSmd2Rb1nLS0tOIC8Mmcy2bfPwIsvxjJvnhkh4K67shk3LnQR33v3GrjtNhtpaQHl\na9nSQ4sWledhXVk4exbWrzexaZOJjAxo395L06ZeGjf2EVOwGECF0LCh5MMPM3nooWzOnBEkJEga\nN/ZSpUp42qNQlIRq1Xz8978OTpwQ2GySqlUl9ev7+OMPD82bV8zMgMul5QuryFQcZ85Q6e7RSy4J\nPFP698+hVi2p+zJjP/9sZvTogBvN44EVK8yMGmVjwQIHF11UsvZHtLFWnJi1/IGKlSXAOzVVcOyY\noHZtH9WqaUGu990Xx/r12pSulPDJJ1aGD88JyXy7xwPTpsWcC5AHsNkk//yni927BW3bylK7e0OJ\nHmIPSsPKlWZuuy1YoEajZPTobMaMcZW44yovOdjtlHmVUziprPoQCqJNFvHx0KVLwemzGjUqRg6b\nNhl5/XUrhw4ZefHFTHr1Cu1U3tmz8M03FqZMsTJ5srNYHnC96ETjxj4SEnykpxsYMqRi43uh5HLw\nerX6x0VRVDWG8xHR06DFweUKfh8bq29rHTQP2siRNrp3T2TuXE0htm83njPU8uIL0QDxzz8NfPZZ\nwKVjtUqefTaLp5+Oo1+/BB59NI4jRyr3lLKeSEyU5J1uBPB6BR9/bGXcuFjS0sLTLoVCUXI2bTIy\nYICdRYss7Nxp5I474jl4MHSP49zKAI89ZuOPP4wsWVK5/DQNGvj43/8cTJ3qoH17/can5WI0wpgx\n2UFlp0BL8jtpUibVq5fczohoY604MWt5UzIYjVL3wZuHDwtGjLCxapVmmH37rQWfT3Ol56drVzeN\nGnlDEneQni7IztZk17ixh5deymLiRCunThkAwaxZMTz6aOmTx4YKvcRglJTLLvPw4YfOc+WZ8jJ/\nviXPSrbiUVnlUN4oOQRQstAItRycTnjppdigJK4ZGSKkNUd37TLy/POBwK7cvvtC6EknrrhCixsO\nx4xNaeTQs6eHn3/OYOpUB++842TmzAwWL86gU6fSzURULvM6BOR1pyYn+7Db9b2KcdEiM3v3Bi5b\njRoSgwGaNvXRoIGHP//UPrv8cjdvvZUZstiE+vW9fPmlA7NZ0qiRl02bjAUMxh9/tLB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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "t = trace[\"halo_position\"].reshape(5000,2)\n", + "\n", + "fig = draw_sky(data)\n", + "plt.title(\"Galaxy positions and ellipcities of sky %d.\" % n_sky)\n", + "plt.xlabel(\"x-position\")\n", + "plt.ylabel(\"y-position\")\n", + "plt.scatter(t[:,0], t[:,1], alpha = 0.015, c = \"r\")\n", + "plt.xlim(0, 4200)\n", + "plt.ylim(0, 4200);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The most probable position reveals itself like a lethal wound.\n", + "\n", + "Associated with each sky is another data point, located in `./data/Training_halos.csv` that holds the locations of up to three dark matter halos contained in the sky. For example, the night sky we trained on has halo locations:" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 1.00000000e+00 1.40861000e+03 1.68586000e+03 1.40861000e+03\n", + " 1.68586000e+03 0.00000000e+00 0.00000000e+00 0.00000000e+00\n", + " 0.00000000e+00]\n" + ] + } + ], + "source": [ + "halo_data = np.genfromtxt(\"data/Training_halos.csv\", \n", + " delimiter = \",\",\n", + " usecols = [1, 2,3, 4,5,6,7,8,9],\n", + " skip_header = 1)\n", + "print(halo_data[n_sky])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The third and fourth column represent the true $x$ and $y$ position of the halo. It appears that the Bayesian method has located the halo within a tight vicinity. " + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "True halo location: 1408.61 1685.86\n" + ] + }, + { + "data": { + "image/png": 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0BgbP2dnC++87ymxda9JEcc892fzrX9lAYKdDh7RVqKx06WLy9tsZ1K9vfUCb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EsvWdm7N3/Tee3aCrfd5qNjxwD16ytlOsFm+3aZTp2SAAmDQTBypI977vEVuGPLn3/KPPxwAocO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\npDh11o4DjaWUWXmuWYH9UspaZTS+UlHS3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GAZgBCiBpB9wWdFIBYL53V1mUzQrZuPBQts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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = draw_sky(data)\n", + "plt.title(\"Galaxy positions and ellipcities of sky %d.\" % n_sky)\n", + "plt.xlabel(\"x-position\")\n", + "plt.ylabel(\"y-position\")\n", + "plt.scatter(t[:,0], t[:,1], alpha = 0.015, c = \"r\")\n", + "plt.scatter(halo_data[n_sky-1][3], halo_data[n_sky-1][4], \n", + " label = \"True halo position\",\n", + " c = \"k\", s = 70)\n", + "plt.legend(scatterpoints = 1, loc = \"lower left\")\n", + "plt.xlim(0, 4200)\n", + "plt.ylim(0, 4200);\n", + "\n", + "print(\"True halo location:\", halo_data[n_sky][3], halo_data[n_sky][4])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Perfect. Our next step is to use the loss function to optimize our location. A naive strategy would be to simply choose the mean:" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ 2312.96009501 1127.87170923]]\n" + ] + } + ], + "source": [ + "mean_posterior = t.mean(axis=0).reshape(1,2)\n", + "print(mean_posterior)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Using the mean:\n", + "Your average distance in pixels you are away from the true halo is 46.0082084242\n", + "Your average angular vector is 1.0\n", + "Your score for the training data is 1.04600820842\n", + "Using a random location: [[ 288 3167]]\n", + "Your average distance in pixels you are away from the true halo is 2908.49191694\n", + "Your average angular vector is 1.0\n", + "Your score for the training data is 3.90849191694\n" + ] + }, + { + "data": { + "text/plain": [ + "3.9084919169390866" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from DarkWorldsMetric import main_score\n", + "\n", + "_halo_data = halo_data[n_sky-1]\n", + "\n", + "nhalo_all = _halo_data[0].reshape(1,1)\n", + "x_true_all = _halo_data[3].reshape(1,1)\n", + "y_true_all = _halo_data[4].reshape(1,1)\n", + "x_ref_all = _halo_data[1].reshape(1,1)\n", + "y_ref_all = _halo_data[2].reshape(1,1)\n", + "sky_prediction = mean_posterior\n", + "\n", + "print(\"Using the mean:\")\n", + "main_score(nhalo_all, x_true_all, y_true_all, \\\n", + " x_ref_all, y_ref_all, sky_prediction)\n", + "\n", + "#what's a bad score?\n", + "random_guess = np.random.randint(0, 4200, size=(1,2))\n", + "print(\"Using a random location:\", random_guess)\n", + "main_score(nhalo_all, x_true_all, y_true_all, \\\n", + " x_ref_all, y_ref_all, random_guess)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is a good guess, it is not very far from the true location, but it ignores the loss function that was provided to us. We also need to extend our code to allow for up to two additional, *smaller* halos: Let's create a function for automatizing our PyMC3. " + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "def halo_posteriors(n_halos_in_sky, galaxy_data,samples = 5e5, burn_in = 500):\n", + " #set the size of the halo's mass\n", + " with pm.Model() as model:\n", + " mass_large = pm.Uniform(\"mass_large\", 40, 180)\n", + " \n", + " mass_small_1 = 20\n", + " mass_small_2 = 20\n", + " \n", + " masses = np.array([mass_large,mass_small_1, mass_small_2], dtype=object)\n", + " \n", + " #set the initial prior positions of the halos, it's a 2-d Uniform dist.\n", + " halo_positions = pm.Uniform(\"halo_positions\", 0, 4200, shape=(n_halos_in_sky,2)) #notice this size\n", + " \n", + " fdist_constants = np.array([240, 70, 70])\n", + " \n", + " _sum = 0\n", + " for i in range(n_halos_in_sky):\n", + " _sum += masses[i]/f_distance(data[:,:2], halo_positions[i, :], fdist_constants[i])*\\\n", + " tangential_distance(data[:,:2], halo_positions[i, :])\n", + " \n", + " mean = pm.Deterministic(\"mean\", _sum)\n", + " \n", + " ellpty = pm.Normal(\"ellipcity\", mu=mean, tau=1./0.05, observed=data[:,2:])\n", + " \n", + " mu, sds, elbo = pm.variational.advi(n=50000)\n", + " step = pm.NUTS(scaling=model.dict_to_array(sds), is_cov=True)\n", + " trace = pm.sample(samples, step=step, start=mu)\n", + " \n", + " burned_trace = trace[burn_in:]\n", + " return burned_trace[\"halo_positions\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "n_sky = 215\n", + "data = np.genfromtxt(\"data/Train_Skies/Train_Skies/\\\n", + "Training_Sky%d.csv\" % (n_sky),\n", + " dtype = None,\n", + " skip_header = 1,\n", + " delimiter = \",\",\n", + " usecols = [1,2,3,4])" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Applied interval-transform to mass_large and added transformed mass_large_interval_ to model.\n", + "Applied interval-transform to halo_positions and added transformed halo_positions_interval_ to model.\n", + "Iteration 0 [0%]: ELBO = 102.73\n", + "Iteration 5000 [10%]: Average ELBO = 113.4\n", + "Iteration 10000 [20%]: Average ELBO = 128.11\n", + "Iteration 15000 [30%]: Average ELBO = 131.97\n", + "Iteration 20000 [40%]: Average ELBO = 132.94\n", + "Iteration 25000 [50%]: Average ELBO = 133.52\n", + "Iteration 30000 [60%]: Average ELBO = 133.95\n", + "Iteration 35000 [70%]: Average ELBO = 134.24\n", + "Iteration 40000 [80%]: Average ELBO = 134.31\n", + "Iteration 45000 [90%]: Average ELBO = 134.43\n", + "Finished [100%]: Average ELBO = 134.31\n", + " [-------100%-------] 5000 of 5000 in 621.1 sec. | SPS: 8.1 | ETA: -0.0" + ] + } + ], + "source": [ + "#there are 3 halos in this file. \n", + "samples = 5000\n", + "traces = halo_posteriors(3, data, samples = samples, burn_in=500)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "image/png": 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lMld2OQLtE1cFiwiOKu8Wo0PXWRFEbX52v1deeWWv/dAotNEYoxFRPPuMy9tvK/bYw2fc\nuPIynflN5PJ5TGDDj7npNM3ZFsQYRBRKFBqDDoLiNYigQrtqpRSuk7LljEZrW64QFPD9ABXGLlWu\ngzKld+K6KdyK93f33Q5HHx018L954IHPscMO/Vf/60CXGRQJoJxyIbXRBh0bVyo2dgLtE+hSPzpK\n9RhzfUV8HNt3ZHCUjX+qlIOS8vYN5pgYbDTyTTaCRx9VfOAD44vHkyZpFi9+hy237AYNylX88hdX\ncPJJJ6NchZfz8GP3dUVIZSvjGJcQvWPta3ytEQNGwFUK5ariGK02Dow2eAWfgh8Uz2dSLk7aKa/b\naALf9ofBvktXOTjheNPGcOONd/Dyw6V2zXwfHHf0h+jOFfD8AGMMSgkOgm8MogRHKZrSKdxsqV+v\n/NWVfPazJ5f63RC2wRTbGL9v5TPZduviNw8gjgKk6vxZ+R2ZQIflowIGYuVVH+Zf7ftUfqTKdRv6\ndqt9G9EagLHhrnTY1ui9RPVUq69RVFtnBnPtqVdXrf6K+kL7mlxBc8UVLVx4YXOPun9+eQefPN7r\nc3tqjaOBol7dlX+LvlujDYEOUAJOyi2bM+vNl5XzQHSvaB6oBu1rCgUfX4fro6NIOQoReOTx6hkH\nxzRZawQiCpt2r3QcR/Qi7EDXOHVeANiFMtA2zZyjyl94nOgAxX9XLqRDhfj9+3PPOGla87pw/PGt\nvPqqw3/9Vyef/nQBkVKZlJPFUz4BBke5pFMZxICIgzEalJBWKXzx0drHVRlMeG3UPmM0RG1Vtr+y\nqSZ8xycICjhOmrSTxte2DqXcHkQNYMOG0rMqZUhXTSTVOESkB5noUUYJSlN1cjRQ7EdjApQa+PuP\nj2PbX4KE54drfA0WevsmG8WmTeXv5eKLO5kx3QNJFWMXKWXfjQ40SimU1gSBJTji1iZqUHrH4ipU\nIBgMgiCOlL3zauNAlJBKuwigtcZ1S0QNKI4vExJvsAsORiM4xTYDHHfch7mRO4rXfvzjh1kyISVi\npkRwBNJKobUm5ZYTNaONbX/IPETC59AGCe+jHFX3mWw98dSqoP0AHKf4TEpT6oOK70gpVcYXItLT\nH7IiSpWTxvAba+TbrUR8DQhidRLYhTnqr0brq3cPKPUT0ONcf8lMtfrjddXqrwjKVbz1msP3v98z\nYUYqZXjvbN3jfG+oN0cOFPXq7vE3R0KiFdg1SAR8H1y3obkzqi+IETWw37WqYWmmtUbHvhWtNcZR\ndYn+mCZrU6dOrXr+zTeFRx5xaG017LortI2rTZxM+JFGAz3QGtHVB5Y2Gj/wYwuxqSodsmTOhIQk\nQIse0oV1xowZZfc3JgBF3XtV3eXFcly/8orDq6/aSfhb32pm7lyf2bN1sYyrHLKZZoKgQMrNIkQS\nNQFxinEJXOVCSLC00UgsYkF8kVaiIDxOO2lwSowrXkc1bNxYqrOlZSbNPTeGfUKjk4woKXue4nnC\nhSkcc4MxRcUJbSPjaMaMGf26z0AJfyPo67PUQksLROKC88/v5qCDPNy0W1ywRYSZM2eWdtmOoAII\nDDjKQSu7A663OSu+4zpJnGqOA1Vbcldqo0JEF8sr5RTJfZx4HHfch8sWChEhlUphxH7zvufw4itp\nXl/jIAK77BKwzTbh4h3ObzNmzMBIuRTCkr2eD1f7mcoXfSrIizGmeF21RbPavFPtPr0hIhtGa0Sp\nEllr4Nut/DZMjOQUibOjQBtEwFGqRHAriGv8/pXH1e5R6zg615++qFV/2Vxbo7/ifZHJwLRpAatW\nlebaCRM011zTwe67lzZXfUGtcTQYMGIw2A1/5T163FfC5cVEa50pCgug9/lSlOCm3KKkDKi7CVdK\nocKNU/y4clzEMabJ2h577FH1/AMPuJx0UisARx6Z58ILu9luplSXkIiUkZTiJFplgBmjy8oWjyMp\nUew8GAhJnNFWZdUIieoPFixYUHb/Yhtq3KfWLiwu8Vi3vnRtV5fw3HMOs2frsjJpJwVuGiFcMst2\ntD3vPViLdCXeeqtUz3vfewATJw7cA3ogk4x9NlPs//5KjioRJ7T1oI1m3rz90UYXNxKN9HlfCf9A\n0Oiz1MOOOwbccH0HbeMMu+3m09xcWqCjdzdvfnmub4PBTZemxXq746FEUWqHQqVS4fdriVOcSNWT\nHkSSu9WvO/z0shauvTaDCRej3Xbz+cMfOpgypURA5s+fDwyMFPRY9JFylZPUXzQHc/GuRooauceC\nBeUpYePkPt5+5SicmLQxjrg62ASWpEViw+h8vG21JH61pID1iF81NKYN6FlXvC+mTjX8/ved3Hef\nw6ZNwrbbambP9tlultVqDDX68sx9navsuy2tXdFxhMoxURUOiNYYA67j1t3kKVeRxkV5VgvnpuqX\nhzFO1mqho6P071tvzaC18KMfdbLlFNPjw4vUCIHWpYm+hqjb7oLjL1wVX3jl+ejLtYtkXP1Rm0QN\nBH1RLdXahcXJVKZClfjIIw4f/ahXl3A1QgoGY5GuRC5X+vfOOwcMgtZxQBgqUtoIoknMYNDaSnUp\nEvOg6B9erW21CH+t9zocUrh6aGuDQw/z69rqxBdto+0417o0DwyGirq/iJMKo1VVaUs94iFKeHtD\nmjPPamHFinIJ3rPPOsXvoj+qwfrtLl9Mh0rVNVwokmIFSpwy6Vqt5+mpDvZRjlv29zKyVkG8wdgy\nVmRXYcdWTgTt9fXH6WCpHHfYQTNrex8/8EuaARkmotaHZ+6LcMLWJbg4YdJog3J62vnWQzSvihN+\njQ1cqlxF2m3cJmfMk7UHH3S4444Ua9cKCxf6LFzoM21a+aR3221pjj66wD99LF914lOuQnR1I9my\ncqJwHZcg/HPcZi2+QLsqsuHQOE6qV4nTYKAvBKHe5B2RqQkTyvvw2WedHmWqtmEQnq+vJGC77Uof\n7uzZ/RPXDzaGgpQ2gspJTGsfFdssRMb9Uf9GanzoSfgN4AUFK32N/hbuYIdTCtcbeiM0Skr2Xyrt\ngK9BGxzX6XW3Oxzozd6oHh57zOlB1AC++MUcU6faOqOFHAbXyDvCUKq6hgt9fYZKdbBy3TID/noS\nv3InDbtxKHPM8H3rjBFTrzYkXRvU92Biv6FHJfmt9szxdaGecKKWjbN1CugfJeorOewPxjRZ23PP\nPTn00DaCkD1ddx18+9tdnHRSnn339bj//tIk9t3vNnPQgR5bblW9rkYHuhKFcqqz5R4LdPjvoZZA\nRCLcRglCI7uwqVM1LS2Gzs7ImNL+hloQ0R8SMHmy/ZCmT9cce+x8hmuCGY2IJrFI5aWUW7ZZiNTy\nYCecQPzieI4T/kitrXVoAB++h2pq/6iukSCnvSH6NsRYeykd6NA2rL6x73CiN3ujeigUepY7+eQc\nX/xinlSMw4kSFi5cOKB2jhU0pPLqBXF1cOlk6W91pUJ1iElRnWpKEiaVGrplvFpfmNB8IvqeezOp\nGQypakR+Iwc+cRSuKa2XleuCte10eqyrvvbJFXJFspZNZxsiaL2NiWhe1eFG13H69050xbwZx+iY\njYYQEVGLsGhRBlGaiy/uorW1NAm+9poqs8MaTihRPTxHRxqipK6Yf5ttDOee2108/uAHvT4Rteij\nqzc4q6EqCegFO+6omTJF8+Mfd7LVVu9eogbhZkI5iEgYBsItO+5tDEZjNRoVcc/m+HGlhHioJMaD\nhjBMhSH0wBtFw6SHnVcDasro+9p7b49LLunk8MMLnHtuN3/8YzsXXNBdlKolGDoUSVlMCNWIvVXl\n3+PHRZstxzo1WMP44f224t9yFF6m2jxe9KINy0WbwH7dUymMCqVi2Hr9wCvet9q6UG1dLfg5fN9D\na4PvexT8HIMBS14FYwL7fYb90hf42sfz87XvMdBGjmY88sgjPc4tWODR1hqwy64Ffv/7dqZNsx3a\n2moG7CU4WrFs2bJBr1MpOOqoAgce6DFpkmb//a2hZLRI+NqvScaKdlMmtJvqZVDHiV1/SMCsWZrF\nizexcKE/JH2xuUGJ4p4V95ap6KNJzakgb7Xim0nxWinuYuNkr5IUDsVG5LnnFIsXu/zhDyluu83l\n0UcV69f3rY7ieIi8IKEYjiGStA1kkRkMRKraqG29SSji39eWW3l89uQcv/1tJ//yLzkWLCiPiRhH\n8m1YDGY/VJOS9YY4GROnnNyVvFoVynWtenWIYLRh6dKlPcZ/9G2bkIEaY/D8PH4Ysqp4vbF2d9r3\nrWp3oKktK/qjzKHPULwP9G9zWE+A0MiYEMpNnxoRJJTdO/Cp10VjWg1aifnzC5x+ejeWBCv22y/g\nz3/exHPPOUyaZJgxo++xYt7N2HZbw1VXddDerpg5UxcXCR2SMKVCr8cKVWVfVGSNird7w6xZiSSh\nGipV8JHdZUOOIDEbzGrlhtIu79lnFYcf3sY775TXv9NOPt/+djfbbadZv16YOtWw444NLJAidl6I\nZIYGdNEJaGAxrgYDfbE32lxU0O8GxG3XTBjk1+hyR7Z164SuLuttGamma3uyltShjXqCVsOaNcLG\njcJOO+mqGpG4naSuMv6VKIwoAhMUw08EgV+cQ2wlptwpYKCOKzUc+IzWiA6dP3RoxlBjvKfdLFob\ntPYRFC6uJZJCWWgtG5Klb9qugcSHNEaHhLO2TfWYTuT+17/+1RQKc3n7bWH8eDuRT57ij5h32lhH\nZJwehLv6aMBXRqCPEzCoHmG/ss4I/Y1mXwurVwv33+9y661pUinDaafl2Xvv0eGEMNToy3sYbVi9\nWjj88HG89lp5e2fPDjjxxDwXXdREV5cwZYrm9tvby5xMakEHhtdfF9auFTZtEpqbYetphmnTggFF\nph9ubM7vdSzCSpcCjEhJMmYMuZzhnvsynHNOM6+95vCLX3Ry9NF9ywJQeZ/eSJzRmiefcjjhhHG8\n9ZZwxx3tVWOkNZLpQRuN5+eL0iAriS9lY7GOEDGbNUf6LQmMNpWG0mYkIlO1si/Ugq99Ar+AmFLw\n9UBstAeDVWEWyVqD307U9yZSTfeRYwSBT+B5aGN46uln3n3ppgD2269yII75Rx4xRDuL+P+j83H0\nzTN1cKLZV8MLLyg+9akWVq4sjYnFi1MsW7apGDB0LGNzlsBss43h979v55xzmlm2zIojXNdw8sl5\nvvWtpmI8sbfeUrz0kuqVrK1cqbjmmgyLFqXLMl5MmaJZtKidPfoZ9HMkMJKhYRL0hFVrlo6N1vie\n5pZbW/ji6S1EngeXXJLl4IM92trKr2+UhPUW2sJozSuvKE46qbW4yXnmGVWVrDUSzkWJwnFSBEEk\nACmPTWbt9UqBzvsrBazcfFRKvCo9b41E0rGSOlJE2bRSOsxCpNJhFoEAXwf4gYdKuTYDiZS8Qivn\nxGrvIt73YkJVbR+/OTGW7Mq71cGgms3auxHDZYsS2TI4SuG6KZwKO6bKso2ImQfb9inqi/Z2OO+8\npjKiBpDLSQ+nlLGIZcuWbX5OABWYM0dz1VWd3HbbJn70o06uvrqDW25xi0QtQtyRqBLLli3jkUcc\nDj20jV/8IltG1MCSvZdfdkZUBdof9MdpafHiZfh+7+V6w6uvCnfc4XLffQ5e/wVFI4ahmC8rA9qu\nfC7DmWeViBrYjUEmU35dkQiE3p+1bN6qxXUr2oqFdmPa9/nHQy4vvVSa86LsLpX2mZGd5PLly+ra\nSbrKJeWmQwLk9CBRtWzv+gJjSl6gVgJVKHve+H2MgsDYTEIFv4AfeNamzstTKOSs93pEao1GG0MQ\neJbwGRNKxWLhh4zG1z5Lly6xNmVV3kV/7BIr0UjfbF6zc4IhRX89NOOIFglXuYPm4ToU3rJr1yru\nvLNn/KmvfCVXdDoZ6xgOJ4ChxoQJhltvTfO1r7Xw+ONuj5hiu+ziM2tW/ff5m9+k2bix+rN/+lN5\n5s4dBAYzChF9756vufNOl298o5kvfKGFlSv7Pw5efllx4omtHH98G0ce2caDD9bJw/UuQtxJxHEc\nli5L4XnlBOjUU/M98hY3SgQqPUYj71Pt+WjPqglzOcVVV2XLrps2TZd5bQaBJvCCol2dTYFUf6NS\nb36OHCEG4rFqcylrdKAJPGuEbwJdRkij+2hKzjVB4OEFHl5QwPe8oiQtWt9EWRVqJCVzQu/4lJsJ\nnScEY8Aib5VSAAAgAElEQVT3PQKjCfyY96nW+LkcQaFQ9Zn7/IxCmB6r9sZyTOsE99xzz5FuwqhA\nI3GDRlMQ06FE1BctLYYddgh44YXoEzB84Qt5Pve58vhTYxV9jb03WvH664orrrDiiO5uYfx4w4YN\ndnFpazP85CddbLFF7QlwwYIFTJiQ5/nnHR54wCWXs/H4DjuswJFHeuy1V9BDLTUWEP/eX3he8ZnP\ntFIoHML//R88/7xi0aKOPof3MAZuuCHNk0+GNkta+NOf0uy/f3cvV44uDEactWqIO4nEY3wCHHpo\ngblzq6gje0mwHitZSuknsZA6ukRM3ljr8sADpSVfKcN225W8NCPSJmE9Sg9dX0RoRMUrWBVhENi2\n+oU8ODY0h3JdAt+361VFbDMT1u+bAN8vkHIyYVpHDSgUCjedJgg8CHNzOo4VNATax2DwAg+tA/bb\nf9+iM4MJSTCA8bVNM+WUvHT7RdZM7w4jY5qsJWgcm7P9Un8wdaph0aIOHn3UxRiYOVMze3YQJv9O\nsLmgs7MU+HXRojTnnJPjhhvS7LhjwFln5dh1196lpLvuqrnhhg7eflvwfZumaostzECd10Y14t/7\nG2+osuC5jz/u8sQTDlOn9k2i+Nprwi9+Ua7Hi6f2S1DCkUcW+NOf0oDhn/85z1e/mmPy5GrpxHr3\n/ix6bsacFyKr/3j5IADfL73no44qsMMOumifFpG2eF7Socw8UcvOrpLARbmUtQjGDxCl8PMF3Ewa\nok2HASKbOdEEfgFC71ubPi6FNgGe9kil0jYvMsrGmRRFYPwy7YKICp3bKP2UTT8X5D1LZkNyaLRG\nHDWgMCqVttnVMHZXYzZPm7VqsZ0GGu+poRgxDdgvjZa4UwNBvC+2395w7LEexx3nsffe7y6iNlZi\namUyBhE7HteuVfzgB1kuvriTyy/vaoioRf3Q1GRD0cyaZZg8eWwTNSj/vksCibuK5954o+9LQ1eX\nsGlT+XWbo2f1cHwbhx7qsXjxJv7+90185zvdbLtt7Tm1N1Vij4gOIkUbLpVybYYDgZZWmDDBfhMT\nJ2q++c0c2WxJRRv9IrWniJT1xWDP/9VUvNVs9CJzDTA4KdeGhEo5xbR4UR/ZvtCIMShjw3cY38cV\nB9dxEcexatLAerEWgkLR7EcpB0HwQ7Vp2I04SnDdFPfdc58laCicVKooZYvuPdDAxHGTlFpIJGuj\nCNVyAAL9zgvYF/TmQVYrP2EjqbIGmk5rsFKWJBh72HJLw7x5PsuXW7XSunUKzxOGMFbomED8e58x\nw7DFFpp160p/b27u+4KczZqyFHRtbYb3v3902ftpbe3q1q8XWloM226rR2STNn487LtvOZFtRCVY\nDdU8N20+0XJj/+nT4Ve/6uTmm1OcemqeOXPi6lWxKsIac+1A8tPWbHeFihcgKBSs52aFRFGJIpXK\nEFDyWFFOZA9pwjhpvs0iEJJJ663qFiVfgR9gcNDG2q0ZAoxyMcaSQDHWNk5EEIJQjWrFaoKgiAUk\nTrnowEfcgdvkFZ+nF5OUMR9nba+99hrpZjSMarFtoEcImWGJ91RJsKq2TdFrPKeBxnyKTxIwNImm\n380Y6ry0w4GlS12OOaYVEMaP19x1VzszZw7MSaS7G554wmHZMpcnn3SZOFFz3HEF9tkn2KxsGht9\nv3/5i8vxx7eitTBhgubPf25n9uy+9aHWcN11ab72tWZaW+Haazv4wAcaI2tRYNittzZDRrTfflv4\n3/9Nc+GFTaTThuOPLzBnjs/hh3tMmTI092wU5cnb++49OdQb2vj8b8JE0E5q4CQlIqj2oNQP0fMb\nRVncsiDw0YFftDGT0Guzo3sTgeehUilSOIhvVaCpdIZANAU/h/YCJDCI42LQKHFwYoTPcV0oSvFA\nBx5owlyfKVzHLVuMB+LhWg8PPfTQuzPO2uaEWrFteot3M9io5mxQrW26Qsdezc5toLZwA0linaA+\nxopTyT77+Pz2tx1cfXWGs8/ODZiorVsHP/tZlksvzRIPrXD11Rn+/Of2HhKR0Yq+vN+FCwvccccm\nXn7Z4b3vDfpM1MCaTH384wX23tunrY2GAhHncrBihcs55zSxerXDDTd0cNBBQyONu/32FN/6VjNT\np2rOPjvHj36U5bbbUmyxBRxxhDeiqu9aydsbJWF9yXDRe1uq3DOUOkUGXEqkZjy3huortlsVvTLj\ndRmjQSkMxobbMB6Ok0IpBSgCHaADK/3qLnRS6MwhQL67Ey+dpjndimhD4HsYB9C2vV4QoAKPVDpr\n/xZoXNeqSI2xnrpKhEAHYCRMPadKuZAdNeDsEf3F5jcz9wGbk81acUAbSvkJoce5/uya+mqDUY1g\nVctPWM3OrTL8x0BjefUniXU9jBVbrYFi2bJl1Yn0ZojmZjj8cJ/f/a6T/ffvG5GqNh7uuy/FpZc2\nQcXip7XQjxBKI4ZG3682GuUEtHf8jY8e1cXsOf0nSy0tsNtuuiGi5nnwf/+X4uMfb+WFF1zyeeHG\nG4dGbOn71gEF4LOfzfOd7zTx5puKV15xOO208nAlQz1HVLP9qpa8fTATofepbbF7Ll2ylKDghXmZ\nBa1tNo9oHeoR301bp59a9VU+Q7RexEMjilI46TSIjezvFzx0oCkUchTyOTyvgFcoUMjn8QoFOrs6\n8fxuugoddHW8w6b2TXR2tdPxzjra31lH+8YNdHVsxLZUo9EEQQFNgPYKBF4BggAHwRFls+Q4bkzq\nBitW3FMkaIOl9qyGejHahp2siYgSkYdE5I/h8UQRuUNEnhWR20VkfKzst0TkORF5WkQ+HDu/l4g8\nJiIrReTS4X6GwUZ8QBspEZLKc5VEbTACWFZDLYIlSlBOKe5OZZwuoEeC9oHG8rL3srncwPSLrCao\njs09KG4lBmv+fP316mPszDO7mTNn85CqQePvd6RI+6OPOpx5ZktZEOP+SPQagevCJz5hDccdxzpD\nRMjlhKee6l88uCeeUNx8c4olS1zWr++9fC3yUi2AbDWtQi0HtMALivHRBoL4PbQfYAJbp5WiharC\nGOKk5fnnFd/4RhNHHtnGk0+qYn1QImVBzCTG1z6en7fpCTGY0IayMjk7xhTjxRkd4BcKePkcea+b\nQiEPYvCDgFyuE88EbOpYw2uvreSNda+x/o3VrH/zNXIbN9HduZFCvpugEKB9g58vUPAKBHkPv+BZ\nCZqm6CXquCmU66Ac+xtqSVqlKrwSI6EG/QrwFDAuPD4X+Isx5mIROQf4FnCuiOwMfAKYA2wD/EVE\ndjT27f8c+Jwx5gER+bOIHGqMub3yRptLnLVqH2W1MnEx92OPKc4/v4lTTilw2GFeXTuaRmPllNm3\nNJgsPW4U6QUFu/OSUs41ouv7SQSsIaiV7Bk02tRO0tsIhjpu0OaCYj/UcSp5N6DaeDj0UI9Vq3Lc\nfHMaY2CvvXxOOy3H+963ecVcazTtVBQ2YP78+cXjoUY+D1demUHrWHJwZViwYOgcEj7ykQJTpuiq\nnq4dHaV2NDpHPPus4ogjxhWv/chH8nzve91Mn14xn8fUgPXMOipVaz3KG4gsx+IOaEGgS8b/gcah\n90C2NWFKDm06CJg/b34ptIfWVqokdgMdtddozdPPOBx7bBtvvWXb//DDLrvsUkBClWJRHY8ual2C\nIAqNoa2KntLcbgJdchgwUZBfg48PhTB5vCsExifrNNPh+kjBpdvbQK6ji1xnHq+7QJMSmlvG4Y8r\noII0rdlxqLSikOtCtEalMvi+h7gOgefZ+G24uCodxnGz0tgDFi7sX3/2AUHg15WsDStZE5FtgCOA\n/wC+Fp4+Gjgw/Pe1WP/xc4GjgN8ZY3zgJRF5DthXRF4G2owxD4TXXAccA/Qga4OBri4beBNg662H\nxnOo8qM0mDBpbWnwxtV/a9YIJ5/cwksvudx9d4o77mhnr71KO5b+GI1X2rfYtFGNDw97T8o+PlcN\nXKXxbov/NtzY3IPiDgW23dbw3e9289Wv5jAGJk40m5VTQRyNvN+RyCX61lsSxhkr4fzzu5k9e+gk\nlxMmwKGH+qxapWhrM7S3l+bUbbbpu0TvmWecMpJ3yy0Z9t034MtfzhfPVXpRig2MX0Q9sw5RgtKU\niB7VN/Vla0cYL63f9mtizVyMMVYNaEyxHUSmMDEHN6M1696G877VUiRqAE1NpbRVom2g3Ug7VAy3\nEYstZs+VpJt2PdJIYDAFDy0G102R9/IEugCi0MbgBwVENCnHJcAQBNBdKOB15iCfo6uQt9kHVEBa\nt9CdyiDaQTS4ksIEAQRCobsTcR1SOo0bRjkYzs2rFUoYdFB7/A/3LP1j4JuUOzhuZYx5E8AY8waw\nZXh+OvBqrNxr4bnpwOrY+dXhuR4YqM3aiy8KX/taM/vuO4799hvH6ae38OKLg99lcXswS9W0/aBD\n0lVpq/bKK6qY301r4eGHKwd5uSqyERuMgapConZGKk+RgUnAIgy2qi6xWbNI+sGiVj8oBVOmGLbc\ncvMlan2BEsU9K+4dtgUqnYZJk4r+hXz7292ceGLPdEtDgVmzNJdf3kEqZe9/3HF5dtuttEhWGxPV\n1I8tLT01ID//eZa1a2MhL3rEQKOH7W89xE1PqtnvRr/Kc/1FRKiUo1Cuw4r77g3jtTmh92d53UZr\nHnk0xdK74x+JDYkSwaoRS9I+kSjQrU3EaR3VBAJNUCiEWQKs2tWEWQsUCj+wuUFxXAKBQncXaCjk\nu/FzObq77P/9fIDnFejOGXDSeD7k8nmrNu3qwvdyoAQcYyMdKEOgfasiJQhTbpXnHx1yO8ZYGqta\nGDbJmogcCbxpjHlERA6qU3TQrCiXLFnCgw8+yIwZMwAYP348u+22W1HMHb2AaseFAnzta/ezdGka\nOAit4ZZbViBS4Oqr56JU/ev7eixKWL5sOVoHzJs/D4AV96xAEBYuPLCs/IYNHwif8C7737vm8bnP\nFVi2bFnZ9cuXL7fxYZRjSdvdd4MICw9Y2PP+oli2bCkA8+fPD4/70P6K65VyBq1/5s2fhzGa5ctX\noEQNqL7HH398UN5Xcjw2jkf7eNAa9thjAU1NcP/9Q3u/xx9/fNieb8stDWeffRuvv6748Ifns9tu\nAf/4x/D176GH+lx66a10dAj/9E/zmDix54IcHc+fNx9tDMuX2+MDFhyAKGHjxiVMntzE228fEl5x\nF11dGq33Kl5vtGFeqF5evnwZgnDAwgMQpM/tX75iOUabcH4Wlq9YXmwfns+y5ctQjsPCA3vO7305\nnj9vPsYYli9fzpNPPVm3PqM1v73+sOLzAxx++Dx23jkoL6/osf4sXbYE3/PYf95+aN9nxT33QmCY\nt99+4ApL71qKEc28+fPxdcDSJX9HlGLf9+9H3uvmgQcfoKA9dt9tJwLf5+GHHiPX3c6MmdPQOs8L\nL69CVJpddt0ZLfDww0/iKsUBBxxAa9tkHrjnAYzRzH3/Pogv3HvPfUgmzQHz5+E4rdy99G5ESVEF\nOhjjz2jN/HnzEKVYvmJFcazdvexuXl61CqMN799/fw455BAqMWxx1kTkP4FPAz7QBLQB/wfsAxxk\njHlTRKYCfzfGzBGRcwFjjPl+eP1i4Hzg5ahMeP544EBjzOmV9xxInLXVq4X99htfZogKsPPOPosX\nt9Pa2q9qe0WtuGRx1eYtf8pw8smlBhxySIH/+Z/OmtdD7/HQomsHogoZCzG7EiQYDdiwAR55xOU3\nv8nw1FMO48cbPv3pPIcd5lVNS5Rg6FAtxmQU6/KxxxSnntrC88+7gOGyy7o48cTy5N5DHQNtoDHa\nBoJNm+Cww8bxzDN2nWltNSxevImdd66vmTFak891EXi+tT8DHKVwscFmtdb42kfnPQLH4PkF8niY\nIEAheEbjKY9CrpvO7g3k83lEGbrW59BeHuVmsAZ4gjgeaZUh25Il7TaRbRvP0w+vYr3cx6RgHw74\n0ALQCnFtn2WbmmhKNdt1Ug0sjVTlM9d7T14+h9+d4+kXXxzZOGvGmPOA8wBE5EDg68aYz4jIxcDJ\nwPeBzwI3h5f8EfitiPwYq+Z8D3C/McaIyEYR2Rd4ADgJ+Mlgt3fCBMOBB3rcdlu5XP7EEwtDRtSg\nuv1IpT3ZFluUfwj77RfUvT7Q5Ua7tey+Bmq/lNg/JUjQd/g+vPSSYt06IZWCyZM1l12W5cors2Xl\n7r/f5Wc/6+CEE7waNTWGJCNI31Ar/iXA7rtrbrqpg5deUjQ3w047VUvGPngx0KqhVoy24UA2C7vt\n5vPMMw5bbaW55pqOIlEzWhfjp1WGu9C+X1RxRsbORhsCrS1pER9RKbwgT2HDO/hNKRwUm/IbQRtS\nmQwFL0e310U+nyfXkaPgd6NcB19pXJ3HSWXJOOC6bRgffAyOE/D0/c9y8+uXE4iPcu6Dv6dY8KH5\nKNfBUSnc0Fbbhqxy+51ZohK9vSclCrxRFLqjCi4CPiQizwKHhMcYY54Cfo/1HP0z8CVT+mLOAK4C\nVgLPGWMWV6u4vzZr2miamn0uuKCLww8v4LqGpibDeed1c+yxhd4raBC5XPXzShROmGAWetqPbTsj\nYPLk0rm5c/261y9fvqLs75t7iIaBILHVskj6wWKk+yGXg+uvT7NgwTgOP3wcH/zgOG69NdWDqEVY\nv35gsd7qxb0a6b4YLajsh2oxJuOYNs0wb17AnnsGNDf37V6VcSn7g2ox2gYLvY2JdBrOOSfHjTe2\nc9tt7bz//aFQQWsbasPXGD/8d8XAdZTN89nR3c3bGzfSkYtiqOUpdHbRsXEtnevW0aELdOfaae/c\ngN7URb6rk/VvvMbGTevY8NY6cm9soHujRxBAITDgZjGZdDEMSqalBae1CV8JPgEbUg/bQLmOQuOz\nTt2HEoNyHFJuCuWmrJ1eKFEzgWbZsuXFXKX9RW/vKSh4dY3ARiSDgTFmCbAk/Pd64IM1yn0P+F6V\n8/8AdhuKtsWlWLO2L3DFFZq33nJQyjB9usHpXzieHnjyScW//Eszu+wS8PnP53nPe2oPgrjXDFjP\npV//upOTT27hpJPy7L57fXf3KNZZoqJMkGB04fnnFV/9ajNxF8HOTsWECZp33in/TnfbzaejQ3HP\nPQ7z5/fPazLJCNI/9EU61qgkZrAyiFTm0RzuyPrbb6/ZfvvqSdkrj6O2KddlzZo3+PvSJVx66aWs\nXr2abbbZhq+ceSb7z51L2s/R2bmJfJBHi0NBPEw+jwB+PkdnQUMQ0N7xNkHQjZOegONkrLQsBY7n\noJrSKDeNZzTKNaTSaRwnzcTUfLQsRxkbdmQLsy8pJ0vKccM4chrlZnGUW5QMxp+jv/3b+3sydetO\ncoNWINB+D5F3X0JYNIozzmjmhhsygLWDu/76DmbMqP0uqtmDrVkjjB9v+rybS5AgQX34vo3GPtTe\niatWKQ46aFxZGIm2NsM553SzYYPwwgsObW2G7bcPeOUVh2uuSTNpkuGvf21nxoy+7/KTXLtDi77Y\njw3XWjMSKErWopRUjk1+HvXF6tWrOeOMM7j77rt7XHvAAQv4z2//K53r1hKoAM/3MMrgGYVT6KIQ\nCCbfSWfOR+c34hHgpNtQLS04TQoT5DAmTTaryLa1kco2WQlbtpl0KkUh8HnloTdZH9zLJOay8LCF\ntGTHFYPfWpvuMLKBYdjsAbXv43fleOL5lUlu0EZQKcUaCpVhoaDLQoA89ZTLjTem+epX8zWvqWYP\ntvXWY5doJ0gwUnjzTeErX2kmn4fPfKbA+94XMGvW0ETWnzVL85vfdPD5z7fw5pv2+25vF669Ns2i\nRe1stx3cfHOKU04pRfpft0547jnVL7JWGbsrIWqDi77Yjw3HWjNSEGXJmZaSzVpXt2L1asV7dvS5\n666/VyVqAHffvYz7H3mM3bafRnfnJvLdHiaTRmVS5H2guxvf99HtHQRBDjeTAgIwNlCuIYUu5Oju\nEAJPkZ0opNuayesCyldkxGXO3B1oS+1JxsnQ5GRJpzIY0UhI1CAMRxXZrw2D1FK5Lm5zdfMHGB02\na0OG/tisDTQ9UiNwXM3ee5eLV3/60yxr1gzNxJnYopTwbu2LyjhRo7EfBsN+p6+o1g+plOHFFx2W\nLElz6qmtHHxwG7ff7pblPBxMHHCAz513buIPf2jn6qs7uPHGdm68sYPttrN/j8esivDOO7Xnit76\nsTJtXITROCZGAgPph77Yjw3HWjNQDLQvnHQaJ51GlOKxx1wOOmgcy+5W/PKXV9S99me/+CVOthVd\nMBgx6EKOoKML6ylgKHR0gRsADmIcxJ2Ak04T5A1aFKKFwM+Rz+fR+W5MYAhyBfxCDnyHiekJNDW1\nkc404TgpUqk0qVS2RNRCCWlE0lbcey8QOkb0w24tcrbo7dp6nqejb3SMAlQa6A82RBQf+lC5R9eG\nDYr165NdboLBx0gkhO4rqgVzHgnyBjBpEnzve11E1r4bNypOOKGVSy4pD3g6mNhmG8PBB/scfbTH\nQQf5TJ9eytG48xyf7363O1baMG1a7aTsWgfWq84v4Ov69qzvdrz6qvDrX6e56KLB2SxXy/EZx/PP\n21yiN9+c4vHHFYE/tGtNI2iUSAy07scec8jnhS9/eTwHH/yZutetXr0a4zgYRzApFxEDgQcF0AVr\n34dykWwKVAqUjzEeQh4p+Hja4LgtOFmHvMnQ3dmJwcGRDOl0Gt8vYIICTjqNm81ipESeMQYxgkIV\nCVuk1i3lKG28r4qq8VCl2t9+di644IJ+Xbg5oLu7+4Ktt956pJvRAyJC2zjNmtcVTz0VMWnD5z+f\nH5IYSlFQ4AT974s1a4THH3coFCQWeX3zQDVyNnO7mSPQknIEgfXaV4owinkJ2mj7RyCfN9xzb5qL\nLmpi220Dpk4dvP6vNR6mTtWIwD33RJHZhXvvTbF6tTB3rj/kOULjtmWOA+99r2bu3IBs1nD66TkW\nLvSr2tNpE6BDwguRKsdpKKr9u22eeOst4YwzWrjiiizLl6fYfXefXXbRA+4Hq15WPfp85UrFRz7S\nxvXXZ7j55jT//d8ZZs4M2HFHzSCF8uozymzsjLEkM9bu/vSFrTOwpCSae4zhT7dkuP/+FO3twhFH\njGfFiqsIaqRXmjlzJkcddwTdQScaHzwDAaB9JPARrdBOCjftYtIpwAPEmgoowWlrJtOaJtOUBRFU\n2qU53UIqnSKbaka5adLpLKQEx03hqNAW3BiM5yPEM0EYtp0+vbyfsN6jDfVHj2es70iwZs0att9+\n++9Unk8kayOELSYJ//ZvNsWK4xjOOitfVd2RYOTxzjtw/vlNfOQj4zjkkDYeeGCQXIKHCdXS1Iwk\nfB/uusvlYx9r4bOfbeGmm1KsWVN9tfI8+NMfmzn2mDYWLbKL3GCgowOeeELx7LPVp8DmZjj99BwX\nXFCSsAHcdFOG667L1Ay7M1iodPwa16Y54giPyy7r4vjjvZo5iq0dlK55nKCEpUtd7rqrlCbp4YeH\nljG98IIqy58ZBJYsPv74yM0n1WzsBlJXUCigPd/Go41JoIzRdMcCzC9f/h722mtuzbrO+NLpGMmT\nbmnBzWRx0i5kBBwXHAc3myaTTpHKZkmls2AEk8tDUCBNhrZ0E5lUExmVYvzE8WyxxUSyrS1knSaM\ngJtNox2F1oZ8IUdgdJG4iqgyCdhA7dQGK7TKmCZrfbFZCwJ4+mnFkiUu//iHw8aNQ9iwEDNmGL7/\n/S4efHATX/9695B5dSa2KCX0py9eeUXxhz9Yz91NmxSnnNLCK6+MvMq6UTVhtThRIzkmVq9WfPKT\nrSxdmubPf05zyimtfPITbaxalS7a70RecU8/leGMM0rG9YORp/PZZ23YnIULx/Gxjz3I+vXVy02a\nBKeemuf66zsZN67UxxdfnOWxxwZ/gY3bFfaXYCtROI6LCDb6epiHsRG8m+aJd96BH/2o3Jg7Ui0P\nVT9MnFhNIiy8/PLwLsNx1WRvRKKyLypVptFxMchtYNWFQaFQ/LvRGr8rx5w5JdOfFSuyHHPMl6u2\n74AFC5i71x6Ib5DAkMlmyU5qo2VCK05LFrIOtLTgtmZxM62I65J1s7iOS4YmMimHVtVEs0nR1jSe\nCdlJTGqaSku2jUxLKy3NbTjKQRNgsBsare0zaaPRaEwUWD5UZa+4994y9XZfshr0phpvFGOarPUF\nf/ubNX489tg2PvShcZx9dvOwLMgtLTBzph5ytUqC/sPzysfBa685PPnkyErXqtl41UMto/KRgOsa\nmpvLF66nn3Y5/YstrF+XsrYjoujudrno+01oXWrz7Nn9iy8W4ZlnFMcc08bvfpeBBuJmNTfDYYd5\nLF7czje/2U1rqwGEl14qnzqrJfruCyrtCqFvCb/jcJVLys3gKGfUGq6PNN55R4opkiLsvPPAxlY9\nGK3ZeXaBr3+9u/IvNe0PBxtPP6344x9dlixJsX6DKgur0QiRKBrdB5ogXyhK0bTnE+RLic9tGQPG\n1qkDn6DgsdWUkv1kZ6cw+70L+fEll7D99tuTTqfZfvvt+fEPf8j3v/sdxjsu6VQTWVfIui6trW24\nzW20ZrO4TZNQTQ5kUqA9HM+gA5+MOKSdNC3pJlqbWpk8fismpMfTpNJIENAqWcZl22hrnYibSuEo\nB1c5ODZnPEYozqkGg8RCjUTerdGvr4RLwrRVA5HSjenQHXvuuWfZca3clWvXCmef3VK2KN90U4Yd\ndtCcd16OEdYaDRhRItkE/euLCRM0qZQpGx+vvjqyC2ClaqtWCrFaGMkxsc02hh/+sIvTTmshTpge\neijFqlWKKVPsornyWZe/3FkSpWWzhl126f+C+vLLipNOKoXIAPjKV/Zj0qTes5LMnq0599wcJ5xQ\nYN06Yaut4gE/S/ZlxhiUps+kuFqwWuUoCMNs0Mc6+5P67d00T3ieEO/yHXf0mTPHjq3B7oeI5LS2\nwOlf7OT97/e5884UGzYIn/qUDQ0z1Fi9Wjj22DbWrrVj4uCDC/zg4k5mzvBrkgjt++y3zz5o35aJ\npGD+u6EAACAASURBVGQRyfO78yjHsdcaW76aFCmqe+utyp3qujqyHPXBD3LwAQeQ1wHZdJqmbApv\nYwdGNJnOHIpW3FQWIwbjduKP07i6nUIO8AOkxcExGkccUC4tzU1MyI4nm27GdTIExuD6ChDSroP4\nghhozrQSBD6iDY7j4mhFoAtEi70oVbaXi8bEcAccjuNds+XqqyQC4PbbU3R1DUPjNhMMRHowUp59\ng4EZMwz//M/lMfAGK5NFb6jVb5Wqrc0tRtNhh3n89KedZLOlsRSldYvw3HN2ko3w9a/n2GGH/o2f\njg74yU8yYcJti4kTNQce2Li3pAhst51m770Dttmm1M5qRKuvEJEeatCBevHW+l4HKgXsDR0dVoL5\nxhujd5c7ebJm333tu29pMVx+eRfTpg1Nf8TtwCaMMxx8UI7vf7+bK67o4sADfZqaGqljYO9szRpV\nJGoAf/tbmu/+ezOd3dUnMu376IIPGnTBLxKx+LOIkjK7LnFUMfhtFK4D7NzkZjNsNcVj+vTS9V3d\nLs1tE5i8xWSmTpnCxIkTyKSbSY1vxU2laUo106payCoXp9snnRcoaDwPRBsyTRlcHEinIJWheUIb\nbVO2omn8eLLZcYjrkM6kcESRMS7iGVwEB8ikMjSlmmhKN5NSLqKBUMIWEczRNqeOrtYMMuI2a1Ul\nESG23NLwr/9abkgMcNBBjX1Iox2DYYMxkIWjP0R5qNCfvkil4AtfyDF7tp3cs1nD3nsP/W64Xr8N\nNEbT3Uvv7vPkP5iEu6UFTjjB429/28R113Vw2WWd3HZbO3PmlOp+9dXSQjJ9esDHPlboN0l+7jmH\nq6/OFI9FDFde2cnatUv7/QyluobGgWMgJLDW9zrUuUHffFO48MIm5s0bx1e+0sy6daOTsE2cCD/5\nSRdXX93B4sWbyr7nwbZZG6iB+WCE3pk0qafpwU03pcs2L3FEaZaWr1hRPI5UgQhWclYhRYtiqkVl\nxCmpEJ1Mmi2nwllnlqQfqTSolEMqkyXT1IyTTkPKwU1ncbPNZNMt0OTidXcSdOdwu/JIu0ezr2ki\nC77CaIfM+FYyk9tIt7aRaW7GbWsjNa6ZpvGTyGbHoVBIAVQAjla4KkXKSZNy0zjKRcJucRy3GLqj\nck4dDfacY1oNGke1aNFxtehRR3lMnNTBf12aZcMGxTHHFDjppDwjKPUcVRhITsGBquxGA2bNMixa\n1MHKlQ6TJhn22GNoyFp8TPbWb42ouow2PaLVG21tMgyNq+0GK49hHEpZ9eLs2dXJ33bb2fvttJPP\ntdd2st12/SeJjz7qUJLSGX7+807mz/e5//5+V1nEYGQFMMaU5Z+M6qpMR9SX+nrUjwzoO+4NQQA3\n35wuJqG/8840q1bl2GKLod/Y9Ac77aTZaaeh3zj2lhOyt1yig/HOZs3SfOMb3Vx4YdyLTWqSaeW6\nVrIWO47/X3t+ycg+THoet+8SpQgKBfycVZUq10VEcfDBHuPHazZuVLSNwxI7Y51hdKARV1A4mBwU\nKBDkcxBAKtVEt+cRtOdRRqFahcAzpMZnGT9xC/yMwTEZmlsmkXItYVSeEAQFXHFDpmMQKc0CxfYa\nVZKoAeK4o06qBu+y3KDxhRAoLj6A1XEYQy6nKBSECRMGvhiNJQwkp2B8oQcSo+caqOynaExG6Gu/\n1XpnOtBlMmQBax9VByORx3DNGmvIP2OGZvr0gc1Tv/pVmnPOaWHrrTU/+Ukn++/vj6qcurXeVTWy\nPdD6hio36PPPKxYuHEcuV6rv1ls3sf/+o5OsjQbUyyVaSoYumBhR7+87e/NN4ac/zXD55VlAaG01\n/OUvm2oS1sjLU7lumfej9n0qJ5BK78igUCDoLhTLi6tws1lEKZbfm+HCC5u49tpOpk0zJVs4gcBo\nCh0d5No7yHd3EHR2E3gFxHEIOjrpWvcOXboARiGtLk0TxtE8eQuU66BaWkinMjhGwNeQ8wkKORwn\nhSMurpvCbcqSbmnGjalpo76OpIkDdQQYKB566KEkN2hcEhFURPbW2keJQzaryWaxoQISQlHEQKQH\nShRErtAVzh0JSqiUpAkgyul3v9XakfdHYjOYeQxrOfpUYuutDVtvPTgL/Yc/7LHLLpvYdlvNttsa\nurpsovbRIjmv9X3FpW39qU9HRMCRsvNDkRv0pZdUGVEDw7hxY1cY0FdUk6DVyiVaTuKMHQEi/Xpn\n0X23nKI477wcxxzjsXatMGNGfcliJUmLIEqVE8wqH1FQKHcmsEoBW27+/nkWLQqYNKl0fbGOwAdf\n8//ZO+8wN6qz7f/OzKitdtcV3BuuFDdwDMGmGzCEjum9BN4QXidAAiGQEHghkEJvIQQ+YkogJJiS\n0A3GhW5YGzAGjG2KcTfevpJmzvn+GI3aSruqK2mt+7p8eUdlNHPmzMwzz3M/922FAggpEbqOLnyg\nQnh79MLr8ePZuokgFv5efVF+D5rQcHv9eLzVCKFhtgaQgQAgMLw+XC4Puq6ja7rNpQs3Q+gJqtLO\nNS3SJVsqF4cwSmtr8oy6uroOCdqx72kJWYJUNyOpJCErSMgKlg1ZPl/19lzkHwpt4ZUuSoF7kArJ\nmgZyGbdUXCqhCd5cvDgjWYh8+RgWi784bJhi770thgxRrFmjcdpp1fzylz7++c/FXfL76aAg8iqa\nregey3UqlDdoY2P8+vbc0ywroW8nu7JwQcc8xkzsmRJ1yBIth1Lx2dqvW2U1N6RpYQVNlKVQlsTn\nlUyZYnHYzCC7jAt2ug/J5kQ6umG62xX3ed1txC07gVo7WNLmjpkCWk00wKhy46vuhdffA29tL/w7\n9qOmdz88vXpQVduLmure9Kzti1f3oqOBKZFtIWRbG5plX/c0l4FQAhWMHgfnuEjTwgqZcVzAxHEp\nhftGt8+sdcyzEZF/dtat4+yPVBLTCiEjqsxg6MUPQCooD0gJ8+YZfPWVxqGHhhgyJD7rkG4GMhSC\nVas02tpsGYw+fZJnLzrKojg37EyQjRxEIkqBv/jBBzpvvOHijTdczJ/vY889NYYNK5+gIh0oqbBM\ny87GOFzFPPLTkiGWwK5piuuua6W2tmA/l1fEZrKUVEkFY9t9rpMMTOxnI7IWKfhricvpZK+iv5O8\nVK6kitg5OdxUu+2RtPchFVLx6xw4WSsrGMLweSPSH8m+19ICn3yiEwzCyBE6PXzgcrtRAQ/KBW5v\nNS6XGysYRBMCn7s30qUj3AK3pwqPtwrNMAiaQazWNrAkmlSoVhOTNlw+H5oUIBXSshCaaYvemhJN\nd2FHnfH83VLLqsF2wFmbMHF8ZDmWZ5PIwVGoiNp3qpukJU1MKxShEQkhMHSj4NydCroHvvxSY/r0\nWgIBwbHHBvjjH1vo0yfz9TzxhIvZs21dwBEjLP70pxb23tvE6+38u8VGKfAXH3zQzS9+EfVr+tOf\nmjnvvM611gqF+np46y2D5mbBlClWzoFjpHsw/L+TPc0nPy0ZvvtOcN55fr76SufPf25mxozk/qWl\niHR4WOl8LrbUqaSMfNZ53flsOkr2HTUeOAGaI+bqIPYYS0siYwJGAehuI267OtrXdJG4nZ01TCRi\n8WKDI4+sBgSTJprcfus2hvffjBWwRXd1w43hcaN0hZKgGToIgeHz4HJ77CxZOJESaGkm2NyCagsh\nLIWmaRiaAYaBu7rKrqgJBbpAd7tQCPue72yrlOhZiN7mE6k4a6UXPuYZlrQihNrYMlPs3zaHhk5L\nMyLBuiVxuYIKOkJDAwQC9jk4d64nxiQ8s3XcfrsvItC7erXOrFnVLF5cHg8M+Sqn5oLETOSf/uRj\n3briSUz85z8uTj21hh//uJpzz/Xz3Xe5bYvzEOoEaI7xtKSwZeeBAxWPPtrE6683cPjh+Q3UGhsp\nqB9ruvIaHX0ukklTxGXFnM/FSlqkEwykUr2PlfKwZLz8TiIX1RF3lZYZyRils6/plnoT9zlVubcj\nbNjgVLigbqnB8Sf25tvNfTC8blw+Hy6fB93tQnd50L1uhKFjVHnsjJlhIHQNhUQ3DHzVNXi8fgy3\n2y6nKoEZCmIFAwSam7Ck3eygufRw0Brmz4bHRXe5ihqodTRe3TrSqKurC5OpLSQKpWTkYhV70xAi\nfFELI5XxsSY0DN1lkxV1vWxKoKVQby8V5DoWmzYJli7VWL1aI9OkdCKP/847PTQ0ZLYOrxdGj04k\n3QsuvtifUcDR0TgUWjS12PzF0aMtdN3Zt/ls3KhFHCk++UTjkUfc/OMfbpYs0QkEUq8nFtmOWUMD\n3HNPVMzxww8N3nsvt8A7lqsoNIHQBQrZ6cNoPq4TvXtDv372GIRC8PrrBhdf7AsLHGeGb74RvPKK\nwa9+5WPmzFp+9KMa/vxnL998k//AOpaHtfitNzsM1lLxtZLdaBP9JPPRaZgYkMVVx5SK8f0UCKVA\nykjQHsl4dcA5iw3AFi5Y2GEAkfie01GZ6v1kiBXKBdi8WeOOu2uQejXeHrUYXruMqhs6Lp8Xd3UV\nLq8vcv1QAtA1lBBohoGnpgpvbS0ut8cuA2sCjDCXTUmUUFgBM5Jh1HU97KObOvPcFffQxM7gRJTH\n43gO0IRmCxNLidJEHHfN4eCIhNJMR9kyTWhoepnk9ivIK774QuOMM/x8/rlBdbUtpHzCCcHUZNkE\n7LCDimgMAbz3nsHatVqcSXhncLvh0ktbeeUVV1zn3YYNGo2NggEDcguw8mGdVOoYNUpyzjmBiB4Y\n2IHF8uUahx1WS1NTuBFD2LZYp5wS7LDEnMuYtbYKtm6N/+zcuW6OOiqUtc1dIldREl/26iqe4Mcf\n65x4YjWWJaiv17j//ua0SvXBoF0a+/GP/WzdGr+dH35oMGmSyZAh6TtPpIuIcn0nwVSqzyTjmcWW\nBhN5a1lvZ0yAJjSB5tRB7fKQ/WeEf2eFRV+F7eNpRbsgU+q6pehQTbotCfusGUbcXEtnX8eNszjo\noBDz5kUrDU8+6eHin/oZNzqIFPax1j3uduVah1YhCD+QAC6vD2EpTBTKspAhExEyEDV++5wKhD+v\nLHSfnXzJlL9bCHQW2BZ/CwsIxxs0VlvNWY5FPkszpWirlH+vu8JmXgqJXMbi/fcNPv/cvlg0NQmu\nvNLPs8+mH7j376848cRYbpTgu+8yn2sTJkiefrqRsWOjN6yjjgqwww7J51yy4zVt72k2p8WUce/l\nwzpJKTvwefVVg6VLdUKhzr/TlXC74cILA+y6qwnsj2EoevZUfPONFgnUwJbvueyyKurqOrZNyGXM\nevRQjBoVnyltakr76ykR2/GZrjVZPq8TlgV/+YsHy7LH86WXXKxdm170+dZbBrNmVbcL1ADGjjUZ\nMya5nEsmXZodIdtxcDJWKlxai92uTEuDHf+OnTFTpolQCs3QwsFGoq1Y/DbYhushrLZgxJA9aTYw\nJsCaPm1ahwFXYpbOKUtmUu7t0QNuuKGFAQOi2yKloLFJt62rPO5Ik0IiYsc5koCxZHgf7eDVam3D\nMkMIwApZKEsiFGjYn01nG7vCN1doGpaV+iGkWwdrEG4q0F1xZc5kF6t8lGZKyVapUIjjS1jSnvxl\nGLRlg2T34GuuqeLbb9O7Cek6HHNMkNiLajb9PULA1KkW//1vE6++2sDLLzdwyy0t9OqVZJuTWNU4\nr0mpMKVExhDS82GdtGSJzowZtZx4Yg0HHVTDY4+5aW3NfD8LiZEjJXPmNHPnnc089lgTY8dK+vVT\nCJF4QARffdVJpiWHMfN64eKL48lYP/yhlXVWLRmKwRPcvFkwf340U2KagpaW9HbqiSfcts5lDIRQ\nnHhigMcea2Lo0PYnTb4DokyQGCQ6XGZnO5JlqnL9PdsSSYuUNiE5n84JnmQ4CBBCRLhlqbYlHWmO\npL8T08maabl3zBjJs882csopAQxDseuutuxLZ+uKaKM5orYRSQ4Ls63Ndlpwe3FXVaO5Xei6gXDC\nHiHQPe6cM535QjtR9ASUxlYWCHV1dTw8p4qtm43IxQoh4rhr+URH/qPFRD7r7U7WIHLDV4ps/eqK\ngVzGYvJkM854HOy282AGjYQTJ1pcfrlzc7YzOtmid2/F7rtbTJlipSzFJsv6KKVYvHhR9FjG/B/p\nGiR7pfT77vNESrRSCi65pIqPPsrS1LOAGDFCMmzYa8yYYWIYdjnm5pvbewQPHNjxeZzrmE2danLF\nFa0YhmLcOJOjj85/Z2o6D6P5vE40N8OmTbG/pfB4Un48Dj//eRuXXdbKoYcGOeaYIPfe28z8+Q3c\nemsLI0YkP1/yGRBlMg7JCPaJ7+eT0O98NtlysiDLaWyI/EvITnVU3tQMI+INmiukhI0bBatWCdat\nE5hJEkgjR0puuaWF999vYO7cprRcSzSh2XI0UqEJ3RbUDQZRpoWSli3OKwS614fh8uCu8qH7XGge\nA6PK004YNxW6grNmN4GkPj+7PWft0kv9NDQKfnJRM2B3fWoinruWL+RT5b1U4fAlIpwJ0TUaTqWA\nceMkjz3WxDnn+CO8szPOCNC/f/oBV1UVnHdegKFDJS0tsPPOhbXi6citwHkvIpYbI5pLmPNEmvwr\nx5VAKY3W1sTPC1as0Jk6tbRth7xeOPHEIOPGWSxY4OK77wRHHx1i99073+5snQbANhX/+c/bmDUr\ngN9PRvMpVyRa8OUL9pRTOF1+Awak/2AyZozkqqsya/3MRJcsn+gsuIroqDmNbUm7O9PXbnPeS7Wv\nqbo9dbcbK2A/BGhuO9uWbpkyFzQ2wpIlBk8/7eLVV918952gVy/FrFlBLrqojWHD4ueExwNDh2YW\naAsFumZExlFZ2OUHJdDdHgy3F81joPs8cd2eogR4arHQdANLpuaMdHudtRkzDsLnU7z2+vcMHd6C\nlBJdd2GEM2351khL10qnnBHhQBG9kRdaw6mUsGaNxsqVGj6fYuxYSd++pX0OpTRzD+s0IWj3XqJ/\nJNBuHQ4S0/f/eMzPz37mj/vMnXc2c9ppxdMyq6A9Cql519AARx5ZzUcf2aXQa65pYfbsQF7Lu4nI\nVN8rb7+Z4O0JtnOAYw8Vy3VIFiClq/HW7nfT3NdIEONovSXJsBUC69cL/vhHLw89lLyrZM6cJo44\nIndCq7N/zjgqJWnbWk+ouQWksH1Je/jx9+mN7Y/btXMkE1iWydKly7Zfb9DWVsHab3WGDLPPG8sy\nbRkOLf+lmXyovJc6hCbQNT2lcnYgAMuW6bz5psHbbxvsuKNkxgyT3Xc3czbkLgUMHy4ZPjyxFJGd\n4XZXIFnWx3lNKom0LPspFN2+mCU8wElL2u3vJO92TCz3jxljctxxQZ56yi4xDBggGT68tLNq2yPM\nkKSpCYJBHcNlNzvk69m1thauvrqNk0822Hlni2OOyb67NV3k8wZcXw+GAX5/x59L5kCgpLIJqoBl\nmmjEPgglL4tGuzcliI47MJ3vpLuvcWXSLgxQli7VUwZqgwZZjBuXn2tCJFOmNFCgaQaa20APuMJ0\np6jeoGkKTMtASnC5KJpoc2MjfPCBwapVGi4X7LCDZNQoi5EjU5+A3TpYq6urAw4CIBAMl3WEsNWK\nRX5LoKWMRYsWFaSbJVXpZ/58g1NPrY4jCT/8MEycaPLoo00MHFi8gK0QY1GOcheLFi1i72l7Y5lm\ndNsBAz2udCqVtNv/RTTrkljyTiz/Dx4sEUJx9dWtWJbN6bvjDi+TJzdTVdV1+5gOCnVulBqUsnXL\nVq/W+e47jY8/1li2zGDrVo1t2wQ+n6K6+jWOOGI6hxwSYsKE3G+k++1nMm9eI/36yZwlZboKoRD8\n9a9v8fjjh3DppW0ce2znmZ/EIEhaEhl5cNPsjI9pB0y6p3104HzX4bs5zQmx7+WCXErEuZwf/ftL\nevaUbNsW/T2/X3HqqQEuuCDAyJH543Q7pV4ne+iuraYpUM3GTTobNrnYtMVF3TKDZctcBAL2ca6q\ngiOOCHLcccFOkwj5vk688oqL88+vjnutulpx7bUtTJyY/DvdOliLRW2treLtEG2zLX9uD2XOXPHI\nI5523VwAS5cafPaZzsCB+ddIygW5ZsWSkfjLgb9nc8xUwrItA6BJ2/0DZYtrShnleCZ2OyZ6mg4a\nBMceG+L006MXI11XbN6sZcxHqSB3rFyp8c9/urn/fk+Ea5kcLpYt8/H114KbbmrNObB2u2HSpPLJ\nqAYC8MwzLn77Wx9KGdTV6WkFaw7sJgELqUA5DzyWRFkWKmRGgrfE4C62YzTx9XwFa7Hr66rs2sSJ\nknnzGvn2W0Fzs6BnT8WAAYpBgySFqsJu2Kjz2WduXnqpmhdecIc7uZNfiw1DcfzxCr+/6x8kkmWZ\nm5oEl13m59VXU3xne+CsHXBAkLvurqdH7yAoia4beIzMjRRLwdewHPDWWzrHHVcTsVZyMGqUyb/+\nlbz1Pha5BsSZBF/J+FmZBmz5WEcxIJWMy6xpmm5zOcPbHuuf63T7GrorrX1raoLHH3dz+eVVgGDg\nQMm8eQ0RdfsKugZbtghOOsnPBx90bG0mhGLvvU0uuCDAnnua7Lhj58eppQUaGwV9+yqn6lfWePNN\nnSOPrIk8aD7wQFPawZrDm7JMCylVmLsmkNIE07JNw8N2T7rXFelCjDeQjw/atBQelfni5n3xhcaX\nX2r4/TB+vEnPnlmvquhYu1bw6qsu/vhHH+vWdTwm/ftLZs9uY/r0EGPHSlyZu/7ljA0bBH/6k5cH\nH/SQGEy++uq8pJy1bh+stbRMYegwk747ttmKxZphm7vqrowDgUTz90I0KHQ13nlHJxgU7LNP/rJd\nStm2PR98YPDmmwZ+v2KffUwmT+7cpDpVQJxuAJdp4OQ0SjgQkJWadSlz1jpChLOGzUOM3fZcH05a\nW23u4vLlOrvtZvGDH5RPlqU74YsvNJYt0/n8c53PPtNpbrblSAYNUowYYdG7twrzCmXa2bRPPtG4\n/nofdXUGZ58d4MwzA2VT6kyGr78WHHFEDd9+a0edQihef72BCRM6tlqKlepQEkzLsjmeKHTDhaZr\nmK2tyKBlixQLW/PQXV2F7nbHNRdI0yTU0oqSCt3twuX3RRoB4vTaYh0CMuzolKYkGJTMf8PLhf9T\nTWOjfb7fc08TJ59cYurVaWLDBsG55/pTeC0rdthBsffeIQ45xGToUIuddiqNsnxTk319fPhhD6+/\n7qK+3n6ovf/++dtfsHbzzTerM886A4CQFQJURGsoMdBKJxgo18xaqnp7IABnnOHn008NXn21NLIe\nyQJiIbS0x72z4CtxLMo1K5YrFixcwLRpe3ca/Hb3sv/2wllLB+mOxfr1gsMOq+arr6LXz+uvb+Gi\ni9I0Ui1B3H+/myuucLoJ5nP++Xtx3bXNuF3JM1iOI0AkK4bCkspuYAuGEJrAcNtdl7ZzQBBlhhAq\nKsSq+2z7JKdTM9TcggzawZvQ7fddfjt6liEz2s0ZDuCkaYIAw+tNK2CTpsSUkro6Nz/6UW3EXQJg\nv/2C/PvfzSSuphzOj2DQfiD55huNYFCgaXamt0cPRZ8+tmRMrtnfQo6DZdki0jaPTrFmzQfbZzeo\npukoJXEZ7vgW6pgbj1QS0wpFbkqpsm6J3Jxyv3lt2iR4800XLS2CNWs0+vUrfuYjmVZdUrHhFGPf\nka5Y5PsJWbBYH8VCBmqlkn2LaqKpiN6gUCLptqXqbu7uQdz2BiUVUtq2Y53NzVWrtLhADeBvf/Nw\n8slBevcu3APf++/rbN0qOOSQ/HJe16zRuO66aErR61Wcc04bbiMsCREy28ldKGmLr8qQhWbooNsJ\nAMsMRZ0iECgzhKbrCI8L0zQR4YhBmhaqLYCn1i6HSssME5kEoCKBWeRfjA5bKNiKpusR2R2ztS2p\nb2YipJRICfff740L1AD22stqF6iVC9xu2HVXya67licfVteJS5SsWZP8c2V6eNKD4w0KHVuuWNK0\nL1TKntCW7MCfKw+2VF2NVE8EDQ1RC5g1a+yLSLF9P5Mdp3S9DaFzNflpe09rZ78U66NYKCSzfSoW\nlJJMmzYtsiwtK6Nt6062aqWeNegKOHNz2rTpaR3/ZDd1wyCJVVf+8MknGsccU8OvflXVzvg+HXTk\nEPDVVxrNzdF13nPPDxg7OhTJnsmQhRUIhm2MohkuZUqQKpwNs0uXuqHbVlAi/OAjhO1DqenhTlAV\nFm61QKmIubvudmN4PWgu3dZZcxno7ngrJNtmyv5bmpb90KpAWSqSeesImqbR0qLxySfxQZ3frzjy\nyOQaiJXzw0YpjEP5RBxZIvamApRdoFVINDREL1CvvmrEe0aaFtKUXRq8Ob8llIg7Tpl6G3YUfOXD\nqDwbFOt3oX0A3j7YjR+nzratVG3VKsgOmc7NESMko0fHP9D+7GdtSb1p84HGRrj5Zi8tLYLGRpHS\n3u3rrwVLl2ps3JgwnzvxDV2/Pvr5q65q5YADQhHJDft7doBlBYJxtlKay0AYOsIIn0+WhSYELrdh\nd4OHxW0dCyjD50XzuFBC2TpgLnckyBKaZhuW+70Y1V4MvzdSLnXWoZTduGB4PehuV/R8DjcudBqs\nGRo9a+G0U6Pl6oEDJU8+2cguu1TO4VJHt45abJ21KFLdVOymg2gwUO5NA4lI5WsW68/mEI9jM0Cm\naWGFOWCxpu35DuCUVFih6G8le7rPV0Zz8eLFccvZGJVng3wYpGcDy7IIWSEsJSPjqgmNN998K2a+\nx2dVUR1nWDPJdJY6usLzr9ThzMXFixfFLadCv36KOXOaOeOMNnbbzeT225uZObNw7hQff6zz9NO2\nsWhtrcLtbj8n33lH5+CDaznggB4cdlg1n3ySkJGKQeLygAGKkSMtHnigiQsvbOOjj+xxsGkCMs6W\nKLapwM6IudAMI1yWtM8tzdDRXHrke2ZbIPIdV5UPV5Uv0g0aG2QJTcPwenFXV8fx0JzOUN3jipRj\nNZeB7nUhjGjXaDq8Nc3QOPmUAM8+28C//93I8883sNdeqekv3e38yPYZuRTGoXtFJZ0gkacWGsRs\npgAAIABJREFUy7kxdCMtDk534urEEi43b7ZLAS4jbJMSo1ivTIUlpe2pGv68o3SfqwBsrCG8gsj6\nCqVV5pRJu5o71pXcOAdSSSzLREGUm2brqUeCX3vjQJgqIuQplQJLpTzG3Y27ub3DmZuQfoPN2LGS\nm29uJRDoXOU/V7z0UrTLb/BgSXW8ligbNgjOP786Yhy/erXBr39dxZw5TfTo0bko7F57mbz0UgO9\nezuNAyFkWBfNDIaQloXucWOZFmZbCN0VLmnGOA1oetSbEuyMmlLSLpFKhRUMIU0Tw+e1s2RWtHEh\nnSArUfTVDt6iy5nIePTpA9OnF5+f3FVYtUrj7bcN5s83aG4WnH56kOnTQ9TUFHvLMkO37gadN2+e\nmjR5UrubSrZdneXaDZoK776rM3NmLWALBL733jYG9jexpETXtLCPmh2oKYhwwZwyI2QvdeHA6d50\ngjZnfeXYlVkqDQQOLGlG1NTBzpi4UuikxXbRSueG08ExLrV9LQS+/lrjs8801q3T0HWYNMksWxJz\nueKbbwT77lsbEfP985+bOffcYFyQ8slyg3337ZHwTcW77zYwalS8hllHQU2sx6TNVbPLoFJKhNuI\nLBuOrEY4y5WolYawgyuzLYAyLVDhbk4Fhi/KSwOiZc5yZfeXOOrqdE47zc+6dfGtoM8918C0aaUZ\nsH7wwXbaDZoMmXQX5uN7pQqPJxqom6bANO1AzNDslL6ua0gkSomoabtUcWWSXMt5Tvem83SfqjEg\nU3R1MFGKllNCaAhNRTJ6iTpq8Z+NdtEmHlNblsCMPPCU4r7mE5s22QKbV1/t4/vvo+f3wIGS115r\nSEswtoL84JtvtDjXhbFjrXbdkX6/oqpKRZqlwC6Xer0xXeFpGp47n5Vhzq6ma2i6jtkWtLlhQsMK\nmgg9iCcms+UEX5rLiGyXputYISvSlGC/ZzcDCD3cOCXIq7VUBVGsXSs4/fTqTkVyywXdYy9SoK6u\nLmnXWracm3Ll6qSqtyeWL+wGJTtw0g09kkHTXTq6riHAzrgBSJWXoCq2e1MP/1Y+ArVU3Y2F4h4U\ns4EgFZzGDE3XMFwu9Ji6d+I4tDsO4eNth2oy7hwqxX3NFonjUF8Pt9/u4ac/9ccFagC77GJSXV2+\n+9oZSoGXk4gvvojO2d69beHeRM7Z0MEhfve7lrjXLr+8LWPhUydYWvzWW2F+mBZW0pAIQ0Q5ZEIg\nNFvnzAoEI80LzvtWMBjRQ9O9boTLQPO47PJngoWUsy+dNQd0ho66XXNBKc6JdLF5s+C779rfo48+\nOpCxiXwpjMN2lVlzMmHZcm66G1fH641/IlXKvmHHBmERrpUGKEESy8+0kSrblcoQPuvfydCrMx9Z\nuHT03QqBLVtgxQodvx/GjLHaKdCn0knrDM4xsWRUYR3sua8JvSj72hVYutTgnnt87V4fONDi//4v\nd7/MCjLDwoVRvto55wYYNEihZDwHTdM1TjghyMiRkro6nQkTLHbf3UwqgtoR5zgSRDmdmy4DszWA\npgkM3YsZCIKuo7t0NJeBFQjaXDUr/DATCtrXGaWQlhkh/hted7hpJ6xjF3YdcHhrcb+dBRIzjbmu\nLxk+/VRjwQKDXXax2GOP9teZUsTAgbZp/GOP2c0pNTWKX/2qleOOC9KnT5E3Lgt0e87ahInjI8vl\nzjHLN1pa4Ljjqnn3XRcej+Ldd+oZ0oFvZy7WTLk6BeTT7zN2XUDeHAy6uvTa3Aw33ugNBxeK665r\n5bzzAvjaxxqdItWYpeJpdlfO2muvGcyaVY1T1/J4FBdd1MbJJwcZPbrCV+tKWBYceWQ1b7/tQtMU\nr7xSz8RJVnj+ZUasV9JptlGRz3d2P4i1glJSYoXCgrdhnpr9hv0waFlWZFmoaHOO7raDTWXJiJSH\nw2lzkMrYPd19i91Oe4V0KpCbCbZuhRNOqObDD12A4vbbWzjllGDBzNjzifp6m3tqWdC7t2LIEJXU\nRL2UsN1y1hwHg+6QCcs3qqrgqKNCvPuuy+6yquk4cM8le5RptivusxlypDrqvExcF1JB7Ps5dKHm\nO0PYGdas0bjnHq/z6/z2tz722MPkhz/MnDib6vikyibnuq+ffmp3aPXurZg40WL48NIIhPbc0+TF\nFxvZuFHg8diaYsOHy7K4MXU3hEIQCNhzbPbsVsbtHLRN1kX6XZQQzTwpGS3hC03rlHPsZL+c7+uO\n43dYtNbJlkkzykGzAkEInxmaEU3t2cGaQAmJ5jZSBlPZZMk663bNFdu2CT780NlewWWXVTF+vMWk\nSaVJ0I9Fjx4wfnxpXFtyRbeOXurq6srScSDf6Kjevttu9gk3YYJFz54dr6czd4AOv5uDzlg2HKlU\nwrgLFy2MrieJllg5lfRaWx17GgciTuagI7TjrKU4PkoqkKCR36z0nXd6uewyP+ecU82hh9Ywf74R\np/vXVUgcB78fpk61OOIIk4MPNhk1avsJ1EqBlxMLjwf23DPE+PEmZ5wRwOXKjiccaRwIfzdxORHO\nOAhNC4vRykjp0vnnCNUKXcPweCJuA3bpU0Q6RSNlTl1D6AKhazQ2CVas0Fi92s74JNvWVMvJ4Gwn\ngoyN3TvDokWLcLvj+c2mKZg3L73rTHdBKZwb228EUwFg85zGjTM57rhgWunhbK2ZihXoQXxQ5mSD\nIlIhThk3Tw0TXYlevWQ7gdCNG7M7pZMdn0JaZPn90XVt2qRxwgnVvPnmdhIVVZAWhIDzzw/yyCNN\nDB1mZk1jiZQ9Iw03etrrcvTNEsuWzv+aYaAZOroebuRxu3D5bb6b80/oWsQ+6pPPvPz4glr23tv+\nt3ix0e73OlruaDs1w0AqW27mmWdcXHmlj2uu8fLyywb19WmtJil22EExY0a86PGCBUa7QLOCwqLb\nc9Z23333Ym9Gl6GtLXqzrq2VnWbKHHz2mUavXqqkJQmy5Ugl42IBWGa8+G86/LuOtiHfHC7LsjmF\nHQk3hkJwxx1ebrghSlK7+eZmzjknmJdtyoWj2Bnee09n5swau6wVxo47Sl58sbFkSqK5QEpYuVLj\n8881DAN23dViyJDSPb/KGcuXa6xcaZcc+/SRjBol44yxIXMeWCIiEh1hZLoeJSUffWxw5JG1NDZG\n5/zpp7dxxx2tednWdesEc+Z4uO02b6R87OCFFxrYc8/so6v339c57LCaiAH8D38YYu7cJsJGDBXk\nEdstZ217wHff2ZyCBx90s3ChC8uCKVNMbruthZ137vzGN3Zs6d8cs+VIJSuharqGbuhR7lo4Y6SE\nShnUdMSb64xT9+GHOoahGDdO4kqzevDJJzoXXVTFmWcGOPzwEIMHt7/Ru1xw6qkB2tpgzhwPe+9t\nMmNGKK1tSgeF7HAdP97i6qtb+b//i7aVbdyosXy5VvbBWmMjPPOMm8svr6KtzR6zU0+1b8rdWUor\n14AoG9TV6Rx5ZE2cEfvIkSZ33NHCD35gRUrYuW6T891suy4bGjWuu64qLlADGDOm/VzPZls3bRJc\neaWPZ5/1tHuvVy+Z84P4pEkW997bzEUX+TFNwfnnByqBWhejG1862nuDdkesWKFx7LHVnHFGNa+/\n7sY0BUoJ3nvPxapV9tNmKdTbi4XEAMPxBnXKfjilPU20K/WtXw+PPOLmpJP83HmXN05cMTaI6YhT\nt3at4MQTqznwwFqefdaV0oQ6EX6/5MsvdX71K3+4Y1cnGX1lwADFr37VxoIFDdx1V3Mke9MZzy+d\nOZFL6bozeL1w9tkB/vCHZlyu6LaFQl1bhi7EufHyyy5mz/ZHAjWAjz4yaG3t4EslgFzGojOz9EKh\nsZG4QA3gyy8NjjqqhvffT6LdkQZSjUM2fDIH69ZpvPaaHTkOHCj52c/auPrqFg44IJTVNiZixQo9\naaDWu7fkySebGDEiu+PhjIVhwDHHhJg/v4EXX2yIPBRuLyiFe2i3Dta6O777TnDqqX6++KJ9gnTM\nGJNdd62QCjoKOIQmIv8cOEHNls1wxRVVzJ7t55VX3Pzud1U8/3z0UbIjF4fY5eZmwZYtGpYluOAC\nfzuOSioMH6645JI2AFauNDjyyBpeey05T0TXbXPtWBJwvozjs+UopoNeveDcc4O8+moDd97ZzD33\nNLHHHkXoMsgj1qzRuOSS9maZZ5wRKLiHZiGRrBkn/v3sA5lcMG6c5IgjAu1etyzBY4/lN/WTLZ8M\n7IcTR1Ln5z9v4//9PzfXX1/FscfW8MADbr75Jrfzq29fyejR0XNnp50s/vznZl56qYHdd8/PfcAw\nYJddJFOnWmXnq9kdUOGslTE+/FDnoINqE15VHHVUkN/+to2ddirMBTMUAk0jqehkuSGVvtjrr+sc\nf3z82O6zT5CnnmpMmmVKxQ/75hvB1Kk9IhySESNM/vOfprTU1VevFsycWRsxqDYMxcMPN3HwwWZa\n5bRsOGuNjfDtt1FdooEDu+/1oRD44AOdGTPi582ee4Z48MHmjBX1SwXpaCTGSk5A/rsSO8L69YL/\n/tfFDTf42LYt/lw59ND8Bv/ZlnqVsnma8+a5GDhQtgvox441+dvfmnPynt2wQbBli31c+vdX9O5d\nnvNte0eFs9YNMWqUxWOPNfH88wZNTRr77hti4kSLMWOsgj3Fr1mjce+9HnbZxeK006LCiOUmkhqn\nZI5ot+0rVrQ/NaZNs1KWAxM5dc549O0jOOigUCQrt3q1QV2dzoABnd9ERoxQPPhgE8cdV0MoJDBN\nwVlnVTN3bmNaWmqZ8vw2bxZcd52PRx6xyyk9e0ouvbSNY48NMmhQ5cKfDgYMkEydamsXVlUpZs9u\n4+STA2UbqEF6GokRTlcXc9bADkzOOy/IoYeGWL9eo60N+vRRSflguSLbfRPCloSZOtXi8881evaU\nkcAS4LPPDM4+289TTzVl3YjSr59q11hRQfdBty6DdnfOWk0NzJwZ4o47WnnwwWbOPjvI5MntA7V8\n1du//x6uu87L/fd7ufzyKr75xrlAF07iId9YtGhRRJXf8btUQrUr9fXvH3+h93oVM2cG09J4U1IR\nDCmWf2qwdJnOWWfFl2nuu89Lc3N627vXXhb339+MI1EeDApOOaWa5ctzO3WTzYlNm0QkUAPYtk3j\nt7+t4uyz/Xz9dfe8VOSbizJggOKRR5p54416Fi5s4Be/aGNoB64gpYRUY5FuST2iP1akLorBgxVT\nplhMn26x884y68x/oflJY8ZIHn64mZoEEfIvvzRYtqy08ielwNUqBZTCOHTPK3AFBcGSJQZPP23f\nzEMhQUvYOzmfxt5ffy245x4Pxx/v59RT/Tz6qJtVq/KbqVNKdrgMMHWqyeGHB9F1xS67mDzzTAO7\n7GKmxf2SUvHSSx4OPLCWww/vwVdfaUyeHCXkvvOOwdat6e2TrsMhh4T4y1+aEcIe14YGjT//2Utj\nY1qrSBs77qiYOrU9cXjJEhevv15aN5FSRt++ivHjJSNGyG7R/VnIRpPtFdOmmbz4YgNnn90WOa+F\nUHH6gxVUEIsKZy1NdGQAvD0gEID/+R8/zzwTJe0uXFjPrrvKnH0/HQSD8JOf+Jk7N54Y3Lu35Ikn\nmthjj/wQZVP5XSaisQG2bhXU1Ch69pRpl3jXrBHst1+PSJu+riuefLKJ006rDrsOwDvvbGP06PTP\nvUAAXnjBxYUX+iMdk88+28D06fltIlm5UuOSS6pYvDheY+SKK1q54oq2vP5WBaWPcqM3lBsCAVi9\nWmPbNoHfrxg1Smbl7ZsMS5bo3Hqrl8MPDzFtmsmwYeUtibO9YLvmrOUaaMXe3JWyQGO7C9jWrdN4\n4YXoDbx/f0nfvo7PXmovzkxgScm2be1f37pV47LLfDz7bBO1if0UWSCV32UslFRU+RVV4ZJyJvv1\n3VotTk/JsgQbNwieeKKRM8+sZscdJT17ZvaQ5PHAkUeGGDGigV/8ooolS1y8+KIr78HaqFGSv/2t\nmWXLdF5/3cWKFRr77mty7LHbV6t+BfnR6qugY3g8dkdrIaBp8PzzLp5/3s0OO0j+/GdbKqS6uiA/\n1+1gmrZg/Bdf6PTrJzPyXLYsaGqC2lryZhzfrSOOurq6dvwkmaTk1RnSKZuVMvJ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UAAAg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\n4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HNBhw4aH33kZOxYD2ef7ee664pYssTZYB216kjqFbh582aGDPHx0UdWiopCT3K/Hw4e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7dbBWLKxbJ1myJHooe/RwAqKePWH6dIvDD7fYvl1QU6OLZsTd2go7d0afVLvtZjNm\njIWUOpRt88Un/zo0DINQoFZsIU9pRjdslGqw1lWxYwe8+aaHP/+5gmXLTCoqnHNi2rQAJ5wQYK+9\nbPx++NOfKrjttn2w7X3jtiGlZo89zmby5BZWrHA09wqduS1VZOts0NAg+PBDg61bJZdcUsOTTzbm\nTQMxn9hvnM2wYRZffBE+r4cMUdTVpfbzjEU66zrrKLQWaCWchgHtNAxIrYOlSBtDGgiEk+lCowUo\nO4Ag2CRgONIfkcK3ADJYTtVagTQQOr2H80h3BXe52Fi2zOCii+IDtQMPDPDLX/ro3r3ou1R0dOo7\nSbE4a9u3h+0+AHr2VHG+aabp2LoUK1AD6NULfvWrZmpr36K6WnPJJS0891wjo0crTjnFH1qvrq70\nLpL5QKISTSG5B6VeClWWheXzoSwrp3HYvh1WrRKsXVs6cjTp4j//Mbngglree8+D3y9oaHiH5ctN\n7rmnimnT6pgzx0RK52ErWXVIKcGnn3q5/PLuHH54NxYvjnex6IjIZk5kS53wejW1tc7fH39s8tZb\nntQfKCIix2G3wZonn2jk/PNb6NVLMWFCgAceaKJ3bx2nSRa7HPteJO8r0brafV8FHJFnnO8QOM4B\nhjQwDRNTmqGAybL9TiLNMLGVjaWtYCNC4nKlKwdiGCbSNNv8vVydtVQdo8XAunWSQCB8j+3RQ/H7\n3zfxyCNNDB4c1IwLjp9t2yhbZeUVnAylwFkrWmZNCPEQcDTwldZ6n+BrNwDnA18HV/u51vr14HvX\nAucCFnCp1npO8PXxwCNAJTBLa31ZsY4hGWKfqm+9tZlBg0ojADr6aAvLambChJ3suqsmKMXD+ee3\n8vTTjhdS376lsa/5RrFJ/+1RCo36PkiaRVSWhfIHicZ+C5VFh0tLC/zrXx5uuqmKlSslpglHHBHg\nyitb2Hff0iljpcJuuyl69FDs2BH/m9i24NZbq5g8uYEf/tDPhAk28+ebPPRQBWvXyiiag4va2s55\n7mSCbLLW1dUwbJjNZ585F6Q77qjkO98J0K9faY2n0orhuyt+/Wubyy9vpbpah7L26WaclFZYwSBC\nConWCinjA3zHdQBAopRCSsdiyhG5dcqWRlBnTQoB0kBio4JBiZIaU8pQhs4J+FJn/NKFI9Tbfrmd\nffax+L//a6KlBXbdVbHnnophw8LXnBCvLmhBJ6WBRCIVncaCrmjeoEKIyUAj8LeYYK1Ba31XzLqj\ngceBicAg4E1gpNZaCyGWAJdord8VQswC/qC1np3oO4vlDbphg+CEE2pZtcrkwgtb+OlPfSVv/uz3\nw9tvm2zfLjnxRD+e0nmwzRsi5TSgsE0TbtCklYq+aIvo0mjev9OOJgm7xxd7rJbP59CuXEgwEzl2\np8CSJQZHHllHbCmirk7z5pv1jBzZMQK2zz+XPPecl8cfr2DjRicrbhiagw6yuOmmFsaNiw5kt22D\nHTsE27dLmpuhtVXg8ThP97vsogsqCdOZ8eyzHi64oDa0/NJL9UyeXKQ2+TSQji9mWzw0dxu2srGV\nUy43pIkQhPw+3c/Zygqu52TFDCkxDW/IDsrdB2ebYVkOf8DvZOWCnwFAg2GYmMGMmCE7N+PJVpZT\nFraD3qfuMSuNkKLgdnT5RLt7g2qt5wshhiR4K9EIHgc8qbW2gDVCiP8BBwgh1gJ1Wut3g+v9DTge\nSBisFQuDBmmeeqqJhgYYPlzFkZVLEV4vfPe7pVeqyyfayjblikjJAiFkOJumw5muQmfVYv+OPWYX\n0jRDmTV3OVM4jSjxp2tDg+DLL2WHCdZGjVJc/bNmzju3mZ07JT4fVFTCgAE21TWCWHZIr17Qq5cG\nSieQ6AzYYw8bIXQoY/nee2ZJBWsJy5wxAVlbGSd3G05QpoNVGOeYHcJ/OPPlriONsBG7a7zuImD7\nQ5ZS2g6AEJiGia0sRHCbStnOA6MOZpmEcN7vxN6ebpZTCBFsopBRZVCtdYfPspXCL3eJEGKpEOIv\nQgiXJjgQWB+xzsbgawOBSPGjDcHXEqKYOmvDhyv23bc0A7VSqLe3F4SUUdyMXMYiMjjTSqECFtpS\nTlkxYIUDpGBMk0smLx3tqlQm27HfK00T6TWdG4PXZOHixRnv09ixNieeGE9SmzbNz557ls5NNi1o\nRZ9eio0b32KPUS0MHdJCZVViLpGzesfVEksXxb5ODB+u+M53AqHl557zUgqyde44xJY1syHWh2yd\nXL6Xy/2K4M64cy4RNyzyO13BXF+glZbWFgK24x0KjpyH+89jeDGkdLo+cTJ7lh3ACprCZzMWpQ53\n7KQhHestIZ3LsIwc5+wz4KUwDu2dG70fuClY3vw18DtgRjvvUxl5gm075dZUnpzF7tbMFrEOBVqr\nqGxW5HGkQ9zN5Lvc74hFZBZNeszQ38nGUppmTiXZfv00d9zRzIwZraxebaAUDBqk2Htvu8PxHt0u\nRkFYYxAS35DL7hSFQU0N/OhHPubM8QCCrVslTU1Ox3wpQArpBDu2hTSyMzKXQoJ0AjIzGIDFllcj\n51xspi7y81prtAbbCqA1BGjFrKhxSnzBjBI4zQWu8bqt3YAu2MAgLKThzX5QigytXLmRtsuYsWOn\nhQ46NjiI9b7taGjXYE1r/U3E4oPAy8G/NwK7Rbw3KPhastcTYuXKlfzoRz9i8ODBAHTv3p2xY8eG\nNFPcaLm8nN/lAw6YzAcfGNx88xK2bhVcddW3+O53AyxdGr3+vLlz0UozedIktO10agopC75/LjL5\nvFaK+QsWOMuTJgGwcPEitK2ZdPDBCEOycPEihGEwZerUnPbv4G99y1l2v2/yJISUeR2PyZMn5/T5\nb33LDi337Vta8y+dZXc8hZQYHg8IWLBgIVLEz79i/B6lsOy+Vszv9/vh/PO/w4MPVtLY+DaLFzdz\n7LGTSmI8Iq9PKJi3YG721ycRM18kzJ83D4Rg6pQ0rhdCMm/+PCzLYsKBE7CsAIsWLaHC9PLtQw9D\nCsnceXNBaw6ePAmBM5+VtvlWcP4uXLAQaUgOnfrtjPbfRfHHfx4azaRJk9Fas3DufIQUaX9+wcIF\naKWZNGkSQggWLFyQcv1CXi/bGt/58+ezbt06ACZMmMDhhx9OLIrWYAAghBgKvKy1Hhtc7q+13hz8\n+3Jgotb6B0KIvYDHgANxypxvEG4wWAz8BHgXeBW4x+0gjUWxGgzKiMa//21y8sm1Uf6cL73UwOTJ\n0Rw5ZVlEETIKSMbPFYmaFYCQREeu2bS2vqucyWk/lH+PwmLDBsGNN1ZRW6v57W9bSqbZqVjXp3SF\ndZVW+C0/Acsf1FuTeEwvXrMipb6b6zUqhFMi7Ci8NbdZwIWAEJ+vMyNZg0HRjlwI8TiwEBglhFgn\nhDgH+K0Q4iMhxFLgEOByAK31cuBpYDkwC/iRDkeVFwMPAZ8D/0sWqEFxOWuljGLW27/5RvDTn1ZF\nBWoAX34Zn4JuDy/SbMfC5aIp23L4aMFSo+H1Yni9ed33Yth+uVmNDRsE27blffMdBunMh85gw5YO\n2ouXM2iQ5q67mrnuOl9JBGohzloerk9tcR0zsXISGkwkpgzaTXkqMaWZUt9NBgM091+mgVp7crVi\ny5btWcbMZByWL5fceWclN9xQyZIlBi0t+dmHoqUxtNY/SPDywynWvxW4NcHr7wFj87hrZeQR9fWC\nL76I1xAaNCj+glLobs18QitHXFcaZlb6aS6nzUVbx1vI8WhuhmXLJH/9azVz5njZbz+LRx9tCnY8\nlpEIpT4/Ozrq6kpPnDvX61M6XMdUHaeRPFilnaBPCIlHeNFCh+2igjy4ZNm59tZIyxZCCqQibc5a\nKWDjRsEpp9SxaZMz3vfeW8kf/9jEKacEcnY5KWoZtNgol0GLj5074dRTa/nPf8KPyDNm+Lj++paE\nXp25or7ecYcotDNELiUR96Lt/u9mZ9ojS7Npk+BPf6rgvvsqcWU4hg61efPNhk4drLV3I8vXXws+\n/tjgnXdM1q41GDXK5pBDAuy3n11UV5Myiod0rhnJtNwiAz3HyD0Q7iyVRjjL65q9t6EHV0Zx4GhR\nRt/oqqs1b79dz+67p9eJ2+46a2V0DXTvDvfe28yLL3pZvVpy7LF+Jk60QoGabcPatTKkRJ2LD/b/\n/ieZMaOabt3gyit9jB9vFSQghOw9ECGsgRb5v9tBWszAYe1ayU9/Ws2//hVda7ruupZOHagpy5FW\ncXXwoLjdnOvWCWbOrGHRouhxv/POSp56qpHvfKdz6x12VaRzzYjs9ozMiinLch7spNORGqlvqLXC\nNMPUi1IwWi/DQc+eGq9X4/eHf6/mZsGXXwp23z23bXfqX7SrctY2bhSsWydCwtfF5h2MHKn46U99\n3H9/M0ccYYXcHNaudWr5Bx/cjSlTunPrrVX4/am3lQqbNwuWLfOwYIGHE0+s4//+r5IdO1J/JhfO\nWra8pViB3GII5saiqQnuvbciIlB7G4ATTmjlkENKK1jQSufN28/Vw4v0h1WWFeIRFePc+M9/zLhA\nzYHg889Lx1e0FLSkSgH5God0rxmuX6eMKH86qreEvI2laYS2FdvMlA89uGTcuvKccJDuOAwfrrjq\nqmiSmmnqvEgblTNrnQzbt8OZZ9awYoXJbbc1c8wxOURDecTq1YKzzqrhk0/CU+6NNzxcc42P3r2z\nm8j9+mk8Hh0y+L3jjip69VKce25h7LOyLaGFPiPCXaSJtlXIUt0nnxj89a8VUa9dcIGPyy7z0adP\n6WTVXG8/yFx1PJEmUyiL6ZaULMspRUUEb5Foboblyw2WLDFZvVoyerTN/vvbjB1rk20j4IABKkqp\n38Uee1h897uBJJ/qvGjvknQxkS3XLUpDUUqE6TQSCNMTVeIMmcILRzEwG5eC9s48dyaYJpx9diuD\nByt+//sqvF7Fddf50i6BpkKZs9bJsGqVYOLEHqHlX/6ymZkzW6moSPGhAqOhAa6+uponn4zeiZkz\nfdx8cwvZXhcCAbj++ioefDDscSmlZtasBg44oHCK+oW42RRaHuKllzycfbbjw9i3r+LOO5s59NBA\nyTluZNuuHxnkgaMYL6SI5gsq5WQmjIioK4JHFAjAww97ueaaaiLLToaheemlBg46KLs55ffDBx8Y\nvP22h08+Mdh1V8W3vx1g7FibgQM77/U3EbqyDEq6Eh3pjlE63qVtQSuF3eoPB2mGkw0sVQmljoT6\nehCCjK+xZc5aF0FNDfTpo9iyxTlpb765ioMOsjjwwPazA1q50uDJJ6NVs3v1Upx9dmvWgRqAx+ME\nfG+8YbJmjTOVlRL87W8VjB/fnHUmJBXinAwiXAxinQyy4bVFLufzJrbffhaPP95ATY1mxAjFgAGl\nGSSEldjDy+kg9qFTa41ARGU1Qy4PSXhEa9dKrr8+OlADsG3BrFmerIM1rxcOPNCOOwed0lPXyDC5\nKPQ8L1VEBlaRfqDue24QB45FFNKR6kg2N5RWtPotTEOEugzT5apFXqNiM8+RbigdCekGwsVEvvnT\npXFUBcLSpUv58kvBv/9t8s47Jhs3ln7rb67o00dz0knh0qfWgquu+k+bXK5CwtGZCY/9gAE2zz7b\nmBfj76FDNf/4RxO77hq+Eb76qodvvkn8W+fKwYg1T4/kQ7mk4GQltlQotObcbrtpjjjCYsoUmwED\ndMlyUYR0fBMF4exYWp9LockUaQEWyyNasHBhaL2aGs2wYfEBmWFojjoqv+XKUNCfxVwpFIoxJ9pD\nWzFTFGIcYiU6bGVhKwtLWSGdNcu2sOyAYyuFhiQZta3bNM89W8GpJ/fijP/Xg6efqmbTJjMtrlrs\nvINobp30RPPhSvU6EYlMtOqyRSmMQ8cLoTPE979fy2efOYc5eLDNE080Mnp0+18YCwXThNNO8/OX\nv1RgWc4N6+OPTVauNJgwoX2yayNGKG6+uZlFi0y+970AkyZZDB6cv99gr70UL7zQyN//7uXBByvZ\nbz+7YP6CsU+hkRc2ZVlRJbZMsgYdSXOu0BBShDw7M/lMuppMycZ31101f/tbEw8/XMHs2R78fsEh\nhwQ462wf+42zyOezbVfNMHXVeS6EdDJqECzXOwGZrSyEcJpMLNvvmLgHvTuTZcoWL/Jw4YW1oeU5\nc7yMG2fxl780Mnx46uteooeCZIFaR0EqrbpYBAKwerXjQTtkiMqaL90e6PSctWnToj22jjmmlT/+\nsblTaxvZNjz0UEWQe+PgsccaOPLI0ur6yzcCAadDtLKSvBuLR5YO3GXnj4iVRPRyV+LjdCZopdm6\nTWDZmm7dAnjMYJODApRGGgbSzK2Lsytzt7oq3FKd0ir0MKK0wlY2AhEK4lyngWQctKee8jBzZm3c\n69df38wVV7Sm3IdSnneZmLZHIl3u3pYtgqee8nLjjVVYlmDGDIcz3Z587kRod7upUsFbb3nZvr1z\nl0MNA44/3s+ll4ZbiEvBxqXQ8Hiccl9BArWY0oE0Tae0FlFSi10ulYtgGZlBa03PnopevQIYhnMD\nUZaNFQigNdiWjbJyy1Lnw8KqLSujMkoLrkSHIc2o16SUCAGmYWAaHrSyQWlEksvYvvvadOsW/5sv\nX972A0SpWqe5DUIaJ/OYiWyPG9gKIZIGan4//P3vXn7xi+pQxenhhyvYvLnjxAKl8UsVCEuXLsUw\non/0ceMsevbM/ma+caPgnXdMnn/ewyOPeHnmGQ+zZpksXmywcqWkqSnXvc4PdtlFc+WVPl54oYEr\nrniNvfZqvwaDUkI23INEJSsXkXyoRMulilLgYJQCIsfB1XcLdca5F32tCfj8qEA4MFK2nZUeXGSA\nlctcKQTnrTwnHBR6HGKDC4/hxZAmQoMOBMAOdmkmCcT33FPxyiv1HHxwAHey9umjuPhiX1rfn8m8\nK9acSNQglAlitepisXKl5JZbqqJe691bp51VK4Vzo9Nz1h5/vJErr6xmwwaD/fYLcNtt2ZdA162T\nHHdcDWvXJh42ITRTp1rMnOlj331t+vVr3xJzbS1MnWohpVWy3X8dAbm4F5TRMRB6sndLMUojhEbi\n3DgNIdCWRmEhPU5W1bZVqGxjINss3aTjFZn+/nZNzltnQaxfp8YJ+rUCZVvYMigxI8CU0Z30SitG\n7tHKI4+2smmTic8n6LuLYshgQUfNv2TbBZ5oXJ/+AAAgAElEQVQuVq82UCp6m5de6qN//45zX+z0\nnLXx48fzzTeCHTsEvXvnZm/U3AzPPeflqquqo+wkEuGQQwLce28TgwZ13vHtSuhKQp5dEcpWKKWx\nbYWybQQaj2milY2ybSCo2abB8BqARImwNIghBIYndRkqF3/ZWJQy96iMthErNeHODX/Ah+UPgBBI\n08D0evB6KqM+63SS2gSsAP6AH20HqKisodJbGVVi7WjIlrOWDt54w+TUU8OCZwceGODBB0vz/tyl\nddb69s0Pj6m6Gn7wA8fr8t13Tf7+dy/Ll5s0N8eOq6aiQgdr46U3GcrIHOUgrbSRazAthEDZNkpr\nlFJg2+iAhWFKlHJuIAiJlBqURguFDj6pZ9Lxm68MbVftquwoSKX7lUhzzZ0bQkq0dO4bWoq431UF\nGxRAoLTC8vsxDAPbH8AyzDaDtVJ+6MymCzxd7L23zYwZPpYsMTn1VD/HH+/vcNWm0vq18oxCeIMa\nhsMZOOMMPy++2MiiRTt5552dvPZaPf/8ZwOvvVbPokX1PPBAE0OHlgbxtxTq7aWCtsZi5054/XWT\nF1/08OGHBvX1RdqxIqMzzYlc+FvuOETquwk0OuCQvC2/hRIK7STT0NrhsgntrIdWSCHSclnIN7k7\n3/zIzjQnckGu49CW7lciqQl3bggpkR4DYUpQKupZ392uCLadC2VT4a3AY5hIKdF2al5yNudJZ5kT\nAwZobruthVmzGvjRj1ozDtQSjUM+PYzTQZfIrBUKVVVO9+Fuu3WsCL2M5AgEBD//eTVr1hiAZtw4\ni5//3MfEiRbdu7f33pUutm4VrF8vqKmBoUNVUbuP88XfkoZAKiez5pQ0FeB4egrTcGQ7hEZZjo+i\nlMLpCjba5qu5KMWMRiwKWY7qCmhL90sIia2sUObNlE75XEiJYZooZWMHbOcBwFZo6cxnZVtYlhWy\nTDM9FahAOEAzzNQnXSF5jqXoIBALKR2Hn1xh2/DZZ5JVqyRbt0p69NAMH24zapSiqqrtz2eLLsFZ\nKyN7lHLavFB46y2Tk0+ujTLenj7dzy9+0cJee5VGtrSU8MUXkvPOq2bpUg9er+aii3xccEHmT6/Z\nIh/8rbCHqMa2AsFSZzBo8RgIaaBtCxEAN91hVHoxvN6U2+1oSOaxWkb6cDNgSmu0VhiGiRlRnlRa\nBZ0KgsGa4XE6QZVT4rRa/U4KFzA8Hoxg9jQQ8GNZjpOGMCQeb0WwazSAYXrwmKnnYqF4jvnwKO1I\nmD3b5MwzawkEIs8Lzc9/7uOii3zUxkvgZYSyzloZGaMULXGKgSlTLB54oInIGsTs2V6mT+/Giy96\nSkaepVTwyScGS5c6T/V+v+Cee6q4//4KfOkpCeSMfGmWOdsSmF6vU4oyJNJrYng8CAFCh83hC0St\nKShWrJD85S9eXn3VTPrb5CqhUIbb6SnQ2na4jlpHlUK1ViGpCaHBavWFbOtUwHI6Q9GOvEfQH1Qr\n5ViwBTXZXHaXx/RSWVnTZqAGhdNYS5hJ7KTYuRN+9avqmEANQPCb31SyYkVuYtmp0KmDtUJw1joi\nsuUdpNIX66hIZyw8Hvje9wI8+2wjvXuHj7mpSXDOOTU884w36HfacZFPLkqiLvv7769k9eriXV6y\n5W+FOWvRnzO8XrxV1Rim1+GkKDCCVmJCSNDCeYgJZp5LXZz2888lxxxTx89+VsMZZ9Ty0UfxN5X5\n8+en9FjtKsjHuSEgSvcrMoAJGba7D8PKeRi2bQuNRiCRhhFlASWkDJUYDWliGEZaXqBx+5XheZLO\nWMTuRzb7lQmKzRWD8Dh06wYzZiR+0unTR9OjR+H2qVMHa2Xkho5gulwoVFbCYYdZzJrVwFln+Qhn\n2QRXXFHNW291DEuIYgQSe+9t069f7PYFfn/BvjJnRI6L+y/YXRDKOjg3A4GUJuB0izr8IxCGk2Gz\n/Y5YrvuvFIO3pib4zW8q2bLFPX8FmzYlPpcjGy3KJdDESPT7OrZRViiDliqAEY5MP9q2kdIIPQQo\n2wo3GASzuq60i8tnc63ODNNTMqXGdBwE8oVcnA7yASHg5JP9PPtsAyed1MqwYTajR9tceWULL7zQ\nwIgRhTvny5y1MlKiK3LWYtHcDMuWGdx7byVz5niwLMHAgYp//aueXXYp3fOnmFpcS5cazJhRzerV\nzs3liCP83Htvc9ZGyYWcd2F+mgo3C7g3xYgxUrZCE0kHUE6HHuGbr7ItpzvU3UdXzDSIyO2117n0\n0UeSQw/tRmTt9oknGpg+vXN7BRcCic4ppR3emPu7ugFLItK9+3mlVWjuGIbpzEUURMh1lBr3a+1a\nwfbtkhEjbOrq2l6/EHDPSRcC0urELgQsC+rrnUpMPsejS+uslZE9unKQ5qK6Gg480GbcuCbWrJGs\nXy/xeqGmpnQDNSiuyv24cTavvtrI6tUOp2b33VVugVqelP6Tbt8N2GyFtnVonkeOkauq7o6jlAbC\nNLEDfie7JiVCh/WxQtsW4aDMXa/Qx5QKX38tiQzUpNTstlthMgCd/eEu9pxSVjCbFuEbrF1NvhiX\nAvfzIUK+EEFTd4UwnfWdXC4l11W5caPgzDNrWbbM4MILW7nqqpacBOazRaGdDjKBaVLUMSid2VAA\nlDlrDjqLVk4+kMtYVFTAHnsopk2zmDrVyksbeCGRqoxdiDnRr5/moINsvvUtmz59sg9kC82VdIMn\ngIVLFoebBogeI7ckKKUMlQS10gjDREgjlJGTHjNUPpWmGdWUU6xjSoVY+a0LLmhl+PD47891TnSW\nhqRU45AoAI0scWqlknK2lHayZ7YKZzSlaaAl2Fph2RZKJRfSjSyzFgvuWKxYYbBsmUMH+POfK/nv\nf9snz9NeZfpSuId26mCtjDK6MgrV/VVoFJor6fCCzAj+mQz/i/tugeExkR7DybJJgTBNtHSe8KXH\nxKjwOn6hLhk8YsyLdUypMGyYols35yY/cWKAiy5qpbKyjQ9lgc7YkBSL2N9XmmYUZysZlywym4YU\nTr+nBIXCUlaUiG5kMBf52WQiu8VAY2N0UHTHHZXt1hUvpMhI27CzoMxZK6OMMkoOhS6naaVCDQFu\n8NbW96TDl0nFE8zlmCzLuc8bWSoDfPSRwbZtgj32sNl118Jc87uqX2lbv6ujq+YHRJDLprGUH4HT\n2ekI5Oqg1prG9JhRfqDu+y6EEHG2UoUWpZ092+T008PELMPQ/Oc/9Qwb1vkC8vZGmbNWRhlldBhO\nUbL9y5e6fuwYpMPnS4cvk8qz011WCr7cKFBa07OHo3qe7Hh8Pli0yOSBByrYsUOy334W3/uen3Hj\n7IzK8Pvsk9qKKB/oqn6lkXxHZVlRr2tBUDBWOlkx4WbHbGRw/hjSxLJacfoLJFLHciel4yHqbjcN\nr9F8B2y77aYwDI1tO/ts2wJV5E7Mro5OfTaVOWsOSqHeXiroymPh3EwUn63wcMstS/j97yv4+9+9\nfP55x7gM5LNt370Rzl+wILTcluRGunyZVFpW69YJfnt7JZOndOOgg3pw8SW1rFptJD2eFSsk3/9+\nLbNne1myxORPf6rkmGPquPnmKr7+Or9loLzoi+XZr7Q9kM04RDasKL8V/tt2gjcZlLWwlT+4bKKU\nw0OTQmBKD6bh/JNCRs3BtqQxCilK647FyJGKmTPD+mJ9+6qS5+zmE4W+b6TDSSxn1sooo4uguVnx\n6qwqLr20htbWaqAagAEDnE7OIUNKu6SRSF1fZGAlkDCrGMEti+3WBBGXxRNSZPSdkdi5E665pprX\nXw+rzb/0UgWtrYK//KWBqsr446msdLrOrCgak+CBByqZONHi+98PZLUvZeQXbnAV+b+Q0nElQEdY\nS3nDQbkMCkoHHwBsy0JKCYYZ0l5zkaiz1EVbmbdEyLRs6vHAjBl+Vq82mD3bw+23N9O/f/h87Aje\noKWKuMxoEpQ5a2WU0UWwaJHB975XR7xXkmbBgnpGjy6dYC1RYKUsG9u2w3pWGZRC2+JTKcuKdBdz\nAsMIgli635WqzPzpp5JJk6L1zgCGDrWZM6ee3r103HfYNrz0kocLL6zBsqLfu+SSFm66qXCeXh2l\nZF4MpApGQuXP4PzRtgoLK0uw7ADKtpGGI2arVMS2pETbNrZlYVsWAonpNTG9FRkFPan2b/NmwapV\nkg0bJIMGaQ78lh/IzsuzsRG2bBHsuqumoiL83V3JGzQXJPqdYjmJH324rMxZK6OMroy1a6P1tlzM\nmNFaMN2tTBByEoCoG194BY1EODdDIaK6LWO3ERtghDMeQc6b1hje6M/bQcsFaZogY0tNbWfx2tJS\nq6mBnj0127dHb+fCC3z06mEjZHz3gGHAsccGGDasgcce8zJ7toemJsGkSRZnnFE4i4j21IUrNYSN\n2RVaBzAMT8iY3R0nJ7vldBdLb9h1QCkLocAQBihAKUzDDN2wtVbh4E3KEA/S2Vb6450o89bYCEuW\nmFx2WQ0bNzrv7b9/gBdebA0FWpl+V20t1NbGZrgTlGHLwVocknELYzOjydCpR7TMWXPQlXlasejK\nY7H//jZjx7r1tLfp1Utx111N/PSnPmpr23XXojS63C7NqPcijNadDsz4ikAynS8nQHO6OV3OmxaO\nZtr8+fNjPqeDWlmZe2S2JV0xeLDimWcaGTcugMejGThQcd+9jXz/xEg7MwdNTfDJJ5KFCw3WrZOM\nG2fz29+28NZbDSxYUM+DDzYxcmR+A+zIc6MryHAkQ+w1QmtXyFahNdh2mFsUOS6hjK9phnh7Li8t\nxEXShPw93Ru16/upgt+jRe7+mhs3Cn796ypOPrkuFKgBXHhhK1VVsXM7+XeVojdoeyAf941k3MJY\nTmIylDNrZZTRyeEGO7uPkDz7bCMbNwo+/LCRadPqGTgwfRpEvjoxk+2ji0gnAa20Y8EjBJEBTeJO\n0UQBhtN1iZQoWznZOdMRtHVLD9HZuNCnHaJ3BscrpIwutSbYx/HjbZ5/vpGdOwUVXpve3f3B73Uu\nxYGAY911yy1VzJ3riJD26qWYM6eB4cNVTmLDmSCdY4mE3w8ffGCwebNk6FDF3nvbmCV8dwkEnHJe\nnz4aTxs2v07mIxCz7GSPUo2T0sEMsDCc9aVIzEUzPY6Bu1YgtOOAkAPWrZNceGE1S5ZEH9hJJ7Vy\n2GEB5zsleeOY5Xt7uaKlxREwL7VEcCpuYSpOYmj9MmetjDI6L/KlfeV2YrrIt3p47H4igsGhCHsl\nCq2BsDVUW9sQhkTrcIjnBpuuNlrYlSCoueaW+4z0dNeSHkcaPK9EOm+WLZk1y8N559WgVHhshdAs\nXFjPHnsUN7uVCWft448d/1GlBIahufPOZr7/fX+7Z2yTYdYskx//uIazzmrl/PNb29Ses5SFHfTy\nlDFdmcnGyeUiue9Lw8A0vQl5SwHbjx1hNWEYBh7DS6ZoaIAbbqjikUeiVY/PPdfH5Zf7Mno462jY\ntg3eeMPDAw9UMnOmj5NOat/mm8ZG+O9/TVpbYcwYm4EDdVqNGGWdtTLK6ILIlz9orp2YbSGRRpey\nY4ITIZApVGET6nwFAzTnfYHUwa7OqA5PJ1hSIiizkIP0RLpk/EQ6bx98EB+oAZxzTvtwCjNpLNi2\nTYT227YFl19eQ48emuOOK71u1dZW+MMfqti+XfL731exYYPkt79tpkeP5J8xg2XLRDfaZOPkZlJC\n5VFpJOQtAcFSqc45K7V8uREVqNXWau66q4lp0wIpj6+jo74ebr21ioceco79uuuqmTy5PqpjtdiY\nP9/kBz9whITHjrX429+aGDKErPl8JZYozC/KnDUHXZmnFYuuNhbJbI4yHYdsOFyZIlajK5vvjNtG\njDaaNGWUVU2Is6YU0jQxvN6iEOkT/S4vveSJC9SOOsrPFVf4qK4u+C7ldG7076+pqIi+MV59dTUb\nN5aeJZDWEPkM8+yzFSxdGs5bJBuHSK5ZOkikjxbLW7KDVlNSBBsWUEgp4xwK0sFHHzkPMj172owc\naXPbbc3Mnl3PSSeFA7VUel6J3stlTrjbs23bcf8ooIjuwoWeUKAG4PWS1zJ8NuPw2GPhLo5lyxxh\n60AOzy6dOlgro4yujnz5g7aHgXK+vjOVl2B7mY8n+l2chgHnhjZkiM39/9fIXb9rYsCA0i9djRih\nuOyyaBmRr7+WrFlTereYykqYNCn6rnnffRU0N+fvO9xABYgK8JKR76UQmIYHQxohYdx04MqGLF8u\nOfroOu65p5J//7uB11+v54ILWqPkeNysnq0UAcuPFeFBatsWAZ8Pq7UV2wrk7D+qgsb0gUAAvxXA\n1ipnIetk+PJLwbXXRpd9J08O0KNH+543kR23AH/9awXr12d/PpQ5awXEqlWC994zWbnSoF8/xZQp\nFqNGdZ2uqjLKyAVtcabyoQMWq6+GCEp3tAOamuCLLyS2BQN2tejdK8yhi/UXDe1uCWmgbdokuOaa\nKl55JXyXmj27nokTC291lSmWLDE48siw5qDLCxw10sp6Trl8JFsH52UCflvkem7glq1GWSRH8/En\nq/jJpQ5B8I036tl///gxt5VFwLaw7QBCSAwp8ZgVCA2BVh/Kcj4jDInh9WCamXPmXLgcPGVrNDrE\nwUvkp5srFi82OOqobqFlITSzZzcwYUL7zrsnnvBy8cXRNg9vvbWTceNSxwBlzlqRsXSpwamn1vLN\nN+GJOXCgzcsvNzJ0aDlgKyN/6IzipW3pfOVLByzTrsdCoqYGxoxR8QK9oc5YFcr+ucKr7nrRY1O4\nrt1UGDBAc9ddLZx4YoCXX/Zy0EEBRo4svUANYO+9bY47zs+LLzqBpdaCVl+8i0Vb88E997QAjdOE\nE7Bag4GaBmkEuzxTdP4l6KRM55x2g3Z/QPD8C+HA6tNPjYTBmsbJoGntfJ+UwbJsOKEb3K4mgTJO\nWuMQ54crnLJzeDn/87GhIXqbV17pY8yY9p93Bx1kMXiwzbp1TnnaMHROFl2d48qeBO3FWauvhyuv\nrIoK1AA2bjTYvLn4HI588rT8fqfjqKOis3HWUpXxUnldlvo4tKXzlS8dsAULF+ZcJm7LUzRTJOMZ\nJrI0ivzf+Tt7/9R8zIk+fTTHHx/goYeamDHDX7Kk9tpauP56H/vu65QC6+o0dXXOOLp+sW39nlHa\ngMHfP1LsFkDZFipgYfv9UduL5IfFcuHSLc2786K5WbB2bbjx5n//i54/yrKwfD60ZWEYJkKAlBLH\n6cqZ84ZhghAobTuZNcNEK8Xcd97JaBzc/TWkiZQSaQhMwwg2aBTm4aF/f8dkHjRXXNHCOee0UlnZ\n5scyQjbnxtChin/8w9FVNE3N7bc352TpV86sFQBNTYIvvojvWuvXTzFgQMfLqlmWU55Ztszg8ce9\nbN4seeSRJnbfveMdS2dDsm7Pjq5A31bGK9OMWKpMRS4ZyUKMc8KuVsLHHPl/7PcVumu3M2HECMWj\njzby4Ycmu+yiGDpUESkkn86cCq0rJErZCMMIZtQkKIVQAqGdxgGlLaTHRAsSKtkn2q67nLDbNPia\nbUNra/g33rAhfO9RloXyW2ze4mHbdklFBew6yKSiwgncnCwfYEqEkhiGB2k6XasimHFra14n2l9p\nmpiGpyjaa3vuqXjzzQaE0IwcqaiqKthXZYwxYxT//GcjO3dK+vdXeNuoLKcKjMuctQJAKfjnPz1c\ndFG4DX/sWIv77mti7NiOFeCsWSN55hkvd91VGbogHHmknz//ualkNZS6EpLpqJUSFytbRAZYGskX\nX0i2bRNUVGgGDtT06mmnrWmWD625RCj2OLfFWSu0Hl5nRyaUgth5pSWOPiBB1wPLQtgg3QKWwAnW\nZHRQLYSI6v7MdL5u3w7f/W43Vq1ygrTjj/fz1782AWD5fMxfVM0FF9axZYtECM0pp/i54ooWRo4M\n70OsP6W2bQwRMY9TzOtCnl8dGX6/8y/d+6Q7jkuXfVTmrBULUjp+fqNH17Nli6CqCoYNU/Tt23EC\n48ZGWLDAw09+Uh1Vzt17b4ubb24uB2olgrayMLHrdSREHs/s103OOac29MAwcqTFHXe0MHGi1eaT\ndL605pLtY4hHphTSU9hLaltBhJACqWgXzlpnQCZZ1thzTwaXVbDzUUrDKYMGS52hbQuSKtkn2m5s\nc0nsPrqesy6qq8N/r91Qyf87oxvNza7nqOCppypYs0by+OON9OwZ3ofIfZKG6XDZYvYpnXHoiNea\nfCIQgHffNbjnnko2bpScfrqfY4/1M2hQ6vt/W+XmTj2q7amz5vXC3nsrDjnE5oAD7HYN1DKtt69Z\nI7nhhipOPz26QWLsWItHH21i+PCOE3TGotS5WtkgVlvMfS0VF6sjjUN9Pdx8c3VUqed//zM5/vja\noCVTaiTjgEHu4xC6+boBoM6eP5cvpJIqSYVSmhP55gFmgkzGIdG55/LVXLFlDBBm2BUjkf5aW9tN\nxWPzeuHww8NSJOPHhyU5lJb4/fH7vWSJyddfRzc9RO6TYZgIQzJ/4YK0MmWJxqEzIZM5sXSpwbHH\n1jFnjpdPPjG5/vpq7rqrEp8v9efaGrvOObJlZI2VKyWnn17Dww9HMjQ1V17ZwmOPNTJ8eMcq43Zl\ndIQLaDo35W7d4JhjEtxxENx9d2Wb+lj50ppLhdgbayq0ZyDSEdBe2nf5QmSmTEiJWVEZJ7acqcBu\nWw01kyaFA7TRo8MZssGDFdde2xK3valTrTif2dh9ElIiDaOkrx+liLffjhe3fvTRCjZtSv3wFLpO\nJUGnLoOOGzeuvXehJDB58uS01lu9WvL//l8Nn38enhYjRljcfXcz++9vU1mhUXbHLq+kOxadHaUw\nDpmQ8884o5UdOwQPPFABEYT5gw6y2yTtuttNtO18jEMmJedSbvwohTkBhS1bp4PYcUjHzzESbRmb\nZ7o9aHuOjR5tcdxxrdg2jBoVDtYqKuC881rZf3+L55/3smWL5JBDAkybZtG7d9sVkkznRDbH1hGQ\nyTj07Rv/cNG/v06r8SHVPO/UwVoZ6aOlBW67rTIUqPXu7TyRHXFEgAEDdBRxWWuNVHTYgK2jYscO\nWL9eIiX07avZZZeOW46GzG7KAwdqfvGLFk46yc/q1ZL6esFuuyn239+Os5Uptu5cJpyd9g5EOgJK\niW+ZyMvTDUJSBSZxWmppbC8V2ppjvXvDb3/bglLO35Ho1g2mTrWZOjU+w9YWMgm+sj22zoapUy32\n3NPis8+cC5NpOv6su+6a2/W6U49k2RvUQTr19p07BbW1mksuaeHZZxv497/rOfdcf8jqJpEkQEdE\noXg5n38umTfPYNUqSVtDEwiQkEeSCl9+Kfjxj6s55JDuTJnSnWnT6njlFQ+NjdntbzrjsHUrPPWU\nhxNPrOG55zzU12f3XcmQikuWCDU1MGGCzSmnBJgxw8/06fGlnCjtq4CF5WvFDlhJ9cZix0ErnbaP\nYWQ5M92Sc6bHXEyUCmetGGXrVIgch1gvz5B+WjAw0VqjlN2mPZM7V5RtRb+ega1TW3Osb19Nv375\nuy4rrZg3b276x5hkrDoDMjk3hg9XPP10I88808AjjzTy9tv1HH641fYH20A5s1YG4KRp77or+ZOX\nECKu3bwMB83N8OMfV/Puux5qajRXXdXCaaf5E2a+3n/f4IYbqqiu1lx+uY8JE+IzQ4mwfr3k1VfD\nNj4bNhiceWYtt9zSxHnn+fF6859RevVVL5dd5khuv/22lyeeaGD69NwvOi4K0UUWKRSrLNvpykOg\nAQMjZTY4k+xxtuXMcudceiiVsYntknT5aAkDkyRZpChpCw1aRGjktXPmaccO+Owzgx07BB4P9Omj\n2G03Ra9emR0jJB+rfKMjlFoHDdIMGpS/ayWUddYKik8/lTz5pBetBTNn+nJOg7Y32svGptTh88Fp\np9Uyd64n9Nqxx7bym9+0RJlwf/21YPr0WtauDafHn366kUMPbfukXrNGcvjhdWzfHlNukZq5c+sZ\nvaeVV62jzZsFhxzSLaob+IQT/Dz4YBOFuodmGmwmmo/ujdHJYiiIkEswTDOlL6GyVaxkWtL1O4OO\nXRnpIVFwEFnyg9SenrFzReFYhWUTbOT7gWzOHJPTTquLem3ECJuLL/Zx4LcCjBjhD53v6fiWFjqQ\nymTcOyK0UnywdGlCnbXOc5Qlhs8/lxx3XB333lvFffdV8v77Hf9Cnq0kQGdHZSXMnBndl/3SSxXc\ncksVW7eGX2tqIsoWxrIEl1xSk5YFmWtdUlcXHfArJfD5RFr2S1YGD3qNjSKBXZrMaBuZINMOwGS2\nSm4JTRgSaYY72YSUCbPBUaXMmPdTZY/TLWeWOz87PhJ1bqYjv+Eidm4YhplRJ6iLQnTJjhhhM3Bg\ntI/mqlUGV1xRw7cP7c5zz1azc6dMOyjKtMs1U3TmUmusuHAsOnWw1l6cteZmuP32SrZsCQ/vl1+2\nX4BTKlyUUkChxmL8eJuTTmqNeu2JJypYtiwcpPfqpRk9Ovpk3LRJsmlTeqfhQQfZvPZaPdde28I+\n+1jstZfF/fc3MmqUnTR42LJF8PrrJjNnVnPMMXVceWUVy5fLNsehtlbTr1/0vk6f7k+r89JFpvyv\nVMtx66fgUAopMbxeDK8HGXzAMIz4EqhWinlz50VY6mjHGshWCJ26gSYdXlVHk6AoXyccpDsO6QYm\n+eLg5csPNxIjRmief76Bo49ujXvP7xdcfPF/eeThamyrNEKFOAHhImXVinFudGlR3PbCF19IXngh\n+q6WT+JnGe0Ln8/x44tE376aX/6yhWOOib7oPfpoRWjd7t3h+utjeYE6o7LiXnsprrrKx6xZDbz2\nWgOnnRagtjbxDWHNGsmFF9bwgx/U8dRTFSxZYvLww5X88Ie17NyZ+uGhf3/NddeF97VPH8VRRwVS\nfCI6i5SpoXimxPt0smBO0ObB8JgJA6/Yi6Ptt7CVAiHQgrT2ORXhuxA3186CUs04umW2tsj0mSIf\nmoeFaE5RWjF0mJ+7727glVd2cs45Pv50SfEAACAASURBVGpro+f9PfdUpZX9LwYyyWh2NLR5zStz\n1vKPt94yOemkMA/AMDTvvFPPXnslvgCUuWAdAxs2CF57zcNTT1Wwzz4W55/fGpcp+/prwbPPern9\n9ioaGgTHHuvnkUeaQu83NcErr3i48soampsFZ5/t46abWgpi3/XrX1dy113x4j577WXx6qsNdO+e\n+vMNDY4a9zffSMaMsRk1KvkNLM4nEYiMQlPxv6K2kSNnLZPtRTUJKI2tdWifpRChrFy2KHsmJkap\njku++VCFkJDJ9zZjPUFtBZs3ediw0aCpUaA1DBmi2GMPRbmnrPBIxVnr+ESqEkRs/Puzn/kYOTJ5\noFbWLyt9bN0KV1xRzZtvOhnT9983+c9/TF56qYFevcLr7bKL5kc/auWoowJs3izimkpqauDkkwNM\nnFhPczMMGqQKEqj5/fDuu/Gnd22t5s47m9sM1ADq6mDKFBuw21w3LkMScxKk0z0cewOKvDG5y9Hr\naAh1qMWXONvq1oxyHJACiYw6F9Mtsdg2fPihwTvvmBgG7LmnzT772PTv37k6P/MVKBRLay7Th+BM\nux9Tf3dhxI/zOY+2bYOKCklFpZNJDNgBQLPrQMXAQRKEQOCUGnMpN3aE7s1SQarftlOPXHtx1kaM\nUOy6q3OCnnlmK2ec0YrHk3jdVNybTDg/qVDmooSR7Vh89pkRCtRcLF9u8tVXiU+hoUMV3/qWzZAh\n8UG6lI4Wz5gxih49nLLqjh3xpdVc4PXCzTc3M358gIoKTf/+il/+spnXX69n4kQ773Mi9iIjTQMZ\nvNjLDDLGIT0qy4rSS1MBK4r7lYoPppXC9vvjXksEdxzcG7q7z4ZMv5Fm+XLJEUfUcfPN1fzqV9Wc\ndlodp5xSy2efyVD5SyuN1epHWXn8kfOMVHMin/y7YmjNZVqGhzD/acGCBVHL2X1/aZfAN2wQnHtu\nLQ8+WEVLi8RWNr6Aj8aWBhp8DfjtALYdYP78+TmVhTPVoytVlMI9tJxZKwCGDlW8/HIDjY0wbJii\nri75usn0y8oZt9LCjh3xY9+/v6JHj9wC6RUrJL/+dRUrVhgMGWJzzjl+9tvPyovMyz77KJ5/vpGd\nOwUtLYInnvBy6ql1/OY3zfTsmfPmo5As+yVIf85GZiOUZYW2497ookytg+tGruP+r23nKT42oxG5\nzfB2dLBmq50sghROJiGDc625WWBZ0et//LHJeefV8M9/NtK3t4UdDNLc/6VpxG2n1BCZSctnNqwY\nWnPuNdU9BqTTDJAKrk2UIHc+VCm5MCTCe++ZzJ3rYe5ck8mTA4zcu4HW1lZs5SdgBVDKoq4qnH7P\nNsuYz2xlV0eZs1YCSJSuz0TzqYzC49NPJdOmdaOlxfl9hNA89lgjRxyRm5bFa6+Z/PCH0dH8XntZ\n/P73zUyYkJ8sTEMD3HRTFQ89VAk4VmLz5tXTv39pnfuuHpWbXQMwvN64YA0RzO64wZghkR6HvB2p\naaWVU36Rphmlc+VypPKllbZ1q2DGjBreeSc+ff7WWzsZM9oXVRUWAsyKDNpq2wFxMgKChGNYqtBK\nY9t26BikEEhPcU3Ji217li4sCy64oCbUBPeLXzRz9vmbqG/eSWvAh5CC6ooaetT0pMJ0rhnZBq+d\nXRetEHj//ffbV2dNCPGQEOIrIcRHEa/1FELMEUKsEELMFkJ0j3jvWiHE/4QQnwohvhvx+nghxEdC\niM+FEL8v1v4XEon0yzLRfCqj8Bg92smW/vjHLVxySQuvvdbAYYflLjo2cqSiT5/op8/ly02OP76O\nDz/Mz+n50UdmKFADJ0vo95fefIrMjBF0HVC2jfSYSI8Z6nR113W7XxHhQC7ypuhKeMQiLviLWD+8\nTjwFIbrbNfx3796ae+5p4pxzfAgRXn/sWMcOSxrRWbTY5baQbefk1q2CLVuy+50TfVd4vB3iea70\njEJCSIHQOqoMX+xSZD46QAuBpiZYvjw8B//9bw9amLQEGvHbzWhlI4UZLCPrnAKszty9WWwUc+Qe\nBqbHvHYN8KbWeg/gLeBaACHEXsApwGjgSOB+EY5W/gicp7UeBYwSQsRuM4SO7A0ayZ/JhPOTCKVQ\nby8VJBuLL78U/Pe/Bu++ayTVxBs/3ubGG33cdJOPAw6wM9IcS4bdd1c89lgj3bpF30iamwXXXVdN\nU1Piz2XCZ3z99eiMz5Ahio8/npv1PucDyrLjOFyu04BGo4VAGCZaONFY5I0vMjCTphmVDUskYZIs\nKBNSsmDRwjj9q0R8p1jP0VgO3W67aX796xbeeaeeZ55p4LnnGnj88UYGDdJI08AwDYQAwzQyKoFm\nyxV77z2DadPqOPzwOh591JuW9ELkuZFozJx/BlqIjLhg7QVpGlEPwekGTZ39eqlUND/2yy8lDTuh\nylODlBIlQWsbKSSLFizKOcAqtFBuMZDPOaG0wlZWxvy9oo2e1no+sD3m5eOAR4N/PwocH/z7WOBJ\nrbWltV4D/A84QAjRH6jTWr8bXO9vEZ/pdCg7BhQHy5dLpk+v47vf7cb06d04+uha/vvf4nGKJk60\nmTOngYsu8mGa0fZUPl8iSYr0ydM+HyxZEl3aO/vsVrp1y9vuZwxl2diWjdYOhysyYJOmCUKGMjdC\nyrgmnNisWoiXlASpREkTZT8SNf3ENiskal6oqoIxYxSHH27x7W9bDBwY3o40DcwKb8ZcNWXZUUF5\nusHaSy95WLvWYP16g8svr+Hyy6vZtCn960iyMUvVEFVqyJcYbWdDRQXU1IR/t0AAlNJ4DS813jq8\nGGjAVtFUnDJyRy4NF+3dYLCL1vorAK31ZiHELsHXBwKLItbbGHzNAjZEvL4h+HpCjBs3Lr9720Ex\nefLk9t6FkkB9Pey55xRsW+NWomwbbr+9ig0bwjfRL74w+f7363jrrXpGjChO6WTUKMWNN7Zwxhmt\nbNokCQRg5Eib3r3jL5eJbpjJiPxSQmVleP0BA2yOPNLPiBHtNydUTNursu2IIEaADBpCy2BWLYng\nLRAnj5DotXBWKP5mnejcSNj0k6BhIXZf8g2tdDCLpUNNRtKTXrAX6UkLMHu2l0mTLC6+uDWpXlbs\nWCTMSiZpiCpVZMMX6+zXy+pqGDrU5qOPnNt/RQVUeDxUeCuxWm2ENPCaXgSagycd1M57mz+sXStZ\nuVIyaJCjG5cJ8jUncmm4KLVHjXIgX0ZB8NFHkhNOqOWww+r44Q9reOMNky1bBEKA1xs/7RoaRNEt\nwjwehxt3+OEWRxxhMWJE4tMhEz6j1wuXXOJwqQYPtvjHP5qSbrdYSMXh0topG5oeD1LKUIdmIiTq\nUMyHZEJiCoJT+tNax3HoChasaR2dRXSbj9Lgr02ZEoib17feWsWaNbntaz7pGWW0H445xh/6+9Bv\n++nT28TrraCqoobaiu54pLdgVk7t4V6xfLnk6KNrOfnkOn7wg5p2s3+MHVMng5leSbS9M2tfCSH6\naa2/CpY4vw6+vhHYLWK9QcHXkr2eEH/4wx+oqalh8ODBAHTv3p2xY8eGomS3Dl3s5UkHT0JrzYIF\nCxBSFPz73Nfa63hLYXndOskHHywAlrJhw2XMmeNl9Og3ufBCHxdeOJVZs7y0tLwTHK1D6ddPsX79\nXObP1yWx/7HLUsG8+fMQCKZMnZJy/UMOmcyiRfWsWDGPxkYNTI6bG+1xPPPmzUVIg6mHTA29r5Xm\n4EmTEFKwcMGClMe3YOFCtNJMnjQptAww6aCDnfWD59eUqVMTfn7+/PksW7aMmTNnxr0vpGDBfEdv\na9LBk1BaO/w2YMrkKc77we8LHc/ceWh06P3I7WmlmDdvHkKk3p+o41uwAI1m0qTJDr9u/nzQzvFq\nWzF//nyElAk/v8ceiksvfZ077qgEvg1AS8s7zJ3byLBhkxJ+3x//+Me0r48CUVLnQz6X3ddKZX8K\nsbzPPjaVlf/G5xNMnz4e0/CweOFiLGUz8cD9AViyaAmffbqCi390cd6+XysVOj/nzZ3X5vmZ7fLW\nrfDkk4sYNsxmn32mcM45NWzcOA+AL744lK++EqxaNS/t7eXzennwpIPRWjF//gLc/NSCBQtZv349\nAsGECRM4/PDDiUVRpTuEEEOBl7XWY4PLtwPbtNa3CyGuBnpqra8JNhg8BhyIU+Z8AxiptdZCiMXA\nT4B3gVeBe7TWryf6vt/97nf63HPPLfRhZYRI/TSIfzothPXU/PnzO31qvy188YXk2GNrgyfsoaHX\n6+o0s2fv5JtvJC+8UMEXX0gGD1aMGmUzaVKAffftmCKObaGU54R7DmzfLmluEVRWQu/eiT1UE8kj\nZCKZkM44pCOjk+q8zsVeKfJ6oJWdkdSIzwfvvmvws59Vs2KFwaGHWvz5z0307Zv4ml/Kc6KYyGYc\nSk2lP539WbLEYPUXkiOP9NOju4jwRdVorTAMk8ULF+d1TuRLLqctvPyyh7POquXii1uYOjXAqaeG\nSbpCaBYvrk/qKpQIhTg3Yq2+hBAY0kwq3VG0YE0I8TjOXbI38BVwA/AC8AxOtmwtcIrWekdw/WuB\n84AAcKnWek7w9f2BR4BKYJbW+tJk39mWzprP5yjTr18vsSwYMECx5552WlY82SLVhb+tQK6M9LBm\njeCPf6xk2DDFAQdY7LGHTU0NfPCBwWmn1fLNN9EXrwMPDHDzzS2cfXYtu+2m2LRJsH69wT/+0cBR\nR+Uuz1FGZti8WfD0014efriCL7+U9O2rOfjgAKed5mf//a2CNkckelhK57xMdV5ne4OKDTqzDfq2\nbRNs3w49e+ooa7Qy2kY6gX+paYnlsj+pgrx8BKTF8oU9//xqnnuuAiE0993XxMUXhz399tsvwPPP\nN7ZrkxUk/52SBWtFK4NqrX+Q5K1pSda/Fbg1wevvAWNz3R/bhuee8/CTn9SgdXhcpk4NcNddTQwf\nXpggNhVBNxPieBnJUVcH8+aZPPigCWimTQtw9dUtVFcrrr22hffeM3nqKW9Idf7DD01691aMGGEz\nb15Y5sK2y2OfDvIt/jl3rsmvflUdWt64UfDMMxU880wFF1zg49prWwryQJXMNURIgVSkzHinOq+z\nUbNP5S2Z6Vj36lUO0rJBuv6ehVTpzyZAyonELmTCdSMDC6f5h6wCtmK4VzQ2OkkYAK0FS5aYDB1q\ns2aNAWiuu87X7oEahB0z0v192z9fW0Ck0ln75hvBjTdWRwVqAHPnenjwwcq8+jRGIhVBt1BCuJ1d\nNygWvXoq7ruviepqDQjefNPLq696OfnkOq644l0+/NDg2mudm/6117Zw771NDBmiueWWFrp3dy50\nXq9m6NDS9XHMFdnOiVhycK6ekYn04oYOVRhG4oelBx6oZNWq+I7IbEnLkeOQSpaiLRmdlOd1FhIS\nybxOCym02tWuE8ngjkO6zSqxpPF8EfOzlXnI5/6ExiJRAJglCi0WbJpOh6uLhQs9HHCAUyG57jof\nBx6YebWkUOdGJhp07d1g0G7o3Vtz4ol+HnigMu69DRskhawOCykSZszSeYIvIzXc7Mi++1o8/ngD\nJ59cRyAgqKmBTZucm/zHH5t8/LEz9Xv1Urz4YgNSwpgxNrNmNbB0qcmwYTZ77905+WrZIlGmIRfP\nyGSZrPHjbV59tYFrrqli6VITQueK5uST/ey6a4Iu0DQyIG0hV1mKZOe1uz+Z7JObjQt5nRrJpUPK\nKAzSzYhmmiFJF9lmyAqxP0IE5XQilksFsdnHykrYc0+b9993rvErV0puucXP6af7GT/eoqamnXc4\nS3Rpb9BNmwRPPeXl7ruraGx0LrITJwa4557mjHVYyigNRHKHlIL33vNw9tm1HHusnzVrDN5807n5\n19RozjyzlbPOamXUqPJvnQ4Sca/ibmgZcFCUrVARJVQpZRRxf/t22LxZ8s03gkBAsMsuiiFDVFwJ\nI5+k5UI0+GQLN6PmGMuHfVEjj61U/Sc7C9pzfEuBCxcZCEH+A9JckWyMHn/cyyWXhKOyN9+sZ/z4\njlEpaXfOWiliwADNpT/xceKJfnZsF1RWQb9+ih49ircPpXRz6IiIHL/QMm6WQzNxfz+vvFLPL35R\njWFofv5zH7btqHYfcYS/Swdqmd6IEmUacuKgaB2dEYvJZPXsCT17KkaPzny/skWq7Fg2cOenk6rX\nGY2R622qAhbKspyA1mNG68m5pvXlrFtBkM7vVahO0EJl7NJFLE9NSgNDllbIkCz7GEthKRStqZjo\n1Gd2Is5aJEfGubFrBg2yGTPWYtRIu+iBWrq2Qbkg03p7Jr6TxUb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1xxxWscc0wjixebhJI7K/VpdHY6HZQvvpj8nGhpIU76YvVqkzfeiA5i3n1XsnBhPSec0MQZ\nZzRw7LFNrF1buNBDComUBkKIogVq3rWxYoXB17/eEBeoAWzaJHnppeKJ//Z4sAYMA5YKId4CXgEe\ndaU4fgN8zS2JHgX8GkBr/Q7wF+Ad4HHg3Ew6QTNBISbYsNG450mpC+Pd2RfgyEwYSN94GIZECkfY\nFZdLpbUTRIWCIce4XKXmsxkBAyNgOPIaKbTL/PCOtVLKaSawnH81OnwTNgwJ6LDYqm3ZKDewjJLA\nKAC/0Fvnju2CYDCynM2bJdu3FyZYy3Rssvlu4t+Xh0tEKZDs2OfSaFRuDgz9DXPmhDjySCc627RJ\nsmBBA/feW1UWYq6lxCefSM4/v45DDx3AsmUmyU7HQbtrZs2K93XeuDEyX3z8sWThwgbefDMSxLS1\nCXbsKOycIIXEkGbRMmoe/HNzLCZPDjFvXvGi+7IrgxYS2XqDZlO+2bBB8PbbBnvs4egmeVWi/lIC\nyhVaaaygheXKSAhpIE1nfAwc/TRlK4IhC1wJBlNKDFNmPY6pylTecfJKfhFjd2eb0BrDNBwvTjdQ\nsyyFRmMEAphuJixdmSvbUtjKlZIjjog20Wtu3sX++1du4L0J2WrqlVonroLEeP99yXHHNbJtW2Ts\nL7qok3PP7YpyXOmrCIXgP/6jlltvdQTRamo0ixe3sM8+8fOPshWr3zE5+eRGtm51/YiF5rHHWjn0\nUKdKceedVVxySTTHaPhwxdNPtzBqVO+LPTo74Y03DB5/vIrXXjNpbFRMmWIzdarFpEk2gwfnv0/l\nrrNWNIR9JjO4YWajb/bAA1VcdVUdpqm58842jj3WQsq+q5HmRz5cHCEFZpWBlDjlUOFd5M6yhACU\nwgyYKE1YcsMrLWezzpS6bl7mU0qntCmlG2Q7HZ/S7fwU3srxsiieEK9GydR6erlo7g0bphk2TPHF\nF864DB+uUto5VZAeiUSTi4043cA0xz2RA0MlWCs9xo9X/PWvrSxc2Mjmzc7433RTLR0dgosv7mLI\nkL59La5bJ/n976vDr7u6BNu2CfbZJ/67QggmTLB47LEW/vlPk81fSA6ZYnHwwRH+WHNzdFlQSs1t\nt7X3ykANHDeemTNtZs7spKvL0cIs1S2+T88GK1aswEqgi5UMmZZvurvh8ccdXpFlCb7//Qb++U8j\nq2WUUtqgkFyUfEvFWilUyOGtaa1RWvkCNUdOwggEMAKmI9MhBRKBFmS8zmSyE0JEOGvecZJSYphO\nds8wXd6D/7gJxxYqvI0kdkFIlKHOpRQ2bJjmZz+LWDpdeGEXw4YV/hzpL/ykdJ6hxRyHbMrj+fIE\nC4H+ck6kQ0vLYh56qJVRoyJBx+9/X8P//V81nZ0pftgHsGmTDDtyOHgx6Xe9OXTcWJuTFwQ590fd\nTJ1qE/DFZyeeGMR72N1nH4uHHmpj+vT40mm5I9G1UVNTukAN+kFmzVYKqQWGdIKpdArr3g05Faqq\nYPRoxfLlzutgUHDLLdXcemuHcwDTLMOfcVG2QtgkndTLraMsVbYqo9/7RGudrKN27JxEjBiuAi0B\nLRyfUF9WKtU6vZtzOJtiGM6fe1UpS2GH7IjQboyDgJQibHMV2UYQARPD+457LFSaDGpslhWtUZaV\nNsNzwglBBg7U2DbMnt37JrZyQm/JWJXSgaGC9JgwQfG3v7Vx0UV1LF3qRB/XXVfDlCkWRx1Vmmuy\nJzLCsaip0QwfnjrBkWr+nzMnxOLFLXR1CUaPVgwd2jszauWAPs9ZO2DCgQjACBhopcMcsnwDnwce\nCHD22Q3h14GA5tVXWxIK78bC40t5QZu3Tck6yjyk570ULrBLtqxstyl+uW5mzQvYDInhlhYTbUPY\nNsoXJKVap7IsJ1jz+D8CjOoqYmU+hMbJ2nkCuzG+pIhIadbPQ9RKgVIYARMQGXPW8P68dVQ4SSVB\nhQtWQT7YuFGwaFE1N9xQg1KCwYMVTz3Vyt57F5dD2lPn7Zo1ksMPb8KynPnsV7/q4Jxzuou+3goi\nKFvpjmLDMCSmaTjdhgBSFKSV/itfsRg4MHIxhUKCHTtS/MCHsEp8VIYpfdksVWBdSKmAVMvKt9NP\nSIkMGOFAyTAMQCfsgguPj7tOfAGbs50KOxjC6g5hhxy7Kce7M1rqw8vm+Z0CLMvCskKOlIgdM/GK\n6IDer+qvbYVAuBOpThn4O0/Gtpsq13GfVVB85NvZmg86O+GttwwWLzb5/HMqnZ69ECNGaC65pItn\nn23l+OO72bZNsH598c+hRBnhUmDcOMUtt7QzbJjioos6M7ZjqqD46NPB2ooVKxxpCJ8mloe4QCgJ\nhyzZ+2PHav74x3YMw3m/qkrT0EBGCPOlYvhRab0zUxTIUwV22XJR0gWJ+UpVCCkxqgJudkon5RT5\n91dIERHOxbnxWV3dWCELy7KxbJebqAApHQkO9+YspMs5E4Lm5mbH89MtpSbiMSX0LBWOYG9ssJgM\n8Vyp6POop7M7/YmflMoztFjjEAzCXXdVMWdOIyed1MjChY189LGZ8HzLFsWS+OhP50QqxI5DIACT\nJtncdlsHr722qyT+rz3FYayqglNOCfHCCy1cemkXa9cuKcl6yx3lcG306WANfN13KYRqk2WSUr5v\nKw6d5viL/eAHXfzf/7Uzdmzmk2dYH8yQSbNU2WSxCilEWkpR01RPkFppVMhGW/FBkgpZzueWHeZ2\nKFdgV5gmIhCINAfECeg6GdcwH02Qdpyd4xUtuptaxy1mP7QGQ6LQznZVSnF9GuvXSy69tA7tEiBX\nrzZ5/gWnKSmfICtdw0QFxUN9PYwbpxkxovjUoZ7MCBsGDB+u/eYvFZQB+jxnbfLkyVHcJ601hpRh\nbS9Iro2W6H0hRF6crWKiFJy1QiMZNyOZvZOQIsxLU5Yd5qEZ1VVOp6a/fElijTuPN+ftnwwYUQTv\nVKTeZJ/Hjpd/v5StwDfhVrT3+j5efNFkwYLGqPeOPDLIA/fvCr/OhTierxVYBT2Hjg7YskVQV0dZ\nSICUQwNDBfHot5y1MMGbSPkutnklWSYp0fu5yDGUConKk7lKhBRClT+z9SR+glS+QE1pHbak8nhp\nTmnLwDAlgaoApmFgSEekNp3NV5g3Zzr/agVW0MIOWqhQ5C9R1iJRSS02A6ss5fQSuBpx0jSivt8X\ntfcqiMaAAQ6n0Y9w+UyTc2asHCQ+Ksge69ZJLrqojkMOGcCcOY28/nrPpq0qGdrehz59pa9YsSLO\nGgiSc5Jiy2CJ3u9NvodeELG0eWnR/Qnz4dEkCoCkl+lyg2H/ay/AE4bErKnGrHbLS8IndpvE5iui\ns+asUyuwlEIBwWAQO+Rk7ezuIHYwM3KtP2DXnj0WoIXAcWgwIpZaZeIRWQwORim1AwuFYnFRxu5t\nR5GzAwHNSSfFW9HkEqwVqzxWDrycckChx2HzZsE559TxwAPV2Lbg008Nzjuvjm3beu7ekWkDQ+Wc\ncFAO49Dn8+dOJsYJulKV9ZLpxcS+n60yeU8iNuunbIXQouDb7S/5ef/mcxPRSjuSG7jBsRDhsnU4\n6+lm18Ien25myzNfzxQqZpKyLYXh0RztSJkgFfwZV++8CO+LXxPOa47I0M2gNyEXt4a+jKYm+I//\n6OBrXwuxfr3B3LkhDj7YRggZXfbP4TqplK16F9aulbzxRrSS/7p1Bm1t9JiFlefc4n9dQXmjz3PW\nJk48CDOGo9ZfEHUDVZFSMBSWa1dIHk0iHTdIHhxnqlmXDHbQJmTbEYHeUNDNyklkwEQYMrwv8bw0\nHRWcaTeL6xfVDQvo9nHP2L6+f7kguVZhPFeo3MSvKygcnn7a5LTTovmL06eH+Mtf2qir66GNonw4\nax99JFm/XjJxol0RzaUfc9ak5y3Zh5BpuclfxhX4HACUxrbsgpWrCsmjSeiQkII/F8m0ufvqEMcy\nW5fSYAgMKUFrTGkQqK1BBkwnUIu5mcby0vyvwQ0STZm4pN6Lyucesilt98b9KzaScUi1BiGNpOdW\nbyojV5Ae48bZLofRQUOD5qqrOns0UIPUkjalQmsrXHZZLaec0sjVV9fS2tpjm1L26NPB2ooVKwB3\nguwjE2C2E7uQgmUvLwtnOcKZK1EYcWBnHYXj0WRy0/cHq3G8QlOmFD5esnhJ+LdhPpwpMatMhOFw\nzIRpRsl+KEsR6g6hrMiEG9UAYTsWVt4fxLtk5CsmXGik42BkS0Aut/3LFKXkovivXds9Z/znYfh7\nPVTtKAdeTjmg0OMwbpzm4YfbuPrqDn71qw6efLKFyZOLr9VWCBT7nPjsM8kTTzgl4nvuqWLVqvLU\nCymHa6PPc9aA8M27WDyaUpYwcvXm9Lh2to7RLMvS2zP58guTSk/HCXTkOuxIR6iK8Ar9QZgQAi2J\n2jetNM5/hJsQonZd4+ighfdDhCVEtBDYSmFaTnAnpUT5JWGUdviA0hHaNYjPBGbiO1suyMVTszft\nX09A2bYb5Aun+QQcceaY87CSlex7OOggm4MO6h0BWimxdatX9wEQPP98gOnTK+OUCH2es/blL08K\nv/Z00goZWKXyyixGEJe/N2d+v88WhRgDj1sBjhG7t/XCcIImaTiWUnbQwiZSho3lKibTzQsHd27W\nw/+5HbKx/cR5oKqmCiEFtutx6h9T6YocG4bs1ZytiqdmYeH3xFVKgYx44hZjXqqggt6AV181mDev\nKfz64IMtHn20tcdLxD2JZJy1/pFZ8+BlTihcx1qyTJf/Bq5shbALYyCfbzdqKbtZC9EhGCUua1mO\ndhkRJwNhGFHfEUoDCmkYCfX0/MfL2/9wNkjFHE9XK00phVKOL6k0I2l6aThcNy10dGAj4iVeehv8\nIsHZZk0rZPl4OOPoXHt4gZmPz1jJSlbQ27B6teSJJ6ro7ITvfKebvfbKPvGz226u6KB77m/ZIujo\nENTV9d0kUq7o04/KK1ascDr7SNxokCirmK1WVDKOld803JOW8PNU8kG2grWx9fZSCd4mCmSzHd9Y\nU/awmTuEMxPed8LBgXYM1EngabqsuTljey+nq1U6F4lSSJzXfpFl4dRUMaXE9OysSjC2+SITDkYu\nBOTeRpYvFRcl7F7hetyaAbPs+H3lwMspB1TGIQJvLPzzttaweLHJvHlN/OpXtdx0Uy2rV+fGNRs6\nVDF+fKTs2dDgWLW9/77EsgqyCwVBOZwTfTpYg4hQaiaCtoluNOmCi6SCujFBG5pw0JbpTaw3ioz6\nEZddcscgmxt5lPJ/WE5DYFSZ4SyX/0YocOU7EE7WK9akPU2g6v9cCBF2TDBcGQ+tfFIdSjvnlyHB\ncH7nWWL1V5QLWb7cENuEI02jJA9MFVTgIVfh8tj74qqVktNOa6CtLf9zd+BAuPLKzvDrGTNCfO97\nDcye3cQll9Ty1lsGXV15r6ZPoF9w1vyaT6lKNLGcJpQOC5lCjvwwTwfMJ9rq8VRScZpKzS0rBrz9\nB2dfE3HCMuF1ZaIH5H1HK4UQvu/kofkGhHlpXqZeuh6lUNEWS4S+cN5WUEFfQz4cVP8819kpOPvs\nep54ojr8eW2t5sUXW9h339wsq3btgrvuqubRRwMcc4zF1VfXRrZTaC6+uIsf/rC7LPxUS4F+zVnz\nZ3j83JDYwC2R96cf2XZOCikwpBEVtCXT3Uq0rnzWnQ7F5hWFb9p++YoEnLFMtzFdwBVxNHAmpe07\nd9DR0UF9fT2DBg/OeT88Xlr4tch9f/oDCs2JDIXgvfckmzZJqqpgzBib0aP7x6RdQQX5wptDdVj4\n23s/fXe3B/88t369wRNPVEV9/otfdDJuXO7eogMGwL/+azdHHx3kX/6lPnr7teCGG2rZvl3w7//e\nyW675byaXo8+nQZYsWJF0if7RCXP2JJmbJYk15uxkE42xnA7BTPKNmiiSqD5BAKx9fZS8IqSittm\nqMWVS0kaYMPGjfz5vvuYe8wxTJk6laPnzuXuu+9mw4YNQPbcg1Tb3Nkl+Phjg+XLTZ56qopHHq3i\nH/8waW423Jb08kWycdi4UbBsmcHq1Xlo5RWQE/nssyZHHtnEwoWNnHRSI3PmNPHSSyaF8p0uBy5K\nMhSLBrF1q+Ctt5xj3BmpQKUdi507nfMjFG9x2qdQzudENvDPodqldHjINFBbunRp1BzY2uKX2oBT\nTunm1FOD5NsobhgwfrzmgQccLbqGhuhz/g9/qOGDD3pOg60czok+n1lLdsNIlrmK7coqZJYg046v\nMBfKfaKRWiCMAmbVipy1g+RZp4zHIGYb7ZCFRsfpq/nx2Wef8aMfnceSJYvD73344YdccMEFzJ49\nm9/97ne57Ytvm7u6HALsxo2SRx4JcN991VhW/P7ccUcbCxb0rrvaihUGZ51Vx8cfm9TXa158cRfj\nxvVcFmvXLrjqqjpsOzK+W7dKvvGNBp59toWJEwsUsZUhiuW1um0bnH9+HU89VYUQmm99K8hPftLJ\nmDHJj3Mo5BDK//3fa9m4UXLWWd1897vd7LlnJcNZzoiaf6UElMOZzEET05sD9xyt2W8/ix07JD/5\nSSdf/3qIwYMLdx7stZfinHO6mTs3xDvvGLzyisH69QYHHWT3aBnUspzu1y1bJCNHqpxLvvmgz3PW\nJk+enPCzcubWFJsLVap9V5ZCKeVooWXgzRrrtRnxNVVoy3ZlOyL6arHaVHfffTcXXHBB0uXfcsst\nfPvb3854+2NLxatXS95+2+Chh6pobg7wrW91s26dwQsvREyahdCce24X557bzR576KTL6gmk4v6t\nWiU57rimKNLwE0+0MG1aYoHK99+XvPGGyZo1kvHjFYccYvGlLxV2ArNtOO+8Ou6/vzrus3vuaWXe\nvMTtYlp7GSDB8OGKmpqCblZJUKw5YOVKyRFHDIh6b9asEHfe2c6gQYnvBatWSb761aaooPmHP+zi\nmms6yYMOWkGRUax5futWgW3DsGF9N3bwY9MmwaJF1fzXf9Vg24Ldd1c8+2wrY8Yknu+UVmjtcKel\nyP6a7bfeoMnQk/Y4aTtMi+yzWIp993dKakHaUk5s2RNv2wChNdIXXGil4jpLt27dyk033ZRyHTfd\ndBPbtm7LePv9y1iD+7AAACAASURBVF/9tuTGG2t48MFqnnmmio4OwR131DBrlpM92203xTnndPHc\nc61cdllXXKBWSjmLzZsFt91WxfPPm+zc6W1Dcvuolha4/PK6qECttlYnfWJ+7z3Jccc18qMf1fPf\n/13LBRfUM2dOE8uXF7ZMYRhw0UVdHHRQdFA2eLBi770TT5QbNghuu62aGTOamDatif/93+peWbYr\n1hxQWwumGX1clywJsHJl8mO3dq0RFaiBQwj//PPCbJPWzjm7Zo3knXckH38se+UxKzcUa54fPFj3\n+kBNaYWtLJRO/YDZ2gq/+U0Nv/lNbfga2L5dsnlz4rFUWqGUjdYapey0y88GfTpY87xBkyEXbk2+\nPJJMbtyFvsgS1duLrbWWrYRDKgN3aRpRY2IkELzt6Ojgs88+S7mOzz77jBdfeikp/y1aSyh6e6qr\nNccdF+K55wJR7w8erHjyyRaWLGnhmms6+fJBFlWBmOXmIGeRz3nmmCPXccopjVx2WR0ffyzj2vWX\nLFkS/v+1aw2WLIlOkZxzTlfSgOiddwy2bYueOjo6BNdfX1PwNvvx4xX33tvGffe1ct117dx2WxuP\nPNKaMIu3caPg/PPrufzyOlpaJKGQ4KabalLyB8uBi5IIxbrRjhmj+O53u+Pe37hRJh2LgQPjz8GG\nBk0gkODLWWDzZsFzz5n89Ke1HHFEEzNmDGDmzAFMm9bEbbdV09GR3/JzRbmeE7kg33m+L42Fh2wC\nqpUrDRYtqgFeDL/X1KQYOjTxvKxjlhX7Oh9UkthZoBCuBJnyxVJ1rfYGZNspmer74ZKdUEhpuF2f\nOiyyq7WmrraOUaNG8eGHHyZdx6hRo6ipqXbkRHzeqNK9nvwcoVi/xnHjFO9/EH+BDhummTrVKRUm\n4xnl0gWbD19pyBDN5MkWb74Z4N57q3n/fcmdd2pGDo+kK/zb4DwlRl7vs4/F6acnJw2PGqXwq457\nqKoib6JxIuyxh2aPPdIrZD72WIAXX4yOIEaPtqmv79ksQK7XbzFcDQIBx9LnxBODPPxwABBIqdln\nHztpNmvCBJu5c4M89ZTXBaj5zW86GD4893F9+23JT35Sx+uvx0d8waDglVfMhEFlIdDW5pyn/dnS\nqD8jYUCVpFzZ3Bx/fl53XUfSEqgQEq3tqNeFQp8O1iZNmpT+S1kg1pVAOG9mdTMt9Y0bYObMmVl9\nvxDIVMIhSp4jhT9iLM9KSIGw3OMgBAN3352LLrooJWftxz/+MUd/bS7KUmCI8HoSZrpcTTX/9sRm\nEpqaFGPHRi7aVE0r2TSq5NsA0tQEF17YzRlnOBv8xhsBrr66lmt/pRm4m4WQklmzZ4e/P3KkIhDQ\nhEKCr30tyLXXdiadjMAxpf7Tn9q55JI6Nm92jskBB1hcdlknVVVJf1ZUbN0q+O1va2Pe1fzyl100\nNSX8CVD8a6NYjQL5YPBgza5dgiuv7CQYFIwbZ3PwwTZVVYnHYuhQzc03d/DPf3azdatg7FiVlyn5\nRx9JFixoZOvWxDeyY44JcvXVnTQ05LyKhFizRvLww1U89liA6mrNiSeGmD8/GGeT1BPzZbmiL45F\nNgHViBHePHgEhqG58spO5s5NXqOXQoIkL85aMvTpYK3QCJt9e3ZDPpeCTG+mpb5x9yTSZQbibmQJ\n5FJSr8AX/Co44ogjmD17NosXL4776uxZs5k9+3DQYKPRIeU4DhAxXE/pGwrsu69i5EibDRsMpNTc\nfnt7lL6QPxD3ypda+LJ1GWZWCqHf9pWvWBx0kMXKlc4l/uCD1cyebfGtb8U/RB5wgOKFF1oIBmHv\nvRUDBiRYoA/V1XD88SEOPrjF1T/T7LFHco5bKSClprraP2aa66/vYOrUnvWsyef6LVZGfeJEm88/\nF1x5pZNauuOOtrRB9tChmq99rTBjaduaIUNUVLDW2Pj/2zvzMDnKOvF/vlXVPfeQgyMXCRAMmHDD\nssuCILCwiEsQV1BgBUFWQBQ5dEVlV9bV3+IuR1DUxeUSUKIcIpdyCSbxAFYYiJwGQkIiSSCQzD3d\nVe/7++Ot6q7u6e7p7umZ6Zl5P8/Tz0xXV1e99fZbb33f76lZvDjFCSek2Hdfn6lTa3KqDC+84PAP\n/9DG1q3Zcz7zjPHVW7Kkl5aWEl+2TCgqEaiOOCLN9dd309Ul7L13wKJFwZDmf0ecnEl2uAEHERM6\nGvTKK6/UZ555Zk2PWSjB7UgGKNQiomfFihV1uUIabsSb8hV+zBfLcxze2vgWTzzxBFdffTXr1q1j\nzpw5XHjhhRx66KHMmjWbZb9Zxt8c9LcQPgQ9x8FrMAJNOQ/H5593eP55j+22MxGQ06dnP8vxM3NC\nrV2UJ6/CsZIfGVusXaXa/NxzDn//9+2kUmZ7Mql5+OEu9torqNsxMRz++EeX225LMn268S9cuDCg\nYXAgaQ4j3Q/V3r/D+V45At6LLxozZFMTXHNNDzvuqEd1TGzcKLz1ltDXJ5lglpkzNe4wY1Q2bhRW\nr3aYOVMzb152brjxxiRf/OJgiWzbbRXLlnXmmHQn4r1RLbYvDNX2Q+QfF+E47pAC26SuYFBL4lUJ\nar3qLXTMoTRx49GfLWLYGqQ8UyUCs2fP5tRTT+VDH/oQPT09tLS0MG3atMEPPydb/zNzuCE0gd3d\nJgruhhtMLoilS7s4+mijb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RUfBxHD7YdkEmbNyravpUWz667BoN8uMz5F0GgkcpdAMila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\n1rf6OOigACd84E+fPnom7fZ2uOSSPvr64I47T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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = draw_sky(data)\n", + "plt.title(\"Galaxy positions and ellipcities of sky %d.\" % n_sky)\n", + "plt.xlabel(\"x-position\")\n", + "plt.ylabel(\"y-position\")\n", + "\n", + "colors = [\"#467821\", \"#A60628\", \"#7A68A6\"]\n", + "\n", + "for i in range(traces.shape[1]):\n", + " plt.scatter(traces[:, i, 0], traces[:, i, 1], c = colors[i], alpha = 0.02)\n", + " \n", + " \n", + "for i in range(traces.shape[1]):\n", + " plt.scatter(halo_data[n_sky-1][3 + 2*i], halo_data[n_sky-1][4 + 2*i], \n", + " label = \"True halo position\", c = \"k\", s = 90)\n", + " \n", + "#plt.legend(scatterpoints = 1)\n", + "plt.xlim(0, 4200)\n", + "plt.ylim(0, 4200);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This looks pretty good, though it took a long time for the system to (sort of) converge. Our optimization step would look something like this:" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(4500, 3, 2)\n", + "[[ 3790.95827012 3794.60133909 3170.00252772 3142.31644835\n", + " 2265.01634741 3623.32277728]]\n", + "Using the mean:\n", + "Your average distance in pixels you are away from the true halo is 175.125422038\n", + "Your average angular vector is 1.0\n", + "Your score for the training data is 1.17512542204\n", + "Using a random location: [[1199 3589]]\n", + "Your average distance in pixels you are away from the true halo is 2522.2681726\n", + "Your average angular vector is 1.0\n", + "Your score for the training data is 3.5222681726\n" + ] + }, + { + "data": { + "text/plain": [ + "3.5222681725978306" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "_halo_data = halo_data[n_sky-1]\n", + "print(traces.shape)\n", + "\n", + "mean_posterior = traces.mean(axis=0).reshape(1,6)\n", + "print(mean_posterior)\n", + "\n", + "\n", + "nhalo_all = _halo_data[0].reshape(1,1)\n", + "x_true_all = _halo_data[3].reshape(1,1)\n", + "y_true_all = _halo_data[4].reshape(1,1)\n", + "x_ref_all = _halo_data[1].reshape(1,1)\n", + "y_ref_all = _halo_data[2].reshape(1,1)\n", + "sky_prediction = mean_posterior\n", + "\n", + "\n", + "print(\"Using the mean:\")\n", + "main_score([1], x_true_all, y_true_all, \\\n", + " x_ref_all, y_ref_all, sky_prediction)\n", + "\n", + "#what's a bad score?\n", + "random_guess = np.random.randint(0, 4200, size=(1,2))\n", + "print(\"Using a random location:\", random_guess)\n", + "main_score([1], x_true_all, y_true_all, \\\n", + " x_ref_all, y_ref_all, random_guess)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## References\n", + "1. Antifragile: Things That Gain from Disorder. New York: Random House. 2012. ISBN 978-1-4000-6782-4.\n", + "1. [Tim Saliman's solution to the Dark World's Contest](http://www.timsalimans.com/observing-dark-worlds)\n", + "2. Silver, Nate. The Signal and the Noise: Why So Many Predictions Fail — but Some Don't. 1. Penguin Press HC, The, 2012. Print." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.core.display import HTML\n", + "def css_styling():\n", + " styles = open(\"../styles/custom.css\", \"r\").read()\n", + " return HTML(styles)\n", + "css_styling()" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/Chapter5_LossFunctions/Ch5_LossFunctions_PyMC_current.ipynb b/Chapter5_LossFunctions/Ch5_LossFunctions_PyMC_current.ipynb new file mode 100644 index 00000000..ec25b027 --- /dev/null +++ b/Chapter5_LossFunctions/Ch5_LossFunctions_PyMC_current.ipynb @@ -0,0 +1,2034 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Chapter 5\n", + "\n", + "`Original content created by Cam Davidson-Pilon`\n", + "\n", + "`Ported to Python 3 and PyMC3 by Max Margenot (@clean_utensils) and Thomas Wiecki (@twiecki) at Quantopian (@quantopian)`\n", + "\n", + "`Ported to PyMC last by Kurisu Chan (@miemiekurisu)`\n", + "____\n", + "\n", + "\n", + "### Would you rather lose an arm or a leg?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Statisticians can be a sour bunch. Instead of considering their winnings, they only measure how much they have lost. In fact, they consider their wins as *negative losses*. But what's interesting is *how they measure their losses.*\n", + "\n", + "For example, consider the following example:\n", + "\n", + "> A meteorologist is predicting the probability of a possible hurricane striking his city. He estimates, with 95% confidence, that the probability of it *not* striking is between 99% - 100%. He is very happy with his precision and advises the city that a major evacuation is unnecessary. Unfortunately, the hurricane does strike and the city is flooded. \n", + "\n", + "This stylized example shows the flaw in using a pure accuracy metric to measure outcomes. Using a measure that emphasizes estimation accuracy, while an appealing and *objective* thing to do, misses the point of why you are even performing the statistical inference in the first place: results of inference. The author Nassim Taleb of *The Black Swan* and *Antifragility* stresses the importance of the *payoffs* of decisions, *not the accuracy*. Taleb distills this quite succinctly: \"I would rather be vaguely right than very wrong.\" " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Loss Functions\n", + "\n", + "We introduce what statisticians and decision theorists call *loss functions*. A loss function is a function of the true parameter, and an estimate of that parameter\n", + "\n", + "$$ L( \\theta, \\hat{\\theta} ) = f( \\theta, \\hat{\\theta} )$$\n", + "\n", + "The important point of loss functions is that it measures how *bad* our current estimate is: the larger the loss, the worse the estimate is according to the loss function. A simple, and very common, example of a loss function is the *squared-error loss*:\n", + "\n", + "$$ L( \\theta, \\hat{\\theta} ) = ( \\theta - \\hat{\\theta} )^2$$\n", + "\n", + "The squared-error loss function is used in estimators like linear regression, UMVUEs and many areas of machine learning. We can also consider an asymmetric squared-error loss function, something like:\n", + "\n", + "$$ L( \\theta, \\hat{\\theta} ) = \\begin{cases} ( \\theta - \\hat{\\theta} )^2 & \\hat{\\theta} \\lt \\theta \\\\\\\\ c( \\theta - \\hat{\\theta} )^2 & \\hat{\\theta} \\ge \\theta, \\;\\; 0\\lt c \\lt 1 \\end{cases}$$\n", + "\n", + "\n", + "which represents that estimating a value larger than the true estimate is preferable to estimating a value below. A situation where this might be useful is in estimating web traffic for the next month, where an over-estimated outlook is preferred so as to avoid an underallocation of server resources. \n", + "\n", + "A negative property about the squared-error loss is that it puts a disproportionate emphasis on large outliers. This is because the loss increases quadratically, and not linearly, as the estimate moves away. That is, the penalty of being three units away is much less than being five units away, but the penalty is not much greater than being one unit away, though in both cases the magnitude of difference is the same:\n", + "\n", + "$$ \\frac{1^2}{3^2} \\lt \\frac{3^2}{5^2}, \\;\\; \\text{although} \\;\\; 3-1 = 5-3 $$\n", + "\n", + "This loss function imposes that large errors are *very* bad. A more *robust* loss function that increases linearly with the difference is the *absolute-loss*\n", + "\n", + "$$ L( \\theta, \\hat{\\theta} ) = | \\theta - \\hat{\\theta} | $$\n", + "\n", + "Other popular loss functions include:\n", + "\n", + "- $L( \\theta, \\hat{\\theta} ) = \\mathbb{1}_{ \\hat{\\theta} \\neq \\theta }$ is the zero-one loss often used in machine learning classification algorithms.\n", + "- $L( \\theta, \\hat{\\theta} ) = -\\theta\\log( \\hat{\\theta} ) - (1- \\theta)\\log( 1 - \\hat{\\theta} ), \\; \\; \\theta \\in {0,1}, \\; \\hat{\\theta} \\in [0,1]$, called the *log-loss*, also used in machine learning. \n", + "\n", + "Historically, loss functions have been motivated from 1) mathematical convenience, and 2) they are robust to application, i.e., they are objective measures of loss. The first reason has really held back the full breadth of loss functions. With computers being agnostic to mathematical convenience, we are free to design our own loss functions, which we take full advantage of later in this Chapter.\n", + "\n", + "With respect to the second point, the above loss functions are indeed objective, in that they are most often a function of the difference between estimate and true parameter, independent of signage or payoff of choosing that estimate. This last point, its independence of payoff, causes quite pathological results though. Consider our hurricane example above: the statistician equivalently predicted that the probability of the hurricane striking was between 0% to 1%. But if he had ignored being precise and instead focused on outcomes (99% chance of no flood, 1% chance of flood), he might have advised differently. \n", + "\n", + "By shifting our focus from trying to be incredibly precise about parameter estimation to focusing on the outcomes of our parameter estimation, we can customize our estimates to be optimized for our application. This requires us to design new loss functions that reflect our goals and outcomes. Some examples of more interesting loss functions:\n", + "\n", + "\n", + "- $L( \\theta, \\hat{\\theta} ) = \\frac{ | \\theta - \\hat{\\theta} | }{ \\theta(1-\\theta) }, \\; \\; \\hat{\\theta}, \\theta \\in [0,1]$ emphasizes an estimate closer to 0 or 1 since if the true value $\\theta$ is near 0 or 1, the loss will be *very* large unless $\\hat{\\theta}$ is similarly close to 0 or 1. \n", + "This loss function might be used by a political pundit whose job requires him or her to give confident \"Yes/No\" answers. This loss reflects that if the true parameter is close to 1 (for example, if a political outcome is very likely to occur), he or she would want to strongly agree as to not look like a skeptic. \n", + "\n", + "- $L( \\theta, \\hat{\\theta} ) = 1 - \\exp \\left( -(\\theta - \\hat{\\theta} )^2 \\right)$ is bounded between 0 and 1 and reflects that the user is indifferent to sufficiently-far-away estimates. It is similar to the zero-one loss above, but not quite as penalizing to estimates that are close to the true parameter. \n", + "- Complicated non-linear loss functions can programmed: \n", + "\n", + " def loss(true_value, estimate):\n", + " if estimate*true_value > 0:\n", + " return abs(estimate - true_value)\n", + " else:\n", + " return abs(estimate)*(estimate - true_value)**2\n", + " \n", + "\n", + "\n", + "- Another example is from the book *The Signal and The Noise*. Weather forecasters have an interesting loss function for their predictions.\n", + "\n", + "\n", + "> People notice one type of mistake — the failure to predict rain — more than other, false alarms. If it rains when it isn't supposed to, they curse the weatherman for ruining their picnic, whereas an unexpectedly sunny day is taken as a serendipitous bonus.\n", + "\n", + "> [The Weather Channel's bias] is limited to slightly exaggerating the probability of rain when it is unlikely to occur — saying there is a 20 percent change when they know it is really a 5 or 10 percent chance — covering their butts in the case of an unexpected sprinkle.\n", + "\n", + "\n", + "As you can see, loss functions can be used for good and evil: with great power, comes great — well you know.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Loss functions in the real world\n", + "\n", + "So far we have been under the unrealistic assumption that we know the true parameter. Of course if we knew the true parameter, bothering to guess an estimate is pointless. Hence a loss function is really only practical when the true parameter is unknown. \n", + "\n", + "In Bayesian inference, we have a mindset that the unknown parameters are really random variables with prior and posterior distributions. Concerning the posterior distribution, a value drawn from it is a *possible* realization of what the true parameter could be. Given that realization, we can compute a loss associated with an estimate. As we have a whole distribution of what the unknown parameter could be (the posterior), we should be more interested in computing the *expected loss* given an estimate. This expected loss is a better estimate of the true loss than comparing the given loss from only a single sample from the posterior.\n", + "\n", + "First it will be useful to explain a *Bayesian point estimate*. The systems and machinery present in the modern world are not built to accept posterior distributions as input. It is also rude to hand someone over a distribution when all they asked for was an estimate. In the course of an individual's day, when faced with uncertainty we still act by distilling our uncertainty down to a single action. Similarly, we need to distill our posterior distribution down to a single value (or vector in the multivariate case). If the value is chosen intelligently, we can avoid the flaw of frequentist methodologies that mask the uncertainty and provide a more informative result.The value chosen, if from a Bayesian posterior, is a Bayesian point estimate. \n", + "\n", + "Suppose $P(\\theta | X)$ is the posterior distribution of $\\theta$ after observing data $X$, then the following function is understandable as the *expected loss of choosing estimate $\\hat{\\theta}$ to estimate $\\theta$*:\n", + "\n", + "$$ l(\\hat{\\theta} ) = E_{\\theta}\\left[ \\; L(\\theta, \\hat{\\theta}) \\; \\right] $$\n", + "\n", + "This is also known as the *risk* of estimate $\\hat{\\theta}$. The subscript $\\theta$ under the expectation symbol is used to denote that $\\theta$ is the unknown (random) variable in the expectation, something that at first can be difficult to consider.\n", + "\n", + "We spent all of last chapter discussing how to approximate expected values. Given $N$ samples $\\theta_i,\\; i=1,...,N$ from the posterior distribution, and a loss function $L$, we can approximate the expected loss of using estimate $\\hat{\\theta}$ by the Law of Large Numbers:\n", + "\n", + "$$\\frac{1}{N} \\sum_{i=1}^N \\;L(\\theta_i, \\hat{\\theta} ) \\approx E_{\\theta}\\left[ \\; L(\\theta, \\hat{\\theta}) \\; \\right] = l(\\hat{\\theta} ) $$\n", + "\n", + "Notice that measuring your loss via an *expected value* uses more information from the distribution than the MAP estimate which, if you recall, will only find the maximum value of the distribution and ignore the shape of the distribution. Ignoring information can over-expose yourself to tail risks, like the unlikely hurricane, and leaves your estimate ignorant of how ignorant you really are about the parameter.\n", + "\n", + "Similarly, compare this with frequentist methods, that traditionally only aim to minimize the error, and do not consider the *loss associated with the result of that error*. Compound this with the fact that frequentist methods are almost guaranteed to never be absolutely accurate. Bayesian point estimates fix this by planning ahead: your estimate is going to be wrong, you might as well err on the right side of wrong." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Example: Optimizing for the *Showcase* on *The Price is Right*\n", + "\n", + "Bless you if you are ever chosen as a contestant on the Price is Right, for here we will show you how to optimize your final price on the *Showcase*. For those who forget the rules:\n", + "\n", + "\n", + "1. Two contestants compete in *The Showcase*. \n", + "2. Each contestant is shown a unique suite of prizes.\n", + "3. After the viewing, the contestants are asked to bid on the price for their unique suite of prizes.\n", + "4. If a bid price is over the actual price, the bid's owner is disqualified from winning.\n", + "5. If a bid price is under the true price by less than $250, the winner is awarded both prizes.\n", + "\n", + "The difficulty in the game is balancing your uncertainty in the prices, keeping your bid low enough so as to not bid over, and trying to bid close to the price.\n", + "\n", + "Suppose we have recorded the *Showcases* from previous *The Price is Right* episodes and have *prior* beliefs about what distribution the true price follows. For simplicity, suppose it follows a Normal:\n", + "\n", + "\n", + "$$\\text{True Price} \\sim \\text{Normal}(\\mu_p, \\sigma_p )$$\n", + "\n", + "\n", + "In a later chapter, we will actually use *real Price is Right Showcase data* to form the historical prior, but this requires some advanced PyMC use so we will not use it here. For now, we will assume $\\mu_p = 35 000$ and $\\sigma_p = 7500$.\n", + "\n", + "We need a model of how we should be playing the *Showcase*. For each prize in the prize suite, we have an idea of what it might cost, but this guess could differ significantly from the true price. (Couple this with increased pressure being onstage and you can see why some bids are so wildly off). Let's suppose your beliefs about the prices of prizes also follow Normal distributions:\n", + "\n", + "$$ \\text{Prize}_i \\sim \\text{Normal}(\\mu_i, \\sigma_i ),\\;\\; i=1,2$$\n", + "\n", + "This is really why Bayesian analysis is great: we can specify what we think a fair price is through the $\\mu_i$ parameter, and express uncertainty of our guess in the $\\sigma_i$ parameter. \n", + "\n", + "We'll assume two prizes per suite for brevity, but this can be extended to any number. \n", + "The true price of the prize suite is then given by $\\text{Prize}_1 + \\text{Prize}_2 + \\epsilon$, \n", + "where $\\epsilon$ is some error term.\n", + "\n", + "We are interested in the updated $\\text{True Price}$ given we have observed both prizes and have belief distributions about them. We can perform this using PyMC. \n", + "\n", + "Lets make some values concrete. Suppose there are two prizes in the observed prize suite: \n", + "\n", + "1. A trip to wonderful Toronto, Canada! \n", + "2. A lovely new snowblower!\n", + "\n", + "We have some guesses about the true prices of these objects, but we are also pretty uncertain about them. I can express this uncertainty through the parameters of the Normals:\n", + "\n", + "\n", + "\\begin{align}\n", + "& \\text{snowblower} \\sim \\text{Normal}(3 000, 500 )\\\\\\\\\n", + "& \\text{Toronto} \\sim \\text{Normal}(12 000, 3000 )\\\\\\\\\n", + "\\end{align}\n", + "\n", + "For example, I believe that the true price of the trip to Toronto is 12 000 dollars, and that there is a 68.2% chance the price falls 1 standard deviation away from this, i.e. my confidence is that there is a 68.2% chance the trip is in [9 000, 15 000].\n", + "\n", + "We can create some PyMC code to perform inference on the true price of the suite." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib inline\n", + "import scipy.stats as stats\n", + "from IPython.core.pylabtools import figsize\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib as mtl\n", + "mtl.style.use(\"ggplot\")\n", + "\n", + "figsize(12.5, 9)\n", + "\n", + "norm_pdf = stats.norm.pdf\n", + "\n", + "plt.subplot(311)\n", + "x = np.linspace(0, 60000, 200)\n", + "sp1 = plt.fill_between(x , 0, norm_pdf(x, 35000, 7500), \n", + " color = \"#348ABD\", lw = 3, alpha = 0.6,\n", + " label = \"historical total prices\")\n", + "p1 = plt.Rectangle((0, 0), 1, 1, fc=sp1.get_facecolor()[0])\n", + "plt.legend([p1], [sp1.get_label()])\n", + "\n", + "plt.subplot(312)\n", + "x = np.linspace(0, 10000, 200)\n", + "sp2 = plt.fill_between(x , 0, norm_pdf(x, 3000, 500), \n", + " color = \"#A60628\", lw = 3, alpha = 0.6,\n", + " label=\"snowblower price guess\")\n", + "\n", + "p2 = plt.Rectangle((0, 0), 1, 1, fc=sp2.get_facecolor()[0])\n", + "plt.legend([p2], [sp2.get_label()])\n", + "\n", + "plt.subplot(313)\n", + "x = np.linspace(0, 25000, 200)\n", + "sp3 = plt.fill_between(x , 0, norm_pdf(x, 12000, 3000), \n", + " color = \"#7A68A6\", lw = 3, alpha = 0.6,\n", + " label = \"Trip price guess\")\n", + "plt.autoscale(tight=True)\n", + "p3 = plt.Rectangle((0, 0), 1, 1, fc=sp3.get_facecolor()[0])\n", + "plt.legend([p3], [sp3.get_label()]);" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Auto-assigning NUTS sampler...\n", + "Initializing NUTS using jitter+adapt_diag...\n", + "Sequential sampling (1 chains in 1 job)\n", + "NUTS: [true_price, first_prize, second_prize]\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + "
\n", + " \n", + " 100.00% [60000/60000 00:21<00:00 Sampling chain 0, 0 divergences]\n", + "
\n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Sampling 1 chain for 10_000 tune and 50_000 draw iterations (10_000 + 50_000 draws total) took 21 seconds.\n" + ] + } + ], + "source": [ + "import pymc as pm\n", + "\n", + "data_mu = [3e3, 12e3]\n", + "\n", + "data_std = [5e2, 3e3] \n", + "\n", + "mu_prior = 35e3\n", + "std_prior = 75e2\n", + "with pm.Model() as model:\n", + " true_price = pm.Normal(\"true_price\", mu=mu_prior, sigma=std_prior)\n", + " \n", + " prize_1 = pm.Normal(\"first_prize\", mu=data_mu[0], sigma=data_std[0])\n", + " prize_2 = pm.Normal(\"second_prize\", mu=data_mu[1], sigma=data_std[1])\n", + " price_estimate = prize_1 + prize_2\n", + " \n", + " logp = pm.logp(pm.Normal.dist(mu=price_estimate, sigma=(3e3)),true_price)\n", + " error = pm.Potential(\"error\", logp)\n", + " \n", + "\n", + " trace = pm.sample(50000, tune=10000,chains=1)\n", + " # burned_trace = trace[10000:]\n", + "\n", + "price_trace = trace.posterior.true_price.data" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "figsize(12.5, 4)\n", + "\n", + "import scipy.stats as stats\n", + "\n", + "x = np.linspace(5000, 40000)\n", + "plt.plot(x, stats.norm.pdf(x, 35000, 7500), c = \"k\", lw = 2, \n", + " label = \"prior dist. of suite price\")\n", + "\n", + "_hist = plt.hist(price_trace[0], bins = 35, density= True, histtype= \"stepfilled\")\n", + "plt.title(\"Posterior of the true price estimate\")\n", + "plt.vlines(mu_prior, 0, 1.1*np.max(_hist[0]), label = \"prior's mean\",\n", + " linestyles=\"--\",color=\"black\")\n", + "plt.vlines(price_trace.mean(), 0, 1.1*np.max(_hist[0]), \\\n", + " label = \"posterior's mean\", linestyles=\"-.\",color=\"black\")\n", + "plt.legend(loc = \"upper left\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notice that because of our two observed prizes and subsequent guesses (including uncertainty about those guesses), we shifted our mean price estimate down about \\$15,000 dollars from the previous mean price.\n", + "\n", + "A frequentist, seeing the two prizes and having the same beliefs about their prices, would bid $\\mu_1 + \\mu_2 = 35000$, regardless of any uncertainty. Meanwhile, the *naive Bayesian* would simply pick the mean of the posterior distribution. But we have more information about our eventual outcomes; we should incorporate this into our bid. We will use the loss function above to find the *best* bid (*best* according to our loss).\n", + "\n", + "What might a contestant's loss function look like? I would think it would look something like:\n", + "\n", + " def showcase_loss(guess, true_price, risk = 80000):\n", + " if true_price < guess:\n", + " return risk\n", + " elif abs(true_price - guess) <= 250:\n", + " return -2*np.abs(true_price)\n", + " else:\n", + " return np.abs(true_price - guess - 250)\n", + "\n", + "where `risk` is a parameter that defines of how bad it is if your guess is over the true price. A lower `risk` means that you are more comfortable with the idea of going over. If we do bid under and the difference is less than $250, we receive both prizes (modeled here as receiving twice the original prize). Otherwise, when we bid under the `true_price` we want to be as close as possible, hence the `else` loss is a increasing function of the distance between the guess and true price." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For every possible bid, we calculate the *expected loss* associated with that bid. We vary the `risk` parameter to see how it affects our loss:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(5000.0, 30000.0)" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "figsize(12.5, 7)\n", + "#numpy friendly showdown_loss\n", + "\n", + "\n", + "def showdown_loss(guess, true_price, risk = 80000):\n", + " loss = np.zeros_like(true_price)\n", + " ix = true_price < guess\n", + " loss[~ix] = np.abs(guess - true_price[~ix])\n", + " close_mask = abs(true_price - guess) <= 250\n", + " loss[close_mask] = -2*true_price[close_mask]\n", + " loss[ix] = risk\n", + " return loss\n", + "\n", + "\n", + "guesses = np.linspace(5000, 50000, 70) \n", + "risks = np.linspace(30000, 150000, 6)\n", + "expected_loss = lambda guess, risk: \\\n", + " showdown_loss(guess, price_trace, risk).mean()\n", + "\n", + "for _p in risks:\n", + " results = [expected_loss(_g, _p) for _g in guesses]\n", + " plt.plot(guesses, results, label = \"%d\"%_p)\n", + " \n", + "plt.title(\"Expected loss of different guesses, \\nvarious risk-levels of \\\n", + "overestimating\")\n", + "plt.legend(loc=\"upper left\", title=\"Risk parameter\")\n", + "plt.xlabel(\"price bid\")\n", + "plt.ylabel(\"expected loss\")\n", + "plt.xlim(5000, 30000)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Minimizing our losses\n", + "\n", + "It would be wise to choose the estimate that minimizes our expected loss. This corresponds to the minimum point on each of the curves above. More formally, we would like to minimize our expected loss by finding the solution to\n", + "\n", + "$$ \\text{arg} \\min_{\\hat{\\theta}} \\;\\;E_{\\theta}\\left[ \\; L(\\theta, \\hat{\\theta}) \\; \\right] $$\n", + "\n", + "The minimum of the expected loss is called the *Bayes action*. We can solve for the Bayes action using Scipy's optimization routines. The function in `fmin` in `scipy.optimize` module uses an intelligent search to find a minimum (not necessarily a *global* minimum) of any uni- or multivariate function. For most purposes, `fmin` will provide you with a good answer. \n", + "\n", + "We'll compute the minimum loss for the *Showcase* example above:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "minimum at risk 30000: 14189.71\n", + "minimum at risk 54000: 13242.36\n", + "minimum at risk 78000: 12143.58\n", + "minimum at risk 102000: 12143.58\n", + "minimum at risk 126000: 11485.25\n", + "minimum at risk 150000: 11041.61\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import scipy.optimize as sop\n", + "\n", + "ax = plt.subplot(111)\n", + "\n", + "\n", + "for _p in risks:\n", + " _color = next(ax._get_lines.prop_cycler)\n", + " _min_results = sop.fmin(expected_loss, 15000, args=(_p,),disp = False)\n", + " _results = [expected_loss(_g, _p) for _g in guesses]\n", + " plt.plot(guesses, _results , color = _color['color'])\n", + " plt.scatter(_min_results, 0, s = 60, \\\n", + " color= _color['color'], label = \"%d\"%_p)\n", + " plt.vlines(_min_results, 0, 120000, color = _color['color'], linestyles=\"--\")\n", + " print(\"minimum at risk %d: %.2f\" % (_p, _min_results))\n", + " \n", + "plt.title(\"Expected loss & Bayes actions of different guesses, \\n \\\n", + "various risk-levels of overestimating\")\n", + "plt.legend(loc=\"upper left\", scatterpoints = 1, title = \"Bayes action at risk:\")\n", + "plt.xlabel(\"price guess\")\n", + "plt.ylabel(\"expected loss\")\n", + "plt.xlim(7000, 30000)\n", + "plt.ylim(-1000, 80000);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As intuition suggests, as we decrease the risk threshold (care about overbidding less), we increase our bid, willing to edge closer to the true price. It is interesting how far away our optimized loss is from the posterior mean, which was about 20 000. \n", + "\n", + "Suffice to say, in higher dimensions being able to eyeball the minimum expected loss is impossible. Hence why we require use of Scipy's `fmin` function.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "______\n", + "\n", + "### Shortcuts\n", + "\n", + "For some loss functions, the Bayes action is known in closed form. We list some of them below:\n", + "\n", + "- If using the mean-squared loss, the Bayes action is the mean of the posterior distribution, i.e. the value $$ E_{\\theta}\\left[ \\theta \\right] $$ minimizes $E_{\\theta}\\left[ \\; (\\theta - \\hat{\\theta})^2 \\; \\right]$. Computationally this requires us to calculate the average of the posterior samples [See chapter 4 on The Law of Large Numbers]\n", + "\n", + "- Whereas the *median* of the posterior distribution minimizes the expected absolute-loss. The sample median of the posterior samples is an appropriate and very accurate approximation to the true median.\n", + "\n", + "- In fact, it is possible to show that the MAP estimate is the solution to using a loss function that shrinks to the zero-one loss.\n", + "\n", + "\n", + "Maybe it is clear now why the first-introduced loss functions are used most often in the mathematics of Bayesian inference: no complicated optimizations are necessary. Luckily, we have machines to do the complications for us. \n", + "\n", + "## Machine Learning via Bayesian Methods\n", + "\n", + "Whereas frequentist methods strive to achieve the best precision about all possible parameters, machine learning cares to achieve the best *prediction* among all possible parameters. Of course, one way to achieve accurate predictions is to aim for accurate predictions, but often your prediction measure and what frequentist methods are optimizing for are very different. \n", + "\n", + "For example, least-squares linear regression is the most simple active machine learning algorithm. I say active as it engages in some learning, whereas predicting the sample mean is technically *simpler*, but is learning very little if anything. The loss that determines the coefficients of the regressors is a squared-error loss. On the other hand, if your prediction loss function (or score function, which is the negative loss) is not a squared-error, like AUC, ROC, precision, etc., your least-squares line will not be optimal for the prediction loss function. This can lead to prediction results that are suboptimal. \n", + "\n", + "Finding Bayes actions is equivalent to finding parameters that optimize *not parameter accuracy* but an arbitrary performance measure, however we wish to define performance (loss functions, AUC, ROC, precision/recall etc.).\n", + "\n", + "The next two examples demonstrate these ideas. The first example is a linear model where we can choose to predict using the least-squares loss or a novel, outcome-sensitive loss. \n", + "\n", + "The second example is adapted from a Kaggle data science project. The loss function associated with our predictions is incredibly complicated. \n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "##### Example: Financial prediction\n", + "\n", + "\n", + "Suppose the future return of a stock price is very small, say 0.01 (or 1%). We have a model that predicts the stock's future price, and our profit and loss is directly tied to us acting on the prediction. How should we measure the loss associated with the model's predictions, and subsequent future predictions? A squared-error loss is agnostic to the signage and would penalize a prediction of -0.01 equally as bad a prediction of 0.03:\n", + "\n", + "$$ (0.01 - (-0.01))^2 = (0.01 - 0.03)^2 = 0.004$$\n", + "\n", + "If you had made a bet based on your model's prediction, you would have earned money with a prediction of 0.03, and lost money with a prediction of -0.01, yet our loss did not capture this. We need a better loss that takes into account the *sign* of the prediction and true value. We design a new loss that is better for financial applications below:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "figsize(12.5, 4)\n", + "\n", + "\n", + "def stock_loss(true_return, yhat, alpha = 100.):\n", + " if true_return * yhat < 0:\n", + " #opposite signs, not good\n", + " return alpha*yhat**2 - np.sign(true_return)*yhat \\\n", + " + abs(true_return) \n", + " else:\n", + " return abs(true_return - yhat)\n", + " \n", + " \n", + "true_value = .05\n", + "pred = np.linspace(-.04, .12, 75)\n", + "\n", + "plt.plot(pred, [stock_loss(true_value, _p) for _p in pred], \\\n", + " label = \"Loss associated with\\n prediction if true value = 0.05\", lw =3) \n", + "plt.vlines(0, 0, .25, linestyles=\"--\")\n", + "\n", + "plt.xlabel(\"prediction\")\n", + "plt.ylabel(\"loss\")\n", + "plt.xlim(-0.04, .12)\n", + "plt.ylim(0, 0.25)\n", + "\n", + "true_value = -.02\n", + "plt.plot(pred, [stock_loss(true_value, _p) for _p in pred], alpha = 0.6, \\\n", + " label = \"Loss associated with\\n prediction if true value = -0.02\", lw =3) \n", + "plt.legend()\n", + "plt.title(\"Stock returns loss if true value = 0.05, -0.02\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note the change in the shape of the loss as the prediction crosses zero. This loss reflects that the user really does not want to guess the wrong sign, especially be wrong *and* a large magnitude. \n", + "\n", + "Why would the user care about the magnitude? Why is the loss not 0 for predicting the correct sign? Surely, if the return is 0.01 and we bet millions we will still be (very) happy.\n", + "\n", + "Financial institutions treat downside risk, as in predicting a lot on the wrong side, and upside risk, as in predicting a lot on the right side, similarly. Both are seen as risky behaviour and discouraged. Hence why we have an increasing loss as we move further away from the true price. (With less extreme loss in the direction of the correct sign.)\n", + "\n", + "We will perform a regression on a trading signal that we believe predicts future returns well. Our dataset is artificial, as most financial data is not even close to linear. Below, we plot the data along with the least-squares line." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "## Code to create artificial data\n", + "N = 100\n", + "X = 0.025*np.random.randn(N)\n", + "Y = 0.5*X + 0.01*np.random.randn(N) \n", + "\n", + "ls_coef_ = np.cov(X, Y)[0,1]/np.var(X)\n", + "ls_intercept = Y.mean() - ls_coef_*X.mean()\n", + "\n", + "plt.scatter(X, Y, c=\"k\")\n", + "plt.xlabel(\"trading signal\")\n", + "plt.ylabel(\"returns\")\n", + "plt.title(\"Empirical returns vs trading signal\")\n", + "plt.plot(X, ls_coef_*X + ls_intercept, label = \"Least-squares line\")\n", + "plt.xlim(X.min(), X.max())\n", + "plt.ylim(Y.min(), Y.max())\n", + "plt.legend(loc=\"upper left\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We perform a simple Bayesian linear regression on this dataset. We look for a model like:\n", + "\n", + "$$ R = \\alpha + \\beta x + \\epsilon$$\n", + "\n", + "where $\\alpha, \\beta$ are our unknown parameters and $\\epsilon \\sim \\text{Normal}(0, \\sigma)$. The most common priors on $\\beta$ and $\\alpha$ are Normal priors. We will also assign a prior on $\\sigma$, so that $\\sigma$ is uniform over 0 to 100." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Auto-assigning NUTS sampler...\n", + "Initializing NUTS using jitter+adapt_diag...\n", + "Multiprocess sampling (4 chains in 4 jobs)\n", + "NUTS: [std, beta, alpha]\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + "
\n", + " \n", + " 100.00% [8000/8000 00:01<00:00 Sampling 4 chains, 0 divergences]\n", + "
\n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 3 seconds.\n" + ] + } + ], + "source": [ + "import pymc as pm\n", + "\n", + "with pm.Model() as model:\n", + " std = pm.Uniform(\"std\", 0, 100)\n", + " \n", + " beta = pm.Normal(\"beta\", mu=0, sigma=100)\n", + " alpha = pm.Normal(\"alpha\", mu=0, sigma=100)\n", + " \n", + " mean = pm.Deterministic(\"mean\", alpha + beta*X)\n", + " \n", + " obs = pm.Normal(\"obs\", mu=mean, sigma=std, observed=Y)\n", + " \n", + " trace = pm.sample()\n", + " # burned_trace = trace[20000:] " + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import arviz as az\n", + "az.plot_trace(trace, var_names=[\"std\", \"beta\", \"alpha\"],figsize=(15, 12))\n", + "az.plot_posterior(trace, var_names=[\"std\", \"beta\", \"alpha\"],figsize=(20, 4));" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It appears the MCMC has converged so we may continue.\n", + "\n", + "For a specific trading signal, call it $x$, the distribution of possible returns has the form:\n", + "\n", + "$$R_i(x) = \\alpha_i + \\beta_ix + \\epsilon $$\n", + "\n", + "where $\\epsilon \\sim \\text{Normal}(0, \\sigma_i)$ and $i$ indexes our posterior samples. We wish to find the solution to \n", + "\n", + "$$ \\arg \\min_{r} \\;\\;E_{R(x)}\\left[ \\; L(R(x), r) \\; \\right] $$\n", + "\n", + "according to the loss given above. This $r$ is our Bayes action for trading signal $x$. Below we plot the Bayes action over different trading signals. What do you notice?\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + }, + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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Mpk+fjoEDB1qs/e2338bKlSsxZcoU3HfffXB3d8ebb75pdiwLUD5jbekxbn7vKRQKs19ui9tuN9ZlYemzcbufbZY+G2V5LSrqs2Fpu6XPhqWfDUT2hDP+RHYqIiICZ8+ehZeXF8LDw83+VK1aFQCwefNm3H///RgxYgSaN2+O2rVrWzw4t0qVKujTpw/mz5+POXPmYOHChcjIyABQ9J+X0Wg06x8cHGz2fMWUSiXatWuHjz/+GHv37kWVKlXw888/AygKU1u3bjV7nJtvBwQEAACuXr1q2nbgwAGz/yy3bNkCvV6PTz75BJGRkahTp84tD74tVhzcL168WOL1qlWrVpn2oTS7d+82e422b98OtVpt9rg3i4iIwNGjR0u8luHh4fD39wdQttfM0n4ePXq01NekOIzcPKY3a9CgATZt2mS2bfPmzaaDbe9FgwYN0KxZM8yfPx/z589H06ZN0bhxY7M+t3pP3guVSoWcnBwARbPOiYmJGD9+PDp06IB69eohNTW1RDgLDw/HQw89hFmzZmH27Nno06ePaaY5MDAQoaGhOHnyZIlxLD4ou1jDhg0xYsQI/PXXX3jllVfw/fffl1rn5s2b0bNnT3Tr1g1NmjRBWFiY2UHH9evXh1arveUBwpY+uzdr0KABjh49avbN1rVr13Dq1Ck0aNDglvcti+PHj5uN27Zt2wAUzYaXpiw/2xo0aICdO3ea7d/trk3QvHlzpKSkYM+ePRbb7+WzsWnTJkiShPr169/yvkT2jsGfyMaysrJMyxWK/5w4cQI9e/ZEzZo10aVLF6xduxbnz5/Hzp07MXHiRKxYsQIAULduXRw+fBgrV67EmTNn8NVXX2HZsmVmj//aa6/hzz//xJkzZ3D06FEsW7YMoaGh8PT0BFB09petW7fi4sWLSEpKKnWGb+XKlZg6dSr27t2LixcvYsWKFbh06ZLpP8Lhw4dj+/btGD16NE6dOoXly5djypQpZo8RHh6O6tWr48MPP8SJEyewZcsWDB8+3GwWrW7dukhMTMScOXNw9uxZzJ8/H998881tX8fw8HD07dsX/fv3x4IFCxAbG4uDBw9i7ty5mDx5cpn2oTTJyckYMmQIjh8/jj/++ANjxoxB//79S8xk3vy6G41GxMTE4N9//8X58+exZcsWjB492hSOyvKa3axHjx6oXr06nnzySaxbtw7nzp3DP//8g8WLFwMAqlevDoVCgT///BMJCQklZpCLjRo1CkuXLsWkSZNw6tQp/Prrr/jwww/x5ptvlpjJvBu9evXCokWLsHDhQrz88sslXptbvSdHjRqFjh073vY5ipd/xcfH48yZM/jmm2+wZs0aPP300wCKXguNRoOvv/4aZ86cwT///IPXX3/d4mzuwIED8f333+PYsWPo16+fWdv48eMxbdo0fPLJJzhy5AhOnjyJFStWmGbzY2Nj8e6772LLli24cOECtm/fjn///feW76u6deti5cqV2LVrF44dO4YBAwaY/ULs4eGBN998Ex9++CFmzJiBU6dO4eDBg5g4caKpT1k+uy+88AL8/f3RrVs37Nu3D3v37kX37t0RHByMbt263fY1vh1JkvDyyy/jyJEj2Lx5M4YMGYIuXbqgdu3apd6nLD/bXn31VSQmJmLAgAE4fvw4/vnnH7NvMi156KGH0LZtW3Tr1g0rV67EuXPnsHXrVsyePRsAoNfr4eHhgbVr1yI+Ph6pqakWH+ftt9/Gvn37MGLECJw4cQKrV6/G0KFD0bNnT9NSMaJKyzaHFhCREEUHiAEo8adu3bpCCCGSkpLEoEGDRNWqVYWLi4uoWrWqiImJEfv27RNCCFFQUCAGDBggdDqd8PT0FD169DAdHFps8ODBonbt2kKr1QpfX1/x2GOPiSNHjpjad+/eLZo1aya0Wq3Fg0KLbdq0SXTo0EHo9Xqh0WhEeHi4mDhxopBl2dRn0aJFIiwsTKjVavHAAw+IFStWmB1oKIQQO3bsMD1f48aNxebNm0sc3Pv++++LgIAA4ebmJjp37ix+/vlns9puPhivmMFgEJMnTxZ169YVLi4uws/PT7Rr1078+uuvZd6Hm0VFRYk+ffqIt956S/j6+goPDw/Rp08fkZ2dbdbnlVdeKXHf8+fPixdeeEHo9XqhVqtFtWrVRM+ePcXZs2fL/JrdfLCmEELExcWJl156Sfj5+QmNRiPq1q1r9vpNnjxZVK1aVSgUChEVFSWEKHlwrxBC/PDDD+K+++4zvbfee+89s4MpLe3XuHHjRPXq1Ut9vYolJiYKFxcXoVKpRHx8vFnb7d6TvXr1uu1z3PzZcXV1FfXr1xefffaZ2UHWS5YsEeHh4UKj0YimTZuKjRs3WjyYvKCgQPj7+4tOnTpZfL7ly5eLBx98ULi6ugpPT0/RpEkT8dFHHwkhhLh69ap4+umnRXBwsFCr1aJKlSqiX79+Zge93uzixYuiU6dOws3NzXQmrr59+5rGS4iig8G//PJLUadOHeHi4iICAgLEc889Z2q39Nm19H45ceKE6Ny5s+lA6C5duojTp0+b2i19nko7EPbmMejYsaP47LPPRFBQkNBqtSImJsZ0tpwb+9zsdj/bhCg6WLdhw4ZCrVaLBg0aiH/++eeWB/cKIURGRoZ47bXXRFBQkHBxcRE1atQQEydONLX/+OOPokaNGkKlUpneY5b2/48//hDNmjUTarVa6PV6MWjQINPB/KXt14IFCwRjFdk7SQguSCOiinH+/HnUrFkT//77710f6Gtr7du3R3h4uGnWkBxTSkoKgoOD8dNPP+HZZ5+1dTmVQu/evXH58mWsW7fO1qUQURnx4F4iInJahYWFuHbtGsaNG4eqVasiJibG1iUREVUYBn8iInJaW7duRYcOHVCzZk3Mnz/f7Mw2RESOhkt9iIiIiIicAM/qQ0RERETkBBj8iYiIiIicAIM/EREREZETcOqDe2+8WIot6fV6s6sqUuXFsXQcHEvHwbF0HBxLx8GxrDjFV8C2hDP+REREREROgMGfiIiIiMgJMPgTERERETkBp17jfzMhBPLy8iDLMiRJstrzXrt2Dfn5+VZ7Pqo4dzqWQggoFApotVqrvueIiIjI+TD43yAvLw8uLi5Qqaz7sqhUKl4t0kHczVgaDAbk5eXB1dW1gqoiIiIi4lIfM7IsWz30E6lUKsiybOsyiIiIyMEx+N+ASy3IVvjeIyIioorG4G9nateuXSGPe+nSJSxfvrxCHrsyW7x4MUaPHg0AmD9/PpYsWVJq35tfw4MHD2LMmDEVXiMRERFReWDwdxL2FPwNBkOFP4fRaLzj+7z88svo2rVrqe03v4ZNmjTBuHHj7qo+IiIiImvjgvZK4Pz58xg9ejSSk5Ph6uqKzz77DOHh4Vi7di2mTZuGgoIC6HQ6TJ8+Hf7+/ti+fTvGjh0LoGgJybJlyzBhwgTExsbi4YcfRteuXTFgwACz55gzZw4WLFgAlUqF2rVr49tvv0VKSgqGDBmC5ORkNG3aFBs3bsTq1auRnZ2NXr16Yf369QCA7777DtnZ2XjzzTexcOFCLFy4EAUFBahZsyamTZsGV1dXvPHGG/Dx8cGRI0fQqFEj9OrVy+I+rVq1ClOnToVCoYCXlxeWLVtmVue2bdvw+eefQ6fT4cyZM4iMjMTEiROhUChQu3ZtDBgwAJs2bcLYsWNx6dIlzJ07FwUFBbj//vsxceJEKJVKLF68GF9//TUCAwMRFhYGtVoNAJgyZQrc3d0xaNAgnDt3DiNHjkRycjKUSiVmzpxZ4jVs2LAhvvvuO8yfPx+pqal48803cfHiRWi1Wnz66aeoX78+pkyZgitXruDixYu4cuUK+vXrh1deecUK7xoiIiIicwz+pZB/mQVx6Vy5PqYUWhOK7v3v+H7vvPMOJk2ahLCwMOzbtw+jRo3CkiVL8MADD2DVqlWQJAk///wzvvnmG3zwwQf47rvvMGHCBLRo0QLZ2dnQaDR47733TCHVkhkzZmD79u3QaDRIT08HAEydOhUPPPAAhg8fjnXr1mHhwoW3rbVz587o2bMnAGDy5MlYtGgR+vbtCwA4e/YsFi9eDKVSieeff97iPn355ZdYuHAhqlSpYqrjZgcOHMCGDRsQEhKCnj174s8//8Tjjz+OnJwc1K1bF2+//TZOnz6NGTNmYMWKFXBxccGoUaOwbNkytGvXDp9//jlWr14NT09PU4C/2dChQzFkyBB07twZeXl5EEKUeA23bdtm6j9lyhQ0bNgQ8+fPx8aNG/H666/j77//BgDExsZiyZIlyM7ORtu2bfHyyy/DxcXltq8lERERUXli8Ldz2dnZ2Lt3LwYOHGjaVlBQAACIi4vDq6++ioSEBBQUFKBatWoAgBYtWuCjjz7C008/jc6dO6Nq1aq3fZ569erhtddew6OPPopHH30UALBjxw7Mnj0bABAdHQ0fH5/bPs7Jkyfx6aefIiMjA9nZ2YiKijK1Pf7441Aqlbfcp4iICAwfPhxPPPEEOnfubPE5mjZtiurVqwMAYmJisGvXLtNjd+nSBQCwZcsWHD58GI899hiAolO16vV67N+/Hy1btoSfnx8A4Mknn8TZs2fNHj8rKwtxcXGm59dqtbfd7127dmHWrFkAgDZt2iA1NRUZGRkAgI4dO0Kj0UCj0UCv1yMxMbFMY0JERER0J0RO1i3bGfxLcTcz8xVBlmV4eXmZZo9vNGbMGAwYMACdOnXCtm3b8MUXXwAAXnvtNXTs2BHr16/HE088gcWLF5e47/Dhw3HkyBEEBQVhwYIFmD9/Pnbs2IG1a9fiyy+/xIYNGwBYPtuMUqk0O/1kXl6e2ePOmTMHDRo0wOLFi7F9+3ZTm5ub2233afLkydi3bx/++ecfdOrUCWvXroWvr69Zn5trKr6t0WhM59AXQqBr164YNWqUWd/Vq1ff9gw6Qohbtpf1PjfWVUypVN7V8QdEREREpRGxxyBPHll04489pfbjwb12ztPTE6GhoVi1ahWAooB59OhRAEBGRgaCgoIAwOxsNOfPn0e9evUwZMgQNGnSBLGxsfDw8EB2drapz9SpU/H3339jwYIFkGUZV69eRevWrfH++++bZusffPBB0xr79evXIy0tDQDg7++PpKQkpKSkID8/H+vWrTM9blZWFgIDA1FYWFjqwcS32qfz58+jWbNmePvtt+Hr64urV6+WuP+BAwdw8eJFyLKM33//HQ888ECJPm3atMH//vc/JCUlAQBSU1Nx+fJl3H///di+fTtSUlJQWFiI//3vfxbrq1KlClavXg0AyM/PR25ubonX8EY3vlbbtm2Dr68vPD09LfYlIiIiuldCCMi/L4Kx/5P/hf7b4Iy/ncnNzUXz5s1NtwcMGIDp06dj1KhR+Oqrr2AwGPDUU0+hQYMGePPNNzFw4EAEBQWhWbNmuHTpEgBg9uzZ2LZtGxQKBerUqYMOHTpAoVBAqVQiOjoazz//vNnBvUajEUOHDkVmZiaEEOjfvz+8vb0xfPhwDBkyBI888ggefPBBBAcHAwBcXFxMy3FCQ0MRHh5ueqy3334bjz/+OEJCQnDfffchK8vyV06l7dMnn3yCc+fOQQiBNm3aoEGDBiXu26xZM0yYMAEnTpxAZGSkxSVBderUwTvvvIMePXpACAGVSoXx48ejefPmePPNN/Hkk08iMDAQjRo1sjgDP23aNLz77rv4/PPPoVKpMHPmTNSrV8/sNbzx2IARI0ZgxIgRaN++PbRaLb788svbjDQRERHRnROZ6ZC//BC4eMZsu+KtCZDqljxu8UaSuJt1DQ7i5tnknJwc03IUa1KpVFY5xeW9ioyMxF9//VVi6Y01bdu27ZYHKdva3Y6lrd57VDq9Xm/6xogqN46l4+BYOg6O5Z0Txw5AnjrWfGOt+6AYOgaS+3+rDG51HCFn/ImIiIiI7JCQjRC//QDx90qz7dIzvSA9+sxtj1u8GYM/ldnOnTttXQJatWqFVq1a2boMIiIiogojUpMhT3kfuHbFbLvivc8h1axz14/L4E9EREREZAfEgZ2QZ4w339iwGRQD34GkvfclwQz+REREREQ2IgwGiEUzITavMdsu9RgAxUOPl+tzMfgTEREREVmZSIyH/OlIIC3lv40qFRSjp0AKqVkhz8ngT0RERERkJfLOTRCzp5hvbN4Kij7DId1w0c+KwAt42ZnQ0FA8/PDDiI6OxiOPPILdu3fbuqRbSk9Pxw8//GC6HR8fj/797eOqxzerXbs2gLLVOGvWLOTm5ppuv/TSS0hPT6/Q+oiIiMgxicICyLOmwNj/SbPQL/V+HcpZv0M5aGSFh36A5/E3u20P5/GvXbs2Tp8+DQDYuHEjvv76ayxdutTqNZXVpUuX0KtXL6xfv94mz28wGKBSle2Lqxtf29u522sW8Dz+joPnmHYcHEvHwbF0HM4yliLuMuSJbwG5Of9t9PCCYuSnkAJLP9/+veB5/CupzMxMeHt7AwCys7PRp08fpKenw2Aw4J133sEjjzyCTz/9FL6+vujXrx8AYNKkSfD398crr7yCb7/9FqtWrUJBQQEeffRRvPXWW8jJycHAgQMRFxcHWZbx+uuv46mnnjJ73oULF2LhwoUoKChAzZo1MW3aNLi6uiIxMREjR47EhQsXAAATJ07E3LlzceHCBTz88MNo164devfubfpFIC8vD6NGjcKhQ4egVCrxwQcfoHXr1li8eDH+/vtv5Obm4vz58+jcuTPef//9EvsfGRmJJ598Etu2bQNQdLXfmjVr4o033oCPjw+OHDmCRo0aoVevXhg9ejSSk5Ph6uqKzz77DOHh4bh48SKGDBkCo9GI9u3bmx73xl9WjEYjxo8fj02bNkGSJLzwwgsQQuDatWvo2rUrdDodfvvtN7NfBGbOnInFixcDAHr06IH+/fvj0qVLePHFFxEZGYndu3cjKCgIc+fOhaura7m/L4iIiMi+yZvXQCyYYbZNatUR0kuDIalcbFQVg3+pZu+5hnOpeeX6mDV1WvSLCLxln7y8PDz88MPIz89HQkICfv31VwCARqPBnDlz4OnpiZSUFDzxxBPo1KkTevTogX79+qFfv36QZRm///47/ve//2HTpk04d+4c/vjjDwgh0Lt3b+zYsQPJyckICgrCggULAAAZGRklaujcuTN69uwJAJg8eTIWLVqEvn37YsyYMXjwwQcxZ84cGI1GZGdn47333sPJkyfx999/AygK1cWKlwD9888/iI2NRY8ePfDvv/8CAI4ePYo1a9ZArVajXbt26NOnD4KDg0vU4uHhgT/++ANLlizBBx98YLpi79mzZ7F48WIolUo8//zzmDRpEsLCwrBv3z6MGjUKS5YswdixY/Hyyy+ja9euZsuRbvTTTz/h0qVLWLNmDVQqFVJTU6HT6fD9999jyZIlJWb8Dx06hF9//RX/+9//IITA448/jpYtW8Lb2xvnzp3DzJkz8emnn2LgwIH4888/8eyzz95yvImIiMgxiPw8yLM+Bw7uMtuuGPgOpIg2NqrKHIO/ndFqtaYQvWfPHrz++utYv349hBCYNGkSdu7cCUmSEB8fj8TERISGhkKn0+HIkSNITExEgwYN4Ovri02bNmHTpk3o1KkTgKKlJOfOncMDDzyAcePGYfz48YiOjkZkZGSJGk6ePIlPP/0UGRkZyM7ORlRUFABg69at+OqrrwAASqUSXl5et1z3vnv3bvTp0wcAEB4ejpCQEJw9exYA0KZNG3h5eQEA6tSpgytXrlgM/jExMaa/P/zwQ9P2xx9/HEqlEtnZ2di7dy8GDhxoaisoKDA9/6xZswAAzz77LMaPv+m8uAC2bNmCl156ybRcSKfTlbo/ALBr1y48+uijpmU5nTt3xs6dO9GpUyeEhoaiYcOGMBgMaNy4sdkvQUREROSYxMUzkMcNN9/oFwDF2xMg+QXYpqhSMPiX4nYz89YQERGBlJQUJCcn459//kFycjL++usvuLi4IDIyEvn5+QCKlpv8+uuvSEhIQPfu3QEAQgi89tpreOmll0o87l9//YX169dj4sSJiIqKwvDh5m/W4cOHY86cOWjQoAEWL16M7du331X9tzp8RK1Wm/6tUChKXRd/46Wob/x3cfCWZRleXl6mX5Zudf87rfFO+2tuOChHqVQiL698vzEiIiIi+yCEKDr3/oY/zbZLHbpA6tYPklJpo8pujWf1sWOxsbEwGo3Q6XTIzMyEXq+Hi4sLtm7disuXL5v6de7cGRs2bMDBgwdNa9nbt2+PxYsXIzs7GwAQFxeHpKQkxMfHw9XVFc8++ywGDRqEw4cPl3jerKwsBAYGorCwEMuXLzdtb9OmjWmpjdFoRGZmJtzd3ZGVlWWx/sjISNP9z5w5gytXrqBWrVp39Br8/vvvpr+bN29eot3T0xOhoaFYtWoVgKIP4tGjRwEALVq0wMqVKwEAy5Yts/j47dq1w4IFC0y/eKSmpgIoWmJkab8efPBBrFmzBrm5ucjJycHq1astfmtCREREjkckJ8LY/0nIA54yC/2KYWOhnPU7FC8MtNvQD3DG3+4Ur/EHikLsl19+CaVSiWeeeQa9evVC586d0aBBA4SHh5vuo1ar0apVK3h7e0N5/c0WFRWF06dP48knnwRQNEP+9ddf4/z58/jkk08gSRJcXFwwceLEEjW8/fbbePzxxxESEoL77rvPFIA//vhjvPPOO/jll1+gUCgwceJEREREoEWLFnjooYfQoUMH9O7d2/Q4vXr1wsiRI9GxY0colUpMnTrVbFa8LAoKCvD4449DlmXMmDHDYp/p06dj1KhR+Oqrr2AwGPDUU0+hQYMG+PjjjzFkyBDMmTMHjz32mMX7vvDCCzh79iyio6OhUqnQs2dP9OnTBz179sSLL76IgIAA/Pbbb6b+jRo1QteuXdGlSxcARd+2NGzYkMt6iIiIHJj890qIX+eU2K74dB4knZ8NKro7PJ3nDezhdJ53Q5ZlPPLII5g5cybCwsLKsTLbuttTatoST+fpOJzlVHPOgGPpODiWjqMyjKWQZciDnwWMxhJtipnLISnsc2afp/N0YKdOnUKvXr3w6KOPOlToJyIiIrIFEXcZ8tjBJbZLT3SH4skXbFBR+WHwr+Tq1Klz1wff2rudO3faugQiIiJyEvKKnyD++LXEdsUn31XYxbasjcGfiIiIiJySMBohD3q6ZIPWFYppv9z27ICVjd0E/wMHDmDevHmQZRkdO3Y0nb+9mBAC8+bNw/79+6HRaDB48GCEhYUhKSkJM2bMQFpaGiRJQnR0dKkHct6OEx/uQDbG9x4REZH1iAuxkD8ZUWK71H0AFB0ft0FF1mEXwV+WZcyZMwfvv/8+/Pz8MGrUKERERCAkJMTUZ//+/YiPj8e0adNw+vRpzJ49GxMmTIBSqcRLL72EsLAw5ObmYuTIkWjcuLHZfcuq+HzyxRdzIrIGg8EAhYJn1iUiIqpo8oJvIDavLrFdMXkuJF+9DSqyLrtIuLGxsQgKCkJgYNFFs1q1aoXdu3ebhfc9e/agXbt2kCQJderUQXZ2NlJTU6HT6UxXW3V1dUVwcDBSUlLuKvhrtVrk5eUhPz/fql/taDQa08W4qHK707EUQkChUECr1VZgVURERM5LFBZAHvxcyYbAYCg/+db6BdmQXQT/lJQU+Pn9dw5UPz8/nD59ukQfvV5v1iclJcUU+gEgISEB586dMzvH/Y3WrVuHdevWAQAmTZpk9ni2dK+n8yT7wbF0HCqVym5+RtC94Vg6Do6l47DGWBYc2Y/UMUNKbPd6fSxc2z9aoc9tr+wi+Fta33zzjPvt+uTl5WHKlCno3bt3qedDj46ORnR0tOm2vZw/tjKcy5bKhmPpODiWjoNj6Tg4lo6jIsfS+O1EYF/JMx4qvvgJkqcXsgFkO/D7yO7P4+/n54fk5GTT7eTkZLOZ/OI+N75BbuxjMBgwZcoUtG3bFpGRkdYpmoiIiIjsgsjLgTy0e8mG8PpQvjvJ+gXZKbsI/rVq1UJcXBwSEhLg6+uLbdu2YdiwYWZ9IiIisHr1arRu3RqnT5+Gm5sbdDodhBD47rvvEBwcjMcfd9yjsImIiIjInDiwE/KM8SW2K157H1KTB2xQkX2zi+CvVCrRt29fjB8/HrIso0OHDggNDcXatWsBAJ06dcL999+Pffv2YdiwYVCr1Rg8uOiKaidPnsTmzZtRrVo1vP322wCAHj16oFmzZjbbHyIiIiKqOMbJ7wKxx0tsV3z9CySt5SXfBEjCiU8gfvXqVVuXAIBrFh0Jx9JxcCwdB8fScXAsHcfdjKXIzIA84sWSDc1bQTloZDlVVvnZ/Rp/IiIiIiJL5G3rIeZ9WWK74q0JkOo2tH5BlRiDPxERERHZFSEE5NEDgcT4Em2Kb36D5KK2QVWVH4M/EREREdkFkZII+d1XSmyXoh6F4sXBNqjIsTD4ExEREZFNyf+sgvhlVontivenQqpeywYVOSYGfyIiIiKyOiHLkAfGWGxTfLccklJp3YKcAIM/EREREVmNOHMC1/q/U2K71OV5KGIsnLWHyg2DPxERERFVOONno4BTR0tsV7w7GVJ4PRtU5HwY/ImIiIioQgiDAfKrz1hsU8xcAUmhsHJFzo3Bn4iIiIjKlbx1HcQP00psl1q0hWLA27wYm40w+BMRERFRuTD2f9LidsWH0yEFV7NuMVQCgz8RERER3TWRmwN5WHeLbYrvV0KSJCtXRKVh8CciIiKiOyb/uQRi+YKSDfWaQDlinPULotti8CciIiKiMit1Oc/HMyBVCbVyNXQnGPyJiIiI6JZEwlXIowdZbFPO+t3K1dDdYvAnIiIiIotKm91HeD0o351s3WLonjH4ExEREZEZLudxTAz+RERERARxeA/kaR9bbONyHsfA4E9ERETkxEpdzuOrh3LyXOsWQ3fNKAusPp2G/lWrltqHwZ+IiIjICZW6nGfiLEj6QCtXQ/fifGoevtkVj5NJeejfoUGp/Rj8iYiIiJyEvPFPiIXfWWzjcp7KJ98g49cjyVh+LBnuaiXeaFnllv0Z/ImIiIgcXKnLeUJrQjn2K+sWQ+XiYHw2vtkZj/isQjwU5oU+9wfAS3vraM/gT0REROSAhGyEPPBpi22Kr36G5OZh5YqoPGTkGTB3XwI2nMtAFU8XfNwxFE2C3Mt0XwZ/IiIiIgciz/0SYvt6i21czlN5CSGw8VwG5uxLQE6BEV0b+KFrQz9oVIoyPwaDPxEREZEDKHU5T/NWUA4aad1iqFzFZRbg213xOBifg7p6LYZEVkN1H80dPw6DPxEREVElJfJyIA/tbrFN8e1SSCoXK1dE5ckgC6w8noJfDidBpZAwsEUgHq3tA4Uk3dXjMfgTERERVTLGN18GMtIstnE5j2M4mZSLb3bG43xaPlqGeqB/RCD83O7tFzkGfyIiIqJKorTlPFLUo1C8ONjK1VBFyCowYuHBRPx1Kg2+riq81y4YkaGe5fLYDP5EREREdkwkxkN+b4DFNsX3KyHd5bIPsi/FB+/O25+AzHwjHqurw4tN9HBzUZbbczD4ExEREdmhUg/WBZfzOJoLafmYuTseRxNyUVevxYcdQhHmqy3352HwJyIiIrIjpS7neaYXFJ2ftXI1VJFyC2X8cjgJq06kwM1FgSGRQYiu5X3XB+/eDoM/ERERkY2J08cgf2r5lJuc3Xc8Qghsv5SJ2XsTkJxjwMO1vPFyU//bXnn3XjH4ExEREdkIl/M4n6sZBfh+zzXsj8tGTZ0G77QJxn3+rlZ5bgZ/IiIiIisrLfArXh0FqVlLK1dD1pBvkLH0WDKWHk2BWimhX/MAPFZHB6XCegdnM/gTERERWYG87R+IeV9ZbOPsvmPbcyULs/ZcQ3xWIdrV8EKfZgHwdbV+DGfwJyIiIqpAXM7jvBKzCzF77zXsuJSFEC81xnUMReMgd5vVw+BPREREVAFKXc4zZiqkarWsXA1ZU75BxvLjKVh6NBkSgJeb+uPJ+3zhorTtNRcY/ImIiIjKibzwO4iNf1ps4+y+4xNCYNulTPywLwEJ2Qa0ruaJ3vcHIMDDxdalAWDwJyIiIrpnXM5D51PzMHtvAg5fy0ENHw0+ia6CRoG2W9ZjCYM/ERER0V0QshHywKcttikmz4Xkq7dyRWQLmflG/HwoEatPp8HdRYFBLQLRKdzHqmfrKSsGfyIiIqI7YHxvAJAYb7GNs/vOwygLrI1Nw8KDicgulPFobR+80NgfnhqlrUsrFYM/ERERURlwOQ8VO3ItB7P2XMP5tHw0DHRD/+YBqKHT2rqs22LwJyIiIiqFyMmG/HoPi22K6b9C0th/2KPyk5hdiHn7ErD1Yib83VR4p21VtAr1hCTZ37IeSxj8iYiIiG7C2X26Ub5BxvJjKVh6LBkA0KOxHk/X84VGpbBxZXeGwZ+IiIjoulIDf2hNKMdavuouOS5ZCPx7PgMLDiQiMafo9Jx9mgXA390+Ts95pxj8iYiIyKmJhKuQRw+y2KaYuQKSonLN6lL5OJaQg7n7EnA6OQ9hOg1eb2V/p+e8Uwz+RERE5JS4nIcsicsswI/7E7D9Uhb8XFV4vWUVtK/pBUUlWcd/K3YT/A8cOIB58+ZBlmV07NgRMTExZu1CCMybNw/79++HRqPB4MGDERYWBgD45ptvsG/fPnh7e2PKlCk2qJ6IiIgqi9ICv9S+MxQ9X7VuMWQ3MvONWHwkCX+dSoVKIeGFxnrEVMJ1/LdiF8FflmXMmTMH77//Pvz8/DBq1ChEREQgJCTE1Gf//v2Ij4/HtGnTcPr0acyePRsTJkwAALRv3x6PPvooZsyYYatdICIiIjsmju6H/OUHFts4u+/cCo0Cf55Kxa9HkpBTKKNjmDdeaOIPX1e7iMnlyi72KDY2FkFBQQgMDAQAtGrVCrt37zYL/nv27EG7du0gSRLq1KmD7OxspKamQqfToX79+khISLBV+URERGSnuJyHSiOEwPZLmfhxfyLiswrRNMgNfZpVjvPx3y27CP4pKSnw8/Mz3fbz88Pp06dL9NHr9WZ9UlJSoNPprFYnERERVQ6lLufp3h+Kjk9YuRqyN6eTczF3bwKOJeaimrcaH3QIwf1V3CvN+fjvll0EfyFEiW03v/Bl6XM769atw7p16wAAkyZNMvtFwpZUKpXd1EL3hmPpODiWjoNj6ThuN5bZS+cj66fvLLYFLt9WUWXRXbDV5zI+Iw/fbbuAv08mQufqgnceCkeXBoFQKRw78Bezi+Dv5+eH5ORk0+3k5OQSM/l+fn5ISkq6ZZ/biY6ORnR0tOn2jY9nS3q93m5qoXvDsXQcHEvHwbF0HKWNZVmW8/A9YF+s/blMzTVg6bFkrD6VBkkCujbwwzMNfOHmokRaSvLtH6ASqVq1aqltdhH8a9Wqhbi4OCQkJMDX1xfbtm3DsGHDzPpERERg9erVaN26NU6fPg03Nzcu8yEiInJipQV+xRsfQWpwv5WrIXuUkWfAsmMp+ONUKgyyQIea3ujRWF9pL8B1r+wi+CuVSvTt2xfjx4+HLMvo0KEDQkNDsXbtWgBAp06dcP/992Pfvn0YNmwY1Go1Bg8ebLr/l19+iWPHjiEzMxODBg3C888/j4ceeshWu0NEREQVxDhjPHBgp8U2HqxLxTLzjVhxPAX/O5mKfIOMqBpe6NZIj6pealuXZlOSsLR43klcvXrV1iUA4NfQjoRj6Tg4lo6DY+kYeHYex1JRn8vsAiN+P5GC30+kIqdQRutqnujRWI9Qb025P5e9svulPkREREQ3E7IMeWCMxTbFuG8gBYVYbCPnk1NoxB8nU7HieAqyCmQ8GOqBHo30Dn1qzrvB4E9ERER2hbP7VFb5Bhl/nErF8mMpyMg3okWwO3o09kctXwZ+Sxj8iYiIyC7cLvBz2RYVKzDKWH06DUuPJiMtz4imVdzxQmM96updbV2aXWPwJyIiIpsROdmQX+9hsU3x5c+Q3D2sXBHZs6wCI9adScPK46lIyTWgUaAb3m2rR/0AN1uXVikw+BMREZHVcTkP3YmrGQX438kU/HM2HXkGgQYBrhjeqgoaB7nburRKhcGfiIiIrIaBn8pKCIFD13Kw6kQK9lzJhlIBtK3uhSfu8+Ua/rvE4E9EREQVSsRdhjx2sMU2xczlkBRKK1dE9izfIGPz+QysOpGKC+n58NYo8XwjP3SurYPOldH1XvDVIyIiogrB2X26Eym5Bvx1KhWrT6chI9+IGj4aDH0wCO1qeEGtVNi6PIfA4E9ERETlqtTAXy0MyjFfWrUWsn+xyXlYdSIFWy5mwCgDLUI88ERdHRoFukGSJFuX51AY/ImIiOieiQM7Ic8Yb7GNs/t0swKDjH/PZ+DPU6k4lpgLrUqBzrV16FJXhyqealuX57AY/ImIiOiucTkP3YkLafn4+0waNp+PRXqeAYEeLnileQA6hnnDXc1jPSoagz8RERHdsdICvxT9JBTd+lm5GrJnOYVGbLmQib9j03AqOQ8qBdCulh7tQl3RONANSgWX81gLgz8RERGVifz7zxCrfrHYxtl9upEQAieScvF3bDq2XsxAnkGgmrcafZsFoENNL4SFBPEqzDbA4E9ERES3xOU8VFZpeQZsPJeOv2PTcTmjAFqVAm2re+HhcB/U8dPyYF0bY/AnIiIii0pdztPvTSgio6xcDdkroyxwMD4ba2PTsftKJgwyUFfviqEPBqF1NS+4uvBUnPaCwZ+IiIhMjJ+OBE4fs9jG2X0qJoTAqeQ8bLmQga0XMpGca4CXRokudXSIDvdBNW+NrUskCxj8iYiIiMt56LaEEDibmo8tFzKw5UImErILoVJIaFbVHa/U8MIDIZ5wUXIpjz1j8CciInJSQpYhD4yx2KZ4/wtI1cOtWxDZHSEELqTlY8uFTGy5mIG4zEIoJaBpFXf0aKzHAyEe8OBpOCsNBn8iIiInw9l9up3L6UVh/98LGbicUQCFBDQMdMMz9f3wYKgnvDQM+5URgz8REZGTYOCnW4nPLDDN7J9LzYcEoH6AK7rUDUSrUE/4uDI2VnYcQSIiIgcm8nIgD+1usU0x5UdIXjorV0T2ongZz45LWdhxORPnUvMBFJ2Rp1/zALSq5gk/NxcbV0nlicGfiIjIAXF2nywxygInk3Kx41Imdl7OQnxWISQA9fxd0aeZP1qFeiHAg2HfUTH4ExERORAGfrpZoVHGofgc7LhcFPbT84xQKSQ0CXLDsw388ECwB5fxOAmOMhERUSUn4i5DHjvYYpvi22WQVPzv3tnkFBqx90o2dlzOxN4r2cg1yNCqFIgIdkdkiCcigt3h5sIDdJ0NfxIQERFVUpzdp2JCCFzJLMDBuBzsvZqFg/E5MMgC3hol2lT3xIOhnmgS5AYXJa+i68wY/ImIiCqZUgO/Ugnld8utWwzZTFquAQfjs3EgPgcH47ORnGMAAAR5uOCxOj54MNQT9+ldoVTwolpUhMGfiIioEpB3boKYPcViG2f3nUOeQcbRa0Uh/2B8Ds6nFZ2Fx0OtQOMgdzQJckPTIHcEeaptXCnZKwZ/IiIiO8blPM7LKAvEpuQVBf24bJxIyoVBBlwUEuoFuOKlpv5oEuSGMJ2Ws/pUJgz+REREdqjUwF+nAZRvT7RuMWQVRlngbGoejiXk4khCDo5ey0F2oQwACNNp8ERdXzSt4o56/q7QqLhWn+4cgz8REZGdkH+YBrF1ncU2zu47nkKjjNjkvKKQn5CL44m5yDMUBf0qni5oWc0TTYPc0TjIDd5aRja6d3wXERER2RiX8ziHfIOME0m5OHo96J9KykWBUQAAqnmr0aGmFxoEuKF+gCuvmEsVgsGfiIjIRkoL/FKX56GIedHK1VB5EkIgJdeAMyl5OJ6Yi6MJuYhNzoVRAAoJqKnT4NHaPkVB398VXpzRJyvgu4yIiMiKjMNfBLIyLLZxdr9yEkLgWlYhzqTm4WxKPs6m5OFMah7S84wAAKUEhPu54ql6vmgY4Ib7/F3hrubFs8j6GPyJiIisgMt5HINRFriaWYAzKXk4m5KHs6n5OJuah+yCorX5Cgmo5q1B86oeCNNpUMtXizBfLbQ8GJfsAIM/ERFRBRFCQB7wlMU2xbAPIDVqbuWK6E5k5htxMT0fF9PycTE9H2dT8nEuNQ/519fluygk1NBp0Kaa1/WAr0F1Hw3UvDou2SkGfyIionLG2f3KJafQiEvpBaaAX/R3AVJyDaY+rioFauo0eDjcpyjk6zQI8dZAxfPnUyVyR8E/JycHV69eRV5entn2hg0blmtRRERElREDv33LN8i4nHFzwM9HQvZ/AV+tlBDqrUHTKm4I9dagurcG1Xw00LupIEkM+VS5lTn4b9y4EXPmzIFWq4Va/d+loCVJwvTp0yukOCIiInsncnMgD+tusU0x4XtI/kFWrogKjUXr8C+k/RfuL6bn41pWIeSiVTpQKSSEeKlxn94NncLVqHY94Ae4u/AquOSwyhz8Fy1ahBEjRuD++++vyHqIiIgqBc7u255RFojLKjAtzSkO+VczCnB9GT4UElDVU42aOi3a1/BGqI8a1b01qOKpZsAnp1Pm4C/LMpo0aVKRtRAREdk9Bn7ryiowIjG7EAnZhci5XIBz11KRkG1AfFYBLqUXwHB9Cl8CEOjhguo+GkSGeKK6jwbVvNUI9lLDhQfbEgG4g+D/1FNPYenSpXj22WehUPADREREzkNcPgf5o9cttilmLIGk1li5IscghEB6nhHXsgtN4T7R9G8DErMLkVMom91HrZTg7+6CIA8XNA1yRzUfDap5axDqrYaGp8wkuqUyB/8//vgDaWlp+P333+Hh4WHW9u2335Z7YURERLbG2f17Y5SLrl6bkPVfqE+4Idgn5RSioHhNznXuLgr4u7sgwN0FDQPdEOCuMt2+LzQQhux0HmRLdJfKHPyHDh1akXUQERHZDQb+2xNCILNARmquAam5hhLBPjG7EEk5BtPBtMV8tEr4u7ugpk6DB0I8EODuAn931fW/XW55RVudmxpJOQz9RHerzMG/fv36FVkHERGRTck7NkDMmWqxzZnCvlEWSMszIDXXWBTq8wxIuR7uU3P/+3dangEG81U4UEiAn2vRDH19f7eimXqPokDv766Cv5sLl+MQ2VCZg7/BYMCyZcuwefNmpKamQqfToV27dnjmmWegUvE6YEREVDk5y+x+vuG/2fmUvOIgbzRtKw74GXlGCAv399IooXNVQeeqQoiXGjpXFXyv39a5FoV6PzcVz5RDZMfKnNh/+uknnDlzBv3794e/vz8SExOxdOlS5OTkoHfv3hVYIhERUfkrNfBrtFBO/9W6xdyFAqOMzHxj0Z+C63/nF23LyC8K9f8FfEOJg2SBohl6nbYouOvdVKjtpy0K8lrzUO+jVcFFyUBPVNmVOfjv2LEDn332GTw9PQEAVatWRc2aNfH2228z+BMRUaUg//AVxNZ/LLbZanbfKAtkFhiRdXOIL/gvyJvaC4zIuN7v5oNib6RWSqbgXt1Hg6ZV3OGrVcHHVWkW6L00Sih4oCyR0yhz8Bei9B8w5eHAgQOYN28eZFlGx44dERMTU+L5582bh/3790Oj0WDw4MEICwsr032JiMi5VdRyHiEE8gwCeQYZeQYZuYUycq//fePtLFOwl82CfVa+EdkWZuKLKSTAU62Ep6boj97NBTV1WnhplPBQK0zbb+zjqVZyHT0RWVTm4N+yZUtMnjwZzz33HPR6PZKSkrB06VK0bNnynouQZRlz5szB+++/Dz8/P4waNQoREREICQkx9dm/fz/i4+Mxbdo0nD59GrNnz8aECRPKdF8iInJOxv5P4toNtw2SArlKDfJbRiP/yZeLQnpctimkm4V3g4y8wpv+NrUL5BbKyDfIFtfDW+KuVpgCupdGiWAvNTw0Snhd32YpyLu5KHjqSiIqN2UO/i+++CKWLl2KOXPmmA7ubd26NZ599tl7LiI2NhZBQUEIDAwEALRq1Qq7d+82C+979uxBu3btIEkS6tSpg+zsbKSmpiIxMfG29yUiospHFsUz6cIUyvNumFG/MYibBfZd25CrVCNPqUFuixHIU6qRq9QgT6mBQXHDf3t/nCv1uSUAWpUCWhcFXFUKuLpI0KoU0GlVqOJZtM3UplLA1UVxU//rfa5vc3dR8KBXIrK5Mgd/lUqFbt26oVu3buVeREpKCvz8/Ey3/fz8cPr06RJ99Hq9WZ+UlJQy3bfYunXrsG7dOgDApEmTzB7PllQqld3UQveGY+k4OJb3ThYCWfkGpOcakJ5XiIw8AzLyzP+dkVeIrHwjcguNyCk0Irfghn/fYgnMzdRKBbS5GXA15kPr6getMR9uxjz4FhRt03V6Am5qJdxclHB1UcBNrYSri9LsbzeX6+3Xl8pw7bv94efScXAsbeOWwf/YsWOm8/cfOXKk1H4NGza8pyIsHT9w81ebpfUpy32LRUdHIzo62nQ7KSnpTkutEMVLp6jy41g6Do7lf4QQyDXIprPGZF0/wDTLdCaZG88q89/2rILSl8EoJMBdrYSnWmEK4z5qBaq4q6/PlEumWfQb/y6eRTfNsKskqIc+B5Uo+UuC1Hc4FC073GYs5et/CgEDIBuA7Fwgu5xeOypf/Fw6Do5lxalatWqpbbcM/nPmzMGUKVMAAN9++63FPpIkYfr06fdQXtEsfXJysul2cnIydDpdiT43vkGK+xgMhtvel4iIipR6CkgLwb043GcVGEtcqOlGrioFPDUK09r0QA8XeNywlr1o/fp/tz3USrir721G3VnOvU9EVJ5uGfyLQz8AzJgxo8KKqFWrFuLi4pCQkABfX19s27YNw4YNM+sTERGB1atXo3Xr1jh9+jTc3Nyg0+ng5eV12/sSETmqQqNcdBGm6xdfSrt+ZdX0vP+CfdYdnAKy+KBSD40Sod6aokB/0xljitu91Eq4q5VWPb87Az8R0d0r8xr/Tz/9FO+8806J7Z9//jneeuuteypCqVSib9++GD9+PGRZRocOHRAaGoq1a9cCADp16oT7778f+/btw7Bhw6BWqzF48OBb3peIqLIqXlpTfFXVlNz/LsJUfNXVtOv/ziywsMQFMM20O8IpIEV+HuTXnrfYpvhwOqTgatYtiIiokipz8D969Ogdbb9TzZo1Q7Nmzcy2derUyfRvSZLQr1+/Mt+XiMjeCCGQmW8sCvJ5pYT663/nW5iZVykk6LRK6FxVqOKpRoMAN9OFmHxNV1hVwkercogzyHB2n4iofN02+C9evBgAYDAYTP8udu3aNfj7+1dMZURElUx2gRGJ2YVIyC5EYrbh+t9Ff1JyDUjLM1hcK++qUkDnWhTow/20RUFeqyoR6j3UznFOdwZ+IqKKcdvgX3zgrCzLZgfRAkVHZD//vOWvX4mIHIkQAhn5RiSYgn0hErINpmCfkF2I7JuW3bgoJPi7q6B3d0EjLzezEF/8bx+tCq4u9rnExppE3CXIY4dYbFN8/QskrZuVKyIicjy3Df7Fa+nr1KljdipMIiJHlJlvxNXMAuxNSsCJK8mIyyzA1cxCXM0oQO5N0/WuKgUC3F3g767CfXpXBHi4XL9d9Le3Vslzwd8GZ/eJiKynzGv8XVxccOHCBVSvXt207fz587h48SLatWtXIcUREVWEnEIj4jMLcSWjAHGZBbiSWWAK+Jn5RlM/hQT4u7ugiqcaD4V5oYqn2izYuzvJ0puKwMBPRGR9ZQ7+ixcvxqeffmq2Ta/X49NPP2XwJyK7k2+QEZ9ViKuZBbiaUYCrxeE+owCpeUazvn5uKlT1VKNVqCeqehUF/QbVAqEpzIKLkstwyovYuxXyd5Mttim+X8lfooiIKliZg39ubi7c3MzXWLq5uSE7m9c3JCLbKDQKXMsuQFzG9YB//U9cRgGScgxmV4311ioR7KlGs6oeqOqpRlUvF1T1VKOKp9riaSz1vm5ISsqx3s44MM7uExHZhzIH/5CQEOzYsQOtWrUybdu1axdCQkIqpDAiohtl5htxNjUPZ1LycC4lH2dS8xCXWQD5hnTvoVag6vXTXFbxUhcF/Osh381FabvinRQDPxGRfSlz8O/ZsycmTpyIbdu2ISgoCPHx8Th8+DBGjRpVkfURkRNKzTXgTEoezqbk4UxqHs6m5CMhu9DU7u+mQpivFq2reV4P9kUz914ahntbkxfPhlhnOdQz7BMR2VaZg/99992HKVOmYMuWLUhKSkJ4eDh69+4NvV5fkfURkQMTQiAhuxBnU/OLQv71sH/jGvyqnmrU0WvRubYPwny1CPPVMuDbIc7uExHZvzIHf6DoYN6YmJgKKoWIHJksBK5mFuBsSv4NM/l5yLp+7nuFBIR6a9C0ijtqXQ/4NXUaLtGxc6UFfunB9lC8MsLK1RAR0a3cMvjPnDkTAwcOBAB8/fXXpZ5x4bXXXiv/yoio0jLIApfT83EmJQ9nUvNxLiUPZ1PzkXf9PPgqhYQaPhq0quaJMJ0WtXy1qO6jsXiQLdkf44dDgSsXLLZxdp+IyH7dMvgHBASY/h0UFFThxRBR5SMLgcvpBTiWmIOzKUVh/0JaPgqvH3WrVUmoqdOiYy1v1NJpEOarRai3BioFT91Y2XA5DxFR5XbL4P/000+b/t21a9cKL4aI7J9RFjiflo8j13JwNCEHxxJzTRe98lArEOarRZe6uuvLdTSo4qGGkiG/Uit1Oc8rw6F4sIOVqyEiort1y+B/5MiRMj1Iw4YNy6UYIrI/hUaB2JRcHE3IxbGEHBxPzEVOYdGSnSAPFzwQ7IEGAa6oH+CGIA8XXoTJQXB2n4jI8dwy+H/77bdmt1NSUiBJEjw9PZGZmQkhBPz8/DB9+vQKLZKIrCffIONUci6OXsvF0YQcnEjKRYGxaNlOqLca7Wp4oUGAG+oHuELv5mLjaqm8MfATETmuWwb/GTNmmP69bNkyZGVloVu3btBoNMjPz8fixYvh6elZ4UUSUcURQuBKRgH2XM3C3ivZOJaYC4MsIAGoqdPgkXAfU9D31t7RicCokhCFhZAHP2uxTfHBNEghNaxbEBERVYgy/y/+xx9/YObMmVCpiu6i0WjwwgsvYODAgWbHAhCR/cs3yDiakIM9V7Kw92o24rOKLo5V3VuDx+vq0CjQDff5u8JDzVNpOjLO7hMROZcyB3+tVovY2Fjcd999pm1nzpyBRqOpkMKIqHwlZhdeD/pZOBifgwKjgFopoUmQG2Lq+aJ5VQ8EeHDpjjNg4Ccick5lDv7dunXDhAkT0Lx5c/j5+SE5ORn79u3DK6+8UpH1EdFdMsgCJxNzTUt4LqTnAwACPVzwcC1vRAR7oEGAG8+d7yREciLkkZZ/Xiu+WgTJzd3KFRERkbWVOfi3a9cOYWFh2LFjB1JTUxEcHIxnn30WISEhFVkfEd2BnEIj9l3Nxo5Lmdh3NRvZhTKUElA/wA19avkjoqoHgr3UPPOOE+HsPhERFbujI/VCQkLwzDPPID09HTqdrqJqIqI7kJZnwK7LWdhxKRMH43NgkAW8NUo8GOqJiGB3NK3iDjcXrtV3Ngz8RER0szIH/+zsbMyePRs7duyASqXCggULsGfPHsTGxqJ79+4VWSMR3eRaVgF2XCoK+yeSciELIMBdhc51fNAyxBP3+bvyollOSJw8Avnz9yy2Kb5fyW96iIicXJmD/6xZs+Du7o5vvvkGI0aMAADUqVMH8+fPZ/AnqmBCCFxIyy8K+5czcS61aL1+dR8Nujb0w4Mhnqip0zDYOSnO7hMRUVmUOfgfPnzY7HSeAODl5YX09PQKKYzI2QkhcDIpD9svZWLHpUzEZxVCAlBX74re9/vjwVBPVPFU27pMsqFSA7+7J5RfLrRuMUREZPfKHPzd3NyQmZlptrY/KSmJa/2JyllcZgE2nkvHxnMZiM8qhEoBNA50xzP1/fBAiAd0rryIljOT166AWDLXYhtn94mI6FbKnCA6duyIKVOmoHv37hBC4NSpU1i0aBEefvjhiqyPyClk5hux5UIGNp7LwImkXEgAGgW5oVsjPSJDPODOC2k5PS7nISKie1Xm4P/UU0/BxcUFc+bMgdFoxLfffovo6Gg89thjFVkfkcMqNArsvZqFjefSsftKNgyyQDVvNV5u6o+oml7Qu/FiWlR64JfadoLi5desXA0REVVmZQr+sizjm2++wcCBA9GlS5eKronIYQkhcCo5DxvOpmPLhQxkFsjw1irRuY4PHqrpzQN0CQBgnPYxcHiPxTbO7hMR0d0qU/BXKBQ4dOgQAwnRXbqWVYCN5zKw8Vw6rmYWQq2UEBnigQ41vdG0ijtPvUkAuJyHiIgqVpmX+nTp0gW//vornn/+ebMz+xCRZQVGGdsvZuLvM+k4fC0HANAw0A3PNvBDq2qevKgWmZS6nKfPG1C0esjK1RARkaMqc4JfvXo10tLS8Mcff8DLy8us7dtvvy33wogqq3Opefg7Ng0bz2cgu0BGoIcLejbWo0OYN/zduW6finB2n4iIrK3MwX/o0KEVWQdRpZZdYMTm8xnY8PdlnEzIgotCQstQTzwc7o2GgW5QcJkcXcfAT0REtlLm4F+nTh0sXboUW7duRWpqKnQ6HVq1aoVnnnmmIusjsltCCBxLyMXfZ9Kw9WImCowC4Xo39I8IQFQNb3hquJSHigiDAfKrln9WKt6fCql6LStXREREzqjMwX/WrFm4evUq+vTpA39/fyQmJmLFihWYPXs2Bg8eXJE1EtmV1FwD1p9Nx7oz6biaWQA3FwUeCvNGdC1vPFgnBMnJybYukewEZ/eJiMielDn47969G19//TXc3d0BACEhIahduzaXAJFTEELgQHwO/jqVit1XsiALoL6/K7o2rIJW1TyhVSkAgGe+IgAM/EREZJ/KHPx9fHyQn59vCv4AUFBQAJ1OVyGFEdmDfIOMjecysOpkCi6lF8Bbq0RMPV90rOWNEC+NrcsjOyIy0yGPeMlim+LLhZDcPa1cERERkbkyB/927dphwoQJePTRR+Hn54fk5GSsWbMG7dq1w5EjR0z9GjZsWCGFEllTck4h/jyVhjWnU5FZICNMp8HrLaugbXVPuCgVti6P7Ahn94mIqLIoc/D/+++/AQDLly8vsb24TZIkTJ8+vRzLI7KuU0m5WHUiFVsvZkAAiAzxwBP3+aK+vyuX8ZAZBn4iIqpsyhz8Z8yYUZF1ENmMURbYfikTv59IxcmkXLi5KPB4XR261NUh0ENt6/LIjogLsZA/GWGxTTFzBSQFvw0iIiL7xUvwktPKzDfi79g0/HEqFUk5BgR5uKB/RAAeCvPmVXXJDGf3iYjIETD4k9O5klGAVSdSsP5sOvKNAo0D3TCwRSAigj14oS0yU2rgD6gC5fiZ1i2GiIjoHjH4k9O4kJaPJUeSsPViJpSShKiaXniirg41dFpbl0Z2RN65CWL2FFyz0MbZfSIiqswY/MnhxSbnYcnRJOy4lAWtSoGn6/niyXq+8NHy7U//4XIeIiJydEw+5LBOJObi1yNJ2Hs1G+5qBbo38sPjdX3hqeH6ffpPaYHf7akeyH+8h5WrISIiqjgM/uRQhBA4kpCDX48k41B8Drw0SrzUxB+d6/jAXc3AT0XkX2ZB/LPKYlvx7L6nXo/8pCRrlkVERFShGPzJIQghsD8uG0uOJONYYi50WiX6NgvAI7V9oFXxFItUhMt5iIjImdk8+GdlZWHq1KlITEyEv78/hg8fDg8PjxL9Dhw4gHnz5kGWZXTs2BExMTEAgO3bt2PJkiW4cuUKJkyYgFq1all5D8iWhBDYdSULS44k43RyHvRuKgyICER0LW9oGPjputICvzTgHShatLFyNURERLZh8+C/YsUKNGrUCDExMVixYgVWrFiBF1980ayPLMuYM2cO3n//ffj5+WHUqFGIiIhASEgIQkND8dZbb+H777+30R6QLQhRdNGtxYeTcT4tH0EeLhgSGYQONb3houQpOQkwTnwbOHvSYhtn94mIyBnZPPjv3r0bH374IQAgKioKH374YYngHxsbi6CgIAQGBgIAWrVqhd27dyMkJAQhISHWLpls7ERiLubuS8DJpFwEe6nxRssqaFfDC0oFAz9xOQ8REVFpbB7809PTodPpAAA6nQ4ZGRkl+qSkpMDPz89028/PD6dPn77j51q3bh3WrVsHAJg0aRL0ev1dVl2+VCqV3dRiz66m5+Hbreex/nQS/NxcMLJjOB6rH2hXgZ9jaRtClpHwrOUlO35fLoCq+p0vAeRYOg6OpePgWDoOjqVtWCX4jxs3DmlpaSW2d+/evUz3F0KU2CbdxRVWo6OjER0dbbqdZCdn7NDr9XZTiz3KKjDityPJWHUyFQoJ6NbID0/X84OriwKpKcm2Ls8Mx9K6yjK7nwYAdzEmHEvHwbF0HBxLx8GxrDhVq1Yttc0qwX/MmDGltnl7eyM1NRU6nQ6pqanw8vIq0cfPzw/Jyf8FvOTkZNO3BOS4DLLAmtNpWHQ4CVn5RjwU5o2eTfTwc3OxdWlkY1zOQ0REdOdsvtQnIiICmzZtQkxMDDZt2oQWLVqU6FOrVi3ExcUhISEBvr6+2LZtG4YNG2aDaskais/U8+P+RFzJKEDjIDf0uT8AYb5aW5dGNiRycyAPs/wtoeLLhZDcPa1cERERUeVi8+AfExODqVOnYv369dDr9RgxYgSAonX9M2fOxKhRo6BUKtG3b1+MHz8esiyjQ4cOCA0NBQDs2rULc+fORUZGBiZNmoQaNWpg9OjRttwlugdnUvIwd18CjlzLQYiXGmPah6B5Vfe7WtpFjoGz+0REROVDEpYW0DuJq1ev2roEAFznBgBJOYX46UAiNp7LgKdGiR6N9egU7gOVHR24WxYcy/Jj68DPsXQcHEvHwbF0HBzLimPzNf5EpckzyFh6NBkrjqdACODp+r54roEf3NVKW5dGNiASrkIePchim2LmckgKvi+IiIjuFoM/2YQQArsuZ2HWnmtIzDGgXXUvvNhUj0APta1LIxuw9ew+ERGRM2DwJ6uLzyzArD3XsOdqNqr7aDCxdVXUD3CzdVlkA6UG/obNoHz9Q6vWQkRE5OgY/MlqCo0ylh1LwW9Hk6GQJPRtFoAudXWVbh0/3Rtx+hjkT0dabOPsPhERUcVh8Cer2B+Xje93x+NqZiHaVPdE32YBPB+/k+FyHiIiItti8KcKlZRTiLl7E7D1Yiaqeqrx0UOhaFrF3dZlkRWVFvil7v2h6PiElashIiJyXgz+VCEMssCqEyn45XASZAH0bKLH0/V84aJU2Lo0sgJ5x0aIOV9YbOPsPhERkW0w+FO5O5qQg+92xeNiegFaBHugf0QAz9bjJLich4iIyH4x+FO5Scsz4Id9CdhwLgMB7iq8FxWMyBBPW5dFVlBa4Fe8NR5S3UZWroaIiIgsYfCneyYLgbWxaZh/IBH5BhnPNfDD8w39oFFxWY8jk//4FWLFTxbbOLtPRERkfxj86Z5cTM/HNzvjcTwxF40C3TCoRSBCvDW2LosqEJfzEBERVU4M/nRXCowylhxJxrJjyXBVKfB6yyroUNMLksRz8jsiIQTkAU9ZbFNMnAVJH2jlioiIiOhOMfjTHTt8LRvf7LyGq5kFaF/DC32bB8Bby7eSI5K/mwyxd6vFNs7uExERVS5Ma1RmmflG/LA/AevOpCPIw4Xn5HdgpS7nkRRQfr/CqrUQERFR+WDwp9sSQuDfC5mYvfcaMvONeKa+L7o30vPgXQcjCgsgD37OYpti2i+QXN2sXBERERGVJwZ/uqVrWQX4btc17IvLRm0/LT56KBQ1dVpbl0XlyPjuK0BKosU2LuchIiJyHAz+ZJFRFvj9RAp+PpQEhSShX/MAPFZHB6WCB+86ilKX89RpAOXbE61bDBEREVU4Bn8q4XRyLmbsjMe51Hw8EOKBARGB8Hd3sXVZVA5ERirkN3tZbFPMXA5JobRyRURERGQtDP5kklsoY+GhRPxxMhXeWhVGtg3Gg6EePEWnA+C594mIiIjBnwAAe65k4btd8UjMMeDR2j54uak/3NWc/a3sSgv8UvSTUHTrZ+VqiIiIyJYY/J1cSq4Bs/dcw9aLmQj1VmPSw9VQL4Bnb6nMxNWLkD94zWIbZ/eJiIicF4O/k5KFwN+x6fhxfwIKjAI9m+jxdD0/uCi5rKey4nIeIiIiuhUGfyd0MT0f3+yMx/HEXDQKdMOrDwQh2Ett67LoLpW6nOeVEVA82N66xRAREZHdYvB3IgVGGb8dTcbSo8lwVSkw7MEgPBTmzYN3KyFx8gjkz9+z2MbZfSIiIrKEwd9JHLmWg292xeNKRgGianihb/MA+Gg5/JUNl/MQERHR3WLyc3BZ+Ub8sD8Bf59JR6CHCz58KBT3V3G3dVl0h0oL/Ir3pkCqWdvK1RAREVFlxODvoIQQ+PdCJmbvvYbMfCOeqe+L7o300KgUti6Nykgc3gN52scW2zi7T0RERHeKwd8BJWYX4ttd8dh7NRu1/bT4sEMowny1ti6LyqjU5TyublBO+8W6xRAREZHDYPB3IEIIrI1Nx7x9CZCFQL/mAXisjg5KBQ/etXdCCMgDnrLYpvj8R0jeOitXRERERI6Gwd9BxGcWYPrOeBy+loPGgW547cEgBHrwFJ32Tt61GWLW5xbbuJyHiIiIyhODfyVnlAX+PJWKBQcSoVRIGBIZhIdr8RSd9q7Uc+93fAKK7v2tXA0RERE5Awb/Suxyej6+3hGPE0m5aF7VHYMjg6B3c7F1WVQKYTRCHvS0xTbFjCWQ1BorV0RERETOhMG/EjLKAiuOp2DRoSRoVBKGt6qCqBpenOW3U/KaZRC//WCxjct5iIiIyFoY/CuZ86l5mLYjHmdS8tAy1AMDWwRB58phtEelLufp1g+K6NIvxEVERERUEZgYK4lCo8DSo8lYcjQJ7i5KvNO2KlpX87J1WXQTkZdb+sW2Zi6HpFBauSIiIiKiIgz+lUBsch6m7YjDhbR8RNXwQr/mAfDScujsifzTNxCbViPBQhuX8xAREZE9YHq0Y4VGGYsOJWH58RT4aFUYHRWMB0I8bV0W3aDU2f1XR0Jq1srK1RARERGVjsHfTp1JycOX267iYnoBomt5o0+zAHiouUzEHoiMNMhvvmyxLXD5NiQlJVm5IiIiIqLbY/C3MwZZ4Lejyfj1cBK8tCqMbR+C5sEeti6LABg/Hw2cPGyxjct5iIiIyN4x+NuRi+n5+HJbHM6k5CGqhhf6RwTCU8NZflsrdTnPu5MhhdezcjVEREREd4fB3w4YZYHlx5Kx8GASXF0UeLdtVbTiGXtsSsRfgTzmVYttnN0nIiKiyojB38biMgswZsNhHLqagcgQDwyODIIPz9hjM8Y3XwYy0ko2ePtC+fkP1i6HiIiIqNwwYdqIEAKrT6dh3r4EuCgVeKNlFbSvyavv2kqpy3nGfQspKNjK1RARERGVPwZ/G0jMLsT0HXE4EJ+DplXc8UHn+lDkZ9q6LKcjYo9BnjzSYhuX8xAREZGjYfC3IiEENpzLwKw91yALgVcfCMQj4T7w99QgicHfakqb3Ue9JlCOGGfdYoiIiIishMHfStJyDfhmVzx2Xs5CfX9XvN6yCoI81bYuy2kIISAPeMpim2LKfEhePtYtiIiIiMjKbB78s7KyMHXqVCQmJsLf3x/Dhw+Hh0fJ89YfOHAA8+bNgyzL6NixI2JiYgAACxYswN69e6FSqRAYGIjBgwfD3d3dyntxazsvZ+LrHfHIK5TRt1kAHq+rg1LBtfzWIPZuhfzdZIttXM5DREREzsTmwX/FihVo1KgRYmJisGLFCqxYsQIvvviiWR9ZljFnzhy8//778PPzw6hRoxAREYGQkBA0btwYL7zwApRKJX766ScsX768xP1tpcAo44d9CfjjVBrCdBoMb10V1bw1ti7LKZS2nEdq/xgUPQdZtxgiIiIiO6CwdQG7d+9GVFQUACAqKgq7d+8u0Sc2NhZBQUEIDAyESqVCq1atTP2aNGkCpbLoIld16tRBSkqK9Yq/hUvp+Xh79QX8cSoNT92nw6ePVGfor2BCNsLY/0mLoV8x/VcoZ/3O0E9EREROy+Yz/unp6dDpdAAAnU6HjIyMEn1SUlLg5+dnuu3n54fTp0+X6Ld+/Xq0atWq4ootAyEE1p1Jx6w916BVKTCmfQgigksuXaLyI69bCbF4jsU2LuchIiIiKmKV4D9u3DikpaWV2N69e/cy3V8IUWLbzee7X7ZsGZRKJdq2bVvq46xbtw7r1q0DAEyaNAl6vb5Mz19WmfkGfPpPLNafTkJEqDfGPFIXevfbH8CrUqnKvRZncO1py7/kefR6De4xL1i5miIcS8fBsXQcHEvHwbF0HBxL27BK8B8zZkypbd7e3khNTYVOp0Nqaiq8vLxK9PHz80NycrLpdnJysulbAgDYuHEj9u7di7Fjx97yAljR0dGIjo423U5KSrrTXSnVicRcTNl6FUk5hXipqT+eqe8L5GYgKff299Xr9eVaiyMT+fmQX+tqsU3x3XJISiVyAeTa6PXkWDoOjqXj4Fg6Do6l4+BYVpyqVauW2mbzpT4RERHYtGkTYmJisGnTJrRo0aJEn1q1aiEuLg4JCQnw9fXFtm3bMGzYMABFZ/tZuXIlPvroI2g01l9DLwuBZUdTsPBQIvRuLpjUqTrq6l2tXoejkxd9D7H+fxbbuJyHiIiI6PZsHvxjYmIwdepUrF+/Hnq9HiNGjABQtK5/5syZGDVqFJRKJfr27Yvx48dDlmV06NABoaGhAIA5c+bAYDBg3LiiCy/Vrl0bAwYMsErtyTmF+HJ7HA7F56BNdU8MfiAI7mqlVZ7bWZR6dp4Bb0PRovRlXURERERkThKWFtA7iatXr971ffdcycJX2+OQb5AxoEUgOoZ533KZ0a3w6y5zIjsL8huW1+grvl9516+zNXAsHQfH0nFwLB0Hx9JxcCwrjl0v9alsCo0yfjyQiFUnUlHDR4O32lRFKE/TWS7kX2ZB/LOqZIOHJ5RTF1q/ICIiIiIHwuB/By5n5GPKlqs4m5qPLnV16H2/P9RKm18KodIrbTmPYvQUSDVqW7kaIiIiIsfE4F8GshD442Qq5h9IhEYp4b2oYESGeNq6rEpNpKVAfru3xTYerEtERERU/hj8b+NaVgGm7YjHkWs5iKjqjiEPVoGvK1+2uyX/vRLi15IX25Jad4Si9+s2qIiIiIjIOTDBlkIIgb/PpGPO3gRIAIY+GHRPB/A6u1KX80yeC8mXF/AgIiIiqmgM/hYk5xRixs547L2ajUaBbhj2YBUEeLjYuqxKR6QkQX63r8U2LuchIiIisi4G/xsIIfDvhUzM3B2PAqNA/4gAPFZHBwVn+e+IvOxHiL+Wltgu9R4GRetoC/cgIiIioorG4H9dep4B3+2+hm0XM1FXr8XrLasi2Ett67IqlVKX83y9GJKWVzMmIiIisiUGfwA7L2Vixq54ZBfIeLmpP2Lq+UKp4Cx/WYjUZMjv9CnZ4K2D8vMfrV8QEREREVnk1ME/q8CI2XuuYcO5DNTUafDxQ1VQQ6e1dVmVgrz+fxCLvi+xXTFkNKSmkTaoiIiIiIhuxamD/7A/ziE114DnG/rh+YZ6uCg5y38rQgjIw7oDebkl2hTfLoOkcuq3ExEREZFdc+qk5qpSYNQj1VHbj+vPb0UkXIU8elCJ7VLn56B45mUbVEREREREd8qpg/8XnWtAo1LYugy7Ja/6BeL3n0tsV3w8A1KVUBtURERERER3y6mDP0N/SUI2Qh74dMkGhaJoOY+CrxkRERFRZeTUwZ/+Iy6ehTzujRLbpef6QPGIhV8EiIiIiKhSYfB3cvIvsyD+WVViu2LSbEh+ATaoiIiIiIgqAoO/ExKFhZAHP1uywde/KPDzSsVEREREDofB34mI08cgfzqyxHap11Ao2jxsg4qIiIiIyFoY/J2APOtziF2bS2xXTPkRkpfOBhURERERkbUx+DsokZcLeWi3kg3Vw6F8/wvrF0RERERENsXg72DE4b2Qp31UYrvi1ZGQmrWyQUVEREREZA8Y/B2E8YsxwPGDJbYrvloEyc3dBhURERERkT1h8K/ERHYm5Dd6lmxoFAHlsLHWL4iIiIiI7BaDfyUk79oMMevzEtsVwz+GVL+p9QsiIiIiIrvH4F+JGMcOAeIuldiumL4EkkZjg4qIiIiIqLJg8LdzIi0F8tu9S2yXWneEovfr1i+IiIiIiColBn87JW/4E+Ln70psV7z3OaSadWxQERERERFVZgz+dkQIUXSwbk5WiTbFt8sgqThcRERERHR3mCTtgCH+Coyvdi2xXXrkaSie62ODioiIiIjI0TD425D8x68QK35C8k3bFR9OhxRczRYlEREREZGDYvC3MiEbIQ982mKbYuYKSAqFlSsiIiIiImfA4G8l4vI5yB+VPAuP9GwvBLw4EElJSTaoioiIiIicBYN/BZMXz4FYt7LEdsWE7yH5B9mgIiIiIiJyRgz+FUAYCiG/+mzJBi8fKD7/EZIkWb8oIiIiInJqDP7lSJw5AXnSOyW2Sy8NgaLdIzaoiIiIiIioCIN/OZDnTIXYsaHEdsXnP0Ly1tmgIiIiIiIicwz+d0nk50F+7fmSDaE1oRz7lfULIiIiIiK6BQb/OySO7IP81YcltisGvgMpoo31CyIiIiIiKgMG/zIyfvkBcHR/ie2KL3+G5O5hg4qIiIiIiMqOwf8WRHYW5DdeKNnQ4H4o3/jI+gUREREREd0lBn8LxJWLkCe8CRTkm21XvPERpAb326gqIiIiIqK7x+B/A3njnxALvyuxXTF9CSSNxgYVERERERGVD6cP/iIvF/L3nwGH95htVwx+D9L9D9qoKiIiIiKi8uXUwd/Y/0nzDQFVoHhzPCRfvW0KIiIiIiKqIE4d/ItJDz8F6bnekBRKW5dCRERERFQhnDr482BdIiIiInIWClsXYEsM/URERETkLGw+45+VlYWpU6ciMTER/v7+GD58ODw8Sl4Q68CBA5g3bx5kWUbHjh0RExMDAPjll1+wZ88eSJIEb29vDB48GL6+vlbeCyIiIiIi+2bzGf8VK1agUaNGmDZtGho1aoQVK1aU6CPLMubMmYP33nsPU6dOxdatW3H58mUAwJNPPonPP/8cn332GZo1a4bffvvNyntARERERGT/bB78d+/ejaioKABAVFQUdu/eXaJPbGwsgoKCEBgYCJVKhVatWpn6ubm5mfrl5+dDkiTrFE5EREREVInYfKlPeno6dDodAECn0yEjI6NEn5SUFPj5+Zlu+/n54fTp06bbixYtwubNm+Hm5oYPPvig1Odat24d1q1bBwCYNGkS9Hr7OG2nSqWym1ro3nAsHQfH0nFwLB0Hx9JxcCxtwyrBf9y4cUhLSyuxvXv37mW6vxCixLYbZ/Z79OiBHj16YPny5Vi9ejWef/55i48THR2N6Oho0+2kpKQyPX9F0+v1dlML3RuOpePgWDoOjqXj4Fg6Do5lxalatWqpbVYJ/mPGjCm1zdvbG6mpqdDpdEhNTYWXl1eJPn5+fkhOTjbdTk5ONn1LcKM2bdpg0qRJpQZ/IiIiIiJnZfM1/hEREdi0aRMAYNOmTWjRokWJPrVq1UJcXBwSEhJgMBiwbds2REREAADi4uJM/fbs2XPL33KIiIiIiJyVzdf4x8TEYOrUqVi/fj30ej1GjBgBoGhd/8yZMzFq1CgolUr07dsX48ePhyzL6NChA0JDQwEACxcuRFxcHCRJgl6vx4ABA2y5O0REREREdkkSlhbQO4mrV6/augQAXOfmSDiWjoNj6Tg4lo6DY+k4OJYV51arX2y+1IeIiIiIiCoegz8RERERkRNg8CciIiIicgIM/kRERERETsCpD+4lIiIiInIWnPG3AyNHjrR1CVROOJaOg2PpODiWjoNj6Tg4lrbB4E9ERERE5AQY/ImIiIiInACDvx2Ijo62dQlUTjiWjoNj6Tg4lo6DY+k4OJa2wYN7iYiIiIicAGf8iYiIiIicgMrWBTiLrKwsTJ06FYmJifD398fw4cPh4eFRot+BAwcwb948yLKMjh07IiYmxtT2119/YfXq1VAqlWjWrBlefPFFK+4BFSuPsQSA33//HT/99BNmz54NLy8vK1VPN7rXsVywYAH27t0LlUqFwMBADB48GO7u7lbeC+d1u8+YEALz5s3D/v37odFoMHjwYISFhZXpvmRddzuWSUlJmDFjBtLS0iBJEqKjo/HYY4/ZZicIwL19LgFAlmWMHDkSvr6+PPNPRRBkFQsWLBDLly8XQgixfPlysWDBghJ9jEajeO2110R8fLwoLCwUb731lrh06ZIQQojDhw+Ljz/+WBQUFAghhEhLS7Na7WTuXsdSCCESExPFJ598Il599VWRnp5urdLpJvc6lgcOHBAGg8H0WJbuTxXjdp8xIYTYu3evGD9+vJBlWZw8eVKMGjWqzPcl67mXsUxJSRFnzpwRQgiRk5Mjhg0bxrG0oXsZy2KrVq0SX375pZg4caI1S3caXOpjJbt370ZUVBQAICoqCrt37y7RJzY2FkFBQQgMDIRKpUKrVq1M/dauXYunnnoKLi4uAABvb2/rFU9m7nUsAeDHH39Ez549IUmS1eqmku51LJs0aQKlUgkAqFOnDlJSUqxXvJO73WcMAPbs2YN27dpBkiTUqVMH2dnZSE1NLdN9yXruZSx1Op1pttjV1RXBwcH8HNrQvYwlACQnJ2Pfvn3o2LGjLcp3Cgz+VpKeng6dTgcA0Ol0yMjIKNEnJSUFfn5+ptt+fn6mH2BxcXE4ceIE3nvvPXzwwQeIjY21TuFUwr2O5Z49e+Dr64saNWpYpV4q3b2O5Y3Wr1+Ppk2bVlitZK4s45KSkgK9Xl+iT1nHlKzjXsbyRgkJCTh37hzCw8MrtmAq1b2O5Q8//IAXX3yRk2IViGv8y9G4ceOQlpZWYnv37t3LdH9h4QRLxW9+WZaRlZWF8ePH48yZM5g6dSqmT5/OD0cFqaixzM/Px7Jly/D+++/fa4lURhX5uSy2bNkyKJVKtG3b9q5qpDtXlnEprU9Z7kvWcy9jWSwvLw9TpkxB79694ebmVv5FUpncy1ju3bsX3t7eCAsLw9GjRyusRmfH4F+OxowZU2qbt7e36WvJ1NRUiwdz+vn5ITk52XQ7OTnZNBvp6+uLyMhISJKE8PBwKBQKZGZm8qDQClJRY3nt2jUkJCTg7bffNm1/9913MXHiRPj4+JT7flDFfi4BYOPGjdi7dy/Gjh3L8GhFtxuX4j5JSUkl+hgMhtvel6znXsYSAAwGA6ZMmYK2bdsiMjLSOkWTRfcyljt27MCePXuwf/9+FBQUIDc3F9OmTcOwYcOsVr8z4FIfK4mIiMCmTZsAAJs2bUKLFi1K9KlVqxbi4uKQkJAAg8GAbdu2ISIiAgDQokULHDlyBABw9epVGAwGeHp6Wm8HyORexrJatWqYPXs2ZsyYgRkzZsDPzw+TJ09m6LeRe/1cHjhwACtXrsS7774LjUZj1dqd3a3GpVhERAQ2b94MIQROnToFNzc36HS6Mt2XrOdexlIIge+++w7BwcF4/PHHbbQHVOxexvKFF17Ad999hxkzZuCNN95Aw4YNGforAC/gZSWZmZmYOnUqkpKSoNfrMWLECHh4eCAlJQUzZ87EqFGjAAD79u3Djz/+CFmW0aFDBzzzzDMAimY0vvnmG1y4cAEqlQovvfQSGjZsaMtdclr3OpY3GjJkCCZOnMhvbmzkXsdy6NChMBgMplOA1q5dGwMGDLDZ/jgbS+Oydu1aAECnTp0ghMCcOXNw8OBBqNVqDB48GLVq1Sr1vmQ7dzuWJ06cwNixY1GtWjXTN249evRAs2bNbLk7Tu1ePpfFjh49ilWrVvF0nhWAwZ+IiIiIyAlwqQ8RERERkRNg8CciIiIicgIM/kREREREToDBn4iIiIjICTD4ExERERE5AQZ/IiIqYePGjWYXP3vppZdw7do1qz1/UlISXnrpJciyXOHP9fzzzyM+Pr7Cn4eIyNYY/ImIKrkhQ4bg0KFDFfocCxYsQGBgYIU+x430ej0WLFgAhYL/TRERlRf+RCUicnBGo9HWJRARkR1Q2boAIiK6e19//TWSkpIwefJkKBQKPPfcc2jZsiVee+01DBo0CEuWLEFAQAA++ugjfPHFFzh+/DgKCgpQo0YN9OvXD6GhoQCKrmL8zTff4NixY6hatSqaNGli9jzPP/88pk2bhqCgIMyYMQMajQaJiYk4fvw4QkJCMGzYMAQFBQEADh48iLlz5yItLQ1t27bFpUuX0K5dO3Ts2LFE/bGxsZg9ezbi4uKgVqvRpk0b9OrVCwkJCXjttdewaNEiKJVKJCQkYMaMGTh37hxq166NKlWqICcnB8OGDTP1HTx4MBYvXoyCggJ06dLFdDXe2NhYzJs3D1euXIFarUZkZCR69eoFlYr/BRKRc+GMPxFRJTZ06FDo9Xq8++67WLBgAZ566ilT27FjxzB16lSMHj0aANC0aVNMmzYNs2fPRs2aNTFt2jRT3zlz5sDFxQUzZ87Eq6++ig0bNtzyebdu3YquXbti3rx5CAoKwi+//AIAyMjIwBdffIEXXngBc+fORdWqVXHq1KlSH2fevHl47LHH8OOPP+Lrr79Gy5YtLfb76quvUKtWLcydOxddu3bFv//+W6LPiRMn8NVXX2HMmDH47bffcPnyZQCAQqFAr169MGfOHHzyySc4cuQI1qxZc8v9IyJyRAz+REQOqmvXrtBqtVCr1QCAhx56CK6urnBxcUHXrl1x4cIF5OTkQJZl7Ny5E926dYNWq0W1atUQFRV1y8eOjIxEeHg4lEol2rRpg/PnzwMA9u/fj5CQEERGRkKpVKJz587w8fEp9XFUKhXi4+ORkZEBrVaLOnXqlOiTlJSEM2fOoFu3blCpVLjvvvvQvHlzi/urVqtRo0YNVK9eHRcuXAAAhIWFoU6dOlAqlQgICEB0dDSOHTtWxleRiMhx8HtOIiIH5efnZ/q3LMtYtGgRduzYgYyMDEiSBKBohr6goABGo9Gsv7+/P44fP17qY98Y5jUaDfLy8gAAqampZo8jSRJ8fX1LfZxBgwZh8eLFGD58OAICAvDcc8+VCPUpKSnw8PCARqMxbdPr9UhKSipTTVevXsX8+fNx5swZ076GhYWVWhMRkaNi8CciclDF4R4AtmzZgj179mDMmDHw9/dHTk4O+vTpAwDw8vKCUqlEcnIygoODAaBEqC4rHx8fpKSkmG4LIcxu36xKlSp44403IMsydu3ahS+++AJz5swx66PT6ZCVlYX8/HxT+L+T+mbPno0aNWrg9ddfh6urK/744w/s2LHjDveMiKjy41IfIqJKzsfHBwkJCbfsk5ubC5VKBQ8PD+Tn52PRokWmNoVCgQceeABLlixBfn4+Ll++jE2bNt1VLc2aNcPFixexa9cuGI1GrFmzBmlpaaX237x5MzIyMqBQKODm5maq50b+/v6oVasWlixZAoPBgFOnTmHv3r1lrik3Nxdubm7QarW4cuUK1q5de1f7RkRU2TH4ExFVcjExMVi6dCl69+6N33//3WKfqKgo+Pv7Y9CgQRgxYgRq165t1v7KK68gLy8PAwYMwIwZM9C+ffu7qsXLywsjRozAwoUL0bdvX1y+fBlhYWFwcXGx2P/AgQMYMWIEXnrpJfzwww94/fXXTcck3Gjo0KE4deoU+vbti19++QWtWrUq9TFv9tJLL2HLli14+eWXMXPmTLRq1equ9o2IqLKThBDC1kUQEZFjkmUZr776KoYOHYqGDRuW2+NOnToVwcHBeP7558vtMYmIHB1n/ImIqFwdOHAA2dnZKCwsxPLlyyGEsHi2njsRGxuL+Ph4yLKMAwcOYM+ePWjRokU5VUxE5Bx4cC8REZWrU6dOYdq0aTAYDAgJCcHbb79tcfnOnUhLS8OUKVOQmZkJPz8/9OvXDzVr1iyniomInAOX+hAREREROQEu9SEiIiIicgIM/kREREREToDBn4iIiIjICTD4ExERERE5AQZ/IiIiIiInwOBPREREROQE/g8AJq4r2SuWCgAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "figsize(12.5, 6)\n", + "from scipy.optimize import fmin\n", + "\n", + "\n", + "def stock_loss(price, pred, coef = 500):\n", + " \"\"\"vectorized for numpy\"\"\"\n", + " sol = np.zeros_like(price)\n", + " ix = price*pred < 0 \n", + " sol[ix] = coef*pred**2 - np.sign(price[ix])*pred + abs(price[ix])\n", + " sol[~ix] = abs(price[~ix] - pred)\n", + " return sol\n", + "\n", + "std_samples = trace.posterior[\"std\"].data\n", + "alpha_samples = trace.posterior.alpha.data\n", + "beta_samples = trace.posterior.beta.data\n", + "\n", + "N = std_samples.shape[1]\n", + "\n", + "noise = std_samples*np.random.randn(N) \n", + "\n", + "possible_outcomes = lambda signal: alpha_samples + beta_samples*signal + noise\n", + "\n", + "\n", + "opt_predictions = np.zeros(50)\n", + "trading_signals = np.linspace(X.min(), X.max(), 50)\n", + "for i, _signal in enumerate(trading_signals):\n", + " _possible_outcomes = possible_outcomes(_signal)\n", + " tomin = lambda pred: stock_loss(_possible_outcomes, pred).mean()\n", + " opt_predictions[i] = fmin(tomin, 0, disp = False)\n", + " \n", + " \n", + "plt.xlabel(\"trading signal\")\n", + "plt.ylabel(\"prediction\")\n", + "plt.title(\"Least-squares prediction vs. Bayes action prediction\")\n", + "plt.plot(X, ls_coef_*X + ls_intercept, label =\"Least-squares prediction\")\n", + "plt.xlim(X.min(), X.max())\n", + "plt.plot(trading_signals, opt_predictions, label =\"Bayes action prediction\")\n", + "plt.legend(loc=\"upper left\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "What is interesting about the above graph is that when the signal is near 0, and many of the possible returns outcomes are possibly both positive and negative, our best (with respect to our loss) prediction is to predict close to 0, hence *take on no position*. Only when we are very confident do we enter into a position. I call this style of model a *sparse prediction*, where we feel uncomfortable with our uncertainty so choose not to act. (Compare with the least-squares prediction which will rarely, if ever, predict zero). \n", + "\n", + "A good sanity check that our model is still reasonable: as the signal becomes more and more extreme, and we feel more and more confident about the positive/negativeness of returns, our position converges with that of the least-squares line. \n", + "\n", + "The sparse-prediction model is not trying to *fit* the data the best (according to a *squared-error loss* definition of *fit*). That honor would go to the least-squares model. The sparse-prediction model is trying to find the best prediction *with respect to our `stock_loss`-defined loss*. We can turn this reasoning around: the least-squares model is not trying to *predict* the best (according to a *`stock-loss`* definition of *predict*). That honor would go the *sparse prediction* model. The least-squares model is trying to find the best fit of the data *with respect to the squared-error loss*.