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Merge branch 'cam-master' into examples-style
Conflicts: ExamplesFromChapters/Chapter3/ClusteringWithGaussians.py
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‎Chapter1_Introduction/Chapter1_Introduction.ipynb‎

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@@ -194,9 +194,9 @@
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"\n",
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"# For the already prepared, I'm using Binomial's conj. prior.\n",
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"for k, N in enumerate(n_trials):\n",
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" sx = plt.subplot(len(n_trials) / 2, 2, k + 1)\n",
197+
" sx = plt.subplot(len(n_trials)/2, 2, k+1)\n",
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" plt.xlabel(\"$p$, probability of heads\") \\\n",
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" if k in [0, len(n_trials) - 1] else None\n",
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" if k in [0, len(n_trials)-1] else None\n",
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" plt.setp(sx.get_yticklabels(), visible=False)\n",
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" heads = data[:N].sum()\n",
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" y = dist.pdf(x, 1 + heads, 1 + N - heads)\n",
@@ -213,7 +213,7 @@
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" y=1.02,\n",
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" fontsize=14)\n",
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"\n",
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"plt.tight_layout();"
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"plt.tight_layout()"
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],
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"language": "python",
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"metadata": {},
@@ -286,14 +286,14 @@
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"input": [
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"figsize(12.5, 4)\n",
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"p = np.linspace(0, 1, 50)\n",
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"plt.plot(p, 2 * p / (1 + p), color=\"#348ABD\", lw=3)\n",
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"# plt.fill_between(p, 2*p/(1+p), alpha=.5, facecolor=[\"#A60628\"])\n",
291-
"plt.scatter(0.2, 2 * (0.2) / 1.2, s=140, c=\"#348ABD\")\n",
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"plt.plot(p, 2*p/(1+p), color=\"#348ABD\", lw=3)\n",
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"#plt.fill_between(p, 2*p/(1+p), alpha=.5, facecolor=[\"#A60628\"])\n",
291+
"plt.scatter(0.2, 2*(0.2)/1.2, s=140, c=\"#348ABD\")\n",
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"plt.xlim(0, 1)\n",
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"plt.ylim(0, 1)\n",
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"plt.xlabel(\"Prior, $P(A) = p$\")\n",
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"plt.ylabel(\"Posterior, $P(A|X)$, with $P(A) = p$\")\n",
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"plt.title(\"Are there bugs in my code?\");"
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"plt.title(\"Are there bugs in my code?\")"
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],
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"language": "python",
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"metadata": {},
@@ -336,12 +336,12 @@
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"colours = [\"#348ABD\", \"#A60628\"]\n",
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"\n",
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"prior = [0.20, 0.80]\n",
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"posterior = [1. / 3, 2. / 3]\n",
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"posterior = [1./3, 2./3]\n",
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"plt.bar([0, .7], prior, alpha=0.70, width=0.25,\n",
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" color=colours[0], label=\"prior distribution\",\n",
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" lw=\"3\", edgecolor=colours[0])\n",
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"\n",
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"plt.bar([0 + 0.25, .7 + 0.25], posterior, alpha=0.7,\n",
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"plt.bar([0+0.25, .7+0.25], posterior, alpha=0.7,\n",
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" width=0.25, color=colours[1],\n",
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" label=\"posterior distribution\",\n",
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" lw=\"3\", edgecolor=colours[1])\n",
@@ -438,7 +438,7 @@
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"plt.ylabel(\"probability of $k$\")\n",
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"plt.xlabel(\"$k$\")\n",
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"plt.title(\"Probability mass function of a Poisson random variable; differing \\\n",
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"$\\lambda$ values\");"
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"$\\lambda$ values\")"
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],
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"language": "python",
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"metadata": {},
@@ -491,14 +491,14 @@
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"lambda_ = [0.5, 1]\n",
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"\n",
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"for l, c in zip(lambda_, colours):\n",
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" plt.plot(a, expo.pdf(a, scale=1. / l), lw=3,\n",
494+
" plt.plot(a, expo.pdf(a, scale=1./l), lw=3,\n",
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" color=c, label=\"$\\lambda = %.1f$\" % l)\n",
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" plt.fill_between(a, expo.pdf(a, scale=1. / l), color=c, alpha=.33)\n",
496+
" plt.fill_between(a, expo.pdf(a, scale=1./l), color=c, alpha=.33)\n",
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"\n",
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"plt.legend()\n",
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"plt.ylabel(\"PDF at $z$\")\n",
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"plt.xlabel(\"$z$\")\n",
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"plt.ylim(0, 1.2)\n",
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"plt.ylim(0,1.2)\n",
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"plt.title(\"Probability density function of an Exponential random variable;\\\n",
