From a49a1b61a7196cf8d4689e8d0174970645f66fa8 Mon Sep 17 00:00:00 2001
From: Eric Schles <ericschles@gmail.com>
Date: Wed, 4 Jan 2017 15:53:48 -0500
Subject: [PATCH 1/2] updating

---
 ...on to Probabilistic Graphical Models.ipynb | 893 +++++++++---------
 notebooks/2. Bayesian Networks.ipynb          |  33 +-
 2 files changed, 483 insertions(+), 443 deletions(-)

diff --git a/notebooks/1. Introduction to Probabilistic Graphical Models.ipynb b/notebooks/1. Introduction to Probabilistic Graphical Models.ipynb
index 8de408d..3f62767 100644
--- a/notebooks/1. Introduction to Probabilistic Graphical Models.ipynb	
+++ b/notebooks/1. Introduction to Probabilistic Graphical Models.ipynb	
@@ -35,7 +35,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "metadata": {
     "collapsed": false
    },
@@ -55,154 +55,142 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>47</th>\n",
+       "      <th>0</th>\n",
        "      <td>5</td>\n",
-       "      <td>3</td>\n",
+       "      <td>4</td>\n",
        "      <td>0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>92</th>\n",
-       "      <td>6</td>\n",
+       "      <th>118</th>\n",
+       "      <td>8</td>\n",
        "      <td>3</td>\n",
-       "      <td>1</td>\n",
+       "      <td>2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>5</td>\n",
-       "      <td>4</td>\n",
-       "      <td>0</td>\n",
+       "      <th>82</th>\n",
+       "      <td>6</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>106</th>\n",
-       "      <td>5</td>\n",
-       "      <td>2</td>\n",
+       "      <th>87</th>\n",
+       "      <td>6</td>\n",
        "      <td>2</td>\n",
+       "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>40</th>\n",
-       "      <td>5</td>\n",
-       "      <td>4</td>\n",
-       "      <td>0</td>\n",
+       "      <th>78</th>\n",
+       "      <td>6</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>5</td>\n",
+       "      <th>116</th>\n",
+       "      <td>6</td>\n",
        "      <td>3</td>\n",
-       "      <td>0</td>\n",
+       "      <td>2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>10</th>\n",
+       "      <th>16</th>\n",
        "      <td>5</td>\n",
        "      <td>4</td>\n",
        "      <td>0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>35</th>\n",
-       "      <td>5</td>\n",
-       "      <td>3</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>22</th>\n",
+       "      <th>10</th>\n",
        "      <td>5</td>\n",
        "      <td>4</td>\n",
        "      <td>0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>123</th>\n",
-       "      <td>6</td>\n",
-       "      <td>3</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>139</th>\n",
+       "      <th>112</th>\n",
        "      <td>7</td>\n",
        "      <td>3</td>\n",
        "      <td>2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>117</th>\n",
-       "      <td>8</td>\n",
-       "      <td>4</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
        "      <th>23</th>\n",
        "      <td>5</td>\n",
        "      <td>3</td>\n",
        "      <td>0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>134</th>\n",
-       "      <td>6</td>\n",
-       "      <td>3</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>39</th>\n",
+       "      <th>19</th>\n",
        "      <td>5</td>\n",
-       "      <td>3</td>\n",
+       "      <td>4</td>\n",
        "      <td>0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>37</th>\n",
-       "      <td>5</td>\n",
+       "      <th>96</th>\n",
+       "      <td>6</td>\n",
        "      <td>3</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>32</th>\n",
-       "      <td>5</td>\n",
-       "      <td>4</td>\n",
-       "      <td>0</td>\n",
+       "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>55</th>\n",
+       "      <th>64</th>\n",
        "      <td>6</td>\n",
        "      <td>3</td>\n",
        "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>57</th>\n",
-       "      <td>5</td>\n",
+       "      <th>68</th>\n",
+       "      <td>6</td>\n",
        "      <td>2</td>\n",
        "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>15</th>\n",
+       "      <th>133</th>\n",
        "      <td>6</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>76</th>\n",
+       "      <td>7</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>48</th>\n",
+       "      <td>5</td>\n",
        "      <td>4</td>\n",
        "      <td>0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>146</th>\n",
-       "      <td>6</td>\n",
-       "      <td>2</td>\n",
+       "      <th>139</th>\n",
+       "      <td>7</td>\n",
+       "      <td>3</td>\n",
        "      <td>2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>114</th>\n",
+       "      <th>128</th>\n",
        "      <td>6</td>\n",
        "      <td>3</td>\n",
        "      <td>2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>102</th>\n",
-       "      <td>7</td>\n",
+       "      <th>105</th>\n",
+       "      <td>8</td>\n",
        "      <td>3</td>\n",
        "      <td>2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>78</th>\n",
+       "      <th>55</th>\n",
