Not adding or removing semicolons, and a fixed a typo

Author: eli-b

Chapter4_TheGreatestTheoremNeverTold/LawOfLargeNumbers.ipynb

@@ -107,7 +107,7 @@
       "\n",
       "plt.ylim(4.35, 4.65)\n",
       "plt.title(\"Convergence of the average of \\n random variables to its \\\n",
-      "expected value\" )\n",
+      "expected value\")\n",
       "plt.ylabel(\"average of $n$ samples\")\n",
       "plt.xlabel(\"# of samples, $n$\")\n",
       "plt.legend();"
@@ -156,8 +156,7 @@
       "figsize(12.5, 4)\n",
       "\n",
       "N_Y = 250  # use this many to approximate D(N)\n",
-      "# use this many samples in the approx. to the variance.\n",
-      "N_array = np.arange(1000, 50000, 2500)\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",
@@ -244,7 +243,7 @@
      "input": [
       "import pymc as pm\n",
       "N = 10000\n",
-      "print np.mean([pm.rexponential(0.5) > 10 for i in range(N)]);"
+      "print np.mean([pm.rexponential(0.5) > 10 for i in range(N)])"
      ],
      "language": "python",
      "metadata": {},
@@ -366,7 +365,7 @@
       "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]];"
+      "print population[np.argsort(-average_across_county)[:10]]"
      ],
      "language": "python",
      "metadata": {},
@@ -498,7 +497,7 @@
       "# adding a number to the end of the %run call with get the ith top photo.\n",
       "%run top_pic_comments.py 2\n",
       "\n",
-      "Image(top_post_url);"
+      "Image(top_post_url)"
      ],
      "language": "python",
      "metadata": {},
@@ -537,7 +536,7 @@
       "for i in comments:\n",
       "    print '\"' + contents[i] + '\"'\n",
       "    print\"upvotes/downvotes: \", votes[i, :]\n",
-      "    print;"
+      "    print"
      ],
      "language": "python",
      "metadata": {},
@@ -581,22 +580,17 @@
       "\n",
       "def posterior_upvote_ratio(upvotes, downvotes, samples=20000):\n",
       "    \"\"\"\n",
-      "    This function accepts the number of upvotes and downvotes a particular comment recieved,\n",
+      "    This function accepts the number of upvotes and downvotes a particular comment 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(\n",
-      "        \"obs\",\n",
-      "        N,\n",
-      "        upvote_ratio,\n",
-      "        value=upvotes,\n",
-      "        observed=True)\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\")[:];"
+      "    return mcmc.trace(\"upvote_ratio\")[:]"
      ],
      "language": "python",
      "metadata": {},
@@ -687,15 +681,15 @@
       "    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, 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 comments\")\n",
+      "plt.title(\"Posterior distributions of upvote ratios on different comments\");\n",
       "order = np.argsort(-np.array(lower_limits))\n",
-      "print order, lower_limits;"
+      "print order, lower_limits"
      ],
      "language": "python",
      "metadata": {},
@@ -767,7 +761,7 @@
       "for i in order[:40]:\n",
       "    ordered_contents.append(contents[i])\n",
       "    print votes[i, 0], votes[i, 1], contents[i]\n",
-      "    print \"-------------\";"
+      "    print \"-------------\""
      ],
      "language": "python",
      "metadata": {},
@@ -905,8 +899,7 @@
       "             xerr=std_err[r_order], xuplims=True, capsize=0, fmt=\"o\",\n",
       "             color=\"#7A68A6\")\n",
       "plt.xlim(0.3, 1)\n",
-      "plt.yticks(np.arange(len(r_order) - 1, -1, -1),\n",
-      "           map(lambda x: x[:30].replace(\"\\n\", \"\"), ordered_contents));"
+      "plt.yticks(np.arange(len(r_order) - 1, -1, -1), map(lambda x: x[:30].replace(\"\\n\", \"\"), ordered_contents));"
      ],
      "language": "python",
      "metadata": {},
@@ -1020,7 +1013,7 @@
       "exp = stats.expon(scale=4)\n",
       "N = 1e5\n",
       "X = exp.rvs(N)\n",
-      "# ...;"
+      "# ..."
      ],
      "language": "python",
      "metadata": {},
@@ -1101,7 +1094,7 @@
       "def css_styling():\n",
       "    styles = open(\"../styles/custom.css\", \"r\").read()\n",
       "    return HTML(styles)\n",
-      "css_styling();"
+      "css_styling()"
      ],
      "language": "python",
      "metadata": {},