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<div id="projectname">SVM Classifier C++<span id="projectnumber"> 1.0.0</span>
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<div id="projectbrief">High-performance Support Vector Machine classifier with scikit-learn compatible API</div>
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<div class="headertitle"><div class="title">Class List</div></div>
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<div class="textblock">Here are the classes, structs, unions and interfaces with brief descriptions:</div><div class="directory">
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<tr id="row_0_" class="even"><td class="entry"><span style="width:0px;display:inline-block;"> </span><span id="arr_0_" class="arrow" onclick="toggleFolder('0_')">▼</span><span class="icona"><span class="icon">N</span></span><b>svm_classifier</b></td><td class="desc"></td></tr>
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<tr id="row_0_0_" class="odd"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classsvm__classifier_1_1DataConverter.html" target="_self">DataConverter</a></td><td class="desc">Data converter between libtorch tensors and SVM library formats </td></tr>
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<tr id="row_0_1_" class="even"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structsvm__classifier_1_1EvaluationMetrics.html" target="_self">EvaluationMetrics</a></td><td class="desc">Model evaluation metrics </td></tr>
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<tr id="row_0_2_" class="odd"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classsvm__classifier_1_1MulticlassStrategyBase.html" target="_self">MulticlassStrategyBase</a></td><td class="desc">Abstract base class for multiclass classification strategies </td></tr>
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<tr id="row_0_3_" class="even"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classsvm__classifier_1_1OneVsOneStrategy.html" target="_self">OneVsOneStrategy</a></td><td class="desc">One-vs-One (OvO) multiclass strategy </td></tr>
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<tr id="row_0_4_" class="odd"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classsvm__classifier_1_1OneVsRestStrategy.html" target="_self">OneVsRestStrategy</a></td><td class="desc">One-vs-Rest (OvR) multiclass strategy </td></tr>
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<tr id="row_0_5_" class="even"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structsvm__classifier_1_1PredictionResult.html" target="_self">PredictionResult</a></td><td class="desc">Prediction result structure </td></tr>
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<tr id="row_0_6_" class="odd"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classsvm__classifier_1_1SVMClassifier.html" target="_self">SVMClassifier</a></td><td class="desc">Support Vector Machine Classifier with scikit-learn compatible API </td></tr>
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<tr id="row_0_7_" class="even"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structsvm__classifier_1_1TrainingMetrics.html" target="_self">TrainingMetrics</a></td><td class="desc">Training metrics structure </td></tr>
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</table>
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</div><!-- directory -->
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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
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<tr id="projectrow">
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<div id="projectname">SVM Classifier C++<span id="projectnumber"> 1.0.0</span>
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||||||
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</div>
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||||||
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<div id="projectbrief">High-performance Support Vector Machine classifier with scikit-learn compatible API</div>
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||||||
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</td>
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||||||
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</tr>
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<div class="qindex"><a class="qindex" href="#letter_D">D</a> | <a class="qindex" href="#letter_E">E</a> | <a class="qindex" href="#letter_M">M</a> | <a class="qindex" href="#letter_O">O</a> | <a class="qindex" href="#letter_P">P</a> | <a class="qindex" href="#letter_S">S</a> | <a class="qindex" href="#letter_T">T</a></div>
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<dd><a class="el" href="classsvm__classifier_1_1DataConverter.html">DataConverter</a> (svm_classifier)</dd></dl>
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<dt class="alphachar"><a id="letter_M" name="letter_M">M</a></dt>
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<dt class="alphachar"><a id="letter_O" name="letter_O">O</a></dt>
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<dd><a class="el" href="classsvm__classifier_1_1OneVsOneStrategy.html">OneVsOneStrategy</a> (svm_classifier)</dd><dd><a class="el" href="classsvm__classifier_1_1OneVsRestStrategy.html">OneVsRestStrategy</a> (svm_classifier)</dd></dl>
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<dt class="alphachar"><a id="letter_S" name="letter_S">S</a></dt>
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<dd><a class="el" href="structsvm__classifier_1_1TrainingMetrics.html">TrainingMetrics</a> (svm_classifier)</dd></dl>
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<div id="projectbrief">High-performance Support Vector Machine classifier with scikit-learn compatible API</div>
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||||||
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</td>
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</tr>
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<li class="navelem"><b>svm_classifier</b></li><li class="navelem"><a class="el" href="classsvm__classifier_1_1DataConverter.html">DataConverter</a></li> </ul>
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</div><!-- top -->
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<div class="headertitle"><div class="title">svm_classifier::DataConverter Member List</div></div>
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</div><!--header-->
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<div class="contents">
|
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<p>This is the complete list of members for <a class="el" href="classsvm__classifier_1_1DataConverter.html">svm_classifier::DataConverter</a>, including all inherited members.</p>
|
||||||
|
<table class="directory">
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|
<tr class="even"><td class="entry"><a class="el" href="classsvm__classifier_1_1DataConverter.html#a46d12ba28c4c5bf6e0fad1122c621fa8">cleanup</a>()</td><td class="entry"><a class="el" href="classsvm__classifier_1_1DataConverter.html">svm_classifier::DataConverter</a></td><td class="entry"></td></tr>
|
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<tr class="odd"><td class="entry"><a class="el" href="classsvm__classifier_1_1DataConverter.html#a46d12ba28c4c5bf6e0fad1122c621fa8">cleanup</a>()</td><td class="entry"><a class="el" href="classsvm__classifier_1_1DataConverter.html">svm_classifier::DataConverter</a></td><td class="entry"></td></tr>
|
||||||
|
<tr class="even"><td class="entry"><a class="el" href="classsvm__classifier_1_1DataConverter.html#a5874904555f26448ed5ae4cf6f370056">DataConverter</a>()</td><td class="entry"><a class="el" href="classsvm__classifier_1_1DataConverter.html">svm_classifier::DataConverter</a></td><td class="entry"></td></tr>
|
||||||
|
<tr class="odd"><td class="entry"><a class="el" href="classsvm__classifier_1_1DataConverter.html#a5874904555f26448ed5ae4cf6f370056">DataConverter</a>()</td><td class="entry"><a class="el" href="classsvm__classifier_1_1DataConverter.html">svm_classifier::DataConverter</a></td><td class="entry"></td></tr>
|
||||||
|
<tr class="even"><td class="entry"><a class="el" href="classsvm__classifier_1_1DataConverter.html#a503eba54e8bb1f370e04b6e24354a32f">from_decision_values</a>(const std::vector< std::vector< double > > &decision_values)</td><td class="entry"><a class="el" href="classsvm__classifier_1_1DataConverter.html">svm_classifier::DataConverter</a></td><td class="entry"></td></tr>
|
||||||
|
<tr class="odd"><td class="entry"><a class="el" href="classsvm__classifier_1_1DataConverter.html#a503eba54e8bb1f370e04b6e24354a32f">from_decision_values</a>(const std::vector< std::vector< double > > &decision_values)</td><td class="entry"><a class="el" href="classsvm__classifier_1_1DataConverter.html">svm_classifier::DataConverter</a></td><td class="entry"></td></tr>
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|
<tr class="even"><td class="entry"><a class="el" href="classsvm__classifier_1_1DataConverter.html#ab3e800a5016a915e9912d5873bb48741">from_predictions</a>(const std::vector< double > &predictions)</td><td class="entry"><a class="el" href="classsvm__classifier_1_1DataConverter.html">svm_classifier::DataConverter</a></td><td class="entry"></td></tr>
|
||||||
|
<tr class="odd"><td class="entry"><a class="el" href="classsvm__classifier_1_1DataConverter.html#ab3e800a5016a915e9912d5873bb48741">from_predictions</a>(const std::vector< double > &predictions)</td><td class="entry"><a class="el" href="classsvm__classifier_1_1DataConverter.html">svm_classifier::DataConverter</a></td><td class="entry"></td></tr>
|
||||||
|
<tr class="even"><td class="entry"><a class="el" href="classsvm__classifier_1_1DataConverter.html#a5460485675613c54596418af3d5057ff">from_probabilities</a>(const std::vector< std::vector< double > > &probabilities)</td><td class="entry"><a class="el" href="classsvm__classifier_1_1DataConverter.html">svm_classifier::DataConverter</a></td><td class="entry"></td></tr>
|
||||||
|
<tr class="odd"><td class="entry"><a class="el" href="classsvm__classifier_1_1DataConverter.html#a5460485675613c54596418af3d5057ff">from_probabilities</a>(const std::vector< std::vector< double > > &probabilities)</td><td class="entry"><a class="el" href="classsvm__classifier_1_1DataConverter.html">svm_classifier::DataConverter</a></td><td class="entry"></td></tr>
|
||||||
|
<tr class="even"><td class="entry"><a class="el" href="classsvm__classifier_1_1DataConverter.html#a26342a8cc8b943f099112040aa960ae6">get_n_features</a>() const</td><td class="entry"><a class="el" href="classsvm__classifier_1_1DataConverter.html">svm_classifier::DataConverter</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
|
||||||
|
<tr class="odd"><td class="entry"><a class="el" href="classsvm__classifier_1_1DataConverter.html#a26342a8cc8b943f099112040aa960ae6">get_n_features</a>() const</td><td class="entry"><a class="el" href="classsvm__classifier_1_1DataConverter.html">svm_classifier::DataConverter</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
|
||||||
|
<tr class="even"><td class="entry"><a class="el" href="classsvm__classifier_1_1DataConverter.html#a16999ded27bc2d42d2ebd9551b00c1cb">get_n_samples</a>() const</td><td class="entry"><a class="el" href="classsvm__classifier_1_1DataConverter.html">svm_classifier::DataConverter</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
|
||||||
|
<tr class="odd"><td class="entry"><a class="el" href="classsvm__classifier_1_1DataConverter.html#a16999ded27bc2d42d2ebd9551b00c1cb">get_n_samples</a>() const</td><td class="entry"><a class="el" href="classsvm__classifier_1_1DataConverter.html">svm_classifier::DataConverter</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
|
||||||
|
<tr class="even"><td class="entry"><a class="el" href="classsvm__classifier_1_1DataConverter.html#a1905f60fef9ffd3a8e8e45e41395352d">get_sparse_threshold</a>() const</td><td class="entry"><a class="el" href="classsvm__classifier_1_1DataConverter.html">svm_classifier::DataConverter</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
|
||||||
|
<tr class="odd"><td class="entry"><a class="el" href="classsvm__classifier_1_1DataConverter.html#a1905f60fef9ffd3a8e8e45e41395352d">get_sparse_threshold</a>() const</td><td class="entry"><a class="el" href="classsvm__classifier_1_1DataConverter.html">svm_classifier::DataConverter</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
|
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|
<tr class="even"><td class="entry"><a class="el" href="classsvm__classifier_1_1DataConverter.html#a9259322e54d2477478a684e99d8e557a">set_sparse_threshold</a>(double threshold)</td><td class="entry"><a class="el" href="classsvm__classifier_1_1DataConverter.html">svm_classifier::DataConverter</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
|
||||||
|
<tr class="odd"><td class="entry"><a class="el" href="classsvm__classifier_1_1DataConverter.html#a9259322e54d2477478a684e99d8e557a">set_sparse_threshold</a>(double threshold)</td><td class="entry"><a class="el" href="classsvm__classifier_1_1DataConverter.html">svm_classifier::DataConverter</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
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||||||
|
<tr class="even"><td class="entry"><a class="el" href="classsvm__classifier_1_1DataConverter.html#a6bb2b4565b27df0db5f229dbd380795e">to_feature_node</a>(const torch::Tensor &sample)</td><td class="entry"><a class="el" href="classsvm__classifier_1_1DataConverter.html">svm_classifier::DataConverter</a></td><td class="entry"></td></tr>
|
||||||
|
<tr class="odd"><td class="entry"><a class="el" href="classsvm__classifier_1_1DataConverter.html#a6bb2b4565b27df0db5f229dbd380795e">to_feature_node</a>(const torch::Tensor &sample)</td><td class="entry"><a class="el" href="classsvm__classifier_1_1DataConverter.html">svm_classifier::DataConverter</a></td><td class="entry"></td></tr>
|
||||||
|
<tr class="even"><td class="entry"><a class="el" href="classsvm__classifier_1_1DataConverter.html#a7e7d8f6102b7a9b3256ff0dc6f536a35">to_linear_problem</a>(const torch::Tensor &X, const torch::Tensor &y=torch::Tensor())</td><td class="entry"><a class="el" href="classsvm__classifier_1_1DataConverter.html">svm_classifier::DataConverter</a></td><td class="entry"></td></tr>
|
||||||
|
<tr class="odd"><td class="entry"><a class="el" href="classsvm__classifier_1_1DataConverter.html#a7e7d8f6102b7a9b3256ff0dc6f536a35">to_linear_problem</a>(const torch::Tensor &X, const torch::Tensor &y=torch::Tensor())</td><td class="entry"><a class="el" href="classsvm__classifier_1_1DataConverter.html">svm_classifier::DataConverter</a></td><td class="entry"></td></tr>
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|
<tr class="even"><td class="entry"><a class="el" href="classsvm__classifier_1_1DataConverter.html#a16e1539ef1266ca9ddd27a2ac5a53b92">to_svm_node</a>(const torch::Tensor &sample)</td><td class="entry"><a class="el" href="classsvm__classifier_1_1DataConverter.html">svm_classifier::DataConverter</a></td><td class="entry"></td></tr>
|
||||||
|
<tr class="odd"><td class="entry"><a class="el" href="classsvm__classifier_1_1DataConverter.html#a16e1539ef1266ca9ddd27a2ac5a53b92">to_svm_node</a>(const torch::Tensor &sample)</td><td class="entry"><a class="el" href="classsvm__classifier_1_1DataConverter.html">svm_classifier::DataConverter</a></td><td class="entry"></td></tr>
|
||||||
|
<tr class="even"><td class="entry"><a class="el" href="classsvm__classifier_1_1DataConverter.html#a66f446e4decfe47bbba37c789f03f729">to_svm_problem</a>(const torch::Tensor &X, const torch::Tensor &y=torch::Tensor())</td><td class="entry"><a class="el" href="classsvm__classifier_1_1DataConverter.html">svm_classifier::DataConverter</a></td><td class="entry"></td></tr>
|
||||||
|
<tr class="odd"><td class="entry"><a class="el" href="classsvm__classifier_1_1DataConverter.html#a66f446e4decfe47bbba37c789f03f729">to_svm_problem</a>(const torch::Tensor &X, const torch::Tensor &y=torch::Tensor())</td><td class="entry"><a class="el" href="classsvm__classifier_1_1DataConverter.html">svm_classifier::DataConverter</a></td><td class="entry"></td></tr>
|
||||||
|
<tr class="even"><td class="entry"><a class="el" href="classsvm__classifier_1_1DataConverter.html#aa0615f3de29958b2c5229d349f2f60ce">validate_tensors</a>(const torch::Tensor &X, const torch::Tensor &y=torch::Tensor())</td><td class="entry"><a class="el" href="classsvm__classifier_1_1DataConverter.html">svm_classifier::DataConverter</a></td><td class="entry"></td></tr>
|
||||||
|
<tr class="odd"><td class="entry"><a class="el" href="classsvm__classifier_1_1DataConverter.html#aa0615f3de29958b2c5229d349f2f60ce">validate_tensors</a>(const torch::Tensor &X, const torch::Tensor &y=torch::Tensor())</td><td class="entry"><a class="el" href="classsvm__classifier_1_1DataConverter.html">svm_classifier::DataConverter</a></td><td class="entry"></td></tr>
|
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<tr class="even"><td class="entry"><a class="el" href="classsvm__classifier_1_1DataConverter.html#ac3af2c9c03cffe2968f29147611e333d">~DataConverter</a>()</td><td class="entry"><a class="el" href="classsvm__classifier_1_1DataConverter.html">svm_classifier::DataConverter</a></td><td class="entry"></td></tr>
|
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<tr class="odd"><td class="entry"><a class="el" href="classsvm__classifier_1_1DataConverter.html#ac3af2c9c03cffe2968f29147611e333d">~DataConverter</a>()</td><td class="entry"><a class="el" href="classsvm__classifier_1_1DataConverter.html">svm_classifier::DataConverter</a></td><td class="entry"></td></tr>
|
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</table></div><!-- contents -->
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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
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<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "https://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
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<div id="top"><!-- do not remove this div, it is closed by doxygen! -->
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<tbody>
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<td id="projectalign">
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<div id="projectname">SVM Classifier C++<span id="projectnumber"> 1.0.0</span>
|
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</div>
|
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<div id="projectbrief">High-performance Support Vector Machine classifier with scikit-learn compatible API</div>
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</td>
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<div id="nav-path" class="navpath">
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<ul>
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<li class="navelem"><b>svm_classifier</b></li><li class="navelem"><a class="el" href="classsvm__classifier_1_1MulticlassStrategyBase.html">MulticlassStrategyBase</a></li> </ul>
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</div><!-- top -->
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<div class="header">
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<div class="headertitle"><div class="title">svm_classifier::MulticlassStrategyBase Member List</div></div>
|
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|
</div><!--header-->
|
||||||
|
<div class="contents">
|
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|
|
||||||
|
<p>This is the complete list of members for <a class="el" href="classsvm__classifier_1_1MulticlassStrategyBase.html">svm_classifier::MulticlassStrategyBase</a>, including all inherited members.</p>
|
||||||
|
<table class="directory">
|
||||||
|
<tr class="even"><td class="entry"><a class="el" href="classsvm__classifier_1_1MulticlassStrategyBase.html#a15bb6eb53e91e604b259b3050bd40e27">classes_</a></td><td class="entry"><a class="el" href="classsvm__classifier_1_1MulticlassStrategyBase.html">svm_classifier::MulticlassStrategyBase</a></td><td class="entry"><span class="mlabel">protected</span></td></tr>
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|
<tr class="odd"><td class="entry"><a class="el" href="classsvm__classifier_1_1MulticlassStrategyBase.html#ad1c4eb746cb1fdd67cf436ff85a9b0f0">decision_function</a>(const torch::Tensor &X, DataConverter &converter)=0</td><td class="entry"><a class="el" href="classsvm__classifier_1_1MulticlassStrategyBase.html">svm_classifier::MulticlassStrategyBase</a></td><td class="entry"><span class="mlabel">pure virtual</span></td></tr>
|
||||||
|
<tr class="even"><td class="entry"><a class="el" href="classsvm__classifier_1_1MulticlassStrategyBase.html#a548af7201b7970abee0c31e7ec07d896">fit</a>(const torch::Tensor &X, const torch::Tensor &y, const KernelParameters &params, DataConverter &converter)=0</td><td class="entry"><a class="el" href="classsvm__classifier_1_1MulticlassStrategyBase.html">svm_classifier::MulticlassStrategyBase</a></td><td class="entry"><span class="mlabel">pure virtual</span></td></tr>
|
||||||
|
<tr class="odd"><td class="entry"><a class="el" href="classsvm__classifier_1_1MulticlassStrategyBase.html#a379c4000227cc46410bfbecce6e80c33">get_classes</a>() const =0</td><td class="entry"><a class="el" href="classsvm__classifier_1_1MulticlassStrategyBase.html">svm_classifier::MulticlassStrategyBase</a></td><td class="entry"><span class="mlabel">pure virtual</span></td></tr>
|
||||||
|
<tr class="even"><td class="entry"><a class="el" href="classsvm__classifier_1_1MulticlassStrategyBase.html#a1740d877a4d634ec1763cb8646f5e172">get_n_classes</a>() const =0</td><td class="entry"><a class="el" href="classsvm__classifier_1_1MulticlassStrategyBase.html">svm_classifier::MulticlassStrategyBase</a></td><td class="entry"><span class="mlabel">pure virtual</span></td></tr>
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|
<tr class="odd"><td class="entry"><a class="el" href="classsvm__classifier_1_1MulticlassStrategyBase.html#a31a0501fa1a6db1d41cbf825b2348e47">get_strategy_type</a>() const =0</td><td class="entry"><a class="el" href="classsvm__classifier_1_1MulticlassStrategyBase.html">svm_classifier::MulticlassStrategyBase</a></td><td class="entry"><span class="mlabel">pure virtual</span></td></tr>
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||||||
|
<tr class="even"><td class="entry"><a class="el" href="classsvm__classifier_1_1MulticlassStrategyBase.html#a8e74cd580feaac0da34d204274a24fea">is_trained_</a></td><td class="entry"><a class="el" href="classsvm__classifier_1_1MulticlassStrategyBase.html">svm_classifier::MulticlassStrategyBase</a></td><td class="entry"><span class="mlabel">protected</span></td></tr>
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<tr class="odd"><td class="entry"><a class="el" href="classsvm__classifier_1_1MulticlassStrategyBase.html#a70f94cfcf8b2bf6d60133c688fe55f9d">predict</a>(const torch::Tensor &X, DataConverter &converter)=0</td><td class="entry"><a class="el" href="classsvm__classifier_1_1MulticlassStrategyBase.html">svm_classifier::MulticlassStrategyBase</a></td><td class="entry"><span class="mlabel">pure virtual</span></td></tr>
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<tr class="even"><td class="entry"><a class="el" href="classsvm__classifier_1_1MulticlassStrategyBase.html#ab5348ee3b83547702ec7903ee7ee2da7">predict_proba</a>(const torch::Tensor &X, DataConverter &converter)=0</td><td class="entry"><a class="el" href="classsvm__classifier_1_1MulticlassStrategyBase.html">svm_classifier::MulticlassStrategyBase</a></td><td class="entry"><span class="mlabel">pure virtual</span></td></tr>
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<tr class="odd"><td class="entry"><a class="el" href="classsvm__classifier_1_1MulticlassStrategyBase.html#a2ab91902f8d6eb216f626ce9ea4be992">supports_probability</a>() const =0</td><td class="entry"><a class="el" href="classsvm__classifier_1_1MulticlassStrategyBase.html">svm_classifier::MulticlassStrategyBase</a></td><td class="entry"><span class="mlabel">pure virtual</span></td></tr>
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<tr class="even"><td class="entry"><a class="el" href="classsvm__classifier_1_1MulticlassStrategyBase.html#a8a5647dd57eed281288f0c9011b11395">~MulticlassStrategyBase</a>()=default</td><td class="entry"><a class="el" href="classsvm__classifier_1_1MulticlassStrategyBase.html">svm_classifier::MulticlassStrategyBase</a></td><td class="entry"><span class="mlabel">virtual</span></td></tr>
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<div id="projectbrief">High-performance Support Vector Machine classifier with scikit-learn compatible API</div>
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<li class="navelem"><b>svm_classifier</b></li><li class="navelem"><a class="el" href="classsvm__classifier_1_1MulticlassStrategyBase.html">MulticlassStrategyBase</a></li> </ul>
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<a href="#pub-methods">Public Member Functions</a> |
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<a href="#pro-attribs">Protected Attributes</a> |
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<a href="classsvm__classifier_1_1MulticlassStrategyBase-members.html">List of all members</a> </div>
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<div class="headertitle"><div class="title">svm_classifier::MulticlassStrategyBase Class Reference<span class="mlabels"><span class="mlabel">abstract</span></span></div></div>
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<p>Abstract base class for multiclass classification strategies.
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<a href="classsvm__classifier_1_1MulticlassStrategyBase.html#details">More...</a></p>
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<p><code>#include <<a class="el" href="multiclass__strategy_8hpp_source.html">multiclass_strategy.hpp</a>></code></p>
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Inheritance diagram for svm_classifier::MulticlassStrategyBase:</div>
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Public Member Functions</h2></td></tr>
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<tr class="memitem:a8a5647dd57eed281288f0c9011b11395" id="r_a8a5647dd57eed281288f0c9011b11395"><td class="memItemLeft" align="right" valign="top"><a id="a8a5647dd57eed281288f0c9011b11395" name="a8a5647dd57eed281288f0c9011b11395"></a>
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virtual </td><td class="memItemRight" valign="bottom"><b>~MulticlassStrategyBase</b> ()=default</td></tr>
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<tr class="memdesc:a8a5647dd57eed281288f0c9011b11395"><td class="mdescLeft"> </td><td class="mdescRight">Virtual destructor. <br /></td></tr>
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<tr class="separator:a8a5647dd57eed281288f0c9011b11395"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:a548af7201b7970abee0c31e7ec07d896" id="r_a548af7201b7970abee0c31e7ec07d896"><td class="memItemLeft" align="right" valign="top">virtual <a class="el" href="structsvm__classifier_1_1TrainingMetrics.html">TrainingMetrics</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="classsvm__classifier_1_1MulticlassStrategyBase.html#a548af7201b7970abee0c31e7ec07d896">fit</a> (const torch::Tensor &X, const torch::Tensor &y, const KernelParameters &params, <a class="el" href="classsvm__classifier_1_1DataConverter.html">DataConverter</a> &converter)=0</td></tr>
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||||||
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<tr class="memdesc:a548af7201b7970abee0c31e7ec07d896"><td class="mdescLeft"> </td><td class="mdescRight">Train the multiclass classifier. <br /></td></tr>
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<tr class="separator:a548af7201b7970abee0c31e7ec07d896"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:a70f94cfcf8b2bf6d60133c688fe55f9d" id="r_a70f94cfcf8b2bf6d60133c688fe55f9d"><td class="memItemLeft" align="right" valign="top">virtual std::vector< int > </td><td class="memItemRight" valign="bottom"><a class="el" href="classsvm__classifier_1_1MulticlassStrategyBase.html#a70f94cfcf8b2bf6d60133c688fe55f9d">predict</a> (const torch::Tensor &X, <a class="el" href="classsvm__classifier_1_1DataConverter.html">DataConverter</a> &converter)=0</td></tr>
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<tr class="memdesc:a70f94cfcf8b2bf6d60133c688fe55f9d"><td class="mdescLeft"> </td><td class="mdescRight">Predict class labels. <br /></td></tr>
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<tr class="separator:a70f94cfcf8b2bf6d60133c688fe55f9d"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:ab5348ee3b83547702ec7903ee7ee2da7" id="r_ab5348ee3b83547702ec7903ee7ee2da7"><td class="memItemLeft" align="right" valign="top">virtual std::vector< std::vector< double > > </td><td class="memItemRight" valign="bottom"><a class="el" href="classsvm__classifier_1_1MulticlassStrategyBase.html#ab5348ee3b83547702ec7903ee7ee2da7">predict_proba</a> (const torch::Tensor &X, <a class="el" href="classsvm__classifier_1_1DataConverter.html">DataConverter</a> &converter)=0</td></tr>
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<tr class="memdesc:ab5348ee3b83547702ec7903ee7ee2da7"><td class="mdescLeft"> </td><td class="mdescRight">Predict class probabilities. <br /></td></tr>
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<tr class="separator:ab5348ee3b83547702ec7903ee7ee2da7"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:ad1c4eb746cb1fdd67cf436ff85a9b0f0" id="r_ad1c4eb746cb1fdd67cf436ff85a9b0f0"><td class="memItemLeft" align="right" valign="top">virtual std::vector< std::vector< double > > </td><td class="memItemRight" valign="bottom"><a class="el" href="classsvm__classifier_1_1MulticlassStrategyBase.html#ad1c4eb746cb1fdd67cf436ff85a9b0f0">decision_function</a> (const torch::Tensor &X, <a class="el" href="classsvm__classifier_1_1DataConverter.html">DataConverter</a> &converter)=0</td></tr>
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||||||
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<tr class="memdesc:ad1c4eb746cb1fdd67cf436ff85a9b0f0"><td class="mdescLeft"> </td><td class="mdescRight">Get decision function values. <br /></td></tr>
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<tr class="separator:ad1c4eb746cb1fdd67cf436ff85a9b0f0"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:a379c4000227cc46410bfbecce6e80c33" id="r_a379c4000227cc46410bfbecce6e80c33"><td class="memItemLeft" align="right" valign="top">virtual std::vector< int > </td><td class="memItemRight" valign="bottom"><a class="el" href="classsvm__classifier_1_1MulticlassStrategyBase.html#a379c4000227cc46410bfbecce6e80c33">get_classes</a> () const =0</td></tr>
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<tr class="memdesc:a379c4000227cc46410bfbecce6e80c33"><td class="mdescLeft"> </td><td class="mdescRight">Get unique class labels. <br /></td></tr>
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<tr class="separator:a379c4000227cc46410bfbecce6e80c33"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:a2ab91902f8d6eb216f626ce9ea4be992" id="r_a2ab91902f8d6eb216f626ce9ea4be992"><td class="memItemLeft" align="right" valign="top">virtual bool </td><td class="memItemRight" valign="bottom"><a class="el" href="classsvm__classifier_1_1MulticlassStrategyBase.html#a2ab91902f8d6eb216f626ce9ea4be992">supports_probability</a> () const =0</td></tr>
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<tr class="memdesc:a2ab91902f8d6eb216f626ce9ea4be992"><td class="mdescLeft"> </td><td class="mdescRight">Check if the model supports probability prediction. <br /></td></tr>
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<tr class="separator:a2ab91902f8d6eb216f626ce9ea4be992"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:a1740d877a4d634ec1763cb8646f5e172" id="r_a1740d877a4d634ec1763cb8646f5e172"><td class="memItemLeft" align="right" valign="top">virtual int </td><td class="memItemRight" valign="bottom"><a class="el" href="classsvm__classifier_1_1MulticlassStrategyBase.html#a1740d877a4d634ec1763cb8646f5e172">get_n_classes</a> () const =0</td></tr>
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<tr class="memdesc:a1740d877a4d634ec1763cb8646f5e172"><td class="mdescLeft"> </td><td class="mdescRight">Get number of classes. <br /></td></tr>
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<tr class="separator:a1740d877a4d634ec1763cb8646f5e172"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:a31a0501fa1a6db1d41cbf825b2348e47" id="r_a31a0501fa1a6db1d41cbf825b2348e47"><td class="memItemLeft" align="right" valign="top">virtual MulticlassStrategy </td><td class="memItemRight" valign="bottom"><a class="el" href="classsvm__classifier_1_1MulticlassStrategyBase.html#a31a0501fa1a6db1d41cbf825b2348e47">get_strategy_type</a> () const =0</td></tr>
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<tr class="memdesc:a31a0501fa1a6db1d41cbf825b2348e47"><td class="mdescLeft"> </td><td class="mdescRight">Get strategy type. <br /></td></tr>
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<tr class="separator:a31a0501fa1a6db1d41cbf825b2348e47"><td class="memSeparator" colspan="2"> </td></tr>
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</table><table class="memberdecls">
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<tr class="heading"><td colspan="2"><h2 class="groupheader"><a id="pro-attribs" name="pro-attribs"></a>
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Protected Attributes</h2></td></tr>
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<tr class="memitem:a15bb6eb53e91e604b259b3050bd40e27" id="r_a15bb6eb53e91e604b259b3050bd40e27"><td class="memItemLeft" align="right" valign="top">std::vector< int > </td><td class="memItemRight" valign="bottom"><a class="el" href="classsvm__classifier_1_1MulticlassStrategyBase.html#a15bb6eb53e91e604b259b3050bd40e27">classes_</a></td></tr>
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<tr class="memdesc:a15bb6eb53e91e604b259b3050bd40e27"><td class="mdescLeft"> </td><td class="mdescRight">Unique class labels. <br /></td></tr>
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<tr class="separator:a15bb6eb53e91e604b259b3050bd40e27"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:a8e74cd580feaac0da34d204274a24fea" id="r_a8e74cd580feaac0da34d204274a24fea"><td class="memItemLeft" align="right" valign="top">bool </td><td class="memItemRight" valign="bottom"><a class="el" href="classsvm__classifier_1_1MulticlassStrategyBase.html#a8e74cd580feaac0da34d204274a24fea">is_trained_</a> = false</td></tr>
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<tr class="memdesc:a8e74cd580feaac0da34d204274a24fea"><td class="mdescLeft"> </td><td class="mdescRight">Whether the model is trained. <br /></td></tr>
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</table>
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<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
|
||||||
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<div class="textblock"><p>Abstract base class for multiclass classification strategies. </p>
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<p class="definition">Definition at line <a class="el" href="multiclass__strategy_8hpp_source.html#l00020">20</a> of file <a class="el" href="multiclass__strategy_8hpp_source.html">multiclass_strategy.hpp</a>.</p>
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</div><h2 class="groupheader">Member Function Documentation</h2>
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<h2 class="memtitle"><span class="permalink"><a href="#ad1c4eb746cb1fdd67cf436ff85a9b0f0">◆ </a></span>decision_function()</h2>
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<td class="memname">virtual std::vector< std::vector< double > > svm_classifier::MulticlassStrategyBase::decision_function </td>
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<td>(</td>
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<td class="paramtype"><a class="el" href="classsvm__classifier_1_1DataConverter.html">DataConverter</a> & </td>
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</table>
|
||||||
|
</td>
|
||||||
|
<td class="mlabels-right">
|
||||||
|
<span class="mlabels"><span class="mlabel">pure virtual</span></span> </td>
|
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</tr>
|
||||||
|
</table>
|
||||||
|
</div><div class="memdoc">
|
||||||
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|
||||||
|
<p>Get decision function values. </p>
|
||||||
|
<dl class="params"><dt>Parameters</dt><dd>
|
||||||
|
<table class="params">
|
||||||
|
<tr><td class="paramname">X</td><td>Feature tensor of shape (n_samples, n_features) </td></tr>
|
||||||
|
<tr><td class="paramname">converter</td><td>Data converter instance </td></tr>
|
||||||
|
</table>
|
||||||
|
</dd>
|
||||||
|
</dl>
|
||||||
|
<dl class="section return"><dt>Returns</dt><dd>Decision function values </dd></dl>
|
||||||
|
|
||||||
|
<p>Implemented in <a class="el" href="classsvm__classifier_1_1OneVsRestStrategy.html#a966b79bc8b6fac0fa78feefc2dd8a878">svm_classifier::OneVsRestStrategy</a>, and <a class="el" href="classsvm__classifier_1_1OneVsOneStrategy.html#a6aae0b5cd72180e94212454da8b777d2">svm_classifier::OneVsOneStrategy</a>.</p>
|
||||||
|
|
||||||
|
</div>
|
||||||
|
</div>
|
||||||
|
<a id="a548af7201b7970abee0c31e7ec07d896" name="a548af7201b7970abee0c31e7ec07d896"></a>
|
||||||
|
<h2 class="memtitle"><span class="permalink"><a href="#a548af7201b7970abee0c31e7ec07d896">◆ </a></span>fit()</h2>
|
||||||
|
|
||||||
|
<div class="memitem">
|
||||||
|
<div class="memproto">
|
||||||
|
<table class="mlabels">
|
||||||
|
<tr>
|
||||||
|
<td class="mlabels-left">
|
||||||
|
<table class="memname">
|
||||||
|
<tr>
|
||||||
|
<td class="memname">virtual <a class="el" href="structsvm__classifier_1_1TrainingMetrics.html">TrainingMetrics</a> svm_classifier::MulticlassStrategyBase::fit </td>
|
||||||
|
<td>(</td>
|
||||||
|
<td class="paramtype">const torch::Tensor & </td>
|
||||||
|
<td class="paramname"><em>X</em>, </td>
|
||||||
|
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|
||||||
|
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|
||||||
|
<td class="paramkey"></td>
|
||||||
|
<td></td>
|
||||||
|
<td class="paramtype">const torch::Tensor & </td>
|
||||||
|
<td class="paramname"><em>y</em>, </td>
|
||||||
|
</tr>
|
||||||
|
<tr>
|
||||||
|
<td class="paramkey"></td>
|
||||||
|
<td></td>
|
||||||
|
<td class="paramtype">const KernelParameters & </td>
|
||||||
|
<td class="paramname"><em>params</em>, </td>
|
||||||
|
</tr>
|
||||||
|
<tr>
|
||||||
|
<td class="paramkey"></td>
|
||||||
|
<td></td>
|
||||||
|
<td class="paramtype"><a class="el" href="classsvm__classifier_1_1DataConverter.html">DataConverter</a> & </td>
|
||||||
|
<td class="paramname"><em>converter</em> </td>
|
||||||
|
</tr>
|
||||||
|
<tr>
|
||||||
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<td></td>
|
||||||
|
<td>)</td>
|
||||||
|
<td></td><td></td>
|
||||||
|
</tr>
|
||||||
|
</table>
|
||||||
|
</td>
|
||||||
|
<td class="mlabels-right">
|
||||||
|
<span class="mlabels"><span class="mlabel">pure virtual</span></span> </td>
|
||||||
|
</tr>
|
||||||
|
</table>
|
||||||
|
</div><div class="memdoc">
|
||||||
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|
||||||
|
<p>Train the multiclass classifier. </p>
|
||||||
|
<dl class="params"><dt>Parameters</dt><dd>
|
||||||
|
<table class="params">
|
||||||
|
<tr><td class="paramname">X</td><td>Feature tensor of shape (n_samples, n_features) </td></tr>
|
||||||
|
<tr><td class="paramname">y</td><td>Target tensor of shape (n_samples,) </td></tr>
|
||||||
|
<tr><td class="paramname">params</td><td>Kernel parameters </td></tr>
|
||||||
|
<tr><td class="paramname">converter</td><td>Data converter instance </td></tr>
|
||||||
|
</table>
|
||||||
|
</dd>
|
||||||
|
</dl>
|
||||||
|
<dl class="section return"><dt>Returns</dt><dd>Training metrics </dd></dl>
|
||||||
|
|
||||||
|
<p>Implemented in <a class="el" href="classsvm__classifier_1_1OneVsRestStrategy.html#aae14da8c0effd04731b5a4a0181eb1b6">svm_classifier::OneVsRestStrategy</a>, and <a class="el" href="classsvm__classifier_1_1OneVsOneStrategy.html#af5ce4aeb191c5feed178b6465eac66f6">svm_classifier::OneVsOneStrategy</a>.</p>
|
||||||
|
|
||||||
|
</div>
|
||||||
|
</div>
|
||||||
|
<a id="a379c4000227cc46410bfbecce6e80c33" name="a379c4000227cc46410bfbecce6e80c33"></a>
|
||||||
|
<h2 class="memtitle"><span class="permalink"><a href="#a379c4000227cc46410bfbecce6e80c33">◆ </a></span>get_classes()</h2>
|
||||||
|
|
||||||
|
<div class="memitem">
|
||||||
|
<div class="memproto">
|
||||||
|
<table class="mlabels">
|
||||||
|
<tr>
|
||||||
|
<td class="mlabels-left">
|
||||||
|
<table class="memname">
|
||||||
|
<tr>
|
||||||
|
<td class="memname">virtual std::vector< int > svm_classifier::MulticlassStrategyBase::get_classes </td>
|
||||||
|
<td>(</td>
|
||||||
|
<td class="paramname"></td><td>)</td>
|
||||||
|
<td> const</td>
|
||||||
|
</tr>
|
||||||
|
</table>
|
||||||
|
</td>
|
||||||
|
<td class="mlabels-right">
|
||||||
|
<span class="mlabels"><span class="mlabel">pure virtual</span></span> </td>
|
||||||
|
</tr>
|
||||||
|
</table>
|
||||||
|
</div><div class="memdoc">
|
||||||
|
|
||||||
|
<p>Get unique class labels. </p>
|
||||||
|
<dl class="section return"><dt>Returns</dt><dd>Vector of unique class labels </dd></dl>
|
||||||
|
|
||||||
|
<p>Implemented in <a class="el" href="classsvm__classifier_1_1OneVsRestStrategy.html#a5e10800b16dbc66fd1c0d5e0a42871f0">svm_classifier::OneVsRestStrategy</a>, and <a class="el" href="classsvm__classifier_1_1OneVsOneStrategy.html#a52f9c3d7d98077d1dec0d6034711b750">svm_classifier::OneVsOneStrategy</a>.</p>
|
||||||
|
|
||||||
|
</div>
|
||||||
|
</div>
|
||||||
|
<a id="a1740d877a4d634ec1763cb8646f5e172" name="a1740d877a4d634ec1763cb8646f5e172"></a>
|
||||||
|
<h2 class="memtitle"><span class="permalink"><a href="#a1740d877a4d634ec1763cb8646f5e172">◆ </a></span>get_n_classes()</h2>
|
||||||
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|
||||||
|
<div class="memitem">
|
||||||
|
<div class="memproto">
|
||||||
|
<table class="mlabels">
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||||||
|
<tr>
|
||||||
|
<td class="mlabels-left">
|
||||||
|
<table class="memname">
|
||||||
|
<tr>
|
||||||
|
<td class="memname">virtual int svm_classifier::MulticlassStrategyBase::get_n_classes </td>
|
||||||
|
<td>(</td>
|
||||||
|
<td class="paramname"></td><td>)</td>
|
||||||
|
<td> const</td>
|
||||||
|
</tr>
|
||||||
|
</table>
|
||||||
|
</td>
|
||||||
|
<td class="mlabels-right">
|
||||||
|
<span class="mlabels"><span class="mlabel">pure virtual</span></span> </td>
|
||||||
|
</tr>
|
||||||
|
</table>
|
||||||
|
</div><div class="memdoc">
|
||||||
|
|
||||||
|
<p>Get number of classes. </p>
|
||||||
|
<dl class="section return"><dt>Returns</dt><dd>Number of classes </dd></dl>
|
||||||
|
|
||||||
|
<p>Implemented in <a class="el" href="classsvm__classifier_1_1OneVsRestStrategy.html#a53abe89ec25c33fd9c32d92ba08d01ed">svm_classifier::OneVsRestStrategy</a>, and <a class="el" href="classsvm__classifier_1_1OneVsOneStrategy.html#a16ee2ae3623767af2165fef2d4b7d039">svm_classifier::OneVsOneStrategy</a>.</p>
|
||||||
|
|
||||||
|
</div>
|
||||||
|
</div>
|
||||||
|
<a id="a31a0501fa1a6db1d41cbf825b2348e47" name="a31a0501fa1a6db1d41cbf825b2348e47"></a>
|
||||||
|
<h2 class="memtitle"><span class="permalink"><a href="#a31a0501fa1a6db1d41cbf825b2348e47">◆ </a></span>get_strategy_type()</h2>
|
||||||
|
|
||||||
|
<div class="memitem">
|
||||||
|
<div class="memproto">
|
||||||
|
<table class="mlabels">
|
||||||
|
<tr>
|
||||||
|
<td class="mlabels-left">
|
||||||
|
<table class="memname">
|
||||||
|
<tr>
|
||||||
|
<td class="memname">virtual MulticlassStrategy svm_classifier::MulticlassStrategyBase::get_strategy_type </td>
|
||||||
|
<td>(</td>
|
||||||
|
<td class="paramname"></td><td>)</td>
|
||||||
|
<td> const</td>
|
||||||
|
</tr>
|
||||||
|
</table>
|
||||||
|
</td>
|
||||||
|
<td class="mlabels-right">
|
||||||
|
<span class="mlabels"><span class="mlabel">pure virtual</span></span> </td>
|
||||||
|
</tr>
|
||||||
|
</table>
|
||||||
|
</div><div class="memdoc">
|
||||||
|
|
||||||
|
<p>Get strategy type. </p>
|
||||||
|
<dl class="section return"><dt>Returns</dt><dd>Multiclass strategy type </dd></dl>
|
||||||
|
|
||||||
|
<p>Implemented in <a class="el" href="classsvm__classifier_1_1OneVsRestStrategy.html#af9e1bd6d08ce3e7afd5279c835ce6cfb">svm_classifier::OneVsRestStrategy</a>, and <a class="el" href="classsvm__classifier_1_1OneVsOneStrategy.html#aae80b4e75459b2aca4f561d62c3c5675">svm_classifier::OneVsOneStrategy</a>.</p>
|
||||||
|
|
||||||
|
</div>
|
||||||
|
</div>
|
||||||
|
<a id="a70f94cfcf8b2bf6d60133c688fe55f9d" name="a70f94cfcf8b2bf6d60133c688fe55f9d"></a>
|
||||||
|
<h2 class="memtitle"><span class="permalink"><a href="#a70f94cfcf8b2bf6d60133c688fe55f9d">◆ </a></span>predict()</h2>
|
||||||
|
|
||||||
|
<div class="memitem">
|
||||||
|
<div class="memproto">
|
||||||
|
<table class="mlabels">
|
||||||
|
<tr>
|
||||||
|
<td class="mlabels-left">
|
||||||
|
<table class="memname">
|
||||||
|
<tr>
|
||||||
|
<td class="memname">virtual std::vector< int > svm_classifier::MulticlassStrategyBase::predict </td>