        "      <td>6</td>\n",
        "      <td>3</td>\n",
        "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>82</th>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>121</th>\n",
        "      <td>6</td>\n",
        "      <td>3</td>\n",
-       "      <td>1</td>\n",
+       "      <td>2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>79</th>\n",
@@ -211,55 +199,73 @@
        "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>71</th>\n",
+       "      <th>132</th>\n",
        "      <td>6</td>\n",
        "      <td>3</td>\n",
-       "      <td>1</td>\n",
+       "      <td>2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>77</th>\n",
+       "      <th>108</th>\n",
        "      <td>7</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>88</th>\n",
+       "      <td>6</td>\n",
        "      <td>3</td>\n",
        "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>20</th>\n",
+       "      <th>93</th>\n",
        "      <td>5</td>\n",
-       "      <td>3</td>\n",
-       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>16</th>\n",
+       "      <th>43</th>\n",
        "      <td>5</td>\n",
        "      <td>4</td>\n",
        "      <td>0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
+       "      <th>74</th>\n",
+       "      <td>6</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
        "      <th>...</th>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>133</th>\n",
-       "      <td>6</td>\n",
+       "      <th>143</th>\n",
+       "      <td>7</td>\n",
        "      <td>3</td>\n",
        "      <td>2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>132</th>\n",
+       "      <th>124</th>\n",
+       "      <td>7</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>127</th>\n",
        "      <td>6</td>\n",
        "      <td>3</td>\n",
        "      <td>2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>38</th>\n",
-       "      <td>4</td>\n",
+       "      <th>67</th>\n",
+       "      <td>6</td>\n",
        "      <td>3</td>\n",
-       "      <td>0</td>\n",
+       "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>144</th>\n",
+       "      <th>141</th>\n",
        "      <td>7</td>\n",
        "      <td>3</td>\n",
        "      <td>2</td>\n",
@@ -271,153 +277,147 @@
        "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>138</th>\n",
+       "      <th>111</th>\n",
        "      <td>6</td>\n",
        "      <td>3</td>\n",
        "      <td>2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>103</th>\n",
+       "      <th>15</th>\n",
        "      <td>6</td>\n",
-       "      <td>3</td>\n",
-       "      <td>2</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>30</th>\n",
+       "      <th>24</th>\n",
        "      <td>5</td>\n",
        "      <td>3</td>\n",
        "      <td>0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>5</td>\n",
+       "      <th>13</th>\n",
+       "      <td>4</td>\n",
        "      <td>3</td>\n",
        "      <td>0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>148</th>\n",
+       "      <th>97</th>\n",
        "      <td>6</td>\n",
        "      <td>3</td>\n",
-       "      <td>2</td>\n",
+       "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>45</th>\n",
-       "      <td>5</td>\n",
+       "      <th>42</th>\n",
+       "      <td>4</td>\n",
        "      <td>3</td>\n",
        "      <td>0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>51</th>\n",
+       "      <th>56</th>\n",
        "      <td>6</td>\n",
        "      <td>3</td>\n",
        "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>56</th>\n",
+       "      <th>61</th>\n",
        "      <td>6</td>\n",
        "      <td>3</td>\n",
        "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>109</th>\n",
-       "      <td>7</td>\n",
+       "      <th>5</th>\n",
+       "      <td>5</td>\n",
        "      <td>4</td>\n",
-       "      <td>2</td>\n",
+       "      <td>0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>110</th>\n",
-       "      <td>6</td>\n",
+       "      <th>122</th>\n",
+       "      <td>8</td>\n",
        "      <td>3</td>\n",
        "      <td>2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>5</td>\n",
-       "      <td>3</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>141</th>\n",
+       "      <th>145</th>\n",
        "      <td>7</td>\n",
        "      <td>3</td>\n",
        "      <td>2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>143</th>\n",
-       "      <td>7</td>\n",
+       "      <th>34</th>\n",
+       "      <td>5</td>\n",
        "      <td>3</td>\n",
-       "      <td>2</td>\n",
+       "      <td>0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>93</th>\n",
-       "      <td>5</td>\n",
+       "      <th>115</th>\n",
+       "      <td>6</td>\n",
+       "      <td>3</td>\n",
        "      <td>2</td>\n",
-       "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>65</th>\n",
-       "      <td>7</td>\n",
+       "      <th>83</th>\n",
+       "      <td>6</td>\n",
        "      <td>3</td>\n",
        "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>24</th>\n",
+       "      <th>9</th>\n",
