Not adding or removing semicolons

Author: eli-b

Chapter6_Priorities/Priors.ipynb

@@ -457,7 +457,7 @@
       "\n",
       "        self.bb_score = np.r_[self.bb_score, bb_score]\n",
       "        self.choices = np.r_[self.choices, choices]\n",
-      "        return;"
+      "        return"
      ],
      "language": "python",
      "metadata": {},
@@ -498,7 +498,7 @@
       "        plt.title(\"Posteriors After %d pull\" % bayesian_strategy.N +\n",
       "                  \"s\" * (bayesian_strategy.N > 1))\n",
       "        plt.autoscale(tight=True)\n",
-      "    return;"
+      "    return"
      ],
      "language": "python",
      "metadata": {},
@@ -521,7 +521,7 @@
       "    plot_priors(bayesian_strat, hidden_prob)\n",
       "    # plt.legend()\n",
       "    plt.autoscale(tight=True)\n",
-      "plt.tight_layout();"
+      "plt.tight_layout()"
      ],
      "language": "python",
      "metadata": {},
@@ -555,7 +555,7 @@
       "from IPython.core.display import HTML\n",
       "\n",
       "# try executing the below command twice if the first time doesn't work\n",
-      "HTML(filename=\"BanditsD3.html\");"
+      "HTML(filename=\"BanditsD3.html\")"
      ],
      "language": "python",
      "metadata": {},
@@ -719,7 +719,7 @@
       "              random_choice]\n",
       "algos = []\n",
       "for strat in strategies:\n",
-      "    algos.append(GeneralBanditStrat(bandits, strat));"
+      "    algos.append(GeneralBanditStrat(bandits, strat))"
      ],
      "language": "python",
      "metadata": {},
@@ -741,7 +741,7 @@
       "\n",
       "plt.title(\"Total Regret of Bayesian Bandits Strategy vs. Random guessing\")\n",
       "plt.xlabel(\"Number of pulls\")\n",
-      "plt.ylabel(\"Regret after $n$ pulls\")\n",
+      "plt.ylabel(\"Regret after $n$ pulls\");\n",
       "plt.legend(loc=\"upper left\");"
      ],
      "language": "python",
@@ -795,7 +795,7 @@
       "\n",
       "plt.title(\"Expected Total Regret of Multi-armed Bandit strategies\")\n",
       "plt.xlabel(\"Number of pulls\")\n",
-      "plt.ylabel(\"Exepected Total Regret \\n after $n$ pulls\")\n",
+      "plt.ylabel(\"Exepected Total Regret \\n after $n$ pulls\");\n",
       "plt.legend(loc=\"upper left\");"
      ],
      "language": "python",
@@ -850,8 +850,8 @@
       "plt.legend([pl1, pl2, pl3],\n",
       "           [\"Upper Credible Bound\", \"Bayesian Bandit\", \"UCB-Bayes\"],\n",
       "           loc=\"upper left\")\n",
-      "plt.ylabel(\"Exepected Total Regret \\n after $\\log{n}$ pulls\")\n",
-      "plt.title(\"log-scale of above\")\n",
+      "plt.ylabel(\"Exepected Total Regret \\n after $\\log{n}$ pulls\");\n",
+      "plt.title(\"log-scale of above\");\n",
       "plt.ylabel(\"Exepected Total Regret \\n after $\\log{n}$ pulls\");"
      ],
      "language": "python",
@@ -926,7 +926,7 @@
       "    bayesian_strat.sample_bandits(i)\n",
       "    plot_priors(bayesian_strat, hidden_prob, lw=2, alpha=0.0, plt_vlines=False)\n",
       "    # plt.legend()\n",
-      "    plt.xlim(0, 0.5);"
+      "    plt.xlim(0, 0.5)"
      ],
