Merge branch 'chap7-style' into examples-style

Author: eli-b

Chapter7_BayesianMachineLearning/DontOverfit.ipynb

@@ -33,7 +33,7 @@
       "url = \"https://dl.dropbox.com/s/lnly9gw8pb1xhir/overfitting.zip\"\n",
       "\n",
       "\n",
-      "results = requests.get(url);"
+      "results = requests.get(url)"
      ],
      "language": "python",
      "metadata": {},
@@ -46,7 +46,7 @@
      "input": [
       "import StringIO\n",
       "z = zipfile.ZipFile(StringIO.StringIO(results.content))\n",
-      "# z.extractall();;"
+      "# z.extractall()"
      ],
      "language": "python",
      "metadata": {},
@@ -57,7 +57,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "z.extractall();"
+      "z.extractall()"
      ],
      "language": "python",
      "metadata": {},
@@ -68,7 +68,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "z.namelist();"
+      "z.namelist()"
      ],
      "language": "python",
      "metadata": {},
@@ -88,7 +88,7 @@
      "collapsed": false,
      "input": [
       "d = z.open('overfitting.csv')\n",
-      "d.readline();"
+      "d.readline()"
      ],
      "language": "python",
      "metadata": {},
@@ -107,7 +107,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "import numpy as np;"
+      "import numpy as np"
      ],
      "language": "python",
      "metadata": {},
@@ -118,7 +118,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "M = np.fromstring(d.read(), sep=\",\");"
+      "M = np.fromstring(d.read(), sep=\",\")"
      ],
      "language": "python",
      "metadata": {},
@@ -129,7 +129,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "len(d.read());"
+      "len(d.read())"
      ],
      "language": "python",
      "metadata": {},
@@ -167,7 +167,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "data = np.loadtxt(\"overfitting.csv\", delimiter=\",\", skiprows=1);"
+      "data = np.loadtxt(\"overfitting.csv\", delimiter=\",\", skiprows=1)"
      ],
      "language": "python",
      "metadata": {},
@@ -193,7 +193,7 @@
       "\n",
       "\"\"\"\n",
       "\n",
-      "data.shape;"
+      "data.shape"
      ],
      "language": "python",
      "metadata": {},
@@ -242,7 +242,7 @@
       "testing_labels = data[ix_testing, 2]\n",
       "\n",
       "print \"training:\", training_data.shape, training_labels.shape\n",
-      "print \"testing: \", testing_data.shape, testing_labels.shape;"
+      "print \"testing: \", testing_data.shape, testing_labels.shape"
      ],
      "language": "python",
      "metadata": {},
@@ -278,7 +278,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "figsize(12, 4);"
+      "figsize(12, 4)"
      ],
      "language": "python",
      "metadata": {},
@@ -290,7 +290,7 @@
      "collapsed": false,
      "input": [
       "hist(training_data.flatten())\n",
-      "print training_data.shape[0] * training_data.shape[1];"
+      "print training_data.shape[0] * training_data.shape[1]"
      ],
      "language": "python",
      "metadata": {},
@@ -322,7 +322,7 @@
      "input": [
       "import pymc as pm\n",
       "\n",
-      "to_include = pm.Bernoulli(\"to_include\", 0.5, size=200);"
+      "to_include = pm.Bernoulli(\"to_include\", 0.5, size=200)"
      ],
      "language": "python",
      "metadata": {},
@@ -333,7 +333,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "coef = pm.Uniform(\"coefs\", 0, 1, size=200);"
+      "coef = pm.Uniform(\"coefs\", 0, 1, size=200)"
      ],
      "language": "python",
      "metadata": {},
@@ -347,7 +347,7 @@
       "@pm.deterministic\n",
       "def Z(coef=coef, to_include=to_include, data=training_data):\n",
       "    ym = np.dot(to_include * training_data, coef)\n",
-      "    return ym - ym.mean();"
+      "    return ym - ym.mean()"
      ],
      "language": "python",
      "metadata": {},
@@ -360,7 +360,7 @@
      "input": [
       "@pm.deterministic\n",
       "def T(z=Z):\n",
-      "    return 0.45 * (np.sign(z) + 1.1);"
+      "    return 0.45 * (np.sign(z) + 1.1)"
      ],
      "language": "python",
      "metadata": {},
@@ -375,7 +375,7 @@
       "\n",
       "model = pm.Model([to_include, coef, Z, T, obs])\n",
       "map_ = pm.MAP(model)\n",
-      "map_.fit();"
+      "map_.fit()"
      ],
      "language": "python",
      "metadata": {},
@@ -394,7 +394,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "mcmc = pm.MCMC(model);"
+      "mcmc = pm.MCMC(model)"
      ],
      "language": "python",
      "metadata": {},
@@ -405,7 +405,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "mcmc.sample(100000, 90000, 1);"
+      "mcmc.sample(100000, 90000, 1)"
      ],
      "language": "python",
      "metadata": {},
@@ -432,7 +432,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "(np.round(T.value) == training_labels).mean();"
+      "(np.round(T.value) == training_labels).mean()"
      ],
      "language": "python",
      "metadata": {},
@@ -452,7 +452,7 @@
      "collapsed": false,
      "input": [
       "t_trace = mcmc.trace(\"T\")[:]\n",
-      "(np.round(t_trace[-500:-400, :]).mean(axis=0) == training_labels).mean();"
+      "(np.round(t_trace[-500:-400, :]).mean(axis=0) == training_labels).mean()"
      ],
      "language": "python",
      "metadata": {},
@@ -471,7 +471,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "t_mean = np.round(t_trace).mean(axis=1);"
+      "t_mean = np.round(t_trace).mean(axis=1)"
      ],
      "language": "python",
      "metadata": {},
@@ -483,7 +483,7 @@
      "collapsed": false,
      "input": [
       "imshow(t_trace[-10000:, :], aspect=\"auto\")\n",
-      "colorbar();"
+      "colorbar()"
      ],
      "language": "python",
      "metadata": {},
@@ -508,7 +508,7 @@
      "input": [
       "figsize(23, 8)\n",
       "coef_trace = mcmc.trace(\"coefs\")[:]\n",
-      "imshow(coef_trace[-10000:, :], aspect=\"auto\", cmap=pyplot.cm.RdBu, interpolation=\"none\");"
+      "imshow(coef_trace[-10000:, :], aspect=\"auto\", cmap=pyplot.cm.RdBu, interpolation=\"none\")"
      ],
      "language": "python",
      "metadata": {},
@@ -531,7 +531,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "include_trace = mcmc.trace(\"to_include\")[:];"
+      "include_trace = mcmc.trace(\"to_include\")[:]"
      ],
      "language": "python",
      "metadata": {},
@@ -543,7 +543,7 @@
      "collapsed": false,
      "input": [
       "figsize(23, 8)\n",
-      "imshow(include_trace[-10000:, :], aspect=\"auto\", interpolation=\"none\");"
+      "imshow(include_trace[-10000:, :], aspect=\"auto\", interpolation=\"none\")"
      ],
      "language": "python",
      "metadata": {},

Chapter7_BayesianMachineLearning/MachineLearning.ipynb

@@ -72,7 +72,7 @@
       "def css_styling():\n",
       "    styles = open(\"../styles/custom.css\", \"r\").read()\n",
       "    return HTML(styles)\n",
-      "css_styling();"
+      "css_styling()"
      ],
      "language": "python",
      "metadata": {},