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" differing $\\lambda$\");"
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],
@@ -642,12 +642,12 @@
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"input": [
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"import pymc as pm\n",
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"\n",
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"alpha = 1.0 / count_data.mean() # Recall count_data is the\n",
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"alpha = 1.0/count_data.mean() # Recall count_data is the\n",
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" # variable that holds our txt counts\n",
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"lambda_1 = pm.Exponential(\"lambda_1\", alpha)\n",
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"lambda_2 = pm.Exponential(\"lambda_2\", alpha)\n",
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"\n",
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"tau = pm.DiscreteUniform(\"tau\", lower=0, upper=n_count_data);"
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"tau = pm.DiscreteUniform(\"tau\", lower=0, upper=n_count_data)"
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],
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"language": "python",
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"metadata": {},
@@ -665,7 +665,7 @@
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"cell_type": "code",
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"collapsed": false,
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"input": [
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"print \"Random output:\", tau.random(), tau.random(), tau.random();"
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"print \"Random output:\", tau.random(), tau.random(), tau.random()"
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],
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"language": "python",
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"metadata": {},
@@ -689,7 +689,7 @@
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" out = np.zeros(n_count_data)\n",
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" out[:tau] = lambda_1 # lambda before tau is lambda1\n",
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" out[tau:] = lambda_2 # lambda after (and including) tau is lambda2\n",
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" return out;"
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" return out"
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],
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"language": "python",
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"metadata": {},
@@ -711,7 +711,7 @@
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"input": [
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"observation = pm.Poisson(\"obs\", lambda_, value=count_data, observed=True)\n",
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"\n",
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"model = pm.Model([observation, lambda_1, lambda_2, tau]);"
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"model = pm.Model([observation, lambda_1, lambda_2, tau])"
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],
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"language": "python",
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"metadata": {},
@@ -731,9 +731,9 @@
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"cell_type": "code",
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"collapsed": false,
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"input": [
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"# Mysterious code to be explained in Chapter 3.\n",
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"### Mysterious code to be explained in Chapter 3.\n",
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"mcmc = pm.MCMC(model)\n",
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"mcmc.sample(40000, 10000, 1);"
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"mcmc.sample(40000, 10000, 1)"
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],
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"language": "python",
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"metadata": {},
@@ -762,7 +762,7 @@
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"input": [
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"lambda_1_samples = mcmc.trace('lambda_1')[:]\n",
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"lambda_2_samples = mcmc.trace('lambda_2')[:]\n",
765-
"tau_samples = mcmc.trace('tau')[:];"
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"tau_samples = mcmc.trace('tau')[:]"
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],
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"language": "python",
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"metadata": {},
@@ -774,7 +774,7 @@
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"collapsed": false,
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"input": [
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"figsize(12.5, 10)\n",
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"# histogram of the samples:\n",
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"#histogram of the samples:\n",
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"\n",
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"ax = plt.subplot(311)\n",
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"ax.set_autoscaley_on(False)\n",
@@ -804,7 +804,7 @@
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"\n",
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"plt.legend(loc=\"upper left\")\n",
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"plt.ylim([0, .75])\n",
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"plt.xlim([35, len(count_data) - 20])\n",
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"plt.xlim([35, len(count_data)-20])\n",
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"plt.xlabel(r\"$\\tau$ (in days)\")\n",
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"plt.ylabel(\"probability\");"
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],
@@ -923,7 +923,7 @@
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"cell_type": "code",
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"collapsed": false,
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"input": [
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"# type your code here.;"
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"#type your code here."