        "      <td>5</td>\n",
        "      <td>3</td>\n",
        "      <td>0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>69</th>\n",
-       "      <td>6</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
        "      <th>25</th>\n",
        "      <td>5</td>\n",
        "      <td>3</td>\n",
        "      <td>0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>140</th>\n",
-       "      <td>7</td>\n",
+       "      <th>37</th>\n",
+       "      <td>5</td>\n",
        "      <td>3</td>\n",
-       "      <td>2</td>\n",
+       "      <td>0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>28</th>\n",
+       "      <th>22</th>\n",
        "      <td>5</td>\n",
-       "      <td>3</td>\n",
+       "      <td>4</td>\n",
        "      <td>0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>135</th>\n",
-       "      <td>8</td>\n",
+       "      <th>50</th>\n",
+       "      <td>7</td>\n",
        "      <td>3</td>\n",
-       "      <td>2</td>\n",
+       "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>131</th>\n",
-       "      <td>8</td>\n",
-       "      <td>4</td>\n",
+       "      <th>137</th>\n",
+       "      <td>6</td>\n",
+       "      <td>3</td>\n",
        "      <td>2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>42</th>\n",
-       "      <td>4</td>\n",
+       "      <th>138</th>\n",
+       "      <td>6</td>\n",
        "      <td>3</td>\n",
-       "      <td>0</td>\n",
+       "      <td>2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>31</th>\n",
+       "      <th>35</th>\n",
        "      <td>5</td>\n",
        "      <td>3</td>\n",
        "      <td>0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>43</th>\n",
+       "      <th>85</th>\n",
+       "      <td>6</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>47</th>\n",
        "      <td>5</td>\n",
-       "      <td>4</td>\n",
+       "      <td>3</td>\n",
        "      <td>0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
@@ -427,72 +427,72 @@
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-       "110       6      3     2\n",
-       "12        5      3     0\n",
-       "141       7      3     2\n",
-       "143       7      3     2\n",
+       "108       7      2     2\n",
+       "88        6      3     1\n",
        "93        5      2     1\n",
-       "65        7      3     1\n",
+       "43        5      4     0\n",
+       "74        6      3     1\n",
+       "..      ...    ...   ...\n",
+       "143       7      3     2\n",
+       "124       7      3     2\n",
+       "127       6      3     2\n",
+       "67        6      3     1\n",
+       "141       7      3     2\n",
+       "98        5      2     1\n",
+       "111       6      3     2\n",
+       "15        6      4     0\n",
        "24        5      3     0\n",
-       "69        6      2     1\n",
-       "25        5      3     0\n",
-       "140       7      3     2\n",
-       "28        5      3     0\n",
-       "135       8      3     2\n",
-       "131       8      4     2\n",
+       "13        4      3     0\n",
+       "97        6      3     1\n",
        "42        4      3     0\n",
-       "31        5      3     0\n",
-       "43        5      4     0\n",
+       "56        6      3     1\n",
+       "61        6      3     1\n",
+       "5         5      4     0\n",
+       "122       8      3     2\n",
+       "145       7      3     2\n",
+       "34        5      3     0\n",
+       "115       6      3     2\n",
+       "83        6      3     1\n",
+       "9         5      3     0\n",
+       "25        5      3     0\n",
+       "37        5      3     0\n",
+       "22        5      4     0\n",
+       "50        7      3     1\n",
+       "137       6      3     2\n",
+       "138       6      3     2\n",
+       "35        5      3     0\n",
+       "85        6      3     1\n",
+       "47        5      3     0\n",
        "\n",
        "[150 rows x 3 columns]"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -520,7 +520,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 5,
    "metadata": {
     "collapsed": false
    },
@@ -528,18 +528,18 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.collections.PathCollection at 0x7f109313ca58>"
+       "<matplotlib.collections.PathCollection at 0x115c2d668>"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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wFeW+qFUuymOjUCjwve+dIp9PEY8PkEi03OqVVyoFisUpAoElXnnlEJ2dndsc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\nWZXNTyV0Zcvbv7+Tl18eXnFCr1SKNDXp/NrX/hHpdJqZiQn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SSY1SqbQtQoHL5cJjeqg1a3d8XylFPBEnnojTarS4dPoSq4UsXp/CtDu4HIW7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OvtZWGmiGG8i2neP9/jSbhcBuHwHi5dusSuXTvxeDzP86kLgnAXEQqEbc+2bYqNBjMLC4Q9HnRd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\nAP8PLrTUOMYf7YoAAAAASUVORK5CYII=\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f10931b5b00>"
+       "<matplotlib.figure.Figure at 0x115150c18>"
       ]
      },
      "metadata": {},
@@ -567,7 +567,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 16,
    "metadata": {
     "collapsed": false
