deploy: d6dc083a5a
This commit is contained in:
rmontanana
587
multiclass__strategy_8cpp_source.html
Normal file
587
multiclass__strategy_8cpp_source.html
Normal file
@@ -0,0 +1,587 @@
|
||||
<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "https://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
|
||||
<html xmlns="http://www.w3.org/1999/xhtml" lang="en-US">
|
||||
<head>
|
||||
<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/>
|
||||
<meta http-equiv="X-UA-Compatible" content="IE=11"/>
|
||||
<meta name="generator" content="Doxygen 1.9.8"/>
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1"/>
|
||||
<title>SVM Classifier C++: src/multiclass_strategy.cpp Source File</title>
|
||||
<link href="tabs.css" rel="stylesheet" type="text/css"/>
|
||||
<script type="text/javascript" src="jquery.js"></script>
|
||||
<script type="text/javascript" src="dynsections.js"></script>
|
||||
<link href="search/search.css" rel="stylesheet" type="text/css"/>
|
||||
<script type="text/javascript" src="search/searchdata.js"></script>
|
||||
<script type="text/javascript" src="search/search.js"></script>
|
||||
<link href="doxygen.css" rel="stylesheet" type="text/css" />
|
||||
</head>
|
||||
<body>
|
||||
<div id="top"><!-- do not remove this div, it is closed by doxygen! -->
|
||||
<div id="titlearea">
|
||||
<table cellspacing="0" cellpadding="0">
|
||||
<tbody>
|
||||
<tr id="projectrow">
|
||||
<td id="projectalign">
|
||||
<div id="projectname">SVM Classifier C++<span id="projectnumber"> 1.0.0</span>
|
||||
</div>
|
||||
<div id="projectbrief">High-performance Support Vector Machine classifier with scikit-learn compatible API</div>
|
||||
</td>
|
||||
</tr>
|
||||
</tbody>
|
||||
</table>
|
||||
</div>
|
||||
<!-- end header part -->
|
||||
<!-- Generated by Doxygen 1.9.8 -->
|
||||
<script type="text/javascript">
|
||||
/* @license magnet:?xt=urn:btih:d3d9a9a6595521f9666a5e94cc830dab83b65699&dn=expat.txt MIT */
|
||||
var searchBox = new SearchBox("searchBox", "search/",'.html');
|
||||
/* @license-end */
|
||||
</script>
|
||||
<script type="text/javascript" src="menudata.js"></script>
|
||||
<script type="text/javascript" src="menu.js"></script>
|
||||
<script type="text/javascript">
|
||||
/* @license magnet:?xt=urn:btih:d3d9a9a6595521f9666a5e94cc830dab83b65699&dn=expat.txt MIT */
|
||||
$(function() {
|
||||
initMenu('',true,false,'search.php','Search');
|
||||
$(document).ready(function() { init_search(); });
|
||||
});
|
||||
/* @license-end */
|
||||
</script>
|
||||
<div id="main-nav"></div>
|
||||
<script type="text/javascript">
|
||||
/* @license magnet:?xt=urn:btih:d3d9a9a6595521f9666a5e94cc830dab83b65699&dn=expat.txt MIT */
|
||||
$(document).ready(function() { init_codefold(0); });
|
||||
/* @license-end */
|
||||
</script>
|
||||
<!-- window showing the filter options -->
|
||||
<div id="MSearchSelectWindow"
|
||||
onmouseover="return searchBox.OnSearchSelectShow()"
|
||||
onmouseout="return searchBox.OnSearchSelectHide()"
|
||||
onkeydown="return searchBox.OnSearchSelectKey(event)">
|
||||
</div>
|
||||
|
||||
<!-- iframe showing the search results (closed by default) -->
|
||||
<div id="MSearchResultsWindow">
|
||||
<div id="MSearchResults">
|
||||
<div class="SRPage">
|
||||
<div id="SRIndex">
|
||||
<div id="SRResults"></div>
|
||||
<div class="SRStatus" id="Loading">Loading...</div>
|
||||
<div class="SRStatus" id="Searching">Searching...</div>
|
||||
<div class="SRStatus" id="NoMatches">No Matches</div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div id="nav-path" class="navpath">
|
||||
<ul>
|
||||
<li class="navelem"><a class="el" href="dir_68267d1309a1af8e8297ef4c3efbcdba.html">src</a></li> </ul>
|
||||
</div>
|
||||
</div><!-- top -->
|
||||
<div class="header">
|
||||
<div class="headertitle"><div class="title">multiclass_strategy.cpp</div></div>
|
||||
</div><!--header-->
|
||||
<div class="contents">
|
||||
<div class="fragment"><div class="line"><a id="l00001" name="l00001"></a><span class="lineno"> 1</span><span class="preprocessor">#include "svm_classifier/multiclass_strategy.hpp"</span></div>
|
||||
<div class="line"><a id="l00002" name="l00002"></a><span class="lineno"> 2</span><span class="preprocessor">#include "svm.h"</span> <span class="comment">// libsvm</span></div>
|
||||
<div class="line"><a id="l00003" name="l00003"></a><span class="lineno"> 3</span><span class="preprocessor">#include "linear.h"</span> <span class="comment">// liblinear</span></div>
|
||||
<div class="line"><a id="l00004" name="l00004"></a><span class="lineno"> 4</span><span class="preprocessor">#include <algorithm></span></div>
|
||||
<div class="line"><a id="l00005" name="l00005"></a><span class="lineno"> 5</span><span class="preprocessor">#include <unordered_map></span></div>
|
||||
<div class="line"><a id="l00006" name="l00006"></a><span class="lineno"> 6</span><span class="preprocessor">#include <unordered_set></span></div>
|
||||
<div class="line"><a id="l00007" name="l00007"></a><span class="lineno"> 7</span><span class="preprocessor">#include <chrono></span></div>
|
||||
<div class="line"><a id="l00008" name="l00008"></a><span class="lineno"> 8</span><span class="preprocessor">#include <cmath></span></div>
|
||||
<div class="line"><a id="l00009" name="l00009"></a><span class="lineno"> 9</span> </div>
|
||||
<div class="line"><a id="l00010" name="l00010"></a><span class="lineno"> 10</span><span class="keyword">namespace </span>svm_classifier {</div>
|
||||
<div class="line"><a id="l00011" name="l00011"></a><span class="lineno"> 11</span> </div>
|
||||
<div class="line"><a id="l00012" name="l00012"></a><span class="lineno"> 12</span> <span class="comment">// OneVsRestStrategy Implementation</span></div>
|
||||
<div class="line"><a id="l00013" name="l00013"></a><span class="lineno"> 13</span> <a class="code hl_function" href="classsvm__classifier_1_1OneVsRestStrategy.html#a30f146a564a9c9681524593cacbb43e7">OneVsRestStrategy::OneVsRestStrategy</a>()</div>
|
||||
<div class="line"><a id="l00014" name="l00014"></a><span class="lineno"> 14</span> : library_type_(SVMLibrary::LIBLINEAR)</div>
|
||||
<div class="line"><a id="l00015" name="l00015"></a><span class="lineno"> 15</span> {</div>
|
||||
<div class="line"><a id="l00016" name="l00016"></a><span class="lineno"> 16</span> }</div>
|
||||
<div class="line"><a id="l00017" name="l00017"></a><span class="lineno"> 17</span> </div>
|
||||
<div class="line"><a id="l00018" name="l00018"></a><span class="lineno"> 18</span> OneVsRestStrategy::~OneVsRestStrategy()</div>
|
||||
<div class="line"><a id="l00019" name="l00019"></a><span class="lineno"> 19</span> {</div>
|
||||
<div class="line"><a id="l00020" name="l00020"></a><span class="lineno"> 20</span> cleanup_models();</div>
|
||||
<div class="line"><a id="l00021" name="l00021"></a><span class="lineno"> 21</span> }</div>
|
||||
<div class="line"><a id="l00022" name="l00022"></a><span class="lineno"> 22</span> </div>
|
||||
<div class="line"><a id="l00023" name="l00023"></a><span class="lineno"> 23</span> TrainingMetrics OneVsRestStrategy::fit(<span class="keyword">const</span> torch::Tensor& X,</div>
|
||||
<div class="line"><a id="l00024" name="l00024"></a><span class="lineno"> 24</span> <span class="keyword">const</span> torch::Tensor& y,</div>
|
||||
<div class="line"><a id="l00025" name="l00025"></a><span class="lineno"> 25</span> <span class="keyword">const</span> KernelParameters& params,</div>
|
||||
<div class="line"><a id="l00026" name="l00026"></a><span class="lineno"> 26</span> DataConverter& converter)</div>
|
||||
<div class="line"><a id="l00027" name="l00027"></a><span class="lineno"> 27</span> {</div>
|
||||
<div class="line"><a id="l00028" name="l00028"></a><span class="lineno"> 28</span> cleanup_models();</div>
|
||||
<div class="line"><a id="l00029" name="l00029"></a><span class="lineno"> 29</span> </div>
|
||||
<div class="line"><a id="l00030" name="l00030"></a><span class="lineno"> 30</span> <span class="keyword">auto</span> start_time = std::chrono::high_resolution_clock::now();</div>
|
||||
<div class="line"><a id="l00031" name="l00031"></a><span class="lineno"> 31</span> </div>
|
||||
<div class="line"><a id="l00032" name="l00032"></a><span class="lineno"> 32</span> <span class="comment">// Store parameters and determine library type</span></div>
|
||||
<div class="line"><a id="l00033" name="l00033"></a><span class="lineno"> 33</span> params_ = params;</div>
|
||||
<div class="line"><a id="l00034" name="l00034"></a><span class="lineno"> 34</span> library_type_ = get_svm_library(params.get_kernel_type());</div>
|
||||
<div class="line"><a id="l00035" name="l00035"></a><span class="lineno"> 35</span> </div>
|
||||
<div class="line"><a id="l00036" name="l00036"></a><span class="lineno"> 36</span> <span class="comment">// Extract unique classes</span></div>
|
||||
<div class="line"><a id="l00037" name="l00037"></a><span class="lineno"> 37</span> <span class="keyword">auto</span> y_cpu = y.to(torch::kCPU);</div>
|
||||
<div class="line"><a id="l00038" name="l00038"></a><span class="lineno"> 38</span> <span class="keyword">auto</span> unique_classes_tensor = torch::unique(y_cpu);</div>
|
||||
<div class="line"><a id="l00039" name="l00039"></a><span class="lineno"> 39</span> classes_.clear();</div>
