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| <div class="title">elastic_net.sql_in</div> </div> |
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| <a href="elastic__net_8sql__in.html">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno"> 1</span> <span class="comment">/* ----------------------------------------------------------------------- */</span><span class="comment">/**</span></div> |
| <div class="line"><a name="l00002"></a><span class="lineno"> 2</span> <span class="comment"> *</span></div> |
| <div class="line"><a name="l00003"></a><span class="lineno"> 3</span> <span class="comment"> * @file elastic_net.sql_in</span></div> |
| <div class="line"><a name="l00004"></a><span class="lineno"> 4</span> <span class="comment"> *</span></div> |
| <div class="line"><a name="l00005"></a><span class="lineno"> 5</span> <span class="comment"> * @brief SQL functions for elastic net regularization</span></div> |
| <div class="line"><a name="l00006"></a><span class="lineno"> 6</span> <span class="comment"> * @date July 2012</span></div> |
| <div class="line"><a name="l00007"></a><span class="lineno"> 7</span> <span class="comment"> *</span></div> |
| <div class="line"><a name="l00008"></a><span class="lineno"> 8</span> <span class="comment"> * @sa For a brief introduction to elastic net, see the module</span></div> |
| <div class="line"><a name="l00009"></a><span class="lineno"> 9</span> <span class="comment"> * description \ref grp_lasso.</span></div> |
| <div class="line"><a name="l00010"></a><span class="lineno"> 10</span> <span class="comment"> *</span></div> |
| <div class="line"><a name="l00011"></a><span class="lineno"> 11</span> <span class="comment"> */</span><span class="comment">/* ----------------------------------------------------------------------- */</span></div> |
| <div class="line"><a name="l00012"></a><span class="lineno"> 12</span> </div> |
| <div class="line"><a name="l00013"></a><span class="lineno"> 13</span> m4_include(`SQLCommon.m4<span class="stringliteral">') --'</span></div> |
| <div class="line"><a name="l00014"></a><span class="lineno"> 14</span> <span class="comment"></span></div> |
| <div class="line"><a name="l00015"></a><span class="lineno"> 15</span> <span class="comment">/**</span></div> |
| <div class="line"><a name="l00016"></a><span class="lineno"> 16</span> <span class="comment">@addtogroup grp_elasticnet</span></div> |
| <div class="line"><a name="l00017"></a><span class="lineno"> 17</span> <span class="comment"></span></div> |
| <div class="line"><a name="l00018"></a><span class="lineno"> 18</span> <span class="comment"><div class="toc"><b>Contents</b><ul></span></div> |
| <div class="line"><a name="l00019"></a><span class="lineno"> 19</span> <span class="comment"><li class="level1"><a href="#about">About</a></li></span></div> |
| <div class="line"><a name="l00020"></a><span class="lineno"> 20</span> <span class="comment"><li class="level1"><a href="#help">Online Help</a></li></span></div> |
| <div class="line"><a name="l00021"></a><span class="lineno"> 21</span> <span class="comment"><li class="level1"><a href="#train">Usage</a></li></span></div> |
| <div class="line"><a name="l00022"></a><span class="lineno"> 22</span> <span class="comment"><li class="level2"><a href="#train">Training Function</a></li></span></div> |
| <div class="line"><a name="l00023"></a><span class="lineno"> 23</span> <span class="comment"><li class="level3"><a href="#optimizer">Optimizer Parameters</a></li></span></div> |
| <div class="line"><a name="l00024"></a><span class="lineno"> 24</span> <span class="comment"><li class="level2"><a href="#output">Output Table</a></li></span></div> |
| <div class="line"><a name="l00025"></a><span class="lineno"> 25</span> <span class="comment"><li class="level2"><a href="#predict">Prediction Function</a></li></span></div> |
| <div class="line"><a name="l00026"></a><span class="lineno"> 26</span> <span class="comment"><li class="level1"><a href="#examples">Examples</a></li></span></div> |
| <div class="line"><a name="l00027"></a><span class="lineno"> 27</span> <span class="comment"><li class="level1"><a href="#seealso">See Also</a></li></span></div> |
| <div class="line"><a name="l00028"></a><span class="lineno"> 28</span> <span class="comment"><li class="level1"><a href="#background">Technical Background</a></li></span></div> |
| <div class="line"><a name="l00029"></a><span class="lineno"> 29</span> <span class="comment"><li class="level1"><a href="#literature">Literature</a></li></span></div> |
| <div class="line"><a name="l00030"></a><span class="lineno"> 30</span> <span class="comment"></ul></div></span></div> |
| <div class="line"><a name="l00031"></a><span class="lineno"> 31</span> <span class="comment"></span></div> |
| <div class="line"><a name="l00032"></a><span class="lineno"> 32</span> <span class="comment">@anchor about</span></div> |
| <div class="line"><a name="l00033"></a><span class="lineno"> 33</span> <span class="comment">@about</span></div> |
| <div class="line"><a name="l00034"></a><span class="lineno"> 34</span> <span class="comment"></span></div> |
| <div class="line"><a name="l00035"></a><span class="lineno"> 35</span> <span class="comment">This module implements elastic net regularization for linear and logistic regression problems. </span></div> |
| <div class="line"><a name="l00036"></a><span class="lineno"> 36</span> <span class="comment"></span></div> |
| <div class="line"><a name="l00037"></a><span class="lineno"> 37</span> <span class="comment">@anchor help</span></div> |
| <div class="line"><a name="l00038"></a><span class="lineno"> 38</span> <span class="comment">@par Online Help </span></div> |
| <div class="line"><a name="l00039"></a><span class="lineno"> 39</span> <span class="comment"></span></div> |
| <div class="line"><a name="l00040"></a><span class="lineno"> 40</span> <span class="comment">View short help messages using the following statements:</span></div> |
| <div class="line"><a name="l00041"></a><span class="lineno"> 41</span> <span class="comment">@verbatim </span></div> |
| <div class="line"><a name="l00042"></a><span class="lineno"> 42</span> <span class="comment">-- Summary of Elastic Net Regularization</span></div> |
| <div class="line"><a name="l00043"></a><span class="lineno"> 43</span> <span class="comment">madlib.elastic_net_train()</span></div> |
| <div class="line"><a name="l00044"></a><span class="lineno"> 44</span> <span class="comment"></span></div> |
| <div class="line"><a name="l00045"></a><span class="lineno"> 45</span> <span class="comment">-- Training function syntax and output table format</span></div> |
| <div class="line"><a name="l00046"></a><span class="lineno"> 46</span> <span class="comment">madlib.elastic_net_train('usage')</span></div> |
| <div class="line"><a name="l00047"></a><span class="lineno"> 47</span> <span class="comment"></span></div> |
| <div class="line"><a name="l00048"></a><span class="lineno"> 48</span> <span class="comment">-- Prediction function syntax</span></div> |
| <div class="line"><a name="l00049"></a><span class="lineno"> 49</span> <span class="comment">madlib.elastic_net_train('predict')</span></div> |
| <div class="line"><a name="l00050"></a><span class="lineno"> 50</span> <span class="comment"></span></div> |
| <div class="line"><a name="l00051"></a><span class="lineno"> 51</span> <span class="comment">-- Syntax for gaussian/linear model</span></div> |
| <div class="line"><a name="l00052"></a><span class="lineno"> 52</span> <span class="comment">madlib.elastic_net_train('gaussian')</span></div> |
| <div class="line"><a name="l00053"></a><span class="lineno"> 53</span> <span class="comment">madlib.elastic_net_train('linear')</span></div> |
| <div class="line"><a name="l00054"></a><span class="lineno"> 54</span> <span class="comment"></span></div> |
| <div class="line"><a name="l00055"></a><span class="lineno"> 55</span> <span class="comment">-- Syntax for binomial/logistic model</span></div> |
| <div class="line"><a name="l00056"></a><span class="lineno"> 56</span> <span class="comment">madlib.elastic_net_train('binomial')</span></div> |
| <div class="line"><a name="l00057"></a><span class="lineno"> 57</span> <span class="comment">madlib.elastic_net_train('logistic')</span></div> |
| <div class="line"><a name="l00058"></a><span class="lineno"> 58</span> <span class="comment"></span></div> |
| <div class="line"><a name="l00059"></a><span class="lineno"> 59</span> <span class="comment">-- Parameter formats for optimizers</span></div> |
| <div class="line"><a name="l00060"></a><span class="lineno"> 60</span> <span class="comment">madlib.elastic_net_train('fista')</span></div> |
| <div class="line"><a name="l00061"></a><span class="lineno"> 61</span> <span class="comment">madlib.elastic_net_train('igd')</span></div> |
| <div class="line"><a name="l00062"></a><span class="lineno"> 62</span> <span class="comment">@endverbatim</span></div> |
| <div class="line"><a name="l00063"></a><span class="lineno"> 63</span> <span class="comment"></span></div> |
| <div class="line"><a name="l00064"></a><span class="lineno"> 64</span> <span class="comment">@anchor train</span></div> |
| <div class="line"><a name="l00065"></a><span class="lineno"> 65</span> <span class="comment">@par Training Function</span></div> |
| <div class="line"><a name="l00066"></a><span class="lineno"> 66</span> <span class="comment">The training function has the following format:</span></div> |
| <div class="line"><a name="l00067"></a><span class="lineno"> 67</span> <span class="comment">@verbatim</span></div> |
| <div class="line"><a name="l00068"></a><span class="lineno"> 68</span> <span class="comment">madlib.elastic_net_train(</span></div> |
| <div class="line"><a name="l00069"></a><span class="lineno"> 69</span> <span class="comment"> tbl_source, tbl_result, col_dep_var, col_ind_var, </span></div> |
| <div class="line"><a name="l00070"></a><span class="lineno"> 70</span> <span class="comment"> regress_family, alpha, lambda_value, standardize, </span></div> |
| <div class="line"><a name="l00071"></a><span class="lineno"> 71</span> <span class="comment"> grouping_col, optimizer := NULL, </span></div> |
| <div class="line"><a name="l00072"></a><span class="lineno"> 72</span> <span class="comment"> optimizer_params := NULL, excluded := NULL, </span></div> |
| <div class="line"><a name="l00073"></a><span class="lineno"> 73</span> <span class="comment"> max_iter := 10000, tolerance := 1e-6)</span></div> |
| <div class="line"><a name="l00074"></a><span class="lineno"> 74</span> <span class="comment">@endverbatim</span></div> |
| <div class="line"><a name="l00075"></a><span class="lineno"> 75</span> <span class="comment"></span></div> |
| <div class="line"><a name="l00076"></a><span class="lineno"> 76</span> <span class="comment">\note It is \b strongly \b recommended that you run</span></div> |
| <div class="line"><a name="l00077"></a><span class="lineno"> 77</span> <span class="comment">\c elastic_net_train() on a subset of the data with a limited</span></div> |
| <div class="line"><a name="l00078"></a><span class="lineno"> 78</span> <span class="comment">\e max_iter before applying it to the full data set with a large</span></div> |
| <div class="line"><a name="l00079"></a><span class="lineno"> 79</span> <span class="comment">\e max_iter. In the pre-run, you can adjust the parameters to get the</span></div> |
| <div class="line"><a name="l00080"></a><span class="lineno"> 80</span> <span class="comment">best performance and then apply the best set of parameters to the whole data</span></div> |
| <div class="line"><a name="l00081"></a><span class="lineno"> 81</span> <span class="comment">set.</span></div> |
| <div class="line"><a name="l00082"></a><span class="lineno"> 82</span> <span class="comment"></span></div> |
| <div class="line"><a name="l00083"></a><span class="lineno"> 83</span> <span class="comment"><DL class="arglist"></span></div> |
| <div class="line"><a name="l00084"></a><span class="lineno"> 84</span> <span class="comment"><DT>tbl_source</DT></span></div> |
| <div class="line"><a name="l00085"></a><span class="lineno"> 85</span> <span class="comment"><DD>Text value. The name of the table containing the training data.</DD></span></div> |
| <div class="line"><a name="l00086"></a><span class="lineno"> 86</span> <span class="comment"></span></div> |
| <div class="line"><a name="l00087"></a><span class="lineno"> 87</span> <span class="comment"><DT>tbl_result</DT></span></div> |
| <div class="line"><a name="l00088"></a><span class="lineno"> 88</span> <span class="comment"><DD>Text value. Name of the generated table containing the output model.</DD></span></div> |
| <div class="line"><a name="l00089"></a><span class="lineno"> 89</span> <span class="comment"></span></div> |
| <div class="line"><a name="l00090"></a><span class="lineno"> 90</span> <span class="comment"><DT>col_dep_var</DT></span></div> |
| <div class="line"><a name="l00091"></a><span class="lineno"> 91</span> <span class="comment"><DD>Text value. An expression for the dependent variable.</DD></span></div> |
| <div class="line"><a name="l00092"></a><span class="lineno"> 92</span> <span class="comment"><DD>Both \e col_dep_var and \e col_ind_var can be valid Postgres</span></div> |
| <div class="line"><a name="l00093"></a><span class="lineno"> 93</span> <span class="comment">expressions. For example, <tt>col_dep_var = 'log(y+1)'</tt>, and <tt>col_ind_var</span></div> |
| <div class="line"><a name="l00094"></a><span class="lineno"> 94</span> <span class="comment">= 'array[exp(x[1]), x[2], 1/(1+x[3])]'</tt>. In the binomial case, you can</span></div> |
| <div class="line"><a name="l00095"></a><span class="lineno"> 95</span> <span class="comment">use a Boolean expression, for example, <tt>col_dep_var = 'y < 0'</tt>.<DD></span></div> |
| <div class="line"><a name="l00096"></a><span class="lineno"> 96</span> <span class="comment"></span></div> |
| <div class="line"><a name="l00097"></a><span class="lineno"> 97</span> <span class="comment"><DT>col_ind_var</DT></span></div> |
| <div class="line"><a name="l00098"></a><span class="lineno"> 98</span> <span class="comment"><DD>Text value. An expression for the independent variables. Use \c '*' to</span></div> |
| <div class="line"><a name="l00099"></a><span class="lineno"> 99</span> <span class="comment">specify all columns of <em>tbl_source</em> except those listed in the</span></div> |
| <div class="line"><a name="l00100"></a><span class="lineno"> 100</span> <span class="comment"><em>excluded</em> string. If \e col_dep_var is a column name, it is</span></div> |
| <div class="line"><a name="l00101"></a><span class="lineno"> 101</span> <span class="comment">automatically excluded from the independent variables. However, if</span></div> |
| <div class="line"><a name="l00102"></a><span class="lineno"> 102</span> <span class="comment">\e col_dep_var is a valid Postgres expression, any column names used</span></div> |
| <div class="line"><a name="l00103"></a><span class="lineno"> 103</span> <span class="comment">within the expression are only excluded if they are explicitly included in the</span></div> |
| <div class="line"><a name="l00104"></a><span class="lineno"> 104</span> <span class="comment">\e excluded argument. It is a good idea to add all column names involved in</span></div> |
| <div class="line"><a name="l00105"></a><span class="lineno"> 105</span> <span class="comment">the dependent variable expression to the <em>excluded</em> string.</DD></span></div> |
| <div class="line"><a name="l00106"></a><span class="lineno"> 106</span> <span class="comment"></span></div> |
| <div class="line"><a name="l00107"></a><span class="lineno"> 107</span> <span class="comment"><DT>regress_family</DT></span></div> |
| <div class="line"><a name="l00108"></a><span class="lineno"> 108</span> <span class="comment"><DD>Text value. The regression type, either 'gaussian' ('linear') or 'binomial' ('logistic').</DD></span></div> |
| <div class="line"><a name="l00109"></a><span class="lineno"> 109</span> <span class="comment"></span></div> |
| <div class="line"><a name="l00110"></a><span class="lineno"> 110</span> <span class="comment"><DT>alpha</DT></span></div> |
| <div class="line"><a name="l00111"></a><span class="lineno"> 111</span> <span class="comment"><DD>Float8 value. Elastic net control parameter, value in [0, 1].</DD></span></div> |
| <div class="line"><a name="l00112"></a><span class="lineno"> 112</span> <span class="comment"></span></div> |
| <div class="line"><a name="l00113"></a><span class="lineno"> 113</span> <span class="comment"><DT>lambda_value</DT></span></div> |
| <div class="line"><a name="l00114"></a><span class="lineno"> 114</span> <span class="comment"><DD>Float8 value. Regularization parameter, positive.</DD></span></div> |
| <div class="line"><a name="l00115"></a><span class="lineno"> 115</span> <span class="comment"></span></div> |
| <div class="line"><a name="l00116"></a><span class="lineno"> 116</span> <span class="comment"><DT>standardize</DT></span></div> |
| <div class="line"><a name="l00117"></a><span class="lineno"> 117</span> <span class="comment"><DD>Boolean value. Whether to normalize the data. Setting this to True usually yields better results and faster convergence. Default: True.</span></div> |
| <div class="line"><a name="l00118"></a><span class="lineno"> 118</span> <span class="comment"></DD></span></div> |
| <div class="line"><a name="l00119"></a><span class="lineno"> 119</span> <span class="comment"></span></div> |
| <div class="line"><a name="l00120"></a><span class="lineno"> 120</span> <span class="comment"><DT>grouping_col</DT></span></div> |
| <div class="line"><a name="l00121"></a><span class="lineno"> 121</span> <span class="comment"><DD>Text value. <em>Not currently implemented. Any non-NULL value is ignored.</em> An expression list used to group the input dataset into discrete groups, running one regression per group. Similar to the SQL <tt>GROUP BY</tt> clause. When this value is null, no grouping is used and a single result model is generated. Default value: NULL.</DD></span></div> |
| <div class="line"><a name="l00122"></a><span class="lineno"> 122</span> <span class="comment"></span></div> |
| <div class="line"><a name="l00123"></a><span class="lineno"> 123</span> <span class="comment"><DT>optimizer</DT></span></div> |
| <div class="line"><a name="l00124"></a><span class="lineno"> 124</span> <span class="comment"><DD>Text value. Name of optimizer, either 'fista' or 'igd'. Default: 'fista'.</DD></span></div> |
| <div class="line"><a name="l00125"></a><span class="lineno"> 125</span> <span class="comment"></span></div> |
| <div class="line"><a name="l00126"></a><span class="lineno"> 126</span> <span class="comment"><DT>optimizer_params</DT></span></div> |
| <div class="line"><a name="l00127"></a><span class="lineno"> 127</span> <span class="comment"><DD>Text value. Optimizer parameters, delimited with commas. The parameters differ depending on the value of \e optimizer. See the descriptions below for details. Default: NULL.</DD></span></div> |
| <div class="line"><a name="l00128"></a><span class="lineno"> 128</span> <span class="comment"></span></div> |
| <div class="line"><a name="l00129"></a><span class="lineno"> 129</span> <span class="comment"><DT>excluded</DT> </span></div> |
| <div class="line"><a name="l00130"></a><span class="lineno"> 130</span> <span class="comment"><DD>Text value. A comma-delimited list of column names excluded from features. </span></div> |
| <div class="line"><a name="l00131"></a><span class="lineno"> 131</span> <span class="comment">For example, <tt>'col1, col2'</tt>. If the \e col_ind_var is an array, \e excluded is a list of the integer array positions to exclude, for example <tt>'1,2'</tt>. If this argument is NULL or an empty string <tt>''</tt>, no columns are excluded.</DD></span></div> |
| <div class="line"><a name="l00132"></a><span class="lineno"> 132</span> <span class="comment"></span></div> |
| <div class="line"><a name="l00133"></a><span class="lineno"> 133</span> <span class="comment"><DT>max_iter</DT></span></div> |
| <div class="line"><a name="l00134"></a><span class="lineno"> 134</span> <span class="comment"><DD>Integer value. The maximum number of iterations that are allowed. Default: 10000.</DD></span></div> |