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Example: Kaggle contest on *Observing Dark World*\n", + "\n", + "\n", + "A personal motivation for learning Bayesian methods was trying to piece together the winning solution to Kaggle's [*Observing Dark Worlds*](http://www.kaggle.com/c/DarkWorlds) contest. From the contest's website:\n", + "\n", + "\n", + "\n", + ">There is more to the Universe than meets the eye. Out in the cosmos exists a form of matter that outnumbers the stuff we can see by almost 7 to 1, and we don’t know what it is. What we do know is that it does not emit or absorb light, so we call it Dark Matter. Such a vast amount of aggregated matter does not go unnoticed. In fact we observe that this stuff aggregates and forms massive structures called Dark Matter Halos. Although dark, it warps and bends spacetime such that any light from a background galaxy which passes close to the Dark Matter will have its path altered and changed. This bending causes the galaxy to appear as an ellipse in the sky.\n", + "\n", + "\n", + "\n", + "\n", + "The contest required predictions about where dark matter was likely to be. The winner, [Tim Salimans](http://timsalimans.com/), used Bayesian inference to find the best locations for the halos (interestingly, the second-place winner also used Bayesian inference). With Tim's permission, we provided his solution [1] here:\n", + "\n", + "1. Construct a prior distribution for the halo positions $p(x)$, i.e. formulate our expectations about the halo positions before looking at the data.\n", + "2. Construct a probabilistic model for the data (observed ellipticities of the galaxies) given the positions of the dark matter halos: $p(e | x)$.\n", + "3. Use Bayes’ rule to get the posterior distribution of the halo positions, i.e. use to the data to guess where the dark matter halos might be.\n", + "4. Minimize the expected loss with respect to the posterior distribution over the predictions for the halo positions: $\\hat{x} = \\arg \\min_{\\text{prediction} } E_{p(x|e)}[ L( \\text{prediction}, x) ]$ , i.e. tune our predictions to be as good as possible for the given error metric.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The loss function in this problem is very complicated. For the very determined, the loss function is contained in the file DarkWorldsMetric.py in the parent folder. Though I suggest not reading it all, suffice to say the loss function is about 160 lines of code — not something that can be written down in a single mathematical line. The loss function attempts to measure the accuracy of prediction, in a Euclidean distance sense, such that no shift-bias is present. More details can be found on the metric's [main page](http://www.kaggle.com/c/DarkWorlds/details/evaluation). \n", + "\n", + "We will attempt to implement Tim's winning solution using PyMC and our knowledge of loss functions." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### The Data\n", + "\n", + "The dataset is actually 300 separate files, each representing a sky. In each file, or sky, are between 300 and 720 galaxies. Each galaxy has an $x$ and $y$ position associated with it, ranging from 0 to 4200, and measures of ellipticity: $e_1$ and $e_2$. Information about what these measures mean can be found [here](https://www.kaggle.com/c/DarkWorlds/details/an-introduction-to-ellipticity), but for our purposes it does not matter besides for visualization purposes. Thus a typical sky might look like the following:" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Data on galaxies in sky 3.\n", + "position_x, position_y, e_1, e_2 \n", + "[[ 1.62690e+02 1.60006e+03 1.14664e-01 -1.90326e-01]\n", + " [ 2.27228e+03 5.40040e+02 6.23555e-01 2.14979e-01]\n", + " [ 3.55364e+03 2.69771e+03 2.83527e-01 -3.01870e-01]]\n" + ] + }, + { + "data": { + "image/png": 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xMfbu3Ysrr7wSIpGo22NnZGRAIBDglVdeQUlJCTZv3ow77rjDb6Zut9txxRVXYNWqVbjhhhuwfv166PX6Tt5Cc+fOxahRo3DPPffg8ssvR0xMTK+vTSwW4/rrr8fhw4dx6NAhXHfddRg/fjyWLFkCAHjggQfw1Vdf4emnn0ZxcTG++OILrFu3DnfffTf4fD7OnTuH+++/H7t27UJFRQX27t2LnTt39njjy8rKwtatW1FbW4umpqYu97n99tthNBrxhz/8ASdOnMCuXbtw7bXXYu7cub4KtL7w4IMP4uuvv8aZM2dw9uxZfPLJJ5BKpb5l246MGjUKGo0G77zzDkpLS/Hhhx/itdde6/P5WvnTn/4EjUaDm266CadOncLmzZvx4IMP9vq8xx57DN999x3uvPNOFBYWoqSkBD///DNuuOEG2Gy2fo+jPU8//TQ2bNiAU6dO4U9/+hMaGhrwpz/9CYB36ZVhGKxevRq7d+9GWVkZfvzxR2zcuLHLY2VkZICmaWzYsAGNjY3dTkZ6ez2tRSZbtmxBWVkZjhw5gp9//rnH78+9996LgoIC3HXXXTh9+jR+/vln/PnPf8bVV1/d7efaFXv27MHjjz+O/fv3o7KyEps3b8axY8e6PPc999yDp59+GqtXr8ZPP/3k2y6RSHDNNdfg7rvvRnp6uu930x3btm3D66+/jqNHj6KyshK7d+/GpZdeCpqmsXbtWt9+5513Hh544IE+vxbCMCJk2WMEwhCh0WjY++67j83Ly2OFQiErFArZ0aNHs3/961/9km63bdvG5ufnswKBgB05ciT75ZdfsiNGjGAfeeQR3z4dk+b/97//sTk5OaxAIGAnTpzIbtu2zS+x/JZbbmGzsrLY5uZm33N27tzJcrlc9rvvvvMb54svvsgCYPfs2dPra2pNcP7oo4/YjIwMls/ns4sWLWLPnTvnt9/777/P5uXlsTwej01OTmb//ve/+5LBa2tr2bVr17IpKSksn89nk5KS2BtvvNFvrOiQKL5x40Y2Ly+P5fP5bOvlo6sk571797Lz5s1jhUIhGxMTw1555ZVsQ0NDp/G3p2My92OPPcaOHTuWlUgkrFwuZ+fPn8/u3Lmzx/floYceYuPj41mxWMyuWLGC/fTTT/2O2TFJnWVZtqqqqlPxwG+//caOGzeO5fP57NixY9nNmzf3mjTPsiy7Y8cO9rzzzmOlUikrFovZvLw89o477vC95wNNmv/uu+/YyZMns3w+nx09ejT7888/+533zJkz7Jo1a1i5XM6KRCI2Pz+f/emnn7o8Jsuy7DPPPMMmJyezNE2zCxYs6HJsvb0em83GXnnllWxmZiYrEAjYuLg49vLLL2crKyt7fI9++ukn32tRq9XsLbfc4pfw35ek+RMnTrArVqxgExISWD6fz6anp7P33HOPr1Cjq2O89tprrEAgYL/99lvfttZijieffLLHMbMsy+7bt4+dP38+q1KpfOe8+uqr2aKiIr/9MjIyBl3sQYhOKJYdZL0ygUAICPfddx82btyI48eP97rvunXr8PHHH+PcuXNDMDJCqNi2bRsWLVqEqqoqpKamhno4UceGDRuwZs0aVFZWIjExMdTDIUQ5nU1LCATCkGIwGHD8+HGsX78eL7zwQqiHQyBEPVarFZWVlXjsscdw1VVXEbFFGBJIDheBEGJWr16NpUuXYvXq1fj9738f6uEQCFHPs88+i3HjxoGmaTz77LOhHg5hmECWFAkEAoFAIBCCDIlwEQgEAoFAIAQZIrgIBAKBQCAQggwRXAQCgUAgEAhBZthUKdbW1gb9HGq1ulszSEJkQj7T6IJ8ntEF+Tyjj2j4TJOTk7vcTiJcBAKBQCAQCEGGCC4CgUAgEAiEIEMEF4FAIBAIBEKQIYKLQCAQCAQCIcgQwUUgEAgEAoEQZIjgIhAIBAKBQAgyRHARCAQCgUAgBBkiuAgEAoFAIBCCDBFcBAKBQCAQCEGGCC4CgUAgEAiEIEMEF4FAIBAIBEKQIYKLQCAQCAQCIcgQwUUgEAgEAoEQZIjgIhAIBAKBQAgyRHARCAQCgUAgBBkiuAgEAoFAIBCCDBFcBAKBQCAQCEGGCC4CgUAgEAiEIEMEF4FAIBAIBEKQIYKLQCAQCAQCIcgQwUUgEAgEAoEQZIjgIhAIBAKBQAgyRHARwgqXhwn1EAgEAoFACDjcUA+AQNDZ3PjwSCMO1VpgdniQrRLi9hmJyFYJQz00AoFAiAg8DIudFUbI+BxMSZGGejiELiARLkJIsbo8eODXCmwtM8Lk8IAFUKKz48W9dUM6jjqTE7+ea0aJzj6k5yUEFp3NjTNNNtjdJFJKGF78eq4ZL+ypw2PbqvHolio4yG8g7CARLkKvsCwLiqKCcuzj9VbUm12dtssFnKCcryNWlwev7a/HrgoTWACJUh7eXD1iSM5N6B8ehgXDsuBxup4nbjrXjDcONsDNsBByadw8LQGLs2MGfD69zQ0pnwMeJzjffQIhkNSanL7/F9RZ8P6RRtw8LTGEIyJ0hAguQrfY3Qxe3V+P3RVGxEl4+L+pCZga4FB1plIAKZ+G2dk2G4sVc3HLtISAnqcrDHY3Ht5chYpmh2+bWsIL+nkJ/WdjsR4fFmrg8rC4c04S5qTLO+3z32NNcDMsAO939z/76pCpEPR7afq3kmZ8VaRFrckFLk3hsnGxuGK8OiCvg0AIFkkyvt/jn88246JRKiTL+d08gzDUkCVFQrd8frwJO8qN8LBAvdmFf++qhcnhCeg5EqR8PL8iE9dMUGNVnhJ3zk7CqyuzkRojCOh5OuJhWDy61V9sAcDvxsUG9byE/nOg2oQ3DjbA6mLgYlh8flzb5X50h0CUh/XO9PvDbyXNeGVfPWpN3qirm2Hx32NNaLa5BzT2aOFAtQmNXUSiCeHDwiw5ZPy2WzrDAodrzSEcEaEjRHARuuV4g9Xvsc3N4LTGFvDzJEj5uGycGjdMScDCrBiIeMH/Wm4rM6BE5y+2rpmgRn6iJOjnJvSPzzoIrO7ys37XRRQqpZ+z+x9O6zttk/BoSPjD91K5p9KIf26vwW0/luJEh2sCIXwQ8zi4qcMSotVF8rjCCbKkSOiWJBkfZ7X2DtuiY8ntSLvIB5cGbpySgBUjlSEcEaErGJbtVMgwKalrUbw0RwEhl8Yv55phdHiwOFuOWWmyfp0vVsxFebuoJ5cGbp+Z2G3eWKShsbhQrneAQ3vFaIK0d0F6qmWS5fSweGZnDd5aPaLbSZHdzeC/x5qQFyfq93tPGDzzM+WgKeCDI41weFjMzei89E4IHURwEbrlqnw1jjdYoW9ZTrlwlDLoS31Dxax0GfR2DzIUAqzIVSAtSl5XtEFTFBRCDprt3qVsMY/G5T0s+87LlGNe5sBvMvfMTcZXRTpUGRxIixFg6YgYJMqiIwfm3cMN+K5DBC9ewsXKUSqsHKUEp+OabAtsu/8bHR5sKzN0Ozn5sFCDn87ogVPAwwtTA57zSeiduRnyiBZaZocbTVYXYkXcoBVrhQoiuAjdkiTj481V2ShqtEIp4iJLGT2+WHPS5V0mXhPCj1unJ+LDQg1ixVzcMCUBseLgRVnFPA6unRgXtOOHktNNndMBGi1uvFvQiN0VRkxOkUJndYPHoZAo5WFxdgwkfA7y1CL8gDahtr/a3K3gOlzTljP0wZFGIrj6iNnhgYBLRU0kdaB8XaTFx8fOwMOwSJXz8bf5KVE1GSaCi9AjAi6NycnkokkIHTPSZJhBlqcGzV9mJuHfu2tRpnd0+tsZrR1nOqQPaCwu/HFKAqYkSyHi0rC15M6V6bv3qrO0yxmqNDhhtLshF5LbTHecaLDi/SONOKe1g0NTWJGrwB+nxIOOsshOX6g3OfFhocYXUa02OvHk9mq8dlF21ES6hrecJhAinDqTE0fqLDhQbUJRg5UYfhK6JTVGgOfOz8Qt0xIg6aUwZWy8CJeO8xYhiHg0Lspri2gJud0/Vy32F1cNFlLZ2B11Jice31aNs1o7WHgrYn84o8ex+uFZmGBsMb5uT63JBW0UVQiTqQeBEIGU6+34z/76TkUNQi6FZTkKXD95eM6SCT3DoSlUNDv8IlGtCDgUxiWIccFIZaelwCvGq1FrcmJXhQkze4g25qlFfhE0DvkOdsuhGnOXEyReN7l00c4IlRCpcj6qjW0GrjFCDmIE0SNToueVDFNcHhZGhxuqKEwwJHTPawcaOoktALC7WXx/Wo+8OBHJUSN0yQ1T4jEmXoxGswsuhkGsmIcMhQDZSmG3rvocmsK9c1Pwx8kuqETd3zbWjlFhc6kBTg+LGAEHmcroyb8JNOkKASj4FyVMS5FgTLwoVEMKKRyawrrFafjwuB7HagyIk/Bw87SEqOr0QARXmGB1eVBvciFewoO0j21tmm1u3PNzOTRWN9Ji+LhzdjJGkIbPw4K1Y1R4eW9dlz47Y+JE3VonEAg8Do35A6zk7K1gIUHKx0MLU/HdKR1Wj1aRKGsPTEiU4IH5KdhSZgDDAjNSpViQGTOsJ85xEh6eWjkGTU1NoR5KUCCCK8SU6+34qFCDgjoLGNbrln11fhwu7YPj+ZYyAzRW7/p2lcGJJ7ZV49WLsiDmDU0fwu4wOzwwOjxIkvGG9cUjmMxKk2FiogQHa8y+SIWYx8HYeDFyYonoJoSOCYkSTCAGwn2CFIR0DcuyOFBtRrpC0KllUSRDBFcIqTY48OBvlX59BBkW+PGMrk+Cq2OGoc7mxq/nmrFm9NC3p3F5GPx6zoAfz+h9TVTHxYvwz6UZQz6W4YKIN/BIRaTTaohaorOj0eyCmMdBllKAKcSGgECIeEr1Djy5o8YbgJgQh0vHRkfLNSK4QsimEoOf2GplVnrfZjyz0mX45JgG7fMum6xDX9FR1GDFy/vqUN+h15ooxJE2QvTh8rD47rQO357UwtTFb+fycbG4ekJ0+GhZXR78XNyMokYr9HY3LE4GIh4NuYCDOAkPyTI+MhQC5CeKwR/m/k2E6KI1b4thgY8KNXB6GFyVH/m/ayK4QshItRA05f1SAd7qlKU5MbhxSkKfnp8k4+P6yfFYf6jRt21cvDgYQ+2WokYr1m2tgtPjH26Ll3Bx87S+vQ4CoS+wLIsntlWhsJuyeZoCRqmjJ+H4ZKMNHx3V+K4P3SHkUpieKsPvxsciVU6S1AmRT3qMAPESHhpbbEW+OK7FmDgxJkZ4bioRXCFkTrocaRcKUNxkg5BLIz9B3G+TwJWjVMhTi3Goxox0Bb/Hku3uONFgxa4KI1aPVvV7vfz1A/WdxNb0VClumpqAOEl09F0khAdOD4uzuq5NN0fHiXD95PioElxTU6R44rx0/HBGj4JaMxyerpWX3c1iR7kRh2rMeP/iHAh68MkiECKFtWNUePNgAwBv9szrB+rxysqsiI7mEsEVYtJjBEgfZOuCnFjhoBKl3zhYjyqDE/uqzXju/Ix+tU5JkvFRbXBCIeRgdLwYq0YpMXqIo2yE4YGAS+M/K7Oxv8oEjcUFAZdGipyPMfHiHq0KIhWGZfG/Ii3K9HZMT5ViQqIETg+LWpMTepsbJocHHpaFXMBFnISLuRnyYSe29lfo8d+DVeDQFC4dG4vc2OgR3MOd83MV2F5m9LWkqje7sKfShIVZMSEe2cCJvqsUod9oWsK2epsb7xxuxH3zUvr83AcXpIJhWVL+TRgSVCJut338wpkX99TiTJMds9NluHiMChJ+7/mNNheDI3UWAMDOChMO1lhw79xkXDgq8l5/MKg0OPC3jeW+CHtBrQX/Pj8TGQqyrBoN0BSFe+cl464N5TA4vM3rD9dYIlpwDel0iGEY3HfffXj66acBAGazGY8//jj+8pe/4PHHH4fZ3Nb49JtvvsGf//xn3HHHHSgsLPRtLy0txd13340///nPePfdd8GyvSQ4EHpF1G5WvK/KhOZ+tlIgYovQX4x297BpQ+T0MNhaZkStyYkvi7T4+6ZKmFpuID0h4XOQ2y5ybXczeHpHDYoahmfrl44cqbX4pTM4PSx+K2kO3YAIAUct5uHJZelIj/Gmujg83V8zNBYXvirS4oMjjShsmaiEG0MquDZs2ICUlLboybfffovx48fj5Zdfxvjx4/Htt98CAKqrq7Fnzx48//zzePDBB/HOO++AYbxv9Pr163HzzTfj5ZdfRn19vZ8YiyScHgaHaswo6SYnZSjJb+eZ42GBgjD9sg4VTVYXqo0OMETMBxSGZfHTGT2u//ocrv3qHH7/5VnsrjSGelhBh8+hkShtW6Yvb3bg37tq+jRZ/P3EOLRfJXQxLN4taOz+CcOIrpaRh4uIj1RcHhZ7K03QWvveYzNVLsDzK7Lw4IIUXD85vst99DY37tpYjg8LNfj6pA6PbKnCw5sr4epBoIWCIRNcWq0WBQUFOO+883zbDh48iAULFgAAFixYgIMHD/q2z549GzweD/Hx8UhMTMS5c+eg1+ths9kwcuRIUBSF+fPn+54TaXx6tAmPb6vGXRvLcdfGctSZnL0/KUgszPL3cqo1hm4soURrdeGvG8pwwzcluO2HMlz9v7N4clMx9FHUPDVUuDwsHtlShbcONUDX8n46POywidacn6vwe1xYb8XeKlOvz8tPlODuOcngt2tvck5nR7OdfCdnpkkxKr5tssihgPOyFaEbEKFXjtSZ8fTOGtzyfSl+Pqvv8/N4HG8lbndFXWV6O4wdosbH6q34/nTfzzEUDJngev/993HNNdf4OY8bDAYold58BKVSCaPRO9vV6XSIjW0zOlOpVNDpdJ22x8bGQqfTDdErCCzOdrXeJTo77v25HOX60ES7JiVJMK5d/y7hMEu8baWg1uLXeNfqYvDTyUbc/XN5v2ZkhM58caIJxzrYOdAUMG+YGLeuHq3C5A4l7QdrzN3s7c/sdDmeWZaBGalS8DkURqgEkBCPO/A4NP5zST6umxiHVXlKPLEkHXlxJGk+nJG25C46PSxeP9CAr4q0ATnuSLUICmHn30QoAxldMSRJ84cPH0ZMTAyys7NRVFTU6/7dhdr7k6/122+/4bfffgMAPP3001Cr1X1+7kDhcrl9Ps9lU4T46Uyb+jY5GTy5sw6fXzcFvBCUvT6zRoEHfjiFs01mXDAhHWrl8LtwnS+W4/MiHTRm/x+p1urGIY0HV09JCtHIIhuj3YWDtRV+27g0hT/Pz8K80clDOpb+/EYDzb8vVmHdz8XYUeK9ycjE4j6PRa0Gpo9MDebwIhIul4ubFowK9TAIfWRuLIv4vfVobLnGfnxUgwkZ8ZiZ2VYIMpDfqBrA82sleGbzOZxp9E5k4qR8/H5WNtSx4ePdNSSC68yZMzh06BCOHDkCp9MJm82Gl19+GTExMdDr9VAqldDr9ZDLvbPd2NhYaLVtylen00GlUnXartVqoVKpujznkiVLsGTJEt/joWiGqVar+3weJQVcna/GJ8fa9m8wOfB9QRkWhKgK4/HFyXB5WPA8FjQ1Dc88rmeWpmH9oUYcrDHD3RKFjBFwkCsbmu9QtPFVkRafHW+C08OCAiDh08hPlOB342KRqeQP+Xvan99oMLh7ZhxW5UpR0ezAjFQZ+U4NklB/nj3RaHahVG+H08MiSylA2iDtf6KFS8eo8NqBegBe0+9//noGr6zM8kW/BvqZxtLAs0tT0WB2wuxkkKEQgMva0NRiKzGUJCd3PZEcEsF11VVX4aqrrgIAFBUV4YcffsBf/vIXfPTRR9i+fTvWrFmD7du3Y9q0aQCAqVOn4uWXX8bKlSuh1+tRV1eHnJwc0DQNkUiE4uJi5ObmYseOHTj//POH4iUEhcvHqyHk0XivoNHnJq0Lcb4QjzO8Kw5jxTz8bX4K7G4GZTo7YlVKKChbRJvthYqfzujxYaHG95iF18zzztlDG9UKN3JjRcQvKooparRi/aEGv/QEAPjd+NioaE8zWJbnKrC3yuSzPNHZ3Pj5bHPA+iUmSPkI1x4nIfXhWrNmDV544QVs2bIFarUad911FwAgLS0Ns2bNwl133QWapnHDDTeApr03vBtvvBGvvfYanE4nJk6ciEmTJoXyJQyaVXkqTE+RYke5EU4Pi6UjFKEeEgHePLbR8WKo1TI0NTl6f8IAMTs9eHFPHY7UWZAi5+OB+Sn9dvsPV7aUGjptkwlI7hEhejlWb8EjW6q6bMd0tin0Fenhwj1zknHPL+WoM3lzY3eWG6OmQXVPUOwwMbKqra0N+jmCFd62ujx4r6ARsWIeLhsbCw49vKNQQ0mwlyye2FbtlzydnyDG40vS/fbxMCx+PKPHWa0No9QiLM9VRETE7eHNlX6J8nIBB88uzwipoAznJShC/wm3z/Otg/X4qbi50/ZEKQ8PLUwly4rt0FpdeG53LYoabYgRcPDhpbkAwu8zHQghXVIkDI6Pjzbh13PeaAFNAZePC03SLyGw2N1eL7b2VHVhyfHZ8SZ8ccKbu7izwoS9VSY8sSQ97A1nb5ySgDcO1KPB7EJOrBB/nByPxCiJ3hEIXfG78WpYnAxON9ngZlhkKASYnCzBedkKiHjhP0kaSmLFPDyxJB0FtRYMlxgCEVwRwL7KNr+er4t0uHRsbNjfbAm9w7AsKApoH2NudVRuz5EORrRFjTbsrzJjVnr/G5UPJRkKAZ5alhHqYRAIQ0aMkIs75wzvHMX+QFMUpqZIQz2MIYNI7gjA3s4t1+ZmfOvehMhGzONgcXZbRaqET3fppJzZRW+4cPOXIRAIBELPkAhXBCAXcGBxtokus7P3PmyEyODW6YmYkCiB2enB9FQp1GJep31+N16Nw7UWXwUrlwamDKNZIYFACE8K6yzITxSTFZc+QgRXGMOwLL47pUODuS2iRQGIk3S+KRPCk4pmBz4/3oRqgxNJch6WZCswLbVNLHFoCvN7cVuPk/Dw0oVZ+O1cM/R2N+ZlyJHRRdQrmNSZnFCLecPeNoRAGEqsLg8OVpsxJUXq86kKF3aUG/Hc7lpid9EPiOAKUxiWxQt76rCj3L+579QUaZdNWwnhh83F4OHNlTDYvRHJCoMD+6vM+MusJL+lxL4gF3BwcYjKpl/YU4ttZUakyvl4alkG5MTagUAYEj491oQfTuuRKOXh2eUZiBGGz7V/R7m3kOunM3r8bpyaVM/3AZLDFaa8caDBT2zNSZfh9hmJuHcuSciMFEwOj09stcIC+PFMeDVU7Ylj9RZsK/N+D6uNTvzvRGSXaxMIkUSpzuvdVW924eW9dSEejT/ntN6xmZ0MihqHRxP6wUIEVxiyoViPX841+x7HCDj445R4LM1RQDBMG0tHIvFSHqands61SpNHjjXC6Q5tMU5phr5NBoEwXGmfG3W41oJqQ/BMmPuLqV0ucU0XdjaEzpC7d5hRb3LincMNvsccCrhnbnKXydSE8Of+eSn4/cQ4jIsXIUMhwNrRKtw8PbiNJ7aUGvDS3tpOy9GBwN2VhTaBQAgKeeq2FlAsgD1Vpu53HmLai8FQt6SLFMJnQZgAAPhfkRbuloJEmgL+OjsZ+Ynh0+2c0D+4NIVLxsbikiHKv/q4UIP/FXlNUreWGqEUcTA+YeDfn46WFOnEKZtAGDLmZMh8v2cAODPACDPLsthbZUKD2YWlOYqAJOCLeTScHm+Uy+khE7G+QCJcYcauCu8MRsKjcfec5F4r2IJBud6OWhIijjgsTg++O63zPWYBHK6xdP+EPjAtRYr8BDEAQMSlcdm46O93Fmy0Vhde3FOLtw7Ww2AnkYHhjtXlQbneDpOjs91PllKICYli3+OBypo3DzbgmZ21eP+IBk9sqx7gUfwhxVv9h7xjYUZenAhqMRdXjFeHxP7hyyItPirUgKaAW6YlYnmuYsjHECoazE6c1tgg5NIYEy+OuEbLZ7X2TjNNaT9eg8vDYE+lCTUmJ1QiLs7LjgGPQ2Pd4jQU1FqQpRKQpe0A8NSOGpxtSTgu1trxzLIMUuHVAsuyqDI4UaKzo6LZAbubAZemkBYjwMIsedTlsO6vNuH53bWwu1lwKGB2ugx/mBzv9zu7c3Yy7vulHI0WN3Jihf0+R43RiY1nm32PT2lsqDM5B93TdJRahFK9N6csSUauC32BCK4Ac6DahIpmBy4dGwtqAGZwjy5OC8Ko+obR4cHnx71VaAwLvFvQiHmZMoh5kSU8+kudyYnX9tfjWENbpY1CyMFzKzIRSV0r4zsIdA4FzO1j+59DNWa8dqAeWmtbxKWy2YGbpiWCQ1N+3mGEgVNvcvrEFuAVyac1NoxNEPfwrOjnrNaGH0/rcaTe0qmytxURjw5JxD+Y/Hq2GXa3d5LkYb29UsubHXju/EyfuFSKuHjhgiwcrbdganL/f4cnGjpXEAYiF3Nuhtwn5NrnmhG6hwiuAHK4qhlPbq8BC69Z5cKs/nkthZoGs9MvQmJ3M6g2ODEyin9MzXY3HthUCX2HpM9muwcaiwt5IRrXQEiW87F0RAw2lRgg4FC4ZXpin5pFn9Xa8NSOal/uYCskPz7wdDUHqzE5h63gsjg9eGlvHfZXm7vdh8+hcPEYVdSJLQAYFSfCoVr/Zf8qgxOHas2Yk972eqV8jt/j/sCw/j/kGAEHyQFoIj8uQYz5mXK4PCwylf2PvA1HiOAKIK/vLvetsR9vsEac4BJ1Ea6P9nvuoRpzJ7EFAFOSJRgVYqHZYPa6u/dnuen2mUm4fJwaciEHwj4uv/x4Wt9JbMn4NC4eQ/K1Ak28hIcYIccviiMLMwfxoeS/x5q6FFsUgCylANNSpbhgpBKKMDL8DCSXjIlFmd6BPZVt1YcUENDXOy1VincLGn2T6SvzA2dSejdp1N0vovNbHAKMdjdONbRdOLoLi4czSTI+UuR8n6eKQsjpsnFyNDE+QYxEKQ/1Le2TFEIOluUocPm42JD2B3vrUAN+OqPHspwY3DYjqV/PjZf2L5+i4/7j4kW4eVpiv49D6B2KonDT1AQ8t7sWDAukyvmYOoz7Yl6Zr0ZajAAaiwssAJmARqZCiGyVcFh0NODQFO6fl4JTGiv2VJrAApieIsXY+MBFPNViHp5Yko4d5UZMSBRjemrf0gwIgYcIrgBR3uxvSBeJFRwcmsJds5Px/J5aWJ0e/GVWUtQlqXYkQcrHaxdlo87kBMMCaTH8AeXeAd6E3zK9A0IujeRBmJseq7fgpxY3+s0lBtw4JSGon8PVE+IwIVECnc2NVDkf2SqyPDAYSnV2HKg2Iy9OhIlJnS055mbIkSLn46zWjtnpsmHdn1LC5/SrMIdhWRTWWcCyQH5i9CzDjo4TY3Rc8F7PKLUo5BF7AhFcAaO5Q0QrWxWZkaGcWCFeuyg71MMYUjg0hdRB+kvtrjDi/SONaLR4lyfzE8V4ZFEauAMI3W8pNfj+72G9VUbBFkHjhmkOUVe4PCxON1nBssDYeHG/l1/+s78eJS0tWaalSHH/vJROoipLKUQWyXvpFyzL4sntNThY411JyFIK8PaVZNmbEDkQwRUg2ke0uDQwOWn4LhMMN4oarPh3yxJRK8fqrXhtfx2SZQIkynjIT5T0eYmkfRUbgICYFA6ERrMLYh7dL2uJSKfR7MLDmyt9S8wjY4V47Lx0iHh9jzC2T1I+WGPGK/vqcBfJdRk0pzU2n9gCgDK9A18U1uKCLBK5IUQGRHAFiNxYIWQCLkwON1aOUpH8l2HEljJDlxV9m0vbWuuIuDR+PykOF4xU9nq89kn8Ej4NtWTof6ZvHqzHhuJmcGngyvw4XDpETvmh5q1D9T6xBXh9snZWGLEsR9HnY0xLkaJM35ZisL3ciHkZcmKtMUi6ah9TobcBRHARIoToTtAZQgRcGv9ePQa3zUjE7yfGhXo4hG5oNLvwz+3VviWfQJAk7T1fy+ZmsP5QAxrMvTv4K9tFSycnSYY8eV9rdWFDcTMAwM0AHxVqcLxhcI71kUKN0dVpm6NjCWcvXDI2FskdjCB3VQa+r+VwIy9OBEGHpdkRsWQpnBA5EMEVQMYlybEsR0FcowPEtjIDjAFuffLK/jocqDbj9QP1ATvmJWNV+PPMRCwZEYNLx6qwMFOGrvKgR6iEfSr3nt4SCRHzaFwbAvHeVYuRX88autgz+pjQIRE7RsjBnIz++R8JuTT+sSgNse2Es83VP9FG6EysmIe/zEqChO+9bU1PleKSCf2r4CVEJsfqLdhQrMdZ7cB6SYYLZEmREJacabLhhT11iBVz8fz5mVAEoOqz1ujEsXqv63KJzg6L0wNJAPKjKIrCkhEKLBmh8G27aoITJxtt0FhcYFhgpFqICYmSPonxq/LjECvmYnKSFAl9iJ4FGpWYCwr+Hmx2z/AQDDdOTYCUz0FRoxXxUh4uH6ceUMVxkoyP/1yUhZ/O6FFpcGJVXu9LyYTemZshx6w0GSwuBnIBBwIuB6ben0aIUFweFi/sqcXudj5l98xJxrwINcElgosQljS05NForW68eqAeDy5IHfQxTze1zY4YFmi0uJAVpIT0BCl/wGKJx6GwcpQqwCPqO7sqTJ0Mb6cPE68oLk3hmgBFFcU8Di4bF0nNocIHD8PihzM67Cw3gUNTGB0nwpX5agi5NDg0NSw8ulrxMCxONFpRWGdBUaMVjWYXzE4GEr43At5+oteKy8OgyepGnIQ3oErpcOHbU1o/sQUAB6rNRHARCIFEyG27SByoNqNUZx+0NYLG4p+fwwmhsWk44vQwKNU5sLXUf/lw7WgllvYjaTzcsTg9ONFgRZKcj/RB2oEQgsNnx5vwxQmt7/GZJhtOaWx4dnlGCEc1tDSYnfjmpA67K00wdrHMb3J4kCDpXJx1tN6Cf+2q9f5dysOTS9Mjtul8YV3n3NGUmKGP+gcKIrgIIaNUZ8eZJhtmpMn8lm08DAteh1nZppJm3KxKHNT5OiY/xwgHPkt2eRh8d0qPZDkPswfY4ywccHoYbCszYl+VCccbrH69NFvpqjosUjlZb8J9P5RCb/eAQwHPLM9Abiypcgs3umq4fKbJhjqTE0kB6AMYzrgZFp8e1eC70/pum0zTlNej73CtBeMT28x1zU4Pnt5RA2tLzmCD2YXPjjXh9pmRmeuWIhfgRGPbykRaDB8r+mGUG24QwUUICdvKDHhxTx1YAB8d1eCFFZlweVj874QWB2vNsDj9xdH+ajNunja4c/I5bTUiEh6NmEH0K3vncCM2nm0GANwxi8Xi7MjqmwkAv5U049OjTdD2IqgWZyuGZkBBhmVZPPnbWehbTIo9rHf5lAiu8GNRdgxOavwTpOMlPMSKo/+W9djWKhyt7yw4YwQczE6XIT9RjGd21gIAfjijw4qRCl/6whmNzSe2Wuloyh1JXDcpDkIuhUqDE9lKAS4dFwsxL3KXk6P/20sIS94taPTlCVmcDP65rRpVRqefn1VGDB8VBq+NgtbqHvTsdqS6bUlydvrA+4l5GBY7K9rK/D880hhxgmtDsR5vHmzo8m9iLg1rSzSQS3urK6OB0002lGn9b2TtTUoJgaXR7MLXJ7WoMTnhdLNIlPKQrRJiYpIEGb30aF2Wo4CQS2NzqQHNNjcyFAJcma/2mzRFI/WmtsIeMY/GzDQpMhQCjE+QIEspAE1RqDa2eby5GWBvlQlrRnt98oRdGPSOiY/cCYWEz8EfpySEehgBgwguwpBjczGdmnu3CqtWxsSJ8JdZSfjrhnLYW27+RocHSYPouzo+QYIkGQ9Guwdrxgw8KZ0FYG4XgdPbPdBYXIjrIp8iXMlSCjArTYpmuwdcmoJCyEFurAh5cSJkKwW4c2M5qgxOzMuQQxYlCcoOd2dxNT9Ck28jgR/O6HxRYMAreLeVeycqWUoBrp8cjwmJnXtNtjI/Uz7sPp86s8s3EbW6GMQIuD4x1UpRg3/kb1uZ0bfPmDgRZqRKsb/a68g/LUWKi0aRCtlwgQguwpDD51CQ8mk/0dKeJSNicOOUBIh4NP44OR6vtXhmDTbJnceh8PTSDLgYdlDiiEtTkAk4fn5VRocnogRXb81yn1qagUM1kVsN1BU5sUIoRTzobd7iiYvylGQ5MYhcNjYW57T2TkuDgLctzz82V+G2GYn9cvGPdsQdIlSbSppx7cQ4n51MncmJNw76ewiW6R2+oiKKovDA/BSc09nBoSjSiD7MIIKLMORwaArXTIjDGx2WtLJbZr357Wa9y3MVsDg9ONZgDUhD8ED4eQHAuHgx9lZ5y5VpCkOWW3KoxozPjjdBwKXx9/kpAfER6wqZgINFEbZM2htSPgevXjoePx+vQrZKiElJ3UdXCINHLuTiqWUZ2F1hxOZSQ5dFGee0dizLCdEAw5AclRAxAg4MLZM5s5NBsdbmmxyZHJ4u24gdb7AiWyWE3ubG1pZWYytHKdFsc2NbuQECDo1JSRIkRnnBQbhDBBchJJw3IgYbzzbDaHcjP1GCORkyTE+RguoiinXx2FhcHGa9/K6bFIej9RZYXQxmpcngcDP4268VaLK4MC9TjmsnxgW8JU9BrRn/3F7tu+BuLG7GpePC630JdzJUYlwSZt+laGdOhhxzMuRweRhUGZyoNjrBoymoJVy/CGOz3Q25gDPkrazCCQ5NYXmuws8So/1S+AiVEGPiRJ2ihhMSxdhXZcJzu2t9opZHUyjT27G1zLuMy6W9baeuGK8e1u9xKCGCixAS+BwaL1+YFephDJgkGR+vrMzCiQYr5qTL8Y/NlTjVchH8+qQOEh4n4GLogyMav9mtLsBtjwiEYMLj0MhWCbtc5nppby22lBqRKOXhT9MTMXEYRx8vHRuLo/VWnGmygUtTSG3nO8WhKfxjURp+OK3DzgojKg1OzE6XwuT04NmdNWgfQKQo+EXA3Qzw+XEtqg1O3DcvZShfEqEFIrgIhAGiFvOwMMu77NaxGfavJYGNPlUbHShvdvhty1Z2XmI9Vm/Bz2ebYXMxWDtG5bc8SyCEIyzLYltLFKa+pbn8v5ZnIFM5PPOPBFwaj52Xhk3nmhEv5XUyLRXxaFw+Xo3Lx6vRYHZCJeLh9h9L/cQWlwZmpclAUV77F3u7KNnuShP2VBpxosEKhgVumBIPXpRXf4YL5F0mEAJAbIeLotMd2N6Drg65L0ohB3M6GK5uKzPgkS1V2F1pQkGdBS/vrQvoGAiEYEBRlF8lrNPD4q1DXVuWDBeEXBoX5akwI7XnsuwEKR+1Jifqzf5dNG6ckoA4iVesPbo4HTEdKo0P1pixo9yIjWeb8dqB4f1eDyVEcBEIAeDKfP+eeTPTBuFf0QUpcj7ULYn5Ii6Nv8xKgqhdRZPW6sKr++v9lhw7GiASAk+dyYnPjjXhi+NNcA2TBt/BYFGWf4FGUaMN5i7a2RA6I+bRaG3MwaMp3DglHitGtllB5MWJ8MrKLCzPUSBWzEWMkIMpyVIIud7rx5ZSA3a38xUkBA+ypEggBID5mXIIuBT2VpqQKOUHPH+Lz6Hx5NJ0FDXaMD5B3MmCoqsKsGnDpOF0KGBZFp8cbcKXRVqfb1KuWkQqHwfI78bH4mCNGTXGNj8+u4eBFNHhARdM4iQ8PLU0A7UmJ6YkS7rsoBEj5OLWGf6t0X451wyN1ZsH+vkJLeZkBM8ChmVZ1Jtd4FAUYsVcn83FcIMILsKAabK6IONzIOCSQCkAzEiV9boEMBgSpHxfC4+OyDssGWTECPB/U6PHoTmcYFkW/9lfj99K2pp80xSQLIscH7ZwQ8zj4LHz0vDu4UYcqbNgRqo0Yhsuh4K8OK9pcX+Ymiz1udpXNDtQWGcJSrHCznIj3i1o9PVkFXAozEiV4Q+T4zqlYkQ7RHARBsSXRVp8XKiBSsTFE0vSkSwn/i6hZFKSBNdOiMNJjRUjY0W4KE8ZNI+u4c6PZ/R+YgsAFmbFdCuGCX1DLeZ1Wz2nt7mhtbrhYVnES3hQBshPbzgzP1OOD440+pLtt5cbAi64GJbFi3vr/JpwOzwsdlQYcU5nw8sXZoPHGT7RLvKtJfQbrdWF/x7TgAWgtbnxQWEjHpifGuphDWsoimpZxiQeU8HEw7D4skjrt210nAi3TCPRxGCwp9KIz45rUdGuQpeCtz/gnbOToVZ3/1xCzyhFXMzNkGN7S7ulgzWWgJ+DpijMSJVid6Wp098aLS5YXR7EcIaPDCFrQYR+c05rR/sivJONnVt3EAjRiMnpQXO7PqBz0mV4dHEaWVYPAmeabHh2Z62f2AK8vUyLGm347HhTaAbWDRuK9fjrhjJc99VZbCjWh3o4feLSsbHgtuRTmRyeoBQq3D0nGX+cHI8spcB3riQZD5ePi4WrK9v8KGb4SEtCwOj4E3GS6izCMEEh5OKmqQlosrowM02GUWrSizFYyPgcSLrpuaoUcbGypSlzk9WFEq0dHJrCuASxr/puqPAwLF7cU4cd7Sr9tpQacMHI8G8ana4Q4A+T4vD24UYAgM3NQBrgZvUcmsLq0SqsHq3CxmI93jzYgDqTC58e0+LTY1rEibmYnirFwqwYjOzl96S1uiCN4LxhIrgI/SY1hg8KbcIrifTnIgwjLhwV/jfSaCBZzsd/VmZjV4URZ7V2sCwQI+QgWyXE3AwZ+Bwap+pNuPX7Ul+FLp9DYXF2DK7MV0PRRbVeMPjvsSY/sQUA2RFk2npRngpuhsXRemun6udAM0otAp9DwdGuolpjdeOn4mb8VNyMBZly/G25osvnnmy04h+bqxAv5eHZZRkBF4ZDARFchH6TKhdgYZYcW8uMoAD8bhxJpCAQCIFHKeLiojxVt383Otx+dihOD4ufzzbjeIMVTy9NhzzIosvNsJ2WDyV8GmvHdD/mcGTtmFisHRP8/M9slRDPrcjEmwcbcLzB2unv28uNMP10Co8sSOr0t+9P6+BiWNQYnfjmlA7XTowL+ngDDRFchAHxl1lJmJoiRayY6+tkTyAQCEPJ9HQF5qTLOiVl1xid+P60HtcE+abs8rCwtDMYVgo5eGBBKon690BajABPLElHmd6O3RUmHG+wokRnh4thwaUppMR0HR083dTWPm13pZEILsLwgaYozA2iUV4gcHoY8GgKFDV8yo4JhOEERVG4e04yRqp1+O6U3uf1RAFQS4J/exPxaNwyLQFH6izIUQmxNEdBLCv6SJZSiKyWpVeGZeFhvD0g4+Li0NTUuSCi/VW8weyCh2EjzkCVfDNCRLPdjY8LNSiotcDuYRAv4eGysbFBdfuNdjwMi31VJmwuNeC0xgaLi4FazMU/l6Qjkcw4CYSohENTWDM6FqvyVDirtcNo9yBBxkN6TOfm7sFgxUilXysdQv+hKQp0LylZKhHXJ6gZ1rt8LCKCi9AXHt9ajXO6thBpmdOBZ3fV4hEejcnJpCVLf9lbZcL7BY2dmrgaHR4IeZFZ0UIgEPoOTVFDXjUaiVGWSKPW6MSZJhtkgrbruJRPgx+BhqlEcIUAD8Oi0uDo8m/VRicmJw/xgCKcL09o8dFRTaftQi6Fe+emBKxayWh3o97sgtHhgcnhAY9DQSHkIlXOh4IsIwwbLE4PKMrbjoYwPKloduDtQw041mDF3AwZ7p3btUP+UOL0MNh0zoAyvR2j40RYmBUT8WKw1ujEbT+WoqNd1+g4ETg0hVKdHR8f1aDG6MTEJAn+MCkeojCeYJO7RAjg0BT+b2oC1h9q8KuwGZcgxrIcRegGFoGU6Oxdiq2pyRL8flI8MhQDX1awuRhsONmAX0/W4bTGClMXfkCAt4/eqjwVrp8cP+BzDQdMDg92VxpRZ3IhP0GMKRHWXNvDsHjzYAM2lTSDAvB/UxMGvJRkcXrwW4kBbobFSLUQ4xMkcHnYYdXmJFKpMznx0G+VMLaYhO6pNIFhWdAhzhV9ZkcNDtV63eI3lRhwusmG22Z0rvaLJOQCDni0v40EABTUWvDrWT0+KNT4fNp+Puv9Xd4yPbGLI4UHRHCFiGU5CkxNkeJkoxUuD4tEGY9U+w0AIZdGbqwQTVY3VCIuxieIsTg7ZlBCCwCKGqx4amcNTH1wXpbxORifQD67nthVYcQbB+p9ovW7Uzq8d3FORCUY/3hGj1/ONfsev1fQiGU5igFFETaebcZHhW0TBSGXgsPNYnqqFHfOTg7rWfpwhmW9JqfGdteFBCkv5GKr1uj0ia1WNpcYcP3k+IiOxEoFHNw3LwXP7KzxC054WOD1gw2dIl/h3vUkcq52UYiqpZcVYeCkyPn49/mZAT+uzc1AwKHQuQOY11wxScrHiFghZqZKMTlZAh6H3CC747eSZryyr95vG49DQcCNrGjOTx38lhweFnY3M6Am4SNU/qXvdrf3zrG/2oy3DzfgzzMjOzIRrZzV2nG6yf+mvnSEIjSDaUdXkdFoqc6emiLFCysy8fkJLfZUmvwaYXckUzk0hRIDhQguAqELpqZI8c7aHNAiOc7VNMLmYiDh05ALuVAIOSGf0UYKDjeD9wsaO22/eIwq4mbe7g7LGnlq0YDEFgBMSpJgWU4Mfj1n6PS3neVG3D4jMWpumNFEtdHp93hsvAhrRofe5DROwsPCTDm2lbc53l80Shlxv7HuSI0R4O45ybh5mgdnNDY4GRaZCgFe21+PYy0GqqlyPq6bFN7eXERwEQg9oJLwka2KnDYd4YbB7vHLfaMpb8PcK8ZHXneCeZlyfHtKBwAQcWncPC1hUMf70/REJEj4+OSYxm9pxNFipikdoJiLRFweFs12N2LF3LCezOS1tKZxeVjMzZDhlmmJQU9Mt7mYPi0x3zE7CTPTZCjV25GnFkVcjmRfkPI5fq/rsfPSfBHHkbGisC8SIIKLQCAEjXgpD7+fGIej9RYky/hYlqOIWAH7+4lxyFYKYHYymJMuG1BlaqPZhSN1Fgi5FPLiRLh0XCxyYgV4dGu1T3SlynlRI7YcbgY/nNajyerC8lyFz+iyFbPTg0+PNWFbqQEWF4PfjY/FVfnhG6VIlvPx3toceFgWMUFsG8SwLD44osHmkmaYnAxiBByMTxRjdZ6q2wbPNEVhVroMs9JlQRtXuEFRVETlPhPBRSAQgsolY2Nxydjg92kDvC1d3itoQLHWjhQZH9dNikdeXPfeTKc0VjSYXRgbL+61cS+HprAgK2bAY9tWZsBLe+t8wooCcMV4Na7IV+OppRn47zENWAC3TAvfKqv+4GZY3P9rBcr0XgucTSXNWJ6jwLIcBTKVQpTq7HhqRw0aLW3eefwIyIUciqbJJTq7L5oKAAaHB7sqTNhbacJfZiVhdroMBbUWHKmzoNnuhoBD48JRyiH3ISP0DyK4CAHBw7DYVNKMGCEXs9KGzwyLED64PAwe3lwJrdXrRm2w2/DE9mq8sSq724jRa/vrUWlwgoLXluW6SXHIjQ3OTeubkzq/pUMWwH+PN2FSsgR5cSI8el56UM4bKnZVGH1iCwDcDPBTcTM2FDdjVroMhXUWWDv0ITw/VxGCkYYfWUohUuX8TjljHhZYf6gBbx9u7FRB7WHZsPADI3RP+E8nCBHBf/bX4fUDDXhuVy2Mdneoh0MYhpTqHT6x1YrJ4UFlc9cmwwB8vncsgOMNVtz3SwXePtQAl6f7SqiBMrYb65C+WI9EIt2V6LPwele1F1tCLo275iRHzVLqYOHSFJ5ZloHF2TEQdqjmNTuZTt8ZmYCDy4YoikwYOCTCRRg0+6tM2FLqrY5xMSwaLC7Ig5jfQCB0RYKUBwHH3yRRwqM75Q2156I8FUr1Dmwp9VYLMizwwxk9TmpseHBBCmLFPS8zAl4T0y2lBnhYFhMSJd2e7/pJcWAYFlvLDLC7WfBoCufnev34opEkWe/vHQCoxVzcPy+l29yk4YpUwMEds5Lwp+kJqDI4sf5QA05pOovYCYli3D4jCfHSvr3f0QLLsmCBsC6y6Ai5KxIGzcazzX6PORH0AyBEDwohF3fOScb6Qw3QWt1Ii+Hj1umJvVZ43T4jETECDr49pUOrVCvR2fH4tmo8uTS919L6dwsa8VuJV7DRlAYXjVLiD5PjO90IeBwat0xPxI1TE6C3uSHh0wMu29daXShusqPZ7obDwyBTIcTEJMmAjhUszs9V4kC1GSc1NmQoBMhUCHCswQqD3Q2GBcQ8CouzFbhivBqyIciLCjUNZic+LmzC+bmKbqOdXcHn0BihEmJaihTVRicYhoVSxMXUFCkWZsl7nFBEKwzL4pEtVTA5PHhqaUbEGAUTwUUYFA43g+MNbQ7HYh49aJd3AmGgzEqTYVaaDHY3AyG3bxdhDk3hD5PjkZ8oxusHGnxJ3GV6Bz48oum1VYit3dIYwwLfndbDzbC4qZvkdy5N9Zqg3xVlejt2lBtxoNrcKbeHS1NYv2YEVGHk3C/i0XhqWQYcbgaCDp+FyeGBmEeHfRl/oPAwLJ7cXoPyZgfKm+14ZWV2v48xlMUn4c6JBiuO1Xv9t7aVGQbcYmuoiQxZSAhbKg0OuNu1GJyRKg36RdTs9KBcb4fVFZ25L4TB01ex1Z7JyVL8Z2UWrpsUh4wY76ThTFPvrUKWd5Ho/fPZZuhsgcllLNXZ8eBvlfjrhnJ8fVLXSWyJeTTump0UVmKrPR3FFuDNORouYgsAdpQbUd6SS1htdAYlR9Dk8OBkoxWnNTY4PV33fY0WjtS1TfL3VnXVDyQ8Cc9fKCFi4LW7aHJpbxPnYMCyLDaebcav55p9lU80BSwZERPxDVoJ4YOAS+PiMbG4eEwszA5Pl2KhIxMSJfi/qfF453CjrwrRwwJGu3vQIujHMzq/47ZHyKWwKCsGV+aru/SEqjI4UNHsgFzAwZh4MbjDSOD0BsOyOFJrQY3JCZYF4iU8TE2RBq15+KFac7tzAw4PAx4nMMuoZXo73jrYgNNNNt/3RMKj8bf5KchPDK9l5kDR1K44ps7k6mHP8IIILsKgSJHzESvmQmd14/+mJgTN1PK53bXYWeE/k2FYb4PWG6YkDCii4T0Gi8+PN6HR4sbyHEWPnk2E4UV//JZWjlJhdJwY35/SocbkxKQkCTIHmVvjYVj891iTn9hSCjnIT5RgaooUM1KlXQrCglozPizU+FkyjFKL8OzyjEGNJ1rQ29x4eHMlqgz+kcJYMRfrFqchPSbwKRHntHbf/yU8OmDVmFUGB+77pcKvsTMAWFwMtpcbo1Zwte+naImglQ4iuAiDgseh8cyyDLgZFkkyflDOUW1wdBJbrVw8JnbAYgsANhTr8dlxLQBge5kBDy1MxeTk6KwaIwSXESoh7pyTHLDjcWgK767NQUWzAzwOBbmA02vV5P4qE57eWdMpIlZlcMDDsMNqGa87NhTrO4ktANBa3fjmpA53zApsxJxlWTRZ26IwubGBm5QeqbN0ElsAIOHTWDkqMvKaBkL7az6pUiQMKwaSANwfEqR8zEiVYn+1NyxPU96eZitHKTEnQz6oY7f3CvKwwIeFGiK4CGGDgEv3yy7hm1O6Lpcfr50YR8RWC/My5dhQrIfZ6Z/nJORSWDJi4J0EuoOiKAi5tO98gbQBWTIiBsVNNhyqscDuZhAv5WFiogSXjYsN+nU5lKjFbdJFxo+cVHQiuILM/ioTZhDn9UHB41D4+4JUmB0e6Gxur9/SIKJa7enYD69M74DW6uqT/xKBEG5MSZbgtMbms7fIUQlx2bhYzCTXIB/pMQK8uWoEtpQZUGt0ggWQIOFhcXbMgPpj9oURKiGO1luRKOUF1E1fzOPgnrkpYFgWHoYFLwJaIwWCqSlSfHHCuzIxPiFylk2J4Aoinx9vwqfHmvC/K0ZGRI+wcEcq4AS8j9l52THYcEaP9kGBQIm5QOJwM6g2OhEr4gbtpkCIfC4bp8ai7BhoLC7ES3hk4tANUgEnaAU+XXHjlAR8c0qHy8fFBkUU0RQFOkgJ/+HIKLUIY+JEKNbasTQn8FHJYEGu3EGi2ujAFyeaAHgT/EjHivBkhEqIayfG4eOjGjAsMCddFpbtRZ7cUYPCllLoBCkPi7LkuGCkssvqNMLwRi3mQU2EVliRrhAEPDdsuPPwolRoLO6I8n0kV+sg8enRJrgZIEbAGbCbNGFouGRsLOaky9BocWFsfN8doIeSRVlyn+BqMLvw2XEtvj6pw+o8FS4ZGxsxTssEAiFweBhve5vhaPkh5nGQoYiseysRXEGgXG/HnkpvVV2GMnLU93AmUcZHYpCqLAPBwqwYWF0M1h9q8CVFOz0s/lekxc4KI/6xKA0p8vAdP4FACBzNNjfeOtSAQzVmUBTwp+mJWJgVOUtrwxUyLQ4Cv5xr9uUEZQe4z5XR4UFls8Nvm87mxoFqE2qNnUudCdHDBSOV+OeSdIzo4HVWb3bh/l8rUGdq+/zrTE5UGxwdD0EgECKcepMTd/9cjt2VJjg8LOxuFru6sc0hhBckwhVgXB7WzzNqcnLgKiiabW7cubEcBrsb/1ySjly1CB8eacT3p/UtXdOB587PDJr5KCH0jIkX47nzM7C3yoRvT+l9rWdMDg++O6XDLdMTobe5cffP5bA6Gdw2IxFLcxShHXSYwLAsmm2R40pNCD88DIsPjjSiWGvH2HgxLhmrGvKUkVf31/s5rQNAtoqspEQCRHAFmMO1ZpgcXudbCZ/GuADmBH1ZpPX1Z9tUYsA3p3Q+byrA67xeb3YSwTXEnNbYkK7gD9mFl6IozE6XY3a6HBqLC8VaG8wOBhOTvN+1Y/UWWFo8f9461ID8RDESpMN3uVFrdeGTo03YXWmE3c1ilFqEp5elh51hosvDYkOxHud0doyJE2FpjmJY5uaEMz+e0eO703oAwCmNDeV6Ox5elNavY3gYFt+f1uFgjRk8mkJenAjLchR9qig1OTw41mD125Yg5eHCCGnePNwhgivA7K1si25NSw5sI+c97Y59otGCBrP/LEfCpzExKXI8SaKBX84247UD9bhsbCyumRg35OePk/A6GRw229taXThbIq6Xjo0d6qGFBQW1Zjy3u9bP5PKs1gaWBRBmWubtww34+WwzAG+z4xONVtw7NyW0gyL4Uaz1b2Z+qNaC0xpbv1qCfXGiydfdAgAK66349ZwBTy5N77Vbh4hHQybg+Cb1o9Qi3Ds3uV/VyizLokzvgIhHQyHkkoKbIYQIrgDiZli/JqULsgbngt4eu5uB1tYmsBxufztpLk3hjplJpCJyCDHa3Xj/SCMAoFRv72XvoSO5w0X7RIN1WAquBrMTz+6shc3t7yi+PEcRlq7rO8qNfo93VZhwzQRn0FpmEfpPx98WADRaXP0SXO0nRK3obG5fSkBPcGkKz5+fiWMNFqTI+BipFvXru2y0u/Hkjhqc0rQJR7mAgyvGq3FhFLcCCheI4Aogx2oNvpm0SsTFhAA2DrU4/X+kiVIe7G4GDjeLESohbp6W0K8WINGEh2Gxo9yIbeVGaK0upMj5+OPk+KAvo31/Wg+ry/t5t1YOluvteHFvHVbnqbAoOzRVQ6PjReDSQKvOqDcPz2KKTecMncTWzAwl/jB56CORfUEl4sLq8v+szM7+N+Y9VGPGzgoj8hPEWJwdAyrMlk47srXUgMO1ZvxuvBppQWgcHUguGqXEphID9C2TXy5N9bs34prRKhysNvtNoAH02TstXsrDEqmiX+ds5d2CRj+xBXgLsd461ACbmxmWE7OhhAiuAHKqvi26tShLHtBZtIhHg6babuyTk6V4Ykk6nB4WkjA06uwJp4fB4VoLaoxOKIQcLMiMAW+ALslWlwf/2FyFs9q2CFOVwQk+TePuuYFrJNwVe6valnhlLQ74bx9uRJnegdcO1GNaijTgzvh9QcrnYE66HNtbIiaqYepMnyDl+X4zsSIufjdejYsmZcBpNoR6aF1yxXg1nttd66twzlAIOlWk9oaHYfHinlqYnAy2lRlxsMaM++elhK3o2lzSjJf31QMAak1OPL8iK2RjqWx2oNnuxth4cbfXbrmQi2eWpePLIi30NjeWjlD0OwKZJOPj+RWZ2FTSjNMaG9wMi+mpMlwwUhGAV9E9Rru7UxS1PV+e0GJ1nnLYtAcKBcPzShwkzjV5jSmFXAqrRwe2bYSYx0GeWoSTGhuEXArLcxXgcWj0dQWxuMkGi4vBxERxyC6+HsabFPy/Ii0M7cLqPJrCggF6yHx4ROMntloJtieVxuJCdTsbjtxYIQx2N4oavQmtTg+LfdUmLBmhCOo4uuOmqQko0dlRbXRifmbglrYjiaU5CkxLlcLNsFCLefjkqAY3fX4U/7kwM9RD65J5mXIkSHnYUW5EjJCD5bnKfif2swAsrrao3t4qM34514zzc8NvuYhlWXx0tMn3uFTngNXlCUlaxPpDDfjxjDcZfsmIGPx5Zveu8AlSPm6bMTjXeIWIi8vGqQd1jP5iczPwdNHYvP3fjQ4PYsVEcAULIrgCSKXeG6pdOUoVlJYrN01LwFsHG3DJ2Fgo+nn8Nw824JzOjqnJEtwzN2XIEyXtbgaPbqnCyQ7hbB5NYdQglkIru/Cayk/0lmsHk+Im/9cxNl4MrdXti0ACXkf4UCEVcPDShVmoMjiQFWAvuEii9XdSY3TiyyItGNY705eHaUukkWrRoFIDuDSFTIUApfq238UXx7VhKbgqmh2+pTkAoKjQOKbvqTT6xBYAbC8z9ii4IhW1mIckGQ91Ju91aWSsECIejaP13knixEQx6b0ZZMLzqhOh2N0exIq5QbvZZymFeGpZxoCeK+R6L2SHai14bGsVnliSPqSJwy/vresktmgKuHN20qAc3m+ckoD3jjSiyeJCkoyPRVkxmJMhC3rJf1W76FasmIsRKqHPE6sVR4f8ob7QYHbiWL3Vuwwm5mJcghjCATbT5tLUsBZb7fnpjM4nhnua5UcD106Mw6Nbq32PtTY3LE5P2KUe1HWYkKTJBeCHYDmrvdgCvMKPZdmwXYYdKByawiOL0rCl1IA4CQ/nZceAQ1NoMDthdTHkWjEEEMEVQBbmqDElnhuWlYKTkqU40egVBCc1NnxxoglX5g9N8rDLw/rlOwFAjkqIW6YnIDd2cIn+2SohHj8vfVDHGAiedqGspSO8y6Edm14L+imUPjzSiK9P6tBeD8QIOfjzjCRMS5UOeKzDHZeHxZbSttwVXhhWKAaSyclSXDE+Fp8f14KF9zvU3+/iUJAo9Y+mLMwOzdK3toOJ6NwMWdSJrVaSZHxcPcH/ut9dcRHDsth0zoBGiwsXj1GFnWCPRIjgCiD/NysDTU1Nve8YAs7LjsGXJ7S+qq0vi3RYmBUzJCXnPA6Fm6cl4Fi9FWoxF5OSpZgU4X5hreJKKeJizWhvZU+ilIcYIceXn5bTj+olh5vBVyd1nbYb7B68sLcWn1yaGxE3ATfDgmXZsEq8LW6y+b73fA49LHyHrsyPw5RkKQrrLZiUJAlLA9UspRDjE8Q43mDF9FQpVucFNw2gO3JjhahvibbFCDi4ZkJ4VrEONe8cbvRF/0p1djyyuH8Gr4TOEME1TFCKuLh8fCw+OKIB4L0xfnasCXfOCW4lXyvn5yrDMo9koMzJkGFvlQl/nBzvu4FzaApLRyjwZZEWsWIuJvdDVAq4NJbnKPDLueZOf8tWCsNebJkdHqw/1IC9VSZ4WOC2GYlYHCJbjI4cb+fMnR3bfQVatDHYfLCh4JFFaWiyukLqNfZ/UxOgEHHBoymszlNBMUyretujs7n9lloL6y0wOTy+amzCwCDfrGHE6jwVDtWYUdSytHiwxgwPw0btDcjmYvDFiSbsqTTB4vRAKeJi5SgVluUM3ptILebh6S7y6a7KV3s92JLE/Y7y3DojETNSpdhXbYLW6gaXpjApSRI2wqU7mm1uPLCpErXtmmcfqjGHzbjbu4PnxvUugpttbtSZnEiS8/tdnELoHzwOFTSx5WFYbC83os7khFLExZx0WZfFTDFCLm6ckhCUMQwGlmWhs7mhEHKH/BrdsSiIYb1LjITBQa4mwwgOTeHeuSl4dGsVyvQOWFwMmqyuqO2z99LeOr/cMZPTidcO1IMFG7RoG4emBuXYPCVFiikpkZWv9fK+Oj+xBWBQlaeBpqxdxd7ElJ5F4JsH67GxuBmtnX/Gxovw19nJndondYRh2bDrzRjNGOxuHKmzwFVpx0g5hQxFZ8PUDcV6vH240ff43cONWDlKiasmqEOSnN8fjtZb8PLeOjRZ3VAKOfi/qQmYkzF0OW4dC35UIm5QKu+HG+QdHGYoRVw8tTQD/z2mQUWzAypR9JYBd9dup8owPJ3Xg0Gz3Y2CWovftlQ5H0tGhEd0y+ry+Bq+A8Dk1BjAYepy3zqTExuKm32PWQAnGm146LdKvL4qu1tB9eUJLT473gQO7RWal4yNDWiXCYI/Z7U2rNtS5evqQcEbHV6Wo/DbT9lhadDFsPjmlA71Zhf+Nj98e1S6PAye213rywXV2z14YU8dxieIh8zOJD9R4tetgjTHDgzhLfMJQUHEo/HHKQl49Lz0ATu8RwI3TImHqEN1VoZCgIvHhCY5NxrhUhTar3aMjBXisfPSwqaiyd6u52hurBDxsu5bx8RJeFAKO4+73uzq0VOtoM4MF8PC7mZxtN6Kf2yuwvO7a+GKdv+JEPHu4Ua/ZuQsgK+KtJ32m5Muw5x0Wafte6tMOKWxdtoeLmitbj9jaMArFsubO3sOBguliIu/zkrGyFghrsxXB93XcLhAIlyEqGVGqgxvrxGjsN4Cu5uBWsxDfqKYLP0EEKmAg0fPS8PxBitGqISYkiwNq4q49vYdvc3SuTSFR89Lx7M7a/y6CMzLkHWyMGjPRaNUKGqs8du2vdwIEY/Gn3ppRkzoP131l+zKy4+iKNw9JxnjEprx5Qmtr3chj6Y6TcTCiQQpD6lyvt93UCbgDNpCp7/My5Rj3jDtUhEsKJYdHplwtbW1QT+HWq0OW1sIwsAgn2lkY3czuPp/xVCLeXj1omwkxsf16fOsNDhQZ/Qmzqf3oaHy96d1+OCIBu52Ao8C8OnluWHpyxfJHKox49mdNXC0RBATpDw8ujitx+R7D8OiWGuD1ckgUykIe0f1OpMT7xY04rTGhnSFAH+YFDfkgitURMM1Nzm56+p/EuEKIAabCy/vrUOp3o5kGR9X5quR1oeLNYFAaCOQLt9CLo3LxqkxJ13Wr8hbeoygT0KrlVV5KkxOkuCLE1rsrzbB7mYxSi0Cjw7fSEqkMjVFirfX5qCo0YrMhFjEcx29VvFxaAqj48RDNMLBUVBrxuFaC+ZlyHHf3JSoTvsYbpAIVwB592gzvjtR73ss4FBYtzgNY+Ij44ceLuwoN+KjwkY4PCzWLUpDtip0LSeiYbYVCTAsi1/ONuO3EgNK9XaMUovw1NL0gPuPDdXnaXMxw8JgNVhsKzPg+9M6qMU8LMiSY05610tb0fb79DAsrvrfWdhbstVzY4X4x8LUsO39GQyi4TPtLsJFrggBRG/zr35zeFh8eiyyvzhDzd5KE57bXYtGizdxdONZfe9PIkQ0Opsbf99UiTdaGqwzrLexcbibvfYEEVuDY1uZESU6B/ZXm/Hszlo8s7NmQL1JI52zWjue3FHjt1RNiFzIVSGAnJ8X32mb3ubuYk9CV7g8LNYfbvDbRiFyb7qE3jHa3bj353Kc6tDYfFUeKUMfzkxM8l8V2FNpwrotVX5FEMHkt5Jm3L2xHE/tqMZvJc1Ddl4OTWF2h8rKUxobdlUYu3kGIZIggiuALMhR45ZpCZC0zG65NIU1o0k5bV8parR2aiQ7KZn4GQWSvVUmvH24AT+c1oXFZOCdgkY0dfjMp6VIcfk4dYhGRAgHLhypRLbSP4fupMaGX7tofdWROpMT5d148PWVo/VWnNPZsa/KjFf21eOvG8pwVmvr/YkB4IbJ8UiW+Sf1t29PRYhciOAKMCtGKvH22hF47vxMvL1mBJZ2MOMjdI/V5V/uPTpOhBmpkeW6Hs5oLC48vaMGP5z2OnDf/F0Jvi7SIpRpnO1NU3k0hasnqPH3BSlR226K0Dd4HBpPLEnHxA79SA/VmHt9rodh8Y8tVag2Dty3auUoJdrnqlcanPjbr5XYWmoY8DH7ilTAwZNLMzAuoS3Kl9mFkz4h8hg+mXhDiJjHQU4sKQXvL+MSJJDxaZicDEbHiXDP3GTimRVAZAIOxDwaVpc3F8bhYfFBoQZ1Zidum5EEAGiyuvDk9mpQoPDs8oygC5/rJsWhsM6CTKUQ8zJkUdtmitB/JHwO1i1KxeZSA7aXGdFkdWNWu+U2m4tBhc6KmiYbuDSF1Bg++BwaqTECCDgUHt9ajRcuyOyXLUepzo5ndtZAxKOxMEuOrWVGtK4muhkW/9lfj9QYftAtGpQiLv65JB3VRgccbhYjQlg4RAgcRHARAkqD2Ylmu2dAvfTkAg5euygbzQ5Pv0ry+4rDzaDK4ITR4W0IG8rqx1Ag5NK4YUo8XtlX77f913MGLMiMwdh4EV7YXYsSnTcyUKq3B/3GsmSEAktGKAJyLI3FhaP1FiTL+KQyOEqgKKrTd+RUoxVfnNCisN6C9qlVfA6F6alS/Gl6IhZkxuB/RVq8fqABd8/pumKslfY2JAV1FtS3dBUo0zswJ12Gs1o7Gi3ebW6GxTcndbhv3tC0BkqVk8hWNEEEFyFg6G1u3PtLBbg0hXfX5gzoGHIhN6Al0AzLoqDWgk0lzThSa/GZJQLAn2cmBuxmHyksGaGAiEvj9YMNMDnalnCrDA64GBYnGtvyVIz2zo7e4QjLsvj4aBO+KtKi9dN96YJMZCqHl6AeDjSYnfjHlio4u2ib5PSw2FVhQqZCgPNHKvD1SS12lBsxK02K2V3YSpxqtOKpHd5o1j1zk5EbK+q0dLe70oRbpiXA6WGxv9qEBrMLWUoigggDgwguQsD49JjG1wPM6vKE3GH7rNaGF/fU+bXIaCVGyMGkpOGZkD8nQ45JyRJsLjGgvNkBpZCLBVlyvHu40W8/mSAylsXfOdyIH87424e077UHIKR5aoTAIRdwka0U4nRT5wR2CsCUZAkuGKmEhM/B3Aw5tpcb8caBBoyNFyOmw0TuzUMNMDg8MDg8eHZnDV69KBsTkySd2up8clSDDy7Jxep2BVBWlwd8Dh1WbawI4Q8RXISAoLW6sLmkLaE01LYx+6pM+NeuGnRl3TMmToQ7ZyeHfXuPQKCxeD+XeCkPi7NjfNvFPA4uyvOvoD3ZzpqBz6GQFQERorNaWyexFSviYnScdym01ujE24cbcKLBCiGXxkPLORjZuZ8xIUIQ8Wg8vSwdxVo7ihqsoPlCOB02qERcjIsX+/VUvHiMCtvLjTA4PHjjYAPub7cMaHMxKNO3JdU3Wtw4q7VjbLwY989LwQObKnyi3eRkUKy1+Zzq3zxYj43FzaAoYHKSBH+ckoAUOck9JPQOEVyEgHCoxoLWKD+X9uYLhZJPjzb5iS0uDUxI9M5+pyRLItpUs6+c1tjw2LYqWFpuHBkKQY/Jt3Wmtln9lGRJRLQUOVDtX7XGoyncNScZHJrCiQYr/rm9ul2RgAe/ntZg5LTYUAyVECAoisIotQij1KIeXckzlUJMSZbgcK0FeypN2Fdlwsw0r9rmcyhwafhdIzQteVrpCgEeOy8dL+2pQ4XBAZoClO2iYzvLjWABsCxwqNaCE43leGB+SqeKSgKhI0RwEQaN2enBgWqT73F6jCDkofZ75iXjUI0ZPJpCrJiL/EQJpPzIWCILBB6Gxcv76nxiCwBqjM4eBZeAQ8PmZkABWDkqMvzj2jcsjhNzcc/cFOTFieBmWLy0t9YntlrJjSc3xeHEJWNicbjFeuTdgsaWiQQNDk0hRyXyW5pUCLkwOz04Xm9FhcGBGCGNZIaHBAkPJzU2KEVcNNvdGKESorC+zRfL7mbw8r46vLkqGzwOcVoidA8RXFHI9jIDSnR2XD0hDoIgRposTg/eOdyIrWUGvyXEgVQoBpr+Nh+ONg5Um1HTIXctXtLzEmp+ohj7q81YM1rl5wEUzizOjkFaDB9uxtssutVGpExvR6PF31A1PYaPteOTYDWSdlHRjpthwaGAsQliTE6SoKDOggazC9+e0uGyFlPdP06Jx4ObKuFiWGQoBODRFG78pgS2DnkItSYXjtRb8ebBBl+Pw45orW5UGZzDrvKZ0D+I4IoytpcZ8PyeOgDePKobpyYE5TwON4NHt1bjTBfJq5OJO3xIYFjWJzhKdP5O21lKAfLiehbCf56ZhEtMzrAQzP2hK+uKOAkPEh4NS0uEa066DLdOT4SYzwHx7I5ufjitw/tHNFCLubhtRqLX663FQuLLIi0WZ8cgVszDKLUIL12YhRMNVszJkGFbmaGT2GpPV2KLz6Hg9LAYpRaSPC5CrxDBFWV8WKjx/f9wrQU3Buk87xU0dim2JHwaExKJ4BoqCmrN+K3EgMI6CywuBvESLu6flwqpoC2yyaMp3DItsddjyQQcjBJEltjqDoWQizdWj8A5rQ0pcj4xVA1TjA4PCussiJNwfUnpg6HR7MI7hxvBAqg3u/Dw5irIBRzkqIQo1tphd7P44IgGd7V4c6XI+T6hdH6uEjVGJ34524wuXCe65KGFqciNFYa8IpsQGRDBFUXUm5x+fem6C38PFoPdjV+66Wm2Jk8V1GVMgheNxYVnd9agWOsfyWq0uKGxurB0hAInGmwAWFwxPg45scNvqUMu4GByMmkNFa54GBb3/1KOWpM3WX1GqhT3zxtcW6d9VUZ01EpGhwcmhwc8GnAxwPZyI1aOUmJkh0gul6Zw87RErB0di5MaK6oMDpTqHDA7PWBYFrUml19O4MJMOfITxMOiAIcQGIjgiiIaWqpsWonrJWdnoJzW2PxytrKVAmgsLsQIuVhFmnUHHb3Njft/qYC2i+bT8zPkmJEqBU1ReGhhaghGNzTUmZw4VGNGsdYOs8MDp8eb7K+1uUFTFGamSXFlflzIizcI3VOmd/jEFgDsrzbj65NaX47VQDjW0HWD6THxIuSpRfjqpA4A8MGRRvxzaUaX+8ZLeYiXxnTa7vIwOFBjhs3FIFXe+xJ9pNFsd8PDsMPCLidUDIngcjqdeOSRR+B2u+HxeDBz5kxcfvnlMJvNeOGFF6DRaBAXF4c777wTUql3RvrNN99gy5YtoGka119/PSZOnAgAKC0txauvvgqn04lJkybh+uuvJzOMFgQdKmTGxgfngpAa400w9bAs5mfIcePUBNSbnUiS8UNuBzEc2FVh7CS2YgQcrB2jwprRqqj/PXx2rAmfHW/qFMloz5dFOhyrt+KZ5RmkH2eYIu/CWPe3EsOgBNfUZAkOdmhwPTFRjHvmpoBLU9hSZoTe5saJRhsO1ZgxNaXvEVAeh8acLhzro4FdFUa8sKcWYh4Hb67ODpsl0jK9HZvONYOmKSwdoUBGhDfxHhLBxePx8Mgjj0AoFMLtduMf//gHJk6ciAMHDmD8+PFYs2YNvv32W3z77be45pprUF1djT179uD555+HXq/H448/jpdeegk0TWP9+vW4+eabkZubi6eeegqFhYWYNGnSULyMsGeESgi5gAOjwwOZgIM1QYo2pcj5eO/iHLBou2jKhjj355zWjl/O6WF2MkiU8nDeiJhh03dsdroMVQYntFYXZC3LZjNSpcNmKdfgcPcotlop1tqxpdQw7No3RQrxUh7mZciws6LNUsY9SMfk80cqIeLReL9AA6vbg2smxPkZ/P5+Yhxe2ustKvrgSCMmJUmC3qB9oDAsixMNVkj5nIBVP1YaHHi/oBEGuwcj1UJcPk4Nk8ODl/bWwc14l1/L9A6MDYNepM02N/72awXsbu93YsMZPW6bkYjzIvj3PCSCi6IoCIXeL4zH44HH4wFFUTh48CDWrVsHAFiwYAHWrVuHa665BgcPHsTs2bPB4/EQHx+PxMREnDt3DnFxcbDZbBg5ciQAYP78+Th48CARXC3wOBT+Pj8Fv5UacNEoZadWFoEklG1fTmts+PumCr/E1h/P6PHwwlTkD4OE/VgxD7fO6D0JPlq5eVoiFmfH4GidFSV6OxxuBh4WMDncvsbbrZzW2IjgCmNum5EEi5NBQZ0FFBCQSeKCrBgsyOq8JAgAi7Lk2FCsx1mtHZUGJ7aWhacgd3kYPLWjBodrLaAp4Mkl6RgdABH0caHG50t2TmfH7koTRsYK/XpTekLdJqSFKqPDJ7YAwMMCbx1qwMw0GSQR6qk4ZDlcDMPg/vvvR319PZYvX47c3FwYDAYolUoAgFKphNFoBADodDrk5ub6nqtSqaDT6cDhcBAb2+YSHRsbC51ON1QvISIYHS8OyA8znDnWYOlUReT0sPj6pG5YCC6C1wqiox0Ey7J4akcN9rdzn08mpfphjYhH45HFaWgwO8GlqaDnD1EUhZumJuC+XyrAAvjihBaLsmLCLsr1U7HeJ4wYFthZYQzIdb1jFNxg9+BQjcX3mKaA7DBp6TVCJYSIS/tZddjdLKqNkWdd08qQCS6apvGvf/0LFosF//73v1FZWdntvt01mu1PA9rffvsNv/32GwDg6aefhlo98LyAvsLlcofkPMOd1ZOk+P50M0wO/zymMcmKgL//5DONLJ5dq8b3x+tRUN2MrFgxfj8tzc/9O5Sf595yHTacbMTpRjNkAi5+NykZy/PiQzKWcGOgH8lAPk+1GlhV48B3J+rRYHbhuB5YMiow3wm3h8H+imaMUIuRKB+4cNlZWeX3mOXwA/K9vWG2CPuqCv0iWu3vqlPSFMhMCY53Y19p/5k+cj4X634+46u4V4l5mJidBAk/Muv9hnzUEokEY8aMQWFhIWJiYqDX66FUKqHX6yGXexMSY2NjodVqfc/R6XRQqVSdtmu1WqhUXYeglyxZgiVLlvged9dvK5D01NeLEDgEAF5YkYGvirQob3aAZb15TReOlAb8/SefaeQxP4WH+SlxAACD3j8CHorP082w+NeuGuyr8k/mfnJTMSaqqLCLrgwUD8PitMaGEr0dcgEH01OlQU++HujnecUYGfaWNaHR4sb7+8sxMXbwnwHDsnhwUyVOamzgcyg8sSR9QJEYlmVRprX4bRsRQwfke6uggCeWpOPlvXWo7tCJAgDOzw78NbS/tP9MR8cAr16UhV0VRjAMsCBLDpuxGV3XooYPycnJXW4fEsFlNBrB4XAgkUjgdDpx/PhxrF69GlOnTsX27duxZs0abN++HdOmTQMATJ06FS+//DJWrlwJvV6Puro65OTkgKZpiEQiFBcXIzc3Fzt27MD5558/FC+BEGbESXi4ZXrk5THVmZw40WCFm2GhFvMwLkEMEW94JLsPV745qe0ktgDvsmioxZbLwwSk/9+BahPeOdyIenObzUOSjIdXV2aH/DV2hZjHwe0zk/DI5iqU6R04UmfBpEE2nz5QbcZJjVcKOD0sPuzBeqInKIpCvJSHuhbLDIWQg3kZgauOHKUW4eULs1Cqt6PJ4sZzu2vhYlisylP2q2pzqFCLeVgzOjoazg+J4NLr9Xj11VfBMAxYlsWsWbMwZcoUjBw5Ei+88AK2bNkCtVqNu+66CwCQlpaGWbNm4a677gJN07jhhhtA096Lwo033ojXXnsNTqcTEydOJAnzhIhh/aEG/HjGv4+fXMDBbTMSMTNNFqJREYJNZXPnSEKGQoD756WEYDReLE4Pnt1Zg8J6K2R8Gr+fFI9lOYoBHetYvQVP76jplFept/WtmjRUTEiU4PxcBTaebcYPp3WDFlwdI0blzY5u9uydayZ4qykTpDzcOzcl4BXIHJpqyYME/s5NgcvDYga5BgUdiu1PYlQEU1tbG/RzkOWn6CNQn2mzzY3rvj7X5d9EXBqfXp5L/KKCxMt767CrwojUGAFunTcCOVLPkJ6/yerCJ0ebUG9yQiniYk66DLPSZSH9vL89pcV7BRq/bbfPSMTSAYiuF/bUYluZsdP2W6YlYMVI5UCH2CcG+/t0uBnctbEctSYn3liVPagWUD+d0eOtQw2+xzECDj68NLeHZ/SMy8Pgf0VabCszosniglLExSi1CKtHqyI2abwvRMN9tLslRbKWQSAMAQoRFwsyu14WGBUnImIrSFQbHNhcaoDDw6JEZ8d9P5zE4ZrOy3vBRC3m4Y5ZSXhqWQbum5eCORnykH/edlfneXZ37bp6Y16GHKKWCAxNAWPiRHh4YWrQxVYgEHBp3DcvBSIu7VetNxAmJUnQPhA1KXlwEbP91WZ8flyLBrMLHhZosrqxu9KEv/1agS2lhkEdmxAaIjPVnzBkMCyLz483od7kwvQ0KWalhXZmHmjqTE4wrDffJNiv6645yZifKcehGjN0Njd4HAqTkiRYkNm1ZxBh8HRcivEwLF7ZV4f3Ls6Jekf+npiTIcNXJ7V+1WoDfTempkjx4aU50FrdkPA5XTrIhzMZCgEeXpiKLWWDEzHJcj7+ND0R7x/RIE3Oxx8mDa4CNTdWCD6H8vuMAK9NxI9ndFicPfjrRqBy+Ah9gwguQo+U6Oz47Li3MnRbuRHTU6W4d24y+BH+IzU7PXhqRw1ONFgBADI+jdtnJgU9l2pqijQsE1OjlTiJtzCh9XMGAL3dg3qzC0my4evRlRYjwD8WpeKlPXXQWN2QCTi4Mn/gtgN8Dh3R72eg/AuXjFAEzEg1QcrHv5ZnYP3hRpxstPr610r59KDaH7Xy4p5abC0zIkclxJ2zk5AaMzw6dYQSksMVQKJh7bkjDWYnbvqu1G/b3AwZ7p0buoTfQPDBkUZ8fdLfMoBDAevXjPAzX4zGz3S4obe58fDmSlQZvEnNQi6NDy/JGTatkHrCw7BotnsFVyROoobL79PmYtBg9kbjMxSCbis/zzTZsL3cK6LmZci6jV5VGRy4/ccy3+NEKQ+vrMwKi+9ANHymIbWFIEQuCVI+8hPEONYuQrCrwoQLRlrDot9WIPGwQKXBGXS3a8LQohRx8ezyDOyqMMHg5mJiHIeIrRY4Q+DuThg8Ih6NzD44wBfUmvFTSyX0h0caccv0riugzU7/wpF6sws7yo1h2eYomiBXHUKv3DknGXFif21eUDu4BNPBYHV5sKvCiK+LtPiqSIt6U+ey+95YladCaoe2L7mxQoyLF8PNsKg2OFBYZ0Fx49AmWBOCg5jHwbIcBf40N7NTSyACIVqYmyH3Je7r7d60iY8KNZ32G6ESQtLB/++0JtztRCMfEuEi9IpKxMUzyzPw4p46X6QrLWbo8zUqDQ58c1KLXRUmv0TSwnoLHj8vvV/HUoq4eG5FJg7VmFFvckEt4SJZxsf6Qw3YU2mEydnav6sKf1+QghmpxKNmOOLysPjulA47KozQWV3gcWjMSpPiivy4iEsOJ0Q/aTEC/G6cGp8ca1uS+7JIC4PdjdtnJvm28Tk0Lh4Ti4+OtokxpYjIgWBD3mFCn4gV8/D4knTUm5xwM+yQJ1h+f1qH9woa0bGRPQVgRa5iQMcUcmnMzZCj2ujAWwcbcLTe2mmfDKUIE0lD7KjDw7AwOT2Q8Tk9OqG/vK8OO8rbe0wx+Km4GY0WFx5amBb8gRII/eSycbEwOT34/nSbyfKmEgPy4kR+S4YXj1WBBYufzuihEHFx0ajwt/GIdIjgCjJWlwdlegcUQi5S5JFbxdNKYggqkX4914x3Djd22i7h0bhjdtKgok97q0x4cU+drzlqe6anSvHohePgNDcP+PiE8MHDsNhVqsX3R2twuNYCu5vBI4tSMTm5+6rRoobOIhzwJjETCOEIRVG4YUoCEqV8vFvQCHfLLPX9gkbMz5T7EuNpisJl49QBqXgk9A0iuILI8QYL/rmtBraWm3m2UoBbZySSHJJ+0jFfLFHKw9IRCqwYqYCE3/2yjs3F4JzOhnqTCzyOt5VFe9Grs7nx/O7aTj43+QlirB2jwuRkKeRCLppIGldEw7AsdpQb8d9jTX69/qalSHoUWwBw8/QEvLK3rt0SszfX77YZST08i0AIPReOUiIvToTPjjfhYLUZJieDZpsH8VKSuh0qiC1EAOlYzvrk9mrsr/a/W8sFHLy6MgtyIdG6fcXlYXCs3goWXl+lDEXPy5kHq83YUKzHsQYL2geuaAp4dHEa8luWCM1OD9YfakCj2YVYMReZSm8pdfv2HuFSolxYZ8HeKhPSYwRYlC2HmEfyh/pCUaMVbx1s6NTXbnF2DG6dnggep3e7T6eHwWmNzdtwXMJDOvErChvC5fcZ7jTb3XC4mUG1LhoqouEzJbYQIWCESthJcBkdHpzU2Eiz4n7A49CY0gezUDfD4qU9ddhR0bmvGwDI+ByktxNrUj4Hd87u+ocRTlhdHjyxrRqulqWBjWf1eOK8dChIkqsfDjeDPZUmmJ0ejI4T4adiPbaU+n8XBFwa105Q46I8VZ+Py+fQPpFOIEQiCjLBDwvIpxBELhkbizK9HXur2kRXjICDEare/VQI/WdLqaFbsTU1WYJbpidG5IXH7GB8YgsAqgxOPLe7Fo8v6V9lZjRT1GDFv3fXQmdzd7vPtBQp7l+WB56TrBETCIShJ/LuPhEEl6bwt/mpKNXZcbTeAiGXxvRUKTEaDBITEsWYlSZFRbMTHpZFqpyP0XEizEqXIVUeuctA8VIekmQ81Jna8o+ONVjRYHZGxBJBsDE5PPjnjmpYnF0nsueohLh2YhwmJkmglgvRRJLyCARCCCCCawjIVgmRTaJaQSdBysff5qeGehhB4Y6ZSfjHliq/BH+Tg0ECacuIw7XmLsXW5CQJVoxUYDrxUBs0GosLZ7U2uBkgVsxFqpyPmAiMFg9Xvj+tQ6PZhQVZclK0FULIL4ZAiABGx4uxblEaPihsRInOjklJEmSrIjdqF0gSJP4RYy4N3Do9EeeRNiUBo9roxMt7630V1wAgE3CQpRBgfKIYExMlyIkVgqZ6L0IIR2wuBkIuBSpCx98bnxxtgt3N4IczeizMkuP2GUl9KhghBBZSpRhAoqG6YqAY7G44PSwkfDqqKujC8TNlWTZqbwwD5XCNGYdqzZAJOFieo+h22T4cP8/BwrIsDHZP0Iso6kxOfHqsCbsqjJ0MiAFAyqcxKUmCGakyTEmRDMl1IBCfZ2sje4WQg4VZMbgyXw1hH3ptHqox4+ezzVgyIibsi6Ae2VyJwnbGzhMSxXh4YVpYiq5o+I2SKkVC0Hh5bx02lxoAeK0XRqiEmJUmw+LsGNIuIggQsdWZKSnSPlWyRhvNdjfWbalCud6Bx85LC2o1ZZKMj7vnJOP3E+OwsViPX0sMMDnamiCbnQx2Vpiws8IELk1hUpIEK3IVmJwsCevvrNbqLbRotnvw7SkdDlSb8ODC1B7zPr89pcX7BRqw8ArecBdcl49X42h9JVp18tF6K/6zvy4iqrSjCeKARhg0nnZBUoYFzmrt+LBQgxu/LcFXRVoMkyBqJ7aVGfDKvjp8cKQRRnv31XORAsuycHqiy2G9VGfHznIjDteYwUTg9/SZHTUo0zvAAp0saHpiMJ9jnISH30+Kx/sX5+CRRak4P1cBVYeJlZthcbDGjMe2VePWH8qwsVgPlyc8398LRirRXg7Wmlx44NdKNJidXe5/rN7iE1sAghpZNDk82FFuxJE6CzxdhRX7yNh4Ma6bFOe3bVuZEWeaSMPqoYSEHwiD5k/TE8Gw6NBzznvR/bBQg2a7GzdMSQjR6IYel4fBs7tqcaDdDVBnc0f0bNLlYfDApkqc1doxOUmC22cmRnS17dF6Cz44okGJzu7bduEoJW6aGjnf08I6C05q2m6YfRE0DMviwU2VOKmxQcSlkRcnwoqRigG1x+LSFCYnSzE5WYpbprEo1TtwtM6CwnoLTmlsvgKPWpMTbxxswE/FevxtXsqQ92Htjbw4Ea7MV+PTdg2fjQ4P3ito7FSE42FYvH6gAe3f6QlBiirWGJ14cFMF9HZvFHGESognlqQNeKl27ZhYsAA+PNImFjeXGDBKTZLohwoiuAiDRsilcfecZJyXHYOfzzbjcK3Zr5ruYI15WAmuz45r/cQWABjsnm72jgz2V5txVusVJwV1Fvx7Vy2eXJoe1ktF3fHDaR3e7qI3pzAM81l6YnuHCU6msnchQwFotHjtRWxuBkfqLDhSZ8HIWCFun5nUaxeHbo9LURihEmKESoiLx8bC5WFRY3SgyuBEjdEJjdUFDkWh3uwakODSWFz44kQTTjRYwefQuGNWUkArv383Xg2ViIv1hxrgaLl2dRUx3F5uRK2pLfKVIudjTnpwlhN/Ktb7xBYAlOjs+OakDldPiOvhWT1z8ZhY5KiE+O+xJpzU2BAjjJ5820iACC5CwJiYJMHEJAmcHganNDYY7B5waGDiMHPp3lNp6rRtfqY8BCMJHB2bNZ/U2HCswRq02X2wqNBZ8W5BZ7GVESPAxWNjQzCigXOiXWNtmvI2W+8NiqLwl1lJeGJbtd+kqFhrx983VeCJJenIUg5eyPA4FDKVQmQG4Fi7K4x4eV+9X4P5Yw2WgFvtLM1RYFyCGL+ea8bJRhtyYzsfv7Cura8rBeBP0xPAoYMj1N1dRCwb2vUCHSj5iRLkJ0rAsGzEVpVGKiSHixBw+BwaExIlmJ8px5x0eY8NpqMRtdh/HnPxGBUWZ8eEaDSBYUKiBB3vK5GY/1FjsPtV2NEAclVCxIg4eGZnDbaXGUI2tu7oLnfH3W77rDQZ1H1c4p2QKMFji9M6RbPMTgZvH2ro8bnntHZsKTWgtN1SbDA5VGPGv3bV+oktDgVM6aXp+EBJkvFx3aR4PLM8Azd2sbysbelkQAH4/cQ4jE8I3oTj/FwF+B2irpOTA3c+IraGHhLhIhAAFNSa8dlxLVweBiPVIqzOUyFZPjAX97/OTsKG4ma4GRbTUqQYlyAO8GiHnngpD8tzFNh4ttm3LRLbJM3KVOJv81JQ3mxHtdGJwzUWnG0nHs5obFiQFR7iuNnuxjM7anBKY0N6jAB/mp6A0fFt36V0hQA6mxuJUh5umda/JfvR8WK8sCIT+6pM+OVcM0p1dlhdDNJ6WO7bXWnEszvb7HUWZclx24xE8DjBm7e/c9g/XwoArp4Q5zdOs8MDUN7eqMFmUZYcFICLRikxI8iVidkqIZ5Yko4fT+thdHqwKEuOhWHy3QwV2pal6UjtIxuZoyYQAsxHhRqU6h0AgFK9A7+VGPD7iXG4YZ6638eKFfNw7cSB51mEKzdMSYBMwMG2MiPSYvhYlBV5y6QURWFWugwOD4PPjms7/T0/MXzE8bcndb6k+AqDA+u2VuPVi7J8kaxbpyfgQLUZCzLlkA9A/HJoCnMy5JiT0bfP8YzGP6K5tcybQ/bXIBWDsCzrt4Qm49P4v6kJPkFc2ezAs3tOYm+5DjRF4Y1V2YiTBLeQY8kIBZYMoaHuKLUIo+aSpHaHm8G/dtXgYI13SXdKsgT3zE2OOM9HIrgIBHgvbK2CC/Au17xb0IjRqWqMDG+LnSGDx6Fw9YS4QSXthgtfFnUWWxkKAe6YFT6VpBqrf76O3c1gV4URa0Z7c80SpHxclKcasvHkJ0rw3Wm937bt5UbcOCUBUkHgb3wUReHvC1JRWG9BeowAM1KlvnZC+6tNeH53Lexub/wrkiw9zA4PPihshM3F4KI8FakS7AO/nmv2iS0AOFxrwduHGvGXWUkhHFX/ITlcBAKA6ybFI7uLKq9fT2tCMBpCsBkT1xbJSpTycOOUeDy/IhOyIAiHgTKtCyNXJoQ2aFOSJVieo+i0nQ7iXWRqihQ3TknAshyFT2wdrDbj2Z01PrEFADNSpUGPbgWKF/bU4tdzBuysMOHBTZURmQs51Bgdnau8I/F9IxEuAgGAiEfj2eWZ+OGMDj+c1kNnc0PAoTArSxnqoQUUN8OistlrlJkk40VcSD5Q3DojEVdNUIMCIBdwwtLeYkGmHCcbbfjlXDMAIFbExaIQFl9QFIVbZyQiP1GMzSUGWF0MLhipGNLvkNXlwSv76tAuhx6xIi5ujBDbGZeHQWF9W6TGxbDYcEbf5yiXy+N189dZ3VCIOJiYJOlzsUQkMyddhm9O6uBqVygS6CrVoYAILgKhBR6HwsVjYrF2tAoWJwMeh0JKYnzE9/UCgFMaKzacacaBGrOv4kvCo/HYeenI6aL8fTgQ7kn/rQLnojwltFY3RqlFEPFCvygxN0OOuX3M+wo028uMMLSLdqTECPHQ/GTESyNDdPA4NIRcGmZnm2Isb3b08Ax/9lSa8NLeOt9jmvLa7lw+LtavoCLayFQK8ejiNHxyTIMmqxtj4kS4qZ+FIuFAeF9xCBHL/moT3i9ohM7mgYhHY1y8CHMy5JieIg2ab02goCgqKDkpoeKToxr874S2U7WXxcX4tWUihCdpMYIeqwejgeImG8xOD0apRT3ayLQGOPgcCguz5LjzvNFwmpuHZpABYm6GHD+3q/btT6RmUrIUShEX+hZ7Cob1GhEXtJjXPrEkDQJu9Fy72jM2QYwnl2aEehiDggguQlDYVW5Crcmb9Gt3tzW1TZHzcf+8lAE7WhP6x+4KI7440TlBnKaA6ybFRVTCrodhUdRoRaXBAQoUpqZIkCAdmHUHITxoMDvxwp46nGqpgJQLOPj3+Rndfq4XjFRgUpIEChEHYh4HciEXTX1vIRkWXD85HiaHB/uqTEiVC3BVft8roeUCDp5emo7Ht1Wj2ujf67FYa8cDmyrx/IqsQA+ZECCI4CIEhSvy1TipsaLJ6t+0ucboxAObKvDGRdkDKmUn9A+G9Yqr9pGB6alSrIqg6iiHm8E3p3TYUKz3a5H0wREKn1yWG1QfKELw8DAs1m2p8k3MAG9y9NF6K5bldC24KIoasD9euCDk0rhvXgo8DDugaH+ijI+XLszCllIDvj+tQ5WhTXiV6BzQ29xQRqhPVbRDPhVCUEiR8/HCBVn49KgG28uNsLZrDWNxMqg2OjGGCK6gMy9TjlFqEerMTvBoCrmxwogSKEa7G+u2VqFE1znPJVMZWa+F4E9Ro9VPbLUyIgKToQfCYFIruDSFZTkKLMtR4Okd1dhb1RbmO62xYVaQ+jsSBge54xGChlzAwS3TE/HHKfE42WhDmd4ODk0hTy3CyEFGVxxuBhTlbSNE6Jl4KS9ikoo78mGhpkuxNTJWiL/PTwnBiEKHh2HBsGzUiMyuLDjWjlYNG8EVKNJiBH6Cq7U5OSH8IIKLEHT4HNrX2DpQPLm9GhUGJx5akDpsq+yGA0oRFxQAFt6l0THxYizPUWBOuizsiy8Cid7mxi3fl8LNsJiRKsXvxqvDPg/ySJ0FVQYHlucoIOB2FolZSiHunpOMbWUG8Dk0lud687MI/aPj9Y8UwoQvRHARIpKyZgcMdg+e3F6N5y/IDPsSf8LAuHpCHFaMVMLmYhAj4ERF9aibYVFYZwHLelsJdSVGusLhZsAC2F1pwsEaM26bkRi2vfUazS48sa0abobFmSYb7p3bdTRyfqYc8zMjr0VUODE5SYpEKQ/1LW2QRsdFRm7mcCQ6YtOEYYdC4BVYWpsbHx4hbvDRjErERYqcHxViy+Vhce/P5Xh8WzWe2F6N234oxTmtvdfnKUVczGvnfeX0sHhxTx02Fut7eFboOFpvgbulUmNXhQmnNNYQjyh64XEo3DUnGUkyHhZkypEXIcUwwxEiuAgRSfvllO3lBjRZSd4CIfwp0dn9enZqrG48uaMaHqb3ZaAbpsQjWdaWi8cCePtwA6oMfTfOHCpMTv9WLEfqLN3sSQgEo9QivLFqBO6akxyWXRMIXojgIkQkczPbqnDcDLClxBDC0RAIfUPVkpPWHq3VjYo+uI0rRFw8uTQDOe2Syt0M8M1JXYBHOXjiOrSbqTY4u9mTQBg+EMEVBpxqtOKToxp8cbzJ5yBM6JmpyVLEidvytnZVmkI4GgKhb8RLebhgpMJvG59D9bnxslLExdPLMnD5uFjEtCyxuvoQHRtqRsf7L2u5w3CMhODQbHPjs2NNWLelCvf9UoFNLb1ACSRpPqQ43Axe3V+P7eVG37Z91SbiFNwHODSFNWNUWH+oEQBQY3SAYVnQJJxOCHNumpaInFgRtpcZwKEprB2j6tIioTt4HApXT4jD78arobG4wrJ5sVrMw+x0Gfa0TIRGxpK8omjHzbD4qkiLL4u0cHraBLbF6cHSHEXoBhZGEMEVQj4s1PiJLQBotnu62ZvQkRW5SuytNOFEow1uBrA6Gb/EaqvLgxKdHWkxgmFVxVhvcuJovRUiHo1ZadKQ+jZ5GBbndHZUNDtgcngg4dOYkChBkiyy3cIHy+LsGCzOHlyFIZemBvU+VhsccHpYJMp4EPMCX5Dwp+mJsLkYWJweLM9VBPz4hL4zUFf7vsKyLF7aU4cdFcZOf1vRIaI7nBk+d6EwZHMXeUfLyUygz3BoCvfPS8FTO2pgdHh8YsvDsPj0WBO+PaWDm2Eh4dFYv2ZEj01xo4W9lSY8v6fWN8NckCnHXXOSQzKWQzVmrD/U4CtXb88fJ8dj9WhVCEZFaDA78cS2alS25FVxKGByshS3zkiEKoAtYeQCDtYtTgvY8Qj952C1Ga8dqIfV5cEt0xKxaJAivzv2Vpk6iS2aAi4ZE4uVo8jvvBUiuEKIWsL19cGiKeDCkUpcMjY2xKOKLORCLp5aluHLEfEwLJ7bXYvd7XK6LC4GRocn6gVXg9mJ53bX+uX0HK4NTWdfjcWFp3bUdJu7U6y1DfGIIoNSnR1vH25ArIiHO+ckBWWJ/LtTOp/YAgAPCxysMeORzZV4+cIsUuUWJWgsLjyzs8Z3PXjtQD1mpEmDEs1s3+OUAjA5WYIr89XIJUvJfhDBFURam+5WGxy4qItmwU8sSce2MgNYFpiSLEV6mDtHhzPclnD51jKDn9gCgGylYFgsYR2tt3ZKoE6LCc13SsSjkSjlodroX51GAZiZJsVNUxNCMq5wRmt1Yd3Wqpablw0Ls+SYkiLt9XnlejtYeJ3b+8KkJCl+Km7utL3S4ITJyUAeBX5nBK+Ibn89cHpYVDQ7MDpOHPBzrRipRF6cCBYng2Q5P6CR0miCvCtB5JmdNThc6/WfOVhjwdtrRvglxyqEXKwZHZyIVonODgmPRuIwEBrt6ZgTJ+HRuG1GUohGM7QohP43Sj6HwnUT40IyFimfgxcvyMTReitKdHZwaW8lXm6scFiI34Hw4xm9X6SgztyzlYLZ6cFT26txotEbLZyUJME/FqX2GhWblirFA/NT8PFRjS/CLuHTuDo/joitKKLZ7l/xTgFIlAbvt9dXwT+cIYIrSJTr7T6xBQB2N4Oj9RbMzQh+G4vPjjXhv8ebwOdQWLc4DWPjAz+jCVfaJ8enyvm4d24yMofJhWBaihSXjo3FgWoTEqR8XJWvRnYIGwHzODSmpkgxtQ9RmvaU6e2oMToRK+YiRyWMmmbNvbGvyn/5N1bUc/Xhx4Uan9gCvOaiJxqsyE/svR/hzDQZZqbJYHZ6YHJ4EC/hDavelMOBSYkSfH5c63s8OVkCJYk8hRTy7geJc7rO7TpE3fRM++G0Dp8db0KchIdlOQqsyFUMOI+i0ezC/4qaAHhDyF+e0GLs4uEjuG6bkYi5GTLECLgYqRYOK5sIiqJw7cQ4XBuiqNZg8TAsHttahcL6tjYwQi6FFblKXDMxzrdsHI04PQxqTW0RLS5NYWxCz7/b9hO6Vuxupl/nlfI5kEZ5buNwZXS8GDdMiccvZ5sxSi3CjVPjQz2kYQ8RXEEiQeo/OxVy6W4voIdqLTA7GZidDrx5sAHH6i24e04KeJz+32DOar0WCa1UhmHbj2Ai5NKYkSrrfUdC2GFyeHC03r/nnt3N4ptTOlhcnrBdGtbZ3Kg3OZGpFAw4IZnPoSHkUrC7vTk38zJkvS7vJcv5aLS0VYDGiriYmNR7dIswfFiVp8KqPFIlGC4Mj1j9/7N3lgGOlefbv86Ju4+7rbu7suziUKylOEX6b2mBGlC8WFtYtEALLbRvhZZSWhx2l3V3350d90zc9Zz3Q2YyydiOxOf5fWGTSSYPk+Sc69zPfV9XEpicJcasvNDBT8Ch8MCiXAgHqHBNz4kWYruaHHhld9uIXtfd6wpXMMBrEgiphlLExcXjVP3+rLcQSwUOtzlx/+f1uPXDc3jw60Y8trEp/DN/kMU/jxnwsy/r8avNzdhYYznv71tWEhrZr9IIceec8w8VfG9WFirUQtAUMClLhMdXFoI/RrZfR0On0486swfeYVYDCYTRQipccYKiKPxyeQHqzV5kS3mDWhKsq1JhU601alR7a70N35qoHnb/UW6vpsjKJPbwEAjD5XuzszFRJ8LGWivOGT1w+hmUqQS4ZWbqbId4Agx+v68DG2ujffSyIqra7x3W4+PT5vDtfS0OtNh8uGnGwP8fd83JxqXjVSiQ84fUUlCgEOCFdSVgWZZYOQyRj0+b8MeDejBsaMjk7jk5WFBEKuKExEAEVxyhKWpITctCLo0nVxXhqc3NqIno/Tpr9AxbcE3KFmOCToRTnW5IeDS+PVU77HUTCMlkUbEci7qGS1JNTDAsi19HTB93oxJycFuEKKwx9u3h/PiMGd+ZNnAvGoemRmTjkUp/n1Tnmzobup0SLJ4gnt/WgoeW5WMuaUMgJAAiuFIElYiL59cU4dOzZuxqdIDLoTAtZ2TN7k+sLMSeZgcmZ4uJHwohrUk1MfH1OWsfsVWsFOCni/Ogicg0XFIix8nOaHNXCZ+DEbRlEmJIkYIfdVHLAvjolIkILkJCIGfjFILHoXHFBM2ovbkEXBpLS+JvP0EgjDXqLT0nazGPxrcmanDZBFWf3qmLqlRgWeCLajNa7T6UqUJ9Wb0FpC/IYGONFU4/gzn5UhQT8+O4ct0ULQ60OmHz9vidmd2BQZ5BIMQOIrgIBAJhiNw0PQuz8qSQ8GlUqEWDThJfPE6Fi8epBt0W/fPhznCv19+OdOKeuTm4gOSpxo1cGR9zC6TYEJFja/MG4Q+yI5oKJxCGAxlpGcOwLIszBjcOtDjIxA6BMAREvJCZ6wSdeMgn6MG2RWsjtreCbCjv7oyB5EzGk97HOoePQTXJ9iQkAFLhGqMEGBaPbWrC8Y7QuL1WzMXjKwuTlr1HIIxFxmlFOBHhFs+wwPYGW5/cVULsmJ0vxbaG6LxVKYk0yhhs3iDeO6RHh8OPeQVSXJpCPmSkwjVG2VxnDYstADC4AnjymyYYXf5BnkUgZA4sy6Le7MHmOis+OWPCoTYn/EH2/E+MIVdP0qBAHm3lktgVjD2WFMsxt6AnbmpOvgRF5EIzY3hzbzs21FhxrMOFtw/o8ZfDncleUhhS4Rqj2COaRrvROwO4+3+1uGm6DpeMU6XchNhoCTIsaCp0BbS7yYHxOhFpUh6jNFq9eGVXG6p72Tfky/l47oIiyIWJOTRK+Bw8t6YYfzyox5E2J7KlPFw6gPkrITZwaAoPLyvAKb0L7gCDaUPIniSkD8f10SbJ/z1lwvVTtCnRo0cE1xhlXoEMfztqgK/XFb0vyOLtA3rUmb24d0FqRqkMF7M7gPcO6bGz0Q4OHYpRsXiCUIu4+MMV5Rmd0RdL/EEWe5vtONrhQpVGiFXlyhH/Lk+AgdkdSEpoMsuyeHpzM9odfau5LTYf6i1eTM1J3KFRJuDgRxnyXUsnJmSNnYzZsUSOlA+rp2eb3s+wsHkDUbYtyYIIrjFKnpyPZy4owr+OG9Fu96Hd6Yc30CO+NtZaMU4rwoWVyuQtMga02Hx4ZGMjjK6u0e8g4PKHqnsmdwBGlx/Zvdz5CX3pcPjw3NYW1JpD2ZxfVgMryhTDDge3eAJ4Z78eO5tsCDAht+8HlxZgvC5xPUsURQ14tbuiVI6ppOJBGEN8cNyILfVWSPkclKgEWFosT2sxev0UDX61uRndtYRSlSAlxBZABNeYplIjwkPLCgCExMe7B/XYWm8L95DsaLSlteBy+YN4fFNTj9jqBQVAQLLnzkuHw4cHv26M+jvmyvjDFlssy+K5rS04FWEIavEE8bejnXhyVVHM1jsUnlldhK/OWXHa4AKXppAn42NWnnTAgHkCIVNpsHrDsXInO9347KwFZSoBvjNVhzkRvW7pwsw8KZ5aXYSvqi0Q8mhcPWl0vpaxhAguAgBALeLi/kV5uG6KFofbnDC4/JhfmN7uyxtrrNA7Bx4CqNIKoRyGE3+NyYP/nDSi3uKFjM/BbbOyUKnJ/GmyV3a39xGtM3Ml2FJnxcw8KWRDnPBqd/ijxFY32iRcfcqFXFw9WQMgdQ7GBML5OGf0oEQliGkbxB2zstBs9Yar1wBQa/biV1uasbBIhh8vyIWAm14XppOyxJiUglU6IrgIUeTL+ciXZ8YW21lD3zy7SK6aOPST7T+OGvD+cUM4hw0APjxpws+X5I90eWlBo8UbNc0KABIejU/OmoGzgIhL4+652Vheqjjv71KJuFAKObB4egY2sqU83DhdF/N1EwiZxoYaC17d3Y5rJ2tww7TYfWcUQi6eXVOMP+zvwKZaa9QxbmejHVZPAE+vLsq4IapkQAQXYUgEGRbbGmzY1WSHL8BiSYkcK8vOf5JNJouKZag2utHWT3P01ZM0Q67g7W224+/HDH3uL1Vl/oQjw/YcfSU8GvMKpdhUawvf5w4weH1PO6bmSM6b2ynk0vjNhSX48pwFJrcfE3ViLCuV94nFIRAI0TAsi38cDR2DttbbYiq4gNB384fzc3HlBDU+OmXCtgYbPF09vSf0bpw1eog3XAwggotwXjqdfjy3tQXnIlyxD7Y5MS1HnDLNiP0xv1CG+YUyvLyrNUokcCjghmnaIf+eXU2OPveN14pw5SgzL9OBEpUQz60pgsPLYGqOGOdMnqi/JRCabG2yeocUlJ6VQhUtb4BBY9e6U/lznGp4AwyMrgDsvmDYtb27D244W/SEoXNK70Zn17Z+h8MPlz8IMS/2Zq0FCgF+MD8Xt83KQq3Jiza7DwohB5UaYcxfayxCvh2EQWFZFr/d3holtoDQVpKEnx7uzN+bnY06sxd1XT0KciEXwymOz8mXYGdj6IpPKeTg8vFqXD5BnXA7g2QxQdfTCzEpS4yLx6nw6Rlz+L4sCTeuB+QOhw+dzgAmZomG3ag/EEfbnXhhRyssniAoAJeMV+GOWdlRj/EGGHx1zoImqw9VWiFWlCrGzHveG7s3iI/PmHCw1Ykakydq2ykSlYiLZSVyfGeqNu36flKZBmtPfxWLkEUL4niNIOZxMDlbjMlkiCSmEMFFGJQOhx+n+8l2u2N2FoRpckAV8zh4fk0xvjpnQb3Fi+Wl8mH1IywskmNWnhSeAAMpnzNmT7rd3Dk7GytLFdjf4oBUQGNpsTwuV9tAKBHhlV1tCLLAOK0QT68uHrWBIcuyYbEFhE5gH582Y1GhLDwOz7IsntzcHO5f+/IccKDVmfE9ewPx8IZGNFi8532c2R3Aplor1lYqkSvLjF7QVKCz1/BPuhx7CdEQwUUYFJWIi0IFH01dY8OlKgFumZGF6bnp5VUk4NKjytQScOmkX7F7AgzqTB602n1w+BhI+TRKVUKUqRNf7q/QCFER522GAMPiD/s7wn46ZwwefHnOjEvGjS4bzeoNRjXud2Pz9dxX38+wwM5GOwwuf1KmKpPNA4vy8NEpI84aQp+/yAoXlwYUAi4qtUJMzZZgSbEsJk79viCTMv19LMsmtWnc7e8J3JYLOEk/FhFGBhFchEERcGm8fFEpGq1eCDg08jJkgjGeNFu98AXZmAihIMNia70Nn1ebcc7oQX9Rf3fMykqpgFaWZRFkMerRdasnAIePibqvuUv4jwalkIsJOlGURYVOzMWUiO0T+QBWF+wYDTosVgrwowV5AELbWZ6I3i0RL7Ynf4c3iOe3teBohwulKgF+ujg/qZPT/+9wJz4+Y4JOwsPPFuejaBRxYPtbHKApYEauZFgCLrKqXqggx+B0hQguwnnh0BRKVaRpciis39GKzfWhpvIVpXL8eGHeiH+XwxvELzc2hnvP+kMh5GBGClUbO51+/GpzM5ptPtw8Q4fLRiEEZQIOaApR1ZShen6djweX5uP940bUmTwoVAhw3RRN1LaoRszDFV0TW91cPE4FnSR9qlv+IIMakxcFcj6kMfq7AQCPQ4HHiV//5r9PGnG0q7pYZ/bihR0teHFdadxebzC21tvwrxNGAECT1YfX9rTh1xeWjOh3dds6AMDKMgXunZ8zZNFVEiHylhTLR/T6hORDBBeBECN2NtrCYgsAvqmz4aIqFapGOE59uN05oNjSirm4oEKJS8apIE2h4YV3DuhR39Xr86eDeszOk464Ksrn0LhighofngyJHimfxrqq2AQ7K4Rc3Dk7e9DH3DozCwuLZDhn9KBYKUirBmKWZfHoxiac7HRDwqPxg/k5WFiUHifqxl69YjUmL9rtPuQkoSfsQGv0hPIZgwcBhh1R9XZbxLFhU60Vi4pkmJ0/NCf3mXkSCLk0ZHx6SJ53hNSECC4CIUb056Le6fKjCiMTXIuL5ciS8HDG4IbNG4SIRyNXyke5WogsaWpWWo60O8P/ZljghN41qm3oG6frUKIUoMXuw6oyxZCsJ2LJOK0oLf2H2h1+nOz6PDr9DF7a2YYKtSglPzdtdh8cviAKFQIIuTSyZXwAzqjHjHZQYqR02150I+HRGOlS3IHo/egdjfYhCy6NmIdnLiiCUsiJ+RYuIXEM+eil1+vx97//HfX19fB4oi0C3njjjZgvjEBIN3qLAZoCyke5FVulFY24QpYMfL2azAayDxgqNEVhGbmiHzXeIItPz5px68ysZC8lTJvdhzf2tuNIe2j7UCXk4IlVRbhqohpb622we0NDDNNzJUnzSVtQKIvy4VtaMrwJ50jGa4U4EzHx3WIbXj9ieRKGYwixZciC6+WXX0Z2djZuuukmCASZ77BNIAyX1eVKbKixotnmAwXghqm6pGyDJJMiBT8qk20suPGnIrkyPspUgqj3otl6fluHROENMHh8UxPaI1IgzJ4gtjeEXNTXryvBN7VW8DgULorRNvJIWFaqQKczgO2NNkzJFuOmUZj2rq5Q4tOzFgS6rkIKyADSmGPIgqu5uRlPPfUUaJqUM2OJ1RPA59UWNFq8YVNJQnoiE3Dw4roSnNC7kC3NnEzK4XDzjCz8anMz/AyLlWXytKrOZRp3zM7GIxsaw5OtqST+D7c7o8RWNyVdAl0n4eHaKUNPg4gnV0/WdAWdj44ihQA/XZyH3+1pB00BV01MncniTKDa6Mb+FgdM7gAqNSKsKFUkbSt6IIYsuCZMmID6+nqUlZXFcz1jipN6F57d2gJbV+l8R6MdZSpB2HxxKDi8wZhOIBFGh4BLY2be0PoyMpHpuRK8fmkpjK4AJg7jc0yIPZOyxHh0RSG+qDaDx6FxXYoIGKDv9jsArCyTY1GaNPaPlPmFMswtCB0fYpWaMNZhWRZv7evA59WW8H1fnbPieIcL9y8a+ZR4PBiy4NLpdHj66acxd+5cKJXKqJ9dd911sV5XxmPxBPDEN03hgNDhEmRY/Hp7C3Y3OfCdqdqUOpgSxjbZUj6ypalTTRmrfHrGjLcPdEAj4uKxlYUDeoslg0qNCI8uL8D2RjtoClhWIsfUnNSxN4knRGjFln0tjiix1U2t2dP3wUlmyILL6/Vi1qxZCAaDMBqN8VzTmGBTrbWP2CpXC4dc3frXCSN2dzVzfnTKhGsna5LqhEwgEFIHhy+I9w7pwbBApyuA1/e047k1xaP+vTUmD9rtPpSphaOO7pmVL8WsIU7pEQgD0b1DFAkF4MoJqbdlO2TB9f3vfz+e6xhzcHqJowq1EL9cXjDk5399zhL+t8vPwOwJJnxknkAAAJsngL0tDngDLJaUyFOqktIfLMuixR4KxBbzaOgkvKjvjssfxP9OmeHyB/Gdabp+c+uCDAunnwGHQkqGuDdYvPBGTIye7nTD5Q+OOPOyze7Db7a3oqYrxJ6mgHvm5mBNhTIWyyUQRsyKUgWarD5srbfBG2QwTiPCxeNUQ7bcSCTDOkO3tbVhx44dMJlMUKvVWLRoEXJzc+O1toxmbaUS7gADg9OPcVoRVpYphhyK3On0w+AKhG9zKECR4ic5QuzxBxnsa3GgxuSFSsTByjJF3EKkB+Kc0YPHNzXC3hXB88/jBrxycSkUMcjSiwdHW2345ac1MEZ8fwBAKeTggnIlLihX4KktzeHsUKWQi6smRTdMdzr9+NmXDTC5Q7+DSwNqEQ9ZEi60Eh6ypTyUKoUoUwuStrXK79UszAJw+pgRfT5YlsWzW1uiwqsZFvii2pKRgsvo8sPmDQ6YruENMPjTQT12Ndkh4XNwywwd5hbIErxKQjccmsKtM7NSyvJkIIZ8VNy/fz9effVVzJw5EzqdDq2trfjFL36BH/7wh5g9e3Y815iRCLg0rh9h35XZHX2yGKcVDVmsETKDE3oXXtzRGiW8N9VaEx6B8srutrDYAgCLJ4ij7S4sKUnN5ufd9eY+YgsIrftfJ4z4vNocld/Y3ySdTsLDm5eV4ctzFmyssaLe4oXe6Yfe6QcQbX4r5dMoUwlRrhZinE6ECVoRlAmoRBd1mYh2Zx6KuPSIK49mTzBKbHVTFefw8mRwutONxzY1wRNgMLdAil8sye9zbP3TQX24Z8jiCeL5ba34/eVlSfMKI6QPQ/7m//3vf8dPf/pTTJ48OXzfiRMn8Mc//pEIrgThD7LocPqQK+NFZcytrVQmdV2ExGJxB/D0lmY4ewU7dzr7Col4EmTYPjEsQP8TaKnCzXMLYbA58PU5Sx+TVgB9wrKzBshOFHBpXDZejcvGq9Fs82JfswN7mx04bXBHmb06fAyOdrhC2YCnQvdlS3kYrxVhcrYYc/KlUMXh7yXg0rh1pg5v7O0AAFwxUQ1BP1ujQ0Ep5GCcVogzhp4m5CnZYtw8I/UrCsPl3yeNYZG6t9mBr85Z+sRJbW+0R90OMCzOmTxxFVwMy+LTM2aIeTQmZolH3T9HSA5D/qabTCZMmDAh6r7x48eTBvoEYXT58csNjWi1+1GlEeLCCiW+PGfBZePVxIl7jHHG4O4jtgDg2hh4BQ0HDk1hgk4UjpABgCXFMkw6T+Zgg8WLr2ssqFQLE/7ZFXBp3Dk7G9dN1uBkpxtNVi9sniAoCpDwOPj7MUP4sXwOhRVl56/UFcgFKJgowJUTNbB5gzjR4cJpgxunOt2oNXng72W33+Hwo8Phx5Z6GygAFRoh5hZIsbBIhgJ57Ixi11aqUKkRwekLjmoCkKYoPLWqCPtaHDC4/JigE6dl3NFQ6OhV0TzU5uwjuLRibtgFHwhtKUeGS8cDCsCxDhf2NIcGpUpVAiwplmNZqRxaUllLG4YsuEpKSvDxxx/jiiuuCN/3ySefoKSkJA7LIvTmb0cNaLWHDgZnjR5My5Hg/euqwOck34j2o1NG7Gp04Lk1RWRSMgFMyBKjRCkIh0TnSHm4ZrIGq8uVCV/LQ8sK8NEpE4wuP6bmSLC89PwC5ff72nFcHxJp2xvteHBpfsJH5RVCLhYUyrCgsKf3ps3uixJcV0/SDLtqIRdwsKBIhgVFod/rDzKoMXlxzuRGrcmLWrMHTVYvuiP6WADVRg+qjR789YgBVRohrpuijVnDb6ziYARcGouLU3ObOJbkyfhR26f2fibgbpuZhV9va4Hdx4BLU/je7Kxwr57R5YfFE0SJUhDTNg+KovDAojy8vKsNOxrtqDN7UWfuxN+OdmJpiRzfmqhBgYKkOqQ6QxZcd9xxB55//nl8/vnn0Gg0MBqNEAgE+NnPfhbP9RG6qDVFe4qcMbpTQmzta3bgTwc7AQCtdv+YdFdPNHIBB+svKkG73Q+GZZN6oJUJOLhxmHEndm9PdW5vswMfnjTh6kmJrc71R66Mj0vHq3C03YULK5QxSX3gcWiM14kwXtdTEfIHWTRZvehwhPq+Ol1+GJyhRm1fkMUnZ8xQi7goI9l5CWdVmQK7mnq2DKfm9K3WTs2R4PdXlKPR4kOBgg8pn4M2uw+v72nHsY5QLuSaCgX+b15sB8oEXBo/W5KPDTUWvHNAD5efQYABNtXasLnOhgWFMtwwTUeOwSnMkAVXfn4+1q9fj7Nnz8JsNkOtVqOiogJcbur2a2QSvRPiky+1Qlfvr+5uC98+0OJAvjz1vE8yEZqikJemB9ZStQANEbl+/z5hxLpKZUrYK9wxKzvur8HjUChTC1NWUHkDDKqNHhhdfvA4FKR8DuQCDrKkvIRPwSaaOQVS3DM3G/89ZUaxUoDLB/ByEvM4YRF9Su/CU5ub4fT3XEhYPX0rY7FidbkSU7MlePtAR3iLkWFDSSW7m+xYXa7E9VO1SeulDDIs/nbUAK2YizUVSjLQFcGw3hEOh9Onj4uQGBYXy3FC39Mr071lkUx+v78D1oiS+wcnjbgsBc3mCKnFNZM12N5gD4f4uvwM6ruyRAnJ50i7E89vawlve3ZDIdTwX6IShEyadSJUaUQjbsZPVdZWqrC2cmjVTYc3iF9tiRZbAHBhnO0ysqQ8PLSsAKc73fjzYX343BBkgS/PWbC9wYY7ZmdjZVni+3v3tzrwwYlQb/fBNid+viQfXCK6AJxHcN13331Yv349AOCee+4Z8HFvvPFGbFdF6MO6SiUsngB2NNgxt0Aac/+bn3xRD4s7gEvGq3DpOPV5r0qMLj++PmeNuo9EVhCGQoFcgNtnZeGtfR3h+8gBOXWYWyDDb9eW4IOuNItuYcwiZJPR7vCHUy64NIUKtRALi2RYUiJP6QnVePBVjaXPZOvl41UJc9AfrxPhmQuKcaTdiQ+OG0PTsACcfgYv72rD3mYHfjg/J6HVY5cvumXgv6dM+FYKtAykAoN+O+66667wv3/4wx/GfTGZjtUTwOlON4qUgmGP9VIUhe9M1eE7U4fXLzMU3P7QFgIA/OlgJ462u/DwsoJBRdfRdhd6D9UvTIGqGyE9uKhKhQI5H5+dtUAr4Wbs1Fu6UqoS4qeL8+HwBbGz0Y4dDTacNrj7xJEFGBanDW6cNrjx7iE9ZuRKcM0kzZAjytIdf4S1iIATMuDsPdWYCKblSDAtR4IzBjc+PWPGnmYHPAEGu5rsaLF58dya4oSJrt6tDh+cMGJdlTLjt6OHwqCCa/z48eF/W61WLFiwoM9jdu/eHftVZSBb6214fU8bPAEWFIAfzM9JylRZf4h4dNTU24FWJ94+0IG75uQM+ByrJ9rzqVDBx83DbJ4mjG2m5kjGTGBxuiLlc7CmQok1FUoEGRa1Zg9O6t2oMXlQZ/agxeZDt+Zg2NCx41CbEz9akIvlw7D8aLJ6cVLvxrJSeb9RSqnKFRPUyJbywKUpTM+RQJrkxI9xWhHGaUXwBhjsb3Fgf6sDrTY/djfZsSpB55txWhGKFT19mi4/gx0NdlyQgakEw2XI9d8333yzX8H11ltvYf78+TFdVKbh8Abx2u62cLYZi1CjcKoILgC4ZJwKr+1pD9/+7KwFCwplA54QIw8sUj6N31xYnHG9HAQCoQcOTaFSI0KlJnLikoHeGYDdG4TdG4SPYUITlgPE4vSHP8ji0Y1NMLkD2FpvxVOri9KmPUHApYclLAeiw+HD344YwLDATTN00A1guDucdS0qlmNRkqw8bpiuxTNbWsK3m22+pKwj1Tiv4OroCPVZMAwDvV4PlmWjfsbnp+ekVCI5bXBHBcmmIhdUKLGvxRGeegGAr89ZBxRc7ogm0e/PzYGIlIsJhFGzo9GGswYPuDSFSVkiTMuRhLf2GZYNe0QNlPOXaHgcetQ2BLVmTziX8rjejU211pS6GI03viCDJ79pDosSvdOP5y8sTvKqRse8AhkuHa/Cx6fNAEJmx/4gCx4nPYR0vDiv4Lr33nvD/+7dx6VUKnHNNdfEflUZRr6cDwqI6nka6hRMInlgUR4e39QUdg6vNXsGfGx35MmiIlnSrqIIhEziw5NGvHeoM3z7gxOATszFz5fmo0QpxC83NOK0IfTdLFYI8P15OVH+XulKb3PRPc2OMSW4TujdURWg0wY3Ohy+pAWfx4o7ZmWDT1P490kTDrU58evtLf1mU44lziu43n//fQDAY489hieeeCLuC8pEcmV83DknG/8+YQTLApeMVw3o75JMBFwav1pdhM/OmrGtwTao2/WsfCkeWpqPOQWJmcZJd74+Z8GeZgdWlsmxsIgIVEJfmL5pTeh0BfDoxib8ZHFeWGwBQIPVi0c3NuK1S8qQJU3vaJccWfT66we50Etn9rc4cKzDhSIFHyvKFOFtU4Ozb0B6piAX9ux87G12YEejHUtTNNg+EQy5h4uIrdFxUZUKFyVhemW4cGgKl45X49LxgwtCLk1hXiGZShwKJ/WucH/c/hYHHllOJ2xsnJA+XDpehaMdThxpd0Xd7/Iz8PgZ8DlUVOC2N8hiU60V10/VJnqpMaVALoBKxIW5a1vR5e9HeaY5OxtteH5ba/j2vhYnfrE0HwCg7dWvJePTA4ampxuCXmkoX9dYiOAaiKeffhoPP/wwAODRRx8dMCePiLHY4w0wONDqgC/IYmqOZMz522QSOyOiQlgAH58xE8FF6IOAS+OxFYXY0WjH9gYbzpk8kPBoLCySYX6hDLd5g/j9/g5EZmFL+JkxqHLFBFU4Imw4Dffpwjd1tqjbu5rsOKV3YUKWGNNzxJjYFQJPAbhzTk7GZNJO69UDfErvHtO9XIOexZctWxb+98qVK+O+GEKIdrsPj21qQntXcr2QS+GpVUWoIl5FaQmn18GzMSIcl5A5/POYAV/XWEBTFG6blYV5BcOvAHNoCktL5P1WAdZVqVClFeHj0yZYPEFMzBIlxfMpHlw2Xg29M4DdTXZ8J80rdv0h78cuotnmw4QsMSiKwpOrirCvxY4ChQBFGRRCnSfnY3KWKBxW72dSe3gs3gwquBYvXhz+9/Lly+O9FkIXfz1qCIstAPAEWHxwwoiHlhXAH2TQ5vCDYVjkyvjEiiENKO+VmTdWr+4ymX8eN+CvRw3h26/vacecfGnM7Q3K1UL8eGFeTH9nKkBTFO6cnY07Z8c/yzIZrKtUYXOdNRyXxKWBCVk9F9A8DpWxvZ33zM3BL75qgN3HoEDOH9PHvyHvU23fvh0lJSUoKChAa2sr3nrrLdA0jTvuuAP5+fnxXOOYo78mShGPhtHlx0+/aICxq9eBR1OYmCXCyjIFFhXJx/QHOZVZUChFhVqIc6ZQM/Cq8sTnmxHihz/I4J/HjFH3WT1BBBmAJm4pBAAVGiGeXl2Mfx43wB9kcdE4FQrkqVnJMrr80Ihj10NWoBDgN2tL8E2dFQvGeN/vkMsj77//PqTSUN/Jn//8Z5SXl2PChAl4++2347a4sUrvbQIZn8ZVEzXg0hQcvp4Raj/D4ki7C+t3tuHnX9Wj3U7M5VIRHofGEysLcdN0He6ek41rSK5YRuH0MX22SsZpRee9ADrR4cKDXzXgux9U43CbM55LJAwDT4DB3mY79jTbUW10IxijbbDxOhEeXVGIp1YXpazweO+QHrf9pwav7W6L6e/NlfHxnam6lPGPSxZDrnDZbDYolUr4fD6cOXMGDzzwADgcDm6//fZ4rm9MsrREDo2Yi8NtTgi5NFaUKcJN879cXoDf7miF1RPtXVNj8uKJb5rw2iVlY9rnJFWRCjgpF+DaZPVia70NN0wjkUyjQSniYpxWiDOGUAVTLuDg3vkDx2IBwBfVZry5tyPszVdr8mB6Lok5SgVe3NEaZQAt5dOYWyDD2kplRmd+ttt9+OiUCQDwdY0Vl01QZ1Q/WSowZMEll8vR3t6OxsZGlJeXg8fjweslzb/xYlKWGJP6CYCdmiPBqxeX4stzFmyus6ElwjDPF2QRYFgiuEYAy7LYWm/D1zVW+IMs7luYi5xhBoynEyc6XHjimyZ4gyyunKgmwbKj5NHlhfimzgqKAhYXyaHsukD6+LQJx/UuTM4SY1W5AmIeB3ua7VFiCwCmEbGVMlxUpcKBVke438rhY7Cp1opNtVZMzRbj3gW5o47eSUUOtDqjJmAPtzmJ4IoxQxZc3/rWt/Dzn/8cNE3jvvvuAwAcO3YMxcXpHUGQjiiEXFw7WYtrJ2vR6fSj3eEDl6ZQrhaCzyFN9MPFH2SwfmcbdjT22Dec6nRnrOBqtHrx9NZmeIMs5AIOEVsxQCrg9PGu8wdZvHNADxbA7iYHPjtrxv0L8/DyrrYosTU9R9xnsIKQPKbnSvDsBcV4cWcr2uzR/bRHO1y477M6PLW6KOO2x2zeQNRtT4r6oTXbvPjXcSOEXBqXjFOhMI1E4ZAF1/Lly8Ph1QJB6H+wsrISP/7xj+OyMMLQ0El4GXm1lSgYlsULO9qwK8Iri6Yw5MgUhy8IEZeOeVXRF2TwRbUFF5QrIeLFTkQHGRYv7WyF0xc6mM7KI5WVWNLp9ONIuxMCDo25BVJoxFwYXKETWavdj8e/aQr/7YFQdM99izJv6jDdqdKK8NolZdjZaMcX1Wac6nSHqz92H4N/nzDiJ4tjNyymd/hxqtOFJqsPEj6Ni6pUCZ9A59HRryfhp+aF2Ft7O3C0I2QO/E1tKOw8XbZ6h+Wm6ff7ceDAAZhMJqjVasyaNSvcSE8gpCPb6m1RYgsAVpQqkHue6lab3YfX97TjWIcLE3QiPLcmtpXefx034p/HjTjc5sSjKwpj9ns/rzajxtTTCjCWXZ9jTaPFi59+WQ9PIHRmVgo5mJsvxVc11vBjHBFiK0vCw1OrCqEUElPjVIQb4YlmcQdwQu+CyR0ATVGYXxib897Rdic+OhXKGozczpuZJ0WxMrGVm0ptdMUuVXM6u6f0gVDawvqdrXj14lLw0mB3Z8jf9LNnz+LZZ59Ffn4+tFotDh48iHfffRcPPvggqqqq4rlGAiFufB1xMgSAaTli3DN3cC8ghy+IxyOMaU91utHp9Me00tg9tXag1Yl6swclMdi+aLB48McD+vDtKo0QM/MGPnEEGBZc0g84ZE51usNiCwAsniC2NdixuFiG7Q09op5DATdM0+GScYmvYhBGhlLExaLi2F2ctNp8eGNfO472inECQhFPiRZbADA1W4xKjRDVRg/mFUhTdpu7UiOM6l1us/txzuTBBF3fnudUY8iC691338Udd9yBRYsWhe/buXMn/vSnP+HZZ58d9LkGgwGvv/46LBYLKIrC6tWrcdFFF8HhcGD9+vXo7OyETqfDfffdF66Y/ec//8GmTZtA0zRuvfVWTJ8+HQBQW1uL119/HT6fDzNmzMCtt96aMTEIhMTT/dHh0sAF5UrcPiv7vOP8f+9lTEtTgEIY2/J7a4TFx45G+6gFF8uyePKbZkRE8eHG6QNPJx5uc+KFHa2YWyDFD+fnjuq1xwqTskTgUIj6G7sDTJ9YLooKnTSI2BqbHO9w4dmtzVHVTiBUUbt5hg6XnSfHNl5QVCjR5GiHE7MHuRBLNt+dpsOBVifs3p5J/ciM0VRmyN/4tra2cA9XN/Pnz0d7e/t5n8vhcHDjjTdi/fr1ePrpp/Hll1+iubkZH330EaZMmYJXXnkFU6ZMwUcffQQAaG5uxs6dO/Hiiy/i4YcfxjvvvAOGCX04//CHP+Cuu+7CK6+8gvb2dhw+fHjo/7cEQi8eWJSHR5YX4O0rKnD33Jwhmcd+UxtdFVtQKIv5sAIvorLUaB39NPChNme4lwgAJupEmJrTf/+W1RPAiztaYfMGsbnOGjMfokynQCHA3XNzwI/4DNEUoOtlIhlggMc3NaG2ywiXMHaweQL41eZoscWlgZVlcrxxaVnSxFY3HQ4f7N4gvjxnQVuK+jrqJDw8tqIAlZrQRehEnQiT+5noT0WGXOHKycnBzp07o+J+du3ahezs80cxqFQqqFQhM0+RSIT8/HyYTCbs27cPjz/+OIBQbuPjjz+O7373u9i3bx8WLlwIHo+HrKws5OTk4Ny5c9DpdHC73eEtzKVLl2Lfvn2YMWPGcP6fCYQwSiEXs4cRJO0NMHBGTO/IBRzcNisr5utSCLkwd3mtGZyB8zz6/GzuFZ67ulw54GM/PWuGtevqMcAAbj8DaT9ZcIS+rKlQYlaeBFvqbPAxLObmh3pxNtdbo3rngizwj2MGPLSsYMi/m2VZ/OekCR+eMoFDAY+uKEzZbR9C/zh8DLKlPDh8QRQqBJiUJcLqciVUouT28QUYFq/ubos6TtAUcPecHFxYqUzewgagUiPCb9eWwB9k0qJ3q5shv8u33HILnnvuOXz++efQarXo7OxEW1sbfvGLXwzrBfV6Perq6lBRUQGr1RoWYiqVCjZb6M02mUyorKwMP0etVsNkMoHD4UCj6TGP1Gg0MJlMw3p9AmE0CLg0ZudJsL/ViVKVAD+cnwttDGMwuilQ8FHfFXLt8gfP8+jzU9OrmtK7QTaSXRH2GHwOFdMpybGARszDVb1Mbh9dXogXdrZG9ewEhlA59AYY8DkUKIrC/06b8d7hzvDP/nvKhPvJhGNakSfn4+WLS5O9jD5srrP2uShjWOC/p00pKbi6SSexBQxDcI0bNw6vvvoqDh48CLPZjFmzZmHmzJnDmlL0eDx44YUXcMstt0AsHrgEyLL9H4gGur8/NmzYgA0bNgAAnnvuOWi18U+g53K5CXkdQuLo7z1df7UGFpcfakn8fLoWlPvDjdZZctHoP1d0ffifShEXM8ry+u19tHkCaLT2bCWMz5IhOytznOiT9R3VAnjjuhzsrDPhUIsVHIrCVVNzoZUN3Bz94ZE2vLC5BhoJH6sqNfj4hCHq5xwef8wfb8gxNzYI2/rm9wLA0gpdwv++mfyeDquOKZVKMXny5LAtxHDEViAQwAsvvIAlS5Zg3rx5AACFQgGz2QyVSgWz2Qy5PDQFotFoYDT2hMF2v17v+41GI9Tq/ve8V69ejdWrV4dvGwyGfh8XS7RabUJeh5A4BntPDe74ve5sHQcyPg27j0GJnDvqz1WVmo9mS6jKtbpMEfU9iuScMboSNjNHkFGf6e73M5ikRIYqGVA1vitHz2uHwWsf8LFmW+hnRqcP/zzcN9tuipaXUe/NSCDH3NiwMIeLKyao8XWNBU4fgxwpD6vKFfjWOGnC/76Z8J7m5fVfeR6y4DIYDHjllVdQXV0NiUQCp9OJiooK3HvvvdDpBr8CZlkWb775JvLz83HJJZeE7589eza2bNmCK664Alu2bMGcOXPC97/yyiu45JJLYDab0dbWhoqKCtA0DZFIhLNnz6KyshJbt27F2rVrh/q/QCCkDUIujQcW5+P9YwZcPmH0jbSry5TYXGdDpUaIaycPnOkojtg+5NBIem9JPHhrXzs+O2tBqUqA783O7jdCKxVYVabAv08a++SmAsB4rQjLiIdaXOhw+NBq98PtD0Iu4GK8TpTx9ig0ReHWmVm4aboOFBW6TYg9FDvEfbonnngCxcXFuP766yEUCuHxePCPf/wD9fX14cb3gTh9+jQeffRRFBUVhbcxvv3tb6OyshLr16+HwWCAVqvF/fffH66affjhh/jmm29A0zRuueWWcGN8TU0Nfve738Hn82H69Om47bbbhmQL0draOpT/zVGRCcqcEE0mvadGlx8qEXfQg2mQYXHHRzUwdZkLSvg0/vytyow54UgUKqx5Y1fYZFLIpfGbtcUpmxl3sNWBZ7e2RI29i7g0XrukBNo4bmknAosngJ2NdvA5FObmSyEfgQFsLL+ftSYP/rC/Ayc7o0vXMgEH9y/MHdSzjhA7MuGYO1CFa8iC6+abb8Y777wDLrfnSxEIBHDbbbfhz3/+c2xWGUeI4CKMhLH4nj6yoQFHO3pOOq9dUppWeWWDoVJrsOK1HVFeWVNzxHhqVVFMfr/dG4SYN/SoJ7M7gCart8ubSwRhP95cJzpceH57S1Sl6//m5WBNhTIma04GRpcf931WH56GlfHpEeUTxur76Q+yuOOjc7D0U00EQvmKT6yMXeIDYWAi39NOpx9v7G1HtpSH70zVQZYm09Kj3lKsrKzEuXPnMH78+PB9NTU1xGWeQMgg/EEWpzqj+7j4Q/AmSxc4NIX5hbKooPLjHS74g+yQPNgGYneTHf84ZkCd2YurJqpx43Tdebdl9jU78Py2Fvi7ym1cGpidL8W1k7VRdg+TssV45aJS/OVIJ7Y32OAJsEOacExldjc5wmILCOUTvr6nHb9dW5KU9XBpIFvK61dw6cRc3DgtcwZH0omdjXYcaA2lbuxpcuC5NcXIkqZvdvCQBVd2djaeffZZzJw5M9y8fujQISxevBjvv/9++HHXXXddXBZKIBDiT6vdFxYAQMhBPyvDwtFvn5WFk3pX2OeMpigwLAtg+IIryLB4a18HvjxnCd+3uc6K/5w0oUorwv/NyxkwpsXg8kf9rQNMSIgcaHHigcV5WFAoC/9MKeLih/NzcdecbNi9QWjiYEWSSPqrVLQn0WiToig8vboIu5scOK53IciwkAs4KFEJsaBQNioxThg5kT2lRncAv93RiufXFKVtusyQBZff7w9PF9psNvB4PMydOxc+n2/AiScCgRBbWJaN68FGwo/e0lpSLE/bg9tAaMQ8PHNBMf58uBM1JjfWVo4s0zDAsHhuawv2tTjC93EowOQOCbkzBjee3tKM1y/pP1h3dbkSh9qc2NPsiLrfz7B4Z39HlODqhs+hoRGnl/dQfywskuHLc2Ic7+jxJZuV5B4pHofGkhI5lpBhhJRhSrY4Ki7rjMGNb+psWFmmSO7CRsiQBdf3v//9eK6DQCAMQovNh/93pBMHW53gcyg8sbIQZXFwGdeKeShTCVBr9qJAzscN0zLTDydPzscvluaP6ne8uqutj9jqHenW4fCj1uzFOK2oz/N5HAoPLs3HN3U2bKix4HSnG0E2VGebWxAtPt47pMc3tVZMz5XgyomapIQbxxIuTeHxFQXYUGNFjcmDYqUA66pUyV4WIcXIkfFxYaUSn521hO/b3jAGBFckb7/9Nu64445Yr4VAIPTDrkY71u9shbfrbO4JAE1Wb1wEFwA8sqIQR9udmFsghZiXHk2qieZgqwOb63ucuTkU8OOFefj0jBmnIwzaaAqDbslSFIWVZQqsLFPAE2BgcgWgEHIg4Uf/3XkcCmZPEN/U2bC5zoarJqpxwzRdUrzEYgWPQxORRTgvt8zIwrEOF5q6DJlbI7aeLe4AttTbwOdQuLBSmfJ2FiOqTW/bti3W6yAQCP1wuM2JX29vCYstILTtF88RdbWIi+WlipQSW94Ag1N6V9iuItn877Q5/G8Jn8ZjKwuxtESOHy7IQYE8ZNfAoyncPEM3ZC8zIZdGnpzfR2wBwKXj1OHfwwL490kTnvymCe6IXE8CIVWxeYPY02yHwdW/o/1gCLg0nrmgGIuKZKApYEauBECoV/KOj2rwx4N6vLmvA4e6mutTmRFVuIYTsUMgEEbOnw7qETmQRlPAXbOz02Y8erT4ggz+esSAL6rN8ARYSHg0/nJ1ZdIrO2IeDa2Yi8XFclw1UQ1Fl4dUgVyA1y8tQ6vNB6mAA3mM3ieZgIP7FubiyW+awxOKh9tdeGRjI55cVZhS4phA6M1nZ8z4+zEDOBSwtESOW2dmhb8zQ0Eu4OBnS/LDCRF7mux4eVcb0m1Yd8gVrvfeew/19fUAgCuvvDJe6yEQCF34g2w4wBoIeRX9Ymk+lpWmZ//CcDG7A/j5lw346JQJnkDoyEpTSLrYAoCfLcnHO1dWDHjiyJPzYya2upmWI8EjywuivLqqjSGzTgIhlem2OQmywDd1NjzweT3qzJ7zPKsvHJqCyx/ES/2IrZFUzxLNkAVXMBjE008/jQceeAAURZHJRAIhzvA4FG6bmYX5hVLcPF2HVy8pw7yCvpNrmYg/yOLRjY2oNXuj7r9mcmY28feGZVk0Wr3Y3mDDzkYbTnS4YPEEMD1Xgt9cWIx8eY/L/KZaG07pXYP8NgIhucwpkGJqTk+EVqcrgF9uaOyT3ToU9jU74OpnK/1QWwZtKd5222245ZZbcOjQIWzbtg0ffvghKisrsXTpUsybNw9CYXwaeMci/iCLo+1OaCW8tJ9GIoyOyyeoY5KlmG58UW1GozXal2lFqRyXjMv8JutOpx9PfdOMBqu3z8+qNEJcM1mDF9aW4OMzJnxyxgyrJ4gaswcTUjQTkkAAgB8tyMXDXzei3RGqRDl8DJ7a3ITXLy2DtJ++xYHw9h4F7iJyNyBVGXK0T2+amprwyiuvoLGxEXw+H4sWLcK1114LtTo1Tw7pEu3jDzJ4dGMTTna6waWBx1YUYmqOJEYrTE06nX7877QJLTYfSlWhE0p/ESfJIPI9Nbj82FoXmkybnC1GVT+j/oTY8MKOVmztmgLk0hRumKbFlRPUo/YES4eopqPtTjyysWnQx/xyWQHmFEgRZFjonX7oJLyMybscDunwfhJ6MLj8ePKbZjREiKNLx6lwx+zs8O3zvaeeAIOPTplAUyGj4BpTqEom4tL4x3WpkXwz6mgfAHC5XNi9eze2bduGhoYGzJs3D7fffju0Wi0++eQTPPPMM/jtb38bkwWPVb6otoTDUwMM8MkZc0YLrlOdLjy+qSnco3Og1QmDy4/7Fvb/gU0mj2xoRKu9p09geo4Y98zNQY4svUOEU5GrJqqhEHCgk/CwsEgGXYa53Q/G1BwJHliUh38cM6DF1td9XcKjIReGKgIcmkIu+fwR0gStmIfn1xTj9/vbsbnOBoYF9rc6cAeyz//kLoRcGtdPCbUWuP1MWHAJual/wTFkwfXCCy/gyJEjmDBhAi644ALMmTMHPF7PQfCmm27CLbfcEo81jimOtEf3YgwUppop/PGAPiy2uulwpGbzY2+38MPtLvxyQyNevKg05g3SY51SlRB3zB67bQpLS+RYUixDg8WLJqsPFk8AAYZFoUKAiVkiMpWY4pwxuLG5zoqZuVLMKUiug36qIeLR+NGCPFw9SYtdjfZRZSNeMk6FT86Y4QuyabHjMKzw6ttvvx1KpbLfn9M0jT/84Q+xWteYxe6NFlhFisy+ejX246u0NEWjNe6YlYXHNjVFTcd0ugL47IwZ108dG83cqU692YPX97SjyepDnpyHi6pUWFmmSHlDxP6gKAolKiFKVKkpPH1BBq/tbsepTjdEPBqz8iRYV6mKS7iwP8ig1e4HhwJ0Et6QopgOtDiwt8WBi6tUKEpwL+wnp83Y2mDDZ2ctWFAoxQOL8kkeYy/y5XxcPVkzqt+hEfNw/6I8fHDciBvSIGB8yILrsssuO+9jBALS4D1apuaIw07VHApYVZ7ZFgDXTdbirX3tCLKhkvBVEzVYV6lM9rL6ZWqOBE+uKsRb+zrCrsdAyKqAkBq8fUCPs12TTzUmL17d3Y6zBg++Py8nyStLPZy+IGzeIPwMCzGPhlrEHZYwbbT4sCXCbb/B4sXHp824fop21CfSSP55zICPTpng7JpMo6lQ9NHtM7OhHeQ6xx1g8EW1BRtqLLh5RhYuG5+4/mKtpOfUuqvJgZd2teKni0cXJUXonwWFsn5zR4HQtG93i85EnSjpubAjMj4lxI8rJ6rRYvOh1e7DNZM0mKDL7MmjCyuVmF8ohd7pR4FcABEvNZrlB2JKtgSvXlyKox0utNp8UIu5mJNPtgxShf4MYb88Z8GVE9Wk16mLw21O/PO4Aac63VHVWpWQg2WlCnxnqnZIFaRSlQDlamG4hwYIBW//5UgnhDwKl4wbvcCpMXnw16PRDdQMG2qWbrH58I9bcgd87px8KdQiLkzuAN45oEe10YMfL8jF7/a2Y0udDbkyHm6blR12Lo8lS0vk+M9JE7r/vNsb7JiabcGFKXoxmam8ta8Dn1dbAABlKgGeXFWUVNPo1D67jUHEvJCj7ksXlWJRcWpurQ1GtdGNX3zVgO9/XIudjbbzPwGAQshFpUaU8mKrG4qiMC1HgnVVKswrkKXldlWmctN0HWT8vp+jQLpZUscJiyeApzY34YTe3cc40uwJ4qNTJvzt6NCm/jg0hcdWFGB8P70zn0eEDY+GAjkfebL+tyi9AWbQ1BMBl8atM7PCt7fW2/DkN03YUGOFn2HRaPXh2S3NaLbF3k6gVCXsY2Hy96OdCJLPYULZWGsN/7vW7MXre9qTuBoiuAgxxOIJ4JENTTjV6UaLzYcXdrTBkiLZd4TMxh9k0WzzgktTeO2SMlw1UY1ihQCVGiF+OD8HhQrS7gCEIlJWlSkx0CWCWsTFkmFc6CmEXDx/YTEeWpaPWXkSqEVcaERcrI1RJUfApfHbtSW4drIGVRohNCIudGIuVpcr8OSqovNuES0tkWNeRNP64V5DSd4gi/+cNMVkrb25eUZWlNmn2RPEOdPwjT4JI6f3RfyuJnufPulEQrYUCTFjb7MD7kCPA3CAYXGi04VFRelXqSP0EGRY7Gi0A0i9gQaWZfHZWQveP2aA1RsEBeDmGTrcPCMLN8+I3+u6/EGcMXhgdPlh8QThDTBg2JCgKVTwMU4r6jeEOtnQFIXvz8vBNZM12N/igMUTgD/IQszjoFwjxJRs8Yj8vOYVyOKWgiDhc3DDNN2Im6LvW5iHX3zVMKAxptUTn4tCHofCQ0sL8MeDHfj6nBUsAEECGufN7gBcfiYqjWCscsk4Ff56JLpi6wsyAJLz3SSCK0m4/EE4vExcJnqShbGfLKusMeSflIn4gyx+s70Fe5odAIBipSCl0g9+u6MV2xvs4dssQiP58eKk3oW/HzPgpN6FQN90kTBCLoUfLcjFwhS92NBJeFhXlfmu/UCoyvHL5QV48KsGdLr6iqtpcfQ5FPFo/N+8XFw9SQOnjwlPnLr9DKqNbozTiobULzccXtnVhlOdbvx6bTGKxnhl9+pJGrTYfNhSZwMLYEGhFBpx8s5JRHAlAbefwfc/roPZHcDUbDF+vDA3qR+CWDErT4p/HOvJ2CxRClChTs2R9v44pXdhQ60VJUoBLqpSpURIcrL572lTWGwBQJvdlzKCa1ejPUpsdROvcO8Aw+KJb5r6+Mb1h5jHgVqU/t/pTEEn4eGp1UV46OtGmLraHNQiLr4zVYPV5cq4v362tKfa5PQF8ZMvGtBq90Er5uLXFxbH7PjPsCyOdrgQYFj865gRDywevYG0yx+EgEOn5fGQpijctzAP356ihdPPoFSV3GMXEVxJ4IzBDXPXl/5ohwvPbm3B82uK0/IDHUmVVoT/m5eDz86aUagQ4PaZWUkfwx0qzVYvfrmxKdxcfdbowQOLUs/tPpH4gww+OhkdUp9KBq9njdGVLAqhK9qBRsRHC5em8MLaEnxdY0WtyYNWuw8MC3Dp0LaXUshFoYKPGbkSTM2RjMmonVQmV8bHMxcU4fltLagze2FyByAXchN+jNpUa0WrPWQrY3AF8O7BzpgIIwCweYLhY9jOJhu+58mCXDjy0/yuJjt+va0FEh6NH87Pxbw4fbfiTaqkgRDBlQQUwuiTVrXRgw011owYGV5TocSaCmWylzFstjfYoybZttXbcNN03aA+P5nOwVYn7L6efTO1iIvxusS6Of/tSCc21loxXivCPfNyokJuV5YpcFLvhsntR5FCgKsnaeIe4FygEERNvhHiQ5Bh8cyWZlQbPZicLca3p2pjMviQK+Pj1xcW471DnfjsrBnHOlxx6z0biN5JGic6XQM8cvhE7nIHGOCMwTMqp/svqi1gWMDuY/Dctha8uK4EpSlqxJsOEMEVJ6yeAMQ8Tr/uwiVKAUpVAtSZe5o4dzTaMkJwpSu93ycWQJPViwnJWU5KYOjV73LlRHVCLTCOtjvx/vFQhW17ox0GVwDPXFAUrgQXKgR4/sLihK2HkDg6HH7sb3UCAHY02nG4zYlHVhTExJeQz6HxvdnZ+PYUbVLsQnrngrr9gzQDDhOlkAMOBQS7/rdqzaMTXIFgz9oYFvjHMQMeXFow2mWOWYgtRIyxuAO4//M63PTvc/juB2fxYa8tGSDk43TP3BxE9kq6YvilIwyfWXnRjbMUMOatBJQRldgZuaHYlkRS02uE/rTBjV1NfXu2MpX/d7gT//dxLZ7Z0oxDbc5kLyehaCVcSCJG+p1+Bk9vaQm3YgyFo+1OPPlNE+76bw0e29TUZ6hHKuBAKUp8zWFugRT8iAu8SOuI0UJTVFS/2GhzaXsPFOxvcZ7XS8wTYGBLovVCKkMEV4zZUGNFjSlUufIEWLx3qBP/Ot7XSHCcVoSHlxUgS8IDBWBVWWZH+KQ6JSoh7p6TDT6HAgXghmnaPleiY435hTJcMUGNW2bo8PCygoRnwfV3YD/dGb8JxFTCG2DwrxNGNNt82NPswOObmvCng/pBjT4zCT6HxmUTop3q7d4gNkUYWQ4Ey7J4a187HtnYhAOtTrQ7/Djc5sTOxtQQ67kyPn6yKA85Uh4q1ELcFuMt6vKIxvB2u2+QR56ftVUqSCOMhBmWxWBF7iDD4qGvG3DnRzU4oY/dVmmmQLYUYwynHwn7+VkLrpnctxloZp4Uv79cAj/Dgt/fEwkJZV2VCqvKFfAG2KTGP6QKHJpKar9SfzVfjXhsHLIEXBr5cj5abD0nzI9OmSATcHD1pNjlFKYy103WwOjy46tzPSKr3XF+AfFFtQWf9eN0X6FJnd6jeYWy+DWgRwiiM0YP2uy+EcdayQUc/HxJPn69vRV2bxBzC6SDthXUmDzhgsNbezvw0sUlJIkjAnKWjzHLSxV9JrkGG1aiKIqIrRSCz6GJ2EoRnL5oyTUxSzRmvKMA4LaZWX2OHf85aRwz8TAUReH/5uXivoW5mJItRpVGOCQLhw01fatgl41XZXwubTdOX892XoBh8e8TfdtahsPUHAneuqwMz60pwk8WDR7A3Wjt6UtusHrj6omXjpAzfYxRibh4fk0xpmSLQVOhicQ7Zmcne1kEwqiweYP4stqC4x2J2yaQRUwkygUc/GpVEYQxNolMZWbnS/HQ0oKoCwBPl6N9JuD0BXGozQnHefp9lpcq8KvVRfjN2hKM6ye3sTeRXksaMRf3LczF7bMScwze1WjH9z+uxZ7m5GxfHml39okv2tfiGODRQ0fC52CCTnzetoLexYNqY/yijIwuPz4+bcKHJ41oHCBFINUYG/X5BJMn5+NXq4vAsOyYLqcGGRanOt1od/jgDbAQ8WgUKQQpVdonDI0dDTa8ua8DQGjK9u652XGvGEzK6jm5Xj5enfY+dSNhToEUr11Sih0NdrTYvJidL014L128eHZrC451uCATcPDAojzMyI2N4/sP5ufi0vFqUBSQJ+Mn1A/tVKcLLTYfnt/agoeXFWBW/sgnBEfCnw919hHkvmDiFHp2r+QU/Sib9geiw+HDT79ogLVLrL93qBPXTdHgO1NHFv+UKIjgiiNjWWwdaXfipZ1tYVfnSAoVoabREuLnkjZMygpVbBkWqLd48csNjbhjVnZct/gmZIlx1UQ1vAEGl/dqoB5LKIVcXDwu87ZST3X5T9m9QfxmWwteuaQU2hg5ricrDaF7QjDIhmKn3risDMpRGI8OB5sn0G84djyji3pTqhKAS1Nhuw1/nMqxB1qdYbHVzfvHjFhSLE/p6fKxU58nJJR/HDX0K7YAoMnqwz+O9Z3cJKQuRUoBLo4QVwEGeHNfR7+2J7Hk5hlZuHNOTsZUdSLxBxl4Agx8QWbMTB9GohD0CBGnn8G/jsf3s5QIJkQYA7v8DP52JHHHud7WQtkSHq6drMEP5uUkbA18Dh01cR8v4TtQRE+nMz4VtVhBKlyEuHD33Bz8bk87TvfTNDleK8KN04lbd7px8wwd2h3+qJ6QPx/qxDiNCJOyx0ZD8mjZ0WDDN3VW1Ju9MLgC6JZZHCrUp6YR8zAxS4QZuRJMy5Fk9DbqijIFPoho6N7VaMc9cxMnDuJBmVqISo0w3Lu0sdaC66ZoEpKVK+RF10+UIi5umJb4LbYbpmlxQu+C0xfEwqLYTWIGGRZfnbMgS8LDrHwpbpymw9+PGcLVtIk6Eaak+HGICC5CXChWhlzAOxw+tNn9cPsZCLgUCuQCZEnHtr9VusLj0PjF0ny8ta89PKrPAnjnYAdeXFea3MWlAUaXHy/vaoO3n56aIAuYPUGYPUGcM3nwv9NmVGqEeHp1EQQZOijwrUlqbK6zhhMN3IHMMH/+1iQNntvaAiBUCf7srAU3To+/8FEIOFAKObB4QlttuUk6ziqEXLx0USkCTKhvN1a8f9yA948ZwedQePuKclw9WYPVFQqc1LsgF3AxQSdK+QuUzPwmExKGP8igze4DM8CWSLaUj+m5EiwokmFmnpSIrTSHS4dG9R9YlId8eahfpcnqG5NbYsNFI+Zh/UWluGScCgXywX2RxDwa47SijA7AFvM4eHxlYXh7aNEoqyFWTwDHO1xJieuJZEGhDBMjthaPjWKyt9nqxW+3t+BnXzbgua0tqDYObLNAURQuitj2X1mePDNtHoeKqdgyuQP4z0kTgNAQQLfdhFLIxcIiOSZni1NebAGkwkUYIQ5vEH86pMe2ehu8QRaTskR45gKSazdWWFoix5JiGWrNXgg4FKgxPCAyHPLlfHyvyybG7g3C5A7A6gnAG2DBsCy4NIU8OR/ZUl5GD90027xQi7goVAiwfl0JTO4A1KOM2Xl2awtOdbpRpODj50vzUSBPXvP0D+fn4hdfhabomqwjsyywuAP42ZcNcEb0Zu1rceCFtcUDDhxdPUkDCoBazE1os/xo8QfZQfs0D7c5o6YtrZ70jA4igoswbJqsXjz5TRP0zp6meKNr6Blnw8HlD6LZ6kPVEPx3CImFoiiUq8mk6UiRCThdHlupO1UVL363px21Ji9+vDAX8wtlMelxau2KsWm0+vDkN8146aISiHnJMTHOk/Px2MpCPL2lOSoTcjgc7XBFiS0gZGS6o9E+oODi0BSundI31SRVsXoCWL+zDUfbnZALOFhVrsQPV/ZNUqjtNX2ZrokTZEuRMCy8AQaPb4oWWwCwrkoZl9fbWGPFT79swD/JVGPGcMbgxmdnzSTgdgyjE/PgDjB4flsLvhlCPuJQyJdFhza/fyy5U4/laiH+cHk5nh1h5X9ythhCbt+qT6o3hg+Hdw7ocajNGe5h/OCEEW/trO/zOEOvC/qyNL3QI4KLMCy+Omfp8+FfUCjFpePi45PUfVL++zEDjnU44/IahMTAsCxe39OGn33ZgLf2deCDfkLdCWODZaVyACFft1d2t8UkweCCCmXU7c/OmuHyJ1fUc2gK0hFGhalFXDx7QTGm50qQLeWhXC3Ez5fkYWoabRWej8gYom42nOnsc19kXF6FWpgwb7NYQwRXCnGsw4mHNzTiZ1/W492DepgH8LFKJpElbh5N4bvTtPjZkvy4NSxmSUJbDQwLvL6nfcDmfELq896hzqgg4niZIhJSnxm5EuR1VaQYFnhhRytsntEd75aVyFEVkWLhC7JoSJPIl4EoUwvxxMpC/P7ycry4rgQLi+TJXlJMuXicqk9eaL6yb/vI5K6qHgXg1pnpaymUnjIxQ3ljbwdabKE+hDMGDzbUWPCTxfmYHqPIi1hw5QQ1siQ8cGkKU3PEcb/SKIkwuGuz+7GnyYEFMfR2GYscaXfijMGN8dqQb00iGt5P6l347ylT1H2ZtDVCGB4UReGOWVl4cnMzgNAU2h/26/HA4rwR/04OTeH+RXl4bFMTOroiZTgZPHiQCczMk+KR5QX45IwZTVYfxutE+OnqcWDctqjHLSmWocOhRalKGBZf6QgRXCmETswNCy4AsPsYvNAVDyHlJ6f5szcCLo2VZYkbN65QC5El4UHf5SD82VkzEVyj4Ei7E49ubArfvmScKjw1F0/+d9qEyHpWpUaI+YXkfRzLzMqXYkmxDNsaQkHPWxtsWFEmx8y8kecP5sr4eHFdCT45YwbDsqgkua0pz8w8adR7rpbw0dsvm6IoXDM5fYYBBoJsKaYQt8/KhrCXyaHNG8TZftzaxwoURWFZSU8Z/bjeBaMrteMbUpmDrdF9cJ+cMeNga8g5fkeDDT/7sgHrd7Sizd4j/P1BdlCvtaFw1tAzZSTj03hgUV5G2x4Qhsbdc3Kgi5g4+9vR0ff1SfkcXD9Fi+9M1RG7EkJKQQRXClGkFODli0owt0CK7sNErow35kfv11Up0a1DGTYUXEoYGf2NUx9sdcIfZPH6nnacMbixud6Gn3/VAIPLj3NGD+76bw3u/l8t7v20bsRZZd0mqaUqAZ5cVYRc2eDGn4SxgVTAwQOL88DrauSpNnpwztg3gJlAyATIlmIMOdluR1OHA5VaUdRUxXDIkfHx8LIC2DwBOP0MsiS8tHDQjScaMQ/LSxXYUBNquG4eoZEgAbigXIlPzpjDPS4AIOHT8DNM1ECE1RPEewc70e7wwdg1vNFk9eHPhzpH1Gfz0LICdLr8KJTzSdWBEMUEnRgPLy/AM1ua4Quy2NNsRwXZCiRkIERwxQBPgMHTW5pxtD002izkUnhwacGomt3lQi7kaXLMcfqC2NfigN7pR6lSiDkFI+/BGIhbZ2bhcJsTBlcAphSc3kwXRDwaT6wsxFv7OnBC70KFWoiLq1QQ8zgoUvDRaO3ZStzbbIenV+7facPIxvdFPBpFirFn8EkYGjNyJXh6dRHe2tfRZ2qNQMgUiOCKAZ+dMYfFFgB4AizePtCB1y4pS+KqEsM3tVa8ua8Dnojg2cvGq3D7rNg2Ykv5HDy4NHQVXEhO3KMiV8bH4ysL+9x/x+xsPL6pCd1uDRwOhSwhJ8rkNkdKtgIJ8aFKK8IL60qSvQwCIW6QHq4Y0O7o29cSzIzg+0FptHjxyu62KLEFACf18Wnyr9AI8eblZbhmct/oh4Gwe4P9musR+jItR4KfLM5DjpQHPofCDVN1uGpi9N/68gmqAZ5NIBAIhMEgFa4YsLpcgQ01FkTuvlw5MT7O66lEtdGN/rwrL6iIn20EnzO0awSWZfHq7nZsrLWCAjA9V4IfL8xNW4fiRLGoSI5FEeaKJ/S9txDJfg+BkAyOtjuxt8WBVpsP3gCDcrUQaytVyJOTqnO6QM4+MaBKK8Jv1pZgX4cfVocTS4rlmJiVvuZsQ2VOgQyVGguqu6aKdGIurp6swdrK5FdBjnW4sLEro40FcKjNiV9va8EzA+SaVRvdeHFHKzRiHv5vXg6Zouuit7xtjbCLIAwPkzuAGqMH7Q4f2h1+dDr9cPkZyAUc3DBNF57kJBB6s35nKzbXRZuBHte7sanWijcvKx9xfFA3LMuSYZYEQARXjChXCzGvqgAGQ2Lz4c4ZPfjXCQPOGjwIsiy0Yh7WVCiwpkI5ap+jU50unDN6MC1HgiJl374puYCD364tQYfDBz/DokCeOr1V/mDf0tsJvRtGlx8aMa/Pz/50UI9Wux+tdj8e3tCI311a1scTbSwi63Ugt5PA6SHT4fDhSLsLJ/QunOp0R02GdsOlKVwzSYMcad/PJIEAAKc73X3EVhiKAj2Kw5Q3wOD1Pe3Y0WhDjpSP/5uXMyaKBcmCCK40xuAKiYPIHiqrJ4g39nrQbvfjlhFmTgUZFi/tasPW+tCXXMKn8e5VFQNu52WnYCP11BwJSlUC1Jl7LCSEXGpAx/6miOk8oyuAbfW2PmG4Y5FsKR80hfDWcZDkHw4Iy7I42uHC3mYHDrY6B60GasVcrCxTYG2lst8LAAKhmxKVABN1IpzsjO6NrdQIcc/cHIh5I69ufXjSiC1dx/lmmw/Pb2vBG5eVjep3EgaGCK40xhtg4RugO79+FKGt/zhmCIstAHD6GBhdgbTaZuNxKDy+ohB/OqTHkXYXpHwat83MgmCAqlWujAdbRPXmaLuLCC6E/o4zcyXY32U2W6JKE6+SBGJw+bHhnBUbay1RE529UYm4mJErwZJiGabnSojTPmFICLk0nl1TjHqzBw0WL8Q8DgoVfOTE4Hh8xhBtMmvxBHHW4Emp/N5MggiuNCZfzsd9C/Pw7iE9jK6eA/0EnQh3zRmZLYMvyODDk9EhwzI+jew03PJQiri4b+HQTDoXFcmjDz7kXBjm6kkaHGxzQsSlyYE4Apc/iHcPdmJjrQWBfq57pHwa47UiTMoWY2auhIjVJGH3BnG0w4npORJIUiSTtj+cviACDAvFAIM9JSphzD9DWZK+x3WlMHX/RukOEVxpztISORYWydDp9MPpCzXgZo1CHLU7/Aj02ja6dWZWxl+NX1ipxM5GO0535VbOziPCopsJWWI8s7oIPA494gSFTOR/p0M5lDI+B0oRF9lSHnKkfOTL+RinFaFIQVz1U4G/HzPg0zNm6MRcPL6qMKV6TYHQNv07Bzrw5TkrAgyLe+ZmJ2zw6NopGuxptsPiCVX35xdKyYVBHCGCKwPg0lTMtvvyZXzoxFx0ugLg0RS+PVWLVeXKmPzuVEbIpfHkqkJsrbdBwKWxNCIwmxASXYRorp+ixfVTtMleBuE8WLqSKTpdATy+sQmvpdhAzO/3d+CLakv49rZ6W8IEl1bMw6uXlOFAiwMiHo15cUgJIfRABBchCg5NYf1Fpag1e1CiFAxY3s5EBFya9G0RCBlGZDtEpyuAf58w4oZpuiSuqIfTne4osQWERFAikQs4WFEWP+9EQg+pI/MJKYNMwMG0HMmYElsEAiEz6V2t/uysuU86RiQsy+LPh/T4+ZcN+MP+DnQ6+9p5ROIPMvjsrBmfnjGjbZg+dcc6nFG3aQpYV5V8H8PzwbIszhjcxCZmmJAzKoFAyBiCDIsvqi1od/jw7alaMt7eiyPtThxuc2JlmWLMZJKWqoSYndczaevwMTipd2FmXv/bZ7VmL/7dNTh02uDGljorfrm8EON1on4ff7Tdhbf2dYRvLyiU4a452VCJzn967b21eeN03YCvk0p0p3gIOBQeW1GISdmk5WAokAoXgUDICJy+IB76uhG/39+B/502Y2+zI9lLSjn+fcKID0+acP/n9djUlcQwFrhjdnaUuNEPUrWS8KJPi3Yfgyc3Nw1YzSnXCKN+964mO378WR1O9YnF6svqciUuHqfC0mI5Hl9Z2Ce7NBXRO/zhFA9vkMULO1rBssSfbygQwUUgEDKC3+1tD0+ZAgAnwyYEvYNsgw2V7ilTX5DFq7vb+mxpZSq5Mj6eXFUIRZflQdEg1b0cGR9z8qOrX04fE+VNGIlSyMWds6NNpi2eIB7b1IRm2+B+iCIejTtnZ+OBxXmYkSaWK+2O6G1TozuARiuJ/BoKRHARCIS051CbE9sb7OHbNIW02JoZKu8c6MC175/FTR9U44UdrdhWb+tj3zIUIrfRGBZ4aWfboP1MmcQ4rQh/uLwcr19SOmh8jT/IoD+tPpDJNACsKlfih/NzwKN7nugNsnhzb8eAz0lX+puIN7sHNvwl9EAEVwZySu8Kj0ITCPHC4Q3ihe2t+M6/zqLVltwr3P+eijbrXVmmgK4fU8d05eIqFeQCDqzeILbW2/DbHa248781+O8p07AqX8tK5ChU9JwwDa4A/tfrb5fJCLg0Cs7Tu/aXw51R29GTskRYV6nEmvNMMK8uV+LFi0owKatH6NeaPYM8Iz3RSXgYp+3x6uLSoSxhwvkhgivD+PCkEb/4uhF/PtyZ7KUQMphOpx8//bIBWxtscPoYWD3JFfjnTD0nNo2Yi1tmjCxHNFXJkfHx/JpiFEeEyBtdAfzxoB73/K8WG2osYIbQR8OhKVw/RRsVpLCryT7g48caDl8Qn/eyabhhqg53z80Zkkt9kUKAZy4oxpuXleG+hbl4enVRnFaaXH66OB8VaiG4NHDLjKw+IfejIcCwcPiCGZnbSqYUM4h6swd/PRISWo3WkWcpEgiD4Q+yeH5bSzicWcKjUaFJ7hWuhEfD7g0iR8rDg0vzY3oCSBXy5Hz85sJivHtIjy+rLQh2nY+M7kBoaqzGip8uyYf6PNNxeqcfkaey5iRXJ1OJg61O+II9fx0Rl0aldvif7VwZP62yZ4eLTsLDC+tKEGRYcOjY9EqeMbjx31Mm7Gs5A1+QhULAwUPLCjKqNYAIrgzin8eN4Uy3TGsYHgkdDh/+c9IEozuABYUyrEwjcz+WZbGx1ordTXbcPTcn4WaI/dHp9OODE0Y0WryoNvZUlBYXy8HjJLdY/vMl+Wi2+TC3QJpSLuKxRsClcdecHFxcpcJfjnRiT5MjLJ5Odrpx/2d1+OXywkEFcFmv6JYAw+KU3kXSBBDKx4zkwkol+En+bKcysRBbLMvirX0dfSqLVm8QTVbvqAXX/hYHtjfYQFEULh+vSmp0ERFcGYLLH8S+lp6+g1xZ8k/QyaTa6MajG5vg8ocU6N5mB7KlPExKg5OKxR3A+p2tONweGiv/fgqI57MGN361pRlWT/QJScqn8Z1pyY+3KVMLUTaG+kgKFAI8uLQAjVYv/nPSiB0NdniDLMyeIJ7Z0ozXLi0d0INsWo4YpSoB6syhKjjDAs9ua8ErF5dCOcbNjiPFaKVGiGsmp75NQ7rzr+PGPmILCPWFjTZi7W9HO/H+MWP49uE2J/50VcWofudoGNvfrgziaLsrqhQ+NSc9RozjxZt7O8Jiq5sWmy/lBVeHw4eHvm6EwRXqiZqdJznvFlG8sbgDeLofsQUAd87OTspJWu/w4+MzJqytVCFfnrlbN+ejSCHAjxbk4ftzGRzrcOFUpxvna+WiKAo/W5yPn35ZD4cv9B2xeoL49Iw5ZSJvkkWVVoSfLMqD0x/EilIFBBlcLU0V9rZE++XRFLCkWI47Z2eP6u/faPVGiS0AMLkDcPmDSTNEJoIrQzgRYbLHpak+PjJjCYZl+0wH0RQwNcXdkI0uPx7d2BQWW1wauHVm8pu/f7e3HZZ+xNa6SiWWlSZ+m7be7MEjG5tg8wZRrBSMacHVDY9DY2aedED39N7kyfl4YmURnvgm9HcEQtYaY11wAcASElyfUO6ak42PT5sRYFjky/m4amYJRMHR+8Od6XT3uW+CTpTU9AkiuDIEW8QJcW6BNCObhocKTVEYrxXhZNcXjksD98zNQU4KN7F6Awx+tbkZ7Y4eB+zrp2jPO8Ieb460O7Gnl2M7BeBbkzT4bhK2EjudIVHaLRJypbF5T23eINrsPozTZk6D7vmo0Ajx0kUl+OdxI/a1ODKqOZmQPlRqRLh/Uc9nT6sSwWAYveCq1AjBoRAeLsmR8nDfwtxR/97RQARXptDV5iPkUrhlBrlKfXhZATbWWuELMpiTL01qo+RQeHlXG2rNPZOlS0vkuGZy7ARNs82L13a3o9bswa9WFaFqiMLiq3OWqNtXTlBjWakcpUn6e762px3WLrEl49MxEQlBhsUjGxrRaPXiuTXFY0p0acQ83DM3B/ckeyEEQowpUQnx/IXF2NfigFrExfJSRdIHaojgyhDm5Euxr9mBH8zPQXaMrvrTGamAg8snqJO9jCGxr9mBHY09XkjTcyW4d35OzH5/g8WLn3/ZAHfXCOvfjxrw2MrCIT33eEfPVvW6SiVuSeIW55Y6Kw639Vz5ripXxmRKan+rA/WWkNjdXGcdU4KLQMhkKjUiVGpS5/tMBFeGsLhYjgWFsph5oqQ7QYbFOwc6cLTDhUeXFyJLmppTmwzL4t1D+vDtKdliPLQ0f0Q2C/uaHThrdOPScSrIuxrZgwyLF7a3hsUWAJw2uIfsn8Pn0KAQxKQsEZptPtz7aR34HAqXjVePeoJoOPiDLP7fEUP4tpRP45pJsZkg293Us2V61pB5zuCpxBfVZmyosWJZiRyXjFOBSoEJXAIhURDBlUEQsdXDu4f0+PSsBUDoIH9TijqP15m9aLb5QAG4aqIaN0zTjeh9/OiUEX86GDK9bbB48dCyAgDAl+csaOhlguvyMzjV6cbk8wwRHG5zQsqnoXcCx/XRDaiH2hwJFVw7Gm3QO3v62747TQdpjPoU9RFhvIPl5RFGz/4WJ6qNHlQbPTjQ6sTDywrA45DjFmFsQGZeCRmHweXHJ2fM4dv9TdilCnIBB1dOUOM3a4tx04ysEYktmyeAvxzuqf4cbe/ZBtxWb+v3OW32wd3F/3K4E49vaorqK+tmok6EWxMsYCPjZyZni7G2Uhmz32339ogscvKPLxURXmmH2px4c197EldDICQWIrgIGcf2BhsiY7gUwtSd2NRJeLhlZtao+gz2tjgQiPgfpumQezMQ8h7rD8sg2YdH25344IQRve2csiQ83D4rC79aXRTeskwU3SPeJUoBfr4kP6ZbUUzE/6lalJpbz5nC0hI5IvuWN9RYSZYjYcxAthQJGUdkozcQ6ovKZJqs0aJqUpY4LEgmZomxt9mONRVKfFFtCUuLwXbO8uV8LC+Ro83hg5BLo0QpwJwCKSZliUEnqecmW8rHeB0X35+bDXmMLU+yJLzw37BiDLnVJ4M8OR9XT9LgHxGGlH8+1IkFhbIkropASAxEcBEyjm73bABQibiYluGu+1mS6KrMuojttp8tyQPLhvr7jutdYWEhH6TqpxHzcN+ivEFf85zRg0/PmkBTFEqUAszIk6BAHj/PsOcvLI7b756WI8GBVidoClhRRkwv4821k7U4Y/DgUNfEaavdh0aLF0XK5HrOEQjxhgguQsahEfd8rO+YNbK+qHRiaYkcn5wxwegK4Mbpuii3cZqiwh5tK0oV+PPhUGP9hFH6V9WaPdhUG9EfdiDk5P+tSRpMz00vgbumQon9rQ7MyZcSS5UEwKEp/GxJHl7b3R62Q3H6U7fPkkCIFURwETKOayZpYHIFsLJMgcXFQ69YnDN6cKzDCbWIi8XF8rQRajIBB69dUgaGZQe1k1hXpcTWehs0Yu6ojUtXlSlwpN2J7Q09/TdHO1w42uHClGwx7p2fm7JWHL0R8Wg8taoo2ctISYIMiy31NrTYfMiW8rC0RB4T80gxj4OfLcnH0XYnOhx+VKWQVxKBEC8olj1f1Glm0NraGvfX0Gq1MBgM538gIeX49IwZbx/oCDfbr61U4p65ORn3njIsCwqISdM5y7L413Ej/nncCD8TfRhRCDh4eHlBypmIZtr7GW/+fcIYrooCgFLIwQ/n52J2imS1kvcz88iE9zQvr/+WDDKlSBjzNFu9UWILAPZk6OQUTVExm/CjKArXTtHijcvKcGGFEvwISwWrN4jntrbAT3yt0hpPIPr9s3iCeH5by4DTrwQCYWCI4CKMeU51utGrQJPSQdephk7Cw/fn5eBPV1bgrjnZWFEqxzitEJOyRH2sJQjpxYWVSkh40acJX5DF5jprklZEIKQvpIeLMObpPR3FoyncOJ0EgA8XqYCDi6pUuKhKleylEGKEVszDU6uL8MqutnDeJADkpEl/Xrpj9wZxrMOJcrWQDHT0YnOdFZ4Ag8VF8pilTsQbIrgIMcHpC+LTM2acNrhRphJgnFaM6bniEWUCJppxWhF+MC8Hm+usUAi5uGqiBhUa4sdEIABAuVqIly4qwdEOFzqdfhTIBRg/yilXwvlptnrxyMYmmNwByAUcvHxxKdQicsoGgCarF+t3tgEA3jvUiTtnZ2NFmSLJqzo/5N0jjBpPgMEjG5tQYwoF/x5odQIwYU6+FL9cXpDcxQ2RCyqUuKBCmexlEAgpCUVRGe9nl2q8tb8DJncoEcLmDWJ/iwNryDEKACDlc0BTAMOGsmFf3tUGX5DFhTGM/IoHqV9+IKQ8fz3SGRZbkexrcaDDQZprCQRC6uD0pb7nl80bjMpEBQC3nwygdKMScTGvoCedgAXw1r52nNK7Bn5SCkAEF2HU7G9xDPiz7is0AoFASDafnDHhux9U44PjxvM/OIkYnP4+903KGjiijGVZHGx1oNHSN2w+U7lrTja0ESbXQRb414nUfl+J4CKMmoH6tGgKyI9j3AuBQCAMlQ6HD3862AmGBT6vNid7OYOSL+dDyO2xWZmaLR60r/SlnW144ptm3PtpHbaMkQlSlYiLp1YVRUWbnSAVLkKmc9vMLJSqBChXCxDp8DQtRxLzoOF0gmFZnNS74A2QrQACIdl8UW1BoMv/xeAK9PEYSyUEXBo/WpCLEqUAq8sVeHBZ/oCPbbR4sbk+FLPFAnj7gB7M2PAzR56cjxfWleDqSRoUyPlYWJTaWaikaZ4waqbnSvBSbimAUD/XP48bUa4W4kcLcpO8suTBsCwe39SEI+0uzMqT4NEVhcleEoEwpjmpd4f/TVMh+5dUZmGRfEgCos0e3Sdr8wZhdAWgk6SudUed2QMpnxOTNcoFHNw4XZcWVj5EcBFiyg3TdLhyohpi3titbAHAl9UWHOlqej3Q6oTR5YdGnLoHQAIh02mNECY6CS9tslLPh5Tf91gbozCJuGDzBvGTLxrAsizumpOT8pOFsYRsKRJizlgXWwCwozE6Gqjd0bcJlkAgJA6Xv2c6cUKKZXyOhgqNELqI5vFSlQDaFL64M7r8CDAsgizwxt527GnOzBi1/iAVLgIhDtSbo20yiGEhgZBccqR8NHdlQKai516QYfF5tRnVRg8q1EKsq1KBO4QqnIBL46dL8vHG3nZQQMq3cugixCAL4I8H9MiV8vHyrjY0WL2YVdCB6Vl8LCuVZ9zFOzkLEAgxhmVZuCMachVCDolCIRCSzLISOf561IDV5QpMzh7YYiFZvLyrDVu6mt8319nQ7vDje7Ozh/TccVoRXrqoNJ7LixlSAQcTdSKc7Az11LU7/HjymyZ0ukIWQrsbzNjdABxud+LBpelhnD1UyJYigRBjKIpCqapnhPvCCiWoXk0VzVYvHtvUhN/va0/08pICMW0kJJurJ2uwfl0Jfjg/9SpAFk8gLLa62VCTufYOV0xUR93uFluRnDH0NdNOd4jgIhDiwHemaiHgUFhUJMO1k7VRPztn9OAXXzficJszIw8qvXnvkB7X//Ms3j7QkeylEMYwNEWhTJ2aGan9XZBI+PE9PTu8Qfz7hBHPb2vB7/a0JzQVZF6BDFPPU2WsysA8W7KlSCDEgZl5Uvzz+nF97nd4g/jVlmbYvaEG3lJVZhvDGl1+fHTKBAD4+LQZ8wtkcdnOYVgWdCqPZhEIg5Ar42NqjjgqzufKCepBnjE6Drc58eLOVlg9PYMELTYvnr6gOG6v2ZufLsnHYxsbUWv2gkOFnOK7kfJo3DozK+rxdWYPTuhd4HNozM6XpmVfbPqtmEBIY94+0AFzRNzR3AJpElcTf07o3WAiDqQ7Gm0xF1xBhsWDXzdgVp4U103Rnv8JBEIK8uDSfHx2xoIOpw9z82WYE6djg8UTwG+2t8Dhi66qCbiJ3fCSCzh4bk0xdjXZQQH4/f4OuP0MJuXK8IM5OmRL+eHHHml34vFNTeFjCZcG1lWpcNvMrLS60CKCK4mwLNunt4eQuVQb3dhc19OnUaoSYE5+Zguu3ujjYI9xrMOFMwYPWmw+XD1JkzH+SoSxhZjHwdWTNXF/neMdrj5ii0tT+O60xBuHCrg0lpcqAADLuv6r1WphMBiiHtdu90dduAWYUMVcwKHTwvC0G9LDlWCCDIt3D+px7T/O4Nb/1OBER2pnP6U7B1sdeO+QHjsbbfAHk9u4/c/jRnQfMyiEIpEyXXBH+gMBiDpoxopjXd8hh49Biy1xfSiE1GVDjQU3flCNm/9djR2NtvM/YQxRqRFCKeyxW5ioE+E3FxanbH8bACwqlvU76b21Pr0GC0iFK8F8dtaM/3T1tHjdAbyyuw1vXV6e5FVlLs9ubYGvqzmgWCnAg0vzkSvjn+dZsccbYHC4zRm+fWGlElNzJAlfR6Ip1wihEnHD26jj4mA4ec7YE9lSa/agSJnZfXGpgMkdQL3ZgyqtqF+n85FSZ/agweLF7DwppCPMYa0ze/D6nvawuH95ZxvGa0XhpIdmqxf/O23GjFwJFhTJYrX0tCFbysdbl5ejyeqFlM9JyvFwuEj5HDy5qhB/PKjH3mZH+L2dV5he7x8RXAnmm15J7u0OP1z+YMYZvMWSBosX1UY3+Bwak7JEw4rIkfE5MHad7BssXjz0dSOevaAIOQk+yDRZfWHhNz1Xgtt6NYRmKnwOje/NzsKLO9qgEHJwQYUi5q9h9fY0/jp9xH4inpjcAbx7UI/tDTYEWeCGqVpcG6O+OU+AwS83NMLhY6AWcfHQsnxUaoYv0E/oXVGVVG+QxTmTJ3zc2NFox5fnLPjynAWLi2X48YI88DijrzS7/EHwaAo8TupvHAm59Ij+tskkW8rHg0sLYHEHUG/xQiPmolCRXhdXRHAlGE8gek8lT8YjYmsATnW68MaeDjRYveH7RFwaz1xQNOTy92UTVPjTwc7wbZM7gLcP6PHL5Yk11NOKudCJuZhbKMOtM3RpcVA+H1ZPANVGD9Qi7qDvx6IiOSboxBBwKEhiWA3pxhNhMhuIx54lAUDo+/j81haYIybbYrkNZXD5w71FJncAv9neilcvLh12MzeFvuJJzOv5HdkRW1PbG+zgc9rwowV5I1w1YPcG8faBDmyps6FCI8Rv15aM+HcRzo9SxMX0NJxQBEgPV8JZWtyT/k5TIFNVA1Bn9uDRjU1RYgsA3AEGHc6hN15fPl6NlWXyqPv2tziictUSgVLExdtXVuDO2dkZIbZO6l2467+1eGpzM+77vB6PbWoa1NxULeLGRWwB0X1hGd4SlzQOtTnx6MamKLE1K0+C2TEc+lAJuYgsNHU4/PjynGXYv2dRkQySCIFVpOBHbWXPyZdCEPFCm2pt+KZ2ZL1A3gCDRzc2YnOdDSxCxy2WJaKf0D/pKRPTmOumaCATcNBq92FhoQyTUjBiIhXY0+wIb8FFMjNXMqzJPoqicO/8XBQqBPjghBFOHwOFkAN+BoieZPLhSWNUfNHhNif+drQTt88aWhRJLOFFTCWKEjzaPhbocPjw3NbmqO9jkYKPe2Ps2C7hc7CoSI6tDT1N7nuaHbhs/PD8qJQiLn61uggfnjRCyufgmsmaqO+7hM/BDdN0+ONBffi+dw7qsahYNuBxodHixf870okakwcqERdz86X41iQN3jmgR62556KwXC3K+EEYwsghgivBUBSFi8epBvy5P8hgT7MDZw1uiHg01lQoh9WzlKr4gwz+e9qMerMHUj4HC4tkgzaNryxV4GCrA9XGkBN7mUqIFWVyrKtUDXvsn6IoXDVRg0vHqdFg8SJHxhtSKCxhYIT9CJtaU3Jc8yMdueUjbLQmDMzbB/RRrRDjtSI8vCwfcmHsTx83zdBhf6sDrq5q6UgjocrUQvxkcf6AP790vApH2p040BoaZLF7gzjU6uy3CdsfZPH4pqZwL6jBFdpKP21w42CrM+qxayuVI1ovYWyQEMH1u9/9DgcPHoRCocALL7wAAHA4HFi/fj06Ozuh0+lw3333QSoNVS7+85//YNOmTaBpGrfeeiumT58OAKitrcXrr78On8+HGTNm4NZbb82oq4mj7U68ursNemePMebWehveuCz9pxi3Ndjxl8M9vVSfV1swTivEvfNzUdBP42OWlIdfX1gCf5AFw7IxMeXjcShUZGBcRDK4epIGB9ucUU3qyZq6LFUJwxFJlXGYghzrHOoSFXwOhYurVPjudF3cLlh0Eh6eWlWEF3a0otXuw/JS+fmfNAJoisJPFufh6S0tON5lKzKQpYg3yMDs6Zv1d7DVicga/Kw8CVaWxX4ohJA5JKT+vnz5cjz00ENR93300UeYMmUKXnnlFUyZMgUfffQRAKC5uRk7d+7Eiy++iIcffhjvvPMOGCZ0UP/DH/6Au+66C6+88gra29tx+PDhRCw/IbTZfXhmS0uU2AKQVi66g1GqEqD3MfqMIdSnZXH3PZh1w+NQCXdAJpyfEpUQL64twRUT1FhaLMf35+YkrR+xoqtxW8Sj0zLuI9W5d0Euvj83B29fUY5bZmbFvTpcoRHid5eW4u/XVg57O3E4iHkcPLWqEHfPycbCItmA/WhSPqffdWgiPObKVALcv2jkjfeEsUFCzmQTJ04MV6+62bdvH5YtWwYAWLZsGfbt2xe+f+HCheDxeMjKykJOTg7OnTsHs9kMt9uNqqoqUBSFpUuXhp+TCexstEf1xAChpvrbZyXePuCE3oV3DnTA308P1UgpVQlx5+zsPqLL6A5gSz0xJkxHcmR83DozCw8szsOFA2ylGFx+vLCjFW/ubY+KNIol3VFBbj+DenPmh4EnmqUlclxYqYQiDluIA0FRVEKmt2mKwroqFX6+JH9Q/7ZbZ2bh4WX5WFupxMIiGW6bmYWfL8nHwiIZbpmhw2/WlsTUj4yQmSTtctBqtUKlCvUyqVQq2Gyhk67JZEJlZWX4cWq1GiaTCRwOBxpNT+yBRqOByWRK7KLjyMQsESQ8Gs6unoWJOhFunK7DxKzEN9Xva3bgf6fN8AdZ3D03J2a/d12VCuN1IvzzuBH7mh3wMyxkAk7StvmMLj8oiiJVkTjytyMGbO0S1Dub7Fi/riTmPYmKCNfs/54240cLYtvMTSAAwNwCGeYWRPd4/XzJwH1iBEJvUu5MM9BI7XBHbTds2IANGzYAAJ577jlotfHf7uByuSN+nSVa4MOyPLRYPZALuciVJ6/XSC51AjDhi2oLrpxZjEk5sXPz1WqBOZUFcPsCcPoZKITcpNgkvL2rAe/tawJNUXjogkpcOL7/SuJo3lMCYPW39fzbE8Q/Ttrw2NpxMX8dPuccfEEW2xts+NmaCZAJ+j+0kfczsyDvZ2w53mbDy1vrcP2MPKyqSk5GYSa/p0kTXAqFAmazGSqVCmazGXJ5qDlSo9HAaDSGH2cymaBWq/vcbzQaoVYPvL+/evVqrF69Ony7dxhmPOgvdHO4aGgAPsBgcMRmUSNAzgn5XLEAXtp0Fs+uKY7p79/VZMfv9rRjeo4EDyxOfN/D6U43/rS3CQDAsCxe2VKDaWqq396UWLynkTh9QfzlcCd8QRa3zcrK+G2ILFH033RTdSdunqqM+f93loSHZlvIzX/TiUYsKuq/2TrW7ychuZD3M3YEGRa//KQGna4Anvn6LCplTFJMuTPhPc3L6/+8lrRu5NmzZ2PLli0AgC1btmDOnDnh+3fu3Am/3w+9Xo+2tjZUVFRApVJBJBLhRKUH+QAATjJJREFU7NmzYFkWW7duxezZs5O1/IymOKKX4WSnG40W7yCPHh6tNh/W72iFzRvE1gYbOodhYhorjnVEj3JbPEE4fIkxQn1rXwc+r7ZgY60V7x3Sn/8Jac4l49RRQjbAhERnrIk0tuw9qk8gEM7P4TYnOl2hPktPgMWBFvI9ijUJqXC99NJLOHnyJOx2O+6++25ce+21uOKKK7B+/Xps2rQJWq0W999/PwCgsLAQCxYswP333w+apnH77beDpkO68I477sDvfvc7+Hw+TJ8+HTNmzEjE8sccFerowOGvaywxM7T89KwZ3ohmfL3DD50ksT5jveOVhFwasgRUmmye6AGBzXU2/N+8zO43ypfzcdvMLLx9oAMMG3Kcj0fP3HidCBu73MKPtpMTBYEwXFrt0bYYVm98hlzGMgkRXD/+8Y/7vf/RRx/t9/6rrroKV111VZ/7y8vLwz5ehPhBURTmF0jxebUFALCr0R4zwXWgNXqrVJWEhvW5BVJ8cKJne3pFqXzYZqojodUeXc3zBVn84JNaPLaiMOGiM5FcPC40LHG03YmZedK49OxNiUhs0DsDMLkDZBiCQBgGvZM9NtZYIRdwsbQkPl5oYxFicETol0gDv05XAOeMox+39wQYtEeIDoWQgxxZ4oXGOK0Id8/JRqlKgEvHqxJmvWHrxzyxyerDnw/pcbDVkdGWBuVqIa6cqInaro4luTI+8iI+S2cM7ri8DoGQqUQmNgBArdmL9Ttb0TqAISxh+JBLQEK/VGlFKFUJUNeVE1ZtdI/avoFLU6AooHvgdHGRLGnGruuqVFhX1ROxZPMEQFMUpHGMhunts9bN1gY7tjbYAYS24K6coMbKMkVCqm6ZxOx8Kf532gwAaLeTkwQh/fEHmYRNcc/Kk4JCR5R7PsMCNSYP8uT8hKwh0yEVrjEEw7J475Aed/znHH7yRT0arYM3w397as9oblsMTmBcmkJ5lyt4loSL70xLzthxJJ1OP376RT1u/Pc53Pjvary1r33YFiRDZWKWGEJutIjSSaKveVpsPry2px2/2d6CIBOfdWQqF1Yo0f3XtXkTMwRBIMSLl3e14tr3z+Lt/R0JeT2dhIdFRX0tgJLR9pGpEME1hvj7UQM+PGlCZ1f46m+3tQ76+HkFMswtCCUE2H0jC5HtzX0L8/CdqVo8t6Y4bA3QbvfhkzMmfHrGDFOc3MgH4lebm3G2a7uUYYHPzlpwuN0Vl9fSSXh45eJSfGeqFldNVOPR5QV4+aJSVPZTOdzV5MDn1ea4rCMdsXmDOKF3ITCICC1QCDAzL5Tn6IzR55UwMvxBFt/UWrG9gaRIjIRTehc21drAsMDHZ8zY1WRPyOsu6CW4siRcTNCRfNJYQaTrGGJjjTXqdoPViwDDhsf2GZbFwVYn9jY7YHD5UaoS4rYZOljcAZSpYtN7ky/nR2XuHWx14OktLeET6buH9HhkeUE4CNkXZHCgxQkuTWFGniSmOW4WTwD1/VheuP3xq45kS/l9MgefXFWIf58ICc7IbUerJ3OrNAdbHWiz+7GiTH5er58Aw+LBrxrQbPOhXC3EkysLB9z6/e40HY60u6Lc5wmJxeQO4Jktzag2ekBTwPQcSVy36jORk53RPYg7G+1YUBg7A+qBmJglhphHw+VnwOdQuHtODmltiCFEcKUwLMvi3UOdcPiCuHKCGgWK0YkeX6/qgFbMDQsYf5DBs1tbcCDCw+hAqxM1Jg9+s7ZkVK87GO8fM0ZVLXxBFv84ZsDUHAn8QRYPf90YrkCN04rw5KpCCGMUZi3lc6LsL4DQFd2M3P5DbOOFmMfBjdN1uHayBofbnDB7QhN2cwYI001n/EEWv9negj3NoWlVkzuAG6cPvrXcYPGiuatxt8bkwe/3dwwYFFymFuLBpfnIkWbu1GcqE2RY/GZbC6ojqsak1jh8+JxokdPhSIxfoVrExdOri3Bc78LMXMmozzmEaIjgSiIsy+L3+ztwqtONIoUAayqU4SBeADjW4cJHp0J5kdsbbHh0RSEmjSJbcVWZIvz7aAq4bWbPdN7n1ZYosdWNd4BG71jRezIGQLgP53CbMyy2gNDk2SdnzLh6kqbPc0YCl6bwxMpCvH2gAwZnABUaIW6aroOIl5yddgGXxrwEXMUmk3cP6cNiCwCs/Uxu9qb3Z3B7gw3fm50N2QBVk9kZKFTThS/PWaKqMzlSHuSkujVsCnsJnUQeksrUQpSpkxctl8kQwZVEnD4Gn521AADqzF5sqbdhdbkCd83JBp8TKut24wmweGVXG16/tGzE22o3TdchT8ZHh8OHOflSTIgQb/Z+mozFPBq3zIyvZcJ3p+lQY/LA0rV9JuHTuHF66DX9TF+xF2vrhGKlAE+tKorp7yT0T63Jg8/ORvellQ/hwF6hEYLPocI+QUEWOKl3Zbw4TTcYlsU/j0VHslwxYeD4tUhYlsX+FieabV6sLFNAIRzbp6Yp2WKUqQSo7ZoS791bRUhPxvanOslIBRxM0IlwKuKKcEONFQanH4+vLESxUgAKCI/ptjv82FZvw4oIj6zhwKEpXFip7Pdn10zWwB1gcLTNBS4HmJItwdpKJXJl8R0HLlML8fvLy3G4zQkGoQNNdzP9lGxJuJ+gm3wynpy27Gi0I3JXWyfmYlX5+T/LfA6N5aVyfHWupwcxWXYihIFptvpgjug7rNQIsaZCed7ndTr9eHZrC2pMoYspb5DF9VMyM7x4qHBoCg8uLcBfDndCJeJgbaXq/E8ipDxEcCWZH8zPwUNfNcIaUWE63O7ClnoblpcqML9Qil1NPVswpw3uEQuuweBzaNwRIzf54TLQVppMwMEvlxfgpZ1t0Dv9mJUnwVUTY7OdSEg8BldPHwqXBu5flAf+ED2GbpqehWqjB3VmL6R8GuO0ZMsj1YjMI82S8PDAorzzNlwbXH48+FVDOMMPwJhKCOhw+HCq043xWhFyel3cZkl5eGBx/72KhPRk7HyyU5QCuQAvrCvB+p2tOKHvqXTVmDxYXqrA7bOycVzvDm/5jbXr+klZYvzhinIEGZZMy6Q5C4tk2NloR56MjzvnZGPiMPoRZQIOnl9TjAOtDhQqBJCP8S2nVKRSI8KKUjlkAg6umaw9b+9WkGHxzJaWKLEl4dNYUjx2omSe39aKGlNomvPicSrcPjMLFKneZizkqJUC6CQ8PHNBMfa3OHC03QmGBS4Zpwr/bP26ErxzQA+Dy4+Lx43N0jIRW+nPvAIZ/nX9uBE/X8ClsbBo7JyM0w0eh8KPFw69IrOz0R7eRgRCF5M/mp+btKGVZODuapdgWODj02YwLHDn7OTsNBDiDxFcKcTsfGm/E1Y6CQ+/WJqfhBURCARCfNjb0tMqQVPA3XNyRjwIYXT5YXIHkCfjQ8JPn6nIeQVS/KdrchwAPj1jxrQcMeYVkCb5TIQILgKBQCAknIKuAZhxWhFumKbFtC6z4+Gw8WwnfretFu1dPlU0BVxcpcIdaVIlunqSBjsa7dA7e/obvz5nIYIrQyGCi0AgEAjnxewO4IzBjUKFICbTwtdN0eKayZoRT5zuarTjuW0tUfd1R+FcOl6FbGnqTzRLBRw8tqIAv9zYFDZg1jsSG29GSBxEcBEIBAJhUPY02fHizjZ4AgwoAP83LwcXDMHy4XyMxt7jQKuj3/vL1QJkSdInaaBAIcBL60rw39MmnO50D8kqhZCeEMFFIBAIhAEJMixe3R0SW0DIF/CvRzpjIrhGw9WTNKi3BVDdGUrIkPBpLCyU4dY0nPRTiri4eUZ8TaaBULQWwII3RDsWQmwhgotAIBAIA9Ji98Hui0598PfKZU0GOTI+3v3ODJxpbAOLkH8XMcTtn06nH3/Y34FDbSFx+qMFuVg8huw3UgUiuAgEAoEwIFkSHmR8Okp0pdLJWiNOn+3DZFBtdOOJTU1R79/xDldKvYdjBVJXJBAIBMKACLk07luYh1wZD0IujbWVyqjge0Lq4g0weGpzc58K5Yy84U+EEkYPqXARCIQxgcHlx4HTekxWhkxUCUNnVr4Us/rxCCSkNtsabLBG5FsCwPxCKbGdSBJEcBEIhDHBF2ct+NcJI0qUAjy5qhCKXvFAAYbF5jor/EEWQTbUoyTmcSDh05DzOVCKuFAIORDzhm6sybIsTujdaLZ5YfcG4QmwkPJpKIRcaMRc5Mn40Iq5adfkTUg/KITig0h1MnkQwUUgZAhWTwBOH4O8GHgkZSJcTkjU1Fu8eHRjE367tjhqWotLU9CKedjTbMe+ZkdUxl8kfA4FpZALpTAkwjQiLrKlPORI+ciW8pAt5YXdzn+7oxXbG+yDrkvIpTA1R4J5BVIsLJINS9ARCIOxolQBmqLg9AUxLVeCIoUg2Usa01AsyyZ/3CQBtLa2xv01tFotDAZD3F+HkDjS5T093ObE01uaEWRYPLWqCJOyhx4MPVY43ObEY5uawrevnazBDdN0Az6+2erFGYMbZwwe1Jg8aLB4hzydJ+PTyJLyoRJx0G73o9Xuw1CeKuPT+NmSfEwdget6JnGgxYHtjTZkS/i4ZrJmwCzVdPl+EoZOJryneXn9Z4qSClcKcqjNif+eMqHV7kOJUoDvzc6GLo2M/AiJxRNg8OKOVviCoTP6hlprSgkuhmXBsskPIJ+UJUa2TIAOuxcA8J+TJlxYqYR2gCm3AoUABQoBVpWHbgcZFk1WL2rNXtSaPagzeVBn8cLZqyEZAOw+BvaIYOZIOBRAURQYlo0SYTwayJcLxry1wR8PdOC/p83h2+N1IkzPHdsClJAZEMGVYnx82oS3D+jDtzscfgQZFo+sKEziqgbH7Wfw0Skjjne4oBXzcMVENUpVwmQvK0yAYVFv9kLCp5ErS+52W7vdhx2NdnBpCqUqAaZki0fdv3Oo1Qmrt6cxtjMily3ZWD0B/OzLBniDLB5YlIsp2ck7cfI4FG6eU4hfbzoHIOQl9eEJI+6ckzOk53NoCiUqIUpUQqxEjxu42R1Ai82HVrsv/N9Wmw/tDj8C/ZS1Qt6Tfe+nu0TYtgYbWmw+lKkFKFYKwB9DJpWb66xRYgtAv39DAiEdIYIrhQgwLP5yuLPP/al+vHlmSzOOdri6brmxt8WB1y4pTQl/nDMGN57f2gKjOwAKwOUT1Lh1kKZRmyeAIAuoRPH5ajy7tQX1Fm/4dp6Mh29P1WFJsWzEwuus0R11O1ua/L97N5vrbOFg4ee3teK1i0uhjNPfdihcMikb/z7chBpT6D3Y1eTAnXNG9ztVIi5UIi4m96oqBhkWeqcfbXYfOhx+dDj8aHf4oXeGxFjvypg3yOKs0YOzxp7KGIcCSlQCVGlEmJglxsw8CaT8zO3x+sex6K0kMY/GeJ0oSashEGILEVwpRIDpmY7qhqaAqydrkrSi86N3+CPEVgiXn8GRdhdWliU3E8wfZMNiCwhFknx0yoTLxqv6FYM1Jg9+/mUD/AyLJcUyfH/e0Cofw2GCThQluFrtfrywoxVH2xX4v3k5IxJdbn/0iXtaCvX/tNh84X/bvUFsa7Dh0vHqpK2HQ1O4d34uHvy6ES4/A5M7gE6nPy5b9hyaQq6MP2BV1eELosPhh97hh8UTgNkTgMUdhMUTgMUThNUTgNUTRI3JixqTF59XW8ChgIlZYlw7WZNxfV7eAIM2e3R19nuzszNaYBLGFkRwpRBCLo0fzMvFX450wuoJYHK2BNdP0WCCLnX6cXoj5tMQcil4Aj1CkQJQpkr+NEyr3RcWW5EMtEWxr8URbore1mCHn2HxwlXZMV3TrTOz0Gr34Uh7tEj9usaKKq0Ia0aQT1epEeLz6tC/ixUCzC9MHb8krTj6EHNc70qq4AKAEpUQj68sxNObm2H1BsHjJKdnSsrnQKrmoFw9+PZ7kGHh9DPwBxlwKAo0TUHMy7xtRgGXRrlagBqTF0Iuje9O0yb9oo1AiCVEcKUYK8oUWJFGBxkpn4MfzMvF63va4Q4wEHFp3Dhdh5IU6OHSSbgQcWm4Az0VoHkFUmRL+684qHttde1ucmBnnQlVMfQIFHBpPLaiEBtrrfjXcSP0Ef1WkZWv4bCsVIHDbS44/UHcOTs7pXp+pvTaZjP3I4CTwTitCG9dXo5mmxdKYWofBjk0BbmAAyDzKz2/Wl2EerMXRUoBqWwRMo7UPtIQ0oIlJXIsKJKhw+GHVsxNGRdvMY+DR5YX4L3DnXD6gpidL8W3p2oHfPyCQhneO6SHI6K3ZnutCVXTlDFdF4emsKZCidXlCpzUu9Fm90HApUdcmeLSFB5Y3P8YcrIZrxOhSiMM9yUVppAPkIhHo1JD+oNSCTGPg4lZqVvRJxBGAxFchJjApSnkp6Dh5qRsMX59YfGQHisTcHDP3Bz8ZnuPZ5sv2HfkP1bQFIXJ2eI+zdaZBEVR+MXSfPx2eys6nH5cOTG524kEAoGQLIjgIhAiWFwsB49D4e9HDfAEGHxrWi6AkW31EUJoxDw8u2ZoojeRmNwBSPl0Sm3BpjOeAIMNNRYcbHUiX87HrTOzxrynGIEQCRFcBEIv5hXIwuGuWq0MBgMRXJnGe4f0+PCkCVyawgSdCFdMUGM2CWceMUaXH09tbkadOfRdOdDqxIWVShTIU2cLmUBINkRwEQiEMUdO1+BEgGFxrMOFYx0uzMiV4AfzcwZ0nif0j8MXxKMbm9AcYQEi5FLQiMjfkUCIhNTSCYTzYHD50Wjxwhbh5k5Iby6oUGBeQXRF61CbE/d+WofqXkayhMH55Iw5SmwBwNpKFUQZaF1BIIwGUuEijBhfkMGX1RacNXowQSfC2kplRvVsGF1+PP7BURxqsYXvK1cLcc/cbDLdlubQFIWfLM7DW/s6sKHGGr7f6WPw1DfNWH9RSUokJaQD+5odUbfnFUhx0/SBQ8EJhP4wuvz4ptaGGaUclGeWp28YcglCGBFBhsXjm5rw9gE9ttbb8Na+Dnx0ypTsZcWUPx/qjBJbQMiN/olNTXD5SbUr3eFzaPxwfi4eW1GA4gi7Cqs3iE211kGeSYikWBn628n4NK6bosEvluYnPaickH58csaMvxzpxP0fncBjGxvh9GXeMZZUuAgjYl+LAyf00VsvB1qduGpi6sYQDZcytRCb62197vcGWfgCLEgBJDOYmSfF9FwJjraHerkcviDmkAb6IfPD+Tn49lQtlEJu0lz7CelPyNw3xOF2Fx7f1IRfrS5KGV/HWJA5/yeEhNLSq2cDAApT0IdrNFw2XoWfrixHnoyH7gv2UpUAP1+Sn9QA5nhy1uDGV+cs8Abi5z+WbIIMC38vfzWaojA9V4Ibp+twz9yclEhKSBcoioJOwiNiizAqlpbIEfkROmv04O0DHclbUBzIzLMGIe7MypPg/x3pRHcsoUrIwbcmZU51CwidSK6YkovFuTwEGRYMy4KXwZ5N3VYJQCgQ+/IJmWdS+s9DLXhzRz28QRZ8DgWViItKjRATdWLMyJUgL8MuGkaKN8Bgb7MDRzuc0Ih4uH6QhAYCIRZoxDzcNEOHPx3sDN/39Tkrrp6kGTCOLd0ggoswIkpUQjy1qghb621QCDm4uEqVsVUfIBTHw0HmXsF/dc4SFlsAMnYic2GpGn/b34ROVwC+IIsOhx8dDj+2N9gBAIUKPhYXyXHROBXkAg58QQaNFh9a7T5YPQG4AwzUIi7KVEKUqgSgMmhIBABYlsXXNVb8+ZAe9q6IqywJEVyExHDFBA1MPg7+e7wdAMAC2NvsSHrgfazI3DMkIe5keizNWMHhC+LdQ/qo+7oboTONAqUIr15Shk/OmPDf02bYewnLJqsPfz9mwIcnjShQ8NFm98Pl7397dXaeBI+sKEzEshOC1RPAiztacbjdFXX/d9JQbLXYfPj0rBkeP4P5hVLMLYhhAj0hrvx0ZTnU/CD+edwIp49J+XD54ZA5/ycEAmFEbKyxwhkR2K0Vc7FghEHa6YCIR+OayVpcOl6Nfc0O7G6242CrM0pYeYMsakyDJwxI+ZxBf55OtNl9ePjrRhjdgaj7vzVRjRVliiStamTsabLjtzta4QuG+h021lrxy2UFmFMwvM+0P8jgq3NWHO1wIsiwuHqSFuN1xA4m3lAUhSsmaLCuUgWrJ4gsaeZMJxHBRSCMcerMnqjbd8zOzuhetW6EXBpLSuRYUiJHkGFRb/HipN6FTqcfh9udaLb60HXODiMTcDApS4Q15UrMypBJRpc/iKc2N0eJLT6HwvdmZ2NNhTJ5CxsBLMvi7QP6sNjq5pzJPSzBVW1047fbW9Hu8Ifv43NojNflx2ythMERcGlkSTPrOEQEF4EwxhF3VWpoKrR9tKBw7G2/cGgK5WohytU904ksy0Lv9MPiCULEo6EUcqNG1zOFz85YoqaOJ2WJcM/cHBQq0m9bmWGBTqe/z/1Ts4fupFln9uDBrxrhZ6JF26Qs0j5BGB1EcBEygn8cNaDa6MZlE9SYlpOhNsVx4ppJGujEXEzKEqNKO7a3THxBBr4ACxGPBoemkC3lIzszClkDohRxME4rQpGCj6UlckzJFqftMACHprCyTIGNXca1HAq4fooWk4bRa/r7fR19xNb0HDHWVSljuVTCGIQILkJGsKHGgk5XAAdanfj2VC2um5J+jb7JQiXi4soMMqwdKR+eMOKvRzvRbUFWIOdjeq4ECwplmJQlGpEICTIsmqxe1Jq9qDN70Grzod3hh8HlR4BhwbCAgEOjUiPEjxbmJiU4e3W5EqvLlQl/3Xhx74JcLC+Vw+gKYHK2GDrJ8P6mbfaeah+XBi4br8Z3p+kyKraMkByI4CJkBPkKATpdAbAA/nbUgBwpD8tK06vZl5BccmV8RPqhNtt8aLb58MkZM7IkPNwwTYvlQ/hM+YMs9rc4sL3Rhv0tTnjOYyLrCzIoVgqgyMDtymQxdRRV7p8vzceuRjs0Yh7mFkiRK8sMD6ihEmRYbKy1Yn6BFPIMmhBMBchfk5ARLCmW4XCbM3z7rX0dmJIjgTqDvcEIsWVBkQyPrSzEm3vbo5qlAUDv9GP9zjbsaLTjF0sGzgrc3WTHG3vbYfEM7mNGAajSCrGoSI7FxTISlJ1CTNCJMUE3dvu1/n3CiL8eNeDzswK8uK4kbbeXUxFyNiJkBCvLFPjqnAVnDKGJO6efwYcnjbhjVnaSV0ZIJ2bkSvDqJaXY0WDH5jorjnW4oiYV9zY7sK3B1m+lK8iw2FhjgbuXbxeHBoQcGnIhByVKARYUyjArX5pRthKEzGFLV35srdmLox0u0hMbQ4jgIiSFg60ObKq1gmGB5aXyURsT0hSFBxbl4YEvGsJmlvtbHERwEYYNn0NjRZkCK8oUcPsZnNC70O7wwRNgoRNzsaCo72fV4gngkQ2NaLT2zRgNMoCTYeD0M2iz+7Gn2YG75+TgwkplAv5vCISh4w8yaI6YWD3U6iSCK4YQwUVIOHua7Hhma0v49o5GO+5bmDuk/pjByJby8diKgrB/jmcAh3ACYaiIeDRmD8Fvi8+hkC/no8nqA3uex3IoCp+cMWF7oy0cE1SmFqBKI4KAm1m+Q4S+6B1+/PukETUmDxgWyJXxIOTSOKl3wRtkcdVENS4Zl5wom17DmdD3Y7FBGDlEcBESztc11n7vG63gAoBKjQgvX1yKHQ02qElfDCFBiHkc/GJpASyeAM4Y3GiweOHxM/AzLLg0BSGXhoBL45PTJnS6Ami0+sLVsM11oS0cLk2hSiPEzDwJVpcroSL9hxmH0eXHfZ/XwRGR7FBjijYe/tNBfdIEF59DgUdTYVsMX5BctMYS8o0mJJwsSd+PXdYwR7cHQ8ilsSqDxtwJ6YNSyMW8AhnmDbBFfmGFEp+dNeOLajP0zugYnQDD4mSnGyc73fjHMQMWFsnx3WlaZEtTY0rOG2DA41DEHmEU1Jq8UWKrP5I5QEFRFLKkvLAR7lhInEgkRHAREs61k7U41elGrTmUVVeo4KdlQC6BMFxEPBrfmqTBlRPVONzmxJF2F451uFBn9kRt5wQYYGu9DUfanXjl4tKkBPie6HBhd7MdZw0etNh9sHuDoClAI+Liggol8bobATPzJJhfKMXuJke/P6cp4KbpugSvKpoFhTJ8cMIIABg/xo2QYw0RXAQAIbO/T86YcbrTjSDL4rLxaqyMU2hth9MPHocGn0PBF2Tx/9u788CoynN/4N8z+0wms2cPISQEBMIOssgiBIsirWiVKkhVtL/aXmqVi1fQ1lat1d6KC4rSWlDrbWmhldaNyiICAgoSImtCFrIvk8lMZpLZZ875/THJkMkCJJk9z+cvZpjMeTNv5pznvMvztDu92Px1I/KTZSjIVdJUShQ6p7dh90UTRHwe7hirQWYMln2JJjyGwZR0Oaak+9aHOT0sKludqGp1wuzwwOHhwADI1UrCXk6o3uLCa8caUGyw9/g/lgOabR7sK2/FXflaGu3qJz6PwYZ5mSht8U07H660oKjRBsBXNP7h6an9LrIdbLeOVmNfeSu8HDA3WxHRtsQbhuO4q63xjAv19fUhP4ZOp4PBYAj5cYLtr6eb8c9zLeianzE9UYS3vpcTkuO9/lUD9vWyjgsAxHwG905Kwveui8wahu5itU+DqdXhwY//XQ6Hx3eqEPMZvPid4cjpUncwVlB/Xt36PVW40Nwz2AJ8+cNmDJNj9ZTkqJjqjIf+rDY74WU5ZCpEUTOFZ7J74GE56GSCsOfhioc+TU9P7/V5GkoY4v5xrgV/P9PS4/lZw0J3l/XDSUkw2DwBiUo7Ob0ctp7UY3yKDCPUsXdBj0elBoc/2AJ8fbTzXAuemJsRwVaRUHlgSjL+fcGIxnYXrC4WGqkAqYlC5GmlmJyWMOQyr4daVhBGi8taHKg2OyHmM5iWIR/UbtdyowN7y1pR2GCFweqGTMTH2tlp/tFYMnAUcA1hHMfhn+d6C7YSsXJi6NYRKCUCPLNwGM40WfFVTTuKm+1oaHPB7vGd3K/PlCOdTupBs/uiCRdb7EiVi5CfLOtXIV8AyFL17ItaszNYzSNRZrROiv+hYDomfFllwfbThoDcWaN1Uvzv4uH9fq9asxNbT+pR2O1GuDOvIRk8CrjiWKvDg3+ea4HVxWLlRF2P3S8M49uG3rmGIE8rwffHaTFr2OCSkF6r8SkJGJ9yOakey3FRuSakzelFi82N7BgdcfvgfEvAjrgRajF+kK/rNYFnb1LkIiweqcJnZa3+5ygZIiGR9bczBmw/3XPqTczv/zn0TJMVLxysg7WX3IX3jNfR6FaQUMAVp2xuL9bvqUJDmy9xXVO7C8/f1POu55mCLBjtHkgEDGTCyJYaicZgCwBO1LVj07EGrJigw/LxOvzjbAs+vWiCLkGAB6YkR33dtfunJOOlL+v9u+AumZx48XAdJqTK8N+z06G6hk0KP7k+BaN0EhTWW5GlFOOOcdGxxo6QoajO4uo12NJIBfjJ9an9ei+Xl8XvDtf3CLYyFSKsmpSEmWG6AR8KKOCKUwcvWfzBFgCcb7bD6WF7ndunAs9XphTzwQH4y2kD3CyHHWd907Atdg+ePVCLN5aOiOriwzdkKcDMAV471ghHl50Rpxtt2LC3Cs/fNPyqfwMMw2BRrgqLKL8ZIVFBzGfg9F7eyLIwR4m78rX9Phd5WSA9UYhyNwu1hI+RWgkW5CgxPUMetTfBsYqutHHqTJMt4LFEwINwAEPNBMhPkUEiYODwcP78NJ1sbhb/KW0N6Zo3ADhe24bXjjVg6Wg17pnQ/2PNzlJgtE6K94uacbDS4h/tqm9zY9vJJqybQ2t2CIkVGQoR/nBbLsqNDgh4DMYkDbwslFTIw/8uzr7q6zwsh+pWJ1LkQiRQ4fUBoYArTsm7fSGup7uVARMLfPX0vqxq61FrDPCtlQu1XeeNaHex+NuZFmQqxAPKj6OVCfHo7HTcM0GHPWVmFBvsaLG5o2J7PyGkf9RSwTXV+QwGl5fF+j1VKDc6IRPysGZmKm7ICk2OrguNbXjvqzpIBDyMSZJidpYCUuHVg0mW43C2yYY6iwvDlGLk93NzUDhQwBWnCnKV+LzCDDfLIT1RhHsjnL041i0dpcaXVW29/l84FpB3LSK746xhUAkJU+S+tRkkNlW3OlHaYgfLAddnyqGMQBZ6MrScarCi3OjbmWxzs9h0rAFjkmS9LkVgOQ5fVrXhvN6Gm0aqkNvPfH3f1LTicMe5dm+5Ge+easad47RYOloNPq/3QYNKkwMvH2lAVZfd0/eM1+HuKKtgQt/UONHu8qKp3Y3hKjEEPAajdVK8uiQbje3ujimx6EioF2kmuwdvfNWAlEQRHpqafM2jfmOSZZianoCT9b4t01IBg6npcuSnyDBneOizMSvEfBhsvpG0arMLjW0upIYpdcZ7p/T4rLQVAJCrkeCmkSrMGZ5II6YRsPVkEz4sNvkfS04yeHrBMIxLjr67+aHE6vJCLOBB0EdAEOus3eo/OjwcDlWasWyMNuD5hjYXfne4Dpc6yrappYJ+B1zfzU/FP4vq0NxxvrM4vdhWqMfBSgs2zMtAUre6uy4vi98eqkNTuzvg+dNNVtyN6Aq46CocBziOw3/vrsTa3ZX46UcVONPkCwoylWJMy5BTsNXFtpN6fFNvxSclJhzpY8SqLz+clITO86ndw2G4SoxbRqlD0Mqe8rSBNc3KTY6wHBcA9pS1wupmYXWzON1kw8Yj9Xjs00oYbO6r/zAJmnN6W0CwBfgufLvO98ylR8KjqMGKDXuqsGJnKf7ns8pINydkpqQlQNgtmHS4A9dX1Jid2LCnyh9sAUD+AG4EVFIhni3IQnJC4HhQudGBp/dXw9JtCcclk7NHsAUgbOmN+oOuxHHA7PSisb0z/YMbv/miDtWtoU9MGY5jBJPV5cWRaov/8eEqyxVe3VO2WoIlXQKsT0tbwYapMtbMbpn/3d7wVeRaNqZnCojKVid+tb8Gnt4WtXXT5vTiWE0b/n3BiMOVFjg9PXP9kKvr63Pr7wjCUOFhOZQY7LCEKHHnjjMG/PrzGpzvKIMUz3/WKqkAK7pMz/EYXyHuTia7B88eqIHJcfmznpDS/yTLndIVImy6NQdLR6vRdbygvs2NHd02LmUoRNDKLgdnAp7vnLV0dHhuhvuDphTjgEoigFLMh7njxOLwsHj9qwb8/ubskB3Tw3J4/LNKLM/X4fvjtFf/gShgsHnQNU4ZyIl45UQdTtS1o6ndDZPdgwt6+4BPKv0xOS0BE1Nl+LYjSW04M/Hfla8Dj2Gw46whoMRPrcWFSpMTI7V9X/C/rvHtruya4ydVLsT/Lh7e77VH7xbq8cUlMzQyIX48PQWjddKr/1AcmZIux3dHq7G71AQP67uwFOSocOe46Jo2iQbHa9vw9jd66K1u3JitwGM39F7bbqD+fEqPf543Bjz3veui7wIfTHeM0yJPJ8HpRhvGJcswqsv37+1vmgKSK2tlAjw6O21Qx5MKefjRtBR87zo1Pr3YirNNNpgcHmi7rRuTi/jYtGQEChuscHlZTEmXR22qo+hsFem3ZWM0eK+o2f/4YosDDW2ukNU9M9k9cHg4bD9twPwRCuiiOA9Vp+6jMaIBpMmQCfn4xfxMPLGnCjY3i5P17b0GXF9cMuPvZ1qwbk56UEYgGIbBuhvS8cdvmpAo5gec7MLh++O0/o0YRQ1WuLwcxiXLMELddx04m9uLl4/WBwRpANDY7sa3jTbM68fC/69r2rDrgu8CZ3J48fT+aryxNKfHeo5499C0FCzP18Li8iJJJhxUzbx49Z9SE7Ycb0LnX9217HDrj5N17T2CrWnpCViYowzqccLFy3K+aTmrC3yGQapciGFKMfg8Bja3F2aH138d6V4dBADqLS4crb68PEMr9ZVuC1ZuwhS5CA9MSb7ia+Rifr/OJ5FCAVecuG2MBqebbDjVpQ6Wye4JWcDVWT7CzXL4qNh01S9ENFBLBWAA/4l4oKNEWSoxnpyfgRcP1aHd1XMewWBz442vGuFmOfz9jAFPzs8ceKO7UEgEEc2XpZIIcMdYLe4Ye20jml4WvabRUEsFmJzWv52d1d1qNzo8HA5XWa65LfFEIRFAcY2jgxzH4WhNG6panfjOSFVM3BgNxrGatoBgCwBuzlMF9RjbzwRmeJ+RKcfjc9JjchOJ28vhfz6rRIUp8PslFfCQp5OgoiPP1zt3jOzz96syO/2f94RUGdbMSKVUM32ggCtO8HkM1s/LwM6zLdhfYUZKgrDHQutgkov54DG+C+qeslasmKCL+rttjVSACR3TcjxmcCfi8SkJeOt7ub2uq/mkxAR3R6Rx0WDv1/vuKWvFVzVtSJULccsoNYYp+x5BinaJYj6emp+Jv55uxiWTEyqJAPkpMqyalIREcf8SJ2areo4S2noJdsllNrcXv/miFuf0vr/BdqcX/296/8q+dOX2svjpRxXgOODOfC0Wj1SBiaIgo9nqxqtHGwKCrcUjVUGvgdrckaIlQcTDHWO0+P44TVR9Dv3BMD1H/gHA7mFxuvFy8myHh+2z9NvktAT89PpUZCpEYVleEcso4IojEgEPqyYlhSXHEo9hoJMJoLd6YHOzONNkC1sSvsH42cw0bCvUY0amfNAnYoWYD/QSOJR0CbK6T6ddSYvNjc1fN/of7ykzY83MVNw4IjanKgBgUloCJvVzNKs30zISMCNTjq9r2wEAQh4zqFxk8Y7jOPz+cL0/2AIA3iBTFlicXv86nbeON+GC3o5HZ6dFTbDxUbExoHTVuGQpfjQtJejHebYgC81WN8Yly4I+XRluAh6D3yzKwv9924z95Wb0theHAa5YZ1ci4GFxkEcR4xUFXGTARumk0Ft9c/ff1LXHRMCVlCDEE3NDOy1X36WGpVJy7SM5Qh4DPgP/Sc/NcnjtWAMyFKIBjVZ6Wa7PRIGxhmEYPDE3A4cqLWiyujE9Q47hqtgd/Qu1PWVmFHZZXgAA09IH9/3UyoRITxShvs0FAPii0oKURCFWDKDUVCicqLv8++anyPCL+ZkhKWc2XCWOq789pUSA/5qRhlUTk3BOb8fFFjs+LDb5R76utCmG9E9sh+ckoq7rsnC7qNHa5+tKDHYcqbKENZVBJFldl3c/ZvXjxKyQCLCg28JblgP+05F0tD9eOVqPhz+siKtcWXwegwU5Stw9XkepEK5ib3lrwOMZmfKgjDR234m382xLwIhuJI1LliJPK8F/zUjFcwXDYn70KdwUEgFmZSUiVyMJmGZcEMMj7NGGRrjIgE3oUtKmoc0Ns8PTY6v/zrMG/N+3vkWm+SkyPL8oK6Rt8rAczuttSBDxI3ZR1soEaOgY5ZrUz7I/D09PgcPDBpQR4vdzyqbF5sYXl3w5xnacacFPZwx83U5XJrsH+8vNuNhih0oiwJJRwV8fQ4KjqkuOvCylCI/MGtwW/U63jFLjZH27fzSJ5YBPS0xRkaJjzczg/I5DXdcdhxIBExO7/2IFBVxkwIarxEiSCfwlGIqb7ZjRJbtvq8ODv3x7eUfP2SYbzjbZQlZUtKHNhV/sq/aXwLlrnDYiNSQnpiagoa0VSTIBbhrZv7tDIZ+Hx+dk4MbsdhQ1WiHiM7itl8SjV9J1xOFgpQU/mpYMIX9wd/vNVjee2FOFFtvlXDuHqyzYdOuIIZeaIVpsP92MT0pM/hxnOpkQORoxrtNJMSFFhostDtyQlYiVE5N6FLMfjHVzMvDLfdW42OKrdnBOb7vKT5BYUmtx+f9900hVvze4kL5RwEUGZVqGHLs7przq2lwB/1dhdKD7JKK5W1mGYOE4DhuP1PuDLQD4d7ExIgHXqklJEPMZ3DJKDdEAA53pmXJMzxzYmpuudc8cHhYNbe5+TW325uMSU0CwBfiK2NaYnRRwRQgHoK1LX+utbuitbnxV49tYkKkQYZROigRRcKfWJAIefntTFv55zoi95a0Yk0Q70+JJe0dC6AyFKCC7PBk8CrjIoNycp/IHXBZHYOb23gq5ZihCk5/FaPegtCWwvmCk1ovLRXysnhr83VHXqnt6Dr118AFXb59lkkyA65IiP5U0VK2YkIQRagn+dtqAyl7KbNVaXHjtWAM+rzDj6QWZAw7+eyPk83D3BB3upgty3BmTLEV1qxMb5mVecXci6T8KuMigZKslmJyWgFMNVtjcgXmRxiXLkKkQ+YeoZ2SGbmeZmM+DgBdYz2zxSFVIjhXtdLLAr3Uwahf+YLwOeqsbx2vbwWMYTE6T4f7JyXRCjrBZwxIxM1OOC812nGqw4nSjDZdMDji7bFCpbHWioc0dVzvrSOis6yiDFC3pPuIJBVxk0FZNSsKZJpsvL1UXfB6D396UhQ+LTVBJ+Lg5L3SJEuViPu4er8NfTxvAAJiXrcAPJ0d/9vtQyFSK/UlpAd8U0GBJBL61ZR6W63XkkkQOwzAYmyzD2GQZVk4EWI5DU7sbLi8HPgOkyIWDXsMXKRanFzz4vt8kPCjQCh0KuMig5Wok+MWNmUjuZS2PUiIISyJWwFdk+bvXacBy3JAeeVGI+Zie4UsSyiC4eXQo2Ip+PIYJWUmvcPmqpg3vntL7d/smCHm4LkmKO8ZqQ7bphpBQi83bHhJ1JqclhGx9Vn9IBLwhHWx1WjFBB7mIh7nZih6pOmJFq92DQ5UWnNfbwHJDI4fbUGGwuXGuyRaQGb6rt79p8gdbAGB1szhZb8VT+6rxcYmx158hJNrF5pmYEHJF2WoJ/vC93JhN/vivCy34vyKDvybljEx50IqAk8ipNTvx5vFGf8mhkRoJNt6S3eN1y/N12HKisdfi53vLzFg6un+pUgiJBhRwERKnYnXdS4XRgXcLmwNSinxd244aszOmi3kPdSdq2/HSkbqA+qJ9jXAtzlNhtE6Cj0pMKDHY0Wz1gMf4qlv8cHJ0lBIipL8o4CKERJWyXvK38RlQAsYY1mx19wi2AOCOsX2PVGWrJfgZZY8ncYQCLkJI2Lm9LA5csqDS5ECGQoyCXKV/N2V+sgwSAS9g9OPuCTqoYnQtGgE+K23tEWwtG6NBQa4qMg0i16TF5oZCLAhJEfDB+KTEhIsGOxbkKINSIzRc6AxGCAkrjuPwzIFanGm6XBLm4xIjfn9zNuQiPtIVIrx083AcqDDDxXKYO1wRFbX6yMDJumS7T5IJ8NC0FMzsUgaMRBeT3YNNxxpQ2GDFA1OSsGyMNtJNCrDrfAuabR58UWnB4pEq/OT6lJhIZ0EBFyEkrKrNroBgCwDq29z4ssqCm/PUAIBhSvGQzaMWj+4Yq8XUdDn4jC9PHIle1a1OPL2/GqaOyiEJUbjrO00h8tfw/aysFXIRLybOF7G5hYmQOLWnrBX3f1CGp/ZVo7C+PdLNCQmdTABRL1MUwSw9Q6LPcJU4bMGWw8PC7h58hYWhxmT34NkDNf5gSyLgYXZW7yORLm/kPt+FI5QBjz84b0RRgzVCrbl2dIYjJIgMNjeKGqywDLBI91c1bTDZPTjbZMOzB2rxYXH85RxKEPHxs5lpAUWVp2fIMT9bEcFWkXhxpMqClTtLsWLnRfxyXzXqO0qLhcKJ2nb85dtmFDfbQ3aMcGE5Di8dqfePHAHA0tFqCHgM/n7GgFa77/lmqxv/vbsSy/92EZ+UmCLS1gU5SkxNv7x2iwPwj3MtEWlLf9CUIiFBsq+8FW8db4KH5SDkMVgzMxU3drsTu5pROilO1vvu1DgA207qoZUKcMPw+ApG5mUrMC0jAZUmJ9RSQcxnRifRo7DBCk9HAq/TTTas+08lnikYhjxtcNcB/uXbZuw467vIf3C+Bb9ZlIUxSbGbBf/DYiPOdpnqH6EW4+7xOrz9TRM+K2vF+WY7fnljJp7eX436jqS0H5UYcetodUTau/aGdDy1t9pfuP2c3ganh4U4CKXMQiV6W0ZIjNlWqPef6N0sh81fN/Z7pGvxSBWUXdIfcADePdUc9ZnWTXYP6i0ueHvLVNkHmZCPsckyCrbIgHlZDsXNdnxeYcbuiyY0tLkwLjkw6LG6Wbz0ZX1Qv0PtLi92nb88+uxhgc8rzEF7/3Bze1l80OX3UYj5eGJuBjhwOFhpAQCcbrTis9JWf7AFIKLTtnIRH79ZlIVFuUrwGEAp5ve6VCGa0AhXBHxSYoJGKsCsPubGSexxe3uuGXF5OdS3uaHoRzoDtVSADfMz8MzntbB3pEXQW92oMbswXBV9i41ZjsOmYw04WGkBy/lq3v1gvA63jaFM4CR0mq1ufFxiwqGqchhtlwOAsUlSPLcoC/vKW/3Z7AGgsd2N0hZH0Ha7Vpmc/ioInRhE98X+Sk7UtcPcuUhexMNT8zORlihCcbPdn56F5YAvLrUG/Nx1SZHdPZwo9i1PWD0lGQwT/YW3aYQrzMpaHPjjN01483hjv0YDhgKO43C02oK/fNsMfbv76j8QRYR8HuZ1m/ZLEPGQper/6M2YJBl+e1MWxnaczOQiHrTS6Lw3OlLVhgOXLP4SLFY3i22Fepyojc8F/ySy3F4OfzrZhIc/LMe/LhgDgi0Bj8Fd+VoIeAyeXjAsYE0gnwFUkuDttstUitC9jvuMTHnQ3j/c2py+oGq0ToKXb872B1IWZ+AIvc0deM3q3FUcaQkifkzU0I3Os3gcO3DJN+xscXpR1+ZCFm2RBuC7Y/3twVpUmHzz8QwDrJgQWyU8fjojFYkSPgrrrVCK+bh/SvKATwI5Ggle+M5wGO0eSAW8qK2JKOljvcSFZhumx/AFiEQfm9uLX39eixJDzwXqyQkCrJuT4R/Bkgh4WHtDOpbna1Ha4kCORoIUefCmrpUSAe6fnIx3T+nBALgrX4upGbH7974wR4n8FBnSE4UBo0TdNyKmJQpR27EJ4aZcJSZHSdJRh4dFq90DlVTQ5zkpGlDAFWbf1F2+868zx0bAZbC5UWpwQJcgCPrC087337CnKmB3THoMrusRC3h4aGoKMDV476mJ0pGtTlMzEjBneCK+rGrzPycR8Pq9WYCQq/m0pLVHsJWtkWHJSAUW5igg7CWtSKYydKkobhujQUGuEkIeE9ULta+FkM8gQ9HznCsWBA7jLR2txkitFBmJIsyNgl3FzVY33vi6EacbrWA5QCJgcPsYLe6eoIt003oV3WfzOONlOeitl4fAvVG+EBoAvqyyYNOxBji9vraumKDDD8YH74/Z7eXwq/01AcGWQszHDbS+7ZpYXV68U6jHOb0dLMfhOp0Uc7MVmJqeEJb1DDyGwbob0nFzng0XDQ7wGN8ORK1MGPJjk6FlZpYcZqcHXpZDslyICSkJuH5UJgwGQ0iOx3EcqlqdSBTz+/x7losGN43FchwqjE6MUIvB7z5HGQVGaqXgMb71WzIhD+OSZZiUFj0jeb/5ota/SxEAHB4O288YMDk9ISqrU1DAFUZGuwddl21F244Kp4fFJyUmeFgOy8frUGt24pWj9ehS0g6fV5iDGnAdrDT7h6g7tTm9ePN4E1ZNSor6EZ5I+7zCjL3ll3dHNba78UWlBTMy5Vg3Jz0syUQZhsH4lASMT4mO6QUSnzIVYjw4NSUsx2qxufHCoTqUtjgg4AHPLMxCfkpwUz7Y3F78an8NLrY4cOc4LVZNir4lFAoxH5PTEnCy3or52b2PIkaK08Oi2uzs8TwDIHGQgXCoRM+nNwR038WmjKJivO1OL57cW433iprxl9MGnGmy4lSDNSDYAny76ILpcJepqE4cfIHE2t2VqAth0sJ4MDE1AcJe7oy/rm3Hv87HX9JUQsJh07EGlLY4APhSPuwpaw36MV492oCLHcc4r7dd5dWR8/NZafjhpCQ8ODW6SueIBTysmpQUsHlBImDwyKw0pPcyPRoNoueKPwR030ask0XPx//nomaUGR3+xxanF7puw+g8BrhzXHCLmMo7so2nyoUw2T3+qUvAl9vpxUO1eH1pTlCPGU+yVGI8UzAMrx6th94auKOo62dJCLk2FUYHihoDA6Bgz0WUttjxdZedvIIom+3oSikR4PtBPu8Hyx1jtbhxhBKlBjvkIj5yNJKo3WAEUMAVVl2nx5JkgqhZ52KwubG3vDXguTS5CNlqMW4fo8GR6jZopAKsmKjDxNTgThs9OisdD031Qi0VoNXhwc6zLdhfbvbnoGpsd8PhYaN650mkdNaLG5cswx9uy8XZJhvO6W1weTkMV4kxLwoWtZLw4ThfkspT9VYI+QzunqDrcdNErs5o75msONjfpe5JUsfFcIb6SNNIBZgxLDbW/FLAFUZqqQBKMR9mp7ffSU9P1Laj3OjADcMTMSzIu26OVLUFrC3L1YiRo5EAAO6fkoz7p4RuKFnIZ/zTlCqJAD+aloJ7JyahwuRAq8OD0TopBVt92H7agA+LjbhznBYrJyZhQmoCJgQ5ICbRqd7iwvvfNuNUvRUyEQ93jdPgq1prQAFfibBj1yzplxyNBGI+4x8hXpijDHrKh9NdRtB4DDB/BN0cDQUUcIXZghwl9pe3Ymk/6k8dq27Di4frAAC7Lhjxm0XBrQvW/Y7uvsmRnauXduyGIYGKm+344HwLPCyHedkKcBwHlgN2nG2B3cPSxXWIqGp1Yt1/KuHqCAjsHhZ/OKFH9wnkwe6gG6o0UgGeWTgMJ+rakaeTYlYIRk9cXRJczctWUHmrIYICrjC7d2ISbhuj6dfuu/0Vrf5/Ozwsdp5twZPzM/t8favdg7+eNkAq5OGBaxidGqb0fdkZAA9MSR7wtOGhSgtON1qxYiLtLgw2p4fFMwdqYOvYeHGy3hqQOuOjYhPG6KRxV+Q6FEpb7HB6OIzSScKyizPYtp82+IOtTt2DrQQRD7fkqcLWpngzJlmGMSG86cvVSKC3tmO0ToqfXJ8asuOQ6EJXxTAT8pl+ByPdKwAV95JpuZPF6cVT+6r9qRYWjFAgWy254vsvGKGEVMBDhkJ01df2Rd/uxitH68FyQJ3FhRe+M3xA7xNvDlVa8EmJCWVGB5IThLhvchJmDuCOudnm9gdbnY7VtCFRxEOby/f8lhNNmJCagEQxjWz0paLFinX/qQLgS8syd7gC98ZY+hGb23vF/9dIBfjljZlRtQs6HnhZDl6OC0qQ/tMZaVic50B+sgzCKF4wT4Ir9m7vhqCx3e60rrSmacdZQ0Beq3bX1au583kMbhh+9cDsSgob2v2B4flmO840Wa/8AyF0pNqC//qoAs8frEWb88oXp1D69wUjNh6pR7HBDg/Lob7NhZeP1MMzgBqa6YkiJHTbfcNywLQua0ssTi92nA1NEsh4kamU+utSurwc9leY8ZMPK7Cv26aRaLZqUhKyVWKI+QzytBKsn5uB745WY3JaAu4er8Xrt47wr8EkwXGsug0rd5binh0X8bvDdVcNeq+mM78VBVtDS0zeAhUVFeGdd94By7IoKCjAsmXLIt2kkLo5T4VDlRZUdWTUXTJK1evrHB4We7vliwlX6glrt8CuxOCISCLMWrMTLx9pgIflUGtx4S/fNuPhCA3Z/+1Mz+BHIuRBcIWM0h+cb0Gt2YVbRqkC1unxGAbXJUlxsj4wkD1YaUGSTODP1P/pxVYsz9fRKFcfRAIenpiXgV/sq/ZPyzk8LF7/qhE1Ztc1TcFHWp5WitduHRHwXH834YSTh+Xg9LBIiOE1ZX8/a/DvnD5a3QaLw4PnFmWBF4ZqDiR+xNwIF8uy2Lp1K5588km88sorOHLkCGprayPdrJCSi/h48TtZeGRmKp6an4FlY3rPiXKqwQqH5/LoSa5GgtQwLcZUSgJPpqZetlaHw1c17QEjSMe71K4MJ47jeuSD4TG44noNi8OD94uasb/CjCc+q+qxdXzFhKQe1QlYzjeK2fm0h+UC6nWSnkbrpHh24TAkJwTejPzrgrHHZx5tXF4WTg8Lt5eFdwAjpeH27wtG/PAfpVi5sxTPHaiBo3sm5RjR/SbprN4esCOUkGsRcyNcZWVlSE1NRUqKb0fW7NmzceLECWRm9r2IPB7IhHwU5Kqu+BqzIzDIWTZGE8IWBRrRbTrySqM4odTa7TOI1Ig9wzB4tmAYdpxpQWO7G7kaMW7JUyNL1XdKDx6P8U/LejngreONGKmV+Aucj9RK8KsFw7DlhG80phMH4GczU/H6V43wckCJwY4FOVQ8+krGJMvw6pIR2H7GgN0XTf6KCp9eNGFhhD87q8uL47XtKDM6UG12os7igtXFwuVle6znTBDxkJIgRLpChOt0UizIUUbN7sTC+nZsK9T7H39Tb8Vnpa24LYznpWC5cYTCn3m+U1O7u49XE9K7mAu4jEYjtNrLIzxarRalpaURbFH06Lrwd162AnOGh2+aIVcjwdgkKc43+xb0Twhy3bFrlSwPTPSYG8G1LJkKMdbekH7Nr5eL+BiuFKOqoz6Yy8vhz6f0+MWNw/yvyU+RYdOtI3CmyYbzeptv/V2WAhkKEZQSAbae1NMW82uUIOLjoakpuH2MBifrrahsdWJ8hP5uO9WYnXjisypY3dc2EmR1sahwOVFhcuLLqjYUG+x4fE5GiFt5bY5U9yzb1dQem6W6bh2lRq3Zhd2lrQB8Gy4mpVHOO9I/MRdwcVzPYXSml3n0ffv2Yd++fQCAF198ETpd8Aou90UgEITlOH25WatFtZVBklyEZePTwl59/te3yvHC3ovIUElx04ThEVnf8IPpKuwuNaPe4kSimI9HFoyGTj3wnGXh7tOfzGWw/uML/seFDTYoVBqIum2UKEgCCrr97Hd0OnxnQnboGxnDeutPHYDRWZFpT3c6HfDCdxPweakBFQYb6i0OtFhdPdI+AICIz0OSXIRsjQy5OhmmZqowLUsV7ib3KUNjBcoDp2gXjc2ATnftOQivJpzfz18sScLdzVaUGawYn65AhpI2JoRCpK+jocRwvUUwUezixYvYuXMnnnrqKQDArl27AAC33377FX+uvr4+5G3T6XQwGGiXWKRZXV4UNVoxPlkGxSC3xkeiT1//qgH7ulyo/rQsF0kJVKIlGGLxO+r2cnCzvulElgMEPN9O5WhfsN3q8OCZz2tQYXJCxGewPF+Lu/KDeyGNxf4kVxYPfZqe3vvMRsyNcOXm5qKhoQF6vR4ajQZHjx7FI488EulmkSiSIOLjhqzYTQD60+tTkSYX4eMSI4apxBRsDXFCPgMhPzrWZfWHSiLAK0tGoKHNBaWED5kw9n4HQoIp5gIuPp+P1atX4/nnnwfLsliwYAGGDRt29R8kJEbweQzuzNfizvzed6MSEktoTSEhPjEXcAHAlClTMGXKlEg3gxBCCCHkmsRcHi5CCCGEkFhDARchhBBCSIhRwEUIIYQQEmIUcBFCCCGEhBgFXIQQQgghIUYBFyGEEEJIiFHARQghhBASYhRwEUIIIYSEGAVchBBCCCEhRgEXIYQQQkiIUcBFCCGEEBJiFHARQgghhIQYBVyEEEIIISFGARchhBBCSIhRwEUIIYQQEmIUcBFCCCGEhBgFXIQQQgghIUYBFyGEEEJIiFHARQghhBASYhRwEUIIIYSEGAVchBBCCCEhRgEXIYQQQkiIUcBFCCGEEBJiDMdxXKQbQQghhBASz2iEK4jWr18f6SaQIKM+jS/Un/GF+jP+xHOfUsBFCCGEEBJiFHARQgghhIQYBVxBtGjRokg3gQQZ9Wl8of6ML9Sf8See+5QWzRNCCCGEhBiNcBFCCCGEhJgg0g2IF0VFRXjnnXfAsiwKCgqwbNmySDeJ9OLNN99EYWEhlEolNm7cCABob2/HK6+8gubmZiQlJeGxxx6DXC4HAOzatQuff/45eDweHnjgAUyaNAkAUFFRgc2bN8PlcmHy5Ml44IEHwDBMpH6tIctgMGDz5s1obW0FwzBYtGgRlixZQn0ao1wuF371q1/B4/HA6/Vi5syZWL58OfVnjGNZFuvXr4dGo8H69euHbn9yZNC8Xi+3Zs0arrGxkXO73dy6deu4mpqaSDeL9OLcuXNceXk5t3btWv9z77//Prdr1y6O4zhu165d3Pvvv89xHMfV1NRw69at41wuF9fU1MStWbOG83q9HMdx3Pr167mSkhKOZVnu+eef5woLC8P+uxCOMxqNXHl5OcdxHGez2bhHHnmEq6mpoT6NUSzLcna7neM4jnO73dyGDRu4kpIS6s8Y99FHH3Gvvvoq98ILL3AcN3TPuTSlGARlZWVITU1FSkoKBAIBZs+ejRMnTkS6WaQXY8eO9d9JdTpx4gTmz58PAJg/f76/706cOIHZs2dDKBQiOTkZqampKCsrg8lkgt1ux6hRo8AwDObNm0f9HSFqtRo5OTkAAKlUioyMDBiNRurTGMUwDCQSCQDA6/XC6/WCYRjqzxjW0tKCwsJCFBQU+J8bqv1JU4pBYDQaodVq/Y+1Wi1KS0sj2CLSH2azGWq1GoDvAm6xWAD4+jUvL8//Oo1GA6PRCD6f36O/jUZjeBtNetDr9bh06RJGjhxJfRrDWJbFE088gcbGRixevBh5eXnUnzHs3Xffxb333gu73e5/bqj2J41wBQHXy0bPmJtbJj301q9Xep5EjsPhwMaNG3H//fdDJpP1+Trq0+jH4/Hw+9//Hlu2bEF5eTmqq6v7fC31Z3Q7efIklEqlfxT6auK9P2mEKwi0Wi1aWlr8j1taWvzRO4l+SqUSJpMJarUaJpMJCoUCQM9+NRqN0Gg0vfa3RqMJe7uJj8fjwcaNGzF37lzMmDEDAPVpPEhISMDYsWNRVFRE/RmjSkpK8M033+DUqVNwuVyw2+3YtGnTkO1PGuEKgtzcXDQ0NECv18Pj8eDo0aOYNm1apJtFrtG0adNw8OBBAMDBgwcxffp0//NHjx6F2+2GXq9HQ0MDRo4cCbVaDalUiosXL4LjOBw6dIj6O0I4jsOWLVuQkZGBpUuX+p+nPo1NFosFVqsVgG/H4pkzZ5CRkUH9GaNWrFiBLVu2YPPmzXj00UeRn5+PRx55ZMj2JyU+DZLCwkK89957YFkWCxYswB133BHpJpFevPrqqzh//jza2tqgVCqxfPlyTJ8+Ha+88goMBgN0Oh3Wrl3rX1j/wQcf4MCBA+DxeLj//vsxefJkAEB5eTnefPNNuFwuTJo0CatXr6Zp5AgoLi7G008/jaysLP/nf8899yAvL4/6NAZVVVVh8+bNYFkWHMdh1qxZuPPOO9HW1kb9GePOnTuHjz76COvXrx+y/UkBFyGEEEJIiNGUIiGEEEJIiFHARQghhBASYhRwEUIIIYSEGAVchBBCCCEhRgEXIYQQQkiIUcBFCCFXYTAYsGrVKrAs2+drVq1ahaampjC2ihASSygtBCGE9NOvf/1rzJ07N6AgLyGEXAmNcBFCCCGEhBiNcBFCol5jYyM2bNiAX/7yl8jJyYHRaMTjjz+OtWvXYty4cQGv/eKLL7B//36MGDECBw8ehFqtxoMPPojx48cD8NVne/vtt1FcXAy5XI7bbrsNixYtAgCUlZXhT3/6ExoaGiASiTBnzhzcd9990Ov1WLNmDbZv344dO3bgX//6FwQCAXg8Hm688UY8+OCDWL58OTZt2oTU1FTYbDZs27YNp06dglgsRkFBAW6//XbweDx/+/Ly8nDgwAHIZDI89NBD/ozahJD4RMWrCSFRLzU1FStXrsTrr7+OF198EW+99Rbmz5/fI9jqVFpaihkzZmDr1q04fvw4XnrpJWzevBlyuRyvvfYahg0bhj/84Q+or6/Hc889h5SUFIwfPx7vvPMOlixZgnnz5sHhcKC6urrHe99zzz0oKSm54pTitm3bYLPZ8MYbb6CtrQ3PP/881Go1Fi5cCMAX2M2fPx9bt27Fvn37sGXLFmzZsiXmSpUQQq4dTSkSQmLCokWLkJqaiieffBImkwn33HNPn69VKpW49dZbIRAIMHv2bKSnp6OwsBAGgwHFxcVYuXIlRCIRsrOzUVBQgEOHDgEABAIBGhsbYbFYIJFIMGrUqH63k2VZHD16FCtWrIBUKkVycjKWLl3qPwYA6HQ6LFq0CDweD/Pnz4fJZILZbO7/h0IIiRkUcBFCYkZBQQFqampw8803QygU4sKFC1i1ahVWrVqFtWvX+l+n0WgCRouSkpJgNBphMpkgl8shlUr9/6fT6WA0GgEADz/8MOrr6/HYY49hw4YNOHnyZL/baLFY4PF4oNPpehy/k0ql8v9bLBYDABwOR7+PRQiJHTSlSAiJCQ6HA++99x4WLlyInTt3YubMmRgzZgzef//9Hq81Go3gOM4fdBkMBkybNg1qtRrt7e2w2+3+oMtgMECj0QAA0tLS8Oijj4JlWRw/fhwvv/wytm7d2uP9rzT1p1AowOfzYTAYkJmZ2eMYhJChiUa4CCEx4Z133sGIESPw8MMPY8qUKfjjH//Y52vNZjN2794Nj8eDY8eOoa6uDpMnT4ZOp8Po0aPx17/+FS6XC1VVVThw4ADmzp0LADh06BAsFgt4PB5kMhkAgMfreZpUKpV95tzi8XiYNWsWtm/fDrvdjubmZnz88cf+YxBChiYa4SKERL0TJ06gqKgIGzduBADcd999ePzxx3H48OFeA5m8vDw0NDTgwQcfhEqlwtq1a5GYmAgA+PnPf463334bP/7xjyGXy3HXXXdhwoQJAICioiL8+c9/htPpRFJSEn7+859DJBL1eP8lS5Zg8+bN2Lt3L+bOnYvVq1cH/P/q1auxbds2rFmzBiKRCAUFBViwYEGwPxZCSAyhtBCEkLjSmXbhueeei3RTCCHEj6YUCSGEEEJCjAIuQgghhJAQoylFQgghhJAQoxEuQgghhJAQo4CLEEIIISTEKOAihBBCCAkxCrgIIYQQQkKMAi5CCCGEkBCjgIsQQgghJMT+PyHIDXPLvjczAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from draw_sky2 import draw_sky\n", + "\n", + "n_sky = 3 #choose a file/sky to examine.\n", + "data = np.genfromtxt(\"data/Train_Skies/Train_Skies/\\\n", + "Training_Sky%d.csv\" % (n_sky),\n", + " dtype = None,\n", + " skip_header = 1,\n", + " delimiter = \",\",\n", + " usecols = [1,2,3,4])\n", + "print(\"Data on galaxies in sky %d.\"%n_sky)\n", + "print(\"position_x, position_y, e_1, e_2 \")\n", + "print(data[:3])\n", + "\n", + "fig = draw_sky(data)\n", + "plt.title(\"Galaxy positions and ellipcities of sky %d.\" % n_sky)\n", + "plt.xlabel(\"x-position\")\n", + "plt.ylabel(\"y-position\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Priors\n", + "\n", + "Each sky has one, two or three dark matter halos in it. Tim's solution details that his prior distribution of halo positions was uniform, i.e.\n", + "\n", + "\\begin{align}\n", + "& x_i \\sim \\text{Uniform}( 0, 4200)\\\\\\\\\n", + "& y_i \\sim \\text{Uniform}( 0, 4200), \\;\\; i=1,2,3\\\\\\\\\n", + "\\end{align}\n", + "\n", + "Tim and other competitors noted that most skies had one large halo and other halos, if present, were much smaller. Larger halos, having more mass, will influence the surrounding galaxies more. He decided that the large halos would have a mass distributed as a *log*-uniform random variable between 40 and 180 i.e.\n", + "\n", + "$$ m_{\\text{large} } = \\log \\text{Uniform}( 40, 180 ) $$\n", + "\n", + "and in PyMC, \n", + "\n", + " exp_mass_large = pm.Uniform(\"exp_mass_large\", 40, 180)\n", + " mass_large = pm.Deterministic(\"mass_large\", np.log(exp_max_large))\n", + "\n", + "(This is what we mean when we say *log*-uniform.) For smaller galaxies, Tim set the mass to be the logarithm of 20. Why did Tim not create a prior for the smaller mass, nor treat it as a unknown? I believe this decision was made to speed up convergence of the algorithm. This is not too restrictive, as by construction the smaller halos have less influence on the galaxies.\n", + "\n", + "Tim logically assumed that the ellipticity of each galaxy is dependent on the position of the halos, the distance between the galaxy and halo, and the mass of the halos. Thus the vector of ellipticity of each galaxy, $\\mathbf{e}_i$, are *children* variables of the vector of halo positions $(\\mathbf{x},\\mathbf{y})$, distance (which we will formalize), and halo masses.\n", + "\n", + "Tim conceived a relationship to connect positions and ellipticity by reading literature and forum posts. He supposed the following was a reasonable relationship:\n", + "\n", + "$$ e_i | ( \\mathbf{x}, \\mathbf{y} ) \\sim \\text{Normal}( \\sum_{j = \\text{halo positions} }d_{i,j} m_j f( r_{i,j} ), \\sigma^2 ) $$\n", + "\n", + "where $d_{i,j}$ is the *tangential direction* (the direction in which halo $j$ bends the light of galaxy $i$), $m_j$ is the mass of halo $j$, $f(r_{i,j})$ is a *decreasing function* of the Euclidean distance between halo $j$ and galaxy $i$. \n", + "\n", + "Tim's function $f$ was defined:\n", + "\n", + "$$ f( r_{i,j} ) = \\frac{1}{\\min( r_{i,j}, 240 ) } $$\n", + "\n", + "for large halos, and for small halos\n", + "\n", + "$$ f( r_{i,j} ) = \\frac{1}{\\min( r_{i,j}, 70 ) } $$\n", + "\n", + "This fully bridges our observations and unknown. This model is incredibly simple, and Tim mentions this simplicity was purposefully designed: it prevents the model from overfitting. \n", + "\n", + "\n", + "### Training & PyMC implementation\n", + "\n", + "For each sky, we run our Bayesian model to find the posteriors for the halo positions — we ignore the (known) halo position. This is slightly different than perhaps traditional approaches to Kaggle competitions, where this model uses no data from other skies nor the known halo location. That does not mean other data are not necessary — in fact, the model was created by comparing different skies. \n" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "import pymc as pm\n", + "import pytensor.tensor as pt\n", + "\n", + "def euclidean_distance(x, y):\n", + " return np.sqrt(((x - y)**2)).sum(axis=1)\n", + "\n", + "def f_distance(gxy_pos, halo_pos, c):\n", + " # foo_position should be a 2-d numpy array\n", + " # T.maximum() provides our element-wise maximum as in NumPy, but instead for theano tensors\n", + " return pt.maximum(euclidean_distance(gxy_pos, halo_pos), c)[:, None]\n", + "\n", + "def tangential_distance(glxy_position, halo_position):\n", + " # foo_position should be a 2-d numpy array\n", + " delta = glxy_position - halo_position\n", + " t = (2*pt.arctan(delta[:,1]/delta[:,0]))\n", + " return pt.stack([-pt.cos(t), -pt.sin(t)], axis=1)\n", + "\n", + "\n", + "with pm.Model() as model:\n", + " #set the size of the halo's mass\n", + " mass_large = pm.Uniform(\"mass_large\", 40, 180)\n", + " \n", + " #set the initial prior position of the halos, it's a 2-d Uniform dist.\n", + " halo_position = pm.Uniform(\"halo_position\", 0, 4200, shape=(1,2))\n", + " \n", + " mean = pm.Deterministic(\"mean\", mass_large /\\\n", + " f_distance(pt.as_tensor(data[:,:2]), halo_position, 240)*\\\n", + " tangential_distance(pt.as_tensor(data[:,:2]), halo_position))\n", + " \n", + " ellpty = pm.Normal(\"ellipcity\", mu=mean, tau=1./0.05, observed=data[:,2:])" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Auto-assigning NUTS sampler...\n", + "Initializing NUTS using advi...\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + "
\n", + " \n", + " 55.92% [27960/50000 00:02<00:02 Average Loss = -149.12]\n", + "
\n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Convergence achieved at 28000\n", + "Interrupted at 27,999 [55%]: Average Loss = -107.23\n", + "Sequential sampling (1 chains in 1 job)\n", + "NUTS: [mass_large, halo_position]\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + "
\n", + " \n", + " 100.00% [6000/6000 00:04<00:00 Sampling chain 0, 0 divergences]\n", + "
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We may cover ADVI, as well as NUTS, in-depth in a later chapter.\n", + "\n", + "Below we plot a \"heatmap\" of the posterior distribution. (Which is just a scatter plot of the posterior, but we can visualize it as a heatmap.)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# t = trace[\"halo_position\"].reshape(5000,2)\n", + "t = trace.posterior.halo_position.data.reshape(5000,2)\n", + "\n", + "fig = draw_sky(data)\n", + "plt.title(\"Galaxy positions and ellipcities of sky %d.\" % n_sky)\n", + "plt.xlabel(\"x-position\")\n", + "plt.ylabel(\"y-position\")\n", + "plt.scatter(t[:,0], t[:,1], alpha = 0.015, c = \"r\")\n", + "plt.xlim(0, 4200)\n", + "plt.ylim(0, 4200);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The most probable position reveals itself like a lethal wound.\n", + "\n", + "Associated with each sky is another data point, located in `./data/Training_halos.csv` that holds the locations of up to three dark matter halos contained in the sky. For example, the night sky we trained on has halo locations:" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1.00000e+00 1.40861e+03 1.68586e+03 1.40861e+03 1.68586e+03 0.00000e+00\n", + " 0.00000e+00 0.00000e+00 0.00000e+00]\n" + ] + } + ], + "source": [ + "halo_data = np.genfromtxt(\"data/Training_halos.csv\", \n", + " delimiter = \",\",\n", + " usecols = [1, 2,3, 4,5,6,7,8,9],\n", + " skip_header = 1)\n", + "print(halo_data[n_sky])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The third and fourth column represent the true $x$ and $y$ position of the halo. It appears that the Bayesian method has located the halo within a tight vicinity. " + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "True halo location: 1408.61 1685.86\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = draw_sky(data)\n", + "plt.title(\"Galaxy positions and ellipcities of sky %d.\" % n_sky)\n", + "plt.xlabel(\"x-position\")\n", + "plt.ylabel(\"y-position\")\n", + "plt.scatter(t[:,0], t[:,1], alpha = 0.015, c = \"r\")\n", + "plt.scatter(halo_data[n_sky-1][3], halo_data[n_sky-1][4], \n", + " label = \"True halo position\",\n", + " c = \"k\", s = 70)\n", + "plt.legend(scatterpoints = 1, loc = \"lower left\")\n", + "plt.xlim(0, 4200)\n", + "plt.ylim(0, 4200);\n", + "\n", + "print(\"True halo location:\", halo_data[n_sky][3], halo_data[n_sky][4])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Perfect. Our next step is to use the loss function to optimize our location. A naive strategy would be to simply choose the mean:" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[2312.20427163 1127.28430251]]\n" + ] + } + ], + "source": [ + "mean_posterior = t.mean(axis=0).reshape(1,2)\n", + "print(mean_posterior)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Using the mean:\n", + "Your average distance in pixels you are away from the true halo is 45.47510107521696\n", + "Your average angular vector is 0.9999999999999999\n", + "Your score for the training data is 1.0454751010752168\n", + "Using a random location: [[ 923 3023]]\n", + "Your average distance in pixels you are away from the true halo is 2389.0398135862033\n", + "Your average angular vector is 1.0\n", + "Your score for the training data is 3.389039813586203\n" + ] + }, + { + "data": { + "text/plain": [ + "3.389039813586203" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from DarkWorldsMetric import main_score\n", + "\n", + "_halo_data = halo_data[n_sky-1]\n", + "\n", + "nhalo_all = _halo_data[0].reshape(1,1)\n", + "x_true_all = _halo_data[3].reshape(1,1)\n", + "y_true_all = _halo_data[4].reshape(1,1)\n", + "x_ref_all = _halo_data[1].reshape(1,1)\n", + "y_ref_all = _halo_data[2].reshape(1,1)\n", + "sky_prediction = mean_posterior\n", + "\n", + "print(\"Using the mean:\")\n", + "main_score(nhalo_all, x_true_all, y_true_all, \\\n", + " x_ref_all, y_ref_all, sky_prediction)\n", + "\n", + "#what's a bad score?\n", + "random_guess = np.random.randint(0, 4200, size=(1,2))\n", + "print(\"Using a random location:\", random_guess)\n", + "main_score(nhalo_all, x_true_all, y_true_all, \\\n", + " x_ref_all, y_ref_all, random_guess)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is a good guess, it is not very far from the true location, but it ignores the loss function that was provided to us. We also need to extend our code to allow for up to two additional, *smaller* halos: Let's create a function for automatizing our PyMC. " + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "def halo_posteriors(n_halos_in_sky, galaxy_data,samples = 5e5, burn_in = 500):\n", + " #set the size of the halo's mass\n", + " with pm.Model() as model:\n", + " mass_large = pm.Uniform(\"mass_large\", 40, 180)\n", + " \n", + " mass_small_1 = 20\n", + " mass_small_2 = 20\n", + " \n", + " masses = np.array([mass_large,mass_small_1, mass_small_2], dtype=object)\n", + " \n", + " #set the initial prior positions of the halos, it's a 2-d Uniform dist.\n", + " halo_positions = pm.Uniform(\"halo_positions\", 0, 4200, shape=(n_halos_in_sky,2)) #notice this size\n", + " \n", + " fdist_constants = np.array([240, 70, 70])\n", + " \n", + " _sum = 0\n", + " for i in range(n_halos_in_sky):\n", + " _sum += masses[i]/f_distance(data[:,:2], halo_positions[i, :], fdist_constants[i])*\\\n", + " tangential_distance(data[:,:2], halo_positions[i, :])\n", + " \n", + " mean = pm.Deterministic(\"mean\", _sum)\n", + " \n", + " ellpty = pm.Normal(\"ellipcity\", mu=mean, tau=1./0.05, observed=data[:,2:])\n", + " \n", + " # mu, sds, elbo = pm.variational.advi(n=50000)\n", + " # step = pm.NUTS(scaling=model.dict_to_array(sds), is_cov=True)\n", + " trace = pm.sample(samples, init='advi', n_init=50000,tune=burn_in,chains=4)\n", + " \n", + " # burned_trace = trace[burn_in:]\n", + " return trace.posterior.halo_positions.data" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "n_sky = 215\n", + "data = np.genfromtxt(\"data/Train_Skies/Train_Skies/\\\n", + "Training_Sky%d.csv\" % (n_sky),\n", + " dtype = None,\n", + " skip_header = 1,\n", + " delimiter = \",\",\n", + " usecols = [1,2,3,4])" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Auto-assigning NUTS sampler...\n", + "Initializing NUTS using advi...\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + "
\n", + " \n", + " 27.26% [13631/50000 00:02<00:07 Average Loss = -129.59]\n", + "
\n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Convergence achieved at 13800\n", + "Interrupted at 13,799 [27%]: Average Loss = -120.66\n", + "Multiprocess sampling (4 chains in 4 jobs)\n", + "NUTS: [mass_large, halo_positions]\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + "
\n", + " \n", + " 100.00% [22000/22000 04:16<00:00 Sampling 4 chains, 39 divergences]\n", + "
\n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Sampling 4 chains for 500 tune and 5_000 draw iterations (2_000 + 20_000 draws total) took 257 seconds.\n", + "There were 9 divergences after tuning. Increase `target_accept` or reparameterize.\n", + "There were 24 divergences after tuning. Increase `target_accept` or reparameterize.\n", + "The acceptance probability does not match the target. It is 0.6235, but should be close to 0.8. Try to increase the number of tuning steps.\n", + "There were 4 divergences after tuning. Increase `target_accept` or reparameterize.\n", + "There were 2 divergences after tuning. Increase `target_accept` or reparameterize.\n", + "The acceptance probability does not match the target. It is 0.8931, but should be close to 0.8. Try to increase the number of tuning steps.\n" + ] + } + ], + "source": [ + "#there are 3 halos in this file. \n", + "samples = 5000\n", + "traces = halo_posteriors(3, data, samples = samples, burn_in=500)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = draw_sky(data)\n", + "plt.title(\"Galaxy positions and ellipcities of sky %d.\" % n_sky)\n", + "plt.xlabel(\"x-position\")\n", + "plt.ylabel(\"y-position\")\n", + "\n", + "colors = [\"#467821\", \"#A60628\", \"#7A68A6\"]\n", + "\n", + "for i in range(traces[0].shape[1]):\n", + " plt.scatter(traces[0][:, i, 0], traces[0][:, i, 1], c = colors[i], alpha = 0.002)\n", + " \n", + " \n", + "for i in range(traces.T.shape[1]):\n", + " plt.scatter(halo_data[n_sky-1][3 + 2*i], halo_data[n_sky-1][4 + 2*i], \n", + " label = \"True halo position\", c = \"k\", s = 90)\n", + " \n", + "#plt.legend(scatterpoints = 1)\n", + "plt.xlim(0, 4200)\n", + "plt.ylim(0, 4200);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You may notice that we use the parameter `chains=4` this time. According to the API document, `Running independent chains is important for some convergence statistics and can also reveal multiple modes in the posterior`. We use 4 independent chains this time to build more confidence." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "for pt in range(traces.shape[0]):\n", + " fig = draw_sky(data)\n", + "\n", + " colors = [\"#467821\", \"#A60628\", \"#7A68A6\"]\n", + "\n", + "\n", + " for i in range(traces[1].shape[1]):\n", + " plt.scatter(traces[pt][:, i, 0], traces[pt][:, i, 1], c = colors[i], alpha = 0.002)\n", + "\n", + "\n", + " for i in range(traces.T.shape[1]):\n", + " plt.scatter(halo_data[n_sky-1][3 + 2*i], halo_data[n_sky-1][4 + 2*i], \n", + " label = \"True halo position\", c = \"k\", s = 90)\n", + "\n", + " #plt.legend(scatterpoints = 1)\n", + " # ax[0].xlim(0, 4200)\n", + " # ax[0].ylim(0, 4200)\n", + " plt.suptitle(\"Galaxy positions and ellipcities of sky %d.\" % n_sky)\n", + " plt.xlabel(\"x-position\")\n", + " plt.ylabel(\"y-position\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This looks pretty good, though it took a long time for the system to (sort of) converge. Our optimization step would look something like this:" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(4, 5000, 3, 2)\n", + "Independent chains: 0\n", + "[[ 779.51700107 347.57575126 2918.9354877 2779.57731108 3001.95900013\n", + " 3617.28296252]]\n", + "Using the mean:\n", + "Your average distance in pixels you are away from the true halo is 4629.09722602648\n", + "Your average angular vector is 1.0\n", + "Your score for the training data is 5.62909722602648\n", + "Using a random location: [[2711 508]]\n", + "Your average distance in pixels you are away from the true halo is 3572.625649924716\n", + "Your average angular vector is 1.0\n", + "Your score for the training data is 4.572625649924716\n", + "Independent chains: 1\n", + "[[ 843.86621797 508.89191769 2692.97496628 2737.46647823 3333.4681748\n", + " 3642.28791824]]\n", + "Using the mean:\n", + "Your average distance in pixels you are away from the true halo is 4463.5876012667\n", + "Your average angular vector is 1.0\n", + "Your score for the training data is 5.463587601266701\n", + "Using a random location: [[ 524 3851]]\n", + "Your average distance in pixels you are away from the true halo is 3173.7457387919403\n", + "Your average angular vector is 1.0\n", + "Your score for the training data is 4.17374573879194\n", + "Independent chains: 2\n", + "[[3826.93963249 3961.83848115 2509.19744264 2044.85494963 2078.93188872\n", + " 1727.1125372 ]]\n", + "Using the mean:\n", + "Your average distance in pixels you are away from the true halo is 131.9944317261983\n", + "Your average angular vector is 1.0\n", + "Your score for the training data is 1.1319944317261983\n", + "Using a random location: [[2635 1141]]\n", + "Your average distance in pixels you are away from the true halo is 2995.400463126759\n", + "Your average angular vector is 1.0\n", + "Your score for the training data is 3.9954004631267592\n", + "Independent chains: 3\n", + "[[3815.8445034 3969.74481299 1817.3991698 1340.05655978 2140.75107775\n", + " 1524.23866576]]\n", + "Using the mean:\n", + "Your average distance in pixels you are away from the true halo is 122.57873507355198\n", + "Your average angular vector is 1.0\n", + "Your score for the training data is 1.1225787350735519\n", + "Using a random location: [[1690 1891]]\n", + "Your average distance in pixels you are away from the true halo is 2869.2377967850625\n", + "Your average angular vector is 1.0\n", + "Your score for the training data is 3.8692377967850624\n" + ] + } + ], + "source": [ + "_halo_data = halo_data[n_sky-1]\n", + "print(traces.shape)\n", + "\n", + "for pt in range(traces.shape[0]): \n", + " print(f\"Independent chains: {pt}\")\n", + " mean_posterior = traces[pt].mean(axis=0).reshape(1,6)\n", + " print(mean_posterior)\n", + "\n", + "\n", + " nhalo_all = _halo_data[0].reshape(1,1)\n", + " x_true_all = _halo_data[3].reshape(1,1)\n", + " y_true_all = _halo_data[4].reshape(1,1)\n", + " x_ref_all = _halo_data[1].reshape(1,1)\n", + " y_ref_all = _halo_data[2].reshape(1,1)\n", + " sky_prediction = mean_posterior\n", + "\n", + "\n", + " print(\"Using the mean:\")\n", + " main_score([1], x_true_all, y_true_all, \\\n", + " x_ref_all, y_ref_all, sky_prediction)\n", + "\n", + " #what's a bad score?\n", + " random_guess = np.random.randint(0, 4200, size=(1,2))\n", + " print(\"Using a random location:\", random_guess)\n", + " main_score([1], x_true_all, y_true_all, \\\n", + " x_ref_all, y_ref_all, random_guess)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## References\n", + "1. Antifragile: Things That Gain from Disorder. New York: Random House. 2012. ISBN 978-1-4000-6782-4.\n", + "1. [Tim Saliman's solution to the Dark World's Contest](http://www.timsalimans.com/observing-dark-worlds)\n", + "2. Silver, Nate. The Signal and the Noise: Why So Many Predictions Fail — but Some Don't. 1. Penguin Press HC, The, 2012. Print." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.core.display import HTML\n", + "def css_styling():\n", + " styles = open(\"../styles/custom.css\", \"r\").read()\n", + " return HTML(styles)\n", + "css_styling()" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "pymc_env", + "language": "python", + "name": "pymc_env" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.4" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/Chapter5_LossFunctions/Ch5_LossFunctions_TFP.ipynb b/Chapter5_LossFunctions/Ch5_LossFunctions_TFP.ipynb new file mode 100644 index 00000000..423002bf --- /dev/null +++ b/Chapter5_LossFunctions/Ch5_LossFunctions_TFP.ipynb @@ -0,0 +1,3187 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "accelerator": "GPU", + "colab": { + "name": "working Ch5_LossFunctions_TFP.ipynb", + "provenance": [], + "collapsed_sections": [] + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "rZQKEjPNGNzG" + }, + "source": [ + "# Probabilistic Programming and Bayesian Methods for Hackers Chapter 5\n", + "\n", + "\n", + " \n", + " \n", + "
\n", + " Run in Google Colab\n", + " \n", + " View source on GitHub\n", + "
\n", + "
\n", + "
\n", + "
\n", + "\n", + "Original content ([this Jupyter notebook](https://nbviewer.jupyter.org/github/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/blob/master/Chapter5_LossFunctions/Ch5_LossFunctions_PyMC2.ipynb#Example:-Kaggle-contest-on-Observing-Dark-World)) created by Cam Davidson-Pilon ([`@Cmrn_DP`](https://twitter.com/Cmrn_DP)) and Tim Salimans ([`@TimSalimans`](https://twitter.com/TimSalimans))\n", + "\n", + "Ported to [Tensorflow Probability](https://www.tensorflow.org/probability/) by Matthew McAteer ([`@MatthewMcAteer0`](https://twitter.com/MatthewMcAteer0)), with help from Bryan Seybold, Mike Shwe ([`@mikeshwe`](https://twitter.com/mikeshwe)), Josh Dillon, and the rest of the TFP team at Google ([`tfprobability@tensorflow.org`](mailto:tfprobability@tensorflow.org)).\n", + "\n", + "Welcome to Bayesian Methods for Hackers. The full Github repository is available at [github/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers](https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers). The other chapters can be found on the project's [homepage](https://camdavidsonpilon.github.io/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/). We hope you enjoy the book, and we encourage any contributions!\n", + "\n", + "---\n", + "### Table of Contents\n", + "- Dependencies & Prerequisites\n", + "- Would you rather lose and arm or a leg?\n", + "- Loss Functions\n", + "- Loss functions in the real world\n", + "- Example: Optimizing for the Showcase on The Price is Right\n", + " - Minimizing our losses\n", + " - Shortcuts\n", + " - Machine Learning via Bayesian Methods\n", + "- Example: Financial prediction\n", + "- Example: Kaggle contest on Observing Dark World\n", + " - Setup\n", + " - Defining our galaxy-plotting function\n", + " - Examining Our Data\n", + " - Priors\n", + " - Training & Tensorflow implemenation\n", + " - Constructing a probabilistic model for the data (observed ellipcities o the galaxies) given the positions of the dark matter halos\n", + " - Using Bayes' rule to get the posterior distribution of the halo positions, i.e. to use the data to guess wherre the dark matter halos might be\n", + " - References\n", + "\n", + "______\n", + "\n", + "## Loss Functions\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "OCfwtJveRRGF" + }, + "source": [ + "### Dependencies & Prerequisites\n", + "\n", + "
\n", + " Tensorflow Probability is part of the colab default runtime, so you don't need to install Tensorflow or Tensorflow Probability if you're running this in the colab. \n", + "
\n", + " If you're running this notebook in Jupyter on your own machine (and you have already installed Tensorflow), you can use the following\n", + "
\n", + "
    \n", + "
  • For the most recent nightly installation: pip3 install -q tfp-nightly
    • \n", + "
  • For the most recent stable TFP release: pip3 install -q --upgrade tensorflow-probability
    • \n", + "
  • For the most recent stable GPU-connected version of TFP: pip3 install -q --upgrade tensorflow-probability-gpu
    • \n", + "
  • For the most recent nightly GPU-connected version of TFP: pip3 install -q tfp-nightly-gpu
    • \n", + "
\n", + "Again, if you are running this in a Colab, Tensorflow and TFP are already installed\n", + "
" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "8ZBkOe7soqtz", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 318 + }, + "outputId": "b6febc20-30ea-48a8-d996-ae3433212c5e" + }, + "source": [ + "#@title Imports and Global Variables { display-mode: \"form\" }\n", + "\"\"\"\n", + "The book uses a custom matplotlibrc file, which provides the unique styles for\n", + "matplotlib plots. If executing this book, and you wish to use the book's\n", + "styling, provided are two options:\n", + " 1. Overwrite your own matplotlibrc file with the rc-file provided in the\n", + " book's styles/ dir. See http://matplotlib.org/users/customizing.html\n", + " 2. Also in the styles is bmh_matplotlibrc.json file. This can be used to\n", + " update the styles in only this notebook. Try running the following code:\n", + "\n", + " import json\n", + " s = json.load(open(\"../styles/bmh_matplotlibrc.json\"))\n", + " matplotlib.rcParams.update(s)\n", + "\"\"\"\n", + "!pip3 install -q wget\n", + "from __future__ import absolute_import, division, print_function\n", + "\n", + "#@markdown This sets the warning status (default is `ignore`, since this notebook runs correctly)\n", + "warning_status = \"ignore\" #@param [\"ignore\", \"always\", \"module\", \"once\", \"default\", \"error\"]\n", + "import warnings\n", + "warnings.filterwarnings(warning_status)\n", + "with warnings.catch_warnings():\n", + " warnings.filterwarnings(warning_status, category=DeprecationWarning)\n", + " warnings.filterwarnings(warning_status, category=UserWarning)\n", + "\n", + "import numpy as np\n", + "import os\n", + "#@markdown This sets the styles of the plotting (default is styled like plots from [FiveThirtyeight.com](https://fivethirtyeight.com/))\n", + "matplotlib_style = 'fivethirtyeight' #@param ['fivethirtyeight', 'bmh', 'ggplot', 'seaborn', 'default', 'Solarize_Light2', 'classic', 'dark_background', 'seaborn-colorblind', 'seaborn-notebook']\n", + "import matplotlib.pyplot as plt; plt.style.use(matplotlib_style)\n", + "import matplotlib.axes as axes;\n", + "from matplotlib.patches import Ellipse\n", + "from mpl_toolkits.mplot3d import Axes3D\n", + "%matplotlib inline\n", + "import seaborn as sns; sns.set_context('notebook')\n", + "from scipy.optimize import fmin\n", + "from IPython.core.pylabtools import figsize\n", + "#@markdown This sets the resolution of the plot outputs (`retina` is the highest resolution)\n", + "notebook_screen_res = 'retina' #@param ['retina', 'png', 'jpeg', 'svg', 'pdf']\n", + "%config InlineBackend.figure_format = notebook_screen_res\n", + "\n", + "import tensorflow as tf\n", + "tfe = tf.contrib.eager\n", + "\n", + "# Eager Execution\n", + "#@markdown Check the box below if you want to use [Eager Execution](https://www.tensorflow.org/guide/eager)\n", + "#@markdown Eager execution provides An intuitive interface, Easier debugging, and a control flow comparable to Numpy. You can read more about it on the [Google AI Blog](https://ai.googleblog.com/2017/10/eager-execution-imperative-define-by.html)\n", + "use_tf_eager = False #@param {type:\"boolean\"}\n", + "\n", + "# Use try/except so we can easily re-execute the whole notebook.\n", + "if use_tf_eager:\n", + " try:\n", + " tf.enable_eager_execution()\n", + " except:\n", + " pass\n", + "\n", + "import tensorflow_probability as tfp\n", + "tfd = tfp.distributions\n", + "tfb = tfp.bijectors\n", + "\n", + " \n", + "def evaluate(tensors):\n", + " \"\"\"Evaluates Tensor or EagerTensor to Numpy `ndarray`s.\n", + " Args:\n", + " tensors: Object of `Tensor` or EagerTensor`s; can be `list`, `tuple`,\n", + " `namedtuple` or combinations thereof.\n", + "\n", + " Returns:\n", + " ndarrays: Object with same structure as `tensors` except with `Tensor` or\n", + " `EagerTensor`s replaced by Numpy `ndarray`s.\n", + " \"\"\"\n", + " if tf.executing_eagerly():\n", + " return tf.contrib.framework.nest.pack_sequence_as(\n", + " tensors,\n", + " [t.numpy() if tf.contrib.framework.is_tensor(t) else t\n", + " for t in tf.contrib.framework.nest.flatten(tensors)])\n", + " return sess.run(tensors)\n", + "\n", + "class _TFColor(object):\n", + " \"\"\"Enum of colors used in TF docs.\"\"\"\n", + " red = '#F15854'\n", + " blue = '#5DA5DA'\n", + " orange = '#FAA43A'\n", + " green = '#60BD68'\n", + " pink = '#F17CB0'\n", + " brown = '#B2912F'\n", + " purple = '#B276B2'\n", + " yellow = '#DECF3F'\n", + " gray = '#4D4D4D'\n", + " def __getitem__(self, i):\n", + " return [\n", + " self.red,\n", + " self.orange,\n", + " self.green,\n", + " self.blue,\n", + " self.pink,\n", + " self.brown,\n", + " self.purple,\n", + " self.yellow,\n", + " self.gray,\n", + " ][i % 9]\n", + "TFColor = _TFColor()\n", + "\n", + "def session_options(enable_gpu_ram_resizing=True, enable_xla=True):\n", + " \"\"\"\n", + " Allowing the notebook to make use of GPUs if they're available.\n", + " \n", + " XLA (Accelerated Linear Algebra) is a domain-specific compiler for linear \n", + " algebra that optimizes TensorFlow computations.\n", + " \"\"\"\n", + " config = tf.ConfigProto()\n", + " config.log_device_placement = True\n", + " if enable_gpu_ram_resizing:\n", + " # `allow_growth=True` makes it possible to connect multiple colabs to your\n", + " # GPU. Otherwise the colab malloc's all GPU ram.\n", + " config.gpu_options.allow_growth = True\n", + " if enable_xla:\n", + " # Enable on XLA. https://www.tensorflow.org/performance/xla/.\n", + " config.graph_options.optimizer_options.global_jit_level = (\n", + " tf.OptimizerOptions.ON_1)\n", + " return config\n", + "\n", + "\n", + "def reset_sess(config=None):\n", + " \"\"\"\n", + " Convenience function to create the TF graph & session or reset them.\n", + " \"\"\"\n", + " if config is None:\n", + " config = session_options()\n", + " global sess\n", + " tf.reset_default_graph()\n", + " try:\n", + " sess.close()\n", + " except:\n", + " pass\n", + " sess = tf.InteractiveSession(config=config)\n", + "\n", + "reset_sess()" + ], + "execution_count": 2, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/html": [ + "

\n", + "The default version of TensorFlow in Colab will soon switch to TensorFlow 2.x.
\n", + "We recommend you upgrade now \n", + "or ensure your notebook will continue to use TensorFlow 1.x via the %tensorflow_version 1.x magic:\n", + "more info.