|
||||
<div class="line"><a id="l00040" name="l00040"></a><span class="lineno"> 40</span> </div>
|
||||
<div class="line"><a id="l00041" name="l00041"></a><span class="lineno"> 41</span> <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i < unique_classes_tensor.size(0); ++i) {</div>
|
||||
<div class="line"><a id="l00042" name="l00042"></a><span class="lineno"> 42</span> classes_.push_back(unique_classes_tensor[i].item<int>());</div>
|
||||
<div class="line"><a id="l00043" name="l00043"></a><span class="lineno"> 43</span> }</div>
|
||||
<div class="line"><a id="l00044" name="l00044"></a><span class="lineno"> 44</span> </div>
|
||||
<div class="line"><a id="l00045" name="l00045"></a><span class="lineno"> 45</span> std::sort(classes_.begin(), classes_.end());</div>
|
||||
<div class="line"><a id="l00046" name="l00046"></a><span class="lineno"> 46</span> </div>
|
||||
<div class="line"><a id="l00047" name="l00047"></a><span class="lineno"> 47</span> <span class="comment">// Handle binary classification case</span></div>
|
||||
<div class="line"><a id="l00048" name="l00048"></a><span class="lineno"> 48</span> <span class="keywordflow">if</span> (classes_.size() <= 2) {</div>
|
||||
<div class="line"><a id="l00049" name="l00049"></a><span class="lineno"> 49</span> <span class="comment">// For binary classification, train a single classifier</span></div>
|
||||
<div class="line"><a id="l00050" name="l00050"></a><span class="lineno"> 50</span> classes_.resize(2); <span class="comment">// Ensure we have exactly 2 classes</span></div>
|
||||
<div class="line"><a id="l00051" name="l00051"></a><span class="lineno"> 51</span> </div>
|
||||
<div class="line"><a id="l00052" name="l00052"></a><span class="lineno"> 52</span> <span class="keyword">auto</span> binary_y = y;</div>
|
||||
<div class="line"><a id="l00053" name="l00053"></a><span class="lineno"> 53</span> <span class="keywordflow">if</span> (classes_.size() == 1) {</div>
|
||||
<div class="line"><a id="l00054" name="l00054"></a><span class="lineno"> 54</span> <span class="comment">// Edge case: only one class, create dummy binary problem</span></div>
|
||||
<div class="line"><a id="l00055" name="l00055"></a><span class="lineno"> 55</span> classes_.push_back(classes_[0] + 1);</div>
|
||||
<div class="line"><a id="l00056" name="l00056"></a><span class="lineno"> 56</span> binary_y = torch::cat({ y, torch::full({1}, classes_[1], y.options()) });</div>
|
||||
<div class="line"><a id="l00057" name="l00057"></a><span class="lineno"> 57</span> <span class="keyword">auto</span> dummy_x = torch::zeros({ 1, X.size(1) }, X.options());</div>
|
||||
<div class="line"><a id="l00058" name="l00058"></a><span class="lineno"> 58</span> <span class="keyword">auto</span> extended_X = torch::cat({ X, dummy_x });</div>
|
||||
<div class="line"><a id="l00059" name="l00059"></a><span class="lineno"> 59</span> </div>
|
||||
<div class="line"><a id="l00060" name="l00060"></a><span class="lineno"> 60</span> <span class="keywordtype">double</span> training_time = train_binary_classifier(extended_X, binary_y, params, converter, 0);</div>
|
||||
<div class="line"><a id="l00061" name="l00061"></a><span class="lineno"> 61</span> } <span class="keywordflow">else</span> {</div>
|
||||
<div class="line"><a id="l00062" name="l00062"></a><span class="lineno"> 62</span> <span class="keywordtype">double</span> training_time = train_binary_classifier(X, binary_y, params, converter, 0);</div>
|
||||
<div class="line"><a id="l00063" name="l00063"></a><span class="lineno"> 63</span> }</div>
|
||||
<div class="line"><a id="l00064" name="l00064"></a><span class="lineno"> 64</span> } <span class="keywordflow">else</span> {</div>
|
||||
<div class="line"><a id="l00065" name="l00065"></a><span class="lineno"> 65</span> <span class="comment">// Multiclass case: train one classifier per class</span></div>
|
||||
<div class="line"><a id="l00066" name="l00066"></a><span class="lineno"> 66</span> <span class="keywordflow">if</span> (library_type_ == SVMLibrary::LIBSVM) {</div>
|
||||
<div class="line"><a id="l00067" name="l00067"></a><span class="lineno"> 67</span> svm_models_.resize(classes_.size());</div>
|
||||
<div class="line"><a id="l00068" name="l00068"></a><span class="lineno"> 68</span> } <span class="keywordflow">else</span> {</div>
|
||||
<div class="line"><a id="l00069" name="l00069"></a><span class="lineno"> 69</span> linear_models_.resize(classes_.size());</div>
|
||||
<div class="line"><a id="l00070" name="l00070"></a><span class="lineno"> 70</span> }</div>
|
||||
<div class="line"><a id="l00071" name="l00071"></a><span class="lineno"> 71</span> </div>
|
||||
<div class="line"><a id="l00072" name="l00072"></a><span class="lineno"> 72</span> <span class="keywordtype">double</span> total_training_time = 0.0;</div>
|
||||
<div class="line"><a id="l00073" name="l00073"></a><span class="lineno"> 73</span> </div>
|
||||
<div class="line"><a id="l00074" name="l00074"></a><span class="lineno"> 74</span> <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = 0; i < classes_.size(); ++i) {</div>
|
||||
<div class="line"><a id="l00075" name="l00075"></a><span class="lineno"> 75</span> <span class="keyword">auto</span> binary_y = create_binary_labels(y, classes_[i]);</div>
|
||||
<div class="line"><a id="l00076" name="l00076"></a><span class="lineno"> 76</span> total_training_time += train_binary_classifier(X, binary_y, params, converter, i);</div>
|
||||
<div class="line"><a id="l00077" name="l00077"></a><span class="lineno"> 77</span> }</div>
|
||||
<div class="line"><a id="l00078" name="l00078"></a><span class="lineno"> 78</span> }</div>
|
||||
<div class="line"><a id="l00079" name="l00079"></a><span class="lineno"> 79</span> </div>
|
||||
<div class="line"><a id="l00080" name="l00080"></a><span class="lineno"> 80</span> <span class="keyword">auto</span> end_time = std::chrono::high_resolution_clock::now();</div>
|
||||
<div class="line"><a id="l00081" name="l00081"></a><span class="lineno"> 81</span> <span class="keyword">auto</span> duration = std::chrono::duration_cast<std::chrono::milliseconds>(end_time - start_time);</div>
|
||||
<div class="line"><a id="l00082" name="l00082"></a><span class="lineno"> 82</span> </div>
|
||||
<div class="line"><a id="l00083" name="l00083"></a><span class="lineno"> 83</span> is_trained_ = <span class="keyword">true</span>;</div>
|
||||
<div class="line"><a id="l00084" name="l00084"></a><span class="lineno"> 84</span> </div>
|
||||
<div class="line"><a id="l00085" name="l00085"></a><span class="lineno"> 85</span> TrainingMetrics metrics;</div>
|
||||
<div class="line"><a id="l00086" name="l00086"></a><span class="lineno"> 86</span> metrics.training_time = duration.count() / 1000.0;</div>
|
||||
<div class="line"><a id="l00087" name="l00087"></a><span class="lineno"> 87</span> metrics.status = TrainingStatus::SUCCESS;</div>
|
||||
<div class="line"><a id="l00088" name="l00088"></a><span class="lineno"> 88</span> </div>
|
||||
<div class="line"><a id="l00089" name="l00089"></a><span class="lineno"> 89</span> <span class="keywordflow">return</span> metrics;</div>
|
||||
<div class="line"><a id="l00090" name="l00090"></a><span class="lineno"> 90</span> }</div>
|
||||
<div class="line"><a id="l00091" name="l00091"></a><span class="lineno"> 91</span> </div>
|
||||
<div class="line"><a id="l00092" name="l00092"></a><span class="lineno"> 92</span> std::vector<int> OneVsRestStrategy::predict(<span class="keyword">const</span> torch::Tensor& X, DataConverter& converter)</div>
|
||||
<div class="line"><a id="l00093" name="l00093"></a><span class="lineno"> 93</span> {</div>
|
||||
<div class="line"><a id="l00094" name="l00094"></a><span class="lineno"> 94</span> <span class="keywordflow">if</span> (!is_trained_) {</div>
|
||||
<div class="line"><a id="l00095" name="l00095"></a><span class="lineno"> 95</span> <span class="keywordflow">throw</span> std::runtime_error(<span class="stringliteral">"Model is not trained"</span>);</div>
|
||||
<div class="line"><a id="l00096" name="l00096"></a><span class="lineno"> 96</span> }</div>
|
||||
<div class="line"><a id="l00097" name="l00097"></a><span class="lineno"> 97</span> </div>
|
||||
<div class="line"><a id="l00098" name="l00098"></a><span class="lineno"> 98</span> <span class="keyword">auto</span> decision_values = decision_function(X, converter);</div>
|
||||
<div class="line"><a id="l00099" name="l00099"></a><span class="lineno"> 99</span> std::vector<int> predictions;</div>
|
||||
<div class="line"><a id="l00100" name="l00100"></a><span class="lineno"> 100</span> predictions.reserve(X.size(0));</div>
|
||||
<div class="line"><a id="l00101" name="l00101"></a><span class="lineno"> 101</span> </div>
|
||||
<div class="line"><a id="l00102" name="l00102"></a><span class="lineno"> 102</span> <span class="keywordflow">for</span> (<span class="keyword">const</span> <span class="keyword">auto</span>& decision_row : decision_values) {</div>
|
||||
<div class="line"><a id="l00103" name="l00103"></a><span class="lineno"> 103</span> <span class="comment">// Find the class with maximum decision value</span></div>
|
||||
