Merge branch 'chap7-style'

Author: eli-b

Chapter7_BayesianMachineLearning/DontOverfit.ipynb

@@ -33,8 +33,7 @@
       "url = \"https://dl.dropbox.com/s/lnly9gw8pb1xhir/overfitting.zip\"\n",
       "\n",
       "\n",
-      "results = requests.get(url)\n",
-      "\n"
+      "results = requests.get(url);"
      ],
      "language": "python",
      "metadata": {},
@@ -47,8 +46,7 @@
      "input": [
       "import StringIO\n",
       "z = zipfile.ZipFile(StringIO.StringIO(results.content))\n",
-      "#z.extractall()\n",
-      "\n"
+      "# z.extractall();;"
      ],
      "language": "python",
      "metadata": {},
@@ -59,7 +57,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "z.extractall()"
+      "z.extractall();"
      ],
      "language": "python",
      "metadata": {},
@@ -70,7 +68,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "z.namelist()"
+      "z.namelist();"
      ],
      "language": "python",
      "metadata": {},
@@ -90,7 +88,7 @@
      "collapsed": false,
      "input": [
       "d = z.open('overfitting.csv')\n",
-      "d.readline()"
+      "d.readline();"
      ],
      "language": "python",
      "metadata": {},
@@ -109,7 +107,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "import numpy as np"
+      "import numpy as np;"
      ],
      "language": "python",
      "metadata": {},
@@ -120,7 +118,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "M = np.fromstring(d.read(), sep=\",\" )"
+      "M = np.fromstring(d.read(), sep=\",\");"
      ],
      "language": "python",
      "metadata": {},
@@ -131,7 +129,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "len(d.read())"
+      "len(d.read());"
      ],
      "language": "python",
      "metadata": {},
@@ -169,7 +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": {},
@@ -195,7 +193,7 @@
       "\n",
       "\"\"\"\n",
       "\n",
-      "data.shape"
+      "data.shape;"
      ],
      "language": "python",
      "metadata": {},
@@ -234,17 +232,17 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "ix_training = data[:,1] == 1\n",
-      "ix_testing = data[:,1] == 0\n",
+      "ix_training = data[:, 1] == 1\n",
+      "ix_testing = data[:, 1] == 0\n",
       "\n",
-      "training_data = data[ ix_training, 5: ]\n",
-      "testing_data = data[ ix_testing, 5: ]\n",
+      "training_data = data[ix_training, 5:]\n",
+      "testing_data = data[ix_testing, 5:]\n",
       "\n",
-      "training_labels = data[ ix_training, 2]\n",
-      "testing_labels = data[ ix_testing, 2]\n",
+      "training_labels = data[ix_training, 2]\n",
+      "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": {},
@@ -280,7 +278,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "figsize( 12, 4 )"
+      "figsize(12, 4);"
      ],
      "language": "python",
      "metadata": {},
@@ -291,8 +289,8 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "hist( training_data.flatten() )\n",
-      "print training_data.shape[0]*training_data.shape[1]"
+      "hist(training_data.flatten())\n",
+      "print training_data.shape[0] * training_data.shape[1];"
      ],
      "language": "python",
      "metadata": {},
@@ -324,7 +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": {},
@@ -335,7 +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": {},
@@ -347,9 +345,9 @@
      "collapsed": false,
      "input": [
       "@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()"
+      "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();"
      ],
      "language": "python",
      "metadata": {},
@@ -361,8 +359,8 @@
      "collapsed": false,
      "input": [
       "@pm.deterministic\n",
-      "def T( z = Z ):\n",
-      "    return 0.45*(np.sign(z) + 1.1)"
+      "def T(z=Z):\n",
+      "    return 0.45 * (np.sign(z) + 1.1);"
      ],
      "language": "python",
      "metadata": {},
@@ -373,11 +371,11 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "obs = pm.Bernoulli( \"obs\", T, value = training_labels, observed = True)\n",
+      "obs = pm.Bernoulli(\"obs\", T, value=training_labels, observed=True)\n",
       "\n",
-      "model = pm.Model( [to_include, coef, Z, T, obs] )\n",
-      "map_ = pm.MAP( model )\n",
-      "map_.fit()"
+      "model = pm.Model([to_include, coef, Z, T, obs])\n",
+      "map_ = pm.MAP(model)\n",
+      "map_.fit();"
      ],
      "language": "python",
      "metadata": {},
@@ -396,7 +394,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "mcmc = pm.MCMC( model )"
+      "mcmc = pm.MCMC(model);"
      ],
      "language": "python",
      "metadata": {},
@@ -407,7 +405,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "mcmc.sample(100000, 90000,1) "
+      "mcmc.sample(100000, 90000, 1);"
      ],
      "language": "python",
      "metadata": {},
@@ -434,7 +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": {},
@@ -454,7 +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": {},
@@ -473,7 +471,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "t_mean = np.round( t_trace).mean(axis=1)\n"
+      "t_mean = np.round(t_trace).mean(axis=1);"
      ],
      "language": "python",
      "metadata": {},
@@ -484,8 +482,8 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "imshow(t_trace[-10000:,:], aspect=\"auto\")\n",
-      "colorbar()"
+      "imshow(t_trace[-10000:, :], aspect=\"auto\")\n",
+      "colorbar();"
      ],
      "language": "python",
      "metadata": {},
@@ -508,9 +506,9 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "figsize( 23, 8)\n",
+      "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": {},
@@ -533,7 +531,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "include_trace = mcmc.trace(\"to_include\")[:]"
+      "include_trace = mcmc.trace(\"to_include\")[:];"
      ],
      "language": "python",
      "metadata": {},
@@ -544,8 +542,8 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "figsize( 23, 8)\n",
-      "imshow(include_trace[-10000:,:], aspect=\"auto\", interpolation=\"none\")"
+      "figsize(23, 8)\n",
+      "imshow(include_trace[-10000:, :], aspect=\"auto\", interpolation=\"none\");"
      ],
      "language": "python",
      "metadata": {},

Chapter7_BayesianMachineLearning/MachineLearning.ipynb

@@ -67,10 +67,12 @@
      "collapsed": false,
      "input": [
       "from IPython.core.display import HTML\n",
+      "\n",
+      "\n",
       "def css_styling():\n",
       "    styles = open(\"../styles/custom.css\", \"r\").read()\n",
       "    return HTML(styles)\n",
-      "css_styling()"
+      "css_styling();"
      ],
      "language": "python",
      "metadata": {},