\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + } + }, + { + "output_type": "stream", + "text": [ + "WARNING:tensorflow:\n", + "The TensorFlow contrib module will not be included in TensorFlow 2.0.\n", + "For more information, please see:\n", + " * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n", + " * https://github.com/tensorflow/addons\n", + " * https://github.com/tensorflow/io (for I/O related ops)\n", + "If you depend on functionality not listed there, please file an issue.\n", + "\n", + "Device mapping:\n", + "/job:localhost/replica:0/task:0/device:XLA_CPU:0 -> device: XLA_CPU device\n", + "/job:localhost/replica:0/task:0/device:XLA_GPU:0 -> device: XLA_GPU device\n", + "/job:localhost/replica:0/task:0/device:GPU:0 -> device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0\n", + "\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "MIClIB5Dz0Mt" + }, + "source": [ + "### Would you rather lose an arm or a leg?\n", + "\n", + "Statisticians can be a sour bunch. Instead of considering their winnings, they only measure how much they have lost. In fact, they consider their wins as *negative losses*. But what's interesting is *how they measure their losses.*\n", + "\n", + "For example, consider the following example:\n", + "\n", + "> A meteorologist is predicting the probability of a possible hurricane striking his city. He estimates, with 95% confidence, that the probability of it *not* striking is between 99% - 100%. He is very happy with his precision and advises the city that a major evacuation is unnecessary. Unfortunately, the hurricane does strike and the city is flooded. \n", + "\n", + "This stylized example shows the flaw in using a pure accuracy metric to measure outcomes. Using a measure that emphasizes estimation accuracy, while an appealing and *objective* thing to do, misses the point of why you are even performing the statistical inference in the first place: results of inference. The author Nassim Taleb of *The Black Swan* and *Antifragility* stresses the importance of the *payoffs* of decisions, *not the accuracy*. Taleb distills this quite succinctly: \"I would rather be vaguely right than very wrong.\" " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "bvbT7rFEzwpw" + }, + "source": [ + "## Loss Functions\n", + "\n", + "We introduce what statisticians and decision theorists call *loss functions*. A loss function is a function of the true parameter, and an estimate of that parameter\n", + "\n", + "$$ L( \\theta, \\hat{\\theta} ) = f( \\theta, \\hat{\\theta} )$$\n", + "\n", + "The important point of loss functions is that it measures how *bad* our current estimate is: the larger the loss, the worse the estimate is according to the loss function. A simple, and very common, example of a loss function is the *squared-error loss*:\n", + "\n", + "$$ L( \\theta, \\hat{\\theta} ) = ( \\theta - \\hat{\\theta} )^2$$\n", + "\n", + "The squared-error loss function is used in estimators like linear regression, UMVUEs and many areas of machine learning. We can also consider an asymmetric squared-error loss function, something like:\n", + "\n", + "$$ L( \\theta, \\hat{\\theta} ) = \\begin{cases} ( \\theta - \\hat{\\theta} )^2 & \\hat{\\theta} \\lt \\theta \\\\\\\\ c( \\theta - \\hat{\\theta} )^2 & \\hat{\\theta} \\ge \\theta, \\;\\; 0\\lt c \\lt 1 \\end{cases}$$\n", + "\n", + "\n", + "which represents that estimating a value larger than the true estimate is preferable to estimating a value below. A situation where this might be useful is in estimating web traffic for the next month, where an over-estimated outlook is preferred so as to avoid an underallocation of server resources. \n", + "\n", + "A negative property about the squared-error loss is that it puts a disproportionate emphasis on large outliers. This is because the loss increases quadratically, and not linearly, as the estimate moves away. That is, the penalty of being three units away is much less than being five units away, but the penalty is not much greater than being one unit away, though in both cases the magnitude of difference is the same:\n", + "\n", + "$$ \\frac{1^2}{3^2} \\lt \\frac{3^2}{5^2}, \\;\\; \\text{although} \\;\\; 3-1 = 5-3 $$\n", + "\n", + "This loss function imposes that large errors are *very* bad. A more *robust* loss function that increases linearly with the difference is the *absolute-loss*\n", + "\n", + "$$ L( \\theta, \\hat{\\theta} ) = | \\theta - \\hat{\\theta} | $$\n", + "\n", + "Other popular loss functions include:\n", + "\n", + "- $L( \\theta, \\hat{\\theta} ) = \\mathbb{1}_{ \\hat{\\theta} \\neq \\theta }$ is the zero-one loss often used in machine learning classification algorithms.\n", + "- $L( \\theta, \\hat{\\theta} ) = -\\theta\\log( \\hat{\\theta} ) - (1- \\theta)\\log( 1 - \\hat{\\theta} ), \\; \\; \\theta \\in {0,1}, \\; \\hat{\\theta} \\in [0,1]$, called the *log-loss*, also used in machine learning. \n", + "\n", + "Historically, loss functions have been motivated from 1) mathematical convenience, and 2) they are robust to application, i.e., they are objective measures of loss. The first reason has really held back the full breadth of loss functions. With computers being agnostic to mathematical convenience, we are free to design our own loss functions, which we take full advantage of later in this Chapter.\n", + "\n", + "With respect to the second point, the above loss functions are indeed objective, in that they are most often a function of the difference between estimate and true parameter, independent of signage or payoff of choosing that estimate. This last point, its independence of payoff, causes quite pathological results though. Consider our hurricane example above: the statistician equivalently predicted that the probability of the hurricane striking was between 0% to 1%. But if he had ignored being precise and instead focused on outcomes (99% chance of no flood, 1% chance of flood), he might have advised differently. \n", + "\n", + "By shifting our focus from trying to be incredibly precise about parameter estimation to focusing on the outcomes of our parameter estimation, we can customize our estimates to be optimized for our application. This requires us to design new loss functions that reflect our goals and outcomes. Some examples of more interesting loss functions:\n", + "\n", + "\n", + "- $L( \\theta, \\hat{\\theta} ) = \\frac{ | \\theta - \\hat{\\theta} | }{ \\theta(1-\\theta) }, \\; \\; \\hat{\\theta}, \\theta \\in [0,1]$ emphasizes an estimate closer to 0 or 1 since if the true value $\\theta$ is near 0 or 1, the loss will be *very* large unless $\\hat{\\theta}$ is similarly close to 0 or 1. \n", + "This loss function might be used by a political pundit whose job requires him or her to give confident \"Yes/No\" answers. This loss reflects that if the true parameter is close to 1 (for example, if a political outcome is very likely to occur), he or she would want to strongly agree as to not look like a skeptic. \n", + "\n", + "- $L( \\theta, \\hat{\\theta} ) = 1 - \\exp \\left( -(\\theta - \\hat{\\theta} )^2 \\right)$ is bounded between 0 and 1 and reflects that the user is indifferent to sufficiently-far-away estimates. It is similar to the zero-one loss above, but not quite as penalizing to estimates that are close to the true parameter. \n", + "- Complicated non-linear loss functions can programmed: \n", + "```python\n", + " def loss(true_value, estimate):\n", + " if estimate*true_value > 0:\n", + " return abs(estimate - true_value)\n", + " else:\n", + " return abs(estimate)*(estimate - true_value)**2\n", + "```\n", + "\n", + "\n", + "- Another example is from the book *The Signal and The Noise*. Weather forecasters have an interesting loss function for their predictions.\n", + "\n", + "\n", + "> People notice one type of mistake — the failure to predict rain — more than other, false alarms. If it rains when it isn't supposed to, they curse the weatherman for ruining their picnic, whereas an unexpectedly sunny day is taken as a serendipitous bonus.\n", + "\n", + "> [The Weather Channel's bias] is limited to slightly exaggerating the probability of rain when it is unlikely to occur — saying there is a 20 percent change when they know it is really a 5 or 10 percent chance — covering their butts in the case of an unexpected sprinkle.\n", + "\n", + "\n", + "As you can see, loss functions can be used for good and evil: with great power, comes great — well you know.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "13afsKrzzqup" + }, + "source": [ + "## Loss functions in the real world\n", + "\n", + "So far we have been under the unrealistic assumption that we know the true parameter. Of course if we knew the true parameter, bothering to guess an estimate is pointless. Hence a loss function is really only practical when the true parameter is unknown. \n", + "\n", + "In Bayesian inference, we have a mindset that the unknown parameters are really random variables with prior and posterior distributions. Concerning the posterior distribution, a value drawn from it is a *possible* realization of what the true parameter could be. Given that realization, we can compute a loss associated with an estimate. As we have a whole distribution of what the unknown parameter could be (the posterior), we should be more interested in computing the *expected loss* given an estimate. This expected loss is a better estimate of the true loss than comparing the given loss from only a single sample from the posterior.\n", + "\n", + "First it will be useful to explain a *Bayesian point estimate*. The systems and machinery present in the modern world are not built to accept posterior distributions as input. It is also rude to hand someone over a distribution when all they asked for was an estimate. In the course of an individual's day, when faced with uncertainty we still act by distilling our uncertainty down to a single action. Similarly, we need to distill our posterior distribution down to a single value (or vector in the multivariate case). If the value is chosen intelligently, we can avoid the flaw of frequentist methodologies that mask the uncertainty and provide a more informative result.The value chosen, if from a Bayesian posterior, is a Bayesian point estimate. \n", + "\n", + "Suppose $P(\\theta | X)$ is the posterior distribution of $\\theta$ after observing data $X$, then the following function is understandable as the *expected loss of choosing estimate $\\hat{\\theta}$ to estimate $\\theta$*:\n", + "\n", + "$$ l(\\hat{\\theta} ) = E_{\\theta}\\left[ \\; L(\\theta, \\hat{\\theta}) \\; \\right] $$\n", + "\n", + "This is also known as the *risk* of estimate $\\hat{\\theta}$. The subscript $\\theta$ under the expectation symbol is used to denote that $\\theta$ is the unknown (random) variable in the expectation, something that at first can be difficult to consider.\n", + "\n", + "We spent all of last chapter discussing how to approximate expected values. Given $N$ samples $\\theta_i,\\; i=1,...,N$ from the posterior distribution, and a loss function $L$, we can approximate the expected loss of using estimate $\\hat{\\theta}$ by the Law of Large Numbers:\n", + "\n", + "$$\\frac{1}{N} \\sum_{i=1}^N \\;L(\\theta_i, \\hat{\\theta} ) \\approx E_{\\theta}\\left[ \\; L(\\theta, \\hat{\\theta}) \\; \\right] = l(\\hat{\\theta} ) $$\n", + "\n", + "Notice that measuring your loss via an *expected value* uses more information from the distribution than the MAP estimate which, if you recall, will only find the maximum value of the distribution and ignore the shape of the distribution. Ignoring information can over-expose yourself to tail risks, like the unlikely hurricane, and leaves your estimate ignorant of how ignorant you really are about the parameter.\n", + "\n", + "Similarly, compare this with frequentist methods, that traditionally only aim to minimize the error, and do not consider the *loss associated with the result of that error*. Compound this with the fact that frequentist methods are almost guaranteed to never be absolutely accurate. Bayesian point estimates fix this by planning ahead: your estimate is going to be wrong, you might as well err on the right side of wrong." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "lfuxjSXhoqty" + }, + "source": [ + "## Example: Optimizing for the *Showcase* on *The Price is Right*\n", + "\n", + "Bless you if you are ever chosen as a contestant on the Price is Right, for here we will show you how to optimize your final price on the *Showcase*. For those who forget the rules:\n", + "\n", + "\n", + "1. Two contestants compete in *The Showcase*. \n", + "2. Each contestant is shown a unique suite of prizes.\n", + "3. After the viewing, the contestants are asked to bid on the price for their unique suite of prizes.\n", + "4. If a bid price is over the actual price, the bid's owner is disqualified from winning.\n", + "5. If a bid price is under the true price by less than $250, the winner is awarded both prizes.\n", + "\n", + "The difficulty in the game is balancing your uncertainty in the prices, keeping your bid low enough so as to not bid over, and trying to bid close to the price.\n", + "\n", + "Suppose we have recorded the *Showcases* from previous *The Price is Right* episodes and have *prior* beliefs about what distribution the true price follows. For simplicity, suppose it follows a Normal:\n", + "\n", + "\n", + "$$\\text{True Price} \\sim \\text{Normal}(\\mu_p, \\sigma_p )$$\n", + "\n", + "\n", + "In a later chapter, we will actually use *real Price is Right Showcase data* to form the historical prior, but this requires some advanced Tensorflow use so we will not use it here. For now, we will assume $\\mu_p = 35 000$ and $\\sigma_p = 7500$.\n", + "\n", + "We need a model of how we should be playing the *Showcase*. For each prize in the prize suite, we have an idea of what it might cost, but this guess could differ significantly from the true price. (Couple this with increased pressure being onstage and you can see why some bids are so wildly off). Let's suppose your beliefs about the prices of prizes also follow Normal distributions:\n", + "\n", + "$$ \\text{Prize}_i \\sim \\text{Normal}(\\mu_i, \\sigma_i ),\\;\\; i=1,2$$\n", + "\n", + "This is really why Bayesian analysis is great: we can specify what we think a fair price is through the $\\mu_i$ parameter, and express uncertainty of our guess in the $\\sigma_i$ parameter. \n", + "\n", + "We'll assume two prizes per suite for brevity, but this can be extended to any number. \n", + "The true price of the prize suite is then given by $\\text{Prize}_1 + \\text{Prize}_2 + \\epsilon$, \n", + "where $\\epsilon$ is some error term.\n", + "\n", + "We are interested in the updated $\\text{True Price}$ given we have observed both prizes and have belief distributions about them. We can perform this using Tensorflow Probability. \n", + "\n", + "Lets make some values concrete. Suppose there are two prizes in the observed prize suite: \n", + "\n", + "1. A trip to wonderful Toronto, Canada! \n", + "2. A lovely new snowblower!\n", + "\n", + "We have some guesses about the true prices of these objects, but we are also pretty uncertain about them. I can express this uncertainty through the parameters of the Normals:\n", + "\n", + "\n", + "\\begin{align*}\n", + "\\text{snowblower} &\\sim \\text{Normal}(3 000, 500 ) \\\\\n", + "\\text{Toronto} &\\sim \\text{Normal}(12 000, 3000 ) \\\\\n", + "\\end{align*}\n", + "\n", + "For example, I believe that the true price of the trip to Toronto is 12 000 dollars, and that there is a 68.2% chance the price falls 1 standard deviation away from this, i.e. my confidence is that there is a 68.2% chance the trip is in [9 000, 15 000].\n", + "\n", + "We can create some TensorFlow code to perform inference on the true price of the suite. " + ] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "kOSDAQY9oqt5", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 648 + }, + "outputId": "c2ddddbf-281a-4a95-b251-0e64bf28009f" + }, + "source": [ + "plt.figure(figsize(12.5, 11))\n", + "\n", + "plt.subplot(311)\n", + "x1 = tf.linspace(start=0., stop=60000., num=250)\n", + "x2 = tf.linspace(start=0., stop=10000., num=250)\n", + "x3 = tf.linspace(start=0., stop=25000., num=250)\n", + "\n", + "historical_prices = tfd.Normal(loc=35000., scale=7500.).prob(x1)\n", + "snowblower_price_guesses = tfd.Normal(loc=3000., scale=500.).prob(x2)\n", + "trip_price_guess = tfd.Normal(loc=12000., scale=3000.).prob(x3)\n", + "\n", + "[\n", + " x1_, x2_, x3_,\n", + " historical_prices_, snowblower_price_guesses_, trip_price_guess_,\n", + "] = evaluate([\n", + " x1, x2, x3,\n", + " historical_prices, snowblower_price_guesses, trip_price_guess,\n", + "])\n", + "\n", + "sp1 = plt.fill_between(x1_, 0, historical_prices_, color=TFColor[3], lw=3, \n", + " alpha=0.6, label=\"historical total prices\")\n", + " \n", + "p1 = plt.Rectangle((0, 0), 1, 1, fc=sp1.get_facecolor()[0])\n", + "plt.legend([p1], [sp1.get_label()])\n", + "\n", + "plt.subplot(312)\n", + "sp2 = plt.fill_between(x2_, 0, snowblower_price_guesses_, color=TFColor[0], \n", + " lw=3, alpha=0.6, label=\"snowblower price guess\")\n", + " \n", + "p2 = plt.Rectangle((0, 0), 1, 1, fc=sp2.get_facecolor()[0])\n", + "plt.legend([p2], [sp2.get_label()])\n", + "\n", + "plt.subplot(313)\n", + "sp3 = plt.fill_between(x3_, 0, trip_price_guess_, color=TFColor[6], lw=3, \n", + " alpha=0.6, label=\"Trip price guess\")\n", + "p3 = plt.Rectangle((0, 0), 1, 1, fc=sp3.get_facecolor()[0])\n", + "plt.legend([p3], [sp3.get_label()]);" + ], + "execution_count": 2, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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jukorr3bpUGxk+m5Px9DT+Hs79ky7amNMmbW2tZt9fp5z/xfGmLsI1v7qPb4F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AD2DMItAACAfcZFTpe+cUmLDy8my6IgUnW+2hvBpXiujfzRfFKKCAAAHF6pTEql0yWlsr25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\nmcgoW8rKvPg62jmn6sWqq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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [], + "image/png": { + "width": 859, + "height": 631 + } + } + } + ] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "w3JI54U8oqt8", + "colab": {} + }, + "source": [ + "data_mu = [3000., 12000.]\n", + "data_std = [500., 3000.]\n", + "\n", + "mu_prior = 35000.\n", + "std_prior = 7500.\n", + " \n", + "def posterior_log_prob(true_price, prize_1, prize_2):\n", + " \"\"\"\n", + " Our posterior log probability, as a function of states\n", + " \n", + " Args:\n", + " true_price_: scalar of true price estimate, taken from state\n", + " prize_1_: scalar of prize 1 estimate, to be added to the prize 1 \n", + " estimate, taken from state\n", + " prize_2_: scalar of prize 2 estimate, to be added to the prize 1 \n", + " estimate, taken from state\n", + " Returns: \n", + " Scalar sum of log probabilities\n", + " Closure over: data_mu, data_std, mu_prior, std_prior\n", + " \"\"\"\n", + " rv_true_price = tfd.Normal(loc=mu_prior, \n", + " scale=std_prior, \n", + " name=\"true_price\")\n", + " rv_prize_1 = tfd.Normal(loc=data_mu[0], \n", + " scale=data_std[0], \n", + " name=\"first_prize\")\n", + " rv_prize_2 = tfd.Normal(loc=data_mu[1], \n", + " scale=data_std[1], \n", + " name=\"second_prize\")\n", + " \n", + " price_estimate = prize_1 + prize_2\n", + " \n", + " rv_error = tfd.Normal(loc=price_estimate, \n", + " scale=3000., \n", + " name='error')\n", + " \n", + " return (\n", + " rv_true_price.log_prob(true_price) +\n", + " rv_prize_1.log_prob(prize_1) + \n", + " rv_prize_2.log_prob(prize_2) + \n", + " rv_error.log_prob(true_price)\n", + " )" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "pOod1lEboquB" + }, + "source": [ + "\n", + "Nice. Now we'll evaluate the result with our `evaluate()` function and see if it matches our expectations." + ] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "3aI85hTgn6lU", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 91 + }, + "outputId": "568036df-a473-4a18-9607-1bd525634834" + }, + "source": [ + "number_of_steps = 50000\n", + "burnin = 10000\n", + "\n", + "[ \n", + " true_price, \n", + " prize_1, \n", + " prize_2 \n", + "], kernel_results = tfp.mcmc.sample_chain(\n", + " num_results=number_of_steps,\n", + " num_burnin_steps=burnin,\n", + " current_state=[\n", + " tf.fill([1], 20000., name='init_true_price'),\n", + " tf.fill([1], 3000., name='init_prize_1'),\n", + " tf.fill([1], 12000., name='init_prize_2')\n", + " ],\n", + " kernel=tfp.mcmc.RandomWalkMetropolis(\n", + " new_state_fn=tfp.mcmc.random_walk_normal_fn(1000.), #specify a new callable that has the appropriate step size\n", + " target_log_prob_fn=posterior_log_prob,\n", + " seed=54),\n", + " parallel_iterations=1,\n", + " name='MCMC_eval')\n", + "\n", + "posterior_price_predictive_samples = true_price[:,0]\n" + ], + "execution_count": 4, + "outputs": [ + { + "output_type": "stream", + "text": [ + "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_probability/python/mcmc/internal/util.py:164: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "Use tf.where in 2.0, which has the same broadcast rule as np.where\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "bu7i6lmKoquC", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 53 + }, + "outputId": "1fc48e41-0fb6-4996-e6da-9177bf61af6f" + }, + "source": [ + "# performing our computations\n", + "# Can take up to 2 minutes in Graph Mode\n", + "[\n", + " posterior_price_predictive_samples_,\n", + " kernel_results_,\n", + "] = evaluate([\n", + " posterior_price_predictive_samples,\n", + " kernel_results,\n", + "])\n", + "\n", + "# For metropolis hastings the acceptance probability should be around 0.234.\n", + "# See https://arxiv.org/pdf/1011.6217.pdf \n", + "print(\"acceptance rate: {}\".format(\n", + " kernel_results_.is_accepted.mean()))\n", + "\n", + "print(\"posterior_price_predictive_sample_ trace:\", \n", + " posterior_price_predictive_samples_)" + ], + "execution_count": 5, + "outputs": [ + { + "output_type": "stream", + "text": [ + "acceptance rate: 0.44302\n", + "posterior_price_predictive_sample_ trace: [15410.804 15667.329 15667.329 ... 21354.152 21460.07 20892.367]\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "j_jDroqFETWA", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 283 + }, + "outputId": "ab286a81-a9ab-4438-a16f-ed8be6c02c29" + }, + "source": [ + "plt.figure(figsize(12.5, 4))\n", + "prices = tf.linspace(start=5000., stop=40000., num=35000)\n", + "prior = tfd.Normal(loc=35000., scale=7500.).prob(prices)\n", + "\n", + "[\n", + " prices_, prior_,\n", + "] = evaluate([\n", + " prices, prior,\n", + "])\n", + "\n", + "plt.plot(prices_, prior_, c=\"k\", lw=2,\n", + " label=\"prior dist. of suite price\")\n", + "\n", + "hist = plt.hist(posterior_price_predictive_samples_, bins=35, density=True, histtype=\"stepfilled\")\n", + "plt.title(\"Posterior of the true price estimate\")\n", + "plt.vlines(mu_prior, 0, 1.1 * np.max(hist[0]), label=\"prior's mean\",\n", + " linestyles=\"--\")\n", + "plt.vlines(posterior_price_predictive_samples_.mean(), 0, 1.1 * np.max(hist[0]),\n", + " label=\"posterior's mean\", linestyles=\"-.\")\n", + "plt.legend(loc=\"upper left\");\n", + "\n" + ], + "execution_count": 6, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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z4AzgN+CNkNNzCsl3NHC+/3JWDPkqApdEyFdsfgDuPaABMAW4xjnn4lV+PASa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\nFV99ELWvov3O37t/Q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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [], + "image/png": { + "width": 852, + "height": 266 + } + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "yZDvbxrMoquG" + }, + "source": [ + "Notice that because of our two observed prizes and subsequent guesses (including uncertainty about those guesses), we shifted our mean price estimate down about $15 000 dollars from the previous mean price.\n", + "\n", + "A frequentist, seeing the two prizes and having the same beliefs about their prices, would bid $\\mu_1 + \\mu_2 = 35000$, regardless of any uncertainty. Meanwhile, the *naive Bayesian* would simply pick the mean of the posterior distribution. But we have more information about our eventual outcomes; we should incorporate this into our bid. We will use the loss function above to find the *best* bid (*best* according to our loss).\n", + "\n", + "What might a contestant's loss function look like? I would think it would look something like:\n", + "\n", + "```python\n", + "def showcase_loss(guess, true_price, risk=80000):\n", + " if true_price < guess:\n", + " return risk\n", + " elif abs(true_price - guess) <= 250:\n", + " return -2*np.abs(true_price)\n", + " else:\n", + " return np.abs(true_price - guess - 250)\n", + "```\n", + "\n", + "where `risk` is a parameter that defines of how bad it is if your guess is over the true price. A lower `risk` means that you are more comfortable with the idea of going over. If we do bid under and the difference is less than $250, we receive both prizes (modeled here as receiving twice the original prize). Otherwise, when we bid under the `true_price` we want to be as close as possible, hence the `else` loss is a increasing function of the distance between the guess and true price." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "tIRbeW9aFLL8", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 313 + }, + "outputId": "078c881b-50e2-4131-ad61-cfb669d15f2e" + }, + "source": [ + "def showdown_loss(guess, size, true_price_, risk_ = 80000):\n", + " \"\"\"Stock Loss function.\n", + "\n", + " Args:\n", + " guess: float32 Tensor, representing a range of guesses for the price, or one guess\n", + " size: int size of guess\n", + " true_price: float32 Tensor of size 50000 x num_guesses_, representing the \n", + " prices from the HMC sampling, broadcast to each of the num_guesses_ guesses \n", + " risk_: a scalar value representing a penalizer for a score going over\n", + " (lower risk indicates more comfort with the price going over)\n", + "\n", + " Returns:\n", + " loss: tensor of shape (true_price.shape,guess.shape), returning the loss function per definition in accompanying text\n", + " \"\"\"\n", + " true_price = tf.transpose(tf.broadcast_to(true_price_,(size,true_price_.shape[0])))\n", + " risk = tf.broadcast_to (tf.convert_to_tensor(risk_,dtype=tf.float32),true_price.shape)\n", + " return tf.where (true_price < guess , risk , \\\n", + " tf.where(tf.abs(true_price - guess) <= 1,-2*tf.abs(true_price),tf.abs(true_price - guess -250)))\n", + " \n", + "num_guesses_ = 70\n", + "num_risks_ = 6\n", + "guesses = tf.linspace(5000., 50000., num_guesses_) \n", + "risks_ = np.linspace(30000, 150000, num_risks_)\n", + "results_cache_ = np.zeros ((num_risks_,num_guesses_))\n", + "\n", + "expected_loss = lambda guess,size, risk: tf.reduce_mean(\n", + " showdown_loss(guess,size, posterior_price_predictive_samples_, risk),axis=0)\n", + "\n", + "risk_num_ = 0\n", + "for _p in risks_:\n", + " results = expected_loss(guesses,num_guesses_,tf.constant(_p,dtype=tf.float32))\n", + " [\n", + " guesses_ ,\n", + " results_\n", + " ] = evaluate([\n", + " guesses,\n", + " results \n", + " ])\n", + " plt.plot(guesses_, results_, label = \"%d\"%_p)\n", + " results_cache_[risk_num_,:] = results_\n", + " risk_num_+=1\n", + "plt.title(\"Expected loss of different guesses, \\nvarious risk-levels of \\\n", + "overestimating\")\n", + "plt.legend(loc=\"upper left\", title=\"Risk parameter\")\n", + "plt.xlabel(\"price bid\")\n", + "plt.ylabel(\"expected loss\")\n", + "plt.xlim(7000, 30000)\n", + "plt.ylim(-1000, 80000);\n" + ], + "execution_count": 7, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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VOI2UWAvgG8CofA03JiVb+gE/lrRvUSEijo6IDYDf56KzI2KDYurBsS8mJdZm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\nnLkEw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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [], + "image/png": { + "width": 875, + "height": 296 + } + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "4zWmZhrFoquH" + }, + "source": [ + "For every possible bid, we calculate the *expected loss* associated with that bid. We vary the `risk` parameter to see how it affects our loss:" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "yNi7TZ2PoquL" + }, + "source": [ + "### Minimizing our losses\n", + "\n", + "It would be wise to choose the estimate that minimizes our expected loss. This corresponds to the minimum point on each of the curves above. More formally, we would like to minimize our expected loss by finding the solution to\n", + "\n", + "$$ \\text{arg} \\min_{\\hat{\\theta}} \\;\\;E_{\\theta}\\left[ \\; L(\\theta, \\hat{\\theta}) \\; \\right] $$\n", + "\n", + "The minimum of the expected loss is called the *Bayes action*. \n", + "\n", + "We'll compute the minimum loss for the *Showcase* example above:" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "7MwzwYAltIhY", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 421 + }, + "outputId": "7e1326fa-eb6f-4bd8-bfa5-6518275b0271" + }, + "source": [ + "ax = plt.subplot(111)\n", + "\n", + "risk_num_ = 0\n", + "\n", + "for _p in risks_: \n", + " color_ = next(ax._get_lines.prop_cycler)\n", + " results_ = results_cache_[risk_num_,:]\n", + " _g = tf.Variable(15000., trainable=True)\n", + "\n", + " loss = -expected_loss(_g,1, tf.constant(_p,dtype=tf.float32))\n", + " optimizer = tf.train.AdamOptimizer(10)\n", + " opt_min = optimizer.minimize(loss, var_list=[_g])\n", + " evaluate(tf.global_variables_initializer())\n", + " min_losses = []\n", + " min_vals = []\n", + " for i in range(500):\n", + " _, l, value_ = evaluate([opt_min, loss, _g])\n", + " min_losses.append(l)\n", + " min_vals.append(value_)\n", + " min_losses = np.asarray(min_losses)\n", + " min_vals = np.asarray(min_vals)\n", + " min_results_ = min_vals[np.argmax(min_losses)]\n", + " plt.plot(guesses_, results_ , color = color_['color'])\n", + " plt.scatter(min_results_, 0, s = 60, \\\n", + " color= color_['color'], label = \"%d\"%_p)\n", + " plt.vlines((min_results_), 0, 120000, color = color_['color'], linestyles=\"--\")\n", + " print(\"minimum at risk %d: %.2f\" % (_p, (min_results_)))\n", + " risk_num_ += 1\n", + " \n", + "plt.title(\"Expected loss & Bayes actions of different guesses, \\n \\\n", + "various risk-levels of overestimating\")\n", + "plt.legend(loc=\"upper left\", scatterpoints = 1, title = \"Bayes action at risk:\")\n", + "plt.xlabel(\"price guess\")\n", + "plt.ylabel(\"expected loss\")\n", + "plt.xlim(7000, 30000)\n", + "plt.ylim(-1000, 80000);\n" + ], + "execution_count": 8, + "outputs": [ + { + "output_type": "stream", + "text": [ + "minimum at risk 30000: 13777.55\n", + "minimum at risk 54000: 12592.70\n", + "minimum at risk 78000: 12157.09\n", + "minimum at risk 102000: 12011.69\n", + "minimum at risk 126000: 11149.57\n", + "minimum at risk 150000: 11149.57\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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jL0jjXs0mtficQ7oXPwHsERFza26hiibsR7nsP5FaWV5K2p8BwL2k4MY7Sc/+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\nFVdmbjpfrJvNyfNFe91FhDRi3KAnaVS/mQ8jq5201jjSfsR65D10wYViR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gAAIABJREFU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\nDh4PfLamynTpFye1PzW5JmlndXu+PqBJnf3q6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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [], + "image/png": { + "width": 875, + "height": 296 + } + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "f_J0GQqAoquO" + }, + "source": [ + "As intuition suggests, as we decrease the risk threshold (care about overbidding less), we increase our bid, willing to edge closer to the true price. It is interesting how far away our optimized loss is from the posterior mean, which was about 20 000. \n", + "\n", + "Suffice to say, in higher dimensions being able to eyeball the minimum expected loss is impossible. \n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "9LykAt1JoquO" + }, + "source": [ + "### Shortcuts\n", + "\n", + "For some loss functions, the Bayes action is known in closed form. We list some of them below:\n", + "\n", + "- If using the mean-squared loss, the Bayes action is the mean of the posterior distribution, i.e. the value \n", + "$$ E_{\\theta}\\left[ \\theta \\right] $$\n", + "\n", + "> minimizes $E_{\\theta}\\left[ \\; (\\theta - \\hat{\\theta})^2 \\; \\right]$. Computationally this requires us to calculate the average of the posterior samples [See chapter 4 on The Law of Large Numbers]\n", + "\n", + "- Whereas the *median* of the posterior distribution minimizes the expected absolute-loss. The sample median of the posterior samples is an appropriate and very accurate approximation to the true median.\n", + "\n", + "- In fact, it is possible to show that the MAP estimate is the solution to using a loss function that shrinks to the zero-one loss.\n", + "\n", + "\n", + "Maybe it is clear now why the first-introduced loss functions are used most often in the mathematics of Bayesian inference: no complicated optimizations are necessary. Luckily, we have machines to do the complications for us. " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "i8noiGrAoquO" + }, + "source": [ + "## Machine Learning via Bayesian Methods\n", + "\n", + "Whereas frequentist methods strive to achieve the best precision about all possible parameters, machine learning cares to achieve the best *prediction* among all possible parameters. Of course, one way to achieve accurate predictions is to aim for accurate predictions, but often your prediction measure and what frequentist methods are optimizing for are very different. \n", + "\n", + "For example, least-squares linear regression is the most simple active machine learning algorithm. I say active as it engages in some learning, whereas predicting the sample mean is technically *simpler*, but is learning very little if anything. The loss that determines the coefficients of the regressors is a squared-error loss. On the other hand, if your prediction loss function (or score function, which is the negative loss) is not a squared-error, like AUC, ROC, precision, etc., your least-squares line will not be optimal for the prediction loss function. This can lead to prediction results that are suboptimal. \n", + "\n", + "Finding Bayes actions is equivalent to finding parameters that optimize *not parameter accuracy* but an arbitrary performance measure, however we wish to define performance (loss functions, AUC, ROC, precision/recall etc.).\n", + "\n", + "The next two examples demonstrate these ideas. The first example is a linear model where we can choose to predict using the least-squares loss or a novel, outcome-sensitive loss. \n", + "\n", + "The second example is adapted from a Kaggle data science project. The loss function associated with our predictions is incredibly complicated. " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "mhqQdIx3ofeh" + }, + "source": [ + "# Example: Financial prediction\n", + "\n", + "\n", + "Suppose the future return of a stock price is very small, say 0.01 (or 1%). We have a model that predicts the stock's future price, and our profit and loss is directly tied to us acting on the prediction. How should we measure the loss associated with the model's predictions, and subsequent future predictions? A squared-error loss is agnostic to the signage and would penalize a prediction of -0.01 equally as bad a prediction of 0.03:\n", + "\n", + "$$ (0.01 - (-0.01))^2 = (0.01 - 0.03)^2 = 0.004$$\n", + "\n", + "If you had made a bet based on your model's prediction, you would have earned money with a prediction of 0.03, and lost money with a prediction of -0.01, yet our loss did not capture this. We need a better loss that takes into account the *sign* of the prediction and true value. We design a new loss that is better for financial applications below:" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "_fMRPjosoquR", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 545 + }, + "outputId": "f8a5a724-4b28-4dfa-9a9a-0a7fe00d7462" + }, + "source": [ + "plt.figure(figsize(12.5, 6.5))\n", + "reset_sess()\n", + "\n", + "def stock_loss(true_return, yhat, alpha=100.):\n", + " \"\"\"\n", + " Stock Loss function\n", + " \n", + " Args:\n", + " true_return: float32 Tensor representing the true stock return\n", + " yhat: float32\n", + " alpha:float32\n", + " \n", + " Returns:\n", + " float: absolute value of the difference\n", + " between `true_return` and `yhat`\n", + " \"\"\"\n", + " if true_return * yhat < 0:\n", + " # opposite signs, not good\n", + " return alpha * yhat ** 2 - tf.sign(true_return) * yhat \\\n", + " + tf.abs(true_return)\n", + " else:\n", + " return tf.abs(true_return - yhat)\n", + "\n", + "\n", + "true_value_1_ = .05\n", + "true_value_2_ = -.02\n", + "pred_ = np.linspace(-.04, .12, 75)\n", + "\n", + "plt.plot(pred_, [evaluate(stock_loss(true_value_1_, p)) for p in pred_],\n", + " label=\"Loss associated with\\n prediction if true value = 0.05\", lw=3)\n", + "plt.vlines(0, 0, .25, linestyles=\"--\")\n", + "plt.xlabel(\"prediction\")\n", + "plt.ylabel(\"loss\")\n", + "plt.xlim(-0.04, .12)\n", + "plt.ylim(0, 0.25)\n", + "\n", + "true_value = -.02\n", + "plt.plot(pred_, [evaluate(stock_loss(true_value_2_, p)) for p in pred_], alpha=0.6,\n", + " label=\"Loss associated with\\n prediction if true value = -0.02\", lw=3)\n", + "plt.legend()\n", + "plt.title(\"Stock returns loss if true value = 0.05, -0.02\");\n" + ], + "execution_count": 9, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Device mapping:\n", + "/job:localhost/replica:0/task:0/device:XLA_CPU:0 -> device: XLA_CPU device\n", + "/job:localhost/replica:0/task:0/device:XLA_GPU:0 -> device: XLA_GPU device\n", + "/job:localhost/replica:0/task:0/device:GPU:0 -> device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0\n", + "\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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JqfVAXeC4Qtjld/5rg0JoS0REREREREREREREROJQEoYlPNp//ck5tytKmYXA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\ndb83jvrOy//NPgRcOOj9WWQ3oAn48DD7tZI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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [], + "image/png": { + "width": 859, + "height": 418 + } + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "HOeAHc5ToquT" + }, + "source": [ + "Note the change in the shape of the loss as the prediction crosses zero. This loss reflects that the user really does not want to guess the wrong sign, especially be wrong *and* a large magnitude. \n", + "\n", + "Why would the user care about the magnitude? Why is the loss not 0 for predicting the correct sign? Surely, if the return is 0.01 and we bet millions we will still be (very) happy.\n", + "\n", + "Financial institutions treat downside risk, as in predicting a lot on the wrong side, and upside risk, as in predicting a lot on the right side, similarly. Both are seen as risky behaviour and discouraged. Hence why we have an increasing loss as we move further away from the true price. (With less extreme loss in the direction of the correct sign.)\n", + "\n", + "We will perform a regression on a trading signal that we believe predicts future returns well. Our dataset is artificial, as most financial data is not even close to linear. Below, we plot the data along with the least-squares line." + ] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "PaDyigdCy85d", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 572 + }, + "outputId": "f891ab38-52ee-4712-e66a-016ad051cbe9" + }, + "source": [ + "# Code for creating artificial \"dummy\" data\n", + "# This is a common strategy for testing our models\n", + "# before applying it to real-world data\n", + "\n", + "reset_sess() # Resets the default tensorflow graph we're using\n", + "\n", + "num_data = 100\n", + "X_data = (0.025 * tfd.Normal(loc=0.,scale=1.).sample(sample_shape=num_data))\n", + "Y_data = (0.5 * X_data + 0.01 * tfd.Normal(loc=0.,scale=1.).sample(sample_shape=num_data))\n", + "\n", + "tf_var_data = tf.nn.moments(X_data, axes=0)[1]\n", + "covar = tfp.stats.covariance(X_data,Y_data, sample_axis=0, event_axis=None)\n", + "ls_coef = covar / tf_var_data\n", + "\n", + "[\n", + " X_data_, Y_data_, ls_coef_,\n", + "] = evaluate([\n", + " X_data, Y_data, ls_coef,\n", + "])\n", + "\n", + "ls_intercept_ = Y_data_.mean() - ls_coef_ * X_data_.mean()\n", + "\n", + "plt.figure(figsize(12.5, 7))\n", + "plt.scatter(X_data_, Y_data_, c=\"k\")\n", + "plt.xlabel(\"trading signal\")\n", + "plt.ylabel(\"returns\")\n", + "plt.title(\"Empirical returns vs trading signal\")\n", + "plt.plot(X_data_, ls_coef_ * X_data_ + ls_intercept_, label=\"Least-squares line\")\n", + "plt.xlim(X_data_.min(), X_data_.max())\n", + "plt.ylim(Y_data_.min(), Y_data_.max())\n", + "plt.legend(loc=\"upper left\");\n" + ], + "execution_count": 10, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Device mapping:\n", + "/job:localhost/replica:0/task:0/device:XLA_CPU:0 -> device: XLA_CPU device\n", + "/job:localhost/replica:0/task:0/device:XLA_GPU:0 -> device: XLA_GPU device\n", + "/job:localhost/replica:0/task:0/device:GPU:0 -> device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0\n", + "\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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6QlLxQTAASC9CLAAAgCFjcQ8Iz3Xd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\nF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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [], + "image/png": { + "width": 856, + "height": 445 + } + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "uxusMfftoquW" + }, + "source": [ + "We perform a simple Bayesian linear regression on this dataset. We look for a model like:\n", + "\n", + "$$ R = \\alpha + \\beta x + \\epsilon$$\n", + "\n", + "where $\\alpha, \\beta$ are our unknown parameters and $\\epsilon \\sim \\text{Normal}(0, 1/\\tau)$. The most common priors on $\\beta$ and $\\alpha$ are Normal priors. We will also assign a prior on $\\tau$, so that $\\sigma = 1/\\sqrt{\\tau}$ is uniform over 0 to 100 (equivalently then $\\tau = 1/\\text{Uniform}(0, 100)^2$)." + ] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "QeBL-LhtoquX", + "colab": {} + }, + "source": [ + "obs_stdev = tf.sqrt(\n", + " tf.reduce_mean(tf.squared_difference(Y_data_, tf.reduce_mean(Y_data_, axis=0)),\n", + " axis=0))\n", + "\n", + "# Let's define the log probability of the bayesian regression function\n", + "def finance_posterior_log_prob(X_data_, Y_data_, alpha, beta, sigma):\n", + " \"\"\"\n", + " Our posterior log probability, as a function of states\n", + " \n", + " Args:\n", + " alpha_: scalar, taken from state of the HMC\n", + " beta_: scalar, taken from state of the HMC\n", + " sigma_: scalar, the standard deviation of , taken from state of the HMC\n", + " Returns: \n", + " Scalar sum of log probabilities\n", + " Closure over: Y_data, X_data\n", + " \"\"\"\n", + " rv_std = tfd.Uniform(name=\"std\", low=0., high=100.)\n", + " rv_beta = tfd.Normal(name=\"beta\", loc=0., scale=100.)\n", + " rv_alpha = tfd.Normal(name=\"alpha\", loc=0., scale=100.)\n", + " \n", + " mean = alpha + beta * X_data_\n", + " rv_observed = tfd.Normal(name=\"obs\", loc=mean, scale=sigma)\n", + " \n", + " return (\n", + " rv_alpha.log_prob(alpha) \n", + " + rv_beta.log_prob(beta) \n", + " + rv_std.log_prob(sigma)\n", + " + tf.reduce_sum(rv_observed.log_prob(Y_data_))\n", + " )" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "YIH8NhDHoquZ", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 91 + }, + "outputId": "3af2b108-cb1f-43bc-c7bb-18779fe6fa64" + }, + "source": [ + "number_of_steps = 30000\n", + "burnin = 5000\n", + "\n", + "# Set the chain's start state.\n", + "initial_chain_state = [\n", + " tf.cast(1.,dtype=tf.float32) * tf.ones([], name='init_alpha', dtype=tf.float32),\n", + " tf.cast(0.01,dtype=tf.float32) * tf.ones([], name='init_beta', dtype=tf.float32),\n", + " tf.cast(obs_stdev,dtype=tf.float32) * tf.ones([], name='init_sigma', dtype=tf.float32)\n", + "]\n", + "\n", + "# Since HMC operates over unconstrained space, we need to transform the\n", + "# samples so they live in real-space.\n", + "# Beta and sigma are 100x and 10x of alpha, approximately, so apply Affine scalar bijector\n", + "# to multiply the unconstrained beta and sigma by 100x and 10x to get back to \n", + "# the problem space\n", + "unconstraining_bijectors = [\n", + " tfp.bijectors.Identity(), #alpha\n", + " tfp.bijectors.AffineScalar(100.), #beta\n", + " tfp.bijectors.AffineScalar(10.), #sigma\n", + "]\n", + "\n", + "# Define a closure over our joint_log_prob.\n", + "unnormalized_posterior_log_prob = lambda *args: finance_posterior_log_prob(X_data_, Y_data_, *args)\n", + "\n", + "# Initialize the step_size. (It will be automatically adapted.)\n", + "with tf.variable_scope(tf.get_variable_scope(), reuse=tf.AUTO_REUSE):\n", + " step_size = tf.get_variable(\n", + " name='step_size',\n", + " initializer=tf.constant(0.5, dtype=tf.float32),\n", + " trainable=False,\n", + " use_resource=True\n", + " )\n", + "\n", + "# Defining the HMC\n", + "kernel=tfp.mcmc.TransformedTransitionKernel(\n", + " inner_kernel=tfp.mcmc.HamiltonianMonteCarlo(\n", + " target_log_prob_fn=unnormalized_posterior_log_prob,\n", + " num_leapfrog_steps=2,\n", + " step_size=step_size,\n", + " state_gradients_are_stopped=True), \n", + " bijector=unconstraining_bijectors)\n", + "\n", + "kernel = tfp.mcmc.SimpleStepSizeAdaptation(\n", + " inner_kernel=kernel, num_adaptation_steps=int(burnin * 0.8))\n", + "\n", + "# Sampling from the chain.\n", + "[\n", + " alpha, \n", + " beta, \n", + " sigma\n", + "], kernel_results = tfp.mcmc.sample_chain(\n", + " num_results = number_of_steps,\n", + " num_burnin_steps = burnin,\n", + " current_state=initial_chain_state,\n", + " kernel=kernel,\n", + " name='HMC_sampling'\n", + ") \n", + " \n", + "# Initialize any created variables for preconditions\n", + "init_g = tf.global_variables_initializer()" + ], + "execution_count": 12, + "outputs": [ + { + "output_type": "stream", + "text": [ + "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/resource_variable_ops.py:1630: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "If using Keras pass *_constraint arguments to layers.\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "euXQbVuxoqua" + }, + "source": [ + "\n", + "Nice. Now we'll evaluate the result with our `evaluate()` function and see if it matches our expectations." + ] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "q4YHPfXToquc", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 53 + }, + "outputId": "3f41599c-992d-4f8d-a960-f5ae3b26b258" + }, + "source": [ + "# Running the Initializer on our model\n", + "evaluate(init_g)\n", + " \n", + "# performing our computations\n", + "# can take up to about 4 mins in graph mode\n", + "[\n", + " alpha_,\n", + " beta_,\n", + " sigma_,\n", + " kernel_results_\n", + "] = evaluate([\n", + " alpha,\n", + " beta,\n", + " sigma,\n", + " kernel_results\n", + "])\n", + "\n", + "print(\"acceptance rate: {}\".format(\n", + " kernel_results_.inner_results.inner_results.is_accepted.mean()))\n", + "\n", + "print(\"final step size: {}\".format(\n", + " kernel_results_.new_step_size[-100:].mean()))" + ], + "execution_count": 13, + "outputs": [ + { + "output_type": "stream", + "text": [ + "acceptance rate: 0.5614\n", + "final step size: 0.001251715118996799\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "jFFZDRcLoVBY", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 674 + }, + "outputId": "e5242163-90aa-4c27-f67d-b10be612c273" + }, + "source": [ + "# plotting the Posterior Samples\n", + "plt.figure(figsize=(15,3))\n", + "plt.plot(np.arange(number_of_steps), sigma_, color=TFColor[6])\n", + "plt.title('HMC sigma (σ) convergence progression', fontsize=14)\n", + "\n", + "plt.figure(figsize=(15,3))\n", + "plt.plot(np.arange(number_of_steps), beta_, color=TFColor[0])\n", + "plt.title('HMC beta (β) convergence progression', fontsize=14)\n", + "\n", + "plt.figure(figsize=(15,3))\n", + "plt.plot(np.arange(number_of_steps), alpha_, color=TFColor[3])\n", + "plt.title('HMC alpha (α) convergence progression', fontsize=14)\n" + ], + "execution_count": 14, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'HMC alpha (α) convergence progression')" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 14 + }, + { + "output_type": "display_data", + "data": { + "image/png": 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5qFXZHfhV5CBPKYC3YyDbaKWU0ooiCP19csWDzxVTueuB7wH1xiBv1o/I5+yt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\nDbH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20JwiEqqkWXNrgTkqQyuHpdWRolLT2M4wDBMr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/SBB8Xelx8D+VX/gTi0L8Sl/R1ycAfcxCjYDgy8Fbj7TmDd2mBtMLFTv15i+tE1LqazcSQZaLyv\npHJoWbEcmDHdeP6/9Hw4Y4k1IkGVFoNJLYL8PHNzgS5dosdbtxr5KTPlt755k5Gj8sfvrc/DVHOi\niwf1pOfHtm2pN7fyg4PcbKB7m+qaWJ0T40WzVIwqr2GYnXDz8qzncjQN3Hs45FJnahb77Gs9Tka0\nqGJtKNp3gBg+EmLoa97Xn31uHAblgzZtgUGDDVlx+f2MUDX/Fv+53l/buSE+B+S9mojBW3c+nZ0N\nHOC9r0CxfIeC7hM4pczKzYWoq7l2Na9/dFSxYnGyzs52XpP9vQZUWAgAoLVrgVeG2KqIPocF75tJ\nadjAzTAMwzCx8NW46pe0qxB4/hnnqM31AfPqvD0KtGMHqLgYGPJCsDbSkY0bQVu3APDI7bPcIcdz\nuqDKHakR+UuRvKxmY/huRf4hF0THTs7n2rRRFEo7quUV3vm/pMWfyM01ItmTxaGHWo/NC0Kzp69K\nOt4vF/5f9Utx1TXudVu0BK74N3DEUerzQRe9d90BKiwE7SgAXhserA0mdmrFR5LchssGhzBvYrBs\nmz67Cj0jEqoxR4dNmQwaPAj0xgjg4QdA0mcj9u8VbDx+PjuVEezP8HL0MXEiiGKHEFaHsxXLQU89\nAXzyYYgDSyKvDgVN+wX00RirGkJNiOCuU8d6XF5mTxmTZig3zjW+90KWHlc5D6ewioR46DH1CR2j\nABM7bhHc8u/MiVR1oGASjthv/+hBAOWVmFHN+SPGWB2n9/wkR3ADhgy45Gwi9t0vehCZK/c+AOKR\nx/Xb1YjgFq1aG/sS197gXlE2cEfuAbpOMdnZcUtNVc3OHeG3qatqYX4OlxQ714tQtCvYeAD377W0\nf0aqvddTTwveN5PSsIGbYRiGYWJhzWrLIS1ZDPz6i7puscaET8XmTdH2MyUSRwvNvzXdo1LlCAIA\nKNOUVHp/tCUHt9+NXlr5l/PJOgopRfn7JyqBjRvdO1kg5RYcMsyIZE8Wch6sl1+MvjYbjsKQIGzf\nHuLeByBuvQM40J9crNi/t7UgYA5u8z1DudBjwieZ9+kuXZXFonETq+xfTc/B/ciDoGuv0qtbXq7/\n/P5oTPVLGv129LXpOV7NroAbPDE6J9C6dTFdHwui9wFJ6zutCBKJc1hf5RyAfvgeeOoJ17yHKc/i\nP0Br1lQf0qyZ0XPJyHuaBEQLU9RoRWVy51FhkK8wJuo8O+W50KqV9joNGwYaUkJo2RKi/xX28sce\n0nekYoJjdiSoLUdw50D0Pdx2iTj+RKmgJq3FGVfMKmqqnDBB0f2OqeYKfp6JqeoMdGl/iDPPhrj5\nNqCB6X7uRz1BIQlu4/6HgKefB3r1dq8nG3oj77GuBHZ2tt54YkGRzk8cdbS9TE5TtEmxPongEgVv\ncUIwB1voRGfv3u1dJwhzZnnXqe8jXziTVtSM1QDDMAzDxAuVN+aX4+xlAPD9+GB9bE2CR3AqoBnN\nQCaDQsZQrDfxp0kTgU0mA3OL8GTz6M9lEFJ+bRTutFbq1AWYM9u9IZ9R5XFHMlzTooWGjDcA2rAh\neiIsGd927YE9u/vfRLji39bjWHJwM+HjtvmUTONx7doQR1o3NER+PnDbQKszzbq/EzywFGJHAciP\nY1RZmdoRSQFNnaLfblAD9/LlhkxzlQyfX4RDHtiEoBvtoqKsLH6bYqlGEAN3n8OAbPX2Dv21AvT1\nl2kbvU8vPOt8sijF5hjxwvx3rvwLaNSo+lBoSJ+mHGefZy9zU2uKIKsGqb7TYSjwxJO+R9g+M9q0\nCZj1W2L6nzgBeGeUu2EjE6msBH6ZGj1WPdelz0UMuAq44MI4D4xJW8zr7oIYZJfNzJxhpJZ6Z5R3\nXZWSx9499ftKdg5uJxo2Ak4/E+ixl6/LzDLmtMnDAR8wDNU68/ue+0BUOU5Z1lh/LNIbWCIM3Co1\nk0aNIUwy9OKmmwHZwcotstwtgtu8BjH3/eEH7uMEAs/bxJEOKndV0MQJ3o2EEcDApCRs4GYYhmGY\nGKCdO+2FO3YYxtnJk6x1A0Zd0Q7JI1M2MmYiu3aBBkkyfqoojQxB3HqHtcDHJrQlkinsSbvZC7yi\nwh452LIlcPiRrk2QyaNYdO8R5uiCoYpyr6iwRGgKouQv+uvUgTDnqQyaW8uEaFADvJZ3Fxke3PF0\nrFi7FnjgXmDwIHVkrxSFlXBJfjl//AknAc2aWZwsaPy3NTcKacZ0X9XpvruBRQvDH0eHjoEuo+nT\nDMeujzQ2kVRcc12w60KAfpsO3PAf/1Ho27cDd98B3HW7kdc80wl6v/dScYm3gXv2LOCuOwx1mQRB\nTqpJGYZ5vUGjXgemTI6ebNo0CSOKkd4HQFxymaWIpv7sfk15uX3dpXoGh+WgGE9UctgBnZZ8sWol\naMx7xnv92Sfx7y+VmPUb6BfTd0yO4AaAPbtXp3MRBx0M9DnU7qDqlLOWqXk0bhJ9PeEHYOKPMacb\noJGvgXbuNH6jf61wrywpWoh99wNOOiV6fFhfx0tF+w5A9x4QVXNRkex83GHQubN3nSrEOQonKyfq\n1gUefwri0ScA83NLN/2UpkS5UKWt08XJufr4EyAu+j8jVVqV84MlZZVbuqQdLrLn++5XHQhBlZXV\n0ey0epX3WAOs0cXF/YB/XeT7Ohth5mZnUgo2cDMMwzBMyFBlBfDdt6D33rGUi4MO8d+YbNwGAJNM\nY8bylSIK/qUMzj++Z3fLIS34PVg7YUt1mg3mFRWWzbBq42uHDvrtmSWSk4XyPSJgYzR6m4RQS7Qn\nGvn9j7BxA+dQduKVIaBXhwGvDAm/7S1bjMX+8JdBmzaCViwHvvzCXq9U2txKtJSsnB8yEjUr56Or\nieogQgCrNDZfJHxFfM/8Tc9AGaNDEk3/1buSSj7RZ0RM2FBFhRGB7ocx74EKC0ElJcCQDJ4LRAhq\n4HaI4K4mzuoS9Now0PZthrqM18a8Lh6OOKJrt3D6STMsKYuyUlRm1o3sbODIoyEaN7aWO33emzYC\no163FStT7aRDhJYqajAR+eRnRqPEafZMl4qZB70+wlqgMjjVqgXcfifEZVcA/aKGLNHvMoj8fIjj\nTgCapKFDCRMfTA42tHs3aMz7wRX7VGze7H7eNFcQvQ8AbvivNer2on7O117a33DeuPMeiEGDgVNP\nj3GwKYCGwVkcdDDEWecAcuoBL3JygOZ7WMt082pnZ+sZw0Vl8Pmfam2TlWX0e+zxwEGHRJ11LrgI\nIicHovkewBEuUdFuqktr1lij0ksVEeROTPPnmCjufQA45jh9hwKndnruk/oKL0xg2MDNMOlAwXZg\n/jzOl8gwqYbLphupDLQ6sqBCGJJxkcmtKmpMJUGUaSgWdLRLHdkgpCg4cfKp8RhRehDipp7IrWXd\nbJs9C2SSj6TI5nXHTvqNyoa3ZOBkAEhgxJk2cgQ9AHz2MSgSPew7AjcNN8H9IASoKkKR/lwWnoEF\nAP5YBLr3TtDAWw0p0Qi/z7PXNd2/RMuW4Y1BF5uBu+p33FXKz73FY+Ms09hRALruaiOK2AVRt15M\n3dDIVwE3SeUIYShaeK0Nliy2HIoE5VsUzZq5V/j8M38NmhwMqCbIlLs4MIk+hzlf52UgS6RjVFBH\nPRmviP0DDw6nn3SmVetkjyA48nf26y/tG/zbtgEP3g+aqSnhHeMmeEJQGZMSkdJHfgZ4GdAyGSdp\n4hYtgcOPsK7bjzoaePZF4MKLEzM2Jj1o29ZWRCrH16B4GTvNEuUqB26HaFVxzXVA+w7R68yR6CmO\nq0y1jsG5/wDgtDPc5bd10VVGy84BantLlNPataDrrwGGvux/jS+n7wCc54R9DjVyjz/8mLtEeyeX\niPhGjSQDt6L/KkSbNpZj0klHYiaWyHYz194QTjtMSsIGboZJdcrKgMceBg0dAmRintlMZ0cB8Oow\nYNQbrg99Jk3Z7DN3mcZEla67GnT/3cbkVgigQUN7pRpg4Kb5c/Urm6SQRNt2QCZIbJkQtWtD5OVB\nNG8OMehp98phRn/stZfFGExvv6muRwRx9LF6baZCVI1qDJUVUYN9KmHerKh6htD4b43/V6008nD6\nIYzFfCoj32Of8/i9+IBefE5dbs7bHsFsdGzYyH4+3tgM3FXH5ZJx6591iRlPqqCTFw4AELt0Oy1d\nAuFidBJ9DgMODqDqIuM1H0iWdPHV17qe9p2yJYQUDWmF29972eUQA65Un/OK5E2ks7RO/ksdpkxy\nP68wMNQ40sGgqwmN+9yei/qTjwx1LA3EOeelx1ynnsKRKhnru9dfS3yfqYJu9GWEBDmIMWlE2NGg\nklMivO57lWYlD4V5R7EvIOrXBw44MMDgUgQ3Z3kPJTtx933h5sI+7gQIHed93QjuKmj+XP9OgrJ6\nGAD0cHGkrVPHW/mvlsuz9Oxzrd9/Bwctcd2NwJHHuPfjheLeK3ymihCdOqfH3IAJDBu4GSbVmTe3\nOtcUTZqY5MEwvhnzPmjOLCM/3LdfJ3s0TNh8qYjSdsNrkSLnrNngIENcAwzcjiicBCwyjZf2D1+m\nO8lQSQkw+Fng4ceBxo0h7nvQubJPA7K4pL/zydPP1N/M0cm3BCRGfjHIGP5YlPhx6GD6btPQIcD6\nfxzPa5HpeaeE1ShEybpXmo1IyVAtcDJwy5EGsYxt924jsjIdDI/FxcCc2frRf2HlJq9b1/ncgCsN\n56Dz/xVbH14GSz+SgWHSoSPEHXdBuCnXqOTTnahp+eLdflc5OYAUxS0iUSktW7m3u2F9jAPTh3zm\nunfE6zvMG5ap4TwYFGH/rlukpIUAzZyh394pp4UwqCSRiOepPK1fszr+faYqbpGLDKND2PfeH76z\nHnvdEyo9IrhV6/h03yfRnA6K/fa3Hh9xFCCp/sVM/frAvQ9418vO9n+/8auu8YM19Y849/xolH5Q\nFBLl4sVXIIaNAPLyQNu3RU+8+Ly6jf17eafPCcKN/4Pws690xlnhj4FJKUL9lhFRPyKaQkQFRFRI\nRDOJ6AYiirkfIrqGiETVv5cd6jxkqqP6V6y6jmFSGpYlT2tolim31bSpyRsIEz7l5aDp05SnhNNm\nm1fE0tIl1uPSUktup2pUEkQ7dhhyRiNfU1+TZjh6ZY541Xos3yPTwdgShFq1owvStu0g7rxHXc/v\novXwIyAOPAgir46lWOTWMhaBbjKppkWidvRzKgQ/KBZZNPqdJAxEg23brMePPGQ93uShIiH/HtJ5\nE1wH1abH3DkJH4bld5OMXF+y4Toyhr32tpar7perVhpRA2730l+ngW65CTR4EDDm/ZiGmhBeHQp6\ndah+/bCMqTrPI0n+T/zfJRB+7uNeMvMK41HC6NIVePIZiEsuU5+PSI0X7gTW/u3eVg2zb+t8d8Td\n90H03AfivAuAXr2NwkYeihGqtDepjld6n1RIfZIAxFnnOJ9s0SJxAwmbSocfd+Q56mNe///snXeY\nFMXWxt/axC5RwhJ2iSJBQXJUwYg556uiImbE8ImiXvWaEXPOXr2YEypiBHMAFcwKigTJccnL5vP9\n0TM73dXVaaZ7pmfm/J5nn5muru6umZ2urqpzznvoX6f60KAUkswUAlHS3diVCByRzSSKz88f8esv\nxoJaJwO3rs9wa3bJ0HuezrvAWCCry9k5nSZCuxJQt+72dXJztTGxF778zFN1kzKSHyn7pN83jTpI\nU75Q9J1iY5mpLJYWKYC+tlNn4HYPSm3NFKqYTEbhW88mhHgYwAsABgH4EsAMAN0BPATg9USM3EKI\nTgDugvup7c8A/qf4C+nqJcPYkMqFKcZfrCbwTHpiIw1sGTVYWaVFX9fVqSOXZBn796cDH75vrldm\nHkDiqcchfvlJi3L4ZKZNw9MAonrlChnxwxyjBJOcizONDXiWRgAVVk4U5du9XTQnR5OSvekWY/lJ\nkRxzdoae4mJv1wKAklLnOkGTTr+R3rsbNk0SnVaqEKtXAW+8Bvwi5YeWJaozDZXCw2MPW9evqgK+\n+gJwygXmVQreEMGdgt9bEylSu3Vr7VUI0Ii9Y+XywtnKFRCTboF46H7gm68sTy+efTr2PuzqQmUb\nILwqNHTr4c+13Rgruu6iORk1aAA6fYy2IHfnPSC3/ZTTbzfVTl8FBcCQYep9b74B3HoTxITLIG6+\n0Fz0IQAAIABJREFUAfja+jeXdfMhiz6HBulk7Tt1BsZfChx4cKzM4Xcj0tFx2kn9KAzKMMlg1EHW\n+/J9lFxNNlb3dtkG7XW7h3Ftd5/67lSRFAO3ZGjIUGOXK1q1SnULmHTH6pnrV9orR4lyhwhuFZn6\nzOxlnDObpMj3cZlOLR6clGQiEdy0ex/XpxQrQ5BG6se5xm1Vag09suN91EHRKX2ODXTYEdY73eZA\nB4BSTmeT6fgymhFCHAfgQgCrAfQhosOJ6BgA3QDMA3AMgPFxnlsAeDrS1ikuD3uLiM5U/J0TTxsY\nJqWwTTRjEJs3pboJjJ/I0dYuED/OhRh/gZZf+6orgKWSLJyUr1P8+APE8mXm87z/LrDgL2B1RGqy\nrg5C3x79IvHHM4DrrnbOYRgmtm6x3x91BKirA+TvJ3HRmNTRvoP7ugrJKADxf/5G0oQlOlHba4T1\nMV49kYFwGJddfEd0+ZVJaIgLnL4vK4P1Yw9DzPjQZNwVSZSnTQlWDhlW5TM/gnh+CsR9d9vno3ad\nuzmCXrElFTLpDRqA+g8AEFFa0EcJ6xfAZMPCm2/UvxXPu512hRtxzURP9SkvT8sr5wdujBVCaE5G\n9zwA7LGnVtaosfuFSqdcgpJzpUiF1HeDBiBF5Iz4+isInTyueO5Z63NkmUS5eGuqese/TrE/MJUL\n10H9jySFGRNhGFckA7sF9HT+DqyccOrqNCPRVRPcnyvN5OpNC/epcEjKFgO3qn8qclCHYBgntqjX\nLMTk24C1axI/v5Mqn36M51YGOt3veTmq+PQxoBtvNY+HW7Y0bjdvkbQ2mYg+o886BzTm7ODaEYEG\nDvLlPEL+fTvMOcR1VxsLOnfRXuV1JpdQ8+bAEUfZ1zn0cOfzDBnKih1ZgF89W/RXPJGIFkQLiWgN\ngKhOxFVxRnGfD2D/yDWWJNJIhklLsmxBh2HShgSjYMTWrYAcWehB5krcfQdw43Vanu7vjXkOxYb1\nwNatQFUlxGuvQKxbF14JZhVOagfffatF5V14LsT99xj3tXPIPxlmLCacyhymVr+VeAfv8uJo1PnC\nbujWMo7IhzBMqt20QZ4UpwqntqqUIKZPg1htY8hOxwg+t1gZFN99R1kspr0V27DKHQZALFroqRni\n999i72W5wWRx3oWghx8Hrr7WWK7/TcmL6bIxXq+WkS5UVgIfzwSuvUpL2eESatBAi5q+5P+A0lLQ\npDsSb4uXe03ug6V7nw46GEpkRzkZKfrHNid2gAi3UZiW857sXpiiR54APfaU8yKhG0NnUFGimxSO\nvHYGOyItUl1W4pFx+s2ms3HXL9L4O7BSbEJtLfDaKxBejL5pZuDGEUeB9DnDkxLBnaVrS38vMJf1\n7Jn8djCZhV1E6803ACtXAs/+F/j6S1enk8do4q2p9o6y+v4xWyTKDzxYc0ZFJHXHHnuq03S0aAnq\n0RMkBMgvx1UrnPJlR5/RRUXAUAtVIz9pHVDakkrjvJBOO92+/okRRUApH7pbhJyqTUWDBra76ZTR\nQBKcCpjU40du7PYABgKoAvCavJ+IPgewAkBbAJ7uZCFEFwB3APgKmtQ5w2QhWToJyVSmvQXM+T7V\nrWD8YN3ahE8hovJ7UTzKSwsi4JUX1Xl4J90MXHKRsSwVud3iwckosHEjcOft6n3ptrilx2LCKcrL\nzYVWn9Mvp6jmzW3bBADwILNVT6rlct3iFBWZLBwjuM33ipg+zf6YZQ4GsXTl118gLlMLRjl+JwDE\nJheT6HRD9fuxM3DL/cp0tWNAaFm1EuKScRCvvQyxfr2WssMtt98FTLoTiObR8xBtQKeOBqmkTlUO\nKADo+BOdTyo7K1ksVonZ39j3+/L/uJlDjuZUozJCABx54Vp61EW9oJycVP2NnYT+9GkQt98K3HCd\n+wV8Fdn+2wBSkwojaGprPTuXpaVUu94wY/HM8BVZASpdxuUqNm0C5v3h7jP8/JNhkx58FMj1N38y\nk4XYzIVFdTXw6ksQs7+BeO5/WlCCE6ooYzu5c3KWKKdduhkL1ivWjdKJpk2B628EjbvYOc/0ZROA\nhx8HDjks2DY1aWK/P+hnk9wHHjDKl9PSyH2MBW3aGrf3Gml/guicIycHdMVVvrTJhJTOTQ+VlAIj\nRvI4MUvww3Wnf+T1dyKycr/9XqrrSESa/L8A8gCMJfK0YjtACDFZCPGEEOJ2IcQxQog0HO0yDDiC\nO8MQ702HeOpxzZuTSW/0kq+JoI9qiseYsGOHMppXlJWZpUidZK7CwhL7vFVixodKj04qDUF+50Tw\n4lFt5a3auXPclzd4N++7v/ZqMSGg0Wca9tExx7m7SLospKWLgTsep5UMXdATDz/g/0m/+VqLvHAi\nncZqtgZu4+9efPSBu3OG5b5+7ZX4jy0qMuZycxkRSW3bAiP2Vt9XFtKVkBeMVMjPA7v2bN9mvU/+\n38iOdSFD3H0HMPOjVDcjfXEzjggibUJtDcSV/2cut4rOBSAiyhpi8ybgmaesz+3UvzjlKM0G0jiC\nmyZYpJHYsgXUwUPqHiA9nVx1aQXEt7ODv578HaUijYofVFRAXDVBU/K6507n+q1bG7fT8bfChI98\n+zmV0Dt52T3noqjmddEyIu1+ragApjwDTL4N+OyTWD2r5/+/TjW2KV0CHuxo3UZzLnDz7EtGxLpu\nXK+Mai4IuL9Zvty4HackuInuPYzbXtdH9P8fqzWlHtZKGlTc2nJfPaXtQSeb0/fQ6WcCE69h43YW\n4ccKV0RUH//Y1ImGinSxqSNzEYB9AFxFRH95bNMRkT89y4UQp0Uiyl0hhDgTwJlu6n722Wf9+vXr\nh/LycqxYscJ1QxlnFiyw8OTPEpqtWQN9zEa2fx/pRneL8o3vvoP1++yX1LYw7nB7j+20di30Q666\nvDzkxBEVU3nHJCw97QwAQPc4osIrduzAto0bUeyi7pJff0V1WKSXbeg47W0UxnGcWLECf6VxH5m3\ndSus3CZMv0siZf+yoKYWiPc72KU7xCWXazK9izQngw6//AxV9ss169Zii+46Tct3oK2inszirdtQ\nE4L/Ud45F2DnJx+13L9gyT9JmxDZ9TnNN26yvbc3rFmDMul4q+dOlH9WLEdVZRpKTzvg9LlV37N8\nzIK//qr/vzdYswadnn/W1bUrbvoPlp0y2vq8IfjNR2m1aROi8SHr163DRl3b2u7YgaZSfTff29/z\n54FCEDnX/Y/f4zqubOBgrHfZx8pU1dTgnwUL0LmqEvI3IBT3WUWbtlj6j920WaNrHUG/bLd+0SLL\nvmDhwkWos5Bx7lpdbThPbX4+FqXg99h85D4o/uIzV3XF66/ir07GZYNOOQJ6t64F8+entVHPiV1y\nc5GjW4T20oc4/W4X//UXapyijTyy0w9zoFqGXLNiuWGsoEffTvHDXCz4/XeQYvG01bp1sMqcWdOo\nMRaVbQQ2bfbc5nTE6n+7YPGS0MrOuvnt7lxUhDxJql48eJ/3ay1enHaL2c0XLTT07X//8QcoQONr\ni7VrodcbqWnYEItDNEZxS5sP30ezyHvx9wKse+E5bO+6C6os0ifttGpVfR+1qU9frE3Dz8y4I6lj\nbpdjRQAQSxY7rlN0rqgwjSVXLFuG8pxcdHj5BTRYtw45NWqnlC1r1mCNi+ctEK55SUbQtBkKzjgL\noq4OlcWtzfOkZctABbF1Pre/Gbf/p9LXXkajOI5zovX330Gv+7R8zRrsKDAGWdh9Fv0zuXD5cnRU\n1Fnfpi2qe/REiT5tWISK/Hwsc/NZSjuYf+PFbZzTOPkA30v+UlpaioZxptPyYxQcdQ2x01WNupW7\nmkkJIboCuB3AHAB3eWjLQmi5uvsBaAagGMB+AD4H0B7Ae0IIL3qanQHs7eZv27ZtzSzOwTCJkUZB\nQYyETUQXZfCiXLYgamMRJWWDhmDx2efFdZ7CNasTkg4XdXWgHHe/p3Tx2M2pTJNIc5+pdcghZCCg\nBTzKyzOcu2il2mmPpIXUKpeOEzXNwjFcqmnaFFt67mpdISwLpDn27YjnnvaUzzLLyKmqqn/f7t23\nXR9XtMpalWWdm2jdJKK/d2WVD4pT5lbUpe9gdeWRR2P9iL3NOyz6gEqpr6NIzsMCN3nibM4rQ1K9\nnMpKbOmh7rPavWej/iL/j1OUy7rOowNE+1dfQu62WGR6VQvj916QiSkFdGzRSS5u6tvP07HrRuxj\nuz/XYzocNxRa9IEmJSEb8lTpWIjQYm4stdO6Eftg4XnjsOic87FuxD5YfvyJoTXsJpWwjFnipGyI\nTzlJ0/B7qGnUyLAtgkohEKGB5Eydt93//iAZNPvtF8N28Zefo/T1Vyy/v+a6fsR1rmKGSTJCoUjS\nfupr6H7vnShatdLSuA0ABRsc8kAzgVLVqhiVrdson0MkpYZYdegR9fOxRWefjxqLXO6NXKbpyKmu\ncq4UBxXt2hm2Pa9h676LOot1rpyqKmzr1kO5Tzk/c8HKw46M6zgmvQmdRqFOmjwfmjS565U7InpO\nUfwpgE+FEK8DOA7AbQAOd3nKJdCM4440bty4H4BmDRs2RLdu3RzrM85EPWGy/vtcaZQbyfrvI52w\niSRq0aULWvD/MlR47nMW/Fn/tnlxMZrL+Y080K2oEOjUOa5jG1AdimXZNQs6lbTzT1o9pKR1H2lh\neKSTTnH9ufz+/DRyb4gvzEOhtnvvi7Z6Y3W3bqDVqyA+mZnU9iVE9x6W+UGT0U5Xfc5S+0jPlt/O\nQouCfOBUhRyaBR1LSjK+H1Dh5n/atVUroK2mRaBKg+D2/NS+A8TyZQCAViNGolUHlc96ipgXG5u0\nLCxES127xd2TTdVdfW9dOvsnh5dk2h1qPS2kkhIIXUoZ2nMvFHTZGXh+Sn1ZA49zvwZFRe7qS84G\nLfbbH/j+O+BPc5/V6J8lrtuQV5Cfmn54lTeFs4bLlqLr4w+DrrsRKC0FGhv95Du9PVXLmZ6pfDur\n/m2zXrujmZf/2TL750bH0lL/nwFF6miL1q1bo7XLtnfesA4YPNhYGOlHo7RqXYxW/SNZ7wYOgjpW\nM3Oh/gMgfvzBVN6tu9t4sOThaV7lMNZxS6jGmG7p3Bl4b3r9Ztf27YEWVpoFiSMWGMUxRV1den5v\nCvK3bcMu+XmAYk4udClDmi37x1ufyqQF6bB+bGrb5s1a2rHCiHZdAs4XhSdbrxeQEAaHszB/R5kA\nHTAKYuaM+u1uPSQDbrduwEEHgwoK0EUIoKgQUKiWlL75Ouihx5TpCA30Hwisij1HfPv/du0KfPBe\n/WaHjh1djx+p2U7GduyyCzDFnParxcBBluvi7ffZ171j8KWXA8/9D+jcGe0OPyJwh7d06G+yDT9c\n16Ku1Y1s6kRXPKyTMMW4GMBIAJOI6Benyh64KfI6SgjhSvOHiJ4lon3c/PXr1+8nH9vKMDH++tO4\n/auftwUTJOKBe6135rnqhpgwo/cQz8sDchN4pLo0pFA3xQJWZaX7/IPbbPJ0homAoxdCiyIKiY46\nBhgxMgWNiaCIGKTmLQBVJPaJJ4NKSixPRUcf62fLEicdor4ccrsBgPjyC2BZRILLjfT4qlUJNiqD\nqfLJA14fYRG2nOc6Q4L4eAawZLGp3BZVRGZtCFQBPDokAC76pPPGgfbeFzT2XND1NwKjzwRk+VOv\n/YjL6Ach5y5u2NDSIUfJ2jXA66+aZdKPPMb9Ofwk3oWmO2/XXkn+jaVfpKYnDGNMn1WfghhjWUUP\neVAZEW9NNRfKfXI6PLeD5OjjQAMHGYqoefMUNcZHPKoUqKBBg50rhZH8fGOeUb/GISqyYX7l4jOK\nsrIkNIRhHJjzHXD1FcBVVwBr1mhltQnco3bKZKwemVz2PcC5ToMGsbFxr96gjhbO0G7m9rq5GR1x\nlIsGukQec3lQ5YGcakMV2d5lZ6BXb+39vvsb97Vt523u0HNX4NbbgXPOT0s1FyZx/JghLIm8drKp\n00Gqa0d01j1KCPGZ/g+xfNjHRMqmK8+gZn7ktQDIOmdfJo0Rc+cYC3QeVEw6k75ynkwE/QQkNzcx\nubMftPuc2newr7d4kbmsqtq9ceHD9z02LInMnQO8+jKwbh1QbZTfokefBLVpk6KGpZhDDrP02qXB\nQ4O//kbFIpCdM8dFl4AGDgIdfiSoR0/jPh8WMH3FYqGczrswyQ2xwaUzlLj1Ju1/5Ubev2xDgo3K\nEKKLSXpspP88UaMz6rhwUkgm4rdfjQWTb9PUI6RIyXqItAXjTz8GvvjM1D8DSCjNhm+8ayPTLUFd\ndgadMhrYf5R9xTZtgH+dCgweApSUamXygo3XRUOnKAwrmu0EHG4juScvOj38IMTMjwxF60bsDQxJ\nwnNDSXyLTaIikpdXVjiRjHwZh2GM6fE347QAGcT9aqVS4fVacloUWba8IjtT2NTTpo22eKtn9Jkp\naYqvtGoFOvcCT4eQTn2DjjsBOO0Mv1uVPPS55wOSmg383MlEerYZcNHnUN/+PjaGYeLk5Rch6uq0\ncc5HH2hlcT6f7RzMAQDtYvupuDiuazAeaNkStOcIUH4B6Njj3R2TiLOwHHgTBty048qrY+sx/aR+\nuf8A/9vEZDR+/PJ/jLz2EkIUEdEORZ3BUl03DLfZVxL52+zhfPrEXWkSwsYw2iKc0Bu1dPJKTBpT\nE4LFYCYx9BOQ3NzEPGOjA0AHr3NlXrHddnMdwS3+XhBO14rVqyCefAwAQOvXQ8jRC0IAu/VWG6Sy\nmSOPBr7/NthrrFtnKhLr11v/jlq0jC2+/vUn8Of82D6XueKThipi/oijzBOsVOLFJjTlWXcL3dkQ\nweMGVc7YRL6b8nLgpx81adFEjFNJRhCBKiut/e7q6oAvP4d45SUAAO1QTPV+/xXYK4VKEwCwzY1Q\nWITTztBkr/3Aa0SpbCB3S0GB/bEby7T+N4JYs9pcZdAQtEqzCFiKfmbZwN08OAnfUKCfJ3hdrJTy\nHNK9DwJPPAox7w+tIAgDd0O1RLnna331JXDiybFt+feq+F1nPU2bproF/jBgIKhPP4hfXAojHnwo\nqHVroG07oHOXYNsWNHoDd6AR3Bb3Y11dWqkjiNdftd7pJgJW38cwTIoQemW9tdEI7jifz04O0WPO\nBk26BQCZnaSYYBh9BnDKqe7ngR06AiuWK3a4WL1LloG7RUvnOlFMykvSbn0EO2COuo53vsRkLQmP\nYohoGYAfoEVGnyDvF0LsDaA9gNUAZsn7Fefbh4iE6g/AjZFqD0fKdvLQ1BMjr38SkYcVEIZJMR2k\niE476RkmffArSoxJHat1i2x5eYktDLTvAKxeDbE6Dungxo2BNJdaEzdcF3tvtbC1k/MjnyISR5kC\nOS1aNrbLDuMTiUTJyc+vsC2eSaoLtO9+wGHB52zyhIe2iHl/uFsYCUO0rd+ojK4yc753jmzULRCQ\n1/HW9ddATHkG4r67jfm7/ZYXDoK6OojnnrXeFzFuA4B48w1znZdfDKZdXtAbCJxIJKXIZsm/etFC\nAJGFGjfEsfBEe+4VufYm60pVLsaVqeyDd41z/tIxIhInG7gTkfEMM1s2a85h5dtjZV5/M712BzXW\nMsTR3vsCRUXGcwTx3cn/n/prWTxvlIu4MM+P5OPd3mfZRCIKUmHDS99cUAAM2yP9jdtA8gzcVvej\nTznQQ4GbMW6mOIUw6YfVmk10fBbvHM3JGFhSAtxxF3D7nbFxFRM8Xpycjz1ePZeodjFmC9CxmsZf\nCtq5K+jEk12tx9WjUNwhvYH84EONO0skx2Mv12IY+CNRDgCTIq+ThRC7RAuFEK0BPBLZvJ0o5sIh\nhLhICDFfCDHFjwYIIToKIU4RQjSQyoUQYrSujTZJcRkmRcyfB1x9JXDf3WbpRzmayGoBgUkvMtHA\nkG3oDSo2nu/UVJGrWGb7duC2mxyrGXK0RampgfjiM+drAKAmTVzVCyU7ucgxGCbDpB84GeMKkrDQ\nq8r77hbZmzxsBu4KySiajO/TKyXtvdV3Y7jIxHGEZOCm0WapUvHU48DCv+3PozeueOxPDJEYekIW\nwa3MO/3VF9YHuBivKNVFko0nh4QEnhW79zGeKfrZdXK5tsQTWRFVv7B7Dobd4NuqGHT2ufEfL/db\n27er66Uz27ZBXHk5xD13QujVT7wadZs3B26dDLrmOuDkU7QyvYJKEHMQq3Nalf/ys7p8g5RCgx2C\nnWngwbkn7GTaON4t+udCkCoFVs9q+XvfvBl46gnguWfd5X9NJo4pGBRjXPn5ERYJXyYjoONPBBUW\ngdz0X1st1DiFAIgg4n0+N2rsXKeoobt6TGpo2hS4535zuZy6RYW+b/c7NVav3pqU+H4u8orrUXXV\nF14EKinVglIOONC4r3Hj+mAVatAAGDosvvYyWYsvK41E9DqARwG0BfCrEOIdIcRUAAsA7AbgLQAP\nSYe1AtADgEXCJs+0APACgHWR/NwvCiHeAbAQwBQARQAeIqLHfboew/jHqy9DbCyDmD8PmP2NcZ/s\nseUg9cGkCWFYDGYSQ+/9nZ9vvShz5FGgU0fbn+vb2WZZbhXDFNk7vPyWuuzsvm5IqDfKDx7iXDls\nizBxQG3bxTacolISkcV3SyKLjXl5oMIiANAm/Y2SEHHuBf13DYQzMqyo0Ft9NwsjLlMapBXygucQ\ni0nxqy9rr/8sAd6aat6vz/dq89una/8DGnexu7aFbSG1ew9TkTIqO0q6OETYtJP+dapcEv91rCLF\nh9pl19KRH4cxKuoc1HNXUEefch2ngkFDQMef6FxPDxEw48OYvHYE8eH7PjYsJMhzwChe1AmiNGig\nRWlF+zH9eCGICFGvEdxWDm8RRYR63EQuZSEUWRimXr2BVhmUTzUd+rEgWKEzYKxcCTz3P+Db2Vpq\nOr/WDLZtg7j2KvW+Tz82bn88A2LOdxBffwV885U/1/fKt7OBaW9pqhZ6nMYkqrHb1q3OdRgmXg44\nUDNMHn9S/OfIydF+8/FSGMI5LOOd3DzQf6SgFzfPxZoQpsbadTdzWfsOwPU3AuMvVasOjL8UdNsd\nwD0PhOdzMGmDb6E0RHQhgFOhyZXvDeAgAH8DuAjAcUQU9Gh1GYA7AcwF0BXA0QBGQfuMrwDYn4jG\nB9wGhokLoffKkiWiOII7M4n+X1evVsq3MGmA/t60k9DJywdG7G1/rrIN9vujqBYEveRzT8eFo9PH\naK85OVoeSTsGDg6+PUFz7vmgps1ALVoAp5zmWJ3atElCo+JECGDsOaDd+wBjzgYKPRprg0Z2EIjH\niBA0Xid3rgzcCRj3wors/GdhPBFL/9EiP2+/VZ0SYpONBHT0Ur16axP0Hj3dtS1svyuvBvd0cYj4\n9VfrfS1bgbpqImPUtGkwBiGrHMQy8eSUi0Zj5OQAV10LmnSnuU55OfDm65rjhqwGBaDSS968INl/\nFOjSy11XF4sWQrzxWoANChF//aku96MP0feJa9Ykfj4ZrwZuq36otTSmkX/LGfj4iovjT9QWgS+6\nJNUt8ZfFi1LdgtTQt1/9W/HFZxBffwnxzFMQV/4fcO3V/qwV2Ci1iNnGTJLiow9iG/r3yWLxIu3z\nvzcdePtN4z6nca6qLwpS9p1hAO0ZO2y4c7oaK+cuISCefTr+64dRhYyJj3YloPa6NG9uAjiSlYNb\nAV14kXrHUUfHd8IWLZITxMFkHL5qRRLRi0S0JxE1JaJGRDSQiB7WS5Pr6t4QyaO9j4fzR48x3UFE\ntIGIriSifYmoAxE1JKJCIupMRCcT0ScJfjyGSQ5LFhu3t0kep2zgzgxqaoCPPoS44VrgmiszU2ox\n09FPsCNGKJJzyQD1g0yS88roEG7v6w7myC3xwxx3xwLhNHA7fXa97GxREWjYHqYqlJen5csdMdLn\nxqWAklJg0mTgltuB5i2c618QGxLR2ATkX4Ni9z7AuIuBIUNT3RIzsqEpjIsDXvOsu4n0SYXBcs0a\nLR1LUGMYuW+zmRiLyy+BsJK4dFiYory8mEHBrdEpbJFCXp0m5MgnJ+bPA778AqhKoqLG7FkQP/9o\nvT83Fxh7LujY44FLLk948YdKY6kDaMJE7U1BAWjM2c4HyxGqbtCne8jJ0SSoZT54D+LDDyA+eA/4\neIZp96b+A71fNwiE0CLRr7m+3umA0RBWst1uUt04sWF97H0Qzma6vp30ai1eI7hlg7bCWYOJ0KJF\n+J4vCSK2WMj3AtbqFZmAzXhCbNoI2KmsuESoVGvckAqnyK9jUePiaymC3GkcqepzymPrLCSrNzGM\nXzRqBNxwi20VcetNwIK/gHff8ffaawNwXGNShli+LLYx93tg2VLg9luB//1X6wOJtPnEypWatP0P\nc2P1k60c1qefpmx2yyTQIC3YhIYOA8LiWMtkDRzzzzDJ4O8FwOpVwOChjounYtkyg3O6WPCXsUIm\nRl5lEhUV2iR11Ur7erU1EFO1iBRRXg6a9bU5DwkTXogg/vg9th0dSB59LGjf/SEmXm7ed8YYYJL9\npMdwibw8c17TXXcDtWgJ4TbiWyaMDjJOaRdkI6Qqj/hd94UvOjgRvBig2rYD3TJJy0GscIBgbJCN\noGFcJy5oALrgIuDlFyE2ljnXnz/PuY4qP2GQLFuqLegAoEMPB46M06PbDp2SRUKqBk595MmnpL9B\nwevCx2efOlah6DmXLoW4726tbP1a4JjjvbbOOzU1zlEv3bprn/vAg/255hVXgT7/VIsE36VbrHzo\nMNCSRRCfWvtVi2VLXQWhUkkJxMrIWFKRnoSOPaF+HAlAS3MUfR+vISOZdOwIjD1Xc/KMQDk57h3+\nsonGPuTM3K1XLDp2zvf+3QtR9Eal/AIAmkFJfDwDdIJCttWqH5LHvZyDO6ug/AKIanO0Le2xJ3DM\n8aBrroSorq7P05kxOCh7iM8/VaTaSBYpWHuyc2xxcrpTOXHq08+sXxdfmxjGDc2bg449HmLq65ZV\nxN13mMt+/y2hy4q//mSBkwxF/PgDaOHfmgPYksWgHrsC1dUQL0xRH5CK1FjRiPOx54KOOiaYxXE1\nAAAgAElEQVSzUqcwaYOvEdwMwyjYsAHirskQz08BVPniZn1tfawqwmjTRv/axvjLn/OBiZcD114F\nvPO2fV15AUc/8WLCTXk5xAXnGMv0jitNmxqjV6KRVp06g26dDACg3fuA9rKONiYh1N78ubnAtf/R\nBo7xEMaFY6/GtnKF2kE8kq+ZRKtiNm77QctWqW6Bmr79gNNOd1VVTHvLuVKy+wGdgVS8N93/qAUA\nqNU9UxOJxNd9NwYnpihFLmWow4zHhQ/xubOBG506a3Vvi+WNEx8mSdZUTu2jwu/FnsJC4KBDgIGD\nzPv8ktUbex5o8FDQ6DMBVcTZqAOdpTB1NPlrvj/t8hN5nHP3falpR9jxw6lGn8fWD4O5jP65kifd\nAyplEd0cl/Ty/k4R3Dlp7mDE2GOhxCS++VpzcB13saaW9S/nFD5pRVhSmRBp6xmGshS0w87APec7\n+2NVEdz6/skPRQyGscNtCiOGcYlB3WT+H9bGbQDYti34BlkhBFDcOv2dwZm0hA3cDBM0D9xT/1a8\nN924b8sWiP89YyiiPUfENhQeqOLvBb42j/EPce9dEJWVEGVlED/ZSGUC5sUeeTLJhBdVZFTHTrH3\nQgCnjAZ16gw65LD6hX8AQMuWoMee0iSbVRKjUfLyICSnB4oacRs2BIYMi6/tYZQodyOprEf/XUex\nkrpkAoF265XqJviDvIBW6UOOw6DY7JwbWgXlaIYGGqyTiE+2RLn0vYp33vY/B+yypbH30bQQhx3h\n/TzRRVArmVTJuEJddvZ+jVQThIHISz8eldbzC8VzjUbuDYosMNIpSTaECJ+eR6WlwNhzgD33sriO\nALr3cH26hvp7JCzIjgdenVP8/B2FGT8WCvVjp0AkynX3YZ7kdGh61lYCn3wc2+65W/1bk1LJ95Ix\nq4NiDMhkDk4Oqz13BY4+FmgVUofEeCkqSnULNOZ+D3HvXVJhEvvZujrggXvt0285KRWpHKefeDT2\nvqQkvrYxjFv0+ZOTBJ2cKoUHJgj0amTU2qhMJmbPsj943dogmsQwoYclyhkmYITVIm5tLfCnYoCu\nX3is9mj4YVKHG+lYPfo8KQDEwr9ZVigdqKuD+OIzQxHts5/ZwDpwkDqyS4+dUTYvD1TcGmLliljZ\nqINi71u4yM2sorZGy9dT3Fot9Z0KvMpPqqIc2Es0uYw+I9Ut8Idu3Y3bLUMsp9WkqWGTuveA+OtP\n5+MeeVx7tnz/rfYHJD+CWxUJvXYNkIiUuIR47n+xjRXLtddDD/ceLR79bqwkLHOkyMRBQ2Kyv2lD\nCg3ca9YAD9yrPf8uvxLYaafEr60ych51LNCoEaiqKvmRcb/9arubdu7q37U8RIuv3fcA/67rF/nS\nUoRXZzUifv67pVEsalv8MNf/Ocd6XY7vAslIKTtVzfwIYvUqXdskZQzd/9UwDgasHT6YzMAvBYx0\no29/4KUXgjv/JndOkuKpJ8yFyXIk2rABuPN2Lee4HQscgj1kp7dPP4aorIxty+M4hvEbH/ox2nU3\niHl/uD+gJ0eNZxTD9gDefhMAILzmV9c7tTNMFsEhTwyTChYvghh3HsTTT5r36QfldvJMTLgo3+Gp\numGixaQPjz9qLiuO0yjmYOA2LeoN3yP2XgjXnrp03gWxw5YuhbhjEnDd1VpUzNtvul708JWyMi1f\nPeA9gnv5cv/bw7iGBg0BmsfpYBE2CgtBB4wCAFDv3bWcsGGlV29Qt+6gvDzQ6WcCpe29Ha9f0Ety\nDm5ZjSLw61VF8nfGs8AUNXBbHWvqt9PQNa1ZM1Cbtr6eUkScCkh2GlmyOPa+rg548TmIDesh1q0F\n7OT1vKBafI+mCUmF7KtV9H+U/Uf5dy0P0uubd+/j33X9IjevXhGE+vT1bqwOoypNWPlnsXOdRFih\nG5vJEdzSM0fIaZwKpehVCycsKixixZ5MZ2icClXpTtOmznUSQVIRpIsuAcmpjdyk+wiS5//nbNwG\nIJxUiKT94pWXjPuz1YmCSS8OONBUZDt2z+XYxYyiWQIOwEE/TxgmpPAMgWGCpNbCcGMXUaRfrPEa\n2cikDqv/NZM5LF4E8bNCer7fgPjOZ+fkoJqk5EsL9W4jsBWTIVFRAfH0ExDvvws896y78/jFTz8C\n/54IXHWFZgiwUaqgM88yFw4aHGDjGBUGSfLu3a0rpiPHnwR6+HHgoktS3RJ7cnKA/7sCuOs+YI+9\ngGYecwjqjQLJjuBWEdaoy+h3Y2U4lBdG01EiOScHuOQy2ypxRRlXVgLl241l0ej9r7+CuPBcCH06\nlt8Vkf3xEIbfs54mxvzGJN+rxa39u9Y6C6UBBeQk/Zsqxo0HTbwGOO9C78cuWpie96CKoH/H8Y5V\n3dKiZex91MEkitNny80F6fvcbVvVx3jIO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LDv/urr2HkNJ4NVKx2rUEFB6tvJMOmIyqir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qliU9Wtlame/sy3YmlzM3JeV6Ns459Ver+1kJ7arh4ZsA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WXZ0hrChib99qrbfi/tULB9YWTBZnYGg1LUbED6XS432l3ONllmUWxtfWpT2pTg1lCV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\nKeVkgMeujtJI/qEZySvRXI3URAUcOWORWoWOyf7WoSh+67uZa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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [], + "image/png": { + "width": 1004, + "height": 213 + } + } + } + ] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "j013lhwnoUxt", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "outputId": "c0bc9df4-f14e-4483-df0e-b0ba30b51ef2" + }, + "source": [ + "# plotting the Posterior Samples\n", + "\n", + "plt.figure(figsize=(15,12))\n", + "plt.subplot(3, 2, 1)\n", + "plt.hist(sigma_, \n", + " bins=100, color=TFColor[6], alpha=0.8)\n", + "plt.ylabel('Frequency')\n", + "plt.title('posterior std (σ) samples', fontsize=14)\n", + "plt.subplot(3, 2, 2)\n", + "plt.plot(np.arange(number_of_steps), \n", + " sigma_, color=TFColor[6], alpha=0.8)\n", + "plt.ylabel('Sample Value')\n", + "plt.title('posterior std (σ) samples', fontsize=14)\n", + "\n", + "plt.subplot(3, 2, 3)\n", + "plt.hist(beta_, \n", + " bins=100, color=TFColor[0], alpha=0.8)\n", + "plt.ylabel('Frequency')\n", + "plt.title('posterior beta (β) samples', fontsize=14)\n", + "plt.subplot(3, 2, 4)\n", + "plt.plot(np.arange(number_of_steps), \n", + " beta_, color=TFColor[0], alpha=0.8)\n", + "plt.ylabel('Sample Value')\n", + "plt.title('posterior beta (β) samples', fontsize=14)\n", + "\n", + "plt.subplot(3, 2, 5)\n", + "plt.hist(alpha_, bins=100, \n", + " color=TFColor[3], alpha=0.8)\n", + "plt.ylabel('Frequency')\n", + "plt.title('posterior alpha (α) samples', fontsize=14)\n", + "plt.subplot(3, 2, 6)\n", + "plt.plot(np.arange(number_of_steps), alpha_, \n", + " color=TFColor[3], alpha=0.8)\n", + "plt.ylabel('Sample Value')\n", + "plt.title('posterior alpha (α) samples', fontsize=14)\n", + "\n", + "#KDE Plots\n", + "warnings.filterwarnings(\"ignore\", category=DeprecationWarning)\n", + "plt.figure(figsize=(15,9))\n", + "plt.subplot(2, 2, 1)\n", + "ax1 = sns.kdeplot(sigma_, \n", + " shade=True, color=TFColor[6], bw=.000075)\n", + "plt.ylabel('Probability density')\n", + "plt.title('KDE plot for std (σ)', fontsize=14)\n", + "plt.subplot(2, 2, 2)\n", + "ax2 = sns.kdeplot(beta_, \n", + " shade=True, color=TFColor[0], bw=.0030)\n", + "plt.ylabel('Probability density')\n", + "plt.title('KDE plot for beta (β) samples', fontsize=14)\n", + "plt.subplot(2, 2, 3)\n", + "ax3 = sns.kdeplot(alpha_, \n", + " shade=True, color=TFColor[3], bw=.0001)\n", + "plt.ylabel('Probability density')\n", + "plt.title('KDE plot for alpha (α) samples', fontsize=14)" + ], + "execution_count": 15, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'KDE plot for alpha (α) samples')" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 15 + }, + { + "output_type": "display_data", + "data": { + "image/png": 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q0rZT3vETVF4rryrnvxoRi2vWHVXOz6la9/LMbHq7emZeA1zTzsZ7enoW0+Zd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Eacc0+Y2b2S7pf0qJl9V17S/ypJnZIenxUPAACA7zY/uDnj9o13byxTJAAA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\njrPo8EVMWziNyTMnl60UVBeyHaYpw0uHu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tH1NVOO84HnAH+dUnpn0fv/C7wsImYCJwFv\nBc6tsn4AUkprgbW1lgPYtGnT7cBwPcuOtcK5QOOXMSBjQGAcyBhQxjhQl8TAiOsAbHzvXsups8u5\niRMnAtDf39/w+ASjqgOLJEmSpA7SzO/F8fxbs1M/e6eWq35Tay+iJm3Ip8uqLLOkZNl61rd0hOsr\n9Fw3IyKmVxj3fY98EdEP/FH+8qIK27uYrPH9ROpofB+hjo3RjqyT0JgyBmQMCIwDGQPKGAfqkhgY\n8fWVje9jp9V37m+gzi7n7r777sMmT57cP3HixPuB3wFce+21ax555JHhqVOnblqzZs219axHvc2Y\nUDnGhUoZEyplTKgc40KleiQmVpFds93c7oKMAz/PpwdGxOSU0qNlljmiZNlqbgAeBWZFxMqU0o1l\nlnlK6fpSSpsi4kZgZb69S+rJB8wFBvO/K3U78mA+nVW7+CN2M9lwdo+Q1wF0ih45F6gJxoCMAYFx\nIGNAGeNAHR4DDdcBREqp9cXRHiLiNODLwM9TSmUHWIyILwAvAN6YUvrgKJdnHdmT8z9IKT19NLel\n7mBMqBzjQqWMCZUyJlSOcaFSxoRGKiJ+BhwKvCql9ImSeccD68jGUF+UUtpRx/r+B/gD4NyU0jtK\n5q0A1gNPAPNSSg8WzTsf+HPg4ymls0ryTQduA6YDB6aUfpW/PxF4iKwB/uSU0nfKlOedwF8B30wp\nnVqr/L3Cc4GMARkDAuNAxoAyxoF6NQb62l2AcWS3O/crLDOSO/clSZIkSepV/5xP3xURO8fYi4i5\nwIfyl+8sbniPiDdExA0RsVtjfWFZIAF/FRFPKcozFbiArH7kQ8UN77n3kT01/6r8pvpCvgHgP8ga\n3r9UaHgHSCk9Dnw1f/lvEbGyeIURcRLw5vzlp6vsA0mSJElSl7HxfYyklG4DrgEmAi8unZ/fub+Y\n7M79K8a2dJIkSZIkdY6U0ueBDwPzgesi4qt5b3HrgQOALwGlPcbtBexLmbHdU0pXAecAQ8DlEfGd\niPgscCPZkxY/Ad5eJt9twB+TNdx/KSJ+GBGfJuvO/WX59LVlPsJbyIaL2xe4PiJ+EBGfjYhrgG+T\nPRX/KeDCuneKJEmSJKnj2fg+tkZ8574kSZIkSeNRSuls4BVkN7IfD5xM1tj9BuCFKaXtI1zfu4Fn\nA98n63nuecC9wN8Ax6eUtlTI9yngWOArwP5kw8U9AfwLcHhK6e4yeW4H1gDnAb8EDsnzLQG+C5yR\nUjrD639JkiRJ6i0D7S7AeJJS+nxEfBh4Hdmd+98DtgEnkHdVx5537kuSJEmSNC6llC4GLq5z2fPI\nGrurLfMt4FsNlOMnwOkjzLMJ+Ps8SZIkSZLGARvfx1hK6eyI+BHwerI79/uBG8jGmPuwd71LkiRJ\nkiRJkiRJUvex8b0NRnLnviRJkiRJkiRJkiSp8znmuyRJkiRJkiRJkiRJTbLxXZIkSZIkSZIkSZKk\nJtn4LkmSJEmSJEmSJElSkxzzffxaC6wDNrS1FOokazEmtKe1GBfa3VqMCe1uLcaE9rQW40K7W4sx\nIclzgYwBGQPKrMU4GO/WYgzIOFCPxkCklNpdBkmSJEmSJEmSJEmSuprdzkuSJEmSJEmSJEmS1CQb\n3yVJkiRJkiRJkiRJapKN75IkSZIkSZIkSZIkNcnGd0mSJEmSJEmSJEmSmmTjuyRJkiRJkiRJkiRJ\nTbLxvcNExBkRcVlEbIqIRyLi6oh4fUQ0dKwi4pSI+E5E3B8RWyLilxHx9ogYrJHvyIj4YkTcHRGP\nRcT6iHh3RAxXydMfEa+LiB9HxIMRsS3P/82IOL2R8qvrY2IgIs6OiCsj4qF8e9dFxN9GxORGyq/2\nx0RE7BURZ0XEhyPiqojYGhEpIj5Y5/ZGHEuqrVvjotl4UmVdHBOHRMT/jYjvR8Q9+e+J+/PXr260\n/OrqmDg1Ij4WEddExJ0R8Xj+u+KqPFamNlJ+Zbo1Liqs66Q8b4qIrzVSfkmjo9XnGo2eiFhbdC4t\nl26okK8vP6ZX58d4U37MX17HNhuKj5H+z9EuEbFvRLwpIi6MiBsiYkd+fF9UR94xPV7RYB1C/hkv\njIg78t8Xt+S/NxbU+ozjQSMx0Oj5Ic/rOaLDRMSEiDghIs7P9+lDkV1rbYyIz0fE02vk91zQ5RqN\nAc8FvSci3hgRn42IX0fEfZHVxd0TEd+LiD+MiKiQr2uOZ6PnkIallEwdkoB/BxLwKPA14IvAQ/l7\nXwD6Rri+t+V5nwC+B3wOuDt/7wpgqEK+l+d5EvAj4DPALfnr9cDcMnkGgO/myzyWb+/TwE/z9xLw\n3nbv425LXR4Tg0Ux8QhwSf4Z7snfuxaY0e593G2pE2ICOL3oe12cPljH9kYcS6bejotm4snUezFB\n9nuisNzDwKXAp4DLis4d3wYmtXsfd1vq1pjI812YL/eb/PhfnG9zc/7+b4H57d7H3Zi6OS7KrGcY\nuBXYkef/Wrv3r8lkylKrzzWmUT9ea9l1vba2TPrnMnn6gS/n+Tblx/XrZPVDCXh/q+Ojkf85pt32\n3/sq/P99UY18Y3q8aLAOATge2JIv9zOyOspf56/vBvZp9zFod2okBho5P+T5PEd0YAJOLDruv8/3\n72eA64ref0cnHBfPBZ0VA54Lei8BtwOPA9cAX82/K1ew6/r6S6X7uJuOZ6PnkKb2absPqmnnwX9h\n0UluddH784Bf5fPeNIL1HZ5/MTYDRxa9PxX4Qb6+PRrDgcVk/5C2A88ven8g/8Il4Itl8v1pPu8W\nYGnJvJOBbfn8Q9u9r7sl9UBMvDufdz2wvOj9YbKK8wRc1O793E2pg2LiaOBDwB8Da4B/pL7Gk4Zi\nydTzcdFQPlNvxkR+PrgaeDEwWDLvScAd+Tr+vt37uZtSN8dEnm8NMK/M+7OKtvff7d7P3Za6PS7K\nrOcCst8YH8bGd5OpY1KrzzWmMTlma/PjcuYI8vwFu67/5xW9vxq4M5/3/DL5GoqPRv/nmHbbh68h\nq7d5CbASWEfthtcxPV40Xh81JS9jAt5QMu897GqEi3Yfhy6MgRGfH/J8niM6MAHPBD4PPK3MvJey\nq6HqGe08Lp4LOjIGPBf0WAKeCkwp8/6BRcfm1d14PBs9hzS9T9t9UE07D/TV+UF+ZZl5xxcFY113\nhOcnzQT8XZl5K/JA20rJk8dF/3guKJNvOtkdLAk4oGTep/L3/7pCeQqNrWe3e193S+rmmAAmkD2x\nuMc/53z+YrI7oHYAq9q9r7sldUpMlFn2POprPGno/GLq7bhoVT5T78ZEyTr+MF/Hje3ez92Uejwm\nnpav44527+duS70UF8Cz8zznA2di47vJ1DGp1eca05gcs7WMoEKd7Kmnu/I8x5WZ/6p83k9bFR+t\n+p9j2m2/raN2w+uYHi8ar6N8Q/7+pRXi9Xf5/FPbvd87KdUZAyM6PxTtc88RXZiAj+b78WPtPC6e\nCzoyBjwXjKME/G2+Hy/uxuPZ6Dmk2eTYWh0gIhYDh5F16/C50vkppR8AG4H5wFF1rG8iWQUUwEVl\n1ncTWRcME4FTS2YXxmYvl+8hsi4nipcr2FqrXLl761xuXOuBmNif7I6jx4Eflsl3O9ldTEF2p5Nq\n6LCYaFSj5xdV0CNxoRYaBzHx83y6eAy21RPGQUw8kU/r/S0qeisuImIG8F9klWd/051/n4kAACAA\nSURBVMp1S2pOq8816lhHA3OB21NKe1z/kx37bcAREbGo8Gaj8dGBv0XGhTYdr0brEKrl2072lFu5\nfBodniO61x7X354Lxp1W1sF4Luhe5epduul4tqVNwsb3znBIPr0+pfRohWWuKlm2mn2BIeD+lNKN\n9a4vIqaTdTNUPL/ecnwrn/5ZRCwtnhERJwPPIOsq9hs1Sy/o/piYmk8fzH/MlFO4EeOwSoXWbjoi\nJhrV5PlFlXV1XGhU9HpMrM6nvx+DbfWKno2JiJgK/F3+8iujua0e1Etx8X5gIfCaKp9FUnu0+lyj\nsfWMiPjXiPjPiPiHiDg5IsrVIxaOXdnrvJTSFrLuSCEbXqQ030jjo2N+i4wzY3q8mqxDqBqTVfKp\nfvWeH8BzRDcrd/3tuWB8qVUH47mgx0XE3sCf5S+L61264ni2s01ioJUrU8P2zqe3VFnm1pJl61nf\nrVWWKbe+5fn0wfyOj5GU4zPACWTjBf02Ii4D7iPr7uEI4HLgrJTSIzVLL+j+mLg7n86NiKkVjnvh\npFdP+dU5MdGo5fm0kfOLKuv2uFDr9WxMREQAb8tf/s9obqvH9ExMRMTRwGvJbiCeS3YX9DDwTbJu\n0FS/noiLiHge8ErgI/kd8pI6S6vPNRpbryzz3q8i4mUppeuK3qv3OK9h9+PcaHx0zO/TcWasj9fy\nfDqiOoS8kn1WjbIaH82r9/wAniO6UkTMJxvOCXa//vZcME5UiYFingt6TES8mqzr9wlkPR4cQ1YH\n8/9SSl8sWrRbjufyfDrmbRI2vneGwlPCm6ssU2i8nDaK62u4HCkbIOFPIuJXwLuAE4tmPwBcQvbk\nu+rT1TGRUvpdRNwKLCWrJD+/OENEnER2YwZk42qotk6JiUaN9fbGi26PC7VeL8fEuWTdWt0F/PMo\nb6uX9FJMrCQbN6zYp4E3V7mIUnldHxcRMRP4D+A2dt2YI6mzdNLvCNXvWuBnwPfIKiOnA4cC/wQ8\nGfheRByaUtqYLz/WdVDGVXt0y3GeWvR3pbzGR+NGen6A7okd5SJiALiQ7EbnS1JKXy2a3S3H03NB\nE2rEAHgu6GXHsnu9yxNkDzv8a8ly3XI82xYHdjuvloiI6RHxNeCdwD+SVY5OIbuz5TtkX9DLIsIT\n2fjx9/n0nyLizyNiUUTMjoiXAxeTjfkBsKM9xZMkdYuIeCVZ9+KPAy9PKd1bI4t6UErpwpRSkN2B\nvQJ4PVnPS7+KiOPaWji1wweABcBrU0oPt7swktQrUkrvSyl9IKX065TS5pTS71NKXweeAlxJ1vvM\nX7e3lJLawfPDuPERsuus24A/bHNZ1B5VY8BzQe9KKb0mr3cZAg4E3gecB1wZEQvbWbZuY+N7Zyjc\nWTGlyjKFOzTqqVhqdH3NlON84DnAuSmld6SUbkopbUkp/W9K6WVkDfBPBt5as/SCHoiJlNIFZE8p\nDpDFx+1k47xfTDZOzL/ki95freDaqVNiolFjvb3xotvjQq3XczERES8GLgC2Ay9LKX1/NLbTw3ou\nJlJKT6SUbk4pfQh4Htnd+BdFxNBobK9HdXVcRMTzgVcAn0gpfbPZ9UkaNW3/n6HWSSk9zq7eh04t\nmjXW9Q3GVXt0y3EuHvawUl7jo8WqnB+ge2JHQES8H/hj4E7ghJTSnSWLdMvx9FzQoDpioCLPBb0j\npfRoSulXKaW/JLuR4snAB4sW6Zbj2bY4sPG9M2zIp8uqLLOkZNl61rd0hOsrjLMwIx8Xpa58EdEP\n/FH+8qIK+S7OpydWmK/dbcinXRkTBSmldwD7AH9JdsfcvwFnAIcBkS9WOv6LytuQT9sdE41qKpZU\n0YZ82q1xodbbkE97IiYi4g/Y9Rvij0rGl1J9NuTTnoiJUimlnwC/JhuL7MjR3l4P2ZBPuzUuXpBP\nnxQR64oTcE4+7+ii96eWX42kUbYhn7bqXKP2uyGfLip6b0M+HelxbjZfR/wWGUc25NOxOl4N1SHk\nQxE9kL+sVFbjY3SUOz+A54iuERHnA/8HuIes0XV9mcU25FPPBT2ozhioxXNB71mbT58XERPyvzfk\n004/nm1rk7DxvTP8PJ8eGBGTKyxzRMmy1dwAPArMioiVFZZ5Sun6UkqbgBtLtlczH1k3IoP535sq\n5Hswn86qMF+76/aY2CnvBeE9KaXXpZTelFL6VH4X3NPyRb5bR/nVITHRqFbEksrq6rjQqOiZmIiI\n08nG8+4DXp1S+nQr1z+O9ExMVHFPPp07RtvrBb0SF4cAx5ekffN5s4reG2jhNiXVr9XnGrXf7Hxa\n/EThNfm07HVe3jPNQfnL4uPcaHx04m+R8WBMj1eTdQhVY7JKPjWn3PkBPEd0hYh4N/DnwH3AiSml\nX1VY1HNBjxpBDNTiuaD3PEA29vsAu9r3uuJ4trNNwsb3DpBSuo0sWCcCLy6dHxHHkz3NcydwRR3r\nexwodL/4ijLrWwEcTTZu6tdLZn+5Sr7pZF17AhQ/eXYfsDX/+6gKxTo6n95crezK9EBMVBURRwFP\nJRs35ss1FhcdFxONanksjXc9EhdqoV6JiYh4HvBZsh/2r0kpfbJV6x5veiUmKsn/fxyWv2zkrvxx\nqdvjIqV0ZkopyiXg1fliXy96/8Fq65M0Olp9rlFHeEk+varovSvIboRbHBHHlcnzYmACcFVKaWPh\nzUbjo9N+i4wXbTpejdYhVMvXD7ysQj41p9z5ATxHdLyIeCdZr6UPAM9KKf2i0rKeC3rTSGKgDp4L\nes9xZPVzD5INKwzddTzb0yaRUjJ1QAJeBCSysbBXFb0/F7g+n/emkjxvILvj4xNl1ncEsAPYDDyl\n6P2pwLp8fe8tk28JsIVsXNXTit4fAD6V5/timXyfy+fdAKwsmXcS8Fg+/5Xt3tfdknogJuYCK8q8\nfwywMS/LKe3ez92UOiUmyqznvHzZD9ZYrqFYMvV2XLQqn6l3YoJsXLCt+TZf0+792Qupm2MiL+Pr\ngOll5i0HvpWv46p27+duS90cFzXyn5nn/1q797HJZGrsXGNq6/FaAzwX6C95fwD4C7JruQScXDL/\nrfn71wNzi95fnR/7BDy/VfHRqv85pt32aWG/vajKMmN6vGi8PmpqUdy9vmTev+TvXwNEu/d7J6Va\nMdDo+SFfxnNEhybgH/P98QBwWJ15PBf0UBppDHgu6L1E9pDkc4GBMvOOJXtyPAHv6cbj2eg5pOn9\n2u4Da9otCD6UH+hHga8CXyDrxj2R3XVRekI7L5+3rsL63pbPfwL4DtkTZHfl710JDFXI9/I8zw7g\nh2Rdvm7I860v/iIV5VlM9lR7Imto/0G+vWvy9xLZmK197d7P3ZS6PCaens+/juzuok+Tdd2RyBpV\nzmz3/u3G1EExcWVRuj1f/s6S9w9tRSyZxkVcNJTP1HsxQfZDu3DD3m1k40qVTe3ex92WujgmlrPr\n9+WVwGfIbvr8CbCNXf8/9rjhz9S7cVHjM52Jje8mU0elkZ5rTG09Vqfnx+U+siHiLiK70W1j/v52\n4C/L5OsHvpIvsyk/xl/Nj3kC/q3V8dHo/xzTzv13aMn/2Yfyfffb4vfbfbxosA6BbNiZLflyV5NV\nrv8qf30PsG+7j0G700hjoNHzQ57Xc0QHJuA0dtXbX0Xl6+9z2n1cPBd0Tgx4Lui9xK5r6AeAS/Jj\n+hV2NYQn4GvA5G49no2eQ5rar+0+sKY9guAM4MdkP3g2Az8DXk+ZRmtqVH7ly5ySnwQfyAP5euDt\nwGCNchwJfInsn9BW4HfAu4HhKnmGgXPJ/pE9RFYhek/+JXh5u/dtt6ZujQmyO4o+SvaDZlO+rRuB\njwD7tHu/dnPqhJhg1z/eaunprYglU+/HRTPxZOqtmGBXQ2vN1O79242pS2NiiOzu+S+T/Y54iKwb\nsbvILgrfAExq977t5tSNcVHj85yZL2/ju8nUQWkk5xpTW4/T3sD7gMvJKtEfy8/l64ELqPJEHNnQ\nlm/Ij+3m/Fj/CDhjtOKjkf85pp377un1/P/thONFg3UIwL5kDQh35vluJauTWtDu/d8JaaQx0Mz5\nIc/vOaLDErt+N9dK6zrhuHgu6IwY8FzQeyk/pu8Avp9/Px7Nj+sG4PPA6b1wPBs9hzSaIt+oJEmS\nJEmSJEmSJElqUF+7CyBJkiRJkiRJkiRJUrez8V2SJEmSJEmSJEmSpCbZ+C5JkiRJkiRJkiRJUpNs\nfJckSZIkSZIkSZIkqUk2vkuSJEmSJEmSJEmS1CQb3yVJkiRJkiRJkiRJapKN75IkSZIkSZIkSZIk\nNcnGd0mSSkTEeRGRIiK1uyz16sYyt0tEHBUROyJiU0TMqrLcvhHxHxHxu4jYEhGPRsRNEXFhRBxT\nYxt7R8S2iNgaEata/ylUS0Ssy78T69pdFkmSJEmdqxuvp7uxzO1iHcD4YB2ApE5i47skSRo3IiKA\nDwABfCCldH+F5Z4LXAv8KbASmAxMAvYGXgH8KCLOqbSdlNLNwCeBicD5rfwMkiRJkiSpNusAJEnt\nYOO7JKktCndoR8R57S6LWqvD78B/IXA48DDwr+UWiIg5ZBfNk4BtwLuA44GjyS7EN5BduP9zRBxZ\nZVv/BGwHTouIo1pUfkmSJEnqOtYB9C7rAHayDkCSBNj4LknSHlJK56WUIqUU7S6LWu5v8+nHK93x\nDrwKmJH//ZqU0jkppR+mlK5MKf0X8Azg0cL8ShtKKd0IfCl/+XdNlluSJEmSNAqsA+hp1gFIksac\nje+SJGlciIhnAgfnLy+ssujx+XQj2d3vu0kpbQCuzF8eUmOzF+XTUyJi3/pKKkmSJEmSmmEdgCSp\nXWx8lyRJ48Wf5NPfppSuqrLcsnz6s5RSpW7z7syn02ts8xvAg2Rd1J1VVyklSZIkSVKzrAOQJLWF\nje+S1GVKx9KKiOkRcW5EXBcRD0fEgxHx44h4TUTUPM9HxKx8nVdHxAMR8VhE3BYRn4uIU+vIf0hE\n/FdE3BARmyNia0RsjIhrI+KjEfGSiBgsWn5DyThg5xaN/VZIaytsa3lE/Eu+7gfzst4aEZ+KiOPL\n5SnKV1j3mfl7p0fEV/OyPhER11bax1XWuyQi3pPv+00R8WhE3BwR/x0Rx9TIu6H4s+b78WMRcVO+\nnhQRM6qto8b6m42Lvog4IyK+FBG358f1gYj4SUS8PSKGy+Q5M99n5xa9V3psU0QsL9nOM/P9+OOI\nuDcituXlvTZ/f2mj+6FoO5OA0/KX/1Nj8an59MEqyyzMpw9UW1FKaSvw1fzlGTW2W1V+TP8630/3\n5fvpvvy79/WIeHNELCuTr+l9HBHr8mO3Ln+9KiI+UhSvG/L4XVaS76CI+Hi+XOHc8uGImFtlW2vz\nbW3IXy/Iv/e/iYgtefm/GxEvbGQ/ltnenPw7/5N8fz4eEXfksX96HfmfHxFfyM9Fj0V2HtwQEVfk\n5X5GK8opSZKk8an0+rQF13rWAVgHUC6/dQDWARSvwzqAyvmtA5BUW0rJZDKZTF2UgPOAlKflwPqi\n16XpO8DkKut6BnB/lfwJ+DwwqUL+/wNsr5E/AfsV5dlQx/Jry2zrTcDWGvk+AvSXybu8aJlXA2vL\n5L223D6usu/OIBvzq1p5/g3oq5C/sB/WAn8KPF4m/4w2xcVi4Ooan+33wBEl+c6s49gmYHmFcldK\nm4EXNPm9Ob5ofafVWHbnsakwfx7wWL7MRXVs+3VF235Sg+XfD7itjn31zhqx0dA+Btbly60DTgQe\nqrCeu8i/78DLqfyd3QAsrLCttUXLHJavs1K5/5vK37GdZa7yuV5S5bMU0peBKWXy9gOfrmPf3tlM\n7JpMJpPJZDKZxnfCOoBq+awDaE1cWAdQIQ7z+dYBWAcA1gGYTKYRpAEkSd3ss8AK4KP53/cDBwB/\nATwZeBbwceBlpRkj4mDgm8Ag2cXzfwBfADYBB+XrOAh4IbCD7Adqaf73kvWisgH4d+DnwH3AFGA1\n2cXO80s2fRIwEbguf/1h4EMly+x2J3FE/Dlwfv7y1/nyvwXuzT//a4CTgdcCDwN/Wfp5i7yZbMyv\ny/P1/AaYRnZhU5eIOIVsvLAgu/h+H9m+3AocDpwDLAHeSHaB9rYqqzsC+EOyscXOB36ar/dosovx\nRjQTF7OAy8gu3reRXQR9j+wYDwJPJ9uH84FvRsShKaVb8+xfIrtgP5vsYhPgSWXKt7Ho7wGyi/gv\nAlcAN5HtsyXAMfm6pgIX59v6df27YTdPK/r7pw2uo3D3/H+R7QuAr9SR7SdFfx/PrtgfiU+SVYg8\nQXZcv0m23yC7A/8I9vyuFbRyHy8ki6kHgf9Lti8nkp0n3gTMBT4aEW8BPkFWAXQ+8Auy88JZwB+R\ndev3r5SJwSJDZBV/M4D3AF8DtpCNsXcOsDfwyvyznVNlPWXld81/muz7dhvwAeB6su4EF5FVrr2M\n7GmJj1NyDgT+DHhp/vflZMflRrIL+Vlk588TgTUjLZskSZJUgXUA1gGUYx3AnqwDsA5gN9YBSBpT\n7W79N5lMJtPIEnvewfrKMstMBL5ftMyzyixzZT5vB3B6mfmTgB8WreO0kvnvyN9/BJhfpbxDlLlr\nvmi959X4vPuz647wd1P5Dtd35ctsB/Ypmbe8ZJ9dVGk9pfu4zLwJ7Lr7eAtwVJllZpNVEBTKs6bM\nMhuKynM9MKtD4uKT+byNwL4VtrU32cVJAj45kv1XZtnlwIQq8xcDt1fa1gj2zzfyddxRx7KFY7O2\nqAxPJ6u4+E3R/vtptTgqWt9AUQx/vIGyryja5htrLLtHHLViH7PrDvJEVuk1p8wy/1K0zN3Aj4Gh\nMst9Nl9mW4X1rC1azzbgmWWWmZF/bxJZZcT+Vcq8rsy82WSVBwm4GJhY4XMXP7FwQsm8wvnxJ8BA\nlf07u9G4NZlMJpPJZDKZsA7AOoDRjQvrAKwDKLfMuqIyWAeQrAMwmUwjS475Lknd7RsppU+UvplS\nehz4Y7KLPoA3FM+PiCOAI/OXn0wpfanMOh4DXkX2oxayO7iLzc+nv00p3VmpgCmlLfm6GvUXZBe7\nvwTOSSntqLDc3wB3kN2F/6oq69sEvK7Kemo5nexCBeBfUkpXli6QUrqPrBs58vK8vsY6z04p3d9g\necppNC6WkXUTBvDmlNJvyq08pXQzWcULwEsjYqjRgqaUNqSUtlWZfzvZBR3AaRERDW6qcMzuaiDv\nOWQVFu8B9snfuwE4tZ44Sik9wa4nOVY0sP35RX//oMa29oijUdjH/yeldE+Z94ufXtkLeE1KaUuZ\n5T6cTwfInu6o5j9TSpeWKfOD7Hqyop/sDvSReB0wDNyTl7PsEyYppQ8DV+UvzyqZXTguP86PcVn5\n+UCSJElqBesAdrEOYBfrAPZkHUDlPNYBWAcgaZTZ+C5J3e2CSjNSSjeR3fUJ8MyIKD7nP6vo749W\nWcfNZN2NATwtIgaLZt+RTw+IiKfUXeKROy2f/k+1i5z8wuKK/GW1H/NfTSk91ER56t13l5FdnJXm\nKXVbSqnqxVQDGo2L55JdxGwjG+Oqmh/m0wlk3ey1RERMj4i9I+LAiDgoIg4ie7oAYDrZHfeNmJNP\nW1XBsR/w5YiodeFYUNju/KpLlXdH0d9nNlH5ADS9jx8Evl1uRn6+eDh/+YtUueu6/y36u1ZFRLVY\n/iFZF29Q/TtWTqF7vm9UqBwoVoj10mNdOC7Pi4i9Rrh9SZIkqRHWAeSsA9iNdQB7sg4gZx0AYB2A\npDFm47skdbda41YV5k9l9x+4B+XTHey6o7OSwl3dg+y64xfgU2TdaA0CP46Ir0XE2RFxcMnFXMPy\nu7ALF0znRkSqlsjGnILqFzf/W2VePQr77o6U0m01li3su2URMa3CMr9osjzlNBoXhQvoCcDWGvu6\neMyyRi4md4qIZRHxgYjYQPZUwk1kTzlcl6f/LFq80Quc2fn0wQbyvpFsrLKVZBVB/0F2oXoMcGk+\n/l8thQvvKSPdeEppA7vudn8LcH1E/ENEnBgRU+tZRwv38fqUUqoyv7B/f1vHMpCNtVjJ49T+vhZi\neb+ImFhjWQAiop9dY7C9qo7zyl/ky5bG+dp8ugq4MSI+HhGvyM9bkiRJ0miwDsA6gHKsA9iTdQDW\nAQDWAUhqDxvfJam73V1jfnH3WrOL/p6VTx+qozu44u7kCvnIuyN7CXAfWddRzwH+neyH8r0R8dk6\nL0iqmdtgvmpdoD1QZV49Cvug1r6HCvuuRLPlKafRuBiN/V1VRDwb+BVZ93f1XLBMbnBThYvFSSPO\nmNmSUroppfTVlNKfkV243ZGv70MRMaHGagrlrtj1Ww0vBy7L/96frIvF7wIPRMQVEfGWSpU7Ld7H\nte4QLzyZUnG5kqdX+qus6/5qXbnlCrEcwMwayxbMIjtnjdRu+yWltJas68VtZE8LnAlcCGyIiA0R\n8cH8iQJJkiSpVawDKM86gOqsAxhJRusAilkHkLMOQNJINHLSkSR1jmp3n456/pTSlyPiUuDFwCnA\n08juDJ2Zv/fiiPgG8KKU0qMNbKL4R/k7gYvqzFd27Kbc9irzRqLZfV/QqvIUa7Rshf39EHDsCPLd\n3sjG8m66Lia7cH+EbDy1b5N1I7apMAZXRDwTuKSQrZFtkd1pPZfKFSAjklJaHxHnAJ8g66LtEKo/\nbVDYbiN33ZNS+j1wXEQ8HXgB8HSyJzAGgKPy9LaIOD2l9JNCvjHex63Wqu9YqeLzyieBdze6opTS\nuRHxUbKKkRPInoSYSlbB8Xrg7Ih4R0rpvMaLK0mSJO1kHUB51gE0xjqAOlkHMCasA5DUM2x8l6Tu\nNg+o1u3ZvKK/7yv6u9D91XBETKpx53txN0t7jJWVUnqYbEymCwAiYjXZuGFvIOvO7FTgn4A/r7KN\nSu4t+nt7SumXDayj1Qr7YF7VpTJV990oajQuCvt7KlnXYltbXbASLwJm5H+/IKX0vQrLteJi+Ray\nC+9674yuR3F3jXtT/cK7sN1bm9lgSmkd+Xh9ETEDeAbwauB5ZPH2hYhYWfSdHst93GqzI2Kgxp3v\nhVhO1P8EyX358gH0NXteybuefDfw7rw7u8OAPwBeR3Y3/LkRcU1K6SvNbEeSJEnCOoB2sA6gdawD\nGCHrAPZgHYCkrmC385LU3Z5SY/4R+XQzcHPR+4Ufmn3sGuOrkiPz6Vaqj+EEZHcDp5Tem6+30B3U\nS2rlq+BmsrGpAJ7a4DparbDvFkbE4hrLFvbdLXkFxVhpNC5+nk/7gKOb2H69dysfmE/vr3JBCLVj\ntB7X59PldXQPV6/i7usqVl5FxHyyC7DicjQtpfRgSumLKaXT2DVe20J2/66M5T5utYnAk2ssU4jl\n3xTu4K8lpbSNXcfh2Iho2V3+KaXtKaWfppTOIXsSqKDRc6AkSZJUzDqAsWcdQG3WARSxDqBh1gFI\n6hk2vktSdzuz0oyIWE52RyzApSml4q7Nvlv091k11vGs/OVlI7kLOqX0AHBN/nKvMosULlQGq6xj\nO/C1/OVxEXFovdsfRfXuu2PJxuUqzTMWzqw0o0ZcfJVdF81vaWL7Oy9CI6Li8WVXDzyTIqLsb5KI\nGAL+qImyFBS6YRsEDq4zz/wa848s+vvGOpf7ScWlmnNJ0d/F37ex3Mej4cxKMyLiacCq/OVIv2Nf\nzqfLgdNHXKo6pJSuYNe4d+XOgZIkSdJInVlphnUAo8Y6gNqsA6i8nHUAI3NmpRnWAUjqJja+S1J3\ne25EvKL0zYiYCHyUXeMa/Xvx/JTSVezqHutVEXFqmXUMAh9n1w/3D5TMf0FEVOy+KyJmkXW9BLvf\nWV3w+3y6stI6cv8MPEHWPdRnI2JFlW1GRDwvIuq9sGrEl9g1vtnbylUG5PulcBdyomT/j4FG4+K3\nwGfyl6dFxN9W20hEzI+I15SZ9fuiv6sd3/X5dIgydwXn3Xd9lOxO7mYVX5jVeiqg4OSIOLfcXdER\nMQy8NX95O9XvZi9s7wng+3Vuu3hbayLikBqLnVT0d/H3bSz38Wh4bT7G3W7y/f+h/OV24CMjXO/7\nycY1BPjPWpV6EfG0iDi+5L0/qvYERUQ8lWy/Q/lzoCRJkjRS1gHsvk3rADLWAezJOgDrAIpZByBp\nTDnmuyR1t6uAT0TEccBngQeB/cguCNbky3w+pfTtMnn/hOziexD4ckR8iOyi8iGybqreCjwpX/Zz\nZcYqehNwUUR8A7gU+DXZeEvDZN1EvYFsfC3Y9QO52OVkY2SdFhGvBX7MrrulH0op3Q2QUro+It5C\nduG/EvjfiPgY8B2yC7xBYDFwFPBCsrtYnwf8ovwua05KaVtE/AnwDWAK8MOIeC/wbbJu+Q4H/gpY\nlmd5T0rp2tEoSxXNxMXZZJ9hFfCOiHgOWQXMdcCjZOOWHUT2NMTJZPv5oyXruLzo7/dGxD+RHavC\nHfUb8jG8Pgv8P7Jj+PGIWEN2gbyJLAbfSFZ582Pg2JHvhl1SSjdGxC/I7ng/AfhwnVnPA06MiI8C\nN+Sf4TCy8QsLd1z/Y0qpWjd7J+bTS1JKD1VZrpI1ZPvnZ2RPJlxDtj/7gKXAGcAL8mV/xu7jzo3Z\nPh4F95DdNf6tiHg/8PX89SHAOWTjSQK8N6X0q5GsOKV0T0S8EvgC2R3pV0TEhWRP2dxKVjm1gKxL\nu9PZta9+ULSaTwDviYgvke2/35F9R+YAxwGvz5d7gl0VcZIkSVIzrAOwDqAc6wBKWAdgHUAx6wAk\njbmUkslkMpm6KJFdCKQ87U3W1VWqkC4Bhqqs65nA/VXyJ+DzwKQyedfVyFdI7weiTP41ZBfa5fKs\nLbP8mcAjdWxvO/CMkrzLi+afOZJ9XGWZM8h+ZFcryweAvgr5N1T6rB0QF3PzZeo5vpdWWMdnquRZ\nXrTcq/NjVmnZT5NdKBdeP72JffTWfB2PAcNVliscm811fP731NjmyqJlz2iw3GfWeSyuA5aWyd/0\nPmbX931djbLWFddF2zqvzLy1+bwNZJVAd1cp+4VAf4Vt1Cwz2Zhs99S5f19Zn2tGZQAAIABJREFU\n4TNUS1uAV7TiO24ymUwmk8lkGp8J6wBqbc86AOsAKn0u6wAa3MdYB5CwDsBkMjWR7HZekrpYSulm\nsjtW/4Gsy6vNwMPAFcBrgWellLZUyX8psBp4B9ndspuAx4GNwP8Az0kpvSil9FiZ7C8nu3P+QuDn\nZHfhbiP7oXkD8DHg6JTSm1JKqcy2rwWOBj5Fdpdp1bHkUkpryS6g/wb4IdmP5Sfy7d1EdjfwW8gu\n6r5fbV2tkFK6GNgHOB/4Jdl+3wrcAnwSODal9MaU0o7RLkuZsjUbF3enlE4guyj5BNndvI+Q7e/7\nyO6q/yBwKrvGAyz1h8DbyO7A3gSU3Q8ppY8DTyN74uIeshj6PfAt4KUppZeRXTS2wgVklSWDZE9I\n1PI5sjj/LrvH23qyi8NjU0pvrZg7U+j67y6ySqxGfAp4NvCvwGVk8b6Z7Lv6e+CbwGuAQ1NKt5Zm\nHuN93FIppavJ7nJ/H9l+f5Ts6ZpLgZeklP4w7T5m4UjX/y2yiqo3kx3n35Pt18fIzkvfBv4vsF9K\n6RMl2Q8ii/GvkH3P7iWLkU3A1cA783wXNVo+SZIkqZh1ANYBVCibdQDlWQdgHUDp+q0DkDQmosxv\nIUlSB4uI84BzAVJKe4xDJamyiPgAWXeIl6eUynaxFhEbyLoM/O+U0plNbKsf+C1Z12hvTyn9v0bX\nNZ5ExFrgVcAtKaXl7S2NJEmS1F7WAUiNsw6g81kHIKkX+eS7JEkaT/6Z7M7pYyLixFoLN+kVZBfd\n9wL/NsrbkiRJkiRJu7MOQJI05mx8lyRJ40ZK6Q7gPfnLc0drO/kd73+Tv/y7lNIjo7UtSZIkSZK0\nJ+sAJEntMNDuAkiSJI2xd5KNy9UXEbNSSvePwjYWAReTjQH4n6OwfkmSJEmSVJt1AJKkMWXjuyRJ\nGldSSluAd4zyNm4FzhvNbUiSJEmSpOqsA5AkjTW7nZckSZIkSZIkSZIkqUmRUmp3GSRJkiRJkiRJ\nkiRJ6mo++S5JkiRJkiRJkiRJUpNsfJckSZIkSZIkSZIkqUnjvvE9IhZHxAci4jcR8WhEPBYR6yPi\nIxGxokq+MyLisojYFBGPRMTVEfH6iKi6TyPilIj4TkTcHxFbIuKXEfH2iBhs/aeTJEmSJEmSJEmS\nJI2FcT3me0QcAlwKzABuB36WzzocWAQ8ApycUrq8JN+/A2cDjwGXANuAE4BpwBeBF6WUdpTZ3tuA\ndwHbgXXAA8DxwBzgSuCElNKWln5IYNOmTT8H9s4/z+9avX5JkiRJGgdWAVOBm4eHhw9pd2GkSqwD\nkCRJkqSmNVwHMN4b3y8Hjgb+C3h9Smlb/v4E4CPAWcAvUkpPLsrzQuDzwJ3AcSml9fn784DvA/sD\nb04pvb9kW4cDPwUeBZ6ZUvpJ/v5U4OvAccD7UkpvafXn3LRp04PAcKvXK0mSJEnj0Kbh4eEZ7S6E\nVIl1AJIkSZLUMiOuAxi33c5HxCSyhneAcwsN7wD533+Tvzw4IoaKsv51Pv2rQsN7nucu4HX5y3PK\ndD9/DhDAuwoN73m+R4BXAzuAsyNiNCpxHhmFddZly5YtbNnS8of5pZYzVtUtjFV1C2NV3cJYVQPa\ndn0l1aljY9RzrowBGQMC40DGgDLGgbokBkZ8fTVuG9/Jun5/oo7lNpM9rU5ELAYOAx4HPle6YErp\nB8BGYD5wVOH9iJgIPDt/eVGZfDcBVwATgVNH8iHq1LZu5jZu3MjGjRvbtXmpbsaquoWxqm5hrKpb\nGKtqgN14q9N1bIx6zpUxIGNAYBzIGFDGOFCXxMCIr6/GbeN7/nT7JfnLv8+7mgd2djv/D/nLj6Vd\nffMX+vS/PqX0aIVVX1WyLMC+wBBwf0rpxhHkkyRJkiRJkiRJkiR1gYF2F6DNzga+BfwJ8OyIuDp/\n/whgJvA+4G1Fy++dT2+pss5bS5Yt/vtWKiuXr6KIOBM4s55l161bt2bNmjVs2bKlbXeQrF+/vvZC\nUgcwVtUtjFV1C2NV3cJYVS2LFi1iaGio9oKSJEmSJGncGteN7ymlmyLiGOATZN3CLy6afTVwWfFY\n8MDUfLq5ymoLff9Pa0G+apYDx9ez4COPdOxwb5IkSZIkSZIkSZLUE8Z143ve8P4F4CHg+cDl+axj\ngfOB/4mIc1NK72hTEavZAPygngWnTp26BhgeGhpi9erVo1qoUoUniMZ6u9JIGavqFsaquoWxqm7x\n/9m78zC5yjLh/98nK1kIEJZAwioElDWCgLihoiKjKIrbqKPO+DoODIwzvq7My2/8yTuIszkooI4i\noBBREFlkUVkSlrAmYd8StpCQfemk0yFL9/P+caro6ura9+X7ua6+Tp1znnPO3d1V1Und574fn6uS\nJEmSJEmqla5NvocQdgSuBSYAb4kxPp+x+7oQwhPAo8DZIYRfxxgXMFidPqHAqdNV7hsytlV6XF4x\nxkuBS0sZ29PTM4sSq+QlSZIkSZIkSZIkSeUb0ewAmugDwK7AfVmJdwBijAuB+0luUHhnavOLqeU+\nBc67V9bYzMd7l3mcJEmSJEmSJEmSJKkNdHPyPZ0I7ykwZl1qOTm1nJ9aHhJCGJfnmKOzxgI8DWwC\nJocQ9s9z3DE5jpMkSZIkSZIkSZIktYFuTr6/kloeFUIYnb0zte2o1OoLADHGl4F5wBjg4zmOOR7Y\nE1gG3JveHmPcAtycWv1MjuNeBxwHbAFurOzbkSRJkiRJkiRJkiQ1Szcn328G+kgq4H8QQhib3pF6\n/EOSVvBrgT9mHPe91PL7IYQDMo7ZDbgotXpejHEg63rnARH4ZgjhmIzjJgK/IPldXBRjXIckSZIk\nSZIkSZIkqa2ManYAzRJjXBFCOB24GPh74CMhhHmp3UcBewCbgb+JMfZkHHd1COHHwGnAYyGEW4Gt\nwAnAJOBa4IIc13swhPAt4PvAnBDC7SRt7Y8HdiOZX/6f6/LNSpIkSZIkSZIkSZLqqpsr34kxXkYy\n1/qvSFq+vzf1tYkkKX9kjPHaHMedTtI+fh5J8vxEYCFwBnBqjLE/z/X+DTgJuINkbviTgVXA/wGO\njzH21fL7kyRJkiRJkiRJkiQ1RtdWvqfFGOcBn6vguJnAzAqOuwW4pdzjJEmSJEmSJEmSJEmtq6sr\n3yVJkiRJkiRJkiRJqgWT75IkSZIkSZIkSVI3WrcO/vxHeOH5ZkcidQST75IkSZIkSZIkSVI3uuhH\n8OAD8OsrkkS8pKqYfJckSZIkSZIkSZK63a8vb3YEUtsb1ewAJElSlnPPybtr782bWfTZzzcwGEmS\nJEmSJEldYe3aZkcgtT0r3yVJkiRJkiRJkqRuMzDQ7AikjmPyXZIkSZIkSZIkSeo2vb3NjkDqOCbf\nJUmSJEmSJEmSpK4Tmx2A1HFMvkuSJEmSJEmSJEmSVCWT75IkSZIkSZIkSZIkVcnkuyRJkiRJkiRJ\nktRt7Dov1ZzJd0mSJEmSJEmSJKnr5Mi+b9nc+DCkDmLyXZIkSZIkSZIkSRIsXtzsCKS2ZvJdkiRJ\nkiRJkiRJEkR70UvVMPkuSZIkSZIkSZIkdZtXbTEv1ZrJd0mSJEmSJEmSJKnTrV0LW7cOrs+6ffiY\nzSbkpWqManYAkiRJkiRJkiRJkupo/jy4+UYYNw6+9GWYuD08t3D4uP7+xscmdRAr3yVJkiRJkiRJ\nkqRONvuOZLlpE8yfn3/c6NGNiUfqUCbfJUmSJEmSJEmSpE7W1zf4eNXK/OMmTqx/LFIHM/kuSZIk\nSZIkSZIkdYtnn2l2BFLHMvkuSZIkSZIkSZIkdYuRI/PvG4iNi0PqQCbfJUmSJEmSJEmSpG4xZkyB\nnSbfpWqYfJckSZIkSZIkSZK6xYgC6cGBgcbFIXUgk++SJEmSJEmSJElStyiUfI9WvkvVMPkuSZIk\nSZIkSZIkdarsavYQ8o81+S5VxeS7JEmSJEmSJEmS1Kn6+4euF0qwm3yXqmLyXZIkSZIkSZIkSepU\n69YNXd9jav6xJt+lqoxqdgCSJKk8e19+GYwdm3vnWWc3NhhJkiRJkiRJrW3TpqHrzz+Xf+zGjfWN\nRepwVr5LkiRJkiRJkiRJnWrixKHrmzfnHzv7jvrGInU4k++SJEmSJEmSJElSpxoYKH3smDH1i0Pq\nAibfJUmSJEmSJEmSpE4Vy0i+H31s/eKQuoDJd0mSJEmSJEmSJKlT5ap8z1cNP8LUoVQNX0GSJEmS\nJEmSJElSp+rPSrTvux/EmHtsvu2SSmLyXZIkSZIkSZIkSepU2W3nR4/OX/lezvzwkoYx+S5JkiRJ\nkiRJkiR1quyEeoz5k+xWvktVMfkuSZIkSZIkSZIkdaphyfcBk+9SnZh8lyRJkiRJkiRJkjrVxo1D\n1yMw0J97bM+6uocjdbJRzQ5AkiS1kHPPKbz/rLMbE4ckSZIkSZKk2hg9euh6jNCfp/L9wQfgvSfW\nPyapQ1n5LkmSJEmSJEmSJHWqXG3nY57ku6SqmHyXJEmSJEmSJEmSOlX2PO6FKt8lVcW285IkqXSF\n2tLbkl6SJLWIEMKngdOAw4GRwNPAJcCPYyy9xCeE8B3gXwoM2Rxj3C7HcZcCny9w3DMxxteXGock\nSZJUlezK94EIq1Y2Jxapw5l8lyRJkiRJHSOEcCFwOvAqcBuwFTgBuAA4IYTwsXIS8CmPAA/n2L61\nyHH3AAtzbF9a5vUlSZKkyuWqfL/xhubEInU4k++SJEmSJKkjhBBOJUm8LwPeEWNckNo+BbgD+Ahw\nJnB+mae+Nsb4nQpC+nmM8dIKjpMkSZJqJ9ec75s2NScWqcM557skSZIkSeoU304tv5lOvAPEGJeT\ntKEH+FYIwc9DJEmS1D2yGz9lV8JLqhn/sylJkiRJktpeCGFP4ChgC3BV9v4Y42xgCbA78ObGR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deAe4cxbMOLLw+DFjh65v3Jh1M0mNPfF4/c6trtJSyfcY4wbgp8BPQwhvAP4G+CxJ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moGlPIDKD+6tXj6iz4pHTm3euVB9S1bWl7+LA/zPWXq0B61vb2D1zPUtqAs1biOwXvV6F0r\nf462NmmKH2jdfY/B97BJkweejxlb+XNjQHtH9P5y6tf0GfHStbRIM2fFm94AQN3J8LcJAAAAAABQ\nb5xzf3TOLfIfrxZJu9w5d6dz7tdplQ8ZdP/viqc58aTiaVpavB5k+W69WXrzzaFpwwLCSYPykjRq\nVPg+VFfSAMnkycXTIH3vOyB+WuekQw8bWD9gTuXLU0/GjfeW7e3eveaoQOORIfPCN0n0vRojGOTf\n/6s5BPxHL5A+cr50znmDt8892rvOra3SCSdW7/yQ9nuf914XMmasNHJkacc98qjw4wJoKATfAQAA\nAAAAUL/Wri2eZkR3vGMFAzKvLZcW/pvXM7tvh/Tbe6Rf/mJob9GcsOB7lN2mJs8TRzAwg6GC8ycj\nm3ZN2MP40Ufy8o6Rzjgrft6s9Xw/51xvqoQLL/aCsiNHSSNGDOzvCwTfszSsfjnGjKn8MfPbWJXz\nPp597uD1XAOKnLY2r3d0cN76UTtLl39BuvRyr1c8qqejQ/qz/+Vdq1mzBu8748zSj7vf/uWVC0Bm\nZOzbBAAAAAAAAJBn/wS9YsP85m7p8celhx6UnnhcWnJv4XTBOX/jCAa/KmXmLOmqq6tz7CTWrJF2\nhM0sEcPGjZUrS1Bw/mRkUzmNKFpapD2mx0+fteD0qJ29qRLyA7j5r6Fa9596d+z8yh8zv/FVknry\nwVMHrwcbi51+RvxjtXfEb2yG8uy8szRrtjQs0AiinNEjaBAGNA2+gQIAAAAAAKB+7b1P9P4kQ4WH\nBWPXvCM99MDA+rZthdP1Fxhe/sUXos9ZNPhV4JiHHDZ0Wz164PfSv3zPe8QM8vUHh/5/q8Cw/5WS\ntV7MKKycRhQtLckCXo3QYIPgu9S9U+WPmT+9SJJ60tU1eL1t2OB15m+vb8HPkXI+V1payysLgMwY\nVjwJAAAAUGHXXRu9vx56cQEAgPpQyflR311TeLtzUk9P8fzBnu/OSbcvis5TSvBrjz2kxx4pni4p\n5yrbs3fxb73l+vXSC8978+QWMnLkn3q4v33sfE3L5ZO83oXVkrVezKg8s2SB0kaoMlHB96ZpkFKg\nUVPZh8y7/ydp0BFszDV2nDRhgrRqlTTnwMqUDekp5x7BZxLQNAi+AwAAAAAAoH4V+7E6rJd6EqtX\nx0u3YoU0Om/+aRcjwFMs+L5+/dBtrVXqHVep4Puzz0j//bPB27ZuDU/f2fmn4LsL9vyrZjCwWu8j\n0lVOnU3a870RgtP5r2H79oHnU6akX5ZaaUvQaGvHDmlYjDBJ/sgnSepkcLQPM+nCT0hvr5YmTIx/\nHNRGsGFeOaNjEHwHmkYDfJsAAAAAAABA0/r9/emda9nSwetxerUvfzV839at0pYtQ7cHg8ZhPcqT\nemNFZY4TDLwX4yKCVv2B0QQqqRECqShzmOeWhD3fGyA4lv8aduwYeN5MQ153dsZPe/998dJt2jjw\nPE7DqyjDhkmTJnOPyoKZMwevh90jzvto8WNxvYGm0bR/7WbWZmYLzOxbZvaYmW00s21mttLMFpnZ\nsUXyn29m95nZBjPr8Y/xObPob3NmdpKZ/drM1ppZr5k9a2Z/Y2YdUfkAAAAAAABQQJq9m2fNHrz+\n8rLieSZMCN+3ZHHh7cEf9+cvKH6eOG75ofTmysocK+jdd8P35fcYbWkZ3Jhgxp7VKY/UGIFUSG1t\npedtaUlWDxpuzvf84HsDvLZqeCBmA678gPu6dfGPX26gHrU1ZOqGkPtJnM8y/gaBptHMf+3zJN0j\n6YuSJkv6naSfSlor6SxJ95rZlwtlNLObJN0q6RBJ90m6W9IsSTdKWhQWgDezv5Z0l6T5kp6QdKek\ncZK+ImmxmXVV6sUBAAAAAABk3tat0tb3otMcOz+dskgDP5z37ZDu+51096+K54mKuzz1RPR5crp3\nilW8WG67pXLHyhc1R33/QPDCmUlzj5F230OaOEk67oTqlEci0IHkwfdGkP/T9I68wGErfw9lefH5\ngecTEwwXT/A929auHbzOsPMAYmjmOd/7Jf2XpBucc4PGljGz8+QF1682s3udc/fm7TtL0iWSVkk6\nxjm31N8+XtK9ks6QdJmkGwLHPETS9ZJ6Jc13zj3sb++WF4Q/RtJXJf1F5V8qAAAAAABAxqxdK/37\nv0bPJS6V1ys2qdwQ6U8/Ld23JGamiMBLLYIy27aVl7+UMgeHnR89Wjr/gvLKEUeSQMepp1WvHKid\npMGu55+VjppbnbKkJbTnexMNO18NL7008Pzll+PnizM9CepX8DtGOZ/bw4eXVxYAmdG0zd2cc791\nzp0dDLz7+34saaG/GvxP4Ep/+aVc4N3Ps1rSZ/3VKwr0fr9Ckkn6ei7w7ufrkfQJeY0BLjGznUt8\nSQAAAAAAAI3jgfuLB94laeUb1S9LjvOD77/8RYI8ET/UhwVl6rmnZH+RQNKSe6Uf3ya9vTovz8C8\n7i5syN5qSNLzfdLk6pUDtbN+fbL0aU5jUS359X47w87XXD3fz1FcV2Cw4vwGLUl8/ov0fAeaSDP3\nfC/mSX85JbfBzKZIOljSNkk/CWZwzi0xs5XyhrE/XNIDfr52SSf7yW4tkO8VM3tQ0lGSTpF0W+Ve\nBgAAQAZdd230/quuTqccAIBMMrPz5TWQ319Sq6QXJf27pO855/qj8sY49qcl/V9/9Sbn3KURaU+S\nN93dIZKGS3pF0o8kfdM5FyOq3OT+8HS8dGkGy/pd8kBKfwmBl87O5HnSsiMi+L70Jen3/vzJb78t\nXfYF7/mmTQNp6jX4sOuutS4BqmFnv5/T2LHSO+94f1tHz/OmjNh1jDd8+DN/GEj/vgNqU85KWp83\nH/nmnoHnjdCwAEhd4DNrVIl9J0eMKL8oADKD5m7hZvrLt/K2Hegvn3PObQnJ92ggrSTNltQlaa1z\nLmxMmkL5AAAAAABAAmZ2k7yG74dIuk/S3ZJmSbpR0qICI9UlOfY0Sd9U9CzeubR/LekuSfMlPSFv\nyrlxkr4iabGZdUVkb279/YN6Shc1dlz1yhK0Y7u08N+S5Sml1+OYsdL79pfa26WTPxidtrs7+fHL\nsSOi199PfjzwfNMm7zo+84fB1zPN4HtPT/E0yIZSG0dM2c1bfvh06eBDpDPPlg45VLr8C9KffVoa\nFuibNqwBAtT5U0s8/dTAc3q+J7P2XenHP5J+/atkn0lB9drgCPEErx9/RwBioOd7AWY2QdLF/up/\n5e3aw1++FpH99UDa/OevK1yhfKHM7GINlDHS4sWL58yZM0e9vb1auXJlnCwVt3Tp0uKJUDemxhnW\nr0FtbeLXjmyhriIrqlVXX4/4bjH1lh9E573gokoXBw2A76soZvLkyeoKDjuJumNmZ0m6RNIqScfk\nposzs/GS7pV0hqTLJN1QwrFN0r/J68jwQ0mhHyhmdoik6yX1Spqfm37OzLrlBeGPkfRVSX+RtBwN\nb+MG6eYfJAt07Ltf/LTt7eXNef7kE17v2SRKHWzh1NOkD55a/If+j10o/d/vlnaOUmx9L37aF56X\n7vj5oE2uNcWfI8eMlV76Y3rnQ/UcfqR05x3J87X6fz/jJ0gnnjywfYTfaGXNmsHpS2+fVf/o+e41\ntohqQJTv9v8amD5j4sTyzonsou0EgBJw5w8ws2GSbpE0StJvnHP53+pyTYk3Rxwi16R2pwrki7K7\npHlxEvbQyhcAAAAA0Byu9JdfygXeJck5t9rMPitpsaQrzOyfShh+/jOSFki6XFKxLphXyPu59uu5\nwLtfjh4z+4SkpZIuMbN/cM4lnJC4wd35P9KGDcnyJOmFVk7vRSn5/NFS+DgJcQJAcV5b2sOlP/pI\n4e2TJ0vBTh8//+mQZP2pBqKYa7lhtLUNXr/gQqm3V7p9UXS+YsH0qVOlFXn9pRq5l3Kz99g9YI40\nf4H3OXDDt4unzwXeJenlZaWfd8ae0siR0saN3ugLyJa29vhpL/qE9MhD0gsvVK88ADKB4PtQ/yzv\nn+kVki6ocVmiLJe0JE7C7u7uOZJGdXV1aebMmUXTV1KuB1Ha50WZOjpqXYLU5XpmdjTha0e2UFeR\nFdWuq5HfLYqck+8lyMf3VaBxmNkUSQdL2ibpJ8H9zrklZrZS0mRJh0t6IMGx95D0DUn3yxu+/u8j\n0rZLynWvvLVAOV4xswclHSXpFEm3xS1HU3j1lWTp2xP8KC5JH5gv3f3rZHnylRKYCxt2vpTh6OtZ\nZ8zRQdIMAMZ9iydOqmoxUAVTp8VLV6y+tQR6gzdy8L3ZR9Bra49/n6qklhbpU5+WVq+KX29RP/Y/\nQLpvibRlizT3mOi0k6dIZ5wtvXDt4O1Hza1e+QDUJYLveczsBkmfkjc83QLn3KpAklwX8hERh8n1\nct9UgXyhnHMLJS2Mk3bDhg2LFbOXPAAAAAAAGXWgv3zOObclJM2j8oLvBypm8N0fbv778n5D+ZRz\nzll0cGa2pC5Ja51zL0eU4yi/HATfc0oJRu+ZsPHUgQdL27ZL/X3SunXSs88ky19S8D2kt/3yV5Mf\nK8xpZxTsZR6qv7/0IPj0GdLjjw3dvn5d0ayuxdINbsYd4OLc86pbDpSvs7O0fC1F6ltr4O+gkYPv\nzz8nnX5mrUtRO8FrXY7gSAzFdHZKu8eabRb1pr1d+vRnpXfXSFN2i5fnhJOkhx/0RlvYc5Y0fnx1\nywig7hB895nZt+QNHfeOvMB7oUkfl/vLqCZquTvw8rxtuedTE+YDAAAAAADx5H7Vfi0iTW5s4SS/\ngF8q6VhJVzjnXkpQjtcj0iQqh5ldLOniOGkXL148Z86cOert7dXK4BDgdSI36kjQ8JVvaFzCnpkb\nt2zR+pDjhRrn/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DyxxrlPwA3AwgpxPlQW51MVlgdw0ijH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dwD9GxAjw+Yi4KzN/Uu+4JEmSJElqZnvUOwBJkiRJklQ3X6FoUf+RegciSZIk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eOOUe8icU2y+jYt7DLvLuSCmdDvwZuUfFBvL3vg/4N+C4lNLDA/GlUko3k88r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\nNtSyYNWCjCNStSlO0Pdv62dqYirDaCRJko7J+QNJqnSPP0bc8cPC+Lr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RdPo6zyl84Ot9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2fWoZcklYViDtD/PrAHuDeE8KbzHRRCeCNwL7AT+MMiXl+ScmKM9H6vN9fuWOfseakULLx6IbXN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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [], + "image/png": { + "width": 1012, + "height": 540 + } + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "gVuArwZcoquf" + }, + "source": [ + "It appears the MCMC has converged so we may continue.\n", + "\n", + "For a specific trading signal, call it $x$, the distribution of possible returns has the form:\n", + "\n", + "$$R_i(x) = \\alpha_i + \\beta_ix + \\epsilon $$\n", + "\n", + "where $\\epsilon \\sim \\text{Normal}(0, 1/\\tau_i) $ and $i$ indexes our posterior samples. We wish to find the solution to \n", + "\n", + "$$ \\arg \\min_{r} \\;\\;E_{R(x)}\\left[ \\; L(R(x), r) \\; \\right] $$\n", + "\n", + "according to the loss given above. This $r$ is our Bayes action for trading signal $x$. Below we plot the Bayes action over different trading signals. What do you notice?\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "kGKdj6B79Y4-", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 408 + }, + "outputId": "b534bc8f-9235-47d6-faa7-339fd9110a15" + }, + "source": [ + "plt.figure(figsize(12.5, 6))\n", + "\n", + "def stock_loss(price, pred, coef=500):\n", + " \"\"\"\n", + " Vectorized Stock Loss function\n", + " \n", + " Args:\n", + " price: A (,) Tensor of prices (independent variable)\n", + " pred: A (1,) Tensor prediction based on the price\n", + " coef: Integer Tensor representing coeficient for Bayes action function\n", + " Returns:\n", + " sol: A (,) array Tensor of data points for Bayes action solution minima\n", + " \"\"\"\n", + " sol = np.zeros_like(price)\n", + " ix = price * pred < 0\n", + " sol[ix] = coef * pred ** 2 - np.sign(price[ix]) * pred + abs(price[ix])\n", + " sol[~ix] = abs(price[~ix] - pred)\n", + " return sol\n", + "\n", + "N = sigma_.shape[0]\n", + "noise = sigma_ * evaluate(tfd.Normal(loc=0., scale=1.).sample(N))\n", + "\n", + "possible_outcomes = lambda signal: alpha_ + \\\n", + " beta_ * signal + \\\n", + " noise\n", + "\n", + "opt_predictions = np.zeros(50)\n", + "trading_signals = np.linspace(X_data_.min(), X_data_.max(), 50)\n", + "for i, signal in enumerate(trading_signals):\n", + " _possible_outcomes = possible_outcomes(signal)\n", + " tomin = lambda pred: stock_loss(_possible_outcomes, pred).mean()\n", + " opt_predictions[i] = fmin(tomin, 0, disp=False)\n", + " \n", + "plt.xlabel(\"trading signal\")\n", + "plt.ylabel(\"prediction\")\n", + "plt.title(\"Least-squares prediction vs. Bayes action prediction\")\n", + "plt.plot(X_data_, ls_coef_ * X_data_ + ls_intercept_, label=\"Least-squares prediction\")\n", + "plt.xlim(X_data_.min(), X_data_.max())\n", + "plt.plot(trading_signals, opt_predictions, label=\"Bayes action prediction\")\n", + "plt.legend(loc=\"upper left\");" + ], + "execution_count": 16, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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qex2rHrfgzVf2qV+Hsv6/h+D9rcmMkXevc+5ivOs+Da/3S2W8c7YUuIPcoRaD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\neikpKUkxMTFq27atoqOjtXLlSg0bNkzTp0/XN99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qauHrt8JvMb/KcPqSwd7VT/9mdU/+JaMrsGCK8uvqqwbKDhlSRpGPiTnCRJkiRJGtHKCa7AqSvp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\nVd2BVVtrSZfHmjqSBceQX7i0O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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [], + "image/png": { + "width": 856, + "height": 391 + } + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "dDQHUSh8oquh" + }, + "source": [ + "What is interesting about the above graph is that when the signal is near 0, and many of the possible returns outcomes are possibly both positive and negative, our best (with respect to our loss) prediction is to predict close to 0, hence *take on no position*. Only when we are very confident do we enter into a position. I call this style of model a *sparse prediction*, where we feel uncomfortable with our uncertainty so choose not to act. (Compare with the least-squares prediction which will rarely, if ever, predict zero). \n", + "\n", + "A good sanity check that our model is still reasonable: as the signal becomes more and more extreme, and we feel more and more confident about the positive/negativeness of returns, our position converges with that of the least-squares line. \n", + "\n", + "The sparse-prediction model is not trying to *fit* the data the best (according to a *squared-error loss* definition of *fit*). That honor would go to the least-squares model. The sparse-prediction model is trying to find the best prediction *with respect to our `stock_loss`-defined loss*. We can turn this reasoning around: the least-squares model is not trying to *predict* the best (according to a *`stock-loss`* definition of *predict*). That honor would go the *sparse prediction* model. The least-squares model is trying to find the best fit of the data *with respect to the squared-error loss*." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "yeH42zITtIGW" + }, + "source": [ + "# Example: Kaggle contest on *Observing Dark World*\n", + "\n", + "\n", + "A personal motivation for learning Bayesian methods was trying to piece together the winning solution to Kaggle's [*Observing Dark Worlds*](http://www.kaggle.com/c/DarkWorlds) contest. From the contest's website:\n", + "\n", + "\n", + "\n", + ">There is more to the Universe than meets the eye. Out in the cosmos exists a form of matter that outnumbers the stuff we can see by almost 7 to 1, and we don’t know what it is. What we do know is that it does not emit or absorb light, so we call it Dark Matter. Such a vast amount of aggregated matter does not go unnoticed. In fact we observe that this stuff aggregates and forms massive structures called Dark Matter Halos. Although dark, it warps and bends spacetime such that any light from a background galaxy which passes close to the Dark Matter will have its path altered and changed. This bending causes the galaxy to appear as an ellipse in the sky.\n", + "\n", + "\n", + "\n", + "\n", + "The contest required predictions about where dark matter was likely to be. The winner, [Tim Salimans](http://timsalimans.com/), used Bayesian inference to find the best locations for the halos (interestingly, the second-place winner also used Bayesian inference). With Tim's permission, we provided his solution [1] here:\n", + "\n", + "1. Construct a prior distribution for the halo positions $p(x)$, i.e. formulate our expectations about the halo positions before looking at the data.\n", + "2. Construct a probabilistic model for the data (observed ellipticities of the galaxies) given the positions of the dark matter halos: $p(e | x)$.\n", + "3. Use Bayes’ rule to get the posterior distribution of the halo positions, i.e. use to the data to guess where the dark matter halos might be.\n", + "4. Minimize the expected loss with respect to the posterior distribution over the predictions for the halo positions: $\\hat{x} = \\arg \\min_{\\text{prediction} } E_{p(x|e)}[ L( \\text{prediction}, x) ]$ , i.e. tune our predictions to be as good as possible for the given error metric.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "innhxnSNJhZu" + }, + "source": [ + "The loss function in this problem is very complicated. For the very determined, the loss function is contained in the file DarkWorldsMetric.py in the parent folder. Though I suggest not reading it all, suffice to say the loss function is about 160 lines of code — not something that can be written down in a single mathematical line. The loss function attempts to measure the accuracy of prediction, in a Euclidean distance sense, such that no shift-bias is present. More details can be found on the metric's [main page](http://www.kaggle.com/c/DarkWorlds/details/evaluation). \n", + "\n", + "We will attempt to implement Tim's winning solution using [Tensorflow Probability](https://medium.com/tensorflow/introducing-tensorflow-probability-dca4c304e245) and our knowledge of loss functions." + ] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "hUVLtGVpIS6s", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 145 + }, + "outputId": "795365f6-9b00-49e8-dd88-32f853ecf840" + }, + "source": [ + "reset_sess()\n", + "\n", + "import wget\n", + "\n", + "# Downloading the zip file containing the Galaxy Data\n", + "url1 = 'https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/blob/master/Chapter5_LossFunctions/data.zip?raw=true'\n", + "filename1 = wget.download(url1)\n", + "filename1\n" + ], + "execution_count": 3, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Device mapping:\n", + "/job:localhost/replica:0/task:0/device:XLA_CPU:0 -> device: XLA_CPU device\n", + "/job:localhost/replica:0/task:0/device:XLA_GPU:0 -> device: XLA_GPU device\n", + "/job:localhost/replica:0/task:0/device:GPU:0 -> device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0\n", + "\n" + ], + "name": "stdout" + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "'data (3).zip'" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 3 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "ORWxu0qjBHiS", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 35 + }, + "outputId": "1fae760b-3033-4f3f-e6ca-3f2429c196dd" + }, + "source": [ + "!unzip -q data.zip -d data" + ], + "execution_count": 4, + "outputs": [ + { + "output_type": "stream", + "text": [ + "replace data/Train_Skies/Train_Skies/Training_Sky1.csv? [y]es, [n]o, [A]ll, [N]one, [r]ename: A\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "JnYd2IbTKLNg" + }, + "source": [ + "We also want to import the data files and Loss functions specific to this Kaggle Competition. You can download the files directly from the [Observing Dark Worlds competition's Data page](https://www.kaggle.com/c/DarkWorlds/data) or, if you already have a Kaggle account, install the [Kaggle API](https://github.com/Kaggle/kaggle-api) and run the following terminal command:\n", + "\n", + "```\n", + "kaggle competitions download -c DarkWorlds\n", + "```\n", + "\n", + "And once the competition information is available locally, we can simply unzip the data." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "l36JzQ3wKWzK" + }, + "source": [ + "\n", + "One last thing to set up is the function we use for plotting galaxies from the files, which we define here:\n", + "\n", + "#### Defining our galaxy-plotting function" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "C1KzXzbjKbmd", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 127 + }, + "outputId": "320a20b3-311d-42d2-bb63-cee08b05ddbb" + }, + "source": [ + "reset_sess() # Resets the default tensorflow graph we're using\n", + "\n", + "def draw_sky(galaxies):\n", + " \"\"\"\n", + " From a given file of galaxy data, \n", + " plots the shapes and positions of\n", + " galaxies.\n", + " \n", + " Args:\n", + " galaxies: 4-column, float32 Numpy array\n", + " containing x-coordinates, y-coordinates,\n", + " and the two axes of ellipcity.\n", + " Returns:\n", + " fig: image of galaxy plot\n", + " \"\"\"\n", + " size_multiplier = 45\n", + " fig = plt.figure(figsize=(10, 10))\n", + " ax = fig.add_subplot(111, aspect='equal')\n", + " n = galaxies.shape[0]\n", + " for i in range(n):\n", + " g = galaxies[i,:]\n", + " x, y = g[0], g[1]\n", + " d = np.sqrt(g[2] ** 2 + g[3] ** 2)\n", + " a = 1.0 / (1 - d)\n", + " b = 1.0 / (1 + d)\n", + " theta = np.degrees( np.arctan2( g[3], g[2])*0.5 )\n", + " \n", + " ax.add_patch(Ellipse(xy=(x, y), width=size_multiplier * a, height=size_multiplier * b, angle=theta))\n", + " ax.autoscale_view(tight=True)\n", + " \n", + " return fig" + ], + "execution_count": 5, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Device mapping:\n", + "/job:localhost/replica:0/task:0/device:XLA_CPU:0 -> device: XLA_CPU device\n", + "/job:localhost/replica:0/task:0/device:XLA_GPU:0 -> device: XLA_GPU device\n", + "/job:localhost/replica:0/task:0/device:GPU:0 -> device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0\n", + "\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "Ds4g4NTmKsFU" + }, + "source": [ + "### Examining Our Data\n", + "\n", + "The dataset is actually 300 separate files, each representing a sky. In each file, or sky, are between 300 and 720 galaxies. Each galaxy has an $x$ and $y$ position associated with it, ranging from 0 to 4200, and measures of ellipticity: $e_1$ and $e_2$. Information about what these measures mean can be found [here](https://www.kaggle.com/c/DarkWorlds/details/an-introduction-to-ellipticity), but for our purposes it does not matter besides for visualization purposes. Thus a typical sky might look like the following:" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "V6ltTec8K6ve", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 271 + }, + "outputId": "fa42925a-6eb1-4186-e1e8-78b4d4fb30e0" + }, + "source": [ + "reset_sess() # Resets the default tensorflow graph we're using\n", + "\n", + "n_sky = 3 #choose a file/sky to examine.\n", + "data = np.genfromtxt(\"data/Train_Skies/Train_Skies/\\\n", + "Training_Sky%d.csv\" % (n_sky),\n", + " dtype = np.float32,\n", + " skip_header = 1,\n", + " delimiter = \",\",\n", + " usecols = [1,2,3,4])\n", + " # It's handy to specify the data type beforehand\n", + "\n", + "galaxy_positions = np.array(data[:, :2], dtype=np.float32)\n", + "gal_ellipticities = np.array(data[:, 2:], dtype=np.float32)\n", + "ellipticity_mean = np.mean(data[:, 2:], axis=0)\n", + "ellipticity_stddev = np.std(data[:, 2:], axis=0)\n", + "num_galaxies = np.array(galaxy_positions).shape[0]\n", + "\n", + "print(\"Data on galaxies in sky %d.\"%n_sky)\n", + "print(\"position_x, position_y, e_1, e_2 \")\n", + "print(data[:3])\n", + "print(\"Number of Galaxies: \", num_galaxies)\n", + "print(\"e_1 & e_2 mean: \", ellipticity_mean)\n", + "print(\"e_1 & e_2 std_dev: \", ellipticity_stddev)\n" + ], + "execution_count": 6, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Device mapping:\n", + "/job:localhost/replica:0/task:0/device:XLA_CPU:0 -> device: XLA_CPU device\n", + "/job:localhost/replica:0/task:0/device:XLA_GPU:0 -> device: XLA_GPU device\n", + "/job:localhost/replica:0/task:0/device:GPU:0 -> device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0\n", + "\n", + "Data on galaxies in sky 3.\n", + "position_x, position_y, e_1, e_2 \n", + "[[ 1.62690e+02 1.60006e+03 1.14664e-01 -1.90326e-01]\n", + " [ 2.27228e+03 5.40040e+02 6.23555e-01 2.14979e-01]\n", + " [ 3.55364e+03 2.69771e+03 2.83527e-01 -3.01870e-01]]\n", + "Number of Galaxies: 578\n", + "e_1 & e_2 mean: [ 0.01398942 -0.00522833]\n", + "e_1 & e_2 std_dev: [0.23272723 0.22050022]\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "021sa-vWK7XK" + }, + "source": [ + "Nice, as we can see above we have the data organized into columns according to their x and y coordinates, and the degrees of elipticity along each axis of the galaxies. If we want to reference the positions directly, we can use the following:" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "4UKLxWPnuUz8", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 625 + }, + "outputId": "fca44498-b248-4e48-b0e8-458c412d80f2" + }, + "source": [ + "fig = draw_sky(data)\n", + "plt.title(\"Galaxy positions and ellipcities of sky %d.\" % n_sky)\n", + "plt.xlabel(\"x-position\")\n", + "plt.ylabel(\"y-position\");" + ], + "execution_count": 7, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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ERERERESyggJhIiIiIiIiIiKSFRQIExERERERERGRrKBAmIiIiIiIiIiIZAUF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whpCxQIIwnP6AlixmIjFh3zNGryedAWwHPb7BjzXS0ONKNU66beKlzRjb+Bo0HGgkmSBsWE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fp+fiqyylywrJBjVc2E2PR8c274CJiIjSh4Ewola4b4QVq09HsM/d9I2CXSfgT9NyMa+U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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [], + "image/png": { + "width": 609, + "height": 608 + } + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "5hIExuMFLI0O" + }, + "source": [ + "\n", + "...beautiful!\n", + "\n", + "\n", + "### Priors\n", + "\n", + "Each sky has one, two or three dark matter halos in it. Tim's solution details that his prior distribution of halo positions was uniform, i.e.\n", + "$$\n", + "\\begin{align}\n", + "& x_i \\sim \\text{Uniform}( 0, 4200)\\\\\n", + "& y_i \\sim \\text{Uniform}( 0, 4200), \\;\\; i=1,2,3\\\\\n", + "\\end{align}\n", + "$$\n", + "Tim and other competitors noted that most skies had one large halo and other halos, if present, were much smaller. Larger halos, having more mass, will influence the surrounding galaxies more. He decided that the large halos would have a mass distributed as a *log*-uniform random variable between 40 and 180 i.e.\n", + "\n", + "$$ m_{\\text{large} } = \\log \\text{Uniform}( 40, 180 ) $$\n", + "\n", + "and in Tensorflow Probability, \n", + "\n", + "```python\n", + "# Log-Uniform Distribution\n", + "mass_large = tfd.TransformedDistribution(\n", + " distribution=tfd.Uniform(name=\"exp_mass_large\", low=40.0, high=180.0),\n", + " bijector=tfb.Exp())\n", + "```\n", + "\n", + "(This is what we mean when we say *log*-uniform.) For smaller galaxies, Tim set the mass to be the logarithm of 20. Why did Tim not create a prior for the smaller mass, nor treat it as a unknown? I believe this decision was made to speed up convergence of the algorithm. This is not too restrictive, as by construction the smaller halos have less influence on the galaxies.\n", + "\n", + "Tim logically assumed that the ellipticity of each galaxy is dependent on the position of the halos, the distance between the galaxy and halo, and the mass of the halos. Thus the vector of ellipticity of each galaxy, $\\mathbf{e}_i$, are *children* variables of the vector of halo positions $(\\mathbf{x},\\mathbf{y})$, distance (which we will formalize), and halo masses.\n", + "\n", + "Tim conceived a relationship to connect positions and ellipticity by reading literature and forum posts. He supposed the following was a reasonable relationship:\n", + "\n", + "$$ e_i | ( \\mathbf{x}, \\mathbf{y} ) \\sim \\text{Normal}( \\sum_{j = \\text{halo positions} }d_{i,j} m_j f( r_{i,j} ), \\sigma^2 ) $$\n", + "\n", + "where $d_{i,j}$ is the *tangential direction* (the direction in which halo $j$ bends the light of galaxy $i$), $m_j$ is the mass of halo $j$, $f(r_{i,j})$ is a *decreasing function* of the Euclidean distance between halo $j$ and galaxy $i$. \n", + "\n", + "The variance, or $\\sigma^2$, was simply estimated to be 0.05 from eyeballing the data. This means the Standard deviation (SD) of the measurement of $e_i$ for the full range of $i$ works out to approximately 0.223607......\n", + "\n", + "Tim's function $f$ was defined:\n", + "\n", + "$$ f( r_{i,j} ) = \\frac{1}{\\min( r_{i,j}, 240 ) } $$\n", + "\n", + "for large halos, and for small halos\n", + "\n", + "$$ f( r_{i,j} ) = \\frac{1}{\\min( r_{i,j}, 70 ) } $$\n", + "\n", + "This fully bridges our observations and unknown. This model is incredibly simple, and Tim mentions this simplicity was purposefully designed: it prevents the model from overfitting. \n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "OVhlxdf1xr-4" + }, + "source": [ + "### Training & Tensorflow implementation\n", + "\n", + "For each sky, we run our Bayesian model to find the posteriors for the halo positions — we ignore the (known) halo position. This is slightly different than perhaps traditional approaches to Kaggle competitions, where this model uses no data from other skies nor the known halo location. That does not mean other data are not necessary — in fact, the model was created by comparing different skies. \n", + "\n", + "***Constructing a prior distribution for the halo positions $p(x)$, i.e. formulate our expectations about the halo positions before looking at the data.***\n", + "\n", + "When constructing our prior and likelihood distributions, we're going to use these to set up a loss function that is very similar to that of a [Variational Auto Encoder](https://github.com/tensorflow/probability/blob/master/tensorflow_probability/examples/vae.py) (although a much lower-dimensional one)." + ] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "nCCZEsmRyak7", + "colab": {} + }, + "source": [ + "def euclidean_distance(x, y):\n", + " \"\"\"\n", + " Calculates the euclidian distance between\n", + " point x and poin y.\n", + " \n", + " Args:\n", + " x: a Tensorflow tensor for element-wise\n", + " calculation\n", + " y: a Tensorflow tensor for element-wise\n", + " calculation\n", + " Returns: \n", + " a Tensor containing the euclidian \n", + " distance between x and y\n", + " \"\"\"\n", + " return tf.sqrt(tf.reduce_sum(tf.squared_difference(x, y), axis=1), name=\"euclid_dist\")\n", + "\n", + "\n", + "def f_distance(gxy_pos, halo_pos, c):\n", + " \"\"\"\n", + " Provides our element-wise maximum as in NumPy, \n", + " but instead for TensorFlow tensors\n", + " \n", + " Args:\n", + " gxy_pos: a 2-d numpy array of observed galaxy\n", + " positions\n", + " halo_pos: a 2-d numpy array with halo positions\n", + " c: a scalar of shape order 0\n", + " Returns: \n", + " Maximum of either the uclidian distance of gxy_pos\n", + " & halo_pos, or the constant c.\n", + " \"\"\"\n", + " return tf.maximum(euclidean_distance(gxy_pos, halo_pos), c, name=\"f_dist\")[:, None]\n", + "\n", + "\n", + "def tangential_distance(glxy_position, halo_position):\n", + " \"\"\"\n", + " Calculates the tangential distance between\n", + " coordinates glxy_position & halo_position.\n", + " \n", + " Args:\n", + " glxy_position: a 2-d numpy array of observed galaxy\n", + " positions\n", + " halo_position: a 2-d numpy array with halo positions\n", + " Returns: \n", + " vectors with direction of dominant halo.\n", + " \"\"\"\n", + " \n", + " x_delta, y_delta = tf.unstack(\n", + " glxy_position - halo_position, num=2, axis=-1)\n", + " angle = 2. * tf.atan(y_delta / x_delta)\n", + " return tf.stack([-tf.cos(angle), -tf.sin(angle)], axis=-1, name='tan_dist')" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "OPJ9YE9-LVrN", + "colab": {} + }, + "source": [ + "def posterior_log_prob(mass_large, halo_pos):\n", + " \"\"\"\n", + " Our posterior log probability, as a function of states\n", + " Closure over: data\n", + " \n", + " Args:\n", + " mass_large: scalar of halo mass, taken from state\n", + " halo_pos: tensor of halo position(s), taken from state\n", + " Returns: \n", + " Scalar sum of log probabilities\n", + " \"\"\"\n", + " rv_mass_large = tfd.Uniform(name='rv_mass_large', low=40., high=180.) \n", + " \n", + " #set the random size of the halo's mass (the big halo for now)\n", + " # We use tfd.Independent to change the batch and event shapes\n", + " rv_halo_pos = tfd.Independent(tfd.Uniform(\n", + " low=[0., 0.],\n", + " high=[4200., 4200.]),\n", + " reinterpreted_batch_ndims=1, name='rv_halo_position')\n", + " ellpty_mvn_loc = (mass_large /\n", + " f_distance(data[:, :2], halo_pos, 240.) *\n", + " tangential_distance(data[:, :2], halo_pos))\n", + " ellpty = tfd.MultivariateNormalDiag(loc=ellpty_mvn_loc, \n", + " scale_diag=[0.223607, 0.223607],\n", + " name='ellpty')\n", + " \n", + " return (tf.reduce_sum(ellpty.log_prob(data[:, 2:]), axis=0) + \n", + " rv_halo_pos.log_prob(halo_pos) + \n", + " rv_mass_large.log_prob(mass_large))\n" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "svjLrY2sLdQW" + }, + "source": [ + "Let's go onto the next part:\n", + "\n", + "***Constructing a probabilistic model for the data (observed ellipticities of the galaxies) given the positions of the dark matter halos: $p(e | x)$***\n", + "\n", + "Given data, we use a Metropolis Random Walk (MRW) Markov chain Monte Carlo method to calculate the precise posterior distribution over the model's parameters. It is possible to use Hamiltoniam Monte Carlo (HMC) for problems like this, but Metropolis is more appropriate for this case due to its comparative simplicity.\n", + "\n", + "Tim's model gives us an approximate posterior to start with. That is, we asume the posterior must be proportional to the normal distribution of distances inferred from galaxy ellipcities.\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "yKp1CAxlxr-_", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 145 + }, + "outputId": "1b7a6a20-a794-4722-b1a7-7635382150ea" + }, + "source": [ + "# Inferring the posterior distribution\n", + "\n", + "number_of_steps = 10000\n", + "burnin = 5000\n", + "\n", + "# Set the chain's start state.\n", + "initial_chain_state = [\n", + " tf.fill([1], 80., name=\"init_mass_large\"),\n", + " tf.fill([1, 2], 2100., name=\"init_halo_pos\")\n", + "]\n", + "\n", + "# Since HMC operates over unconstrained space, we need to transform the\n", + "# samples so they live in real-space.\n", + "unconstraining_bijectors = [\n", + " tfp.bijectors.Identity(),\n", + " tfp.bijectors.Identity()\n", + "]\n", + "\n", + "# Define a closure over our joint_log_prob.\n", + "unnormalized_posterior_log_prob = lambda *args: posterior_log_prob( *args)\n", + "\n", + "# Initialize the step_size. (It will be automatically adapted.)\n", + "with tf.variable_scope(tf.get_variable_scope(), reuse=tf.AUTO_REUSE):\n", + " step_size = tf.get_variable(\n", + " name='step_size',\n", + " initializer=tf.constant(0.06, dtype=tf.float32),\n", + " trainable=False,\n", + " use_resource=True\n", + " )\n", + "\n", + "kernel = tfp.mcmc.HamiltonianMonteCarlo(\n", + " target_log_prob_fn=unnormalized_posterior_log_prob,\n", + " num_leapfrog_steps=6,\n", + " step_size=step_size)\n", + "\n", + "kernel = tfp.mcmc.TransformedTransitionKernel(\n", + " inner_kernel=kernel,\n", + " bijector=unconstraining_bijectors)\n", + "\n", + "kernel = tfp.mcmc.SimpleStepSizeAdaptation(\n", + " inner_kernel=kernel, num_adaptation_steps=int(burnin * 0.8))\n", + "\n", + "# Sampling from the chain.\n", + "[\n", + " mass_large, \n", + " posterior_predictive_samples\n", + "], kernel_results = tfp.mcmc.sample_chain(\n", + " num_results = number_of_steps,\n", + " num_burnin_steps = burnin,\n", + " current_state=initial_chain_state,\n", + " kernel=kernel)" + ], + "execution_count": 10, + "outputs": [ + { + "output_type": "stream", + "text": [ + "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/resource_variable_ops.py:1630: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "If using Keras pass *_constraint arguments to layers.\n", + "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_probability/python/distributions/uniform.py:182: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "Use tf.where in 2.0, which has the same broadcast rule as np.where\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "Xpl5Lw0tZmWx" + }, + "source": [ + "We new have the setup for our probabilistic model. We can now go onto the third step:\n", + "\n", + "\n", + "#### Using Bayes’ rule to get the posterior distribution of the halo positions, i.e. use to the data to guess where the dark matter halos might be.\n", + "\n", + "We're going to take the results of the outputs of the Markov chain, take the mean and standard deviation, and then use those to create a lower-dimensional multivariate normal distribution of halo distributions. This will be our posterior predictive distribution.\n", + "\n", + "First, we can create the convenience functions to set up our session" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "VDsln5XWxr_K" + }, + "source": [ + "Now we'll eval the results using our `evaluate()` function and see if it matches our expectations." + ] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "fI4tDYE5xr_K", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 305 + }, + "outputId": "ec6ac23d-d122-4a5b-b6ee-2dbb9d91729b" + }, + "source": [ + "\n", + "# Initializing our variables\n", + "init_g = tf.global_variables_initializer()\n", + "evaluate(init_g)\n", + "\n", + "# performing our computations\n", + "[\n", + " posterior_predictive_samples_,\n", + " kernel_results_,\n", + "] = evaluate([\n", + " posterior_predictive_samples,\n", + " kernel_results,\n", + "])\n", + "\n", + "print(\"acceptance rate: {}\".format(\n", + " kernel_results_.inner_results.inner_results.is_accepted.mean()))\n", + "\n", + "print(\"final step size: {}\".format(\n", + " kernel_results_.new_step_size[-100:].mean()))\n", + "\n", + "print(\"posterior_predictive_samples_ value: \\n {}\".format(\n", + " posterior_predictive_samples_))" + ], + "execution_count": 11, + "outputs": [ + { + "output_type": "stream", + "text": [ + "acceptance rate: 0.587\n", + "final step size: 20.03693389892578\n", + "posterior_predictive_samples_ value: \n", + " [[[2320.5542 1087.7014]]\n", + "\n", + " [[2335.7424 1106.9401]]\n", + "\n", + " [[2315.682 1150.1962]]\n", + "\n", + " ...\n", + "\n", + " [[2335.8533 1114.5752]]\n", + "\n", + " [[2320.6536 1138.3337]]\n", + "\n", + " [[2320.6536 1138.3337]]]\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "Ak2DGvKmLqyv" + }, + "source": [ + "Below we plot a \"heatmap\" of the posterior predictive distribution. (Which is just a scatter plot of the posterior, but we can visualize it as a heatmap.)" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "-5HddB8d1A7X", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 625 + }, + "outputId": "e356a2c5-6e70-49e7-f266-15a65714ee8c" + }, + "source": [ + "t = posterior_predictive_samples_.reshape(number_of_steps,2)\n", + "fig = draw_sky(data)\n", + "plt.title(\"Galaxy positions and ellipcities of sky %d.\" % n_sky)\n", + "plt.xlabel(\"x-position\")\n", + "plt.ylabel(\"y-position\")\n", + "plt.scatter(t[:,0], t[:,1], alpha = 0.015, c = \"#F15854\") # Red\n", + "plt.xlim(0, 4200)\n", + "plt.ylim(0, 4200);\n" + ], + "execution_count": 12, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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OGPMW8H9YhfOPwKq7tElEvhZrZMioI0tGIyKzgK+xanTtiHWS3EzncRnMlol1rCe1HXTu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tkOh4PBgwfH3F50PQdXAuT3n97kdXC0b+cFeHJnSbPfG9c7j8GDk2sso2t7GVQ3f7VnVHDz\npD70iDJoINpr4IA7hC8cPQNlzvACBvdv3WqcSA5yHojP8cAd7fj4WzeW0tD69hBvWPHqfjPvllmo\nC8R3g3bOyB4M7hY7w+xI8joQ8hoQ0HVfB/fYvVw8vzzu0spCu4E/zyxkcBr2cOyqr4HWSHTm2E+B\nEwA38CPgX1rro16SSqkrgKcat/0J8GAiD0Ip9QQNmWvFwEyt9ZFX+wdTqaJ1kj6Y7dV0bFdH7xeR\n1voZ4Jl4tq2urv6YOLPMhBAinYzKNXNikYWlzYz4Pr7Q0glHFF20wNfpvW1Rvx9LWYzpnFYjzOxp\na/XjCyEOyY0y2CLewFimSXF8QfKdp4QQorOd2svGAxOyuWdFdcxplVO6WXhqag69M6URf7pLdKe/\ny2gonbxea/1sc4ExAK31czQEzxQJzupSSj0G3ERDf7OZWuvmimd3NP7ZN8pD9T5i26Z/79PC/Q72\nNnM1NuePdz8hhBDt7P+OPfq0XGAzMD4JbzonRgjYKeCmUW0bkFDmjV4udslAB1kWaRDclQXDmgiX\nbiLBTu7R8myvI317sAOLMfkGhwghRDK4bmQmS+cUcuUQB0OyTTS9hOmVYeSSgXY+nl3AO2cUSGBM\nAInPHOtHQ474i3Fs+wLwFw41q28zpdQjwG1AOXCK1np9hE1XNf45UilljzCxcsIR2wJ8A3iAXKXU\nwAgTK48/cj+tdbVSaisNEysnAAvi2U8IIUT7m9zNyg9HZPDn9YfaQv5qQjYGlXw3nbN627AbFZ7Q\n4QGMa0dkMKmo7TfbkRgU/DDFp5OK5lX6wtyytJJ5O70ENXR3GPj2IAfXj8wkz9b6TEQR3RWDHfx2\nbS0HPK3r45dvM3DH2OQq+xZCiGQzxGXmick5AIS1ptQTJttiwJaEE8lF50v0EnAV4NVaR581DzRu\n4wGqE/HESqmHaCjRrARO1VqvjfLcu2mYmmkBLmzmsabR0BetGFjWZD8/8F7jPy9rZr8BNJSK+oF5\nR3x7bpT9soDZjf/8T6TjFkII0T4emujivglZDMoycevoTC4dlJx9tfJsRn432XXY187tZ+O+Cdlt\nfuxoJZlhDT9YUok7KA35u5prF1cwd4f3f2Un+91hHltbx0lzS/m6Qkbat5cMs4FftfJ9azbAH6e4\nJHgp0povpFla7OPFzfX8/utafr2qhifX1fHOTg9ryv34Q5IFKw5nUIoih1ECYyKiRGeOfQJcqJQa\nESVrCwCl1EgaJkm+39YnVUrdT0Pf1CoaAmPxZF/9GngNeFgptVRrvaXxsQqBJxu3eUhrfeSdwEPA\necAdSqn3tdbLG/fLBP5BQ8DxSa111RH7/Q64DrhSKfWm1vqtxv1MNGTQZQFvxvq5CSGEaB83jnJy\n46jkz8S4eKADk4LPiv2c2cfGKb0S0wdsgNOEw6RwR2jO8XVFgNuXVfPU1JyEPJ/ofJuqAny0p/kB\nD3vdIc6YV8pzM3KZLr3m2sXFAx2sKvMflrUai90Iz83IS9j7XohU4gtpXtnq5r1dXhbv91EfpZlU\ntkVxfn87N41y0j9LSuaEELEl+kxxP3AW8LRSapbWutmssMZMqb/T0Lj/vrY8oVLqHOD/Gv+5BbhR\nNV8K843W+qGD/9Bav66UeoqGgNXXSqn5QICGCZdZwJvAH498EK31CqXUncDDwFKl1EIagnLTgELg\niybH03S/3Uqpq4HngDeVUp8C+2iY7Nm38divbflPQAghRLq5YICDCwYkNrvNaFB8d6iDJ9dFvlF/\nfZubXx+fjcsqvce6gs9L/ETLragLar77cQWLzymkr1NuLtvDg8dns70myAcRgpRHmlBokcBYiqrx\nh/lwj5d1FQGUglE5Zo4vtNBLeh3F5f3dHn68rJo99dH7Yx5U7df8c6ObN7Z7mHt6PuPyk6+PqBAi\nuST6bFwD/ICGzKtvGoNPnwB7G7/fg4Yg0nWADfg+UKeUOqrBvdZ6V5zPmdvk7+Mb/2vOJzRkfTV9\njusbg1Q/ajwuIw19xf4BPNVM1tjB/R5RSq0Fbqehh5gN2Ab8HviN1rrZKxyt9UtKqW3AXcBkYCKw\nG3gUeCBSMFEIIYToCHeNy+KFzW6q/c2HTAJhWFHq51S5Oe8S4um9X+3X3Lq0in+fnt/+B5SGDErx\n9Mm5XDq/nCXFR0/NPdLi/X6Wl/g4vrD9egyKxFuw18uNn1ayz334rYVRweWDHdw7XhYdonljm5ur\nP6ls1b41fs15H5Sx8ZLuWGWAhRBtorUmQiJSl5Do4Nj2Jn/PAn4RY/sXInxdE+exaa2fAZ6JZ9sI\n+79IfAMEjtzvfVpREqq1/gKY09L9hBBCiPZW6glTFyEwdtCeuvhW7UXy6xmlz1xTC/f5JCDTjjLN\nBt44LZ+bl1bx0hZ3zO2fWlcvv4sU8sFuLxfPL2/2eyENz25ys/SAn4/OKpAAWTMOuEPctuzIbjUt\nU+XX7KsPSXmlEG2wZL+P6z+tpNIbpmeGkW8PdnDt8Mwu1cMt0WeIRP1kus5PWAghhEgRL21xEyv0\nZZJ7ty5jancrWRZFTYyAKMAb2zxJHZBZVxFg4T4vvhD0cBj49uCMdnuusNY8u9HNe7s9bKwKUhMI\nU2gzUmg30Ndp4qTuVk7tZSOnBYEOi1Hx1NQcJhRYuGdFddReShurZVBCqgiFNXd+ETuws7k6yFUf\nV/DaqXkYDXIb1NSm6mDEbOZ4jcs30ztTBlgI0RY3fFrJ7sYF0o3VQX7xZQ3PbXLzzPRcRuWaO/no\nEiOhwTGttVwyCyGEEClqwV5vzG3McuPWZViNirP62OPKVnprp4eHJ7libtfR3tvl4bdr61heenhJ\notGguHhg4qfOVnhDXLqggi9KDn++Sl+QjdWwpNjP85vdWAxwai8bd47LYnQLbhquGpbBGX1s3PdV\nDS9vdRNuJiZQF5ApfKlid32I7bXxZdsu3OfjnV1ezu1nb+ejSi2jcs0U2g2UeFo3LTnbovjrSTmY\n5LNLiFZbW+5nZzOVA1tqgpzzfhlvzcrvEgEyCWYJIYQQAk9Q83VF7IyUbg65dOhKbhyVSTwVEfvd\nYfyh5AnKlHtDXLmonEsXVBwVGAOo9IWpDYTZXRdM6HE/t9l9VGCsOf4wzNvlZdpbJdzwaSUH3PGX\nI3d3GHlyag6LZhdwyUA7WZZDvyAF3DYm+afqigYlnpaVob+53dNOR5K6cqwG3jw9n3xbyz97zulr\nY+mcIgZnp/5NuxCdKVpwusIX5pz3y1gXxzVkspPCayGEEEJwwBMiEGNh3qCQiV9dzIgcM7cd4+SR\n1bUxt02W0Ni8nR5uWVpFqTfyC/bJdXXc+UXDnCOnWTGjp5VrhmcypVvbSkNbmrUV1vD8Zjcf7vHy\n6il5jG3B++eYPAt/PimXUFizrjKAQSl6ZRilL1UKyba07He1sy7YTkeS2kbkmPny/CKe2VjPq1vd\nrK9q/udkUDA+38JpvW2c2cfGiBwJigmRCLGC0xW+MFcuqmDxuQU4Urj/RocEx5RSbwAurfXMjng+\nIYQQQrSML47smgkFlhbf7Inkd8cxTjZUBnh7Z+Sy2hE5pqSY9PbAyhoeXRM7kLerSflHbUAzd4eX\nt3Z4uWOskzvHZbX6+b89yMFv1tS2OFBY4gkz54My3j+rgGGult2wGw2KMXkSlE5FQ11mBmYZ2VoT\nXwZZgV36YkXishq4ZYyTW8Y48QQ1e+qD7K4LURvQZJoVhXYjfTONZMlnlBAJNzjbhMXQkBUdyZaa\nIA+srOWB47M77sASrKPOHicCJ3fQcwkhhBCihWJljQFcNFB64XRFRoPi2em53DAyM+I2PxwR+Xsd\nIaw1N31WGVdgLBINPLS6lpfj6LEWSf8sE9eNbF2z/yq/5sGVNa1+bpGaftaCYOxoyXSKi92kGJxt\nZkZPG+f2szOzp43RuWYJjAnRTjLMBmb1tsXc7i/r69hYlbrllXIGEUIIIQSF9uiXBH0yjVzRjhMA\nRecyKMX9x2fz0sxcJhYeylKyGOD2MZlcPjjxze3jFdaa65ZU8q9NrQ9qNfWLL6upjScaHMEDE7L5\n4YjWvRfe2+2l0tf65xap54IBDm4eFTu4XGg3cH0rA69CCNHerhgS+/wU1PD0N/UdcDTtQ3qOCSGE\nEHEIhjWL9vlYWuxjS02Q3plGpnSzcmafrpFNVWg3MsBpZFuEyWo/HevEkgRldaJ9ndHHzhl97JR6\nQoR0Q7+uDHPnrqU+sLKGV7YmrlH5AU+Yd3Z6uXRQ6wJ+SikemujipO5Wfr6ihi018feJMiqwxXgf\nzd3hYUNlgGyLgWk9rNI3qQv4xfgsXFYDj6yuxdNMCfswl4m/nJRDrk3KKoUQyWlmTyv9nEZ2xJjA\n+9o2N/dNyE6KVgwtJcExIYQQIopQWPPyVje/WVPL9iMuCJ5cV8+Dx2dzfZRytFRy2eAM7mum7Ov8\n/nYua2UgQaSmZOl9NG+nh8fX1iX8cddXtr3s48w+dk7tZeMf39Tz1w11MftKWY1w/4Rs7FHGg36w\n28uViyoO+9qYXDPXjsjgkoEOjIbUu9kQDZmZt45xcukgB29s97C23M+2miBFdiNTulv53tCMlLyR\nFEKkD4NS/Pr4bC5dUBF1u0qf5oPdXs7pl3qLxxIcE0IIISJYXebnmsWVbK6OnBny4hZ3lwmO3TI6\nk6UHfCzY6wNAARcPtPOHKTkoJTduomNtqQ5w3ZLKuJrfn9/PxtydXuKYK5FQZoPi2hGZXDsik9Vl\nfubv9bGqzM8BT4hidxhPUNMjw8ix+WauG5kZsxl/qffoANvaigA/+rSKP6+v54kTXRxbIM35U1U3\nh5EfdZHPCyFE+jmjj51vDbDz+rbo2dxryv0SHIviVaD1o4GEEEKIDvbi5npuXVaFL8aQMa07+G68\nHRkNiudm5DJ3h5e99SHO62dnYLaso4nOccvSKmoCsd9f5/az8ddpuVR9VM7Cfb64HvvEosQHmMbm\nWxib37bHPT5K4OvrigCnzCvl+8MyuOe4LJydXO4qhBAi/fxmkov1FQHWV0VeON7vjt5bc1tNkE3V\nAQpsRoa6TGQmyedZQq94lVKnaK3nH/l1rfXNiXweIYQQoj09ta6Ou5ZXx7XtgKyuFTxymAyt7sUk\nRKJ8tMfLp8X+mNtdNtjBEye6MBkUDxyfzdS5JQRjxNNyrIrJ3awJOtLEGuIyM7WbhSUR/t/DGv66\noZ6P9nh5YWae9CMTQgjRoVxWA2+dkc/s98rYECFAlmFuvtpg7g4Pj62pZW3FodYGvTKMvHNGPv2c\nnX89negQ3YdKqW1KqV8opfom+LGFEEKIdrdgr5f/WxFfYAzge0NlupgQifanddH7jBkU3DnWyZ+m\n5GBq7MM1PMfMfROyiVYAbFDw+8k5ZFmSY5W6OQ8cn02UtmQAbK8Ncdo7pby7K3GDCoQQQoh45NuM\nvH1GPsflN79Ac3Yf22H/rvKFufrjCq5cVHFYYAxgT32I366tbbdjbYlEXxm4gX7Az4GtSqmPlFKX\nKKWSc3lOCCGEaGJ7TZCrPq4gHGel5NRuFmb0tMXeUKSdQFizZL+P362t5cFVNczd4aHGH73MQByy\nsixy1tgAp5H3zsjnznFHd+y4bmQmL5+SR3/n0QMFRrhMvHpKHrP7JncflDF5Fm4Z7Yy5XV1Qc8XC\nCp7+JvEDC4QQQiQXX0hz31fVXLu4guuXVPLKVjf1gc67rsi3GfnwrAIenphNN/uhsNIF/e1MaZKd\nvbU6yJS5JbyxPfJizn8r2j4kJxESnbtWBFwCfA84EZgJzACqlVIvAv/UWn+V4OcUQgghEuKeFdVU\n++OLjPVwGPj7tNx2PiKRatzBMH9ZX88f/ltHhe/wi9axeWbmn13wv0wn0Tx/SFPTzPvQqOCqoRnc\nOyELhyny+u7pvW2c3rsbGyoDfFrc0INsUJaJaT2sGFJksMRd45ysrwrw7i5v1O1CGm5fVo1JKa6U\nLFYhhOiy/rahjseaTG9+cYubbIviqqEZ/PgYJxmd0LfL2GQoTZUvTG0gTO/MQyGmPXVBZr9fyr4Y\nPcg6McZ3mIT+BLXW9Vrrp7XWU4ChwMPAfsAFXAcsV0qtUUrdqJSSOwohhBBJY0dtkHkxbkQPshnh\n+Rl5FDmOzk4R6esf39Qz9vUD3PtVzVGBMYDV5QHWlifH6mgysxgV5zWZcpVrNXDN8Ay+PL+IR09w\nRQ2MNTU8x8w1wzO5Zngm03vaUiYwBg03HE9Py2ViYXwN/m9bViUllkII0YU1F0Cq9mt++3UdJ75Z\nwsf74ruGbS8uq+GwwJgnqLl4fnnMwBjAqNzk6J/ZbuFFrfVmrfVdQB/gbOA/QAAYDfwO2KuUekUp\nNUvJfHghhBCdbNFeH/HkjGVZFK+cksexUabKifRS4w9z2YJybltWRYkn+kWg1SiXPPH4x8k5fHNx\nN7Ze2o1Nl3Tj0Uku+nex4Rex2E2Kl0/JY2gcE2NDGq7+uJLlJfFN6xRCCJFaemZEXpDdWRdizgfl\n3PRZJZ5YU2k6yB/+W8u6ysgTLZvyhZIjdazdc++01mGt9bta628BPYFbgPWAFfgWMA/Y2djEv1t7\nH48QQgjRnKCOfTExwmVi/lkFTOshfcZEg731IWbNK40r69BhUvTOlGzDeCil6OYwkmczpnUZao7V\nwLwzIzc9bsoT0nxnYQVVzWQtCiGESG1n9rGRbYn+efivTW7Ofq+UEk+og46qefvdIZ74Ov5+mMtL\nkyOrvqMLU/vRUG7ZA9CAavyvFw1N/LcppX7RwcckhBBCMChKVkqe1cC947NYMLuQIa7kSP0Wna/G\nH+bCD8tYH2GU+ZEuG+RI6imJIjkdnAo2u2/soHyxJ8zPv4x/2q4QQojUkGk2cNOo2MNavioLcPq8\nUnbVxXdt0h6e+LqW+hZksO2uC/FlaeRBPB2l3a/QlFIFSqlblVJrgeXAD4EcYA1wAw2BsiuApYAN\n+LlS6qftfVxCCCFEU9N72rh3fBYDsxoyeywGmFBg5t7xWay5sIibRzuxm9I3g0UcLhTWXP1xRdyB\nsSyL4idjY1/UivQT1jrmxDGHycC/pufyf+OcxKrMfX6zm/WVybEKL4QQInF+NDKTwXGU2m+vDXHG\nvDL21XdOBtl7cfbwberNKNMsO0q7tJR8tAAAIABJREFUNG9QShmAs4CrgDMbn0cBNcBLwN+01iub\n7PIC8IJS6mrgb8APgEfa49iEEEKISG4e7eTm0U5q/GEyTApjGpdzieh+s7aWj/bG19/JpOCZk3Mp\ntEtJZbrbUh1g7g4vS4p97KgNUuULUxPQhDWYDZBvM9An08RQl4npPaxM72HDZW1Yy1ZK8ZOxWZza\ny8YtS6tYHWG4Q1jD69vc/Py47I78XxNCCNHObCbFCzNyOeWdUmoC0TOz9rpDXL6wnHfPKMDWgYu7\nFd4QO+taHpT7qqzzM8cSGhxTSo0AvgdcDhTSEBCDhqywvwGvaq0jhgS11k8rpR4G+ibyuIQQQoiW\nkNI3EU2ZN8TvW9BL4+FJ2czoKX3q0lkwrLnji2qe/qY+4jaBMOx3h9nv9vNFiZ9/bXJjVDC+wMLV\nwzK4oL8do0ExNt/CgrML+MuGeh5cWUNdM6Ura2QqqhBCdElDXGb+Oi2Hby+oIByjcnFlWYCbl1by\nl5NyO+bgaAjgGRQxj+1IG5Ig4znRV///BW4DioBy4HFghNZ6itb62WiBsSbq2uG4RJLzhTTPbarn\ngg/LmPzmAW5dWkm5t3MbCQohhBDNeW6TO65eGgr4+XFZXD0ss/0PSiS16z+tjBoYiySk4YsSPz9Y\nXMnUt0pY2diTxWhQXD8yk9UXFvF/45xHDXoYlSO9EYUQoqua1dvOveOz4tr2la0entnY8s+f1nKY\nDAxuxXTpKr+mNkaLgfbWHmWV84G/A29qrVsT/ptMO5V7iuRU5Qtz4UdlrGgypWJdZZDF+328cVo+\n/ZzyckhnwbBmQ1WQrdVBij0hemcYOaHIQq5NypOEEJ1jY1Xsy5sss+IPU3I4t5+9A45IJLvlJW0v\nF1lfGeT8D8tYNLuQ/o03Hvk2Iz8Zm8WPj3GypjzAAU8Yh0kxuZulzc8nhBAied04yonZoLjri2pi\nLdf96qsa5vSz/69Mv709fqKLyxeWU+lrOLIMk2JSkYUFMdpRlHnCOM2dlyeV6KhDf631zrY8gNZ6\nb6IORiQ/rTWXLig/LDB20NaaENcvqeTdMws64chEZ/OFNH9dX8eT6+vY7z58FcFigHuOzeKGUZko\nJT2hEmlzdYBHV9eyeL+PuoDGaVFMKrQytbuVWb1t9MiQoKQQ/hgLm6f3tvHQ8dn/C2AIce3wTO5a\n3vYpklV+zS1Lq5g7K/+wryvVUG4phBAiffxwRCZFdgPXLakkWtFVhS/M09/Uc/sxHTMYaHI3K+su\n6sb++jA1gTAjc8xU+8MMebk4aiDPEmviTDtLaFiurYExkX7e2ull2YHIq6lLD/hZnQTN+UTHKveG\nOPWdUu75suaowBg03Jje82UNty+TcfWJtOyAj0n/KeHVbR6KPWHqgpr97jD/2eHhtmVVHPN6MTd+\nWkmxW0qeRXq7YrCD5nrbntzDykdnFfDKKXkSGBOHuW5kJtcOz0jIY7WwjYsQQogu7Lz+Dt47s4Bh\nrujXHW9sd3fQETVwmAwMzDYxLt+CxagosBuZUBB9ESejk6fCS28v0Wm01jywsibmdkuK45sGJrqG\nMm+I2e+VsbYidtnSPzfWs70m2AFHlR6e+LqOUJS7rkAYntvsZvwbB/hPB3/ACpFMpve0sXROIX+Y\n7OKnY5385aQc1l/UjTdPz2dCoWTviOY9PMnFvDPyObGo9a+RwdkmHj9BplAKIYQ4ZFy+hcXnFPLj\nY5xEqkosbibhoKPN6R+91YSjk4NjsqwpOs1/K4Nsqo4d2Cj1dP4bWXScny2vZn1VfAEvDawq80uG\nRoLE+3FUF9Rc9XEle+tD3DCqY9KzhUg2Q1xmhrik6blomcndrLx7ZgEbqwJ8tMfLwr0+lh7wRS2H\nARjmMjGnn53rRmaSLdN0hRBCHMFiVNx9bBZz+tl5eHUN7+7yHrbo7bJ0fiuaOf3s3L2iutlJlv2c\nxk4vq5Q7StFpDk5ciqXcJ8GxdLG7Lsjr2+IZanuIrZNXGLqSwdkm3tsd37YauHtFDUaluG6kTOIT\nQoiWGOoyM9Rl5oZRTvwhza66ILvqQpR5w9T4w2SaDeRYDbgsiv5ZJgrt0u9RiFSwuTrAAytrCWtN\nd4eR7wzJYGSuLKSIjjMq18xzM/LYVx/i7Z0eit0hjEpx2WBHZx8aPTKMXDrIwQubj65Amdbd2glH\ndDgJjolOs7s+vr5FRXZZIU0Xa8sDza4kRDM0Wy44EuWqYRn8ZUMdvha0FPvlV9Wc0ccmU2WF6ARV\nvjDrKxtK0PtkGumRYcQgQ0pSjsWoGJRtZpB8nrUbf0hT4gkR1FBkN2KXhTXRTi78qJwdtYcupP66\noZ5vDbDz0MRs8mTSuuhAPTKMXDsi+Rawf3lcFh/s9lLmPTwB5uKBnR+8k7sZ0WmyzfFdmByTJ/1b\n0oUvWsOrZszqbWNgtpzGEqWf08Q9x2Zx94rYvQAP8oXgT+vqeHSSqx2PTAjRVLE7xE2fVTJ/r++w\nBYUMk+KcfnauH5nJaMlUEIJ99SEeW1vLc5vq/zdl1qQaMiuOL7RwVh8b03rYOvcgU9gn+7z8aV0d\no3LNfGuAgxE56X3eqfSFDwuMQUOm/WvbPHxZ6ufVU/MYLEFwkeYK7Eaen5HL1R9XsrdxyNdtYzI5\nsZtkjok0lh9HiYDDpDgpCVIsRcdoSf+eng4jD02UpsSJdsMoJ56g5oFVtXHvsynOHnFCiLbbWh3k\n9HdLj1pxBagPal7a4ublLW6uG5nBfeOzMRokQ0akJ6013/qw7Kg+pkENq8sDrC4P8NcN9Qxzmbh9\njJMLBthTOvNyU1WADVVBtlQH2ecOYTVCvs3IwCwT07pbcVkTX4nxwmY3H+7x8eEeH4+vrePigXYe\nOD6b/DTNkCr1RE69317bMIn9uRl5TJV7G5HmJhVZ+WxOIc9uqqfQbuTigdEb9XcUCY6JTjMuP3Yg\n5NrhGeS0w4e5SE6jcs2c3cfGO7u8Ubfrk2nkrVn5UsrXTn4yNoueGUZuXVYVV4mlt4UZf0KI1nts\nbW2zgbGmNPDkuno2VQX55/RcnJFGVwnRhe2oDcU14OebqiDXLK7k2U31PD0tlyJHagV2/r3NzeNf\n1/HfKFO+zQaY0s3KpYMcXDjAjkpQELDfEQORXtnqYcFeH3+c4mJW7+S42e1IA7JM2IxEHLBR5ddc\nMr+ceWfkMzZfKmNEenNZDdw8OrkGe8nVkug0w1xmpnSL/MGQm4RvGNH+/j4tl2uHZzT7PYdJce3w\nDD6eXSCBsXb27cEZfD6niHP72WJOsYz0+xJCJN7q8viG2QDM3+vjp59Xt+PRCHG4Mm+IT4t9vLvL\nw9wdHubt9PDJPh/73S1oZpkgfTKNOONs4QHwabGfqW+V8Mm+6At0yeTBVTVc9Ull1MAYQCAMi/b5\n+MHiSs58r4w9dYnJ+J7aTBlUmTfMJfMr+OWX1WidXotnJoNq9mfSVH1Qc+mCcspijagVQkS1ttzP\nI6truOHTSn62vIqP93kJt/GcI3eXolM9OsnFuR+UUeI5fBXcZVG8empeu6SAi+RmMykenuTiu0Mz\nWLzfx+66EN0cBgZlm5hU2D5lAaJ5/bNMPDs9j01VAd7e6WXxfh/LS/x4QhqTguMLLdwx1in9WoTo\nQD0dRtZXxn9j+9IWN9cMy+DYAslSEIlX7g3xylYPH+7xsq4iQGmUrMZcq4EROSam97BxySAHPTPa\nN0PLaFCc1svGG9vjn4Jd4glz4UflvHl6flL0v4lm/h4vj6yOvwXCQcsO+Lnwo3L+MQJMbbykmtLN\nwuhcM183E5z73dd17HeH+OOUHMxNyrt31gZ5YGUNq8sDlHhCDM42cX5/B98bmtElJpBfMSSDj/b6\nom6z3x3m2sWVvHFafgcdlRBdy5/W1XHPiurD+q4+ua6e4wssvHVGPjZj684lEhwTnWp4jpkPzyrg\nxk8rWXbAT5bFwOy+Nm4e7WRAlrw809nwHDPD07yxa7IY4jJzu8vM7cc4CWuNLwRWIyndm0WIVDWx\nyBrzxutIi/b5JDgmEkprzf0ra3hyXT2eOEvrK3xhPi3282mxn4dW1/DtQQ4emeTC2sqbmHj8emI2\nnxX7KPZEL0Vuyh+Gqz6uYNl5RUnd2mN5afxZpEfaUBXkpX0mrujVtgwypRR3jnVy2cKKZr//ylYP\nZd4wz07PJdNsYGt1kJnvlFDlP/SaWVEaYEVpNa9sdfPcjFx6Z6b29f85/ewcm29mZVn0bL4Fexsy\nLM/sk37lp0K0xdbqID8/IjB20PJSP6vL/Ewqat3iRvKe8UXa6Oc08fYZBZRc2YPNl3Tjick5EhhL\nUlW+MO/v9vCbNbW8stVNpS/+i03RNRiUwm5SEhgTopNcOyKDXi3MuNmdoBIqIQ66bVkVj62tizsw\ndqRAGJ7d5OY7iyoINHeHkyCFdiMvn5JHXguDXMWeMC9tcbfTUSXGyDYuIG5zJ+Y28Ky+dqZGaZOy\nYK+P2e+XUeYN8fMvqw8LjDW1ujzA9LdL2V6T+uerXxyXFdd2939V0+YyMCHSze//W0u0j55gG25P\nJTgmkoZBKZmqlcT+tqGOka8Wc8n8Cu5fWcO1iysZ93oxS/a3LINBCCFE6znNBp6dntuim33p0SgS\naVtNkH9uTEzg6IPdXha1MBOypcbmW1hybiEnFLUse/Kz4uS+vjm3n52ZPVtf+nlMVuJ6Xj0yyUW0\nAZWrygLMfq+MeTEGLpV5w1yzuCLle5VN62HjwgGxM8LWVwV5fVv8Zb9CCJot404UCY4JIWJ6eHUN\nP/m8mvrg4RcrVX7NBR+WSYBMCCE60HEFFhbOLmCEK3bQq7vDwHeHytAMkTguiyIjQb2hDAqKHO1/\nO9Ijw8jbs/K5a5wz7ib9mS1o5t9Z/nFyLuf2a3nfzyuHOJjTLXHBseE5Zu6fkB11mw1xTA4F+LI0\n0KI+ccnqtye6GJod+xz92tbkzlAUItm0JTMsFgmOCSGiWlvu59EoDV/9YbhnhUxDE0KIjtTXaWLh\n7EIenpjNoAitCGb0sPLJOYUyyEQkVK7NyEun5GFvY68wBTxxootj8jqmH57JoLhjbBZrvlXE3cdm\nRS1PHppt4o6x8ZXGdaZsi4Fnp+fxxml5nNvPFjNoOSTbxB+nuPjtia6EH8v3h2cyu29iBvTM25k6\nE0MjyTQbeOXUPPJt0c+/nx3wE2rH0mIhupphOe2XDS959kKIqO78oppgjM/s1eUBvqkKMMwlDfSF\nECKRSjwh5u308nmJj9qAxmlWXNDfwam9rNhMimtHZHLtiEy2VAfYURtid12ITLPi2HwLA+PIWhCi\nNU7qbmXh7AJ+s6aWt3d68LdgJV8BJ/ewctsYJ1O7d/xEyFybkR8f4+THxzjZURtkeYmfVWV+3EGN\n02xgfIGFc/rZUqq35syeNmb2tOENaj4t9rG5OkiJJ4QnpCmwGRmUbWJItqndBx39YXIOq8pK2FPf\ntqy0T/b7CGudUr+D5vRzmnj3jHwuml/OjtrmfybuoKbYE2736a1CdBWz+9p5dWv7ZJfKVZMQIqJS\nT4hlB+KbhrS7LiTBMSHSUKknRI7VgEl6RibUl6V+fvVVDZ8W+46ayPTKVg8nFFmYe3o+lsbsnUHZ\nZgZld/1zsDeoqfCFqQ+GcQc1WkOWxUC2RZFtkddhRxqeY+bpk3Op8IZYtM/HusoA6yoCbKgKUhsI\nEwiB0QAZJsUQl5kROSaGu8xM62GlT5JMJOznNNHPaeKigY7OPpSEsJkUp/SycUqvznl+l9XA09Ny\nmP1+WYsCpkeq8IXxhcCeHC+TNhniMjP/7AIuW1DBFyVHX1NbjdDNLtm9QsTrlJ42Cu0GSlowhThe\nXeCUI4RoLyvLAsSb6F3mlcmVQqSLsNb8aV0dz250s6UmSIHNwF9PymF6z8SU1KQzT1Dz8xXV/P2b\n+qjn32UH/Ly0xc2VXbSfWLE7xNryAOsrG/5bVxlge20Id4xUZqdZ0SfTyDF5FiYVWZjRw0qvJAnE\ndFW5NiMXDHBwQWcfiEgKE4us/GlKDj9YXBn3NeSRHCYVtcF/qsm3GXlrVj6//7qWJ76uo67JeWxK\nN2uXH0i2aK+XP6+v41sDHFzYRQLRovPYTYrfTHLxnUUVCX9suVoQQkTkb0EPhBxr1/5gF0I0qA+E\nuerjCj7Yc2gQR6k3zG3LqlhxfpFk7rSBN6i5eH45i+MccrLfnbiG2smg2B3i5S1u3tzhYXV566ZR\n1QY06yqDrKsM8uIWNwYFlw1y8IvxWeR3pbttIZLYhQMd7K4P8auvalq1/9TuVlSKl1QeyWpU/GRs\nFtcMz+StnR6K3SEsBsXVw7vmAsdBFd4QVy6qoCag+WCPjyp/mGuGZ3b2YYkUd04/Oz8d6+SRKH2x\nW0OCYyJh9tWHCGpNjtWA0yzpwV1BrCaiBxkVTCjomIa6QojOde3iysMCYwdtrw3x/m4vZ/eNPb5e\nHM0f0ly+MP7AGIDT0jU+awNhzf1f1fCndXUxe1y2VFjDc5vd7KgN8vYZBYl9cCFERLeNcVLmDfHk\nuvoW7WdU8Mvjkn8YQmu5rAa+MyR2QKzKF2bRPi9l3jAze9oYEGHwSrL72zf11AQOndh/trya6T2s\nadEGQLSvn43LYlSOmRs/q6Tan5iLh9R8l4mk4g1qbvqskle3NTTGMxtgTj87/3dsFv2c8hJLZQOz\nTBgVhGKcb07paSVPVuSTgtYNv6yutuIqksPfNtTxzq7IU8S21wY78Gi6lsfX1jJ/b/yBMYOCs/p0\njTLWyxdW8MHu9p1OZ2njZEUhRMs9MCGbWr/muc3uuLZ3mhWPTnK1++CAZLey1M93FlX8b7CBoprT\netv4y9SclJs+/OkRCz6BMNy3soZnp+d10hGJruScfnZOKLLwylY3S/b7QCm6t2G4hUQuRJv9a1P9\n/wJj0HDSe22bh3d3eXlxZi7TenSNi/d0VGg3cnZfG3N3RL5pyTApHp6U+JHgomXWVQT4+ZfVLDvg\nx2Jo6PnxvaEOZvWWLB6RGJurA9yzojrqNr6uVeXXYap8Yf7437oW7XPFYEeXWIAKa81nLciWa40z\n+9j442T5nBKioyml+P1kF5lmxVPrI2eQFdkNPDIxm8ndrWlf/ry9JshZ75XhabIyrYEPdnuZ80EZ\n756Zj8OUOgGyDVVHL5q9vdPL1uqgTFQWCVFgN3LDKCc3jHK2+bFS550lktbfvmn+w66+sXfKh+28\nGiza173js+nuaP5UYVDwyKTsLnGDlsrm7/Ey691SFuz14Q5qqvyaD3Z7uWR+BXcvjx7MECJej66u\nxRsj+FUoE7daZfF+32ENmmM5Lt/MfROy2/GIOo5BKZ6ZnkuvNqz0NsduVMzpZ+ffp+Xx4sw8ctP8\nhluIzqKU4tcTXfxqfBaR8jcPeMJU+nXaB8YA7l9Zc1hgrKnV5QF+u7ZlCymdqT4QbnZgV1jDq9vi\nyybsaCtK/Fwyv5xZ80q5fkllQzaSSBtyFSvabGtN5DIabwi+s6icTVWta6wrOl8/p4n5ZxdybP7h\nKe6Ds03857Q8LhvctRuJJrtKX5jvf1JBbaD5C6k/rqvj+c0t6/chxJH21Yd4Y7sn5nbH5Uvvwdao\nDcQ/7XdMrpk3Tssnq4v0GwM4pZeNFecX8fvJLub0s9MjwoJMJAro7zRyVh8bPznGyYszc9n67W48\nMz2XGTJBVYikcNNoJ38+KYdIbYkfX1tLoAWDoLoib1Azd0f0z9qnv6nHHUyNCfHRpgu3dyl9a2yt\nDnL2+6W8v9vL5yV+XtziZvb7Zdz4aSW+WD1mRJcg6R6izYyqYQUgEm8Ibvysig/Okka4qapnhpGF\nswvZVBVgR22IkblmeiZ4lV+0zjMb66mK0YTy5ytqOKO33CCK1ntpiztm78FMk2J4jlxWtEavjPh+\nbpcMtPPwJBfZXSgwdpDdpPjOkIz/Naou9YTYWx+i2h+myq+p9oep9ocxGxQOkyLTpMi1Gci1GhiY\nZSJDBgGJNFHtD7OpKkiB3ZBymfsXD3TQzW7gB4srOeA5PMCzqy7E5wf8TO1u7aSj63xba4Ixh5JU\n+ML8e7uHy1NgcdocZXr1mvIA+90hujuS537iruVVzbaHeG6zG19Y89eTcjv+oESHSq0zqkhKA7NM\nfNNMPXlTX5T4mbvDw7n9pP9RKhviMjPEld5NUpPNFyX+mNtU+MK8uMXNrPS93hRt9Flx7LKCmb2s\nGNJ0EIQnqHl8bS1v7fBQ7guTYVJcNSyDa4ZnxNUbZloPK5cOcvDSlqPLTBRwQpGFu8ZlpdVNY4Hd\nSIE9eW6ahOhsnqDmu4vKD5sW3DfTyHeHZvCjkZkpM3RiWg8bn80p5KbPqnj3iAEvX5Skd3BsS5Rq\nnKZWlga4fHA7H0wCGKN8/Gngw91erhyaHEE+f0izaF/ka51Xt3oY5qrltjFt72slkpcss4k2i3da\n1itbk7O2XIhUVuKJrwP6kdOChGiJlWWxg7BXD8vsgCNJThd9VMaja2rZWB2kzBtmZ12IX3xZw9S5\nJeyqi+9m56mpOSyaXcA1wzI4v7+dywc7eOyEbDZc3I13zyxI6xtGIQQ8trb2sMAYwM66EPd+VcPk\nuSV8si/5ytQiybcZeXFmHs/NyGWYS3I1DoqSaHWYeD9XOps5xoLZsgPJc216wBMiVoeDB1fWsKVa\nWgV1ZXI2Em02u6+dx+JoDrlorw93MJxSE1aESHaeOJt4b62RMYKidXbWBmOW7o4vMHNSmgZvXt7i\nZklx88HDrTUhTp9XytzT8+PKuh2Xb2Gc9G0TQjRjZWnkRYrN1UHO+7Cch47P5gcjUmehYnZfO7P7\n2llXEWBrTZBTe6V3C4g+mfFly6ZKazarEWxGIg7zWVeZPEE+SxyRyaCGx9bW8dTUnA44ItEZJEqR\nxrTWlHhC7KwN4m3BlKwjjc23xHVT5AlpVsRRAiaEiN+wOMtcc6ypUW4hko8/xlW4ScGvj3d10NEk\nn39tij7wYr87zFWfVBJMlbsZIURSKmlm6l9TYQ0//aKav21InWmGB43MNXNOPzt2U3pfqwxzmcmM\n42cwMDs18luUUvTOjHysm6oDaJ0cn41FDiMFttihkf9sd1PlS42BCKLlJDiWhkJhzfOb6zn2jQMM\nebmYY14/wMCX9vOdheXM2xl7GllzHpmUjTWOxY5Y2QdCiJaZ3jO+bJ0i6d0jWilW8/e7j81iQmH6\nZjvtqI298v3figAvbJbWAuKQffUh/vFNPbctreKBlTW8tcPTpoVK0fWNzo1vMeynn1ezvKRjy9XW\nVQR4ZHUN1y2p5JpPKnhkdQ0H3JKx3lJWo+KywY6Y2w1JkeAYwICsyMfqC8E+d/IEmuJ5j3lDsKZc\nkj26KgmOpaFbl1Vxw6dVbK899KFVH9S8tdPLZQsruGxBOdX+lp2ohrnMPDAhO+Z29hRpFipEqrho\ngIOBWbEDXzN7pnepgmi9ApuB/s7mX2Ozetu4eXTqlPC0h3jjGbEyzET6+GSfj7GvF3Pbsir+sbGe\nR9fU8p1FFYx+rZiHV9dQEakGSaS1eHv8auDWpVXtnq0aCmv+8U094984wOS5JTy4qpaXtrh5bZuH\nB1fVMvq1Yl7fJosCLXX9yEyiJY9ZjaTUgLMRMXrK7U6i/mln943v5/p1RWL7jvlDWrLLk4QEx9LM\np8U+/rUp+gfVvF1ezn2/DH+oZW/S7w/P5NFJ2UQ6n7ssisnd0je7QIj2YDMpHj8heklbrwwjlwyK\nvRIpRHOUUtw5Luuor393iIPnZuSi0nRC5UH5cZRhAKytCEhmkADg7hXVNLcGWeoN8+tVtUz4dwnv\n725dJr/ous7sY2NcfnzZY+sqgzz9TfsF5NdXBpj+dim3LauKOGHRH4Y7Pq/G18L7iXTX12niV1ES\nDm4e7aQwhaoBRsXIxvIm0evj4oH2uD7Td9QmbgHj7uXV9HhuH8e9cYDFMjyr00lwLM18fiC+NNDV\n5QF++VV1ix//muGZ/PWkHHKtR7+0rhuZSYZZXnJCJNq0HjaempqDrZlrpT6ZRl47NS/t+3iItrlo\ngJ3HT3AxvYeV60dm8NasfH43OQdzvKO1urB4A16BcMMNpRCxpgyX+8JcOr+Cx9bUdtARiVRgUIrf\nnuAi3iKMF7e0T9bWFwd8nPZOKWvjyJ4p94Vj9q0UR7t+ZCa/Pj6bbMuhX7ZBwc/GOblrrLMTj6zl\nTupujTqFM5g8VZVkmA3cEcfPN9OcmGuff22q54/r6gjqhsmz575fxls7ZGGkM6VOwbJIiJ1x9EY5\n6Kl19Vw5JIOhcTb8PujCgQ5O623j+c1uNlQG8IY0p/eyceFAyVwRor1cOsjBhAIzL21xs/SAH39I\nc0KRlVvGZJLfXNRMiBZQSnHVsAyuGpbR2YeSdFrShkCGNQsgrqCyBu5bWYPLqrh6WHqXLotDxuZb\n+PExTh5eHTtwurY8QI0/TFaMvpEtsbLUz4UflVMX56KAUUGGLM61ynUjM7lyqIN3dnpxBzUTCiyM\njLPvXDIpsBsZnWtiTXnz96BbagKcQvK0/rhmeCYrSvy8ui1ykCreYVixPHLE+1gDNy+tZHI3C3ly\n7d4pJDiWZnrHOSIYGt6gb+/0tjg4Bg0NnH80Ui7mhOhIg7LN3HNc7N5/QojEqAuEKffFd5NoUNEb\nE4v0cUYfG3/bEF/J251fVDMyx8ykoviGr4iu765xWVT7w/x5ffTXkIaEljTWB8JcvrCcmkD8j3lm\nHxuGNC+9bwuHycBFXSC5IFp22EOrarlogIPcJAoG/X5yDrvrQyxrpuKqb6YxIT3f1lUE2FN/dBZx\npU/z6JpaHpqYvlPAO5OsYaaZWb1bFpnfWCUlIEIIIURzDrRgytblgx1kSmsBAVw3InrD7aYCYbh2\ncaU0axaHeWiii3vHZ0WdFJ9nNVCQwN5Uf91Q36LJgiYFP2umX6VIL4GwZmuEvnQAVX7NLUurOvCI\nYrOZFHNPz+fW0ZmHvceyzIpt+kSdAAAgAElEQVTfT3YlpFXJV2WRWx29utVDQM75nUKu0tLMmDwL\nFw2IP9rdw5E8UXwhhBAimbjjzMro4TDwy+PkJlE0GJBl4t44JnwftLMuxLxd3nY8IpGKbh7tZNmc\nImb2bD6r8IZRia3g+M/2+HshKeA3J7gYnpN6ZYAisTZWBYk1gPetnV6WJFkzeotR8Yvx2ay/qBvv\nnJHPMyfnsubCbkzrkZgS0Gpf5EBzhS/Mgr1yzu8Mkt+fhh470cXyUn9ckzaOyZMPNSGEEKI5WTGa\n8hoV/GB4Bj85xplUJSOi8/1oZCZryqL3tWnqg93ehJTyiK5lQJaJN07LZ3WZnw/3eNlUHaRXhpET\niqyc3sJqkViqWtBf8aGJ2Xx3qPSoFBCKMwPq+c31TO2efOXjeTYjU7ol/vPbE2Nx7d/bPMzqLef8\njibBsTTkNBt4a1Y+ly+oiDpp5sQiC3P6y5tSCCGEaE52jEbX5/S182vpGyIi+P3kHIIa/h1HRk5Y\nS4mNiGxsvoWx+ZZ2fY5cq4FdddEX1nOtBh47IZvz+qd+n6x4PfF1La9scTOlu5WbRmXSK1Nurw+q\n9IW5/fP4SiY/2uNDa41Kkx513hjBsc9LIpddivYj79401SfTxILZBTy1ro6Xt7hZX3WoFtxuVHx3\nqIM7xmZJE00hhBARBcOah1bVsqk6QFDDxEIL5/W30ydNbg6yLQqzoaEvVHPO7ps8E7hE8rGZFE9P\ny+HYfDO/+qqGaIk53aTNRcrbUxfk7Z1esi2KETnmdg9mJdp9E7K54MOyZl+nTrPikoEO7hznTKsp\ne/vdIX75ZQ0aWF8V5OWtbp6bnpuw0rtUFgprzn2/LGoiRlMVvjC1AU2WJT3uPTNijK/eVReiNhDG\n2UV6le6tD/HPjfV8vK9h+irAAKeJM/vYmNPfjiNJxnmnx9WraJbZoLhptJObRjvZWh1ka00Qq1Ex\nKteUVh9sQgghWufFLW5+s/bQKPJ3d3m596saZvW2cf+E7C4/nVEpxfgCS7MTrbLMilN7yQ2SiE4p\nxQ2jnMzua+fJdXW8sNlNXfDwjIJBWSZuTHD/KNGxPj/g44IPy6lv8rs9taeVx050pcxiwtTuVr6+\nsBv/2FjPytKGc16PDCOn97Yxo4cNWwKalKea1WV+mr5ba/yai+aX8+LMPGb2TO/z/+vbPXEHxg6q\n8YfJipGR3VX0dca+195ZG2JUbur/PD7Z5+XSBRX/C4odtL4yyDu7vDy4qpanp+UwMQmmMqfG2Vi0\nu4HZJgZmy8tBtF4grNlcHWR9ZYAdtSG8IY0C7CZFD4eR0blmhrpMmAzpd/EkRFcVaiaDIKwbgmQL\n93q5ZbSTW8c4sRq77vv+zN62ZoNjN412ps1Fvmi7vk4TD09ycde4LBbt8zY2sdZM7W5lSjdrl34P\npYP7VtYcFhgD+Givjxlvl/LemfkMzk6NHr9FDiN3yQTK/2muNM4XgssXVLBgdgEj0nggwdMb6lu8\nT5wzbrqEIXHcd5d6QkBqv4ZWlfm5eH551KEMe+pDnPVeGY+f6OI7Qzq3V6FEQ4QQrRYIa17f5uH5\nzfUsL/FHLC06yGaEcfkWLh3k4OKBDrnYFyLF9Ymy8ukNwUOra3lju4dnTs5lZG5qX+BFculgB39c\nV8cBz6ET4MgcEzdJpo9oBZfVkFb9mtLBztognxU33z+ozBvmvA/Kef/MfOlVlYIilTt7QpprPqlg\n4ezCtLzW9QY1K8ta1jMrx6rok5k+lUvDXGZsRqIGjSxd4LXz1Pq6mNNKAYIafrysivEFlk4NKsuS\nphCiVbbVBJnw7wNct6SSz4pjB8ag4QNg2QE/N31WxbGvH2DZgeQa2yyEaJlp3a30jnExu7k6yOnz\nSnl3V3xT+VJNvs3Iy6fkkdFYUnRWHxtvz8rvEhe1Qoi2K/dGv0DaUx/i8oUVMnQhBQ2Nkv2zrjLI\nr76q6cCjSR7fVDX0IW2Jc/ra06YZPzQEvk6L0XrB1gWuI1aXxV9a6w/Do6trY2/YjiQ4JoRosQPu\nEOe8X8aO2jiWAiLY6w5x8fxyvm5hPwIhRPIwGRQ/i6PEpi6ouWJhBc9ubHmZRSoYl29h0yXd+Or8\nIl6YmUeu9O0UQjQyx3GDu7o8wLMb3R1wNCKRcm1GBkcJkD25ro5Pi9NvIfi/lS27th+cbeLe8dnt\ndDTJ61sDomcJ27tAH7+eGS27Hlpd3rlTOiU4JoRosSXFPvbUtz4wdlCNX/NMF71ZFiJdXDLQzrTu\nsZuohjTcvLSKl7Z0zRvADLNBencKIY4yJNuEI46b3EfX1OBPp6ZLXcS5/ewRv6eBu5dXo9MsK7DK\nF7ucpE+mEZdFMaefnddOzcNlTb+wxOm9bWRHmM5pNcLALjDUaFx+y0ok9ybg/rIt0u9VKIRos1G5\nZuwJSvXtCid+IdKZUopnpucyMCu+1cGbP6vkcympFilIa021P8yuuiBfVwT4/ICPr0r9fF0RYHtN\nkEpfWErjxFGsRsWJRZaY2+1zh1kq58aUc16U4Bg0ZAXO3eHtoKNJDvGUR95zbBY7LuvBM9Nz6edM\nz3sBq1Fx+xhns9+bWNg1BrFcNyKTHGv8/x9j82KfK9tTer4ShRBtMsxl5p/Tc7h8QUWLewo0dflg\nB9eN6NypJEKItsuxGnj5lDxOeaeUan/0k4I/DJcvrGD+2QVpe0Eskl+1P8yS/T6WHvCxpjzAN5VB\nKv1hwjE+8xQNZSRDXSaGZJsY6mqY1HxsvqVL3OiI1jmtl435e2MHvhbu9XFyj+h9iERyGZlrZmi2\niY3VwYjb/GZtLXP6Rw+idSVOc+xznQyvb3DdyEz+s8PDqiN6c105JPUGs4S1psQTPmxQRYHdyN+n\n5XLlwgrqYtw0GhTcPLpzhxlJ5pgQolVm9bbz+XmFXDrI8f/s3Xd8lIX9wPHPc/sul1x2wgYhbAFR\nQARRQQXEWWu1btva1lnb2tb+tNM6am21dmhtte49cYEFRBBUBJE9wg4jkJ3b8/n9cYmE5FaSS3KX\n+75fr7zO3D1P7jE8ee55vs93tDuLbGy+nsdn5PHItNyMar4pRG9WZtPz/KwCrAmUD1V7Qtz0SV03\nbJUQ7eMJqNy/tpGxr1Ry5ZJa/rnJyYpKHzXe+IExCJdR7XcGWXzAy6Obndy2sp6571cz5IVDXLqo\nhpd2uHAmMsFG9CpXlFkoMsW/7Fqegf2peoNrR8S+0bux1s+SA5mTPTYggT5TfaJM+sw0eo3CczML\nmF56NGPq2uEWLo7TjyzVfFXtY+Y7VYx8uZJT3jpMg+/o59ysfiY+Or+IsTGmlmsU+Mf0POYN6tkg\nstyyFUJ02DCbnkdPzeP+KTZWV/lYX+NnfY2fnY0BXAGVoKpi0CiUWrSMztMxOk/PxEIDY2IcHIUQ\n6Wt6qZF35xZyyf9qqIozoW1FpY/5e9ycH6ckRYju9Ns1DTy2Ofm9MF0BlYUVHhZWePjlKoX7Judy\n2bD0uvgRHZel13DnxBxuW1kfc7l4ky1FarpmhIUH19mpidFr69Vdbmb2y4yswEnFBrRKuNdoNHIt\ncFS/LC3vzCnkvX0e9BqFmf3i93FNJV9V+5jzfhWepnZhm+sCfH9ZHS+fWfD1MmU2PUvPK2JBhYf5\ne91sahrIZtAqTC0xcO3wLIbn9vw+IcExIUSn2QwaZvUzMStDPvSFENFNKDSw5LwiLl9cG3ca7cMb\n7BIcEyklvxuaQtd5VX64vI5qT5Cbx0buNyN6n2uGW1hQ4WFBRfQMokQa94vUY9FpuO14K79a3Rh1\nmff3ufEFczFkQHm1Va9hXIG+Talgs4FWLTaDFLC1pCgK5/Zw1lRHeIMq1y+r+zow1mxhhYctdX5G\n5R0NeOk04f/HVP7/lL1SCCGEEEk1wKrjw3lF/GScFX2MM431NX5pYC5Sys/GZ/PHKTayuiFIsdve\ns1O5RPdSFIWnTs9nWmn0htOTi3u2GbXouOtHWekfo5ywwaey5GBqlVYedgXZ4lDY4VSOKYNLhnMG\nRL9hfqbcTO81ntjqpDxKv73FaVhKLMExIYQQQiSdWafw6xNtLL+gmKlRJrUFVKhySxmRSB2KovCD\n0VbWX1LCA1NsTC0Jlwclk0kLN42x8vuTcpL7g0XKM+kUXjqzgDkRAgdaBX58fGplEjb4QgQSabYn\nMOkU/jw1N+YyC2NkDXYXd0DlwXV2Zrx9hJEvV3L1V2a+vdbMsBcPcdmiGt7d607K+1w/ykquoe3B\nU6vAD2QYV6/x7PbobQjWx6keSEVSVilEBlNVlQUVHhYf8LKl3k8gBCcXG7iizJISdd9CiPQ3MlfP\n+3MLWXLQy9PbnCw64MXVNLHo3IEmSqQpr0hBBSYt3x9t5fujrdR7Q3x+xMe2ej/bGgJsq/ezvT5A\noz+xoIFBAyNy9Ywr0DOtxMCsfrLfZ7JsfXi67wf73Dy93cUBZxCzVuGXJ2QzJCd1Ls3uXtPAIxsd\nqCqU2XT8eFw23xoqffJimT3AxHdHZvHE1sgBg0210SdadofdjQEuXVTD9giZPv4QX5f9fmOImcdO\nzetUCWiuUcMfT87lhuV1xwwzuXWslRFyjdErrKnysaU++j7tTPAzMpWkzhFYCNFt/CGVl3e6eGSD\no80H5OdHfDy/w8Wqi4rJN8nJuxCi8xRF+bovoTugUu0JYtEp3dLfSYjOyjVqmD3AxOxW2T713hD1\nvhD13hDOgIovqOIPQZZewWbQYDOEH7P1ikxmFm3MHWhm7sDU7L2zvd7Pn9c7vv5+S324wfYbu938\n57Q8rLHq5TPcHybZWH7IGzEAtdfRc8ExVVW5bmltxO1q7Y3dbrJ0Cn+bntep97x0qIUSs4Yfr6zH\n4Ve5bVw2N42xdupnitTx6i5XzNcD6Rcbk+CYEJlme72f731cFzPVtdoT4pNKnzTKFkIknVmnMMAq\npx8i/eUaNeQaNZBalXAihfx5nZ0P93s4f7CZa4Zb0iao9PruyKV1Cyo8XLmkllfOLMiIxvIdYdYp\nPHVGPue8X0W979joQLL7erXHq7vcfFWTeJnbiztc/HR8NoOzO/d5fXpfE2u/WdqpnyGS56AzSL0v\nhKpC3ywteZ24SbnqiC/m68E0LMmWs1MhMsh7e938YFkdjgRC+XLOI4QQQgjRcQ+tt+MIqHx+xMff\nNth5+ox8ppQYe3qz4qr1RA/iLD3o5Xsf1/LUGfloJCMyotF5et6cXcjFH9ZQ6z36uxzag2WzKyq9\n7Vo+oMI7e9zckmJ98ETHvLvXzZ/W2VnXIkCqUcLtdC7I0zKzsH0DYkKqyqa62MHWAlN63AxoKf22\nWAjRIctrNVz1UW1CgTGF8Ad7OguEVH6ysp7bVtRxwCkTwYQQQgjRvTQtrrQq3SHOW1DNKztjlyKl\nAlucbJL5ez38Y5Mj5jKZ7oRCAx/OK2RKiwmkPVmR0ZEwpozL6R2e3OrkqiW1xwTGAEIqrDzs4xdb\njdxTbsAbTDzTq8IRxBvn8mpYCvVQTJQEx4TIANsdCndtM5JoduvZA0wp1RS2I17e6eLJbU6e2u7i\nlLcOsyQNxwkLIYQQIn31azV4wReCHyyr4/HNqR1YGpkb/xzw3i/tVPRgD610MMymZ+G8IpacW8Qb\nZxfws/E9l4V1QQcCcy0DeyI9rany8dNP64l3CfjWYR1XLK5BVRO7WEykRHhcQfrtPxIcE6KXcwVC\n/HSLEVcwsXtGZq3Cb05M//Hynx4+Wgff4FO5ekktG9JwpLAQovPW1YRPDr+9qIYrF9fIsUAI0S3O\n6m9q85wK/OLzBt7eE7mvVyo4u7+JeDOZ3EGVu79s7J4NSnMTiwzM7Gfq0TLUM/qZ2hXsOnegiZPT\noARYxPbSDlfcwFizRQe8vLQzseOSThN7X9YqcFJR+lUhSXBMiF7uP1ucVHoT+1NXgEdPzUv7kkqA\nI+5jc30dAZVrltRg90uSuBCZotEX4vsf13La/Cqe2OrkgwoP7+7zMO/9KlwBORYIIbrWRUMiZ+uo\nwA+X1bG6KnZD656SY9BwZr+2gb3W3tztps4rx9J08cKs/IQCFicW6vnLKbndsEWiqx1wta+1zHPl\nzoSW08WJ857Z30RBvAh7CpLgmBC93BNbEzvIAdw1MYcLo5zIpZssXdvD2y57kLtWNfTA1gghulul\nK8g5H1Tzyq62d0Eb/eox2aVCCNEVTig0MDovcomiO6jy7UU17LGnZmnilcMtcZfxh+CdvambASeO\nVWDS8sE5RTw0NZfJRcdmkSnAxEI9f5iUw8J5RRSb0y+wIdoa0s5po7saEzse5Rhih5GuLIt//EhF\nEhwTParWE2SfIyDZPF1oryP+HQOdAn+cYuOnPdgLIdmKzZEPb8+Wu9gcZ7qKECK97W4McPZ7VWyM\nUT65rT41L0iFEL3LjWOsUV+r8oS4bFEN7gSGJXW3OQPMnFISvwzvjd0SHEsneo3CdSOz+PDcIrZc\nWspT4z08Mc7DtstKWXJeMTePzY5bMifSx/TS9vX98iTYlL/UoqWPJfK1VqlZw5wB8TNPU5EEx0S3\nU1WV13e5OOn1wxz3YiXjXj3M4OcPcea7R/jDmsaUvYOWjoIhNe50miHZ4btIPxgd/eQtHQ2Mcqck\npMKvv5DsMSF6K6c/xOWLa9gX58ZAtJM6IYRIpm8PtTDCFj17Y2t9gLtS9Lzkz1NziZMgwlfVkoWb\nrvpYtIzJDjEuJySZYr3U3IFmTu+beO+4SH0S27vsfVNs6NM0wCpnhqLb/W2jg+9+XMeOFmmbQRVW\nV/l5cL2dSW8c5o7P6yWbLAm0GoXvjMyK+NqwHB2PTMvl84tKmNQLp9HEajq66ICXpQdleqUQvdEv\nVzWwJYGssHH5ve+4J4RIPVqNwp0TYw86emKrk4UVqXdeMipPz92TbDGXqfepBBIdhy6E6HaPz8hL\naBhDH4uGX05IfCjbrWOt5OiPDYJdVWbhoiHpWVIJEhwTPeDhDbHHV/tD8NhmJzPfqeKgs31NBEVb\nD0yx8cBIL5f28fPN48z8bHw2b5xdwKpvFHP18CwM2vSM7MdzQoG+zQG7pXtkwpLohZz+EF9V+9ib\noRm4yw95eWa7K+5yJxTqGRojk0MIIZLp/MFmJhbGboT+45V1NPhS78bwD0ZbuX1c9LYbRkk4EiKl\nFZu1vDe3kFvGWqP+vQ4yh3h3ThFDchI/Nxpm0/OvGXkUmTTYDAp/mZrLX6el9yAHOTMU3c6bYC1z\neUOAcz6oYv6cQgZaZVftKK1G4YzCIGcUBikry+/pzek2Wo3C1BIDC/d7I77+RZWftdU+TiiU7BGR\n/vbYA9z8SR0rKn1fj+wekq3lJ+OyuWp45OzR3uih9faElrspRg8gIYToCr+fZOPcD6qjvn7QFeKu\nVQ38bXpeN25VYu46MQdPUOXvm9re4J7d3yQ9qoRIcTqNwt2TbPz4eCsLKjxsbOq/bNAoHBeqZkpu\nqEM3DecONFM+sHcMcwPJHBM9oChKo/RI9tiDXPtRLSFV0rVF+83oG7tu/j/tmOQpRKpq9IWY814V\nn7QIjAHstge5ZUU9ly2qoc6betkIyba5zs+Sg5GD4S31z9Jy4eDecyInhEgP00uNXBNnAuRz5a6Y\ng0R60h8m23j5zAKOyz6aepKtV+RmgxBpJN+k5fKyLO6dnMu9k3P57Uk2puaFkPh2mATHRLe7Ylj7\n6pC/rPbzpkzCER0wb6Ap5kCCt3e7cUpvO5HmntrmpNIdfT9eUOHh8sU1+Ht5T5intyUW7H5wqk2y\nHIQQPeLuSTb6Z0WvQ1SBu9ekZnN+gNkDTHzxjRLWfKOE184qYO03S5hSknizbyGESGUSHBPd7qax\nVgZZ29egYP5eCY6J9hucrePkGGPIHQGVd/amXgNcIdrj5Z3xe2x9etjHn9ZFLjl0+EO8tdvNM9ud\nvLnblbbDUL6oij8x7boRFuYMkKwxIUTPyDFoePTUvJhZGgv3e/nscPws2J6i1SgMtek4s7+JQpM0\nHBNC9B4SHBPdzqLT8Obswph3zlqr8/bujAfRda4qi52p+OF+CY6J9JZoyeRjmx207vW8ttrHpDcO\nc+3SWm5dUc91S+sY/XIlD3zViJpm5ex77LEHuEwtMXD/lPRuFCuESH+n9jHyo7GxSxF/t0aGBgkh\nRHeT4JjoEcfl6Hh3buExfQtiKW5HnzIhWvrGEAsFxuj7zyeVqXt3VohE5BgSOz42+lQ+qT16zFVV\nlVtX1HPIdWzEzO5XuXetnWs+qsUdaBsg29kQ4NYVdQx6/iDFTx9g+ttHeGmHi0APl23mGaOnYswZ\nYOKNswsx9tLpvEKI9HLXxBzO7h+9HPHTw76Uzh4TQojeSCIOoscMztbxyYXF/HScFYsu+gWLVafw\n8/HRR0gLEYtJp3DdiOjT+o64Q2ypS83mt0Ik4qz+sQdPtLSm4ejH/pKDXjbEaPw8f2+4V1nLDLJ3\n97o5+a3DPLPdRYNPxReCjbV+fri8jgsXVuMK9FxJ5ml92v4eFOC7I7N4fmY+5hifM0II0Z20GoUn\nT8/n+Hx91GWeK49fMi+EECJ5JDgmepRFp+FXJ9pY980SHj4llwsHmykxa8jWKwywavn+qCzWXFzC\n8NzoJw9CxHPL8VbyY2SPLTskd2dF+rrkuMR7aPlCRwNEmxMICn900MvjW5xfL3/9x3VEa0n2SaWP\n2z/tuUbSP5uQzaQiPQrhmyqzB5j4cF4Rf56ai1Ya8AshUoxVr+HlMwvoa4l8fjJ/r7vXD1IRQohU\nouvpDRACoMis5doRWVwbI8NHiI6yGTT8bHw2v1wV+cJ9bXX8Rt5CpKpxBQYuHGzmrT3xB5cUG49G\nthK95rp3bSOXl1n4xyYH7mDslV7a6eLOiTn0a0dPyWTpY9Hyv3OLCakqGkWCYUKI1Nc3S8vLZxVy\n3gdV1PuOPb42+lRWVno5rW/i2cFCCCE6TjLHMsgXR3yc9Pphrvuolv9udeKLc5EjRG/yvVFZUXvc\n7XfGbuQtRKp7+JRchtti3+8yaOCCkqP7erzlmzX4VBbs8zA/geBbSIVXE5ie2ZUkMCaESCfH5+tZ\nMK+Ifpa25yi74wwaEUIIkTwSHMsg3qDKjsYAb+5x8+NP65kx/wjlDdJrScSWbhProtFrFO6ZbIv4\nWq2n5/okCZEMuUYN784tZM6A6BkGN4+1Umw8+vd8el8T5gQb1L+2y4Xdn9ixYI1kYgohRLuMzNWz\n+LwiphQbjnneGWEoihBCiK4hwbEMtrU+wNVLavFKBplosrXez29XNzD97SMc98Ihip8+QOmzB5nz\nXhWPb3YQSvNA2dyBZq4f2bZ01yN/A6IXKDZreenMAv41I49TSgyYtJClU5hYqOeRabn8+sRjg8Nm\nncLZA6JPS2upvDGQ8Hb4JNYshBDtVmrR8u7cQh4+JZcRNh0FRg0z+yZ2jBZCCNF50nMsw22pD/DQ\nejt3nJDT05sielCjL8QvPm/gxR2Ry6E+O+LjsyM+lhz08p/T8rDq0zeufs9kG5vr/ayoPJrdUpZg\neZkQ6eDSoRYuHWohGFLRKKDEKDP81cQcFlR48Map3DG0o6F9H3P6Hh+EEKIn6TWK9OAVQogeImew\ngoX7PT29CaIHravxMe3tI1EDYy0tqPDw53X2btiqrmPQKjw/s+CY8rNZ/aTZreh9tBolZmAMYJhN\nz+9Oilxu3NKoXD2TihKbGnzhkMSnZwohhBBCCJEKJF1CcMQlNTCZqsod5NL/1VDpTnwf+KDCw28S\nuJhOZblGDS+dWcDCCg+HXEGuKrP09CYJ0WN+ONpKIKTy69WNUSdY3jLWysY6P19U1cf8WQOtWmb0\nkTIgIYQQQgiRXiQ4Jji+ILFsANH7/G5NY7sCYwD5xt6TcDo7RvNyITLJzWOzGZKt4+efNXDAdWyN\n5a1jrUwsMnBCoZ5Xd7pYXhm54b4G+PeMPJkWKYQQQggh0o4ExwSz+0uAIFN9dMDb7nVaT1ISsa2t\n9vH0Nidb6wOowLgCPVOLDZw/2IyuHX2chOhq8waZOau/iUUHPGytD2DRKUwqMnBiUfhvXlEUbhhj\nZXllbcT1NQqMzZebLUIIIYQQIv1IcCzDnVCo58rhUlKWiez+UJsMkXjyjRpuGGPtoi3qXVRV5ber\nG/n7Jgcth2F+fsTHv7c4GbbWziPTcjmlVErQROowaBXOGWjmnIGRX2/wRZ/sGlBhRaWPsyUjUwgh\nhEg7rkCI9/Z6WHTAw7b6AAddQew+lQKThmKzhsHZOuYOMHH+YDNGrdzgFb2PBMcy2KQiPS/MKkAv\n2SsZKVuvYViOjh2NgYSWt+gUnj4jn2Kztou3rHd4bLOTv250RH19R2OACxZW89bsQqYlIUDm9Ifw\nh8L91IToKtWe2AH1ZYe8EhwTQggh0sghj8JDy+uYv8eNI9D2Jth+Z5D9ziBfVvt5Y7ebX33RwAuz\nCphYJNUkoneR4FiG0SgwJk/PrWOtfPM4c9xJZqJ3u3tSDtctrSXO9S6TivQ8dmo+Q21yyEjUgwlM\n9fSH4KoltSw5r4jB2R373fqCKjd/Uscbu92EgLF5em4cY+WyYZIRKpLP7o+eOQZQnmCwXYh0sc8R\nYHdjgApnkAPOIIddIWq94S+DJpxR3cei5YoyC8NzpaxYCJFeFlZpuafcgDsUf2p9s0p3iIs+rGbB\nOUWMypPjnug95Eo3g0wqNnDwyr6YdBIQE2FzB5p5d24RP1pRx6a6Yy9qFWBSkYErh1u4YpgFrWQY\nJqzeG6LGm9igg1pviIfW2/nrtLwOvdePP63nlV3ur79fX+vnh8vrWFPt44EpNgmAi6Syxvn8OOBs\nX6m2EKnCH1LZWh9gQ42P9bV+NtT62Vjrj1lK3NIBV5D/nJbfxVsphBDJ89lhL3dt61j1QoNP5fXd\nbu5KweCYqqrUeEMUmqTaRbRPrwiOKYoyApgDTAJOAoYTvra/RFXV16Ks8xRwTYwfu01V1ZFR1tUA\nNwDXASOBILAe+Keqqizq31wAACAASURBVC/G2dbLm9YdB2iBrcB/gUdVVW3f2MB2ktpwWFfj46MD\nXlZXhU9+3QEVvQYKTFpOLjZwRj8jM/oYseozpzTtpCIDKy4sYa89wOY6P0atglWvMCRbR5GUUHZI\ng699f8rz97p56JTcdk/5+7LKx/Plke/0/XuLk1G5er4zMqtdP1OIWPJNsY+NdZ4u/RgTImkOuYKs\nqPSystLH6iofW+v9tPPQ/bXT+xq5f4otuRsohBBd7JerGjq1frY+ta4tj7iD/PLzBhZWeHAEVPKN\nGiYVG/jF+GwpARUJ6RXBMcLBph91cN0VwI4Izx+KtLCiKFrgDeB8oBH4EDACs4AXFEU5WVXViNui\nKMo/gBsBD7AY8Det93dglqIo3+zqAFmm2mMP8PPP6vlwf+TpjAddITbU+vn3Vic2g8Lt47O5aYy1\n3cGK7rC2OhwQWVnpxaRTGJaj4/bx2Z0u5xiUrWNQB0v7xLEGWLXkGzXUJpg9VudV8QXB1M5f/4L9\nnpiv/2Z1A2f3N9LfKv+uIjn6Z8UOmDsC8hEmUlONJ8jSg14+PuRl+SEvu+2dz3LMNSj8fpKNq4fL\nTQghRPopr+94K4S+Fg3fS6EbsKuOeLlySS1H3EfPQ2q9IRZWeFi838Ofp+ZyzYjU2V6RmnrLFdNG\n4E/AamAN8ARwWoLr/kdV1afa8V63EQ6MbQZmqqp6GEBRlDJgOXCroihLVFV9u+VKiqJcTDgwVgnM\nUFW1vOn5EuAj4CLgFuCv7dgWkYBqT5C571dxyJXYRVuDT+VXXzTy6WEfL8wq6OKtS5w3qPLb1Q08\nttlJyyKPL6v9LNjvYfG5RZTZUi+1ORNpFIXLhpn55yZnQstbdUqHyp231ftjvm73q9z3lZ1/TO9Y\nyaYQrQ2Pc4yxGTIn61akvjVVPt7d62bRAS8ba/0kViAZ3wCrlhtGW7lquIXsDMo0F0L0LrP6G3l7\nT+wbrZEUmTT89/R8slLk+Ofwh7j2o2MDYy0FVLj9s3pOLDIwNl+ulUR0qbFHd5Kqqv9RVfXnqqq+\noqrqzq56n6assZ83fXtDc2CsaRvKgV80fXtnhNV/2fT4i+bAWNN6hwlnvgHc0VSyKZLoJyvrEw6M\ntfT+Pg/PbE8suNHVqtxBZr5zhEdbBcaaNfpUntmeeCNN0fXumJDDqNzE7j9MKelYqrcxgT5wb+92\n44kweUiIjuibpaUsxmCOwjhll0J0pZCqsrLSyx2f1zP2lUpmvVvFQxscbEhCYEwBZvUz8tzMfL66\nuIQbx1glMCaESGuPnZrPSbbEs2j1GvjWcWZWXFjMlJLOT1pPlv9udXIwzrWePwQ/WlHXTVsk0lVv\nyRzrLlOBYmC/qqrLIrz+KvBvYJKiKP1UVT0AoChKf+BEwNe0zDFUVf1YUZQDQD/gZGBlF21/RlpT\nFTu7JpYXyl09Xi7hCah8a1FNm4b5rS2o8HD3JOl5kipyDBpeOrOAOXGyFo1aOvzvlpVArwdHQGXl\nYS8z+5k69B5CtHZ6XyPlDZGPRxIcE90tpKosP+Rl/l4P7+51czhK5kBHjS/Qc/4gMxcfZ+7wVGEh\nhEhFZp3C38d6+ahGy1KnjU8rvTS2mkqdY1A4Pl/PRYPNfGOImfwUbHL/+RFfQsutqfazxx6QY7mI\nSvYMOENRlHGAFTgMfAL8L0rvrxOaHr+I9INUVXUpirIJmND0daDVeptUVXVHWrfpZ/ZrWlaCY0k0\nPFfHAVfHeoukQqPJX69uYG11/ABfICTZQalmULaOlReW8Ps1DTy73UXrBK5x+Xr+Pj2X0R2c9HNc\ngh/uOxsDzOzXobcQoo1Z/Yz8e0vkrNqhOXJaIbpHpSvIM9udPLPdxf4kTkltntR83mAT5w2SgJgQ\nonfTKnBmYZAbpoZbybgCIY64Q3iCKkUmDQUpGAxrbUdj4r3T1tX45bguopI9A66O8NxmRVEuU1V1\nQ6vnhzQ97o3x8/YRDowNafFcouu1XDYmRVGuBa5NZNmlS5dOmDBhAi6XiwMHDsRfoZe5uY/CykoT\n3lD7Al16ReWqonrKy3suBbfKq/DkVhPh0/XYirQ+ysvLYy4T73XRNW4sgityYatDwzanBp0CZVkh\nTrC50NU2UF7bsZ9bFlQAc9zl9lZWUa47OmNE9gPRmX1gsAp9jSYOettmiY2ghvLyqs5smugm6Xoc\n2OJQeG6/nsU1WoJqcm5g6RWV8TkhzigIcnpBkGJjuE2BvxLKK5PyFikrXfcDkTyyDwhoux9ogdqm\nr1QX8JlItFvU4UOHKPcn74ZKb9JbjgX9+vXDYrF0aN1MDo59Rbh5/yLCgakcYCJwDzAeWKQoysTm\n0sgm1qbHWI2oHE2P2UlYL5bBJDh0wOFwxF+oFxtoVnl4tJffbDdwxJfYgdOiVbljqI9R1p7Nxnrl\nkI5Agif/p+XLgT6V2fQwJS/ElLzklfwMzVIZkRVimzP2fu2TAYIiibQKXD/Qz+/Kj+03kqdXmZIr\nO5voGnvdCg/sNLCqvvNZDAoqZVkqk3KDTMkNckJOiDRIjhAibQRU6MCcISE6ZJQ1xA5XYtd4gyxy\nniKiy9jgmKqqD7d6ygm8pyjK/4CPCff++iVwc3dvW4L2EN7OuKxW6wTAZrFYKCsr69KNSlVlwAUn\nhHhqm4sP9rn5/IivTcBAIVwSdPYAI7eMzaaPpefPlJevrwTiB71yDQo/njYIiy7yB0PznYBM/ffv\nzR7I8nDBwpqYy5w8pISy4yyyH4ik7QO3DVPZEKjjjd3hTgEK8PD0AkYNjp/JKHpWOh4H/rrBzn1f\nNeLp4D0gvQbG5uuZVmLklFIDp5QYyTVmdn+8dNwPRHJ1xT4QUlVu+qSe13a5GG7Tcd4gMz8el41R\nK5GyrlLrCbJwv5cqd5ASi5aZfY0UmRO/hukNx4LvW728s6A67nKlZg0zjx+KPoGBVpmkN+wDyZKx\nwbFoVFX1KYpyH/A2cE6rl5tTsGJ1aG/OErMnYb1Y2/kU8FQiyzY0NCwlwSyz3syi03DjGCs3jrHi\nCoRYX+On1hsiqIZ7i00oMKTUybKqquyxJ3YlcNMYa9TAmOjdTutr4urhlqjTSvUaOEOa8YskUxSF\nJ07LY0yeniUHPVxynIULJDAmusB/tjj4zerGhJfPMSiMzdNzfL6ecQXhx5G5egxycS5El/v4oJcX\nd4TPRzbVBdhUZ+fdfR5empVPf6tcdiaTL6jy2zUNPLnVecyNgzyjwitnFjKpuGOT0NPRqX2MXFFm\n4fnyyOfCzf5vYo4ExkRMcpSKbGvTY+sW1nuaHgfFWHdAq2U7s57oIhadhpNTaARxJEEVNEr4MZZT\nSw38ZFyi1biiN7p3so3djQGWV7ad1vOLCTnkpVDQV/QeiqLw0/HZ/HS8HH9E1zm5xMjFQ8zssQeo\n8YZwBVTyDBryTRqKTBr6W7X0z9Ix0KplbL6eQVYtiiIXP0L0hNd2t507trHWz5z3q1lyXhHF7cho\nEtHttQe4dmltxIFddV6Vu75oYOG8oh7Ysp5z72Qb++yRz4UV4OaxVq4eHitPRQgJjkVT0PTYulnX\nl02PkyKtpCiKBRjb9O3aFi81//cYRVHMUSZWTmq1rMhwOo3CaX2MLDrgjbrMhAI9z80qQCt3QTKa\nVa/hjdmF/PErOy+WuzjgCmLQwPdGZXG7BC6EEGlsbL6eJ07P7+nNEEIkoMYTuZ/TfmeQ65bW8vbs\nQnRyztoph1xBznm/mgOu6NUlnx/xsdceYFAGTWW0GTS8NbuQJ7c5eXuPm3U1fvwhlRMKDfz4+GzO\nHiBVFCK+zPmLaZ9vNT1+0er5T4EqoL+iKDNUVV3W6vVLAD3wRctG/qqqViiK8iXhhv+XAM+0XElR\nlNOA/kBl03sIAYSDG0sOegm1yh5rvgPyq4k5UioiANBrFO6amMNdE3PYaw9QYNJg1UvGmBBCCCG6\nR7Y++jnpikofv1ndyD2Tbd24Rb1LMKTynaW1MQNjzao8IQZl2P1RrUbh+lFWrh9lJaSqKCCZxKJd\nMvLKSVGUCYqinKsoirbV8zpFUX4K3Nr01EMtX1dVNQg80PTto4qiFLdYtwy4v+nbeyK87X1Nj39U\nFGVYi/WKgX82fXu/qqoyQkN8bc4AMy/MyueUEgN9LRr6WbRcPszCh/OKuHuSTQJjIqJB2ToJjAkh\nhBCiWw20xi6b/OcmB2ur25a9icQ8vd3Fp4cT+/0VZHhLDY2iSGBMtFuvyBxTFGUiRwNMAKObHu9V\nFOX25idVVT256T8HA28CtU0ZXUcIl1IeD/QFQsDPVVVdGOHtHgJmAOcB5YqiLCacLXYmYAL+pqrq\n261XUlX1NUVRHgVuADYoirII8AOzgBzgLeDv7f+/F73dnAFm5gyQRtdCCCGEECJ1ndHPxJ/Xt+5K\nc5QK/PqLBt6Zm1n9sJLl8S3Rf7ctlZg1cQOVQoi2ekVwjHBwaUqE56PNI10H/BWYTDiQdirh4/V+\n4L/AP1RVXRNpRVVVg4qiXAjcCFwHzAaCwBrgn6qqvhBtI1VVvVFRlE+AmwhPj9QSbv7/JPCoZI0J\nIYQQQggh0tHJxQZyDQr1vujTpJZX+vjssDfpg7F8QbVXV1R8Uulla30goWWvGZEl/YiF6IBeERxT\nVXUp4TZMiS6/G7itE+8XIpzl1e5Mr6bgWdQAmhBCCCGEEEKkG51G4ez+Jl7ZFWn22FH/2epMWnDs\nbxvtvL7LzfpaPxatwvhCPVcPz+KS48xoklhW5wqE2FQbYGdjgAFWLScU6rHouq908bMEyykNGrhW\npjIK0SG9IjgmhBBCCCGEEKJn/WC0NW5wbNF+D4GQ2unJlX9eZ+fuLxu//t4RUFlR6WNFpY+/rLPz\n+0k2ZidhSuF+R4CZ71ZxxH20yMeggTP6Grn1+GymlSY3Cy6SKnf8JvwAd07MoW+WlFR2py11fv74\nlZ2t9X7G5Om5vMzCrH4yHTMdZXanPiGEEEKINLWx1s+bu128tdtNsPVYYyG6yEFnkNd2ufiq2ocv\nKPudONaJRQbO7Bc7WFTvU/nsSOcb87+4wxX1tW0NAS5bVMOD6+ydfp/Xd7uPCYwB+EKwcL+XeR9U\nM/f9KpYe9HT6fWIpMscPeJ0/yMStY61duh3iWB8f9DL7/Sre2uNma32A13e7ufjDGn72aX1Pb5ro\nAAmOCSGEEEKkkfIGP+d9UMX0t49w3dI6rl1ay3c/ruvpzRIZYI89wNS3DvO9j+s4/Z0qTnjtMK/v\nih6gEJnpdyfZiJcUtvSgt1PvEQyp7HXE7sGlAn/4spF7WmSXdUSpJXZg6tPDPi5cWMPNn9Rh93dN\nC+mLh8QeznXNcAuPz8iXCY3dyOkPcf2yWhoj9Nj791Ynf/qqc/ud6H4SHBNCCCGESBMf7HMz4+0q\nllcem3Uxf6+bak9iZTdCdNTSg14aWlwIHnAF+e7HdVz/cS1+yV4UTcbk6/n5+OyYyxxwdu54pdUo\nZOsTu5R9cJ2dFZUdD8ZNLNQn1Nz6uXIXZ8yvYlOtv8PvFc2QHB23RMgKKzBqeHxGHn+dlodJJ4Gx\n7vTqrrYZhS09tMEhn8tpRoJjQgghhBBpYMkBD9curcUdoZQtpMKKys6XKQkRy8YoF/2v7nJz3Ue1\nBCRAJpr8YkI2s/tHL690Bzq/r8yMU77ZTAVu/qQOV6BjWV1lNj2XDI2dudVsR2OAuR9U8VV18o/H\nd0+y8d7cQu6dbOP28dk8NzOfzZeW8q2hlqS/l4jvkzgBV1dA5ZENjm7aGpEMEhwTQgghhEhxq454\nuXJJLd4YN6GN0oNZdLECU/RLh3f3efjuxxIgE2GKovCvGfmU2SLPf+tj6fxl6PdHZcUt32y22x7k\ntTiDAmK5d7KNAmNi29zoU7now+qoweTOmFZq5MYxVu6amMO5g8wYtZIt1lMOJpD9+Mbuju9zovtJ\ncEwIIYQQIoU5/CG+93EdrjiZFoOzZQi56Frj8vUxX397j4c/dLK/k+g9co0aFp5TyPRSQ5vXLk1C\nttPkYiPXj8xKePkFFR1vml9o0vLyWQVk6xMLRtV5VS5cWM3+OH3RRPqq8sTPRNzvDCY8aVT0PAmO\nCSGEEEKksD982cg+R+yT6yKThmE5EhxLdw5/iF990cCli2q4+MNqfryyjme2O9nRkPwMlI6YVmqM\nm6H41w0Olh/qXLN10Xvkm7TMn1PIQ1NzObOfkX4WLXdMyGZCYduAWUf88oRsCoyJBaz2NHYuUHVS\nkYFXziogK8HeXtWeELetlKmFvVWimYTxPr9F6pCzKCGEEEKIFFXe4Oc/W5xxl7t0qAVdovVFImUt\nO+TlbxuP7VHz323haZD9s7RcMNjMVcMtjMyNncHVVXKNGi45zsJz5dEnVKrAj1bUsfLCEmkQLgDQ\nKArXjcziunZkeSWixhPk24tqqfEmVso7MAnZtVNLjLx8VgHXLKmlxhs/c2jRAS+LD3iY1c/U6fcW\nqWVMvp7PjsTvLWdNMNtQ9DzJHBNCCCFEt/IEVJYc8PDpYckuiefJrU4S6Vt95XBpyNwbnFJipChK\nX6/9ziD/2OTg5DePcPa7VTyz3YnD37EG451x45i2E/Na22UP8u8t0ohadJ2QqnLd0jpWVSXe+H5m\n38Qa+MczvdTIx+cXMbUksey3d/ZI36neaFaCAyH6WKQhaLqQ4JgQQgghukUgpPLndXaGvXiIb3xY\nw9z3q3lpR/QMlEznD6m8sjP+RdWsfsYeyyQSyZVr1PDwKblxl1tV5ePWFfWMfKmSO1c1cNjVfWU7\no/P0nJXAReG/tjilOb/oMn9Z72BZjPLdlrk62XqFm8ZYuX5U8jLX+lt1vDe3kHsm28iLU9a52y5l\ndb3R3AEmRufGzkbsn6UlxyAhl3Qh/1JCtOAJqNy3tpGyFw/xE+kRIIQQSXPIFWTeB9Xc/WUjjhap\nUB8d7HiD5N7uwwpP3LIdgwbum2zrpi0S3WHeIDOPTMtNaAqfI6Dyj00Oxr9WyR2f13db4+f7p+Ri\nipMMsd8Z5C3JmBFdYGdDgPvXxh78UGbTsez8IhadW8Suy/twz2QbipLc8jaNEg66bbiklLtPyqHU\nHPnSenSedDLqjRRF4Y4TcmIuc+2I5JYSi64lwTEhmjj8Ic56r4o/fmWnyhPiyW1O1rQjVVsIIURk\n+x0Bznq3is8j9OZw+iWzJJovq+N/Bt1xQg7DJWus17l6eBbPnpGPNcGeXZ4gPLbZyQmvHeYPXzbi\n7OJyy6E2HXdOjH1RCPD0tvj98oRor79vssctNy8yaxhXYOCkIgP6Lu7HaNVruOX4bNZdUsorZxbw\no7FWZvc3ctlQM/dPsfH7SXIDo7c6f7CZH42NXGo+OlfHD0dLcCydSBhbiCY3Lq9jQ+2x06D+u83J\niUXJmaYjhBCZqNoT5KIPa9jvjJzRMjxOSUImi/Y7azZvoCnqSblIf/MGmfnwXB3XfFRLeUNiU/Yc\nAZUH19l5eaeLh0/J7dIm4Mdlx++js7rKjz+kdnlwQmQOVyDEa7viZySOsHX/TQOjVuHsASbOHiDN\n9zPJ7ybZOC5Hx6ObHWytD6AAZw8w8e8ZeVj16ZeLVOsJ4guBzaDBnGFDVeSMVAjg+XIn8/e2Le3Z\nbe/cyGchhMhkjb4QF39YE/PCfnppchok90baGCVAZ/Yz8t/T89FK0KFXG52nZ/n5xTywrpFHNjgS\nGs4AUOEIcvGHNVw61Mz9U3LJMyb/Au29ffEHariDKnvsAcp6IFAheqdVR3zYE8g4Hik3XkQ3umZE\nFteMyGJ3YwCbQSE/Xt15CjnsVfiwSsvOfTV8We3jkOto5vGJhXp+MSEnYwK+6RfKFCLJvEGVe76M\n3LfAIeU+QgjRYT9cXse6Gn/U13MMigTHYjgzStPzuQNMPDuzAINWAmOZwKRT+PWJNpacV8T4gvYF\nmV7e6WbyG4eZ3wW9v+L1w2vWAwM1RS+2tT7+jWuNAucMzIyLeZFahuTo0iYwtq7Gx9VLarjgCxOP\n7DHw3j7PMYExgDXVfr61qIb39mZG/0gJjomM93y5i4OuyGduqsTGRBJ5AireoOxUqazGE6TGI1Ol\nOiOkqtj9IZ7Y6uD9fbGb7X9nRJYEeGI4e4CJEwuPBkP6WbQ8emoeL55ZkHGlDgLGFRhYcm4Rd0/K\nwWZI/N+/yhPi6o9q+f2aBkJJPLFJpKxSA1GblAvREYmUGJ/Vz0h/q2SOCRGJL6hy56oGTp9fxfy9\nHoLE/zx5L875XCpQk/D5JkcNkdECIZWHN9ijvt4vKz0i/yJ17W4M8MRWJ6/scnHEHUIBZvQx8r1R\nWZw3yNzTmyeavLXbzT83OVhd7UMDfHR+McfnSxlQe3xV7ePetY18dtiHJ6jGPdWy6BRuln5ZMVn1\nGv53bhHra/yUWLSUmjVJn7Ym0otWo3DL2GyuKsvirxvs/GuLE1eCtZZ/We9gV2OQx2fkJWVb5g0y\n8+jm2A33Q8C3F9fy9uxCTBLQFUkQb39XgJ+My+6ejREizexo8POdpXWsr42e1R/JhHZmLXeXfY4A\nz2538cpOF/ud4TDfx+cXM6aD5/ASHBMZ7Z29bvY5omeJDLPJn4joGFVVue8rOw+usxNqcR6nAh8f\n8vLxIS/3TLZx0xgJDvSkCkeAW1fU89HBo71zQsCuxoAExxKkqip/We/g3rWNtCcx8toRFgrTpPSg\nJ2kUhQmFMhhGHCvXqOE3J9m4cYyVv2908MRWJ44EgmRv7XHjCar8fiB0Ns46vdTIyFxd3DK3z4/4\nuGVFHf8+Lb9zbygE4QzaWG4da2VKiZTrC9HaHnuAeR9Uc9jdvlp3kxbmpFiZsqqqPLDOzgNf2duc\ne9YlWPIfieQ5i4z2epxpN2USHBMddOMn9Tzw1bGBsdZ+80UDVW4p4espb+9xM+3tI8cExpqNzpO/\n/UTduqKeu79sX2AsR6/wo7FyZ1+Izioya/ndJBsbvlXKvZNtjE6gCfmCCg+vVSbnGPfg1NwECnLg\n1V1uFlRkRs8a0bWmlka/WXBSkZ5fnZjTjVsjRHrwBVWuXlLb7sAYwH2TcxmYQmXKqqryg2V13Le2\nbWCssyQ4JjKWO6Cy+EDsSUvDJTgmOuCFcicv7nDFXS6gwmdHfN2wRaK1f2xycO1HtTT62n6qDsnW\nMjRH/vYT8bvVDTxbHn9fb+33k2yUxLn7L4RIXJ5Rw41jrKy8qITF5xZx7XAL2froYav/ViTnGDe9\n1MgVZZaElr390wYc0p1fdNKsfibOijCsZHqpgVfPKkQnE3yFaGPRAU+7SykV4P9OyOa6kVlds1Ed\n9OQ2J6/ESXDpKDn7Fxlr+SEv7hjhZqtOYaKUsoh2qveG+M3qyNNPI0mksaxIHlVVueuLRv6xyRF1\nmZvGWNFIX6e4/rnJwUMbov8eo5nZ18g1wxO7mM4Eqqqy5KCXA84glxxnkUb7otNOLDJwYpGB+6bk\nsvKwl48PevmiyseGGj+OgIpBA6fkJS9I9YdJNpYd8sZsUwGw3xnkbxsd/PIEyewRnfP36Xn8enUD\nKyp9jMnTMXuAmWtHWOSzW4goPoqTENJaiVnDo6fmMbNfapVTHnQG+W07rrPaS4JjImMtOxT7IDGj\nr1GmqIl2m7/XTZUn8YuOgVbJnuku/pDKDcvreC3G3aaBVi1XD0+tO2Sp6LPDXu76oqHd6xWZNDw2\nI0+ayjdZV+PjhmV1bG7q2bTPEeSuiRI4EMlh1inM6mdiVtPFTfMkL0VRKC8vT9r75Bo1vHZWAbPf\nr6LOG7vG5bntLu6YkC3HANEpJRYt/5ohPeyESFSeKbGCQZ0Clwy1cPeknJTsC/vyThd2f5JrKVuQ\nskqRsbbVx04tPV8mCYoOWFudeJmkToFTS6VpbHcINfUniBUYA/jVxBwJisfhDqjcuLwuZj+9SLJ0\nCi/MKqDYnHonWz3h/X1u5r5f/XVgDGBjO0sehGgPRVG6LCg1PFfPm2cXkmuI/fMPuIKsq5H9XAgh\nutONo63M7Bv9mqPIEOK2462subiER0/NS8nAGIQHvHQlyRwTGau8MXo5m0kL56TYVA6RHrzt6K9/\nwxir9F3qJrd/2sAbu2MHxs4daOKSoVLuF8+D6xrZZW/fIAmDBp6bmc+kYilVh/AwiO8uraX1cEFP\nsjvLCtGNJhQa+OCcIr6ztJYtMSZYdqQhtBBCiI7LNWp4Y3Yhq454WVnpo94XQqdROD5fT77jAKVG\nlbKyAT29mXH523tntp0kOCYykjeoxuyNcdXwLHIMklgp2u+kIgMvJNCMv3+WljsmyLS+7vDndXae\n3OaMuUwfi4ZHpuV20xalr/2OAH/b2L4+Y1oF/n1aPmekWN+KnvJVtY8fLGsbGAMZAiPS36g8PUvO\nK+aOz+t5envkz8KiBMt7hBBCJNfkYiOTi4/NICsvT58bc8fn6+MO1OsM+XQSGemQKxi1JMiggR+N\ntXbvBoleY/YAEzkxJoRBuK/V/DmFZOnlENzVXt/l4g9fxm7cqQCPnppHfoqmkKeSx7c48bUj6SPf\nqOHVswq4YLCUqQPUeoJc9VEtnij3ZianQWad3R9iyQEPla72ZQ+KzGHWKfx1Wh5vzy5kZl8jLYcH\nnlJiYGy+vuc2TgghRNq6aIiZeHOLLJ0YbCS3KIVo5bJhFvpb5U9DdEy/LC3PzMzn6iW1NLZqGGnU\nwsVDLNwz2UaeUQJjXW1Hg59bV9QT737YzWOtnN43vbKaVFVl6UEvm+r8uAIq3xmZ1eX9IZz+EE9v\nj52B10wB5g408eDJufTNkqAjQDCk8p2P66iIkrWsUeCMGP1AUkG1J8g3FtawvtZPsVnDonOLGCif\nlyKK0/oaOa2vkUOuIPsdQXSa8F1/nUb6OgohhGi/8QUG/jY9j9s/rcfZKgU/W69w+TAL4ws6fgNG\nzmhERsqOktmTrVe4fbyUuonOOb2viS2XlvLmHjefHfahU6AsV89lQ80USHZSt/CHVK5fVtfmg7O1\n2f2N/PbE9JoOtUMrxQAAIABJREFUuKnWz60r6lhTfbSp9Qs7XHx5cUmXjrF/aaeLBl/s3+c1wy2c\n3tfIlGKjBMVaeXiDg6UHo5cCzOxrTOnjg6qqfOt/4cAYwBF3iMsX17L8/CKZPChi6mPR0kf6awoh\nhEiCbw+zcM5AEy/vcLGl3o++qXfaRUPMWDtZlSPBMZGR8o0asvVKm1Gw90+xyV1wkRRZeg1XlmVx\nZVlWT29KRrpvbSNrq2NPRBtfoOfJ0/PRplEWw4IKN9d8VNtm8MMee5BVR3ycXNJ1mUcL9nlivj6h\nQM+DU3PRp9Hvs7vsagzwp3Wxy3t/ODq1y/nf2evhy1Z/Uxtr/Sw75OO0FM94E0IIIUTvYTNo+H4X\nnDdJXY/ISIqicGXZsVPpfjAqiyskkCFE2ltR6eXhDbGbxg/N0fLaWQVp1fdtyQFPxMBYs5cSGATR\nGauqoo/PHpyt5eUzCyQwFsXPPquP2mcMoMymY1a/1A4wPbY58t/Uu3tjT4EVQgghhEgHkiIjMtaN\nY6wsO+Slwady96QcLhpiib+SECKlhVSVn31aH3XgBoQnhb45u5Aic/qU+ayu8nHF4uiBMYCF+z2o\nqtolJW41nmDUkspis4Y3zi6kRMqmIlpY4Yk7WekHo7JSujSxxhNk5eHIwdFPKrtuapQQQgghRHeR\n4JjIWAOsOlZcWNLTmyF6sZ0NAR7b7GDBfg9Tig08fEpup2vhRWwv73SzuT4Q9fXReTpeO6uwx/th\n2f0h/EE1oQmZrkCI739cizsYu9/XIVeIpQe9nNEv+cMFbAYNRi1tgnOlZoU7J2azaL+HjXV+djcG\n8IXAqFUYZNVy1XBLm5HhmURVVX67uiHmMv0sWi4vS+2bM6uORM8a3O+UqZVCNFu038PfNjooMmsY\nn6/n2pFZZMvnvhBCpAUJjgkhRBf4pNLLZf+rwdHUEL7C4SbfqOGBk3N7eMt6L1VVeWi9Perrp5Ya\neG5WATZDz12orK328fs1jayo9OILhacTPjMzP+bF0x/X2tllTywA8eJOV5cEx3QahbF5+mOGAOQb\nNVR5QtyyInrw59lyF9ePyuJPGbrfLznoZUuMYC3Ar0/KwaJL7YvnWMExu1/F7g9JAEBkPFcgxLcX\n1+APhb9/bZebv250cO9kG98amtoBcCGEENJzTAghkm5rvZ/LFx8NjDX7z1Yn+x2xL5RFx31S6WN7\nQ+Tf7zeGmHn97MIeDYy9v8/NnPer+OhgODAG8NFBL99bWktIjZwVttce4J9Rej1F8lmU0rdk+OUJ\nOQyyatE2Vf/VekPESWYD4MOK2I38e7PHNsX+tzu52MC3jjN309Z03OoY/eYAKl2SPSbE9vrA14Gx\nZtWeEN9fVsf/rapHjXKcF0IIkRokOCaEEEl224p6GiP0ZwqpHJN5I5Lry+q2F/BmrcL9U2w8cVoe\nBm3P9XTa2RDguqWRe4Yt3O9l2aHIfZue2e5sc7EVyz5HkENJDlRUOALcuLyOSxfVsNcRTCgg1tI1\nIzJz0El5g59FMXqN6TXw0Cm5Kd1rrFlFnNJJe5R+dEJkkmh9GQH+ucnJzSvqCcZqiCmEEKJHSXBM\nCCGS6N29bj6LUYJUIZljXabGc2wU6Yy+RpZfUMQPR1t7PADxs8/qYzbT/9/+tkGUYEjlhQ5MoPzs\ncPIapP9jk4OT3jjMCztc7Q6KAYzM1XFDF4zaTgdPbnUS61d2x4QcRuXpu217OqPWEztCa+zBwLMQ\nqWJsvo5YfwnPl7u4YXldt22PEEKI9pGeY0IIkSQhVeX3axpjLiNVFV3nmuFZVLqDlJq1zBtoYkpJ\najSCX1jhYcnB2AGr7fVtMwqXHfJyyNWOtLEm2+L0uEqEP6Ry0/I6Xtnl7vDPmFpi4PmZ+Zh1mRk4\nWRijnPScgSZ+Mi59goatS8Rbs+oz899YiJYKTFrG5uvZUBs9Q/yVXW6OL7Bzy9jsbtwyIYQQiZDg\nmBBCJEmsnlfNSiw9OyWxNxtq0/H4jPye3ow2ni93xl1GHyHzZm1Nx0pwD7s7X1Z5+6f1HQ6MGbVw\n42grd07MQafJzKDJfkcg6hCFYTk6Hjs1r8ezGRMVCKnEqwQrMkshghAAV5RZuOPz2BNqf7e6kWkl\nRiYWGbppq4QQQiRCzmaEECJJ3t4TP5hQKsGxjBIIqSyJ0XeqWVaE7Kr9jo4FuWq97c82a2lznZ9n\ny9tfzqlR4NvDLHzxjRJ+c5ItYwNjED2wmaNXeHZmPjk9OBiivXQahewYmWFFJk3KT9sUortcOzyL\n0jjB4oAKP1xeR0D6jwkhREqRsxkhhEiSRfvjT+UbmiMJu5lke0MgbkkaQL+stkFTYwfjqC5/5y64\nPj7ojZsp1FK2XuHKMgsrLijm0VPzGGiVfbzR1zZAmWtQeHN2Ydr0GWspVlB/UrFkvwjRzKRTuG1c\n/JLJ7Q0BXt7Z/psQQgghuo4Ex4QQIgnqvSH2xsn0KbPpIgZBRO9ljxAkieS0Pm37o53V3xRx2bP6\nxe6l1tlqvW8MMTM6L3aAq8ik4aLBZv57eh5bLy3l79Pz0jLo01UKTMeeXg3O1vLBOUWcmKZlVH1i\nBMemSHBMiGNcOzyL4bb4NwkeXGeX7DEhhEghcntXCCGS4KArfgncvIGRgx2i93IlkDWWb9QwPUJw\n7NQ+Rs4ZaOL9fR4UYFSejrsm5jCzr4mBzx8kWtytyNy5AGyJRcvKC0tYUellZaUXu19Fo0CeUcPY\nfD1j8/TSOy+OM/qamFpi4JAryOXDLHx/lJVcY/rejxycrWXZocivSXBMiGOZdAqPz8jjrPeq8Me4\nP7LbHuT13W4uHWrpvo0TQggRlQTHhBAiCSoTCI59S06AM45VHz8gcvNYK/oI/bn0GoUXZhVQ4QhQ\nZNJiatGXbEKBgVVVvog/r9iUnCDMtFIj00qTM/HTG1TZUuenwadSaNIwJEfbq/tUGbUKH5xT1NOb\nkTSn9zHyzPa2JWADrVomS3BMiDYmFBq4d7KNn30Wuzn/23skOCaEEKlCgmNCCJEE9jh9ni4cbGa0\nlJ1lnOPz9Vh0StQMsuE2HbeMtcb8GQMi9PCaUhI9OFbYycyxZHtlp4sfrajHHTz6O1AI99+bPcDE\nOQPDWVaaNJnemInOGmAiS6fgbLUf/2C0Vf7dhIji+lFWdjYGeGxz9InFKyvjD2wRQgjRPXrvbVsh\nhOhGJTGmU+k18JsTc7pxa0SqMOkUvjHEHPE1s1bhkWm5EbPG4jm9b/SMruPzUysI+8x25zGBMQAV\n2NEY4B+bHMz7oJrhL1Vy24o6NtVGnvIoela2XsP1o7KOee64bC3fHZEVZQ0hBMD9U3K5fXz0Bv31\nPpX6Tk4YFkIIkRwSHBNCiCQYlacnWojjxtFWhsiUyox1/xQbY1o1uC80aXj17AJOLulY2eIZfY0M\nzm6bIZalU1KuB9SZ/eL32qv2hHhqu4tpbx/hwoXVfCLZFCnn1yfmMHtA+N9ydJ6O988pOqbUVwgR\n2V0Tc/jDpBy0Ef5cbAYlrfsRCiFEbyJXa0IIkQQ2g4aLhph5Y7f7mOfnDjDxa8kay2hWvYb5cwp5\nZruLHY0BppcamTPARF4nLog0isJtx2dz28r6Y56fN8iEMdIVWA/64Wgrz5Y72dkYvy8fwNKDXpYe\n9HJqqYEHp+YyIje1MuEylUZReGlWPhXOIMWteuBlHFUNfzVTlM6PiRW92s1js5leauRnn9XzRdXR\nDNlIk4qFEEL0DAmOCSFEktw/xcbKSi+V7hBWncJVwy38fpINbQfK5jpiV2OAL6t9NPpURuTqOKXE\ngCIXbCmhwKTlx+Oil9Z0xJVlFhZUeFhQ4QGgyKTh1xNTLxBr0im8NbuQcz+oZq8jsQAZwPJKHzPm\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kgoOLZw09OCuEEDkxDJWNFU0EnIJ+6O5RkyhDYYhFxzZjLCngU2WeDruaXKmZVFDPYpVm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\nwcpGfFJup4EVSSxUy5S2/rUnccN/CCGJR2FMnDEM8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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [], + "image/png": { + "width": 611, + "height": 608 + } + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "LCk0Wd5SL2Ah" + }, + "source": [ + "The most probable position reveals itself like a lethal wound.\n", + "\n", + "Associated with each sky is another data point, located in `./data/Training_halos.csv` that holds the locations of up to three dark matter halos contained in the sky. For example, the night sky we trained on has halo locations:" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "xSnboDWINzs9", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 53 + }, + "outputId": "5c564c09-44da-4b09-d37c-02f7432ba48f" + }, + "source": [ + "halo_data = np.genfromtxt(\"data/Training_halos.csv\", \n", + " delimiter = \",\",\n", + " usecols = [1, 2, 3, 4, 5, 6, 7, 8, 9],\n", + " skip_header = 1)\n", + "print(halo_data[n_sky])\n" + ], + "execution_count": 13, + "outputs": [ + { + "output_type": "stream", + "text": [ + "[1.00000e+00 1.40861e+03 1.68586e+03 1.40861e+03 1.68586e+03 0.00000e+00\n", + " 0.00000e+00 0.00000e+00 0.00000e+00]\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "HIccg51VN1bp" + }, + "source": [ + "The third and fourth column represent the true x and y position of the halo. It appears that the Bayesian method has located the halo within a tight vicinity." + ] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "zpRSL5sZ1ZdY", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 643 + }, + "outputId": "787a88d5-242d-41d1-ccd4-061d4a99ad24" + }, + "source": [ + "fig = draw_sky(data)\n", + "plt.title(\"Galaxy positions and ellipcities of sky %d.\" % n_sky)\n", + "plt.xlabel(\"x-position\")\n", + "plt.ylabel(\"y-position\")\n", + "plt.scatter(t[:,0], t[:,1], alpha = 0.015, c = \"#F15854\") # Red\n", + "plt.scatter(halo_data[n_sky-1][3], halo_data[n_sky-1][4], \n", + " label = \"True halo position\",\n", + " c = \"k\", s = 70)\n", + "plt.legend(scatterpoints = 1, loc = \"lower left\")\n", + "plt.xlim(0, 4200)\n", + "plt.ylim(0, 4200);\n", + "\n", + "print(\"True halo location:\", halo_data[n_sky][3], halo_data[n_sky][4])\n" + ], + "execution_count": 14, + "outputs": [ + { + "output_type": "stream", + "text": [ + "True halo location: 1408.61 1685.86\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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I/PQ3SCkV08NLW/nN/AbaQ5K9vm3289b6Dj6cPYjBhYlnun6x2ZtQYAzg2k+a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tkOh4PBgwfH3F50PQdXAuT3n97kdXC0b+cFeHJnSbPfG9c7j8GDk2sso2t7GVQ3f7VnVHDz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\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ls3MPzgohRFYMQ2VjReIBp4APenrVJMpgCKKRsc0YS/B7VZmnw64mV2omFdSzWKUZvyg4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\nyKh98POojSjSBPzCP8a/HIxyUVtQfClJHVBbUEIJuMY/xnflszyzbUOUh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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [], + "image/png": { + "width": 611, + "height": 608 + } + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "4qTGj4xGMD3o" + }, + "source": [ + "Perfect. Our next step is to use the loss function to optimize our location. A naive strategy would be to simply choose the mean:" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "Nh9yUghy1dkp", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 53 + }, + "outputId": "d0d1c8db-c704-4bc9-c2af-16037393a908" + }, + "source": [ + "mean_posterior = t.mean(axis=0).reshape(1,2)\n", + "print(\"Mean Posterior: \\n {}\".format(mean_posterior[0]))\n" + ], + "execution_count": 15, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Mean Posterior: \n", + " [2324.5498 1122.9581]\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "bmdi8Oiz0tJd" + }, + "source": [ + "but we also want to determine how good this mean score is. We can use the [DarkWorldsMetric.py](https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/blob/master/Chapter5_LossFunctions/DarkWorldsMetric.py) script to judge that." + ] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "EhqC4ZzxVbba", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 145 + }, + "outputId": "bff984b6-7d77-405d-9b84-f6726349a284" + }, + "source": [ + "reset_sess()\n", + "\n", + "import wget\n", + "url = 'https://raw.githubusercontent.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/master/Chapter5_LossFunctions/DarkWorldsMetric.py'\n", + "filename = wget.download(url)\n", + "filename" + ], + "execution_count": 16, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Device mapping:\n", + "/job:localhost/replica:0/task:0/device:XLA_CPU:0 -> device: XLA_CPU device\n", + "/job:localhost/replica:0/task:0/device:XLA_GPU:0 -> device: XLA_GPU device\n", + "/job:localhost/replica:0/task:0/device:GPU:0 -> device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0\n", + "\n" + ], + "name": "stdout" + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "'DarkWorldsMetric (1).py'" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 16 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "rh_R7W5l1hGD", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 197 + }, + "outputId": "0b8ec192-f1ff-4ad0-b5c4-b4ee6585179b" + }, + "source": [ + "from DarkWorldsMetric import main_score\n", + "\n", + "halo_data_sub = halo_data[n_sky-1]\n", + "\n", + "nhalo_all = halo_data_sub[0].reshape(1,1)\n", + "x_true_all = halo_data_sub[3].reshape(1,1)\n", + "y_true_all = halo_data_sub[4].reshape(1,1)\n", + "x_ref_all = halo_data_sub[1].reshape(1,1)\n", + "y_ref_all = halo_data_sub[2].reshape(1,1)\n", + "sky_prediction = mean_posterior\n", + "\n", + "print(\"Using the mean:\", sky_prediction[0])\n", + "main_score(nhalo_all, x_true_all, y_true_all,\n", + " x_ref_all, y_ref_all, sky_prediction)\n", + "\n", + "#what's a bad score?\n", + "random_guess = tfd.Independent(tfd.Uniform(\n", + " low=[0., 0.],\n", + " high=[4200., 4200.]),\n", + " reinterpreted_batch_ndims=1, \n", + " name='rv_halo_position').sample()\n", + "random_guess_ = evaluate([random_guess])\n", + "\n", + "print(\"\\n Using a random location:\", random_guess_[0])\n", + "main_score(nhalo_all, x_true_all, y_true_all,\n", + " x_ref_all, y_ref_all, random_guess_)" + ], + "execution_count": 17, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Using the mean: [2324.5498 1122.9581]\n", + "Your average distance in pixels you are away from the true halo is 41.93538113267255\n", + "Your average angular vector is 1.0\n", + "Your score for the training data is 1.0419353811326726\n", + "\n", + " Using a random location: [ 825.1095 4039.4138]\n", + "Your average distance in pixels you are away from the true halo is 3311.9013851878894\n", + "Your average angular vector is 1.0\n", + "Your score for the training data is 4.311901385187889\n" + ], + "name": "stdout" + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "4.311901385187889" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 17 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "9JgVnP8jOui8" + }, + "source": [ + "This is a good guess, it is not very far from the true location, but it ignores the loss function that was provided to us. We also need to extend our code to allow for up to two additional, *smaller* halos: Let's create a function for automatizing our Tensorflow workflow from before. \n", + "\n", + "First let's reset our Tensorflow Graph and import the new dataset:\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "BrpRo49n1yyZ", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 271 + }, + "outputId": "b636a085-f4d4-41c8-c8f7-2d7c84279a67" + }, + "source": [ + "reset_sess()\n", + "\n", + "n_sky = 215 #choosing a file/sky to examine.\n", + "data = np.genfromtxt(\"data/Train_Skies/Train_Skies/\\\n", + "Training_Sky%d.csv\" % (n_sky),\n", + " dtype = np.float32,\n", + " skip_header = 1,\n", + " delimiter = \",\",\n", + " usecols = [1,2,3,4])\n", + " # It's handy to specify the data type beforehand\n", + "\n", + "galaxy_positions = np.array(data[:, :2], dtype=np.float32)\n", + "gal_ellipticities = np.array(data[:, 2:], dtype=np.float32)\n", + "ellipticity_mean = np.mean(data[:, 2:], axis=0)\n", + "ellipticity_stddev = np.std(data[:, 2:], axis=0)\n", + "num_galaxies = np.array(galaxy_positions).shape[0]\n", + "\n", + "print(\"Data on galaxies in sky %d.\"%n_sky)\n", + "print(\"position_x, position_y, e_1, e_2 \")\n", + "print(data[:3])\n", + "print(\"Number of Galaxies: \", num_galaxies)\n", + "print(\"e_1 & e_2 mean: \", ellipticity_mean)\n", + "print(\"e_1 & e_2 std_dev: \", ellipticity_stddev)\n" + ], + "execution_count": 18, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Device mapping:\n", + "/job:localhost/replica:0/task:0/device:XLA_CPU:0 -> device: XLA_CPU device\n", + "/job:localhost/replica:0/task:0/device:XLA_GPU:0 -> device: XLA_GPU device\n", + "/job:localhost/replica:0/task:0/device:GPU:0 -> device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0\n", + "\n", + "Data on galaxies in sky 215.\n", + "position_x, position_y, e_1, e_2 \n", + "[[ 3.90340e+03 1.38480e+03 -4.93760e-02 1.73814e-01]\n", + " [ 1.75626e+03 1.64510e+03 4.09440e-02 1.90665e-01]\n", + " [ 3.81832e+03 3.18108e+03 1.97530e-01 -2.10599e-01]]\n", + "Number of Galaxies: 449\n", + "e_1 & e_2 mean: [ 0.01484613 -0.02457484]\n", + "e_1 & e_2 std_dev: [0.20280695 0.20415685]\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "lQB0dpy81p6f", + "colab": {} + }, + "source": [ + "def multi_posterior_log_prob(mass_large_, halo_pos_):\n", + " \"\"\"\n", + " Our modified posterior log probability, as a function of states\n", + " Closure over: data\n", + " \n", + " Args:\n", + " mass_large_: scalar of halo mass, taken from state\n", + " halo_pos_: tensor of halo position(s), taken from state\n", + " Closure over: \n", + " data\n", + " Returns: \n", + " Scalar sum of log probabilities\n", + " \"\"\"\n", + " # set the random size of the halo's mass (we have multiple)\n", + " rv_mass_large = tfd.Uniform(name='rv_mass_large', low=40., high=180.) \n", + " rv_mass_small_1 = 20.\n", + " rv_mass_small_2 = 20.\n", + " \n", + " # set the initial prior positions of the halos, \n", + " # these are a set of 2-d Uniform distributions\n", + "\n", + " rv_halo_pos = tfd.Independent(tfd.Uniform(name=\"rv_halo_positions\",\n", + " low=tf.to_float(np.reshape(\n", + " np.tile([0., 0.],\n", + " n_halos_in_sky),\n", + " [n_halos_in_sky, 2])),\n", + " high=tf.to_float(np.reshape(\n", + " np.tile([4200., 4200.],\n", + " n_halos_in_sky),\n", + " [n_halos_in_sky, 2]))),\n", + " reinterpreted_batch_ndims=1) # notice this size\n", + " \n", + " fdist_constants = np.array([240., 70., 70.])\n", + " \n", + " # For our calculations of ellipcity derived from halo position, we derive means based\n", + " # on the sum of the means of forces from multiple halos\n", + " \n", + " mean_sum = 0\n", + " mean_sum += (mass_large_[0] / f_distance(data[:,:2], halo_pos_[0, :], fdist_constants[0]) *\n", + " tangential_distance(data[:,:2], halo_pos_[0, :]))\n", + " mean_sum += (rv_mass_small_1 / f_distance(data[:,:2], halo_pos_[1, :], fdist_constants[1]) *\n", + " tangential_distance(data[:,:2], halo_pos_[1, :]))\n", + " mean_sum += (rv_mass_small_2 / f_distance(data[:,:2], halo_pos_[2, :], fdist_constants[2]) *\n", + " tangential_distance(data[:,:2], halo_pos_[2, :]))\n", + " \n", + " ellpty = tfd.MultivariateNormalDiag(loc=(mean_sum), scale_diag=[0.223607, 0.223607], name='ellpty')\n", + "\n", + " return (tf.reduce_sum(ellpty.log_prob(data[:, 2:]), axis=0) + \n", + " rv_halo_pos.log_prob(tf.to_float(halo_pos_[0, :]))[0] + \n", + " rv_halo_pos.log_prob(tf.to_float(halo_pos_[1, :]))[1] +\n", + " rv_halo_pos.log_prob(tf.to_float(halo_pos_[2, :]))[2] + \n", + " rv_mass_large.log_prob(tf.to_float(mass_large_[0][0])))\n" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "IsfBD04hxr_y", + "cellView": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 91 + }, + "outputId": "646103da-19b5-4556-d41f-90cef74f1a25" + }, + "source": [ + "#@title ## Inferring the posterior distribution\n", + "number_of_steps = 10000 #@param {type:\"slider\", min:2000, max:20000, step:100}\n", + "#@markdown (Default is 2500).\n", + "burnin = 2500 #@param {type:\"slider\", min:0, max:4000, step:100}\n", + "#@markdown (Default is 2000).\n", + "leapfrog_steps=6 #@param {type:\"slider\", min:1, max:9, step:1}\n", + "#@markdown (Default is 6).\n", + " \n", + "# We have three halos in the sky instead of one\n", + "n_halos_in_sky = 3\n", + "\n", + "# Set the chain's start state.\n", + "initial_chain_state = [\n", + " tf.constant([80., 20., 20.], shape=[n_halos_in_sky, 1], \n", + " dtype=tf.float32, name=\"init_mass_large_multi\"),\n", + " tf.constant([1000., 500., 2100., 1500., 3500., 4000.], \n", + " shape=[n_halos_in_sky,2], \n", + " dtype=tf.float32, name=\"init_halo_pos_multi\")\n", + "]\n", + "\n", + "# Since HMC operates over unconstrained space, we need to transform the\n", + "# samples so they live in real-space.\n", + "unconstraining_bijectors = [\n", + " tfp.bijectors.Identity(),\n", + " tfp.bijectors.Identity()\n", + "]\n", + "\n", + "# Define a closure over our joint_log_prob.\n", + "unnormalized_posterior_log_prob = lambda *args: multi_posterior_log_prob( *args)\n", + "\n", + "# Initialize the step_size. (It will be automatically adapted.)\n", + "with tf.variable_scope(tf.get_variable_scope(), reuse=tf.AUTO_REUSE):\n", + " step_size = tf.get_variable(\n", + " name='step_size',\n", + " initializer=tf.constant(0.6, dtype=tf.float32),\n", + " trainable=False,\n", + " use_resource=True\n", + " )\n", + "\n", + "# Defining the HMC\n", + "kernel=tfp.mcmc.TransformedTransitionKernel(\n", + " inner_kernel=tfp.mcmc.HamiltonianMonteCarlo(\n", + " target_log_prob_fn=unnormalized_posterior_log_prob,\n", + " num_leapfrog_steps=leapfrog_steps,\n", + " step_size=step_size,\n", + " #step_size_update_fn=tfp.mcmc.make_simple_step_size_update_policy(),\n", + " state_gradients_are_stopped=True),\n", + " bijector=unconstraining_bijectors)\n", + "\n", + "kernel = tfp.mcmc.SimpleStepSizeAdaptation(\n", + " inner_kernel=kernel, num_adaptation_steps=int(burnin * 0.8))\n", + "\n", + "# Sampling from the chain.\n", + "[\n", + " mass_large, \n", + " halo_pos\n", + "], kernel_results = tfp.mcmc.sample_chain(\n", + " num_results = number_of_steps,\n", + " num_burnin_steps = burnin,\n", + " current_state=initial_chain_state,\n", + " kernel=kernel)" + ], + "execution_count": 20, + "outputs": [ + { + "output_type": "stream", + "text": [ + "WARNING:tensorflow:From :26: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "Use `tf.cast` instead.\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "QMn0V1Zj4qhc", + "colab": {} + }, + "source": [ + "large_halo_pos = halo_pos[:,0]\n", + "small1_halo_pos = halo_pos[:,1]\n", + "small2_halo_pos = halo_pos[:,2]\n", + "\n", + "# Initializing our variables\n", + "init_g = tf.global_variables_initializer()" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "SFE7hvhWxr_4", + "colab": {} + }, + "source": [ + "# Can take up to 3 minutes in Graph Mode\n", + "# Running the Initializer on our model\n", + "evaluate(init_g)\n", + " \n", + "# performing our computations\n", + "[\n", + " large_halo_pos_,\n", + " small1_halo_pos_,\n", + " small2_halo_pos_,\n", + " kernel_results_\n", + "] = evaluate([\n", + " large_halo_pos,\n", + " small1_halo_pos,\n", + " small2_halo_pos,\n", + " kernel_results\n", + "])" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "DiQbMPv_iHFH", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 53 + }, + "outputId": "0afb0bf5-e037-4cd5-90eb-7f50538ea16a" + }, + "source": [ + "print(\"acceptance rate: {}\".format(\n", + " kernel_results_.inner_results.inner_results.is_accepted.mean()))\n", + "\n", + "print(\"final step size: {}\".format(\n", + " kernel_results_.new_step_size[-100:].mean()))" + ], + "execution_count": 24, + "outputs": [ + { + "output_type": "stream", + "text": [ + "acceptance rate: 0.4699\n", + "final step size: 2.2313740253448486\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "AOh30jd719rq", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 625 + }, + "outputId": "163ee136-feab-4626-fa6d-e7e676099c0d" + }, + "source": [ + "fig = draw_sky(data)\n", + "plt.title(\"Galaxy positions and ellipcities of sky %d.\" % n_sky)\n", + "plt.xlabel(\"x-position\")\n", + "plt.ylabel(\"y-position\")\n", + "\n", + "# Hex for red, purple, and yellow-orange\n", + "colors = [\"#F15854\", \"#B276B2\", \"#FAA43A\"]\n", + "\n", + "\n", + "t1 = large_halo_pos_\n", + "t2 = small1_halo_pos_\n", + "t3 = small2_halo_pos_\n", + "\n", + "plt.scatter(t1[:,0], t1[:,1], alpha = 0.015, c = colors[0])\n", + "plt.scatter(t2[:,0], t2[:,1], alpha = 0.015, c = colors[1])\n", + "plt.scatter(t3[:,0], t3[:,1], alpha = 0.015, c = colors[2])\n", + " \n", + "for i in range(3):\n", + " plt.scatter(halo_data[n_sky-1][3 + 2*i], halo_data[n_sky-1][4 + 2*i], \n", + " label = \"True halo position\", c = \"k\", s = 90)\n", + " \n", + "#plt.legend(scatterpoints = 1)\n", + "plt.xlim(0, 4200)\n", + "plt.ylim(0, 4200);\n" + ], + "execution_count": 25, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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9MhYb+yxY+vz6gQrP+/XWSdfpzQh/fe6apsvsGjDArgPDrgFDafc6eMvVw3z2\ntkLb273saas5dnV2Vn0zFoZd+LNg2nFSJo4ZhrHTMViOuPi2AoWa4zVHdLO2c0f3gzE1f9xUabks\nnMp2YhjGTsllD5SnXOelh3TRm7UxlwzDMIzZ86lbxqcljAFsKjUpWzCMnRwTxwzD2Kko1xzP//Ug\nf9pcBeCSOwpcdEI//7T/Lmf1nXduG6q1XBaZNmYYS5IrN7QWvQF6s8IbHt69QL0xDMMwdmUGyxH/\n8efRabd7WN8SkxmiCGgU9FKQsh/nlxtL7Ko1DGNX5yM3jW4TxgDGQserrxzi4L4MR1h+wbRYP9Za\nHOszV4lhLEkeLE7+a/z5j+xjIG9f6A3DMIzZc93mkPHa9H5RPXIgw4G9s5AZFlKoiiKgCpUaRJq9\nhXOQ6oBcGshCKg0i87N/Y6fCxDHDMHYqvvm34g7zyhG88Y9D/Oppuy1Cj5Yu9xda30Svzps4ZhhL\nkVrcetlzD+zgZYd2LVxnDMMwjF2ayX5obUYmgIse108wEzEpEaoiIKr/UE6QUcGKrBfJZG7EqqgK\nlQrUxvTvqArEuv2gAGEGUt2Q74B0FghMJNvFMXHMaMnGYsSFN48xUonpzgQ864AOTlxjo9sZ88cd\nwyEPFpvf+V27OeS6zVWOs3DPtnlokryHtV3mLDGMpcjarhR3jex4s3LEijT//bj+ReiRsZjcNRJy\n1aYqf3qoylAlZmU+4MQ1OZ5zQAdiN3GGYcySI6dRtdGbET5z0oqZBfFHVRXHorJmf6RSXhBzUClA\n4CAVQZyCdN43moVYFUVQHofqCNQqqCjm9ykCcRlqZciEUKxCR58XyATEfmDeVTFxzGhKLXac9pPN\n3Dtev7n+wh0FTlqb41OPX8GedmNtzAON11szvvm3oolj02BLubXFZN9uew8bxlLkRQd1ct512+e/\nHL97lq89YYDOtH1hXy5cvanCe68f5apN1R2WffnOIv97yzhfecIA+3TbV33DMGbO49bkeMZ+eX6w\nfvLBYA5fkeZLpwxwUN8MIlCiqC6MEUA2Cy5WocpFKkrFoV+eB1eFdAoEZixWVcegMgS1ArgUpHO6\nr1QaXABO0HLLUchGUElB0AtBGnDmINtFsW9RRlOu3FBpKlRcsaHCk378ELcMhovQK2NXpxBOnmnw\nf3cXbZTFaTBZ+dUBs8mCMAxj0TjnkC6evHeeTACPXJXh/cf18uPTV1nO2DIhdo63XT3M6T/d0lQY\nS7hpa8g7rhlZwJ4ZhrGrcsm6AT706D5279heOuhKC6fumePLpwzw+2fsNrkw5pwXvOK68LWNal0Y\nCzIQRzqFFahVVbSSAGKBuKa0nd4AACAASURBVApBBIjOd27CthqIY4hrDZP/YlytQLWk4pgTL75V\nAecdbEVwIVrf6aC0FarjUBmHsASu1nqfxpLG7o6Mplz1UOsvXA8WY8742Wa+f9oqHmkuHmMOGQsn\nUXOAoYrjtqGQo1baddcO6QBandJH2XvXMJYkfdmAb526kih2pAL75Xo5UYsdr7lyiG//o9TW+pc/\nOPnIpoZhGO0gIrz6iG5efUQ3943X2FqO2as7xaqpfpSJYxWwiMEBsYPk/5b4kshYF1OLIZtR4Sys\ngCvpfGKIBM0By0GtCGQg67flvJAmfn/EKqKlHMSB37gnSOu65XGoDgIRZLp9H0UdaQ4Vv2JRF1kQ\nQFSCyhYtv8z3Qi2n7TJpLIds18LEMaMpXenJ3+SjVceLLxvkymestl+rjTkjm5r6n8stQzUTx9ok\nE0hTp10uhQnbhrHEMWFs+fHOP420LYwB9GbtGjEMY27ZuzvN3t1TrBTHQA2iGKJQXVsu9q6wGPCj\nP4qoEFUtq1gW1bxTrKaClAtUnEpKJ6OC39YoBLGOKBlX6o4y59SBJmmQGpBXwQ0vcoUVdYRF4xCO\nQ7rL9yFR6ALdHgKSUjHPJe4zpy6zEIhyXnNLg3RAOm05ZLsIJo4tUwbLEX/YVKVUczjgiBWZ7QIX\n2ym5eqAY8ZarR/j8uoF57KmxnNirjSy7e8enN2rOcqaVxn3sqiy5NoRIwzAMY+fgtqGQz91emFab\nI1fMIPvHMAxjNiTli1FFxTEaSijDCqS8KCbo/FpRBTQEUp3q4oqKQApSObyVywflZ4BQRbIyPqC/\n5Msu034/MYRj6ipL+RKKjL+vTaW0pDIqqfjVWN7pQn0uQYN7rKL7rlZ03/l+tpVzhiWQLKQAsjpI\ngDnIljwmji0z7hmr8a4/jfCz+8pEEwwlRw1k+MSJ/Ry9Msu6PXJ0p4Xx2lQZUCVefYSNIGjMDYf2\nT/2RNDhJyLyxPb3ZgNFwx+zAx+1u71fDMIylxNfuKjKdyE0Bzj26Z976YxiGsQNx7CcfzxP4YHtx\nqPgVaGliXEZVJVHhqTYIqbyWUtZqEKR8xhiouOa3EQvgtAQzXVBBSwI/pVX8imsa6J/Ua8axClnb\nRrgUkDxIUV1m6U4dlTIpz0xlVeSKSrqtqAqEkOsBct6tVtWcMheoCAgqtqXt+/VSx/x/y4jhSswp\nP9rMj+/dURgDuHkw5Ok/38L1m6v0ZAKee2BnW9v93G3jc9xTY7myMp9inylGUezK2K8y7dLKAXrm\nvh0L3BPDMAxjNmwqTT6a80Ref2Q3j9k9N0+9MQxjWTJpqD5ATYWmKFL3VVRVwSkpi0z+jmsacB+N\noZliFShshuJmqAxCOAjVgm6rVoG4qG6yqKhurlQOqlWfaea808vp9hMXmWRRqaOmU1zRTDHxDrVU\nB0gEoRfZakWQmLpLLdYRMsNIRTmXHF+lnl2WSuu+amWfV2Yh/UsdE8eWEbcMhQxWJnfdjFQdr7pi\nCOccLz+si3YiTS5dX2KwPL0vbYbRimftP7lw05W2j612OXJgR3Hs0K6YY1bZL1uGYRhLifQ0MubO\nPbqH9z6qbx57YxjGsiARw+JIhaJkFMlkcnF9xMiazwur1TQ7rFpQx1atCLWSllRGNd1OFIKrQHUE\nwlGQnJYxlgpQKUClCJVRqI2qSyssQrUI40MwuhGKG6CyVdeJyxBkgciPeNmAZH1WWBpqkZrJIi9q\nkfLOr3Gfb4YvCa3qNitlKA5DWNa+VSoQDvm8slGohbqPVLY+oEBk0S9LHbvLXEa0K2b/bbTGTVtD\nDl+R4RWHdk25fjmCqycZ3dIwpsPzHza5Y3EgZx9b7XJ8E9fAi/YKF6EnhmEYxmx41WFdTBUVOZAL\n+MQJ/bz72N6F6ZRhGLsmztXLDGsVncKyD8uP6jeViUgWVaEwDOMPQmkDFDdCYQvUxqA8rBlg1UEV\nl6pj6uIKx9X5le2BVJeWN4ofYbIWqjBWHtOpVIDiEJQ3wshGGHoQCkMqkIUjfgRL6o42Gm56Az8K\npSTB+jV1oDkAUWdYuaLur2oVigV1sRU2wNhmqI6q+JYK9NirQ1AZgbig7jfnNEctqgJmFlnqWOaY\n0ZQka+z84/r4/cYKtwxNroSvH7MPA2NuOLQ/w6NXZ/nT5uaC6/FrzPXULqfvnWe/ntS29+cxvRGn\nrrL3qmEYxlLjmFVZfnD6Kt55zQg3D9Z/5AgEDutP88z9OnjFYd302w9IhmHMBudzulwIkRfJnC+H\nBKCm4fSBzwQrjUJ1KxQH9e8kaD+Xg1BAun24fagClYvVRYaDTAeUhnyZYuzLI0vehVUGV9b1U2kV\n1ahBlNWRKMtVDeavhdCbB5dRp5jzo09KEtPiPxNrMQQ+Q6xWUeGuOq7rRRUf1F8EclAahvIgZHKQ\n20P7l/ayiWSACOJxqOUgqOgxW+rLLoGJY0ZTklEDO9LC1564kmf+YsukAphVWBtzyYcf08eTfrJ5\nh2y8Q/vTHNpvo2+1SyYQLjy+n7MvG+SQ/jQfObA4pfPAMIyFY7gSk01Bp5WLG21w4poclz99NXeO\n1NhQiAhEOGZVhr6sXT+GYcwBSW5XraqC1rayStH8/NiXTMZVSMVQHoXSZhjfAqWRunMrSEEpApeF\nvhUQZ7V0slKBdE1daIFo+WTaB/M7p8IWTksVCWBsSHPBYlExLt0NmaRfEaR6tEyyvBXS/ZBLQxAC\nOfwwkkCsxyNOnWFRWY8jLGh5Z61YD/8v1cCNaj8lC5l+PR+BP3YJ/CADGX+etoJU9djSnd5JF+s6\nxpLExDFjBw7pS28Xir5fT5pfPnU1z/v1Vm7Y0rwk69Q9LfTVmDuOXZ3lvEf28u/XjW6blxJ4v2Wo\nTJsn7pln/VlryaaEu+4aWezuGMay5zt/L/KlOwvctDVkLNRfADIB9GUDDu1P89jdc5y6Z45H75Yl\nsGHhjQkEIhzan7EfigzDmHuiUPO3osQ95rQEUrL1jC0XqlA0Ogy1ES1vDEvq7ooKXvjyGV/hCIxs\ngN5VkO7RnLGooi6sTEbLFSsA3umVyoJLaXsnQAiFrRB0Qlc/1MZ94L5TV1uqWx1b1ZJuIxNBMKD9\nS6XR0TBDLYmsjKrI5bLgCirwBRnoyEKlpCJbNg9hSreVy2h4vwSaW0aoIp1UtF3N6bFkKpDKQOfu\nUMnqMgDSJpItQUwcM3bgQ4/p2+EL+W4dKX58+io+8OdRPn97gWpDrv9pe+U4xL6kGXPM6x/ew+Er\nMrzn2hGKNcdbj+nhSXvlp25o7EDW7GKGseg45zjn8kF+sL68w7Iwhi3lmN9vrPL7jVU+etMYe3en\neMOR3Zx9cBc5ew8bhmEY80kUaslhtaxB9c6PKFkrUi+l9NEmcRGirVp+WI2gI68CU6WmIlQYatlj\nksFV2ASZPq88BNC/u5ZGZgMd7ZGs318Bsh1a4lgpqxDlapCOdVk6p6WRtQpkuqAcQk+nbrMyBkEe\nqKrwFocqnAWRZp5JoKJbOK7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5Dt1ONgc57yBLZ1AnWNb3qwL0oKJYXvuJoCLjENqJCuq4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g7ICllSGe3dbMJb2gQXiy7fNFeHNfoNM1r+0JKDiWoX4wKZcrhnvY2hAmFDU5qcyp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qFuHviujrk73DGl126vl+6VQgghRDrz+DVztjaGfd6n4ZrPa/hgVhEqTUop/HNFfVyBsWY7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\nF+P+LZ13HR1VqPG7GSV9MCIhRCboSgLwzoDOt5f6em8weeCYMjuzz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W2TFh2L\nzUI0EEWP68TDcYL+ILFIDJo4sPNkJnJBW1MbtgIbNtOG0hShQAin17kve0yInval8V5OGuDg72sD\njCzUuHiElFoQQojeFDNMXt8dYd72MHtCOoM8GucMcXLaIGd/D61DeRkcU0pNIhGMmmeapr7f7Vbg\nm8A32m+6f//HmaapK6X+D/gV8IBS6lTTNOvaH3sEcE/7oXd18G3vBp4E7lVKvWWa5sb2x1UCf24/\n5h7TNDOpn5QQGe+f61I1gT2QVXJlhThAW8xgeV2U+rDBqEIrE0psOKUOT0bZ14nSBIXCaE+TjRPH\nYrWgx3WCwSCRUCRRgywbdooFSXTa9Cj8ph+P6UGP6+gRHTSwZsD0tKMgnVLyfyPbjS+xcd+Jxf09\nDCGEyHkNYZ0r5jeysiF2wO0Prg1wQpWdu6cWManc3k+j61j/zz56gFLqWD4JMAGMb//6S6XUd/be\naJrm9Pa/DgeeAZraM7rqSGylnAgMJNHw/DbTNF/u4NvdD8wCPgVsUEq9SmIqegaJHkx/ME1zzsEP\nMk3zKaXUA8DNwAdKqQVADDgdKASeBf7Y9Z9eiPzVEjGoD6cfTz6iKCdOeUIctlUNUe5d1caru8IH\nbEmeVGbjlfMrsGsSBMgEewNjpmFiURbiehw9qhMnjg0boXCIQCCAxbCg6zpxI55o79NGovB9JnIB\njsRfo3oUu7ITDUWxu+zEI3GsTiuGYfRbx8p9wUgODJAppRL/VhIkE0IIITrzuYVNhwTG9lq6J8oF\n8xqYfVYZ06ocfTyy5HLlSrEQmNbB7cn6kb4P/A6YSiKQNpPEVGgn8E/gT6Zprujoge3ZYxcDXwGu\nB84msYFhBfBn0zQfSzZI0zS/opRaAnyVRPdIjUTx/4eAByRrTIiuiXdh+41Dg2uPkC2VIr/tDCn+\nvM3GgoZ6Ovrfs6oxxtI9UU4emDkTlXy1f2AMlSjCrywKi2bBErYQjoSJRCMQgnA4jLIqPC4PvpAP\nSoCa/v4JOmAh3PP/CwAAIABJREFUERwzAQ/YrDY0pWHREsE9AwM9pmPHjmEkOm/2pQN+58D+Fdz3\n3qYsChNTAmRCCCFEEh81x3izNnXLbH/c5DOvNfHOJZWUOTOjTXVOBMdM03wdDmlCk+r4LcAth/H9\nDBJZXl3O9GoPniUNoAkh0lfu1JhQYmVNc7zTYz97hIeBnsw48QrRHx7dEODWlU4iRuqPS69NLvoz\nwn6BMdM0EzntBhhWA4vDguk3Mf0m4WAiMKZiinAknJgNRfp78EnYSYzPC2jgcXtQusKpOYlH4tjK\nbJiaiR7T0dDQTX1fgKyzYNSS2ghztoZY2xxjd0DHAIZ6rQzzapwx2Mn5Q51YLcmf4+Bg5CHfr/11\nMA1TAmRCCCFECnO3hdI6riFs8NC6AN+dVNjLI0pPTgTHhBD56+pRbu5Y3prymCnlNn52XGacdNOx\nJ6izO6ijgKNKbSkv6ITojG6YfP8dH39bF6CzdSSHBiMKJIjc3w7ezmfEExlVpkoU5I/Go+joGFYD\nq92aKGgfDhGNRiFAoih/JooBcVCawu10Y1EWDItBNBSlYEAByqawWW2YmOhRHYvVgmk1U25pfGxD\ngN994Odj36GLJFvbEmVl/70hyKhCjQdnFjOlMkkR4FSBsXZKJYJiewNk6S/LCiGEEPkjrKe/u+cf\n6wLcekwBlgxYcJLgmBAiq31lgpflDVHmbD20wI4i0b79jycV47VlRzX+BTvDXLewiWA88aFS4bRw\nw1gPX5ngpdCeHT+DyBymafLFRc08szW9FbyrRrkpzZDU9nxnGEYiUymWyBozdCOx/TCqY8YSARpT\nmRiagambGFEDs8UEX3+PPAU7UJwIQoXjYWzKRnFxMYbNwLAYaBbtk8YDhpH4ueMGFpU492lWbV/G\nVtww+fqbLfx3YzCtb72pVeeaV5t55dwyhhZaD9iyeXAwcn+pumeapmSPCSGEEAcb4E5/LlkbMqgN\nGhmxw0eCY0KIrKZZFP88pZS528M8tTnI+40xvDYLk8psfGWCl/ElyVu3tcUMHl0f5I3aCFta4wz1\naswa6OTL4zz9lq31uw/a9gXGAOrDBvesauN/m4I8cmopR5dlVlcXkdl+tqI17cCY16q4fXL2ZFjm\nqr1b9/6fvTcPlyyt6zw/73KWWO6WWxW1U9TCKpuFLCVLC4LQSI+CrSA8LmOPjc3YTyvt1sOoqP20\nKOLeLrTdo6OCztDiCIogCsWiIBR7sVNQUFWZefMuEXG2d5s/3oi4WVm53KzMm3kz8/3kE09sJ+Ke\nOPdmxInv+X6/v9nJOx+nUIookDkTpzpaawk24KzD1pZm3MDJ6z3OP4robFuC/qBPURaYYOjnfbIs\ni4KgDwgvEEHgTHSPOdzcQSd07F37lY9Nti2MzTjYeF7zkRG//pQVPP7+AtnRHWNHFfN7v1UJK4SI\n60hcj0QikUgkEvflW6/r8VMf2KB121v+dJxmO0kSxxKJxAWPFILnX9vj+df2trV86wK/f8eE135k\nxGq79aXnU+uWv7mr5cOHO37/aXt2anVPyh3rx+9P+9LI8ey/Osxrn7zMd93QP8drlbgQ+cPPTHjd\nx8bbWlYK+N2nrZzWkb7E2WcmyHg3dYN5T2Aq2gSwJgpinekINmCsoa1a6nEdhaeCKD7tVjwwgOFg\nSFZmFP2CTGZkeYY1FhEEWmpcmIpheLAgpcRZRwgBaSWj4PitT2zvb/tY7qqi2Ci1vJ9ANuPo3wOB\nrd437usk8+L8TdVMJBKJRGK3cqCn+PYH9/njbRzEWikEV+4C1xjEuUGJROIi4I51w3e/Y5WX/d0q\nr7l9k8PNNqX6S4y7K8dz3nKIn/qnjfsIY0fz51+oWT/BfTtNoU7sWKtd4N++e40/+uxu/vab2A3c\nXTl+/B+3n6979S1LPPea7YnLiZ3h6EL42WUx/Td3MDlPO24JJmBri7MOZ1x0ljniIc/dav7LgWuh\nXC5RfcXK8goqU/MOLyUUzjtcFz+7vPdI5HwQgdQSoQSmNXzucMda98COMj/jigJkjGtynLf5+whj\n8xujY0wIEcv4fZg//mhXWSKRSCQSicjP3rLINcNTi14vur5/0u8/55LkHEskLgJCCLzwbavcNYlf\nKt58Z8NvfGLMa5+0zLdfn1xGM+4cWZ7/14f58vjUwuEXR5bHFuc+wvjEy3L+/Asnj8H98HvWuXqg\neNoVJyiWTlzyvPajIyZ2e+LBDz9yyA89YrjDa5Q4JVNhzIfYseW8m948LeFvOoQX2MZiGoNUEmss\nTdOAJYpjlij4qOn1M+QepXlHb4Hbiz6HlaYRgn3OcoNpeVRX89R6vL0dySXgCtBKs1AsoAtNYxq0\n0BSDAussVKAzjSscGKIwpsHLLXeX99FRd5X05AJOVx87UEq+5/r7vm967+fCV/DxCefC2NQ1dmyv\nmFSSQBTIlFAEkbrHEolEIpE4mn2l4s3P2cd3/O0qnznO0ByIQ6B+9NEL53jNTkxyjiUSFwG33dPN\nhbEZG13g+/9hjd98gNGTi43OBV78jtVtCWMAw+z8fNHZTjTUBfjev1/jYJ3cgYnj8yefPbWNPZPw\nS09c4mduWToHa5Q4GUdH9gQiijMquqWUVFHACYLJeILKFCIIJJIgQzzMaYAGqKbnZ/jWsC4VP7dy\nOf/b/mt4w8IePp2XrCrNRCruzAre0V/kdcuX8UP7r+ZTWXHyJ5NABlnIcMbRujYOD6gCUkm8ihM3\nnXeYxhBswDaWrumw1qKlxluP7Sy+8fjWk3nDqx92egcvrhlI/p+nLdEXzB1fM9fXTNgKYcu1dyJh\nbPa7klLOXWQzUS2RSCQSicQW1y1o3vuvDvC6Jy9z3VHT0A/0JN9zU5+3/8v9HOjtjkglJOdYInFR\n8Kk1c8L7/tM/bcDNim/ef2kLKb94+4hPrB3/qMWxXDtU3Lh04iL/neT515Y8ak/Gx46c+HcKcKT1\n/MKHNnndU1bO0ZolLhRGxjM+hWvsugXF7z11D7ccSAMedgsz0cU7P5/IeGzfmHCCtm1xwYEHpRRC\nC0IvRFHME91jZ8BBpfnJPVdwUJ/6PfBunfPqPQ/iVw99hf3+BJ8xA1BLChccvbKHlBIpJDZYspCR\nhxxvovgXVKBrOuhA93UUsKyPUzltvGw6g0Tybcue7gbNa++0nOQjkAOF4DuvKXjFw/os9zQ+TAcc\nyKOimxCFMBmHAMSrUzfZUcLlTDibOcdm9wtEmlyZSCQSicRx0FLwPTcP+J6bB6y3nk3juXqgduVn\nZhLHEomLgJ4+8ZtLAH76MzkPKlpuPHertKv4/IbldR8bbXv573/oYAfX5uRIIXj1LYv8q79ZPeWy\nf/jZih98xJCHLp8fIS+xOwkBSgXHqx1c1oHvu9rw4994Hfku6XdIRLyPHVazzrGZ+DJzN81EGACl\nVXRSeU8WMjrTxa6xGjhyZuvxPxb2bEsYmzGWit9b2sdPrt17/AUWIF/M6Zd9ev1edL4JgQrTjrGJ\nQ/TiJEpNdIkFAr7x6L6OxfnOIxA479CZjgIa8N1X53z7VRlvO+y5Y+y5c2w51ASWNDy4r3jcXsFz\nrulRSIWUUWBUWuHxhDagini02rmtnrOZeKaUQvgoUNaTGHUPIbrdEHHaZiDMRTNB+v+USCQSicTJ\nWC4ky8XuDS8mcSyRuAi4Znjy/8omCH7ucznPf2wgk5feDvwbvlCxzfolHrsv4+XnuX/p6VeUPP/a\nkr+8sznpci7Aaz8y4nfP02TNxO5kMZe89knL/JfbR3xl4sglPHZfzvOuKXmKuoehJglju4wQYjTP\nm61y9+BDjCR6YtzQB0QmojhEFyOGUyFJ9AWhCXCGKfpDUvOe8vTf/z6TnaD/UAJLkPdz8ixnsDAA\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dWVKeptX5YiWTgn/3yAWeXdyD9XDTjTdcUlMqE4lE4nzivQcPrnXY1kZhbCp6BQLIWO4e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wTExYzf28AS1wXLj2xcFPVRGibIVArNBDrBAUFRrud3ToirEtr8PON3Tz/LoGvV0ymQ+aTIgl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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [], + "image/png": { + "width": 611, + "height": 608 + } + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "1kfhERKKPdp-" + }, + "source": [ + "This looks pretty good, though it took a long time for the system to (sort of) converge. Our optimization step would look something like this:" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "q23126YI2CQ5", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 215 + }, + "outputId": "e311a121-2e13-498b-ed9a-da6ac31e3141" + }, + "source": [ + "from DarkWorldsMetric import main_score\n", + "\n", + "halo_data_sub = halo_data[n_sky - 1]\n", + "\n", + "\n", + "halo_lar_mean_ = np.mean(large_halo_pos_,axis=0)\n", + "halo_sm1_mean_ = np.mean(small1_halo_pos_,axis=0) \n", + "halo_sm2_mean_ = np.mean(small2_halo_pos_,axis=0)\n", + "\n", + "mean_posterior = [np.concatenate([halo_lar_mean_, halo_sm1_mean_, halo_sm2_mean_])]\n", + "\n", + "nhalo_all = halo_data_sub[0].reshape(1, 1)\n", + "x_true_all = halo_data_sub[3].reshape(1, 1)\n", + "y_true_all = halo_data_sub[4].reshape(1, 1)\n", + "x_ref_all = halo_data_sub[3].reshape(1, 1)\n", + "y_ref_all = halo_data_sub[4].reshape(1, 1)\n", + "sky_prediction1 = mean_posterior[0][:2]\n", + "sky_prediction2 = mean_posterior[0][2:4]\n", + "sky_prediction3 = mean_posterior[0][4:]\n", + "\n", + "print(\"Using the means:\", \n", + " sky_prediction1, \n", + " sky_prediction2, \n", + " sky_prediction3)\n", + "main_score([1], x_true_all, y_true_all,\n", + " x_ref_all, y_ref_all, [sky_prediction3])\n", + "\n", + "# what's a bad score?\n", + "print(\"\\n\")\n", + "random_guess = np.random.randint(0, 4200, size=(1, 2))\n", + "print(\"Using a random location:\", random_guess[0])\n", + "main_score([1], x_true_all, y_true_all,\n", + " x_ref_all, y_ref_all, random_guess)\n" + ], + "execution_count": 26, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Using the means: [1063.85 203.07939] [2047.1882 1213.6409] [3659.7612 3881.318 ]\n", + "Your average distance in pixels you are away from the true halo is 70.93141113865642\n", + "Your average angular vector is 1.0\n", + "Your score for the training data is 1.0709314111386563\n", + "\n", + "\n", + "Using a random location: [1063 1080]\n", + "Your average distance in pixels you are away from the true halo is 3889.2443937736803\n", + "Your average angular vector is 1.0\n", + "Your score for the training data is 4.88924439377368\n" + ], + "name": "stdout" + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "4.88924439377368" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 26 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "8DC8sam2Q65-" + }, + "source": [ + "## References\n", + "[1] Antifragile: Things That Gain from Disorder. New York: Random House. 2012. ISBN 978-1-4000-6782-4.\n", + "\n", + "[2] [Tim Saliman's solution to the Dark World's Contest](http://www.timsalimans.com/observing-dark-worlds)\n", + "\n", + "[3] Silver, Nate. The Signal and the Noise: Why So Many Predictions Fail — but Some Don't. 1. Penguin Press HC, The, 2012. Print." + ] + } + ] +} \ No newline at end of file diff --git a/Chapter5_/DarkWorldsMetric.py b/Chapter5_LossFunctions/DarkWorldsMetric.py similarity index 88% rename from Chapter5_/DarkWorldsMetric.py rename to Chapter5_LossFunctions/DarkWorldsMetric.py index a2e47e77..73a171ab 100644 --- a/Chapter5_/DarkWorldsMetric.py +++ b/Chapter5_LossFunctions/DarkWorldsMetric.py @@ -23,33 +23,33 @@ def calc_delta_r(x_predicted,y_predicted,x_true,y_true): """ Compute the scalar distance between predicted halo centers and the true halo centers. Predictions are matched to the closest halo center. - Notes: It takes in the predicted and true positions, and then loops over each possile configuration and finds the most optimal one. + Notes: It takes in the predicted and true positions, and then loops over each possible configuration and finds the most optimal one. Arguments: x_predicted, y_predicted: vector for predicted x- and y-positions (1 to 3 elements) x_true, y_true: vector for known x- and y-positions (1 to 3 elements) Returns: radial_distance: vector containing the scalar distances between the predicted halo centres and the true halo centres (1 to 3 elements) - true_halo_idexes: vector containing indexes of the input true halos which matches the predicted halo indexes (1 to 3 elements) + true_halo_indexes: vector containing indexes of the input true halos which matches the predicted halo indexes (1 to 3 elements) measured_halo_indexes: vector containing indexes of the predicted halo position with the reference to the true halo position. e.g if true_halo_indexes=[0,1] and measured_halo_indexes=[1,0] then the first x,y coordinates of the true halo position matches the second input of the predicted x,y coordinates. """ - num_halos=len(x_true) #Only works for number of halso > 1 + num_halos=len(x_true) #Only works for number of halos > 1 num_configurations=mt.factorial(num_halos) #The number of possible different comb configurations=np.zeros([num_halos,num_configurations],int) #The array of combinations #I will pass back - distances = np.zeros([num_configurations],float) #THe array of the distances + distances = np.zeros([num_configurations],float) #The array of the distances #for all possible combinations radial_distance=[] #The vector of distances #I will pass back #Pick a combination of true and predicted - a=['01','012'] #Input for the permutatiosn, 01 number halos or 012 + a=['01','012'] #Input for the permutations, 01 number halos or 012 count=0 #For the index of the distances array true_halo_indexes=[] #The tuples which will show the order of halos picked predicted_halo_indexes=[] - distances_perm=np.zeros([num_configurations,num_halos],float) #The distance between eac + distances_perm=np.zeros([num_configurations,num_halos],float) #The distance between each #true and predicted #halo for every comb true_halo_indexes_perm=[] #log of all the permutations of true halos used @@ -58,14 +58,14 @@ def calc_delta_r(x_predicted,y_predicted,x_true,y_true): for perm in it.permutations(a[num_halos-2],num_halos): which_true_halos=[] which_predicted_halos=[] - for j in xrange(num_halos): #loop through all the true halos with the - #predicted halos indexed by perm[j] + for j in range(num_halos): #loop through all the true halos with the + distances_perm[count,j]=np.sqrt((x_true[j]-x_predicted[int(perm[j])])**2\ +(y_true[j]-y_predicted[int(perm[j])])**2) #This array logs the distance between true and - #predicted halo for ALL configruations + #predicted halo for ALL configurations - which_true_halos.append(j) #logthe order in which I try each true halo + which_true_halos.append(j) #log the order in which I try each true halo which_predicted_halos.append(int(perm[j])) #log the order in which I true #each predicted halo true_halo_indexes_perm.append(which_true_halos) #this is a tuple of tuples of @@ -109,14 +109,14 @@ def calc_theta(x_predicted, y_predicted, x_true, y_true, x_ref, y_ref): # Angle at which the halo is at - #with respect to the reference poitn + #with respect to the reference point phi[x_true != x_ref] = np.arctan((y_predicted[x_true != x_predicted]-\ y_true[x_true != x_predicted])\ /(x_predicted[x_true != x_predicted]-\ x_true[x_true != x_predicted])) # Angle of the estimate #wrt true halo centre - #Before finding the angle with the zero line as the line joiing the halo and the reference + #Before finding the angle with the zero line as the line joining the halo and the reference #point I need to convert the angle produced by Python to an angle between 0 and 2pi phi =convert_to_360(phi, x_predicted-x_true,\ y_predicted-y_true) @@ -141,7 +141,7 @@ def convert_to_360(angle, x_in, y_in): theta: the angle in the range 0:2pi """ n = len(x_in) - for i in xrange(n): + for i in range(n): if x_in[i] < 0 and y_in[i] > 0: angle[i] = angle[i]+mt.pi elif x_in[i] < 0 and y_in[i] < 0: @@ -165,7 +165,7 @@ def get_ref(x_halo,y_halo,weight): """ Gets the reference point of the system of halos by weighted averaging the x and y coordinates. Arguments: - x_halo, y_halo: Vector num_halos referrin to the coordinates of the halos + x_halo, y_halo: Vector num_halos referring to the coordinates of the halos weight: the weight which will be assigned to the position of the halo num_halos: number of halos in the system Returns: @@ -190,7 +190,7 @@ def main_score( nhalo_all, x_true_all, y_true_all, x_ref_all, y_ref_all, sky_pre r=np.array([],dtype=float) # The array which I will log all the calculated radial distances angle=np.array([],dtype=float) #The array which I will log all the calculated angles #Load in the sky_ids from the true - num_halos_total=0 #Keep track of how many halos are iput into the metric + num_halos_total=0 #Keep track of how many halos are input into the metric @@ -204,13 +204,13 @@ def main_score( nhalo_all, x_true_all, y_true_all, x_ref_all, y_ref_all, sky_pre x_predicted=np.array([],dtype=float) y_predicted=np.array([],dtype=float) - for i in xrange(nhalo): - x_predicted=np.append(x_predicted,float(sky[0])) #get the predictd values + for i in range(nhalo): + x_predicted=np.append(x_predicted,float(sky[0])) #get the predicted values y_predicted=np.append(y_predicted,float(sky[1])) #The solution file for the test data provides masses #to calculate the centre of mass where as the Training_halo.csv #direct provides x_ref y_ref. So in the case of test data - #we need to calculae the ref point from the masses using + #we need to calculate the ref point from the masses using #Get_ref() x_ref=x_ref_all[selectskyinsolutions] @@ -219,7 +219,7 @@ def main_score( nhalo_all, x_true_all, y_true_all, x_ref_all, y_ref_all, sky_pre num_halos_total=num_halos_total+nhalo - #Single halo case, this needs to be separately caluclated since + #Single halo case, this needs to be separately calculated since #x_ref = x_true if nhalo == 1: #What is the radial distance between the true and predicted position @@ -238,7 +238,7 @@ def main_score( nhalo_all, x_true_all, y_true_all, x_ref_all, y_ref_all, sky_pre #true positions. These are found by matching up the true halos to #the predicted halos such that the average of all the radial distances #is optimal. it also contains indexes of the halos used which are used to - #show which halo has been mathced to which. + #show which halo has been matched to which. r_index_index = calc_delta_r(x_predicted, y_predicted, x_true, \ y_true) @@ -260,18 +260,20 @@ def main_score( nhalo_all, x_true_all, y_true_all, x_ref_all, y_ref_all, sky_pre # Find what the average distance the estimate is from the halo position av_r=sum(r)/len(r) - #In order to quanitfy the orientation invariance we will express each angle - # as a vector and find the average vecor + #In order to quantify the orientation invariance we will express each angle + # as a vector and find the average vector #R_bar^2=(1/N Sum^Ncos(theta))^2+(1/N Sum^Nsin(theta))**2 N = float(num_halos_total) angle_vec = np.sqrt(( 1.0/N * sum(np.cos(angle)) )**2 + \ ( 1.0/N * sum(np.sin(angle)) )**2) - W1=1./1000. #Weight the av_r such that < 1 i a good score > 1 isnt so good. + W1=1./1000. #Weight the av_r such that < 1 is a good score > 1 is not so good. W2=1. metric = W1*av_r + W2*angle_vec #Weighted metric, weights TBD - print 'Your score for the training data is', metric + print('Your average distance in pixels you are away from the true halo is', av_r) + print('Your average angular vector is', angle_vec) + print('Your score for the training data is', metric) return metric @@ -302,7 +304,7 @@ def main(user_fname, fname): - num_halos_total=0 #Keep track of how many halos are iput into the metric + num_halos_total=0 #Keep track of how many halos are input into the metric sky_prediction = c.reader(open(user_fname, 'rb')) #Open the result.csv @@ -312,12 +314,11 @@ def main(user_fname, fname): with open(user_fname, 'r') as f: header = float((f.readline()).split(',')[1]) #try and make where the #first input would be - #a float, if succed its - #not a header - print 'THE INPUT FILE DOESNT APPEAR TO HAVE A HEADER' + #a float, if succeed it + #is not a header + print('THE INPUT FILE DOES NOT APPEAR TO HAVE A HEADER') except : - print 'THE INPUT FILE APPEARS TO HAVE A HEADER, SKIPPING THE FIRST LINE' - + print('THE INPUT FILE APPEARS TO HAVE A HEADER, SKIPPING THE FIRST LINE') skip_header = sky_prediction.next() @@ -329,7 +330,7 @@ def main(user_fname, fname): if does_it_exist > 0: #If it does then find the matching solutions to the sky_id selectskyinsolutions=true_sky_id.index(sky_id)-1 else: #Otherwise exit - print 'Sky_id does not exist, formatting problem: ',sky_id + print('Sky_id does not exist, formatting problem: ',sky_id) sys.exit(2) @@ -340,8 +341,8 @@ def main(user_fname, fname): x_predicted=np.array([],dtype=float) y_predicted=np.array([],dtype=float) - for i in xrange(nhalo): - x_predicted=np.append(x_predicted,float(sky[2*i+1])) #get the predictd values + for i in range(nhalo): + x_predicted=np.append(x_predicted,float(sky[2*i+1])) #get the predicted values y_predicted=np.append(y_predicted,float(sky[2*i+2])) #The solution file for the test data provides masses #to calculate the centre of mass where as the Training_halo.csv @@ -355,7 +356,7 @@ def main(user_fname, fname): num_halos_total=num_halos_total+nhalo - #Single halo case, this needs to be separately caluclated since + #Single halo case, this needs to be separately calculated since #x_ref = x_true if nhalo == 1: #What is the radial distance between the true and predicted position @@ -374,7 +375,7 @@ def main(user_fname, fname): #true positions. These are found by matching up the true halos to #the predicted halos such that the average of all the radial distances #is optimal. it also contains indexes of the halos used which are used to - #show which halo has been mathced to which. + #show which halo has been matched to which. r_index_index = calc_delta_r(x_predicted, y_predicted, x_true, \ y_true) @@ -396,20 +397,20 @@ def main(user_fname, fname): # Find what the average distance the estimate is from the halo position av_r=sum(r)/len(r) - #In order to quanitfy the orientation invariance we will express each angle - # as a vector and find the average vecor + #In order to quantify the orientation invariance we will express each angle + # as a vector and find the average vector #R_bar^2=(1/N Sum^Ncos(theta))^2+(1/N Sum^Nsin(theta))**2 N = float(num_halos_total) angle_vec = np.sqrt(( 1.0/N * sum(np.cos(angle)) )**2 + \ ( 1.0/N * sum(np.sin(angle)) )**2) - W1=1./1000. #Weight the av_r such that < 1 i a good score > 1 isnt so good. + W1=1./1000. #Weight the av_r such that < 1 is a good score > 1 is not so good. W2=1. metric = W1*av_r + W2*angle_vec #Weighted metric, weights TBD - print 'Your average distance in pixels you are away from the true halo is', av_r - print 'Your average angular vector is', angle_vec - print 'Your score for the training data is', metric + print('Your average distance in pixels you are away from the true halo is', av_r) + print('Your average angular vector is', angle_vec) + print('Your score for the training data is', metric) if __name__ == "__main__": diff --git a/Chapter5_LossFunctions/README.md b/Chapter5_LossFunctions/README.md new file mode 100644 index 00000000..40371130 --- /dev/null +++ b/Chapter5_LossFunctions/README.md @@ -0,0 +1,4 @@ +Chapter 5: Would you rather lose an arm or a leg? +==== + +### [Read it online here](http://nbviewer.ipython.org/urls/raw.github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/master/Chapter5_LossFunctions/Ch5_LossFunctions_PyMC2.ipynb) diff --git a/Chapter5_LossFunctions/data.zip b/Chapter5_LossFunctions/data.zip new file mode 100644 index 00000000..9a79b6b2 Binary files /dev/null and b/Chapter5_LossFunctions/data.zip differ diff --git a/Chapter5_/data/Test_haloCounts.csv b/Chapter5_LossFunctions/data/Test_haloCounts.csv similarity index 100% rename from Chapter5_/data/Test_haloCounts.csv rename to Chapter5_LossFunctions/data/Test_haloCounts.csv diff --git a/Chapter5_/data/Train_Skies/Train_Skies/Training_Sky1.csv b/Chapter5_LossFunctions/data/Train_Skies/Train_Skies/Training_Sky1.csv similarity index 100% rename from Chapter5_/data/Train_Skies/Train_Skies/Training_Sky1.csv rename to Chapter5_LossFunctions/data/Train_Skies/Train_Skies/Training_Sky1.csv diff --git a/Chapter5_/data/Train_Skies/Train_Skies/Training_Sky10.csv b/Chapter5_LossFunctions/data/Train_Skies/Train_Skies/Training_Sky10.csv similarity index 100% rename from Chapter5_/data/Train_Skies/Train_Skies/Training_Sky10.csv rename to 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Chapter5_LossFunctions/data/Train_Skies/Train_Skies/Training_Sky98.csv diff --git a/Chapter5_/data/Train_Skies/Train_Skies/Training_Sky99.csv b/Chapter5_LossFunctions/data/Train_Skies/Train_Skies/Training_Sky99.csv similarity index 100% rename from Chapter5_/data/Train_Skies/Train_Skies/Training_Sky99.csv rename to Chapter5_LossFunctions/data/Train_Skies/Train_Skies/Training_Sky99.csv diff --git a/Chapter5_/data/Training_halos.csv b/Chapter5_LossFunctions/data/Training_halos.csv similarity index 100% rename from Chapter5_/data/Training_halos.csv rename to Chapter5_LossFunctions/data/Training_halos.csv diff --git a/Chapter5_/draw_sky2.py b/Chapter5_LossFunctions/draw_sky2.py similarity index 74% rename from Chapter5_/draw_sky2.py rename to Chapter5_LossFunctions/draw_sky2.py index b16b649a..6f4ca58f 100644 --- a/Chapter5_/draw_sky2.py +++ b/Chapter5_LossFunctions/draw_sky2.py @@ -2,20 +2,19 @@ from matplotlib.patches import Ellipse import numpy as np -def draw_sky( galaxies ): +def draw_sky(galaxies): """adapted from Vishal Goklani""" size_multiplier = 45 - fig = plt.figure(figsize=(9,9)) - fig.patch.set_facecolor("blue") + fig = plt.figure(figsize=(10,10)) ax = fig.add_subplot(111, aspect='equal') n = galaxies.shape[0] - for i in xrange(n): + for i in range(n): _g = galaxies[i,:] x,y = _g[0], _g[1] d = np.sqrt( _g[2]**2 + _g[3]**2 ) a = 1.0/ ( 1 - d ) b = 1.0/( 1 + d) - theta = np.degrees( np.arctan2( _g[2], _g[3])*0.5 ) + theta = np.degrees( np.arctan2( _g[3], _g[2])*0.5 ) ax.add_patch( Ellipse(xy=(x, y), width=size_multiplier*a, height=size_multiplier*b, angle=theta) ) ax.autoscale_view(tight=True) diff --git a/Chapter6_Priorities/BanditsD3.html b/Chapter6_Priorities/BanditsD3.html new file mode 100644 index 00000000..8249ba63 --- /dev/null +++ b/Chapter6_Priorities/BanditsD3.html @@ -0,0 +1,90 @@ + + + + + + + + + +
