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ieegPymc3Version1.ipynb
{
"cells": [
{
"metadata": {},
"cell_type": "markdown",
"source": "# NIH Seizure Prediction using Bayesian Logistic Regression and Pymc3\n\n\nCode and documentation for my solution (51th place) for the Kaggle Melbourne University AES/MathWorks/NIH Seizure Prediction challenge : https://www.kaggle.com/solomonk\n\n### A 2016 Kaggle competition.\n\nhttps://www.kaggle.com/c/melbourne-university-seizure-prediction"
},
{
"metadata": {
"collapsed": false,
"trusted": true
},
"cell_type": "code",
"source": "%reset -f\n%matplotlib inline\n\nimport numpy as np\nimport pandas as pd\nimport pymc3 as pm\nimport seaborn as sns\nimport os,sys,inspect\ncurrentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))\nparentdir = os.path.dirname(currentdir)\ndirToInclude=parentdir +'/features/'\nsys.path.insert(0,dirToInclude)\n\nimport IeegConsts\nfrom IeegConsts import *\nfrom IeegFeatures import *\n\nimport pandas\nimport numpy as np\nimport pandas as pd\nfrom sklearn import cross_validation\nfrom sklearn import metrics\nfrom sklearn.metrics import roc_auc_score, log_loss, roc_auc_score, roc_curve, auc\nfrom sklearn.cross_validation import StratifiedKFold, ShuffleSplit, cross_val_score, train_test_split\nimport matplotlib.pyplot as plt\n\nimport theano.tensor as tt\n\n%matplotlib inline\n\nnp.set_printoptions(precision=4, threshold=10000, linewidth=100, edgeitems=999, suppress=True)\n\npd.set_option('display.max_columns', None)\npd.set_option('display.max_rows', None)\npd.set_option('display.width', 100)\npd.set_option('expand_frame_repr', False)\npd.set_option('precision', 6)\n \nfrom matplotlib.pylab import rcParams\nrcParams['figure.figsize'] = 12, 4\n\ntrain_dir=TRAIN_DATA_FOLDER_IN_ALL\ntest_dir=TEST_DATA_FOLDER_IN_ALL \n\n\nieegFeatures= IeegFeatures(train_dir, True)\ndf_cols_train=ieegFeatures.ieegGenCols()\nprint(len(df_cols_train))\n# F_NAME_TRAIN= TRAIN_FEAT_BASE + TRAIN_PREFIX_ALL +'-feat_TRAIN_df.hdf'\n# X_df_train=pandas.read_hdf(F_NAME_TRAIN, engine='python')\n\nX_df_train= pd.read_hdf(TRAIN_FEAT_BASE + TRAIN_PREFIX_ALL \n + 'X_df_train.hdf', 'data',format='fixed',complib='blosc',complevel=9)\n\n# X_df_train.drop('Unnamed: 0', axis=1, inplace=True)\n\n\nn=16\nlast_cols=list()\nfor i in range(1, n_psd + 1):\n last_cols.append('psd_{}'.format(i)) \nfor i in range(1, 16 + 1):\n last_cols.append('var_{}'.format(i)) \nfor i in range(1, 16 + 1):\n last_cols.append('kurt_{}'.format(i))\nfor i in range(1, n_corr_coeff + 1):\n last_cols.append('corcoef_{}'.format(i))\nfor i in range(1, n + 1):\n last_cols.append('hurst_{}'.format(i))\n# for i in range(1, n_plv+ 1):\n# last_cols.append('plv_{}'.format(i)) \n# for i in range(1, n + 1):\n# last_cols.append('mean_{}'.format(i))\n# for i in range(1, n + 1):\n# last_cols.append('median_{}'.format(i))\n# for i in range(1, n + 1):\n# last_cols.append('std_{}'.format(i))\n\nX_df_train_SINGLE=X_df_train\n\n\nX_df_train_SINGLE.drop('id', axis=1, inplace=True)\nX_df_train_SINGLE.drop('file', axis=1, inplace=True)\nX_df_train_SINGLE.drop('patient_id', axis=1, inplace=True)\n\nX_df_train_SINGLE = X_df_train_SINGLE.loc[X_df_train_SINGLE['file_size'] > 100000]\nX_df_train_SINGLE.drop('file_size', axis=1, inplace=True)\nX_df_train_SINGLE.drop('sequence_id', axis=1, inplace=True)\nX_df_train_SINGLE.drop('segment', axis=1, inplace=True)\n\nanswers_1_SINGLE = list (X_df_train_SINGLE[singleResponseVariable].values)\nX_df_train_SINGLE = X_df_train_SINGLE.drop(singleResponseVariable, axis=1)\n\nX_df_train_SINGLE=X_df_train_SINGLE[last_cols]\nX_df_train_SINGLE=X_df_train_SINGLE.apply(lambda x: pandas.to_numeric(x, errors='ignore'))\nX_df_train_SINGLE.head(5)",
"execution_count": 1,
"outputs": [
{
"output_type": "stream",
"text": "Starting:ieegFeatures:2017-02-11 22:08:18.068192\nCols:1239\n1239\n",
"name": "stdout"
},
{
"output_type": "execute_result",
"data": {
"text/plain": " psd_1 psd_2 psd_3 psd_4 psd_5 psd_6 psd_7 psd_8 psd_9 psd_10 psd_11 psd_12 psd_13 psd_14 psd_15 psd_16 psd_17 psd_18 psd_19 psd_20 psd_21 psd_22 psd_23 psd_24 psd_25 psd_26 psd_27 psd_28 psd_29 psd_30 psd_31 psd_32 psd_33 psd_34 psd_35 psd_36 psd_37 psd_38 psd_39 psd_40 psd_41 psd_42 psd_43 psd_44 psd_45 psd_46 psd_47 psd_48 psd_49 psd_50 psd_51 psd_52 psd_53 psd_54 psd_55 psd_56 psd_57 psd_58 psd_59 psd_60 psd_61 psd_62 psd_63 psd_64 psd_65 psd_66 psd_67 psd_68 psd_69 psd_70 psd_71 psd_72 psd_73 psd_74 psd_75 psd_76 psd_77 psd_78 psd_79 psd_80 psd_81 psd_82 psd_83 psd_84 psd_85 psd_86 psd_87 psd_88 psd_89 psd_90 psd_91 psd_92 psd_93 psd_94 psd_95 psd_96 psd_97 psd_98 psd_99 psd_100 psd_101 psd_102 psd_103 psd_104 psd_105 psd_106 psd_107 psd_108 psd_109 psd_110 psd_111 psd_112 psd_113 psd_114 psd_115 psd_116 psd_117 psd_118 psd_119 psd_120 psd_121 psd_122 psd_123 psd_124 psd_125 psd_126 psd_127 psd_128 psd_129 psd_130 psd_131 psd_132 psd_133 psd_134 psd_135 psd_136 psd_137 psd_138 psd_139 psd_140 psd_141 psd_142 psd_143 psd_144 psd_145 psd_146 psd_147 psd_148 psd_149 psd_150 psd_151 psd_152 psd_153 psd_154 psd_155 psd_156 psd_157 psd_158 psd_159 psd_160 psd_161 psd_162 psd_163 psd_164 psd_165 psd_166 psd_167 psd_168 psd_169 psd_170 psd_171 psd_172 psd_173 psd_174 psd_175 psd_176 psd_177 psd_178 psd_179 psd_180 psd_181 psd_182 psd_183 psd_184 psd_185 psd_186 psd_187 psd_188 psd_189 psd_190 psd_191 psd_192 var_1 var_2 var_3 var_4 var_5 var_6 var_7 var_8 var_9 var_10 var_11 var_12 var_13 var_14 var_15 var_16 kurt_1 kurt_2 kurt_3 kurt_4 kurt_5 kurt_6 kurt_7 kurt_8 kurt_9 kurt_10 kurt_11 kurt_12 kurt_13 kurt_14 kurt_15 kurt_16 corcoef_1 corcoef_2 corcoef_3 corcoef_4 corcoef_5 corcoef_6 corcoef_7 corcoef_8 corcoef_9 corcoef_10 corcoef_11 corcoef_12 corcoef_13 corcoef_14 corcoef_15 corcoef_16 corcoef_17 corcoef_18 corcoef_19 corcoef_20 corcoef_21 corcoef_22 corcoef_23 corcoef_24 corcoef_25 corcoef_26 corcoef_27 corcoef_28 corcoef_29 corcoef_30 corcoef_31 corcoef_32 corcoef_33 corcoef_34 corcoef_35 corcoef_36 corcoef_37 corcoef_38 corcoef_39 corcoef_40 corcoef_41 corcoef_42 corcoef_43 corcoef_44 corcoef_45 corcoef_46 corcoef_47 corcoef_48 corcoef_49 corcoef_50 corcoef_51 corcoef_52 corcoef_53 corcoef_54 corcoef_55 corcoef_56 corcoef_57 corcoef_58 corcoef_59 corcoef_60 corcoef_61 corcoef_62 corcoef_63 corcoef_64 corcoef_65 corcoef_66 corcoef_67 corcoef_68 corcoef_69 corcoef_70 corcoef_71 corcoef_72 corcoef_73 corcoef_74 corcoef_75 corcoef_76 corcoef_77 corcoef_78 corcoef_79 corcoef_80 corcoef_81 corcoef_82 corcoef_83 corcoef_84 corcoef_85 corcoef_86 corcoef_87 corcoef_88 corcoef_89 corcoef_90 corcoef_91 corcoef_92 corcoef_93 corcoef_94 corcoef_95 corcoef_96 corcoef_97 corcoef_98 corcoef_99 corcoef_100 corcoef_101 corcoef_102 corcoef_103 corcoef_104 corcoef_105 corcoef_106 corcoef_107 corcoef_108 corcoef_109 corcoef_110 corcoef_111 corcoef_112 