| <div class="line"><a name="l00135"></a><span class="lineno"> 135</span> <span class="comment"></span></div> |
| <div class="line"><a name="l00136"></a><span class="lineno"> 136</span> <span class="comment"><DT>tolerance</DT> </span></div> |
| <div class="line"><a name="l00137"></a><span class="lineno"> 137</span> <span class="comment"><DD>Float value. The criteria to end iterations. Both the</span></div> |
| <div class="line"><a name="l00138"></a><span class="lineno"> 138</span> <span class="comment">'fista' and 'igd' optimizers compute the average difference between the</span></div> |
| <div class="line"><a name="l00139"></a><span class="lineno"> 139</span> <span class="comment">coefficients of two consecutive iterations, and when the difference is smaller</span></div> |
| <div class="line"><a name="l00140"></a><span class="lineno"> 140</span> <span class="comment">than \e tolerance or the iteration number is larger than \e max_iter, the</span></div> |
| <div class="line"><a name="l00141"></a><span class="lineno"> 141</span> <span class="comment">computation stops. The default is 1e-6.</DD> </span></div> |
| <div class="line"><a name="l00142"></a><span class="lineno"> 142</span> <span class="comment"></DL></span></div> |
| <div class="line"><a name="l00143"></a><span class="lineno"> 143</span> <span class="comment"></span></div> |
| <div class="line"><a name="l00144"></a><span class="lineno"> 144</span> <span class="comment">@anchor optimizer </span></div> |
| <div class="line"><a name="l00145"></a><span class="lineno"> 145</span> <span class="comment">@par Optimizer Parameters </span></div> |
| <div class="line"><a name="l00146"></a><span class="lineno"> 146</span> <span class="comment">Optimizer parameters are supplied in a string containing a comma-delimited</span></div> |
| <div class="line"><a name="l00147"></a><span class="lineno"> 147</span> <span class="comment">list of name-value pairs. All of these named parameters are optional, and</span></div> |
| <div class="line"><a name="l00148"></a><span class="lineno"> 148</span> <span class="comment">their order does not matter. You must use the format "<param_name> = <value>"</span></div> |
| <div class="line"><a name="l00149"></a><span class="lineno"> 149</span> <span class="comment">to specify the value of a parameter, otherwise the parameter is ignored.</span></div> |
| <div class="line"><a name="l00150"></a><span class="lineno"> 150</span> <span class="comment"></span></div> |
| <div class="line"><a name="l00151"></a><span class="lineno"> 151</span> <span class="comment">When the \ref elastic_net_train() \e optimizer argument value is \b 'fista', the \e optimizer_params argument has the following format:</span></div> |
| <div class="line"><a name="l00152"></a><span class="lineno"> 152</span> <span class="comment">@verbatim</span></div> |
| <div class="line"><a name="l00153"></a><span class="lineno"> 153</span> <span class="comment"> 'max_stepsize = ..., eta = ..., warmup = ..., warmup_lambdas = ..., </span></div> |
| <div class="line"><a name="l00154"></a><span class="lineno"> 154</span> <span class="comment"> warmup_lambda_no = ..., warmup_tolerance = ..., use_active_set = ..., </span></div> |
| <div class="line"><a name="l00155"></a><span class="lineno"> 155</span> <span class="comment"> activeset_tolerance = ..., random_stepsize = ...'</span></div> |
| <div class="line"><a name="l00156"></a><span class="lineno"> 156</span> <span class="comment">@endverbatim</span></div> |
| <div class="line"><a name="l00157"></a><span class="lineno"> 157</span> <span class="comment"></span></div> |
| <div class="line"><a name="l00158"></a><span class="lineno"> 158</span> <span class="comment"><DL class="arglist"></span></div> |
| <div class="line"><a name="l00159"></a><span class="lineno"> 159</span> <span class="comment"><DT>max_stepsize</dt></span></div> |
| <div class="line"><a name="l00160"></a><span class="lineno"> 160</span> <span class="comment"><DD>Initial backtracking step size. At each iteration, the algorithm first tries</span></div> |
| <div class="line"><a name="l00161"></a><span class="lineno"> 161</span> <span class="comment"><em>stepsize = max_stepsize</em>, and if it does not work out, it then tries a</span></div> |
| <div class="line"><a name="l00162"></a><span class="lineno"> 162</span> <span class="comment">smaller step size, <em>stepsize = stepsize/eta</em>, where \e eta must</span></div> |
| <div class="line"><a name="l00163"></a><span class="lineno"> 163</span> <span class="comment">be larger than 1. At first glance, this seems to perform repeated iterations for even one step, but using a larger step size actually greatly increases the computation speed and minimizes the total number of iterations. A careful choice of \e max_stepsize can decrease the computation time by more than 10 times.</span></div> |
| <div class="line"><a name="l00164"></a><span class="lineno"> 164</span> <span class="comment">The default is 4.0.</DD></span></div> |
| <div class="line"><a name="l00165"></a><span class="lineno"> 165</span> <span class="comment"><DT>eta</DT></span></div> |
| <div class="line"><a name="l00166"></a><span class="lineno"> 166</span> <span class="comment"><DD>If stepsize does not work \e stepsize / \e eta is tried. Must be greater than 1. The default is 2.</DD></span></div> |
| <div class="line"><a name="l00167"></a><span class="lineno"> 167</span> <span class="comment"></span></div> |
| <div class="line"><a name="l00168"></a><span class="lineno"> 168</span> <span class="comment"><DT>warmup</DT></span></div> |
| <div class="line"><a name="l00169"></a><span class="lineno"> 169</span> <span class="comment"><DD>If \e warmup is True, a series of lambda values, which is</span></div> |
| <div class="line"><a name="l00170"></a><span class="lineno"> 170</span> <span class="comment">strictly descent and ends at the lambda value that the user wants to calculate,</span></div> |
| <div class="line"><a name="l00171"></a><span class="lineno"> 171</span> <span class="comment">is used. The larger lambda gives very sparse solution, and the sparse</span></div> |
| <div class="line"><a name="l00172"></a><span class="lineno"> 172</span> <span class="comment">solution again is used as the initial guess for the next lambda's solution,</span></div> |
| <div class="line"><a name="l00173"></a><span class="lineno"> 173</span> <span class="comment">which speeds up the computation for the next lambda. For larger data sets,</span></div> |
| <div class="line"><a name="l00174"></a><span class="lineno"> 174</span> <span class="comment">this can sometimes accelerate the whole computation and may be faster than</span></div> |
| <div class="line"><a name="l00175"></a><span class="lineno"> 175</span> <span class="comment">computation on only one lambda value. The default is False.</DD></span></div> |
| <div class="line"><a name="l00176"></a><span class="lineno"> 176</span> <span class="comment"></span></div> |
| <div class="line"><a name="l00177"></a><span class="lineno"> 177</span> <span class="comment"><DT>warmup_lambdas</D></span></div> |
| <div class="line"><a name="l00178"></a><span class="lineno"> 178</span> <span class="comment"><DD>The lambda value series to use when \e warmup is True. The default is NULL, which means that lambda values will be automatically generated.</DD></span></div> |
| <div class="line"><a name="l00179"></a><span class="lineno"> 179</span> <span class="comment"></span></div> |
| <div class="line"><a name="l00180"></a><span class="lineno"> 180</span> <span class="comment"><DT>warmup_lambda_no</DT></span></div> |
| <div class="line"><a name="l00181"></a><span class="lineno"> 181</span> <span class="comment"><DD>How many lambdas are used in warm-up. If \e warmup_lambdas is not NULL, this value is overridden by the number of provided lambda values. The default is 15. </DD></span></div> |
| <div class="line"><a name="l00182"></a><span class="lineno"> 182</span> <span class="comment"></span></div> |
| <div class="line"><a name="l00183"></a><span class="lineno"> 183</span> <span class="comment"><DT>warmup_tolerance</DT></span></div> |
| <div class="line"><a name="l00184"></a><span class="lineno"> 184</span> <span class="comment"><DD>The value of tolerance used during warmup. The default is the same as the \e tolerance argument. </DD></span></div> |
| <div class="line"><a name="l00185"></a><span class="lineno"> 185</span> <span class="comment"></span></div> |
| <div class="line"><a name="l00186"></a><span class="lineno"> 186</span> <span class="comment"><DT>use_active_set</DT></span></div> |
| <div class="line"><a name="l00187"></a><span class="lineno"> 187</span> <span class="comment"><DD>If \e use_active_set is True, an active-set method is used to</span></div> |
| <div class="line"><a name="l00188"></a><span class="lineno"> 188</span> <span class="comment">speed up the computation. Considerable speedup is obtained by organizing the</span></div> |
| <div class="line"><a name="l00189"></a><span class="lineno"> 189</span> <span class="comment">iterations around the active set of features&mdash;those with nonzero coefficients.</span></div> |
| <div class="line"><a name="l00190"></a><span class="lineno"> 190</span> <span class="comment">After a complete cycle through all the variables, we iterate on only the active</span></div> |
| <div class="line"><a name="l00191"></a><span class="lineno"> 191</span> <span class="comment">set until convergence. If another complete cycle does not change the active set,</span></div> |
| <div class="line"><a name="l00192"></a><span class="lineno"> 192</span> <span class="comment">we are done, otherwise the process is repeated. The default is False. </DD></span></div> |
| <div class="line"><a name="l00193"></a><span class="lineno"> 193</span> <span class="comment"></span></div> |
| <div class="line"><a name="l00194"></a><span class="lineno"> 194</span> <span class="comment"><DT>activeset_tolerance</DT></span></div> |
| <div class="line"><a name="l00195"></a><span class="lineno"> 195</span> <span class="comment"><DD>The value of tolerance used during active set</span></div> |
| <div class="line"><a name="l00196"></a><span class="lineno"> 196</span> <span class="comment">calculation. The default is the same as \c tolerance.</DD> </span></div> |
| <div class="line"><a name="l00197"></a><span class="lineno"> 197</span> <span class="comment"></span></div> |
| <div class="line"><a name="l00198"></a><span class="lineno"> 198</span> <span class="comment"><DT>random_stepsize</DT></span></div> |
| <div class="line"><a name="l00199"></a><span class="lineno"> 199</span> <span class="comment"><DD>Whether to add some randomness to the step size. Sometimes, this can speed</span></div> |
| <div class="line"><a name="l00200"></a><span class="lineno"> 200</span> <span class="comment">up the calculation. The default is False.</DD></span></div> |
| <div class="line"><a name="l00201"></a><span class="lineno"> 201</span> <span class="comment"></DL></span></div> |
| <div class="line"><a name="l00202"></a><span class="lineno"> 202</span> <span class="comment"></span></div> |
| <div class="line"><a name="l00203"></a><span class="lineno"> 203</span> <span class="comment">When the \ref elastic_net_train() \e optimizer argument value is \b 'igd', the \e optimizer_params argument has the following format:</span></div> |
| <div class="line"><a name="l00204"></a><span class="lineno"> 204</span> <span class="comment">@verbatim</span></div> |
| <div class="line"><a name="l00205"></a><span class="lineno"> 205</span> <span class="comment">'stepsize = ..., step_decay = ..., threshold = ..., warmup = ..., </span></div> |
| <div class="line"><a name="l00206"></a><span class="lineno"> 206</span> <span class="comment"> warmup_lambdas = ..., warmup_lambda_no = ..., warmup_tolerance = ..., </span></div> |
| <div class="line"><a name="l00207"></a><span class="lineno"> 207</span> <span class="comment"> parallel = ...' </span></div> |
| <div class="line"><a name="l00208"></a><span class="lineno"> 208</span> <span class="comment">@endverbatim</span></div> |
| <div class="line"><a name="l00209"></a><span class="lineno"> 209</span> <span class="comment"></span></div> |
| <div class="line"><a name="l00210"></a><span class="lineno"> 210</span> <span class="comment"><DL class="arglist"></span></div> |
| <div class="line"><a name="l00211"></a><span class="lineno"> 211</span> <span class="comment"><DT>stepsize</DT></span></div> |
| <div class="line"><a name="l00212"></a><span class="lineno"> 212</span> <span class="comment"><DD>The default is 0.01.</DD></span></div> |
| <div class="line"><a name="l00213"></a><span class="lineno"> 213</span> <span class="comment"><DT>step_decay</DT></span></div> |
| <div class="line"><a name="l00214"></a><span class="lineno"> 214</span> <span class="comment"><DD>The actual setpsize used for current step is (previous stepsize) / exp(setp_decay). The default value is 0, which means that a constant stepsize is used in IGD.</DD></span></div> |
| <div class="line"><a name="l00215"></a><span class="lineno"> 215</span> <span class="comment"><DT>threshold</DT></span></div> |
| <div class="line"><a name="l00216"></a><span class="lineno"> 216</span> <span class="comment"><DD>When a coefficient is really small, set this coefficient to be 0. The default is 1e-10.</DD></span></div> |
| <div class="line"><a name="l00217"></a><span class="lineno"> 217</span> <span class="comment"><DD>Due to the stochastic nature of SGD, we can only obtain very small values for</span></div> |
| <div class="line"><a name="l00218"></a><span class="lineno"> 218</span> <span class="comment">the fitting coefficients. Therefore, \e threshold is needed at the end of</span></div> |
| <div class="line"><a name="l00219"></a><span class="lineno"> 219</span> <span class="comment">the computation to screen out tiny values and hard-set them to </span></div> |
| <div class="line"><a name="l00220"></a><span class="lineno"> 220</span> <span class="comment">zeros. This is accomplished as follows: (1) multiply each coefficient with the</span></div> |
| <div class="line"><a name="l00221"></a><span class="lineno"> 221</span> <span class="comment">standard deviation of the corresponding feature; (2) compute the average of</span></div> |
| <div class="line"><a name="l00222"></a><span class="lineno"> 222</span> <span class="comment">absolute values of re-scaled coefficients; (3) divide each rescaled coefficient</span></div> |
| <div class="line"><a name="l00223"></a><span class="lineno"> 223</span> <span class="comment">with the average, and if the resulting absolute value is smaller than</span></div> |
| <div class="line"><a name="l00224"></a><span class="lineno"> 224</span> <span class="comment">\e threshold, set the original coefficient to zero.</DD></span></div> |
| <div class="line"><a name="l00225"></a><span class="lineno"> 225</span> <span class="comment"><DT>warmup</DT></span></div> |
| <div class="line"><a name="l00226"></a><span class="lineno"> 226</span> <span class="comment"><DD>If \e warmup is True, a series of lambda values, which is</span></div> |
| <div class="line"><a name="l00227"></a><span class="lineno"> 227</span> <span class="comment">strictly descent and ends at the lambda value that the user wants to calculate,</span></div> |
| <div class="line"><a name="l00228"></a><span class="lineno"> 228</span> <span class="comment">is used. The larger lambda gives very sparse solution, and the sparse</span></div> |
| <div class="line"><a name="l00229"></a><span class="lineno"> 229</span> <span class="comment">solution again is used as the initial guess for the next lambda's solution,</span></div> |
| <div class="line"><a name="l00230"></a><span class="lineno"> 230</span> <span class="comment">which speeds up the computation for the next lambda. For larger data sets,</span></div> |
| <div class="line"><a name="l00231"></a><span class="lineno"> 231</span> <span class="comment">this can sometimes accelerate the whole computation and may be faster than</span></div> |
| <div class="line"><a name="l00232"></a><span class="lineno"> 232</span> <span class="comment">computation on only one lambda value. The default is False.</DD></span></div> |
| <div class="line"><a name="l00233"></a><span class="lineno"> 233</span> <span class="comment"><DT>warmup_lambdas</DT></span></div> |
| <div class="line"><a name="l00234"></a><span class="lineno"> 234</span> <span class="comment"><DD>An array of lambda values to use for warmup. The default is Null.</DD></span></div> |
| <div class="line"><a name="l00235"></a><span class="lineno"> 235</span> <span class="comment"><DT>warmup_lambda_no</DT></span></div> |
| <div class="line"><a name="l00236"></a><span class="lineno"> 236</span> <span class="comment"><DD>The number of lambdas used in </span></div> |
| <div class="line"><a name="l00237"></a><span class="lineno"> 237</span> <span class="comment">warm-up. The default is 15. If \e warmup_lambdas is not NULL, this argument is overridden by the size of the \e warmup_lambdas array.</DD></span></div> |
| <div class="line"><a name="l00238"></a><span class="lineno"> 238</span> <span class="comment"><DT>warmup_tolerance</DT></span></div> |
| <div class="line"><a name="l00239"></a><span class="lineno"> 239</span> <span class="comment"><DD>The value of tolerance used during warmup.The default is the same as \c tolerance.</DD></span></div> |
| <div class="line"><a name="l00240"></a><span class="lineno"> 240</span> <span class="comment"><DT>parallel</DT></span></div> |
| <div class="line"><a name="l00241"></a><span class="lineno"> 241</span> <span class="comment"><DD>Whether to run the computation on multiple segments. The default is True.</DD></span></div> |
| <div class="line"><a name="l00242"></a><span class="lineno"> 242</span> <span class="comment"><DD>SGD is a sequential algorithm in nature. When running in a distributed</span></div> |
| <div class="line"><a name="l00243"></a><span class="lineno"> 243</span> <span class="comment">manner, each segment of the data runs its own SGD model and then the models</span></div> |
| <div class="line"><a name="l00244"></a><span class="lineno"> 244</span> <span class="comment">are averaged to get a model for each iteration. This averaging might slow</span></div> |
| <div class="line"><a name="l00245"></a><span class="lineno"> 245</span> <span class="comment">down the convergence speed, although we also acquire the ability to process</span></div> |
| <div class="line"><a name="l00246"></a><span class="lineno"> 246</span> <span class="comment">large datasets on multiple machines. This algorithm, therefore, provides the</span></div> |
| <div class="line"><a name="l00247"></a><span class="lineno"> 247</span> <span class="comment">\e parallel optionto allow you to choose whether to do parallel computation.</span></div> |
| <div class="line"><a name="l00248"></a><span class="lineno"> 248</span> <span class="comment"></DD> </span></div> |
| <div class="line"><a name="l00249"></a><span class="lineno"> 249</span> <span class="comment"></DL></span></div> |
| <div class="line"><a name="l00250"></a><span class="lineno"> 250</span> <span class="comment"></span></div> |
| <div class="line"><a name="l00251"></a><span class="lineno"> 251</span> <span class="comment">@anchor output</span></div> |
| <div class="line"><a name="l00252"></a><span class="lineno"> 252</span> <span class="comment">@par Output Table</span></div> |
| <div class="line"><a name="l00253"></a><span class="lineno"> 253</span> <span class="comment">The output table produced by the elastic_net_train() function has the following columns:</span></div> |
| <div class="line"><a name="l00254"></a><span class="lineno"> 254</span> <span class="comment"></span></div> |
| <div class="line"><a name="l00255"></a><span class="lineno"> 255</span> <span class="comment"><DL class="arglist"></span></div> |
| <div class="line"><a name="l00256"></a><span class="lineno"> 256</span> <span class="comment"><DT>family</DT></span></div> |
| <div class="line"><a name="l00257"></a><span class="lineno"> 257</span> <span class="comment"><DD>The regression type: 'gaussian' or 'binomial'.</DD></span></div> |
| <div class="line"><a name="l00258"></a><span class="lineno"> 258</span> <span class="comment"><DT>features</DT></span></div> |