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+ + diff --git a/Chapter6_Priorities/Ch6_Priors_PyMC2.ipynb b/Chapter6_Priorities/Ch6_Priors_PyMC2.ipynb new file mode 100644 index 00000000..64a621a0 --- /dev/null +++ b/Chapter6_Priorities/Ch6_Priors_PyMC2.ipynb @@ -0,0 +1,2534 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Chapter 6\n", + "\n", + "____\n", + "\n", + "This chapter of [Bayesian Methods for Hackers](https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers) focuses on the most debated and discussed part of Bayesian methodologies: how to choose an appropriate prior distribution. We also present how the prior's influence changes as our dataset increases, and an interesting relationship between priors and penalties on linear regression." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Getting our priorities straight\n", + "\n", + "\n", + "Up until now, we have mostly ignored our choice of priors. This is unfortunate as we can be very expressive with our priors, but we also must be careful about choosing them. This is especially true if we want to be objective, that is, not to express any personal beliefs in the priors. \n", + "\n", + "### Subjective vs Objective priors\n", + "\n", + "Bayesian priors can be classified into two classes: *objective* priors, which aim to allow the data to influence the posterior the most, and *subjective* priors, which allow the practitioner to express his or her views into the prior. \n", + "\n", + "What is an example of an objective prior? We have seen some already, including the *flat* prior, which is a uniform distribution over the entire possible range of the unknown. Using a flat prior implies that we give each possible value an equal weighting. Choosing this type of prior is invoking what is called \"The Principle of Indifference\", literally we have no prior reason to favor one value over another. Calling a flat prior over a restricted space an objective prior is not correct, though it seems similar. If we know $p$ in a Binomial model is greater than 0.5, then $\\text{Uniform}(0.5,1)$ is not an objective prior (since we have used prior knowledge) even though it is \"flat\" over [0.5, 1]. The flat prior must be flat along the *entire* range of possibilities. \n", + "\n", + "Aside from the flat prior, other examples of objective priors are less obvious, but they contain important characteristics that reflect objectivity. For now, it should be said that *rarely* is a objective prior *truly* objective. We will see this later. \n", + "\n", + "#### Subjective Priors\n", + "\n", + "On the other hand, if we added more probability mass to certain areas of the prior, and less elsewhere, we are biasing our inference towards the unknowns existing in the former area. This is known as a subjective, or *informative* prior. In the figure below, the subjective prior reflects a belief that the unknown likely lives around 0.5, and not around the extremes. The objective prior is insensitive to this." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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mzJl08pOSkqze87///e+cOnWKJk2a4OHhQdeuXenWrZu1DRFx+fhwVTltvKXN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\nODss4aJq8kh4TWHYug3T6tUorbH074d+6AEwVVQFqhCitOsZCS/3J1J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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib inline\n", + "import numpy as np\n", + "from IPython.core.pylabtools import figsize\n", + "import matplotlib.pyplot as plt\n", + "import scipy.stats as stats\n", + "\n", + "figsize(12.5, 3)\n", + "colors = [\"#348ABD\", \"#A60628\", \"#7A68A6\", \"#467821\"]\n", + "\n", + "x = np.linspace(0, 1)\n", + "y1, y2 = stats.beta.pdf(x, 1, 1), stats.beta.pdf(x, 10, 10)\n", + "\n", + "p = plt.plot(x, y1,\n", + " label='An objective prior \\n(uninformative, \\n\"Principle of Indifference\" )')\n", + "plt.fill_between(x, 0, y1, color=p[0].get_color(), alpha=0.3)\n", + "\n", + "p = plt.plot(x, y2,\n", + " label=\"A subjective prior \\n(informative)\")\n", + "plt.fill_between(x, 0, y2, color=p[0].get_color(), alpha=0.3)\n", + "\n", + "p = plt.plot(x[25:], 2 * np.ones(25), label=\"another subjective prior\")\n", + "plt.fill_between(x[25:], 0, 2, color=p[0].get_color(), alpha=0.3)\n", + "\n", + "plt.ylim(0, 4)\n", + "\n", + "plt.ylim(0, 4)\n", + "leg = plt.legend(loc=\"upper left\")\n", + "leg.get_frame().set_alpha(0.4)\n", + "plt.title(\"Comparing objective vs. subjective priors for an unknown probability\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The choice of a subjective prior does not always imply that we are using the practitioner's subjective opinion: more often the subjective prior was once a posterior to a previous problem, and now the practitioner is updating this posterior with new data. A subjective prior can also be used to inject *domain knowledge* of the problem into the model. We will see examples of these two situations later." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Decision, decisions...\n", + "\n", + "The choice, either *objective* or *subjective* mostly depends on the problem being solved, but there are a few cases where one is preferred over the other. In instances of scientific research, the choice of an objective prior is obvious. This eliminates any biases in the results, and two researchers who might have differing prior opinions would feel an objective prior is fair. Consider a more extreme situation:\n", + "\n", + "> A tobacco company publishes a report with a Bayesian methodology that retreated 60 years of medical research on tobacco use. Would you believe the results? Unlikely. The researchers probably chose a subjective prior that too strongly biased results in their favor.\n", + "\n", + "Unfortunately, choosing an objective prior is not as simple as selecting a flat prior, and even today the problem is still not completely solved. The problem with naively choosing the uniform prior is that pathological issues can arise. Some of these issues are pedantic, but we delay more serious issues to the Appendix of this Chapter (TODO)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We must remember that choosing a prior, whether subjective or objective, is still part of the modeling process. To quote Gelman [5]:\n", + "\n", + ">...after the model has been fit, one should look at the posterior distribution\n", + "and see if it makes sense. If the posterior distribution does not make sense, this implies\n", + "that additional prior knowledge is available that has not been included in the model,\n", + "and that contradicts the assumptions of the prior distribution that has been used. It is\n", + "then appropriate to go back and alter the prior distribution to be more consistent with\n", + "this external knowledge.\n", + "\n", + "If the posterior does not make sense, then clearly one had an idea what the posterior *should* look like (not what one *hopes* it looks like), implying that the current prior does not contain all the prior information and should be updated. At this point, we can discard the current prior and choose a more reflective one.\n", + "\n", + "Gelman [4] suggests that using a uniform distribution with large bounds is often a good choice for objective priors. Although, one should be wary about using Uniform objective priors with large bounds, as they can assign too large of a prior probability to non-intuitive points. Ask yourself: do you really think the unknown could be incredibly large? Often quantities are naturally biased towards 0. A Normal random variable with large variance (small precision) might be a better choice, or an Exponential with a fat tail in the strictly positive (or negative) case. \n", + "\n", + "If using a particularly subjective prior, it is your responsibility to be able to explain the choice of that prior, else you are no better than the tobacco company's guilty parties. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Empirical Bayes\n", + "\n", + "While not a true Bayesian method, *empirical Bayes* is a trick that combines frequentist and Bayesian inference. As mentioned previously, for (almost) every inference problem there is a Bayesian method and a frequentist method. The significant difference between the two is that Bayesian methods have a prior distribution, with hyperparameters $\\alpha$, while empirical methods do not have any notion of a prior. Empirical Bayes combines the two methods by using frequentist methods to select $\\alpha$, and then proceeds with Bayesian methods on the original problem. \n", + "\n", + "A very simple example follows: suppose we wish to estimate the parameter $\\mu$ of a Normal distribution, with $\\sigma = 5$. Since $\\mu$ could range over the whole real line, we can use a Normal distribution as a prior for $\\mu$. How to select the prior's hyperparameters, denoted ($\\mu_p, \\sigma_p^2$)? The $\\sigma_p^2$ parameter can be chosen to reflect the uncertainty we have. For $\\mu_p$, we have two options:\n", + "\n", + "1. Empirical Bayes suggests using the empirical sample mean, which will center the prior around the observed empirical mean:\n", + "$$\n", + "\\mu_p = \\frac{1}{N} \\sum_{i=0}^N X_i \n", + "$$\n", + "\n", + "2. Traditional Bayesian inference suggests using prior knowledge, or a more objective prior (zero mean and fat standard deviation).\n", + "\n", + "Empirical Bayes can be argued as being semi-objective, since while the choice of prior model is ours (hence subjective), the parameters are solely determined by the data.\n", + "\n", + "Personally, I feel that Empirical Bayes is *double-counting* the data. That is, we are using the data twice: once in the prior, which will influence our results towards the observed data, and again in the inferential engine of MCMC. This double-counting will understate our true uncertainty. To minimize this double-counting, I would only suggest using Empirical Bayes when you have *lots* of observations, else the prior will have too strong of an influence. I would also recommend, if possible, to maintain high uncertainty (either by setting a large $\\sigma_p^2$ or equivalent.)\n", + "\n", + "Empirical Bayes also violates a theoretical axiom in Bayesian inference. The textbook Bayesian algorithm of:\n", + "\n", + ">*prior* $\\Rightarrow$ *observed data* $\\Rightarrow$ *posterior* \n", + "\n", + "is violated by Empirical Bayes, which instead uses \n", + "\n", + ">*observed data* $\\Rightarrow$ *prior* $\\Rightarrow$ *observed data* $\\Rightarrow$ *posterior*\n", + "\n", + "Ideally, all priors should be specified *before* we observe the data, so that the data does not influence our prior opinions (see the volumes of research by Daniel Kahneman *et. al* about [anchoring](http://en.wikipedia.org/wiki/Anchoring_and_adjustment) )." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Useful priors to know about\n", + "\n", + "### The Gamma distribution\n", + "\n", + "A Gamma random variable, denoted $X \\sim \\text{Gamma}(\\alpha, \\beta)$, is a random variable over the positive real numbers. It is in fact a generalization of the Exponential random variable, that is:\n", + "\n", + "$$ \\text{Exp}(\\beta) \\sim \\text{Gamma}(1, \\beta) $$\n", + "\n", + "This additional parameter allows the probability density function to have more flexibility, hence allowing the practitioner to express his or her subjective priors more accurately. The density function for a $\\text{Gamma}(\\alpha, \\beta)$ random variable is:\n", + "\n", + "$$ f(x \\mid \\alpha, \\beta) = \\frac{\\beta^{\\alpha}x^{\\alpha-1}e^{-\\beta x}}{\\Gamma(\\alpha)} $$\n", + "\n", + "where $\\Gamma(\\alpha)$ is the [Gamma function](http://en.wikipedia.org/wiki/Gamma_function), and for differing values of $(\\alpha, \\beta)$ looks like:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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yLzV49OhR7rjjDnr16sXQoUPJyckhNTUVu921y3ppaSmff/45N954Y8v+gLBw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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "figsize(12.5, 5)\n", + "gamma = stats.gamma\n", + "\n", + "parameters = [(1, 0.5), (9, 2), (3, 0.5), (7, 0.5)]\n", + "x = np.linspace(0.001, 20, 150)\n", + "for alpha, beta in parameters:\n", + " y = gamma.pdf(x, alpha, scale=1. / beta)\n", + " lines = plt.plot(x, y, label=\"(%.1f,%.1f)\" % (alpha, beta), lw=3)\n", + " plt.fill_between(x, 0, y, alpha=0.2, color=lines[0].get_color())\n", + " plt.autoscale(tight=True)\n", + "\n", + "plt.legend(title=r\"$\\alpha, \\beta$ - parameters\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### The Wishart distribution\n", + "\n", + "Until now, we have only seen random variables that are scalars. Of course, we can also have *random matrices*! Specifically, the Wishart distribution is a distribution over all [positive semi-definite matrices](http://en.wikipedia.org/wiki/Positive-definite_matrix). Why is this useful to have in our arsenal? (Proper) covariance matrices are positive-definite, hence the Wishart is an appropriate prior for covariance matrices. We can't really visualize a distribution of matrices, so I'll plot some realizations from the $5 \\times 5$ (above) and $20 \\times 20$ (below) Wishart distribution:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import pymc as pm\n", + "\n", + "n = 4\n", + "for i in range(10):\n", + " ax = plt.subplot(2, 5, i + 1)\n", + " if i >= 5:\n", + " n = 15\n", + " plt.imshow(pm.rwishart(n + 1, np.eye(n)), interpolation=\"none\",\n", + " cmap=plt.cm.hot)\n", + " ax.axis(\"off\")\n", + "\n", + "plt.suptitle(\"Random matrices from a Wishart Distribution\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "One thing to notice is the symmetry of these matrices. The Wishart distribution can be a little troubling to deal with, but we will use it in an example later." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### The Beta distribution\n", + "\n", + "You may have seen the term `beta` in previous code in this book. Often, I was implementing a Beta distribution. The Beta distribution is very useful in Bayesian statistics. A random variable $X$ has a $\\text{Beta}$ distribution, with parameters $(\\alpha, \\beta)$, if its density function is:\n", + "\n", + "$$f_X(x | \\; \\alpha, \\beta ) = \\frac{ x^{(\\alpha - 1)}(1-x)^{ (\\beta - 1) } }{B(\\alpha, \\beta) }$$\n", + "\n", + "where $B$ is the [Beta function](http://en.wikipedia.org/wiki/Beta_function) (hence the name). The random variable $X$ is only allowed in [0,1], making the Beta distribution a popular distribution for decimal values, probabilities and proportions. The values of $\\alpha$ and $\\beta$, both positive values, provide great flexibility in the shape of the distribution. Below we plot some distributions:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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ZkkQLv5J9W4U3JG6ENyRuhLcaxo7bA4UjZqDo9IGYUrtl6D8K9K79le0Xc3GU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WAAAg\nAElEQVREMcDR839i76mf4Q+MtsiNNaWxbfkzJERnh29y8xRJglZrUDc9thPiFSxa2JajYlOmkihZ\n6iEzB/AFvOw78yqHanYiSqPBSFF6GX+x459Jic2c8H0BxyBd3/ssXuV2JM3467igEjBk6zBk6lBc\n/ShdRuY2uV72OTNJycpFanSTOPcKh76GMHABAOnO70P2ndM+z3mnifaLEt871UW3Mxgkm9QKnimJ\nQztSSBgVrScpNxa3w4tzOPhI3O8N0Fbbg2PYRXSSUc5KzyKyJvr69A4188Ler1F5cSeiFJQiCYKC\nlYUPcOeKz2A0xIV5huHjdjTRN0MQwKKD4oTgj0SwecuVIkRPAGr7RA5cDjDolkiQddNzhvmuib4e\nSoWS/JRSCtOW0dJbj8MdlGL0W7vYV/0aWpWW/JQShJGstCRJuJr66HphNz7FRlCOuYYrwJClI3qZ\nEW28ZsFIN2RN9OwgSRKXWry8c9hOZ+9oslOrhnWlGpbkqVFN8omHYG9HGKgNDozJkLZ+2uc77zTR\nL9b20zAUvHNRAE8WxmC8qm+6RqemeFPONVnptvM9dNT1klOWRsGaDDR6+RG5zOwTEP0cOfcS+0//\nclz2Oc6cztayZ0iIzgrj7BYWcXrYkQebM+F0F5zsgJGmqHgDweYtB5tl3bTM3CA9Ppe/euBf2F/9\nGu+fexNREvH5Pfxu/39yrG4vn9/xdWJtJgYPN+DpHAbGBM+CiCFDjyFHj1IrF+XLTC+SJNHa5eN4\ntXNc4SBMLfs8bp+xRYTeEaamK3NKznGi084Pq3pC4x1ZZjam3rj9qdft41JFG30t47XSKo2S/NUZ\n5K5IQ6WePxXGMpFNz2ATrx76Nu19F0LLFIKSFYUPsLzgfpSKOXdfO6/wi3C+F050BK3yribNJLA9\nR8maNCVqWR8qE8F09F/m5SPP0TnYgoDAIhaxTdhOOlfZ4EleNKp6zBs2oNTJwbPM9NPVFwyeO8Zk\nnmGS2ucb4RlC8c4zAEgKNTx9DJSa253uOOaNJrrf5ecfyttw+oN6r+JYHU8Vxkw6KzTYZeNyVQe2\nfue45dooDYXrMskqTUYhe0vLzBAB0U/52T9w4PSvCYij/pfxlky2lj1DvEWWvEQSV5q3VHRA/QS6\naZMGNmUp2ZKlwqKTg2mZyMTv81F7+DDxrVEkkzR+peRF6diH2n8I8yP/jqC9cUJKRmaqDAz7OXHW\nSVO7b9xypQKKslQszlKhUd/e+VN47y8RHJ0ASA/+DpLKbmt/V3OzIHpOpL1ESeJnZ3tDAXSMVsnj\nedFTeqwak2wi+t5C+lqHuFzVicsWlIR4HF7O7r3EpYpWcpenkVmaLGump5FTx4+wcu3065TmEp39\n9bxW/m06++tDyxSCkpVFD1GWv0POPk/AucoqSldM78lwKggCZEcHfwZcwWC6ujvoMw1g88Lb9QHe\nvRRgZaqC7TkqsqPlm/Bwc/JEJavWrAj3NMJPQIJGB6oaK6W23KtW+dA49qG2vY4gDqK9+1/lAJqg\nJvqOsmXhnsacR5Ikuvr8VF9009TuZWyeVhAgL01Jaa4a/XQ1u4otgpEgmp4z0x5E34w5cfXe02zl\nfH+wa5sAfCA/Gt0tNFIRBIGEzBji06PpauynuboLryt4h+Syeqh5v5HaI81kliaRU5aGMUY/nb+G\nzALDH/Dy/pnfcujM86HCQYCE6Gy2lT1DrPnWu4vJzB6xerg3DzZnQdWIbto2ImUPSHCiXeREu5fc\nGIHtOSqWJytQLpBiLJkIwyfCJTuct4FzvO5UUkKnoZnU+u+gCQwCcCjKTEXLOzxjTiTLPLGDh4zM\nZAgEJBravFTXuegdDFyzPjNJydJ8VbBt9zQixS5CaD0QHPRUAZ+Y1v3fjIiXc7TbvPzTkXZ8I6Xz\nm9OM3JNpnpZ9B/wiHRd7aT3fjd9z7X96cl4cucvTiMuwyMVEMlOirfc8rx36P/QMNYWWKRVqVi96\nhKW5d6NQyDr8uYooQV0/VLRDm+3a9dE62JKlYmOmEpPcWlxmNnAFoM4GdXbwjm/PjUqAbANkatHs\n+TuUXdUA9ChV/EdCGj6FAoWg4L6ce3go9wHUsie9zBRweUTON7ipueTG4bo2nkyJU7A0X02seYae\n1FmbUez/WwAkfTw8tS+Y8p4m5rScwy9K/PhMTyiATjGo2J5umrb9K1UKMoqTSC1MoOfyAO21vSFb\nPICuhn66GvoxxRnIKEkifVEiOqN22j5fZv7h83vYd/oXHDn3EtIYv9bk2AK2ln2CaKNsoTTXUQiw\nOD7402kLSj3O941a5A254fU6Pzvr/axKVbAtW0WWLPWQmQmsPrhgg4Yxve2voFFAjgEyDaBWoDr2\no1AALSFwefEDSIPnQfIjSiI7G9+hsruKT5Y+TX603B1V5vqIokR7j4+6yx4a27wErspBKhSQnayk\nKFNFtGmGz32mdCSVAcHvRHD1IdnbwZQ+s585hojORP+pboA3G4OuGioBvrA0gSTDzN0lS5LEUJeN\nttpeBjus124gQEJmDBnFSSTnx8muHpNgIWmiGzsreaP8uwzYRjuHqZRa1hU/QUn21pBHq8zNCbcm\neqrYvVDZCZVd4PRduz43RmBbtooVKbLUYyZZMJroPk9QstHihKsv4QYl5ERBuh5GHGQUje+j3fts\naBNfyRP4ix6gz9XPK41vcdnWElonILA9cyuPFzyCTqWbhV8mMpA10TdnYNhP3WUP9c2eCbPOOg0U\nZKjIT1dN2a7udhCOfAOhN2hxJ235NuQ/OG37nrOZ6LoBN281jtrS3ZNlntEAGoKa6ZgUMzEpZpzD\nbtrreuluHEAMjGQUJehtHqS3eRCVRklqQTxpixOJS49GIV8YFywuj43dFT/m1MU3xy1Pi1/MlmVP\nY45KCNPMZGYLoyaomV6fEWwtfrIDOsa0Fm8clGgc9PHn87AxU8mmLBXRsquHzFQQJWh1wQUr9Hqv\nXW9WQZ4RkrXjHmcLQy1oDn4nNA6klOEvvA+AeH0cnyl+mhPdp3in5T28og8Jib0t+6nqOcPTJR+j\nNL54xn81mcjF7gzQ2Oal7rKHvgm0zgAxJoGiTBWZycqwJAn+f3tvHiTJdd93fl4eVVlX393T03P0\n3DdmBhgAg5MgSJAEJUoKyeJSp2XLu5YVltexsVpau/Jq5fCKK23sriWbslfWWozQ6rTEFcUDJECZ\nIEEQ5wBzX5jBzPQcPX2fdWVl5nv7R1ZVV/UxfXdXd79PRMY7M+tVVVbWN3/5e7+nmg6URTR9Z5ZU\nRM/GrJZoIcQfAT8M9CmlHprcvhyW6Jwv+Zev36U/F8YUrFzWe6XxvYCB2yP03hxitDc9bR/bsWjf\n1Uz7nmZaOxu1hXoDcbnrNb7+xv/FeG6wXBexYjx5+LMc2P6s9qXfwNwbD8X05QpXjxKGgOPtBs91\nmuzTC7hoHkShOFnwyjhkphExrRHYlYCmyFRfUC9H9Cu/jDHSBYBMtOE+/xsQiU85zLA7wlduvMS1\n0Q+r6p/qeILP7f9JkhEdwWOjMDwWcPNegZt3C/QN+dP2iUZCl40dmy0aU2J1r2F9ZzDeDBcUVE0H\n4Mf/askOveg40UKIZ4E08McrJaL/0/l+vlecseOYgl851kZDdPWFaT5doO/WEL03hsoh8iZjWgat\nnY2072mmfVezXhVxnTKeHeSlt36Xi7e+W1W/o/1hnj36sySchtUZmKbmSBfC1RBP94T5yXSkBB/p\nNHliq4ljaTGtKTLuwZV0KKD9Sf/TAuhwQreNuhn+Y5TCfvV/xfrwO2HRsHE/+uuohpmjcCilODNw\nnq93vULOz5Xr6yIpfvrA53is/YS+4VuHKKXoG/K5ebfAzXsFRsbltP0MA7a2muzYbLK52aidJ/Be\nFvHSzyFQKGHAz70BkcTSHHopFlsRQuwAvrYSIvq93gy/935vufzZPQ0ca51617yaKKUYH8zSd3OI\ngTuj5TB509GwKUVrZwOtnY00ddRtuAVd1ptPtFKK09de4uV3fp9cYSI0QyxaxzMP/Qy7Nus/maVg\nrflEz4VAwgdD8F433J5mykXUhJNbTT7SabJ1uWayr3PWvE+0UtCdDyNt3MtPbY+IcKJgZzw8YR6A\neeHLRN78YrlcOPGPCDqfntMwxgtpvt71MucHL1XVH2s9ys8d/CmaYk0z7Ll22Wg+0eOZgLu9Hnd7\nPO72euQL02tBIaCt0WD7JpPtm8xFL46yXIhX/zvE2C0A1Iv/EbY8uSTHXRER3fdDv7LYca5ZFJBr\n6WCs8wDj2/fjNs7s/2p4BRL3b5HovkGy+ybRkX5q83RcOi7JDIeMpbkjXG1GGyRvfsKnZ1v1b2bP\nBYPHXrOI5tf7t7lyrKfzRrNyrNXzxohGqTt8nIbjjxFpbJ7S7g70MfL+24xdPofyp3+8Xkm8Ocue\nj9+mNJd54HoD907NPzLQ7V0Bb73gk63w5LAKcOJ1i/1nDQy1fq55a/XcmSuBHSXT3km6YyfpLbsp\nNLTM2Fd4BVL3PiTVdYW6O9cwC9Pc0NUYW0700LI3nEfXd6mJ++faluzYbS99cfkmFv71X/81F71u\nWkX4SCmOwQ7DKZ+Ml2QGYN2WL8sM9F3j0EA37e99h7OORaZ9G5uOPE+upYOu+1cA6NxyCGlHuGD6\nsG07nU+8iJnP0nPp+zjDvRzLesQG7nM5GK+p97fYcqmuVsazkLIUEDwR5ezJgIF7OeiCps4YyVFo\n+1uPpj5B1LBrZry6rMsbtVyqq5XxzFb+IBUhuecAH3n2hzHsCKe6LsFYL492HkIpxetvvUL62hV2\n94/M+fimHfDjT/chDHinB9wxm/ozbQsaX/p6nv23IPN8hA+OSYa6QhePtz8W48ODgpavF6gbFTXz\neS6mfMhI1NR4Flv2YknO1MXINbbRsf9p8k2b6Oq+DEBnUUB33QufNHRuOYSZS9N37jvEe29zYngM\nI/C5JDPcr5H3M1t5vCfBjVT4O3loc4b75xZ+vDCfpV+FXgY/duYMH//4x5kObYleRoKIQ3rzDtJb\ndpHeshsv1fjA/sL3iPfdJd57h9jAPeL93Vj5zAP30SwvfZslb3zCZ6Rl4nciJBx+z+TYmya2v34s\nMRqNZvkRlkVq32Hqj50g1rFtSnuQzzF64TSjZ07hjQ7P79iGZNfzd0i2hmLXdw0+eHknXnbxc3N6\nt0jeeMFntHnStfCUyfG3TCx9LVw1lDDIN7WRa+kg27aVzKbteHUPdrkRvk+8t4vkvRsku2/gDPWu\n6SfjhhVw5CeulZ++XPzKHvz80gSge5AleklEdN3o4ib9eUrxhTuK3qJr8f6I5L+ql0u56ExNkM8H\njI56jIx4jI97+JMni0yD4xjU19vU10eor7eoq7OxFrDk+Wpx+sIZHj6y9nxb836WV+98mff7vltV\n3+xs5pktP0xzbPPqDGyDcPHSFQ4fOrDaw1gVMoHBhWySs9kUY8HUP4G4IXm0PscT9VnaItOHnNqo\nnD5/iYcfqs2QbCILdp/AHgARTP1zk47Ca1IEDcBCLvFKkjz7b4n2vhsWEWQO/iJ+w/7FDbwCXwW8\nPvo+r4++T8DE5LMWu5HPtf8QB5LLt3rxcvP25SucPFj71xylYDwvGEiXNoOhjCCY1bVG0eBIWpMB\nbcmAlkTAepuiFbn0f2COfwCAe+hX8Ts+uehj+l5AttNeuDuHEOLPgeeAZiHEHeA3lFJfWvTIKnh5\neEJAR4Ti06n1J6ABHMfEcUw2bXJQSpHPB4yN+YyPe4yP+7ju1Bmx+bwkn3fp7Z2IBhKPm6RSFqmU\nXU4dR4fJWgqUUlwafIdv3/5LMt5oud4ybE60Pc/B5scw9KIpmmUkYUpOpsZ4PDnGLdfhTDbFjXwM\nVbQTZaXBa8MJXhtOsCtW4GR9lqPJPLY+LWuPAKwhsPsF5vjU67MSiqAO/CaFjMOCTYFKEb/yJ2UB\nDZDv/KElFdAAljD5aMNjHEns4euD36PLvQ/AgDfM79/5Ux6pO8xPtH2SenvpVhbeyPgBjOQEQxnB\nUMZgOCsYzgq8aW7CJmMIRVNc0hwPBXNTPGC9R9+VDYfLItocPLUkIno2lmTFwsVYou8XQit0yZ7y\n6WTAY/HFjWmt4roB4+M+6XS4ZTI+c/16bFuQTNokk1bVFonof9a5Mpjr4Vu3/oRbY5er6rel9vLk\n5k+TjNSv0sg0G53xwORCNsm5bJLxaazTjiF5JJXj8focW53ZJ55plhEFRiYUztbgDFbniMJvVPiN\nLMmSZ87Nb5D44M/KZbf9GXI7fmRq3OglRCnF6fQVvj3yBnk5EbvRMaJ8pvV5nm18VBsc5oiUMJYX\njOQEo9kwHc4KxnKifPM8G3Fb0hgPaIyFwrkhJtedpXk2ROYOzoV/DYCy68h+5C9BLO7OYTZL9KqK\naKkU/6Zb8WFx4ucWS/EPGwNqJfTgaiOlIpcLyoI6nfbJZuf3+DYSMUgmLRIJi0TCLKeOY2rLdRFf\nevyg+xu82f1NAjUhQGJWkic2f4oddQf1Z6WpCaSCLtfhXDbF9QrrdCUdUY/H63I8Upcjbm5Mg8Sq\n4IE9CFa/wMxOY3WmwuqcYOFW50lEun9A6vy/L5cLzUfJ7v0ZWCEBmw6yvDL0Buez16rqtzmb+an2\nH2Z7rGNFxrEWKPihK8ZoTjBWTEeyYV7OI9JJ1JQ0xCSNcUljLKAxLnEs/VtHKZzT/wPCC2OI5h77\nt8j6xbno1LSI/v6o4s8Hwtc3UPw3TQGbanYh8tqgJKwzmVBQl9IgmN/3aBgQj1vE46GwjsdNYjGT\neNxaUteQWveJ/nDkAi/f+lOG3b5ynUBwsPkxHmn7KBEzuoqj27hsZJ/ouZIOTC5kE1zIJhkJpk4c\ns4TiSDLPY3U59sYLG8I4seI+0RLMUbAGBNYwiGmEUNnq3AAs8dpb1uBF6t77HYQKjSt+aifpQ/81\nGCu/yNeN3F1eGnqNQX/CDU4AzzQ+xmdanyduOis+pvmwVD7RXhAK5dI2Vtpygpw33x+hIhlR1McC\nGhxJfUxS74SCWdt1psf+8EtYA28CUNj19/F2/dyijrdon+jlYsRX/M3QhPB7Kq60gJ4DhiGK1uSJ\nD0sphetKstmAXC4gl/OLaYCcfuEhpKTsNgLVqy8KQVFQm8RiFrGYWbXZ9iov8bkEjLqD/N3tv+TK\n0HtV9a2xLTzV8Wk9cVBT8yTNgCdSY5xMjnGnEOVCNskHuTh+cVaarwRnxmOcGY9RbwWcqMvxWF2O\nVj0ZcXEoMLJF4TwAxjRRKZRQBPXgNy7S1/kBmOO3SZ35N2UBHcQ2kTnwC6sioAF2xbbyTzo+xw9G\nT/P90fcJCFDA94ff5czYJX6s7QUeqz+Kscb/OwIJGVeQdiHtivJWEs3uAqOUxGxJKiqpcyR1pdSR\nrKE4AjWBrD8MRRFtDp5atIiejVWzRP9hj+R0MXpbk6n4J00BesXbpaUkrkuCOpcLyOfDzfMW/r2b\npsBxDGIxszxZ0nGMiryJadbml+lLj7fuv8wPur+BX+HHFzEcHm3/GPsbH1nzNwiajUteCq7kEpzP\nJun1pn+KssMp8GhdjmOpPDHt7jFnRAGskrtGbvprRBBTBI0Kvx5YxklcRm6A+rd/E8MNQ+BJu47x\nh/4pKvrgMKorxZA3yjeHvs/1/J2q+p2xrXy2/dNsc2rTSKEU5DzIFgQZV5ApCDLuRDntLsSaPIEh\nFImIJBlVpKKSZCQUyqmoXPeT/lYMbxzn/V8NlwDHIPvcX8EiJrrWpDvHuYzi/+6ZeN2fbwjYGdEX\n85XE9yX5fEAuF6auG5DPS1x3cQK7hGUJHMckGjXKaTQaiu1o1CQSMYhGDYwVfMZ8bfgsr3T9OSNu\nf1X97oaHeLz9E8SsxAx7ajRrj37P5kI2yaVcgpyceo0uuXs8ksqzP+FSo/e9q4sfRtewBgXmWOjq\nNRlphWHp/AaFWgGPBSM/SN27X8DM9gCgTIfxw7+MTNSWMFVKcTl7g28N/4DxYGK9AwE83fAon2l7\nnoQZW6GxQCGAXEGURXK2IMgVJvJZLyzPxzd5OkKhrIhHQpGciCiS0VAox23thrESRC98ASNzC4D8\nQ/+SYNNHFnysmnPnKEjFfx6YEGnHHakF9CpgWQbJpEEyObUtCFSVsC4UQnHtuhLXlXPyv/Z9RTrt\nc/HqJTq3zOyjaNuiSlRHowaRSFiu3BYjuIfyvXy76y+4PnKuqr7J2cQTm1+kPbF9QcfVLB/aJ3rx\ntNoez9cP85G6YW7mY1zIJbmRjyGLQrDS3SNpBjycynOiLseWqL9m/+iXxCdagjkM9qDAHJnez7kc\nmq5BIZMsi7vGdBi5/lBA5/qK4zDJ7P/7NSegAYQQHErsZk9sO6+NvsebY2eRSBTw+sgpTo9f5DOt\nH+OphocXFMVDqXCiXt4PhXHeE+SLaa4oiHMV+dnEcde9B/9XVbwycVsRjyjitiyniaJgjmmhvOoE\nDUfKItocfHdRIno2VlxEf3sEhooBEOJC8UJyBqddzaphmlP9rivxfVkW1IWCpFAIKvLhNtcHHJ6n\n8Ly5heSyLIFtV4vriXLYNrEJpFHgrZ6XePv+K1VRNyKmw4m259nf9IgOwaRZ95gC9sRy7InlyAYG\nl3PhZMR+P1Lukw5Mvj+S4PsjCdoiPifqcjycytNkbxD/6dIEwSGBNQRCTh9dQyZC4RzUsazuGtNh\nZPuoe/e3MPMD4XiESXbvz+LX71nZgcyTiGHzQuMTPJw8wLeGXi+7eGSCHH/Z8w3eGHmfn2j7JJ1O\nJ64PrifC1A/TUBxP5Mt1PqhFWo2nHa+piNmSmK2KW2hBjtmhdTlmqw0xSXctI+sPw72vA6FfNEot\nW7jHFXXnGPIU/+qOouQt8JlUwCMxbYVebyiliuK4WlhXlj1PLonbyLSvj2Qg8j73nG/jG+mKFkGH\nOMG+yMeI2QksS2FaYBZTo1Q2w1RbEzTrmX7P5lI2weVcgrSc/oZ5h1Pg4bocx5J5kusthJYEcyx0\n1bCGp4/nDEU/5/pwoqBanTl7GJke6k79FmZ+CChaoPf9PH5T7azOqFRo7fUCgRcYeIGBHxhV5YIn\nuBN8yDn1bXJitGr/xsIRtuY/RVQ+eLnqhWIZCsdSRK1QGDu2ImYpHFvhWKE4dmylJ/KtB1SA895/\njwiyAGSf+ANUcueCDlVT7hx/MzQhoNstxXFnnV2UNUD4GC8SEUQiBokHuBlXiu2SwC6VfV9Wtc1V\ncI+bN7kd+wY5635VfcLfyvbcj5AItpIG0tPvXoVhKkyzKK7NUGwbxbSy3jBD4W0UBbhhVuxrajGu\nqU1abY/n6kd4tm6E267DpVyCa/k4nppQEbfyEW7lI/xtXx374qGgPpx0cYw1eu0uCeeSxXkG4Swj\nxegaDQq1ylEujXQ39ad+C8MdAUAJi8z+X8BvXJrVCEPxC740CAKBLw38QOAV00CGQrhU75cEshRl\noVzqPzfL8MMc5Ag90de477yGEuFTwuHIBUbsK2xyn2Zz/jlMZv/gLSMUxZWbYymipsKxZVk0O5bC\n0hP3Ng7CJKg/iFWMvmUOnsJfoIiejRUT0ddzivcqlMunknpRlY3A+ctneejgsWnbKsX2bCil8P1w\nC0V2dTrm9XE5+Ab9XKjaz5Z1bM19iibvKIL5mRhkIJABUFjciSqMkqAuivBS3pioM0wwjGnyRmXf\niXphhOJ8vQp07RO9chgCdjh5djh5CnKI6/k4l3MJbrlOeTEXieBKNsqVbBRLKA4mXI6nchxMuNTS\noqjT+kQHYI6ANVy0OE/jqgEg7aJwri9OEKyB35Y5foe6U1/AKISLRyjDZmTPPySX2E+QFwQyFLlh\nKvClqMgX64OJvF/s7wdhviSc57oq3lJhYNPhfpzmwgnuxV5mKBLOV1HCp8f5HkPR9zioPsZe8zhR\nS5QFcsSsTpdyRb5TH1zl0X1Lu0y6ZvWQ9YehKKKtwVP4nZ9dltdZEREtVfVkwsNRSWfkATtoNJMQ\nQmDbAtsOY1iXyAcZTg1+k3PD30Uy4fdsCov9dU+wN3ESIaPIIF8UxYLAD8WxDMI/mJJYlr4o95Ez\n/NEuBCUFvgQWERpphiNPCG1DIYp5USxX5YsW8TCtbAtF/uSyMEJxJSrbxfoX7xudiKE4FM9wKJ4h\nGxhczce5kktwrzARdsJXgvNph/Nph4iQHEq6HEvmOZBwsWtFUHtgjYQWZ3N0+smBMCGcg3qFnKNw\nViqMFSxlaMGVcpqtWF/qF1S0TZcPgolyqS5R6OKZsf8NQ40X31KU79qfp+/uwSX8oJYWIRSWIbHN\nAMuQWIbENBW2EWCZstgWppZZ6vcU/XIH38u8SY8fRk4qiDRnxVfps97mxYbn2eMsjxVRs34JGg6X\n88bwBQhysAzRYFbEJ/r1McWf9YevY6H4p80B9frRimYR+LLAueFXeW/wZVyZrWrrTBzmocaPErfq\nFnx8pSgLbSknRHdYDvOqIl+qV5X9pUAtoRivJYRQE4LaCMM6iclC21BlwV2qMwxATK0PNzWpDJTq\nRXW9MIpthK8TjmnyfqU+apq6Sf1A3xg8gFHf5Eou9J8e8Ke3gEQNyeGEy9FUnv1xtxz3X6nqjaL7\nAAoUoGRFqibl1YS7gVLV9XJS2SpAMg9JV5Dwpg9HB5BHMSRgQCjGKo5dEr+qUhxPrpesiNW2IzjN\nM+4XsckB4BHj1ejn6TeXx1IqhMIUEtMIRbBZEsClsikxhSoL4VJ7SQxP7LdwPaGU4rJ7ndfS75KW\nmaq2PdEdvNjwPB2R9sW+Vc0GInruNzFy3QDkj/9rgpaT8z7GqvtEZwPFVwcnflhPJ6QW0JoFI5Xk\n6uhbvD3wNdL+cFVbc7SD400v0BzdsujXEYLypMPwb31hqPKfbyislSyKbVkhvGWlAK/uU9q3MlVS\nFEXI6qk+pQSqKnDDelGgqkpoV6bAlLYwURNvX1R8EqIqqfqIxKRy+dhLyBT7iCJVOXgAACAASURB\nVJp0JqtJ2crylLygTrk8jhv6z6rQZaC0c/ixhDvdVXBvRgm7tDSZsDkCHTbUPSDQ9aiv6Pag24PR\n8nlbg+esUhzwv8nD3p9iFD/PAnG+E/01Bs09GEWha4hwM4vC1RQSw1DlvGkU24Qq1supbRWCuRZc\nK4UQHHL2sie6g3cz53g3exaf8Mu67t7ii71f4nj8MJ+o/wiNVsMqj1azFggajpRFtDlwakEiejaW\nXUS/NKxIF6PY1RuKp+JrdEKKZkE8yCd6Piil6Mpc4M3+rzDo3qtqS1qNHG38KFvi+2tutUEhCN0s\nTAU2LEaQT6ZSoFeKazVZgKtiubJNVbYVRXHJqjd5n0n5lRDvc4/ZutSIsrV07t9UbZ1zK4EBZZG3\nkkQEtFnQbsMmG6KT1N+prks82nkIpRTDAdwrhMI5s6SRVFXZ1ckQE09YjKKbVGWbYVT0LddV9K2o\nM/HY3vPHtAx9r/xKnt1Af+cvcChuY4hbG+JpSUTYPJ08wdHYAd7IvMeF/Aeo4rl2JnuR89krPJF8\nhOfrniJuxpfsdbVP9PpD1h+G+68AYbzo5WBZRXRPQfHdiig2LyQl9ga4CGiWlnvZa7zd/1W6c9eq\n6qNGnMMNz7IrdQxDbLzHG1UCHVhKgf4gKh/LzyS2w7aK+qL4Lj+6V4AU5XzJ7aXUN572SDV7E69R\n2h+m1qmJcZXFr5okhst9RFXdRJu+MC0Hpec4qmy0FkXXAYoW1UrXmmq3HYr1dQJaDUGLENTDjDfK\nAYpxIfnQDBgRiiAKIqloBlonuQkJURLAqiyES2J4ol1VC+WK9qXG8NK0XfkisbEr5To3vp2hnT8L\ndgpzFW5YVpuUmeBTdR/hRPwhvp9+lw8LXQAEBPwg/S7vZc7xdOpxnk49hmOscggVTU0iU3tQRgQh\nCxi5bkS2GxXvWNLXWDafaKUUv39fcSl06WK7rfiFhmBD3Elrloae3A3e7v8qd7JXquotYbO//iT7\n6h7H1hdPzRJRvhROEd6lelFVX9U2KV/qX9V9UvvkY808sFna53JNFTN0m1wvJl6s6lo9nZvKdH0F\noGBYRbjhxbkRpBiWM88i77Bd9kVz7HeyNJvhSomGD07GwMmYOGkDc4YwdACBqXBjEjcucR3JPAPw\n1AR29j6bLv8udr63XJdtPM7wth8HY5UCU9cgdws9vJZ+m26/r6o+Zjh8JPUETyZPEDF0xAJNNZGr\n/w5z5DwA7v5fwd/2o/Paf9V8oi9kKQtoULyY1AJaMzf68rd5u/9rdGXOV9ULDHaljnO44Rkc8wEB\nqDWaBVApBKe/VM3X4LDxrIclminQbBV4jBGGA5sbfoJbXoJ+6VT16/ai9BUi3B+O80iQ44hfoPkB\nMeEVCi9aFM4xiR9Rc7uJqFFiwxdovfrvMYOJydGj7Z8gvemjeqbrJLZG2vnpxh/leuEW30+/y1AQ\nPubOyTwvj36X18ff4bm6JzmZeBhb33xoigT1h8si2hw8NW8RPRvLIqIDpfhyxWTCRxxFuz6nNyTz\n8YkeyN/jnYGvcyN9uqpeIOhMPsSh+qdJ2npCyUbgytWrHNiv/RPXA42mxwlzhBPREdLSpMuLk8vF\naC4Y7PcK7PE9HmQ/DAxFoSia3ZhEPcBz69zVyxzdX7sh4MpIj8auL9PQ/a2JKmEz3PlZ8g1HVnFg\ntY0Qgr3RneyOdHI5f503Mu8zKsMQgBmZ5aWR/8Lr42/zXOopHk0ewxZzlzjaJ3p9IhuOQOgJhDl8\nBmQBlvCJxbKI6DfHoc8L81GheD65pLM6NOuM3txNTg1+k5vpc1PaticOcbjhGVJ28yqMTKPRLBoF\nUd8glTfZkTN5wlVYMjdj9wD40LK5ZEW4ZEe4a5pssTz2WHn2kqNdeTURTWKh2NluWj/4A6KZrnJd\nYNcxuPPn8eKLjyy0ETCEweHYPg44e7iY/4A3M+8zXgyLNxak+drIK3x37A2erTvJ44nj2s1jA6Oc\nNmS0FcPtRwR5jJGLyKaHl+z4S+4TXZCK/+W2KocR+lgi4JnExn2sqZmZe9lrnBp4iTvZy1Patsb3\nc7jhWeojraswMo1Gs2AUOJ5B0jVJ5E2SeRNbPthZ2TUlvRHBRcvmDTNJv5hZ9CREEApqK8duO09M\nrJH/F6VI9X6Xppt/jiEL5ep8ai/D238SaadWcXBrG18FnMtd4e3sGTKT1g2IGzGeTj3Gk8kTOIYz\nwxE06xn71p9j9b4KgN/+MdwjvzbnfWfziV5yEf3KsOIrQ+Exk4binzUHOiKHpoxSituZS5wa/Cb3\nc9entG+J7+NQ/dM0RnVQfY1mTaAg5hmhYHZD0WzNIpp9Q5KNSDKRgGw0wDcn/oeUgkFlc0PGuSHj\n3FdRZnJ8Fii2mAV2W3l2W3m2mAUeEC561TC8cVqu/xGJoQlXNSVMRjteJNPyZHH1IM1i8ZTP2dxl\n3s2emyKmHRHlidQJnk4+RmIJQ+Npah+R6cK58FsAKGGTffZPINI4p31XVERnA8X/fFuRK3pv/FAq\n4NHYGrESaJaFkk90oAKuj53izNDf0e/eqeojEGxLHORg/VPa8qwBtE90LSMkJAqhlTnhhps5S+zw\nQCiykSDcogEFc+4TAnPKoEvGuCnjdMkYOWZ2is5/eJrj+/ez28qzy3JpMvxVn58XGz5Hy7U/wvJG\nynWe08ZQ5+fwY5tXcWTrF1/5XMh/wDuZs4zJdFWbLWxOJB7i6eRjNNtN5XrtE72+iV78bYz0DQAK\ne34Rb8dPzWm/FY3O8crIhIBuMhUPO1pAb3Q86fL+4MucHX6VjD9S1WZg0Jl8iAP1T5CquJhpNJoa\nQYEdiLJYjrsm8YIx63qEvlDkiqI5FwlwrYVH0YgJyQEzwwEzg1TQpyLclHFuyRg9k6zUBQRX/DhX\n/NDSWC98dlp5dlouO608dcbKzc8x3SGab/4ZicFTVfXplicY7fi0Dl+3jFjC4njsEA85B7icv87b\n2TMMF6N5eMrjrfT7vJ1+n4OxfTybOsn2iPZFX+/4bc8RKYpo6+438Do/Gy60sEiWzBI94oe+0KXo\nRH+vLuCwFtEblrHCAGeHv8Ol0R/gSbeqzRQWO5PH2F9/koRVv0oj1Gg0kxES4gWTuGuE1mbXxA5m\ndzXwDEkuIsnZoXAuLEI0z4ecMrgjHbqKVurxWexCLYZXFtWdpktiOUS19KnvfoWGO3+LUXHtC6wE\nw9v+Hm79gaV/Tc0DkUrygXuTt7Nn6PeHprRvi3TwdOpxDsf2Y2rXmvWJ9HBOfx7hhxNQ88f+FUHr\nk7PutmKW6JeGJwR0u6U4FNUCeqOhlOJe9irnhr/HzfSZ8lKtJRwjwZ66E+xOPUxU+6RpNKtLcQJg\nybocd01i3uxWZgDXCgVzLhKQtWXo07wKbhMxIdlnZtlnZlEKhpVNl4rRJWPclQ7epNVXBqTNQMHm\n3UI4ia/NKNBpuewoiurkIkW1M3KZ5hv/L5Fcd1V9tvFhRjte1JMHVwlDGBxwdrM/uovbXjfvZs9x\nq3C33H6n0M1fDH6FBrOeJ5KPcCJxVPtNrzcMG7/1Gez7LwNg3f3anET0bCyJiO4rKN4Ymyh/LCFX\n3Q9Ns3K4QZYro29xfuR7jBR6q9qGunLs2LONfXWP05k4jGks60rzmnWC9oleYoph5uKuQawQiuZY\nYXZfZgApFDlbkrcDcrYkFwmYZd7gqiAE9Fw/z8N7D/CwOUagoFdFuSNj3JYO95VDMEnp98kIfYVI\nWVS3GB6dlst202W75dIg5rZImOkO03TrL0kOvFVV7zmbGNn6oxSSO5fsfWoWjhCCzsgWOiNbGPCH\neC97gUv5awRIhrpy0AnfGn2Vvxt9jYfiBzmZfIRtkY4Zl5rXrC2Ctuew7r+CQGENnqKwBMuAL4mi\n+dqwonT/vsOW7I5oK/RGoD9/h/PD3+ODsXfwVWFK+yZnB1ub2nmy46P6IqTRrBRFC3OsYBAvmMTm\nIZghDDeXtwNykTBdjD/zamIK6BAuHYbLScBXgntFUX1XOvSqKHLSGytZqt8jCUBK+Gy3Cmw3XbZZ\nLpsMryr6h1EYpeHeS6R6voMhvXK9NCKMtb9ApvXJJfG71Cw9LVYTn6r7CM8kH+V09hL/RbxbbvMJ\nOJ29wOnsBTrsTZxMPsKx+CEdb3qNo5wWZMOR8gqG1r2v4+39x4s65pL4RP/a+xNXlV9s9Nmq50us\nW9wgy7WxU1wefYPe/K0p7ZaIsCP5ELtTj1AfaVn5AWo0GwhDQqwslA1inolTMDDmqHo9Q5K3S1tA\n3pY1aWVeDjwl6FZR7laI6smW6slEkGwxC+xRfTzd92U6+16pivkMkG04yuiWH0Ladcs5fM0S4ymf\nq/kbnMldosfvn9LuiChH44c4kTjK1shmbRhaoxgj54le/XcAKDtF9pk/AzM6Y/8Vjc5xICq1gF6H\nKCW5k73C5dE3uTF+hkB5U/rU263sqTvB9sRhbH23rtEsLUXrcsnC7HihcI7MYdJfCb8kmK0J4RyY\nG/epoS0UnSJPp5EHQkv1fRXlnnTollHuK4fCJJ/qiD/Ooz3/mU8MfoWoyle1DcU66dn8GRKprVhr\nZQEYTRlbWByJ7eNIbB89Xj9ncpe4kv8Qn3DluLxyeSdzmncyp2mzWjiROMrxxGFSZnKVR66ZD7L+\nMDLaguEOILxxrN7v4Xd8csHHWzIRLVA8n9DLe68nRgp9XBl9iyujb5L2h6e0GxhsTRxgT+oEzdEt\n096Za99WzULYsOeNgogvQouyZxAtGMQ8g6g3d+syTFiY3Q0mmC9du8KhvQuLfmEJxTaRZ1tRVEsF\nAypCt4qSzo9yYOAVnh76BrFJS5bfcvbwN22/wJnUkyAEZiBpJ8sWkWGryLBVpGkjt6aXKt8InL1+\ng2N7dgHQbrfyov0czyVPcjF/jbO5SwwHExO/+vwBvjn6HV4efZV9zm4eSRzlQGw3ltBzfmoeYRC0\nPYdx58tAOMGwJkT0MUfRqs+fNc+4N8S1sVNcGz9Ff/72tH0aIpvYmTzK9sQhHWVDo1kAQkG0KI6d\nim2+YlmhcK1QLLtFK7O7gVwylhMDye7x9znZ/x3qRs8jJkUbuh/dwV+3/QNO1T1D5ezDAIN7JLmn\nkrxT3MUmYDNZOkSGDhGmbeS0xbrGiRkOj8Yf4kTsCHe9Hi7kP+AD9wae8gGQKK7kr3Mlfx1HRDkU\n28fR+EF2OzswtS98zeK3PoV1928Rysccu4oxehVZvzCjzZL4RP/6+/ArzQH1+pxZk2T8Ua6Pvc+1\n8Xfpyd2Ytk/UiLE9cZidqaM0RDat8Ag1mjVIcaGSqG+UBXPUD8VyxBdzCiVXiWeEArksmi25YvGY\nNxKmn6Vx8A1a+l/FcXuntLvRNgZaP0Y6dRBfmPSS4B4pukWS+yQZFrG5vQ6STeToEBk2iyztIks7\nWWIiWOq3pFlCCtLjqnuDi/kPuOv1TNsnbsQ4EjvAQ/GD7Ixuw9Cxp2sO+8MvYQ28CYC3+ZMUDv/q\ntP1WZNnvr1xWfDKlXTnWEqOFfm6kz3Jz/Cz3c9enxHQGMDBpj+1iR/IhNsf36DtrjWYyCqySUPYF\nkSqhbMw5IkYlviFxLVUUyeHmWtq6vKwoSXL8Co1D79Aw/A6mnBptKJ3Yy0jTSTLJvfAAUZTDoocE\n3SS5XxTW42LmiUuTacAtC+r2orhuxsXUVuuaY9gf5WL+Gpfz1xmV49P2SRkJDsT2cii2l13ODmzt\n8lETiPQNnIu/DYAyIuEEw8jUycArIqKHbgc4+gJf0ygl6c13cbMonIcK96ftJxBsiu1ke+IgHbF9\nRExnUa+7YX1bNYuils4bISEShNbjqB+K46gnwtQ3MBYglBUKz1ShSC6lWiwvmnn5RCtFPHODxqG3\naRg+he2PTekSGA6jDQ8z0ngSL9q84HGlseklQQ8JekSSXhKMiLlfW00kLeTZJLK0iRzt5GgTWZpw\nta/1ElHpEz1flFL0+gNcyX/IVfcG4zIzbb+IsNnr7OJgbC8HnN3EtTvk6qEU0YtfwMh0AeDu/cf4\nnT85pduKROfQAro2yflp7mavcDtzia70BbLB1D+JEm1OJ9sSB9ka36/9nDUbikqRHPENbF8Uy6F1\neS7LXs9EIEpCWeKVrMvF/AK0t2axKEkse5uGkfdoGHqXaGFg2m5utI3hpicYqz+KMuZuRZ6JJB5J\nRtjNCKWHfjkVuoL0kqBPJOgjzgBxgmms3AEGvcTpVXEqHxqWxHWryNFaSkWOFvJEhX46vFIIIWi3\nW2m3W3kueZJ7Xi9X3Q+5mr9JVk1MRC0oj4u5q1zMXUUg6IxuZZ+ziz3OTjrsdgwdNm/lEAK/7Tki\nN/8YAPvu1/G3/8QDnzJNe5ilsERn72ofrlogkB73cze4nbnEnexl+vN3YBo3DQBTWGxydtAR30dH\nfA+OmVjZwWo0K0HRL7kkjKdL7UWafktC2TMVnhn6KRfM0LKsrcqrj+mnSY1dom70PKmxC9j+9I/d\nfTPJeP0RxuqOko9tZTWW3Q0QDBKjj3hZWPcTn5c7SIk6CjSLPC3kaBF5mnFpETmacPWExhVCKkm3\n18v1wm0+dG9VRfiYTNyIsTu6g73OTvY4O2iw6ldwpBuUwMU5/S8QQRaA/OHPE2x+oarLirhzaBG9\nOviyQE/uJvdzH9Kdu8797PVpVw4sETXibI7vYUt8L5ucnViGDuqtWaMoMBVYJSEchBbjkmAu54P5\nT+Cb+lKqKJBDkVxOi2JZC+XaQkiPWPZ2KJzHzhPP3JwSWaNEYDiM1x1mvO4hsomd87ZCrRR5TAaI\n0U+cAREK6wHipMX8Y/ILFPUUaBJ5mnBpEi5N5MtpjLktda6ZH0ophoIRrrtdfOh20e33PbB/i9XE\nzuh2dkS30hndRqNZrxd4WQasrr/C7vk2AMqwyT/yvyMbDpfbtYheR+T8ND25G3TnrtGdvU5//jaS\nmT97gaAp2sEmZyftsZ00RTtWfJZwLfm2amofUZyo98GVqxzbdRArMLCkKAtiq0IcL8QXeTpKItk3\nJkSyXyWYdQSMWsb00yTS10mkr3Plg/M829CLUQxBNh2+mSCT3MN43WGyib0oY+1O9MpjMkSMQWIM\niDAdJMYwDnKB1/ooPo0UaBAuDbg0CpdGXBpEgQZcEvjrUmQvxid6IWRkllvuXboK97jl3SM7Kf74\nZOrMFJ2RreyIbmNHdCttditmjd70rSn8DNGLv42RDyPxKLuO3GO/h4pvCZtXcsVCzdLhBln68rfp\ny3eVt3FvcNb9klYjm2I7aXd20hrbTsRY3MRAjWZRKLCkwAoElhSYgcAulYt1E/lQMAPkhmLsSi2N\nb75vKHyjUhyH5Ym8FslrBdPP4uTuEMveIZa7TSJzEyc/MUm6KwfGpKfgCkE+tpVMci+Z5F7yTkfN\nWpzni0NAB2k6SFd57gUIRlWUIRyGiDEswnQIh1GiD3RVcbHowaJHFX9/k+xsFpI6CtQXRXV9MV9P\ngZQoUEeBBL6e8DgLCSPO4dg+Dsf2oZRiIBjiVuEeXYW73C30lFdKLDEWjHM+d5nzucsA2MKmw97E\nlkg7WyKb2RLZTIvVpP2q54uVoLD/vyV68bcR/jjCG8M5/evkHvs9iMzuUqMt0auMVJIxr59Bt5sh\nt5tBt5v+/G1Gvf457V9nN9MS3UaLs5XW6DYSdsMyj1izIVFgSjClKG9WKQ2mlkvieCEh3uaKFKoo\nkENxHKZyUllP4FuLCFkg6vYRzffi5O4RKwrnmSYCTqZgN5GLd5JJ7iGT2IO09GTpEj6CUaKM4DCM\nw4hwGCEa5nHwliCUqYEkhUcdHilRIFWRJvFICY8UHgk87Z89Db7yue/1c8/r4a7XQ7fXS0F5s+4X\nFRE2RzbRbrexqTjRsc1uIaaNabMixm8Qvfx/Ioqfc1B/mPwjv4MvTe3OUQsUZJ6xwgAjhV5GvX6G\n3PsMud0MFXoI5vDjgDBuc0NkUyiYnW20RLfqSBqaOSEUGFJMCGElqsvlDUwlJtWF9Yv1LZ4LCkVQ\nFMbl1KwQy0VhHBgKKdAW5DWMEeSJFAaxC4NE3X6i+Z5QNLu92IWhGf2YJ6MwyMc6yMW2k4uHW2Cl\nlnn06xNFGOe6JLJHiTIqomFKlDGiuEsc5ziGT6IorhMizCfwSBbzcXziwide7GdvQNEtlaTfH6oQ\n1X2kZwijNx31Zoo2u4VNdistVhPNVhPNViN1Zkpbriswht4ncu0Pytcef9NzZPZ/nuyO6MJFtBDi\nReB3ARP4f5RSv1PZrkV0iC890v4waW+YcX+ItDfMmDfASKGP0ULfA8PLTYfAoCHSRmOkncboZpoi\n7dRFWtfcgifaJ3oBKDCKotdQYCiBURS3pbxRFLqlfmZZJId1YVrcT4p5LSW9tG9FERhhBIvAmNh8\nY6YyIOYZ71dTWyiJ5Y9jeWPY3iiWP4rtjWEXhokUBokUhrALg1jFGfHzOjQmrtOGG20n72zGdTaT\nj3WgjHCC3YUPb3Jk986lfkeaClwMxogyTpQxIoyJME0TYbyY5sTyTVq3CUJhjY8jwnystImgnHcI\ncESAU8oTYCNn9GRZaZ/oxZIJs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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "figsize(12.5, 5)\n", + "\n", + "params = [(2, 5), (1, 1), (0.5, 0.5), (5, 5), (20, 4), (5, 1)]\n", + "\n", + "x = np.linspace(0.01, .99, 100)\n", + "beta = stats.beta\n", + "for a, b in params:\n", + " y = beta.pdf(x, a, b)\n", + " lines = plt.plot(x, y, label=\"(%.1f,%.1f)\" % (a, b), lw=3)\n", + " plt.fill_between(x, 0, y, alpha=0.2, color=lines[0].get_color())\n", + " plt.autoscale(tight=True)\n", + "plt.ylim(0)\n", + "plt.legend(loc='upper left', title=\"(a,b)-parameters\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "One thing I'd like the reader to notice is the presence of the flat distribution above, specified by parameters $(1,1)$. This is the Uniform distribution. Hence the Beta distribution is a generalization of the Uniform distribution, something we will revisit many times.\n", + "\n", + "There is an interesting connection between the Beta distribution and the Binomial distribution. Suppose we are interested in some unknown proportion or probability $p$. We assign a $\\text{Beta}(\\alpha, \\beta)$ prior to $p$. We observe some data generated by a Binomial process, say $X \\sim \\text{Binomial}(N, p)$, with $p$ still unknown. Then our posterior *is again a Beta distribution*, i.e. $p | X \\sim \\text{Beta}( \\alpha + X, \\beta + N -X )$. Succinctly, one can relate the two by \"a Beta prior with Binomial observations creates a Beta posterior\". This is a very useful property, both computationally and heuristically.\n", + "\n", + "In light of the above two paragraphs, if we start with a $\\text{Beta}(1,1)$ prior on $p$ (which is a Uniform), observe data $X \\sim \\text{Binomial}(N, p)$, then our posterior is $\\text{Beta}(1 + X, 1 + N - X)$. \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Example: Bayesian Multi-Armed Bandits\n", + "*Adapted from an example by Ted Dunning of MapR Technologies*\n", + "\n", + "> Suppose you are faced with $N$ slot machines (colourfully called multi-armed bandits). Each bandit has an unknown probability of distributing a prize (assume for now the prizes are the same for each bandit, only the probabilities differ). Some bandits are very generous, others not so much. Of course, you don't know what these probabilities are. By only choosing one bandit per round, our task is devise a strategy to maximize our winnings.\n", + "\n", + "Of course, if we knew the bandit with the largest probability, then always picking this bandit would yield the maximum winnings. So our task can be phrased as \"Find the best bandit, and as quickly as possible\". \n", + "\n", + "The task is complicated by the stochastic nature of the bandits. A suboptimal bandit can return many winnings, purely by chance, which would make us believe that it is a very profitable bandit. Similarly, the best bandit can return many duds. Should we keep trying losers then, or give up? \n", + "\n", + "A more troublesome problem is, if we have a found a bandit that returns *pretty good* results, do we keep drawing from it to maintain our *pretty good score*, or do we try other bandits in hopes of finding an *even-better* bandit? This is the exploration vs. exploitation dilemma.\n", + "\n", + "### Applications\n", + "\n", + "\n", + "The Multi-Armed Bandit problem at first seems very artificial, something only a mathematician would love, but that is only before we address some applications:\n", + "\n", + "- Internet display advertising: companies have a suite of potential ads they can display to visitors, but the company is not sure which ad strategy to follow to maximize sales. This is similar to A/B testing, but has the added advantage of naturally minimizing strategies that do not work (and generalizes to A/B/C/D... strategies)\n", + "- Ecology: animals have a finite amount of energy to expend, and following certain behaviours has uncertain rewards. How does the animal maximize its fitness?\n", + "- Finance: which stock option gives the highest return, under time-varying return profiles.\n", + "- Clinical trials: a researcher would like to find the best treatment, out of many possible treatment, while minimizing losses. \n", + "- Psychology: how does punishment and reward affect our behaviour? How do humans learn?\n", + "\n", + "Many of these questions above are fundamental to the application's field.\n", + "\n", + "It turns out the *optimal solution* is incredibly difficult, and it took decades for an overall solution to develop. There are also many approximately-optimal solutions which are quite good. The one I wish to discuss is one of the few solutions that can scale incredibly well. The solution is known as *Bayesian Bandits*.\n", + "\n", + "\n", + "### A Proposed Solution\n", + "\n", + "\n", + "Any proposed strategy is called an *online algorithm* (not in the internet sense, but in the continuously-being-updated sense), and more specifically a reinforcement learning algorithm. The algorithm starts in an ignorant state, where it knows nothing, and begins to acquire data by testing the system. As it acquires data and results, it learns what the best and worst behaviours are (in this case, it learns which bandit is the best). With this in mind, perhaps we can add an additional application of the Multi-Armed Bandit problem:\n", + "\n", + "- Psychology: how does punishment and reward affect our behaviour? How do humans learn?\n", + "\n", + "\n", + "The Bayesian solution begins by assuming priors on the probability of winning for each bandit. In our vignette we assumed complete ignorance of these probabilities. So a very natural prior is the flat prior over 0 to 1. The algorithm proceeds as follows:\n", + "\n", + "For each round:\n", + "\n", + "1. Sample a random variable $X_b$ from the prior of bandit $b$, for all $b$.\n", + "2. Select the bandit with largest sample, i.e. select $B = \\text{argmax}\\;\\; X_b$.\n", + "3. Observe the result of pulling bandit $B$, and update your prior on bandit $B$.\n", + "4. Return to 1.\n", + "\n", + "That's it. Computationally, the algorithm involves sampling from $N$ distributions. Since the initial priors are $\\text{Beta}(\\alpha=1,\\beta=1)$ (a uniform distribution), and the observed result $X$ (a win or loss, encoded 1 and 0 respectfully) is Binomial, the posterior is a $\\text{Beta}(\\alpha=1+X,\\beta=1+1−X)$.\n", + "\n", + "To answer our question from before, this algorithm suggests that we should not discard losers, but we should pick them at a decreasing rate as we gather confidence that there exist *better* bandits. This follows because there is always a non-zero chance that a loser will achieve the status of $B$, but the probability of this event decreases as we play more rounds (see figure below).\n", + "\n", + "Below we implement Bayesian Bandits using two classes, `Bandits` that defines the slot machines, and `BayesianStrategy` which implements the above learning strategy." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "from pymc import rbeta\n", + "\n", + "\n", + "class Bandits(object):\n", + "\n", + " \"\"\"\n", + " This class represents N bandits machines.\n", + "\n", + " parameters:\n", + " p_array: a (n,) Numpy array of probabilities >0, <1.\n", + "\n", + " methods:\n", + " pull( i ): return the results, 0 or 1, of pulling \n", + " the ith bandit.\n", + " \"\"\"\n", + "\n", + " def __init__(self, p_array):\n", + " self.p = p_array\n", + " self.optimal = np.argmax(p_array)\n", + "\n", + " def pull(self, i):\n", + " # i is which arm to pull\n", + " return np.random.rand() < self.p[i]\n", + "\n", + " def __len__(self):\n", + " return len(self.p)\n", + "\n", + "\n", + "class BayesianStrategy(object):\n", + "\n", + " \"\"\"\n", + " Implements a online, learning strategy to solve\n", + " the Multi-Armed Bandit problem.\n", + " \n", + " parameters:\n", + " bandits: a Bandit class with .pull method\n", + " \n", + " methods:\n", + " sample_bandits(n): sample and train on n pulls.\n", + "\n", + " attributes:\n", + " N: the cumulative number of samples\n", + " choices: the historical choices as a (N,) array\n", + " bb_score: the historical score as a (N,) array\n", + " \"\"\"\n", + "\n", + " def __init__(self, bandits):\n", + "\n", + " self.bandits = bandits\n", + " n_bandits = len(self.bandits)\n", + " self.wins = np.zeros(n_bandits)\n", + " self.trials = np.zeros(n_bandits)\n", + " self.N = 0\n", + " self.choices = []\n", + " self.bb_score = []\n", + "\n", + " def sample_bandits(self, n=1):\n", + "\n", + " bb_score = np.zeros(n)\n", + " choices = np.zeros(n)\n", + "\n", + " for k in range(n):\n", + " # sample from the bandits's priors, and select the largest sample\n", + " choice = np.argmax(rbeta(1 + self.wins, 1 + self.trials - self.wins))\n", + "\n", + " # sample the chosen bandit\n", + " result = self.bandits.pull(choice)\n", + "\n", + " # update priors and score\n", + " self.wins[choice] += result\n", + " self.trials[choice] += 1\n", + " bb_score[k] = result\n", + " self.N += 1\n", + " choices[k] = choice\n", + "\n", + " self.bb_score = np.r_[self.bb_score, bb_score]\n", + " self.choices = np.r_[self.choices, choices]\n", + " return" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Below we visualize the learning of the Bayesian Bandit solution." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "figsize(11.0, 10)\n", + "\n", + "beta = stats.beta\n", + "x = np.linspace(0.001, .999, 200)\n", + "\n", + "\n", + "def plot_priors(bayesian_strategy, prob, lw=3, alpha=0.2, plt_vlines=True):\n", + " # plotting function\n", + " wins = bayesian_strategy.wins\n", + " trials = bayesian_strategy.trials\n", + " for i in range(prob.shape[0]):\n", + " y = beta(1 + wins[i], 1 + trials[i] - wins[i])\n", + " p = plt.plot(x, y.pdf(x), lw=lw)\n", + " c = p[0].get_markeredgecolor()\n", + " plt.fill_between(x, y.pdf(x), 0, color=c, alpha=alpha,\n", + " label=\"underlying probability: %.2f\" % prob[i])\n", + " if plt_vlines:\n", + " plt.vlines(prob[i], 0, y.pdf(prob[i]),\n", + " colors=c, linestyles=\"--\", lw=2)\n", + " plt.autoscale(tight=\"True\")\n", + " plt.title(\"Posteriors After %d pull\" % bayesian_strategy.N +\n", + " \"s\" * (bayesian_strategy.N > 1))\n", + " plt.autoscale(tight=True)\n", + " return" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + 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20r9tJwN7GyFMbkPerCryF9VQsGgBeZfPQvLyho75FI4NQNOFPJr6hBMD3u/6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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "hidden_prob = np.array([0.85, 0.60, 0.75])\n", + "bandits = Bandits(hidden_prob)\n", + "bayesian_strat = BayesianStrategy(bandits)\n", + "\n", + "draw_samples = [1, 1, 3, 10, 10, 25, 50, 100, 200, 600]\n", + "\n", + "for j, i in enumerate(draw_samples):\n", + " plt.subplot(5, 2, j + 1)\n", + " bayesian_strat.sample_bandits(i)\n", + " plot_priors(bayesian_strat, hidden_prob)\n", + " # plt.legend()\n", + " plt.autoscale(tight=True)\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that we don't really care how accurate we become about the inference of the hidden probabilities — for this problem we are more interested in choosing the best bandit (or more accurately, becoming *more confident* in choosing the best bandit). For this reason, the distribution of the red bandit is very wide (representing ignorance about what that hidden probability might be) but we are reasonably confident that it is not the best, so the algorithm chooses to ignore it.\n", + "\n", + "From the above, we can see that after 1000 pulls, the majority of the \"blue\" function leads the pack, hence we will almost always choose this arm. This is good, as this arm is indeed the best.\n", + "\n", + "Below is a D3 app that demonstrates our algorithm updating/learning three bandits. The first figure shows the raw counts of pulls and wins, and the second figure is a dynamically updating plot. I encourage you to try to guess which bandit is optimal, prior to revealing the true probabilities, by selecting the `arm buttons`." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + "\n", + " \n", + "\n", + "\n", + " \n", + "
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Denote this best bandit's probability of $w_{opt}$. Our score should be relative to how well we would have done had we chosen the best bandit from the beginning. This motivates the *total regret* of a strategy, defined as:\n", + "\n", + "\\begin{align}\n", + "R_T & = \\sum_{i=1}^{T} \\left( w_{opt} - w_{B(i)} \\right)\\\\\\\\\n", + "& = Tw^* - \\sum_{i=1}^{T} \\; w_{B(i)} \n", + "\\end{align}\n", + "\n", + "\n", + "where $w_{B(i)}$ is the probability of a prize of the chosen bandit in the $i$th round. A total regret of 0 means the strategy is attaining the best possible score. This is likely not possible, as initially our algorithm will often make the wrong choice. Ideally, a strategy's total regret should flatten as it learns the best bandit. (Mathematically, we achieve $w_{B(i)}=w_{opt}$ often)\n", + "\n", + "\n", + "Below we plot the total regret of this simulation, including the scores of some other strategies:\n", + "\n", + "1. Random: randomly choose a bandit to pull. If you can't beat this, just stop. \n", + "2. Largest Bayesian credible bound: pick the bandit with the largest upper bound in its 95% credible region of the underlying probability. \n", + "3. Bayes-UCB algorithm: pick the bandit with the largest *score*, where score is a dynamic quantile of the posterior (see [4] )\n", + "3. Mean of posterior: choose the bandit with the largest posterior mean. This is what a human player (sans computer) would likely do. \n", + "3. Largest proportion: pick the bandit with the current largest observed proportion of winning. \n", + "\n", + "The code for these are in the `other_strats.py`, where you can implement your own strategy very easily." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "figsize(12.5, 5)\n", + "from other_strats import GeneralBanditStrat, bayesian_bandit_choice, max_mean, lower_credible_choice, \\\n", + " upper_credible_choice, random_choice, ucb_bayes, Bandits\n", + "\n", + "# define a harder problem\n", + "hidden_prob = np.array([0.15, 0.2, 0.1, 0.05])\n", + "bandits = Bandits(hidden_prob)\n", + "\n", + "# define regret\n", + "\n", + "\n", + "def regret(probabilities, choices):\n", + " w_opt = probabilities.max()\n", + " return (w_opt - probabilities[choices.astype(int)]).cumsum()\n", + "\n", + "# create new strategies\n", + "strategies = [upper_credible_choice,\n", + " bayesian_bandit_choice,\n", + " ucb_bayes,\n", + " max_mean,\n", + " random_choice]\n", + "algos = []\n", + "for strat in strategies:\n", + " algos.append(GeneralBanditStrat(bandits, strat))" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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CaY+iYOb1hFKqkdmr+imGs7N1vSnuvJe0qFkv5ZRS/sAnGD2Qv1nVS12lVBelVFVzJKXb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\n1jrZWXgJhl9ARWAtUE1bFWDWrFn6mWe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3cpQ6GzqOV+VC60vzooe2U7Psp4S2LAU7nrbd\n+HvgHXs5nrGX4R17BcblLUCU+aN1RRXaGRsCIjI8D3F0anXDXg4cOMA3vvENXnrpJSoqKjDGMGfO\nHCTDvZGb6t+/PydPniQQCNQ3EA4cOIDDkehBGTBgAFVVDb0uIsLHH3/MgAEDmj1mNudNNWjQIDZu\n3JjTPnWxLV26tNG6AwcOcMkll2Q8x1//+te09YMHD2bYsGGsW7cu5/MrpZRqP7Fj+wm88zChrX8m\n/unutO3G7cdXcQveiQtxnzMT49CbZiiVLznNETDG/C9jzP/M9K+9AsxFR50jUKe2thZjDL169cK2\nbZ544gl27MjuWW1Dhgxh0qRJ/PCHPyQajbJ69Wpee+21+u0LFy7k9ddfZ9WqVUSjUR588EG8Xi8V\nFRVtFv+NN97Im2++yUsvvUQsFuP48eNs27btjPtdcsklfPTRRzz//PPEYjFeeOEFdu3axeWXX55W\n9vbbb+fBBx9k8+bNiAh//etfOXjwIFOnTqVLly788pe/JBgMEo/Hqaqq4v3332+zz6c6L+2xU7nQ\n+tIw8ffEH+/i0+9PoXbFL9MaAa4RMyi/6Wf0/f+20+3G/41n5IWdrhGgdUUVWq7/xw2h8TChAcDn\ngRfbLKJObPTo0Xz1q1/l8ssvx7IsFi1axMyZM+u3p06aTV1X57e//S333nsv5557LtOmTeOWW26p\nv9PPyJEjeeihh/j2t7/N4cOHmTBhAk8++WSLtytNPXamczc1ePBgnnnmGb773e/y9a9/nfLycr7z\nne/UzzNoLvaePXuyePFi/u3f/o1vfvObnHvuuSxevLjR/II6Cxcu5MSJE9xzzz0cPnyYoUOH8tBD\nDzF48GAWL17Md7/7XaZMmUI4HGbkyJGteoCZUkqp1okd3Utw/dME1z1F/Ni+9AIuL95xV9Ll4vtw\nDenYnXdKlQKT6/CPtAMYcwVwq4jc0TYhtd5Pf/pTueuuu9LWHzp0qH4yrlKlSOt4+9JxvCoXna2+\n2LXHCW1ZSnDD80R2V2Ys4x41B//n7sJ7wXyM25/nCItXZ6sr6uxs3LiR+fPnt+nTYtviGtzrgD76\nVSmllOokJBYhsruSwNrFhDYvgXg0rYzxd8c75jLK5t6La/CEAkSplDqTnBoCxphzmqzyA18C9rfm\n5MaYvcApIA5ERaTCGNMTeBoYBuwFbhaRk8nyDwB3Jct/TUSWpR6vo88R6AieffZZvvnNb6atHzJk\nCO+8804BIlKq/WmPncpFqdYXu+YYoarXCe94g/DO5Ugww0MmjYXngkvwTV+Ed9yVJX/Xn7NVqnVF\ndRy5XhFoOt0/AGwC7mzl+QWYKyKp9++8H3hdRH5sjPl2cvl+Y8wYYBEwBhgEvGGMGSUidivPrVrh\npptuqr/fv1JKqdImkQChqmUE3nuUyAdvNlvONWwqvsnX4520EEd3HaaoVEeRU0NARNrjScRNxzpd\nA8xJvn8EeJNEY2AhsFhEosBeY8xuoAJYXbdja58joJRSLdFxvCoXHb2+2OFaIh+sJPDuHwnvfgdi\n4YzlHD2G4Bn/BXzTbsY9dHKeoywNHb2uqPw5dvpIuxy30PfpEhI9+3Hg1yLyW6CfiNR92iNAv+T7\ngaQk/cBBElcGzsjtdnPs2DF69ux5xjvfKNXRBAKB+udFKKVUa0g8SmjrK4Q2LSFctQyJBNILGQvX\n0Cl4x38Bz/lzcQ6eqH9TlWonx04fYcf+DVQd2EDV/g1UnzzAv17y2zY/T65zBDzAd4BbSCTmh4Cn\ngO+LSKgV5/+ciBw2xvQBXjfG7EzdKCJijGnptkaNtu3evZt7772XoUOHAtCtWzfGjx/PhRdeSE1N\nDdu3b8fhcNCtWzeA+ltr6rIud9RlEaFXr1707duXysrE3Trqepd0ue2WL7zwwqKKR5eLe7kj1ZcZ\nI7oTqPw9q15bgh04QUV/AFibfLB7RX9w9j+f912T8FxwCXO+cEPD/vveKXj8uqzLpbJ8KnCC8kEO\nqg5sYMXK5Ryv+QSA4/tDBD9LTMbf1HsT8+fPpy3ldPtQY8zDwCjgP0lMEB4K/A9gl4h8+awCMeZ7\nQA3wdyTmDVQbYwYAK0VktDHmfgAR+WGy/KvA90RkTd0xli9fLjo0SCmllGqe2DaRXauoWf4LIh++\nlbGMo+9IvBMW4J91B85ew/IcoVKlL1OPf0tcTg/fmPtgwW8fei1wroicSC5vN8asAT4CcmoIGGP8\ngENEThtjyoDLgP8AlpCYfPyj5OtLyV2WAE8aY35GYkjQSGBt6jF1joDKhY7NVNnSuqJyUaz1JX7q\nCIHVjxNc8wTxY3vTtltd++KfdTveSdfiGjg2/wF2QsVaV1Tba03iP2rgBMYMncqYIdM4b8BYtm7Z\n1uZx5doQOEzilqEnUtb5SAwRylU/4MXk+EIn8ISILDPGrAeeMcbcTfL2oQAiUmWMeQaoAmLAvXK2\nT0NTSimlSpjEY0Q+eofA6scz3+/fWHjGXUnZ3H/APbwC4yj01EGlSkNbJP4up7vd48x1aND9wK3A\ng8ABEkOD7gWeBNbVlRORFW0bZnZ0aJBSSqnOTkSI7FpFYPXjhHe8kfF+/8bXDd+0mymbe68O/VGq\nDeQj8S+GJwt/Jfn6QMo6k1z/lZR1I84mKKWUUkrlxq45RmD90wTXPEHs8I6MZVwjKij73N14JyzA\nuH15jlCp0tFRevzPJKeGgIgMb6c42oTOEVC50LGZKltaV1Qu8llfJBIkvHMFgdWPEd65HOx4Whmr\n2wC8E67GP+NWXIMn5CUulR393dJxlEri35QOBlRKKaU6mOih7QTe/SPBdU8j4Zq07cZdhq/iFvwz\nvoRz8AS9379SOSrVxL+pnOYIFDudI6CUUqpU2aHThLa8TOCdh4nu25CxjHPwRPyz7sQ3+Tosf7c8\nR6hUx9UREv9imCOglFJKqTwRESK73yG4bjGhTUuQSG1aGUfvc/BOWoh/5m04e+sUPaWy0RES/3wo\nqYaAzhFQudCxmSpbWldULs62vogIsSMfEN72KsH1zxCr3pleyOHGO/4L+Gf/De6RF+nQnw5Kf7fk\njyb+mZ11Q8AYswA4LCKZr1MqpZRS6owkHiW0aQm1b/2K6P6NGcs4+5+Pb/ot+CpuwdG1T54jVKrj\n0MQ/O62aI2CMeRiYC2wCHgO6icgf2zSyVtA5AkoppToaiYYIrH2KmmX/G/uzw+kFHC580xfhn3kH\nrmFTtfdfqQw6Q+JfTHMEXgHuBmYBdwDpgxaVUkoplZHEIkR2vU1o6yuEtryMXXO0cQGHG8/Yy/CO\n+wLeCV/A8pYXJlClilRnSPzzobUNgbgkLiW8m/xXFHSOgMqFjs1U2dK6onLRUn2JfbKbwLt/JLDu\nKaT2eNp2q0sf/Bfehf9zX8bRtW97h6oKTH+3ZE8T//bR2obANGPMnSSGBS0XkfTnlyullFIKiccS\nD/1652HCVcsylrG6D6Lswrsp+/w9GLc/zxEqVXw08c+P1s4RuBfYCVwKzANOisgVbRxbznSOgFJK\nqWIRrzma6P1/5w8Zx/5b3Qfhm3gNnvFfwD1iBsZRUjfyUyonmvifWTHNEVgN9BWRBwCMMdp9oZRS\nqtOLffpXghueJbLrbSJ710E82riAMXjGXoF/9t/gGX0xxnIUJlClCkwT/+LQqoaAiGxsshxom3DO\njs4RULnQsZkqW1pXVEtEhMjOFdS+9RDhnctZWw0V/RuXsbr0wVfxRfwzvoSz36jCBKqKTmf63aKJ\nf3HS65BKKaVUK0g0RHDTn6hd+SCxQ9szlnENnULZ5/8e76SFGE1iVCeiiX/H0Ko5AsVK5wgopZRq\nT3bgM8I73yC0eQnhnSuRcE1aGff58/DPuBX3ObNwdB9YgCiVyj9N/NtfwecIGGMsEbHbMgCllFKq\nmIkdJ7JnDaENzxNY8wTEI2lljNuPb+ZtlF34tzj7nleAKJXKL038S0PWDQFjjBM4bYzpLiLhdoyp\n1XSOgMpFZxqbqc6O1pXOKXb8AKFNLxJ45w/Ej+3LWMbR51z8Fbfgn/03WGU9Aa0vKnsdqa5o4l+a\nsm4IiEjMGLML6A183H4hKaWUUoVhh2sJrn6c4IZnie7fmLGMc+A4vBMW4J14Nc7+ozGmTa/UK1UU\nNPHvHHKaI2CM+Vfgi8AvgQNA/c4isqLNo8uRzhFQSinVGvGaowQqf0/t27/N+MRf4++Ob9K1eCdd\ni3vkRZr8q5KjiX/xK/gcAeDe5Ov3MmwbcZaxKKWUUnljh04T3vEGwY0vEN7+GtixxgUsB55Rc/BO\nvh7f5Gv1ib+qpGjiryDHhoCIDG+nONqEzhFQuehIYzNVYWldKR0SCRD+cBXBDc8S2vJy+gO/AEev\nYZTN+0d8k6+rH/efC60vKlv5rCua+KtMcn6OgDHmMhLDg/qKyAJjzDSgvBiGBimllFJNiW0T2bOa\n0IbnCG58HgmdzljONWxawz3/HfqYHdWxaeKvspHrHIH7gH8Cfgc8ICLlxphxwG9EZHY7xZg1nSOg\nlFIKwA7XEPlwFeEPVhLa+mfszw5nLOccMAbvxKvxTblBb/upOjRN/EtfMcwR+AYwX0T2JCcOA+wA\nRrdlUEoppVRrxI7tI1D5ewLvPYqETmUs4+h9Dt5xV+Cb/kVcg8blOUKl2oYm/qot5NoQ6ELibkGp\n3EBRPFdA5wioXOg4XpUtrSvFzQ7XEtr4PIHVjxHdtyFjGausV6Lnf+pNuM6Z2a53/dH6orKVS13R\nxF+1h1wbAm8D9wPfT1l3H7CyNSc3xjiA9cBBEbnaGNMTeBoYBuwFbhaRk8myDwB3AXHgayKyrDXn\nVEop1fFJPEp03wZCm5cQXP8sdu2xtDKOPufiHXcFnjGX4z5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IlTHmGaAKiAH3SjFcylBK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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# train 10000 times\n", + "for strat in algos:\n", + " strat.sample_bandits(10000)\n", + "\n", + "#test and plot\n", + "for i, strat in enumerate(algos):\n", + " _regret = regret(hidden_prob, strat.choices)\n", + " plt.plot(_regret, label=strategies[i].__name__, lw=3)\n", + "\n", + "plt.title(\"Total Regret of Bayesian Bandits Strategy vs. Random guessing\")\n", + "plt.xlabel(\"Number of pulls\")\n", + "plt.ylabel(\"Regret after $n$ pulls\");\n", + "plt.legend(loc=\"upper left\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Like we wanted, Bayesian bandits and other strategies have decreasing rates of regret, representing that we are achieving optimal choices. To be more scientific so as to remove any possible luck in the above simulation, we should instead look at the *expected total regret*:\n", + "\n", + "$$\\bar{R_T} = E[ R_T ] $$\n", + "\n", + "It can be shown that any *sub-optimal* strategy's expected total regret is bounded below logarithmically. Formally:\n", + "\n", + "$$ E[R_T] = \\Omega \\left( \\;\\log(T)\\; \\right)$$\n", + "\n", + "Thus, any strategy that matches logarithmic-growing regret is said to \"solve\" the Multi-Armed Bandit problem [3].\n", + "\n", + "Using the Law of Large Numbers, we can approximate Bayesian Bandit's expected total regret by performing the same experiment many times (500 times, to be fair):" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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znMgRwfj5980j+5DJGCillFJKKXU0jDHUHGygvKTG+WqpBag+0NDl8IaHBhAx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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# this can be slow, so I recommend NOT running it.\n", + "trials = 200\n", + "expected_total_regret = np.zeros((1000, 3))\n", + "\n", + "for i_strat, strat in enumerate(strategies[:-2]):\n", + " for i in range(trials):\n", + " general_strat = GeneralBanditStrat(bandits, strat)\n", + " general_strat.sample_bandits(1000)\n", + " _regret = regret(hidden_prob, general_strat.choices)\n", + " expected_total_regret[:, i_strat] += _regret\n", + "\n", + " plt.plot(expected_total_regret[:, i_strat] / trials, lw=3, label=strat.__name__)\n", + "\n", + "plt.title(\"Expected Total Regret of Multi-armed Bandit strategies\")\n", + "plt.xlabel(\"Number of pulls\")\n", + "plt.ylabel(\"Exepected Total Regret \\n after $n$ pulls\");\n", + "plt.legend(loc=\"upper left\");" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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9/vx5nn76aZo1a4aPjw+tWrXimWee4cwZW7+65s2b4+XlhY+PD/7+/gwZMoTo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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure()\n", + "[pl1, pl2, pl3] = plt.plot(expected_total_regret[:, [0, 1, 2]], lw=3)\n", + "plt.xscale(\"log\")\n", + "plt.legend([pl1, pl2, pl3],\n", + " [\"Upper Credible Bound\", \"Bayesian Bandit\", \"UCB-Bayes\"],\n", + " loc=\"upper left\")\n", + "plt.ylabel(\"Exepected Total Regret \\n after $\\log{n}$ pulls\");\n", + "plt.title(\"log-scale of above\");\n", + "plt.ylabel(\"Exepected Total Regret \\n after $\\log{n}$ pulls\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Extending the algorithm \n", + "\n", + "\n", + "Because of the Bayesian Bandits algorithm's simplicity, it is easy to extend. Some possibilities are:\n", + "\n", + "- If interested in the *minimum* probability (eg: where prizes are a bad thing), simply choose $B = \\text{argmin} \\; X_b$ and proceed.\n", + "\n", + "- Adding learning rates: Suppose the underlying environment may change over time. Technically the standard Bayesian Bandit algorithm would self-update itself (awesome) by noting that what it thought was the best is starting to fail more often. We can motivate the algorithm to learn changing environments quicker by simply adding a *rate* term upon updating:\n", + "\n", + " self.wins[ choice ] = rate*self.wins[ choice ] + result\n", + " self.trials[ choice ] = rate*self.trials[ choice ] + 1\n", + "\n", + " If `rate < 1`, the algorithm will *forget* its previous wins quicker and there will be a downward pressure towards ignorance. Conversely, setting `rate > 1` implies your algorithm will act more risky, and bet on earlier winners more often and be more resistant to changing environments. \n", + "\n", + "- Hierarchical algorithms: We can setup a Bayesian Bandit algorithm on top of smaller bandit algorithms. Suppose we have $N$ Bayesian Bandit models, each varying in some behavior (for example different `rate` parameters, representing varying sensitivity to changing environments). On top of these $N$ models is another Bayesian Bandit learner that will select a sub-Bayesian Bandit. This chosen Bayesian Bandit will then make an internal choice as to which machine to pull. The super-Bayesian Bandit updates itself depending on whether the sub-Bayesian Bandit was correct or not. \n", + "\n", + "- Extending the rewards, denoted $y_a$ for bandit $a$, to random variables from a distribution $f_{y_a}(y)$ is straightforward. More generally, this problem can be rephrased as \"Find the bandit with the largest expected value\", as playing the bandit with the largest expected value is optimal. In the case above, $f_{y_a}$ was Bernoulli with probability $p_a$, hence the expected value for a bandit is equal to $p_a$, which is why it looks like we are aiming to maximize the probability of winning. If $f$ is not Bernoulli, and it is non-negative, which can be accomplished a priori by shifting the distribution (we assume we know $f$), then the algorithm behaves as before:\n", + "\n", + " For each round, \n", + " \n", + " 1. Sample a random variable $X_b$ from the prior of bandit $b$, for all $b$.\n", + " 2. Select the bandit with largest sample, i.e. select bandit $B = \\text{argmax}\\;\\; X_b$.\n", + " 3. Observe the result,$R \\sim f_{y_b}$, of pulling bandit $B$, and update your prior on bandit $B$.\n", + " 4. Return to 1\n", + "\n", + " The issue is in the sampling of the $X_b$ drawing phase. With Beta priors and Bernoulli observations, we have a Beta posterior — this is easy to sample from. But now, with arbitrary distributions $f$, we have a non-trivial posterior. Sampling from these can be difficult.\n", + "\n", + "- There has been some interest in extending the Bayesian Bandit algorithm to commenting systems. Recall in Chapter 4, we developed a ranking algorithm based on the Bayesian lower-bound of the proportion of upvotes to the total number of votes. One problem with this approach is that it will bias the top rankings towards older comments, since older comments naturally have more votes (and hence the lower-bound is tighter to the true proportion). This creates a positive feedback cycle where older comments gain more votes, hence are displayed more often, hence gain more votes, etc. This pushes any new, potentially better comments, towards the bottom. J. Neufeld proposes a system to remedy this that uses a Bayesian Bandit solution.\n", + "\n", + "His proposal is to consider each comment as a Bandit, with the number of pulls equal to the number of votes cast, and number of rewards as the number of upvotes, hence creating a $\\text{Beta}(1+U,1+D)$ posterior. As visitors visit the page, samples are drawn from each bandit/comment, but instead of displaying the comment with the $\\max$ sample, the comments are ranked according to the ranking of their respective samples. From J. Neufeld's blog [7]:\n", + "\n", + " > [The] resulting ranking algorithm is quite straightforward, each new time the comments page is loaded, the score for each comment is sampled from a $\\text{Beta}(1+U,1+D)$, comments are then ranked by this score in descending order... This randomization has a unique benefit in that even untouched comments $(U=0,D=0)$ have some chance of being seen even in threads with 5000+ comments (something that is not happening now), but, at the same time, the user is not likely to be inundated with rating these new comments. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Just for fun, though the colors explode, we watch the Bayesian Bandit algorithm learn 35 different options. " + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 0.0431 0.0745 0.1187 0.0098 0.0945 0.0438 0.0442 0.0059 0.0749\n", + " 0.023 0.0543 0.025 0.1231 0.0148 0.0164 0.2688 0.0073 0.0564\n", + " 0.0031 0.0698 0.0478 0.1657 0.0091 0.0384 0.2236 0.1548 0.0562\n", + " 0.0209 0.024 0.0197 0.0788 0.0572 0.1207 0.0405 0.0679]\n" + ] + }, + { + "data": { + "image/png": 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NnWAQ5M2pzLQ4WYNreS3e3U301jdRvGpWTqWTUygUmSd8cicA5slLht0f6vLR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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "figsize(12.0, 8)\n", + "beta = stats.beta\n", + "hidden_prob = beta.rvs(1, 13, size=35)\n", + "print(hidden_prob)\n", + "bandits = Bandits(hidden_prob)\n", + "bayesian_strat = BayesianStrategy(bandits)\n", + "\n", + "for j, i in enumerate([100, 200, 500, 1300]):\n", + " plt.subplot(2, 2, j + 1)\n", + " bayesian_strat.sample_bandits(i)\n", + " plot_priors(bayesian_strat, hidden_prob, lw=2, alpha=0.0, plt_vlines=False)\n", + " # plt.legend()\n", + " plt.xlim(0, 0.5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Eliciting expert prior\n", + "\n", + "Specifying a subjective prior is how practitioners incorporate domain knowledge about the problem into our mathematical framework. Allowing domain knowledge is useful for many reasons, for example:\n", + "\n", + "- Aids the speed of MCMC convergence. For example, if we know the unknown parameter is strictly positive, then we can restrict our attention there, hence saving time that would otherwise be spent exploring negative values.\n", + "- More accurate inference. By weighing prior values near the true unknown value higher, we are narrowing our eventual inference (by making the posterior tighter around the unknown) \n", + "- Express our uncertainty better. See the *Price is Right* problem in Chapter 5.\n", + "\n", + "Of course, practitioners of Bayesian methods are not experts in every field, so we must turn to domain experts to craft our priors. We must be careful with how we elicit these priors though. Some things to consider:\n", + "\n", + "1. From experience, I would avoid introducing Betas, Gammas, etc. to non-Bayesian practitioners. Furthermore, non-statisticians can get tripped up by how a continuous probability function can have a value exceeding one.\n", + "\n", + "2. Individuals often neglect the rare *tail-events* and put too much weight around the mean of distribution. \n", + "\n", + "3. Related to above is that almost always individuals will under-emphasize the uncertainty in their guesses.\n", + "\n", + "Eliciting priors from non-technical experts is especially difficult. Rather than introduce the notion of probability distributions, priors, etc. that may scare an expert, there is a much simpler solution. \n", + "\n", + "### Trial roulette method \n", + "\n", + "\n", + "The *trial roulette method* [8] focuses on building a prior distribution by placing counters (think casino chips) on what the expert thinks are possible outcomes. The expert is given $N$ counters (say $N=20$) and is asked to place them on a pre-printed grid, with bins representing intervals. Each column would represent their belief of the probability of getting the corresponding bin result. Each chip would represent an $\\frac{1}{N} = 0.05$ increase in the probability of the outcome being in that interval. For example [9]:\n", + "\n", + "> A student is asked to predict the mark in a future exam. The figure below shows a completed grid for the elicitation of a subjective probability distribution. The horizontal axis of the grid shows the possible bins (or mark intervals) that the student was asked to consider. The numbers in top row record the number of chips per bin. The completed grid (using a total of 20 chips) shows that the student believes there is a 30% chance that the mark will be between 60 and 64.9.\n", + "\n", + "\n", + "\n", + "\n", + "From this, we can fit a distribution that captures the expert's choice. Some reasons in favor of using this technique are:\n", + "\n", + "1. Many questions about the shape of the expert's subjective probability distribution can be answered without the need to pose a long series of questions to the expert - the statistician can simply read off the density above or below any given point, or that between any two points.\n", + "\n", + "2. During the elicitation process, the experts can move around the chips if unsatisfied with the way they placed them initially - thus they can be sure of the final result to be submitted.\n", + "\n", + "3. It forces the expert to be coherent in the set of probabilities that are provided. If all the chips are used, the probabilities must sum to one.\n", + "\n", + "4. Graphical methods seem to provide more accurate results, especially for participants with modest levels of statistical sophistication." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Example: Stock Returns\n", + "\n", + "\n", + "Take note stock brokers: you're doing it wrong. When choosing which stocks to pick, an analyst will often look at the *daily return* of the stock. Suppose $S_t$ is the price of the stock on day $t$, then the daily return on day $t$ is :\n", + "\n", + "$$r_t = \\frac{ S_t - S_{t-1} }{ S_{t-1} } $$\n", + "\n", + "The *expected daily return* of a stock is denoted $\\mu = E[ r_t ] $. Obviously, stocks with high expected returns are desirable. Unfortunately, stock returns are so filled with noise that it is very hard to estimate this parameter. Furthermore, the parameter might change over time (consider the rises and falls of AAPL stock), hence it is unwise to use a large historical dataset. \n", + "\n", + "Historically, the expected return has been estimated by using the sample mean. This is a bad idea. As mentioned, the sample mean of a small sized dataset has enormous potential to be very wrong (again, see Chapter 4 for full details). Thus Bayesian inference is the correct procedure here, since we are able to see our uncertainty along with probable values.\n", + "\n", + "For this exercise, we will be examining the daily returns of the AAPL, GOOG, TSLA and AMZN. Before we pull in the data, suppose we ask our a stock fund manager (an expert in finance, but see [10] ), \n", + "\n", + "> What do you think the return profile looks like for each of these companies?\n", + "\n", + "Our stock broker, without needing to know the language of Normal distributions, or priors, or variances, etc. creates four distributions using the trial roulette method above. Suppose they look enough like Normals, so we fit Normals to them. They may look like: " + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/camerondavidson-pilon/.virtualenvs/data/lib/python2.7/site-packages/matplotlib/axes/_subplots.py:69: MatplotlibDeprecationWarning: The use of 0 (which ends up being the _last_ sub-plot) is deprecated in 1.4 and will raise an error in 1.5\n", + " mplDeprecation)\n" + ] + }, + { + "data": { + "image/png": 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WxwiEvKRTebZteovdodSNk8qwFbQBoZSNIs+8QnZsAl/POlze5hgfWwv+jd3k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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "figsize(11., 5)\n", + "colors = [\"#348ABD\", \"#A60628\", \"#7A68A6\", \"#467821\"]\n", + "\n", + "normal = stats.norm\n", + "x = np.linspace(-0.15, 0.15, 100)\n", + "\n", + "expert_prior_params = {\"AAPL\": (0.05, 0.03),\n", + " \"GOOG\": (-0.03, 0.04),\n", + " \"TSLA\": (-0.02, 0.01),\n", + " \"AMZN\": (0.03, 0.02),\n", + " }\n", + "\n", + "for i, (name, params) in enumerate(expert_prior_params.items()):\n", + " plt.subplot(2, 2, i + 1)\n", + " y = normal.pdf(x, params[0], scale=params[1])\n", + " #plt.plot( x, y, c = colors[i] )\n", + " plt.fill_between(x, 0, y, color=colors[i], linewidth=2,\n", + " edgecolor=colors[i], alpha=0.6)\n", + " plt.title(name + \" prior\")\n", + " plt.vlines(0, 0, y.max(), \"k\", \"--\", linewidth=0.5)\n", + " plt.xlim(-0.15, 0.15)\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that these are subjective priors: the expert has a personal opinion on the stock returns of each of these companies, and is expressing them in a distribution. He's not wishful thinking -- he's introducing domain knowledge.\n", + "\n", + "In order to better model these returns, we should investigate the *covariance matrix* of the returns. For example, it would be unwise to invest in two stocks that are highly correlated, since they are likely to tank together (hence why fund managers suggest a diversification strategy). We will use the *Wishart distribution* for this, introduced earlier." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import pymc as pm\n", + "\n", + "n_observations = 100 # we will truncate the the most recent 100 days.\n", + "\n", + "prior_mu = np.array([x[0] for x in expert_prior_params.values()])\n", + "prior_std = np.array([x[1] for x in expert_prior_params.values()])\n", + "\n", + "inv_cov_matrix = pm.Wishart(\"inv_cov_matrix\", n_observations, np.diag(prior_std ** 2))\n", + "mu = pm.Normal(\"returns\", prior_mu, 1, size=4)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next we pull historical data for these stocks:" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# I wish I could have used Pandas as a prereq for this book, but oh well.\n", + "import datetime\n", + "import ystockquote as ysq\n", + "\n", + "stocks = [\"AAPL\", \"GOOG\", \"TSLA\", \"AMZN\"]\n", + "\n", + "enddate = \"2015-04-27\"\n", + "startdate = \"2012-09-01\"\n", + "\n", + "stock_closes = {}\n", + "stock_returns = {}\n", + "CLOSE = 6\n", + "\n", + "for stock in stocks:\n", + " x = np.array(ysq.get_historical_prices(stock, startdate, enddate))\n", + " stock_closes[stock] = x[1:, CLOSE].astype(float)\n", + "\n", + "# create returns:\n", + "\n", + "for stock in stocks:\n", + " _previous_day = np.roll(stock_closes[stock], -1)\n", + " stock_returns[stock] = ((stock_closes[stock] - _previous_day) / _previous_day)[:n_observations]\n", + "\n", + "dates = list(map(lambda x: datetime.datetime.strptime(x, \"%Y-%m-%d\"), x[1:n_observations + 1, 0]))" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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07pFSai5wJ9Dm7MLr1q1j9erVhIeHA+Dt7c3YsWOJioqySEMvNGVlZaSkpDSX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\n12jCuk2r+GHJLH5ZOZ/MrLyX+ZQtXY4D6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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "figsize(12.5, 4)\n", + "\n", + "for _stock, _returns in stock_returns.items():\n", + " p = plt.plot((1 + _returns)[::-1].cumprod() - 1, '-o', label=\"%s\" % _stock,\n", + " markersize=4, markeredgecolor=\"none\")\n", + "\n", + "plt.xticks(np.arange(100)[::-8],\n", + " list(map(lambda x: datetime.datetime.strftime(x, \"%Y-%m-%d\"), dates[::8])),\n", + " rotation=60);\n", + "\n", + "plt.legend(loc=\"upper left\")\n", + "plt.title(\"Return space\")\n", + "plt.ylabel(\"Return of $1 on first date, x100%\");" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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Krd9cTPFqDEQ2xRRrlaleyBPTeRZTrBoDkU0xxZqGgTYgrgMOBSYCS4CrUyuR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\nKU/aT1OdPn06AM3NzTQ1NQ36iaMyMDH1qY4pVo2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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "figsize(11., 5)\n", + "returns = np.zeros((n_observations, 4))\n", + "\n", + "for i, (_stock, _returns) in enumerate(stock_returns.items()):\n", + " returns[:, i] = _returns\n", + " plt.subplot(2, 2, i+1)\n", + " plt.hist(_returns, bins=20,\n", + " density=True, histtype=\"stepfilled\",\n", + " color=colors[i], alpha=0.7)\n", + " plt.title(_stock + \" returns\")\n", + " plt.xlim(-0.15, 0.15)\n", + "\n", + "plt.tight_layout()\n", + "plt.suptitle(\"Histogram of daily returns\", size=14);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Below we perform the inference on the posterior mean return and posterior covariance matrix. " + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " [-----------------100%-----------------] 150000 of 150000 complete in 195.3 sec" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/camerondavidson-pilon/.virtualenvs/data/lib/python2.7/site-packages/pymc/Model.py:93: UserWarning: The MCMC() syntax is deprecated. Please pass in nodes explicitly via M = MCMC(input).\n", + " 'The MCMC() syntax is deprecated. Please pass in nodes explicitly via M = MCMC(input).')\n" + ] + } + ], + "source": [ + "obs = pm.MvNormal(\"observed returns\", mu, inv_cov_matrix, observed=True, value=returns)\n", + "\n", + "model = pm.Model([obs, mu, inv_cov_matrix])\n", + "mcmc = pm.MCMC()\n", + "\n", + "mcmc.sample(150000, 100000, 3)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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dlUYHPJ2yYZPLY60Qj9uw7FNY2lGIFFt/FNto0ci6iIiIiEhIKWe9wCgPzS/F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\nM/sb4F3gKgDn3Btm9hDwBtAB3OSCDN2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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "figsize(12.5, 4)\n", + "\n", + "# examine the mean return first.\n", + "mu_samples = mcmc.trace(\"returns\")[:]\n", + "\n", + "for i in range(4):\n", + " plt.hist(mu_samples[:, i], alpha=0.8 - 0.05 * i, bins=30,\n", + " histtype=\"stepfilled\", density=True,\n", + " label=\"%s\" % list(stock_returns.keys())[i])\n", + "\n", + "plt.vlines(mu_samples.mean(axis=0), 0, 500, linestyle=\"--\", linewidth=.5)\n", + "\n", + "plt.title(\"Posterior distribution of $\\mu$, daily stock returns\")\n", + "plt.legend();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "(Plots like these are what inspired the book's cover.)\n", + "\n", + "What can we say about the results above? Clearly TSLA has been a strong performer, and our analysis suggests that it has an almost 1% daily return! Similarly, most of the distribution of AAPL is negative, suggesting that its *true daily return* is negative.\n", + "\n", + "\n", + "You may not have immediately noticed, but these variables are a whole order of magnitude *less* than our priors on them. For example, to put these one the same scale as the above prior distributions:" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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BX3/9dYYMGQJAv379GDFiBKNHJzObNTY2AvT6dG5bveSnUulp06ZlsvxUnvWRH5Vn98sv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\nYVD0/cC97t7lnQV3f42k4rg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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "figsize(11.0, 3)\n", + "for i in range(4):\n", + " plt.subplot(2, 2, i + 1)\n", + " plt.hist(mu_samples[:, i], alpha=0.8 - 0.05 * i, bins=30,\n", + " histtype=\"stepfilled\", density=True, color=colors[i],\n", + " label=\"%s\" % list(stock_returns.keys())[i])\n", + " plt.title(\"%s\" % list(stock_returns.keys())[i])\n", + " plt.xlim(-0.15, 0.15)\n", + "\n", + "plt.suptitle(\"Posterior distribution of daily stock returns\")\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Why did this occur? Recall how I mentioned that finance has a very very low signal to noise ratio. This implies an environment where inference is much more difficult. One should be careful about over-interpreting these results: notice (in the first figure) that each distribution is positive at 0, implying that the stock may return nothing. Furthermore, the subjective priors influenced the results. From the fund managers point of view, this is good as it reflects his updated beliefs about the stocks, whereas from a neutral viewpoint this can be too subjective of a result. \n", + "\n", + "Below we show the posterior correlation matrix, and posterior standard deviations. An important caveat to know is that the Wishart distribution models the *inverse covariance matrix*, so we must invert it to get the covariance matrix. We also normalize the matrix to acquire the *correlation matrix*. Since we cannot plot hundreds of matrices effectively, we settle by summarizing the posterior distribution of correlation matrices by showing the *mean posterior correlation matrix* (defined on line 2)." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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mNt/MDjCzsWZ2fsybY2Zz4v+3xu0HmtkMM1szKKfhtEyZ1tAuGq7b/KhMrHHy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\nx3FawDunKcjSapoXWVpN8yJLq2leFNk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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "inv_cov_samples = mcmc.trace(\"inv_cov_matrix\")[:]\n", + "mean_covariance_matrix = np.linalg.inv(inv_cov_samples.mean(axis=0))\n", + "\n", + "\n", + "def cov2corr(A):\n", + " \"\"\"\n", + " covariance matrix to correlation matrix.\n", + " \"\"\"\n", + " d = np.sqrt(A.diagonal())\n", + " A = ((A.T / d).T) / d\n", + " #A[ np.diag_indices(A.shape[0]) ] = np.ones( A.shape[0] )\n", + " return A\n", + "\n", + "\n", + "plt.subplot(1, 2, 1)\n", + "plt.imshow(cov2corr(mean_covariance_matrix), interpolation=\"none\",\n", + " cmap=plt.cm.hot)\n", + "plt.xticks(np.arange(4), stock_returns.keys())\n", + "plt.yticks(np.arange(4), stock_returns.keys())\n", + "plt.colorbar(orientation=\"vertical\")\n", + "plt.title(\"(mean posterior) Correlation Matrix\")\n", + "\n", + "plt.subplot(1, 2, 2)\n", + "plt.bar(np.arange(4), np.sqrt(np.diag(mean_covariance_matrix)),\n", + " color=\"#348ABD\", alpha=0.7)\n", + "plt.xticks(np.arange(4) + 0.5, stock_returns.keys());\n", + "plt.title(\"(mean posterior) variances of daily stock returns\")\n", + "\n", + "plt.tight_layout();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Looking at the above figures, we can say that it is likely that TSLA has an above-average volatility (looking at the return graph this is quite clear). The correlation matrix shows that there are no strong correlations present, but perhaps GOOG and AMZN express a higher correlation (about 0.30). \n", + "\n", + "With this Bayesian analysis of the stock market, we can throw it into a Mean-Variance optimizer (which I cannot stress enough to not use with frequentist point estimates) and find the minimum. This optimizer balances the tradeoff between a high return and high variance.\n", + "\n", + "$$ w_{opt} = \\max_{w} \\frac{1}{N}\\left( \\sum_{i=0}^N \\mu_i^T w - \\frac{\\lambda}{2}w^T\\Sigma_i w \\right)$$\n", + "\n", + "where $\\mu_i$ and $\\Sigma_i$ are the $i$th posterior estimate of the mean returns and the covariance matrix. This is another example of loss function optimization." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Protips for the Wishart distribution\n", + "\n", + "If you plan to be using the Wishart distribution, read on. Else, feel free to skip this. \n", + "\n", + "In the problem above, the Wishart distribution behaves pretty nicely. Unfortunately, this is rarely the case. The problem is that estimating an $NxN$ covariance matrix involves estimating $\\frac{1}{2}N(N-1)$ unknowns. This is a large number even for a modest $N$. Personally, I've tried performing a similar simulation as above with $N = 23$ stocks, and ended up giving considering that I was requesting my MCMC simulation to estimate at least $\\frac{1}{2}23*22 = 253$ additional unknowns (plus the other interesting unknowns in the problem). This is not easy for MCMC. Essentially, you are asking you MCMC to traverse a 250+ dimensional space. And the problem seemed so innocent initially! Below are some tips, in order of supremacy:\n", + "\n", + "1. Use conjugancy if it applies. See section below.\n", + "\n", + "2. Use a good starting value. What might be a good starting value? Why, the data's sample covariance matrix is! Note that this is not empirical Bayes: we are not touching the prior's parameters, we are modifying the starting value of the MCMC. Due to numerical instability, it is best to truncate the floats in the sample covariance matrix down a few degrees of precision (e.g. instability can cause unsymmetrical matrices, which can cause PyMC to cry.). \n", + "\n", + "3. Provide as much domain knowledge in the form of priors, if possible. I stress *if possible*. It is likely impossible to have an estimate about each $\\frac{1}{2}N(N-1)$ unknown. In this case, see number 4.\n", + "\n", + "4. Use empirical Bayes, i.e. use the sample covariance matrix as the prior's parameter.\n", + "\n", + "5. For problems where $N$ is very large, nothing is going to help. Instead, ask, do I really care about *every* correlation? Probably not. Furthermore ask yourself, do I really really care about correlations? Possibly not. In finance, we can set an informal hierarchy of what we might be interested in the most: first a good estimate of $\\mu$, the variances along the diagonal of the covariance matrix are secondly important, and finally the correlations are least important. So, it might be better to ignore the $\\frac{1}{2}(N-1)(N-2)$ correlations and instead focus on the more important unknowns.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Conjugate Priors\n", + "\n", + "Recall that a $\\text{Beta}$ prior with $\\text{Binomial}$ data implies a $\\text{Beta}$ posterior. Graphically:\n", + "\n", + "$$ \\underbrace{\\text{Beta}}_{\\text{prior}} \\cdot \\overbrace{\\text{Binomial}}^{\\text{data}} = \\overbrace{\\text{Beta}}^{\\text{posterior} } $$ \n", + "\n", + "Notice the $\\text{Beta}$ on both sides of this equation (no, you cannot cancel them, this is not a *real* equation). This is a really useful property. It allows us to avoid using MCMC, since the posterior is known in closed form. Hence inference and analytics are easy to derive. This shortcut was the heart of the Bayesian Bandit algorithm above. Fortunately, there is an entire family of distributions that have similar behaviour. \n", + "\n", + "Suppose $X$ comes from, or is believed to come from, a well-known distribution, call it $f_{\\alpha}$, where $\\alpha$ are possibly unknown parameters of $f$. $f$ could be a Normal distribution, or Binomial distribution, etc. For particular distributions $f_{\\alpha}$, there may exist a prior distribution $p_{\\beta}$, such that:\n", + "\n", + "$$ \\overbrace{p_{\\beta}}^{\\text{prior}} \\cdot \\overbrace{f_{\\alpha}(X)}^{\\text{data}} = \\overbrace{p_{\\beta'}}^{\\text{posterior} } $$ \n", + "\n", + "where $\\beta'$ is a different set of parameters *but $p$ is the same distribution as the prior*. A prior $p$ that satisfies this relationship is called a *conjugate prior*. As I mentioned, they are useful computationally, as we can avoided approximate inference using MCMC and go directly to the posterior. This sounds great, right?\n", + "\n", + "Unfortunately, not quite. There are a few issues with conjugate priors.\n", + "\n", + "1. The conjugate prior is not objective. Hence it is only useful when a subjective prior is required. It is not guaranteed that the conjugate prior can accommodate the practitioner's subjective opinion.\n", + "\n", + "2. There typically exist conjugate priors for simple, one dimensional problems. For larger problems, involving more complicated structures, hope is lost to find a conjugate prior. For smaller models, Wikipedia has a nice [table of conjugate priors](http://en.wikipedia.org/wiki/Conjugate_prior#Table_of_conjugate_distributions).\n", + "\n", + "Really, conjugate priors are only useful for their mathematical convenience: it is simple to go from prior to posterior. I personally see conjugate priors as only a neat mathematical trick, and offer little insight into the problem at hand. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Jefferys Priors\n", + "\n", + "Earlier, we talked about objective priors rarely being *objective*. Partly what we mean by this is that we want a prior that doesn't bias our posterior estimates. The flat prior seems like a reasonable choice as it assigns equal probability to all values. \n", + "\n", + "But the flat prior is not transformation invariant. What does this mean? Suppose we have a random variable $\\bf X$ from Bernoulli($\\theta$). We define the prior on $p(\\theta) = 1$. " + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "figsize(12.5, 5)\n", + "\n", + "x = np.linspace(0.000, 1, 150)\n", + "y = np.linspace(1.0, 1.0, 150)\n", + "lines = plt.plot(x, y, color=\"#A60628\", lw=3)\n", + "plt.fill_between(x, 0, y, alpha=0.2, color=lines[0].get_color())\n", + "plt.autoscale(tight=True)\n", + "plt.ylim(0, 2);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, let's transform $\\theta$ with the function $\\psi = log \\frac{\\theta}{1-\\theta}$. This is just a function to stretch $\\theta$ across the real line. Now how likely are different values of $\\psi$ under our transformation." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "figsize(12.5, 5)\n", + "\n", + "psi = np.linspace(-10, 10, 150)\n", + "y = np.exp(psi) / (1 + np.exp(psi)) ** 2\n", + "lines = plt.plot(psi, y, color=\"#A60628\", lw=3)\n", + "plt.fill_between(psi, 0, y, alpha=0.2, color=lines[0].get_color())\n", + "plt.autoscale(tight=True)\n", + "plt.ylim(0, 1);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Oh no! Our function is no longer flat. It turns out flat priors do carry information in them after all. The point of Jeffreys Priors is to create priors that don't accidentally become informative when you transform the variables you placed them originally on.\n", + "\n", + "Jeffreys Priors are defined as:\n", + "\n", + "$$p_J(\\theta) \\propto \\mathbf{I}(\\theta)^\\frac{1}{2}$$\n", + "$$\\mathbf{I}(\\theta) = - \\mathbb{E}\\bigg[\\frac{d^2 \\text{ log } p(X|\\theta)}{d\\theta^2}\\bigg]$$\n", + "\n", + "$\\mathbf{I}$ being the *Fisher information*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Effect of the prior as $N$ increases\n", + "\n", + "In the first chapter, I proposed that as the amount of our observations or data increases, the influence of the prior decreases. This is intuitive. After all, our prior is based on previous information, and eventually enough new information will shadow our previous information's value. The smothering of the prior by enough data is also helpful: if our prior is significantly wrong, then the self-correcting nature of the data will present to us a *less wrong*, and eventually *correct*, posterior. \n", + "\n", + "We can see this mathematically. First, recall Bayes Theorem from Chapter 1 that relates the prior to the posterior. The following is a sample from [What is the relationship between sample size and the influence of prior on posterior?](http://stats.stackexchange.com/questions/30387/what-is-the-relationship-between-sample-size-and-the-influence-of-prior-on-poste)[1] on CrossValidated.\n", + "\n", + ">The posterior distribution for a parameter $\\theta$, given a data set ${\\bf X}$ can be written as \n", + "\n", + "$$p(\\theta | {\\bf X}) \\propto \\underbrace{p({\\bf X} | \\theta)}_{{\\rm likelihood}} \\cdot \\overbrace{ p(\\theta) }^{ {\\rm prior} } $$\n", + "\n", + "\n", + "\n", + ">or, as is more commonly displayed on the log scale, \n", + "\n", + "$$ \\log( p(\\theta | {\\bf X}) ) = c + L(\\theta;{\\bf X}) + \\log(p(\\theta)) $$\n", + "\n", + ">The log-likelihood, $L(\\theta;{\\bf X}) = \\log \\left( p({\\bf X}|\\theta) \\right)$, **scales with the sample size**, since it is a function of the data, while the prior density does not. Therefore, as the sample size increases, the absolute value of $L(\\theta;{\\bf X})$ is getting larger while $\\log(p(\\theta))$ stays fixed (for a fixed value of $\\theta$), thus the sum $L(\\theta;{\\bf X}) + \\log(p(\\theta))$ becomes more heavily influenced by $L(\\theta;{\\bf X})$ as the sample size increases. \n", + "\n", + "There is an interesting consequence not immediately apparent. As the sample size increases, the chosen prior has less influence. Hence inference converges regardless of chosen prior, so long as the areas of non-zero probabilities are the same. \n", + "\n", + "Below we visualize this. We examine the convergence of two posteriors of a Binomial's parameter $\\theta$, one with a flat prior and the other with a biased prior towards 0. As the sample size increases, the posteriors, and hence the inference, converge." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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3UZmb32BdQzsfwob1w3TdUEzXDSU4sbukpQjRikjHW7iD1prs7GzMZvOVKwuP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hXfnzlJ4M6Rbs9vrfUYFmZgyK5t7xCfibnY+3EquDP317iG/25TS5zkJ0JJGRkTz66KOu9Isv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\nFlM4ldWandu38kblSWb/8BaX+PuXtKQl3f7p2uOMjAwAkpKSSEl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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "figsize(12.5, 15)\n", + "\n", + "p = 0.6\n", + "beta1_params = np.array([1., 1.])\n", + "beta2_params = np.array([2, 10])\n", + "beta = stats.beta\n", + "\n", + "x = np.linspace(0.00, 1, 125)\n", + "data = pm.rbernoulli(p, size=500)\n", + "\n", + "plt.figure()\n", + "for i, N in enumerate([0, 4, 8, 32, 64, 128, 500]):\n", + " s = data[:N].sum()\n", + " plt.subplot(8, 1, i + 1)\n", + " params1 = beta1_params + np.array([s, N - s])\n", + " params2 = beta2_params + np.array([s, N - s])\n", + " y1, y2 = beta.pdf(x, *params1), beta.pdf(x, *params2)\n", + " plt.plot(x, y1, label=r\"flat prior\", lw=3)\n", + " plt.plot(x, y2, label=\"biased prior\", lw=3)\n", + " plt.fill_between(x, 0, y1, color=\"#348ABD\", alpha=0.15)\n", + " plt.fill_between(x, 0, y2, color=\"#A60628\", alpha=0.15)\n", + " plt.legend(title=\"N=%d\" % N)\n", + " plt.vlines(p, 0.0, 7.5, linestyles=\"--\", linewidth=1)\n", + " #plt.ylim( 0, 10)#" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Keep in mind, not all posteriors will \"forget\" the prior this quickly. This example was just to show that *eventually* the prior is forgotten. The \"forgetfulness\" of the prior as we become awash in more and more data is the reason why Bayesian and Frequentist inference eventually converge as well." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Bayesian perspective of Penalized Linear Regressions\n", + "\n", + "There is a very interesting relationship between a penalized least-squares regression and Bayesian priors. A penalized linear regression is a optimization problem of the form:\n", + "\n", + "$$ \\text{argmin}_{\\beta} \\;\\; (Y - X\\beta)^T(Y - X\\beta) + f(\\beta)$$\n", + "\n", + "for some function $f$ (typically a norm like $|| \\cdot ||_p^p$). \n", + "\n", + "We will first describe the probabilistic interpretation of least-squares linear regression. Denote our response variable $Y$, and features are contained in the data matrix $X$. The standard linear model is:\n", + "\n", + "\\begin{equation}\n", + "Y = X\\beta + \\epsilon\n", + "\\end{equation}\n", + "\n", + "where $\\epsilon \\sim \\text{Normal}( {\\bf 0}, \\sigma{\\bf I })$. Simply, the observed $Y$ is a linear function of $X$ (with coefficients $\\beta$) plus some noise term. Our unknown to be determined is $\\beta$. We use the following property of Normal random variables:\n", + "\n", + "$$ \\mu' + \\text{Normal}( \\mu, \\sigma ) \\sim \\text{Normal}( \\mu' + \\mu , \\sigma ) $$\n", + "\n", + "to rewrite the above linear model as:\n", + "\n", + "\\begin{align}\n", + "& Y = X\\beta + \\text{Normal}( {\\bf 0}, \\sigma{\\bf I }) \\\\\\\\\n", + "& Y = \\text{Normal}( X\\beta , \\sigma{\\bf I }) \\\\\\\\\n", + "\\end{align}\n", + "\n", + "In probabilistic notation, denote $f_Y(y \\; | \\; \\beta )$ the probability distribution of $Y$, and recalling the density function for a Normal random variable (see [here](http://en.wikipedia.org/wiki/Normal_distribution) ):\n", + "\n", + "$$ f_Y( Y \\; |\\; \\beta, X) = L(\\beta|\\; X,Y)= \\frac{1}{\\sqrt{ 2\\pi\\sigma} } \\exp \\left( \\frac{1}{2\\sigma^2} (Y - X\\beta)^T(Y - X\\beta) \\right) $$\n", + "\n", + "This is the likelihood function for $\\beta$. Taking the $\\log$:\n", + "\n", + "$$ \\ell(\\beta) = K - c(Y - X\\beta)^T(Y - X\\beta) $$\n", + "\n", + "where $K$ and $c>0$ are constants. Maximum likelihood techniques wish to maximize this for $\\beta$, \n", + "\n", + "$$\\hat{ \\beta } = \\text{argmax}_{\\beta} \\;\\; - (Y - X\\beta)^T(Y - X\\beta) $$\n", + "\n", + "Equivalently we can *minimize the negative* of the above:\n", + "\n", + "$$\\hat{ \\beta } = \\text{argmin}_{\\beta} \\;\\; (Y - X\\beta)^T(Y - X\\beta) $$\n", + "\n", + "This is the familiar least-squares linear regression equation. Therefore we showed that the solution to a linear least-squares is the same as the maximum likelihood assuming Normal noise. Next we extend this to show how we can arrive at penalized linear regression by a suitable choice of prior on $\\beta$. \n", + "\n", + "#### Penalized least-squares\n", + "\n", + "In the above, once we have the likelihood, we can include a prior distribution on $\\beta$ to derive to the equation for the posterior distribution:\n", + "\n", + "$$P( \\beta | Y, X ) = L(\\beta|\\;X,Y)p( \\beta )$$\n", + "\n", + "where $p(\\beta)$ is a prior on the elements of $\\beta$. What are some interesting priors? \n", + "\n", + "1\\. If we include *no explicit* prior term, we are actually including an uninformative prior, $P( \\beta ) \\propto 1$, think of it as uniform over all numbers. \n", + "\n", + "2\\. If we have reason to believe the elements of $\\beta$ are not too large, we can suppose that *a priori*:\n", + "\n", + "$$ \\beta \\sim \\text{Normal}({\\bf 0 }, \\lambda {\\bf I } ) $$\n", + "\n", + "The resulting posterior density function for $\\beta$ is *proportional to*:\n", + "\n", + "$$ \\exp \\left( \\frac{1}{2\\sigma^2} (Y - X\\beta)^T(Y - X\\beta) \\right) \\exp \\left( \\frac{1}{2\\lambda^2} \\beta^T\\beta \\right) $$\n", + "\n", + "and taking the $\\log$ of this, and combining and redefining constants, we arrive at:\n", + "\n", + "$$ \\ell(\\beta) \\propto K - (Y - X\\beta)^T(Y - X\\beta) - \\alpha \\beta^T\\beta $$\n", + "\n", + "we arrive at the function we wish to maximize (recall the point that maximizes the posterior distribution is the MAP, or *maximum a posterior*):\n", + "\n", + "$$\\hat{ \\beta } = \\text{argmax}_{\\beta} \\;\\; -(Y - X\\beta)^T(Y - X\\beta) - \\alpha \\;\\beta^T\\beta $$\n", + "\n", + "Equivalently, we can minimize the negative of the above, and rewriting $\\beta^T \\beta = ||\\beta||_2^2$:\n", + "\n", + "$$\\hat{ \\beta } = \\text{argmin}_{\\beta} \\;\\; (Y - X\\beta)^T(Y - X\\beta) + \\alpha \\;||\\beta||_2^2$$\n", + "\n", + "This above term is exactly Ridge Regression. Thus we can see that ridge regression corresponds to the MAP of a linear model with Normal errors and a Normal prior on $\\beta$.\n", + "\n", + "3\\. Similarly, if we assume a *Laplace* prior on $\\beta$, ie. \n", + "\n", + "$$ f_\\beta( \\beta) \\propto \\exp \\left(- \\lambda ||\\beta||_1 \\right)$$\n", + "\n", + "and following the same steps as above, we recover:\n", + "\n", + "$$\\hat{ \\beta } = \\text{argmin}_{\\beta} \\;\\; (Y - X\\beta)^T(Y - X\\beta) + \\alpha \\;||\\beta||_1$$\n", + "\n", + "which is LASSO regression. Some important notes about this equivalence. The sparsity that is a result of using a LASSO regularization is not a result of the prior assigning high probability to sparsity. Quite the opposite actually. It is the combination of the $|| \\cdot ||_1$ function and using the MAP that creates sparsity on $\\beta$: [purely a geometric argument](http://camdp.com/blogs/least-squares-regression-l1-penalty). The prior does contribute to an overall shrinking of the coefficients towards 0 though. An interesting discussion of this can be found in [2].\n", + "\n", + "For an example of Bayesian linear regression, see Chapter 4's example on financial losses." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### References\n", + "\n", + "1. Macro, . \"What is the relationship between sample size and the influence of prior on posterior?.\" 13 Jun 2013. StackOverflow, Online Posting to Cross-Validated. Web. 25 Apr. 2013.\n", + "\n", + "2. Starck, J.-L., , et al. \"Sparsity and the Bayesian Perspective.\" Astronomy & Astrophysics. (2013): n. page. Print.\n", + "\n", + "3. Kuleshov, Volodymyr, and Doina Precup. \"Algorithms for the multi-armed bandit problem.\" Journal of Machine Learning Research. (2000): 1-49. Print.\n", + "\n", + "4. Gelman, Andrew. \"Prior distributions for variance parameters in hierarchical models.\" Bayesian Analysis. 1.3 (2006): 515-533. Print.\n", + "\n", + "5. Gelman, Andrew, and Cosma R. Shalizi. \"Philosophy and the practice of Bayesian statistics.\" British Journal of Mathematical and Statistical Psychology. (2012): n. page. Web. 17 Apr. 2013.\n", + "\n", + "6. http://jmlr.csail.mit.edu/proceedings/papers/v22/kaufmann12/kaufmann12.pdf\n", + "\n", + "7. James, Neufeld. \"Reddit's \"best\" comment scoring algorithm as a multi-armed bandit task.\" Simple ML Hacks. Blogger, 09 Apr 2013. Web. 25 Apr. 2013.\n", + "\n", + "8. Oakley, J. E., Daneshkhah, A. and O’Hagan, A. Nonparametric elicitation using the roulette method. Submitted to Bayesian Analysis.\n", + "\n", + "9. \"Eliciting priors from experts.\" 19 Jul 2010. StackOverflow, Online Posting to Cross-Validated. Web. 1 May. 2013. .\n", + "\n", + "10. Taleb, Nassim Nicholas (2007), The Black Swan: The Impact of the Highly Improbable, Random House, ISBN 978-1400063512" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.core.display import HTML\n", + "\n", + "\n", + "def css_styling():\n", + " styles = open(\"../styles/custom.css\", \"r\").read()\n", + " return HTML(styles)\n", + "css_styling()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Bayesian Rugby\n", + "Note: This submission comes from Peadar Coyle and is our first 'guest' example. \n", + "Peadar is known as @springcoil on Twitter and is an Irish Data Scientist with a Mathematical focus, he is currently based in Luxembourg. \n", + "I came across the following blog post on http://danielweitzenfeld.github.io/passtheroc/blog/2014/10/28/bayes-premier-league/ \n", + "I quote from him, about his realization about Premier League Football -\n", + "_It occurred to me that this problem is perfect for a Bayesian model. We want to infer the latent parameters (every team's strength) that are generating the data we observe (the scorelines). Moreover, we know that the scorelines are a noisy measurement of team strength, so ideally, we want a model that makes it easy to quantify our uncertainty about the underlying strengths.\n", + "\n", + "_So I googled 'Bayesian football' and found this paper, called 'Bayesian hierarchical model for the prediction of football results.' The authors (Gianluca Baio and Marta A. Blangiardo) being Italian, though, the 'football' here is soccer._\n", + "\n", + "_In this post, I'm going to reproduce the first model described in the paper using pymc. While they used Seria A in their paper, I'm going to use the 2013-2014 Premier League._\n", + "\n", + "Since I am a rugby fan I decide to apply the results of the paper [Bayesian Football](http://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=3&cad=rja&uact=8&ved=0CC8QFjAC&url=http%3A%2F%2Fwww.statistica.it%2Fgianluca%2FResearch%2FBaioBlangiardo.pdf&ei=0m3aVKK2KMm6UarSgYgM&usg=AFQjCNGiEg26H58zDiEIx3C7diUzfq3bJQ&sig2=yICsOBSJBniJNzlLW-H86g&bvm=bv.85464276,d.d24) to the Six Nations.\n", + "\n", + "## Acquiring the data\n", + "The first step was to acquire the data - which I created in a csv file from data I got on wikipedia and sports websites. To be honest a lot of this turned out to be manual entry. But this is fine for T=6 teams :) \n", + "\n", + "We largely follow the code of the website cited above, with only a few small changes. We do less wrangling because I personally curated the data. \n", + "\n", + "Remark: Here we use Pandas whereas we didn't use this before. " + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import os\n", + "import math\n", + "import warnings\n", + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import pymc # I know folks are switching to \"as pm\" but I'm just not there yet" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "DATA_DIR = os.path.join(os.getcwd(), 'data/')" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " team i\n", + "0 Wales 0\n", + "1 France 1\n", + "2 Ireland 2\n", + "3 Scotland 3\n", + "4 Italy 4" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data_file = DATA_DIR + 'results_2014.csv'\n", + "df = pd.read_csv(data_file, sep=',')\n", + "df.head()\n", + "teams = df.home_team.unique()\n", + "teams = pd.DataFrame(teams, columns=['team'])\n", + "teams['i'] = teams.index\n", + "teams.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we need to do some merging" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " home_team away_team home_score away_score i_home i_away\n", + "0 Wales Italy 23 15 0 4\n", + "1 France England 26 24 1 5\n", + "2 Ireland Scotland 28 6 2 3\n", + "3 Ireland Wales 26 3 2 0\n", + "4 Scotland England 0 20 3 5" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = pd.merge(df, teams, left_on='home_team', right_on='team', how='left')\n", + "df = df.rename(columns = {'i': 'i_home'}).drop('team', 1)\n", + "df = pd.merge(df, teams, left_on='away_team', right_on='team', how='left')\n", + "df = df.rename(columns = {'i': 'i_away'}).drop('team', 1)\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "observed_home_goals = df.home_score.values\n", + "observed_away_goals = df.away_score.values\n", + "home_team = df.i_home.values\n", + "away_team = df.i_away.values\n", + "num_teams = len(df.i_home.drop_duplicates())\n", + "num_games = len(home_team)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we need to prepare the model for PyMC" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "g = df.groupby('i_away')\n", + "att_starting_points = np.log(g.away_score.mean())\n", + "g = df.groupby('i_home')\n", + "def_starting_points = -np.log(g.away_score.mean())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# The model\n", + "The league is made up by a total of T= 6 teams, playing each other once \n", + "in a season. We indicate the number of points scored by the home and the away team in the g-th game of the season (15 games) as $y_{g1}$ and $y_{g2}$ respectively. \n", + "\n", + "The vector of observed counts $\\mathbb{y} = (y_{g1}, y_{g2})$ is modelled as independent Poisson:\n", + "$y_{gi}| \\theta_{gj} \\tilde\\;\\; Poisson(\\theta_{gj})$\n", + "where the theta parameters represent the scoring intensity in the g-th game for the team playing at home (j=1) and away (j=2), respectively.\n", + "\n", + "\n", + "We model these parameters according to a formulation that has been used widely in the statistical literature, assuming a log-linear random effect model:\n", + "$$log \\theta_{g1} = home + att_{h(g)} + def_{a(g)} $$\n", + "$$log \\theta_{g2} = att_{a(g)} + def_{h(g)}$$\n", + "the parameter home represents the advantage for the team hosting the game\n", + "and we assume that this effect is constant for all the teams and\n", + "throughout the season. In addition, the scoring intensity is \n", + "determined jointly by the attack\n", + "and defense ability of the two teams involved, represented by the parameters att and def, respectively.\n", + "In line with the Bayesian approach, we have to specify some suitable prior distributions for all the \n", + "random parameters in our model. The variable $home$ \n", + "is modelled as a fixed effect, assuming a standard \n", + "flat prior distribution. We use the notation of describing the Normal distribution in terms of mean \n", + "and the precision. \n", + "$home \\tilde\\; Normal(0,0.0001)$\n", + "\n", + "\n", + "Conversely, for each t = 1, ..., T, the team-specific effects are modelled as exchangeable from a common distribution:\n", + "$att_t \\tilde\\; Normal(\\mu_{att}, \\tau_{att})$\n", + "and $def_t \\tilde\\; Normal(\\mu_{def}, \\tau_{def})$\n", + "\n", + "Note that they're breaking out team strength into attacking and defending strength. A negative defense parameter will sap the mojo from the opposing team's attacking parameter.\n", + "\n", + "I made two tweaks to the model. It didn't make sense to me to have a $\\mu_{att}$ when we're enforcing the sum-to-zero constraint by subtracting the mean anyway. So I eliminated $\\mu_{att}$ and $\\mu_{def}$\n", + "\n", + "Also because of the sum-to-zero constraint, it seemed to me that we needed an intercept term in the log-linear model, capturing the average goals scored per game by the away team.\n", + "This we model with the following hyperprior.\n", + "$$intercept \\tilde\\; Normal(0, 0.001)$$\n" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " [-----------------100%-----------------] 200000 of 200000 complete in 87.0 sec" + ] + } + ], + "source": [ + "#hyperpriors\n", + "home = pymc.Normal('home', 0, .0001, value=0)\n", + "tau_att = pymc.Gamma('tau_att', .1, .1, value=10)\n", + "tau_def = pymc.Gamma('tau_def', .1, .1, value=10)\n", + "intercept = pymc.Normal('intercept', 0, .0001, value=0)\n", + "#team-specific parameters\n", + "atts_star = pymc.Normal(\"atts_star\", \n", + " mu=0, \n", + " tau=tau_att, \n", + " size=num_teams, \n", + " value=att_starting_points.values)\n", + "defs_star = pymc.Normal(\"defs_star\", \n", + " mu=0, \n", + " tau=tau_def, \n", + " size=num_teams, \n", + " value=def_starting_points.values) \n", + "# trick to code the sum to zero constraint\n", + "@pymc.deterministic\n", + "def atts(atts_star=atts_star):\n", + " atts = atts_star.copy()\n", + " atts = atts - np.mean(atts_star)\n", + " return atts\n", + "\n", + "@pymc.deterministic\n", + "def defs(defs_star=defs_star):\n", + " defs = defs_star.copy()\n", + " defs = defs - np.mean(defs_star)\n", + " return defs\n", + "\n", + "@pymc.deterministic\n", + "def home_theta(home_team=home_team, \n", + " away_team=away_team, \n", + " home=home, \n", + " atts=atts, \n", + " defs=defs, \n", + " intercept=intercept): \n", + " return np.exp(intercept + \n", + " home + \n", + " atts[home_team] + \n", + " defs[away_team])\n", + " \n", + "@pymc.deterministic\n", + "def away_theta(home_team=home_team, \n", + " away_team=away_team, \n", + " home=home, \n", + " atts=atts, \n", + " defs=defs, \n", + " intercept=intercept): \n", + " return np.exp(intercept + \n", + " atts[away_team] + \n", + " defs[home_team]) \n", + "\n", + "\n", + "home_points = pymc.Poisson('home_points', \n", + " mu=home_theta, \n", + " value=observed_home_goals, \n", + " observed=True)\n", + "away_points = pymc.Poisson('away_points', \n", + " mu=away_theta, \n", + " value=observed_away_goals, \n", + " observed=True)\n", + "\n", + "mcmc = pymc.MCMC([home, intercept, tau_att, tau_def, \n", + " home_theta, away_theta, \n", + " atts_star, defs_star, atts, defs, \n", + " home_points, away_points])\n", + "map_ = pymc.MAP( mcmc )\n", + "map_.fit()\n", + "mcmc.sample(200000, 40000, 20)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "# Diagnostics#\n", + "\n", + "Let's see if/how the model converged. The home parameter looks good, and indicates that home field advantage amounts to goals per game at the intercept.\n", + "\n", + "We can see that it converges just like the model for the Premier League in the other tutorial.\n", + "\n", + "I wonder and this is left as a question if all field sports have models of this form that converge.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Plotting home\n" + ] + }, + { + "data": { + "image/png": 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6D1bwuQiLNAGsqWMWLw43Kn4pqA0tbcYYY6JV8ADLmCiUys0CYdx1V7FLYIwx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\nDrNZncOxHJzyZvUfLKq7CE8DFqrqgcCxwN0i0ji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p8fErIsa8AZOBB4EDganAvcD8UW1O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\nzMwsY/8PnbeUZsdhMY4AAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pymc.Matplot.plot(home)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It looks like we have decent parameters and that the intercept was a good idea. I'm not so sure about how autocorrelated the Tau terms are but that is for me me to brush up on my Bayesian models. \n", + "\n", + "# Simulation#\n", + "Now we pull in some observed data (i.e. the table from last year) and include some remarks about Qualification" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "observed_season = DATA_DIR + 'table_2014.csv'\n", + "df_observed = pd.read_csv(observed_season, encoding = 'iso-8859-1')\n", + "df_observed.loc[df_observed.QR.isnull(), 'QR'] = ''" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "def simulate_season():\n", + " \"\"\"\n", + " Simulate a season once, using one random draw from the mcmc chain. \n", + " \"\"\"\n", + " num_samples = atts.trace().shape[0]\n", + " draw = np.random.randint(0, num_samples)\n", + " atts_draw = pd.DataFrame({'att': atts.trace()[draw, :],})\n", + " defs_draw = pd.DataFrame({'def': defs.trace()[draw, :],})\n", + " home_draw = home.trace()[draw]\n", + " intercept_draw = intercept.trace()[draw]\n", + " season = df.copy()\n", + " season = pd.merge(season, atts_draw, left_on='i_home', right_index=True)\n", + " season = pd.merge(season, defs_draw, left_on='i_home', right_index=True)\n", + " season = season.rename(columns = {'att': 'att_home', 'def': 'def_home'})\n", + " season = pd.merge(season, atts_draw, left_on='i_away', right_index=True)\n", + " season = pd.merge(season, defs_draw, left_on='i_away', right_index=True)\n", + " season = season.rename(columns = {'att': 'att_away', 'def': 'def_away'})\n", + " season['home'] = home_draw\n", + " season['intercept'] = intercept_draw\n", + " season['home_theta'] = season.apply(lambda x: math.exp(x['intercept'] + \n", + " x['home'] + \n", + " x['att_home'] + \n", + " x['def_away']), axis=1)\n", + " season['away_theta'] = season.apply(lambda x: math.exp(x['intercept'] + \n", + " x['att_away'] + \n", + " x['def_home']), axis=1)\n", + " season['home_goals'] = season.apply(lambda x: np.random.poisson(x['home_theta']), axis=1)\n", + " season['away_goals'] = season.apply(lambda x: np.random.poisson(x['away_theta']), axis=1)\n", + " season['home_outcome'] = season.apply(lambda x: 'win' if x['home_goals'] > x['away_goals'] else \n", + " 'loss' if x['home_goals'] < x['away_goals'] else 'draw', axis=1)\n", + " season['away_outcome'] = season.apply(lambda x: 'win' if x['home_goals'] < x['away_goals'] else \n", + " 'loss' if x['home_goals'] > x['away_goals'] else 'draw', axis=1)\n", + " season = season.join(pd.get_dummies(season.home_outcome, prefix='home'))\n", + " season = season.join(pd.get_dummies(season.away_outcome, prefix='away'))\n", + " return season\n", + "\n", + "\n", + "def create_season_table(season):\n", + " \"\"\"\n", + " Using a season dataframe output by simulate_season(), create a summary dataframe with wins, losses, goals for, etc.\n", + " \n", + " \"\"\"\n", + " g = season.groupby('i_home') \n", + " home = pd.DataFrame({'home_goals': g.home_goals.sum(),\n", + " 'home_goals_against': g.away_goals.sum(),\n", + " 'home_wins': g.home_win.sum(),\n", + " 'home_losses': g.home_loss.sum()\n", + " })\n", + " g = season.groupby('i_away') \n", + " away = pd.DataFrame({'away_goals': g.away_goals.sum(),\n", + " 'away_goals_against': g.home_goals.sum(),\n", + " 'away_wins': g.away_win.sum(),\n", + " 'away_losses': g.away_loss.sum()\n", + " })\n", + " df = home.join(away)\n", + " df['wins'] = df.home_wins + df.away_wins\n", + " df['losses'] = df.home_losses + df.away_losses\n", + " df['points'] = df.wins * 2\n", + " df['gf'] = df.home_goals + df.away_goals\n", + " df['ga'] = df.home_goals_against + df.away_goals_against\n", + " df['gd'] = df.gf - df.ga\n", + " df = pd.merge(teams, df, left_on='i', right_index=True)\n", + " df = df.sort_index(by='points', ascending=False)\n", + " df = df.reset_index()\n", + " df['position'] = df.index + 1\n", + " df['champion'] = (df.position == 1).astype(int)\n", + " df['relegated'] = (df.position > 5).astype(int)\n", + " return df \n", + " \n", + "def simulate_seasons(n=100):\n", + " dfs = []\n", + " for i in range(n):\n", + " s = simulate_season()\n", + " t = create_season_table(s)\n", + " t['iteration'] = i\n", + " dfs.append(t)\n", + " return pd.concat(dfs, ignore_index=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/maxwell/anaconda3/envs/bayes/lib/python3.5/site-packages/ipykernel/__main__.py:63: FutureWarning: by argument to sort_index is deprecated, pls use .sort_values(by=...)\n" + ] + } + ], + "source": [ + "simuls = simulate_seasons(10000)" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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Dc+4eA/AYAK7B4XlQVjNHcM8CfxgRrwLeBMyVdBQwH7g5Io4EVgALACQdA5wJ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\nwJmZWS05wJmZWS39f6x9fDYiTOXnAAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ax = simuls.points[simuls.team == 'Ireland'].hist(figsize=(7,5))\n", + "median = simuls.points[simuls.team == 'Ireland'].median()\n", + "ax.set_title('Ireland: 2013-14 points, 1000 simulations')\n", + "ax.plot([median, median], ax.get_ylim())\n", + "plt.annotate('Median: %s' % median, xy=(median + 1, ax.get_ylim()[1]-10));" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ax = simuls.gf[simuls.team == 'Ireland'].hist(figsize=(7,5))\n", + "median = simuls.gf[simuls.team == 'Ireland'].median()\n", + "ax.set_title('Ireland: 2013-14 scores for, 1000 simulations')\n", + "ax.plot([median, median], ax.get_ylim())\n", + "plt.annotate('Median: %s' % median, xy=(median + 1, ax.get_ylim()[1]-10));" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/maxwell/anaconda3/envs/bayes/lib/python3.5/site-packages/ipykernel/__main__.py:3: FutureWarning: by argument to sort_index is deprecated, pls use .sort_values(by=...)\n", + " app.launch_new_instance()\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "g = simuls.groupby('team')\n", + "df_champs = pd.DataFrame({'percent_champs': g.champion.mean()})\n", + "df_champs = df_champs.sort_index(by='percent_champs')\n", + "df_champs = df_champs[df_champs.percent_champs > .05]\n", + "df_champs = df_champs.reset_index()\n", + "\n", + "fig, ax = plt.subplots(figsize=(8,6))\n", + "ax.barh(df_champs.index.values, df_champs.percent_champs.values)\n", + "\n", + "for i, row in df_champs.iterrows():\n", + " label = \"{0:.1f}%\".format(100 * row['percent_champs'])\n", + " ax.annotate(label, xy=(row['percent_champs'], i), xytext = (3, 10), textcoords = 'offset points')\n", + "ax.set_ylabel('Team')\n", + "ax.set_title('% of Simulated Seasons In Which Team Finished Winners')\n", + "_= ax.set_yticks(df_champs.index + .5)\n", + "_= ax.set_yticklabels(df_champs['team'].values);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Unfortunately it seems that in most of the Universes England come top of the Six Nations. \n", + "And as an Irish man this is firm proof that I put Mathematical rigour before patriotism :) \n", + "\n", + "This is a reasonable result, and I hope it proved a nice example of Bayesian models in Rugby Analytics. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.0" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/Chapter6_Priorities/Ch6_Priors_PyMC3.ipynb b/Chapter6_Priorities/Ch6_Priors_PyMC3.ipynb new file mode 100644 index 00000000..1492c085 --- /dev/null +++ b/Chapter6_Priorities/Ch6_Priors_PyMC3.ipynb @@ -0,0 +1,1913 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Chapter 6\n", + "`Original content created by Cam Davidson-Pilon`\n", + "\n", + "`Ported to Python 3 and PyMC3 by Max Margenot (@clean_utensils) and Thomas Wiecki (@twiecki) at Quantopian (@quantopian)`\n", + "\n", + "\n", + "---\n", + "\n", + "This chapter of [Bayesian Methods for Hackers](https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers) focuses on the most debated and discussed part of Bayesian methodologies: how to choose an appropriate prior distribution. We also present how the prior's influence changes as our dataset increases, and an interesting relationship between priors and penalties on linear regression." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Getting our priorities straight\n", + "\n", + "\n", + "Up until now, we have mostly ignored our choice of priors. This is unfortunate as we can be very expressive with our priors, but we also must be careful about choosing them. This is especially true if we want to be objective, that is, not to express any personal beliefs in the priors. \n", + "\n", + "### Subjective vs Objective priors\n", + "\n", + "Bayesian priors can be classified into two classes: *objective* priors, which aim to allow the data to influence the posterior the most, and *subjective* priors, which allow the practitioner to express his or her views into the prior. \n", + "\n", + "What is an example of an objective prior? We have seen some already, including the *flat* prior, which is a uniform distribution over the entire possible range of the unknown. Using a flat prior implies that we give each possible value an equal weighting. Choosing this type of prior is invoking what is called \"The Principle of Indifference\", literally we have no prior reason to favor one value over another. Calling a flat prior over a restricted space an objective prior is not correct, though it seems similar. If we know $p$ in a Binomial model is greater than 0.5, then $\\text{Uniform}(0.5,1)$ is not an objective prior (since we have used prior knowledge) even though it is \"flat\" over [0.5, 1]. The flat prior must be flat along the *entire* range of possibilities. \n", + "\n", + "Aside from the flat prior, other examples of objective priors are less obvious, but they contain important characteristics that reflect objectivity. For now, it should be said that *rarely* is a objective prior *truly* objective. We will see this later. \n", + "\n", + "#### Subjective Priors\n", + "\n", + "On the other hand, if we added more probability mass to certain areas of the prior, and less elsewhere, we are biasing our inference towards the unknowns existing in the former area. This is known as a subjective, or *informative* prior. In the figure below, the subjective prior reflects a belief that the unknown likely lives around 0.5, and not around the extremes. The objective prior is insensitive to this." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "image/png": 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ja9GRm5cBqO1RMtu0An47fxq2byd4536OvfoBJ/xKcX+TOnQZNZwS9R5gl7n4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\nol1TPv30U9566y2OHz8OQEFBAWfOnHFsL0vUAXx9fQHIz8/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MnwVKqWSlVKJS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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib inline\n", + "import numpy as np\n", + "import scipy.stats as stats\n", + "from IPython.core.pylabtools import figsize\n", + "import matplotlib.pyplot as plt\n", + "\n", + "figsize(12.5,3)\n", + "colors = [\"#348ABD\", \"#A60628\", \"#7A68A6\", \"#467821\"]\n", + "\n", + "x = np.linspace(0,1)\n", + "y1, y2 = stats.beta.pdf(x, 1,1), stats.beta.pdf(x, 10,10)\n", + "\n", + "p = plt.plot(x, y1, \n", + " label='An objective prior \\n(uninformative, \\n\"Principle of Indifference\")')\n", + "plt.fill_between(x, 0, y1, color = p[0].get_color(), alpha = 0.3)\n", + "\n", + "p = plt.plot(x,y2 ,\n", + " label = \"A subjective prior \\n(informative)\")\n", + "plt.fill_between(x, 0, y2, color = p[0].get_color(), alpha = 0.3)\n", + "\n", + "p = plt.plot(x[25:], 2*np.ones(25), label = \"another subjective prior\")\n", + "plt.fill_between(x[25:], 0, 2, color = p[0].get_color(), alpha = 0.3)\n", + "\n", + "plt.ylim(0,4)\n", + "\n", + "plt.ylim(0, 4)\n", + "leg = plt.legend(loc = \"upper left\")\n", + "leg.get_frame().set_alpha(0.4)\n", + "plt.title(\"Comparing objective vs. subjective priors for an unknown probability\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The choice of a subjective prior does not always imply that we are using the practitioner's subjective opinion: more often the subjective prior was once a posterior to a previous problem, and now the practitioner is updating this posterior with new data. A subjective prior can also be used to inject *domain knowledge* of the problem into the model. We will see examples of these two situations later." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Decision, decisions...\n", + "\n", + "The choice, either *objective* or *subjective* mostly depends on the problem being solved, but there are a few cases where one is preferred over the other. In instances of scientific research, the choice of an objective prior is obvious. This eliminates any biases in the results, and two researchers who might have differing prior opinions would feel an objective prior is fair. Consider a more extreme situation:\n", + "\n", + "> A tobacco company publishes a report with a Bayesian methodology that retreated 60 years of medical research on tobacco use. Would you believe the results? Unlikely. The researchers probably chose a subjective prior that too strongly biased results in their favor.\n", + "\n", + "Unfortunately, choosing an objective prior is not as simple as selecting a flat prior, and even today the problem is still not completely solved. The problem with naively choosing the uniform prior is that pathological issues can arise. Some of these issues are pedantic, but we delay more serious issues to the Appendix of this Chapter (TODO)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We must remember that choosing a prior, whether subjective or objective, is still part of the modeling process. To quote Gelman [5]:\n", + "\n", + ">... after the model has been fit, one should look at the posterior distribution\n", + "and see if it makes sense. If the posterior distribution does not make sense, this implies\n", + "that additional prior knowledge is available that has not been included in the model,\n", + "and that contradicts the assumptions of the prior distribution that has been used. It is\n", + "then appropriate to go back and alter the prior distribution to be more consistent with\n", + "this external knowledge.\n", + "\n", + "If the posterior does not make sense, then clearly one had an idea what the posterior *should* look like (not what one *hopes* it looks like), implying that the current prior does not contain all the prior information and should be updated. At this point, we can discard the current prior and choose a more reflective one.\n", + "\n", + "Gelman [4] suggests that using a uniform distribution with large bounds is often a good choice for objective priors. Although, one should be wary about using Uniform objective priors with large bounds, as they can assign too large of a prior probability to non-intuitive points. Ask yourself: do you really think the unknown could be incredibly large? Often quantities are naturally biased towards 0. A Normal random variable with large variance (small precision) might be a better choice, or an Exponential with a fat tail in the strictly positive (or negative) case. \n", + "\n", + "If using a particularly subjective prior, it is your responsibility to be able to explain the choice of that prior, else you are no better than the tobacco company's guilty parties. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Empirical Bayes\n", + "\n", + "While not a true Bayesian method, *empirical Bayes* is a trick that combines frequentist and Bayesian inference. As mentioned previously, for (almost) every inference problem there is a Bayesian method and a frequentist method. The significant difference between the two is that Bayesian methods have a prior distribution, with hyperparameters $\\alpha$, while empirical methods do not have any notion of a prior. Empirical Bayes combines the two methods by using frequentist methods to select $\\alpha$, and then proceeds with Bayesian methods on the original problem. \n", + "\n", + "A very simple example follows: suppose we wish to estimate the parameter $\\mu$ of a Normal distribution, with $\\sigma = 5$. Since $\\mu$ could range over the whole real line, we can use a Normal distribution as a prior for $\\mu$. How to select the prior's hyperparameters, denoted ($\\mu_p, \\sigma_p^2$)? The $\\sigma_p^2$ parameter can be chosen to reflect the uncertainty we have. For $\\mu_p$, we have two options:\n", + "\n", + "1. Empirical Bayes suggests using the empirical sample mean, which will center the prior around the observed empirical mean:\n", + "\n", + "$$ \\mu_p = \\frac{1}{N} \\sum_{i=0}^N X_i $$\n", + "\n", + "2. Traditional Bayesian inference suggests using prior knowledge, or a more objective prior (zero mean and fat standard deviation).\n", + "\n", + "Empirical Bayes can be argued as being semi-objective, since while the choice of prior model is ours (hence subjective), the parameters are solely determined by the data.\n", + "\n", + "Personally, I feel that Empirical Bayes is *double-counting* the data. That is, we are using the data twice: once in the prior, which will influence our results towards the observed data, and again in the inferential engine of MCMC. This double-counting will understate our true uncertainty. To minimize this double-counting, I would only suggest using Empirical Bayes when you have *lots* of observations, else the prior will have too strong of an influence. I would also recommend, if possible, to maintain high uncertainty (either by setting a large $\\sigma_p^2$ or equivalent.)\n", + "\n", + "Empirical Bayes also violates a theoretical axiom in Bayesian inference. The textbook Bayesian algorithm of:\n", + "\n", + ">*prior* $\\Rightarrow$ *observed data* $\\Rightarrow$ *posterior* \n", + "\n", + "is violated by Empirical Bayes, which instead uses \n", + "\n", + ">*observed data* $\\Rightarrow$ *prior* $\\Rightarrow$ *observed data* $\\Rightarrow$ *posterior*\n", + "\n", + "Ideally, all priors should be specified *before* we observe the data, so that the data does not influence our prior opinions (see the volumes of research by Daniel Kahneman *et. al* about [anchoring](http://en.wikipedia.org/wiki/Anchoring_and_adjustment))." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Useful priors to know about\n", + "\n", + "### The Gamma distribution\n", + "\n", + "A Gamma random variable, denoted $X \\sim \\text{Gamma}(\\alpha, \\beta)$, is a random variable over the positive real numbers. It is in fact a generalization of the Exponential random variable, that is:\n", + "\n", + "$$ \\text{Exp}(\\beta) \\sim \\text{Gamma}(1, \\beta) $$\n", + "\n", + "This additional parameter allows the probability density function to have more flexibility, hence allowing the practitioner to express his or her subjective priors more accurately. The density function for a $\\text{Gamma}(\\alpha, \\beta)$ random variable is:\n", + "\n", + "$$ f(x \\mid \\alpha, \\beta) = \\frac{\\beta^{\\alpha}x^{\\alpha-1}e^{-\\beta x}}{\\Gamma(\\alpha)} $$\n", + "\n", + "where $\\Gamma(\\alpha)$ is the [Gamma function](http://en.wikipedia.org/wiki/Gamma_function), and for differing values of $(\\alpha, \\beta)$ looks like:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "image/png": 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s2YiNjUVYWBieeuop+VhJSUnyEoWlpaW4++67ERYWhvHjx6OoqAgpKSnQarUA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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "figsize(12.5, 5)\n", + "gamma = stats.gamma\n", + "\n", + "parameters = [(1, 0.5), (9, 2), (3, 0.5), (7, 0.5)]\n", + "x = np.linspace(0.001 ,20, 150)\n", + "for alpha, beta in parameters:\n", + " y = gamma.pdf(x, alpha, scale=1./beta)\n", + " lines = plt.plot(x, y, label = \"(%.1f,%.1f)\"%(alpha,beta), lw = 3)\n", + " plt.fill_between(x, 0, y, alpha = 0.2, color = lines[0].get_color())\n", + " plt.autoscale(tight=True)\n", + " \n", + "plt.legend(title=r\"$\\alpha, \\beta$ - parameters\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### The Wishart distribution\n", + "\n", + "Until now, we have only seen random variables that are scalars. Of course, we can also have *random matrices*! Specifically, the Wishart distribution is a distribution over all [positive semi-definite matrices](http://en.wikipedia.org/wiki/Positive-definite_matrix). Why is this useful to have in our arsenal? (Proper) covariance matrices are positive-definite, hence the Wishart is an appropriate prior for covariance matrices. We can't really visualize a distribution of matrices, so I'll plot some realizations from the $5 \\times 5$ (above) and $20 \\times 20$ (below) Wishart distribution:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "n = 4\n", + "for i in range(10):\n", + " ax = plt.subplot(2, 5, i+1)\n", + " if i >= 5:\n", + " n = 15\n", + " plt.imshow(stats.wishart.rvs(n+1, np.eye(n)), interpolation=\"none\", \n", + " cmap = \"hot\")\n", + " ax.axis(\"off\")\n", + " \n", + "plt.suptitle(\"Random matrices from a Wishart Distribution\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "One thing to notice is the symmetry of these matrices. The Wishart distribution can be a little troubling to deal with, but we will use it in an example later." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### The Beta distribution\n", + "\n", + "You may have seen the term `beta` in previous code in this book. Often, I was implementing a Beta distribution. The Beta distribution is very useful in Bayesian statistics. A random variable $X$ has a $\\text{Beta}$ distribution, with parameters $(\\alpha, \\beta)$, if its density function is:\n", + "\n", + "$$f_X(x | \\; \\alpha, \\beta ) = \\frac{ x^{(\\alpha - 1)}(1-x)^{ (\\beta - 1) } }{B(\\alpha, \\beta) }$$\n", + "\n", + "where $B$ is the [Beta function](http://en.wikipedia.org/wiki/Beta_function) (hence the name). The random variable $X$ is only allowed in [0,1], making the Beta distribution a popular distribution for decimal values, probabilities and proportions. The values of $\\alpha$ and $\\beta$, both positive values, provide great flexibility in the shape of the distribution. Below we plot some distributions:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "image/png": 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Lt0TCU3saV2nonLR8/tAVV413BzbFZCTh2tEYTMaA3E8SaSGEEEKIc9zVJ2k4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vSwh7oDXv2Yx3BP1b1QwoHoXhhz1L1wpZBCYLd2UejcRLF7C/mebQ/WG75P4v5t0n3XSfdfJzF8\nd3W/IReSI3/3CnosFMCXvr6zIdc9zG2Rnq+Q3kEopB+Zrc93v/tdmc0t3Pc3kJLf6ZPcjCbibDUD\n/kFrMGMC8mrHtn1yOZdczmV83MV1H/z3tyyNlhazsmWzJqa5eiJBvHPuDI8eWX2+rq5v84O+v+G1\nu99GMuXK0WJ18vSmH6MntXUFR7f2OX/hEocPHVjpYawIpUDjfCHF2UKGEW/mJOu4FnAiU+SJ1iKb\nrPoz6tcr77x3gUcfObTSw6iLKIF5X2AMgubNfLgFMYnXIfFagYU8TqUkdeE/EL/zYqUqv/dncTsf\nXfigp49RBrw+8R4vjr2BK6euvRYjw0/3fIqjmf2r1rf/9YuX+MDB5r/nSAmTNgxNagxNCoYmNUby\nYkZkoDp7krUCutI+3WmfrpSPscambMWu/Dv0kbcBsPf+Mt72n2rIccc3aQt37RBC/DnwHNAhhLgF\n/G9Syj9uyMiqeGmciojWkfxYZu2JaADL0unu1unujiOlxLYDxsddJiY8xsddbHtmgHPbDrh/3+b+\n/anp/omETiZjRJtJNmsQj+ur9gbWbFwePcO3b/45OWe4UqcJnWNdT3O08yl0bY3dfRRNRVwLOJme\n4ERqgtuOxdl8hiulJEH0ar4UaLySS/FKLsXWuMMTLUWOZUrEtVVsTVqrBKCPRgJ6fOb9WSLxM+C1\nS4I0D+vCXEPi+n+qEdGlTc81VEQDaELjyewxDiZ38rXhH3C1dAuAnDfBH/X9JUfSe/mpnk/REZsZ\nXk/x8PgBjBUFI3nBaF4wUtAYzQsc/8EXihCStkRAZ8qnI+nTkfKJrfFHl99yuCKkjeE3Gyak56Kh\nKxsu1CI94kn++S2JHQ3l2ZTPs6n1+UBwnICJCZfJSY/JSY983iOYqa3rYhiCTMYgnTZIpcI0nTaI\nxTQlsOdJzh7m273/kcujtTPVe5LbeHrzj9Jizf2qTKFYKgq+xvlimnfzaUb9mVbqmAg4nilxqqXI\n9ri7Jg0RqwmtAMagwBwCUc/6bEi8donfBrIBkV2tvu+TPveFStnpeJTC3s/MjCvdQKSUnC9c45sj\nPyQfFCv1pjD4ZOeHeL7jSQyxxpVbgwgkTJQEuaJgrBBtxXCTcn7/w7gR0JYMaE/4tCd92pMB622d\nOGGPED/zTwCQwqDw3JdATyz6uHNZpFdcSEsp+cN7knejCaEduuQftfsY6iEAhH+fYtGvEdaFgs/D\n/NtMU1REdTIZiuxUSice19GaJQbkCuMHHm/c+w4/6PsKbjA1I97SEzy+4aPsbT2mfowomgIp4bZj\n8W4hw5ViEr+OCbM75nEqW+BktkTGmOcvccXi8cAYBnNQoOfrW5+DdGh99jMsyvpcjTl4hsw7/8fU\n8t/ZPeQP/kPQlmdhsKJv852x13h78kJN/YZYFz+z4VPsTe1YlnGsBlx/SjCPVwnnXFEQzFMwA5i6\npC3h05YIaEuGacJcnwbI6Vjv/hZasR+A0vHP4XeeWvQxm1pIvzMp+fcDU2P4xVaP7Wr9kjkJglBc\nFwp+JKw98nkf33+4/6UQkEzqpFKhwE4mdZJJnUSisSK72X2kb09c4Rs3/ozBYl9N/d7WYzy+4aPE\njeQKjWx9s559pOdLwde4UEzxXiHNsDfzxqkhOZS2eTxb5EDKZgVW7l12lt1HWoI+HlqfjVEQdfxU\nAyO0PHttEtng55ueu07Lm59D+KHrn5fcyOTh/waMeGNPNA/u2Pf4z8MvMeAO19SfajnKT3Z/jIyR\nWvYxPQyN8pF2fZgsCSZswUQpFMzjkXAuLGBCZioW0BIPaIn7tCbCfMKU6q3TLBi9f4V57wUA3K0/\ngbP/Vxd9zEX5SC8lBV/yl0NT4u9E3Fcieh5omoisygZdXeGMVCkljhNQKPgUi34ktD2KRX9W1xAp\nIZ/3yed9oHa5NSEgHtdniOtEItzWgrvIpJvjxVtf4t2hl2vqW60untr0o2oyoaLpSeoBj6UnOJma\n4K4b471CmkvFFK4M3+cGCM5Nxjk3GSet+5zIlng8W2SjmqC4aEQBzCGBMQSaW8f6LCLf57bF+z7P\nhla4T/btf10R0X6sNbREr4CIBthibeCXN/4Ub0y8x4tjb+JIF4A3cu9ybuIyP9r1YZ5uO4kmVre/\ngR9A3oG8LZi0BflIMJeFc6nO9TAfLCMgGw/IWrXpOlvDbdEErYchEtL68OklP9+KWqT/42DAD8bD\nfEpIfrXDJ766v19Nx3SBXSqFW7HozytiyGxoGhVhHY+XN60mbxjN+c/0A4+3Bl7kpb6vYPtTfn2G\nMHm051kOd5xCU359ilWKEwjeLyU5V0jT59QXVJstl8ezRR7NFknp6nXwvHHBHAZjqL7rBkBgSby2\nKPLGEpqqhDNOy+v/DL1wLzyvnmDyyK8QJJtjxcFxb5Jvjb7MhcL1mvot1gZ+esMn2ZXctkIjmxsp\noeRCwRHkHUHejvJ2WJ60BQUHFvrLSCBJxSRpKyBjBaStUDBn4sGanwi4bAQu8bf+W0QQ/pArfPBP\nkcnFLc7SlK4dV4uS3+2fOvdPZX0OxdUNfTnxfVkjrkulANsO84sR2WUMQ2BZoai2LA3L0rGsUGxX\nl5fTT/tm7hLf7v3/GIz8p8psy+zniY2fIB1rWbaxKBRLzYhncK6Q5kIhxWQwU9XpSA6mbE5mixxM\n2TTpb9+VxQdjNBLPORB1BJTUJV5L5LoRZ0msz9UIZ4Ls6X+BMXEzPL8wmDz0y/jZHUt74gVwtXiL\nr4/8gFFvvKb+8Zaj/ETXR2gxM8syDilDl4uiKyg6oTguOIJCJJqL5bLDQ/kq10MISdKUpGMBqVhQ\nI5yTMYmamrT0xN7/N+hj5wCwD/xjvC0/vqjjNZ2Q9qXkt+9I+qM5XXtjAZ9pWZvh7lYrvi8jUR2K\na9uuToOH8sfu7bvA9s2z+yyGgjsU1rFYKLqr01hMj1INfYFOnuP2CN+99ZdcGHmzpj4ba+eJjZ9k\nS2b3go6rWDqUj3TjCCT02nHOFdJcLdWfoJjQwqgfJ7OrO+pHQ3ykA9BzYAzP7vdcdt3wWxs7cfBB\nCDtH9vRvY0zeDseBoLDv53E7Zl3mYcXxpMcr42f5Qe5tvKrY03Etxqc6n+XZ9lPoC3gLWC2OSy6U\nXEExSsv5oiMq4tl/gEB+0LOq6szEjdCynIwFJM0wTcVC8az8l1ce/d73iPX+BQBe55PYx//Zoo7X\ndD7SPxynIqJNJD+yRmNGr2Z0XUQTEOu3e16A4wQVYe04AY7jV+WDeUcW8TyJ55V9tR88rrKojsU0\nTFNEqTYjNU0BusebAy/wSv/XaqJxGMLkePczHO54QsWEVqx5NAE74yV2xkuUAo1LxdD14547tepX\nMdB4NZfk1VySDtPjRKbEiWyRrtiDv5drgvKkwWGBMQJilji9fkLit0m8LMv+BBX2KNk3fxsjH06M\nlgiKu3+qqUU0gCEMPtRykmOpfXx79JWKu0cpcPjy/Rd4dewd/m73x9mb3IPtge0JHA9sV1CqpKFY\ntr0p0Wx7i7ce18PUJAkzFMSJmCRZzpuSVCwgYUg09famqQlaD0NvmNdHz0DggtaAOJN1WHaL9KQv\n+a1bkkI0Ae75lM/T6zRm9FpGSonnyYqodt0pgV1d1wgXkrrnRzJqvsedxLdwtLGath7xCPtjHydl\nZtENiW6AYUg0g7CsU6lXP/AUa5kRz+BCIcWFYppxv74q3GK5nMiGC760rLVQemXxPBKJ5zrxniHy\ne26V+C00POrGfNFKI2Tf/Dx64S4QWaL3fBq368TKDKgOUoZvP1xfw/U1vCh1fVGpc32NPreXs/Lb\n5EVtdI8Wdx9bij9CIuhakvHpQhI3JZYRCWUjLMeNUCTHzYC4IdXkvrWAlFhn/xc0ewiA4ol/TdB+\nbMGHayqL9NdGpkR0qyZ5IqlE9FpECIFpCkxTIzVHxKNqwV0W1mE6s+x5cl5W7rx+h1uJr5E3btXU\nJ/wethV+nIy/kwJQmMfn0PRQWGtVAlvTQdcj4a2HgrtSV9Wnsq+uBLmiOWk3PJ7O5ngqk6PPsThf\nTHG5mMKWU+a2O7bJnUGTrw5m2J1weDRb4pF0ieRqnaQYgD4Rimd9pP5S3QCBGQpnrzXye15BtOJQ\nKKKLAwBINAp7P4Pb2ZiwomUB7Acani/w5khdX4QCuVwXCeWwjzZPC/ER9nOA+9Zr9Me/SyDCt4U5\n8zI54yrdzgfYVHoeQz449KiuSSw9FMdxcyoflkNhHI/KSiCvI4QgaDmMdv/7QBi9YzFCes5TLadF\nus+WfP6OpHzGn2nxOWCt0puxYt68d/Esjxxc/AUspcT3Ja4r8bxgRjrhjHHJ/xr3eLtmPyNIsqn0\nUbqcxxCszJ1UaGVRXRbZoGlTQrtSr5XTqE6ratem9hNRHyHWrkhXPtIrgyfhWinJxWKKG6VEXX9q\nHcm+lM3xTInDKZt4E4nquj7SAehjYIxGPs+zuG0EhsTPhn7PQYJl83ueC61wn+zpz6MXBwGQQmN8\n189TaD2GFwj8yqbhB2JGXbns+dPKgYZfJZLlCn1YV0zQF/8OQ7G3QExdR6ZMsF8+xz7tMRKmhmVI\nYpFIjhmhYI4ZsqETZE9ffp/H9u1v3AEVK4o2egbr8r8FwE/vovTEv1vwsZrCIi2l5IvDUyJ6hxmw\nP9Y8N19F8yOEwDAEhgFUCWLHL/HOyPd4p/QCHlN+0AKNvZnH2J95CkMm8D2bwBcEvsD3IfDKeRHV\nU2kvlxv1JJWBwAuABcYXnePIFeEttEhsa0RCW0b10/NTfeuVy/2FFvrWinK7mGpfywJ+vWMI2J8o\nsD9RoBRoXCkmuVhMcsuZCkfhI7iYj3MxH8cQkgORqD6YsrG0Jrmve2BE4lkfqz9hEKrEc4skSDKv\nr7yUEATh5lflpQxjDAdVm/+A/IzUn8rH3Xs8nfs8ugxdIHwMfmD+On13T8LdBv6tGohAYugBhhZt\nelApm5pfKZtaVb3+GKNyGy/lX+W2G34wVxQ5J77BPeN1Ppn9MLsTe1f92gWK5SXIHkAKHSF99Mnr\nCHsYaXU0/DzLZpE+Myn5QrSCoSBcBrx7RZeDUax2fOlzfuwHvDn0NYr+RE3bpsRejrU/T8ZsX/Dx\npQQZUCOsA18QBLPnpQ++L6b2C8J8U5i2GowQckpUa2FZqymHArwsuqsF+Kz1YmY90+tFVb0W/mWF\nkFG/6n2q8sia+rCuNl9pU9Rl0te5VAwt1QNVkxSrMYXkYKrE0YzNwZRNLLIwSlm7IUESfr/qpnJa\nnZxyP6i0VddFZd2BtA2ZkiDl1g9VB2AjGREwJCS5quMGwdTxgqpyWSRXBPMyfJ+7/Et8yP4/iRPe\n23xMXrL+O/r1pVklViDRtQBdkxhaMCNvlMv6VLm86bqs5DWx8IgVUkquOr18f/J1xvzacHnbY1v4\nZOuH2W5tacCnVawXYhd+B33iMgD2od/A2/TxBR1nxS3SbiD50vCUYH8sESgRrVgwUkquTbzNq4P/\niZw7WNPWYnbxaPvH6E5sX/R5hIgsu3r05F8gFUEeVAvs0EodBCCrBHdZmFfaqupnpBJYghnr8/9c\n4Q+HKdaKCp0S3TAlrmvSKvEdJnLq44uqv4SoSWr+RGJaufocjWKGnUROu5LltGx1eUZekJIOJ3FC\nf1oZugiU9wv/LOFO9yQMMLuQbSRtOmw0YYMJrcbs55vww5Cr/S6MVq7b5rxmd3nf55TzR+iEA/WI\n8X3rv+ee/giaCMWqrskonSrrWoBertNkJT+9rVKOUkMETRGFQgjBXmsHO2Nbead4nlfzb1dWR+x1\n7vCH9/+MQ4l9fLzlObrNxlsWFWuPoPVIRUjrw6cXLKTnYlnk7HdzMByFjkwIyXMqSse6olE+0gB9\nhcu8cv+vGSjdrKlP6BkeaXuWbanDTbf8bK0gh8WI8ulMF+kyEtjVIlzKKlFesd5N9Q+m18k6fafl\nl0PAzz+ma6MRFaspzPe/1ZyCbCkRgNHAa3m+GEB3JJw3mBCftrrF6d4LPLY9vKN+WZQAACAASURB\nVG7GPEmfG4ZbnWhowJHwx5YmpuYqaNFbFk2bqp9Kp1ylyu5XmpBV+XLqs2Xgr+gZ+nrlTJ6e4v72\nz7I3nWW/uLku3poYQufx5FEOx/fxWv4dzhQvEBD+Ay8UL3OpeIWTqWN8pOUZsnq6YedVPtJrD7/l\nMObtvwZAH34LpB8+kBvIkgvpMU/yrdGpm+1zqYBEc+kcxSpgoNjL60N/w638+Zp6U1gcbH2KvZmT\n6Nr6e82xlCJ9Liqv5+U04S2nBDmySoRXXunPXq7uj4TkpEumw605D3LKAjpjn+pxMdVWU2bqnFDb\ndz2K4eWg+n1O+O8QoRuQACNKQyt/rfsOTOUzAjo1QZcQtADaLGoyQDJJwHXNJ6dJ3Fjo9tMmoF3U\nTtAVlMtTolhM61MtgCt9Ku5Ejf07Cb9E1+U/JDXyTqXOjfcwvOuzyFgb+gr8aFlpklqc5zNPciJ5\nmB9OnuaSfQ0I/89v5s9wpnCeJ9Mn+VDmAyT1B0f4UKw/ZHIL0swi3HGEN4E2fpmg5WBDz7HkPtJ/\nMhDwxmSY79YDfrk9UMtjKubNUOkOrw99lRuTZ2vqNXT2Zh/jYMuTxPTECo1OsdaoL7Cr60VNfU1b\nvXJkua9UTe9bp67+wB7QPp97qpil2wz3k6mT1YjFei4r9fpG/cYCkxtekutehqGgvk81QI/hsC9e\nYL9VpNsIV1TUPLAKGvFJjXhex5glRB1AoEnsREApGeAkAuQqNNTo9jA9F38PK3+7UlfM7md0+2eQ\n+ux/u/XGPXeQlybf4JbbX1NviRgfzDzO05lTJLQVjlWoaDrMa3+MMfQqAM6uz+Lu+vmHPsaK+Uhf\nL8mKiAb4REatMa+YHyP2Xd4Y+ipXJ96e1iLYnjrMkbYPkTJaVmRsirXLdDE4k4c1PKw/K2KZNs2l\nzchxghzjgcF1N8UNL8WAPxX9A2DAizE4YXJ3NMlxv8RRz6bLnXs6nxsLsBMBdjLAjcn5/ZBoUqzx\nK3Rf+r8w3KnJdRNdzzC+6ROh+VtRYYPZxU+3/gi9Th8v5d/gvhdGM7Glw4vjL/PqxFs8kz3FB9OP\nYWnqB4giJGg9DJGQ1odPL0hIz8WSCWkpJV8cmnqIHLACdqpwd+uSh/GRHnMGeHPo61wefwM5TYRs\nSR7gSOszZGOdSzFMRZNx6f33ObBf+SuuBbKax3Erx3ErRyHQuekmKdhxWks6+12XvZ5LfI4fHb6Q\nOJFwdhIBwRwuju++f5Gj+xv76nZJkAGtd75K662vICL/X4nG2NafpNDx2AoPrnkRQrDD2sL22GYu\n2zd4Jf8Ww364em1Jlngh9xIvT7zJhzJP8ET6BDFt/ktRKh/ptYmfPRS6lCHRcpfAHQcz27DjL5mQ\nPpOHm3aY15F8LL3GlpZVNJShUh9vDX+DqxNvzRDQmxJ7OdL2IVpj3Ss0OoVCsSgkxDxBpmSwvaRz\nqhRgBKVZuwfADd3gohnjvGnRqxtsMFz2GEX2yhKbpbOq324apSG6rnyB+PjlSl2gJxje+V/gpHet\n4MhWD0II9sd3sdfawSX7Oq/k36qEzCsERb6Ze5GXJl7jqczjPJE+qVw+1jNmGpnagcjfQBCgD7+N\nv+G5hh1+SXykfSn53G3JQBi1hicSPh/PKGu0YiYDxZucHv7GDB9ogA3xXRxp+xDt1sYVGJlCoVgw\nEixPkC4ZpEo6aVsn5s/tpuBqAQMxuGjGeEVPcVfM/mo+IXx2GyX2GiV2GyXS2uox1KQGX6Pj2p+i\n+8VKnZ3cxuj2T+NbbSs4stVNIAPOl67wav5txoPJmjZLWDyZOclT6cdJqUmJ6xLjzlcx+74KgNfx\nGPajn3+o/ZfdR/q1CSoi2hKSp1W4O8U0+gpXOD38DW7nL8xo64nv5HDr03TGVeB9hWJVICHuaqRs\nnXQknM0HCGdPSAoxn4LlU4j5uHro67yNItvIMRKYXA8SXA+S9Mt4zSIoRalzzk1xzk0BsFFz2G2G\nonqrbjNHOOkVQ3hFOq7/GZnBVyp1Eo2JDc8z0fNsw0NyrTc0ofFIYj+H4ns4V7rM6/kzFUFtS5u/\nHX+Flyfe5FTqOM9kP0BWz6zwiBXLid/5JEbffw5X3hw+jVPoQyY3N+TYDRfSTiD52siUcH4yGZBU\n8yXWNWUf6UAGXJ84w5nR73CveH1Gv02JvRxqfUpZoBWA8pFuZoSEpK2TqtqMWZbgLuMLSdEsC+cA\n2wjmnCTYrrm0ay6PMU5JatwK4twMktwIEhSmPbruBjHu2jF+aGcpXHuHY/sPsDuyVndq3orHXrbG\nL9N15d9jlqYWkPJibYxu/zROatsKjmztoQudY4mDHInv52LpKm8UzjDi5wBwpcvLk2/y2uTbHE8d\n5unMKXrMrsq+ykd67SLjnQStj6CPvQuAeedrOPt+uSHHbriQfmkcxqJVo1JC8kRCWaPXO650ODvy\nImdHv8u4OzStVbA1eYBDrU/REuuqu79CoVhZDE+EgtnRSdo6SVtDe0CojIcVznMRFwH79AL79AJS\nwqCMcTNIcCNIcldaNdZqD8EVL8EVLwyLmREeOw072kq0av5sp2k4mjNOe+9fkrn/w5r6Qttxxrb8\nHaSu/HaXCl1oHEns41B8D5ftG7xeOMOgNwKAj89b+Xd5K/8u++K7eDpzit3WjpUdsGLJ8XqerQhp\no/+bOLt/ERoQXrKhQrro1y6+8qFUQExZo9ctk+4o747+LeeNH2DfL9S0CTS2p45wsPVJMmb7Co1Q\n0cwoa/TKICQkHK3G4vwg/2YIXTWKMZ9ibPHCec7xCegWDt2awyly2FJwO0jQG23sPl7Tf0IavOsa\nvBu5gbRpLrsMm516ie2GTWYp/KtlQObei7T1fgndn7r3BVqcsa0/QbGtMSu9Kh6MJjQOxHez39rF\ndecWr+XPcNe7X2m/XLrO5dJ1NpjdPL35cTzpYyg3mzVJ0HKYwOpEs4cQ3iTGwN/ibfrEoo/b0MmG\nf3td8M0wCg2tmuRXO3z0JvRVUywdUkruFa/z3tj3uTr+FgG11h9Ti7M7/Sh7s4+RMBq3tKtCoVgA\nEixPI2lHwtnRiTsPtjYDOHpAwfQpxgKKVT7OK82YNOgNEtwKEtwO4tjMLYo6NJcdhs123WaHYZNd\npMXamrhGx7X/FyvfW1NfbDlEbvOP4cdaF3V8xeKQUtLvDnC6+B5X7Jsz2jNamlPp4zyePq78qNcg\nRv+3MG9/CQA/u4/SqT+Y137LNtnwe7mp/IfTgRLR6wgnKHF5/E3OjX6fIftOTdtIb5GtuzeyL3uK\nnelHMB4irqdi/aJ8pBtMFIIu6egkHZ2ErZF0dHT54Bt1gKRkhoK5ZAYUTR+/SY12/VfPcWzvAY7p\nEwQS7ssYtyNR3SfjeNRa14cDk2HH5C3CH/ZtmssO3WabYbNNd2ifp4+15k7Q1vtFMgMvIapCeHqx\ndsa2/Dh2Vl3LzYAQgs2xDWyObWDUy/F28RzvFS/j4THSW4Tt8N3xH/Li+CscSuzjifQJdlrbECvt\naK9oCF7XUxh3voKQHvr4ZbTc+wQti/tuNlRIO9G9o1sPOGIp3+j1wIh9l3NjL3Ep9ypOnbiwHdZm\nNrVu4oObn0dTq3QpFMuDBMvVSLjaQ4tmCK3NZcFcXEI3jaVGE7BBOGzQHB4nhyfhnoxzK4hzJ0hw\nT1r40z7YaGAyGpi844bCOiV8tlYJ6426U2Mk0txJWvq/Rbb/BbSqe6AUBhM9zzHR/Qxo5rJ8XsXD\n0Wa08JHMU3wwdZJ3i5f4lvZGpS0g4FzxEueKl+gyOngifYJHU0eIq3jUqxszjd/xeGXJcOPOV3EW\nKaQb6trxT94O7y4/2+KzVwnpNYsTlLg2/jYXc6/SX7wyo10XBttSh9mTOUGbtWEFRqhQrB+0AOKO\nRsLVSTgaCUcn4Wpo8xTNnhZQMgKKsVA8l0yfYJ385vWk4K60uBPE6Qvi9EsLn7k/vEHAJt1htxzl\nqaEvs2vg6zUxoQGK2f3kNv84vqXmf6wmfOlzxb7JmeIF7rj3ZrTHhMmRxAFOpo6yw9qqrNSrFDF5\ng/j53wZAajEKT/85xOZe6XBZ40hvNQP2qKXA1xxSSu4Wr3Ix9ypXx9/ClfaMPmmjjT3Zk+xIPUJM\nzUZXKBpLtMhJPBLKCUcj7upY3vxVrycktulHgjncPK05fJtXAkNItooSW7XQkuxJGJBWJKrj9AfW\nDB9r0y/w6P2/5hNDXyQV5GvactYm+jf+KImWXZhCPQdXG7rQORDfzYH4bga9Ec4WL3K+dAVXhgtj\nONLl7cJ7vF14j3ajlRPJo5xIPUKr0bjlphVLj0ztIEhtR8v3IgIH4+638Lb/9IKP13CL9C+1eWxV\nb7HWDBPuCO/nXuNi7lVy7uCMdoFgY2Ive7Mn6I7vqPsLXfm6KhbCur1uIl/muBsK5TANt/lamSFc\nKdA2Aux1JpovXLnEob0HGnIsKWFYmvTLODk7z56h7/DMyFdJ+xM1/fqtbXy5+7O8mX0WKTQ0JD0U\n2CzybBGTbBF5eiiiK3Hd1Jy9ep1je2qXaHcChwulq5wpXmDIH52xjwB2Wzs4mTrKwcQ+YsqNZ1Wg\nD75M7PqfAhAkNlL84B/DHO6ny2aR3hcLlIheA+S9HFfH3+LKxOm6C6cAZIwOdmaOsj11REXfUCgW\ngJAQczXinoZVJZYfVjBLJI4uI8HsV8TzPCLWKR6AQLI9f56Tg9+jdfRtBLWh8gZjm/hy12d5pfV5\nZFXItADBXVLclSlOy24gdAnZQIFNIh9tBXooKMt1kxPTYhxPHuJY4iD3vEHOlS5zqXQNWzoASOCq\nfZOr9k1iwuRAYg9Hk4fYF9+FIZZk8WhFA/DbH0P2/hXCL6AV76IPv4Xf+fiCjtVQi/TNmz7d6rpZ\nlRS9Sa5NvsOV8dP0FS4DM68LU1hsTR1iZ+YR2mOblH+YQvEgJBiBwHJDsWx5GnFXYLk6licQD2ke\n9spWZiPANiW2EeAYAQ+huxXzQPNLtI28Tuf975Eo9c1od8w2hrueZ7zlEXxhMEiSO2ToF2nukmZY\nJOd3HgK6KbJJFNgoCmwgTJPCa/RHUjQQV3pctW9yrniZXnfm9QEQFxaHEvs4mjzI7vgOdBWbuukw\nev8K894LAHidH8A+/s9n7TuXRbqhQrpwZ/lWjFIsngl3hBuTZ7kx8S53Cu8jmbkwgUDQHd/BzvQj\nbEruw1CvrRSKWqrEcswTWN6UaLZcbd6RMqrxRCSSzVAol8XzepkEuCLIgNTkVdpG36Bt5PUZEwgB\nCokdjHZ8gMnMQZhDGNnooUWaNHcjcZ0T8583ksVhQ5Ww7hEFOilhKOt10zHuT3K+dJmLpWuM+GN1\n+yS1BAfieziU2Mee+A5iKgRsUyBKA8TP/iYAEkHxqT9FJuoHSFBCWgGEEwaH7NtcnzjLzcl3GbRv\nz9q3y9rGtvQhtiT3Y+nzs67Mxrr1dVUsima6bkTktxzztMpmeWXxvDCxDKEfcyiUJU5kXXYM5Zax\nGB7KR1pKEsVbtI28QevIG8TcmT6wgTAZbznOaPsHcOI9Cx5XAYMBUtwjzT2RYoAUIyIx7/01Ajqw\n6REFekSRbor0iAId2Mr3ukHU85GeL1JKhvwRLpWuc6l0jVwwUbefIQz2WDs4mNjLgcReMnpqMUNW\nLJLYpd9Hz50HwNnxadw9/2XdfssatUPRXNh+gTuF97mdv8jNyfeY9GY+KMp0WJvYljrMluQB5fes\nWFeUhbJZFsp+WTSHqek/vBtGGV9IHD3ANcI0FMtSuWSsFFISL/XROvoWrSNvELcH6nZzzHZG259g\nvPU4gT5/wTsbSTx2kmMnuYrnnC11BkhyjzSDIsl9UgySwKtj7Q7QGCTBoExwrko36wS0Y9MlinRR\npEuU6BZFOikRF8q4tVwIIegyOuhKd/B06jHueUO8X7rG+/Z1Jqqiu3jS41LpKpdKVxGj32BLbBP7\n4rvYG9/J5tgmdLXewrLi9TxbEdJm3zdxd/0CPOQbA2WRXmP40megeIPb+Yvcyl/gfukmso6/M4BA\nozu+nc3JfWxK7iGpQvgo1iISTF+EWySSa1JPYC7SZ8IXElcPBbKrBzi6xDUCHD2yLivBvKJofoHM\n+EWyuffIjJ+va3kG8PUEE5nDjLccpZjcPucs/qUiAEZIcJ8k90WK+yQZJPlQriFlMjh0UKJTlOgQ\npak8JTXJcZmQUnLPG+Sq3ctVu5fhOpE/ysSFxa74dvbEd7I3vpMOo20ZR7pOkQHWmf8ZzRkBwN73\nX+Nt+3szuinXjjWMH7jcL/XSX7xGf+Eq/cUruHVWGCxjCouNyT1sTu5lQ2IXpmYt42gVigYiQauI\nZA3Ti9Iq0VzOL9SaPHUqiadJ3Eggu3pkZY7KvkCJ5WZCeiQKt8lMXCSbO0dq8hqC+s+nQMSYyBxk\nouUo+fTuOX2fVxIHjaFIVA+JBINRfkI8/D1cIMni0IZNhyjRJmzay3lsUsxvWXTFwzPmjXPVCUV1\nn3tvVkMXQJveyk5rKzusreywttBhtKtJ/kuA0fcNzDtfBkAKDfvY/47feaqmjxLSawjbL3CveJ3+\n4lXuFq4yULqJL+ee4d0W28CGxC564jvojG9BW+YHRTP5uipWARIMX3Dl0vsc3X0Q0xcYkUA2yiLZ\n1zB8sWDf5JmnrBLKusQri+RKuvbjL69mNL9AavI6qcmrXLp8lmdaB9ADZ9b+vhYnn9rDRMth8ul9\nyFU8+ctGY4QEwyQYFgmGSDJMnFES+Au0qMfwacWmTdi04kTpVDmFi7YGvw+L8ZFeCMWgRK/Tx03n\nDjedPianLfAznbSWYru1hR3WFnZYW9lgdqtoII3At7Eu/g5avhcAqccpnfxdguyeShflI71KcfwS\ng/Yt7hd7uV8Kt3qLokwnqWfpSexkQ2In3fHti54sqFAsiiiqheEL9Cg1pqVm1Gb6AiNysyiNJNid\nacy164kpcezpZdEc4JWF8zpYqGStoPklEsXbJAq3SRRvk8xfJ17sR0SWvVsF0Ot4qZWsjeQz+8in\n91JMbGlay/PDYhGwkTwbyddELQ2AnLQYJcEIcUZEnJEonyOOnMOy6aCHriUy+v5Ns7fpBGRxaMGh\nVYRpi7BpwSEjXLI4pNeo2G4kCS1eWUlRSsmIn6M3EtW3nX5cao1kk0Ge88X3OV98HwADnY2xHjbH\nNrA5tpHN5ka6zA7lZ/2w6Bb2vl/DOv8v0JxhhF/COvublB7/fWS8+4G7K4t0EyBlwLg7zIjdz7Dd\nz7DTz1DpNqPOAPXiOU8nbbTRaW2hK76NzvgW0kabev2jaDwSdAl6JHr1IBTCldSvXzaCpbsWA6aE\n8VQaTCtLNalvFSICl5g9SNweIF7sqwhny74/r/1do4Vicjv59B7y6T34RmaJR7x68BGME2OUOGPE\nGRVRisUYcZwGLCQikGQIRXVGOGRwyQiXDGE+LdwwxVVh/ergS5973hB3nLv0uQP0ufcqi8DMhSlM\nNprdbDS76TG7oq2TpDKoPRBR6Me68C8RUejLIL2T4mO/C0ZKuXY0C27gkHMGybn3yTmDjDr3GLb7\nGLHv4s3jCwLhBMGWWBdd1lY641vptLaoCBuKeSEkaIFAD0CXoeDVAiqiWA9EVD+trrKxaF/j+SCR\n+AL8shDWJH4kiH2tViQHyjd5VaP5NqYzTMwZiUTzPazSAFZpgJgzVLEyPwiJwLY2UExtp5jYRjG5\nDc9sWeLRr00kUMIgh1XZxoTFeCSyx4lREo1dTyCORwqXNB4p4U7LeyRxSQovynuYBOvOhzsMrzdK\nn3OPO+49+t0BxoPJee+f0VIVYd1httNptNFhtNGiZ9GUBbuClrtE7P3fR8hQz/rtJygd/xzjW2KL\nE9JCiE8CvwdowH+QUv7L6X2UkAZfeky6o0x6o0y6o0y4I4y7Q4w598m5g+S9+sHaZ0MgyJqdtFsb\naYttpN3aQIvZja6tLo8c5SO9AKKJdJoMxa4mRViOxG6lLhLFWhC264FAi8RwdV9diodadrqxHyUU\nvH6VKC5vM8thP8RDxgNWNBcyQPfymF4Ow81huuMYbo6YO0rMHsZ0Rog5Ixj+/IVA5dAIHKuLUnwj\ndrQV45uRejjp7ty1GxzZvbPRn0hRhYPGOBYTxBjHYpwYEyIsl7dig8V2NQYByUhgJ/BJCI8E3rS8\nRxyfuPDDNCrH5hDhy+0jvVgKQYkBd5ABb4h77iD3vKEH+llPR0en3WilIxLWrUaWFr2FVj1Di9FC\nSkuirbNfLfrgq8Su/3Gl7G76JEMf/Y2F+0gLITTgD4CPAP3Am0KIr0gpLzVozE2NlBInKFH0xyl4\nExS8cYp+bZr3xphwRyj4E8zHFaMelpYka3bSEusKN7OL1ljPmlhJ8Nbt26tbSEeiVkgQkaAViNDC\nW11XSaeEb7lOkwJRLYintdWI5RUUvQ/CF5JAhKK3LI5rNgFBnbqFWI1779xSQnqlkT66X6xsWiWf\nx/Dy6F4ew5uMypNReQLTHUfUWSn1oU4NeGYrTqwTx+qkZG3Ajm/EsbqQc9wXb/TfVUJ6iYkR0EmR\nTqpWf5z26POkYLJKWOcxyYswn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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "figsize(12.5, 5)\n", + "\n", + "params = [(2, 5), (1, 1), (0.5, 0.5), (5, 5), (20, 4), (5, 1)]\n", + "\n", + "x = np.linspace(0.01, .99, 100)\n", + "beta = stats.beta\n", + "for a, b in params:\n", + " y = beta.pdf(x, a, b)\n", + " lines = plt.plot(x, y, label = \"(%.1f,%.1f)\"%(a,b), lw = 3)\n", + " plt.fill_between(x, 0, y, alpha = 0.2, color = lines[0].get_color())\n", + " plt.autoscale(tight=True)\n", + "plt.ylim(0)\n", + "plt.legend(loc = 'upper left', title=\"(a,b)-parameters\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "One thing I'd like the reader to notice is the presence of the flat distribution above, specified by parameters $(1,1)$. This is the Uniform distribution. Hence the Beta distribution is a generalization of the Uniform distribution, something we will revisit many times.\n", + "\n", + "There is an interesting connection between the Beta distribution and the Binomial distribution. Suppose we are interested in some unknown proportion or probability $p$. We assign a $\\text{Beta}(\\alpha, \\beta)$ prior to $p$. We observe some data generated by a Binomial process, say $X \\sim \\text{Binomial}(N, p)$, with $p$ still unknown. Then our posterior *is again a Beta distribution*, i.e. $p | X \\sim \\text{Beta}( \\alpha + X, \\beta + N -X )$. Succinctly, one can relate the two by \"a Beta prior with Binomial observations creates a Beta posterior\". This is a very useful property, both computationally and heuristically.\n", + "\n", + "In light of the above two paragraphs, if we start with a $\\text{Beta}(1,1)$ prior on $p$ (which is a Uniform), observe data $X \\sim \\text{Binomial}(N, p)$, then our posterior is $\\text{Beta}(1 + X, 1 + N - X)$. \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Example: Bayesian Multi-Armed Bandits\n", + "*Adapted from an example by Ted Dunning of MapR Technologies*\n", + "\n", + "> Suppose you are faced with $N$ slot machines (colourfully called multi-armed bandits). Each bandit has an unknown probability of distributing a prize (assume for now the prizes are the same for each bandit, only the probabilities differ). Some bandits are very generous, others not so much. Of course, you don't know what these probabilities are. By only choosing one bandit per round, our task is devise a strategy to maximize our winnings.\n", + "\n", + "Of course, if we knew the bandit with the largest probability, then always picking this bandit would yield the maximum winnings. So our task can be phrased as \"Find the best bandit, and as quickly as possible\". \n", + "\n", + "The task is complicated by the stochastic nature of the bandits. A suboptimal bandit can return many winnings, purely by chance, which would make us believe that it is a very profitable bandit. Similarly, the best bandit can return many duds. Should we keep trying losers then, or give up? \n", + "\n", + "A more troublesome problem is, if we have found a bandit that returns *pretty good* results, do we keep drawing from it to maintain our *pretty good score*, or do we try other bandits in hopes of finding an *even-better* bandit? This is the exploration vs. exploitation dilemma.\n", + "\n", + "### Applications\n", + "\n", + "\n", + "The Multi-Armed Bandit problem at first seems very artificial, something only a mathematician would love, but that is only before we address some applications:\n", + "\n", + "- Internet display advertising: companies have a suite of potential ads they can display to visitors, but the company is not sure which ad strategy to follow to maximize sales. This is similar to A/B testing, but has the added advantage of naturally minimizing strategies that do not work (and generalizes to A/B/C/D... strategies)\n", + "- Ecology: animals have a finite amount of energy to expend, and following certain behaviours has uncertain rewards. How does the animal maximize its fitness?\n", + "- Finance: which stock option gives the highest return, under time-varying return profiles.\n", + "- Clinical trials: a researcher would like to find the best treatment, out of many possible treatment, while minimizing losses. \n", + "- Psychology: how does punishment and reward affect our behaviour? How do humans learn?\n", + "\n", + "Many of these questions above are fundamental to the application's field.\n", + "\n", + "It turns out the *optimal solution* is incredibly difficult, and it took decades for an overall solution to develop. There are also many approximately-optimal solutions which are quite good. The one I wish to discuss is one of the few solutions that can scale incredibly well. The solution is known as *Bayesian Bandits*.\n", + "\n", + "\n", + "### A Proposed Solution\n", + "\n", + "\n", + "Any proposed strategy is called an *online algorithm* (not in the internet sense, but in the continuously-being-updated sense), and more specifically a reinforcement learning algorithm. The algorithm starts in an ignorant state, where it knows nothing, and begins to acquire data by testing the system. As it acquires data and results, it learns what the best and worst behaviours are (in this case, it learns which bandit is the best). With this in mind, perhaps we can add an additional application of the Multi-Armed Bandit problem:\n", + "\n", + "- Psychology: how does punishment and reward affect our behaviour? How do humans learn?\n", + "\n", + "\n", + "The Bayesian solution begins by assuming priors on the probability of winning for each bandit. In our vignette we assumed complete ignorance of these probabilities. So a very natural prior is the flat prior over 0 to 1. The algorithm proceeds as follows:\n", + "\n", + "For each round:\n", + "\n", + "1. Sample a random variable $X_b$ from the prior of bandit $b$, for all $b$.\n", + "2. Select the bandit with largest sample, i.e. select $B = \\text{argmax}\\;\\; X_b$.\n", + "3. Observe the result of pulling bandit $B$, and update your prior on bandit $B$.\n", + "4. Return to 1.\n", + "\n", + "That's it. Computationally, the algorithm involves sampling from $N$ distributions. Since the initial priors are $\\text{Beta}(\\alpha=1,\\beta=1)$ (a uniform distribution), and the observed result $X$ (a win or loss, encoded 1 and 0 respectfully) is Binomial, the posterior is a $\\text{Beta}(\\alpha=1+X,\\beta=1+1−X)$.\n", + "\n", + "To answer our question from before, this algorithm suggests that we should not discard losers, but we should pick them at a decreasing rate as we gather confidence that there exist *better* bandits. This follows because there is always a non-zero chance that a loser will achieve the status of $B$, but the probability of this event decreases as we play more rounds (see figure below).\n", + "\n", + "Below we implement Bayesian Bandits using two classes, `Bandits` that defines the slot machines, and `BayesianStrategy` which implements the above learning strategy." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "rand = np.random.rand\n", + "\n", + "class Bandits(object):\n", + " \"\"\"\n", + " This class represents N bandits machines.\n", + "\n", + " parameters:\n", + " p_array: a (n,) Numpy array of probabilities >0, <1.\n", + "\n", + " methods:\n", + " pull( i ): return the results, 0 or 1, of pulling \n", + " the ith bandit.\n", + " \"\"\"\n", + " def __init__(self, p_array):\n", + " self.p = p_array\n", + " self.optimal = np.argmax(p_array)\n", + " \n", + " def pull(self, i):\n", + " #i is which arm to pull\n", + " return np.random.rand() < self.p[i]\n", + " \n", + " def __len__(self):\n", + " return len(self.p)\n", + "\n", + " \n", + "class BayesianStrategy(object):\n", + " \"\"\"\n", + " Implements a online, learning strategy to solve\n", + " the Multi-Armed Bandit problem.\n", + " \n", + " parameters:\n", + " bandits: a Bandit class with .pull method\n", + " \n", + " methods:\n", + " sample_bandits(n): sample and train on n pulls.\n", + "\n", + " attributes:\n", + " N: the cumulative number of samples\n", + " choices: the historical choices as a (N,) array\n", + " bb_score: the historical score as a (N,) array\n", + " \"\"\"\n", + " \n", + " def __init__(self, bandits):\n", + " \n", + " self.bandits = bandits\n", + " n_bandits = len(self.bandits)\n", + " self.wins = np.zeros(n_bandits)\n", + " self.trials = np.zeros(n_bandits)\n", + " self.N = 0\n", + " self.choices = []\n", + " self.bb_score = []\n", + "\n", + " \n", + " def sample_bandits(self, n=1):\n", + " \n", + " bb_score = np.zeros(n)\n", + " choices = np.zeros(n)\n", + " \n", + " for k in range(n):\n", + " #sample from the bandits's priors, and select the largest sample\n", + " choice = np.argmax(np.random.beta(1 + self.wins, 1 + self.trials - self.wins))\n", + " \n", + " #sample the chosen bandit\n", + " result = self.bandits.pull(choice)\n", + " \n", + " #update priors and score\n", + " self.wins[choice] += result\n", + " self.trials[choice] += 1\n", + " bb_score[k] = result \n", + " self.N += 1\n", + " choices[k] = choice\n", + " \n", + " self.bb_score = np.r_[self.bb_score, bb_score]\n", + " self.choices = np.r_[self.choices, choices]\n", + " return " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Below we visualize the learning of the Bayesian Bandit solution." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "figsize(11.0, 10)\n", + "\n", + "beta = stats.beta\n", + "x = np.linspace(0.001,.999,200)\n", + "\n", + "def plot_priors(bayesian_strategy, prob, lw = 3, alpha = 0.2, plt_vlines = True):\n", + " ## plotting function\n", + " wins = bayesian_strategy.wins\n", + " trials = bayesian_strategy.trials\n", + " for i in range(prob.shape[0]):\n", + " y = beta(1+wins[i], 1 + trials[i] - wins[i])\n", + " p = plt.plot(x, y.pdf(x), lw = lw)\n", + " c = p[0].get_markeredgecolor()\n", + " plt.fill_between(x,y.pdf(x),0, color = c, alpha = alpha, \n", + " label=\"underlying probability: %.2f\" % prob[i])\n", + " if plt_vlines:\n", + " plt.vlines(prob[i], 0, y.pdf(prob[i]) ,\n", + " colors = c, linestyles = \"--\", lw = 2)\n", + " plt.autoscale(tight = \"True\")\n", + " plt.title(\"Posteriors After %d pull\" % bayesian_strategy.N +\\\n", + " \"s\"*(bayesian_strategy.N > 1))\n", + " plt.autoscale(tight=True)\n", + " return" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "image/png": 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al8A0NqEhIucCtwAXisinIrJRRJa03SfIGLMS2C0iO4A/Al8NY5Ujlit2IKOu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\nAW7MIKyXFlAUNqDWum9lyLYXs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fI2B3jwwcN3/P9z8uRJXnjhBQAK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hdXaeaOb3dpSmzfNy+NRNCya1j6NhPHohVuvzNcAmEUkVS0tvAY6McI9i4585VIai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\nreNU1jlo6Whg8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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "hidden_prob = np.array([0.85, 0.60, 0.75])\n", + "bandits = Bandits(hidden_prob)\n", + "bayesian_strat = BayesianStrategy(bandits)\n", + "\n", + "draw_samples = [1, 1, 3, 10, 10, 25, 50, 100, 200, 600]\n", + "\n", + "for j,i in enumerate(draw_samples):\n", + " plt.subplot(5, 2, j+1) \n", + " bayesian_strat.sample_bandits(i)\n", + " plot_priors(bayesian_strat, hidden_prob)\n", + " #plt.legend()\n", + " plt.autoscale(tight = True)\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that we don't really care how accurate we become about the inference of the hidden probabilities — for this problem we are more interested in choosing the best bandit (or more accurately, becoming *more confident* in choosing the best bandit). For this reason, the distribution of the red bandit is very wide (representing ignorance about what that hidden probability might be) but we are reasonably confident that it is not the best, so the algorithm chooses to ignore it.\n", + "\n", + "From the above, we can see that after 1000 pulls, the majority of the \"blue\" function leads the pack, hence we will almost always choose this arm. This is good, as this arm is indeed the best.\n", + "\n", + "Below is a D3 app that demonstrates our algorithm updating/learning three bandits. The first figure are the raw counts of pulls and wins, and the second figure is a dynamically updating plot. I encourage you to try to guess which bandit is optimal, prior to revealing the true probabilities, by selecting the `arm buttons`." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + "\n", + " \n", + "\n", + "\n", + " \n", + "