    },
@@ -575,11 +575,11 @@
     {
      "data": {
       "text/plain": [
-       "array([1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 0,\n",
-       "       1, 0, 1, 1, 0, 1, 1])"
+       "array([2, 2, 1, 1, 0, 2, 2, 2, 0, 2, 1, 1, 0, 2, 2, 2, 2, 1, 0, 0, 2, 2, 0,\n",
+       "       0, 2, 2, 2, 2, 2, 2])"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -597,7 +597,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 17,
    "metadata": {
     "collapsed": false
    },
@@ -605,10 +605,10 @@
     {
      "data": {
       "text/plain": [
-       "0.56666666666666665"
+       "0.59999999999999998"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -617,6 +617,40 @@
     "classifier.score(X_test, y_test)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Just for fun, let's look at what a Support Vector Machine classifier would look like, before moving onto Probabilistic Graphical Models."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "prediction [0 2 1 2 0 2 1 1 0 1 0 0 0 0 1 1 0 1 1 1 2 0 2 1 1 1 2 0 0 2]\n",
+      "0.7\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.svm import SVC\n",
+    "from sklearn.cross_validation import train_test_split\n",
+    "\n",
+    "X_train, X_test, y_train, y_test = train_test_split(data.ix[:, ['length', 'width']].values, data.type.values, test_size=0.2)\n",
+    "clf = SVC()\n",
+    "clf.fit(X_train, y_train)\n",
+    "print(\"prediction\",clf.predict(X_test))\n",
+    "print(clf.score(X_test, y_test))"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -629,7 +663,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 28,
    "metadata": {
     "collapsed": true
    },
@@ -640,7 +674,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 29,
    "metadata": {
     "collapsed": false
    },
@@ -660,154 +694,142 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>47</th>\n",
+       "      <th>0</th>\n",
        "      <td>5</td>\n",
-       "      <td>3</td>\n",
+       "      <td>4</td>\n",
        "      <td>0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>92</th>\n",
-       "      <td>6</td>\n",
+       "      <th>118</th>\n",
+       "      <td>8</td>\n",
        "      <td>3</td>\n",
-       "      <td>1</td>\n",
+       "      <td>2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>5</td>\n",
-       "      <td>4</td>\n",
-       "      <td>0</td>\n",
+       "      <th>82</th>\n",
+       "      <td>6</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>106</th>\n",
-       "      <td>5</td>\n",
-       "      <td>2</td>\n",
+       "      <th>87</th>\n",
+       "      <td>6</td>\n",
        "      <td>2</td>\n",
+       "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>40</th>\n",
-       "      <td>5</td>\n",
-       "      <td>4</td>\n",
-       "      <td>0</td>\n",
+       "      <th>78</th>\n",
+       "      <td>6</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>5</td>\n",
+       "      <th>116</th>\n",
+       "      <td>6</td>\n",
        "      <td>3</td>\n",
-       "      <td>0</td>\n",
+       "      <td>2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>10</th>\n",
+       "      <th>16</th>\n",
        "      <td>5</td>\n",
        "      <td>4</td>\n",
        "      <td>0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>35</th>\n",
-       "      <td>5</td>\n",
-       "      <td>3</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>22</th>\n",
+       "      <th>10</th>\n",
        "      <td>5</td>\n",
        "      <td>4</td>\n",
        "      <td>0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>123</th>\n",
-       "      <td>6</td>\n",
-       "      <td>3</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>139</th>\n",
+       "      <th>112</th>\n",
        "      <td>7</td>\n",
        "      <td>3</td>\n",
        "      <td>2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>117</th>\n",
-       "      <td>8</td>\n",
-       "      <td>4</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
        "      <th>23</th>\n",
        "      <td>5</td>\n",
        "      <td>3</td>\n",
        "      <td>0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>134</th>\n",
-       "      <td>6</td>\n",
-       "      <td>3</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>39</th>\n",
+       "      <th>19</th>\n",
        "      <td>5</td>\n",
-       "      <td>3</td>\n",
+       "      <td>4</td>\n",
        "      <td>0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>37</th>\n",
-       "      <td>5</td>\n",
+       "      <th>96</th>\n",
+       "      <td>6</td>\n",
        "      <td>3</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>32</th>\n",
-       "      <td>5</td>\n",
-       "      <td>4</td>\n",
-       "      <td>0</td>\n",
+       "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>55</th>\n",
+       "      <th>64</th>\n",
        "      <td>6</td>\n",
        "      <td>3</td>\n",
        "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>57</th>\n",
-       "      <td>5</td>\n",
+       "      <th>68</th>\n",
+       "      <td>6</td>\n",
        "      <td>2</td>\n",
        "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>15</th>\n",
+       "      <th>133</th>\n",