| <div class="line"><a name="l00259"></a><span class="lineno"> 259</span> <span class="comment"><DD>An array of the features (independent variables) passed into the analysis.</DD></span></div> |
| <div class="line"><a name="l00260"></a><span class="lineno"> 260</span> <span class="comment"><DT>features_selected</DT></span></div> |
| <div class="line"><a name="l00261"></a><span class="lineno"> 261</span> <span class="comment"><DD>An array of the features selected by the analysis.</DD></span></div> |
| <div class="line"><a name="l00262"></a><span class="lineno"> 262</span> <span class="comment"><DT>coef_nonzero</DT></span></div> |
| <div class="line"><a name="l00263"></a><span class="lineno"> 263</span> <span class="comment"><DD>Fitting coefficients for the selected features.</DD></span></div> |
| <div class="line"><a name="l00264"></a><span class="lineno"> 264</span> <span class="comment"><DT>coef_all</DT></span></div> |
| <div class="line"><a name="l00265"></a><span class="lineno"> 265</span> <span class="comment"><DD>Coefficients for all selected and unselected features</DD></span></div> |
| <div class="line"><a name="l00266"></a><span class="lineno"> 266</span> <span class="comment"><DT>intercept</DT></span></div> |
| <div class="line"><a name="l00267"></a><span class="lineno"> 267</span> <span class="comment"><DD>Fitting intercept for the model.</DD> </span></div> |
| <div class="line"><a name="l00268"></a><span class="lineno"> 268</span> <span class="comment"><DT>log_likelihood</DT></span></div> |
| <div class="line"><a name="l00269"></a><span class="lineno"> 269</span> <span class="comment"><DD>The negative value of the first equation above (up to a constant depending on the data set).</DD></span></div> |
| <div class="line"><a name="l00270"></a><span class="lineno"> 270</span> <span class="comment"><DT>standardize</DT></span></div> |
| <div class="line"><a name="l00271"></a><span class="lineno"> 271</span> <span class="comment"><DD>Boolean value. Whether the data was normalized (\e standardize argument was True). </span></div> |
| <div class="line"><a name="l00272"></a><span class="lineno"> 272</span> <span class="comment"><DT>iteration_run</DT></span></div> |
| <div class="line"><a name="l00273"></a><span class="lineno"> 273</span> <span class="comment"><DD>The number of iterations executed.</span></div> |
| <div class="line"><a name="l00274"></a><span class="lineno"> 274</span> <span class="comment"></DL></span></div> |
| <div class="line"><a name="l00275"></a><span class="lineno"> 275</span> <span class="comment"></span></div> |
| <div class="line"><a name="l00276"></a><span class="lineno"> 276</span> <span class="comment">@anchor predict</span></div> |
| <div class="line"><a name="l00277"></a><span class="lineno"> 277</span> <span class="comment">@par Prediction Function</span></div> |
| <div class="line"><a name="l00278"></a><span class="lineno"> 278</span> <span class="comment">The prediction function has the following format:</span></div> |
| <div class="line"><a name="l00279"></a><span class="lineno"> 279</span> <span class="comment">@verbatim</span></div> |
| <div class="line"><a name="l00280"></a><span class="lineno"> 280</span> <span class="comment">madlib.elastic_net_predict( </span></div> |
| <div class="line"><a name="l00281"></a><span class="lineno"> 281</span> <span class="comment"> '<regress_family>',</span></div> |
| <div class="line"><a name="l00282"></a><span class="lineno"> 282</span> <span class="comment"> coefficients,</span></div> |
| <div class="line"><a name="l00283"></a><span class="lineno"> 283</span> <span class="comment"> intercept,</span></div> |
| <div class="line"><a name="l00284"></a><span class="lineno"> 284</span> <span class="comment"> ind_var</span></div> |
| <div class="line"><a name="l00285"></a><span class="lineno"> 285</span> <span class="comment"> ) FROM tbl_result, tbl_new_source </span></div> |
| <div class="line"><a name="l00286"></a><span class="lineno"> 286</span> <span class="comment">@endverbatim</span></div> |
| <div class="line"><a name="l00287"></a><span class="lineno"> 287</span> <span class="comment">The above function returns a double value for each data point. When predicting with binomial models, the return value is 1 if the predicted result is True, and 0 if the prediction is False.</span></div> |
| <div class="line"><a name="l00288"></a><span class="lineno"> 288</span> <span class="comment"></span></div> |
| <div class="line"><a name="l00289"></a><span class="lineno"> 289</span> <span class="comment"><DL class="arglist"></span></div> |
| <div class="line"><a name="l00290"></a><span class="lineno"> 290</span> <span class="comment"><DT>regress_family</DT></span></div> |
| <div class="line"><a name="l00291"></a><span class="lineno"> 291</span> <span class="comment"><DD>The type of regression, either 'gaussian' ('linear') or 'binomal' ('logistic').</DD></span></div> |
| <div class="line"><a name="l00292"></a><span class="lineno"> 292</span> <span class="comment"><DT>coefficients</DT></span></div> |
| <div class="line"><a name="l00293"></a><span class="lineno"> 293</span> <span class="comment"><DD>Fitting coefficients, as a DOUBLE array.</DD></span></div> |
| <div class="line"><a name="l00294"></a><span class="lineno"> 294</span> <span class="comment"><DT>intercept</DT></span></div> |
| <div class="line"><a name="l00295"></a><span class="lineno"> 295</span> <span class="comment"><DD>The intercept for the model.</DD></span></div> |
| <div class="line"><a name="l00296"></a><span class="lineno"> 296</span> <span class="comment"><DT>ind_var</DT></span></div> |
| <div class="line"><a name="l00297"></a><span class="lineno"> 297</span> <span class="comment"><DD>Independent variables, as a DOUBLE array.</DD></span></div> |
| <div class="line"><a name="l00298"></a><span class="lineno"> 298</span> <span class="comment"><DT>tbl_result</DD></span></div> |
| <div class="line"><a name="l00299"></a><span class="lineno"> 299</span> <span class="comment"><DD>The name of the output table from the training function.</DD></span></div> |
| <div class="line"><a name="l00300"></a><span class="lineno"> 300</span> <span class="comment"><DT>tbl_new_source</DT></span></div> |
| <div class="line"><a name="l00301"></a><span class="lineno"> 301</span> <span class="comment"><DD>The name of the table containing new data to predict.</DD></span></div> |
| <div class="line"><a name="l00302"></a><span class="lineno"> 302</span> <span class="comment"></DL></span></div> |
| <div class="line"><a name="l00303"></a><span class="lineno"> 303</span> <span class="comment"></span></div> |
| <div class="line"><a name="l00304"></a><span class="lineno"> 304</span> <span class="comment">There are several different formats of the prediction function:</span></div> |
| <div class="line"><a name="l00305"></a><span class="lineno"> 305</span> <span class="comment"></span></div> |
| <div class="line"><a name="l00306"></a><span class="lineno"> 306</span> <span class="comment">-# </span></div> |
| <div class="line"><a name="l00307"></a><span class="lineno"> 307</span> <span class="comment">@code</span></div> |
| <div class="line"><a name="l00308"></a><span class="lineno"> 308</span> <span class="comment">SELECT madlib.elastic_net_gaussian_predict (</span></div> |
| <div class="line"><a name="l00309"></a><span class="lineno"> 309</span> <span class="comment"> coefficients, intercept, ind_var</span></div> |
| <div class="line"><a name="l00310"></a><span class="lineno"> 310</span> <span class="comment">) FROM tbl_result, tbl_new_source LIMIT 10;</span></div> |
| <div class="line"><a name="l00311"></a><span class="lineno"> 311</span> <span class="comment">@endcode</span></div> |
| <div class="line"><a name="l00312"></a><span class="lineno"> 312</span> <span class="comment"></span></div> |
| <div class="line"><a name="l00313"></a><span class="lineno"> 313</span> <span class="comment">-# </span></div> |
| <div class="line"><a name="l00314"></a><span class="lineno"> 314</span> <span class="comment">@code</span></div> |
| <div class="line"><a name="l00315"></a><span class="lineno"> 315</span> <span class="comment">SELECT madlib.elastic_net_binomial_predict (</span></div> |
| <div class="line"><a name="l00316"></a><span class="lineno"> 316</span> <span class="comment"> coefficients, intercept, ind_var</span></div> |
| <div class="line"><a name="l00317"></a><span class="lineno"> 317</span> <span class="comment">) FROM tbl_result, tbl_new_source LIMIT 10;</span></div> |
| <div class="line"><a name="l00318"></a><span class="lineno"> 318</span> <span class="comment">@endcode</span></div> |
| <div class="line"><a name="l00319"></a><span class="lineno"> 319</span> <span class="comment">\n</span></div> |
| <div class="line"><a name="l00320"></a><span class="lineno"> 320</span> <span class="comment">This returns 10 BOOLEAN values.</span></div> |
| <div class="line"><a name="l00321"></a><span class="lineno"> 321</span> <span class="comment"></span></div> |
| <div class="line"><a name="l00322"></a><span class="lineno"> 322</span> <span class="comment">-#</span></div> |
| <div class="line"><a name="l00323"></a><span class="lineno"> 323</span> <span class="comment">@code</span></div> |
| <div class="line"><a name="l00324"></a><span class="lineno"> 324</span> <span class="comment">SELECT madlib.elastic_net_binomial_prob (</span></div> |
| <div class="line"><a name="l00325"></a><span class="lineno"> 325</span> <span class="comment"> coefficients, intercept, ind_var</span></div> |
| <div class="line"><a name="l00326"></a><span class="lineno"> 326</span> <span class="comment">) FROM tbl_result, tbl_new_source LIMIT 10;</span></div> |
| <div class="line"><a name="l00327"></a><span class="lineno"> 327</span> <span class="comment">@endcode</span></div> |
| <div class="line"><a name="l00328"></a><span class="lineno"> 328</span> <span class="comment">\n</span></div> |
| <div class="line"><a name="l00329"></a><span class="lineno"> 329</span> <span class="comment">This returns 10 probability values for True class.</span></div> |
| <div class="line"><a name="l00330"></a><span class="lineno"> 330</span> <span class="comment"></span></div> |
| <div class="line"><a name="l00331"></a><span class="lineno"> 331</span> <span class="comment">Alternatively, you can use another prediction function that stores the prediction</span></div> |
| <div class="line"><a name="l00332"></a><span class="lineno"> 332</span> <span class="comment">result in a table. This is useful if you want to use elastic net together with the </span></div> |
| <div class="line"><a name="l00333"></a><span class="lineno"> 333</span> <span class="comment">general cross validation function.</span></div> |
| <div class="line"><a name="l00334"></a><span class="lineno"> 334</span> <span class="comment">@code</span></div> |
| <div class="line"><a name="l00335"></a><span class="lineno"> 335</span> <span class="comment">SELECT madlib.elastic_net_predict(</span></div> |
| <div class="line"><a name="l00336"></a><span class="lineno"> 336</span> <span class="comment"> 'tbl_train_result',</span></div> |
| <div class="line"><a name="l00337"></a><span class="lineno"> 337</span> <span class="comment"> 'tbl_data',</span></div> |
| <div class="line"><a name="l00338"></a><span class="lineno"> 338</span> <span class="comment"> 'col_id', -- ID associated with each row</span></div> |
| <div class="line"><a name="l00339"></a><span class="lineno"> 339</span> <span class="comment"> 'tbl_predict' -- Prediction result</span></div> |
| <div class="line"><a name="l00340"></a><span class="lineno"> 340</span> <span class="comment">);</span></div> |
| <div class="line"><a name="l00341"></a><span class="lineno"> 341</span> <span class="comment">@endcode</span></div> |
| <div class="line"><a name="l00342"></a><span class="lineno"> 342</span> <span class="comment">You do not need to specify whether the model is "linear" or "logistic" because this information is already included in the result table.</span></div> |
| <div class="line"><a name="l00343"></a><span class="lineno"> 343</span> <span class="comment"></span></div> |
| <div class="line"><a name="l00344"></a><span class="lineno"> 344</span> <span class="comment">@anchor examples</span></div> |
| <div class="line"><a name="l00345"></a><span class="lineno"> 345</span> <span class="comment">@examp</span></div> |
| <div class="line"><a name="l00346"></a><span class="lineno"> 346</span> <span class="comment">-# Create an input data set.</span></div> |
| <div class="line"><a name="l00347"></a><span class="lineno"> 347</span> <span class="comment">@verbatim</span></div> |
| <div class="line"><a name="l00348"></a><span class="lineno"> 348</span> <span class="comment">sql> DROP TABLE IF EXISTS houses;</span></div> |
| <div class="line"><a name="l00349"></a><span class="lineno"> 349</span> <span class="comment">sql> CREATE TABLE houses (id INT, tax INT, bedroom INT, bath FLOAT, price INT,</span></div> |
| <div class="line"><a name="l00350"></a><span class="lineno"> 350</span> <span class="comment"> size INT, lot INT);</span></div> |
| <div class="line"><a name="l00351"></a><span class="lineno"> 351</span> <span class="comment">sql> COPY houses FROM STDIN WITH DELIMITER '|';</span></div> |
| <div class="line"><a name="l00352"></a><span class="lineno"> 352</span> <span class="comment"> 1 | 590 | 2 | 1 | 50000 | 770 | 22100</span></div> |
| <div class="line"><a name="l00353"></a><span class="lineno"> 353</span> <span class="comment"> 2 | 1050 | 3 | 2 | 85000 | 1410 | 12000</span></div> |
| <div class="line"><a name="l00354"></a><span class="lineno"> 354</span> <span class="comment"> 3 | 20 | 3 | 1 | 22500 | 1060 | 3500</span></div> |
| <div class="line"><a name="l00355"></a><span class="lineno"> 355</span> <span class="comment"> 4 | 870 | 2 | 2 | 90000 | 1300 | 17500</span></div> |
| <div class="line"><a name="l00356"></a><span class="lineno"> 356</span> <span class="comment"> 5 | 1320 | 3 | 2 | 133000 | 1500 | 30000</span></div> |
| <div class="line"><a name="l00357"></a><span class="lineno"> 357</span> <span class="comment"> 6 | 1350 | 2 | 1 | 90500 | 820 | 25700</span></div> |
| <div class="line"><a name="l00358"></a><span class="lineno"> 358</span> <span class="comment"> 7 | 2790 | 3 | 2.5 | 260000 | 2130 | 25000</span></div> |
| <div class="line"><a name="l00359"></a><span class="lineno"> 359</span> <span class="comment"> 8 | 680 | 2 | 1 | 142500 | 1170 | 22000</span></div> |
| <div class="line"><a name="l00360"></a><span class="lineno"> 360</span> <span class="comment"> 9 | 1840 | 3 | 2 | 160000 | 1500 | 19000</span></div> |
| <div class="line"><a name="l00361"></a><span class="lineno"> 361</span> <span class="comment"> 10 | 3680 | 4 | 2 | 240000 | 2790 | 20000</span></div> |
| <div class="line"><a name="l00362"></a><span class="lineno"> 362</span> <span class="comment"> 11 | 1660 | 3 | 1 | 87000 | 1030 | 17500</span></div> |
| <div class="line"><a name="l00363"></a><span class="lineno"> 363</span> <span class="comment"> 12 | 1620 | 3 | 2 | 118600 | 1250 | 20000</span></div> |
| <div class="line"><a name="l00364"></a><span class="lineno"> 364</span> <span class="comment"> 13 | 3100 | 3 | 2 | 140000 | 1760 | 38000</span></div> |
| <div class="line"><a name="l00365"></a><span class="lineno"> 365</span> <span class="comment"> 14 | 2070 | 2 | 3 | 148000 | 1550 | 14000</span></div> |
| <div class="line"><a name="l00366"></a><span class="lineno"> 366</span> <span class="comment"> 15 | 650 | 3 | 1.5 | 65000 | 1450 | 12000</span></div> |
| <div class="line"><a name="l00367"></a><span class="lineno"> 367</span> <span class="comment">\.</span></div> |
| <div class="line"><a name="l00368"></a><span class="lineno"> 368</span> <span class="comment">@endverbatim</span></div> |
| <div class="line"><a name="l00369"></a><span class="lineno"> 369</span> <span class="comment">-# Train the model.</span></div> |
| <div class="line"><a name="l00370"></a><span class="lineno"> 370</span> <span class="comment">@verbatim</span></div> |
| <div class="line"><a name="l00371"></a><span class="lineno"> 371</span> <span class="comment">sql> DROP TABLE IF EXISTS houses_en;</span></div> |
| <div class="line"><a name="l00372"></a><span class="lineno"> 372</span> <span class="comment">sql> SELECT madlib.elastic_net_train( </span></div> |
| <div class="line"><a name="l00373"></a><span class="lineno"> 373</span> <span class="comment"> 'houses', 'houses_en', 'price', 'array[tax, bath, size]', </span></div> |
| <div class="line"><a name="l00374"></a><span class="lineno"> 374</span> <span class="comment"> 'gaussian', 0.5, 0.1, true, null, 'fista', </span></div> |
| <div class="line"><a name="l00375"></a><span class="lineno"> 375</span> <span class="comment"> '', </span></div> |
| <div class="line"><a name="l00376"></a><span class="lineno"> 376</span> <span class="comment"> null, 10000, 1e-6);</span></div> |
| <div class="line"><a name="l00377"></a><span class="lineno"> 377</span> <span class="comment">@endverbatim</span></div> |
| <div class="line"><a name="l00378"></a><span class="lineno"> 378</span> <span class="comment">-# View the resulting model.</span></div> |
| <div class="line"><a name="l00379"></a><span class="lineno"> 379</span> <span class="comment">@verbatim</span></div> |
| <div class="line"><a name="l00380"></a><span class="lineno"> 380</span> <span class="comment">-- Turn on expanded display to make it easier to read results.</span></div> |
| <div class="line"><a name="l00381"></a><span class="lineno"> 381</span> <span class="comment">sql> \x on</span></div> |
| <div class="line"><a name="l00382"></a><span class="lineno"> 382</span> <span class="comment">sql> SELECT * from houses_en;</span></div> |
| <div class="line"><a name="l00383"></a><span class="lineno"> 383</span> <span class="comment">@endverbatim</span></div> |
| <div class="line"><a name="l00384"></a><span class="lineno"> 384</span> <span class="comment">-# Use the prediction function to evaluate residuals. </span></div> |
| <div class="line"><a name="l00385"></a><span class="lineno"> 385</span> <span class="comment">@verbatim</span></div> |
| <div class="line"><a name="l00386"></a><span class="lineno"> 386</span> <span class="comment">sql> SELECT *, price - predict as residual FROM (</span></div> |
| <div class="line"><a name="l00387"></a><span class="lineno"> 387</span> <span class="comment"> SELECT houses.*, </span></div> |
| <div class="line"><a name="l00388"></a><span class="lineno"> 388</span> <span class="comment"> madlib.elastic_net_predict(</span></div> |
| <div class="line"><a name="l00389"></a><span class="lineno"> 389</span> <span class="comment"> 'gaussian', m.coef_nonzero, m.intercept, array[tax,bath,size]</span></div> |
| <div class="line"><a name="l00390"></a><span class="lineno"> 390</span> <span class="comment"> ) as predict</span></div> |
| <div class="line"><a name="l00391"></a><span class="lineno"> 391</span> <span class="comment"> FROM houses, houses_en m) s;</span></div> |
| <div class="line"><a name="l00392"></a><span class="lineno"> 392</span> <span class="comment">@endverbatim</span></div> |
| <div class="line"><a name="l00393"></a><span class="lineno"> 393</span> <span class="comment"></span></div> |
| <div class="line"><a name="l00394"></a><span class="lineno"> 394</span> <span class="comment">@anchor seealso</span></div> |
| <div class="line"><a name="l00395"></a><span class="lineno"> 395</span> <span class="comment">@sa File elastic_net.sql_in documenting the SQL functions.</span></div> |
| <div class="line"><a name="l00396"></a><span class="lineno"> 396</span> <span class="comment">@sa grp_validation</span></div> |
| <div class="line"><a name="l00397"></a><span class="lineno"> 397</span> <span class="comment"></span></div> |
| <div class="line"><a name="l00398"></a><span class="lineno"> 398</span> <span class="comment">@anchor background</span></div> |
| <div class="line"><a name="l00399"></a><span class="lineno"> 399</span> <span class="comment">@par Technical Background</span></div> |
| <div class="line"><a name="l00400"></a><span class="lineno"> 400</span> <span class="comment"></span></div> |
| <div class="line"><a name="l00401"></a><span class="lineno"> 401</span> <span class="comment">Elastic net regularization seeks to find a weight vector that, for any given training example set, minimizes:</span></div> |