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Denote this best bandit's probability by $w_{opt}$. Our score should be relative to how well we would have done had we chosen the best bandit from the beginning. This motivates the *total regret* of a strategy, defined:\n", + "\n", + "\\begin{align}\n", + "R_T & = \\sum_{i=1}^{T} \\left( w_{opt} - w_{B(i)} \\right)\\\\\\\\\n", + "& = Tw^* - \\sum_{i=1}^{T} \\; w_{B(i)} \n", + "\\end{align}\n", + "\n", + "\n", + "where $w_{B(i)}$ is the probability of a prize of the chosen bandit in the $i$ round. A total regret of 0 means the strategy is matching the best possible score. This is likely not possible, as initially our algorithm will often make the wrong choice. Ideally, a strategy's total regret should flatten as it learns the best bandit. (Mathematically, we achieve $w_{B(i)}=w_{opt}$ often)\n", + "\n", + "\n", + "Below we plot the total regret of this simulation, including the scores of some other strategies:\n", + "\n", + "1. Random: randomly choose a bandit to pull. If you can't beat this, just stop. \n", + "2. Largest Bayesian credible bound: pick the bandit with the largest upper bound in its 95% credible region of the underlying probability. \n", + "3. Bayes-UCB algorithm: pick the bandit with the largest *score*, where score is a dynamic quantile of the posterior (see [4] )\n", + "3. Mean of posterior: choose the bandit with the largest posterior mean. This is what a human player (sans computer) would likely do. \n", + "3. Largest proportion: pick the bandit with the current largest observed proportion of winning. \n", + "\n", + "The code for these are in the `other_strats.py`, where you can implement your own very easily." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "figsize(12.5, 5)\n", + "from other_strats import *\n", + "\n", + "#define a harder problem\n", + "hidden_prob = np.array([0.15, 0.2, 0.1, 0.05])\n", + "bandits = Bandits(hidden_prob)\n", + "\n", + "#define regret\n", + "def regret(probabilities, choices):\n", + " w_opt = probabilities.max()\n", + " return (w_opt - probabilities[choices.astype(int)]).cumsum()\n", + "\n", + "#create new strategies\n", + "strategies= [upper_credible_choice, \n", + " bayesian_bandit_choice, \n", + " ucb_bayes , \n", + " max_mean,\n", + " random_choice]\n", + "algos = []\n", + "for strat in strategies:\n", + " algos.append(GeneralBanditStrat(bandits, strat))" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "image/png": 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9xKZNm7BYLCQlJbF9+3YuXLhAlSpVaNSokfV8du3aZe0htz1fW5YtW0ZkZCQJ\nCQnMmDGDXr16WRv96WkrVqxIhw4dmDx5Mjdv3kRrzenTpzPVpT1Dhw5l7ty5HDp0CDAmMNsaGlnR\nt29fFi9ezJEjR0hOTmbq1Kk0bdo004iEUorBgwc7rLvs6kkQBEEQhOKP1pqUM/u5viiCi1Nqc/3T\nJzIZAx6BYZQZ8O98k6HYGQRFdQ7BtGnTWLt2LcHBwaxatSrDBFswGrtly5YlNDSUiIgI/v3vf1sn\nFAP069ePmTNnUqNGDX777Tc+/vhjAEqWLMnKlStZtWoVoaGhhIaG8tZbb2U7eTgr3nrrLUJDQ+nY\nsSPVq1fnrbfesq4EZN8jHxAQwKJFi6wrIzVs2JC5c+da08+fP599+/ZRvXp1Zs2axaBBgzIcb5uf\nUorw8HCeffZZQkNDSU1NZfr06Q7Tfvjhh6SmptKqVStCQkIYMWKE1cUnK3r16sXYsWMZOXIkgYGB\nDB06lOvXrzs8L1vat2/PpEmTGDZsGHXr1uXs2bP85z//cShXVnWXUz0JhQfpwRNyg+iL4CyiK3cn\n2mIh+cRW4paN5fJbjbjybmcS9y2D28l/JXL3xDusL/dGrMD/xfX4tn4i3+RRzizJWJTYuHGjduQy\nFBMT45SvfGFk+/btREREZFgK05bRo0cTEBDA5MmTC1gyoTBSlHVdEARBEIorWmtSzx0k6eAaEvcv\nxxLn2BugxH118Kr3MH5tRuBeNiBD3IEDB+jYsWOef5FV5hAIgiA4gfj5CrlB9EVwFtGV4o3WmtsX\nfyfpwCoS9y8n7coZh+mUTxm863bDt91IPAMLfl5rsTMI7kZyM4E2J1q3bu3Qf/7dd9+lb9++eVZO\nQTNu3DiWL1+eaf+AAQN45513XCCRIAiCIAjFlbTr50nY8xVJ+5dz+2KkwzTK7158GvbEu3EfPIOb\no0o4Xvq9IBCXIUEoZoiuC4IgCELBY0mOJ/nIjyQeWEXy0XVgScuURvmUwbvew3g3eBSv0C4o99z1\nzYvLkCAIgiAIgiAUIrTWpJzYQuK+5SQd+gadfCtTGuXph+f9HfAJ64t33a4oD28HObmWYmcQyBwC\nQRDyA/HzFXKD6IvgLKIrRQ9tsZAStYPEAytI/vV7LPFXHKbzrNEWn5ZD8K7fHTevkgUsZe4odgaB\nIAiCIAiCIOQ1qTFHSNz7FYm/fI3leozDNO7la+DTpB8+TfpSonx1h2kKI8XOICiq3yEQBKFwIz14\nQm4QfRG9+Y9XAAAgAElEQVScRXSlcGNJukHS4R+J//lDbkf/6jCN8rsXn7B++IT1xqNa8zxd7KWg\nKHYGgSAIgiAIgiDcKZakmyQfXUfSwTUkHV2f8WNhJm5+/ng37IlPk354BLdAuRXtb/26THqlVC2l\n1C9KqQPmb5xS6nml1D1KqXVKqd+VUj8ppcrYHDNJKXVCKXVMKdXFUb4HDx4suJPIQxo1asSWLVtc\nLQZgLD26Y8eOfMt/yZIldO/ePd/yz66swMBAzp49m2/554Z3332XMWPG5JksQv6ybds2V4sgFCFE\nXwRnEV0pPKTG/s71r17g4qu1uf75MyT9+l1GY8DDG+8Gj3LP04up8OZhygyYjWf1VkXeGAAXjhBo\nrSOBxgBKKTcgGvgaeBnYoLX+l1JqIjAJeFkpFQoMAOoAVYANSqmauritm1oIyE9jIJ2CHE6zLcvW\nGMirLzzf6bm8+OKLf6tcQRAEQRD+HjolgeTj/yN+63xSTmx1mKZEQH18GvXCp+VQ3EuVL2AJC4bC\n4jLUCYjSWp9TSvUC2pv7PwN+xjASegJfaa1vA6eVUieA5sBu24xkDoEgCPmB+PkKuUH0RXAW0ZWC\nx5J0k+Qj60g8sJLkyJ8hNSlTmhL3hRouQY0fo0TFWgUvZAFTWMY4woHF5v+KWuuLAFrrWKCCuT8A\nOGdzzHlzX7HhwIEDtGrViurVq/Pcc8+RkpJCXFwcgwYNolatWlSvXp1BgwZx4cIFANasWcNDDz2U\nIY8PPviAoUOHApCSksKrr75KgwYNqFOnDuPHjyc52Rj6unr1KoMGDSI4OJjq1avz6KOPWvOwdV86\ncOAAXbt2JTg4mLp16zJx4kRu375tTevv78/ChQtp1qwZISEhTJgwwalztVgsTJw4kWrVqtGyZcsM\n7lKLFy+mZcuWBAYG0qRJExYuXGiN2759O/Xq1eODDz6gdu3a1K1bl8WLF1vjr127xuDBgwkKCqJz\n586cOnUqQ7n+/v6cPn2azz77jBUrVvD+++8TGBjI448/nq2858+fZ9iwYdSqVYuaNWvy8ssvW+O0\n1rz22muEhIQQFhbGhg0brHGxsbE8/vjjVK9enWbNmvH5559b42bOnElERIQ1vGvXLrp160ZwcDAN\nGjTgq6++ArK/joIgCIIgOEdq9K9cW/gkFydX5/oXz5B85MeMxoBSeNXrjv9z31NuwlZKdZtwVxgD\nUAhGCJRSHhi9/xPNXfYuQLlyCbrT7xD8WKl1ro/Jjm6xuXe7WbFiBatWrcLX15eBAwfyzjvv8Oyz\nz/L444+zcOFCbt++zXPPPceECRP44osvePjhhxk3bhwnTpygZs2aACxfvpyXXnoJgDfeeIOzZ8+y\nbds23N3dGTlyJLNmzWLKlCl88MEHBAQEEBUVhdaavXv3OpTJ3d2dadOmERYWxvnz5+nfvz8LFixg\n1KhR1jTr1q1j06ZNxMXF8dBDD9GtW7dMhoo9+/fv57HHHiMqKopvvvmGYcOGcejQIcqUKUP58uVZ\ntmwZgYGB7Ny5k/79+9OkSRPq168PwKVLl7h16xZHjx5l06ZNjBgxgkcffZTSpUszfvx4fHx8+P33\n3zl16hT9+vWjWrVq1nLT3XuGDx/Onj17nHIZslgsDBo0iPbt2zN//nzc3Nz45ZdfMpzL4MGDiYqK\nYuHChbzwwgscOXIEgKeeeop69epx/Phxfv/9d/r06UNISIi1RyhdnnPnzjFgwADee+89evbsyc2b\nNzl//nyO11EoOGStcCE3iL4IziK6kr9Ykm6SuG8ZiXuWkHr2gMM0JSrVxqtuN3xbj6CEf2ABS1g4\ncLlBADwM7Nda/2mGLyqlKmqtLyqlKgGXzP3ngao2x1Ux92Vg8+bN7Nu3j8BA44KWKVOG+vXrExIS\nkn9nkEc888wz3HfffQCMHTuWSZMmMXnyZGvvvZeXFy+++CKPPfYYAJ6envTu3Ztly5bxyiuvcOzY\nMc6dO0fXrl0B+OKLL9i2bRulS5cG4IUXXmDUqFFMmTKFEiVKcPHiRc6cOUNwcDAtW7Z0KFPDhg2t\n/6tUqcLw4cPZvn17BoNgzJgxlCpVilKlStG2bVsOHz6co0FQvnx5ax69e/fmgw8+YN26dfTv35/O\nnTtb07Vq1YoOHTqwc+dOq0Hg6enJSy+9hJubG507d8bPz48TJ07QuHFjvvvuO3bs2IG3tzd16tRh\n0KBB7Ny505rfnUw52b9/PxcvXuTNN9/EzZw41KJFC2t8YGAgQ4YMAWDgwIGMHz+ey5cvk5KSwt69\ne1m+fDkeHh7Uq1ePoUOH8tVXX2V6+K9cuZIHH3yQ3r17A1C2bFnKli0LZH8dHREXF0flypWBvyar\npZcnYQlLWMISLlzhdAqLPMUhrG+n8L8l75MStYtGibvRybfYE2vUc/NKxu/+1EA8a7TloSFjKVE+\nxDj+2Fnatg10ufy24fT/6XMgmzZtSseOHclrlKvn5CqllgA/aq0/M8Mzgata65nmpOJ7tNbpk4q/\nBFpguAqtBzJNKt64caN2NEIQExNjbSQ5wtUjBI0aNWLWrFnWxvDx48fp1KkTf/zxB5MmTbL2wGut\niY+P5/Llyyil2LdvHyNHjuTAgQO89dZbxMXFMXv2bP78809q165NmTLWRZqwWCxorTlz5gy3bt1i\n5syZfP/99yilGDZsGC+88IJVljlz5tCuXTuioqKYMmUKBw8eJDExkbS0NBo2bMh3330HGC44+/fv\nt/bCOzNRd8mSJSxYsCCDa82IESNo3Lgxzz//POvXr2fWrFlERUVhsVhISkri+eefZ9KkSWzfvp2I\niAh+++23DHU3Z84c7r//fkJDQzl37hw+Pj4ALFy4kOXLl/P9999nktfZScWrV69m7ty5GeS1PZdF\nixZZ87ct48qVKwwePJjff//dGrdw4UK+/fZbVq5cycyZMzl9+jQfffQRL730Er6+vrz55psZ8s/p\nOjoiJ10XBEEQhOKG1prUM/tJ+vVbEvctx3IjNnMidw+863enZMcxeFRtmDm+CHDgwAE6duyY5yuz\nuHSEQCnlizGheKTN7pnAMqXUk8AZjJWF0FofVUotA44CqcCzebnC0J24+OQ16S4iYLiQVKpUiblz\n53Ly5Ek2btxIuXLlOHz4MA8++CBaa5RSNG3aFA8PD3bu3MmKFSv45JNPAKNR6uvry44dO6hUqVKm\nskqWLMnUqVOZOnUqx48fp1evXoSFhfHAAw9kSDd+/HgaNGjAggUL8PX1Zd68eXz77bd/+1zT50Gk\nEx0dTffu3UlJSWHEiBHMmzeP7t274+bmxtChQ53q2S9Xrhzu7u6cP3+eGjVqABnr1B5nVwcKCAgg\nOjoai8ViHSFwhkqVKnHt2jXi4+Px8/MDjPNMHwWyL+PAgcxDmTldR0EQBEG4m0k9f5iE3YtIPvwj\naVcdLyvuXqEmfg88g09YX9z87ilgCYsGLp1UrLVO0FqX11rftNl3VWvdSWtdW2vdRWt93SZuuta6\nhta6jtZ6naM8i+p3CAAWLFhATEwM165d491336V3797Ex8fj7e1NqVKluHbtGjNnzsx0XHh4OBMm\nTMDT09PqyqKUYujQoUyePJk//zS8sWJiYti0aRNg+P2nT7gtWbIkJUqUwN3dPVPeN2/epFSpUvj6\n+hIZGcmnn36aJ+d6+fJl5s+fz+3bt1m9ejUnTpygS5cupKSkkJKSgr+/P25ubqxfv57//e9/TuXp\n5uZGjx49mDlzJomJiRw/fpwlS5Zkmb5ChQpZ9rLb0qRJEypWrMibb75JQkICycnJ7N69O8fjAgIC\naN68OVOnTiU5OZkjR46waNEiwsPDM6Xt168fmzdvZs2aNaSlpXHt2jUOHz6c43UUCg774X1ByA7R\nF8FZRFdyjyX+Grc2vc/lGa35c1Y7ErbMz2QMuJWuiF+H/4f/mHWUn7QLvweeFmMgGwrLKkN3PUop\n+vXrR9++fWnSpAkhISGMGzeOUaNGkZiYSM2aNenWrRudOnXKdOyAAQM4duwYAwYMyLD/jTfeICQk\nhC5dulCtWjX69u1LVFQUAFFRUfTu3ZvAwEAefvhhnnrqKVq3bm2VJZ2pU6eyfPlyAgMDGTt2rNXH\n3Vbu7MJZ0bRpU06ePEmNGjWYPn06n332GWXKlKFkyZLMmDGDESNGEBISwtdff83DDz+cY92lM3Pm\nTG7dukWdOnV47rnnMq0eZJt2yJAhHD9+nJCQEIYNG5Zl/m5ubixevJiTJ0/SoEED6tevz+rVq52S\n55NPPuHMmTOEhoYyfPhwJk2alGkUBoz5GUuXLmXu3LmEhITQvn1768Tk119/PcvrKAiCIAh3A9pi\nIfnEVq5/OZqLb9Tl5jevczv2eIY0yrs0Ps0Hcc/TX1LhtUOU7vUWntWaFui3j4oqLp9DkNfc6RyC\nokxSUhK1a9fm559/Jjg42NXiCC6mOOu6IAiCcHeRGnOExD1LSPzlayxxFzIn8PDGu153fJsPwrNG\nG5SHd8ELWYAUyzkEQt6wYMECwsLCxBgQBEEQBKHIk3YtmoSdn5F0+EduxxxxmKZElQb4tXkS77A+\nuHmVLGAJix/FziC40+8QFFXSv8y8aNEiF0uSkXHjxrF8+fJM+wcMGMA777zjAomyJzo62uoyZc/O\nnTsJCChW38AT7oBt22StcMF5RF8EZxFdMbAkxJF8YjNJB1aS9Ov3oC2Z0ijvUvg0DcenWTgegWHi\nCpSHFDuD4G6jsE6inj17NrNnz3a1GE5TpUoV6xq/giAIgiDkPzo1icRfviZx1yJSTu8BS1rmRO6e\neNfrik+LIXjV7oByl6ZrflDsajW9x1wQBCEvkR48ITeIvgjOcrfpik67TUrUDpIOribx0Dfo+KsO\n03lWb41vu5F43f+QuAQVAMXOIBAEQRAEQRAKD5bkWyQf20DKiW0kHfoWy63LmRMphUfVxnjd3wHv\nBj3wqNKg4AW9iyl2BsHdNodAEISCQfx8hdwg+iI4S3HVFW2xkHJyJ0mHviFx/3J0wnWH6dzvqYpv\n26fwaT4I91LlC1hKIZ1iZxAIgiAIgiAIruH2xRMk7FtK0oFVpF057TCNW+lKeNfvjndYHzyDW6Dc\nMn8YVShYip1BIHMIBEHID4pjD56Qf4i+CM5SHHRFp6WSfGwD8f/7gJSoHQ7TuPsH4d2oN173P4Rn\n9VZiBBQyip1BcLfRs2dPBgwYwJAhQ5w+5ty5czRq1IjLly/j5iYfqxYEQRAEIfekXYsmfst8EnZ9\ngU6MyxSvfMrg0+gxvBs/hmeNB1DS5ii0FDuDQOYQOIes3SsIuaO4+vkK+YPoi+AsRU1XLPHXSDry\nI0kHV5N8fFPmpULd3PGq0wmfpgPwrtsN5enjGkGFXFHsDAJBEARBEAQh79ApCSQeWEXS4R9JPrYB\n0lIypXErG4BP48fwa/8P3MtWdoGUwt+h2I3dFNU5BP7+/pw+fdoaHj16NNOmTbOGf/jhB9q3b09Q\nUBBNmzZl06ZN1rhTp07RqVMngoKCGDp0KHFxmYft7NFa88UXX1C3bl3q1q3L3LlzrXEHDhyga9eu\nBAcHU7duXSZOnMjt27cBmDBhAq+++mqGvB5//HHmzZsHQGxsLMOHD6dWrVqEhYUxf/78DPl27NiR\noKAg6tSpkykfQSjMFKUePMH1iL4IzlJYdUVbLKTGHOHm9//k4psNiPvqeZIP/5DJGPCs2Y57nl5M\nhdcOUbrXVDEGiigyQmDyzuQf8zS/8dO65Sp9di48+/fv59lnn+Xzzz+nXbt2xMbGcuvWLWv80qVL\nWblyJYGBgURERDBx4kRrAz07tm/fzv79+zl58iSPPfYYDRo0oF27dri7uzNt2jTCwsI4f/48/fv3\nZ8GCBYwaNYqBAwcydOhQpk6dCsDVq1fZsmULc+bMQWvN4MGDeeSRR/jvf//L+fPn6d27NzVr1qRD\nhw5MmjSJiIgI+vfvT0JCAseOHctVHQmCIAiCkL+kXYsmfvM8EvZ+leVHwzyqNsa7US+8GzxKifIh\nBSyhkB8UuxGCgwcPulqEO0JrnWXcl19+yZAhQ2jXrh0AlSpVokaNGtb48PBwateujY+PD5MnT2bN\nmjXZ5pfOxIkT8fb2JjQ0lMGDB7Ny5UoAGjZsSJMmTVBKUaVKFYYPH8727dsBCAsLo3Tp0mzevBmA\nVatW0aZNG/z9/dm/fz9Xrlxh3LhxuLu7ExgYyNChQ1m1ahUAHh4enDx5kqtXr+Lr60uTJk3urLIE\nwQVs27bN1SIIRQjRF8FZCoOu6NspJB1dz7XPn+HS202J//nDTMaA+z1VKfXo65SfvIdy4zZSsuPz\nYgwUI2SEoAhw/vx5unTpkmV8QECA9X/VqlVJSUnhypUrlCtXLstjlFJUrlw5w3HpPfZRUVFMmTKF\ngwcPkpiYSFpaGg0bNrSmHThwIMuWLaN9+/YsW7aMf/zjHwBER0dz4cIFQkKMB4TWGovFQuvWrQF4\n//33mTZtGi1atCAoKIgJEyZke16CIAiCIOQP2mIhJWoHCdv/S/LxTeikG5nSuPn541mzLd6NHsO7\n/iMod2k2FleK3ZW90zkEuXXxyWt8fX1JSEiwhi9dumRt6AcEBHDq1Kksjz1//rz1/7lz5/D09MTf\n3z/HMs+fP28daYiOjqZSpUoAjB8/ngYNGrBgwQJ8fX2ZN28e3377rfW4/v3707ZtW44cOcKJEyfo\n3r27Vc5q1aqxZ88eh+UFBwfzySefAPDNN9/wxBNPEBUVhY+PrEAgFH4Kq5+vUDgRfRGcpaB1xZIc\nT+Ler4j/+UPS/nTctvAIakrJri/hdX9HWSr0LkGuciGhfv36rFy5EovFwoYNG9ix468PewwZMoTF\nixezdetWtNZcuHCBEydOWOOXLVtGZGQkCQkJzJgxg169euW4rKjWmnfeeYfExESOHTvG4sWL6dOn\nDwA3b96kVKlS+Pr6EhkZyaeffprh2MqVK9OoUSMiIiLo0aMHXl5eADRp0oSSJUsyZ84ckpKSSEtL\n49ixY/zyyy8ALF++nCtXrgBQunRplFLyHQRBEARByGe01qSc2sP1pWO49Gptbqx4KZMx4FY2AL8O\n/49y4zdT7sV1eId2FmPgLqLYXemiOodg2rRprF27luDgYFatWsUjjzxijQsLC2Pu3LlMnjyZoKAg\nevbsSXR0NGC4/oSHh/Pss88SGhpKamoq06dPz7E8pRStW7emadOm9O3bl+eee4727dsDMHXqVJYv\nX05gYCBjx46ld+/emY4fNGgQx44dY+DAgdZ9bm5uLFmyhN9++43GjRtTq1YtxowZw82bNwHYuHEj\nrVu3JjAwkFdeeYUFCxZYjQlBKOwUBj9foegg+iI4S37qik5NImHvUv6c1Z4r73Ujcefn6JS/vBGU\nTxl8Ww2n3IStVHj9V0r3eguPKvXzTR6h8KKcmXxalJg9e7Z+8sknM+2PiYnJ4DMv/D127txJREQE\nhw4dcrUogh2i6/lDUft4kOBaRF8EZ8lrXbEkx5MSuZnko+tJ/GUVOulmpjTuFWri1+ZJfFo+jptX\nyTwrW8h/zCXc8/zrsjKHQMg1qampzJs3j2HDhrlaFEEoMKRxJ+QG0RfBWfJCVyzxV0k+/j8S9y8j\nOXIL3E7OnMjDB5/GvfFtOQSP4BY5uhYLdxfFziAQDFasWMHYsWMz7a9atap1CdE7ITIyko4dO1K/\nfn1GjRr1d0QUBEEQBOEO0WmppJzYSsKeJSQd+gbSUh2mc/cPwqfFEPzaPImb3z0FLKVQVCh2BsHB\ngwcJCwtztRgup1+/fvTr1y/P861Vqxbnzp3L83wFobAjLiBCbhB9EZwlN7qiLWmknNpN0sFvSPrl\nayy3LjtMV+K+OnjV6YxXnU541mgjowFCjhQ7g0AQBEEQBKE4kXL2FxJ3f0nSoW+zNAI8AsPwCu2M\nT9NwSpSrVrACCkWeYmcQyBwCQRDyA+ntFXKD6IvgLFnpitaa5OMbid/wHilRjl193crch3fDnvg2\nC8ejqrR/hDvHpQaBUqoM8B+gHmABngQigaVAEHAaGKC1jjPTTzLT3AZe0Fqvc4HYgiAIgiAI+ULa\nrT9JPvIT8Vs/4Xb0r5nilW9ZvBs8ik/jPnjWaCtfDxbyBFdr0XvAD1rr/kqpEoAfMBnYoLX+l1Jq\nIjAJeFkpFQoMAOoAVYANSqma2m7dVJlDIAhCfiA+4UJuEH0RnGXbtm20ad7EGA3Y/l9SIreAtmRM\n5FYCn7A++DQfhGdIK1QJT9cIKxRbXGYQKKVKAw9orZ8A0FrfBuKUUr2A9mayz4CfgZeBnsBXZrrT\nSqkTQHNgdwGLLgiCIAiC8LewJN0k5Y/t3PzxAy5+cxCdEp85kYcPvq2GUrLD/8P9nioFL6Rw1+DK\nEYJg4E+l1KdAQ2AfMAaoqLW+CKC1jlVKVTDTBwA7bY4/b+7LgMwhEAQhP5DeXiE3iL4IjrAk3SDp\nl69J2LWI1HOHwHKbRoD9J2I9gpvjXb87Ps0H416ynCtEFe4yXGkQlADCgNFa631KqXcxRgLs74vi\n9SllQRAEQRDuGtKuRZN0dAPJx9aT/PvPkJroMJ17+Rp4N+yBb6vhlPAPLFghhbseVxoE0cA5rfU+\nM7wSwyC4qJSqqLW+qJSqBFwy488DVW2Or2Luy8B7772Hn58fgYHGzVSmTBnq169PSEhIfp2HIBQq\n4uLiqFy5MmD4psJfvZUSvvNw+v/CIo+EC3dY9OXuDqfFxbLpi3dIObGNMBUJwJ5YAGheCWvY3b8a\nHlUa8NDwCeyKuoJSiramMVCYzkfCrgun/z979iwATZs2pWPHjuQ1ym5OboGilNoMPKO1jlRKvQ74\nmlFXtdYzzUnF92it0ycVfwm0wHAVWg9kmlQ8e/Zs/eSTT2YqKyYmxtpIEoTijOh6/iCTRIXcIPpy\n95F28zLJh9eSdHgtyUfXQRbtqxKV6+HTLBzf5oNw87tXdEXIFQcOHKBjx455/qU5V44QADwPfKmU\n8gBOAiMAd2CZUupJ4AzGykJorY8qpZYBR4FU4Fl7YwDufA7BwH81uaPjsuKrCftzlb5Ro0Y89dRT\nLFu2jDNnztC7d2+mTJnC6NGj2bVrF02bNmXhwoWULl2aESNGsGvXLpKSkqhXrx6zZs3i/vvvJzU1\nlU6dOjFkyBCeeeYZLBYLjzzyCB07dmT8+PFZlj1z5kyOHz+Ol5cXP/zwA0FBQSxcuJBvv/2Wjz76\nCC8vL+bMmcODDz4IwI0bN5gyZQobNmzAzc2NQYMGMXnyZJRSnD59mjFjxnD48GHc3Nzo0KEDs2bN\nonTp0tbzfPrpp1m6dCnR0dF07NiRDz/8EE9PWTFBKNzIC1vIDaIvdw8pZw+QdPAb4rd+4tgdyK0E\nnjXb4h3aBa/QzpQoXz1DtOiKUBhwc2XhWutDWutmWutGWus+Wus4rfVVrXUnrXVtrXUXrfV1m/TT\ntdY1tNZ1iuM3CL777jtWr17Nnj17+PHHHwkPD+f111/njz/+wGKx8PHHHwPQuXNn9u/fT2RkJA0a\nNGDUqFEAeHh4MG/ePGbMmEFkZCTvvvsuFouFcePG5Vj2unXrGDhwIKdPn6Z+/fr069cPrTVHjx5l\n/PjxvPjii9a0o0ePxtPTkwMHDrB582Z+/vlnPv/8c8D4kMqLL77I8ePH2bVrFzExMcycOTNDWWvW\nrGHlypUcPHiQw4cPs3jx4ryqQkEQBEHId9JuXOTG929zeXpLrvy7E/Gb5mQyBjxrtKVM+P9R4a2j\n+P9jFX7tIzIZA4JQWHD1CEGeU5S/QzBy5Ej8/f0BaNmyJRUqVKBu3boAPPLII2zduhWAwYMHW4+Z\nMGEC8+bN4+bNm5QqVYo6deowbtw4hg4dyp9//snGjRtRKueRpZYtW1pHAHr16sV3333HmDFjUErR\np08fxo4dy40bN0hKSmLDhg2cPn0aLy8vvL29iYiI4PPPP2f48OEEBwcTHBwMwL333ss//vEPZs2a\nlaGsiIgIKlQwFo/q1q0bhw8f/nsVJwgFgAzrC7lB9KX4YUm8QdJv35O4fwUpkZszfysAKHFfKD7N\nB+FdtyslKtRwKl/RFaEwUOwMgjslty4++UH58uWt/318fDKEvb29uXXrFhaLhalTp/LNN99w5Yox\nAUkpxdWrVylVqhQAAwcO5O2336Znz55Uq1bNqbLTG+jpZfn7+1sNCR8fH7TWxMfHc+HCBVJTU6lT\npw5gjAhoralSpQoAly9fZtKkSezcuZP4+HgsFgtly5bN9jwvXryYi1oSBEEQhIJBa03y0fUk7l9O\n0m/fQ2pS5kQePuaXgx/Dq05n+XKwUCQpdlpb3L9DsGLFCtauXcuaNWuoUqUKN27cIDg4GNvpFOPH\nj6dr165s2rSJ3bt306JFizwrPyAgAG9vb6KiohyOPEydOhU3Nzd27txJ6dKl+eGHH5g4cWKelS8I\nrkJ68ITcIPpStEk3BG5t/D9ST+7KnEApPINb4tvuGbxDu6A8fTOncRLRFaEwUOwMguJOfHw83t7e\nlClThvj4eN56660MDfOlS5fy66+/smXLFtauXcuzzz7L1q1b8fW984eVLRUrVqRDhw5MnjyZyZMn\nU7JkSc6cOUNMTAytW7fm1q1blClThpIlSxITE8P777+fJ+UKgiAIQn5jSb5F0qFvif/fB9y+cDRT\nfImA+viE9cUnrI98OVgoVrh0UnF+cPDgQVeLcEfY97Zn5fcfHh5OlSpVqFu3Lm3atKF58+bWuOjo\naKZMmcJHH32Er68vffv2pXHjxrzyyit5Kt+HH35IamoqrVq1IiQkhBEjRljdfiZMmMChQ4eoVq0a\ngwcPpkePHk6dlyAUdmzXhBaEnBB9KTrotFSSjq7n+lfPc+mN+sQtHp3RGHBzx7f1E5Qbv5nyL22m\nZMfn89QYEF0RCgMu/Q5BfiDfIRDudkTX8weZ+CfkBtGXwk/a9RgStn9Kwu4vsdyIzRSvPP3wbT0c\nvxAzo4wAACAASURBVAf/gXvZgHyTQ3RFyA3F9TsEeU5xn0MgCIJrkBe2kBtEXwont6+eI/noepIO\nrSHlj20OPx7mXi4Y3xaP49t6BG5+9+S7TKIrQmGg2BkEgmMGDBjArl2ZJ0aNHTuWMWPGuEAiQRAE\nQch/tMVC8rENxP/8ASkntjpM41a6Ij5N+uPd4BE8gpqh3IqdR7UgZEuxMwiK8ncI8pNly5a5WgRB\nKNLIsL6QG0RfXE/azUskHfiahJ0LuR37e+YESuFZ4wF82zyBd73uqBKeBS8koitC4SBXBoFSqgNw\nWmt9Sil1HzADsACTtNaZHfAEQRAEQRAKkNsXI7m1cQ6Je7/K/PEwN3c8a7bDu25XvBv2wL3Mfa4R\nUhAKGbkdIfgQ6Gr+n23+JgLzgZ55JdTfQeYQCIKQH0gPnpAbRF8KDp12m5RTu0k+8hPJR9dx+2Jk\npjTKqyS+rYbh1z6i0C0XKroiFAZyaxAEaK3PKqVKYBgGQUAKEJPnkgmCIAiCIDhAW9JIObGFxP0r\nSDryEzr+qsN0HiEt8WkyAJ/GvXHzLVPAUgpC0SG3BsENpVRFoB5wVGt9SynlCXjkvWh3hswhEAQh\nPxA/XyE3iL7kD2k3L5G4dykJ2z8l7cppx4k8vPGq/RC+rYbhXbdLgcp3J4iuCIWB3BoE7wN7AU8g\nfWmaNsDxvBRKEARBEAQBzFWCjv5EwvZPST6+KfO8AMCtzH141+2GV90ueNV8AOXp6wJJBaHokiuD\nQGs9Uyn1NZCmtY4yd58Hns5zye4QmUOQmZkzZ3Lq1CnmzZvnalEYPXo0AQEBTJ48OdfHDhgwgL59\n+xIeHp4PkglC9kgPnpAbRF/+PmlxscZowI5PSbt6NlO88imDT7OB+DTph0fVxkV2qVDRFaEwkKNB\noJR6KIv9QXkvjpBfKJXnH7UrcGTpVEEQhOKNJTme5OMbSdj+qfHNAAejAZ7VWxuGQFhflKePC6QU\nhOKHMyMEC5xIo4GQvylLnlAc5hCkpaXh7u7uajEEQbBB/HyF3CD64jxaa1JObCFh5xckH/kRnZKQ\nKY3yvcf4enDbpyjhX7z6I0VXhMJAjuNrWutgJ7ZCYQz8HS6MuTdPt9zSqFEj5syZwwMPPEDVqlWZ\nPXs2TZo0ITAwkNatW/P9999b0y5ZsoTu3bvz2muvERISQlhYGBs2bLDGnz17lh49ehAUFETfvn25\nejXj6gtr166ldevWhISE0KtXLyIjIzPI8f777/PAAw8QGBjICy+8wOXLlxkwYACBgYH06dOHGzdu\n5Hg+u3btolu3bgQHB9OgQQO++uora9z169cZOHAggYGBdOnShTNnzljjdu/eTadOnQgODqZTp07s\n2bPHGtezZ08WLVpkDX/22We0bNnSWke//fYbALGxsQwfPpxatWoRFhbG/PnznbkEgiAIQgGSdutP\n4jd/zJ8z23D1w94k/bIqozGgFJ7V21Bm8AdUfOM3Svd6q9gZA4JQWCiaDnfZUJTnEKxatYply5Zx\n6tQpatasydq1azn7/9m78zipqjPx/59Te1UDDbLLKovKJg00CIii4r4bCS4TlzCJMU5MZjL5Jppl\nMvN9zfwSZybfmUkySUzURBP3fYkxKKLYimwNiiwKKkuzLw3SXXvd5/dHVXdXdVVDVVNdWz/vl7y6\n7r3n3vtUe7rqPPeec+727Xz3u9/ljjvuYN++fa1l6+vrOfXUU/nkk0+46667+Na3vtW67atf/SpT\npkxhy5YtfOc73+Gxxx5r3bZlyxZuv/12fvrTn7J582bmzZvHTTfdRDQabS3z8ssv8/zzz7NixQpe\nffVVrr/+en784x+zZcsWLMvivvvuO+b7aGhoYMGCBXzta19jy5YtLF26lEmTJrVuf+6557j77rvZ\nunUrp5xyCv/6r/8KxBOFG2+8kTvuuINPPvmEr3/969xwww0cPnw47RzPP/88//Ef/8F9993H9u3b\nefTRR+nTpw8iwk033cQZZ5zBxo0bef7557nvvvtYsmRJ7v9DlEqiV/BULrS+ZCYihD9bzqH7/4Z9\n/zSez5+7h+ie1HlJ7APG0uOif2TAP71P37tewjfjxooeJKx1RZWC4yYExpjzs/lXiGAr3de+9jUG\nDx6M2+3mqquuYsCAAQBcc801jBo1ivr6+tayw4YN40tf+hLGGG644Qb27NnD/v37aWhoYO3atdxz\nzz04nU5mzZrFJZdc0rrf888/z0UXXcQ555yD3W7nrrvuIhAIpFyJv/322+nbty+DBg1i5syZTJs2\njQkTJuByubj88stbr8R35Omnn+bcc8/l2muvxW6307t3byZMmNC6/fLLL6empgabzcb8+fNbj7do\n0SJGjx7N/PnzsdlsXHfddYwdO5ZXX3017Rx/+tOf+OY3v8nkyZMBGDlyJEOHDqW+vp6DBw/yj//4\nj9jtdoYPH87NN9/Ms88+24n/I0oppfIhemgHRxf9J/t/MpOD/3MpoQ//AlbbhSjjqsJ39lfp93+W\n0v+e9+h52Q9K7gFiSlUyHUOQMPi/Mz/UpJBOPvnk1tePP/44v/71r9m+PT6zgt/v5+DBg63bW5IF\nAK83PqiqubmZAwcO0Lt379Z1EE8edu2KPztuz549DBs2rHWbMYYhQ4awe/fu1nX9+/dPOXbyssfj\noamp6ZjvY+fOnZxyyikdbk+O3efz0dzcnDG2ltiTYzveOXbs2MHu3bsZNSpeHUUEy7KYPXv2MWNW\n6ni0n6/KhdYXsIJHCax6ksCKx4jsWJtxgLDzlBl4a69PPDisdxGiLD6tK6oUHDchEJGOW3Yqr1pm\nAmpoaOAf/uEfeOGFF5gxYwYAc+fORUSOe4xBgwZx+PBhAoFAa1LQ0NCALTEd26BBg9i4cWPKPjt3\n7kxJRk7UkCFDUu5mZGvQoEGtCVCLhoYGLrjggozn+OyzzzKuHzlyZModD6WUUoUhYT+hj5cSWv9X\nAvXPIKH0C0jG3QPPlGuomnsHzsHjixClUqq9nMYQGGP+b0f/uirAXJXzGIIWzc3N2Gw2+vbti2VZ\nPPLII2mN+I4MHTqUmpoafvrTnxKJRHjvvfdSutxcc801vPbaa7z99ttEo1F+8Ytf4PF4mD59et7i\nnz9/Pm+99RYvvPACsViMxsZGPvzww+Pud+GFF/Lpp5/yzDPPEIvFePbZZ/n4449Tujy1uPnmm/nl\nL3/J+++/D8Bnn31GQ0MD06ZNo0ePHvz85z8nGAwSi8XYuHEja9asydv7U92TXsFTuehO9UVEiOze\nyNFX72Xvv5xB4/034V/2UGoyYAyu086l982/ZcC/fEjvG36uyUBCd6orqnTl+qTiYe2WBwFzgefy\nE073lfycgNNOO40777yTiy66CLvdzvXXX8/MmTOz3v+3v/0td955J6NHj2b69OnceOONHDlyBIAx\nY8bwm9/8hu9+97vs2bOHSZMm8eijj+JwONKOk2k5G0OHDuWJJ57gRz/6Ed/85jeprq7mBz/4ARMn\nTjzmfn369OGxxx7jnnvu4Tvf+Q6jRo3i8ccfp3fv3mmxXH311TQ2NnL77beze/duhg8fzm9+8xuG\nDh3KY489xg9/+EOmTJlCOBxmzJgx/OAHP8j5fSillMpMrBjhj94kuP5VQuv/SqyxIWM5+4CxVM35\nW7zTvoitqk+Bo1RKZctk0w3lmAcw5hLgRhG5NT8hnZif/exnsnDhwrT1u3btymu3GKVKldb1rqH9\nfFUuKrW+RA/tILj6KfzLHs749GAAe59heGquxj3+IlxjzqqIB2N2pUqtK6pr1NfXM2/evLz/UeV6\nhyCTRcATeTiOUkoppUqMRIIEN7xGYNnDhDYtzljGuHy4Tj8fb801eM64HONwFzhKpdSJyCkhMMa0\nn0nIB9wE7OjMyY0xW4EjgAVERGSGMaYP8QRjBLAVWCAiRxLl7wEWAlHgWyKyqP0xK2EMQTl4+umn\n+fa3v522ftiwYbzzzjtFiEiprqVX8FQuKqG+WE0Haa57AH/d/VhNB9K2G18fvLUL8Ey8FNfoWRi7\nswhRlr9KqCuq/OV6h2AL8SlGW25V+IE1QGe7C1nAuSLSmLTubuB1Efl3Y8z3gHuAu40x44EFwDhg\nKPC6MWasnGifJ9Up8+fPZ/78+cUOQymlVB6JCJFP36P53d8T/OBliARTCxiD+7Tz8NRcjXfqdRX9\nwDClupOcEgIRyfeTjQ3pMx1dTXygMsBDwJvEk4SrgMdFJApsNcZsBmYAy5N37uxzCJRS6li0n6/K\nRbnVl+iBzwisfILguleI7kqfFc7Wewi+GTfgnXETjn46G3k+lVtdUcXTFDjSZcfOxxiCEyHAa8aY\nGHCfiNwPDBSRvQAisscY0/IUqyHAsqR9dybWZcXtdnPw4EFOOukkHeCkKpbf78dutxc7DKVUGZBY\nhOC6PxN470+EPloCGW64O4ZOpsd538BTc5V2CVKqwJoCR9jUsIb121ezccdqtu37mP9zwW+75Fy5\njiFwAT8kPm5gMLALeBz4NxEJHmvfDpwlIruNMf2BRcaYj4gnCcly6hK0ZcsW7rzzToYPHw5AdXU1\nkyZNYs6cOTQ1NbF+/XrsdjvV1dUArdNx6rIuV8Ly3r17aW5uZuDAgUD8yhO09VHV5c4vz5kzp6Ti\n0eXSXi7V+iIizBgoBFY9wdK/voSEmpgxCABW7In/nDHMg7d2AWtdU7D3G8XZ084umfh1WZcreTkQ\naqbPcDfrt69m8ZJF7G1sQBAObQ8SOBIBYG2/tcybN498y2naUWPMA8BpwL8B24gP/P0+sFlE0uf6\nzCUQY34MNAFfIT6uYK8xZhCwRETGGWPuBkRE7k2UfxX4sYikdBlavHixaJchpZRSqk1kzyYCKx4j\n+OGrxPZtTi+QGBvgnXEj7nEXYvP2KnyQSnUzme4AyDGug9uMne/M+01JTDt6DTBaRA4nljcYY5YT\nH2ycU0JgjPEBNhFpMsZUARcB/wK8CNwG3Et8sPILiV1eBB4xxvwX8a5CY4AV7Y+rYwhULrTvpsqW\n1hWVi2LXF4lFiWxdSXDTYkIbXiO6c13GcrbeJ+ObcRPemTfjOKn9s0dVIRS7rqjC6UwCcMqg0xk/\nrJbxw6dx2pDJbFr/cZfElmtCsIf4VKOHk9Z5gd2dOPdA4DljjCTieEREFhljVgFPGmMWEr8LsQBA\nRDYYY54ENgAR4E6dYUgppZRqEzu8E/+yPxJY8Rixxswzght3D7zTb8BbuwDn8KkYW77nC1FKQX4S\nAJ+7R0FizbXL0N3Exw/8AmgAhgF/BzwKrGwpJyJv5DfM7GmXIaWUUt2JxCIE176If/mfCG9emnFw\nMHYnngkX4629Htdpc7EVqJGhVHdSiASgVJ5U/LXEz++3W39H4h/EBwG3f4CZUkoppfLECjUTXPs8\nwbUvENryDkQCaWWMrw+eM67AM/4iXKeeg83TswiRKlW5yukOwPHklBCISMlPPqxjCFQutO+mypbW\nFZWLrqov0YPbaF7yvwRWPYkEP89YxnXqXHyzb8Uz8VKMw533GFR+6WdL+aikBKC9XO8QKKWUUqqA\nYo0NBNY8S3DNC0R2rMlYxt5nKJ5pX6Rqzt9i731ygSNUqjJVcgLQXk5jCMqBjiFQSilV7qzmQwTX\nvUJg5ROEP3knYxl7v1H4Zt+CZ/LVOPqOKHCESlWeckgASmUMgVJKKaW6QKzpAKEP/4J/xWNEtq4C\nK5peyObAfepcqs79Oq5Tz9UZgpQ6AeWQABRKxSUEOoZA5UL7bqpsaV1Ruci2vkQP7SC8aTHBda8Q\n+vgtiEXSCxkbrlPPwTt1Pp5Jl2PzVXdBxKpY9LOlcDQB6NgJJwTGmCuAvSKy8riFlVJKqW5OIkFC\nm96gue4Bwh8t6bCcc0QtnslX4J22AHv1oAJGqFRl0AQge50aQ2CMeRCYC7wPPAz0FpE/5De0ztEx\nBEoppUpRrLGB5roH8L/3R6T5UMYyzhHT8NRcg3fKtTo4WKkcdYcEoNTGEPxZRBYaY2YBtwJNeYxJ\nKaWUqghWqIngB38m+P6LhDYsAiuWWsAYXGPOxjPxEtwTL9XBwUrloDskAIXS2YQgCiAiy4Bl+Qvn\nxOkYApUL7bupsqV1ReXizWcfoiZcT7D+WSTcnLbd3mcYninX4pt9G45+IwsfoCoZ+tmSPU0Auk5n\nE4LpxphbgT8Bi0XkSB5jUkoppcpO7PAu/MsfIbj2BY6s2UAgQ7d/19izqZp7B+7xF2Fs9sIHqVQZ\n0QSgcDo7huBOYBNwIXA+0Cgil+Q5tk7RMQRKKaUKxQo1EVj5JKEPXyH00ZsgVloZ+4Cx+KbfgOeM\ny3EMPLXwQSpVJjQBOL5SG0PwHtBfRO4BMMZ48xeSUkopVbpEhPCny/DXPUBo/V+RsD+9kN2F54wr\nqDr7KzhPORNj8v79rVTZ0wSgdHQqIRCR+nbLgfyEc+J0DIHKhfbdVNnSuqKspoME1r6Af9lDRHeu\ny1jGNWYOvlm3sPJIL845/6ICR6jKUXf6bNEEoHRV3IPJlFJKqXyRcAD/iscIrHqCyLZVkKGbrX3A\nWKpm34b7jCtwnDQMAFtdXaFDVarkaAJQPjo1hqCU6RgCpZRSJyqyawPNS3+TmCUoQ5cgpxdf7QJ8\nZ38Fx+Dx2iVIKTQBKISSGENgjLGJZBgxpZRSSpW52OGdBN9/mUD900S2rU4vYGw4R07HO+VavLUL\nsPl6Fz5IpUqIJgCVI+uEwBhjB5qMMb1FJNSFMZ0QHUOgctGd+m6qE6N1pTJJOEBw42sElj9KaNPi\n9AeHAfb+o/Gd9WV8tddj69E3q+NqfVHZKqe6oglA5co6IRCRmDHmY6AvsKvrQlJKKaW6VuzIHgIr\nH6f5zV9hNR1IL2B34pl0GVVz78A5coZ2CVLdkiYA3UdOYwiMMd8FbgD+B2iAtlohIm/kPbpO0DEE\nSimlMpFIkOCGRQRWP01o4+sQCaaVcY2Zg6fmGrw1V2d9N0CpSqEJQOkriTEEwNcTP/+53XoBRp1w\nNEoppVQeiRUjsnUl/hWPElzzPBJqSitj630yvhk34Z1+PY7+o4sQpVLFoQmAapFTQiAip3RVIPmi\nYwhULsqp76YqLq0r5UNEiHz6Hv7lfyL44auIvzFjOcfQyVSd/RW8U67FuHx5jUHri8pWIeuKJgCq\nIzk/h8AYcyHxbkMDRORKY8w0oLpUugwppZTqnmJHdhNc9wqB5Y8S2bEmYxl7v1PwTr0O77T5OAae\nWuAIlSosTQBUtnIdQ3AX8C3gfuAeEak2xkwAficis7soxpzoGAKllOo+JBom+MHL+Jc9THjz0oxl\nbL0G4R5/Ab4ZN+E85UwdIKwqliYAla9UxhD8PTBPRLYaY76XWLcJOC2/YSmllFKZSdhPcOPrBNc8\nR2jTG0jwaHohhxtv7RfxzbwF54hpmgSoiqQJgMqXXBOCnsCOxOuWGucEwnmL6ATpGAKVC+3nq7Kl\ndaW4RITIZ8vxr3iUwOpnIBJIL2Sz4xp9Fp4zrsA75dqizhKk9UVlK5e6ogmA6iq5JgRLgbuBf0ta\n901gSWcDMMbYgFVAg4hcZYzpAzwBjAC2AgtE5Eii7D3AQiAKfEtEFnX2vEoppUpf7Mhu/Mv+SGD5\nn4g1NmQsYz9pON7p1+ObeTP2PkMLHKFSXUcTAFUouY4hGAy8BPQDhgCfAkeBK0RkT6cCMOYfgGlA\nr0RCcC9wUET+PdEtqY+I3G2MGQ88AkwHhgKvA2Ol3RvQMQRKKVXerOZDBDe8RvCDlwmtfzXz04MH\njMVbcxWeKdfiGDROuwSpiqAJgDqekhhDICK7jTHTiTfKRxDvPrRCRKzOnNwYMxS4jPgdh28nVl8N\nzE28fgh4k/hdiauAx0UkCmw1xmwGZgDLO3NupZRSpSW6/xOaXvsvAqueBCuatt14euGpuQrfmV/C\nOXK6JgGq7GkCoEpFTgmBMeY7IvKfwIrEv5b13xaR/9eJ8/8X8H+A6qR1A0VkL4CI7DHGDEisHwIs\nSyq3M7EuhY4hULnQfr4qW1pXuobV3Ejww1fwv/sQkW2rMpZxjZ6N76wv4znjSozDVeAIO0fri8ok\nUwJwcJufk0Z4M5bXBEAVSq5jCP4J+M8M638I5JQQGGMuB/aKyFpjzLnHKJp9nybgrbfeYtWqVQwf\nPhyA6upqJk2a1PrBXFdXB6DLugzAunXrSioeXdbl7rA8e8oEQpveYMkTvyKy431mDIzfZF6R6Hg6\nYxA4h9WwxnEGrlEzmXvlDSUVvy7rcrbLgVAzfYa7Wb99NYuXLGJvYwN9RngAOLQtdWD8oW0BjLFR\ne+ZUxg+rJbzPxbD+o7ngvAtbj1e/a21JvT9d7vrlltfbt28HoLa2lnnz5pFvWY0hMMacn3j5EnAF\nkHyfdhTwIxEZkdOJjfn/gC8RHyDsJT6D0XNALXCuiOw1xgwClojIOGPM3YCIyL2J/V8FfiwiKV2G\ndAyBUkqVHivUHH968IpHCX7wZ4hlmJzO5sA9bh5V596Ja8wc7RKkyo52AVJdrdhjCB5I/PQADyat\nF2APcFeuJxaR7wPfBzDGzAX+UURuNsb8O3AbcC9wK/BCYpcXgUeMMf9FvKvQGJK6LSmllCotYlmE\nt9QRWPEogfdfyjxVqDE4h0/FM+kKfDO/VNSpQpXKlSYAqlIcNyEwxnxDRE5JvH5URG7q4ph+Cjxp\njFkIbAMWAIjIBmPMk8AGIALc2X6GIdAxBCo3dXXaz1dlR+tK9qL7thB8/yX8yx4idmh7xjLOYTW4\nJ16Kt/Z6HH2HFzjCrqf1pTJ1RQKgdUWVgmzuEPwb8MvE6yu6IggReQt4K/H6EHBBB+V+AvykK2JQ\nSinVeRIJEqh/hualvyW6c13GMvb+o3GfPg/frFtwnjy+wBEqlTu9A6C6i+OOITDGrAHeANYD/wv8\nXaZyIvJgpvWFpmMIlFKqcCJ7PiL04V9ofvt3WEd2p2033mo8NVdTNfvLOIaeoeMCVEnTBECVumKO\nIbge+C5wI+AEbs5QRkgdW6CUUqpCxY7uJ7j2BfzLHiK6a316AacH92nn45l8Jd7JV2FcmadUVKrY\nNAFQKu64CYGIfAx8BcAYs1hE8j/XUR7pGAKVC+27qbLV3etK7PBOgu+/RGDNc0S2rsxYxtZrIFVz\nv45v1i3YfL0LHGFp6e71pVSVYgKgdUWVgmxnGQJAROYZYwYSf0JwP5KmHy2VLkNKKaXyQ0QIb3qD\n5rd/R2jj65DpofROL57xF+CecAmeyVdi06ulqoSUYgKgVCnK6jkErYWNuQb4E7AZmEB8XMFEoE5E\nzuuSCHOkYwiUUqrzRITorvUE179KYOUTxPZ/kl7IZsc5cjreKV/AW7sAm7dX4QNVKgNNAFSlK/Zz\nCFr8K/BlEXnKGNMoIlOMMV8mnhwopZQqU1ZzI/5lD+F/74/EDnyWsYxr7Nl4aq7BO/kqfV6AKgma\nACiVH7kmBMNF5Kl26x4i/nCy7+QnpBOjYwhULrTvpspWpdaVyO6N+N99iMCKR5FQU9p24+6Bb+aX\n8M35Co7+o4oQYXmq1PpSbJWYAGhdUaUg14RgnzFmoIjsBbYaY2YBBwB7/kNTSinVFazmRgIrH8e/\n6kmiDe+nbTfuHrjHzcMz6XI8ky7DuHxFiFKpykwAlCpFuY4h+B6wRUSeMcbcAvwWsICficiPuijG\nnOgYAqWUyix6cBv+ugdornsAIoG07Y6Bp1I17+/xTrkG4/QUIULV3WkCoNSxlcQYAhG5N+n1w8aY\nN4EqEdmY78CUUkqdmNYBwuv+THDtC0T3bEovZHfimXgJvtlfxjX2HIzNVvhAVbelCYBSpSHXLkMp\nRGR7vgLJFx1DoHKhfTdVtsqprsSO7iOw6in8yx4mtm9zxjKOkydSdfbf4pl8DTZfdYEjrHzlVF8K\nSROAdFpXVCk4oYRAKaVUaZBoiOAHf8b/3h8Jb367g2cGeHCPPQfvjBvwTL4aY/J+11mpFJoAKFUe\nchpDUA50DIFSqjuxQk34l/6O5rd/h/X5nrTtxt0D9/iL8Ey6DPeEi7G5q4oQpeouNAFQqmuVxBgC\npZRSxSeWRWjj6wRWP0VowyIkeDS1gDG4Rs3CW7sAz5RrsXl6FidQVfE0AVCqMuSUEBhjviMi/5lh\n/bdF5P/lL6zO0zEEKhfad1NlqxTqikTDBFY9SdMbv8g4NsDWayC+2bfhm/kl7L2HFCFC1aIU6ktX\n0AQg/yq1rqjykusdgn8C0hIC4IdASSQESilVaWJH9hBY9STNS+/DOrI7bbu9/xh6nP8NvNO+iHF5\nixChqlSaACjVPWSVEBhjzk+8tBtjzgOS+y6NAo6m71UcNTU1xQ5BlRG9KqOyVYy6Etm1geY3/5fA\n6qchFknZZjw98c26Be/U63AMnawDhEtMuX62aAJQeOVaV1RlyfYOwQOJnx7gwaT1AuwF7spnUEop\n1V1ZTQfxL3uIQP0zRHenP+LF1nMAVXO/ju+sL2Pz9ipChKqSaAKglIIsEwIROQXAGPOwiNzStSGd\nGB1DoHKhfTdVtrqyrohlEd5SR2Dl4wTWvpDxKcLOkdPxzbwZ77T5+hThMlCqny2aAJSeUq0rqnvJ\n9UnFtxhjLgRuAAaIyJXGmFqgl4i80SURKqVUBRIRons2Enz/ZQIrHyd2cGt6IYcbz4SLqTr367hO\nObPgMarypwmAUiobOT2HwBjzDeDvgfuBe0Sk2hgzAfidiMzuohhzos8hUEqVKhEhunMdwQ//QvCD\nPxPd9WHGco6hZ1B1zh14Jl+BTRtjKgeaAChV2UrlOQT/AMwTka3GmO8l1m0CTstvWEopVTmswOf4\n33sY/7sPEdv/ScYyxluNd+p1eKffgHPENB0krLKiCYBSKh9yTQh6AjsSr1s+cZxAOG8RnSAdQ6By\noX03VbY6U1eiezfTXPcAgRWPIqGm9AJ2F55Jl+I54wo8Ey/TKUMrSFd9tmgCUHn0e0iVglwTVK2C\nigAAIABJREFUgqXA3cC/Ja37JrAkbxEppVQZE8sitP5Vmt/6DeEtdWnbjbsH7omX4Jl4Ke7T5+lM\nQeqYNAFQShVCrmMIBgMvAf2AIcCnxJ9BcIWI7OmSCHOkYwiUUoUmsQjBda8QWPEokW31WM0H08rY\nB4ylx3l34p22QO8EqA5pAqCUOpZSGUOwF5ie+DeCePehFSJi5TswpZQqdZb/MIGVT9D85q+INe5I\nL2Cz455wCVWzb8N12nkYm63wQaqSpgmAUqoUZJ0QGGPsQBPQW0RWACtO5MTGGDfxLkiuRBxPi8i/\nGGP6AE8QTzi2AgtE5Ehin3uAhUAU+JaILGp/XB1DoHKhfTdVtlrqikTDBNY8R3Dt84Q2LYFY+hAq\n4+mFb/ZtVJ3zVey9hxQhWlVsHX22aAKg2tPvIVUKsk4IRCRmjPkY6AvsOtETi0jIGHOeiPgTycY7\nxpi/ANcBr4vIvydmMroHuNsYMx5YAIwDhgKvG2PGSi59npRSqpOswOc0vfFzmt+6D+vI7rTttqq+\neGffiq92AfZ+p2DsziJEqUqNJgBKqXKQa5ehR4CXjTH/AzTQNtMQnXkwmYj4Ey/diVgEuBqYm1j/\nEPAm8YHMVwGPi0gU2GqM2QzMAJYnH7OmpibXMFQ3pldl1LFED24jtPF1QhteY+ymxRy1YmllHEMn\n45t1K97a+frMANWaAHwSWs3Lf/iVJgDquPR7SJWCXBOCryd+/nO79QKMyvXkxhgbsBoYDfyviKw0\nxgwUkb0AIrLHGDMgUXwIsCxp952JdUoplTcS9hNY/TTNb/2G6J5NGcvYeg3EN+creKd+AUe/Uwoc\noSolegdAKVUJckoIRCSv33yJwchTjDG9gOcSTz1u/0maU5eg//mf/6Gqqorhw4cDUF1dzaRJk1oz\n8Lq6+DSAuqzLAL/+9a+1fugys2snE1q/iLdefITwp+8xvW8QgBWJudNmDIq/dgwYi3viJcxb+H2M\nw53Yf2fR49flwi0HQs30Ge5m/fbVLF6yiL2NDfQZ4QHg0LYAACeN8La+NsZG7ZlTGT+slvA+F8P6\nj+aC8y5sPV79rrUl9f50ufDLLetKJR5dLq3lltfbt28HoLa2lnnz5pFvOU072pWMMT8C/MBXgHNF\nZK8xZhCwRETGGWPuBkRE7k2UfxX4sYikdBn62c9+JgsXLix0+KpM1dXpYK7uLHrgM5qX/pbA8kcy\nPzjM4cY9Zg7ucRewqrkfcy+7rvBBqqLq7B0A55G+XH3ZfL0DoI5Lv4dULkpi2lFjzP/tYFOI+JiC\nV1u6+2RxrH5ARESOGGO8wIXAT4EXgduAe4FbgRcSu7wIPGKM+S/iXYXGkGGmIx1DoHKhH8LdjxX8\nnOD7L+F/5/dEttdnLGPvPxrfrFvwzbwFm68aaBvYpCqbdgFShabfQ6oU5JQQAKcC1xJviO8AhhEf\n2PsScCXwK2PMdSLyahbHGgw8lBhHYAOeEJFXjDHvAU8aYxYC24jPLISIbDDGPAlsACLAnTrDkFIq\nG9GD2wmtf5Xg+lcJb3kHYpG0Mo6Bp+KpuRrPxEtxDJ2MMXm/AKNKkCYASimVe0JgA24QkedaVhhj\nrgZuEpGZxphbiV/lP25CICLrgLQHBojIIeCCDvb5CfCTYx1Xn0OgcqG3aiuXiBD84CX8dQ8S3rw0\ncyG7E/dp51F19ldxnX7+MZMArSuVoVAJgNYXlS2tK6oU5JoQXAzc2G7dy8AfE6//BPziRINSSqnO\nijUdIFj/LP53HiS69+OMZRxDJuGdeh3eM2/C3qNfgSNUhaR3AJRS6vhyTQg+IT716C+T1t2RWA/Q\nj/jA4KLRMQQqF3pVpnJE926m+a1f41/+SHqXIGNwnXounkmX45l4caeeHqx1pTyUSgKg9UVlS+uK\nKgW5JgRfAZ5NPEG45TkAMeALie2nAT/KX3hKKXVskYZ1NC35BcH6Z0Gs1I0ON74ZN1F13t/h6J/z\no1JUGSiVBEAppcpZTgmBiNQbY8YCM4GTgd3AMhGJJLYvBTrorFsYOoZA5UL7bpYnCfsJrnuF5rd+\nk3GmIOfwqXjP/BLeqV/A5u2Vl3NqXSkN5ZIAaH1R2dK6okpBrncIAM4lPo5ggIhcYYypNcb0EpE3\n8huaUkqliu7/BP87v8e/8nGk+VDadtdp59Hjgr/HNWaOzhJUIcolAVBKqXKW04PJjDF3Ad8C7gfu\nEZHqxNOFfycis7soxpwsXrxY9A6BUpUjenA7wbXPE1z7ApEda9IL2F14aq6i6qyFuEbNLHyAKq80\nAVBKqY6VxIPJgL8H5onI1sQ4AoBNxMcOKKVU3oS3r6H5jZ8TfP+l9LEBgL3PULxnfgnfWV/G3rN/\nESJU+aAJgFJKFV+uCUFP4g8kA1o/sZ1AOG8RnSAdQ6ByoX03S4tEggTqnyGw4jHCn7ybXsDmwD1u\nHr7Zt+EedyHGZitYbFpX8qO7JABaX1S2tK6oUpBrQrAUuBv4t6R13wSW5C0ipVS3E2s6gL/uAfx1\nD2I17U/b7jp1Lt7aBXgmXIKtqk8RIlSd1V0SAKWUKme5jiEYDLxE/HkDQ4BPgaPAFSKyp0sizJGO\nIVCqPFjBzwmseprQxtcJffwmRIKpBWx2PFO+QI/z78I5ZGJRYlS50wRAKaW6TkmMIRCR3caY6cB0\nYATx7kMrRDJ08FVKqXbEsgh/uozQuj/jX/4IEjyaVsZWPZiqs7+Kt3YB9t4nFyFKlQtNAJRSqvzl\nPO2oxG8prEj8wxgzyRjzTyLyxXwH1xk6hkDlQvtuFobVdJDAqidpfvcPxPZtzljGOayGqvP+Ds/k\nqzH2zsyI3LW0rsRpApAdrS8qW1pXVCnI6lvXGOMD7gFqgM3APxPvNvQz4ELgoS6KTylVpkSE0IZF\n+N/+HaGP3sw8U1D/MVTN+Vvc4+Zh7z9anx1QgjQBUEqpypfVGAJjzO+BKcBfgUuBvcDpxBOB/xaR\nA10ZZC50DIFSxSXhAP53/9Dh3QDj6Yl36nw8ky7Dddq5GJu9CFGqjmgCoJRSpavYYwguBmpEZJ8x\n5hfAdmCuiLyd74CUUuVHYhFCm94guOZ5gh++kj42wBicw6bgm3ULnqlfwKYNxpKhCYBSSqlsE4Ie\nIrIPQEQajDFNpZoM6BgClQvtu3liYk0H8L/zewLv/YlY44607cZVhW/WLfjOuR1H3xFFiDB/KqWu\naAJQGJVSX1TX07qiSkG2CYHDGHMe0HqLov2yiLyR59iUUiVILIvwlrcJrHiMwNoXIBpKK2PvM5Sq\n8+/CO+MmbO6qIkSpWmgCoJRS6niyHUOwFY7xDRKffGhUvoI6ETqGQKn8E8si0vA+gZWPE3z/RazP\n96aVMd5qfDNvxlNzNc7hU3WAcJFoAqCUUpWrqGMIRGRkvk+slCp90X1baK67n8CqpxB/Y8YyzmFT\nqDr363jOuALj9BQ4QqUJgFJKqRNVepN9nyAdQ6ByoX0300kkSHDDIvxLf0v4k3czljFVJ+Gdeh3e\n6TfgGj6lwBEWR6nUFU0AykOp1BdV+rSuqFJQcQmBUip3EosQ+uhNAqufJrTuFSTcnFbG1qMf7nEX\n4J1+Pa4xc3S60ALRBEAppVRXy2oMQTnRMQRKZS92eBfNb99PYMWjWEf3pRew2XGPv4iqsxbiOv18\nHRdQAJoAKKWU6kixn0OglKoQIkJk22r8dQ8QqH8GrGhaGXu/UXgmX0nVOV/DXj2oCFF2H5oAKKWU\nKraKSwh0DIHKRXfpuykiRD5bTvD9Fwl++Cqxg1vTyth6DcI79Qt4pl6Hc1iN3g1oJ191RROA7qG7\nfLaoE6d1RZWCiksIlFJtYod30vzO7wnWP5sxCQBwjppJj3PvxD3hEoxdPxLyTRMApZRSpa5oYwiM\nMUOBh4GBgAX8TkR+bozpAzwBjAC2AgtE5Ehin3uAhUAU+JaILGp/XB1DoLo7sSwi21fjf+cPBFY/\nlbFLkPH0wnPGFfhm34ZrZG3hg6xgmgAopZTqKpU4hiAKfFtE1hpjegCrjTGLgC8Dr4vIvxtjvgfc\nA9xtjBkPLADGAUOB140xY6XSRkUr1UmxI3toXvJL/CsfR5oPpRdwevFOuw7PpCtwn3qOPjMgTzQB\nUEopVe6KlhCIyB5gT+J1kzFmI/GG/tXA3ESxh4A3gbuBq4DHRSQKbDXGbAZmAMuTj6tjCFQuyr3v\npogQ3bORwIrH8b/z+4zThbpGzcI76xY8ky7F5ulVhCgrQ0td0QRAZaPcP1tU4WhdUaWgJDoMG2NG\nAjXAe8BAEdkL8aTBGDMgUWwIsCxpt52JdUp1O9EDn+Ff/gjBNc8RO/BZ2nZbVV/cEy7GN/NLuEbN\nLEKElaMlAfhr/bO8vOVXmgAopZSqOEVPCBLdhZ4mPiagyRjT/ps2py5BNTU1eYtNVb5yuioTa2wg\n+MHLBNe9QnhLXcYyjsHj6XnFj3CPuxBjsxU4wspwzDsAwfTymgCoTMrps0UVl9YVVQqKmhAYYxzE\nk4E/isgLidV7jTEDRWSvMWYQ0PK0pJ3AsKTdhybWpXj66ae5//77GT58OADV1dVMmjSp9Q+uri7e\nkNJlXS6H5bffWkJk6ypqAssJfbSEFbvjDdMZiUcDrNgDxunh7HkX4516HSuPVGMaDXMSyUCx4y+H\n5UComT7D3azfvprFSxaxt7GBPiPi4ysObQsAcNIIb+uyMTZqz5zK+GG1hPe5GNZ/NBecd2Hr8ep3\nrS2p96fLuqzLuqzL5bvc8nr79u0A1NbWMm/ePPKtqE8qNsY8DBwQkW8nrbsXOCQi9yYGFfcRkZZB\nxY8AZxLvKvQakDao+Gc/+5ksXLiwcG9ClbW6utLruxk7vIvg+y8R+uQdwlvqEP/h9ELGhuvUufhm\n34pn/EU6QDgHnR0D4DzSl6svm693AFRWSvGzRZUmrSsqFxU3y5Ax5izgb4B1xpg1xLsGfR+4F3jS\nGLMQ2EZ8ZiFEZIMx5klgAxAB7tQZhlSlaHt68P0E6p/NOFUoxuAaew6eM67EM+lS7NWDCx9oGcrX\nIOC6ujqmjDqrgJErpZRShVHUOwRdQZ9DoMqJ1dxIc939+N/9A9aR3RnL2PsMxTPti/hm3Yqj7/AC\nR1h+dBYgpZRSlari7hAo1V2JCJHPlhOof4bAiseQsD+tjGvULDxTrsU1ejaOweMwJu9/+xVDEwCl\nlFLqxFRcQqDPIVC5KGTfzciejwh+8BKBVU8R27c5bbvx9cYz8VJ8c76Ca/iUgsRUjoqVAGg/X5UL\nrS8qW1pXVCmouIRAqVIi0TDB9a/iX/pbwp+8m7GMY/B4qs77Bt5p12HszgJHWPr0DoBSSinVtXQM\ngVJ5JrEoka0rCX7wMv5VTyDNh9LKGHcPPFOuxTttPq4xc7RLUBJNAJRSSqnMdAyBUiXM8h8mtHkp\n4U1LCK7/K9bne9IL2Ry4x83DU3MN3slXYly+wgdagjQBUEoppYqr4hICHUOgcnEifTet4OcE176A\nf8VjRD5bAWJlLGfrPQTfjBvwzf4y9t4nn0i4FaFcEwDt56tyofVFZUvriioFFZcQKNWVWmYIan7n\n9wTffxGioYzlbD364Z54Kd7JV+E67VyMzV7gSEtHuSYASimlVHehYwiUOg4RIdrwAcEP/0Kg/hli\n+z9JL2QMzmFTcJ92Hq7Tz8M1cnq3HSCsCYBSSinVNXQMgVIFZjUfwr/sYfwrHss4TSiA4+QJeKff\ngLd2Afae/QscYWnQBEAppZQqbxWXEOgYApWL9n03RYTwJ+8SWPUEgdXPQCSQto9xVeGZcg2+sxZ2\ny+cFdNcEQPv5qlxofVHZ0rqiSkHFJQRKdUbs8C78yx8hsPIJYgc+Tdtu3D3wTLoM94SLcY+/EFsZ\nNmg7q7smAEoppVR3UXEJQU1NTbFDUGVComGm9Wzk0P1/Q2jDIrBiaWUcQyZRNfcOvDVXd5tpQjUB\nyEyv4KlcaH1R2dK6okpBxSUESh1PrLGB5roHCSx/BKtpf9p24+mJd+p8vNOvxzlyesU/NEwTAKWU\nUqp7q7iEQMcQqEws/2ECq58iuO4VwlvqWu8GrNgDMwbFy7jGzME36xY8ky6r6LsBmgB0jvbzVbnQ\n+qKypXVFlYKKSwiUaiHREKFNbxBY8xzBD/6ccYCwreokqi68DV/t9TgGji1ClF1PEwCllFJKHYs+\nh0BVnFhjA81v/gr/yscR/+GMZVxjz6Hq7K/innAxxl5ZebEmAEoppVRl0ucQKHUMscYGAmtfIPjB\ny0S2roAMia7j5In4Zt+KZ+Kl2HufXIQou4YmAEoppZQ6ERWXEOgYgu5DrBihjYvxL3uI0Pq/glhp\nZex9huGZ+gW8U67FOfSMtO3l2HdTE4DiKMe6oopH64vKltYVVQoqLiFQlS92ZDf+ugfxL38E6/M9\n6QWMDdfYs6k653bc4y/G2GyFDzKPNAFQSimlVFfSMQSqLIhlEdr4Ov53HiS0aXHGZwa4xp6Dd8o1\nuCddjr1n/yJEmR+aACillFIqEx1DoLql6IGtBFY9SWDl48QObk3bbuvRH2/tF/HNvg3HgDGFDzAP\nNAFQSimlVDFVXEKgYwjKn1gWwQ9epPmNXxLZXp+xjGv0WfjO+SqeiZdi7M5On6sYfTc1AShP2s9X\n5ULri8qW1hVVCiouIVDlK3poB8H6Z/CveIzYvs1p242nJ75Zt+CbdWtZ3Q3QBEAppZRSpUzHEKii\nih7cRmjjYoLvv0B489vpBWwO3Kefj7f2i3gmXoZxeQsfZI40AVBKKaVUV9AxBKoiiGUR/mgJwY2v\nEdq0JOOdAADj7oFvzt/S47xvYOvRt8BR5kYTAKWUUkqVs4pLCHQMQWmy/IcJrH4K/7KHie5an7mQ\nseE+7Vw8U76AZ/KV2Dw9uzyuzvTd1ASge9J+vioXWl9UtrSuqFJQtITAGPMAcAWwV0TOSKzrAzwB\njAC2AgtE5Ehi2z3AQiAKfEtEFhUjbpW9lrsB/lVPElr3ZyTsTy/k9OIecxbu0+fhmXxlST5BWBMA\npZRSSlWyoo0hMMbMAZqAh5MSgnuBgyLy78aY7wF9RORuY8x44BFgOjAUeB0YKxmC1zEExRc7sptA\n/bP43/k9sQOfphdwevDNvBnPhItxjZ6NcXoKH+QxaAKglFJKqVJUcWMIRKTOGDOi3eqrgbmJ1w8B\nbwJ3A1cBj4tIFNhqjNkMzACWFyhcdRwiQmTHWvxL7yNQ/yxY0bQyjpMn4Jv9ZbxTr8Pmqy5ClJlp\nAqCUUkqp7qzUxhAMEJG9ACKyxxgzILF+CLAsqdzOxLo0OoagsCJ7PiKw+imCa18ktn9L2nbj6YXv\nzJvwTr8Bx5BJGJP3pDZnyQnA4iWLiPQ6qAmAOi7t56tyofVFZUvriioFpZYQtFdZc6JWiFhjA8H1\nfyWw+mkin2W+SeMcNRPf9BvwTL0Om7uqwBGmOtYdgEONAU7qlTqVqSYASimllOpOSi0h2GuMGSgi\ne40xg4B9ifU7gWFJ5YYm1qXZsmULd955J8OHDwegurqaSZMmtWbfdXV1ALqc4/LM04cQXPMcb774\nKLH9nzJjUPz3vWJP/OeMQYDTy1rvDDyTr+S86xYWLd5AqJk+w92tdwD2NjbQZ0R8nMKhbQEAThrR\nlgQ0bg9Re+ZUxg+rJbzPxbD+o7ngvAtbj1e/a23Rf/+6XPzlOXPmlFQ8ulzay1pfdFmXdTkfyy2v\nt2/fDkBtbS3z5s0j34r6YDJjzEjgJRGZlFi+FzgkIvd2MKj4TOJdhV5DBxV3OREh8tlyjr56L+GP\n38pcyObAM+lSvNO+iPv08zEuX2GDRMcAKKWUUqp7qLhBxcaYR4Fzgb7GmO3Aj4GfAk8ZYxYC24AF\nACKywRjzJLABiAB3ZkoGQMcQ5EOssQH/uw8RWPtCxnEB2J24x56Ne9yFeCZfgb13xuEcXSafCUBd\nXR2+UZoMqOOrq9N+vip7Wl9UtrSuqFJQtIRARG7qYNMFHZT/CfCTrouoexMrFp8laNlDBFY+CbFw\nagGbHfe4C/BO+QLuiRdj8/QqWGx6B0AppZRSqusUtctQV9AuQ7mJHtwWvxuw/BGspv1p2427B54p\n11A19w6cg8cXJCZNAJRSSiml0lVclyFVPLEjewhtfI3A6qcJb347YxnniGlUnX8XnnEXdPm4AE0A\nlFJKKaWKp+ISAh1DkE6sGJGGDwiueZbQ5reJNnyQsZytRz/c4y7EN+tmnKec2WXPDCilBED7bqps\naV1RudD6orKldUWVgopLCFSb6MHt+N97mOCqp4g17shcyNhwn34+vtm34R5/Ecae/ypRSgmAUkop\npZRKpWMIKoyIEP50Gc1L/pfQ+r+CWOmFbA5co2fhHn8h3ilfwN775LzGoAmAUkoppVT+6RgCdUwS\nDhD84GWal95HZHt92nbj64Nn/IV4aq7BNXo2Nm/+ZgnSBEAppZRSqnxVXELQncYQSDRE+NPlhD95\nh+a3f4f4D6eVcZ06F99ZC/GMvxDj9OTlvJWUAGjfTZUtrSsqF1pfVLa0rqhSUHEJQaUTK0Zo42IC\nKx4ltPF1JOxPL+Rw4639Ij3OuwvHwLEnfM5KSgCUUkoppVQqHUNQJqL7ttBcdz/B+mexmg5kLGM/\naTjemTfjm30r9h79On0uTQCUUkoppUqPjiHohiQaJrTxdZrrHiD80ZKMZez9RuEacxbuU8/Bc8aV\nGIcr5/NoAqCUUkop1X1VXEJQCWMIIg0fEFj1JIFVT2V8erCtR3+80+bjPfNvcAwel/PzAjQBaKN9\nN1W2tK6oXGh9UdnSulIYIsLRI0H27zlKJBwjFrWIxSyCgQixqEVLh5nknjOtLyXRShLi7aX4fxmW\n2+0rEItZWJa0Hldadkw5ftJxEtvb4kldHjYuj7+UJBWXEJQrsSxCGxbRvOR/CX/yTnoBY3BPuISq\nsxbiOu08jM2W9bE1AVBKKaXKi4jQfDRENJqYPjyl0dhxg1EktYHa1nBNLCeVt2LSeswOjwXJ7dy2\nBnOmcxy3fOK8VtsJk9rHqftnir217LHef+qyFbPYt/sou3ccpunzUMe/8DIxbNyALjmujiEoIolF\nCH+6nNCHfyH44SvEDm5LK2OrHoy39np8s2/D0Xd4VsfVBEAppYqrtVGWaICl/hRiUcGyrNbGkYjE\nf1rxn+FQFLHSr0rGj51YCR023I7VaOuofCxmEQnFSCqWfMK2dakbU46TYbeUHbIpl7FZknzlNUMc\nxzpGy5Xg9ldwWxdbf1Xtfme029YuroxXkjtcn+G9ZTh2y/ZgIMLenUcI+CMolez8+QN0DEGliOzZ\nhH/p7wh+8FLmAcI2B56aq/FO+yLu088/7tODNQFQqnsTS7CSGpQtjcvkBmZLYzS+Q+ptbrFSb2Uf\n90pjUmPSar0d3hpNWoOuo4ZT+21J7bHWwmkNs5yP1XEjzxLBilmJBmO8gR6LWoTDMcLBKOFwlGjY\nIhKJEvBHsGItv1er9XVLeRINfUvafm9KqdLidNkZNKQab5ULh9OGw2HD6XbgdNggqft1y8uWLtnx\nHyb+04BJKpSyLWX/+DqbzWB32FKOR/IPYxI/wSQO3n45ed/myK58/TpSVFxCUKpjCCz/YYLvv0jz\nO78n2vB+xjLG0xPfrFupOud27H2GdngsTQDyR/tuFk/AH2bvzs/5/HCAUDDa2riyYvHGWSgYabut\nnJDaADz2VceUq4dkaCjmcCwR2PjxWsadWnPi521/4Pa7pe/SYdlIKMbeXUeIRjI8kVwV1badGxgx\nZHyxw1Bl4Fh1xeV24PE6UhuNmRqQyY3Tdutbysa3m5TGrs3W1jJNbYwml0s+QKZGbPvltkZs8v7J\nx7PZbEkxtd+e3NBObUR31GBubYx30Lju1cfLycN7029gz7b3XKbq6zUhKDtiWYQ+eoPAe38kuO4v\nYEXTytiqB+OZcDHuSZfhHns2xuFOK6MJgKoERxoD7NzayM5tjTRsbeTgvqZih5SThp2HsIf3FDsM\nVWaMLd7QaWmItfy0220YW7xB1tIwS152uuzY7fHWXVtjp90VQ8jccDtGo61tv/ZXH+OxuT2OtKuY\n8ZcZrmy2bUwtk7wuU9srOY5sjtFuOVM8qeVST2qzxX+fqZtMWmzJ29LPbdLiSNmWdqxjvY/UY61Z\nG2RqzRkp5ewOG/0H9aT3ST5MmTdgVXmouISgpqam2CEQO7of/zsPElj5BLGDW9MLONy4x11A1Tm3\n4xo9G2Ozp2zWBKBwyv3uQMsMCal9abPt19quT2um/qxJBVKPlSjZQX9YEQj6wzQe8LcmAEePBDv3\nJktEqV/tTW1cxq/AGRNfb7fbOryS1/6KYrxI+vr0xmf8Rcrt8NZgcm9stS5n1RDL0BjMsqGX0rg2\n8d+NzR7/2fLa5bLj9jhxuOw4nXacLjserxOH04Yt8Xu22dteu9yORMM/udF/cc4zwKnuacKUIcUO\nQanKSwiKKbp3M83vPIj/3T9ANH0ku3PYFDxTr8U3/UZsPfq2rtcEoHsLBSPs3HaYhq2HOHIoQCzR\nZSYWtYhFY0Sj8T7aViy9b3jz0VBat5pyYbMZBpzci5P6V+H1ORONsXgjy243uD3O1oZmmoxXKU37\nzZmvKKatz75s26kyN/QyrT7mlcJ2O2W6Ynq8857Ur4refX0Z41FKKaWyUXEJQaHHEEg0ROijt2h+\n4xcZpws13mp8M27EO/NmnIPjk8c2BY6wafObmgCUgGKMIfA3hWnYeoiGrfEr5/t3f565L3qFcbrs\nnDy8N0NG9GHoyD4MGlaNy1U+H0E63kTlQuuLypbWFVUKyufbuISICNG9HxP84CX8b9+PdXRfWhnn\nsBp8Z38Vb801NMfCvN+whvWLX9EEoBv6/HC873zD1kZ2fHaIQ/ub83p8r8+Z1q0i+Upy6swHqVer\nO+qWkd5PueW1Sb2S3v48SeXcHgc9qz0MHFLN0JF9GDC4JzZ79s/PUEoppVRh6HMIcmC0Vo6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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#train 10000 times\n", + "for strat in algos:\n", + " strat.sample_bandits(10000)\n", + " \n", + "#test and plot\n", + "for i,strat in enumerate(algos):\n", + " _regret = regret(hidden_prob, strat.choices)\n", + " plt.plot(_regret, label = strategies[i].__name__, lw = 3)\n", + "\n", + "plt.title(\"Total Regret of Bayesian Bandits Strategy vs. Random guessing\")\n", + "plt.xlabel(\"Number of pulls\")\n", + "plt.ylabel(\"Regret after $n$ pulls\");\n", + "plt.legend(loc = \"upper left\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Like we wanted, Bayesian bandits and other strategies have decreasing rates of regret, representing we are achieving optimal choices. To be more scientific so as to remove any possible luck in the above simulation, we should instead look at the *expected total regret*:\n", + "\n", + "$$\\bar{R}_T = E[ R_T ] $$\n", + "\n", + "It can be shown that any *sub-optimal* strategy's expected total regret is bounded below logarithmically. Formally,\n", + "\n", + "$$ E[R_T] = \\Omega \\left( \\;\\log(T)\\; \\right) $$\n", + "\n", + "Thus, any strategy that matches logarithmic-growing regret is said to \"solve\" the Multi-Armed Bandit problem [3].\n", + "\n", + "Using the Law of Large Numbers, we can approximate Bayesian Bandit's expected total regret by performing the same experiment many times (500 times, to be fair):" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "image/png": 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QQpsAaVqJLqUsnGqchZaaDbU2ZrMZh8Ph2c7Nza3VQC4uLqa8vNzTwM7IyCA1\nNdVzPDMz07NeVlZGcXExCQkJJCYmcvHFF7Nly5ZG8/ZlNqHo6GiCg4M5evRorXwbSycxMZFZs2ax\ndu3aeudlZGQ0eD9NOVZ73196ejqBgYFER0fX8s1ITEwkODiYw4cPt2iGpMTERNLS0nw+301CQgLp\n6emebbvdTmFhYT3lq6mya6qcNB2Ljj4XuqZjoetL56aqqgZboYopUFxgp7jQSyEoqsBZWX3aeQQF\n+xMeEUJW/o+cf96FJxUB4zfEHNBhZ/vTnH3oMahOwLnnnsuWLVtwuVx8/PHHfPHFF7WOSyl57LHH\nqKqqYseOHXz00UceR2KAjz76iK+++gqn08nq1asZNWoU3bt358orr+Tw4cNs3LiR6upqqqqq+O67\n7zh48GCL5BNCMGfOHFasWEF2djYul4tvvvmGqqoqj3zezJgxg//7v//jk08+weVyUVFRwfbt2zlx\n4gRJSUkMGzbMcz9ffvmlp2fd+3692bhxIwcOHMDhcPDYY49x7bXXej6i7nPj4+O57LLLWL58OaWl\npUgpOXr0aL2yrMvcuXN55pln+P777wHlTO2thDTG9ddfzxtvvMGePXuorKxk1apVjBo1qt5IhhCC\nG2+8scGya6qcNBqNRtO21FS7yD1Rwp7vMvn0/f1s+vM3/GnNpzz98Ee89PQ23nl1F5++/yO7vzzO\nkf155GeX+aQoCJMgPCKYpF6RpA7vzgWX9ubya1OZNm8E8+4ezV0PTuDuhy7n5nsu5pKJ/ZlwdSqj\nxqTQf3ACCYlWzJZArShozihdamShs/osrF69moULF/Liiy8yZcqUWs6+oBrCERERpKamYjab+d3v\nfudxbgaYPn06a9asYefOnQwdOpQ//elPAISGhrJlyxZWrFjBypUrkVIyePBgfv3rX7dYxkcffZRH\nH32UCRMm4HA4GDx4sGdWproftcTERF577TUefvhhbr/9dvz9/RkxYgRPPvkkAM8//zwLFy6kT58+\nnHfeecyePbvWDEfe6QkhmDVrFgsXLuTQoUOMGTOG3/3udw2e+8c//pFHHnmEiy66CLvdTq9evbjn\nnnuavK9rr72WoqIi5s+fz4kTJ0hOTmb9+vUkJSU1+bEeN24cy5YtY968edhsNs4//3xefPHFBuV6\n9NFHWbVqVb2ya66cNB0H3UusaQm6vnQcXC5pxBpQsQVKSyooyneQnWmjIKcM1ykEHPP3NxmjAMGG\nWdBJ86DKPtcYAAAgAElEQVTwyJAW+QvouqLpCAhfpo48W9i6datsyAwpKyvLJ9v8jsj27dtZsGBB\nrek8vbnzzjtJTExk+fLlZ1gyTUekM9d1jUajORWqnDWGyZBD/RY6PGZEJUXlLVYI3CMDEVFmtUSb\nvUyEgnXPv6bd2LVrFxMmTGj1ytelRhZO1WdBo9FomkLboGtagq4vrUt1tcszrah7lEAFIivHVuSg\nrKTylNO2RoYQmxBGTEIYcd3UrzUyBL8zNJOQriuajkCXUha6Iq3ZuzF69OgG7fXXrl3L9ddf32r5\nnGkWLVrEpk2b6u2fOXOmNvnRaDSadsY77oB7hKCowHFSGSithNMwkrCEBREeEUyYNdiIOxBCbLcw\n4rqFExwS0Ho3otF0UrQZEto0Q9N10HVdo9F0NFwuib200jM6YPMEHzOmHrV5xx1oOcIksEaGeEyG\nlPlQCNYoM9aoEAIDdb+p5uxAmyFpNBqNRqPpdDgrqykprlBBx4rKKbFVeMyFSouVU7Gr5jQ6LgWE\nhQeryMPGCIE10lAIIs2ERQSfMbMhjeZMUeOSHCuqYG+unb25dvbl2Lm7b9vk1aWUBe2zoNFo2gJt\nV6xpCWdbfXFWVlNUoMyDbEUOQzGooNQIRFZRXnXaeQQG+RMeGUxEpBlrtJlIY7FGmgmzBuPnf3Yq\nA2dbXdGcOqWV1ezLtbMv18HenDJ+zHPgOI0Rt5bQpZQFjUaj0Wg0LUdKiaPMSUFuGQV5dvVrLI6y\n049IHGIO8AQcC48MwRoRXCsImfYd0HQlXFKSXlzB3hxj1CDXwfHiinaTp0spC501zoJGo+nY6J4/\nTUvoyPWlsqKawjylEBTl2Q1HYuVQfKqRif38BKHW4NrxBiJCPA7FYRHB2m+gETpyXdG0HnZnDftz\n7ewzTIr25zooc9Y0e12U2Z/UuFBS48wMjLdQmXmgTeTTb6dGo9FoNF0IzyhBXhmFuXb1a4wWnMo0\noyY/QUSkmYgYMxGRZsIjgwmznlQKzJZAhEnHHdBoQI0aZBRXsi9PKQf7cuwcLapodkIvPwF9os2k\nxlsYGGchNc5CXGhArVkvd2W2jcxdSlnorD4Lw4YNY926dYwdO7a9RWH06NE8+eSTjB49uk3Sf/PN\nN3n11Vd5//332yT9pvJKTk5m27ZtJCcnt0n6LWHt2rUcO3aM3//+960ii6Zt0XbFmpZwpuqLik5c\nTkGuXY0WeP2eih9BYJAfUbGhRMVaiI4LJSYulOi4UMIjQzBpZaBN0N+Wzk9pZTX7cx3sy7WzP8/3\nUYOIYH8GxiulIDXeQr8YM8Ht5JvTpZQFzenzxRdftHkeZzLypXdex48f96y3VuTrU72Xe++997Ty\n1Wg0XYOK8ioVmdg7OnHRyQBlLY1ObPITREZblEIQayEixkKkMd2oOVRHJtZomsJ7hiK3WVG6rfnR\nOpOAlKgQz4hBaryFbmEd533rUsqC9lnQaDRtge7507SE06kvJcXlpKcVkpFWRHpaIcUFjlNKJyDQ\nzzNCEB1rIcr4tUaZ9TSjHQj9benYFJVXeUYN9uXaOZDvoNyHGYqswf4MjDMzMM7COXEWzok1ExLg\ndwYkPjX0F6GTsGvXLi666CL69OnD3XffjdPpxGazMXv2bPr370+fPn2YPXs2J06cAODdd99l/Pjx\ntdJ49tlnmTt3LgBOp5MHH3yQIUOGMHDgQBYvXkxlpdJ+CwsLmT17NikpKfTp04errrrKk8awYcP4\n/PPPPTJdeeWVpKSkMGjQIJYuXUp19UkHuOjoaF566SXOO+88evfuzZIlS3y6V5fLxdKlS+nVqxcX\nXnihJz+AN954gwsvvJDk5GRGjhzJSy+95Dm2fft2Bg8ezLPPPsuAAQMYNGgQb7zxhud4UVERN954\nIz179uSKK64gLS2tVr7R0dEcPXqUl19+mc2bN/OHP/yB5ORk5syZ06S8mZmZzJs3j/79+9OvXz8e\neOABzzEpJQ899BC9e/dmxIgRfPzxx55j2dnZzJkzhz59+nDeeefxyiuveI6tWbOGBQsWeLa//PJL\nJk2aREpKCkOGDOGvf/0r0PRz1Gg0nRtXjYu87FL+tzODDzb/l+ef+IznH/+MDzb9j//tzPBJUTBb\nAknqFcmQ85K4bMo5XP/TkcxfMo57HrqcuXeOZvKMIVxwaR/6pcYTFRuqFQWNphGqXZIDeQ7e3ZPH\nb/99lJvf2sOs13/g4Y+O8Nfvc/j+RFmDioKfgP4xZq5NjWHppT15eWYqG+cM5tGJfZg9LIHh3cM6\ntKIAXWxk4VR9Fv6V0Lr2+ZOyW27Ks3nzZt5++23MZjM33HADTz75JAsXLmTOnDm89NJLVFdXc/fd\nd7NkyRJeffVVfvKTn7Bo0SIOHjxIv379ANi0aRP3338/AL/61a84fvw427Ztw8/Pj/nz5/PEE0+w\ncuVKnn32WRITEzl8+DBSSr755psGZfLz82P16tWMGDGCzMxMZsyYwYYNG7jjjjs853z44Yd88skn\n2Gw2xo8fz6RJk+opMXX59ttvmTp1KocPH+bvf/878+bN4/vvv8dqtRIbG8vGjRtJTk5mx44dzJgx\ng5EjR3LuuecCkJubS1lZGXv37uWTTz7hlltu4aqrriI8PJzFixcTEhLCjz/+SFpaGtOnT6dXr16e\nfN3DfTfffDNff/21T2ZILpeL2bNnM27cOJ5//nlMJhPfffddrXu58cYbOXz4MC+99BK/+MUv2LNn\nDwA/+9nPGDx4MPv37+fHH3/kuuuuo3fv3p6eJLc86enpzJw5k6effpprrrmG0tJSMjMzm32OmjOH\ntivWtISG6ouUkuJCB9kZNmMpISerhOqqpm2b/fwEEdEWImOUqZA1yow1MgSrMeVoQGDHboRomkZ/\nW9qPfLuTfV6jBgfzHTh9CB4YbQ5gYJzFM3LQL8ZMUCePA9KllIXOzO233063bt0AuO+++1i2bBnL\nly/39PoHBQVx7733MnXqVAACAwOZNm0aGzduZMWKFezbt4/09HSuvPJKAF599VW2bdtGeHg4AL/4\nxS+44447WLlyJf7+/uTk5HDs2DFSUlK48MILG5Rp6NChnvWkpCRuvvlmtm/fXktZ+OUvf0lYWBhh\nYWGMGTOGH374oVllITY21pPGtGnTePbZZ/nwww+ZMWMGV1xxhee8iy66iMsuu4wdO3Z4lIXAwEDu\nv/9+TCYTV1xxBRaLhYMHDzJ8+HDee+89vvjiC4KDgxk4cCCzZ89mx44dnvSkbHkE0W+//ZacnBwe\neeQRTCb1Mbjgggs8x5OTk7npppsAuOGGG1i8eDF5eXk4nU6++eYbNm3aREBAAIMHD2bu3Ln89a9/\nrffHsGXLFi699FKmTZsGQEREBBEREUDTz1Gj0XRcykoqyM4sITu9mBMZNnIyS3xyOvYP8COxZwRJ\nvaLokRJJQo8I/Dt5Q0SjaW+c1S4OFjjYl+tgvzF9ab69+fcxwE/QL9pcy6QoLjTwDEh8ZulSykJn\n9lno3r27Z71Hjx5kZ2dTUVHBsmXLPD33UkrsdjtSSoQQzJo1i/nz57NixQo2bdrE1KlT8ff3Jz8/\nH4fDwWWXXeZJ0+VyeRrLd999N2vWrOH6669HCMG8efP4xS9+UU+mw4cPs3LlSnbv3k15eTk1NTW1\nFAiAuLg4z3pISAhlZWXN3qtbKfK+X7d51UcffcQTTzzB4cOHcblcVFRUkJqa6jk3MjLS02h352m3\n28nPz6empqZWOSYlJTUrS3NkZmbSo0ePWnl6U/f+Aex2OwUFBURGRmI2m2vd5+7duxvMIyUlpd7+\n5p6j5syhe/40jeGsrK4dxCynjLzsar58/1Ofrg8NDyIhyUq3HhH0SIkkPtGqTYW6EPrb0vpIKcku\ndXqiIe/Ps3O4oJxqHyYDSAgLVEpBrFIO+kSHENAF3scupSycKqdiNtTauM1OQJmlJCQk8Mwzz3Dk\nyBG2bt1KTEwMP/zwA5deeqlHWRg1ahQBAQHs2LGDzZs388ILLwDKNt9sNvPFF1+QkJBQL6/Q0FBW\nrVrFqlWr2L9/P9deey0jRozgkksuqXXe4sWLGTJkCBs2bMBsNrN+/Xr+8Y9/nPa9uhUDNxkZGUye\nPBmn08ktt9zC+vXrmTx5MiaTiblz5/rUOI6JicHPz4/MzEz69u0L1C7Tuvg6A0FiYiIZGRm4XK5G\nFYaGSEhIoKioCLvdjsViAdR91lWU3Hns2rWr3v7mnqNGozlzOCurKcgtI99LKSjILaOkBVFXg0MC\nSEiynlwSwwkND25DqTWasx+7s4YDeSfNifbnObBVNB9gMMjfxDmxZs4xTIrOibUQZe6akcS7lLLQ\nWeMsAGzYsIGJEycSEhLC2rVrmTZtGna7neDgYMLCwigqKmLNmjX1rps1axZLliwhMDDQYx4jhGDu\n3LksX76cxx9/nJiYGLKysti/fz/jx4/nww8/pF+/fqSkpBAaGoq/vz9+fvXtXktLSwkLC8NsNnPg\nwAH+8pe/EBMTc9r3mpeXx/PPP8+tt97Ke++9x8GDB5k4cSJOpxOn00l0dDQmk4mPPvqIf//73wwc\nOLDZNE0mE1dffTVr1qxh3bp1HDt2jDfffJOePXs2eH5cXBzHjh1rNt2RI0cSHx/PI488wtKlS/Hz\n82P37t21TJEaIjExkfPPP59Vq1bxyCOPcOjQIV577TWPQufN9OnTWbt2Le+++y5XXXUVJSUlZGZm\nekyXGnuOmjOHtivuOlRWVBmjBHalGBhKQanNd6XgWOZe+vQ6l/ju4ST0sNItUSkH1qiQDjNVoqZj\noL8tLcMlJenFFbV8DY75EPAMIMkaZPgaKOWgV2QIfjp+CNDFlIXOihCC6dOnc/3115OTk8PkyZNZ\ntGgRxcXFzJ8/n379+tGtWzcWLlzIBx98UOvamTNnsnr16nozEf3qV7/i8ccfZ+LEiRQWFtKtWzdu\nvfVWxo8fz+HDh1myZAmFhYVYrVZ+9rOfeYKwef+RrVq1il/+8pesW7eOIUOGMG3aNP7zn//Ukrvu\nffjCqFGjOHLkCH379iU+Pp6XX34Zq9UKwGOPPcYtt9yC0+lk0qRJ/OQnP2m27NysWbOGu+66i4ED\nB9KvXz/mzJnDtm3bGjz3pptu4pZbbvE4HHvPVOSNyWTijTfe4IEHHmDIkCGYTCauv/76RpUF7zxe\neOEF7rvvPlJTU4mMjGTZsmX1Rm9AmUu99dZbPPjgg9xzzz1YrVZWrFjB4MGDefjhh3niiScafI4a\njebUqayopiC3lHxDGSjILSM/p+URjk0mQUS0mZh4FcAsOi6UQ0dNTJ4yAVMXMF/QaNqSkopqT6Az\n96iB3YeAZ6GBfpxjjBa4f8ODdZO4MURXsm/eunWrbGhkISsrq5Yt+9lERUUFAwYM4NNPP23Q7l3T\ntTib67pGcypUOWsoyFOKQEFOGfk5peTnllHaAvMhOBnMzFspiI4LJTLajJ92QNZoTpsal+RoUXmt\nUYOMlgQ8MxSDgXEWEq1BmM7CUbxdu3YxYcKEVr8xrUad5WzYsIERI0ZoRUGj0XRpXDUubMXl5J0o\nJfdEKfnZatSguMiBTzYKBn5+gshYCzFxtZWCiGgdzEyjaU3qBjz7Mc9BRXXzAc8igv0ZGK9MiVKN\nqUs7ehyDjk6XUhY6s8/CqeCe/em1115rZ0lqs2jRIjZt2lRv/8yZM3nyySfbQaKmycjI8Jhh1WXH\njh0kJiaeYYk0HQ1tV9z+SJfE4XBiL62krKSSkuJybEXlFOXbKcyzU1zowOXDHOluTH6CKI9SEKZG\nDOJDiYgMOW3zIV1fNL7SVepKtUtypLCcfTl2j3JwotTZ7HV+AvrGKDOi1HjljJwQGqh9f1qZLqUs\ndDUamoazI/DUU0/x1FNPtbcYPpOUlMTx48fbWwyNRoMyGyrKt1OQp5yMC3LLKMyzU1Rgb5Ey4EYI\nDJ+CMKLjQomJDyUmPozIGD1SoNG0FYWOKvZ6KQYHfAx4FmMOUKMGxtSlfc+CgGedgS6lLHTmOAsa\njabj0hV6/s40NdUuCvPt5GUrk6G8nDIKckpbNBVpXULDg4iKDSWuWxixCWHEJIQRFWsh4AybKOj6\novGVs6GuuKTkWFEFe3Ls7MkpY0+OnWwfRg1qBTyLV7MUxVrOvoBnnYEupSxoNBqNpmMhpaSspJK8\n7FKlGOSo38K8lo8UBAX7YwkLIjQsiLCIEMIjgomKsRAZayEqxkJgkP7L02jamvKqGn7Mc3iUg325\nvs1QFB8a6ImE3JUCnnUGutSXs6v5LGg0mjNDV7ErPl2cldXkGzMO5Z0oJS+nlPzsMirKq3xOQ5gE\nEVEhRMWGEh1rIcrtZBzbeZQBXV80vtIZ6kq+3WkoBko5OFxQTnPBkAP9BP1jzQyKsxhBz7puwLPO\nQOf4smo0Go2m0+BySYoLHeSdODlSkJ9dRnGho0XphEeGEBsfSkyCcjCOTQgjMtqipyLVaNoJ9/Sl\nbuVgb46dnLLmTYoiQ/wZFG8hNT6UQfEW+upRg05Fl1IWtM+CRqNpCzp6z19bUlVVQ362mo40N6uE\n3BMl5GWXUl3V/BSHbgKD/IlNcPsRKKUgJj6MoLM0SFJXri+altHedcXhrGF/nt2jHOzPteNo5t0W\nQM/IYAbFWxhkKAcJYXqGos7M2fkl1gBwzTXXMHPmTG666Safr0lPT2fYsGHk5eVhMmmtX6PRKFwu\nSWFeGdmZJRTmllFU4KAwz05hXhm+xvYUJkFUjIVYt0JgKAhh1mDdkNBoOgC5ZU725JSx11AOjhQ2\nb1IU5Cc4J85CarxFjR7EWQjtJCaBGt/oUk9T+yz4hv7T1mhaRmewK24JUkpKisvJzijhREYx2Rk2\ncjJLqPLBSdGNJSyI2IRQj0IQGx9GVFwo/tqE6KyrL5q2oy3rSo0R28B7lqJ8e/P+Q9HmAGPUQI0c\n9I4Owd+k2w1nM51SWRBCmICdQIaU8hohRCTwFtATOArMlFLa2lFEjUaj6TQ4ypxkZ9o4kV5MdmYJ\n2Rk2yu3N2yEDICAy2kxct3DiuocT3z2c2G5hWEKD2lZojUbTIuzOGvblun0N1CxFzUVEFkBKVLDH\n12BQvIV4HfTsjOGqrqbaVkaVrZRqWylVtlKqvLdLyk7+ltgxLZ7TJnJ0SmUB+AWwFwg3th8APpZS\nPi6EWAosM/bVorP6LERHR/Ptt9/Sq1cvAO68804SExNZvnw5AO+//z5r1qzh6NGjxMbG8vjjjzN+\n/HgA0tLSuPzyyzl48CBjx47lmWeewWq1NpmflJJXX32Vxx9/HICf//zn3HXXXQDs2rWLZcuWceDA\nAcxmM1dddRW/+c1v8Pf3Z8mSJQQFBbFq1SpPWnPmzOGSSy5hwYIFZGdns3TpUnbs2EFoaCgLFixg\n/vz5nnTvv/9+Dh06hNlsZvr06bXS0Wg6Mp2pl9hV4yI700bmsWJOpNvIzrRRUlTu07WWsCASkqzE\nJoSpKUljzETHhXaaWYg6Cp2pvmjal1OtK1JKcsqcXo7IZaQVVtCcxWCwv4mBcWYGxYeSasQ2sASe\n2TgkZxtSSmocFVQVl6ilqKTh9eJStW0rVdu2UmrKWjYpRJxWFhRCiCRgMvAb4D5j97XAOGP9ZeBT\nGlAWTpUnl/+rtZICYPHqSS06vykN/ttvv2XhwoW88sorjB07luzsbMrKyjzH33rrLbZs2UJycjIL\nFixg6dKlrF+/vtk8t2/fzrfffsuRI0eYOnUqQ4YMYezYsfj5+bF69WpGjBhBZmYmM2bMYMOGDdxx\nxx3ccMMNzJ0719PILyws5PPPP2fdunVIKbnxxhuZMmUKf/7zn8nMzGTatGn069ePyy67jGXLlrFg\nwQJmzJiBw+Fg3759LSojjUbTMM7KajVqcLyYrOPFpKcV4aysbva6oGB/4hOtdEuykmAsoeFBukdR\no+mAVLskhwsctWYpKnA0b1IUYwmo5YjcOyoEP21S1CBSSqpL7Y039htq+BeX4CwuQTp9nx66I9Lp\nlAVgLXA/4N09Hi+lzAGQUmYLIeIaurCz+izIJrwHX3/9dW666SbGjh0LQEJCQq3js2bNYsCAAQAs\nX76cSy+9lOeee67ZP/ylS5cSHBxMamoqN954I1u2bGHs2LEMHTrUc05SUhI333wz27dv54477mDE\niBGEh4fz2WefMW7cON5++20uvvhioqOj2blzJwUFBSxatAiA5ORk5s6dy9tvv81ll11GQEAAR44c\nobCwkKioKEaOHHlKZaXRtAcdwQa9psZFUb5dxTEwIh7nZ5di82HUwM/fRFy3MLolRRiKQTiR0RaE\nbjS0CR2hvmg6B43VldLKai+TIjv78xxUNmNSZBLQOyqk1hSmcaFdLyKyrKmhqsTedGPf3dAvsnka\n/tW2UmSN735brYIQ+IeHEmANIyAi7OS61ViPCMM/PIwAayj+YaFktJEYnUpZEEJMAXKklLuFEJc2\ncWrLwn52YjIzM5k4cWKjxxMTEz3rPXr0wOl0UlBQQExMTKPXCCHo3r17revcPf2HDx9m5cqV7N69\nm/LycmpqamopEDfccAMbN25k3LhxbNy4kZ///OcAZGRkcOLECXr37g0oBcjlcjF69GgA/vCHP7B6\n9WouuOACevbsyZIlS5q8L42mK1NT4yI/u5SsdBs5mTZyT5RSkFNKjY8Rj8OswST3iaZ7DysJPSKI\niQ/FT895rtF0SKSUnCh1epyQ9+TYOV7UvEmROcDEQK9Zis6JtWA+i0yKXFXVDfbk1+/lr7NtK8Pn\nKdxaCVNQIAGR4QREGEsD64ER4fhHhKl9VkMBCA9FtGBmyoxdu9pE/k6lLAAXA9cIISYDIUCYEOJV\nIFsIES+lzBFCJAC5DV186NAhFi5cSHJyMgBWq5Vzzz3X04BtjJaaDbU2ZrMZh+Ok3Vpubq5HCUhM\nTCQtLa3RazMzMz3r6enpBAYGEh0d3WyemZmZ9O3bF1ANffeIxeLFixkyZAgbNmzAbDazfv16/vGP\nf3iumzFjBmPGjGHPnj0cPHiQyZMne+Ts1asXX3/9dYP5paSk8MILLwDw97//nZ/+9KccPnyYkJCQ\nZmXV+I7NZvMogtu2bQNO2sTq7VPfHjNmTJumX2qr4L13P6Qgr4zo0D7kZNk4fPQHAHompgJwLHNv\ng9spPQYRFWehyJ5GdHwoV119BZExFrZv305plY2h3ZPbvfy62nZb1xe93bm3q2pcbHz/E44WlVPT\nfRB/eOMHjv2wE4DwPsr3suTw7nrbUSEBXHLJGAbFW6g4+j0JoUGMHTvUk/6utI5xf3W3ayoq+ezD\nj6gudTCqdz+qikv44quvqS6zMzQqgariEr45sI/qMjupwoyzqITdeZm4yitINVkA2OuyA7T59rlh\nsQREhLHf34lfqIVRKf0IiAznf6X5+IdauGjESAIiwvn2+BH8wyyMHX8ZARHh7Pj2Gx/L47yT2+nN\nn+9eP378OACjRo1iwoQJtDaiKROXjowQYhywyJgN6XGgQEq5xnBwjpRS1vNZ2Lp1q2zIDCkrK6tW\nT3pHY/LkyVx00UWsWLGCTz75hJtvvpk777yT5cuXs2vXLqZPn87LL7/MmDFjPD4L/fr145prriEt\nLY0tW7aQlJTEnXfeSVBQUJM+C+44CzNmzGDt2rUcPXqUqVOn8vzzzzNu3Dguv/xyJk2axOLFizlw\n4ABz584lJiaGf/7zn540rrvuOvLy8hg+fDjr1q0DwOVycfnllzN16lTmz59PQEAABw4coKKiguHD\nh7Np0ybGjx9PdHQ0n376KXPmzOHIkSMEBekZVVqTjl7XNVDlrCEn00ZWupqd6ER6MWUllT5dG2YN\nPhntOF5NWRoVqyMeazQdGbdJ0Q/ZatTgxzw7zmZGCU0C+kabDZMiNXIQY2lfkyLpcqmZeopsVBXZ\ncBbaVI9+kU2Z8xR6rXvtd5X79n1rTZQJj9Gr79Wz32TvvzUMU2DAGZe1JezatYsJEya0uv1oZxtZ\naIzHgI1CiFuBY8DMhk7qrD4Lq1evZuHChbz44otMmTKFKVOmeI6NGDGCZ555huXLl3Ps2DHi4+N5\n/PHH6devH0IIZs2axcKFCzl06BBjxozhd7/7XbP5CSEYPXo0o0aNQkrJ3Xffzbhxyn981apV/PKX\nv2TdunUMGTKEadOm8Z///KfW9bNnz+bnP/85a9as8ewzmUy8+eabrFy5kuHDh+N0Ounbty8rVqwA\nYOvWraxcuZLy8nJ69OjBhg0btKKg6TScqg26dEmKCuycSLeRla5mJ8rLLkU2FwUJCI8IpluPCLr1\niPBMVxoc0rH/yDQK7bPQdZFSkl3q9MQ2+CHHzrGiikbPLzm8m/A+w7AE+nlmKRoUb2FArJmQgLYz\nKXJVVeMsLKaqoBhnQTFVRcpRt6rIRlWhDafR2FcNfmO9uBRcvkduP21MJgLcZjueRn1YrQZ+YAMN\nf39rKCb/s6X5e2botCMLp8JTTz0lb7311nr7dW9r67Jjxw4WLFjA999/396iaOqg63rb4Gvjr8pZ\nQ3aGjcxjRWQeL+bE8WIqypufJSMg0I+ERCvdkq10NxQES5hWpjsrWlnoOtSdpWhPThmFjuZnI0sI\nC2RQvAVT5h6m/2Q8PSODMZ3CTGS1evvdtvu2UqPBbzv563bkNc6pLilrPvFWQvj7ERBpbaA3P6zB\nxr573T/M0iJ7/q6AHlloBTprnIXORFVVFevXr2fevHntLYpGc8ZorOFnL61UisGxIjKPFZObVYLL\nh1GD6LhQuvWw0q1HBN17RBAdZ8GkHZDPGrSicPbiHfjsh+wyn2cp6httZlDCyajI0Wb3KGEvz3lS\nSmrKHDgLi3EW2KgqLMZZaFMjAHV+PceLSs5ob79/mEU1/CPDCYyynlyPNNajvNYjrQRGheNnMesp\nmTs4XUpZ0Cg2b97MfffdV29/jx492L59+ymne+DAASZMmMC5557LHXfccToiajSdDncU5JzMEnKz\nSlSqD7MAACAASURBVMg5UeJTsLMQc4BhTqSUg4QkqzYn0mg6Cfl2p+FroEyK0grLaa4/wBxgUn4G\n1gDOCaymh6jEVFKMM+sYzh+KKSywke3d8C+0edZlVfOjEq2CyURgZDiB0ZEERFkJjI6obfITZTUa\n/eEERkUQEKVGBkwBull5NtKlnmpn9VlobaZPn8706dNbPd3+/fuTnp7e6ulqNB0NKSW2onKyjheT\ndayYTz/9jIiQFJ+ujYq1kNgzksSeEST2jCQiWveqdTW0GVLnpMYlOVpUXivwWU6ZEwBTdTUh5Xai\n7GWEOOwEO8oIMdajnQ7iaiqIqHQQ4ihD2EpxFhXjKq8kD8hrIs+9LrtnRp5Txbu337vBHxhl9Wr0\nW9Ux93q4BeF39kyzqjk9upSyoNFoNKeCyyXJyy4lI62QjKNFZB0vxl56cgYPW2E5EYn1r/P3N5GQ\nZKW7oRh0T44gxNz1giBpNJ0JWVNDVVEJtpxCDh3J4djRXLIz8ijOKcS/tJQQux2ro4zxjjJC7HZC\nHGUEVTbupOzN6c774xcSfLKnP8pKYFSE0ehXv+5efs/xSGuHn8FH0/HpUsqC9lnQaDS+UF1VQ05W\nCZnHikhPKyLzaBHOysaH/3smpmIyCeITw0lItBLXPZz4xHCi43SwM0199KjCmUNKSY2j/KQNf34R\nzgLj12Pio37L84upLChGlpYhvCZ/iTCW1kYEBtRu4Ndp+AdEWxnlvS/Sip85uA0k0WiapkspC40R\nFBREQUEBUVFR2hxAc9bicDjw08PKDVJSbJgUGUvuiRJczcxzHhjkT/dkK92TlUlRtx4RBAbpT6pG\n05a4qqrVdJ3GlJ7uparQWC90bxsOv0UluCqdLcrjVFoBws+vlv3+yZ7/RpSBaKt27NV0GrrUP1tj\nPgvR0dGUlZWRlZWlX1yNB5vNhtVqbW8xWg0/Pz/i4uLaW4x2R0pJcYGD9LRCjh8uJONooU9Bzyxh\nQfRIiSSpVxSJPSOJjg/FZFLfi23bttGzr+4t1viG9lk4ictZZfT0F1KZX6R6/POLVI9/QTHOArXu\nVgqqbaVtLpMUgopgM+UWC4SHExgdQXh8JDHdYohIiFSN/jpmQP7hoW0yjaeuK5qOQJdSFpoiNDSU\n0NDQ9hZD04E4cuQIAwcObG8xNK1AVVUNGWmFpP2Yz6F9OZQUN29fHBVjoVtyBEm9IklKiSQiSvcC\najTN4aquVgG88otUQz+/iMr8IqoKik8qAwUnzYHOROO/xt8fhzmUcrMFhyWM8tAw9Wu2UG4Jo9xi\nodwcigwPI7lnHP17x5HaPZxzYi2YA/VorEbTpYKybd26VerZkDSasx8pJYV5dtIO5HP0YB4ZaUVU\nNzHXeUCgH92SrHRPjqBbcoR2RNZoDKTLpRr/7ga+oQQ01PB3FhRRVVQCbdmuEELNzx8d4Vn8Iq2U\nhVjI9Q8mXQZzuCaA/IBgykMslFtCqQ4IhAYUfXfgs9Q4FdugZ2QwfibdIaDpvOigbBqNRtMEzspq\n0tMKSTuQz5Ef85qMcRAY5EdSryh69FZLXEKYDnqm6TLImhqcBcVU5uRTkZ1PZU4+zrzC+gpAvmr8\ny5qathPGZPI0+oNiowiMiVRLdIRh228sbtOfiDBKq6QKembENjiQ76CqGR8jf5Ogb3SIim8QH0pq\nvMUr8JlGo2mKLqUs6DgLmpagbUU7NlVVNZw4XszxI4UcP1xAdoatyejIUbEWUvrH0HtALEkpUa06\nS5GuK5qW0Bb1xT0CUJlboEx/cgvUep6xnl+IM7dQ7Su0tV1UXyGUo2+0avAHxkQSFBNJQHQEQR5F\nINKjFAREhDVp6y+lJKukkp05dvbsLWFPzgmO+2BGGBbkp0YMEpRy0D/GTJB/5+sQ0N8WTUegSWVB\nCHGNlPLvDey/Skr5XtuJpdFoNPUpK6kg7UA+h/blcuxQPtVVjTd4AoP86Nk3hpT+MfTqF0N4RMgZ\nlFSjOX2klFSXlBkN/0LV+5/npQDkFirHYENBkNVtMwLgbw3zNPybUwACIsMx+Z96P2RVjYtDBUbg\ns+wy9uTYKa5oPmpx9/AgBsVbPEuPiGBM2sdIo2kVmvRZEEKUSCnDG9hfKKWMalPJ2gDts6DRdC6q\nqmrIPFrEscMFHDtUQG5WSZPnx3ULI7lvNL37x5LYMxK/TtiTqDn7qbaXq8Z+dh6VOf/P3n2Ht3ld\nhx//XoAT4N6blChSkxI1LFu2PGLFo4nd7OGkTZrVpHGTjqSZTZr0l6RN0owmbUazt2fsON5blmRr\nWaIoiZIoUeImuAcAkiAB3N8fLwiR4gAgESBIns/z8BHeFy/wXtlXEg7uOef6vvXv7vMHBGM9/bi6\n+3B196HHxsMyhtiMNOJzMojPyyI+J8t4nJ1xMSiYCAAy08K6qZfD5aauy8lJm7Er8pluJ64AKUVm\nBRVZFl9gkMT6XCvpklIkRGRrFpRSBb6HJqVUPlPbDq8EQmtaLIQQQdBeTVfHEI3njOCgrakfzxyF\nyemZFkrKMylemUHJykwsSVKULBaG9nr9dQCuTl8KUI8RALi6fSlA3b24bD247c6wjCEmNZn4nAzi\nsjKMD/85mcRlG0FAfHYGcTmZxvOZ6ZhiI5+FrLWm0zHGCZuTOl/NQVP/KIHKoZPizL5aA+OnMttK\ngnwRIETEzPa3RSv4//y2XfLcAPDFsI0ojKRmQYRCckUjwz44yoX6bprO9dLc0MvI8OzfpJpMisKy\ndFauzqZ8bQ4ZWdYIjnR2MleWLu3x4OrpNwIAWw+urh7fr70XC4S7ehnr6gu6ELjO62SdKbi5a7Za\nLn7wz0onfuIDf/bEuQxfgJCOOSH+Sn6r887j1TT1jxqFyDYHJ2xOeub48z1hokvRxKpBafryTSmS\nv1tENJgtWEjEWE3YDdww6bzWWsuqghDismmt6Wwf4lxdF+dPd9HVMXef9YxsK6XlmZRWZFG8IoP4\nhGXVl0GEidftNnL/bd24unoZnTEY6MXV3TfvxcAqJoaEgjwS8rKIz80yvvX3/cRnp/tXA+KyMoix\nLp5amzG3lzM9w/7AoK7LiXNs7gDKpKA8M5ENvsBgfW4SmVZJKRIimgS1z4JSKhso0lofDf+Qwkdq\nFoRYGFprOtuGOHPCRv0JG4N9s7c1tVjjKF2VSemqTErKM6UwWYTM7XAy2t7NaEcXo+1djHYYj13t\nXUZgYOthrKd/3vcDiE1PMT7852ReTAHKmZQClJ1BfG4WsekpS2KDP7vL7UsncnLC5qC+e5jxOTqS\nASTGmlibY2WDLzBYk2MhMVY2PhNiPizIPgu+eoXfATsx6hSSlFJvBl6rtf7ofA9GCLF0eL0aW+sA\nZ+u6qD9uY3CWfQ9MZkXxigxWVGZRWp5FVl7SkvggJebfRHeg0faJIKDrYlDQ0YXL93i+awJiM9KI\nz800VgJyMi8WBedlEZ+b6S8QjrY0oPnW5RjjZKeD4zajU1FjEPUG6YkxbMhLYkOulQ15SazMSJSN\nz4RYZAKt5/8fsBe4DejynXsB+FY4BxUuUrMgQiG5oqEbHRmn8WwPF+q7OX+mhxHnzFmLcfExrFqb\nw6p1OZSuylr0qUUyV66c1prxvsFpAcBoezeuSY89w7OvSoVEKWMjsFxfKtDkYCDXFwj4VgjmuxvQ\nYpgvXq1pHhjlhM1YNTjZ6aTTETgLuSjVaGFalZfE+twkClLiJPi/AothroilL9C/0DuAN2qtPUop\nDaC17ldKpYd/aEKIxcBpd1F/spNzdZ00n+9Dz5KGEJ8QQ/naHFZX5VG6KosY6WaybEx0Cpq2GtDe\nOSkY6Mbrmp+SOFN8HAn52cTn55BYmEN8fg4J+Tkk5GcRn5tNQl4WcdkZC9IRKFqNe7yc7Rkx6g06\njeDA7gpcb7Aq08KGPKtRc5BnJT1R6g2EWGoC/U3ZA5QBDRMnlFKVGN2SFp3q6uqFHoJYROTbnNk5\n7S7qT9g4c8JGa2M/s+UiWKxxrFyTTcX63CUdICznuaI9HlxdfZPqAy4GA66ObuOcrRs9HnhjrWCY\nExNIKDQ+/Mfn55BQkO0LBC4+js1Ijepvs6NhvoyMezjZ6eS4zcFJm5PT3U7GAuxvEB9jYl2OhfW5\nSWzIs7I2xyr1BmEWDXNFiEDBwneAR5RSXwHMSqk3AV9gkaYhCSEu32D/COfqOjl7spPWptkDhLyi\nVFauzmZFZRZ5hakoyU9etLTWjPcPMdLSwWhbJyPtnYy2djLa1ulLE+rGZesJumVoIDHJViMIuDQA\nKMglIT+bhIIcYlKkpuVyjLm9nOpyUtPh4Fi7ndPdw7gDFCOnJsQYhch5SVTlWSnPtBAjf56FWHbm\nDBa01j9WSg0AH8ZYZfg48A2t9T2RGNx8k5oFEQrJFYWBvmFOH+vg7MlOOmfbPVlBUVk6lRvyqFiX\nS3JqQmQHGQUW81wZH7Qz0mpjtNXGcEsHI03tDDe1M9LczkiLDY9zeF7uE5ue4gsAsokvyPE/Tpj0\nOCY5OvbNCLdIzBeX28vpLifHO53Udtip6wy8clCQEudbNTAKkotS4yUwW2CL+e8WsXTMGiwopczA\np4Fvaa3vjdyQhBALyTU6TsOpbo6/2krL+b4Zr1EKisoyqKzKo3J9Ltbkpd0FZjEbH7Qz0tLBSHMH\nw01tjDR3GMdtnYy22ualc1BcZhoJBRO1AZMCAN8KQXxe9qLaL2Axco55jE5FHUa3ovqewCsHZekJ\nbMpP8hcjy/4GQoiZzLnPglKqD8jUwWzGsAjIPgtCzGzYOUbDqS7qT3bSfK4HzwzfQJrMitLyTCrW\n51K+NgdrkgQI0cA7Ns5oeyfO860Mn29huPniqsBISwfuIccVvb/ZkkhiUR6JxXkkFOaRUJRLYkEO\nCYW5RoCQm7XkW4ZGo77hcU7YjMDgRKeD870jAduYFqXGU52fzKaCJDbmJ0kxshBLzILsswD8Hngf\n8PP5vrEQYmGNjbmpP27j5NF2Wi/0zbg/lVJQVpnN2k35lK/JJj5BPlxEmvZ6cXX1+tODhpvajMe+\noMDV2XtFm4uZEuONYKAoj4SiPCwl+VhKi0gsySexpGDJbCC2mGmtsdnHOO7bGfm4zUHbkCvg60rS\nEvydiqoLksiyxkVgtEKIpSZQsLAW+Ful1KeAFiaVNGqtbw3nwMJBahZEKJZqrmh/r5Oa/c0cP9zG\nmGvmDjW5BSlUbMhl/ebCZVmDEKornSue4VFfilA7w41tRs1AU5sRELR04B29/JaiRjCQj8X34T+x\nJJ/E4nx/gBCbmSbBQIQFmi8er6axf8S/x8GJTie9w+NzvqdJQXlmIlV5F2sO0mTlYNFbqv8OicUl\nULBwn+9HCLFIaa3p6XRwrq6Lc3WzFyoXlKRRuSGXVetyScuwRHiUS5/b4TQCgcY2hi+0Mtzk+/VC\nK6PtXYHfYDZKkZCfTWJJPtbyEixlhSQWXwwK4rLSJRiIcmNuL6e7h327Izuo63QyPO6d8zWxZsWa\nbCsb8owN0NbmWLHGSRtTIcT8m7NmYamRmgWxnPR1O6ir6eBMbQf9vTN3tEnPslC1rZh11fkkpcgK\nwpVyO0cYvtAyKSho8dcSuDp7Lvt9Y9NTSCwpwFJaSGJJPpbSAt9xAQkFufO+w7AIL7vLTV2n019z\ncLZnmPEAxciWWBPrfDsjV+UlUZllIW6J7lsihLg8C1KzoJR61yxPuTA2ZntVaz0/O+0IIa6Y0+6i\nrqaduqPtdNvsM15jMitWVGSxeUcppeWZsg9CiLxuN6OtNpznmnGeb8HZ0Myw79fLXSFQZjMJRblY\nVhRhKSk0goGyQn9QEJuSNM+/CxFJ3c4xf2Bw0uagsX80YDFyhiWGqtwk/x4HZemJmOXPqhBiAQRK\nQ/oYsAUYANqAQiANOA6UAk6l1Ju01kfDOsp5IjULIhSLJVfU4/Fyob6HE4dbaTjTjZ7hG8rYODMr\nV2ezal0OK1dLoXIgWmvGevpxNjQbwUBDC86GJpznjVWDS3cjrvM6WWeae48AFRuDpdS3OlBWiKWs\nEEtZEdaVxSQW58vqwBKhtaZ5YNQIDDqNguROx9Sak6GGGlLKq6ecK0qN99UbGAXJeclxkj4mFs2/\nQ2JpCxQs7Af+CPyX1lor42+uTwAFwL8AXwK+D8hMFiLCum12Th5po66mnWHH9ALYmBgTKyqzWVud\nz8rV2cTESj7zpdzOEYYbW3Gea2b4fDPOBmOFwHm+BffgzCszc1FmM4mlBVhXFBkBwYoirCuKsaws\nJrE4D1NMoL9yxWIz7vFyrnfEKET2BQhDrrl3tDYpWJ1t8e+OLMXIQohoFsw+C9laa8+kc2agW2ud\noZRKAGxa67TwD/XKSc2CWOzcbi/1x20ceaUJW+vgjNcUlaWzYWshlRvyiIuXD6cA4wNDOM424ai/\ngKP+As76Rhz1jYy2dV7W+8XnZWFdWYKlvBjrimKsq0qwrCzGUlqIKVb+my9lI+Meo97AV3NwusuJ\nK8DOyPExJtbmWNiQa9QbrMmxkCjBuxBini3UPgs9wK3AE5PO3QL0+h7HAXN/hSKEuGJDAyPUHmql\n9mALw87pqwjW5Hg2bClk/dZCMrLmTodZysZ6+nHUN/qCgkZ/YODq6g384kuYrRas5cVYy0uMn1Ul\nWFaWYF1ZREzS8v1vvNwMjrp99QbGysG53mEC1CKTmhDD+lyrv4XpqiwLMVJvIIRYpAIFC/8E3KeU\nOoixz0IxsB24y/f8tcCPwze8+SU1CyIUC50rOuwc48xxG6dq2mlvHpj2vDnGxKq1OazfUkjZqkxM\n5uXRGUV7vYy0duI824jjbKPv1yac55oY75t5tWU2/rSh8hKsK4uxlBvBgLW8lPi8rKBzxhd6roj5\n0+WY2PzMCA6aBkYDviYvOY4NE8FBXhLFqfFzzh2ZLyJYMldENJgzWNBaP6aUqgTuwKhTeBl4t9ba\n5nv+SeDJsI9SiGVifMxDw6ku6o6101jfg3eGrzCTUuKpvrqEjduLsSzhHVm11oy2dWI/1YCzvhH7\nmQs4fSsGnuGRkN7LlBCHtbyUpMoykirLsFaUkVS5AktZoRQWL2Naa1oGXVNWDi4tRr6UAlZkJPhW\nDYyCZNkZWQixlAW1z4JSKhsoWixdj2YjNQsiGnk9XprP91FX087Zk52Mj03P7FMmRcnKDDZtL2bV\n2pwlt4rgHXfjPNfE0Il67CfOGr+ePMv4QGhFxubEBKwVpSRVriBpte+nsozE4nyUWXLElzuv1lzo\nG6G2w2hjetzmYHB07u7fMSZFZZbFv/nZulwryVILJISIQgu1z0IB8FuMbkdjQJJS6s3Aa7XWH53v\nwQixXGit6Wofoq6mnVPHOmbsZgTGrsprN+WzuiofS9LS+PbS7XBir2tg6LgREAydOIvjzHm8rrm/\n0Z0sNiP14gpBRRnWVaUkVZSSUJiLMi2tQEpcPo9X09A7Qm2HnVrfyoFjhmB8svgYE+tyLL42pkms\nybGSIJufCSGWsUBfj/wY2AvcBkzsNvQC8K1wDipcpGZBhCIcuaIjw2PUHW3n+Kut9NgcM16TkWVl\nbXU+azcVkJZpmdf7R5LWGldXL/bj9QydPOtfMRi+0Br0e8SkJJG8bhXJa1Zirby4UhCfnRHGkYdO\n8oqjg9urqe8eptZmp7bDQV2nk+Fx75yvSY43+9OJqvKSIlKMLPNFBEvmiogGgYKFHcAbtdYepZQG\n0Fr3K6XSwz80IZYG7dU0n++l9lAr5+o68czQZtGaHM+ajXmsrS4gtyBl0W3GpD0enOdb/CsFE+lE\nYz39Qb9HQmEuKRsqSN5Qafy6vpLE4rxF999CRM6Yx8vprmFqbQ6Odzio63Lics8dHKQnxrAxL4mq\nfKONaWl6AiaZY0IIMatgWqeWAQ0TJ3wFz8F/NRhFqqurA18khM+VfpvjtLs4friV2sOtDPVPL8iN\niTVRuT6PdZsLKCnPxLRIWit6hkexnz6P/WQ9Q8fPMnSyHkddA56RwF1jwOhAZK0oJWVDJckbKvyB\nQVx6SphHHj7yzV9kjLq9nOpycrzDQW2Hg1PdTsYD7HGQZY31Bwcb85IoCtCpKBJkvohgyVwR0SBQ\nsPAd4BGl1FcAs1LqTcAXWKRpSEKEm/Zqmhp6OXawhYZTXTN2M8orSqVqWxFrNuYTnxDdhZJjvQNG\nCtGkVCLHuSbwzv3t7QSz1ULy+lWkrK/wBQaVJK1egTkhPswjF0vByLiHk52+4MDm4Ez3MO4Amxzk\nJsWxMT/J+MlLIi85bsGDAyGEWMwCtU79sVJqAPgwxirDx4FvaK3vicTg5pvULIhQhJIrOj7u4cTh\nVg7va2Swb/oqQkJiLOuqC6jaVkR2fvJ8D3VejPX0M3CkjsGjdQydqGfoRD2uju6gXx+fm0XyemOl\nYGLVwFJWuCwKjiWveH44xzz+Nqa1HQ7qewJvgFaYEs9GX0rRxvwkchZBIwCZLyJYMldENAj4tabW\n+l7g3snnlFJmrXXQOzcrpV4DNGqtLyil8oH/BLzAZyf2bBBiMXLaXdQcaKZmfzMjw+PTni8sTWfT\n1cVUrs8lJjZ6Wndqrxfn2Sb6D9XSf6CW/oPHGGlqD+7FSmEtL/YFBpX+FYNoKzoW0W9o1M2JTiMw\nqO1wcL5vJGBwUJKWMCWtKNMq+2QIIUQ4BbXPgv9ipWKA9wOf1lqXh/C6U8BtWutmpdTvfadHgGyt\n9V+GMuArIfssiPnS1T7E4X2NnK7twHtJznR8QgzrNxeycXsRWbnRsYrgdY0xeOw0/Qdr6T9Yy8Ch\nWsb7hwK+zpQQR/KacpKrKv2pRMlrVxFjTYzAqMVSMzAyznGb07fPgZ0LfaME+hdoRXqCsXLgWz1I\nT5TgQAghZhLRfRaUUuXAj4Bq4CxGgLAK+B9gEPhyiPcp9AUKMRhtWEsx9m0I8qtMIRae1pqmc70c\n2nOBpnO9055PTktg23VlbLyqmNi4hV1FGOsfYuDQcfoP1TJwsJbBmlMB9zEwxceRvKGCtC3rSd20\nhuQNlVhXlWCKie66ChG9eofH/fUGxzscNA3MXQRvUrAyI9GfVlSVl0RKlNf1CCHEUjfb38LfB7qB\nvwXeCTwCeIC/11o/ehn3GVJK5QIbgDqttUMpFQdE9CsiqVkQoZjIFfW4vdSfsHFozwW6OqbvKFxQ\nksaWa0upXJ+7IDsra60Zae5g4JCxatB/4BiOMxcCvi42I4307VWkb99E2vYqUqtWY4qP/nzvaCR5\nxYYux5hv1cBIK2obcs15vUlBRZaFjb56gw15SVgXONCOBJkvIlgyV0Q0mC1Y2A4Uaa1HlVLPYqwm\nrNBaN13mfb4PHALigH/0nbsOOB3Kmyil4oGXfO8TAzygtf6yb9+HezFWLBqBt2utBy9zrEIA4Bga\nZfeTZzjxahsjzqnfyisFlRvy2LazjPzitIiOS3s82E81GLUGB47Rf/AYLltPwNdZVhaTflUV6Vdv\nIm37RqzlJdIlRlw2rTU2x5i/jWmtzYHNPvfqVYxJUZll8XcrWpdjxbIMggMhhFjMZqxZUEoNaa1T\nJh33aa2vqHrRtz+DR2vdMOk4Xmt9PMT3sWith5VSZmAfRoemtwC9WutvKKU+DaRrrT9z6WulZkEE\n4vVqzp/ppmZ/M41np38Aj4k1UbW1iK07y0jLiMzuylprHKfP07v3MH37jtD3Sg3uwekrHJOpGDMp\nVatJ860cpG/fKAXI4oporWkfcvkDg9oOB93O6UX9k8WaFWuyrf42pmtzrSTELP3uWEIIsRAiWrMA\nxCmlPjfpOOGSY7TWXwvlRlrr+rmOQ3ifYd/DeIzxa+ANwI2+878CXgSmBQtCzMY97qH2cCuH9lzA\nPkNedXJqAlXbiqi+pgSLNfypOiMtHfTuOWz87H2Vse6+Oa83J1lI27bBHxikbl4nRcjiimitaR4Y\n9QcHx20O+obdc74m3qxYl2ulKj+ZjXlJrMm2ECfBgRBCLGqzBQsPA1WTjv90yXHAFkpKqZuDGYDW\n+vlgrpv0vibgVaAc+F+t9SGlVK7WutP3fjalVM5Mr5WaBXGpsTE3tQdbOLSnEad9an51U3sdN73m\nRqqvLmZFZXZYd1ge6x2gb98RevYcom/PYYYb2+a8Pj4nk/RrqknfvpH0qzeSvG4VyizpHAtlKeQV\ne7WmsW/Uv2pw3OZgcHTu4CAx1sT6XKt/j4PKLAuxC1C3s9gshfkiIkPmiogGMwYLWut3zsN7/yyI\nazSwMpQ31Vp7gc1KqRTgIaXUeqYHLzMGM7t37+bw4cOUlJQAkJqaSlVVlf8P4t69ewHkeBkcd7QO\ncs9vHqHlfB8F2asBaGqrA2BNxSaqthVhOnGG3PIRytfkzPv9PcOjPPHTXzN0/AylF3qwnzhLndcJ\nwDqTFWDKcWx6Cs2VeaRsXM1t730X1vIS9u3bhwMorVq94P895XjxHe/Zs4ee4XFiSzZytN3Oi7tf\nwjnuJaW8GoChhhqAKccJMSau37mTqvwk3E3HKUqN54YbNvnf/8DZ6Pn9ybEcL4XjCdEyHjmOruOJ\nx83NzQBs27aNXbt2Md9C2mch2iilvgAMAx8EbtJadyql8oAXtNZrL71eahaWt4nWpwdfukBzw/TW\np0kp8Vx1/Qo2bi8mNgwbqLm6++h+Zh9dT+2h56VDeEdm7xRjTkwg/ZpNZO7cRuYN20heX7EsdkIW\n4aO1psM+xrF2OzW+ouTeGTYSnCw53uxfNajKS2JlRiLmMK6wCSGEuHyRrlm4YuFIQ1JKZQHjWutB\npVQicAvGbtCPAH8DfB14L0balBAAjI97OFXTzqv7mujtckx7Pi3TwlU7y1i/tYiYecyv1lrjOHOB\nrqf30vXUHgaP1MEswbkym0ndss4IDq7fRtrW9dLGVFyxDrtRkHys3c6xIAqSUxNi/MXIG/OTKE1P\nwCQds4QQYlkLW7BAeNKQ8oFf+eoWTMC9WuvHlVL7gfuUUu8HmoC3z/RiqVlYXhxDo9QcaOHYTSS0\nTwAAIABJREFUgWZGLvkGVZkUazbmUX11CQUlaTO2EN27N/RcUa/bTf+BWrqe3kP3U3vnrD2wVpSS\nddPVZF5/FRk7qolJtoZ0LxE9LmeuhEOXY4yadrsRIHQ46HTM3crUGmdmQ66VzYXJbC5Ipiw9Qdrp\nRkC0zBcR/WSuiGgQtmBBa70iDO95HJj2aV9r3Qe8dr7vJxanzvYhXt3XyOnaDryeqd/kx8aZ2bC1\nkK3XlpGWOT+tT8eHHPS8cICup/fQ89wrjA/M0tbUZCL96k3k3LaTnFt3Yl1ZPC/3F8tXj3OMmnYH\nxzqMAKEjwD4HllgTG/KS2JSfxKb8ZMozJa1ICCHE3KbVLCilrg3mhVrrl8MyojCSmoWlra2pn5ef\nO0fTuen1CMlpCWzZUUrVtiISEq984/CRVhtdT+2l6+k99L18FD3unvE6s9VC9s3XkHPbTrJu3kFc\nRuoV31ssX73D49R22H0BgoP2ADskJ8SY2JBnZVN+Mpvyk6jIskhwIIQQS1QkaxYeDOJ1GigI9iZK\nqX+f9Y20/mKw7yPETDpaBtj37LkZN1ErLE1jy7VlVKzLwXSFLR2d51vofOwFbI++yNCx2TcfTyjI\nIefWnWTftpPMa7dI7YG4bP0j4xxrN4qRazrstA7OHRzEmxXrJ60cVGZbiJHgQAghxBWYFixorfPD\ncJ9L8y3yMDZReygM95qV1CwsLZ3tQ+x79iznT3dPOa8UrK7KY+t1ZeQXp132++/du5fq7EJsj75A\n52MvYq87N+u1KRtXk3PrTnJu20nyhkrJ+15m5iuveHjMQ63NwdF2OzVtdi70T98gcLI43yZoEysH\nq7Nln4PFQPLQRbBkrohoEM4CZz+t9fsuPaeUuh24KxL3F0tLf4+Tl56q5+zJzinnlYK11QXsuLmc\n9MzLKxbWWmM/UY/tsRepve+PONpnrj9QsTFk7txKzm3Xk33LdSQW5l7W/cTyNubxcrrLydF2B0fb\n7JzuduKdo5t1rEmxNsfKpgJj5WBNjoU4CQ6EEEKE0Zz7LCilrMDnMVYBsgD/16Va68orurHR0ahf\nax2xJG6pWVjcRobHeOX5Bmr2N+Od/IlKwZqqPHbcvIrMnKSQ31drjf3kWToeegbboy8w0tQ+43Wm\n+DiybtpO7h2vIefWncSmJl/ub0UsUx6vpqFvhJo2O0fb7ZywOXB5Zv872KzwBQfGysHaHCvx89je\nVwghxNKxUPss/C9QCfw38FOMzc/+GXgglJsopS5tj2oB3gW0hPI+YnnyeLwcO9DMy881MDoytQVq\nxfpcrt21iuy80D+4Dze20vHQM3Q89CyO+gszXmNOTCBr1w7y7ngN2a/dQUyStDcVwdNa0zbk4kib\nnRrfXgd2l2fO15RnJrK5IJnqAmMjtMQwbBAohBBCBCtQsPAXQJXWuksp9WOt9b1KqVcwiqC/GcJ9\nLk32HgZqMDZQixipWVh8OloGeOqhE/TYpm6mVliazk2vWx1yTcJY3yC2R56j/YEnGTh8YsZrzEkW\ncm7bSfOKbG756AcwWxIue/xieZicV9zrHOdou93/0xNgI7SClDg2Fxj7HGwqSCY1ISLZoWIBSR66\nCJbMFRENAv2rFANM9KF0KKVSgFZgdSg30VrLurkIyZjLzZ6n6zm6v9noveWTlmHhhtsrqVifG3QR\nsXfcTfez+2i77wm6n315xjan5sQEcv7iBvLfeAtZN16FKT4O+969EiiIgOwuN8dtDmpebuFom52W\nAB2L0hNjqPYFB5sLkslNlm5ZQggholegYKEWuB54EXgZ+C7gABpCuYlSKg74V4yC5gKgHbgH+KrW\neu52H/Oouro6UrcSV6DhdBfP/qkO++DFqRETa+baXeVsubaMmCBzth1nLtD6h0dpf+BJxnr6pz2v\nYsxk3XQ1+W+5lZxbryfGmjjlefk2R8zE5fZystPB0XYHNe12zvYM49W5MDC9dS8YG6FtzE8ygoPC\nZErTZJfk5U7+bhHBkrkiokGgYOEjkx7/A0bqUSnwNyHe50cYtQ8fB5p87/E5oBB4f4jvJZYox9Ao\nzz96mvoTtinnyyqyeO0b1pGWEXjH5fEhB7Y/PUvrHx5j8MjJGa9J3bKegrfcRv4bdhGXlT4vYxdL\nl8erqe8Z5qivKLmuy8n4HEXJsSajnelEcFApG6EJIYRYxAIFCwla62MAWusO4K8AlFIbQ7zPG4By\nrfWA77hOKXUAo5YhYsGC1CxEJ6/Hy9H9zex79ixjk4o/Ey2xvOaOtazdlD/nN7Ha66XvlRra/vAo\ntsdewDsyPQ0kPj+bwrf/BYVvfx3W8pKgxiW5osuT1pqmgVF/cFDb4WB43Dvr9QpI7T3NbTffyOaC\nJNbnJknHIjEn+btFBEvmiogGgYKFPUDKDOdfBDJCuI8NowPSwKRziUBHCO8hlqDWC308++e6aQXM\n67cUcNPr1pBomT2fe6Stk/b7Hqf1nsdmbHeqYmPIvf0GCu+6g6wbr0KZpauMmFmnfcxfkHys3U7f\nyPS6lsmKU+PZXJhMta+l6bFDTnZeFfSm9kIIIcSiEWifBbvWOvmSc8XAq1rrnKBvotRnMFqlfh+j\nQLoYuBv4PXBo4jqt9fMhjT5Ess9C9LAPjrLn6Xrqjk79kJ+RZWXXX66ldFXWjK/TXi/dz71C888f\npOfFAzDD/E1et4rCd91BwZtuJS7z8ndwFkvX4KibY+12jrQbLU3bh8bmvD7LGusvSK4uSCLLKkXJ\nQgghoktE91lQSo1j9KAxK6Uu/VfUDHwjxPt82Pfr5y45/xEu1kVo4NL9GMQSYx8c5cDu8xw/3IrH\nfTG1IybWzI6by9l2XRnmGVI4xocctN3zGM0/f4DhxrZpz8ekJlPw5lspvOsOUqoqpYBUTDEy7jE6\nFrU7ONpup6F3ZM7rk+PNbMpPorogmS2FyRSmxMucEkIIsSzNloa0ASMVdzdwg++x9v10Tao9CIrW\nesWVDHK+SM3CwvG4vRzee4FXXmjAfUn+d+WGXG563RpS0hKnvc5xronmnz1A272P4xm+5AOeUmTe\nsI2iu+4g5/YbMCfEz+uYJVd08Rr3eDnTPezfDO1Ul5M5apKJNys25Bkdi6oLkynPSAypKFnmigiF\nzBcRLJkrIhrMGCxorc/4HuZOnFNKpWutp/efFCKAlgt9PPPwSfq6nVPO5xWlsvOWCsoqpqYcaa+X\nnhcO0PTT++l5Yf+094tJTaborjsoef9bsZTkh3XsYnHwas2FvhFfUbKD4zYHo+7Zi5JNCtZkW6ku\nSGJLYTJrcqzEmaUoWQghhLhUoJoFK/Ad4N1AAjAC/A74hNbaMesLo5TULETWsHOMl548w4lXp6YN\n5eQnc/1tlZRVZE1J7fCMuGh/4Aka/+9enGebpr1f0uoVlH7wbeS/+bZpeyKI5UVrTfuQUZRc027n\nWIeDwdG5i5JXpCdQXWjUHVTlJWGNk4J3IYQQS0dEaxYm+R6QA+zg4v4I/w/4b+AD8z0YsTRor+bE\nkTZ2P3GG0ZFx//nYODM7b6lg8zUlmCZ9i+vq7qP5F3+k+Zd/ZLzvkgw3pci5bSelH3wbGddtlbzx\nZaxveJwaX8eimnYHnY65i5Jzk+J8ex0kUZ2fTLolNkIjFUIIIZaOQMHC64CKSasItUqpvwLOhndY\n4SE1C+HX02nnmYfraGuamrFWsT6Xm+9YS3Jqgv+c42wjjT/6A+0PPIXXNfWDX0yylaJ33UnJ+9+C\npbQwImO/lOSKLqwxt5fjNgevttk53DpEY//cm72nJsRQXZDk71qUnzK/NSxzkbkiQiHzRQRL5oqI\nBoGChTEgDZiccpQGjM98efCUUncAnVrrQwEvFlHP4/HyyvMNHNx9Hq/3YmpbSnoiu+5cS/mai512\n7acaaPjuL7E98vy01qcJRXmUfejtFL3rTmKSrREbv1h4WmuaB0b9wcHxDgeuOaqSE2NNbMwzOhZt\nLkimLCMBk6w8CSGEEPMqULDwS+AppdQ3uZiG9EngF5dzM6XUz4EbgWPAr4H1TNpnIdyqq6sjdatl\nZaB3mMfuO0ZHy6D/nMmkuOr6FVzzmnJifbnh9rpznPv2L+h89IVp75FavZayj9xF7h03YYoJNC0j\nQ77NCT+7y83RNjuHW+282jZEt3P27yFiTIq1OVY2+1YPVudYiQmhY1E4yVwRoZD5IoIlc0VEg0Cf\nyr4MdAIfBAqAduCHvp/L8ZjW+v1KqR3Ae5m6YiEWGa01dUfbefaROsbHPP7zhaXp3PLGdWTlGvv5\nDR0/w7lv/4KuJ16a9h7Zu3aw8uPvIW37RqlHWAbcXs2ZLieH2+y82jpEfc8w3jlamhalxrO1MIVt\nRclszE8iMVaKkoUQQohImm1Tts9orf9Ta+0FfuD7mQ9uAK31K8Ar8/SeQZOahfnjGh3n2UfqOFXT\n4T9nMil23lrBVTtXoEwK5/kWzv7Hj7H9efrG3Dm37aT8n99P6qY1kRx2SCRX9MpprWkddHGkzdgt\n+Vi7neHx2VuaWuPMbC5IYmtRClsLk8lLjlzdwZWQuSJCIfNFBEvmiogGs60sfA74zzDc7yql1HuB\n3wLPaa0HA71ARBetNWdPdvLcn0/htLv859OzLLz+HZvIK0zF1d1Hw3d+ScuvH0K7PVNen/v6myj/\nx/eSUrU60kMXEdI/Mu7b78DOkTb7nKlFJgUVWRa2FaWwzbffQSiboQkhhBAivGbcZ0EpZddaJ8/7\nzZT6KHAauAW4GejXWt8+3/eZjeyzcGWGnWM889BJztZ1Tjm/YWshN9+xFrPHTeP/3cP5//ktHsfw\nlGtyX3cjqz75AZLXrYrkkEUEjLm91NocxupB2xDn++buWpRtjWWbb+WguiCZlIToqFERQgghFrNI\n77MQo5R6HzDrDbXWP7+M++0HsrXWnwVQSsnOWotE64U+Hr33GI6hi6sJ1uR4dt25lsoNeXQ9vY+6\nz/4Xo21TA4n0a6pZ/cW7SduyPtJDFmHUMeTiYMsQh1qHONZun7NrkSXWxKb8ZLYUGj9FqfFSnyKE\nEEIsErMFC7HAe+Z4nQZCDha01kcuOR4J9T2uhNQshG6iJeqBFxumdDndeFURN9y+GgYHOPrBz0/r\ncGStKGP1Fz5K9i3XLdoPhpIrepHHq6nrcnKgeZD9zUM0D8y+emBWsDbXypaCZDYXJrMme+mnFslc\nEaGQ+SKCJXNFRIPZgoVhrfVrIjoSEXX6uh08dl8tnW1D/nOJllhuf2sVKysyaf7Vw9R/7YdTUo5i\nM1Kp/NxHKHzn66OmBaq4PM4xD4dahjjQMsjBliHsLs+s1050LdpalMzGvCQscdK1SAghhFgKZqtZ\nGNJapyzAeMJKahaCd6qmnacfPjmlJWrRinRe//ZN6NYWTn7y6wwerZvymsJ3vp7VX7ibuMy0SA9X\nzJO2QRf7mwc50DLI8Q4Hs2UXxZkVmwuSuao4hauKUiK6W7IQQgghpot0zULzfN5EKWXytWEVUc7r\n1bz01BkO72n0nzObFTtvraR6cy7nv/0zGn98D9pzMYiwriph3dc/ReZ1EogtNh6v5mSng/3NQ+xv\nHqR10DXrtZmWWK4uSeGaklSqC5JJiDFFcKRCCCGEWAgzBgta6w3zdQOllBlwKKXStNazfxKJAKlZ\nmNuYy81j99XScKrLfy4908Kdd1WjztSx76ZPMNpq8z+n4mIp//h7WPmxv8YUH7cQQw6rpZorane5\nOdw6xP7mIQ61DOEYmz29qDLL4g8QVmUmLtr6k3BbqnNFhIfMFxEsmSsiGoQ9qVxr7VFK1QOZGDtA\niyg0NDDCQ785QneH3X+ufG0Ot9xaxoWvfY+2ex+fcn3GtVtY941/IWlVaaSHKi5Dy8Covzj5RKdj\n1l2T482KLYUpXFOSwvbiVDKtsZEdqBBCCCGiyow1C/N+E6U+BbwT+G+gFaObEgBa6+nb+4aJ1CzM\nrKNlgId/e3TKJmtXXb+CNXF91P3zf+Dq7PGfj81IZc2/fYyCt/+FfMscxdxezQmbwx8gtA3NvqiX\nZY3lmuJUrilNYVN+MvGSXiSEEEIsOpGuWZhvf+f79UuXnNfAygiNQczgxKutPPunOtxuo6TEZFLs\nel0lsY88yNFf/nHKtXl/uYt1X/tn4rLSF2KoIoChUTeHWo3ag8OtdpxzpBetzrZwTUkq15SksDJD\n0ouEEEIIMbNpwYJSKqgP71rr88HeRGu9IpRBhYvULFzkcXt5/tFTHDvY4j+XkBjLa6/NovvTn8d5\nttF/Pi47g/Vf/xdyX3fjAox04UR7rqjWmpYBF/tbBtnfPEhdp3PW9KKEGBNbCpO5piSV7cUpZFgk\nvWg+RftcEdFF5osIlswVEQ1mWlk4h/GNv2JSutAMxyE1UldK3YKRipSjtb5TKbUVSI1kGpIwOIZG\neeT3NbQ3D/jPZeZY2RbTQdP7/h09Nu4/n3P79az/5qeJz85YiKGKS4x7vJywOdnfMsiB5kHah8Zm\nvTYnKZZrSlK5ujiVTflJxEl6kRBCCCFCNC1Y0Fr7P1Eopd4HvBYjfagJKAW+CDwXyk2UUh8D/gH4\nKfBW3+lR4PvAtZcx7stSXV0dqVtFrbamfh75fc2U+oSKygxyn3mAtuf2+s+ZExNY+9V/ovCuO5Zt\nikq0fJszNOrmYMtEetEQw+MzdyFWwJociz9AWJGRsGz/30VatMwVsTjIfBHBkrkiokGgmoX/B1Ro\nrUd8x2eVUh8G6oFfhnCffwR2aa0blVKf9p07DawOZbDiyhw/3MozD5/E68tVUQq2ViTi+da/M9Dd\n578uZeMaNv3wS1jLSxZqqMua1pqWQRf7m3zpRV2zpxclxprY6utedFVxCumJkl4khBBCiPkTKFgw\nAWXAqUnnSgkxBQlIBiaS4yc+9sQCs+dQhMFyrlk4+NJ5Xnqy3n+ckBjLJncjzs/8bMp1ZX/3Lio/\n+2FMcfKhM5K5ohPdi/b7uhe1z9G9KDcpzlg9KElhY34ScWZJL1poklcsQiHzRQRL5oqIBoGChe8A\nzyulfoHxYb8Y+Bvf+VC8BHwG+Oqkcx8HXgjxfUSItNbseaqegy9d8J/Lykyg5IUHcB4+4j8Xl53B\nxu9/gaybrl6IYS5LDtdE96K5N0dTwNocK9eUGpujlaZJepEQQgghIiPgPgtKqduBtwEFQAdwn9b6\nyZBuolQ+8GcgCygEzgN24A6ttW2u186n5bbPgterefZPJ6k91Oo/l5tmJvfX38Pb0+s/l3XzDqr+\n+/NSxBwBHXYjveiV5kGOdzjwzNG9aFuR0b1I0ouEEEIIEciC7bPgCwxCCg5meI8OpdRVwFUYaUwt\nwEGt9cyVmuKKud1eHr/vGPUnOv3n8uJdZHzv23jdRrcjFRvD6i/cTekH34YySSpLOHi15mzPMC83\nDbK/aZAL/aOzXpttjfXtfSDdi4QQQggRHeYMFpRS8Rjdj+4CMrXWqUqpW4FKrfX/BHsTpdQntdb/\nBRz0/Uyc/2et9bcvb+ihWy41C2MuN3/63VGazl1cPcgb6STzFz9B+eKz+PxsNv/sa6RtWb9Qw4x6\nl5srOub2UtNhNwKE5kH6ht2zXluZZeGa0lR2yOZoi5rkFYtQyHwRwZK5IqJBMDULhcC7gSd85076\nzgcdLGAEHP81w/l/BSIWLCwHw44x/vjrV7G1DvrP5dvOkPH4vUx8DE2/ZhPVP/mqpB3No6FRNwda\nBnmlydg9edQ986JZrFmxucBIL9pRkkqmVdKLhBBCCBG9AgULbwJWaa2dSikvgNa6TSlVGMybK6Vu\n9j00K6VeA0z+2nQlRt1CxCz1fRYG+0d44BeH6O8Z9p8rOHOA9H1P+f/Dl7z/raz58scxxQbMQFv2\nAn2b02F38UqTESActzlmbW+aEm/mal9wsLUomcTYUJuJiWgn3/yJUMh8EcGSuSKiQaBPjGOXXqOU\nygZ6Z758mom+nAnAzyed14AN+FiQ7yMC6LbZefCXh3FMarlZePBJ0k8YWV8qxsy6//gExX/9xoUa\n4pLQYXfx0vkBdp/v51zvyKzXFabEs6M0lR2lqazLsWI2SXqREEIIIRafQMHC/cCvlFL/BP6uRt8F\n7gn0xkqpv9dar/A9/r3W+l1XOtgrtVRrFlob+3no16/iGjVy401KU/jcg6Q21gEQm55C9U+/RuZ1\nS+/3Hk4TuaJdjjFeOt/P7gsDnOkenvHaifamEwFCcWq81B8sI5JXLEIh80UES+aKiAaBgoXPAV8H\njgMW4CzwE+Dfg3jvr3KxruGOyx3gZEqpIuDXQC7gBX6itf6eUioduBej01Ij8Hat9eCsb7SENJzu\n4s+/r8Hty5GPUV6KHvstSbZGAKwVpWz9zTexlBUt4CgXn17nOHsu9PNAXz11Xc4Zr4k1KTYXJrOj\n1OhglGmR+gMhhBBCLC0B91nwX2ikH/XoIF+glDoKPI9REP2/wN0zXae1/vlM52d5zzwgT2tdo5RK\nAl4F3gC8D+jVWn9DKfVpIF1r/ZlLX7/U9lk4V9fJI7+vwetLlo/HTdHDPyexz9i6InXzOrb+7lvE\nZaQu5DAXjf7hcfY0DvDi+X5O2pzMNNHNCrYWpXDjyjR2lKSSFC+1H0IIIYRYeAuyz4JSqk9rnQGg\nte6edL5La50T4L3fAXwKo+1qLPDXM1yjmVrLMCffBm4232OHUuoUUIQRMNzou+xXwIsYO0YvWS3n\n+/jzPcf8gUKi10XRg/9HvL0fgMzrt7H5F/9BTJJ1IYcZ9QZGxtnbOMhLF/qp7Zi5SNmkYHNBMjeu\nTOfa0lRSEiRAEEIIIcTyEOhTz7S8CqVULBCwnYvWuh74oO81z2mtd13WCGehlCoDqoH9QK7WutN3\nX5tSasZAZqnULHS2D/HQb47g8aUeJbpHKLn/h8SOOADIff1NbPrBlzDFxy3kMKPW0KibfU2D7D7f\nT027fdYAIav/DHfd8VquK00lTXZQFnOQvGIRCpkvIlgyV0Q0mDFYUErtwfjWP0Ep9dIlTxcBL4dy\nkzAECknAA8A/+FYYLv24N2Oq1O7duzl8+DAlJSUApKamUlVV5f+DuHfvXoCoPnYMjXK+xsSYy01T\nWx0xnjFes/8lYkcc1HmdZO3awa0//ndMMTFRMd5oOXaOefi/B5/iWIeDztQKPBqGGmoASCk3Wura\nG2pYkZHIO1+/i+vL0rjnV/tJ7TlN2pqFH78cy7Ecy7EcL7/jCdEyHjmOruOJx83NzQBs27aNXbvm\n9SM3MEvNglLqvRgNXn4IfGTSUxroBJ7XWo+HdCOlcoHtQBaT9lsIpWbB9z4xwKPAE1rr//adOwXc\npLXu9NU1vKC1Xnvpaxd7zcKYy83vfrif3i5jBcHsHmPFn39OQn8XAGV/9y5Wf/Fu6cLjM+bxcrB5\niGfO9XG4ZYjxWTZCWJdj5caVadywIl02SRNCCCHEohTRmgWt9a8AlFL7tdanr/QmSqk3Ar/F6Ka0\nHqPoeQOwlxBqFnx+DtRNBAo+jwB/g9G56b3An65wyFFHezWP31frDxSU10PJk7/zBwqVn/8IK/7+\nr5d9oKC15kz3MM+c7ePF8/3YXZ4Zr1udbeHGlencsCKNnCRJ1xJCCCGEmIkpwPMfVUpdO/mEUupa\npdR3Q7zPV4D3aa03A07fr3+L0c0oaEqp64B3AzcrpY4qpY4opW7HCBJuUUqdAXYB/znT62tqakIc\ndvR4+flznDvV5T8u3PMI1q4WUIp13/gUKz/2nmUdKHQ7x7jnmI0PPnCKjz9Sz59P9UwLFCqyEvng\nVQX8+h3r+P4bVvPWqpw5A4VLl4GFmI3MFREKmS8iWDJXRDSYcWVhkruAT15y7lXgYeAfQ7hPidb6\n/kvO/Qqjs9Gl7z8rrfU+Zi+ufm0I41lU6k/YeOX5Bv9x5olXSGs4joqNYeP3v0j+G5fsb31OI+Me\n9jUO8szZPmra7TMWquQmxbFrVTqvrcigKDUh4mMUQgghhFjMAgULmumrD+YZzgXSpZSa6FjUqJTa\nAfQQRFel+VRdXR3J282LzvYhHr//uP/Y2tZA3qFnUWYzm3/2NXJu3bmAo4s8r9Yc73DwzNk+9jQO\nMDLunXZNYqyJ68vSuKUig6r8JEyXueIyUUgkRCAyV0QoZL6IYMlcEdEgULCwB/iKUupTWmuvUsoE\nfMl3PhQ/AXYCDwLfAV7A2IH5WyG+z7LitLt4+DdHcI8b6TRxQ30Uv/AgCqj6ny8sq0ChbdDFs+f6\nePZsH52OsWnPK6C6IJlbKjK4riyVxNiIxqFCCCGEEEtSoGDhHzA6D3UopZqAEqADuDOUm2itvz7p\n8a+VUi8CVq31qdCGe2UW0z4L7nEPD//2CPbBUQBMY6OUPHMPMWOjbPj2Zyl4060LPMLwc7jc7L4w\nwDP1fdR1OWe8pjg1nlsqM7i5PGPeC5X37pX+1iI4MldEKGS+iGDJXBHRYM5gQWvdqpTagtHytBho\nAQ5qrafnfoRAa918Ja9f6rTWPPXHE3S0DBonvF6KX3iQhMEeVn/hboreFVKstqh4teZIm52nzvTy\ncvMg457plQjJ8WZuWpnOLRUZrM62LOvCbiGEEEKIcAq0sgBGXUEsYNJa71dKWZVSaK1n/qo3ii2W\nmoUDu89z6liH/zjv4NMktzWw4u53s+Ludy/gyMJnaNTNU/W9PHa6h/ah6WlGZgXbi1O5pSKD7SUp\nxJlDLZsJnXybI4Ilc0WEQuaLCJbMFREN5gwWlFJVGHsYuDB2br4XuBFjL4N3hH10y9C5U13sffqs\n/zj9zKtk1h2k6F13UvmvH13Akc2/iT0R/nyqhxfP98+4irAqM5FbKjK4qTyd9ETZME0IIYQQIpIC\nfT37Q+CLWus1wMSOzbsxipUXnWjfZ6G3y8Hj9x3zH1s6Gsl/5QlybrmOdd/4lyWTbjPq9vLkmV7+\n/k9n+Pgj9Txztm9KoJAUZ+bNG7L58ZvX8IM3reFNG3IWJFCQ/tYiWDJXRChkvohgyVyy2+aoAAAg\nAElEQVQR0SBQGtJ6jJ2XwWijitbaqZRKDOuoliHX6Dh/+u1RxnwbicXa+yl5/n5S15Wz6UdfxhQT\nTMZYdGsdHOXRUz08Xd+HY2z6zsoVWYn85bpsblyZTkJM+NOMhBBCCCHE3AJ9Am0EtgKHJ04opbYD\n58I4prCJ1poFr1fz53uO0ddjlIEo9zglz92HJSmeLb/8OjFWywKP8PJ5vJoDLYM8UtfDkTb7tOfj\nzIqbVqZz57osVmdbF2CEs5NcUREsmSsiFDJfRLBkrohoEChY+ALwmFLqR0CcUuqzwEeAD4V9ZMvI\nS0+dobG+x39cuOcRLEM9VN//PRKL8hZwZJdveMzDE2d6+VNdNzb79ILlgpQ47liTxa2VmaQkLP5V\nEyGEEEKIpWjOXA+t9aPA7UA2Rq1CKfBmrfXTERjbvIvGmoXTxzo4vKfRf5x9bA9pF06y9iv/RMaO\nzQs3sMvU6xznZwfbePc9J/nxgbYpgYJJwY6SVL52ezk/f9s63roxN6oDBckVFcGSuSJCIfNFBEvm\niogGAT+paa2PAkurDU+U6O918tRDJ/zHyc1nyHn1BYr++g0Uv/dNCziy0DX2j/BAbRfPN/Tj9k7t\napQcb+Z1a7K4Y00Wucnzu3GaEEIIIYQIH6X19HaV/ieVigP+FbgLKADagXuAr2qtRyMywnn03HPP\n6WjZwdnj8fKHHx/A1mpsvBY32Ev5Iz8lc/Nqtj/wfUxx0d8mVGtNbYeD+493cbBlaNrzRanxvHlD\nDrdUZBAvBctCCCGEEGFz5MgRdu3aNe+tMwOtLPwQWA18HGjCSEP6HFAIvH++B7OcvPJ8gz9QUB4P\nxS8+iDU7lc0/+1rUBwoer2Zf0wD313Zxpnt42vPrc628tSqHHaWpmJZIu1chhBBCiOUo0Ne9bwTu\n0Fo/obWu01o/AbzBd37RiZaahbamfg682OA/zjnyAlZnH5t/8Z/EZ2cs4MjmNub28uipHj7wwCm+\n8lzjlEBBATvLUvnunZV8585KritLW/SBguSKimDJXBGhkPkigiVzRUSDQCsLNsACDEw6lwh0hG1E\nS9zYmJvH769lIvvL2tFI1olX2PC//0bqpjULOrbZ2F1uHj3Vw0MnuhkYdU95Ls6suLUik7dUZVOY\nmrBAIxRCCCGEEOEQKFj4DfCkUur7QCtQDNwN/FopdfPERVrr58M3xPkTDfss7Hv2HIN9IwCYXCMU\nvvQwZR96OwVvvnWBRzZd//A49x/v4rHTPYyMe6c8lxRn5s51WbxxffaC7K4cCdLfWgRL5ooIhcwX\nESyZKyIaBAoWPuz79XOXnP+I7weMnZ1XzueglqqOlgGO7Gv0H+cfeIrsyiJW/2t0NZvqGx7n3tpO\nHj/Vg8sztQA+yxrLWzbk8BerM7HEmRdohEIIIYQQIhLmDBa01isiNZBIqKmpYaG6IXncXp588IQ/\n/SiptYGs7gts+v0vo6ag2Tnm4b5jnfzxZDcu99SVhNL0BN6+MYebVqYTa14enY327t0r3+qIoMhc\nEaGQ+SKCJXNFRIOA+ywopWKBa4ACrfW9SikrgNbaGe7BLSUHdp+nt8sBgGl8jIKXH6Xq25/GUpK/\nwCODUbeXR+q6ufdYJ3aXZ8pzqzIT+asteVxTIp2NhBBCCCGWm0D7LFQBjwAuoEhrnaSUeh3wXq31\nOyI0xnmzUPssDPQO84vv7sHjS+nJ3/8kGzdmU/Xdz0d8LJONe7w8caaX39fY6BueWri8MiORv9mW\nz9XFKSgJEoQQQgghotpC7rPwRa31b5RS/b5zu4GfzPdAlrLnH63zBwqJ3W0UjdpY+9WvLdh4PF7N\ns+f6+O0RG52OsSnP5SfH8d6t+dxUni4rCUIIIYQQy1yg5PP1wG99jzX4048SwzmocFmIfRYaTndx\n/kyPcaA1BQeeYtP/fJEYqyXiY/Fqze7z/XzowVN866XmKYFChiWGj11bxE/fupabV2VIoID0txbB\nk7kiQiHzRQRL5oqIBoFWFhqBrcDhiRNKqe3AuTCOaclwu708//AJ/3F6/RHWv/Nm0rasi/hYDrUM\n8bND7Zz3tW2dkBJv5p2bcrlzXTbxMcujcFkIIYQQQgQnULDwBeAxpdSPgDil1GcxWqZ+KOwjC4NI\n77Nw5OVGBoeMb+9NrhFW9jew6hP/EtExtA+5+OErrRxoGZpy3hJr4q1VObxpQw5WaYE6I+lAIYIl\nc0WEQuaLCJbMFRENArVOfVQpdTtGcLAbKAXerLV+NRKDW8ycdhcvP30GMNJ5cmpeYvN/fRJTfFxE\n7j885uEPNTb+eKKbce/FIvZ4s+KN67N528ZcUhICNsMSQgghhBDLWMC8E631Ua31R7XWr9daf0Rr\n/aqvneqiE8mahT2P1eH2GoFC/EA3W3aujEj6kVdrnq7v5f3313FvbZc/UFDA7ZWZ/Ood6/nA9kIJ\nFIIguaIiWDJXRChkvohgyVwR0WDOT4xKqWeA92itOyad2wj8BtgU5rEtWt02OyeO2cBXJFxy7iCV\n3/x62O9b1+nkB6+0Ut8zPOX8mmwLd19bxOpsa9jHIIQQQgghlo5AXy8fAY4ppf4euB/4NPAp4HPh\nHlg4RKpm4bl7X/UHCkmt57j6H94S1u5HPc4xfnaonefO9U85n2mJ5QNXFXDzKmmDejkkV1QES+aK\nCIXMFxEsmSsiGgSqWfi0UupR4NfAN4B2YLvWWrohzeLC6U5aO0eNA6+XtaYucl93Y1juNe7x8tCJ\nbn571Mao2+s/H2tWvLUqh3duyiUxVoqXhRBCCCHE5QmmV+YKIAXoBqxAQlhHFEbhrlnwejXP3OPv\nMkvG+Vqu+vKHw7ID8tE2Ox/542l+eqh9SqCwsyyNn751Le/bViCBwhWSXFERLJkrIhQyX0SwZK6I\naBCoZuEBYANwu9b6kFLqbuAlpdR/aK2/GZERLiJHXzjN0JjxAd007uLqq/OxlBbO6z36h8f5wf5W\ndp8fmHK+LD2Bj+4oorogeV7vJ4QQQgghli+ltZ79SaV+AHxCaz0y6Vwl8But9dURGN+8eu655/SW\nLVvC8t5jLjc/+rfHGTMZrVGLGo/w9l/MX6tUrTXPnevnh/tbsbs8/vOWWBPv2ZrPX67LJsYkdQlC\nCCGEEMvRkSNH2LVr17x/GAxUs/DRGc7VK6Wune+BLHYv/f5lf6AQ4xziNX97y7wFCr3Ocb67t3na\nxmo3l6fzoasLybQsyk62QgghhBAiys1Ys6CU+t4lxx+45JL7wjaiMApXzYJjcOT/t3fv0XWVZR7H\nv0+SJr2kTektvaRpS6HlsnoBSqEUHKDKRVB0jWVRQEFxHGZUFEcUdBwEHJVREEfHmaVcREQYUBAY\nXYIUKAJyqSW0UOiF0oZe0pY2vSRpmzR55o+9c3J6epLsU5KcnezfZy2W+93n7H3ec/IzPU/2++6X\npW/tTLWnsoXy00983+d1d55avZ3PPfTmAYVCeWkx3z1nMteeMVGFQjfSWFGJSlmRXCgvEpWyInHQ\n3pWFy4Gr0to/AO5Ia3+ouzrUGy28YxEthcGX9v61W/i7b85/3+esbWjiJy+s57m1B85N+OgxI7ji\nRE1eFhEREZHu116xkDneqU8Mhu+OdRa2VG9n1ZYWKAgu0swY6wysKD/k87W486cV27j95Y3UNbbN\nTSgvLearH6hkhiYw9xjd31qiUlYkF8qLRKWsSBy0VyxkznpufxZ0wv35rr9AQQkApVvf5eRbFxzy\nuWp27+OWZ6t5bVPdAfs/fNRwPjd7HAOLdTVBRERERHpOe+ssFJnZGWZ2ppmdmaXdK7+1dvWchU2r\na9i0ryTVnnPCCPoNLs35PO7O4yu3ceVDbx1QKIwZXMz3zpnMl0+tVKGQBxorKlEpK5IL5UWiUlYk\nDtq7srAFuDOtvS2jvaXbetSLLLrneYJ16mDo1nVMuyFzHnjntjcEdzp6sbptAnOBwfzp5Vx63GhK\niqKsmyciIiIi0vWyFgvuPrGH+9EjunLOwtY1NazfNyB1bWb2aRMo6NfhnWgP8sK6Hdz6bDW70tZN\nGDekhK+dPoGjRw3qsr7KodFYUYlKWZFcKC8SlbIicZDbt1tJeebOZ6BgKABDdmxi2iWfinxsU3ML\nt7+8kYff2HrA/guOGckVs8fSX1cTRERERCQGEvWttKvmLGxbs4nqpra5CSfOrcQKo80p2LR7H1c/\ntuqAQmHEoH7cfO4RfP6UChUKMaKxohKVsiK5UF4kKmVF4kBXFg7Bol88iReOBGBQ/XZmLLgo0nFv\nbqnnW4+/fcCwozkTyvjqByoZXKIfhYiIiIjES6K+oXbFnIWdazextmkIFAftE0+uoKCg86sBL7+7\nk5uefId9zcFdaIsKjM/OHsvHjx2JWZ9YxqLP0VhRiUpZkVwoLxKVsiJxkKhioSs8e/tCWopHADBg\n7y6Ov/CsTo95YuU2bv1LNS3hahVl/Yu44UOHc0y5JjGLiIiISHz1ugHyZnaHmW02s6Vp+w4zsyfM\nbIWZPW5mZdmOfb9zFhq21vJ2w8BU+7gZoygobP8jdHceeG0zP3y2rVAoLy3mRx85UoVCL6CxohKV\nsiK5UF4kKmVF4qDXFQvAXcDZGfuuBZ5096nAU8B13fHCz93+JPv7B8VC8b46Zl9yWrvPbXHnf17a\nwO2vbEztO3xYf277yBQqyvp3R/dERERERLpUrysW3P05oDZj9wXA3eH23cDHsh37fuYsNNbv4a3N\nbe1pkwZSVJT9DkhNzS3c/Mw6Hn697Y5H00eX8sPzjmT4oH6H3AfpWRorKlEpK5IL5UWiUlYkDvrK\nnIVR7r4ZwN1rzGxUV7/Ai79cSOPAwQAU7dvDKZefm/V5DY3N3LjwHZZs2J3ad+rEoVx7+gSKdVtU\nE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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#this can be slow, so I recommend NOT running it. \n", + "\n", + "trials = 500\n", + "expected_total_regret = np.zeros((10000, 3))\n", + "\n", + "for i_strat, strat in enumerate(strategies[:-2]):\n", + " for i in range(trials):\n", + " general_strat = GeneralBanditStrat(bandits, strat)\n", + " general_strat.sample_bandits(10000)\n", + " _regret = regret(hidden_prob, general_strat.choices)\n", + " expected_total_regret[:,i_strat] += _regret\n", + " plt.plot(expected_total_regret[:,i_strat]/trials, lw =3, label = strat.__name__)\n", + " \n", + "plt.title(\"Expected Total Regret of Multi-armed Bandit strategies\")\n", + "plt.xlabel(\"Number of pulls\")\n", + "plt.ylabel(\"Exepected Total Regret \\n after $n$ pulls\");\n", + "plt.legend(loc = \"upper left\");" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "image/png": 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0009z9OhRi2vCw8N56aWX2LBhAx07dgSgV69e5vaW9Lvmpl69ely5coUmTZoA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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure()\n", + "[pl1, pl2, pl3] = plt.plot(expected_total_regret[:, [0,1,2]], lw = 3)\n", + "plt.xscale(\"log\")\n", + "plt.legend([pl1, pl2, pl3], \n", + " [\"Upper Credible Bound\", \"Bayesian Bandit\", \"UCB-Bayes\"],\n", + " loc=\"upper left\")\n", + "plt.ylabel(\"Exepected Total Regret \\n after $\\log{n}$ pulls\");\n", + "plt.title( \"log-scale of above\" );\n", + "plt.ylabel(\"Exepected Total Regret \\n after $\\log{n}$ pulls\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Extending the algorithm \n", + "\n", + "\n", + "Because of the Bayesian Bandits algorithm's simplicity, it is easy to extend. Some possibilities:\n", + "\n", + "- If interested in the *minimum* probability (eg: where prizes are a bad thing), simply choose $B = \\text{argmin} \\; X_b$ and proceed.\n", + "\n", + "- Adding learning rates: Suppose the underlying environment may change over time. Technically the standard Bayesian Bandit algorithm would self-update itself (awesome) by noting that what it thought was the best is starting to fail more often. We can motivate the algorithm to learn changing environments quicker by simply adding a *rate* term upon updating:\n", + "\n", + " self.wins[choice] = rate*self.wins[choice] + result\n", + " self.trials[choice] = rate*self.trials[choice] + 1\n", + "\n", + " If `rate < 1`, the algorithm will *forget* its previous wins quicker and there will be a downward pressure towards ignorance. Conversely, setting `rate > 1` implies your algorithm will act more risky, and bet on earlier winners more often and be more resistant to changing environments. \n", + "\n", + "- Hierarchical algorithms: We can setup a Bayesian Bandit algorithm on top of smaller bandit algorithms. Suppose we have $N$ Bayesian Bandit models, each varying in some behavior (for example different `rate` parameters, representing varying sensitivity to changing environments). On top of these $N$ models is another Bayesian Bandit learner that will select a sub-Bayesian Bandit. This chosen Bayesian Bandit will then make an internal choice as to which machine to pull. The super-Bayesian Bandit updates itself depending on whether the sub-Bayesian Bandit was correct or not. \n", + "\n", + "- Extending the rewards, denoted $y_a$ for bandit $a$, to random variables from a distribution $f_{y_a}(y)$ is straightforward. More generally, this problem can be rephrased as \"Find the bandit with the largest expected value\", as playing the bandit with the largest expected value is optimal. In the case above, $f_{y_a}$ was Bernoulli with probability $p_a$, hence the expected value for a bandit is equal to $p_a$, which is why it looks like we are aiming to maximize the probability of winning. If $f$ is not Bernoulli, and it is non-negative, which can be accomplished apriori by shifting the distribution (we assume we know $f$), then the algorithm behaves as before:\n", + "\n", + " For each round, \n", + " \n", + " 1. Sample a random variable $X_b$ from the prior of bandit $b$, for all $b$.\n", + " 2. Select the bandit with largest sample, i.e. select bandit $B = \\text{argmax}\\;\\; X_b$.\n", + " 3. Observe the result,$R \\sim f_{y_a}$, of pulling bandit $B$, and update your prior on bandit $B$.\n", + " 4. Return to 1\n", + "\n", + " The issue is in the sampling of $X_b$ drawing phase. With Beta priors and Bernoulli observations, we have a Beta posterior — this is easy to sample from. But now, with arbitrary distributions $f$, we have a non-trivial posterior. Sampling from these can be difficult.\n", + "\n", + "- There has been some interest in extending the Bayesian Bandit algorithm to commenting systems. Recall in Chapter 4, we developed a ranking algorithm based on the Bayesian lower-bound of the proportion of upvotes to total votes. One problem with this approach is that it will bias the top rankings towards older comments, since older comments naturally have more votes (and hence the lower-bound is tighter to the true proportion). This creates a positive feedback cycle where older comments gain more votes, hence are displayed more often, hence gain more votes, etc. This pushes any new, potentially better comments, towards the bottom. J. Neufeld proposes a system to remedy this that uses a Bayesian Bandit solution.\n", + "\n", + "His proposal is to consider each comment as a Bandit, with the number of pulls equal to the number of votes cast, and number of rewards as the number of upvotes, hence creating a $\\text{Beta}(1+U,1+D)$ posterior. As visitors visit the page, samples are drawn from each bandit/comment, but instead of displaying the comment with the $\\max$ sample, the comments are ranked according to the ranking of their respective samples. From J. Neufeld's blog [7]:\n", + "\n", + " > [The] resulting ranking algorithm is quite straightforward, each new time the comments page is loaded, the score for each comment is sampled from a $\\text{Beta}(1+U,1+D)$, comments are then ranked by this score in descending order... This randomization has a unique benefit in that even untouched comments $(U=1,D=0)$ have some chance of being seen even in threads with 5000+ comments (something that is not happening now), but, at the same time, the user is not likely to be inundated with rating these new comments. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Just for fun, though the colors explode, we watch the Bayesian Bandit algorithm learn 15 different options. " + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 6.78509844e-03 9.68721665e-02 3.16101371e-02 8.88059449e-02\n", + " 3.32812651e-02 6.59572621e-02 7.91635546e-02 5.97822577e-02\n", + " 1.17088549e-01 1.29945195e-05 3.66798062e-02 5.77077187e-02\n", + " 4.32774140e-02 6.94914246e-02 1.22741733e-01 5.13528129e-02\n", + " 3.29414904e-01 5.13320236e-02 5.35031763e-02 1.57610420e-02\n", + " 1.94570205e-02 1.11759388e-01 3.23349076e-02 2.04068995e-02\n", + " 1.47822753e-01 8.24022697e-03 3.20395660e-04 4.45643230e-03\n", + " 6.42090321e-03 7.29322919e-02 8.18486095e-02 5.05066236e-03\n", + " 1.73946201e-01 6.48018322e-02 7.70657954e-03]\n" + ] + }, + { + "data": { + "image/png": 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+dbFctq6h1KculivGXt/1qYtl69/fhlCf2vr9LWuvpk+fbvN9p5Cydgc9hIeH\ny6lTp1b7OGazmcNvP4Rv3E+UYqRk9Ae0HntJ6W/nWB2VylsbYnAwCMLHtadTgFu161ZRRESE7r6m\nUDHbP73FCxAZGUlYWJio73rUBCHEjcBoKeU0y/KdwBVSyv9YlwsPD5czk9vwZusE7rr1vPtlVtw7\nmZ4eyynIc8LrpT34ttDSI/7c8T3/W/c2XVr14cXbFrD8aCqTfzpMO18Xtj3UGyEa7o9Rj9e2ilkf\n9Bizre12g84htmYwGOj8+HzSWt2EkVJMKx8m+q+Fl32cUe2bML6zH8Vmyatro8nIr/mv8/R28YGK\nWQ/0Fq8digEGCCGchXZ3GgYcrlgoNDSU/BIHluQUVXqQwW99SF6uC86uheya+XD5+hE9JuDi6Mah\n2F2cTDzM1e19ae7hyPG0fCJOZ9ZSSDVDj9e2ilkf9BizrRp8DrE1g8FA58fmkxZ0i5ZTvPJhTi7/\n/rKP8+CAFnQJcCMlt5g31p1qNFMDKYqi2EpKuR1YAuwG9gICWFBZWVeHIiIS2/HCp+HnbXP39SZK\nhgHQzmEjKce1MRmuTu5c1WMiAH9s/x8mg+BOyxRsX0Um1nQ4iqIoNarB5xBXZDAY6PzoR6QF34aR\nUpxWPcrJP767rGM4GA28ENYaHxcTe+Jz+HLHeQOtq8U6v0UvVMz2T2/x2iMp5ctSys5Syh5Syrul\nlOd9RbZnzx6u9d8HwF+F7Tl96vR5xxn6zsfk5rji7FLE3tf/nZd4bN/bMRqMbD26huSMM9zZqxkG\nAX8eSSW5ih7nhkCP17aKWR/0GLOtGlUPcRltoN0HpLWZjIFSnNY8dtnzFPu5OfL8VW0wCPhpfzIb\no9NrqbaKoiiNy3NXTaKNZxrRWb68/vfS87a7ergTZbgagHaOm0g8egoAP89mXNl5DGZZyvKd39PC\n04nR7X0pNkv15DpFURq0RpNDXJHBYKDzw++SFnKndlO89nFO/Pb1ZR2jR3N37r+iBQDhG2OISS+o\nkbrpMWdHxWz/9BavXoWGhhLcOphrnKIA+DO5J98t/vG8csPC3ycn2x0nl2IOvvlI+fpx/e4EYN3+\nZWTnZ3BP72YAfLs7CXMtD+K2lR6vbRWzPugxZls1yh7iMgaDgc7/eYe0tndjwIzz309w4tevLusY\nk7r6MzzEm/xiM7PWnCS3qHYf2qEoitIYvPrADIY2jyK/xIFFWeenO7h6uHPcNAaAds5bidl1EIDg\ngPb0bDMF47zSAAAgAElEQVSQwuICVu9ewlUhPrTycuJURgFrT6hv4hRFaZgaXQ5xRQaDgc7/F05a\nuynaTfH6J4j66eNL3l8IwWNDgmjt40xcZiGza2CQnR5zdlTM9k9v8eqVdZt9u7c7bg5FbEpsy7Of\nzjuv7Ij33icryxNH52JOvv94+fpx/e8CYGXkIkpLC5napzkAn+9MqOXa20aP17aKWR/0GLOtqn1D\nLIQwCCEihRC/1USFbGEwGOg8/S3SO0zDgMR90wsc/XbuJe/v4mBk1qgQPJyMbIvN4ptdDbPRVhRF\nqUs333AT4/33AvBnfmcOHDh4znZnZ2dOetwAQDv3Xez+eQUA3YL60aZpJzLz0lh/4DcmhzbFyShY\nczydk2n5dRuEoijKJaiJHuJHgENVbaytHOKKDAYDXabPIbPHYwB4Rs7m8IIXLnn/QE8nXrAMslu4\nN4l1J9Jsrosec3ZUzPZPb/HqVcU2e9a4yXT0SSYux4t5O9adV37se+GkZDTF6GCmYNnLgPbN28QB\n2gOZftv2LV5OcH1XfyTwRQPscNDjta1i1gc9xmyrat0QCyFaAtcAn9dMdaqv09SZZPWfiUTgfehj\nDr7/CGaz+ZL27dXCgwcHtAS0QXbHUvJqs6qKoigNXkCAP+MdYxFIlid0550vz09JS247DWmGYO+j\nbAj/FIB+HUYQ6Nuas1kJbD68kvv7BQLw/Z4kNVZDUZQGp7o9xO8ATwJVJt3u2bOHmNjYap7m8nS8\n/THyhs3BjAHfk//jUPj9mEsurQGe0MWPsR2bUFQqmbX6JKl5l/8kOz3m7KiY7Z/e4tWrysZ9PDft\nEUa3OEiJ2cgvBQGkpZ47OG7kzMc5kxGCMIDnvo8AMAgDEwbcA8CvW7+iezNX+rX0IKuwlJ8OpNR6\nHJdDj9e2ilkf9BizrWy+IRZCXAskSSn3oD3xqNLnRm/YsIEJNw7h2glXM3v2bObPn3/OGxQREVEr\ny+0m3UfR6A/ZmmjgxK5fODx3MqVFRRfdf9OmTfSSp+jW1I2zecVM/2AJ6zZsvKzz79+/v9bjU8tq\nWS3X/PL8+fOZPn06s2fPZvbs2bU+KLgxeax7H5q5ZXMwrRnPL/v6vO2lI56mtETQzCeOFU+/BMCg\nzmPw82xGfNopdkatZ1pfbXDdZzvikQ10CjZFUfRJ2NooCSHeACYDJYAL4AH8IqW8y7rc2rVr5dw1\n0xCl0CqjJVOmzqFzx07Vrfcli1m3DLHsQUwUkuYzhA5PL8TB2eWi+6XnF/OfX4+SklvM1e19mTE0\nCCEqvedXFMVORUZGEhYWpqtf/LVr18revXtXuu3ZBfP4NHkALqZiXguKY8rtd56z/Z+7htPOdx/p\nmT6EzD+Is7MzKyMX89WaOYQ07cxLd3xDzw92kpxbzLLJ3RjS2rsuQlIURUdsbbdt7iGWUj4npQyS\nUoYAtwJ/V7wZLtMixR9phJgmcbzzzT28MvcpW0972YJGTMBwy/8oEq74pv9D1OsTKMrJvuh+Pi4O\nvHJ1CE5GwaqoNH7an1wHtVUURWm43rz/CYY3P0Z+iQM/5hjIzzt3nEXTe+dSXGjCxyuddY9qD+sY\n0f06vNyacDLpMIditjLFMgXb/G3xdV5/RVGUqtTJPMThb62gY+lg3LKMZHkXc9i8lieeGse2Xdtq\n+/QAtBg4Eqe7FlMoPPDJ3smJN64lL+3sRfdr28SVJ4cHA/DF9ngiojMu6XzWX8HqhYrZ/uktXr26\nWJrI1MCmeDvlszM5mGd/OHeAXYdh/TmWPxCA9iV/kpmciqODM+P6TQbg500LmNJbm4JtZVQax1Mb\nxhRsery2Vcz6oMeYbVUjN8RSyg1SyusuVOblZ9/jvnHv0uqs1jsQ55fAZ0sf5qU3H62JKlxUs15X\n4j5tGfkGX7zzDhA3+2qy4k5ddL+hbXyY2q85Epiz/hRHU3Jrva6KoigN1bix1zLBZx8Ay9JD+e2P\n38/ZHvrKfPLznHFzz2PbMw8CMCr0JjxdfTiecID45F3c1D0ACXy6XfUSK4rSMBhnzZpVqyfIz8+f\n1by5dhPcqmUrrh51Owc2H6cwI5Ycr1JSRQxblv2KybkFIcFtarUurv7NoMNoMnb9hUdxLJnbfsEc\nPBRXv2YX3K9rUzfO5hZz9GweW2MyGdrGB3cnY5Xlg4KCarrqDZ6K2f7pLV6AhIQEQkJCXq7vetSl\n/Pz8WV+mxjE0oEWVZYZ1CuWfE1s5meVHmojj1t69yre5enmw9bf9NHM8io+MIzNgNH5BLUHC/tPb\nSMqIY8rQW/hyVyJHz+YxpU8zXByqbk/rgh6vbRWzPugxZlvb7VpPmajMrKfe4pE7PicotSXCDGf8\nk/l+9dPMfP3/av3cPm060vyp1WQ6d8C1NIWcT68jYfv6C+4jhOC/g1sRGuhOen4JM1edUPNoKopi\ntz4+uYWtKVX33rq4unKbh8TFVMzGhPY8s+DcxzqP+OCT8kc6x3z4XwBG9boJDxdvouL3U5y7nxEh\n3uQVm/kmMrFWY1EURbkUdZJDXJluXbszd84yujqOwSvdkTyPUqIctvL4k1fz54rfK92nprgHNKfN\nCytJ9+iHk8ym5IfbOLVqyQX3MRkEL4a1IcjbmVPpBby2NpoSc+UzdOgxZ0fFbP/0Fq9e7dmzh3xz\nNpO3/UVuSWGV5e65bTITArT2fVlOV9at31C+zdnZmWj/uwEI8drPxnc+w9nRhXH9tVkplmxawEP9\ntQd1fLYzgaLSS3t4Um3R47WtYtYHPcZsq3rpIbb2wozXeWradwSlBWMogXj/VH7a8grPv3wv2dkX\nnw3CVk7uXnSY+RtpAaMxUYjD8geJ+un8JzBZc3cy8eroELycTew6k81Hm2PVXJqKotgdR+FMWkki\nQ9cuvWC51ybcQ/cm8STlefDhyePnbBsz52Xi0tpgMEq8974NwOheN+Ph4kVU/D78DEfo6OdKQnYR\nvxxsWA/qUBRFf+o0h7gqvj6+jBp5C8d3JVCYfJocrxLSHRLZ+tcSTsZk0r/3gFqpm8Fowm/Q9cRG\nJeCavhfH2L+Ji8ujSeiwKucc9nAy0a2pO2tPpHEkJQ9nk4GuzdzPKaPHnB0Vs/3TW7yg3xziPPcm\nHMyOI6MkjV2pudwU1L7Ssi6uLhQe3sbmXE+iMgNIPbqYUX2uLN+eXNIK55O/4uGWQ8Rvp+l83URA\nyyWOTzvNtb1v4K+oNE6kFTClT7N6m+tdj9e2ilkf9Bhzo8ohrsrTj87i1Wd+p3VmBxwKIcU/h62J\n/+OZ526utcc/GwwGuv73PTJDnwDA+8AHHH53+gUf9dylqRtPD9OmY/t8RzxrotJqpW6Koij1YX6/\n4XRx7wDA+rP7mXc4ssqyD941tTx14pfMHufMOtHrpmuIytE6NDoUL+NsTDyje9+Mt1sTTiYeoq3L\nIZp7OHIkJY/Vx9MrPb6iKEpdqLcc4qr4+TRh9us/MjRoGk2TPSlxhFPeJ3j945t59a1naqmW0Ome\n58gb9halGPGNWczhN2+muKDqOTKHhvjw4ABtFHb4xtPsissq36bHnB0Vs/3TW7x6VdZmrx8+kaYO\nLSmRxbwbFXHBQXZzbr6XPv4xpBW4siAx7ZwHdvR4dQF5uS64uuWzf+Z9ODm4MHHgvQD8svkTHuzf\nFID3NsfVYlQXpsdrW8WsD3qM2VYNqofY2rS7H+S9eetoW9AXlxwDmT5FHCpdzRNPXsvajetq5Zxt\nJ92LecKXFOOCb+o6jr8ymtyzSVWWv75bADd2D6BUwitro4k6m1dlWUVRlMbEZDLx57CJeBh9yTNn\nM3nbcjILKx9k5+Huyd0+Ag/HQjYntj3ngR1+QYEcc7oBgPbu29n53S+E9ZiEn2czYs+eoLPrAbyc\njWyNzWJbbFalx1cURaltDSKH+ELCho3Hw9ie5D17yXbJJcs9hyNH1rAjYhdXDRtfgzXVeLXpSH7T\ngeTvX4FH0WlSNy+FNkNxbRJQafleLTyIzyok6mw+W05nMri1N13bh9R4vRo6PeYp6S1mvcUL+s0h\nLmuzfRydaeHsxaqkaHLMGfwSm8BD7btXul+Prt05fXgpe3NacrLIH/8z2+jRVSvb/pqxnPjxWzzc\nssk+uJdWN/wfzo5u7Dq+kfjU4wzucj1b4nJIyy/mhq7+dRZrGT1e2ypmfdBjzHaRQ1yVsKEjmDd3\nOV2No7Qp2tzNHHfaySNPXMUX335W4+dr1utKmjy6iizHNriXxJP98bWc2by60rIGIZgxNIhegR6k\n55fw7IoTZOQX13idFEVR6sPNwR24tWVfBAZiC6MZtW5ZlWXfvP0hrmx2kuwiJ77NkGTn/Nvjm913\nBuZSQQuf06x44nmGdRtHM58gEjNi6eq+E2eTgb+OaYOVFUVR6lqDyyG+kBeenM3z0xfTOqMtpiJI\nCshk/elPeOb520hMTKix8wB4BYXQ+oW1pHv2x0lmIxffwfGln1da1sFo4MWRbWjbxIX4rELuf38J\n+cX6enCHHvOU9Baz3uLVq8ra7Hf7DKWPVxcAIjMP8dCO9ZXu6+Lqyv3NfPB1zmNnchDPLPq3zRzy\n8FROZPYEICT9O/IzcrhlyEMArNz5Bbd19wLgnU21M4D6QvR4bauY9UGPMduqUfQQWwtq1YrZbyzm\nioDJ+Ke4U+wEp7yOMevd63ll7lM1ei4nT286vvAbaS0mYaQEtw1PceiT5zGbz59E3s3RyOuj29LU\n3ZGYjAJeXRtNcT1PNq8oilJTVo2YQLBzCBIzv8Tv5JOo/ZWWu27ceCZ57gPg15RefPj1gvJtrR/5\ngMJ8Rzw8stn6+BQGdBxF22ZdychNpbtLBCaD4OeDKZxIq3pAs6IoSm1o8DnEVbmiz0CGDLyRQ//s\noaAwmRyvElJkNJuX/UJmjgNdO3erkfMYjEb8Bo7nTEIJTklbcTm7g5jd+/Dtdy0Gk+mcsi4ORvq3\n8mRLujPRaQXEZRYyqLU3hnqaW7Mu6TFPSW8x6y1eUDnEFd3ZugP/iz5FTmkGW1ITuMK3FUFuHueV\nu7rPlew4voqozKacMcJwLxO+fk3wCgxgyx9HaOZwmCbGWA4nh9B3xGg2HvyDM2eP0Kv9OPYll5Bb\nVMo1HZvUdqjl9Hhtq5j1QY8x23UOcVU8PDx4/aUvmDxyLq3OBiLMEO9/lj/2z+PZmXdwNj21Rs5j\nMBjoPPUFCq5+nxIcaZK8gqhXx5KXdv7TlVp6OfPmmLa4OhjYGJ3BexHqaXaKotgHN5MTvw2egIfR\nhzxzFndt+5PkKqanfLpHT4I90zmW7s+szWvK14/99HOS0ptjdDDjvH4WXYP70bPNleQX5dLBYTUG\nAYv3pxCTUVBXYSmKojSuHOKqhA0dwVtzf6en63U0OetCkYsk2uMIL8wZX6NzF4dccwemyT9RYPDG\nO3cvZ94IIy3q0HnlEo9E8trotjgZBSuOpfLZ9ni7vynWY56S3mLWW7x6dbE2u5OXL+E9R+EkXEkv\nSWbEuiWUlJScV65f337c6hKNyVDK8jPdeWbBvPJtBcNnUVpsoKlPAn89cB+3D3sYgWDb4aVMam+m\nxCx5tw7nJdbjta1i1gc9xmyrRt1DXNEzj7zE7Jl/EZLXHedcAxm+hRw0r2bGk2NY+sfSGjlH875D\n8H54JdkOwbiXxJH18Rhi1v1+Xrluzdx5cWQIJoNgyf5kftxT9XzGiqIodUUI4SWE+EkIcVgIcVAI\nccXlHuPGoPbc22YABowkFMYwfP2vlZZ7etp/ub7ZbgB+yurJ4p9/AmDAlJs4kjsYgM7id0qOZTGk\n6zWUmktoxa8I4Ps9SZzJqnzeY0VRlJomarvncu3atbJ37961eo7K/LVmBWtXfsCZJolIAzjlCwJL\nOzPjsXfx86l+blpBRjon374dn6xtmDGQ3esJOtz5FAbDuZ8xNp5M5411pzBL+L+BLZlQD3NsKopi\nm8jISMLCwuxqEIAQ4mtgg5TyKyGECXCVUpbPj3Y5bfYNEX+x7qz2WOcRfr35efDY88pk52Rx0w+/\nsT25Nb39Y1l84xh8m/iQk5ZB3BO98PLM5HRaRzp8tIzHPp9EYXEBToHP8fupptzbpzlvjW1bE2Er\niqITtrbbdtVDbG3syDHMe+tPejhei+9ZZwpdJNHuh5g5+zpef/v5ah/f2duHji/+TlrInRgw47V7\nLofn3n3e456HhvjwyKBWAHy0JY7VUTWT16woinK5hBCewBAp5VcAUsoS65vhy/Xz4LF0dOsAwIaz\ne3lk18bzyni4ezI90As/l1wiU1rxzG/fAuDu601c8HSkGYJ8jrL91Q+ZcMUUALzzFyEw8+3uRGJV\nLrGiKHXALnKIL+TZx19hzswVhOR2wzlPkN6kgANFK5jx5BgWLvmhWsc2mkx0/e975A6bSwkO+Cb+\nyYmXR7Jq2bnpGWM7+XF//0AAwjfGsO5EerXO2xDpMU9JbzHrLV471QY4K4T4SggRKYRYIIRwsS5w\nuW32PyMm0cKpNWZKWRi3nbcP7z6vzHXjxnOz+wEAlsb34vUF7wIw8qUnOZnRAyGgbdpXDGpxNX6e\nzUlKP8H4lgcoNkveiqj9eYn1eG2rmPVBjzHbym57iK15eHjwxsvfcNOAl2iZoj2C+Yx/Cn8efptn\nnr+FEydPVOv47Sbdh2nyEvINvnjlHyZj4WMkRm46p8yNPZpyV+9mmCXMWX+KjdH2d1OsKEqDZwJ6\nAx9JKXsDeUC1Rh6bTCa2jroRP4dAimUh86I2sCz2/Db1tQdmML7FXkqlgYV57Vm3fgMAbZ/+nPw8\nZ9zdczn84v1MHv4IAIasn3EUefy4N4mTal5iRVFqmd3mEF/Im+/M4kzCWs76aY8I9cg00cylD6++\n8HG1jpsdH8uZD27FK/8wJThSNPwN2k6cek6Zr3fG88OeJIwCZo5sw5XB3tU6p6IotcfecoiFEE2B\nLVLKEMvyYOBpKeX4sjIPPfSQzMjIKJ+/1MvLi+7duzN4sDYIrqzHqeJyq149GP73QjIPRuJscGf5\ntKcI9Qk4p/zpU6eZ+OF3nM72ZWBfNxbeNpG9e/ax7f0PuT1wFeZSwVJxH3t9j5LtGouz7zX8tiOQ\nESHe/Pz0bRc8v1pWy2pZn8v79+8nMzMTgJiYGPr27cuMGTMuu93W5Q0xQHZ2NnPefZyEwr3kemqP\nWfZPcSc4eDRP/Oc5m49bXJDP0Xem0SRpOQBpbe+m80PzMJiMAEgp+XJHPIv2JWMyCF4a2YYrgryq\nH5CiKDXO3m6IAYQQG4BpUspjQoiX0AbVPV22vTpt9qaUeG7d8gu55kx8TE3ZMvIOApzPycjgqx/+\nxytnmpNZ6MyNLXaxYIrWI7zzrt608D1FRpY3PL2IV5Y9hBAGNhc/Q44MYNMDveno52pz3Iqi6EOD\nHVRX3znEVfHw8OC1mZ/x2G1fEJzeBlMRpPjnEJn9M08+NY6/1qyw6bgOzi6kDbqfzNAnMWPA98Q3\nHHnl2vKHeAghmNovkBu6+VNilryyJpqdcTaPaWkw9JinpLeY9RavHfsv8L0QYg/QE3jDemN12uxB\n/oG80X0kTsKF9JIkhq1dTEGFOYqn3H4nt3rtQSD5Jb4XMxeEA+A++QMKCxzw9swg4Y2XGNFjImZZ\nyjDv5ZglvLH+tM31uhg9XtsqZn3QY8y20kUO8YV069qdOW8uIazNQzRP9sVsgFi/BBZFvMCzMyfb\n9LQ7g8FAp3uepXTCFxQKd3yythP/+lASd28GtJvi+69owYQu/hSbJbNWnyTyTOO/KVYUpeGTUu6V\nUvaTUoZKKa+XUmbW5PHvbNOJB9sOwiQcSCqOo//qRec9uOPN+59gYovdmKWBH7O688NPi+h09SAO\nGW4CoKPnNoK3m3BxdKMwZzeBpsP8fiSVnWeya7KqiqIo5XSbMlGVV956ioSzm0hvok3145XmSDPv\nAbz8zDs2HS89+ijJn9yJZ+FxSnEgb+BMOtzyH0BLn/hgUxx/HDmLo1Ewa1QIfVt61lgsiqJUjz2m\nTFxMTbXZD+5Yx5Iz2zBTSohLO3aOvuWc7Wmp6dz283J2JAfTrUkC/xvbn+DWwWy7qz9BvsfJzvZg\n/03P8tO+z3B0DuTPrCcYFOzLssndEEJXb4miKJehzlMmhBBOQohtQojdQoj9lly0Ru/FJ+cy94UV\nhOR1xyXHQKZvEUcNG3nkiat45+O5l308nzYdCXlpHWnNx2OkGI8tL3Jwzt0U5+UihOA/g1pybacm\nFJVKXlp1km0xNdpZoyiKUi8+6TeCMP9QQHAy/zhD1vx8znbfJj481saPQPcsDqQ257l12rgL/4c+\noSDPCQ+PbPx+WEIznyCKCuLp6PQPEaczWXsyox6iURTF3tl8QyylLARGSCl7AaHAWCFE/4rlGmoO\n8YV4eHjwxqyvmTr6bYJSW2IshqSATHZkLuKJp65hybLFF9y/Ys6Og6sbXZ/+huyBr2jzFSf8zsmX\nR5AefRSDEPx3UCsmdPGj2Cx5eU00m041vgZfj3lKeotZb/HqVU222YsGjWGAd3cADuYcYfT6Zeds\nHzNqNJPdjuNoLOGvM9157PN5hAzszRHXyQC0945kWJT2pLrWYjnOpPLK36cw1/A3m3q8tlXM+qDH\nmG1VrRxiKWWe5b9OaPNb1m7+RR0bMmgIc+csY3DzKeX5xXF+SSzbN5enn7uJAwf3X9bxOtzyHxzu\nXkqusSmehcfJfH8Up9f8jBCC6QNblg+0e21tNBtPqnmKFUVp/JYPH093j84A7Mg4yE0R5w5Yfmba\nf7kpQHv88+KzfXjny48Z++5bRKd1Rhige9pCBvgPxWwupJfzLxxIymHJgZQ6j0NRFPtWrRxiIYQB\n2AW0RZvo/dmKZdauXSuX/rGJJx+9G0/Pxp0f+/rbzxOfuJFUy/zFrtlGmhm78OyMD/Dw8Ljk4+Sl\npXDq3TvxydqORJDRcRqdpr2OMBr4cmcCi/YmYRDw1LBgrmrnW1vhKIpyESqHuOb0X7WI43nHERiY\nFNifz/uHlW/Lz8vjroU/sDa+Ey3cM5nTyUivVu3Ie3cYrm75nEjrxpd9BHmFOewtuQej2xVsn94H\nVwdjjddTUZTGrV6mXZNSmi0pEy2BK4QQXSqWWbJkCRuXr+HmGx9n5Oh7GDfxNlauXFm+PSIi4pwu\n/Ya8/PzjrzNh5ExcTgTjmeFAnkcpO1O2c/f/DealNx+55ONFHjpKpxf/JL3jA2xPlERtWMCRl8eS\nm5xAh4ITXGGIsTzR7jTv/Li8wcSvltWyvS/Pnz+f6dOnM3v2bGbPnt0oU74aqs1X3UArpzZIzCyL\n38mjuzaWb3NxdeXVgYPp6pvImRwv5p3KoMjNgSjf+5BmCPE5wLXHOwHQzWEpydkZfLz1TH2FoiiK\nHaqxWSaEEDOBXCnl29brw8PDZezxQNys1uUIyDcWYjTG8tjDt+Hv26RG6lCXEhMTeO+jJ0kwHKXA\nzQxAQIoHrYJGMbDXsPKnqFzM6bW/Iv94BCeZTb7BB4cJ79Nq2LV8vzuRb3YlAPDQgBZM6hZQa7HU\nhIiIiEuO2V7oLWa9xQv67CEODw+XU6dOvXhBGxSUlNBn1Y8kFMVgxIE7g67k7d7/XlNLlv7CzGgX\nkvI8GBV4iK9vnczOB6+hne8+CvMdWdRmFLsNh4gtHUS86SZ2PuhPE5cskOlAFlrmngsIVxCBYGgF\nwumi9dLjta1i1gc9xmxru22y9YRCCD+gWEqZKYRwAUYBsysre+/0Hnz0+S+Yza1wLXXCXYJ7iROU\ntOPj8B3kGQsxGWJ59L+N5+a4WbPmvPnqd/yz6R9+/z2cePdYkv2zScn5hd2f/En0mQe485a7L3qc\n4LCJZLbvQfwn9+CddwC59C4OHZjKrdNex8lkYMG2M8zfeoacolIm92qmphtSFKXRcjaZ2H71zfRd\ntZCkoji+i92Co0EwO3QQADdOup4Tn73Pe4U9WB3fhRkLP+W1NxeRPHMAXp6ZjD4cScANfvQIiaJT\ni4/wMJZAUdXnkxi0G2NjN6SxHxi6gHCoo2gVRWlMbO4hFkJ0B75BS7swAIuklK9XLFcxHy0lLZV3\nPlhIaUlLXMzazXGZXAF5hiKMhjimT5tIy5aBNtWtPnzx7Wcc3PcTCX6pSAOYiiAwuyUjRj3E2JFj\nLrp/aVERRxY8h8/xrxBIMly7Efjg12zJ9+LdCC2FYmJXfx4c0AKDuilWlDqhxx7iupg7PrekkL4r\nF5JUHIdJODKt9WBe7zmwfPuMz+fxVeIADMLMQ347GFvqRquDL2ByMBOVP4Dh72iPcD6e7k2AezAe\nzv4gvIASIB9kDphjQZ5BYC4/rsQVjAORDleDoW2txqgoSv2wtd2u1wdzZGVlMeedbygpbYVzqQse\nVnXJA3JNxRhFLPdNvZaQ4OBarWdNefOdWcTHryPFPwcAx3xB88I23Hbzc4SG9rro/rHr/6Dkt//i\nbM6gUHggx8wlrsNoZq87RbFZMrK9LzOGBGE06OpvtKLUC3VDXHsyCwvpv+ZHUorPYBKOTG87lFnd\nrijfPuWrD1l2JhQvpwLeuuIvmi6Npp1cj7lUsNUrjL8DT7E1YyzNW97A73d2r/zbM1kI5lOI0l1Q\nugMhY//dZGiHNI0D40AQanCeotgLW9tt46xZs2qhOv9aunTprF69Kr8RdHJyImz4FYwa0Yk+/QL4\ne+ffZONKqXTAHXAzG3Ex+xC56yzL1x/m7w3raNnCB78mDXfmhSEDh+Pp2p7M03nI1CRyvErIdE5n\n194/2Pb3Bjq074+Xl1eV+3u17oCp540k7d2Be+EpTMf/hIREBo2dwObYHI6dzeNkWj6Dgr0a1E1x\nREQEQUFB9V2NOqW3mPUWL0BCQgIhISEv13c96tKF2uya5GwycUdQR348HUNOaQaR6WcoKDExLKAF\nACPbObP99CGiMgM4kN6UGycZSF+bjLdbGk3SE/jH0AQX5yi2p3WhdRM/ugS4nX8SYQKDHxi7g8MY\npLy+F10AACAASURBVHEQYALzGYRMQpRuhdKtRGw+RVBwb9DRt296/H1WMeuDre12tWaZqEmenp68\n+uLDzHllEk88fSVZLlGkmPLIEuAC+Jc60KS4NUu+juLpmX/w7EsL2LYtsr6rXaWXn3mHOTNX0K6o\nH57pDuS7m4n2OMIrn9zI87OmcjY9tcp9PZq1oNNLf5HR7WHMGPGNWYTHJ+N4qVsp7o5GtpzO5PmV\nJ8gtKq3DiBRFUWqWr7MLW8Juwc8hkGJZyIcn/uHVA9uh+C88jK/z/si1tPdOITrLlye3tMVw5zxy\nc1xxdcvn3ugCDBTT1fQjL645Qc6ltIeGFkjHe5AuCzA7PoAU/ggZhyj+CVHwFJQeqv2gFUVpkOo1\nZeJSZGVlEf7BNxQUtsCh1A1vq/oWAZnGUoQhmSv6NmfS+Ivn6taHs+mpvP3eDJJKDpHrqTXaXmmO\nBLj34qlH5lxwDuP4revI/+lBXEtTKMaFxH7P8LYcRlpBKSG+zrw2ui1+bo51FYqi6IpKmagbyQX5\nDF77I2eLE3AQTnzePY0JzY4hTWP5dqkjr53xJyXPnRGBR3kgJpWuqf/P3lmHR3Gtf/xzZtY37kQI\nJBAguHuBKnXvrXtLvbfeWzfaX+3W5ba33lIXbpFStLhbkCAhIe66WZ85vz+WBihSCAmW/TxPnmR2\nzp457+7MyTvvfM/7voiiShbXD+fbrsVs8p/H+UOu4okTOxzcgaUPtDkI348IWRl4SR2GNF4diCwH\nCRLkmOOY1BA3h6effxunOwGDFkLkLkP3A7WKBKWKjh1g3PVXttgxW4qcbTn89+PHKVG3NKVqi660\nEhszhKcefGWf73NWV5D71k1E1QTydpZFjOSDTg+x1W0l1m7k+bHppEZaD4sNQYK0JYIO8eGj3NXI\nGXMnsM1VjkEYubtjCo/2ugyAl//7Nm9UZeL0mTgnaRVXzP2DHuGz0HwKXxgHsbJdFcu1B5g+7kw6\nRTdjLpQe8E9E+H5B4EViQRqvAMNpII6aB6lBggQ5AI5IYY4DoaUT2z/5yB28+MxFjB8/Fn/MNioM\ndVQpoAIxuiDGH0Pd1hj+9dhvPPTEt7z07/db9PgHwq6J/nclPS2dF56bwLjT3yC1pgNGj6AqxkU2\ns7nrgdE8+8oehf4AsEXF0u3xn3AMfw4fVuJr53H/yqs4RVtFRaOPe37dQlapozVN+lv2ZfPxTFuz\nua3Z21Y5IsVIpCRe+YxFw6bTJzQGv/Tx+rZ8xi2dBcADN97BNZGrUIXO/4r6MnHoEEprklCNOhc1\nrCPSqdJV+ZIHp26iOUGe+QuWgfESpOUNpDoIgRvF9xHC8wToxS1t7VFBW7yegzYH2R/H9K3vw/fe\nxovP/IMXnhtLWKdKKg2VVCoSHYjWIdYfjlLZgUcf/Y2HnviRJ8e/RX19/ZEeNkOHDOPFF37kgr6P\nklKZhOqD8tgG1uu/c9f9Yxj/70f3eI+iKHS++DZC7pxJnTUTq17DlRvv5/ayt/G4nTw8dStzc2uO\ngDVBggQJcoj4JyK0WZhUA5NGDqGdqT0afn4sXspVi6YDMP7m+7g8YRkA31QMYmLvi3E2WgkJdTAu\nz0mYzKeg4Ht+Wl/Z/HEosUjzg+im+5FEIPRshPt+8E2DVn6aGiRIkCPLMSeZOBB+/vU3liwvQepx\nhGsquyps64XAo7pQ1cKjpkreJ1/9l/VrfqIkogxtR6mU+PIwEhJH8q97n9mjveb3s/njpwjb8B8U\nNCpM7Xkr5V/kWzO45RioahckyLFCUDJxGPAvQXhfQSDRTQ+AYTBuv5+B07+lyJOHQGFMTB9+GHE6\nADd88iY/F/XDavDxeN0kzmr4DNWgs6ZmAB9n1rDF8ABzbr+AcEuz604FkA0I7ycILSBVk0o/pPk2\nEBGHanGQIEFakaM27Vpubu5T7dq1a9Vj/JVuXTpx8pi+nHJiBqaQelZt20ijsKNIZUc6NwNWLYqF\ni4qZ+sdGZs2ZTbtYG3FxsYd1nH/St1c/Tj3lCmpzFby5BTjMjThCPZRpW5j3y7es3ZDLiCGjm9or\nikJs/xNpjB2GY8M8In0FnFAzDb8w8HV1MvUenf5JocECHkGCHCJtMe3aYZ2z9W0Iz/8h8KMbrwDj\nyQAYFIWbO2bybX4xdf4acp2l/FFayxUdunBql56s3T6LzXXxrAlvT2alk2TDFuLNxVDZg6Lwlaxz\n9Oe0jEOcz4UZDIORIhm0tQiZD/65gYIeSjDoECTI0cpRm3btiOjRdmHw4H688PTNvPjsWVwxrhvV\nphwqDB4cAuwSYv2BdG6/TMjnoccn868nP2bS1OmHdMzmanZuuPomXnl5CqMTbyCpIg5Fg7K4OlZ5\nfuWf95/Ei28+u1v7dv1HkPrUAqqSLkDFzyVl/+WR3HtZtGo1T07fhvMwpmVrizqltmZzW7O3rXLY\n5mzpQnheReBBqmPAcN5uuw0GA8tPvoR0aydAsrh2LcOn/4DVZuP1sWcxMG47lS47T3W7lK213REK\nnKQsol+Fn0VZH7G08MDlcfs9tw3DkJZXkUomglqE52nwTTzmJRRt8XoO2hxkfxzTGuKDJS01leef\nup0XnzmXex8aRp0lkOu4VgjMQKymEu1LZN08jYcfm8rDT3zNq29+cNjHefO1t/Hqy1MZkXANSRWx\nKDqUxtWy2vkL99x/Mq+8/XxTW1NIKD0e+C/esz7ErUSQ4VzHc1tvInz1p9z7v2zKHd7DPv4gQYIE\n+TuE70uELEOKVKTp5r0WxTAYDCw77R/0CO0GwMbGTfT97Wvi4mJ5rl8G3aNKKWiI4KG+d1FVF43J\n7Ofy6i108S7goYmT8fj1PfpsFkoM0vwk0nA+Ah3F9wXC+zLIxpbpP0iQIEec41JD3Byee/EdGhpj\nUfQwIvXd7xRqBPjVRkyGIh7457WEhYUd1rG99cFr5G75jZKoSqQKQoeEykjatR/Fg3c93tSusbKU\nvPfvJKpyJgCbbD35tuND3H3uKLrurYpTkCBB9ktQQ9xKaGtRPM8gMSAt/wdKh799yzlzJ7GgOguJ\nToIphfknXsyK+fN5ZGMjOXUxjDFk8Uru01isXrZXp/N/3UMxRl1Kr9CVOD0NeHwuPD43RtWExWTD\nag4hMiSGhIgU4iOTSYnpRPvYThhU4/4H4l+K8L6NwIkU7ZDm+0FJbZnPpZWRMrDoXJegSTAqoAal\ndUGOM9pMHuLDwX8//YqtuRpSjyFcU3ZblOcQ4FK8KEoxl5w/jL59eh22cb35n1fJ2zKNkpgqpAJI\niK8IJy5xKI/eO76p3bYpX8GMx7HqtXiEmZ8SbqDfBf9kdOdgovkgQQ6GoEPcCshGhPs+hKxEN14G\nxgsP+K3XLZ7JpNLlaPiJNrZj2qgLWTl9Js/kmyhsiOBm11TurH8fRZWsqB3Ma12jsVBLqFJyQP0b\nDWY6xHWha3Jf+nQcSpfkPnt3kPUShOcVhNyOxIQ03QKGEw7YjtbGrUnyHZL8RklBo6TKDbVeSa0X\n/H/5l29RwWaACJMgwQrxVkGyTZAWKrAa2tSpH+Q44ah1iF999VV5/fXXt+oxWpMlS1by85TlSNkO\nm2bEvsvH5QHqVQ2hVNK9q52rLrsICGh2RowY0Wpjeu29lyjYNpPSqEp0NfBabEUI0bEDmwp8NFaW\nkfv+XURXBvTQm209KBjxHJefPrJZi+10XcPpbcTlaUTT/Wi6H13X0KWOpmssW7KcQYMHYDCYMKom\nDKoRo2rCaDBiMdpQFLXF7D9aaO3v+WijrdkLbdMhbu05W3jeR2gzkEonpHk8iIObG/61eiEfb1+E\nT7oJV2M5qXY1kSV+JhnOp8wZyqsl7zDWOA2pw+/ukUzI7MdbF19EiMWG0WDGr/lwe504PQ1U1pdS\nVlvI4oVLIKqBkprtux3LYrTRq+NQhnU7lb5pwzEbdyn6IT0I74cIbU5g03Au0nj5QdvTUpQ6JWtr\ndLJqJDn1gUjw3lAARUB51gKiewxnXx6AABJtkBGu0CtSkBEmUJVj+1Joi3NYW7S5ufP2IealOf4Z\nPLgfgwcHoiUV1VW88dYEfFoSRs1GhAzojtHiKcmCf63/DanUUVuzrFVPwHtufRB4kPc+fpucjVMo\niSijItZBBbO5/cGRREf044E7n6PHY9+ybcrXaDMfI8O5jg7TL2HCphs595bHCbWa0aVOraOSqoYy\nqhrKqG4ob/pd46jA6Wmg0d2A0+PA5d2/Vq56u4ufN+27QpTNHILdEobdHBr4bQkj3BZJVGgckSGx\nRIbEErXjt90Shgg+xgsS5PhDzwNtJhIVabq9Wc7jMz0G4KzN5fv6Cuq0CqaFZ9Ldv4HLGhfzmT6C\n+9rdTsq2IrqHreMkw0Iqtul8nzWcx8aevM8+k0QvRowYgcNdT07JOrLylrI6dyGFlTks3TyTpZtn\nYjZaGZRxIif2Op+uyX0Qwow03Y70d0b4Pkb4J4IsQpruBnF4Koe6/JJllToLyiXbHTtdWwVobxek\nhghS7IGob4RJEGECsxqYW+drBoYNNeDWoNEPVR5JqVNS5oLtjYH+ipxQ5NSZXQJWFXpFCQbHKnQN\nF8EsRkGOO4KSiUPgpX+/T1VtOEKPIFIX7Dq11wmBV3WiqkXcdsMFJCcntto4vvj2M9au+IGSsGL8\nO/QdkVUWokN78tDdLyNcDax5/w7SqucAsNXahVlJaWzz5eHxuQ/4OFaTHas5BKNqRFVUFKGiKGrT\n35rux6d58Wle/H5f4G+/F7fPeVD2WIw24iKSiI9I3vkTmUy7yFSiw+JRgqVUgxwm2mKEuNXmbCkR\nnmcR+lqk4Uyk6bqD7mLp5ll8PutVKutLqQzPZHO7ITj0WkzCynWpQ4hYtpx3qrvjciv8lncHieEl\nuJwW3ozqz1W3f0D/lINLJ1dZX8KSTbNYmD2NnJL1Ta8nRnXg5D4XMrrnOdjMIaBl7ciY4QgsEjQ/\nDErrpfGs80qmF+nMLdPx7ggFW1XoHSXoEamQGSGwHaLcwasFnOL1tZK11TrFrp37Ik0wNE7hhASF\nCFObujyCHAMctZKJ49kh3pUff5nMslXlSD2OUE3Fsss+F+BQNYRSTr+eUVxy0bmtMoafJ/3Movmf\nUWorxGsJfK/hNSaiLZnkGNbRTRdcUldBpL8GPwamh3dkWZSNyIgkokLjiAqNJzosnuiQQNQ2xBqO\nzRyKzRyC1dR82YOuazg9DhzuehrdDTR66ml011PXWE21o5waR+WOqHQ51Q0V+3WgzUYrydFpJMfs\n+IlOIyW2E9Gh8cGocpAWJ+gQtyDaKhTPeCR2pPVtEKEH/NYGVy2fzHiJhRunAZASk84/Rt6GGt2N\nixf+SrW/DAWVE2N7033dRj6o6YvF5WJK4a2EhTZQWxfBM51G8ulDH2M2NG8eK60p4I91vzJ77S/U\nNlYBgSDBib3P5/T+lxITAsLzAkIWIwlHmh8EtUuzjrUv6rySKYU6C8r0Ji1w5zDBiHiFvlECk9p6\np2q5KxCNXlSuU+kJvKYKGBAjOKmdSvuQNnWZBDmKOWod4mNdQ9wcJnz9HeuyK9FkEhbNTOguH7EO\n1CigK/XYLGXcc8c1h5y1wuGqI2v7EtbkLia7YCW+Kish9T5KzXl4bIGDh9YaiDFkkNCpI+3zshlS\nPQOAGmMiIWf+H+1Hn3VIY2hJnZLDVUdZbeHuPzWFFNdsp27HP6K/EmoNp0N8Vzo2/XQjLiKpVaPJ\nbU2b1dbshbbpELfKnC01hPt+hCxAN14NxnMO+K1r8xbzzuQnqGuswmy0cNmouzi178VN13a128XI\n2T9Q4skHoHtIV0bn5PFxTT9Sa4uZUHMvZquPopr2fHHiNbxxzT0AeN1OnM56fG4XCxYt4oQTRhIW\nkYDBuP8sE37Nx8qcefy24hs2FKwAQFVURvU4hwuGXkas+atAFBwD0nRbiyy28+mS2SU6Uwp13DvS\ny/eNEpye3HxHtLnXsy4lW+slc0p1VlXJJg1yz0jBmckKHUKP3id4bXEOa4s2BzXERxHtUxK5/LJL\nAKivr+e1tz/H6Y5D0cOI0CFaB/QwcITx6ouLcCseFKWE884Y2KRX3h+6rpFTuoE1uYtYk7uQrSXr\nkXLnEgqT2Uxcz+4MlP0p3LCaMnUbDRF+GthAaU42xb5UytMfoH/hdyR5tsMvV7N24VjSb/439piE\n1vpYDpgQazgh1nDS23XfY1+Dq5bCylwKK3MorNpGYWUO+RVbaHDVkZW3hKy8JU1tbeYQOif2JCOx\nFxlJvUlv1z3weDNIkCCHF20WQhYgRRwYTj+gt0gpmbbqOz6f+Sq61OiW3I9xpz9BQmTKbu2iLFbW\nnHIZI2b/zObGzax3ZFPXvgPXiZV8Ivsw3j+Ox73vkRSZz5CNX3DDC1PxChc+4QIRcOeqt7v4Jieg\n+1WlCZNuxUYIYaqdWEsUHePTyOw6iPTMIRiMRgZlnMigjBPJLd3IpGVfsjD7d2at/Zk/1v3Kib3P\n4YqhI7Ep8xDeN9FlGRgu2mue5QMhu07nqxyNih3qtl6RgvNSVRJtR+Y+TRGCjHBBRrhClTvgqM8r\nCyzmy6rR6Bmpc157lSR7m7qPDHIcEJRMHGa++2EiK7OqkHo8IZrKrksvfECdIkGpITbGwX133dy0\nT5c6mwrXsCh7Gks2zaTOWd20T1UMdE3uQ6+OQ+mZOniPXJobN2Xz+VfjqfRvpiHCD4DRA4n1CfS0\nWhnZMA+T9OEWYfiHP0z6BTejKEfvXf5fkVJS1VBKblk2uaXZ5JZlk1eWTU1j5W7tBIKU2E5kJAUc\n5IzEXsRHJAelFkH2SVuMELf4nC19CPftCFmNbroXDMP+9i1+zcenM19mxuofATh/6A1cPOKWv33i\nc968yawoXU238mrinZWg5eFTHGS6ndxQXYYCfBkRywpbCEiBggEhFQQKutDQxf4LGam6hRgZS6o9\nmX6dBzJk9EVYrHaKq7fz08IPWbDhNyQSg2rkjlOHMTStACEkUh2DNI0DceAxKLcm+Xm7zh+lgWBH\nghUu7qDSPfLom5sbfJIZxTpzSnQ8eiBDxdA4wTnt1aDGOMhh56iVTAQd4n2za9YKg2Yj8i9fRa0A\nn+pCiO1UhfxOgyxr2hcXkUSfjsPo3XEY3dsPwGKy/e3xKmuqeP3tR6huWE91TGCFhKJB10obp6p1\npHoLAaix9yH+2jeJ7tyj5Yw9AlQ1lLGlaC2bitayuXgNeWXZaPru5azDbVF0S+lPj9RB9EgdGHSQ\ng+zG8egQCyEUYDlQKKXcQ7vQ4nO2fxaK912kaI+0vPq3kVKf38trEx9kZc48jKqJcac/wYjM/UeV\nNU1jxuSPmL1xNvnkogtf0z6DHoLQ2zOmppjTfavQpWCmZwwXvvoRdnv47kP1+airLqW4YBOFhVso\nqSykuK6EMm81taIan7J7th1VmomXifSIyeC0k66EMCs/LviQRdm/I5EMTg/j7lNjMag6UukVKOKB\nAFm9o8pdI8hdVquhgLCxrSGCj3PiqPSoKALOTFYYm6Qc9WnPGnwBjfMfpTq6BJMCZyQrnJSoYDzK\nxx7k+OGodYjbooa4uZqdt979mKJSI1JG71EQxA00qBpCqaBDB41x115/SI7bUy/dT03FCspi6gO3\n81JyUrmfk/QKrNKDHyMNGdfS6drHMdn+XmZwLOiUvD43OaUb2Vy8hs1Fa9lSvJZ6Z81ubaJD4+me\nOpAeqYPo3n4A0aHx++zvWLC5JWlr9sJx6xDfA/QHwvbmELfonC11hPtehCxEN90JhlH7be7XfLz2\ny4OsyJlLiCWchy56g86JPffZ3uf18ON3/2ZGwRwc6s4nQqFaIlX2zuSE2ykJC+UMTyjTt2Xwn83P\n0T9iDZpfMFM/m6vf+QQhxAGf2zkblrBk+XQ2lW+hwFeEU91l/pCCKL0dvSK7MHTYmczJm0Jp1XyG\npNs5s084JoNAoiD2mSEYpIRZlafxfdGV6BhItuRxbeqXpNi9IJKRSkqgKp6SDuLQ5F+teT2XuSS/\nbNdYVR3wL+IscGmaSmbEkY1ut8U5rC3aHNQQH+NIKRl73kCmr/qeRdnvUuC0kew/HyFTMGsWwqTE\noqmgJVC/BR59fBqa4sColnD3nZcTGxV9UMf7s4DHK28/T8n2uZREVjAz3shiLZ6LK2rprdcTuflD\n8h//H+rJT5J62iXHlIxib5iMFrql9KVbSl8g8JkXV+exPn856/OXsSF/OVUNZcxdN4m56yYB0C4y\nle6pA+jVYQg9UwdjNQdLYAc5dhFCJANnAOOBe1v9gNoKhCxEihhQh++3qV/z8frEh1iRMxe7JYzH\n/vEeHeL3nqVB0zT+98Pr/LptasApVcGo2+lpyeTs0ZfTrc8JZNdVc+78ifh9xfzPUsPJXXMYx2NM\n2PQwGZE5nOidzISH7uGKl14/YHPSMweTnjm4aXvLusXMXTSRtZXZlCtFVKvFzKkv5o+pc0mU7Tgh\nNYkze+qYdqRAE+hICYhoUCIBOwgLIHBrRj7PP5kVNRkAnBQ7mwvafYpR8QZWY7MFscsDLimSQemC\nVLuD2gtExAHb0drEWwXjuhrIrtX5Jlej1AVvbtAYHKtzSQcVu/G4uscMcpwQlEwcYXx+L/M2TOH3\nld+RV76p6fXeHYdySp+L6Zs+HFUx8NyL79DQGIPQw4nU2S3n8c60bpV06mjgxmuvOOhxfPr1J6xf\n/Qtl9iK8Fkm6x8VFNdUk6AFNXU3YIOKveuWYl1HsD13qFFRsZd32ZazbvpSNBSt3SwH3p1a7T8fh\n9EkfTnJ0WlBecZxzvEWIhRDfE3CGw4H7WlsyIdyPIvRN6MZrwbjvTDZSSt6e9BgLNv4WcIYveZeO\nCd322nb9ytn8Z9oblKsFAFj0MIZHDebySx/EHha1W1u338/ImT+S49oKwFARy5aNyXyV/SDJEcV4\n3UZWp93LuQ8+1HwjZQP4F1Fd+ju/zchjSambMqW0acGeUbfTWU0kKtrFmWN00uPNeHyCRnE7EWGj\nAaj2SN7Z6KfIGSilfFW6Sv8YBaQGsgZkOegFCJkPei7ouQh8uw9DdAC1P9IwCERasxfxtTR+XTKz\nWGdSoY5PhzAjXJ6u0ifq2A6wBDl6OWolE0GHeO94fC5mrf2FX5d8TrWjHAikDhvd81xO6n3BHiup\nd+XnX39jyfJCpIzHqhkJ2eUrlECtAn7FiaoUc+N1Z5OWmnrA45o5dzbTp71PuZKLO8TP8MZ6Tm+o\nwSolGir16VeRft2TmEPC/76zYxy/5iO3LJusvCWsyV3I5uKs3bJ5RIfG0ydtOH3ShtGj/aBg9Pg4\n5HhyiIUQZwKnSynvEEKMJuAQn/3Xdrfeequsra2lffv2AISHh9OzZ8+mx67z588H+PvtodEonseZ\nt9CNNN/DiJEn7bP93HWTWVv/OxajjTPSbyExKnWP/oYMHsw7/7mPKRtnIdGJTYlgZMQwMjqPxWS2\n7Hc8T2YtYW07Pxp+Om6uoTEvmu9dHxETXs28PCMVvW7j8n/eTXFBAXNmzcDrctE5rQOaz0321hxU\ng5mePXtiCwkjv6SU2LhYTj0lA+Gbyvx5vwB+Rg6LRGJi/pIoSorCKKypZa1jM4WFxQBEpVqJ9icS\npbk5dYjG8CGRrC7uy9a8EfyvAEIyh5Nghf7Vi4k0i/1/vtLPiGGJoG9kwbwpoOcxclggr/O8hTUg\nwhkx8jykYQzzF24/sO+rlbc79x/OF1s1Fi4IbJ954gj+0VFl9ZIFR8X4gtvH7nZWVhZ1dXUA5Ofn\nM2DAAO67776jzyEOaoh3x+Vp5PfV3zN52ZdN+tWUmHTOHnQ1Q7qegslgPqhj1dfX8/q7n9HojEPo\nYUToYjcdTJP2WFSTEOfln3fccED9lpQU89YHT1DbuBFvZCNn1Vcz2OUAoEGx4R/yBF0vurFJRtES\nOiWf34HbX4VXr0fT3fh1N5ruRpcedN2LxA/IgFMqJIFzN3D+ClSEMCCEAUUYUIQRgQEhTKiKAUVY\nMCmhmA1RmNUwFHX/uUb3hsNVx9q8JazeNp81uYvI3VhEVOqOVE2KgcyU/vTvPIoBnUYRE3bk09e1\nNEEt2rGNEOJ54ErAD1iBUOAnKeXVu7ZrqTlbeP4PoS1HGi5Emi7bZ7slm2by2sQHEQjuv+Df9O+0\nZ97e7ZtX8/KPT1CpFgHQkQxuv/hRkjse+BOr8euW8e62hbh0Bx0VC/b1iXyw9WHCQxv4I9dCn/Yh\nhKuVf9/RDhqVEKpM8dRaYmi0xSIjOhCZ1J+eA4YTGx8DBKQd82dM4Pc1v5Erc5oW+9lkJCPTErl4\ntJtZdecxpfwiuoQr3NxFxd6cCnPSC/oGhLYMtKUIuVPbLJV0pDoaDCN2K4ZyJK5nXQbyF/+8PRAt\nDjXC5WkqfaMPT7S4Lc5hbdHmwx4h3qFF+xyIJ6Bw+lBK+eZf2wUd4gAuTyNTln/FlBVf0+iuByAt\nIZMLht5Av04ntFgBiTl/LGTarCx0mdCkPd6VQOYKN4oo5pLzR9C3T6/99pdT5eT9/z6Dr2I5ttAS\nzndUkewLyCjy1RgMp/0f/U+9YK82e3311Hu34fKV49Vq0PV6kA4UGjEqTozCjUlxY1bcWFUXRsW3\ntyG0OLoUeHQLHs2CV5rx6mb80oRfmtGkDSlsCBGKQQnHpEZhNcYSYkzCZAzbpQ+dH379GjXGyept\nC9lasm636HHH+K707xRwjlPjMo4LaUVwYj1+EEKMYh+SiRaZs/UKhPs2QEVa/wNi70+Ucks38uSE\nG/D6PVwx+m7OHnT1Hm2m/vIeX2V/hV9xYdRtXNzpYs656K4DHoqjwcG836fg3DwLm3srT/Q5lxxR\nT5wQZKyP5bXcp1jX4CLDFoEzLAK/RcWt2NGEAU0xICSo0odBerDoLuz+OkK1egzSv3fTUSi0dKTE\nnok/rg9pfUbQq39fasoLmPDT66xwrMGrNABgkBZ6xXQgs0ciBVU2Lh91N+H2qL32e8BIHfTNPbxK\nNgAAIABJREFUCO0P8C9AEJB9SQygDkAaTgKlN/MXLDxi13OFW/L5Vo0t9YH/T0NiBZemqVhasdIe\ntM05rC3afCQc4gQgQUq5WggRAqwAzpVSZu/arq1LJnRdY3bWRL6b/35TlbUuyX24YOgN9OowtNUd\npfGvvEtDfSTICMI0sVvmCi9Qp+ogaomMqOXhe2/bax8un8a7iwrZuHwSUaXT6GDI5lRXNXapowHr\n1EQqB5zF4JNiMIgGrGodoYY6Qg0NBzVWv67i0m14dTOaNOCXBjRpQN/xW6ICAokI/JZih05OItAR\n6ChoCKEF/hYayo7fBuHDqHixKC4sqqdZn6VHM+PQQnFqoXj0UPwyDEWJwKjGoBBJXnEpy7YsZk3u\nIjy+namUYsLaMaDTCfTvPJpuyX13yxEd5OimLTrELTFnC++3CP/3SHU40nzPXts4PQ4e/vRyyuuK\nGN3zHMaNfWKP+fCjD//F9OoZIHRitRQevORZUtL3nXXiT7w+H7MnT8K99kd61M7Bpu9cC+AWJh4Y\n9CCzrC4ihGD4OjvP5r+A2eqlpj4S27if6TB4l0CBrEN4v0JoswKbWPCLM8gv7kPe5u3UFG/DX5OL\nvSGXOHceCZ58DOye3rFejSDP3p26uKFkDDqZmRuXsq5sNi7yAp+XNJBhS8Xpq+WMk29ndM9zWuZ/\ng/SAtgzhnwP62qYMF1IkIw1nBLJ+iIN7KtlS6FLyR6nOTzuixbEWuDHDQGqwBHSQQ+SIa4iFEL8A\nb0kpZ+76elt2iNfkLuLL2a9RUJkDQOfEnlx2wp1ktu9/RMazavVavvt5PrpMxKhZiPjLV18vwKN4\nUUQZgwamcP7ZYwGQmkaNZwu/b85nwupIlIYy+pR+Tj/PMvr761AAlxAsEYkUdRjMgzfWAgEHt84f\niVMLwavb8UsbUthRRCgGJQKTGo7REI5ZjcSiRmNUbAhVpbXRNS9urRaPvwavVodPd+DXHGh6I5ps\nQMhGFBoxKE7MihOb0ojd0IBR2XtEaFca/TYa/BE4/KHUe0yU17nIr6hia2ExeSWl2C1hDOw8msFd\nTqZn6qCgc3yUc7w6xPvjkOdsqe0oxFGJbn4S1L07sO9OfoK56yfTIa4Lz175KUbDztt1v8/Hi2/d\nQpZ/NQC9jf144I53/7ascnlZOXO+fpsuRT8S5ytper3InEpexBDsGWMYccrphIaFcv2SmUwuXY1Z\nujltvZFH8//d5BTbb/mZ1IE9wD8N4fsagTMQYTWcjjSeDyJsn2Oorqph2bw51OUsJbwui9TGDYRr\ntbu1KbSkkR17AmXWRIoaFlEi8gAQUiXRn0hsdBTXXfoM8RHJ+7X3oNCrQJuD8E9DyEBhJ0kIGE5B\nGsaCcnCZilqKEqfkv5sDCwpVAee1D+QtVo6Dp2pBjgxH1CEWQnQA5gA9pJSOXfe1RcnET5O/Y5Nz\nHmtyFwIQG57IZSfcydCupxxVj85fe/sjyspNSBlNmKawa5xAJ7A4TxgbSUvJ57KzFxFi1yl1hPPi\nonPIqmiP9Hk4rfItBlfPo7rEwaAEqFINzFLiKLH04dxzbmfIwKFHyrwWRWoaLq2KRl8Jbl8JHn8l\nyxatZNDgaExKA3a1njBjDab9yD68uolqbxS1Hjs1LoVqh4bfF0pC+CD6dBizm0NwNBJ89NY2OOQ5\nW1uJ4nkeKeKRlrdgL3KwhRun8eavj2AymHnhmq9Iiu7YtM/n9fDYa9ewXWxBSIXTYk7j2hue2+8h\nc3NyWfHdy/Sp+BW7HiieUWWIJTvmFFKGX87gkXuvjvfN9k08vHY2jnWr+IeSwgPb3sZs9VJdH0nk\nbReSnLkNAKn0QZquAyXpoD8Ov99P1so1bF0+g5DyxXSpX4Z1l4h1o2JnXthI1tq9lOh5gewUUiHB\nm8jg7kP5x3kPoCgtGCiQftAWs2DuB4wc8qecQgV1ONJ4YbNsPFR8uuSnPJ3ZO6ryZUYIru2kEtbC\nVe7a4hzWFm0+Yg7xDrnEHOBZKeXEv+4/55xzpN1uP/QVy8fAtsfn4vn3/8XvcyeTOjAMq8lON/so\nBmacyJhRY474+Pa2PXP2JGrd2aR2FiyaYWHlijp0v4Xu7TJRgO1FGwBISMqkQdUpL1tOeEwNncee\nx6StYVRvWU2s3UjPwh+4zLqMbSUBqUBsiplJ1mi2b4skLrE3r7309lFhb0tu//n3n9tS05gx9384\nvWX0HBCFXy9n9eK1mNQGThlhIdTQEFgBDowcFgnQtN1jQBrV3igWLKrHoMZwwgmnEmXtwfIl649a\ne4/0eFpj+7333iMrK6tpvoqLi2vWauVjmUN1iIXnZYS2BN14ORgv2GN/ZX0JD35yKU6PgxtPfYST\n+1zYtC/gDF/NdrEVRZq4uss1jD3vln0eq6qimpmfPc+A4m+anMzNtl7UdruK0y6+Covl728yS5wO\nRr33CpVpBq7c5uHeLR9gtvioro8kfNwJJPe/BdRBh5zGbFqhxs/5OormZmjJdGw5v5Nau4Qkz/am\nNuvM6UwNT6VY3Q5CR0iFZH8Kl552E/0H7L9a38Eyf948RgyLRfgng7YkkCMZAeowpPEiUPad6ai1\nWFut89lWjUZ/YMHd9Z1VurVgMY+26By2RZuPiEMshDAAk4CpUso39tamrUgmsvKW8OHv4ymvLUII\nhVP6XMhFw8cRZos80kPbDbevhjLHfLy+zYQZ8ok3F6OIvyy880WwfFMKC2a3x9EYjVkzEfaX08Qh\nwKX4EUY32yOiqLVHclnPaFIXv0Fy7lfYZGDh3QqrnckhkRhqIwgPy+Sfd4wnJvLIPJo70ji95dS5\nt+DyFeLXylGpJNRQTbSpYp9yjGpvNLW+OLwyDqMhmUhLd8LN6YdFWhKkbUaID2nOlrUI1zhA7lhM\nt/v8J6Vk/He3sW77Uvp3GsX957/a9NRs18iwIo3c3OtWRp9+zV4P4/f7mfjpu3TLfotIf2BtxvqQ\nQSjD7uLEM85o1tAvWzCZWZUbuSS3ivuyP8Zs8VFVH4nrkg8YOPakZvX5JzOKNX7I0xHA1Z1Uhsbt\ndPJWLF5Kzvzv6Fgxi2RPHgDbDfH8GJZBgbkIhESRBtJlOrde8RSJKRmHNJa9opcj/D+DfzaCwFwk\n1SE7HOMOLX+8/VDjkXy6RWNTfWC1yNkpCmOTgxKKIAfOkXKIPwcqpZT7rHh0vDvEDlcdX8x+jT/W\n/QpA+9jOjBv7BOntMo/wyALompdixwJcnrXY1XzaWQpRdyl35NdVyjxJOLR2qGoHoq39CLel79HP\ny6/9h6oaO1JGEqKpWHfZ92fuY031opncjBrUjuS1XxNd8B0GdPzAfHsYM0IikE4Tsf729B94EZdc\ncGmr238soGkecqsWUVa/ClWpIdziJdriINpUuVdHudFvo8qbgEuPRVETCTVnEG3thaoemcUxxzNB\nh/gg8f2C4vsSqQ5EmvcsdjF//RTenvw4odZwXr3hx6aAgaZpPPnqNWxlI4o0cmPPWzjxjGv3eoj1\na7Io/uEBejQsBSDX2oW6Afdw+oWXNG/MAPo2hOdVppep3JzVg1NztvBw9uc7nOIo1g9+mH+Mu7FZ\nXS8o0/kiJzDnXpWuMjx+3xHPRX/Mo3DJD2RUzSTOU8xmYzK/hLWnxBzQQ6u6mV7mHtx6zbOERe27\nrHyz0SsR/l/AP2MXx3gQ0njZYY0Y61IypVBncoGOBHpECK7rHKxwF+TAOBJZJoYDc4EsAj6RBB6R\nUv62a7vjVUMspWRR9nQ+m/kydc5qjKqJC4ffxFkDr2LxoiVH9BGF01tOacMc0LNJMG8jxLBT1q1L\nQYk7BYeWgtnUlQT7CEzG0H13thcqqqt48+0JeP0JKHoo4ToUFW0gNSlwE+AD6hRJKLkM93xDF986\nANwIZoeG84c9HJ+uEF8dSVh0D+6/41lCQw9uDEcDrfUoqry2iCWbZ7JsywxsNg9d2rcjKcpKrN1H\nvKVyt+/zTzy6iXJPIk4tAUVNIdLak0hzlxaNJAcfvbUNmj1nS4lw342Qxejmh0EdsNvuRncD9/73\nAuqc1dxy+pOM7rkzwcXrb93JYtdChDRwY49bOOnM6/bo3u/38/OH/2bAltex6G4aFTurOt3BuTfd\ni+lvFtvtF/9cFswZz8hhoUiRxnb3rZw2dylDcpfyZPaXmC0+6hpCmZZ+I3c/8vhBdb2qSueDTRoS\n+EdHhTHtDux69Pt8rF7wBu7Vs0jMX8tmYwKTQuOoNJUBgep3Q8MGcv01T2GxN2/u3O/1rFch/P8D\n/3QEXiQKqKOQxktAiW3W8ZrD+hqdj7cEJBRRZhjXRSU1pPkSirY4h7VFm5s7bxv+vsnekVIuYPcK\nwm2GRncDH04bz+JN0wHoltyPm8Y+RmLUgVeEa2nqXDmUOWZhE1tIsubRybozJ26VN4ZKbxpGQxfi\nQ4bRzn5oE1psVDTPPnFn0/acPxbyyeelVBjSmrJXxOgCSGO58RHWqXn0831DR20tpzfUckJDA9PC\nwlkYKykR87j3/04mWqbSb8AFwagxEBeRxNmDrubsQVdTXlvEwuxp/DBnGvkVWwHo0C6B/hmdSUuI\nJdLqJspUTpSpihRrHpAHLAa+x9EYQqW3HW69HSZDKtG2PoRajtw5GuQ4R+YiZDGSCFD67rH723nv\nUOespktyH07osbOM8zdfvsBi10KQggtSLtirM1xaUsaK/9zOiNpA6rPVYSNJv+xlLu52CPIBqSN8\n3yD8PwF+pOEUpPE62ltNTB6ZwBkNVh7qYeKFtZ8RHtrAmbnvM/4FN4/+a/wBdb+pTuejzQFn+Mzk\nA3eGAQxGIwNG3w8jBuOufYeaBXD5yiLKHGamhoVSb6hkrmMOS948l1Gxw7n6mif+NgPHQaFEBxYR\nGs8D3w+BiLE2G7R5YBiLNF6w30wbLUX3SIVHews+2KSR55C8nKVxSUfJyHjlqFqgHuT4IFi6+SDZ\nVLSGt359lMr6EixGG1eOuYcTe5/XYoU1DoZ6dx5lDTOwKdk7nKEAmlQodqfSqKcTaRtKjKXHYdWc\nvvDe19RVGJE+KzZdxb7jFIvTNtLH9w2x+hYAaoWVadZQloTbkEKg+CG2OpzwqO48cOfzx2TUuDUp\nqMxh4cZpLNj4G+W1RU2vx0UkMab3SXRpH4dBrcQsSokxF+81D3SNN5JqXyJ+krCbuxJnG4jBYN2j\nXZAAbTFC3Nw5W3i/RPh/QRrGIk27ywtyStbz2BfXIITCi9dOICW2EwBzf/+S91a9iRQaw+wjuev2\n1/fod/7M2YRMu4t4bxFuxcKKLg9w0bi95zY+YKQH4X0ToS1BoiCN14Fx90Vr8/Nquezn+fS2r+Pf\nS98hNKQRt8vEx6k38uBdDxIasm+HsNgpeTnLj0uD0QkK/+h4CA6ctg7heQmBk7y8VBZPdNDoyGO+\nXaPREFiUG+KP44S40Vx9454ylRZBLw3cPGiBRagSK9J4DhjOOSx5jH265Ic8nT92ZKEYHCu4Ik3F\n1MqFPIIcmxzxPMT74nhxiHVdY+KST/l+/n/QpUZaQiZ3nf08CZGHdyWu01tOUd0UbMp6kix5TQvi\nfLqRAlcnNNGdhLAx2E2toC87CNx+nU+WF/Pz6hLSHOVENLqRfiuhfkG6tpI+vm+JkIUAVCoxzDDH\nsiTcBUrgHLbXq8Ro7enW60yuvWzPiFFbRkpJTul6FmyYxuLs36lp3FluNiUmnWHdxjKs6ymYzE5q\nXFlo2nZsaimxpuI9ipJ4dSNlnmScWiJGQzqx9oHYze0Ot0lHLUGH+ACRckfu4XJ08zOgZu6yS/LY\nl9eQU7KeswZexZVj/glAYe46Hvn2NrxKIxkikyfv/RT1Lzfu//vyU3qsfBSr7qLAkoY4520GDhty\naAZKR6CstJ6NxIY03wdq7702/XpNGXf9uoLBUat4aflHRIbV4vMYeDf2Zs474yyGDNlzLPVeyYtZ\nfqo80DdKcFMX9dAXhOkFCM94hKxEinbUuu/i+0+/oK52A2us5XiVQKq5WG8KA6OHcPlN92MwNPsB\n8H7GkRsoUqIH8kNLEYU0XgHqyL2m12tpllbofJmj4dUhxQ7juhiIsbSpyzPIAdDceVt96qmnWmE4\nO/n555+f6tt3z8dnxxLVDeW8+sv9zM6aiERy1sCruPPs8fvMIDF//vymtE0tga55ya//neqGCUQp\nXxJvzibcWItfGtjuzKDSPxq7/S5iQs8kytYDkxrSYsc+UP5qs0ERDEwOo39KBAsbFLKNoZRGhNK+\nZzTFVaWsVk+lTqQQr28nQlbSw1/JAGcoUu9MqeLFbfNQZ60lp2Ypcyd+w4KlS+nVcwg2q+2w27Yv\nWvp7PlCEEESFxtEnbRhnDLiczJT+GFQj5XXFVNaXsj5/Gb+t/IZNBZuwmzrRLfEiYsPPQRjOptzX\nhWJXJFUeG7rUCTM2EG6sJcZUQKRhDWY5lQrHH5Q0rKbalY8uBVY1BqEoR8zeI0lJSQlpaWlPH+lx\nHE6aNWfrOSj+iUgRCcZrd3OOlmyeydTlE4iwR3PveS9jUI34vB4e//BWGtQqorREnr/7M4ym3SON\n3735NIPXP4dJ+lgZcSJ97v2B9IxOh2acXo3wPI2QOUgRjbQ8DWrGPs/tngkhKMLMD9kqOXEe+hdV\nEmZ3MNCxgi+2WFlcmssJvXcWWvJqkrc2ahS7oEOI4LauKgalBRw2EQ7qMNCyELIQi2Ep/YaPI6nH\nqZRtKCbEaaFWrafRUMNWTzar5/xO0aqNdOjaF4vFstcum3U9i0gwnIBUMkHPR8hihLYU9FUgkkGJ\nOXRb90OSXdArUmFjnU6JCxZX6CTbBHHWA/uM2+Ic1hZtbu683Qq3kMcXq7ct5J3Jj9HgqiPcFsVt\nZz5N7457T/Le0lQ7N1DlmE68aR0dTTX8WXd5uzMNr+hNYuipdAg5fAscmkNmvJ13z+vCD1nlfLmq\nlHlFHsI6jOTmwUmc0vlSstdfydwvx9Pfs5AYvYALHQWMdnZkkXkwK+y11Bq3UB5XTzlLuP+Ns4hx\nxZOUOox7bnvwSJt2VKAoKt1TB9I9dSDXn/IQa3IXsXDjNJZv/YOtJevYWrKOL2a9Sq8OQxnZ/Uz6\ndxpJXMjO6F+Dp4iKxuX4/DnY1GISzEXEmcuIM5cBq4BfaGgMpcKTQmGtj3KHmRhrH5Rghb0guyC0\nRYE/1CEgdkZ5Nd3Pt3PfBeDCYTdjMQVuaN98/14q1UKMuo2HLnwWk2Xnja7f72fiy3cwsuw7AOYn\nXcv597x06BFPvWyHM1yOFElI8+MH5MDdNyKFgjoP360+jxfSirhvm4WU8ALu8PyXCQsv5FqTxqeX\n3oouJZ9u1ch1SKJMcGvXFn6kr0QhLc+A5zWEvgo8z5AadQsP3P0Sk5Z+gWv2L+iamRLjNrZZtrPd\nXUrZaytJtnZk8IV3kdZ5z+xBzUbtgbT8H1Kbi/BNQOhbEZ7HkOpQpPFKUFrvCWWSXfCvXgY+2aKR\nVSN5e6PG2e0lY5OCqdmCHBpBycQ+kFIyaekXTJj7FlLq9O44lFvPeJoIe+vm0NU1HwX1v4G2iFTr\nliZJRLU3mnJvD2JCTiHK1rVVx9BaFNW5eWNBAauLA1kSercL4Y5hyaRGWvE6HWz79g1Maz/AKgPa\n1zKlI2uMp7PG6qXSvBKvWtPUV5gjhlCtIz27DOHaq689EuYc1bi9LlZs/YP5G6ayJncRugykfbKa\n7AzKOJER3c+ge0r/PSpg+fxOKhqX4vBuwkQhsebCPbTIjX47ZZ72eGlPuKUXsbZ+x62DHJRMHABS\nIty3IWQFuvlZULvt7GvNT3w4bTwJESm8csP3GFQj03/9Lx9teB+AazKu5/Tzb2tq7/X5mPziTQyp\n/B9+YWBp5qNceNPdh26UXorwPImQVUilE9L8KIgDX6Pg1yVXfreBP7ZuY4R4k7u2OOgUsQmAufXD\nmDDmIvqm9qXUkIlFhQd6GEiyt9JpIzWE7zOEf0pg03A+0ngZRdXbeX/q0zhyi3ApNmqNAUmaRQtj\nlEMlwhRHwuhbGTpqZAuPx43wTQT/xB0ZKQxgOGvHwrvWe6KnS8nUQp1JO1Kz9Y4KVLezGtrU5Rpk\nLwQ1xC2I1+fmg2njmb8hMOFcOOxmLhx+U6sunGv0llFUN5FY40qiTQFNqE83st3VDaNxCEmhY44L\np0NKyYyt1fxncRH1Hg1VwPk94riybwI2k4rX0UDOd29gWfcxFr0WgDIliYXmC8lTInBYsqg1rUcX\ngRLJijQS6eyEWctEVcI486R+jB51eCL4xwp1jdUsyv6deRumkFOyvun1qJA4hmeOZUTmGaTGdd7r\ne6WmUeXeQK1rFYrMI8aUT4Sxdrc2jX4bZZ72+Egl1NKDOFt/FPXoLkN9oAQd4gNA24zieSSgJ7W8\n3ySX8Phc/PPD86lxVHDX2S8wrNupVJUVcM8nV+JVHPQ1DeChf/6nqRu328u0l65nUPUUvMLImv4v\ncs6V1x66QXrJDme4Gql03eEMH/xCUqdP4+IJ61lVkMsQwxvcvFGlV8QKhIAttWk83v9+wlMVnht2\nHr1iDsP575uG8H0UqDCnDkaa7kKXBqau+IZv571DvCOUKqPEqQYKl4T7Yji3vgaTMRKt382cdM65\nLasz1isD0WJtLgCScKTpUlBP3O2pQUuTVaPzyWYNpwbxFhjX1UCirU1dskH+QlBD3EJUN5Tzwvd3\nsiZ3IWajlbvOfp5T+118UCuED0azU9mYRXHdx0Qrn5Ng3oxNdVLjjSTfPRyr7U7iw84m3JKOaMla\n9q3AgdoshCA92sbYLtE0ejU2V7rYUN7I9C3VRNuMpMeHEdtvFPbh11NaKaB8IxGygm7+JWQqxYSF\ndKGucRQhrmjAi8dYi8tUSYN5I25DHvk5NcyeUcaMOTnMmvMHXn8tndPTjqjNRxqLyUqnxB6c1Pt8\nhnUbS4gljKqGMirrS9lctIYZq39g6eZZuLxOYsLbYTPv1KALRcFmiifK1pv1q1Q6pd9KtdaLQmco\n1R4LCl7CjA1EmiqJMeUQqizC7Z1KUf0KKpxb8Wpu7IaEo/783RdBDfHfI/yTEPpmMJwIhp2O9JTl\nE1i2ZTYd4rpwzcn3I4Rg/Lu3USmKCdfieebOj1ENgZt8v9/Pb/93DYOqp+IRZtYP+TdnX37VoRvT\nFBmuRird9ukMH8i1bFQVzu4azbQcD2sbMqlIWkVIcQeS1QJibDWMzlvEt64xLPMuxmiPJDM86tDH\nvz/UTqBkgLYcIfNAX41QB5KRPJihXU8lq2YNpXU5dPAk0qjoOA11rLFJygin39Yfmfz1xxQ1GEjL\n7LnHYsZmIWxgGIxU+oIsRMgihLYCtOWgJLda/uJ4q6BfjMLmP3XF5TpxFrFXp/hYmbNbkrZoc3Pn\n7VZ3iBctWnTMOMRbirMY/+2tFNdsJzY8kUcveY/uqQP+/o1/IT8//29PwJL6BVTWf0SS8QdiTMUY\nhMZ2ZyeqtNOJC/snMSFDMBlaP89jS3EgNu+K2aAwpH04g1PCyal2UVDnYX5eLVmlDjJibUSH24np\nOwr7iBuaHOMQfzHJjlX01LcwsP/JXHnjC6xbUIap1oBfacRjbMBhzKPOvARNKcCmh1OTE8HU2Xn8\nPmcds+b8QYOjkm5dDnFhTjNtPhoItUbQvf0Axva7lF4dh2BUjZTXFlFRX0LW9iVMXT6BjQUrkFIS\nF5GI0bBzoVN+fj6pHTpgNcYSZetNlH0UFss5VPl6UegMo9prQeAjzFjf5CCHKYtxe6dSWL+SCmcu\nEgWrGotQDn+awubQFh3ig5qzpUT43kfgRBqvbdLkur1OXpv4EF6/h1vPeJp2ke2Z/NM7zKmejpAq\n94z5F0kddkorfnrxDoZU/oJHWNg08m1Ov/gfh26IXrWLTCITaX5kn5HhA72WzQaFM7tE8+tmJ+sa\nutPYsRDdfy4d65f8P3vnHVhFmb3/z8ytuTe994SE0AkgTSAg0puigL27YF/L+l23/tyqu6urrr33\ngopYQJr03nsnkN7rzS25deb9/TEhCgYIJCgCz3/3zty33Jk575nzPuc5hAS7uKJ2GXNqhrPLX8wb\nJQe4OrUT5rOh9nAUcjzo+oOyHUmUgLIWdN0JtqQzrMdEwoIjWVe+EjngJcmfgkPnwq63s9FiptZm\nYVLdHEpXfsTaIy46dOuDoT20jOUo0I1AyCmg5jYl3i0HtQTkjiBZ297HcbDqJQbFyNR4BUUu2FYr\n8CrQOUw6hlf8S7TZbcWFOOcztdsXKRNNWLNvAa8v+Dt+xUfXlL48Mvk/J1SROFMIRaHI8R1yYDlp\nljwA/KqegsaehFonEGP9Zbw4tDdUIVh0sJa3N5dh9yrIElzRNZpbLkkg1KwtJj6ng7zZL2PY9Q4W\nRaOUNOqi8WffScbU+7H7fbzw6l+x1x+kOqIeRa/pVUpCT7i/C5H+XoT5OwF6GmQIyB4kqrkkO5Zr\np03+uaZ+TiCg+NmRt441++az9fAq/IoPAIPeRN/MYQztPoFeHQahbyVlp77xIHXuLaDkE2UsJtJY\ne8xxmz+cam8a6DKJtgwiLKhDu8+pvXCRMnEKqPnInt8ipAiE+fVmusT8LZ/wwbJnyErM5u83vUN9\nTQmPvH0LXtnBIMsQHnrgheYmPn/uTwwtfJUAOnYMeIYrbry17ZMQDUiex5FEKULOQpgePyOaxIlQ\nZvcy+aM9ZGR0ICI0hJQ1b3DtwX8THOLC79PxnDyDVV0vxZpayJCUvvyr91mmcQk7kvdpJHU/AhPC\n+BDoBwBQ1VDGm4v+ye6CjYT7g7AQS5m+CCSBXg1igCuEKx1bcBoi2Zd8PSNvfIiomHaKbgsvBOYg\n+b9q4hcbQT8ZYbjqrOgXCyFYXqHyRb6KiuYQT++kI+RiyecLChc5xG3AvM0f8+HyZwEY0+cabh3x\naKsX/9ZAKAoFDXOxsJwEs1ZQwaOYKXT3ITZkyjntEPyUsHsCvLe1nPkHalAFBBt13NR62fHXAAAg\nAElEQVQnniu7RWPQaQttwOcl/5u3YdNrBPu1pBGPFIq7802kX/sIlsholiz/jqXLPqDBW0BdlBua\nHgudYiIi0INIf29CAulIyAjAJkFA50WSquncMYQ7bmmH6NQvFC6Pg42HlrJm73z2FW9t/j4kKIxB\nXcYwtPtEOib0OC0KUV3jAeoaNyGLPOJMhT9K0qvyxlHvT0Wv70R8cA5BxnNHOeWiQ3wK+Gcj+2ci\ndCMRpnsB7QXrwTcmU+eo5LdTnqNvx2H8v6dvIVfsI0yJ5cVHZjerSnzx+nMM2f8PANZ1/0v7JNCJ\nRiTPX5FEHkJK1aTVTiOBrlVdCMHr+/3ssEk43W4a9z5GfM0e7imqJSa8EqHCMtcw/i/jQfp32Ue+\nOYx3Bo5iQPRZ1PgWfiTf60jKCgQSwnAD6K8GSdIcxd3f8OGyZ3H7XKQFYnGgo05fDoBZCWe0I8Dw\nxt24dKHsjJ/CwGkPk9qhnSKLajWS/0MkZZ02VCkaYbgVdIPgLChD5DaovHlIwe6HCKNW8jk95Jex\nK3URbcc56xA/88wz4s477zyrfZwphBB8uuolvtn4HgC3XP4IE/vf3OZ2j9YOF4pCkX0BJrGURHMx\nAHZ/KOXefiSFT8NijG1zX+cK2rNeen6dm9c3lrKtVHOckkJN3DUwiUtTQ5sdMTWgULBwJr7VLxHm\nPQSAnyAcHaaSfPXDhKVqvOFX3nmBwtxV1FOCPcLf3IfJaybMn02k2h+rmoDE989OgwQ+2Y8k1xMS\n4uTXd91MaOiP6Svne434Gns5a/cvYvXe+ZTUHKGu0E1kWhBx4cnkdJtATrfxJESe3oKpKn6qG7fT\n4NmBiUISzQWYflAsRBUS5Z4UnEoqZmM34oMHY9D/9LraR3EhOsSnY7Mlz5+R1AOoxt+CfiAAK3bP\n4bUFfyM5OpOn7viUDctn8cLmp5GQeWzYX+kzaAIAS+fOJWvZdAzCz+r0B7j24b+3ffDCj+R9Eknd\njZDiEeZ/aNq5p8DpPsurK1Q+zlPQS4JdBw6xt7SGodZPsDZu466DFjpF7gGgyJbMLalPkJTWSHVM\nKUmhmXyTM/Hs0SiEgMDXyP6PtY+6QQjjfc3R8TpHFW8ueoLteWuoK2jk8oy+HPJX4G5S8An3xzOl\noYKevny8kpltMRPpMukhumX3aJ/xKfuQfO9onGfQqCzGO0Bu/6BQvVfwxkFNBk8vwQ0ZOkTuuvPa\nZreE832dagnnbFLducohVtQAbyx6gkXbPkOWdNw78W+M7j2tXdouKipCCj2Ay/06aeYVhOjt2P2h\n5HuGExXyW2JDczDo2p9H9XOiPXlKEUEGRnaMoHOMhdyaRkrtXlbk1bO30klaRBBRFgOSLBPRKZvI\nkXdiM3fHXlqI1VdMkG0X3vVvU7xlC4o1gRETr2H0qGu5YtR0cjeVY6xyEVBceCx+XMZSakybcfjX\n4xF1eKRojFiwAFahw6Ja0bsjWLWmmAUrDrF05VZWrl5Oeno8EeHh5z03y2IKoUtyb0b3nkb/rOFU\nV9ahmhqpcVSwv3gri7Z9xo68dfgUH7FhSZgMp96SlmQdwaYkoqx9CbeOBN0ESj2JlLstuBWJEP3R\nQiFFhOu2ovrnUWLfSqUrD0VIWHSxPyn/+CKH+CQQDiT/u4AOjHeDZEAVKi/O/RMOt42bL3+E5IgM\nnpz1e7yyk56G3lx77aMA7N+9j9BvbsOiulgffRXTHvtxyebThhBIvpeR1M0IwrXIcCsLRZzOs5zv\n0KKPKnBrRz2/yg5nUa6NHfauxIc0sjPqMJayHiTJJURYG7i27juWOftT7u1KpOUgfz28i0q3j1Hx\nZ8F2SBLouiKkDqBsQxL5oGzVKvFJwQSZrAzpOo74iBQ27VpHXXA1Br1ML31PqgM2GvU2tltgl6k3\nHb02ujm3Ytj+ASs3bsemSyQ5rY2VWeUY0I9EyFGg5Gq858BSJFGnJQi2I40iSC9xaYyEKwD5TsGu\nekFVaTFDuqaiu4D0is/3daolXOQQnwZ8fg/Pz/0jWw+vxKg38cjkp+iT2T5vUOX2tSi+b0ht4gg7\nA8GUeC8lLfx6TIbwdunjQkJAFczdV81H2ytweDUt3eEZ4dzeL5HE0GONZ/nmVdQteJ7wupXIaBxi\nm6UH5px7SB1zLbqmqExNfS0vvvYPnHUHqA6uwRf0/TMQUWvGIndCMlyKXqRgVWSOd/N8gF0WCLkR\nWapk5GXdGTXisrP2H5xLUFWFvUVbWL13HpsOLcfjbwRAJ+vITh9ETrfx9Mu6rFXOcUvw+OupdK7B\n5z9ImL6IeHPZMccb/GFU+TqAnEV8cA5WU1Kb53QyXIgR4lbb7MAaZN//EHJPhPkvAGzJXcF/v3qU\n6NB4/jfjaz754J/Mr/4Wo2rl+V/NJCImifo6GweeGke65xAHrb0Z8Kd5WCxt5/dKvo+RAl8hMDc5\nw+1YiKIJDr/gyZ0B6n1wWbzMDRmaOoPGKd7NkTo3l4atJtQ9m76l8Vzt2ok1uBHFL/OWehMvJF1D\nv5RC8sML0RuSeSo7hyuSz44KDmopkvc/SKIMgRVhehh037/o2Jw1vLPkP2w6tAyAHtHZqHWC/co+\nhKQgCT0Z3g7cWr+WUKGVht4VOgTDwHsYMXFi28cnnEj+WRBYoEnHYUUYrgX9WJDaN4K+rkrlkyMK\nAaFVELyrs45I0wX1WF9QOGcpE+eaQ+zyOHj6y0c4ULIdqzmU3019nk5J2W1u19aYS51rJh2tuwBo\nVCwUeQaSGn4DZsNZlt+5AGD3BPh0ZyXf7KvGrwh0EkzqGs2NfeKJCDqW712fn0vZ1/8jpPBrDLgB\ncOkTULJvI33K3ZiCw5rP3X/wAB999hxOey41EQ0oP2gqotZMqC4Z2ZKN8KUiRAQm1UDocc+MCthk\nUCQvklxDeoqBe6a3Q2LQOY7vi3/MZ2f+hubiH2aDhf6dLien2zh6pA1AJ5/54ubwFFLlXAfqYeJM\n+YQa7M3HVCFR4U3CEUjHbOxOQnAOen37JU7BRYf4ZJC8LyApq1ANt4HhCgD+30e3k1u2m9tH/pYB\nqUN46O2b8MsuJsVN5ubbHicQCLDgiZvoV7+YKkM8EfctbB+eamAJsu81BDLC9IdjHL/2gioEL+xT\nONAg6BAs8WiPY8syf+8Ue+gZso+UwLuENuiYUdhAfISWO7LD1pMZ6X/GaIVOWXvYio8sawafDx5P\nsvUsUIOEC8n3IpKypYlXfCPorzqGt7vh4BLeWfxv7I316HUGxqROYPeRPRTLRwAwqBa6+NK5rn4J\nVqHRmw5ae+PsOZ1x065thwqCxUi+95DUndqQpWSNRqHr1bZ2j0ORU/DawQB1XgjRw/TOOjqHXeQV\nn484Zx3ic4lD7HQ38MTn95FfeYDI4Fj+cO1LpES3LYrg8ddRVP8BGZaNGGU/flXPrOWxXDn2z+cV\nR/hU+Kl4SlVOHx9sLWdxbh0CCDLITO0Ry9SesViNx2ppemz1FHz1CoY9H2JRqgDwSVac6VNImvxr\nwtOPlV9bvXY1Cxa/i8uVT02EHeWonRcQWWsm1JBMVudReDwGiksVSotrSE3sRZgKx5tVuyThlf1I\nUj0Wi42H77utRR7yLwknu8ZHi3+s2beAw+V7mr8Ps0YxqMtocrqNJzO++2kl4x0PoShUNW6lwbON\nIKmARHMhBvl7XrhXMVHmScdHBhGW/kSZuyO1UV/1QnSIW2WzhYLkno6EA9X8PMhJHCnfy58+vBWr\nOZSX75nPf1+8nz3KTsKVeF5+bA46nY5Zrz1DzoEn8Epmyq78hMGXD2/7gJX9WklmAqjGe0E/8rSb\naI39+rpQYWGpSogB/pitJ6KFCGOV08d1n+5lZ4WLtKBSLjG8hdNVy4wDkXQL24Ykg90Rwm8i/8D6\nsB70TS6kMKIQl2TlkvBMZg0e1/78YqFC4Atkv1YKW+guRRjvZc3a7c1zdrhtfLziBVbs/gaAxMg0\ncsKH8F3uMmy6CgAsSiTZgTSuqF+IRdV2hgrNHSnLuo0JN83AbG5DMRIhND1l//tIoqJpnP0Rhts0\nabl2wuIVq9kXM4j9DQIZuDpNZlSi3Ca7dK7jIoe49bhgOMQuj4Mnm5zh+PAUHr/hzdNOCPohVMXH\n4bqPsKpvkhSUi05SOeLqAab78doyyOzQ9dSNnEf4qXhKVqOOwenh5KSHU+PyUVDvYVeFk/kHahAI\nMqOCmhUp9OYgovsMI+zyu6hWkmisKMLqLyPItgvfhrcoXr8at7ASmtYJSZJIS01j5PDJjB9zO+Gi\nE7ZD5RjqPbjNXhqDA9jMdeQ5tuAs3Y1JqiUyysITf7+bBrWAg6WHccom3BjQC5p4yDIW1YrBG8Xa\nNcXMX57L4hU7Wb5iBW6PjU5Z7b+lezZxsmt8tPjHiF5Xk9NtAiFBYdQ7a6ixl3O4fA/Ldn3N2v0L\ncXrsRIbEEhwU1mI7J4Mky038436EWUYRkMZQ7I6kymNGUQOEGRuIMNYSbTyMVVpJg3sZJfbd2DxV\nGPVRGHWnH4G7yCE+AdTDyMoChBQHhutAkvh8zasUVB1kTJ9rCPUY+Pzg+wDcc+mvSc3oweZ1G+i4\n+mH0IsCGLo8x7pob2z5YtbrJGXYj9JPAMOWMmjmV/dpRq/JpvooM3NdVR7K15cii1ahjavcYdpQ7\n2VltpFr0oUd4AWvDKlBqe5PqK8ca3MhE33Ji613MFJdhsMfTI6Ka9e4i3sjbzyG7i0lJ6Wc0jxYh\nSaDr/gNecQEoGygqjSI1rTsAJoOZflmX0S2lH7lluymrK2Rv/W4G9BhOL103Cu3luHU2SvQV7Arq\nii14KHHeEuJ9paRULadw1SesOtRAcqdszGbzmY1RTgL9aIRkbtIvLoLAd0jCC3IWSG1XfiovKWZa\n3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JOPVvyP/lmXU3awkFI5j5763jw4/X/k/jOHBF8xaxNuZtrvXmi5vdZCCCTf80jKGoSU\nijD/q13L/B69txVV8OxehSMOQadQiYe669q9zK9PUXls4RE+2F4JwPReKlLtqxRWHUKWdFxZ2of+\nrm+xWLU8iEJbBs/2uJMlHq2AVFZEDbEpFaxVakCSkNERb0zmD936cVN6y8mhJ5vzMVCrkXwvIKla\nBFjor9CKebQgeeb02Jm15jW+2z4LIVRNjWLYA1yaNYqPP3qCVXXr8Mvazly4Es9VXa+gc8+xbPny\nJXpVzCZE0YITtYYY9iVOY+i1D5y+MoUIQGAhkv9zzZFHB/oJCMM0kH6c+9FaG+ZXBTPzFNZVacZ0\ncKzEdR10mH6BFIpfytrcnrjoEAPfbHyPmStfRK8z8IdrXqJ7ar9W/1aLCn9AorkEgEPOS0gKn0GQ\nMea0x3Eh3oC/tDkX2Tx8vrOSpYfrUJoegc4xFqb0iGVoh/CTalLWHNhJ5cK32LfxK3LitKixQMIW\n3BtD72tIHXP9SSkVR/HN/K/ZuOkb3M5iakNt+MzfP4sGL0Q2hGG1JtO120huue62Vs/t8y++Ycfu\nUhQRgySsBCkyLaUFOiVwyypITnRyNTlDOp9U1eLnvsaKGmBv0RbW7/+OTYeW4fI6mo9lxndnUJfR\nXNpl9BmryLSE89AhjgfihRA7JEkKBrYCk4UQB46ec1Kb3VyuuTeq6U/87r3rKao+zE3d7+TjPe8i\nIfPvq99g49yZDC1+k3JjCll/XkNI6Il3PlqFwGJk3+tNZZn/0+Yo4PE4em9/nq+wrFwl3KgV3wg1\nnp1LL4Tg3W0V/H5RHgFVMDApiMmJK1m58xMEgq5BXbl8cxmZ4TuRZYHfq2OTPJy/Z95KSaOWSNYn\nthRjfAMbVW0HRUIi1pjMnek9+G23U+/KnvB5FgoEvkbyf6aVVJbSEab7QW6Z+1tYlct7S55if8k2\nADLiu3HL5b8hyZrIe588ySbnVgKy5txHKglc3eMq+g2ZxuKZr5JZ9BmJ3iIA3HIQO6InkDn2Xnr1\nPc1dZdGA5JsJylIkBIJQhPEG0I04hl98OjZMCMGaSsHnBQp+FeKCYHonPSm/MArFz223fw6csw7x\nT0WZWLV3Hq/MexwJiQevfJJBXca06ndCUThc9x4ZQUswyH7qfRHYmEJa+PizPPFHBRcAACAASURB\nVOKLOBdQ4/IxZ18N8w7U4PBqHLdoq4HJ3WIY3zmKUPOJOW4Bn5fi5V/TuPFTwurWokOjQ/gxYY8Z\nTtiQm0jKGY+sP/WWa1FxMW9/9AyN9Yex6atxhAeOOR5WZyRUxGAJz+BXNz9KakrrI6J2u523P5hF\nZTUIEYmsBhGswvHxNYGmjeyTFSTZgU6qYciQLuek9FtA8bMzfz3rDyxmS+6KZhk3gM7JvRncZSwD\nO49ss8bx+eYQHw9Jkr4GXhRCLD363clstuR9DUlZgmq4iYK6bvz+/RsJNocR50ngCAfoSFduHPd7\nLO+OwSQ8bBv0EhOva2MBDjUfyfNHJPyoxgdBP6xt7Z0Am6tV3s5V0EnwaA8dGSFnf5t8XWEDM74+\nSLnDR5hZxx8Huti+51nqHJWYDEFMrB9M9+KviAzTpCHrGyL4OmMab5pH0+DVZNKGJObRGO1lh1qN\nQFvPI/XxjInL4IU+Q89c71k5pEWLRUVT5HUKwjClxWixEIL1B77jo+X/o86pKbj0z7qcGy77NRZM\nvPfxE2xxbSMga3SJKCWRqb2mkjP6JhZ9/gnB+z6gi2s7ACoyu0JzMPb/FSOvuOL0xqzmIfneQVK1\n9zshJSIMN4FuwBnTa0pdgrcPadJ7egmmpMtcHn9+V7f7peNncYglSXobmARUCiGyWzrnp3CId+av\n56nZD6GoCreOeJQJ/VpngB3eYursr9HBehCAQ84+JEfci9kQeTaHexHnIDwBlSW5dXy1p4riBi8A\nJr3M6KxIruoeQ2r4yasvNdZVU7zwI8Tu2YS5933/vRyNJ2080cOuJ6bXwJPyjX+I5155isqSrTj9\n5dRFuJqLgUBT9NgeisWcSEqHgdx354OnPd/qulpeeW0mbm8oQg1H38RHPt51P8ZJlhxIcg1DzzEn\n2ef3sD1vLesOLGLbkTX4A9r1kySZrsl9GNh5FP2zLicy5PR3e85nh1iSpHRgBdBDCNFcxeSkDrH7\nASRRgWp6kg9Wfsv8LR8zImU0y4uWIVD5y5j/UvD18/RuWMmO0GGM//vXbRukaGziDZcjdCMRpnvb\n1t4JUOoS/Gd3AJ8K13eQGZ7w0yVO1zb6+fXcXBbm1gFwa69gUpVP2XRoMQBZMT0YssZPV+MSjKYA\nQoW8hm582vcKvnQNxKPo0UkqlyfnUh0psUepRml6OQ/VRdEnPJX3BowkzHQGFBPh0QpkBBZoH6WT\nR4s9Pjffbv6QuZvex+v3oJN1jO49jSmDZxBwOXlv5r/Z2rgDpckxjlaSueaSaVw29hbWLF1G/ZrX\n6W1bjl5o4z8S1JWqrJuZcMMdmINaWQFPCFDWI/k/+b4MtNypqbDHmSUb+xTBFwUqqyq1BOmeERK3\nddQRbDgvTcMvHj+XQ5wDOIEPTuQQn23KRH7Ffv428y48/kauGHArNw1/qFW/K6yfR5Q8m1CDHVfA\nQrl/MhlRU9tlTBfiFsX5MmdVCLaWOPhqbxVbSr7fkr8kKYSJXaIZlBbWTKc40Zxrc/dQ+d37mPLm\nYlG+17t16pPxZ4wndsSNRHf5cbLKibB+wzrmLfkAT0MRNmPtMYl5ACE2PWGBaILC0rnuqnvo0b3n\n6U4bgI0btzF30VoCgSiECEGvGggV3zvJhaX7SEvqdoyTrNEtahg8sBOTxrcxaaod4Pa62HpkFev3\nf8eO/HUoqvZfSUh0SspmQKeRDOw8gujQ1nEVz1eHuIkusQL4hxDimx8eu/LKK4XVam2WagoLC6Nn\nz57kDO6K7Lmb1esaUYy/5eNdT9LQWEdwURxFooDslO5c3mEqga/uwy8Z6PaHpXTL7sGaNWsAmp+V\nVn8eMgTJ9xxrVs8DKY4hI94FyXTm7Z3g89KVq/nfop0kTbqbgTESHSvWI0lSu7Xfms9CCPabM3l8\nST6+gl2khJl4fFocizc/T96+EnQ6PTf3vJaOq+dS7s1FkqBPhI59vsH8L7ojWxoyUJN7YdQFyHYt\nwBGiJ79LFD7hhgPFGKUgMi8ZwjO9BxM4mNd8rXNyclo3XrWAof3XI4lKVq9rAN0whgz/PUiGFs+3\nN9ooVraxYvccagsbMRmDmHH9g4y75DoWz5/HghWfUB9fjiJ5qSt0E6ZGM33cTYyaNJ3PZ35G3rqv\nuTZoI8GKg00VYNNHENT3BgZPuZuC4sJW3j8DIbCEtStfBVwMHRzB6g0xCP0okOPO6Hptq1V5+stV\neBXI6juEWzvqqN+77qzfH235/Oqrr2rP7zkynrPxeffu3TQ0NACazFy/fv149NFHf3rKhCRJacDc\nn8MhrrFX8KcPb6XBVUtOt/HcN/HvyKco6+gPNFJQ9zKdgzcCUOzugDXoLsItrU++OxXOF+fwdHA+\nzrmw3s1Xe6tZkluHr4loHBmkZ2znKCZ0jiZ356aTzlkNKJRtWELD+s+xlC3FLOzNx+zGDJSsCSSM\nuomIDqeno/r8a09TUbINl7ecujDHMQVBdH6IsFmxGmIJierMPXf+5kdFQU4H23fsYvY3KwkokZSW\nlJKakH2Mk3wUx0aSnchyDf37ZnL1FePOuO+2wuVxsO3IajYdWsqO/PXNkWPQOMcDOo9gYKeRJ03I\nOx8dYkmS9MC3wAIhxPPHHz+hzQ6sQva9gJAvYVvpMP4z+yHSgtModpShSn5+fcnvMS9+gkRvEatT\nZnDto/9p20D9i5D9b5413jBoL8CvH1RYvGINl1w6hMd66n9W7dndFU6mf3WQ3Fo3Zr3M73KiMDk+\nY8Vu7Z0lJTqToYcy6Vj5ORFhWoKpy2lhq3ksM/vksLq8IwIJg6wwIuEArkgT2yQHjapme3ToiTMm\ncWeH7gyodZ2ezf5RtDgNYbwbdCfWQS+syuWTlc+zM389AJHBsUwZPIPhPa+gvrKEdz7/N7u8u1Ak\nHwBhShxjO4zgyqkP0WBzsPTTV8gq/ox4n6Yk4ZOM7IgYScSgOxjW2l0q4YbAXCT/HNasKydncCTo\nhiMM14J8+rtGdV7BO4cUDju0NWFYnMzUdPmcTbg7H9fmU+Fn4xCfyiE+W5QJn9/DXz+ZTl7lfrqn\n9uMP17x0yspWdY378HteJ8Fc2lRt7jIyo6Yj64wn/d1FXNhweAMsya1j3oFaimzaVp8E9EsOZVLX\naAakhKI7SRIeaBXxSlbPx7lpFsFVK5sl3AAagrpCpwnEX34d4emnV5J0/8EDzPzyZdz1BTToqrFH\n+I85bnJLhDtCsZgTiE3qxSP3PnZa7beEHzrJQoT+KJJ8FAJwSOCVFSSpEUmqJT0tmLvvvLnNYzhd\neHyNbM9bw8aDS9metwav39N8LC22EwM6jeDSzqNIijp2K/g8dYg/AGqEEL9p6fiJbLbkfQVJWYZq\nuIX/zV/PhoOL6a3LZoeyiyglictCLmXo4f9SaUwk88/r25ZIp+Y16Q37UY0Pg/7sLOgLSxS+LlKx\n6OAPvfTEmH/+S+3yKfx+UR4f79RUKPolhfBIXzsLN/yXSlsJEhIjuk0maU4+XVhIkEW7l2tsMWyO\nv4K5Wd1YVZ7V7BhfHn8AfZiRDSaVukBFcz8R+jj6RSTzat/LiDSfhuyZshfJ9wqSqEQggX6UxtM9\nSensnfnrmLnyJQqqNHpifHgK1+Tcw6CuY6irKOaDWU+zrXFHc/KdVYlkeEIO11z3f8h6Iws++wjr\ngU/p7tzc3OZhS3eqMq5h7HV3tk7fWtiQ/LMh8B0SCgID6MchDFeBFNb6+aO9SH1XqjK3WEUREGOG\n2zvqyAy9KM92LuCcdYjvvfdeYbPZfrz91sbtpZ0Ni1izbwFqrZXpY/7ImJHjTvr75G61xBtms3Vj\nKfZAGL0GP0Ji6NBzItx/8fMv47MQgg/mLGFDUQNF1o74VYH9yA7CTHpuumIU4zpHcWjHplO2F/B5\nSfNW07jlCw7uXY0OPwOahBGW1yYjEvsx9s5HiO7c87TH+6e//oGS4t1ERnixWWyU1mqyR5Fp2oLn\nOuAl2BdMh45d6d59BGkJHdrl/7EGh/LlnJUUFtQisJCamE2oCiWlGp86LUnj7hWW7sMjQVRyV4Ts\nobx0M0FmH//8xx+IiYz6Sa6nP+DFmiix6dAyFi2Zj9fvaf5/6vfq0DlD6NXtEsKsUcTGxp7R1tu5\nCkmShgCrgN1o7ywC+KMQYuHRc07oELvvQxJVNMp/YcbLdxHw+zCLcNw6G1fETuWSPe8QHqhjffY/\nmXLnfWc+SNGI5HlMS+bSj9aikGcB+2wqL+5TEMD9XXT0jDy3nJlFuXX8Zv5hyh0+jDqJ/xscR7w6\nj/lbPkYVClZzKFd2vJGQz78my7oJvUFBCKiwpbAleRILMzJYVZGFKmR0ksrQ+MMkmXysiAqhwluK\ngvbybJIsJJkTeKhTb27p0ErZNuFF8n8BgTlNzmWYVhxDN/SEyWuqUNl4cAmfr36N8nqN9pAak8V1\nQ+/jksyhOG01fPzZf1hv24JX1uhqJjWUIREDuPH6xwgOi2LLuo0UrXiL7JqFWFXNtjXoI9gdO4ke\nE+6ha4+upx67Wo7k/xRJWatNBVOTY3zlaTvGJS7Bu7kBShu1IMmYJK3ss+EUAZKLOLs4Zx3is0GZ\nmLvpAz5e8TwmQxD/uPndk2oNa9rCr9A5WLv5811diAl7EIsxtl3H9ENciFsUF9qcGzwBXvxsAUeC\nMim1NyVxAb0SgxmdFUlOejhBhlMn5nidDZQsmY1n11xC6jZg4PttfYchlUD6aKJyphLTc0CrE/Ka\nf+9w8Pr7L1JbsZtGbwX1IY5jpN0AQusNhAYiMQUn0avnSK6dcv0J2zvda7x3zz4+/XIp/kA4gjBk\n1diiugWAD3DKoMh+JOzodHWMHt6X4ZcNbnV/ZwJ/wMfuwo1sOrSMzbkrcHm+p7XEhidxe7+/nncR\n4lOhRZut1iB77kFgYXXBdbz07eP0lDuzWz2ISQ1hvNSHy8rfJz+oCwP+serMlQ2EQPI9i6Ss1xK4\nzE+0q97wUdR4BP/aFcAVgInJMhFF689J+9XgCfD4knw+3KFFi3vGWXl8qJ71O19hT6H28p0Snclo\neTQhy94lNewgsk4gVCixZbAzcxKLUpJZVZFFQNXsUf/YQrJ1VSy2u6jNDMelNjT3F6mPp094Eq/3\na2XUWC1G8r3+vaqD3ANhnHFSeouiBli551u+WPsGdQ5tXpkJ3Zk6eAZ9MnLwNjr59NOnWVW1jkad\nRgsxqFb6WHpy41UPEp/amerKGpbPeoMOxV+Q7C3Q2kVmT+gQAt2uYczU6zAafrxjfIwNU/OQfJ8h\nqVu1sWP+gWP84yp9J4JfFXxbrPJdqYoAkixwW0c9qcHnhtm40NZm+Hkd4nQ0h7jFTJ72doh35K3j\nP7MfQgiV31z1NAM6jTjhuQ5PIQ3Ol0m15KEKidzGEWRGTkc+BbWirbgQb8ALdc5DhgxhR7mT+Qdq\nWFfYgL+Ja2zWywztEM7orEiyE4KRWyHR42t0UrpiDo3b5xBcs+YYWoVLF4c3ZSSh/SaROGAEOuPp\n03wqKsp568PncdQdxqVUUh/eiHKc3xJqMxDij8AcnEi3bsO56ZpbjplvW69xdV0tb7zzGQ6HCSEi\nkNQgzEIiuAUzpKJRLnw/EeUioPjZV7yVjQeXsjl3OfbGeh4b9eZFhxggsA7Z9yxC7s1/F9jYnLuc\nmEAy1foS+ugHck3JNwSpbnYNe5OxU9qQnOxfiOx/q4k3/DTI7V8d1KMInt6tRfW6h0vc31XHurVr\nz2n7tTLfxkPf5lLU4EUnwT0DEhmVmMfsNS9Q1aDxawd0Gkmv0kxCNr5JcngekgyqIlHU0JkDXSez\nLD6E5VVdaPRrtiPFtooRnY24O3dnpbOKal85alN5ZZMURIIpkQeysrkz8xTKDEIFZQWS70MkHAj0\nTcUxprZYHOMofAEvS3bM5psN79LQqClsZMR1ZcrgGfTtOIyA38dXs55jcdGK5pLQktCTKWUxdeiN\n9Bk0gUAgwNK536Du+IieDavRoalA1Bji2B83kZ7jfnVM1LhFG6Yc1gp7qJqOsuYYj29yjFtP+zls\nV3n/sEK1B2RgZKLMFSnyz8pJhwtzbf65VCY+AYYDUUAl8BchxLs/PKc9OcRldYX8+cNbafQ6mTbk\nbqYNueuE55Y2LCeUjwgzNGD3h1IvriclvHXaxBdxEWcChzfAqnwbiw/Vsa/K1fx9bLCBkR0jGZ0V\nSXJY66SDAh4PpWsX4tjyFdaKVZjF91EcrxSMK+pSzD3GkTh8MkHhZ5Y0t2PHdr6c/w6ehiKcohZb\nuPsYeTfQFCxC/RGYrYlkdR7K7TfccUZ9nQrvfvgZuYdrUUUUQgRjUHWECm1hOR4uCdySQMheJKkB\no97O1VdcRp/eLW5SnRFUVSG3bDeuKvWCc4hbstmS712kwDx88tXc8crLhDbqqTU0IiFzhbc7l9d9\nw56QgYz+x4Iz71g90sQbDqAaHwH9kDbOpIUuhODNgwrb6wRxZvhdth6L/pdxeZ0+hX8uL+DNzeUI\nINZq4M+XJWLxLOKbje82yZzpGdlrCslbjUTvf4/48GIkSXOMixuyKO40lcVJRlbUdaHGrTmrMRYn\nORGH6B2ZyKxgmYLGcpyKrbnfCH0cnYJj+W/vIXQ/ma0RdiTfR0jKMu0joQjDdaAfdUxxjOPh9btZ\nsuNL5m56H5tL01tOj+3MlMHT6Zc1HKEKFs99kwX7F1GpK2r+XYySwtiskYybfA96g4GD+w6wc/7b\ndKmcR4xf40qryOwNGYi78zTGTr3h5NJtSi6Sf9ZxjvEEhOGKVjvGXkUwp0hlWbkWLY42aaWfu4Sf\nW3Sc8x3nfWGORq+DP394O2V1BfTPupxHrnrqhIoSh2s+IMM8D72sUOxOJ9hyP2FBLesmXsRFnA2U\nNnhYcrieJbl1VDp9zd93jrFwWUYEwzqEExvcuiivEghQtmEJ9s1zMJWuwhooaz6mosNu7YnUcSQx\nOZOJyupxxmPWHOT38NgLcak12MLdP4oghzToCfGFYw6KJSahJ9NvvpeQkDZWITsBNm7cxreL1h5D\nuQhRoaV/TQGckoRPDoDkRqaeiHCFe6bfSGho67c/j8f5mFR3KrToEHv+iKQeYm/VBP72+cuk+TtQ\naMgnRcniwcqlyAgKr5rN4OGXnVmnwtXEG65E6Mdq2+5nAfOKFeYWq5h18PtsPfFBv7xLu73Mwe+/\ny2Nzkyxk36QQ/pwTzN5D77N673wEApPBzPi+NxKxzE5c/sfERWg2Q6hQ1pBGefo1fJdqYqM7k8O2\naAAMskJO/GEuCTip79+P+eVHqPKVNUeNdeiJMsRzaVQiz/bOOTGlQjmM5H/vB8UxUhHG20F38hdW\nn9/Dkp1fMnfj+9S7agBIjspg0oBbyOk2Hr3OwO4tS/hi+QfkqodQJY0DbVEiGBTVj+uveZSQiBh8\nfj+Lv5yFvPdzetjXom8af50+mn2x4+k86g6yL+l94oEoh5oixju08WMG/RiEfhLIratRkO9Q+eiI\nQmnTJt+gGIlp6TqsF3WLfxKcsw5xe1AmVKHy3y9/w7Yjq0mJzuQfN7+H2Wj58XmKjyO1zzdLqh10\nDiQj8gF0+tPIoG0HXIhbFBfn3DJUIdhd7mTJ4TpW5dtw+9XmY93jrAzrEM6wjAiiLK2n8dQc2En1\n2q/hyFJCG/ch832bTn0yvsQhBPcaS8KgURgtrci+PgH27N3NrDlv427QHOQj9lrCM459lsyNEmGu\nEIIMMVjC07lp2t1kZmSecZ+nQnVdLW+8/SkOpxkhIkCYMak6QoSgJevnBVzN3GQnslxH185x3HLD\ntFb1dyE6xD+y2cKP5L4FiQAvLU9j/e6VqJhQZA8j3T2YVD+XrRGjmPSXz8+sQyGQfM8gKRsQUocm\n3nD7K/9sr1V5/aCCBNzXVUfPiO8DKr80+6UKwazd1fx1WT6VTs0xvKlXHHdmB1i69U22HF4JoCXe\nDbwNy3e1RBz+iMSwQo7GkRblRhHb7QY2ZoWzXY5kXWUGqtAOdous4BJjIUO6DeAtbx1HXJXYAtXN\n/RulIGKNcVyVnMnjXfv9mDPeXBzjQySh/U7IfRHGm0E+eZVNX8DLsl1fM2fj+80c48jgWCb0u5GR\nvaYQZLJSWZrHp189z1bnTnxNCXh61UxXYxemXH4LXfsMByAv9whb5r5NVsVc4n2lbKqAAfFw0NqL\nmpSJ5Ey+hfiEuJYHohxsihgfdYz1oLsMYZgMcuIpr5GiCr4rU5lXrBIQEGKAqWk6BsRIraLQtRd+\nafd2e+C8doi/2fAuM1e9RLA5jCdu/YC48OQfneP2VVNtf5Z0Sy4BVUeeZwIdo29rU79nigvxBrw4\n51PDE1DZVNzAyjwbm4oa8DbxjSWgZ3wwwzLCGZoeTsRpOMeumkrKV83Bu3chwXUbj+EdBzDgCOmF\nnDmc6AETiOySfdqJeT/ExzM/4VDhZjwNxbgDNdiCnfiCjrUfOj+ENQRhlSIxhSQyqN9EJo47zfKr\nZ4AVK9exZMVWAko4QoQiCRMWFX782qzheNqFQdfApLE5DBx4bGT0okMMKAeRvX9CJYnbXt9Cgj2C\ngqByQpVY/l/lFmQEZdd+S//Bl55Zh/4FyP63EQQhzE+dFd5wqUvw1O4AXhWmpMmMSTp2C/+Xar8c\n3gDPrCnm1Y1l+FWBxSBzz4BEJqbXM3fDq+wr1hLGQi0RTOx3E2Eb/Zi3vkNKWC5bqgUD4qHBHk6B\nPJTywUNZIXlZXdMJm1d78bUafAyJyaWP4iV22GW8Xnjo/7P33mFyXFXe/+dWdY6Tc9CMpBnlLNmW\nkYNkwMbGNuAABkxYFn4sG1lYYN9lYZfl3X0JC+wSFliCF2xM8jrJQbYcZMmSlTzKYTQzmtHk0Dl3\nV93fH92aoGDNSKM0U5/nqWe6u25V3dNVdebbt849h65E35iJeE7FS4WthI/OmMOnG04ZBZYpyKxH\npP+IIJFN06auyeUALntL2zJamtcPPc+T2/+HzsEWABxWF29fei+3LruffFcxiXiUx//wPV7u3ExQ\n7Rvetkir5Iaa67jz7j/H5nSTyWR44YnHOfDsf3Gvez8WmX1qlxRWDnjfhpzzHm65+x5stjP8ENNb\nEOnHQduGQOZsuAZpuhvUc6fJ7ItLft2i0RzK+sp6t+D9deolm3R3tV7bF8IVK4gvNGTiSGcT//Sb\nT6JLjS/c8x8srT89rmwotg+SP6LY2k8k48Knf4iqvCuntKyBwanE0xrbOrLieEdnaHgyniKy4nh1\nrZfVtXmUusc/UpZJJenZ9hKhpg2YujbjSbaMWR9Ti0mUrMY+fx1lq2/FUVB0QTaEw2F+9Ivv4h84\nQiLeR9gaJOLVxjaS4AmacaU8WO3FuPPr+fgDf0ZZ2eSLnjPx45//mvb2ELosQEoXqjTh1uFMPzlG\nJvHpIBIIEeK+uxZNO0F8ms9OP4WSfoi+yBz+4pfPkp8uw2/uZWF8Hh/3r2d33jpu/+rvz+9g2jFE\n8h9yccOfBdPkZxWJpLMZJYaScE2x4KOzVMQlHKG7FBwbivOVjW08ezQ7Oc1rU/nLaytZXXqCJ7b+\nF6292fSHDquLdy67n6rWfNj4I2pchzBZsvdsMm6hLbEU+faP81Sigz2ZSpoGRwafGvP7WW47zrtr\nF7K1zMP/nmihL9WbrYaXw6XmUWYt4v3VDfzl7IUjI8fSn8sB/CKCDBIVTGuRpntAees5ELrUaWrd\nwpNvPMThzjcBUBUT1815O7cufz+zyrNhYptf/A3rdz9Ju2xFFxkAzLqDeZY5vHfdgzQuXgNAb08f\nrz3+ECUnnqEhtnf4OH5TIQcLb6HiuvefOfRH70aknwDtVUSuNLZU5mVDKdTlbxknrUvJ9gHJY+0a\noXR2EORtpQp31ShG+eeLwJQUxOF4gC/+8gGGwn28e9VH+OBNf3lam47A8xQrD+M0xehPlqFYP0OB\nYxy5CA0MrhCiKY2t7UFebfWzqytMRh+5J+sL7Dlx7GVmoX1C/8jDPZ30bnma1OGNOIe2Y5Ujpah1\nBGHrbLSKa3AtXEv5qrVYXBceC/yr3z7E4cObSUV7iAgfwbwk+in/J0wp8ITsOMnH4iyjvm4Vf/Lg\nxYkZPRMjYRfWXNiFHYuu4D7DJL6195RMe0Eskt9GaFt54WApjz23lyFLDCFN/ENfH3l6mK57n2bV\n9ddN/EAyikh8HiH7kaZbkZZPTKIVWTRd8r2DGkdDklqn4G8XqJd91v/FZEdniH95uZ3X2rMjuKUu\nM5+9voplBe08vf2XHMqNGFvNNtYtfh/LrCvp/e/vUGd6A4czK2y1jKArVE+w+k6Ozi5lu8ywZWg2\n/kR21NisaFxT0sY8fZAP3Xw33xpqY7uvh6F0P2k5kjbSqXgptRbzvup6Pt+wNCuO9X5E+vc5Uann\nimO8M1ccI++c9h3t2svTO37FjuZXkDIbKja7YiG3Lns/1zSuw6SaGexp5w9Pfp8dvj1E1aHhbYu1\nam6csZo77v4MNnt2QuGeXbs58tIjNAw8T2muGh5Ah20m7cW30HjTvSxefop+0YcQmfWQeQFB9juT\nohRpeheYbgZxtudSEM9I1p/QealXR5fgMMGd1QpryhTUKfYj7XJyxQri8w2ZkFLyrcc+y66WTcyu\nWMhXPvDT0yrRtQw9Qr3tCVSh0RZtpMT7WeyW8y9TO1lMx0cUhs2TQziZYfuJEFvbg+zoDI2JOS5x\nmbmuxst1tV4WlbsxTSD5u57R6Gt6Hf/OZxEdm3HHDqEyMpqrYSLsmIusXo13yTpKl6/BZBmb//V8\n7H1j1xs888KjJENdJFKDhOxhYm79tHb2iIIn5sZmKcTmruSG6+9i3Q03T+hYF8rm17ezYeMbpDIu\npPSCtHH/e8qmnSA+1WeL+KcQcogv/W6QdIeXDlsnZak6vjD4Ervz1nL7V/8w8YNIiUh9E6FtR4r6\nXNzw5KbDlFLyqxaN1/slHjN8aZGJfOuZT+VU8l9SSl5tC/Ivrxxnd3cEX3DDpgAAIABJREFUgAq3\nhc9cW8m1pX1s2PlL3mzdgq89TnGdi2sabmHt3Dvp+M6vqY69QFHeSKxwOOymXV+J6z1/yh/69nFA\nlrFzoGY41jjfFueawhYWZJJ88J4P8dUjTWz3dzOU6iclR6pBOhQPJZYi1pbW8E8LV+BUBhDp3yK0\nbElniQVMNyNNd4Fy7hoBA8EeNrz5O17a879Ek9kf+vmuYtYtfi83L7qLQncpmqax+cVHeLbpGdpp\nRYoMvvY4pdWFzDLN5NYVd7JizV2oqkomk+HlZ9cTbfojC/yv4NQjw8dqs8+hs3QtC9a+n3mLRk1a\nljHIvIzIPIOQfTk7HLmR73e+ZehPT0zy2zaNw8Gs/iqzw921KovzxaQ/vZhK1/Z4mXKCeP2Oh/nV\ny/+O0+rm3z76G4q9IxeX1DSODf2YRlc2vcuRyLXMLPzLK6YE83S8AA2bJ5+UptPUHWZre5CtHUF8\nsczwOodZYVmlh5VVblZUeyh2TuzaT0aC9G7bSGT/y5h63sCdbEEw4gvS2Ii45kPVStzz1lC6Yg3b\ndzdNir2/ePi/aWl9g2S4jxg+gp44mVO7L8EdMuFKurFasvHIl0MkT/sYYn0IJfEpMrqVD37/MBbc\nJJUw9/lsXJM4TOf7nuKaNecR5pBej5L+BRJHLm74reNJz4dnTmg8eULHrMBn56vUuc8ePz8V/ZeU\nkvVHhvi/r3ZweCA7tyDfbuKTKyu4tTbELx79Bv3KYXSZ/WE8s2w+ty1/P6mnjmE7+FuqXUeHwym0\njEJXqA5/+e0Els/nlUgXb8ZqaQ4UDx+vyh1gmec48zX48D0f4qvNb7JlsIuBVP+YsAqLsJFvLmau\nu4ivzitnsWsDQsuWZJYouRjju885+Q4gkYqz+eAzPLfrUTqHWgEQQmHZzDWsW/weltStRlFU+rpa\n+eOTP+DZvVtw1o2Ut3dqBSx0zeHOWz5M/dxVAAQDQV5+8neYW55jXngrNn1E2B9zzKendB1L3vEA\nDXMbcl+0BtouROZphH5w5PtXFmWFsbrijOEUUkqafJI/HtcYzA2sz3QL3lOrMGsSS0BPxWv7XFyx\ngvh8QiaO9eznKw//CZqe4W/f8y1Wzh75J6hrKVqH/p0G185csY13Mrt48h+1GRhcSehScnQgxtb2\nIK+3B2kPJMasn5FvY0WVh5VVHuaXObGoE3Oo0cE++ra9QOzQK1j7t+NKd45Zr2EiYm9AK1+Oc84a\nSlbcdMExyCfp7e3hF7/5MSHfMZLxfqLmECFPGnnq/xAJrpAJV9KFzVKI1V3Ommtv55abL15+8eko\niMf47FxBjk6/h+/+rJsO2wBWzcvX+/awz/M2bvvnJyZ+AK0ZkfxyLm74c2A6z8l4b8G2fp1fHstm\nlPjUHJUlV1hZ5kuJLiXPN/v4zpZOdnZlR1MdZoUHl5bxwHyVvcee4KU9jxNJZMMs8pyFrFv8PhY4\nltHxg+9RrW8l3+sf3l8saudEagHW6z/KVpOPN4Vgp7+OvthIyFWtx89S93EWSpUP3/sA/3R0L5v6\nOxlKDxHTR6pBCgR5pmLqHW7+pn6Q24o2oSrZJ0hSXYU03QlK41nLQZ9ESsn+jh1sbHqMHc0vo+nZ\nwYNCdylrF93NjQvvpMhThqZpvPHqY2zYvZ5jmWNklJxQl4IivYKVpUu4892fIr84W2nPN+TnlSd+\ng/34BuaFtw1PxtMRHHMspK/0Ruasec9IGje9FZF+FrQtCFK5XRcgTbeAessZ07ZldMlrfdlsFJHc\nmMfiAsFdNSoVjmnleiaNKSOIo4kwX3zoAQaC3dy6/P18dN3nh9el0iG6A9+g3nmYtG6mPXkP9YUX\nUBXJwOAqpSecZFdnmB2dIZq6w2NCK2wmhSUVLlZUeVha4abKa53wY7hQVzsDO14icex1TP27caeO\njxlBlgjClhlkSpZhm7Wa4pVr8VTWTpp9TU1v8uSG3xAPnSCZHCJqChPypM4hkguwuCpYvuRm7nrX\n3ZPSj+kuiEXqIUTmKdbvyfDsRp1+SxeLopV8LLiZQ2//FWtvv31iO5ehXL7hQaTpNqTlTya9/4cC\nOt8/pKFJuL9O4ebyc5dQnw5IKdnaEeK7r3fyYktW4CoC3tVQyINL8iH6Os/tfnQ4o4NAsKjuOtYu\nupvo/x7AdvD3VDqPYLGOPKnyBQvoEsspf+AzPNayiyMWOzt9dcNFPyA7crzY08HMdIJP3vFBnoj0\n8XDHUXoSfgKZAeQov+JUPMxyOnlvaT/3Vx6izBpFKvVI0+2grh5XWE0w6uPV/U+xcc9j9AU6h21Z\nULuKNQtuZ9XstdgsdhLxKM8//VNea9lCl2hHiuxouCJNVDGDa2pX8M7bPo7Lmw3DHOgbZNNTj+Dq\n2MDc8A7McmSkucM2k46C6yldegerb74Jk5qAzCuIzAaEzOWARgF1GVJdC+oyEGNT1cUzkhe6dV7s\n1knp2Yl31xQLbq1Sr8p82ZeTK1YQTyRkQkrJd574AtuPbqSudA7//MFfYDZln6VGk12EIt+m0t5B\nTHMwqH2Eqrx1F7Pr5810fERh2Hz5SGs6B/qi7OwMsbMzRKtv7OhxgcPEknI3iyvcLKlwUe62nmVP\nZyfmG2T9Qz9injKI2rsTV/zomBhkyJWXzpuHWrUMz9xrKV50LWbH2Uu3TpT9B/bx2DO/Ih7sJJkY\nJGoOE/akTpu0B+CIKDjjDmyKF7O9CG9BHR+57xMTzm4xHQXxaJ99siDHv/5hiKaeKBKdz/WHiFnL\nuOH/bprYjqWGSH4doe9FKrOQ1q9Netxwa1jnewc0kjrcUqFwz4zxieEr5V6+VOzrjfDlXzzF67Jm\neBJvQ5Gdjy8rY0nBCbYefIIdzS+T0bKiz+so4IYFd7Ci+Hpaf/BzSqObKfV0oajZbXVNMBAqo8+8\njOJ7PsHj7bs4anay01eHLzEyycxrTbCssJ36jJ/7lt2IWlfD1w7u4nB4AF96kPSouGOBoMySx6o8\nuK24lztKe3DabkGa3wEi/5w26lLnQMdONjY9xs5jr5DR0vja45TPKuCaxnXcOP8O5tYsRxEKgz3t\nPLn+J+wc2INP7RnehyLNVMhqVlUv4x23fYy8gmxoT09XD68/83vsna8wJ/wGdn0kJGTQXEJz3vXY\nGt7BTe96Nw5bCyLzPGg7EDmfKfGC6Qakae1poSHBVHbi3eY+HZ2sMF5eJLitUqXSOXFXNN2ubZgi\ngvilvY/zk+e+ht3i5F8/8jBl+dkLJRA7ip78LkXWfvypfJKm/48S1/KL2e0LYjpegIbNVw6D0RS7\nusLs7AyxpztCIJEZs77UZWFJhYvF5VmBXDTO+OPR9qZiEfp2vkb44GvQuR1X5ABmkmPaa6hErDPR\nihdinbGC/EXXkz9r3gXlQj6V/Qf28fgzDxMNniCZHCSmhgh7UqdV2ANQM+AOWbBnXFitBVhcJSya\nt4Z77rrvrPuf1oJ4VEGOL/17ihZLFwXpMr48sJU3ln2Dux+cWKiaSD2CyDyWLelr++Y5021NlK6o\n5NsHMsQycG2x4MFZ6rgLIFyp9/LFZPPmzcxavIr/ebOXh97spSecfcTvsqjcu6CYuxuthHybeHnf\nE8OjxgD1pXO5ft6tVPhLGXj0p1TJ3eR7fcPrR4vj8vs+yZPHd9JstrA/XE1baCRkwKJmWFrUyUyl\nn+X2Yu677z6+sm8nmwY6GUwFCWaGkKOKDpmEmTq7l9V5KVYXSu6ouh67ZSmcpWLtaCKJENsOv8Aj\nj/+SmGOk0meBu5TrGm/hurnvYGbZfIQQHNm3hede/R0Hw0cJqv3DbYU0US6rWVG+mFvf9VEKirP6\nJBwK8+ozj5NpfoGG4FbyMyOZLaKKi2b3cqLl17PwhptpnN2FyLyEkCMhaVKZhVRvBNN1YzJtDCQk\nz3dpbO2X5LJysrRAcFvVxHIYT8dr+4oVxOMNmegPdvN3P7+fRDrGZ27/GmvmvwvI5hg2pf6DfIuf\n3kQ5VvtnjTLMBgbjREpJeyDBnu4ITd1h9vZGCCfHjuyWuy3ML3Uyv8zF/FInNXm2CVdS0lIpBg/t\nJnBgK+n2XVh8+3GlT4wJswBICjcx1xwoX4Jj1koKF16Lu/z0QjsXQm9vDw/97r8J+tpIxwdJ6EGi\n9hgx1+nZLSCb4cIVt2MTeZjtBTg8lbz9pju5Zvk101IQD/ts7RhK8osMhBX+4b+j+M293BwsYFW6\nj/lf34XFPIHR3cx2lNQ3kChI65dBXTipfR5ISL61L0MwDYvyBZ+aoxpprCZAWtNZf8THz3Z2s6Vj\nJMa3ocjOBxaWsKqkn73H1rPtyIvEU1EgO4I7r2Y518+9FevOOPHXfkul2I/X4x8O+dU1gS9cTJ+Y\nh+uGD7BbDbFXC3M4Wc6+wQpG15es9fiZ6+6iKh3hrgXXkTdvDv92aDd7Av0EMgEiWmBMny3CSo3N\nTbXdwnxvDX/duPrs5aRH0ePr4LUD69l0YD2DoZHR4GJvBdc2vp3Vc97OjNI5CCFo3r+N5zf9lv3B\nowTU3uG2QqqU6lUsKGxk3Y33UNeYHaBLpdNs3rAB375nqfNvpiLZMebYJ2z1dHhX4WpcxpqbUrhs\n2xBkJzxKFFAWIk1rQF01nL7Nl5Rs6MqOGGdy7nRenmBducK8vMnPSjEVuKoFsS51vv7bT3OgYyer\nGtbyN3d9AyEE/ZGdOLQf4jGH6IzXkOf+Ig7LuVOyGBgYnBldSlqH4jT1RNjTHWZfb4RYeqxQdFtV\n5pU4mV/mZH6pi8YiBxbTxEd1EwE/A01bCB/Zhux+E0f4EDY9cFq7uJJPwtWALJmHfcZS8udfg7d2\n5qSOJAP879P/S9O+V0hGekml/MTUCGF3Eu1Muk6CM6zymff+1/QVxOkNKOmf8Mz2FL98oxuBwpd7\ne9k/69Pc95kvjX+Hejci8UUEMXTzh8F816T2dyAh+ff9GfwpaPQI/nyeinkCKQkNxnKwP8oje/r4\n/f4BBqLZkAlFwM31+dw7z0up6RC7mjewu+W14ZAKVTGxoHYlK2evxbo7RmrT7ykX+8kbJY4hm8at\nNzObRNWNxJYv4GVfK20UstdXRShlG26nCp35hb3MtPZSl9b55N0fZGsyxE9aD9Ie9RHMBIiOSo0G\noGKiyJKP1+xhWV4pn5u7jHqX96x26lLnWPd+th7ewLYjL+KPjKSbK82rYsXsm1g5+yYaKhahKCpt\nR3bx/Mu/Ya//CD6lB8SIdnJpRcy01XDd3DWsvukeLLasmH1z+y6atz1D3sBWGiJNWEeFhCQUG83O\npYTKFzNzhYPFc/diUnMTCjGDuhyprgZ1KQg7wZTkhS6dTX3ZGGPIpmtbW65wbbEypfNrT5QrVhCP\nJ2Ti2V2/4aGN38LjyOebH/sdXmcBPaEt5PETnKYo7bF6ir1fwmY+d9zQlcB0fERh2Hx1oumS4/44\n+3ujHOiLsL8vymA0PaaNWRHUF9qx9hzg1nU30VjsoNJrnfAosq7rBNtb8O3ZTKJtB8rAfpyxFszE\nT2ubEk6ijtnI4nlYa5eQ17iC/IYFqKYzxEJcAL29Pfzq9z8n4GslFRskqYWIWWNE3RmkAn93y0+n\nnSA+6bNF6seIzAt8/WeCPfFWypJlfDpwiLIvv4k37+xCYwwyjkh8CSE7keq1SMvfnjNjwEQYLYZn\nubNi2HYewmAq3MsT5Vw2pzWdl1oDPLKnj+eO+kjnYo1tJoVbZuVz2ywb+XIPO48+z4GOncOFMgSC\nhspFrJx9M3nNZmIbH6dE20eRuxfVNKI3UkkTA9FKhszzcKy5i02pHtrMgpZkKfuHytHkyA9ijyXB\n/PxuqlQfNRnBh95xN1tFnKc7m+iMRziRiOPPhMb0XyBwqfl4TG6q7V7eXVFLY4+ftTeeXoVOlzpH\nOpvYengDbxzZSDA2EgLiceSzbOYNrJx9EwtrV2Ex2zjRso8XXn6UfYOH6RNd6GLEZ5p0GxWiisVl\n87ll7QcorZ4NZEMrXnv+GeLHXqYmsJ2q5PExffCbCmlzLyJZVU3DshjzZw2hqiInjhcj1WtBXUEk\n42Rzn84rvTqBbJQLDhOsKVW4sUyh4JRc29Px2r5qBXH30HG+8NADpDNJPnv3N1nVsJbOwEZK1F9g\nUxO0RedQkf9FzCbXRe3nZDIdL0DD5qlDfyTF/t6sOD7YF6HNl0ACoZYmPDOz6YWcFpWGIgeNxdll\nTrGTQufEJ0jpGQ1/2yECB3aQ6GhC9B/EET06pqreSTJYiVpryeTNQi2dg7NuIfmNy3CWVU76aPKh\nI4d5/JlHeeeNd05fQZz4AkJv4U+/FyWo9nNrwIvLu4B7vvAf49uRlIjUdxDa60hRhbT9K4hzP9Ie\nL31xyXcPXLgYhql7L78VE7HZF0vzxwMDPHZgkDc6R4TnSXF8a52JPPax//gm9h1/g7SWGm5TUTCD\nJfXXU0c94d9voDC6k1J7B1Z7aswxwmEXA+kZBF2LEG+7kVcj7XSYXRwOV3A8NDZdmd2UZl5+DzWW\nQSrTGe6//gYihW280NvDmyELByIaA6ngmAwWAOqRXrzzl5BvdjHfW8ifzpzH9cUVY9rousaRrr3s\nbH6ZHcdeoT8wUsHOYrIyv2YFi+uvZ0ndasryq4lFgrz64iPsaNlOW6qDuDrqKZgUePRiaq0VLK5d\nwttuvnd4Yt6hfQfZv/lpXL1bqY/swXNKSMiguYR273wy1SXMWZZi3sxgtuS1Mh+prkQTy9kdKOKl\nbp22SNZOQTacYnWJwuICgUkR0/LavmIF8VuFTGh6hq8+8gmau/exZv7tfOb2f6bDv54K88NYlBQt\n0QXU5H8B1TR5TtTAwGBiRFMaRwdiHBmMcqQ/xpGBGIOx9GntCuwmZhY6mFVoZ2ahnZmFDso9lvMa\nSQ53Hsd3YDvxtjeRffuxhZtx6INnbJ8UbuL2OvSC2Zgq5uGqW0jh3OXY8i78idK0jSFeuggR/xAt\nJxT+/okjKNLMP/V2kv7Yc2Ordb0V6adQ0g8hsSFt/w+UyknrY0dE8p8HM4QzFy6GDSZGVyjJU4cH\neeLg0BhxrAq4ttrD2jobtdajnOjdQlPrluGYY8iWjJ5fs5IFFSvhhXasrVsp4SgF7sHhjBUAUodw\nxMNgppqgcwF9jXPY60zTo7o5Fi2jNTh2QqZJ0Zib30etbZASPcqKcslNq3rZ5HexyVfCnpCZtniM\nmB47zR674sKpesg3O2n05PP+6lm8qzI7T0lKSedgCzuaX2Fn8yu09h0as21ZXjVL6lezuG41c6qW\nYrc6ObD7ZV7Z9jSHg8cYVHqG07kBCKmQp5dSZ69i2ayVrL7pHhwuL6l0mp1bXqdz7yt4BncxM7oH\nlzZ2UGDQXMIJdwPx0jLKG1VWLh7AYqsCdTktset5ua+aN30Mxxm7TXBNscLqUmXa5TO+KgXx49t+\nzqObfkCBq4Rvfvx3DMReptryCGYlQ3NkKXWFn79iqs8ZGBiMMBhNcWQgNrwcHYwRTWmntXOYFeoL\nRgRyfaGdmjwbtvOISY4O9uI73ESsdS/p3kOY/M3YE8exyNP/yQHE1CKSjhlIbx2m0tk4qufgnbUA\nV0XNuEeUp60gXlKAkvgc337UyhuDh6lOlHNLRvDOf35qfDvRdiKS30CgT3rxjcNBnf86rJHQsqNh\nn2w0xPDl4qQ4fvaIj60nQsMp3ABm5Nl4+yw3cz3dkNjLoY5ttPcfHbN9vquY+TUrqVfr0Z/eSV54\nH8WmDtyu0JjkEVJCJOJiKF1NwD6XQMNC3vSk6TE5aI2XcsRfPFxO+iSFthizvX1UWnzUOoPcNq8D\ne4GZDYO1vO7LY38EBlJBNMZm4QGwCjsu1YvX7GSG08u6kkoerG8klQixp20rTa2vs+/4tuGy0QCK\nUKkvm8v8mhXMq1lOY+US0tEoW179I2+27uJ4soug0j8m9lhIE4V6KbWOShbULuKa6++goLiaVDrN\nG69uomf/q+QN7aI+uhenHh3Tx6Sw0e6czVBBLbYZbpYsS+EtXsI2/81sGaijOz4SXjbDJVhZJFhW\nqJy1fPlU4ooVxGcLmWjvb+bv/+dDaHqGL937ffLyBqkyP4xZyXAksoqZhX+Dok5ujspLxXR8RGHY\nPPV5K3t1KekNpzg2FKNlKE7LUJxjQ7Ex5aZPIoAyt4XafBu1+XZq82zMyLdRnWfDOkGhrOs6oROt\nBI40Ee84gN53CFOoBWeyA5XTR7EB0tiJWavQPDMQhbOwVjTgrptP/sy5p+VNno6C+Nvf/rb8+IMz\nUFI/5OP/GSIihrjb78S95M+4/f73n3sHeisi8Y8IEkjTvUjL/ZPWt+0DOv9zTCMjYWWR4COzVEyT\nMIFuut3LMPk2BxMZXmr1s6HZx4stfoZG3fuqgKUVblZXalSYDhMO7eFI5y5CMf+YfZR4K2msWkJF\nuAjTpgPkRY5QpHbgcQdRlLFaJRG3EEiU4KeagHcWe+qL6fJ66M7kczRYOib/8UlKh15n7sIiSiwB\napwhrq/rw1xk47n+GWwNuOmIZwhkgqRl6rRtFVScqhen6qTA4qDB5WWVw4Tbt5+DJ3bQ1jtSBhtA\nVVTqy+Yzv2YF82tW0FC5iIhvkNde/QN7T+ylI9VNRD3lqZcUuPRCKkwlzC6exYolN9Ow8HrSaY3d\n27bSeeB17IN7qIoeoCzVxan0Wqrocc0gXlSKWl2Nv+IdPL8rRt6CNUDW987yCFYUCZYWKHgsU9O1\nna/fntwZKuMko6X54TP/iKZnuGXJ+8jL81FlfmRYDM8q/FuEalQXMjC4WlCEoMJjpcJj5Ya6kVAF\nfyxNi29EIB/3JegMJugJp+gJp9g2KsWTIqDcbc0J5axIrsmzUeGxYjef2R8oikJe7SzyamcB9wx/\nrqVS+FsPEW47RKLrENpAC6ZQG7bECawygjfZDAPNMPACHAYN6EeQUItI2irRPdWohfWw7N0X6yu7\nohF6G/tbbEREGybdRp0WYfn77jn3hvogIvmvWTGs3oA0nz3H80TQpeTxdp0N3dmJWzeXK9w7Q5lw\nOI7BxcNrM/GeecW8Z14xmi7Z1R3mxWN+Nh0PZPOid4XZ2QVQg900g+UVH2RxXYBC0zES4f00d+2m\nP9hFfzAn9GaAzexgZvk7qJU1eLZ0UBBqplBtx+sIYLOnKLN3UkYnsJVrWyEadeJPlxFQZ9BXWMv+\n2kJ6XF46koUc9ZfSF3PT190w3OdvHYAie5Q69yCVNj8rrCEWFPkprCnguUAFOwIq/ckkUT1CQo8Q\n1nyENR+9KTgYgccBs7DizH8b9sK341JNePUkrnAron0jzd17ae7ey+Pbfo6qmJhR0sisigXcPP9e\nZlcsRA9F2Lz5CY70HqEz1UdIGSSiDnJUDnK0/yDrNzyJ+TkHRZRQ4yinoWouS+/4KhW1c2htbmHf\ntldId+6mJLyP2vgRylKdlPk6wQccBZ3vIYcKKelbQH/BAjoLV9NWsoLmkJdHW3UavYKF+YJFBQrF\nNuNeuiwhE3/c8hN+v+XHlORV8jfv+yTV1oexKGmORlYws/Dzhhg2MJjCpDWdrlCSdn+Cdn+C4/4E\nx/1xukNJ9LO4oyKHmUpvVnBXeq1U5v5WuK0TTgkX6e8h0LyfWMdB0n3N4GvFGmvHnulFOWUSTs+D\nL067EeKNGzfKpXN+z3/+oZPXew8yI1HBMu9y7vurr771hjKMSHwFITuQyrxsvuFJqEQXz0h+dlRj\nf0CiAPfVZWfTG/lXrx7CyQxbO0K80hZg0/EAB/vHhjkJYF6xjWXFAUrN7chkM31DB0fE8SgqCmqZ\n4Z1N8e4k+b0nyNdOkG/uw+UMn1ajQ+oQizkIpQsJUEGft4Ij1VU055fQqxXSEiwak+7tJBY1Q63b\nT4UjQJE5RLEpSqFNZah0BjsSZvqScSJajKgWJC2Tp22ftUnBobixKQ4sKJjTUWyxbvL9B/DGTgDg\ntnuZVb4gu1QspNxZycFdL7OnZTfHI50M0k9aOT0kzKq7KaCQKkcZs8sbWLpsLfkldezaupX+Y7sw\nDx2gNHqUqmQbJnn6U7oBSzndzln0eubSnzePocIF2MsbWVhsY1GBoN4truo83ldsyMSpgrhzsJUv\n/PID2VCJB/6G+Xnrc2J4OfWFn7tqwyQMDAwujJSm0xlI0h6IczwnljsCCXrDqTGxiaMRQInLMkYo\nl7ktlLktlLosuKzjfwiWSSQItDcTaT9CorcFbbCN5E2fnJ6CuOEbfOqHQUL0c0fAy9o/+zUVVRVn\n30jGEcl/QujHkKICafs6CPcF96UlpPPzZo2hJDhN8MlGlUbv5GYUMbj0DEbTbO8M8UZniG0nQjR1\nR4bTup3EZVFZXJxhtrsbN60kokfpHjw8JoPFSYo8ZcwQtVQfSFMU7CKfLryWQez2+BkL2aWTJkKJ\nPEJaMQFLCV0FZRwpr+Sws5KOWAmdkTOnFVSFTo07QIXDR5E5jJcEZsz05xXR5rARyCSJaXHiepT4\nKXmSR2MWNuyKE7OwYJYSs5bAmvDjjBynQffTUFxHbUkDNYUz0YfCHD3aRNvQcXrTgwSVwTFp3k5i\n0Z3kywJKrIVU51fRULeQGTNXcOTgUfqad2D1H6Q02kxl8vgZRXJamOm219HjamDA00DKO4Oiqnlc\nu3gutfm2q+oH6BUriEfHEOtS56uPfIKjXXv46K3v45b6/ViUFEcjy6gv/PyUEcNGPNr0YLrZfLns\n1XRJfyRFVyhJVzBJZzBJVyhBdyhJbzh11lFlyKaHK3VZKHVbKMv9LXWNXzBP1xjidTe8wBceO4oi\nzbw3Uc89X3nk7BvIJCL5dYR+EClKkNavXXBZZk1Knu3UWX9CRwI1TsGfNqoX7bHudLuX4cqyOZ7W\naOqJ8MaJELt7IrzZHaErdPrIq92kMz9/iGpbD26lCz15HH+ojXTmDG0tTmqpZlabhaKQH6/WR4u/\nixtr4pjMp08ABsikVKIJN2EtH5+pmF5PGS1FlezLr+N4ppSOUN6e304cAAAgAElEQVSY6nqjcZpT\nVLkClNiCFKgRnHoGzWJh0OnkhM1KUKaIa3FieoiMPPP8hiwCu+LEKuyYhQmzrmPNxCkUaebYzNxY\nWIo3GKW7s412Xyc9qQGCyiCaOP2HAlIQPS6YVVtDibWQ2oJqairnkM7Y8XcdQR86ijfSTlm8naJ0\n35m/E2Giz1rFgL2WoKMa6a2jpLKBuYuXvvWP5MvIVRFD/GLTHzjatYd3rrqWdcNieOmUEsMGBgaT\ni6oIyj1Wyj1WVpxS5Tmt6fRFUlmRHEzSHUrSF0nRF07RG0kRTWm0+uK0+k4v/gFZwVzkNFPsNFPo\nMFPktFDkNFPkMFN0HnmVpwrP78jaXpYqwrvsLSbFyQQi+c2cGC5AWr9ywWK4PaLzcItORzQrPd5Z\nqfDuamVSJs8ZXJnYzSrX1Xi5rmZkZLY/kqKpJ8Lu7jBNPREO9MfoCiXZOVDMToqBRbmWOlV2H7Pc\nAxSauzFlOohH24inghzmMIcrgVzGP1+7i2drylnmr2fGoEZ+2o+bIdyqH6c9jMmi4bUE8BKgijYW\nSWAAZD8kExZiKRcR6cVvLqTfUUKbp4I97lm8KWcQTDs44i/hCKdX01WFTpkzzEx7mHyzCaeaQjVD\nymzCZzHRpSqESZPQYyT0CHE9QpxRI8wKtAE7k3F+3R3CKhxYiyowF9diRsUuoTYSpDw0iCs2SCY5\nRED6iSkBkkqUHuU4Penj7OnbBTnda9FduKWHfFcRJaVzKHIWIjVQ40Gs4V68sV5KEp0UpvupTByn\nMnEc/EAXcBB4AY6qHgYt5fit5cTtlUhPNa6SempmNTKzsRGb7erKEnbJQiaGwn187mf3snh2DZ++\nQWBX4zRHF1NX8EVDDBsYGEw6UkqCicwYgdwXTo15n8zob7mPf1smp90I8caNG+VPX/gqftHFmnAl\nn/rKY5jOVCFQhrIT6PRmJB6k7Z9BqTq93TiJZiRPduhs6s2OCudb4COzVObkGSESBlkC8QyHBqIc\n7I9xsD/Kgf7s68gZUj6aieBV+qhxDlFkGcBOHzLVTSI5cIY9g9QkDYESGgNuipIxPLoPt+LDZQlh\ntSXPGHpxkkxaIZm0EdOcRPEQMOUxYCnghK2cg9Za3jDNxmfJe0vbih0RSu1h8swxHJYUZpNEmiFu\nhpAi6RMa/VqSFGdOM3kqVmHHLs2Uh1MUR4O44j7M6SF0fYiUCIzJjzz2ixBYpRundOFVXXhUO4ri\nQsGGM52kItFDRfw4JYkTWM8SPw3ZEIwhcyl+SwkRcwlJewm4yrEXVFJUMYP6xgZKSk//8TAZXPEj\nxA9t/BaNNWV8co0JuxqhJTqfuoIvGGLYwMDgoiCEIM9uJs9uprHYedr6k4J5KJZmIJpmMJpmKJZm\nMJpiMJrOFR8588jyVMdPN0IqeNyNZxbD+gAi+TWE7EaK4uwEOuX8Hp8mNMlL3TovduvENFCAWyoU\nbq9WjPzCBmPIs5tOG0mWUtIZTHJwIEbzUIxWXyKb+tFnoTvsYjA8c8w+VJI4RR9O0UeRxUe+2Yed\nQVRtgObC7DKCC3BhTpqYP1RAVdRKYTqJWw/hEkGcphB2WwyTWcdkjuEkRjGjttfgpH7NpFQSqaxo\nDuMmpHrwmfLoVYtoM5Vz3FJGq62c/eZSOEuedIuaodIepcAWw21JYrekMZl1dJNOStWJqhmCMkW/\njBHUYySFIOCGQ24rUJ5bQOiS4lia4miSvGQcVzqGSQuA7ictAiSVEElC+E7aMFo7WwVWixubdxUm\n4cGkuLFgw6VlKEiHKU/1Ux9vozzTm816kerMbjc2wx4acExx4jcXEzSXELEWk7KXIpwlWN0leIrK\nKa2spnpGDS73palUfNEFcVNTE8IbIxA/yufeVYzLFOB4rIHq/C9O2aIbV1Js1qXCsHnqM9XsHS2Y\nZ57lKf/u3bsvbaeuAJqamkBIilKlXHvPp05voDUhkt9HEECKWqT1/4BScHq7cxBMSTb36bzUoxPN\nzfFp9Arum6FS6by0QniqXdvjYarYLISgOi+bx/yds8deh7G0RpsvwbFc2NSWzZtJls+nM+ShK1RL\nT0JCYqS9SgK7GMLBIHYxhFMM4jUHsDsCNNn9NOn9o/ZuBYqRmqQ67KU26KYoKfBmkrj0KA4Rwa6E\nsZtiWG1JTBYNlyWKiygl9HMa6eyia4JUykwyYyOu24ngIig8+BQvQyKfIbOXPnMBfeYCWi2FdFqL\nSZ9BS6lCJ8+WwNr9JiVz6rFbUljNGRRzBl3VyDjTJFwpAkqGHjSiUickdVIZSXE8Q0E8iSeZwJGO\nY9EiqHoEKUNkRHhYMAMMJ+ZRcl+JFXDbUeQcTNKDCRcWHNilGZem49USFKRDlKUGKdKD5Kd6qUge\nRznLHMQw0KO6CZnyCZvyiZoKSFryydgKUZzFmF2FuLxFeAtLKCkvp6S8bDyXzRm5IEEshLgV+C7Z\nr+JnUsr/d2qbY8eO0eXeyF/dWk6eeZAT8TrKvF/ENIXLMe/bt29KOJqJYNg89Zlu9kJWHK5bt+5y\nd2PSGK/PJg8qM25mz5k1skKmEelHEJlstTqpLEBaPw/i9NH3s5HRJUeCktf7dd70yeEJkTPdgjtr\nlMuWQWI6XtvTwWaHWWV+qZP5pdlr1LY3wKcfzMYea7qkN5LiRDBJZzCR+5vkRLCc7nB2LkJ7LDNm\ndFQlgY0ANhHAKgLY8GMTAYY8QQ56w9hEGDNhBDrZW8wLeJGapDaYR03YSXFC4NGS2PUEdmJYRQyb\nGsNiSmKxpFBNOjZ7ChspvIQYDvo9lUxuiWVDNjJpM0nNQlK3EZN2ojiIY+fJVj+rgrMJKy5Cqgu/\nyY1f9TBk9uA3l9NvziOsOoZHpc2KRtqaJGpJgSVFypnGYs6gmtIINQ0ygSUZx5yMoqTiKOk4Qosh\ntBhSxtBlBE1E0EWSlBggxQAxIJD9ArMMyz8n4ERIE6p0oOJAwY4qbKjCghkFmy6xo+HQ03i0JPmZ\nEMXRdsqHBsnTw6inpMocQtC06Jvn5bfPWxALIRTg+8A6oBvYIYR4Qkp5eHS7aDTKX6wtpNjaTU+i\nkgL3F7GYPed72KuCYDB4ubtwyTFsnvpMN3sB9uzZc7m7MGlMxGfjFVSXrch+IPVsKeb0o9kcwyhI\n8/1guhvEuXPG+5KS5pDkgF9nr1+SyAkMBVhaILixXKHRIy5rWqfpeG1Pd5tVRWTzmXusUH1mTZLS\ndPojKXojaXrDSfoi6exchEiK/kiKoXgGXyxNZyxNKHlSOeuYiWEVISyEsYgwVsIc94awerPvLYBZ\nZDBjwiRswEguZG/URk3IQ3HCgjclcWsp7HoCKwksIrcoScxqCrMphcms5UI2kthIkh1THeENBe5w\nHT/duNyINGRv8UxGJZMxkdHNpHUzaWkmJU2ksJDCQhIrSWEhLmwkhJWYYicqbMRUOxGlhIhqJ6Q6\nCZkcxBULQtWx2+JYTAlMagyLiKDIGIqMIckuOjEyIooUGTIiRIbQ6f1URv01kR2BBqAQZBEKVhRp\nRWBDCAsCC3nn6bcvZIR4FdAspWwHEEI8CtwFHD61YaW9m6FUEQ7HZ7FbLmwGsoGBgYHBeTFun+3N\nFPOeD98PmVcR6ScR2U2yadUsfw1qw5j2mi4JprNhEAMJ6IlLemOS4xGJ/5RsUJUOWFaosLpEId9q\nxAgbXLlYVIUqr40qrw1467zaaU3HH88wFE/ji2XnJvhiaYbiI69DSY1QMkMwkRl+HUkmUWUMMzHM\nIorZEsNcHB15TwyziGEiMbyoIoGJJCYSKDqUhp2UxpzkJ014MwKXrmHRM1hkGl+ik3Z/HmaSmEhh\nVlKYlDQmJY1qymAyaSiqxGzRMFs04OwT5c6JZFhkA4ye/yd10HUFXRPoUsm+liqarpARKhlhIq2q\npBWVtKqSUhU0ARnl5CLRhE5G6GQUnYzQyAgNXYhsmLMQaAh0AR25WOmJciGCuBI4Mep9J1mHO4be\n3l5CaQ/S8pe4bbUXcLirh46OjsvdhUuOYfPUZ7rZOwUZt89eUT6bn7X2InGiyw+iY0WnGCnysv/M\nyJDWJYkMJDSInWXCOoBDhZkewWyPYHGBQqn9yhPB0/HaNmyeXMyqQonLQolrYnOjpJREUtqwQA4l\nRv5G0xqxtEYspRNLa0TTOrGURiydfR9LZoimE3TlR0mmYqQyMdKZGJl0LCeaE/iOPcWOOTeikkIl\njUI6+1qkUUhhUdKUyzTVSYXClIInCbY0WDUwZ3RUXUPVNFSZQdUzqDKdfU06u4gMqkijkkFVMqiK\nhqJoKEJHUSSKqiMUiVBAVXRUE4ydpfdWXw6nT+obB/82sebDXPRJdTNnzuT//GMU+DEAixcvZsmS\nJRf7sJeVFStWTLvJOIbNU5/pYG9TU9OYMAmnc/zxsVOFmTNn0tkThf/+NTDaZ3fmllEoueVcyYLi\n2aWrL5vG9EpjOlzbp2LYfGViBgpzC5BVaW+p1EycjFU+E01lpRdVc52HXp10Jstvn3ceYiHEtcBX\npZS35t5/EZBnmqRhYGBgYHB5MXy2gYGBwdm5kCm9O4BZQohaIYQFeD/w5OR0y8DAwMBgkjF8toGB\ngcFZOO+QCSmlJoT4c2ADIyl8Dk1azwwMDAwMJg3DZxsYGBicnYteutnAwMDAwMDAwMDgSmbSsqAL\nIW4VQhwWQhwVQnzhLG3+QwjRLIRoEkJc1TPrzmWvEKJRCPG6ECIhhPjs5ejjZDMOmx8QQuzJLZuF\nEAsvRz8nk3HYfGfO3jeFENuFENdfjn5OJuO5l3PtVgoh0kKI917K/l0MxnGebxRCBIQQu3PLP1yO\nfk4m081ng+G3p4PfNny24bNz6yfus6WUF7yQFdbHgFqykySbgDmntLkNWJ97fQ2wbTKOfTmWcdpb\nBCwHvgZ89nL3+RLZfC3gzb2+9Wo+xxOw2THq9ULg0OXu98W2eVS7jcDTwHsvd78vwXm+EXjycvf1\nEts8ZXz2BGw2/PZVfJ4Nn2347FFtJuyzJ2uEeDjhu5QyDZxM+D6au4D/AZBSvgF4hRClk3T8S805\n7ZVSDkopd5EtrjgVGI/N26SUJ0sBbSOb9/RqZjw2j0o9jgvQL2H/LgbjuZcB/gL4A9B/KTt3kRiv\nzVdeAt3zZ7r5bDD89nTw24bPNnz2aCbksydLEJ8p4fupN9WpbbrO0OZqYTz2TjUmavMngGcvao8u\nPuOyWQhxtxDiEPAU8PFL1LeLxTltFkJUAHdLKX/E1BCJ4722r8uFDqwXQsy7NF27aEw3nw2G34ap\n77cNn2347NFMyGdf9MIcBtMPIcTNwMeAt13uvlwKpJSPA48LId4G/Avw9svcpYvNd4HRMVtTwcGe\ni11AjZQyJoS4DXgcaDjHNgYGVw3TyW8bPtvw2WdiskaIu4CaUe+rOL0gURdQfY42VwvjsXeqMS6b\nhRCLgJ8Ad0op/ZeobxeLCZ1nKeVmoF4IUXCxO3YRGY/NK4BHhRBtwD3AD4QQd16i/l0MzmmzlDJy\n8lGrlPJZwDwNzvNU8tlg+G2Y+n7b8NmGzwbOz2dPliAeT8L3J4EHYbhiUkBK2TdJx7/UTDTB/VT4\nNXZOm4UQNcAfgQ9LKVsuQx8nm/HYPHPU62WARUrpu7TdnFTOabOUsj631JGNSfszKeXVXOBhPOe5\ndNTrVWRTVk7p88zU8tlg+O3p4LcNn234bOD8fPakhEzIsyR8F0J8Krta/kRK+YwQ4l1CiGNAlOyj\nmauS8dibOxk7ATegCyH+CpgnpYxcvp6fP+OxGfgyUAD8UAghgLSUctXl6/WFMU6b3yeEeBBIAXHg\nvsvX4wtnnDaP2eSSd3KSGafN9wghPg2kyZ7n+y9fjy+c6eazwfDbTAO/bfhsw2dzAT7bKMxhYGBg\nYGBgYGAwrZm0whwGBgYGBgYGBgYGVyOGIDYwMDAwMDAwMJjWGILYwMDAwMDAwMBgWmMIYgMDAwMD\nAwMDg2mNIYgNDAwMDAwMDAymNYYgNjAwMDAwMDAwmNYYgtjAwMDAwMDAwGBaYwhiAwMDAwMDAwOD\naY0hiA0MDAwMDAwMDKY1hiA2MDAwMDAwMDCY1hiC2MDAwMDAwMDAYFpjCGIDAwMDAwMDA4NpjSGI\nDYYRQtwohNCEEBWXuy9vhRDiXiHEMSFEWgjx88vdn6sJIUStEEIXQqwe9ZkuhHjgcvbLwMDg3Bg+\n2uAkp/ptIUSbEOLvL2efrnYMQXyREEL8InfB6jmncFwI8SMhRMEkHuOFSXY2W4ByKWX3JO5zwggh\nnhVCZIQQt51hnQL8DHgUqAb+SgjxQSGEfgn6tVYIsUkIERBCDAkhNgghlp/SxiWE+KkQYlAIERFC\nPCOEqD+ljUkI8Q0hRLcQIiaEeE0Isexi938U8hIey8DgisTw0efPleijhRBvE0L8QQhxIudXjwoh\nviKEsJzS7jtCiG1CiKgQInWWfY3LRwsh/i533SSEELuFEG+/WPYZXHwMQXxx2QSUArXAXwDvBR66\nrD06C0IIk5QyI6Xsv8D9iJxDPN/ta4EbgW8CnzpDkwrABTwrpeyVUoYBwSSJPCGE+SyfVwNPAbuB\nFcAaIAg8J4Swj2r6a+Bmsuf6+lzfXhBCWEe1+RbwMeBPc/tqBV4UQpRMhg3jQFyi4xgYXOkYPnri\n21+RPpqsvz0GfACYC/wd8GfAd05ppwAPAz98i8Oc00cLIf4a+Arwf4DFwAvAU0KIBRM0yeBKQUpp\nLBdhAX4BbDjls78H0oA1974BWA+Ec8uTwMxR7d25/fQACaAD+Nao/euANurvDbl1JcAvgX4gBLwG\nrBm13xtz27wrty5G1rGd/LxiVNtrgVdzbXxkHUnxqPVfAZqB+4BDQApoBOYBzwF+IAIcAD44ju/t\na8DvgXIgTnY05OS6j5zB5hvP8NnPR23zF7l+xYEjuXOgjlrfljvmD4BBYOtZ+nVXbt/OUZ8tyB1z\nYe797Nz7daPa5OXO3YOjzmkc+JNRbZTcOf7Ht/hePpK7dtYB+3P72AYsHtXmo0D6lO0qc306eW3U\n5t6vHtVGBx4Y9f4TwMHcMYaAV0ZfE8ZiLFNhwfDRU8pHn6WvfwMMnGXdR4DUGT4fl48GOoGvnbLt\n9tG2nWHfJ7+LO4A3csfZB9x8hjYVp2ybJvd/JPf+VL/dBvz9qPd3kR3AiebO8Zj/F8Zy+mKMEF9a\nEmRvLJMQwkb2F6WF7GjjDWR/VT8nhDDl2n8dWAK8G5jFiEMD+CuyjvJ3ZEc4yoHXc/t9GXAA78xt\n/wywQQjReEp/vgX8G9lf00/lPhv+FS/E/8/eeYfHVV17+11T1ItlyZLl3nsRxgUwYMAQWkJIclNw\nSIEk9wZCQoAvNyHJTXJvyg3kmoQ0UiCEFEoCoSSEEky1cQHbsoUb7l2S1cuozcz6/jgzsixL1mg0\n/ez3eeaRzplT1pp9Zs8+6/z2WlICvIDVyS/E+hLPweoMezIKuAn4JFYnexR4BKvzOiewz+1YX8p+\nEREncCPwoKoeD/jxmR6bPAosxoo2vC/g8xrglsD7wc/h1sDxvhM471eBGYH1/w58q9epvwhUBWy9\noR/zNmJ1Xv8eeJyWiRU92A3sDGyzFOvH5uXgTqragNVJnh9YtRCrzV/osY0f61oIbtMfDuAu4PPA\nIuAE8I8e0Wel7yhMyJGZgATkPqxrbxrWdfmHUPc3GJIc00efgQTvo/uiAGtAOBjOZoA+WkQmYH2m\nL/Ta93kG7scBVgLfwWr79ViR5ZIe7w8pmh441l+wbo5mYX1uPwG8QzluyhPvEXmqvugVfcC6KPcA\nawLLn8G6Ky/osU0x1l3+9YHlpzjz3ea/er+PFSU8BDh6rV8F3BP4P3gHuqLXNsuw7t5HBZa/GziW\nq8c28wL7nh9Y/jbWl2x0r2M10ONuNsTP7APAMUACyx8F9vfapq8I58cBX6/tMrE6wvf0Wv8JoL7H\n8n7gXyHatxg4gHWn7sOKok7o8f6dwJE+9vsL8PfA/9cF9nX12uZuoOIM5/5UYL+LeqwbhhW1uqHH\nNp299htUhBi4FutHMSfe3yHzMq9ovkwfnXp9dK/jzMSStd3Uz/v9RYgH7KOBcwPbTOm1zc1A8xls\nCrbrp3usc2L9rvx3X23cY7uQI8RYA20fMC7W36tkfpkIcXS5WESaRcQDbMXqbK8PvDcL2K6q3Xfk\namnDdgGzA6t+CXxYRLaKyE9E5AoRGUj/uRDrDrwxcO5mEWnGumud2mM7Bd4a4FizgHWq2n1Xqapb\nsTqZ2T22q1LVo732/T/gARF5JTCx4awBzgVWxPXPGvhGA08Dw/qauBECs7E63Cd6fQ6/BnJFpLDH\nthsGOpiIjMD6AX0aa2B8HlYk6DkRyQ7DvnBZF/xHrejzDk5ti6HyL6yO9YCIPCIin+v1WRkMqYTp\no1Okj+6JiEzFit4+rKr3hWFbNFFO7cd9WP5Fsh/fCrwIbBORv4nIl0RkTASPn5KYAXF0WYd1tz4D\nyFDVK1R1f6g7q+qLWLN0vw+kY03YWjVAh+vAilzOwxL6B18zsTqzngz2UVJ/nHYcVf0eVuf+GNYX\nfZ2I/E9/BwhM1HgP8GWxZnx3YUU/87AeoQ2W4LX9b5z6OczBkgLUncn+PrgFKypyq6puVtX1WJGE\ncVhRErA0ZkV9tE9J4D16/B15hm3Cpa9Z3P1NQOkTVW3FemR4LdYP/+eBPSH+WBoMyYbpo1Onjw7a\nOQdLU/13Vb0pDLtC6aOPY8lCotmPd19DgUmQIY/XVNWvqldiTfDeAHwIeFdErhqibSmNGRBHlzZV\n3a+qh3rewQfYBsySHil+Arqf6Vgie8CKAqrqY4Ev9tXARVhRAbD0qs5ex30bmIT12GZfr1flIO3f\nBpzTQy+HiMwH8nva2B+qekBVf6WqH8HShJ2pc/ocff9IXAdcLSKlZ9i3M2Bbzx+hbVh6wMl9fA77\nekQ4QiWb0/VXitV5Bc+7BmsAeklwAxEZBizB0hKCpUXuxNIOBrcR4NIe25yJc3odeyaWr2BN0HEG\notlBzmaQejS1WK2q31HVs7E6eJOn2JCKmD46dfpoRGQR1iTgR1X1C4PdP8CAfbSqHsCSjlzea98r\ngNUDmcmp/bgT66ljz35csDTKQc4ijOxAqvq2qv5QVZdh3SQMRn9tO8yAOH48jDWh4TEROSswmelR\n4DCW5hQR+Z6IfEBEpgUeAV2PdUd+KHCM/cDZIjJJRAoDneKfA+ufFZHLxCrEsFhEviYi1/Q4f39f\nrp7rf4519/97EZktIudjTbB6TVXf7M8xEckWkZ+LyMUiMiEQXbyCk1/43ts7sb6oj6rqDlXd3uP1\nF6zJFJ/pa98enwPA+0WkSESyA5HOHwA/EJGbA5/hLBH5qIj88AzH6o9nsH4c/zdwrLlYs8SDky1Q\n1d2B7e4TkQtFpAyrnbvbVK0URL8K2HW1iMzCkmJkAL8JwY67ReSCwPn/gDVD/ZHAexuwNI8/FJEp\nInIF8F+DcVJErhGRL4vIAhEZKyIfAMbQT9sZDCmM6aNPbp/wfbSIXAi8hKXrvktESoKvXttNDtw0\njA8szw+8smFQffSPgNvEyrE8PWDzPOCeEMz9mohcKSIzAucqwprMDJZs5yDwncBxzw8cM+Q8ziJy\nroh8M3BdjRWR5QHbTD9+JqIpULbziz5S+vSxzVTgH1iDmiYsPdakHu9/E0sL1IQ10ekV4Nwe70/E\nuhtu5tSUPgVYKWoOY92BHwaeIJByhf5F+6etx7pzfRXrkVUd8EegqMf73wbe7XWcdKxOfy/WBJRK\nrEHb6H4+h2sD553az/v3EJi4gdWJ+egxYaPHNpWcntLnRqzUMx6sFGJrgf/o8f4+eqSqGaC9PoD1\niLUB64fypZ7tEdgmG0sDV4M1OH22Z5sGtnFizRw/FrDrDeCsAc79KayoxaWcTIm2ll5pdIArsTq9\n1sBxL+t1bZz2+QWWg5PqLsCa3FMVsG0X8JV4f5/My7wi/cL00SnVRwfa09fr5ef0yXyv9LFdd9sE\ntgmpjwa+gjUhri3gw6UD2Bhsv/diPSlow0qjeUmv7RZh6cdbgc2czGDUc1Jdd7/d+3PCekLxbMD+\nNqwbkh/Sa6KgeZ36Cs4UNRgMCYyIfAr4raqmDbixwWAwGBIOEVmGlZZzrMa52qDhdIxkwmAwGAwG\ngyE2mEqhCYoZEBsMBoPBYDDEBvNYPkExkgmDwWAwGAwGg61xDbzJ0Fi5cqWWlZVF+zQJRXl5Ocbn\n1MduPtvNX7B8vuOOO2z1iNP02fbA+GwP7OpzOP121AfEW7ZsYdm2bzP6u7vIyMqK9ukSghdffJEF\nCxbE24yYYnxOfezmL8BDDz0UbxNizpYtW7jxxhvjbUZMseO1bXwOneYu5StvWWmqLyxxsGJy79TS\niYsd2zncfjvqGuLKykqyfK1sePav0T5VwnDo0KGBN0oxjM+pj938tSuVlYOtDZH82PHaNj6HzlHP\nSWnp/paQ0wEnBHZs53CJ2aQ6z9H1sTqVwWAwGAwGQ0Q42qo9/odOn5l7lYpEfUB8+eVWZcOMtiPR\nPlXCsGKF/arcGp9TH7v5CzB//vx4mxBzgn22nbDjtW18Dp2eEWI/cKg1eQbEdmzncPvtqA+Ig2Lu\n/Gb7hO3PP//8eJsQc4zPqY/d/AVsNxkF7OmzHa9t43PoHG21/pZkWn8PtCTPgNiO7RxuHxb1AXF5\neTlecVHsOcyBHe9E+3QJwerVq+NtQswxPqc+dvPXrpSXl8fbhJhjx2vb+BwaflWOtVkD4KXF1pDp\nQHPyDIjt2M7hMuCAWEQeEJEqEdnax3t3iIhfRIaf6RhV2eNxoOx8/fGh2GowGAwGg8EQM060Q5cf\nCtJg1rDAgDiJIsSG0AklQvwgcJqoTETGAJcBB8+0c1lZGU05YwFwNu8Jw8Tkw46PKIzPqY/d/E1W\nRCRdRNaLyGYRqRCRbwfWF4jIiyKyS0ReEJH8vvY3kgl7YF4KVL4AACAASURBVHwOjSMBvfCYbKE0\nC9IcUNNhpWJLBuzYzuEy4IBYVVcD9X289WPgK6GcpCNrHADZLfaZWGewL8c9SmuSdJaG1ENVO4CL\nVfUsoAy4UkQWA18DXlLV6cDLwJ1xNNNgSAqOBSbUjcoSnCKMz7HqPSSTbMIQGmFpiEXkGuCwqlYM\ntG15eTnDp1wEQHHzfjo7OsI5ZVJhR82O8dnipWM+/rvcy/+Ue6lqS60O045tnKyoqifwbzpWASYF\n3g8EM9Y/BFzb175GQ2wPjM+hcSQwIB6TZQ2EJwQHxEkim7BjO4fLoCvViUgm8HUsuUT36v62f+21\n19iQmUnOuxlk+JvpuPUm3n/9jd1h/GBjpdJyRUVFQtkTi+UgiWJPrJeXLl3Kkwf9/On5N6wPYt5S\n7tnm5fzGdRSkS9ztM8uhLd93331UVFQwbpz1VKu4uJjly5eTbIiIA9gITAZ+oapviUiJqlYBqGql\niBTH1UiDIQkI5iAe1WtAvD9JBsSG0BHVgRtVRMYDf1fVeSIyB3gJ8GANhMcAR4HFqlrde99Vq1bp\n4w/9jasK1zG5bhPlE1dw5a0/j6wXBkMcUVX+uNfHm9WKQ+C6SU42nPCzu0kpSIPb57gYkTHosuqG\nBGDTpk0sX748aRtPRPKAJ4EvAW+o6vAe79WqamHvfW666SZtaGjovinIz89n7ty5CXPTYpbNcqyW\n233Kit++hkPgsc8tw+kQ/vnKG9y/y8eEBUtZucjFmjVrEsZeuy5XVFTQ2NgIWJX5Fi5cyB133DHo\nfjvUAfEErAHx3D7e2w8sUNW+dMasWrVKX/6ve5h3iYu5h59hZ8n5XHznM4O102BIWCrq/fxihw+3\nA/5jupM5BQ7afcrPt/vY06yMzoJvznchkrTjKtuS7ANiABH5L6wAxmeBi1S1SkRGAq+o6sze269a\ntUoXLFgQazMNhoRjX7Ofuyt8jMmCb5a5ASsA8tW3vTR1wX+f5aIkM6m7h5Qk3H47lLRrDwNvAtNE\n5JCI3NBrE+UMkony8nLGvnsUf940AAqazpiUIiXoLSOwA3b2ecMJq7b9lWMczCmwvlIZTuGWWU5y\n3XDUA4da42ZmxLBjGycjIlIUzCARkLhdBuwAngE+HdjsU8DTfe1vNMT2wPg8MMGCHKOzTw5xRISx\ngeXKJJgnYsd2DpdQskysUNVRqpququNU9cFe709S1bozHSO/uYO32oroEjclbYfZ+faGodptMCQE\n7T6lvM7qFBcXnfp1ynAKCwutdcFBs8EQA0qBV0SkHFgPvKCq/wTuAi4TkV3AcuCHcbTRYEh4giWb\nR2edGvPLtYLFtHbF2iJDNIlZ6eb2XfupzJ0EwP71f4v2aeNKUNtiJ+zqc3mt0uWHKXlCUR864cUj\nrHVv1/jxhyBPSmTs2MbJiKpWqOoCVS1T1Xmq+v3A+jpVvVRVp6vqe1S1oa/9TR5ie2B8Hpj+BsQ5\nbmu5xZv4fbod2zlcoj4gDjJ830GacsYDkNayL1anNRiiyvpA5HdJUd9fpQk5wogMaOyCXY2J33ka\nDAaDwaK+w+qze0+KznFZf1tMhDiliPqAOKhHG//uMTqyJgKQ15zaOmI7anbs6PPzr7zBzkbFJbCg\nqG8ZvYh0SymSXTZhxza2I0ZDbA+MzwPT4rX+ZrtPXZ9MEWI7tnO4xCRCXFuQRU5rJ1s8xfgRRrbu\n5/ih1B4UG1KfXY2KArMLhGxX/xNaF4+wvmab65ROX+J3oAaDwWB3fH6l3WdlDMh0nvqeiRCnJjHR\nEB+ZNgaAtv2VVGeNw6Vetj7/p2ifOm7YUbNjR59bJp4LwJIRZ/4alWQK47OFdh9U1CfvgNiObWxH\njIbYHhifz0xrMDrsAof01hBbf4MR5ETGju0cLjGJEDdOsrTDw/ceoiHX+l+adsTi1AZDVDjuUQ63\nWpGDuQUDpzsMTq7bUJPcsgmDwWCwA639yCUAcgJPBFu7kjfAYTidmGiIi5csBGDc7mO0ZVrVj3Jb\nDkX71HHDjpodu/m8q9FP9dY1zCkQ3I6BB8QLAzribfVKlz85O1G7tbFdMRpie2B8PjOtAX1wX3K4\nZIoQ27GdwyUmEeIvf+x9nCjKIbutix0tRQCUNO2ltaklFqc3GCLO3mars5ySF1oxnPw0oTQTvAqH\nW5NzQGwwGAx2oaWHZKI3WS5LW+zxgi/J02kaThKzPMRHAzrixsPN1KaXkOlvY90zv4/26eOCHTU7\ndvN5f7NSPG8pk3JD/wpNyrUGz3uakrMDtVsb2xWjIbYHxuczEyy60deA2CFClssq0+tJ8CixHds5\nXGISIX567UPdOuLC3Qepy7PSr3mrN8fi9AZDRGnsVGo6IN0Bo7JC329ynvV1C0aXDQaDwZCYBCUT\nOf1kEOqWTZhMEylDTDTET299hukXLwNgwu7jtGRY0eKs1tTUEdtRs2Mnn/cFBrSy502cEppkAmBy\nIEK8r0nRJHzMZqc2tjNGQ2wPjM9n5kyT6uDkQDnRcxHbsZ3DJSYR4obWKj5xxQUcGVNAepePPY35\nAJQ07qGzoyMWJhgMESM4IC7NDH0wDFCcAbkuaPZCdXs0LDMYDAZDJGjpCk6q6/t9EyFOPWKiIU7z\ntvF6xT84Pt2STTTs99DoLiDX28ibT/4x2ibEHDtqduzkc3BAfPUlg/NZRJgUmISXjLIJO7WxnTEa\nYntgfD4znu5Jdf1IJoLFOYyGOGWISYQY4Nmtz9A6ZTIApbsOUp0/FYD2Y+tiZYLBMGS6/MrBFmsw\nOzF3cBFiOCmb2Ntk8hEbDAZDotJf2eYgwfLNJhdx6hATDTHA/voDXHvdtXS5HIw+XEeTezQA2a2p\nV8LZjpodu/h8uFXxKozMhM3r1gx6/ylJHCG2SxvbHaMhtgfG5zMz0KS67CQp32zHdg6XmESI/QjO\njkbGDPdzYHIJDoV9NdkAjGx4l3aPJxZmGAxDJiiXmBxGdBhgbLbgEqhsM5EFg8FgSFTOlHYNTkaI\nE31SnSF0YqIh9qcPw4Hy6JrfcWKalXLNs7eZBnchOb5m1j6dWjpiO2p27OJzcEA8KdcRls9uhzAh\nJzmjxHZpY7tjNMT2wPjcP6p6MstEfwPiJIkQ27GdwyUmEeJJwycAsPlYBcyaAcDYnYc5kT8FgM7j\nG2JhhsEwJFS1exA7KcwIMcDkJJZNGAwGQ6rT4beqirodkOYcIA9xgk+qM4TOgANiEXlARKpEZGuP\ndXeLyA4RKReRJ0Qkr7/9y8vLef9ZHwKgva2GL3z6AzTnpFFY56GeYgCyW1JLR2xHzY4dfK7vhMZO\nq2xnSWb4PndPrEuyAbEd2thgNMR2wfjcP0G5RE4/0WHrvYBkIsGlb3Zs53AJJUL8IHB5r3UvArNV\ntQzYDdx5pgMsnXMlna4s3P4uXq94nIMzxgJwsDIDgJGNu42O2JDw7A8MYCfmCI5BFOToTTC6fLBF\n8SVhgQ6DwWBIZQaSS4CJEKciAw6IVXU1UN9r3UuqGswbtQ4Y09/+QT3a8JyRALz07ivUTZkAgG9X\nA/VpI8j2NbP2yd8P3voExY6aHTv4fNRjDV7HZlsD2nB9znELhenQ5YeqtoiZF3Xs0MYGoyG2C8bn\n/glOlOsvBzFAptMaQLX7wOtP3MCGHds5XCKhIb4ReG6gjS6acgEAJ5qPMeqC8wCYtOsYJ3InAdBZ\n9XYETDEYokdwQDw6K/zocJAxgUH10dbE7UgNBoPBjngGyEEMVqGlYJS41USJU4IzPBAYGBH5BtCl\nqg/3t829995LRkYmY8eN5t31jaSlNeAcvo0jYwoYc6SetXv9NLRDUcEB4KTeJXhXk4zLFRUV3HTT\nTQljTyyWg+sSxZ5oLB/zKNVb13DU42TRpRec5vtgjjd63LlsqVNeen01HSWOhPBvoOWh+Jssy/fd\ndx8VFRWMGzcOgOLiYpYvX46dKC8vZ8GCBfE2I6asXr3adpE043P/nCzbfObgR44LmrqsTBP5aREx\nMeLYsZ3DRTQEDaOIjAf+rqrzeqz7NPA54BJV7ehv35UrV+rcqRez6IKJXP/L9+FtOcbokvnkv5TF\n4n9tYt9lk/lA4bO0OTIZ/o0tDCssioBb8cWOF2Cq+9zhU7683osI/HSJC5dDhuTzplo/v9nlY/Yw\n4YuzhnRfGjNSvY37YtOmTSxfvnzojwSSiJUrV+qNN94YbzNiih2vbeNz/zx72MffD/u5YrSDa8c7\n+91u5Ttedjcpt812Mj0/ZoV/B4Ud2zncfjvUFpTAy1oQuQL4CnDNmQbDYOnRdm49DsCiMVbU4UD9\nAdpmTAMga+sJTmSMItPfxoanHhis/QmJ3S4+SH2fj3sUxapQ53IMTUMMJ2UXRzzJI5lI9TZOFURk\njIi8LCLbRKRCRL4YWP9tETkiIpsCryv62t9oiO2B8bl/ghKInDNIJiA5chHbsZ3DJZS0aw8DbwLT\nROSQiNwA/AzIAf4V6Fh/eaZjVB1toq6mlY9f+Hl84sDd2chlV82nNdPNyKomajPGW+eq2zJ0jwyG\nKHA0kARlVAT0wwAjMiDdYaVxS/S0PYakwwvcrqqzgXOBW0RkRuC9e1R1QeD1fPxMNBgSl9YQJtWB\nqVaXaoSSZWKFqo5S1XRVHaeqD6rqVFUd36Njvbm//YM5LXduOU7RsFIkYzgAr+58nP0zrfRrJxqs\nMs7DmvYO3aMEwI55/1Ld574m1A3FZ4dI9+A6WaLEqd7GqYKqVqpqeeD/FmAHMDrw9oB3dCYPsT0w\nPvdPywBlm4NkJ0GE2I7tHC4xE73s3HIcVWVuySwAdpzYRe00K8NE7Q4fPhyUNu9l3ztbz3QYgyEu\nHItghokgwUwTR0ymCUOUEJEJQBmwPrDqlkBBpftFJD9uhhkMCUzIkgmTiziliPpsnrKyMtYeq6eu\nppXq482sWPoZvn7gdaS9jpKlZ8GjLzFqezXHF01mTMtu3n31T0yac3e0zYoqdtTspLrPwQFxT8nE\nUH0enWX9TZbUa6nexqmGiOQAjwO3qmpLQNr2P6qqIvI94B7gM73327NnDzfffHN3po38/Hzmzp2b\nMJlAopkpJ5HsMcuRXw41E9K773pJm7GULJeccfscl1C9dQ1bDgofnbgs7v71tRxclyj2RGO5oqKC\nxsZGAA4dOsTChQvDyg4UUpaJobBq1SqtPZzOlvWHWXTBRJZdOZ0P3bscd0cDC6deTv4PtjL2cB2O\nG4cz1/MmO0su5OI7n4qqTQbDYGjuUr7ylpd0B/x4iWtIVep6sqfJz/+942NsNnxj/gChCENcSNYs\nEyLiAv4BPKeq9/bx/mmZg4KsWrVK7ZZ2zWDoye0buvB44f8Wubp1wn3xTr2fn+/wMWuY8KUkyRZk\nB6KdZSJsysvLmTmvFICdW4+jfmXy8IkAbDxazrGZ1v81R60kfoWNe6JtUtSxo2YnlX3uGR3uORge\nqs9B+cVxD/gSuNJRkFRu4xTkd8D2noNhERnZ4/0PAu/0taPRENsD43Pf+FVp81pi+6wBxrgns0wk\nbv9tx3YOl5hoiEePLyA3P4PmxnaOHmrgw4uvB8DbVkPrzCkA6KYm2hxZjGg/xobnn46FWQZDSBzt\nHhBH9riZLqEoHbwKVe2RPbbBvojIUuDjwCUisrlHirW7RWSriJQDy4Db4mqowZCAeLygQKaLAZ8G\ndmeZSOBJdYbQifqAuKysDHEI0+dZwYkdW46xaPpFdKbl4lQfw6fV0pyTRuEJD1UZVrS4bucL0TYr\nqthRa5nKPgc1vqOzT+0cI+Hz6CSaWJfKbZxKqOoaVXWqapmqnhVMsaaqn1TVeYH116pqVV/7mzzE\n9sD43DfBCXIDZZiA5JhUZ8d2DpeYZZmYVTYKgF1bK/F6/UwosDJMbK3cxN651kC4odFKv5bdnPyy\nCUPqcCyQgziSGSaCjMlKngGxwWAwpDqtAflDzgA5iMHKJe8S6PRDp8/04clOTDTEAMWleYwYmUt7\nWxf7d53go4tWANDpOUHdDEs20bjDB0Bpw05am1qibVrUsKNmJ1V99qv2mWECIuNzMPXa0STIRZyq\nbWw4FaMhtgfG575pHUSEWETITvAosR3bOVxiWnx71llWlHjb5qMsmXUpne5cXOolbXYnnS4HeTta\nqXWXkO1rYc1ffxFL0wyGPqnrgA4/5Lkh9wyzjcNltIkQGwwGQ8LQLZkIMfFPMpRvNoRGTDTEQWbO\nL0UE9u06QZunk/EFEwDY37iRfTNH41Co9ZVYhtVtjrZpUcOOmp1U9bm/6DBExueiYAnnrsSeqQyp\n28aGUzEaYntgfO6boGRioLLNQRK9fLMd2zlcYhohzsnLYPyUIvw+ZefWSj6y6DoAOjwnqA5km2g4\naN1uFTTsjqVpBkOfHG+zOrnSzOikonWIMDJw7Mq2xOxQDQaDwS4MRjLRc7tWEyFOemKmIQ4yOyCb\n2L75KOfOvpxOdw4u9dIyy3o+4Xi7jU5JY1TrfspfezHa5kUFO2p2UtXn6sAgtSTz9Pci5fPIQDq3\n456IHC5qpGobG07FaIjtgfG5bwY/ILYCGp4EnVRnx3YOl5hGiAGmzCrBnebk+OFG6k60MDYgm6jz\nb+TghELcHUqV0yoZWrnZVKwzxJfqQH7g4ihFiOFk9Pm4iRAbDAZDXGkNSB/OVKGuJ5lO629bgk6q\nM4ROTDXEAO40J9PmWDmJt28+xscC2SbaW6s5Ojsgm6ixQmbZzckpm7CjZidVfa4KRogzoqMhBpJG\nMpGqbWw4FaMhtgfG574JSh9CjRAHq9l5EnRAbMd2DpeYR4gBZi8IZps4xpKZ76Ezzco2cXymlYe4\nc1MnAKMadtJQWxMPEw0G2rxKU5eVZ7IgPXrnKQ1M2KtMgtRrBoPBkMoEJ8eFOqkuOCBu80XLIkOs\niLmGGGDshOHkD8+kubGdg3tqmFwYyEOc9jbVxTm4qqHWWUyWr5X1f/t1tE2MOHbU7KSizyflEn2X\n8IyUz0UZ1qC7rhPaE1SHBqnZxobTMRpie2B87hvPIDXEmUENcYJmmbBjO4dLXCLE4hDmLBgDQMXb\nR/nkeZ9BATpq2Vc2FYCG5mEAOOu3xMNEg6F7Ql1xH3KJSOIUoTjD+r8qwWUTBoPBkMoEB8RZoUom\nnKfuZ0heYq4hDjLn7NGIwJ4dVUweeTa+9GE41M/hmbkAtG+znj8U1e+KtokRx46anVT0ubo9mGGi\n7wFxJH0eGZBNJHKmiVRsY8PpGA2xPTA+n45PlQ4/CJDuDO2YmUENcYJKJuzYzuEy4IBYRB4QkSoR\n2dpjXYGIvCgiu0TkBRHJH+yJc/MzmDDVykm8o/wYs0tmAdCQ9TY1hVn49jtpJ4OStsOs/cfjgz28\nwTBkqmIUIYaTmSYSfWKdwWAwpCrBTBGZzr5lcn2RFZBMtCWoZMIQOqFEiB8ELu+17mvAS6o6HXgZ\nuLO/nc+kR5u7MCCb2HiEGy68GT+Cq6OeffOnoH4HNR3FADS9+88QzEwc7KjZSUWfqwIa4r5yEENk\nfe6OECfwgDgV29hwOkZDbA+Mz6cTnBiXGaJcAk5KJhI17Zod2zlcBhwQq+pqoL7X6vcDDwX+fwi4\nNpyTT55RTGaWm5rKFtL9oyCzEAEOBmQTre9aV9qwhuSTTRiSG1WNmYYYekSITaYJg8FgiAttg9QP\nw8nBc6vP+t0wJC/haoiLVbUKQFUrgeL+NjyTHs3pcjArULnunbePsHD0WQDU5WykIT+Dzndd+HAy\numkXO9/eEKapsceOmp1U87m5y4oWZDgh1933NpH0uTjD0q2daAevPzE71VRrY0PfGA2xPTA+n06w\n2lymM/QgiNshuB3gV+j0D8m8qGDHdg6XSE2qC/sXfO7CsQBsLz/G9UtvwStO0rsa2T1/Ev4uJzVd\nxTjxs3/NHyJkqsEwMN0T6jIECVFLNhTSnEJRBvg5me7NYDAYDLGjW0M8iAgxnKxWZzJNJDeDbPZu\nqkSkRFWrRGQkUN3fhvfeey/Z2dmMG2eVY87Pz2fu3Lnddy07d5fT6jtMdudYThxUWqsy8LXVsH9m\nLoteh3XlPkrHwvBCSzYR1MME90/E5YqKCm666aaEsScWy8F1iWLPUJcdU88DoHn7GlY3O/vcvrfv\nQz3/yExh24bVPF/v4MarL0yozyMa/ibi8n333UdFRUV3f1VcXMzy5cuxE+Xl5SxYsCDeZsSU1atX\n2y6SZnw+neCANjPEDBNBslzQFHiqWDAE+6KBHds5XCQUzYuITAD+rqpzA8t3AXWqepeIfBUoUNWv\n9bXvypUr9cYbbzzj8beXH+Off9lK8ag8ZObbPLvxITolk4/+1k2Rt4VZ1+yl3ZFJ7lfepqi0dJAu\nxh47XoCp5vOTB328cNTPe8c6eO/YvnvHSPv8xAEf/zrm531jHVzdzznjSaq1cShs2rSJ5cuXR/8R\nQQIRSp+datjx2jY+n85Lx3w8fsDPJaUOPjIx9D747gov+5qV/zfHyZS8uJR36Bc7tnO4/XYoadce\nBt4EponIIRG5AfghcJmI7AKWB5b7JBQ92rTZJWRmuak+1sRFk1fQ6cwgTdvYuWAyXR439V3DyfC3\n8faTPw/ZsXhit4sPUs/n4IS6kjNMqIu0z6UJnmki1drY0DdGQ2wPjM+n0xZmhDiRJRN2bOdwCSXL\nxApVHaWq6ao6TlUfVNV6Vb1UVaer6ntUtWEoRrjcTmafPRqAbRuPUzpsPAB7Z1nlu1oOpQOQ3lgx\nlNMYDCFTFdAQF/dTlCMalAbSux03mSYMBoMh5gSLawwmy0TP7dsStDiHITSiHtsPNafl/EXW5Lqd\nWyv5yFnXA+DJeIva4Vm07bEGxKW122j3JHAprwB2zPuXSj77VTkRmNgWLKncF5H2eWRg8F3VZtmQ\naKRSGxv6x+QhtgfG59MJFtfIdA0uEBIszuFJwOIcdmzncEkYsUtBUTbjJhfi7fKR2zGbrrR8XHjZ\nefZk2urTafHmkO+t5/VH7o23qYYUp6ETuvyQ5x58xzgUMl1Cfhp4FWo7YnZag8FgMBD+pLrMBC/O\nYQiNqA+IB6NHm7/YihKXrzvE7JLZAOyb4QOE5kNZALhOvBVxGyONHTU7qeRzd8nmAeQS0fA5kUs4\np1IbpzIiMkZEXhaRbSJSISJfCqwvEJEXRWSXiLwgIvl97W80xPbA+Hw6bUOUTHgSUDJhx3YOl4SJ\nEANMmVVMTl46dSdaef/Uz+ITB17XZipLcmnZYwksS2srkkI2YUheqron1MX+3CXdsonEGxAbkgYv\ncLuqzgbOBb4gIjOArwEvqep04GXgzjjaaDAkHN2SiUEU5oCTTxITUTJhCJ2E0RADOJ0O5i+28n8e\n3uEjLasEEdh59kQ8dRl4vFkM66rl9UcTO9uEHTU7qeRzsDDGQBHiaPg8MjCxLhEjxKnUxqmMqlaq\nanng/xZgBzAGeD/wUGCzh4Br+9rfaIjtgfH5dMKeVJfAkgk7tnO4JFSEGGDe4jE4ncLendVcNvEK\nAA5PagSEpgNB2cT6OFpoSHW6JRNnSLkWLUq6JRMxP7UhBQnkkC8D1gElqloF1qAZKI6fZQZD4hFu\npbpElkwYQifcSnUhM1g9WnZOOtPnlbJ98zHG6SV0uh4jjXc5OH4UWfs9jJwCI2sr6OzoIC09PUpW\nDw07anZSyefuss1x0BB3Z5pIwNRrqdTGdkBEcoDHgVtVtUVEel9UfV5ke/bs4eabb+63umi8qwlG\ns9pmItljliO/fKZqquctXUq7D6q3rmGjz8mFF1wQ8vGr2vyQfS5tXk0of4P0LM4Rb3uisVxRUUFj\nYyMAhw4dYuHChWFVGA2pUt1QWLVqlQ62DOjxI438+Zdrych0s3/snzlavYWSQ0u48vHNTPvgQTLT\n2nmn7Jtc9unbo2S1wa54/cqX1nlR4KfnuHA7Yhsl9qty23ovHX5YuchFtttWRdISjmStVCciLuAf\nwHOqem9g3Q7gIlWtEpGRwCuqOrP3vuH02QZDsuPxKrdv8JLhhJ8scQ9q3+o25VubvRSlw/fOHty+\nhsgTtUp1QyUcPVrpmHxKx+bT3tbFZaWfxI9QPWovXoeD5gOWyNJRtTbSpkYMO2p2UsXnmg7wA8PT\nGXAwHA2fHSKUBHTEweIgiUKqtLFN+B2wPTgYDvAM8OnA/58Cnu5rR6MhtgfG51MJt0odJLZkwo7t\nHC4JpyEOcta5VrW64ztcODJHoK4ads4dQ9PhXABG1m6ls8MkazVElu6SzTGsUNebbh2xSaZiCAMR\nWQp8HLhERDaLyCYRuQK4C7hMRHYBy4EfxtNOgyGRCDflGpyahzgRiyoZQiOh8hD3ZPqckeTkpVNb\n3cK5RRcDsLtsGK0nMmnvSGd45wle/XNiFumwo9YyVXwezIS6aPk8MkFzEadKG6c6qrpGVZ2qWqaq\nZ6nqAlV9XlXrVPVSVZ2uqu9R1Ya+9jd5iO2B8flUPGFWqQNwOoR0hyXK70iwKLEd2zlcEjZC7HQ5\nuqPEI1svpNOZQWNBBa1ZaTTtzwbAXZ24sglDcnIy5Vr8bChJ0AGxwWAwpCrdKdfCkEzAychyW4IN\niA2hk5Aa4iDzF4/Fnebk8L4GxufOAkcHFQsn0nAwD4DRNeU01NZEytSIYUfNTqr43C2ZCCFCHC2f\nRyZocY5UaWPDmTEaYntgfD6VcFOuBQnuFyz/nCjYsZ3DJWEjxAAZmW7mnD0agEUZH8aPcHCGl7b6\ndDwtGeR6G1n3l5/E2UpDKhGcyDZQUY5oUpwBApxot7JeGAyx5oq/vhZvEwyGmOIJs0pdkCynqVaX\n7CSshjjI2UsnIAKVu5W0zFF4sndQWZJH4/4cALLqNkXCzIhiR81OKvjc7lMaOsEpUBhCiuto+Zzm\nFArTrWwXNe1ROUVYpEIbGwamrKyMt/f58Pv98TYlZtjx2jY+n0r7ECbV9dwv0TJN2LGdwyWhI8QA\nw4ZnMWVWCX6fsiTrakSg4pzx3bKJsbVbOHZgX5ytbUPcHAAAIABJREFUNKQCJwKDzxEZVvqzeGJ0\nxIZ44u9K45l9R+JthsEQMzxDSLsGJyUTiVi+2RAaCa0hDrLoggkA6NFJdLlzOD7+IB5POq21GWT4\n29j6zM+GfI5IYkfNTir4XD3Iks3R9DkRB8Sp0MaGgQn22Q+9cyjOlsQOO17bxudTafNZfW1WGFkm\nIHElE3Zs53BJ+AgxwKhxBYyZUEBnu48Zaefgd9WwrWwsDQesKHFuw5Y4W2hIBapCLNkcC0YGslwk\n0oDYYC/ePtgWbxMMhpjhSdFJdYbQSXgNcZDFyyYBUNKwDK+42Ds/i4bDeajCuPp32LZ+TUTOEwns\nqNlJBZ+7I8QhDoij6fPJTBNRO8WgSYU2NgxMWVkZiJ/W5gy219bH25yYYMdr2/h8KkOpVAeJm3bN\nju0cLkMaEIvIbSLyjohsFZE/i0hapAzrzcRpRRSX5tLZ6mBE2gya8iuoysyj+Xg2LvVyZM3vonVq\ng00IDj5LMuJrB5xanENN5SNDjPkv532A8Kste+NtisEQE05KJsLbP1ElE4bQCXtALCKjgC8CC1R1\nHuACPtZ7u0jltBSR7ijxtLarUYefinPGU7c/H4ARNZsjcp5IYEfNTir4XD1IyUQ0fc51Wwni23zQ\n1BW10wyKVGhjw8CUl5dzYf16UC+r9vRZzC7lsOO1bXw+lZOT6sKTzGUmaITYju0cLkOVTDiBbBFx\nAVnAsaGb1D/T5oxkWGEWvqY80l1jOTK5hoZjOXg7nZR6DvDyn34ZzdMbUpiWLqXVC+kOyHPH2xrr\nBrAkQQt0GFKfUR0NXOlew/EaNy1dCXJHZjBEkbZIpV0zGuKkJewBsaoeA1YCh4CjQIOqvtR7u0hp\niAEcDmHxhRMBmNF1Bd60I2yfNZaGA7nWBkdOO31csKNmJ9l9ru4uyGENRkMh2j6XJNjEumRvY0No\nBPvsS3xrwe/k/ordcbYo+tjx2jY+n0RVh552LUElE3Zs53AJ814IRGQY8H5gPNAIPC4iK1T14Z7b\nPf7449x///2MGzcOgPz8fObOndvdSMFwfqjL9Z79VDfsZgRT0MLhvDGqnYzV6XxwGow9sYmnv3Eu\n7o4GFhV2IC4XG7tG4x59Fhd95Ks4MksGfT6zbI9l17TzAGjZ/iarm51xt+f8889nZKZQvXU1rx11\ncOGHL4y7PXZYvu+++6ioqOjur4qLi1m+fDl2ZH7zDsj28rcdVXx5wax4m2MwRI0OPyiQ5gCnI8y0\nayYPcdIj4U7YEZF/Ay5X1c8Flj8BLFHVW3put3LlSr3xxhuHbGhPNq09yMt/30FD3tvs5kne+6cC\nli7cRuawDvaPv4SJB18+3d40J9kXXkH2pT/GkVEUUXt6s3r1atvdlSW7z08f8vHcET9XjXFwzbjQ\nQgTR9rm81s+vdvmYPUz44qyw710jRrK3cThs2rSJ5cuXxz8PXwxZuXKlLtnxLYZ3ebit5Cusci6m\n6v9djMORFFk6w8KO17bx+SR1HcrXN3rJT4O7FoanmfN4lds3eMlwwk+WJIDuLoAd2zncfnsoPdwh\n4BwRyRDrGfNyYMcQjhcy8xaOIScvneGNM/hCQxWVS0Z3T67T5joKbvhvim7/E8P/YyU5y6/APa4E\n7fTR8tKznPj+XDxr7kRtVJbUMDBBnW4i5CAOMjIBi3MY7EGTswSAS/1v4utM54k99inSYbAfwahu\nVphyCYAMJwhWCWi/yQyUlAxFQ7wBeBzYDGzBuhZ+03u7SGqIg7jcThYvHclFHT9laruHJQWbOHF0\nGOqH8XVb2dkwHfe4q0ifeQO573uYott3MPzmn+MeOwJ/cweNf/01DQ9egL+9JuK2gT01O8nu84mg\nhngQKdei7bNVQhrqOqDTF/8ONtnb2BAaZWVlODdbN2Pzm7eDenlgy8E4WxVd7HhtG59PEky5lhlm\nlToAhwgZgQF1Iskm7NjO4TKkZ2Cq+t+qOlNV56nqp1Q1JtOR1dvB+O03UOp/hzbyeKzQzca5U2g8\nmoMTP9Vv/eG0fdKnraDwth0MW3Ebku6ivWIHtfcswlu1LhYmGxIYVe3OQRxqUY5Y4HQIIzIsbVt1\ne7ytMdiJuq1uWpzpjGmv5z2u9Ww+6MVvnqoZUhRPBCLE0KNaXYKlXjOERtRFYZHKQxxE/X4aH7mK\nzh3v4nen83LG13GylH3z/dTuLQBgTNUGDjyymiMPvcmxRzdQ+dRmal/ZSUdlExmLvknRl5/ANSIP\nb3UjNT++hs49f4mojXbM+5fMPjd0QqcfclyQPYgIQSx8TiTZRDK3sSF0ysvLQYX2dqs/vdS/mq72\ndJ47GNWsmnHFjte28fkkwZRr4ZZtDpKdgBPr7NjO4ZJ0syQ6tvyEto2bkTQnwz/3M7zDplLasozW\nrN1UZI6lo8VNQVcNO48+RWd1M+2H6/Hsrqbx7YMc+/N6Dv/2DZr2llJwy1oyZk9F273U/eYLdLz7\n8MAnN6QkJ1OuJU50OEhJhslFbIgPLeVW4dGgbOLXm/fH2SKDIToEU6WFW5QjSHfqtQSQuBkGT9QH\nxJHUEGtHPU1P3wNA7tWfJHPav3HOxZNxaw6FXWdRsbSU2j3DrPdbNzHq+nMo/chCiq+ZT/7CCThz\n0vE2ttGwdi9H/7QT5+JHyDxrHtrpo/63t9Kx86GI2GlHzU4y+9w9oW6QJZtj4XMiRYiTuY0NoVNW\nVkbmpHQa92XicViyife63uCtAx3xNi1q2PHaNj6fpHtS3RAjxJkJGCG2YzuHS1JFiFtevAVfgwdX\n6XCyzv9f/F4fIw5Wke2AUW3LqCmqYH/DSPw+mFC/hXe2vE7m+EJypo+k8OLpjPv8Mko/upD0UcPw\neTqpeXE3rY7vknlWGdrlo+7+O+jc/Ui83TTEmKA+N5EyTAQZmWX9TYQBscE+1MyfDSq0NVoBhst8\nq+nwZPDakco4W2YwRJ5ISSaCGuTWBBoQG0InaTTE3uoNtLz2AgD5H/oBONzUPL+NjoN1zM53keEv\nZLh/GuuWTKPxSC4OlPqKUwe3IkLmuEJGrVjMiKvn4sxOp6PSQ0PD18iYXwZeP3W/+zJdx18fkq12\n1Owks8/VbeFJJmLh80nJRPxT+SRzGxtCp7y8nMfnfBgcULfeKpd4duM20tXDLzbtjbN10cGO17bx\n+SRByUTWECUTwTkoiVStzo7tHC7xz/YfIk1PfgG8fjIXzCNtykeoW72Hlh3HEbeTxdctZt9jW2mr\nXs72CQ9RuamQgvHNjD3+Gp/48Qo61AMoae7hDMstYdbomXxm2ccYO3kp1f98B8+eaupr7iB/yrfo\n3LOf+l+voPDLq3AOmx5vtw0xoKo75VriRYiz3UKuG5q7rMl/w9PjbZEhGRCRB4D3AlWqOi+w7tvA\n54DqwGZfV9Xn+9q/Nj0PR1kRnk0naHFkU9DVygr38zy276qY2G8wxJKITaoL1OMwEeLkJCk0xJ37\nn6Fjx14k3UXuNb+medsxGtbuBYGSa+aTUZLH+ZdNw625pJPO765xU+Vyk+9vY1bTJhzewzi8R/C2\nbaWm+l+8vvmnfOLeK7npj//Jnlk+hl84FcRFU8c3cI0qwtfgof4378XfdsIyQNtBO0C9EEKUzo6a\nnWT12afKiYBkYjA5iCF2PieKjjhZ29imPAhc3sf6e1R1QeDV52A42Gevn3cpILQdyQXgvI638LRk\n8PrRqiiZHD/seG0bn0/iiZCGOJhlojWBIsR2bOdwSYoIsWf1TwDIWnIBfh1LzQtrACi8ZAZZk0YA\n8FrVy1Tk/RKftIIT3iaPq6nlkjYnIy78CgC7K/dQ1XAUT+teHP5aGute52dPryYzdyFfu/QLpL9a\nSavzW2QN/zpdx2pp/OMlFKxYisNxsoCH4gLHFHDMRp1zwTETZIjJCw1xo7Yd/AoFaZA2xMdl0aIk\nE3Y3WZP/Zg2LtzWGZEBVV4vI+D7eCvkiXzvhXM7J/ys1G7MYMQrOatxF8fATrFyvXPjBkghaazDE\nl+AkuMwh/pQHJROtManIYIg0Ca8h9rccom3LVgCyzv8qta/uQn1+cmaWkr/A6u+/+sh3eXHDSnzS\nSo53PFnesbTsmIuv08G49uNMPFzNZy/+GHdd901+f9N9PHLbc1yy8HYcmXMApb15A9/61838dvha\nssuOU7CiDMlw0b79KC2vvoXiQklDcSB4Ef9OxPsEjo7vIO23QtcLVgQ5gB01O8nqc1AuEc6Eulj5\nfDJCHJPT9UuytrHhFG4RkXIRuV9E8vvaoLy8HHeWC6/LRdWiuXS2ptHizSXT7+XjzmdZt68z5Yp0\n2PHaNj6fJBKV6qBnhHhIh4kodmzncEn4LBOetT8Ar5/0aRPoapuMZ0814nYy/KJp+Hw+Pn3fTRw8\n/BSCn8y8c7hq3J1MaLuGLYsyqN1n9feuA8+eckyn08m/X/JxHv7iQ1x3yfdQ1wQc2sq+yqf46vYN\nVGfmkHvlEhBofmk/bbvPRbMeRrP+gj/zIfxpX0Nd70WlGNFKHF2/Rdpugq4XQVPrhyLV6Z5Ql4D6\n4SAlCSKZMCQ9vwQmqWoZUAnc09+GMwstHdGL894HQOO2bAAWtW2iqz2dJ/YciratBkPMiFSEOCsY\nIU4gyYQhdKIumRiKhlh9Xjxr/wlA5tIbqHl5JwDDzpmEKyeDWx/6Bu3NG1BcTJv4Qb774a9SX9vK\ngV11pLmFLTqdS3Udk2s38aPfP0H9yPmoQqbbwdSiLKaPyOKyuZdyzZxGHlz9Ii9ufhdPyz5ufzif\nK9Kv4+rpLnw7X6Ph4e9TVHI2rpHng2SDayHKQtBPoL71iPcpxL8P6foN6lvN+ef9R0Q+u8Hiaenk\nyIE6TlQ2U1PVgqelA3eaE3eai6zsNMZMLGDcpEKycyM/MytZdUrBlGvFmYPf12iIDcmEqp7osfhb\n4O99bbdnzx6aNmzgeGMmR33KsKJ2Ju1y8m/zhblNB5jU/g/ufnQMH/7WLcDJCFTw+kjW5SCJYo9Z\njvzy+eeff9r7b7zxBvu2+RgxbynZrqEdP8cN1VvX4HEDZRfF3d8gq1evTojPP1rLFRUVNDY2AnDo\n0CEWLlzI8uXLGSyiUU7ltGrVKl2wYEFY+7a/8yvq7/86zmFZpL3vNepe3oMrP5MxNy7lV6/8idc2\n/wxBKZtxA1+75pbu/Z5+4h22bH6bw/oqd+x6mfwxLWwZ+R7uKfrqaedwOfycN2YnV0/ZAjqN/336\nWRzeQyjCVPc13CpP4z2wH9fIAopu24ik9yHiVAXfWqTzAYRGFDfqvh5cV4FEN/KoqhzZX8+WDYd4\nd1sV/hAq5BSNzKFsyThmLxiN221v/fNPtnnZ2ah8YYaTucMT84GJX5UvrfPiVfjxYteQH+sZQmfT\npk0sX748KT9wEZkA/F1V5waWR6pqZeD/24BFqrqi937BPvtjf9tIXZ2Dc3au4bw/PczEy4+TV9DI\n0yOW8U3XFzn2/5aR5rR3/2FIftq8ym0bvKQ74N5z3EM6VrtP+fJ6L24H/GyIxzKET7j9dkJriD2r\nHwAg85wraHjzIACFF01n7b7NvFr+AIKSV3BB92C4qd3LL948zP21HbgpxZkBu5snADD1+GruPDuH\nH1wxmf9cNp73zypkRmE9foXXD83iqy9fx70bz+PGq+4jr+ACBGVP19P8j/9cKMjGW1lP05PX9W2o\nCLjOQzN/gjovYfWb1Ti6HkQ6f3qKtjjSVB9v4s/3reOx+zewc2sl6lfGTS5k0YUTuerD8/joZxfz\noU+fzfuuK+OCy6cxYWoRLreTmsoWXnp6O7+561XeXLWHzo6hC56SVacUbg5iiJ3PDhFKAhHseEaJ\nk7WN7YiIPAy8CUwTkUMicgNwt4hsFZFyYBlwW1/7Bvvsry4qRUR4a/JiHPkuTpTnAbCkaTPaCb+t\n2B0TX2KBHa9t47NFUO8b1P8OhXQHuAS6/NCZIOWb7djO4ZKwWSZ8tRV07NoLTsE/7NP421tJHzUM\n/9gc7v3ld3BoK6RN4WefuguAfbVtfP2FPdR5vDicDtKnFzNm16U8O28Vs6t3k1XgwfPySi6+41eg\nymXjHkN8q6j2jOb5gzfx/G4vx5o6uGf1caaNuInRORM4cvgRTnjX8p3sJdzZ9CqsW0/a1LvJPPs/\n+zZactH0m1G3A2U14nsD2g+j6f8JjuKIfTbeLh9rX9nLW6/vx+9XsnLSmLdoLPMWjSFvWP/P/pcs\nm4TP62f39ireemM/VUebeHPVHio2HuHSa2YxeUbkbEwGOn1KXSc4BIoSPL9vaaZw1KMcb4OJufG2\nxpDo9BX5xUrFFjJnjR1FRu4J2prcHFh4NuNWraPDn87IjiauL3iOh7ZcyRfKZkTIYoMhPgT1vtkR\nCOiKCFkuaOqyBtpp5gFKUpGweYjbK34HChkzptG03VqXf/Z4vv3E3Th8x/E7CvjBih+RnpZORWUL\ndzy7mzqPl1nF2dz3gRncumI+o4sm4MhN40D1GAAmHXqJdo8HvM8gvlUoaRQV3ML1Z8/goY/M5vYL\nxlGY5ebdGg8VrcsYPuZ2/JJFo38/d5XMp12Exr/8H94Tb5/R9vOXfR7N+AEqIxE9gLR/HfwHwvoc\netNY38affrmW9a/uw6/KWeeO47N3XMj5l00942A4iNPlYMa8Uq6/+Vw++tnFlIzKo7mhnSf/sIm/\nP1JOm6czLLuSUV8azD88Ih2cjsFHiGPpc2mWZd9xT/yiDsnYxobB07PPXjEtDYCX514OCPU7rSjx\nRe1vsueog7r26D0BiyV2vLaNzxYnI8SRUUYFI82eoT94jQh2bOdwSUzRJNCx7RUAXBOX0VXbijM7\nnaph7Rw5/jIAi2Z8iEnF41h7sJE7n9tDa6eP8yfkc/dVU5g4PBOn08HFV89gdMfFPD1rPp2tLoZJ\nHf+6/06k608AaNoXwTkVsAZEV0wv5HcfnsknF4zE7RT2eqZB0TfwSz71NPDjkim0dvqpf3AF2tV6\nZgcc49CMu1DHHIQGpP3b4NsxpM+k6lgTD/9qHTVVLRQUZXHdvy9h+ftmkZY++EC/iDB20nA+ftM5\nXHTVDFxuJ7sqKvnjz9+k6mjjkOxMFqrbw5dLxJpRgQHxsTgOiA3246NlM0nLdlFXVELLnDHU7c5D\ngbMbdzFRj/O/67fF20SDYUgEcwZHQjIBVnVRgBaTaSLpSEgNsd9znI59h0HA03IRALnzx/C9p+7G\noR7UPZHbr/x3dp1o5bur9tPpU66aUcg3LplImuukSxOmFjF75jT8eVkcqrKixOP2voS3y4ff/XFw\nnXvauTPdTq5fUMp9185gWlEWDb4SmvK+hs8xnGqHl5+NGENDZR1NT/b1RNKiW7Mj2Wj611HnYoRW\npOO74Ns06M8DYP+7J3j0N+tpbe5g7MThfPymcxk9viCsY/XE4XSw8PwJ3PDlpYwck09TQzuP/Ho9\n2zYdHdRxklGnVDXElGux9DkRIsTJ2MaGwdO7zz6v1Hpq9MqCq+lqc9Nam4Nb/XzC8QxPVNTGw8SI\nY8dr2/hs0S2ZSNEIsR3bOVwSMkLcsf0h8Clp40fRuscBDmFj+lE8TW+hCB9d+lk8XuV7qw7g9StX\nzyjk1qVj+3zsfdFVMxjnvZhnpi3A2+GgWI7zwqNjwXXtGW0YV5DBT66ZxicXjATXCJpzv4bPUUyl\ny8mvCkupXbuGts0/HdgZSUPT7kCdyxE6kY67wTe4m4SDe2p48o+b6Or0MbOslA/dsJCMzMjOYM0v\nyOJjn1vM3IVj8Hr9PPd4BW+8+C7RzkIST4IT1ErCSLkWa0ZkWJM16jqtWdEGQ6z4ytJ5uDJc7J4y\nB19pDie2W/ndz2naSEO9m9UpWMrZYB+6JRMR+klNxOIchtAY0oBYRPJF5K8iskNEtonIkt7bhKMh\n7njHyj3sGHU2qJI9tZgH1vwawU9a1nyuXXQ5P3rtIFUtnUwryuKmc8cg/aQ3GzY8i3MvnIMvp4Rj\n1aMAKN7+Rkjp0FwO4foFpfzvFVPIzS6mKe8/8TmKOepO49eFJVT95Qd4a7aett9pmh1xommfR11X\nWJXuOu4GX0VIn0XV0Uae+tNm/D5LL3zVh+fhckXnPsbldnL5B+dw2ftnIQ5h/av7eOnp7fj9Aw/A\nklGnVBWo/DYyTMlELH129sg0cTxOmSaSsY0Ng6d3n+12u5k0vB0cDjYsvoymYzl0eV2Mb6vho+4X\n+cHaXXGyNHLY8do2PltEMsuEdZxg+ebECFzYsZ3DZagjq3uBf6rqTGA+MDSRLKDeNtp3WgU4PI2W\npOG1jP3QsQsljTvedwePV1Sz7lATOWlOvrF8AmnOM7uxZNlwpmfN4Z9TluD3CqPlAE898OOQbTpr\ndC6/uHY600pG0Zx7Bz4p5GBaBr/NGk7lgx9DfSFMRBNB3TeirssCkeIfgm/7GXepr23lid9vpKvT\nx4x5pVxy9cx+B/6RZP6Scbz/42fhdDnYsuEw//zLFnze1KrAp6rdA8twB8SxZlS3bCLOhhhsx13L\n5+F0u9g4dylkuqjdaUWJr2h/hQ17vXT6fHG20GAIj2hJJkyEOPkIe0AsInnABar6IICqelW1qfd2\ng9UQd+5+FG334hqRT0fDRNyF2TzzrlVQKTtvAcPzJ/HgW8cA+Mqy8ZSGUHUtTX/PFVeuw5M+lWPV\npQAUbnqU99/5OJd+9XGuvfOvfPK//srt33+SP/75NfbtPl0/W5yTxsqrp3LB1Mk0592BSh570zP5\nXZtS+9SNp2zbr2ZHHKj7c6jzYoQOa1Ds39/npu1tXTzx4EY8rZ2Mn1LIlf82FwkjE0K4TJlZzIc+\nfTZp6U52bq3kH49twe/rf1CcbDqlpi5o90GWC3LDfFQWa59L4zyxLtna2BAeffXZ2enpjCxopzMj\nk+1nn0vtnmH4VVjQ8C5zfHv56eadcbA0ctjx2jY+W0R8Ul2ClW+2YzuHy1AixBOBGhF5UEQ2ichv\nRGTIasyOd54EwDlmDgA1o4QuzzYU4ZMXfoLfrj+KT+HK6YWcOz5/wOP5u7bz58cO86U/z+aPziX8\nqOAG/D5hins3re4mNuWO5PXsUv6RXsrvdQS37ney8LH9TP36P/nct5/ghec24gtEP9JcDu68eAIf\nXjCXxrz/h4N0tmdk8YeKDbRsfTQ0B8VhySec5yF4kPbvg/9UDZ6q8tzjFTTUeSgeldcdrY014yYV\n8pHPLCY9w8XubVU8/8Q7aAjyiWSgZ3Q4FlH3SDAqEMmOl2TCYG9+dPFsHC4n6xYux9vhpulIDi6U\n6/Xv/H7z8XibZzCERaQlE1kmQpy0DOUScAELgC+o6tv/n73zDo+iWv/458y2tE2vpIeEQELvIE06\nCgrotWBH7L13xd+9XlHsDVAUvV4V9aJXwEYvofeaUEJCCuk9m2TbnN8fS5CSQAgJCTf7eR4edrJn\nTpmZPfPOO9/zvkKI94BngVdOLnT48GHuv/9+IiIiAPDy8qJLly515qWWqsrqP7egVkJfm0Mu8frq\nOZSWV+Ab1xWjZwJLVy3ARatw+5QpZ+x/8vbAgQP5acEGpi9cyTG3eAhPAGCzqrD4oD9XdSrg7wUf\nszL8LkzVVvyC48gsrWJ9SjIZOg+KYnqxAE8W/HczxvmrmJIQy0NTBnLk6AHigYeG9uPjlY9j3/sK\nf6DF/b//YGrEMDbtPXzKQao3D/dlD4G5gqSkNSAe5LLhc0F4kZSURMruHEqzjBhctATGVLF5y8YW\nyxN+OH0P4YlWju7Rsn/nMQ6k7qL3oCgGDx7cIv1pqm1be8f1VbF/HUllmkbVN2jQoIva/xA3Qf7u\ndVTpgIRhF/34XezxtsT2rFmz2LNnz4n5KjAwkBEjRtCWqG/dh7/RjQBvC3m2QLK6JOB2oArv8AoG\nlm5nOhb2F5WQ4HfhkW9agraos3SO2UFTSyY8dLX1Nkl1F0xbPM+NRTQ2ioAQIgjYIKWMOb49CHhG\nSjnh5HLLly+XPXv2bFCd1uwVFM68FsXDgMnzc1QvV54v/weKLGVA1wfYbxpAWkkNU/uEcEO34Hrr\nyUzP4/ZZSewwBgHgU13O7X4WbhjXk8oy+P2Pt7it6BuEIlka8zi3PfLiKfvb7XbWr93Pj2sPsaTK\nQL67wxOts1kZay/iuev70LFzJKtSS3j/z+9xqfgMKWCMcOf2J1c33OMoqxA1ryBkGlKJRRpeJTuj\nivmfbUaqkok39yA2IahhdTUzGUeK+OnLbdhsKn2HRDNkbHxLd+mCmH/EzqpclcmRCqNDL410QqqU\nPLLJhlWFd/pqcWuiCdxJ/Wzfvp0RI0a0qQN9tjk7u6SUO/+bQWD6YabMmUmHcem4epmZGzSZ7Z1u\n5+eJzpuvk0uLJzZbMdlgZh8tRt2F/9SzTJJ/7LLRzg1e7t600aCcNIzGztuNfg8vpcwDMoUQHY7/\naQRwxiqx89EQWw45tMLa8GgQGv6r24siS1EVf2LCryCtpIYgDz2TE+tPMfzHb1sZ+tkOdhiDMNaY\neMywlJ2P1/DS4xOJ6xRB977h+PuPJqsiAiEgcf+PZ9Sh0WgYPKwLH7w0mf2vjeOrrjr6VeZi1epY\nZAhm8II07pm+gDidlaeuvAFP95EALFEr+erTqQ3X7Ag3pMsLSBGIUA9jLvuExfN3IlVJ70FRrcYY\nBod84qqbeqAogs1r0ti5MeOU7y81nVJuEyyou9hjVoQguDbSRAvoiC+1c+ykcZxtzg718cbHy0pu\neDQFcdEUHnB4hIdUbmBNShU1tktzcV1bvLadY3Y4GWrjBddKHS6U1iaZaIvnubFcqDD1YeAbIcRO\nHFEm/nkhlVnTHCmRVZ3D+7i1dA0A7YL68e+dBQDc2afdKck3TuYf7y7m5q0mSl096FKRTdKUr3n5\nwTyMvuNPlBGK4Nrrh7EqdhSqHdq5ZDJ7xgv19klRFCZc1Y/fZ1zLinFBjKrOQVUUftQG0efzPfz5\n4xpuv+JpYnXhSCFYVrqLxT/MZv9zb7Pn0deGLY/WAAAgAElEQVTYee/L7Hv6TQ69OZeML3+iaN12\nbJUnZbkT3kjDc0jcWPmHhooyMyHhXgwe06HePrUUMfEBjJqUCMDyRftJTc5v4R41nlqDuHah2qVC\nyHED/lh1C3fESZvl74MjUTQKawZeSclRT2wWDR1MOVwj1/Lapr0t3T0nThpMtQ0k4KpxhLZsCjxq\nDWIr/9Nx/P8XuaBnIinlLqDP2co0NA6xVFXMaUcAqKnqSrJLCVQfQWKgZ8cp/HtXNR383Rga413n\n/q++vYj3q31AgVvI463nV2PQ1aDq7gShP6Wsb4AHA/vfRsb3K4lyT6X/wZ+A187Zx+694vi+Vxyb\nNyTz8i/72OwRzIc1Bn7+eBVX742g87Ac9mps5IQm8+cWK52SMuuuSAjcYyPxG9SLwHFD8B3Qg/Sj\nD7B3dwlarY1xk8xozhFKrqXo0iuM8pJqNqxIZdH8XdxwV1+Cw7wuKZ1SjV1SanEkuvA7d5CSemmJ\nMTtCr8kW8RBfSufYSeM515wdG+CPj9dRjsZ2oiSkHUWHighKLGKCeQkP7xjC3y+7SB1tQtrite0c\nc9MvqAPQKY57i02CVQV9Cyvy2uJ5biytxuqyF+9ELa9GuOqwVsWzTDgSXujcOrI63RHu69ougXXq\nc2d++JvDGAb+EVTN+8+GYNDlI0UwaIbU2d7AyzuxLvoq7DaFIM885r54T4P7mhDgxptVKTyz6AuC\nSgvJ8gni48HXsTNzCh3MNswCdvbJRLx2J10/eplOrz1O+8fuIGzKBDy7dkRoNZgOpZMxbwFbr3uE\npd0m8ttXjkgTlw3dhp/nbLC33jBGA0fEktizHTarnf/+ezumCnNLd+m8yK36K0OdcolEmKilXQuH\nXnPiBOCty9ujaLWsGzCOwoM+qKqgd+khupbv5de0rJbunhMnDaKpF9QBCCFOZL1rLbIJJw2j2Q3i\nhmqILamLAdAGhYDQcMzsMIijQi4js8yMv5uOQdFneodnzV3K62WeADzvXcH900YibAsBkNoJIOp+\nPNNoFG6b+igHFUd4t0FZf5KSdvSsfaxKz2LnPS+RNHgKx374je5Zh/gsYyW3FqagtdtY0a4X/7G/\ngu2IGbMw83XJf5DDexJ557XEPXMXnd95joFLvmDU4WX0//VTYh69DY/4aLI6DaBG0eNakE1o0YG/\nstmprTMlqhCC0RM7ExrpQ2W5mYXf7mDN6jUt3a0Gk3tcbhB0gQk5WkKbFXIiOYdTQ+ykeWjInB3q\n402Aj4WDnXtQYgyk+Ihj4fEU20JeSzrY3F1sctrite0cc9Onba6lNSXnaIvnubG0Gg+x9ch6AFSX\nGFK0JQh7DqpwxWLoD8BVif5oT0tMsWrFLl7OdsghHnUr4ckHx4GajFAPI/EE7bCzthkQbORo1zuw\nWrR4eZez4/2n6iwn7XbSZn9H0uW3kPvLcoRWQ9gtVzN4/XwG/fA+7703jd/HhhBbUUiuNpA18g40\ndhdsaj5PfPUEVTU1p9SnGPR49+pMh2fvIerLDymJ74WCSuSu5ex5LJ2CNTUIyrHlPY9qLmvM4Wx2\nNFqFq6Z0x8PTQPbRUraftsiuNXNCP3yJZKg7GT8D6BUos7aewO9O2ibvjkhAGPSsG3IFBSm+SAn9\nS/ZCVjqZ5ZUt3T0nTs5Jc0gmHPW1ruQcThpGsxvEDdUQW9IdXgWb7MZynWNhhtalAztzLRg0givi\n/U8pX1RQxr1Ls7BrNPzNlsfLjzuivdV6h9GOAXFugehtU29lr9dAAPqUJTH75wWnfG9KzWDjhHs5\nMP1D1GozIZNHM3TTf+g88xncIkNPlOvVL561r17BvbpCZGhvVtsfR5UGsO7njs+eR61DXK/aVZYv\ncmS77jc8jnFr5pH49gsceMedioNW9F5llK2bSs4vS5FSIqVEranAVpiONTcFW0EqtuJM1KqyFhHv\nuxsNJ5KG2Mr82b2lHs10K6MpIkxAy2izHJEmWkY24dSitQ0aOmf7G90I9TWT3K0vhS4hlGV5oJd2\n7lR/4rGVu5q5l01LW7y2nWP+y2Bt6hCWtQZ2pbVJq20UbfE8N5Ymfi5qHGplJra8UtAq1Ji6kyU+\nRwDu3gMpNMOIOF88Xf7qqqqq3PXOEvLdHV7Z916+8vgX2Qj7ViQ6pG5sg9pWFEHYpFep/nIc7l41\nhP8yCyZdA0DByo3suudlbOWVGEICSHzzaQJH1b9ixOCi55/PTGTUsp3cv6aCbeJuemk/Qaleza2f\n/pOv737+FA30zs2ZFORW4OnjSt+hMWh0GsJuuJLQv40lf8kChPlT9JYyTJvvI/NPKzoXM6iWOtsW\nBg80vuFo/GPQR/REF9kTXXgPFFfPBh2HxhIS7s3IqxP4c8Feli/cj3+QkXYRdS98bC2cMIgvsQgT\ntYS5Q4bJEe8yrnlPrxMnZ+XdUV24/sdDrBt6JcGrsvAOr2Rw8TbeTc6kfKwFT4P+3JU4cdJC1KZt\n9mhyD7Hj/6pWIJlw0nBahYbYkvYLANpAfw5Rg7BnI3Ehx9oVgImJAaeU//DTpaxyD8HFauaLm7rh\n6u4CgLA64hijHQbi3Gmda+natxu7QkcD0Fm/m6defY70OfPZdtOT2MorCbpiKINWf3NWY/hkdC6V\nrLl/ID2Ehb12R0Y9a9nP3DVv1glPblWlhXVLDwFw+RUd0ek0SLuNmr2/Uzb/IeTa5yj7bjvlf6Si\nMRWj01eAakGiRfEOQxMYh8Y/GsU7FKF3R5orseUkY97zKxW//p3iTyaR90J7ij6cQOWKD7HlH663\nvxdKl15h6L0Lsdslv3yzg8rymnPv1ELYVUl+DQggyOXC6mopbVa4u8OQzzJdXA+xU4vWNjif2PGe\nri4kBNWQ0rU3x0Q4lfmuGO1mHlB/4Nm1l46XuC1e284x/yWZaKoYxLW0JslEWzzPjaVVeIitqasd\nH4xRrKzZDypg6IhF1dEtxIMoH9cTZQ+nZDIjRwc6mB4p6dw12vGFrAC7Y2GX1I7nfBn76McceWY9\nvm6FXLP7N5Yu3kK4qtL+8anEPjkVoZzfs0NAsC/fPz2V999+lPm2ccRof6es4F888KkbH999G2uX\nHMRcYyMqzo+YSC2Vy96nat3n2Ev+WqGtCWiPITYafUgW2kBPtj9eRcl2G4ZAPzq99hjBE4Y7xisl\nsqoUe0km1twUrEe3Yz26FWvWLiyp67CkrqNi4SvoInri2ncKrj0no7g1rRe3e/8Ijh0wkJVWwsJv\nd3LdtL5o64kX3ZIU1IAqj2txNZeqh9jR70zTOQo6cXIRmDG8O1f/kMKGYVcSuuYoscMzGV68gU92\npGIb3gvtec6dTpxcLGoNVo8myFB3Mq1pUZ2ThqOZPn16szZQXV09PSQk5KxlKn6fjlpWiRowhh8s\nySDL0XlPxEQ7pvQIJs7f7UTZaW8vIdXFm6GmHGY+dVKWaNsyFHUbUukKugl1tHJ2dHo9a1MLCS/Z\njKd7BVvD+jPqkUeIvPPahqdiPk5ERATgSOrRt2sc7Ve+wwptIm6aHMpq9rNhvYaaNDuKIhgVtRXz\nt7dhTl6GrClH4x+Dx/CH8LrmDTzGPotL5+vQh9jQGrMInRxCWUoQ5XuOkrtoBaYjmfgN6oXGxYDQ\nu6LxDELXLhGXhJG4DbgV98H3oAvtAloD9uIM7MVHMe9fgmn1bOzFmWj8otAYA84xmoYRGRlJdHwA\nB3bnUpRfSbXJQvuO9WcUbCkOl0u2FklijIJ+ARd2o649zxcbNw38ma1issGYUOWihY5rqfG2JDk5\nOcTExLza0v24mDRkzj4ZjUZDiSmH9SKE6E27CfDIx+hag9HVxFJ9IkPCWk/Gzfpoi9e2c8yQlKdS\nUAOXBSkXHHXoZHKqYU+JJNgVuvm27ANhWzzPjZ23W/zRXZpLsWblgYCUqnikPROJngK1K1pFMCjq\nL+nD779uZZV7CHqbhZm3DzipEomwLXd81I5sXD+kpFO2kTRdJxStZGzOSt48uumCxgag8Yxj0A23\n8G7BUqQtGL2opND+LTaqiFdXoFv7D6SlCkPHEfjc/T0Bz2/GY+SjaIM6nDDEpW4qUumEoi2n96f+\nJL75GBpXF3J+WsK64bdSlLStzrYVNy9ce07G55Y5BP09Be9bPkUfPwzsFqo3fk3hG5dRPPtaLEc2\nXvA4Adw9DFx9s2OR3a7NmezfcaxJ6m1KmmpBXUviqhUEuDgCv+c6M9Y5aQU8OKA7ek8ta0dNJG+v\nYwH0iKIN/LzBmbnOSeul1oPb1BpiD6eH+JKkxTXE1ow/wC7R+HuRRBEAUt8BKfT0DjNiNDiuLJvV\nxotrsgG43aWc2PiwvypRDyFkhiPUmuasifPqJWX6B2R8+RM5OR2wqwp+4aVctmoVO/bWbWyejdM1\nOy5d7qdd/268VrAJvd0Lu6aAg+7f8o3iiyW8P34P/4bvvT/ikjCqbmmG0CENTyKFP0IeIuL6fAYu\nm4dXjwRqsvPY8reHSXn1I1Rz3QvuAITeDdde1+J3308EPLcJt0F3gs4Vc8oKij64gqJZ12BJ33Le\nYz19zMGhXoyY0AmAJf/dR2FeRaPrbA5ymnBBXUtqs8LcamUTF0+j5tSitQ3OR0N8Mg93N3IkoRsH\nXDqf0BJPq/6RT3e3/rjEbfHado4ZTNbmiTLh1ooM4rZ4nhtLi3uIrVmO+MPCsx1pHF/45eJIljEs\nxudEuQ8+XUaa0Q//qnJeuG/UKXUI2zLHB+0wEOcfYTtj3gKOzvkeodMyZOo97A8cCkBPzz3864sv\nsVouPHaK8ep/Y/R1p2/FeHSqkSptGlnuqVyddxdHqhrwelJ4IQ3PINEj7Ctxj9hDv4Wzaf+EQ9+c\nPutbNoybRkVy6jmr0gbG4nXtTIKm78Fj9JMIgweWAyspem8MJV/ciq3gyAWNtUvvMBJ6ODLZLfx2\nJxZzK5gVjlO7EC3M7RwFWznh7hffIHZy6SCE+FwIkSfE8ZSfjr/5CCGWCCEOCCH+FOI8Vh43gFEd\nY/D0UVkzdjJ5+/7yEs9dsbUpm3HipMmoajYPsWN+rrI65+dLiRaPQ2zL3u/4XxuOye7wJJSLLhg0\nggGRjvm6pLCc93McXX2+oytGL/e/KpBVYF/n+NgIuUTByo0kv/geAJ3ffo6A4f3pcus7lEtvXL3N\nXL93JU++94/zqvP0uH9qdTmlXz9Aqq0fNSTQpWICoKWdZhs6ny2M/v4AixY2QJ6hRCP1DwIgrP9C\nUfYR99Q0+i2ajVt0GBX7D7Nh7J1k/vuXBsUlVtx9MV7xPIEv78R95GMIvRs1uxdTMGMA5T8/j1rV\n8KQgJ49ZCMHIqxPwC/SguMDEkp/3tUic5NOxqZLcakeEiZAm8BC3ZHzHsBaINOGMZ3lJMQ8Yc9rf\nngWWSSnjgRXAc3Xt2NA4xHXxwcg48tt3YIffgBNe4rtN81u9l7gtXtttfcx2KamyO+4Hrk1sENd6\niCtbgS+oLZ7nxtLyHuJjjnTJO21hCGlCFT6oSiD9I7xw1TnSLr/35SoqXNxIqMjn1puHnlqBbS0C\nM1JJAKXdebVdkZzKrrtfQtrtxDx6G6HXjQMgJCKSw3HXAxAWl0PCxt0sXvpr48aXs5/Cd0Zg2rec\nnZaJAFxWs5o7y/ORCNpr/sTTdTd37KzhzQ9/O3eF2oFI7TUIVIT5XVBz8O6ZyMBlXxJ20wRUs4V9\nT77Bnkdew17VsPBnirsvnuNfIuD5zbj2vRFUG6bVsymY0Z/qnf9tlDGr12u5akp3dHoNKbtz2LWp\n5ZN25FWDXYK/C7hcohEmajnhIa6SreJhw0nrQkqZBJSc9uerga+Of/4KmNjU7YZ4edI52EzSqIlk\n73EsphtVuIEfV6xs6qacOLkgqo8bq65amnxh8slxiJ3z86VDi2qIpc2MNd8xZ2+wOqQOdn08CMHQ\n9g65hKmymq9LHMHdH+sbjHKaxraxi+lslSZ2TH0OW4WJ4AnDiXv6rlO+v/LB1zni0QWNTjJEbOP3\npb+TmZHToLprNTs1+5ZQ9O4Y7AWppPreQpXwJSDYg47RWXSuLGO83fFDSdR+j7fmMDPKPJn68n8w\n19SvBQaQuuuRmt4IKhHmN0BWoXV3o/Pbz9Hlg5dQXA0c++E3No6/G9ORhhuiGu92eE/5GP8nVqKL\n6oNankfpl1Mp+exGbMVnr6cunZJfoAdjJjnkLyt/TSY3q2XTUGdV1colmmbya0ltlrfe8ZqvygbF\n5ovTplOLdskTKKXMA5BS5gJ1hoFprIa4lpljelMZGcqG9mMozXRkr5ta/j2zdx+4oHqbk7Z4bbf1\nMVc2U9pmcIT01CmOhc8WtenrPx/a4nluLC3qIbblrwebiuLlRrp0xN81axNx0yn0DXOk4Jrz1WpK\nXT2IqShk0qT+p1agZiDkESTuoOl/evX1IqVk31NvUpWWhTEhli4fvFTnYjbD0KexSD3e4ZVcu34D\n//zyTWqqG6Ynrtr0LSWf34S0mFC638g+xeF9HjwmHu+bv0NxNzAyL42uwh+BSg/dF3iqmfxXH8y4\nVxaRl1Ncf+VCQeofRoowhMxCWD4A6fjVhV43jgG/zcUtJtwhoRgzldxfVzX42ADowrri9/DveF77\nFsLFiHn/EgpnDKBy5cdI+/m9A+rYLYTu/SKw2yULv9vZ4OPXHNTKC0LdL23vMDhkKWEneYmdOGkE\ndV44q1ev5v7772fGjBnMmDGDWbNmnXJTTUpKOuf2ENteNoycQPrBSDYdA9f9O1m+fGGD97/Y23v2\n7GlV/XFuN/921fEYxEV71jdL/bWG9vI1LTvePXv2tIrj3Zzbs2bNOjFf3X///Y1+qBfN7c5fvny5\n7NmzZ53fVW/+O6XfvosMj+QxuwsCCyXebzEsLprnLo/CYrHS7ZU/yXP35q1wC1NvG35q5y3fIGw/\nIzUjkYZ7G9ynrG8Xsffx19G4uTJgyRd4xEbWW/aPt++gW+YvWKu0LMocSMGIAbz8+NMoSt1GlZQS\n0/L3qVj8fwC4j3qcfa43sG7ZYdpFeHPjPf0QQmBOnkfxp0+gAjNCh1KgZmATPuwsv4diQwjBplK+\nuSaeHr3j6h+ImouoeRZBJVI7GamfcuIrW4WJPY++Rt5xYzjq3hvp8OJ9KNrzexy2l+VS/vNz1Ow8\nnk0wrBveN3yALqxLg+uw2VS+m7ORvOxy2ncMYOLNPRH1HL/m5IP9NvaXSu6J19DDr8XVQhfMgnQ7\nS4+pjA9XGB+uaenu/E+yfft2RowYcUk+QQkhIoFFUsqux7eTgWFSyjwhRDCwUkrZ6fT9zjZnnw/X\nLdhG+JL13JH9Ln6xpezwiuXQ1V/xSM8zmnTi5KKzu1jlkxQ7id6ChxKa3k38951WsqvghW7aExI3\nJxeHxs7bLWoVWLMdqT13aUIRWLBrQpCKN32Oe4f/9e0a8ty9CTaVcuuUIafuLFWwr3V81A5ucJsV\nKUfY/8I7ACTMePKsxjDA4Ls+5JhLNDo3G0O1uyjJOML8+XXriaWUVP72T4cxLASek2egH/4MW5PS\nARg0Ku5EbGFDpzvwGDUBRcJTRRvQK+FoZQndvL4hrCybXHdvxi88yoIF6+vvnBKMNDyOREHYfgLb\nuhNfaY3udJ/7Gh1ffRih1ZA++zu23fg4luLzky1ovILxuX0ePnfNR+MTji1rF4XvjqRy2ftI1d6g\nOrRahaumdMfFVUdqSgFbktLOqw9NRXZthIn/kcnJGWnCyTkQx//VshC4/fjn24BfmrPxT8clkDJg\nCHsKu2K3CXqUHebA8q+wqS38DtmJE/6KMNEckglHvcfTNzsjTVwytKiG2HbMESJsq2oEwKp1eA56\nhRpRVZVPkisBuCtcg1Z32lWrpiBkIVL4g9Iwj4NqsbL7/umo1WbaXXfFiUV0Z8Pd0wNTzwexSS1+\n7cuYvGEzKw79xMak/WeUrfzjDSqXvs3mPAXvWz7FfcjdbE1Kx1xjIyLGl4j2fqeU9xjzOYa4SHTV\nFl6ypoASgE7NJDbkdxIKjlKtd+HufTZee28xan03EU1XpO42AITlY1D/CpkmhCDqnhvou+Aj9P4+\nFK3dyoZxdzYoNNvpuCSOxv/Z9Y74xXYrFYtfpeijCdiKHIsiT36FURdePm6M+5vDq7x2ySEy084i\nCWkGKqySMisYFEfa5qbgXGNubsIuskHc0uN10nCEEN8C64EOQogMIcQdwAxglBDiADDi+PYZXKiG\nuBZvN1dGd5D8MfoWCpIdc9+0ov/w9PKzPOS3EG3x2m7rY648Lplwb+K0zbW0lvTNbfE8N5aW9RDn\n5AJwWK1ybOs6Eevnio+bjt9/3Uq60Q/v6kruve3yM/YVx73DaC4D0bBhHPnwayr2H8Y1sh0Jrz/e\n4H4Oue4OksMdEYyiumcz8I8cvlzxHkcO5J8oU7HkLSr/fBOEgseox3HteQ1VlRa2rUsH4LJRZ0of\nhEaL160LUDxd8cjL50m9ihTu6G178I/exoDCdKSi8HalN7e/soCa6npWT2mvQGouR2BBmN8EWXrK\n1z79ujHgzy/w7NqR6qPH2Hjl3eT9trrB469FMbjjde1MfO75AcUzGOuRjRS+OZiqTd80aCVt+46B\n9B0SjVQli+fvwlRxkVaDAdnHdbbt3MRFS3Xc3AS5gk5xLKoz2ZxeCCd/IaWcIqVsJ6U0SCkjpJTz\npJQlUsqRUsp4KeVoKU+bKJqBRwZ2p7xLDKsMY7GYtITUFBO69RNKz5JEyImTi0Gze4iPp0Rwzs2X\nDhdsEAshFCHEdiHEwrq+ry+mpb3sIGqlGbNBR416DInApo2n93G5xJcbHJ7Ha9xrcHV3OXVnaQXb\nBsfHBsolKpJTSX3vSwA6v/M8Wvfzy8wwYNoH5LhGone30dc/hYAUC3O+/4TCvEpMq2ZR+ds/QSh4\n3zybEVOfB2BrUhpWi53oDv6ERvrUWa/GGIPPre+CIgg9spWbXDoh0eJiWY2mfRYjq3LQ2awsNoQw\nevqvHMssOLMSIZD6u5FKB4QsRJjfchyjk3ANDaLfL7MImTwae1U1O6Y+x+G3v0A24vWlS6eRBDyT\nhEu3q5DmSsq+e4jEA3OwVxaec99Bo+IIi/LBVGFm8fe7UNWLM1k0h1yipeM7aoQg1O3ixSNu6fE6\nuThcSBziunh/RBRJ468nbb9DnjY5fxmPffosixZ9xqJFc1m+9FuyMw7X/xbsItAWr+22PubK47fI\n5jKIjcc9z+Utt44caJvnubE0hYf4EeBM/cA5sGU5PJQpXsEIVOyaSKTiRu8wT/Jyilmj90dIlWkT\n61jcYd/lWEgmIkCJOmdbqs3GnkdeQ1pthN82Cb/Lzn/BiNHbh/JuD2DFIZ0Yd2AbZaYs5nz6Fbm/\nvAWA140f4trrWgCqTBZ2bMwAYODIsyyMA/Sx12G84joA+mb9zCC9I4ScW/WPmMJKGeVSg091BXuN\ngQz/ZDNbN6acWYnQIQ1PIYUvQk1BWD6H07y2GlcDXT9+hfiXHgAhODxzLjvvehGbqeq8j4fi7ov3\n7fPwunm2IxLF3t8ofHMI5kNnfz2jaBTG39ANNw89mUeKWb/s0Hm33RhqPcShl3iGutOJOG7gp1c4\nvRBOWicRvj609z/AvJHhHNS54CJtjM9ZwDfJs/kmeRaf7XibJ+Zfz71vj2Dm7AfYtnlZixrHTtoG\ntVEm3Js4bXMtXsc9xGXOlyGXDBdkEAshwoArgLn1lalPj2bNdqTzTNb5OrZ1cbjpFBKC3Jn3wwas\nWh09K/OJT4g4s137GqDh3uH0Wd9RvjsFl9Ag4l+6v0H71MWwG6aRHDEBgPC+OVy5cD8HWcVCt4fQ\nj/s/3PreCDg0O9vWpZ/wDoeEnTtDqvvwj3FJiEOabVxf9R3ttWMB8KicS7FXCUPCjcSWF5Dv7sVV\nv2fz/fd1GJ7CB6l/+nh652Vg+/PMIkIQ/cBN9Pr3W2g9Pcj7dRUbx99D1dHs8z4eQgjcel9HwDPr\n2CESUMtzKf5kIhV/vHHWBXceni6Mv74bQsDGVUc4cqAOr3cTk2Vy/N+UIddagzYrxugYz5HK5jeI\nW8N4nTQ/TaUhBijMP8aMWfeQkf4xZvej/OTthx0YUFVBiD2Y3p796eASjxtulMtytpVvZOaqZ3j0\n3fH8snAOlpqGJRe6UNritd3Wx9yccYgBvA2OubnU0rLOirZ4nhvLhXqI3wWeop54lmfDdiwZgCM4\nkm7YNdH0aGdEQfLDMceVelOi35k7ymqwO4xpNOd+FVCVkcPhtz8HIPGtZ9B6uJ9jj7Nz+QOfkO7V\nGa1BJb5bBgMXV7HLbSWLUjtiszqMQHONlR3HJR8Dhsc2qF6hKHjd/DMaXw/s+aU8Iv7AVzMIgQ1j\nxUfkkkfXbtEMM+VQozNw/0GVV99edKYnRROL1DtC0AnrPLDvrbO9gBEDGPD7XNxjI6hMTmXD2Dsp\nStraqGOi8QnDOOk1PEY/AUgq/3iD4lmTsZfVn8gkor3fCV31bz/spry0ulFtNwS7lORU13qI/zf0\nw7VEHzeI0yqcGeuctC5WLJvPk19ex86KrejRMzLsGorcXqLgoD8CmFa0j7+l/Mh9R/7g1WPJ3FtS\nQ3+LG27SQL49j+9SPuXRjyawasWPTo+xkybnLw9x89TvfdxD3NIGsZOGo5k+fXqjdhRCXAmESCm/\nePXVV6OBgdOnT//u9HJff/319AULFpwIDr1nzx7MZjM+e7/EbjLzRbGW6jIzMmQKE7tEsfyHBfyU\nUYzRYGT23YPYtHkjGRkZREQ4PMVJaz4n8+hmIiK7g24CSUlJp35/2vbXNz9ARno6XSdfScyDt5yz\n/Lm2N2zcyMFiA8GVuzG6mziSXkFutg+ZIclYMyIpLE8lZVcuNeUGouL8sGpzGly/0BnZUqIlbdsa\nQiwlDAvx5OcjPlSUZOPltodCuqBYTXQ+todDnlFssrmyct7XtPOwERMT/Vd9WYKI8CCEmkzS2j/J\nyDISERl/Rnt6Xy/SQr3ISE/HPT2PnFNqErIAACAASURBVAVL2FmYTZFBITIy8ryOz+DBQzDEDWFr\niTvpyTsIMh2keusPbMmRZJdb69w/NMKH1avXkp52lKpSHYk9Qlm/ft0FnZ+6tncfyuCANgxfA3ik\nb2iy+iMiIpqkfxeyvWNjEuuSj4JfBAMCFbZvbPrj15rG29zbs2bNYt68eSfmq4qKCgYOHPgqbYjq\n6urpISEhF1TH8mXzmbv9LWxYiXdJYIwhmr77/k2YpoQ/7FfSzboFo7aa3d5dcFVVXOzVBNgq6FxV\nwLDKQoKtVrJ17hSJarYeS2Lb1hXE+HbAxy+4iUZ5KrXnvy3R1sf8e7ZKtR3GhGmaRzYhYNkxFQmM\nDm25OPFt8Tzn5OQQExNz3vN2oxNzCCH+CdwM2ABXwAj8JKW89eRydQV5l+ZScp9rT6Gi4bXAcFRh\npNT7Hf51QyLPzVzEL/pgrrfnMeuVa85s1/wWwr4RVXc76MaftY8FKzaybcrjaNzdGJz0HS4hAY0a\n6yl9t1ko/mQSW6v96ZqzCKnC4RUR/DSsL2qQnsuj7yAztQSL2caN9/SrdzHd2ajZ9SElX74CEqzx\nN/FSVTlm+15U4U255zOE+obRtzKX6elg1unpVJHP9/cPIizypEys0o4wz0CoO5AiAunyGgjXusdk\nt3Nwxqekffg1AKE3jidxxpMoBn1jDhH2inxKv74Hy0GHTtx9xCMYr3geodGdUba6ysK/PlpPRWkN\nPQdEMnxC0wft31qoMvegna4+gvs7NZM7oAX5ONnGnhLJnR009PG/9BOOtCYu5cQcjeVCE3NsXPcb\nH6x7BRWVYf4j6Ju6khCTI9TjMY+OzI19mVHzP2BAx42oUjC/761cPUhQnKtQml6DPjWNyMId6NQa\nNrkZ+dXTlypFQUFhfNT1XDf5YbTaxs1NTpzU8ugmKzV2eKevFrdmMIjtUvLgBhsS+Li/Fk0LJKNq\nq1z0xBxSyuePh/OJAW4AVpxuDEPdejRrzhpQJYc9HZIImzaacG8XXO1WluAwIKeN7VxHozVg3+74\nrOl31v6pZgvJL74LQOzjdzSNMSwlZQuexnJkA92rtrI/ZDhCgciBxxj76y5MlnJ2HtjKoSO7iYjx\nbZQxDODS7SGMYx0PA/q0+bygT0SjiUWRpRgr3iGrJI+N7sF8OdgP/6pyko2BDJ+zlQ1J+/6qRGiQ\nhkeRoh1CZiAsH51I73w6QqMh/oX76Db7VRRXA9nfLWbzNQ9Sk3fuqBG1nKxT0hgD8b13AcYrXwSh\nYFr+PkUfjsdeknXGfq5ueq66sTuKRrB9w1EO7MltcJsNpblSNrcWbVa0x3EdcTMvrGst43XSvFyI\nhnjPjrV8vO7/UFHprotj3J6vCDGlUuQSSvqkj3G5+yrenzSP5ZMeojDDB0VIRu1YjI9LDv27pDN2\nQi7DH3Ul7MWBHBsymmh8eCkvg4GmclRUFqZ/x7MfXM/RI8lNOOLGX9s11VZSU/LZti6dDStSWf37\nAVb/foDt69M5tD+PgtwK5EWKpHO+tMXfc+2YLXZJjR20AlyayXmrEQLP489tZS0YaaItnufG0iLu\nMlvONgAO6n0AMzZtDN3aGVnwy2aq9QbiKwro1a8OfbB9BwILUokD5ewGbtqc+VQdycQ9LpLIu65r\nkn5XrZtH9YZ/gc4Fnzu/ppd7BAffG084B4npe4zR//EibVIwkIrBRYuU8kRmuvPFfdQcbDkHqN65\nF++iD3jK8++8ofyCRs3EWPEux3iab6UPP97chfu+2kKKMZDJS/OYmVHIzVOGOioR7kjDM1DzHMK+\nCazfIPW31NtmyMRRuMVEsOOOZyndupcNY++k5xev49Uj4bz7LxRHPGZ9zABK/jUNa/oWCmYOwfvG\nj3DpcsWp7YZ7M2xcR1YsTubPn/YQEGLE1//CtN4nU5u44n9NP1xLzEk6YidOWorSonzeW/oCVqx0\n0kZx69ElCOBQ1ES8x4VwWfgCAFQJN4/Yxo/772FazUwCXIr494caioY8iMVWhZ9LLrHe6fTun0bY\nqPbs25zAwFUH6Vp0jPleAWSRwYv/uZ1buz/CqNFTzt6pZhlnFbs2Z5J2sIDCvMpzlndz1xMZ60d0\nhwDiEoPQ6Z1p1luakuORH7z1NGtcem+doMwiKbVIfA3/m/ef/yUarSE+menTpx+tSz8MdevRanbM\nwZKWykJjMNXCSrXrOCZ2TeRfS/dxVG/kdj8rQ/p3OKMuYf0RITOR2vGgia+3P+b8InZOexFptdF9\nzt9xjwm/wBGCJX0rpV9NBanifdMnuHQagYubGzn2EJQjyzG6V2LzCuKY9xA03jVkF6chK41Ed/Bv\nlFEshMDQaRLm5G+wFZThbdxNHA+ySRxGUXPQWQ9QRC92l8JHN3Ynfd0uDhq8+b1EoXzjDi4f0MHR\nrvAEJQbs6xBqMhIP0NQfBs4lyJ+QyaMp276PypQjHPvPH7iGB2NMOPviwPp0ShrfcNz63IAt7yC2\nY/uo2fETsroMfdxghPLXjSE4zIuifBP5ORVkphWT2CMUjebCX/9LKfkhXcWqwjWRmiZ9NdZatFnu\nWliSrVJhhdGhCppmmuBby3gvJo3Vol3KNFZDPOub50mrSSVUCea+zHVokOzr9QDdry0nxu8YVVY9\nuwsHsSTzLn5I7sTvMoCE5DTCvTOJr07ltYIBLMv3JynTh4WHopm9owfz9vQlXQnBt583HkZ/Bqce\nwYSVbJ2WHbkbyNi7lx6JQ9Hq6pdQSCmxFJZQtjOZotWbKNm8i9Lt+yjdvo/qzBxUs5WYTvHnlIgd\nPVzIsoX7WbE4mWMZpVSZLGi0Cu3CvWnfKZCoWD9iOgYQEeOLj7877h4GrFY7pkoLhXmVHNqfx86N\nGZjKzXj6uOLm3rKyj7b4e64dc3aVZGOBpJ2bYGBQ88nM9paq5FVDoo9CSAs5ZNrieW7svN0yHuKC\nDGxAiXBEFrBroolyU9ikd0gorh/b7cydpPkkuUT/s9Z/aOZc7FXVBI4djN/g3hfcX7WyiJIv7wC7\nFbfBd5+INQzQe9SV/JG6mS4pH9M+YAfZts1kH8hnS/8dKFv0SAkjrurUOKPY4I3PnT9R+O4obFm5\nxMXP4u7KB5gj5qC1p+FZ8Q55PM6LKzN544krif9iGR9VezPL4seBFxfw5bPj8fB0BU03pP4+hOUj\nhHUeUviCtv5jaAjwpc+PH5D84rtk/uu/7H7w/yjfd5j4F+9DaM7fu6F4+OEz7VtMqz6hYtGrmFbP\nxpK2Ge/bPkfr51i8J4RgzOTOFOSUU5hbybKF+xl3bZfzbut08mscqTM9deDbRCmbWxuuWkGIGxyr\ngoxKSXtPpyfCycVlx5aVbCpJQiM13Ji7Cy0quztcw+hJKWgUyaHiCNbk3sucrRUcLPxLijWz32PM\n3naQoNBCvih8hbljfqJKNZBZVsOhomoKquDX1Ah+TY1Ar6iM6jaM7gW7mJz/Cwu9vNlctp70j6/h\nhZs+JSjkrxu/arVRlLSVvMUryf8zCUthyWk9lihaFSkF0i4AgVv7CPyH9sV/WF98L+uF1t2x5qKs\npIqVv6ZweL8jM6lWqxDfNYTOvUIJCfdGq63foJJSUpRv4ujhQlJ255CTWcb2DUfZvuEo8V2CGTQ6\nDh+/pnsb5qRhlNZ6iJv5nuClE4B0Rpq4RGgSD/HZ+Pnnn6f36NHjlL9VLvknmWaVDe5G7EowHn7j\nMRw5wuIKHR0qCnj6xjr0wfZtKPY1SKU96CbW217lgTT2PjEDoSj0mPc6el/vC+q/VO2UfHErtuzd\n6CJ743PrZ6d4NgFie1/Ous2HCa7eT6htG4fW5GJUE0mJXkn10QAsZXqi4wMaZRQrroHoo2Ko3roI\ntSCP4HAzIZab2EEyGjUHnS2FEtGLNekmHru2Nz3KclieZ+GQize/LtvN8ChPfPw8QYlCokFR94B9\nCyiJZ5WdCI2GwFGXoQ/wpXDVJko376Z0+z4CRw5E43LmLJKUlHTWJ1EhBProvhjiL8dyYBW2vANU\nb/4ObWAs2iDH2wCtViE8xpd927PJyy7H6OVCUDvP8z5mJ7OvVLKzWBLvJegb0LSvKs815otJpkmS\nYXKkpo4xNo/HozWN92LRFj3Edc3ZZ8NSU8MbPz5ElazicpOJ3tWl7Asbzeg7TWgU+CNtHM+uHstX\nO0spqrIR7mXg+WGRvDm2PQ8PjeLDVA96FKzC09WEb94WHnnwKW7uHsxDA0IZ39GPWD9X7KokrcTC\nwVI/1loS2O45gl6mUhSRRZGoYfWun4hyi8PfM5C0Wd+yc9qLZP37F8r3HASbCe9oO8F9oF3vSoK7\nFhHcOY+ghEKCEosISiwmzbuYDsGZWDO3UbT0N9I//Z6y5DySi7T8sfgwhXmV6PQaBo6I5crru9Gp\nWzu8fFxRzrFQSgiBm4eedhHedO0TTvtOgSAlhfmVFORWsGtTJpXlZkLCvS+6lKIt/p5rx7y3RCWl\nTJLorZDg3Xwe4swqyYEySZi7oFMztnM22uJ5buy8fdHPkLTbsBeVcVTnMKps2mgSg9z5Zb8jOcOY\neowWYT+eqvkc3uED//gEVJWwm6/CIzbygvtbueRtLAdWorj74XPHPEQdq5stZhupXEeaZhBaYSWk\nWyH9tx0gKrkTB92/ZNO2bfz2w27s9sbF0tTHTMb7hscBsCcvpZvnWqaKaUjhg9Z2BM+KdyirruSp\nXw+RMLQ7v4wLJdBUxkFjAMO/2sviRZscFWknI7WjEVgR5jdAPXOR2+lE3DaJPj9+gM7Xm6JVm9kw\nbhqVB9IaNQ4AfVRv/J9ajaHzFciackq+uJWyn55F2swABAQbGXmVQ7O8fOF+8nPKG90W/KWrrY3X\n+79KtIfjp9zcC+ucODmd739+lwK1AG9Vx7jyPNK9ujJ0mh1FEczdfTv3/ZHIjhwToZ4G3r8ylq33\n9+LuPu2I8nFBr9fz0APXslRMREroZNrHnNcdcdQVIegc5MF9/UL5+eYu7H64D9OHRxHt48Kxai8+\n097Bn/JV3KxGqoWVmUsf5+M7J3Do9TlYS0oI7O1B4u0aOl9zhIjeqXj5HEInjqHIcgR2hE6P0OkQ\nQqLRqbj6mPGNLie0Rz5hg46xS6Nl675ybDaVSF+Y+shA+g9rj6tb46UOQe08GT2pM3c+PpjOvUKR\nUrJrcybz3l3Lvu3ZzljiF4mTNcTNibeudSTncNIwmt1DfLoeTS3Zh2nlPFYb/cjRajEbhnB5+87M\nPVyNTaPlnStjCQg6LTqDtCAssxHYkPp7QHjU2VbRuu0cen0OGnc3enzx+olXXo3FnLqesu8eBAE+\nd36NLqxrneW2rU8nNaUAc7sBuFi3kehejFtANV4rzOSEJJIRsBRrVjuKj6nEJgQ1ShurCx2CUA5j\nOZQMJfsJDIoh1Dr6JE9xMpXaPqw8UsGgruE82i+ETWv3ku7uy39zbJg272TogA4ITQ+Q6QiZDvZt\noL2s3nBstbiGhxBy1XCKN+zAdDCd7P/8gbFjDO7t/3rqPJ8nUKFzwaXHJBRXL8yH1mJN34I5eTn6\nDkNR3LwJbOdJeWk1udnlHD1cRKfuIeh0jfOeLMpUKbPAuFAFf5emNYpb01O3ToHVuSo1dhjZrnk8\nTa1pvBeLtughPh8NcXFBDh+tno6Kyp1F2XipAuPN3fH3t/Hsqpt4fZ0f1TaVUbE+LJiSSL9wrzPC\nT3m6upAZGknN0nX4GYuJKj7EaiWajnGnLub1NGjpF+7JtN4hdA5yJ6vMzJFyLQcYiocsw02TRVZI\nDebOroztVoSn8RCKrQAUDfroeFy7x+IxLBDPUVF4jm6P58gojJdHYhwWSYfRYbgkBKAP86RIF8Of\nlY9ToonBICsYYn6XDrlzKPjPAqzVLhi7dG30YulaDC46YhOCiO8STHGBiaL8Sg7vz+dYRilhUT64\nuJ4ZorKpaYu/5xPxx/Mc2t5BQc2r7a2wSTYVSDx0ggGBLeMhbovn+ZLxENvyHdnQ0nQOI8ymjaZg\nbyrVegMxFYUkdIk+cyd1D4JqpIgCpe7A7FJKDvzfRwDEPHgThgDfC+qnaiqm9F93gVRxH/kYhvjL\n6yxnMdvYssbhMR18RTeUka9RqvPDw7+aiH45jPp5L14FHUjxmMu+g3tZMG8rNdWNi8HiPnIObv37\ngU1FyZhFgkvOSZ7iNLwq3sZqq+bVZUfYWy34/R8TuUMpQCoKH5n9uPr5nygqrETqH0MqHRCyAFHz\nGsiqc7btGh5Cv19mE3zVCOyVVWy/7RlS3/+q0R4NIQTuw+7D7+Hf0PhGYM3cSeFbQ6netRCAEVcl\nEBhipLS4ikXf7WqUd92qSrJMEgFEevxve4iDXMFV4/B8lJid3ggnF4fFy77Eho04s51YSw1pnYcQ\nHWPm7+uuZvZ2hyTr2SERfHd9Aj5nMfIm9uzKitFPU1ZqxGCw0P6X5ygpO13360CjCCZ09OfP27ow\nyzOf2IJsdqk3ss92A1IqJBsreddopNw7AuPY4QQ9M4CAaSF4jXLDpYMPGt/2CNeBSO01qLqpSNf7\nET5PoIt9gtyAJ1ic8Qgm/AkKyOOaXrMJ0R1E52rHNyQdsfUxDk9NpGDx901y/PwCPfjb1N6Mu7YL\nrm46jh4u4qsP1pO861iT1O+kbkocLyTxaWYPsZfTQ3xJ0ewG8ekxLW35+6gWghKNRKJFq49g7WHH\nIosxfnUbLcK2GQCprT/2cMGSJMp3pWAI9CPqnhsvqM9SSkq/fRC1LAddVB+MY5+pt+zOTRlUV1lp\nF+FNVJw/PS8fwzKPyVQrbnhHVBDaLZ8rvkvGszieFONcDmYk892cTY1KVSwUBc9r/4uhU3tktRV9\n/kw6GcqYKqahCm80tjS8Kt5C2quYuTqDX1KKefv5SXwSB26WGtZ5BDP0nTVs2ZSGNDyLFCEImY4w\n/9MR4/kcaN1d6Tbn/4h73vFK89Drc9g57QVsFaZGxzrUR/bC/8lVGLpciaypoHTe7ZQteAatsDHx\nlp64uevJSC1ixeLzjzuaaZLYJQS7OhaeNTWtKb6jIsQJWUhzySZa03idNB8NjUNcU21idebvAIyu\nyCfXNYLLJks+2zmQdzc7HBvvXhnL00MiGhTa6pU7rmFBx4exmTX4uRay45VJ9Za115jZdc/LeP3z\nLV798QPeKf8WFzWcLbYHMUsPsjV23jLoOOgLioeBkupIjpaN4kDxQxyrfhKL5hGk/kbQXQHakSRt\n1JCS3I2f5xux2RQ69wrlxgdvpN3kzwj5+0f43nUV0tcfAA/vPGzL7iPt/o5Ubv+tQcfqbAghSOwZ\nyh2PDiY2IRCL2cav3+/mtx93YzHbLrj++miLv+faMdcaqN765nWU1C7aK7M0azNnpS2e58Zy0T3E\n9oLDZBzXD9s1EcT7urFOcSx8u25k4pk7SLtjERiApk+ddUopOfzW5wBEP3wLGjeXC+pjVdJczPv+\nQLh64X3LZ3VmWINTvcMDR8SeeI3WY8QEDsXfhU1oCexYQlBsEVd+k4xHSRwpxs84WrSfb2dvpCCn\n4rz7JrQGfG5fij4qBLW8BpeS1+iotXCnuOu4UZyOZ8UMhFrGnE3ZzNqYxd/+dhlL/tae6Ioijrn7\ncNXvx/jo043Ydc8jhR9CTUGYZzgieZyrfSFo//Ct9PzqDbRGd/J+XcX6MVOpOtp4j4bi5o3P1H/h\nOel10OioWvsZhe+Nxc2Wy8RbeqDRCHZtymTHhqPnVW9b0Q/XEnc8usSBMqc3wknzs3z5fEyYCLVa\niLaY8bi6ExvywnlxdT9UCc8NjeDWHueXavmFZ59guZfDEO6o7ua7F247o4ylpJyt1z9K7qIVuAVr\n6HqnlTGW+fxY9Ag3ee5ll/oI5WooFRqVmSsz+edngns+X8VTX3/Ky98+xmNzJ3HrOwO5f9YVvP7j\nQ/yYNIeNm3bw6/e7UFVJ78FRjJnc2RHKTYlB6CdjSPySdi9twv/J17G6h6HaBS76fMq/upnMl/pg\nzdt6wcfTzUPP1Tf1YNTERLQ6hf07jvHvTzZQXGi64Lqd/IVNlVRYQcCJxBnNhZvGIWersUON3Tkv\nt3YuuobYtGomO0wWDri4YdF3J6wygHU2NyIqi5l+y4AzK1BTUOx/IEUQ6KZAHZ6G/D/XcvTT7zEE\n+dP1g5dQdI2PJmfNTXGEWFNteN88G0NM/V7presc2uF2Ed4MGh13wiCOiIggtvcwNhzIJLh0N54h\nJuyVWkKTqkiPj+eY5xJ0VSGkbq/BL8ADv8C6NdH1IbQuuHSdjHn/fOwFZRhcNuOpvZwY+rBNHERR\n89BbtmPXdWN/IRwpqmZCn0hu7R9B8uqdHDB4sbJKx/al+xnVazIuum0ImQFquiOknTi3BtW9fQRB\n4y+neP0OTIfSUdbubFC84nrHJAT6qN4YOo3AcnA19uNRKLzbd8KvU08O7c8j/XAR7cK98fZza1Cd\nK3NUjlXB4CCFSI+mf/ZrbdosrYB1+ZIqu2R4SNPriFvbeC8GTg1x3dhVOx8vfgmTNHF1eRHV/l2I\nHOnJFT9MwWQV3N4zmFdHRDVKa5s4dCxbFy8hUJdHWGUqP+wqodeQkY6+ZeWy5doHKd99AK8ElYj+\nh9GYiyk36PC+qTNXjarhusRUFqWPpLTagruSS66tlBA64GLUodXqAIFNtVJtMZFXmkleqh2f8sEA\nmAP34tG+EKOrJ17up8nuhCsar954Db8XbVxnStZt4f/ZO+/4KOr0j79ntpckm94LKYSSQOgdVEAU\nRVGxF852iop6enpnuRNPTz27/u7sir2ggqKAVCmhhZJAeu99k2yym91sm/n9EUCQlkRBFN6vF68X\nm53vd+Y7s/OdZ57v53keldiBwt2GbcPHuFu2oBs0BUHh0+sxH9iFIBAW6UfS4FCqy1ppbe4kL7OO\noFDjr1qsCE7P+zkmJoY2J6ytlzCp4dzIE5vZQxAEtjZJ2D0wNkTEqDr5zpnT8Tr/fjTE5mbq9yVR\n9yoiqWywADDFeOSlIcHbLZdAMfqIxvAh3uH516HQ9T2xoOxxYfl4Hri70I2+Bl3axUfd1uX0sHPT\n4d7hgznvnlfZG9OdIi5ydCNB4RZmfVSEb9tgio0f0Cjv4dtPMtmytqTX5T1FfTgB81agCDTiaWjF\n4HmMeBHu5c+giEIhmTF2PI2OarZWtXP/98W4lGo++/ccngx1oHG7WKMLZ8ILhaTvuB4ZHwRpN4Lr\n5W6vfA8wxEczbtnbRMw5D6+ji713Pk7eQy8gufpep1IdM5yg+9ejHXJht4Ti/ZuILH2J0ZNikCWZ\n7z7L6rHH5CcP8W8TzHCyiTUK6BXQ3AXmrjPeiDOcODK2rKDJ24if10uao5PI6cHcsXImli4Fk+P8\neO68hD4HnqlUKvwfeI/GzjCUGi/nlH3A2//3Gl2NZjIuu4vO4kr0wzqIGVKESvbQFG4i+p4xFAsq\nXlvbyTNLWzB2vIlWtGGWxiEIEg1CIQ2WANReNU73T3I1P3cy/eyXAlCtXUG26zMWpf+PBxZeyV/e\nuYzFW96h0fKzbDyCgD7pAhJe24ty6rNYzf6IChnXrk3UPTQKZ/6jIPd+9e9gAkOMXDtvLEmDQ3F2\neVjy0W62ris9ZctA/55o2yeX8D/Bcon9+KnP6Ih/L5xUDbHs7sRr6aRe+ZNBvMfTLW+YkXaEanKy\nDPsMYlkx+oj9N63chDWnGE1oENHXHd2A7Qm2lc/hqdmDIiAG30ufOua2mdt+0g7HJgYe8t3Bmp0Z\n971HTtRMREEmZnw9AcEdzPqwgICmoZQaPqNRs5kta0tY+llWr/ViCt8kAu/4DoW/AU+dGR/pMcJE\niQe9NyMoExDlDjRtz+InFFPS4mD+0kJKW+zccet0VlwUTZy1hXqDiUvWyzz11vl4JR2CdzuC6/96\nbBQr9FpS/+8fdN46C0Gtomrh12yffQeO2sZejeVgRL0fphs/wPey/4BCjT39XQZkzyMhwdD9cPhg\n13EDE61uGbMT1CJE9Myh3GtONW2WKAgk+3VPvgUnQDZxqo33DCeGnmiIl+/+FIDJtnZqAgayV4xj\nVXk0BpXIqxcmHZZJorcMj49nx0VPY3MY0Pt0MS7jRT675wkclbX4jGkmKbkOEWhOTWBjUiS3f17H\nK6uaWZ/fRENbAxqVlqGhGmYOMKJVT0CSFSiVeRTbTUT5X8k1k+9mZvKdJDmuQ0BBTttiGrWH/r7r\nWytYlP4697x1MY98eANb81fh8R4674RcfAvxb+ZgVc3GaVUhehy0vvka5jcmIFm+PjCPyrKM5PYg\nSz0PDlZrlFx0TRoTz+2uLLp5TTHffpr5q+mKT8f7OT09/aeiHCepUOD+/fxWOuLT8Tr3lZNaqc5r\n3o0kyTTs8xCHCia2GfVo3E7OmXoEfbBciSA3IeMH4uGlnA/2DsfPv/6IBSN6iqt8O7Y1L4EgYLr2\ndUTt0QtC9MQ7fDBT5r/LppevY1D9WmIn18GmCC78JI8VVw6nOmI5XYoW5NwLsbTYmX39MPz8e27B\nKQKHEnDHN7T89yLcNY34xD4Gin/xiOcG/qP6Erc7B7nlRYICb8PcmcZ93xfzyDlxjB6RxMakKO55\nfhlL1GE8Z4sg/ckLePeWlUREpINLsy/F3fHfmQRBIHTGJFIuuZisWx+hfXcuW6bNJfXlRwiZManH\nY/l5n4ZJt6KOG0Xb+zfhrctmZMtNWPxfoqXFztJPs7jsTyOOmsJuv3c41iicsFLGpyIDTAKZrTL5\nFomJJ7Ak6RlOX2oqCinuKkQtyYyzW6k/qz/3rZkOwOPT+hFj+mUxHPu5ddbFPFGey9zClwkMbGVQ\nwwbqLohjqE8hMgKrw0JZ0SJBS3eWnOSoNIbHT2Rg9AjiwwaiPCj24/Nln7A453/4KwrJb+mg6MeL\nOMcZB5LEkFFRjAm6nrSRz1LWkE9ZQx65lTvIr8nEK3Ubn6UNubzy3UNolFrOGjKbyyfehnHfM0Jp\n0NH/ufeoX7yc5oUPEBhXj7uwIzJc2wAAIABJREFUhoYn5qEb9wJlX5swb7Iie/Y5GUQRUaNCHWBC\nHeSPJiQQfb8oDPHRGBJj8E3pj8rU3bcgCIw9K4GQcF+WfbGHkrwmPn5tK7OvH/6rSyhOFw4E1GlO\nznPBtL9a3ZnsP6c8J1VD7Cr7lurszWw0+uEVA/CxDCVXMDDKYWbu+UfI8etZhSDlgmISKA/3EDf9\nsJHKtxehCQ0i9f/+gajsm30vOW20vj4H2d6GYeo96Mded8zt92uHI2NNTJyedJhB/HPNjkKpJGrU\nxezO2UVIZzm+MTacbWpit7ZjDhlKfdBObOoa1K0JFGQ1ERbl1yujWDREoB00ga49X+NtsqAz7UBW\nTGCiewQZGitubzWSYyc+Gh3tcj/Wl7WhVoqkRftx8TmDCa8qY2OTi3JdAJ/s6E94i5mU5CyQ20Ax\n/IhSlZ8TExODNjyYiDnnYS0ow5ZfSv03a3BbOgicMAJB2TetlsIvDN3oq/G2VCDVZhLeuZFK7dm0\ntLjpsDhIHBRyxBeS7c0SxR0yIwJPXCWiU1GbpVcI/Ngg0eGG6RHiL86XejCn4nhPNGc0xIezbNUH\nFFqySXN0EqEM5m2/y8hsCmVirB/PzIj/1X5zsiyDzsLaaoHB9gJMPu3YzDraQmP41sfLZrWeYB8N\ns0Zfx23nP8mFo65jQNQwAn1DEX9WTTSl/xBGho1lY94alGILWgowOGMQA/y5du4Y+sXFoVFpCfOP\nZmD0cCanXMisUdczKGYkKqWaRksNbq8Lr+ShtD6H7zI+JK88A2OhlZa3llLwz1eo+Ww51jot7dU+\naPxcaPROPFVmAlLsBM8MwpIr47VJIMvIHi8eayfORjP2smrad+XSvGYLdYtWUP7fj6lbvArL7lzc\nlg5UJl9CEkLpnxJKVWm3rjg/q56w6N49J37O6Xg/x8TEsMssU26TGR4okOB74p0GdQ6ZPItMuF4g\nxf/kOylOx+vc13n7hBvE5eXlBybXruyF5FaWskdnxKNMwtwST63Wl2uDJCaOSTqsreBaiIAFWX0V\niIdO0LIss2feY7iaWun/0G34jz5y0Yye0P71g7iKNqCMSMH/hrcQxKMb1i6nh+8/y8LjljjvshRM\nPaxDr1AqiRw1m125mYTayvCN6aSrQ0VkRgd2/WDqwgswa3PwsSdRtLsNWZaJivVH6OHSo2iMQjto\nHF17l3QbxT7b8arGMcGVxl6djMNTjtyVg05ow65IZXddJ5WWLkZF+TJyWDwXhCrZnlFElSGA7y0D\nyf1RxdmDNqHTNoBiZI88xdAtoQi/ZDpKo57Wzbuw7Myhee0WAiYMRx3g16M+fo6g0qAdehGKwFjk\noh8I6dpFhXIijQ0OvF6J2MSgw9osr5FoccK0iBObeP1UQ6+Erc3dBvHQAPGAfu0MfeN0NIgPnrOP\nxMKV/8EqdTCzo42qtBm8UDwFnVLkq2tSjplruDfYnTbeWPE4tU+/T//vW8maMpwkeynBvmaqKiLY\nm9qfuVNCufX8DxkcexYG7fED2fwDQjh70IVs3rEZh6IJs3oPa11hfJbVwvh+oQQbD/VsKxRKQk1R\njEiczKwxc0nrNwGLrZkmSy0yMs3WBra0bWW3XIBsthJi0+M/aghB06fh1g6jdXcNen8r2OxgbiLx\ngXAGPPcgifc/Sb87ryPquosJv+RcgqeOwzc1GW1kKKJahbu1HZe5DVt+Kc2r0ql8exG1X65Aamxi\nyIR4OpUGmhts5GfVY/T95eXtTzc2N0nUO2B8iEik4cTPjy1OyGyR8VfDyKAzq3Yng1M2qO5gPZqn\nseyngDoxghxtdxTvBZMHHN5QakSQK5DRgph62NdNP2zs1g6HBRF13UV9Pr6unB9wbP0QFGpM17+B\noDy27CJzayUOu5vIWBMxCYFH3OZomh21RsOkez8hN2IaCkEidnw9psR2JqwrZNjGJDxyJ9m+/6Nd\nUcrWdaUsendHr/IVK8PGEzj/WxQmPe6aZgyuf6DStXJf13QG+1yJjALsmzDZXkAnOthUbuHupUVU\nWbpIHhTDj0/N5l59G0qvh++0Ixj7v2tZ+UP+vkC7Y+vWDh6zIIr0m3cNY797E11sBB3ZRWyZfiO1\nX67o8Vh+jiAI6EdfTdADGwiLDmRS10sIspeMDeXs3lx6yLYur0xpR3dBjv6+J27COxW1WYIgMHCf\njji/vW+lwo/GqTjeM/z6HEtDXF1RQJ2nBq0kESIbWOzsXrm7bXR3KeZfg+b2ev758Y00L1rNgCwF\njnNamWDZRLr/eADSInYxZXUH5R23o1T2vACTLMvs3NhIvPVGwlxjQPAwWPURLvsWJv/zAx5fnYX7\nKAWAnLVNeN/6kZH/LOKaV5UM36RA2wkI0OkHW8/18tndXmr+mkbiP24n5fm/M2TRGpo75tBRZwCP\nF8sXWbQtvB/Z8iBKXQv6mHBMwwcROnMK8XddR+pLDzNu+dtMK17NuFULGfjv+wg5fzJKXyOOyjqq\n3v2KrKvuwfjUo8R5GpEkmZWLc9iworBPwXan4/18iIa47wrLXnFAQ9z3WPNfxOl4nfvKSX1d8Zhr\nfwqoc5jo1OgI7Wxn0JC4wzc+kF1iOAiHeh1kWabkhfcAiJ9/Q5+1w15rM+2f3wOAz4X/QBU+6Jjb\nu5wedmyqAGD81MOlEj1BrdEw+Z6PDgTaxY6sJ2hoG2m7KjjrmwAUHj8KjQup1/9IdUUrH/7fFopy\nGnrcvzJ4JAF3fY8iwIinrgVd+8OojDVcaxvKTL95SIIB0V2EuvUJQtTNVFm6uPvbQtLLLSgUCv55\n3yyWzwgj3mqm0RDA1Xuv5q5nPDjaepan+GD8hg1iwpoPCJs9Da/dQfb8J9hz5wLc7X2PwFYG9SPw\n7uX0P/d8xrjfAWDdsiJy1u84sE1xh4xHhmiD8JukufmtGbhPIlJgOaNZO8PhCIJQIQjCHkEQMgVB\nyOhN200Z3wMwuMtORuxU1lXFYVQruGts5K9ybKX1uTz68Vxce8sZul1J3mUdjA1uxCTZsYY6yPJJ\nRRBhTORmDC8+wyvLe/6wz9lVy56MalRKFX+b+xRzEv+EIEOCYiWx4hr+b3sr419fT1Zdx4E21vxS\n9t71LzaOvZyKNz/H3daBf/8krrjoft6av4YF17xLYngKAA6XjUXpr3Pjy5P5YO3zuH2UDPvoNTRT\nn6NmdyRet4gzv5mm5z+la/dcBNcXIB9uJYlqFX5Dkom9eQ7DFz7D1PwVjPn+TfrNvx5DUhze9g6M\nH75JRPp3CJLEjk3lLH57Cy7XiSvi8Udiv5b3ZGWZ2F/844yG+NTnpGqIrcv+zTKND3ZRgbN9NDWq\nMKZh4ZJzDi/IIbg/RpDNyKrLQDxUA3NAOxwWROqrj/ZJOyzLMpaP/oynZg/qpEn4zXn+uAbujk3l\nlBU2Exnrz4TpRw+mO55mR6FUkjD+UrbklxHcno9fsA3ZT4EqRya6SKRqQAxt2h20ayrx6exPcXYL\nnVYnMQmBRw0iOxhRH4Z26EycBYvxNrWjdKejCB9MpCWWeONIdnqKUEiNeDq3EuIfh9kVxIZyCy6P\nxNBwH6Kig7lhYj9atu1hj6RnryKWRT/qSXR+RULSSBAOfwE52phFjZrQC85CFxmGeWMG1r1F1C9e\nhXFAPPq4vj1EBVFEkziRsKR4XHu/oUFKpLTEik/Tj4QMSmNjI5TZZMaGiAeMwxPBqarN8lHB6jqJ\n9n064l8rqPBUHe+J5I8omXj88cfvBsbLsvzyggUL3v7598fSEL/3w9PYZCszO1pZGHgrNXY/5o+N\nYkb/nntqj8auko385+t78LZZGZGhJXOmjRu8DWiRsQ+JZfp1ERTHTKK5sJUwj5kon2raV1XzuS6c\nswfGHbPv5nor336SiSTJzLg0hYQBIQwaOIZoRQy7Kjdi8m8nXMimwD6EhVkWmpvN+L/2LoV/fw5r\nXgmCIBJ+yXRSX3qYxAdvxX9ECgq9jiDfMM4ZeglTUi6ipqWMJkstkuylpD6H73d8RF1bJcMvuILQ\nyZdQ+nERSrkZtdZJ194GPM270MQWIKgTQDxc9rUfQRTRRYQSNHkUsTddRtisc1D4GBD2ZKIuyacj\npj9tNomcFbvxa6rAlBzbo2fi6Xg/R0VHs7hKQgYuif315sZjoRJgRa2ES4Lzo37duI6ecDpe51NW\nMrEfydGMw+akRaFERqTJ1V3nftrA4MM3lttBKkRG2e0hPvgrSaLk+V/uHXZs/xhnzgoErS+ma/6H\nIB77VDjsLjL2VaWbMO34mSV6woy/vEl2wp9wCyrCo5uInGEmyGrjkndqCGwYiV0oJdP0MjZVJXsy\nqvnov1uoq2rrUd8KUzKBd2/aV9HOgVjyL4zhO4m36XlIfQeiKhlRtmOte4kI+XsEWeaLvU38dVkx\nDVYnWp2GFx+5hEXjfIiytVJjDOHKrOnc+M8PaGms6NU4BUEg6poLmbDmA/xGDKarromdV95L3kMv\n4OnsfQnr/aj7jeashx5naEgFsqBkdXY4Oc/dSp65u8+BptPPOwzgoxKINoBbgpKOM16JMxyGQB/m\n/sryAuq9dWgliWr1WLY1R+OjUXDH2IhffEA7izfw4jcP4HI7GVxpYtv0TuY6GjHIEmJiBIlXxCOL\nSZw/4C80XrWQHJ/+iEqZsf02k/bms8z/cPVR+3Z2eVj6aSYej0TKiEhShv/0Ij5m3Pk8fuk7+Al+\n6MQGpiifwl8u5N18BzcEDyc/JomYm+Ywaesihr62AL+0gUec+4P9wnnkiv/x2rzljEyagoCAJEts\nyV/J3W/O4p3it4n64D+4ou+mdlcIkkfAkdVA04tLcObcgeB6F+SezYXG5H4kPzKPKTu+5uxX72Oc\nuwS1tY1OnYnlu+wsP/vPFP77dRw1PV9ZPF2wukGSwUcJql+YGrCnqBUCeiV4ZbD9RrKJM/SMk6Yh\n9jbvoFGpQhYEvGIY5b6hKLxeZk4fdngj704EJBBTQDg0irbph01Yc/dph6+d1adj8pjL6Vj8MAB+\nc55D4R913Dbb15fhcnqISwo8qnZ4P73R7Jw3/wXyB9+DTeFDkKmFmIuaMKq6mPVpNsk7UxG8TvIN\nb1NtXI65uYNP39zOuu/ze5SLUtRHEDBvM9pBicgON97MF/GLWoafU8kCzw346SchINHV9g2mzpcw\nqR3kNnZy++IC1pa0AjB1+jC2/+NcbhbrUXg9fKtJY8yruXz8ydJej9mQEMOYb18n6aHbEFRKqhZ+\nzZZpc2nbmd3j8/VzFDpfpt1zG6nJCiRBzZr22bQ2OlDJbuL1Pcul3FdOZW3W/swae1t/PYP4VB7v\nGXqFDKwWBGGHIAi3/vzLo2mIN2d8B0BKVycf+s4BYN7oiF8cSLezeAMvffsgXslDUmcImQPauLa9\nmVCPG2VoIKHXJIIiElnzMAg6rk9Jpmj22+T4JKFQyozpv52ZHzzF7W/8gMN1aLJXWZZZtSSHthY7\nQWFGps46XBYXn5TKJYPvI1FMRBbdjFS9wUi+pFHhwxOzbuHNcTPxhBzBcXMEAnxC+OslL/LfecsY\nlzwdAQEZmYyitfz1k6tZMbYB5f2PU7YtlU6zFsnmpHVhFu1fv4zcfhd4d/f4vAkKBYGTRjLu1Qe4\n8Z8zCTbIeAy+FE28jD1LM9gweg6ZNz9My+bd3Rk7fsbpeD+vXr8JOHn64f3s1xFbfoNcxKfjde4r\nfc5DLAhCFPAhEApIwNuyLL96tO09jVkH9MMuKRRJVJBibcI/6PAIWcGzrxiH8tCyybIk/WLtsCx5\nsXwyD9nViTZtNtoRc47bpsPiIHNbFQCTZiT3ep/HY8YtD7Pl2wTsW58khFpUs9zUbAxm3MYiwqpD\n2HyBQKN6M83+hQzquI7dW6Akr5FzL0khLunoS20AgsaE6eZ0rIsvo3PzZlzbP8Z/VB1ttTfzN8f5\nfBOQwLa2LxBdechN/yQuYh4V9nj+s76SHdUdzJ8QjcGg5bmHL+eq7VnM/7qAAt8o7i6HLx/6lJdv\nPot+iT33EIlKJQn3zCV42nj23vUvbPmlbL9oHv3uuIbE+2/uU6VBQRA49/ppeBbtJn9vMxHbsxAT\nvXRsfx6/q15BFXG4JOePzvBAgZW1sLtF4vJ+IuJplIv5DMdlgizL9YIgBNNtGOfLsnzgqblhwwZ2\n7tx5YKnVz8+P1NRUttesB6C1XMVerQXfxDjmjYk88MCdOHEiQK8+7yrZyKOv3oUkeUlJ6EexsYH4\nfDOdDgdCvI7A6/uzebcXWT2diZN9DrQfCmya+QbOH+bjLcxDDtzDlZ8/xl8cbqYkCkT7m5g4cSJ7\ntlez6od1KFUiN/3lFlRqxWHHs+brb1j/n/8ypaqToCviWe7NBVZzSXguy+X7+ej7bJauWc9rd17O\n+f0Dezy+ey5+hqvb63jytQcpqNlNQKyOnSUbWFX5A/EXDOCCynOJ3LOVquBmhG9rGVe0Av85lWQ0\npSArz2fipBm9Op/X/W0Gq77JYfl3a6gYkMoYUzDysvX8+N0y9P2iuOjv9xA26xy2bNt2yI/hl1y/\n39tnqwea9m7G6CPA0Cknbf9tFV6IH0+7SyY9ffNJHX92dvZvdr5P1ufs7Gza29sBqKqqYuTIkUyd\nOpXeIhzpzbFHDQUhDAiTZTlLEAQjsAu4WJblgoO3W7t2rTx8+HCsK27io60b2Wj0o9U5jZ3CLG4W\nzTz38OxDO5YdCI6bAA+y7i0Q/A981bh8A5k3PYQmPJjJWxf1ySC2rX4J67InEH3DCP7bZkSD/3Hb\n/PB1Njm7ahkwJIwLr0rr9T57SlHmLlq+/Qtxlhy8iFQXRtCeacCuVbHq8oFYgncioaCf91yCrBMQ\nEBg8PJKzZiaj0x+/7E7nj3fTsfRjkEEzsD8dXQ8iuQyUmRy81fEeorcWGQXGwAtpFGbhkiDUqObv\nZ8UyOMwIgMdl58XX3uLltlS6VBo0bie3Gm08dMcMdIbeRZlLThfFz71D+WufgiShj4tk8HN/I3DS\nyD6dP0mS+d97e3CWNSAIXqY5niSEEgxn3YlxxgOImtMnkb0sy/xjtwezE+5PUZB0EvJt/hHZvXs3\nU6dO/cO+TQiC8BhglWX5xf1/2z9nH0xtVQn3f34lWsmLx3UVy8WJ/HlUOM/MSOjzvotq9/LEF7fj\n9jgJ9Ymk0VrLQJuDWzsaEQQIvGEImuQwZM0CUBzZEfHs1j3Erv4rk1t3IctQuTeSz8c/wJg5Izkn\nJJLP3tiG1ytzwZVDGDj00Bd3WZKo/vAbCv/1P7x2B6oAP5IfvZOqKIk3tj2NCxcmj0yR+xqyFGMB\nmJXszzPnJRLu07tnT21LOR/9+BJZZZsP+XuiGMPw76tIi61D598dtGycGI3P9CFguBkUU3qUB/7A\nmGSZjI3lbFpZBECMupOAxR/gaTIDoIuJIO72q4m66gIU+l8nK8jviR/rvXxRLjE5VOSahL7lxu8L\nH5Z42NIkc228gklhZ+biE01f5+0+XxlZlhtkWc7a938bkA8cNUrKY645kHKt3R0CwMTBRwjc8GYi\n4O6uTHeQMfxreIddVZlYVzwNgOnq/+uRMWxutJG7uxZRFJgw/fBcyb8m/YeNYMh9y8mNmI4Cibjk\nGkJndmD0Opn9UTaDt6eg8OqoVKxgb8DruJWt5O6uZeFL6ezdUY10nNQ7hrNfxf+mfyFolDjzizDY\nH0AbWEu8Rcfjyjsx6sch4KWz5VuM1heI8XHSaHNx/7Ji3smoxemRUKr1PHjP3Wy6opKJ1jycKg3/\ndQYy4sk1fPFFOlIvSpOKGjXJj97B2O/ewDggHntFLTsuv5u985/A1WLp9fkTBKgZMghrZBiyrOBH\n/SM0CAPoXPcq5v9MoCvv6DrDPxqCIDA8sPv23mU+oyM+QzeCIOj3OTAQBMEAnAvkHK/dnpzupeZ4\np4sf9xVJmjs8rM/HUddSwbNf34vb4yTQJ5RGay1Bdg83trUgIGM8Kw5tciCyev5RjWGAB8cNpWnW\nq3wfNBlBgLihtdyU+Th5r61g4Tvb8Xplho6OPswY7mo0s/Oqv5D39+fx2h2EXTyVSZs+I+qaCxk/\n+SKeuuJ9QsVQLEqBSM0n3Ox+Fg1OvitsY/RrO3htey2eXqQ6iwzsx9/nvMrTN3zM4JifqrKWSFUs\nmgmvhfSjpDoAWQJbejXN//0RT9lTCM4nQWrs8X4EQWDMlHguuiYNpUqkymXAfOfDJD7zN/Tx0Tiq\n6sh/+AXWj7yUkhcX4mpt73HffwROdsq1/ezPNNFyJtPEKc2v8qoiCEIckAZs//l3+/VokqXpgGSi\nVpmAKHmZMvlwPZewL92arDi0Ml3jio3d2uHwYKKuubDXxyg5O7F8dBtIHvST/4xmYM/c6Rt/KESW\nYcioaPx7WITjl2h2DL5Gpj34BVmJN+MQdYT51hN7aROakC5GbS5m5idqdLYhuKVaMn1eps53FbZO\nO6uW5PLxa1upKW89Zv/a1LsIuvdrFEE+eBrbEIsfwjd2CxoXPOyYxcigPyEJOnDm01H1d+K1O5Bl\nWLS3idsW55NZZwVBJGHwTSz99wDeT/6SaFsTDa3VzCuG8x9azJ7dJb0as2lECuNXv0/Sw7cjatTU\nfbmCTZOupvbLFUfUvh2NOju0ewQcowYxKC0Cj6Rkvf4R6oIvwdtaRdtbV9L2/o142+t7dXxH41TX\nZo3YlwR+d4uE1MeVoIM51cd7hh4RCqQLgpAJbAO+k2V51cEbHElDnFWxBQCHFItDUjMmyoeBwX1b\ncbHYzDz91XxsXe346PxpsTai7ZS5s6IFlcKNOjEE36lxyMqLQTn+uP3NHzYQLn6W90IvRkIgpH8b\nlykXoeuy06lRMHDSoX4a84YMtkydS8vGHagCTKS99SS262egDjQd2CYqLpmn533BKL/xuEWRakMt\nt0j3Mcm9hU43PLq6nLPe3s226o6fH84x6Rc2kH9c9QaPXf3OgXRtADXBTv43xo/3xQhsDhWeZjvN\nr+/CunIJ2O4F93cg9zwmon9KGFfdOgaDj4aaSgvrW/wZvPht0t75N35pA3G3Wlj6zMtsGHkp+f94\n+bQJwNuxpXsOO1kp1/azvzhUnf3kG8Rn5u2e84vTru3zNiwHHpJl+bDoqGeeeWbB6tWrydi5k221\nEh2NXho8g0lUKJk/ayjp6elUVVV169VkN5vX/5uqahsxCfNB8CE9PZ3KigosT7+Lq7mVjivOoc2g\nOqBvO6T9MT6bdr6Gq3Adu1zRtI+4g9h+8cdtX17UzGcfLqXT2crc289FrVH2aH/Z2dmMGjWqV8f3\n889T59xCblcE27N2Y29vISW1gy4fPUWFNoJ31WPQD6MtuJ3KmmxK3VuI8I/D1apn2XerydlbSMrQ\nZLQ61RH7r2kTSL70QTy1P7Alq4Xq/AwGjGiiq3MYnfnN+HvjqfaxI0pN1OWkI7TuISJuHHU2gcU/\nrGNvYRkTh/ZHo+5Hc0sH50Z8ilDeSrlqAJUtNXy0o5jyzGpGJIawZ+/uHo03tl8cAWOGUh7pS2VV\nFcaqZppWbGTDylWYlTIJqYOPe/4yzBLrN6YT6qjhtstHY+90sW3bNva0hZI46RJ8zZvYsiuH4lUL\niTRpUUWnsXnL1j5dn5iYGKqqqg7860v7E/3ZTwWL12yivqaa4f1jCdQKv6i/U328v8bn119/nYUL\nF5KdnU16ejpWq5Xx48f/YdKuLViwwLJgwYI3FixY8OaCBQteX7BgwWFPy61bty4YNuynYGdJknj/\nx//gFrzkcgXNhPHIWbGkhBp7vX+Xx8lTi+6ipqUMg9YXW1c72i6RW3d2EB7RgeijJ+imFATdkG7v\ncA8rZI4IDaQ9YgTvVnsZ25mLSdFElGcX3m2VvNkVglXdxZDQQEqee5fcv/4Hr91B4KSRjPryFUzD\nBx/yG9iPSq1h/MiZ+Nt9yGnYRbNSJkixhyvs28hTD6eiU8knexqpaXcyJtoXvarnS/DBfuGcPWQ2\nSRGpVDeX0G7vdmQ0+ihJ9zfiY4NIuQtXhQVnQSOaqAoU+jwQE0EwHaf3boy+WgYMCae6rIWWpk7y\n99STdE4ag+dfScC4YVQUFWOsMdO+O5eq977CXl6LPj4aTdDxV05/r3ybVYk3IJqzw0SCtCfPKBaB\nDQ0SbgmmRpw8qQZwxN/2H52+pl3rs4YYQBAEJfA9sEKW5VeOtM3atWvlYWlprH8kljf9Q+iSY9no\nvo8rvI288dhlh27szUR0/htZiEHWHZC00bBsPVk3P9xn7XBXzgra3rkWFGqC7l/boyArr1fig1c3\n09rcyZTzkxk1qV+v9vlr0drcROZ7dzCofh0AZk8ITauMuDvUmAMN/Di7H51+uwAwiP1J7piD6DGg\nVIqMnNSPUZPi0GiPHAUuez10rrkd6w+LQQZVTBhOv7/hbA3Dq4C3jVuotKxEwI0k+BASeR3lXSNx\nSzL+OiV3jotiUj8TAu0IzheprSzhbx+OZbk2DVkQ0bm6mGuw8eCfp2LyP35p1QPHJcvULVpBwYJX\ncbd1ICgURM+9hMQHbkHtf/Qypa/mecizyPwpUcHYEBFZlklfXcz29WUATJwcSlLVs7jyVgKgCE7E\nd/aTaAZNP+m5IU8WSyq9rKyVmBImcnX8yZ2I/wj80TXER+LnGuLy0hwe+nouOi98630ZX42C/HvH\noOuFAQjd9/Xryx9jY+4ytCo9XW47aknJ5d93MWJEDYIgE3hjGpr+CcjaZw+RzPWUdXsqKPpsLWc7\nX8RXrsfrEinZGcuScbfROHoQ1/7t76i8Mol/vZmEe+ciKHo2htqqEl79+u9UussRZJnJNht5wqV8\noTkfj6zATyPy4ORYbh4ZjroHueJ/fl4yitbx2cb/0tBW1R2iLkK808F1LWb88YAo4HN2HMYp8Qja\nS5BVc0A4ftwIdBeUWrZoL6X5TYiiwNRZAxkyOhpBEOjILab8f5/Q8O1aZG+3BzrkvEnEz78e04iU\n4/T8++Mfu900d8GCYUqUnRKrAAAgAElEQVTCdCfvtvbKMvdu9+CW4MXRSvTK02pKOen0dd7+RR7i\nxx9//H2gUpblfx1tm/Ly8gVhPi52bP6QAq0eizeZenkIf07QMfRnFeoE9xIEuRyU54Ki+2aUJYm9\n8x7D1dxK/4fn4T/q8DLOx8Lb0UjbG5cjux34XPw4utQLetQua1sleVn1mAL1zJwzBPEk5Sz8OTqD\ngfiJV7C5vBNtRykBmDEl2bAbDKhKvAze3Yzs6U9zhBqXWEGNZhtKvQttZzS1Fe3szahBlmVCwn1R\nKA+dqAVRRJ14Eer4aJwF6/A2WxA71mIYZMDVlshIZzQxgaPJdFchSk3YO3aikysJD06jziaysdxC\nQXMniUH++Bmn4uvr4rKJK5kiFlGY50uVPpidkp4PNpbizCtixJAYlKrjJzYRBAHflCSirr4Qr91B\n+54C2nfnUvPpUhR6Hb6p/Q/LG93hkvm8TEIQ4NoEBWqFgCAIxCYEolIrqCxpoaqyE7n/LJKmnoun\nZg/e5hK6dn+Fu3IXyqihKIzHztjxe8SgFNjUKNHqlJkWcfKTwv/e+SMW5jge5eXlhxTmWL/xK3Jb\nMhE94RQxibnDwzi//7FTTx6JFbs+Y2nGByhEBW6vC5Wg4tzFEqNT6lCqvRgnxWAYE92dXk2M7nX/\nDruLHz/LxubU84PvOEyqfMI9rQRFW0jKy8Sz18l3d/yVftdOZsrcS4+be/5gfP0COHvUbLqqLZR0\nFFChURNIPvOtP1CuG0ilx8S6MguLc5uJNmlIDND1+F4TBIGooHjOHXY5IaZIKpoKsTtsWGUVm319\nMEgS0S4nrnILjtwm1KE1KI07QQwD8fgZfhRKkeTUMNxuL7WVFsoKm+mwOIhLDEIfHkTYBWcRMec8\nZK8Xa0EptoJyaj79jtYtmWhCA9HFRv4h5g1ZlllSJSHJMDtWRHkSn+miIJDVItPuhhSTQOBJ9E6f\njvR13u6zQSwIwgTgJcDw+OOP37bvX+WCBQsOEZAuWbJkQWp4JxuzVlGl1lLrHYGFeJ67aAC+fgdp\n0GQPgut1BFzI6ltB8AO6M0tUvfslmvBghrz6KIKy514JWZaxvH8jnvpc1P2n4HfZcz26se2dLr79\nJBOvR+L8OakE9nJpMD09/VdfokgcdTaWkElU1BYRYq8iwN+CapACe5OC0OIOBmS56PAbitXfTAel\n1Oi3o9doUNpCqSptY+/OGgSBbsP4Zx4MZWAqupGz8TSsw9PQgrc6C2NMHl71cPwtRs4SRlLqq8Pi\nLAdPHXbLekINAm5FApUWF8sKzGRlbGfs0EtRqxOJDt/G3LOySemoI6csgFpDAOlODR+vyUNZUcmQ\nlBgUPfDMKPRagqeNJ3TmFDpLq+gsqsC8diuNy9ajj48+pNLdliaJHItMir/AxNBD+46M9Scg2EBp\nQTP11e20uINJvekBlL4BuCt24mkowL7lfaTOVtQxwxHUuh5dkxNxnX9tfFWQYZZoc0GSn/CLlgl/\nD+P9tTkdDeIlS5YcIpn4bOWLtHhbKJBmYCGWVy5MItjQM+/kfrIrM3ht2T+RkZFlGVEQmbbOl5Gm\nGowhdlQRvgRcOQhZMxeUE3p9zLIk893ne2is6SAsyo9750/l7uZ4cJkZYK/AGOKgn1BC+LK9/KBP\n5jOLk3PjAtDse0HvyW9bFBUMTZ1Ef/0Assu306TwkKsTmW3/gWldZRQYB1LZqWJxrplt1R0MDjEQ\nauz5eRIEkbiQZM4ddjkmn0BKmvMRWrooUugp8NES5+pCb3PSuaser7UNbXQ2gli5LwD92HpuQRCI\nSwrCz19HRbGZxtoOVq1Yx7CRA9Hp1ahMPgRPG0/UtRchqpRY80roLK2i7quVNK/ejMrPF0NiTK9e\nIk412lzwyQ+bCI2MYWb0yV8tK7dJVHdCjFGgn8/JO49n5u2e80uyTGyWZVkhy3KaLMvDZFkeLsvy\nD0fa1tNcgFnZvWxvI5QIWxuRMSGHbiTlIGBDFqIOeAe6q9K9C0DC3Tcgano3Cds3vY2zYC2C3h/T\nta/1+GbevLoYZ5eH2MRA4gf0LCH7ySApbRhTHllOVtKfsagCCRSbGDC1Ar8ZLnSSi3OWZXPRB1pM\njaMRvQ7KWMqOwOdw+Odh73SyYUUh77ywkd1bKnC7Dg3QUPgm4H/rdvzm3IqgUuDMzUddMx+/+PUo\nJbi9Yzx/8n8QlPHdFe7MX6FueYxkYzEA6ZUWbvwyjyX5MbjVzyOLKVw4o4pt//iUV2J2E2lro9Fg\n4uEGHUMfW8krb6yky+Hs0bh9BiYw6stXGbbwaXSxEdgKy9l55b1kzJmPZVd3gHxGc7f0Z3TQka/x\ngCHhXH7TKLQ6FWWFzXz69m7cqXMJfnQn+vF/AlnGvvEtmp4YhvWHZ5G6rH28SqcWgiAwYl+2ic2N\nPc8AcoYzAHg9Hiq7uiVHDfJgUkJUDArpXTBdq7WJV5c+hCR7EYVuQ2Rq0wAGttYTENeBoFLif+VA\n0IwHZe8DpgF2pFdQVtCMVqdi1tVDUXu9PL9pMxuLknky7DYsKj2GIAcjxu/h1jVPkLbwc/70URYv\npmf2el9Dhk/m+du+ZlLQVCRBYLWPP9k+JfyfeR63yd/hq+5iY0U7Z72TxV3fFVHT3tWr/pUKFecO\nu4JXb1vKxRfdjdLXB6nUwJuqSFYb/ZAAR0Ytlc9upX339whd94L7K5CPX/Vh8PBIrp03Dv9APZZW\nOx/9bwtFOT8F1GmCA+j/8O1M2bWE/o/MQx0cQMfeQrL+/CibJl1D9SdLkZy/QXWJX4FKW/czIsb4\n23hnowzd+63pPJNp4lTlFwfVHQ+Hw7HAv2kNS5tqcYgKSr3nMU7ycOk5h2aY+EkuMeOAXKJx2Xqq\n3vuq2zv8Su+8w+76fNrevxEkL/7Xv4U6dkSP2tVUtLF2aR6iQmD2dcMxGHufn+VEv40ljZ6GI3Y6\nRTVlBNkq8de1Yhjsxq7Voy7zMiC7EVNTGPUxcUjKaprJpt6QTYAmEKnDl4qiFvZmVONyegkMNaJW\nd3tJBEFAFTMd7dDJeKrX4mlqx1u1C2NUNrJ+CCaLH2czHEtgJLVdlYhSM/b2zejkSuKTp9HQqWRn\njZWN5U6C/KYR6WdClPNIG1TOLePr8C0PoKBZosFgYoNdzQdrC7DnFpM2MAq15tjVrgRBwJgUR/T1\nF6M06GjfU0BncSU1n35HVUUzm5PHoRHhukTFUZfCfE06kgaHUlnSQmtzJ3mZdYTGhhI2+VK0Qy7A\n21KJp6EQV0k6jq0fgahAFZmKoDjysf1e3rqDtQI/1kvUO2B8iIiuj/q138t4f01ORw+xw+E4IJko\nLtjNmuKlIOkplGYxb3Q0Y2P8etyXV/Lw3OK/UNdagVKhwit5GOs3muR3soifXIuokDHN7o+mfyqy\n5qEe62IPpqaijeVf7gUZZl2dhr/Cxc6r/kLLhgxGNDQTetW1/M01hiSxjEhPC35RNuIcRUQt30uG\nK4Q3WwUmDAglNqBnwWoAao2W0cOm0183kIKKTJrFLnbqjQx2ZnFf2/d0BERR6g1lT4ODd3fVY7a7\nSQk1YtT0/BmmVKhIjkpj6sjLsIYpacjNR8zRsiVUT7DCSaDLhTu3mfLsJlRhleiNO0EIASH8mLmL\nDUYNg4dHIrv1NNVbKcxuwOn0EBMfcEAaqNCo8R8zlJgbL0MbEYKtqAJHRS3Nq9Kp+fx7kGV8BsYj\nqnt/vX4rtjdL1OujGRkkMsDv5Hu63RJsbZYRBYFJoSdv/2fm7Z5zwg3i8vLyBbrKJSzptCMjUiTN\n5voIJaNHHJTQXfYguN7YJ5e4BQQ/ZEliz+2P4TK3kfzIPEwje64dll0OWt+6Aqm9Ht3Y6zBOvadH\n7TweicUf7MJhdzPmrAQGDDlCnuRTBL+gYPpNvJKMZi0uay1BrnqCAlpRpCjpsivwLesidWcrKlsc\n5ohwvIpKmsiizpCFn8aIYA2ktsJC5tYqrBYH/kF6dPuWQUVjFLrRtyHqm3CV5eBpNCO2r8KQ7MLZ\nnswgRyjjtOPJ18p0umrAU4+97UcCNTZ0hmTqbTLryyxsrw0j0HcqkYZyVIpaxgzN49aJ/oTUGCms\nt1FvMLHZqWHh+mJaMvMY1C8Yo8+x5QqiUon/mKFEX38xgkKkY28hOckjMacOI6FkL8N9PKgDjv7A\n1unVDBoWSWuTjeYGK/l7ulOwxaQmox91JeqkSXiaS/E2FeMq/BF7xmeIGgPKiMEI4u8zKE2vFKiz\ny9TZQSXCANPvd9nzZHM6GsQHa4hXrfuEwvYcmrxDaJCH8vzMRAJ6Uar5y/Q3SM9bjkJU4pU89A9N\nYcRLlfQbVYbGx402JRifcweB9jEQe6/ht9tcfPneDlxdHkZN6kecsoMdl9+NvbwaXWwEoxa9wvDJ\no7hsxEBur43HLrsZ4CjD6NNFWFQjiTv3YNxVx1feUL5qbGd0qA4/Xc8kUwCh4bGcM/xSnLUWyjqK\nqFKrydMpubjtR650bMccFEtZVwC76my8t6ue9i4PqaFG9OqezyUqpYbBMSMZN+lSasKduFaXYs33\noTJcIFLswrfTiXNnHVkl9SjC9uCnLwYxBoSAo/apVIr0TwlDq1NRVdpCXaWFqtIWouMD0B50fUWV\nEr+0gcTceCmGpFjs5TXYy2to2ZBB1Qff4G63YkyKQ+lz6hc9WlkrYXbCtHCRMP3J9xJrlbCqVsLu\ngfMiz8RznEhOWYN4yZIlC/RtG9ksyXQRQKV0Dk9MiyM49KAIYikb0bsaWYgE1ZUgCDQsXUf1wq/7\n5B1u//KvuPLXoAhOwP/mjxCUPXuL3bquhOLcRgKCDFxw5RDEXkYL7+dkanbih44heMINbCtrRGer\nIUAyExzZhiJZhcsCAaV2Une2obbGYQ6PxKusxEwuNYZdGPUqVJ1hNNXZyNxWRWNtO1qdCpO/HkGh\nQB13Htph0/A2peNpbMFbU4jOsAF1TDi0RjDOnUCc32j2ymZayspRaavwWNcToHWi0CZSZ5X4scxF\nRsNognziiNBnoxLLGJ5Swq3njCS6HoqqW6k3mMjw6Hh7Ry2563YR66MiPOLYQTsKrYbASSOJvGYW\n3/sl06XW0f+/z9HyzH+xFVegj4tEE3LkPpT7gkwEQaC6vJXqslZqKtqISwxEH94P3ZhrUcWOwNNQ\niLe5FGfuSuwZnyEIClQRgw54jH9P2iw/NWxpkql3yJwdJqLoQ0DJ72m8vxano0F8sIb445XP0i53\nUCpNI9QUwd+m9Lwy3d6Kbby98kkAZFkiyDeci9IjCJB34B9nRfTTEvSnoWC4BxS9C5aGbt3w0k+z\naKq3EhFjYqhUw55bH8Fr7SRg4ghGffEK+phuw16nVHJLWgIb1Qm8YE4iUSwnwtOKX6SNaHUlXd9s\nxKdFwbsOf5bXNDA50oT+OKtW+1EqVaSlTiYtaAwlZVk0yVaydQZalA5uNC/jbKmE9rAISjv9yaix\n8t7uehwuicGhhl6latOodAxNnMDgWZdQY+yAL+poqPGlM9xDqMJFYLsd2856VpdU4jSmE+7bjKBI\nPKq+ePPmzYydOISYhEAqis20NHWSs6sGo6+W4DCfQww2QRTxGZhA9A2z8UsbiKO2EXtZNZaMvVS+\n+yX2smp00eFoQk/NwGRZlllUIVGbtZkbxsT1eZXsl6AWBTY3SnR6YFSwiFF1co7hzLzdc064Qbx1\n69YFtG1kr0JJuxyLo3Mg/7pyxKE3m/sbBLmsWy6hTEXyeNjz50dxt7aT/M+7MI04fpq0/dgzPse2\n4mlQaQm8/WuU/lE9amdutLH8y73IMlx87TBMPSzCcSROdt4/hVJJ4pjzccSfT259A8bOOkxCK0Fx\nFuivwdkqEFjeSerOVjQdsZgjopCU1bTKhVTrt6E0ONA5I7E0u8jPqicvsw6P24t/oAGtfzTa4beg\nCtfhrtiB19yBXLsFY+Qe8EnE1xLMOdJQGrw6Wn3ciJIZT1cRcudGgvQSgjqeWquXdWU+7Go6iwCd\nSKSxEIW8gyGDO7h5+nSSWhzUl9dTrfOjUGHkw5JO1izLQN9uITk5AvEY2u9aWcOaNg1GUeI8cw62\n3GJseaVUf/gNll05aMKC0UWHH/Y2LggC0fEBRMSY9j0MbORm1hEYYiAg2IgyOAH9uLkow5Jx1+cj\ntVTgLFiLfcsHyF4XqvBBVNc3/W4mGn81ZLfJmJ0QqBX6pKM7k8/y9GB/HmK3y8VHW15BEmQKPHO4\nYUQ8k/v1LBVam62ZpxbdidPdrZ/VqHTc6nc13q8WET26YV9p5lSU4ZeB6pK+Hee6UrJ31qDVqxjW\nsoeKZ14HSSL2lssZ8t9/ojToD2szOSqUs1L7c2tNEp0KkQRnBb5aBzYfK+PtRcSv2EGbQ8tb7UbW\n1dRwdkzggcC74xEQFMY5Y+bga9NT2pRPs8JLht4HldzIvPqlDNU1Yw0OpdTqx9bqDt7ZUU+TzUX/\nQD0mXc/2AaBV60kZOoX46y+juqkK11ftNLXqEcO6CBTdxFlstOaY+aSgCIu8mkhTF2r1gMPkKPvv\nZ1+TjsHDI7G02Gmut1KS10RLk43oft0Zeg5GEAQMCTFEXX0hQWePwWOzYyssx5pbQvVH39K6NROV\nvy/6uMhTKgDP7Oz2zsot1VwzKu43884Wtss0dUGir0DESfJSn5m3e85J0RBX5X1OsVpDszSIhK5Q\nrp5+UH5D2btPLuFEVt8EgonaL5ZT+9n36GIjSH3pYYQeemrd9flY3rsevG78rngB7aDpPWrn9Up8\n89FurO1dDBkVxbBxsX0Z6gF+qx+fX0AgCeMvwRI5lcLGenw76zCJrYT0a0Pqr8NtgYAyO6k7W/Bt\nDKc9MBmntharXE6dJp0OQxV+ikDcHTqqSlvZvaWSpnorWr2awIFnYxh/E1CKu7oMT1MLgnktxn4V\neMRY0pSpnCONwO2fQKW7EVEy43bkg30zwXoZWRlHrRXWVUSzsWYCSsFDrE8OKnkNgwZouX7GxZxr\nEGnPK6VcVlOj8+M7s8zHK3NozCogMcKEn+n/2zvv+DquMn8/Z2ZuL7q66r1akuXe7dgpdhLiFAgh\nhdDZAAvZBZa2P2Bhl14WCMsufSkBAiQkZNOdxI7T3O2425ItS7Z6L1fS7Xdmzu+PK/cmO26x7/P5\njO7cmXNnztHMfOedM+953+OjfSzvMDkQlCzMVbn+3ddQ8O5bQUCwfj+hfc10Pvo8/S+vx5LuxVVR\ndJxI+zKc1M7Ip7drlIGeIHu2dzESiFBU7kezaFjyJuJc+BEsBZMxBlow+vcT37eK8OrfUZjpQcut\nRrGPP8byxUIIgVWFbYOS/qjkmtwzf2V3pYkqXJkG8UEf4n31m3m58VmiZgb7zZv4wdJKsscRNcE0\nDR544vO09+8/tOz+RV8i8oXfULZwP5rVxLO4FOeca5HWz4I4c1ekxroeVjxVB8DE5vWEHnsSYdGY\n/KMvUvEvHzqlMZZut3H/rEo22sr5Su8Mcm19LFI7cWdEySnpo2zfbkpWbKMv7uJ/hxy81tHO1YV+\n7JbT9xgrQqGycho3THsXia4gzSONdFo01ri8FEb2c3/nk1Q5hwlnZbI/mMaWziC/eaOThv4wpen2\nM4pKYbXaqV78NgrfexttdU2MPBUlErFgy4mQRYLpwyMEmob4w64dHBh+Bp99kHTPZMSYYXzk9Wyx\nqFRPySUt3UFL0wB9XaPs2tyO1+cgI9t9Qq2w52eT+/Yl5N99MyiC4N4DhJta6XpiBZ1/fxEzFsdV\nUYzqtI+7TeeL+oBky4BkVlUJc7MunutbZ1jSOCrJcYgL5sec0u3xc0F8iPfu/TvtVhtd5myuTyvg\n2gVH5KY3d4y5S+SD5V7MeIKtH/0K+kiQ2u98Fu+UqnHtx4yOMPjLOzFHenDMfjfum7887hv+6hUN\n7N3Zjcdn5/b3zUA7w4Dzlxrp2TlULLyLTt98Dgz0khbqxKcOklU6hFYjMGLgPhCnZnsvpXuchF1T\nCKbFiIt2epQ36HBtxekEaySbod4odds62flGO6GIwDfjXjIX34EMbkPv7MLo7kQNrMBdsh9DlFAx\nWsIScw6htGLaEl1Jwzhchwi/gkcbwWovpSdsZ31HOcua5hNJaJR6NuHgGfLyXNx+/W18oCYLUb+X\n/SMJel1pbNQd/O+2Pl5//g3UgUGqqvJQVZWILvlTk0HChHvLFHw2gcXrJmvxfIo+eAeqy8FofRPh\nA+10P72Sjr8tw4jGcFUUo7kO+wlabRq10/Ox2S20HRikp2OE+m1dZGS7SM9wIYRAy6nCMf8DWCsW\nYAx3Y/TuI9G8kdDrv0bvrEPxZKGmF13SfmG5Dljdk/Sjq/QKslKxME/LlWgQH/QhfunVv7InsIte\nczKaq5avXT9hXOf342t/y2u7ngGSZe9e+HEyfrKRjIz1uDKjWIq8+O6aC46vg3LmD5P9PUEe/+Nm\nTENS2LgJ+8svYM1MZ/Zff0zO0mvGvZ2FBdncO7OSbw2WsyJRRp7aRUFiEE9OmLz8bsrrdlL08nZ6\nYw5+02/j6ZYuJngEud7T19litTFtytUsLLuB/pY2OmLtHLDZWe3yUhRp5ONdTzHD3gP5PvaH/Ozu\njfCHLd1saBshza5Slu5AGaeWWF1uKt9+G9m3XkvL6/sYfM1ESoEtI0qumWBecJRw5zB/2VPPa02P\nYeoN5GVMxqId3Q4hBNn5Xmqm5tHfPcpAb4iGXT30dY9SUJKOzX7iXmxLmoesxfMp/vC7sPrTCB9o\nJ9LaycDrm2j53WOE9rdhy83CnnfxIjat6zXZPyqZm6VQdREG1B0kqMOWAYlDhblZl04P+uXGJWsQ\nP/HEE1/vCG1gULPQalzLRydPYELV4WDiIvEIQrYiLTeDOpnWPz5B95MrcFeVUfv9L4zrtYs0DQJ/\nuI9E80a03BrSP/JnlHH6Dbc2DbD8yd0IAXd8cBb+rDc/OOBS8dnJLiymfNE9NDum0jIyjCvci08E\nyCgYxjUpge7U0NpMyvb0MWmzDkYVQV8uCa2FIbmPdsdqRlzNOBUnMuilq3WYHRvbaNhnIKreS841\nS7Ak6tF7+li3vYti46WkYayUUhUs4zo5lxFPAZ3GIIo5gIzvh9BLuOggzVNIf9TLjt5inmyYS8eo\nlzTLZrKtj+P22Fi86G18/JoqKno7CLR2025x0Wrz8twg/P6lPRxYt4sW4aFDuJngFcfFlVQdNvwL\nZlD84TuxZWcQ3t9GpK2LwdWbafnto4QaW7Bl+bHnZyNEMolHfrGPqkk5dLcPM9gXon5bFwO9QQpK\nfFhtWtIwzijFOedebLU3sm5nE7mJdvTuPUQ2Pkx0xzMgFLScCeP2W7+QKEKQMGHviKQ7DAtzxBkZ\n8JfKeX0huRIN4oM+xH9f/t/0m0N0mPO5ecosbpyQfdrf7m7ZxK+eP/zvml99A4v3lxJa9SA5tYMI\nq0rGR2ai+L8KavkZ1y0aSfDY7zYRCsbxtdST9epTpE2tZs7ff4pn4vj9mw/itGi8b1IJ/f2DfE9b\nSrvqI9/sIJNRvPkh8vK6qKjbScXyjSQCOo+Oenm4ZQglMcTk3NMbeG6Pj6tm30Ktdzo9Hc30GP1j\nhnEa2dEWPtjxHIuszVgLPByI+Nk3mODx3f08vL2XcMKkwu/APc4BePasDMreewfeyTW0LGukf4sF\nhMTuj5FvJLgqPIo2EmVZezuPb32UtauepKi4lAxv4VE6YHdYqJ2Rj9tjo715kL7uIDs2taGqCrmF\naSdNUqXarKTPmULJfXeSNqMWfSRIqLGV0d37aP/L0/QuX4OUEmdZIeoZhlB9s7zQbjIQg8zWtUyb\n8ObeAL8ZFAGvjqVwvuECpXBO6fb4uSA+xHsjm4gpCvv1pXzvXXOwO8ZCmckQIv4rwATrJzEiKts+\n9hWMUITaH/w/PNXjE8zR575DZMOfEc50Mv75KVTv6YUbkgk4Hvv9GyRiBguWVDB5ZsHpfzQOLjWf\nndzSCsoX3k2o4jZ29QyhRgOkGwOk+4fx1YaQhRbUoQRZDaNM2jJIYWMacdtEIl6VmNJMv7qdduc6\n4o5eXDKd2IiNtv2DbN0BnY7bsE9ZQnB4B/lGAKOnK9ljXLgXYfNRNTqZJeZsLO4aWmQQ0+wDvQNj\n9GVsej1pDo0RI5f9gXxe3D+dV1sricf3UGB/GLdtmElTFvLeG2fxnhIHloZG2oej9DrT2I6LV1oD\ntPUOktW8lzKXOHqg5hiK1YJv5iSK77sL3+zJGMEQocZWgnWNdDz8LL3Pv44Zi+MozkdzOXC6rEye\nWYDVrtHREqCvazR5M9AUsvMP3wzUtDx6rMXU3P1vCJsLvbcRY6CFWN1ywqt/ixHoRHFnonhzL6le\n4yK3YF2fSU8U/LYz8yW+1M7rC8GVaBAf9CH+2+v/RVQYNBk38fWb5pHvPXUIykBogO/87X5iiQgA\npdnVfGLSp2j84pcpXdCGUCD9zolYqz4B2pIzrpehJ13bejpHsA90U7ziEQruuIGZv/8+1ozxh0w7\nEfH+Pr5/x3X0+sv4ZNccInY72bKbTIJ480PklvRQ1rGHmhfXYGsP8ErUx4PtMTa2tzInN/20A/Cy\ncgq5bt4d1Hqn0d/ZQbfeQ7M12WPsiXVyV8dy3q5vxpnnZMiSRuuIhVXNw/x6Yyf1fWEyHBpFabbT\naokQAldlCaX33YU9v4COlR30bdNQVIkjPUqhEefq0AjZsRjrOod5ueMVXt/5MCPhbtI9hXid6Ye2\nk1uYxsTp+YwEovR1j9LSOEBjXQ/pmU58/uP9sw/VQVFwVRSTf+dN5N91E8JiIdTUQqSlg74Va2j5\nzd8I7mvGkubGUXj+9dEcG1CnS5gtOqgsu3gGsVOD5Z3JgXWL8xSsFyBbXkq3x4+Q8vwGiV65cqX8\nwUsfw5Qq9UP/xs7vv/PwysSLKInfIJUpSPvX2PfD39L0wO/xTq1hwYu/G9eFEtnyOIE/fQwUFf8n\n/o6t6tpx1cs0TGdFPRoAACAASURBVJ54aAsHGvopKPHx7o/OPeuoEm81ouEwrzz4Tfw9qygeqT+0\nfERPY2i/m2CdHT2aFPjGCdnsmZXLYE4jpjoEgKl4yJbVZAdn4UgUIsbyuxR5e5guH8LTs4WDR85S\nmI3pv57g4FKQVvZoQzyhrGIkthlBIrk94UJxziZhX8KoTD6UqMJgQcE+3la+i9mFRWj2t4NSgGma\nLH9hCz+tC7JZtxHXDycYqRrt49ZcjbtunMLEyaUnbX+krYu2Pz9F+5+fJj4QAECoKpnXzSX/7qVk\n33QNqsPGSCDCymfqaarvBcDnd3LN0iomTMo57tyUepzojmcIrfotiQMbDi3XcqpwzHkPjtl3o/pO\nn2b1QrCxz+T3+ww8FvjmDO2ijLh+q7Blyxauv/76K+oftHLlSjmxppr7fnoNJoIt8pvs/eLNp3yF\nb0qT7z32SXY2J899r9PPt979Wxrv/RL5Ja9g98ZxzszFd8/7kbYvgTgzrZVS8uyfN7O3vh8tHKRi\n2R+Y8q8foOSj95xzg8o0Tf5n2x5+/GozH4gt4/rQa0wMdiTrYUKgzUP/Xj/16TPYNXMB+6dMx+Kz\nsLRE4RPzpo5rHw11m/n7q79kR/BwYpCJ0TALQyMU6SovFr6T19Nm82pXKaZM/q9KfXbumpzFPVOy\nqcwYX2g4M6HT8bfnaPrxg5jD7WRPGiC9dJiDL15bHXZW2L3stjuRQlCWXcyiSXeyoOYm/J7DPeD7\n9/ax8pk6hgeTDzvl1Vlce3M1Gdnjy+JqRGL0PPcK7Y88x+DqzYeWO4ryKHj3LeTdeROusvENgD9T\nusOSr2/TSbfC92aPP2Tg+eJ7O3RagpLPTVIvqvvG5czZ6vYFM4iDMgfn8D/w6HfvPrzz6JcQZiOm\n9dNEeqpZtehezEiMuU/+Av/86afddrxpHQO/uhMSUbx3fA/XtR8fd71efraeLWtbsDssfOCTV5GW\nPv7Yk5cTrz/2R+IHnqOwdzNp+pjBi6A/nE2oTiPU4sRIqOiKoLE6lwOTM+nPa8PQepJlhQM/leRG\nZuGMlqGg4TG7qOVFyuKvoZrJbHSKx45WPo/gyNsx4gX0EuYZ+04a4xsRRteh+hhaMZrrKobEPMwx\n/0K3JcLCogauLdWZXjQTU0zlK1slgZjJpO461m5tZJXmJ2Y53INVHBxksUfn9gUVXH3t5BOmijZj\ncXpfXE3H31+g/+V1yDHjWnU7yb1tMbm3X0/Gwlk0Hwjw6rI9DPaFAMgrSmPhDRMoqcw44c040bmb\nyIa/Etn8GGawP7lQKFirrsUx+x7sk5aiOMef3OBcI6XkR7sMmkYlN+Qp3FX21vaZP59cqQaxMIb5\nz1e+wKiZh7fwszz8/utP+Zsn1v2Ov636BQCaYuHf3/Nr5K9fxdz5Y9JLRtGyXGR+8haE90cgztxv\n+KU/r2dbXQAlEadqzeMs+OG/kLFw5lm1b7yYpslPtu3hZ+s6uGZkHe+IL2fuUD0qyXtmaMDO0P40\nenqyqKuZx+6Z8+mrnEC6J84HJ6ZzU83pXTj27dnGs6seZPPQenR0ADL0BAtDI8wJh9jrm80LOTfy\ncriW7vBh43Nmvpt7pmRzR23muNJom7E47Q8/S9N//xFjqJPMqiEyJgTQLMnslUGnlZftXtZZnUQV\nFYFgQv4k5lbdwNyqJWT7CtATBpvXtrDh1SbiMQMhoHZGAQuWVJyyx/hYwi2dyYHzf3uOaEfPoeXe\naTXkvfMGct9xPY6CnHFv73Rs6DN5cJ/BdL/gEzXjj+ZxvnioUWdNr+SeMoUleSntPR9csgbxAw88\nIJf3/5xeczI3Ou7hy/9ya3KF2YYS/SwSJ9LxG7b/8/fo+r/l5L59CdN/8+3TbjfRWcfAT29FRoZx\nXvVhvHc/MO6egm3rW3lpLBvd3ffNoajs5AHMz4bVq1ezaNGic7rN881oYIg1f/0BroE3KB3cjiaT\n4mxIhcBoOqEGC6NtbvSYhingQHkWTVNy6C8YJGY9wFBrBF+pBwcF5MRr8UZqcBlOyvU1VOnL8cmx\nHhYh0ArzwTuX0OhNGLqXN7RuViqbGIlvQ5AM0yRRMC0VSMccRpU5yDHj2GMNU1tqpU+bTrFL58tT\n7QghGB0O8fhTG3lmTz/rtXQi1sMjm32RIIuUUa6vymTpDdPIyTv+eMf7h+h6aiWdjz3P8LbDveaa\n10322xaSufQaurzFrF/VQiSUTF0aMtr40EfvOKlhLI0EsfqVRDY9THTXi2CMpTxVLdiqrsU+7e3Y\nJ9+C4j51zOXzQWtQ8r0dOkLAv0/TyBtHCKC34nn9ZrkSDeIHHnhAGmIfK/uW02HM4yM3foEPzz65\n+1p92xa+8fA/wpih+Imbv8bE9jRav/MJCmf3gKaS/alFqEX/A0rRGdfn5Z8tY0unAqbJxKbVLPnJ\nv5xTgwlOf24/1tDMt1c1kt7TxIfMp1k0tAW3kXzYNw3BSKeboQNeWuNl7J42n32TpjNSWIjXneBd\nFS7umVZzyv0HBnp58eU/82rzMoZksmNCk5IpkRCzIkGKE7Ai51bWpc/h9aEKgomkEawKWFTq49Zq\nPzdXZVBwGrcWM56g+9lXaP71I2zYsYmrJyTIqh7C6hp7W6cKWvxeXsBJg2Y7lPGuNLuauVVLmDPh\nOvz2QtauTIa8M02Joggmzypg7rXlZ2QYS9NkYPVmOh9dRs/zqzBC4UPr0udNI/f2G8i55Rrs4/DV\nPhWPHjB4ucvk9mIFT/O6i65hq7pN/rLfoNYn+HTt+TfQU7o9fi6YQdxsLOY7s9/H9TcmA76L+J8Q\n+tNI7UYGd1zFhts+jmKzsmjVw4eCqZ8MfbCNgf9eijnchW3qbaR/+MFxZxFr3tfP43/cjDQlS++a\ncs78ho/krX4C7t6whva1fyQtUEfh8B5Ukr0IphQEwulEmiwEOx1EAzZAMOB38mquFcusfAL+A5hq\n0g1BCjfpZjn+SDUTonFqE2soMLaijvWEmIpGIrcKxTUXPXIdo6ZgmXUPu81tJPQGxNh+DxrHOGYS\n1OZQWDUfVdMY7GhkkreZuUV+5hZPIsOd7EGJRmK8uHwbz21t49W4k36n91DbhDSZEBxggVdy47Qi\nFi+egsN1dFig4L5muv5vBT3Pv0Zwz+HQUYrDRvqSqwhMvYo9ASsN+3dSUlBLdp6H2VeXUT0lF/Uk\nbjdmaJDI1ieIbnuKeNPa5LtXAEXFWrkI+5RbsU28AS2z9E0fv/HylyaDVT0mRS7418kaVvXU+vFW\nP6/PhivVIG4cfJz9Zjf1+l288KnPkec5saEVCA3w/x68l5HwIAC3zH4fd5bezfb33k3p3Iak3/C9\nk7HP+TGos8+oHnowxPP/8RB77aUATIod4G3fuQ/Vfmqj72wY77ndMBTga6vr2FrfyXuM5cyLbGbK\nyAEOXvV6VCXQ6mG4w0ObWcK+mhk01U6jr6wCp9tgTrbg/jkT8TpOHIrM0HXWr1vG8m2PsTdSd2i5\nyzCYHg0xMxLEgZ8Xc29mrX06GwJF6Obhe9+MPDe3VGdwS7WfmkznSTuJpJS88Ns/kbuhgZ7nXyUt\nf5iMigCe3MNGacJrpz4jjWfjNvrk4X1keHKYVnYV1ZkLGGlKp2FHL1ImbefqKbnMvaac7HzviXZ7\nUoxIjL6Va+l68iX6XlqDGY0fWuedVkP2TVeTfdMiPLWVZ+wi88OdOk2jkk/XqgzuWnvRNSyUkHzx\nDR1Dwndmafht51deUro9fi6Yy0R94m5Wf/JTeNJcyVTNkU8gCGBYv836237I8LZ6yj/zIaq+dGq3\nByPQycDP34nR14i14ir8n/g7wjK+OIctjQM88dAW9ITBvGvLufqm8YV0u5Kp37SeljV/wjtcT2Gg\nDotMHFoX1e0Eux2E2uwEu12Heo+byzJpr8ykvwCC3mZMdRgAgZdcPY+ZQZ1J0WbyzMZD20oIB6Pe\nGuKuScB8YjhYqTVQz46jjGMAQytAOKcTlDXo2gQQyafsivRR5hbZmV5QzsQcP3ZNwTRNNq7fw7Or\n97F6QGe3MwNDPfxUbkvEmRgdYrZPZWFtHtdeU4sv/fAr3VBTKz3LXqPn+dcZ3rL7cB0sVoJX30Rv\n6RRiJLfn9tqYMruQqXOK8KSd/Jw0RvuI7XyOyPZniO9bBaZ+aJ2aVYGt5npsE6/HVrkQYR1/j8uZ\nEtaTvcR9UZidIfhIlXpJDQC8FLgSDeKVK1fKny3/FGElyn7zH9l4Ek3WjQTffPjjNHRuB2Bq6Xw+\nf/P32XbXB8gvX41mM3EtKsJ7+zfA8s4TbuNkDG+t48XvPkrrhPkAzCowue6fbr5kzk/TNPnd7kZ+\nsakNT/d+7mE5c0a3URwZOFTGiCuMdLkY6fDQNZpHQ+UMWitraK+oxsjwkOGM8a7yNG4/SWjRzvb9\nvLb2Cda3vUyP0X1ouV9PMDUapjYaxmb6WJ51C2+4p/DGaDER/bCPbFGajevKfFxX7uPaUh9+54n9\nZyNtXbQ++H90PPY8hLvxlw/jLxvG4kzqkgQSeR72eNysiFlpTxx+6FcVleqMReREFxHscB56zi8s\nTWfGghIqa7NP2klwMvRgiN4XV9P9zMv0v7YRMxI7tM5ekEP2TVeTuXge/qtmnDD5ypEYUvLZDTpx\nE340R7tg2eFOx28bdN7ol7y9SOHWopTbxLnmkjeIm4IfZtM3P5VcqK9DiT+AFIW0/O1q6r70ALbc\nTK5e88gpT3B9oIXBX7wTY6AFLX8yGZ98Zty+mAca+njqz1vRdZPJswq46Y7JiAswwvNyorl+F/Uv\n/R7HaAM5Q3vxJQaOWh+MuIh2WQj3Ogj1OYiHLJhC0FHgo70ii/5CC8PpfSQs7QgBfkPlqojJ1NAg\nWUbg0HZMBP1qFQHHZGK2aYTJY6e9kT2WfeiJeoSMHiorsSFtE4ir1cS0Ggy1GISKppjUZJpMzc9k\nWn4mtdkubJrCcCDIipd2sHJ3F+sjGi3HuCsopkFFaJAZTpM5JeksmFVGzaQSFEUh2tlL74ur6Htl\nA4NrtmCEwpiqSqBiKgNTFhBLS6YtFQLKKjOYPKeI8ppsNO3kNwQzNER09wvE6pYT2/sqMjJ8eKVm\nw1qxAGvFQmwVV2EpnjHuh7/x0hmW/OcOnZgJ7yxWWFqYEucjuVIN4h+s+BgmCpasL/OX+951wnIP\nvvRDXtzyCAA5vkK+/YE/ceDz38MbfRCbN4F1QgYZ930W7PcfevV+OsxYnH0/fpCNr+6nZ9ZiAK5e\nkMO8t884N407D7SMjPLAG3t5vq6fecE3WGysZ3Jw71HGsTQh2Ock2ONktNdFs30CLWUTaa2ooady\nAsJtJcMZY3GBg/dMrcZyRBIQ0zRp3LuN1zY9yaae1YzIwxphNw0mxiJMiobJj6u8lv421rtmsSlR\nTiB+eFyMAKbnubmuzMei0jRmF3jw2I5+XW/qOv2vbKDzsRfoXf46Lv8QGeUBPHkhFPWwnWDme2nN\n9LJa2tgyaHLQhLCYaRQkriUjNhNhJuvv8tiYPLOASbMK8GeeeUhTIxJjYNUb9C5fRd/yNcR6D/9P\nhabimz2ZjKvnkHHNHNKmT0Q5Jqvgpj6T3+0zyHPA12Zc/AF1B6kLmPxPnUGGDb41Uxt3zOkU4+OS\nNYgPukxEAv/EU9/9CEgTEf0CQrYSHbmT1xb8BiMSZfpvv0PubYtPuh29Zx8Dv3gn5nAXluKZ+D/+\nGIprfGlEm+p7efqvWzEMydQ5hdx4+6TzagxfCa8o4rEY6578C9GO1XhGmujau4erchJHlYnFrYR7\nbET67YSH7ESG7JgJlRG3lY6STHqLvAzlCEa93XhpY0oszJRYjLJY+NDAFYAoXnrUGnrViXS6J9Gr\nGvSrexlQ9mLQfdQ+JTZMazkxtRpdq0LXikHY0RSTcr9CTVY6NdlearKdFHhtdLT28crqetY29rMl\nrNDk8mMe437jikWojg8zxaswuzyD2dPKqCjPYfkf/krlYIz+VzcwvGMvobxSBmtmM1JSDWPbsGBQ\nnG2helYxlfMqsVpP7jMmDZ1E62Zi9S8Rq3+ZRNvWowtoNqwls5JGctl8LCWzUJxvLtwUwPZBk1/u\nMRDAx6pVZmac2IC/Es7rY7kSDeKDmj1iFvK+G7/P+2ZNPK7Mqt3L+Plz/w6Ay+bhux/6M0M/eRS1\n4QHc2REUv4vsz9yP8Hxx3JnohrfWsf3z/0lDxkQCE5KDqpfcUsXMRWcer/hMOVfndnswyE82N7Bs\n7yAlA7tZKtdRG9nDxNG2ozTNSAhC/UkDebjfQ4tzAp2FFXQWl9FbUkY4Jwu3PU61V3JnTQHTCpNu\nhIaus2vHWjbtWsmO3k30mocHpQkpKUrEqIhHqYjFGBaVrPJexw57Nbuj+SSOcK1QBJSM7uP6665h\nXpGXeYVeCtMOu6IkAiN0Pf0ynY8uY2T7drz5QXxFo8cZx0qmk0COlz1WO6vDKm2jJoq0kRGfTnZs\nPg7zcAhUT5Zg4tR8ps2sIC39zN98SdNkeNse+laspv+1TcmxHubhN4eq20n63Gmkz52Cb/YUvNNr\n+X6jRkcY3lehcnWOcslomCklX92sMxiHz9Sq1PjOX7SJS6XNF5JL2iB+vu83TLd9ha985jbQ16LE\nf4wkg/XvNRlav5u8O9/GtJ9//aTbiDWtJfDghzGD/VjK5+P/x0dQ7Kf3UZKmZN0rTax9uREkzJhf\nzJK3Tzzvr92uxBNw2bPPYOnbgxzcjSvUSvZwE25j9Lhy0ZCV6ICN8KCdaMBGdNhGIqIx4rHTnZ9O\nf56XkWwLds8IU406ZsS78ZnGUduICCc9SiVDSjUdWh4tVhiydjGiNZMQ/UeVlQhMNRddLUXXytC1\nUgy1CIQFjxWqspxUZrgp8zsoz3DgNROsWVPP6roudgZ09qhuAo7jQws54lGyDmxkRlkVk7JdTMrz\nUBIfRexpoOeNvbTH7AQqphDNOOwPr+gJ/LEhijJVJswoImtG9aHEICfCGO0j3rSGeONa4k1r0bvq\njiujZpZjKZ6BpXgG1uKZaAVTUGxn3hPzXJvBM20mArinTGHxCUY/X4nn9ZVsELcbC3j2Mz/B5zj6\nIW5v+za++cg/YpgGqqLxjff9DuWxDRjr/wN3dgRsVnI+9wGU7O+COH2vXHwgQMP3fkXz31fSuuRu\nwrnFaKrg1nunM2HSuR08dzLOx7ndE47w+11NPL+vj1BHG7cY65moNzAh1ExhdPCosqYhiARsRAbs\nhAcd9MWy2e+voTevmL68QgKFhYQy/TjtOmUekyXFfpZUFNPT2czGLcvZ2raWpmgD5hGuZUJKChNx\nKuJR8uM63WIi611XU2+ZQKOejdFaB0VTDpXPcWtMz/MwNdfN9Dw3U3Pd5HusxHr66V2+ht4XVjG0\nfgOejABpxaN4ckOolsP7QxEoRV4GMjzsUW2sGYbhQBEZ8Zn441NQOcL32zmCr9Cksjab2qoastLy\nzvi+nBgeZXDtFgZef4OBVZsINbYe/f+fcxXrvvw9XNEgnxnZTPrkCWztbOHqa8afzfB88kyrwXPt\nJnMyBR+pOn+D61K6PX4uiMvEV5d/nS/N+zbXXDcJEf08QrbTu3oqb3zwBWx5WSx65SEsvuMNXCkl\n4dd+xcjT/wGmga1mCb5/+OO4bviRcJxlj+7gQEM/CFh4fSXzF1dcMj5olzvxWIz1Tz9CuGMDtkgH\nnmAH2eG2o3yQD6InFGLDNqLDVqLDNmIjVmIhK4mQhf40FwPZHkay3Jg+DZ8rSonWy0RjLx4ODwAZ\nVDz0qgW0q0W0Wz30aCbDWi8RtQeEedT+JApSyUZXC9G1Agw1OZlKFhZNocRnp9zvpNhnp8BrRQwO\n0binja0HBtk5arBfO7GRDOCJhilMBClWdYpklLxwGGfUJKG6ifqOSBhjmjj6O/EMdZFlN8gr8JBe\nU4pnYjnuqrITJhowQ0PED6wn3rSW+P4NJDp2gh47upBQkkZy/kS0vFq0vFos+bWoGaWnHHgqpeS5\ndpNn25L/qyV5CneVKlf8q7wr0SA+6ObWa97Cy1/61lHr2voa+bc/fYDEWMSUz9/xAHnr+4g8/5mk\nMWyxkP3pu1ELfwji1KEsjWiMtoeepOnHDzLgyaNz4a3oDjduj5U7PjSbnDMcmHWps7V3gD/sPsDr\nB4Zx9+3nBnMT1YlGJoRbyI8OHVfeiCtEh21ExjoOhmNptNnL6cwqZyA7l5GsbEbz8wj7PLhtOgWO\nCAWJVuzD+2ju30VbouUoAxnAbRgUJ2LkJnQSRhaNltnstk6jnhJC8nh3LL9DYWqOh5psF9WZTird\ngsy6OmIvraL/9fVYZCee3BCe3BAOf/RozxghENkuQlluDlg87AhMYnCoBk+8+ijjOKr0E7a14sxI\nkF/ioSi/mPyMUgoyykhz+sd9z4529jK0cTtDG3cS2LSD5+79FAMTpzLpDz9nwtOPAqC6nHgnT8A7\npQrvlGo8kypxVZSgOs79QM3TMRCVfHWLjirgP+douFLx4M8Zl7RB/PkXf8ayT/8Ch+0NlPhPMOJp\nrJi5DzOsM/uR/yLzunnH/c4MDTL8+BeJbnkcANeST+G59d8R6qmfpKQpqdveyesvNBAajeFwWrj1\n3dMonZB5XtqXYvwEBvrZ+sKjxPp2YYt24gz34A91nrAnGUBKSIQ1YkEr8aCFeNBKbOwzGrbQ73Iz\nmOUlmuFEejSsLhOvNUKO2ke52YKVEJ2am0ZrOm0WD/2ahRE1QVQJwgkvFQuCTBBZmGo2CTWHuJZH\nwpKDEGnk+xwU+ezkeazYQ0FG23ro7A7QPBqnUbfQbvccFQv5WEpCw0zR4xQjSNOsiCOTE0iJfagH\nZ3crzt42PIkg6Vke3OUFOMuLcZUX4iwtxFGYiyXDhxACaSTQu+pJtG4h3rqVROtW9O56OKZHPdk0\nB1pWOVpWOWpWJVpWBVp2BWpWBYrrcNi49b0mDzUZGBIqPYJ7y1UKXVeuUF/JBrHV+X7+9MnPHlre\nN9zFvz54D9F48kH0Y2/7CmUrWjG3/wBXZhSpWcj57IdQ878F4uTXgRlPJGPi/uQPhAaDdM27ieHK\nZEKLwrJ0bnv3NNzec+srfymyubefJ/d1sK5tiJGuTubFtlFj7qco1s6EUDs+PXzC3yUiWrLTIGgh\nNmolGHfTYymgw1XKYHouoz4/0Uw/wQwPYVc/Fn0fWmw/xJoxZOS47dlNg1w9gVvXSJgZ9IhyGtWp\nNCjVBDlx51OmUzAhw0GJBlkDPaQ1NuLZ8QbF8T3kpffhzoxgT4sdn3/FopHwe9lvn8qB6HQGQpPA\nPHofUaWPUa2ZUe0Ahn2AzOw0irIqyE4rIDstnyxfAVnefHzuDJSTJHhpHDH50S4Dh9T52MbHiG7b\nxcjOBqKdvccXVhScJfm4q0pxVZXhrirFXVmCo7QQa/r5fSj7yW6dPcOSO0oUbipIjd84V1yyBvED\nDzwgH+raySv/9iNE9LMI2cnub0Ro+eMQxffdRe13P3dUeWmaRDY9zOjTX8cMDSCsLtLe+1Mc008/\nSrmzNZlAobM1OUArv9jHre+edsGTblyJryjOts3xWIw9G9fSVfc6hFqxxvpwRAfxRPrwxQdQjunh\nOBJTFyQi2thkIRHWjviuEcRBv9PLkNdNPM2B6VTRHKDZY8Tto0Tto4S0CEHVYERViCunuBakiia9\nKCIdQToIH31tQdLLpqOrGahWPz5vOmkygTIcIBQIMhBK0J0QdKl2ehxejCOSg1ilpNA0KDIMik2D\nPNPk2Ec90zQxwqOIkUFsQz14+jpJ7+vAnQjhKMjFUZCDvSDn8GdhLrbsNDQRwBzej95Vj95VR6Kr\nDjPQedKmCasLNb0ANb0QJb2Q5sx5/NlxG0HsCCRXZ8NtxRZ2bFxzxZ3XV6JBnHRz+zW3LXiAD12T\nPN5dg6189c8fIhQdAeAji79I4YPP4jafR7MbSNVKzhc+jZp7cp/haHcfbQ89RftDTxEeCjIwaR4D\nU6/C0KxoFoVrbqpixvySizLY+VLQ7HAiwQvNnaxu72d7zyiiu5Ha6B4qzHZy9W5KI93kRQewyBM8\n8I6RiKpJHQwn9TAWtTIi0xjSsui159LuTyeQqRP1jtLZ20haiQ4cbyQDaNLEa4Bm2tHNNIbJpUuU\n0SbKGCWTOG7geIPUqejkyRA5w/1k97WQa/aSZ+uj0NFDntpHpjmEayzevIlCv1JBj3Uy3VotA2Yl\nBkc/TBnEiKjdhNWuQ1NU7UOoJn53Flm+AvLTS8j2FeD3ZONzZfBCoIbGkINbCgTvKDmsrK88u4zJ\nLj8jOxsY2bGX4N79hPe3I40T/0+1NA/OkgKcpcnJUZyHPS8be14WttwsLOneN/XWedugya/Gxm98\npEpldua59yW+FM7tC81FMYiFEEuBn5C8Kn4npfzPY8vcf//9crco4+mv2RH6U4TbTV5b0k320sVM\n+9U3ULTkySpNk1j9CoIv/ReJAxsBsFYuIu3uB9ByJpy0DnrCYO/Obraub6W7PTn61um2cs3SaiZN\nz78o4vrLX/6S+++//4Lv92JyLtocTEg29ZssazMZ1cES6mdB/yv4A3WISA+WxCC22BCuyABpsX7s\n5omF/FhMXaDHVfSoihFX0aMaekxFj6kYMZWEoRGx2Bhx2BnwWBjyKAx4BMMOg5hTJ2aLY6j6cdtt\n3higdO4Rrg1SQZN2FOlAxYUi3QjSECINKdNISAe6YSWhW4npVsKGjREsBFQrI1YH2UKMGccG2aaJ\n7yTXpg6MmiYhQyeeiKHHIijREFp4FEtwGGdwGLceI00T+F1WMnxOsrIduNMNrK4oFm0EofcjIt2Y\nox0QP4Gvt8XLq5P+lY2V9yEVDcVMMPTwV/jU/DSqzE4sngxUTxaKJwvFPfbp8qM4fAiHd9xxwS91\nfv/73/P5z3/+sjGIx6vZ27x1PPbZx3DZLKzbs4KfPftVjLHwgB8svYeyp/9GWkZz8hW530f2p76B\n6nv/cdEka0hSIQAADlRJREFUEiNB+pYnQ2j1rVxHxO0nUDmVodrZGFrS8CmpzOCGd9SSfhZRCM4V\nl7JmNwwFWNHSzfbeYRp7h0nrr6cs3kKB0UOW3kdBvI/c6CC+RBCFU9/PpQRjTPse2mNyd76dqK4R\nsFoYdNrod2sMuBSGnAZBq0FEUYkJgTyh0SdQcCKli4R0E5Y+hmQ2o2QQly4SuEhIJ3Fc6Dg40nh2\nESVbDJMthshmAJ8xTFp8mLTEMB7TjiYy0MkjpuQSEyceOGwyQkLpJ6oMElIHiaiDxNUgasldKMV3\nIGWM0Oa7cGomXkc6Pncme1Z3cvu7b8HrTMfjSMNl9+JSXah9IWgZJNHYSaihmfCBdsLNHUclCzkR\nit2KPTdpHNvzsrDnZWPLzcSa4cPiT8Pq92H1p2Hx+1Cd9hMaz8+2GTzbZqII+Mdqlen+c2sUX8rn\n9vnibHX7rD25RfJ978+A64FOYJMQ4ikp5Z4jy4VCId43P4rQX8TUJXVfHyLz2gVM+8XXEaqK3tNA\ntG454TUPYvQfAEBxZ+F957exz7rruBPINEyGBsK07R/kwL5+WpsGSMSTT3c2u8a0eUXMu7YCm/3i\npWgcHh4+faHLjLNps2FKuiLQHJRsGzCpG5aYY3pe6RHcMzWXYvd7T/r71r31NO/YSHigGaJ9KMYI\nlsQolvgo9tgwrlgAlz6CRUtg1XSszuON2lMho2AEFcyEgp5QSZgKMRRiiiCqCh7qjnFrmyRsE4Ss\ngqhFEBeCuFBICEFMCBJj87oAixAYFoFuFdgRuIQgUyoILCimBlgwpZ1haWcQG7p0oOJExYld2nGa\nNlzSikNa0KSNdIsFxWZDcVlRcCLIRZEaEpWQUOkTggiCiBBEhSCmm+hDJqaRwNR1SMQQ8RhaIoLN\nDOMgjFuN4VYjeIwIru2bKWtqoLF0KV2ZM2iNePhx5qexmRGKhndR0rWFosAy8kb34o92YCNxyBNF\n2DwIpw/FkYbi9CXn7V4UhxdhdSFsLoTVmZxsruOWKQeXWR2g2S6a7//27dsvyn7PB2ei2XHvTFSR\n4JfLvsNru54BwGvCfR0mhc0/RM1MhtuyTp+M//1/RGhlAOijIYa31zO0fjtDG7bT98Zuwum5hLOL\nGLn1I0Qzcg/tp7AsnYU3TDjnmULPhktZs6vSfVSlH2kUvg2AqG6wpXeAHf1DLBsM0jwQQPQ3kRFq\nJ1vvI8MYIksfJDc+iD8+jDcRwmFG0ewGmt0gbgNfURCA3CN3GB+bxpASEqZCFIWoUIioCiFNIawl\ntS42pnlxIYgrY59HLhMKcRQSWNFNBwnpII6TmHSSwMGA4qAHB3FrBnFbITHFQVS4iQs7hlSwmEEy\nDEGOhCxDIdPU8JgaKl5sphebWU7akdK+C9j1CoaSAPP9SBHEJIQpQgw0NLBzxQ50ESIugsTVCDEl\nQkxEiYoYQhXYJtmwTrNh0WxYRBZWqWJJCLSoR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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "figsize(12.0, 8)\n", + "beta = stats.beta\n", + "hidden_prob = beta.rvs(1,13, size = 35)\n", + "print(hidden_prob)\n", + "bandits = Bandits(hidden_prob)\n", + "bayesian_strat = BayesianStrategy(bandits)\n", + "\n", + "for j,i in enumerate([100, 200, 500, 1300]):\n", + " plt.subplot(2, 2, j+1) \n", + " bayesian_strat.sample_bandits(i)\n", + " plot_priors(bayesian_strat, hidden_prob, lw = 2, alpha = 0.0, plt_vlines=False)\n", + " #plt.legend()\n", + " plt.xlim(0, 0.5)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Eliciting expert prior\n", + "\n", + "Specifying a subjective prior is how practitioners incorporate domain knowledge about the problem into our mathematical framework. Allowing domain knowledge is useful for many reasons:\n", + "\n", + "- Aids speeds of MCMC convergence. For example, if we know the unknown parameter is strictly positive, then we can restrict our attention there, hence saving time that would otherwise be spent exploring negative values.\n", + "- More accurate inference. By weighing prior values near the true unknown value higher, we are narrowing our eventual inference (by making the posterior tighter around the unknown) \n", + "- Express our uncertainty better. See the *Price is Right* problem in Chapter 5.\n", + "\n", + "plus many other reasons. Of course, practitioners of Bayesian methods are not experts in every field, so we must turn to domain experts to craft our priors. We must be careful with how we elicit these priors though. Some things to consider:\n", + "\n", + "1. From experience, I would avoid introducing Betas, Gammas, etc. to non-Bayesian practitioners. Furthermore, non-statisticians can get tripped up by how a continuous probability function can have a value exceeding one.\n", + "\n", + "2. Individuals often neglect the rare *tail-events* and put too much weight around the mean of distribution. \n", + "\n", + "3. Related to above is that almost always individuals will under-emphasize the uncertainty in their guesses.\n", + "\n", + "Eliciting priors from non-technical experts is especially difficult. Rather than introduce the notion of probability distributions, priors, etc. that may scare an expert, there is a much simpler solution. \n", + "\n", + "### Trial roulette method \n", + "\n", + "\n", + "The *trial roulette method* [8] focuses on building a prior distribution by placing counters (think casino chips) on what the expert thinks are possible outcomes. The expert is given $N$ counters (say $N=20$) and is asked to place them on a pre-printed grid, with bins representing intervals. Each column would represent their belief of the probability of getting the corresponding bin result. Each chip would represent an $\\frac{1}{N} = 0.05$ increase in the probability of the outcome being in that interval. For example [9]:\n", + "\n", + "> A student is asked to predict the mark in a future exam. The figure below shows a completed grid for the elicitation of a subjective probability distribution. The horizontal axis of the grid shows the possible bins (or mark intervals) that the student was asked to consider. The numbers in top row record the number of chips per bin. The completed grid (using a total of 20 chips) shows that the student believes there is a 30% chance that the mark will be between 60 and 64.9.\n", + "\n", + "\n", + "\n", + "\n", + "From this, we can fit a distribution that captures the expert's choice. Some reasons in favor of using this technique are:\n", + "\n", + "1. Many questions about the shape of the expert's subjective probability distribution can be answered without the need to pose a long series of questions to the expert - the statistician can simply read off density above or below any given point, or that between any two points.\n", + "\n", + "2. During the elicitation process, the experts can move around the chips if unsatisfied with the way they placed them initially - thus they can be sure of the final result to be submitted.\n", + "\n", + "3. It forces the expert to be coherent in the set of probabilities that are provided. If all the chips are used, the probabilities must sum to one.\n", + "\n", + "4. Graphical methods seem to provide more accurate results, especially for participants with modest levels of statistical sophistication." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Example: Stock Returns\n", + "\n", + "\n", + "Take note stock brokers: you're doing it wrong. When choosing which stocks to pick, an analyst will often look at the *daily return* of the stock. Suppose $S_t$ is the price of the stock on day $t$, then the daily return on day $t$ is :\n", + "\n", + "$$r_t = \\frac{ S_t - S_{t-1} }{ S_{t-1} } $$\n", + "\n", + "The *expected daily return* of a stock is denoted $\\mu = E[ r_t ]$. Obviously, stocks with high expected returns are desirable. Unfortunately, stock returns are so filled with noise that it is very hard to estimate this parameter. Furthermore, the parameter might change over time (consider the rises and falls of AAPL stock), hence it is unwise to use a large historical dataset. \n", + "\n", + "Historically, the expected return has been estimated by using the sample mean. This is a bad idea. As mentioned, the sample mean of a small sized dataset has enormous potential to be very wrong (again, see Chapter 4 for full details). Thus Bayesian inference is the correct procedure here, since we are able to see our uncertainty along with probable values.\n", + "\n", + "For this exercise, we will be examining the daily returns of the AAPL, GOOG, MSFT and AMZN. Before we pull in the data, suppose we ask our a stock fund manager (an expert in finance, but see [10] ), \n", + "\n", + "> What do you think the return profile looks like for each of these companies?\n", + "\n", + "Our stock broker, without needing to know the language of Normal distributions, or priors, or variances, etc. creates four distributions using the trial roulette method above. Suppose they look enough like Normals, so we fit Normals to them. They may look like: " + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "image/png": 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KyJHyzw+7HatpfNnJGYrptGMrMAGISHkUIkN2DRvKGeNlzf8ppYa8Mn/OK+UE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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "figsize(11., 5)\n", + "colors = [\"#348ABD\", \"#A60628\", \"#7A68A6\", \"#467821\"]\n", + "\n", + "normal = stats.norm\n", + "x = np.linspace(-0.15, 0.15, 100)\n", + "\n", + "expert_prior_params = {\"AAPL\":(0.05, 0.03),\n", + " \"GOOG\":(-0.03, 0.04), \n", + " \"TSLA\": (-0.02, 0.01), \n", + " \"AMZN\": (0.03, 0.02), \n", + " }\n", + "\n", + "for i, (name, params) in enumerate(expert_prior_params.items()):\n", + " plt.subplot(2, 2, i+1)\n", + " y = normal.pdf(x, params[0], scale = params[1])\n", + " #plt.plot( x, y, c = colors[i] )\n", + " plt.fill_between(x, 0, y, color = colors[i], linewidth=2,\n", + " edgecolor = colors[i], alpha = 0.6)\n", + " plt.title(name + \" prior\")\n", + " plt.vlines(0, 0, y.max(), \"k\",\"--\", linewidth = 0.5)\n", + " plt.xlim(-0.15, 0.15)\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that these are subjective priors: the expert has a personal opinion on the stock returns of each of these companies, and is expressing them in a distribution. He's not wishful thinking -- he's introducing domain knowledge.\n", + "\n", + "In order to better model these returns, we should investigate the *covariance matrix* of the returns. For example, it would be unwise to invest in two stocks that are highly correlated, since they are likely to tank together (hence why fund managers suggest a diversification strategy). We will use the *Wishart distribution* for this, introduced earlier." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's get some historical data for these stocks. We will use the covariance of the returns as a starting point for our Wishart random variable. This is not empirical bayes (as we will go over later) because we are only deciding the starting point, not influencing the parameters." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "# I wish I could have used Pandas as a prereq for this book, but oh well.\n", + "import datetime\n", + "import collections\n", + "import ystockquote as ysq\n", + "import pandas as pd\n", + "\n", + "n_observations = 100 # we will truncate the the most recent 100 days.\n", + "\n", + "stocks = [\"AAPL\", \"GOOG\", \"TSLA\", \"AMZN\"]\n", + "\n", + "enddate = \"2015-04-27\"\n", + "startdate = \"2012-09-01\"\n", + "\n", + "CLOSE = 6\n", + "\n", + "stock_closes = pd.DataFrame()\n", + "\n", + "for stock in stocks:\n", + " x = np.array(ysq.get_historical_prices(stock, startdate, enddate))\n", + " stock_series = pd.Series(x[1:,CLOSE].astype(float), name=stock)\n", + " stock_closes[stock] = stock_series\n", + "\n", + "stock_closes = stock_closes[::-1]\n", + "stock_returns = stock_closes.pct_change()[1:][-n_observations:]\n", + " \n", + "dates = list(map(lambda x: datetime.datetime.strptime(x, \"%Y-%m-%d\"), x[1:n_observations+1,0]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And here let's form our basic model:" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Applied log-transform to c and added transformed c_log_ to model.\n", + "Added new variable c to model diagonal of Wishart.\n", + "Added new variable z to model off-diagonals of Wishart.\n" + ] + } + ], + "source": [ + "import pymc3 as pm\n", + "import theano.tensor as tt\n", + "from theano.tensor.nlinalg import matrix_inverse, diag, matrix_dot\n", + "\n", + "prior_mu = np.array([x[0] for x in expert_prior_params.values()])\n", + "prior_std = np.array([x[1] for x in expert_prior_params.values()])\n", + "\n", + "init = stock_returns.cov()\n", + "\n", + "with pm.Model() as model:\n", + " cov_matrix = pm.WishartBartlett(\"covariance\", np.diag(prior_std**2), 10, testval = init)\n", + "\n", + " mu = pm.Normal(\"returns\", mu=prior_mu, sd=1, shape=4)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here are the returns for our chosen stocks:" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "image/png": 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9x7dyJGMfmsbvxzt37M6rd9umd0ZCQgKxsbGNzjdv7wrBSa21V719zkC61trf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7HmNjwix/B+nZVsq5601Bh+eO/QrWzdZht7yjvYUdJO7jnxJ756NQviA1icGEhyqOGyn3ub3crT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\nIOk7wDXAKEmy/TfgubKo/xD4aRk3yPY75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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "figsize(12.5, 4)\n", + "\n", + "cum_returns = np.cumprod(1 + stock_returns) - 1\n", + "cum_returns.index = dates[::-1]\n", + "cum_returns.plot()\n", + "\n", + "plt.legend(loc = \"upper left\")\n", + "plt.title(\"Return space\")\n", + "plt.ylabel(\"Return of $1 on first date, x100%\");" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + }, + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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at+4PDwy+3u8XsuXR1b3mbV+/sdSymbUKNyBKlEv/uXbOuX7rjl7TEw+dwbYd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\nByKfPGWNoZFfGS4EfmZmofg+/xJCWBunWCIi0samHd4qIiL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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "figsize(11., 5 )\n", + "\n", + "for i, _stock in enumerate(stocks):\n", + " plt.subplot(2,2,i+1)\n", + " plt.hist(stock_returns[_stock], bins=20,\n", + " normed = True, histtype=\"stepfilled\",\n", + " color=colors[i], alpha=0.7)\n", + " plt.title(_stock + \" returns\")\n", + " plt.xlim(-0.15, 0.15)\n", + "\n", + "plt.tight_layout()\n", + "plt.suptitle(\"Histogram of daily returns\", size =14);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Below we perform the inference on the posterior mean return and posterior covariance matrix. " + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " [-------100%-------] 5000 of 5000 in 40.4 sec. | SPS: 123.8 | ETA: 0.0" + ] + } + ], + "source": [ + "with model:\n", + " obs = pm.MvNormal(\"observed returns\", mu=mu, cov=cov_matrix, observed=stock_returns)\n", + " step = pm.NUTS()\n", + " trace = pm.sample(5000, step=step)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "image/png": 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uQPijbP1Rtv4oW3+UrT/K1q/uMI76okWLmDNnDrNnz2bJkiXs2rWr+b3evXtz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\niIiEkGrU85TqzvxRtv4oW39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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "figsize(12.5,4)\n", + "\n", + "#examine the mean return first.\n", + "mu_samples = trace[\"returns\"]\n", + "\n", + "for i in range(4):\n", + " plt.hist(mu_samples[:,i], alpha = 0.8 - 0.05*i, bins = 30,\n", + " histtype=\"stepfilled\", density=True, \n", + " label = \"%s\" % stock_returns.columns[i])\n", + "\n", + "plt.vlines(mu_samples.mean(axis=0), 0, 500, linestyle=\"--\", linewidth = .5)\n", + "\n", + "plt.title(\"Posterior distribution of $\\mu$, daily stock returns\")\n", + "plt.legend();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "(Plots like these are what inspired the book's cover.)\n", + "\n", + "What can we say about the results above? Clearly TSLA has been a strong performer, and our analysis suggests that it has an almost 1% daily return! Similarly, most of the distribution of AAPL is negative, suggesting that its *true daily return* is negative.\n", + "\n", + "\n", + "You may not have immediately noticed, but these variables are a whole order of magnitude *less* than our priors on them. For example, to put these one the same scale as the above prior distributions:" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "image/png": 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k6WHgJknnAU8BZ7UykWYD9cS3rmFt55JWJ8OspepdB8L6kEJ/uhRiBI+BKJIU\nYmyViFgQEWMi4piIOCoiJuT7l0fEKRFxaEScFhErW53WVknl+itqnOufXU5s3LR52+tAFEcqcTaC\nKxBmZmZmZlYzVyCarDSQpchSiBHYvIhc0aWQnynEaO0rlesvlThLA6iLLJW8TCXORvAYCLMq1m/s\nZtaSF9nYHTy7+uVWJ8fMzMysbTStBULSGZLmSnosn3EjSSn0pytijN3dwW1znuMn05fy3EsbAI+B\nKJIUYmxHLhcyqVx/qcRZPgYigE3r1rUuMU2SSl6mEmcjNKUCIWkY8D3gdOBI4IOSDmvGZ7W70pzC\nRZZCjAArOh9rdRIGRQr5mUKM7cblwitSuf5SifOJ1Ss2P18zbwELrvxpC1PTHKnkZSpxNkKzWiCO\nB+ZHxFMRsQG4ETizSZ/V1kqLdxRZCjECbHhpddX9wyS6I+hu0ZoqjZZCfqYQYxtyuZBL5forYpzP\nT/kTG55bscW+NRtf6eYam7rZsPKFwU5W0xUxL6tJJc5GaFYFYm/g6bLtRfk+s8L530eW8fXfLeSh\np4tXaJg1kMsFG/KW3vZbNqx8sdXJMGs5D6Juss7OzlYnoemKFONLL29i2eqXGSYhwXbbvLJY40vP\nL91ie/P+DZt4acMmNmzqHsykNk2R8rMnKcRo7SuV669IcXZv2MC6xcuIjRsZtv12W7y27OW1W+xb\n+/RSFt14G7ufeiLb7zZysJPaFEXKy96kEmcjNKsCsRjYr2x7n3zfZjNmzODaa6/dvD169GjGjBnT\npOS0zrHHHsu0acUefFvUGN+z25bbR7/vbYzZe03PJ6xYwLQVC5qbqEFQ1PwsV9QYZ8yYwcyZMzdv\njx49mvHjx7cwRVvos1yANMqGol5/lQoZ59mnbdV14/QZRzGs4hpdBix7agE8NfTLBChoXlZRxDib\nVS4omtBvW9I2wDxgPLAEmAp8MCLmNPzDzMys7blcMDMrjqa0QETEJkmfASaRjbO42oWEmVm6XC6Y\nmRVHU1ogzMzMzMysmJq2kByApJGSJkmaJ+lOSSN6OO5qSV2SZlXsv1zSIknT8scZzUzvQDUgzprO\nb6V+xFh1oah2zstaFreS9B1J8yXNkDSmP+e2iwHEeUzZ/oWSZkqaLmnq4KW6//qKU9Khku6XtE7S\nxf05t13UGWPL8zKFsiGFcgFcNrhsaP3vSS1SKBdgkMuGiGjaA5gIfDF//iVgQg/HjQPGALMq9l8O\nXNzMNLagnEs9AAAgAElEQVRJnDWd3+4xklVIHwf2B7YDZgCHtXNe9pbmsmPeDvwmf34C8ECt57bL\no5448+0ngZGtjqNBcb4WeCPwr+XX5FDJz3pibJe8TKFsSKFcqDWdLhva87ek3jjz7Zb/njQoxiFd\nLtQb50DysqktEGSLBJWm07gWeG+1gyJiCrCi2mvA1vNmtp9646zp/BarJY19LRTVjnlZy+JWZwLX\nAUTEg8AISaNqPLdd1BMnZHnX7N+LRugzzoh4LiL+BGzs77ltop4YoT3yMoWyIYVyAVw2uGxo/e9J\nX1IoF2CQy4ZmZ/ruEdEFEBFLgd0H8B6fyZvMrmrXJlzqj7MR31Oz1ZLGvhaKase8rGVxq56OGUoL\nYw0kzsVlxwRwl6SHJH2iaamsXz15MlTys950tkNeplA2pFAugMsGlw2t/z3pSwrlAgxy2VD3LEyS\n7gJGle/KE/FPPSSuP64EvhIRIemrwH8AHxtQQuvU5Dgbff6ApJKXDdCOd8ua7cSIWCLpdWQ/MHPy\nO6c29AxKXqbwe5JCuQBp5GWDuGxw2TCU9Ssv665ARMSpPb2WDwwbFRFdkvYgW1ulP+/9bNnmD4Ff\nDzCZdWtmnEC95zdEA2LscaGodsrLCrUsbrUY2LfKMdvXcG67qCdOImJJ/vdZSb8kayptx0KipsXK\nmnDuYKornYOVlymUDSmUC+CyIeeyocoxQ6RsSKFcgEEuG5rdhelW4KP5848At/RyrKiovec/RiXv\nBx5pZOIaqK44+3l+q9SSxoeAQyTtL2l74G/y89o5L3tMc5lbgb8FkPQmYGXeZF/Lue1iwHFK2knS\n8Hz/zsBptE/+VepvnpT/Wxwq+TngGNsoL1MoG1IoF8Blg8sGWv570pcUygUY7LKh1tHWA3kArwHu\nJlt9dBKwa75/T+C2suNuAJ4B1gOdwLn5/uuAWWQjyX8FjGpmelsYZ9Xz2+nRjxjPyI+ZD1xatr9t\n87JamoFPAZ8sO+Z7ZLMbzATG9hVvOz4GGidwYJ5v04GOoR4nWVeMp4GVwPL83+LwoZSfA42xXfKy\nAb+Zbft70sAY275c6GecLhva9DHQONvl96QRMfb0m1m0vOwpzoHkpReSMzMzMzOzmrX71FtmZmZm\nZtZGXIEwMzMzM7OauQJhZmZmZmY1cwXCzMzMzMxq5gqEmZmZmZnVzBUIMzMzMzOrmSsQZmZmZmZW\nM1cgzMzMzMysZq5AmJmZmZlZzVyBMDMzMzOzmrkCYWZmZmZmNXMFwszMzMzMauYKhBWepG5Jm/K/\n1R5P5se9RtJ3JD0paZ2kZZLulfTXZe91jaRJNXzm8ZI2SnqwmbGZmVn/SdpL0npJiyQNq3jtd3nZ\n8O9Vzrswf+2xsn3Vypjy7b/Ij/tRvj2h4j33zve/pVnxmjWaKxCWgj2APfO/fwkEMCbf3gM4Lj/u\nF8A44BPA64HTgRuA3QbwmZ8CrgQOlnR0PYk3M7OG+xhwK7ASeHfFawE8BXxY0rYVr30CWFixr7yM\nKT1eDzwO/BEo3UgKYC3w95L2rfKZZkNG5T8Ms8KJiGWl55KW50+fq9g/AngL8K6ImJzvfhqY3t/P\nk7QL8NfACWT/xv4OuGBgqTczs0aSJLIKxKeBI8lu+NxScdhk4K3A+4Cf5+eNA/YB/jvfD2xZxpR9\nxn8B2wPvi4iXy166HxgOXAF8qPyUuoIyG2RugTDLrAZeBM6UtFOd7/VhYE5EPAr8CDhH0o51vqeZ\nmTXGO8j+c/9/wI+B8ZL2qzimG7ga+GTZvk+QtUq/1NubS/oacArZDanKykUAnwc+KGnsgCMwazFX\nIMyAiNgE/C3ZXaUVkh6S9J+S3jqAt/s4cE3+vlOBxcAHG5ZYMzOrxyeAn0REd0QsIWtt+HiV464B\n3iLpAEm7Ah8AftDbG0v6EPAF4OyIeKTaMRFxH1mLx1ZjLMyGClcgzHIRcQuwN9nYh5uBw4HJkr5b\n63tIOiE/76dlu68jayI3M7MWkrQ38E7g2rLdPwY+VjmYOq9c3E5W4fgwMDsiZvTy3m8CfghcGhG3\n9ZGULwHjJL2r/1GYtZ7HQJiViYgNwO/yx0RJ/wh8RdI3IqKzhrf4FLAdsCzrZgtkfVsl6eiImNX4\nVJuZWY0+RnbzdLrKfqTzfe9m67EQPyDryrQc+M+e3jTvAvVL4IaI+GZfiYiI+ZL+G5hI1qXKbEhx\nC4RZ7+bmf1/X14H54OmzyAZMjy57HA38AbdCmJm1TF5hOA/4N7KZ+Mp/p29ky/EOJXcALwP7smXL\ncvn77kxW8ZhH/37nvwzslX+uZ2GyIcUtEJairWa7kPQa4H/J+rzOJJva7yjga8CTQHmz9XBJoyve\nYh3ZoLlNwI8iYn3F+18P/Lukz0fE2kYFYmZmNXsH2SxKP4iIReUvSPoRcLuk/cv3R0RIOhIYFhFr\nenjf64FRwDnAbls2bACwKiLWVe6MiOfyNSH+ZSDBmLWSKxCWomp3elYD95G1HhwC7AgsAe4EvpYP\nsi45AZhWcf48skrErysrD7lfAN8jG0z9P3Wl3szMBuITwAOVlYfcPWTdlD5GRRnRS8Wh1HWptI5E\nRw+HnUs2Fq6a/yQrd/buOdlm7UcRvbeaSboaeBfQFRFHV7x2CfAN4LURsTzfdxlZE+FG4MKI6HPV\nXjMzG1p6KhskfZbsP0Qbgd9ExKX5fpcNZmYFUcsYiGvIZqXZgqR9gFPJVmss7TucrA/44cDbgStV\npS3PzMyGvK3KBkknk92NPSoijiKfptJlg5lZsfRZgYiIKcCKKi99i2yu43JnAjdGxMaIWAjMB46v\nN5FmZtZeeigbzgcmRMTG/Jjn8v0uG8zMCmRAszBJeg/wdERU9vfbG3i6bHsx7tdnZpaKN5AtvPWA\npN9KemO+32WDmVmB9HsQtaQdgX8g675kZmZWsi0wMiLeJOk44OfAQS1Ok5mZNdhAZmE6GDgAmJn3\nYd0HmCbpeLK7SvuVHbtPvm8r559/fjzxxBPsscceAOy8884ccsghjBkzBoAZM7JZM4f6dmlfu6Sn\nGduVsbY6Pc3afvzxx/nABz7QNulp1nYK+XnzzTcX9vdm5syZLF26FIDTTz+dSy65ZDDHGjxNNuMY\nEfGQpE2SdsNlg39L2iA9zdpOIT9L+9olPS4b+nd9rlmTTSS2dOnShpULfc7CBCDpALLpKY+q8toC\nYGxErJB0BNl8yCeQNU/fBbw+qnzI5MmTY+zYsfWlfgiYMGECl156aauT0VQpxAjwlS9/lQs/ezHb\nvWpbdtp5+1Ynp2lSyM8UYgSYNm0a48ePb1oForJskPRJYO+IuFzSG4C7ImJ/lw1bSuX6c5zFkUKM\nkEacjSoX+hwDIekG4H7gDZI6JZ1bcUiQL8wVEbOBm4DZwO3ABdUKiJR0dna2OglNl0KMAHNmP87k\n2+bwXNeLrU5KU6WQnynE2Gw9lA3/AxwkqQO4AfhbcNlQKZXrz3EWRwoxQjpxNkKfXZgi4uw+Xj+o\nYvsK4Io602XWFtav28CiBSvo7g7Wr93Q6uSYtZO1wDbAvIo1gj5ctkZQ5UQbyVYazMyKpJYWiKsl\ndUmaVbbv65LmSJoh6X8l7VL22mWS5uevn9ashA8VZ5/da/2rEIocY/emYM6sZ+j40yKOHT2+1ckZ\nFEXOz5IUYhwEXiNogFK5/hxncaQQI6QTZyMMdCG5ScCRETGGbD7vywDyfq4uJMqMGzeu1UlouhRi\nBDjs9aNbnYRBkUJ+phBjs3mNoIFL5fpLMc45T09jQdfcFqamOVLMS+vdgBaSi4i7I6I733yAbEYN\ngPfgQmILU6ZMaXUSmi6FGAHmzp/Z6iQMihTyM4UYW8FrBNUmlesvxTiXrHiaVWuWtzA1zZFiXlrv\nBjKNa6XzgJ/mz/cG/lj2WtKFhJlZKrxGkJlZOuqqQEj6R2BDRPy0z4MTlUJzWAoxgrswFUkKMbZA\nQ9YIuvnmm7nqqqvYb7/s8BEjRnDUUUdtzrPSHcKhvl3SLulpxva4cePaKj3N3C55ZPpcdtlxCWMO\n+vO2Sp+3a9su7WuX9DRiu6Ojg1WrVgHZLFPHHnss48fXP6az1nUg9ieb6/vosn0fBT4BvC0i1uf7\nLgUiIibm23cAl0fEg5Xvef7558fKlSsLX0h4e2hvv/GY45l822xmPTINyCoRx447gM5n5rRF+rzt\n7fLt0vPSVITHHntsUxeS8xpBZlu6Z9YtvGb46zZXIMzaTaPWgRjQQnKSzgC+CbwlIp4vO86FRIXy\nmmxRFTnGtWteZvJts3l5/Sbmzp+5uQKx30G7tTppTVPk/CxJIUZo7kJy+ToQJwO7AV1kN4uuKXv9\nSeDYiFieb18GfAzYAFwYEZOqvW8KZUMq11+KcRa1ApFiXhZVo8qFbfs6oLyQkNQJXE7Wz3V74K58\nkqUHIuKCiJgtqbRY0AYSXyzIzKzAtloHQtLXgXcD64HpwMaKc1wemJkVQE0tEM2Qwl0mG/rKWyBK\nit4CYcXR5BaIccBq4LqyCsQpwD0R0S1pAlmX1svKWqePIxv/cDeJt05bMRW1BcKKo1HlwkAXkhsp\naZKkeZLulDSi7DUvJGdmVnCe4tvsFS+8tIJHOx/m+ReWtDopZoNioAvJXQrcHRGHAvfgheR6VD64\nsahSiBG8DkSRpBBjGzgPuD1/7nUgyqRy/aUU5/oN65g88xcsWdHZ6uQ0RUp5abUZ0EJyZKuKXps/\nvxZ4b/7cd5nMzBLnKb7NzIqtz0HUPdg9IroAImKppN3z/V5IrkLRR/NDGjGC14EokhRibJV8iu93\nAG8r270Y2Ldsex+8DkRbpacZ2+PGpbcOxII5i9ll7SyvAzFEt0v72iU9jdhuq3UgJC2PiNeUvf58\nROwm6bvAHyPihnz/VcDtEfGLyvf0QDkbCjyI2oayZg6iBk/xbVby7Kol/PTe7wLwliPf5UHU1rYG\nbRrXHnRJGhURXZL2AJbl+32XqcpdiaLfhamMtdXpaeT2G4/JeuDNnT+TzsWPc9rJf9lW6XN+Dmz7\n+9//fmF/b6ZM2XIhuUbcaarGU3wPXPkdziJLKc5Djzq41cloqpTyMoU4G2GgC8lNBJZHxERJXwJG\nRsSlvsu0tRQuxiLH6IXkiimFGKHp07heDbwL6CprnR4J/AzYH1gInBURq/LXLiMbWL0RLySXxPWX\nUpyHHXUIN9z7HQAO32cs+7/u9Rywx+Fsv+32LU5dY6SUl0WPczCncb0BuB94g6ROSecCE4BTJc0D\nxufbRMRsoHSX6XYSv8sEFP5ChDRiBI+BKJIUYhwEnqFvgFK5/lKJ83UHDOfBx+7evD1n0TT+MOf/\n6O7e0MJUNVYqeZlKnI3QZxemiDi7h5dO6eH4K4Ar6kmUmZm1t4iYko+PK3cmcFL+/Frgd2SVis0z\n9AELJZVm6HtwkJJr1jQr1zzPE0tntzoZZoOqlnUgeiTpIkmPSJol6XpJ2/e2yFyKyvsmF1UKMYLX\ngSiSFGJskS1m6APKZ+jzOhC5VK6/VOKcNe3RVieh6VLJy1TibISBDqJG0l7AZ4HDIuJlST8DPggc\nQdaE/fV8fMRlZHegzMwsLf3uwprCBBsdHR1tlR5v17f9xGML2GvsjkA2jSvAnx2zS9ukrxHbJe2S\nnmZtd3R0tFV6GvV707JpXKuemFUg/giMAV4EfgF8B/gecFLZDE2/i4jDKs9PYaCcDX2extWGskGY\nxrVyiu85wMllv/+/jYjDJV0KRERMzI+7A7g8IrbqwuSywYaaB+dN5sHHJm+xb+cdduGckz7LDtvv\n3KJUmVU3aIOoexIRz5DN991J1hy9KiLuBkb10IRtVggrl69l0cLldC1+odVJMWs15Y+SW4GP5s8/\nAtxStv9v8m6uBwKHAFMHK5FmZtZYA65ASNqVbMDc/sBewM6SzmHrJuukZ2FKoT9dCjHCK2MgHp/d\nxdR7F7Bg/rMtTlFzpJCfKcTYbJ6hb+BSuf5SidNjIIojlTgbYcBjIMhmYXoyIpYDSPol8Of0vMjc\nFlLo51quXdLj7f5tVy4kV5rKde78mTz3wnDedPLBbZVeb6fbz7VkypTBWUiutxn6JF0EfAy4V1IH\ncC7wX8BbyW46XSLpwdIaEWZmNrTUMwbieOBq4DhgPdmc4A8B+1FlkbnK893P1dpZd3cQEby8fiOT\nf73lGIiSvfbbdXMFwqwdNXsMRDX5+LgpbDnBxu1kE2w8XzbBhssGKwSPgbChpFHlwoBbICJiqqSb\ngenAhvzvD4BXAzdJOg94imzxILMhpWvxKjr+tAigauXBzHq1DVm31m5gR7JxcpdRfY0Is8KJ6GbN\n+jV0dwc77TC81ckxa7i61oGIiC9HxOERcXREfCQiNkTE8og4JSIOjYjTImJloxI7FKXQn66IMUZ3\nsPqF9ax+Yf3mfV4HojhSiLFVPMFG31K5/lKJs9oYiJfWr+bGe79H57OPtSBFjZdKXqYSZyPUMwaC\nfJG4q4A/A7qB84DHgJ+R9XNdCJzlfq5mZmmomGBjFfDz/kywkcL4OK8DUaztautAHHj43mzq3sif\nps7guadeaqv0DmS7pF3S4/Fx/fu9aat1IAAk/Qj4fURcI2lbYGfgH3A/VxvinnlqBQ/8/slej/EY\nCGt3LRoD8QHg9Ij4RL79YeBNwNuoskZE5fkuG2yoqTYGouTU0R/g8P18PVv7aPk6EJJ2Af4iIq4B\niIiNeUvDmWT9W8n/vrfeRJqZ2ZDRCbxJ0g6SRDad62x6XiPCzMyGmHrGQBwIPCfpGknTJP1A0k64\nn+sWUuhPl0KM4DEQRZJCjK0SEVOBX5NN4b2W7CbSdLJpXL8o6WXgC8CVLUtki6Vy/aUSp9eBKI5U\n4myEeioQ2wJjgf8XEWOBNWQzanghOTOztO0FXBgROwCvBR4F/g6YGBHbA98ALmhh+szqtu7ll3hq\n2XzWrHuh1UkxG3T1DKJeBDwdEQ/n2/9LVoHwQnKJbY8bN66t0tOI7akPP8Dc+Uu2WDiuXJEXkiti\nflZul/a1S3oaOdBxypTBWUiuJ2XdWz8KWfdWYJWkM/E0rsCW12GRFT3O9RvW8ZuHf8Kw125odVKa\nruh5WZJKnI1Q7yDq3wOfiIjHJF0O7JS/5IXkbEjzIGorghYNoh5NtibQbGA08DDwOWBxRIwsO255\nRLym8nyXDTZUrFqznOt//202buq5AuFB1NZuWj6IOvf3wPWSZpAVFF8DJgKnSppHNnhuQp2fMaSl\n0J8uhRjBYyCKJIUYW8jdW/uQyvWXSpylqVuLLJW8TCXORqinCxMRMRM4rspLp9TzvmZmNmS5e2sf\n214HojjbT85exNKnnuPAw/cGtlwHAmDaQzN4vtPrQAyVba8DUbu6ujABSBpG1kS9KCLeI2kkNSwk\n52Zqa2fuwmRF0IouTODurZYGd2GyoahdujABXEjW17XkUuDuiDgUuAe4rAGfYWZmQ4e7t5qZFVhd\nFQhJ+wDvAK4q2+2F5Mqk0J8uhRjBYyCKJIUYWynv3noC0A1sm7dCR75N/txjIAoulTg9BqI4Uomz\nEeptgfgW2YJA5QWBF5IzMzO3TpsNegdCs8Ex4AqEpHcCXRExg97/iSR7lwnSmFM4hRiBzWtCFF0K\n+ZlCjK3k1unepXL9pRJnacB0NfOf6WDaE38YxNQ0Ryp5mUqcjVDPLEwnAu+R9A5gR+DVkn4MLPVM\nG94e6ts9LSRXvl3UheS8PXS3S89buZBcrtQ6PaJs3xat05LcOm2Ft3DZPF54aQVjD/6LVifFrKHq\nnoUJQNJJwCX5LExfB573TBuZKVNeWe22qIoYY7VZmObOn7lFK0RRZ2EqYn5WSiFGaNlCcu8E3h4R\nn5F0MnBxXjasqFhI7vmI2K3y/BTKhlSuvyLHuei5J+lauYg/zp3EE7Of7rUV4jXDd+dDb/3cIKau\n8Yqcl+VSiLNR5UI9LRA9mQDcJOk84CngrCZ8hpmZtSe3Tvex7XUghv728L3EfXPuYMGcxb2uA7Fg\nzmKe3/EleCttlf6BtG62U3qate11IGrXkBaIgUjhLpMNXV4HwoqgVetAlLh12opqxpP3ce+jv6np\n2CK0QFhxtHwdCEn7SLpH0qOSOiT9fb5/pKRJkuZJulPSiL7ey8zMCm8CXgfCzKwQ6pnGdSNZ39Yj\ngTcDn5Z0GJ6qbwuVzX9FlEKMsPU6EC+v38jy59bw/LLVvLy+55VIh5oU8jOFGFul/OYS8D3g7vwl\nrwORS+X6SyVOrwNRHKnE2QgDrkBExNJ8ClciYjUwB9gHT9VnQ9iqFWtZ/uxq1q/f2Oexz3Wt5ne3\nz+X3d8xj3dq+jzdLhG8umZkVXEMGUUs6ABgDPICn6ttC0UfzQ7FifHLeMhY89lzV17wORHGkEGOr\n5AuILs2fr5ZUfnPppPywa4HfkVUqkpPK9ZdKnL3NwFQUqeRlKnE2Qr0rUSNpOHAzcGHeElHZLJ1s\nM7WZWcp6u7kEJH1zycxsKKurBULStmS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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "figsize(11.0,3)\n", + "for i in range(4):\n", + " plt.subplot(2,2,i+1)\n", + " plt.hist(mu_samples[:,i], alpha = 0.8 - 0.05*i, bins = 30,\n", + " histtype=\"stepfilled\", density=True, color = colors[i],\n", + " label = \"%s\" % stock_returns.columns[i])\n", + " plt.title(\"%s\" % stock_returns.columns[i])\n", + " plt.xlim(-0.15, 0.15)\n", + " \n", + "plt.suptitle(\"Posterior distribution of daily stock returns\")\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Why did this occur? Recall how I mentioned that finance has a very very low signal to noise ratio. This implies an environment where inference is much more difficult. One should be careful about over-interpreting these results: notice (in the first figure) that each distribution is positive at 0, implying that the stock may return nothing. Furthermore, the subjective priors influenced the results. From the fund managers point of view, this is good as it reflects his updated beliefs about the stocks, whereas from a neutral viewpoint this can be too subjective of a result. \n", + "\n", + "Below we show the posterior correlation matrix, and posterior standard deviations. An important caveat to know is that the Wishart distribution models the *inverse covariance matrix*, so we must invert it to get the covariance matrix. We also normalize the matrix to acquire the *correlation matrix*. Since we cannot plot hundreds of matrices effectively, we settle by summarizing the posterior distribution of correlation matrices by showing the *mean posterior correlation matrix* (defined on line 2)." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + }, + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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YzC7u0w1yzuXqmdVrmDZvZbOr0edOGTPMG9m94Hd8rIGPk52fVriqrpjDazRk\ndBG3US3jik8k9FgTf0EbImmYma0ws644/2VgId2HS6ya5lZkPgZvNh63+nnMslm+YHazq+Aq8Ea2\ncy2h/tuqu7rUMq54uszydBlJewBtQDKNbbKkLklXx4tNnXPODQDeyK6B52Tnx3Oys3q9ysM1W0wV\nmQ78U2KUlsuBPc2sDVgBtFzaSJHzZIvM41Y/j1k2ux5wcLOr4Croi3GynXO95r3VDVZ1XPE4vXu5\nMpI2IzSwf2ZmN5YKmNnTifJXATeXe/Hp06dz9dVXM2JEqMKQIUM48MADNzQ6Sj+j+7RP+3Rzph9d\n9SpsGbqJSukZpcZtf5+uJ16dnZ1MnToVgBEjRjB06NBeDl/b2mRmza5Drjo6OqynIfyymDFjRq69\n2ccPyv8HhOTY23l5b87rg5CTnWdv9gU5rqtkHfn2Zh8zYQJnnn027e3tmXJzOzo6rL398iplvph5\n/Q4kDQYWEy58/BthlKNJZrYwUWYCcKaZHS/pMGCKmR0Wn7sBeMbMzk6td7iZrYj/fxk4xMxOTb9+\nR0eHjR07tkFb1zs+Bm82RY5bUUfKmDvzXg469N0NW39vRsqYv+Llwl74uHzB7Ib1Zp8yZhijh2+b\nefk5c+YM6HOT92Q71xL84sZGqmVccTO7RdIESUuIQ/gBSDoC+BjwF0lzCcOLlobq+4GkNsLQfo8C\nZ/T1tjmXVtSRMpY//ByLt2xcvXykDNfXvJFdA8/Jzo/nZGfl6SKNVm1c8Tg9ucxy91BhtzGzT+ZZ\nx2Yoam9s0Xnc6ue5xdl43IrLG9nOtQTvyXbOOedaiY8uUgMfJzs/Pk52Vj6En2sOH7s4G49b/Xy8\n52w8bsXlPdnOtQTvyXbOOedaifdk18BzsvPjOdlZ1d+TLelYSYskPSjpnDLPv1nSTfFGKX+R9OmG\nVd+1LM8tzsbjVj/PLc7G41Zc3pPtXEuorydb0iDgUsKQdE8CsyTdaGaLEsXOBOab2QmSdgIWS/ov\nM/P8E+ecc66XvCe7Bp6TnR/Pyc6q7p7sQ4GHzOwxM1sDTAMmpsoYsF38fzvgWW9guzTPLc7G41Y/\nzy3OxuNWXN6T7VxLqPvW6bsCjyemnyA0vJMuBW6S9CSwLfDRzNVzzjnnXDfek10Dz8nOj+dkZ9WQ\n0UWOAeaa2S7AQcBlkrLf2sv1S55bnI3HrX6eW5yNx624vCfbuZbQPSd7xoy1zJixsXG93XZdtLe3\nJ4ssB0bNRguZAAAgAElEQVQkpneL85I+A3wfwMwelrQU2A/4c27Vds455wYo78mugedk58dzsrN6\nrdtj/Pi1nH8+Gx5tbW3pBWYBoySNlLQFcApwU6rMY8DRAJKGAfvQGm+R60OeW5yNx61+nlucjcet\nuPplT/YHB+f7g/+zZuwo5ba+/1mXfzNuxowZuae1HJdzHAFWEVp2eXn56hxXFs1YBOP3y299a94K\nd/d6LfWlhJjZOkmTgdsJX6avMbOFks4IT9uVwHeB6yQ9EBf7mpmt6nVVnXPOOdc/G9l5y7OB3Sit\nkDcOsEOzK1CDPBvYucnwxczMbgP2Tc27IvH/3wh52c5V5LnF2Xjc6ue5xdl43IrLG9nOtYJi5rA4\n55xzrgLPya7Bs2bNrkJVrZA3DiFdpOhmLKpeps+tqfJwrkE8tzgbj1v9PLc4G49bcXlPtnOtwHuy\nnXPOuZbijewaeE52fjwnOyPvrXZN4rnF2Xjc6ue5xdl43IrLG9nOtQLvyXbOOedaiudk18BzsvPj\nOdkZeU62axLPLc7G41Y/zy3OxuNWXN7Idq4VrKvycL0m6VhJiyQ9KOmcCmUukfSQpC5JbXHebpL+\nKGm+pL9I+lKi/PaSbpe0WNLvJQ3pq+1xzjnXXN7IroHnZOfHc7Iz8p7shpI0CLiUMG74aGCSpP1S\nZY4D9jKzvYEzgJ/Gp9YCZ5vZaODdwJmJZb8O3GFm+wJ/BL7R8I3JmecWZ+Nxq5/nFmfjcSsub2Q7\n1wq8kd1ohwIPmdljZrYGmAZMTJWZCNwAYGb3A0MkDTOzFWbWFee/DCwEdk0sc338/3rgxMZuhnPO\nuaLwRnYNPCc7P56TnZGnizTarsDjiekn2NhQrlRmebqMpD2ANuC+OGuoma0EMLMVwNDcatxHPLc4\nG49b/Ty3OBuPW3H56CLOtQLvrS48SdsC04F/MrPVFYoV/xu7c865XHgjuwaek50fz8nOKENvtaRj\ngSmEX6yuMbOLUs9/FfgYoeG3ObA/sJOZPd/b6rag5cCIxPRucV66zO7lykjajNDA/pmZ3ZgoszKm\nlKyUNBx4qtyLT58+nauvvpoRI0IVhgwZwoEHHrghr7fUK9qM6XHjxjX19Vt5uqQo9SlNz515L8sf\nfm5DLm+pJ7S/TzNmQub4PbrqVdhyz0JtTzIXe/mC2Q1bfz3x6uzsZOrUqQCMGDGCoUOH0t7ezkAl\na4FUiHp0dHTYxe9/f7Or0aOb165tdhVqctzgwc2uQlW3Xd3sGlS35q0TuHvLs2lvb8/0ba2jo8Pa\ntzm65zKr7+i2/ngh34NAO/AkMAs4xczKJsNI+iBwlpn1/EL9lKTBwGJCvP4GzAQmmdnCRJkJwJlm\ndrykw4ApZnZYfO4G4BkzOzu13ouAVWZ2URyxZHsz+3r69Ts6Omzs2LGN2jznupm/4mWmzVvZ7Gr0\nuVPGDGP08G0zLesxy2bOnDmZz339Qa452ZKGSvq5pCWSZkm6R9LE+Nw4SfdLWihpgaTTUsuennju\nPklHJJ4bLOl7cWitOfHRZ1fpe052fjwnO6P1VR6bquVCvqRJwC9yrHFLMbN1wGTgdmA+MM3MFko6\nQ9LpscwtwFJJS4ArgC8AxGPVx4CjJM2Nx6dj46ovAt4vqdSAv7BPNywHnlucjcetfp5bnI3Hrbjy\nThf5HXCtmX0MQNLuwAmShgE/B04ws3mSdgBul/SEmd0ae9FOAw43s+ckHQT8TtIhZvYU8K+EC4ZG\nm9kaSdsAX8m57s4V1xt1L1HuQr5DyxWUtBVwLHBmlqr1F2Z2G7Bvat4VqenJZZa7Byj7s4+ZrQIG\n5K8Dzjk30OXWky3pKOB1M7uqNM/MHjezywgn72vNbF6cvwr4GmEMWeL/XzWz5+Lzc4HrCOPNbgV8\nHpgce+Qws9Vm9i951b0az8nOj+dkZ1R/T3Y9PgR0DtBcbFeFj/ecjcetfj7eczYet+LKsyd7NDCn\nh+euS837c5xfadnZwCeBUcBjZvZKPtV0rgWlerJnPBAeJdsd1JW+uKSWC/lKTmEAp4o455xzjdCw\ncbIlXRpvPTyT3g9blbyg69Mx73GZpPQ4tg3hOdn58ZzsjFI91+PfDuefuvHR1taWXmIWMErSSElb\nEBrSN6ULxdt8HwncmH7OOfDc4qw8bvXz3OJsPG7FlWdP9nzgI6UJM5scc69nA7cB7wRuTpR/Z1ym\ntOzBwIzE8wfH+UuA3SVtE9NErgOuk/QAZfIgp0+fzjwztorTmwNvZmPKR6nBXM/0i8COcX1Zlk9P\nz5gxY0N6R6lx3NvpkrzWV5ouNYpLaR69nX4x5/WVGsSlFI88pruW9X59ADMWw6PPwPqt59F23CY9\nzfWpNk72lt0nzWydpNKFfKUh/BZKOiM8bVfGoicCvzezV7NXzjnnnHNpuQ7hJ+le4LrSxUKSRhAa\nzu8G7gcmxgsfdwRuBc43s1skfQj4FnCcma2S1Ea4iPJQM3tK0oXAMOAfzOz1ONzWfOADZrYsWQcf\nwi8/PoRfPnIZwm9VlSH8drhjQA+T1Op8CD/Xl3w4uvp5zLIZ6EP45T26yInAFElfA54GVgNfizdi\n+DhwlaTtYtl/j0NiYWY3S9oF+D9J64GXgI/FkUUgNMAvAP4q6UXgVeB6wvi/zvV/vb+40TnnnHN9\nKNecbDNbaWaTzGwvMzvMzNrNbHp8rtPMDjWz/ePjytSyV5jZfmZ2gJm9Kw6LVXpurZl9w8z2NrOD\nzWycmX3fzPqkS9hzsvPjOdkZvVHl4VyDeG5xNh63+nlucTYet+Ly26o71wq8J9s555xrKd7IroGP\nk50fHyc7o2oXPjrXID7eczYet/r5eM/ZeNyKyxvZzrWCdc2ugHPOOefq0bBxsvsTz8nOj+dkZ7Sm\nysO5BvHc4mw8bvXz3OJsPG7F5T3ZzrUC78l2zjnnWoo3smvgOdn58ZzsjLy32jWJ5xZn43Grn+cW\nZ+NxKy5vZDvXCrwnu9+bv+LlZlehz+20zeYM227L6gWdc64FeSO7Bs+aFb43O3mr9iJbRfF7s2cs\nKmBvtvdk93tFvZvc8gWzG9ZTdsqYYf22kd3Z2em92XVq5L7Wn3ncissb2c61Am9kO+eccy3FRxep\nQdF7scFzsvNUuF5sCOkiPT3KkHSspEWSHpR0ToUy4yXNlfRXSXc2pO6upXkPWTbei10/39ey8bgV\nl/dkO9cK6uzJljQIuBRoB54EZkm60cwWJcoMAS4DPmBmyyXtlF+FnXPOuYHNe7Jr4ONk58fHyc6o\n/p7sQ4GHzOwxM1sDTAMmpsqcCvzazJYDmNkzDam7a2k+Bm82Pk52/Xxfy8bjVlzeyHauFdR/M5pd\ngccT00/EeUn7ADtIulPSLEmfyLnWLaXG9JpLJD0kqUvSQYn510haKemBVPnzJD0haU58HNvo7XDO\nOVcM3siugedk58dzsjPKkJNdg82AscBxwLHAP0sa1duqtqJEes0xwGhgkqT9UmWOA/Yys72BM4Cf\nJJ6+Ni5bzsVmNjY+bsu/9o3l+Z7ZeE52/Xxfy8bjVlyek+1cK0j1Vs94MjxKtntbF+3t7ckiy4ER\niend4rykJ4BnzOw14DVJdwFjgCV5VbuFbEivAZBUSq9JJg9NBG4AMLP7JQ2RNMzMVppZp6SRFdZd\n/G/pzjnncuc92TXwnOz8eE52Ruu7P8YPh/PHbny0tbWll5gFjJI0UtIWwCnATakyNwLjJA2WtDXw\nLmBhYzeksGpJr0mXWV6mTDmTY3rJ1fFi05bi+Z7ZeE52/Xxfy8bjVlzeyHauFbxR5ZFiZuuAycDt\nwHxgmpktlHSGpNNjmUXA74EHgPuAK81sQcO3ZWC5HNjTzNqAFcDFTa6Pc865PtIv00Xe04ie5xzX\nOWGzxoT9Bzmv79Z1xb+X99sGD252FaoaPwE+cXYvV5LhZjQx/3ff1LwrUtM/BH7Ym6r1E7Wk1ywH\ndq9SphszezoxeRVwc7ly06dP584Fj/PmnXcBYIutt2XnPfbdkGtZ6qlqxvSuBxzcsPUzZgKwsde3\nlMfcX6ZLilKf0vTcmfey/OHnCrF/9eV0b/a3R1e9ClvuWajtSeZiJ+/6mPf664lXZ2cnU6dOBWDE\niBEMHTo0nco4oMhaIBWiHh0dHTbr6KObXY0ezWiBCykBblm7ttlVqGrPlmhkT+ATZ59Ne3t7pje+\no6PD2q/seZ/uOP2OzOt3IGkwsJgwrvjfgJnAJDNbmCgzATjTzI6XdBgwxcwOSzy/B3CzmR2YmDfc\nzFbE/78MHGJmp6Zfv6Ojw377dMtlkvTaKWOGMXr4tpmXX/nS6zyzeuDdDnWnbTbv1e3o5694mWnz\nVuZYo9bQm/3NY5bNnDlzBvS5qV/2ZOftEWDPZleiilVm7NACjfcZM2YUfiSU14A3NbsSaQOvHdGn\nzGydpFJ6zSDgmlJ6TXjarjSzWyRNkLQEWA18prS8pKnAeGBHScuA88zsWuAHktoI2fSPEkYlaSnJ\nHrKieWb1msI2fBoZt1PGDOtVI7uoiryvFZnHrbi8ke1cK1jf7Ar0fzWm10yusOwmvdNx/idzq6Bz\nzrmW4o3sGhS9FxtoiV5saI3xvAvXiw1lL250ri94D1k2Hrf6ecyy8bgVlzeynWsF3pPtnHPOtRQf\nwq8GjzS7AjVY1SIXsLbCeN6vNbsC5dQ5hJ9zefExeLPxuNXPY5aNx624vCfbuVbgPdnOOedcS/FG\ndg08Jzs/npOdkY8u4prE8z2z8bjVz2OWjcetuLyR7VwrKP59gZxzzjmX4DnZNfCc7Px4TnZGa6o8\nnGsQz/fMxuNWP49ZNh634vKebOdagV/c6JxzzrUU78mugedk58dzsjNaX+VRhqRjJS2S9KCkc8o8\nf6Sk5yXNiY9vNaz+rmV5vmc2Hrf6ecyy8bgVl/dkO9cK6kwJkTQIuBRoB54EZkm60cwWpYreZWYn\n5FJH55xzzm3gPdk18Jzs/HhOdkbrqjw2dSjwkJk9ZmZrgGnAxDLlWuMnENc0nu+Zjcetfh6zbDxu\nxeWNbOdaQf0XPu4KPJ6YfiLOS3u3pC5J/yvpgDyr7Jxzzg1kDU0XkbQD0AEY8FZCn9tT8enfAX/P\nxr64M8xslqQ7ga+Y2Zwy65sCnGRmuzWy3mmek50fz8nOqDFD+M0GRpjZK5KOI3wm92nIK7mW5fme\n2Xjc6ucxy8bjVlwNbWSb2SrgIABJ3wZeNrOLJR0G/AhoM7O1sTG+RU/rkiTgRGCZpCPN7E+NrLtz\nhZLqrZ7xBsxIzNuuq4v29vZkkeXAiMT0bnHeBmb2cuL/WyVdLmmH+Ll1zjnnXC/0ZbpIsqv1rcAz\nZrYWQmPczFZUWX488FfgJ8CpDalhBZ6TnR/Pyc4olYM9fjCc/6aNj7a2tvQSs4BRkkZK2gI4Bbgp\nWUDSsMT/hwLyBrZL83zPbDxu9fOYZeNxK65m5WTfDoyIw4tdJum9NSwzCZhK+El7gqTBDa2hc0VS\nZ062ma0DJhM+a/OBaWa2UNIZkk6PxU6S9FdJc4EpwEcbvRnOOefcQNGUIfzMbLWkscB7gKOAaZK+\nbmY3lCsvaXNgAvDluOxM4Bjglr6or+dk58dzsjPKkJNtZrcB+6bmXZH4/zLgst5WzfVvnu+Zjcet\nfh6zbDxuxdW0cbLNzIC7gLsk/QX4JFC2kU1oUA8B/hJzs7cCXqFMI3v69OnMBLaP028CdmFjQ7mU\n+tHM6VVmGxrFpTSPok6X0jtKjeOiTZdSO0oN46JMl/5fC3TOm8eBm+ZM18dvne6cc861lKY0siXt\nA6w3syVxVhvwWLJIapFJwOfM7Fdx+a2BpZLeZGbdUmhPOukkRv70pxVfO90rXcv0I1Wer3d6WaLX\nOd0DnXW61HDPa30l6Z7n3k6n5/V2fele5zymX6vyfK3Tpf/HjRlTLme6Pt7Idk2yfMFs7ynLwONW\nP49ZNh634mpWTva2wPUxH7QL2B84P/H8/0haFh+/IpUaYmavAHcDH+rDOjvXNPXfi8bVq9pt6GOZ\nSyQ9FMcWPygx/xpJKyU9kCq/vaTbJS2W9HtJQxq9Hc4554qhz3qyzew7if/nAEdUKPe+Gtd3Uk5V\nq8pzsvPjOdnZeEd2Y9VyG/o4lvheZra3pHcRRjo6LD59LfBjNk15+zpwh5n9IDbcvxHntQzvIcvG\n41Y/j1k2Hrfi8js+OtcCvCe74Wq5Df1EYiPazO4HhpSGQTSzTuC5MuudCFwf/7+eMNa/c865AcAb\n2TXwcbLz4+NkZ1P/XdVdnWq5DX26zPIyZdKGmtlKgHgvgKG9rGef8zF4s/G41c9jlo3HrbiaNrqI\nc6523lvdb7TGt2HnnHO95o3sGnhOdn48Jzsb761uuKq3oY/Tu1cpk7ZS0jAzWylpOPBUuULTp0/n\nzgWP8+addwFgi623Zec99t2Qa1nqqWrG9K4HHNyw9TNmAgCdnZ0AjBs3rq7p7Ue1NT0+PW5flPf6\n5868l+d22KrueJWm5868l+UPP9f0+PT5+9GL/e3RVa/ClnsWanuSudjJEUbyXn898ers7GTq1KkA\njBgxgqFDh/Zu+NoWJ2uRNINadXR02Kyjj252NXo0o0UaxLesXdvsKlS15+Di3/hz/IQJfOLss2lv\nb8/0xnd0dNh+VfbpRXfckXn9DuIdZBcTLnz8GzATmGRmCxNlJgBnmtnxkg4DppjZYYnn9wBuNrMD\nE/MuAlaZ2UXxwsftzWyTCx87Ojrst08PvIFHThkzjNHDt828/PwVLzNt3soca9QaPG7Z9CZuHrNs\n5syZM6DPTZ6TXQPPyc6P52Rn4znZjVXLbejN7BbC+PxLgCuAL5aWlzQV+D9gnzj06GfiUxcB75dU\nasBf2GcblRPP98zG41Y/j1k2Hrfi8nQR51rA+mZXYACodhv6OD25wrKnVpi/Cij2T2vOOecawhvZ\nNfCc7Px4TnY2bzS7Am7A8jF4s/G41c9jlo3Hrbg8XcS5FpAlXaSWOxjGcodIWiPpw3nX2znnnBuo\nvJFdA8/Jzo/nZGdT781oEncwPAYYDUyStF+FchcCv29MzV2r83zPbDxu9fOYZeNxKy5vZDvXAjL0\nZNdyB0OAfwSmU2FoOeecc85l443sGnhOdn48Jzub9VUeZVS9g6GkXYATzewnQGvsQK7Peb5nNh63\n+nnMsvG4FZdf+OhcC2jQhY9TgGSutje0nXPOuZx4T3YNPCc7P56TnU2653o2cE3i0dXVlV6kljsY\nvhOYJmkpcBJwmaQTcq+8a2me75mNx61+HrNsPG7F5T3ZzrWAdE/26PgoGdTWll5kFjBK0kjCHQxP\nASYlC5jZhkwoSdcS7lZ4U151ds455wYyb2TXwHOy8+M52dlUuxlN+icpM1snqXQHw0HANaU7GIan\n7cr0IjlV1fUznu+Zjcetfh6zbDxuxdUvG9nfbXYFqnjxytZoz+y1WfF3j6Xri38vxDVr1nD33Xf3\nbh1Vni/3TtVyB8PE/M9mrJpzzjnnyvCc7BqUG4e4aGYsbnYNavNqC+SOFzFvvN5xsp3Li+d7ZuNx\nq5/HLBuPW3EVv6vSOVe1J9s555xzxeKN7BoMbnYFajB+3+plimCrFsgdL2LeeIOG8HOuKs/3zMbj\nVj+PWTYet+LyRrZzLaD4mefOOeecS/Kc7Bq0Qs6r52Tnp4g52Rluq+5cLjzfMxuPW/08Ztl43IrL\ne7KdawGt8EXPOeeccxt5I7sGnpOdH8/JzsZ7q12zeL5nNh63+nnMsvG4FZc3sp1rAd6T7ZxzzrUW\nz8muQSs0cDwnOz+ekz0wSTpW0iJJD0o6p0KZSyQ9JKlLUlu1ZSWdJ+kJSXPi49i+2JY8eb5nNh63\n+nnMsvG4FZf3ZDvXAlrhi14rkzQIuBRoB54EZkm60cwWJcocB+xlZntLehfwU+CwGpa92Mwu7svt\ncc4513zek10Dz8nOj+dkZ+M92Q13KPCQmT1mZmuAacDEVJmJwA0AZnY/METSsBqWLf5O3wPP98zG\n41Y/j1k2Hrfi8ka2cy0gy23Vq6U/SDpB0jxJcyXNlHREo+rfAnYFHk9MPxHn1VKm2rKTY3rJ1ZKG\n5Fdl55xzReaN7Bq0wk/1npOdn/6Qk51IYTgGGA1MkrRfqtgdZjbGzA4CPgdc3aDq91e19FBfDuxp\nZm3ACqDl0kY83zMbj1v9PGbZeNyKy3OynWsBGVJCNqQwAEgqpTBsyDE2s1cS5bdlYN9YcjkwIjG9\nW5yXLrN7mTJbVFrWzJ5OzL8KuLnci0+fPp07FzzOm3feBYAttt6WnffYd8PPwKWTaH+bZswEADo7\nOwEYN25cXdPbj2or1PYkp59+dHHD1j935r08t8NWdcerND135r0sf/i5QsUrqYj726OrXoUt9yxU\nvErTTz+6uKHrrydenZ2dTJ06FYARI0YwdOhQ2tvbGahkLdCzWI+Ojg6bePTRza5Gj168qtk1qM1e\npxc/lfSRdcX/nWHNmjXcfffdtLe3Zwp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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "cov_samples = trace[\"covariance\"]\n", + "mean_covariance_matrix = cov_samples.mean(axis=0)\n", + "\n", + "def cov2corr(A):\n", + " \"\"\"\n", + " covariance matrix to correlation matrix.\n", + " \"\"\"\n", + " d = np.sqrt(A.diagonal())\n", + " A = ((A.T/d).T)/d\n", + " #A[ np.diag_indices(A.shape[0]) ] = np.ones( A.shape[0] )\n", + " return A\n", + "\n", + "\n", + "plt.subplot(1,2,1)\n", + "plt.imshow(cov2corr(mean_covariance_matrix) , interpolation=\"none\", \n", + " cmap = \"hot\") \n", + "plt.xticks(np.arange(4), stock_returns.columns)\n", + "plt.yticks(np.arange(4), stock_returns.columns)\n", + "plt.colorbar(orientation=\"vertical\")\n", + "plt.title(\"(mean posterior) Correlation Matrix\")\n", + "\n", + "plt.subplot(1,2,2)\n", + "plt.bar(np.arange(4), np.sqrt(np.diag(mean_covariance_matrix)),\n", + " color = \"#348ABD\", alpha = 0.7)\n", + "plt.xticks(np.arange(4) + 0.5, stock_returns.columns);\n", + "plt.title(\"(mean posterior) standard deviations of daily stock returns\")\n", + "\n", + "plt.tight_layout();\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Looking at the above figures, we can say that likely TSLA has an above-average volatility (looking at the return graph this is quite clear). The correlation matrix shows that there are not strong correlations present, but perhaps GOOG and AMZN express a higher correlation (about 0.30). \n", + "\n", + "With this Bayesian analysis of the stock market, we can throw it into a Mean-Variance optimizer (which I cannot stress enough, do not use with frequentist point estimates) and find the minimum. This optimizer balances the tradeoff between a high return and high variance.\n", + "\n", + "$$ w_{opt} = \\max_{w} \\frac{1}{N}\\left( \\sum_{i=0}^N \\mu_i^T w - \\frac{\\lambda}{2}w^T\\Sigma_i w \\right)$$\n", + "\n", + "where $\\mu_i$ and $\\Sigma_i$ are the $i$th posterior estimate of the mean returns and the covariance matrix. This is another example of loss function optimization." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Protips for the Wishart distribution\n", + "\n", + "If you plan to be using the Wishart distribution, read on. Else, feel free to skip this. \n", + "\n", + "In the problem above, the Wishart distribution behaves pretty nicely. Unfortunately, this is rarely the case. The problem is that estimating an $NxN$ covariance matrix involves estimating $\\frac{1}{2}N(N-1)$ unknowns. This is a large number even for modest $N$. Personally, I've tried performing a similar simulation as above with $N = 23$ stocks, and ended up giving considering that I was requesting my MCMC simulation to estimate at least $\\frac{1}{2}23*22 = 253$ additional unknowns (plus the other interesting unknowns in the problem). This is not easy for MCMC. Essentially, you are asking you MCMC to traverse 250+ dimensional space. And the problem seemed so innocent initially! Below are some tips, in order of supremacy:\n", + "\n", + "1. Use conjugancy if it applies. See section below.\n", + "\n", + "2. Use a good starting value. What might be a good starting value? Why, the data's sample covariance matrix is! Note that this is not empirical Bayes: we are not touching the prior's parameters, we are modifying the starting value of the MCMC. Due to numerical instability, it is best to truncate the floats in the sample covariance matrix down a few degrees of precision (e.g. instability can cause unsymmetrical matrices, which can cause PyMC3 to cry.). \n", + "\n", + "3. Provide as much domain knowledge in the form of priors, if possible. I stress *if possible*. It is likely impossible to have an estimate about each $\\frac{1}{2}N(N-1)$ unknown. In this case, see number 4.\n", + "\n", + "4. Use empirical Bayes, i.e. use the sample covariance matrix as the prior's parameter.\n", + "\n", + "5. For problems where $N$ is very large, nothing is going to help. Instead, ask, do I really care about *every* correlation? Probably not. Further ask yourself, do I really really care about correlations? Possibly not. In finance, we can set an informal hierarchy of what we might be interested in the most: first a good estimate of $\\mu$, the variances along the diagonal of the covariance matrix are secondly important, and finally the correlations are least important. So, it might be better to ignore the $\\frac{1}{2}(N-1)(N-2)$ correlations and instead focus on the more important unknowns.\n", + "\n", + "**Another thing** to note is that the implementation of the Wishart distribution has changed in from PyMC to PyMC3. Wishart distribution matrices are required to have certain mathematical characteristics that are very restrictive. This makes it so that it is impossible for MCMC methods to propose matrices that will be accepted in our sampling procedure. With our model here we sample the Bartlett decomposition of a Wishart distribution matrix and use that to calculate our samples for the covariance matrix (http://en.wikipedia.org/wiki/Wishart_distribution#Bartlett_decomposition)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Conjugate Priors\n", + "\n", + "Recall that a $\\text{Beta}$ prior with $\\text{Binomial}$ data implies a $\\text{Beta}$ posterior. Graphically:\n", + "\n", + "$$ \\underbrace{\\text{Beta}}_{\\text{prior}} \\cdot \\overbrace{\\text{Binomial}}^{\\text{data}} = \\overbrace{\\text{Beta}}^{\\text{posterior} } $$ \n", + "\n", + "Notice the $\\text{Beta}$ on both sides of this equation (no, you cannot cancel them, this is not a *real* equation). This is a really useful property. It allows us to avoid using MCMC, since the posterior is known in closed form. Hence inference and analytics are easy to derive. This shortcut was the heart of the Bayesian Bandit algorithm above. Fortunately, there is an entire family of distributions that have similar behaviour. \n", + "\n", + "Suppose $X$ comes from, or is believed to come from, a well-known distribution, call it $f_{\\alpha}$, where $\\alpha$ are possibly unknown parameters of $f$. $f$ could be a Normal distribution, or Binomial distribution, etc. For particular distributions $f_{\\alpha}$, there may exist a prior distribution $p_{\\beta}$, such that:\n", + "\n", + "$$ \\overbrace{p_{\\beta}}^{\\text{prior}} \\cdot \\overbrace{f_{\\alpha}(X)}^{\\text{data}} = \\overbrace{p_{\\beta'}}^{\\text{posterior} } $$ \n", + "\n", + "where $\\beta'$ is a different set of parameters *but $p$ is the same distribution as the prior*. A prior $p$ that satisfies this relationship is called a *conjugate prior*. As I mentioned, they are useful computationally, as we can avoided approximate inference using MCMC and go directly to the posterior. This sounds great, right?\n", + "\n", + "Unfortunately, not quite. There are a few issues with conjugate priors.\n", + "\n", + "1. The conjugate prior is not objective. Hence only useful when a subjective prior is required. It is not guaranteed that the conjugate prior can accommodate the practitioner's subjective opinion.\n", + "\n", + "2. There typically exist conjugate priors for simple, one dimensional problems. For larger problems, involving more complicated structures, hope is lost to find a conjugate prior. For smaller models, Wikipedia has a nice [table of conjugate priors](http://en.wikipedia.org/wiki/Conjugate_prior#Table_of_conjugate_distributions).\n", + "\n", + "Really, conjugate priors are only useful for their mathematical convenience: it is simple to go from prior to posterior. I personally see conjugate priors as only a neat mathematical trick, and offer little insight into the problem at hand. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Jefferys Priors\n", + "\n", + "Earlier, we talked about objective priors rarely being *objective*. Partly what we mean by this is that we want a prior that doesn't bias our posterior estimates. The flat prior seems like a reasonable choice as it assigns equal probability to all values. \n", + "\n", + "But the flat prior is not transformation invariant. What does this mean? Suppose we have a random variable $\\textbf X$ from Bernoulli($\\theta$). We define the prior on $p(\\theta) = 1$. " + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "figsize(12.5, 5)\n", + "\n", + "x = np.linspace(0.000 ,1, 150)\n", + "y = np.linspace(1.0, 1.0, 150)\n", + "lines = plt.plot(x, y, color=\"#A60628\", lw = 3)\n", + "plt.fill_between(x, 0, y, alpha = 0.2, color = lines[0].get_color())\n", + "plt.autoscale(tight=True)\n", + "plt.ylim(0, 2);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, let's transform $\\theta$ with the function $\\psi = log \\frac{\\theta}{1-\\theta}$. This is just a function to stretch $\\theta$ across the real line. Now how likely are different values of $\\psi$ under our transformation." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "figsize(12.5, 5)\n", + "\n", + "psi = np.linspace(-10 ,10, 150)\n", + "y = np.exp(psi) / (1 + np.exp(psi))**2\n", + "lines = plt.plot(psi, y, color=\"#A60628\", lw = 3)\n", + "plt.fill_between(psi, 0, y, alpha = 0.2, color = lines[0].get_color())\n", + "plt.autoscale(tight=True)\n", + "plt.ylim(0, 1);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Oh no! Our function is no longer flat. It turns out flat priors do carry information in them after all. The point of Jeffreys Priors is to create priors that don't accidentally become informative when you transform the variables you placed them originally on.\n", + "\n", + "Jeffreys Priors are defined as:\n", + "\n", + "$$p_J(\\theta) \\propto \\mathbf{I}(\\theta)^\\frac{1}{2}$$\n", + "$$\\mathbf{I}(\\theta) = - \\mathbb{E}\\bigg[\\frac{d^2 \\text{ log } p(X|\\theta)}{d\\theta^2}\\bigg]$$\n", + "\n", + "$\\mathbf{I}$ being the *Fisher information*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Effect of the prior as $N$ increases\n", + "\n", + "In the first chapter, I proposed that as the amount of our observations or data increases, the influence of the prior decreases. This is intuitive. After all, our prior is based on previous information, and eventually enough new information will shadow our previous information's value. The smothering of the prior by enough data is also helpful: if our prior is significantly wrong, then the self-correcting nature of the data will present to us a *less wrong*, and eventually *correct*, posterior. \n", + "\n", + "We can see this mathematically. First, recall Bayes Theorem from Chapter 1 that relates the prior to the posterior. The following is a sample from [What is the relationship between sample size and the influence of prior on posterior?](http://stats.stackexchange.com/questions/30387/what-is-the-relationship-between-sample-size-and-the-influence-of-prior-on-poste)[1] on CrossValidated.\n", + "\n", + ">The posterior distribution for a parameter $\\theta$, given a data set ${\\textbf X}$ can be written as \n", + "\n", + "$$p(\\theta | {\\textbf X}) \\propto \\underbrace{p({\\textbf X} | \\theta)}_{{\\textrm likelihood}} \\cdot \\overbrace{ p(\\theta) }^{ {\\textrm prior} } $$\n", + "\n", + "\n", + "\n", + ">or, as is more commonly displayed on the log scale, \n", + "\n", + "$$ \\log( p(\\theta | {\\textbf X}) ) = c + L(\\theta;{\\textbf X}) + \\log(p(\\theta)) $$\n", + "\n", + ">The log-likelihood, $L(\\theta;{\\textbf X}) = \\log \\left( p({\\textbf X}|\\theta) \\right)$, **scales with the sample size**, since it is a function of the data, while the prior density does not. Therefore, as the sample size increases, the absolute value of $L(\\theta;{\\textbf X})$ is getting larger while $\\log(p(\\theta))$ stays fixed (for a fixed value of $\\theta$), thus the sum $L(\\theta;{\\textbf X}) + \\log(p(\\theta))$ becomes more heavily influenced by $L(\\theta;{\\textbf X})$ as the sample size increases. \n", + "\n", + "There is an interesting consequence not immediately apparent. As the sample size increases, the chosen prior has less influence. Hence inference converges regardless of chosen prior, so long as the areas of non-zero probabilities are the same. \n", + "\n", + "Below we visualize this. We examine the convergence of two posteriors of a Binomial's parameter $\\theta$, one with a flat prior and the other with a biased prior towards 0. As the sample size increases, the posteriors, and hence the inference, converge." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "image/png": 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wWiFES5IOuHCWkpISCgsLpQPuxjw8PIiKimr039AlcsCVUpuB17TW2+pvb24O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\nPsq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V7Hr4eVe55+UXENQ30fB6dhVb+TanwXLzif74m417cX3ljaXMufH4adb8YqMJnzaZ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\niyk4ksL7p/dx87BbsCjlFr9/KUtZyq1X3rt3L/n5+QCcPHmShIQ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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "figsize(12.5, 15)\n", + "\n", + "p = 0.6\n", + "beta1_params = np.array([1.,1.])\n", + "beta2_params = np.array([2,10])\n", + "beta = stats.beta\n", + "\n", + "x = np.linspace(0.00, 1, 125)\n", + "data = stats.bernoulli.rvs(p, size=500)\n", + "\n", + "plt.figure()\n", + "for i,N in enumerate([0,4,8, 32,64, 128, 500]):\n", + " s = data[:N].sum() \n", + " plt.subplot(8,1,i+1)\n", + " params1 = beta1_params + np.array([s, N-s])\n", + " params2 = beta2_params + np.array([s, N-s])\n", + " y1,y2 = beta.pdf(x, *params1), beta.pdf( x, *params2)\n", + " plt.plot(x,y1, label = r\"flat prior\", lw =3)\n", + " plt.plot(x, y2, label = \"biased prior\", lw= 3)\n", + " plt.fill_between(x, 0, y1, color =\"#348ABD\", alpha = 0.15) \n", + " plt.fill_between(x, 0, y2, color =\"#A60628\", alpha = 0.15) \n", + " plt.legend(title = \"N=%d\" % N)\n", + " plt.vlines(p, 0.0, 7.5, linestyles = \"--\", linewidth=1)\n", + " #plt.ylim( 0, 10)#\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Keep in mind, not all posteriors will \"forget\" the prior this quickly. This example was just to show that *eventually* the prior is forgotten. The \"forgetfulness\" of the prior as we become awash in more and more data is the reason why Bayesian and Frequentist inference eventually converge as well." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Bayesian perspective of Penalized Linear Regressions\n", + "\n", + "There is a very interesting relationship between a penalized least-squares regression and Bayesian priors. A penalized linear regression is a optimization problem of the form:\n", + "\n", + "$$ \\text{argmin}_{\\beta} \\;\\; (Y - X\\beta)^T(Y - X\\beta) + f(\\beta)$$\n", + "\n", + "for some function $f$ (typically a norm like $|| \\cdot ||_p^p$). \n", + "\n", + "We will first describe the probabilistic interpretation of least-squares linear regression. Denote our response variable $Y$, and features are contained in the data matrix $X$. The standard linear model is:\n", + "\n", + "\\begin{equation}\n", + "Y = X\\beta + \\epsilon\n", + "\\end{equation}\n", + "\n", + "where $\\epsilon \\sim \\text{Normal}( {\\textbf 0}, \\sigma{\\textbf I })$. Simply, the observed $Y$ is a linear function of $X$ (with coefficients $\\beta$) plus some noise term. Our unknown to be determined is $\\beta$. We use the following property of Normal random variables:\n", + "\n", + "$$ \\mu' + \\text{Normal}( \\mu, \\sigma ) \\sim \\text{Normal}( \\mu' + \\mu , \\sigma ) $$\n", + "\n", + "to rewrite the above linear model as:\n", + "\n", + "\\begin{align}\n", + "& Y = X\\beta + \\text{Normal}( {\\textbf 0}, \\sigma{\\textbf I }) \\\\\\\\\n", + "& Y = \\text{Normal}( X\\beta , \\sigma{\\textbf I }) \\\\\\\\\n", + "\\end{align}\n", + "\n", + "In probabilistic notation, denote $f_Y(y \\; | \\; \\beta )$ the probability distribution of $Y$, and recalling the density function for a Normal random variable (see [here](http://en.wikipedia.org/wiki/Normal_distribution) ):\n", + "\n", + "$$ f_Y( Y \\; |\\; \\beta, X) = L(\\beta|\\; X,Y)= \\frac{1}{\\sqrt{ 2\\pi\\sigma} } \\exp \\left( \\frac{1}{2\\sigma^2} (Y - X\\beta)^T(Y - X\\beta) \\right) $$\n", + "\n", + "This is the likelihood function for $\\beta$. Taking the $\\log$:\n", + "\n", + "$$ \\ell(\\beta) = K - c(Y - X\\beta)^T(Y - X\\beta) $$\n", + "\n", + "where $K$ and $c>0$ are constants. Maximum likelihood techniques wish to maximize this for $\\beta$, \n", + "\n", + "$$\\hat{ \\beta } = \\text{argmax}_{\\beta} \\;\\; - (Y - X\\beta)^T(Y - X\\beta) $$\n", + "\n", + "Equivalently we can *minimize the negative* of the above:\n", + "\n", + "$$\\hat{ \\beta } = \\text{argmin}_{\\beta} \\;\\; (Y - X\\beta)^T(Y - X\\beta) $$\n", + "\n", + "This is the familiar least-squares linear regression equation. Therefore we showed that the solution to a linear least-squares is the same as the maximum likelihood assuming Normal noise. Next we extend this to show how we can arrive at penalized linear regression by a suitable choice of prior on $\\beta$. \n", + "\n", + "#### Penalized least-squares\n", + "\n", + "In the above, once we have the likelihood, we can include a prior distribution on $\\beta$ to derive to the equation for the posterior distribution:\n", + "\n", + "$$P( \\beta | Y, X ) = L(\\beta|\\;X,Y)p( \\beta )$$\n", + "\n", + "where $p(\\beta)$ is a prior on the elements of $\\beta$. What are some interesting priors? \n", + "\n", + "1\\. If we include *no explicit* prior term, we are actually including an uninformative prior, $P( \\beta ) \\propto 1$, think of it as uniform over all numbers. \n", + "\n", + "2\\. If we have reason to believe the elements of $\\beta$ are not too large, we can suppose that *a priori*:\n", + "\n", + "$$ \\beta \\sim \\text{Normal}({\\textbf 0 }, \\lambda {\\textbf I } ) $$\n", + "\n", + "The resulting posterior density function for $\\beta$ is *proportional to*:\n", + "\n", + "$$ \\exp \\left( \\frac{1}{2\\sigma^2} (Y - X\\beta)^T(Y - X\\beta) \\right) \\exp \\left( \\frac{1}{2\\lambda^2} \\beta^T\\beta \\right) $$\n", + "\n", + "and taking the $\\log$ of this, and combining and redefining constants, we arrive at:\n", + "\n", + "$$ \\ell(\\beta) \\propto K - (Y - X\\beta)^T(Y - X\\beta) - \\alpha \\beta^T\\beta $$\n", + "\n", + "we arrive at the function we wish to maximize (recall the point that maximizes the posterior distribution is the MAP, or *maximum a posterior*):\n", + "\n", + "$$\\hat{ \\beta } = \\text{argmax}_{\\beta} \\;\\; -(Y - X\\beta)^T(Y - X\\beta) - \\alpha \\;\\beta^T\\beta $$\n", + "\n", + "Equivalently, we can minimize the negative of the above, and rewriting $\\beta^T \\beta = ||\\beta||_2^2$:\n", + "\n", + "$$\\hat{ \\beta } = \\text{argmin}_{\\beta} \\;\\; (Y - X\\beta)^T(Y - X\\beta) + \\alpha \\;||\\beta||_2^2$$\n", + "\n", + "This above term is exactly Ridge Regression. Thus we can see that ridge regression corresponds to the MAP of a linear model with Normal errors and a Normal prior on $\\beta$.\n", + "\n", + "3\\. Similarly, if we assume a *Laplace* prior on $\\beta$, ie. \n", + "\n", + "$$ f_\\beta( \\beta) \\propto \\exp \\left(- \\lambda ||\\beta||_1 \\right)$$\n", + "\n", + "and following the same steps as above, we recover:\n", + "\n", + "$$\\hat{ \\beta } = \\text{argmin}_{\\beta} \\;\\; (Y - X\\beta)^T(Y - X\\beta) + \\alpha \\;||\\beta||_1$$\n", + "\n", + "which is LASSO regression. Some important notes about this equivalence. The sparsity that is a result of using a LASSO regularization is not a result of the prior assigning high probability to sparsity. Quite the opposite actually. It is the combination of the $|| \\cdot ||_1$ function and using the MAP that creates sparsity on $\\beta$: [purely a geometric argument](http://camdp.com/blogs/least-squares-regression-l1-penalty). The prior does contribute to an overall shrinking of the coefficients towards 0 though. An interesting discussion of this can be found in [2].\n", + "\n", + "For an example of Bayesian linear regression, see Chapter 4's example on financial losses." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### References\n", + "\n", + "1. Macro, . \"What is the relationship between sample size and the influence of prior on posterior?.\" 13 Jun 2013. StackOverflow, Online Posting to Cross-Validated. Web. 25 Apr. 2013.\n", + "\n", + "2. Starck, J.-L., , et al. \"Sparsity and the Bayesian Perspective.\" Astronomy & Astrophysics. (2013): n. page. Print.\n", + "\n", + "3. Kuleshov, Volodymyr, and Doina Precup. \"Algorithms for the multi-armed bandit problem.\" Journal of Machine Learning Research. (2000): 1-49. Print.\n", + "\n", + "4. Gelman, Andrew. \"Prior distributions for variance parameters in hierarchical models.\" Bayesian Analysis. 1.3 (2006): 515-533. Print.\n", + "\n", + "5. Gelman, Andrew, and Cosma R. Shalizi. \"Philosophy and the practice of Bayesian statistics.\" British Journal of Mathematical and Statistical Psychology. (2012): n. page. Web. 17 Apr. 2013.\n", + "\n", + "6. http://jmlr.csail.mit.edu/proceedings/papers/v22/kaufmann12/kaufmann12.pdf\n", + "\n", + "7. James, Neufeld. \"Reddit's \"best\" comment scoring algorithm as a multi-armed bandit task.\" Simple ML Hacks. Blogger, 09 Apr 2013. Web. 25 Apr. 2013.\n", + "\n", + "8. Oakley, J. E., Daneshkhah, A. and O’Hagan, A. Nonparametric elicitation using the roulette method. Submitted to Bayesian Analysis.\n", + "\n", + "9. \"Eliciting priors from experts.\" 19 Jul 2010. StackOverflow, Online Posting to Cross-Validated. Web. 1 May. 2013. .\n", + "\n", + "10. Taleb, Nassim Nicholas (2007), The Black Swan: The Impact of the Highly Improbable, Random House, ISBN 978-1400063512" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.core.display import HTML\n", + "def css_styling():\n", + " styles = open(\"../styles/custom.css\", \"r\").read()\n", + " return HTML(styles)\n", + "css_styling()" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/Chapter6_Priorities/Ch6_Priors_PyMC_current.ipynb b/Chapter6_Priorities/Ch6_Priors_PyMC_current.ipynb new file mode 100644 index 00000000..ca8d760e --- /dev/null +++ b/Chapter6_Priorities/Ch6_Priors_PyMC_current.ipynb @@ -0,0 +1,1986 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Chapter 6\n", + "`Original content created by Cam Davidson-Pilon`\n", + "\n", + "`Ported to Python 3 and PyMC3 by Max Margenot (@clean_utensils) and Thomas Wiecki (@twiecki) at Quantopian (@quantopian)`\n", + "\n", + "`Ported to PyMC last by Kurisu Chan (@miemiekurisu)`\n", + "\n", + "---\n", + "\n", + "This chapter of [Bayesian Methods for Hackers](https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers) focuses on the most debated and discussed part of Bayesian methodologies: how to choose an appropriate prior distribution. We also present how the prior's influence changes as our dataset increases, and an interesting relationship between priors and penalties on linear regression." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Getting our priorities straight\n", + "\n", + "\n", + "Up until now, we have mostly ignored our choice of priors. This is unfortunate as we can be very expressive with our priors, but we also must be careful about choosing them. This is especially true if we want to be objective, that is, not to express any personal beliefs in the priors. \n", + "\n", + "### Subjective vs Objective priors\n", + "\n", + "Bayesian priors can be classified into two classes: *objective* priors, which aim to allow the data to influence the posterior the most, and *subjective* priors, which allow the practitioner to express his or her views into the prior. \n", + "\n", + "What is an example of an objective prior? We have seen some already, including the *flat* prior, which is a uniform distribution over the entire possible range of the unknown. Using a flat prior implies that we give each possible value an equal weighting. Choosing this type of prior is invoking what is called \"The Principle of Indifference\", literally we have no prior reason to favor one value over another. Calling a flat prior over a restricted space an objective prior is not correct, though it seems similar. If we know $p$ in a Binomial model is greater than 0.5, then $\\text{Uniform}(0.5,1)$ is not an objective prior (since we have used prior knowledge) even though it is \"flat\" over [0.5, 1]. The flat prior must be flat along the *entire* range of possibilities. \n", + "\n", + "Aside from the flat prior, other examples of objective priors are less obvious, but they contain important characteristics that reflect objectivity. For now, it should be said that *rarely* is a objective prior *truly* objective. We will see this later. \n", + "\n", + "#### Subjective Priors\n", + "\n", + "On the other hand, if we added more probability mass to certain areas of the prior, and less elsewhere, we are biasing our inference towards the unknowns existing in the former area. This is known as a subjective, or *informative* prior. In the figure below, the subjective prior reflects a belief that the unknown likely lives around 0.5, and not around the extremes. The objective prior is insensitive to this." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib inline\n", + "import numpy as np\n", + "import scipy.stats as stats\n", + "from IPython.core.pylabtools import figsize\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib as mtl\n", + "mtl.style.use(\"ggplot\")\n", + "\n", + "figsize(12.5,3)\n", + "colors = [\"#348ABD\", \"#A60628\", \"#7A68A6\", \"#467821\"]\n", + "\n", + "x = np.linspace(0,1)\n", + "y1, y2 = stats.beta.pdf(x, 1,1), stats.beta.pdf(x, 10,10)\n", + "\n", + "p = plt.plot(x, y1, \n", + " label='An objective prior \\n(uninformative, \\n\"Principle of Indifference\")')\n", + "plt.fill_between(x, 0, y1, color = p[0].get_color(), alpha = 0.3)\n", + "\n", + "p = plt.plot(x,y2 ,\n", + " label = \"A subjective prior \\n(informative)\")\n", + "plt.fill_between(x, 0, y2, color = p[0].get_color(), alpha = 0.3)\n", + "\n", + "p = plt.plot(x[25:], 2*np.ones(25), label = \"another subjective prior\")\n", + "plt.fill_between(x[25:], 0, 2, color = p[0].get_color(), alpha = 0.3)\n", + "\n", + "plt.ylim(0,4)\n", + "\n", + "plt.ylim(0, 4)\n", + "leg = plt.legend(loc = \"upper left\")\n", + "leg.get_frame().set_alpha(0.4)\n", + "plt.title(\"Comparing objective vs. subjective priors for an unknown probability\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The choice of a subjective prior does not always imply that we are using the practitioner's subjective opinion: more often the subjective prior was once a posterior to a previous problem, and now the practitioner is updating this posterior with new data. A subjective prior can also be used to inject *domain knowledge* of the problem into the model. We will see examples of these two situations later." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Decision, decisions...\n", + "\n", + "The choice, either *objective* or *subjective* mostly depends on the problem being solved, but there are a few cases where one is preferred over the other. In instances of scientific research, the choice of an objective prior is obvious. This eliminates any biases in the results, and two researchers who might have differing prior opinions would feel an objective prior is fair. Consider a more extreme situation:\n", + "\n", + "> A tobacco company publishes a report with a Bayesian methodology that retreated 60 years of medical research on tobacco use. Would you believe the results? Unlikely. The researchers probably chose a subjective prior that too strongly biased results in their favor.\n", + "\n", + "Unfortunately, choosing an objective prior is not as simple as selecting a flat prior, and even today the problem is still not completely solved. The problem with naively choosing the uniform prior is that pathological issues can arise. Some of these issues are pedantic, but we delay more serious issues to the Appendix of this Chapter (TODO)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We must remember that choosing a prior, whether subjective or objective, is still part of the modeling process. To quote Gelman [5]:\n", + "\n", + ">... after the model has been fit, one should look at the posterior distribution\n", + "and see if it makes sense. If the posterior distribution does not make sense, this implies\n", + "that additional prior knowledge is available that has not been included in the model,\n", + "and that contradicts the assumptions of the prior distribution that has been used. It is\n", + "then appropriate to go back and alter the prior distribution to be more consistent with\n", + "this external knowledge.\n", + "\n", + "If the posterior does not make sense, then clearly one had an idea what the posterior *should* look like (not what one *hopes* it looks like), implying that the current prior does not contain all the prior information and should be updated. At this point, we can discard the current prior and choose a more reflective one.\n", + "\n", + "Gelman [4] suggests that using a uniform distribution with large bounds is often a good choice for objective priors. Although, one should be wary about using Uniform objective priors with large bounds, as they can assign too large of a prior probability to non-intuitive points. Ask yourself: do you really think the unknown could be incredibly large? Often quantities are naturally biased towards 0. A Normal random variable with large variance (small precision) might be a better choice, or an Exponential with a fat tail in the strictly positive (or negative) case. \n", + "\n", + "If using a particularly subjective prior, it is your responsibility to be able to explain the choice of that prior, else you are no better than the tobacco company's guilty parties. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Empirical Bayes\n", + "\n", + "While not a true Bayesian method, *empirical Bayes* is a trick that combines frequentist and Bayesian inference. As mentioned previously, for (almost) every inference problem there is a Bayesian method and a frequentist method. The significant difference between the two is that Bayesian methods have a prior distribution, with hyperparameters $\\alpha$, while empirical methods do not have any notion of a prior. Empirical Bayes combines the two methods by using frequentist methods to select $\\alpha$, and then proceeds with Bayesian methods on the original problem. \n", + "\n", + "A very simple example follows: suppose we wish to estimate the parameter $\\mu$ of a Normal distribution, with $\\sigma = 5$. Since $\\mu$ could range over the whole real line, we can use a Normal distribution as a prior for $\\mu$. How to select the prior's hyperparameters, denoted ($\\mu_p, \\sigma_p^2$)? The $\\sigma_p^2$ parameter can be chosen to reflect the uncertainty we have. For $\\mu_p$, we have two options:\n", + "\n", + "1. Empirical Bayes suggests using the empirical sample mean, which will center the prior around the observed empirical mean:\n", + "\n", + "$$ \\mu_p = \\frac{1}{N} \\sum_{i=0}^N X_i $$\n", + "\n", + "2. Traditional Bayesian inference suggests using prior knowledge, or a more objective prior (zero mean and fat standard deviation).\n", + "\n", + "Empirical Bayes can be argued as being semi-objective, since while the choice of prior model is ours (hence subjective), the parameters are solely determined by the data.\n", + "\n", + "Personally, I feel that Empirical Bayes is *double-counting* the data. That is, we are using the data twice: once in the prior, which will influence our results towards the observed data, and again in the inferential engine of MCMC. This double-counting will understate our true uncertainty. To minimize this double-counting, I would only suggest using Empirical Bayes when you have *lots* of observations, else the prior will have too strong of an influence. I would also recommend, if possible, to maintain high uncertainty (either by setting a large $\\sigma_p^2$ or equivalent.)\n", + "\n", + "Empirical Bayes also violates a theoretical axiom in Bayesian inference. The textbook Bayesian algorithm of:\n", + "\n", + ">*prior* $\\Rightarrow$ *observed data* $\\Rightarrow$ *posterior* \n", + "\n", + "is violated by Empirical Bayes, which instead uses \n", + "\n", + ">*observed data* $\\Rightarrow$ *prior* $\\Rightarrow$ *observed data* $\\Rightarrow$ *posterior*\n", + "\n", + "Ideally, all priors should be specified *before* we observe the data, so that the data does not influence our prior opinions (see the volumes of research by Daniel Kahneman *et. al* about [anchoring](http://en.wikipedia.org/wiki/Anchoring_and_adjustment))." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Useful priors to know about\n", + "\n", + "### The Gamma distribution\n", + "\n", + "A Gamma random variable, denoted $X \\sim \\text{Gamma}(\\alpha, \\beta)$, is a random variable over the positive real numbers. It is in fact a generalization of the Exponential random variable, that is:\n", + "\n", + "$$ \\text{Exp}(\\beta) \\sim \\text{Gamma}(1, \\beta) $$\n", + "\n", + "This additional parameter allows the probability density function to have more flexibility, hence allowing the practitioner to express his or her subjective priors more accurately. The density function for a $\\text{Gamma}(\\alpha, \\beta)$ random variable is:\n", + "\n", + "$$ f(x \\mid \\alpha, \\beta) = \\frac{\\beta^{\\alpha}x^{\\alpha-1}e^{-\\beta x}}{\\Gamma(\\alpha)} $$\n", + "\n", + "where $\\Gamma(\\alpha)$ is the [Gamma function](http://en.wikipedia.org/wiki/Gamma_function), and for differing values of $(\\alpha, \\beta)$ looks like:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "figsize(12.5, 5)\n", + "gamma = stats.gamma\n", + "\n", + "parameters = [(1, 0.5), (9, 2), (3, 0.5), (7, 0.5)]\n", + "x = np.linspace(0.001 ,20, 150)\n", + "for alpha, beta in parameters:\n", + " y = gamma.pdf(x, alpha, scale=1./beta)\n", + " lines = plt.plot(x, y, label = \"(%.1f,%.1f)\"%(alpha,beta), lw = 3)\n", + " plt.fill_between(x, 0, y, alpha = 0.2, color = lines[0].get_color())\n", + " plt.autoscale(tight=True)\n", + " \n", + "plt.legend(title=r\"$\\alpha, \\beta$ - parameters\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### The Wishart distribution\n", + "\n", + "Until now, we have only seen random variables that are scalars. Of course, we can also have *random matrices*! Specifically, the Wishart distribution is a distribution over all [positive semi-definite matrices](http://en.wikipedia.org/wiki/Positive-definite_matrix). Why is this useful to have in our arsenal? (Proper) covariance matrices are positive-definite, hence the Wishart is an appropriate prior for covariance matrices. We can't really visualize a distribution of matrices, so I'll plot some realizations from the $5 \\times 5$ (above) and $20 \\times 20$ (below) Wishart distribution:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "image/png": 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98MAD2/38OnfurIKCAt12223auHGjpkyZoqqqKp122mn/8vEByC+KY2AHa9Gihc455xyNGDFC+++/v6688kp17dpVrVq10htvvKEjjzxyu8d67LHH9Morr6h58+YaPny4zjnnnGy277776pFHHtEll1yikpISVVVVqaqqaps/d5prd9xxhx577DEVFRVp4MCB6tev3xb5sGHD1L9/fzVt2nS7fr6zbt26qqqq0oIFC7THHnto9913z3Y/6NOnj66++mqddtppKi4u1gEHHJDt8LFy5UoNHDhQzZo1U7t27bTrrrtmP8X/Nh5//HG9//77Ki0tVZ8+fTR8+HD16tXrW4+32bdZF927d9fHH3+8RW/fjh07at26derUqVP2R3K+6bnnnlNFRYUKCws1ZMgQPfHEE7X+OMvXuWu6LdtznTd3mCguLlaPHj20cuVKzZw5UwceeOBW227atEl33nmnSktL1bx5c7344osaNWqUJKlnz56qqKhQ69atVVJSYo9ts9atW6tZs2YqLS3VmWeeqXvvvVfl5eWSpMsvv1wNGjRQq1at1L9//63+Ryj1/Bo0aKAJEybo2WefVUlJiS666CKNHTs2OzaA/xyZsD3/vgsAAAD8D8AnxwAAAEBEcQwAAABEFMcAAABARHEMAAAARBTHAAAAQERxDAAAAEQUxwAAAEBEcQwAAABEFMcAAABARHEMAAAARBTHAAAAQERxDAAAAEQUxwAAAEBEcQwAAABEFMcAAABARHEMAAAARBTHAAAAQERxDAAAAEQUxwAAAEBEcQwAAABEFMcAAABARHEMAAAARBTHAAAAQERxDAAAAEQUxwAAAEBEcQwAAABE9ZLph5mddBhbKijNy7Rak6d5Jem16vzM2ymE3E7wXH7WkPrmZ1o1zdO8klQ/T/O+l8M1NCQ/66ffr/IyrRrlZ1pJ0sMn5GniP+Rw/SzP0/3nvPxMq7/laV5JBXl6D1uTy/ew1/O0fn6fn2l1X57mlaQ+eZr3nm2vHz45BgAAACKKYwAAACCiOAYAAAAiimMAAAAgojgGAAAAIopjAAAAIKI4BgAAACKKYwAAACCiOAYAAAAiimMAAAAgojgGAAAAIopjAAAAIKI4BgAAACKKYwAAACCiOAYAAAAiimMAAAAgojgGAAAAIopjAAAAIKI4BgAAACKKYwAAACCiOAYAAAAiimMAAAAgojgGAAAAIopjAAAAIKI4BgAAACKKYwAAACCiOAYAAAAiimMAAAAgojgGAAAAokwIIdQWFmQyO/NYstbUfki5VZ6f5ytJKs3TvH/J8bkuytM5XdUtL9P+KDM1L/NK0rg8zZvL12u/PN2DBuVlVmlOnuaVpKvzNG9O7/cn5un+84c8raBr78nPvJI+/kV+5m2Zy/Xz0zytn5/NyMu0f8l0zcu8ktTzxjxN/LNtrx8+OQYAAAAiimMAAAAgojgGAAAAIopjAAAAIKI4BgAAACKKYwAAACCiOAYAAAAiimMAAAAgojgGAAAAIopjAAAAIKI4BgAAACKKYwAAACCiOAYAAAAiimMAAAAgojgGAAAAIopjAAAAIKI4BgAAACKKYwAAACCiOAYAAAAiimMAAAAgojgGAAAAIopjAAAAIKI4BgAAACKKYwAAACCiOAYAAAAiimMAAAAgojgGAAAAIopjAAAAIKI4BgAAAKJMCCHUmu6W2YmH8jVF+ZlW/ztP80q6+7/yM++QxOXfIdrmZw396IO8TKsHH8rPvJKk4/M0b0nu1lD/TH7Wz2F5mVWqydO8kvSnPM07MZf3oHy9h52Tn2n1Zp7mlfTRhPzM2zqX66d1ftbPX5blZVr1zHU9kFCQp3v9mlqeM58cAwAAABHFMQAAABBRHAMAAAARxTEAAAAQURwDAAAAEcUxAAAAEFEcAwAAABHFMQAAABBRHAMAAAARxTEAAAAQURwDAAAAEcUxAAAAEFEcAwAAABHFMQAAABBRHAMAAAARxTEAAAAQURwDAAAAEcUxAAAAEFEcAwAAABHFMQAAABBRHAMAAAARxTEAAAAQURwDAAAAEcUxAAAAEFEcAwAAABHFMQAAABBRHAMAAAARxTEAAAAQURwDAAAAEcUxAAAAEGVCCKG28LVMZmceS1ano/Myre5+IT/zStKQi/M08chaL/+OsVd+1lDBwrxMqzWf5GdeSVJJjq9lPpyYp/UzIS/Tqnt+ppUkTaz9reA/VkGe3sPydPvRpjzNK0mt/z9cPxqUn/WjFvmZtmBEfuaVpDVhcp5m/l/bfJRPjgEAAICI4hgAAACIKI4BAACAiOIYAAAAiCiOAQAAgIjiGAAAAIgojgEAAICI4hgAAACIKI4BAACAiOIYAAAAiCiOAQAAgIjiGAAAAIgojgEAAICI4hgAAACIKI4BAACAiOIYAAAAiCiOAQAAgIjiGAAAAIgojgEAAICI4hgAAACIKI4BAACAiOIYAAAAiCiOAQAAgIjiGAAAAIgojgEAAICI4hgAAACIKI4BAACAiOIYAAAAiCiOAQAAgCgTQgj5PggAAADg3wGfHAMAAAARxTEAAAAQURwDAAAAEcUxAAAAEFEcAwAAABHFMQAAABBRHAMAAAARxTEAAAAQURwDAAAAEcUxAAAAEFEcAwAAABHFMQAAABBRHAMAAAARxTEAAAAQURwDAAAAEcUxAAAAEFEcAwAAABHFMQAAABBRHAMAAAARxTEAAAAQURwDAAAAEcUxAAAAEFEcAwAAABHFMQAAABBRHAMAAAARxTEAAAAQURwDAAAAUb10vFs6Lq9O5+vN7KXpeOKMdH7s3mb8FiZ/2+SSVvwznc8x+880+RW/Tuc1F6fzuq3MBD1N/lgwG3xHUzLJuODo9O5lZvg+Jj/R5FeY/Hcmf9fkkvTfJm9q8ntNXtfkE02+q8mdk0MO19C69Ppp2SS9++tm+JdMfnK3dP7R1HTe34x/h8klqb7Jx5l86J7mGBam8xPM+LNM3tDkJ+Vy/WjfZPpaZn4yf8WMvr/JnzT5pSZ3197lkrTH4el86avp3L2HDTD5zSZfZPKDTX5GHtfPOrN+GpvFv/iLdO7WX9+SdD5peTrvdZSZQFLV9HT+idl/o8n/ZPKnDkjnVaYI62DGL69l/fDJMQAAABBRHAMAAAARxTEAAAAQURwDAAAAEcUxAAAAEFEcAwAAAFEmhEQflPJ0GyU9b0avTMc1poXQO2Z414bpR6en808fNwPIt8opPiadvzY5nXcy/3syd1M6d62EMiPMBjfktpVbQSa9htaYPi8jt6dXUcJQk//S5O+Z3HTikyS9ZXLX6ua67Zgjpa3JXzT5pyZ/I4etlFqa9fNxGJkeoO7g7zR/pXn9/dbsv4vJ3fqSpL+ZPN1sSjK3IP3E5K7VlusmeZfJb8nh+nnNrJ9OIX0DfiqTXgBFZn7Xqs+1WXRtrj4zuSTVmPxGk5tOb1pi8vtMfrvJ/2Dys3O4ftaZ9dM4pPvkHZFJnz1XQrnXXvmh6by3uXn8ybXDlTR8QTo/3+zv6rS+5gZ2h2m5+4YZ/2HTCk5v0MoNAAAASKI4BgAAACKKYwAAACCiOAYAAAAiimMAAAAgojgGAAAAIopjAAAAIEr3OS4zfY5daX2iyU3/Or2Sjl/7ZzrvYIb/yOSStM/B6XzdP9J5493T+dIP0rntEdgnnX88Pp23zGGPSEmqMH0iB5n9B49N5yPPSefm9GimyZ80+Y9NLvle2a7d9iqTu3VcbfJ+Jm9h8gtyuIY+MuuntbsH1aSPbZIZ31lv8mdM/oPtmGOOyW8x+bkmd63Q051cfa9n16u1Xw7Xzyi3fsz+J5sbwNBT07lptW/v765HdXeTS1Ijk/c3N6iOphG7u7+5e2QDk//e5FW5fA9rlF4/R3yR3v0lc2xnmPU5LT28ik3u+nCbt1dJ0iEmf9Tkro++u75PmPxOk7sao1ct14hPjgEAAICI4hgAAACIKI4BAACAiOIYAAAAiCiOAQAAgIjiGAAAAIgojgEAAICoXjItTe9cMyOd13V9jCcuSedD2ibjThPTu7+xIJ2/nI4lSfv8Mp2vPzqdv2f6GFd0S+d9P0/nrpFhy13M/jnm+gw7ro/x4JDu4np55qFk/qaZP70CbStuSf4clJvc9UI9ZTuOIWVgu3T+PdOo9oLvOH+Ke+4nm9z1Me4V0ldnfibdKNz1kB7dMJ3fYPqkSr5Psbl8thdzM9PI9LVZ6dydA9MKPqf2N7lp4Wv7GA83jap/+Gw6d9fOTG97kEuSOQRNMSfhUrP/OyZ391h3/3LrN5cWm9fn82Z/18f4sfDDZD44U5XMR15mDuCRdDxqudlf0pob0/nTplH6Xc3TeaX5vor30rGeOiGdT5xgBqgFnxwDAAAAEcUxAAAAEFEcAwAAABHFMQAAABBRHAMAAAARxTEAAAAQURwDAAAAUSaEEGoLJ5oefXuZwctNfzudZfK7v5fOe/49Gc99Ib276zEsSYunpvM9SswApoffkE3p3LRR1knHpfM1f0znBbVf/h1ipllD3zf7v27yO0x+15/T+bj/SudLzfimBbAkaZXJG5n8JybvYnLzKtKPTe5a8fbL5Rrqnl4/leb1+VMzvOszu094K5mPzuyXzE2LUHt8knSMyct3Nxt0NrnppX7i5HTueu0OMfmBOVw/F5n7z7tm/7tN/t8mH2Pyh03uvirgCJNLvhe7Wx7PmNz1OXa94j8xuXsPvCWH6+d3Zv0caPbvZfITTT7SNPm92xRhQ0yf9Snb0WfdXR/Xh7qpyY8yubn9qNLk00zet5b1wyfHAAAAQERxDAAAAEQUxwAAAEBEcQwAAABEFMcAAABARHEMAAAARBTHAAAAQJTsc6wO6R5/Dy5ID36wmbzT3mYD1yDxL+nafkUm3UTY9ZCUpC4HpPMhc9J5dzO+6xXd8YF0/tfz0vmRrtHqz3Lb57jS9Il0PXaLTH6PyX9k8n53pvOZV6bzN834ku+VfN056fyisek83e3br8E1Jh9ZaDZYlbs19JFZP67H5hsmd+vL3SMuCPcm879mLkzmu5nxJansYrPBS+l40qx07no9m1uQTje5OTxdlMM+tfPM+ulg9nd9iN1rz93fr7gsnY/8ZTqfaMaXpM9M/rzJLzN5fZNvNLnrFWxa9WpgLvust0ivH5Wl44q/pfO5l6Xzu3+ZzoeEQekN2qbfIWd+kN5dkg47NJ2faJ6j66Ptej1XpFvJ+xexK0RrqYH45BgAAACIKI4BAACAiOIYAAAAiCiOAQAAgIjiGAAAAIgojgEAAICI4hgAAACI0n2Oj/huPf5WPp7OF6VjW7mXmrxZSHfgfD9jDlBS2Q/MBi+a3DVS/afJzfwvT0jnXVwTyg257XPs+tSONvu7PrNNTL6/yStNfpi5vjNdE2FJV5h8mmkk+7DpZT3NjH+syV0v17tNXpbDPqO9zfp5wux/jclHmyaqLb9I5+PN+EeGPZL5xMxiM4LvI9vLNCoeZW60Lcz4fS8zG9yXjq9am87vyOH6edesH3d/ecTkp5r8WZO7Hr+DB6TzB8eYAWTfpnWDyb9n8gKT9zG561U/2OSH5XD9TDLr5w6z/+cmf7nEbLDK5O7FOzUdX+AacUsabY7xseXp/AzzJn2yuT/cno7V/pfpfNpl6byylvXDJ8cAAABARHEMAAAARBTHAAAAQERxDAAAAEQUxwAAAEBEcQwAAABEFMcAAABAlO5zvGu6x9+npkfvp2Zy1yJ4YLd0/rLp4dfajF9m+utJUoHp0bfmxnQ+e0Q672iamI7emM4vOCSdz5+VzvfJYY9ISfqr6RPpemA+afJXTF5tctfD0/VJPuxws4GkI15N5+eb/T8y+Rkmd32My0x+lMnb53ANvWHWzwaz/xKT/83k7h5yvMnfNPmxrlGuJE1Px/PNInf95F0v33NN3qN5Op9r3icqcrh+Fpv1Y06tbTN7gMkXmHycyU8x+Y/2NhtI9kUw1PTyPsEMf6HJ3T20rsmdB3P5HlZpvuvB3JznmwUw2Uzvzp3rMf0bk4/+b7OBpBNNo+GzzP5zTH6MySsL0/mQ1en8HDN+J/ocAwAAAGkUxwAAAEBEcQwAAABEFMcAAABARHEMAAAARBTHAAAAQERxDAAAAEQUxwAAAECU/BKQFaaBumveXXyw2eCX6Xjx0el8D9eBvW06LnjW7C9pTfhFMp+UuSaZ9zJfZLLSfJFJ8e7pXO6LTHYx+Xu5/RKQI8waqjL7zzN5C5Onr57keui7JVJjckl66fp0fvdN6fxxM/5eJjc93O0XIdxg8ndy2IR/nlk/7ouEPjH56SY338Gj8ovT+aRfp/NepWYCSfpVOq4x3xTxFzN8kcndl4i42/BnJj8yl1/i0Dm9fn5rvqBnkxn+HZOb7z/RbJP/3OQ9G5oNJGlhOp5m1qBbogeZfKXJDzP53SavzOH6qTL3n7+b/W8zufsSsd+ZLxHre6iZ4P10fOJys7+kP4SZyfyqTPoKui/66mLyB00+0HwJ0RrzIizgS0AAAACANIpjAAAAIKI4BgAAACKKYwAAACCiOAYAAAAiimMAAAAgojgGAAAAomSf42mmx1/lMenB101O5+vTsZqVpPMhpkff3U3MBFeaXNIk02ew1z/S+QbT69n1ir7V5Ncdlc5Xmia2xbnsMSrpWLOGXA9D1+ez3ORvmnzUOWaD7un4wfPM/pJWmXyIuQY/N+fwLjP+0yYfYPJ3TS9fjczdGhpunru5PDrO5Pea/GzXZ9w12jY9Nue7JsKS2pu8rmumu0c63rA6nbtevhWml/pvP0/nZ+fwHrTUrJ8jzf7v10/nU0wjbHd5TzX5/zJ5L5NLknmbtn2CrzXn8B4z/jSTu/VlygiNyuH6edA8995m/3SHYM+9d/ze5P1Mbpa3JOkVk99xodngyXRcZe5fb5jh9zf5DJPfSp9jAAAAII3iGAAAAIgojgEAAICI4hgAAACIKI4BAACAiOIYAAAAiCiOAQAAgKheKnQ9+pqYBoSdTI/Q9z5I581M/zvX41RF6Xi26WEsSb26pXPXx7hBaJnMR2c+9geRsiQdux64Q7/b7JbrI3udyWtM/pLJbzb5RWPTeWeTf2TGl6QJJl9lemneYNbgmqnpvNrMP8zkQ36dzu8eaQb4Dobumc5vWJjOzzXju17r6pyOJ/2fdO56uHZw88v3yu1l+hjrsXT8/gnp/A9m+Dmuj3ErM0AOufewV03e0fQxvtTs/6zJ3f3reyY3l06S1NTkro/xLaaPbUdzk3/ZzO960buvK8glc/nt9XP5Xc3TuesBbG5POsOcvKFrzQCS+rgNTB9jPZGO9/qvdO7Wx0l7p/MXFpgBasEnxwAAAEBEcQwAAABEFMcAAABARHEMAAAARBTHAAAAQERxDAAAAEQUxwAAAECU7HN8helvqkvS8VLTx7jC9G8dYvq3uh6mMj0CO9Z3A0grzTEUmP1dH+MLXBPaI9Nxlenz+lMzfK7V/Y77uz7Cp5i8i8nLTL7B5DeYXJLmmNz1onZ9jG95PJ3/6PR07p7jESbPpTvM+v6J2d/1yW52iNnA9PBtZ3b/zOSPmFySBpl8w+p07voY72Pu87tdnM5NO3u9vCydu9fodzHA5M+b3L1FuD7WLj/K5O79xbQgluT7xLrn6PoY9zPvsz83vXzvMr3MR5p7QC79yeRP7ZvOr3g7nVeac3e0mX8vk59s+hhfbvaX/OvT9WJ2fYwrQnEy/1VmZTIfavoYm0tUKz45BgAAACKKYwAAACCiOAYAAAAiimMAAAAgojgGAAAAIopjAAAAIKI4BgAAAKJMCCHUFtZkMsmd55nBXX/Fvgen86f/kc5PesBM8Id0PHqC2V/SBaaJ582ml7NznenxqPdqvTySpN7mGk00w9et/fLvEPeb43On7ymTX2ry8SY/0OSdTf6GySXpfJO/Z3LXCtv14nwwlCbziZn0DKPN+H/I4Rqab9bPF2b/k03+msnPNnm5yW+9LJ1P+aUZQJJpE6wDTG5ug9rN5P3Db5J5yKRXeHpvaWAO188os37MW4wONbl7jzMtWJV+Zfpe/veYXJIeNLl7Di+b3K3PG8z79EXnpfMlZvyqXL6HHZheP3eYJvZdzfDu3t/I5H33S+fvvpXO2xeaCSTdb/qof2L2d9914M7B6GvS+dxfpPP9zfiZWtYPnxwDAAAAEcUxAAAAEFEcAwAAABHFMQAAABBRHAMAAAARxTEAAAAQURwDAAAAUbLPsVqne/wF0+Aw08fMXmTyT9PxX/+Yzl1/vS6HmA0kaZHJXRM906Sxyow/0gz/p3B6Mg+Zx5N5bT3+dpSnTJ/RX5n9jzH5De3S+dPm/E4247sem66PruR7VV5n8mEmd32OzzD5sWPT+VnnpPNHcriGxpn1c7TZ3/XQbGryO0w+2OQdm5gN3OKQNPef6bxil3Q+7vN0blq56wiTZ0yz3ZpB6TyXvdavNuvnerO/65O+t8mvNPlZJp9pcvceJ0mzTX6DyaeZ/C7Tq/+ihel8VPotSj3Sb3GaksP1U2XWz+/N/qtM/tQJ6fwj810Mrc3+6pmOh1xm9pd0d/N0/rS5P51kXiRDTTPwU9OxKsz9bYq5P/agzzEAAACQRnEMAAAARBTHAAAAQERxDAAAAEQUxwAAAEBEcQwAAABEFMcAAABAlO5zfEa6x5/r8fvxjem8pek/t8b0rysw4+sX6Xj+RrO/pH1MD8eVpofjXWb8n5p8k8nd/91kwrVmi5tN/t24PsdDzf6uj+xok19t8n6F6fz91em8xowvSd83+TsXp/Mhv07n+5rxXR9k1278keA6nR5l8m/vabN+XjH7dzT5ZyZ3PX7dmXF9ls81ueSP0c1xdqt0/rLpV/+GGf9HJq8b3F1qe15F385jZv24mZ80+XqTH2ryvUze0eSmxbQkv8ZMm2G5Vt1lJnf3H9cLeMp5ZoPf5K7P8TyzfsoPSO8/aU46dyXIGpP3NTXQtBHp3F1bSSo3+c9M7l4j7v3LtEnXiybv8azZ4Pv0OQYAAACSKI4BAACAiOIYAAAAiCiOAQAAgIjiGAAAAIgojgEAAICI4hgAAACI0n2OAQAAgP9B+OQYAAAAiCiOAQAAgIjiGAAAAIgojgEAAICI4hgAAACIKI4BAACA6P8CMM3yzQ2ENPUAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "n = 4\n", + "for i in range(10):\n", + " ax = plt.subplot(2, 5, i+1)\n", + " if i >= 5:\n", + " n = 15\n", + " plt.imshow(stats.wishart.rvs(n+1, np.eye(n)), interpolation=\"none\", \n", + " cmap = \"hot\")\n", + " ax.axis(\"off\")\n", + " \n", + "plt.suptitle(\"Random matrices from a Wishart Distribution\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "One thing to notice is the symmetry of these matrices. The Wishart distribution can be a little troubling to deal with, but we will use it in an example later." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### The Beta distribution\n", + "\n", + "You may have seen the term `beta` in previous code in this book. Often, I was implementing a Beta distribution. The Beta distribution is very useful in Bayesian statistics. A random variable $X$ has a $\\text{Beta}$ distribution, with parameters $(\\alpha, \\beta)$, if its density function is:\n", + "\n", + "$$f_X(x | \\; \\alpha, \\beta ) = \\frac{ x^{(\\alpha - 1)}(1-x)^{ (\\beta - 1) } }{B(\\alpha, \\beta) }$$\n", + "\n", + "where $B$ is the [Beta function](http://en.wikipedia.org/wiki/Beta_function) (hence the name). The random variable $X$ is only allowed in [0,1], making the Beta distribution a popular distribution for decimal values, probabilities and proportions. The values of $\\alpha$ and $\\beta$, both positive values, provide great flexibility in the shape of the distribution. Below we plot some distributions:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "figsize(12.5, 5)\n", + "\n", + "params = [(2, 5), (1, 1), (0.5, 0.5), (5, 5), (20, 4), (5, 1)]\n", + "\n", + "x = np.linspace(0.01, .99, 100)\n", + "beta = stats.beta\n", + "for a, b in params:\n", + " y = beta.pdf(x, a, b)\n", + " lines = plt.plot(x, y, label = \"(%.1f,%.1f)\"%(a,b), lw = 3)\n", + " plt.fill_between(x, 0, y, alpha = 0.2, color = lines[0].get_color())\n", + " plt.autoscale(tight=True)\n", + "plt.ylim(0)\n", + "plt.legend(loc = 'upper left', title=\"(a,b)-parameters\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "One thing I'd like the reader to notice is the presence of the flat distribution above, specified by parameters $(1,1)$. This is the Uniform distribution. Hence the Beta distribution is a generalization of the Uniform distribution, something we will revisit many times.\n", + "\n", + "There is an interesting connection between the Beta distribution and the Binomial distribution. Suppose we are interested in some unknown proportion or probability $p$. We assign a $\\text{Beta}(\\alpha, \\beta)$ prior to $p$. We observe some data generated by a Binomial process, say $X \\sim \\text{Binomial}(N, p)$, with $p$ still unknown. Then our posterior *is again a Beta distribution*, i.e. $p | X \\sim \\text{Beta}( \\alpha + X, \\beta + N -X )$. Succinctly, one can relate the two by \"a Beta prior with Binomial observations creates a Beta posterior\". This is a very useful property, both computationally and heuristically.\n", + "\n", + "In light of the above two paragraphs, if we start with a $\\text{Beta}(1,1)$ prior on $p$ (which is a Uniform), observe data $X \\sim \\text{Binomial}(N, p)$, then our posterior is $\\text{Beta}(1 + X, 1 + N - X)$. \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Example: Bayesian Multi-Armed Bandits\n", + "*Adapted from an example by Ted Dunning of MapR Technologies*\n", + "\n", + "> Suppose you are faced with $N$ slot machines (colourfully called multi-armed bandits). Each bandit has an unknown probability of distributing a prize (assume for now the prizes are the same for each bandit, only the probabilities differ). Some bandits are very generous, others not so much. Of course, you don't know what these probabilities are. By only choosing one bandit per round, our task is devise a strategy to maximize our winnings.\n", + "\n", + "Of course, if we knew the bandit with the largest probability, then always picking this bandit would yield the maximum winnings. So our task can be phrased as \"Find the best bandit, and as quickly as possible\". \n", + "\n", + "The task is complicated by the stochastic nature of the bandits. A suboptimal bandit can return many winnings, purely by chance, which would make us believe that it is a very profitable bandit. Similarly, the best bandit can return many duds. Should we keep trying losers then, or give up? \n", + "\n", + "A more troublesome problem is, if we have found a bandit that returns *pretty good* results, do we keep drawing from it to maintain our *pretty good score*, or do we try other bandits in hopes of finding an *even-better* bandit? This is the exploration vs. exploitation dilemma.\n", + "\n", + "### Applications\n", + "\n", + "\n", + "The Multi-Armed Bandit problem at first seems very artificial, something only a mathematician would love, but that is only before we address some applications:\n", + "\n", + "- Internet display advertising: companies have a suite of potential ads they can display to visitors, but the company is not sure which ad strategy to follow to maximize sales. This is similar to A/B testing, but has the added advantage of naturally minimizing strategies that do not work (and generalizes to A/B/C/D... strategies)\n", + "- Ecology: animals have a finite amount of energy to expend, and following certain behaviours has uncertain rewards. How does the animal maximize its fitness?\n", + "- Finance: which stock option gives the highest return, under time-varying return profiles.\n", + "- Clinical trials: a researcher would like to find the best treatment, out of many possible treatment, while minimizing losses. \n", + "- Psychology: how does punishment and reward affect our behaviour? How do humans learn?\n", + "\n", + "Many of these questions above are fundamental to the application's field.\n", + "\n", + "It turns out the *optimal solution* is incredibly difficult, and it took decades for an overall solution to develop. There are also many approximately-optimal solutions which are quite good. The one I wish to discuss is one of the few solutions that can scale incredibly well. The solution is known as *Bayesian Bandits*.\n", + "\n", + "\n", + "### A Proposed Solution\n", + "\n", + "\n", + "Any proposed strategy is called an *online algorithm* (not in the internet sense, but in the continuously-being-updated sense), and more specifically a reinforcement learning algorithm. The algorithm starts in an ignorant state, where it knows nothing, and begins to acquire data by testing the system. As it acquires data and results, it learns what the best and worst behaviours are (in this case, it learns which bandit is the best). With this in mind, perhaps we can add an additional application of the Multi-Armed Bandit problem:\n", + "\n", + "- Psychology: how does punishment and reward affect our behaviour? How do humans learn?\n", + "\n", + "\n", + "The Bayesian solution begins by assuming priors on the probability of winning for each bandit. In our vignette we assumed complete ignorance of these probabilities. So a very natural prior is the flat prior over 0 to 1. The algorithm proceeds as follows:\n", + "\n", + "For each round:\n", + "\n", + "1. Sample a random variable $X_b$ from the prior of bandit $b$, for all $b$.\n", + "2. Select the bandit with largest sample, i.e. select $B = \\text{argmax}\\;\\; X_b$.\n", + "3. Observe the result of pulling bandit $B$, and update your prior on bandit $B$.\n", + "4. Return to 1.\n", + "\n", + "That's it. Computationally, the algorithm involves sampling from $N$ distributions. Since the initial priors are $\\text{Beta}(\\alpha=1,\\beta=1)$ (a uniform distribution), and the observed result $X$ (a win or loss, encoded 1 and 0 respectfully) is Binomial, the posterior is a $\\text{Beta}(\\alpha=1+X,\\beta=1+1−X)$.\n", + "\n", + "To answer our question from before, this algorithm suggests that we should not discard losers, but we should pick them at a decreasing rate as we gather confidence that there exist *better* bandits. This follows because there is always a non-zero chance that a loser will achieve the status of $B$, but the probability of this event decreases as we play more rounds (see figure below).\n", + "\n", + "Below we implement Bayesian Bandits using two classes, `Bandits` that defines the slot machines, and `BayesianStrategy` which implements the above learning strategy." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "rand = np.random.rand\n", + "\n", + "class Bandits(object):\n", + " \"\"\"\n", + " This class represents N bandits machines.\n", + "\n", + " parameters:\n", + " p_array: a (n,) Numpy array of probabilities >0, <1.\n", + "\n", + " methods:\n", + " pull( i ): return the results, 0 or 1, of pulling \n", + " the ith bandit.\n", + " \"\"\"\n", + " def __init__(self, p_array):\n", + " self.p = p_array\n", + " self.optimal = np.argmax(p_array)\n", + " \n", + " def pull(self, i):\n", + " #i is which arm to pull\n", + " return np.random.rand() < self.p[i]\n", + " \n", + " def __len__(self):\n", + " return len(self.p)\n", + "\n", + " \n", + "class BayesianStrategy(object):\n", + " \"\"\"\n", + " Implements a online, learning strategy to solve\n", + " the Multi-Armed Bandit problem.\n", + " \n", + " parameters:\n", + " bandits: a Bandit class with .pull method\n", + " \n", + " methods:\n", + " sample_bandits(n): sample and train on n pulls.\n", + "\n", + " attributes:\n", + " N: the cumulative number of samples\n", + " choices: the historical choices as a (N,) array\n", + " bb_score: the historical score as a (N,) array\n", + " \"\"\"\n", + " \n", + " def __init__(self, bandits):\n", + " \n", + " self.bandits = bandits\n", + " n_bandits = len(self.bandits)\n", + " self.wins = np.zeros(n_bandits)\n", + " self.trials = np.zeros(n_bandits)\n", + " self.N = 0\n", + " self.choices = []\n", + " self.bb_score = []\n", + "\n", + " \n", + " def sample_bandits(self, n=1):\n", + " \n", + " bb_score = np.zeros(n)\n", + " choices = np.zeros(n)\n", + " \n", + " for k in range(n):\n", + " #sample from the bandits's priors, and select the largest sample\n", + " choice = np.argmax(np.random.beta(1 + self.wins, 1 + self.trials - self.wins))\n", + " \n", + " #sample the chosen bandit\n", + " result = self.bandits.pull(choice)\n", + " \n", + " #update priors and score\n", + " self.wins[choice] += result\n", + " self.trials[choice] += 1\n", + " bb_score[k] = result \n", + " self.N += 1\n", + " choices[k] = choice\n", + " \n", + " self.bb_score = np.r_[self.bb_score, bb_score]\n", + " self.choices = np.r_[self.choices, choices]\n", + " return " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Below we visualize the learning of the Bayesian Bandit solution." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "figsize(11.0, 10)\n", + "\n", + "beta = stats.beta\n", + "x = np.linspace(0.001,.999,200)\n", + "\n", + "def plot_priors(bayesian_strategy, prob, lw = 3, alpha = 0.2, plt_vlines = True):\n", + " ## plotting function\n", + " wins = bayesian_strategy.wins\n", + " trials = bayesian_strategy.trials\n", + " for i in range(prob.shape[0]):\n", + " y = beta(1+wins[i], 1 + trials[i] - wins[i])\n", + " p = plt.plot(x, y.pdf(x), lw = lw)\n", + " c = p[0].get_markeredgecolor()\n", + " plt.fill_between(x,y.pdf(x),0, color = c, alpha = alpha, \n", + " label=\"underlying probability: %.2f\" % prob[i])\n", + " if plt_vlines:\n", + " plt.vlines(prob[i], 0, y.pdf(prob[i]) ,\n", + " colors = c, linestyles = \"--\", lw = 2)\n", + " plt.autoscale(tight = \"True\")\n", + " plt.title(\"Posteriors After %d pull\" % bayesian_strategy.N +\\\n", + " \"s\"*(bayesian_strategy.N > 1))\n", + " plt.autoscale(tight=True)\n", + " return" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "hidden_prob = np.array([0.85, 0.60, 0.75])\n", + "bandits = Bandits(hidden_prob)\n", + "bayesian_strat = BayesianStrategy(bandits)\n", + "\n", + "draw_samples = [1, 1, 3, 10, 10, 25, 50, 100, 200, 600]\n", + "\n", + "for j,i in enumerate(draw_samples):\n", + " plt.subplot(5, 2, j+1) \n", + " bayesian_strat.sample_bandits(i)\n", + " plot_priors(bayesian_strat, hidden_prob)\n", + " #plt.legend()\n", + " plt.autoscale(tight = True)\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that we don't really care how accurate we become about the inference of the hidden probabilities — for this problem we are more interested in choosing the best bandit (or more accurately, becoming *more confident* in choosing the best bandit). For this reason, the distribution of the red bandit is very wide (representing ignorance about what that hidden probability might be) but we are reasonably confident that it is not the best, so the algorithm chooses to ignore it.\n", + "\n", + "From the above, we can see that after 1000 pulls, the majority of the \"blue\" function leads the pack, hence we will almost always choose this arm. This is good, as this arm is indeed the best.\n", + "\n", + "Below is a D3 app that demonstrates our algorithm updating/learning three bandits. The first figure are the raw counts of pulls and wins, and the second figure is a dynamically updating plot. I encourage you to try to guess which bandit is optimal, prior to revealing the true probabilities, by selecting the `arm buttons`." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + "\n", + " \n", + "\n", + "\n", + " \n", + "
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Denote this best bandit's probability by $w_{opt}$. Our score should be relative to how well we would have done had we chosen the best bandit from the beginning. This motivates the *total regret* of a strategy, defined:\n", + "\n", + "\\begin{align}\n", + "R_T & = \\sum_{i=1}^{T} \\left( w_{opt} - w_{B(i)} \\right)\\\\\\\\\n", + "& = Tw^* - \\sum_{i=1}^{T} \\; w_{B(i)} \n", + "\\end{align}\n", + "\n", + "\n", + "where $w_{B(i)}$ is the probability of a prize of the chosen bandit in the $i$ round. A total regret of 0 means the strategy is matching the best possible score. This is likely not possible, as initially our algorithm will often make the wrong choice. Ideally, a strategy's total regret should flatten as it learns the best bandit. (Mathematically, we achieve $w_{B(i)}=w_{opt}$ often)\n", + "\n", + "\n", + "Below we plot the total regret of this simulation, including the scores of some other strategies:\n", + "\n", + "1. Random: randomly choose a bandit to pull. If you can't beat this, just stop. \n", + "2. Largest Bayesian credible bound: pick the bandit with the largest upper bound in its 95% credible region of the underlying probability. \n", + "3. Bayes-UCB algorithm: pick the bandit with the largest *score*, where score is a dynamic quantile of the posterior (see [4] )\n", + "3. Mean of posterior: choose the bandit with the largest posterior mean. This is what a human player (sans computer) would likely do. \n", + "3. Largest proportion: pick the bandit with the current largest observed proportion of winning. \n", + "\n", + "The code for these are in the `other_strats.py`, where you can implement your own very easily." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "figsize(12.5, 5)\n", + "from other_strats import *\n", + "\n", + "#define a harder problem\n", + "hidden_prob = np.array([0.15, 0.2, 0.1, 0.05])\n", + "bandits = Bandits(hidden_prob)\n", + "\n", + "#define regret\n", + "def regret(probabilities, choices):\n", + " w_opt = probabilities.max()\n", + " return (w_opt - probabilities[choices.astype(int)]).cumsum()\n", + "\n", + "#create new strategies\n", + "strategies= [upper_credible_choice, \n", + " bayesian_bandit_choice, \n", + " ucb_bayes , \n", + " max_mean,\n", + " random_choice]\n", + "algos = []\n", + "for strat in strategies:\n", + " algos.append(GeneralBanditStrat(bandits, strat))" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#train 10000 times\n", + "for strat in algos:\n", + " strat.sample_bandits(10000)\n", + " \n", + "#test and plot\n", + "for i,strat in enumerate(algos):\n", + " _regret = regret(hidden_prob, strat.choices)\n", + " plt.plot(_regret, label = strategies[i].__name__, lw = 3)\n", + "\n", + "plt.title(\"Total Regret of Bayesian Bandits Strategy vs. Random guessing\")\n", + "plt.xlabel(\"Number of pulls\")\n", + "plt.ylabel(\"Regret after $n$ pulls\");\n", + "plt.legend(loc = \"upper left\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Like we wanted, Bayesian bandits and other strategies have decreasing rates of regret, representing we are achieving optimal choices. To be more scientific so as to remove any possible luck in the above simulation, we should instead look at the *expected total regret*:\n", + "\n", + "$$\\bar{R}_T = E[ R_T ] $$\n", + "\n", + "It can be shown that any *sub-optimal* strategy's expected total regret is bounded below logarithmically. Formally,\n", + "\n", + "$$ E[R_T] = \\Omega \\left( \\;\\log(T)\\; \\right) $$\n", + "\n", + "Thus, any strategy that matches logarithmic-growing regret is said to \"solve\" the Multi-Armed Bandit problem [3].\n", + "\n", + "Using the Law of Large Numbers, we can approximate Bayesian Bandit's expected total regret by performing the same experiment many times (500 times, to be fair):" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#this can be slow, so I recommend NOT running it. \n", + "\n", + "trials = 500\n", + "expected_total_regret = np.zeros((10000, 3))\n", + "\n", + "for i_strat, strat in enumerate(strategies[:-2]):\n", + " for i in range(trials):\n", + " general_strat = GeneralBanditStrat(bandits, strat)\n", + " general_strat.sample_bandits(10000)\n", + " _regret = regret(hidden_prob, general_strat.choices)\n", + " expected_total_regret[:,i_strat] += _regret\n", + " plt.plot(expected_total_regret[:,i_strat]/trials, lw =3, label = strat.__name__)\n", + " \n", + "plt.title(\"Expected Total Regret of Multi-armed Bandit strategies\")\n", + "plt.xlabel(\"Number of pulls\")\n", + "plt.ylabel(\"Exepected Total Regret \\n after $n$ pulls\");\n", + "plt.legend(loc = \"upper left\");" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure()\n", + "[pl1, pl2, pl3] = plt.plot(expected_total_regret[:, [0,1,2]], lw = 3)\n", + "plt.xscale(\"log\")\n", + "plt.legend([pl1, pl2, pl3], \n", + " [\"Upper Credible Bound\", \"Bayesian Bandit\", \"UCB-Bayes\"],\n", + " loc=\"upper left\")\n", + "plt.ylabel(\"Exepected Total Regret \\n after $\\log{n}$ pulls\");\n", + "plt.title( \"log-scale of above\" );\n", + "plt.ylabel(\"Exepected Total Regret \\n after $\\log{n}$ pulls\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Extending the algorithm \n", + "\n", + "\n", + "Because of the Bayesian Bandits algorithm's simplicity, it is easy to extend. Some possibilities:\n", + "\n", + "- If interested in the *minimum* probability (eg: where prizes are a bad thing), simply choose $B = \\text{argmin} \\; X_b$ and proceed.\n", + "\n", + "- Adding learning rates: Suppose the underlying environment may change over time. Technically the standard Bayesian Bandit algorithm would self-update itself (awesome) by noting that what it thought was the best is starting to fail more often. We can motivate the algorithm to learn changing environments quicker by simply adding a *rate* term upon updating:\n", + "\n", + " self.wins[choice] = rate*self.wins[choice] + result\n", + " self.trials[choice] = rate*self.trials[choice] + 1\n", + "\n", + " If `rate < 1`, the algorithm will *forget* its previous wins quicker and there will be a downward pressure towards ignorance. Conversely, setting `rate > 1` implies your algorithm will act more risky, and bet on earlier winners more often and be more resistant to changing environments. \n", + "\n", + "- Hierarchical algorithms: We can setup a Bayesian Bandit algorithm on top of smaller bandit algorithms. Suppose we have $N$ Bayesian Bandit models, each varying in some behavior (for example different `rate` parameters, representing varying sensitivity to changing environments). On top of these $N$ models is another Bayesian Bandit learner that will select a sub-Bayesian Bandit. This chosen Bayesian Bandit will then make an internal choice as to which machine to pull. The super-Bayesian Bandit updates itself depending on whether the sub-Bayesian Bandit was correct or not. \n", + "\n", + "- Extending the rewards, denoted $y_a$ for bandit $a$, to random variables from a distribution $f_{y_a}(y)$ is straightforward. More generally, this problem can be rephrased as \"Find the bandit with the largest expected value\", as playing the bandit with the largest expected value is optimal. In the case above, $f_{y_a}$ was Bernoulli with probability $p_a$, hence the expected value for a bandit is equal to $p_a$, which is why it looks like we are aiming to maximize the probability of winning. If $f$ is not Bernoulli, and it is non-negative, which can be accomplished apriori by shifting the distribution (we assume we know $f$), then the algorithm behaves as before:\n", + "\n", + " For each round, \n", + " \n", + " 1. Sample a random variable $X_b$ from the prior of bandit $b$, for all $b$.\n", + " 2. Select the bandit with largest sample, i.e. select bandit $B = \\text{argmax}\\;\\; X_b$.\n", + " 3. Observe the result,$R \\sim f_{y_a}$, of pulling bandit $B$, and update your prior on bandit $B$.\n", + " 4. Return to 1\n", + "\n", + " The issue is in the sampling of $X_b$ drawing phase. With Beta priors and Bernoulli observations, we have a Beta posterior — this is easy to sample from. But now, with arbitrary distributions $f$, we have a non-trivial posterior. Sampling from these can be difficult.\n", + "\n", + "- There has been some interest in extending the Bayesian Bandit algorithm to commenting systems. Recall in Chapter 4, we developed a ranking algorithm based on the Bayesian lower-bound of the proportion of upvotes to total votes. One problem with this approach is that it will bias the top rankings towards older comments, since older comments naturally have more votes (and hence the lower-bound is tighter to the true proportion). This creates a positive feedback cycle where older comments gain more votes, hence are displayed more often, hence gain more votes, etc. This pushes any new, potentially better comments, towards the bottom. J. Neufeld proposes a system to remedy this that uses a Bayesian Bandit solution.\n", + "\n", + "His proposal is to consider each comment as a Bandit, with the number of pulls equal to the number of votes cast, and number of rewards as the number of upvotes, hence creating a $\\text{Beta}(1+U,1+D)$ posterior. As visitors visit the page, samples are drawn from each bandit/comment, but instead of displaying the comment with the $\\max$ sample, the comments are ranked according to the ranking of their respective samples. From J. Neufeld's blog [7]:\n", + "\n", + " > [The] resulting ranking algorithm is quite straightforward, each new time the comments page is loaded, the score for each comment is sampled from a $\\text{Beta}(1+U,1+D)$, comments are then ranked by this score in descending order... This randomization has a unique benefit in that even untouched comments $(U=1,D=0)$ have some chance of being seen even in threads with 5000+ comments (something that is not happening now), but, at the same time, the user is not likely to be inundated with rating these new comments. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Just for fun, though the colors explode, we watch the Bayesian Bandit algorithm learn 15 different options. " + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.01316003 0.0470422 0.10278464 0.02769084 0.0282016 0.02893928\n", + " 0.13485387 0.02336669 0.02246445 0.03354184 0.05025746 0.12199161\n", + " 0.01499722 0.01608101 0.12336017 0.03335515 0.0600726 0.05795924\n", + " 0.0643701 0.03606559 0.09241004 0.05762127 0.15399929 0.02956034\n", + " 0.10352865 0.25244598 0.01693866 0.00323096 0.06597221 0.00389394\n", + " 0.13672946 0.17195421 0.06365645 0.06637099 0.01518599]\n" + ] + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "figsize(12.0, 8)\n", + "beta = stats.beta\n", + "hidden_prob = beta.rvs(1,13, size = 35)\n", + "print(hidden_prob)\n", + "bandits = Bandits(hidden_prob)\n", + "bayesian_strat = BayesianStrategy(bandits)\n", + "\n", + "for j,i in enumerate([100, 200, 500, 1300]):\n", + " plt.subplot(2, 2, j+1) \n", + " bayesian_strat.sample_bandits(i)\n", + " plot_priors(bayesian_strat, hidden_prob, lw = 2, alpha = 0.0, plt_vlines=False)\n", + " #plt.legend()\n", + " plt.xlim(0, 0.5)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Eliciting expert prior\n", + "\n", + "Specifying a subjective prior is how practitioners incorporate domain knowledge about the problem into our mathematical framework. Allowing domain knowledge is useful for many reasons:\n", + "\n", + "- Aids speeds of MCMC convergence. For example, if we know the unknown parameter is strictly positive, then we can restrict our attention there, hence saving time that would otherwise be spent exploring negative values.\n", + "- More accurate inference. By weighing prior values near the true unknown value higher, we are narrowing our eventual inference (by making the posterior tighter around the unknown) \n", + "- Express our uncertainty better. See the *Price is Right* problem in Chapter 5.\n", + "\n", + "plus many other reasons. Of course, practitioners of Bayesian methods are not experts in every field, so we must turn to domain experts to craft our priors. We must be careful with how we elicit these priors though. Some things to consider:\n", + "\n", + "1. From experience, I would avoid introducing Betas, Gammas, etc. to non-Bayesian practitioners. Furthermore, non-statisticians can get tripped up by how a continuous probability function can have a value exceeding one.\n", + "\n", + "2. Individuals often neglect the rare *tail-events* and put too much weight around the mean of distribution. \n", + "\n", + "3. Related to above is that almost always individuals will under-emphasize the uncertainty in their guesses.\n", + "\n", + "Eliciting priors from non-technical experts is especially difficult. Rather than introduce the notion of probability distributions, priors, etc. that may scare an expert, there is a much simpler solution. \n", + "\n", + "### Trial roulette method \n", + "\n", + "\n", + "The *trial roulette method* [8] focuses on building a prior distribution by placing counters (think casino chips) on what the expert thinks are possible outcomes. The expert is given $N$ counters (say $N=20$) and is asked to place them on a pre-printed grid, with bins representing intervals. Each column would represent their belief of the probability of getting the corresponding bin result. Each chip would represent an $\\frac{1}{N} = 0.05$ increase in the probability of the outcome being in that interval. For example [9]:\n", + "\n", + "> A student is asked to predict the mark in a future exam. The figure below shows a completed grid for the elicitation of a subjective probability distribution. The horizontal axis of the grid shows the possible bins (or mark intervals) that the student was asked to consider. The numbers in top row record the number of chips per bin. The completed grid (using a total of 20 chips) shows that the student believes there is a 30% chance that the mark will be between 60 and 64.9.\n", + "\n", + "\n", + "\n", + "\n", + "From this, we can fit a distribution that captures the expert's choice. Some reasons in favor of using this technique are:\n", + "\n", + "1. Many questions about the shape of the expert's subjective probability distribution can be answered without the need to pose a long series of questions to the expert - the statistician can simply read off density above or below any given point, or that between any two points.\n", + "\n", + "2. During the elicitation process, the experts can move around the chips if unsatisfied with the way they placed them initially - thus they can be sure of the final result to be submitted.\n", + "\n", + "3. It forces the expert to be coherent in the set of probabilities that are provided. If all the chips are used, the probabilities must sum to one.\n", + "\n", + "4. Graphical methods seem to provide more accurate results, especially for participants with modest levels of statistical sophistication." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Example: Stock Returns\n", + "\n", + "\n", + "Take note stock brokers: you're doing it wrong. When choosing which stocks to pick, an analyst will often look at the *daily return* of the stock. Suppose $S_t$ is the price of the stock on day $t$, then the daily return on day $t$ is :\n", + "\n", + "$$r_t = \\frac{ S_t - S_{t-1} }{ S_{t-1} } $$\n", + "\n", + "The *expected daily return* of a stock is denoted $\\mu = E[ r_t ]$. Obviously, stocks with high expected returns are desirable. Unfortunately, stock returns are so filled with noise that it is very hard to estimate this parameter. Furthermore, the parameter might change over time (consider the rises and falls of AAPL stock), hence it is unwise to use a large historical dataset. \n", + "\n", + "Historically, the expected return has been estimated by using the sample mean. This is a bad idea. As mentioned, the sample mean of a small sized dataset has enormous potential to be very wrong (again, see Chapter 4 for full details). Thus Bayesian inference is the correct procedure here, since we are able to see our uncertainty along with probable values.\n", + "\n", + "For this exercise, we will be examining the daily returns of the AAPL, GOOG, MSFT and AMZN. Before we pull in the data, suppose we ask our a stock fund manager (an expert in finance, but see [10] ), \n", + "\n", + "> What do you think the return profile looks like for each of these companies?\n", + "\n", + "Our stock broker, without needing to know the language of Normal distributions, or priors, or variances, etc. creates four distributions using the trial roulette method above. Suppose they look enough like Normals, so we fit Normals to them. They may look like: " + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "figsize(11., 5)\n", + "colors = [\"#348ABD\", \"#A60628\", \"#7A68A6\", \"#467821\"]\n", + "\n", + "normal = stats.norm\n", + "x = np.linspace(-0.15, 0.15, 100)\n", + "\n", + "expert_prior_params = {\"AAPL\":(0.05, 0.03),\n", + " \"GOOG\":(-0.03, 0.04), \n", + " \"TSLA\": (-0.02, 0.01), \n", + " \"AMZN\": (0.03, 0.02), \n", + " }\n", + "\n", + "for i, (name, params) in enumerate(expert_prior_params.items()):\n", + " plt.subplot(2, 2, i+1)\n", + " y = normal.pdf(x, params[0], scale = params[1])\n", + " #plt.plot( x, y, c = colors[i] )\n", + " plt.fill_between(x, 0, y, color = colors[i], linewidth=2,\n", + " edgecolor = colors[i], alpha = 0.6)\n", + " plt.title(name + \" prior\")\n", + " plt.vlines(0, 0, y.max(), \"k\",\"--\", linewidth = 0.5)\n", + " plt.xlim(-0.15, 0.15)\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that these are subjective priors: the expert has a personal opinion on the stock returns of each of these companies, and is expressing them in a distribution. He's not wishful thinking -- he's introducing domain knowledge.\n", + "\n", + "In order to better model these returns, we should investigate the *covariance matrix* of the returns. For example, it would be unwise to invest in two stocks that are highly correlated, since they are likely to tank together (hence why fund managers suggest a diversification strategy). We will use the *Wishart distribution* for this, introduced earlier." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's get some historical data for these stocks. We will use the covariance of the returns as a starting point for our Wishart random variable. This is not empirical bayes (as we will go over later) because we are only deciding the starting point, not influencing the parameters." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "# I wish I could have used Pandas as a prereq for this book, but oh well.\n", + "import datetime\n", + "import collections\n", + "#import ystockquote as ysq\n", + "import pandas as pd\n", + "import pandas_datareader as pdr\n", + "\n", + "n_observations = 100 # we will truncate the the most recent 100 days.\n", + "\n", + "stocks = [\"AAPL\", \"GOOG\", \"TSLA\", \"AMZN\"]\n", + "\n", + "enddate = \"2015-04-27\"\n", + "startdate = \"2012-09-01\"\n", + "\n", + "CLOSE = 6\n", + "\n", + "stock_closes = pd.DataFrame()\n", + "\n", + "for stock in stocks:\n", + " data = pdr.get_data_yahoo(stock,start=startdate,end=enddate)[\"Close\"]\n", + " #x = np.array(ysq.get_historical_prices(stock, startdate, enddate))\n", + " #stock_series = pd.Series(x[1:,CLOSE].astype(float), name=stock)\n", + " stock_closes[stock] = data\n", + "\n", + "stock_closes = stock_closes[::-1]\n", + "stock_returns = stock_closes.pct_change()[1:][-n_observations:]\n", + " \n", + "#dates = list(map(lambda x: datetime.datetime.strptime(x, \"%Y-%m-%d\"), x[1:n_observations+1,0]))\n", + "dates = stock_returns.index.to_list()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And here let's form our basic model:" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Added new variable covariance_c to model diagonal of Wishart.\n", + "Added new variable covariance_z to model off-diagonals of Wishart.\n" + ] + } + ], + "source": [ + "import pymc as pm\n", + "import pytensor.tensor as pt\n", + "from pytensor.tensor.nlinalg import matrix_inverse, matrix_dot\n", + "from pytensor.tensor.basic import diag\n", + "prior_mu = np.array([x[0] for x in expert_prior_params.values()])\n", + "prior_std = np.array([x[1] for x in expert_prior_params.values()])\n", + "\n", + "init = stock_returns.cov()\n", + "\n", + "with pm.Model() as model:\n", + " cov_matrix = pm.WishartBartlett(\"covariance\", np.diag(prior_std**2), 10, initval = init)\n", + "\n", + " mu = pm.Normal(\"returns\", mu=prior_mu, sigma=1, shape=4)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here are the returns for our chosen stocks:" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "figsize(12.5, 4)\n", + "\n", + "cum_returns = np.cumprod(1 + stock_returns) - 1\n", + "cum_returns.index = dates[::-1]\n", + "cum_returns.plot()\n", + "\n", + "plt.legend(loc = \"upper left\")\n", + "plt.title(\"Return space\")\n", + "plt.ylabel(\"Return of $1 on first date, x100%\");" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + }, + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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vhpjdu3dr5syZatasmS6//PJKx3jq1CktWrRIzZs3l3RulpevvvpK+/fvV0REhCRp5MiR+vrrr/XSSy8pOTnZMTVlo0aNKjV9qGEYev755x3fThVr3ry5MjIyJElXXHGF+vfvr08//VRPPvmkJOmXX37R2LFjHX8MxsTEVC5pP/f888/r4osv1tVXX606dero3XffVYMGDRwjFy1bttQXX3yhQYMGKSQkRDabTY888ogSExM1depUDRw4ULm5uUpPT3d8Vi+//HL16NFDo0aN0ssvv6zQ0FCNGzdO9erVq/APj8suu0xvvvmmPvnkE8XGxurvf/+7srOzPVL8jRs3TnfccYc6dOig7t27a/ny5XrnnXdc/sPl4Ycf1pAhQ5SYmKjOnTvrgw8+0DfffON0E/Vll12mt99+Wx07dtSJEyf02GOPVWp603vvvVc9e/bU6dOn9cEHH7jcPjw8XI8++qgeffRRGYahm266ScePH9fXX3+tgIAAjRw5UhdddJHq1q2rFStWqGXLlgoNDVVERIS6du2qZ599Vm+88YZuuukmffTRR/ryyy8rHOUyo6LPYW2wY8cOFRUVqU2bNk7rr7/+em3atEmS1KJFC23ZskXbt29XUVGR2rZtW+q+itcX34i+bds23XvvvaVuGxoaqtatWzu2/e9//6vWrVs7jRj961//Uv/+/R3Lxf1VWQICArRo0SKFhYVJOjeafPToUS1evFhBQef+xJo0aZJWrlypefPmafbs2Y7/L5GRkZXqKy7sn4p16tTJMaPXpZdeqgULFujTTz/V3XffraNHjyo/P199+/Z1TNVcHVM2w38xAoFqtXfvXv3rX//S0KFDHeuGDRumBQsW6MyZM07bvvrqq07bDR48WOvWrdN//vMfp+1+/vlnhYWFqV69emrRooUMw9DHH39cpTngmzRp4vTLOTc3V4WFhYqOjna6nvrtt9/W9u3bK32cCyUmJpZYd+GD0KKjo7V//37H8qOPPqoRI0aoc+fOmjp1qmMkBM7Cw8P13HPP6dprr1X79u21ceNGffLJJ45v4J566int2bNHrVu3dhSD7du31/vvv68PP/xQ7dq10/jx4zV+/Hg98MADjv2++eabuuSSS9S5c2f17dtXd911ly666CKFhoaWG899992nfv36aeDAgUpMTNSuXbv0yCOPeCT3lJQUvfTSS8rIyFBcXJxeeOEFzZ07V7fccotL+ym+hnzSpEm65pprtHnzZo0dO9ZpmzfeeEPHjx9Xhw4d1L9/f91zzz0uP6laOje1bvH72rp1a5fbS9K0adM0depUzZo1S23btlW3bt304YcfOqZvDQoK0osvvqjXX39dUVFR+vOf/yxJ6tGjh6ZMmaJJkyapQ4cO2rVrl9sefFfR57A2KOvSv8WLF2vjxo0aOXKk4zLNii4TrMpleKXtu0uXLo7L3U6dOlXhSHabNm0cxYN0rq/Yt2+fGjZs6NRXfPHFF27rKy7sn4qV11dYrVaNGDFCPXr0UK9evZSWlsbsX6ga79y7jdpq2rRphiQjMDDQ6UeS0wwZxbNUBAQElNjuwQcfdGw3ZcoUo3nz5sb27duNn376yThx4kSJY7o6C9OUKVOM1q1bO61LS0szGjZsaGzfvr3Ezy+//GIYRtmzMD355JNGTEyM07pVq1Y5zcKxYMECIzAwsEQsSUlJxvDhw53WTZs2rcSsNz/99JMxZ84c47bbbjPq1KljTJo0yXS+cK+DBw8aderUcZoRCK77448/DKvVarz99tveDgVudujQISMgIMCYPn16qa+f/zuueNunnnqq1G1zcnIMScbSpUsNwzCMq666yujatWup2548edKoV6+eMWbMGMMwzs3IVa9ePeN///tfqdsHBgYaCxYsKDOPoUOHOs2aZhiGMWrUKKNdu3al9hW//fabYRhlz8J09913l4j9jTfeMM7/U620/skwSu/nhg8fbiQlJTmt27x5s5Genm707NnTCAoKcpoxDHAFIxCoNkVFRXr99dc1ceLEEjPoDBo0yOlm6nnz5qlbt276/vvvnbZ74YUXtGjRIscNc9K5GxZjY2MVExPjsW/xEhISdOTIEZ06darE9dTF3wSVdT31RRddpAMHDjitd/coQUxMjFJTU/XBBx/oqaee0ssvv+zW/aNsq1at0tKlS/Xzzz/r66+/1p133imbzaaePXt6O7QaqaioSHv37tWUKVNUt25d3XHHHd4OCW7WqFEj9erVSy+99JIKCgpMbTtnzhwdPXq0xOtPP/20mjRpom7dukmSBg0apFWrVumbb74pse0LL7ygP/74w3FJ0l133aU//vijzAcUVkZCQoJ+/vlnNWjQoERfUTwjV3l9xYX3T7m7r2jXrp3Gjh2rTz75pNzZDYGKcA8Eqs3y5cu1e/du3XfffSWGX++++25169ZNu3btUoMGDfTBBx/o1VdfdXryq3Ru1orx48fr/fff15AhQ0wfe9++fSVugrTZbKavO+7atavsdrtuvfVWzZw5U1dddZXy8/O1du1ahYaG6t5775XNZlNYWJg+/fRTtW3bViEhIbJarerSpYv++OMP/fWvf9Xw4cO1fv16zZkzx3Ts5Tl+/Lgef/xx3XbbbWrVqpWOHDmi5cuXu3xzLCrv9OnTeuKJJ/Tzzz+rXr166tixo3JycsqcoQfl2717t1q1aqVLLrlECxYsqNT9E/B9c+fO1Q033KBrrrlGU6dO1dVXX62wsDBt27ZN//rXv5xump8zZ46uv/56de3aVdOnT1fbtm21b98+ZWRk6PPPP1dmZqZj8oyHH35Yy5YtU9++fZWWlqbOnTvr1KlTWrJkiaZPn67Jkyc7HriYmJioyZMna9KkSdq5c6f69++vVq1aqaCgQMuXL1dRUZHLN+/fddddysjIUJ8+fTRjxgxddtll2r9/v1atWqU2bdooJSVFLVq0UEBAgP7973/rzjvvVEhIiCIiImS32zVz5kz97W9/U69evbRq1SpTE4eYsWPHDr322mu65ZZb1KxZM/3+++/64osv1L59e7fsH7WQt4dAUHv07dvX+NOf/lTqa2fOnDGaNGliTJo0yXj++eeNkJAQo6CgoNRtb7/9dscDsMoazj1fixYtHA+bO//nvvvuK3X7svb5xx9/GI8//rjRsmVLo06dOkaTJk2MHj16GCtXrnRs89ZbbxktW7Y0goKCnC4zmj9/vtGqVSsjNDTU6Nmzp/Hee++55RKmkydPGgMGDDBatmxphISEGJGRkUa/fv1KPIQJAHzNwYMHjccee8y44oorjNDQUCM0NNRo06aNMXr06BKX9/z+++9Gamqq0bx5c6NOnTpG48aNjVtvvdVYv359if3+73//M5555hmjbdu2RkhIiBEWFmZ06tSpzMsKly1bZvTs2dNo3LixERgYaNhsNqNnz57GO++8U+6D5Eq7hMkwDCMvL88YNWqUERUVZdSpU8eIiooyUlJSnGKdOXOmERUVZQQEBDhdZjR9+nQjKirKqF+/vtG/f3/jb3/7m1suYfr999+Nv/zlL0Z0dLQRHBxsXHzxxcaIESOMI0eOlJkfUB6LYVTzU08AAAAA1FjcAwEAAADANAoIAAAAAKZRQAAAAAAwjQICAAAAgGkUEAAAAABMq/bnQFz4kBR/ZLPZlJeX5+0wPI48/U9tybW25Fn84KqagL7Bf5CnfyFP/+KufoERCAAAAACmUUAAAAAAMK3aL2ECAPiHoqIijR8/Xo0aNdL48eN1/PhxZWRk6ODBg4qMjNSYMWMUFhbm7TABAG7GCAQAoFL+/e9/Kzo62rGcmZmp+Ph4vfjii4qPj1dmZqb3ggMAeAwFBADAZYcOHdL69euVnJzsWJebm6ukpCRJUlJSknJzc70VHgDAg7iECaiC6av3uLT9E52beSgSoHq9+eabGjRokE6ePOlYV1BQIKvVKkmyWq06evRome2zsrKUlZUlSUpLS5PNZvNswD4gKCiIPGuwL+940Gl5e4BFRUWGY/mG91+q7pCqhb+ezwvVljzdhQICAOCS7777ThEREYqJidGWLVsqtQ+73S673e5Yrg3TJ9aWaSL9Nc/CwkKn5eDgYKd1/piz5L/n80K1JU93TeNKAQEAcMm2bdv07bffasOGDSosLNTJkyf14osvKiIiQvn5+bJarcrPz1eDBg28HSoAwAMoIAAALhk4cKAGDhwoSdqyZYv++c9/6qGHHtKiRYuUnZ2tlJQUZWdnKzEx0cuRAgA8gZuoAQBukZKSok2bNumhhx7Spk2blJKS4u2QAAAewAgEAKDS2rZtq7Zt20qSwsPDNXnyZC9HBADwNEYgAAAAAJhGAQEAAADANC5hAgAAqKLNwyeV+3r8/BnVFAngeYxAAAAAADCNAgIAAACAaRQQAAAAAEyr8B6IvLw8zZkzR0eOHJHFYpHdblfv3r11/PhxZWRk6ODBg4qMjNSYMWMUFhZWHTEDAAAA8JIKC4jAwEANHjxYMTExOnnypMaPH68rr7xSq1evVnx8vFJSUpSZmanMzEwNGjSoOmIGAAAA4CUVXsJktVoVExMjSapbt66io6N1+PBh5ebmKikpSZKUlJSk3Nxcz0YKAAAAwOtcmsb1wIED2rlzp2JjY1VQUCCr1SrpXJFx9OhRjwQIVKfpq/eUWBccvF+FhYVeiAYAAMD3mC4gTp06pfT0dA0bNkz16tUzfYCsrCxlZWVJktLS0mSz2VyPsoYJCgoizxoqOHh/iXUWS4CCg4Pdsn9ff7/88ZyWprbkCQCAJ5gqIM6cOaP09HR16tRJHTt2lCRFREQoPz9fVqtV+fn5atCgQalt7Xa77Ha7YzkvL88NYfs2m81GnjVUaSMNwcHBbhuB8PX3yx/PaWlqS55RUVHeDgEA4IcqvAfCMAy98sorio6O1s033+xYn5CQoOzsbElSdna2EhMTPRclAAAAAJ9Q4QjEtm3blJOTo+bNm2vcuHGSpAEDBiglJUUZGRlatWqVbDabxo4d6/FgAQAAAHhXhQXEFVdcoSVLlpT62uTJk90eEAAAAADfxZOoAQAAAJhGAQEAAADANAoIAAAAAKZRQAAAAAAwjQICAAAAgGkUEAAAAABMM/UkagAAzldYWKgpU6bozJkzOnv2rP70pz+pX79+On78uDIyMnTw4EFFRkZqzJgxCgsL83a4AAA3ooAAALisTp06mjJlikJDQ3XmzBlNnjxZV199tdatW6f4+HilpKQoMzNTmZmZGjRokLfDBQC4EZcwAQBcZrFYFBoaKkk6e/aszp49K4vFotzcXCUlJUmSkpKSlJub680wAQAewAgEAKBSioqK9Pjjj2vfvn3q0aOHLr30UhUUFMhqtUqSrFarjh496uUoAQDuRgEBAKiUgIAAPffcczpx4oRmzZql3bt3m26blZWlrKwsSVJaWppsNpunwvQZQUFB5FmDBQcHOy0HBFhKrCtPTX1P/PV8Xqi25OkuFBAAgCqpX7++4uLitHHjRkVERCg/P19Wq1X5+flq0KBBqW3sdrvsdrtjOS8vr7rC9RqbzUaeNVhhYaHTcnBwcIl15amp74m/ns8L1ZY8o6Ki3LIf7oEAALjs6NGjOnHihKRzf1ht3rxZ0dHRSkhIUHZ2tiQpOztbiYmJ3gwTAOABjEAAAFyWn5+vOXPmqKioSIZh6LrrrlOHDh102WWXKSMjQ6tWrZLNZtPYsWO9HSoAwM0oIAAALmvRooWeffbZEuvDw8M1efJkL0QEAKguXMIEAAAAwDQKCAAAAACmUUAAAAAAMK3CeyDmzp2r9evXKyIiQunp6ZKkJUuWaOXKlY7p+QYMGKD27dt7NlIAAAAAXldhAdG5c2f17NlTc+bMcVrfp08f9e3b12OBAQAAAPA9FV7CFBcXp7CwsOqIBQAAAICPq/Q0ritWrFBOTo5iYmI0ZMgQigwAAACgFqhUAdG9e3fdfvvtkqTFixdr4cKFSk1NLXXbrKwsZWVlSZLS0tJks9kqGWrNERQURJ41VHDw/hLrLJYABQcHu2X/vv5++eM5LU1tyRMAAE+oVAHRsGFDx7+Tk5M1c+bMMre12+2y2+2O5by8vMocskax2WzkWUMVFhaWWBccHFzq+srw9ffLH89paWpLnlFRUd4OAagxNg+f5O0QgBqjUtO45ufnO/69bt06NWvWzG0BAQAAAPBdFY5AzJ49W1u3btWxY8c0atQo9evXT1u2bNGuXbtksVgUGRmpkSNHVkesAAAAALyswgJi9OjRJdZ17drVE7EAAAAA8HE8iRoAAACAaRQQAAAAAEyjgAAAAABgWqUfJAfUBNNX7/F2CAAAAH6FEQgAAAAAplFAAAAAADCNAgIAAACAaRQQAAAAAEyjgAAAAABgGgUEAAAAANOYxhUA4LK8vDzNmTNHR44ckcVikd1uV+/evXX8+HFlZGTo4MGDioyM1JgxYxQWFubtcAEAbkQBAQBwWWBgoAYPHqyYmBidPHlS48eP15VXXqnVq1crPj5eKSkpyszMVGZmpgYNGuTtcAEAbsQlTAAAl1mtVsXExEiS6tatq+joaB0+fFi5ublKSkqSJCUlJSk3N9ebYQIAPIACAgBQJQcOHNDOnTsVGxurgoICWa1WSeeKjKNHj3o5OgCAu3EJEwCg0k6dOqX09HQNGzZM9erVM90uKytLWVlZkqS0tDTZbDZPhegzgoKCyNOHBQcHu7R9QIDFpTY18T2Rau75dFVtydNdKCAAAJVy5swZpaenq1OnTurYsaMkKSIiQvn5+bJarcrPz1eDBg1KbWu322W32x3LeXl51RKzN9lsNvL0YYWFhS5tHxwc7FKbmvieSDX3fLqqtuQZFRXllv1wCRMAwGWGYeiVV15RdHS0br75Zsf6hIQEZWdnS5Kys7OVmJjorRABAB7CCAQAwGXbtm1TTk6OmjdvrnHjxkmSBgwYoJSUFGVkZGjVqlWy2WwaO3aslyMFALhbhQXE3LlztX79ekVERCg9PV2SmOcbAGq5K664QkuWLCn1tcmTJ1dzNACA6lRhAdG5c2f17NlTc+bMcazLzMxknm94xfTVe7wdQpW4Gv8TnZt5KBIAAIDKqfAeiLi4uBKjC8zzDQAAANROlbqJmnm+AQAAgNrJ4zdRM9e3//JGnsHB+6v1eJJksQS4PD+4u1T3+8tnFwAAVKRSBYTZeb4l5vr2Z97I09V5ut3B1bm+3am6318+u/7FXfN9AwBwvkpdwsQ83wAAAEDtVOEIxOzZs7V161YdO3ZMo0aNUr9+/ZjnGwAAAKilKiwgRo8eXep65vkGAAAAap9KXcIEAAAAoHaigAAAAABgGgUEAAAAANMoIAAAAACY5vEHyQEAAHjb5uGTvB0C4DcYgQAAAABgGgUEAAAAANMoIAAAAACYRgEBAAAAwDQKCAAAAACmUUAAAAAAMI0CAgAAAIBpFBAAAAAATKOAAAAAAGAaT6IGALhs7ty5Wr9+vSIiIpSeni5JOn78uDIyMnTw4EFFRkZqzJgxCgsL83KkAAB3YwQCAOCyzp07a+LEiU7rMjMzFR8frxdffFHx8fHKzMz0TnAAAI+igAAAuCwuLq7E6EJubq6SkpIkSUlJScrNzfVGaAAAD+MSJgCAWxQUFMhqtUqSrFarjh49Wua2WVlZysrKkiSlpaXJZrNVS4zeFBQURJ5eFBwc7Nb9BQRYXNqnL74nZvjq+XS32pKnu1SpgLj//vsVGhqqgIAABQYGKi0tzV1xAQD8mN1ul91udyzn5eV5MZrqYbPZyNOLCgsL3bq/4OBgl/bpi++JGb56Pt2ttuQZFRXllv1UeQRiypQpatCggTtiAQDUYBEREcrPz5fValV+fj59AwD4Ke6BAAC4RUJCgrKzsyVJ2dnZSkxM9HJEAABPqPIIxIwZMyRJ3bp1cxqOLsZ1rv7LG3kGB++v1uNJksUS4PZrZ82q7veXzy7Mmj17trZu3apjx45p1KhR6tevn1JSUpSRkaFVq1bJZrNp7Nix3g4TAOABFsMwjMo2Pnz4sBo1aqSCggJNnz5dd999t+Li4spt8/vvv1f2cDVGbbmOzht5Tl+9p1qPJ7l+nWtN8kTnZk7LfHb9i7uuda0O9A3+w1fz3Dx8klv352rfED9/hluPX1189Xy6W23J0139QpUuYWrUqJGkc9e9JiYmaseOHW4JCgAAAIBvqnQBcerUKZ08edLx702bNql58+ZuCwwAAACA76n0PRAFBQWaNWuWJOns2bO68cYbdfXVV7srLgAAAAA+qNIFRJMmTfTcc8+5MxYAAIBKcfc9DtWtovhr6j0U8E9M4woAAADANAoIAAAAAKZRQAAAAAAwjQICAAAAgGkUEAAAAABMo4AAAAAAYFqlp3EFLjR99R5vhwAAAAAPYwQCAAAAgGmMQAAAAHhYTX/QHXA+RiAAAAAAmEYBAQAAAMA0CggAAAAApnEPBAAA8HncQwD4DkYgAAAAAJhGAQEAAADANAoIAAAAAKZxD0Qt4uqTop/o3MxDkaA24XNXPt4fAEBNQwEBAACq5PwbnIODg1VYWOj0evz8GS7tAyhNRZ8RM58zuEeVCoiNGzdqwYIFKioqUnJyslJSUtwUFgCgpqJvAAD/Vul7IIqKijR//nxNnDhRGRkZ+vLLL/Xrr7+6MzYAQA1D3wAA/q/SBcSOHTvUtGlTNWnSREFBQbr++uuVm5vrztgAADUMfQMA+D+LYRhGZRp+/fXX2rhxo0aNGiVJysnJ0fbt2zV8+HCn7bKyspSVlSVJSktLq2K4AABfRt8AAP6v0iMQpdUdFoulxDq73a60tDSlpaVp/PjxlT1cjUKe/qW25CnVnlzJ03PoG8pGnv6FPP0Lebqm0gVE48aNdejQIcfyoUOHZLVa3RIUAKBmom8AAP9X6QKidevW2rt3rw4cOKAzZ85o7dq1SkhIcGdsAIAahr4BAPxfpadxDQwM1D333KMZM2aoqKhIXbp0UbNm5T/gyG63V/ZwNQp5+pfakqdUe3IlT8+hbygbefoX8vQv5OmaSt9EDQAAAKD2qfQlTAAAAABqHwoIAAAAAKZV+h6Ishw/flwZGRk6ePCgIiMjNWbMGIWFhZXYbu7cuVq/fr0iIiKUnp7uWL9kyRKtXLlSDRo0kCQNGDBA7du3d3eYVVbVPM229zazcW7cuFELFixQUVGRkpOTlZKSIsn3z2dZcRczDEMLFizQhg0bFBISotTUVMXExJhq60uqkuf999+v0NBQBQQEKDAw0Kfn7K8oz99++01z587Vzp071b9/f/Xt29d0W19SlTy9cT7pF5zV9H5B8u++obb0CxJ9QzH6hkqcT8PNFi1aZHz88ceGYRjGxx9/bCxatKjU7bZs2WL89NNPxtixY53WL1682Fi6dKm7w3K7quZptr23mYnz7NmzxgMPPGDs27fPOH36tPHoo48ae/bsMQzDt89neXEX++6774wZM2YYRUVFxrZt24wJEyaYbusrqpKnYRhGamqqUVBQUN1hu8xMnkeOHDG2b99uvPvuu06fS387n2XlaRjeOZ/0C85qer9gGP7bN9SWfsEw6BvOR9/g+vl0+yVMubm5SkpKkiQlJSUpNze31O3i4uJ89psVM6qap9n23mYmzh07dqhp06Zq0qSJgoKCdP311/tsPuczE/e3336rm266SRaLRZdddplOnDih/Pz8GpVzVfKsSczkGRERodjYWAUGBrrc1ldUJU9voV9wVtP7Bcl/+4ba0i9I9A3no29wndsvYSooKHA8NMhqtero0aMu72PFihXKyclRTEyMhgwZ4pMdSlXzdMf7VB3MxHn48GE1btzYsdy4cWNt377dseyr57OiuIu3sdlsTtscPnzYVFtfUZU8i8/9jBkzJEndunXz2anuqnJO/O18VqS6zyf9QvW0r07+2jfUln5Bom/wdNvqVt19Q6UKiGnTpunIkSMl1vfv378yu3PSvXt33X777ZKkxYsXa+HChUpNTa3yfivDk3n6kqrmaZQyE7DFYpHkW+fzQuXFXdE2Ztr6iqrkKZ37fDRq1EgFBQWaPn26oqKiFBcX55lgq6Aq58Tfzmd5PHU+6Rf8q1+QamffUFv6BYm+wdNtq1t19w2VKiD++te/lvlaRESE8vPzZbValZ+f77hByqyGDRs6/p2cnKyZM2dWJkS38GSeVW3vTlXNs3Hjxjp06JBj+dChQ45vJ3zpfF6ovLjP3yYvL6/ENmfOnKmwra+oSp6S1KhRI0nnPguJiYnasWOHT3YSZvL0RNvqVtVYPXU+6Rf8q1+QamffUFv6BYm+wdNtq1t19w1uvwciISFB2dnZkqTs7GwlJia61P78a+vWrVtX4RNMvaWqeVa1fXUxE2fr1q21d+9eHThwQGfOnNHatWuVkJAgybfPZ3lxF0tISFBOTo4Mw9B///tf1atXT1ar1VRbX1GVPE+dOqWTJ09Kkk6dOqVNmzapefPm3kijQlU5J/52PsvirfNJv1A97auTv/YNtaVfkOgbPN22ulV33+D2J1EfO3ZMGRkZysvLk81m09ixYxUWFqbDhw9r3rx5mjBhgiRp9uzZ2rp1q44dO6aIiAj169dPXbt21UsvvaRdu3bJYrEoMjJSI0eO9Mlqr6p5ltXe15jNc/369XrrrbdUVFSkLl266NZbb5Uknz+fpcX96aefSjo3xG4YhubPn6/vv/9ewcHBSk1NVevWrcts66sqm+f+/fs1a9YsSdLZs2d144031ug8jxw5ovHjx+vkyZOyWCwKDQ3V888/r3r16vnV+Swrz2PHjnnlfNIv+Fe/IPl331Bb+gWJvkGib6hs3+D2AgIAAACA/+JJ1AAAAABMo4AAAAAAYBoFBAAAAADTKCAAAAAAmEYBAQAAAMA0CggAAAAAplFAAAAAADCNAgIAAACAaRQQAAAAAEyjgAAAAABgGgUEAAAAANMoIAAAAACYRgGBamOxWMr9admypSTp0KFDeuihh9SqVSuFhIQoMjJSnTp10nvvvefY17Bhw2S3200dNz4+XoGBgdq0aZMn0nIYMWKEOnfu7NFjAIA/2bdvn0JDQ9W0aVOdPn26xOudO3eWxWLRI488UuK12bNny2KxKDY21rGuon4mOztbkjR16lRZLBb95S9/KbHfli1bavr06W7M8v+LjY3V1KlTPbJvoDpRQKDa7N271/GzdOlSSdK6desc63JzcyVJt912m3JycjRv3jz997//1fLlyzVgwAAdOnTI5WOuXbtWBw4c0PDhw/Xqq69WKu7CwsJKtasKbxwTAKrbG2+8oT59+qhx48aOfuFCzZs311tvvVXi9+Jrr72mFi1aOK07v58p/tmxY4diY2PVsWNHdezY0bFtaGioli5dqtWrV1c5D/oJ1DYUEKg2TZs2dfw0atRIkhQZGelYFxkZqSNHjig7O1vTp09X9+7d1aJFC3Xo0EGpqal64IEHXD7mvHnzdNddd2nEiBF6++239ccff1TYxmKx6MUXX9TAgQMVERGhu+66S5L02Wef6YYbblDdunUVHR2tu+++21HUTJ06VfPnz1d2drbjm64333zTsb+3337b6Rh2u13Dhg1zLLds2VJPPPGEUlNT1bhxY91www1avXq1LBaLPvvsM910002qV6+e4uLitGLFCqd9Pf3004qJiXGM1vTo0UMnT550+b0CgOpUVFSk1157TUOHDtXQoUPL/JInOTlZ4eHh+vjjjx3r1qxZoz179uiOO+5w2vb8fqZp06Zq0qSJxo0bp//973/KzMxUaGioY9vo6Gj169dPY8eOVVFRkem4i383L1u2TDfeeKNCQ0Mdsb/00ku64oorFBoaqksvvVQzZszQmTNnJJ0bTfnpp5/05JNPOvqJXbt2Ofb366+/Oh0nKCjI0Y/s2rVLFotF77zzjnr37q369etr4sSJmjp1qmJjY7V06VJdccUVql+/vrp06aKffvrJsZ+jR4/q7rvvVtOmTRUSEqJmzZpp7NixpvMFSkMBAZ8SFham8PBwLV26VCdOnKjSvvLz8/X+++9r6NChuvbaaxUdHa0lS5aYavvkk0/quuuu0/r16zVjxgytWrVKf/7zn9W/f39t2rRJmZmZ2rVrl/7yl7/IMAw9+uijGjhwoK677jrHt1533nmnS/G++OKLuuiii/TVV1/prbfecqx/9NFHNXHiRH3//fdKSEjQnXfeqSNHjkiSPvroI6WlpemFF17Q9u3b9dlnn6lXr14uHRcAvOHTTz/ViRMn1Lt3bw0ePFirV6/Wzz//XGK7gIAADR8+XK+99ppj3auvvqqBAweqfv365R5j0qRJ+vTTT/WPf/xDTZs2LfH6zJkz9cMPPzj9zjXrkUce0WOPPaYffvhBKSkpmjp1qmbNmqVnnnlGP/zwg1544QXNmzdPTz75pKRzv69btmypRx55xNFPNGvWzKVjPv744xo4cKA2b96s+++/X9K5UZeXX35Z77zzjtauXasjR47onnvucbR54okntH79ei1dulTbt2/X4sWL1aZNG5fzBZwYgBd88cUXhiRj586dJV776KOPjMaNGxt16tQxOnToYDz00EPGypUrnbYZOnSokZycXO4xZs+ebVx99dWO5ZkzZxrXXXddhbFJMu655x6ndUlJScbjjz/utO6XX34xJBkbNmwwDMMwhg8fbiQlJZW6v0WLFjmtS05ONoYOHepYbtGihdG1a1enbT7//HNDkvHhhx861u3du9eQZCxfvtwwDMN4/vnnjUsvvdQoLCysMC8A8CUpKSnG6NGjHcu9evUyJkyY4LRNUlKSMXz4cOP333836tSpY+zYscPIz8836tata3z33XfGlClTjNatW5e6/0WLFhkBAQFGZmZmidfObzd+/Hjj4osvNo4fP24Yxrnfx9OmTSsz7uLfzQsXLnSsO3HihFG3bl3jk08+cdr2rbfeMiIiIhzLrVu3NqZMmVLq/vbs2eO0PjAw0FiwYIFhGIaxc+dOQ5Lx1FNPlcgjMDDQOHDggGPde++9Z1gsFuPkyZOGYRhG3759nfobwB0YgYDP+ctf/qLffvtNy5cv12233aatW7cqOTnZ8W2LWa+++qqGDh3qWB48eLDWrVun//znPxW2vfbaa52Wc3NzNXv2bIWFhTl+4uLiJEnbt293KS6zxyx29dVXO/7dtGlTBQYGav/+/ZKkfv366fTp02rRooWGDRumRYsW6dixY26JBwA8Ze/evfrXv/7l9Dt62LBhWrBggeOSn/NdfPHF6t27t+bPn6+FCxeqTZs2at++fZn7//rrrzVixAg988wz+vOf/1xuLBMnTtTZs2c1c+ZMl3I4/3f2li1bdPLkSd12221O/cR9992ngoICHTx40KV9mzlmsaioKEVGRjqWo6OjZRiGDhw4IElKTU3VBx98oHbt2unhhx/WJ5984tIlW0BpKCDgk0JCQtS1a1dNmDBBn332maZNm6a5c+dq165dptqvWbNGW7du1SOPPKKgoCAFBQWpWbNmOnv2rKmbqS8cFi8qKtLjjz+ujRs3Ov1s3769wkuGLBaLDMNwWlfabCNlDcUHBweXWFf8yz86Olo//vij3njjDV100UWaNm2aLr/8cu3Zs6fcmADAm+bPn68zZ84oISHB8Tt64MCB2rdvn/7xj3+U2mbkyJFasGCB5s2bp5EjR5a57927dyslJUX9+/fXY489VmEs4eHhmjZtmmbNmlXiPoTynP87u/h38vvvv+/UR2zevFnbt2933PdXmoCAc3+Knd9PnD17ttQ/8kvrJy7sIywWi1NMPXr00O7duzVp0iSdOnVKgwYNUteuXXX27FmzqQIlUECgRii+XtPstzjz5s1Tt27d9P333zv9Mn/hhRe0aNEil28yTkhI0JYtWxQbG1viJywsTNK5X+Kl/UK+6KKL9PvvvzuW//e//2nr1q0uHb88ISEh6tmzp5599llt3rxZf/zxhzIzM922fwBwp6KiIr3++uuaOHFiiS9lBg0aVOaXPD179lRISIh++eUXDRw4sNRtTpw4ob59++rSSy91aea94cOHKzY2VhMmTKhUTm3btlVoaKh+/vnnUvuJwMBASaX3ExdddJEkOfUTGzduLPHFU1U0atRIAwYM0Lx587Rs2TJlZ2e7tR9C7RPk7QCA8x06dEi33Xab7r77bl111VVq2LCh/vOf/2jChAlq1aqV0+U8x48f18aNG53ah4aG6qKLLtIHH3ygV199Ve3atXN6vVWrVho/frzef/99DRkyxHRcTz31lLp3764xY8Zo6NChCg8P1/bt2/X+++/rb3/7m+rWratWrVrp/fff15YtW9SkSROFh4crJCREdrtdr7zyim666SaFh4drxowZbpt+b/78+SoqKtK1116rhg0bauXKlTp27Jjj8ioA8DXLly/X7t27dd9996l58+ZOr919993q1q2bdu3a5Xg2ULGAgABt3rxZRUVFCg8PL7FfwzB01113ad++fXr77bd1+PDhEtsUX1p0ocDAQKWnp6tHjx6ljvpWJCwsTBMnTtTEiRMlSd26ddOZM2e0efNmbdiwwXF5VKtWrfTll19q9+7dqlevnho1aqTY2Fi1aNFCU6dOVUZGhvLy8jRx4kTHSEJVTZo0SR06dFDbtm0VEBCgd955R2FhYSXee8AVjEDAp4SFhen666/XnDlz1LVrV7Vp00YPPfSQunbtquzsbNWpU8ex7TfffKNrrrnG6SclJUVvvfWWDMMo9brX+vXrq0+fPi4/E6JLly5atWqVNm/erE6dOunKK6/UmDFjFB4e7ohp+PDhSkxM1PXXX6/IyEjHg+9mzZqldu3aqUePHurVq5duuukmJSYmVuFd+v+sVqsWLFigzp07q02bNnr++ef16quvKjk52S37BwB3mzdvnjp27FjqH7BJSUmKjIzU66+/Xmrb8PBwRURElPra7t27tXTpUu3fv1/x8fG6+OKLS/zMmjWrzLi6deum3r1763//+1+l8vrrX/+qjIwMvf7667rqqqt04403KiMjw6kQevLJJ1VQUKDLL79ckZGR2r17t4KCgrR48WIdOHBA11xzje6//37NmDHDcWlTVYWGhmry5Mnq0KGDEhIStGnTJn3yySdlvo+AGRbDnWNkAAAAAPwaIxAAAAAATKOAAAAAAGAaBQQAAAAA0yggAAAAAJhGAQEAAADANAoIAAAAAKZV+4Pkzn/Sor+y2WzKy8vzdhgeR57+p7bkWlvyjIqK8nYIptE3+A/y9C/k6V/c1S8wAgEAAADANAoIAAAAAKZRQAAAAAAwjQICAAAAgGnVfhM14KuW/X1zqeuDg4NVWFjotK5P//jqCAkA4GNm//MxSaX3DaNvedYbIQHVjhEIAAAAAKZRQAAAAAAwrcJLmPLy8jRnzhwdOXJEFotFdrtdvXv31vHjx5WRkaGDBw8qMjJSY8aMUVhYWHXEDAAAAMBLKiwgAgMDNXjwYMXExOjkyZMaP368rrzySq1evVrx8fFKSUlRZmamMjMzNWjQoOqIGQAAAICXVHgJk9VqVUxMjCSpbt26io6O1uHDh5Wbm6ukpCRJUlJSknJzcz0bKQAAAACvc2kWpgMHDmjnzp2KjY1VQUGBrFarpHNFxtGjR0ttk5WVpaysLElSWlqabDZbFUP2fUFBQeRZAwUHB5e63mIJKPGaP+V9Pn87p2WpLXkCAOAJpguIU6dOKT09XcOGDVO9evVMH8But8tutzuW8/LyXIuwBrLZbORZA104HV+x0qbq86e8z+dv57QstSXPqKgoj+27rPvjlixZopUrV6pBgwaSpAEDBqh9+/YeiwPwJcVTvJaGKV7hT0wVEGfOnFF6ero6deqkjh07SpIiIiKUn58vq9Wq/Px8R2cBAPB/Zd0fJ0l9+vRR3759vRwhAMBTKrwHwjAMvfLKK4qOjtbNN9/sWJ+QkKDs7GxJUnZ2thITEz0XJQDAp5R1fxwAwP9VOAKxbds25eTkqHnz5ho3bpykc0PSKSkpysjI0KpVq2Sz2TR27FiPBwsA8D3n3x/3448/asWKFcrJyVFMTIyGDBnCFN8A4GcqLCCuuOIKLVmypNTXJk+e7PaAAAA1x4X3x3Xv3l233367JGnx4sVauHChUlNTS7Rjgg3/5e95Fk+qYbFYypx8ozQ19T3x9/NZrLbk6S4uzcIEAECx0u6Pa9iwoeP15ORkzZw5s9S2TLDhv/w9z+JJNUqbYKM8NfU98ffzWay25OmuyTUqvAcCAIALlXV/XH5+vuPf69atU7NmzbwRHgDAgxiBAAC4rKz747788kvt2rVLFotFkZGRGjlypJcjBQC4GwUEAMBlZd0fxzMfUNOV9ywHAOdwCRMAAAAA0yggAAAAAJhGAQEAAADANO6BACph2d83m962T/94D0YCAABQvRiBAAAAAGAaBQQAAAAA0yggAAAAAJhGAQEAAADANAoIAAAAAKZRQAAAAAAwjQICAAAAgGkUEAAAAABMo4AAAAAAYBpPogYAAH5n9j8fK/O10bc8W42RAP6HAgIA4LK8vDzNmTNHR44ckcVikd1uV+/evXX8+HFlZGTo4MGDioyM1JgxYxQWFubtcAEAbkQBgRpn2d83m962T/94D0YC1F6BgYEaPHiwYmJidPLkSY0fP15XXnmlVq9erfj4eKWkpCgzM1OZmZkaNGiQt8MFALhRhfdAzJ07VyNGjNAjjzziWLdkyRLdd999GjdunMaNG6f169d7NEgAgG+xWq2KiYmRJNWtW1fR0dE6fPiwcnNzlZSUJElKSkpSbm6uN8MEAHhAhSMQnTt3Vs+ePTVnzhyn9X369FHfvn09FhgAoGY4cOCAdu7cqdjYWBUUFMhqtUo6V2QcPXq01DZZWVnKysqSJKWlpclms1VbvN4SFBREntUoODi4zNfKi6+8duezWCymt63omL7MV86np9WWPN2lwgIiLi5OBw4cqI5YAAA1zKlTp5Senq5hw4apXr16ptvZ7XbZ7XbHcl5enifC8yk2m408q1FhYWGZr5UXX3ntzhccHGx624qO6ct85Xx6Wm3JMyoqyi37qfQ9ECtWrFBOTo5iYmI0ZMgQbpIDgFrmzJkzSk9PV6dOndSxY0dJUkREhPLz82W1WpWfn68GDRp4OUoAgLtVqoDo3r27br/9dknS4sWLtXDhQqWmppa6LcPU/stbeXpqyLis/VosAS4dsyoxeBufXZhlGIZeeeUVRUdH6+abb3asT0hIUHZ2tlJSUpSdna3ExEQvRgkA8IRKFRANGzZ0/Ds5OVkzZ84sc1uGqf2Xt/L01JBxWft1dZi6KjF4G59d/+KuoerSbNu2TTk5OWrevLnGjRsnSRowYIBSUlKUkZGhVatWyWazaezYsR6LAQDgHZUqIIqHpyVp3bp1atasmVuDAgD4tiuuuEJLliwp9bXJkydXczQAgOpUYQExe/Zsbd26VceOHdOoUaPUr18/bdmyRbt27ZLFYlFkZKRGjhxZHbECAAAA8LIKC4jRo0eXWNe1a1dPxAIAAADAx1X4IDkAAAAAKEYBAQAAAMA0CggAAAAAplFAAAAAADCNAgIAAACAaRQQAAAAAEyjgAAAAABgGgUEAAAAANMoIAAAAACYVuGTqAFPW/b3zd4OAQAAACYxAgEAAADANEYgAAAumzt3rtavX6+IiAilp6dLkpYsWaKVK1eqQYMGkqQBAwaoffv23gwTAOABFBAAAJd17txZPXv21Jw5c5zW9+nTR3379vVSVACA6sAlTAAAl8XFxSksLMzbYQAAvIARCACA26xYsUI5OTmKiYnRkCFDKDIAwA9RQAAe5sosU336x3swEsCzunfvrttvv12StHjxYi1cuFCpqamlbpuVlaWsrCxJUlpammw2W7XF6S1BQUHkWY2Cg4PLfK28+Mprdz6LxWJ624qO6ct85Xx6Wm3J010oIAAAbtGwYUPHv5OTkzVz5swyt7Xb7bLb7Y7lvLw8T4bmE2w2G3lWo8LCwjJfKy++8tqdLzg42PS2FR3Tl/nK+fS02pJnVFSUW/ZDAQEAcIv8/HxZrVZJ0rp169SsWTMvRwT4jtn/fKzM10bf8mw1RgJUHQUEAMBls2fP1tatW3Xs2DGNGjVK/fr105YtW7Rr1y5ZLBZFRkZq5MiR3g4TAOABFBAAAJeNHj26xLquXbtWfyAAgGpXYQFR2sOCjh8/royMDB08eFCRkZEaM2YMM20AAAAAtUCFBURpDwvKzMxUfHy8UlJSlJmZqczMTA0aNMijgQKV4coMSAAAAKhYhQ+SK+1hQbm5uUpKSpIkJSUlKTc31zPRAQAAAPAplXoSdUFBgWOmDavVqqNHj7o1KAAAAAC+yeM3UfOwIP/lrjxdeRCPN1gsAdUWo7c/N3x2AQBARSpVQERERDjm+87Pz1eDBg3K3JaHBfkvd+XpyoN4vMHVhwVVhbc/N3x2/Yu7HhgEAMD5KnUJU0JCgrKzsyVJ2dnZSkxMdGtQAAAAAHxThSMQpT0sKCUlRRkZGVq1apVsNpvGjh1bHbECAAAA8LIKC4jSHhYkSZMnT3Z3LAAAAAB8XKUuYQIAAABQO1FAAAAAADCNAgIAAACAaR5/DgQAAIAvmf3Px7wdgkvKi3f0Lc9WYyTAOYxAAAAAADCNEQgAgMvmzp2r9evXKyIiQunp6ZKk48ePKyMjQwcPHlRkZKTGjBmjsLAwL0cKAHA3RiAAAC7r3LmzJk6c6LQuMzNT8fHxevHFFxUfH6/MzEzvBAcA8CgKCACAy+Li4kqMLuTm5iopKUmSlJSUpNzcXG+EBgDwMAoIAIBbFBQUyGq1SpKsVquOHj3q5YgAAJ7APRAAgGqXlZWlrKwsSVJaWppsNpuXI/K8oKAg8qxGwcHBHt2/xWJx2zEqer/KO46n32tfOZ+eVlvydBcKCACAW0RERCg/P19Wq1X5+flq0KBBmdva7XbZ7XbHcl5eXnWE6FU2m408q1FhYaFH9x8cHOy2Y1T0fpV3HE+/175yPj2ttuQZFRXllv1wCRMAwC0SEhKUnZ0tScrOzlZiYqKXIwIAeAIjEAAAl82ePVtbt27VsWPHNGrUKPXr108pKSnKyMjQqlWrZLPZNHbsWG+HCQDwAAoIAIDLRo8eXer6yZMnV28gAIBqxyVMAAAAAEyjgAAAAABgGgUEAAAAANMoIAAAAACYRgEBAAAAwDQKCAAAAACmUUAAAAAAMK1Kz4G4//77FRoaqoCAAAUGBiotLc1dcQEAAADwQVV+kNyUKVPUoEEDd8QCAAAAwMdxCRMAAAAA06o8AjFjxgxJUrdu3WS320u8npWVpaysLElSWlqabDZbVQ/p84KCgsjTBcHBwW6IxnMsloBqi9Hbnxs+uwAAoCJVKiCmTZumRo0aqaCgQNOnT1dUVJTi4uKctrHb7U6FRV5eXlUOWSPYbDbydEFhYaEbovGc4ODgaovR258bPrv+JSoqytshAB41+5+PeTsEt/CXPFB7VOkSpkaNGkmSIiIilJiYqB07drglKAAAAAC+qdIFxKlTp3Ty5EnHvzdt2qTmzZu7LTAAAAAAvqfSlzAVFBRo1qxZkqSzZ8/qxhtv1NVXX+2uuAAANRRTfAOAf6t0AdGkSRM999xz7owFqPWW/X2z6W379I/3YCRA1TDFNwD4L6ZxBQAAAGBaladxBQDgQkzxXVJtmT64OvP05jTgFovFJ6Yh9/R7zecWpaGAAAC4FVN8l662TB9cnXl6cxrw6pziuzyefq/53PoXd03vzSVMAAC3YopvAPBvFBAAALdhim8A8H9cwgSPcGU2IVQOMzbBFzHFNwD4PwoIAIDbMMU3APg/LmECAAAAYBoFBAAAAADTuIQJAAB4zex/PubtEAC4iBEIAAAAAKZRQAAAAAAwjQICAAAAgGkUEAAAAABMo4AAAAAAYBoFBAAAAADTKCAAAAAAmMZzIAAAgKTyn8kw+pZnqzESmFXZ52hU5XzyOQEFRA2x7O+bPbLfPv3jPbJf+Bazn5/g4GB1u/Vyr8Ygee5zWRxDcHCwCgsLvRYHAAA1GZcwAQAAADCtSiMQGzdu1IIFC1RUVKTk5GSlpKS4KSwAQE1F3wAA/q3SIxBFRUWaP3++Jk6cqIyMDH355Zf69ddf3RkbAKCGoW8AAP9X6QJix44datq0qZo0aaKgoCBdf/31ys3NdWdsAIAahr4BAPxfpQuIw4cPq3Hjxo7lxo0b6/Dhw24JCgBQM9E3AID/sxiGYVSm4VdffaXvv/9eo0aNkiTl5ORox44duueee5y2y8rKUlZWliQpLS2tiuECAHwZfQMA+L9Kj0A0btxYhw4dciwfOnRIVqu1xHZ2u11paWlKS0vT+PHjK3u4GoU8/UttyVOqPbmSp+fQN5SNPP0LefoX8nRNpQuI1q1ba+/evTpw4IDOnDmjtWvXKiEhwS1BAQBqJvoGAPB/lZ7GNTAwUPfcc49mzJihoqIidenSRc2aNXNnbACAGoa+AQD8X5WeA9G+fXu1b9/e9PZ2u70qh6sxyNO/1JY8pdqTK3l6Fn1D6cjTv5CnfyFP11T6JmoAAAAAtU+l74EAAAAAUPtU6RKm0hw/flwZGRk6ePCgIiMjNWbMGIWFhZXYbu7cuVq/fr0iIiKUnp7uWL9kyRKtXLlSDRo0kCQNGDDApaHw6lLVPM229zazcW7cuFELFixQUVGRkpOTlZKSIsn3z2dZcRczDEMLFizQhg0bFBISotTUVMXExJhq60uqkuf999+v0NBQBQQEKDAw0Ken3Kwoz99++01z587Vzp071b9/f/Xt29d0W19SlTy9cT7pF5zV9H5B8u++obb0CxJ9QzH6hkqcT8PNFi1aZHz88ceGYRjGxx9/bCxatKjU7bZs2WL89NNPxtixY53WL1682Fi6dKm7w3K7quZptr23mYnz7NmzxgMPPGDs27fPOH36tPHoo48ae/bsMQzDt89neXEX++6774wZM2YYRUVFxrZt24wJEyaYbusrqpKnYRhGamqqUVBQUN1hu8xMnkeOHDG2b99uvPvuu06fS387n2XlaRjeOZ/0C85qer9gGP7bN9SWfsEw6BvOR9/g+vl0+yVMubm5SkpKkiQlJSUpNze31O3i4uJ89psVM6qap9n23mYmzh07dqhp06Zq0qSJgoKCdP311/tsPuczE/e3336rm266SRaLRZdddplOnDih/Pz8GpVzVfKsSczkGRERodjYWAUGBrrc1ldUJU9voV9wVtP7Bcl/+4ba0i9I9A3no29wndsvYSooKHA8NMhqtero0aMu72PFihXKyclRTEyMhgwZ4pMdSlXzdMf7VB3MxHn48GE1btzYsdy4cWNt377dseyr57OiuIu3sdlsTtscPnzYVFtfUZU8i8/9jBkzJEndunXz2ZkqqnJO/O18VqS6zyf9QvW0r07+2jfUln5Bom/wdNvqVt19Q6UKiGnTpunIkSMl1vfv378yu3PSvXt33X777ZKkxYsXa+HChUpNTa3yfivDk3n6kqrmaZQykZfFYpHkW+fzQuXFXdE2Ztr6iqrkKZ37fDRq1EgFBQWaPn26oqKiFBcX55lgq6Aq58Tfzmd5PHU+6Rf8q1+QamffUFv6BYm+wdNtq1t19w2VKiD++te/lvlaRESE8vPzZbValZ+f77hByqyGDRs6/p2cnKyZM2dWJkS38GSeVW3vTlXNs3Hjxjp06JBj+dChQ45vJ3zpfF6ovLjP3yYvL6/ENmfOnKmwra+oSp6S1KhRI0nnPguJiYnasWOHT3YSZvL0RNvqVtVYPXU+6Rf8q1+QamffUFv6BYm+wdNtq1t19w1uvwciISFB2dnZkqTs7GwlJia61P78a+vWrVvns08wrWqeVW1fXczE2bp1a+3du1cHDhzQmTNntHbtWiUkJEjy7fNZXtzFEhISlJOTI8Mw9N///lf16tWT1Wo11dZXVCXPU6dO6eTJk5KkU6dOadOmTWrevLk30qhQVc6Jv53PsnjrfNIvVE/76uSvfUNt6Rck+gZPt61u1d03uP1BcseOHVNGRoby8vJks9k0duxYhYWF6fDhw5o3b54mTJggSZo9e7a2bt2qY8eOKSIiQv369VPXrl310ksvadeuXbJYLIqMjNTIkSN9stqrap5ltfc1ZvNcv3693nrrLRUVFalLly669dZbJcnnz2dpcX/66aeSzg2xG4ah+fPn6/vvv1dwcLBSU1PVunXrMtv6qsrmuX//fs2aNUuSdPbsWd144401Os8jR45o/PjxOnnypCwWi0JDQ/X888+rXr16fnU+y8rz2LFjXjmf9Av+1S9I/t031JZ+QaJvkOgbKts38CRqAAAAAKbxJGoAAAAAplFAAAAAADCNAgIAAACAaRQQAAAAAEyjgAAAAABgGgUEAAAAANMoIAAAAACYRgEBAAAAwLT/B3Df168t4UH0AAAAAElFTkSuQmCC", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "figsize(11., 5 )\n", + "\n", + "for i, _stock in enumerate(stocks):\n", + " plt.subplot(2,2,i+1)\n", + " plt.hist(stock_returns[_stock], bins=20,\n", + " density = True, histtype=\"stepfilled\",\n", + " color=colors[i], alpha=0.7)\n", + " plt.title(_stock + \" returns\")\n", + " plt.xlim(-0.15, 0.15)\n", + "\n", + "plt.tight_layout()\n", + "plt.suptitle(\"Histogram of daily returns\", size =14);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Below we perform the inference on the posterior mean return and posterior covariance matrix. " + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Auto-assigning NUTS sampler...\n", + "Initializing NUTS using jitter+adapt_diag...\n", + "Sequential sampling (1 chains in 1 job)\n", + "NUTS: [covariance_c, covariance_z, returns]\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + "
\n", + " \n", + " 100.00% [6000/6000 00:26<00:00 Sampling chain 0, 0 divergences]\n", + "
\n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Sampling 1 chain for 1_000 tune and 5_000 draw iterations (1_000 + 5_000 draws total) took 26 seconds.\n" + ] + } + ], + "source": [ + "with model:\n", + " obs = pm.MvNormal(\"observed returns\", mu=mu, cov=cov_matrix, observed=stock_returns)\n", + " # step = pm.NUTS()\n", + " # We use chains=1 for quicker progress\n", + " trace = pm.sample(5000, tune=1000, chains=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "figsize(15,6)\n", + "\n", + "#examine the mean return first.\n", + "mu_samples = trace.posterior.returns.data[0]\n", + "\n", + "for i in range(4):\n", + " plt.hist(mu_samples[:,i], alpha = 0.8 - 0.05*i, bins = 30,\n", + " histtype=\"stepfilled\", density=True, \n", + " label = \"%s\" % stock_returns.columns[i])\n", + "\n", + "plt.vlines(mu_samples.mean(axis=0), 0, 500, linestyle=\"--\", linewidth = .5)\n", + "\n", + "plt.title(\"Posterior distribution of $\\mu$, daily stock returns\")\n", + "plt.legend();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "(Plots like these are what inspired the book's cover.)\n", + "\n", + "What can we say about the results above? Clearly TSLA has been a strong performer, and our analysis suggests that it has an almost 1% daily return! Similarly, most of the distribution of AAPL is negative, suggesting that its *true daily return* is negative.\n", + "\n", + "\n", + "You may not have immediately noticed, but these variables are a whole order of magnitude *less* than our priors on them. For example, to put these one the same scale as the above prior distributions:" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "figsize(15.0,8)\n", + "for i in range(4):\n", + " plt.subplot(2,2,i+1)\n", + " plt.hist(mu_samples[:,i], alpha = 0.8 - 0.05*i, bins = 30,\n", + " histtype=\"stepfilled\", density=True, color = colors[i],\n", + " label = \"%s\" % stock_returns.columns[i])\n", + " plt.title(\"%s\" % stock_returns.columns[i])\n", + " plt.xlim(-0.15, 0.15)\n", + " \n", + "plt.suptitle(\"Posterior distribution of daily stock returns\")\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Why did this occur? Recall how I mentioned that finance has a very very low signal to noise ratio. This implies an environment where inference is much more difficult. One should be careful about over-interpreting these results: notice (in the first figure) that each distribution is positive at 0, implying that the stock may return nothing. Furthermore, the subjective priors influenced the results. From the fund managers point of view, this is good as it reflects his updated beliefs about the stocks, whereas from a neutral viewpoint this can be too subjective of a result. \n", + "\n", + "Below we show the posterior correlation matrix, and posterior standard deviations. An important caveat to know is that the Wishart distribution models the *inverse covariance matrix*, so we must invert it to get the covariance matrix. We also normalize the matrix to acquire the *correlation matrix*. Since we cannot plot hundreds of matrices effectively, we settle by summarizing the posterior distribution of correlation matrices by showing the *mean posterior correlation matrix* (defined on line 2)." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + }, + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "cov_samples = trace.posterior.covariance.data[0]\n", + "mean_covariance_matrix = cov_samples.mean(axis=0)\n", + "\n", + "def cov2corr(A):\n", + " \"\"\"\n", + " covariance matrix to correlation matrix.\n", + " \"\"\"\n", + " d = np.sqrt(A.diagonal())\n", + " A = ((A.T/d).T)/d\n", + " #A[ np.diag_indices(A.shape[0]) ] = np.ones( A.shape[0] )\n", + " return A\n", + "\n", + "\n", + "plt.subplot(1,2,1)\n", + "plt.imshow(cov2corr(mean_covariance_matrix) , interpolation=\"none\", \n", + " cmap = \"hot\") \n", + "plt.xticks(np.arange(4), stock_returns.columns)\n", + "plt.yticks(np.arange(4), stock_returns.columns)\n", + "plt.colorbar(orientation=\"vertical\")\n", + "plt.title(\"(mean posterior) Correlation Matrix\")\n", + "\n", + "plt.subplot(1,2,2)\n", + "plt.bar(np.arange(4), np.sqrt(np.diag(mean_covariance_matrix)),\n", + " color = \"#348ABD\", alpha = 0.7)\n", + "plt.xticks(np.arange(4) + 0.5, stock_returns.columns);\n", + "plt.title(\"(mean posterior) standard deviations of daily stock returns\")\n", + "\n", + "plt.tight_layout();\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Looking at the above figures, we can say that likely TSLA has an above-average volatility (looking at the return graph this is quite clear). The correlation matrix shows that there are not strong correlations present, but perhaps GOOG and AMZN express a higher correlation (about 0.30). \n", + "\n", + "With this Bayesian analysis of the stock market, we can throw it into a Mean-Variance optimizer (which I cannot stress enough, do not use with frequentist point estimates) and find the minimum. This optimizer balances the tradeoff between a high return and high variance.\n", + "\n", + "$$ w_{opt} = \\max_{w} \\frac{1}{N}\\left( \\sum_{i=0}^N \\mu_i^T w - \\frac{\\lambda}{2}w^T\\Sigma_i w \\right)$$\n", + "\n", + "where $\\mu_i$ and $\\Sigma_i$ are the $i$th posterior estimate of the mean returns and the covariance matrix. This is another example of loss function optimization." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Protips for the Wishart distribution\n", + "\n", + "If you plan to be using the Wishart distribution, read on. Else, feel free to skip this. \n", + "\n", + "In the problem above, the Wishart distribution behaves pretty nicely. Unfortunately, this is rarely the case. The problem is that estimating an $NxN$ covariance matrix involves estimating $\\frac{1}{2}N(N-1)$ unknowns. This is a large number even for modest $N$. Personally, I've tried performing a similar simulation as above with $N = 23$ stocks, and ended up giving considering that I was requesting my MCMC simulation to estimate at least $\\frac{1}{2}23*22 = 253$ additional unknowns (plus the other interesting unknowns in the problem). This is not easy for MCMC. Essentially, you are asking you MCMC to traverse 250+ dimensional space. And the problem seemed so innocent initially! Below are some tips, in order of supremacy:\n", + "\n", + "1. Use conjugancy if it applies. See section below.\n", + "\n", + "2. Use a good starting value. What might be a good starting value? Why, the data's sample covariance matrix is! Note that this is not empirical Bayes: we are not touching the prior's parameters, we are modifying the starting value of the MCMC. Due to numerical instability, it is best to truncate the floats in the sample covariance matrix down a few degrees of precision (e.g. instability can cause unsymmetrical matrices, which can cause PyMC3 to cry.). \n", + "\n", + "3. Provide as much domain knowledge in the form of priors, if possible. I stress *if possible*. It is likely impossible to have an estimate about each $\\frac{1}{2}N(N-1)$ unknown. In this case, see number 4.\n", + "\n", + "4. Use empirical Bayes, i.e. use the sample covariance matrix as the prior's parameter.\n", + "\n", + "5. For problems where $N$ is very large, nothing is going to help. Instead, ask, do I really care about *every* correlation? Probably not. Further ask yourself, do I really really care about correlations? Possibly not. In finance, we can set an informal hierarchy of what we might be interested in the most: first a good estimate of $\\mu$, the variances along the diagonal of the covariance matrix are secondly important, and finally the correlations are least important. So, it might be better to ignore the $\\frac{1}{2}(N-1)(N-2)$ correlations and instead focus on the more important unknowns.\n", + "\n", + "**Another thing** to note is that the implementation of the Wishart distribution has changed in from PyMC to PyMC3. Wishart distribution matrices are required to have certain mathematical characteristics that are very restrictive. This makes it so that it is impossible for MCMC methods to propose matrices that will be accepted in our sampling procedure. With our model here we sample the Bartlett decomposition of a Wishart distribution matrix and use that to calculate our samples for the covariance matrix (http://en.wikipedia.org/wiki/Wishart_distribution#Bartlett_decomposition)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Conjugate Priors\n", + "\n", + "Recall that a $\\text{Beta}$ prior with $\\text{Binomial}$ data implies a $\\text{Beta}$ posterior. Graphically:\n", + "\n", + "$$ \\underbrace{\\text{Beta}}_{\\text{prior}} \\cdot \\overbrace{\\text{Binomial}}^{\\text{data}} = \\overbrace{\\text{Beta}}^{\\text{posterior} } $$ \n", + "\n", + "Notice the $\\text{Beta}$ on both sides of this equation (no, you cannot cancel them, this is not a *real* equation). This is a really useful property. It allows us to avoid using MCMC, since the posterior is known in closed form. Hence inference and analytics are easy to derive. This shortcut was the heart of the Bayesian Bandit algorithm above. Fortunately, there is an entire family of distributions that have similar behaviour. \n", + "\n", + "Suppose $X$ comes from, or is believed to come from, a well-known distribution, call it $f_{\\alpha}$, where $\\alpha$ are possibly unknown parameters of $f$. $f$ could be a Normal distribution, or Binomial distribution, etc. For particular distributions $f_{\\alpha}$, there may exist a prior distribution $p_{\\beta}$, such that:\n", + "\n", + "$$ \\overbrace{p_{\\beta}}^{\\text{prior}} \\cdot \\overbrace{f_{\\alpha}(X)}^{\\text{data}} = \\overbrace{p_{\\beta'}}^{\\text{posterior} } $$ \n", + "\n", + "where $\\beta'$ is a different set of parameters *but $p$ is the same distribution as the prior*. A prior $p$ that satisfies this relationship is called a *conjugate prior*. As I mentioned, they are useful computationally, as we can avoided approximate inference using MCMC and go directly to the posterior. This sounds great, right?\n", + "\n", + "Unfortunately, not quite. There are a few issues with conjugate priors.\n", + "\n", + "1. The conjugate prior is not objective. Hence only useful when a subjective prior is required. It is not guaranteed that the conjugate prior can accommodate the practitioner's subjective opinion.\n", + "\n", + "2. There typically exist conjugate priors for simple, one dimensional problems. For larger problems, involving more complicated structures, hope is lost to find a conjugate prior. For smaller models, Wikipedia has a nice [table of conjugate priors](http://en.wikipedia.org/wiki/Conjugate_prior#Table_of_conjugate_distributions).\n", + "\n", + "Really, conjugate priors are only useful for their mathematical convenience: it is simple to go from prior to posterior. I personally see conjugate priors as only a neat mathematical trick, and offer little insight into the problem at hand. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Jefferys Priors\n", + "\n", + "Earlier, we talked about objective priors rarely being *objective*. Partly what we mean by this is that we want a prior that doesn't bias our posterior estimates. The flat prior seems like a reasonable choice as it assigns equal probability to all values. \n", + "\n", + "But the flat prior is not transformation invariant. What does this mean? Suppose we have a random variable $\\textbf X$ from Bernoulli($\\theta$). We define the prior on $p(\\theta) = 1$. " + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "image/png": 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8ev6pp57KjBkzrsP4AABw86u5PWZgYCBz587NnDlz0tjYmBUrVuTQoUOXXX/gwIHcd99913RIAACYzGqW9uHh4ZTL5dHX5XI5w8PDl1z7ySef5OjRo7n33nvHHO/q6srXv/719PX1XeW4AAAw+dTcHlOtVi86ViqVLrn21VdfzZIlS8Zsjdm6dWtmzZqVM2fO5Nlnn828efOydOnSi67t6+sbLfXd3d2ZOXNmvffAJNbQ0CAr1E1eqJesMB7ywo1Qs7SXy+UMDQ2Nvh4aGkpra+sl1x44cCArV64cc2zWrFlJkpaWltxzzz0ZGBi4ZGnv6OhIR0fH6OvTp0/XdQNMbjNnzpQV6iYv1EtWGA95oV5zruLamttjFi9enJMnT+bUqVMZGRnJwYMH097eftG6jz/+OMeOHRtz7vz58zl37tzo16+99loWLlx4FeMCAMDkU/NJe0NDQzZs2JCurq5UKpWsWbMmCxYsyN69e5MknZ2dSZJXXnklX/rSl9LU1DR67ZkzZ7J9+/YkyYULF7Jy5crcdddd1+E2AADg5lWqXmrTegG8+YMfTvQIfA74lSTjIS/US1YYD3mhXku+vPqKr/WJqAAAUHBKOwAAFJzSDgAABae0AwBAwSntAABQcEo7AAAUnNIOAAAFp7QDAEDBKe0AAFBwSjsAABSc0g4AAAWntAMAQMEp7QAAUHBKOwAAFJzSDgAABae0AwBAwSntAABQcEo7AAAUnNIOAAAFp7QDAEDBKe0AAFBwSjsAABSc0g4AAAWntAMAQMEp7QAAUHCN9Sw6evRoent7U6lUsnbt2qxbt27M+TfeeCPf+MY3Mnv27CTJ8uXL85WvfKWuawEAgM9Ws7RXKpX09PRky5YtKZfL2bx5c9rb2zN//vwx6+6888488cQTV3QtAABweTW3xwwMDGTu3LmZM2dOGhsbs2LFihw6dKiub3411wIAAP+jZmkfHh5OuVwefV0ulzM8PHzRuuPHj+fxxx/Ptm3b8s4774zrWgAA4PJqbo+pVqsXHSuVSmNef+ELX8iePXvS1NSU/v7+fPOb38yuXbvquvZTfX196evrS5J0d3dn5syZ9czPJNfQ0CAr1E1eqJesMB7ywo1Qs7SXy+UMDQ2Nvh4aGkpra+uYNdOmTRv9+u67705PT08++OCDuq79VEdHRzo6OkZfnz59uu6bYPKaOXOmrFA3eaFessJ4yAv1mnMV19bcHrN48eKcPHkyp06dysjISA4ePJj29vYxa06fPj36VH1gYCCVSiXTp0+v61oAAOCz1XzS3tDQkA0bNqSrqyuVSiVr1qzJggULsnfv3iRJZ2dnfvzjH2fv3r1paGjI1KlT89hjj6VUKl32WgAAoH6l6qU2nhfAmz/44USPwOeAX0kyHvJCvWSF8ZAX6rXky6uv+FqfiAoAAAWntAMAQMEp7QAAUHBKOwAAFJzSDgAABae0AwBAwSntAABQcEo7AAAUnNIOAAAFp7QDAEDBKe0AAFBwSjsAABSc0g4AAAWntAMAQMEp7QAAUHBKOwAAFJzSDgAABae0AwBAwSntAABQcEo7AAAUnNIOAAAFp7QDAEDBKe0AAFBwSjsAABSc0g4AAAXXWM+io0ePpre3N5VKJWvXrs26devGnN+/f39eeOGFJElTU1M2btyY2267LUny8MMPp6mpKVOmTElDQ0O6u7uv6Q0AAMDNrmZpr1Qq6enpyZYtW1Iul7N58+a0t7dn/vz5o2tmz56dp59+Os3NzTly5Ei+853vZNu2baPnn3rqqcyYMeP63AEAANzkam6PGRgYyNy5czNnzpw0NjZmxYoVOXTo0Jg1S5YsSXNzc5Lk9ttvz9DQ0PWZFgAAJqGaT9qHh4dTLpdHX5fL5bz11luXXf/SSy9l2bJlY451dXUlSe6///50dHRc6awAADAp1Szt1Wr1omOlUumSa19//fXs27cvzzzzzOixrVu3ZtasWTlz5kyeffbZzJs3L0uXLr3o2r6+vvT19SVJuru7M3PmzHrvgUmsoaFBVqibvFAvWWE85IUboWZpL5fLY7a7DA0NpbW19aJ1b7/9dp577rls3rw506dPHz0+a9asJElLS0vuueeeDAwMXLK0d3R0jHkKf/r06XHdCJPTzJkzZYW6yQv1khXGQ16o15yruLbmnvbFixfn5MmTOXXqVEZGRnLw4MG0t7ePWTM4OJjt27fnkUceybx580aPnz9/PufOnRv9+rXXXsvChQuvYlwAAJh8aj5pb2hoyIYNG9LV1ZVKpZI1a9ZkwYIF2bt3b5Kks7Mz3/ve9/Lhhx/m+eefH72mu7s7Z86cyfbt25MkFy5cyMqVK3PXXXddv7sBAICbUKl6qU3rBfDmD3440SPwOeBXkoyHvFAvWWE85IV6Lfny6iu+1ieiAgBAwSntAABQcEo7AAAUnNIOAAAFp7QDAEDBKe0AAFBwSjsAABSc0g4AAAWntAMAQMEp7QAAUHBKOwAAFJzSDgAABae0AwBAwSntAABQcEo7AAAUnNIOAAAFp7QDAEDBKe0AAFBwSjsAABSc0g4AAAWntAMAQMEp7QAAUHBKOwAAFJzSDgAABae0AwBAwTXWs+jo0aPp7e1NpVLJ2rVrs27dujHnq9Vqent7c+TIkdxyyy3ZtGlTFi1aVNe1AADAZ6v5pL1SqaSnpydPPvlkvvWtb+XAgQN59913x6w5cuRI3nvvvezatStf+9rX8vzzz9d9LQAA8NlqlvaBgYHMnTs3c+bMSWNjY1asWJFDhw6NWXP48OGsWrUqpVIpd9xxRz766KO8//77dV0LAAB8tpqlfXh4OOVyefR1uVzO8PDwRWva2touWlPPtQAAwGeruae9Wq1edKxUKtW1pp5rP9XX15e+vr4kSXd3d5Z8eXWt0SBJMmeiB+BzRV6ol6wwHvLC9VbzSXu5XM7Q0NDo66GhobS2tl60ZnBw8KI19Vz7qY6OjnR3d6e7uztPPPHEuG+EyUlWGA95oV6ywnjIC/W6mqzULO2LFy/OyZMnc+rUqYyMjOTgwYNpb28fs6a9vT0vv/xyqtVqjh8/nmnTpqW1tbWuawEAgM9Wc3tMQ0NDNmzYkK6urlQqlaxZsyYLFizI3r17kySdnZ1ZtmxZ+vv78+ijj2bq1KnZtGnTZ14LAADUr673ab/77rtz9913jznW2dk5+nWpVMrGjRvrvraWjo6Oca1n8pIVxkNeqJesMB7yQr2uJiul6qX+tSgAAFAYNfe0AwAAE6uu7THXw9GjR9Pb25tKpZK1a9dm3bp1Y85Xq9X09vbmyJEjueWWW7Jp06YsWrRoYoZlwtXKy/79+/PCCy8kSZqamrJx48bcdtttN35QJlytrHxqYGAgf/VXf5U/+7M/y7333ntjh6Qw6snLG2+8ke9+97u5cOFCpk+fnr/927+98YMy4Wpl5eOPP86uXbsyNDSUCxcu5Hd/93ezZs2aiRmWCbVnz5709/enpaUlO3bsuOj8FXfc6gS4cOFC9ZFHHqm+99571V/84hfVv/zLv6y+8847Y9a8+uqr1a6urmqlUqm++eab1c2bN0/EqBRAPXn56U9/Wj179my1Wq1W+/v75WWSqicrn657+umnq9u2bav+67/+6wRMShHUk5cPP/yw+thjj1X/67/+q1qtVqunT5+eiFGZYPVk5R//8R+r//AP/1CtVqvVM2fOVL/61a9Wf/GLX0zEuEywN954o/qzn/2s+ud//ueXPH+lHXdCtscMDAxk7ty5mTNnThobG7NixYocOnRozJrDhw9n1apVKZVKueOOO/LRRx/l/fffn4hxmWD15GXJkiVpbm5Oktx+++1jPh+AyaOerCTJP/3TP2X58uWZMWPGBExJUdSTl3/5l3/J8uXLRz/1u6WlZSJGZYLVk5VSqZTz58+nWq3m/PnzaW5uzpQpdiFPRkuXLh3tJJdypR13QtI0PDyccrk8+rpcLmd4ePiiNZ/+IXm5NUwO9eTlf3vppZeybNmyGzEaBVPvny2vvPLKmHfAYnKqJy8nT57Mhx9+mKeffjpf//rX86Mf/ehGj0kB1JOVBx54IP/xH/+RP/3TP81f/MVf5I//+I+Vdi7pSjvuhOxpr17iDWtKpdK41zA5jCcLr7/+evbt25dnnnnmeo9FAdWTle9+97v5wz/8Q3+ZUldeLly4kJ///Of567/+6/z3f/93tmzZkttvvz3z5s27UWNSAPVk5Sc/+Ul++Zd/OX/zN3+T//zP/8zWrVvzK7/yK5k2bdqNGpPPiSvtuBNS2svl8pjtC0NDQ2ltbb1ozeDg4GeuYXKoJy9J8vbbb+e5557L5s2bM3369Bs5IgVRT1Z+9rOf5e/+7u+SJB988EGOHDmSKVOm5Nd//ddv6KxMvHr/Lpo+fXqamprS1NSUO++8M2+//bbSPsnUk5V9+/Zl3bp1KZVKmTt3bmbPnp0TJ07ki1/84o0el4K70o47IY+aFi9enJMnT+bUqVMZGRnJwYMH097ePmZNe3t7Xn755VSr1Rw/fjzTpk1T2iepevIyODiY7du355FHHvGX6SRWT1Z27949+t+9996bjRs3KuyTVL1/F/30pz/NhQsX8sknn2RgYCC33nrrBE3MRKknK21tbfn3f//3JMnp06dz4sSJzJ49eyLGpeCutONO2Icr9ff35+///u9TqVSyZs2a/P7v/3727t2b5H8+bbVaraanpyc/+clPMnXq1GzatCmLFy+eiFEpgFp5+fa3v51/+7d/G90j1tDQkO7u7okcmQlSKyv/2+7du/Nrv/Zr3vJxEqsnLy+++GL27duXKVOm5Dd/8zfz27/92xM5MhOkVlaGh4ezZ8+e0X9Q+Hu/93tZtWrVRI7MBNm5c2eOHTuWs2fPpqWlJevXr8/IyEiSq+u4PhEVAAAKzr/EAgCAglPaAQCg4JR2AAAoOKUdAAAKTmkHAICCU9oBAKDglHYAACg4pR0AAAru/wEk9F0uRGLAtwAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "figsize(12.5, 5)\n", + "\n", + "x = np.linspace(0.000 ,1, 150)\n", + "y = np.linspace(1.0, 1.0, 150)\n", + "lines = plt.plot(x, y, color=\"#A60628\", lw = 3)\n", + "plt.fill_between(x, 0, y, alpha = 0.2, color = lines[0].get_color())\n", + "plt.autoscale(tight=True)\n", + "plt.ylim(0, 2);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, let's transform $\\theta$ with the function $\\psi = log \\frac{\\theta}{1-\\theta}$. This is just a function to stretch $\\theta$ across the real line. Now how likely are different values of $\\psi$ under our transformation." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "figsize(12.5, 5)\n", + "\n", + "psi = np.linspace(-10 ,10, 150)\n", + "y = np.exp(psi) / (1 + np.exp(psi))**2\n", + "lines = plt.plot(psi, y, color=\"#A60628\", lw = 3)\n", + "plt.fill_between(psi, 0, y, alpha = 0.2, color = lines[0].get_color())\n", + "plt.autoscale(tight=True)\n", + "plt.ylim(0, 1);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Oh no! Our function is no longer flat. It turns out flat priors do carry information in them after all. The point of Jeffreys Priors is to create priors that don't accidentally become informative when you transform the variables you placed them originally on.\n", + "\n", + "Jeffreys Priors are defined as:\n", + "\n", + "$$p_J(\\theta) \\propto \\mathbf{I}(\\theta)^\\frac{1}{2}$$\n", + "$$\\mathbf{I}(\\theta) = - \\mathbb{E}\\bigg[\\frac{d^2 \\text{ log } p(X|\\theta)}{d\\theta^2}\\bigg]$$\n", + "\n", + "$\\mathbf{I}$ being the *Fisher information*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Effect of the prior as $N$ increases\n", + "\n", + "In the first chapter, I proposed that as the amount of our observations or data increases, the influence of the prior decreases. This is intuitive. After all, our prior is based on previous information, and eventually enough new information will shadow our previous information's value. The smothering of the prior by enough data is also helpful: if our prior is significantly wrong, then the self-correcting nature of the data will present to us a *less wrong*, and eventually *correct*, posterior. \n", + "\n", + "We can see this mathematically. First, recall Bayes Theorem from Chapter 1 that relates the prior to the posterior. The following is a sample from [What is the relationship between sample size and the influence of prior on posterior?](http://stats.stackexchange.com/questions/30387/what-is-the-relationship-between-sample-size-and-the-influence-of-prior-on-poste)[1] on CrossValidated.\n", + "\n", + ">The posterior distribution for a parameter $\\theta$, given a data set ${\\textbf X}$ can be written as \n", + "\n", + "$$p(\\theta | {\\textbf X}) \\propto \\underbrace{p({\\textbf X} | \\theta)}_{{\\textrm likelihood}} \\cdot \\overbrace{ p(\\theta) }^{ {\\textrm prior} } $$\n", + "\n", + "\n", + "\n", + ">or, as is more commonly displayed on the log scale, \n", + "\n", + "$$ \\log( p(\\theta | {\\textbf X}) ) = c + L(\\theta;{\\textbf X}) + \\log(p(\\theta)) $$\n", + "\n", + ">The log-likelihood, $L(\\theta;{\\textbf X}) = \\log \\left( p({\\textbf X}|\\theta) \\right)$, **scales with the sample size**, since it is a function of the data, while the prior density does not. Therefore, as the sample size increases, the absolute value of $L(\\theta;{\\textbf X})$ is getting larger while $\\log(p(\\theta))$ stays fixed (for a fixed value of $\\theta$), thus the sum $L(\\theta;{\\textbf X}) + \\log(p(\\theta))$ becomes more heavily influenced by $L(\\theta;{\\textbf X})$ as the sample size increases. \n", + "\n", + "There is an interesting consequence not immediately apparent. As the sample size increases, the chosen prior has less influence. Hence inference converges regardless of chosen prior, so long as the areas of non-zero probabilities are the same. \n", + "\n", + "Below we visualize this. We examine the convergence of two posteriors of a Binomial's parameter $\\theta$, one with a flat prior and the other with a biased prior towards 0. As the sample size increases, the posteriors, and hence the inference, converge." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "figsize(12.5, 15)\n", + "\n", + "p = 0.6\n", + "beta1_params = np.array([1.,1.])\n", + "beta2_params = np.array([2,10])\n", + "beta = stats.beta\n", + "\n", + "x = np.linspace(0.00, 1, 125)\n", + "data = stats.bernoulli.rvs(p, size=500)\n", + "\n", + "plt.figure()\n", + "for i,N in enumerate([0,4,8, 32,64, 128, 500]):\n", + " s = data[:N].sum() \n", + " plt.subplot(8,1,i+1)\n", + " params1 = beta1_params + np.array([s, N-s])\n", + " params2 = beta2_params + np.array([s, N-s])\n", + " y1,y2 = beta.pdf(x, *params1), beta.pdf( x, *params2)\n", + " plt.plot(x,y1, label = r\"flat prior\", lw =3)\n", + " plt.plot(x, y2, label = \"biased prior\", lw= 3)\n", + " plt.fill_between(x, 0, y1, color =\"#348ABD\", alpha = 0.15) \n", + " plt.fill_between(x, 0, y2, color =\"#A60628\", alpha = 0.15) \n", + " plt.legend(title = \"N=%d\" % N)\n", + " plt.vlines(p, 0.0, 7.5, linestyles = \"--\", linewidth=1)\n", + " #plt.ylim( 0, 10)#\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Keep in mind, not all posteriors will \"forget\" the prior this quickly. This example was just to show that *eventually* the prior is forgotten. The \"forgetfulness\" of the prior as we become awash in more and more data is the reason why Bayesian and Frequentist inference eventually converge as well." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Bayesian perspective of Penalized Linear Regressions\n", + "\n", + "There is a very interesting relationship between a penalized least-squares regression and Bayesian priors. A penalized linear regression is a optimization problem of the form:\n", + "\n", + "$$ \\text{argmin}_{\\beta} \\;\\; (Y - X\\beta)^T(Y - X\\beta) + f(\\beta)$$\n", + "\n", + "for some function $f$ (typically a norm like $|| \\cdot ||_p^p$). \n", + "\n", + "We will first describe the probabilistic interpretation of least-squares linear regression. Denote our response variable $Y$, and features are contained in the data matrix $X$. The standard linear model is:\n", + "\n", + "\\begin{equation}\n", + "Y = X\\beta + \\epsilon\n", + "\\end{equation}\n", + "\n", + "where $\\epsilon \\sim \\text{Normal}( {\\textbf 0}, \\sigma{\\textbf I })$. Simply, the observed $Y$ is a linear function of $X$ (with coefficients $\\beta$) plus some noise term. Our unknown to be determined is $\\beta$. We use the following property of Normal random variables:\n", + "\n", + "$$ \\mu' + \\text{Normal}( \\mu, \\sigma ) \\sim \\text{Normal}( \\mu' + \\mu , \\sigma ) $$\n", + "\n", + "to rewrite the above linear model as:\n", + "\n", + "\\begin{align}\n", + "& Y = X\\beta + \\text{Normal}( {\\textbf 0}, \\sigma{\\textbf I }) \\\\\\\\\n", + "& Y = \\text{Normal}( X\\beta , \\sigma{\\textbf I }) \\\\\\\\\n", + "\\end{align}\n", + "\n", + "In probabilistic notation, denote $f_Y(y \\; | \\; \\beta )$ the probability distribution of $Y$, and recalling the density function for a Normal random variable (see [here](http://en.wikipedia.org/wiki/Normal_distribution) ):\n", + "\n", + "$$ f_Y( Y \\; |\\; \\beta, X) = L(\\beta|\\; X,Y)= \\frac{1}{\\sqrt{ 2\\pi\\sigma} } \\exp \\left( \\frac{1}{2\\sigma^2} (Y - X\\beta)^T(Y - X\\beta) \\right) $$\n", + "\n", + "This is the likelihood function for $\\beta$. Taking the $\\log$:\n", + "\n", + "$$ \\ell(\\beta) = K - c(Y - X\\beta)^T(Y - X\\beta) $$\n", + "\n", + "where $K$ and $c>0$ are constants. Maximum likelihood techniques wish to maximize this for $\\beta$, \n", + "\n", + "$$\\hat{ \\beta } = \\text{argmax}_{\\beta} \\;\\; - (Y - X\\beta)^T(Y - X\\beta) $$\n", + "\n", + "Equivalently we can *minimize the negative* of the above:\n", + "\n", + "$$\\hat{ \\beta } = \\text{argmin}_{\\beta} \\;\\; (Y - X\\beta)^T(Y - X\\beta) $$\n", + "\n", + "This is the familiar least-squares linear regression equation. Therefore we showed that the solution to a linear least-squares is the same as the maximum likelihood assuming Normal noise. Next we extend this to show how we can arrive at penalized linear regression by a suitable choice of prior on $\\beta$. \n", + "\n", + "#### Penalized least-squares\n", + "\n", + "In the above, once we have the likelihood, we can include a prior distribution on $\\beta$ to derive to the equation for the posterior distribution:\n", + "\n", + "$$P( \\beta | Y, X ) = L(\\beta|\\;X,Y)p( \\beta )$$\n", + "\n", + "where $p(\\beta)$ is a prior on the elements of $\\beta$. What are some interesting priors? \n", + "\n", + "1\\. If we include *no explicit* prior term, we are actually including an uninformative prior, $P( \\beta ) \\propto 1$, think of it as uniform over all numbers. \n", + "\n", + "2\\. If we have reason to believe the elements of $\\beta$ are not too large, we can suppose that *a priori*:\n", + "\n", + "$$ \\beta \\sim \\text{Normal}({\\textbf 0 }, \\lambda {\\textbf I } ) $$\n", + "\n", + "The resulting posterior density function for $\\beta$ is *proportional to*:\n", + "\n", + "$$ \\exp \\left( \\frac{1}{2\\sigma^2} (Y - X\\beta)^T(Y - X\\beta) \\right) \\exp \\left( \\frac{1}{2\\lambda^2} \\beta^T\\beta \\right) $$\n", + "\n", + "and taking the $\\log$ of this, and combining and redefining constants, we arrive at:\n", + "\n", + "$$ \\ell(\\beta) \\propto K - (Y - X\\beta)^T(Y - X\\beta) - \\alpha \\beta^T\\beta $$\n", + "\n", + "we arrive at the function we wish to maximize (recall the point that maximizes the posterior distribution is the MAP, or *maximum a posterior*):\n", + "\n", + "$$\\hat{ \\beta } = \\text{argmax}_{\\beta} \\;\\; -(Y - X\\beta)^T(Y - X\\beta) - \\alpha \\;\\beta^T\\beta $$\n", + "\n", + "Equivalently, we can minimize the negative of the above, and rewriting $\\beta^T \\beta = ||\\beta||_2^2$:\n", + "\n", + "$$\\hat{ \\beta } = \\text{argmin}_{\\beta} \\;\\; (Y - X\\beta)^T(Y - X\\beta) + \\alpha \\;||\\beta||_2^2$$\n", + "\n", + "This above term is exactly Ridge Regression. Thus we can see that ridge regression corresponds to the MAP of a linear model with Normal errors and a Normal prior on $\\beta$.\n", + "\n", + "3\\. Similarly, if we assume a *Laplace* prior on $\\beta$, ie. \n", + "\n", + "$$ f_\\beta( \\beta) \\propto \\exp \\left(- \\lambda ||\\beta||_1 \\right)$$\n", + "\n", + "and following the same steps as above, we recover:\n", + "\n", + "$$\\hat{ \\beta } = \\text{argmin}_{\\beta} \\;\\; (Y - X\\beta)^T(Y - X\\beta) + \\alpha \\;||\\beta||_1$$\n", + "\n", + "which is LASSO regression. Some important notes about this equivalence. The sparsity that is a result of using a LASSO regularization is not a result of the prior assigning high probability to sparsity. Quite the opposite actually. It is the combination of the $|| \\cdot ||_1$ function and using the MAP that creates sparsity on $\\beta$: [purely a geometric argument](http://camdp.com/blogs/least-squares-regression-l1-penalty). The prior does contribute to an overall shrinking of the coefficients towards 0 though. An interesting discussion of this can be found in [2].\n", + "\n", + "For an example of Bayesian linear regression, see Chapter 4's example on financial losses." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### References\n", + "\n", + "1. Macro, . \"What is the relationship between sample size and the influence of prior on posterior?.\" 13 Jun 2013. StackOverflow, Online Posting to Cross-Validated. Web. 25 Apr. 2013.\n", + "\n", + "2. Starck, J.-L., , et al. \"Sparsity and the Bayesian Perspective.\" Astronomy & Astrophysics. (2013): n. page. Print.\n", + "\n", + "3. Kuleshov, Volodymyr, and Doina Precup. \"Algorithms for the multi-armed bandit problem.\" Journal of Machine Learning Research. (2000): 1-49. Print.\n", + "\n", + "4. Gelman, Andrew. \"Prior distributions for variance parameters in hierarchical models.\" Bayesian Analysis. 1.3 (2006): 515-533. Print.\n", + "\n", + "5. Gelman, Andrew, and Cosma R. Shalizi. \"Philosophy and the practice of Bayesian statistics.\" British Journal of Mathematical and Statistical Psychology. (2012): n. page. Web. 17 Apr. 2013.\n", + "\n", + "6. http://jmlr.csail.mit.edu/proceedings/papers/v22/kaufmann12/kaufmann12.pdf\n", + "\n", + "7. James, Neufeld. \"Reddit's \"best\" comment scoring algorithm as a multi-armed bandit task.\" Simple ML Hacks. Blogger, 09 Apr 2013. Web. 25 Apr. 2013.\n", + "\n", + "8. Oakley, J. E., Daneshkhah, A. and O’Hagan, A. Nonparametric elicitation using the roulette method. Submitted to Bayesian Analysis.\n", + "\n", + "9. \"Eliciting priors from experts.\" 19 Jul 2010. StackOverflow, Online Posting to Cross-Validated. Web. 1 May. 2013. .\n", + "\n", + "10. Taleb, Nassim Nicholas (2007), The Black Swan: The Impact of the Highly Improbable, Random House, ISBN 978-1400063512" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.core.display import HTML\n", + "def css_styling():\n", + " styles = open(\"../styles/custom.css\", \"r\").read()\n", + " return HTML(styles)\n", + "css_styling()" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "pymc_env", + "language": "python", + "name": "pymc_env" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.4" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/Chapter6_Priorities/Ch6_Priors_TFP.ipynb b/Chapter6_Priorities/Ch6_Priors_TFP.ipynb new file mode 100644 index 00000000..beb649db --- /dev/null +++ b/Chapter6_Priorities/Ch6_Priors_TFP.ipynb @@ -0,0 +1,2953 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "working Ch6_Priors_TFP.ipynb", + "provenance": [], + "collapsed_sections": [ + "NVFx5oT5h30M" + ] + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "NFaKtWALh3zH" + }, + "source": [ + "# Probabilistic Programming and Bayesian Methods for Hackers Chapter 6\n", + "\n", + "\n", + " \n", + " \n", + "