corcoef_113 corcoef_114 corcoef_115 corcoef_116 corcoef_117 corcoef_118 corcoef_119 corcoef_120 hurst_1 hurst_2 hurst_3 hurst_4 hurst_5 hurst_6 hurst_7 hurst_8 hurst_9 hurst_10 hurst_11 hurst_12 hurst_13 hurst_14 hurst_15 hurst_16\n0 -0.406786 -0.506428 -0.550631 -0.602230 -0.117925 -0.258156 -0.292530 -0.310629 -0.464966 -2.681048 -0.672553 -0.735941 -0.558759 -0.644890 -0.688039 -0.669862 -0.510144 -0.598658 -0.646677 -0.670835 -0.129998 -0.251415 -0.323000 -0.227784 -0.639318 -2.760449 -0.665167 -0.659974 -0.590483 -0.588708 -0.647818 -0.715215 -0.629282 -0.647044 -0.621452 -0.675702 -0.426132 -0.442531 -0.483979 -0.434602 -0.750417 -2.766089 -0.708479 -0.729438 -0.629134 -0.464072 -0.620423 -0.708196 -0.707625 -0.711356 -0.642788 -0.672846 -0.478605 -0.479591 -0.520513 -0.456928 -0.790443 -2.769022 -0.732106 -0.730806 -0.646176 -0.473156 -0.634019 -0.703660 -0.763965 -0.765547 -0.680584 -0.695172 -0.550166 -0.547159 -0.539212 -0.476176 -0.818632 -2.770506 -0.785050 -0.764124 -0.638319 -0.486804 -0.654367 -0.688834 -0.905803 -0.866233 -0.841806 -0.849490 -0.760451 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<th>psd_50</th>\n <th>psd_51</th>\n <th>psd_52</th>\n <th>psd_53</th>\n <th>psd_54</th>\n <th>psd_55</th>\n <th>psd_56</th>\n <th>psd_57</th>\n <th>psd_58</th>\n <th>psd_59</th>\n <th>psd_60</th>\n <th>psd_61</th>\n <th>psd_62</th>\n <th>psd_63</th>\n <th>psd_64</th>\n <th>psd_65</th>\n <th>psd_66</th>\n <th>psd_67</th>\n <th>psd_68</th>\n <th>psd_69</th>\n <th>psd_70</th>\n <th>psd_71</th>\n <th>psd_72</th>\n <th>psd_73</th>\n <th>psd_74</th>\n <th>psd_75</th>\n <th>psd_76</th>\n <th>psd_77</th>\n <th>psd_78</th>\n <th>psd_79</th>\n <th>psd_80</th>\n <th>psd_81</th>\n <th>psd_82</th>\n <th>psd_83</th>\n <th>psd_84</th>\n <th>psd_85</th>\n <th>psd_86</th>\n <th>psd_87</th>\n <th>psd_88</th>\n <th>psd_89</th>\n <th>psd_90</th>\n <th>psd_91</th>\n <th>psd_92</th>\n <th>psd_93</th>\n <th>psd_94</th>\n <th>psd_95</th>\n <th>psd_96</th>\n <th>psd_97</th>\n <th>psd_98</th>\n <th>psd_99</th>\n <th>psd_100</th>\n <th>psd_101</th>\n <th>psd_102</th>\n <th>psd_103</th>\n <th>psd_104</th>\n <th>psd_105</th>\n <th>psd_106</th>\n <th>psd_107</th>\n <th>psd_108</th>\n <th>psd_109</th>\n <th>psd_110</th>\n <th>psd_111</th>\n <th>psd_112</th>\n <th>psd_113</th>\n <th>psd_114</th>\n <th>psd_115</th>\n <th>psd_116</th>\n <th>psd_117</th>\n <th>psd_118</th>\n <th>psd_119</th>\n <th>psd_120</th>\n <th>psd_121</th>\n <th>psd_122</th>\n <th>psd_123</th>\n <th>psd_124</th>\n <th>psd_125</th>\n <th>psd_126</th>\n <th>psd_127</th>\n <th>psd_128</th>\n <th>psd_129</th>\n <th>psd_130</th>\n <th>psd_131</th>\n <th>psd_132</th>\n <th>psd_133</th>\n <th>psd_134</th>\n <th>psd_135</th>\n <th>psd_136</th>\n <th>psd_137</th>\n <th>psd_138</th>\n <th>psd_139</th>\n <th>psd_140</th>\n <th>psd_141</th>\n <th>psd_142</th>\n <th>psd_143</th>\n <th>psd_144</th>\n <th>psd_145</th>\n <th>psd_146</th>\n <th>psd_147</th>\n <th>psd_148</th>\n <th>psd_149</th>\n <th>psd_150</th>\n <th>psd_151</th>\n <th>psd_152</th>\n <th>psd_153</th>\n <th>psd_154</th>\n <th>psd_155</th>\n <th>psd_156</th>\n <th>psd_157</th>\n <th>psd_158</th>\n <th>psd_159</th>\n <th>psd_160</th>\n <th>psd_161</th>\n <th>psd_162</th>\n <th>psd_163</th>\n <th>psd_164</th>\n <th>psd_165</th>\n <th>psd_166</th>\n <th>psd_167</th>\n <th>psd_168</th>\n <th>psd_169</th>\n <th>psd_170</th>\n <th>psd_171</th>\n <th>psd_172</th>\n <th>psd_173</th>\n <th>psd_174</th>\n <th>psd_175</th>\n <th>psd_176</th>\n <th>psd_177</th>\n <th>psd_178</th>\n <th>psd_179</th>\n <th>psd_180</th>\n <th>psd_181</th>\n <th>psd_182</th>\n <th>psd_183</th>\n <th>psd_184</th>\n <th>psd_185</th>\n <th>psd_186</th>\n <th>psd_187</th>\n <th>psd_188</th>\n <th>psd_189</th>\n <th>psd_190</th>\n <th>psd_191</th>\n <th>psd_192</th>\n <th>var_1</th>\n <th>var_2</th>\n <th>var_3</th>\n <th>var_4</th>\n <th>var_5</th>\n <th>var_6</th>\n <th>var_7</th>\n <th>var_8</th>\n <th>var_9</th>\n <th>var_10</th>\n <th>var_11</th>\n <th>var_12</th>\n <th>var_13</th>\n <th>var_14</th>\n <th>var_15</th>\n <th>var_16</th>\n <th>kurt_1</th>\n <th>kurt_2</th>\n <th>kurt_3</th>\n <th>kurt_4</th>\n <th>kurt_5</th>\n <th>kurt_6</th>\n <th>kurt_7</th>\n <th>kurt_8</th>\n <th>kurt_9</th>\n <th>kurt_10</th>\n <th>kurt_11</th>\n <th>kurt_12</th>\n <th>kurt_13</th>\n <th>kurt_14</th>\n <th>kurt_15</th>\n <th>kurt_16</th>\n <th>corcoef_1</th>\n <th>corcoef_2</th>\n <th>corcoef_3</th>\n <th>corcoef_4</th>\n <th>corcoef_5</th>\n <th>corcoef_6</th>\n <th>corcoef_7</th>\n <th>corcoef_8</th>\n <th>corcoef_9</th>\n <th>corcoef_10</th>\n <th>corcoef_11</th>\n <th>corcoef_12</th>\n <th>corcoef_13</th>\n <th>corcoef_14</th>\n <th>corcoef_15</th>\n <th>corcoef_16</th>\n <th>corcoef_17</th>\n <th>corcoef_18</th>\n <th>corcoef_19</th>\n <th>corcoef_20</th>\n <th>corcoef_21</th>\n <th>corcoef_22</th>\n <th>corcoef_23</th>\n <th>corcoef_24</th>\n <th>corcoef_25</th>\n <th>corcoef_26</th>\n <th>corcoef_27</th>\n <th>corcoef_28</th>\n <th>corcoef_29</th>\n <th>corcoef_30</th>\n <th>corcoef_31</th>\n <th>corcoef_32</th>\n <th>corcoef_33</th>\n <th>corcoef_34</th>\n <th>corcoef_35</th>\n <th>corcoef_36</th>\n <th>corcoef_37</th>\n <th>corcoef_38</th>\n <th>corcoef_39</th>\n <th>corcoef_40</th>\n <th>corcoef_41</th>\n <th>corcoef_42</th>\n <th>corcoef_43</th>\n <th>corcoef_44</th>\n <th>corcoef_45</th>\n <th>corcoef_46</th>\n <th>corcoef_47</th>\n <th>corcoef_48</th>\n <th>corcoef_49</th>\n <th>corcoef_50</th>\n <th>corcoef_51</th>\n <th>corcoef_52</th>\n <th>corcoef_53</th>\n <th>corcoef_54</th>\n <th>corcoef_55</th>\n <th>corcoef_56</th>\n <th>corcoef_57</th>\n <th>corcoef_58</th>\n <th>corcoef_59</th>\n <th>corcoef_60</th>\n <th>corcoef_61</th>\n <th>corcoef_62</th>\n <th>corcoef_63</th>\n <th>corcoef_64</th>\n <th>corcoef_65</th>\n <th>corcoef_66</th>\n <th>corcoef_67</th>\n <th>corcoef_68</th>\n <th>corcoef_69</th>\n <th>corcoef_70</th>\n <th>corcoef_71</th>\n <th>corcoef_72</th>\n <th>corcoef_73</th>\n <th>corcoef_74</th>\n <th>corcoef_75</th>\n <th>corcoef_76</th>\n <th>corcoef_77</th>\n <th>corcoef_78</th>\n <th>corcoef_79</th>\n <th>corcoef_80</th>\n <th>corcoef_81</th>\n <th>corcoef_82</th>\n <th>corcoef_83</th>\n <th>corcoef_84</th>\n <th>corcoef_85</th>\n <th>corcoef_86</th>\n <th>corcoef_87</th>\n <th>corcoef_88</th>\n <th>corcoef_89</th>\n <th>corcoef_90</th>\n <th>corcoef_91</th>\n <th>corcoef_92</th>\n <th>corcoef_93</th>\n <th>corcoef_94</th>\n <th>corcoef_95</th>\n <th>corcoef_96</th>\n <th>corcoef_97</th>\n <th>corcoef_98</th>\n <th>corcoef_99</th>\n <th>corcoef_100</th>\n <th>corcoef_101</th>\n <th>corcoef_102</th>\n <th>corcoef_103</th>\n <th>corcoef_104</th>\n <th>corcoef_105</th>\n <th>corcoef_106</th>\n <th>corcoef_107</th>\n <th>corcoef_108</th>\n <th>corcoef_109</th>\n <th>corcoef_110</th>\n <th>corcoef_111</th>\n <th>corcoef_112</th>\n <th>corcoef_113</th>\n <th>corcoef_114</th>\n <th>corcoef_115</th>\n <th>corcoef_116</th>\n <th>corcoef_117</th>\n <th>corcoef_118</th>\n <th>corcoef_119</th>\n <th>corcoef_120</th>\n <th>hurst_1</th>\n <th>hurst_2</th>\n <th>hurst_3</th>\n <th>hurst_4</th>\n <th>hurst_5</th>\n <th>hurst_6</th>\n <th>hurst_7</th>\n <th>hurst_8</th>\n <th>hurst_9</th>\n <th>hurst_10</th>\n <th>hurst_11</th>\n <th>hurst_12</th>\n <th>hurst_13</th>\n <th>hurst_14</th>\n <th>hurst_15</th>\n <th>hurst_16</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>-0.406786</td>\n <td>-0.506428</td>\n <td>-0.550631</td>\n <td>-0.602230</td>\n <td>-0.117925</td>\n <td>-0.258156</td>\n <td>-0.292530</td>\n <td>-0.310629</td>\n <td>-0.464966</td>\n <td>-2.681048</td>\n <td>-0.672553</td>\n <td>-0.735941</td>\n <td>-0.558759</td>\n <td>-0.644890</td>\n <td>-0.688039</td>\n <td>-0.669862</td>\n <td>-0.510144</td>\n <td>-0.598658</td>\n <td>-0.646677</td>\n <td>-0.670835</td>\n <td>-0.129998</td>\n <td>-0.251415</td>\n <td>-0.323000</td>\n <td>-0.227784</td>\n <td>-0.639318</td>\n <td>-2.760449</td>\n <td>-0.665167</td>\n <td>-0.659974</td>\n <td>-0.590483</td>\n <td>-0.588708</td>\n <td>-0.647818</td>\n <td>-0.715215</td>\n <td>-0.629282</td>\n <td>-0.647044</td>\n <td>-0.621452</td>\n <td>-0.675702</td>\n <td>-0.426132</td>\n <td>-0.442531</td>\n <td>-0.483979</td>\n <td>-0.434602</td>\n <td>-0.750417</td>\n <td>-2.766089</td>\n <td>-0.708479</td>\n <td>-0.729438</td>\n <td>-0.629134</td>\n <td>-0.464072</td>\n <td>-0.620423</td>\n <td>-0.708196</td>\n <td>-0.707625</td>\n <td>-0.711356</td>\n <td>-0.642788</td>\n <td>-0.672846</td>\n <td>-0.478605</td>\n <td>-0.479591</td>\n <td>-0.520513</td>\n <td>-0.456928</td>\n <td>-0.790443</td>\n <td>-2.769022</td>\n <td>-0.732106</td>\n <td>-0.730806</td>\n <td>-0.646176</td>\n <td>-0.473156</td>\n <td>-0.634019</td>\n <td>-0.703660</td>\n <td>-0.763965</td>\n <td>-0.765547</td>\n <td>-0.680584</td>\n <td>-0.695172</td>\n <td>-0.550166</td>\n <td>-0.547159</td>\n <td>-0.539212</td>\n <td>-0.476176</td>\n <td>-0.818632</td>\n <td>-2.770506</td>\n <td>-0.785050</td>\n <td>-0.764124</td>\n <td>-0.638319</td>\n <td>-0.486804</td>\n <td>-0.654367</td>\n <td>-0.688834</td>\n <td>-0.905803</td>\n <td>-0.866233</td>\n <td>-0.841806</td>\n <td>-0.849490</td>\n <td>-0.760451</td>\n <td>-0.696508</td>\n <td>-0.704839</td>\n <td>-0.673899</td>\n <td>-0.937149</td>\n <td>-2.788111</td>\n <td>-0.922047</td>\n <td>-0.953263</td>\n <td>-0.767587</td>\n <td>-0.691095</td>\n <td>-0.780723</td>\n <td>-0.759457</td>\n <td>-1.181694</td>\n <td>-1.112317</td>\n <td>-1.080482</td>\n <td>-1.044585</td>\n <td>-0.970119</td>\n <td>-0.848156</td>\n <td>-0.901840</td>\n <td>-0.927819</td>\n <td>-1.146751</td>\n <td>-2.809118</td>\n <td>-1.091821</td>\n <td>-1.153706</td>\n <td>-0.992040</td>\n <td>-0.950499</td>\n <td>-0.943695</td>\n <td>-0.874682</td>\n <td>-1.422331</td>\n <td>-1.389611</td>\n <td>-1.338669</td>\n <td>-1.274514</td>\n <td>-1.122098</td>\n <td>-1.014736</td>\n <td>-1.053481</td>\n <td>-1.121061</td>\n <td>-1.423202</td>\n <td>-2.849218</td>\n <td>-1.344742</td>\n <td>-1.423175</td>\n <td>-1.194762</td>\n <td>-1.215790</td>\n <td>-1.194936</td>\n <td>-1.162727</td>\n <td>0.615413</td>\n <td>0.465645</td>\n <td>0.394319</td>\n <td>0.333944</td>\n <td>0.734827</td>\n <td>0.507590</td>\n <td>0.499734</td>\n <td>0.471931</td>\n <td>0.564459</td>\n <td>0.117440</td>\n <td>0.326137</td>\n <td>0.306081</td>\n <td>0.306713</td>\n <td>0.156820</td>\n <td>0.166570</td>\n <td>0.143397</td>\n <td>0.512056</td>\n <td>0.373415</td>\n <td>0.298273</td>\n <td>0.265338</td>\n <td>0.722755</td>\n <td>0.514332</td>\n <td>0.469264</td>\n <td>0.554776</td>\n <td>0.390108</td>\n <td>0.038039</td>\n <td>0.333523</td>\n <td>0.382048</td>\n <td>0.274988</td>\n <td>0.213003</td>\n <td>0.206791</td>\n <td>0.098044</td>\n <td>0.330776</td>\n <td>0.269807</td>\n <td>0.294938</td>\n <td>0.250845</td>\n <td>0.369017</td>\n <td>0.274044</td>\n <td>0.281546</td>\n <td>0.327668</td>\n <td>0.246239</td>\n <td>0.030195</td>\n <td>0.253611</td>\n <td>0.295587</td>\n <td>0.231769</td>\n <td>0.326421</td>\n <td>0.217546</td>\n <td>0.114852</td>\n <td>-0.181280</td>\n <td>-0.103608</td>\n <td>-0.003335</td>\n <td>-0.014493</td>\n <td>-0.353738</td>\n <td>-0.240287</td>\n <td>-0.187718</td>\n <td>-0.227108</td>\n <td>-0.143868</td>\n <td>-0.007843</td>\n <td>-0.079912</td>\n <td>-0.086461</td>\n <td>-0.043219</td>\n <td>0.113418</td>\n <td>0.010754</td>\n <td>0.016808</td>\n <td>1330.445710</td>\n <td>1053.480839</td>\n <td>1012.823856</td>\n <td>882.276599</td>\n <td>5324.886292</td>\n <td>3438.669283</td>\n <td>2822.905996</td>\n <td>3031.208239</td>\n <td>949.112785</td>\n <td>0.365631</td>\n <td>707.979865</td>\n <td>596.306308</td>\n <td>1072.018943</td>\n <td>1306.998679</td>\n <td>937.363732</td>\n <td>935.186251</td>\n <td>2.666569</td>\n <td>6.968982</td>\n <td>3.161490</td>\n <td>1.738240</td>\n <td>1.444126</td>\n <td>18.737641</td>\n <td>33.201227</td>\n <td>40.104324</td>\n <td>3.765897</td>\n <td>30.769902</td>\n <td>1.565525</td>\n <td>1.709295</td>\n <td>2.556811</td>\n <td>3.083199</td>\n <td>1.464667</td>\n <td>2.148079</td>\n <td>0.178701</td>\n <td>0.032578</td>\n <td>-0.023397</td>\n <td>-0.152288</td>\n <td>-0.219400</td>\n <td>-0.201431</td>\n <td>-0.190916</td>\n <td>0.204805</td>\n <td>0.006099</td>\n <td>0.081204</td>\n <td>0.049197</td>\n <td>-0.009776</td>\n <td>-0.070802</td>\n <td>-0.063187</td>\n <td>-0.074768</td>\n <td>0.220537</td>\n <td>0.081216</td>\n <td>-0.179154</td>\n <td>-0.259667</td>\n <td>-0.271336</td>\n <td>-0.228816</td>\n <td>0.135671</td>\n <td>0.116379</td>\n <td>0.205116</td>\n <td>0.137675</td>\n <td>-0.019435</td>\n <td>-0.069349</td>\n <td>-0.026827</td>\n <td>-0.071847</td>\n <td>0.210955</td>\n <td>-0.159451</td>\n <td>-0.245452</td>\n <td>-0.239983</td>\n <td>-0.167325</td>\n <td>0.007277</td>\n <td>0.011734</td>\n <td>0.167109</td>\n <td>0.104715</td>\n <td>-0.002921</td>\n <td>-0.062695</td>\n <td>-0.046157</td>\n <td>-0.062296</td>\n <td>-0.169828</td>\n <td>-0.228330</td>\n <td>-0.229710</td>\n <td>-0.120309</td>\n <td>-0.001547</td>\n <td>0.064140</td>\n <td>0.139054</td>\n <td>0.209108</td>\n <td>0.004949</td>\n <td>-0.047816</td>\n <td>-0.013022</td>\n <td>-0.013767</td>\n <td>-0.016553</td>\n <td>-0.148324</td>\n <td>-0.176761</td>\n <td>-0.166190</td>\n <td>-0.022829</td>\n <td>-0.180159</td>\n <td>-0.186908</td>\n <td>-0.120251</td>\n <td>-0.150716</td>\n <td>-0.152404</td>\n <td>-0.142461</td>\n <td>0.354104</td>\n <td>0.136804</td>\n <td>-0.234881</td>\n <td>-0.070225</td>\n <td>-0.273721</td>\n <td>-0.272838</td>\n <td>-0.240637</td>\n <td>-0.232917</td>\n <td>-0.275701</td>\n <td>-0.222007</td>\n <td>0.337664</td>\n <td>-0.211292</td>\n <td>-0.057565</td>\n <td>-0.260903</td>\n <td>-0.268919</td>\n <td>-0.218403</td>\n <td>-0.237076</td>\n <td>-0.278634</td>\n <td>-0.220479</td>\n <td>-0.198198</td>\n <td>-0.036452</td>\n <td>-0.208113</td>\n <td>-0.195315</td>\n <td>-0.202388</td>\n <td>-0.227254</td>\n <td>-0.264204</td>\n <td>-0.178708</td>\n <td>-0.049883</td>\n <td>0.126748</td>\n <td>0.116390</td>\n <td>0.055399</td>\n <td>-0.036598</td>\n <td>-0.025073</td>\n <td>-0.024066</td>\n <td>-0.049266</td>\n <td>0.031814</td>\n <td>0.016276</td>\n <td>0.028395</td>\n <td>0.088016</td>\n <td>0.058297</td>\n <td>0.276228</td>\n <td>0.031344</td>\n <td>-0.044703</td>\n <td>-0.039266</td>\n <td>0.000437</td>\n <td>0.044788</td>\n <td>-0.014861</td>\n <td>0.011477</td>\n <td>0.072533</td>\n <td>0.106350</td>\n <td>0.118652</td>\n <td>0.088373</td>\n <td>0.493077</td>\n <td>0.194405</td>\n <td>0.354023</td>\n <td>0.474543</td>\n <td>0.344651</td>\n <td>0.312167</td>\n <td>0.568701</td>\n <td>0.411647</td>\n <td>0.388849</td>\n <td>0.264564</td>\n <td>0.256256</td>\n <td>0.495332</td>\n <td>0.611394</td>\n <td>0.347852</td>\n <td>0.331746</td>\n <td>0.501788</td>\n <td>0.473050</td>\n <td>0.391432</td>\n <td>0.359969</td>\n </tr>\n <tr>\n <th>1</th>\n <td>-0.428051</td>\n <td>-0.566576</td>\n <td>-0.635108</td>\n <td>-0.672799</td>\n <td>-0.256426</td>\n <td>-0.372712</td>\n <td>-0.317646</td>\n <td>-0.404852</td>\n <td>-0.479488</td>\n <td>-2.690905</td>\n <td>-0.720872</td>\n <td>-0.778956</td>\n <td>-0.560340</td>\n <td>-0.700978</td>\n <td>-0.691731</td>\n <td>-0.715577</td>\n <td>-0.417979</td>\n <td>-0.647573</td>\n <td>-0.697937</td>\n <td>-0.670199</td>\n <td>-0.292538</td>\n <td>-0.322548</td>\n <td>-0.347250</td>\n <td>-0.277774</td>\n <td>-0.667934</td>\n <td>-2.740056</td>\n <td>-0.661574</td>\n <td>-0.690016</td>\n <td>-0.623905</td>\n <td>-0.589920</td>\n <td>-0.679789</td>\n <td>-0.771444</td>\n <td>-0.612797</td>\n <td>-0.640917</td>\n <td>-0.636664</td>\n <td>-0.640541</td>\n <td>-0.502826</td>\n <td>-0.474716</td>\n <td>-0.480880</td>\n <td>-0.469672</td>\n <td>-0.725687</td>\n <td>-2.745536</td>\n <td>-0.664675</td>\n <td>-0.692026</td>\n <td>-0.589230</td>\n <td>-0.488217</td>\n <td>-0.596600</td>\n <td>-0.682200</td>\n <td>-0.709069</td>\n <td>-0.710412</td>\n <td>-0.686045</td>\n <td>-0.677144</td>\n <td>-0.565640</td>\n <td>-0.513876</td>\n <td>-0.526703</td>\n <td>-0.516816</td>\n <td>-0.774404</td>\n <td>-2.758830</td>\n <td>-0.715076</td>\n <td>-0.711834</td>\n <td>-0.596229</td>\n <td>-0.430723</td>\n <td>-0.572233</td>\n <td>-0.684438</td>\n <td>-0.776716</td>\n <td>-0.806387</td>\n <td>-0.763084</td>\n <td>-0.727851</td>\n <td>-0.639602</td>\n <td>-0.570051</td>\n <td>-0.592727</td>\n <td>-0.567083</td>\n <td>-0.814626</td>\n <td>-2.772359</td>\n <td>-0.767065</td>\n <td>-0.751058</td>\n <td>-0.622363</td>\n <td>-0.450963</td>\n <td>-0.600960</td>\n <td>-0.682608</td>\n <td>-0.919301</td>\n <td>-0.930214</td>\n <td>-0.913473</td>\n <td>-0.894954</td>\n <td>-0.852568</td>\n <td>-0.710257</td>\n <td>-0.763854</td>\n <td>-0.761351</td>\n <td>-0.947070</td>\n <td>-2.782325</td>\n <td>-0.932717</td>\n <td>-0.959682</td>\n <td>-0.763956</td>\n <td>-0.695249</td>\n <td>-0.787622</td>\n <td>-0.767490</td>\n <td>-1.227845</td>\n <td>-1.222355</td>\n <td>-1.169036</td>\n <td>-1.106684</td>\n <td>-1.059154</td>\n <td>-0.901919</td>\n <td>-0.944014</td>\n <td>-0.952874</td>\n <td>-1.192869</td>\n <td>-2.814676</td>\n <td>-1.129484</td>\n <td>-1.171308</td>\n <td>-0.982528</td>\n <td>-0.906194</td>\n <td>-0.977814</td>\n <td>-0.920432</td>\n <td>-1.466409</td>\n <td>-1.442010</td>\n <td>-1.393084</td>\n <td>-1.318627</td>\n <td>-1.200591</td>\n <td>-1.060743</td>\n <td>-1.056533</td>\n <td>-1.169781</td>\n <td>-1.446816</td>\n <td>-2.839578</td>\n <td>-1.372801</td>\n <td>-1.427087</td>\n <td>-1.210371</td>\n <td>-1.217941</td>\n <td>-1.219325</td>\n <td>-1.220321</td>\n <td>0.618679</td>\n <td>0.485593</td>\n <td>0.387613</td>\n <td>0.315243</td>\n <td>0.687265</td>\n <td>0.422889</td>\n <td>0.527013</td>\n <td>0.441787</td>\n <td>0.573319</td>\n <td>0.107294</td>\n <td>0.299179</td>\n <td>0.273774</td>\n <td>0.299294</td>\n <td>0.087059</td>\n <td>0.180658</td>\n <td>0.121686</td>\n <td>0.628751</td>\n <td>0.404596</td>\n <td>0.324784</td>\n <td>0.317843</td>\n <td>0.651154</td>\n <td>0.473052</td>\n <td>0.497408</td>\n <td>0.568865</td>\n <td>0.384873</td>\n <td>0.058143</td>\n <td>0.358477</td>\n <td>0.362714</td>\n <td>0.235729</td>\n <td>0.198118</td>\n <td>0.192600</td>\n <td>0.065819</td>\n <td>0.359662</td>\n <td>0.336351</td>\n <td>0.327431</td>\n <td>0.306036</td>\n <td>0.377840</td>\n <td>0.275827</td>\n <td>0.311445</td>\n <td>0.330987</td>\n <td>0.284924</td>\n <td>0.039459</td>\n <td>0.307191</td>\n <td>0.332190</td>\n <td>0.254153</td>\n <td>0.318847</td>\n <td>0.273614</td>\n <td>0.154859</td>\n <td>-0.269089</td>\n <td>-0.068246</td>\n <td>0.002647</td>\n <td>-0.011806</td>\n <td>-0.273313</td>\n <td>-0.197225</td>\n <td>-0.185963</td>\n <td>-0.237878</td>\n <td>-0.099950</td>\n <td>-0.018684</td>\n <td>-0.051286</td>\n <td>-0.030524</td>\n <td>0.018424</td>\n <td>0.120729</td>\n <td>0.081014</td>\n <td>0.089040</td>\n <td>1452.257995</td>\n <td>899.164212</td>\n <td>823.909800</td>\n <td>802.319827</td>\n <td>3459.216856</td>\n <td>2657.810182</td>\n <td>2670.563703</td>\n <td>2588.839317</td>\n <td>919.439123</td>\n <td>0.347581</td>\n <td>731.671687</td>\n <td>661.900286</td>\n <td>1423.068986</td>\n <td>1568.033791</td>\n <td>1143.074468</td>\n <td>879.163979</td>\n <td>3.047194</td>\n <td>7.240983</td>\n <td>4.372952</td>\n <td>2.604562</td>\n <td>2.639642</td>\n <td>21.375989</td>\n <td>16.903039</td>\n <td>19.801985</td>\n <td>4.652647</td>\n <td>35.961740</td>\n <td>2.177266</td>\n <td>2.657048</td>\n <td>4.372113</td>\n <td>3.978509</td>\n <td>1.861551</td>\n <td>1.916273</td>\n <td>0.158984</td>\n <td>0.006843</td>\n <td>-0.020018</td>\n <td>-0.114797</td>\n <td>-0.187727</td>\n <td>-0.183429</td>\n <td>-0.153786</td>\n <td>0.175157</td>\n <td>0.010284</td>\n <td>0.052897</td>\n <td>-0.014611</td>\n <td>-0.064157</td>\n <td>-0.117036</td>\n <td>-0.133033</td>\n <td>-0.121117</td>\n <td>0.199643</td>\n <td>0.116175</td>\n <td>-0.163561</td>\n <td>-0.226921</td>\n <td>-0.222678</td>\n <td>-0.194723</td>\n <td>0.107515</td>\n <td>0.126857</td>\n <td>0.184516</td>\n <td>0.111269</td>\n <td>-0.061203</td>\n <td>-0.125231</td>\n <td>-0.091346</td>\n <td>-0.106738</td>\n <td>0.258751</td>\n <td>-0.143910</td>\n <td>-0.213731</td>\n <td>-0.205557</td>\n <td>-0.162809</td>\n <td>0.005357</td>\n <td>0.008838</td>\n <td>0.167182</td>\n <td>0.112151</td>\n <td>-0.043900</td>\n <td>-0.113255</td>\n <td>-0.071328</td>\n <td>-0.098273</td>\n <td>-0.141045</td>\n <td>-0.211931</td>\n <td>-0.213553</td>\n <td>-0.140393</td>\n <td>-0.014006</td>\n <td>0.060842</td>\n <td>0.151790</td>\n <td>0.248345</td>\n <td>-0.035642</td>\n <td>-0.116842</td>\n <td>-0.094239</td>\n <td>-0.068171</td>\n <td>0.047842</td>\n <td>-0.159501</td>\n <td>-0.132093</td>\n <td>-0.119789</td>\n <td>-0.039888</td>\n <td>-0.155188</td>\n <td>-0.133624</td>\n <td>-0.096723</td>\n <td>-0.129859</td>\n <td>-0.148303</td>\n <td>-0.128713</td>\n <td>0.266662</td>\n <td>0.050960</td>\n <td>-0.196714</td>\n <td>-0.059094</td>\n <td>-0.228505</td>\n <td>-0.223246</td>\n <td>-0.177460</td>\n <td>-0.188110</td>\n <td>-0.210657</td>\n <td>-0.183980</td>\n <td>0.248465</td>\n <td>-0.184424</td>\n <td>-0.047883</td>\n <td>-0.198102</td>\n <td>-0.205756</td>\n <td>-0.154796</td>\n <td>-0.195820</td>\n <td>-0.221138</td>\n <td>-0.193138</td>\n <td>-0.157629</td>\n <td>-0.034454</td>\n <td>-0.178085</td>\n <td>-0.166659</td>\n <td>-0.135418</td>\n <td>-0.194642</td>\n <td>-0.207960</td>\n <td>-0.151013</td>\n <td>-0.056442</td>\n <td>0.073630</td>\n <td>0.055497</td>\n <td>-0.007204</td>\n <td>-0.085205</td>\n <td>-0.104699</td>\n <td>-0.048605</td>\n <td>-0.043361</td>\n <td>0.032085</td>\n <td>0.024517</td>\n <td>0.010900</td>\n <td>0.068934</td>\n <td>0.041464</td>\n <td>0.238677</td>\n <td>-0.037642</td>\n <td>-0.121946</td>\n <td>-0.120850</td>\n <td>-0.050691</td>\n <td>-0.025828</td>\n <td>-0.101934</td>\n <td>-0.091659</td>\n <td>-0.011054</td>\n <td>-0.006813</td>\n <td>-0.002555</td>\n <td>0.026854</td>\n <td>0.464664</td>\n <td>0.188227</td>\n <td>0.363075</td>\n <td>0.502679</td>\n <td>0.401603</td>\n <td>0.414200</td>\n <td>0.488810</td>\n <td>0.622529</td>\n <td>0.485241</td>\n <td>0.571458</td>\n <td>0.406541</td>\n <td>0.615575</td>\n <td>0.687594</td>\n <td>0.409496</td>\n <td>0.531784</td>\n <td>0.434593</td>\n <td>0.765483</td>\n <td>0.494393</td>\n <td>0.491168</td>\n </tr>\n <tr>\n <th>2</th>\n <td>-0.438473</td>\n <td>-0.388276</td>\n <td>-0.560804</td>\n <td>-0.462901</td>\n <td>-0.261924</td>\n <td>-0.412540</td>\n <td>-0.530027</td>\n <td>-0.607979</td>\n <td>-0.674493</td>\n <td>-0.713967</td>\n <td>-0.710172</td>\n <td>-0.723321</td>\n <td>-0.598466</td>\n <td>-0.773537</td>\n <td>-0.741306</td>\n <td>-0.744617</td>\n <td>-0.648960</td>\n <td>-0.595146</td>\n <td>-0.648578</td>\n <td>-0.649621</td>\n <td>-0.392382</td>\n <td>-0.425151</td>\n <td>-0.550321</td>\n <td>-0.508957</td>\n <td>-0.794122</td>\n <td>-0.822105</td>\n <td>-0.802749</td>\n <td>-0.803596</td>\n <td>-0.627584</td>\n <td>-0.527127</td>\n <td>-0.652850</td>\n <td>-0.811482</td>\n <td>-0.559530</td>\n <td>-0.478087</td>\n <td>-0.490636</td>\n <td>-0.533479</td>\n <td>-0.510440</td>\n <td>-0.583750</td>\n <td>-0.647979</td>\n <td>-0.686542</td>\n <td>-0.791141</td>\n <td>-0.825084</td>\n <td>-0.751450</td>\n <td>-0.687138</td>\n <td>-0.624905</td>\n <td>-0.485618</td>\n <td>-0.654331</td>\n <td>-0.784637</td>\n <td>-0.654161</td>\n <td>-0.568703</td>\n <td>-0.505873</td>\n <td>-0.532767</td>\n <td>-0.559091</td>\n <td>-0.647034</td>\n <td>-0.714812</td>\n <td>-0.753031</td>\n <td>-0.823205</td>\n <td>-0.820382</td>\n <td>-0.738886</td>\n <td>-0.652164</td>\n <td>-0.659866</td>\n <td>-0.503283</td>\n <td>-0.688057</td>\n <td>-0.788743</td>\n <td>-0.785134</td>\n <td>-0.680696</td>\n <td>-0.549041</td>\n <td>-0.554762</td>\n <td>-0.613011</td>\n <td>-0.687526</td>\n <td>-0.759987</td>\n <td>-0.774439</td>\n <td>-0.868717</td>\n <td>-0.823551</td>\n <td>-0.744845</td>\n <td>-0.680407</td>\n <td>-0.620970</td>\n <td>-0.451936</td>\n <td>-0.653109</td>\n <td>-0.771946</td>\n <td>-0.877637</td>\n <td>-0.784635</td>\n <td>-0.765896</td>\n <td>-0.777874</td>\n <td>-0.786298</td>\n <td>-0.849431</td>\n <td>-0.915044</td>\n <td>-0.947412</td>\n <td>-0.949799</td>\n <td>-0.922733</td>\n <td>-0.860079</td>\n <td>-0.859587</td>\n <td>-0.610814</td>\n <td>-0.514078</td>\n <td>-0.701109</td>\n <td>-0.777309</td>\n <td>-1.178028</td>\n <td>-1.095816</td>\n <td>-1.054415</td>\n <td>-1.014244</td>\n <td>-0.986959</td>\n <td>-0.985453</td>\n <td>-1.088277</td>\n <td>-1.190281</td>\n <td>-1.230308</td>\n <td>-1.236988</td>\n <td>-1.154182</td>\n <td>-1.145834</td>\n <td>-0.998370</td>\n <td>-0.907247</td>\n <td>-1.004930</td>\n <td>-0.979935</td>\n <td>-1.428880</td>\n <td>-1.364952</td>\n <td>-1.297947</td>\n <td>-1.237569</td>\n <td>-1.054132</td>\n <td>-1.113059</td>\n <td>-1.231489</td>\n <td>-1.363413</td>\n <td>-1.510361</td>\n <td>-1.500478</td>\n <td>-1.358628</td>\n <td>-1.392567</td>\n <td>-1.255594</td>\n <td>-1.165068</td>\n <td>-1.221994</td>\n <td>-1.208271</td>\n <td>0.563889</td>\n <td>0.524655</td>\n <td>0.325820</td>\n <td>0.417272</td>\n <td>0.613218</td>\n <td>0.499598</td>\n <td>0.463052</td>\n <td>0.444107</td>\n <td>0.393296</td>\n <td>0.338068</td>\n <td>0.272525</td>\n <td>0.256221</td>\n <td>0.164258</td>\n <td>-0.105926</td>\n <td>0.085670</td>\n <td>0.122293</td>\n <td>0.353402</td>\n <td>0.317786</td>\n <td>0.238046</td>\n <td>0.230551</td>\n <td>0.482760</td>\n <td>0.486987</td>\n <td>0.442759</td>\n <td>0.543129</td>\n <td>0.273668</td>\n <td>0.229931</td>\n <td>0.179948</td>\n <td>0.175946</td>\n <td>0.135140</td>\n <td>0.140484</td>\n <td>0.174126</td>\n <td>0.055428</td>\n <td>0.344518</td>\n <td>0.345249</td>\n <td>0.367767</td>\n <td>0.336182</td>\n <td>0.316437</td>\n <td>0.279592</td>\n <td>0.292698</td>\n <td>0.323815</td>\n <td>0.239590</td>\n <td>0.227718</td>\n <td>0.234561</td>\n <td>0.295782</td>\n <td>0.139791</td>\n <td>0.199161</td>\n <td>0.173257</td>\n <td>0.088665</td>\n <td>-0.008884</td>\n <td>0.027464</td>\n <td>0.129721</td>\n <td>0.105631</td>\n <td>-0.166323</td>\n <td>-0.207395</td>\n <td>-0.150061</td>\n <td>-0.219314</td>\n <td>-0.034078</td>\n <td>-0.002212</td>\n <td>0.054614</td>\n <td>0.119837</td>\n <td>0.004651</td>\n <td>0.058676</td>\n <td>-0.000870</td>\n <td>0.033237</td>\n <td>2656.218991</td>\n <td>1535.532773</td>\n <td>1214.674003</td>\n <td>1946.472638</td>\n <td>2661.030179</td>\n <td>1732.844809</td>\n <td>1066.889208</td>\n <td>944.611286</td>\n <td>583.431453</td>\n <td>584.678465</td>\n <td>725.139566</td>\n <td>639.098452</td>\n <td>1373.657623</td>\n <td>1883.493652</td>\n <td>1005.484401</td>\n <td>742.213566</td>\n <td>0.749912</td>\n <td>7.486165</td>\n <td>4.812114</td>\n <td>1.285146</td>\n <td>1.231140</td>\n <td>1.518840</td>\n <td>3.409387</td>\n <td>2.827718</td>\n <td>1.183922</td>\n <td>2.380808</td>\n <td>1.954354</td>\n <td>2.532612</td>\n <td>1.832488</td>\n <td>3.483692</td>\n <td>0.901819</td>\n <td>1.961327</td>\n <td>-0.029160</td>\n <td>-0.061747</td>\n <td>0.192742</td>\n <td>-0.160926</td>\n <td>-0.184992</td>\n <td>-0.210974</td>\n <td>-0.170999</td>\n <td>0.025638</td>\n <td>-0.085231</td>\n <td>-0.148064</td>\n <td>-0.124346</td>\n <td>-0.094917</td>\n <td>-0.140349</td>\n <td>-0.138500</td>\n <td>-0.221177</td>\n <td>0.112207</td>\n <td>-0.040638</td>\n <td>-0.157796</td>\n <td>-0.182545</td>\n <td>-0.193836</td>\n <td>-0.146527</td>\n <td>0.006995</td>\n <td>0.032771</td>\n <td>0.040653</td>\n <td>0.003278</td>\n <td>-0.069760</td>\n <td>-0.129927</td>\n <td>-0.113316</td>\n <td>-0.130260</td>\n <td>0.138613</td>\n <td>-0.159326</td>\n <td>-0.173303</td>\n <td>-0.164666</td>\n <td>-0.097393</td>\n <td>-0.094210</td>\n <td>-0.000567</td>\n <td>0.048494</td>\n <td>0.012564</td>\n <td>-0.073928</td>\n <td>-0.149313</td>\n <td>-0.140981</td>\n <td>-0.147593</td>\n <td>-0.185277</td>\n <td>-0.184865</td>\n <td>-0.220040</td>\n <td>-0.124713</td>\n <td>-0.162265</td>\n <td>-0.127757</td>\n <td>-0.102602</td>\n <td>0.031571</td>\n <td>-0.103355</td>\n <td>-0.147154</td>\n <td>-0.119620</td>\n <td>-0.221497</td>\n <td>0.144652</td>\n <td>0.033266</td>\n <td>-0.054917</td>\n <td>-0.135020</td>\n <td>-0.158212</td>\n <td>-0.138838</td>\n <td>-0.159000</td>\n <td>-0.072380</td>\n <td>-0.100799</td>\n <td>-0.133940</td>\n <td>-0.097326</td>\n <td>0.390705</td>\n <td>0.037903</td>\n <td>-0.146170</td>\n <td>-0.167650</td>\n <td>-0.160586</td>\n <td>-0.154301</td>\n <td>-0.103053</td>\n <td>-0.120547</td>\n <td>-0.140514</td>\n <td>-0.103375</td>\n <td>0.286846</td>\n <td>-0.082819</td>\n <td>-0.128839</td>\n <td>-0.101643</td>\n <td>-0.114714</td>\n <td>-0.064995</td>\n <td>-0.130834</td>\n <td>-0.150770</td>\n <td>-0.070582</td>\n <td>-0.038292</td>\n <td>-0.076110</td>\n <td>-0.050995</td>\n <td>-0.027173</td>\n <td>-0.020764</td>\n <td>-0.123348</td>\n <td>-0.118128</td>\n <td>-0.025818</td>\n <td>0.279122</td>\n <td>0.071281</td>\n <td>0.072518</td>\n <td>0.022651</td>\n <td>-0.117495</td>\n <td>-0.123846</td>\n <td>-0.025361</td>\n <td>0.341524</td>\n <td>0.142416</td>\n <td>-0.019938</td>\n <td>-0.140631</td>\n <td>-0.158554</td>\n <td>-0.051268</td>\n <td>0.197225</td>\n <td>-0.030536</td>\n <td>-0.145669</td>\n <td>-0.177705</td>\n <td>-0.043935</td>\n <td>-0.011806</td>\n <td>-0.141013</td>\n <td>-0.146454</td>\n <td>-0.034084</td>\n <td>-0.167929</td>\n <td>-0.096414</td>\n <td>-0.004234</td>\n <td>0.441489</td>\n <td>0.178188</td>\n <td>0.407435</td>\n <td>0.236761</td>\n <td>0.419352</td>\n <td>0.262515</td>\n <td>0.232381</td>\n <td>0.196593</td>\n <td>0.279796</td>\n <td>0.205667</td>\n <td>0.184826</td>\n <td>0.258989</td>\n <td>0.246480</td>\n <td>0.192713</td>\n <td>0.360385</td>\n <td>0.268366</td>\n <td>0.271937</td>\n <td>0.228565</td>\n <td>0.213197</td>\n </tr>\n <tr>\n <th>3</th>\n <td>-0.111201</td>\n <td>-0.041987</td>\n <td>-0.313406</td>\n <td>-0.228682</td>\n <td>-0.245675</td>\n <td>-0.030617</td>\n <td>-0.180695</td>\n <td>-0.312091</td>\n <td>-0.233526</td>\n <td>0.009591</td>\n <td>-0.279750</td>\n <td>-0.301803</td>\n <td>-0.203881</td>\n <td>-0.209715</td>\n <td>-0.205457</td>\n <td>-0.254544</td>\n <td>-0.330172</td>\n <td>-0.221672</td>\n <td>-0.476177</td>\n <td>-0.453048</td>\n <td>-0.550985</td>\n <td>-0.417036</td>\n <td>-0.528471</td>\n <td>-0.600747</td>\n <td>-0.413666</td>\n <td>-0.398714</td>\n <td>-0.542718</td>\n <td>-0.581673</td>\n <td>-0.472075</td>\n <td>-0.445944</td>\n <td>-0.471322</td>\n <td>-0.551943</td>\n <td>-0.662568</td>\n <td>-0.692448</td>\n <td>-0.868319</td>\n <td>-0.821175</td>\n <td>-0.896454</td>\n <td>-0.761788</td>\n <td>-0.864188</td>\n <td>-0.902398</td>\n <td>-0.738648</td>\n <td>-0.756345</td>\n <td>-0.805154</td>\n <td>-0.824320</td>\n <td>-0.664092</td>\n <td>-0.679323</td>\n <td>-0.750961</td>\n <td>-0.830958</td>\n <td>-0.697661</td>\n <td>-0.752281</td>\n <td>-0.928897</td>\n <td>-0.873393</td>\n <td>-0.946171</td>\n <td>-0.814563</td>\n <td>-0.931151</td>\n <td>-0.953419</td>\n <td>-0.780086</td>\n <td>-0.816081</td>\n <td>-0.868454</td>\n <td>-0.895429</td>\n <td>-0.729425</td>\n <td>-0.741979</td>\n <td>-0.815945</td>\n <td>-0.901900</td>\n <td>-0.733869</td>\n <td>-0.810772</td>\n <td>-0.962738</td>\n <td>-0.927516</td>\n <td>-1.014153</td>\n <td>-0.868144</td>\n <td>-0.992215</td>\n <td>-0.999176</td>\n <td>-0.828851</td>\n <td>-0.885969</td>\n <td>-0.915254</td>\n <td>-0.965826</td>\n <td>-0.822768</td>\n <td>-0.819871</td>\n <td>-0.876483</td>\n <td>-0.970698</td>\n <td>-0.972177</td>\n <td>-1.062594</td>\n <td>-1.156289</td>\n <td>-1.116091</td>\n <td>-1.288333</td>\n <td>-1.179984</td>\n <td>-1.264226</td>\n <td>-1.247310</td>\n <td>-1.083451</td>\n <td>-1.085929</td>\n <td>-1.178520</td>\n <td>-1.229726</td>\n <td>-1.104440</td>\n <td>-1.064691</td>\n <td>-1.102844</td>\n <td>-1.237834</td>\n <td>-1.339655</td>\n <td>-1.480199</td>\n <td>-1.520626</td>\n <td>-1.407563</td>\n <td>-1.617144</td>\n <td>-1.543949</td>\n <td>-1.609962</td>\n <td>-1.547593</td>\n <td>-1.381859</td>\n <td>-1.360007</td>\n <td>-1.522461</td>\n <td>-1.622398</td>\n <td>-1.390270</td>\n <td>-1.358110</td>\n <td>-1.394830</td>\n <td>-1.581343</td>\n <td>-1.565151</td>\n <td>-1.734503</td>\n <td>-1.791662</td>\n <td>-1.650791</td>\n <td>-1.810555</td>\n <td>-1.802295</td>\n <td>-1.858161</td>\n <td>-1.760692</td>\n <td>-1.490321</td>\n <td>-1.535764</td>\n <td>-1.744210</td>\n <td>-1.874748</td>\n <td>-1.571948</td>\n <td>-1.623168</td>\n <td>-1.601572</td>\n <td>-1.777521</td>\n <td>1.006954</td>\n <td>1.181037</td>\n <td>0.987916</td>\n <td>1.009138</td>\n <td>1.176661</td>\n <td>1.294287</td>\n <td>1.222866</td>\n <td>1.059909</td>\n <td>0.973988</td>\n <td>1.211287</td>\n <td>1.037546</td>\n <td>1.081314</td>\n <td>1.020372</td>\n <td>0.977363</td>\n <td>1.019290</td>\n <td>1.121932</td>\n <td>0.787984</td>\n <td>1.001352</td>\n <td>0.825146</td>\n <td>0.784772</td>\n <td>0.871351</td>\n <td>0.907869</td>\n <td>0.875091</td>\n <td>0.771252</td>\n <td>0.793848</td>\n <td>0.802983</td>\n <td>0.774579</td>\n <td>0.801444</td>\n <td>0.752178</td>\n <td>0.741134</td>\n <td>0.753425</td>\n <td>0.824533</td>\n <td>0.421398</td>\n <td>0.475432</td>\n <td>0.388355</td>\n <td>0.366721</td>\n <td>0.471007</td>\n <td>0.513187</td>\n <td>0.480061</td>\n <td>0.423903</td>\n <td>0.426102</td>\n <td>0.385358</td>\n <td>0.460572</td>\n <td>0.493782</td>\n <td>0.488029</td>\n <td>0.443142</td>\n <td>0.415544</td>\n <td>0.481244</td>\n <td>-0.366585</td>\n <td>-0.525920</td>\n <td>-0.436791</td>\n <td>-0.418051</td>\n <td>-0.400344</td>\n <td>-0.394682</td>\n <td>-0.395030</td>\n <td>-0.347350</td>\n <td>-0.367746</td>\n <td>-0.417625</td>\n <td>-0.314007</td>\n <td>-0.307662</td>\n <td>-0.264148</td>\n <td>-0.297992</td>\n <td>-0.337881</td>\n <td>-0.343289</td>\n <td>3788.115611</td>\n <td>5446.828809</td>\n <td>1691.364780</td>\n <td>2308.655231</td>\n <td>1710.880902</td>\n <td>4515.610336</td>\n <td>2227.635213</td>\n <td>1331.450809</td>\n <td>2275.862726</td>\n <td>5136.964324</td>\n <td>1586.126659</td>\n <td>1376.815749</td>\n <td>2241.370570</td>\n <td>2339.214348</td>\n <td>2251.594604</td>\n <td>1757.943299</td>\n <td>2.040104</td>\n <td>2.225392</td>\n <td>0.739700</td>\n <td>1.147698</td>\n <td>1.072190</td>\n <td>2.500653</td>\n <td>0.793764</td>\n <td>0.409903</td>\n <td>0.840943</td>\n <td>0.632733</td>\n <td>0.553273</td>\n <td>0.550425</td>\n <td>1.570896</td>\n <td>0.646849</td>\n <td>0.687921</td>\n <td>0.347896</td>\n <td>0.642901</td>\n <td>0.274792</td>\n <td>-0.066169</td>\n <td>-0.233990</td>\n <td>-0.180758</td>\n <td>-0.177053</td>\n <td>-0.112920</td>\n <td>-0.219090</td>\n <td>-0.108098</td>\n <td>-0.101637</td>\n <td>-0.229437</td>\n <td>-0.227107</td>\n <td>-0.255041</td>\n <td>-0.275551</td>\n <td>-0.379040</td>\n <td>0.510787</td>\n <td>0.016690</td>\n <td>-0.255037</td>\n <td>-0.158896</td>\n <td>-0.062680</td>\n <td>-0.035647</td>\n <td>-0.296388</td>\n <td>-0.283800</td>\n <td>-0.070270</td>\n <td>-0.272400</td>\n <td>-0.276680</td>\n <td>-0.331240</td>\n <td>-0.361764</td>\n <td>-0.382152</td>\n <td>0.062699</td>\n <td>-0.176201</td>\n <td>-0.132981</td>\n <td>0.018451</td>\n <td>0.205976</td>\n <td>-0.257594</td>\n <td>-0.189507</td>\n <td>-0.105161</td>\n <td>-0.189763</td>\n <td>-0.243678</td>\n <td>-0.291848</td>\n <td>-0.318517</td>\n <td>-0.311132</td>\n <td>-0.156172</td>\n <td>-0.054545</td>\n <td>0.038934</td>\n <td>0.077202</td>\n <td>-0.246424</td>\n <td>-0.152501</td>\n <td>-0.072676</td>\n <td>0.078642</td>\n <td>-0.233161</td>\n <td>-0.176286</td>\n <td>-0.099320</td>\n <td>0.110287</td>\n <td>0.631025</td>\n <td>0.419345</td>\n <td>-0.078530</td>\n <td>-0.174900</td>\n <td>-0.115546</td>\n <td>-0.258702</td>\n <td>-0.215993</td>\n <td>-0.184341</td>\n <td>-0.108651</td>\n <td>-0.054307</td>\n <td>-0.029026</td>\n <td>0.545307</td>\n <td>-0.039585</td>\n <td>-0.259957</td>\n <td>-0.179956</td>\n <td>-0.283438</td>\n <td>-0.225450</td>\n <td>-0.314569</td>\n <td>-0.247017</td>\n <td>-0.201914</td>\n <td>-0.125843</td>\n <td>0.273577</td>\n <td>-0.276813</td>\n <td>-0.282466</td>\n <td>-0.278067</td>\n <td>-0.230892</td>\n <td>-0.315863</td>\n <td>-0.279530</td>\n <td>-0.253046</td>\n <td>-0.112160</td>\n <td>-0.188416</td>\n <td>-0.163674</td>\n <td>-0.048980</td>\n <td>0.062526</td>\n <td>-0.156347</td>\n <td>-0.209395</td>\n <td>-0.182854</td>\n <td>0.011055</td>\n <td>0.427245</td>\n <td>0.177390</td>\n <td>0.088372</td>\n <td>0.268092</td>\n <td>0.100422</td>\n <td>-0.017090</td>\n <td>-0.070929</td>\n <td>-0.021371</td>\n <td>0.031837</td>\n <td>-0.091824</td>\n <td>-0.026530</td>\n <td>0.056613</td>\n <td>-0.251510</td>\n <td>0.477342</td>\n <td>0.044892</td>\n <td>-0.102662</td>\n <td>-0.161943</td>\n <td>0.237846</td>\n <td>0.030722</td>\n <td>-0.154910</td>\n <td>-0.114277</td>\n <td>0.472173</td>\n <td>0.476516</td>\n <td>0.306272</td>\n <td>0.260913</td>\n <td>0.709682</td>\n <td>0.116640</td>\n <td>0.170123</td>\n <td>0.335260</td>\n <td>0.291863</td>\n <td>0.326536</td>\n <td>0.257931</td>\n <td>0.469382</td>\n <td>0.175357</td>\n <td>0.220069</td>\n <td>0.398464</td>\n <td>0.263109</td>\n <td>0.274553</td>\n <td>0.245795</td>\n <td>0.289799</td>\n <td>0.272079</td>\n <td>0.321547</td>\n <td>0.246082</td>\n <td>0.324690</td>\n </tr>\n <tr>\n <th>4</th>\n <td>-0.770331</td>\n <td>-0.584485</td>\n <td>-0.502479</td>\n <td>-0.454269</td>\n <td>-0.553190</td>\n <td>-0.636482</td>\n <td>-0.795933</td>\n <td>-0.820530</td>\n <td>-0.787236</td>\n <td>-0.671795</td>\n <td>-0.796073</td>\n <td>-0.793696</td>\n <td>-0.679834</td>\n <td>-0.628853</td>\n <td>-0.670575</td>\n <td>-0.794874</td>\n <td>-0.928855</td>\n <td>-0.881467</td>\n <td>-0.786080</td>\n <td>-0.976289</td>\n <td>-0.934422</td>\n <td>-1.029379</td>\n <td>-1.072448</td>\n <td>-1.145465</td>\n <td>-1.016527</td>\n <td>-0.975320</td>\n <td>-1.063246</td>\n <td>-1.115494</td>\n <td>-0.958634</td>\n <td>-0.902233</td>\n <td>-0.986790</td>\n <td>-1.188340</td>\n <td>-1.047846</td>\n <td>-1.020852</td>\n <td>-1.043965</td>\n <td>-1.165375</td>\n <td>-1.101895</td>\n <td>-1.233633</td>\n <td>-1.199967</td>\n <td>-1.297162</td>\n <td>-1.037072</td>\n <td>-1.144614</td>\n <td>-1.268083</td>\n <td>-1.374482</td>\n <td>-1.146901</td>\n <td>-1.116260</td>\n <td>-1.229882</td>\n <td>-1.395987</td>\n <td>-1.084449</td>\n <td>-1.059271</td>\n <td>-1.090673</td>\n <td>-1.209574</td>\n <td>-1.136210</td>\n <td>-1.260735</td>\n <td>-1.212954</td>\n <td>-1.316720</td>\n <td>-1.116572</td>\n <td>-1.194359</td>\n <td>-1.319347</td>\n <td>-1.425663</td>\n <td>-1.177851</td>\n <td>-1.173634</td>\n <td>-1.270851</td>\n <td>-1.439431</td>\n <td>-1.101896</td>\n <td>-1.073788</td>\n <td>-1.113964</td>\n <td>-1.211854</td>\n <td>-1.131726</td>\n <td>-1.244887</td>\n <td>-1.215078</td>\n <td>-1.339792</td>\n <td>-1.171134</td>\n <td>-1.210906</td>\n <td>-1.361289</td>\n <td>-1.462588</td>\n <td>-1.211765</td>\n <td>-1.203163</td>\n <td>-1.298599</td>\n <td>-1.474930</td>\n <td>-0.923572</td>\n <td>-1.080045</td>\n <td>-1.095923</td>\n <td>-1.285582</td>\n <td>-1.134799</td>\n <td>-1.274666</td>\n <td>-1.171211</td>\n <td>-1.363859</td>\n <td>-1.139129</td>\n <td>-1.235459</td>\n <td>-1.429862</td>\n <td>-1.517777</td>\n <td>-1.294699</td>\n <td>-1.278452</td>\n <td>-1.367044</td>\n <td>-1.531927</td>\n <td>-0.899367</td>\n <td>-1.100151</td>\n <td>-0.985105</td>\n <td>-1.557288</td>\n <td>-1.298830</td>\n <td>-1.503771</td>\n <td>-1.473174</td>\n <td>-1.561458</td>\n <td>-1.428877</td>\n <td>-1.340960</td>\n <td>-1.552326</td>\n <td>-1.626884</td>\n <td>-1.371516</td>\n <td>-1.371110</td>\n <td>-1.431025</td>\n <td>-1.634748</td>\n <td>-1.281053</td>\n <td>-1.315199</td>\n <td>-1.357246</td>\n <td>-1.756855</td>\n <td>-1.486056</td>\n <td>-1.726523</td>\n <td>-1.678698</td>\n <td>-1.748864</td>\n <td>-1.621325</td>\n <td>-1.442570</td>\n <td>-1.695904</td>\n <td>-1.850529</td>\n <td>-1.549643</td>\n <td>-1.467518</td>\n <td>-1.627333</td>\n <td>-1.829001</td>\n <td>0.140970</td>\n <td>0.505496</td>\n <td>0.534510</td>\n <td>0.946256</td>\n <td>0.655926</td>\n <td>0.737801</td>\n <td>0.500527</td>\n <td>0.630986</td>\n <td>0.473039</td>\n <td>0.613219</td>\n <td>0.690718</td>\n <td>0.775217</td>\n <td>0.651577</td>\n <td>0.693462</td>\n <td>0.727282</td>\n <td>0.785428</td>\n <td>-0.017554</td>\n <td>0.208515</td>\n <td>0.250909</td>\n <td>0.424236</td>\n <td>0.274694</td>\n <td>0.344904</td>\n <td>0.224013</td>\n <td>0.306051</td>\n <td>0.243748</td>\n <td>0.309694</td>\n <td>0.423545</td>\n <td>0.453419</td>\n <td>0.372777</td>\n <td>0.420082</td>\n <td>0.411067</td>\n <td>0.391962</td>\n <td>-0.162730</td>\n <td>0.043468</td>\n <td>-0.040567</td>\n <td>0.212531</td>\n <td>0.092561</td>\n <td>0.135059</td>\n <td>0.089004</td>\n <td>0.133562</td>\n <td>0.161324</td>\n <td>0.108517</td>\n <td>0.174601</td>\n <td>0.152609</td>\n <td>0.153288</td>\n <td>0.164773</td>\n <td>0.134974</td>\n <td>0.146634</td>\n <td>-0.145176</td>\n <td>-0.165047</td>\n <td>-0.291476</td>\n <td>-0.211705</td>\n <td>-0.182133</td>\n <td>-0.209845</td>\n <td>-0.135009</td>\n <td>-0.172489</td>\n <td>-0.082424</td>\n <td>-0.201176</td>\n <td>-0.248945</td>\n <td>-0.300811</td>\n <td>-0.219489</td>\n <td>-0.255309</td>\n <td>-0.276093</td>\n <td>-0.245328</td>\n <td>511.591893</td>\n <td>717.759756</td>\n <td>1305.924328</td>\n <td>2609.476978</td>\n <td>470.554463</td>\n <td>300.968428</td>\n <td>200.910476</td>\n <td>164.800668</td>\n <td>232.670311</td>\n <td>353.349813</td>\n <td>185.055518</td>\n <td>163.490757</td>\n <td>327.573035</td>\n <td>412.032264</td>\n <td>270.711882</td>\n <td>147.534822</td>\n <td>1.996694</td>\n <td>0.228024</td>\n <td>0.450064</td>\n <td>-0.254879</td>\n <td>0.542542</td>\n <td>1.087637</td>\n <td>0.792145</td>\n <td>1.881766</td>\n <td>1.114670</td>\n <td>0.304191</td>\n <td>1.542368</td>\n <td>0.757896</td>\n <td>1.059704</td>\n <td>0.859168</td>\n <td>2.562184</td>\n <td>0.349247</td>\n <td>0.030753</td>\n <td>-0.024299</td>\n <td>-0.145883</td>\n <td>-0.127290</td>\n <td>-0.178721</td>\n <td>-0.186140</td>\n <td>-0.145188</td>\n <td>0.021860</td>\n <td>0.015256</td>\n <td>-0.026582</td>\n <td>-0.087564</td>\n <td>-0.020789</td>\n <td>-0.033702</td>\n <td>-0.080075</td>\n <td>-0.124249</td>\n <td>-0.320325</td>\n <td>0.248216</td>\n <td>-0.056074</td>\n <td>-0.059749</td>\n <td>-0.080378</td>\n <td>-0.013758</td>\n <td>0.015854</td>\n <td>-0.313705</td>\n <td>-0.134662</td>\n <td>-0.250060</td>\n <td>-0.123679</td>\n <td>-0.293216</td>\n <td>-0.207075</td>\n <td>-0.225572</td>\n <td>-0.473004</td>\n <td>-0.000651</td>\n <td>-0.194882</td>\n <td>-0.098219</td>\n <td>-0.236044</td>\n <td>-0.178006</td>\n <td>0.137542</td>\n <td>-0.019509</td>\n <td>0.158804</td>\n <td>-0.074843</td>\n <td>0.072406</td>\n <td>0.119957</td>\n <td>0.102874</td>\n <td>-0.164157</td>\n <td>0.035398</td>\n <td>-0.130333</td>\n <td>0.026446</td>\n <td>-0.028035</td>\n <td>-0.372921</td>\n <td>-0.191441</td>\n <td>-0.358372</td>\n <td>-0.231236</td>\n <td>-0.294606</td>\n <td>-0.297595</td>\n <td>-0.263418</td>\n <td>0.393418</td>\n <td>0.083894</td>\n <td>-0.097702</td>\n <td>-0.102853</td>\n <td>-0.121281</td>\n <td>-0.099799</td>\n <td>-0.083224</td>\n <td>-0.132804</td>\n <td>-0.160676</td>\n <td>-0.118069</td>\n <td>-0.146844</td>\n <td>0.321721</td>\n <td>0.107047</td>\n <td>-0.091751</td>\n <td>-0.202066</td>\n <td>-0.117705</td>\n <td>-0.134136</td>\n <td>-0.173693</td>\n <td>-0.222429</td>\n <td>-0.202671</td>\n <td>-0.117897</td>\n <td>0.502476</td>\n <td>-0.154050</td>\n <td>-0.136416</td>\n <td>-0.045410</td>\n <td>0.040682</td>\n <td>-0.139774</td>\n <td>-0.169549</td>\n <td>-0.097835</td>\n <td>0.093651</td>\n <td>-0.101272</td>\n <td>-0.115679</td>\n <td>-0.014920</td>\n <td>0.094516</td>\n <td>-0.141819</td>\n <td>-0.142176</td>\n <td>-0.142847</td>\n <td>0.135922</td>\n <td>0.136635</td>\n <td>0.067097</td>\n <td>-0.102046</td>\n <td>0.079685</td>\n <td>-0.058121</td>\n <td>-0.077385</td>\n <td>-0.178405</td>\n <td>0.142252</td>\n <td>0.097217</td>\n <td>0.088642</td>\n <td>0.179863</td>\n <td>0.101274</td>\n <td>0.058654</td>\n <td>-0.056705</td>\n <td>0.032192</td>\n <td>0.021425</td>\n <td>0.084679</td>\n <td>0.061646</td>\n <td>0.076973</td>\n <td>0.121898</td>\n <td>0.093407</td>\n <td>0.485742</td>\n <td>0.242453</td>\n <td>0.087940</td>\n <td>-0.063777</td>\n <td>0.188335</td>\n <td>0.040613</td>\n <td>0.212929</td>\n <td>0.435515</td>\n <td>0.292176</td>\n <td>0.311181</td>\n <td>0.236584</td>\n <td>0.269141</td>\n <td>0.365435</td>\n <td>0.430709</td>\n <td>0.268305</td>\n <td>0.350697</td>\n <td>0.300376</td>\n <td>0.470037</td>\n <td>0.158063</td>\n <td>0.384128</td>\n <td>0.254672</td>\n <td>0.270050</td>\n <td>0.276992</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"metadata": {},
"execution_count": 1
}
]
},
{
"metadata": {
"collapsed": true,
"trusted": true
},
"cell_type": "code",
"source": "y=answers_1_SINGLE\nX=X_df_train_SINGLE\n\n# Normalize variables\nX_norm = (X - X.mean())/X.std()\n\nlr_best_params = {'penalty': 'l2', 'C': 100, 'solver': 'newton-cg', 'fit_intercept': False}\nlr = LogisticRegression(**lr_best_params)\nlr.fit(X_norm, y)\n#Store LR coeefs\nlr_coeefs=lr.coef_ \n\nk = (X_df_train_SINGLE.shape[1])",
"execution_count": 2,
"outputs": []
},
{
"metadata": {
"collapsed": false,
"trusted": true
},
"cell_type": "code",
"source": "with pm.Model() as logistic_model: \n μ = pm.Normal('μ', 0, sd=10)\n b = pm.Laplace('b', 0.0, b=0.1, shape=k) \n p = pm.math.invlogit(μ + tt.dot(X_norm, b)) \n likelihood = pm.Bernoulli('likelihood', p, observed=y)",
"execution_count": 3,
"outputs": []
},
{
"metadata": {
"collapsed": false,
"trusted": true
},
"cell_type": "code",
"source": "niter=3000\nwith logistic_model:\n trace_logistic_model = pm.sample(niter, n_init=50000)",
"execution_count": 4,
"outputs": [
{
"output_type": "stream",
"text": "Auto-assigning NUTS sampler...\nInitializing NUTS using advi...\nAverage ELBO = -8,546.2: 100%|██████████| 50000/50000 [02:06<00:00, 396.60it/s]\nFinished [100%]: Average ELBO = -7,615.1\n100%|██████████| 3000/3000 [1:00:51<00:00, 1.08s/it] \n",
"name": "stderr"
}
]
},
{
"metadata": {
"collapsed": false,
"trusted": true
},
"cell_type": "code",
"source": "ax = pm.traceplot(trace_logistic_model[-1000:], figsize=(12,len(trace_logistic_model.varnames)*1.5), \n lines={k: v['mean'] for k, v in pm.df_summary(trace_logistic_model[-1000:]).iterrows()})",
"execution_count": 5,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": "<matplotlib.figure.Figure at 0x13a4b8e80>",
"image/png": 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iFxid7QGQUr6/JVIpFKuQjK7z8LOn+PbDh4kl0mwbaud9N29b0T4L5xtup40P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\nNl0uv7BJaiqu3Jpi5/o6njGmO5Nz7fLznL/yEouWy9t32uRnCi7N3Oel1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},
"metadata": {}
}
]
}
],
"metadata": {
"_draft": {
"nbviewer_url": "https://gist.github.com/ca3eb59c03ade9052d2d2dbcd7345f1b"
},
"anaconda-cloud": {},
"gist": {
"id": "ca3eb59c03ade9052d2d2dbcd7345f1b",
"data": {
"description": "Untitled1.ipynb",
"public": true
}
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3",
"language": "python"
},
"latex_envs": {
"bibliofile": "biblio.bib",
"cite_by": "apalike",
"current_citInitial": 1,
"eqLabelWithNumbers": true,
"eqNumInitial": 0
},
"language_info": {
"name": "python",
"version": "3.6.0",
"mimetype": "text/x-python",
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"pygments_lexer": "ipython3",
"nbconvert_exporter": "python",
"file_extension": ".py"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
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