<div class="line"><a id="l00104" name="l00104"></a><span class="lineno"> 104</span> <span class="keyword">auto</span> max_it = std::max_element(decision_row.begin(), decision_row.end());</div>
|
||||
<div class="line"><a id="l00105" name="l00105"></a><span class="lineno"> 105</span> <span class="keywordtype">int</span> predicted_class_idx = std::distance(decision_row.begin(), max_it);</div>
|
||||
<div class="line"><a id="l00106" name="l00106"></a><span class="lineno"> 106</span> predictions.push_back(classes_[predicted_class_idx]);</div>
|
||||
<div class="line"><a id="l00107" name="l00107"></a><span class="lineno"> 107</span> }</div>
|
||||
<div class="line"><a id="l00108" name="l00108"></a><span class="lineno"> 108</span> </div>
|
||||
<div class="line"><a id="l00109" name="l00109"></a><span class="lineno"> 109</span> <span class="keywordflow">return</span> predictions;</div>
|
||||
<div class="line"><a id="l00110" name="l00110"></a><span class="lineno"> 110</span> }</div>
|
||||
<div class="line"><a id="l00111" name="l00111"></a><span class="lineno"> 111</span> </div>
|
||||
<div class="line"><a id="l00112" name="l00112"></a><span class="lineno"> 112</span> std::vector<std::vector<double>> OneVsRestStrategy::predict_proba(<span class="keyword">const</span> torch::Tensor& X,</div>
|
||||
<div class="line"><a id="l00113" name="l00113"></a><span class="lineno"> 113</span> DataConverter& converter)</div>
|
||||
<div class="line"><a id="l00114" name="l00114"></a><span class="lineno"> 114</span> {</div>
|
||||
<div class="line"><a id="l00115" name="l00115"></a><span class="lineno"> 115</span> <span class="keywordflow">if</span> (!supports_probability()) {</div>
|
||||
<div class="line"><a id="l00116" name="l00116"></a><span class="lineno"> 116</span> <span class="keywordflow">throw</span> std::runtime_error(<span class="stringliteral">"Probability prediction not supported for current configuration"</span>);</div>
|
||||
<div class="line"><a id="l00117" name="l00117"></a><span class="lineno"> 117</span> }</div>
|
||||
<div class="line"><a id="l00118" name="l00118"></a><span class="lineno"> 118</span> </div>
|
||||
<div class="line"><a id="l00119" name="l00119"></a><span class="lineno"> 119</span> <span class="keywordflow">if</span> (!is_trained_) {</div>
|
||||
<div class="line"><a id="l00120" name="l00120"></a><span class="lineno"> 120</span> <span class="keywordflow">throw</span> std::runtime_error(<span class="stringliteral">"Model is not trained"</span>);</div>
|
||||
<div class="line"><a id="l00121" name="l00121"></a><span class="lineno"> 121</span> }</div>
|
||||
<div class="line"><a id="l00122" name="l00122"></a><span class="lineno"> 122</span> </div>
|
||||
<div class="line"><a id="l00123" name="l00123"></a><span class="lineno"> 123</span> std::vector<std::vector<double>> probabilities;</div>
|
||||
<div class="line"><a id="l00124" name="l00124"></a><span class="lineno"> 124</span> probabilities.reserve(X.size(0));</div>
|
||||
<div class="line"><a id="l00125" name="l00125"></a><span class="lineno"> 125</span> </div>
|
||||
<div class="line"><a id="l00126" name="l00126"></a><span class="lineno"> 126</span> <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i < X.size(0); ++i) {</div>
|
||||
<div class="line"><a id="l00127" name="l00127"></a><span class="lineno"> 127</span> <span class="keyword">auto</span> sample = X[i];</div>
|
||||
<div class="line"><a id="l00128" name="l00128"></a><span class="lineno"> 128</span> std::vector<double> sample_probs;</div>
|
||||
<div class="line"><a id="l00129" name="l00129"></a><span class="lineno"> 129</span> sample_probs.reserve(classes_.size());</div>
|
||||
<div class="line"><a id="l00130" name="l00130"></a><span class="lineno"> 130</span> </div>
|
||||
<div class="line"><a id="l00131" name="l00131"></a><span class="lineno"> 131</span> <span class="keywordflow">if</span> (library_type_ == SVMLibrary::LIBSVM) {</div>
|
||||
<div class="line"><a id="l00132" name="l00132"></a><span class="lineno"> 132</span> <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> j = 0; j < classes_.size(); ++j) {</div>
|
||||
<div class="line"><a id="l00133" name="l00133"></a><span class="lineno"> 133</span> <span class="keywordflow">if</span> (svm_models_[j]) {</div>
|
||||
<div class="line"><a id="l00134" name="l00134"></a><span class="lineno"> 134</span> <span class="keyword">auto</span> sample_node = converter.to_svm_node(sample);</div>
|
||||
<div class="line"><a id="l00135" name="l00135"></a><span class="lineno"> 135</span> <span class="keywordtype">double</span> prob_estimates[2];</div>
|
||||
<div class="line"><a id="l00136" name="l00136"></a><span class="lineno"> 136</span> svm_predict_probability(svm_models_[j].get(), sample_node, prob_estimates);</div>
|
||||
<div class="line"><a id="l00137" name="l00137"></a><span class="lineno"> 137</span> sample_probs.push_back(prob_estimates[0]); <span class="comment">// Probability of positive class</span></div>
|
||||
<div class="line"><a id="l00138" name="l00138"></a><span class="lineno"> 138</span> } <span class="keywordflow">else</span> {</div>
|
||||
<div class="line"><a id="l00139" name="l00139"></a><span class="lineno"> 139</span> sample_probs.push_back(0.0);</div>
|
||||
<div class="line"><a id="l00140" name="l00140"></a><span class="lineno"> 140</span> }</div>
|
||||
<div class="line"><a id="l00141" name="l00141"></a><span class="lineno"> 141</span> }</div>
|
||||
<div class="line"><a id="l00142" name="l00142"></a><span class="lineno"> 142</span> } <span class="keywordflow">else</span> {</div>
|
||||
<div class="line"><a id="l00143" name="l00143"></a><span class="lineno"> 143</span> <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> j = 0; j < classes_.size(); ++j) {</div>
|
||||
<div class="line"><a id="l00144" name="l00144"></a><span class="lineno"> 144</span> <span class="keywordflow">if</span> (linear_models_[j]) {</div>
|
||||
<div class="line"><a id="l00145" name="l00145"></a><span class="lineno"> 145</span> <span class="keyword">auto</span> sample_node = converter.to_feature_node(sample);</div>
|
||||
<div class="line"><a id="l00146" name="l00146"></a><span class="lineno"> 146</span> <span class="keywordtype">double</span> prob_estimates[2];</div>
|
||||
<div class="line"><a id="l00147" name="l00147"></a><span class="lineno"> 147</span> predict_probability(linear_models_[j].get(), sample_node, prob_estimates);</div>
|
||||
<div class="line"><a id="l00148" name="l00148"></a><span class="lineno"> 148</span> sample_probs.push_back(prob_estimates[0]); <span class="comment">// Probability of positive class</span></div>
|
||||
<div class="line"><a id="l00149" name="l00149"></a><span class="lineno"> 149</span> } <span class="keywordflow">else</span> {</div>
|
||||
<div class="line"><a id="l00150" name="l00150"></a><span class="lineno"> 150</span> sample_probs.push_back(0.0);</div>
|
||||
<div class="line"><a id="l00151" name="l00151"></a><span class="lineno"> 151</span> }</div>
|
||||
<div class="line"><a id="l00152" name="l00152"></a><span class="lineno"> 152</span> }</div>
|
||||
<div class="line"><a id="l00153" name="l00153"></a><span class="lineno"> 153</span> }</div>
|
||||
<div class="line"><a id="l00154" name="l00154"></a><span class="lineno"> 154</span> </div>
|
||||
<div class="line"><a id="l00155" name="l00155"></a><span class="lineno"> 155</span> <span class="comment">// Normalize probabilities</span></div>
|
||||
<div class="line"><a id="l00156" name="l00156"></a><span class="lineno"> 156</span> <span class="keywordtype">double</span> sum = std::accumulate(sample_probs.begin(), sample_probs.end(), 0.0);</div>
|
||||
<div class="line"><a id="l00157" name="l00157"></a><span class="lineno"> 157</span> <span class="keywordflow">if</span> (sum > 0.0) {</div>
|
||||
<div class="line"><a id="l00158" name="l00158"></a><span class="lineno"> 158</span> <span class="keywordflow">for</span> (<span class="keyword">auto</span>& prob : sample_probs) {</div>
|
||||
<div class="line"><a id="l00159" name="l00159"></a><span class="lineno"> 159</span> prob /= sum;</div>
|
||||
<div class="line"><a id="l00160" name="l00160"></a><span class="lineno"> 160</span> }</div>
|
||||
<div class="line"><a id="l00161" name="l00161"></a><span class="lineno"> 161</span> } <span class="keywordflow">else</span> {</div>
|
||||
<div class="line"><a id="l00162" name="l00162"></a><span class="lineno"> 162</span> <span class="comment">// Uniform distribution if all probabilities are zero</span></div>
|
||||
<div class="line"><a id="l00163" name="l00163"></a><span class="lineno"> 163</span> std::fill(sample_probs.begin(), sample_probs.end(), 1.0 / classes_.size());</div>
|
||||
<div class="line"><a id="l00164" name="l00164"></a><span class="lineno"> 164</span> }</div>
|
||||
<div class="line"><a id="l00165" name="l00165"></a><span class="lineno"> 165</span> </div>
|
||||
<div class="line"><a id="l00166" name="l00166"></a><span class="lineno"> 166</span> probabilities.push_back(sample_probs);</div>
|
||||
<div class="line"><a id="l00167" name="l00167"></a><span class="lineno"> 167</span> }</div>
|
||||
<div class="line"><a id="l00168" name="l00168"></a><span class="lineno"> 168</span> </div>
|
||||
<div class="line"><a id="l00169" name="l00169"></a><span class="lineno"> 169</span> <span class="keywordflow">return</span> probabilities;</div>