\n", + " Run in Google Colab\n", + " \n", + " View source on GitHub\n", + "
\n", + "
\n", + "
\n", + "
\n", + "\n", + "Original content ([this Jupyter notebook](https://nbviewer.jupyter.org/github/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/blob/master/Chapter6_Priorities/Ch6_Priors_PyMC2.ipynb)) created by Cam Davidson-Pilon ([`@Cmrn_DP`](https://twitter.com/Cmrn_DP))\n", + "\n", + "Ported to [Tensorflow Probability](https://www.tensorflow.org/probability/) by Matthew McAteer ([`@MatthewMcAteer0`](https://twitter.com/MatthewMcAteer0)), with help from Bryan Seybold, Mike Shwe ([`@mikeshwe`](https://twitter.com/mikeshwe)), Josh Dillon, and the rest of the TFP team at Google ([`tfprobability@tensorflow.org`](mailto:tfprobability@tensorflow.org)).\n", + "\n", + "Welcome to Bayesian Methods for Hackers. The full Github repository is available at [github/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers](https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers). The other chapters can be found on the project's [homepage](https://camdavidsonpilon.github.io/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/). We hope you enjoy the book, and we encourage any contributions!\n", + "\n", + "---\n", + "### Table of Contents\n", + "- Dependencies & Prerequisites\n", + "- Getting our priorities straight\n", + " - Subjective vs Objective priors\n", + " - Subjective Priors\n", + " - Decisions, decisions...\n", + " - Empirical Bayes\n", + "- Useful priors to know about\n", + " - The Gamma distribution\n", + " - The Wishart distribution\n", + " - The Beta distribution\n", + "- Example: Bayesian Multi-Armed Bandits\n", + " - Applications\n", + " - A Proposed Solution\n", + " - A Measure of Good\n", + " - Extending the algorithm\n", + "- Eliciting expert prior\n", + " - Trial roulette method\n", + " - Example: Stock Returns\n", + " - Protips for the Wishart distribution\n", + "- Conjugate Priors\n", + "- Jeffreys Priors\n", + "- Effect of ther prior as N increases\n", + " - Bayesian perspective of Penalized Linear Regressions\n", + " - References\n", + "\n", + "---\n", + "\n", + "This chapter of [Bayesian Methods for Hackers](https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers) focuses on the most debated and discussed part of Bayesian methodologies: how to choose an appropriate prior distribution. We also present how the prior's influence changes as our dataset increases, and an interesting relationship between priors and penalties on linear regression." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "3r8evK75Hyay" + }, + "source": [ + "### Dependencies & Prerequisites\n", + "\n", + "
\n", + " Tensorflow Probability is part of the colab default runtime, so you don't need to install Tensorflow or Tensorflow Probability if you're running this in the colab. \n", + "
\n", + " If you're running this notebook in Jupyter on your own machine (and you have already installed Tensorflow), you can use the following\n", + "
\n", + "
    \n", + "
  • For the most recent nightly installation: pip3 install -q tfp-nightly
    • \n", + "
  • For the most recent stable TFP release: pip3 install -q --upgrade tensorflow-probability
    • \n", + "
  • For the most recent stable GPU-connected version of TFP: pip3 install -q --upgrade tensorflow-probability-gpu
    • \n", + "
  • For the most recent nightly GPU-connected version of TFP: pip3 install -q tfp-nightly-gpu
    • \n", + "
\n", + "Again, if you are running this in a Colab, Tensorflow and TFP are already installed\n", + "
" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "YRorv1tNh3zJ", + "outputId": "198eae9c-ab52-47f8-893d-d7df6eb66337", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 280 + } + }, + "source": [ + "#@title Imports and Global Variables { display-mode: \"form\" }\n", + "\"\"\"\n", + "The book uses a custom matplotlibrc file, which provides the unique styles for\n", + "matplotlib plots. If executing this book, and you wish to use the book's\n", + "styling, provided are two options:\n", + " 1. Overwrite your own matplotlibrc file with the rc-file provided in the\n", + " book's styles/ dir. See http://matplotlib.org/users/customizing.html\n", + " 2. Also in the styles is bmh_matplotlibrc.json file. This can be used to\n", + " update the styles in only this notebook. Try running the following code:\n", + "\n", + " import json\n", + " s = json.load(open(\"../styles/bmh_matplotlibrc.json\"))\n", + " matplotlib.rcParams.update(s)\n", + "\"\"\"\n", + "!pip3 install -q pandas_datareader\n", + "!pip3 install -q wget\n", + "from __future__ import absolute_import, division, print_function\n", + "\n", + "#@markdown This sets the warning status (default is `ignore`, since this notebook runs correctly)\n", + "warning_status = \"ignore\" #@param [\"ignore\", \"always\", \"module\", \"once\", \"default\", \"error\"]\n", + "import warnings\n", + "warnings.filterwarnings(warning_status)\n", + "with warnings.catch_warnings():\n", + " warnings.filterwarnings(warning_status, category=DeprecationWarning)\n", + " warnings.filterwarnings(warning_status, category=UserWarning)\n", + "\n", + "import numpy as np\n", + "import os\n", + "#@markdown This sets the styles of the plotting (default is styled like plots from [FiveThirtyeight.com](https://fivethirtyeight.com/))\n", + "matplotlib_style = 'fivethirtyeight' #@param ['fivethirtyeight', 'bmh', 'ggplot', 'seaborn', 'default', 'Solarize_Light2', 'classic', 'dark_background', 'seaborn-colorblind', 'seaborn-notebook']\n", + "import matplotlib.pyplot as plt; plt.style.use(matplotlib_style)\n", + "import matplotlib.axes as axes;\n", + "from matplotlib.patches import Ellipse\n", + "import scipy.stats as stats\n", + "rand = np.random.rand\n", + "beta = stats.beta\n", + "from mpl_toolkits.mplot3d import Axes3D\n", + "import pandas_datareader.data as web\n", + "%matplotlib inline\n", + "\n", + "import seaborn as sns; sns.set_context('notebook')\n", + "from IPython.core.pylabtools import figsize\n", + "#@markdown This sets the resolution of the plot outputs (`retina` is the highest resolution)\n", + "notebook_screen_res = 'retina' #@param ['retina', 'png', 'jpeg', 'svg', 'pdf']\n", + "%config InlineBackend.figure_format = notebook_screen_res\n", + "\n", + "import tensorflow as tf\n", + "tfe = tf.contrib.eager\n", + "\n", + "# Eager Execution\n", + "#@markdown Check the box below if you want to use [Eager Execution](https://www.tensorflow.org/guide/eager)\n", + "#@markdown Eager execution provides An intuitive interface, Easier debugging, and a control flow comparable to Numpy. You can read more about it on the [Google AI Blog](https://ai.googleblog.com/2017/10/eager-execution-imperative-define-by.html)\n", + "use_tf_eager = False #@param {type:\"boolean\"}\n", + "\n", + "# Use try/except so we can easily re-execute the whole notebook.\n", + "if use_tf_eager:\n", + " try:\n", + " tf.enable_eager_execution()\n", + " except:\n", + " pass\n", + "\n", + "import tensorflow_probability as tfp\n", + "tfd = tfp.distributions\n", + "tfb = tfp.bijectors\n", + "\n", + " \n", + "def evaluate(tensors):\n", + " \"\"\"Evaluates Tensor or EagerTensor to Numpy `ndarray`s.\n", + " Args:\n", + " tensors: Object of `Tensor` or EagerTensor`s; can be `list`, `tuple`,\n", + " `namedtuple` or combinations thereof.\n", + "\n", + " Returns:\n", + " ndarrays: Object with same structure as `tensors` except with `Tensor` or\n", + " `EagerTensor`s replaced by Numpy `ndarray`s.\n", + " \"\"\"\n", + " if tf.executing_eagerly():\n", + " return tf.contrib.framework.nest.pack_sequence_as(\n", + " tensors,\n", + " [t.numpy() if tf.contrib.framework.is_tensor(t) else t\n", + " for t in tf.contrib.framework.nest.flatten(tensors)])\n", + " return sess.run(tensors)\n", + "\n", + "class _TFColor(object):\n", + " \"\"\"Enum of colors used in TF docs.\"\"\"\n", + " red = '#F15854'\n", + " blue = '#5DA5DA'\n", + " orange = '#FAA43A'\n", + " green = '#60BD68'\n", + " pink = '#F17CB0'\n", + " brown = '#B2912F'\n", + " purple = '#B276B2'\n", + " yellow = '#DECF3F'\n", + " gray = '#4D4D4D'\n", + " def __getitem__(self, i):\n", + " return [\n", + " self.red,\n", + " self.orange,\n", + " self.green,\n", + " self.blue,\n", + " self.pink,\n", + " self.brown,\n", + " self.purple,\n", + " self.yellow,\n", + " self.gray,\n", + " ][i % 9]\n", + "TFColor = _TFColor()\n", + "\n", + "def session_options(enable_gpu_ram_resizing=True, enable_xla=True):\n", + " \"\"\"\n", + " Allowing the notebook to make use of GPUs if they're available.\n", + " \n", + " XLA (Accelerated Linear Algebra) is a domain-specific compiler for linear \n", + " algebra that optimizes TensorFlow computations.\n", + " \"\"\"\n", + " config = tf.ConfigProto()\n", + " config.log_device_placement = True\n", + " if enable_gpu_ram_resizing:\n", + " # `allow_growth=True` makes it possible to connect multiple colabs to your\n", + " # GPU. Otherwise the colab malloc's all GPU ram.\n", + " config.gpu_options.allow_growth = True\n", + " if enable_xla:\n", + " # Enable on XLA. https://www.tensorflow.org/performance/xla/.\n", + " config.graph_options.optimizer_options.global_jit_level = (\n", + " tf.OptimizerOptions.ON_1)\n", + " return config\n", + "\n", + "\n", + "def reset_sess(config=None):\n", + " \"\"\"\n", + " Convenience function to create the TF graph & session or reset them.\n", + " \"\"\"\n", + " if config is None:\n", + " config = session_options()\n", + " global sess\n", + " tf.reset_default_graph()\n", + " try:\n", + " sess.close()\n", + " except:\n", + " pass\n", + " sess = tf.InteractiveSession(config=config)\n", + "\n", + "reset_sess()" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + " Building wheel for wget (setup.py) ... \u001b[?25l\u001b[?25hdone\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "text/html": [ + "

\n", + "The default version of TensorFlow in Colab will soon switch to TensorFlow 2.x.
\n", + "We recommend you upgrade now \n", + "or ensure your notebook will continue to use TensorFlow 1.x via the %tensorflow_version 1.x magic:\n", + "more info.

\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + } + }, + { + "output_type": "stream", + "text": [ + "WARNING:tensorflow:\n", + "The TensorFlow contrib module will not be included in TensorFlow 2.0.\n", + "For more information, please see:\n", + " * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n", + " * https://github.com/tensorflow/addons\n", + " * https://github.com/tensorflow/io (for I/O related ops)\n", + "If you depend on functionality not listed there, please file an issue.\n", + "\n", + "Device mapping:\n", + "/job:localhost/replica:0/task:0/device:XLA_CPU:0 -> device: XLA_CPU device\n", + "\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "48d58kVDh3zI" + }, + "source": [ + "## Getting our priorities straight\n", + "\n", + "\n", + "Up until now, we have mostly ignored our choice of priors. This is unfortunate as we can be very expressive with our priors, but we also must be careful about choosing them. This is especially true if we want to be objective, that is, not to express any personal beliefs in the priors. \n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "Cxl9I5wvB4oQ" + }, + "source": [ + "### Subjective vs Objective priors\n", + "\n", + "Bayesian priors can be classified into two classes: *objective* priors, which aim to allow the data to influence the posterior the most, and *subjective* priors, which allow the practitioner to express his or her views into the prior. \n", + "\n", + "What is an example of an objective prior? We have seen some already, including the *flat* prior, which is a uniform distribution over the entire possible range of the unknown. Using a flat prior implies that we give each possible value an equal weighting. Choosing this type of prior is invoking what is called \"The Principle of Indifference\", literally we have no prior reason to favor one value over another. Calling a flat prior over a restricted space an objective prior is not correct, though it seems similar. If we know $p$ in a Binomial model is greater than 0.5, then $\\text{Uniform}(0.5,1)$ is not an objective prior (since we have used prior knowledge) even though it is \"flat\" over [0.5, 1]. The flat prior must be flat along the *entire* range of possibilities. \n", + "\n", + "Aside from the flat prior, other examples of objective priors are less obvious, but they contain important characteristics that reflect objectivity. For now, it should be said that *rarely* is a objective prior *truly* objective. We will see this later." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "X5YKEsldB1Tz" + }, + "source": [ + "#### Subjective Priors\n", + "\n", + "On the other hand, if we added more probability mass to certain areas of the prior, and less elsewhere, we are biasing our inference towards the unknowns existing in the former area. This is known as a subjective, or *informative* prior. In the figure below, the subjective prior reflects a belief that the unknown likely lives around 0.5, and not around the extremes. The objective prior is insensitive to this." + ] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "Ip59tSbbpVG-", + "outputId": "32140ce4-4d73-47d6-cf64-3816ed2b6e9d", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 446 + } + }, + "source": [ + "plt.figure(figsize(12.5, 7))\n", + "\n", + "colors = [TFColor[1], TFColor[2], TFColor[3], TFColor[4]]\n", + "\n", + "x = tf.linspace(start=0., stop=1., num=50)\n", + "obj_prior_1 = tfd.Beta(1., 1.).prob(x)\n", + "subj_prior_1 = tfd.Beta(10., 10.).prob(x)\n", + "subj_prior_2 = 2 * tf.ones(25)\n", + "\n", + "[\n", + " x_, obj_prior_1_, subj_prior_1_, subj_prior_2_,\n", + "] = evaluate([\n", + " x, obj_prior_1, subj_prior_1, subj_prior_2,\n", + "])\n", + "\n", + "p = plt.plot(x_, obj_prior_1_, \n", + " label='An objective prior \\n(uninformative, \\n\"Principle of Indifference\")')\n", + "plt.fill_between(x_, 0, obj_prior_1_, color = p[0].get_color(), alpha = 0.3)\n", + "\n", + "p = plt.plot(x_, subj_prior_1_ ,\n", + " label = \"A subjective prior \\n(informative)\")\n", + "plt.fill_between(x_, 0, subj_prior_1_, color = p[0].get_color(), alpha = 0.3)\n", + "\n", + "p = plt.plot(x_[25:], subj_prior_2_, \n", + " label = \"another subjective prior\")\n", + "plt.fill_between(x_[25:], 0, 2, color = p[0].get_color(), alpha = 0.3)\n", + "\n", + "plt.ylim(0,4)\n", + "\n", + "plt.ylim(0, 4)\n", + "leg = plt.legend(loc = \"upper left\")\n", + "leg.get_frame().set_alpha(0.4)\n", + "plt.title(\"Comparing objective vs. subjective priors for an unknown probability\");" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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2WbcYYU0peEh7RYzwGsDnfvi/o8K6hbYp6XJZzLwbBS26Lk5QduNd5+/3w18r5nqD45Wd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\n3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xxzDL/97W8544wz4o4kSVK9knhzOelFswt16YiT6/yUVao9soNGEZWtzRQ2rSe927pN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\nCGFnCOE/hBD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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [], + "image/png": { + "width": 824, + "height": 429 + } + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "ds1NsX9ah3zO" + }, + "source": [ + "The choice of a subjective prior does not always imply that we are using the practitioner's subjective opinion: more often the subjective prior was once a posterior to a previous problem, and now the practitioner is updating this posterior with new data. A subjective prior can also be used to inject *domain knowledge* of the problem into the model. We will see examples of these two situations later." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "s5rHtoNqh3zP" + }, + "source": [ + "### Decision, decisions...\n", + "\n", + "The choice, either *objective* or *subjective* mostly depends on the problem being solved, but there are a few cases where one is preferred over the other. In instances of scientific research, the choice of an objective prior is obvious. This eliminates any biases in the results, and two researchers who might have differing prior opinions would feel an objective prior is fair. Consider a more extreme situation:\n", + "\n", + "> A tobacco company publishes a report with a Bayesian methodology that retreated 60 years of medical research on tobacco use. Would you believe the results? Unlikely. The researchers probably chose a subjective prior that too strongly biased results in their favor.\n", + "\n", + "Unfortunately, choosing an objective prior is not as simple as selecting a flat prior, and even today the problem is still not completely solved. The problem with naively choosing the uniform prior is that pathological issues can arise. Some of these issues are pedantic, but we delay more serious issues to the Appendix of this Chapter." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "RoP5bOuSh3zP" + }, + "source": [ + "We must remember that choosing a prior, whether subjective or objective, is still part of the modeling process. To quote Gelman [5]:\n", + "\n", + ">... after the model has been fit, one should look at the posterior distribution\n", + "and see if it makes sense. If the posterior distribution does not make sense, this implies\n", + "that additional prior knowledge is available that has not been included in the model,\n", + "and that contradicts the assumptions of the prior distribution that has been used. It is\n", + "then appropriate to go back and alter the prior distribution to be more consistent with\n", + "this external knowledge.\n", + "\n", + "If the posterior does not make sense, then clearly one had an idea what the posterior *should* look like (not what one *hopes* it looks like), implying that the current prior does not contain all the prior information and should be updated. At this point, we can discard the current prior and choose a more reflective one.\n", + "\n", + "Gelman [4] suggests that using a uniform distribution with large bounds is often a good choice for objective priors. Although, one should be wary about using Uniform objective priors with large bounds, as they can assign too large of a prior probability to non-intuitive points. Ask yourself: do you really think the unknown could be incredibly large? Often quantities are naturally biased towards 0. A Normal random variable with large variance (small precision) might be a better choice, or an Exponential with a fat tail in the strictly positive (or negative) case. \n", + "\n", + "If using a particularly subjective prior, it is your responsibility to be able to explain the choice of that prior, else you are no better than the tobacco company's guilty parties. " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "DD_MDbroh3zQ" + }, + "source": [ + "### Empirical Bayes\n", + "\n", + "While not a true Bayesian method, *empirical Bayes* is a trick that combines frequentist and Bayesian inference. As mentioned previously, for (almost) every inference problem there is a Bayesian method and a frequentist method. The significant difference between the two is that Bayesian methods have a prior distribution, with hyperparameters $\\alpha$, while empirical methods do not have any notion of a prior. Empirical Bayes combines the two methods by using frequentist methods to select $\\alpha$, and then proceeds with Bayesian methods on the original problem. \n", + "\n", + "A very simple example follows: suppose we wish to estimate the parameter $\\mu$ of a Normal distribution, with $\\sigma = 5$. Since $\\mu$ could range over the whole real line, we can use a Normal distribution as a prior for $\\mu$. How to select the prior's hyperparameters, denoted ($\\mu_p, \\sigma_p^2$)? The $\\sigma_p^2$ parameter can be chosen to reflect the uncertainty we have. For $\\mu_p$, we have two options:\n", + "\n", + "**Option 1**: Empirical Bayes suggests using the empirical sample mean, which will center the prior around the observed empirical mean:\n", + "\n", + "$$ \\mu_p = \\frac{1}{N} \\sum_{i=0}^N X_i $$\n", + "\n", + "**Option 2**: Traditional Bayesian inference suggests using prior knowledge, or a more objective prior (zero mean and fat standard deviation).\n", + "\n", + "Empirical Bayes can be argued as being semi-objective, since while the choice of prior model is ours (hence subjective), the parameters are solely determined by the data.\n", + "\n", + "Personally, I feel that Empirical Bayes is *double-counting* the data. That is, we are using the data twice: once in the prior, which will influence our results towards the observed data, and again in the inferential engine of MCMC. This double-counting will understate our true uncertainty. To minimize this double-counting, I would only suggest using Empirical Bayes when you have *lots* of observations, else the prior will have too strong of an influence. I would also recommend, if possible, to maintain high uncertainty (either by setting a large $\\sigma_p^2$ or equivalent.)\n", + "\n", + "Empirical Bayes also violates a theoretical axiom in Bayesian inference. The textbook Bayesian algorithm of:\n", + "\n", + ">*prior* $\\Rightarrow$ *observed data* $\\Rightarrow$ *posterior* \n", + "\n", + "is violated by Empirical Bayes, which instead uses \n", + "\n", + ">*observed data* $\\Rightarrow$ *prior* $\\Rightarrow$ *observed data* $\\Rightarrow$ *posterior*\n", + "\n", + "Ideally, all priors should be specified *before* we observe the data, so that the data does not influence our prior opinions (see the volumes of research by Daniel Kahneman *et. al* about [anchoring](http://en.wikipedia.org/wiki/Anchoring_and_adjustment))." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "zby_E57oBuH5" + }, + "source": [ + "## Useful priors to know about" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "8pQLkxcih3zQ" + }, + "source": [ + "### The Gamma distribution\n", + "\n", + "A Gamma random variable, denoted $X \\sim \\text{Gamma}(\\alpha, \\beta)$, is a random variable over the positive real numbers. It is in fact a generalization of the Exponential random variable, that is:\n", + "\n", + "$$ \\text{Exp}(\\beta) \\sim \\text{Gamma}(1, \\beta) $$\n", + "\n", + "This additional parameter allows the probability density function to have more flexibility, hence allowing the practitioner to express his or her subjective priors more accurately. The density function for a $\\text{Gamma}(\\alpha, \\beta)$ random variable is:\n", + "\n", + "$$ f(x \\mid \\alpha, \\beta) = \\frac{\\beta^{\\alpha}x^{\\alpha-1}e^{-\\beta x}}{\\Gamma(\\alpha)} $$\n", + "\n", + "where $\\Gamma(\\alpha)$ is the [Gamma function](http://en.wikipedia.org/wiki/Gamma_function), and for differing values of $(\\alpha, \\beta)$ looks like:" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "rUXIzxfFh3zR", + "outputId": "0a6cd386-6024-4f0f-d7f4-3f0defbf2820", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 431 + } + }, + "source": [ + "parameters = [(1, 0.5), (9, 2), (3, 0.5), (7, 0.5)]\n", + "x = tf.cast(tf.linspace(start=0.001 ,stop=20., num=150), dtype=tf.float32)\n", + "\n", + "plt.figure(figsize(12.5, 7))\n", + "for alpha, beta in parameters:\n", + " [ \n", + " y_, \n", + " x_ \n", + " ] = evaluate([\n", + " tfd.Gamma(float(alpha), float(beta)).prob(x), \n", + " x,\n", + " ])\n", + " lines = plt.plot(x_, y_, label = \"(%.1f,%.1f)\"%(alpha, beta), lw = 3)\n", + " plt.fill_between(x_, 0, y_, alpha = 0.2, color = lines[0].get_color())\n", + " plt.autoscale(tight=True)\n", + "\n", + "plt.legend(title=r\"$\\alpha, \\beta$ - parameters\");" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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e1LlmXlqvR59pC+RHQElMIQOnZ+Er74IxP2s/VJCD1YgJI8eMql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\nkbv54sjdzbJMXOeO8gT3LIxzZuE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4WwAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [], + "image/png": { + "width": 836, + "height": 414 + } + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "8Af9T7Oph3zT" + }, + "source": [ + "### The Wishart distribution\n", + "\n", + "Until now, we have only seen random variables that are scalars. Of course, we can also have *random matrices*! Specifically, the Wishart distribution is a distribution over all [positive semi-definite matrices](http://en.wikipedia.org/wiki/Positive-definite_matrix). Why is this useful to have in our arsenal? (Proper) covariance matrices are positive-definite, hence the Wishart is an appropriate prior for covariance matrices. We can't really visualize a distribution of matrices, so I'll plot some realizations from the $5 \\times 5$ (above) and $20 \\times 20$ (below) Wishart distribution:" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "3y8PyXdfJ5F7", + "outputId": "e1abff0b-1d5d-4fe3-f670-679d436e8e91", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 605 + } + }, + "source": [ + "reset_sess()\n", + "\n", + "n = 4\n", + "print(\"output of the eye function \\n(a commonly used function with Wishart Distributions): \\n\", np.eye(n))\n", + "\n", + "plt.figure(figsize(12.5, 7))\n", + "for i in range(10):\n", + " ax = plt.subplot(2, 5, i+1)\n", + " if i >= 5:\n", + " n = 15\n", + " [\n", + " wishart_matrices_ \n", + " ] = evaluate([ \n", + " tfd.Wishart(df=(n+1), scale=tf.eye(n)).sample() \n", + " ])\n", + " plt.imshow( wishart_matrices_, \n", + " interpolation=\"none\", \n", + " cmap = \"hot\")\n", + " ax.axis(\"off\")\n", + "\n", + "plt.suptitle(\"Random matrices from a Wishart Distribution\");" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Device mapping:\n", + "/job:localhost/replica:0/task:0/device:XLA_CPU:0 -> device: XLA_CPU device\n", + "\n", + "output of the eye function \n", + "(a commonly used function with Wishart Distributions): \n", + " [[1. 0. 0. 0.]\n", + " [0. 1. 0. 0.]\n", + " [0. 0. 1. 0.]\n", + " [0. 0. 0. 1.]]\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [], + "image/png": { + "width": 797, + "height": 426 + } + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "fdT3MMLOh3zW" + }, + "source": [ + "One thing to notice is the symmetry of these matrices. The Wishart distribution can be a little troubling to deal with, but we will use it in an example later." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "t_shyPsXh3zX" + }, + "source": [ + "### The Beta distribution\n", + "\n", + "You may have seen the term `beta` in previous code in this book. Often, I was implementing a Beta distribution. The Beta distribution is very useful in Bayesian statistics. A random variable $X$ has a $\\text{Beta}$ distribution, with parameters $(\\alpha, \\beta)$, if its density function is:\n", + "\n", + "$$f_X(x | \\; \\alpha, \\beta ) = \\frac{ x^{(\\alpha - 1)}(1-x)^{ (\\beta - 1) } }{B(\\alpha, \\beta) }$$\n", + "\n", + "where $B$ is the [Beta function](http://en.wikipedia.org/wiki/Beta_function) (hence the name). The random variable $X$ is only allowed in [0,1], making the Beta distribution a popular distribution for decimal values, probabilities and proportions. The values of $\\alpha$ and $\\beta$, both positive values, provide great flexibility in the shape of the distribution. Below we plot some distributions:" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "0VSxcnhuOtrK", + "outputId": "e46db278-8014-4581-8542-652b33dbfa70", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 485 + } + }, + "source": [ + "reset_sess()\n", + "\n", + "params = [(2, 5), (1, 1), (0.5, 0.5), (5, 5), (20, 4), (5, 1)]\n", + "x = tf.cast(tf.linspace(start=0.01 ,stop=.99, num=100), dtype=tf.float32)\n", + "\n", + "plt.figure(figsize(12.5, 7))\n", + "for alpha, beta in params:\n", + " [ \n", + " y_, \n", + " x_ \n", + " ] = evaluate([\n", + " tfd.Beta(float(alpha), float(beta)).prob(x), \n", + " x,\n", + " ])\n", + " lines = plt.plot(x_, y_, label = \"(%.1f,%.1f)\"%(alpha, beta), lw = 3)\n", + " plt.fill_between(x_, 0, y_, alpha = 0.2, color = lines[0].get_color())\n", + " plt.autoscale(tight=True)\n", + "plt.ylim(0)\n", + "plt.legend(title=r\"$\\alpha, \\beta$ - parameters\");" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Device mapping:\n", + "/job:localhost/replica:0/task:0/device:XLA_CPU:0 -> device: XLA_CPU device\n", + "\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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G9vr+F6aWkpSkpKLI6G6N4Gft6Hq//tRdP9lre0of1XOi2MqLA4NafhzJBEMoFYIr4OERUm85kt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V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\nykPcB6TX47apjwQw29Rnfi6Os+11t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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [], + "image/png": { + "width": 813, + "height": 414 + } + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "4LaufPdOh3za" + }, + "source": [ + "One thing I'd like the reader to notice is the presence of the flat distribution above, specified by parameters $(1,1)$. This is the Uniform distribution. Hence the Beta distribution is a generalization of the Uniform distribution, something we will revisit many times.\n", + "\n", + "There is an interesting connection between the Beta distribution and the Binomial distribution. Suppose we are interested in some unknown proportion or probability $p$. We assign a $\\text{Beta}(\\alpha, \\beta)$ prior to $p$. We observe some data generated by a Binomial process, say $X \\sim \\text{Binomial}(N, p)$, with $p$ still unknown. Then our posterior *is again a Beta distribution*, i.e. $p | X \\sim \\text{Beta}( \\alpha + X, \\beta + N -X )$. Succinctly, one can relate the two by \"a Beta prior with Binomial observations creates a Beta posterior\". This is a very useful property, both computationally and heuristically.\n", + "\n", + "In light of the above two paragraphs, if we start with a $\\text{Beta}(1,1)$ prior on $p$ (which is a Uniform), observe data $X \\sim \\text{Binomial}(N, p)$, then our posterior is $\\text{Beta}(1 + X, 1 + N - X)$. \n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "Z1Jkp2odh3za" + }, + "source": [ + "## Example: Bayesian Multi-Armed Bandits\n", + "*Adapted from an example by Ted Dunning of MapR Technologies*\n", + "\n", + "> Suppose you are faced with $N$ slot machines (colourfully called multi-armed bandits). Each bandit has an unknown probability of distributing a prize (assume for now the prizes are the same for each bandit, only the probabilities differ). Some bandits are very generous, others not so much. Of course, you don't know what these probabilities are. By only choosing one bandit per round, our task is devise a strategy to maximize our winnings.\n", + "\n", + "Of course, if we knew the bandit with the largest probability, then always picking this bandit would yield the maximum winnings. So our task can be phrased as \"Find the best bandit, and as quickly as possible\". \n", + "\n", + "The task is complicated by the stochastic nature of the bandits. A suboptimal bandit can return many winnings, purely by chance, which would make us believe that it is a very profitable bandit. Similarly, the best bandit can return many duds. Should we keep trying losers then, or give up? \n", + "\n", + "A more troublesome problem is, if we have found a bandit that returns *pretty good* results, do we keep drawing from it to maintain our *pretty good score*, or do we try other bandits in hopes of finding an *even-better* bandit? This is the exploration vs. exploitation dilemma.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "lg1gD8ILBjo-" + }, + "source": [ + "### Applications\n", + "\n", + "\n", + "The Multi-Armed Bandit problem at first seems very artificial, something only a mathematician would love, but that is only before we address some applications:\n", + "\n", + "- Internet display advertising: companies have a suite of potential ads they can display to visitors, but the company is not sure which ad strategy to follow to maximize sales. This is similar to A/B testing, but has the added advantage of naturally minimizing strategies that do not work (and generalizes to A/B/C/D... strategies)\n", + "- Ecology: animals have a finite amount of energy to expend, and following certain behaviours has uncertain rewards. How does the animal maximize its fitness?\n", + "- Finance: which stock option gives the highest return, under time-varying return profiles.\n", + "- Clinical trials: a researcher would like to find the best treatment, out of many possible treatment, while minimizing losses. \n", + "- Psychology: how does punishment and reward affect our behaviour? How do humans learn?\n", + "\n", + "Many of these questions above are fundamental to the application's field.\n", + "\n", + "It turns out the *optimal solution* is incredibly difficult, and it took decades for an overall solution to develop. There are also many approximately-optimal solutions which are quite good. The one I wish to discuss is one of the few solutions that can scale incredibly well. The solution is known as *Bayesian Bandits*.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "TmgKDLEsBl51" + }, + "source": [ + "### A Proposed Solution\n", + "\n", + "\n", + "Any proposed strategy is called an *online algorithm* (not in the internet sense, but in the continuously-being-updated sense), and more specifically a reinforcement learning algorithm. The algorithm starts in an ignorant state, where it knows nothing, and begins to acquire data by testing the system. As it acquires data and results, it learns what the best and worst behaviours are (in this case, it learns which bandit is the best). With this in mind, perhaps we can add an additional application of the Multi-Armed Bandit problem:\n", + "\n", + "- Psychology: how does punishment and reward affect our behaviour? How do humans learn?\n", + "\n", + "\n", + "The Bayesian solution begins by assuming priors on the probability of winning for each bandit. In our vignette we assumed complete ignorance of these probabilities. So a very natural prior is the flat prior over 0 to 1. The algorithm proceeds as follows:\n", + "\n", + "For each round:\n", + "\n", + "1. Sample a random variable $X_b$ from the prior of bandit $b$, for all $b$.\n", + "2. Select the bandit with largest sample, i.e. select $B = \\text{argmax}\\;\\; X_b$.\n", + "3. Observe the result of pulling bandit $B$, and update your prior on bandit $B$.\n", + "4. Return to 1.\n", + "\n", + "That's it. Computationally, the algorithm involves sampling from $N$ distributions. Since the initial priors are $\\text{Beta}(\\alpha=1,\\beta=1)$ (a uniform distribution), and the observed result $X$ (a win or loss, encoded 1 and 0 respectfully) is Binomial, the posterior is a $\\text{Beta}(\\alpha=1+X,\\beta=1+1-X)$.\n", + "\n", + "To answer our question from before, this algorithm suggests that we should not discard losers, but we should pick them at a decreasing rate as we gather confidence that there exist *better* bandits. This follows because there is always a non-zero chance that a loser will achieve the status of $B$, but the probability of this event decreases as we play more rounds (see figure below).\n", + "\n", + "Below we implement Bayesian Bandits using two classes, `Bandits` that defines the slot machines, and `BayesianStrategy` which implements the above learning strategy." + ] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "ytA8EtbBh3zc", + "outputId": "bd6aad2d-1f54-4b86-a84f-f1a88449e1cf", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 71 + } + }, + "source": [ + "reset_sess()\n", + "\n", + "class Bandits(object):\n", + " \"\"\"\n", + " This class represents N bandits machines.\n", + "\n", + " parameters:\n", + " arm_true_payout_probs: a (n,) Numpy array of probabilities >0, <1.\n", + "\n", + " methods:\n", + " pull( i ): return the results, 0 or 1, of pulling \n", + " the ith bandit.\n", + " \"\"\"\n", + " def __init__(self, arm_true_payout_probs):\n", + " self._arm_true_payout_probs = tf.convert_to_tensor(\n", + " arm_true_payout_probs,\n", + " preferred_dtype=tf.float32,\n", + " name='arm_true_payout_probs')\n", + " self._uniform = tfd.Uniform(low=0., high=1.)\n", + " assert self._arm_true_payout_probs.shape.is_fully_defined()\n", + " self._shape = np.array(\n", + " self._arm_true_payout_probs.shape.as_list(),\n", + " dtype=np.int32)\n", + " self._dtype = tf.convert_to_tensor(\n", + " arm_true_payout_probs,\n", + " preferred_dtype=tf.float32).dtype.base_dtype\n", + "\n", + " @property\n", + " def dtype(self):\n", + " return self._dtype\n", + " \n", + " @property\n", + " def shape(self):\n", + " return self._shape\n", + "\n", + " def pull(self, arm):\n", + " return (self._uniform.sample(self.shape[:-1]) <\n", + " self._arm_true_payout_probs[..., arm])\n", + " \n", + " def optimal_arm(self):\n", + " return tf.argmax(\n", + " self._arm_true_payout_probs,\n", + " axis=-1,\n", + " name='optimal_arm')\n" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Device mapping:\n", + "/job:localhost/replica:0/task:0/device:XLA_CPU:0 -> device: XLA_CPU device\n", + "\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "8rWqI4Bc7b2A", + "colab": {} + }, + "source": [ + "class BayesianStrategy(object):\n", + " \"\"\"\n", + " Implements a online, learning strategy to solve\n", + " the Multi-Armed Bandit problem.\n", + " \n", + " parameters:\n", + " bandits: a Bandit class with .pull method\n", + " \n", + " methods:\n", + " sample_bandits(n): sample and train on n pulls.\n", + " \"\"\"\n", + " \n", + " def __init__(self, bandits):\n", + " self.bandits = bandits\n", + " dtype = bandits._dtype\n", + " self.wins_var = tf.Variable(\n", + " initial_value=tf.zeros(self.bandits.shape, dtype))\n", + " self.trials_var = tf.Variable(\n", + " initial_value=tf.zeros(self.bandits.shape, dtype))\n", + " \n", + " def sample_bandits(self, n=1):\n", + " return tf.while_loop(\n", + " cond=lambda *args: True,\n", + " body=self._one_trial,\n", + " loop_vars=(tf.identity(self.wins_var),\n", + " tf.identity(self.trials_var)),\n", + " maximum_iterations=n,\n", + " parallel_iterations=1)\n", + " \n", + " def make_posterior(self, wins, trials):\n", + " return tfd.Beta(concentration1=1. + wins,\n", + " concentration0=1. + trials - wins)\n", + " \n", + " def _one_trial(self, wins, trials):\n", + " # sample from the bandits's priors, and select the largest sample\n", + " rv_posterior_payout = self.make_posterior(wins, trials)\n", + " posterior_payout = rv_posterior_payout.sample()\n", + " choice = tf.argmax(posterior_payout, axis=-1)\n", + "\n", + " # Update trials.\n", + " one_hot_choice = tf.reshape(\n", + " tf.one_hot(\n", + " indices=tf.reshape(choice, shape=[-1]),\n", + " depth=self.bandits.shape[-1],\n", + " dtype=self.trials_var.dtype.base_dtype),\n", + " shape=tf.shape(wins))\n", + " trials = tf.assign_add(self.trials_var, one_hot_choice)\n", + "\n", + " # Update wins.\n", + " result = self.bandits.pull(choice)\n", + " update = tf.where(result, one_hot_choice, tf.zeros_like(one_hot_choice))\n", + " wins = tf.assign_add(self.wins_var, update)\n", + "\n", + " return wins, trials" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "5pwDKmkah3zd" + }, + "source": [ + "Below we visualize the learning of the Bayesian Bandit solution." + ] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "mNlSH9J-PgzB", + "outputId": "4cf45fcf-6ccc-48d3-a10c-25c66304c8bb", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 996 + } + }, + "source": [ + "reset_sess()\n", + "\n", + "hidden_prob_ = np.array([0.85, 0.60, 0.75])\n", + "bandits = Bandits(hidden_prob_)\n", + "bayesian_strat = BayesianStrategy(bandits)\n", + "\n", + "\n", + "draw_samples_ = np.array([1, 1, 3, 10, 10, 25, 50, 100, 200, 600])\n", + "\n", + "def plot_priors(bayesian_strategy, prob, wins, trials, \n", + " lw = 3, alpha = 0.2, plt_vlines = True):\n", + " ## plotting function\n", + " for i in range(prob.shape[0]):\n", + " posterior_dists = tf.cast(tf.linspace(start=0.001 ,stop=.999, num=200), dtype=tf.float32)\n", + " y = tfd.Beta(concentration1 = tf.cast((1+wins[i]), dtype=tf.float32) , \n", + " concentration0 = tf.cast((1 + trials[i] - wins[i]), dtype=tf.float32))\n", + " y_prob_i = y.prob(tf.cast(prob[i], dtype=tf.float32))\n", + " y_probs = y.prob(tf.cast(posterior_dists, dtype=tf.float32))\n", + " [ \n", + " posterior_dists_,\n", + " y_probs_,\n", + " y_prob_i_,\n", + " ] = evaluate([\n", + " posterior_dists, \n", + " y_probs,\n", + " y_prob_i,\n", + " ])\n", + " \n", + " p = plt.plot(posterior_dists_, y_probs_, lw = lw)\n", + " c = p[0].get_markeredgecolor()\n", + " plt.fill_between(posterior_dists_, y_probs_,0, color = c, alpha = alpha, \n", + " label=\"underlying probability: %.2f\" % prob[i])\n", + " if plt_vlines:\n", + " plt.vlines(prob[i], 0, y_prob_i_ ,\n", + " colors = c, linestyles = \"--\", lw = 2)\n", + " plt.autoscale(tight = \"True\")\n", + " plt.title(\"Posteriors After %d pull\" % N_pulls +\\\n", + " \"s\"*(N_pulls > 1))\n", + " plt.autoscale(tight=True)\n", + " return\n", + "\n", + "plt.figure(figsize(11.0, 12))\n", + "for j,i in enumerate(draw_samples_):\n", + " plt.subplot(5, 2, j+1) \n", + " evaluate(tf.global_variables_initializer())\n", + " [wins_, trials_] = evaluate(bayesian_strat.sample_bandits(i))\n", + " N_pulls = int(draw_samples_.cumsum()[j])\n", + " plot_priors(bayesian_strat, hidden_prob_, wins=wins_, trials=trials_)\n", + " #plt.legend()\n", + " plt.autoscale(tight = True)\n", + "plt.tight_layout()" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Device mapping:\n", + "/job:localhost/replica:0/task:0/device:XLA_CPU:0 -> device: XLA_CPU device\n", + "\n", + "WARNING:tensorflow:From :51: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "Use tf.where in 2.0, which has the same broadcast rule as np.where\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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TxTb/29nmP7tZ0eYPIawjKzCsAZ4C/lLrnGqnXwlnX5H8V1r3L01c+yTGeBrY23p4tjb/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\ndtsVxmxZNPadONHehiWtlfSmDzGBlWGtht+cUKowpvSRcYV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YroOEblOkgREREREelzzlz1+U5d841fmCYlGXLMlY7n6h99juieHcRqthA5VIfh0n6/+T6x6s3E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BvuN5leaPx4r8SF2R14LlF6p4TLs9uxpOLy4tAnAmHMS/Mod/ZY7AsrP0ri1XverBVcgRm7hB\nbOLG1rpSMMpvezv5KNzDR+FuPgqfBRqqul9xfOTdWRy6fLnE9ZX7FNevMGje5FVVPtAzr6wNbpfO\nkTbP0xFqo1vOTBRCCCFeSOVgiMSlyyQuXcaTXCM6Pkp0bBTfWnxf23Ft5Gm48h4NV96jGI6SGLpM\nfHiEXGu7TEEixIson8M1PYU59dAJFaYnMbLrh75b7fM5YUJjcyVUaHIaMT/pRKj99b0X4lApZaBc\nNRiuGqBva73WFrqc3BE6VCofDrvZ9Ob+7XWswvpjTaed6oc6DHdDpQJie6nMmPR+eMFpDVczBr+1\n6uJryy6mNg7+/93nK/OjdUV+oKZI5DRVLVRBORAm0zlApnNga51RLOBffbQjfJjFF1/AsKobyruz\nKX4ge5MfSNzcWlcYrSfTcZ5M5wDprgusn+lDu2UqtdNIQgZxKMq2zfXVCdLpq/Sra1w08gd+to2W\nulhRg7SGOmiLeGmr7lCFEEIIcYIVYzWsjHyWlctv4FtdJjo2Smx8FPd6Zl/b8WRSNL//TZrf/yb5\nukYSwyPEhy9TqG969g8LIU4e28ZYnMecfIhr6iHm1MMjacxsRyJYTc3b1QlNTehIVIJL8UJRykS5\n6zDcdeDv31qvdRldXtuackmXE5XlGodf+QBO9cMqVnn1seoHlBvDVf+x8KER5W5AGUFnKilx6pRs\n+KOkyW+vmry56mL+gFMhAcRMm/+opsifrC0y4LfkbXsH2+Ml23qWbOvZ7ZWWhS+xSGBlFn+l4iGw\nModZ3Kjqvr2pVby3/oj6W3/kjMV0kW3rId15gUzneTKdFyjUNMrn7CkgIYOoGtvWjK7Nspi8yll9\nlX4zdaDmzQCTpWamGaIx2EVDOCjFU0IIIcTLTik2GprYaGhi6Y0vEpifJTY2SuTBPVyF/f2x448v\n0/b2m7S9/SbZtg7iwyMkLr5OKRI7pMELIZ6XyqQxpyYxpyqhwvQkqlA41H3awRB2SwtWSwtWcyt2\nSzM6GDrUfQpxkinlQrkbMNy7/0J3pl1KYpcT6FLcWVYCCHTxaAanS5/Q+wEwAtvBg2tnFUQDyvAe\nzfjEnqXL8PaayW+vuvhG3EWyfPCDyx6l+Xy0xJ+oKfJGpIRLjlPvnWmxOx7bAAAgAElEQVSy0dDG\nRkMbXKis0zaeVJzAyhz+5VkCS9MElmZwFfJV261hlbcbS/+hs64QqSWzI3RYb+/Ddstr96SRkEE8\nt4nUMpOJa7SVv0una4XOAwbLC+Vaxu0hwoEe2oJhBg159xdCCCHEEyhFrq2DXFsHC5//MqGZSaJj\no0QmxzHKpX1tKvhohuCjGdp/72tkus8RH7rM2uCrWP7AIQ1eCPFMto2xuIDr4TjmwwnMyQnM1ZXD\n3aU/sB0otLRiN7egw+FD3acQLwpn2qVaDFct+Hq31mut0XZ2u+JhRwCh7cOfymyLncMuTGMXph8f\nuxnBcNWh3PWVSghnqdz1UgFxRLSGu1mDtxImv58w+XbKpKyf79/91WCJP1Fb5PuiJcIv2XRIh0oZ\nFGMNFGMNJDebTGuNN7myFTgElmbwL89ilqsXMHrTid3VDoZJtq2XTOd5p+Kha4BCTZNUOxwzCRnE\ngcxnk9xbuU5N6SN6XbM0woGeTQkrxKg1hMfbS2e0lgEJFoQQQgixD9o0yXT3kunuRZWKRCYfEB27\nQ3hmEmXvfeoGpTWRh/eJPLxP52//Oqm+QeLDI6T6h7A9Mi+sEIeqVMKcmcI18QDz4TiuhxOofO7Q\ndqd9PqxmJ1CwK0uZ8kiI6lNKocwQmCFMb8eu+7RdqAQOOyofSnG0leQo+j5sjcNKY1lpKEw+fqfy\nOaHDjuBhM4hQZlR6QDyH9TK8mzT5/biLtxImcwds3LzTxUCZL8eKfClWpMkjwcKRUYpCTSOFmkbW\nzo846+zKVEtLMwQWpwkuTeNbncewrars0rAtwrP3Cc/ep/WPvg441Q7p7ovO5ewQ2ZYuMA44vYo4\nEAkZxJ7FN7LcWrmFb+MKA+Y4I0of6BmUtb3cKg9ie87RFWmgX170QgghhKgC7faQOneB1LkLmPk8\nkYl7xMZGCc7P7ms7hmVRc+8mNfduYnm8rA1cIn5phPTZ809u5CqE2BeVXcd8OOFUKkw8wJydRpWr\n21xykzYMJ0hobcNqbcVqbUXHaiRQEOKYKcOL6WkBT8uu9U7fh8rUS+U17PIa2lrDLifArt6ULHui\nN7CLc9jFucfvUy6nAmKzEbWrHsNdVwkj6lBKDrftZGsYzRq8vWbyVtzkvZRJ6TmrFQAG/GW+XFPk\nS9ESrd6j6Asi9sQw2ahvY6O+jcTgZwBQ5RL+1Uf88pUVXs9Mcjn9kAu5RxhVChW96QQNN75Fw41v\nAVD2BUh3XdgKHjId56Wh9CGTdz3xVOulAteX76JzV7hg3uF1ZR3oWVPUJrdKA+Rc/XSFWugx5akn\nhBBCiMNj+f2sXXyVtYuv4s6kiI7fIzp2B//q8r62YxYL1N/4kPobH1IKhkhcfJ3E8GXW28/KQUoh\n9kJrVHwV18Q4rocPMB8+wFx8wpzpVWJHo5VAoQ2rrQ27qRlc8reHEKeF0/fBqRb4OG1vOKHDrvBh\nDV1OgN7fdInPTZexS0tQWsJ6LPtQKFcNhquuMhVTHYarthJK1KLM8AtfBaE1jOcU30qafCvp4ltr\nJonn6K2wSaEZClp8MVrkj0VLnJFg4dTQLje55i5+pe3VrXWhcp7vNt0guDBFcGGS4MIkro1sVfbn\n2shRe+8jau99BIBtullv7yPVPUT67EXSXRewAjItYjXJty3xmKJlcW1ljFzmCgPGTS4ZhQM9U2yt\nuFPqIWEM0B5qpyMiiaEQQlRLvqGJcuWsT5ccPDlxSkbjcQ/hRBrwHfEZiBWlcJTV1z7F6mufwptY\nJTo2SmxsFE86ua/tuLPrNH3wLk0fvEshVkd8+DKJ4RHyTa2HNHIhTiGtMRbncY2PYT64j2viAUY6\ndTi7cruxWlqdMKESLOiQNGYW4kWlDN8nVD9osLM7AojEjgAiCRz1gWiNLiewygksxh+/W7kwXLWV\nPhZ1W0vDXYty1e25F4Td0kGx6Mx77zkBUztO5SuhwprJt5Imi8XqBCkupRkJlflitMjnoyXq3TIV\n0ml23r+zctHNens/6+39zs3N/g6L26GDf/URSj///7lhlYhMjRKZGoW3fwOtFLnmLtLdg1vBQzHW\n8OwNiU+kdBX+owSkUql3gC+Qy2IsPaGU7oSzbJtb8SniqWv0cZUa8+BNmMZK7cyrQZqDndR5/FUc\n5em3mlgFoL728bMyhNgkzxOxV/JcEXslz5Un0Br/0gLRsTtEH9zDnTv4WVO5plbiwyMkhi5TrKmr\n4iCP1pn6NsKBKMC70Wj0i8c8nFNn8+8BXcpi5+ePezhHZ7NJ8/h9XA/GMB+MYaxnDmVXVl2dEya0\ntWG1nsFuaADjdJ0NvLi0CEBzU/Mxj0ScZPI8qR6tbbSVrjSfroQQllP9oK3Dea96bsqzHUJUqiCU\na7saAsO/FULMzDpTQna0tx/pEG0N93MGH6QMvpNypj+a3qje+3HMtPlMpMTnIiXeiJQIyWyVz+20\nvq8YxQKBpWmCC5Nb4YM7fziN4zdqmkj1DJPquUSq9xKF2qZD2c9J1hl2UeM14AB/D8ipjy8x29aM\npReYS1yj0/6IHjNBzwHfuGfLDTzUQ9QGumkOh6mp7lCFEEIIIapLKfLNreSbW1n83JcIzk0TGxsl\n8nAMs1jY16YCS/ME3vo67W99nUzHWRLDIyQGX6MckhJs8QKy7Uqlwn2nWmFiDGO9+n/sa9N0eiic\nacc604HV1gZ+OYFJCLE/ShkoVwxcMT5+uEPrciWAWMMup9BWErucRFtJdDkFVKdJ7b7pInZpEUqL\nT5iKiUpDaid08FtubBWhlI1jmDGUq+ZQpmPKWnAlbfKdlMEHaZPvpk2SVZj+aJNCcyFg8Ua4xGcj\nJc4HLEyZlVIAtsfLevs51tvPOSu0xpNadSodKqGDf+URSj9/xZJvbQnfR2/R9NFbAGzUNpPquUSy\n9xKpnmGKNVKt/jQSMryEpjNxHsSv0Vj6iC7XAm0KHvu03YNlK8p9a4iAv5f2YJQLhnwCCCGEEOIU\nMgyyHd1kO7qZL38/4ekJomOjhKceYFj7O8AQnnlIeOYhHb/zVVI950kMj7A2cAnb6zukwQtxyGwb\nY2EzVLiPOTGOkT2EUMHnp9zeXgkVzmA3t0gvBSHEoVLKhXLVgqv2CQGEjbbWK4FDEntzWQkh0MVj\nGbMzuA3s4iPs4iN8ABo2lt/Z8QAT5YphuGqc3hBmZbl52xVDGZ/8vURrmNxQXE2bfJA2+DBlcnPd\nwKK6x3wips1nwmXeiJT4dLhErUyDJPZCKYqxBoqxBtYGvgcAo7jhhA7zDwk9miC4OIlRfv4+Lb7E\nIr7EIk3f/QYA+bpWp9Kh16l0KEalSnwn+db2kljOZ7i9fINw8QrnXQ/5HjjQ/37a9nO7PITh6aMr\nUsf5U1aeLIQQQgjxNNrlIt3TT7qnH6OwQeThGLGxUYJz0/uaD1bZNrHxUWLjo9guN8nzQ8SHR0j1\nXUC73If4GwjxnLTGWHiEa+yeEyo8GMd4junEPokdq8FqdwIF60wHdl2dNFMXQpwYTgVEBFwR8Hbs\nus/pAZHfCh62KiAqYQR27phGvclCl+NY5fgnP8TwO6GDWUNW1fCoXMu9Qh1XcvV8K1PPWKEG6yBn\noz6FS2kuBsq8HirzmUiJi1KtIKrE9vjIdA6Q6RwAQFll/MtzhOYfEHz0kND8RFUaSvvj8/jj8zR/\n+HsA5OvbSPUOk+x5hVTvMKXI6Z02tRokZHiBpYsbXF++jZm/wqB5lxGlD/Q/vmG7uVm+QNHdT3eo\niT5TJsMTQgghxIvP9vpIDgyTHBjGlV0n8uAesbFRAkv7m2/fKJeovX2V2ttXKfv8rA2+Rnz4Mpmu\nvlM3n7x4MalEHNf9u85l7B5GJl3V7WulsJuanSqFSrCgZToxIcQppZQCM4BpBsDT+tj92i5WgocU\n2nIuzvU02kqBfv4zrJ+bnccu5oF5fEAP0KPgTwaBIFhasWTFeFSuY9GKsWjVsGDFWCzXbN1eLNdQ\n4JNPnDDQDAQsXg+VGQmVuBQs45fDSeIIaNNFrqWLXEsXvA5oG19iieD8hFPpMP8Qb/opIdwe+Vcf\n4V99RPN3fheAXMOZSpXDKyT7XqUcjDz3Pk4TCRleMPlyiWsr9yitX2XQvMmrqnyg/+WyNrhdOkfa\nPE9HqI1uOeNOCCGEEC+xcjBE4tJlEpcu40mtER0bJTo2im9tf3+guDbyNFx5j4Yr71EMR0kMXSY+\nPEKutV3O4hZHRmXXMcfu4xpzggVzZbmq29eGgd3SSrmzE6u9A+tMO3g8Vd2HEEKcVMrwoIxGDPfj\n87dvV0FUQodKELF9Ow2Uj37QH2MqTatrjVbX2lMft2YFWbBqWLBqWCzHKKkoQXeEJl+IjkAInyuK\nrQLyHUccL2WwUdfCRl0L8aHPAeDOrFUChwlC8xP4VhdQPN+UXYGVOQIrc7R8+020UmTbekj2vcba\nudfIdA9iu73V+G1OLAkZXgBl2+b66gTp9FXOq2sMGfkD/8+OlrpZVhc4E+qgLeKlrbpDFUIIUSU9\nv/Ev6Sw7f4C4XC4m/uxfOuYRiZ1i2X+163Yy+OePaSQny5970Lvr9r/ufXBMI3k+xWgNKyOfZeXy\nG/hWl4mOOdMiudcz+9qOJ5Oi+f1v0vz+N8nXNZIYHiE+fJlCfdMhjVy8tIoFXA8ncN0fxbx/D3Nu\nZl/Tfz2LNgys1jasjg6sjk6stjMSKgghxBPsqoKg5bH7nRAi54QOWwHEdhihrTTw/A1uq6XGzFJj\nZrnA3O479P/P3p3HSVaV9x//PFXV+zY7s+8DwwwwwzKAqIAL7nGJazQkGIMGNa4xEpefxp9RMSRx\n55doDGqCBk3cQRTZRFBA9hmGGZiVmWGW7p7el1qe3x/31kxNUVVdXV3dVd39fb9e93Wr7lanbp3q\nvk8995wD9KUfxkhGWklG2khZG8lIemolFWkjaa2kIi246f/GdHDp4ye2ZPzOKaO7fi6XeMtMOtee\nQ+facwCIDvbR/NQTND+1nZanttNwZN+Yjm/u4fGeYPGt15OK1dC14jSOnnwWR9ecSd+i1VOuRbOS\nDJNUKuVs6dzL00fvZ6XfzynRrpIGbwbYEV/AHk5nXtMy5rY0Mbe8RRURkXHQcPhgpYsgBdSkyntX\n8FTx2GBDpYtQXmYMzj2JwbkncfCCi2ncv5cZ27bQ+sRWYkODozpUQ/shFt36cxbd+nP6Fi6l/YxN\ndJx+NvHWGeNUeJnSkkmie3cf6wIpuvNJLFG+O2M9EiG5aBHJJctILguTCjVq+SwiMlZBEqKJaLTp\nhK6YuhIRHh+oY3t/jP2DQ3QP95A6eoCaxgQLYp0sjHWwKNrO4lg7zZGhCr6DZzISxFIdxFIdBbdL\nUU8q0kIy0kLKWoN5pIWktZCKtIbzFlLWBDa1fpydTrYOVOdP0cn6pmMDOgNEB3pp3hcmHfZuo6H9\nwJiOH0nEmbn9AWZufwCAeGMrR9cE3SodPfkshmY/M+k42VTnJyt5Pdl1kJ0dD7IocS/LYodZVuLf\n1QOJWWxPnU5r4yoWtbSyvrzFFBEREZlezOhftJT+RUs5cOElNO3dGSQcdmwnkhhd38tN+/fQtH8P\nS276X3qWr6H9jE10rj+TZEPjOBVepgJrP0LNY5uJPfYose2PYwMDZTu2R6PPbKmgpIKISFn1Jo2d\ng7XsHKxlx2AtuwZreHKwlm0DdRyK5/r5biM8Ywgdp9UGWBRrDxMPHcE81n7s8YJoJzGrntYQaREG\niaQGiaUOF9zOiZCy5jAZ0RK2iDiejMh8rNYRUqpkQzNdqzfStXojALH+nmNJh+a922joeHpMx6/p\n72buQ3cw96E7ABiYveBYwqFr9cZJOZ6DkgyTwL6+ozx++EFmxu9jdWwv86CkT64j2cyW5OnU1q1m\nWdssTo2oTzwRERGRcvNolN7lq+ldvhqLD9O68wnatm2mZc9OLFV8UG/utO7cRuvObSz72ffoWrOe\n9jM20XXK6aTUFY0MDxHbvo3YY5uJPbaZ6KGxBbuZ3IzUwkUkli8nuWw5yYWLlFQQERkjdzgUj7J3\nqIZdQ7VhQqGGHWFiIXciYbSMbm+kO97IY/ElObeIkOKk6FEWxTpYGG1nSayddXWHWVVzhAWxDtrs\nKDWMrkXmRDJSRL2baPIZGZZnSFEXtn5oJhlpImXNpCLNx+ZJayZlTeGyJjCNTC25JRpbgiTAmjMB\niPV1n9DSob5zbD0NNLQfoKH9AAt+dwNuRu/iNXSecg6dazfRs3QtRKu/birJUKXaB/t45PAjNAze\nx7rYdjZBSZ9WX6qORxLrSdWezPLWuZwSqf5KKSIiIjJVeE0tXSevo+vkdUQHBmh9cisztm2haf/e\nUR0nkkwyc+vDzNz6MMnaOjpP3UD7hk30rFyLT4KgQ8rAncjT+4lt2Uxs62ZiT2wraxdIyblzSS5f\nQWLZCpJLl0Ld1B6cUESk3JIO+4dj7B2qyTsNeWW7+amzFKvqBjm5HtY3NrCuYTZr6pqojQQJCQeO\nAnicaKqHiPcS8R6iqb7wcS+RVDj3fqr91tUIQ0RSQ8ARSI68fcoag8RDJDP50JyxrJlUpImUtZCy\nBnXbNI0lmlqD8RVOPgsIkw5PbaNl7zZadm+lrqdw92CFmHtwnL3bWHrzdSTqmzh68ll0rt1E59pz\nGG6bU663UVZKMlSR3vgQDx56DO+/j3XRLZxtyZI+oWGP8kj8VPpjp7C8eQGrovqYRURERCot2dBA\n52ln0nnamdT0dNO2/THatm2m4cjoxvCIDg8x56F7mPPQPcSbmuk47Ww6zjiH3iUrwao93JdR6e8j\n9vhjYTdIm4kc7SzboVOtrUFSYfkKksuW483NZTu2iMhU4g49yQgHhmMcGI7xdLyG/cMxDgzFODBc\nw9PxYPnB4RjJKvrZfWHNMCfXD7KmfpA19QOcXD/IktphosUU0WpIRmeRZFb+bTxJxPuPJR6ix5IQ\nfScsM8qXEB9vwfvphyLGV3MsSERYU5h4aAyfN5KKBI/dGjPWBZNaS0xNiaZWjp5yDkdPOQfcqe06\nQuvurbTs3Urz3m3EhkrvxjI22Mech3/DnId/A0DfghXHEg7dy9fjsepobapfnytsOJnkgcPb6O/5\nA+siD7EhMlzSp5Jy49H4ajojp7KkeTFLW9WEXkRERKRaxVtaOXLWeRw56zzqOo7Qtm0LM7Ztobb7\n6KiOU9PXy0m/v52Tfn87QzNmBQNGn7GJgZMWjryzVJ9UiuieXce7QNq1A3Mvy6G9vp7EsuXHEgs+\nc6aSUiIyrQ2n4Eg8xpFElMPxGIfjUY7EYxyKx3h6OJiCxEINfanqvWN9/lAna/v3M3fVfNbUBUmF\n1fWDtETHedwFiwZ39NMC+X43d8cYOtb6Iep9x1tCpHqJeF849WOU5//dRDGcqAeJFEZxqoMBrhuP\nJyUiGckJa8SzkhKpSBNOnf5nTyZmDM+Yy5EZczmy4bmQStF4aA8tex6nZc9WmvbvIJIqomlNHk0H\ndtJ0YCeLb72eRF0DXas3Hks6DM2aX8Y3MjpKMlRAMpXikfZdtHc9wBruZ120t+RPYlt8CfvtNOY3\nLWVBawOTfyxyERERkellaNYcDp1/IYfOey4NBw/Qtm0zbU9spaa/b1THqTvawcI7bmLhHTfRP28h\nHWecQ/sZmxieOXucSi7lYH19wWDNmx8m9tgWIn29ZTmux2IkFy85llRInXQSRKr3RzIRkbFwh/6U\n0ZmInjAdCRMHmQmEI/EgqXA0OXnuKI/gLKodZmXdECvqhlheN8QLf/p11vbvZ0aiH4BHn3tlhUuZ\ngxlOPcloPUnmEM+3nTvmAyckHYIWEeFj7yOSCucMT+Q7KLv0ANdQfHc6wWDX9bg1kLKGcF4fJCWs\nnlTm8kj6cf2xZU513Ok+bUUi9M9fTv/85Rw898VE4kM07XuSlj1badnzOI1H9pV86NjQALM3383s\nzXcD0D93MZ2nbqLzlE10rzqdVM3EdX9ZFUkGM3szcAVwBkH+cyvwH8A17j7q1KuZvQT4AHAOUA/s\nAL4LXO3uQ+Uq92ikUs627gM81fEAy1L3sSrawaoS/5/tScxjp5/GrMYVzG9pYWZ5iyoiIiIilWDG\nwPyFDMxfyNPPeQFN+/YwY9tmWp/cRnR4dJewjYf203jzT1h880/oWbqSjjM20bH+LBLNLeNU+OpT\n7hijbNyJHNhPbPPD1Dz6MNGdT5attUJyzlySK1eRWLmS5JKlEKuKcE9EpCju0JcyuhNRupMRepIR\nupNRepIRjoZJg4549NjjzkSEjsTx55Ue72CsDGd+TZyltcMsqR1ice0wS+uGWVo7xNLaYWojJ/6v\nOK37iQqVdByY4dZIkkaSzC28rcefmXhITyc8n3ytI/IJBrvuB+8vaX8nytyaOpLUEeluDpMPjWGi\nIp2QqMOtHre6MKFRl7G8DqdWY1CUSaqmjp7l6+hZvg4IxnMIxmAIWjrU9pTePWbj4adoPPwUi+74\nIclYLV1rNtKx7jw6Tz2PoZnzyvUWcqr4VaeZfRV4JzAI/BqIAy8AvgK8wMxeN5ogwMz+FriKYEiX\n24BO4CLg08ArzOwF7iV+K0uwu6edJ9ofYF78PpbHDrDIyN+MrIBDyTYeT55OY8NqljS1sS6iZlIi\nIiIiU1YkQt+S5fQtWc7+i15My+4nadu2hZZdTxBJjq55dcueHbTs2cHSG75P16q1dJyxic5TN5Cq\nqx+nwldeuWOMMRseJrb98WOJhUhn6YMBZvL6+mBMhZUrSaxYhbe2luW4IiLFiqegPxWhLxmhPxWh\nP2X0JTOfR+hORE5MGiSC+bFliSCR0JOMkKqiMQ3GQ0MkyYKaOAtq4iypHWJJ7TBLwkTCwpr4MxIJ\nkoPVkLI2UrQV/n3tWOuIgTApMYCF80jG3HwgSE5QkXuSJ4SRpMb6qaEfkqX/gJ2iLn8SIr2cujwJ\ni1qcGmZHovR7HYNeg6OkBQTjOXSuPYfOtcF4DnWdB2ndtYXWXVto3vcEkWRp45pEE8PMeuweZj12\nD/Bl+haspGPduXSceh49y9ZCpLytuSqaZDCz1xJc/D8NXOju28PlJwG3Aq8B/hr4YpHHOwf4HNAP\nPN/dfx8ubwZ+DlwI/APw/vK+kxMdGujh0UMP0Tp8H6fEdnIulHSmu1MNPJo4nUjtGpa3zmatmjeL\niIiITDsei9G96hS6V51CZGiQ1h3bmLFtC01P7R7VHfCWSjFj+xZmbN9CKlbD0bWn037GJrrWrKua\nAePKodwxRsnl6OygZvPDxDY/QuzxrVh87N07OJBauIjEipUkVq4itXChukASkRMkHQZTxlAqwkDK\nGEoZgx4J5iljMBUJ1rsd224wlfHYg30GwuX9yQh9qQj9SQvnkWPz/pQxPMlbD5RbazQRJhGGWVgb\nzmviLKgN5m3RpLrWnygntI4ooutITxLxwTARkZWESCcmUhnLJnm3TaWIMAQ+RNS7Sz7Gw0uOPx5I\n1VJ7tCZMQNQGc6vJeFx7bF3q2OOaZ6w7vl/s2ByLBd1ETbbWF2YMzZrP4VnzOXzW87H4MC1Pbadl\nd5B0qD96uORDNx3YQdOBHSz59feIN7bSeeq5QSuHU84m2dA85qJXuiXD34XzD6cv/gHc/aCZXUHQ\nEuFKM/tykXcaXQkYcFU6wRAer9fM3gpsB95pZn/v7qMbVa9IXYMHqD3wcTaZl3R2B1M1PJxYx3DN\nKaxoPok10cnTR6CIiIiIjK9UXT1HTz2Do6eeQayvl9YntjJj2xYaD+4f1XEiiTizHr2fWY/eT6K+\ngc71Z8Gf/jU0to1TySdUuWOMUbH2IzR/8e+J7n+qLMdLNTeTDJMKieUroLGxLMcVmU7cgyRd0iHh\nRhJIupFySGAk3Uh6sCy9Lr1tKnyecEiSsV3G84QbqXDfRLgulbEuPY9nTMMpI+6c8DzhxrAbPQOz\nibsR6Wgg4TCctT4RPj/22CEeJhMSrl+wx0PMUsyNJZgbizOvJsG8mjjzYnHm1cSZGwuf18RpUEuE\nycuipKwJaKKoNqOeCJMRg5gPhi0lBsPnAzTGHzxh8xR1U7q1RCkaIsPgw+CjG4dsNJxImJiIQUYS\nwi14TPpxuM3xbWuylkfBosGcKG7hPHO5RcPXi4FFCm9LpKgEiNfU0r1iPd0r1rMPqD16mNbdW2jd\n9RjNe7cRTZSW7Krp72beH25m3h9uxiMRulacTue684i+5NVQV9q1ZsWSDGa2GDgbGAa+n73e3W83\ns33AIuB84K4RjlcLvDR8+l85jrfDzO4Gng28DLhuTG8gj2brZ9BG908l4REeiZ9MT/RUljYvZMUU\nupNMRERERMZHoqmZjg3n0LHhHGq7OmnbtoW2bVuo72wf1XFigwPM/cNv6b/k9aRmzR+n0k6McscY\npYh0do4pwZCwKI/NWc2DJ63jwfnr2N26iGO3vWYctlAjlnyrCkUpBdcV+NGy5GOWVI5CxytzGUs4\nv8WsGxo+CYC69tqsdQXKn+eg5T73hV4r2K/wOU6R/jE/+PH+2LLM5+Hcw57Sjy+3cNvgsYePPfyx\n/oRtCepkquA2Fi4vXO6qN2EdPU9fM6IJZsUSzAzns2JJZseCx+kkwryaODOiSdRrtZzAYqSslRS5\nuyrMTjK0t1wBnsJ8iAgnJiQixx6fuOxY8qK4tIfkYKSwsAUGUPif4AQLuouKhEmPCMF988HciYTX\nf+nn4eNIBFYY3SuMbl9CND5MdHCQ6OAAkfhw2E0YkDEde378hbMLAmxhZtcWoj3roXlDSe+nki0Z\nzgznm919IM829xIEAGcycgBwCtAIdLj7kwWO9+zweOOSZBiNLfEVHLJ1LG5eyuLWiRvtW0RERESm\nluG2mRze9GwOn3MB9UcOBQmH7Y9R21t6c/ZJqtwxxoQ4UDuDG2dt4Oezz+TXM0+jN9YQrEgA5Rm+\nQapNvtopIiWLWYq2aPKEqTWaCJMGyTCJkGBWmFCYEUtQo8SBTCO5wMoAACAASURBVCSL4NZAkobR\n7ecJzIeJMIj5UJCoCOdB0mIoa/lguH34mIkbhkqKF3wuKYxE7uRHMQmRKNAEySZIlmGMi/p6K2Uo\nYaCySYYV4Xx3gW32ZG1bzPH2FNhmNMfDzC4DLitm2+3btz9r7ty5RJpXUn/m5/NuN+C1DKYaiFDD\nWjPWFnNwmTJmr6x0CWQyUD2RYvRf+YUTns+ed1KFSiK51EdOvBaYnZpboZKEr18lf1d+tnjWCc9n\n15Z2l4yMYBUkznsR7UA0Pkzt4CA1Q4NYqnCkklq6Ov1wdaHtqlxZY4xS4oHU0tXP+BudS0+sno5Y\nM+01zfRG61mIcTlweTEvJiIyRRlO1Jx0pyOZj2PmREkRNSfovMSJWurY44hFsIr3Cj5xFA9Ut2qJ\nB4Kb2I+1HyPdVsws43Hm8mPrT2h3NpnbhMkoRJqPBY6jjgcq+dc3PaJEoY63esN5SwWOB7AcuKiY\nDWtrg+auVtNMdOYZebdr5nhBRURESpWaveCE58X+Y5OJos8nl+c8Y8mCHFvJeMhuJT2CyXy5Wu6Y\nYDmjjAdoaiZ16sYRt28KpyUjbSgiIpKD4oFqp89HJrVRxwPTJ8Vbml3A7cVseOjQobMbGhqitbW1\nHcAT41oqmbQefPDBjb29vW3Nzc1dGzdufHDkPWQ6Uj2RYqmuSLFUV6RIqwkCip2VLkgV2YXiASkj\n/T2WYqieSLFUV6RYqitSpJLjgUomGdJ3EDUV2CadNempwPFw92uBa4vZVqQYF1988W0Ed8M96O4X\nV7Y0Uq1UT6RYqitSLNUVmUbKGhMoHpBy099jKYbqiRRLdUWKpboi423sI0KUblc4X1Zgm3Tr4V0F\ntsk+3tIyHU9ERERERCaXXeG8XDGGiIiIiIiMoJJJhgfC+Xozyzes+qasbQvZCgwAs8xsVZ5tzh3F\n8UREREREZHIpd4whIiIiIiIjqFiSwd33AvcDtcDrs9eb2UXAYuBp4O4ijjcM3Bg+fUuO460EngUM\nAz8vueAiIiIiIlKVyh1jiIiIiIjIyCrZkgHgs+H8KjNbnV5oZvOAr4VPP+fuqYx17zazrWb27RzH\n+xzgwIfN7NyMfZqBbxK836+5+9Eyvw8REREREakOo44xRERERESkdBVNMrj7D4BrgPnAI2b2UzP7\nX2A7sA74EfCVrN3mAKeQY+wFd78XuBJoBO4ys1+a2fXAkwSDm/we+Og4vR0REREREamwEmMMERER\nEREpUazSBXD3d5rZncC7CBIBUYLxFb4JXDPaO4zc/fNm9jDwQYL+VuuBHcCXgKvdfaic5RcRERER\nkepS7hhDRERERETyq3iSAcDdrwOuK3LbTwKfHGGbXwC/GHPBRERERERkUhpNjCEiIiIiIqWr9JgM\nIiIiIiIiIiIiIiIySSnJICIiIiIiIiIiIiIiJamK7pJEppFrgduAXRUthVS7a1E9keJci+qKFOda\nVFdERKrBtejvsYzsWlRPpDjXoroixbkW1RUZR+bulS6DiIiIiIiIiIiIiIhMQuouSURERERERERE\nRERESqIkg4iIiIiIiIiIiIiIlERJBhERERERERERERERKYmSDCIiIiIiIiIiIiIiUhIlGURERERE\nREREREREpCRKMoiIiIiIiIiIiIiISEmUZBAZAzN7s5n9xsy6zKzXzO4zs3eZWdHfLTOLmNkFZvZp\nM7vLzDrNLG5mB83sBjN79Xi+Bxl/5agnBY79djPzcPpKOcorlVPuumJmUTP7KzO7w8zazWzQzPaa\n2U/N7I/KXX6ZOOWsK2Y208w+Y2aPmFmfmQ2Z2W4z+46ZbRyP8ouITBWKB6QYigekWIoHpFiKB6Ta\nmLtXugwik5KZfRV4JzAI/BqIAy8AWoAfAq9z91QRx1kNbA+fdgD3AZ3ASmBTuPxa4C9cX9hJp1z1\nJM+xlwGPAM2AAV9193eXo9wy8cpdV8xsNnAjwd+RDuBuoA9YApwJ/Je7/2U534NMjHLWFTNbCvwG\nWAocAX4fHncjsApIAG9y9/8p89sQEZn0FA9IMRQPSLEUD0ixFA9IVXJ3TZo0jXICXgs4cABYk7H8\nJGBLuO69RR5rFcE/hZcA0ax1FwG94fHeWun3raly9STHsQ24Oawf14bH+kql37Om6qgrBC0Vfxvu\n9wWgPmt9C3B6pd+3pqqoK9eF+/wcaMyqQ58M1x0Bair93jVp0qSpmibFA5omup7kOLbigSk0KR7Q\nVMG6onhAU1kmtWQQKYGZ3QecDfy5u387a91FwG3A08AiL/GulIzjfQz4v8At7v6CsRxLJtZ41hMz\nuwL4GvAeYDbwCXTn0qRV7rpiZu8A/h/wM3dXM+gpZBzqygFgPnCBu9+dtS4K9AANwHp331KWNyEi\nMgUoHpBiKB6QYikekGIpHpBqpTEZREbJzBYT/EEfBr6fvd7dbwf2EfyRPr8ML/lAOF9chmPJBBnP\nemJmK4DPA3cC6nd1khunupIOLv+5HGWU6jBOdWVohPXpu1GOFHk8EZEpT/GAFEPxgBRL8YAUS/GA\nVDMlGURG78xwvtndB/Jsc2/WtmOxJpwfKMOxZOKMSz0xMwO+CcSAt7mao00FZa0rZrYAOA1IAneb\n2clm9nEz+1cz+6yZvSSsRzL5jMfflV+E84+ZWWN6YVhHPg40Aj9x90OjLayIyBSmeECKoXhAiqV4\nQIqleECqVqzSBRCZhFaE890FttmTtW1Jwj/w7wmfapCdyWW86sm7gYuBK919WwnlkupT7rpyejhv\nB64guMst8//9lcBdZvYaXShOOuPxd+VjBAHIy4DdZvY7gruZNgDLgP8kGFRORESOUzwgxVA8IMVS\nPCDFUjwgVUstGURGrzmc9xXYpject4zxtb5G8I9hC/BvYzyWTKyy1xMzWwV8DrgPuLr0okmVKXdd\nmZUx/2eCZrTrgFbg+cBjwAXkaF4rVa/sf1fc/QhBvfgWMAd4BcFgcquBHcDt7t5TUmlFRKYuxQNS\nDMUDUizFA1IsxQNStZRkEKlSZvZx4M+BLuAN7j5SP3kyhWU0i64haBadrHCRpHql/7fHgDvd/c3u\n/pi797j7rcCLgAHgQjN7XsVKKVXBzNYS9PX9YuBSYAEwA3gBQfDydTP7ZuVKKCIyfSkekEyKB2QU\nFA9I0RQPSLkoySAyeumscFOBbdLZ5ZKyvWb2AeBT4Wu91N03l3Icqahy15P3ABcCn3X3h8dSMKk6\n5a4rmdt8PXuluz8F/Dx8qqBicilrXTGzGEHXG6uBP3b3/3T3p929y91vAS4BDgJvVQAqInICxQNS\nDMUDUizFA1IsxQNStTQmg8jo7QrnywpssyRr26KZ2V8D/0RwZ8Er3P3u0R5DqsKucF6uevKacH6J\nmV2UtW55ehszOw3odfdXFHFMqQ67wnm56srOPI9zbTO/iONJ9dgVzstVV84jaDq/I9f/GnfvMLMb\ngcuAFwK3FltQEZEpblc4VzwghewK54oHZCS7wrniARnJrnCueECqjpIMIqP3QDhfb2YN7j6QY5tN\nWdsWxczeBXwJGARe6e63l15MqbDxqifPKrBuYTh1jeJ4UnnlriuPEzRrbQJm59lmTjjvzbNeqlO5\n68rScF7ob8bRcD6rwDYiItON4gEphuIBKZbiASmW4gGpWuouSWSU3H0vcD9QC7w+e314V8li4Gmg\n6LuOzOyvgK8AQ8Cr3f3mshRYKqLc9cTdL3Z3yzUBfx9u9tVw2YzyvRMZb+NQV+LAz8KnL8hxvBqC\npvYQDBook8Q4/P/ZH87Xmlm+vxvnh/N8d8GJiEw7igekGIoHpFiKB6RYigekminJIFKaz4bzq8xs\ndXqhmc0DvhY+/Zy7pzLWvdvMtprZt7MPZmaXh/sNAa9x95vGr+gygcpaT2RKK3dd+SyQAt5uZi/O\n2CcKXAWsAvYBPyzv25AJUM66cjdBYNEA/LuZtWbsEzGzjxEEFQmCvlpFROQ4xQNSDMUDUizFA1Is\nxQNSldRdkkgJ3P0HZnYNcAXwiJndDMQJ7hJoBX5EcBdSpjnAKQQZ5WPMbCPwr4ARZIbfaGZvzPGy\nR9z9b8r6RmRclbOeyNRW7rri7g+Z2fuALwI3mtk9wFPAmcBKguawr8/TvFaqWDnrirsPm9llwI+B\nPwYuMrN7CfoA3wisIAhO3+fuT47bmxIRmYQUD0gxFA9IsRQPSLEUD0i1UpJBpETu/k4zuxN4F3AR\nEAW2At8ErsnMGo9gBkFAAbA2nHLZDSiomGTKWE9kiit3XXH3L5vZIwR/N84HzgIOAP8GfNbdd5Wx\n+DKByllX3P1XZrYB+ADwfOBigpauB4HvAV9099+V9x2IiEwNigekGIoHpFiKB6RYigekGpm7V7oM\nIiIiIiIiIiIiIiIyCWlMBhERERERERERERERKYmSDCIiIiIiIiIiIiIiUhIlGURERERERERERERE\npCRKMoiIiIiIiIiIiIiISEmUZBARERERERERERERkZIoySAiIiIiIiIiIiIiIiVRkkFERERERERE\nREREREqiJIOIiIiIiIiIiIiIiJRESQYRERERERERERERESmJkgwiIiIiIiIiIiIiIlISJRlERERE\nRERERERERKQkSjKIiIiIiIiIiIiIiEhJlGQQEREREREREREREZGSKMkgIiIiIiIiIiIiIiIlUZJB\nRERERERERERERERKoiSDiIiIiIiIiIiIiIiUREkGEREREREREREREREpiZIMIiIiIiIiIiIiIiJS\nEiUZRERERERERERERESkJEoyiIiIiIiIiIiIiIhISZRkEBERERERERERERGRkijJICIiIiIiIiIi\nIiIiJVGSQURERERERERERERESqIkg4iIiIiIiIiIiIiIlERJBhERERERERERERERKYmSDCIiIiIi\nIiIiIiIiUhIlGUREREREREREREREpCRKMoiIiIiIiIiIiIiISEmUZBARERERERERERERkZIoySAi\nIiIiIiIiIiIiIiVRkkFEZBIws11m5mZ2caXLUi3MbJOZ/dTMjphZKjw/n6x0uaR0ZnZZ+DnelmPd\nbeG6yya+ZCIiIiKVpXjgmRQPTD1mdnH4Oe7Kse5afcYi1UtJBhGpiIwLhOyp28weNLN/NLPFVVDO\nT4bTjEqXZaoys7lmNhx+/l1m1lDEPmuA24BXADOBI8BBoDdcf1n4uW0cx6KPmZmdbWafCn9AP2Rm\ncTPrMLPfmNl7zKy+wL6X5fkOZU69E/l+RERERIqleEDSpnk8kO97kDn9bIRjtJrZp83sMTPrN7N2\nM/u1mb1uot6HiEis0gUQkWkvDnSEjw2YC2wIp780sz9y9zsrVTjgE+H8WuBoBcvxJDAI9FewDOPl\nzUBN+LgVeDXw3RH2eTvQCPwGeKW7Z382lwEXAbuAB8tV0HIys7cA/5mxKAV0EwRJzwmnd5jZi9x9\nX4FDZX6HsvWVo6wiIiIi40jxQHEUD5xo0scDWfoIEyQ5dObbKUzE3QGsCBf1EpzD5wPPN7Nr3P2d\n5SyoiEguaskgIpV2l7vPD6eTgGbgzwgu4GcA3y/mTpapzt1f4O5r3f2eSpdlHPx5OP961vNC1ofz\n63MEFJNFDUGQ+HWCIKDR3WcSBAV/TRBorAP+x8yswHEyv0PZ06rxfhMiIiIiY6R4oAiKB55hKsQD\nma4ucE1/aa4dwhjhBwQJhl3As929BWgB/pbgJqYrzOzyCXoPIjKNKckgIlXF3fvd/TvAe8JF8wnu\nZJEpyMxOB84E9gEfILjz5oVmtnCEXdOB5mTuDuguYKW7v93db3X3IQB373H3rwDvCrc7D7iwUoUU\nERERmUiKB6aXaR4PjNWrCGKFFPAad78LwN0H3f0fgS+F233KzGorVEYRmSaUZBCRanU9wcUSwNnZ\nK83sJDP7JzPbGvY72WVm95jZB82sLt9BzexVZnaDmR3M6P/+cTP7rpm9MWO7a83MM3bdmdUv5rU5\njj3XzD5rZo+YWa+Z9ZnZo2b2D2Y2K095jg3gZmaLzOxrZrbDzIbM7MFc2+U5zqjPR0b/n580szoz\n+6iZPWxmPeHyGeF2kbBP01vD/j3jZnbYzDab2TfN7CX5zncR0ncpfdfde4EfAVHgTwudLyB9Hv4j\n4zPZFZbTCZpGZ6/PN4BYrZm924JxEDrCc787fG+n5ilHUeeuEHff5u4HC2xyHTAcPn7Gd2A8ZdXL\npWb2DTPba2aDZrbTzK42s7Y8+444QPNI9bmE8q4ws2vMbJuZDYTfgd1hWf7OzOaU43VERERkQike\nUDyQ93wxBeKBMnhLOL/Z3XN1CXU14ASJuueP5sCZ1/RmNtPM/iWsl4Nm9pSZ/ZuZLciz74gDNBcT\nM4yyvPMsGMfl0fB7N2hB/HKXBWPgLSvH64hIfhqTQUSqkrsPmdkRYB5B9zHHmNm5wI1A+kK9B6gF\nNoXTpRb0Y38oa79/AD6SsaiH4A6Yk8PpecB/h+u6CAYOOyl8fgRIZuzblXXs5wA/zijTMEFQtD6c\nLjWzS9z98Txv+WTg+8Acgi504nm2e4ZSz0eGeoJ+PM8NXze7n9fvEPSTmtZF8JnMIejOZx3wi2LL\nm1HuKMcvjK8L5/9FEFD8OfD5HLsdDss7i6C7oW5gIGPdAMHnlmt9epvMMiwgOHcbwkUpgm6KlgJv\nBf7EzN7i7v+b522MdO5K5u5xM+sBZhMEWpWwmiDAn0twl5gDy4EPAq8yswvd/UCFygaAmZ1FMOhf\nS7gozvHPcClBgPkAJdRRERERqRzFA4oHmObxQBGeF85vyrXS3feZ2WbgNIIkQynXw7OBe4FVBOcx\nASwCLgdebWYXuftjJRy3bMIEwt1AOumRJPjcFwGLgWcB+4H/V5ECikwTaskgIlXJgn5X54ZPj2Ys\nn0lwd8ss4BHgXHdvJei79fUEg2JtILg4zTzecuDK8Olngbnu3uruDQSBy+uAn6e3d/f3uvv8jENs\nyuoX870Zx14G/DQs0zXAGoJgpQk4HfglsAT43/BCOpd/Ag4Q9KPZ5O7NYZlGOk8lnY8s7yIIat4E\nNLv7DIIfkvvM7EKCgCIJvB9oDdfXAwsJBlQrdSC+FxPcVfOYuz8QLrsZOASsM7Nzsndw903h53JX\nuOi9GZ/JJnf/7wLr57v7pvSxzKyGIBDcAPwauACoD8/fQuAL4fv8jpnlG9sg77kr6YxkMLP1BBf1\nAI8W2HR9eBfZQHjn1KPhnUYrCuxTrKsJgsjnhv27NhF0V3CEIAHxrTK8xlhdTZBg+D1wlrvXhmNb\nNBEE1V8g60cAERERqX6KBxQPTLN44C1h64nhsDXFb83sb82sNdfGZjaP47HC5gLH3RLO142yPGkf\nJ7jW/iOC99dM0IpkJ8H38/vheaykTxAkGJ4g6Ga21t1nEXwHTwc+DTxdueKJTA9KMohItXobkB7s\n9vcZy99NcAFxFHiRu98L4O5Jd/8BwcUdBP14ZjYJPZfgb95Wd/+Iux9Jr3D3w+7+P+7+thLL+g8E\ng9J9zt3f6e5PuHsqnB4luCB7mODC7jV5jpEALkn3oxmW64kiXrvU85GpGXhjeEE+HO6/293jwPnh\nNr9y9y+4e0+43t39gLt/y93/pohy5pJuGp2+awl3T3D87rFiBnwbiz8n+BH6N8BL3f3u8D0Tvrf3\nA/8KNBIEVLkUOndj9elwvocg6MlnDnAqwV1T9QR3yr0P2Gxmby6wXzHqCM7NnQBhnf4x8IZw/SXh\nXXuVlK6j780ITtP9Od/n7u9397srVDYREREpneIBxQPTKR5YTfA59hLUpQuAq4BHzGxDju0zuyra\nX+C46XU5uzYqQivwWnf/mbunANz9duClBK111gNvLLD/REjX0Y+5+28yyjnk7o+6+8fd/UcVLJ/I\ntKAkg4hUDQssN7O/4XjT2N0EdwWlpe/m+Ya7P+NuBHf/JUFTSTj+QygEzSUB2syssYxlbiS4QygF\n/HOubcKLzR+ETy/Jc6hve+H++fMp9XxkejjcLpf0eZtnZmX7nxH2UfrK8Ol1WavTd1n9iY3vAGXp\noOWLBYKAdFnyfW6Fzl3JzOxyjg9w+P50wJJlP8FdO6cR3HE1myDIeTnBHUsNwLfCu89KdX2u4Nbd\nb+X43WEj3mE3ztJ1tNTASURERKqE4gHFA6HpFA/cD1xB0D1TfXgH/izgrwgSR0uBG81sdtZ+TRmP\nB8gv3X1Tc4nl+036hqNMHnT7la7TigdERGMyiEjFXWQnDqiW6QDw6vQPrOEF5mnhulsLHPMWgn4X\nz8pY9nugg+DC424z+yrB3Tg7x1J4gkHoagn6qn/EzPJt1xDOl+RZP+o7rcd4Pop97V8T3KFyFnCb\nmf0bcIu7F7pbphhvIrjr/nfuviNzhbv/3syeJOj38+XAD8f4Ws9gZjGCu9kA/jWsD7mkm7OX7XMb\niZldBHw5fPpVz9P/axjM/DJr2RBwg5n9FriP4I6ozxHcCVWK2wqsuz08br56NVFuIOgv99tm9jWC\n7gL+UKbWJCIiIjL+FA8EFA+EplM84O5fyrHsaFime4DfEdTZD3LieCIT5bYC624n6EqrGuKB84Cr\nzGwNQfLjd+5eKPkiImWmlgwiUmlxgoG5DhL0k/gk8Cvgb4H17v5gxrazOP53a1+BYz4VztN9uOLu\nncClBH2SnkHQ7HWHmR0ws2+FP+yWIn23hBEMCpdvSvelme+uqcN5lhdS8vko9rXdfTvBnTUDwHMJ\nBn3bZ2Y7zewaMztzdEU+5hlNo7Ok7xj6sxKPP5JZBMEgBH2Z5vvc5oTbNGQfIFTK55ZX2O/sTwi6\nKfoh8N7Ce+Tm7l3AZ8Kn55vZnELbF1CoXqXX5atXE+VDBK0qWoAPEwR63WZ2i5ldEfbnLCIiItVL\n8UBA8cCJpmU8kCnsCvR74dM/ylqdOeZDoevddH3rLbEYkyEeuIoghqoF3kmQVOs2s7vM7ENhqxkR\nGWdKMohIpd3lxwfhWuDuq939Re7+j2EgkE/9aF/I3W8AVgBvB64n6G5mPsGFa/qunNFK/x3tcncr\nYro4z3GSJbx2plGfj2Jf292/SXDe3kcwMFo7wWBmfwX8wcxGdUeNmZ3M8X4zv2Rmnj0B/ydc//Ix\n/EBeSOb/vzOL+ezyHGesn9sxZnYGcBNBAPpL4E3uPpbjp/suNoLPb0py93bgOQRN2L8EPEAQYDwP\n+BrwqJktrlwJRUREZASKBwKKB6Z5PJBH+pp+ZdbyzJYkCwvsn153oGwlqjLh2AuvImit83mC1h+e\n8XxbnnEtRKSMlGQQkcmkg6CvUwj6pswn/YPiM+4qcfcud/+6u7/R3RcRDFT19XD15Wb28lGWKd1v\naquZtY1y37Ea8/kolrsfdPcvuvurCe5UOZfgTnsD/m/4A3mxRjOAWw3wJ6PYvljtHA8ICp27CWFm\nawnu2JtFMPDca/KMwzDRiglYsutVIpwXCnTL+l3xwM3u/l53P4vgjrN3EHxHVgL/Us7XExERkYpR\nPHAixQOlq6p4YLTc/TCQHrx8fYFN14XzLSW+1KSIBwDc/Xfu/mF3fxYwk6De7CGor98o9+uJyImU\nZBCRSSP80fXR8OnzCmz6/HB+fxHH3OLubye42wEgu5l0un/YfHeu3EdwEWXAS0Z6vXIaj/NR5Ou6\nu99LMMDdUwT/S55TzL7hYHGXhk/fRXDxl2/6cLjdaIKQtHSwlfNzC/vrvy98+tISjl82ZraKoK/b\necC9wMvdvb/wXkU5L+PxrhKPUajbgPS67Hp1NJznbD1gZquBcW2y7O6d7v5vHO+3ttTuD0RERKSK\nKB44keKBgiZNPDCC9DV9rrFD0uNw5ByU2szSSTQI4o1SjEc80AScWmJ5iuLufe7+PYJWSwBnh68r\nIuNESQYRmWx+EM4vM7MF2SvN7EUEzSIhaAKdXl6bvW2W9KBQdVnLu8N5zh9F3b0H+J/w6afMrCXf\nC5hZzMyaRyjHaJV0PopV6LyFXfmkB9fNPm/5PI9g0LQkcL27H803cbz/0bPNrNDdObkU/NxC14bz\ny0ZqPmtmM0f5+kUxsyUEF/wLgYeAF4d1aqT98o4oGK5vBa4Mn94T3ulUijeaWXbTbMzsQuDZ4dPv\nZ61+JJy/Ms8xr8yzfNTMLBIO2pdPvu+1iIiITF6KB06keCC3qo8Hirim30AwQDbAz3Nskh7P4kV5\nyv8BgiTLAQoPDF7IRWZ2QY6yrQFeFz7NFw+8yMxytWZ4P2W8Ph/hu53+XhvHx+AQkXGgJIOITDZf\nIbhIagB+EQ6Ui5lFzey1HL8Qvdndb8nY7wozu8nM3px58W1mM8I+RC8OF92U9Xqbw/mfmVk0T5mu\nJGiqfDJwl5m9xMxqwuObma0xsw8AW4FzSnjPhZR6Por1GTP7gZm92sxmpRea2Ulm9iWCvlmdoKuf\nYqTvQrrD3Y8U2tDd93D87qLR3r2U/tz+uECz9X8nuGOtHrjFzC4Pf5wHwMzmm9lbzOx2ShyAuRAz\nmwfcDCwjaL58yQj9DmdaZma/M7O3mdmx5t1mVmtmLwF+S1AfU8DfjaGYw8CN6cAi/FH/jzgezP7K\n3X+btc8PCOrE6Wb2xfRAa2Y2L6wzlwLlaKkBwfgVT5jZR83s9PR3NCznC4B/CLfL/l6LiIjI5KV4\n4ESKB3Kr+ngA+FMz+76ZvTLr3LaZ2eUEAxjXAoeAq3Ps/2OCMRsiwA/N7Pxw/zoz+yDBGBoAnxhD\nV6zdwP+a2cvSSREzey5wI0GiYDPPTF79lODH/bnAt8O4J/2+Pgp8EugqsTy5PGpmnzGzTemEQ/i9\nOxf4crjNvaOItUSkFO6uSZMmTRM+Edw14sBtJex7LsFFvIdTN8FFTPr5Q8C8rH3el7HegV6gM2vZ\nv+Z4rbdmrB8AdhN0PXN11nabgH0Z2w4T9JE5lPUaF2XttytcfvEI7znvdqWcj6zP4JMFXvcLWeXv\nCo+fuewjRX5uzeF5d+BdRe5zZbj9fiCasfy2cPllefZbxtfcfQAAIABJREFUm3Hu4+Fnswu4M2u7\necCdGe8lSdA/ay8nvsdPjPbcFfHe/k/WeX26wPTFrH2XZ5VvIKxvwxnL+oBLSyxbur79JUFQ40AP\nQXIgffztwII8+/9zVvk6CRIeCeCyfPU5XJfz70Kuz5zgzrTM1xkOP79ExrIngcWlfk6aNGnSpEmT\npvGZUDyQ3i/ndVGOcuTdrpTzkfUZfLLA6yoeGL944LKs1+gOXzuVsWw3cFaBYywGdmRs3xO+3/Tz\na0osW/r8fhB4InzcHx4/fexDwLo8+78n6711hufWCeKgnJ8fQbLPgV3F1leC7pnSr5MIz2FmXHQY\nOKPUz0mTJk3FTWrJICKTjrvfQzCA1b8A2wgGA0sQ3OXyIeA8dz+Utdt1wOXAfwOPEVx4NRPc9fMT\n4JXu/o4cr/Uf4X73hK+xhODO8zlZ291LcCH7YeAugovSGQQXYvcBXyIIKG4f27t/phLPR7H+heAC\n8cfhsY3gjpW9BOfyQnf/TJHHeh3QRHCh98Mi90k3PV9Anr5Gc3H3reH2vyAIhOYTfG6Ls7Y7RNCX\n6FuAGwguQNNN3LcC3wbeAHyu2Ncehcz/wa3ASQWm7LuvDhJ8LtcDjxPUszaO17erCC74vzPGMj5B\ncLfdNwnOY5QgOPsn4Bx3P5Bnvw8C7yQIaAcJPvObgOe7+7VjLFOmbuAVBMHvPRz//PoIxrf4KLDR\n3Z8q42uKiIhIhSkeeEYZFQ9kmSTxwK3Ax8My7gyXtRIkp24hSIyd5u55x9IIr3M3Ap8JyxsjSATc\nCrzB3a8YYxnbCZJYXyCIQWoJEj5fJ7jOzjmgtLt/CXgjQUuRfoLY57fAa9z9U2MsU7ZXAZ8Nj7+f\n4Hs9DDxM8Lmtd/eHy/yaIpLF3L3SZRAREZEqYma7CIKw57n7bZUtjYiIiIiITCQzu40g+fLWMt8k\nJCJTlFoyiIiIiIiIiIiIiIhISZRkEBERERERERERERGRkijJICIiIiIiIiIiIiIiJVGSQURERERE\nRERERERESqKBn0VEREREREREREREpCRqySAiIiIiIiIiIiIiIiVRkkFEREREREREREREREqiJIOI\niIiIiIiIiIiIiJRESQYRERERERERERERESlJrNIFmCq6uroeAFYAvcATFS6OiIiIiMhorQaagZ1t\nbW1nVrowk43iARERERGZ5EqOB5RkKJ8VQFs4LapwWURERERESrWi0gWYpBQPiIiIiMhUMOp4QEmG\n8uklCChE8urv7wegsbGxwiWRaqZ6IsVSXZFiqa5ILu4pUt2PQyoOQKR5JVbTDMF1rYye4gEZkf4e\nSzFUT6RYqivVJ5Fy9vYmOdCfJOUT//p1UWNFS5TZ9RHM7Nhy1RUZpVHHA0oylM8T6I4lGcG+ffsA\nWLNmTYVLItVM9USKpboixVJdkVwS+29k+PEvH3tef+bnic48A9TVT6kUD8iI9PdYiqF6IsVSXake\nw0nn37f28fmHuukcqkB2Ictz5tdy1XkzWD+rBlBdkVEbdTygJIOIiIiIyDTjqQTx3ddXuhgiIiIi\nk96t+wb54N1H2dGTHPW+CxsjnDqzhsVNUWbWRZhRG6E2CsNJGEg6R4dS7OxJsLM7ya7eBPFUcce9\n8+lhLvzJIT5wRgsf3tgy6nKJjJaSDCIiIiIi00zi4C344MFKF0NERERk0jo6lOLj93bxne39Re9T\nH4ULTqrjogV1bJxTQ1ttpOh9+xPOfYeH+e3TQ9x1cJieeOEWE0mHf3yoh98cGOIjS40F9ZVvYSFT\nl5IMIiIiIiLTiHuS+K7/rnQxRERERCatX+4d5H13dbK/v7imBetmxnjdigaePb+OuqiNvEMOjTHj\nwgV1XLigjqGk86unBrl+xwB7egu3oPjdoWHe0l7Px9YMo96SZLwoySAiIiIiMo0kD96BD+yrdDFE\nREREJp14yvn7+7r5yubixsV97vxa3rSq8djYCOVSFzVesayBly2t53cHh7lmSx97+/InG3qSxoe3\n1tHV0M2HNrScMCi0SDkoySAiIiIiMk24pxje/b3cKyPlDX5FREREppKnehP8xW2d3HN4eMRtT5sZ\n44r1zayfOb7XVxEzLphfx6Z5tfzPzgG+9Xg/A8n83SJ95oEedvcm+cIFM6iJKNEg5aMkg4iIiIjI\nNJE8fBfetzvnOquZMcGlEREREZkcbts/yF/c1knHUOHukebWR3j3+mYuXFA7oa0FaiLGm1Y18sJF\ndXzugR7uOxLPu+1/be9nf1+Sbz1vFq2jGBNCpBDVJBERERGRacDdie/6bs51VjcXYk0TXCIRERGR\n6nft43289pftIyYYXrG0nv+4eCYXLayrWHdEc+qjfP78Nv7q1CYKDf1w6/4hXvmLIxwd4T2JFEtJ\nBhERERGRaSDZfg+p3idzrosueJH65hURERHJkEw5H72ni/fddZQCPRAxtz7C1ee38TcbWmiuqfxP\nrREz3rS6ka88ewZz6/OX58H2OH/8yyN0DSvRIGNX+ZovIiIiIiLjqlArBmpnEZ21aWILJCIiIlLF\n+hMpLr21g6+OMMDz+fNq+cZFMzlnbu0Elax4p86s4WvPmcGq1mjebe4/Eud1vzxCtxINMkZKMoiI\niIiITHGpzgdIdW/NuS42/xIskj/4FBEREZlOuoZTvPaX7dywZzDvNhHg8rVNfObcVtqqeFyDuQ1R\nvvzsGZzRksy7zb2H47z+V+30xpVokNJV77dARERERETGzN0Z3nld7pU1bUTnnDexBRIRERGpUkcG\nk7zyF0e4++Bw3m1aaoyrn9XGW9Y0EpkE3U02xiK8b+Uwz56ZyLvN7w8N87bbO0mmCvQLJVKAkgwi\nIiIiIlNYqvMBUl2P5lwXm/9CLFIzwSUSERERqT77+pK87IYjPNQez7vNoqYoX3vODM6aU33dIxUS\nM7h8aZwXLa7Lu81Newe58p4u3JVokNFTkkFEREREZIpyd4Z3fDv3ylgz0TkXTGyBRERERKrQvr4k\nr7jxMNu68t/tv2FWMMbBkubYBJasfCIGH97YwgsX5U80fP2xPq7Z0jeBpZKpQkkGEREREZEpKtl+\nT4GxGF6IRSfXXXgiIiIi5XagP8krf3GYnT35xy24cEEt/3h+W1WPv1CMqBlXbmzheQvzJxo+ek8X\nP9s9MIGlkqlgcn8zREREREQkJ3cnnq8VQ00r0bnPndgCiYiIiFSZg/3BGAxPdudPMLx0ST3/56xW\naqPVP/5CMWIR4yNntnDm7NxdZjrwjjs6efxo/m6jRLIpySAiIiIiMgUlD/+WVO+TOdfF5r9IrRhE\nRERkWjsymORVNx1he4Eukl6/soEPbWgmFpkaCYa0mojxqXNaWdoczbm+L+H82S0d9MZTE1wymayU\nZBARERERmWLcUwzv/M/cK2tmEJ2rsRhERERk+uqJp3j9r9rZejR/guFPVzfyznVNRGxqJRjSWmoj\nXHVeGzNrc7+/x7sSvO+uoxoIWoqiJIOIiIiIyBSTPHQH3rcr57rYwhdjkdzN40VERESmuqGkc+kt\nHTxwJH93QH+yuoG3rW3EpmiCIW1BY5TPnNtGTZ5fiH+wY4BvbNVA0DIyJRlERERERKYQTyXztmKw\n2llEZ58/wSUSERERqQ7JlPOOOzq5bf9Q3m3esLKBt69tmvIJhrRTZ9bwvtOb867/yD1d3H94eAJL\nJJORkgwiIiIiIlNI4uCteP9TOddFF74Ui8QmuEQiIiIilefuXPn7Ln60ayDvNn+8ooEr1k2fBEPa\ny5c28LIl9TnXxVNw+R0d9Gl8BilASQYRERERkSnCUwniO/8r5zqrm0d09qYJLpGIiIhIdfjalj6+\nXqDrn0sW1fHu9dMvwZD23tObWd2a+2aUJ7uTfOK+7gkukUwmSjKIiIiIiEwRiad/hQ8eyLkutvCl\nmEUnuEQiIiIilffT3QN87J6uvOvPn1fLhze2TNlBnotRFzX+/pxWmmK5z8E3tvbxq6cGJ7hUMlko\nySAiIiIiMgV4apj4zu/mXGf184nMOmuCSyQiIiJSeX84PMzbb+/E86w/bWaMT57dSiwyfRMMaYua\nonxoQ0ve9e++s5OOweQElkgmCyUZRERERESmgMT+m/ChQznXxRa+DDNd+ouIiMj0src3wZ/8up2B\nZO4Uw7LmKJ89t436PHfvT0cXL6zjkkV1OdcdHEjx/ruPTnCJZDKYspGGmTWY2d+a2b1mdtTM+s1s\np5l938yeXenyiYiIiIiUiycGiO+6Luc6a1hEZOaGCS6RiIiISGX1xVO8+dcdHBrIPWDxzFrjc+e1\n0VI7ZX8eLdl7Tm9mXn3u8/LjXYP8dHf+wbNlepqS3yIzWwE8DFwFLAJuBX4OHAZeDTyvcqUTERER\nESmv+N4f4sOdOdfFFr1crRhERERkWnF33nXnUR7piOdcXxuBz5zbxoJGjVeVS0tNhL87M3+3SR+6\n+yhdw7mTNzI9Tblow8yagF8Bq4ArgSXu/hp3f727nwvMB66vZBlFRERERMrFh7uI7/lBznXWuJRI\n22kTXCIRERGRyvqnh3v50a7cd9sb8LGzWjl1Zs3EFmqSOXNOLW9Y2ZBz3dMDKT71h+4JLpFUsymX\nZAA+RpBg+Kq7X+XuJ4xG4u7t7r6tMkUTERERESmv4d3fg2R/znWxxa/CTH0Mi4iIyPRxw54BPn1/\n/h/A335qExcuyD3mgJzobWubWNSUu7XHv2/t43cHhya4RFKtplSSwcxqgcvDp/9cybKIiIiIiPx/\n9u47Pq6zyv/457l3ZtRlSVaX3GI7duwUp0IqpFFTYENJYPklhA6hJCRZlrqEtrtA6AQ2sBgIARKy\ndAiQkIR00hPbibtcZKtYvU259z6/P8Ypju7Ysi1dSaPv+/WalzRznjtzbI9tzT33OWeiBSNteNt/\nHxpzyg/DLT804oxEREREJs+Gvgzv+Ud4C0mAs5sKuHBh+NX5U5r1MUESE6TBhg+xnggFruHKI0tz\nxj98by+pHEO1ZWaJTXYC4+xYYDbQaq3dbIw5Bng9UAu0A3+11t4zmQmKiIiIiIyX9KafgPVCY7Hm\n8yLOZmowxnwQOBU4guzngHKgF3gCWAn8zNoIP52LiIhIJIYyAW/7ezcDmfD/5pdWxLjyqLKpt8vT\neiRSrRSMtJBIthDPdOJmuohluoh5fZggjeH5Ri0Wh8ApInCL8OKzySTq8RJ1pBMNpIoXky5ohnGc\nx3V0dYJXzynkz9uSo2Jr+zy+tWqQK4/KPb9BZoZ8KzIcsftrqzHmK8BHXxT/lDHmN8C/WmuH9vVk\nxphLgEvG8sJ33nnnihUrVjA8PExra+t+pCwz0fr16yc7BZkG9D6RsdJ7RcZK75X8Ektvp6b9DsI+\nJicLD6OzK4CurWN+vvp5lRTG8+ID4r+RLS6sAu4DhoB5wBnAmcAbjDH/Yq3VtEIREZE8Ya3lw/f1\n8nRv+MUXswscPndcOQXu5BcYjD9M0fDTFA2uonjwKQqSmzA5LhoJPZ4ANxjCDYaIZ3ZRNLx2j7jv\nFJEqXsxIyXKGyo4nVbTwoHN+77IS7m9P0ZseXcC59skBLlxYRHNpvp1mlv2Rb3/6Vbu/Hg2cAHwd\n+DbQBZwGfBd43e6vF4/h+eYDLxvLCw8ODu5nqiIiIiIiB66873cYRn/QszgMlZ4yCRlNGRcCj734\noiJjzHLgduB8sp8FfjQJuYmIiMgE+P7TQ/xqU/ig57gD1xxfTk1R+GyBKLiZbkr7H6C0736KBlft\nsTNh3F8rGKF48EmKB59kdvvP8WJVFDuH0ZY4HuwcOICdHLMSDh88vJTPPTowKjbsWT7zcD8/fHlV\nyJEyU+RbkeHZvUBx4AZr7eUviP3OGLMD+CfwNmPMNdbajft4vhbgrrG8cGlp6QpgVnFxMYsXL97P\ntGWmePYKUr1HZG/0PpGx0ntFxkrvlfzjdz9OctvTobFY7Wk0zT1yv5/TKciPAYi52qNaa1cbY74D\nXAOcjYoMIiIieeGfHSk++c++nPEPLi9leWU8woyyjD9MWd+9lHffTtHwmshf/1kxr5sm7qUpfS/p\ntTfRX3UW/ZVn4Mf3ryhwRmMBt25L8lBnZlTsls0jXLo0xcn1+fHzpOy/fCsyvLCcdv2Lg9bah40x\njwDHkd2hsNcig7V2Jdm+rfvU19d3J2Pc9SAiIiIicqCstaQ3/m9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u6591Grsa3q75CxNs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FbcPbE5OPljV2ju0g40yidZ6ivvmeqMWfzewk7QsMSNLR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\nZq/J2269mXnero8UzCW7PqHtY83sI2Z2t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O2W9P0q2/ijhGX3mdlr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kyCCEEEJMUCODGNcupgIK7dcw2q5idLWhnMJfLkdFxzFbL0Pr5Tu2a38Qd8Vq3BVrcVfcg7Pq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\n5VpPTzk3l+udnuWc+VwPY8zbgLflcm5XV9ftq1atwqlcT+muv8z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" + ] + }, + "metadata": { + "tags": [], + "image/png": { + "width": 780, + "height": 851 + } + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "qkO2vdVHh3zh" + }, + "source": [ + "Note that we don't really care how accurate we become about the inference of the hidden probabilities — for this problem we are more interested in choosing the best bandit (or more accurately, becoming *more confident* in choosing the best bandit). For this reason, the distribution of the red bandit is very wide (representing ignorance about what that hidden probability might be) but we are reasonably confident that it is not the best, so the algorithm chooses to ignore it.\n", + "\n", + "From the above, we can see that after 1000 pulls, the majority of the \"blue\" function leads the pack, hence we will almost always choose this arm. This is good, as this arm is indeed the best.\n", + "\n", + "Below is a D3 app that demonstrates our algorithm updating/learning three bandits. The first figure are the raw counts of pulls and wins, and the second figure is a dynamically updating plot. I encourage you to try to guess which bandit is optimal, prior to revealing the true probabilities, by selecting the `arm buttons`." + ] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "q6YL7mvvQbKW", + "outputId": "a108700f-86c3-4284-c01c-6784159f9a9d", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 89 + } + }, + "source": [ + "# Getting the HTML file for the simulated Bayesian Bandits\n", + "reset_sess()\n", + "\n", + "import wget\n", + "url = 'https://raw.githubusercontent.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/master/Chapter6_Priorities/BanditsD3.html'\n", + "filename = wget.download(url)\n", + "filename" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Device mapping:\n", + "/job:localhost/replica:0/task:0/device:XLA_CPU:0 -> device: XLA_CPU device\n", + "\n" + ], + "name": "stdout" + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "'BanditsD3 (3).html'" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 14 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "mcak8nERh3zh", + "outputId": "bbea6e23-f22c-466c-9bf2-e5aa6d69884e", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 405 + } + }, + "source": [ + "from IPython.core.display import HTML\n", + "\n", + "#try executing the below command twice if the first time doesn't work\n", + "HTML(filename = \"BanditsD3.html\")" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + "\n", + " \n", + "\n", + "\n", + " \n", + "
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\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 15 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "aYfdd4jBh3zk" + }, + "source": [ + "Deviations of the observed ratio from the highest probability is a measure of performance. For example, in the long run, we can attain the reward/pull ratio of the maximum bandit probability if we are optimal. Long-term realized ratios less than the maximum represent inefficiencies. (Realized ratios larger than the maximum probability is due to randomness, and will eventually fall below). \n", + "\n", + "### A Measure of *Good*\n", + "\n", + "We need a metric to calculate how well we are doing. Recall the absolute *best* we can do is to always pick the bandit with the largest probability of winning. Denote this best bandit's probability by $w_{opt}$. Our score should be relative to how well we would have done had we chosen the best bandit from the beginning. This motivates the *total regret* of a strategy, defined:\n", + "$$\n", + "\\begin{align}\n", + "R_T & = \\sum_{i=1}^{T} \\left( w_{opt} - w_{B(i)} \\right)\\\\\n", + "& = Tw^* - \\sum_{i=1}^{T} \\; w_{B(i)} \n", + "\\end{align}\n", + "$$\n", + "\n", + "where $w_{B(i)}$ is the probability of a prize of the chosen bandit in the $i$ round. A total regret of 0 means the strategy is matching the best possible score. This is likely not possible, as initially our algorithm will often make the wrong choice. Ideally, a strategy's total regret should flatten as it learns the best bandit. (Mathematically, we achieve $w_{B(i)}=w_{opt}$ often)\n", + "\n", + "\n", + "Below we plot the total regret of this simulation, including the scores of some other strategies:\n", + "\n", + "1. Random: randomly choose a bandit to pull. If you can't beat this, just stop. \n", + "2. Largest Bayesian credible bound: pick the bandit with the largest upper bound in its 95% credible region of the underlying probability. \n", + "3. Bayes-UCB algorithm: pick the bandit with the largest *score*, where score is a dynamic quantile of the posterior (see [4] )\n", + "4. Mean of posterior: choose the bandit with the largest posterior mean. This is what a human player (sans computer) would likely do. \n", + "5. Largest proportion: pick the bandit with the current largest observed proportion of winning. \n", + "\n", + "The code for these are in the `other_strats.py`, where you can implement your own very easily.\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "TTUtPs3gO6u3", + "outputId": "d3d97868-5c01-47c4-b90e-35f7c90bfc8f", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 35 + } + }, + "source": [ + "url = 'https://raw.githubusercontent.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/master/Chapter6_Priorities/other_strats.py'\n", + "filename = wget.download(url)\n", + "filename" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "'other_strats.py'" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 11 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "FNK3FQH8h3zk", + "outputId": "b8a647db-5a8e-4d4f-eeb0-98ee9dc24b8e", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 354 + } + }, + "source": [ + "plt.figure(figsize(12.5, 5))\n", + "from other_strats import *\n", + "\n", + "#define a harder problem\n", + "hidden_prob = np.array([0.15, 0.2, 0.1, 0.05])\n", + "bandits = Bandits(hidden_prob)\n", + "\n", + "#define regret\n", + "def regret(probabilities, choices):\n", + " w_opt = probabilities.max()\n", + " return (w_opt - probabilities[choices.astype(int)]).cumsum()\n", + "\n", + "#create new strategies\n", + "strategies= [upper_credible_choice, \n", + " bayesian_bandit_choice, \n", + " ucb_bayes , \n", + " max_mean,\n", + " random_choice]\n", + "algos = []\n", + "for strat in strategies:\n", + " algos.append(GeneralBanditStrat(bandits, strat))\n", + " \n", + "#train 10000 times\n", + "for strat in algos:\n", + " strat.sample_bandits(10000)\n", + " \n", + "#test and plot\n", + "for i,strat in enumerate(algos):\n", + " _regret = regret(hidden_prob, strat.choices)\n", + " plt.plot(_regret, label = strategies[i].__name__, lw = 3)\n", + "\n", + "plt.title(r\"Total Regret of Bayesian Bandits Strategy vs. Random guessing\")\n", + "plt.xlabel(r\"Number of pulls\")\n", + "plt.ylabel(r\"Regret after $n$ pulls\");\n", + "plt.legend(loc = \"upper left\");" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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+1gQmmllDP28NM7sF7x4VrZ6U5/flV2b2DzPraV6vq1Ce7uTf4350zq2MUtZS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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [], + "image/png": { + "width": 846, + "height": 337 + } + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "NJB_1onah3zo" + }, + "source": [ + "Like we wanted, Bayesian bandits and other strategies have decreasing rates of regret, representing we are achieving optimal choices. To be more scientific so as to remove any possible luck in the above simulation, we should instead look at the *expected total regret*:\n", + "\n", + "$$\\bar{R}_T = E[ R_T ] $$\n", + "\n", + "It can be shown that any *sub-optimal* strategy's expected total regret is bounded below logarithmically. Formally,\n", + "\n", + "$$ E[R_T] = \\Omega \\left( \\;\\log(T)\\; \\right) $$\n", + "\n", + "Thus, any strategy that matches logarithmic-growing regret is said to \"solve\" the Multi-Armed Bandit problem [3].\n", + "\n", + "Using the Law of Large Numbers, we can approximate Bayesian Bandit's expected total regret by performing the same experiment many times (500 times, to be fair):" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "HyJfLEEbh3zo", + "outputId": "53bb81fc-79b9-4b37-814c-ab509f6cc4bd", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 354 + } + }, + "source": [ + "# This can be slow, so I recommend NOT running it.\n", + "# Estimated time for Graph Mode: 16 minutes.\n", + "\n", + "trials = tf.constant(500)\n", + "expected_total_regret = tf.zeros((10000, 3))\n", + "\n", + "[\n", + " trials_,\n", + " expected_total_regret_,\n", + "] = evaluate([\n", + " trials,\n", + " expected_total_regret,\n", + "])\n", + "\n", + "for i_strat, strat in enumerate(strategies[:-2]):\n", + " for i in range(trials_):\n", + " general_strat = GeneralBanditStrat(bandits, strat)\n", + " general_strat.sample_bandits(10000)\n", + " _regret = regret(hidden_prob, general_strat.choices)\n", + " expected_total_regret_[:,i_strat] += _regret\n", + " plt.plot(expected_total_regret_[:,i_strat]/trials_, lw =3, label = strat.__name__)\n", + " \n", + "plt.title(\"Expected Total Regret of Multi-armed Bandit strategies\")\n", + "plt.xlabel(\"Number of pulls\")\n", + "plt.ylabel(\"Exepected Total Regret \\n after $n$ pulls\");\n", + "plt.legend(loc = \"upper left\");" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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1ieQpiluXhW9pE65fRdP9sXMkXUWycqVxXL+Iu9brgmvh8C3ffhb8/VS65Y5L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\n3DNaHccbSautqn5GN4M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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [], + "image/png": { + "width": 853, + "height": 337 + } + } + } + ] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "WCyPsXM-h3zr", + "outputId": "7f3ca4c9-445e-42db-f2f6-8cfe102910ba", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 340 + } + }, + "source": [ + "# This cell is dependent on the previous one\n", + "# If you didn't run the previous one, you don't have to run this one\n", + "plt.figure()\n", + "\n", + "[pl1, pl2, pl3] = plt.plot(expected_total_regret_[:, [0,1,2]], lw = 3)\n", + "\n", + "plt.xscale(\"log\")\n", + "plt.legend([pl1, pl2, pl3], \n", + " [\"Upper Credible Bound\", \"Bayesian Bandit\", \"UCB-Bayes\"],\n", + " loc=\"upper left\")\n", + "plt.ylabel(r\"Exepected Total Regret after $\\log{n}$ pulls\");\n", + "plt.title( r\"log-scale of above\" );\n", + "plt.ylabel(r\"Exepected Total Regret after $\\log{n}$ pulls\");" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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596Jzbo1z7oBz7g/n3AzgdGAh3l5bDxzViI+OhsDZuXnFx8fnPOVGjrrrrrsu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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [], + "image/png": { + "width": 860, + "height": 323 + } + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "IbZ7ODFfh3zt" + }, + "source": [ + "### Extending the algorithm \n", + "\n", + "\n", + "Because of the Bayesian Bandits algorithm's simplicity, it is easy to extend. Some possibilities:\n", + "\n", + "- If interested in the *minimum* probability (eg: where prizes are a bad thing), simply choose $B = \\text{argmin} \\; X_b$ and proceed.\n", + "\n", + "- Adding learning rates: Suppose the underlying environment may change over time. Technically the standard Bayesian Bandit algorithm would self-update itself (awesome) by noting that what it thought was the best is starting to fail more often. We can motivate the algorithm to learn changing environments quicker by simply adding a *rate* term upon updating:\n", + "\n", + " self.wins[choice] = rate*self.wins[choice] + result\n", + " self.trials[choice] = rate*self.trials[choice] + 1\n", + "\n", + " If `rate < 1`, the algorithm will *forget* its previous wins quicker and there will be a downward pressure towards ignorance. Conversely, setting `rate > 1` implies your algorithm will act more risky, and bet on earlier winners more often and be more resistant to changing environments. \n", + "\n", + "- Hierarchical algorithms: We can setup a Bayesian Bandit algorithm on top of smaller bandit algorithms. Suppose we have $N$ Bayesian Bandit models, each varying in some behavior (for example different `rate` parameters, representing varying sensitivity to changing environments). On top of these $N$ models is another Bayesian Bandit learner that will select a sub-Bayesian Bandit. This chosen Bayesian Bandit will then make an internal choice as to which machine to pull. The super-Bayesian Bandit updates itself depending on whether the sub-Bayesian Bandit was correct or not. \n", + "\n", + "- Extending the rewards, denoted $y_a$ for bandit $a$, to random variables from a distribution $f_{y_a}(y)$ is straightforward. More generally, this problem can be rephrased as \"Find the bandit with the largest expected value\", as playing the bandit with the largest expected value is optimal. In the case above, $f_{y_a}$ was Bernoulli with probability $p_a$, hence the expected value for a bandit is equal to $p_a$, which is why it looks like we are aiming to maximize the probability of winning. If $f$ is not Bernoulli, and it is non-negative, which can be accomplished apriori by shifting the distribution (we assume we know $f$), then the algorithm behaves as before:\n", + "\n", + " For each round, \n", + " \n", + " 1. Sample a random variable $X_b$ from the prior of bandit $b$, for all $b$.\n", + " 2. Select the bandit with largest sample, i.e. select bandit $B = \\text{argmax}\\;\\; X_b$.\n", + " 3. Observe the result,$R \\sim f_{y_a}$, of pulling bandit $B$, and update your prior on bandit $B$.\n", + " 4. Return to 1\n", + "\n", + " The issue is in the sampling of $X_b$ drawing phase. With Beta priors and Bernoulli observations, we have a Beta posterior — this is easy to sample from. But now, with arbitrary distributions $f$, we have a non-trivial posterior. Sampling from these can be difficult.\n", + "\n", + "- There has been some interest in extending the Bayesian Bandit algorithm to commenting systems. Recall in Chapter 4, we developed a ranking algorithm based on the Bayesian lower-bound of the proportion of upvotes to total votes. One problem with this approach is that it will bias the top rankings towards older comments, since older comments naturally have more votes (and hence the lower-bound is tighter to the true proportion). This creates a positive feedback cycle where older comments gain more votes, hence are displayed more often, hence gain more votes, etc. This pushes any new, potentially better comments, towards the bottom. J. Neufeld proposes a system to remedy this that uses a Bayesian Bandit solution.\n", + "\n", + "His proposal is to consider each comment as a Bandit, with the number of pulls equal to the number of votes cast, and number of rewards as the number of upvotes, hence creating a $\\text{Beta}(1+U,1+D)$ posterior. As visitors visit the page, samples are drawn from each bandit/comment, but instead of displaying the comment with the $\\max$ sample, the comments are ranked according to the ranking of their respective samples. From J. Neufeld's blog [6]:\n", + "\n", + " > [The] resulting ranking algorithm is quite straightforward, each new time the comments page is loaded, the score for each comment is sampled from a $\\text{Beta}(1+U,1+D)$, comments are then ranked by this score in descending order... This randomization has a unique benefit in that even untouched comments $(U=1,D=0)$ have some chance of being seen even in threads with 5000+ comments (something that is not happening now), but, at the same time, the user is not likely to be inundated with rating these new comments. " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "dz-kHC8qh3zt" + }, + "source": [ + "Just for fun, though the colors explode, we watch the Bayesian Bandit algorithm learn 15 different options. " + ] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "MU7I5gGiFc6m", + "colab": {} + }, + "source": [ + "# To avoid any conflicts with our 'other_strats.py' contents, we re-define our\n", + "# classes here so you can run this notebook in one 'run all' call\n", + "class Bandits(object):\n", + " \"\"\"\n", + " This class represents N bandits machines.\n", + "\n", + " parameters:\n", + " arm_true_payout_probs: a (n,) Numpy array of probabilities >0, <1.\n", + "\n", + " methods:\n", + " pull( i ): return the results, 0 or 1, of pulling \n", + " the ith bandit.\n", + " \"\"\"\n", + " def __init__(self, arm_true_payout_probs):\n", + " self._arm_true_payout_probs = tf.convert_to_tensor(\n", + " arm_true_payout_probs,\n", + " preferred_dtype=tf.float32,\n", + " name='arm_true_payout_probs')\n", + " self._uniform = tfd.Uniform(low=0., high=1.)\n", + " assert self._arm_true_payout_probs.shape.is_fully_defined()\n", + " self._shape = np.array(\n", + " self._arm_true_payout_probs.shape.as_list(),\n", + " dtype=np.int32)\n", + " self._dtype = self._arm_true_payout_probs.dtype.base_dtype\n", + "\n", + " @property\n", + " def dtype(self):\n", + " return self._dtype\n", + " \n", + " @property\n", + " def shape(self):\n", + " return self._shape\n", + "\n", + " def pull(self, arm):\n", + " return (self._uniform.sample(self.shape[:-1]) <\n", + " self._arm_true_payout_probs[..., arm])\n", + " \n", + " def optimal_arm(self):\n", + " return tf.argmax(\n", + " self._arm_true_payout_probs,\n", + " axis=-1,\n", + " name='optimal_arm')\n", + " \n", + "class BayesianStrategy(object):\n", + " \"\"\"\n", + " Implements a online, learning strategy to solve\n", + " the Multi-Armed Bandit problem.\n", + " \n", + " parameters:\n", + " bandits: a Bandit class with .pull method\n", + " \n", + " methods:\n", + " sample_bandits(n): sample and train on n pulls.\n", + " \"\"\"\n", + " \n", + " def __init__(self, bandits):\n", + " self.bandits = bandits\n", + " dtype = self.bandits.dtype.base_dtype\n", + " self.wins_var = tf.Variable(\n", + " initial_value=tf.zeros(self.bandits.shape, dtype))\n", + " self.trials_var = tf.Variable(\n", + " initial_value=tf.zeros(self.bandits.shape, dtype))\n", + " \n", + " def sample_bandits(self, n=1):\n", + " return tf.while_loop(\n", + " cond=lambda *args: True,\n", + " body=self._one_trial,\n", + " loop_vars=(tf.identity(self.wins_var),\n", + " tf.identity(self.trials_var)),\n", + " maximum_iterations=n,\n", + " parallel_iterations=1)\n", + " \n", + " def make_posterior(self, wins, trials):\n", + " return tfd.Beta(concentration1=1. + wins,\n", + " concentration0=1. + trials - wins)\n", + " \n", + " def _one_trial(self, wins, trials):\n", + " # sample from the bandits's priors, and select the largest sample\n", + " rv_posterior_payout = self.make_posterior(wins, trials)\n", + " posterior_payout = rv_posterior_payout.sample()\n", + " choice = tf.argmax(posterior_payout, axis=-1)\n", + "\n", + " # Update trials.\n", + " one_hot_choice = tf.reshape(\n", + " tf.one_hot(\n", + " indices=tf.reshape(choice, shape=[-1]),\n", + " depth=self.bandits.shape[-1],\n", + " dtype=self.trials_var.dtype.base_dtype),\n", + " shape=tf.shape(wins))\n", + " trials = tf.assign_add(self.trials_var, one_hot_choice)\n", + "\n", + " # Update wins.\n", + " result = self.bandits.pull(choice)\n", + " update = tf.where(result, one_hot_choice, tf.zeros_like(one_hot_choice))\n", + " wins = tf.assign_add(self.wins_var, update)\n", + "\n", + " return wins, trials" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "1Ftd1BZCh3zt", + "outputId": "8039a912-eafd-4858-eeee-0bc50ced4c02", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 609 + } + }, + "source": [ + "# Now we run our code\n", + "plt.figure(figsize(12.0, 8))\n", + "\n", + "hidden_prob = tfd.Beta(1., 13.).sample(sample_shape = (35))\n", + "[ hidden_prob_ ] = evaluate([ hidden_prob ])\n", + "print(hidden_prob_)\n", + "bandits = Bandits(hidden_prob_)\n", + "bayesian_strat = BayesianStrategy(bandits)\n", + "\n", + "draw_samples_2 = tf.constant([100, 200, 500, 1300])\n", + "[draw_samples_2_] = evaluate([draw_samples_2])\n", + "\n", + "for j,i in enumerate(draw_samples_2_):\n", + " plt.subplot(2, 2, j+1) \n", + " evaluate(tf.global_variables_initializer())\n", + " [wins_, trials_] = evaluate(bayesian_strat.sample_bandits(i))\n", + " N_pulls = int(draw_samples_2_.cumsum()[j])\n", + " plot_priors(bayesian_strat, hidden_prob_, wins=wins_, trials=trials_,\n", + " lw = 2, alpha = 0.0, plt_vlines=False)\n", + " plt.xlim(0, 0.5)\n" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "[0.01218424 0.0568153 0.00441008 0.03084338 0.2003062 0.04682478\n", + " 0.03857084 0.14390767 0.07413073 0.00383715 0.04700143 0.2294181\n", + " 0.17437263 0.02050903 0.03244106 0.04025097 0.03778872 0.00711611\n", + " 0.03300583 0.0216572 0.01763294 0.01303744 0.03785267 0.03087961\n", + " 0.01221027 0.10118557 0.1115974 0.06856678 0.10960134 0.1346299\n", + " 0.1686299 0.01256515 0.06999508 0.04691529 0.367356 ]\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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ihYF4vkvQTy1W6ZWHrqjL3W+zvfoNUJvCQiVaadHGKUGr0zI07YV/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XEjXVVVzteVUVC6m6OVB3B4ifvGRSYyM2U+qXGzMOh8L+T25hnSaXp+ttPzo24fSltp775c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\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aSlVlvIXfQW4LToCsn/IQhuyHw2pD0YXjvPzuc0FlN+Ru4I4bw7vPSNm18wLHn9gakpzc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\nccG538APxv/DzC5J8dz/jh84tOAHW/lAAXAlfkC/FvjRoK9QREREZPR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628QvX1tfLdcK+saXzp3If3y719c4vjBwBcmA4cOBA3ZePUqVM7RbJDCv2i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hOTmZqKgozObA/7sgmpCiijGmXAmCgOqoBS0wxZTm7UFu34rm7kIMz0MwnH0I7CkPnZiYGCRJ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DnaokNpjx6e2hDno6jQ7Vap65U51ellb5ObDTVOXjpr539P2fhteRBvuq9lp3MiRc54ar1R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\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NDrMEnymK4PH8N4nvf0G3TnIsw7TgS0jNdVh//CWdxwaExs+9X/xnsFin8lYhwk5wzSYI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\nf3WWHeVZSuU6GgvjMsSjoojzCo7zMuIqhf0JpUOKuIr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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [], + "image/png": { + "width": 798, + "height": 484 + } + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "l1klOI7hDcOy" + }, + "source": [ + "## Eliciting expert prior\n", + "\n", + "Specifying a subjective prior is how practitioners incorporate domain knowledge about the problem into our mathematical framework. Allowing domain knowledge is useful for many reasons:\n", + "\n", + "- Aids speeds of MCMC convergence. For example, if we know the unknown parameter is strictly positive, then we can restrict our attention there, hence saving time that would otherwise be spent exploring negative values.\n", + "- More accurate inference. By weighing prior values near the true unknown value higher, we are narrowing our eventual inference (by making the posterior tighter around the unknown) \n", + "- Express our uncertainty better. See the *Price is Right* problem in Chapter 5.\n", + "\n", + "plus many other reasons. Of course, practitioners of Bayesian methods are not experts in every field, so we must turn to domain experts to craft our priors. We must be careful with how we elicit these priors though. Some things to consider:\n", + "\n", + "1. From experience, I would avoid introducing Betas, Gammas, etc. to non-Bayesian practitioners. Furthermore, non-statisticians can get tripped up by how a continuous probability function can have a value exceeding one.\n", + "\n", + "2. Individuals often neglect the rare *tail-events* and put too much weight around the mean of distribution. \n", + "\n", + "3. Related to above is that almost always individuals will under-emphasize the uncertainty in their guesses.\n", + "\n", + "Eliciting priors from non-technical experts is especially difficult. Rather than introduce the notion of probability distributions, priors, etc. that may scare an expert, there is a much simpler solution. " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "TW3MuT7ph3zw" + }, + "source": [ + "\n", + "\n", + "### Trial roulette method \n", + "\n", + "\n", + "The *trial roulette method* [7] focuses on building a prior distribution by placing counters (think casino chips) on what the expert thinks are possible outcomes. The expert is given $N$ counters (say $N=20$) and is asked to place them on a pre-printed grid, with bins representing intervals. Each column would represent their belief of the probability of getting the corresponding bin result. Each chip would represent an $\\frac{1}{N} = 0.05$ increase in the probability of the outcome being in that interval. For example [8]:\n", + "\n", + "> A student is asked to predict the mark in a future exam. The figure below shows a completed grid for the elicitation of a subjective probability distribution. The horizontal axis of the grid shows the possible bins (or mark intervals) that the student was asked to consider. The numbers in top row record the number of chips per bin. The completed grid (using a total of 20 chips) shows that the student believes there is a 30% chance that the mark will be between 60 and 64.9.\n", + "\n", + "\n", + "From this, we can fit a distribution that captures the expert's choice. Some reasons in favor of using this technique are:\n", + "\n", + "1. Many questions about the shape of the expert's subjective probability distribution can be answered without the need to pose a long series of questions to the expert - the statistician can simply read off density above or below any given point, or that between any two points.\n", + "\n", + "2. During the elicitation process, the experts can move around the chips if unsatisfied with the way they placed them initially - thus they can be sure of the final result to be submitted.\n", + "\n", + "3. It forces the expert to be coherent in the set of probabilities that are provided. If all the chips are used, the probabilities must sum to one.\n", + "\n", + "4. Graphical methods seem to provide more accurate results, especially for participants with modest levels of statistical sophistication." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "9amNroash3zw" + }, + "source": [ + "### Example: Stock Returns\n", + "\n", + "\n", + "Take note stock brokers: you're doing it wrong. When choosing which stocks to pick, an analyst will often look at the *daily return* of the stock. Suppose $S_t$ is the price of the stock on day $t$, then the daily return on day $t$ is :\n", + "\n", + "$$r_t = \\frac{ S_t - S_{t-1} }{ S_{t-1} } $$\n", + "\n", + "The *expected daily return* of a stock is denoted $\\mu = E[ r_t ]$. Obviously, stocks with high expected returns are desirable. Unfortunately, stock returns are so filled with noise that it is very hard to estimate this parameter. Furthermore, the parameter might change over time (consider the rises and falls of AAPL stock), hence it is unwise to use a large historical dataset. \n", + "\n", + "Historically, the expected return has been estimated by using the sample mean. This is a bad idea. As mentioned, the sample mean of a small sized dataset has enormous potential to be very wrong (again, see Chapter 4 for full details). Thus Bayesian inference is the correct procedure here, since we are able to see our uncertainty along with probable values.\n", + "\n", + "For this exercise, we will be examining the daily returns of the AAPL, GOOG, MSFT and AMZN. Before we pull in the data, suppose we ask our a stock fund manager (an expert in finance, but see [9] ), \n", + "\n", + "> What do you think the return profile looks like for each of these companies?\n", + "\n", + "Our stock broker, without needing to know the language of Normal distributions, or priors, or variances, etc. creates four distributions using the trial roulette method above. Suppose they look enough like Normals, so we fit Normals to them. They may look like: " + ] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "e2Ao0_kHFxgc", + "outputId": "fa65e3f9-aa39-441b-96f9-68a0b9b2e452", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 508 + } + }, + "source": [ + "plt.figure(figsize(11., 7))\n", + "colors = [TFColor[3], TFColor[0], TFColor[6], TFColor[2]]\n", + "\n", + "expert_prior_params_ = {\"GOOG\":(-0.03, 0.04), \n", + " \"AAPL\":(0.05, 0.03), \n", + " \"AMZN\": (0.03, 0.02), \n", + " \"TSLA\": (-0.02, 0.01),}\n", + "\n", + "for i, (name, params) in enumerate(expert_prior_params_.items()):\n", + " x = tf.linspace(start=-0.15, stop=0.15, num=100)\n", + " plt.subplot(2, 2, i+1)\n", + " y = tfd.Normal(loc=params[0], scale = params[1]).prob(x)\n", + " [ x_, y_ ] = evaluate([ x, y ])\n", + " plt.fill_between(x_, 0, y_, color = colors[i], linewidth=2,\n", + " edgecolor = colors[i], alpha = 0.6)\n", + " plt.title(name + \" prior\")\n", + " plt.vlines(0, 0, y_.max(), \"k\",\"--\", linewidth = 0.5)\n", + " plt.xlim(-0.15, 0.15)\n", + "plt.tight_layout()" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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C3dDdnV5c2WxoNm0WEgtTU9BImlI3dMGdSKfqrDNyIrKlxe5842iZqWpEteGc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\noNFwS60K1Wq3NEZ7/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DKEMWSJj9p9NXnieQ4JAAAABbBS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\nAtWxKpXRCp2FDs2ZJt12l4WLCzR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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [], + "image/png": { + "width": 780, + "height": 491 + } + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "dEAv79VVh3zz" + }, + "source": [ + "Note that these are subjective priors: the expert has a personal opinion on the stock returns of each of these companies, and is expressing them in a distribution. He's not wishful thinking -- he's introducing domain knowledge.\n", + "\n", + "In order to better model these returns, we should investigate the *covariance matrix* of the returns. For example, it would be unwise to invest in two stocks that are highly correlated, since they are likely to tank together (hence why fund managers suggest a diversification strategy). We will use the *Wishart distribution* for this, introduced earlier." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "lQmwe9Agh3z0" + }, + "source": [ + "Let's get some historical data for these stocks. We will use the covariance of the returns as a starting point for our Wishart random variable. This is not empirical bayes (as we will go over later) because we are only deciding the starting point, not influencing the parameters." + ] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "kQYQK919Hdbs", + "outputId": "34a0896a-8ad7-411b-8e45-50cf927a2828", + "cellView": "both", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 215 + } + }, + "source": [ + "#@title External Stock Data\n", + "import datetime\n", + "import collections\n", + "import pandas_datareader.data as web\n", + "import pandas as pd\n", + "\n", + "n_observations = 100 #@param {type:\"slider\", min:50, max:200, step:10}\n", + "#@markdown We will truncate the the most recent 100 days by default.\n", + "stock_1 = \"GOOG\" #@param {type:\"string\"}\n", + "stock_2 = \"AAPL\" #@param {type:\"string\"}\n", + "stock_3 = \"AMZN\" #@param {type:\"string\"}\n", + "stock_4 = \"TSLA\" #@param {type:\"string\"}\n", + "stocks = [stock_1, stock_2, stock_3, stock_4]\n", + "\n", + "start_date = \"2015-09-01\" #@param {type:\"date\"}\n", + "end_date = \"2018-04-27\" #@param {type:\"date\"}\n", + "\n", + "CLOSE = 2\n", + "\n", + "stock_closes = pd.DataFrame()\n", + "\n", + "for stock in stocks:\n", + " stock_data = web.DataReader(stock,'yahoo', start_date, end_date)\n", + " dates = stock_data.index.values\n", + " x = np.array(stock_data)\n", + " stock_series = pd.Series(x[1:,CLOSE].astype(float), name=stock)\n", + " stock_closes[stock] = stock_series\n", + " \n", + "stock_closes = stock_closes[::-1]\n", + "stock_returns = stock_closes.pct_change()[1:][-n_observations:]\n", + "dates = dates[-n_observations:]\n", + "stock_returns_obs = stock_returns.values.astype(dtype=np.float32)\n", + "print (stock_returns[:10])" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + " GOOG AAPL AMZN TSLA\n", + "99 0.000252 0.040504 0.002425 0.022456\n", + "98 0.013630 0.015911 -0.009048 0.017082\n", + "97 0.000028 -0.028467 -0.015485 0.023693\n", + "96 -0.029602 -0.015918 -0.025733 -0.015869\n", + "95 -0.019326 -0.020194 -0.016075 -0.010667\n", + "94 0.021333 0.034806 0.022557 0.046690\n", + "93 -0.015655 -0.022457 -0.008404 -0.046668\n", + "92 0.018908 0.018295 0.013998 0.016284\n", + "91 0.036108 0.024091 0.070022 0.048464\n", + "90 -0.012547 0.002293 0.007038 -0.001934\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "8v4Jr5Psh3z6" + }, + "source": [ + "And here let's form our basic model:" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "rA2nnMW6Hx04", + "outputId": "7f598489-548d-41b1-c065-2d71252ff54f", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 179 + } + }, + "source": [ + "expert_prior_mu = tf.constant([x[0] for x in expert_prior_params_.values()], dtype=tf.float32)\n", + "expert_prior_std = tf.constant([x[1] for x in expert_prior_params_.values()], dtype=tf.float32)\n", + "\n", + "true_mean = tf.to_float(stock_returns.mean())\n", + "print(\"Observed Mean Stock Returns: \\n\", evaluate(true_mean),\"\\n\")\n", + "true_covariance = tf.to_float(stock_returns.cov().values)\n", + "print(\"\\n Observed Stock Returns Covariance matrix: \\n\", evaluate(true_covariance))" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Observed Mean Stock Returns: \n", + " [-0.00147148 0.00154825 -0.00154284 0.00275819] \n", + "\n", + "\n", + " Observed Stock Returns Covariance matrix: \n", + " [[0.00033305 0.00017166 0.00032086 0.00023813]\n", + " [0.00017166 0.00034335 0.0002082 0.00021941]\n", + " [0.00032086 0.0002082 0.00042362 0.00028964]\n", + " [0.00023813 0.00021941 0.00028964 0.0006579 ]]\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "ybjhB13Ch3z9" + }, + "source": [ + "Here are the returns for our chosen stocks:" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "wNEjA6F9GJoB", + "outputId": "fb67c21c-ea87-48cc-c13b-9b02d6cc8332", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 310 + } + }, + "source": [ + "plt.figure(figsize(12.5, 4))\n", + "\n", + "cum_returns = np.cumprod(1 + stock_returns) - 1\n", + "cum_returns.index = dates#[::-1]\n", + "cum_returns.plot()\n", + "\n", + "plt.legend(loc = \"upper left\")\n", + "plt.title(\"Return space\")\n", + "plt.ylabel(\"Return of $1 on first date, x100%\");" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + }, + { + "output_type": "display_data", + "data": { + "image/png": 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zEj+v0rjHczKZm/iZ/3hYz2u5dfgjhIWGA3As/SDvfv0cOXnZAUQvEriAKqOc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\naZhE+/GZvNDPo9VsI6JrLLtqU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i8qCDjgqxgMDAyuHNJzqy/QBUFgdO+z+7sYGBicG9Pe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qbxxokFfHBqIT+amLFVgah2hw/189LMfAr87hxhG86bXcffF7dw1hu1LG1w+1FZCh49Io8D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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [], + "image/png": { + "width": 849, + "height": 275 + } + } + } + ] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "I0142HOhGZZC", + "outputId": "74ab17f8-6568-484e-f8f4-1ccc7442146a", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 512 + } + }, + "source": [ + "plt.figure(figsize(11., 7))\n", + "\n", + "for i, _stock in enumerate(stocks):\n", + " plt.subplot(2,2,i+1)\n", + " plt.hist(stock_returns[_stock], bins=20,\n", + " normed = True, histtype=\"stepfilled\",\n", + " color=colors[i], alpha=0.7)\n", + " plt.title(_stock + \" returns\")\n", + " plt.xlim(-0.15, 0.15)\n", + "\n", + "plt.tight_layout()\n", + "plt.suptitle(\"Histogram of daily returns\", size =14);" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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LiHhzRPw8Im6oj2/Xkn6X+t3/6/q7/IaIuDAi3hgR27SkPbZ+5+1cHzq35ftwebtydCnr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\nvq3D8T8CLm/z+FVDKJskSZI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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [], + "image/png": { + "width": 780, + "height": 495 + } + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "XliwM2I0h30E" + }, + "source": [ + "Below we perform the inference on the posterior mean return and posterior covariance matrix. " + ] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "Ox3yBj3DIAvX", + "colab": {} + }, + "source": [ + "def stock_joint_log_prob(observations, prior_mu, prior_scale_diag, loc, scale_tril):\n", + " \"\"\"MVN with priors: loc=Normal, covariance=Wishart.\n", + "\n", + " Args:\n", + " observations: `[n, d]`-shaped `Tensor` representing Bayesian Gaussian\n", + " Mixture model draws. Each sample is a length-`d` vector.\n", + " prior_mu: Expert Prior Mu\n", + " prior_scale_diag: Expert Prior scale (diagonal)\n", + " loc: `[K, d]`-shaped `Tensor` representing the location parameter of the\n", + " `K` components.\n", + " scale_tril: `[K, d, d]`-shaped `Tensor` representing `K` lower\n", + " triangular `cholesky(Covariance)` matrices, each being sampled from\n", + " a Wishart distribution.\n", + "\n", + " Returns:\n", + " log_prob: `Tensor` representing joint log-density over all inputs.\n", + " \"\"\"\n", + " rv_loc = tfd.MultivariateNormalDiag(loc=prior_mu,\n", + " scale_identity_multiplier=1.)\n", + " rv_cov = tfd.Wishart(\n", + " df=10,\n", + " # scale_tril = chol(diag(prior_scale_diag**2)) is equivalent to\n", + " scale_tril=tf.diag(prior_scale_diag), \n", + " # For computational reasons, let's make all calcultions in Cholesky form.\n", + " input_output_cholesky=True) \n", + " rv_observations = tfd.MultivariateNormalTriL(\n", + " loc=loc,\n", + " scale_tril=scale_tril)\n", + " return (rv_loc.log_prob(loc) +\n", + " rv_cov.log_prob(scale_tril) +\n", + " tf.reduce_sum(rv_observations.log_prob(observations), axis=-1))\n", + " " + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "bDmQXEN3h30E", + "colab": {} + }, + "source": [ + "num_results = 30000\n", + "num_burnin_steps = 5000\n", + "\n", + "\n", + "# Set the chain's start state.\n", + "initial_chain_state = [\n", + " expert_prior_mu,\n", + " tf.diag(expert_prior_std),\n", + "]\n", + "\n", + "# Set the unconstraining bijectors.\n", + "unconstraining_bijectors = [\n", + " tfb.Identity(),\n", + "# Maps between a positive-definite matrix and a numerically stabilized, \n", + "# lower-triangular form.\n", + " tfb.ScaleTriL() \n", + "]\n", + "\n", + "# Define a closure over our joint_log_prob.\n", + "unnormalized_posterior_log_prob = lambda *args: stock_joint_log_prob(\n", + " stock_returns_obs, expert_prior_mu, expert_prior_std, *args)\n", + "\n", + "# Initialize the step_size. (It will be automatically adapted.)\n", + "with tf.variable_scope(tf.get_variable_scope(), reuse=tf.AUTO_REUSE):\n", + " step_size = tf.get_variable(\n", + " name='stock_step_size',\n", + " initializer=tf.constant(0.5, dtype=tf.float32),\n", + " trainable=False,\n", + " use_resource=True\n", + ")\n", + "\n", + "kernel=tfp.mcmc.TransformedTransitionKernel(\n", + " inner_kernel=tfp.mcmc.HamiltonianMonteCarlo(\n", + " target_log_prob_fn=unnormalized_posterior_log_prob,\n", + " num_leapfrog_steps=2,\n", + " step_size=step_size,\n", + " state_gradients_are_stopped=True),\n", + " # Since HMC operates over unconstrained space, we need to transform\n", + " # the samples so they live in real-space.\n", + " bijector=unconstraining_bijectors)\n", + "\n", + "kernel = tfp.mcmc.SimpleStepSizeAdaptation(\n", + " inner_kernel=kernel, num_adaptation_steps=int(num_burnin_steps * 0.8))\n", + "\n", + "\n", + "# Sample from the chain.\n", + "[\n", + " stock_return_samples,\n", + " chol_covariance_samples,\n", + "], kernel_results = tfp.mcmc.sample_chain(\n", + " num_results=num_results,\n", + " num_burnin_steps=num_burnin_steps,\n", + " current_state=initial_chain_state,\n", + " kernel=kernel)\n", + "\n", + "mean_chol_covariance = tf.reduce_mean(chol_covariance_samples, axis=0)\n", + "\n", + "# Initialize any created variables.\n", + "init_g = tf.global_variables_initializer()\n" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "xNpzHKy_SvmO", + "outputId": "a4c9e246-49a5-43a7-d0ec-6d4b81e1c757", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 53 + } + }, + "source": [ + "# Can take up to 1 minute in Graph Mode\n", + "evaluate(init_g)\n", + "[\n", + " stock_return_samples_,\n", + " chol_covariance_samples_,\n", + " mean_chol_covariance_,\n", + " kernel_results_\n", + "] = evaluate([\n", + " stock_return_samples,\n", + " chol_covariance_samples,\n", + " mean_chol_covariance,\n", + " kernel_results\n", + "])\n", + "print(\"acceptance rate: {}\".format(\n", + " kernel_results_.inner_results.inner_results.is_accepted.mean()))\n", + "\n", + "print(\"final step size: {}\".format(\n", + " kernel_results_.new_step_size[-100:].mean()))\n" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "acceptance rate: 0.4810333333333333\n", + "final step size: 0.0013554277829825878\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "LYDbCkha2pAw", + "outputId": "4c2cf308-416e-4afe-f872-0929da268b99", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 284 + } + }, + "source": [ + "plt.figure(figsize(12.5, 4))\n", + "\n", + "# examine the mean return first.\n", + "# mean_return_samples_ is a 4-column data frame collected for each stock\n", + "mu_samples_ = stock_return_samples_\n", + "\n", + "for i in range(4):\n", + " plt.hist(mu_samples_[:,i], alpha = 0.8 - 0.05*i, bins = 30,\n", + " histtype=\"stepfilled\", color=colors[i], density=True, \n", + " label = \"%s\" % stock_returns.columns[i])\n", + "\n", + "plt.vlines(mu_samples_.mean(axis=0), 0, 500, linestyle=\"--\", linewidth = .5)\n", + "\n", + "plt.title(r\"Posterior distribution of $\\mu$, daily stock returns\")\n", + "plt.xlim(-0.010, 0.010)\n", + "plt.legend();" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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gK1eu3OpWfZlg1tixYznssMOarKNPnz5ccsklAFx//fXUf+TQlClTWLRoEb16\n9eKHP2z0SUBbaN++faNBNjVtWwSeMqHAv9Vb91dgGbBnCGFoI2VOA9oDf4sxLs6sjDEuJBW06pDO\ns4UQwjBgT2BJug1JkiRJkiRJkpSHJ554grq6Ovr168dBBx2UdfnHH3+curo6PvnJT3LwwQc3mzeE\nsOmPTh577LFN66urq3nhhRcAGD16dIttZup45513+Mc//rFFXwBOOumkVgXQlL28A08hhEEhhP8M\nIZQ1WF8eQvgucEF61S8zaTHGjcDV6Y+3hRB2q1duP+B/0h9/3kiTV6ZfrwohfKJeud2AX6U//k+M\nsS7XbZIkSZIkSZIkSSmZ2962FDRqqfwhhxzSqvyZdl5//XU2bNgAwOzZs6mrq6NDhw7079+/xTp2\n2WUX+vTps0X7mXrqt6HCKy9AHX2B3wMfhhBeAd4ndXu9/sDuQB1waYzxiQblfgkMBU4A5oUQZpC6\nyukYoBNwc4zxoYaNxRinhRBuA74BvBZC+BOwHjga+BjwIHBLAbZLkiRJytkNN9yQV7qk0uKcTZbj\nL0lSsj788EMgFcxpzJ///Gd+97vfbbX++9//PnvttdemZzXttttuW+VpTCZfXV0dVVVV7Lrrrpvq\n6NGjB+XlrQtt7LbbbixYsGCLZ0Vl3je1Lb/+9a83BacyOnXqxC9/+ctG82trhQg8zQZuBD4NHAgc\nAURgEXAXcGuM8e8NC8UYN4YQTgLGAWOB44CNwN+BX8UYJzXVYIxxXAjhOeCbwDCgDJgL3Anc5tVO\nkiRJStqYMWPySpdUWpyzyXL8JUkqbXPnzmXy5MlbrT///PPZa6+9sq6v/jOZ8pFLPc888wzTp0/f\nYl2XLl0MPGUh78BTjPFt4KIcy9aRujop6yuU0oGpJoNTkiRJUpIqKyuprq7OOV1SaXHOJsvxlyQp\nWTvttBMAH3zwQaPp48aNY9y4cZs+9+/fn4ULF25V/v33329Ve8uWLQOgXbt29OjRA4Cdd94ZgKqq\nKjZs2NCqq54y9WTKZt4vXry4yW2ZNGlz2OGdd95h4MCBreqzNsv7GU+SJEmSJEmSJGn7lQm+vPrq\nqzmVHzRoEAAvv/xyq/K/8sorAPTr129TgGnAgAGEEFi3bt1Wt8JrzLJly1iwYMEW7Wfqgdy3RS0z\n8CRJkiRJkiRJkpp03HHHEULg9ddf54033si6/PHHH0+7du345z//uSmo1JQYI/fdd9+mchk9evRg\n8ODBAI3e1q+hTB19+vThoIMO2rR++PDhADz44IOsX78+uw1Rqxh4kiRJkorguOOOyytdUmlxzibL\n8ZckKVmf+MQnGDFiBAAXX/ALm/YAACAASURBVHwx69aty6r8Pvvss6n89773PdauXdtk3l//+te8\n8cYbdOvWjfPOO2+LtIsvvhiA8ePHN3v11IIFC7jmmmsAuOiiiwghbEobOXIke+yxB0uWLOFnP/tZ\nVtuh1jHwJEmSJBXBlClT8kqXVFqcs8ly/CVJSt51111Hnz59eOGFFxgxYgRz5sxpNN8//vEPVq1a\ntdX6a6+9lj333JNXXnmF0047jXfeeWeL9PXr13PDDTfwgx/8gBACN910E7vuuusWeY499ljOPfdc\nNmzYwKmnnsqjjz66VTsvvPACJ5xwAitXruS4445j7NixW6R37NiRO++8kw4dOnDTTTdxwQUXsGTJ\nkq3qiTHy0ksvtTgu2lrLT9+SJEmSlLWRI0c2e6K0pXRJpcU5myzHX5Kk5O288848+eSTjB07lr/+\n9a8MHTqUffbZhwMOOICuXbvy0Ucf8a9//Yt58+YBMHToUHr37r1F+ccee4zRo0fz7LPPcsghh3DY\nYYfRu3dvVq1axUsvvURVVRVdunThxhtv5OSTT260H9dccw2dOnXiV7/6FV/5ylfYa6+9GDBgAOXl\n5cydO5c333wTgFNPPZVbbrlli6udMj7zmc/wyCOPcM4553DPPfcwceJE+vfvT58+fejUqRNVVVXM\nmTOHpUuXUlZWxsiRI4swotsvA0+SJElSETzxxBN5pUsqLc7ZZDn+kiSVhl69evHYY4/xxz/+kfvv\nv5+XXnqJZ555htraWj72sY+xzz77MG7cOL70pS9x6KGHblW+d+/ezJw5k2nTpvHAAw8we/ZsXnnl\nFSoqKujbty/nnnsu5513Hj179myyD+3atePnP/85X/7yl7nrrrt45pln+POf/8zGjRvZbbfdGDly\nJGeeeSaHH354s9vymc98hldeeYUpU6bw2GOPMWfOHObOnUuMkZ122olPfvKTnH/++Zx66qnstdde\neY/djsTAkyRJkiRJkiRJrXDp4Tsl3YWScOyxx3LsscfmVDZzBVG+VxH179+f66+/Pq86OnbsyFln\nncVZZ52VVz3aks94kiRJkiRJkiRJUkEYeJIkSZKKoLq6Oq90SaXFOZssx1+SJKntMPAkSZIkFcH4\n8ePzSpdUWpyzyXL8JUmS2g4DT5IkSVIRXHTRRXmlSyotztlkOf6SJElth4EnSZIkSZIkSZIkFYSB\nJ0mSJEmSJEmSJBWEgSdJkiSpCCZPnpxXuqTS4pxNluMvSZLUdhh4kiRJkopg0KBBeaVLKi3O2WQ5\n/pIkSW2HgSdJkiSpCD75yU/mlS6ptDhnk+X4S5IktR0GniRJkiRJkiRJklQQBp4kSZIkSZIkSZJU\nEAaeJEmSpCI4++yz80qXVFqcs8ly/CVJktoOA0+SJElSEdx44415pUsqLc7ZZDn+kiRJbYeBJ0mS\nJKkIhg0blle6pNLinE2W4y9JktR2lCfdAUmSJGl7NHv27LzSJZUW52yyHH9JUsm4/pqke5Cdiy8p\nSrV1dXUMGDCARYsWsfPOOzN37lzat29f8HL9+/dn4cKFW6zr2LEjPXv2ZMiQIXzzm99kwIABW6R/\n4xvfYPLkyVx22WVcfvnluW2g8uIVT5IkSZIkSZIkqdWeeuopFi1aBMDy5cuZPn16UcsdffTRjBo1\nilGjRvH5z3+e2tpapkyZwlFHHcX999+f20aoaAw8SZIkSUXQq1evvNIllRbnbLIcf0mSSsuECRMA\n2H333QGYOHFiUctddNFF3Hbbbdx2223cd999zJo1i9NPP50NGzbwne98h6qqqmw3QUVk4EmSJEkq\ngrlz5+aVLqm0OGeT5fhLklQ6qqqqmD59OiEE7rjjDsrKypgxYwbvvfdeUco1pnPnzlx33XV06dKF\nlStXMmPGjFw3R0Vg4EmSJEkqgiuvvDKvdEmlxTmbLMdfkqTSMXXqVGprazn88MMZMmQIRx11FBs3\nbmTy5MlFKdeUbt26se+++wJs9RwoJcvAkyRJklQEV111VV7pkkqLczZZjr8kSaUjc7u80aNHA3DG\nGWcALd82L9dyzVm1ahUAHTp0yLkOFZ6BJ0mSJEmSJEmS1KLZs2fz2muv0a1bN0aMGAHA8OHD6dGj\nB2+99RbPP/98Qcs1Z86cObzzzjsA9O/fP8ctUjEYeJIkSZIkSZIkSS3KXLV00kknUVFRAUDHjh05\n7bTTtkgvVLnGVFdXM336dM4880zq6uro378/hx9+eG4bpKIw8CRJkiQVwcyZM/NKl1RanLPJcvwl\nSUpebW0t06ZNAzbfJi8j8/mhhx5i9erVBSlX3wknnEBlZSWVlZX07duX0aNH88477zBw4EAmTpxI\nu3aGOkpJedIdkCRJkiRJkiRJpe0Pf/gDVVVV7LvvvgwePHiLtIEDB9KvXz9ef/11HnjgAc4666y8\ny9V39NFHs9tuuwGpK6V69erFkCFDGDp0KCGEAm+p8mXgSZIkSSqCI488kurq6pzTJZUW52yyHH9J\nkpKXuR3eypUrOf7447dK/+CDDwCYOHHiFgGkXMvVd9FFF3HEEUfktwHaZgw8SZIkSZIkSZKkJi1a\ntGjTrW+XLVvGsmXLmsz74osvMm/ePPbbb7+cy6lt88aHkiRJkiRJkiSpSZMmTaKuro6hQ4dSXV3d\n5HLyyScDm69yyrWc2jYDT5IkSVIRXHbZZXmlSyotztlkOf6SJCUnxsikSZMAGDlyZLN5M+lTpkxh\n48aNOZdT2+at9iRJkqQiuPzyy/NKl1RanLPJcvwlSUrOs88+y/z58+ncuTMnnnhis3mPOeYYdtll\nF5YsWcKTTz6Zc7nhw4fn3e97772XGTNmNJl+ySWXcNxxx+XdjrbmFU+SJElSERxwwAF5pUsqLc7Z\nZDn+kiQlJ3P7uy9+8Yt069at2bzl5eWccsopBSmXr3fffZeXX365yeWDDz4oSDvaWogxJt2HNmPF\nihUzgWFJ90NK2rx58wB80J+E80Gqz/mwpcrKSqqrq3NOV9vmfNj+OGdzU6i54Phre+Bvg7RZKc6H\nhQsXAtC7d++Ee6LtQTb7U01NDRUVFQBPd+/e/ciidmwb8YonSZIkSZIkSZIkFYSBJ0mSJKkIBg4c\nmFe6pNLinE2W4y9JktR2GHiSJEmSiuDpp5/OK11SaXHOJsvxlyRJajsMPEmSJElFcOGFF+aVLqm0\nOGeT5fhLkiS1HQaeJEmSpCK4++6780qXVFqcs8ly/CVJktoOA0+SJEmSJEmSJEkqCANPkiRJkiRJ\nkiRJKggDT5IkSVIRvPnmm3mlSyotztlkOf6SJElth4EnSZIkqQhmzZqVV7qk0uKcTZbjL0mS1HYY\neJIkSZKKYNSoUXmlSyotztlkOf6SJElth4EnSZIkSZIkSZIkFYSBJ0mSJEmSJEmSJBWEgSdJkiSp\nCG644Ya80iWVFudsshx/SZKktsPAkyRJklQEY8aMyStdUmlxzibL8ZckSWo7ypPugCRJkrQ9qqys\npLq6Oud0SaXFOZssx1+SVCp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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [], + "image/png": { + "width": 847, + "height": 267 + } + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "2HfSPPBKh30I" + }, + "source": [ + "(Plots like these are what inspired the book's cover.)\n", + "\n", + "What can we say about the results above? Clearly TSLA has been a strong performer because the distribution is mostly above zero. Similarly, most of the distribution of AMZN is negative, suggesting that its *true daily return* is negative.\n", + "\n", + "You may not have immediately noticed, but these variables are a whole order of magnitude *less* than our priors on them. For example, to put these on the same scale as the above prior distributions:" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "ivxBl93vh30I", + "outputId": "28174012-ab10-4e26-dd53-18d7e575c955", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 512 + } + }, + "source": [ + "plt.figure(figsize(11.0, 7))\n", + "\n", + "for i in range(4):\n", + " plt.subplot(2,2,i+1)\n", + " plt.hist(mu_samples_[:,i], alpha = 0.8 - 0.05*i, bins = 30,\n", + " histtype=\"stepfilled\", density=True, color = colors[i],\n", + " label = \"%s\" % stock_returns.columns[i])\n", + " plt.title(\"%s\" % stock_returns.columns[i])\n", + " plt.xlim(-0.15, 0.15)\n", + " \n", + "plt.suptitle(r\"Posterior distribution of daily stock returns\")\n", + "plt.tight_layout()" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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h46rnjqmG+xmPXYH/jYhzKa2+f0v5HliRsh93Zuyi4x8p3z1159fuH1T1Xrma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\nrRnK/1Z7btPq9vLMvKdLvov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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [], + "image/png": { + "width": 780, + "height": 495 + } + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "HwOhEcsfh30J" + }, + "source": [ + "Why did this occur? Recall how I mentioned that finance has a very very low signal to noise ratio. This implies an environment where inference is much more difficult. One should be careful about over-interpreting these results: notice (in the first figure) that each distribution is positive at 0, implying that the stock may return nothing. Furthermore, the subjective priors influenced the results. From the fund managers point of view, this is good as it reflects his updated beliefs about the stocks, whereas from a neutral viewpoint this can be too subjective of a result. \n", + "\n", + "Below we show the posterior correlation matrix, and posterior standard deviations. An important caveat to know is that the Wishart distribution models the *covariance matrix* (although it can also be used to model the inverse covariance matrix). We also normalize the matrix to acquire the *correlation matrix*. Since we cannot plot hundreds of matrices effectively, we settle by summarizing the posterior distribution of correlation matrices by showing the *mean posterior correlation matrix* (defined on line 2)." + ] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "eRN-k-VuVN5n", + "outputId": "9bb8efa9-b0f3-47a4-ff9d-da0190bc6cf5", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 508 + } + }, + "source": [ + "mean_covariance_matrix = tf.matmul(mean_chol_covariance_, mean_chol_covariance_, adjoint_b=True)\n", + "mean_covariance_matrix_ = evaluate(mean_covariance_matrix)\n", + "\n", + "def cov2corr(A):\n", + " \"\"\"\n", + " A: covariance matrix input\n", + " Returns:\n", + " A: correlation matrix output\n", + " \"\"\"\n", + " d = tf.sqrt(tf.matrix_diag_part(A))\n", + " A = tf.transpose(tf.transpose(A)/d)/d\n", + " return A\n", + "\n", + "\n", + "plt.subplot(1,2,1)\n", + "plt.imshow(evaluate(cov2corr(mean_covariance_matrix_)) , interpolation=\"none\", \n", + " cmap = \"hot\") \n", + "plt.xticks(evaluate(tf.range(4.)), stock_returns.columns)\n", + "plt.yticks(evaluate(tf.range(4.)), stock_returns.columns)\n", + "plt.colorbar(orientation=\"vertical\")\n", + "plt.title(\"(mean posterior) Correlation Matrix\")\n", + "\n", + "plt.subplot(1,2,2)\n", + "plt.bar(evaluate(tf.range(4.)), evaluate(tf.sqrt(tf.matrix_diag_part(mean_covariance_matrix_))),\n", + " color = \"#5DA5DA\", alpha = 0.7)\n", + "plt.xticks(evaluate(tf.range(4.)), stock_returns.columns);\n", + "plt.title(\"(mean posterior) standard deviations of daily stock returns\")\n", + "\n", + "plt.tight_layout();" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [], + "image/png": { + "width": 774, + "height": 491 + } + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "pU9pYDdyh30M" + }, + "source": [ + "Looking at the above figures, we can say that likely TSLA has an above-average volatility (looking at the return graph this is quite clear). The correlation matrix shows that there are not strong correlations present, except perhaps GOOG and AMZN expressing a much higher correlation of about 0.80; if you look back a few cells at the original plot of the returns over time, you'll see how closely they follow each other.\n", + "\n", + "With this Bayesian analysis of the stock market, we can throw it into a Mean-Variance optimizer (which I cannot stress enough, do not use with frequentist point estimates) and find the minimum. This optimizer balances the tradeoff between a high return and high variance.\n", + "\n", + "$$ w_\\text{opt} = \\max_{w} \\frac{1}{N}\\left( \\sum_{i=0}^N \\mu_i^T w - \\frac{\\lambda}{2}w^T\\Sigma_i w \\right)$$\n", + "\n", + "where $\\mu_i$ and $\\Sigma_i$ are the $i$th posterior estimate of the mean returns and the covariance matrix. This is another example of loss function optimization." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "NVFx5oT5h30M" + }, + "source": [ + "### Protips for the Wishart distribution\n", + "\n", + "If you plan to be using the Wishart distribution, read on. Else, feel free to skip this. \n", + "\n", + "In the problem above, the Wishart distribution behaves pretty nicely. Unfortunately, this is rarely the case. The problem is that estimating an $NxN$ covariance matrix involves estimating $\\frac{1}{2}N(N-1)$ unknowns. This is a large number even for modest $N$. Personally, I've tried performing a similar simulation as above with $N = 23$ stocks, and ended up giving up considering that I was requesting my MCMC simulation to estimate at least $\\frac{1}{2}23*22 = 253$ additional unknowns (plus the other interesting unknowns in the problem). This is not easy for MCMC. Essentially, you are asking you MCMC to traverse 250+ dimensional space. And the problem seemed so innocent initially! Below are some tips, in order of supremacy:\n", + "\n", + "1. Use conjugancy if it applies. See section below.\n", + "\n", + "2. Use a good starting value. What might be a good starting value? Why, the data's sample covariance matrix is! Note that this is not empirical Bayes: we are not touching the prior's parameters, we are modifying the starting value of the MCMC. Due to numerical instability, it is best to truncate the floats in the sample covariance matrix down a few degrees of precision (e.g. while TFP holds up to instability and unsymmetrical matrices much better than higher-level tools like PyMC, it's stilll better to try and avoid them for the sake of model accuracy). \n", + "\n", + "3. Provide as much domain knowledge in the form of priors, if possible. I stress *if possible*. It is likely impossible to have an estimate about each $\\frac{1}{2}N(N-1)$ unknown. In this case, see number 4.\n", + "\n", + "4. Use empirical Bayes, i.e. use the sample covariance matrix as the prior's parameter.\n", + "\n", + "5. For problems where $N$ is very large, nothing is going to help. Instead, ask, do I really care about *every* correlation? Probably not. Further ask yourself, do I really really care about correlations? Possibly not. In finance, we can set an informal hierarchy of what we might be interested in the most: first a good estimate of $\\mu$, the variances along the diagonal of the covariance matrix are secondly important, and finally the correlations are least important. So, it might be better to ignore the $\\frac{1}{2}(N-1)(N-2)$ correlations and instead focus on the more important unknowns.\n", + "\n", + "**Another thing** to note is that Wishart distribution matrices are required to have certain mathematical characteristics that are very restrictive. This makes it so that it is impossible for MCMC methods to propose matrices that will be accepted in our sampling procedure. With our model here we sample the Bartlett decomposition of a Wishart distribution matrix and use that to calculate our samples for the covariance matrix (http://en.wikipedia.org/wiki/Wishart_distribution#Bartlett_decomposition)." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "iRX-m7quh30M" + }, + "source": [ + "## Conjugate Priors\n", + "\n", + "Recall that a $\\text{Beta}$ prior with $\\text{Binomial}$ data implies a $\\text{Beta}$ posterior. Graphically:\n", + "\n", + "$$ \\underbrace{\\text{Beta}}_{\\text{prior}} \\cdot \\overbrace{\\text{Binomial}}^{\\text{data}} = \\overbrace{\\text{Beta}}^{\\text{posterior} } $$ \n", + "\n", + "Notice the $\\text{Beta}$ on both sides of this equation (no, you cannot cancel them, this is not a *real* equation). This is a really useful property. It allows us to avoid using MCMC, since the posterior is known in closed form. Hence inference and analytics are easy to derive. This shortcut was the heart of the Bayesian Bandit algorithm above. Fortunately, there is an entire family of distributions that have similar behaviour. \n", + "\n", + "Suppose $X$ comes from, or is believed to come from, a well-known distribution, call it $f_{\\alpha}$, where $\\alpha$ are possibly unknown parameters of $f$. $f$ could be a Normal distribution, or Binomial distribution, etc. For particular distributions $f_{\\alpha}$, there may exist a prior distribution $p_{\\beta}$, such that:\n", + "\n", + "$$ \\overbrace{p_{\\beta}}^{\\text{prior}} \\cdot \\overbrace{f_{\\alpha}(X)}^{\\text{data}} = \\overbrace{p_{\\beta'}}^{\\text{posterior} } $$ \n", + "\n", + "where $\\beta'$ is a different set of parameters *but $p$ is the same distribution as the prior*. A prior $p$ that satisfies this relationship is called a *conjugate prior*. As I mentioned, they are useful computationally, as we can avoided approximate inference using MCMC and go directly to the posterior. This sounds great, right?\n", + "\n", + "Unfortunately, not quite. There are a few issues with conjugate priors.\n", + "\n", + "1. The conjugate prior is not objective. Hence only useful when a subjective prior is required. It is not guaranteed that the conjugate prior can accommodate the practitioner's subjective opinion.\n", + "\n", + "2. There typically exist conjugate priors for simple, one dimensional problems. For larger problems, involving more complicated structures, hope is lost to find a conjugate prior. For smaller models, Wikipedia has a nice [table of conjugate priors](http://en.wikipedia.org/wiki/Conjugate_prior#Table_of_conjugate_distributions).\n", + "\n", + "Really, conjugate priors are only useful for their mathematical convenience: it is simple to go from prior to posterior. I personally see conjugate priors as only a neat mathematical trick, and offer little insight into the problem at hand. " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "boR5QieIh30N" + }, + "source": [ + "## Jefferys Priors\n", + "\n", + "Earlier, we talked about objective priors rarely being *objective*. Partly what we mean by this is that we want a prior that doesn't bias our posterior estimates. The flat prior seems like a reasonable choice as it assigns equal probability to all values. \n", + "\n", + "But the flat prior is not transformation invariant. What does this mean? Suppose we have a random variable $\\textbf X$ from Bernoulli($\\theta$). We define the prior on $p(\\theta) = 1$. " + ] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "dAizKLxwh30N", + "outputId": "6dedfbb2-d820-4d67-9434-1af57e5f331e", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 326 + } + }, + "source": [ + "plt.figure(figsize(12.5, 5))\n", + "\n", + "x = tf.linspace(start=0.000 ,stop=1, num=150)\n", + "y = tf.linspace(start=1.0, stop=1.0, num=150)\n", + "\n", + "[\n", + " x_, y_\n", + "] = evaluate([\n", + " x, y\n", + "])\n", + "\n", + "lines = plt.plot(x_, y_, color=TFColor[0], lw = 3)\n", + "plt.fill_between(x_, 0, y_, alpha = 0.2, color = lines[0].get_color())\n", + "plt.autoscale(tight=True)\n", + "plt.ylim(0, 2);\n" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [], + "image/png": { + "width": 840, + "height": 309 + } + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "tfk_ynA6h30P" + }, + "source": [ + "Now, let's transform $\\theta$ with the function $\\psi = \\log \\frac{\\theta}{1-\\theta}$. This is just a function to stretch $\\theta$ across the real line. Now how likely are different values of $\\psi$ under our transformation." + ] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "CsaJ4hkWh30P", + "outputId": "982b676e-e5ed-4219-8055-b6b18edce6d4", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 326 + } + }, + "source": [ + "plt.figure(figsize(12.5, 5))\n", + "\n", + "psi = tf.linspace(start=-10. ,stop=10., num=150)\n", + "y = tf.exp(psi) / (1 + tf.exp(psi))**2\n", + " \n", + "[psi_, y_] = evaluate([psi, y])\n", + " \n", + "lines = plt.plot(psi_, y_, color=TFColor[0], lw = 3)\n", + "plt.fill_between(psi_, 0, y_, alpha = 0.2, color = lines[0].get_color())\n", + "plt.autoscale(tight=True)\n", + "plt.ylim(0, 1);" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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Jknq/gl0NppS+EBGPAl8EzgXKgZXAj4EfNmxF1IoRZAkigFm5qSmv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\n6rkqKnJjI0447O1UXV3fbW++C9/t22HnjqKOd9S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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [], + "image/png": { + "width": 836, + "height": 309 + } + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "LFRbEVl_h30Q" + }, + "source": [ + "Oh no! Our function is no longer flat. It turns out flat priors do carry information in them after all. The point of Jeffreys Priors is to create priors that don't accidentally become informative when you transform the variables you placed them originally on.\n", + "\n", + "Jeffreys Priors are defined as:\n", + "\n", + "$$p_J(\\theta) \\propto \\mathbf{I}(\\theta)^\\frac{1}{2}$$\n", + "$$\\mathbf{I}(\\theta) = - \\mathbb{E}\\bigg[\\frac{d^2 \\text{ log } p(X|\\theta)}{d\\theta^2}\\bigg]$$\n", + "\n", + "$\\mathbf{I}$ being the *Fisher information*" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "vPAasBXPh30Q" + }, + "source": [ + "## Effect of the prior as $N$ increases\n", + "\n", + "In the first chapter, I proposed that as the amount of our observations or data increases, the influence of the prior decreases. This is intuitive. After all, our prior is based on previous information, and eventually enough new information will shadow our previous information's value. The smothering of the prior by enough data is also helpful: if our prior is significantly wrong, then the self-correcting nature of the data will present to us a *less wrong*, and eventually *correct*, posterior. \n", + "\n", + "We can see this mathematically. First, recall Bayes Theorem from Chapter 1 that relates the prior to the posterior. The following is a sample from [What is the relationship between sample size and the influence of prior on posterior?](http://stats.stackexchange.com/questions/30387/what-is-the-relationship-between-sample-size-and-the-influence-of-prior-on-poste)[1] on CrossValidated.\n", + "\n", + ">The posterior distribution for a parameter $\\theta$, given a data set ${\\textbf X}$ can be written as \n", + "\n", + "$$p(\\theta | {\\textbf X}) \\propto \\underbrace{p({\\textbf X} | \\theta)}_{{\\textrm likelihood}} \\cdot \\overbrace{ p(\\theta) }^{ {\\textrm prior} } $$\n", + "\n", + "\n", + "\n", + ">or, as is more commonly displayed on the log scale, \n", + "\n", + "$$ \\log( p(\\theta | {\\textbf X}) ) = c + L(\\theta;{\\textbf X}) + \\log(p(\\theta)) $$\n", + "\n", + ">The log-likelihood, $L(\\theta;{\\textbf X}) = \\log \\left( p({\\textbf X}|\\theta) \\right)$, **scales with the sample size**, since it is a function of the data, while the prior density does not. Therefore, as the sample size increases, the absolute value of $L(\\theta;{\\textbf X})$ is getting larger while $\\log(p(\\theta))$ stays fixed (for a fixed value of $\\theta$), thus the sum $L(\\theta;{\\textbf X}) + \\log(p(\\theta))$ becomes more heavily influenced by $L(\\theta;{\\textbf X})$ as the sample size increases. \n", + "\n", + "There is an interesting consequence not immediately apparent. As the sample size increases, the chosen prior has less influence. Hence inference converges regardless of chosen prior, so long as the areas of non-zero probabilities are the same. \n", + "\n", + "Below we visualize this. We examine the convergence of two posteriors of a Binomial's parameter $\\theta$, one with a flat prior and the other with a biased prior towards 0. As the sample size increases, the posteriors, and hence the inference, converge." + ] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "6Q4-vPI_h30R", + "outputId": "5e07cef3-dbd0-4768-9844-e7d3e0dabc9a", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 784 + } + }, + "source": [ + "p = 0.6\n", + "beta1_params = tf.constant([1.,1.])\n", + "beta2_params = tf.constant([2,10])\n", + "\n", + "\n", + "data = tfd.Bernoulli(probs=p).sample(sample_shape=(500))\n", + "[\n", + " beta1_params_, \n", + " beta2_params_, \n", + " data_,\n", + "] = evaluate([\n", + " beta1_params, \n", + " beta2_params, \n", + " data\n", + "])\n", + "\n", + "plt.figure(figsize(12.5, 15))\n", + "plt.figure()\n", + "for i, N in enumerate([0, 4, 8, 32, 64, 128, 500]):\n", + " s = data_[:N].sum() \n", + " plt.subplot(8,1,i+1)\n", + " params1 = beta1_params_ + np.array([s, N-s])\n", + " params2 = beta2_params_ + np.array([s, N-s])\n", + " x = tf.linspace(start=0.00, stop=1., num=125)\n", + " y1 = tfd.Beta(concentration1 = tf.to_float(params1[0]), \n", + " concentration0 = tf.to_float(params1[1])).prob(tf.to_float(x))\n", + " y2 = tfd.Beta(concentration1 = tf.to_float(params2[0]), \n", + " concentration0 = tf.to_float(params2[1])).prob(tf.to_float(x))\n", + " [x_, y1_, y2_] = evaluate([x, y1, y2])\n", + " plt.plot(x_, y1_, label = r\"flat prior\", lw =3)\n", + " plt.plot(x_, y2_, label = \"biased prior\", lw= 3)\n", + " plt.fill_between(x_, 0, y1_, color =\"#5DA5DA\", alpha = 0.15) \n", + " plt.fill_between(x_, 0, y2_, color =\"#F15854\", alpha = 0.15) \n", + " plt.legend(title = \"N=%d\" % N)\n", + " plt.vlines(p, 0.0, 7.5, linestyles = \"--\", linewidth=1)\n", + " plt.ylim( 0, 20)\n" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + }, + { + "output_type": "display_data", + "data": { + "image/png": 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E4y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\ncEgAU9/jTj1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E2uN1jCp08sVJfj5Z6aPQnZ1p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0779u0Ljdu3bx+DBw8mPT2dOXPmcO+991K7dm2CgoJo3bo18+bNo2vXrpw8eZIxY8Z4+Coq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\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Fli9frqlTpyolJUVXXnml3n//fbVv377eY7v07NlTM2fO1PXXX6+srCy99dZbmjNnjvr0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\nvu7VNOtLwnpmVZm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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [], + "image/png": { + "width": 820, + "height": 749 + } + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "8K7Ps7Myh30S" + }, + "source": [ + "Keep in mind, not all posteriors will \"forget\" the prior this quickly. This example was just to show that *eventually* the prior is forgotten. The \"forgetfulness\" of the prior as we become awash in more and more data is the reason why Bayesian and Frequentist inference eventually converge as well." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "XUkMJLSUh30T" + }, + "source": [ + "### Bayesian perspective of Penalized Linear Regressions\n", + "\n", + "There is a very interesting relationship between a penalized least-squares regression and Bayesian priors. A penalized linear regression is a optimization problem of the form:\n", + "\n", + "$$ \\text{argmin}_{\\beta} \\;\\; (Y - X\\beta)^T(Y - X\\beta) + f(\\beta)$$\n", + "\n", + "for some function $f$ (typically a norm like $|| \\cdot ||_p^p$). \n", + "\n", + "We will first describe the probabilistic interpretation of least-squares linear regression. Denote our response variable $Y$, and features are contained in the data matrix $X$. The standard linear model is:\n", + "\n", + "\\begin{equation}\n", + "Y = X\\beta + \\epsilon\n", + "\\end{equation}\n", + "\n", + "where $\\epsilon \\sim \\text{Normal}( {\\textbf 0}, \\sigma{\\textbf I })$. Simply, the observed $Y$ is a linear function of $X$ (with coefficients $\\beta$) plus some noise term. Our unknown to be determined is $\\beta$. We use the following property of Normal random variables:\n", + "\n", + "$$ \\mu' + \\text{Normal}( \\mu, \\sigma ) \\sim \\text{Normal}( \\mu' + \\mu , \\sigma ) $$\n", + "\n", + "to rewrite the above linear model as:\n", + "$$\n", + "\\begin{align}\n", + "& Y = X\\beta + \\text{Normal}( {\\textbf 0}, \\sigma{\\textbf I }) \\\\\n", + "& Y = \\text{Normal}( X\\beta , \\sigma{\\textbf I }) \\\\\n", + "\\end{align}\n", + "$$\n", + "In probabilistic notation, denote $f_Y(y \\; | \\; \\beta )$ the probability distribution of $Y$, and recalling the density function for a Normal random variable (see [here](http://en.wikipedia.org/wiki/Normal_distribution) ):\n", + "\n", + "$$ f_Y( Y \\; |\\; \\beta, X) = L(\\beta|\\; X,Y)= \\frac{1}{\\sqrt{ 2\\pi\\sigma} } \\exp \\left( \\frac{1}{2\\sigma^2} (Y - X\\beta)^T(Y - X\\beta) \\right) $$\n", + "\n", + "This is the likelihood function for $\\beta$. Taking the $\\log$:\n", + "\n", + "$$ \\ell(\\beta) = K - c(Y - X\\beta)^T(Y - X\\beta) $$\n", + "\n", + "where $K$ and $c>0$ are constants. Maximum likelihood techniques wish to maximize this for $\\beta$, \n", + "\n", + "$$\\hat{ \\beta } = \\text{argmax}_{\\beta} \\;\\; - (Y - X\\beta)^T(Y - X\\beta) $$\n", + "\n", + "Equivalently we can *minimize the negative* of the above:\n", + "\n", + "$$\\hat{ \\beta } = \\text{argmin}_{\\beta} \\;\\; (Y - X\\beta)^T(Y - X\\beta) $$\n", + "\n", + "This is the familiar least-squares linear regression equation. Therefore we showed that the solution to a linear least-squares is the same as the maximum likelihood assuming Normal noise. Next we extend this to show how we can arrive at penalized linear regression by a suitable choice of prior on $\\beta$. \n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "pxcl7cMcCsRd" + }, + "source": [ + "#### Penalized least-squares\n", + "\n", + "In the above, once we have the likelihood, we can include a prior distribution on $\\beta$ to derive to the equation for the posterior distribution:\n", + "\n", + "$$P( \\beta | Y, X ) = L(\\beta|\\;X,Y)p( \\beta )$$\n", + "\n", + "where $p(\\beta)$ is a prior on the elements of $\\beta$. What are some interesting priors? \n", + "\n", + "1\\. If we include *no explicit* prior term, we are actually including an uninformative prior, $P( \\beta ) \\propto 1$, think of it as uniform over all numbers. \n", + "\n", + "2\\. If we have reason to believe the elements of $\\beta$ are not too large, we can suppose that *a priori*:\n", + "\n", + "$$ \\beta \\sim \\text{Normal}({\\textbf 0 }, \\lambda {\\textbf I } ) $$\n", + "\n", + "The resulting posterior density function for $\\beta$ is *proportional to*:\n", + "\n", + "$$ \\exp \\left( \\frac{1}{2\\sigma^2} (Y - X\\beta)^T(Y - X\\beta) \\right) \\exp \\left( \\frac{1}{2\\lambda^2} \\beta^T\\beta \\right) $$\n", + "\n", + "and taking the $\\log$ of this, and combining and redefining constants, we arrive at:\n", + "\n", + "$$ \\ell(\\beta) \\propto K - (Y - X\\beta)^T(Y - X\\beta) - \\alpha \\beta^T\\beta $$\n", + "\n", + "we arrive at the function we wish to maximize (recall the point that maximizes the posterior distribution is the MAP, or *maximum a posterior*):\n", + "\n", + "$$\\hat{ \\beta } = \\text{argmax}_{\\beta} \\;\\; -(Y - X\\beta)^T(Y - X\\beta) - \\alpha \\;\\beta^T\\beta $$\n", + "\n", + "Equivalently, we can minimize the negative of the above, and rewriting $\\beta^T \\beta = ||\\beta||_2^2$:\n", + "\n", + "$$\\hat{ \\beta } = \\text{argmin}_{\\beta} \\;\\; (Y - X\\beta)^T(Y - X\\beta) + \\alpha \\;||\\beta||_2^2$$\n", + "\n", + "This above term is exactly Ridge Regression. Thus we can see that ridge regression corresponds to the MAP of a linear model with Normal errors and a Normal prior on $\\beta$.\n", + "\n", + "3\\. Similarly, if we assume a *Laplace* prior on $\\beta$, ie. \n", + "\n", + "$$ f_\\beta( \\beta) \\propto \\exp \\left(- \\lambda ||\\beta||_1 \\right)$$\n", + "\n", + "and following the same steps as above, we recover:\n", + "\n", + "$$\\hat{ \\beta } = \\text{argmin}_{\\beta} \\;\\; (Y - X\\beta)^T(Y - X\\beta) + \\alpha \\;||\\beta||_1$$\n", + "\n", + "which is LASSO regression. Some important notes about this equivalence. The sparsity that is a result of using a LASSO regularization is not a result of the prior assigning high probability to sparsity. Quite the opposite actually. It is the combination of the $|| \\cdot ||_1$ function and using the MAP that creates sparsity on $\\beta$: [purely a geometric argument](http://camdp.com/blogs/least-squares-regression-l1-penalty). The prior does contribute to an overall shrinking of the coefficients towards 0 though. An interesting discussion of this can be found in [2].\n", + "\n", + "For an example of Bayesian linear regression, see Chapter 4's example on financial losses." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "m6fFxaLFh30V" + }, + "source": [ + "## References\n", + "\n", + "[1] Macro, . \"What is the relationship between sample size and the influence of prior on posterior?.\" 13 Jun 2013. StackOverflow, Online Posting to Cross-Validated. Web. 25 Apr. 2013.\n", + "\n", + "[2] Starck, J.-L., , et al. \"Sparsity and the Bayesian Perspective.\" Astronomy & Astrophysics. (2013): n. page. Print.\n", + "\n", + "[3] Kuleshov, Volodymyr, and Doina Precup. \"Algorithms for the multi-armed bandit problem.\" Journal of Machine Learning Research. (2000): 1-49. Print.\n", + "\n", + "[4] Gelman, Andrew. \"Prior distributions for variance parameters in hierarchical models.\" Bayesian Analysis. 1.3 (2006): 515-533. Print.\n", + "\n", + "[5] Gelman, Andrew, and Cosma R. Shalizi. \"Philosophy and the practice of Bayesian statistics.\" British Journal of Mathematical and Statistical Psychology. (2012): n. page. Web. 17 Apr. 2013.\n", + "\n", + "[6] James, Neufeld. \"Reddit's \"best\" comment scoring algorithm as a multi-armed bandit task.\" Simple ML Hacks. Blogger, 09 Apr 2013. Web. 25 Apr. 2013.\n", + "\n", + "[7] Oakley, J. E., Daneshkhah, A. and O’Hagan, A. Nonparametric elicitation using the roulette method. Submitted to Bayesian Analysis.\n", + "\n", + "[8] \"Eliciting priors from experts.\" 19 Jul 2010. StackOverflow, Online Posting to Cross-Validated. Web. 1 May. 2013. .\n", + "\n", + "[9] Taleb, Nassim Nicholas (2007), The Black Swan: The Impact of the Highly Improbable, Random House, ISBN 978-1400063512" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "RD-EgGMch30V", + "colab": {} + }, + "source": [ + "from IPython.core.display import HTML\n", + "def css_styling():\n", + " styles = open(\"../styles/custom.css\", \"r\").read()\n", + " return HTML(styles)\n", + "css_styling()\n" + ], + "execution_count": 0, + "outputs": [] + } + ] +} \ No newline at end of file diff --git a/Chapter6_Priorities/README.md b/Chapter6_Priorities/README.md new file mode 100644 index 00000000..92133949 --- /dev/null +++ b/Chapter6_Priorities/README.md @@ -0,0 +1,4 @@ +Chapter 6: Getting our priorities straight +=========== + +### [Read it online here](http://nbviewer.ipython.org/urls/raw.github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/master/Chapter6_Priorities/Ch6_Priors_PyMC2.ipynb) diff --git a/Chapter6_Priorities/d3bandits.js b/Chapter6_Priorities/d3bandits.js new file mode 100644 index 00000000..98af25ea --- /dev/null +++ b/Chapter6_Priorities/d3bandits.js @@ -0,0 +1,334 @@ + var ARMS = [0,0,0,0,0,0]; + var _PROBS = [ rbeta(2,9), rbeta(5,9), rbeta(5,9) ]; + var BB_RUN = 0; + + d3.select( "#reveal-div" ) + .selectAll("p") + .data( _PROBS ) + .enter() + .append("p") + .text( function(d,i){ return "Arm " + (i + 1) + ": " +d.toFixed(4); } ) + + + function pdfbeta(x_array,a,b){ + //x is an array + _beta = Beta(a,b); + function _pdfbeta(x){ + return ( Math.pow(x,a-1)*Math.pow(1-x, b - 1) )/_beta + } + + return x_array.map( _pdfbeta ) + } + + function Beta(a,b){ + //stirlings approx + // use logs and exponentials to avoid underflow + // with logs is still giving me errors + + //var n = Math.pow(a, a - 0.5)*Math.pow(b, b-0.5) + + var log_n = Math.log(a)*(a - 0.5) + Math.log(b)*( b-0.5) + //var d = Math.pow( a + b, a+ b-0.5) + var log_d = Math.log( a + b)*(a+ b-0.5) + return Math.sqrt( 2*Math.PI)*Math.exp(log_n - log_d) + } + + + function rbeta(a,b){ + //from Simulation and MC, Wiley + + var p = a/b; + if (Math.min(a,b) <= 1){ + var lambda = Math.min(a,b) + }else{ + var lambda = Math.sqrt( (2*a*b - a - b)/(a+b-2) ) + } + + while (1){ + var R1 = Math.random(); + var R2 = Math.random(); + var y = Math.pow( ( 1./R1 - 1.), 1./lambda ); + if ( 4*R1*R2*R2 < (Math.pow(y, a - lambda)*Math.pow( (1.+ p)/(1 + p*y) , a + b ) )){ + return (p*y)/(1+ p*y) + } + } + } + + function rbeta_array( arm_counts){ + // to be used with ARMS with uniform prior. + samples = [] + for (var i=0; i < arm_counts.length/2; i++){ + samples.push( + rbeta(arm_counts[2*i + 1]+1, 1+arm_counts[2*i] - arm_counts[2*i+1] ) + ) + } + return samples + } + + function draw_arm( p ){ + if ( Math.random() < p){ return 1 } else { return 0 } + } + + + function update_arm( arm_number ){ + var result = draw_arm(_PROBS[arm_number] ); + ARMS[2*arm_number] += 1; + ARMS[2*arm_number+1] += result; + redraw(arm_number); + return + } + + + function bayesian_bandits(){ + //for (var i = 0; i < n_pulls; i++ ){ + //sample from Beta distributions + var samples = rbeta_array(ARMS); + var select = samples.indexOf( d3.max( samples) ); + update_arm( select ); + if (BB_RUN < 300){ + BB_RUN += 1; + window.setTimeout( bayesian_bandits, 100 ) + } + else{ + return + } + //} + } + + var x_array = []; + var _N = 100; + var max_data = 10 + for ( var i =0; i < _N; i++ ){ + x_array.push( .01*i ) + } + + var colors = ["#348ABD", "#A60628", "#7A68A6"]; + var fill_colors = [ "rgba(52, 128, 189,0.1)", "rgba(166, 6, 40, 0.1 )", "rgba( 122, 104, 166,0.1 )"]; + + var w = 600, + h = 150, + margin = 15, + y = d3.scale.linear().domain([0, max_data]).range([h - margin,0 + margin ]), + x = d3.scale.linear().domain([0,_N]).range([0 + margin, w - margin]) + + var vis = d3.select("#beta-graphs") + .append("svg:svg") + .attr("width", w ) + .attr("height", h ) + + var g = vis.append("svg:g") + + var line = d3.svg.line() + .x(function(d, i) { return x(i); }) + .y(y) + + + for ( var i =0; i < 3; i++){ + var _data = pdfbeta(x_array, 1 + ARMS[2*i+1],1+ARMS[2*i] - ARMS[2*i+1] ); + g.selectAll('path.line') + .data( [_data] ) + .enter() + .append("svg:path") + .attr("stroke", colors[i] ) + //.attr("fill", fill_colors[i] ) + //.attr("fill", fill_colors[i] ) + //.attr("stroke-width", 0 ) + .attr("d", line ) + .attr("id", "line-" + i ); + } + + + g.append("svg:line") + .attr("x1", x(0)) + .attr("y1", y(0)) + .attr("x2", x(w)) + .attr("y2", y(0)) + + g.append("svg:line") + .attr("x1", x(0)) + .attr("y1", y(0)) + .attr("x2", x(0)) + .attr("y2", y(max_data)) + + g.selectAll(".xLabel") + .data( d3.range(0,1.2,.2) ) + .enter().append("svg:text") + .attr("class", "xLabel") + .text(String) + .attr("x", function(d) { return x(100*d) }) + .attr("y", h) + .attr("text-anchor", "middle") + .attr("dy", 0.0 ) + /* + g.selectAll(".yLabel") + .data(y.ticks(4)) + .enter().append("svg:text") + .attr("class", "yLabel") + .text(String) + .attr("x", 0) + .attr("y", function(d) { return y(d) }) + .attr("text-anchor", "right") + .attr("dy", 4) + */ + g.selectAll(".xTicks") + .data(x.ticks(5)) + .enter().append("svg:line") + .attr("class", "xTicks") + .attr("x1", function(d) { return x(d); }) + .attr("y1", y(0)) + .attr("x2", function(d) { return x(d); }) + .attr("y2", y(-0.1)) + + vis.append("text") + .attr("x", (w / 2)) + .attr("y", 15 ) + .attr("text-anchor", "middle") + .style("font-size", "17px") + .text("Posterior Distributions"); + + /* + g.selectAll(".yTicks") + .data(y.ticks(4)) + .enter().append("svg:line") + .attr("class", "yTicks") + .attr("y1", function(d) { return -1 * y(d); }) + .attr("x1", x(-0.3)) + .attr("y2", function(d) { return -1 * y(d); }) + .attr("x2", x(0)) + */ + + + + + + + var data = ARMS; + var labellist = ["Arm 1", "", "Arm 2", "", "Arm 3", ""]; + + var w_bar = 600, + h_bar = 170, + labelpad = 50, + x_bar = d3.scale.linear().domain([0, 100]).range([0, w_bar]), + y_bar = d3.scale.ordinal().domain(d3.range(data.length)).rangeBands([0, h_bar], .2); + + var vis = d3.select("#paired-bar-chart") + .append("svg:svg") + .attr("width", w_bar + 40) + .attr("height", h_bar + 20) + .append("svg:g") + + var bars = vis.selectAll("g.bar") + .data(data) + .enter().append("svg:g") + .attr("class", "bar") + .attr("transform", function(d, i) { return "translate(" + labelpad + "," + y_bar(i) + ")"; }) + + + bars.append("svg:rect") + .attr("fill", function(d, i) { return (i%2)? colors[i]: fill_colors[i]; } ) //Alternate colors + .attr("width", function(d,i){ return x_bar(d)*0.5 }) + .attr("height", y_bar.rangeBand()); + + bars.append("svg:text") + .attr("x", 0) + .attr("y", 10 + y_bar.rangeBand() / 2) + .attr("dx", -6) + .attr("dy", ".50em") + .attr("text-anchor", "end") + .text(function(d, i) { return labellist[i]; }); + + var counts = bars.append("svg:text") + .attr("x", 0) + .attr("y", 10 + y_bar.rangeBand() / 2) + .attr("dx", -6) + .attr("dy", "-.40em") + .attr("text-anchor", "end") + .text(function(d, i) { return ""; }); + + + var rules = vis.selectAll("g.rule") + .data(x.ticks(10)) + .enter().append("svg:g") + .attr("class", "rule") + .attr("transform", function(d) { return "translate(" + x_bar(d) + ", 0)"; }); + + + rules.append("svg:line") + .attr("y1", 0) + .attr("y2", h_bar) + .attr("x1", labelpad) + .attr("x2", labelpad) + .attr("stroke", "white") + .attr("stroke-opacity", .3); + + + + function redraw(arm_number){ + + var _data = [] + for ( var i =0; i < 3; i++){ + _data.push( pdfbeta(x_array, 1 + ARMS[2*i+1],1+ARMS[2*i] - ARMS[2*i+1] ) ); + + } + //update what is max. + max_data = d3.max( [ + 10, + d3.max(_data[0]), + d3.max(_data[1]), + d3.max(_data[2]) ]) + + y = d3.scale.linear().domain([0, max_data]).range([h - margin,0 + margin ]) + line = d3.svg.line() + .x(function(d, i) { return x(i); }) + .y(y) + + for ( var i =0; i < 3; i++){ + g.select("#line-" + i) + .data( [_data[i]] ) + .attr("d", line ) + } + + + + + + + bars.data(ARMS) + .enter().append("svg:g") + .attr("class", "bar") + .attr("transform", function(d, i) { return "translate(" + labelpad + "," + y(i) + ")"; }); + + + bars.append("svg:rect") + .attr("fill", function(d, ix) {_ix = Math.floor(ix/2); return (ix%2)? fill_colors[_ix]: colors[_ix]; } ) //Alternate colors + .attr("width", function(d,i){ return x_bar(d)*0.5 }) + .attr("height", y_bar.rangeBand()); + + + counts + .attr("x", 0) + .attr("y", 10 + y_bar.rangeBand() / 2) + .attr("dx", function( d,i) { return !(i%2) ? 50 + 3.2*data[i] : 67 + 3.2*data[i] ;}) + .attr("dy", "-.40em") + .attr("text-anchor", "end") + .text(function(d, i) { return !(i%2) ? data[i] + " pulls" : data[i] + " rewards" ;}); + + //update scoreboard + var rewards = ARMS[1] + ARMS[3] + ARMS[5]; + var pulls = ARMS[0] + ARMS[2] + ARMS[4]; + document.getElementById("rewards").innerHTML = rewards ; + document.getElementById("pulls").innerHTML = pulls ; + document.getElementById("ratio").innerHTML = (rewards/pulls).toFixed(3) ; + + } + + + d3.select( "#reveal-div" ) + .selectAll("p") + .data( _PROBS ) + .enter() + .append("span") + .attr( "style", "margin-left:15; margin-right: 30px; margin-top:0" ) + .text( function(d,i){ return d.toFixed(4) ; } ) + //redraw() //to initialize + \ No newline at end of file diff --git a/Chapter6_Priorities/data/data.csv b/Chapter6_Priorities/data/data.csv new file mode 100644 index 00000000..47ece4c5 --- /dev/null +++ b/Chapter6_Priorities/data/data.csv @@ -0,0 +1,7 @@ +Pos,Team,Pl,w,D,L,PF,PA,DF,TF,TA,Pts +1,Ireland,5,4,0,1,132,49,83,16,4,8 +2,England,5,4,0,1,138,65,73,14,5,8 +3,Wales,5,3,0,2,122,79,43,11,6,6 +4,France,5,3,0,2,101,100,1,9,10,6 +5,Scotland,5,1,0,4,47,138,-91,4,15,2 +6,Italy,5,0,0,5,63,172,-109,7,21,0 diff --git a/Chapter6_Priorities/data/data.txt b/Chapter6_Priorities/data/data.txt new file mode 100644 index 00000000..faadafa9 --- /dev/null +++ b/Chapter6_Priorities/data/data.txt @@ -0,0 +1,12 @@ + +Possition Nation Games Points Tries Table +points +P:q +layed Won Drawn Lost For Against Difference +1 Ireland 5 4 0 1 132 49 +83 16 8 +2 England 5 4 0 1 138 65 +73 14 8 +3 Wales 5 3 0 2 122 79 +43 11 6 +4 France 5 3 0 2 101 100 +1 9 6 +5 Scotland 5 1 0 4 47 138 −91 4 2 +6 Italy 5 0 0 5 63 172 −109 7 0 + diff --git a/Chapter6_Priorities/data/results_2014.csv b/Chapter6_Priorities/data/results_2014.csv new file mode 100644 index 00000000..620d129b --- /dev/null +++ b/Chapter6_Priorities/data/results_2014.csv @@ -0,0 +1,16 @@ +home_team,away_team,home_score,away_score +Wales,Italy,23,15 +France,England,26,24 +Ireland,Scotland,28,6 +Ireland,Wales,26,3 +Scotland,England,0,20 +France,Italy,30,10 +Wales,France,27,6 +Italy,Scotland,20,21 +England,Ireland,13,10 +Ireland,Italy,46,7 +Scotland,France,17,19 +England,Wales,29,18 +Italy,England,11,52 +Wales,Scotland,51,3 +France,Ireland,20,22 diff --git a/Chapter6_Priorities/data/table_2014.csv b/Chapter6_Priorities/data/table_2014.csv new file mode 100644 index 00000000..bed25eaa --- /dev/null +++ b/Chapter6_Priorities/data/table_2014.csv @@ -0,0 +1,7 @@ +Position,Team,Points,QR +1,�Ireland,8,winners +2,�England,8,triple_crown +3,�Wales,6, +4,�France,6, +5,�Scotland,2, +6,�Italy,0,wooden_spoon diff --git a/Chapter6_Priorities/data/table_2014.ods b/Chapter6_Priorities/data/table_2014.ods new file mode 100644 index 00000000..1bab1908 Binary files /dev/null and b/Chapter6_Priorities/data/table_2014.ods differ diff --git a/Chapter6_Priorities/other_strats.py b/Chapter6_Priorities/other_strats.py new file mode 100644 index 00000000..f13fa1e8 --- /dev/null +++ b/Chapter6_Priorities/other_strats.py @@ -0,0 +1,124 @@ +#other strats. +# TODO: UBC strat, epsilon-greedy + +import scipy.stats as stats +import numpy as np + +rand = np.random.rand +beta = stats.beta + + +class GeneralBanditStrat(object): + + """ + Implements a online, learning strategy to solve + the Multi-Armed Bandit problem. + + parameters: + bandits: a Bandit class with .pull method + choice_function: accepts a self argument (which gives access to all the variables), and + returns and int between 0 and n-1 + methods: + sample_bandits(n): sample and train on n pulls. + + attributes: + N: the cumulative number of samples + choices: the historical choices as a (N,) array + bb_score: the historical score as a (N,) array + + """ + + def __init__(self, bandits, choice_function): + + self.bandits = bandits + n_bandits = len(self.bandits) + self.wins = np.zeros(n_bandits) + self.trials = np.zeros(n_bandits) + self.N = 0 + self.choices = [] + self.score = [] + self.choice_function = choice_function + + def sample_bandits(self, n=1): + + score = np.zeros(n) + choices = np.zeros(n) + + for k in range(n): + #sample from the bandits's priors, and select the largest sample + choice = self.choice_function(self) + + #sample the chosen bandit + result = self.bandits.pull(choice) + + #update priors and score + self.wins[choice] += result + self.trials[choice] += 1 + score[k] = result + self.N += 1 + choices[k] = choice + + self.score = np.r_[self.score, score] + self.choices = np.r_[self.choices, choices] + return + + +def bayesian_bandit_choice(self): + return np.argmax(np.random.beta(1 + self.wins, 1 + self.trials - self.wins)) + +def max_mean(self): + """pick the bandit with the current best observed proportion of winning """ + return np.argmax(self.wins / (self.trials +1)) + +def lower_credible_choice( self ): + """pick the bandit with the best LOWER BOUND. See chapter 5""" + def lb(a,b): + return a/(a+b) - 1.65*np.sqrt((a*b)/( (a+b)**2*(a+b+1))) + a = self.wins + 1 + b = self.trials - self.wins + 1 + return np.argmax(lb(a,b)) + +def upper_credible_choice(self): + """pick the bandit with the best LOWER BOUND. See chapter 5""" + def lb(a,b): + return a/(a+b) + 1.65*np.sqrt((a*b)/((a+b)**2*(a+b+1))) + a = self.wins + 1 + b = self.trials - self.wins + 1 + return np.argmax(lb(a,b)) + +def random_choice(self): + return np.random.randint(0, len(self.wins)) + + +def ucb_bayes(self): + C = 0 + n = 10000 + alpha =1 - 1./((self.N+1)) + return np.argmax(beta.ppf(alpha, + 1 + self.wins, + 1 + self.trials - self.wins)) + + + + +class Bandits(object): + """ + This class represents N bandits machines. + + parameters: + p_array: a (n,) Numpy array of probabilities >0, <1. + + methods: + pull( i ): return the results, 0 or 1, of pulling + the ith bandit. + """ + def __init__(self, p_array): + self.p = p_array + self.optimal = np.argmax(p_array) + + def pull(self, i): + #i is which arm to pull + return rand() < self.p[i] + + def __len__(self): + return len(self.p) diff --git a/Chapter6_Priorities/ystockquote.py b/Chapter6_Priorities/ystockquote.py new file mode 100644 index 00000000..22e7234f --- /dev/null +++ b/Chapter6_Priorities/ystockquote.py @@ -0,0 +1,170 @@ +# +# ystockquote : Python module - retrieve stock quote data from Yahoo Finance +# +# Copyright (c) 2007,2008,2013 Corey Goldberg (cgoldberg@gmail.com) +# +# license: GNU LGPL +# +# This library is free software; you can redistribute it and/or +# modify it under the terms of the GNU Lesser General Public +# License as published by the Free Software Foundation; either +# version 2.1 of the License, or (at your option) any later version. +# +# Requires: Python 2.7/3.2+ + + +__version__ = '0.2.2' + + +try: + # py3 + from urllib.request import Request, urlopen + from urllib.parse import urlencode +except ImportError: + # py2 + from urllib2 import Request, urlopen + from urllib import urlencode + + +def _request(symbol, stat): + url = 'http://finance.yahoo.com/d/quotes.csv?s=%s&f=%s' % (symbol, stat) + req = Request(url) + resp = urlopen(req) + return str(resp.read().decode('utf-8').strip()) + + +def get_all(symbol): + """ + Get all available quote data for the given ticker symbol. + + Returns a dictionary. + """ + values = _request(symbol, 'l1c1va2xj1b4j4dyekjm3m4rr5p5p6s7').split(',') + return dict( + price=values[0], + change=values[1], + volume=values[2], + avg_daily_volume=values[3], + stock_exchange=values[4], + market_cap=values[5], + book_value=values[6], + ebitda=values[7], + dividend_per_share=values[8], + dividend_yield=values[9], + earnings_per_share=values[10], + fifty_two_week_high=values[11], + fifty_two_week_low=values[12], + fifty_day_moving_avg=values[13], + two_hundred_day_moving_avg=values[14], + price_earnings_ratio=values[15], + price_earnings_growth_ratio=values[16], + price_sales_ratio=values[17], + price_book_ratio=values[18], + short_ratio=values[19], + ) + + +def get_price(symbol): + return _request(symbol, 'l1') + + +def get_change(symbol): + return _request(symbol, 'c1') + + +def get_volume(symbol): + return _request(symbol, 'v') + + +def get_avg_daily_volume(symbol): + return _request(symbol, 'a2') + + +def get_stock_exchange(symbol): + return _request(symbol, 'x') + + +def get_market_cap(symbol): + return _request(symbol, 'j1') + + +def get_book_value(symbol): + return _request(symbol, 'b4') + + +def get_ebitda(symbol): + return _request(symbol, 'j4') + + +def get_dividend_per_share(symbol): + return _request(symbol, 'd') + + +def get_dividend_yield(symbol): + return _request(symbol, 'y') + + +def get_earnings_per_share(symbol): + return _request(symbol, 'e') + + +def get_52_week_high(symbol): + return _request(symbol, 'k') + + +def get_52_week_low(symbol): + return _request(symbol, 'j') + + +def get_50day_moving_avg(symbol): + return _request(symbol, 'm3') + + +def get_200day_moving_avg(symbol): + return _request(symbol, 'm4') + + +def get_price_earnings_ratio(symbol): + return _request(symbol, 'r') + + +def get_price_earnings_growth_ratio(symbol): + return _request(symbol, 'r5') + + +def get_price_sales_ratio(symbol): + return _request(symbol, 'p5') + + +def get_price_book_ratio(symbol): + return _request(symbol, 'p6') + + +def get_short_ratio(symbol): + return _request(symbol, 's7') + + +def get_historical_prices(symbol, start_date, end_date): + """ + Get historical prices for the given ticker symbol. + Date format is 'YYYY-MM-DD' + + Returns a nested list (first item is list of column headers). + """ + params = urlencode({ + 's': symbol, + 'a': int(start_date[5:7]) - 1, + 'b': int(start_date[8:10]), + 'c': int(start_date[0:4]), + 'd': int(end_date[5:7]) - 1, + 'e': int(end_date[8:10]), + 'f': int(end_date[0:4]), + 'g': 'd', + 'ignore': '.csv', + }) + url = 'http://ichart.yahoo.com/table.csv?%s' % params + req = Request(url) + resp = urlopen(req) + content = str(resp.read().decode('utf-8').strip()) + days = content.splitlines() + return [day.split(',') for day in days] \ No newline at end of file diff --git a/Chapter7_BayesianMachineLearning/DontOverfit.ipynb b/Chapter7_BayesianMachineLearning/DontOverfit.ipynb new file mode 100644 index 00000000..10bc46cd --- /dev/null +++ b/Chapter7_BayesianMachineLearning/DontOverfit.ipynb @@ -0,0 +1,614 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Implementation of Salisman's Don't Overfit submission" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "From [Kaggle](http://www.kaggle.com/c/overfitting)\n", + ">In order to achieve this we have created a simulated data set with 200 variables and 20,000 cases. An ‘equation’ based on this data was created in order to generate a Target to be predicted. Given the all 20,000 cases, the problem is very easy to solve – but you only get given the Target value of 250 cases – the task is to build a model that gives the best predictions on the remaining 19,750 cases." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import gzip\n", + "import requests\n", + "import zipfile\n", + "\n", + "url = \"https://dl.dropbox.com/s/lnly9gw8pb1xhir/overfitting.zip\"\n", + "\n", + "\n", + "results = requests.get(url)" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import StringIO\n", + "z = zipfile.ZipFile(StringIO.StringIO(results.content))\n", + "# z.extractall()" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "z.extractall()" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "['overfitting.csv']" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "z.namelist()" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "'case_id,train,Target_Practice,Target_Leaderboard,Target_Evaluate,var_1,var_2,var_3,var_4,var_5,var_6,var_7,var_8,var_9,var_10,var_11,var_12,var_13,var_14,var_15,var_16,var_17,var_18,var_19,var_20,var_21,var_22,var_23,var_24,var_25,var_26,var_27,var_28,var_29,var_30,var_31,var_32,var_33,var_34,var_35,var_36,var_37,var_38,var_39,var_40,var_41,var_42,var_43,var_44,var_45,var_46,var_47,var_48,var_49,var_50,var_51,var_52,var_53,var_54,var_55,var_56,var_57,var_58,var_59,var_60,var_61,var_62,var_63,var_64,var_65,var_66,var_67,var_68,var_69,var_70,var_71,var_72,var_73,var_74,var_75,var_76,var_77,var_78,var_79,var_80,var_81,var_82,var_83,var_84,var_85,var_86,var_87,var_88,var_89,var_90,var_91,var_92,var_93,var_94,var_95,var_96,var_97,var_98,var_99,var_100,var_101,var_102,var_103,var_104,var_105,var_106,var_107,var_108,var_109,var_110,var_111,var_112,var_113,var_114,var_115,var_116,var_117,var_118,var_119,var_120,var_121,var_122,var_123,var_124,var_125,var_126,var_127,var_128,var_129,var_130,var_131,var_132,var_133,var_134,var_135,var_136,var_137,var_138,var_139,var_140,var_141,var_142,var_143,var_144,var_145,var_146,var_147,var_148,var_149,var_150,var_151,var_152,var_153,var_154,var_155,var_156,var_157,var_158,var_159,var_160,var_161,var_162,var_163,var_164,var_165,var_166,var_167,var_168,var_169,var_170,var_171,var_172,var_173,var_174,var_175,var_176,var_177,var_178,var_179,var_180,var_181,var_182,var_183,var_184,var_185,var_186,var_187,var_188,var_189,var_190,var_191,var_192,var_193,var_194,var_195,var_196,var_197,var_198,var_199,var_200\\r\\n'" + ] + }, + "execution_count": 69, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "d = z.open('overfitting.csv')\n", + "d.readline()" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "M = np.fromstring(d.read(), sep=\",\")" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "23919756" + ] + }, + "execution_count": 71, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(d.read())" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "np.fromstring?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "data = np.loadtxt(\"overfitting.csv\", delimiter=\",\", skiprows=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "There are also 5 other fields,\n", + "\n", + "case_id - 1 to 20,000, a unique identifier for each row\n", + "\n", + "train - 1/0, this is a flag for the first 250 rows which are the training dataset\n", + "\n", + "Target_Practice - we have provided all 20,000 Targets for this model, so you can develop your method completely off line.\n", + "\n", + "Target_Leaderboard - only 250 Targets are provided. You submit your predictions for the remaining 19,750 to the Kaggle leaderboard. \n", + "\n", + "Target_Evaluate - again only 250 Targets are provided. Those competitors who beat the 'benchmark' on the Leaderboard will be asked to make one further submission for the Evaluation model.\n", + "\n", + "\n" + ] + }, + { + "data": { + "text/plain": [ + "(20000L, 205L)" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "print \"\"\"\n", + "There are also 5 other fields,\n", + "\n", + "case_id - 1 to 20,000, a unique identifier for each row\n", + "\n", + "train - 1/0, this is a flag for the first 250 rows which are the training dataset\n", + "\n", + "Target_Practice - we have provided all 20,000 Targets for this model, so you can develop your method completely off line.\n", + "\n", + "Target_Leaderboard - only 250 Targets are provided. You submit your predictions for the remaining 19,750 to the Kaggle leaderboard. \n", + "\n", + "Target_Evaluate - again only 250 Targets are provided. Those competitors who beat the 'benchmark' on the Leaderboard will be asked to make one further submission for the Evaluation model.\n", + "\n", + "\"\"\"\n", + "\n", + "data.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "training: (250L, 200L) (250L,)\n", + "testing: (19750L, 200L) (19750L,)\n" + ] + } + ], + "source": [ + "ix_training = data[:, 1] == 1\n", + "ix_testing = data[:, 1] == 0\n", + "\n", + "training_data = data[ix_training, 5:]\n", + "testing_data = data[ix_testing, 5:]\n", + "\n", + "training_labels = data[ix_training, 2]\n", + "testing_labels = data[ix_testing, 2]\n", + "\n", + "print \"training:\", training_data.shape, training_labels.shape\n", + "print \"testing: \", testing_data.shape, testing_labels.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Develop Tim's model\n", + "\n", + "He mentions that the X variables are from a Uniform distribution. Let's investigate this:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "figsize(12, 4)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "50000\n" + ] + }, + { + "data": { + "image/png": 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LmJAL2InyDAAAAFhEeYbjMasGE3IBE3IBE3IBO1GeAQAAAIsoz3A8ZtVgQi5gQi5gQi5g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\nwKL/D71Max2EVz3vAAAAAElFTkSuQmCC\n" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "hist(training_data.flatten())\n", + "print training_data.shape[0] * training_data.shape[1]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "looks pretty right" + ] + }, + { + "cell_type": "code", + "execution_count": 127, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import pymc as pm\n", + "\n", + "to_include = pm.Bernoulli(\"to_include\", 0.5, size=200)" + ] + }, + { + "cell_type": "code", + "execution_count": 128, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "coef = pm.Uniform(\"coefs\", 0, 1, size=200)" + ] + }, + { + "cell_type": "code", + "execution_count": 129, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "@pm.deterministic\n", + "def Z(coef=coef, to_include=to_include, data=training_data):\n", + " ym = np.dot(to_include * training_data, coef)\n", + " return ym - ym.mean()" + ] + }, + { + "cell_type": "code", + "execution_count": 130, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "@pm.deterministic\n", + "def T(z=Z):\n", + " return 0.45 * (np.sign(z) + 1.1)" + ] + }, + { + "cell_type": "code", + "execution_count": 132, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Warning: Stochastic to_include's value is neither numerical nor array with floating-point dtype. Recommend fitting method fmin (default).