        "      <td>6</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>76</th>\n",
+       "      <td>7</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>48</th>\n",
+       "      <td>5</td>\n",
        "      <td>4</td>\n",
        "      <td>0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>146</th>\n",
-       "      <td>6</td>\n",
-       "      <td>2</td>\n",
+       "      <th>139</th>\n",
+       "      <td>7</td>\n",
+       "      <td>3</td>\n",
        "      <td>2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>114</th>\n",
+       "      <th>128</th>\n",
        "      <td>6</td>\n",
        "      <td>3</td>\n",
        "      <td>2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>102</th>\n",
-       "      <td>7</td>\n",
+       "      <th>105</th>\n",
+       "      <td>8</td>\n",
        "      <td>3</td>\n",
        "      <td>2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>78</th>\n",
+       "      <th>55</th>\n",
        "      <td>6</td>\n",
        "      <td>3</td>\n",
        "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>82</th>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>121</th>\n",
        "      <td>6</td>\n",
        "      <td>3</td>\n",
-       "      <td>1</td>\n",
+       "      <td>2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>79</th>\n",
@@ -816,214 +838,226 @@
        "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>71</th>\n",
+       "      <th>132</th>\n",
        "      <td>6</td>\n",
        "      <td>3</td>\n",
-       "      <td>1</td>\n",
+       "      <td>2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>77</th>\n",
+       "      <th>108</th>\n",
        "      <td>7</td>\n",
-       "      <td>3</td>\n",
-       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>20</th>\n",
-       "      <td>5</td>\n",
+       "      <th>88</th>\n",
+       "      <td>6</td>\n",
        "      <td>3</td>\n",
-       "      <td>0</td>\n",
+       "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>16</th>\n",
+       "      <th>93</th>\n",
        "      <td>5</td>\n",
-       "      <td>4</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>44</th>\n",
+       "      <th>43</th>\n",
        "      <td>5</td>\n",
        "      <td>4</td>\n",
        "      <td>0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>147</th>\n",
+       "      <th>74</th>\n",
        "      <td>6</td>\n",
        "      <td>3</td>\n",
-       "      <td>2</td>\n",
+       "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>100</th>\n",
-       "      <td>6</td>\n",
-       "      <td>3</td>\n",
-       "      <td>2</td>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>101</th>\n",
+       "      <th>134</th>\n",
        "      <td>6</td>\n",
        "      <td>3</td>\n",
        "      <td>2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>52</th>\n",
-       "      <td>7</td>\n",
+       "      <th>90</th>\n",
+       "      <td>6</td>\n",
        "      <td>3</td>\n",
        "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>19</th>\n",
+       "      <th>21</th>\n",
        "      <td>5</td>\n",
        "      <td>4</td>\n",
        "      <td>0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>5</td>\n",
-       "      <td>4</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>137</th>\n",
+       "      <th>113</th>\n",
        "      <td>6</td>\n",
-       "      <td>3</td>\n",
+       "      <td>2</td>\n",
        "      <td>2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>64</th>\n",
+       "      <th>70</th>\n",
        "      <td>6</td>\n",
        "      <td>3</td>\n",
        "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>80</th>\n",
+       "      <th>103</th>\n",
        "      <td>6</td>\n",
+       "      <td>3</td>\n",
        "      <td>2</td>\n",
-       "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>68</th>\n",
-       "      <td>6</td>\n",
+       "      <th>109</th>\n",
+       "      <td>7</td>\n",
+       "      <td>4</td>\n",
        "      <td>2</td>\n",
-       "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>97</th>\n",
+       "      <th>73</th>\n",
        "      <td>6</td>\n",
        "      <td>3</td>\n",
        "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>127</th>\n",
-       "      <td>6</td>\n",
+       "      <th>107</th>\n",
+       "      <td>7</td>\n",
        "      <td>3</td>\n",
        "      <td>2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>67</th>\n",
-       "      <td>6</td>\n",
+       "      <th>120</th>\n",
+       "      <td>7</td>\n",
        "      <td>3</td>\n",
-       "      <td>1</td>\n",
+       "      <td>2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>48</th>\n",
+       "      <th>12</th>\n",
        "      <td>5</td>\n",
-       "      <td>4</td>\n",
+       "      <td>3</td>\n",
        "      <td>0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>50</th>\n",
-       "      <td>7</td>\n",
-       "      <td>3</td>\n",
+       "      <th>62</th>\n",
+       "      <td>6</td>\n",
+       "      <td>2</td>\n",
        "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>136</th>\n",