|
||||
<div class="line"><a id="l00170" name="l00170"></a><span class="lineno"> 170</span> }</div>
|
||||
<div class="line"><a id="l00171" name="l00171"></a><span class="lineno"> 171</span> </div>
|
||||
<div class="line"><a id="l00172" name="l00172"></a><span class="lineno"> 172</span> std::vector<std::vector<double>> OneVsRestStrategy::decision_function(<span class="keyword">const</span> torch::Tensor& X,</div>
|
||||
<div class="line"><a id="l00173" name="l00173"></a><span class="lineno"> 173</span> DataConverter& converter)</div>
|
||||
<div class="line"><a id="l00174" name="l00174"></a><span class="lineno"> 174</span> {</div>
|
||||
<div class="line"><a id="l00175" name="l00175"></a><span class="lineno"> 175</span> <span class="keywordflow">if</span> (!is_trained_) {</div>
|
||||
<div class="line"><a id="l00176" name="l00176"></a><span class="lineno"> 176</span> <span class="keywordflow">throw</span> std::runtime_error(<span class="stringliteral">"Model is not trained"</span>);</div>
|
||||
<div class="line"><a id="l00177" name="l00177"></a><span class="lineno"> 177</span> }</div>
|
||||
<div class="line"><a id="l00178" name="l00178"></a><span class="lineno"> 178</span> </div>
|
||||
<div class="line"><a id="l00179" name="l00179"></a><span class="lineno"> 179</span> std::vector<std::vector<double>> decision_values;</div>
|
||||
<div class="line"><a id="l00180" name="l00180"></a><span class="lineno"> 180</span> decision_values.reserve(X.size(0));</div>
|
||||
<div class="line"><a id="l00181" name="l00181"></a><span class="lineno"> 181</span> </div>
|
||||
<div class="line"><a id="l00182" name="l00182"></a><span class="lineno"> 182</span> <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i < X.size(0); ++i) {</div>
|
||||
<div class="line"><a id="l00183" name="l00183"></a><span class="lineno"> 183</span> <span class="keyword">auto</span> sample = X[i];</div>
|
||||
<div class="line"><a id="l00184" name="l00184"></a><span class="lineno"> 184</span> std::vector<double> sample_decisions;</div>
|
||||
<div class="line"><a id="l00185" name="l00185"></a><span class="lineno"> 185</span> sample_decisions.reserve(classes_.size());</div>
|
||||
<div class="line"><a id="l00186" name="l00186"></a><span class="lineno"> 186</span> </div>
|
||||
<div class="line"><a id="l00187" name="l00187"></a><span class="lineno"> 187</span> <span class="keywordflow">if</span> (library_type_ == SVMLibrary::LIBSVM) {</div>
|
||||
<div class="line"><a id="l00188" name="l00188"></a><span class="lineno"> 188</span> <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> j = 0; j < classes_.size(); ++j) {</div>
|
||||
<div class="line"><a id="l00189" name="l00189"></a><span class="lineno"> 189</span> <span class="keywordflow">if</span> (svm_models_[j]) {</div>
|
||||
<div class="line"><a id="l00190" name="l00190"></a><span class="lineno"> 190</span> <span class="keyword">auto</span> sample_node = converter.to_svm_node(sample);</div>
|
||||
<div class="line"><a id="l00191" name="l00191"></a><span class="lineno"> 191</span> <span class="keywordtype">double</span> decision_value;</div>
|
||||
<div class="line"><a id="l00192" name="l00192"></a><span class="lineno"> 192</span> svm_predict_values(svm_models_[j].get(), sample_node, &decision_value);</div>
|
||||
<div class="line"><a id="l00193" name="l00193"></a><span class="lineno"> 193</span> sample_decisions.push_back(decision_value);</div>
|
||||
<div class="line"><a id="l00194" name="l00194"></a><span class="lineno"> 194</span> } <span class="keywordflow">else</span> {</div>
|
||||
<div class="line"><a id="l00195" name="l00195"></a><span class="lineno"> 195</span> sample_decisions.push_back(0.0);</div>
|
||||
<div class="line"><a id="l00196" name="l00196"></a><span class="lineno"> 196</span> }</div>
|
||||
<div class="line"><a id="l00197" name="l00197"></a><span class="lineno"> 197</span> }</div>
|
||||
<div class="line"><a id="l00198" name="l00198"></a><span class="lineno"> 198</span> } <span class="keywordflow">else</span> {</div>
|
||||
<div class="line"><a id="l00199" name="l00199"></a><span class="lineno"> 199</span> <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> j = 0; j < classes_.size(); ++j) {</div>
|
||||
<div class="line"><a id="l00200" name="l00200"></a><span class="lineno"> 200</span> <span class="keywordflow">if</span> (linear_models_[j]) {</div>
|
||||
<div class="line"><a id="l00201" name="l00201"></a><span class="lineno"> 201</span> <span class="keyword">auto</span> sample_node = converter.to_feature_node(sample);</div>
|
||||
<div class="line"><a id="l00202" name="l00202"></a><span class="lineno"> 202</span> <span class="keywordtype">double</span> decision_value;</div>
|
||||
<div class="line"><a id="l00203" name="l00203"></a><span class="lineno"> 203</span> predict_values(linear_models_[j].get(), sample_node, &decision_value);</div>
|
||||
<div class="line"><a id="l00204" name="l00204"></a><span class="lineno"> 204</span> sample_decisions.push_back(decision_value);</div>
|
||||
<div class="line"><a id="l00205" name="l00205"></a><span class="lineno"> 205</span> } <span class="keywordflow">else</span> {</div>
|
||||
<div class="line"><a id="l00206" name="l00206"></a><span class="lineno"> 206</span> sample_decisions.push_back(0.0);</div>
|
||||
<div class="line"><a id="l00207" name="l00207"></a><span class="lineno"> 207</span> }</div>
|
||||
<div class="line"><a id="l00208" name="l00208"></a><span class="lineno"> 208</span> }</div>
|
||||
<div class="line"><a id="l00209" name="l00209"></a><span class="lineno"> 209</span> }</div>
|
||||
<div class="line"><a id="l00210" name="l00210"></a><span class="lineno"> 210</span> </div>
|
||||
<div class="line"><a id="l00211" name="l00211"></a><span class="lineno"> 211</span> decision_values.push_back(sample_decisions);</div>
|
||||
<div class="line"><a id="l00212" name="l00212"></a><span class="lineno"> 212</span> }</div>
|
||||
<div class="line"><a id="l00213" name="l00213"></a><span class="lineno"> 213</span> </div>
|
||||
<div class="line"><a id="l00214" name="l00214"></a><span class="lineno"> 214</span> <span class="keywordflow">return</span> decision_values;</div>
|
||||
<div class="line"><a id="l00215" name="l00215"></a><span class="lineno"> 215</span> }</div>
|
||||
<div class="line"><a id="l00216" name="l00216"></a><span class="lineno"> 216</span> </div>
|
||||
<div class="line"><a id="l00217" name="l00217"></a><span class="lineno"> 217</span> <span class="keywordtype">bool</span> OneVsRestStrategy::supports_probability()<span class="keyword"> const</span></div>
|
||||
<div class="line"><a id="l00218" name="l00218"></a><span class="lineno"> 218</span><span class="keyword"> </span>{</div>
|
||||
<div class="line"><a id="l00219" name="l00219"></a><span class="lineno"> 219</span> <span class="keywordflow">if</span> (!is_trained_) {</div>
|
||||
<div class="line"><a id="l00220" name="l00220"></a><span class="lineno"> 220</span> <span class="keywordflow">return</span> params_.get_probability();</div>
|
||||
<div class="line"><a id="l00221" name="l00221"></a><span class="lineno"> 221</span> }</div>
|
||||
<div class="line"><a id="l00222" name="l00222"></a><span class="lineno"> 222</span> </div>
|
||||
<div class="line"><a id="l00223" name="l00223"></a><span class="lineno"> 223</span> <span class="comment">// Check if any model supports probability</span></div>
|
||||
<div class="line"><a id="l00224" name="l00224"></a><span class="lineno"> 224</span> <span class="keywordflow">if</span> (library_type_ == SVMLibrary::LIBSVM) {</div>
|
||||
<div class="line"><a id="l00225" name="l00225"></a><span class="lineno"> 225</span> <span class="keywordflow">for</span> (<span class="keyword">const</span> <span class="keyword">auto</span>& model : svm_models_) {</div>
|
||||
<div class="line"><a id="l00226" name="l00226"></a><span class="lineno"> 226</span> <span class="keywordflow">if</span> (model && svm_check_probability_model(model.get())) {</div>
|
||||
<div class="line"><a id="l00227" name="l00227"></a><span class="lineno"> 227</span> <span class="keywordflow">return</span> <span class="keyword">true</span>;</div>
|
||||
<div class="line"><a id="l00228" name="l00228"></a><span class="lineno"> 228</span> }</div>
|
||||
<div class="line"><a id="l00229" name="l00229"></a><span class="lineno"> 229</span> }</div>
|
||||
<div class="line"><a id="l00230" name="l00230"></a><span class="lineno"> 230</span> } <span class="keywordflow">else</span> {</div>
|
||||
<div class="line"><a id="l00231" name="l00231"></a><span class="lineno"> 231</span> <span class="keywordflow">for</span> (<span class="keyword">const</span> <span class="keyword">auto</span>& model : linear_models_) {</div>
|
||||
<div class="line"><a id="l00232" name="l00232"></a><span class="lineno"> 232</span> <span class="keywordflow">if</span> (model && check_probability_model(model.get())) {</div>
|
||||
<div class="line"><a id="l00233" name="l00233"></a><span class="lineno"> 233</span> <span class="keywordflow">return</span> <span class="keyword">true</span>;</div>
|
||||
<div class="line"><a id="l00234" name="l00234"></a><span class="lineno"> 234</span> }</div>
|
||||