|
||||||
|
<td>(</td>
|
||||||
|
<td class="paramtype">const torch::Tensor & </td>
|
||||||
|
<td class="paramname"><em>X</em>, </td>
|
||||||
|
</tr>
|
||||||
|
<tr>
|
||||||
|
<td class="paramkey"></td>
|
||||||
|
<td></td>
|
||||||
|
<td class="paramtype"><a class="el" href="classsvm__classifier_1_1DataConverter.html">DataConverter</a> & </td>
|
||||||
|
<td class="paramname"><em>converter</em> </td>
|
||||||
|
</tr>
|
||||||
|
<tr>
|
||||||
|
<td></td>
|
||||||
|
<td>)</td>
|
||||||
|
<td></td><td></td>
|
||||||
|
</tr>
|
||||||
|
</table>
|
||||||
|
</td>
|
||||||
|
<td class="mlabels-right">
|
||||||
|
<span class="mlabels"><span class="mlabel">pure virtual</span></span> </td>
|
||||||
|
</tr>
|
||||||
|
</table>
|
||||||
|
</div><div class="memdoc">
|
||||||
|
|
||||||
|
<p>Predict class labels. </p>
|
||||||
|
<dl class="params"><dt>Parameters</dt><dd>
|
||||||
|
<table class="params">
|
||||||
|
<tr><td class="paramname">X</td><td>Feature tensor of shape (n_samples, n_features) </td></tr>
|
||||||
|
<tr><td class="paramname">converter</td><td>Data converter instance </td></tr>
|
||||||
|
</table>
|
||||||
|
</dd>
|
||||||
|
</dl>
|
||||||
|
<dl class="section return"><dt>Returns</dt><dd>Predicted class labels </dd></dl>
|
||||||
|
|
||||||
|
<p>Implemented in <a class="el" href="classsvm__classifier_1_1OneVsRestStrategy.html#a771903a821d5380ddd5d0b3a912e7df9">svm_classifier::OneVsRestStrategy</a>, and <a class="el" href="classsvm__classifier_1_1OneVsOneStrategy.html#ab60df2d9b6069a73369b0bf9d3675662">svm_classifier::OneVsOneStrategy</a>.</p>
|
||||||
|
|
||||||
|
</div>
|
||||||
|
</div>
|
||||||
|
<a id="ab5348ee3b83547702ec7903ee7ee2da7" name="ab5348ee3b83547702ec7903ee7ee2da7"></a>
|
||||||
|
<h2 class="memtitle"><span class="permalink"><a href="#ab5348ee3b83547702ec7903ee7ee2da7">◆ </a></span>predict_proba()</h2>
|
||||||
|
|
||||||
|
<div class="memitem">
|
||||||
|
<div class="memproto">
|
||||||
|
<table class="mlabels">
|
||||||
|
<tr>
|
||||||
|
<td class="mlabels-left">
|
||||||
|
<table class="memname">
|
||||||
|
<tr>
|
||||||
|
<td class="memname">virtual std::vector< std::vector< double > > svm_classifier::MulticlassStrategyBase::predict_proba </td>
|
||||||
|
<td>(</td>
|
||||||
|
<td class="paramtype">const torch::Tensor & </td>
|
||||||
|
<td class="paramname"><em>X</em>, </td>
|
||||||
|
</tr>
|
||||||
|
<tr>
|
||||||
|
<td class="paramkey"></td>
|
||||||
|
<td></td>
|
||||||
|
<td class="paramtype"><a class="el" href="classsvm__classifier_1_1DataConverter.html">DataConverter</a> & </td>
|
||||||
|
<td class="paramname"><em>converter</em> </td>
|
||||||
|
</tr>
|
||||||
|
<tr>
|
||||||
|
<td></td>
|
||||||
|
<td>)</td>
|
||||||
|
<td></td><td></td>
|
||||||
|
</tr>
|
||||||
|
</table>
|
||||||
|
</td>
|
||||||
|
<td class="mlabels-right">
|
||||||
|
<span class="mlabels"><span class="mlabel">pure virtual</span></span> </td>
|
||||||
|
</tr>
|
||||||
|
</table>
|
||||||
|
</div><div class="memdoc">
|
||||||
|
|
||||||
|
<p>Predict class probabilities. </p>
|
||||||
|
<dl class="params"><dt>Parameters</dt><dd>
|
||||||
|
<table class="params">
|
||||||
|
<tr><td class="paramname">X</td><td>Feature tensor of shape (n_samples, n_features) </td></tr>
|
||||||
|
<tr><td class="paramname">converter</td><td>Data converter instance </td></tr>
|
||||||
|
</table>
|
||||||
|
</dd>
|
||||||
|
</dl>
|
||||||
|
<dl class="section return"><dt>Returns</dt><dd>Class probabilities for each sample </dd></dl>
|
||||||
|
|
||||||
|
<p>Implemented in <a class="el" href="classsvm__classifier_1_1OneVsRestStrategy.html#a55639e5adaadcd6414b50d5ebf0d1cd2">svm_classifier::OneVsRestStrategy</a>, and <a class="el" href="classsvm__classifier_1_1OneVsOneStrategy.html#ae62e8b24115042d1119e76f3302f6992">svm_classifier::OneVsOneStrategy</a>.</p>
|
||||||
|
|
||||||
|
</div>
|
||||||
|
</div>
|
||||||
|
<a id="a2ab91902f8d6eb216f626ce9ea4be992" name="a2ab91902f8d6eb216f626ce9ea4be992"></a>
|
||||||
|
<h2 class="memtitle"><span class="permalink"><a href="#a2ab91902f8d6eb216f626ce9ea4be992">◆ </a></span>supports_probability()</h2>
|
||||||
|
|
||||||
|
<div class="memitem">
|
||||||
|
<div class="memproto">
|
||||||
|
<table class="mlabels">
|
||||||
|
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|
||||||
|
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|
||||||
|
<table class="memname">
|
||||||
|
<tr>
|
||||||
|
<td class="memname">virtual bool svm_classifier::MulticlassStrategyBase::supports_probability </td>
|
||||||
|
<td>(</td>
|
||||||
|
<td class="paramname"></td><td>)</td>
|
||||||
|
<td> const</td>
|
||||||
|
</tr>
|
||||||
|
</table>
|
||||||
|
</td>
|
||||||
|
<td class="mlabels-right">
|
||||||
|
<span class="mlabels"><span class="mlabel">pure virtual</span></span> </td>
|
||||||
|
</tr>
|
||||||
|
</table>
|
||||||
|
</div><div class="memdoc">
|
||||||
|
|
||||||
|
<p>Check if the model supports probability prediction. </p>
|
||||||
|
<dl class="section return"><dt>Returns</dt><dd>True if probabilities are supported </dd></dl>
|
||||||
|
|
||||||
|
<p>Implemented in <a class="el" href="classsvm__classifier_1_1OneVsRestStrategy.html#a200300198628ac119eac09e62ff62336">svm_classifier::OneVsRestStrategy</a>, and <a class="el" href="classsvm__classifier_1_1OneVsOneStrategy.html#a8875d29cb8666af10e0fb5634e08c0c1">svm_classifier::OneVsOneStrategy</a>.</p>
|
||||||
|
|
||||||
|
</div>
|
||||||
|
</div>
|
||||||
|
<h2 class="groupheader">Member Data Documentation</h2>
|
||||||
|
<a id="a15bb6eb53e91e604b259b3050bd40e27" name="a15bb6eb53e91e604b259b3050bd40e27"></a>
|
||||||
|
<h2 class="memtitle"><span class="permalink"><a href="#a15bb6eb53e91e604b259b3050bd40e27">◆ </a></span>classes_</h2>
|
||||||
|
|
||||||
|
<div class="memitem">
|
||||||
|
<div class="memproto">
|
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<td class="memname">std::vector<int> svm_classifier::MulticlassStrategyBase::classes_</td>
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<p>Unique class labels. </p>
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<p class="definition">Definition at line <a class="el" href="multiclass__strategy_8hpp_source.html#l00092">92</a> of file <a class="el" href="multiclass__strategy_8hpp_source.html">multiclass_strategy.hpp</a>.</p>
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<h2 class="memtitle"><span class="permalink"><a href="#a8e74cd580feaac0da34d204274a24fea">◆ </a></span>is_trained_</h2>
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<td class="memname">bool svm_classifier::MulticlassStrategyBase::is_trained_ = false</td>
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<p>Whether the model is trained. </p>
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<p class="definition">Definition at line <a class="el" href="multiclass__strategy_8hpp_source.html#l00093">93</a> of file <a class="el" href="multiclass__strategy_8hpp_source.html">multiclass_strategy.hpp</a>.</p>
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<hr/>The documentation for this class was generated from the following file:<ul>
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<li>include/svm_classifier/<a class="el" href="multiclass__strategy_8hpp_source.html">multiclass_strategy.hpp</a></li>
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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
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|
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|
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<div id="projectbrief">High-performance Support Vector Machine classifier with scikit-learn compatible API</div>
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<li class="navelem"><b>svm_classifier</b></li><li class="navelem"><a class="el" href="classsvm__classifier_1_1OneVsOneStrategy.html">OneVsOneStrategy</a></li> </ul>
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<p>This is the complete list of members for <a class="el" href="classsvm__classifier_1_1OneVsOneStrategy.html">svm_classifier::OneVsOneStrategy</a>, including all inherited members.</p>
|
||||||
|
<table class="directory">
|
||||||
|
<tr class="even"><td class="entry"><a class="el" href="classsvm__classifier_1_1MulticlassStrategyBase.html#a15bb6eb53e91e604b259b3050bd40e27">classes_</a></td><td class="entry"><a class="el" href="classsvm__classifier_1_1MulticlassStrategyBase.html">svm_classifier::MulticlassStrategyBase</a></td><td class="entry"><span class="mlabel">protected</span></td></tr>
|
||||||
|
<tr class="odd"><td class="entry"><a class="el" href="classsvm__classifier_1_1OneVsOneStrategy.html#a6aae0b5cd72180e94212454da8b777d2">decision_function</a>(const torch::Tensor &X, DataConverter &converter) override</td><td class="entry"><a class="el" href="classsvm__classifier_1_1OneVsOneStrategy.html">svm_classifier::OneVsOneStrategy</a></td><td class="entry"><span class="mlabel">virtual</span></td></tr>
|
||||||
|
<tr class="even"><td class="entry"><a class="el" href="classsvm__classifier_1_1OneVsOneStrategy.html#af5ce4aeb191c5feed178b6465eac66f6">fit</a>(const torch::Tensor &X, const torch::Tensor &y, const KernelParameters &params, DataConverter &converter) override</td><td class="entry"><a class="el" href="classsvm__classifier_1_1OneVsOneStrategy.html">svm_classifier::OneVsOneStrategy</a></td><td class="entry"><span class="mlabel">virtual</span></td></tr>
|
||||||
|
<tr class="odd"><td class="entry"><a class="el" href="classsvm__classifier_1_1OneVsOneStrategy.html#a52f9c3d7d98077d1dec0d6034711b750">get_classes</a>() const override</td><td class="entry"><a class="el" href="classsvm__classifier_1_1OneVsOneStrategy.html">svm_classifier::OneVsOneStrategy</a></td><td class="entry"><span class="mlabel">inline</span><span class="mlabel">virtual</span></td></tr>
|
||||||
|
<tr class="even"><td class="entry"><a class="el" href="classsvm__classifier_1_1OneVsOneStrategy.html#a16ee2ae3623767af2165fef2d4b7d039">get_n_classes</a>() const override</td><td class="entry"><a class="el" href="classsvm__classifier_1_1OneVsOneStrategy.html">svm_classifier::OneVsOneStrategy</a></td><td class="entry"><span class="mlabel">inline</span><span class="mlabel">virtual</span></td></tr>
|
||||||
|
<tr class="odd"><td class="entry"><a class="el" href="classsvm__classifier_1_1OneVsOneStrategy.html#aae80b4e75459b2aca4f561d62c3c5675">get_strategy_type</a>() const override</td><td class="entry"><a class="el" href="classsvm__classifier_1_1OneVsOneStrategy.html">svm_classifier::OneVsOneStrategy</a></td><td class="entry"><span class="mlabel">inline</span><span class="mlabel">virtual</span></td></tr>
|
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|
<tr class="even"><td class="entry"><a class="el" href="classsvm__classifier_1_1MulticlassStrategyBase.html#a8e74cd580feaac0da34d204274a24fea">is_trained_</a></td><td class="entry"><a class="el" href="classsvm__classifier_1_1MulticlassStrategyBase.html">svm_classifier::MulticlassStrategyBase</a></td><td class="entry"><span class="mlabel">protected</span></td></tr>
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||||||
|
<tr class="odd"><td class="entry"><a class="el" href="classsvm__classifier_1_1OneVsOneStrategy.html#a6d4b060383169010dda4197a0bffa020">OneVsOneStrategy</a>()</td><td class="entry"><a class="el" href="classsvm__classifier_1_1OneVsOneStrategy.html">svm_classifier::OneVsOneStrategy</a></td><td class="entry"></td></tr>
|
||||||
|
<tr class="even"><td class="entry"><a class="el" href="classsvm__classifier_1_1OneVsOneStrategy.html#ab60df2d9b6069a73369b0bf9d3675662">predict</a>(const torch::Tensor &X, DataConverter &converter) override</td><td class="entry"><a class="el" href="classsvm__classifier_1_1OneVsOneStrategy.html">svm_classifier::OneVsOneStrategy</a></td><td class="entry"><span class="mlabel">virtual</span></td></tr>
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||||||
|
<tr class="odd"><td class="entry"><a class="el" href="classsvm__classifier_1_1OneVsOneStrategy.html#ae62e8b24115042d1119e76f3302f6992">predict_proba</a>(const torch::Tensor &X, DataConverter &converter) override</td><td class="entry"><a class="el" href="classsvm__classifier_1_1OneVsOneStrategy.html">svm_classifier::OneVsOneStrategy</a></td><td class="entry"><span class="mlabel">virtual</span></td></tr>
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<tr class="even"><td class="entry"><a class="el" href="classsvm__classifier_1_1OneVsOneStrategy.html#a8875d29cb8666af10e0fb5634e08c0c1">supports_probability</a>() const override</td><td class="entry"><a class="el" href="classsvm__classifier_1_1OneVsOneStrategy.html">svm_classifier::OneVsOneStrategy</a></td><td class="entry"><span class="mlabel">virtual</span></td></tr>
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<tr class="odd"><td class="entry"><a class="el" href="classsvm__classifier_1_1MulticlassStrategyBase.html#a8a5647dd57eed281288f0c9011b11395">~MulticlassStrategyBase</a>()=default</td><td class="entry"><a class="el" href="classsvm__classifier_1_1MulticlassStrategyBase.html">svm_classifier::MulticlassStrategyBase</a></td><td class="entry"><span class="mlabel">virtual</span></td></tr>
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<tr class="even"><td class="entry"><a class="el" href="classsvm__classifier_1_1OneVsOneStrategy.html#af9a653b62502e296d0d18092be56344f">~OneVsOneStrategy</a>() override</td><td class="entry"><a class="el" href="classsvm__classifier_1_1OneVsOneStrategy.html">svm_classifier::OneVsOneStrategy</a></td><td class="entry"></td></tr>
|
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|
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<div id="projectbrief">High-performance Support Vector Machine classifier with scikit-learn compatible API</div>
|
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<li class="navelem"><b>svm_classifier</b></li><li class="navelem"><a class="el" href="classsvm__classifier_1_1OneVsOneStrategy.html">OneVsOneStrategy</a></li> </ul>
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<a href="#pub-methods">Public Member Functions</a> |
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<a href="classsvm__classifier_1_1OneVsOneStrategy-members.html">List of all members</a> </div>
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<div class="headertitle"><div class="title">svm_classifier::OneVsOneStrategy Class Reference</div></div>
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<div class="contents">
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<p>One-vs-One (OvO) multiclass strategy.
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<a href="classsvm__classifier_1_1OneVsOneStrategy.html#details">More...</a></p>
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<p><code>#include <<a class="el" href="multiclass__strategy_8hpp_source.html">multiclass_strategy.hpp</a>></code></p>
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<div class="dynheader">
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Inheritance diagram for svm_classifier::OneVsOneStrategy:</div>
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<area shape="rect" title="One-vs-One (OvO) multiclass strategy." alt="" coords="5,93,196,133"/>
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Collaboration diagram for svm_classifier::OneVsOneStrategy:</div>
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<center><span class="legend">[<a href="graph_legend.html">legend</a>]</span></center></div>
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<table class="memberdecls">
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<tr class="heading"><td colspan="2"><h2 class="groupheader"><a id="pub-methods" name="pub-methods"></a>
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Public Member Functions</h2></td></tr>
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<tr class="memitem:a6d4b060383169010dda4197a0bffa020" id="r_a6d4b060383169010dda4197a0bffa020"><td class="memItemLeft" align="right" valign="top"><a id="a6d4b060383169010dda4197a0bffa020" name="a6d4b060383169010dda4197a0bffa020"></a>
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 </td><td class="memItemRight" valign="bottom"><b>OneVsOneStrategy</b> ()</td></tr>
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<tr class="memdesc:a6d4b060383169010dda4197a0bffa020"><td class="mdescLeft"> </td><td class="mdescRight">Constructor. <br /></td></tr>
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<tr class="separator:a6d4b060383169010dda4197a0bffa020"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:af9a653b62502e296d0d18092be56344f" id="r_af9a653b62502e296d0d18092be56344f"><td class="memItemLeft" align="right" valign="top"><a id="af9a653b62502e296d0d18092be56344f" name="af9a653b62502e296d0d18092be56344f"></a>
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 </td><td class="memItemRight" valign="bottom"><b>~OneVsOneStrategy</b> () override</td></tr>
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<tr class="memdesc:af9a653b62502e296d0d18092be56344f"><td class="mdescLeft"> </td><td class="mdescRight">Destructor. <br /></td></tr>
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<tr class="separator:af9a653b62502e296d0d18092be56344f"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:af5ce4aeb191c5feed178b6465eac66f6" id="r_af5ce4aeb191c5feed178b6465eac66f6"><td class="memItemLeft" align="right" valign="top"><a class="el" href="structsvm__classifier_1_1TrainingMetrics.html">TrainingMetrics</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="classsvm__classifier_1_1OneVsOneStrategy.html#af5ce4aeb191c5feed178b6465eac66f6">fit</a> (const torch::Tensor &X, const torch::Tensor &y, const KernelParameters &params, <a class="el" href="classsvm__classifier_1_1DataConverter.html">DataConverter</a> &converter) override</td></tr>
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<tr class="memdesc:af5ce4aeb191c5feed178b6465eac66f6"><td class="mdescLeft"> </td><td class="mdescRight">Train the multiclass classifier. <br /></td></tr>
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<tr class="separator:af5ce4aeb191c5feed178b6465eac66f6"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:ab60df2d9b6069a73369b0bf9d3675662" id="r_ab60df2d9b6069a73369b0bf9d3675662"><td class="memItemLeft" align="right" valign="top">std::vector< int > </td><td class="memItemRight" valign="bottom"><a class="el" href="classsvm__classifier_1_1OneVsOneStrategy.html#ab60df2d9b6069a73369b0bf9d3675662">predict</a> (const torch::Tensor &X, <a class="el" href="classsvm__classifier_1_1DataConverter.html">DataConverter</a> &converter) override</td></tr>
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||||||
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<tr class="memdesc:ab60df2d9b6069a73369b0bf9d3675662"><td class="mdescLeft"> </td><td class="mdescRight">Predict class labels. <br /></td></tr>
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<tr class="separator:ab60df2d9b6069a73369b0bf9d3675662"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:ae62e8b24115042d1119e76f3302f6992" id="r_ae62e8b24115042d1119e76f3302f6992"><td class="memItemLeft" align="right" valign="top">std::vector< std::vector< double > > </td><td class="memItemRight" valign="bottom"><a class="el" href="classsvm__classifier_1_1OneVsOneStrategy.html#ae62e8b24115042d1119e76f3302f6992">predict_proba</a> (const torch::Tensor &X, <a class="el" href="classsvm__classifier_1_1DataConverter.html">DataConverter</a> &converter) override</td></tr>
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||||||
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<tr class="memdesc:ae62e8b24115042d1119e76f3302f6992"><td class="mdescLeft"> </td><td class="mdescRight">Predict class probabilities. <br /></td></tr>
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<tr class="separator:ae62e8b24115042d1119e76f3302f6992"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:a6aae0b5cd72180e94212454da8b777d2" id="r_a6aae0b5cd72180e94212454da8b777d2"><td class="memItemLeft" align="right" valign="top">std::vector< std::vector< double > > </td><td class="memItemRight" valign="bottom"><a class="el" href="classsvm__classifier_1_1OneVsOneStrategy.html#a6aae0b5cd72180e94212454da8b777d2">decision_function</a> (const torch::Tensor &X, <a class="el" href="classsvm__classifier_1_1DataConverter.html">DataConverter</a> &converter) override</td></tr>
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||||||
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<tr class="memdesc:a6aae0b5cd72180e94212454da8b777d2"><td class="mdescLeft"> </td><td class="mdescRight">Get decision function values. <br /></td></tr>
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<tr class="separator:a6aae0b5cd72180e94212454da8b777d2"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:a52f9c3d7d98077d1dec0d6034711b750" id="r_a52f9c3d7d98077d1dec0d6034711b750"><td class="memItemLeft" align="right" valign="top">std::vector< int > </td><td class="memItemRight" valign="bottom"><a class="el" href="classsvm__classifier_1_1OneVsOneStrategy.html#a52f9c3d7d98077d1dec0d6034711b750">get_classes</a> () const override</td></tr>
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||||||
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<tr class="memdesc:a52f9c3d7d98077d1dec0d6034711b750"><td class="mdescLeft"> </td><td class="mdescRight">Get unique class labels. <br /></td></tr>
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<tr class="separator:a52f9c3d7d98077d1dec0d6034711b750"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:a8875d29cb8666af10e0fb5634e08c0c1" id="r_a8875d29cb8666af10e0fb5634e08c0c1"><td class="memItemLeft" align="right" valign="top">bool </td><td class="memItemRight" valign="bottom"><a class="el" href="classsvm__classifier_1_1OneVsOneStrategy.html#a8875d29cb8666af10e0fb5634e08c0c1">supports_probability</a> () const override</td></tr>
|
||||||
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<tr class="memdesc:a8875d29cb8666af10e0fb5634e08c0c1"><td class="mdescLeft"> </td><td class="mdescRight">Check if the model supports probability prediction. <br /></td></tr>
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<tr class="separator:a8875d29cb8666af10e0fb5634e08c0c1"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:a16ee2ae3623767af2165fef2d4b7d039" id="r_a16ee2ae3623767af2165fef2d4b7d039"><td class="memItemLeft" align="right" valign="top">int </td><td class="memItemRight" valign="bottom"><a class="el" href="classsvm__classifier_1_1OneVsOneStrategy.html#a16ee2ae3623767af2165fef2d4b7d039">get_n_classes</a> () const override</td></tr>
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||||||
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<tr class="memdesc:a16ee2ae3623767af2165fef2d4b7d039"><td class="mdescLeft"> </td><td class="mdescRight">Get number of classes. <br /></td></tr>
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<tr class="separator:a16ee2ae3623767af2165fef2d4b7d039"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:aae80b4e75459b2aca4f561d62c3c5675" id="r_aae80b4e75459b2aca4f561d62c3c5675"><td class="memItemLeft" align="right" valign="top">MulticlassStrategy </td><td class="memItemRight" valign="bottom"><a class="el" href="classsvm__classifier_1_1OneVsOneStrategy.html#aae80b4e75459b2aca4f561d62c3c5675">get_strategy_type</a> () const override</td></tr>
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||||||
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<tr class="memdesc:aae80b4e75459b2aca4f561d62c3c5675"><td class="mdescLeft"> </td><td class="mdescRight">Get strategy type. <br /></td></tr>
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<tr class="separator:aae80b4e75459b2aca4f561d62c3c5675"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="inherit_header pub_methods_classsvm__classifier_1_1MulticlassStrategyBase"><td colspan="2" onclick="javascript:toggleInherit('pub_methods_classsvm__classifier_1_1MulticlassStrategyBase')"><img src="closed.png" alt="-"/> Public Member Functions inherited from <a class="el" href="classsvm__classifier_1_1MulticlassStrategyBase.html">svm_classifier::MulticlassStrategyBase</a></td></tr>
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<tr class="memitem:a8a5647dd57eed281288f0c9011b11395 inherit pub_methods_classsvm__classifier_1_1MulticlassStrategyBase" id="r_a8a5647dd57eed281288f0c9011b11395"><td class="memItemLeft" align="right" valign="top">
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virtual </td><td class="memItemRight" valign="bottom"><b>~MulticlassStrategyBase</b> ()=default</td></tr>
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<tr class="memdesc:a8a5647dd57eed281288f0c9011b11395 inherit pub_methods_classsvm__classifier_1_1MulticlassStrategyBase"><td class="mdescLeft"> </td><td class="mdescRight">Virtual destructor. <br /></td></tr>
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<tr class="separator:a8a5647dd57eed281288f0c9011b11395 inherit pub_methods_classsvm__classifier_1_1MulticlassStrategyBase"><td class="memSeparator" colspan="2"> </td></tr>
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</table><table class="memberdecls">
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<tr class="heading"><td colspan="2"><h2 class="groupheader"><a id="inherited" name="inherited"></a>
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Additional Inherited Members</h2></td></tr>
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<tr class="inherit_header pro_attribs_classsvm__classifier_1_1MulticlassStrategyBase"><td colspan="2" onclick="javascript:toggleInherit('pro_attribs_classsvm__classifier_1_1MulticlassStrategyBase')"><img src="closed.png" alt="-"/> Protected Attributes inherited from <a class="el" href="classsvm__classifier_1_1MulticlassStrategyBase.html">svm_classifier::MulticlassStrategyBase</a></td></tr>
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<tr class="memitem:a15bb6eb53e91e604b259b3050bd40e27 inherit pro_attribs_classsvm__classifier_1_1MulticlassStrategyBase" id="r_a15bb6eb53e91e604b259b3050bd40e27"><td class="memItemLeft" align="right" valign="top">std::vector< int > </td><td class="memItemRight" valign="bottom"><a class="el" href="classsvm__classifier_1_1MulticlassStrategyBase.html#a15bb6eb53e91e604b259b3050bd40e27">classes_</a></td></tr>
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<tr class="memdesc:a15bb6eb53e91e604b259b3050bd40e27 inherit pro_attribs_classsvm__classifier_1_1MulticlassStrategyBase"><td class="mdescLeft"> </td><td class="mdescRight">Unique class labels. <br /></td></tr>
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<tr class="separator:a15bb6eb53e91e604b259b3050bd40e27 inherit pro_attribs_classsvm__classifier_1_1MulticlassStrategyBase"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:a8e74cd580feaac0da34d204274a24fea inherit pro_attribs_classsvm__classifier_1_1MulticlassStrategyBase" id="r_a8e74cd580feaac0da34d204274a24fea"><td class="memItemLeft" align="right" valign="top">bool </td><td class="memItemRight" valign="bottom"><a class="el" href="classsvm__classifier_1_1MulticlassStrategyBase.html#a8e74cd580feaac0da34d204274a24fea">is_trained_</a> = false</td></tr>
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<tr class="memdesc:a8e74cd580feaac0da34d204274a24fea inherit pro_attribs_classsvm__classifier_1_1MulticlassStrategyBase"><td class="mdescLeft"> </td><td class="mdescRight">Whether the model is trained. <br /></td></tr>
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<tr class="separator:a8e74cd580feaac0da34d204274a24fea inherit pro_attribs_classsvm__classifier_1_1MulticlassStrategyBase"><td class="memSeparator" colspan="2"> </td></tr>
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</table>
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<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
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<div class="textblock"><p>One-vs-One (OvO) multiclass strategy. </p>
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<p class="definition">Definition at line <a class="el" href="multiclass__strategy_8hpp_source.html#l00171">171</a> of file <a class="el" href="multiclass__strategy_8hpp_source.html">multiclass_strategy.hpp</a>.</p>
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</div><h2 class="groupheader">Member Function Documentation</h2>
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<h2 class="memtitle"><span class="permalink"><a href="#a6aae0b5cd72180e94212454da8b777d2">◆ </a></span>decision_function()</h2>
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<td class="memname">std::vector< std::vector< double > > svm_classifier::OneVsOneStrategy::decision_function </td>
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<td>(</td>
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<td class="paramtype">const torch::Tensor & </td>
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<td class="paramtype"><a class="el" href="classsvm__classifier_1_1DataConverter.html">DataConverter</a> & </td>
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<td class="paramname"><em>converter</em> </td>
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<td>)</td>
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<span class="mlabels"><span class="mlabel">override</span><span class="mlabel">virtual</span></span> </td>
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</div><div class="memdoc">
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<p>Get decision function values. </p>
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<dl class="params"><dt>Parameters</dt><dd>
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<table class="params">
|
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<tr><td class="paramname">X</td><td>Feature tensor of shape (n_samples, n_features) </td></tr>
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<tr><td class="paramname">converter</td><td>Data converter instance </td></tr>
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</dl>
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<dl class="section return"><dt>Returns</dt><dd>Decision function values </dd></dl>
|
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<p>Implements <a class="el" href="classsvm__classifier_1_1MulticlassStrategyBase.html#ad1c4eb746cb1fdd67cf436ff85a9b0f0">svm_classifier::MulticlassStrategyBase</a>.</p>
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<h2 class="memtitle"><span class="permalink"><a href="#af5ce4aeb191c5feed178b6465eac66f6">◆ </a></span>fit()</h2>
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<td class="memname"><a class="el" href="structsvm__classifier_1_1TrainingMetrics.html">TrainingMetrics</a> svm_classifier::OneVsOneStrategy::fit </td>
|
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|
<td>(</td>
|
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<td class="paramtype">const torch::Tensor & </td>
|
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<td></td>
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<td class="paramtype">const torch::Tensor & </td>
|
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<td class="paramtype">const KernelParameters & </td>
|
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<td class="paramtype"><a class="el" href="classsvm__classifier_1_1DataConverter.html">DataConverter</a> & </td>
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|
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<td>)</td>
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<p>Train the multiclass classifier. </p>
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<dl class="section return"><dt>Returns</dt><dd>Training metrics </dd></dl>
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<p>Implements <a class="el" href="classsvm__classifier_1_1MulticlassStrategyBase.html#a548af7201b7970abee0c31e7ec07d896">svm_classifier::MulticlassStrategyBase</a>.</p>
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<h2 class="memtitle"><span class="permalink"><a href="#a52f9c3d7d98077d1dec0d6034711b750">◆ </a></span>get_classes()</h2>
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<td class="memname">std::vector< int > svm_classifier::OneVsOneStrategy::get_classes </td>
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<td>(</td>
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<p>Get unique class labels. </p>
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<dl class="section return"><dt>Returns</dt><dd>Vector of unique class labels </dd></dl>
|
||||||
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|
||||||
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<p>Implements <a class="el" href="classsvm__classifier_1_1MulticlassStrategyBase.html#a379c4000227cc46410bfbecce6e80c33">svm_classifier::MulticlassStrategyBase</a>.</p>
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<p class="definition">Definition at line <a class="el" href="multiclass__strategy_8hpp_source.html#l00197">197</a> of file <a class="el" href="multiclass__strategy_8hpp_source.html">multiclass_strategy.hpp</a>.</p>
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<h2 class="memtitle"><span class="permalink"><a href="#a16ee2ae3623767af2165fef2d4b7d039">◆ </a></span>get_n_classes()</h2>
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<td class="memname">int svm_classifier::OneVsOneStrategy::get_n_classes </td>
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<td>(</td>
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<p>Get number of classes. </p>
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<dl class="section return"><dt>Returns</dt><dd>Number of classes </dd></dl>
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||||||
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<p>Implements <a class="el" href="classsvm__classifier_1_1MulticlassStrategyBase.html#a1740d877a4d634ec1763cb8646f5e172">svm_classifier::MulticlassStrategyBase</a>.</p>
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<p class="definition">Definition at line <a class="el" href="multiclass__strategy_8hpp_source.html#l00201">201</a> of file <a class="el" href="multiclass__strategy_8hpp_source.html">multiclass_strategy.hpp</a>.</p>
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<h2 class="memtitle"><span class="permalink"><a href="#aae80b4e75459b2aca4f561d62c3c5675">◆ </a></span>get_strategy_type()</h2>
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<td class="memname">MulticlassStrategy svm_classifier::OneVsOneStrategy::get_strategy_type </td>
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<p>Get strategy type. </p>
|
||||||
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<dl class="section return"><dt>Returns</dt><dd>Multiclass strategy type </dd></dl>
|
||||||
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|
||||||
|
<p>Implements <a class="el" href="classsvm__classifier_1_1MulticlassStrategyBase.html#a31a0501fa1a6db1d41cbf825b2348e47">svm_classifier::MulticlassStrategyBase</a>.</p>
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<p class="definition">Definition at line <a class="el" href="multiclass__strategy_8hpp_source.html#l00203">203</a> of file <a class="el" href="multiclass__strategy_8hpp_source.html">multiclass_strategy.hpp</a>.</p>
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<h2 class="memtitle"><span class="permalink"><a href="#ab60df2d9b6069a73369b0bf9d3675662">◆ </a></span>predict()</h2>
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||||||
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|
||||||
|
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<p>Predict class labels. </p>
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|
||||||
|
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|
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<dl class="section return"><dt>Returns</dt><dd>Predicted class labels </dd></dl>
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<p>Implements <a class="el" href="classsvm__classifier_1_1MulticlassStrategyBase.html#a70f94cfcf8b2bf6d60133c688fe55f9d">svm_classifier::MulticlassStrategyBase</a>.</p>
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<h2 class="memtitle"><span class="permalink"><a href="#ae62e8b24115042d1119e76f3302f6992">◆ </a></span>predict_proba()</h2>
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<td class="memname">std::vector< std::vector< double > > svm_classifier::OneVsOneStrategy::predict_proba </td>
|
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<td>(</td>
|
||||||
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<td class="paramtype">const torch::Tensor & </td>
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<p>Predict class probabilities. </p>
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<dl class="section return"><dt>Returns</dt><dd>Class probabilities for each sample </dd></dl>
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||||||
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<p>Implements <a class="el" href="classsvm__classifier_1_1MulticlassStrategyBase.html#ab5348ee3b83547702ec7903ee7ee2da7">svm_classifier::MulticlassStrategyBase</a>.</p>
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<h2 class="memtitle"><span class="permalink"><a href="#a8875d29cb8666af10e0fb5634e08c0c1">◆ </a></span>supports_probability()</h2>
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<td class="memname">bool svm_classifier::OneVsOneStrategy::supports_probability </td>
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<p>Check if the model supports probability prediction. </p>
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<dl class="section return"><dt>Returns</dt><dd>True if probabilities are supported </dd></dl>
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<p>Implements <a class="el" href="classsvm__classifier_1_1MulticlassStrategyBase.html#a2ab91902f8d6eb216f626ce9ea4be992">svm_classifier::MulticlassStrategyBase</a>.</p>
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<hr/>The documentation for this class was generated from the following file:<ul>
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<div id="projectbrief">High-performance Support Vector Machine classifier with scikit-learn compatible API</div>
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<li class="navelem"><b>svm_classifier</b></li><li class="navelem"><a class="el" href="classsvm__classifier_1_1OneVsRestStrategy.html">OneVsRestStrategy</a></li> </ul>
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<p>This is the complete list of members for <a class="el" href="classsvm__classifier_1_1OneVsRestStrategy.html">svm_classifier::OneVsRestStrategy</a>, including all inherited members.</p>
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<table class="directory">
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<tr class="even"><td class="entry"><a class="el" href="classsvm__classifier_1_1MulticlassStrategyBase.html#a15bb6eb53e91e604b259b3050bd40e27">classes_</a></td><td class="entry"><a class="el" href="classsvm__classifier_1_1MulticlassStrategyBase.html">svm_classifier::MulticlassStrategyBase</a></td><td class="entry"><span class="mlabel">protected</span></td></tr>
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<tr class="odd"><td class="entry"><a class="el" href="classsvm__classifier_1_1OneVsRestStrategy.html#a966b79bc8b6fac0fa78feefc2dd8a878">decision_function</a>(const torch::Tensor &X, DataConverter &converter) override</td><td class="entry"><a class="el" href="classsvm__classifier_1_1OneVsRestStrategy.html">svm_classifier::OneVsRestStrategy</a></td><td class="entry"><span class="mlabel">virtual</span></td></tr>
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<tr class="even"><td class="entry"><a class="el" href="classsvm__classifier_1_1OneVsRestStrategy.html#aae14da8c0effd04731b5a4a0181eb1b6">fit</a>(const torch::Tensor &X, const torch::Tensor &y, const KernelParameters &params, DataConverter &converter) override</td><td class="entry"><a class="el" href="classsvm__classifier_1_1OneVsRestStrategy.html">svm_classifier::OneVsRestStrategy</a></td><td class="entry"><span class="mlabel">virtual</span></td></tr>
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<tr class="odd"><td class="entry"><a class="el" href="classsvm__classifier_1_1OneVsRestStrategy.html#a5e10800b16dbc66fd1c0d5e0a42871f0">get_classes</a>() const override</td><td class="entry"><a class="el" href="classsvm__classifier_1_1OneVsRestStrategy.html">svm_classifier::OneVsRestStrategy</a></td><td class="entry"><span class="mlabel">inline</span><span class="mlabel">virtual</span></td></tr>
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<tr class="even"><td class="entry"><a class="el" href="classsvm__classifier_1_1OneVsRestStrategy.html#a53abe89ec25c33fd9c32d92ba08d01ed">get_n_classes</a>() const override</td><td class="entry"><a class="el" href="classsvm__classifier_1_1OneVsRestStrategy.html">svm_classifier::OneVsRestStrategy</a></td><td class="entry"><span class="mlabel">inline</span><span class="mlabel">virtual</span></td></tr>
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<tr class="odd"><td class="entry"><a class="el" href="classsvm__classifier_1_1OneVsRestStrategy.html#af9e1bd6d08ce3e7afd5279c835ce6cfb">get_strategy_type</a>() const override</td><td class="entry"><a class="el" href="classsvm__classifier_1_1OneVsRestStrategy.html">svm_classifier::OneVsRestStrategy</a></td><td class="entry"><span class="mlabel">inline</span><span class="mlabel">virtual</span></td></tr>
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<tr class="even"><td class="entry"><a class="el" href="classsvm__classifier_1_1MulticlassStrategyBase.html#a8e74cd580feaac0da34d204274a24fea">is_trained_</a></td><td class="entry"><a class="el" href="classsvm__classifier_1_1MulticlassStrategyBase.html">svm_classifier::MulticlassStrategyBase</a></td><td class="entry"><span class="mlabel">protected</span></td></tr>
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<tr class="odd"><td class="entry"><a class="el" href="classsvm__classifier_1_1OneVsRestStrategy.html#a30f146a564a9c9681524593cacbb43e7">OneVsRestStrategy</a>()</td><td class="entry"><a class="el" href="classsvm__classifier_1_1OneVsRestStrategy.html">svm_classifier::OneVsRestStrategy</a></td><td class="entry"></td></tr>
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<tr class="even"><td class="entry"><a class="el" href="classsvm__classifier_1_1OneVsRestStrategy.html#a771903a821d5380ddd5d0b3a912e7df9">predict</a>(const torch::Tensor &X, DataConverter &converter) override</td><td class="entry"><a class="el" href="classsvm__classifier_1_1OneVsRestStrategy.html">svm_classifier::OneVsRestStrategy</a></td><td class="entry"><span class="mlabel">virtual</span></td></tr>
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<tr class="odd"><td class="entry"><a class="el" href="classsvm__classifier_1_1OneVsRestStrategy.html#a55639e5adaadcd6414b50d5ebf0d1cd2">predict_proba</a>(const torch::Tensor &X, DataConverter &converter) override</td><td class="entry"><a class="el" href="classsvm__classifier_1_1OneVsRestStrategy.html">svm_classifier::OneVsRestStrategy</a></td><td class="entry"><span class="mlabel">virtual</span></td></tr>
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<tr class="even"><td class="entry"><a class="el" href="classsvm__classifier_1_1OneVsRestStrategy.html#a200300198628ac119eac09e62ff62336">supports_probability</a>() const override</td><td class="entry"><a class="el" href="classsvm__classifier_1_1OneVsRestStrategy.html">svm_classifier::OneVsRestStrategy</a></td><td class="entry"><span class="mlabel">virtual</span></td></tr>
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<tr class="odd"><td class="entry"><a class="el" href="classsvm__classifier_1_1MulticlassStrategyBase.html#a8a5647dd57eed281288f0c9011b11395">~MulticlassStrategyBase</a>()=default</td><td class="entry"><a class="el" href="classsvm__classifier_1_1MulticlassStrategyBase.html">svm_classifier::MulticlassStrategyBase</a></td><td class="entry"><span class="mlabel">virtual</span></td></tr>
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<tr class="even"><td class="entry"><a class="el" href="classsvm__classifier_1_1OneVsRestStrategy.html#acfd698dd6cc0a988ac642a00d1f0b970">~OneVsRestStrategy</a>() override</td><td class="entry"><a class="el" href="classsvm__classifier_1_1OneVsRestStrategy.html">svm_classifier::OneVsRestStrategy</a></td><td class="entry"></td></tr>
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<div id="projectname">SVM Classifier C++<span id="projectnumber"> 1.0.0</span>
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<div id="projectbrief">High-performance Support Vector Machine classifier with scikit-learn compatible API</div>
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<li class="navelem"><b>svm_classifier</b></li><li class="navelem"><a class="el" href="classsvm__classifier_1_1OneVsRestStrategy.html">OneVsRestStrategy</a></li> </ul>
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<a href="#pub-methods">Public Member Functions</a> |
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<a href="classsvm__classifier_1_1OneVsRestStrategy-members.html">List of all members</a> </div>
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<div class="headertitle"><div class="title">svm_classifier::OneVsRestStrategy Class Reference</div></div>
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<p>One-vs-Rest (OvR) multiclass strategy.
