Revert "Ending code cells with a semicolon."

This reverts commit 4876681.

Author: CamDavidsonPilon

Chapter3_MCMC/IntroMCMC.ipynb

@@ -67,7 +67,7 @@
       "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\");"
+      "plt.title(\"Uniform prior landscape; alternate view\")\n"
      ],
      "language": "python",
      "metadata": {},
@@ -113,7 +113,7 @@
       "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\");"
+      "plt.title(\"$Exp(3), Exp(10)$ prior landscape; \\nalternate view\")\n"
      ],
      "language": "python",
      "metadata": {},
@@ -168,7 +168,7 @@
       "                        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, :]);"
+      "L = np.dot(likelihood_x[:, None], likelihood_y[None, :])\n"
      ],
      "language": "python",
      "metadata": {},
@@ -232,7 +232,7 @@
       "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);"
+      "plt.ylim(0, 5)\n"
      ],
      "language": "python",
      "metadata": {},
@@ -324,7 +324,7 @@
       "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], \"...\";"
+      "print data[:10], \"...\"\n"
      ],
      "language": "python",
      "metadata": {},
@@ -378,7 +378,7 @@
       "\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], \"...\";"
+      "print assignment.value[:10], \"...\"\n"
      ],
      "language": "python",
      "metadata": {},
@@ -423,19 +423,17 @@
       "to a set of parameters, located in the (1,2) arrays `taus` and `centers`.\n",
       "\"\"\"\n",
       "\n",
-      "\n",
       "@pm.deterministic\n",
       "def center_i(assignment=assignment, centers=centers):\n",
       "    return centers[assignment]\n",
       "\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], \"...\";"
+      "print \"Assigned precision: \", tau_i.value[:4], \"...\"\n"
      ],
      "language": "python",
      "metadata": {},
@@ -460,7 +458,7 @@
       "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, taus, centers]);"
+      "model = pm.Model([p, assignment, taus, centers])\n"
      ],
      "language": "python",
      "metadata": {},
@@ -483,7 +481,7 @@
      "collapsed": false,
      "input": [
       "mcmc = pm.MCMC(model)\n",
-      "mcmc.sample(50000);"
+      "mcmc.sample(50000)\n"
      ],
      "language": "python",
      "metadata": {},
@@ -535,18 +533,18 @@
       "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",
+      "     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",
+      "     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",
+      "     color=\"#467821\", lw=lw)\n",
       "plt.xlabel(\"Steps\")\n",
       "plt.ylim(0, 1)\n",
-      "plt.legend();"
+      "plt.legend()\n"
      ],
      "language": "python",
      "metadata": {},
@@ -582,7 +580,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "mcmc.sample(100000);"
+      "mcmc.sample(100000)\n"
      ],
      "language": "python",
      "metadata": {},
@@ -615,9 +613,9 @@
       "\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",
+      "     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",
+      "     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",
@@ -626,7 +624,7 @@
       "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\");"
+      "plt.xlabel(\"Steps\")\n"
      ],
      "language": "python",
      "metadata": {},
@@ -673,7 +671,7 @@
       "             histtype=\"stepfilled\")\n",
       "    # plt.autoscale(tight=True)\n",
       "\n",
-      "plt.tight_layout();"
+      "plt.tight_layout()\n"
      ],
      "language": "python",
      "metadata": {},
@@ -706,12 +704,12 @@
       "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",
+      "       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",
+      "       [\"%.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\");"
+      "plt.title(\"Posterior labels of data points\")\n"
      ],
      "language": "python",
      "metadata": {},
@@ -741,12 +739,12 @@
       "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",
+      "        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\");"
+      "plt.xlabel(\"value of data point\")\n"
      ],
      "language": "python",
      "metadata": {},
@@ -790,7 +788,7 @@
       "posterior_p_mean = mcmc.trace(\"p\")[:].mean()\n",
       "\n",
       "plt.hist(data, bins=20, histtype=\"step\", normed=True, color=\"k\",\n",
-      "         lw=2, label=\"histogram of data\")\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",
@@ -802,7 +800,7 @@
       "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\");"
+      "plt.title(\"Visualizing Clusters using posterior-mean parameters\")\n"
      ],
      "language": "python",
      "metadata": {},
@@ -853,7 +851,7 @@
       "\n",
       "plt.plot(ex_mcmc.trace(\"x\")[:])\n",
       "plt.plot(ex_mcmc.trace(\"y\")[:])\n",
-      "plt.title(\"Displaying (extreme) case of dependence between unknowns\");"
+      "plt.title(\"Displaying (extreme) case of dependence between unknowns\")\n"
      ],
      "language": "python",
      "metadata": {},
@@ -942,7 +940,7 @@
       "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();"
+      "print \"Probability of belonging to cluster 1:\", v.mean()\n"
      ],
      "language": "python",
      "metadata": {},
@@ -1036,7 +1034,7 @@
       "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();"
+      "plt.legend()\n"
      ],
      "language": "python",
      "metadata": {},
@@ -1082,7 +1080,7 @@
       "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.\");"
+      "plt.title(\"Autocorrelation plot of $y_t$ and $x_t$ for differing $k$ lags.\")\n"
      ],
      "language": "python",
      "metadata": {},
@@ -1142,7 +1140,7 @@
       "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.\");"
+      "at differing $k$ lags.\")\n"
      ],
      "language": "python",
      "metadata": {},
@@ -1189,7 +1187,7 @@
       "from pymc.Matplot import plot as mcplot\n",
       "\n",
       "mcmc.sample(25000, 0, 10)\n",
-      "mcplot(mcmc.trace(\"centers\", 2), common_scale=False);"
+      "mcplot(mcmc.trace(\"centers\", 2), common_scale=False)\n"
      ],
      "language": "python",
      "metadata": {},
@@ -1312,7 +1310,7 @@
       "def css_styling():\n",
       "    styles = open(\"../styles/custom.css\", \"r\").read()\n",
       "    return HTML(styles)\n",
-      "css_styling();"
+      "css_styling()\n"
      ],
      "language": "python",
      "metadata": {},