Ending code cells with a semicolon.

Author: eli-b

Chapter5_LossFunctions/LossFunctions.ipynb

@@ -215,7 +215,7 @@
       "                       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()])\n"
+      "plt.legend([p3], [sp3.get_label()]);"
      ],
      "language": "python",
      "metadata": {},
@@ -260,7 +260,7 @@
       "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\")[:]\n"
+      "price_trace = mcmc.trace(\"true_price\")[:];"
      ],
      "language": "python",
      "metadata": {},
@@ -300,7 +300,7 @@
       "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]), label=\"posterior's mean\", linestyles=\"-.\")\n",
-      "plt.legend(loc=\"upper left\")\n"
+      "plt.legend(loc=\"upper left\");"
      ],
      "language": "python",
      "metadata": {},
@@ -381,7 +381,7 @@
       "plt.legend(loc=\"upper left\", title=\"Risk parameter\")\n",
       "plt.xlabel(\"price bid\")\n",
       "plt.ylabel(\"expected loss\")\n",
-      "plt.xlim(5000, 30000)\n"
+      "plt.xlim(5000, 30000);"
      ],
      "language": "python",
      "metadata": {},
@@ -434,7 +434,7 @@
       "plt.xlabel(\"price guess\")\n",
       "plt.ylabel(\"expected loss\")\n",
       "plt.xlim(7000, 30000)\n",
-      "plt.ylim(-1000, 80000)\n"
+      "plt.ylim(-1000, 80000);"
      ],
      "language": "python",
      "metadata": {},
@@ -594,7 +594,7 @@
       "true_value = -.02\n",
       "plt.plot(pred, [stock_loss(true_value, _p) for _p in pred], alpha=0.6, 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"
+      "plt.title(\"Stock returns loss if true value=0.05, -0.02\");"
      ],
      "language": "python",
      "metadata": {},
@@ -642,7 +642,7 @@
       "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\")\n"
+      "plt.legend(loc=\"upper left\");"
      ],
      "language": "python",
      "metadata": {},
@@ -695,7 +695,7 @@
       "mcmc = pm.MCMC([obs, beta, alpha, std, prec])\n",
       "\n",
       "mcmc.sample(100000, 80000)\n",
-      "mcplot(mcmc)\n"
+      "mcplot(mcmc);"
      ],
      "language": "python",
      "metadata": {},
@@ -783,6 +783,7 @@
       "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",
@@ -816,7 +817,7 @@
       "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\")\n"
+      "plt.legend(loc=\"upper left\");"
      ],
      "language": "python",
      "metadata": {},
@@ -905,7 +906,7 @@
       "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.ylabel(\"y-position\");"
      ],
      "language": "python",
      "metadata": {},
@@ -1012,7 +1013,7 @@
       "\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) * tangential_distance(glx_pos, h_pos)\n"
+      "    return mass / f_distance(glx_pos, h_pos, 240) * tangential_distance(glx_pos, h_pos);"
      ],
      "language": "python",
      "metadata": {},
@@ -1028,7 +1029,7 @@
       "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)\n"
+      "mcmc.sample(200000, 140000, 3);"
      ],
      "language": "python",
      "metadata": {},
@@ -1070,7 +1071,7 @@
       "plt.ylabel(\"y-position\")\n",
       "scatter(t[:, 0], t[:, 1], alpha=0.015, c=\"r\")\n",
       "plt.xlim(0, 4200)\n",
-      "plt.ylim(0, 4200)\n"
+      "plt.ylim(0, 4200);"
      ],
      "language": "python",
      "metadata": {},
@@ -1099,7 +1100,7 @@
       "                          delimiter=\",\",\n",
       "                          usecols=[1, 2, 3, 4, 5, 6, 7, 8, 9],\n",
       "                          skip_header=1)\n",
-      "print halo_data[n_sky]\n"
+      "print halo_data[n_sky];"
      ],
      "language": "python",
      "metadata": {},
@@ -1139,7 +1140,7 @@
       "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"
+      "print \"True halo location:\", halo_data[n_sky][3], halo_data[n_sky][4];"
      ],
      "language": "python",
      "metadata": {},
@@ -1170,7 +1171,7 @@
      "collapsed": false,
      "input": [
       "mean_posterior = t.mean(axis=0).reshape(1, 2)\n",
-      "print mean_posterior\n"
+      "print mean_posterior;"
      ],
      "language": "python",
      "metadata": {},
@@ -1208,7 +1209,7 @@
       "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, x_ref_all, y_ref_all, random_guess)\n",
-      "print\n"
+      "print;"
      ],
      "language": "python",
      "metadata": {},
@@ -1307,7 +1308,7 @@
       "\n",
       "    mcmc = pm.MCMC([ellpty, mean, halo_positions, mass_large])\n",
       "    mcmc.sample(samples, burn_in, thin)\n",
-      "    return mcmc.trace(\"halo_positions\")[:]\n"
+      "    return mcmc.trace(\"halo_positions\")[:];"
      ],
      "language": "python",
      "metadata": {},
@@ -1323,7 +1324,7 @@
       "                     dtype=None,\n",
       "                     skip_header=1,\n",
       "                     delimiter=\",\",\n",
-      "                     usecols=[1, 2, 3, 4])\n"
+      "                     usecols=[1, 2, 3, 4]);"
      ],
      "language": "python",
      "metadata": {},
@@ -1338,7 +1339,7 @@
       "samples = 10.5e5\n",
       "traces = halo_posteriors(3, data, samples=samples,\n",
       "                         burn_in=9.5e5,\n",
-      "                         thin=10)\n"
+      "                         thin=10);"
      ],
      "language": "python",
      "metadata": {},
@@ -1383,7 +1384,7 @@
       "\n",
       "# plt.legend(scatterpoints=1)\n",
       "plt.xlim(0, 4200)\n",
-      "plt.ylim(0, 4200)\n"
+      "plt.ylim(0, 4200);"
      ],
      "language": "python",
      "metadata": {},
@@ -1434,7 +1435,7 @@
       "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, x_ref_all, y_ref_all, random_guess)\n",
-      "print\n"
+      "print;"
      ],
      "language": "python",
      "metadata": {},
@@ -1514,7 +1515,7 @@
       "def css_styling():\n",
       "    styles = open(\"../styles/custom.css\", \"r\").read()\n",
       "    return HTML(styles)\n",
-      "css_styling()\n"
+      "css_styling();"
      ],
      "language": "python",
      "metadata": {},