|
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<a href="classsvm__classifier_1_1OneVsRestStrategy.html#details">More...</a></p>
|
||||||
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<p><code>#include <<a class="el" href="multiclass__strategy_8hpp_source.html">multiclass_strategy.hpp</a>></code></p>
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Inheritance diagram for svm_classifier::OneVsRestStrategy:</div>
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<div class="center"><img src="classsvm__classifier_1_1OneVsRestStrategy__inherit__graph.png" border="0" usemap="#asvm__classifier_1_1OneVsRestStrategy_inherit__map" alt="Inheritance graph"/></div>
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Collaboration diagram for svm_classifier::OneVsRestStrategy:</div>
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Public Member Functions</h2></td></tr>
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<tr class="memitem:a30f146a564a9c9681524593cacbb43e7" id="r_a30f146a564a9c9681524593cacbb43e7"><td class="memItemLeft" align="right" valign="top"><a id="a30f146a564a9c9681524593cacbb43e7" name="a30f146a564a9c9681524593cacbb43e7"></a>
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 </td><td class="memItemRight" valign="bottom"><b>OneVsRestStrategy</b> ()</td></tr>
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<tr class="memdesc:a30f146a564a9c9681524593cacbb43e7"><td class="mdescLeft"> </td><td class="mdescRight">Constructor. <br /></td></tr>
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<tr class="separator:a30f146a564a9c9681524593cacbb43e7"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:acfd698dd6cc0a988ac642a00d1f0b970" id="r_acfd698dd6cc0a988ac642a00d1f0b970"><td class="memItemLeft" align="right" valign="top"><a id="acfd698dd6cc0a988ac642a00d1f0b970" name="acfd698dd6cc0a988ac642a00d1f0b970"></a>
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 </td><td class="memItemRight" valign="bottom"><b>~OneVsRestStrategy</b> () override</td></tr>
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<tr class="memdesc:acfd698dd6cc0a988ac642a00d1f0b970"><td class="mdescLeft"> </td><td class="mdescRight">Destructor. <br /></td></tr>
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<tr class="separator:acfd698dd6cc0a988ac642a00d1f0b970"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:aae14da8c0effd04731b5a4a0181eb1b6" id="r_aae14da8c0effd04731b5a4a0181eb1b6"><td class="memItemLeft" align="right" valign="top"><a class="el" href="structsvm__classifier_1_1TrainingMetrics.html">TrainingMetrics</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="classsvm__classifier_1_1OneVsRestStrategy.html#aae14da8c0effd04731b5a4a0181eb1b6">fit</a> (const torch::Tensor &X, const torch::Tensor &y, const KernelParameters &params, <a class="el" href="classsvm__classifier_1_1DataConverter.html">DataConverter</a> &converter) override</td></tr>
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<tr class="memdesc:aae14da8c0effd04731b5a4a0181eb1b6"><td class="mdescLeft"> </td><td class="mdescRight">Train the multiclass classifier. <br /></td></tr>
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<tr class="separator:aae14da8c0effd04731b5a4a0181eb1b6"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:a771903a821d5380ddd5d0b3a912e7df9" id="r_a771903a821d5380ddd5d0b3a912e7df9"><td class="memItemLeft" align="right" valign="top">std::vector< int > </td><td class="memItemRight" valign="bottom"><a class="el" href="classsvm__classifier_1_1OneVsRestStrategy.html#a771903a821d5380ddd5d0b3a912e7df9">predict</a> (const torch::Tensor &X, <a class="el" href="classsvm__classifier_1_1DataConverter.html">DataConverter</a> &converter) override</td></tr>
|
||||||
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<tr class="memdesc:a771903a821d5380ddd5d0b3a912e7df9"><td class="mdescLeft"> </td><td class="mdescRight">Predict class labels. <br /></td></tr>
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<tr class="separator:a771903a821d5380ddd5d0b3a912e7df9"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:a55639e5adaadcd6414b50d5ebf0d1cd2" id="r_a55639e5adaadcd6414b50d5ebf0d1cd2"><td class="memItemLeft" align="right" valign="top">std::vector< std::vector< double > > </td><td class="memItemRight" valign="bottom"><a class="el" href="classsvm__classifier_1_1OneVsRestStrategy.html#a55639e5adaadcd6414b50d5ebf0d1cd2">predict_proba</a> (const torch::Tensor &X, <a class="el" href="classsvm__classifier_1_1DataConverter.html">DataConverter</a> &converter) override</td></tr>
|
||||||
|
<tr class="memdesc:a55639e5adaadcd6414b50d5ebf0d1cd2"><td class="mdescLeft"> </td><td class="mdescRight">Predict class probabilities. <br /></td></tr>
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<tr class="separator:a55639e5adaadcd6414b50d5ebf0d1cd2"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:a966b79bc8b6fac0fa78feefc2dd8a878" id="r_a966b79bc8b6fac0fa78feefc2dd8a878"><td class="memItemLeft" align="right" valign="top">std::vector< std::vector< double > > </td><td class="memItemRight" valign="bottom"><a class="el" href="classsvm__classifier_1_1OneVsRestStrategy.html#a966b79bc8b6fac0fa78feefc2dd8a878">decision_function</a> (const torch::Tensor &X, <a class="el" href="classsvm__classifier_1_1DataConverter.html">DataConverter</a> &converter) override</td></tr>
|
||||||
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<tr class="memdesc:a966b79bc8b6fac0fa78feefc2dd8a878"><td class="mdescLeft"> </td><td class="mdescRight">Get decision function values. <br /></td></tr>
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<tr class="separator:a966b79bc8b6fac0fa78feefc2dd8a878"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:a5e10800b16dbc66fd1c0d5e0a42871f0" id="r_a5e10800b16dbc66fd1c0d5e0a42871f0"><td class="memItemLeft" align="right" valign="top">std::vector< int > </td><td class="memItemRight" valign="bottom"><a class="el" href="classsvm__classifier_1_1OneVsRestStrategy.html#a5e10800b16dbc66fd1c0d5e0a42871f0">get_classes</a> () const override</td></tr>
|
||||||
|
<tr class="memdesc:a5e10800b16dbc66fd1c0d5e0a42871f0"><td class="mdescLeft"> </td><td class="mdescRight">Get unique class labels. <br /></td></tr>
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<tr class="separator:a5e10800b16dbc66fd1c0d5e0a42871f0"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:a200300198628ac119eac09e62ff62336" id="r_a200300198628ac119eac09e62ff62336"><td class="memItemLeft" align="right" valign="top">bool </td><td class="memItemRight" valign="bottom"><a class="el" href="classsvm__classifier_1_1OneVsRestStrategy.html#a200300198628ac119eac09e62ff62336">supports_probability</a> () const override</td></tr>
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||||||
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<tr class="memdesc:a200300198628ac119eac09e62ff62336"><td class="mdescLeft"> </td><td class="mdescRight">Check if the model supports probability prediction. <br /></td></tr>
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<tr class="separator:a200300198628ac119eac09e62ff62336"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:a53abe89ec25c33fd9c32d92ba08d01ed" id="r_a53abe89ec25c33fd9c32d92ba08d01ed"><td class="memItemLeft" align="right" valign="top">int </td><td class="memItemRight" valign="bottom"><a class="el" href="classsvm__classifier_1_1OneVsRestStrategy.html#a53abe89ec25c33fd9c32d92ba08d01ed">get_n_classes</a> () const override</td></tr>
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||||||
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<tr class="memdesc:a53abe89ec25c33fd9c32d92ba08d01ed"><td class="mdescLeft"> </td><td class="mdescRight">Get number of classes. <br /></td></tr>
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<tr class="separator:a53abe89ec25c33fd9c32d92ba08d01ed"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:af9e1bd6d08ce3e7afd5279c835ce6cfb" id="r_af9e1bd6d08ce3e7afd5279c835ce6cfb"><td class="memItemLeft" align="right" valign="top">MulticlassStrategy </td><td class="memItemRight" valign="bottom"><a class="el" href="classsvm__classifier_1_1OneVsRestStrategy.html#af9e1bd6d08ce3e7afd5279c835ce6cfb">get_strategy_type</a> () const override</td></tr>
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<tr class="memdesc:af9e1bd6d08ce3e7afd5279c835ce6cfb"><td class="mdescLeft"> </td><td class="mdescRight">Get strategy type. <br /></td></tr>
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<tr class="separator:af9e1bd6d08ce3e7afd5279c835ce6cfb"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="inherit_header pub_methods_classsvm__classifier_1_1MulticlassStrategyBase"><td colspan="2" onclick="javascript:toggleInherit('pub_methods_classsvm__classifier_1_1MulticlassStrategyBase')"><img src="closed.png" alt="-"/> Public Member Functions inherited from <a class="el" href="classsvm__classifier_1_1MulticlassStrategyBase.html">svm_classifier::MulticlassStrategyBase</a></td></tr>
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<tr class="memitem:a8a5647dd57eed281288f0c9011b11395 inherit pub_methods_classsvm__classifier_1_1MulticlassStrategyBase" id="r_a8a5647dd57eed281288f0c9011b11395"><td class="memItemLeft" align="right" valign="top">
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virtual </td><td class="memItemRight" valign="bottom"><b>~MulticlassStrategyBase</b> ()=default</td></tr>
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<tr class="memdesc:a8a5647dd57eed281288f0c9011b11395 inherit pub_methods_classsvm__classifier_1_1MulticlassStrategyBase"><td class="mdescLeft"> </td><td class="mdescRight">Virtual destructor. <br /></td></tr>
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<tr class="separator:a8a5647dd57eed281288f0c9011b11395 inherit pub_methods_classsvm__classifier_1_1MulticlassStrategyBase"><td class="memSeparator" colspan="2"> </td></tr>
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</table><table class="memberdecls">
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<tr class="heading"><td colspan="2"><h2 class="groupheader"><a id="inherited" name="inherited"></a>
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Additional Inherited Members</h2></td></tr>
|
||||||
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<tr class="inherit_header pro_attribs_classsvm__classifier_1_1MulticlassStrategyBase"><td colspan="2" onclick="javascript:toggleInherit('pro_attribs_classsvm__classifier_1_1MulticlassStrategyBase')"><img src="closed.png" alt="-"/> Protected Attributes inherited from <a class="el" href="classsvm__classifier_1_1MulticlassStrategyBase.html">svm_classifier::MulticlassStrategyBase</a></td></tr>
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<tr class="memitem:a15bb6eb53e91e604b259b3050bd40e27 inherit pro_attribs_classsvm__classifier_1_1MulticlassStrategyBase" id="r_a15bb6eb53e91e604b259b3050bd40e27"><td class="memItemLeft" align="right" valign="top">std::vector< int > </td><td class="memItemRight" valign="bottom"><a class="el" href="classsvm__classifier_1_1MulticlassStrategyBase.html#a15bb6eb53e91e604b259b3050bd40e27">classes_</a></td></tr>
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||||||
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<tr class="memdesc:a15bb6eb53e91e604b259b3050bd40e27 inherit pro_attribs_classsvm__classifier_1_1MulticlassStrategyBase"><td class="mdescLeft"> </td><td class="mdescRight">Unique class labels. <br /></td></tr>
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<tr class="separator:a15bb6eb53e91e604b259b3050bd40e27 inherit pro_attribs_classsvm__classifier_1_1MulticlassStrategyBase"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:a8e74cd580feaac0da34d204274a24fea inherit pro_attribs_classsvm__classifier_1_1MulticlassStrategyBase" id="r_a8e74cd580feaac0da34d204274a24fea"><td class="memItemLeft" align="right" valign="top">bool </td><td class="memItemRight" valign="bottom"><a class="el" href="classsvm__classifier_1_1MulticlassStrategyBase.html#a8e74cd580feaac0da34d204274a24fea">is_trained_</a> = false</td></tr>
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||||||
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<tr class="memdesc:a8e74cd580feaac0da34d204274a24fea inherit pro_attribs_classsvm__classifier_1_1MulticlassStrategyBase"><td class="mdescLeft"> </td><td class="mdescRight">Whether the model is trained. <br /></td></tr>
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<tr class="separator:a8e74cd580feaac0da34d204274a24fea inherit pro_attribs_classsvm__classifier_1_1MulticlassStrategyBase"><td class="memSeparator" colspan="2"> </td></tr>
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</table>
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<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
|
||||||
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<div class="textblock"><p>One-vs-Rest (OvR) multiclass strategy. </p>
|
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<p class="definition">Definition at line <a class="el" href="multiclass__strategy_8hpp_source.html#l00099">99</a> of file <a class="el" href="multiclass__strategy_8hpp_source.html">multiclass_strategy.hpp</a>.</p>
|
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</div><h2 class="groupheader">Member Function Documentation</h2>
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<a id="a966b79bc8b6fac0fa78feefc2dd8a878" name="a966b79bc8b6fac0fa78feefc2dd8a878"></a>
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||||||
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<h2 class="memtitle"><span class="permalink"><a href="#a966b79bc8b6fac0fa78feefc2dd8a878">◆ </a></span>decision_function()</h2>
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<tr>
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<td class="memname">std::vector< std::vector< double > > svm_classifier::OneVsRestStrategy::decision_function </td>
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<td>(</td>
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<td class="paramtype">const torch::Tensor & </td>
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<td class="paramname"><em>X</em>, </td>
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<td></td>
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<td class="paramtype"><a class="el" href="classsvm__classifier_1_1DataConverter.html">DataConverter</a> & </td>
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<td class="paramname"><em>converter</em> </td>
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<tr>
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<td>)</td>
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<span class="mlabels"><span class="mlabel">override</span><span class="mlabel">virtual</span></span> </td>
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</div><div class="memdoc">
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<p>Get decision function values. </p>
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<dl class="params"><dt>Parameters</dt><dd>
|
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|
<table class="params">
|
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|
<tr><td class="paramname">X</td><td>Feature tensor of shape (n_samples, n_features) </td></tr>
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<tr><td class="paramname">converter</td><td>Data converter instance </td></tr>
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</table>
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</dd>
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</dl>
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<dl class="section return"><dt>Returns</dt><dd>Decision function values </dd></dl>
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<p>Implements <a class="el" href="classsvm__classifier_1_1MulticlassStrategyBase.html#ad1c4eb746cb1fdd67cf436ff85a9b0f0">svm_classifier::MulticlassStrategyBase</a>.</p>
|
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||||||
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<h2 class="memtitle"><span class="permalink"><a href="#aae14da8c0effd04731b5a4a0181eb1b6">◆ </a></span>fit()</h2>
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<tr>
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<td class="memname"><a class="el" href="structsvm__classifier_1_1TrainingMetrics.html">TrainingMetrics</a> svm_classifier::OneVsRestStrategy::fit </td>
|
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|
<td>(</td>
|
||||||
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<td class="paramtype">const torch::Tensor & </td>
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<td></td>
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<td class="paramtype">const torch::Tensor & </td>
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<td></td>
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<td class="paramtype">const KernelParameters & </td>
|
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<td class="paramtype"><a class="el" href="classsvm__classifier_1_1DataConverter.html">DataConverter</a> & </td>
|
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<td class="paramname"><em>converter</em> </td>
|
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<tr>
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<td>)</td>
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<span class="mlabels"><span class="mlabel">override</span><span class="mlabel">virtual</span></span> </td>
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</div><div class="memdoc">
|
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|
||||||
|
<p>Train the multiclass classifier. </p>
|
||||||
|
<dl class="params"><dt>Parameters</dt><dd>
|
||||||
|
<table class="params">
|
||||||
|
<tr><td class="paramname">X</td><td>Feature tensor of shape (n_samples, n_features) </td></tr>
|
||||||
|
<tr><td class="paramname">y</td><td>Target tensor of shape (n_samples,) </td></tr>
|
||||||
|
<tr><td class="paramname">params</td><td>Kernel parameters </td></tr>
|
||||||
|
<tr><td class="paramname">converter</td><td>Data converter instance </td></tr>
|
||||||
|
</table>
|
||||||
|
</dd>
|
||||||
|
</dl>
|
||||||
|
<dl class="section return"><dt>Returns</dt><dd>Training metrics </dd></dl>
|
||||||
|
|
||||||
|
<p>Implements <a class="el" href="classsvm__classifier_1_1MulticlassStrategyBase.html#a548af7201b7970abee0c31e7ec07d896">svm_classifier::MulticlassStrategyBase</a>.</p>
|
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||||||
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<h2 class="memtitle"><span class="permalink"><a href="#a5e10800b16dbc66fd1c0d5e0a42871f0">◆ </a></span>get_classes()</h2>
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<td class="memname">std::vector< int > svm_classifier::OneVsRestStrategy::get_classes </td>
|
||||||
|
<td>(</td>
|
||||||
|
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|
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|
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|
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|
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<span class="mlabels"><span class="mlabel">inline</span><span class="mlabel">override</span><span class="mlabel">virtual</span></span> </td>
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|
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||||||
|
<p>Get unique class labels. </p>
|
||||||
|
<dl class="section return"><dt>Returns</dt><dd>Vector of unique class labels </dd></dl>
|
||||||
|
|
||||||
|
<p>Implements <a class="el" href="classsvm__classifier_1_1MulticlassStrategyBase.html#a379c4000227cc46410bfbecce6e80c33">svm_classifier::MulticlassStrategyBase</a>.</p>
|
||||||
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||||||
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<p class="definition">Definition at line <a class="el" href="multiclass__strategy_8hpp_source.html#l00125">125</a> of file <a class="el" href="multiclass__strategy_8hpp_source.html">multiclass_strategy.hpp</a>.</p>
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||||||
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||||||
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||||||
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<h2 class="memtitle"><span class="permalink"><a href="#a53abe89ec25c33fd9c32d92ba08d01ed">◆ </a></span>get_n_classes()</h2>
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<td class="memname">int svm_classifier::OneVsRestStrategy::get_n_classes </td>
|
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|
<td>(</td>
|
||||||
|
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|
||||||
|
<td> const</td>
|
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|
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|
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|
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|
||||||
|
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|
||||||
|
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||||||
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<span class="mlabels"><span class="mlabel">inline</span><span class="mlabel">override</span><span class="mlabel">virtual</span></span> </td>
|
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|
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|
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|
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|
||||||
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|
||||||
|
<p>Get number of classes. </p>
|
||||||
|
<dl class="section return"><dt>Returns</dt><dd>Number of classes </dd></dl>
|
||||||
|
|
||||||
|
<p>Implements <a class="el" href="classsvm__classifier_1_1MulticlassStrategyBase.html#a1740d877a4d634ec1763cb8646f5e172">svm_classifier::MulticlassStrategyBase</a>.</p>
|
||||||
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||||||
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<p class="definition">Definition at line <a class="el" href="multiclass__strategy_8hpp_source.html#l00129">129</a> of file <a class="el" href="multiclass__strategy_8hpp_source.html">multiclass_strategy.hpp</a>.</p>
|
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||||||
|
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<h2 class="memtitle"><span class="permalink"><a href="#af9e1bd6d08ce3e7afd5279c835ce6cfb">◆ </a></span>get_strategy_type()</h2>
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<td class="memname">MulticlassStrategy svm_classifier::OneVsRestStrategy::get_strategy_type </td>
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<td>(</td>
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<p>Get strategy type. </p>
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<dl class="section return"><dt>Returns</dt><dd>Multiclass strategy type </dd></dl>
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<p>Implements <a class="el" href="classsvm__classifier_1_1MulticlassStrategyBase.html#a31a0501fa1a6db1d41cbf825b2348e47">svm_classifier::MulticlassStrategyBase</a>.</p>
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<p class="definition">Definition at line <a class="el" href="multiclass__strategy_8hpp_source.html#l00131">131</a> of file <a class="el" href="multiclass__strategy_8hpp_source.html">multiclass_strategy.hpp</a>.</p>
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<h2 class="memtitle"><span class="permalink"><a href="#a771903a821d5380ddd5d0b3a912e7df9">◆ </a></span>predict()</h2>
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<td class="memname">std::vector< int > svm_classifier::OneVsRestStrategy::predict </td>
|
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<td>(</td>
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<td class="paramtype">const torch::Tensor & </td>
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<td class="paramname"><em>X</em>, </td>
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<td class="paramtype"><a class="el" href="classsvm__classifier_1_1DataConverter.html">DataConverter</a> & </td>
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<td class="paramname"><em>converter</em> </td>
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<p>Predict class labels. </p>
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<dl class="params"><dt>Parameters</dt><dd>
|
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|
<table class="params">
|
||||||
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<tr><td class="paramname">X</td><td>Feature tensor of shape (n_samples, n_features) </td></tr>
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<dl class="section return"><dt>Returns</dt><dd>Predicted class labels </dd></dl>
|
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<p>Implements <a class="el" href="classsvm__classifier_1_1MulticlassStrategyBase.html#a70f94cfcf8b2bf6d60133c688fe55f9d">svm_classifier::MulticlassStrategyBase</a>.</p>
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<h2 class="memtitle"><span class="permalink"><a href="#a55639e5adaadcd6414b50d5ebf0d1cd2">◆ </a></span>predict_proba()</h2>
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<td class="memname">std::vector< std::vector< double > > svm_classifier::OneVsRestStrategy::predict_proba </td>
|
||||||
|
<td>(</td>
|
||||||
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<td class="paramtype">const torch::Tensor & </td>
|
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||||||
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<td class="paramtype"><a class="el" href="classsvm__classifier_1_1DataConverter.html">DataConverter</a> & </td>
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<p>Predict class probabilities. </p>
|
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<dl class="params"><dt>Parameters</dt><dd>
|
||||||
|
<table class="params">
|
||||||
|
<tr><td class="paramname">X</td><td>Feature tensor of shape (n_samples, n_features) </td></tr>
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||||||
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<tr><td class="paramname">converter</td><td>Data converter instance </td></tr>
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</dd>
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|
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<dl class="section return"><dt>Returns</dt><dd>Class probabilities for each sample </dd></dl>
|
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||||||
|
<p>Implements <a class="el" href="classsvm__classifier_1_1MulticlassStrategyBase.html#ab5348ee3b83547702ec7903ee7ee2da7">svm_classifier::MulticlassStrategyBase</a>.</p>
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<a id="a200300198628ac119eac09e62ff62336" name="a200300198628ac119eac09e62ff62336"></a>
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<h2 class="memtitle"><span class="permalink"><a href="#a200300198628ac119eac09e62ff62336">◆ </a></span>supports_probability()</h2>
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<div class="memitem">
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<td class="memname">bool svm_classifier::OneVsRestStrategy::supports_probability </td>
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<td>(</td>
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<p>Check if the model supports probability prediction. </p>
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<dl class="section return"><dt>Returns</dt><dd>True if probabilities are supported </dd></dl>
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<p>Implements <a class="el" href="classsvm__classifier_1_1MulticlassStrategyBase.html#a2ab91902f8d6eb216f626ce9ea4be992">svm_classifier::MulticlassStrategyBase</a>.</p>
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<hr/>The documentation for this class was generated from the following file:<ul>
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<li>include/svm_classifier/<a class="el" href="multiclass__strategy_8hpp_source.html">multiclass_strategy.hpp</a></li>
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<div id="projectbrief">High-performance Support Vector Machine classifier with scikit-learn compatible API</div>
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<li class="navelem"><b>svm_classifier</b></li><li class="navelem"><a class="el" href="classsvm__classifier_1_1SVMClassifier.html">SVMClassifier</a></li> </ul>
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<p>This is the complete list of members for <a class="el" href="classsvm__classifier_1_1SVMClassifier.html">svm_classifier::SVMClassifier</a>, including all inherited members.</p>
|
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<table class="directory">
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<tr class="even"><td class="entry"><a class="el" href="classsvm__classifier_1_1SVMClassifier.html#a4c91072ea0d3d9b97ba458ff7d0898b8">cross_validate</a>(const torch::Tensor &X, const torch::Tensor &y, int cv=5)</td><td class="entry"><a class="el" href="classsvm__classifier_1_1SVMClassifier.html">svm_classifier::SVMClassifier</a></td><td class="entry"></td></tr>
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<tr class="odd"><td class="entry"><a class="el" href="classsvm__classifier_1_1SVMClassifier.html#ad153c0537998eae5fbca5fd0b5ead2b7">decision_function</a>(const torch::Tensor &X)</td><td class="entry"><a class="el" href="classsvm__classifier_1_1SVMClassifier.html">svm_classifier::SVMClassifier</a></td><td class="entry"></td></tr>
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<tr class="even"><td class="entry"><a class="el" href="classsvm__classifier_1_1SVMClassifier.html#a38a9b020b9f4f9254920c97a3a047e9b">evaluate</a>(const torch::Tensor &X, const torch::Tensor &y_true)</td><td class="entry"><a class="el" href="classsvm__classifier_1_1SVMClassifier.html">svm_classifier::SVMClassifier</a></td><td class="entry"></td></tr>
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<tr class="odd"><td class="entry"><a class="el" href="classsvm__classifier_1_1SVMClassifier.html#a7e6648c4d2bac92bb00381076ea92db3">fit</a>(const torch::Tensor &X, const torch::Tensor &y)</td><td class="entry"><a class="el" href="classsvm__classifier_1_1SVMClassifier.html">svm_classifier::SVMClassifier</a></td><td class="entry"></td></tr>
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<tr class="even"><td class="entry"><a class="el" href="classsvm__classifier_1_1SVMClassifier.html#af0fea42cdfc9416ed854b0d4aefa82b9">get_classes</a>() const</td><td class="entry"><a class="el" href="classsvm__classifier_1_1SVMClassifier.html">svm_classifier::SVMClassifier</a></td><td class="entry"></td></tr>
|
||||||
|
<tr class="odd"><td class="entry"><a class="el" href="classsvm__classifier_1_1SVMClassifier.html#a2ade33562381e34cbe4b04089545a715">get_feature_importance</a>() const</td><td class="entry"><a class="el" href="classsvm__classifier_1_1SVMClassifier.html">svm_classifier::SVMClassifier</a></td><td class="entry"></td></tr>
|
||||||
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<tr class="even"><td class="entry"><a class="el" href="classsvm__classifier_1_1SVMClassifier.html#a55338ab396bd5da923b6acbef8ed783a">get_kernel_type</a>() const</td><td class="entry"><a class="el" href="classsvm__classifier_1_1SVMClassifier.html">svm_classifier::SVMClassifier</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
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<tr class="odd"><td class="entry"><a class="el" href="classsvm__classifier_1_1SVMClassifier.html#a14c2f7917c8a91154c09160288509f2c">get_multiclass_strategy</a>() const</td><td class="entry"><a class="el" href="classsvm__classifier_1_1SVMClassifier.html">svm_classifier::SVMClassifier</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
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<tr class="even"><td class="entry"><a class="el" href="classsvm__classifier_1_1SVMClassifier.html#a75d501339e2e2273082b0838e9caadcd">get_n_classes</a>() const</td><td class="entry"><a class="el" href="classsvm__classifier_1_1SVMClassifier.html">svm_classifier::SVMClassifier</a></td><td class="entry"></td></tr>
|
||||||
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<tr class="odd"><td class="entry"><a class="el" href="classsvm__classifier_1_1SVMClassifier.html#a780afcb2ad618e46541aff8a44e9c7b4">get_n_features</a>() const</td><td class="entry"><a class="el" href="classsvm__classifier_1_1SVMClassifier.html">svm_classifier::SVMClassifier</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
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<tr class="even"><td class="entry"><a class="el" href="classsvm__classifier_1_1SVMClassifier.html#a7c39ec09b15186dcb4f04ae7171d23bb">get_parameters</a>() const</td><td class="entry"><a class="el" href="classsvm__classifier_1_1SVMClassifier.html">svm_classifier::SVMClassifier</a></td><td class="entry"></td></tr>
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||||||
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<tr class="odd"><td class="entry"><a class="el" href="classsvm__classifier_1_1SVMClassifier.html#a38173e5cf0f6a4620f032fd54c28d592">get_svm_library</a>() const</td><td class="entry"><a class="el" href="classsvm__classifier_1_1SVMClassifier.html">svm_classifier::SVMClassifier</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
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<tr class="even"><td class="entry"><a class="el" href="classsvm__classifier_1_1SVMClassifier.html#a0b8c77f81d84489b2da0d080773a2970">get_training_metrics</a>() const</td><td class="entry"><a class="el" href="classsvm__classifier_1_1SVMClassifier.html">svm_classifier::SVMClassifier</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
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<tr class="odd"><td class="entry"><a class="el" href="classsvm__classifier_1_1SVMClassifier.html#afed66a704dfb38cc7d080d3337d10194">grid_search</a>(const torch::Tensor &X, const torch::Tensor &y, const nlohmann::json &param_grid, int cv=5)</td><td class="entry"><a class="el" href="classsvm__classifier_1_1SVMClassifier.html">svm_classifier::SVMClassifier</a></td><td class="entry"></td></tr>
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<tr class="even"><td class="entry"><a class="el" href="classsvm__classifier_1_1SVMClassifier.html#a71a85ab7893e7e2b40763db34096d8bb">is_fitted</a>() const</td><td class="entry"><a class="el" href="classsvm__classifier_1_1SVMClassifier.html">svm_classifier::SVMClassifier</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
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<tr class="odd"><td class="entry"><a class="el" href="classsvm__classifier_1_1SVMClassifier.html#a583f5743acf5e6b850e079b9190989f1">load_model</a>(const std::string &filename)</td><td class="entry"><a class="el" href="classsvm__classifier_1_1SVMClassifier.html">svm_classifier::SVMClassifier</a></td><td class="entry"></td></tr>
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<tr class="even"><td class="entry"><a class="el" href="classsvm__classifier_1_1SVMClassifier.html#a209902805c75e8f22c55575adfedc7be">operator=</a>(const SVMClassifier &)=delete</td><td class="entry"><a class="el" href="classsvm__classifier_1_1SVMClassifier.html">svm_classifier::SVMClassifier</a></td><td class="entry"></td></tr>
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<tr class="odd"><td class="entry"><a class="el" href="classsvm__classifier_1_1SVMClassifier.html#a49b6a4a5ae8a8e0eaf24221482be3d6a">operator=</a>(SVMClassifier &&) noexcept</td><td class="entry"><a class="el" href="classsvm__classifier_1_1SVMClassifier.html">svm_classifier::SVMClassifier</a></td><td class="entry"></td></tr>
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<tr class="even"><td class="entry"><a class="el" href="classsvm__classifier_1_1SVMClassifier.html#a5c998d5574b3b6afe003b23ed02ed1d1">predict</a>(const torch::Tensor &X)</td><td class="entry"><a class="el" href="classsvm__classifier_1_1SVMClassifier.html">svm_classifier::SVMClassifier</a></td><td class="entry"></td></tr>
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<tr class="odd"><td class="entry"><a class="el" href="classsvm__classifier_1_1SVMClassifier.html#ab4ef3c839e085ece646cdd2501a51f67">predict_proba</a>(const torch::Tensor &X)</td><td class="entry"><a class="el" href="classsvm__classifier_1_1SVMClassifier.html">svm_classifier::SVMClassifier</a></td><td class="entry"></td></tr>
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<tr class="even"><td class="entry"><a class="el" href="classsvm__classifier_1_1SVMClassifier.html#aa2bd5715c9e54e3fb465a9bcbf2e9c8a">reset</a>()</td><td class="entry"><a class="el" href="classsvm__classifier_1_1SVMClassifier.html">svm_classifier::SVMClassifier</a></td><td class="entry"></td></tr>
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<tr class="odd"><td class="entry"><a class="el" href="classsvm__classifier_1_1SVMClassifier.html#ab8a0bd35705825e80a7567b576d47359">save_model</a>(const std::string &filename) const</td><td class="entry"><a class="el" href="classsvm__classifier_1_1SVMClassifier.html">svm_classifier::SVMClassifier</a></td><td class="entry"></td></tr>
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<tr class="even"><td class="entry"><a class="el" href="classsvm__classifier_1_1SVMClassifier.html#a0479c57489c14be4a5ca79368086f7f6">score</a>(const torch::Tensor &X, const torch::Tensor &y_true)</td><td class="entry"><a class="el" href="classsvm__classifier_1_1SVMClassifier.html">svm_classifier::SVMClassifier</a></td><td class="entry"></td></tr>
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<tr class="odd"><td class="entry"><a class="el" href="classsvm__classifier_1_1SVMClassifier.html#adb01e761fea07c709f3a0e315d3d0e06">set_parameters</a>(const nlohmann::json &config)</td><td class="entry"><a class="el" href="classsvm__classifier_1_1SVMClassifier.html">svm_classifier::SVMClassifier</a></td><td class="entry"></td></tr>
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<tr class="even"><td class="entry"><a class="el" href="classsvm__classifier_1_1SVMClassifier.html#a3f8b4e932f075b267507ad77a499a135">supports_probability</a>() const</td><td class="entry"><a class="el" href="classsvm__classifier_1_1SVMClassifier.html">svm_classifier::SVMClassifier</a></td><td class="entry"></td></tr>
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<tr class="odd"><td class="entry"><a class="el" href="classsvm__classifier_1_1SVMClassifier.html#a3ed45cdbc3fc5d947320177f42115dcf">SVMClassifier</a>()</td><td class="entry"><a class="el" href="classsvm__classifier_1_1SVMClassifier.html">svm_classifier::SVMClassifier</a></td><td class="entry"></td></tr>
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<tr class="even"><td class="entry"><a class="el" href="classsvm__classifier_1_1SVMClassifier.html#a2afb41f77de4e8de6368d274a30191ec">SVMClassifier</a>(const nlohmann::json &config)</td><td class="entry"><a class="el" href="classsvm__classifier_1_1SVMClassifier.html">svm_classifier::SVMClassifier</a></td><td class="entry"><span class="mlabel">explicit</span></td></tr>
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<tr class="odd"><td class="entry"><a class="el" href="classsvm__classifier_1_1SVMClassifier.html#a90b2f18dd2cfeb23cf1375f265e22db0">SVMClassifier</a>(KernelType kernel, double C=1.0, MulticlassStrategy multiclass_strategy=MulticlassStrategy::ONE_VS_REST)</td><td class="entry"><a class="el" href="classsvm__classifier_1_1SVMClassifier.html">svm_classifier::SVMClassifier</a></td><td class="entry"></td></tr>
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<tr class="even"><td class="entry"><a class="el" href="classsvm__classifier_1_1SVMClassifier.html#a377b6082ac4153be3197ef70c1c82984">SVMClassifier</a>(const SVMClassifier &)=delete</td><td class="entry"><a class="el" href="classsvm__classifier_1_1SVMClassifier.html">svm_classifier::SVMClassifier</a></td><td class="entry"></td></tr>
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<tr class="odd"><td class="entry"><a class="el" href="classsvm__classifier_1_1SVMClassifier.html#ae2eafdc66d1907c145efffd186dfff3f">SVMClassifier</a>(SVMClassifier &&) noexcept</td><td class="entry"><a class="el" href="classsvm__classifier_1_1SVMClassifier.html">svm_classifier::SVMClassifier</a></td><td class="entry"></td></tr>
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<tr class="even"><td class="entry"><a class="el" href="classsvm__classifier_1_1SVMClassifier.html#a233584f6696969ce1a402624fd046146">~SVMClassifier</a>()</td><td class="entry"><a class="el" href="classsvm__classifier_1_1SVMClassifier.html">svm_classifier::SVMClassifier</a></td><td class="entry"></td></tr>
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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
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<div id="projectname">SVM Classifier C++<span id="projectnumber"> 1.0.0</span>
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</div>
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<div id="projectbrief">High-performance Support Vector Machine classifier with scikit-learn compatible API</div>
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<ul>
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<li class="navelem"><b>svm_classifier</b></li><li class="navelem"><a class="el" href="classsvm__classifier_1_1SVMClassifier.html">SVMClassifier</a></li> </ul>
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<a href="#pub-methods">Public Member Functions</a> |
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<a href="classsvm__classifier_1_1SVMClassifier-members.html">List of all members</a> </div>
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<div class="headertitle"><div class="title">svm_classifier::SVMClassifier Class Reference</div></div>
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<div class="contents">
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<p>Support Vector Machine Classifier with scikit-learn compatible API.