<div class="line"><a id="l00235" name="l00235"></a><span class="lineno"> 235</span> }</div>
|
||||
<div class="line"><a id="l00236" name="l00236"></a><span class="lineno"> 236</span> }</div>
|
||||
<div class="line"><a id="l00237" name="l00237"></a><span class="lineno"> 237</span> </div>
|
||||
<div class="line"><a id="l00238" name="l00238"></a><span class="lineno"> 238</span> <span class="keywordflow">return</span> <span class="keyword">false</span>;</div>
|
||||
<div class="line"><a id="l00239" name="l00239"></a><span class="lineno"> 239</span> }</div>
|
||||
<div class="line"><a id="l00240" name="l00240"></a><span class="lineno"> 240</span> </div>
|
||||
<div class="line"><a id="l00241" name="l00241"></a><span class="lineno"> 241</span> torch::Tensor OneVsRestStrategy::create_binary_labels(<span class="keyword">const</span> torch::Tensor& y, <span class="keywordtype">int</span> positive_class)</div>
|
||||
<div class="line"><a id="l00242" name="l00242"></a><span class="lineno"> 242</span> {</div>
|
||||
<div class="line"><a id="l00243" name="l00243"></a><span class="lineno"> 243</span> <span class="keyword">auto</span> binary_labels = torch::ones_like(y) * (-1); <span class="comment">// Initialize with -1 (negative class)</span></div>
|
||||
<div class="line"><a id="l00244" name="l00244"></a><span class="lineno"> 244</span> <span class="keyword">auto</span> positive_mask = (y == positive_class);</div>
|
||||
<div class="line"><a id="l00245" name="l00245"></a><span class="lineno"> 245</span> binary_labels.masked_fill_(positive_mask, 1); <span class="comment">// Set positive class to +1</span></div>
|
||||
<div class="line"><a id="l00246" name="l00246"></a><span class="lineno"> 246</span> </div>
|
||||
<div class="line"><a id="l00247" name="l00247"></a><span class="lineno"> 247</span> <span class="keywordflow">return</span> binary_labels;</div>
|
||||
<div class="line"><a id="l00248" name="l00248"></a><span class="lineno"> 248</span> }</div>
|
||||
<div class="line"><a id="l00249" name="l00249"></a><span class="lineno"> 249</span> </div>
|
||||
<div class="line"><a id="l00250" name="l00250"></a><span class="lineno"> 250</span> <span class="keywordtype">double</span> OneVsRestStrategy::train_binary_classifier(<span class="keyword">const</span> torch::Tensor& X,</div>
|
||||
<div class="line"><a id="l00251" name="l00251"></a><span class="lineno"> 251</span> <span class="keyword">const</span> torch::Tensor& y_binary,</div>
|
||||
<div class="line"><a id="l00252" name="l00252"></a><span class="lineno"> 252</span> <span class="keyword">const</span> KernelParameters& params,</div>
|
||||
<div class="line"><a id="l00253" name="l00253"></a><span class="lineno"> 253</span> DataConverter& converter,</div>
|
||||
<div class="line"><a id="l00254" name="l00254"></a><span class="lineno"> 254</span> <span class="keywordtype">int</span> class_idx)</div>
|
||||
<div class="line"><a id="l00255" name="l00255"></a><span class="lineno"> 255</span> {</div>
|
||||
<div class="line"><a id="l00256" name="l00256"></a><span class="lineno"> 256</span> <span class="keyword">auto</span> start_time = std::chrono::high_resolution_clock::now();</div>
|
||||
<div class="line"><a id="l00257" name="l00257"></a><span class="lineno"> 257</span> </div>
|
||||
<div class="line"><a id="l00258" name="l00258"></a><span class="lineno"> 258</span> <span class="keywordflow">if</span> (library_type_ == SVMLibrary::LIBSVM) {</div>
|
||||
<div class="line"><a id="l00259" name="l00259"></a><span class="lineno"> 259</span> <span class="comment">// Use libsvm</span></div>
|
||||
<div class="line"><a id="l00260" name="l00260"></a><span class="lineno"> 260</span> <span class="keyword">auto</span> problem = converter.to_svm_problem(X, y_binary);</div>
|
||||
<div class="line"><a id="l00261" name="l00261"></a><span class="lineno"> 261</span> </div>
|
||||
<div class="line"><a id="l00262" name="l00262"></a><span class="lineno"> 262</span> <span class="comment">// Setup SVM parameters</span></div>
|
||||
<div class="line"><a id="l00263" name="l00263"></a><span class="lineno"> 263</span> svm_parameter svm_params;</div>
|
||||
<div class="line"><a id="l00264" name="l00264"></a><span class="lineno"> 264</span> svm_params.svm_type = C_SVC;</div>
|
||||
<div class="line"><a id="l00265" name="l00265"></a><span class="lineno"> 265</span> </div>
|
||||
<div class="line"><a id="l00266" name="l00266"></a><span class="lineno"> 266</span> <span class="keywordflow">switch</span> (params.get_kernel_type()) {</div>
|
||||
<div class="line"><a id="l00267" name="l00267"></a><span class="lineno"> 267</span> <span class="keywordflow">case</span> KernelType::RBF:</div>
|
||||
<div class="line"><a id="l00268" name="l00268"></a><span class="lineno"> 268</span> svm_params.kernel_type = RBF;</div>
|
||||
<div class="line"><a id="l00269" name="l00269"></a><span class="lineno"> 269</span> <span class="keywordflow">break</span>;</div>
|
||||
<div class="line"><a id="l00270" name="l00270"></a><span class="lineno"> 270</span> <span class="keywordflow">case</span> KernelType::POLYNOMIAL:</div>
|
||||
<div class="line"><a id="l00271" name="l00271"></a><span class="lineno"> 271</span> svm_params.kernel_type = POLY;</div>
|
||||
<div class="line"><a id="l00272" name="l00272"></a><span class="lineno"> 272</span> <span class="keywordflow">break</span>;</div>
|
||||
<div class="line"><a id="l00273" name="l00273"></a><span class="lineno"> 273</span> <span class="keywordflow">case</span> KernelType::SIGMOID:</div>
|
||||
<div class="line"><a id="l00274" name="l00274"></a><span class="lineno"> 274</span> svm_params.kernel_type = SIGMOID;</div>
|
||||
<div class="line"><a id="l00275" name="l00275"></a><span class="lineno"> 275</span> <span class="keywordflow">break</span>;</div>
|
||||
<div class="line"><a id="l00276" name="l00276"></a><span class="lineno"> 276</span> <span class="keywordflow">default</span>:</div>
|
||||
<div class="line"><a id="l00277" name="l00277"></a><span class="lineno"> 277</span> <span class="keywordflow">throw</span> std::runtime_error(<span class="stringliteral">"Invalid kernel type for libsvm"</span>);</div>
|
||||
<div class="line"><a id="l00278" name="l00278"></a><span class="lineno"> 278</span> }</div>
|
||||
<div class="line"><a id="l00279" name="l00279"></a><span class="lineno"> 279</span> </div>
|
||||
<div class="line"><a id="l00280" name="l00280"></a><span class="lineno"> 280</span> svm_params.degree = params.get_degree();</div>
|
||||
<div class="line"><a id="l00281" name="l00281"></a><span class="lineno"> 281</span> svm_params.gamma = (params.get_gamma() == -1.0) ? 1.0 / X.size(1) : params.get_gamma();</div>
|
||||
<div class="line"><a id="l00282" name="l00282"></a><span class="lineno"> 282</span> svm_params.coef0 = params.get_coef0();</div>
|
||||
<div class="line"><a id="l00283" name="l00283"></a><span class="lineno"> 283</span> svm_params.cache_size = params.get_cache_size();</div>
|
||||
<div class="line"><a id="l00284" name="l00284"></a><span class="lineno"> 284</span> svm_params.eps = params.get_tolerance();</div>
|
||||
<div class="line"><a id="l00285" name="l00285"></a><span class="lineno"> 285</span> svm_params.C = params.get_C();</div>
|
||||
<div class="line"><a id="l00286" name="l00286"></a><span class="lineno"> 286</span> svm_params.nr_weight = 0;</div>
|
||||
<div class="line"><a id="l00287" name="l00287"></a><span class="lineno"> 287</span> svm_params.weight_label = <span class="keyword">nullptr</span>;</div>
|
||||
<div class="line"><a id="l00288" name="l00288"></a><span class="lineno"> 288</span> svm_params.weight = <span class="keyword">nullptr</span>;</div>
|
||||
<div class="line"><a id="l00289" name="l00289"></a><span class="lineno"> 289</span> svm_params.nu = 0.5;</div>
|
||||
<div class="line"><a id="l00290" name="l00290"></a><span class="lineno"> 290</span> svm_params.p = 0.1;</div>
|
||||
<div class="line"><a id="l00291" name="l00291"></a><span class="lineno"> 291</span> svm_params.shrinking = 1;</div>
|
||||
<div class="line"><a id="l00292" name="l00292"></a><span class="lineno"> 292</span> svm_params.probability = params.get_probability() ? 1 : 0;</div>
|
||||
<div class="line"><a id="l00293" name="l00293"></a><span class="lineno"> 293</span> </div>
|
||||
<div class="line"><a id="l00294" name="l00294"></a><span class="lineno"> 294</span> <span class="comment">// Check parameters</span></div>
|
||||
<div class="line"><a id="l00295" name="l00295"></a><span class="lineno"> 295</span> <span class="keyword">const</span> <span class="keywordtype">char</span>* error_msg = svm_check_parameter(problem.get(), &svm_params);</div>
|
||||
<div class="line"><a id="l00296" name="l00296"></a><span class="lineno"> 296</span> <span class="keywordflow">if</span> (error_msg) {</div>
|
||||
<div class="line"><a id="l00297" name="l00297"></a><span class="lineno"> 297</span> <span class="keywordflow">throw</span> std::runtime_error(<span class="stringliteral">"SVM parameter error: "</span> + std::string(error_msg));</div>
|
||||
<div class="line"><a id="l00298" name="l00298"></a><span class="lineno"> 298</span> }</div>
|
||||
<div class="line"><a id="l00299" name="l00299"></a><span class="lineno"> 299</span> </div>
|
||||