| <div class="line"><a name="l00402"></a><span class="lineno"> 402</span> <span class="comment">\f[\min_{w \in R^N} L(w) + \lambda \left(\frac{(1-\alpha)}{2} \|w\|_2^2 + \alpha \|w\|_1 \right)\f]</span></div> |
| <div class="line"><a name="l00403"></a><span class="lineno"> 403</span> <span class="comment">where \f$L\f$ is the metric function that the user wants to minimize. Here \f$ \alpha \in [0,1] \f$</span></div> |
| <div class="line"><a name="l00404"></a><span class="lineno"> 404</span> <span class="comment">and \f$ lambda \geq 0 \f$. If \f$alpha = 0\f$, we have the ridge regularization (known also as Tikhonov regularization), and if \f$\alpha = 1\f$, we have the LASSO regularization.</span></div> |
| <div class="line"><a name="l00405"></a><span class="lineno"> 405</span> <span class="comment"></span></div> |
| <div class="line"><a name="l00406"></a><span class="lineno"> 406</span> <span class="comment">For the Gaussian response family (or linear model), we have</span></div> |
| <div class="line"><a name="l00407"></a><span class="lineno"> 407</span> <span class="comment">\f[L(\vec{w}) = \frac{1}{2}\left[\frac{1}{M} \sum_{m=1}^M (w^{t} x_m + w_{0} - y_m)^2 \right]</span></div> |
| <div class="line"><a name="l00408"></a><span class="lineno"> 408</span> <span class="comment">\f]</span></div> |
| <div class="line"><a name="l00409"></a><span class="lineno"> 409</span> <span class="comment"></span></div> |
| <div class="line"><a name="l00410"></a><span class="lineno"> 410</span> <span class="comment">For the Binomial response family (or logistic model), we have</span></div> |
| <div class="line"><a name="l00411"></a><span class="lineno"> 411</span> <span class="comment">\f[</span></div> |
| <div class="line"><a name="l00412"></a><span class="lineno"> 412</span> <span class="comment">L(\vec{w}) = \sum_{m=1}^M\left[y_m \log\left(1 + e^{-(w_0 +</span></div> |
| <div class="line"><a name="l00413"></a><span class="lineno"> 413</span> <span class="comment"> \vec{w}\cdot\vec{x}_m)}\right) + (1-y_m) \log\left(1 + e^{w_0 +</span></div> |
| <div class="line"><a name="l00414"></a><span class="lineno"> 414</span> <span class="comment"> \vec{w}\cdot\vec{x}_m}\right)\right]\ ,</span></div> |
| <div class="line"><a name="l00415"></a><span class="lineno"> 415</span> <span class="comment">\f]</span></div> |
| <div class="line"><a name="l00416"></a><span class="lineno"> 416</span> <span class="comment">where \f$y_m \in {0,1}\f$.</span></div> |
| <div class="line"><a name="l00417"></a><span class="lineno"> 417</span> <span class="comment"></span></div> |
| <div class="line"><a name="l00418"></a><span class="lineno"> 418</span> <span class="comment">To get better convergence, one can rescale the value of each element of x</span></div> |
| <div class="line"><a name="l00419"></a><span class="lineno"> 419</span> <span class="comment">\f[ x' \leftarrow \frac{x - \bar{x}}{\sigma_x} \f]</span></div> |
| <div class="line"><a name="l00420"></a><span class="lineno"> 420</span> <span class="comment">and for Gaussian case we also let</span></div> |
| <div class="line"><a name="l00421"></a><span class="lineno"> 421</span> <span class="comment">\f[y' \leftarrow y - \bar{y} \f]</span></div> |
| <div class="line"><a name="l00422"></a><span class="lineno"> 422</span> <span class="comment">and then minimize with the regularization terms.</span></div> |
| <div class="line"><a name="l00423"></a><span class="lineno"> 423</span> <span class="comment">At the end of the calculation, the orginal scales will be restored and an</span></div> |
| <div class="line"><a name="l00424"></a><span class="lineno"> 424</span> <span class="comment">intercept term will be obtained at the same time as a by-product.</span></div> |
| <div class="line"><a name="l00425"></a><span class="lineno"> 425</span> <span class="comment"></span></div> |
| <div class="line"><a name="l00426"></a><span class="lineno"> 426</span> <span class="comment">Note that fitting after scaling is not equivalent to directly fitting.</span></div> |
| <div class="line"><a name="l00427"></a><span class="lineno"> 427</span> <span class="comment"></span></div> |
| <div class="line"><a name="l00428"></a><span class="lineno"> 428</span> <span class="comment">@anchor literature</span></div> |
| <div class="line"><a name="l00429"></a><span class="lineno"> 429</span> <span class="comment">@literature</span></div> |
| <div class="line"><a name="l00430"></a><span class="lineno"> 430</span> <span class="comment"></span></div> |
| <div class="line"><a name="l00431"></a><span class="lineno"> 431</span> <span class="comment">[1] Elastic net regularization. http://en.wikipedia.org/wiki/Elastic_net_regularization</span></div> |
| <div class="line"><a name="l00432"></a><span class="lineno"> 432</span> <span class="comment"></span></div> |
| <div class="line"><a name="l00433"></a><span class="lineno"> 433</span> <span class="comment">[2] Beck, A. and M. Teboulle (2009), A fast iterative shrinkage-thresholding algorithm for linear inverse problems. SIAM J. on Imaging Sciences 2(1), 183-202.</span></div> |
| <div class="line"><a name="l00434"></a><span class="lineno"> 434</span> <span class="comment"></span></div> |
| <div class="line"><a name="l00435"></a><span class="lineno"> 435</span> <span class="comment">[3] Shai Shalev-Shwartz and Ambuj Tewari, Stochastic Methods for l1 Regularized Loss Minimization. Proceedings of the 26th International Conference on Machine Learning, Montreal, Canada, 2009.</span></div> |
| <div class="line"><a name="l00436"></a><span class="lineno"> 436</span> <span class="comment"></span></div> |
| <div class="line"><a name="l00437"></a><span class="lineno"> 437</span> <span class="comment">*/</span></div> |
| <div class="line"><a name="l00438"></a><span class="lineno"> 438</span> </div> |
| <div class="line"><a name="l00439"></a><span class="lineno"> 439</span> ------------------------------------------------------------------------</div> |
| <div class="line"><a name="l00440"></a><span class="lineno"> 440</span> <span class="comment"></span></div> |
| <div class="line"><a name="l00441"></a><span class="lineno"> 441</span> <span class="comment">/**</span></div> |
| <div class="line"><a name="l00442"></a><span class="lineno"> 442</span> <span class="comment"> * @brief Interface for elastic net</span></div> |
| <div class="line"><a name="l00443"></a><span class="lineno"> 443</span> <span class="comment"> *</span></div> |
| <div class="line"><a name="l00444"></a><span class="lineno"> 444</span> <span class="comment"> * @param tbl_source Name of data source table</span></div> |
| <div class="line"><a name="l00445"></a><span class="lineno"> 445</span> <span class="comment"> * @param tbl_result Name of the table to store the results</span></div> |
| <div class="line"><a name="l00446"></a><span class="lineno"> 446</span> <span class="comment"> * @param col_ind_var Name of independent variable column, independent variable is an array</span></div> |
| <div class="line"><a name="l00447"></a><span class="lineno"> 447</span> <span class="comment"> * @param col_dep_var Name of dependent variable column</span></div> |
| <div class="line"><a name="l00448"></a><span class="lineno"> 448</span> <span class="comment"> * @param regress_family Response type (gaussian or binomial)</span></div> |
| <div class="line"><a name="l00449"></a><span class="lineno"> 449</span> <span class="comment"> * @param alpha The elastic net parameter, [0, 1]</span></div> |
| <div class="line"><a name="l00450"></a><span class="lineno"> 450</span> <span class="comment"> * @param lambda_value The regularization parameter</span></div> |
| <div class="line"><a name="l00451"></a><span class="lineno"> 451</span> <span class="comment"> * @param standardize Whether to normalize the variables (default True)</span></div> |
| <div class="line"><a name="l00452"></a><span class="lineno"> 452</span> <span class="comment"> * @param grouping_col List of columns on which to apply grouping</span></div> |
| <div class="line"><a name="l00453"></a><span class="lineno"> 453</span> <span class="comment"> * (currently only a placeholder)</span></div> |
| <div class="line"><a name="l00454"></a><span class="lineno"> 454</span> <span class="comment"> * @param optimizer The optimization algorithm, 'fista' or 'igd'. Default is 'fista'</span></div> |
| <div class="line"><a name="l00455"></a><span class="lineno"> 455</span> <span class="comment"> * @param optimizer_params Parameters of the above optimizer,</span></div> |
| <div class="line"><a name="l00456"></a><span class="lineno"> 456</span> <span class="comment"> * the format is 'arg = value, ...'. Default is NULL</span></div> |
| <div class="line"><a name="l00457"></a><span class="lineno"> 457</span> <span class="comment"> * @param exclude Which columns to exclude? Default is NULL</span></div> |
| <div class="line"><a name="l00458"></a><span class="lineno"> 458</span> <span class="comment"> * (applicable only if col_ind_var is set as * or a column of array,</span></div> |
| <div class="line"><a name="l00459"></a><span class="lineno"> 459</span> <span class="comment"> * column names as 'col1, col2, ...' if col_ind_var is '*';</span></div> |
| <div class="line"><a name="l00460"></a><span class="lineno"> 460</span> <span class="comment"> * element indices as '1,2,3, ...' if col_ind_var is a column of array)</span></div> |
| <div class="line"><a name="l00461"></a><span class="lineno"> 461</span> <span class="comment"> * @param max_iter Maximum number of iterations to run the algorithm</span></div> |
| <div class="line"><a name="l00462"></a><span class="lineno"> 462</span> <span class="comment"> * (default value of 10000)</span></div> |
| <div class="line"><a name="l00463"></a><span class="lineno"> 463</span> <span class="comment"> * @param tolerance Iteration stopping criteria. Default is 1e-6</span></div> |
| <div class="line"><a name="l00464"></a><span class="lineno"> 464</span> <span class="comment"> */</span></div> |
| <div class="line"><a name="l00465"></a><span class="lineno"> 465</span> CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.elastic_net_train (</div> |
| <div class="line"><a name="l00466"></a><span class="lineno"> 466</span>  tbl_source TEXT,</div> |
| <div class="line"><a name="l00467"></a><span class="lineno"> 467</span>  tbl_result TEXT,</div> |
| <div class="line"><a name="l00468"></a><span class="lineno"> 468</span>  col_dep_var TEXT,</div> |
| <div class="line"><a name="l00469"></a><span class="lineno"> 469</span>  col_ind_var TEXT,</div> |
| <div class="line"><a name="l00470"></a><span class="lineno"> 470</span>  regress_family TEXT,</div> |
| <div class="line"><a name="l00471"></a><span class="lineno"> 471</span>  alpha DOUBLE PRECISION,</div> |
| <div class="line"><a name="l00472"></a><span class="lineno"> 472</span>  lambda_value DOUBLE PRECISION,</div> |
| <div class="line"><a name="l00473"></a><span class="lineno"> 473</span>  standardize BOOLEAN,</div> |
| <div class="line"><a name="l00474"></a><span class="lineno"> 474</span>  grouping_col TEXT,</div> |
| <div class="line"><a name="l00475"></a><span class="lineno"> 475</span>  optimizer TEXT,</div> |
| <div class="line"><a name="l00476"></a><span class="lineno"> 476</span>  optimizer_params TEXT,</div> |
| <div class="line"><a name="l00477"></a><span class="lineno"> 477</span>  excluded TEXT,</div> |
| <div class="line"><a name="l00478"></a><span class="lineno"> 478</span>  max_iter INTEGER,</div> |
| <div class="line"><a name="l00479"></a><span class="lineno"> 479</span>  tolerance DOUBLE PRECISION</div> |
| <div class="line"><a name="l00480"></a><span class="lineno"> 480</span> ) RETURNS VOID AS $$</div> |
| <div class="line"><a name="l00481"></a><span class="lineno"> 481</span> PythonFunction(elastic_net, elastic_net, <a class="code" href="elastic__net_8sql__in.html#a735038a5090c112505c740a90a203e83" title="Interface for elastic net. ">elastic_net_train</a>)</div> |
| <div class="line"><a name="l00482"></a><span class="lineno"> 482</span> $$ LANGUAGE plpythonu;</div> |
| <div class="line"><a name="l00483"></a><span class="lineno"> 483</span> </div> |
| <div class="line"><a name="l00484"></a><span class="lineno"> 484</span> ------------------------------------------------------------------------</div> |
| <div class="line"><a name="l00485"></a><span class="lineno"> 485</span> -- Overloaded functions</div> |
| <div class="line"><a name="l00486"></a><span class="lineno"> 486</span> CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.elastic_net_train (</div> |
| <div class="line"><a name="l00487"></a><span class="lineno"><a class="code" href="elastic__net_8sql__in.html#a735038a5090c112505c740a90a203e83"> 487</a></span>  tbl_source TEXT,</div> |
| <div class="line"><a name="l00488"></a><span class="lineno"> 488</span>  tbl_result TEXT,</div> |
| <div class="line"><a name="l00489"></a><span class="lineno"> 489</span>  col_ind_var TEXT,</div> |
| <div class="line"><a name="l00490"></a><span class="lineno"> 490</span>  col_dep_var TEXT,</div> |
| <div class="line"><a name="l00491"></a><span class="lineno"> 491</span>  regress_family TEXT,</div> |
| <div class="line"><a name="l00492"></a><span class="lineno"> 492</span>  alpha DOUBLE PRECISION,</div> |
| <div class="line"><a name="l00493"></a><span class="lineno"> 493</span>  lambda_value DOUBLE PRECISION,</div> |
| <div class="line"><a name="l00494"></a><span class="lineno"> 494</span>  standardization BOOLEAN,</div> |
| <div class="line"><a name="l00495"></a><span class="lineno"> 495</span>  grouping_columns TEXT,</div> |
| <div class="line"><a name="l00496"></a><span class="lineno"> 496</span>  optimizer TEXT,</div> |
| <div class="line"><a name="l00497"></a><span class="lineno"> 497</span>  optimizer_params TEXT,</div> |
| <div class="line"><a name="l00498"></a><span class="lineno"> 498</span>  excluded TEXT,</div> |
| <div class="line"><a name="l00499"></a><span class="lineno"> 499</span>  max_iter INTEGER</div> |
| <div class="line"><a name="l00500"></a><span class="lineno"> 500</span> ) RETURNS VOID AS $$</div> |
| <div class="line"><a name="l00501"></a><span class="lineno"> 501</span> BEGIN</div> |
| <div class="line"><a name="l00502"></a><span class="lineno"> 502</span>  PERFORM MADLIB_SCHEMA.elastic_net_train($1, $2, $3, $4, $5, $6, $7, $8,</div> |
| <div class="line"><a name="l00503"></a><span class="lineno"> 503</span>  $9, $10, $11, $12, $13, 1e-6);</div> |
| <div class="line"><a name="l00504"></a><span class="lineno"> 504</span> END;</div> |
| <div class="line"><a name="l00505"></a><span class="lineno"> 505</span> $$ LANGUAGE plpgsql VOLATILE;</div> |
| <div class="line"><a name="l00506"></a><span class="lineno"> 506</span> </div> |
| <div class="line"><a name="l00507"></a><span class="lineno"> 507</span> CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.elastic_net_train (</div> |
| <div class="line"><a name="l00508"></a><span class="lineno"> 508</span>  tbl_source TEXT,</div> |
| <div class="line"><a name="l00509"></a><span class="lineno"> 509</span>  tbl_result TEXT,</div> |
| <div class="line"><a name="l00510"></a><span class="lineno"> 510</span>  col_ind_var TEXT,</div> |
| <div class="line"><a name="l00511"></a><span class="lineno"> 511</span>  col_dep_var TEXT,</div> |
| <div class="line"><a name="l00512"></a><span class="lineno"> 512</span>  regress_family TEXT,</div> |
| <div class="line"><a name="l00513"></a><span class="lineno"> 513</span>  alpha DOUBLE PRECISION,</div> |
| <div class="line"><a name="l00514"></a><span class="lineno"> 514</span>  lambda_value DOUBLE PRECISION,</div> |
| <div class="line"><a name="l00515"></a><span class="lineno"> 515</span>  standardization BOOLEAN,</div> |
| <div class="line"><a name="l00516"></a><span class="lineno"> 516</span>  grouping_columns TEXT,</div> |
| <div class="line"><a name="l00517"></a><span class="lineno"> 517</span>  optimizer TEXT,</div> |
| <div class="line"><a name="l00518"></a><span class="lineno"> 518</span>  optimizer_params TEXT,</div> |
| <div class="line"><a name="l00519"></a><span class="lineno"> 519</span>  excluded TEXT</div> |
| <div class="line"><a name="l00520"></a><span class="lineno"> 520</span> ) RETURNS VOID AS $$</div> |
| <div class="line"><a name="l00521"></a><span class="lineno"> 521</span> BEGIN</div> |
| <div class="line"><a name="l00522"></a><span class="lineno"> 522</span>  PERFORM MADLIB_SCHEMA.elastic_net_train($1, $2, $3, $4, $5, $6, $7, $8,</div> |
| <div class="line"><a name="l00523"></a><span class="lineno"> 523</span>  $9, $10, $11, $12, 10000);</div> |
| <div class="line"><a name="l00524"></a><span class="lineno"> 524</span> END;</div> |
| <div class="line"><a name="l00525"></a><span class="lineno"> 525</span> $$ LANGUAGE plpgsql VOLATILE;</div> |
| <div class="line"><a name="l00526"></a><span class="lineno"> 526</span> </div> |
| <div class="line"><a name="l00527"></a><span class="lineno"> 527</span> CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.elastic_net_train (</div> |
| <div class="line"><a name="l00528"></a><span class="lineno"> 528</span>  tbl_source TEXT,</div> |
| <div class="line"><a name="l00529"></a><span class="lineno"> 529</span>  tbl_result TEXT,</div> |
| <div class="line"><a name="l00530"></a><span class="lineno"> 530</span>  col_ind_var TEXT,</div> |
| <div class="line"><a name="l00531"></a><span class="lineno"> 531</span>  col_dep_var TEXT,</div> |
| <div class="line"><a name="l00532"></a><span class="lineno"> 532</span>  regress_family TEXT,</div> |
| <div class="line"><a name="l00533"></a><span class="lineno"> 533</span>  alpha DOUBLE PRECISION,</div> |
| <div class="line"><a name="l00534"></a><span class="lineno"> 534</span>  lambda_value DOUBLE PRECISION,</div> |
| <div class="line"><a name="l00535"></a><span class="lineno"> 535</span>  standardization BOOLEAN,</div> |
| <div class="line"><a name="l00536"></a><span class="lineno"> 536</span>  grouping_columns TEXT,</div> |
| <div class="line"><a name="l00537"></a><span class="lineno"> 537</span>  optimizer TEXT,</div> |
| <div class="line"><a name="l00538"></a><span class="lineno"> 538</span>  optimizer_params TEXT</div> |
| <div class="line"><a name="l00539"></a><span class="lineno"> 539</span> ) RETURNS VOID AS $$</div> |
| <div class="line"><a name="l00540"></a><span class="lineno"> 540</span> BEGIN</div> |
| <div class="line"><a name="l00541"></a><span class="lineno"> 541</span>  PERFORM MADLIB_SCHEMA.elastic_net_train($1, $2, $3, $4, $5, $6, $7, $8,</div> |
| <div class="line"><a name="l00542"></a><span class="lineno"> 542</span>  $9, $10, $11, NULL);</div> |
| <div class="line"><a name="l00543"></a><span class="lineno"> 543</span> END;</div> |
| <div class="line"><a name="l00544"></a><span class="lineno"> 544</span> $$ LANGUAGE plpgsql VOLATILE;</div> |
| <div class="line"><a name="l00545"></a><span class="lineno"> 545</span> </div> |
| <div class="line"><a name="l00546"></a><span class="lineno"> 546</span> CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.elastic_net_train (</div> |
| <div class="line"><a name="l00547"></a><span class="lineno"> 547</span>  tbl_source TEXT,</div> |
| <div class="line"><a name="l00548"></a><span class="lineno"> 548</span>  tbl_result TEXT,</div> |
| <div class="line"><a name="l00549"></a><span class="lineno"> 549</span>  col_ind_var TEXT,</div> |
| <div class="line"><a name="l00550"></a><span class="lineno"> 550</span>  col_dep_var TEXT,</div> |
| <div class="line"><a name="l00551"></a><span class="lineno"> 551</span>  regress_family TEXT,</div> |
| <div class="line"><a name="l00552"></a><span class="lineno"> 552</span>  alpha DOUBLE PRECISION,</div> |
| <div class="line"><a name="l00553"></a><span class="lineno"> 553</span>  lambda_value DOUBLE PRECISION,</div> |
| <div class="line"><a name="l00554"></a><span class="lineno"> 554</span>  standardization BOOLEAN,</div> |
| <div class="line"><a name="l00555"></a><span class="lineno"> 555</span>  grouping_columns TEXT,</div> |
| <div class="line"><a name="l00556"></a><span class="lineno"> 556</span>  optimizer TEXT</div> |
| <div class="line"><a name="l00557"></a><span class="lineno"> 557</span> ) RETURNS VOID AS $$</div> |