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<a href="classsvm__classifier_1_1SVMClassifier.html#details">More...</a></p>
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<p><code>#include <<a class="el" href="svm__classifier_8hpp_source.html">svm_classifier.hpp</a>></code></p>
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<table class="memberdecls">
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<tr class="heading"><td colspan="2"><h2 class="groupheader"><a id="pub-methods" name="pub-methods"></a>
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Public Member Functions</h2></td></tr>
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<tr class="memitem:a3ed45cdbc3fc5d947320177f42115dcf" id="r_a3ed45cdbc3fc5d947320177f42115dcf"><td class="memItemLeft" align="right" valign="top"><a id="a3ed45cdbc3fc5d947320177f42115dcf" name="a3ed45cdbc3fc5d947320177f42115dcf"></a>
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 </td><td class="memItemRight" valign="bottom"><b>SVMClassifier</b> ()</td></tr>
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<tr class="memdesc:a3ed45cdbc3fc5d947320177f42115dcf"><td class="mdescLeft"> </td><td class="mdescRight">Default constructor with default parameters. <br /></td></tr>
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<tr class="separator:a3ed45cdbc3fc5d947320177f42115dcf"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:a2afb41f77de4e8de6368d274a30191ec" id="r_a2afb41f77de4e8de6368d274a30191ec"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="classsvm__classifier_1_1SVMClassifier.html#a2afb41f77de4e8de6368d274a30191ec">SVMClassifier</a> (const nlohmann::json &config)</td></tr>
|
||||||
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<tr class="memdesc:a2afb41f77de4e8de6368d274a30191ec"><td class="mdescLeft"> </td><td class="mdescRight">Constructor with JSON parameters. <br /></td></tr>
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<tr class="separator:a2afb41f77de4e8de6368d274a30191ec"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:a90b2f18dd2cfeb23cf1375f265e22db0" id="r_a90b2f18dd2cfeb23cf1375f265e22db0"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="classsvm__classifier_1_1SVMClassifier.html#a90b2f18dd2cfeb23cf1375f265e22db0">SVMClassifier</a> (KernelType kernel, double C=1.0, MulticlassStrategy multiclass_strategy=MulticlassStrategy::ONE_VS_REST)</td></tr>
|
||||||
|
<tr class="memdesc:a90b2f18dd2cfeb23cf1375f265e22db0"><td class="mdescLeft"> </td><td class="mdescRight">Constructor with explicit parameters. <br /></td></tr>
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<tr class="separator:a90b2f18dd2cfeb23cf1375f265e22db0"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:a233584f6696969ce1a402624fd046146" id="r_a233584f6696969ce1a402624fd046146"><td class="memItemLeft" align="right" valign="top"><a id="a233584f6696969ce1a402624fd046146" name="a233584f6696969ce1a402624fd046146"></a>
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 </td><td class="memItemRight" valign="bottom"><b>~SVMClassifier</b> ()</td></tr>
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<tr class="memdesc:a233584f6696969ce1a402624fd046146"><td class="mdescLeft"> </td><td class="mdescRight">Destructor. <br /></td></tr>
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 </td><td class="memItemRight" valign="bottom"><b>SVMClassifier</b> (const <a class="el" href="classsvm__classifier_1_1SVMClassifier.html">SVMClassifier</a> &)=delete</td></tr>
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<tr class="memdesc:a377b6082ac4153be3197ef70c1c82984"><td class="mdescLeft"> </td><td class="mdescRight">Copy constructor (deleted - models are not copyable) <br /></td></tr>
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<a class="el" href="classsvm__classifier_1_1SVMClassifier.html">SVMClassifier</a> & </td><td class="memItemRight" valign="bottom"><b>operator=</b> (const <a class="el" href="classsvm__classifier_1_1SVMClassifier.html">SVMClassifier</a> &)=delete</td></tr>
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<tr class="memdesc:a209902805c75e8f22c55575adfedc7be"><td class="mdescLeft"> </td><td class="mdescRight">Copy assignment (deleted - models are not copyable) <br /></td></tr>
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 </td><td class="memItemRight" valign="bottom"><b>SVMClassifier</b> (<a class="el" href="classsvm__classifier_1_1SVMClassifier.html">SVMClassifier</a> &&) noexcept</td></tr>
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<tr class="memdesc:ae2eafdc66d1907c145efffd186dfff3f"><td class="mdescLeft"> </td><td class="mdescRight">Move constructor. <br /></td></tr>
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<tr class="memitem:a49b6a4a5ae8a8e0eaf24221482be3d6a" id="r_a49b6a4a5ae8a8e0eaf24221482be3d6a"><td class="memItemLeft" align="right" valign="top"><a id="a49b6a4a5ae8a8e0eaf24221482be3d6a" name="a49b6a4a5ae8a8e0eaf24221482be3d6a"></a>
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<a class="el" href="classsvm__classifier_1_1SVMClassifier.html">SVMClassifier</a> & </td><td class="memItemRight" valign="bottom"><b>operator=</b> (<a class="el" href="classsvm__classifier_1_1SVMClassifier.html">SVMClassifier</a> &&) noexcept</td></tr>
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<tr class="memdesc:a49b6a4a5ae8a8e0eaf24221482be3d6a"><td class="mdescLeft"> </td><td class="mdescRight">Move assignment. <br /></td></tr>
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<tr class="memitem:a7e6648c4d2bac92bb00381076ea92db3" id="r_a7e6648c4d2bac92bb00381076ea92db3"><td class="memItemLeft" align="right" valign="top"><a class="el" href="structsvm__classifier_1_1TrainingMetrics.html">TrainingMetrics</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="classsvm__classifier_1_1SVMClassifier.html#a7e6648c4d2bac92bb00381076ea92db3">fit</a> (const torch::Tensor &X, const torch::Tensor &y)</td></tr>
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<tr class="memdesc:a7e6648c4d2bac92bb00381076ea92db3"><td class="mdescLeft"> </td><td class="mdescRight">Train the SVM classifier. <br /></td></tr>
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<tr class="separator:a7e6648c4d2bac92bb00381076ea92db3"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:a5c998d5574b3b6afe003b23ed02ed1d1" id="r_a5c998d5574b3b6afe003b23ed02ed1d1"><td class="memItemLeft" align="right" valign="top">torch::Tensor </td><td class="memItemRight" valign="bottom"><a class="el" href="classsvm__classifier_1_1SVMClassifier.html#a5c998d5574b3b6afe003b23ed02ed1d1">predict</a> (const torch::Tensor &X)</td></tr>
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<tr class="memdesc:a5c998d5574b3b6afe003b23ed02ed1d1"><td class="mdescLeft"> </td><td class="mdescRight">Predict class labels for samples. <br /></td></tr>
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<tr class="separator:a5c998d5574b3b6afe003b23ed02ed1d1"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:ab4ef3c839e085ece646cdd2501a51f67" id="r_ab4ef3c839e085ece646cdd2501a51f67"><td class="memItemLeft" align="right" valign="top">torch::Tensor </td><td class="memItemRight" valign="bottom"><a class="el" href="classsvm__classifier_1_1SVMClassifier.html#ab4ef3c839e085ece646cdd2501a51f67">predict_proba</a> (const torch::Tensor &X)</td></tr>
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<tr class="memdesc:ab4ef3c839e085ece646cdd2501a51f67"><td class="mdescLeft"> </td><td class="mdescRight">Predict class probabilities for samples. <br /></td></tr>
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<tr class="separator:ab4ef3c839e085ece646cdd2501a51f67"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:ad153c0537998eae5fbca5fd0b5ead2b7" id="r_ad153c0537998eae5fbca5fd0b5ead2b7"><td class="memItemLeft" align="right" valign="top">torch::Tensor </td><td class="memItemRight" valign="bottom"><a class="el" href="classsvm__classifier_1_1SVMClassifier.html#ad153c0537998eae5fbca5fd0b5ead2b7">decision_function</a> (const torch::Tensor &X)</td></tr>
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<tr class="memdesc:ad153c0537998eae5fbca5fd0b5ead2b7"><td class="mdescLeft"> </td><td class="mdescRight">Get decision function values. <br /></td></tr>
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<tr class="separator:ad153c0537998eae5fbca5fd0b5ead2b7"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:a0479c57489c14be4a5ca79368086f7f6" id="r_a0479c57489c14be4a5ca79368086f7f6"><td class="memItemLeft" align="right" valign="top">double </td><td class="memItemRight" valign="bottom"><a class="el" href="classsvm__classifier_1_1SVMClassifier.html#a0479c57489c14be4a5ca79368086f7f6">score</a> (const torch::Tensor &X, const torch::Tensor &y_true)</td></tr>
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<tr class="memdesc:a0479c57489c14be4a5ca79368086f7f6"><td class="mdescLeft"> </td><td class="mdescRight">Calculate accuracy score on test data. <br /></td></tr>
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<tr class="separator:a0479c57489c14be4a5ca79368086f7f6"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:a38a9b020b9f4f9254920c97a3a047e9b" id="r_a38a9b020b9f4f9254920c97a3a047e9b"><td class="memItemLeft" align="right" valign="top"><a class="el" href="structsvm__classifier_1_1EvaluationMetrics.html">EvaluationMetrics</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="classsvm__classifier_1_1SVMClassifier.html#a38a9b020b9f4f9254920c97a3a047e9b">evaluate</a> (const torch::Tensor &X, const torch::Tensor &y_true)</td></tr>
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<tr class="memdesc:a38a9b020b9f4f9254920c97a3a047e9b"><td class="mdescLeft"> </td><td class="mdescRight">Calculate detailed evaluation metrics. <br /></td></tr>
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<tr class="separator:a38a9b020b9f4f9254920c97a3a047e9b"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:adb01e761fea07c709f3a0e315d3d0e06" id="r_adb01e761fea07c709f3a0e315d3d0e06"><td class="memItemLeft" align="right" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="classsvm__classifier_1_1SVMClassifier.html#adb01e761fea07c709f3a0e315d3d0e06">set_parameters</a> (const nlohmann::json &config)</td></tr>
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<tr class="memdesc:adb01e761fea07c709f3a0e315d3d0e06"><td class="mdescLeft"> </td><td class="mdescRight">Set parameters from JSON configuration. <br /></td></tr>
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<tr class="separator:adb01e761fea07c709f3a0e315d3d0e06"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:a7c39ec09b15186dcb4f04ae7171d23bb" id="r_a7c39ec09b15186dcb4f04ae7171d23bb"><td class="memItemLeft" align="right" valign="top">nlohmann::json </td><td class="memItemRight" valign="bottom"><a class="el" href="classsvm__classifier_1_1SVMClassifier.html#a7c39ec09b15186dcb4f04ae7171d23bb">get_parameters</a> () const</td></tr>
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<tr class="memdesc:a7c39ec09b15186dcb4f04ae7171d23bb"><td class="mdescLeft"> </td><td class="mdescRight">Get current parameters as JSON. <br /></td></tr>
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<tr class="separator:a7c39ec09b15186dcb4f04ae7171d23bb"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:a71a85ab7893e7e2b40763db34096d8bb" id="r_a71a85ab7893e7e2b40763db34096d8bb"><td class="memItemLeft" align="right" valign="top">bool </td><td class="memItemRight" valign="bottom"><a class="el" href="classsvm__classifier_1_1SVMClassifier.html#a71a85ab7893e7e2b40763db34096d8bb">is_fitted</a> () const</td></tr>
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||||||
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<tr class="memdesc:a71a85ab7893e7e2b40763db34096d8bb"><td class="mdescLeft"> </td><td class="mdescRight">Check if the model is fitted/trained. <br /></td></tr>
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<tr class="separator:a71a85ab7893e7e2b40763db34096d8bb"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:a75d501339e2e2273082b0838e9caadcd" id="r_a75d501339e2e2273082b0838e9caadcd"><td class="memItemLeft" align="right" valign="top">int </td><td class="memItemRight" valign="bottom"><a class="el" href="classsvm__classifier_1_1SVMClassifier.html#a75d501339e2e2273082b0838e9caadcd">get_n_classes</a> () const</td></tr>
|
||||||
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<tr class="memdesc:a75d501339e2e2273082b0838e9caadcd"><td class="mdescLeft"> </td><td class="mdescRight">Get the number of classes. <br /></td></tr>
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<tr class="separator:a75d501339e2e2273082b0838e9caadcd"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:af0fea42cdfc9416ed854b0d4aefa82b9" id="r_af0fea42cdfc9416ed854b0d4aefa82b9"><td class="memItemLeft" align="right" valign="top">std::vector< int > </td><td class="memItemRight" valign="bottom"><a class="el" href="classsvm__classifier_1_1SVMClassifier.html#af0fea42cdfc9416ed854b0d4aefa82b9">get_classes</a> () const</td></tr>
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||||||
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<tr class="memdesc:af0fea42cdfc9416ed854b0d4aefa82b9"><td class="mdescLeft"> </td><td class="mdescRight">Get unique class labels. <br /></td></tr>
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<tr class="separator:af0fea42cdfc9416ed854b0d4aefa82b9"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:a780afcb2ad618e46541aff8a44e9c7b4" id="r_a780afcb2ad618e46541aff8a44e9c7b4"><td class="memItemLeft" align="right" valign="top">int </td><td class="memItemRight" valign="bottom"><a class="el" href="classsvm__classifier_1_1SVMClassifier.html#a780afcb2ad618e46541aff8a44e9c7b4">get_n_features</a> () const</td></tr>
|
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<tr class="memdesc:a780afcb2ad618e46541aff8a44e9c7b4"><td class="mdescLeft"> </td><td class="mdescRight">Get the number of features. <br /></td></tr>
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<tr class="separator:a780afcb2ad618e46541aff8a44e9c7b4"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:a0b8c77f81d84489b2da0d080773a2970" id="r_a0b8c77f81d84489b2da0d080773a2970"><td class="memItemLeft" align="right" valign="top"><a class="el" href="structsvm__classifier_1_1TrainingMetrics.html">TrainingMetrics</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="classsvm__classifier_1_1SVMClassifier.html#a0b8c77f81d84489b2da0d080773a2970">get_training_metrics</a> () const</td></tr>
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<tr class="memdesc:a0b8c77f81d84489b2da0d080773a2970"><td class="mdescLeft"> </td><td class="mdescRight">Get training metrics from last fit. <br /></td></tr>
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<tr class="separator:a0b8c77f81d84489b2da0d080773a2970"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:a3f8b4e932f075b267507ad77a499a135" id="r_a3f8b4e932f075b267507ad77a499a135"><td class="memItemLeft" align="right" valign="top">bool </td><td class="memItemRight" valign="bottom"><a class="el" href="classsvm__classifier_1_1SVMClassifier.html#a3f8b4e932f075b267507ad77a499a135">supports_probability</a> () const</td></tr>
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<tr class="memdesc:a3f8b4e932f075b267507ad77a499a135"><td class="mdescLeft"> </td><td class="mdescRight">Check if the current model supports probability prediction. <br /></td></tr>
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<tr class="separator:a3f8b4e932f075b267507ad77a499a135"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:ab8a0bd35705825e80a7567b576d47359" id="r_ab8a0bd35705825e80a7567b576d47359"><td class="memItemLeft" align="right" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="classsvm__classifier_1_1SVMClassifier.html#ab8a0bd35705825e80a7567b576d47359">save_model</a> (const std::string &filename) const</td></tr>
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<tr class="memdesc:ab8a0bd35705825e80a7567b576d47359"><td class="mdescLeft"> </td><td class="mdescRight">Save model to file. <br /></td></tr>
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<tr class="separator:ab8a0bd35705825e80a7567b576d47359"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:a583f5743acf5e6b850e079b9190989f1" id="r_a583f5743acf5e6b850e079b9190989f1"><td class="memItemLeft" align="right" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="classsvm__classifier_1_1SVMClassifier.html#a583f5743acf5e6b850e079b9190989f1">load_model</a> (const std::string &filename)</td></tr>
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<tr class="memdesc:a583f5743acf5e6b850e079b9190989f1"><td class="mdescLeft"> </td><td class="mdescRight">Load model from file. <br /></td></tr>
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<tr class="separator:a583f5743acf5e6b850e079b9190989f1"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:a55338ab396bd5da923b6acbef8ed783a" id="r_a55338ab396bd5da923b6acbef8ed783a"><td class="memItemLeft" align="right" valign="top">KernelType </td><td class="memItemRight" valign="bottom"><a class="el" href="classsvm__classifier_1_1SVMClassifier.html#a55338ab396bd5da923b6acbef8ed783a">get_kernel_type</a> () const</td></tr>
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<tr class="memdesc:a55338ab396bd5da923b6acbef8ed783a"><td class="mdescLeft"> </td><td class="mdescRight">Get kernel type. <br /></td></tr>
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<tr class="separator:a55338ab396bd5da923b6acbef8ed783a"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:a14c2f7917c8a91154c09160288509f2c" id="r_a14c2f7917c8a91154c09160288509f2c"><td class="memItemLeft" align="right" valign="top">MulticlassStrategy </td><td class="memItemRight" valign="bottom"><a class="el" href="classsvm__classifier_1_1SVMClassifier.html#a14c2f7917c8a91154c09160288509f2c">get_multiclass_strategy</a> () const</td></tr>
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<tr class="memdesc:a14c2f7917c8a91154c09160288509f2c"><td class="mdescLeft"> </td><td class="mdescRight">Get multiclass strategy. <br /></td></tr>
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<tr class="separator:a14c2f7917c8a91154c09160288509f2c"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:a38173e5cf0f6a4620f032fd54c28d592" id="r_a38173e5cf0f6a4620f032fd54c28d592"><td class="memItemLeft" align="right" valign="top">SVMLibrary </td><td class="memItemRight" valign="bottom"><a class="el" href="classsvm__classifier_1_1SVMClassifier.html#a38173e5cf0f6a4620f032fd54c28d592">get_svm_library</a> () const</td></tr>
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<tr class="memdesc:a38173e5cf0f6a4620f032fd54c28d592"><td class="mdescLeft"> </td><td class="mdescRight">Get SVM library being used. <br /></td></tr>
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<tr class="separator:a38173e5cf0f6a4620f032fd54c28d592"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:a4c91072ea0d3d9b97ba458ff7d0898b8" id="r_a4c91072ea0d3d9b97ba458ff7d0898b8"><td class="memItemLeft" align="right" valign="top">std::vector< double > </td><td class="memItemRight" valign="bottom"><a class="el" href="classsvm__classifier_1_1SVMClassifier.html#a4c91072ea0d3d9b97ba458ff7d0898b8">cross_validate</a> (const torch::Tensor &X, const torch::Tensor &y, int cv=5)</td></tr>
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<tr class="memdesc:a4c91072ea0d3d9b97ba458ff7d0898b8"><td class="mdescLeft"> </td><td class="mdescRight">Perform cross-validation. <br /></td></tr>
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<tr class="separator:a4c91072ea0d3d9b97ba458ff7d0898b8"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:afed66a704dfb38cc7d080d3337d10194" id="r_afed66a704dfb38cc7d080d3337d10194"><td class="memItemLeft" align="right" valign="top">nlohmann::json </td><td class="memItemRight" valign="bottom"><a class="el" href="classsvm__classifier_1_1SVMClassifier.html#afed66a704dfb38cc7d080d3337d10194">grid_search</a> (const torch::Tensor &X, const torch::Tensor &y, const nlohmann::json &param_grid, int cv=5)</td></tr>
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<tr class="memdesc:afed66a704dfb38cc7d080d3337d10194"><td class="mdescLeft"> </td><td class="mdescRight">Find optimal hyperparameters using grid search. <br /></td></tr>
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<tr class="separator:afed66a704dfb38cc7d080d3337d10194"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:a2ade33562381e34cbe4b04089545a715" id="r_a2ade33562381e34cbe4b04089545a715"><td class="memItemLeft" align="right" valign="top">torch::Tensor </td><td class="memItemRight" valign="bottom"><a class="el" href="classsvm__classifier_1_1SVMClassifier.html#a2ade33562381e34cbe4b04089545a715">get_feature_importance</a> () const</td></tr>
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<tr class="memdesc:a2ade33562381e34cbe4b04089545a715"><td class="mdescLeft"> </td><td class="mdescRight">Get feature importance (for linear kernels only) <br /></td></tr>
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<tr class="separator:a2ade33562381e34cbe4b04089545a715"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:aa2bd5715c9e54e3fb465a9bcbf2e9c8a" id="r_aa2bd5715c9e54e3fb465a9bcbf2e9c8a"><td class="memItemLeft" align="right" valign="top"><a id="aa2bd5715c9e54e3fb465a9bcbf2e9c8a" name="aa2bd5715c9e54e3fb465a9bcbf2e9c8a"></a>
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void </td><td class="memItemRight" valign="bottom"><b>reset</b> ()</td></tr>
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<tr class="memdesc:aa2bd5715c9e54e3fb465a9bcbf2e9c8a"><td class="mdescLeft"> </td><td class="mdescRight">Reset the classifier (clear trained model) <br /></td></tr>
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<tr class="separator:aa2bd5715c9e54e3fb465a9bcbf2e9c8a"><td class="memSeparator" colspan="2"> </td></tr>
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</table>
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<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
|
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<div class="textblock"><p>Support Vector Machine Classifier with scikit-learn compatible API. </p>
|
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<p>This class provides a unified interface for SVM classification using both liblinear (for linear kernels) and libsvm (for non-linear kernels). It supports multiclass classification through One-vs-Rest and One-vs-One strategies. </p>
|
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<p class="definition">Definition at line <a class="el" href="svm__classifier_8hpp_source.html#l00021">21</a> of file <a class="el" href="svm__classifier_8hpp_source.html">svm_classifier.hpp</a>.</p>
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</div><h2 class="groupheader">Constructor & Destructor Documentation</h2>
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<a id="a2afb41f77de4e8de6368d274a30191ec" name="a2afb41f77de4e8de6368d274a30191ec"></a>
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<h2 class="memtitle"><span class="permalink"><a href="#a2afb41f77de4e8de6368d274a30191ec">◆ </a></span>SVMClassifier() <span class="overload">[1/2]</span></h2>
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<div class="memitem">
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<div class="memproto">
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<table class="mlabels">
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<tr>
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<td class="mlabels-left">
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<table class="memname">
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<tr>
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<td class="memname">svm_classifier::SVMClassifier::SVMClassifier </td>
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<td>(</td>
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<td class="paramtype">const nlohmann::json & </td>
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<td class="paramname"><em>config</em></td><td>)</td>
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<td></td>
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</tr>
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</table>
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</td>
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<td class="mlabels-right">
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<span class="mlabels"><span class="mlabel">explicit</span></span> </td>
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</tr>
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</table>
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<p>Constructor with JSON parameters. </p>
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|
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<h2 class="memtitle"><span class="permalink"><a href="#a90b2f18dd2cfeb23cf1375f265e22db0">◆ </a></span>SVMClassifier() <span class="overload">[2/2]</span></h2>
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<td class="memname">svm_classifier::SVMClassifier::SVMClassifier </td>
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<td>(</td>
|
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<td class="paramtype">KernelType </td>
|
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<td class="paramname"><em>kernel</em>, </td>
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<td class="paramtype">double </td>
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<td class="paramname"><em>C</em> = <code>1.0</code>, </td>
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<td class="paramkey"></td>
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<td></td>
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<td class="paramtype">MulticlassStrategy </td>
|
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<td class="paramname"><em>multiclass_strategy</em> = <code>MulticlassStrategy::ONE_VS_REST</code> </td>
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<td></td>
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<td>)</td>
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<p>Constructor with explicit parameters. </p>
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<dl class="params"><dt>Parameters</dt><dd>
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<table class="params">
|
||||||
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<tr><td class="paramname">kernel</td><td>Kernel type </td></tr>
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<tr><td class="paramname">C</td><td>Regularization parameter </td></tr>
|
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<tr><td class="paramname">multiclass_strategy</td><td>Multiclass strategy </td></tr>
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<h2 class="groupheader">Member Function Documentation</h2>
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<h2 class="memtitle"><span class="permalink"><a href="#a4c91072ea0d3d9b97ba458ff7d0898b8">◆ </a></span>cross_validate()</h2>
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<td class="memname">std::vector< double > svm_classifier::SVMClassifier::cross_validate </td>
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<td>(</td>
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<td class="paramtype">const torch::Tensor & </td>
|
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<td class="paramname"><em>X</em>, </td>
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<td class="paramtype">int </td>
|
||||||
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<td class="paramname"><em>cv</em> = <code>5</code> </td>
|
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<td></td>
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<td>)</td>
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||||||
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<p>Perform cross-validation. </p>
|
||||||
|
<dl class="params"><dt>Parameters</dt><dd>
|
||||||
|
<table class="params">
|
||||||
|
<tr><td class="paramname">X</td><td>Feature tensor </td></tr>
|
||||||
|
<tr><td class="paramname">y</td><td>Target tensor </td></tr>
|
||||||
|
<tr><td class="paramname">cv</td><td>Number of folds (default: 5) </td></tr>
|
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|
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|
||||||
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|
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|
</dl>
|
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<dl class="section return"><dt>Returns</dt><dd>Cross-validation scores for each fold </dd></dl>
|
||||||
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||||||
|
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|
||||||
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<h2 class="memtitle"><span class="permalink"><a href="#ad153c0537998eae5fbca5fd0b5ead2b7">◆ </a></span>decision_function()</h2>
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<td class="memname">torch::Tensor svm_classifier::SVMClassifier::decision_function </td>
|
||||||
|
<td>(</td>
|
||||||
|
<td class="paramtype">const torch::Tensor & </td>
|
||||||
|
<td class="paramname"><em>X</em></td><td>)</td>
|
||||||
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|
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<p>Get decision function values. </p>
|
||||||
|
<dl class="params"><dt>Parameters</dt><dd>
|
||||||
|
<table class="params">
|
||||||
|
<tr><td class="paramname">X</td><td>Feature tensor of shape (n_samples, n_features) </td></tr>
|
||||||
|
</table>
|
||||||
|
</dd>
|
||||||
|
</dl>
|
||||||
|
<dl class="section return"><dt>Returns</dt><dd>Tensor with decision function values </dd></dl>
|
||||||
|
<dl class="exception"><dt>Exceptions</dt><dd>
|
||||||
|
<table class="exception">
|
||||||
|
<tr><td class="paramname">std::runtime_error</td><td>if model is not fitted </td></tr>
|
||||||
|
</table>
|
||||||
|
</dd>
|
||||||
|
</dl>
|
||||||
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||||||
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||||||
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||||||
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||||||
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<h2 class="memtitle"><span class="permalink"><a href="#a38a9b020b9f4f9254920c97a3a047e9b">◆ </a></span>evaluate()</h2>
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<td class="memname"><a class="el" href="structsvm__classifier_1_1EvaluationMetrics.html">EvaluationMetrics</a> svm_classifier::SVMClassifier::evaluate </td>
|
||||||
|
<td>(</td>
|
||||||
|
<td class="paramtype">const torch::Tensor & </td>
|
||||||
|
<td class="paramname"><em>X</em>, </td>
|
||||||
|
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|
||||||
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|
||||||
|
<td class="paramkey"></td>
|
||||||
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<td></td>
|
||||||
|
<td class="paramtype">const torch::Tensor & </td>
|
||||||
|
<td class="paramname"><em>y_true</em> </td>
|
||||||
|
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|
||||||
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|
||||||
|
<td></td>
|
||||||
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<td>)</td>
|
||||||
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||||||
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|
||||||
|
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|
||||||
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</div><div class="memdoc">
|
||||||
|
|
||||||
|
<p>Calculate detailed evaluation metrics. </p>
|
||||||
|
<dl class="params"><dt>Parameters</dt><dd>
|
||||||
|
<table class="params">
|
||||||
|
<tr><td class="paramname">X</td><td>Feature tensor of shape (n_samples, n_features) </td></tr>
|
||||||
|
<tr><td class="paramname">y_true</td><td>True labels tensor of shape (n_samples,) </td></tr>
|
||||||
|
</table>
|
||||||
|
</dd>
|
||||||
|
</dl>
|
||||||
|
<dl class="section return"><dt>Returns</dt><dd>Evaluation metrics including precision, recall, F1-score </dd></dl>
|
||||||
|
|
||||||
|
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|
||||||
|
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|
||||||
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<a id="a7e6648c4d2bac92bb00381076ea92db3" name="a7e6648c4d2bac92bb00381076ea92db3"></a>
|
||||||
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<h2 class="memtitle"><span class="permalink"><a href="#a7e6648c4d2bac92bb00381076ea92db3">◆ </a></span>fit()</h2>
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<td class="memname"><a class="el" href="structsvm__classifier_1_1TrainingMetrics.html">TrainingMetrics</a> svm_classifier::SVMClassifier::fit </td>
|
||||||
|
<td>(</td>
|
||||||
|
<td class="paramtype">const torch::Tensor & </td>
|
||||||
|
<td class="paramname"><em>X</em>, </td>
|
||||||
|
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|
||||||
|
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|
||||||
|
<td class="paramkey"></td>
|
||||||
|
<td></td>
|
||||||
|
<td class="paramtype">const torch::Tensor & </td>
|
||||||
|
<td class="paramname"><em>y</em> </td>
|
||||||
|
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|
||||||
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|
||||||
|
<td></td>
|
||||||
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<td>)</td>
|
||||||
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|
||||||
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||||||
|
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|
||||||
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|
||||||
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|
||||||
|
<p>Train the SVM classifier. </p>
|
||||||
|
<dl class="params"><dt>Parameters</dt><dd>
|
||||||
|
<table class="params">
|
||||||
|
<tr><td class="paramname">X</td><td>Feature tensor of shape (n_samples, n_features) </td></tr>
|
||||||
|
<tr><td class="paramname">y</td><td>Target tensor of shape (n_samples,) with class labels </td></tr>
|
||||||
|
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|
||||||
|
</dd>
|
||||||
|
</dl>
|
||||||
|
<dl class="section return"><dt>Returns</dt><dd>Training metrics </dd></dl>
|
||||||
|
<dl class="exception"><dt>Exceptions</dt><dd>
|
||||||
|
<table class="exception">
|
||||||
|
<tr><td class="paramname">std::invalid_argument</td><td>if input data is invalid </td></tr>
|
||||||
|
<tr><td class="paramname">std::runtime_error</td><td>if training fails </td></tr>
|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
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|
||||||
|
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|
||||||
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||||||
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<a id="af0fea42cdfc9416ed854b0d4aefa82b9" name="af0fea42cdfc9416ed854b0d4aefa82b9"></a>
|
||||||
|
<h2 class="memtitle"><span class="permalink"><a href="#af0fea42cdfc9416ed854b0d4aefa82b9">◆ </a></span>get_classes()</h2>
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||||||
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||||||
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||||||
|
<td class="memname">std::vector< int > svm_classifier::SVMClassifier::get_classes </td>
|
||||||
|
<td>(</td>
|
||||||
|
<td class="paramname"></td><td>)</td>
|
||||||
|
<td> const</td>
|
||||||
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|
||||||
|
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|
||||||
|
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|
||||||
|
|
||||||
|
<p>Get unique class labels. </p>
|
||||||
|
<dl class="section return"><dt>Returns</dt><dd>Vector of unique class labels </dd></dl>
|
||||||
|
|
||||||
|
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|
||||||
|
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|
||||||
|
<a id="a2ade33562381e34cbe4b04089545a715" name="a2ade33562381e34cbe4b04089545a715"></a>
|
||||||
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<h2 class="memtitle"><span class="permalink"><a href="#a2ade33562381e34cbe4b04089545a715">◆ </a></span>get_feature_importance()</h2>
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||||||
|
<td class="memname">torch::Tensor svm_classifier::SVMClassifier::get_feature_importance </td>
|
||||||
|
<td>(</td>
|
||||||
