Merge branch 'misc-style' into examples-style

Author: eli-b

Prologue/Prologue.ipynb

@@ -192,7 +192,7 @@
       "def css_styling():\n",
       "    styles = open(\"../styles/custom.css\", \"r\").read()\n",
       "    return HTML(styles)\n",
-      "css_styling();"
+      "css_styling()"
      ],
      "language": "python",
      "metadata": {},

sandbox/ABCtests.ipynb

@@ -18,7 +18,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "import pymc as pm;"
+      "import pymc as pm"
      ],
      "language": "python",
      "metadata": {},
@@ -40,7 +40,7 @@
       "    if np.array_equal(fake_obs, obs):\n",
       "        return uni\n",
       "    else:\n",
-      "        return None;"
+      "        return None"
      ],
      "language": "python",
      "metadata": {},
@@ -51,7 +51,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "mcmc = pm.MCMC([uni, fake_obs, accept]);"
+      "mcmc = pm.MCMC([uni, fake_obs, accept])"
      ],
      "language": "python",
      "metadata": {},
@@ -62,7 +62,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "mcmc.sample(10000);"
+      "mcmc.sample(10000)"
      ],
      "language": "python",
      "metadata": {},
@@ -89,7 +89,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "samples = mcmc.trace('accept')[:];"
+      "samples = mcmc.trace('accept')[:]"
      ],
      "language": "python",
      "metadata": {},
@@ -100,7 +100,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "hist(samples[samples > 0]);"
+      "hist(samples[samples > 0])"
      ],
      "language": "python",
      "metadata": {},
@@ -127,7 +127,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "samples[:1000];"
+      "samples[:1000]"
      ],
      "language": "python",
      "metadata": {},

sandbox/CommitDataForChapter1.ipynb

@@ -21,7 +21,7 @@
       "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;"
+      "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": {},
@@ -35,9 +35,9 @@
       "count_data = weekly_totals\n",
       "n_count_data = len(weekly_totals)\n",
       "\n",
-      "plt.bar(range(n_count_data), weekly_totals)\n",
+      "plt.bar(range(n_count_data), weekly_totals);\n",
       "print weekly_totals\n",
-      "print n_count_data;"
+      "print n_count_data"
      ],
      "language": "python",
      "metadata": {},
@@ -71,7 +71,7 @@
       "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);"
+      "tau = pm.DiscreteUniform(\"tau\", lower=0, upper=n_count_data)"
      ],
      "language": "python",
      "metadata": {},
@@ -87,7 +87,7 @@
       "    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;"
+      "    return out"
      ],
      "language": "python",
      "metadata": {},
@@ -100,7 +100,7 @@
      "input": [
       "observation = pm.Poisson(\"obs\", lambda_, value=count_data, observed=True)\n",
       "\n",
-      "model = pm.Model([observation, lambda_1, lambda_2, tau]);"
+      "model = pm.Model([observation, lambda_1, lambda_2, tau])"
      ],
      "language": "python",
      "metadata": {},
@@ -113,7 +113,7 @@
      "input": [
       "# Mysterious code to be explained in Chapter 3.\n",
       "mcmc = pm.MCMC(model)\n",
-      "mcmc.sample(40000, 10000, 1);"
+      "mcmc.sample(40000, 10000, 1)"
      ],
      "language": "python",
      "metadata": {},
@@ -142,7 +142,7 @@
      "input": [
       "lambda_1_samples = mcmc.trace('lambda_1')[:]\n",
       "lambda_2_samples = mcmc.trace('lambda_2')[:]\n",
-      "tau_samples = mcmc.trace('tau')[:];"
+      "tau_samples = mcmc.trace('tau')[:]"
      ],
      "language": "python",
      "metadata": {},
@@ -188,7 +188,7 @@
       "plt.legend(loc=\"upper left\")\n",
       "plt.ylim([0, .75])\n",
       "plt.xlabel(\"$\\tau$ (in days)\")\n",
-      "plt.ylabel(\"probability\");"
+      "plt.ylabel(\"probability\")"
      ],
      "language": "python",
      "metadata": {},
@@ -211,7 +211,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "n_count_data;"
+      "n_count_data"
      ],
      "language": "python",
      "metadata": {},
@@ -230,7 +230,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "lambda_2_samples;"
+      "lambda_2_samples"
      ],
      "language": "python",
      "metadata": {},