\n" + ] + } + ], + "source": [ + "obs = pm.Bernoulli(\"obs\", T, value=training_labels, observed=True)\n", + "\n", + "model = pm.Model([to_include, coef, Z, T, obs])\n", + "map_ = pm.MAP(model)\n", + "map_.fit()" + ] + }, + { + "cell_type": "code", + "execution_count": 133, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "mcmc = pm.MCMC(model)" + ] + }, + { + "cell_type": "code", + "execution_count": 176, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[****************100%******************] 100000 of 100000 complete\n" + ] + } + ], + "source": [ + "mcmc.sample(100000, 90000, 1)" + ] + }, + { + "cell_type": "code", + "execution_count": 177, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.73999999999999999" + ] + }, + "execution_count": 177, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "(np.round(T.value) == training_labels).mean()" + ] + }, + { + "cell_type": "code", + "execution_count": 178, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.72399999999999998" + ] + }, + "execution_count": 178, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "t_trace = mcmc.trace(\"T\")[:]\n", + "(np.round(t_trace[-500:-400, :]).mean(axis=0) == training_labels).mean()" + ] + }, + { + "cell_type": "code", + "execution_count": 179, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "t_mean = np.round(t_trace).mean(axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 180, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 180, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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imA4ACBzHdAAAAiJu7exMZQEOHQCQk1mcWprRKklSXHPaqhHNaJXm1aIY1wbLGlaHpzZH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\nKoM1J5YaGxtVXV2tubk5LS0taW5uTtu3b9f3v/99Pfjgg5KkBx98UC+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XaYIF79mRC/mc8GsJpMMv5dzIF68kVkSwTa8SMWQHKMbfxhn4npXPFeFVhNCa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7M/xQ5aBZLnFdHaGCuyQ8WBuOQvg3MmPy8WRp95WgvJ0afEWXuFE5fV2XBgogIDj2NAgsmp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xGxExhIpaptCLMlSBBedXGc3ngzuM6mqfuwTf1KEWIhjxrAxRionJyaZMradYhVd0EfCV\no86JwYxw8s4B99fiHdgDpiJpAOsGFYgi2D9QCJ1a1OtsxNdM/L9LmQSr2+sV4pPWc7XPXAAaB+Xr\nEJVL18qClG2162PZ6WMXyKpNR+WgSflTTnBMlEESiTYpr3V9mDMqlqL92RHnYVVxo03XQK4o/6jW\nWi1+/pRsV4UIFM2AzzcH9QrlKjmCwbExZufU3m08fqXE3oFAuzWvL+xS4kaFR2uQ0FFIKEJcKpFC\nMkp2OsH8KrBiNp5LnsgHRlyuslvOKO7Xrg8Tgbgks1TFDT+Q2q6laAy0vqvOIzKVBCIjALP6KVod\nnfdPc1MVCGj8toTfsIHUPPeKn67SK/zMeWA8bQugDN1PZnePioMpF7Fs1xciqEY6PSg/y8Njyvcp\nOS6JSzQZjXqmE5erHJdkaiNTyEIyWjCpop2t9JqpQkumyN8JvaiOIYLXznp5Usrzg0mFMah0tIec\nuOzBInfayohksg3ASdBCECzvHxKXdG2KKwzb60U1qUeE7ZtlfjEVTFLQ9qQhgNPeYi7N2hiwf+yA\na1evHv2bkIWS47Fykx1s8wbT2SoXD1ptrrReWisHOgeC5L7WlhO+f3LMJjvcKk5mOdOJptr410i9\n3dgDCQ2uCgfUKk5GYncRzDOr7mUA3rzyD2qfjaSYgbVxCdyXERHjynZAJbTjrOebT5X3DUx0CJvL\nZuE6W31mV6+8+EjihwVI6tEOhLa4C/QWEYxGRdGe4GQnBSbjWX2boMNzTei5tlFUvKXavmGvdSib\nKDmk3j8K3RiietSRkY1EprBCFS8yuwswO2IAOFoQUzimhgy3cPJ77q+XaWao8+2J7XX8LcUPTnaS\neOM/XXjoX0pubCpeCb+FWsIVgIM6yehe9n7KvtbMoAyi9ZTiQMU/i4m7horhPUr0KCV2B9nbgLyx\nirco2b0UNBKjfeikpBZuMZbI11M+IIq+Vv5/ZWoPPPjp7eLn7Wf4mU1ulf3qLnBPrp1jQNgAjpHt\n9fA+kS7G1K4504nLVaPsr44l6vkjyNobLCaQaQ7izzkGbb+cgd8UiUNKKlKgpQb+FgTn411uYV7O\nyu+mJQR9VBKgZCpxiChdMTinlYkTSbxM6mfiOZMD3N+qT4JOYZNXxL8jEIboimfGSLQ8iP5QjPN9\nIuwWjjG2gkAA7CSapJMDv2dxLBL/LbTbRERMYG1S1dFqE++fREscjlmag6qFuw2bv8PNPLzA65lq\nx6o1cuYVQuqgNgARra2EHFFzkxBfPSnEl4gcAYdNJS6pTQ7/v0BPEjevSnYSCqL3FLddPwmidqRA\naZnBiUd7QH+Tx+yWSFDWnqc3FEX2xP0JE7eZggXimeExiZQgjhHFkbKW2Lfp14jj8nyyqji59LJ4\nQQgdGH8KJNGallHCCgwwP4AEpfFuHNQvtevPRWGzdm7ee5+LOttGYROpqcCnU49lH7qFpBI7jKfR\nXS4sUoKoS7ywItYicJFub6/bN7FwEQLBXnWGQ1tI6ony5/RulE/Zh04JKZAGj8yhcqL8SX+DueYH\n58vrhhLn6RoFNDJ6Nhc2FcdmeZxPKXGcTIlBgBQn2d9eQuI4EdwXwQW3zqDsH9F69rjl/0wnLlc5\nLh0i61oOqQhuH6pVOfwoWaYCG6qKC0QDOaBqSqKanlFpc5BY052yY2CJTRkwbHJyiUfx8ESAUAEk\nlkQ7GSIPS+QYrEdhLEm52UhQZ9px3rU3Xr0RX7zPcXkRETJ5y7h0MiEJIxN38JzHkLhWRmtzSwRT\nmYh43GRFAoocQxVM0LxRwVxm4oiSuuo+CXEkBW0qTbWKjyDZqQpOtSa7IWZlJ7O1qC8EkTProK1I\nhVqdpy8UsilBnSlasqrQfv3V1+Laiy889hC6f9ViSYVFWQwntI8wKlL1IWhSzzJzrXe4wsiUOi4J\nF3WHItCj92mADgiJ5qAHqRCnkiMOx2UfCl6NifOAqe6GPqARW4mJe5XQckQzFtDeS3NgV8SUG6C2\nrIx5SR++/5NyXG4Oy3GQ4wEp9OjgXLl4ReN5sC3oBQwOcFqD1dgkGwC9wYSS8MlG41n5rHWx5AAA\nIABJREFUGoN1p1sJksqQHFQt5NQqTwCaCC66Lju8n9HYcJDytNSOhH9Qq4SdLUBDsctU6I2M75S7\n3wYUuxh7vfKPqMuSfIB/U4jLBw+m03r4b4PH3MomkziPkxwik/xmxuZXfX4VZAzLzofasCk4zORx\n7CoeP1gwVJCR2RI+AiebJUPqTSY0DceQHDNSAVZG4jxOLEt8oRERY/huRvxmo/p2YApMIkT3iIF2\noWT78cRtt906+mx+92b1eWqRMBRMu0btBgcGhxSacLJonA/OMaqstgqtCK7JZNXSmDeDyoMkxQoV\n78TvHQhnqmQKDR9AfyrJ5xsAfI2EomxrAfcvEJeomkn8boJ/F6/LCECG20/gMSR00AGMmtMOvOys\nrI3t7iN/I6oM9nrVPkamOKwI7bN1UbWKl8fNBIo3Kpag5LUjzsPqrLlhARUQVYKUgAI0nByuNrVv\nD2EPoLEhfepTNizgijibCrtyrwX+SyfZ79hu5R4UIdpBYaCpxOm9u+V5rsJQAgqsFgnbvbYsGj6w\nXcHBXGvOukm3qXxKFPox1mAyNTfnVAw31pPeen1XJnUyKjCAk9egIiU1NygAD/m7DuJw1hZCgDCf\nKaHnjFllhKClDsNscR7HaNwibzygdyMY9a9jF9g3DQ5uZWcycfnAVjkuM+NcZeSw0KC0HKZkcyrk\ntaY27AVwMqECpgDi0yajJhg5uYqwPhOJNYWk7kxVcxITp17QUr7/meBLdFp+yEgZT3FcIkKG0LjG\n3FSIDmxVF5Xe2rj9+DS7/MJLR59R27NCYlHi0kL2QnJGOVLIGWwEevRqVIIaRUuM4NzRTWPH3Pgt\ncVAmFhiVwMWaRah7PEaI8xBCSrXDO7XuWlVx4j2TRgnNqJ8DTjeAs88pTjhCNWVyXK6u21evvvTo\nVygcRUIzgkYD9hoHWa/fJaEQ4PziNVMxWHHG1hrtM8qcVnHpAyVyzPUIDZnou0uOS2MOOjzwZJlI\nINXdILt1as+DSDzjt8R7rgWkqFHpdF5Rd8PqevLskySK+Khl8qwqX4NofihtI7kHiTPWGP+UIFfP\nBf0zpalA5zcSV46qOJkquC8TK7uUBJ0LhKLjuwyAHz8C4hBjzCiBPuUHZZm6ZkKpqrFJcQ11Mqrz\nj+HZqKHUlIj2mU5crppTZSBTCBlS+cJ25GQS37MqDiMTl6Toaqgd0ybjcHEoJCKiioznT4hDFTSS\nAAaZQoE4GyZyuziBLlya2kdozKj3fACJYC4q1DvsGtVF7VvNVNqcZBsZJgFlMAlOthibNAYmBpE9\nmeIqo/Hc7uc5JaqFm56NVuaDqqWYm5nCQTQG1H1SAEJjVlKvwDCXbd8IEKrnzCUjQbcIQaMg7jNT\nnZJMrefURq044SihQk/S4RMP8cxqC35KUKoNKKZMvtiIaEToIbMjyDHNNV/eH8f38toxlR/uoCFp\nbaA2/mzE5WkjOFu0Ngq0W3YsVDy9eCx92utVQgcKLrTXKe+Y+JSlS42UVYaoXuK6pVrFyQ8hn0Zx\nY5M1FQUvwG9xZp+iM8s08huUf5S5mtAe3BF7EPE1qjHbpUIxxHVTsdfTaWTnUQMISlXwJN9NvWek\nzIFjhoJ2gFwt9cyoRpD9LM904nKV49Lxfx1+K0JcZjqfDQAkfYMATD1/XEix5bD+vTjvUiltZiIu\nh+QwJVa5KDkaYaqZEapJIS7BMNEiHjFtSo4IEprxXFTFCNGDDgwekm3Hl59vvnojnrvPcekEBrXr\npgomHa4q4l505oaDeMXNf5KXOFVWK8ByFoyemXKyRvvl58m/Vf9cqEUnImLgoJoq54akcSFxnh6j\nkKhNjUwVFai9Vx5D+5aRBEtN9q34IC+/8mpcvfLiYw+h4pFCrtD9K55lMocw37FMvyUzmHAQl7NR\n3vkVqo46YhxBo3vQvugkGqWvk5i5cVrFne6aJrrP1Jo9AtS/2rcWwI1Mdk6g7mk9UUvjSZ7Y371/\nK77wBPMOPzASJ8q22uKRwwFOatsR9UABxT9L5nQE1XIiRjAFm1YVT0TWGveZ6Tc42iVkTo7ICdEp\nDlQcn2SqQLEAaiCFuCQ6oRYMGRXvOh4dXRrN2X+T4jyr1jdGGLUwy/PQRG4ANhyR65hmmtqwmzAL\ncSlskSgoRDF4F8iyIyIG5zIZMMumUFiTvfImm9nuo3gMZ/CdRFwS8S9RBSgkHizY57A9gZV7VYKc\nW6XLg+Z4BXKxWKZW0h9Ypqq4cqScdZsMx7NIgmXztdUaOSbKyaJHpoRGMoWj+ltlnmOVaCClS+Kr\nbK1xy+HaEtp+xXrmJJtq29FkwAaJy/borjik7prVWCa/gcjaXbt7UE72OIhfTKquJkBarUf+JgQv\n8RwrsbEgvkLV3QAOuCxQQMLdETVsoiPH4VlX828GAijqXqrpb8RUJu6/1nlRVIDfIw5utUd776x+\nPUcahUxxHuHrUNtzdPP2YI3spRZW4YfCukEmOyUaKl6Q0RhUo4/2DZVsJAosSt6rDsc2ibFm7ifC\nSJxGCaCQzWGdc0y1149hD1ZGnQ8OstehOaO4UtJMVVJ8EOJZmRMHKR0EMiepir8l1jMSSNuFa1bj\nfAs40J1nlm1nMnH54CV/qr129O9Tf1RGdZbsQ8hD5Bm0gyrVSLLaduiIiJ/eK0e6CtKMBNtAFh7h\nCWrgb1H8JVrl1XclyxbnwXZQleiB4Jw2jL6owE/2yyg5dZ+UnHEqfY6NSDl5l5MTtQml4xXI565c\nfewxypGg89PmI3n85iCYoKrDcH6Hw4eChsWIEZeZ7Wt0yWot8Xj0iJJAtENWOvpatZMUqvk97++U\nxwauc2I/7QLcwglmFKrpIgRn74Ezp9rnqFNh0a9XFSdTYzmT+kMZIQspALHEeVb2mZeuXj1RCoeS\nY6pVfA0EdQ5EYEDoNWo5jAgMtMinUug9QmFIsasGDDuVgkUbVCGm1qdRc4Poh9S+TeZweNGTkTQW\ncDtqPmOHE1HpKFgb+HqL3Tt8zCkbBeFqbAy3zxc/pz1dhQ2cOBFzA/zt1fbqFzaeOtH5SVXcMeUH\nzgF0QJHDQhRCntgsF8PVtkF8xg43LxWPVCFquZsXOzqAIAJkSZqr6rOwUVJbIU6xvVysgah4bsR7\ntGzeNcRTsEBgIG6dJPBE7JtUqN4Fv22sdDhgOG00xLWv7EwmLkuWmbGWXF0w+Rd79/LOLxblzPZi\ny8BhUYsiOXMUAKvELS1kDjpBOQwd2DAwoadeGtyParsm4l3H6HHKd0YqcyrQhfahzLm5LzYShOhD\nQk21cBNyhlCV0pyWOwMF0RmWnTxlte9GKkDSPDcqMTIJBEZLY3vIQTsG+haXa31CkUxVLVE510gQ\nO0bJRoUQHAKPVWcJLkZDHQyZa5PiZV3SfE4UNFNFJasQBaaQI5QIdlplkUZgXo92UfxaZLQHjoxO\nHWmQuKQ2QcccgT4yxwclddwIbqFT+0ZtG7tqUybBP3UM7jWwB0jEJXyuiiqpQiuJiEvZ3UDzOVHs\nShWvMkVKCaEmEZeQ0FFLY2Zh6QB8HeUbOO39tf6OLB7BM3OAMhRTqblJc3BgJBQdjktnDxgfAIDA\nKOxS55tjCkDi5AIcejg8P/zUmtjrFlDwoLXZAUmoNctZGwbnN4ufr3Uh2azAEMbS1FT26kwnLlc5\nLh1zqqOo8pVY0Zb6F8aCnalQTYHW1OHYhxYdZahaqu6RnCnhZGU6DAODQ6V2VVBtko5jSsHJdIcV\nmimpTck+tY+SYtu6cBiodGA55hAYKS5RRA8Kpc0Pam+8eiO++OJLj/+PlebUR6hVfDrmRAc5UxJt\nU2tG4lgpsZ+20RagxGEyjZK9c1E8mo4B1bNGVXNOXM5gs3HoQtQ4r20Vn+zWV9TbU54bSD4PpsS5\nZlC8Wbu0zb8HCNquWM9275aLcYRGt5IJK10fL7/yWly98kL9b9w3Rdg/OFf+nHh5IzihRi2PntWT\n7zt8lbRvOmuzmkuEuJTCBJWJaOUDkH/mcFySOM+OGGcO4jLTaGzIxHElxU2EULw3/DOnGErPU82N\n+axu3qpEh1pryE5SWHrr3Zvx7FOHyuKyVT6xeDS6zaCL3np576bYTQKFqBBmTA0qYDtFAIdmrrtW\nThopc+LqLhSWHcQlrcEqp0DvUyEOHaFebCM3ku0Uh3zsiXJrdQSv28SZmq1CTsneruh8ai3y/BAn\nrUQFbFqDKdZ5XBrsTCcuP6hZSCBKXIIz71SsGhATtW3ZrU/Q1qqjqrdCm4zidUAzVMXxp8RYGsNv\nkWhPRMQSkgNkSvzAccAzE7d0acqPmk/LjhFxSEVwcNRqUeJaLPCAxhwIj2mf9mUnaAQag+NB83y5\nfPgZBACOci8dIpMzUNRQgQGOjURkuSO001lnhwWPaYhInPwSlVDIvDZaG/oboqJOatNEpJ+JAhKm\nnnNt4lKagax0/BMyemcK9T++V543KqEyqeTXsoqqqwWyVgsLZo+chxKKSlEYrk3RVdJap+gyqLXR\nWQLpeVqq4jAHPeE0/o6SXZnjX+1B+0RvMK3fNyip7aC6lE9J51F+Wy3HpSPMoZ4zzSdH1C/TMhGn\nqXuGsNW9fjaen6iY0AF/VwqrEjeyEGlc/xgUqeD/K55hivechB6Z6gZwUHKZNGOKrYHMioXBamP3\nCI8DmURj1BJEe+pysld9fjIVO1Fuh8ZTKrI9uPNpLpLqdM30mKcjLqxTIlwN/yH4DtnP5kwnLlfR\nliLJjKacSTJcSBI5LhO7+iIiF1JNXF3y/HBDJE6j/GKqZihI+5IqPWKy1DrNKgByhCFqE4eS8wUq\nPRYnnBFo7hNPjGqHBbXdqeEULJf1yRFKqKiqJXIYCefHEZRZtcsvPERbEjG+40jRrcgk6KjsMKgW\nkWG3fCIpmgFGl9buc6UT256H9YlLGhsDkekggnHN5QqfC8J2EkFyfGxCo/agHTyC9yBM2lx4En9r\n/k7efqbmcy3JuGzrIRqFxFZxtWcsRxBMCqTDYLseITCBMegEQGTLlfXkpS/9748UOmsRh0pkgr5T\nt0KokoFAQ9NeswbvUzeXwBhITA6pZ+YYBS0dUaSkoiPxnilzEFe0bt4FHsWDM4zgd6w1A1U1YYQe\nrFWBPjymnhuaUM8KCdYzCphkTlITKT5W9vPLH790ot+6C5X1Z6qvShv5AVNYm7c2eZ+hVnErQdyA\nqr0yJznjxGgUi0oKNvicOhmlqjnEe9ODeiFAdc2DAeypibz1ox1e5xZb5fMQup46C7JN5S5qO1xU\n7EZjU63BSLVo8H8qO9OJy0cMRGPkIUaGcETIkeQHf5om9wTjOdcmiFUwja0DKtKHhay9ztB9J3FC\nNoLFf3ud0asKCVMymYBIDFqoTTQimEYA7n9DtEKOdt4vfq6U4aYkNLN5EY+pNRXjUFLVatODZLMM\nmiE4ykQvKj4iFqbgtXGSqGbIyVZ+/tRe297gFlpEyNCmbInz4CF8n+KZkaAI/ZZGSEHxyGiFw3Ns\n8ZylivZ4xu+5BwkNFRjUJi4ne4YPIBKXmfxSlNTsrTPVDiqRC9GStXMgZpFZjRUISxqbtAbIdmT4\nbs1o++6vcXBOyXsOzsU8v5nHtZ7p00o+bTiPEvuqDU5V0uA8tLbSe4ngtcEBCWQ2WDl8yrg/q7oF\nzEFF8UHtoOo5Z9oF6AhQ/lltq+w5lTh3gALg0znFg4sgdKOSgIgqE3MTr41EyG5zcsjhRiajNUCh\noTvE/WfsZ07iyuEm7w2amU9kDlKbhHrVTxGIybEF7KnSD6/kjaauD2XqWfY2yr7bYiwSl9DJRzn9\nzfPsHxIYQvnUTUm0nOnE5SMcl4AEVANPKj1WGrWVOC23NIkicoNDx1qzckJtA5BTEczVRWgPtVhR\ndZyQSxHCARcBEFUNCdGhHFZKtilej8luHdxdOsxGmxgluyY7fF2tSvRQN/j/d3vlBZPQm2fB1slh\nExsWJqFGZS7R45vFN1+9Ec/d57ik9maVOCOnlUjBFaqPW87YkSLUrULvkdG9KDVJFDQS74wq2r3E\nin5TquKZ4jR98c5IhXNyr7yeLMTaTMgRx5QjVfs2nWJXa8KcwbUcl7XFroiI7hrvDbOD8hzYuMBJ\n5R4EtJnjbNVevnEjrr70EHVO/pZTPKJCgOK4tIwCaic4TqR4wUBf7AFkSpyHUB09QT2BhT1qRRPJ\nsQuQ0HGMitQqOYJCM+KZObzpqNxriIYQn7VKKHe3y6Sxy04e95sqNh3AuJ2LBD0hxSk5N9kRRVri\nQMcjmBd01ad766c349knH4+6nEBhTz0zineUEB3FDrRqqPWE4r3aomJExHJS3h+d9VzyvxL34LCe\n45KsKxD8c9hs9SMrHzMdGchq2IPaSgwVxplaNzMFytowC1W3FPkUFNP01xkMQUb7WQQj2LuCx5Ke\ncwvufybmplOLHjQEej6TicsH77LT+mAZXGodUIMFpd6J4NqogGa3iteafKaQIN4TkQmR6Dr8n1YF\nLlHNy7pmcH66G1yxqR03kqvroLxhO4HRSCE6IHGJ70ygd/uAhlWq4mTT/TJCSCXUyMlRbf8kQOG0\nii/2ys/5+CtbLlc+Q7RRrqp1ral2AzqNxZMDw9m5f8doDirEKx2jUBCgZxP77wsRpESHgQpOsu2W\nOBb3wTEesADM5rC8bkgaCSM2rvUtFsbapPjdVOKi1qhItvv2Dh5DLV9tUaTE30JhhlxPdrpXHk+d\nQTk5I9v7aW5K3ivghZRCM+XBSa3ijjkCcRiYGSJgSgXYaROja0OEitHFE4JihPZH4uDORAdFcAut\nY7mq4ipohrXO6ZYzKiH7wL+rxt+M1kAq7It9+xIm4vmYnR+9W/x8FfHY6baP/lZop3fuldfG8wYY\nQBXpyA+gVKf6LZo3Vqs4rLMqOUY5gqVImtHYHGxdEBdXNqQ/UmrXcG1DsQbRu5mO6tu7yRZAZRUR\ncQBiTyreRzFUokYz1kxJjQbr2SaM52xV8TkklQkkEBGxpOKNkQvYhXVD/dYMEqQfCY7LB5P5U+21\nhxM7kSvKscU+BwC1phzjJkydfQmJS+WW1QYnKiyyHEBK6ACqLYIXLGfxwXOItuv+Vp4SNT1P5eTQ\n5jsWnB9kVLWetzmhNYfqsCRrNpQu0WjMiNmBimliw+bzn4xf7fkrKxyX43rEFRk9S9kqvl5GVCxm\n/4rHkKiUbBMEo6Fhod736tdzRD2rVnFYZxzHXCEXUnVmjEQw3WdmUlmqZsJ4zlTuHWwLZDv5J6D2\nHRGxBwrdjtF+1iXEcUR0h+U1aCTQNmuAus1s+VvtlLh69dqjX1VmmxWH1eBcPRKPkjNrBvfiASHk\nxLNEkQ6HLoYodoz1VBXCMNkr3qUlNgS2IBSKeGZ0P7vAcZlvQLHSVKs4CfE5QpCJSA0VOqmAnqy2\n7VaN8zYg6NXdn2SuPXtCjkvqCHJ8DdVd4YA7yAhZnNlyqsV58tCYswkXlsloNmcKADnmvOO5uH+k\nZBC3uU97d0Mt+WSE7M6cF8oUz3NrXv5uDMluRVfkxBR0jNMRo+xMJi6zFi1KqCgBilo1s0x15jNh\n0MK3ZqCKMpOAGjkBqpUKoAOoAlp8VABM46wnEJepJniHyChBJatW4MwSH4zae1FMQSRbafOh37K4\nJ0VavUXBhDgPbphwjJPncZwccmad37JIyY1An4y4YCIegyA+RXN4YqQ4S+VrU46cFxxDEgQFGzgB\ngHQhiQIwEfWoY2tuiIJrD8TryNR+SohLJc5DiXBJywLjJjNvqSheyHcjfjHJp53IMaoKrtlO+4dt\nNGezTdIiVT4zFOgLVlt2OnUyA2CVNCGUWKZPbZniiwROuGXyuk1GfNLqmVFChfZaKQ4Ex6gEAHLS\nGXElJSeyjWIkFGkVsSNdc2beTomE0hx0EpqZqDJV1JlN8qh0qKiRvc5QvK0MY24Dwe1Y7TPITlwi\nuEqsQXQNPaLYUWK08PxVjoxeWeOIy6997WvxjW98I9rtdnzuc5+LP//zP4+9vb346le/Gj/84Q/j\nM5/5TPzVX/1VnD9//uj//9mf/Vl0Op344z/+4/jVX/3ViIj41re+Fb/zO78To9EovvKVr8Qf/dEf\n4TkfLFqrHJdLcGYlbBWgrlYAAsmhptoUlWUu8kvgw3GCRppEljiPERioyVLbqqocCVKaZN3ciMH5\nOj4UEsCJiGgBt4hEKEF1WAYTxDML74wEQyK4RYFasSIiesRHBHyZypDfSz0zGALUdi6NUCjHbnGV\n45Les3JyWJwF3tkdcS8teP+i0krDti+UJsko2dpa3xJHlVuxHCPVTFXUmY7KaxOhrQ6tPDbU3KwF\nqkvRmu0yl2rnFieBh4DQmN8qO8YKhTOH56yEu1DNUAQgipevZGsXBPUH8Lg56ryUnHMcY8mLCkUq\nJbZH1X4UMzH8o9nK7d+48XK89NLVxx5DPH6qCE6oIic5RSiMiMAEWSZdh4NCWYzKa71TjHfuRVFf\n1CL71NkvbgJ6cyTa9Gg9gaGxlpzsPTCoVE7dKNCfODx6xNks9g3wN1ULKyWoHMFDx6chDuJVZPXJ\nOS4NKhNDiI94OQnAITnYM5OtsGYoxCXtZwd3WaAOOS4NLtkLEIep+HTzQn23HnUq9KGLShmNDcl1\nD0XasfAPEFnYBgS9eM9WRxLMDVIVz072qm4ZMrqGEXQ4qmdGS6BCcC+WZ6BV/Ac/+EH8yZ/8SXz3\nu9+NwWAQX/3qV+Mv/uIv4tvf/nZ8+ctfjt///d+PP/iDP4ivf/3r8fWvfz2+853vxF/+5V/Gd77z\nnXj77bfjV37lV+Kf//mfo9Vqxe/+7u/Gn/7pn8bly5fjK1/5Svzt3/5t/Pqv/3rxvFkclxS0SE4y\n+Dyzai4rcE3JMpEJJAwfUpkENG5RngMm5VLMFadVlYzGmSKMV/yXJctGG3FwUj/OuZrDx9B8IvL7\nCNFWQa1QjmBDvWaK1daGPC3H3nO71Tr6zOF2IZ8xs9ih1PRomI3v1VeNEYmouExhPLU3ONmJ5OOO\naqiBRibL3BukaA2gQJTNKgPthXDyDybloEEK8RnPufZx1rYpR2gaiU7l7zmOsRKII6O2/4iI5RJa\n+xKTcKvb6WL56N8ozmIgLjMt8/4VJxtaon+aXYynd+MktWvPIc04hl6zI6bjWFPtiGhiDbIKuInG\nnV88N6i9nJJtaj2hJIDyKTPnAJkjzpPZquyg7ayijqEqTuB+NWbo2ThzE4vxiuPSaW/HaybBTYPK\nSQGFpuW8gsyF0JqauNcpQJIjKESWmdZRuZDpXh1dgUIWt6ycQ31XomNyRdne3o5erxf7+/vR6XRi\nf38/PvGJT8TXvva1uH79ekRE/PZv/3Z86Utfiq9//evxN3/zN/Gbv/mb0ev14jOf+Ux89rOfjTfe\neCM+/elPx87OTly+fDkiIn7rt34r/vqv/xoTlw/sSFE8vIw5wWDVokzomQ4Q7zrV6cx2k2wj3h+F\nEKLNn4Im1Y5LAYAkpCUesblwWCoJdpWDQe9zdJOrdpvPKDzmyc8REbE0NnlyJkZ3uDpOqGcUJxJT\nowPJvqGYTzQGsVV8Ws/HdFzVe9VS5+0Jg6bnX1zhuISKslrPaNTSbSqRBZq2PaGmSIqOQ4FeI6NL\nW4zKytURQoXY4Aslvk5Hs1Y55nSfjqo1mlgy1p48X/z8x3/3P/CY3mdhDxhAUh/Q2xER+9TCLK6Z\nnplCadbajPgFI5gTbkMR9t+uOr9q4Z0Dn7I6hvYApXZOCWpC139QcZ6ToC0jIjqD8nqi1mxSzlX+\nCdldwaW5HJeDCRLnUW3nmcWLNojTZCcuraAF6V/qAyPkphZ7APk0VKSlNSuCOQ6tBIRKHMJ7y0S7\nqEIMfee8fydxhgACVdikDi9YA+85qMYPOGVPgraM8NCLTuKUxtkARN3UGS4BGrpnPDMq7Kv11OFF\nzS0g1//WdEz6AILmCq7ZEU6j31LznPwAFVJNQGyLYicF4KC2540ny91FEaHJaQu2MPh/1fyjtX7Z\n4gsbnFfdZz9r3S4/M4rdlDWFu5OR0MWLF+P3fu/34lOf+lSsra3Fr/3ar8WXv/zlePfdd+Opp56K\niIinnnoq3n33sB3vnXfeieeee+7o+GeeeSbefvvt6PV68cwzzxx9/slPfjLefvvtD+N+HjHKTKuF\nJ7V9B0xNViXAgMeQ+hYsFnJwQXJKkq+TMAIETeoRU8uVRDuRkysWklrHSAVg5MxSS0VExHS/LnFC\niKLDE+UR2cvxB2PDmTKT/XLbqUo0PIEJ8voWDdr81FqN34nqJKqKw/2rOH+6U26vV+hhWhss6odJ\n+fzK+TkAHrm5SgJVmoW43Con5yLYmRgaA50CIycJrubmTiXiUfLP0jMTc7M6ABA8RVuQoHUQr5Ib\nudLLokTX4Y+Vz9OaskBcraq4SigtANHgFFbVu6QxmCk4KNHAcD9Omx4VApTD7qAHieJjYATN4x1Y\na43k1BIKuw6yV7fwlq+5jyrM9aZGH/mUi907eAyNQSqsHsD8i/D48jZBOEsZvTfkgLcSpwKJRmuQ\n0ZFCe43jtygqm+F6+dpo3dxQhW3wddRWT7+GHPRG7OTEtAolieAOuE+1b2a2ihPi12kVVzEFxY4z\ngThmPxz+v/D1WOhG8ZZD9ynMTbUH0LVpajY4JjFBregNHH+7BT7d7qg8nrXWQflzpbdCGhmdiVCP\nB8TlLiS7+2Ke147ZCFGkMxLkymTi8nvf+1784R/+YfzgBz+Ic+fOxW/8xm/EN77xjUf+T6vVMiGl\nbP/n+N240O7F7cU0nm4P4un24Cg58vLLL0dExNWrh5X4/zY5RNv8x94h78N3pw//7vY6sfujf4yI\niM1PfS4iInZ/9I/x1ns349mnDqtYb717MyLi6O/v3g80/tf+xiO//3/cv7aX/+G/H57/l/5DRES8\n+eP3IiLiuWcOE7nf/H/ffeTvHywOB9Jn2mtHf7/1+ivx7PNXDs//+iuH57//99+fVD4+AAAgAElE\nQVTvHCY0/rf7vG3/9zE189Lvdabzn7neB3+/+c7h9V3+xMeO/v7+9ZfjpfvP78b95/ng7//r7/+f\nw/u78sLh/b7yWkQ8RGK9vTxcnD/ZGh79vfPDf4gLv/DLERFx+5//PiIiLvzCL0drMIwb3/6Xw9//\nxX93eL5v/0v8ZP1GXH7hEEn25ms3Dq/v/t93v/dfIyLiY//hP0VExHv//b9ERMS895mIiNj54T9E\nRMTWp3/p6O/r39qPa7/8ixERcf3vvx0REdd++RejvbYR1791+P6vff7w/T/4++n7fDRv/eT++//4\ng/Fw6/B6nn7i8Pp+/H5ERDyoef7jfW7Gz91Hmf3jaDcu/Zdvxhfuv7+/u/8+v/D8lVhMZvHNf70/\nHn7u/vi4//evffrpiIh45Z9+FBERV/79p/D5rto/vHn4Pn7p8gtHf3c7P41rz33+8P6++a3D+73/\n94/uj5dP3R8vD/5+EGgdv///uns4/j5/8RAp9K1btx/+3WrH9dffOvz95589PN/rb8V3dzfjF589\nvJ5vv3V4fb/47AuxjIhXXzl8vy/eV8d+8Pd0/xCNOr31/YiI6F38+YiI+G9/9/rh+Z578fD833z1\nkb8fzM//2Fs/+nt250fRPX/4/GZ3Dp/ng79f/sd/ioiIq5/794/8/SufPRw/1988HK/XLh+O3zeO\nxuPh+3zztVeO/l4uI966//ez979/67VX4p3v/ehoPTi+Pvx/7L3Jk13Xde65bn+zQaJhI0gAJaqz\nJIqdSIIQiU7P7w0qPHA4XBEauCIcHnnogQdyhKb+F55Vk/JA4ZknFVE1qIoKVUQJIKUniVZjybJk\nyaQkkiJEEgSQiexuW4N7z8XNi/P7kPvjxsmUiDXKc2+ee87ZZ++1V/OtbxX6aHF9/k9Tg+HyTyYo\ntouPf2oyPlcm1y+6if9v/+s/xOefeDJeOH8h6o16XPnZryMi4sJnPxYREVd+9uu4+pu347mHTkRE\nxCvvTOZvcfzL4eR9f6Y5Ga+fDybjdzQm139tOh8+Pp0f/zEo13+Pt1eiNh7HN78zWY+Xzk7W5ze/\n8/3Y/u2vo3vysxERsXN1oj+K45dfmuiXL5ydvL8ffGfyPs9PN8xX3p3e74MnZsdvjnfumP+nat1o\n1GrxxvT49PT7N8Y78fLr1+Pi05+bjOcP/30yntPjl3/x/03G89GPRETEt3/124iI+NP/8l8n9//9\nn0ye55nHZ8c/3t2MJzqT5//x7uT5i+MfTdffU9P196Pvfiua12/E02sTrqAfrk/mdXG89cbk95c+\nMtFP27+d6KfCmSjT51uDfnx6+r5+MX0fn24uR3OpGT+Yci994ejk94vjyWyYcKJGxIwXdfH9Fsd/\nNH3/P52+38em7/unvc1Y+Y/fzPTRvH6qNerxytsTffHcFAVSHO9Os+Prr/0oIiLWPv5URPD++MjU\nKLz88mS9Xzz3wuz4d/++FY88MdEvr/94om8eeeJMtBr1+OF0/jw9nU+z4+n7WPz+x7tTfd1ZveN4\n2Bve8X5/vLsZr4934pHp/Hp9Ot+K42I/XtyfLzUm8/nylYl+uHhhoh+++Z2pfinRz7V6rdQ++f6N\nG/HMlDP8+zcmwZVnjh2LWqMeL/18sv7Pf2byxovjF6b7ycu/eD0iIs59+pGIiPjGtyfz74VHJ99/\n+1dvzY67x7p7fr+43ubrP9lzP/P3t3P1p3fcb0REozZZT9+a6vcXp/rrB9P1UPY8ERE/mu43T62u\nzY77c7yW//AP/z2efPLJ2fF3r0724+dPPrjneLA2mde7b0/0e+fhib7//k5hD97eL4rjwc6g9Pon\nv/fybP0srqfh+iTh3lg7tee4vTSxJ+btn+L4mz/YvMP+KI7L7Inf/Oi78akvnI2IiF/+4DsREbPj\nH65P7ve5h6f68u2J/nyqOZl/ZfqZ5nOt0YgrP53svxcem+y/V376Wrz1+ttoH5N90hst3TG+xXH/\n2qvRPD6Zr4Prk/naPP6xGA1H8a/bk+d/cmny/MXxZDZFfPOVib136bkncbwibiepRxuT+V0/8uHZ\n8a//9bvxiacn4/fqDyfj+Ymnz8a434sr//6ryfN/7tHJ80+PC/382nT/LKq//uV/TNb3vP0VERFr\nn5yM70+m+urxM7PjH91aj6en86uwr55eXYtGsx43fjmZL8c+NZkvxfHw7ATosfj+v/PmVJ8u2JPF\n8ctTfX1uqr9f/o/fxGijHc0Tk+cbvDd5vuaJRyN6d67H4vj54WQ8F+29y/86taem9sq8/dLf3LlD\n/7z8i9djOY7HpTNPT37vexP7vjj+bUzmz0eiu+e4CA79y7Wp/fnAxB59Yzx5H6drS3cc93b6d+w/\n66/9KPrXXovWA5P53b82tTenx1tv/usd/x9xO0n0w6k/9vSRyfv79XT//Px0v/y36f75+fZK1Bu1\nO/7/hxvrcfOlKzN9uKgfv/Wfb0yOP3l6z3Gxn7/y7nvx85sb8b98cvJJmb1QHG/uDuL6dP4cn86n\n67/8QfxisB2fmM7fV6fzuTgu7P/F+fTh6fwve57Xf3P1Dv+oOC6zjzdfu4r6/+2fTfTV6cefi4iI\nN37yyuQ9TAOav5zaP5+a2kO/HGzF6e/9aI+9FjG13+qNuPLTqb/52NTf/Omr8bOr3y5d/xERt6b+\n5OrUnyyOozMZ7523Jvtd98OPzY5/uH4znp3aq/8ytV+fffBE9DfXo/fuZD20H5ysj+K4CLgX8+dj\n0/dHx4V8//p0vI4fmx1vvv7jWD41ef6tNyfPv3zq8WjV2T76o+nvLdpDZc8XERGTn4+XfzmZj+c+\ndXp2/G9gH48GPd4fn5g8X6G/zp6axiOm/tb56f7+0pXLs+N6ox43pvGAY5+c6Isb//nD+OYrjZm/\ntui//et0P3hyuj8Ux5+bPv/ifv7ya1cnj1vi74w/PIrRrel+sjrdT269FW/+2ytx6vOT+frmv03m\na3G8c3Xif3RPfm7PcRHsW5zPM/to7v0Wx6PhqFQ/3Bz9MI5+YjIeN1+djE9x/C/XJufPv6+IiKPF\nel6YH2X2Z3E8HN0Zn/ret1+KVwfb8Znp/v7z6X5fHBfxucX3WQR1F+fHm1N9fyq68WbsxM9j8r4e\n2NoNVY8tA5evvPJKvPjii/HAAxOj5c///M/j29/+dpw8eTKuXr0aJ0+ejLfeeisefnhS9nrq1Kl4\n/fXXZ+e/8cYbcfr06Th16lS88cYbez4/deoUXvd/7kyU6HxzniI0euni3tKhImBZdjwej2Plkcdn\nf0dErDzyeJypX5n9T2GQFVJM4MXj4cZkAz338Yf3HBcOWSGLx8UGM39cTIKI2PN3xG2HaPH45biB\nv/e5VmcWHS8MxuK4CFgW8vxHHorTc2N4YWE8i4Dl4vH/OX0BH63tzQJ8tNaN6x99IvrTBjXFAujv\nDqJ/cz2+eHqyofVvTjbAL55+MN4+f7v89cW5vyNuG2iFHC0M9p9MNtQiYFnIkY89GZe+cDu4WwQw\nIyalK0UAqShjKY7/7f+ZLKjCYendmhhqxQZcSHH8nz+ZXOOJhbLYJ7qrcfKL52bZrmemAbbReBy9\nja14Zgrd7m1MFnhxXGR0C4O5kFML41scF2ijQuEU8vTZc3Gp+Zvbzz91kAv56MJ8WTwuApaFPDY1\nCLev79x5XKvHpRf3vp9LL56N9Ru30WtPPn/7/tr1WvyXhfk1O/7v342I2wHL2e8t/P/88XAc8UfN\n5dnfEZPj2vJDs6x6bXky34e97Rj3duLCZyYGfJGNLY5nvz/d8Ap5anr/u9MOEYvHX5i+3wKV84Uv\nnovPxL/HaHMyv89/crrBTY8LfVTcb3FcZA0vPV1sqdPxubT3+b/w9FNx7sJkjYyGo5lDUASez336\nkXjjjWuz/y8MgkIeby2XHv9qWkZfrOdCXzy+oD8W9UnhEBdy6ewzsfz/3ib4Xj69d31euHCp9Hj8\nz1+LiNsBy0Kee/BE/Lj27ux4fj30x+P40LQwuz+9nw9FJy48/qmZ43ph6lAVx2cfORkRt8erOI7B\nRF9devKzdxzvdG7vAU/M/T0aj+OJMy/M/o6IeOLMC7F0/Pb8f2bu74iIzsmJQViUsRXHRSLoQ1OH\nbf74o3MZ5SKAGTHJzhYOcJGpLY4L+eK5vfr04wvrvTguDKn55yuOLz3+idnx/N/9zV48NeUG7W9O\n9GlxvH1rctx66HN7jml/rPcn6/FLxfqbO76ydDvb/0fPvjD7e3cwjM89+8XZ3xExOy6ep0hw3H6e\nVTwejcbx+en8Lt7P51vLcW0uD/vI3PyrN2rxhSN7x3vxuAhYFlIEqGK6/yweF/tlIasffSKe+d3/\nPTsuHLxCioDl4vFwZ/J7hX4o5NwnT+FxrVGbBQQKefaB47H26O01PP93RPn+G3F7PXzx3Pk9x1+Y\nBvALdNfi8VML8/ep1bU4d/7FiPFkPT79xOcnNsj0+OxH9lKsFMej65P13jo+2U9GhX6rT97f5nT9\nzx83WvU71uszx4/FqRfOz3isCvusOC4M7kKK44Jf74HP7NWPD3zmmbh05ja6/tKZp2Z/1xq1eHKB\na/fJlSPRnrOBFu2hxf16djxtgDKzf+aO/6+5OfzIgn1RBCznj3/zxjuz4y8+sne8yT6Z348Xjxtr\np2fozsbaxKEaD4fRaDfiC+29zSFmx8X+uGDPPLlgHy8eFwHL+eMn52ym+b9rjUZcfPyTe/6/OP7e\neOKwFfZS8Ty0n/3zjyb+TRGwLOSRx8/s0dGL+roIWNLx4vsvAkx0XAQs5487P799zcbDf3T7y96d\n+qU4LpqNFfZ/gaQpApZRcjzqD2YJkgJ9+cKjH45jzz4548a89OxUf0yPi4BlIYvHi/rpdEz3s/Gd\nx4PeKJZPTfRpQWmxfOqJaL11m8ahCFgWQuNf+/H/HhERX1gYn88v+Ifzx6PheDYfi8Drk8tH4vPC\n33nuocnz9dY39xy/9+7k+OyHH4xGsz4rXS/z/wrp7fRj5fTjs78jIlZOPx6na90Zl2KR8C2OaT79\n6hfThM5UP8309cqR+PCcjzTvL9VrEWfP7d3/zp47H9dWjsRw2ii3+L3i+PPP7/U3j06Pi/sr1t/8\n8ZfmdOj83zEazhL6RRXehc9+LP7l4edn+vtjT07o6opjev/vvTXx95YfeWrP98uPPBXP/u5/zI6f\nnbNfR4PeDDBRIMyK40I+tvD+5u27iIhP1yfHhX032y/njougZUTs+Xs4HscT0/Er7PXiuFgBi/ZQ\n50NT/3g6XsVxxCQgVwTACjn3qdPRXP757Hg+XjHY2YxoH7v9d8TseDycBOien+5XRWLgv31prz6d\nP+7vDmbzuYgvrJx+PC4915q93yKhFaNh1Oo1jJ8UNE/PTvfz4pjiPYUUAcv5448+cVvHz/89Hg1n\nCdPZeM7r27gdsCykzP4oZLjTi7OnHp79HRFx9tTDcaTx1MxePfLoZH4Wx//1+cf2/F5x/NOp/ljU\nP+2pvd6bVsDNH28PhvH41N/Zntrbj595IQat5Rn9w6cL+3l6XABuivlXHI9H/0dERNRXT06PJ793\nak7fn4ru7HhlWVEs3SVw+dnPfjb+/u//Pra3t6Pb7cY3vvGNeP7552NlZSW+/vWvx9/93d/F17/+\n9fizP/uziIj40z/90/iLv/iL+Nu//dt488034xe/+EU8//zzUavVYm1tLb7zne/E888/H//0T/8U\nf/M3fyNvLGIvx6UqLSOh7piqTBjLro1yB0eqaM4jKwcSeQwjjOY84jsivlXdr8YAQ1bvTHbPTpSb\nUPbavM5lgq2VtNK2XTHGtRp00xPvjCDqsmkR8LgRf4hTBLIquoqTEAyd5oUSq8mCU6a3Tz6UImip\nhDr2RaQ3p5BE7tjVnfkqaTyHOSk5RIkG8sQYc4NKlVXJcWo5cITHE5NKGO5U9iqOTdprh5tQPije\n2TJwI7cEJYNVRp44B/uC3mDchDUgnpM6zjvi8BLuXC8vbVPce1W3Dry4oP+w9B0a4SnOXuyOasyl\ntlGSv2PQZRQO/x2SkcdQ8aKSSA5uh+PS4AvLKdQ0o5+xc6wq1e4C95hD/WCNP3UtMaS+lN4FOacQ\nVYGSEawzZVO3V9Kb2u2HlqGonomIUGq+t1uuT1QXYBJZqpw4BxX34LJBl0Ey6qV3FW8AL2eqbRAR\n0YY+GEqw4aQwApGXVe5b5d+1lwXHIwjZgXUo4Y6I2AFfGNgVIiJiF7t3p+sm5Lh8IJ1ihugNZKm8\nYW93jpX3Dmj3RHk30HUsg1/dFP4+2duKfzb1zdA0v5sPJC2Up556Kv7yL/8ynnvuuajX6/HMM8/E\nX//1X8fGxkZ8+ctfjn/8x3+MRx99NP75n/85IiIee+yx+PKXvxyPPfZYNJvN+NrXvjYzAr/2ta/F\nX/3VX8X29nb8yZ/8yV0b8xw2IV6F7Nd5n2T2+7qG+hKCU2rzw25+tMuqICgpZTUutPkLRxe7rBnj\nT8Hmpuqml7ELKgWUlNDGOFRceTA3qGmJCqZQJ+pb5JhFxCoEgYiPxiGlVyT7K2RkZWw+YHE4CX6t\nVDdLzj9oqKKMPJoD7dWM+tTguHQSUcQhJHkUYWycRjM64ca/l3r9wU65o0ccXhEqsQQ3Rg3VIoLM\nEjXPkEs2Y6ORkbKyKbEq5qYyGst/Kr0DJr3LyTnlny8f4bWxC3xlObtq14wkNfE1qnme09ZaUg44\ndSc1eLccXkL8LRgzZ1zUvkX7sJM8dpo99WgPaHPCjd7NKiRVcnL1RbBN5bwb7hxsrFnZbZmaSeRs\nZsLf0fMMoTlWBHMc0m/lbPYWEdHslrf2U8G+VJGcdKCbWoJ/lmyqoippUbA7dDAgo2noOVobThdo\nCZTJ2ASLGrQpPdfbKuead4Ktzn5C61w9f2etXNc6Ksjh7M2pT8nWUYFbEsVxSZzBA+EjNyDpSEOj\n5hkl6ZSvQd/lbBAXcZfAZUTEV77ylfjKV76y57MTJ07EN77xjdL//+pXvxpf/epX7/j82WefjR//\n+MdJNzdfKo5WthDalBSqsQtKtpYxa5iZElQQv4IhoX6sbWRgYJxp4Sm26mPL0OlSBcG2y7sKt06c\n5HNI+SJKN69hOtxiNGaZyE3JyI4T8a8i5abr3IKOtrSWIiJG0IGyKbstl8+B1srR0s8dFEpH3DPN\nQOUAocC9LXY1v3L5Nh9tH+YgbVYRnAGzAuTr1+7+TwuCiMvEZjIRIpsn0DlNaJBV66T3AudNmefs\nKKNDK9dmRmkfKd8DFKKEu/2mPz8FgRQSsE4dTcU5Co2XKhRsG4k1UweUICWVFBKuBkm63jrvM7s3\nyzthU+fwiIguzAEjBscyN2cuv/RyXDw/X94L9hn81IoK9oI+VdOCmhnkFOVoc8I1fa+jc2j9K1HB\nkWaHEovV4HedwAUFYpV9klPonh00rOM0IiJeOOcIVICEZ25xgjAPHi+fmxQ4VI7+NiDYlVADz3nE\n4yvvvLcHdUlCLrKqoiG/ZvPtcp8qgucg2Xoy4QfiNBSi+aeSatyJXiRisEFcuh9APo1s9kZdtTOq\nJrInIkSDvG45QjAiYgANbZQ0W9CJHOx9hRKmp6Gqk4iIeLD845xJWqWzCHEZb/PvpTb9VRVhhLiU\nySNCEFcduDwsUjMCl47Qosw98Hz9e28YqbEcg5GhRp+UvFO+RkKOaURErVO+YTglMtxR10Fi8jkK\nCZMsGdeG7CoO19mB4ExGcIgUCoLqkwChY6AHc5bpLV5jNB7PPsOy54zlaxJRYDznzqB8nFtqnoHk\n3AFGmxv4XepaV8bHEJ7fQdbmTp6kiqQkwHuD5zScWQdRoLPDaQoqtQv53aSK96kQBfT0/C4j2jUo\nOcpYKDJu3J4b43pj7zHcG9GFSLuF0C4itUs2hUTcgQOQMW6eFdXmiHoWdLQNVBmuGeFo50ZDpgoN\njUqq5HSOs4qwNXLa+44QEqneypds6Kzxb+Wk/nCE8AtOqbiTVHASBNShOT11wqJotsh1ahh7vWOH\nO3sAJc8cuhznPSOyWqKxK3IG6frwubLpKHhL89x5/xL0gMjW9HumMxTNn+pSz0Jo1LwJ30MduJzn\nuBwj9yKf7wz8ALJD9SPHSj//vRQV6ALHxBHkYxJCRqZ0WiEDYyHhMopyTIeJgUtl5ObkI5LORCLH\npVMKJ/lPCfFmoE0oCEeoxsn1AQWRFY29dywvXLxNVq2yoCRZYy20KVZUokJPTyWPEWINGkaGmhsk\nZMypteGsmwryXZKSgASdWbEHOQ6QI6m2tOIkJPskxNzs7aa9NKxgEKLQ2CRqbY5hbmZ9Y43bRu7F\nL/3xnq9SS7vUWmLO1vSy57aI3NI5dG9WaWNGyKs1z2RBCDy/kbxyZICOpii5I+QIcXI5e6DaA6rK\n+oKMKUkpxqy5dO/RyCrhRyXJCnSi1m2qcMKT75kqn+ZlP2jLCN5SJYLcKFUmIX9b2Yc0z61gK9yz\nSlwQx6XDTe6IY7c1gP/WCzSVi9pnncofsgOphDsiorMEifI6oFSNOSNtOvARqkJc0hpU56TaR33g\nxY2I6BvBZtSAH1TEJYkTyLei/xUhLqsqn0Ex0Hu0kJzNjwwJSZhPAZ2MSDQlpDCV05gahHLQRo4c\nNKpLXZ02eRW44gtlbIxhkL/nlJzvTK3ZOpT8RdzAcxxkIQlmTVuqhPlg5/PvoziJgJzce+TMKMN0\nNLr3+tEJKNcE2mc8Oli9cVilqpANVbc45qEKgFjNUUDIMXEQl8hxaSTIHMmpM9TTK/qXVMlKiXCY\nhfwA4QehHVJVtRysgbq459QghAoM1AxbAxHkhhLKiF/IalM66zyn3VgVejmnnlfiBOiyXt+Ym3SO\n0qfs8+d7fp1wy8eZytc42HiPc/2ca9OVQx243MNxaZSWyYYuINhoBgieHQV/mI2fYXJfKJbBdnr5\nliPUaGPcS+eccYQcWhU0IW4b/P/MuiJnWQ8ZBkBREhERQ3g3SikSZ2ZriblVSMjIUN1hkeS/y4hL\nLL3G8sG913jpypU4P+2s29so56tzUFWOjI2SfBpPr2ds+Vg6jXaqCjbnzDQqXtDUTsiytFPMZ5Le\nLhntgMIQDVhQnwgl6MRaUgORcszAOSe7ISKi1Umbt0PgElai9iBqjkLdaSMiWsRnfY9smsuXL8fF\nKcdvBDsTdHnFY7r1bvm7cXgMnXJkujXlGA0AIeIgLimo7iCrleR06J0gyGqnfH9UupnsELq601Ve\nSV7n2NiDKAqmUKpkh2SMqA1F0KLVAWSv2Ou3AfVLgZaGMmqpQZxYm/vxA/bLcYnXqCjOQIFjhay+\nsVVuCT7gNC4D+1wlLpCvEfY5JYMe7/UkFpdnxmQ8cpMb/iHRtURE7K6X82nnjIFJZHG+y8gGrqki\n0ZNGhRvNZ6o6GEFDrQhRESLWJhVs5g7qH+rA5R6pKGvnwGNThSZRhO7cmkuU09iHJb6inEbYGDrH\nyx3g6+IRf/1uWtOayQ2AWhJKMRXurrgwKAO2A80PHMk9LYlge+k4l67QvCGOy/VdEbiFMn5VPkVK\nub9d3mUv6ukBzWVhmG4DX6HstEmGAQRu7+C4HI3v6sTUodNpBM+bnMas4sSrIjmnKCGwy57guExN\nRuXOmjoZzd9ulOsai65h5UjyOdSEaARrZtTm4CgFLknPRETUYd9S8zx1nLfFvMBScdE4ajRI60I5\nEMTrZOQqx5hKpZUxTcGBnPpk3gQajPcek9M2NJxGorLpic2WgkDvwfqLCJwD60awk4LNVtUBBYEz\nV11Y/FbQBEZ1YcXrUxBSJLZTy/hviWA/gSEUsIKu02gbnWsrQoKhGIFLQkOrEmKyQxQHeioar73K\nc9nx3bChzFwEoN6q3z4Wt9sBDmqnOY+kC8GyX+g2LnTjEdiDjhqBQ0q4HKWS44joQ8JV2rQZ7T16\nN0rPUam4lnzl1SSDHW7oNGhCklQ8J5Ux1zvltqOkDeSvUGivp2SHU6kgaRRgPqvy7sZquc9LyRPV\nO2QZ1qCym2k6fSACl8XAfKo5R9FrbH5Oog8dvYpKxR2jMdVpQCcrAlPKS8LI6sIEHwLxstL7VHKl\nAloktVY+jkvHYM5ZCpWb49LhsaLkQRXB/gjlaDul4tRRVjjtlAHLSDy8ePUXL1yYfdZbLzcMcjqa\nzPvG46xoDJZgPW8JbplUqQmOUTTMDTQ2bdi5m7Y4wUaam5bAXqdoBPo75YEj7AI95IBeo1buaOTk\ncHIkd4lMqtpWAfUG6SaFIAe0i6TAhrXuNAbYj1y4cHHPMaMQ8gVUFXiOdGBOTjTpGNAcaBqmfEbk\njrpnfDcqOJHx3lQyMlUIjZuTKzGCA2oqQJ3auEoK7SeOH1QR6KQK2RLAitZqp/Tz96sbzzz8wOxv\nNfq74G855rlam4Pd8vlETXAUbz6tG4fj0qEmI45LGbiF75ptolJiYd5+pU/TKU5y2qiEoG0CUCmC\n/Qp1Wy1AypMOVH4IbptGgLwqofmsYgHDW+UgHqbR4DFTTa3w+hWZ6IcycFkqgPZy1qNy9NEAyxgx\nPvCOgcJjIhtjVRhm9Dz9TSM4QEpZQJrJyB2LDFDOLmekFFQ2JRXuXx3HpRgX7CqeViYawcb0joOo\nyNg10okB1jrlBmuEUOTw/tVbdtAWJHRfKjiCKFF8/9y4yTGkHNcQO7E7negNoXnuBMHUvkVoB0cw\n0CDQe7VeogEo9iA0sowxy2lIySlrJI+aiV2VR6JUnJDFikaCxqbV5fuifShn3NJJUmK3b1mKRRQn\nRqdVw9HO2ZzHEghONRNpbCJ0sgUblKnmjXBvDjXTFiHhANWphGIjuQOXNAdUGX9qYwZLhB+kKj9y\niXr91FhV/15aqaxK7BJKVAnZJ85vEYJXrk14zvYqr40GJEkIwKAQgpRwq6q8XfqVILRvUBWZEuc5\ncyI+af45ukQ2OwNbR40+JQOJ5kkFyGkNSH+HkpQw/iNRXeMI3ZtC6tM55MtI6fEAACAASURBVB+0\nBRpZUTPhOdXQYx/uwOWrw634RGOCuhzU040pVb9PQi+rfvSB0s9zS07jA39LOFkE3VZlcsTvhJkR\nsR5WwTBQ8GgyQEcb3DSEODccISSQUqT99bSSeIJtR2j0Eskgsat5BPNokfG3Kh3zdKXYWSvXAQ1C\nPBrBnNW2KN+i57/2u+RzyDFb3BTnOS47x8rLAG69+Q5en/Yeui9VClUH7sOlVZ5/N6EUxzHM6W2O\nNtfxnAEkT+ora8nXt5qzGEgw5FcTAY2cqGcy2FTgjDsHl8+NWg/oHSJiNC53AJQOHMFaV8ZXO7Gh\njwo2j4GDuyHQwKmIfCcwoBot0XzeFWjom++V71sOVxfJ/FSe139KqAGHSriS7Fh2o0gGgx2E80lV\npKwAn3czX3WJIzdE0Ij0SR/K3iO4isFBVeE1DI7LGrychgIDwOcq2M2ISzHO8B2W6isTkJ5HjNn2\n29dLPz9q2KeUQHfSc4Md3mvIdiXfybFbnKTOfGL3e1evxZmTE/9TgV4oEeaUXavgWBM6odN+4mDa\nZGIXbY1yfeI05xoKtBnyQhrgplbO5pVGwqu3dTP9OqDr1fMTBZm0qWhtgj5TXKo5heIddSMRpsTp\nXdBaSUP9DsSYObzNdaDs+UCUit9rsbpi3byW7foqo56z7NP5rT7cmyK5p2wzlmkaPrbMMlGmQ5yC\nZcfGmFGZplKkVqk2yNgo3yGSf2WY0Wu7disdVUZKXp2zfb08CNX62NHyE0bCYIZ35vjfqnwLS0Fg\n812cMoPR7c9WIHCpAkp0fQo09OBdRnAzB4UGptK6bdFohqQL+2juJlzkHDvN3kicUiilT6hxFK0n\n5X+Nd8vHs7fJc2O8Un5vY7hlFWhBB0iV0MLnMtCRiNBRyTu6s/qJD+E5u9u/Tbq+EkJitcDJjIi4\nAYi3nXVOBDSb5bqWVICDDplPBI5jvPcYAtTEc9wV18dSeaNrqayiAfuE7m1bONqUvMvaAMWwTdTj\nYyO8ZXb0CFVTB0iHCkJiRYgMXMIp8Dlx9SlRyGKkTDLsUxobVfVAdFJqr6UkiWOfkh2qZjmW8HaZ\n65xBF4BEE8mjNpSKO6i6+SR169YWJq3nZXmpXDfcFMhmrEgxfFQCcCje/B6sTdVTIVWc6pah5Dmm\nxHI1lAhN0IEaWZtW3q70zAYknLrHT+I5w95v8DuS3W3Yn2HOKA50RxqJpf8KcUnTWVMSgA4Smy0l\nj2gNbAkAF/kUOQEcrhzqwGWBtozggJqKQVbBiWXxaoiJV0m5hzRyyyfrLaEUcpa+U6BjoMqKKNMh\nFAkpcmfzoYWsgoCUtSSR+3jGaS7nHyg/MrIbkTfLQg2Vdm+9V/r5uCcyYPCeVTwJwZMCVYVCjREW\nrvHCuQuzz3IGu0lvkjMfwQ5I7uY0JFh2LjZFTJ4IGomc0mwDStgwphWCO2dpFTqgwpilRMRwI10H\nkDOZsxw+In3MdpSRSV8MeM3u3uIGUWWibA2a56oUipKRqpkKOXQO4Jf2x/nXf/bFC7Eff4TmrLqv\n7pHyQMOugbg8IZDqpOsdlCo2AMnII+ggPXLrM9LpuAYUEhASPhQcVYL2qZGIUk4rOroZA5eKt73W\nz5gMdPoTEL+bOIcSiwrtQxVe9G6GIuHagGqd98txefb0w/v6vx74SNJHhqS3aixKPhJRMhwRyGpa\nm54+SddbFARsWGX/6bYO2RRqbWKzL4d+yLhn2reGu9wgr7dJ/KsHGwRzgp1kn+YOzmGpuIEgdujs\ntlTMhQQoHS2eZSGHOnA5Lw7vD3YTFL9FxjRlGh3OndxuPhP8wkYufosMsKWKSEfo+jWllEH5Opwb\nJCo7zU07WMFQCSuJLAWtpc9BQh03WiILD074Dhg/4xqPcWupPIOsNjIyGluUUa+nB/rUPkpz03GA\naJNTexI1psgZbG+p5jyNdLoOGjNHb+I66y6Xfu5KKv+mlbwy1KlCXKoy6lzShMRBREQbiNRJagPW\nf+1meSJA6UCHW0dVEZSJcgzop5oKoZRo6KqulfRNc4kTZPQ8quQJDWA8I11k7oyqG8BpUsFmCjQ4\nidhjAj1IQmXsTDwidEBGxKVDSaDQyAMoH5OlfQZKkMRpMkBrg/ZNcmaVVJXwsxxqmk+K4xIC3o7d\ngNcQ3xFQRZawgh9A9nFrRTSoQ4oNQfFi9AEgQe5FY5op/4jeMyWWqVIqtxD9laKL2YWgqlqbFCNw\nOC4JWeo058nZB0HZGlQt1OgYzYmE2twENOB45ATuyj/P3XAxp5BfcWxNVCsBUIB0o+xqDmpLNuKD\nBOoHKnA5z3F54JIR7eFkZ6sSurVNo1TcCag4fCT4bgTiMicfBhmzA5FpHOymcWzKzFQj3zJGRIeQ\nOmxy6qfImZFKkcqHkONSjHFGzo1xPx8p82Jg4KUrl+P8QmfdRVFBwNS3eV2UipMoPiAStTZIuHGa\nQYmxxdnhnMLOhMGVJSI6WbuKgygkVhO5YUFvieZIyCGknAkqVZYJJ/yqVEROB3+rJpyZVGNONUfq\nbZRzT6o92EL8ZaSyoeDAfID6Wy9diRfP353jkptgpd/XhuLTBpGBKzCqOhAcUIFTrOIAjlVHFPUI\nyYrQTRQ4I660CIX6zljGL9YGVtHQ/2dGXKaCEZTUWxm510QQFNGgBhqYfstrZsL3nOpvqG7bqWCI\nye/dfd1+542394W6zBm4kpRRiSWk6rdyNrXCpJaxNlW3adq3HI7DPlVxKX2aMdiGa8MJdotKDdLb\neXnjDXtGXD814ZObx5H8um2BhKS1ho2zxNrsGs2+AviMP1Cl4vNCTWOUkDMl4bHQTEI5IKmiStid\nUpDUshKlxymoKp02GM9mN53z5ehyufKzkK1LR/A7J3BCQu9TclwmOgdqzjjNecjI2rrGAZ06OBNk\nfGA3z2BeSAXdb69AE6aM2Ry199E8Vw0wMKABfJGL16/Vbn/W3yrPXCvjN9UsVAmKcbN8PSu0HekT\nVXKVKjVRqk8dcndvMGE/CZGf686ExNeYfHldqp34ouV2So2jROCMg9fwoB1OSNI6U2Pm0MIsJQYB\nyMmIYPukuc2UBLsb5XxEjtDcaC2z3VKHoPJQoURr5b/npGKdRhckZBgrLllCSI0kJ1z5d8T7FRHJ\nHbJzUu8oIZ5lp3GfCoKTQ6s6NI8AQUuJCDVixD9ZX2b7kORYt3yeEXIvgvcAFZzA8najOY/FvUfJ\ncIX6JqRuxqC6bFzVSXeOX32nXD87fhjrM77ppYePl37+3s9+Pft7NBzuiyaIAjcOn7bs0Az6ga6v\n1jleP6MKVI9PFQQqBEXr2ekqTX61simJNo0Qr+r3CEDiiPqtEVQF1sS72YBGgFEv59l2umArO3hI\nlQJUFWoE56TvkDFJTGXfqux8GXRAqt0c8QFpzlNEdD9WX7prRF59r7ImJFiO2MkXuFTBGadxUKqo\nS1CwSRmmFLhScHMSMgBVp1Mq1W2KxgjYtdBQFjSeOZvzqDLJMXXAEEJG1s1fc2OGEaCkNnfKN+yj\nHQFDh03h7U1GSbYwCAYbZj09A6rQ0H1KBIjgBHaIhsDt4oq5OIe2rEHgaADjH8GlksRZe9UI6CsU\nVqdZ/vwqOEACPxU1VdoKzhQZ344oB7QOuvGEgc5QDsAq/J6T0SYE8cabN/ikR8s/RlTdg5/An+q+\nVR7QU/tmlyheMu6nx0U9OnHf1dYewHNQb4E4HLeNtWP4HY3M7i0OqPZA1dHadGQ+cHfhokabz64P\n1CPSbgFeyi0DQa6as1CSau30WvJ1MOHjoNogQO1wxTl6Zij2AAqqkqirUxCwfoTXBs0bCiq/J+wW\npyR8C+65Eg78iIghNG/scpKQkvG1Yb69VqGqyN8j3RARcWIlbZ4tP8jlsJvvQLDd0I3zY3nmweP7\nAjp87MHyZKBkmYL51AKQQATz81PgyKluIxoNJaS3dKk4NE7rpvsO3cSOzhH8nAqNPgIOZtkMFd5z\nHflv+foUCFeBuzHcc1e8Zwp2YeM2oRtvwRx8VTScJKHAZaNdDuxQInnLwd5bEnGtemJIbwe4RyOY\ntkz1Oxktlds0TlBfyaEMXJYJjZWzWJWQkqNOq46oe87paNGmpOwoMswUEouI6Vsr6QuZuKL6O6Ls\nl5SvaIyAPCXE0yOen5w2heoknhhHas3y31IJKIcrisaZ5oxE9hrNUTpr0ExhA5rz9Muz2RG8KW4L\npew4Z6hPwGlUZSBDMF6VYUgOGH2uuMrG7XLDuL/D16dGF06CBs8QgUtEoYhO8CTUII648iI4O69Q\nEJTRVKWFJDk5fJQDTnET7A4rgnZkzDrPohJRToY+WTI2TVFIOAxoiOQhOU39zZt4TmeNdWouoSB0\nRMR1MOYJ1afUzACSoQ4pPQWaIljXdI7APYubzlkpMhZ0Dani0A44ti6i2gSi4zhU8YQw6Z3nqUKc\nwCXNP8mjh0EDdkDba/ee1kvtm+2l9GDTLVhPlCBQ498zaHbo3aQ274yIuAbXP62CQ7A/Kt+Zqlho\nOVOCKCJvqTiJAn1QQyfVDBarGwCNrcSxaeg6DluQgxJESgAVuIR123ZsMECQywa+IEqf0NjsGOhF\nEmWfEv/t9orwt26U+9Vk0wyEDUC+W19Vf4K969AoKDnUgcvXhtvx8cYki5Ez0agcwM5auZKlKL8D\n55W8ChmdKbo3dYWdwf6QYPu6vuFokyEhocYGDDl1nNX/08aoNv9WomEi+WsMYn5yGogQWf4WfK6A\nK0sPfKT08+sCWbtM5Q475WW/tRYjbkkoMxehS99JaA4olOC8XLlyOS7cheNSNU0hXUO23KZ6aUYQ\nhhoj7BrzjAKHsXoCz8mJUHEC1ySOY8zk/xGdxOeUZddb5ajr7lFORBHqFkvOBAqH56zYtw0dKBue\nlYjFSyr2piY1FYOhIYcxIm95b88IXKpmS6kyn7zZj/6LiBiIjqZ4DtgaTqOrm1sCUQBzYEBcmmKa\ndY+D3eB0bgYkqCNqbVAJIdnajuRGdFDjLqKEUOX99E1boHQdXj4SotKRgeOMvJQ5kzfql7CEVzjn\nFITI6YcpoTGbp7L53tVrceYkI/cLIZ5dlYwmUQ11FGVMmahKkQZE29pGszNK7KtKDbJbmopjMqNN\nuQJgjLqo7lg9Wr4HVNVVnKS1XF7CHRHRagAgSei5VAow1WyR2DJWMgbOnXiHihGQbuiJ5CVW8hGd\nn7ABqqKsceRQBy4PQloQBKiK4/KgBUlcK5rDyCOYmdyVSLadTYkcatmZLjEDIcc/o2HooJCI+0+h\nB2nDbIrrE0LGEtjJHDooFYR0qAdSxUGu0JxdbvI6o2yaI4rkngSzo8Jpx+SNkR0ncXSGZWSK95zV\nxoBF4KDE0QEUc6kB7/Og902VnWee6bzcPiRoABsBHVnylbFJoYO6zuk0km7OSdg/uRChvg/YDoQ9\n2NmzrKYpFZU9O5fB9Xx4TfesMs5pb2e0G5QQ4kkFZ0aJtBAK8ezM55xrgNCLuuElVOWp5jxoOwPP\ns0qsk26sqIFt1kY3VsPFwxscIiGub8X1T6L0aSr/pgIDEBpVrg2q5Dzg5spjYxNCH12MGYHYDkMn\n9kMduCzQlhGctVRGpgPdpbJLVSJx0JJq/yonl8r0FCHrLi4KQJuJiU8k94oPbLS5Ufp5fUXwe2VU\nPoQq6wiEUqrBQiTSERFjID9X84KcRpUdpY0Jy23E2uyuPVT6uURuwHUaa+ncMrSTqXKLBtm4hpFP\nxvTi2rw0x/E2gjmjAgCpukERfI8b0DhLIeFAnyh+MxLqcqcEu5Mmogaca0SIxgzi9+jXVEl6TluK\nmlYondUUCIHSa+wyVUS9lt40owqRTQ4M57y/VY5sRD4qETimsiKVcCW0h3T0Cb0EnHiOzE+lP760\nP47LVL5QJQ7i8tZ2un3olH07fHEosG85FEsK7TLYLq+IyNl8wGqDK7uK0+fVOK09cBoV1yEFlGg9\ne3RBxh4MdkNu2TXWYBMqAqhSQCXPqYpETZn9oJ73g7ZU4iAud25yRQzRae0Y12mLRHkuUerM6cTu\ncNaSOMkrur6jm+pAsaISlDScCtlMgXAVA2sLBGeqECBLBTspcOmUpDsyAHq8RpcnNNF10Nzo74p1\nTo2rMs4zVw5l4DIpoisraImw32iOkzFwqYJQBy0Eq1dOG5W15ERnWBPfCShVgJCLqJBkHQQ7zhvl\n0Ng5WswZMqapAU4Er010tA0jO7V8NCI8qgKYm0ozYEBDOCCpm4wq3aDgTENcn5qWOOIg7mid7Yfs\n/vdJciIuqRGdarZGcTsyzMdQOqRE7ZtVZIFl4BJQomPB5YvoPVAnaj/NuZ/UhTOdijh0giPOm6T7\nqoqr0EmEkn3qlGiNs3LZOghy/s6x3dLRO8o+hS+M5zy8lntmga7iqmlSTns/p6hEjGp0USa5K2io\nWueg0dhO8uKgkVjc0yHvfZGuz81xWIU4peKk65Vt0N8st7dVM9ScVZY0N3PaBzkD2hFeM0aaTzTM\nqeX4EXfp0QIgqtxyKAOXhbw63IpPNCYR5Krq7ZGTC+SwbtauUOBClsnB544BTCIXGGaURdnrAfM3\npDqaMmgzSldwdH3laJJzRMGpw8yRQULo2QgvO0tCCO7FS1y+fDkuTlGXY8h0q3fGyJHyzxXiEoMz\nihYTPlfIXhLaMGvG/Kdu446otUwlGpJjkt4ZdRSOapwG2W0Y7F9EiGXcGyK8LLAMRKYKLAKFeMRS\nKNi2ZODYcIBIb1CjG/lbw3J95jjg85zwixyXWMVhODnknDvlY7TOHVEJP5WkyiWOTevEWZTeJFvj\noBO+Vdk0DsclOa056Q2UEMdgVTIw0MjEC0lCVGIR6b6jkvkk/ffevhZnHn5/qMtUUTZlKsWGQokT\nsjgrslwI2mcSiQeAKGPN0jmSFihrsA0AFMLWoFurtzgZTWvDAjZk5OXsGohL/v9q9gZJQQc+Gvl1\nKnDdokSAureKKpMP1gq4L/flvtyX+3Jf7st9uS/35b7cl/tyX+7Lfbkv9+W+3JcSOdSIywJtGcHd\n/JQ4mYkGcXVhpt8oR1UltBWUCKjEEGWgVF6SovlOmRhlmocKcUkkuqIxQk6OS+SkE7xv/Q0uITxI\nkVmjXjkfxmBUnmlzSsVlpTKsDUTjKtQANSARz4+omowlDYsNcC5dOD9Dc1HnYEdn4JxVSxaQjfWm\nQHxWQGTtlCcQT1NuoZFRqK4+IBdUWVHG5ogo6vqpvD/qnRHawaEKUCVvqagOWVZEDaLazHOcKg4S\nTmXUHfSeQ8CfKvM648VzF96XDmmpVwbPrzguc3ZhHRq0LFUgDnOXL9KYKZRwLSOXJDUZcISWTNtY\nF2oPoDmv3j/putyNLUmwtLGi8sFWBypSxPNvJ/JiqqoH0qdq39qPPtkv2pK7ijv7pkB1JeoghUSl\nruIWZZMhVN6te6FWYNMKFexUCqRf30Bc73IneryOGErkra4fL/1YmTM5uYmHiShtJQ5KU12/DpVs\n2FW8s4q/teWgnitqxHaoA5fzUlXVKSqlikrCq+gypsayCvJxeX3qWJcZgpzTOMcSCeGYHDi3CVzf\naRiA11DfOdwq0JwEN1mVVDASDjlLNLDcIbPiJ3VCTy/tRdG9G68PY9bfTF/POPwVbZZVCekTxT+b\nlcfJaNox2EzkyhHOLO0BShyjPfWcrKXlVQmUcEdwEEQmCRPFsWfUOKcG7pQ+a0CA0mExcDp9OoFj\n2rdzBvocUY+C5fXi5eTk7KwieTb8A9uDkPpCBGgVUKAKsWzqjOvGCUKMIdjjUH+QrZXbpaQmcRSg\nVQAOZ68nsehSqIRWBIfonLqRPXZABxRsdXx3p1TceWO011nxBouDufz6R8XcpLEhP/Aw9C6htal6\nR5CgHyLOIcqg3HIoA5fFgL023J51Fne4EEhhK4OJZJzRmFeTqAoOIyVbRsesHYjMqy6oJJSBqwOJ\n9URgsYhgJzktOTlL1Yadeh2pFI2MNgUoNxUKBBQ5OQZqze63q/ae60C2u76c3ujDEUJcKt2QzH22\n4DBcvvJSXLxwPiI8wnQSS81A4FIFdKvQZrVhPt2cWxx9glye4qU5FQkotDaFPk9OREjEZbnebgmD\n1Qk2pRrN8k2Coz9ulXd5jIgY7gKiAE5xqjvGPUZBIEJIdAel4MT4HhHpv3TlSpy/cOGu59D8UyhZ\nSoQ5Ivn1pO2SJsQNnDPQ56DRHV7QnGLxqYtzKHhOdlgVwdGI6ppH4vWFbjhocQKH7aW0tan2YEIW\nOgGNedDDK+++F889eCL5N2b35ST1BEpy1E8LTjQFL+itjEAJSmrJZiIHnIxEXlpxzhIgi533jNVq\nYlic7vFOzAX3DbCpHRM4Z1d5A9chxYmfpK6BkfCdWqDr1GOOoalbzkqViEMauCwTMgyq6mQ2TlTW\nStQ9O2ZJahBCloobi58amjhdsYisWSEusTPfmBdLViJtcIAVOiTVOcjdnKd3q3w8pTOfqHzUPCeI\nuqJRIEez2WW4Owpsfqo5zVYFTsO4sbcxxrjenH1GzpljFNApcp5R4FLM8w6UkTtIrJy6vtFNb0BC\nHRAlogACR05Zi8rO5ywVp86xvY0tPic1OCF0FgUolZFZBeJS68by51Hd09srR+GnyvWMao5Ee41C\nQXWPlTcOGu5AQDUE4ipjOej8a6nV9h6TkAO2JBYaBQFVgzaSgVH2XU/saByR126h/byxlK4bNUoW\nOr6vpAd0Kdhu0SjssD6jaUPJ2KXM1COpTWMqE6HnsTmHYZ864pSKU2KZ5llnjak/bl1Np3+itTEf\nHKnVavsKypKPnLtSgN4zXWZ3nYMjhBLNec9q6Bqwb44MT7y/mz7PHX2eM0niBJQweUOl3cEBYhmL\nQAow2LfEe6Yg3NJxbp5IeuOgg92qe/322zdKPyfbVelGssPlyoDApdO9XMmhDlwWaEslyplwYMgY\nBCCeHsNgokUUEbElOFSqkA4sCtV9q5+xhJaUsi5Doc1fGCzYgTEjqk0gOlIdfZW1dcrESPluqvIt\n2jCcrBUYbAqNTI6mxXEJ71mtzVEPxiYjjcGi//vC+YuzzwZb5UGwnB0Y15QzTagyY/45XcVRRNAE\ny5qMpAqJChxT2a0TaFM8kqmxa3l9AyHW37mV9P/jzgpfvlFuACvDlPRjZZQclG4XaXiaGw6yms7J\nOc8jGCV6r0qEFtGWhDirQ7BdzXNyGtcNfarQFrVW+b2pDsUkOQOXOUt71dq0rpMIX1Hqj4KAo82N\npGtE5KWsUnZTzuBETsSrKhVvUsA7NxQJhBI+6p6pcy4FCtWadRIRtDbm1/nZUw/t67dozkhuZpCt\ndzkI1V4rLwmg6w+2eQ9qZ8y4WkAZCDY2xX2R77S9kTE4I97ZLtyzindUQUGngqDkoyigCF4H/K2m\nWH/EmdoXc5P8yuQqOlOYs5jHrHO8HMSzDYnVWo3HjBK4asXu1ssDwWSfuXIoA5dJTp0idx0YAR0K\naC2xo5UqubkQyGmtgsg9QgUbDSRYToOtzdmUnEKZiZwNQGS5g2GYkkOvAtQkFOzuiaABveWbImtJ\nBkNV5PO0btU8I5QYbfICvBcBKEFFL0HzhoxZZfyM6+UBrV2x+dPjOGgbpzkLJZZyltxJ8nlCLxp6\nTp3joMRIaD01BUq1MyonTK+tAxLQcGatUiCxB/Z20wIqvxXzvArn3GoEuJlOmK/vgcq38gVIZSlS\nYqLYSV53q7KbrABpeoCcJKcOVLqpDoFbqzkRlekpLlOyacR6on2TtloneavOWc2Y2MtapqfoOsje\nPWD+T2UfpgbOZKDDoNnK2ezMmYMkLWGfUZNIEhU0o1Jxxz4i3UjB6Qj2g8aK/ghLhdPHvw/P7wQa\nK2rDYdFMkY2s9ufe5nrp57XmR0o/V2AIi7cawWrkhxpBWDHPaW4qWprROC2oPdhNAxzcTdq1+815\n4tXh1qyzOMKTVRfiCiLjORF6rqQqEmd/c5TV0MiAUeCyv3WTT6qXZyRHO1y6QZnTnMFeVY6aakxm\nbQwTEe2V8nvbVM4M7IwrsGG0VQdMA9VFG9MA0V4f4h+DZ1EBIMzOg2MW8f55KS9fvhwXL16MCDYY\nFeIv1QCUwSFANio9S9d3nNZb8G6GK8z/NOqRYWig2shpVcjqjIFLBznhCHG2qr2uCYapExyhjLaT\nndco1bR3IN+ZEbhrEcVFRvCiGv+ta+WomkGD980mJHBrg3wB0vacsTGv/5RQRl+tmd313dLPB8ID\npH17pJLksG4c5xT1llibdGdVJHWU7FwXcyYx2KXsCQroqIAWUQwQl/C2RYvEz9huQhWRsW9ZiV0o\n+asqOuLQ3zhIqM+fKqfrIJFzFkQlXGvduwNivvf2tX13Fi8TmYw2mnSSTUXrTCWpqXOx42861Y80\nZ5TdQNKUqINyyenXOUm6nEi43Y338LvaMYfnlbqKU7M3fv+UiFK+C+31Q6r6MN5l7oog8hHJdh6I\n8v4+rCe5NiGB+oHluKyqYVNqKY6lLIWCqaK0TQ2ls/kvJTqth6E5KxmAjmG4A7ycUhLHQDrtY8OY\nBe2zLQxGClwsAfeaLB8DuH9XZMBpbdYGhmEOitRpTpWz5K62MDFqMZ59NoRNSW2+qQagExzr75YH\nAKqScYspReowN3vrzG92WCVn/x0psKcp3YhoZDpHIMSq2h9Sy/Xl0qDnUaiuNsxbCFxawXbBi0lI\nbSsZmzGgMf/+x7G/+UDIJRVsPgqJMOf5qeQwIpAUyqlIyZkoH0IAwmkK4IhDF4JcaTXegyk4Iksb\n4dXk1MFWR10hyIHt8IvRelYBYkjU14ykjtOklLjf6hkRlwolrdCYJHVIBM0/f71eu2dNW8l/VWuT\nSrKbcI+yIoU4Y429TlWEkKDtLDZ7ehfEsarkOlR9POwgLlWAPGuAlK7B75nWjcVbn4iEVEKIV0ec\n2IkCttB3ipqLeNCpd4ST1JJ7IHG9f5AClwXaMkI44Ir3yuDPoAVeXRwLTQAAIABJREFU30dmbL+i\nSsVzdg5GEdls6himAloUuHQcLUJcqswQlluIxULvGakChOInfSWD2jntEMMwpI1EGtOJXcUbgsaX\ngn0OjQIqX+XkwTnq+qj8RbkPlqLsAl/lwiXOXbh4V2dJGdOphsG2SpwYJV+Y6TQSNDgO4r7oOt0H\n1pKvjzau1HOAIDecVsW/WsuY2ctJsUHIbvXOSAdJ/lljPFOD9NIupUxzIx/q3lkz6pxtQA+Nj6cj\nDhGh9T7lwoW7oy0jvOQRdbtVZYL0/LoxQ/l1cA9WEw3t4IPlilP7jAocpQrxCNaB5zyCqWyUkG6g\nx2xkpooYGskjEpqzct8i/Sx0Fv5eRRyXFLgYAC9vRMTNbagugGfpbTIcngJa0qbdRyLizIf2h7bM\n2cBWBRtpbLBSYo3tie2M/OxkazvPr/w9en4HcbdDiRhh9GflOc4YUFK/NUKORf49SrggElIlFWA8\nnUQUNXRyRAHVyK9UXJ7E87sM56imx44vPqxVlPSs5CoZhCaYWsJOBJ6Ugio7ThXHyVKSvPhEoIuU\n/Kow/ujXHI5Hi6uNSrFEsJNKIRzDkAxj5TRmRdZm7OjqZDqxM+MOk99TBzraYJS0lsvLfZxs0i2x\n+fF7Fht2og5S/00IGSXIyZUxcO6Ms4NOoPI91RgEuy1XVfIGhkFuvsac+a4xdEKXfImJU6A2zBsc\nicx7aplYRPICDZy1aQbsgeqdEdqgvVrOVxrBCcRxxj0op0juRVg0jm7sC77UWrN8DlgI2iYEWzMG\nh6yqEzXOMGcocKwEbSqjMYpqqkfJExqZnhE4twIqFZWKOw0fq6DNUlegEk6FON0AHUjzzOJl1SVu\npR879pHT6AaDfaLsmd4zBe7qYl4Q6CWnKDAGUtzIfQMaARrxBtQzytbLmKSmBoGOYAVJRAxhf8zZ\n00IJTUEJ1AAZgA7ITedG4sS1sNGOQdkm6XcqKgs7nNbmVPZyXKafTy9Ywu5p3DN2DqZmLhEVdkEF\noeCMWuB1RKJRBjL9vhwuDgc55BhfFIhWgUsq+yUhtF8E37PFS6r4cChzT+jVVgd/q5+ZFDhZjExj\nzm5ylE1bfGfzHG+O00JGDu0vap4RCkOtGbKxHGQ5Bo4ksvpgOYgxO6zKPWDQHN4lR0bb5Uk6KkOJ\niAi4NQwQOyhxNWbwuTImG4nrmQLnEcEIJRFQqrfSjcZUUe+MqigUcqJJzknGBhzzw7xfjksHOUK2\nloM0UHtDTlTLaADrJmPgsiqOSxUcSRXi5ZXnGO+F3rLjgCt9xg0vFUInY6k4Ii4P1j/RKhjQa+L5\nCfGHCU/F/QhDo+bGfnyUV955L557iHm8CyHOVDVmZIc5SYUB7CdqD8LGnsZ6IqR4zkCfEgqCOqLu\nmMrrLfRgRo5Ltc4czlr0+SnYb1zDocairVbZADQHlR9kxSIA3EJ7jUJcVrVuHDnUgct5oTmh5qrD\n6zAE5zAnj50SJ5qekyvHQTxSps/hcLI642U0prymHRBo2GGl4JRjkahyxFRRilwhWMtEoXBoPREh\ncASXArSOQ5MLQ9T8HxLiz+kMCSiIxRUznvuMNiVHSGdQA5yIwB3byTSSnlVCG2lNBE0a7fLgkOOc\n50y4KKOAHA1VIkJOQ06RiMvEHJGD6HFEGYYOegaFSsUzOgYOSri5xC/GsRvQOckYOJufyqOF49R1\nqypFiMdt20APOpK19P+AOze3rOaN6XsN/7totEMBhQMOwklHtyL0DorB2UuNIRw0thPooMSeFaB2\nqGyMsmcKuM/7gePx+H01mnUCEGrfTO0qriqFyHfMWREkEZdgUw0E9QfNTac5jxVsPGjdYIi119G6\nzQgGUGuD9hRGFuelJCB7T53TXEr00UVci3xxNc4S+JJRDnXgcp7jEkXMFXrBzqboILRI5AJzSl4S\nRZXpEapJLXDMDsPEV3MbM3Ciq3it/WD5dUR5P1ICQEBRN0BJV1jNlXw8cmOjFIg4RxzZoc7NKgMH\n62lbBLRa0J0wZ8nlhjCyji/lQ0jVAKK/SGR/6fyLM3RabyOdroJ0DQYB1Vw2eOzoOjtGKRRyjIrO\neFjWZNBYkGEiM6PGMqPtSaHxHYQCCQXi20fEfgw5Ghxn2YW6/BwZZ6jd+4SfbFw2LNd1owbr+VE/\nrYoj1WGMiGiJfcahBSFxGnDsR/bLcUmigv3t1XIjf9NYSxJtAxOXrq+k0cnXAIUkK/9w8P6srkPJ\nWKyiWuIyRULV1VeP4TlITZUxaKBsSuoqLseM+LSdwB3ZAUIJ50zGO81oKNimkg23qFQc3k1njauI\nNt9jHwWFOvfOBcHOfrjcv1kUCgKqajmyKcjWjlBcnuX/r+Y5JRVkAj3RplPUbEO4Dn2uZDSoJmhD\nYlXYZSwVz92ApUE0O7CfOH74L27x89PzUMLJqcijqtQIbnamJGdjPYrFqHFuw/PkpuY61IHLeXEW\nZdYqwYzBkYOWsQhAdJrlG7niMDpC0HUDIUabryJ4p6wlBYcivOwoiWPLDhR6qUTk/IfsuHImyDBp\nKeUP49yFUpBxmwMdS8dOln6ulCLxkrao0Uo9vds1kRhHiI3JUDRYIqQQpxC4sDpwwilqndP7H/SM\ngIpxzxRooqBRBHdVdpDVjhCHjJMdVigIp+SFhLKwQ+GYknNAzqwOtJS/M2ocFxGx3KQkpWhOk5jA\nVKXihCpS+aFUQ18hrmk+KzQ4lw+J9XTAHJdkmFNwTNErIIJfvGd6/r6orogAOwSmH5V8Ksk5/qoT\nPYnaNxyHVqHos13DqJQgG6CdeT+hAKlTKk5rQyYpDQQ1JlYMNDCBS5aErUUBMuJTj+C15oBekDIJ\nz4iodcttZLJ1lZyARIja5qgqTnUVp3WzZVQrWhyHGYMg1Il+2Bd7PfGfGgkfTDiLPYjmrFMq7Qjd\nmrIbHOqB5lJ5Q2Ty6yWfNei6rrHXU4A6N8cl+XtLD4i1CSzAq5AgaHREYt1YZtjULjNl16EOXM5z\nXOZckqqEmRS2VQ4Kcpi5A3JCfVsrnJ0kIaXsZIbGW9wcZmAgvg5S1KZUM4x2ctqOinKH5KyJ6hwM\nxqRDME735fBeqbWJ3eMTkVMpcvnKS3HxwvmI4ESAhSA3pL5djihQiFcKjuw4TbhAVGMKCvaoxmHo\ntBDaRzj6Y+ocLF6Zx02bfg5JDbgXJdon4w7dJxSIMAwdIyt1nB1Ua3vMOigVKS7nGTlTO5y8IVSL\nui9ELxGNhEW9cvvvRY5LrOIAXa+b85RPAMVBTqKCI+N++Xvb3UhPrJGo6oZU6a1vJZ/jNJxUyaPU\nZkNyD0K+SBFQgeehoHZOuqYID9mZk5sUbUoBBuitl1eEpAahpYhhcTgGd6EkmIKgjbZAG60AGlrd\nwD66x7/y7nvx3IN357i8uVVuhz4obGqF+MJzQD+2YD33N9N1k2PP0PxXnMX0nhVKlPSWg9J09IZz\nDj0PclYL2SQuVdWcpwc+WvLVvSRVE/TpusgsI+KyIpojR3avl/eOcGJOBBRQKmPLsCkdOdSBy3lx\nECXOy6LNhzZsJ5KsePycjSSnEMffirivTcgOEjpCKV6rqziNpzCy3g9nzB2Xt5gH0p7T2azUVKKM\nruS4hM8p2FjrCQdoXL4pKMQjSX+TSnSEegNFSvD4CM4oK0kOdiw6bLXa7DNy2hV6mHQg3ddxhbYB\nB0RlWql0YGU5veyeAlcKcUlGplN2i4Fr1bjMyA47e11qwkluMzDPBluMEh+2y8/pQSmOKu9v1MsT\nXuoZydFXgTOJLi67htMFuM9jtrP+Dlyo/GOH49YpHVKOQQc4lMaN8nmu9jkKHM6jcWvj4b7KoJ3A\nJSWpB4r5Aa6jylFrrbSSr4awKTHhYtB4kG5USR0SNc6W05IYuHQa0Ix7Qp/B41AQTqFkaWScZmsq\n2MsBFRgbsQVjqbiQ1IaTjsiGRtg5l9ff9q205IFKEjfasJ4q4iR0kNokRKUVEdEHO4BQXWrMiMZB\n2kCgH3OWo0ruQdjT2p10vUlDo6qouOFmRs5qIV3SM8KmI39TmWA7N34H3zxS+mlOuqQItoMaiGx3\nkk18z5QIajzE16G1ScCn4S7vgUdhPr+JZ7Dk5AaPOOSBy3mOS+ScEXMFS6GEMa0U9h+KqAwoBQcU\nH9bboJTIkFIm6QMQON5+77d8Uv1RuD5bZrvr+dAOZJmqa3QfOJJ0CRnQFYEbEkKcKuVPinwTyuRU\nYwoyplU2a+vd8o2x16DA5QP4W8RZS2gvV3BfImduwWG7ePHS7O/N35UjiFtGGQbdl2pmQaX/KtCB\nRpZB1UCZ3tHycTyHEGe1BtALVCSOkal4p0g/8P4oSmTWytEdKknXgTnY2ARnppum/yLYYI6ICBjP\nesbOxe8Kwn5CvI0EXUarW95UrDYy+N2IrkPos00sE+RmZ1QOp2hBUmV+LC+9cBYpKvacA/pU7Wdk\n6zkBauW0RZTPdXJ0VKADux0bpb0U1FeUECRqnDHYa6G9qBSN7XZMaqyk66CAILpT8qqS54QQcoT2\nZ+U014blNp2iX0IdlLFxV0PsW04gOJWXUNlafUA8qqcfba6Xfj4/z888fNuOVXYD8UUqjkuaAwol\nOdwu/46CI6rsnewWGYRKDFAqMIQTbCLZ2Ur3w8jeVmuTKAGsJkxGQMnBVjXADhuKd9kjQAp0wr4u\n1j+ZjjfFObRvkQ0kqTcMoWaYKkFBPPQOLRABdXS/E/wqqxzqwOV+RA0UZeCcbte0WBxRpUi7FSAu\nlSFBqBbF70ULySkTowVGyKWIsOoE198qh1R/JOPKI3h8RER7LZ+jl7OjqzQYCD1G8HCBNhruljt6\nam30N8vXYGsJAgBiUx5TpslJDMnAXeJviaTCyDDMU7u9yvcPDROU0zig8mpDNyD34w5TQtSh7Lm1\nzGUtyJWFwTHeRutGEIpEBXvp3jCjr4IjgBCTaJ9EB0Anz8rfmapUkNy8ICs5eengeRTqHEurQDU5\npeJKcK8/Xs4/HCFK6yriuCQhHdQX6ozWk4Os765xoxciwd6+nsZzHRHR6ELlj8UjCImgjE1WIjhJ\naXVhpvknHLDjhO7fMZqdwR6IncuFKJ1JwVZZXo/vE8ZGLVmaTypATPugFVSHhKcoLiUklEqsDqBU\nlFCCas52j5fzxUmOSUJ1GfOJGg19SJzjVPj1Nsr3NLLPdm8ygIMqnDaN/awJzUwUGAFjBCoRk5GX\nRwWVSY5ApUBViEvntxod6NAty/jL1wb1B1C+C8X6JJUM8XnDOU7iRM0zB0G8crIcdHALqnWIRzSC\nkwpqnmFPt4zzLOKQBi6LgXltuB0fb0yMe8pAKt8jJ3R3TJu/IbIJraEUsayFIM3CyKUhOyE20lVQ\npKNNIwgIzzJyAseZO1mRYNMQBfdvZlx6hmFIZMkya5c4nrUBGyy0KSmleA0y2rsb78EZBqJCCBlA\nyjDmtQnraWFtznNcOrqB3ie9SVlyDGiTBjUaCi4Vd5BwtJzGTaNzs1G+SGj0hiqvh9iEzFrCGlDI\nBQr4swoShvl2eYmKCmhQIJTmbH3zGv5Wf1QeOFNJDcrcK8Mw1WeTNBqAUBrXRFO5RK4mFSAfAuq+\nvswJshXqGikJ22GvwzPen1x+6eW4eP7c7etkJHkn+0jxSdM7a4FjFsFB1e7RckoEhagYbJbvqTlR\nbYT0UKIaXTWoyYFuRZ52jshrU2xAcVzSWid1Ql3Ac4ukJaFGdM6aoSSlAhCQVIS4dJqj7EAyHGks\nxKZBHceVTVvvlCev5pGd3736bjx/8u6dxXvISafKnqkqUdwz7ENOV/ENsGlkAh3ms9MMloJNslSc\nAlcCqJIqslQc7k3Z7szbDmhshYQkG0DY1DSfFDUXJnbhOqoTPY2m3oLSGv45zXnUPCNqIKlP2gAu\nInqDpaP8W3Bvyg7mruJ5O84fysBlmTjZBGciYRl5RnJRVfZroUETRRm5RLDswNCxo6z4rVVAFNRF\n2fcI0HvNo7zZZ+XDgMdRnfmIi4JEzf9xxrmpue8gO0zdFI1yUGeetVZA+RoGuwQcUuDqqChJzyiU\n7SeDOYLnOSExZQaYGnCozRfG0+maiapRcOBRgJi6eSqhZ1EGMxkmSv8Qul2hVIliwdpOjCQRBbQQ\noaIad9GcFcrJ6aiZmxOpTOq98iBwhBG4VO+fkpQCde404usKuoJcMp7jaxzXG3uOUzsnK33WXs7H\nuyRtTbg3Rf1wkOLYoBKFAc8vUWX74DXdrzjAanLOdmlsDFWiAjpbBhqVxGosaiB4ca81qIwQ8SgG\nemWtPOGSE+2jmnouPwTPL36PADHz++aoP5odK/t41eBYpECssilJBx8F301Rs1GSxqLrAI5Vpc4I\ncdsyEus9QSVD8h6scxU49hqYQgIZdHPuLtBk76urtMGvs5InIIoaKxVxWZWQra2EluBI6OYTS+Xj\nfFVcfjeResOVQx24LNCWETzBlVJyXnAVHJcKHp1TnPIxZ8xSrz8WRq6jlFGEY5jaAEMZ805DoVRU\ng9rIa07jHuB9kQYDOSBGgoA2BZmggO+wM54IwFDQQGUAd3aodIGNvOSEyx0cl7c76u6ul28yineJ\nYj3MGSwyzU1+TjwHycfz0UhQY5AINsBUYwaSnBz7sskAjVlGlKoSVXZJkpokVChZSp7lltT4jOpq\nTlLb5cBlA9A2Af5PbmeCETKq02YFhukc2uvil/54f+cYHJeEUFOl4li+ZswNopKRVK70bgxUG9ln\nfRGc+X0UsrcdjkvaHql8U0lOW1uJ0xwJk+FKNwCCNSvHpbKDKYGu0GMQbOLfErykMAdkOew+koRn\nPvT+EuOODaAQf6m2m6LMomo9RyigWlUX6GEvvW+CkzylxK5sxpp8lXRReqYNSTqLF9LRZ/C5rKLJ\n3Ak7lyj7mMroHZPa8XfodX4guoqXbU40wVXIiJypoTBMlx8sdybGO+UOiMNDoJAjjiR3oZWIy/Lf\nosxQBAd7GsA5opSVE7jMuShw8xNBYCSxFUHw1A6Mas44HSBJlgwUyE0o4VaBLnIAFZH2FgV0EFUk\nSgdgzqiAkuNnYKk2oIRHNVbJuPmLXSlVN0jkzLDcMKurcg/4ygnCIdilxUEwCk6MtkTH+0QhbqWI\nwFJxJc724FQkkBBKr3uCGxoll9wN0gdGlsjAOusLZK/Ktpf+lqMAbr2LX0ne5hJR65zK9xQ6oQu6\nYecmdDuPiPFHyjt6EkLLqiARKACq4mhBEPio2E92rpfrALkHQFCf+PUiApH/1IVYSWqlhhLsKm4k\n752SO9W8kOiMnIY+hMauH2FeUpoDPfgtJ3mrgmA3odGH4rnl60CwXRBg056qklqbV4GyJyPHpXr9\nfQhCKiobairnSBsaiyrznPSzkzzAiiCjVLwJ6MmIiOYKBEeI3w5/iSngctoz6vkxqSEDWsRZTF2w\nWVTTWxLPR4bnNJrzEC2IQjaPIHSoQEx0b4TsVr4OfaXiGiTEi+rkZ9Qe0IMGWT2VWAVAhtO6AyvM\nVANdQDB/IAKXhbw63Jp1FlcTPKcMYeBz1+iTOGTJOYWMPHVb2LWRuoor44Ma/ahGK5C1HO9wp0+H\nRiBVZFfx42mlqtIxMNYGzTNdvlW+BqgBi0P5oowM1SCqVJSyBGfSQVU5EU3aYBYf8fLly3tQl6Xn\niCBgaskNdRqOCKt7PQ1NVeUWhCpS3H8k/Czp5O+OOOX1JA7aRz2nCl6X/lbLGH8xlNi4Sa3nnLQs\nYLXWgCsuIqIPHWXxEkbJp0IWU+MabOYREU0KtmUs7Z1HXO5H/0V4iShKYDuqSRPzw15LVQ+qaUsH\n5pPRHIl0o9M0xwk00PNHpKP0JL8awv7zIdidruIKpat4Tvn3MiKyhd5Kvn5GxKUjkoM8cQ9Q9E8U\nVHaeft4+euXd9+K5BydNN9Q6o4CWTMRQTwex2dJ7xgSBiJpQtZpTKo62nioiaxL3Y/p6zklJoBI0\nSzTPFG86fO7ENSiBKys14N0oxOmwV+6/O/1GaD5JxCXYW8SioXiu8Roq2OrEgiARQntgL9EGjfBQ\nwh9YjkvKzChumQEYYAoFgCUC1IU4szjOac7OxceB10BJqtGm/n1AjW5UEMpRvrRhG6kJMgxUEHD7\n3fKu5iRdBQ/v50OPyfmX2FVc+TJUJtkUk3kZxpM2uAgOjlCw20FV1TqMLE1HPO49Hs191oFmDooP\nh+YmzfJVMf7UJV4ZTKi2MwaNnI66By0WZ7CYm6vJKCnxnpfLSyiVY9wDJJjTUIqaYOXuKp66NpXB\nRqWVo1s38BxsKgYV5KqZCJbviX2TEJfKyKT3WcvZNXI+QTIa7DlG+hm4Z3rGCFGRYwThllbTaTTI\ndpX2FOlTp3wO1pOiHiFRa8lxWmoZA+HUmEDZ9A4SKlVU8s4JhJKQ7Sz3YNhTFWcuPk/GwKXkKwR/\nT/kOy6uE6iq/ZwVGIFGJiBo0QptPRjea9dmxshty0mwtQ3fiiIgGNAAhUZUCx5bvPc+vsulpD3Bg\nLf2dNJ8uQjV25THbNpoQkdDaUNd3Aldb18p9NMcOrrWIZozPoXHeUd3jIUBKydsqwFARE31AQqXi\n29SESiTDaQ9yXDcH2avkUAcuC7RlRERDAs7LhYKQCtXYh5I31VH1oCV1IilHn0qS1TVoY26ulZPr\nqvV9BAimscOYkFqXz3E2DBLqdquIvJeOryZfJ6eQ0UrlyI6oFUsbpgrOYLCZHCPDYVKIS/xKdA7G\neUYd6xae/8VzF2YbSGetXPkT96UjawoF0y4PBCsOISaFTtfnpLZrGLgWiQijAY0jDpUIiTKMMLFn\nCBkzTpkiikCIkaNRFTc0iRVrFzooNaBDBntExAioR8iQjRDrSTj6A0KCNe7N3Lh46Ut7viI7zDKM\n4fmXBYLdQw6UT5zdDQqCCMRlEygBMlYkOYhLWaYH74ZK4SICA2dOIqRNDZ3EeiKhqzs850qQxw4o\nIZTgnFWmLgUbRfLK4XnF38oYOFZIOCyhBeWo5l8T0NAyzgP2wfzzn5nrKK6SKtuwbmWpNHUBlvZZ\n2hpUNhChB53gGM0ZSoRGcMJvYDTaaXXTfTpKkKjxbzfBd6qoKpXshlTqmwhNo9DfLg8EEzWaAnCR\nahJsGSg5S8Ud6SlwEfg1xMGsqmsoFqTGmahUcsuhDlzuEQNVk7MckTik/tCEgnA3RSkUIe5qy+VO\nk1JWG8aGwRw+eaP8JE55cc75NIYggJPNkpIYhJHcOvDO1JJtE4cO/JbjZCqOzWvE5WmgjZx7y4lS\nJJFoI9iZJaIA35lBSo4cl0bTIIFeyyn0np0qEDVmCpFdLgI9CEikVF5eV8gA76rGWbJUt1xSkXVO\naEKhulJ5f5wSKRU4JVqIgUCOEKophuX7tmWDCbRdajkslcNHMC2QrG6Ad9ZUnL2J+6ZC2+UM6JBz\n3DQQl7JJJvGCKpQ47DVO+Ryuc7E2uCScPq/GYXMkKwe8sKmxtPKAE05KiBcTS6hVtZ6xNskOcZIH\n5Ic5CHL1LMNExJ+ihOgN8tlHpM+UfUoUdCrcQHuaY9OfqKAZsBLnnpG3XzVDJaCMWDJUSZczSedU\nCuRN0BixA3V9CB4jl6vQ53QZ5dfnBFAoOZTRuELRvjbc3tNZPJeormiEaqJMv8MrkzuglDr3NX9Q\nucYmQt4I3hiczr0nlsvHXzlTtTp03RMG20rGEgVyqBWqLKcDYvERWa0G0+65Peaymv7mzdLPVcnr\nbjInm+DRg03J6WjscM+RLC6zl65cifMXLujri80iNdYpOV+A41JSAsDPtcX6I/1Iv6U6VJOod4aE\n9caaIbSB2gPonTWFA5C6pdQVcmZppfxzibYBlCYhOoxgs+OAEUrbEVV2jOl2gUJAR5/KsVUizggq\n3zL4pJugn2uGA4TBAcFxOQQ7bAjBsW2xBxNfnTPPWlApMhFqGpLOcZkV9QyiKkUcwcCl0GcUvGZO\nPhWETKd/SkV8OYFLh7ffqVSwEJcGUITepxNoQP5V8VtUQqkCt4SecgLkVnUFvJv567/yznvx3ENc\nul0IBS4lbzmIQvyNEntUS8osAyVKQnuDsgGoqdoAWSFZHNQ/7cGyVBuCvUoo5uEkNbiKjO95l2j7\nxHVS703NGVLPDrJ3AAnPBiBhlehECDVUSvdRHTQuqQClGjkR8AHluKwKh0tZ+NQskxJV8pYzmi8u\ngl816+lcENRQJ0CPO5vSALq6RzBfYVWCpNRGF1gSZbBZ/FbUGc4wchBxK4zfwU46L2cXkCCtJSjR\nUATjGTk3at3yQE8Ez3XklqndeVx8Rgagw7tEsg76LyIQVaUkJw6FjIz6zgaeM+oDSrYixKUjZBio\nBiDtREdLoTcJ2SfJ1wlBjTBZ0dBoBMhesW/QDqD0ZqrR6hi5SjDhQurcQWmLc6gkX5VWtskJzchJ\nWFv4+/1YRCrhSqgmNc8QcSmRM5D0dtCD27A2M6LqHJ715MZ5cZfnT0RcqqAB8XuNR2w3YvIMTLph\nZp5lCoQqmzJrMhyeRwV7cX8wxibnszjBGdq3qKFXRER/EyoVxB7U2g/HYL22r/EgjssVY20qarRm\nIselEqehUKo4LjU2oQv20RutdJBVKs92RMQqdHyvqjkP2UHKPnSammFzHrDdZQNhGJvVjLywuQV9\ndEFLUofeEfRqJBjBWDc7lHT/IHUVvxdoy7uJ6tD7QZbDPCpOCV3OslvkBRXlHsoALRMZ7LUMQ+K2\nyTcuVMKuRMHgsdkWbr5ijHN24Mwoi3d1cQ5tmbVrKEhXEsUc7JghcuYQl6I54iAuqxCJIE8tezZ0\nw0GL1LKwNnKisS0xUEAScUlzEAKX7zcRu5+O4q5QECw7IwesDQehkVOcRoSHVdQry9roJtsv/eEJ\n2icVNc8j9KSSVP2k7ON7RSd25mGoKFsQhdROFb3XpzUncVDCVUnWsl+r+jL9Ojnfc05Rz0/LpmbQ\niTkyzgihqIprnXz0oVpPBs8oiTPNqmJM+b3xHqi8WfNLO1lWv9OMAAAgAElEQVTgg3XOUwNaEQLV\nRb8l0avlykJlhoaJ3UnVa/H4WMiZYsVnlUqDkGGc8xpKnDI9mmdElaCENtJhjdULETkrJBjdM3Fc\nRqQrcYevVDXAwPlsZKAa3bQOmBGCSBv+f1MZ/wbisooVoErRqHyoDuXQ1vWlwUqd+WSRTPI9VMHL\n3oT5FxEx2sjHf0ndw3MbjA7agYQCsXWhG5D+JJ35AGW0uY7f0fM3IGsvxaiIcQKHqXuA2oIJWai6\n15NNQ1x5h1lo3+jdSq9gUchWejdOOS6J5eSK91xFqbjze04QKCfaRY3zQftOTuk9lX2SqPG3Grhm\nXBsPQId0q+xaUI+QDqYmONQcS0nOvdkRK3ZglV3nu75TDkzrWSW16J5VA12azhIlSb8H9+wkHJ0+\nDDlFcYBTIzYV7CWhudE0mh4roTv7QCEu98NxqbYdJwNHG0b72Fryb5GojEkV2SnFcbnY1bgQZcjR\nObVuPg9MdSwb9aAcFMpEIyKWjue7ty4gJ6hDvSM5SyciIhpQCqGMT1I+2FW+z92e26vHxd2lCXXz\nG/dFOTp1NRfjjA4tdHpVQg19HI5LJUikDf8vHTZAYqnGFDQ3ZGMGECxRcQJaTuA4NUEUvGYcI4s4\n+XILJYKUY0YoPSw7FYjL4aic+sBBGqiASqrIDpRUWiloTGhuWDx2cM5AOKBHHK4k0g8ZUVWN8e05\nc/nKlT2ocxJ6NccFSrkO31GDwgh+Z8vAzR0RESMoryVnTrwWKserDfPZGk6puAo0ECXC7rpIdoCN\nimWaAiVM5aijm0w/RPYWJU9yBy6JfqcyAd70mrDDMdiREV0vaRwSaXkiIlbW0riW1R5MiWVHmnPl\nwN996914/sOTzuLOduZQnCj0aHMlzXfqC85c2tNz07JUITn5IpXc3Lr3TRKdJIQKXBIatyGQkJ0j\n5byuTsNLUk1qnlURuJT9CQh0ofoQwBykqtABlONHRHRgDigdXNW2dagDl/OSs3xHOQZN4I9w+KUc\ncRCXJGRIKP+LAjc556OTS1Mdy+rAuUJNJnIL6ZFOolGkJHepOHH4WF07DWdWdTPjc9IIpmWZZs71\nbBjmxLGpOC6rkCVD/6hgLyFYHUoCmk0KcYloyIr0OektaWTB81CyISLzHKFGO8KZoXHOST1x0FLV\no6ANIBwjcjQaIqnSp73e6V4OgSanq3htruy8NhruOSZBjlVxTnsFUP+GM6mD6tS9HZr2SMckI/cf\noVczT3Tah2k/t0SsDWoAolD3NASkm51Ao6oucQKhpDdyNudx+OSdxKKDOKPmPEpS9dNIIDTJ35J3\nBfN2sHN7nIe9wZ5jkpx8kSpJOdzHvexXMLFtzBma/wQsicgLbnLECkRj93g+h+ZgTiSc8umwQVJF\nNBI0NCpwmUx/lDnYTvNZcd0ndxUXCT/uKs6Xp5H+QDXnmUdbOqrCKR1AgtmM3AFVxSHIMJV8QJRp\ndm7A4NdCUnRRckeIS5Udzm2cl18jnSeGRBofEDhT53DmmBu9kPJBREMHmuaI3+oI43OYsTOeI1iq\nKhDMjgE2L/NoSzImc3KVrRuGnCq3ok3OoVHAUc5s/KTem5Wddso9hM6qolRcofeSO6rK5jxQXp/Z\nMEydguoJa0SjIAw2aoKTs+pCdid1giNkzcIepAIQOJ/nxvLii1/cF0UF7QHqGVuAHHJMgx3lTIA4\nyEZCYThCNsjIeBZHVJJ+DIg/PoG/2qU9TaHnYNrSdMrNO0e/53SuVmhUFES8sq3VWILrGDQSThCA\n+HeV06yCx2Wigu1kn6k3hpU3c8H2Z44fk3z5hSxBFcumeETHD0oNKqtAXwN0kBNsdThGaa9VqDaS\nlmjSSeI8JwUuZbdn+NzxndCmV7rBUI9DahIIOrAqNDIFDutOV3EZOE3Xm/QOMHAshOztg6ZxiDjk\ngct5cVwW8o1kZz4i2ReBs983UWuVJrgsyc/oUO4MypVCa/konkOIy6oEAYeKyLudtvQcFIgjPaO8\nnYxsNS2wTFIFW6GrOCIrjU25MuJlCKorjk0KQqhSHDKMqOxVusUQQFBd7uibnIjLcSN9/Q9vcSfy\nnEKlGMpgdYKaqbFTmZ1fPpJ8/eQgnEApN+rlOkjpBmvMEs9xSsWVpGahnSZ0CnVOHSD7WzfxnNTm\nPI44Zc91ojER72zUA31m2DMttQDBpnQqMkYDCpAb/MMZq3uk0wxdYGWCKLGKYTzmtUT2Sc6EZ1UN\nM5QzS7oWOcANxKUSTKA2jIoUYzypaYV6z6lJIhlsdxCXIA6VDol6QgoqqmQ4BWiZSkcFLtMRl6nr\ntimuT/uZ6h5P4ugT7tDNe9DR5XJ710FcqlJhEifYh3aDSGrgeIIOzN1SgtCIjn1AovQPJSkVShir\nTMF0VDYo2YdKOs17v9dGHPLA5X44LpUYiT425oxMpyMO2iJ1wSqb4P0ixPYINuc5+Ij9QUvd4EUk\nydlVOWcZhGPLd0TWisi/yTFyxJn/ijM2VRavfuXK5bhwYdJZ9151rbyXgiUqGeeZE+ioqpFAVudY\nODM5wYg5yzpUaV2qqGCvU0GbinboO2OsOOEy2hROeTk9v0Qo0Zhl1IHz8s2Xvx2Xzr1w1//jBm0s\nOXWAg5zZD4pqUWo0zg6qDQM9RtBIJSkVZQtJYuCsVhN2Q0biLXrNTuDSKRRQgbOcNFNM/cBz9qBp\nQZymFQcu+9gDvve7a3HmQ3fvLE4AEtnAFuwwpRtTfVQHPZsT1VUZGMEwQug5lQ4+cP5bEEoeRog5\nU1GpOI1mzmCnM89z6/PU5HZV1Yq55ffGE3Ycs+Eg/aT+JpfK5pLc23uqvaDWqmOAU3kv8k4p5Awo\nbMmJCItvvMPNWYjfKqco9GIjESWqoN6K44/ECVDmVHJtQNB2hbKm0bQcI4LUC0MS521G8vnFK4zn\nPlOdHkkoEIvTSUyL2qBcN9ZFeT85EzkRBRbaqJOOoKfAuQooMxLMcHRVdjZj5JL4Vx1KAgzOiEAL\nPYszZgodcDSRY08ClIizNukKWqSeozLFHWgMEx5yAtHV9yhwecf1KXmVsdOoUybooDAsHViBD25V\nXah9g+wzRX2BvNXp8yyrbqwoNkb0O0o4oGSsTQooiMAlUYnkTOzKSgVjzAY9SoYDelXsgalVVJOT\nMqKeqfLJ+C0HcUmi9Mky6EAnoITvTCU8ieZK0Xwhl6xhHyH1o7GfGKXijjg8+E4QDgOhMM65q25w\n36IGeYYf6NDJEbI8gm3EBsaNWZ8jxYqQ0fg+4nJfaMvc5jJyfmTcYKqSnNx3SnCCNw1FTllDY+Kr\n7EMVHJc5x185UzkRl864NClrKM4hhekss5wNC9Tj51xOHNTfe3zu/MWsaLq7Se5yWPwpYzCdt4zG\npGFkOgGNA+dfrUiSn9MINkunNfnX0sWZ/Qed0VaciOw0HTCiY07PXHrx7PvSO44D7AQhHd1AYjXn\nsXgEc6Khs/3URBKfRzlg9G6cRjPYsOAQ8H5llYoSETkld3OMg7pGxF4k5PMfeXBf5zgBekweCFwF\nlsTTGhAJwkEF46mCUwdtNzFfpOg2TRzgRqm4U11Ddphl64i9HX/PuGeaZ5KtxGggW4VIc4gSfhBQ\nzN00p6pt8FAHLufFa7RjEK8CYXq945esL4p6t1nLPUCcrUIFNH63VW4AjpfKA4fOZjHYVVwc0FVc\nKJ6cQUUHpZoqyplySmWHUMIpy9cIPUbayghOKCODuBxbXWgCVBc8hqCwCVUXITK34jlTiayVniM0\nuKMz6ClVOWwNdky1nrczlrWgkSEynejoG8Fu8vMl71VGw2AI6JCIiH5OVBFkbVWgo72chmCt7d4S\n36abJTQHc4bgHu4Iw5wc/YxN/ZRQ5n6wyYjLbThHNsKrMouSII7TRDYA0XFF8Hp2kBuE9lEBCNQ1\nGQNNTjWGg97tbRrd60EkjyHdm9BnyNdHJxjLQvkniJ7L2rhLQbTSE345bWrHd3M4LmWH3hJRvLTE\nmauEqhtyivLdaA6otdk5ltYcZmudf2vboMsgHUz2iUIv5+wd0DQQt7fAPs5J2aUEx1KsZSpvb5If\nFhEtqnAUtjs256H/F++S3vOqmBs0NjmBMqrqgJC9EnHZXS79vL8Owe5dtg/JplZ7/RJwXOaWQx24\nnOe49NAOv39Z0JyGCYkaFXKAq6KvIaPdKgcWQo1ecooyvlL5CmVwFJwW7UwRKbdsQcjflciwxs/Y\n3ykPXKgmB1UE9asSRFwuHL905cqss7jDC5nMfyu+c0q+6PpOmSSXt6d3+jxocexS9f5z5k4Iwa2M\n2bqE6h6c5NQYuffAVANYcfkiX6HQmfQ81GhJSkUcl7QHOAkCGpuWeC/OddKRjQIhBHZDzqoLR6wu\nwAe9nxsOKDlzuZPXVSA4ta0H+6ZaGxXw1TnloJY+A3F4aWVX8R4HDgr53tVrceakz3GpBPVpQ9A4\nJOoz1ehmmLHsl0Ql28hHU74b7bVVcXnm5LjMWRGS20fPmvQH/axUINlbNP+d9+/4dLITOdE8GXzm\nijaNhErFc8uhDlzOS0674KADmkonO8ZcstFUESGuk4GlsvOhQFzWmsfKvxBKuQGwCnIm1Xuhe1bd\nnhvdtEyrDEIO0w2zERgTknMDEKyr3XI10uwzx6hT+k9KPjUzp8RCrhlOOxkMi13Fm/Xbn7WPlGfT\nNn/H40xCdyy5ysCZ2d3m8afRdCgJcGaK+d+kdWaUCbYMI4PmpspakjHrZOGzNlsT0tuFTqMQoB63\ny+dyRMRwVI4slnaUYTOnjs1HoZunEsWznOo0qP0UA5QycJmv5AuD3Y4xP7eea6PB3mPYn+meu8LW\no6SGMtjpOgqpT1qQ+EKVCVhFIkbZLSRqLQ22y5OUMkmf2pxHzNkWoGpq7fSu7vSYOXk0Ixgl5jRz\ncGwtREJVRH1BPpq0T5BPm6vlCPVM+lQlXInjUiaDwT6Yr64YDkay2qIQCmh1xNrETvTCPiPfhXj4\nVbCXEJfKPqK1nhP0Q9ynSrpG3wQa5qqaR5Ko69O7GQlqNkySjAVnLuwbVMWi9iD6ToPO0wKnTlhF\nJY9or1fxq9HWeunn2/0Plf+/8J1pzHR1RXqA1JFDHbh8Px3FI5isWUW5e7fgRWYs+VJxxkbbQTUl\nOqdihVF2akmNGWyYtaX0Z+mAwsyZNY3Iy1XjZNt7N0UZc6oYJdk5BY12kZ2ngI5yAClw03DKbSwH\ntFwooKfE4bjEjcwIaNFTqgzwuAENSMR7Jv7Tzlr6O3NAfcTx53SidxB3IwiQOr/lNM0gkTprUD5m\n1HwhIqK+Qk3VANHR4r19OCpHoajAsVPylRqIlvMPHP2a7Cqez5hDFIhRvig7B1fRoGwuEXTx3At7\nTGFazyqxSdInWiDl6MN6VhyXqY62k2xwGvRRaWtuJGQDaJbUdZznwevTu1GJbTilS6W1AgVFtmbO\nMlUlI1rP6jWDTaMCxH2gpSCKGSWOfU66SZaKQ+CM9GlD8DVS80T19PspFT/z8N3RlhFshzs0DkSZ\nFsHPSTowayPGSC9vls1gDV1HQb3e7r3fzyMirkGMwmnO43Fcln/ucUKKYCMlHBCly1ehPf2o4IWp\nguNS+W5YXWEE9cmm6FNwODyUKp3zgQpczouzyTcMh76K8pWqqnqcrA1tPs7mR+K8Sxm4JEJakVGn\nLGBOwnpZinPAwp3xRIkElUlRuUWTudKasClVwReqxEKoCcMcp4CB+FOBo1yi1vm4Ub6e1JohDdQE\nlK4SfDcC8UoG4GDXGUvYyDOjoGjO7AgkVNY9pQnIEbGftID/cQRcutSFO4KJ1J3mPJrHPG1/3FF7\nIOmAjAilelfwiGbUDWo9N8HQHzcympJi36B35iQ2yQZYFs4MJWnakuf27qiqPZ8rri5yaCuqoiGR\n+wYkiSipMTmJ+FfBbhHInQ4CGPg9kw6mz50u4EoIQKDuObWruEx4GlUk7VVG0ecSWdpJjT1FAKKZ\nGFQjbvgIMxEB+sQBsCzBs5TXL0yEfBRNC1Ou62nOUnVbBK8bx9/EpkEqEQW6VnGf0nXaigMbBDnw\nxX7ywGq+gBqWPYv3T/tTq7uC5xA1W03s26TrKBms1hm958UKt/1Io1lRw01YZyrYTgFC8tEdu0na\nwRXJoQ5cznNcet2/qOROcKJVwDGpYjNOCWXOUnFSpKppR84yGewAqSL2B9zxnZSigyqzxHBayGlz\naBQwcCnOqUPgQsWNc3aCp00xZ5MTJZg1XphL8xyXDcrAiXFJHTLZnGcETbjE2iQD1EkQ0W95VAnp\nwUanMQkFOrTOTk8e5WQ/IbST2hsHwqErv4bBnyOhK/d+3UoH1Hge0oE5hcoXIzx+LRLnfe5Hvvny\nt+LSuRfv+n9kgMuOsmDsOInV3KXCJLQH5JQqbOCIu+wBqfNJDD9R+Tio+5yiksR0z951jOekwKWw\ntUeA1K8qqJ6zcRgGjsQ+h6X6hpqd/63vXn03nj95987iN6BJ6vHM1Gjc1Kz8OruGPjloAIMjzvyj\nPThnoyslORvNWGJcP2cndJW7odL3qhoUkg5S9gkhuCmukpPjNCIvpaOSP5xuF/flvtyX+3Jf7st9\nuS/35b7cl/tyX+7Lfbkv9+W+3Jc/GDnUiMt5jkuKmKu8hGobT0LcHmNoG++UYx9wA0hZBkIEy4rk\nHkn+DY6IPmSaFTqFOvONbr6H5xDvCpL/i9QMTTM1N4gPiETxsTl8UE6jD5o32AlecD9uX79a+jmV\ndUVEtKDcIGtzHkNnjIEQOSI9c7x49RcvXJA6LkKXFaVef02WvMH77zG/HOkTR2gcxoJfj3jciKcp\nghGsiAY35oxDSaAQcjm3lDFkmhXaq7eV1iBKdah30GsOw0cqsuym6o5ag/J6oZua3dWk64fTOVsg\nGmgO1iUvJ6DrM3YVH8+hAC5euLAHTEfvjJADm+Idd46Ul6SrPYhK66SeJd7wY+XXV/M/J5UNSQ+Q\nW0okcgXWgOLRI9sBqwscT0asJ3qfpIIJ7aZEVbcgb7zq6o1zHegdjL1Z2fR4bxXM2QimsbA40A2h\npjVqb6bmbfN67rmHTuxrryIuV1Xa6fCjkzhVkW0ou83JsuUg6J3Ks9SqkwhRRSR8R6IEUGNGv+ZU\nfSwZMQ/mhuWb7m/eLP3cacRH8qBqtgW6jnqnUIPK3CJ5WUEHN2Gdd46cwJ+6BZytytbpwSTM3aPk\nUAcu56Wq7qgD4BEb72xWcv1KeBFF6QYFyNT+hvxOsIjUExLnieoMSIpsLLsN55v69Pg54f6tzJB+\n4teSRNowb44u0+YnuLqA45GauUTwOyPDNDcMnsS5DkL63+/N7FPoOtR8ICKwe7cKHJMxrZxWEqc5\nT87ujFQiQsHRCA40OGW6ymDIWFnIPDmivH5cSwyqiWAzNtMQgoEGdQuJc0OV6jcgrK50AzUtwQi9\n+C0MaBnmhHJmkHsvZzmo+C16Z2QfKGcGOwcLW5OCM1sbgkkuORmaTtifU7Yz26A0n3ZFt+HU8mKL\n45LWX/AcoIAqdUfOLQfdbVgJ6qCMtqsyT4ivUtknO+ucdC4TlfB37P3+RnlzjCYEQasqoW6tCKAI\nNkdJb87TG5Svm5w0Jg79k+zcjMGZ9Pd/wmhctGHYzqmiEmTYCV01wSJAWE3YR7CnY2NT/KW8gs3W\njL4FSsjWcXLElAwdCNAJibKPaKXlHptDHbic57iksVLvECeY2GCwM11bEOMniua4PFiS9ZugYCxj\n1pislGl2mvMctKjgTKqR01dZa0OR0b0pI6MGwYYWIlRYwXWP3p2z545fA6ORDFOHC0UZOWg0yu6k\n788Am+e43HirvBO91+iGPhdOu8ElSUNGHKu5hbgsnQCAQ21DgY4l4QBRgOymyOjnzOvVoHs8JfUi\nIurA50vvuTdmpUVBgN9H3iulgyjhMt4GG0QgIfH6Yp+hKaj22hGR+cOccWS+gmC/HJcYbBdTpnOs\nHPH6juwQDU67QGlip0/DaW2tQMNBUd1AwgGI5J/SaHDRUIZPSjNqGi0OQmJFiLGesGmQEWhRCLWc\nnKnE1Sa9P3hOQuNHsK5RFREkFDiSTrMVoCLQA/ghQqFQ80R1V3Sd+QD1d377Tpz9yEPiVyZCvtOa\nmJtYXSKUAFWL0f7cXuZ1RvNc7fX0zhSfMwmtQeo2HyG4kY35d1w0LiKh9+x0FSfR3JfwzoTdsLte\n/l1tKLrXQ1CtDgknBWwgzmDZ0Ad0HTWiHA7S36UCqtE7oMqvCB4bEmqSG8EgNpXA36rIrzuUgcvi\nXQ7Ht/92AgBDKjsWWctmh1rQ53shmuP/3jtnSllQdlqF2dBoA8NQvUnqKCsF3k2tw13OKEBMxrzK\nwBEasrPGXc1TO0QrI9chmafnV8EJejNU3q86xvWgDGBdwO0JJUqOtsoA0pgtL6nscEaScZizi4HT\nwXg8+6y9Ur6eVKfLVFEBNXIm1TjT86u1kSqKkoA2fwcdQc/idHuX6D1Y631hzGcN6kH5ltMcixIk\n6n4Rda8cfeP5VefQMlHBmQFkj5aOHMdzNt/5TfkXiRXkETzPe5tpJfx3k3a3XAftjoj83YiCza/n\n0XBfQTlychSipbWSMRmt7BZ4Nzs30uhi7nIDyaeQDeChwcV1wAFU1Av4W9QF2bHPReASu/1mRBbX\nm+nj7OxbWX0XUSrOiMt0N9Pxgyh50BKUHP2dcsQjIVvHEoxA9ywqjyCxOo+4bLSbiMCcF/LDJI0D\n3LNKLLdgONfAd74JFE8RTLFwGs/g+UxUNrJBG6xBlYjC38rcBImE7CNH6gboh8ZTJTy3jDJqQlyO\nNstR0g62agl8KiVk0wyMqlxlU1MgvgXrLIITSwQGUO9sB/xdVcWyAddpZAT+RRzSwGUh++G4zC04\nkTJCXZV+O+hS8a6hFIlzwxHsUC27igNfpZPpN4Tmptr8s5b8NI1MI1y/KYwM5rikMkXZBrj002WR\ngdxIhIKMjTWrOC6dwCUKOE2LHCFnXjg/+8wxplNFqh96/8AxG8EIVqdUnKQ2TH/PKjtPyN4WdeYT\na5nKJLWaL//ylph/WYH6UL4ly+RAP9cNRAE5YIrnl55fDYsKHpeJkzwdNzlA3zkKKJrhz8p/S+gz\nLHsW8xz5tYQz0wa0QTsjV9q8nrl0/tzCV5BYBQNc2Y2EHDoiHIOcXVjrhq3V3ywvSXcCahQES10X\ndzuH5pMTICUdpJbmakZaIEIPOojLwyykt5RuwCqGjPy3SoiySvkOg+3yYAPxht/4DZdWchAina5i\nOEc/c+bhB/Ycp0rOsuuIwDgsgU7aouyc1k1ON1g9vhNspHdGSDwl2d9NooxgbahScdrqFcUM2k6C\n4mMASQXynWS/E5hPToKEuHRzU5MpHn6UxHtQ93xiqXyc3xa2HiFbc8uhDlzOixO3pKyJQ0icNWuZ\ntZVCXqHAkYJh34IgRO1YevkYNvpR6DlyWm68jefkDJyQKKWYyq+Wm+OVgqq764KrC8QJ6DWXytGw\niuOyBdkxhLuPuDkTBbVVYwYyzBT/bep7U0NJzYnUOyOHkjZyhVAaN8uzZq0uI5tzztu+U6pNzoyi\nCyEOI2QKEIFL0FsqoEJjpozc1MCRDlCXf9xZ46xpA+YGybZACdMaRC7lYNS3M84kqnEVqnqBFkxG\nygsbhOZg58QankO6QfFJb4KuoddpVZAYQThymhz9syHQIWSHkDMz+bJcbzucaA1AvDpCutEJXDqy\nrp4/leMyo30eIfQjBOEcjksVNOEEfvqcsTi4BxAgFyhVDFxmRKk6SH31/L2t8sofqu5xKLNU2X9O\nzlq6jlrP5AcoGoudG+UofgpCjgUYwil7JnFKxTkIlX4DO5vpgSay6dSSJX/bEe4PUBGXrljPtKfT\nela6gcr4G2LfTtWbqqkhnqOa7sI7oEriiIjx8rHSzyl51xYVQVtQESERzNTvI/P+fKgDl/Mcl47Q\nZFVIOAwCVIS4dIKqObNT5ByqyYrQdaM5D6JtVo7iOdToRPGSpvICquAEdU7evs5ItN56WgmfQhs5\ny5ieX3IPAsdhbwCoImGwdlbLu5ltiuv3N8vXIPMUpaN0W+I9H4ExU0o5FSm+uJS+9dKVePH8hOOS\nulY22jzPUh13Vb43Wipfg5S1nVw/6fJSqERhtMsoCDJmczoMipeYxka9F/pOdXNMpdiQ8xICLcox\nGGyVB+9HZPyITdBB8Dt2durafMhAVNRyGmzSMC63WxptPmeVAsRqr6VO5PcoF/vNl78dl869cPv6\niFAq14Fq38xZ9XBEoIoIwdyFruJKqLTUCQ7lbB7oBDtbFTXcJCofiSymCQ2JCAfVSWspIm85KDYB\nE6KQ4iS9DbBpDf5VChypeba+XW4fKvuMEosUUJBNSpUOAKHy5vkA9TzHpePrqWArPadKUt66Ws61\nTk///7d3LjFWVlseX6fOox5UUTyreBRayENAgXBDuO1VvAME44TIhEgcEMWJxpFOHKoTcewjMcYB\nI8WJ4gRCYoKx7ZsmnUCn05AryUWDiPSVlwVFvU6dHtCWBb1/f/mWm1OlrN+o6lSdc77znb3XXnvt\n/1pr+AofrFPA32NPKNAjAy0Z14AZ3cXtOa1PKg5AtkHWzKXHHd2eyW8YunS+8GspyD5TuQq1nlCP\nCCVgInEL+fueBjQqVZz8E1XiqDSatsELZqQDmqOQdm9mdhHsqfKbO8CeebrXK6Z14HIyTfJx7npo\nA5wzAKHADpDq9IPk7sKQNCMlX53aeU7OxRtleylZYBpTxUElK1J4yZlUTYiKNjPwlArwFNi2jB34\nUv/+a1eUcwOam2ZUvvAcKmWdfwJPDSFPh+yczRwIdQpP85lsYO7KL56vs2hQ35NWpg5P0D7BU9Qa\niCnUIzw3KDgjsxvgi8s6/Can47aUb6tGHhayFxtTspseleZVcPL/7xULv15hHGtQs6BxLtMkM/o0\nNAQ8KhBKyVeZGn8oPOmQTUoVJ/usvmfZ9LMgOQ9CbrLhc7YAABjdSURBVLJN9fHf5OOpZ2LNWPF+\npEb17NFzporT/Xet201SnZPoRe1Pc5as8thAX/AcxozKiCl44JLbo2c1anOCISQUyChgl9AU0KKL\n9OO50+inZeDy5w+/vPLLwPUsCdhVXKSp0QnU8OX0KZMH2Zxnir1MLvDsUMg4dpO4kAlVVWO8I/2H\njCpZBQXb1DjDUxv4f1IN3HiSo9EInNrIk06wmANUKsDRhZrk6WZmXTQ2qDmPSGH2vD+qxBxyLzo1\nvPX2//nhzROPUc0TT9MUWmAuCcVro5xWb6ngHL2Pp6s4LoqdrBAjRYNHcYkBRceGxdNVXEFKebpm\n2T2eglOk9rLiJ6oqtZ0c8ymOv8iGUngK7ag/7IEC8SodmcaA+i7ptJ8Cmi4nf9I689e//MtNv2MX\nXqxzXVxxOSQ27dgh3BG44nsjUt7Iboo1qOihsycdVkG+mwpcNiDYRWtdSaihWymgITbGdG2Ncj7l\nSF2MM057bk4QsEGbdqH6xr1Lk3baqhM0QWWGxn8qPgc8PkVLR3rvMjm75qH7Ft/Wa9GYUWW+CBWE\nxcamcMtor2HGPrXHByI/TIkR6LOUVfmfjCqeVtg7q3UrZz1dDM6Jz++pcelZUmodab++AWswBUfN\nWI1aNPNSMQYNAr1gqrgqc1WwZJP6zmiYKcXl3I4739zabJoGLlOotIrCryUmvqcmWlFyux451ZCt\n8GKyC27GgI5HOeSq4QOfEzdGjo32sHCk6NTSAxWMl8+h9Ha1woxR4AKCcK1d+FLl1rSBVekOhcso\nOIwlNZMx441+WdQJQWADoFJ+KVW8pVy8czBtzK6IYHujmv7OVKo4Bag8tVRz0iyVKnXT06qufKp3\neh9Pszt1z8hpw+CQbDQ01SHKNDXRgRJXuhEe56i69hRzdUBjwKNCUnazMCLQwQHa9CGlqmHF41nV\nnfIoztIPU81iBSrFhaoN40kZbaCyJ757RgrafM5uw9NsC65LNSWgYKsKjuSEghPqYN3TCZzwNI6i\n71m5p9TsStXsxWwtmBsq2D42yCV7CCpz1Tz/hMosibWehApwa4ZEySxiPGNKhmzqB/OWuo2b8T7E\nk62FB4HiOZ56uoTy3YuSOx24ToFAh4Ib/RNHmSeC7KwXXuvFk8Bu0+cfvsp9IFTZNIJ6VNxVgcvJ\nNS5dmzZYyJQMvHVmsYXEs8DIXstYW6U58mTXfXaknBFYvwMUal6KqqQ80n3lzBfdtMhaXUIJRZDi\nTaogYENbboEOlA5jpRwWcrRR8ecYf2qeeYI92GwKApe3zqW/ffWlPfTwjRqXDQhcet6fUErA0mhx\nB5QC0Uq9RhRtmKAgJWZucqZIdIsgDAm+XAHKUVAwC5tJyhXa5KgDClxP8Bl8RqY2ukXXurra6BM1\nodIUWQRJhNqJlCOVbm7OcxHWgEpbJz6nszv9PavvsyilsV/szBf/9u/217/8eeJ3VU82hVL7kIK4\nXOJBQw64Wjdo3SQ8gXt1eEnjnDZGnk63nueotSZnejFt2lSaokellhPXGIDvEwMKIiFpDI5iqmI9\nq0DjKAr25obU5RgAMfYdPWnHpLiU+z2w6VTjUkHlCmSwF2ssCsUd+CFkA0cGeaBRtp4rVbxZDWWA\nnB2qlbAlZ+DSE2yj71mV5kJbP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ovWp8elmVnt3rRtXLCO7/NMuJ9rl7E9o1HbNpj+LGXR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\ncBkEQRAEQRAEQRAEQRAEwbTjfwF7q3fz2qxTFwAAAABJRU5ErkJggg==\n" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "figsize(23, 8)\n", + "coef_trace = mcmc.trace(\"coefs\")[:]\n", + "imshow(coef_trace[-10000:, :], aspect=\"auto\", cmap=pyplot.cm.RdBu, interpolation=\"none\")" + ] + }, + { + "cell_type": "code", + "execution_count": 183, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "include_trace = mcmc.trace(\"to_include\")[:]" + ] + }, + { + "cell_type": "code", + "execution_count": 184, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 184, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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ad97Ro0fnfXz37t2xe/fuix6//vrr4/HHH190uBqONQF9VkMVBPqphvoH9FcNNRBg1lB3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\nAAAAANBPTTcua3YAgFQ1OwB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Wb/3WeY8NqnkPPvhg3H777bF+/foopcTmzZvnDvAArEbz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\nwMsxXAIAAAAAL8dwCQAAAAC8nD9FAWBsJd2iIQAAAABJRU5ErkJggg==\n" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "figsize(23, 8)\n", + "imshow(include_trace[-10000:, :], aspect=\"auto\", interpolation=\"none\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.10" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/Chapter7_BayesianMachineLearning/MachineLearning.ipynb b/Chapter7_BayesianMachineLearning/MachineLearning.ipynb new file mode 100644 index 00000000..7aba4583 --- /dev/null +++ b/Chapter7_BayesianMachineLearning/MachineLearning.ipynb @@ -0,0 +1,179 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "List of topics to cover:\n", + "\n", + "- Bayesian solution to overfitting\n", + " - Salisman's solution to the Don't Overfit\n", + "- Predictive distributions; \"how do I evaluate testing data?\"\n", + "- model fitting, BIC + visualization tools\n", + "- Gaussian Processes\n", + "\n", + "\n", + "Would be nice/cool to cover:\n", + "\n", + "- classification models (using the books text)\n", + "- Bayesian networks?" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.core.display import HTML\n", + "\n", + "\n", + "def css_styling():\n", + " styles = open(\"../styles/custom.css\", \"r\").read()\n", + " return HTML(styles)\n", + "css_styling()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.10" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/Chapter7_BayesianMachineLearning/auc.py b/Chapter7_BayesianMachineLearning/auc.py new file mode 100644 index 00000000..cde567ab --- /dev/null +++ b/Chapter7_BayesianMachineLearning/auc.py @@ -0,0 +1,61 @@ +#contributed by Ben Hammer, 2013 + + +def tied_rank(x): + """ + Computes the tied rank of elements in x. + + This function computes the tied rank of elements in x. + + Parameters + ---------- + x : list of numbers, numpy array + + Returns + ------- + score : list of numbers + The tied rank f each element in x + + """ + sorted_x = sorted(zip(x,range(len(x)))) + r = [0 for k in x] + cur_val = sorted_x[0][0] + last_rank = 0 + for i in range(len(sorted_x)): + if cur_val != sorted_x[i][0]: + cur_val = sorted_x[i][0] + for j in range(last_rank, i): + r[sorted_x[j][1]] = float(last_rank+1+i)/2.0 + last_rank = i + if i==len(sorted_x)-1: + for j in range(last_rank, i+1): + r[sorted_x[j][1]] = float(last_rank+i+2)/2.0 + return r + +def auc(actual, posterior): + """ + Computes the area under the receiver-operator characteristic (AUC) + + This function computes the AUC error metric for binary classification. + + Parameters + ---------- + actual : list of binary numbers, numpy array + The ground truth value + posterior : same type as actual + Defines a ranking on the binary numbers, from most likely to + be positive to least likely to be positive. + + Returns + ------- + score : double + The mean squared error between actual and posterior + + """ + r = tied_rank(posterior) + num_positive = len([0 for x in actual if x==1]) + num_negative = len(actual)-num_positive + sum_positive = sum([r[i] for i in range(len(r)) if actual[i]==1]) + auc = ((sum_positive - num_positive*(num_positive+1)/2.0) / + (num_negative*num_positive)) + return auc \ No newline at end of file diff --git a/ExamplesFromChapters/Chapter1/SMS_behaviour.py b/ExamplesFromChapters/Chapter1/SMS_behaviour.py new file mode 100644 index 00000000..05d5378f --- /dev/null +++ b/ExamplesFromChapters/Chapter1/SMS_behaviour.py @@ -0,0 +1,28 @@ +import pymc as pm +import numpy as np + +count_data = np.loadtxt("../../Chapter1_Introduction/data/txtdata.csv") +n_count_data = len(count_data) + +alpha = 1.0 / count_data.mean() # recall count_data is + # the variable that holds our txt counts + +lambda_1 = pm.Exponential("lambda_1", alpha) +lambda_2 = pm.Exponential("lambda_2", alpha) + +tau = pm.DiscreteUniform("tau", lower=0, upper=n_count_data) + + +@pm.deterministic +def lambda_(tau=tau, lambda_1=lambda_1, lambda_2=lambda_2): + out = np.zeros(n_count_data) + out[:tau] = lambda_1 # lambda before tau is lambda1 + out[tau:] = lambda_2 # lambda after tau is lambda2 + return out + +observation = pm.Poisson("obs", lambda_, value=count_data, observed=True) +model = pm.Model([observation, lambda_1, lambda_2, tau]) + + +mcmc = pm.MCMC(model) +mcmc.sample(100000, 50000, 1) diff --git a/ExamplesFromChapters/Chapter2/ABtesting.py b/ExamplesFromChapters/Chapter2/ABtesting.py new file mode 100644 index 00000000..bccef2e7 --- /dev/null +++ b/ExamplesFromChapters/Chapter2/ABtesting.py @@ -0,0 +1,39 @@ +""" +This is an example of using Bayesian A/B testing + +""" + +import pymc as pm + +# these two quantities are unknown to us. +true_p_A = 0.05 +true_p_B = 0.04 + +# notice the unequal sample sizes -- no problem in Bayesian analysis. +N_A = 1500 +N_B = 1000 + +# generate data +observations_A = pm.rbernoulli(true_p_A, N_A) +observations_B = pm.rbernoulli(true_p_B, N_B) + + +# set up the pymc model. Again assume Uniform priors for p_A and p_B +p_A = pm.Uniform("p_A", 0, 1) +p_B = pm.Uniform("p_B", 0, 1) + + +# define the deterministic delta function. This is our unknown of interest. + +@pm.deterministic +def delta(p_A=p_A, p_B=p_B): + return p_A - p_B + + +# set of observations, in this case we have two observation datasets. +obs_A = pm.Bernoulli("obs_A", p_A, value=observations_A, observed=True) +obs_B = pm.Bernoulli("obs_B", p_B, value=observations_B, observed=True) + +# to be explained in chapter 3. +mcmc = pm.MCMC([p_A, p_B, delta, obs_A, obs_B]) +mcmc.sample(20000, 1000) diff --git a/ExamplesFromChapters/Chapter2/FreqOfCheaters.py b/ExamplesFromChapters/Chapter2/FreqOfCheaters.py new file mode 100644 index 00000000..9c2a0a10 --- /dev/null +++ b/ExamplesFromChapters/Chapter2/FreqOfCheaters.py @@ -0,0 +1,17 @@ +import pymc as pm + +p = pm.Uniform("freq_cheating", 0, 1) + + +@pm.deterministic +def p_skewed(p=p): + return 0.5 * p + 0.25 + +yes_responses = pm.Binomial( + "number_cheaters", 100, p_skewed, value=35, observed=True) + +model = pm.Model([yes_responses, p_skewed, p]) + +# To Be Explained in Chapter 3! +mcmc = pm.MCMC(model) +mcmc.sample(50000, 25000) diff --git a/ExamplesFromChapters/Chapter2/ORingFailure.py b/ExamplesFromChapters/Chapter2/ORingFailure.py new file mode 100644 index 00000000..f216e526 --- /dev/null +++ b/ExamplesFromChapters/Chapter2/ORingFailure.py @@ -0,0 +1,32 @@ +import numpy as np +import pymc as pm + + +challenger_data = np.genfromtxt( + "../../Chapter2_MorePyMC/data/challenger_data.csv", + skip_header=1, usecols=[1, 2], missing_values="NA", delimiter=",") +# drop the NA values +challenger_data = challenger_data[~np.isnan(challenger_data[:, 1])] + + +temperature = challenger_data[:, 0] +D = challenger_data[:, 1] # defect or not? + +beta = pm.Normal("beta", 0, 0.001, value=0) +alpha = pm.Normal("alpha", 0, 0.001, value=0) + + +@pm.deterministic +def p(temp=temperature, alpha=alpha, beta=beta): + return 1.0 / (1. + np.exp(beta * temperature + alpha)) + + +observed = pm.Bernoulli("bernoulli_obs", p, value=D, observed=True) + +model = pm.Model([observed, beta, alpha]) + +# mysterious code to be explained in Chapter 3 +map_ = pm.MAP(model) +map_.fit() +mcmc = pm.MCMC(model) +mcmc.sample(260000, 220000, 2) diff --git a/ExamplesFromChapters/Chapter3/ClusteringWithGaussians.py b/ExamplesFromChapters/Chapter3/ClusteringWithGaussians.py new file mode 100644 index 00000000..647553f7 --- /dev/null +++ b/ExamplesFromChapters/Chapter3/ClusteringWithGaussians.py @@ -0,0 +1,41 @@ +import numpy as np +import pymc as pm + + +data = np.loadtxt("../../Chapter3_MCMC/data/mixture_data.csv", delimiter=",") + + +p = pm.Uniform("p", 0, 1) + +assignment = pm.Categorical("assignment", [p, 1 - p], size=data.shape[0]) + +taus = 1.0 / pm.Uniform("stds", 0, 100, size=2) ** 2 # notice the size! +centers = pm.Normal("centers", [150, 150], [0.001, 0.001], size=2) + +""" +The below deterministic functions map an assignment, in this case 0 or 1, +to a set of parameters, located in the (1, 2) arrays `taus` and `centers.` +""" + + +@pm.deterministic +def center_i(assignment=assignment, centers=centers): + return centers[assignment] + + +@pm.deterministic +def tau_i(assignment=assignment, taus=taus): + return taus[assignment] + +# and to combine it with the observations: +observations = pm.Normal("obs", center_i, tau_i, + value=data, observed=True) + +# below we create a model class +model = pm.Model([p, assignment, taus, centers]) + + +map_ = pm.MAP(model) +map_.fit() +mcmc = pm.MCMC(model) +mcmc.sample(100000, 50000) diff --git a/ExamplesFromChapters/README.md b/ExamplesFromChapters/README.md new file mode 100644 index 00000000..bde59920 --- /dev/null +++ b/ExamplesFromChapters/README.md @@ -0,0 +1,5 @@ +## Read Me + + +Included is all PyMC examples and models *out of context*, this is for users to easily view the entire Python program. + diff --git a/LICENSE.txt b/LICENSE.txt new file mode 100644 index 00000000..08a0c072 --- /dev/null +++ b/LICENSE.txt @@ -0,0 +1,22 @@ +Copyright (c) 2013 Cameron Davidson-Pilon + +Permission is hereby granted, free of charge, to any person +obtaining a copy of this software and associated documentation +files (the "Software"), to deal in the Software without +restriction, including without limitation the rights to use, +copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the +Software is furnished to do so, subject to the following +conditions: + +The above copyright notice and this permission notice shall be +included in all copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, +EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES +OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND +NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT +HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, +WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING +FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR +OTHER DEALINGS IN THE SOFTWARE. diff --git a/Prologue/Prologue.ipynb b/Prologue/Prologue.ipynb new file mode 100644 index 00000000..ff85f923 --- /dev/null +++ b/Prologue/Prologue.ipynb @@ -0,0 +1,286 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Probabilistic Programming and \n", + "=====\n", + "\n", + "Bayesian Methods for Hackers \n", + "=======" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "###Probabilistic Programming & Bayesian Methods for Hackers \n", + "#### *Using Python and PyMC*\n", + "\n", + "\n", + "\n", + "\n", + "The Bayesian method is the natural approach to inference, yet it is hidden from readers behind chapters of slow, mathematical analysis. The typical text on Bayesian inference involves two to three chapters on probability theory, then enters what Bayesian inference is. Unfortunately, due to mathematical intractability of most Bayesian models, the reader is only shown simple, artificial examples. This can leave the user with a *so-what* feeling about Bayesian inference. In fact, this was the author's own prior opinion.\n", + "\n", + "\n", + "
\n", + "\n", + "After some recent success of Bayesian methods in machine-learning competitions, I decided to investigate the subject again. Even with my mathematical background, it took me three straight-days of reading examples and trying to put the pieces together to understand the methods. There was simply not enough literature bridging theory to practice. The problem with my misunderstanding was the disconnect between Bayesian mathematics and probabilistic programming. That being said, I suffered then so the reader would not have to now. This book attempts to bridge the gap.\n", + "\n", + "If Bayesian inference is the destination, then mathematical analysis is a particular path towards it. On the other hand, computing power is cheap enough that we can afford to take an alternate route via probabilistic programming. The latter path is much more useful, as it denies the necessity of mathematical intervention at each step, that is, we remove often-intractable mathematical analysis as a prerequisite to Bayesian inference. Simply put, this latter computational path proceeds via small intermediate jumps from beginning to end, where as the first path proceeds by enormous leaps, often landing far away from our target. Furthermore, without a strong mathematical background, the analysis required by the first path cannot even take place.\n", + "\n", + "*Bayesian Methods for Hackers* is designed as a introduction to Bayesian inference from a computational/understanding-first, and mathematics-second, point of view. Of course as an introductory book, we can only leave it at that: an introductory book. For the mathematically trained, they may cure the curiosity this text generates with other texts designed with mathematical analysis in mind. For the enthusiast with less mathematical-background, or one who is not interested in the mathematics but simply the practice of Bayesian methods, this text should be sufficient and entertaining.\n", + "\n", + "\n", + "The choice of PyMC as the probabilistic programming language is two-fold. As of this writing, there is currently no central resource for examples and explanations in the PyMC universe. The official documentation assumes prior knowledge of Bayesian inference and probabilistic programming. We hope this book encourages users at every level to look at PyMC. Secondly, with recent core developments and popularity of the scientific stack in Python, PyMC is likely to become a core component soon enough.\n", + "\n", + "PyMC does have dependencies to run, namely NumPy and (optionally) SciPy. To not limit the user, the examples in this book will rely only on PyMC, NumPy, SciPy and Matplotlib.\n", + "\n", + "\n", + "Contents\n", + "------\n", + "\n", + "(The below chapters are rendered via the *nbviewer* at\n", + "[nbviewer.ipython.org/](http://nbviewer.ipython.org/), and are read-only and rendered in real-time.\n", + "Interactive notebooks + examples can be downloaded by cloning! )\n", + "\n", + "\n", + "* [**Prologue:**](http://nbviewer.ipython.org/urls/raw.github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/master/Prologue/Prologue.ipynb) Why we do it.\n", + "\n", + "* [**Chapter 1: Introduction to Bayesian Methods**](http://nbviewer.ipython.org/urls/raw.github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/master/Chapter1_Introduction/Ch1_Introduction_PyMC3.ipynb)\n", + " Introduction to the philosophy and practice of Bayesian methods and answering the question \"What is probabilistic programming?\" Examples include:\n", + " - Inferring human behaviour changes from text message rates.\n", + " \n", + "* [**Chapter 2: A little more on PyMC**](http://nbviewer.ipython.org/urls/raw.github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/master/Chapter2_MorePyMC/Ch2_MorePyMC_PyMC3.ipynb)\n", + " We explore modeling Bayesian problems using Python's PyMC library through examples. How do we create Bayesian models? Examples include:\n", + " - Detecting the frequency of cheating students, while avoiding liars.\n", + " - Calculating probabilities of the Challenger space-shuttle disaster.\n", + " \n", + "* [**Chapter 3: Opening the Black Box of MCMC**](http://nbviewer.ipython.org/urls/raw.github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/master/Chapter3_MCMC/Ch3_IntroMCMC_PyMC3.ipynb)\n", + " We discuss how MCMC operates and diagnostic tools. Examples include:\n", + " - Bayesian clustering with mixture models\n", + " \n", + "* [**Chapter 4: The Greatest Theorem Never Told**](http://nbviewer.ipython.org/urls/raw.github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/master/Chapter4_TheGreatestTheoremNeverTold/Ch4_LawOfLargeNumbers_PyMC3.ipynb)\n", + " We explore an incredibly useful, and dangerous, theorem: The Law of Large Numbers. Examples include:\n", + " - Exploring a Kaggle dataset and the pitfalls of naive analysis\n", + " - How to sort Reddit comments from best to worst (not as easy as you think)\n", + " \n", + "* [**Chapter 5: Would you rather loss an arm or a leg?**](http://nbviewer.ipython.org/urls/raw.github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/master/Chapter5_LossFunctions/Ch5_LossFunctions_PyMC3.ipynb)\n", + " The introduction of Loss functions and their (awesome) use in Bayesian methods. Examples include:\n", + " - Solving the Price is Right's Showdown\n", + " - Optimizing financial predictions\n", + " - Winning solution to the Kaggle Dark World's competition.\n", + " \n", + "* [**Chapter 6: Getting our *prior*-ities straight**](http://nbviewer.ipython.org/urls/raw.github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/master/Chapter6_Priorities/Ch6_Priors_PyMC3.ipynb)\n", + " Probably the most important chapter. We draw on expert opinions to answer questions. Examples include:\n", + " - Multi-Armed Bandits and the Bayesian Bandit solution.\n", + " - what is the relationship between data sample size and prior?\n", + " - estimating financial unknowns using expert priors.\n", + " \n", + " We explore useful tips to be objective in analysis, and common pitfalls of priors. \n", + " \n", + " \n", + "**More questions about PyMC?**\n", + "Please post your modeling, convergence, or any other PyMC question on [cross-validated](http://stats.stackexchange.com/), the statistics stack-exchange.\n", + " \n", + " \n", + "Using the book\n", + "-------\n", + "\n", + "The book can be read in three different ways, starting from most recommended to least recommended: \n", + "\n", + "1. The most recommended option is to clone the repository to download the .ipynb files to your local machine. If you have IPython installed, you can view the \n", + "chapters in your browser *plus* edit and run the code provided (and try some practice questions). This is the preferred option to read\n", + "this book, though it comes with some dependencies. \n", + " - IPython 0.13 is a requirement to view the ipynb files. It can be downloaded [here](http://ipython.org/)\n", + " - For Linux users, you should not have a problem installing Numpy, Scipy, Matplotlib and PyMC. For Windows users, check out [pre-compiled versions](http://www.lfd.uci.edu/~gohlke/pythonlibs/) if you have difficulty. \n", + " - In the styles/ directory are a number of files (.matplotlirc) that used to make things pretty. These are not only designed for the book, but they offer many improvements over the default settings of matplotlib and the IPython notebook.\n", + "2. The second, preferred, option is to use the nbviewer.ipython.org site, which display IPython notebooks in the browser ([example](http://nbviewer.ipython.org/urls/raw.github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/master/Chapter1_Introduction/Chapter1.ipynb)).\n", + "The contents are updated synchronously as commits are made to the book. You can use the Contents section above to link to the chapters.\n", + " \n", + "3. **PDF versions are coming.** PDFs are the least-preferred method to read the book, as pdf's are static and non-interactive. If PDFs are desired, they can be created dynamically using Chrome's builtin print-to-pdf feature.\n", + " \n", + "\n", + "Installation and configuration\n", + "------\n", + "If you would like to run the IPython notebooks locally, (option 1. above), you'll need to install the following:\n", + "\n", + "- IPython 0.13 is a requirement to view the ipynb files. It can be downloaded [here](http://ipython.org/ipython-doc/dev/install/index.html)\n", + "- For Linux users, you should not have a problem installing Numpy, Scipy and PyMC. For Windows users, check out [pre-compiled versions](http://www.lfd.uci.edu/~gohlke/pythonlibs/) if you have difficulty. \n", + " - also recommended, for data-mining exercises, are [PRAW](https://github.com/praw-dev/praw) and [requests](https://github.com/kennethreitz/requests). \n", + "- In the styles/ directory are a number of files that are customized for the notebook. \n", + "These are not only designed for the book, but they offer many improvements over the \n", + "default settings of matplotlib and the IPython notebook. The in notebook style has not been finalized yet.\n", + "\n", + "\n", + "Contributions and Thanks\n", + "-----\n", + "\n", + "\n", + "Thanks to all our contributing authors, including (in chronological order):\n", + "\n", + "- [Cameron Davidson-Pilon](http://www.camdp.com)\n", + "- [Stef Gibson](http://stefgibson.com)\n", + "- [Vincent Ohprecio](http://bigsnarf.wordpress.com/)\n", + "- [Lars Buitinck](https://github.com/larsman)\n", + "- [Paul Magwene](http://github.com/pmagwene) \n", + "- [Matthias Bussonnier](https://github.com/Carreau)\n", + "- [Jens Rantil](https://github.com/JensRantil)\n", + "- [y-p](https://github.com/y-p)\n", + "- [Ethan Brown](http://www.etano.net/)\n", + "- [Jonathan Whitmore](http://jonathanwhitmore.com/)\n", + "- [Mattia Rigotti](https://github.com/matrig)\n", + "- [Colby Lemon](https://github.com/colibius)\n", + "- [Gustav W Delius](https://github.com/gustavdelius)\n", + "- [Matthew Conlen](http://www.mathisonian.com/) \n", + "- [Jim Radford](https://github.com/radford)\n", + "- [Vannessa Sabino](http://baniverso.com/)\n", + "- [Thomas Bratt](https://github.com/thomasbratt)\n", + "- [Nisan Haramati](https://github.com/nisanharamati)\n", + "- [Thomas Bratt](https://github.com/thomasbratt)\n", + "- [Robert Grant](https://github.com/bgrant)\n", + "- [Yaroslav Halchenko](https://github.com/yarikoptic)\n", + "- [Alex Garel](https://github.com/alexgarel)\n", + "\n", + "\n", + "We would like to thank the Python community for building an amazing architecture. We would like to thank the \n", + "statistics community for building an amazing architecture. \n", + "\n", + "Similarly, the book is only possible because of the [PyMC](http://github.com/pymc-devs/pymc) library. A big thanks to the core devs of PyMC: Chris Fonnesbeck, Anand Patil, David Huard and John Salvatier.\n", + "\n", + "One final thanks. This book was generated by IPython Notebook, a wonderful tool for developing in Python. We thank the IPython \n", + "community for developing the Notebook interface. All IPython notebook files are available for download on the GitHub repository. \n", + "\n", + "\n", + "\n", + "####Contact\n", + "Contact the main author, Cam Davidson-Pilon at cam.davidson.pilon@gmail.com or [@cmrndp](https://twitter.com/cmrn_dp)\n", + "\n", + "\n", + "![Imgur](http://i.imgur.com/Zb79QZb.png)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.core.display import HTML\n", + "\n", + "\n", + "def css_styling():\n", + " styles = open(\"../styles/custom.css\", \"r\").read()\n", + " return HTML(styles)\n", + "css_styling()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.11" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/Prolouge/Untitled0.ipynb b/Prolouge/Untitled0.ipynb deleted file mode 100644 index 4220cc62..00000000 --- a/Prolouge/Untitled0.ipynb +++ /dev/null @@ -1,103 +0,0 @@ -{ - "metadata": { - "name": "Untitled0" - }, - "nbformat": 3, - "nbformat_minor": 0, - "worksheets": [ - { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Probabilistic Programming \n", - "=======\n", - "and Bayesian Methods for Hackers\n", - "========\n", - "### *Using Python and PyMC*\n", - "\n", - "\n", - "Bayesian method is the natural approach to inference, yet it is hidden from readers behind chapters of slow, mathematical, analysis. The typical text on Bayesian inference involves two to three chapters on probability theory, then enters what Bayesian inference is. Unfortunately, due to mathematical intractability of most Bayesian models, the reader is only shown simple, artificial examples. This can leave the user with a \"so-what\" feeling about Bayesian inference. In fact, this was the author's own prior opinion.\n", - "\n", - "After some recent success of Bayesian methods in machine-learning competitions, I decided to investigate the subject again. Even with my mathematical background, it took me three straight-days of reading examples and trying to put the pieces together to understand how the method works so well. That being said, I suffered then so the reader would not have to now. The problem with my misunderstanding was the disconnect between Bayesian mathematics and probabilistic programming. This book attempts to bridge the gap.\n", - "\n", - "If Bayesian inference is the destination, then mathematical analysis is a path to it. On the other hand, computing power is cheap enough that we can afford to take an alternate route via probabilistic programming. The path is much more useful, as it denies the necessity of mathematical intervention at each step, that is, we remove often-intractable mathematical analysis as a prerequisite to Bayesian inference. Simply put, this path proceeds via small intermediate jumps from beginning to end, where as the first path proceeds by enormous leaps, often landing far away from our target. Furthermore, with a tuned-mathematical background, the analysis required by the first path cannot even take place.\n", - "\n", - "*Probabilistic Programming and Bayesian Methods for Hackers* is designed as a introduction to Bayesian methods and inference from a computation/understanding-first, and mathematical-second, point of view. Of course as an introductory book, we can only leave it at that: an introductory book. For the mathematically trained, they may supplement this text with other texts designed with mathematical analysis in mind. For the enthusiast with less mathematical-background, or one who is not interested in the mathematics but simply the practice of Bayesian methods, this text should be sufficient.\n", - "\n", - "The choice of PyMC as the probabilistic programming language is two-fold. As of this writing, there is currently no central resource for examples and explanation in the PyMC universe. The official documentation assumes prior knowledge of Bayesian inference and probabilistic programming. We hope this book encourages users at every level to look at PyMC. Secondly, with recent core developments and popularity of the scientific stack in Python, PyMC is likely to become a core component of the stack.\n", - "\n", - "PyMC does have some dependencies to run, namely NumPy and (optionally) SciPy. To not limit the user, the examples in this book will rely only on PyMC, NumPy and SciPy only.\n", - "\n", - "Development\n", - "------\n", - "\n", - "This book has an unusual development design. The content is open-sourced, meaning anyone can be an author. \n", - "Authors submit content or revisions using the GitHub interface. After a major revision or addition, we collect all the content, compile it to a \n", - "PDF, and increment the version of *Probabilistic Programming and Bayesian Methods for Hackers*. \n", - "\n", - "\n", - "Contributions and Thanks\n", - "-----\n", - "\n", - "\n", - "Thanks to all our contributing authors, including (in chronological order):\n", - "\n", - "- [Cameron Davidson-Pilon](http://www.camdp.com)\n", - "- Andrew Hand\n", - "- [Stef Gibson](http://stefgibson.com)\n", - " \n", - "\n", - "\n", - "We would like to thank the Python community for building an amazing architecture. We would like to thank the \n", - "statistics community for building an amazing architecture. \n", - "\n", - "One final thanks. This book was generated by IPython Notebook, a wonderful tool for developing in Python. We thank the IPython \n", - "community for developing the Notebook interface. All IPython notebook files are available for download on the GitHub repository. \n", - "\n", - "\n", - "\n", - "### How to contribute\n", - "\n", - "####What to contribute?\n", - "\n", - "- The current chapter list is not finalized. If you see something that is missing (MCMC, MAP, Bayesian networks, good prior choices, Potential classes etc.),\n", - "feel free to start there. \n", - "- Cleaning up Python code and making code more PyMC-esque.\n", - "- Giving better explainations\n", - "- Contributing to the IPython notebook styles.\n", - "\n", - "\n", - "####Installation and configuration\n", - "\n", - "- IPython 0.14 is a requirement to view the ipynb files. It can be downloaded [here](http://ipython.org/ipython-doc/dev/install/index.html)\n", - "- For Linux users, you should not have a problem installing Numpy, Scipy and PyMC. For Windows users, check out [pre-compiled versions](http://www.lfd.uci.edu/~gohlke/pythonlibs/) if you have difficulty. \n", - "- In the styles/ directory are a number of files that are customized for the *pdf version of the book*. \n", - "These are not only designed for the book, but they offer many improvements over the \n", - "default settings of matplotlib and the IPython notebook. The in notebook style has not been finalized yet.\n", - "- Currently the formatting of the style is not set, so try to follow what has been used so far, but inconsistencies are fine. \n", - "\n", - "####Commiting\n", - "\n", - "- All commits are welcome, even if they are minor ;)\n", - "- If you are unfamiliar with Github, you can email me contributions to the email below.\n", - "\n", - "####Contact\n", - "Contact the main author, Cam Davidson-Pilon at cam.davidson.pilon@gmail.com or [@cmrndp](https://twitter.com/cmrn_dp)\n", - "\n" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [], - "language": "python", - "metadata": {}, - "outputs": [] - } - ], - "metadata": {} - } - ] -} \ No newline at end of file diff --git a/README.md b/README.md index 9152c969..302c042c 100644 --- a/README.md +++ b/README.md @@ -1,100 +1,228 @@ -Probabilistic Programming and Bayesian Methods for Hackers -======== -## *Using Python and PyMC* +# [Bayesian Methods for Hackers](http://camdavidsonpilon.github.io/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/) +#### *Using Python and PyMC* -Bayesian method is the natural approach to inference, yet it is hidden from readers behind chapters of slow, mathematical, analysis. The typical text on Bayesian inference involves two to three chapters on probability theory, then enters what Bayesian inference is. Unfortunately, due to mathematical intractability of most Bayesian models, the reader is only shown simple, artificial examples. This can leave the user with a "so-what" feeling about Bayesian inference. In fact, this was the author's own prior opinion. +The Bayesian method is the natural approach to inference, yet it is hidden from readers behind chapters of slow, mathematical analysis. The typical text on Bayesian inference involves two to three chapters on probability theory, then enters what Bayesian inference is. Unfortunately, due to mathematical intractability of most Bayesian models, the reader is only shown simple, artificial examples. This can leave the user with a *so-what* feeling about Bayesian inference. In fact, this was the author's own prior opinion. -After some recent success of Bayesian methods in machine-learning competitions, I decided to investigate the subject again. Even with my mathematical background, it took me three straight-days of reading examples and trying to put the pieces together to understand how the method works so well. That being said, I suffered then so the reader would not have to now. The problem with my misunderstanding was the disconnect between Bayesian mathematics and probabilistic programming. This book attempts to bridge the gap. +After some recent success of Bayesian methods in machine-learning competitions, I decided to investigate the subject again. Even with my mathematical background, it took me three straight-days of reading examples and trying to put the pieces together to understand the methods. There was simply not enough literature bridging theory to practice. The problem with my misunderstanding was the disconnect between Bayesian mathematics and probabilistic programming. That being said, I suffered then so the reader would not have to now. This book attempts to bridge the gap. -If Bayesian inference is the destination, then mathematical analysis is a path to it. On the other hand, computing power is cheap enough that we can afford to take an alternate route via probabilistic programming. The path is much more useful, as it denies the necessity of mathematical intervention at each step, that is, we remove often-intractable mathematical analysis as a prerequisite to Bayesian inference. Simply put, this path proceeds via small intermediate jumps from beginning to end, where as the first path proceeds by enormous leaps, often landing far away from our target. Furthermore, with a tuned-mathematical background, the analysis required by the first path cannot even take place. +If Bayesian inference is the destination, then mathematical analysis is a particular path towards it. On the other hand, computing power is cheap enough that we can afford to take an alternate route via probabilistic programming. The latter path is much more useful, as it denies the necessity of mathematical intervention at each step, that is, we remove often-intractable mathematical analysis as a prerequisite to Bayesian inference. Simply put, this latter computational path proceeds via small intermediate jumps from beginning to end, where as the first path proceeds by enormous leaps, often landing far away from our target. Furthermore, without a strong mathematical background, the analysis required by the first path cannot even take place. -*Probabilistic Programming and Bayesian Methods for Hackers* is designed as a introduction to Bayesian methods and inference from a computation/understanding-first, and mathematical-second, point of view. Of course as an introductory book, we can only leave it at that: an introductory book. For the mathematically trained, they may supplement this text with other texts designed with mathematical analysis in mind. For the enthusiast with less mathematical-background, or one who is not interested in the mathematics but simply the practice of Bayesian methods, this text should be sufficient. +*Bayesian Methods for Hackers* is designed as an introduction to Bayesian inference from a computational/understanding-first, and mathematics-second, point of view. Of course as an introductory book, we can only leave it at that: an introductory book. For the mathematically trained, they may cure the curiosity this text generates with other texts designed with mathematical analysis in mind. For the enthusiast with less mathematical background, or one who is not interested in the mathematics but simply the practice of Bayesian methods, this text should be sufficient and entertaining. -The choice of PyMC as the probabilistic programming language is two-fold. As of this writing, there is currently no central resource for examples and explanation in the PyMC universe. The official documentation assumes prior knowledge of Bayesian inference and probabilistic programming. We hope this book encourages users at every level to look at PyMC. Secondly, with recent core developments and popularity of the scientific stack in Python, PyMC is likely to become a core component of the stack. +The choice of PyMC as the probabilistic programming language is two-fold. As of this writing, there is currently no central resource for examples and explanations in the PyMC universe. The official documentation assumes prior knowledge of Bayesian inference and probabilistic programming. We hope this book encourages users at every level to look at PyMC. Secondly, with recent core developments and popularity of the scientific stack in Python, PyMC is likely to become a core component soon enough. -PyMC does have some dependencies to run, namely NumPy and (optionally) SciPy. To not limit the user, the examples in this book will rely only on PyMC, NumPy and SciPy only. +PyMC does have dependencies to run, namely NumPy and (optionally) SciPy. To not limit the user, the examples in this book will rely only on PyMC, NumPy, SciPy and Matplotlib. -Examples from the book: +Printed Version by Addison-Wesley ------ +
-1. Inferring human behaviour from SMS message rates. -2. Solving the Price is Right Showdown. -3. Implementing Kaggle winning solutions. -4. Exploring Github's social datasets. -5. Aerospace data, specifically the Challenger Spacecraft explosion. -6. Proving smoking really does kill people. +**Bayesian Methods for Hackers is now available as a printed book!** You can pick up a copy on [Amazon](http://www.amazon.com/Bayesian-Methods-Hackers-Probabilistic-Addison-Wesley/dp/0133902838). What are the differences between the online version and the printed version? + - Additional Chapter on Bayesian A/B testing + - Updated examples + - Answers to the end of chapter questions + - Additional explanation, and rewritten sections to aid the reader. + + +Contents +------ + +See the project homepage [here](http://camdavidsonpilon.github.io/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/) for examples, too. + + +The below chapters are rendered via the *nbviewer* at +[nbviewer.jupyter.org/](http://nbviewer.jupyter.org/), and is read-only and rendered in real-time. +Interactive notebooks + examples can be downloaded by cloning! + +### PyMC2 + +* [**Prologue:**](http://nbviewer.jupyter.org/urls/raw.github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/master/Prologue/Prologue.ipynb) Why we do it. + +* [**Chapter 1: Introduction to Bayesian Methods**](http://nbviewer.jupyter.org/urls/raw.github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/master/Chapter1_Introduction/Ch1_Introduction_PyMC2.ipynb) + Introduction to the philosophy and practice of Bayesian methods and answering the question, "What is probabilistic programming?" Examples include: + - Inferring human behaviour changes from text message rates + +* [**Chapter 2: A little more on PyMC**](http://nbviewer.jupyter.org/urls/raw.github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/master/Chapter2_MorePyMC/Ch2_MorePyMC_PyMC2.ipynb) + We explore modeling Bayesian problems using Python's PyMC library through examples. How do we create Bayesian models? Examples include: + - Detecting the frequency of cheating students, while avoiding liars + - Calculating probabilities of the Challenger space-shuttle disaster + +* [**Chapter 3: Opening the Black Box of MCMC**](http://nbviewer.jupyter.org/urls/raw.github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/master/Chapter3_MCMC/Ch3_IntroMCMC_PyMC2.ipynb) + We discuss how MCMC operates and diagnostic tools. Examples include: + - Bayesian clustering with mixture models + +* [**Chapter 4: The Greatest Theorem Never Told**](http://nbviewer.jupyter.org/urls/raw.github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/master/Chapter4_TheGreatestTheoremNeverTold/Ch4_LawOfLargeNumbers_PyMC2.ipynb) + We explore an incredibly useful, and dangerous, theorem: The Law of Large Numbers. Examples include: + - Exploring a Kaggle dataset and the pitfalls of naive analysis + - How to sort Reddit comments from best to worst (not as easy as you think) + +* [**Chapter 5: Would you rather lose an arm or a leg?**](http://nbviewer.jupyter.org/urls/raw.github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/master/Chapter5_LossFunctions/Ch5_LossFunctions_PyMC2.ipynb) + The introduction of loss functions and their (awesome) use in Bayesian methods. Examples include: + - Solving the *Price is Right*'s Showdown + - Optimizing financial predictions + - Winning solution to the Kaggle Dark World's competition + +* [**Chapter 6: Getting our *prior*-ities straight**](http://nbviewer.jupyter.org/urls/raw.github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/master/Chapter6_Priorities/Ch6_Priors_PyMC2.ipynb) + Probably the most important chapter. We draw on expert opinions to answer questions. Examples include: + - Multi-Armed Bandits and the Bayesian Bandit solution. + - What is the relationship between data sample size and prior? + - Estimating financial unknowns using expert priors + + We explore useful tips to be objective in analysis as well as common pitfalls of priors. + +### PyMC3 + +* [**Prologue:**](http://nbviewer.jupyter.org/urls/raw.github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/master/Prologue/Prologue.ipynb) Why we do it. + +* [**Chapter 1: Introduction to Bayesian Methods**](http://nbviewer.jupyter.org/urls/raw.github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/master/Chapter1_Introduction/Ch1_Introduction_PyMC3.ipynb) + Introduction to the philosophy and practice of Bayesian methods and answering the question, "What is probabilistic programming?" Examples include: + - Inferring human behaviour changes from text message rates + +* [**Chapter 2: A little more on PyMC**](http://nbviewer.jupyter.org/urls/raw.github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/master/Chapter2_MorePyMC/Ch2_MorePyMC_PyMC3.ipynb) + We explore modeling Bayesian problems using Python's PyMC library through examples. How do we create Bayesian models? Examples include: + - Detecting the frequency of cheating students, while avoiding liars + - Calculating probabilities of the Challenger space-shuttle disaster + +* [**Chapter 3: Opening the Black Box of MCMC**](http://nbviewer.jupyter.org/urls/raw.github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/master/Chapter3_MCMC/Ch3_IntroMCMC_PyMC3.ipynb) + We discuss how MCMC operates and diagnostic tools. Examples include: + - Bayesian clustering with mixture models + +* [**Chapter 4: The Greatest Theorem Never Told**](http://nbviewer.jupyter.org/urls/raw.github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/master/Chapter4_TheGreatestTheoremNeverTold/Ch4_LawOfLargeNumbers_PyMC3.ipynb) + We explore an incredibly useful, and dangerous, theorem: The Law of Large Numbers. Examples include: + - Exploring a Kaggle dataset and the pitfalls of naive analysis + - How to sort Reddit comments from best to worst (not as easy as you think) + +* [**Chapter 5: Would you rather lose an arm or a leg?**](http://nbviewer.jupyter.org/urls/raw.github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/master/Chapter5_LossFunctions/Ch5_LossFunctions_PyMC3.ipynb) + The introduction of loss functions and their (awesome) use in Bayesian methods. Examples include: + - Solving the *Price is Right*'s Showdown + - Optimizing financial predictions + - Winning solution to the Kaggle Dark World's competition + +* [**Chapter 6: Getting our *prior*-ities straight**](http://nbviewer.jupyter.org/urls/raw.github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/master/Chapter6_Priorities/Ch6_Priors_PyMC3.ipynb) + Probably the most important chapter. We draw on expert opinions to answer questions. Examples include: + - Multi-Armed Bandits and the Bayesian Bandit solution. + - What is the relationship between data sample size and prior? + - Estimating financial unknowns using expert priors + + We explore useful tips to be objective in analysis as well as common pitfalls of priors. + + + + +**More questions about PyMC?** +Please post your modeling, convergence, or any other PyMC question on [cross-validated](http://stats.stackexchange.com/), the statistics stack-exchange. + + Using the book ------- -The book can be read in three different ways. The most traditional approach is to read the chapters as PDFs contained in the `previews` folder. The content -in these PDFs is not guarunteed to be the most recent content as the PDFs are only compiled periodically. Similarly, the book will not be -interactive. +The book can be read in three different ways, starting from most recommended to least recommended: -The second option is to use the nbviewer website, which display ipython notebooks in the browser ([example](http://nbviewer.ipython.org/urls/raw.github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/master/Chapter1_Introduction/Chapter1_Introduction.ipynb)). -In each chapter's folder is a README that links to the nbviewer url. These are not interactive either. - -The final option is to fork the repository and download the .ipynb files to your local machine. If you have IPython installed, you can view the +1. The most recommended option is to clone the repository to download the .ipynb files to your local machine. If you have Jupyter installed, you can view the chapters in your browser *plus* edit and run the code provided (and try some practice questions). This is the preferred option to read this book, though it comes with some dependencies. + - Jupyter is a requirement to view the ipynb files. It can be downloaded [here](http://jupyter.org/). Jupyter notebooks can be run by `(your-virtualenv) ~/path/to/the/book/Chapter1_Introduction $ jupyter notebook` + - For Linux users, you should not have a problem installing NumPy, SciPy, Matplotlib and PyMC. For Windows users, check out [pre-compiled versions](http://www.lfd.uci.edu/~gohlke/pythonlibs/) if you have difficulty. + - In the styles/ directory are a number of files (.matplotlirc) that used to make things pretty. These are not only designed for the book, but they offer many improvements over the default settings of matplotlib. +2. The second, preferred, option is to use the nbviewer.jupyter.org site, which display Jupyter notebooks in the browser ([example](http://nbviewer.jupyter.org/urls/raw.github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/master/Chapter1_Introduction/Ch1_Introduction_PyMC2.ipynb)). +The contents are updated synchronously as commits are made to the book. You can use the Contents section above to link to the chapters. + +3. PDFs are the least-preferred method to read the book, as PDFs are static and non-interactive. If PDFs are desired, they can be created dynamically using the [nbconvert](https://github.com/jupyter/nbconvert) utility. +Installation and configuration +------ + + +If you would like to run the Jupyter notebooks locally, (option 1. above), you'll need to install the following: + +- Jupyter is a requirement to view the ipynb files. It can be downloaded [here](http://jupyter.org/install.html) +- Necessary packages are PyMC, NumPy, SciPy and Matplotlib. + - For Linux/OSX users, you should not have a problem installing the above, [*except for Matplotlib on OSX*](http://www.penandpants.com/2012/02/24/install-python/). + - For Windows users, check out [pre-compiled versions](http://www.lfd.uci.edu/~gohlke/pythonlibs/) if you have difficulty. + - also recommended, for data-mining exercises, are [PRAW](https://github.com/praw-dev/praw) and [requests](https://github.com/kennethreitz/requests). +- New to Python or Jupyter, and help with the namespaces? Check out [this answer](http://stackoverflow.com/questions/12987624/confusion-between-numpy-scipy-matplotlib-and-pylab). + +- In the styles/ directory are a number of files that are customized for the notebook. +These are not only designed for the book, but they offer many improvements over the +default settings of matplotlib and the Jupyter notebook. The in notebook style has not been finalized yet. + + Development ------ This book has an unusual development design. The content is open-sourced, meaning anyone can be an author. -Authors submit content or revisions using the GitHub interface. After a major revision or addition, we collect all the content, compile it to a -PDF, and increment the version of *Probabilistic Programming and Bayesian Methods for Hackers*. +Authors submit content or revisions using the GitHub interface. +### How to contribute -Contributions and Thanks ------ +#### What to contribute? +- The current chapter list is not finalized. If you see something that is missing (MCMC, MAP, Bayesian networks, good prior choices, Potential classes etc.), +feel free to start there. +- Cleaning up Python code and making code more PyMC-esque +- Giving better explanations +- Spelling/grammar mistakes +- Suggestions +- Contributing to the Jupyter notebook styles -Thanks to all our contributing authors, including (in chronological order): -- [Cameron Davidson-Pilon](http://www.camdp.com) -- [Stef Gibson](http://stefgibson.com) -- [Vincent Ohprecio](http://bigsnarf.wordpress.com/) - +#### Commiting -We would like to thank the Python community for building an amazing architecture. We would like to thank the -statistics community for building an amazing architecture. +- All commits are welcome, even if they are minor ;) +- If you are unfamiliar with Github, you can email me contributions to the email below. + +Reviews +------ +*these are satirical, but real* -One final thanks. This book was generated by IPython Notebook, a wonderful tool for developing in Python. We thank the IPython -community for developing the Notebook interface. All IPython notebook files are available for download on the GitHub repository. +"No, but it looks good" - [John D. Cook](https://twitter.com/JohnDCook/status/359672133695184896) +"I ... read this book ... I like it!" - [Andrew Gelman](http://www.andrewgelman.com/2013/07/21/bayes-related) +"This book is a godsend, and a direct refutation to that 'hmph! you don't know maths, piss off!' school of thought... +The publishing model is so unusual. Not only is it open source but it relies on pull requests from anyone in order to progress the book. This is ingenious and heartening" - [excited Reddit user](http://www.reddit.com/r/Python/comments/1alnal/probabilistic_programming_and_bayesian_methods/) -### How to contribute -####What to contribute? -- The current chapter list is not finalized. If you see something that is missing (MCMC, MAP, Bayesian networks, good prior choices, Potential classes etc.), -feel free to start there. -- Cleaning up Python code and making code more PyMC-esque. -- Giving better explainations -- Contributing to the IPython notebook styles. +Contributions and Thanks +----- -####Installation and configuration +Thanks to all our contributing authors, including (in chronological order): -- IPython 0.14 is a requirement to view the ipynb files. It can be downloaded [here](http://ipython.org/ipython-doc/dev/install/index.html) -- For Linux users, you should not have a problem installing Numpy, Scipy and PyMC. For Windows users, check out [pre-compiled versions](http://www.lfd.uci.edu/~gohlke/pythonlibs/) if you have difficulty. -- In the styles/ directory are a number of files that are customized for the *pdf version of the book*. -These are not only designed for the book, but they offer many improvements over the -default settings of matplotlib and the IPython notebook. The in notebook style has not been finalized yet. -- Currently the formatting of the style is not set, so try to follow what has been used so far, but inconsistencies are fine. +Authors | | | | +--- | --- | --- | --- +[Cameron Davidson-Pilon](http://www.camdp.com) | [Stef Gibson](http://stefgibson.com) | [Vincent Ohprecio](http://bigsnarf.wordpress.com/) |[Lars Buitinck](https://github.com/larsman) +[Paul Magwene](http://github.com/pmagwene) | [Matthias Bussonnier](https://github.com/Carreau) | [Jens Rantil](https://github.com/JensRantil) | [y-p](https://github.com/y-p) +[Ethan Brown](http://www.etano.net/) | [Jonathan Whitmore](http://jonathanwhitmore.com/) | [Mattia Rigotti](https://github.com/matrig) | [Colby Lemon](https://github.com/colibius) +[Gustav W Delius](https://github.com/gustavdelius) | [Matthew Conlen](http://www.mathisonian.com/) | [Jim Radford](https://github.com/radford) | [Vannessa Sabino](http://baniverso.com/) +[Thomas Bratt](https://github.com/thomasbratt) | [Nisan Haramati](https://github.com/nisanharamati) | [Robert Grant](https://github.com/bgrant) | [Matthew Wampler-Doty](https://github.com/xcthulhu) +[Yaroslav Halchenko](https://github.com/yarikoptic) | [Alex Garel](https://github.com/alexgarel) | [Oleksandr Lysenko](https://twitter.com/sash_ko) | [liori](https://github.com/liori) +[ducky427](https://github.com/ducky427) | [Pablo de Oliveira Castro](https://github.com/pablooliveira) | [sergeyfogelson](https://github.com/sergeyfogelson) | [Mattia Rigotti](http://neurotheory.columbia.edu/~mrigotti/) +[Matt Bauman](https://github.com/mbauman) | [Andrew Duberstein](http://www.andrewduberstein.com/) | [Carsten Brandt](http://cebe.cc/) | [Bob Jansen](http://web2docx.com) + [ugurthemaster](https://github.com/ugurthemaster) | [William Scott](https://github.com/williamscott) | [Min RK](http://twitter.com/minrk) | [Bulwersator](https://github.com/Bulwersator) + [elpres](https://github.com/elpres) | [Augusto Hack](https://github.com/hackaugusto) | [Michael Feldmann](https://github.com/michaf) | [Youki](https://github.com/Youki) + [Jens Rantil](http://jensrantil.github.io) | [Kyle Meyer](http://kyleam.com) | [Eric Martin](http://ericmart.in) | [Inconditus](https://github.com/Inconditus) + [Kleptine](https://github.com/Kleptine) | [Stuart Layton](https://github.com/slayton) | [Antonino Ingargiola](https://github.com/tritemio) | [vsl9](https://github.com/vsl9) + [Tom Christie](https://github.com/tom-christie) | [bclow](https://github.com/bclow) | [Simon Potter](http://sjp.co.nz/) | [Garth Snyder](https://github.com/GarthSnyder) + [Daniel Beauchamp](http://twitter.com/pushmatrix) | [Philipp Singer](http://www.philippsinger.info) | [gbenmartin](https://github.com/gbenmartin) | [Peadar Coyle](https://twitter.com/Springcoil) + +We would like to thank the Python community for building an amazing architecture. We would like to thank the +statistics community for building an amazing architecture. + +Similarly, the book is only possible because of the [PyMC](http://github.com/pymc-devs/pymc) library. A big thanks to the core devs of PyMC: Chris Fonnesbeck, Anand Patil, David Huard and John Salvatier. + +One final thanks. This book was generated by Jupyter Notebook, a wonderful tool for developing in Python. We thank the IPython/Jupyter +community for developing the Notebook interface. All Jupyter notebook files are available for download on the GitHub repository. -####Commiting -- All commits are welcome, even if they are minor ;) -- If you are unfamiliar with Github, you can email me contributions to the email below. -####Contact +#### Contact Contact the main author, Cam Davidson-Pilon at cam.davidson.pilon@gmail.com or [@cmrndp](https://twitter.com/cmrn_dp) diff --git a/previews/chp1Feb3.pdf b/previews/chp1Feb3.pdf deleted file mode 100644 index c6250376..00000000 Binary files a/previews/chp1Feb3.pdf and /dev/null differ diff --git a/previews/chp2Feb3.pdf b/previews/chp2Feb3.pdf deleted file mode 100644 index 8ab1dfbb..00000000 Binary files a/previews/chp2Feb3.pdf and /dev/null differ diff --git a/previews/prolougeFeb3.pdf b/previews/prolougeFeb3.pdf deleted file mode 100644 index c5353d2b..00000000 Binary files a/previews/prolougeFeb3.pdf and /dev/null differ diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 00000000..c8604dce --- /dev/null +++ b/requirements.txt @@ -0,0 +1,10 @@ +ipython>=2.0 +matplotlib>=1.2.1 +numpy>=1.7.1 +pymc>=5.0.1 +pyzmq>=13.1.0 +scipy>=0.12.0 +tornado>=3.0.2 +wsgiref>=0.1.2; python_version < '3.0' +praw>=2.0.0 +jinja2 diff --git a/sandbox/ABCtests.ipynb b/sandbox/ABCtests.ipynb new file mode 100644 index 00000000..0c302a7b --- /dev/null +++ b/sandbox/ABCtests.ipynb @@ -0,0 +1,259 @@ +{ + "metadata": { + "name": "ABCtests" + }, + "nbformat": 3, + "nbformat_minor": 0, + "worksheets": [ + { + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## ABC" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "import pymc as pm" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 1 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "obs = np.array([0, 0, 1])\n", + "\n", + "uni = pm.Uniform(\"prop\", 0, 1)\n", + "fake_obs = pm.Bernoulli(\"fake_obs\", uni, size=3)\n", + "\n", + "\n", + "@pm.deterministic\n", + "def accept(uni=uni, fake_obs=fake_obs, obs=obs):\n", + " if np.array_equal(fake_obs, obs):\n", + " return uni\n", + " else:\n", + " return None" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 132 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "mcmc = pm.MCMC([uni, fake_obs, accept])" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 133 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "mcmc.sample(10000)" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + " \r", + "[****************100%******************] 10000 of 10000 complete" + ] + }, + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "\n" + ] + } + ], + "prompt_number": 134 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "samples = mcmc.trace('accept')[:]" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 135 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "hist(samples[samples > 0])" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "pyout", + "prompt_number": 136, + "text": [ + "(array([ 47., 118., 158., 125., 141., 95., 74., 48., 33., 10.]),\n", + " array([ 0.01977077, 0.11150485, 0.20323893, 0.29497301, 0.38670709,\n", + " 0.47844116, 0.57017524, 0.66190932, 0.7536434 , 0.84537748,\n", + " 0.93711155]),\n", + " )" + ] + }, + { + "output_type": "display_data", + "png": 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a/sandbox/Chapter10_/More hacking with PyMC.ipynb b/sandbox/Chapter10_/More hacking with PyMC.ipynb new file mode 100644 index 00000000..d0377f34 --- /dev/null +++ b/sandbox/Chapter10_/More hacking with PyMC.ipynb @@ -0,0 +1,908 @@ +{ + "metadata": { + "name": "More hacking with PyMC" + }, + "nbformat": 3, + "nbformat_minor": 0, + "worksheets": [ + { + "cells": [ + { + "cell_type": "code", + "collapsed": false, + "input": [ + "figsize(12.5, 4)\n", + "import scipy.stats as stats;" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 2 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Chapter 11 \n", + "___________\n", + "## More Hacking with PyMC\n", + "\n", + "This chapter introduces useful or advanced techniques with PyMC including building your own stochastic variables, user-defined steps etc." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "##### Example: Real-time Github Popularity Measures\n", + "\n", + "\n", + "Most of you are likely familar with the git-repository website Github. An observed phenomenon on Github is the *scale-ness* of the popularity of repositories. Here, for lack of a better measure, we use the numbers of *stars* and *forks* to measure popularity. This is not a bad measure, but it can ignore page-views, downloads and tends to overemphasize older repositories. Since we will be studying *all* repositories and not a single one, the absense of these measures is not as relevant. \n", + "\n", + "Contained in this folder is a Python script for scrapping data from Github on the popularity of repos. The script requires the `Requests` and `BeautifulSoup` libraries, but if you don't have that installed, provided in the `./data` folder is the same data from a previous date (Feburary 18, 2013 at last pull). The data is the fraction of repositories with stars equal to or greater than $2^k,\\; k = 0,...,15$ and the fraction of repositories with forks equal to or than $2^k,\\; k = 0,...,15$.\n", + " " + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "run github_datapull.py;" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "Scrapping data from Github. Sorry Github...\n", + "The data is contained in variables `foo_to_explore` and `repo_with_foo`\n", + "\n", + "stars first...\n", + "number of repos with greater than or equal to 0 stars: 2738541" + ] + }, + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "\n", + "number of repos with greater than or equal to 1 stars: 1704779" + ] + }, + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "\n", + "number of repos with greater than or equal to 2 stars: 493529" + ] + }, + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "\n", + "number of repos with greater than or equal to 4 stars: 212099" + ] + }, + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "\n", + "number of repos with greater than or equal to 8 stars: 106973" + ] + }, + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "\n", + "number of repos with greater than or equal to 16 stars: 58101" + ] + }, + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "\n", + "number of repos with greater than or equal to 32 stars: 31877" + ] + }, + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "\n", + "number of repos with greater than or equal to 64 stars: 17370" + ] + }, + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "\n", + "number of repos with greater than or equal to 128 stars: 9239" + ] + }, + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "\n", + "number of repos with greater than or equal to 256 stars: 4578" + ] + }, + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "\n", + "number of repos with greater than or equal to 512 stars: 2150" + ] + }, + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "\n", + "number of repos with greater than or equal to 1024 stars: 872" + ] + }, + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "\n", + "number of repos with greater than or equal to 2048 stars: 286" + ] + }, + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "\n", + "number of repos with greater than or equal to 4096 stars: 84" + ] + }, + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "\n", + "number of repos with greater than or equal to 8192 stars: 22" + ] + }, + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "\n", + "number of repos with greater than or equal to 16384 stars: 5" + ] + }, + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "\n", + "number of repos with greater than or equal to 32768 stars: 1" + ] + }, + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "\n", + "\n", + "forks second...\n", + "number of repos with greater than or equal to 0 forks: 2738548" + ] + }, + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "\n", + "number of repos with greater than or equal to 1 forks: 334539" + ] + }, + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "\n", + "number of repos with greater than or equal to 2 forks: 159206" + ] + }, + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "\n", + "number of repos with greater than or equal to 4 forks: 74836" + ] + }, + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "\n", + "number of repos with greater than or equal to 8 forks: 36532" + ] + }, + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "\n", + "number of repos with greater than or equal to 16 forks: 17948" + ] + }, + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "\n", + "number of repos with greater than or equal to 32 forks: 8394" + ] + }, + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "\n", + "number of repos with greater than or equal to 64 forks: 3841" + ] + }, + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "\n", + "number of repos with greater than or equal to 128 forks: 1580" + ] + }, + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "\n", + "number of repos with greater than or equal to 256 forks: 605" + ] + }, + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "\n", + "number of repos with greater than or equal to 512 forks: 222" + ] + }, + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "\n", + "number of repos with greater than or equal to 1024 forks: 69" + ] + }, + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "\n", + "number of repos with greater than or equal to 2048 forks: 17" + ] + }, + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "\n", + "number of repos with greater than or equal to 4096 forks: 4" + ] + }, + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "\n", + "number of repos with greater than or equal to 8192 forks: 2" + ] + }, + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "\n", + "number of repos with greater than or equal to 16384 forks: 0" + ] + }, + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "\n", + "number of repos with greater than or equal to 32768 forks: 0" + ] + }, + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "\n" + ] + } + ], + "prompt_number": 1 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "plt.plot((stars_to_explore), repo_with_stars, label=\"stars\")\n", + "plt.plot((forks_to_explore), repo_with_forks, label=\"forks\")\n", + "plt.legend(loc=\"lower right\")\n", + "plt.title(\"Popularity of Repos (as measured by stars and forks)\")\n", + "plt.xlabel(\"$K$\")\n", + "plt.ylabel(\"number of repos with stars/forks $K$\")\n", + "plt.xlim(-200, 35000);" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "pyout", + "prompt_number": 3, + "text": [ + "(-200, 35000)" + ] + }, + { + "output_type": "display_data", + "png": 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Kzz//fK0BlQ87MBMA3njjDezevRvXr1/HqlWr8OKLL2p0l7mf+/2C9TC/gALV42ecnZ3R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\nRERERGbzX8VMdv/FLvLEAAAAAElFTkSuQmCC\n" + } + ], + "prompt_number": 3 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Clearly, we need to adjust the scale of this plot as most of the action is hidden. The number of repos falls very quickly. We will put it on a log-log plot." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "plt.plot(log2(stars_to_explore + 1), log2(repo_with_stars + 1), 'o-', label=\"stars\")\n", + "plt.plot(log2(forks_to_explore + 1), log2(repo_with_forks + 1), 'o-', label=\"forks\",)\n", + "plt.legend(loc=\"upper right\")\n", + "plt.title(\"Log-Log plot of Popularity of Repos (as measured by stars and forks)\")\n", + "plt.xlabel(\"$\\log{K}$\")\n", + "plt.ylabel(\"$\\log$(number of repos with stars/forks < K )\");" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "pyout", + "prompt_number": 4, + "text": [ + "" + ] + }, + { + "output_type": "display_data", + "png": 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Tg3379mHu3Lkqj9d8EwkJCdiwYQPu3buHhw8fYvXq1fD3969y5gagYT9Vkagu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+ } + ], + "prompt_number": 4 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Both characteristics look like a straight line plotted on a log-log plot. What does this mean? Denote the fraction of repos with greater than or equal to $k$ stars (or forks) $P(k)$. So in the above plot, $\\log{P(k)}$ on the y-axis and $\\log{k}$ is on the x-axis. The above linear relationship can be written as:\n", + "\n", + "$$ \\log_2{P(k)} = \\beta\\log_2{k} + \\alpha$$\n", + "\n", + "rearranging by taking both sides to the power of 2:\n", + "\n", + "$$ P(k) = 2^\\alpha k^{\\beta} = C k^{\\beta}, \\;\\; C = 2^{\\alpha}$$\n", + "\n", + "This relationship is very interesting. It is called a *power-law*, and occurs very freqently in social datasets. Why does it occur so frequently in social datasets? It has much to do with a \"winner-take-all\" or \"winner-take-most\" effect. Winners in a power-law enviroment are components that seem take a disproportiante amount of the popularity, and keep winning. In term of popularity of repos, *winning repos* are repos that are very good quailty (intially are winners), and are shared/talked about often (keep winning). \n", + "\n", + "\n", + "The above plot is also telling us that the majority of repos have very few stars and forks, only a handful have hundreds, and an incredibly small number have thousands. This is not-so obvious after browsing Github's website, where you see some repos with 36000+ stars, but fail to see the millions that do not have any stars (as they are not popular, they won't be common on your tour of the site.)\n", + "\n", + "Distributions like this are also said to have *fat-tails*, i.e. the probability does not drop quickly as we extend into the tail of the dataset, but most of the probability is still centered near zero. \n", + "\n", + "\n", + "The heaviness of the tail and strength of \"winner-take-all\" effect are both influenced by the $\\beta$ parameter. The small the $\\beta$, the more pronounced these effects. Below is a list of distributions that follow a power-law and an approximate $\\beta$ exponent [1]. Recall though that *we never observe these numbers*, we must infer them from the data.\n", + "\n", + "\n", + "
PhenomenonAssumed Exponent
Frequency of word use-1.2
Number of hits on website-1.4
US book sales-1.5
Intensity of wars-0.8
New worth of Americans-1.1
Github Stars??
\n", + "\n", + "\n", + "\n", + "### The estimation problem\n", + "\n", + "It is very easy to *overestimate* the true paramter $\\beta$. This is because the tail events (the events of 500+ stars) are very rare. For example, suppose in our Github dataset we only observe 100 samples. With very high probability (about 30%), all of these samples will have less than 31 stars. This is because\n", + "approximately 99% ( Number of all repos - Number of repos with greater than 31 stars)/(Number of all repos) of all repos have less than 31 stars. Thus, we would have no samples in our dataset from the *tail* of the distribution. If I then told you that there existed a repo with 36000+ stars, you would call me crazy, as it would be about 1000 times larger than your observed most popular repo. You would assign a very large $\\beta$ exponent to your dataset (recall large $\\beta$ means thinner tails). Similarly, with the same 30% probability we would not see repos more popular than 64 stars if we had a sample of 1000. Taking this to its logical conclusion, how confident should we be that there might not exist a theoretical repo that can attain 72000+ stars, or 150000+ stars, one which would push an estimated $\\beta$ down even more. \n", + "\n", + "\n", + "### Yule-Simon distribution\n", + "\n", + "The \n", + "\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "from scipy.special import beta\n", + "import pymc as pm\n", + "\n", + "\n", + "param = pm.Exponential(\"param\", 1)\n", + "\n", + "\n", + "@pm.stochastic(dtype=int, observed=True)\n", + "def yule_simon(value=repo_with_stars, rho=param):\n", + " \"\"\"test\"\"\"\n", + "\n", + " def logp(value, rho):\n", + " return np.log(rho) + np.log(beta(value, rho + 1))\n", + "\n", + " def random(rho):\n", + " W = stats.expon.rvs(scale=1. / rho)\n", + " return stats.geom.rvs(np.exp(-W))\n", + "\n", + "\n", + "model = pm.Model([param, yule_simon])\n", + "mcmc = pm.MCMC(model)\n", + "\n", + "mcmc.sample(10000, 8000);" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "ename": "TypeError", + "evalue": "yule_simon: computed log-probability [-20.51503062 -19.93158602 -18.31405136 -17.21386783 -16.32349938\n -15.53050299 -14.75010755 -13.96101721 -13.13877723 -12.2264853\n -11.23694781 -10.06769225 -8.63087616 -7.0237458 -5.33941252\n -3.44559118 -1.4738842 ] cannot be cast to float", + "output_type": "pyerr", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 8\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 9\u001b[0m \u001b[1;33m@\u001b[0m\u001b[0mmc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstochastic\u001b[0m\u001b[1;33m(\u001b[0m \u001b[0mdtype\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mint\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mobserved\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mTrue\u001b[0m \u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 10\u001b[1;33m \u001b[1;32mdef\u001b[0m \u001b[0myule_simon\u001b[0m\u001b[1;33m(\u001b[0m \u001b[0mvalue\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mrepo_with_stars\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrho\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mparam\u001b[0m \u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 11\u001b[0m \u001b[1;34m\"\"\"test\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 12\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32mc:\\Python27\\lib\\site-packages\\pymc\\InstantiationDecorators.pyc\u001b[0m in \u001b[0;36minstantiate_p\u001b[1;34m(__func__)\u001b[0m\n\u001b[0;32m 147\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0minstantiate_p\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0m__func__\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 148\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mparents\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_extract\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0m__func__\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkeys\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'Stochastic'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 149\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0m__class__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mvalue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mparents\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mparents\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 150\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 151\u001b[0m \u001b[0mkeys\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;34m'logp'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'random'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'rseed'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32mc:\\Python27\\lib\\site-packages\\pymc\\PyMCObjects.pyc\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, logp, doc, name, parents, random, trace, value, dtype, rseed, observed, cache_depth, plot, verbose, isdata, check_logp, logp_partial_gradients)\u001b[0m\n\u001b[0;32m 714\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mcheck_logp\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 715\u001b[0m \u001b[1;31m# Check initial value\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 716\u001b[1;33m \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlogp\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfloat\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 717\u001b[0m \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Stochastic \"\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;34m\"'s initial log-probability is %s, should be a float.\"\u001b[0m \u001b[1;33m%\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlogp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__repr__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 718\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32mc:\\Python27\\lib\\site-packages\\pymc\\PyMCObjects.pyc\u001b[0m in \u001b[0;36mget_logp\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 833\u001b[0m \u001b[0mlogp\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfloat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlogp\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 834\u001b[0m \u001b[1;32mexcept\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 835\u001b[1;33m \u001b[1;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;34m': computed log-probability '\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlogp\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;34m' cannot be cast to float'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 836\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 837\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mlogp\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[0mlogp\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mTypeError\u001b[0m: yule_simon: computed log-probability [-20.51503062 -19.93158602 -18.31405136 -17.21386783 -16.32349938\n -15.53050299 -14.75010755 -13.96101721 -13.13877723 -12.2264853\n -11.23694781 -10.06769225 -8.63087616 -7.0237458 -5.33941252\n -3.44559118 -1.4738842 ] cannot be cast to float" + ] + } + ], + "prompt_number": 83 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "def logp(value, rho):\n", + " return np.log(rho) + np.log(beta(value, rho + 1))\n", + "\n", + "beta(repo_with_stars, 1.3);" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "pyout", + "prompt_number": 84, + "text": [ + "array([ 3.96781274e-09, 7.12048348e-09, 3.60230434e-08,\n", + " 1.08508004e-07, 2.64859823e-07, 5.86390404e-07,\n", + " 1.28195491e-06, 2.82711390e-06, 6.44529816e-06,\n", + " 1.60819963e-05, 4.33570545e-05, 1.39960612e-04,\n", + " 5.90762159e-04, 2.95765600e-03, 1.59980669e-02,\n", + " 1.06728840e-01, 7.69230769e-01])" + ] + } + ], + "prompt_number": 84 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Exercises:\n", + "1. Distributions like the Normal distribution have very skinny tails. Compare the PDFs of the Normal versus a power-law distribution." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "x = np.linspace(1, 50, 200)\n", + "plt.plot(x, exp(-(x - 1) ** 2), label=\"Normal distribution\")\n", + "plt.plot(x, x ** (-2), label=r\"Power law, $\\beta = -2$\")\n", + "plt.plot(x, x ** (-1), label=r\"Power law, $\\beta = -1$\")\n", + "plt.legend();" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "pyout", + "prompt_number": 20, + "text": [ + "" + ] + }, + { + "output_type": "display_data", + "png": 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oImJiYrztJHFxcWRkZDB27FjvaK7JZGL58uXs2LGD0aNHk5WVxaOPPkp1dbXf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\nEvg4W1VBVawJ3fZeRUSivV1UKErcHQ7PIsJRhES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+ } + ], + "prompt_number": 20 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### References\n", + "1. Taleb, Nassim. The Black Swan. 1st edition. New York: Random House, 2007. Print." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "from IPython.core.display import HTML\n", + "\n", + "\n", + "def css_styling():\n", + " styles = open(\"../styles/custom.css\", \"r\").read()\n", + " return HTML(styles)\n", + "css_styling();" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "html": [ + "\n", + "" + ], + "output_type": "pyout", + "prompt_number": 1, + "text": [ + "" + ] + } + ], + "prompt_number": 1 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "import pymc as pm\n", + "\n", + "beta = pm.Uniform(\"beta\", -100, 100)\n", + "\n", + "\n", + "@pm.observed\n", + "def survival(value=y_, beta=beta):\n", + " return np.sum([value[i - 1] * np.log((i + 0.) ** beta - (i + 1.) ** beta) for i in range(1, 99)]);" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 167 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "model = pm.Model([survival, beta])\n", + "#map_ = pm.MAP( model )\n", + "# map_.fit()\n", + "\n", + "mcmc = pm.MCMC(model)\n", + "mcmc.sample(50000, 40000);" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + " \r", + "[****************100%******************] 50000 of 50000 complete" + ] + }, + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "\n" + ] + } + ], + "prompt_number": 168 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "from pymc.Matplot import plot as mcplot\n", + "mcplot(mcmc);" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "Plotting beta\n" + ] + }, + { + "output_type": "display_data", + "png": 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AE++CeL8pbY8fP15v5/cLicCfDl6De0MB/jyaL7P/xt0WJNUNQtLN+7hxJQOhHg4mMR76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8kmMPzg5t7dDWFn3t0NYObe3Q1t0Y5gEAAACPYphPcuzB2aGtHdraoq8d2tqh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\n" + } + ], + "prompt_number": 150 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "y = [(a >= i).sum() for i in range(1, 100)];" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 165 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "y_ = -np.diff(y)\n", + "print y_\n", + "\n", + "print y;" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "[41264 5572 1638 646 313 181 101 70 48 47 22 14\n", + " 14 14 7 11 4 6 4 0 1 0 2 0\n", + " 0 1 2 1 3 0 2 2 1 0 1 1\n", + " 2 1 0 1 0 0 0 0 0 0 0 0\n", + " 1 1 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0]\n", + "[50000, 8736, 3164, 1526, 880, 567, 386, 285, 215, 167, 120, 98, 84, 70, 56, 49, 38, 34, 28, 24, 24, 23, 23, 21, 21, 21, 20, 18, 17, 14, 14, 12, 10, 9, 9, 8, 7, 5, 4, 4, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n" + ] + } + ], + "prompt_number": 166 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "b = -2.3;" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 112 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "np.sum([y_[i - 1] * np.log((i + 0.) ** b - (i + 1.) ** b) for i in range(1, 7)]) + y[-1] * np.log(7);" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "pyout", + "prompt_number": 113, + "text": [ + "-13526.483069774908" + ] + } + ], + "prompt_number": 113 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "y_;" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "pyout", + "prompt_number": 114, + "text": [ + "array([48930, 940, 103, 19, 7, 1])" + ] + } + ], + "prompt_number": 114 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "np.append(y_, y[-1]);" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "pyout", + "prompt_number": 116, + "text": [ + "array([48930, 940, 103, 19, 7, 1, 0])" + ] + } + ], + "prompt_number": 116 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "mc.Uninformative?" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 129 + } + ], + "metadata": {} + } + ] +} \ No newline at end of file diff --git a/sandbox/Chapter10_/data/gh_forks.csv b/sandbox/Chapter10_/data/gh_forks.csv new file mode 100644 index 00000000..afd0d589 --- /dev/null +++ b/sandbox/Chapter10_/data/gh_forks.csv @@ -0,0 +1,34 @@ +0.000000000000000000e+00 +1.000000000000000000e+00 +2.000000000000000000e+00 +4.000000000000000000e+00 +8.000000000000000000e+00 +1.600000000000000000e+01 +3.200000000000000000e+01 +6.400000000000000000e+01 +1.280000000000000000e+02 +2.560000000000000000e+02 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00000000..bc6ea09d --- /dev/null +++ b/sandbox/Chapter10_/data/gh_stars_02112013.csv @@ -0,0 +1,34 @@ +0.000000000000000000e+00 +1.000000000000000000e+00 +2.000000000000000000e+00 +4.000000000000000000e+00 +8.000000000000000000e+00 +1.600000000000000000e+01 +3.200000000000000000e+01 +6.400000000000000000e+01 +1.280000000000000000e+02 +2.560000000000000000e+02 +5.120000000000000000e+02 +1.024000000000000000e+03 +2.048000000000000000e+03 +4.096000000000000000e+03 +8.192000000000000000e+03 +1.638400000000000000e+04 +3.276800000000000000e+04 +9.241220000000000000e+05 +2.138996000000000000e+06 +2.337881000000000000e+06 +2.461031000000000000e+06 +2.531702000000000000e+06 +2.571640000000000000e+06 +2.595213000000000000e+06 +2.608768000000000000e+06 +2.616559000000000000e+06 +2.621043000000000000e+06 +2.623388000000000000e+06 +2.624607000000000000e+06 +2.625171000000000000e+06 +2.625367000000000000e+06 +2.625423000000000000e+06 +2.625440000000000000e+06 +2.625444000000000000e+06 diff --git a/sandbox/Chapter10_/github_datapull.py b/sandbox/Chapter10_/github_datapull.py new file mode 100644 index 00000000..51380d42 --- /dev/null +++ b/sandbox/Chapter10_/github_datapull.py @@ -0,0 +1,72 @@ + +try: + import numpy as np + from requests import get + from bs4 import BeautifulSoup + + + + + stars_to_explore = ( 2**np.arange( -1, 16 ) ).astype("int") + forks_to_explore = ( 2**np.arange( -1, 16 ) ).astype("int") + repo_with_stars = np.ones_like( stars_to_explore ) + repo_with_forks = np.ones_like( forks_to_explore ) + + URL = "https://github.com/search" + print "Scrapping data from Github. Sorry Github..." + print "The data is contained in variables `foo_to_explore` and `repo_with_foo`" + print + print "stars first..." + payload = {"q":""} + for i, _star in enumerate(stars_to_explore): + payload["q"] = "stars:>=%d"%_star + r = get( URL, params = payload ) + soup = BeautifulSoup( r.text ) + try: + h3 = soup.find( class_="sort-bar").find( "h3" ).text #hopefully the github search results page plays nicely. + value = int( h3.split(" ")[2].replace(",", "" ) ) + except AttributeError as e: + #there might be less than 10 repos, so I'll count the number of display results + value = len( soup.findAll(class_= "mega-icon-public-repo" ) ) + + repo_with_stars[i] = value + print "number of repos with greater than or equal to %d stars: %d"%(_star, value ) + + #repo_with_stars = repo_with_stars.astype("float")/repo_with_stars[0] + + + print + print "forks second..." + payload = {"q":""} + for i, _fork in enumerate(stars_to_explore): + payload["q"] = "forks:>=%d"%_fork + r = get( URL, params = payload ) + soup = BeautifulSoup( r.text ) + try: + h3 = soup.find( class_="sort-bar").find( "h3" ).text #hopefully the github search results page plays nicely. + value = int( h3.split(" ")[2].replace(",", "" ) ) + except AttributeError as e: + #there might be less than 10 repos, so I'll count the number of display results + value = len( soup.findAll(class_= "mega-icon-public-repo" ) ) + + repo_with_forks[i] = value + print "number of repos with greater than or equal to %d forks: %d"%(_fork, value ) + + #repo_with_forks = repo_with_forks.astype("float")/repo_with_forks[0] + + np.savetxt( "data/gh_forks.csv", np.concatenate( [forks_to_explore, repo_with_forks], axis=1) ) + np.savetxt( "data/gh_stars.csv", np.concatenate( [stars_to_explore, repo_with_stars], axis=1) ) + +except ImportError as e: + print e + print "requests / BeautifulSoup not found. Using data pulled on Feburary 11, 2013" + _data = np.genfromtxt( "data/gh_forks.csv", delimiter = "," ) #check this. + forks_to_explore = _data[:,0] + repo_with_forks = _data[:,1] + + _data = np.genfromtxt( "data/gh_stars.csv", delimiter = "," ) #check this. + stars_to_explore = _data[:,0] + repo_with_stars = _data[:,1] + + + \ No newline at end of file diff --git a/sandbox/CommitDataForChapter1.ipynb b/sandbox/CommitDataForChapter1.ipynb new file mode 100644 index 00000000..bcc8acf2 --- /dev/null +++ b/sandbox/CommitDataForChapter1.ipynb @@ -0,0 +1,261 @@ +{ + "metadata": { + "name": "CommitDataForChapter1" + }, + "nbformat": 3, + "nbformat_minor": 0, + "worksheets": [ + { + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To replace the data in chapter 1 with this real time data." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "from requests import get\n", + "response = get('https://api.github.com/repos/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/stats/commit_activity').json()\n", + "weekly_totals = np.array(map(lambda x: x['total'], response))\n", + "weekly_totals = weekly_totals[np.where(weekly_totals)[0]] # gives me 52 weeks, but project started < 1 year ago so it backwards fills with 0s" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 6 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "count_data = weekly_totals\n", + "n_count_data = len(weekly_totals)\n", + "\n", + "plt.bar(range(n_count_data), weekly_totals);\n", + "print weekly_totals\n", + "print n_count_data" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "[13 16 7 26 5 46 25 6 4 28 19 22 13 28 7 10 8 15 13 6 36 5 4 8 8\n", + " 3 2 11 20 9 1]\n", + "31\n" + ] + }, + { + "output_type": "display_data", + "png": 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wk8xil5dFHjk8q7K4G1nk0TelGwAAGlO6B8JOMotdXhZ55PCsyuJuZJFH35RuAABoTOke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\nPvOZz1SV+7EaXioL92N1fPe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+ } + ], + "prompt_number": 27 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "import pymc as pm\n", + "\n", + "\n", + "alpha = 1.0 / count_data.mean() # Recall count_data is the\n", + " # variable that holds our txt counts\n", + "\n", + "lambda_1 = pm.Exponential(\"lambda_1\", alpha)\n", + "lambda_2 = pm.Exponential(\"lambda_2\", alpha)\n", + "\n", + "tau = pm.DiscreteUniform(\"tau\", lower=0, upper=n_count_data)" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 12 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "@pm.deterministic\n", + "def lambda_(tau=tau, lambda_1=lambda_1, lambda_2=lambda_2):\n", + " out = np.zeros(n_count_data)\n", + " out[:tau] = lambda_1 # lambda before tau is lambda1\n", + " out[tau:] = lambda_2 # lambda after tau is lambda2\n", + " return out" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 13 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "observation = pm.Poisson(\"obs\", lambda_, value=count_data, observed=True)\n", + "\n", + "model = pm.Model([observation, lambda_1, lambda_2, tau])" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 14 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "# Mysterious code to be explained in Chapter 3.\n", + "mcmc = pm.MCMC(model)\n", + "mcmc.sample(40000, 10000, 1)" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + " \r", + "[****************100%******************] 40000 of 40000 complete" + ] + }, + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "\n" + ] + } + ], + "prompt_number": 15 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "lambda_1_samples = mcmc.trace('lambda_1')[:]\n", + "lambda_2_samples = mcmc.trace('lambda_2')[:]\n", + "tau_samples = mcmc.trace('tau')[:]" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 16 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "figsize(12.5, 10)\n", + "# histogram of the samples:\n", + "\n", + "ax = plt.subplot(311)\n", + "ax.set_autoscaley_on(False)\n", + "\n", + "plt.hist(lambda_1_samples, histtype='stepfilled', bins=30, alpha=0.85,\n", + " label=\"posterior of $\\lambda_1$\", color=\"#A60628\", density=True)\n", + "plt.legend(loc=\"upper left\")\n", + "plt.title(r\"Posterior distributions of the variables \\\n", + " $\\lambda_1,\\;\\lambda_2,\\;\\tau$\")\n", + "\n", + "plt.xlabel(\"$\\lambda_1$ value\")\n", + "\n", + "ax = plt.subplot(312)\n", + "ax.set_autoscaley_on(False)\n", + "\n", + "plt.hist(lambda_2_samples, histtype='stepfilled', bins=30, alpha=0.85,\n", + " label=\"posterior of $\\lambda_2$\", color=\"#7A68A6\", density=True)\n", + "plt.legend(loc=\"upper left\")\n", + "\n", + "plt.xlabel(\"$\\lambda_2$ value\")\n", + "\n", + "plt.subplot(313)\n", + "\n", + "\n", + "#w = 1.0 / tau_samples.shape[0] * np.ones_like(tau_samples)\n", + "plt.hist(tau_samples, bins=n_count_data, alpha=1,\n", + " label=r\"posterior of $\\tau$\",\n", + " color=\"#467821\", rwidth=2.)\n", + "plt.xticks(np.arange(n_count_data))\n", + "\n", + "plt.legend(loc=\"upper left\")\n", + "plt.ylim([0, .75])\n", + "plt.xlabel(\"$\\tau$ (in days)\")\n", + "plt.ylabel(\"probability\")" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "pyout", + "prompt_number": 29, + "text": [ + "" + ] + }, + { + "output_type": "display_data", + "png": 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CqLs09XRDpw/rbvz0MT4Zhpl93qyB192Vi/nKw2zlYbbyMFt5mK1psehG3r6N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+ } + ], + "prompt_number": 29 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "n_count_data" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "pyout", + "prompt_number": 21, + "text": [ + "31" + ] + } + ], + "prompt_number": 21 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "lambda_2_samples" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "pyout", + "prompt_number": 28, + "text": [ + "array([ 7.7774429 , 7.7774429 , 7.7774429 , ..., 8.21626815,\n", + " 8.21626815, 8.80155957])" + ] + } + ], + "prompt_number": 28 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [], + "language": "python", + "metadata": {}, + "outputs": [] + } + ], + "metadata": {} + } + ] +} \ No newline at end of file diff --git a/sandbox/GithubUsers.ipynb b/sandbox/GithubUsers.ipynb new file mode 100644 index 00000000..20671284 --- /dev/null +++ b/sandbox/GithubUsers.ipynb @@ -0,0 +1,244 @@ +{ + "metadata": { + "name": "GithubUsers" + }, + "nbformat": 3, + "nbformat_minor": 0, + "worksheets": [ + { + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Example: Number of Github users\n", + "\n", + "\n", + "This is a fun example. Suppose we wish to predict how many sign-ups there are on Github.com. Officially, Github does not release an up-to-date count, and at last offical annoucment (January 2013) the count was 3 million. What if we wish to measure it today? We *could* [extrapolate future numbers from previous annoucements](http://redmonk.com/dberkholz/2013/01/21/github-will-hit-5-million-users-within-a-year/), but this uses little data and we could potentially be off by hundreds of thousands, and you are essentially just curve fitting complicated models. \n", + "\n", + "Instead, what we are going to use is `user id` numbers from real-time feeds on Github. The script `github_events.py` will pull the most recent 300 events from the [Github Public Timeline feed](https://github.com/timeline) (we'll be accessing data using their API). From this, we pull out the `user ids` associated with each event. We run the script below and display some output:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "%run github_events.py\n", + "\n", + "\n", + "print \"Some User ids from the latest events (push, star, fork etc.) on Github.\"\n", + "print ids[:10]\n", + "print\n", + "print \"Number of unique ids found: \", ids.shape[0]\n", + "print \"Largest user id: \", ids.max()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "ename": "KeyError", + "evalue": "'id'", + "output_type": "pyerr", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32mc:\\Python27\\lib\\site-packages\\IPython\\utils\\py3compat.pyc\u001b[0m in \u001b[0;36mexecfile\u001b[1;34m(fname, glob, loc)\u001b[0m\n\u001b[0;32m 169\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 170\u001b[0m \u001b[0mfilename\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfname\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 171\u001b[1;33m \u001b[1;32mexec\u001b[0m \u001b[0mcompile\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mscripttext\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfilename\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'exec'\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mglob\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mloc\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 172\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 173\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mexecfile\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0mwhere\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32mC:\\Users\\Cameron\\Dropbox\\My Work\\Probabilistic-Programming-and-Bayesian-Methods-for-Hackers\\Chapter2_MorePyMC\\github_events.py\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 22\u001b[0m \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mloads\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtext\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 23\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mevent\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 24\u001b[1;33m \u001b[0mids\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m(\u001b[0m \u001b[0mevent\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"actor\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"id\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 25\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m+=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 26\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mKeyError\u001b[0m: 'id'" + ] + }, + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "Some User ids from the latest events (push, star, fork etc.) on Github.\n", + "[1524995 1978503 1926860 1524995 3707208 374604 37715 770655 502701\n", + " 4349707]" + ] + }, + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "\n", + "\n" + ] + }, + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "Number of unique ids found: 300\n", + "Largest user id: " + ] + }, + { + "output_type": "stream", + "stream": "stdout", + "text": [ + " 2085773151\n" + ] + } + ], + "prompt_number": 1 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "figsize(12.5, 3)\n", + "plt.hist(ids, bins=45, alpha=0.9)\n", + "plt.title(\"Histogram of %d Github User ids\" % ids.shape[0]);\n", + "plt.xlabel(\"User id\")\n", + "plt.ylabel(\"Frequency\");" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'figsize' is not defined", + "output_type": "pyerr", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mfigsize\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m12.5\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhist\u001b[0m\u001b[1;33m(\u001b[0m \u001b[0mids\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbins\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m45\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0malpha\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m0.9\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtitle\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Histogram of %d Github User ids\"\u001b[0m\u001b[1;33m%\u001b[0m\u001b[0mids\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m)\u001b[0m\u001b[1;33m;\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mxlabel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"User id\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mylabel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Frequency\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m;\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mNameError\u001b[0m: name 'figsize' is not defined" + ] + } + ], + "prompt_number": 2 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There are some users with multiple events, but we are only interested in unique `user ids`, hence why we have less than 300 ids. Above I printed the largest `user id`. Why is this important? If Github assigns `user ids` serially, which is a fair assumption, then we **know** that there are certainly more users than that number. Remember, we are only looking at less than 300 individuals out of a much larger population, so it is unlikely that we would have sampled the most recent sign-up. \n", + "\n", + "At best, we can only estimate the total number of sign-ups. Let's get more familar with this problem. Consider a fictional website that we wish to estimate the number of users:\n", + "\n", + "1. Suppose we sampled only two individuals in a similar manner to above: the ids are 3 and 10 respectively. Would it be likely that the website has millions of users? Not very. Alternatively, it is more likely the website has less than 100 users. \n", + "\n", + "2. On the other hand, if the ids were 3 and 34 989, we might be more willing to guess there could possibly thousands, or millions of user sign-ups. We are not very confident in an estimate, due to a lack of data.\n", + "\n", + "3. If we sample thousands of users, and the maximum `user id` is still 34 989, then is seems likely that the total number of sign ups is near 35 000. Hence our inference should be more confident.\n", + "\n", + "\n", + "We make the following assumption:\n", + "\n", + "**Assumption:** Every user is equally likely to perform an event. Clearly, looking at the above histogram, this assumption is violated. The participation on Github is skewed towards early adopters, likely as these early-adopting individuals have a) more invested in Github, and b) saw the value earlier in Github, therefore are more interested in it. The distribution is also skewed towards new sign ups, who likely signed up just to push a project. \n", + "\n", + "To create a Bayesian model of this is easy. Based on the above assumption, all `user_ids` sampled are from a `DiscreteUniform` model, with lower bound 1 and an unknown upperbound. We don't have a strong belief about what the upper-bound might be, but we do know it will be larger than `ids.max()`. \n", + "\n", + "Working with such large numbers can cause numerical problem, hence we will scale everything by a million. Thus, instead of a `DiscreteUniform`, we will used a `Uniform`:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "FACTOR = 1000000.\n", + "\n", + "import pymc as pm\n", + "\n", + "upper_bound = pm.Uniform(\"n_sign_ups\", ids.max() / FACTOR, (ids.max()) / FACTOR + 1)\n", + "obs = pm.Uniform(\"obs\", 0, upper_bound, value=ids / FACTOR, observed=True)\n", + "\n", + "# code to be examplained in Chp. 3.\n", + "mcmc = pm.MCMC([upper_bound, obs])\n", + "mcmc.sample(100000, 45000)" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "ename": "ZeroProbability", + "evalue": "Stochastic obs's value is outside its support,\n or it forbids its parents' current values.", + "output_type": "pyerr", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mZeroProbability\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[0mupper_bound\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mUniform\u001b[0m\u001b[1;33m(\u001b[0m \u001b[1;34m\"n_sign_ups\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mids\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m/\u001b[0m\u001b[0mFACTOR\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mids\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m/\u001b[0m\u001b[0mFACTOR\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m \u001b[0mobs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mUniform\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"obs\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mupper_bound\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mids\u001b[0m\u001b[1;33m/\u001b[0m\u001b[0mFACTOR\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mobserved\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mTrue\u001b[0m \u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 7\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 8\u001b[0m \u001b[1;31m#code to be examplained in Chp. 3.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32mc:\\Python27\\lib\\site-packages\\pymc\\distributions.pyc\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, *args, **kwds)\u001b[0m\n\u001b[0;32m 268\u001b[0m \u001b[0mrandom\u001b[0m \u001b[1;33m=\u001b[0m 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272\u001b[0m \u001b[0mnew_class\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32mc:\\Python27\\lib\\site-packages\\pymc\\PyMCObjects.pyc\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, logp, doc, name, parents, random, trace, value, dtype, rseed, observed, cache_depth, plot, verbose, isdata, check_logp, logp_partial_gradients)\u001b[0m\n\u001b[0;32m 714\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mcheck_logp\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 715\u001b[0m \u001b[1;31m# Check initial value\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 716\u001b[1;33m \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlogp\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfloat\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 717\u001b[0m \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Stochastic \"\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;34m\"'s initial log-probability is %s, should be a float.\"\u001b[0m \u001b[1;33m%\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlogp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__repr__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 718\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32mc:\\Python27\\lib\\site-packages\\pymc\\PyMCObjects.pyc\u001b[0m in \u001b[0;36mget_logp\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 846\u001b[0m \u001b[1;32mraise\u001b[0m \u001b[0mZeroProbability\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0merrmsg\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;34m\"\\nValue: %s\\nParents' values:%s\"\u001b[0m \u001b[1;33m%\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_value\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_parents\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 847\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 848\u001b[1;33m \u001b[1;32mraise\u001b[0m \u001b[0mZeroProbability\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0merrmsg\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 849\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 850\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mlogp\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mZeroProbability\u001b[0m: Stochastic obs's value is outside its support,\n or it forbids its parents' current values." + ] + } + ], + "prompt_number": 3 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "from scipy.stats.mstats import mquantiles\n", + "\n", + "samples = mcmc.trace(\"n_sign_ups\")[:]\n", + "\n", + "hist(samples, bins=100,\n", + " label=\"Uniform prior\",\n", + " density=True, alpha=0.8,\n", + " histtype=\"stepfilled\", color=\"#7A68A6\");\n", + "\n", + "quantiles_mean = np.append(mquantiles(samples, [0.05, 0.5, 0.95]), samples.mean())\n", + "print \"Quantiles: \", quantiles_mean[:3]\n", + "print \"Mean: \", quantiles_mean[-1]\n", + "plt.vlines(quantiles_mean, 0, 33,\n", + " linewidth=2, linestyles=[\"--\", \"--\", \"--\", \"-\"],\n", + " )\n", + "plt.title(\"Posterior distribution of total number of Github users\")\n", + "plt.xlabel(\"number of users (in millions)\")\n", + "plt.legend()\n", + "plt.xlim(ids.max() / FACTOR - 0.01, ids.max() / FACTOR + 0.12);" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'mcmc' is not defined", + "output_type": "pyerr", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mscipy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstats\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmstats\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mmquantiles\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0msamples\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmcmc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtrace\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"n_sign_ups\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m hist(samples, bins = 100, \n", + "\u001b[1;31mNameError\u001b[0m: name 'mcmc' is not defined" + ] + } + ], + "prompt_number": 4 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Above we have plotted the posterior distribution. Note that there is no posterior probability assigned to the number of users being less than `ids.max()`. That is good, as it would be an impossible situation. \n", + "\n", + "The three dashed vertical bars, from left to right, are the 5%, 50% and 95% quantitle lines. That is, 5% of the probability is before the first line, 50% before the second and 95% before the third. The 50% quantitle is also know as the *median* and is a better measure of centrality than the mean for heavily skewed distributions like this one. The solid line is the posterior distribution's mean.\n", + "\n", + "So what can we say? Using the data above, there is a 95% chance that there are less than 4.4 million users, and is probably around 4.36 million users. I was wondering how accurate this figure was. At the time of this writing, it seems a bit high considering only five months prior the number was at 3 million:\n", + "\n", + "\n", + "

Last night @github crossed the 3M user mark #turntup

— Rick Bradley (@rickbradley) January 15, 2013
\n", + "\n", + "\n", + "\n", + "I thought perhaps the `user_id` parameter was being used liberally to users/bots/changed names etc, so I contacted Github Support about it:\n", + "\n", + "

@cmrn_dp User IDs are assigned to new users/organizations, whether they\u2019re controlled by humans, groups, or bots.

— GitHub Support (@GitHubHelp) May 6, 2013
\n", + "\n", + "\n", + "\n", + "So we may be overestimating by including organizations, which perhaps should not be counted as users. TODO: estimate the number of organizations. Any takers?" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [], + "language": "python", + "metadata": {}, + "outputs": [] + } + ], + "metadata": {} + } + ] +} \ No newline at end of file diff --git a/sandbox/SpaceShuttleBayesFactor.ipynb b/sandbox/SpaceShuttleBayesFactor.ipynb new file mode 100644 index 00000000..a26ccc13 --- /dev/null +++ b/sandbox/SpaceShuttleBayesFactor.ipynb @@ -0,0 +1,332 @@ +{ + "metadata": { + "name": "SpaceShuttleBayesFactor" + }, + "nbformat": 3, + "nbformat_minor": 0, + "worksheets": [ + { + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "###Bayes Factor for Log Regression/" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#####Is there really a relationship between failure and temperature?\n", + "\n", + "An critism of our above analysis is that *assumed* that the relationship followed a logistic model, this we implictly assumed that the probabilities change over temperature. Let's look at the data again. (Top figure)\n", + "\n", + "Could it be that infact this particular sequence of defects occured by chance? After all, I can produce similar plots. (Bottom figure) This might explain the large overlap in temperatures." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "figsize(12.5, 6)\n", + "subplot(211)\n", + "plt.scatter(challenger_data[:, 0], challenger_data[:, 1], s=75,\n", + " color=\"k\", alpha=0.5)\n", + "plt.yticks([0, 1])\n", + "plt.ylabel(\"Damage Incident?\")\n", + "plt.title(\"(Real) Defects of the Space Shuttle O-Rings vs temperature\")\n", + "\n", + "subplot(212)\n", + "n = challenger_data.shape[0]\n", + "plt.scatter(challenger_data[:, 0], stats.bernoulli.rvs(0.6, size=n),\n", + " s=75, color=\"k\", alpha=0.5)\n", + "plt.yticks([0, 1])\n", + "plt.ylabel(\"Damage Incident?\")\n", + "plt.xlabel(\"Outside temperature (Farhenhit)\")\n", + "plt.title(\"(Artificial) Defects of the Space Shuttle O-Rings vs \\\n", + "temperature\")" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'figsize' is not defined", + "output_type": "pyerr", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mfigsize\u001b[0m\u001b[1;33m(\u001b[0m \u001b[1;36m12.5\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m6\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2\u001b[0m \u001b[0msubplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m211\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3\u001b[0m plt.scatter( challenger_data[:,0], challenger_data[:,1], s = 75, \\\n\u001b[0;32m 4\u001b[0m color=\"k\", alpha = 0.5) \n\u001b[0;32m 5\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0myticks\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mNameError\u001b[0m: name 'figsize' is not defined" + ] + } + ], + "prompt_number": 1 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Introducing the Bayes factor \n", + "\n", + "The *Bayes factor* is a measure comparing two models. In our example, on the one hand we believe that temperature plays an important role in the probability of O-ring failures. On the other hand, we believe that there is no significant connection, and the pattern appears by coincidence. We can compare these model using the ratio of the *probabilities of observing the data, given the model*:\n", + "\n", + "$$ \\frac{ P( \\text{observations} | \\text{Model}_1 ) }{ P( \\text{observations} | \\text{Model}_2 ) }$$\n", + "\n", + "\n", + "Calculating this is not at all obvious. For simplicity, let's select a set of parameters from the posterior distribution (which is tantamount to selecting a particular model)." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "alpha_hat = alpha_samples[0, 0] # select the first alpha sample\n", + "beta_hat = beta_samples[0, 0] # select the first beta sample\n", + "\n", + "p_hat = logistic(temperature, beta_hat, alpha_hat)\n", + "print \"estimates of probability at observed temperature, defects: \"\n", + "print np.array(zip(p_hat, temperature, D))[:3, :]\n", + "print \"...\"" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'alpha_samples' is not defined", + "output_type": "pyerr", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0malpha_hat\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0malpha_samples\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;31m#select the first alpha sample\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2\u001b[0m \u001b[0mbeta_hat\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbeta_samples\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;31m#select the first beta sample\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[0mp_hat\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlogistic\u001b[0m\u001b[1;33m(\u001b[0m \u001b[0mtemperature\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbeta_hat\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0malpha_hat\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[1;32mprint\u001b[0m \u001b[1;34m\"estimates of probability at observed temperature, defects: \"\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mNameError\u001b[0m: name 'alpha_samples' is not defined" + ] + } + ], + "prompt_number": 2 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To calculate the numerator in the Bayes factor, we start by **assuming a model**, in our case assume `alpha_hat`, `beta_hat` are true, and calculate the probability of the defects we observe, equal to the product of:\n", + "\n", + "$$ P(\\; \\text{Ber}(\\; p(t_i \\; | \\; \\hat{\\beta}, \\hat{\\alpha} )\\; ) = D_i \\; ),\\;\\; i=1..N $$\n", + "\n", + "For example, using the output above, the first computation, $i=0$, would look like \n", + "\n", + "\\begin{align}\n", + "& p = p(t_1 \\; | \\; \\hat{\\beta}, \\hat{\\alpha} ) = 0.667 \\\\\\\\\n", + "& d = D_0 = 0 \\\\\\\\\n", + "& \\Rightarrow \\; P( \\; \\text{Ber}(p) = d ) = ?? \n", + "\\end{align}\n", + "\n", + "The probability of this ocurring is $(1-0.667) = 0.333\\; $ (Recall the definition of a Bernoulli random variable $\\text{Ber}(p)$: it is equal to $1$ with probability $p$ and equal to 0 with probability $1-p$). As each observation is independent, we multiply all the observations together. A clever way to do this for a specific $i$ is, recalling the $D_i$ can only be 1 or 0:\n", + "\n", + "$$\\left( p(t_1 \\; | \\; \\hat{\\beta}, \\hat{\\alpha} )\\right)^{D_i} \\left( 1 - p(t_1 \\; | \\; \\hat{\\beta}, \\hat{\\alpha} ) \\right)^{(1-D_i)}$$\n", + "\n", + "\n", + "(it is possible that the quantity can overflow, so it is recommended to take the $\\log$ of the above::\n", + "\n", + "$$ D_i\\log( p(t_1 \\; | \\; \\hat{\\beta}, \\hat{\\alpha} ) ) + (1-D_i)\\log( 1- p(t_1 \\; | \\; \\hat{\\beta}, \\hat{\\alpha} ) ) $$\n", + "\n", + "and sum the terms instead of multiplying. Be sure to use this transform for both models you are comparing.)" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "print p_hat\n", + "_product = p_hat ** (D) * (1 - p_hat) ** (1 - D)\n", + "prob_given_model_1 = _product.prod()\n", + "print \"probability of observations, model 1: %.10f\" % prob_given_model_1" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'p_hat' is not defined", + "output_type": "pyerr", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mprint\u001b[0m \u001b[0mp_hat\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2\u001b[0m \u001b[0m_product\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mp_hat\u001b[0m\u001b[1;33m**\u001b[0m\u001b[1;33m(\u001b[0m \u001b[0mD\u001b[0m \u001b[1;33m)\u001b[0m\u001b[1;33m*\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m-\u001b[0m\u001b[0mp_hat\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m**\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m-\u001b[0m\u001b[0mD\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[0mprob_given_model_1\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_product\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprod\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[1;32mprint\u001b[0m \u001b[1;34m\"probability of observations, model 1: %.10f\"\u001b[0m\u001b[1;33m%\u001b[0m\u001b[0mprob_given_model_1\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mNameError\u001b[0m: name 'p_hat' is not defined" + ] + } + ], + "prompt_number": 3 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The second model we are comparing against is that $\\beta=0$, i.e. there is no relationship between probabilites and temperature. We perform the same calculations as above. Notice that `beta=0` here in the below PyMC code:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "beta = 0\n", + "alpha = pm.Normal(\"alpha\", 0, 0.001, value=0)\n", + "\n", + "\n", + "@pm.deterministic\n", + "def p(temp=temperature, alpha=alpha, beta=beta):\n", + " return 1.0 / (1. + np.exp(beta * temperature + alpha))\n", + "\n", + "\n", + "observed = pm.Bernoulli(\"bernoulli_obs\", p,\n", + " value=D, observed=True)\n", + "\n", + "model = pm.Model({\"observed\": observed, \"beta\": beta, \"alpha\": alpha})\n", + "\n", + "# mysterious code to be explained in Chapter 3\n", + "map_ = pm.MAP(model)\n", + "map_.fit()\n", + "mcmc = pm.MCMC(model)\n", + "mcmc.sample(260000, 220000, 3)\n", + "######\n", + "\n", + "alpha_samples_model_2 = mcmc.trace(\"alpha\")[:, None]\n", + "alpha_hat = alpha_samples_model_2[0] # use the first 'model'\n", + "beta_hat = 0\n", + "\n", + "p_hat = logistic(temperature, beta_hat, alpha_hat)\n", + "print \"estimates of probability at observed temperature, defects: \"\n", + "print np.array(zip(p_hat, temperature, D))[:3, :]\n", + "print\n", + "print \"Notice the probability is constant for all temperatures?\"" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'mc' is not defined", + "output_type": "pyerr", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[0mbeta\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0malpha\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mNormal\u001b[0m\u001b[1;33m(\u001b[0m \u001b[1;34m\"alpha\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0.001\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m0\u001b[0m \u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 3\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[1;33m@\u001b[0m\u001b[0mmc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdeterministic\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mp\u001b[0m\u001b[1;33m(\u001b[0m \u001b[0mtemp\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtemperature\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0malpha\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0malpha\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbeta\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbeta\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mNameError\u001b[0m: name 'mc' is not defined" + ] + } + ], + "prompt_number": 4 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "# compute the probability of observations given the model_2\n", + "\n", + "_product = p_hat ** (D) * (1 - p_hat) ** (1 - D)\n", + "prob_given_model_2 = _product.prod()\n", + "print \"probability of observations, model 2: %.10f\" % prob_given_model_2" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'p_hat' is not defined", + "output_type": "pyerr", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;31m#compute the probability of observations given the model_2\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0m_product\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mp_hat\u001b[0m\u001b[1;33m**\u001b[0m\u001b[1;33m(\u001b[0m \u001b[0mD\u001b[0m \u001b[1;33m)\u001b[0m\u001b[1;33m*\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m-\u001b[0m\u001b[0mp_hat\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m**\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m-\u001b[0m\u001b[0mD\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 4\u001b[0m \u001b[0mprob_given_model_2\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_product\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprod\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[1;32mprint\u001b[0m \u001b[1;34m\"probability of observations, model 2: %.10f\"\u001b[0m\u001b[1;33m%\u001b[0m\u001b[0mprob_given_model_2\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mNameError\u001b[0m: name 'p_hat' is not defined" + ] + } + ], + "prompt_number": 5 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "print \"Bayes factor = %.3f\" % (prob_given_model_1 / prob_given_model_2)" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'prob_given_model_1' is not defined", + "output_type": "pyerr", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mprint\u001b[0m \u001b[1;34m\"Bayes factor = %.3f\"\u001b[0m\u001b[1;33m%\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mprob_given_model_1\u001b[0m\u001b[1;33m/\u001b[0m\u001b[0mprob_given_model_2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[1;31mNameError\u001b[0m: name 'prob_given_model_1' is not defined" + ] + } + ], + "prompt_number": 7 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Is this good? Below is a chart that can be used to compare the computed Bayes factors to a degree of confidence in M1. \n", + "\n", + "
Bayes factorSupporting M1
<1:1 Negative (supports M2)
1:1 to 3:1 Barely worth mentioning
3:1 to 10:1 Substantial
10:1 to 30:1 Strong
30:1 to 100:1 Very strong
> 100:1 Decisive
\n", + "\n", + "We are not done yet. If you recall, we selected only one model, but recall we actually have a distribution of possible models (sequential pairs of ($\\beta, \\alpha$) from the posterior distributions). So to be correct we need to average over samples from the posterior:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "p = logistic(temperature[None, :], beta_samples, alpha_samples)\n", + "_product = p ** (D) * (1 - p) ** (1 - D)\n", + "prob_given_model_1 = _product.prod(axis=1).mean()\n", + "print \"expected prob. of obs., given model 1: %.10f\" % prob_given_model_1\n", + "\n", + "\n", + "p_model_2 = logistic(temperature[:, None],\n", + " np.zeros_like(temperature), alpha_samples_model_2)\n", + "\n", + "_product = p_model_2 ** (D) * (1 - p_model_2) ** (1 - D)\n", + "prob_given_model_2 = _product.prod(axis=1).mean()\n", + "print \"expected prob. of obs., given model 2: %.10f\" % prob_given_model_2\n", + "print\n", + "print \"Bayes factor: %.3f\" % (prob_given_model_1 / prob_given_model_2)" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'logistic' is not defined", + "output_type": "pyerr", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mp\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlogistic\u001b[0m\u001b[1;33m(\u001b[0m \u001b[0mtemperature\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mNone\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbeta_samples\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0malpha_samples\u001b[0m \u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2\u001b[0m \u001b[0m_product\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mp\u001b[0m\u001b[1;33m**\u001b[0m\u001b[1;33m(\u001b[0m \u001b[0mD\u001b[0m \u001b[1;33m)\u001b[0m\u001b[1;33m*\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m-\u001b[0m\u001b[0mp\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m**\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m-\u001b[0m\u001b[0mD\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[0mprob_given_model_1\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_product\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprod\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[1;32mprint\u001b[0m \u001b[1;34m\"expected prob. of obs., given model 1: %.10f\"\u001b[0m\u001b[1;33m%\u001b[0m\u001b[0mprob_given_model_1\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mNameError\u001b[0m: name 'logistic' is not defined" + ] + } + ], + "prompt_number": 9 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It looks we have a pretty good model, and temperature *is* significant. Look at you, you're a rocket scientist now." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [], + "language": "python", + "metadata": {}, + "outputs": [] + } + ], + "metadata": {} + } + ] +} \ No newline at end of file diff --git a/sandbox/Sum of Gaussian Distributions + noise.ipynb b/sandbox/Sum of Gaussian Distributions + noise.ipynb new file mode 100644 index 00000000..4e672231 --- /dev/null +++ b/sandbox/Sum of Gaussian Distributions + noise.ipynb @@ -0,0 +1,272 @@ +{ + "metadata": { + "name": "Sum of Gaussian Distributions + noise" + }, + "nbformat": 3, + "nbformat_minor": 0, + "worksheets": [ + { + "cells": [ + { + "cell_type": "code", + "collapsed": false, + "input": [ + "import pymc as pm\n", + "import numpy as np\n", + "import pylab as pl\n", + "\n", + "\n", + "def GaussFunc(x, amplitude, centroid, sigma):\n", + " return amplitude * np.exp(-0.5 * ((x - centroid) / sigma) ** 2)\n", + "\n", + "wavelength = np.arange(5000, 5050, 0.02)\n", + "\n", + "# Profile 1\n", + "centroid_one = 5025.0\n", + "sigma_one = 2.2\n", + "height_one = 0.8\n", + "profile1 = GaussFunc(wavelength, height_one, centroid_one, sigma_one, )\n", + "\n", + "# Profile 2\n", + "centroid_two = 5027.0\n", + "sigma_two = 1.2\n", + "height_two = 0.5\n", + "profile2 = GaussFunc(wavelength, height_two, centroid_two, sigma_two, )\n", + "\n", + "# Measured values\n", + "noise = np.random.normal(0.0, 0.02, len(wavelength))\n", + "combined = profile1 + profile2 + noise\n", + "\n", + "# If you want to plot what this looks like\n", + "pl.plot(wavelength, combined, label=\"Measured\")\n", + "pl.plot(wavelength, profile1, color='red', linestyle='dashed', label=\"1\")\n", + "pl.plot(wavelength, profile2, color='green', linestyle='dashed', label=\"2\")\n", + "pl.title(\"Feature One and Two\")\n", + "pl.legend()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "pyout", + "prompt_number": 1, + "text": [ + "" + ] + }, + { + "output_type": "display_data", + "png": 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D8HRS8Odhmt6AUqVudB1Fu9AhdGE+xAowgeA2Hz68jHeqLiGfPLlOmcqnn0Zy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\n+VkSgJ8A99JCKuRbUUi66c81D5mSyvac83emvUq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+ } + ], + "prompt_number": 1 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "# Unknowns\n", + "\n", + "est_centroid_one = pm.Uniform(\"est_centroid_one\", 5000, 5050)\n", + "est_centroid_two = pm.Uniform(\"est_centroid_two\", 5000, 5050)\n", + "\n", + "est_sigma_one = pm.Uniform(\"est_sigma_one\", 0, 5)\n", + "est_sigma_two = pm.Uniform(\"est_sigma_two\", 0, 5)\n", + "\n", + "est_height_one = pm.Uniform(\"est_height_one\", 0, 5)\n", + "est_height_two = pm.Uniform(\"est_height_two\", 0, 5)\n", + "\n", + "std_deviation = 1. / mc.Uniform(\"std\", 0, 1) ** 2\n", + "\n", + "# Set up the inference\n", + "\n", + "\n", + "@pm.deterministic(trace=False)\n", + "def est_profile_1(x=wavelength, centroid=est_centroid_one, sigma=est_sigma_one, height=est_height_one):\n", + " return GaussFunc(x, height, centroid, sigma)\n", + "\n", + "\n", + "@pm.deterministic(trace=False)\n", + "def est_profile_2(x=wavelength, centroid=est_centroid_two, sigma=est_sigma_two, height=est_height_two):\n", + " return GaussFunc(x, height, centroid, sigma)\n", + "\n", + "\n", + "@pm.deterministic(trace=False)\n", + "def mean(profile_1=est_profile_1, profile_2=est_profile_2):\n", + " return profile_1 + profile_2\n", + "\n", + "\n", + "observations = pm.Normal(\"obs\", mean, std_deviation, value=combined, observed=True)" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 5 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "model = pm.Model([est_centroid_one,\n", + " est_centroid_two,\n", + " est_height_one,\n", + " est_height_two,\n", + " est_sigma_one,\n", + " est_sigma_two,\n", + " std_deviation])\n", + "\n", + "map_ = pm.MAP(model)\n", + "map_.fit()" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 17 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "mcmc = pm.MCMC(model)\n", + "mcmc.sample(70000, 60000)" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + " \r", + "[****************100%******************] 70000 of 70000 complete" + ] + }, + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "\n" + ] + } + ], + "prompt_number": 18 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "mcplot(mcmc)" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "Plotting est_height_one\n", + "Plotting" + ] + }, + { + "output_type": "stream", + "stream": "stdout", + "text": [ + " est_sigma_two\n", + "Plotting" + ] + }, + { + "output_type": "stream", + "stream": "stdout", + "text": [ + " est_height_two\n", + "Plotting" + ] + }, + { + "output_type": "stream", + "stream": "stdout", + "text": [ + " est_centroid_two\n", + "Plotting" + ] + }, + { + "output_type": "stream", + "stream": "stdout", + "text": [ + " est_centroid_one\n", + "Plotting" + ] + }, + { + "output_type": "stream", + "stream": "stdout", + "text": [ + " est_sigma_one\n", + "Plotting" + ] + }, + { + "output_type": "stream", + "stream": "stdout", + "text": [ + " std\n" + ] + }, + { + "output_type": "display_data", + "png": 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ERISFhRlVFiFztbBSpayWobm8Un6vlZSUaGlJIGhH2/NLo72+oKAA3t4tAbeK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lFjzElnPF+HCSJwb1tqGvXy6qwXu/5imVrc27ilmTo/Hb7WqlNhV9WzTUBQuH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\nyeNXrlzh22+/ZdasWQC4ubmZdLy0pnK/Sy3QSl20Ug/QVl3sLSIigk2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7WIQchukY0guq8JyXneqGBuSLUw9x/mElvnzeF7KPekntnCok97z4sFKp89Xd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DBmHy5MmIiIjQvEUSKCtsZ2slua+psfjHxawn2NRKQSBj+mTM8fv4/HKO+LE7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z/3hw84Gu8Fmu9dngPEIWDH5IZXI8aZTB1cWRV3nB8lSJYIjTlDxVVU+XZbl8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\nwrlz5xAREYGkpCR88803AIAmTZrg/v376Ny5MxiGgZ2dHdtjV/N8NY+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xiG9jwY3BMAxK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HWEwPN7w9NhAA8z2N7NYRH00Ooa3beq4U312mJ3wdHtAJHZzsMTbEAxHeHQW3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B1DiC1vzYUbakHS7IER9UAsuRkk+T8JlzPqQkiz1wSWXOnKF/u9ZUmiGLkKls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+ } + ], + "prompt_number": 19 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "mcplot?" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 20 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [], + "language": "python", + "metadata": {}, + "outputs": [] + } + ], + "metadata": {} + } + ] +} \ No newline at end of file diff --git a/sandbox/github_events.py b/sandbox/github_events.py new file mode 100644 index 00000000..85dd9a4a --- /dev/null +++ b/sandbox/github_events.py @@ -0,0 +1,27 @@ +#github_events.py + +try: + from json import loads + + import numpy as np + from requests import get + +except ImportError as e: + raise e + + +URL = "https://api.github.com/events" + +#github allows up to 10 pages of 30 events, but we will only keep the unique ones. +ids = np.empty(300, dtype=int) + +k = 0 +for page in range(10,0, -1): + + r = get( URL, params = {"page":page} ) + data = loads(r.text) + for event in data: + ids[k] = ( event["actor"]["id"] ) + k+=1 + +ids = np.unique( ids.astype(int) ) \ No newline at end of file diff --git a/styles/bmh_matplotlibrc.json b/styles/bmh_matplotlibrc.json new file mode 100644 index 00000000..aaa31dca --- /dev/null +++ b/styles/bmh_matplotlibrc.json @@ -0,0 +1,22 @@ +{ + "lines.linewidth": 2.0, + "axes.edgecolor": "#bcbcbc", + "patch.linewidth": 0.5, + "legend.fancybox": true, + "axes.prop_cycle": cycler('color', [ + "#348ABD", + "#A60628", + "#7A68A6", + "#467821", + "#CF4457", + "#188487", + "#E24A33" + ]), + "axes.facecolor": "#eeeeee", + "axes.labelsize": "large", + "axes.grid": true, + "patch.edgecolor": "#eeeeee", + "axes.titlesize": "x-large", + "svg.fonttype": "path", + "examples.directory": "" +} diff --git a/styles/codemirror.css b/styles/codemirror.css deleted file mode 100644 index 476d682f..00000000 --- a/styles/codemirror.css +++ /dev/null @@ -1,107 +0,0 @@ -.CodeMirror { - line-height: 1em; - font-family: monospace; -} - -.CodeMirror-scroll { - overflow: auto; - height: 300px; - /* This is needed to prevent an IE[67] bug where the scrolled content - is visible outside of the scrolling box. */ - position: relative; -} - -.CodeMirror-gutter { - position: absolute; left: 0; top: 0; - z-index: 10; - background-color: #f7f7f7; - border-right: 1px solid #eee; - min-width: 2em; - height: 100%; -} -.CodeMirror-gutter-text { - color: #aaa; - text-align: right; - padding: .4em .2em .4em .4em; - white-space: pre !important; -} -.CodeMirror-lines { - padding: .4em; -} - -.CodeMirror pre { - -moz-border-radius: 0; - -webkit-border-radius: 0; - -o-border-radius: 0; - border-radius: 0; - border-width: 0; margin: 0; padding: 0; background: transparent; - font-family: inherit; - font-size: 12px; - padding: 0; margin: 0; - white-space: pre; - word-wrap: normal; -} - -.CodeMirror-wrap pre { - word-wrap: break-word; - white-space: pre-wrap; -} -.CodeMirror-wrap .CodeMirror-scroll { - overflow-x: hidden; -} - -.CodeMirror textarea { - outline: none !important; -} - -.CodeMirror pre.CodeMirror-cursor { - z-index: 10; - position: absolute; - visibility: hidden; - border-left: 1px solid black; -} -.CodeMirror-focused pre.CodeMirror-cursor { - visibility: visible; -} - -div.CodeMirror-selected { background: #d9d9d9; } -.CodeMirror-focused div.CodeMirror-selected { background: #d7d4f0; } - -.CodeMirror-searching { - background: #ffa; - background: rgba(255, 255, 0, .4); -} - -/* Default theme */ - -.cm-s-default span.cm-keyword {color: #708;} -.cm-s-default span.cm-atom {color: #219;} -.cm-s-default span.cm-number {color: #164;} -.cm-s-default span.cm-def {color: #00f;} -.cm-s-default span.cm-variable {color: black;} -.cm-s-default span.cm-variable-2 {color: #05a;} -.cm-s-default span.cm-variable-3 {color: #085;} -.cm-s-default span.cm-property {color: black;} -.cm-s-default span.cm-operator {color: black;} -.cm-s-default span.cm-comment {color: #a50;} -.cm-s-default span.cm-string {color: #a11;} -.cm-s-default span.cm-string-2 {color: #f50;} -.cm-s-default span.cm-meta {color: #555;} -.cm-s-default span.cm-error {color: #f00;} -.cm-s-default span.cm-qualifier {color: #555;} -.cm-s-default span.cm-builtin {color: #30a;} -.cm-s-default span.cm-bracket {color: #cc7;} -.cm-s-default span.cm-tag {color: #170;} -.cm-s-default span.cm-attribute {color: #00c;} -.cm-s-default span.cm-header {color: #a0a;} -.cm-s-default span.cm-quote {color: #090;} -.cm-s-default span.cm-hr {color: #999;} -.cm-s-default span.cm-link {color: #00c;} - -span.cm-header, span.cm-strong {font-weight: bold;} -span.cm-em {font-style: italic;} -span.cm-emstrong {font-style: italic; font-weight: bold;} -span.cm-link {text-decoration: underline;} - -div.CodeMirror span.CodeMirror-matchingbracket {color: #0f0;} -div.CodeMirror span.CodeMirror-nonmatchingbracket {color: #f22;} diff --git a/styles/custom.css b/styles/custom.css new file mode 100644 index 00000000..ac5a409f --- /dev/null +++ b/styles/custom.css @@ -0,0 +1,76 @@ + + diff --git a/styles/initmathjax.js b/styles/initmathjax.js deleted file mode 100644 index 57e82e60..00000000 --- a/styles/initmathjax.js +++ /dev/null @@ -1,86 +0,0 @@ -//---------------------------------------------------------------------------- -// Copyright (C) 2008-2011 The IPython Development Team -// -// Distributed under the terms of the BSD License. The full license is in -// the file COPYING, distributed as part of this software. -//---------------------------------------------------------------------------- - -//============================================================================ -// MathJax initialization -//============================================================================ - -var IPython = (function (IPython) { - - var init_mathjax = function () { - if (window.MathJax) { - // MathJax loaded - MathJax.Hub.Config({ - TeX: { - extensions: ["AMSmath.js"] - }, - tex2jax: { - inlineMath: [ ['$','$'], ["\\(","\\)"] ], - displayMath: [ ['$$','$$'], ["\\[","\\]"] ] - }, - displayAlign: 'center', // Change this to 'center' to center equations. - "HTML-CSS": { - styles: {'.MathJax_Display': {"margin": 4}} - } - }); - } else if (window.mathjax_url != "") { - // Don't have MathJax, but should. Show dialog. - var dialog = $('
') - .append( - $("

").addClass('dialog').html( - "Math/LaTeX rendering will be disabled." - ) - ).append( - $("

").addClass('dialog').html( - "If you have administrative access to the notebook server and" + - " a working internet connection, you can install a local copy" + - " of MathJax for offline use with the following command on the server" + - " at a Python or IPython prompt:" - ) - ).append( - $("
").addClass('dialog').html(
-                        ">>> from IPython.external import mathjax; mathjax.install_mathjax()"
-                    )
-                ).append(
-                    $("
").addClass('dialog').html(
-                        "This will try to install MathJax into the IPython source directory."
-                    )
-                ).append(
-                    $("
").addClass('dialog').html(
-                        "If IPython is installed to a location that requires" +
-                        " administrative privileges to write, you will need to make this call as" +
-                        " an administrator, via 'sudo'."
-                    )
-                ).append(
-                    $("
").addClass('dialog').html(
-                        "When you start the notebook server, you can instruct it to disable MathJax support altogether:"
-                    )
-                ).append(
-                    $("
").addClass('dialog').html(
-                        "$ ipython notebook --no-mathjax"
-                    )
-                ).append(
-                    $("
").addClass('dialog').html(
-                        "which will prevent this dialog from appearing."
-                    )
-                ).dialog({
-                    title: "Failed to retrieve MathJax from '" + window.mathjax_url + "'",
-                    width: "70%",
-                    modal: true,
-                })
-        } else {
-            // No MathJax, but none expected. No dialog.
-        };
-    };
-
-
-    // Set module variables
-    IPython.init_mathjax = init_mathjax;
-
-    return IPython;
-
-}(IPython));
\ No newline at end of file
diff --git a/styles/matplotlibrc b/styles/matplotlibrc
index a2127748..9394e67d 100644
--- a/styles/matplotlibrc
+++ b/styles/matplotlibrc
@@ -76,7 +76,7 @@ lines.linewidth   : 2.0     # line width in points
 # http://matplotlib.org/api/artist_api.html#module-matplotlib.patches
 # information on patch properties
 patch.linewidth        : 0.5     # edge width in points
-patch.facecolor        : 348ABD, A60628, 7A68A6, 467821, CF4457, 188487, E24A33
+patch.facecolor        : blue
 patch.edgecolor        : eeeeee
 patch.antialiased      : True  
 
@@ -199,7 +199,7 @@ text.hinting_factor : 8 # Specifies the amount of softness for hinting in the
 #mathtext.it  : serif:italic
 #mathtext.bf  : serif:bold
 #mathtext.sf  : sans
-#mathtext.fontset : cm # Should be 'cm' (Computer Modern), 'stix',
+mathtext.fontset : cm # Should be 'cm' (Computer Modern), 'stix',
                        # 'stixsans' or 'custom'
 #mathtext.fallback_to_cm : True  # When True, use symbols from the Computer Modern
                                  # fonts when a symbol can not be found in one of
@@ -237,7 +237,9 @@ axes.labelsize      : large  # fontsize of the x any y labels
 #axes.unicode_minus  : True    # use unicode for the minus symbol
                                # rather than hyphen.  See
                                # http://en.wikipedia.org/wiki/Plus_and_minus_signs#Character_codes
-axes.color_cycle    : 348ABD, A60628, 7A68A6, 467821, CF4457, 188487, E24A33  # color cycle for plot lines
+#axes.prop_cycle : cycler('color', ['1f77b4', 'ff7f0e', '2ca02c', 'd62728', '9467bd', '8c564b', 'e377c2', '7f7f7f', 'bcbd22', '17becf'])
+axes.prop_cycle    : cycler('color', ['348abd', 'a60628', '7a68a6', '467821','d55e00',  'cc79a7', '56b4e9', '009e73', 'f0e442', '0072b2'])
+                                            # color cycle for plot lines
                                             # as list of string colorspecs:
                                             # single letter, long name, or
                                             # web-style hex
@@ -300,7 +302,7 @@ legend.fancybox      : True  # if True, use a rounded box for the
 ### FIGURE
 # See http://matplotlib.org/api/figure_api.html#matplotlib.figure.Figure
 figure.figsize   : 11, 8    # figure size in inches
-#figure.dpi       : 80      # figure dots per inch
+figure.dpi       : 100     # figure dots per inch
 #figure.facecolor : 0.75    # figure facecolor; 0.75 is scalar gray
 #figure.edgecolor : white   # figure edgecolor
 #figure.autolayout : False  # When True, automatically adjust subplot
@@ -350,7 +352,7 @@ figure.figsize   : 11, 8    # figure size in inches
 # the default savefig params can be different from the display params
 # Eg, you may want a higher resolution, or to make the figure
 # background white
-#savefig.dpi        : 100      # figure dots per inch
+savefig.dpi        : 300      # figure dots per inch
 #savefig.facecolor  : white    # figure facecolor when saving
 #savefig.edgecolor  : white    # figure edgecolor when saving
 #savefig.format     : png      # png, ps, pdf, svg
diff --git a/styles/notebook.css b/styles/notebook.css
deleted file mode 100644
index e0be47bb..00000000
--- a/styles/notebook.css
+++ /dev/null
@@ -1,429 +0,0 @@
-/**
- * Primary styles
- *
- * Author: IPython Development Team
- */
-
-@font-face {
-    font-family: "Computer Modern";
-    src: url('http://mirrors.ctan.org/fonts/cm-unicode/fonts/otf/cmunss.otf');
-}
-/**
-@font-face {
-    font-family: "Computer Modern";
-    src: url('http://mirrors.ctan.org/fonts/cm-unicode/fonts/otf/cmunsx.otf');
-    font-weight: bold;
-}
-@font-face {
-    font-family: "Computer Modern";
-    src: url('http://mirrors.ctan.org/fonts/cm-unicode/fonts/otf/cmunsi.otf');
-    font-style: italic, oblique;
-}
-@font-face {
-    font-family: "Computer Modern";
-    src: url('http://mirrors.ctan.org/fonts/cm-unicode/fonts/otf/cmunbxo.otf');
-    font-weight: bold;
-    font-style: italic, oblique;
-}
-*/
- 
- 
-body {
-    overflow: hidden;
-}
-
-span#save_widget {
-    padding: 5px;
-    margin: 0px 0px 0px 300px;
-    margin: 0px 0px 0px 300px;
-    display:inline-block;
-}
-
-span#notebook_name {
-    height: 1em;
-    line-height: 1em;
-    padding: 3px;
-    border: none;
-    font-size: 146.5%;
-}
-
-
-
-
-.ui-menubar-item .ui-button .ui-button-text {
-    padding: 0.4em 1.0em;
-    font-size: 100%;
-}
-
-.ui-menu {
-  -moz-box-shadow:    0px 6px 10px -1px #adadad;
-  -webkit-box-shadow: 0px 6px 10px -1px #adadad;
-  box-shadow:         0px 6px 10px -1px #adadad;
-}
-
-.ui-menu .ui-menu-item a {
-    border: 1px solid transparent;
-    padding: 2px 1.6em;
-}
-
-.ui-menu .ui-menu-item a.ui-state-focus {
-    margin: 0;
-}
-
-.ui-menu hr {
-    margin: 0.3em 0;
-}
-
-#menubar_container {
-    position: relative;
-}
-
-#notification {
-    position: absolute;
-    right: 3px;
-    top: 3px;
-    height: 25px;
-    padding: 3px 6px;
-    z-index: 10;
-}
-
-#toolbar {
-    padding: 3px 15px;
-}
-
-#cell_type {
-    font-size: 85%;
-}
-
-
-div#main_app {
-    width: 100%;
-    position: relative;
-}
-
-span#quick_help_area {
-    position: static;
-    padding: 5px 0px;
-    margin: 0px 0px 0px 0px;
-}
-
-.help_string {
-    float: right;
-    width: 170px;
-    padding: 0px 5px;
-    text-align: left;
-    font-size: 85%;
-}
-
-.help_string_label {
-    float: right;
-    font-size: 85%;
-}
-
-div#notebook_panel {
-    margin: 0px 0px 0px 0px;
-    padding: 0px;
-}
-
-div#notebook {
-    overflow-y: scroll;
-    overflow-x: auto;
-    width: 100%;
-    /* This spaces the cell away from the edge of the notebook area */
-    padding: 5px 5px 15px 15px;
-    margin: 0px;
-    background-color: white;
-}
-
-div#pager_splitter {
-    height: 8px;
-}
-
-div#pager {
-    padding: 15px;
-    overflow: auto;
-    display: none;
-}
-
-div.ui-widget-content {
-    border: 1px solid #aaa;
-    outline: none;
-}
-
-.cell {
-    border: 1px solid transparent;
-}
-
-div.cell {
-    width: 600px;
-    padding: 5px 5px 5px 0px;
-    /* This acts as a spacer between cells, that is outside the border */
-    margin: auto;
-}
-
-div.code_cell {
-    background-color: white;
-}
-
-/* any special styling for code cells that are currently running goes here */
-div.code_cell.running {
-}
-
-div.prompt {
-    /* This needs to be wide enough for 3 digit prompt numbers: In[100]: */
-    width: 11ex;
-    /* This 0.4em is tuned to match the padding on the CodeMirror editor. */
-    padding: 0.4em;
-    margin: 0px;
-    font-family: monospace;
-    text-align:right;
-    display: none;
- }   
-
-div.input {
-    page-break-inside: avoid;
-    width: 600px;
-}
-
-/* input_area and input_prompt must match in top border and margin for alignment */
-div.input_area {
-    color: black;
-    border: 1px solid #ddd;
-    border-radius: 3px;
-    background: #f7f7f7;
-}
-
-div.input_prompt {
-    color: navy;
-    border-top: 1px solid transparent;
-}
-
-div.output_wrapper {
-    /* This is a spacer between the input and output of each cell */
-    margin-top: 5px;
-    margin-left: 5px;
-    /* FF needs explicit width to stretch */
-    width: 100%;
-    /* this position must be relative to enable descendents to be absolute within it */
-    position: relative;
-}
-
-/* class for the output area when it should be height-limited */
-div.output_scroll {
-  /* ideally, this would be max-height, but FF barfs all over that */
-  height: 24em;
-  /* FF needs this *and the wrapper* to specify full width, or it will shrinkwrap */
-  width: 100%;
-  
-  overflow: auto;
-  border-radius: 3px;
-  box-shadow: inset 0 2px 8px rgba(0, 0, 0, .8);
-}
-
-/* output div while it is collapsed */
-div.output_collapsed {
-  margin-right: 5px;
-}
-
-div.out_prompt_overlay {
-  height: 100%;
-  padding: 0px;
-  position: absolute;
-  border-radius: 3px;
-}
-
-div.out_prompt_overlay:hover {
-  /* use inner shadow to get border that is computed the same on WebKit/FF */
-  box-shadow: inset 0 0 1px #000;
-  background: rgba(240, 240, 240, 0.5);
-}
-
-div.output_prompt {
-    color: darkred;
-    /* 5px right shift to account for margin in parent container */
-    margin: 0 5px 0 -5px;
-}
-
-/* This class is the outer container of all output sections. */
-div.output_area {
-    padding: 0px;
-    page-break-inside: avoid;
-}
-
-/* This class is for the output subarea inside the output_area and after
-   the prompt div. */
-div.output_subarea {
-    padding: 0.4em 0.4em 0.4em 0.4em;
-}
-
-/* The rest of the output_* classes are for special styling of the different
-   output types */
-
-/* all text output has this class: */
-div.output_text {
-    text-align: left;
-    color: black;
-    font-family: monospace;
-}
-
-/* stdout/stderr are 'text' as well as 'stream', but pyout/pyerr are *not* streams */
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-    padding-top: 0.0em;
-    padding-bottom: 0.0em;
-}
-div.output_stdout {
-}
-div.output_stderr {
-    background: #fdd; /* very light red background for stderr */
-}
-
-div.output_latex {
-    text-align: left;
-    color: black;
-}
-
-div.output_html {
-}
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-div.output_png {
-}
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-}
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-    background-color: white;
-    padding: 5px 5px 5px 5px;
-    width: 600px;
-    line-height: 145%;
-    font-family: "Computer Modern", futurabook, Verdana, Arial, sans-serif;
-    margin:auto;
-}
-
-div.text_cell_input {
-    color: black;
-    border: 1px solid #ddd;
-    border-radius: 3px;
-    background: #f7f7f7;
-}
-
-div.text_cell_render {
-    font-family: "Computer Modern", futurabook, Verdana, Arial, sans-serif;
-    outline: none;
-    resize: none;
-    width:  inherit;
-    border-style: none;
-    padding: 5px;
-    color: black;
-}
-
-/* The following gets added to the  if it is detected that the user has a
- * monospace font with inconsistent normal/bold/italic height.  See
- * notebookmain.js.  Such fonts will have keywords vertically offset with
- * respect to the rest of the text.  The user should select a better font. 
- * See: https://github.com/ipython/ipython/issues/1503
- *
- * .CodeMirror span {
- *      vertical-align: bottom;
- * }
- */
-
-.CodeMirror {
-    line-height: 1.231;  /* Changed from 1em to our global default */
-    font-family: Consolas, monospace;
-
-}
-
-.CodeMirror-scroll {
-    height: auto;     /* Changed to auto to autogrow */
-    /*  The CodeMirror docs are a bit fuzzy on if overflow-y should be hidden or visible.*/
-    /*  We have found that if it is visible, vertical scrollbars appear with font size changes.*/
-    overflow-y: hidden;
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-}
-
-/* CSS font colors for translated ANSI colors. */
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-.ansiblack {color: black;}
-.ansired {color: darkred;}
-.ansigreen {color: darkgreen;}
-.ansiyellow {color: brown;}
-.ansiblue {color: darkblue;}
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-.ansigrey {color: grey;}
-.ansibold {font-weight: bold;}
-
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-.completions select {
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-option.context {
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-.completions p b {
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-.completions p {
-    background: #DDF;
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-pre.dialog {
-    background-color: #f7f7f7;
-    border: 1px solid #ddd;
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-}
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-.shortcut_descr {
-}
-
-/* Word-wrap output correctly.  This is the CSS3 spelling, though Firefox seems
-   to not honor it correctly.  Webkit browsers (Chrome, rekonq, Safari) do.
- */
-pre, code, kbd, samp { white-space: pre-wrap; }
-
-#fonttest {
-    font-family: monospace;
-}
-
-.js-error {
-    color: darkred;
-}
\ No newline at end of file
diff --git a/to_latex_pdf.sh b/to_latex_pdf.sh
new file mode 100755
index 00000000..376d30eb
--- /dev/null
+++ b/to_latex_pdf.sh
@@ -0,0 +1,4 @@
+find Prologue Chapter* -name "*.ipynb" | grep -v "PyMC2" | xargs ipython3 nbconvert --to pdf --template article
+
+# merge all files:
+pdfjoin Prologue.pdf Ch*.pdf DontOverfit.pdf MachineLearning.pdf