|
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|
||||||
|
<td> const</td>
|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
|
||||||
|
<p>Get feature importance (for linear kernels only) </p>
|
||||||
|
<dl class="section return"><dt>Returns</dt><dd>Tensor with feature weights/importance </dd></dl>
|
||||||
|
<dl class="exception"><dt>Exceptions</dt><dd>
|
||||||
|
<table class="exception">
|
||||||
|
<tr><td class="paramname">std::runtime_error</td><td>if not supported for current kernel </td></tr>
|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
|
||||||
|
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|
||||||
|
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|
||||||
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||||||
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<h2 class="memtitle"><span class="permalink"><a href="#a55338ab396bd5da923b6acbef8ed783a">◆ </a></span>get_kernel_type()</h2>
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||||||
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||||||
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|
||||||
|
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|
||||||
|
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|
||||||
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|
||||||
|
<td class="memname">KernelType svm_classifier::SVMClassifier::get_kernel_type </td>
|
||||||
|
<td>(</td>
|
||||||
|
<td class="paramname"></td><td>)</td>
|
||||||
|
<td> const</td>
|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
<span class="mlabels"><span class="mlabel">inline</span></span> </td>
|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
|
||||||
|
<p>Get kernel type. </p>
|
||||||
|
<dl class="section return"><dt>Returns</dt><dd>Current kernel type </dd></dl>
|
||||||
|
|
||||||
|
<p class="definition">Definition at line <a class="el" href="svm__classifier_8hpp_source.html#l00187">187</a> of file <a class="el" href="svm__classifier_8hpp_source.html">svm_classifier.hpp</a>.</p>
|
||||||
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|
||||||
|
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|
||||||
|
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||||||
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||||||
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<h2 class="memtitle"><span class="permalink"><a href="#a14c2f7917c8a91154c09160288509f2c">◆ </a></span>get_multiclass_strategy()</h2>
|
||||||
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||||||
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||||||
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|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
<td class="memname">MulticlassStrategy svm_classifier::SVMClassifier::get_multiclass_strategy </td>
|
||||||
|
<td>(</td>
|
||||||
|
<td class="paramname"></td><td>)</td>
|
||||||
|
<td> const</td>
|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
<span class="mlabels"><span class="mlabel">inline</span></span> </td>
|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
|
||||||
|
<p>Get multiclass strategy. </p>
|
||||||
|
<dl class="section return"><dt>Returns</dt><dd>Current multiclass strategy </dd></dl>
|
||||||
|
|
||||||
|
<p class="definition">Definition at line <a class="el" href="svm__classifier_8hpp_source.html#l00193">193</a> of file <a class="el" href="svm__classifier_8hpp_source.html">svm_classifier.hpp</a>.</p>
|
||||||
|
|
||||||
|
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|
||||||
|
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|
||||||
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<h2 class="memtitle"><span class="permalink"><a href="#a75d501339e2e2273082b0838e9caadcd">◆ </a></span>get_n_classes()</h2>
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|
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|
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|
||||||
|
<td class="memname">int svm_classifier::SVMClassifier::get_n_classes </td>
|
||||||
|
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|
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||||||
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|
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|
||||||
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||||||
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<p>Get the number of classes. </p>
|
||||||
|
<dl class="section return"><dt>Returns</dt><dd>Number of classes (0 if not fitted) </dd></dl>
|
||||||
|
|
||||||
|
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|
||||||
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|
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<h2 class="memtitle"><span class="permalink"><a href="#a780afcb2ad618e46541aff8a44e9c7b4">◆ </a></span>get_n_features()</h2>
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<td class="memname">int svm_classifier::SVMClassifier::get_n_features </td>
|
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|
<td>(</td>
|
||||||
|
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||||||
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||||||
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<p>Get the number of features. </p>
|
||||||
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<dl class="section return"><dt>Returns</dt><dd>Number of features (0 if not fitted) </dd></dl>
|
||||||
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||||||
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<p class="definition">Definition at line <a class="el" href="svm__classifier_8hpp_source.html#l00155">155</a> of file <a class="el" href="svm__classifier_8hpp_source.html">svm_classifier.hpp</a>.</p>
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<h2 class="memtitle"><span class="permalink"><a href="#a7c39ec09b15186dcb4f04ae7171d23bb">◆ </a></span>get_parameters()</h2>
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||||||
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<td class="memname">nlohmann::json svm_classifier::SVMClassifier::get_parameters </td>
|
||||||
|
<td>(</td>
|
||||||
|
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<p>Get current parameters as JSON. </p>
|
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<dl class="section return"><dt>Returns</dt><dd>JSON object with current parameters </dd></dl>
|
||||||
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|
||||||
|
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<a id="a38173e5cf0f6a4620f032fd54c28d592" name="a38173e5cf0f6a4620f032fd54c28d592"></a>
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<h2 class="memtitle"><span class="permalink"><a href="#a38173e5cf0f6a4620f032fd54c28d592">◆ </a></span>get_svm_library()</h2>
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<td class="memname">SVMLibrary svm_classifier::SVMClassifier::get_svm_library </td>
|
||||||
|
<td>(</td>
|
||||||
|
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|
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|
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|
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|
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|
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<p>Get SVM library being used. </p>
|
||||||
|
<dl class="section return"><dt>Returns</dt><dd>SVM library type </dd></dl>
|
||||||
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|
||||||
|
<p class="definition">Definition at line <a class="el" href="svm__classifier_8hpp_source.html#l00199">199</a> of file <a class="el" href="svm__classifier_8hpp_source.html">svm_classifier.hpp</a>.</p>
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<a id="a0b8c77f81d84489b2da0d080773a2970" name="a0b8c77f81d84489b2da0d080773a2970"></a>
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<h2 class="memtitle"><span class="permalink"><a href="#a0b8c77f81d84489b2da0d080773a2970">◆ </a></span>get_training_metrics()</h2>
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<td class="memname"><a class="el" href="structsvm__classifier_1_1TrainingMetrics.html">TrainingMetrics</a> svm_classifier::SVMClassifier::get_training_metrics </td>
|
||||||
|
<td>(</td>
|
||||||
|
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|
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|
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|
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|
||||||
|
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|
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|
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|
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|
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|
||||||
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|
||||||
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|
||||||
|
<p>Get training metrics from last fit. </p>
|
||||||
|
<dl class="section return"><dt>Returns</dt><dd>Training metrics </dd></dl>
|
||||||
|
|
||||||
|
<p class="definition">Definition at line <a class="el" href="svm__classifier_8hpp_source.html#l00161">161</a> of file <a class="el" href="svm__classifier_8hpp_source.html">svm_classifier.hpp</a>.</p>
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<a id="afed66a704dfb38cc7d080d3337d10194" name="afed66a704dfb38cc7d080d3337d10194"></a>
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|
<h2 class="memtitle"><span class="permalink"><a href="#afed66a704dfb38cc7d080d3337d10194">◆ </a></span>grid_search()</h2>
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<td class="memname">nlohmann::json svm_classifier::SVMClassifier::grid_search </td>
|
||||||
|
<td>(</td>
|
||||||
|
<td class="paramtype">const torch::Tensor & </td>
|
||||||
|
<td class="paramname"><em>X</em>, </td>
|
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|
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|
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|
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|
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|
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|
||||||
|
<td class="paramtype">const torch::Tensor & </td>
|
||||||
|
<td class="paramname"><em>y</em>, </td>
|
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|
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|
||||||
|
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|
||||||
|
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|
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|
||||||
|
<td class="paramtype">const nlohmann::json & </td>
|
||||||
|
<td class="paramname"><em>param_grid</em>, </td>
|
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|
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||||||
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|
||||||
|
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|
||||||
|
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|
||||||
|
<td class="paramtype">int </td>
|
||||||
|
<td class="paramname"><em>cv</em> = <code>5</code> </td>
|
||||||
|
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|
||||||
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|
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|
<td></td>
|
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<td>)</td>
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|
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|
||||||
|
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|
||||||
|
|
||||||
|
<p>Find optimal hyperparameters using grid search. </p>
|
||||||
|
<dl class="params"><dt>Parameters</dt><dd>
|
||||||
|
<table class="params">
|
||||||
|
<tr><td class="paramname">X</td><td>Feature tensor </td></tr>
|
||||||
|
<tr><td class="paramname">y</td><td>Target tensor </td></tr>
|
||||||
|
<tr><td class="paramname">param_grid</td><td>JSON object with parameter grid </td></tr>
|
||||||
|
<tr><td class="paramname">cv</td><td>Number of cross-validation folds </td></tr>
|
||||||
|
</table>
|
||||||
|
</dd>
|
||||||
|
</dl>
|
||||||
|
<dl class="section return"><dt>Returns</dt><dd>JSON object with best parameters and score </dd></dl>
|
||||||
|
|
||||||
|
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|
||||||
|
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|
||||||
|
<a id="a71a85ab7893e7e2b40763db34096d8bb" name="a71a85ab7893e7e2b40763db34096d8bb"></a>
|
||||||
|
<h2 class="memtitle"><span class="permalink"><a href="#a71a85ab7893e7e2b40763db34096d8bb">◆ </a></span>is_fitted()</h2>
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|
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|
<td class="memname">bool svm_classifier::SVMClassifier::is_fitted </td>
|
||||||
|
<td>(</td>
|
||||||
|
<td class="paramname"></td><td>)</td>
|
||||||
|
<td> const</td>
|
||||||
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
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|
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|
||||||
|
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|
||||||
|
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|
||||||
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|
||||||
|
<p>Check if the model is fitted/trained. </p>
|
||||||
|
<dl class="section return"><dt>Returns</dt><dd>True if model is fitted </dd></dl>
|
||||||
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|
||||||
|
<p class="definition">Definition at line <a class="el" href="svm__classifier_8hpp_source.html#l00137">137</a> of file <a class="el" href="svm__classifier_8hpp_source.html">svm_classifier.hpp</a>.</p>
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<a id="a583f5743acf5e6b850e079b9190989f1" name="a583f5743acf5e6b850e079b9190989f1"></a>
|
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|
<h2 class="memtitle"><span class="permalink"><a href="#a583f5743acf5e6b850e079b9190989f1">◆ </a></span>load_model()</h2>
|
||||||
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|
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|
||||||
|
<td class="memname">void svm_classifier::SVMClassifier::load_model </td>
|
||||||
|
<td>(</td>
|
||||||
|
<td class="paramtype">const std::string & </td>
|
||||||
|
<td class="paramname"><em>filename</em></td><td>)</td>
|
||||||
|
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|
||||||
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|
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|
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|
||||||
|
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|
||||||
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|
||||||
|
<p>Load model from file. </p>
|
||||||
|
<dl class="params"><dt>Parameters</dt><dd>
|
||||||
|
<table class="params">
|
||||||
|
<tr><td class="paramname">filename</td><td>Path to load the model from </td></tr>
|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
<dl class="exception"><dt>Exceptions</dt><dd>
|
||||||
|
<table class="exception">
|
||||||
|
<tr><td class="paramname">std::runtime_error</td><td>if loading fails </td></tr>
|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
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|
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|
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||||||
|
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|
||||||
|
<h2 class="memtitle"><span class="permalink"><a href="#a5c998d5574b3b6afe003b23ed02ed1d1">◆ </a></span>predict()</h2>
|
||||||
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||||||
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|
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|
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|
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|
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|
||||||
|
<td class="memname">torch::Tensor svm_classifier::SVMClassifier::predict </td>
|
||||||
|
<td>(</td>
|
||||||
|
<td class="paramtype">const torch::Tensor & </td>
|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
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|
||||||
|
<p>Predict class labels for samples. </p>
|
||||||
|
<dl class="params"><dt>Parameters</dt><dd>
|
||||||
|
<table class="params">
|
||||||
|
<tr><td class="paramname">X</td><td>Feature tensor of shape (n_samples, n_features) </td></tr>
|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
<dl class="section return"><dt>Returns</dt><dd>Tensor of predicted class labels </dd></dl>
|
||||||
|
<dl class="exception"><dt>Exceptions</dt><dd>
|
||||||
|
<table class="exception">
|
||||||
|
<tr><td class="paramname">std::runtime_error</td><td>if model is not fitted </td></tr>
|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
|
||||||
|
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|
||||||
|
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|
||||||
|
<a id="ab4ef3c839e085ece646cdd2501a51f67" name="ab4ef3c839e085ece646cdd2501a51f67"></a>
|
||||||
|
<h2 class="memtitle"><span class="permalink"><a href="#ab4ef3c839e085ece646cdd2501a51f67">◆ </a></span>predict_proba()</h2>
|
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||||||
|
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|
||||||
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|
||||||
|
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|
||||||
|
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|
||||||
|
<td class="memname">torch::Tensor svm_classifier::SVMClassifier::predict_proba </td>
|
||||||
|
<td>(</td>
|
||||||
|
<td class="paramtype">const torch::Tensor & </td>
|
||||||
|
<td class="paramname"><em>X</em></td><td>)</td>
|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
|
||||||
|
<p>Predict class probabilities for samples. </p>
|
||||||
|
<dl class="params"><dt>Parameters</dt><dd>
|
||||||
|
<table class="params">
|
||||||
|
<tr><td class="paramname">X</td><td>Feature tensor of shape (n_samples, n_features) </td></tr>
|
||||||
|
</table>
|
||||||
|
</dd>
|
||||||
|
</dl>
|
||||||
|
<dl class="section return"><dt>Returns</dt><dd>Tensor of shape (n_samples, n_classes) with class probabilities </dd></dl>
|
||||||
|
<dl class="exception"><dt>Exceptions</dt><dd>
|
||||||
|
<table class="exception">
|
||||||
|
<tr><td class="paramname">std::runtime_error</td><td>if model is not fitted or doesn't support probabilities </td></tr>
|
||||||
|
</table>
|
||||||
|
</dd>
|
||||||
|
</dl>
|
||||||
|
|
||||||
|
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|
||||||
|
</div>
|
||||||
|
<a id="ab8a0bd35705825e80a7567b576d47359" name="ab8a0bd35705825e80a7567b576d47359"></a>
|
||||||
|
<h2 class="memtitle"><span class="permalink"><a href="#ab8a0bd35705825e80a7567b576d47359">◆ </a></span>save_model()</h2>
|
||||||
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
<td class="memname">void svm_classifier::SVMClassifier::save_model </td>
|
||||||
|
<td>(</td>
|
||||||
|
<td class="paramtype">const std::string & </td>
|
||||||
|
<td class="paramname"><em>filename</em></td><td>)</td>
|
||||||
|
<td> const</td>
|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
|
||||||
|
<p>Save model to file. </p>
|
||||||
|
<dl class="params"><dt>Parameters</dt><dd>
|
||||||
|
<table class="params">
|
||||||
|
<tr><td class="paramname">filename</td><td>Path to save the model </td></tr>
|
||||||
|
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|
||||||
|
</dd>
|
||||||
|
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|
||||||
|
<dl class="exception"><dt>Exceptions</dt><dd>
|
||||||
|
<table class="exception">
|
||||||
|
<tr><td class="paramname">std::runtime_error</td><td>if saving fails </td></tr>
|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
|
||||||
|
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|
||||||
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<h2 class="memtitle"><span class="permalink"><a href="#a0479c57489c14be4a5ca79368086f7f6">◆ </a></span>score()</h2>
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<td class="memname">double svm_classifier::SVMClassifier::score </td>
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<td>(</td>
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<td class="paramtype">const torch::Tensor & </td>
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<p>Calculate accuracy score on test data. </p>
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<dl class="params"><dt>Parameters</dt><dd>
|
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|
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<tr><td class="paramname">X</td><td>Feature tensor of shape (n_samples, n_features) </td></tr>
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<dl class="section return"><dt>Returns</dt><dd>Accuracy score (fraction of correctly predicted samples) </dd></dl>
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<dl class="exception"><dt>Exceptions</dt><dd>
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<tr><td class="paramname">std::runtime_error</td><td>if model is not fitted </td></tr>
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<h2 class="memtitle"><span class="permalink"><a href="#adb01e761fea07c709f3a0e315d3d0e06">◆ </a></span>set_parameters()</h2>
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<td class="memname">void svm_classifier::SVMClassifier::set_parameters </td>
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<td>(</td>
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<td class="paramtype">const nlohmann::json & </td>
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<p>Set parameters from JSON configuration. </p>
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<dl class="params"><dt>Parameters</dt><dd>
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<h2 class="memtitle"><span class="permalink"><a href="#a3f8b4e932f075b267507ad77a499a135">◆ </a></span>supports_probability()</h2>
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<td class="memname">bool svm_classifier::SVMClassifier::supports_probability </td>
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<td>(</td>
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<p>Check if the current model supports probability prediction. </p>
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<dl class="section return"><dt>Returns</dt><dd>True if probabilities are supported </dd></dl>
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</div>
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<hr/>The documentation for this class was generated from the following file:<ul>
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<li>include/svm_classifier/<a class="el" href="svm__classifier_8hpp_source.html">svm_classifier.hpp</a></li>
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<div id="projectname">SVM Classifier C++<span id="projectnumber"> 1.0.0</span>
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|
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<div id="projectbrief">High-performance Support Vector Machine classifier with scikit-learn compatible API</div>
|
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<li class="navelem"><a class="el" href="dir_68267d1309a1af8e8297ef4c3efbcdba.html">src</a></li> </ul>
|
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|
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|
||||||
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<div class="headertitle"><div class="title">data_converter.cpp</div></div>
|
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|
||||||
|
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|
||||||
|
<div class="fragment"><div class="line"><a id="l00001" name="l00001"></a><span class="lineno"> 1</span><span class="preprocessor">#include "svm_classifier/data_converter.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>
|
||||||
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<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>
|
||||||
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<div class="line"><a id="l00004" name="l00004"></a><span class="lineno"> 4</span><span class="preprocessor">#include <stdexcept></span></div>
|
||||||
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<div class="line"><a id="l00005" name="l00005"></a><span class="lineno"> 5</span><span class="preprocessor">#include <iostream></span></div>
|
||||||
|
<div class="line"><a id="l00006" name="l00006"></a><span class="lineno"> 6</span><span class="preprocessor">#include <cmath></span></div>
|
||||||
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<div class="line"><a id="l00007" name="l00007"></a><span class="lineno"> 7</span> </div>
|
||||||
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<div class="line"><a id="l00008" name="l00008"></a><span class="lineno"> 8</span><span class="keyword">namespace </span>svm_classifier {</div>
|
||||||
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<div class="line"><a id="l00009" name="l00009"></a><span class="lineno"> 9</span> </div>
|
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|
||||||
|
<div class="line"><a id="l00010" name="l00010"></a><span class="lineno"><a class="line" href="classsvm__classifier_1_1DataConverter.html#a5874904555f26448ed5ae4cf6f370056"> 10</a></span> <a class="code hl_function" href="classsvm__classifier_1_1DataConverter.html#a5874904555f26448ed5ae4cf6f370056">DataConverter::DataConverter</a>()</div>
|
||||||
|
<div class="line"><a id="l00011" name="l00011"></a><span class="lineno"> 11</span> : n_features_(0)</div>
|
||||||
|
<div class="line"><a id="l00012" name="l00012"></a><span class="lineno"> 12</span> , n_samples_(0)</div>
|
||||||
|
<div class="line"><a id="l00013" name="l00013"></a><span class="lineno"> 13</span> , sparse_threshold_(1e-8)</div>
|
||||||
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<div class="line"><a id="l00014" name="l00014"></a><span class="lineno"> 14</span> {</div>
|
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<div class="line"><a id="l00015" name="l00015"></a><span class="lineno"> 15</span> }</div>
|
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<div class="line"><a id="l00016" name="l00016"></a><span class="lineno"> 16</span> </div>
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<div class="foldopen" id="foldopen00017" data-start="{" data-end="}">
|
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<div class="line"><a id="l00017" name="l00017"></a><span class="lineno"><a class="line" href="classsvm__classifier_1_1DataConverter.html#ac3af2c9c03cffe2968f29147611e333d"> 17</a></span> <a class="code hl_function" href="classsvm__classifier_1_1DataConverter.html#ac3af2c9c03cffe2968f29147611e333d">DataConverter::~DataConverter</a>()</div>
|
||||||
|
<div class="line"><a id="l00018" name="l00018"></a><span class="lineno"> 18</span> {</div>
|
||||||
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<div class="line"><a id="l00019" name="l00019"></a><span class="lineno"> 19</span> <a class="code hl_function" href="classsvm__classifier_1_1DataConverter.html#a46d12ba28c4c5bf6e0fad1122c621fa8">cleanup</a>();</div>
|
||||||
|
<div class="line"><a id="l00020" name="l00020"></a><span class="lineno"> 20</span> }</div>
|
||||||
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|
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|
<div class="line"><a id="l00021" name="l00021"></a><span class="lineno"> 21</span> </div>
|
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<div class="foldopen" id="foldopen00022" data-start="{" data-end="}">
|
||||||
|
<div class="line"><a id="l00022" name="l00022"></a><span class="lineno"><a class="line" href="classsvm__classifier_1_1DataConverter.html#a66f446e4decfe47bbba37c789f03f729"> 22</a></span> std::unique_ptr<svm_problem> <a class="code hl_function" href="classsvm__classifier_1_1DataConverter.html#a66f446e4decfe47bbba37c789f03f729">DataConverter::to_svm_problem</a>(<span class="keyword">const</span> torch::Tensor& X,</div>
|
||||||
|
<div class="line"><a id="l00023" name="l00023"></a><span class="lineno"> 23</span> <span class="keyword">const</span> torch::Tensor& y)</div>
|
||||||
|
<div class="line"><a id="l00024" name="l00024"></a><span class="lineno"> 24</span> {</div>
|
||||||
|
<div class="line"><a id="l00025" name="l00025"></a><span class="lineno"> 25</span> <a class="code hl_function" href="classsvm__classifier_1_1DataConverter.html#aa0615f3de29958b2c5229d349f2f60ce">validate_tensors</a>(X, y);</div>
|
||||||
|
<div class="line"><a id="l00026" name="l00026"></a><span class="lineno"> 26</span> </div>
|
||||||
|
<div class="line"><a id="l00027" name="l00027"></a><span class="lineno"> 27</span> <span class="keyword">auto</span> X_cpu = ensure_cpu_tensor(X);</div>
|
||||||
|
<div class="line"><a id="l00028" name="l00028"></a><span class="lineno"> 28</span> </div>
|
||||||
|
<div class="line"><a id="l00029" name="l00029"></a><span class="lineno"> 29</span> n_samples_ = X_cpu.size(0);</div>
|
||||||
|
<div class="line"><a id="l00030" name="l00030"></a><span class="lineno"> 30</span> n_features_ = X_cpu.size(1);</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">// Convert tensor data to svm_node structures</span></div>
|
||||||
|
<div class="line"><a id="l00033" name="l00033"></a><span class="lineno"> 33</span> svm_nodes_storage_ = tensor_to_svm_nodes(X_cpu);</div>
|
||||||
|
<div class="line"><a id="l00034" name="l00034"></a><span class="lineno"> 34</span> </div>
|
||||||
|
<div class="line"><a id="l00035" name="l00035"></a><span class="lineno"> 35</span> <span class="comment">// Prepare pointers for svm_problem</span></div>
|
||||||
|
<div class="line"><a id="l00036" name="l00036"></a><span class="lineno"> 36</span> svm_x_space_.clear();</div>
|
||||||
|
<div class="line"><a id="l00037" name="l00037"></a><span class="lineno"> 37</span> svm_x_space_.reserve(n_samples_);</div>
|
||||||
|
<div class="line"><a id="l00038" name="l00038"></a><span class="lineno"> 38</span> </div>
|
||||||
|
<div class="line"><a id="l00039" name="l00039"></a><span class="lineno"> 39</span> <span class="keywordflow">for</span> (<span class="keyword">auto</span>& nodes : svm_nodes_storage_) {</div>
|
||||||
|
<div class="line"><a id="l00040" name="l00040"></a><span class="lineno"> 40</span> svm_x_space_.push_back(nodes.data());</div>
|
||||||
|
<div class="line"><a id="l00041" name="l00041"></a><span class="lineno"> 41</span> }</div>
|
||||||
|
<div class="line"><a id="l00042" name="l00042"></a><span class="lineno"> 42</span> </div>
|
||||||
|
<div class="line"><a id="l00043" name="l00043"></a><span class="lineno"> 43</span> <span class="comment">// Extract labels if provided</span></div>
|
||||||
|
<div class="line"><a id="l00044" name="l00044"></a><span class="lineno"> 44</span> <span class="keywordflow">if</span> (y.defined() && y.numel() > 0) {</div>
|
||||||
|
<div class="line"><a id="l00045" name="l00045"></a><span class="lineno"> 45</span> svm_y_space_ = extract_labels(y);</div>
|
||||||
|
<div class="line"><a id="l00046" name="l00046"></a><span class="lineno"> 46</span> } <span class="keywordflow">else</span> {</div>
|
||||||
|
<div class="line"><a id="l00047" name="l00047"></a><span class="lineno"> 47</span> svm_y_space_.clear();</div>
|
||||||
|
<div class="line"><a id="l00048" name="l00048"></a><span class="lineno"> 48</span> svm_y_space_.resize(n_samples_, 0.0); <span class="comment">// Dummy labels for prediction</span></div>
|
||||||
|
<div class="line"><a id="l00049" name="l00049"></a><span class="lineno"> 49</span> }</div>
|
||||||
|
<div class="line"><a id="l00050" name="l00050"></a><span class="lineno"> 50</span> </div>
|
||||||
|
<div class="line"><a id="l00051" name="l00051"></a><span class="lineno"> 51</span> <span class="comment">// Create svm_problem</span></div>
|
||||||
|
<div class="line"><a id="l00052" name="l00052"></a><span class="lineno"> 52</span> <span class="keyword">auto</span> problem = std::make_unique<svm_problem>();</div>
|
||||||
|
<div class="line"><a id="l00053" name="l00053"></a><span class="lineno"> 53</span> problem->l = n_samples_;</div>
|
||||||
|
<div class="line"><a id="l00054" name="l00054"></a><span class="lineno"> 54</span> problem->x = svm_x_space_.data();</div>
|
||||||
|
<div class="line"><a id="l00055" name="l00055"></a><span class="lineno"> 55</span> problem->y = svm_y_space_.data();</div>
|
||||||
|
<div class="line"><a id="l00056" name="l00056"></a><span class="lineno"> 56</span> </div>
|
||||||
|
<div class="line"><a id="l00057" name="l00057"></a><span class="lineno"> 57</span> <span class="keywordflow">return</span> problem;</div>
|
||||||
|
<div class="line"><a id="l00058" name="l00058"></a><span class="lineno"> 58</span> }</div>
|
||||||
|
</div>
|
||||||
|
<div class="line"><a id="l00059" name="l00059"></a><span class="lineno"> 59</span> </div>
|
||||||
|
<div class="foldopen" id="foldopen00060" data-start="{" data-end="}">
|
||||||
|
<div class="line"><a id="l00060" name="l00060"></a><span class="lineno"><a class="line" href="classsvm__classifier_1_1DataConverter.html#a7e7d8f6102b7a9b3256ff0dc6f536a35"> 60</a></span> std::unique_ptr<problem> <a class="code hl_function" href="classsvm__classifier_1_1DataConverter.html#a7e7d8f6102b7a9b3256ff0dc6f536a35">DataConverter::to_linear_problem</a>(<span class="keyword">const</span> torch::Tensor& X,</div>
|
||||||
|
<div class="line"><a id="l00061" name="l00061"></a><span class="lineno"> 61</span> <span class="keyword">const</span> torch::Tensor& y)</div>
|
||||||
|
<div class="line"><a id="l00062" name="l00062"></a><span class="lineno"> 62</span> {</div>
|
||||||
|
<div class="line"><a id="l00063" name="l00063"></a><span class="lineno"> 63</span> <a class="code hl_function" href="classsvm__classifier_1_1DataConverter.html#aa0615f3de29958b2c5229d349f2f60ce">validate_tensors</a>(X, y);</div>
|
||||||
|
<div class="line"><a id="l00064" name="l00064"></a><span class="lineno"> 64</span> </div>
|
||||||
|
<div class="line"><a id="l00065" name="l00065"></a><span class="lineno"> 65</span> <span class="keyword">auto</span> X_cpu = ensure_cpu_tensor(X);</div>
|
||||||
|
<div class="line"><a id="l00066" name="l00066"></a><span class="lineno"> 66</span> </div>
|
||||||
|
<div class="line"><a id="l00067" name="l00067"></a><span class="lineno"> 67</span> n_samples_ = X_cpu.size(0);</div>
|
||||||
|
<div class="line"><a id="l00068" name="l00068"></a><span class="lineno"> 68</span> n_features_ = X_cpu.size(1);</div>
|
||||||
|
<div class="line"><a id="l00069" name="l00069"></a><span class="lineno"> 69</span> </div>
|
||||||
|
<div class="line"><a id="l00070" name="l00070"></a><span class="lineno"> 70</span> <span class="comment">// Convert tensor data to feature_node structures</span></div>
|
||||||
|
<div class="line"><a id="l00071" name="l00071"></a><span class="lineno"> 71</span> linear_nodes_storage_ = tensor_to_linear_nodes(X_cpu);</div>
|
||||||
|
<div class="line"><a id="l00072" name="l00072"></a><span class="lineno"> 72</span> </div>
|
||||||
|
<div class="line"><a id="l00073" name="l00073"></a><span class="lineno"> 73</span> <span class="comment">// Prepare pointers for problem</span></div>
|
||||||
|
<div class="line"><a id="l00074" name="l00074"></a><span class="lineno"> 74</span> linear_x_space_.clear();</div>
|
||||||
|
<div class="line"><a id="l00075" name="l00075"></a><span class="lineno"> 75</span> linear_x_space_.reserve(n_samples_);</div>
|
||||||
|
<div class="line"><a id="l00076" name="l00076"></a><span class="lineno"> 76</span> </div>
|
||||||
|
<div class="line"><a id="l00077" name="l00077"></a><span class="lineno"> 77</span> <span class="keywordflow">for</span> (<span class="keyword">auto</span>& nodes : linear_nodes_storage_) {</div>
|
||||||
|
<div class="line"><a id="l00078" name="l00078"></a><span class="lineno"> 78</span> linear_x_space_.push_back(nodes.data());</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> </div>
|
||||||
|
<div class="line"><a id="l00081" name="l00081"></a><span class="lineno"> 81</span> <span class="comment">// Extract labels if provided</span></div>
|
||||||
|
<div class="line"><a id="l00082" name="l00082"></a><span class="lineno"> 82</span> <span class="keywordflow">if</span> (y.defined() && y.numel() > 0) {</div>
|
||||||
|
<div class="line"><a id="l00083" name="l00083"></a><span class="lineno"> 83</span> linear_y_space_ = extract_labels(y);</div>
|
||||||
|
<div class="line"><a id="l00084" name="l00084"></a><span class="lineno"> 84</span> } <span class="keywordflow">else</span> {</div>
|
||||||
|
<div class="line"><a id="l00085" name="l00085"></a><span class="lineno"> 85</span> linear_y_space_.clear();</div>