      "language": "python",
      "metadata": {},
@@ -1044,7 +1044,7 @@
       "    plt.title(name + \" prior\")\n",
       "    plt.vlines(0, 0, y.max(), \"k\", \"--\", linewidth=0.5)\n",
       "    plt.xlim(-0.15, 0.15)\n",
-      "plt.tight_layout();"
+      "plt.tight_layout()"
      ],
      "language": "python",
      "metadata": {},
@@ -1081,7 +1081,7 @@
       "prior_std = np.array([x[1] for x in expert_prior_params.values()])\n",
       "\n",
       "inv_cov_matrix = pm.Wishart(\"inv_cov_matrix\", n_observations, np.diag(prior_std ** 2))\n",
-      "mu = pm.Normal(\"returns\", prior_mu, 1, size=4);"
+      "mu = pm.Normal(\"returns\", prior_mu, 1, size=4)"
      ],
      "language": "python",
      "metadata": {},
@@ -1122,7 +1122,7 @@
       "    _previous_day = np.roll(stock_closes[stock], -1)\n",
       "    stock_returns[stock] = ((stock_closes[stock] - _previous_day) / _previous_day)[:n_observations]\n",
       "\n",
-      "dates = map(lambda x: datetime.datetime.strptime(x, \"%Y-%m-%d\"), x[1:n_observations + 1, 0]);"
+      "dates = map(lambda x: datetime.datetime.strptime(x, \"%Y-%m-%d\"), x[1:n_observations + 1, 0])"
      ],
      "language": "python",
      "metadata": {},
@@ -1141,7 +1141,7 @@
       "\n",
       "plt.xticks(np.arange(100)[::-8],\n",
       "           map(lambda x: datetime.datetime.strftime(x, \"%Y-%m-%d\"), dates[::8]),\n",
-      "           rotation=60)\n",
+      "           rotation=60);\n",
       "\n",
       "plt.legend(loc=\"upper left\")\n",
       "plt.title(\"Return space\")\n",
@@ -1210,7 +1210,7 @@
       "model = pm.Model([obs, mu, inv_cov_matrix])\n",
       "mcmc = pm.MCMC()\n",
       "\n",
-      "mcmc.sample(150000, 100000, 3);"
+      "mcmc.sample(150000, 100000, 3)"
      ],
      "language": "python",
      "metadata": {},
@@ -1292,7 +1292,7 @@
       "    plt.xlim(-0.15, 0.15)\n",
       "\n",
       "plt.suptitle(\"Posterior distribution of daily stock returns\")\n",
-      "plt.tight_layout();"
+      "plt.tight_layout()"
      ],
      "language": "python",
      "metadata": {},
@@ -1346,7 +1346,7 @@
       "plt.subplot(1, 2, 2)\n",
       "plt.bar(np.arange(4), np.sqrt(np.diag(mean_covariance_matrix)),\n",
       "        color=\"#348ABD\", alpha=0.7)\n",
-      "plt.xticks(np.arange(4) + 0.5, stock_returns.keys())\n",
+      "plt.xticks(np.arange(4) + 0.5, stock_returns.keys());\n",
       "plt.title(\"(mean posterior) variances of daily stock returns\")\n",
       "\n",
       "plt.tight_layout();"
@@ -1566,7 +1566,7 @@
       "    plt.fill_between(x, 0, y2, color=\"#A60628\", alpha=0.15)\n",
       "    plt.legend(title=\"N=%d\" % N)\n",
       "    plt.vlines(p, 0.0, 7.5, linestyles=\"--\", linewidth=1)\n",
-      "    #plt.ylim( 0, 10)#;"
+      "    #plt.ylim( 0, 10)#"
      ],
      "language": "python",
      "metadata": {},
@@ -1718,7 +1718,7 @@
       "def css_styling():\n",
       "    styles = open(\"../styles/custom.css\", \"r\").read()\n",
       "    return HTML(styles)\n",
-      "css_styling();"
+      "css_styling()"
      ],
      "language": "python",
      "metadata": {},