| <div class="line"><a name="l00558"></a><span class="lineno"> 558</span> BEGIN</div> |
| <div class="line"><a name="l00559"></a><span class="lineno"> 559</span>  PERFORM MADLIB_SCHEMA.elastic_net_train($1, $2, $3, $4, $5, $6, $7, $8,</div> |
| <div class="line"><a name="l00560"></a><span class="lineno"> 560</span>  $9, $10, NULL::TEXT);</div> |
| <div class="line"><a name="l00561"></a><span class="lineno"> 561</span> END;</div> |
| <div class="line"><a name="l00562"></a><span class="lineno"> 562</span> $$ LANGUAGE plpgsql VOLATILE;</div> |
| <div class="line"><a name="l00563"></a><span class="lineno"> 563</span> </div> |
| <div class="line"><a name="l00564"></a><span class="lineno"> 564</span> CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.elastic_net_train (</div> |
| <div class="line"><a name="l00565"></a><span class="lineno"> 565</span>  tbl_source TEXT,</div> |
| <div class="line"><a name="l00566"></a><span class="lineno"> 566</span>  tbl_result TEXT,</div> |
| <div class="line"><a name="l00567"></a><span class="lineno"> 567</span>  col_ind_var TEXT,</div> |
| <div class="line"><a name="l00568"></a><span class="lineno"> 568</span>  col_dep_var TEXT,</div> |
| <div class="line"><a name="l00569"></a><span class="lineno"> 569</span>  regress_family TEXT,</div> |
| <div class="line"><a name="l00570"></a><span class="lineno"> 570</span>  alpha DOUBLE PRECISION,</div> |
| <div class="line"><a name="l00571"></a><span class="lineno"> 571</span>  lambda_value DOUBLE PRECISION,</div> |
| <div class="line"><a name="l00572"></a><span class="lineno"> 572</span>  standardization BOOLEAN,</div> |
| <div class="line"><a name="l00573"></a><span class="lineno"> 573</span>  grouping_columns TEXT</div> |
| <div class="line"><a name="l00574"></a><span class="lineno"> 574</span> ) RETURNS VOID AS $$</div> |
| <div class="line"><a name="l00575"></a><span class="lineno"> 575</span> BEGIN</div> |
| <div class="line"><a name="l00576"></a><span class="lineno"> 576</span>  PERFORM MADLIB_SCHEMA.elastic_net_train($1, $2, $3, $4, $5, $6, $7, $8,</div> |
| <div class="line"><a name="l00577"></a><span class="lineno"> 577</span>  $9, <span class="stringliteral">'FISTA'</span>);</div> |
| <div class="line"><a name="l00578"></a><span class="lineno"> 578</span> END;</div> |
| <div class="line"><a name="l00579"></a><span class="lineno"> 579</span> $$ LANGUAGE plpgsql VOLATILE;</div> |
| <div class="line"><a name="l00580"></a><span class="lineno"> 580</span> </div> |
| <div class="line"><a name="l00581"></a><span class="lineno"> 581</span> CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.elastic_net_train (</div> |
| <div class="line"><a name="l00582"></a><span class="lineno"> 582</span>  tbl_source TEXT,</div> |
| <div class="line"><a name="l00583"></a><span class="lineno"> 583</span>  tbl_result TEXT,</div> |
| <div class="line"><a name="l00584"></a><span class="lineno"> 584</span>  col_ind_var TEXT,</div> |
| <div class="line"><a name="l00585"></a><span class="lineno"> 585</span>  col_dep_var TEXT,</div> |
| <div class="line"><a name="l00586"></a><span class="lineno"> 586</span>  regress_family TEXT,</div> |
| <div class="line"><a name="l00587"></a><span class="lineno"> 587</span>  alpha DOUBLE PRECISION,</div> |
| <div class="line"><a name="l00588"></a><span class="lineno"> 588</span>  lambda_value DOUBLE PRECISION,</div> |
| <div class="line"><a name="l00589"></a><span class="lineno"> 589</span>  standardization BOOLEAN</div> |
| <div class="line"><a name="l00590"></a><span class="lineno"> 590</span> ) RETURNS VOID AS $$</div> |
| <div class="line"><a name="l00591"></a><span class="lineno"> 591</span> BEGIN</div> |
| <div class="line"><a name="l00592"></a><span class="lineno"> 592</span>  PERFORM MADLIB_SCHEMA.elastic_net_train($1, $2, $3, $4, $5, $6, $7, $8,</div> |
| <div class="line"><a name="l00593"></a><span class="lineno"> 593</span>  NULL);</div> |
| <div class="line"><a name="l00594"></a><span class="lineno"> 594</span> END;</div> |
| <div class="line"><a name="l00595"></a><span class="lineno"> 595</span> $$ LANGUAGE plpgsql VOLATILE;</div> |
| <div class="line"><a name="l00596"></a><span class="lineno"> 596</span> </div> |
| <div class="line"><a name="l00597"></a><span class="lineno"> 597</span> CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.elastic_net_train (</div> |
| <div class="line"><a name="l00598"></a><span class="lineno"> 598</span>  tbl_source TEXT,</div> |
| <div class="line"><a name="l00599"></a><span class="lineno"> 599</span>  tbl_result TEXT,</div> |
| <div class="line"><a name="l00600"></a><span class="lineno"> 600</span>  col_ind_var TEXT,</div> |
| <div class="line"><a name="l00601"></a><span class="lineno"> 601</span>  col_dep_var TEXT,</div> |
| <div class="line"><a name="l00602"></a><span class="lineno"> 602</span>  regress_family TEXT,</div> |
| <div class="line"><a name="l00603"></a><span class="lineno"> 603</span>  alpha DOUBLE PRECISION,</div> |
| <div class="line"><a name="l00604"></a><span class="lineno"> 604</span>  lambda_value DOUBLE PRECISION</div> |
| <div class="line"><a name="l00605"></a><span class="lineno"> 605</span> ) RETURNS VOID AS $$</div> |
| <div class="line"><a name="l00606"></a><span class="lineno"> 606</span> BEGIN</div> |
| <div class="line"><a name="l00607"></a><span class="lineno"> 607</span>  PERFORM MADLIB_SCHEMA.elastic_net_train($1, $2, $3, $4, $5, $6, $7, True);</div> |
| <div class="line"><a name="l00608"></a><span class="lineno"> 608</span> END;</div> |
| <div class="line"><a name="l00609"></a><span class="lineno"> 609</span> $$ LANGUAGE plpgsql VOLATILE;</div> |
| <div class="line"><a name="l00610"></a><span class="lineno"> 610</span> </div> |
| <div class="line"><a name="l00611"></a><span class="lineno"> 611</span> ------------------------------------------------------------------------</div> |
| <div class="line"><a name="l00612"></a><span class="lineno"> 612</span> <span class="comment"></span></div> |
| <div class="line"><a name="l00613"></a><span class="lineno"> 613</span> <span class="comment">/**</span></div> |
| <div class="line"><a name="l00614"></a><span class="lineno"> 614</span> <span class="comment"> * @brief Help function, to print out the supported families</span></div> |
| <div class="line"><a name="l00615"></a><span class="lineno"> 615</span> <span class="comment"> */</span></div> |
| <div class="line"><a name="l00616"></a><span class="lineno"> 616</span> CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.elastic_net_train ()</div> |
| <div class="line"><a name="l00617"></a><span class="lineno"> 617</span> RETURNS TEXT AS $$</div> |
| <div class="line"><a name="l00618"></a><span class="lineno"> 618</span> PythonFunction(elastic_net, elastic_net, elastic_net_help)</div> |
| <div class="line"><a name="l00619"></a><span class="lineno"> 619</span> $$ LANGUAGE plpythonu;</div> |
| <div class="line"><a name="l00620"></a><span class="lineno"> 620</span> </div> |
| <div class="line"><a name="l00621"></a><span class="lineno"> 621</span> ------------------------------------------------------------------------</div> |
| <div class="line"><a name="l00622"></a><span class="lineno"> 622</span> <span class="comment"></span></div> |
| <div class="line"><a name="l00623"></a><span class="lineno"> 623</span> <span class="comment">/**</span></div> |
| <div class="line"><a name="l00624"></a><span class="lineno"> 624</span> <span class="comment"> * @brief Help function, to print out the supported optimizer for a family</span></div> |
| <div class="line"><a name="l00625"></a><span class="lineno"> 625</span> <span class="comment"> * or print out the parameter list for an optimizer</span></div> |
| <div class="line"><a name="l00626"></a><span class="lineno"> 626</span> <span class="comment"> *</span></div> |
| <div class="line"><a name="l00627"></a><span class="lineno"> 627</span> <span class="comment"> * @param family_or_optimizer Response type, 'gaussian' or 'binomial', or</span></div> |
| <div class="line"><a name="l00628"></a><span class="lineno"> 628</span> <span class="comment"> * optimizer type</span></div> |
| <div class="line"><a name="l00629"></a><span class="lineno"> 629</span> <span class="comment"> */</span></div> |
| <div class="line"><a name="l00630"></a><span class="lineno"> 630</span> CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.elastic_net_train (</div> |
| <div class="line"><a name="l00631"></a><span class="lineno"> 631</span>  family_or_optimizer TEXT</div> |
| <div class="line"><a name="l00632"></a><span class="lineno"> 632</span> ) RETURNS TEXT AS $$</div> |
| <div class="line"><a name="l00633"></a><span class="lineno"> 633</span> PythonFunction(elastic_net, elastic_net, elastic_net_help)</div> |
| <div class="line"><a name="l00634"></a><span class="lineno"> 634</span> $$ LANGUAGE plpythonu;</div> |
| <div class="line"><a name="l00635"></a><span class="lineno"> 635</span> </div> |
| <div class="line"><a name="l00636"></a><span class="lineno"> 636</span> ------------------------------------------------------------------------</div> |
| <div class="line"><a name="l00637"></a><span class="lineno"> 637</span> ------------------------------------------------------------------------</div> |
| <div class="line"><a name="l00638"></a><span class="lineno"><a class="code" href="elastic__net_8sql__in.html#add7e2592fb0bd9f2eb1d8b176c80b5d0"> 638</a></span> ------------------------------------------------------------------------</div> |
| <div class="line"><a name="l00639"></a><span class="lineno"> 639</span> <span class="comment"></span></div> |
| <div class="line"><a name="l00640"></a><span class="lineno"> 640</span> <span class="comment">/**</span></div> |
| <div class="line"><a name="l00641"></a><span class="lineno"> 641</span> <span class="comment"> * @brief Prediction and put the result in a table</span></div> |
| <div class="line"><a name="l00642"></a><span class="lineno"> 642</span> <span class="comment"> * can be used together with General-CV</span></div> |
| <div class="line"><a name="l00643"></a><span class="lineno"> 643</span> <span class="comment"> * @param tbl_model The result from elastic_net_train</span></div> |
| <div class="line"><a name="l00644"></a><span class="lineno"> 644</span> <span class="comment"> * @param tbl_new_source Data table</span></div> |
| <div class="line"><a name="l00645"></a><span class="lineno"> 645</span> <span class="comment"> * @param col_id Unique ID associated with each row</span></div> |
| <div class="line"><a name="l00646"></a><span class="lineno"> 646</span> <span class="comment"> * @param tbl_predict Prediction result</span></div> |
| <div class="line"><a name="l00647"></a><span class="lineno"> 647</span> <span class="comment"> */</span></div> |
| <div class="line"><a name="l00648"></a><span class="lineno"> 648</span> CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.elastic_net_predict (</div> |
| <div class="line"><a name="l00649"></a><span class="lineno"> 649</span>  tbl_model TEXT,</div> |
| <div class="line"><a name="l00650"></a><span class="lineno"> 650</span>  tbl_new_source TEXT,</div> |
| <div class="line"><a name="l00651"></a><span class="lineno"> 651</span>  col_id TEXT,</div> |
| <div class="line"><a name="l00652"></a><span class="lineno"><a class="code" href="elastic__net_8sql__in.html#a6f6ee3def49b5e9a1d3375fe41427dc0"> 652</a></span>  tbl_predict TEXT</div> |
| <div class="line"><a name="l00653"></a><span class="lineno"> 653</span> ) RETURNS VOID AS $$</div> |
| <div class="line"><a name="l00654"></a><span class="lineno"> 654</span> PythonFunction(elastic_net, elastic_net, elastic_net_predict_all)</div> |
| <div class="line"><a name="l00655"></a><span class="lineno"> 655</span> $$ LANGUAGE plpythonu;</div> |
| <div class="line"><a name="l00656"></a><span class="lineno"> 656</span> </div> |
| <div class="line"><a name="l00657"></a><span class="lineno"> 657</span> ------------------------------------------------------------------------</div> |
| <div class="line"><a name="l00658"></a><span class="lineno"> 658</span> <span class="comment"></span></div> |
| <div class="line"><a name="l00659"></a><span class="lineno"> 659</span> <span class="comment">/**</span></div> |
| <div class="line"><a name="l00660"></a><span class="lineno"> 660</span> <span class="comment"> * @brief Prediction use learned coefficients for a given example</span></div> |
| <div class="line"><a name="l00661"></a><span class="lineno"> 661</span> <span class="comment"> *</span></div> |
| <div class="line"><a name="l00662"></a><span class="lineno"> 662</span> <span class="comment"> * @param regress_family model family</span></div> |
| <div class="line"><a name="l00663"></a><span class="lineno"> 663</span> <span class="comment"> * @param coefficients The fitting coefficients</span></div> |
| <div class="line"><a name="l00664"></a><span class="lineno"> 664</span> <span class="comment"> * @param intercept The fitting intercept</span></div> |
| <div class="line"><a name="l00665"></a><span class="lineno"> 665</span> <span class="comment"> * @param ind_var Features (independent variables)</span></div> |
| <div class="line"><a name="l00666"></a><span class="lineno"> 666</span> <span class="comment"> *</span></div> |
| <div class="line"><a name="l00667"></a><span class="lineno"> 667</span> <span class="comment"> * returns a double value. When regress_family is 'binomial' or 'logistic',</span></div> |
| <div class="line"><a name="l00668"></a><span class="lineno"> 668</span> <span class="comment"> * this function returns 1 for True and 0 for False</span></div> |
| <div class="line"><a name="l00669"></a><span class="lineno"> 669</span> <span class="comment"> */</span></div> |
| <div class="line"><a name="l00670"></a><span class="lineno"><a class="code" href="elastic__net_8sql__in.html#a3578608204ac9b2d3442ff42977f632b"> 670</a></span> CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.elastic_net_predict (</div> |
| <div class="line"><a name="l00671"></a><span class="lineno"> 671</span>  regress_family TEXT,</div> |
| <div class="line"><a name="l00672"></a><span class="lineno"> 672</span>  coefficients DOUBLE PRECISION[],</div> |
| <div class="line"><a name="l00673"></a><span class="lineno"> 673</span>  intercept DOUBLE PRECISION,</div> |
| <div class="line"><a name="l00674"></a><span class="lineno"> 674</span>  ind_var DOUBLE PRECISION[]</div> |
| <div class="line"><a name="l00675"></a><span class="lineno"> 675</span> ) RETURNS DOUBLE PRECISION AS $$</div> |
| <div class="line"><a name="l00676"></a><span class="lineno"> 676</span> DECLARE</div> |
| <div class="line"><a name="l00677"></a><span class="lineno"> 677</span>  family_name TEXT;</div> |
| <div class="line"><a name="l00678"></a><span class="lineno"> 678</span>  binomial_result BOOLEAN;</div> |
| <div class="line"><a name="l00679"></a><span class="lineno"> 679</span> BEGIN</div> |
| <div class="line"><a name="l00680"></a><span class="lineno"> 680</span>  family_name := lower(regress_family);</div> |
| <div class="line"><a name="l00681"></a><span class="lineno"> 681</span> </div> |
| <div class="line"><a name="l00682"></a><span class="lineno"> 682</span>  IF family_name = <span class="stringliteral">'gaussian'</span> OR family_name = <span class="stringliteral">'linear'</span> THEN</div> |
| <div class="line"><a name="l00683"></a><span class="lineno"> 683</span>  RETURN MADLIB_SCHEMA.elastic_net_gaussian_predict(coefficients, intercept, ind_var);</div> |
| <div class="line"><a name="l00684"></a><span class="lineno"> 684</span>  END IF;</div> |
| <div class="line"><a name="l00685"></a><span class="lineno"> 685</span> </div> |
| <div class="line"><a name="l00686"></a><span class="lineno"> 686</span>  IF family_name = <span class="stringliteral">'binomial'</span> OR family_name = <span class="stringliteral">'logistic'</span> THEN</div> |
| <div class="line"><a name="l00687"></a><span class="lineno"> 687</span>  binomial_result := MADLIB_SCHEMA.elastic_net_binomial_predict(coefficients, intercept, ind_var);</div> |
| <div class="line"><a name="l00688"></a><span class="lineno"> 688</span>  IF binomial_result THEN</div> |
| <div class="line"><a name="l00689"></a><span class="lineno"> 689</span>  <span class="keywordflow">return</span> 1;</div> |
| <div class="line"><a name="l00690"></a><span class="lineno"> 690</span>  ELSE</div> |
| <div class="line"><a name="l00691"></a><span class="lineno"> 691</span>  <span class="keywordflow">return</span> 0;</div> |
| <div class="line"><a name="l00692"></a><span class="lineno"><a class="code" href="elastic__net_8sql__in.html#aaddbfdfea4207d38ab7e2cd3db876bdc"> 692</a></span>  END IF;</div> |
| <div class="line"><a name="l00693"></a><span class="lineno"> 693</span>  END IF;</div> |
| <div class="line"><a name="l00694"></a><span class="lineno"> 694</span> </div> |
| <div class="line"><a name="l00695"></a><span class="lineno"> 695</span>  RAISE EXCEPTION <span class="stringliteral">'This regression family is not supported!'</span>;</div> |
| <div class="line"><a name="l00696"></a><span class="lineno"> 696</span> END;</div> |
| <div class="line"><a name="l00697"></a><span class="lineno"> 697</span> $$ LANGUAGE plpgsql IMMUTABLE STRICT;</div> |
| <div class="line"><a name="l00698"></a><span class="lineno"> 698</span> </div> |
| <div class="line"><a name="l00699"></a><span class="lineno"> 699</span> ------------------------------------------------------------------------</div> |
| <div class="line"><a name="l00700"></a><span class="lineno"> 700</span> <span class="comment"></span></div> |
| <div class="line"><a name="l00701"></a><span class="lineno"> 701</span> <span class="comment"> /**</span></div> |
| <div class="line"><a name="l00702"></a><span class="lineno"> 702</span> <span class="comment"> * @brief Prediction for linear models use learned coefficients for a given example</span></div> |
| <div class="line"><a name="l00703"></a><span class="lineno"> 703</span> <span class="comment"> *</span></div> |
| <div class="line"><a name="l00704"></a><span class="lineno"> 704</span> <span class="comment"> * @param coefficients Linear fitting coefficients</span></div> |
| <div class="line"><a name="l00705"></a><span class="lineno"> 705</span> <span class="comment"> * @param intercept Linear fitting intercept</span></div> |
| <div class="line"><a name="l00706"></a><span class="lineno"> 706</span> <span class="comment"> * @param ind_var Features (independent variables)</span></div> |
| <div class="line"><a name="l00707"></a><span class="lineno"> 707</span> <span class="comment"> *</span></div> |
| <div class="line"><a name="l00708"></a><span class="lineno"> 708</span> <span class="comment"> * returns a double value</span></div> |
| <div class="line"><a name="l00709"></a><span class="lineno"> 709</span> <span class="comment"> */</span></div> |
| <div class="line"><a name="l00710"></a><span class="lineno"> 710</span> CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.elastic_net_gaussian_predict (</div> |
| <div class="line"><a name="l00711"></a><span class="lineno"> 711</span>  coefficients DOUBLE PRECISION[],</div> |
| <div class="line"><a name="l00712"></a><span class="lineno"> 712</span>  intercept DOUBLE PRECISION,</div> |
| <div class="line"><a name="l00713"></a><span class="lineno"> 713</span>  ind_var DOUBLE PRECISION[]</div> |
| <div class="line"><a name="l00714"></a><span class="lineno"> 714</span> ) RETURNS DOUBLE PRECISION AS</div> |
| <div class="line"><a name="l00715"></a><span class="lineno"> 715</span> <span class="stringliteral">'MODULE_PATHNAME'</span>, <span class="stringliteral">'__elastic_net_gaussian_predict'</span></div> |
| <div class="line"><a name="l00716"></a><span class="lineno"> 716</span> LANGUAGE C IMMUTABLE STRICT;</div> |
| <div class="line"><a name="l00717"></a><span class="lineno"> 717</span> </div> |
| <div class="line"><a name="l00718"></a><span class="lineno"> 718</span> ------------------------------------------------------------------------<span class="comment"></span></div> |
| <div class="line"><a name="l00719"></a><span class="lineno"> 719</span> <span class="comment">/**</span></div> |
| <div class="line"><a name="l00720"></a><span class="lineno"> 720</span> <span class="comment"> * @brief Prediction for logistic models use learned coefficients for a given example</span></div> |