|
||||||
|
<div class="line"><a id="l00086" name="l00086"></a><span class="lineno"> 86</span> linear_y_space_.resize(n_samples_, 0.0); <span class="comment">// Dummy labels for prediction</span></div>
|
||||||
|
<div class="line"><a id="l00087" name="l00087"></a><span class="lineno"> 87</span> }</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="comment">// Create problem</span></div>
|
||||||
|
<div class="line"><a id="l00090" name="l00090"></a><span class="lineno"> 90</span> <span class="keyword">auto</span> linear_problem = std::make_unique<problem>();</div>
|
||||||
|
<div class="line"><a id="l00091" name="l00091"></a><span class="lineno"> 91</span> linear_problem->l = n_samples_;</div>
|
||||||
|
<div class="line"><a id="l00092" name="l00092"></a><span class="lineno"> 92</span> linear_problem->n = n_features_;</div>
|
||||||
|
<div class="line"><a id="l00093" name="l00093"></a><span class="lineno"> 93</span> linear_problem->x = linear_x_space_.data();</div>
|
||||||
|
<div class="line"><a id="l00094" name="l00094"></a><span class="lineno"> 94</span> linear_problem->y = linear_y_space_.data();</div>
|
||||||
|
<div class="line"><a id="l00095" name="l00095"></a><span class="lineno"> 95</span> linear_problem->bias = -1; <span class="comment">// No bias term by default</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> <span class="keywordflow">return</span> linear_problem;</div>
|
||||||
|
<div class="line"><a id="l00098" name="l00098"></a><span class="lineno"> 98</span> }</div>
|
||||||
|
</div>
|
||||||
|
<div class="line"><a id="l00099" name="l00099"></a><span class="lineno"> 99</span> </div>
|
||||||
|
<div class="foldopen" id="foldopen00100" data-start="{" data-end="}">
|
||||||
|
<div class="line"><a id="l00100" name="l00100"></a><span class="lineno"><a class="line" href="classsvm__classifier_1_1DataConverter.html#a16e1539ef1266ca9ddd27a2ac5a53b92"> 100</a></span> svm_node* <a class="code hl_function" href="classsvm__classifier_1_1DataConverter.html#a16e1539ef1266ca9ddd27a2ac5a53b92">DataConverter::to_svm_node</a>(<span class="keyword">const</span> torch::Tensor& sample)</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> validate_tensor_properties(sample, 1, <span class="stringliteral">"sample"</span>);</div>
|
||||||
|
<div class="line"><a id="l00103" name="l00103"></a><span class="lineno"> 103</span> </div>
|
||||||
|
<div class="line"><a id="l00104" name="l00104"></a><span class="lineno"> 104</span> <span class="keyword">auto</span> sample_cpu = ensure_cpu_tensor(sample);</div>
|
||||||
|
<div class="line"><a id="l00105" name="l00105"></a><span class="lineno"> 105</span> single_svm_nodes_ = sample_to_svm_nodes(sample_cpu);</div>
|
||||||
|
<div class="line"><a id="l00106" name="l00106"></a><span class="lineno"> 106</span> </div>
|
||||||
|
<div class="line"><a id="l00107" name="l00107"></a><span class="lineno"> 107</span> <span class="keywordflow">return</span> single_svm_nodes_.data();</div>
|
||||||
|
<div class="line"><a id="l00108" name="l00108"></a><span class="lineno"> 108</span> }</div>
|
||||||
|
</div>
|
||||||
|
<div class="line"><a id="l00109" name="l00109"></a><span class="lineno"> 109</span> </div>
|
||||||
|
<div class="foldopen" id="foldopen00110" data-start="{" data-end="}">
|
||||||
|
<div class="line"><a id="l00110" name="l00110"></a><span class="lineno"><a class="line" href="classsvm__classifier_1_1DataConverter.html#a6bb2b4565b27df0db5f229dbd380795e"> 110</a></span> feature_node* <a class="code hl_function" href="classsvm__classifier_1_1DataConverter.html#a6bb2b4565b27df0db5f229dbd380795e">DataConverter::to_feature_node</a>(<span class="keyword">const</span> torch::Tensor& sample)</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> validate_tensor_properties(sample, 1, <span class="stringliteral">"sample"</span>);</div>
|
||||||
|
<div class="line"><a id="l00113" name="l00113"></a><span class="lineno"> 113</span> </div>
|
||||||
|
<div class="line"><a id="l00114" name="l00114"></a><span class="lineno"> 114</span> <span class="keyword">auto</span> sample_cpu = ensure_cpu_tensor(sample);</div>
|
||||||
|
<div class="line"><a id="l00115" name="l00115"></a><span class="lineno"> 115</span> single_linear_nodes_ = sample_to_linear_nodes(sample_cpu);</div>
|
||||||
|
<div class="line"><a id="l00116" name="l00116"></a><span class="lineno"> 116</span> </div>
|
||||||
|
<div class="line"><a id="l00117" name="l00117"></a><span class="lineno"> 117</span> <span class="keywordflow">return</span> single_linear_nodes_.data();</div>
|
||||||
|
<div class="line"><a id="l00118" name="l00118"></a><span class="lineno"> 118</span> }</div>
|
||||||
|
</div>
|
||||||
|
<div class="line"><a id="l00119" name="l00119"></a><span class="lineno"> 119</span> </div>
|
||||||
|
<div class="foldopen" id="foldopen00120" data-start="{" data-end="}">
|
||||||
|
<div class="line"><a id="l00120" name="l00120"></a><span class="lineno"><a class="line" href="classsvm__classifier_1_1DataConverter.html#ab3e800a5016a915e9912d5873bb48741"> 120</a></span> torch::Tensor <a class="code hl_function" href="classsvm__classifier_1_1DataConverter.html#ab3e800a5016a915e9912d5873bb48741">DataConverter::from_predictions</a>(<span class="keyword">const</span> std::vector<double>& predictions)</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> <span class="keyword">auto</span> options = torch::TensorOptions().dtype(torch::kInt32);</div>
|
||||||
|
<div class="line"><a id="l00123" name="l00123"></a><span class="lineno"> 123</span> <span class="keyword">auto</span> tensor = torch::zeros({ <span class="keyword">static_cast<</span>int64_t<span class="keyword">></span>(predictions.size()) }, options);</div>
|
||||||
|
<div class="line"><a id="l00124" name="l00124"></a><span class="lineno"> 124</span> </div>
|
||||||
|
<div class="line"><a id="l00125" name="l00125"></a><span class="lineno"> 125</span> <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = 0; i < predictions.size(); ++i) {</div>
|
||||||
|
<div class="line"><a id="l00126" name="l00126"></a><span class="lineno"> 126</span> tensor[i] = <span class="keyword">static_cast<</span><span class="keywordtype">int</span><span class="keyword">></span>(predictions[i]);</div>
|
||||||
|
<div class="line"><a id="l00127" name="l00127"></a><span class="lineno"> 127</span> }</div>
|
||||||
|
<div class="line"><a id="l00128" name="l00128"></a><span class="lineno"> 128</span> </div>
|
||||||
|
<div class="line"><a id="l00129" name="l00129"></a><span class="lineno"> 129</span> <span class="keywordflow">return</span> tensor;</div>
|
||||||
|
<div class="line"><a id="l00130" name="l00130"></a><span class="lineno"> 130</span> }</div>
|
||||||
|
</div>
|
||||||
|
<div class="line"><a id="l00131" name="l00131"></a><span class="lineno"> 131</span> </div>
|
||||||
|
<div class="foldopen" id="foldopen00132" data-start="{" data-end="}">
|
||||||
|
<div class="line"><a id="l00132" name="l00132"></a><span class="lineno"><a class="line" href="classsvm__classifier_1_1DataConverter.html#a5460485675613c54596418af3d5057ff"> 132</a></span> torch::Tensor <a class="code hl_function" href="classsvm__classifier_1_1DataConverter.html#a5460485675613c54596418af3d5057ff">DataConverter::from_probabilities</a>(<span class="keyword">const</span> std::vector<std::vector<double>>& probabilities)</div>
|
||||||
|
<div class="line"><a id="l00133" name="l00133"></a><span class="lineno"> 133</span> {</div>
|
||||||
|
<div class="line"><a id="l00134" name="l00134"></a><span class="lineno"> 134</span> <span class="keywordflow">if</span> (probabilities.empty()) {</div>
|
||||||
|
<div class="line"><a id="l00135" name="l00135"></a><span class="lineno"> 135</span> <span class="keywordflow">return</span> torch::empty({ 0, 0 });</div>
|
||||||
|
<div class="line"><a id="l00136" name="l00136"></a><span class="lineno"> 136</span> }</div>
|
||||||
|
<div class="line"><a id="l00137" name="l00137"></a><span class="lineno"> 137</span> </div>
|
||||||
|
<div class="line"><a id="l00138" name="l00138"></a><span class="lineno"> 138</span> <span class="keywordtype">int</span> n_samples = <span class="keyword">static_cast<</span><span class="keywordtype">int</span><span class="keyword">></span>(probabilities.size());</div>
|
||||||
|
<div class="line"><a id="l00139" name="l00139"></a><span class="lineno"> 139</span> <span class="keywordtype">int</span> n_classes = <span class="keyword">static_cast<</span><span class="keywordtype">int</span><span class="keyword">></span>(probabilities[0].size());</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> <span class="keyword">auto</span> tensor = torch::zeros({ n_samples, n_classes }, torch::kFloat64);</div>
|
||||||
|
<div class="line"><a id="l00142" name="l00142"></a><span class="lineno"> 142</span> </div>
|
||||||
|
<div class="line"><a id="l00143" name="l00143"></a><span class="lineno"> 143</span> <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i < n_samples; ++i) {</div>
|
||||||
|
<div class="line"><a id="l00144" name="l00144"></a><span class="lineno"> 144</span> <span class="keywordflow">for</span> (<span class="keywordtype">int</span> j = 0; j < n_classes; ++j) {</div>
|
||||||
|
<div class="line"><a id="l00145" name="l00145"></a><span class="lineno"> 145</span> tensor[i][j] = probabilities[i][j];</div>
|
||||||
|
<div class="line"><a id="l00146" name="l00146"></a><span class="lineno"> 146</span> }</div>
|
||||||
|
<div class="line"><a id="l00147" name="l00147"></a><span class="lineno"> 147</span> }</div>
|
||||||
|
<div class="line"><a id="l00148" name="l00148"></a><span class="lineno"> 148</span> </div>
|
||||||
|
<div class="line"><a id="l00149" name="l00149"></a><span class="lineno"> 149</span> <span class="keywordflow">return</span> tensor;</div>
|
||||||
|
<div class="line"><a id="l00150" name="l00150"></a><span class="lineno"> 150</span> }</div>
|
||||||
|
</div>
|
||||||
|
<div class="line"><a id="l00151" name="l00151"></a><span class="lineno"> 151</span> </div>
|
||||||
|
<div class="foldopen" id="foldopen00152" data-start="{" data-end="}">
|
||||||
|
<div class="line"><a id="l00152" name="l00152"></a><span class="lineno"><a class="line" href="classsvm__classifier_1_1DataConverter.html#a503eba54e8bb1f370e04b6e24354a32f"> 152</a></span> torch::Tensor <a class="code hl_function" href="classsvm__classifier_1_1DataConverter.html#a503eba54e8bb1f370e04b6e24354a32f">DataConverter::from_decision_values</a>(<span class="keyword">const</span> std::vector<std::vector<double>>& decision_values)</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> <span class="keywordflow">if</span> (decision_values.empty()) {</div>
|
||||||
|
<div class="line"><a id="l00155" name="l00155"></a><span class="lineno"> 155</span> <span class="keywordflow">return</span> torch::empty({ 0, 0 });</div>
|
||||||
|
<div class="line"><a id="l00156" name="l00156"></a><span class="lineno"> 156</span> }</div>
|
||||||
|
<div class="line"><a id="l00157" name="l00157"></a><span class="lineno"> 157</span> </div>
|
||||||
|
<div class="line"><a id="l00158" name="l00158"></a><span class="lineno"> 158</span> <span class="keywordtype">int</span> n_samples = <span class="keyword">static_cast<</span><span class="keywordtype">int</span><span class="keyword">></span>(decision_values.size());</div>
|
||||||
|
<div class="line"><a id="l00159" name="l00159"></a><span class="lineno"> 159</span> <span class="keywordtype">int</span> n_values = <span class="keyword">static_cast<</span><span class="keywordtype">int</span><span class="keyword">></span>(decision_values[0].size());</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="keyword">auto</span> tensor = torch::zeros({ n_samples, n_values }, torch::kFloat64);</div>
|
||||||
|
<div class="line"><a id="l00162" name="l00162"></a><span class="lineno"> 162</span> </div>
|
||||||
|
<div class="line"><a id="l00163" name="l00163"></a><span class="lineno"> 163</span> <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i < n_samples; ++i) {</div>
|
||||||
|
<div class="line"><a id="l00164" name="l00164"></a><span class="lineno"> 164</span> <span class="keywordflow">for</span> (<span class="keywordtype">int</span> j = 0; j < n_values; ++j) {</div>
|
||||||
|
<div class="line"><a id="l00165" name="l00165"></a><span class="lineno"> 165</span> tensor[i][j] = decision_values[i][j];</div>
|
||||||
|
<div class="line"><a id="l00166" name="l00166"></a><span class="lineno"> 166</span> }</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> tensor;</div>
|
||||||
|
<div class="line"><a id="l00170" name="l00170"></a><span class="lineno"> 170</span> }</div>
|
||||||
|
</div>
|
||||||
|
<div class="line"><a id="l00171" name="l00171"></a><span class="lineno"> 171</span> </div>
|
||||||
|
<div class="foldopen" id="foldopen00172" data-start="{" data-end="}">
|
||||||
|
<div class="line"><a id="l00172" name="l00172"></a><span class="lineno"><a class="line" href="classsvm__classifier_1_1DataConverter.html#aa0615f3de29958b2c5229d349f2f60ce"> 172</a></span> <span class="keywordtype">void</span> <a class="code hl_function" href="classsvm__classifier_1_1DataConverter.html#aa0615f3de29958b2c5229d349f2f60ce">DataConverter::validate_tensors</a>(<span class="keyword">const</span> torch::Tensor& X, <span class="keyword">const</span> torch::Tensor& y)</div>
|
||||||
|
<div class="line"><a id="l00173" name="l00173"></a><span class="lineno"> 173</span> {</div>
|
||||||
|
<div class="line"><a id="l00174" name="l00174"></a><span class="lineno"> 174</span> validate_tensor_properties(X, 2, <span class="stringliteral">"X"</span>);</div>
|
||||||
|
<div class="line"><a id="l00175" name="l00175"></a><span class="lineno"> 175</span> </div>
|
||||||
|
<div class="line"><a id="l00176" name="l00176"></a><span class="lineno"> 176</span> <span class="keywordflow">if</span> (y.defined() && y.numel() > 0) {</div>
|
||||||
|
<div class="line"><a id="l00177" name="l00177"></a><span class="lineno"> 177</span> validate_tensor_properties(y, 1, <span class="stringliteral">"y"</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> <span class="comment">// Check that number of samples match</span></div>
|
||||||
|
<div class="line"><a id="l00180" name="l00180"></a><span class="lineno"> 180</span> <span class="keywordflow">if</span> (X.size(0) != y.size(0)) {</div>
|
||||||
|
<div class="line"><a id="l00181" name="l00181"></a><span class="lineno"> 181</span> <span class="keywordflow">throw</span> std::invalid_argument(</div>
|
||||||
|
<div class="line"><a id="l00182" name="l00182"></a><span class="lineno"> 182</span> <span class="stringliteral">"Number of samples in X ("</span> + std::to_string(X.size(0)) +</div>
|
||||||
|
<div class="line"><a id="l00183" name="l00183"></a><span class="lineno"> 183</span> <span class="stringliteral">") does not match number of labels in y ("</span> + std::to_string(y.size(0)) + <span class="stringliteral">")"</span></div>
|
||||||
|
<div class="line"><a id="l00184" name="l00184"></a><span class="lineno"> 184</span> );</div>
|
||||||
|
<div class="line"><a id="l00185" name="l00185"></a><span class="lineno"> 185</span> }</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> </div>
|
||||||
|
<div class="line"><a id="l00188" name="l00188"></a><span class="lineno"> 188</span> <span class="comment">// Check for reasonable dimensions</span></div>
|
||||||
|
<div class="line"><a id="l00189" name="l00189"></a><span class="lineno"> 189</span> <span class="keywordflow">if</span> (X.size(0) == 0) {</div>
|
||||||
|
<div class="line"><a id="l00190" name="l00190"></a><span class="lineno"> 190</span> <span class="keywordflow">throw</span> std::invalid_argument(<span class="stringliteral">"X cannot have 0 samples"</span>);</div>
|
||||||
|
<div class="line"><a id="l00191" name="l00191"></a><span class="lineno"> 191</span> }</div>
|
||||||
|
<div class="line"><a id="l00192" name="l00192"></a><span class="lineno"> 192</span> </div>
|
||||||
|
<div class="line"><a id="l00193" name="l00193"></a><span class="lineno"> 193</span> <span class="keywordflow">if</span> (X.size(1) == 0) {</div>
|
||||||
|
<div class="line"><a id="l00194" name="l00194"></a><span class="lineno"> 194</span> <span class="keywordflow">throw</span> std::invalid_argument(<span class="stringliteral">"X cannot have 0 features"</span>);</div>
|
||||||
|
<div class="line"><a id="l00195" name="l00195"></a><span class="lineno"> 195</span> }</div>
|
||||||
|
<div class="line"><a id="l00196" name="l00196"></a><span class="lineno"> 196</span> }</div>
|
||||||
|
</div>
|
||||||
|
<div class="line"><a id="l00197" name="l00197"></a><span class="lineno"> 197</span> </div>
|
||||||
|
<div class="foldopen" id="foldopen00198" data-start="{" data-end="}">
|
||||||
|
<div class="line"><a id="l00198" name="l00198"></a><span class="lineno"><a class="line" href="classsvm__classifier_1_1DataConverter.html#a46d12ba28c4c5bf6e0fad1122c621fa8"> 198</a></span> <span class="keywordtype">void</span> <a class="code hl_function" href="classsvm__classifier_1_1DataConverter.html#a46d12ba28c4c5bf6e0fad1122c621fa8">DataConverter::cleanup</a>()</div>
|
||||||
|
<div class="line"><a id="l00199" name="l00199"></a><span class="lineno"> 199</span> {</div>
|
||||||
|
<div class="line"><a id="l00200" name="l00200"></a><span class="lineno"> 200</span> svm_nodes_storage_.clear();</div>
|
||||||
|
<div class="line"><a id="l00201" name="l00201"></a><span class="lineno"> 201</span> svm_x_space_.clear();</div>
|
||||||
|
<div class="line"><a id="l00202" name="l00202"></a><span class="lineno"> 202</span> svm_y_space_.clear();</div>
|
||||||
|
<div class="line"><a id="l00203" name="l00203"></a><span class="lineno"> 203</span> </div>
|
||||||
|
<div class="line"><a id="l00204" name="l00204"></a><span class="lineno"> 204</span> linear_nodes_storage_.clear();</div>
|
||||||
|
<div class="line"><a id="l00205" name="l00205"></a><span class="lineno"> 205</span> linear_x_space_.clear();</div>
|
||||||
|
<div class="line"><a id="l00206" name="l00206"></a><span class="lineno"> 206</span> linear_y_space_.clear();</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> single_svm_nodes_.clear();</div>
|
||||||
|
<div class="line"><a id="l00209" name="l00209"></a><span class="lineno"> 209</span> single_linear_nodes_.clear();</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> n_features_ = 0;</div>
|
||||||
|
<div class="line"><a id="l00212" name="l00212"></a><span class="lineno"> 212</span> n_samples_ = 0;</div>
|
||||||
|
<div class="line"><a id="l00213" name="l00213"></a><span class="lineno"> 213</span> }</div>
|
||||||
|
</div>
|
||||||
|
<div class="line"><a id="l00214" name="l00214"></a><span class="lineno"> 214</span> </div>
|
||||||
|
<div class="line"><a id="l00215" name="l00215"></a><span class="lineno"> 215</span> std::vector<std::vector<svm_node>> DataConverter::tensor_to_svm_nodes(<span class="keyword">const</span> torch::Tensor& X)</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> std::vector<std::vector<svm_node>> nodes_storage;</div>
|
||||||
|
<div class="line"><a id="l00218" name="l00218"></a><span class="lineno"> 218</span> nodes_storage.reserve(X.size(0));</div>
|
||||||
|
<div class="line"><a id="l00219" name="l00219"></a><span class="lineno"> 219</span> </div>
|
||||||
|
<div class="line"><a id="l00220" name="l00220"></a><span class="lineno"> 220</span> <span class="keyword">auto</span> X_acc = X.accessor<float, 2>();</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> <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i < X.size(0); ++i) {</div>
|
||||||
|
<div class="line"><a id="l00223" name="l00223"></a><span class="lineno"> 223</span> nodes_storage.push_back(sample_to_svm_nodes(X[i]));</div>
|
||||||
|
<div class="line"><a id="l00224" name="l00224"></a><span class="lineno"> 224</span> }</div>
|
||||||
|
<div class="line"><a id="l00225" name="l00225"></a><span class="lineno"> 225</span> </div>
|
||||||
|
<div class="line"><a id="l00226" name="l00226"></a><span class="lineno"> 226</span> <span class="keywordflow">return</span> nodes_storage;</div>
|
||||||
|
<div class="line"><a id="l00227" name="l00227"></a><span class="lineno"> 227</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> std::vector<std::vector<feature_node>> DataConverter::tensor_to_linear_nodes(<span class="keyword">const</span> torch::Tensor& X)</div>
|
||||||
|
<div class="line"><a id="l00230" name="l00230"></a><span class="lineno"> 230</span> {</div>
|
||||||
|
<div class="line"><a id="l00231" name="l00231"></a><span class="lineno"> 231</span> std::vector<std::vector<feature_node>> nodes_storage;</div>
|
||||||
|
<div class="line"><a id="l00232" name="l00232"></a><span class="lineno"> 232</span> nodes_storage.reserve(X.size(0));</div>
|
||||||
|
<div class="line"><a id="l00233" name="l00233"></a><span class="lineno"> 233</span> </div>
|
||||||
|
<div class="line"><a id="l00234" name="l00234"></a><span class="lineno"> 234</span> <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i < X.size(0); ++i) {</div>
|
||||||
|
<div class="line"><a id="l00235" name="l00235"></a><span class="lineno"> 235</span> nodes_storage.push_back(sample_to_linear_nodes(X[i]));</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> nodes_storage;</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> std::vector<svm_node> DataConverter::sample_to_svm_nodes(<span class="keyword">const</span> torch::Tensor& sample)</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> std::vector<svm_node> nodes;</div>
|
||||||
|
<div class="line"><a id="l00244" name="l00244"></a><span class="lineno"> 244</span> </div>
|
||||||
|
<div class="line"><a id="l00245" name="l00245"></a><span class="lineno"> 245</span> <span class="keyword">auto</span> sample_acc = sample.accessor<float, 1>();</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="comment">// Reserve space (worst case: all features are non-sparse)</span></div>
|
||||||
|
<div class="line"><a id="l00248" name="l00248"></a><span class="lineno"> 248</span> nodes.reserve(sample.size(0) + 1); <span class="comment">// +1 for terminator</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="keywordflow">for</span> (<span class="keywordtype">int</span> j = 0; j < sample.size(0); ++j) {</div>
|
||||||
|
<div class="line"><a id="l00251" name="l00251"></a><span class="lineno"> 251</span> <span class="keywordtype">double</span> value = <span class="keyword">static_cast<</span><span class="keywordtype">double</span><span class="keyword">></span>(sample_acc[j]);</div>
|
||||||
|
<div class="line"><a id="l00252" name="l00252"></a><span class="lineno"> 252</span> </div>
|
||||||
|
<div class="line"><a id="l00253" name="l00253"></a><span class="lineno"> 253</span> <span class="comment">// Skip sparse features</span></div>
|
||||||
|
<div class="line"><a id="l00254" name="l00254"></a><span class="lineno"> 254</span> <span class="keywordflow">if</span> (std::abs(value) > sparse_threshold_) {</div>
|
||||||
|
<div class="line"><a id="l00255" name="l00255"></a><span class="lineno"> 255</span> svm_node node;</div>
|
||||||
|
<div class="line"><a id="l00256" name="l00256"></a><span class="lineno"> 256</span> node.index = j + 1; <span class="comment">// libsvm uses 1-based indexing</span></div>
|
||||||
|
<div class="line"><a id="l00257" name="l00257"></a><span class="lineno"> 257</span> node.value = value;</div>
|
||||||
|
<div class="line"><a id="l00258" name="l00258"></a><span class="lineno"> 258</span> nodes.push_back(node);</div>
|
||||||
|
<div class="line"><a id="l00259" name="l00259"></a><span class="lineno"> 259</span> }</div>
|
||||||
|
<div class="line"><a id="l00260" name="l00260"></a><span class="lineno"> 260</span> }</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">// Add terminator</span></div>
|
||||||
|
<div class="line"><a id="l00263" name="l00263"></a><span class="lineno"> 263</span> svm_node terminator;</div>
|
||||||
|
<div class="line"><a id="l00264" name="l00264"></a><span class="lineno"> 264</span> terminator.index = -1;</div>
|
||||||
|
<div class="line"><a id="l00265" name="l00265"></a><span class="lineno"> 265</span> terminator.value = 0;</div>
|
||||||
|
<div class="line"><a id="l00266" name="l00266"></a><span class="lineno"> 266</span> nodes.push_back(terminator);</div>
|
||||||
|
<div class="line"><a id="l00267" name="l00267"></a><span class="lineno"> 267</span> </div>
|
||||||
|
<div class="line"><a id="l00268" name="l00268"></a><span class="lineno"> 268</span> <span class="keywordflow">return</span> nodes;</div>
|
||||||
|
<div class="line"><a id="l00269" name="l00269"></a><span class="lineno"> 269</span> }</div>
|
||||||
|
<div class="line"><a id="l00270" name="l00270"></a><span class="lineno"> 270</span> </div>
|
||||||
|
<div class="line"><a id="l00271" name="l00271"></a><span class="lineno"> 271</span> std::vector<feature_node> DataConverter::sample_to_linear_nodes(<span class="keyword">const</span> torch::Tensor& sample)</div>
|
||||||
|
<div class="line"><a id="l00272" name="l00272"></a><span class="lineno"> 272</span> {</div>
|
||||||
|
<div class="line"><a id="l00273" name="l00273"></a><span class="lineno"> 273</span> std::vector<feature_node> nodes;</div>
|
||||||
|
<div class="line"><a id="l00274" name="l00274"></a><span class="lineno"> 274</span> </div>
|
||||||
|
<div class="line"><a id="l00275" name="l00275"></a><span class="lineno"> 275</span> <span class="keyword">auto</span> sample_acc = sample.accessor<float, 1>();</div>
|
||||||
|
<div class="line"><a id="l00276" name="l00276"></a><span class="lineno"> 276</span> </div>
|
||||||
|
<div class="line"><a id="l00277" name="l00277"></a><span class="lineno"> 277</span> <span class="comment">// Reserve space (worst case: all features are non-sparse)</span></div>
|
||||||
|
<div class="line"><a id="l00278" name="l00278"></a><span class="lineno"> 278</span> nodes.reserve(sample.size(0) + 1); <span class="comment">// +1 for terminator</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> <span class="keywordflow">for</span> (<span class="keywordtype">int</span> j = 0; j < sample.size(0); ++j) {</div>
|
||||||
|
<div class="line"><a id="l00281" name="l00281"></a><span class="lineno"> 281</span> <span class="keywordtype">double</span> value = <span class="keyword">static_cast<</span><span class="keywordtype">double</span><span class="keyword">></span>(sample_acc[j]);</div>
|
||||||
|
<div class="line"><a id="l00282" name="l00282"></a><span class="lineno"> 282</span> </div>
|
||||||
|
<div class="line"><a id="l00283" name="l00283"></a><span class="lineno"> 283</span> <span class="comment">// Skip sparse features</span></div>
|
||||||
|
<div class="line"><a id="l00284" name="l00284"></a><span class="lineno"> 284</span> <span class="keywordflow">if</span> (std::abs(value) > sparse_threshold_) {</div>
|
||||||
|
<div class="line"><a id="l00285" name="l00285"></a><span class="lineno"> 285</span> feature_node node;</div>
|
||||||
|
<div class="line"><a id="l00286" name="l00286"></a><span class="lineno"> 286</span> node.index = j + 1; <span class="comment">// liblinear uses 1-based indexing</span></div>
|
||||||
|
<div class="line"><a id="l00287" name="l00287"></a><span class="lineno"> 287</span> node.value = value;</div>
|
||||||
|
<div class="line"><a id="l00288" name="l00288"></a><span class="lineno"> 288</span> nodes.push_back(node);</div>
|
||||||
|
<div class="line"><a id="l00289" name="l00289"></a><span class="lineno"> 289</span> }</div>
|
||||||
|
<div class="line"><a id="l00290" name="l00290"></a><span class="lineno"> 290</span> }</div>
|
||||||
|
<div class="line"><a id="l00291" name="l00291"></a><span class="lineno"> 291</span> </div>
|
||||||
|
<div class="line"><a id="l00292" name="l00292"></a><span class="lineno"> 292</span> <span class="comment">// Add terminator</span></div>
|
||||||
|
<div class="line"><a id="l00293" name="l00293"></a><span class="lineno"> 293</span> feature_node terminator;</div>
|
||||||
|
<div class="line"><a id="l00294" name="l00294"></a><span class="lineno"> 294</span> terminator.index = -1;</div>
|
||||||
|
<div class="line"><a id="l00295" name="l00295"></a><span class="lineno"> 295</span> terminator.value = 0;</div>
|
||||||
|
<div class="line"><a id="l00296" name="l00296"></a><span class="lineno"> 296</span> nodes.push_back(terminator);</div>
|
||||||
|
<div class="line"><a id="l00297" name="l00297"></a><span class="lineno"> 297</span> </div>
|
||||||
|
<div class="line"><a id="l00298" name="l00298"></a><span class="lineno"> 298</span> <span class="keywordflow">return</span> nodes;</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> </div>
|
||||||
|
<div class="line"><a id="l00301" name="l00301"></a><span class="lineno"> 301</span> std::vector<double> DataConverter::extract_labels(<span class="keyword">const</span> torch::Tensor& y)</div>
|
||||||
|
<div class="line"><a id="l00302" name="l00302"></a><span class="lineno"> 302</span> {</div>
|
||||||
|
<div class="line"><a id="l00303" name="l00303"></a><span class="lineno"> 303</span> <span class="keyword">auto</span> y_cpu = ensure_cpu_tensor(y);</div>
|
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<div class="line"><a id="l00304" name="l00304"></a><span class="lineno"> 304</span> std::vector<double> labels;</div>
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<div class="line"><a id="l00305" name="l00305"></a><span class="lineno"> 305</span> labels.reserve(y_cpu.size(0));</div>
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<div class="line"><a id="l00306" name="l00306"></a><span class="lineno"> 306</span> </div>
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<div class="line"><a id="l00307" name="l00307"></a><span class="lineno"> 307</span> <span class="comment">// Handle different tensor types</span></div>
|
||||||
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<div class="line"><a id="l00308" name="l00308"></a><span class="lineno"> 308</span> <span class="keywordflow">if</span> (y_cpu.dtype() == torch::kInt32) {</div>
|
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<div class="line"><a id="l00309" name="l00309"></a><span class="lineno"> 309</span> <span class="keyword">auto</span> y_acc = y_cpu.accessor<int32_t, 1>();</div>
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<div class="line"><a id="l00310" name="l00310"></a><span class="lineno"> 310</span> <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i < y_cpu.size(0); ++i) {</div>
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<div class="line"><a id="l00311" name="l00311"></a><span class="lineno"> 311</span> labels.push_back(<span class="keyword">static_cast<</span><span class="keywordtype">double</span><span class="keyword">></span>(y_acc[i]));</div>
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<div class="line"><a id="l00312" name="l00312"></a><span class="lineno"> 312</span> }</div>
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<div class="line"><a id="l00313" name="l00313"></a><span class="lineno"> 313</span> } <span class="keywordflow">else</span> <span class="keywordflow">if</span> (y_cpu.dtype() == torch::kInt64) {</div>
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<div class="line"><a id="l00314" name="l00314"></a><span class="lineno"> 314</span> <span class="keyword">auto</span> y_acc = y_cpu.accessor<int64_t, 1>();</div>
|
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<div class="line"><a id="l00315" name="l00315"></a><span class="lineno"> 315</span> <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i < y_cpu.size(0); ++i) {</div>
|
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<div class="line"><a id="l00316" name="l00316"></a><span class="lineno"> 316</span> labels.push_back(<span class="keyword">static_cast<</span><span class="keywordtype">double</span><span class="keyword">></span>(y_acc[i]));</div>
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<div class="line"><a id="l00317" name="l00317"></a><span class="lineno"> 317</span> }</div>
|
||||||
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<div class="line"><a id="l00318" name="l00318"></a><span class="lineno"> 318</span> } <span class="keywordflow">else</span> <span class="keywordflow">if</span> (y_cpu.dtype() == torch::kFloat32) {</div>
|
||||||
|
<div class="line"><a id="l00319" name="l00319"></a><span class="lineno"> 319</span> <span class="keyword">auto</span> y_acc = y_cpu.accessor<float, 1>();</div>
|
||||||
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<div class="line"><a id="l00320" name="l00320"></a><span class="lineno"> 320</span> <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i < y_cpu.size(0); ++i) {</div>
|
||||||
|
<div class="line"><a id="l00321" name="l00321"></a><span class="lineno"> 321</span> labels.push_back(<span class="keyword">static_cast<</span><span class="keywordtype">double</span><span class="keyword">></span>(y_acc[i]));</div>
|
||||||
|
<div class="line"><a id="l00322" name="l00322"></a><span class="lineno"> 322</span> }</div>
|
||||||
|
<div class="line"><a id="l00323" name="l00323"></a><span class="lineno"> 323</span> } <span class="keywordflow">else</span> <span class="keywordflow">if</span> (y_cpu.dtype() == torch::kFloat64) {</div>
|
||||||
|
<div class="line"><a id="l00324" name="l00324"></a><span class="lineno"> 324</span> <span class="keyword">auto</span> y_acc = y_cpu.accessor<double, 1>();</div>
|
||||||
|
<div class="line"><a id="l00325" name="l00325"></a><span class="lineno"> 325</span> <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i < y_cpu.size(0); ++i) {</div>
|
||||||
|
<div class="line"><a id="l00326" name="l00326"></a><span class="lineno"> 326</span> labels.push_back(y_acc[i]);</div>
|
||||||
|
<div class="line"><a id="l00327" name="l00327"></a><span class="lineno"> 327</span> }</div>
|
||||||
|
<div class="line"><a id="l00328" name="l00328"></a><span class="lineno"> 328</span> } <span class="keywordflow">else</span> {</div>
|
||||||
|
<div class="line"><a id="l00329" name="l00329"></a><span class="lineno"> 329</span> <span class="keywordflow">throw</span> std::invalid_argument(<span class="stringliteral">"Unsupported label tensor dtype"</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> </div>
|
||||||
|
<div class="line"><a id="l00332" name="l00332"></a><span class="lineno"> 332</span> <span class="keywordflow">return</span> labels;</div>
|
||||||
|
<div class="line"><a id="l00333" name="l00333"></a><span class="lineno"> 333</span> }</div>
|
||||||
|
<div class="line"><a id="l00334" name="l00334"></a><span class="lineno"> 334</span> </div>
|
||||||
|
<div class="line"><a id="l00335" name="l00335"></a><span class="lineno"> 335</span> torch::Tensor DataConverter::ensure_cpu_tensor(<span class="keyword">const</span> torch::Tensor& tensor)</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> <span class="keywordflow">if</span> (tensor.device().type() != torch::kCPU) {</div>
|
||||||
|
<div class="line"><a id="l00338" name="l00338"></a><span class="lineno"> 338</span> <span class="keywordflow">return</span> tensor.to(torch::kCPU);</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> </div>
|
||||||
|
<div class="line"><a id="l00341" name="l00341"></a><span class="lineno"> 341</span> <span class="comment">// Convert to float32 if not already</span></div>
|
||||||
|
<div class="line"><a id="l00342" name="l00342"></a><span class="lineno"> 342</span> <span class="keywordflow">if</span> (tensor.dtype() != torch::kFloat32) {</div>
|
||||||
|
<div class="line"><a id="l00343" name="l00343"></a><span class="lineno"> 343</span> <span class="keywordflow">return</span> tensor.to(torch::kFloat32);</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="keywordflow">return</span> tensor;</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> </div>
|
||||||
|
<div class="line"><a id="l00349" name="l00349"></a><span class="lineno"> 349</span> <span class="keywordtype">void</span> DataConverter::validate_tensor_properties(<span class="keyword">const</span> torch::Tensor& tensor,</div>
|
||||||
|
<div class="line"><a id="l00350" name="l00350"></a><span class="lineno"> 350</span> <span class="keywordtype">int</span> expected_dims,</div>
|
||||||
|
<div class="line"><a id="l00351" name="l00351"></a><span class="lineno"> 351</span> <span class="keyword">const</span> std::string& name)</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> <span class="keywordflow">if</span> (!tensor.defined()) {</div>
|
||||||
|
<div class="line"><a id="l00354" name="l00354"></a><span class="lineno"> 354</span> <span class="keywordflow">throw</span> std::invalid_argument(name + <span class="stringliteral">" tensor is not defined"</span>);</div>