<div class="line"><a id="l00300" name="l00300"></a><span class="lineno"> 300</span> <span class="comment">// Train model</span></div>
|
||||
<div class="line"><a id="l00301" name="l00301"></a><span class="lineno"> 301</span> <span class="keyword">auto</span> model = svm_train(problem.get(), &svm_params);</div>
|
||||
<div class="line"><a id="l00302" name="l00302"></a><span class="lineno"> 302</span> <span class="keywordflow">if</span> (!model) {</div>
|
||||
<div class="line"><a id="l00303" name="l00303"></a><span class="lineno"> 303</span> <span class="keywordflow">throw</span> std::runtime_error(<span class="stringliteral">"Failed to train SVM model"</span>);</div>
|
||||
<div class="line"><a id="l00304" name="l00304"></a><span class="lineno"> 304</span> }</div>
|
||||
<div class="line"><a id="l00305" name="l00305"></a><span class="lineno"> 305</span> </div>
|
||||
<div class="line"><a id="l00306" name="l00306"></a><span class="lineno"> 306</span> svm_models_[class_idx] = std::unique_ptr<svm_model>(model);</div>
|
||||
<div class="line"><a id="l00307" name="l00307"></a><span class="lineno"> 307</span> </div>
|
||||
<div class="line"><a id="l00308" name="l00308"></a><span class="lineno"> 308</span> } <span class="keywordflow">else</span> {</div>
|
||||
<div class="line"><a id="l00309" name="l00309"></a><span class="lineno"> 309</span> <span class="comment">// Use liblinear</span></div>
|
||||
<div class="line"><a id="l00310" name="l00310"></a><span class="lineno"> 310</span> <span class="keyword">auto</span> problem = converter.to_linear_problem(X, y_binary);</div>
|
||||
<div class="line"><a id="l00311" name="l00311"></a><span class="lineno"> 311</span> </div>
|
||||
<div class="line"><a id="l00312" name="l00312"></a><span class="lineno"> 312</span> <span class="comment">// Setup linear parameters</span></div>
|
||||
<div class="line"><a id="l00313" name="l00313"></a><span class="lineno"> 313</span> parameter linear_params;</div>
|
||||
<div class="line"><a id="l00314" name="l00314"></a><span class="lineno"> 314</span> linear_params.solver_type = L2R_L2LOSS_SVC_DUAL; <span class="comment">// Default solver for C-SVC</span></div>
|
||||
<div class="line"><a id="l00315" name="l00315"></a><span class="lineno"> 315</span> linear_params.C = params.get_C();</div>
|
||||
<div class="line"><a id="l00316" name="l00316"></a><span class="lineno"> 316</span> linear_params.eps = params.get_tolerance();</div>
|
||||
<div class="line"><a id="l00317" name="l00317"></a><span class="lineno"> 317</span> linear_params.nr_weight = 0;</div>
|
||||
<div class="line"><a id="l00318" name="l00318"></a><span class="lineno"> 318</span> linear_params.weight_label = <span class="keyword">nullptr</span>;</div>
|
||||
<div class="line"><a id="l00319" name="l00319"></a><span class="lineno"> 319</span> linear_params.weight = <span class="keyword">nullptr</span>;</div>
|
||||
<div class="line"><a id="l00320" name="l00320"></a><span class="lineno"> 320</span> linear_params.p = 0.1;</div>
|
||||
<div class="line"><a id="l00321" name="l00321"></a><span class="lineno"> 321</span> linear_params.nu = 0.5;</div>
|
||||
<div class="line"><a id="l00322" name="l00322"></a><span class="lineno"> 322</span> linear_params.init_sol = <span class="keyword">nullptr</span>;</div>
|
||||
<div class="line"><a id="l00323" name="l00323"></a><span class="lineno"> 323</span> linear_params.regularize_bias = 0;</div>
|
||||
<div class="line"><a id="l00324" name="l00324"></a><span class="lineno"> 324</span> </div>
|
||||
<div class="line"><a id="l00325" name="l00325"></a><span class="lineno"> 325</span> <span class="comment">// Check parameters</span></div>
|
||||
<div class="line"><a id="l00326" name="l00326"></a><span class="lineno"> 326</span> <span class="keyword">const</span> <span class="keywordtype">char</span>* error_msg = check_parameter(problem.get(), &linear_params);</div>
|
||||
<div class="line"><a id="l00327" name="l00327"></a><span class="lineno"> 327</span> <span class="keywordflow">if</span> (error_msg) {</div>
|
||||
<div class="line"><a id="l00328" name="l00328"></a><span class="lineno"> 328</span> <span class="keywordflow">throw</span> std::runtime_error(<span class="stringliteral">"Linear parameter error: "</span> + std::string(error_msg));</div>
|
||||
<div class="line"><a id="l00329" name="l00329"></a><span class="lineno"> 329</span> }</div>
|
||||
<div class="line"><a id="l00330" name="l00330"></a><span class="lineno"> 330</span> </div>
|
||||
<div class="line"><a id="l00331" name="l00331"></a><span class="lineno"> 331</span> <span class="comment">// Train model</span></div>
|
||||
<div class="line"><a id="l00332" name="l00332"></a><span class="lineno"> 332</span> <span class="keyword">auto</span> model = train(problem.get(), &linear_params);</div>
|
||||
<div class="line"><a id="l00333" name="l00333"></a><span class="lineno"> 333</span> <span class="keywordflow">if</span> (!model) {</div>
|
||||
<div class="line"><a id="l00334" name="l00334"></a><span class="lineno"> 334</span> <span class="keywordflow">throw</span> std::runtime_error(<span class="stringliteral">"Failed to train linear model"</span>);</div>
|
||||
<div class="line"><a id="l00335" name="l00335"></a><span class="lineno"> 335</span> }</div>
|
||||
<div class="line"><a id="l00336" name="l00336"></a><span class="lineno"> 336</span> </div>
|
||||
<div class="line"><a id="l00337" name="l00337"></a><span class="lineno"> 337</span> linear_models_[class_idx] = std::unique_ptr<::model>(model);</div>
|
||||
<div class="line"><a id="l00338" name="l00338"></a><span class="lineno"> 338</span> }</div>
|
||||
<div class="line"><a id="l00339" name="l00339"></a><span class="lineno"> 339</span> </div>
|
||||
<div class="line"><a id="l00340" name="l00340"></a><span class="lineno"> 340</span> <span class="keyword">auto</span> end_time = std::chrono::high_resolution_clock::now();</div>
|
||||
<div class="line"><a id="l00341" name="l00341"></a><span class="lineno"> 341</span> <span class="keyword">auto</span> duration = std::chrono::duration_cast<std::chrono::milliseconds>(end_time - start_time);</div>
|
||||
<div class="line"><a id="l00342" name="l00342"></a><span class="lineno"> 342</span> </div>
|
||||
<div class="line"><a id="l00343" name="l00343"></a><span class="lineno"> 343</span> <span class="keywordflow">return</span> duration.count() / 1000.0;</div>
|
||||
<div class="line"><a id="l00344" name="l00344"></a><span class="lineno"> 344</span> }</div>
|
||||
<div class="line"><a id="l00345" name="l00345"></a><span class="lineno"> 345</span> </div>
|
||||
<div class="line"><a id="l00346" name="l00346"></a><span class="lineno"> 346</span> <span class="keywordtype">void</span> OneVsRestStrategy::cleanup_models()</div>
|
||||
<div class="line"><a id="l00347" name="l00347"></a><span class="lineno"> 347</span> {</div>
|
||||
<div class="line"><a id="l00348" name="l00348"></a><span class="lineno"> 348</span> <span class="keywordflow">for</span> (<span class="keyword">auto</span>& model : svm_models_) {</div>
|
||||
<div class="line"><a id="l00349" name="l00349"></a><span class="lineno"> 349</span> <span class="keywordflow">if</span> (model) {</div>
|
||||
<div class="line"><a id="l00350" name="l00350"></a><span class="lineno"> 350</span> svm_free_and_destroy_model(&model);</div>
|
||||
<div class="line"><a id="l00351" name="l00351"></a><span class="lineno"> 351</span> }</div>
|
||||
<div class="line"><a id="l00352" name="l00352"></a><span class="lineno"> 352</span> }</div>
|
||||
<div class="line"><a id="l00353" name="l00353"></a><span class="lineno"> 353</span> svm_models_.clear();</div>
|
||||
<div class="line"><a id="l00354" name="l00354"></a><span class="lineno"> 354</span> </div>
|
||||
<div class="line"><a id="l00355" name="l00355"></a><span class="lineno"> 355</span> <span class="keywordflow">for</span> (<span class="keyword">auto</span>& model : linear_models_) {</div>
|
||||
<div class="line"><a id="l00356" name="l00356"></a><span class="lineno"> 356</span> <span class="keywordflow">if</span> (model) {</div>
|
||||
<div class="line"><a id="l00357" name="l00357"></a><span class="lineno"> 357</span> free_and_destroy_model(&model);</div>
|
||||
<div class="line"><a id="l00358" name="l00358"></a><span class="lineno"> 358</span> }</div>
|
||||
<div class="line"><a id="l00359" name="l00359"></a><span class="lineno"> 359</span> }</div>
|
||||
<div class="line"><a id="l00360" name="l00360"></a><span class="lineno"> 360</span> linear_models_.clear();</div>
|
||||
<div class="line"><a id="l00361" name="l00361"></a><span class="lineno"> 361</span> </div>
|
||||
<div class="line"><a id="l00362" name="l00362"></a><span class="lineno"> 362</span> is_trained_ = <span class="keyword">false</span>;</div>
|
||||
<div class="line"><a id="l00363" name="l00363"></a><span class="lineno"> 363</span> }</div>
|
||||
<div class="line"><a id="l00364" name="l00364"></a><span class="lineno"> 364</span> </div>
|
||||
<div class="line"><a id="l00365" name="l00365"></a><span class="lineno"> 365</span> <span class="comment">// OneVsOneStrategy Implementation</span></div>
|
||||
<div class="line"><a id="l00366" name="l00366"></a><span class="lineno"> 366</span> OneVsOneStrategy::OneVsOneStrategy()</div>
|
||||