+       "      <th>114</th>\n",
        "      <td>6</td>\n",
        "      <td>3</td>\n",
        "      <td>2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>107</th>\n",
+       "      <th>106</th>\n",
+       "      <td>5</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>86</th>\n",
        "      <td>7</td>\n",
        "      <td>3</td>\n",
-       "      <td>2</td>\n",
+       "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>108</th>\n",
+       "      <th>75</th>\n",
        "      <td>7</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>59</th>\n",
-       "      <td>5</td>\n",
+       "      <th>65</th>\n",
+       "      <td>7</td>\n",
        "      <td>3</td>\n",
        "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>122</th>\n",
-       "      <td>8</td>\n",
+       "      <th>125</th>\n",
+       "      <td>7</td>\n",
        "      <td>3</td>\n",
        "      <td>2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>96</th>\n",
+       "      <th>53</th>\n",
        "      <td>6</td>\n",
-       "      <td>3</td>\n",
+       "      <td>2</td>\n",
        "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>41</th>\n",
+       "      <th>117</th>\n",
+       "      <td>8</td>\n",
        "      <td>4</td>\n",
        "      <td>2</td>\n",
-       "      <td>0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>33</th>\n",
-       "      <td>6</td>\n",
+       "      <th>57</th>\n",
+       "      <td>5</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>46</th>\n",
+       "      <td>5</td>\n",
        "      <td>4</td>\n",
        "      <td>0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>73</th>\n",
+       "      <th>95</th>\n",
        "      <td>6</td>\n",
        "      <td>3</td>\n",
        "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>84</th>\n",
-       "      <td>5</td>\n",
+       "      <th>99</th>\n",
+       "      <td>6</td>\n",
        "      <td>3</td>\n",
        "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>75</th>\n",
-       "      <td>7</td>\n",
+       "      <th>32</th>\n",
+       "      <td>5</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>94</th>\n",
+       "      <td>6</td>\n",
        "      <td>3</td>\n",
        "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>105</th>\n",
-       "      <td>8</td>\n",
+       "      <th>129</th>\n",
+       "      <td>7</td>\n",
        "      <td>3</td>\n",
        "      <td>2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>36</th>\n",
+       "      <th>69</th>\n",
        "      <td>6</td>\n",
-       "      <td>4</td>\n",
-       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>53</th>\n",
+       "      <th>102</th>\n",
+       "      <td>7</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>149</th>\n",
        "      <td>6</td>\n",
+       "      <td>3</td>\n",
        "      <td>2</td>\n",
-       "      <td>1</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -1032,72 +1066,72 @@
       ],
       "text/plain": [
        "     length  width  type\n",
-       "47        5      3     0\n",
-       "92        6      3     1\n",
-       "5         5      4     0\n",
-       "106       5      2     2\n",
-       "40        5      4     0\n",
-       "6         5      3     0\n",
-       "10        5      4     0\n",
-       "35        5      3     0\n",
-       "22        5      4     0\n",
-       "123       6      3     2\n",
-       "139       7      3     2\n",
-       "117       8      4     2\n",
-       "23        5      3     0\n",
-       "134       6      3     2\n",
-       "39        5      3     0\n",
-       "37        5      3     0\n",
-       "32        5      4     0\n",
-       "55        6      3     1\n",
-       "57        5      2     1\n",
-       "15        6      4     0\n",
-       "146       6      2     2\n",
-       "114       6      3     2\n",
-       "102       7      3     2\n",
-       "78        6      3     1\n",
+       "0         5      4     0\n",
+       "118       8      3     2\n",
        "82        6      3     1\n",
-       "79        6      3     1\n",
-       "71        6      3     1\n",
-       "77        7      3     1\n",
-       "20        5      3     0\n",
+       "87        6      2     1\n",
+       "78        6      3     1\n",
+       "116       6      3     2\n",
        "16        5      4     0\n",
-       "..      ...    ...   ...\n",
-       "44        5      4     0\n",
-       "147       6      3     2\n",
-       "100       6      3     2\n",
-       "101       6      3     2\n",
-       "52        7      3     1\n",
+       "10        5      4     0\n",
+       "112       7      3     2\n",
+       "23        5      3     0\n",
        "19        5      4     0\n",
-       "4         5      4     0\n",
-       "137       6      3     2\n",
+       "96        6      3     1\n",
        "64        6      3     1\n",
-       "80        6      2     1\n",
        "68        6      2     1\n",
-       "97        6      3     1\n",
-       "127       6      3     2\n",
-       "67        6      3     1\n",
+       "133       6      3     2\n",
+       "76        7      3     1\n",
        "48        5      4     0\n",