|
||||||
|
<div class="line"><a id="l00355" name="l00355"></a><span class="lineno"> 355</span> }</div>
|
||||||
|
<div class="line"><a id="l00356" name="l00356"></a><span class="lineno"> 356</span> </div>
|
||||||
|
<div class="line"><a id="l00357" name="l00357"></a><span class="lineno"> 357</span> <span class="keywordflow">if</span> (tensor.dim() != expected_dims) {</div>
|
||||||
|
<div class="line"><a id="l00358" name="l00358"></a><span class="lineno"> 358</span> <span class="keywordflow">throw</span> std::invalid_argument(</div>
|
||||||
|
<div class="line"><a id="l00359" name="l00359"></a><span class="lineno"> 359</span> name + <span class="stringliteral">" must have "</span> + std::to_string(expected_dims) +</div>
|
||||||
|
<div class="line"><a id="l00360" name="l00360"></a><span class="lineno"> 360</span> <span class="stringliteral">" dimensions, got "</span> + std::to_string(tensor.dim())</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> }</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> <span class="keywordflow">if</span> (tensor.numel() == 0) {</div>
|
||||||
|
<div class="line"><a id="l00365" name="l00365"></a><span class="lineno"> 365</span> <span class="keywordflow">throw</span> std::invalid_argument(name + <span class="stringliteral">" tensor cannot be empty"</span>);</div>
|
||||||
|
<div class="line"><a id="l00366" name="l00366"></a><span class="lineno"> 366</span> }</div>
|
||||||
|
<div class="line"><a id="l00367" name="l00367"></a><span class="lineno"> 367</span> </div>
|
||||||
|
<div class="line"><a id="l00368" name="l00368"></a><span class="lineno"> 368</span> <span class="comment">// Check for NaN or Inf values</span></div>
|
||||||
|
<div class="line"><a id="l00369" name="l00369"></a><span class="lineno"> 369</span> <span class="keywordflow">if</span> (torch::any(torch::isnan(tensor)).item<bool>()) {</div>
|
||||||
|
<div class="line"><a id="l00370" name="l00370"></a><span class="lineno"> 370</span> <span class="keywordflow">throw</span> std::invalid_argument(name + <span class="stringliteral">" contains NaN values"</span>);</div>
|
||||||
|
<div class="line"><a id="l00371" name="l00371"></a><span class="lineno"> 371</span> }</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> <span class="keywordflow">if</span> (torch::any(torch::isinf(tensor)).item<bool>()) {</div>
|
||||||
|
<div class="line"><a id="l00374" name="l00374"></a><span class="lineno"> 374</span> <span class="keywordflow">throw</span> std::invalid_argument(name + <span class="stringliteral">" contains infinite values"</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> }</div>
|
||||||
|
<div class="line"><a id="l00377" name="l00377"></a><span class="lineno"> 377</span> </div>
|
||||||
|
<div class="line"><a id="l00378" name="l00378"></a><span class="lineno"> 378</span>} <span class="comment">// namespace svm_classifier</span></div>
|
||||||
|
<div class="ttc" id="aclasssvm__classifier_1_1DataConverter_html_a16e1539ef1266ca9ddd27a2ac5a53b92"><div class="ttname"><a href="classsvm__classifier_1_1DataConverter.html#a16e1539ef1266ca9ddd27a2ac5a53b92">svm_classifier::DataConverter::to_svm_node</a></div><div class="ttdeci">svm_node * to_svm_node(const torch::Tensor &sample)</div><div class="ttdoc">Convert single sample to libsvm format.</div><div class="ttdef"><b>Definition</b> <a href="data__converter_8cpp_source.html#l00100">data_converter.cpp:100</a></div></div>
|
||||||
|
<div class="ttc" id="aclasssvm__classifier_1_1DataConverter_html_a46d12ba28c4c5bf6e0fad1122c621fa8"><div class="ttname"><a href="classsvm__classifier_1_1DataConverter.html#a46d12ba28c4c5bf6e0fad1122c621fa8">svm_classifier::DataConverter::cleanup</a></div><div class="ttdeci">void cleanup()</div><div class="ttdoc">Clean up all allocated memory.</div><div class="ttdef"><b>Definition</b> <a href="data__converter_8cpp_source.html#l00198">data_converter.cpp:198</a></div></div>
|
||||||
|
<div class="ttc" id="aclasssvm__classifier_1_1DataConverter_html_a503eba54e8bb1f370e04b6e24354a32f"><div class="ttname"><a href="classsvm__classifier_1_1DataConverter.html#a503eba54e8bb1f370e04b6e24354a32f">svm_classifier::DataConverter::from_decision_values</a></div><div class="ttdeci">torch::Tensor from_decision_values(const std::vector< std::vector< double > > &decision_values)</div><div class="ttdoc">Convert decision values back to PyTorch tensor.</div><div class="ttdef"><b>Definition</b> <a href="data__converter_8cpp_source.html#l00152">data_converter.cpp:152</a></div></div>
|
||||||
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<div class="ttc" id="aclasssvm__classifier_1_1DataConverter_html_a5460485675613c54596418af3d5057ff"><div class="ttname"><a href="classsvm__classifier_1_1DataConverter.html#a5460485675613c54596418af3d5057ff">svm_classifier::DataConverter::from_probabilities</a></div><div class="ttdeci">torch::Tensor from_probabilities(const std::vector< std::vector< double > > &probabilities)</div><div class="ttdoc">Convert probabilities back to PyTorch tensor.</div><div class="ttdef"><b>Definition</b> <a href="data__converter_8cpp_source.html#l00132">data_converter.cpp:132</a></div></div>
|
||||||
|
<div class="ttc" id="aclasssvm__classifier_1_1DataConverter_html_a5874904555f26448ed5ae4cf6f370056"><div class="ttname"><a href="classsvm__classifier_1_1DataConverter.html#a5874904555f26448ed5ae4cf6f370056">svm_classifier::DataConverter::DataConverter</a></div><div class="ttdeci">DataConverter()</div><div class="ttdoc">Default constructor.</div><div class="ttdef"><b>Definition</b> <a href="data__converter_8cpp_source.html#l00010">data_converter.cpp:10</a></div></div>
|
||||||
|
<div class="ttc" id="aclasssvm__classifier_1_1DataConverter_html_a66f446e4decfe47bbba37c789f03f729"><div class="ttname"><a href="classsvm__classifier_1_1DataConverter.html#a66f446e4decfe47bbba37c789f03f729">svm_classifier::DataConverter::to_svm_problem</a></div><div class="ttdeci">std::unique_ptr< svm_problem > to_svm_problem(const torch::Tensor &X, const torch::Tensor &y=torch::Tensor())</div><div class="ttdoc">Convert PyTorch tensors to libsvm format.</div><div class="ttdef"><b>Definition</b> <a href="data__converter_8cpp_source.html#l00022">data_converter.cpp:22</a></div></div>
|
||||||
|
<div class="ttc" id="aclasssvm__classifier_1_1DataConverter_html_a6bb2b4565b27df0db5f229dbd380795e"><div class="ttname"><a href="classsvm__classifier_1_1DataConverter.html#a6bb2b4565b27df0db5f229dbd380795e">svm_classifier::DataConverter::to_feature_node</a></div><div class="ttdeci">feature_node * to_feature_node(const torch::Tensor &sample)</div><div class="ttdoc">Convert single sample to liblinear format.</div><div class="ttdef"><b>Definition</b> <a href="data__converter_8cpp_source.html#l00110">data_converter.cpp:110</a></div></div>
|
||||||
|
<div class="ttc" id="aclasssvm__classifier_1_1DataConverter_html_a7e7d8f6102b7a9b3256ff0dc6f536a35"><div class="ttname"><a href="classsvm__classifier_1_1DataConverter.html#a7e7d8f6102b7a9b3256ff0dc6f536a35">svm_classifier::DataConverter::to_linear_problem</a></div><div class="ttdeci">std::unique_ptr< problem > to_linear_problem(const torch::Tensor &X, const torch::Tensor &y=torch::Tensor())</div><div class="ttdoc">Convert PyTorch tensors to liblinear format.</div><div class="ttdef"><b>Definition</b> <a href="data__converter_8cpp_source.html#l00060">data_converter.cpp:60</a></div></div>
|
||||||
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<div class="ttc" id="aclasssvm__classifier_1_1DataConverter_html_aa0615f3de29958b2c5229d349f2f60ce"><div class="ttname"><a href="classsvm__classifier_1_1DataConverter.html#aa0615f3de29958b2c5229d349f2f60ce">svm_classifier::DataConverter::validate_tensors</a></div><div class="ttdeci">void validate_tensors(const torch::Tensor &X, const torch::Tensor &y=torch::Tensor())</div><div class="ttdoc">Validate input tensors.</div><div class="ttdef"><b>Definition</b> <a href="data__converter_8cpp_source.html#l00172">data_converter.cpp:172</a></div></div>
|
||||||
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<div class="ttc" id="aclasssvm__classifier_1_1DataConverter_html_ab3e800a5016a915e9912d5873bb48741"><div class="ttname"><a href="classsvm__classifier_1_1DataConverter.html#ab3e800a5016a915e9912d5873bb48741">svm_classifier::DataConverter::from_predictions</a></div><div class="ttdeci">torch::Tensor from_predictions(const std::vector< double > &predictions)</div><div class="ttdoc">Convert predictions back to PyTorch tensor.</div><div class="ttdef"><b>Definition</b> <a href="data__converter_8cpp_source.html#l00120">data_converter.cpp:120</a></div></div>
|
||||||
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<div class="ttc" id="aclasssvm__classifier_1_1DataConverter_html_ac3af2c9c03cffe2968f29147611e333d"><div class="ttname"><a href="classsvm__classifier_1_1DataConverter.html#ac3af2c9c03cffe2968f29147611e333d">svm_classifier::DataConverter::~DataConverter</a></div><div class="ttdeci">~DataConverter()</div><div class="ttdoc">Destructor - cleans up allocated memory.</div><div class="ttdef"><b>Definition</b> <a href="data__converter_8cpp_source.html#l00017">data_converter.cpp:17</a></div></div>
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<div id="projectbrief">High-performance Support Vector Machine classifier with scikit-learn compatible API</div>
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<li class="navelem"><a class="el" href="dir_d44c64559bbebec7f509842c48db8b23.html">include</a></li><li class="navelem"><a class="el" href="dir_daf582bc00f2bbc6516ddb6630e28009.html">svm_classifier</a></li> </ul>
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<div class="headertitle"><div class="title">data_converter.hpp</div></div>
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<div class="fragment"><div class="line"><a id="l00001" name="l00001"></a><span class="lineno"> 1</span><span class="preprocessor">#pragma once</span></div>
|
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<div class="line"><a id="l00002" name="l00002"></a><span class="lineno"> 2</span> </div>
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<div class="line"><a id="l00003" name="l00003"></a><span class="lineno"> 3</span><span class="preprocessor">#include "types.hpp"</span></div>
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<div class="line"><a id="l00004" name="l00004"></a><span class="lineno"> 4</span><span class="preprocessor">#include <torch/torch.h></span></div>
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<div class="line"><a id="l00005" name="l00005"></a><span class="lineno"> 5</span><span class="preprocessor">#include <vector></span></div>
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<div class="line"><a id="l00006" name="l00006"></a><span class="lineno"> 6</span><span class="preprocessor">#include <memory></span></div>
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<div class="line"><a id="l00007" name="l00007"></a><span class="lineno"> 7</span> </div>
|
||||||
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<div class="line"><a id="l00008" name="l00008"></a><span class="lineno"> 8</span><span class="comment">// Forward declarations for libsvm and liblinear structures</span></div>
|
||||||
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<div class="line"><a id="l00009" name="l00009"></a><span class="lineno"> 9</span><span class="keyword">struct </span>svm_node;</div>
|
||||||
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<div class="line"><a id="l00010" name="l00010"></a><span class="lineno"> 10</span><span class="keyword">struct </span>svm_problem;</div>
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||||||
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<div class="line"><a id="l00011" name="l00011"></a><span class="lineno"> 11</span><span class="keyword">struct </span>feature_node;</div>
|
||||||
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<div class="line"><a id="l00012" name="l00012"></a><span class="lineno"> 12</span><span class="keyword">struct </span>problem;</div>
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<div class="line"><a id="l00013" name="l00013"></a><span class="lineno"> 13</span> </div>
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<div class="line"><a id="l00014" name="l00014"></a><span class="lineno"> 14</span><span class="keyword">namespace </span>svm_classifier {</div>
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<div class="line"><a id="l00015" name="l00015"></a><span class="lineno"> 15</span> </div>
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<div class="foldopen" id="foldopen00023" data-start="{" data-end="};">
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<div class="line"><a id="l00023" name="l00023"></a><span class="lineno"><a class="line" href="classsvm__classifier_1_1DataConverter.html"> 23</a></span> <span class="keyword">class </span><a class="code hl_class" href="classsvm__classifier_1_1DataConverter.html">DataConverter</a> {</div>
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||||||
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<div class="line"><a id="l00024" name="l00024"></a><span class="lineno"> 24</span> <span class="keyword">public</span>:</div>
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<div class="line"><a id="l00028" name="l00028"></a><span class="lineno"> 28</span> <a class="code hl_function" href="classsvm__classifier_1_1DataConverter.html#a5874904555f26448ed5ae4cf6f370056">DataConverter</a>();</div>
|
||||||
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<div class="line"><a id="l00029" name="l00029"></a><span class="lineno"> 29</span> </div>
|
||||||
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<div class="line"><a id="l00033" name="l00033"></a><span class="lineno"> 33</span> <a class="code hl_function" href="classsvm__classifier_1_1DataConverter.html#ac3af2c9c03cffe2968f29147611e333d">~DataConverter</a>();</div>
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<div class="line"><a id="l00034" name="l00034"></a><span class="lineno"> 34</span> </div>
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<div class="line"><a id="l00041" name="l00041"></a><span class="lineno"> 41</span> std::unique_ptr<svm_problem> <a class="code hl_function" href="classsvm__classifier_1_1DataConverter.html#a66f446e4decfe47bbba37c789f03f729">to_svm_problem</a>(<span class="keyword">const</span> torch::Tensor& X,</div>
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||||||
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<div class="line"><a id="l00042" name="l00042"></a><span class="lineno"> 42</span> <span class="keyword">const</span> torch::Tensor& y = torch::Tensor());</div>
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||||||
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<div class="line"><a id="l00043" name="l00043"></a><span class="lineno"> 43</span> </div>
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<div class="line"><a id="l00050" name="l00050"></a><span class="lineno"> 50</span> std::unique_ptr<problem> <a class="code hl_function" href="classsvm__classifier_1_1DataConverter.html#a7e7d8f6102b7a9b3256ff0dc6f536a35">to_linear_problem</a>(<span class="keyword">const</span> torch::Tensor& X,</div>
|
||||||
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<div class="line"><a id="l00051" name="l00051"></a><span class="lineno"> 51</span> <span class="keyword">const</span> torch::Tensor& y = torch::Tensor());</div>
|
||||||
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<div class="line"><a id="l00052" name="l00052"></a><span class="lineno"> 52</span> </div>
|
||||||
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<div class="line"><a id="l00058" name="l00058"></a><span class="lineno"> 58</span> svm_node* <a class="code hl_function" href="classsvm__classifier_1_1DataConverter.html#a16e1539ef1266ca9ddd27a2ac5a53b92">to_svm_node</a>(<span class="keyword">const</span> torch::Tensor& sample);</div>
|
||||||
|
<div class="line"><a id="l00059" name="l00059"></a><span class="lineno"> 59</span> </div>
|
||||||
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<div class="line"><a id="l00065" name="l00065"></a><span class="lineno"> 65</span> feature_node* <a class="code hl_function" href="classsvm__classifier_1_1DataConverter.html#a6bb2b4565b27df0db5f229dbd380795e">to_feature_node</a>(<span class="keyword">const</span> torch::Tensor& sample);</div>
|
||||||
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<div class="line"><a id="l00066" name="l00066"></a><span class="lineno"> 66</span> </div>
|
||||||
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<div class="line"><a id="l00072" name="l00072"></a><span class="lineno"> 72</span> torch::Tensor <a class="code hl_function" href="classsvm__classifier_1_1DataConverter.html#ab3e800a5016a915e9912d5873bb48741">from_predictions</a>(<span class="keyword">const</span> std::vector<double>& predictions);</div>
|
||||||
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<div class="line"><a id="l00073" name="l00073"></a><span class="lineno"> 73</span> </div>
|
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<div class="line"><a id="l00079" name="l00079"></a><span class="lineno"> 79</span> torch::Tensor <a class="code hl_function" href="classsvm__classifier_1_1DataConverter.html#a5460485675613c54596418af3d5057ff">from_probabilities</a>(<span class="keyword">const</span> std::vector<std::vector<double>>& probabilities);</div>
|
||||||
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<div class="line"><a id="l00080" name="l00080"></a><span class="lineno"> 80</span> </div>
|
||||||
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<div class="line"><a id="l00086" name="l00086"></a><span class="lineno"> 86</span> torch::Tensor <a class="code hl_function" href="classsvm__classifier_1_1DataConverter.html#a503eba54e8bb1f370e04b6e24354a32f">from_decision_values</a>(<span class="keyword">const</span> std::vector<std::vector<double>>& decision_values);</div>
|
||||||
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<div class="line"><a id="l00087" name="l00087"></a><span class="lineno"> 87</span> </div>
|
||||||
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<div class="line"><a id="l00094" name="l00094"></a><span class="lineno"> 94</span> <span class="keywordtype">void</span> <a class="code hl_function" href="classsvm__classifier_1_1DataConverter.html#aa0615f3de29958b2c5229d349f2f60ce">validate_tensors</a>(<span class="keyword">const</span> torch::Tensor& X, <span class="keyword">const</span> torch::Tensor& y = torch::Tensor());</div>
|
||||||
|
<div class="line"><a id="l00095" name="l00095"></a><span class="lineno"> 95</span> </div>
|
||||||
|
<div class="line"><a id="l00100" name="l00100"></a><span class="lineno"><a class="line" href="classsvm__classifier_1_1DataConverter.html#a26342a8cc8b943f099112040aa960ae6"> 100</a></span> <span class="keywordtype">int</span> <a class="code hl_function" href="classsvm__classifier_1_1DataConverter.html#a26342a8cc8b943f099112040aa960ae6">get_n_features</a>()<span class="keyword"> const </span>{ <span class="keywordflow">return</span> n_features_; }</div>
|
||||||
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<div class="line"><a id="l00101" name="l00101"></a><span class="lineno"> 101</span> </div>
|
||||||
|
<div class="line"><a id="l00106" name="l00106"></a><span class="lineno"><a class="line" href="classsvm__classifier_1_1DataConverter.html#a16999ded27bc2d42d2ebd9551b00c1cb"> 106</a></span> <span class="keywordtype">int</span> <a class="code hl_function" href="classsvm__classifier_1_1DataConverter.html#a16999ded27bc2d42d2ebd9551b00c1cb">get_n_samples</a>()<span class="keyword"> const </span>{ <span class="keywordflow">return</span> n_samples_; }</div>
|
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|
<div class="line"><a id="l00107" name="l00107"></a><span class="lineno"> 107</span> </div>
|
||||||
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<div class="line"><a id="l00111" name="l00111"></a><span class="lineno"> 111</span> <span class="keywordtype">void</span> <a class="code hl_function" href="classsvm__classifier_1_1DataConverter.html#a46d12ba28c4c5bf6e0fad1122c621fa8">cleanup</a>();</div>
|
||||||
|
<div class="line"><a id="l00112" name="l00112"></a><span class="lineno"> 112</span> </div>
|
||||||
|
<div class="line"><a id="l00117" name="l00117"></a><span class="lineno"><a class="line" href="classsvm__classifier_1_1DataConverter.html#a9259322e54d2477478a684e99d8e557a"> 117</a></span> <span class="keywordtype">void</span> <a class="code hl_function" href="classsvm__classifier_1_1DataConverter.html#a9259322e54d2477478a684e99d8e557a">set_sparse_threshold</a>(<span class="keywordtype">double</span> threshold) { sparse_threshold_ = threshold; }</div>
|
||||||
|
<div class="line"><a id="l00118" name="l00118"></a><span class="lineno"> 118</span> </div>
|
||||||
|
<div class="line"><a id="l00123" name="l00123"></a><span class="lineno"><a class="line" href="classsvm__classifier_1_1DataConverter.html#a1905f60fef9ffd3a8e8e45e41395352d"> 123</a></span> <span class="keywordtype">double</span> <a class="code hl_function" href="classsvm__classifier_1_1DataConverter.html#a1905f60fef9ffd3a8e8e45e41395352d">get_sparse_threshold</a>()<span class="keyword"> const </span>{ <span class="keywordflow">return</span> sparse_threshold_; }</div>
|
||||||
|
<div class="line"><a id="l00124" name="l00124"></a><span class="lineno"> 124</span> </div>
|
||||||
|
<div class="line"><a id="l00125" name="l00125"></a><span class="lineno"> 125</span> <span class="keyword">private</span>:</div>
|
||||||
|
<div class="line"><a id="l00126" name="l00126"></a><span class="lineno"> 126</span> <span class="keywordtype">int</span> n_features_; </div>
|
||||||
|
<div class="line"><a id="l00127" name="l00127"></a><span class="lineno"> 127</span> <span class="keywordtype">int</span> n_samples_; </div>
|
||||||
|
<div class="line"><a id="l00128" name="l00128"></a><span class="lineno"> 128</span> <span class="keywordtype">double</span> sparse_threshold_; </div>
|
||||||
|
<div class="line"><a id="l00129" name="l00129"></a><span class="lineno"> 129</span> </div>
|
||||||
|
<div class="line"><a id="l00130" name="l00130"></a><span class="lineno"> 130</span> <span class="comment">// Memory management for libsvm structures</span></div>
|
||||||
|
<div class="line"><a id="l00131" name="l00131"></a><span class="lineno"> 131</span> std::vector<std::vector<svm_node>> svm_nodes_storage_;</div>
|
||||||
|
<div class="line"><a id="l00132" name="l00132"></a><span class="lineno"> 132</span> std::vector<svm_node*> svm_x_space_;</div>
|
||||||
|
<div class="line"><a id="l00133" name="l00133"></a><span class="lineno"> 133</span> std::vector<double> svm_y_space_;</div>
|
||||||
|
<div class="line"><a id="l00134" name="l00134"></a><span class="lineno"> 134</span> </div>
|
||||||
|
<div class="line"><a id="l00135" name="l00135"></a><span class="lineno"> 135</span> <span class="comment">// Memory management for liblinear structures</span></div>
|
||||||
|
<div class="line"><a id="l00136" name="l00136"></a><span class="lineno"> 136</span> std::vector<std::vector<feature_node>> linear_nodes_storage_;</div>
|
||||||
|
<div class="line"><a id="l00137" name="l00137"></a><span class="lineno"> 137</span> std::vector<feature_node*> linear_x_space_;</div>
|
||||||
|
<div class="line"><a id="l00138" name="l00138"></a><span class="lineno"> 138</span> std::vector<double> linear_y_space_;</div>
|
||||||
|
<div class="line"><a id="l00139" name="l00139"></a><span class="lineno"> 139</span> </div>
|
||||||
|
<div class="line"><a id="l00140" name="l00140"></a><span class="lineno"> 140</span> <span class="comment">// Single sample storage (for prediction)</span></div>
|
||||||
|
<div class="line"><a id="l00141" name="l00141"></a><span class="lineno"> 141</span> std::vector<svm_node> single_svm_nodes_;</div>
|
||||||
|
<div class="line"><a id="l00142" name="l00142"></a><span class="lineno"> 142</span> std::vector<feature_node> single_linear_nodes_;</div>
|
||||||
|
<div class="line"><a id="l00143" name="l00143"></a><span class="lineno"> 143</span> </div>
|
||||||
|
<div class="line"><a id="l00149" name="l00149"></a><span class="lineno"> 149</span> std::vector<std::vector<svm_node>> tensor_to_svm_nodes(<span class="keyword">const</span> torch::Tensor& X);</div>
|
||||||
|
<div class="line"><a id="l00150" name="l00150"></a><span class="lineno"> 150</span> </div>
|
||||||
|
<div class="line"><a id="l00156" name="l00156"></a><span class="lineno"> 156</span> std::vector<std::vector<feature_node>> tensor_to_linear_nodes(<span class="keyword">const</span> torch::Tensor& X);</div>
|
||||||
|
<div class="line"><a id="l00157" name="l00157"></a><span class="lineno"> 157</span> </div>
|
||||||
|
<div class="line"><a id="l00163" name="l00163"></a><span class="lineno"> 163</span> std::vector<svm_node> sample_to_svm_nodes(<span class="keyword">const</span> torch::Tensor& sample);</div>
|
||||||
|
<div class="line"><a id="l00164" name="l00164"></a><span class="lineno"> 164</span> </div>
|
||||||
|
<div class="line"><a id="l00170" name="l00170"></a><span class="lineno"> 170</span> std::vector<feature_node> sample_to_linear_nodes(<span class="keyword">const</span> torch::Tensor& sample);</div>
|
||||||
|
<div class="line"><a id="l00171" name="l00171"></a><span class="lineno"> 171</span> </div>
|
||||||
|
<div class="line"><a id="l00177" name="l00177"></a><span class="lineno"> 177</span> std::vector<double> extract_labels(<span class="keyword">const</span> torch::Tensor& y);</div>
|
||||||
|
<div class="line"><a id="l00178" name="l00178"></a><span class="lineno"> 178</span> </div>
|
||||||
|
<div class="line"><a id="l00184" name="l00184"></a><span class="lineno"> 184</span> torch::Tensor ensure_cpu_tensor(<span class="keyword">const</span> torch::Tensor& tensor);</div>
|
||||||
|
<div class="line"><a id="l00185" name="l00185"></a><span class="lineno"> 185</span> </div>
|
||||||
|
<div class="line"><a id="l00192" name="l00192"></a><span class="lineno"> 192</span> <span class="keywordtype">void</span> validate_tensor_properties(<span class="keyword">const</span> torch::Tensor& tensor, <span class="keywordtype">int</span> expected_dims, <span class="keyword">const</span> std::string& name);</div>
|
||||||
|
<div class="line"><a id="l00193" name="l00193"></a><span class="lineno"> 193</span> };</div>
|
||||||
|
</div>
|
||||||
|
<div class="line"><a id="l00194" name="l00194"></a><span class="lineno"> 194</span> </div>
|
||||||
|
<div class="line"><a id="l00195" name="l00195"></a><span class="lineno"> 195</span>} <span class="comment">// namespace svm_classifier</span></div>
|
||||||
|
<div class="ttc" id="aclasssvm__classifier_1_1DataConverter_html"><div class="ttname"><a href="classsvm__classifier_1_1DataConverter.html">svm_classifier::DataConverter</a></div><div class="ttdoc">Data converter between libtorch tensors and SVM library formats.</div><div class="ttdef"><b>Definition</b> <a href="data__converter_8hpp_source.html#l00023">data_converter.hpp:23</a></div></div>
|
||||||
|
<div class="ttc" id="aclasssvm__classifier_1_1DataConverter_html_a16999ded27bc2d42d2ebd9551b00c1cb"><div class="ttname"><a href="classsvm__classifier_1_1DataConverter.html#a16999ded27bc2d42d2ebd9551b00c1cb">svm_classifier::DataConverter::get_n_samples</a></div><div class="ttdeci">int get_n_samples() const</div><div class="ttdoc">Get number of samples from last conversion.</div><div class="ttdef"><b>Definition</b> <a href="data__converter_8hpp_source.html#l00106">data_converter.hpp:106</a></div></div>
|
||||||
|
<div class="ttc" id="aclasssvm__classifier_1_1DataConverter_html_a16e1539ef1266ca9ddd27a2ac5a53b92"><div class="ttname"><a href="classsvm__classifier_1_1DataConverter.html#a16e1539ef1266ca9ddd27a2ac5a53b92">svm_classifier::DataConverter::to_svm_node</a></div><div class="ttdeci">svm_node * to_svm_node(const torch::Tensor &sample)</div><div class="ttdoc">Convert single sample to libsvm format.</div><div class="ttdef"><b>Definition</b> <a href="data__converter_8cpp_source.html#l00100">data_converter.cpp:100</a></div></div>
|
||||||
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<div class="ttc" id="aclasssvm__classifier_1_1DataConverter_html_a1905f60fef9ffd3a8e8e45e41395352d"><div class="ttname"><a href="classsvm__classifier_1_1DataConverter.html#a1905f60fef9ffd3a8e8e45e41395352d">svm_classifier::DataConverter::get_sparse_threshold</a></div><div class="ttdeci">double get_sparse_threshold() const</div><div class="ttdoc">Get sparse threshold.</div><div class="ttdef"><b>Definition</b> <a href="data__converter_8hpp_source.html#l00123">data_converter.hpp:123</a></div></div>
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<div class="ttc" id="aclasssvm__classifier_1_1DataConverter_html_a26342a8cc8b943f099112040aa960ae6"><div class="ttname"><a href="classsvm__classifier_1_1DataConverter.html#a26342a8cc8b943f099112040aa960ae6">svm_classifier::DataConverter::get_n_features</a></div><div class="ttdeci">int get_n_features() const</div><div class="ttdoc">Get number of features from last conversion.</div><div class="ttdef"><b>Definition</b> <a href="data__converter_8hpp_source.html#l00100">data_converter.hpp:100</a></div></div>
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<div class="ttc" id="aclasssvm__classifier_1_1DataConverter_html_a46d12ba28c4c5bf6e0fad1122c621fa8"><div class="ttname"><a href="classsvm__classifier_1_1DataConverter.html#a46d12ba28c4c5bf6e0fad1122c621fa8">svm_classifier::DataConverter::cleanup</a></div><div class="ttdeci">void cleanup()</div><div class="ttdoc">Clean up all allocated memory.</div><div class="ttdef"><b>Definition</b> <a href="data__converter_8cpp_source.html#l00198">data_converter.cpp:198</a></div></div>
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<div class="ttc" id="aclasssvm__classifier_1_1DataConverter_html_a503eba54e8bb1f370e04b6e24354a32f"><div class="ttname"><a href="classsvm__classifier_1_1DataConverter.html#a503eba54e8bb1f370e04b6e24354a32f">svm_classifier::DataConverter::from_decision_values</a></div><div class="ttdeci">torch::Tensor from_decision_values(const std::vector< std::vector< double > > &decision_values)</div><div class="ttdoc">Convert decision values back to PyTorch tensor.</div><div class="ttdef"><b>Definition</b> <a href="data__converter_8cpp_source.html#l00152">data_converter.cpp:152</a></div></div>
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<div class="ttc" id="aclasssvm__classifier_1_1DataConverter_html_a5460485675613c54596418af3d5057ff"><div class="ttname"><a href="classsvm__classifier_1_1DataConverter.html#a5460485675613c54596418af3d5057ff">svm_classifier::DataConverter::from_probabilities</a></div><div class="ttdeci">torch::Tensor from_probabilities(const std::vector< std::vector< double > > &probabilities)</div><div class="ttdoc">Convert probabilities back to PyTorch tensor.</div><div class="ttdef"><b>Definition</b> <a href="data__converter_8cpp_source.html#l00132">data_converter.cpp:132</a></div></div>
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<div class="ttc" id="aclasssvm__classifier_1_1DataConverter_html_a5874904555f26448ed5ae4cf6f370056"><div class="ttname"><a href="classsvm__classifier_1_1DataConverter.html#a5874904555f26448ed5ae4cf6f370056">svm_classifier::DataConverter::DataConverter</a></div><div class="ttdeci">DataConverter()</div><div class="ttdoc">Default constructor.</div><div class="ttdef"><b>Definition</b> <a href="data__converter_8cpp_source.html#l00010">data_converter.cpp:10</a></div></div>
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<div class="ttc" id="aclasssvm__classifier_1_1DataConverter_html_a66f446e4decfe47bbba37c789f03f729"><div class="ttname"><a href="classsvm__classifier_1_1DataConverter.html#a66f446e4decfe47bbba37c789f03f729">svm_classifier::DataConverter::to_svm_problem</a></div><div class="ttdeci">std::unique_ptr< svm_problem > to_svm_problem(const torch::Tensor &X, const torch::Tensor &y=torch::Tensor())</div><div class="ttdoc">Convert PyTorch tensors to libsvm format.</div><div class="ttdef"><b>Definition</b> <a href="data__converter_8cpp_source.html#l00022">data_converter.cpp:22</a></div></div>
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<div class="ttc" id="aclasssvm__classifier_1_1DataConverter_html_a6bb2b4565b27df0db5f229dbd380795e"><div class="ttname"><a href="classsvm__classifier_1_1DataConverter.html#a6bb2b4565b27df0db5f229dbd380795e">svm_classifier::DataConverter::to_feature_node</a></div><div class="ttdeci">feature_node * to_feature_node(const torch::Tensor &sample)</div><div class="ttdoc">Convert single sample to liblinear format.</div><div class="ttdef"><b>Definition</b> <a href="data__converter_8cpp_source.html#l00110">data_converter.cpp:110</a></div></div>
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<div class="ttc" id="aclasssvm__classifier_1_1DataConverter_html_a7e7d8f6102b7a9b3256ff0dc6f536a35"><div class="ttname"><a href="classsvm__classifier_1_1DataConverter.html#a7e7d8f6102b7a9b3256ff0dc6f536a35">svm_classifier::DataConverter::to_linear_problem</a></div><div class="ttdeci">std::unique_ptr< problem > to_linear_problem(const torch::Tensor &X, const torch::Tensor &y=torch::Tensor())</div><div class="ttdoc">Convert PyTorch tensors to liblinear format.</div><div class="ttdef"><b>Definition</b> <a href="data__converter_8cpp_source.html#l00060">data_converter.cpp:60</a></div></div>
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<div class="ttc" id="aclasssvm__classifier_1_1DataConverter_html_a9259322e54d2477478a684e99d8e557a"><div class="ttname"><a href="classsvm__classifier_1_1DataConverter.html#a9259322e54d2477478a684e99d8e557a">svm_classifier::DataConverter::set_sparse_threshold</a></div><div class="ttdeci">void set_sparse_threshold(double threshold)</div><div class="ttdoc">Set sparse threshold (features with absolute value below this are ignored)</div><div class="ttdef"><b>Definition</b> <a href="data__converter_8hpp_source.html#l00117">data_converter.hpp:117</a></div></div>
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<div class="ttc" id="aclasssvm__classifier_1_1DataConverter_html_ab3e800a5016a915e9912d5873bb48741"><div class="ttname"><a href="classsvm__classifier_1_1DataConverter.html#ab3e800a5016a915e9912d5873bb48741">svm_classifier::DataConverter::from_predictions</a></div><div class="ttdeci">torch::Tensor from_predictions(const std::vector< double > &predictions)</div><div class="ttdoc">Convert predictions back to PyTorch tensor.</div><div class="ttdef"><b>Definition</b> <a href="data__converter_8cpp_source.html#l00120">data_converter.cpp:120</a></div></div>
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Files</h2></td></tr>
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top"><a href="data__converter_8hpp_source.html"><span class="icondoc"></span></a> </td><td class="memItemRight" valign="bottom"><b>data_converter.hpp</b></td></tr>
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Copyright (C) 1997-2020 by Dimitri van Heesch
|
||||||
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|
||||||
|
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|
||||||
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and associated documentation files (the "Software"), to deal in the Software without restriction,
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|
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|
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furnished to do so, subject to the following conditions:
|
||||||
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||||||
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The above copyright notice and this permission notice shall be included in all copies or
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substantial portions of the Software.
|
||||||
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||||||
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING
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|
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|
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|
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|
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|
function updateStripes()
|
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|
{
|
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|
$('table.directory tr').