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],
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"language": "python",
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"metadata": {},
@@ -941,7 +941,7 @@
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"cell_type": "code",
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"collapsed": false,
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"input": [
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"# type your code here.;"
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"#type your code here."
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],
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"language": "python",
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"metadata": {},
@@ -959,7 +959,7 @@
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"cell_type": "code",
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"collapsed": false,
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"input": [
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"# type your code here.;"
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"#type your code here."
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],
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"language": "python",
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"metadata": {},
@@ -973,7 +973,7 @@
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"### References\n",
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"\n",
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"\n",
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"- [1] Gelman, Andrew. N.p.. Web. 22 Jan 2013. [N is never large enough](http://andrewgelman.com/2005/07/n_is_never_large).\n",
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"- [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",
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"- [2] Norvig, Peter. 2009. [The Unreasonable Effectiveness of Data](http://static.googleusercontent.com/media/research.google.com/en//pubs/archive/35179.pdf).\n",
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"- [3] Patil, A., D. Huard and C.J. Fonnesbeck. 2010. \n",
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"PyMC: Bayesian Stochastic Modelling in Python. Journal of Statistical \n",
@@ -987,12 +987,10 @@
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"collapsed": false,
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"input": [
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"from IPython.core.display import HTML\n",
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"\n",
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"\n",
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"def css_styling():\n",
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" styles = open(\"../styles/custom.css\", \"r\").read()\n",
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" return HTML(styles)\n",
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"css_styling();"
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"css_styling()"
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],
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"language": "python",
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"metadata": {},
@@ -1004,6 +1002,22 @@
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" font-family: \"Computer Modern\";\n",
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" src: url('http://9dbb143991406a7c655e-aa5fcb0a5a4ec34cff238a2d56ca4144.r56.cf5.rackcdn.com/cmunss.otf');\n",
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" }\n",
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" @font-face {\n",
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" font-family: \"Computer Modern\";\n",
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" font-weight: bold;\n",
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" src: url('http://9dbb143991406a7c655e-aa5fcb0a5a4ec34cff238a2d56ca4144.r56.cf5.rackcdn.com/cmunsx.otf');\n",
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" }\n",
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" @font-face {\n",
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" font-family: \"Computer Modern\";\n",
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" font-style: oblique;\n",
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" src: url('http://9dbb143991406a7c655e-aa5fcb0a5a4ec34cff238a2d56ca4144.r56.cf5.rackcdn.com/cmunsi.otf');\n",
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" }\n",
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" @font-face {\n",
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" font-family: \"Computer Modern\";\n",
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" font-weight: bold;\n",
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" font-style: oblique;\n",
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" src: url('http://9dbb143991406a7c655e-aa5fcb0a5a4ec34cff238a2d56ca4144.r56.cf5.rackcdn.com/cmunso.otf');\n",
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" }\n",
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" div.cell{\n",
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" width:800px;\n",
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" margin-left:16% !important;\n",
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" .warning{\n",
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" color: rgb( 240, 20, 20 )\n",
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" } \n",
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"</style>\n"
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"</style>\n",
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"<script>\n",
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" MathJax.Hub.Config({\n",
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" TeX: {\n",
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" extensions: [\"AMSmath.js\"]\n",
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" },\n",
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" tex2jax: {\n",
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" inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ],\n",
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" displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ]\n",
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" },\n",
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" displayAlign: 'center', // Change this to 'center' to center equations.\n",
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" \"HTML-CSS\": {\n",
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" styles: {'.MathJax_Display': {\"margin\": 4}}\n",
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" }\n",
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" });\n",
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"</script>\n"
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],
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"metadata": {},
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"output_type": "pyout",
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"prompt_number": 1,
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"text": [
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"<IPython.core.display.HTML at 0x10f034850>"
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"<IPython.core.display.HTML at 0x103dff550>"
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]
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}
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],

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