| <div class="line"><a name="l00721"></a><span class="lineno"> 721</span> <span class="comment"> *</span></div> |
| <div class="line"><a name="l00722"></a><span class="lineno"> 722</span> <span class="comment"> * @param coefficients Logistic fitting coefficients</span></div> |
| <div class="line"><a name="l00723"></a><span class="lineno"> 723</span> <span class="comment"> * @param intercept Logistic fitting intercept</span></div> |
| <div class="line"><a name="l00724"></a><span class="lineno"> 724</span> <span class="comment"> * @param ind_var Features (independent variables)</span></div> |
| <div class="line"><a name="l00725"></a><span class="lineno"> 725</span> <span class="comment"> *</span></div> |
| <div class="line"><a name="l00726"></a><span class="lineno"> 726</span> <span class="comment"> * returns a boolean value</span></div> |
| <div class="line"><a name="l00727"></a><span class="lineno"> 727</span> <span class="comment"> */</span></div> |
| <div class="line"><a name="l00728"></a><span class="lineno"> 728</span> CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.elastic_net_binomial_predict (</div> |
| <div class="line"><a name="l00729"></a><span class="lineno"> 729</span>  coefficients DOUBLE PRECISION[],</div> |
| <div class="line"><a name="l00730"></a><span class="lineno"> 730</span>  intercept DOUBLE PRECISION,</div> |
| <div class="line"><a name="l00731"></a><span class="lineno"> 731</span>  ind_var DOUBLE PRECISION[]</div> |
| <div class="line"><a name="l00732"></a><span class="lineno"><a class="code" href="elastic__net_8sql__in.html#a96db4ff4ba3ea363fafbf6c036c19fae"> 732</a></span> ) RETURNS BOOLEAN AS</div> |
| <div class="line"><a name="l00733"></a><span class="lineno"> 733</span> <span class="stringliteral">'MODULE_PATHNAME'</span>, <span class="stringliteral">'__elastic_net_binomial_predict'</span></div> |
| <div class="line"><a name="l00734"></a><span class="lineno"> 734</span> LANGUAGE C IMMUTABLE STRICT;</div> |
| <div class="line"><a name="l00735"></a><span class="lineno"> 735</span> </div> |
| <div class="line"><a name="l00736"></a><span class="lineno"> 736</span> ------------------------------------------------------------------------<span class="comment"></span></div> |
| <div class="line"><a name="l00737"></a><span class="lineno"> 737</span> <span class="comment">/**</span></div> |
| <div class="line"><a name="l00738"></a><span class="lineno"> 738</span> <span class="comment"> * @brief Compute the probability of belonging to the True class for a given observation</span></div> |
| <div class="line"><a name="l00739"></a><span class="lineno"> 739</span> <span class="comment"> *</span></div> |
| <div class="line"><a name="l00740"></a><span class="lineno"> 740</span> <span class="comment"> * @param coefficients Logistic fitting coefficients</span></div> |
| <div class="line"><a name="l00741"></a><span class="lineno"> 741</span> <span class="comment"> * @param intercept Logistic fitting intercept</span></div> |
| <div class="line"><a name="l00742"></a><span class="lineno"> 742</span> <span class="comment"> * @param ind_var Features (independent variables)</span></div> |
| <div class="line"><a name="l00743"></a><span class="lineno"> 743</span> <span class="comment"> *</span></div> |
| <div class="line"><a name="l00744"></a><span class="lineno"> 744</span> <span class="comment"> * returns a double value, which is the probability of this data point being True class</span></div> |
| <div class="line"><a name="l00745"></a><span class="lineno"> 745</span> <span class="comment"> */</span></div> |
| <div class="line"><a name="l00746"></a><span class="lineno"> 746</span> CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.elastic_net_binomial_prob (</div> |
| <div class="line"><a name="l00747"></a><span class="lineno"> 747</span>  coefficients DOUBLE PRECISION[],</div> |
| <div class="line"><a name="l00748"></a><span class="lineno"> 748</span>  intercept DOUBLE PRECISION,</div> |
| <div class="line"><a name="l00749"></a><span class="lineno"> 749</span>  ind_var DOUBLE PRECISION[]</div> |
| <div class="line"><a name="l00750"></a><span class="lineno"><a class="code" href="elastic__net_8sql__in.html#aa78cde79f1f2caa7c5b38f933001d793"> 750</a></span> ) RETURNS DOUBLE PRECISION AS</div> |
| <div class="line"><a name="l00751"></a><span class="lineno"> 751</span> <span class="stringliteral">'MODULE_PATHNAME'</span>, <span class="stringliteral">'__elastic_net_binomial_prob'</span></div> |
| <div class="line"><a name="l00752"></a><span class="lineno"> 752</span> LANGUAGE C IMMUTABLE STRICT;</div> |
| <div class="line"><a name="l00753"></a><span class="lineno"> 753</span> </div> |
| <div class="line"><a name="l00754"></a><span class="lineno"> 754</span> ------------------------------------------------------------------------</div> |
| <div class="line"><a name="l00755"></a><span class="lineno"> 755</span> <span class="comment">/* Compute the log-likelihood for one data point */</span></div> |
| <div class="line"><a name="l00756"></a><span class="lineno"> 756</span> CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__elastic_net_binomial_loglikelihood (</div> |
| <div class="line"><a name="l00757"></a><span class="lineno"> 757</span>  coefficients DOUBLE PRECISION[],</div> |
| <div class="line"><a name="l00758"></a><span class="lineno"> 758</span>  intercept DOUBLE PRECISION,</div> |
| <div class="line"><a name="l00759"></a><span class="lineno"> 759</span>  dep_var BOOLEAN,</div> |
| <div class="line"><a name="l00760"></a><span class="lineno"> 760</span>  ind_var DOUBLE PRECISION[]</div> |
| <div class="line"><a name="l00761"></a><span class="lineno"> 761</span> ) RETURNS DOUBLE PRECISION AS</div> |
| <div class="line"><a name="l00762"></a><span class="lineno"> 762</span> <span class="stringliteral">'MODULE_PATHNAME'</span>, <span class="stringliteral">'__elastic_net_binomial_loglikelihood'</span></div> |
| <div class="line"><a name="l00763"></a><span class="lineno"> 763</span> LANGUAGE C IMMUTABLE STRICT;</div> |
| <div class="line"><a name="l00764"></a><span class="lineno"> 764</span> </div> |
| <div class="line"><a name="l00765"></a><span class="lineno"> 765</span> ------------------------------------------------------------------------</div> |
| <div class="line"><a name="l00766"></a><span class="lineno"> 766</span> -- Compute the solution <span class="keywordflow">for</span> just one step ------------------------------</div> |
| <div class="line"><a name="l00767"></a><span class="lineno"> 767</span> ------------------------------------------------------------------------</div> |
| <div class="line"><a name="l00768"></a><span class="lineno"><a class="code" href="elastic__net_8sql__in.html#a308718fd5234bc1007b971a639aadf71"> 768</a></span> </div> |
| <div class="line"><a name="l00769"></a><span class="lineno"> 769</span> CREATE TYPE MADLIB_SCHEMA.__elastic_net_result AS (</div> |
| <div class="line"><a name="l00770"></a><span class="lineno"> 770</span>  intercept DOUBLE PRECISION,</div> |
| <div class="line"><a name="l00771"></a><span class="lineno"> 771</span>  coefficients DOUBLE PRECISION[],</div> |
| <div class="line"><a name="l00772"></a><span class="lineno"> 772</span>  lambda_value DOUBLE PRECISION</div> |
| <div class="line"><a name="l00773"></a><span class="lineno"> 773</span> );</div> |
| <div class="line"><a name="l00774"></a><span class="lineno"> 774</span> </div> |
| <div class="line"><a name="l00775"></a><span class="lineno"> 775</span> ------------------------------------------------------------------------</div> |
| <div class="line"><a name="l00776"></a><span class="lineno"> 776</span> </div> |
| <div class="line"><a name="l00777"></a><span class="lineno"> 777</span> <span class="comment">/* IGD */</span></div> |
| <div class="line"><a name="l00778"></a><span class="lineno"> 778</span> </div> |
| <div class="line"><a name="l00779"></a><span class="lineno"> 779</span> CREATE FUNCTION MADLIB_SCHEMA.__gaussian_igd_transition (</div> |
| <div class="line"><a name="l00780"></a><span class="lineno"> 780</span>  state DOUBLE PRECISION[],</div> |
| <div class="line"><a name="l00781"></a><span class="lineno"> 781</span>  ind_var DOUBLE PRECISION[],</div> |
| <div class="line"><a name="l00782"></a><span class="lineno"> 782</span>  dep_var DOUBLE PRECISION,</div> |
| <div class="line"><a name="l00783"></a><span class="lineno"> 783</span>  pre_state DOUBLE PRECISION[],</div> |
| <div class="line"><a name="l00784"></a><span class="lineno"> 784</span>  lambda DOUBLE PRECISION,</div> |
| <div class="line"><a name="l00785"></a><span class="lineno"> 785</span>  alpha DOUBLE PRECISION,</div> |
| <div class="line"><a name="l00786"></a><span class="lineno"> 786</span>  dimension INTEGER,</div> |
| <div class="line"><a name="l00787"></a><span class="lineno"> 787</span>  stepsize DOUBLE PRECISION,</div> |
| <div class="line"><a name="l00788"></a><span class="lineno"> 788</span>  total_rows INTEGER,</div> |
| <div class="line"><a name="l00789"></a><span class="lineno"> 789</span>  xmean DOUBLE PRECISION[],</div> |
| <div class="line"><a name="l00790"></a><span class="lineno"> 790</span>  ymean DOUBLE PRECISION,</div> |
| <div class="line"><a name="l00791"></a><span class="lineno"> 791</span>  step_decay DOUBLE PRECISION</div> |
| <div class="line"><a name="l00792"></a><span class="lineno"> 792</span> ) RETURNS DOUBLE PRECISION[]</div> |
| <div class="line"><a name="l00793"></a><span class="lineno"> 793</span> AS <span class="stringliteral">'MODULE_PATHNAME'</span>, <span class="stringliteral">'gaussian_igd_transition'</span></div> |
| <div class="line"><a name="l00794"></a><span class="lineno"> 794</span> LANGUAGE C IMMUTABLE;</div> |
| <div class="line"><a name="l00795"></a><span class="lineno"> 795</span> </div> |
| <div class="line"><a name="l00796"></a><span class="lineno"> 796</span> --</div> |
| <div class="line"><a name="l00797"></a><span class="lineno"> 797</span> </div> |
| <div class="line"><a name="l00798"></a><span class="lineno"> 798</span> CREATE FUNCTION MADLIB_SCHEMA.__gaussian_igd_merge (</div> |
| <div class="line"><a name="l00799"></a><span class="lineno"> 799</span>  state1 DOUBLE PRECISION[],</div> |
| <div class="line"><a name="l00800"></a><span class="lineno"> 800</span>  state2 DOUBLE PRECISION[]</div> |
| <div class="line"><a name="l00801"></a><span class="lineno"> 801</span> ) RETURNS DOUBLE PRECISION[] AS</div> |
| <div class="line"><a name="l00802"></a><span class="lineno"> 802</span> <span class="stringliteral">'MODULE_PATHNAME'</span>, <span class="stringliteral">'gaussian_igd_merge'</span></div> |
| <div class="line"><a name="l00803"></a><span class="lineno"> 803</span> LANGUAGE C IMMUTABLE STRICT;</div> |
| <div class="line"><a name="l00804"></a><span class="lineno"> 804</span> </div> |
| <div class="line"><a name="l00805"></a><span class="lineno"> 805</span> --</div> |
| <div class="line"><a name="l00806"></a><span class="lineno"> 806</span> </div> |
| <div class="line"><a name="l00807"></a><span class="lineno"> 807</span> CREATE FUNCTION MADLIB_SCHEMA.__gaussian_igd_final (</div> |
| <div class="line"><a name="l00808"></a><span class="lineno"> 808</span>  state DOUBLE PRECISION[]</div> |
| <div class="line"><a name="l00809"></a><span class="lineno"> 809</span> ) RETURNS DOUBLE PRECISION[] AS</div> |
| <div class="line"><a name="l00810"></a><span class="lineno"> 810</span> <span class="stringliteral">'MODULE_PATHNAME'</span>, <span class="stringliteral">'gaussian_igd_final'</span></div> |
| <div class="line"><a name="l00811"></a><span class="lineno"> 811</span> LANGUAGE C IMMUTABLE STRICT;</div> |
| <div class="line"><a name="l00812"></a><span class="lineno"> 812</span> </div> |
| <div class="line"><a name="l00813"></a><span class="lineno"> 813</span> <span class="comment">/*</span></div> |
| <div class="line"><a name="l00814"></a><span class="lineno"> 814</span> <span class="comment"> * Perform one iteration step of IGD for linear models</span></div> |
| <div class="line"><a name="l00815"></a><span class="lineno"> 815</span> <span class="comment"> */</span></div> |
| <div class="line"><a name="l00816"></a><span class="lineno"> 816</span> CREATE AGGREGATE MADLIB_SCHEMA.__gaussian_igd_step(</div> |
| <div class="line"><a name="l00817"></a><span class="lineno"> 817</span>  <span class="comment">/* ind_var */</span> DOUBLE PRECISION[],</div> |
| <div class="line"><a name="l00818"></a><span class="lineno"> 818</span>  <span class="comment">/* dep_var */</span> DOUBLE PRECISION,</div> |
| <div class="line"><a name="l00819"></a><span class="lineno"> 819</span>  <span class="comment">/* pre_state */</span> DOUBLE PRECISION[],</div> |
| <div class="line"><a name="l00820"></a><span class="lineno"> 820</span>  <span class="comment">/* lambda */</span> DOUBLE PRECISION,</div> |
| <div class="line"><a name="l00821"></a><span class="lineno"> 821</span>  <span class="comment">/* alpha */</span> DOUBLE PRECISION,</div> |
| <div class="line"><a name="l00822"></a><span class="lineno"> 822</span>  <span class="comment">/* dimension */</span> INTEGER,</div> |
| <div class="line"><a name="l00823"></a><span class="lineno"> 823</span>  <span class="comment">/* stepsize */</span> DOUBLE PRECISION,</div> |
| <div class="line"><a name="l00824"></a><span class="lineno"> 824</span>  <span class="comment">/* total_rows */</span> INTEGER,</div> |
| <div class="line"><a name="l00825"></a><span class="lineno"> 825</span>  <span class="comment">/* xmeans */</span> DOUBLE PRECISION[],</div> |
| <div class="line"><a name="l00826"></a><span class="lineno"> 826</span>  <span class="comment">/* ymean */</span> DOUBLE PRECISION,</div> |
| <div class="line"><a name="l00827"></a><span class="lineno"> 827</span>  <span class="comment">/* step_decay */</span> DOUBLE PRECISION</div> |
| <div class="line"><a name="l00828"></a><span class="lineno"> 828</span> ) (</div> |
| <div class="line"><a name="l00829"></a><span class="lineno"> 829</span>  SType = DOUBLE PRECISION[],</div> |
| <div class="line"><a name="l00830"></a><span class="lineno"> 830</span>  SFunc = MADLIB_SCHEMA.__gaussian_igd_transition,</div> |
| <div class="line"><a name="l00831"></a><span class="lineno"> 831</span>  m4_ifdef(`GREENPLUM<span class="stringliteral">', `prefunc = MADLIB_SCHEMA.__gaussian_igd_merge,'</span>)</div> |
| <div class="line"><a name="l00832"></a><span class="lineno"> 832</span>  FinalFunc = MADLIB_SCHEMA.__gaussian_igd_final,</div> |
| <div class="line"><a name="l00833"></a><span class="lineno"> 833</span>  InitCond = '{0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0}<span class="stringliteral">'</span></div> |
| <div class="line"><a name="l00834"></a><span class="lineno"> 834</span> <span class="stringliteral">);</span></div> |
| <div class="line"><a name="l00835"></a><span class="lineno"> 835</span> <span class="stringliteral"></span></div> |
| <div class="line"><a name="l00836"></a><span class="lineno"> 836</span> <span class="stringliteral">CREATE AGGREGATE MADLIB_SCHEMA.__gaussian_igd_step_single_seg (</span></div> |
| <div class="line"><a name="l00837"></a><span class="lineno"> 837</span> <span class="stringliteral"> /* ind_var */ DOUBLE PRECISION[],</span></div> |
| <div class="line"><a name="l00838"></a><span class="lineno"> 838</span> <span class="stringliteral"> /* dep_var */ DOUBLE PRECISION,</span></div> |
| <div class="line"><a name="l00839"></a><span class="lineno"> 839</span> <span class="stringliteral"> /* pre_state */ DOUBLE PRECISION[],</span></div> |
| <div class="line"><a name="l00840"></a><span class="lineno"> 840</span> <span class="stringliteral"> /* lambda */ DOUBLE PRECISION,</span></div> |
| <div class="line"><a name="l00841"></a><span class="lineno"> 841</span> <span class="stringliteral"> /* alpha */ DOUBLE PRECISION,</span></div> |
| <div class="line"><a name="l00842"></a><span class="lineno"> 842</span> <span class="stringliteral"> /* dimension */ INTEGER,</span></div> |
| <div class="line"><a name="l00843"></a><span class="lineno"> 843</span> <span class="stringliteral"> /* stepsize */ DOUBLE PRECISION,</span></div> |
| <div class="line"><a name="l00844"></a><span class="lineno"> 844</span> <span class="stringliteral"> /* total_rows */ INTEGER,</span></div> |
| <div class="line"><a name="l00845"></a><span class="lineno"> 845</span> <span class="stringliteral"> /* xmeans */ DOUBLE PRECISION[],</span></div> |
| <div class="line"><a name="l00846"></a><span class="lineno"> 846</span> <span class="stringliteral"> /* ymean */ DOUBLE PRECISION,</span></div> |
| <div class="line"><a name="l00847"></a><span class="lineno"> 847</span> <span class="stringliteral"> /* step_decay */ DOUBLE PRECISION</span></div> |
| <div class="line"><a name="l00848"></a><span class="lineno"> 848</span> <span class="stringliteral">) (</span></div> |
| <div class="line"><a name="l00849"></a><span class="lineno"> 849</span> <span class="stringliteral"> SType = DOUBLE PRECISION[],</span></div> |
| <div class="line"><a name="l00850"></a><span class="lineno"> 850</span> <span class="stringliteral"> SFunc = MADLIB_SCHEMA.__gaussian_igd_transition,</span></div> |
| <div class="line"><a name="l00851"></a><span class="lineno"> 851</span> <span class="stringliteral"> -- m4_ifdef(`GREENPLUM'</span>, `prefunc = MADLIB_SCHEMA.__gaussian_igd_merge,<span class="stringliteral">')</span></div> |
| <div class="line"><a name="l00852"></a><span class="lineno"> 852</span> <span class="stringliteral"> FinalFunc = MADLIB_SCHEMA.__gaussian_igd_final,</span></div> |
| <div class="line"><a name="l00853"></a><span class="lineno"> 853</span> <span class="stringliteral"> InitCond = '</span>{0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0}<span class="stringliteral">'</span></div> |
| <div class="line"><a name="l00854"></a><span class="lineno"> 854</span> <span class="stringliteral">);</span></div> |
| <div class="line"><a name="l00855"></a><span class="lineno"> 855</span> <span class="stringliteral"></span></div> |
| <div class="line"><a name="l00856"></a><span class="lineno"> 856</span> <span class="stringliteral">--</span></div> |
| <div class="line"><a name="l00857"></a><span class="lineno"> 857</span> <span class="stringliteral"></span></div> |
| <div class="line"><a name="l00858"></a><span class="lineno"> 858</span> <span class="stringliteral">CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__gaussian_igd_state_diff (</span></div> |
| <div class="line"><a name="l00859"></a><span class="lineno"> 859</span> <span class="stringliteral"> state1 DOUBLE PRECISION[],</span></div> |
| <div class="line"><a name="l00860"></a><span class="lineno"> 860</span> <span class="stringliteral"> state2 DOUBLE PRECISION[]</span></div> |
| <div class="line"><a name="l00861"></a><span class="lineno"> 861</span> <span class="stringliteral">) RETURNS DOUBLE PRECISION AS</span></div> |
| <div class="line"><a name="l00862"></a><span class="lineno"> 862</span> <span class="stringliteral">'</span>MODULE_PATHNAME<span class="stringliteral">', '</span>__gaussian_igd_state_diff<span class="stringliteral">'</span></div> |
| <div class="line"><a name="l00863"></a><span class="lineno"> 863</span> <span class="stringliteral">LANGUAGE C IMMUTABLE STRICT;</span></div> |
| <div class="line"><a name="l00864"></a><span class="lineno"> 864</span> <span class="stringliteral"></span></div> |
| <div class="line"><a name="l00865"></a><span class="lineno"> 865</span> <span class="stringliteral">--</span></div> |
| <div class="line"><a name="l00866"></a><span class="lineno"> 866</span> <span class="stringliteral"></span></div> |
| <div class="line"><a name="l00867"></a><span class="lineno"> 867</span> <span class="stringliteral">CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__gaussian_igd_result (</span></div> |
| <div class="line"><a name="l00868"></a><span class="lineno"> 868</span> <span class="stringliteral"> in_state DOUBLE PRECISION[],</span></div> |
| <div class="line"><a name="l00869"></a><span class="lineno"> 869</span> <span class="stringliteral"> feature_sq DOUBLE PRECISION[],</span></div> |
| <div class="line"><a name="l00870"></a><span class="lineno"> 870</span> <span class="stringliteral"> threshold DOUBLE PRECISION,</span></div> |
| <div class="line"><a name="l00871"></a><span class="lineno"> 871</span> <span class="stringliteral"> tolerance DOUBLE PRECISION</span></div> |