-       "50        7      3     1\n",
-       "136       6      3     2\n",
-       "107       7      3     2\n",
+       "139       7      3     2\n",
+       "128       6      3     2\n",
+       "105       8      3     2\n",
+       "55        6      3     1\n",
+       "4         5      4     0\n",
+       "121       6      3     2\n",
+       "79        6      3     1\n",
+       "132       6      3     2\n",
        "108       7      2     2\n",
-       "59        5      3     1\n",
-       "122       8      3     2\n",
-       "96        6      3     1\n",
-       "41        4      2     0\n",
-       "33        6      4     0\n",
+       "88        6      3     1\n",
+       "93        5      2     1\n",
+       "43        5      4     0\n",
+       "74        6      3     1\n",
+       "..      ...    ...   ...\n",
+       "134       6      3     2\n",
+       "90        6      3     1\n",
+       "21        5      4     0\n",
+       "113       6      2     2\n",
+       "70        6      3     1\n",
+       "103       6      3     2\n",
+       "109       7      4     2\n",
        "73        6      3     1\n",
-       "84        5      3     1\n",
+       "107       7      3     2\n",
+       "120       7      3     2\n",
+       "12        5      3     0\n",
+       "62        6      2     1\n",
+       "114       6      3     2\n",
+       "106       5      2     2\n",
+       "86        7      3     1\n",
        "75        7      3     1\n",
-       "105       8      3     2\n",
-       "36        6      4     0\n",
+       "65        7      3     1\n",
+       "125       7      3     2\n",
        "53        6      2     1\n",
+       "117       8      4     2\n",
+       "57        5      2     1\n",
+       "46        5      4     0\n",
+       "95        6      3     1\n",
+       "99        6      3     1\n",
+       "32        5      4     0\n",
+       "94        6      3     1\n",
+       "129       7      3     2\n",
+       "69        6      2     1\n",
+       "102       7      3     2\n",
+       "149       6      3     2\n",
        "\n",
        "[120 rows x 3 columns]"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 29,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1108,7 +1142,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 30,
    "metadata": {
     "collapsed": false
    },
@@ -1138,7 +1172,7 @@
        "dtype: float64"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 30,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1151,7 +1185,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 31,
    "metadata": {
     "collapsed": false
    },
@@ -1159,11 +1193,11 @@
     {
      "data": {
       "text/plain": [
-       "array([1, 1, 0, 2, 1, 1, 1, 0, 0, 1, 0, 1, 1, 2, 1, 0, 2, 2, 1, 2, 0, 1, 0,\n",
-       "       2, 0, 2, 2, 0, 0, 0])"
+       "array([2, 2, 1, 1, 2, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 2, 2, 0, 1, 1, 0, 0, 0,\n",
+       "       0, 2, 1, 1, 0, 1, 0])"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 31,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1185,7 +1219,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 32,
    "metadata": {
     "collapsed": false
    },
@@ -1193,13 +1227,13 @@
     {
      "data": {
       "text/plain": [
-       "array([False, False,  True,  True,  True, False, False,  True,  True,\n",
-       "       False,  True,  True,  True,  True, False,  True,  True,  True,\n",
-       "        True, False,  True,  True,  True,  True,  True,  True,  True,\n",
+       "array([ True,  True, False,  True,  True,  True, False,  True,  True,\n",
+       "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
+       "       False,  True,  True,  True,  True,  True, False, False, False,\n",
        "        True,  True,  True], dtype=bool)"
       ]
      },
-     "execution_count": 25,
+     "execution_count": 32,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1211,7 +1245,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 33,
    "metadata": {
     "collapsed": false
    },
@@ -1220,7 +1254,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.766666666667\n"
+      "0.8\n"
      ]
     }
    ],
@@ -1332,8 +1366,9 @@
   }
  ],
  "metadata": {
+  "anaconda-cloud": {},
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python [default]",
    "language": "python",
    "name": "python3"
   },
@@ -1347,7 +1382,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.4.5"
+   "version": "3.5.2"
   }
  },
  "nbformat": 4,
diff --git a/notebooks/2. Bayesian Networks.ipynb b/notebooks/2. Bayesian Networks.ipynb
index ad66879..c0b673a 100644
--- a/notebooks/2. Bayesian Networks.ipynb	
+++ b/notebooks/2. Bayesian Networks.ipynb	
@@ -2,13 +2,16 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 9,
    "metadata": {
     "collapsed": true
    },
    "outputs": [],
    "source": [
-    "from IPython.display import Image"
+    "from IPython.display import Image\n",
+    "#fix found here: http://stackoverflow.com/questions/26930515/module-not-found-on-ipython-notebook\n",
+    "import sys\n",
+    "sys.path.append('/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/')"
    ]
   },
   {
@@ -65,7 +68,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 10,