<div class="line"><a id="l00367" name="l00367"></a><span class="lineno"> 367</span> : library_type_(SVMLibrary::LIBLINEAR)</div>
|
||||
<div class="line"><a id="l00368" name="l00368"></a><span class="lineno"> 368</span> {</div>
|
||||
<div class="line"><a id="l00369" name="l00369"></a><span class="lineno"> 369</span> }</div>
|
||||
<div class="line"><a id="l00370" name="l00370"></a><span class="lineno"> 370</span> </div>
|
||||
<div class="line"><a id="l00371" name="l00371"></a><span class="lineno"> 371</span> OneVsOneStrategy::~OneVsOneStrategy()</div>
|
||||
<div class="line"><a id="l00372" name="l00372"></a><span class="lineno"> 372</span> {</div>
|
||||
<div class="line"><a id="l00373" name="l00373"></a><span class="lineno"> 373</span> cleanup_models();</div>
|
||||
<div class="line"><a id="l00374" name="l00374"></a><span class="lineno"> 374</span> }</div>
|
||||
<div class="line"><a id="l00375" name="l00375"></a><span class="lineno"> 375</span> </div>
|
||||
<div class="line"><a id="l00376" name="l00376"></a><span class="lineno"> 376</span> TrainingMetrics OneVsOneStrategy::fit(<span class="keyword">const</span> torch::Tensor& X,</div>
|
||||
<div class="line"><a id="l00377" name="l00377"></a><span class="lineno"> 377</span> <span class="keyword">const</span> torch::Tensor& y,</div>
|
||||
<div class="line"><a id="l00378" name="l00378"></a><span class="lineno"> 378</span> <span class="keyword">const</span> KernelParameters& params,</div>
|
||||
<div class="line"><a id="l00379" name="l00379"></a><span class="lineno"> 379</span> DataConverter& converter)</div>
|
||||
<div class="line"><a id="l00380" name="l00380"></a><span class="lineno"> 380</span> {</div>
|
||||
<div class="line"><a id="l00381" name="l00381"></a><span class="lineno"> 381</span> cleanup_models();</div>
|
||||
<div class="line"><a id="l00382" name="l00382"></a><span class="lineno"> 382</span> </div>
|
||||
<div class="line"><a id="l00383" name="l00383"></a><span class="lineno"> 383</span> <span class="keyword">auto</span> start_time = std::chrono::high_resolution_clock::now();</div>
|
||||
<div class="line"><a id="l00384" name="l00384"></a><span class="lineno"> 384</span> </div>
|
||||
<div class="line"><a id="l00385" name="l00385"></a><span class="lineno"> 385</span> <span class="comment">// Store parameters and determine library type</span></div>
|
||||
<div class="line"><a id="l00386" name="l00386"></a><span class="lineno"> 386</span> params_ = params;</div>
|
||||
<div class="line"><a id="l00387" name="l00387"></a><span class="lineno"> 387</span> library_type_ = get_svm_library(params.get_kernel_type());</div>
|
||||
<div class="line"><a id="l00388" name="l00388"></a><span class="lineno"> 388</span> </div>
|
||||
<div class="line"><a id="l00389" name="l00389"></a><span class="lineno"> 389</span> <span class="comment">// Extract unique classes</span></div>
|
||||
<div class="line"><a id="l00390" name="l00390"></a><span class="lineno"> 390</span> <span class="keyword">auto</span> y_cpu = y.to(torch::kCPU);</div>
|
||||
<div class="line"><a id="l00391" name="l00391"></a><span class="lineno"> 391</span> <span class="keyword">auto</span> unique_classes_tensor = torch::unique(y_cpu);</div>
|
||||
<div class="line"><a id="l00392" name="l00392"></a><span class="lineno"> 392</span> classes_.clear();</div>
|
||||
<div class="line"><a id="l00393" name="l00393"></a><span class="lineno"> 393</span> </div>
|
||||
<div class="line"><a id="l00394" name="l00394"></a><span class="lineno"> 394</span> <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i < unique_classes_tensor.size(0); ++i) {</div>
|
||||
<div class="line"><a id="l00395" name="l00395"></a><span class="lineno"> 395</span> classes_.push_back(unique_classes_tensor[i].item<int>());</div>
|
||||
<div class="line"><a id="l00396" name="l00396"></a><span class="lineno"> 396</span> }</div>
|
||||
<div class="line"><a id="l00397" name="l00397"></a><span class="lineno"> 397</span> </div>
|
||||
<div class="line"><a id="l00398" name="l00398"></a><span class="lineno"> 398</span> std::sort(classes_.begin(), classes_.end());</div>
|
||||
<div class="line"><a id="l00399" name="l00399"></a><span class="lineno"> 399</span> </div>
|
||||
<div class="line"><a id="l00400" name="l00400"></a><span class="lineno"> 400</span> <span class="comment">// Generate all class pairs</span></div>
|
||||
<div class="line"><a id="l00401" name="l00401"></a><span class="lineno"> 401</span> class_pairs_.clear();</div>
|
||||
<div class="line"><a id="l00402" name="l00402"></a><span class="lineno"> 402</span> <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = 0; i < classes_.size(); ++i) {</div>
|
||||
<div class="line"><a id="l00403" name="l00403"></a><span class="lineno"> 403</span> <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> j = i + 1; j < classes_.size(); ++j) {</div>
|
||||
<div class="line"><a id="l00404" name="l00404"></a><span class="lineno"> 404</span> class_pairs_.emplace_back(classes_[i], classes_[j]);</div>
|
||||
<div class="line"><a id="l00405" name="l00405"></a><span class="lineno"> 405</span> }</div>
|
||||
<div class="line"><a id="l00406" name="l00406"></a><span class="lineno"> 406</span> }</div>
|
||||
<div class="line"><a id="l00407" name="l00407"></a><span class="lineno"> 407</span> </div>
|
||||
<div class="line"><a id="l00408" name="l00408"></a><span class="lineno"> 408</span> <span class="comment">// Initialize model storage</span></div>
|
||||
<div class="line"><a id="l00409" name="l00409"></a><span class="lineno"> 409</span> <span class="keywordflow">if</span> (library_type_ == SVMLibrary::LIBSVM) {</div>
|
||||
<div class="line"><a id="l00410" name="l00410"></a><span class="lineno"> 410</span> svm_models_.resize(class_pairs_.size());</div>
|
||||
<div class="line"><a id="l00411" name="l00411"></a><span class="lineno"> 411</span> } <span class="keywordflow">else</span> {</div>
|
||||
<div class="line"><a id="l00412" name="l00412"></a><span class="lineno"> 412</span> linear_models_.resize(class_pairs_.size());</div>
|
||||
<div class="line"><a id="l00413" name="l00413"></a><span class="lineno"> 413</span> }</div>
|
||||
<div class="line"><a id="l00414" name="l00414"></a><span class="lineno"> 414</span> </div>
|
||||
<div class="line"><a id="l00415" name="l00415"></a><span class="lineno"> 415</span> <span class="keywordtype">double</span> total_training_time = 0.0;</div>
|
||||
<div class="line"><a id="l00416" name="l00416"></a><span class="lineno"> 416</span> </div>
|
||||
<div class="line"><a id="l00417" name="l00417"></a><span class="lineno"> 417</span> <span class="comment">// Train one classifier for each class pair</span></div>
|
||||
<div class="line"><a id="l00418" name="l00418"></a><span class="lineno"> 418</span> <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = 0; i < class_pairs_.size(); ++i) {</div>
|
||||
<div class="line"><a id="l00419" name="l00419"></a><span class="lineno"> 419</span> <span class="keyword">auto</span> [class1, class2] = class_pairs_[i];</div>
|
||||
<div class="line"><a id="l00420" name="l00420"></a><span class="lineno"> 420</span> total_training_time += train_pairwise_classifier(X, y, class1, class2, params, converter, i);</div>
|
||||
<div class="line"><a id="l00421" name="l00421"></a><span class="lineno"> 421</span> }</div>
|
||||
<div class="line"><a id="l00422" name="l00422"></a><span class="lineno"> 422</span> </div>
|
||||
<div class="line"><a id="l00423" name="l00423"></a><span class="lineno"> 423</span> <span class="keyword">auto</span> end_time = std::chrono::high_resolution_clock::now();</div>
|
||||
<div class="line"><a id="l00424" name="l00424"></a><span class="lineno"> 424</span> <span class="keyword">auto</span> duration = std::chrono::duration_cast<std::chrono::milliseconds>(end_time - start_time);</div>
|
||||
<div class="line"><a id="l00425" name="l00425"></a><span class="lineno"> 425</span> </div>
|
||||
<div class="line"><a id="l00426" name="l00426"></a><span class="lineno"> 426</span> is_trained_ = <span class="keyword">true</span>;</div>
|
||||
<div class="line"><a id="l00427" name="l00427"></a><span class="lineno"> 427</span> </div>
|
||||
<div class="line"><a id="l00428" name="l00428"></a><span class="lineno"> 428</span> TrainingMetrics metrics;</div>
|
||||
<div class="line"><a id="l00429" name="l00429"></a><span class="lineno"> 429</span> metrics.training_time = duration.count() / 1000.0;</div>
|
||||
<div class="line"><a id="l00430" name="l00430"></a><span class="lineno"> 430</span> metrics.status = TrainingStatus::SUCCESS;</div>
|
||||
<div class="line"><a id="l00431" name="l00431"></a><span class="lineno"> 431</span> </div>
|
||||
<div class="line"><a id="l00432" name="l00432"></a><span class="lineno"> 432</span> <span class="keywordflow">return</span> metrics;</div>
|
||||
<div class="line"><a id="l00433" name="l00433"></a><span class="lineno"> 433</span> }</div>
|
||||
<div class="line"><a id="l00434" name="l00434"></a><span class="lineno"> 434</span> </div>
|
||||
<div class="line"><a id="l00435" name="l00435"></a><span class="lineno"> 435</span> std::vector<int> OneVsOneStrategy::predict(<span class="keyword">const</span> torch::Tensor& X, DataConverter& converter)</div>
|
||||
<div class="line"><a id="l00436" name="l00436"></a><span class="lineno"> 436</span> {</div>