| <div class="line"><a name="l00872"></a><span class="lineno"> 872</span> <span class="stringliteral">) RETURNS MADLIB_SCHEMA.__elastic_net_result AS</span></div> |
| <div class="line"><a name="l00873"></a><span class="lineno"> 873</span> <span class="stringliteral">'</span>MODULE_PATHNAME<span class="stringliteral">', '</span>__gaussian_igd_result<span class="stringliteral">'</span></div> |
| <div class="line"><a name="l00874"></a><span class="lineno"> 874</span> <span class="stringliteral">LANGUAGE C IMMUTABLE STRICT;</span></div> |
| <div class="line"><a name="l00875"></a><span class="lineno"> 875</span> <span class="stringliteral"></span></div> |
| <div class="line"><a name="l00876"></a><span class="lineno"> 876</span> <span class="stringliteral">------------------------------------------------------------------------</span></div> |
| <div class="line"><a name="l00877"></a><span class="lineno"> 877</span> <span class="stringliteral"></span></div> |
| <div class="line"><a name="l00878"></a><span class="lineno"> 878</span> <span class="stringliteral">/* FISTA */</span></div> |
| <div class="line"><a name="l00879"></a><span class="lineno"> 879</span> <span class="stringliteral"></span></div> |
| <div class="line"><a name="l00880"></a><span class="lineno"> 880</span> <span class="stringliteral">CREATE FUNCTION MADLIB_SCHEMA.__gaussian_fista_transition (</span></div> |
| <div class="line"><a name="l00881"></a><span class="lineno"> 881</span> <span class="stringliteral"> state DOUBLE PRECISION[],</span></div> |
| <div class="line"><a name="l00882"></a><span class="lineno"> 882</span> <span class="stringliteral"> ind_var DOUBLE PRECISION[],</span></div> |
| <div class="line"><a name="l00883"></a><span class="lineno"> 883</span> <span class="stringliteral"> dep_var DOUBLE PRECISION,</span></div> |
| <div class="line"><a name="l00884"></a><span class="lineno"> 884</span> <span class="stringliteral"> pre_state DOUBLE PRECISION[],</span></div> |
| <div class="line"><a name="l00885"></a><span class="lineno"> 885</span> <span class="stringliteral"> lambda DOUBLE PRECISION,</span></div> |
| <div class="line"><a name="l00886"></a><span class="lineno"> 886</span> <span class="stringliteral"> alpha DOUBLE PRECISION,</span></div> |
| <div class="line"><a name="l00887"></a><span class="lineno"> 887</span> <span class="stringliteral"> dimension INTEGER,</span></div> |
| <div class="line"><a name="l00888"></a><span class="lineno"> 888</span> <span class="stringliteral"> total_rows INTEGER,</span></div> |
| <div class="line"><a name="l00889"></a><span class="lineno"> 889</span> <span class="stringliteral"> max_stepsize DOUBLE PRECISION,</span></div> |
| <div class="line"><a name="l00890"></a><span class="lineno"> 890</span> <span class="stringliteral"> eta DOUBLE PRECISION,</span></div> |
| <div class="line"><a name="l00891"></a><span class="lineno"> 891</span> <span class="stringliteral"> use_active_set INTEGER,</span></div> |
| <div class="line"><a name="l00892"></a><span class="lineno"> 892</span> <span class="stringliteral"> is_active INTEGER,</span></div> |
| <div class="line"><a name="l00893"></a><span class="lineno"> 893</span> <span class="stringliteral"> random_stepsize INTEGER</span></div> |
| <div class="line"><a name="l00894"></a><span class="lineno"> 894</span> <span class="stringliteral">) RETURNS DOUBLE PRECISION[]</span></div> |
| <div class="line"><a name="l00895"></a><span class="lineno"> 895</span> <span class="stringliteral">AS '</span>MODULE_PATHNAME<span class="stringliteral">', '</span>gaussian_fista_transition<span class="stringliteral">'</span></div> |
| <div class="line"><a name="l00896"></a><span class="lineno"> 896</span> <span class="stringliteral">LANGUAGE C IMMUTABLE;</span></div> |
| <div class="line"><a name="l00897"></a><span class="lineno"> 897</span> <span class="stringliteral"></span></div> |
| <div class="line"><a name="l00898"></a><span class="lineno"> 898</span> <span class="stringliteral">--</span></div> |
| <div class="line"><a name="l00899"></a><span class="lineno"> 899</span> <span class="stringliteral"></span></div> |
| <div class="line"><a name="l00900"></a><span class="lineno"> 900</span> <span class="stringliteral">CREATE FUNCTION MADLIB_SCHEMA.__gaussian_fista_merge (</span></div> |
| <div class="line"><a name="l00901"></a><span class="lineno"> 901</span> <span class="stringliteral"> state1 DOUBLE PRECISION[],</span></div> |
| <div class="line"><a name="l00902"></a><span class="lineno"> 902</span> <span class="stringliteral"> state2 DOUBLE PRECISION[]</span></div> |
| <div class="line"><a name="l00903"></a><span class="lineno"> 903</span> <span class="stringliteral">) RETURNS DOUBLE PRECISION[] AS</span></div> |
| <div class="line"><a name="l00904"></a><span class="lineno"> 904</span> <span class="stringliteral">'</span>MODULE_PATHNAME<span class="stringliteral">', '</span>gaussian_fista_merge<span class="stringliteral">'</span></div> |
| <div class="line"><a name="l00905"></a><span class="lineno"> 905</span> <span class="stringliteral">LANGUAGE C IMMUTABLE STRICT;</span></div> |
| <div class="line"><a name="l00906"></a><span class="lineno"> 906</span> <span class="stringliteral"></span></div> |
| <div class="line"><a name="l00907"></a><span class="lineno"> 907</span> <span class="stringliteral">--</span></div> |
| <div class="line"><a name="l00908"></a><span class="lineno"> 908</span> <span class="stringliteral"></span></div> |
| <div class="line"><a name="l00909"></a><span class="lineno"> 909</span> <span class="stringliteral">CREATE FUNCTION MADLIB_SCHEMA.__gaussian_fista_final (</span></div> |
| <div class="line"><a name="l00910"></a><span class="lineno"> 910</span> <span class="stringliteral"> state DOUBLE PRECISION[]</span></div> |
| <div class="line"><a name="l00911"></a><span class="lineno"> 911</span> <span class="stringliteral">) RETURNS DOUBLE PRECISION[] AS</span></div> |
| <div class="line"><a name="l00912"></a><span class="lineno"> 912</span> <span class="stringliteral">'</span>MODULE_PATHNAME<span class="stringliteral">', '</span>gaussian_fista_final<span class="stringliteral">'</span></div> |
| <div class="line"><a name="l00913"></a><span class="lineno"> 913</span> <span class="stringliteral">LANGUAGE C IMMUTABLE STRICT;</span></div> |
| <div class="line"><a name="l00914"></a><span class="lineno"> 914</span> <span class="stringliteral"></span></div> |
| <div class="line"><a name="l00915"></a><span class="lineno"> 915</span> <span class="stringliteral">/*</span></div> |
| <div class="line"><a name="l00916"></a><span class="lineno"> 916</span> <span class="stringliteral"> Perform one iteration step of FISTA for linear models</span></div> |
| <div class="line"><a name="l00917"></a><span class="lineno"> 917</span> <span class="stringliteral"> */</span></div> |
| <div class="line"><a name="l00918"></a><span class="lineno"> 918</span> <span class="stringliteral">CREATE AGGREGATE MADLIB_SCHEMA.__gaussian_fista_step(</span></div> |
| <div class="line"><a name="l00919"></a><span class="lineno"> 919</span> <span class="stringliteral"> /* ind_var */ DOUBLE PRECISION[],</span></div> |
| <div class="line"><a name="l00920"></a><span class="lineno"> 920</span> <span class="stringliteral"> /* dep_var */ DOUBLE PRECISION,</span></div> |
| <div class="line"><a name="l00921"></a><span class="lineno"> 921</span> <span class="stringliteral"> /* pre_state */ DOUBLE PRECISION[],</span></div> |
| <div class="line"><a name="l00922"></a><span class="lineno"> 922</span> <span class="stringliteral"> /* lambda */ DOUBLE PRECISION,</span></div> |
| <div class="line"><a name="l00923"></a><span class="lineno"> 923</span> <span class="stringliteral"> /* alpha */ DOUBLE PRECISION,</span></div> |
| <div class="line"><a name="l00924"></a><span class="lineno"> 924</span> <span class="stringliteral"> /* dimension */ INTEGER,</span></div> |
| <div class="line"><a name="l00925"></a><span class="lineno"> 925</span> <span class="stringliteral"> /* total_rows */ INTEGER,</span></div> |
| <div class="line"><a name="l00926"></a><span class="lineno"> 926</span> <span class="stringliteral"> /* max_stepsize */ DOUBLE PRECISION,</span></div> |
| <div class="line"><a name="l00927"></a><span class="lineno"> 927</span> <span class="stringliteral"> /* eta */ DOUBLE PRECISION,</span></div> |
| <div class="line"><a name="l00928"></a><span class="lineno"> 928</span> <span class="stringliteral"> /* use_active_set */ INTEGER,</span></div> |
| <div class="line"><a name="l00929"></a><span class="lineno"> 929</span> <span class="stringliteral"> /* is_active */ INTEGER,</span></div> |
| <div class="line"><a name="l00930"></a><span class="lineno"> 930</span> <span class="stringliteral"> /* random_stepsize */ INTEGER</span></div> |
| <div class="line"><a name="l00931"></a><span class="lineno"> 931</span> <span class="stringliteral">) (</span></div> |
| <div class="line"><a name="l00932"></a><span class="lineno"> 932</span> <span class="stringliteral"> SType = DOUBLE PRECISION[],</span></div> |
| <div class="line"><a name="l00933"></a><span class="lineno"> 933</span> <span class="stringliteral"> SFunc = MADLIB_SCHEMA.__gaussian_fista_transition,</span></div> |
| <div class="line"><a name="l00934"></a><span class="lineno"> 934</span> <span class="stringliteral"> m4_ifdef(`GREENPLUM'</span>, `prefunc = MADLIB_SCHEMA.__gaussian_fista_merge,<span class="stringliteral">')</span></div> |
| <div class="line"><a name="l00935"></a><span class="lineno"> 935</span> <span class="stringliteral"> FinalFunc = MADLIB_SCHEMA.__gaussian_fista_final,</span></div> |
| <div class="line"><a name="l00936"></a><span class="lineno"> 936</span> <span class="stringliteral"> InitCond = '</span>{0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0}<span class="stringliteral">'</span></div> |
| <div class="line"><a name="l00937"></a><span class="lineno"> 937</span> <span class="stringliteral">);</span></div> |
| <div class="line"><a name="l00938"></a><span class="lineno"> 938</span> <span class="stringliteral"></span></div> |
| <div class="line"><a name="l00939"></a><span class="lineno"> 939</span> <span class="stringliteral">--</span></div> |
| <div class="line"><a name="l00940"></a><span class="lineno"> 940</span> <span class="stringliteral"></span></div> |
| <div class="line"><a name="l00941"></a><span class="lineno"> 941</span> <span class="stringliteral">CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__gaussian_fista_state_diff (</span></div> |
| <div class="line"><a name="l00942"></a><span class="lineno"> 942</span> <span class="stringliteral"> state1 DOUBLE PRECISION[],</span></div> |
| <div class="line"><a name="l00943"></a><span class="lineno"> 943</span> <span class="stringliteral"> state2 DOUBLE PRECISION[]</span></div> |
| <div class="line"><a name="l00944"></a><span class="lineno"> 944</span> <span class="stringliteral">) RETURNS DOUBLE PRECISION AS</span></div> |
| <div class="line"><a name="l00945"></a><span class="lineno"> 945</span> <span class="stringliteral">'</span>MODULE_PATHNAME<span class="stringliteral">', '</span>__gaussian_fista_state_diff<span class="stringliteral">'</span></div> |
| <div class="line"><a name="l00946"></a><span class="lineno"> 946</span> <span class="stringliteral">LANGUAGE C IMMUTABLE STRICT;</span></div> |
| <div class="line"><a name="l00947"></a><span class="lineno"> 947</span> <span class="stringliteral"></span></div> |
| <div class="line"><a name="l00948"></a><span class="lineno"> 948</span> <span class="stringliteral">--</span></div> |
| <div class="line"><a name="l00949"></a><span class="lineno"> 949</span> <span class="stringliteral"></span></div> |
| <div class="line"><a name="l00950"></a><span class="lineno"> 950</span> <span class="stringliteral">CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__gaussian_fista_result (</span></div> |
| <div class="line"><a name="l00951"></a><span class="lineno"> 951</span> <span class="stringliteral"> in_state DOUBLE PRECISION[]</span></div> |
| <div class="line"><a name="l00952"></a><span class="lineno"> 952</span> <span class="stringliteral">) RETURNS MADLIB_SCHEMA.__elastic_net_result AS</span></div> |
| <div class="line"><a name="l00953"></a><span class="lineno"> 953</span> <span class="stringliteral">'</span>MODULE_PATHNAME<span class="stringliteral">', '</span>__gaussian_fista_result<span class="stringliteral">'</span></div> |
| <div class="line"><a name="l00954"></a><span class="lineno"> 954</span> <span class="stringliteral">LANGUAGE C IMMUTABLE STRICT;</span></div> |
| <div class="line"><a name="l00955"></a><span class="lineno"> 955</span> <span class="stringliteral"></span></div> |
| <div class="line"><a name="l00956"></a><span class="lineno"> 956</span> <span class="stringliteral">------------------------------------------------------------------------</span></div> |
| <div class="line"><a name="l00957"></a><span class="lineno"> 957</span> <span class="stringliteral">------------------------------------------------------------------------</span></div> |
| <div class="line"><a name="l00958"></a><span class="lineno"> 958</span> <span class="stringliteral">------------------------------------------------------------------------</span></div> |
| <div class="line"><a name="l00959"></a><span class="lineno"> 959</span> <span class="stringliteral"></span></div> |
| <div class="line"><a name="l00960"></a><span class="lineno"> 960</span> <span class="stringliteral">/* Binomial IGD */</span></div> |
| <div class="line"><a name="l00961"></a><span class="lineno"> 961</span> <span class="stringliteral"></span></div> |
| <div class="line"><a name="l00962"></a><span class="lineno"> 962</span> <span class="stringliteral">CREATE FUNCTION MADLIB_SCHEMA.__binomial_igd_transition (</span></div> |
| <div class="line"><a name="l00963"></a><span class="lineno"> 963</span> <span class="stringliteral"> state DOUBLE PRECISION[],</span></div> |
| <div class="line"><a name="l00964"></a><span class="lineno"> 964</span> <span class="stringliteral"> ind_var DOUBLE PRECISION[],</span></div> |
| <div class="line"><a name="l00965"></a><span class="lineno"> 965</span> <span class="stringliteral"> dep_var BOOLEAN,</span></div> |
| <div class="line"><a name="l00966"></a><span class="lineno"> 966</span> <span class="stringliteral"> pre_state DOUBLE PRECISION[],</span></div> |
| <div class="line"><a name="l00967"></a><span class="lineno"> 967</span> <span class="stringliteral"> lambda DOUBLE PRECISION,</span></div> |
| <div class="line"><a name="l00968"></a><span class="lineno"> 968</span> <span class="stringliteral"> alpha DOUBLE PRECISION,</span></div> |
| <div class="line"><a name="l00969"></a><span class="lineno"> 969</span> <span class="stringliteral"> dimension INTEGER,</span></div> |
| <div class="line"><a name="l00970"></a><span class="lineno"> 970</span> <span class="stringliteral"> stepsize DOUBLE PRECISION,</span></div> |
| <div class="line"><a name="l00971"></a><span class="lineno"> 971</span> <span class="stringliteral"> total_rows INTEGER,</span></div> |
| <div class="line"><a name="l00972"></a><span class="lineno"> 972</span> <span class="stringliteral"> xmean DOUBLE PRECISION[],</span></div> |
| <div class="line"><a name="l00973"></a><span class="lineno"> 973</span> <span class="stringliteral"> ymean DOUBLE PRECISION,</span></div> |
| <div class="line"><a name="l00974"></a><span class="lineno"> 974</span> <span class="stringliteral"> step_decay DOUBLE PRECISION</span></div> |
| <div class="line"><a name="l00975"></a><span class="lineno"> 975</span> <span class="stringliteral">) RETURNS DOUBLE PRECISION[]</span></div> |
| <div class="line"><a name="l00976"></a><span class="lineno"> 976</span> <span class="stringliteral">AS '</span>MODULE_PATHNAME<span class="stringliteral">', '</span>binomial_igd_transition<span class="stringliteral">'</span></div> |
| <div class="line"><a name="l00977"></a><span class="lineno"> 977</span> <span class="stringliteral">LANGUAGE C IMMUTABLE;</span></div> |
| <div class="line"><a name="l00978"></a><span class="lineno"> 978</span> <span class="stringliteral"></span></div> |
| <div class="line"><a name="l00979"></a><span class="lineno"> 979</span> <span class="stringliteral">--</span></div> |
| <div class="line"><a name="l00980"></a><span class="lineno"> 980</span> <span class="stringliteral"></span></div> |
| <div class="line"><a name="l00981"></a><span class="lineno"> 981</span> <span class="stringliteral">CREATE FUNCTION MADLIB_SCHEMA.__binomial_igd_merge (</span></div> |
| <div class="line"><a name="l00982"></a><span class="lineno"> 982</span> <span class="stringliteral"> state1 DOUBLE PRECISION[],</span></div> |
| <div class="line"><a name="l00983"></a><span class="lineno"> 983</span> <span class="stringliteral"> state2 DOUBLE PRECISION[]</span></div> |
| <div class="line"><a name="l00984"></a><span class="lineno"> 984</span> <span class="stringliteral">) RETURNS DOUBLE PRECISION[] AS</span></div> |
| <div class="line"><a name="l00985"></a><span class="lineno"> 985</span> <span class="stringliteral">'</span>MODULE_PATHNAME<span class="stringliteral">', '</span>binomial_igd_merge<span class="stringliteral">'</span></div> |
| <div class="line"><a name="l00986"></a><span class="lineno"> 986</span> <span class="stringliteral">LANGUAGE C IMMUTABLE STRICT;</span></div> |
| <div class="line"><a name="l00987"></a><span class="lineno"> 987</span> <span class="stringliteral"></span></div> |
| <div class="line"><a name="l00988"></a><span class="lineno"> 988</span> <span class="stringliteral">--</span></div> |
| <div class="line"><a name="l00989"></a><span class="lineno"> 989</span> <span class="stringliteral"></span></div> |
| <div class="line"><a name="l00990"></a><span class="lineno"> 990</span> <span class="stringliteral">CREATE FUNCTION MADLIB_SCHEMA.__binomial_igd_final (</span></div> |
| <div class="line"><a name="l00991"></a><span class="lineno"> 991</span> <span class="stringliteral"> state DOUBLE PRECISION[]</span></div> |
| <div class="line"><a name="l00992"></a><span class="lineno"> 992</span> <span class="stringliteral">) RETURNS DOUBLE PRECISION[] AS</span></div> |
| <div class="line"><a name="l00993"></a><span class="lineno"> 993</span> <span class="stringliteral">'</span>MODULE_PATHNAME<span class="stringliteral">', '</span>binomial_igd_final<span class="stringliteral">'</span></div> |
| <div class="line"><a name="l00994"></a><span class="lineno"> 994</span> <span class="stringliteral">LANGUAGE C IMMUTABLE STRICT;</span></div> |
| <div class="line"><a name="l00995"></a><span class="lineno"> 995</span> <span class="stringliteral"></span></div> |
| <div class="line"><a name="l00996"></a><span class="lineno"> 996</span> <span class="stringliteral">/*</span></div> |
| <div class="line"><a name="l00997"></a><span class="lineno"> 997</span> <span class="stringliteral"> * Perform one iteration step of IGD for linear models</span></div> |
| <div class="line"><a name="l00998"></a><span class="lineno"> 998</span> <span class="stringliteral"> */</span></div> |
| <div class="line"><a name="l00999"></a><span class="lineno"> 999</span> <span class="stringliteral">CREATE AGGREGATE MADLIB_SCHEMA.__binomial_igd_step(</span></div> |
| <div class="line"><a name="l01000"></a><span class="lineno"> 1000</span> <span class="stringliteral"> /* ind_var */ DOUBLE PRECISION[],</span></div> |
| <div class="line"><a name="l01001"></a><span class="lineno"> 1001</span> <span class="stringliteral"> /* dep_var */ BOOLEAN,</span></div> |
| <div class="line"><a name="l01002"></a><span class="lineno"> 1002</span> <span class="stringliteral"> /* pre_state */ DOUBLE PRECISION[],</span></div> |
| <div class="line"><a name="l01003"></a><span class="lineno"> 1003</span> <span class="stringliteral"> /* lambda */ DOUBLE PRECISION,</span></div> |
| <div class="line"><a name="l01004"></a><span class="lineno"> 1004</span> <span class="stringliteral"> /* alpha */ DOUBLE PRECISION,</span></div> |
| <div class="line"><a name="l01005"></a><span class="lineno"> 1005</span> <span class="stringliteral"> /* dimension */ INTEGER,</span></div> |
| <div class="line"><a name="l01006"></a><span class="lineno"> 1006</span> <span class="stringliteral"> /* stepsize */ DOUBLE PRECISION,</span></div> |