sandbox/GithubUsers.ipynb

@@ -30,7 +30,7 @@
       "print ids[:10]\n",
       "print\n",
       "print \"Number of unique ids found: \", ids.shape[0]\n",
-      "print \"Largest user id: \", ids.max();"
+      "print \"Largest user id: \", ids.max()"
      ],
      "language": "python",
      "metadata": {},
@@ -87,7 +87,7 @@
      "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.title(\"Histogram of %d Github User ids\" % ids.shape[0]);\n",
       "plt.xlabel(\"User id\")\n",
       "plt.ylabel(\"Frequency\");"
      ],
@@ -144,7 +144,7 @@
       "\n",
       "# code to be examplained in Chp. 3.\n",
       "mcmc = pm.MCMC([upper_bound, obs])\n",
-      "mcmc.sample(100000, 45000);"
+      "mcmc.sample(100000, 45000)"
      ],
      "language": "python",
      "metadata": {},
@@ -176,7 +176,7 @@
       "hist(samples, bins=100,\n",
       "     label=\"Uniform prior\",\n",
       "     normed=True, alpha=0.8,\n",
-      "     histtype=\"stepfilled\", color=\"#7A68A6\")\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",

sandbox/SpaceShuttleBayesFactor.ipynb

@@ -45,7 +45,7 @@
       "plt.ylabel(\"Damage Incident?\")\n",
       "plt.xlabel(\"Outside temperature (Farhenhit)\")\n",
       "plt.title(\"(Artificial) Defects of the Space Shuttle O-Rings vs \\\n",
-      "temperature\");"
+      "temperature\")"
      ],
      "language": "python",
      "metadata": {},
@@ -87,7 +87,7 @@
       "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 \"...\";"
+      "print \"...\""
      ],
      "language": "python",
      "metadata": {},
@@ -140,7 +140,7 @@
       "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;"
+      "print \"probability of observations, model 1: %.10f\" % prob_given_model_1"
      ],
      "language": "python",
      "metadata": {},
@@ -198,7 +198,7 @@
       "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?\";"
+      "print \"Notice the probability is constant for all temperatures?\""
      ],
      "language": "python",
      "metadata": {},
@@ -224,7 +224,7 @@
       "\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;"
+      "print \"probability of observations, model 2: %.10f\" % prob_given_model_2"
      ],
      "language": "python",
      "metadata": {},
@@ -246,7 +246,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "print \"Bayes factor = %.3f\" % (prob_given_model_1 / prob_given_model_2);"
+      "print \"Bayes factor = %.3f\" % (prob_given_model_1 / prob_given_model_2)"
      ],
      "language": "python",
      "metadata": {},
@@ -292,7 +292,7 @@
       "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);"
+      "print \"Bayes factor: %.3f\" % (prob_given_model_1 / prob_given_model_2)"
      ],
      "language": "python",
      "metadata": {},

sandbox/Sum of Gaussian Distributions + noise.ipynb

@@ -42,7 +42,7 @@
       "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();"
+      "pl.legend()"
      ],
      "language": "python",
      "metadata": {},
@@ -96,7 +96,7 @@
       "    return profile_1 + profile_2\n",
       "\n",
       "\n",
-      "observations = pm.Normal(\"obs\", mean, std_deviation, value=combined, observed=True);"
+      "observations = pm.Normal(\"obs\", mean, std_deviation, value=combined, observed=True)"
      ],
      "language": "python",
      "metadata": {},
@@ -116,7 +116,7 @@
       "                  std_deviation])\n",
       "\n",
       "map_ = pm.MAP(model)\n",
-      "map_.fit();"
+      "map_.fit()"
      ],
      "language": "python",
      "metadata": {},
@@ -128,7 +128,7 @@
      "collapsed": false,
      "input": [
       "mcmc = pm.MCMC(model)\n",
-      "mcmc.sample(70000, 60000);"
+      "mcmc.sample(70000, 60000)"
      ],
      "language": "python",
      "metadata": {},
@@ -155,7 +155,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "mcplot(mcmc);"
+      "mcplot(mcmc)"
      ],
      "language": "python",
      "metadata": {},