|
||||
<div class="line"><a id="l00437" name="l00437"></a><span class="lineno"> 437</span> <span class="keywordflow">if</span> (!is_trained_) {</div>
|
||||
<div class="line"><a id="l00438" name="l00438"></a><span class="lineno"> 438</span> <span class="keywordflow">throw</span> std::runtime_error(<span class="stringliteral">"Model is not trained"</span>);</div>
|
||||
<div class="line"><a id="l00439" name="l00439"></a><span class="lineno"> 439</span> }</div>
|
||||
<div class="line"><a id="l00440" name="l00440"></a><span class="lineno"> 440</span> </div>
|
||||
<div class="line"><a id="l00441" name="l00441"></a><span class="lineno"> 441</span> <span class="keyword">auto</span> decision_values = decision_function(X, converter);</div>
|
||||
<div class="line"><a id="l00442" name="l00442"></a><span class="lineno"> 442</span> <span class="keywordflow">return</span> vote_predictions(decision_values);</div>
|
||||
<div class="line"><a id="l00443" name="l00443"></a><span class="lineno"> 443</span> }</div>
|
||||
<div class="line"><a id="l00444" name="l00444"></a><span class="lineno"> 444</span> </div>
|
||||
<div class="line"><a id="l00445" name="l00445"></a><span class="lineno"> 445</span> std::vector<std::vector<double>> OneVsOneStrategy::predict_proba(<span class="keyword">const</span> torch::Tensor& X,</div>
|
||||
<div class="line"><a id="l00446" name="l00446"></a><span class="lineno"> 446</span> DataConverter& converter)</div>
|
||||
<div class="line"><a id="l00447" name="l00447"></a><span class="lineno"> 447</span> {</div>
|
||||
<div class="line"><a id="l00448" name="l00448"></a><span class="lineno"> 448</span> <span class="comment">// OvO probability estimation is more complex and typically done via</span></div>
|
||||
<div class="line"><a id="l00449" name="l00449"></a><span class="lineno"> 449</span> <span class="comment">// pairwise coupling (Hastie & Tibshirani, 1998)</span></div>
|
||||
<div class="line"><a id="l00450" name="l00450"></a><span class="lineno"> 450</span> <span class="comment">// For simplicity, we'll use decision function values and normalize</span></div>
|
||||
<div class="line"><a id="l00451" name="l00451"></a><span class="lineno"> 451</span> </div>
|
||||
<div class="line"><a id="l00452" name="l00452"></a><span class="lineno"> 452</span> <span class="keyword">auto</span> decision_values = decision_function(X, converter);</div>
|
||||
<div class="line"><a id="l00453" name="l00453"></a><span class="lineno"> 453</span> std::vector<std::vector<double>> probabilities;</div>
|
||||
<div class="line"><a id="l00454" name="l00454"></a><span class="lineno"> 454</span> probabilities.reserve(X.size(0));</div>
|
||||
<div class="line"><a id="l00455" name="l00455"></a><span class="lineno"> 455</span> </div>
|
||||
<div class="line"><a id="l00456" name="l00456"></a><span class="lineno"> 456</span> <span class="keywordflow">for</span> (<span class="keyword">const</span> <span class="keyword">auto</span>& decision_row : decision_values) {</div>
|
||||
<div class="line"><a id="l00457" name="l00457"></a><span class="lineno"> 457</span> std::vector<double> class_scores(classes_.size(), 0.0);</div>
|
||||
<div class="line"><a id="l00458" name="l00458"></a><span class="lineno"> 458</span> </div>
|
||||
<div class="line"><a id="l00459" name="l00459"></a><span class="lineno"> 459</span> <span class="comment">// Aggregate decision values for each class</span></div>
|
||||
<div class="line"><a id="l00460" name="l00460"></a><span class="lineno"> 460</span> <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = 0; i < class_pairs_.size(); ++i) {</div>
|
||||
<div class="line"><a id="l00461" name="l00461"></a><span class="lineno"> 461</span> <span class="keyword">auto</span> [class1, class2] = class_pairs_[i];</div>
|
||||
<div class="line"><a id="l00462" name="l00462"></a><span class="lineno"> 462</span> <span class="keywordtype">double</span> decision = decision_row[i];</div>
|
||||
<div class="line"><a id="l00463" name="l00463"></a><span class="lineno"> 463</span> </div>
|
||||
<div class="line"><a id="l00464" name="l00464"></a><span class="lineno"> 464</span> <span class="keyword">auto</span> it1 = std::find(classes_.begin(), classes_.end(), class1);</div>
|
||||
<div class="line"><a id="l00465" name="l00465"></a><span class="lineno"> 465</span> <span class="keyword">auto</span> it2 = std::find(classes_.begin(), classes_.end(), class2);</div>
|
||||
<div class="line"><a id="l00466" name="l00466"></a><span class="lineno"> 466</span> </div>
|
||||
<div class="line"><a id="l00467" name="l00467"></a><span class="lineno"> 467</span> <span class="keywordflow">if</span> (it1 != classes_.end() && it2 != classes_.end()) {</div>
|
||||
<div class="line"><a id="l00468" name="l00468"></a><span class="lineno"> 468</span> <span class="keywordtype">size_t</span> idx1 = std::distance(classes_.begin(), it1);</div>
|
||||
<div class="line"><a id="l00469" name="l00469"></a><span class="lineno"> 469</span> <span class="keywordtype">size_t</span> idx2 = std::distance(classes_.begin(), it2);</div>
|
||||
<div class="line"><a id="l00470" name="l00470"></a><span class="lineno"> 470</span> </div>
|
||||
<div class="line"><a id="l00471" name="l00471"></a><span class="lineno"> 471</span> <span class="keywordflow">if</span> (decision > 0) {</div>
|
||||
<div class="line"><a id="l00472" name="l00472"></a><span class="lineno"> 472</span> class_scores[idx1] += 1.0;</div>
|
||||
<div class="line"><a id="l00473" name="l00473"></a><span class="lineno"> 473</span> } <span class="keywordflow">else</span> {</div>
|
||||
<div class="line"><a id="l00474" name="l00474"></a><span class="lineno"> 474</span> class_scores[idx2] += 1.0;</div>
|
||||
<div class="line"><a id="l00475" name="l00475"></a><span class="lineno"> 475</span> }</div>
|
||||
<div class="line"><a id="l00476" name="l00476"></a><span class="lineno"> 476</span> }</div>
|
||||
<div class="line"><a id="l00477" name="l00477"></a><span class="lineno"> 477</span> }</div>
|
||||
<div class="line"><a id="l00478" name="l00478"></a><span class="lineno"> 478</span> </div>
|
||||
<div class="line"><a id="l00479" name="l00479"></a><span class="lineno"> 479</span> <span class="comment">// Convert scores to probabilities</span></div>
|
||||
<div class="line"><a id="l00480" name="l00480"></a><span class="lineno"> 480</span> <span class="keywordtype">double</span> sum = std::accumulate(class_scores.begin(), class_scores.end(), 0.0);</div>
|
||||
<div class="line"><a id="l00481" name="l00481"></a><span class="lineno"> 481</span> <span class="keywordflow">if</span> (sum > 0.0) {</div>
|
||||
<div class="line"><a id="l00482" name="l00482"></a><span class="lineno"> 482</span> <span class="keywordflow">for</span> (<span class="keyword">auto</span>& score : class_scores) {</div>
|
||||
<div class="line"><a id="l00483" name="l00483"></a><span class="lineno"> 483</span> score /= sum;</div>
|
||||
<div class="line"><a id="l00484" name="l00484"></a><span class="lineno"> 484</span> }</div>
|
||||
<div class="line"><a id="l00485" name="l00485"></a><span class="lineno"> 485</span> } <span class="keywordflow">else</span> {</div>
|
||||
<div class="line"><a id="l00486" name="l00486"></a><span class="lineno"> 486</span> std::fill(class_scores.begin(), class_scores.end(), 1.0 / classes_.size());</div>
|
||||
<div class="line"><a id="l00487" name="l00487"></a><span class="lineno"> 487</span> }</div>
|
||||
<div class="line"><a id="l00488" name="l00488"></a><span class="lineno"> 488</span> </div>
|
||||
<div class="line"><a id="l00489" name="l00489"></a><span class="lineno"> 489</span> probabilities.push_back(class_scores);</div>
|
||||
<div class="line"><a id="l00490" name="l00490"></a><span class="lineno"> 490</span> }</div>
|
||||
<div class="line"><a id="l00491" name="l00491"></a><span class="lineno"> 491</span> </div>
|
||||
<div class="line"><a id="l00492" name="l00492"></a><span class="lineno"> 492</span> <span class="keywordflow">return</span> probabilities;</div>
|
||||
<div class="line"><a id="l00493" name="l00493"></a><span class="lineno"> 493</span> }</div>
|
||||
<div class="line"><a id="l00494" name="l00494"></a><span class="lineno"> 494</span> </div>
|
||||
<div class="line"><a id="l00495" name="l00495"></a><span class="lineno"> 495</span> std::vector<std::vector<double>> OneVsOneStrategy::decision_function(<span class="keyword">const</span> torch::Tensor& X,</div>
|
||||
<div class="ttc" id="aclasssvm__classifier_1_1OneVsRestStrategy_html_a30f146a564a9c9681524593cacbb43e7"><div class="ttname"><a href="classsvm__classifier_1_1OneVsRestStrategy.html#a30f146a564a9c9681524593cacbb43e7">svm_classifier::OneVsRestStrategy::OneVsRestStrategy</a></div><div class="ttdeci">OneVsRestStrategy()</div><div class="ttdoc">Constructor.</div></div>
|
||||
</div><!-- fragment --></div><!-- contents -->
|
||||
<!-- start footer part -->
|
||||
<hr class="footer"/><address class="footer"><small>
|
||||
Generated on Sun Jun 22 2025 11:25:27 for SVM Classifier C++ by <a href="https://www.doxygen.org/index.html"><img class="footer" src="doxygen.svg" width="104" height="31" alt="doxygen"/></a> 1.9.8
|
||||
</small></address>
|
||||
</body>
|
||||
</html>
|
||||
Reference in New Issue
Block a user