| <div class="line"><a name="l01007"></a><span class="lineno"> 1007</span> <span class="stringliteral"> /* total_rows */ INTEGER,</span></div> |
| <div class="line"><a name="l01008"></a><span class="lineno"> 1008</span> <span class="stringliteral"> /* xmeans */ DOUBLE PRECISION[],</span></div> |
| <div class="line"><a name="l01009"></a><span class="lineno"> 1009</span> <span class="stringliteral"> /* ymean */ DOUBLE PRECISION,</span></div> |
| <div class="line"><a name="l01010"></a><span class="lineno"> 1010</span> <span class="stringliteral"> /* step_decay */ DOUBLE PRECISION</span></div> |
| <div class="line"><a name="l01011"></a><span class="lineno"> 1011</span> <span class="stringliteral">) (</span></div> |
| <div class="line"><a name="l01012"></a><span class="lineno"> 1012</span> <span class="stringliteral"> SType = DOUBLE PRECISION[],</span></div> |
| <div class="line"><a name="l01013"></a><span class="lineno"> 1013</span> <span class="stringliteral"> SFunc = MADLIB_SCHEMA.__binomial_igd_transition,</span></div> |
| <div class="line"><a name="l01014"></a><span class="lineno"> 1014</span> <span class="stringliteral"> m4_ifdef(`GREENPLUM'</span>, `prefunc = MADLIB_SCHEMA.__binomial_igd_merge,<span class="stringliteral">')</span></div> |
| <div class="line"><a name="l01015"></a><span class="lineno"> 1015</span> <span class="stringliteral"> FinalFunc = MADLIB_SCHEMA.__binomial_igd_final,</span></div> |
| <div class="line"><a name="l01016"></a><span class="lineno"> 1016</span> <span class="stringliteral"> InitCond = '</span>{0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0}<span class="stringliteral">'</span></div> |
| <div class="line"><a name="l01017"></a><span class="lineno"> 1017</span> <span class="stringliteral">);</span></div> |
| <div class="line"><a name="l01018"></a><span class="lineno"> 1018</span> <span class="stringliteral"></span></div> |
| <div class="line"><a name="l01019"></a><span class="lineno"> 1019</span> <span class="stringliteral">CREATE AGGREGATE MADLIB_SCHEMA.__binomial_igd_step_single_seg (</span></div> |
| <div class="line"><a name="l01020"></a><span class="lineno"> 1020</span> <span class="stringliteral"> /* ind_var */ DOUBLE PRECISION[],</span></div> |
| <div class="line"><a name="l01021"></a><span class="lineno"> 1021</span> <span class="stringliteral"> /* dep_var */ BOOLEAN,</span></div> |
| <div class="line"><a name="l01022"></a><span class="lineno"> 1022</span> <span class="stringliteral"> /* pre_state */ DOUBLE PRECISION[],</span></div> |
| <div class="line"><a name="l01023"></a><span class="lineno"> 1023</span> <span class="stringliteral"> /* lambda */ DOUBLE PRECISION,</span></div> |
| <div class="line"><a name="l01024"></a><span class="lineno"> 1024</span> <span class="stringliteral"> /* alpha */ DOUBLE PRECISION,</span></div> |
| <div class="line"><a name="l01025"></a><span class="lineno"> 1025</span> <span class="stringliteral"> /* dimension */ INTEGER,</span></div> |
| <div class="line"><a name="l01026"></a><span class="lineno"> 1026</span> <span class="stringliteral"> /* stepsize */ DOUBLE PRECISION,</span></div> |
| <div class="line"><a name="l01027"></a><span class="lineno"> 1027</span> <span class="stringliteral"> /* total_rows */ INTEGER,</span></div> |
| <div class="line"><a name="l01028"></a><span class="lineno"> 1028</span> <span class="stringliteral"> /* xmeans */ DOUBLE PRECISION[],</span></div> |
| <div class="line"><a name="l01029"></a><span class="lineno"> 1029</span> <span class="stringliteral"> /* ymean */ DOUBLE PRECISION,</span></div> |
| <div class="line"><a name="l01030"></a><span class="lineno"> 1030</span> <span class="stringliteral"> /* step_decay */ DOUBLE PRECISION</span></div> |
| <div class="line"><a name="l01031"></a><span class="lineno"> 1031</span> <span class="stringliteral">) (</span></div> |
| <div class="line"><a name="l01032"></a><span class="lineno"> 1032</span> <span class="stringliteral"> SType = DOUBLE PRECISION[],</span></div> |
| <div class="line"><a name="l01033"></a><span class="lineno"> 1033</span> <span class="stringliteral"> SFunc = MADLIB_SCHEMA.__binomial_igd_transition,</span></div> |
| <div class="line"><a name="l01034"></a><span class="lineno"> 1034</span> <span class="stringliteral"> -- m4_ifdef(`GREENPLUM'</span>, `prefunc = MADLIB_SCHEMA.__binomial_igd_merge,<span class="stringliteral">')</span></div> |
| <div class="line"><a name="l01035"></a><span class="lineno"> 1035</span> <span class="stringliteral"> FinalFunc = MADLIB_SCHEMA.__binomial_igd_final,</span></div> |
| <div class="line"><a name="l01036"></a><span class="lineno"> 1036</span> <span class="stringliteral"> InitCond = '</span>{0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0}<span class="stringliteral">'</span></div> |
| <div class="line"><a name="l01037"></a><span class="lineno"> 1037</span> <span class="stringliteral">);</span></div> |
| <div class="line"><a name="l01038"></a><span class="lineno"> 1038</span> <span class="stringliteral"></span></div> |
| <div class="line"><a name="l01039"></a><span class="lineno"> 1039</span> <span class="stringliteral">--</span></div> |
| <div class="line"><a name="l01040"></a><span class="lineno"> 1040</span> <span class="stringliteral"></span></div> |
| <div class="line"><a name="l01041"></a><span class="lineno"> 1041</span> <span class="stringliteral">CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__binomial_igd_state_diff (</span></div> |
| <div class="line"><a name="l01042"></a><span class="lineno"> 1042</span> <span class="stringliteral"> state1 DOUBLE PRECISION[],</span></div> |
| <div class="line"><a name="l01043"></a><span class="lineno"> 1043</span> <span class="stringliteral"> state2 DOUBLE PRECISION[]</span></div> |
| <div class="line"><a name="l01044"></a><span class="lineno"> 1044</span> <span class="stringliteral">) RETURNS DOUBLE PRECISION AS</span></div> |
| <div class="line"><a name="l01045"></a><span class="lineno"> 1045</span> <span class="stringliteral">'</span>MODULE_PATHNAME<span class="stringliteral">', '</span>__binomial_igd_state_diff<span class="stringliteral">'</span></div> |
| <div class="line"><a name="l01046"></a><span class="lineno"> 1046</span> <span class="stringliteral">LANGUAGE C IMMUTABLE STRICT;</span></div> |
| <div class="line"><a name="l01047"></a><span class="lineno"> 1047</span> <span class="stringliteral"></span></div> |
| <div class="line"><a name="l01048"></a><span class="lineno"> 1048</span> <span class="stringliteral">--</span></div> |
| <div class="line"><a name="l01049"></a><span class="lineno"> 1049</span> <span class="stringliteral"></span></div> |
| <div class="line"><a name="l01050"></a><span class="lineno"> 1050</span> <span class="stringliteral">CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__binomial_igd_result (</span></div> |
| <div class="line"><a name="l01051"></a><span class="lineno"> 1051</span> <span class="stringliteral"> in_state DOUBLE PRECISION[],</span></div> |
| <div class="line"><a name="l01052"></a><span class="lineno"> 1052</span> <span class="stringliteral"> feature_sq DOUBLE PRECISION[],</span></div> |
| <div class="line"><a name="l01053"></a><span class="lineno"> 1053</span> <span class="stringliteral"> threshold DOUBLE PRECISION,</span></div> |
| <div class="line"><a name="l01054"></a><span class="lineno"> 1054</span> <span class="stringliteral"> tolerance DOUBLE PRECISION</span></div> |
| <div class="line"><a name="l01055"></a><span class="lineno"> 1055</span> <span class="stringliteral">) RETURNS MADLIB_SCHEMA.__elastic_net_result AS</span></div> |
| <div class="line"><a name="l01056"></a><span class="lineno"> 1056</span> <span class="stringliteral">'</span>MODULE_PATHNAME<span class="stringliteral">', '</span>__binomial_igd_result<span class="stringliteral">'</span></div> |
| <div class="line"><a name="l01057"></a><span class="lineno"> 1057</span> <span class="stringliteral">LANGUAGE C IMMUTABLE STRICT;</span></div> |
| <div class="line"><a name="l01058"></a><span class="lineno"> 1058</span> <span class="stringliteral"></span></div> |
| <div class="line"><a name="l01059"></a><span class="lineno"> 1059</span> <span class="stringliteral">------------------------------------------------------------------------</span></div> |
| <div class="line"><a name="l01060"></a><span class="lineno"> 1060</span> <span class="stringliteral"></span></div> |
| <div class="line"><a name="l01061"></a><span class="lineno"> 1061</span> <span class="stringliteral">/* Binomial FISTA */</span></div> |
| <div class="line"><a name="l01062"></a><span class="lineno"> 1062</span> <span class="stringliteral"></span></div> |
| <div class="line"><a name="l01063"></a><span class="lineno"> 1063</span> <span class="stringliteral">CREATE FUNCTION MADLIB_SCHEMA.__binomial_fista_transition (</span></div> |
| <div class="line"><a name="l01064"></a><span class="lineno"> 1064</span> <span class="stringliteral"> state DOUBLE PRECISION[],</span></div> |
| <div class="line"><a name="l01065"></a><span class="lineno"> 1065</span> <span class="stringliteral"> ind_var DOUBLE PRECISION[],</span></div> |
| <div class="line"><a name="l01066"></a><span class="lineno"> 1066</span> <span class="stringliteral"> dep_var BOOLEAN,</span></div> |
| <div class="line"><a name="l01067"></a><span class="lineno"> 1067</span> <span class="stringliteral"> pre_state DOUBLE PRECISION[],</span></div> |
| <div class="line"><a name="l01068"></a><span class="lineno"> 1068</span> <span class="stringliteral"> lambda DOUBLE PRECISION,</span></div> |
| <div class="line"><a name="l01069"></a><span class="lineno"> 1069</span> <span class="stringliteral"> alpha DOUBLE PRECISION,</span></div> |
| <div class="line"><a name="l01070"></a><span class="lineno"> 1070</span> <span class="stringliteral"> dimension INTEGER,</span></div> |
| <div class="line"><a name="l01071"></a><span class="lineno"> 1071</span> <span class="stringliteral"> total_rows INTEGER,</span></div> |
| <div class="line"><a name="l01072"></a><span class="lineno"> 1072</span> <span class="stringliteral"> max_stepsize DOUBLE PRECISION,</span></div> |
| <div class="line"><a name="l01073"></a><span class="lineno"> 1073</span> <span class="stringliteral"> eta DOUBLE PRECISION,</span></div> |
| <div class="line"><a name="l01074"></a><span class="lineno"> 1074</span> <span class="stringliteral"> use_active_set INTEGER,</span></div> |
| <div class="line"><a name="l01075"></a><span class="lineno"> 1075</span> <span class="stringliteral"> is_active INTEGER,</span></div> |
| <div class="line"><a name="l01076"></a><span class="lineno"> 1076</span> <span class="stringliteral"> random_stepsize INTEGER</span></div> |
| <div class="line"><a name="l01077"></a><span class="lineno"> 1077</span> <span class="stringliteral">) RETURNS DOUBLE PRECISION[]</span></div> |
| <div class="line"><a name="l01078"></a><span class="lineno"> 1078</span> <span class="stringliteral">AS '</span>MODULE_PATHNAME<span class="stringliteral">', '</span>binomial_fista_transition<span class="stringliteral">'</span></div> |
| <div class="line"><a name="l01079"></a><span class="lineno"> 1079</span> <span class="stringliteral">LANGUAGE C IMMUTABLE;</span></div> |
| <div class="line"><a name="l01080"></a><span class="lineno"> 1080</span> <span class="stringliteral"></span></div> |
| <div class="line"><a name="l01081"></a><span class="lineno"> 1081</span> <span class="stringliteral">--</span></div> |
| <div class="line"><a name="l01082"></a><span class="lineno"> 1082</span> <span class="stringliteral"></span></div> |
| <div class="line"><a name="l01083"></a><span class="lineno"> 1083</span> <span class="stringliteral">CREATE FUNCTION MADLIB_SCHEMA.__binomial_fista_merge (</span></div> |
| <div class="line"><a name="l01084"></a><span class="lineno"> 1084</span> <span class="stringliteral"> state1 DOUBLE PRECISION[],</span></div> |
| <div class="line"><a name="l01085"></a><span class="lineno"> 1085</span> <span class="stringliteral"> state2 DOUBLE PRECISION[]</span></div> |
| <div class="line"><a name="l01086"></a><span class="lineno"> 1086</span> <span class="stringliteral">) RETURNS DOUBLE PRECISION[] AS</span></div> |
| <div class="line"><a name="l01087"></a><span class="lineno"> 1087</span> <span class="stringliteral">'</span>MODULE_PATHNAME<span class="stringliteral">', '</span>binomial_fista_merge<span class="stringliteral">'</span></div> |
| <div class="line"><a name="l01088"></a><span class="lineno"> 1088</span> <span class="stringliteral">LANGUAGE C IMMUTABLE STRICT;</span></div> |
| <div class="line"><a name="l01089"></a><span class="lineno"> 1089</span> <span class="stringliteral"></span></div> |
| <div class="line"><a name="l01090"></a><span class="lineno"> 1090</span> <span class="stringliteral">--</span></div> |
| <div class="line"><a name="l01091"></a><span class="lineno"> 1091</span> <span class="stringliteral"></span></div> |
| <div class="line"><a name="l01092"></a><span class="lineno"> 1092</span> <span class="stringliteral">CREATE FUNCTION MADLIB_SCHEMA.__binomial_fista_final (</span></div> |
| <div class="line"><a name="l01093"></a><span class="lineno"> 1093</span> <span class="stringliteral"> state DOUBLE PRECISION[]</span></div> |
| <div class="line"><a name="l01094"></a><span class="lineno"> 1094</span> <span class="stringliteral">) RETURNS DOUBLE PRECISION[] AS</span></div> |
| <div class="line"><a name="l01095"></a><span class="lineno"> 1095</span> <span class="stringliteral">'</span>MODULE_PATHNAME<span class="stringliteral">', '</span>binomial_fista_final<span class="stringliteral">'</span></div> |
| <div class="line"><a name="l01096"></a><span class="lineno"> 1096</span> <span class="stringliteral">LANGUAGE C IMMUTABLE STRICT;</span></div> |
| <div class="line"><a name="l01097"></a><span class="lineno"> 1097</span> <span class="stringliteral"></span></div> |
| <div class="line"><a name="l01098"></a><span class="lineno"> 1098</span> <span class="stringliteral">/*</span></div> |
| <div class="line"><a name="l01099"></a><span class="lineno"> 1099</span> <span class="stringliteral"> Perform one iteration step of FISTA for linear models</span></div> |
| <div class="line"><a name="l01100"></a><span class="lineno"> 1100</span> <span class="stringliteral"> */</span></div> |
| <div class="line"><a name="l01101"></a><span class="lineno"> 1101</span> <span class="stringliteral">CREATE AGGREGATE MADLIB_SCHEMA.__binomial_fista_step(</span></div> |
| <div class="line"><a name="l01102"></a><span class="lineno"> 1102</span> <span class="stringliteral"> /* ind_var */ DOUBLE PRECISION[],</span></div> |
| <div class="line"><a name="l01103"></a><span class="lineno"> 1103</span> <span class="stringliteral"> /* dep_var */ BOOLEAN,</span></div> |
| <div class="line"><a name="l01104"></a><span class="lineno"> 1104</span> <span class="stringliteral"> /* pre_state */ DOUBLE PRECISION[],</span></div> |
| <div class="line"><a name="l01105"></a><span class="lineno"> 1105</span> <span class="stringliteral"> /* lambda */ DOUBLE PRECISION,</span></div> |
| <div class="line"><a name="l01106"></a><span class="lineno"> 1106</span> <span class="stringliteral"> /* alpha */ DOUBLE PRECISION,</span></div> |
| <div class="line"><a name="l01107"></a><span class="lineno"> 1107</span> <span class="stringliteral"> /* dimension */ INTEGER,</span></div> |
| <div class="line"><a name="l01108"></a><span class="lineno"> 1108</span> <span class="stringliteral"> /* total_rows */ INTEGER,</span></div> |
| <div class="line"><a name="l01109"></a><span class="lineno"> 1109</span> <span class="stringliteral"> /* max_stepsize */ DOUBLE PRECISION,</span></div> |
| <div class="line"><a name="l01110"></a><span class="lineno"> 1110</span> <span class="stringliteral"> /* eta */ DOUBLE PRECISION,</span></div> |
| <div class="line"><a name="l01111"></a><span class="lineno"> 1111</span> <span class="stringliteral"> /* use_active_set */ INTEGER,</span></div> |
| <div class="line"><a name="l01112"></a><span class="lineno"> 1112</span> <span class="stringliteral"> /* is_active */ INTEGER,</span></div> |
| <div class="line"><a name="l01113"></a><span class="lineno"> 1113</span> <span class="stringliteral"> /* random_stepsize */ INTEGER</span></div> |
| <div class="line"><a name="l01114"></a><span class="lineno"> 1114</span> <span class="stringliteral">) (</span></div> |
| <div class="line"><a name="l01115"></a><span class="lineno"> 1115</span> <span class="stringliteral"> SType = DOUBLE PRECISION[],</span></div> |
| <div class="line"><a name="l01116"></a><span class="lineno"> 1116</span> <span class="stringliteral"> SFunc = MADLIB_SCHEMA.__binomial_fista_transition,</span></div> |
| <div class="line"><a name="l01117"></a><span class="lineno"> 1117</span> <span class="stringliteral"> m4_ifdef(`GREENPLUM'</span>, `prefunc = MADLIB_SCHEMA.__binomial_fista_merge,<span class="stringliteral">')</span></div> |
| <div class="line"><a name="l01118"></a><span class="lineno"> 1118</span> <span class="stringliteral"> FinalFunc = MADLIB_SCHEMA.__binomial_fista_final,</span></div> |
| <div class="line"><a name="l01119"></a><span class="lineno"> 1119</span> <span class="stringliteral"> InitCond = '</span>{0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0}<span class="stringliteral">'</span></div> |
| <div class="line"><a name="l01120"></a><span class="lineno"> 1120</span> <span class="stringliteral">);</span></div> |
| <div class="line"><a name="l01121"></a><span class="lineno"> 1121</span> <span class="stringliteral"></span></div> |
| <div class="line"><a name="l01122"></a><span class="lineno"> 1122</span> <span class="stringliteral">--</span></div> |
| <div class="line"><a name="l01123"></a><span class="lineno"> 1123</span> <span class="stringliteral"></span></div> |
| <div class="line"><a name="l01124"></a><span class="lineno"> 1124</span> <span class="stringliteral">CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__binomial_fista_state_diff (</span></div> |
| <div class="line"><a name="l01125"></a><span class="lineno"> 1125</span> <span class="stringliteral"> state1 DOUBLE PRECISION[],</span></div> |
| <div class="line"><a name="l01126"></a><span class="lineno"> 1126</span> <span class="stringliteral"> state2 DOUBLE PRECISION[]</span></div> |
| <div class="line"><a name="l01127"></a><span class="lineno"> 1127</span> <span class="stringliteral">) RETURNS DOUBLE PRECISION AS</span></div> |
| <div class="line"><a name="l01128"></a><span class="lineno"> 1128</span> <span class="stringliteral">'</span>MODULE_PATHNAME<span class="stringliteral">', '</span>__binomial_fista_state_diff<span class="stringliteral">'</span></div> |
| <div class="line"><a name="l01129"></a><span class="lineno"> 1129</span> <span class="stringliteral">LANGUAGE C IMMUTABLE STRICT;</span></div> |
| <div class="line"><a name="l01130"></a><span class="lineno"> 1130</span> <span class="stringliteral"></span></div> |
| <div class="line"><a name="l01131"></a><span class="lineno"> 1131</span> <span class="stringliteral">--</span></div> |
| <div class="line"><a name="l01132"></a><span class="lineno"> 1132</span> <span class="stringliteral"></span></div> |
| <div class="line"><a name="l01133"></a><span class="lineno"> 1133</span> <span class="stringliteral">CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__binomial_fista_result (</span></div> |
| <div class="line"><a name="l01134"></a><span class="lineno"> 1134</span> <span class="stringliteral"> in_state DOUBLE PRECISION[]</span></div> |
| <div class="line"><a name="l01135"></a><span class="lineno"> 1135</span> <span class="stringliteral">) RETURNS MADLIB_SCHEMA.__elastic_net_result AS</span></div> |
| <div class="line"><a name="l01136"></a><span class="lineno"> 1136</span> <span class="stringliteral">'</span>MODULE_PATHNAME<span class="stringliteral">', '</span>__binomial_fista_result<span class="stringliteral">'</span></div> |
| <div class="line"><a name="l01137"></a><span class="lineno"> 1137</span> <span class="stringliteral">LANGUAGE C IMMUTABLE STRICT;</span></div> |
| <div class="line"><a name="l01138"></a><span class="lineno"> 1138</span> <span class="stringliteral"></span></div> |
| <div class="line"><a name="l01139"></a><span class="lineno"> 1139</span> <span class="stringliteral"></span></div> |
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