    "metadata": {
     "collapsed": false
    },
@@ -76,7 +79,7 @@
        "True"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -136,7 +139,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 11,
    "metadata": {
     "collapsed": false
    },
@@ -144,14 +147,14 @@
     {
      "data": {
       "text/plain": [
-       "[<TabularCPD representing P(D:2) at 0x7f82b084c208>,\n",
-       " <TabularCPD representing P(I:2) at 0x7f82b0840eb8>,\n",
-       " <TabularCPD representing P(G:3 | I:2, D:2) at 0x7f82b084c128>,\n",
-       " <TabularCPD representing P(L:2 | G:3) at 0x7f82b0840e80>,\n",
-       " <TabularCPD representing P(S:2 | I:2) at 0x7f82b0840d30>]"
+       "[<TabularCPD representing P(D:2) at 0x104b5f5f8>,\n",
+       " <TabularCPD representing P(I:2) at 0x104b5f630>,\n",
+       " <TabularCPD representing P(G:3 | I:2, D:2) at 0x104b5f780>,\n",
+       " <TabularCPD representing P(L:2 | G:3) at 0x104b5f710>,\n",
+       " <TabularCPD representing P(S:2 | I:2) at 0x104b79128>]"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -209,7 +212,8 @@
     }
    ],
    "source": [
-    "model.get_cardinality('G')"
+    "model.get_cardinality('G')\n",
+    "help(model.get_cardinality)"
    ]
   },
   {
@@ -605,8 +609,9 @@
   }
  ],
  "metadata": {
+  "anaconda-cloud": {},
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python [default]",
    "language": "python",
    "name": "python3"
   },
@@ -620,7 +625,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.4.4"
+   "version": "3.5.2"
   }
  },
  "nbformat": 4,

From 0d60ea810cabbfacd8c9a3cf99211e85e6a15c66 Mon Sep 17 00:00:00 2001
From: Eric Schles <ericschles@gmail.com>
Date: Thu, 5 Jan 2017 10:42:41 -0500
Subject: [PATCH 2/2] updating

---
 ...on to Probabilistic Graphical Models.ipynb | 27 ++++++++++---------
 notebooks/2. Bayesian Networks.ipynb          | 21 ++++++++-------
 2 files changed, 25 insertions(+), 23 deletions(-)

diff --git a/notebooks/1. Introduction to Probabilistic Graphical Models.ipynb b/notebooks/1. Introduction to Probabilistic Graphical Models.ipynb
index 3f62767..22d6a38 100644
--- a/notebooks/1. Introduction to Probabilistic Graphical Models.ipynb	
+++ b/notebooks/1. Introduction to Probabilistic Graphical Models.ipynb	
@@ -567,7 +567,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 41,
    "metadata": {
     "collapsed": false
    },
@@ -575,11 +575,11 @@
     {
      "data": {
       "text/plain": [
-       "array([2, 2, 1, 1, 0, 2, 2, 2, 0, 2, 1, 1, 0, 2, 2, 2, 2, 1, 0, 0, 2, 2, 0,\n",
-       "       0, 2, 2, 2, 2, 2, 2])"
+       "array([1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 2, 0, 0, 2, 1, 1, 1, 0,\n",
+       "       1, 1, 1, 1, 1, 1, 2])"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 41,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -597,7 +597,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 63,
    "metadata": {
     "collapsed": false
    },
@@ -605,10 +605,10 @@
     {
      "data": {
       "text/plain": [
-       "0.59999999999999998"
+       "0.73333333333333328"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 63,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -626,7 +626,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 67,
    "metadata": {
     "collapsed": false
    },
@@ -635,8 +635,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "prediction [0 2 1 2 0 2 1 1 0 1 0 0 0 0 1 1 0 1 1 1 2 0 2 1 1 1 2 0 0 2]\n",
-      "0.7\n"
+      "prediction [1 1 1 0 0 2 1 1 1 0 2 1 2 1 0 1 1 2 2 0 0 0 0 0 1 2 0 2 2 2]\n",
+      "0.733333333333\n"
      ]
     }
    ],
@@ -1142,7 +1142,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 50,
    "metadata": {
     "collapsed": false
    },
@@ -1172,7 +1172,7 @@
        "dtype: float64"
       ]
      },
-     "execution_count": 30,
+     "execution_count": 50,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1245,7 +1245,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 52,
    "metadata": {
     "collapsed": false
    },
@@ -1254,6 +1254,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "30\n",
       "0.8\n"
      ]
     }
diff --git a/notebooks/2. Bayesian Networks.ipynb b/notebooks/2. Bayesian Networks.ipynb
index c0b673a..feed992 100644
--- a/notebooks/2. Bayesian Networks.ipynb	
+++ b/notebooks/2. Bayesian Networks.ipynb	
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 12,
    "metadata": {
     "collapsed": true
    },
@@ -195,20 +195,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 15,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "3"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
+     "ename": "AttributeError",
+     "evalue": "'BayesianModel' object has no attribute 'get_cardinality'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-15-48469127faf9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_cardinality\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'G'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mhelp\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_cardinality\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mAttributeError\u001b[0m: 'BayesianModel' object has no attribute 'get_cardinality'"
+     ]
     }
    ],
    "source": [