|
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|
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|
||||||
|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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$(this).show();
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
// the clicked row
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var currentRow = $('#row_'+id);
|
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|
// all rows after the clicked row
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||||||
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var rows = currentRow.nextAll("tr");
|
||||||
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|
||||||
|
var re = new RegExp('^row_'+id+'\\d+_$', "i"); //only one sub
|
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|
||||||
|
// only match elements AFTER this one (can't hide elements before)
|
||||||
|
var childRows = rows.filter(function() { return this.id.match(re); });
|
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|
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|
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|
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// replace right arrow by down arrow for current row
|
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||||||
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||||||
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||||||
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|
||||||
|
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|
||||||
|
<div id="projectbrief">High-performance Support Vector Machine classifier with scikit-learn compatible API</div>
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|
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|
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|
||||||
|
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||||||
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||||||
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||||||
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<div class="contents">
|
||||||
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|
||||||
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|
||||||
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<title>SVM Classifier C++: Class Members</title>
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<div id="projectname">SVM Classifier C++<span id="projectnumber"> 1.0.0</span>
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<div id="projectbrief">High-performance Support Vector Machine classifier with scikit-learn compatible API</div>
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/* @license magnet:?xt=urn:btih:d3d9a9a6595521f9666a5e94cc830dab83b65699&dn=expat.txt MIT */
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<div class="contents">
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<div class="textblock">Here is a list of all documented class members with links to the class documentation for each member:</div>
|
||||||
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|
||||||
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<h3><a id="index_a" name="index_a"></a>- a -</h3><ul>
|
||||||
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<li>accuracy : <a class="el" href="structsvm__classifier_1_1EvaluationMetrics.html#abe60e87c99b8b3c4499e602d8c26847a">svm_classifier::EvaluationMetrics</a></li>
|
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</ul>
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||||||
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<h3><a id="index_c" name="index_c"></a>- c -</h3><ul>
|
||||||
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<li>classes_ : <a class="el" href="classsvm__classifier_1_1MulticlassStrategyBase.html#a15bb6eb53e91e604b259b3050bd40e27">svm_classifier::MulticlassStrategyBase</a></li>
|
||||||
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<li>cleanup() : <a class="el" href="classsvm__classifier_1_1DataConverter.html#a46d12ba28c4c5bf6e0fad1122c621fa8">svm_classifier::DataConverter</a></li>
|
||||||
|
<li>confusion_matrix : <a class="el" href="structsvm__classifier_1_1EvaluationMetrics.html#a843fbb476ae6bf24d7f72dabc3ef334a">svm_classifier::EvaluationMetrics</a></li>
|
||||||
|
<li>cross_validate() : <a class="el" href="classsvm__classifier_1_1SVMClassifier.html#a4c91072ea0d3d9b97ba458ff7d0898b8">svm_classifier::SVMClassifier</a></li>
|
||||||
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</ul>
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||||||
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|
||||||
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<h3><a id="index_d" name="index_d"></a>- d -</h3><ul>
|
||||||
|
<li>DataConverter() : <a class="el" href="classsvm__classifier_1_1DataConverter.html#a5874904555f26448ed5ae4cf6f370056">svm_classifier::DataConverter</a></li>
|
||||||
|
<li>decision_function() : <a class="el" href="classsvm__classifier_1_1MulticlassStrategyBase.html#ad1c4eb746cb1fdd67cf436ff85a9b0f0">svm_classifier::MulticlassStrategyBase</a>, <a class="el" href="classsvm__classifier_1_1OneVsOneStrategy.html#a6aae0b5cd72180e94212454da8b777d2">svm_classifier::OneVsOneStrategy</a>, <a class="el" href="classsvm__classifier_1_1OneVsRestStrategy.html#a966b79bc8b6fac0fa78feefc2dd8a878">svm_classifier::OneVsRestStrategy</a>, <a class="el" href="classsvm__classifier_1_1SVMClassifier.html#ad153c0537998eae5fbca5fd0b5ead2b7">svm_classifier::SVMClassifier</a></li>
|
||||||
|
<li>decision_values : <a class="el" href="structsvm__classifier_1_1PredictionResult.html#a1ba501fdd3da8d3c4b99285b2f71c1d9">svm_classifier::PredictionResult</a></li>
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</ul>
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|
|
||||||
|
<h3><a id="index_e" name="index_e"></a>- e -</h3><ul>
|
||||||
|
<li>evaluate() : <a class="el" href="classsvm__classifier_1_1SVMClassifier.html#a38a9b020b9f4f9254920c97a3a047e9b">svm_classifier::SVMClassifier</a></li>
|
||||||
|
</ul>
|
||||||
|
|
||||||
|
|
||||||
|
<h3><a id="index_f" name="index_f"></a>- f -</h3><ul>
|
||||||
|
<li>f1_score : <a class="el" href="structsvm__classifier_1_1EvaluationMetrics.html#a0eca4a2bc01318bb641f384abd0a47bc">svm_classifier::EvaluationMetrics</a></li>
|
||||||
|
<li>fit() : <a class="el" href="classsvm__classifier_1_1MulticlassStrategyBase.html#a548af7201b7970abee0c31e7ec07d896">svm_classifier::MulticlassStrategyBase</a>, <a class="el" href="classsvm__classifier_1_1OneVsOneStrategy.html#af5ce4aeb191c5feed178b6465eac66f6">svm_classifier::OneVsOneStrategy</a>, <a class="el" href="classsvm__classifier_1_1OneVsRestStrategy.html#aae14da8c0effd04731b5a4a0181eb1b6">svm_classifier::OneVsRestStrategy</a>, <a class="el" href="classsvm__classifier_1_1SVMClassifier.html#a7e6648c4d2bac92bb00381076ea92db3">svm_classifier::SVMClassifier</a></li>
|
||||||
|
<li>from_decision_values() : <a class="el" href="classsvm__classifier_1_1DataConverter.html#a503eba54e8bb1f370e04b6e24354a32f">svm_classifier::DataConverter</a></li>
|
||||||
|
<li>from_predictions() : <a class="el" href="classsvm__classifier_1_1DataConverter.html#ab3e800a5016a915e9912d5873bb48741">svm_classifier::DataConverter</a></li>
|
||||||
|
<li>from_probabilities() : <a class="el" href="classsvm__classifier_1_1DataConverter.html#a5460485675613c54596418af3d5057ff">svm_classifier::DataConverter</a></li>
|
||||||
|
</ul>
|
||||||
|
|
||||||
|
|
||||||
|
<h3><a id="index_g" name="index_g"></a>- g -</h3><ul>
|
||||||
|
<li>get_classes() : <a class="el" href="classsvm__classifier_1_1MulticlassStrategyBase.html#a379c4000227cc46410bfbecce6e80c33">svm_classifier::MulticlassStrategyBase</a>, <a class="el" href="classsvm__classifier_1_1OneVsOneStrategy.html#a52f9c3d7d98077d1dec0d6034711b750">svm_classifier::OneVsOneStrategy</a>, <a class="el" href="classsvm__classifier_1_1OneVsRestStrategy.html#a5e10800b16dbc66fd1c0d5e0a42871f0">svm_classifier::OneVsRestStrategy</a>, <a class="el" href="classsvm__classifier_1_1SVMClassifier.html#af0fea42cdfc9416ed854b0d4aefa82b9">svm_classifier::SVMClassifier</a></li>
|
||||||
|
<li>get_feature_importance() : <a class="el" href="classsvm__classifier_1_1SVMClassifier.html#a2ade33562381e34cbe4b04089545a715">svm_classifier::SVMClassifier</a></li>
|
||||||
|
<li>get_kernel_type() : <a class="el" href="classsvm__classifier_1_1SVMClassifier.html#a55338ab396bd5da923b6acbef8ed783a">svm_classifier::SVMClassifier</a></li>
|
||||||
|
<li>get_multiclass_strategy() : <a class="el" href="classsvm__classifier_1_1SVMClassifier.html#a14c2f7917c8a91154c09160288509f2c">svm_classifier::SVMClassifier</a></li>
|
||||||
|
<li>get_n_classes() : <a class="el" href="classsvm__classifier_1_1MulticlassStrategyBase.html#a1740d877a4d634ec1763cb8646f5e172">svm_classifier::MulticlassStrategyBase</a>, <a class="el" href="classsvm__classifier_1_1OneVsOneStrategy.html#a16ee2ae3623767af2165fef2d4b7d039">svm_classifier::OneVsOneStrategy</a>, <a class="el" href="classsvm__classifier_1_1OneVsRestStrategy.html#a53abe89ec25c33fd9c32d92ba08d01ed">svm_classifier::OneVsRestStrategy</a>, <a class="el" href="classsvm__classifier_1_1SVMClassifier.html#a75d501339e2e2273082b0838e9caadcd">svm_classifier::SVMClassifier</a></li>
|
||||||
|
<li>get_n_features() : <a class="el" href="classsvm__classifier_1_1DataConverter.html#a26342a8cc8b943f099112040aa960ae6">svm_classifier::DataConverter</a>, <a class="el" href="classsvm__classifier_1_1SVMClassifier.html#a780afcb2ad618e46541aff8a44e9c7b4">svm_classifier::SVMClassifier</a></li>
|
||||||
|
<li>get_n_samples() : <a class="el" href="classsvm__classifier_1_1DataConverter.html#a16999ded27bc2d42d2ebd9551b00c1cb">svm_classifier::DataConverter</a></li>
|
||||||
|
<li>get_parameters() : <a class="el" href="classsvm__classifier_1_1SVMClassifier.html#a7c39ec09b15186dcb4f04ae7171d23bb">svm_classifier::SVMClassifier</a></li>
|
||||||
|
<li>get_sparse_threshold() : <a class="el" href="classsvm__classifier_1_1DataConverter.html#a1905f60fef9ffd3a8e8e45e41395352d">svm_classifier::DataConverter</a></li>
|
||||||
|
<li>get_strategy_type() : <a class="el" href="classsvm__classifier_1_1MulticlassStrategyBase.html#a31a0501fa1a6db1d41cbf825b2348e47">svm_classifier::MulticlassStrategyBase</a>, <a class="el" href="classsvm__classifier_1_1OneVsOneStrategy.html#aae80b4e75459b2aca4f561d62c3c5675">svm_classifier::OneVsOneStrategy</a>, <a class="el" href="classsvm__classifier_1_1OneVsRestStrategy.html#af9e1bd6d08ce3e7afd5279c835ce6cfb">svm_classifier::OneVsRestStrategy</a></li>
|
||||||
|
<li>get_svm_library() : <a class="el" href="classsvm__classifier_1_1SVMClassifier.html#a38173e5cf0f6a4620f032fd54c28d592">svm_classifier::SVMClassifier</a></li>
|
||||||
|
<li>get_training_metrics() : <a class="el" href="classsvm__classifier_1_1SVMClassifier.html#a0b8c77f81d84489b2da0d080773a2970">svm_classifier::SVMClassifier</a></li>
|
||||||
|
<li>grid_search() : <a class="el" href="classsvm__classifier_1_1SVMClassifier.html#afed66a704dfb38cc7d080d3337d10194">svm_classifier::SVMClassifier</a></li>
|
||||||
|
</ul>
|
||||||
|
|
||||||
|
|
||||||
|
<h3><a id="index_h" name="index_h"></a>- h -</h3><ul>
|
||||||
|
<li>has_probabilities : <a class="el" href="structsvm__classifier_1_1PredictionResult.html#aeb863c05f5761ba1925166b73e3f4da6">svm_classifier::PredictionResult</a></li>
|
||||||
|
</ul>
|
||||||
|
|
||||||
|
|
||||||
|
<h3><a id="index_i" name="index_i"></a>- i -</h3><ul>
|
||||||
|
<li>is_fitted() : <a class="el" href="classsvm__classifier_1_1SVMClassifier.html#a71a85ab7893e7e2b40763db34096d8bb">svm_classifier::SVMClassifier</a></li>
|
||||||
|
<li>is_trained_ : <a class="el" href="classsvm__classifier_1_1MulticlassStrategyBase.html#a8e74cd580feaac0da34d204274a24fea">svm_classifier::MulticlassStrategyBase</a></li>
|
||||||
|
<li>iterations : <a class="el" href="structsvm__classifier_1_1TrainingMetrics.html#a7ad692cf23590fe1be348902031bbc5a">svm_classifier::TrainingMetrics</a></li>
|
||||||
|
</ul>
|
||||||
|
|
||||||
|
|
||||||
|
<h3><a id="index_l" name="index_l"></a>- l -</h3><ul>
|
||||||
|
<li>load_model() : <a class="el" href="classsvm__classifier_1_1SVMClassifier.html#a583f5743acf5e6b850e079b9190989f1">svm_classifier::SVMClassifier</a></li>
|
||||||
|
</ul>
|
||||||
|
|
||||||
|
|
||||||
|
<h3><a id="index_o" name="index_o"></a>- o -</h3><ul>
|
||||||
|
<li>objective_value : <a class="el" href="structsvm__classifier_1_1TrainingMetrics.html#a1b41ef88d2dfb85b7f587292e6b83b7f">svm_classifier::TrainingMetrics</a></li>
|
||||||
|
<li>OneVsOneStrategy() : <a class="el" href="classsvm__classifier_1_1OneVsOneStrategy.html#a6d4b060383169010dda4197a0bffa020">svm_classifier::OneVsOneStrategy</a></li>
|
||||||
|
<li>OneVsRestStrategy() : <a class="el" href="classsvm__classifier_1_1OneVsRestStrategy.html#a30f146a564a9c9681524593cacbb43e7">svm_classifier::OneVsRestStrategy</a></li>
|
||||||
|
<li>operator=() : <a class="el" href="classsvm__classifier_1_1SVMClassifier.html#a209902805c75e8f22c55575adfedc7be">svm_classifier::SVMClassifier</a></li>
|
||||||
|
</ul>
|
||||||
|
|
||||||
|
|
||||||
|
<h3><a id="index_p" name="index_p"></a>- p -</h3><ul>
|
||||||
|
<li>precision : <a class="el" href="structsvm__classifier_1_1EvaluationMetrics.html#ad2ee81c9b36abcfb04598110402c242f">svm_classifier::EvaluationMetrics</a></li>
|
||||||
|
<li>predict() : <a class="el" href="classsvm__classifier_1_1MulticlassStrategyBase.html#a70f94cfcf8b2bf6d60133c688fe55f9d">svm_classifier::MulticlassStrategyBase</a>, <a class="el" href="classsvm__classifier_1_1OneVsOneStrategy.html#ab60df2d9b6069a73369b0bf9d3675662">svm_classifier::OneVsOneStrategy</a>, <a class="el" href="classsvm__classifier_1_1OneVsRestStrategy.html#a771903a821d5380ddd5d0b3a912e7df9">svm_classifier::OneVsRestStrategy</a>, <a class="el" href="classsvm__classifier_1_1SVMClassifier.html#a5c998d5574b3b6afe003b23ed02ed1d1">svm_classifier::SVMClassifier</a></li>
|
||||||
|
<li>predict_proba() : <a class="el" href="classsvm__classifier_1_1MulticlassStrategyBase.html#ab5348ee3b83547702ec7903ee7ee2da7">svm_classifier::MulticlassStrategyBase</a>, <a class="el" href="classsvm__classifier_1_1OneVsOneStrategy.html#ae62e8b24115042d1119e76f3302f6992">svm_classifier::OneVsOneStrategy</a>, <a class="el" href="classsvm__classifier_1_1OneVsRestStrategy.html#a55639e5adaadcd6414b50d5ebf0d1cd2">svm_classifier::OneVsRestStrategy</a>, <a class="el" href="classsvm__classifier_1_1SVMClassifier.html#ab4ef3c839e085ece646cdd2501a51f67">svm_classifier::SVMClassifier</a></li>
|
||||||
|
<li>predictions : <a class="el" href="structsvm__classifier_1_1PredictionResult.html#a1a3e5f634777de08f2e6cd71ee4e8891">svm_classifier::PredictionResult</a></li>
|
||||||
|
<li>probabilities : <a class="el" href="structsvm__classifier_1_1PredictionResult.html#a22d86775346acc5545affd204eba47fd">svm_classifier::PredictionResult</a></li>
|
||||||
|
</ul>
|
||||||
|
|
||||||
|
|
||||||
|
<h3><a id="index_r" name="index_r"></a>- r -</h3><ul>
|
||||||
|
<li>recall : <a class="el" href="structsvm__classifier_1_1EvaluationMetrics.html#a78b1a0c50c4722bf73941a64e3a15e8f">svm_classifier::EvaluationMetrics</a></li>
|
||||||
|
<li>reset() : <a class="el" href="classsvm__classifier_1_1SVMClassifier.html#aa2bd5715c9e54e3fb465a9bcbf2e9c8a">svm_classifier::SVMClassifier</a></li>
|
||||||
|
</ul>
|
||||||
|
|
||||||
|
|
||||||
|
<h3><a id="index_s" name="index_s"></a>- s -</h3><ul>
|
||||||
|
<li>save_model() : <a class="el" href="classsvm__classifier_1_1SVMClassifier.html#ab8a0bd35705825e80a7567b576d47359">svm_classifier::SVMClassifier</a></li>
|
||||||
|
<li>score() : <a class="el" href="classsvm__classifier_1_1SVMClassifier.html#a0479c57489c14be4a5ca79368086f7f6">svm_classifier::SVMClassifier</a></li>
|
||||||
|
<li>set_parameters() : <a class="el" href="classsvm__classifier_1_1SVMClassifier.html#adb01e761fea07c709f3a0e315d3d0e06">svm_classifier::SVMClassifier</a></li>
|
||||||
|
<li>set_sparse_threshold() : <a class="el" href="classsvm__classifier_1_1DataConverter.html#a9259322e54d2477478a684e99d8e557a">svm_classifier::DataConverter</a></li>
|
||||||
|
<li>support_vectors : <a class="el" href="structsvm__classifier_1_1TrainingMetrics.html#a3963a55a5d40a0e25d8ce94a5b81a227">svm_classifier::TrainingMetrics</a></li>
|
||||||
|
<li>supports_probability() : <a class="el" href="classsvm__classifier_1_1MulticlassStrategyBase.html#a2ab91902f8d6eb216f626ce9ea4be992">svm_classifier::MulticlassStrategyBase</a>, <a class="el" href="classsvm__classifier_1_1OneVsOneStrategy.html#a8875d29cb8666af10e0fb5634e08c0c1">svm_classifier::OneVsOneStrategy</a>, <a class="el" href="classsvm__classifier_1_1OneVsRestStrategy.html#a200300198628ac119eac09e62ff62336">svm_classifier::OneVsRestStrategy</a>, <a class="el" href="classsvm__classifier_1_1SVMClassifier.html#a3f8b4e932f075b267507ad77a499a135">svm_classifier::SVMClassifier</a></li>
|
||||||
|
<li>SVMClassifier() : <a class="el" href="classsvm__classifier_1_1SVMClassifier.html#a3ed45cdbc3fc5d947320177f42115dcf">svm_classifier::SVMClassifier</a></li>
|
||||||
|
</ul>
|
||||||
|
|
||||||
|
|
||||||
|
<h3><a id="index_t" name="index_t"></a>- t -</h3><ul>
|
||||||
|
<li>to_feature_node() : <a class="el" href="classsvm__classifier_1_1DataConverter.html#a6bb2b4565b27df0db5f229dbd380795e">svm_classifier::DataConverter</a></li>
|
||||||
|
<li>to_linear_problem() : <a class="el" href="classsvm__classifier_1_1DataConverter.html#a7e7d8f6102b7a9b3256ff0dc6f536a35">svm_classifier::DataConverter</a></li>
|
||||||
|
<li>to_svm_node() : <a class="el" href="classsvm__classifier_1_1DataConverter.html#a16e1539ef1266ca9ddd27a2ac5a53b92">svm_classifier::DataConverter</a></li>
|
||||||
|
<li>to_svm_problem() : <a class="el" href="classsvm__classifier_1_1DataConverter.html#a66f446e4decfe47bbba37c789f03f729">svm_classifier::DataConverter</a></li>
|
||||||
|
<li>training_time : <a class="el" href="structsvm__classifier_1_1TrainingMetrics.html#af8eee57134f3fe0fab23b60fe33cb4de">svm_classifier::TrainingMetrics</a></li>
|
||||||
|
</ul>
|
||||||
|
|
||||||
|
|
||||||
|
<h3><a id="index_v" name="index_v"></a>- v -</h3><ul>
|
||||||
|
<li>validate_tensors() : <a class="el" href="classsvm__classifier_1_1DataConverter.html#aa0615f3de29958b2c5229d349f2f60ce">svm_classifier::DataConverter</a></li>
|
||||||
|
</ul>
|
||||||
|
|
||||||
|
|
||||||
|
<h3><a id="index__7E" name="index__7E"></a>- ~ -</h3><ul>
|
||||||
|
<li>~DataConverter() : <a class="el" href="classsvm__classifier_1_1DataConverter.html#ac3af2c9c03cffe2968f29147611e333d">svm_classifier::DataConverter</a></li>
|
||||||
|
<li>~MulticlassStrategyBase() : <a class="el" href="classsvm__classifier_1_1MulticlassStrategyBase.html#a8a5647dd57eed281288f0c9011b11395">svm_classifier::MulticlassStrategyBase</a></li>
|
||||||
|
<li>~OneVsOneStrategy() : <a class="el" href="classsvm__classifier_1_1OneVsOneStrategy.html#af9a653b62502e296d0d18092be56344f">svm_classifier::OneVsOneStrategy</a></li>
|
||||||
|
<li>~OneVsRestStrategy() : <a class="el" href="classsvm__classifier_1_1OneVsRestStrategy.html#acfd698dd6cc0a988ac642a00d1f0b970">svm_classifier::OneVsRestStrategy</a></li>
|
||||||
|
<li>~SVMClassifier() : <a class="el" href="classsvm__classifier_1_1SVMClassifier.html#a233584f6696969ce1a402624fd046146">svm_classifier::SVMClassifier</a></li>
|
||||||
|
</ul>
|
||||||
|
</div><!-- contents -->
|
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|
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<hr class="footer"/><address class="footer"><small>
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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
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<div id="projectname">SVM Classifier C++<span id="projectnumber"> 1.0.0</span>
|
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|
</div>
|
||||||
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<div id="projectbrief">High-performance Support Vector Machine classifier with scikit-learn compatible API</div>
|
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</td>
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</tbody>
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<div id="main-nav"></div>
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</div><!-- top -->
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<!-- window showing the filter options -->
|
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<div id="MSearchSelectWindow"
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onmouseover="return searchBox.OnSearchSelectShow()"
|
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onmouseout="return searchBox.OnSearchSelectHide()"
|
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onkeydown="return searchBox.OnSearchSelectKey(event)">
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</div>
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|
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<div id="MSearchResultsWindow">
|
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<div class="SRPage">
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<div class="SRStatus" id="Loading">Loading...</div>
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</div>
|
||||||
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||||||
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<div class="contents">
|
||||||
|
<div class="textblock">Here is a list of all documented functions with links to the class documentation for each member:</div>
|
||||||
|
|
||||||
|
<h3><a id="index_c" name="index_c"></a>- c -</h3><ul>
|
||||||
|
<li>cleanup() : <a class="el" href="classsvm__classifier_1_1DataConverter.html#a46d12ba28c4c5bf6e0fad1122c621fa8">svm_classifier::DataConverter</a></li>
|
||||||
|
<li>cross_validate() : <a class="el" href="classsvm__classifier_1_1SVMClassifier.html#a4c91072ea0d3d9b97ba458ff7d0898b8">svm_classifier::SVMClassifier</a></li>
|
||||||
|
</ul>
|
||||||
|
|
||||||
|
|
||||||
|
<h3><a id="index_d" name="index_d"></a>- d -</h3><ul>
|
||||||
|
<li>DataConverter() : <a class="el" href="classsvm__classifier_1_1DataConverter.html#a5874904555f26448ed5ae4cf6f370056">svm_classifier::DataConverter</a></li>
|
||||||
|
<li>decision_function() : <a class="el" href="classsvm__classifier_1_1MulticlassStrategyBase.html#ad1c4eb746cb1fdd67cf436ff85a9b0f0">svm_classifier::MulticlassStrategyBase</a>, <a class="el" href="classsvm__classifier_1_1OneVsOneStrategy.html#a6aae0b5cd72180e94212454da8b777d2">svm_classifier::OneVsOneStrategy</a>, <a class="el" href="classsvm__classifier_1_1OneVsRestStrategy.html#a966b79bc8b6fac0fa78feefc2dd8a878">svm_classifier::OneVsRestStrategy</a>, <a class="el" href="classsvm__classifier_1_1SVMClassifier.html#ad153c0537998eae5fbca5fd0b5ead2b7">svm_classifier::SVMClassifier</a></li>
|
||||||
|
</ul>
|
||||||
|
|
||||||
|
|
||||||
|
<h3><a id="index_e" name="index_e"></a>- e -</h3><ul>
|
||||||
|
<li>evaluate() : <a class="el" href="classsvm__classifier_1_1SVMClassifier.html#a38a9b020b9f4f9254920c97a3a047e9b">svm_classifier::SVMClassifier</a></li>
|
||||||
|
</ul>
|
||||||
|
|
||||||
|
|
||||||
|
<h3><a id="index_f" name="index_f"></a>- f -</h3><ul>
|
||||||
|
<li>fit() : <a class="el" href="classsvm__classifier_1_1MulticlassStrategyBase.html#a548af7201b7970abee0c31e7ec07d896">svm_classifier::MulticlassStrategyBase</a>, <a class="el" href="classsvm__classifier_1_1OneVsOneStrategy.html#af5ce4aeb191c5feed178b6465eac66f6">svm_classifier::OneVsOneStrategy</a>, <a class="el" href="classsvm__classifier_1_1OneVsRestStrategy.html#aae14da8c0effd04731b5a4a0181eb1b6">svm_classifier::OneVsRestStrategy</a>, <a class="el" href="classsvm__classifier_1_1SVMClassifier.html#a7e6648c4d2bac92bb00381076ea92db3">svm_classifier::SVMClassifier</a></li>
|
||||||
|
<li>from_decision_values() : <a class="el" href="classsvm__classifier_1_1DataConverter.html#a503eba54e8bb1f370e04b6e24354a32f">svm_classifier::DataConverter</a></li>
|
||||||
|
<li>from_predictions() : <a class="el" href="classsvm__classifier_1_1DataConverter.html#ab3e800a5016a915e9912d5873bb48741">svm_classifier::DataConverter</a></li>
|
||||||
|
<li>from_probabilities() : <a class="el" href="classsvm__classifier_1_1DataConverter.html#a5460485675613c54596418af3d5057ff">svm_classifier::DataConverter</a></li>
|
||||||
|
</ul>
|
||||||
|
|
||||||
|
|
||||||
|
<h3><a id="index_g" name="index_g"></a>- g -</h3><ul>
|
||||||
|
<li>get_classes() : <a class="el" href="classsvm__classifier_1_1MulticlassStrategyBase.html#a379c4000227cc46410bfbecce6e80c33">svm_classifier::MulticlassStrategyBase</a>, <a class="el" href="classsvm__classifier_1_1OneVsOneStrategy.html#a52f9c3d7d98077d1dec0d6034711b750">svm_classifier::OneVsOneStrategy</a>, <a class="el" href="classsvm__classifier_1_1OneVsRestStrategy.html#a5e10800b16dbc66fd1c0d5e0a42871f0">svm_classifier::OneVsRestStrategy</a>, <a class="el" href="classsvm__classifier_1_1SVMClassifier.html#af0fea42cdfc9416ed854b0d4aefa82b9">svm_classifier::SVMClassifier</a></li>
|
||||||
|
<li>get_feature_importance() : <a class="el" href="classsvm__classifier_1_1SVMClassifier.html#a2ade33562381e34cbe4b04089545a715">svm_classifier::SVMClassifier</a></li>
|
||||||
|
<li>get_kernel_type() : <a class="el" href="classsvm__classifier_1_1SVMClassifier.html#a55338ab396bd5da923b6acbef8ed783a">svm_classifier::SVMClassifier</a></li>
|
||||||
|
<li>get_multiclass_strategy() : <a class="el" href="classsvm__classifier_1_1SVMClassifier.html#a14c2f7917c8a91154c09160288509f2c">svm_classifier::SVMClassifier</a></li>
|
||||||
|
<li>get_n_classes() : <a class="el" href="classsvm__classifier_1_1MulticlassStrategyBase.html#a1740d877a4d634ec1763cb8646f5e172">svm_classifier::MulticlassStrategyBase</a>, <a class="el" href="classsvm__classifier_1_1OneVsOneStrategy.html#a16ee2ae3623767af2165fef2d4b7d039">svm_classifier::OneVsOneStrategy</a>, <a class="el" href="classsvm__classifier_1_1OneVsRestStrategy.html#a53abe89ec25c33fd9c32d92ba08d01ed">svm_classifier::OneVsRestStrategy</a>, <a class="el" href="classsvm__classifier_1_1SVMClassifier.html#a75d501339e2e2273082b0838e9caadcd">svm_classifier::SVMClassifier</a></li>
|
||||||
|
<li>get_n_features() : <a class="el" href="classsvm__classifier_1_1DataConverter.html#a26342a8cc8b943f099112040aa960ae6">svm_classifier::DataConverter</a>, <a class="el" href="classsvm__classifier_1_1SVMClassifier.html#a780afcb2ad618e46541aff8a44e9c7b4">svm_classifier::SVMClassifier</a></li>
|
||||||
|
<li>get_n_samples() : <a class="el" href="classsvm__classifier_1_1DataConverter.html#a16999ded27bc2d42d2ebd9551b00c1cb">svm_classifier::DataConverter</a></li>
|
||||||
|
<li>get_parameters() : <a class="el" href="classsvm__classifier_1_1SVMClassifier.html#a7c39ec09b15186dcb4f04ae7171d23bb">svm_classifier::SVMClassifier</a></li>
|
||||||
|
<li>get_sparse_threshold() : <a class="el" href="classsvm__classifier_1_1DataConverter.html#a1905f60fef9ffd3a8e8e45e41395352d">svm_classifier::DataConverter</a></li>
|
||||||
|
<li>get_strategy_type() : <a class="el" href="classsvm__classifier_1_1MulticlassStrategyBase.html#a31a0501fa1a6db1d41cbf825b2348e47">svm_classifier::MulticlassStrategyBase</a>, <a class="el" href="classsvm__classifier_1_1OneVsOneStrategy.html#aae80b4e75459b2aca4f561d62c3c5675">svm_classifier::OneVsOneStrategy</a>, <a class="el" href="classsvm__classifier_1_1OneVsRestStrategy.html#af9e1bd6d08ce3e7afd5279c835ce6cfb">svm_classifier::OneVsRestStrategy</a></li>
|
||||||
|
<li>get_svm_library() : <a class="el" href="classsvm__classifier_1_1SVMClassifier.html#a38173e5cf0f6a4620f032fd54c28d592">svm_classifier::SVMClassifier</a></li>
|
||||||
|
<li>get_training_metrics() : <a class="el" href="classsvm__classifier_1_1SVMClassifier.html#a0b8c77f81d84489b2da0d080773a2970">svm_classifier::SVMClassifier</a></li>
|
||||||
|
<li>grid_search() : <a class="el" href="classsvm__classifier_1_1SVMClassifier.html#afed66a704dfb38cc7d080d3337d10194">svm_classifier::SVMClassifier</a></li>
|
||||||
|
</ul>
|
||||||
|
|
||||||
|
|
||||||
|
<h3><a id="index_i" name="index_i"></a>- i -</h3><ul>
|
||||||
|
<li>is_fitted() : <a class="el" href="classsvm__classifier_1_1SVMClassifier.html#a71a85ab7893e7e2b40763db34096d8bb">svm_classifier::SVMClassifier</a></li>
|
||||||
|
</ul>
|
||||||
|
|
||||||
|
|
||||||
|
<h3><a id="index_l" name="index_l"></a>- l -</h3><ul>
|
||||||
|
<li>load_model() : <a class="el" href="classsvm__classifier_1_1SVMClassifier.html#a583f5743acf5e6b850e079b9190989f1">svm_classifier::SVMClassifier</a></li>
|
||||||
|
</ul>
|
||||||
|
|
||||||
|
|
||||||
|
<h3><a id="index_o" name="index_o"></a>- o -</h3><ul>
|
||||||
|
<li>OneVsOneStrategy() : <a class="el" href="classsvm__classifier_1_1OneVsOneStrategy.html#a6d4b060383169010dda4197a0bffa020">svm_classifier::OneVsOneStrategy</a></li>
|
||||||
|
<li>OneVsRestStrategy() : <a class="el" href="classsvm__classifier_1_1OneVsRestStrategy.html#a30f146a564a9c9681524593cacbb43e7">svm_classifier::OneVsRestStrategy</a></li>
|
||||||
|
<li>operator=() : <a class="el" href="classsvm__classifier_1_1SVMClassifier.html#a209902805c75e8f22c55575adfedc7be">svm_classifier::SVMClassifier</a></li>
|
||||||
|
</ul>
|
||||||
|
|
||||||
|
|
||||||
|
<h3><a id="index_p" name="index_p"></a>- p -</h3><ul>
|
||||||
|
<li>predict() : <a class="el" href="classsvm__classifier_1_1MulticlassStrategyBase.html#a70f94cfcf8b2bf6d60133c688fe55f9d">svm_classifier::MulticlassStrategyBase</a>, <a class="el" href="classsvm__classifier_1_1OneVsOneStrategy.html#ab60df2d9b6069a73369b0bf9d3675662">svm_classifier::OneVsOneStrategy</a>, <a class="el" href="classsvm__classifier_1_1OneVsRestStrategy.html#a771903a821d5380ddd5d0b3a912e7df9">svm_classifier::OneVsRestStrategy</a>, <a class="el" href="classsvm__classifier_1_1SVMClassifier.html#a5c998d5574b3b6afe003b23ed02ed1d1">svm_classifier::SVMClassifier</a></li>
|
||||||
|
<li>predict_proba() : <a class="el" href="classsvm__classifier_1_1MulticlassStrategyBase.html#ab5348ee3b83547702ec7903ee7ee2da7">svm_classifier::MulticlassStrategyBase</a>, <a class="el" href="classsvm__classifier_1_1OneVsOneStrategy.html#ae62e8b24115042d1119e76f3302f6992">svm_classifier::OneVsOneStrategy</a>, <a class="el" href="classsvm__classifier_1_1OneVsRestStrategy.html#a55639e5adaadcd6414b50d5ebf0d1cd2">svm_classifier::OneVsRestStrategy</a>, <a class="el" href="classsvm__classifier_1_1SVMClassifier.html#ab4ef3c839e085ece646cdd2501a51f67">svm_classifier::SVMClassifier</a></li>
|
||||||
|
</ul>
|
||||||
|
|
||||||
|
|
||||||
|
<h3><a id="index_r" name="index_r"></a>- r -</h3><ul>
|
||||||
|
<li>reset() : <a class="el" href="classsvm__classifier_1_1SVMClassifier.html#aa2bd5715c9e54e3fb465a9bcbf2e9c8a">svm_classifier::SVMClassifier</a></li>
|
||||||
|
</ul>
|
||||||
|
|
||||||
|
|
||||||
|
<h3><a id="index_s" name="index_s"></a>- s -</h3><ul>
|
||||||
|
<li>save_model() : <a class="el" href="classsvm__classifier_1_1SVMClassifier.html#ab8a0bd35705825e80a7567b576d47359">svm_classifier::SVMClassifier</a></li>
|
||||||
|
<li>score() : <a class="el" href="classsvm__classifier_1_1SVMClassifier.html#a0479c57489c14be4a5ca79368086f7f6">svm_classifier::SVMClassifier</a></li>
|
||||||
|
<li>set_parameters() : <a class="el" href="classsvm__classifier_1_1SVMClassifier.html#adb01e761fea07c709f3a0e315d3d0e06">svm_classifier::SVMClassifier</a></li>
|
||||||
|
<li>set_sparse_threshold() : <a class="el" href="classsvm__classifier_1_1DataConverter.html#a9259322e54d2477478a684e99d8e557a">svm_classifier::DataConverter</a></li>
|
||||||
|
<li>supports_probability() : <a class="el" href="classsvm__classifier_1_1MulticlassStrategyBase.html#a2ab91902f8d6eb216f626ce9ea4be992">svm_classifier::MulticlassStrategyBase</a>, <a class="el" href="classsvm__classifier_1_1OneVsOneStrategy.html#a8875d29cb8666af10e0fb5634e08c0c1">svm_classifier::OneVsOneStrategy</a>, <a class="el" href="classsvm__classifier_1_1OneVsRestStrategy.html#a200300198628ac119eac09e62ff62336">svm_classifier::OneVsRestStrategy</a>, <a class="el" href="classsvm__classifier_1_1SVMClassifier.html#a3f8b4e932f075b267507ad77a499a135">svm_classifier::SVMClassifier</a></li>
|
||||||
|
<li>SVMClassifier() : <a class="el" href="classsvm__classifier_1_1SVMClassifier.html#a3ed45cdbc3fc5d947320177f42115dcf">svm_classifier::SVMClassifier</a></li>
|
||||||
|
</ul>
|
||||||
|
|
||||||
|
|
||||||
|
<h3><a id="index_t" name="index_t"></a>- t -</h3><ul>
|
||||||
|
<li>to_feature_node() : <a class="el" href="classsvm__classifier_1_1DataConverter.html#a6bb2b4565b27df0db5f229dbd380795e">svm_classifier::DataConverter</a></li>
|
||||||
|
<li>to_linear_problem() : <a class="el" href="classsvm__classifier_1_1DataConverter.html#a7e7d8f6102b7a9b3256ff0dc6f536a35">svm_classifier::DataConverter</a></li>
|
||||||
|
<li>to_svm_node() : <a class="el" href="classsvm__classifier_1_1DataConverter.html#a16e1539ef1266ca9ddd27a2ac5a53b92">svm_classifier::DataConverter</a></li>
|
||||||
|
<li>to_svm_problem() : <a class="el" href="classsvm__classifier_1_1DataConverter.html#a66f446e4decfe47bbba37c789f03f729">svm_classifier::DataConverter</a></li>
|
||||||
|
</ul>
|
||||||
|
|
||||||
|
|
||||||
|
<h3><a id="index_v" name="index_v"></a>- v -</h3><ul>
|
||||||
|
<li>validate_tensors() : <a class="el" href="classsvm__classifier_1_1DataConverter.html#aa0615f3de29958b2c5229d349f2f60ce">svm_classifier::DataConverter</a></li>
|
||||||
|
</ul>
|
||||||
|
|
||||||
|
|
||||||
|
<h3><a id="index__7E" name="index__7E"></a>- ~ -</h3><ul>
|
||||||
|
<li>~DataConverter() : <a class="el" href="classsvm__classifier_1_1DataConverter.html#ac3af2c9c03cffe2968f29147611e333d">svm_classifier::DataConverter</a></li>
|
||||||
|
<li>~MulticlassStrategyBase() : <a class="el" href="classsvm__classifier_1_1MulticlassStrategyBase.html#a8a5647dd57eed281288f0c9011b11395">svm_classifier::MulticlassStrategyBase</a></li>
|
||||||
|
<li>~OneVsOneStrategy() : <a class="el" href="classsvm__classifier_1_1OneVsOneStrategy.html#af9a653b62502e296d0d18092be56344f">svm_classifier::OneVsOneStrategy</a></li>
|
||||||
|
<li>~OneVsRestStrategy() : <a class="el" href="classsvm__classifier_1_1OneVsRestStrategy.html#acfd698dd6cc0a988ac642a00d1f0b970">svm_classifier::OneVsRestStrategy</a></li>
|
||||||
|
<li>~SVMClassifier() : <a class="el" href="classsvm__classifier_1_1SVMClassifier.html#a233584f6696969ce1a402624fd046146">svm_classifier::SVMClassifier</a></li>
|
||||||
|
</ul>
|
||||||
|
</div><!-- contents -->
|
||||||
|
<!-- start footer part -->
|
||||||
|
<hr class="footer"/><address class="footer"><small>
|
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|
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
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|
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|
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|
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functions_vars.html
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<tr id="projectrow">
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|
||||||
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<div id="projectname">SVM Classifier C++<span id="projectnumber"> 1.0.0</span>
|
||||||
|
</div>
|
||||||
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<div id="projectbrief">High-performance Support Vector Machine classifier with scikit-learn compatible API</div>
|
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||||||
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|
||||||
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||||||
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||||||
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<div class="contents">
|
||||||
|
<div class="textblock">Here is a list of all documented variables with links to the class documentation for each member:</div><ul>
|
||||||
|
<li>accuracy : <a class="el" href="structsvm__classifier_1_1EvaluationMetrics.html#abe60e87c99b8b3c4499e602d8c26847a">svm_classifier::EvaluationMetrics</a></li>
|
||||||
|
<li>classes_ : <a class="el" href="classsvm__classifier_1_1MulticlassStrategyBase.html#a15bb6eb53e91e604b259b3050bd40e27">svm_classifier::MulticlassStrategyBase</a></li>
|
||||||
|
<li>confusion_matrix : <a class="el" href="structsvm__classifier_1_1EvaluationMetrics.html#a843fbb476ae6bf24d7f72dabc3ef334a">svm_classifier::EvaluationMetrics</a></li>
|
||||||
|
<li>decision_values : <a class="el" href="structsvm__classifier_1_1PredictionResult.html#a1ba501fdd3da8d3c4b99285b2f71c1d9">svm_classifier::PredictionResult</a></li>
|
||||||
|
<li>f1_score : <a class="el" href="structsvm__classifier_1_1EvaluationMetrics.html#a0eca4a2bc01318bb641f384abd0a47bc">svm_classifier::EvaluationMetrics</a></li>
|
||||||
|
<li>has_probabilities : <a class="el" href="structsvm__classifier_1_1PredictionResult.html#aeb863c05f5761ba1925166b73e3f4da6">svm_classifier::PredictionResult</a></li>
|
||||||
|
<li>is_trained_ : <a class="el" href="classsvm__classifier_1_1MulticlassStrategyBase.html#a8e74cd580feaac0da34d204274a24fea">svm_classifier::MulticlassStrategyBase</a></li>
|
||||||
|
<li>iterations : <a class="el" href="structsvm__classifier_1_1TrainingMetrics.html#a7ad692cf23590fe1be348902031bbc5a">svm_classifier::TrainingMetrics</a></li>
|
||||||
|
<li>objective_value : <a class="el" href="structsvm__classifier_1_1TrainingMetrics.html#a1b41ef88d2dfb85b7f587292e6b83b7f">svm_classifier::TrainingMetrics</a></li>
|
||||||
|
<li>precision : <a class="el" href="structsvm__classifier_1_1EvaluationMetrics.html#ad2ee81c9b36abcfb04598110402c242f">svm_classifier::EvaluationMetrics</a></li>
|
||||||
|
<li>predictions : <a class="el" href="structsvm__classifier_1_1PredictionResult.html#a1a3e5f634777de08f2e6cd71ee4e8891">svm_classifier::PredictionResult</a></li>
|
||||||
|
<li>probabilities : <a class="el" href="structsvm__classifier_1_1PredictionResult.html#a22d86775346acc5545affd204eba47fd">svm_classifier::PredictionResult</a></li>
|
||||||
|
<li>recall : <a class="el" href="structsvm__classifier_1_1EvaluationMetrics.html#a78b1a0c50c4722bf73941a64e3a15e8f">svm_classifier::EvaluationMetrics</a></li>
|
||||||
|
<li>support_vectors : <a class="el" href="structsvm__classifier_1_1TrainingMetrics.html#a3963a55a5d40a0e25d8ce94a5b81a227">svm_classifier::TrainingMetrics</a></li>
|
||||||
|
<li>training_time : <a class="el" href="structsvm__classifier_1_1TrainingMetrics.html#af8eee57134f3fe0fab23b60fe33cb4de">svm_classifier::TrainingMetrics</a></li>
|
||||||
|
</ul>
|
||||||
|
</div><!-- contents -->
|
||||||
|
<!-- start footer part -->
|
||||||
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<hr class="footer"/><address class="footer"><small>
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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
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</small></address>
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</body>
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</html>
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<div id="projectname">SVM Classifier C++<span id="projectnumber"> 1.0.0</span>
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<div id="projectbrief">High-performance Support Vector Machine classifier with scikit-learn compatible API</div>
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<div class="line"><span class="comment">/* Class not documented with doxygen comments */</span></div>
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<div class="line"><span class="comment">/*! Class that is inherited using public inheritance */</span></div>
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<div class="line"><span class="keyword">class </span>Inherited : <span class="keyword">public</span> PublicBase,</div>
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|
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</div><!-- fragment --><p> This will result in the following graph:</p>
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<center><img src="graph_legend.png" alt="" class="inline"/></center><p>The boxes in the above graph have the following meaning: </p>
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A filled gray box represents the struct or class for which the graph is generated. </li>
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A box with a black border denotes a documented struct or class. </li>
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A blue arrow is used to visualize a public inheritance relation between two classes. </li>
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||||||
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A purple dashed arrow is used if a class is contained or used by another class. The arrow is labelled with the variable(s) through which the pointed class or struct is accessible. </li>
|
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|
||||||
|
A yellow dashed arrow denotes a relation between a template instance and the template class it was instantiated from. The arrow is labelled with the template parameters of the instance. </li>
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|
||||||
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|
||||||
|
<div id="projectbrief">High-performance Support Vector Machine classifier with scikit-learn compatible API</div>
|
||||||
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||||||
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||||||
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||||||
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||||||
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<tr id="row_0_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classsvm__classifier_1_1DataConverter.html" target="_self">svm_classifier::DataConverter</a></td><td class="desc">Data converter between libtorch tensors and SVM library formats </td></tr>
|
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<tr id="row_1_" class="odd"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structsvm__classifier_1_1EvaluationMetrics.html" target="_self">svm_classifier::EvaluationMetrics</a></td><td class="desc">Model evaluation metrics </td></tr>
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<tr id="row_2_" class="even"><td class="entry"><span style="width:0px;display:inline-block;"> </span><span id="arr_2_" class="arrow" onclick="toggleFolder('2_')">▼</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classsvm__classifier_1_1MulticlassStrategyBase.html" target="_self">svm_classifier::MulticlassStrategyBase</a></td><td class="desc">Abstract base class for multiclass classification strategies </td></tr>
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<tr id="row_2_0_" class="odd"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classsvm__classifier_1_1OneVsOneStrategy.html" target="_self">svm_classifier::OneVsOneStrategy</a></td><td class="desc">One-vs-One (OvO) multiclass strategy </td></tr>
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<tr id="row_2_1_" class="even"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classsvm__classifier_1_1OneVsRestStrategy.html" target="_self">svm_classifier::OneVsRestStrategy</a></td><td class="desc">One-vs-Rest (OvR) multiclass strategy </td></tr>
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<tr id="row_3_" class="odd"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structsvm__classifier_1_1PredictionResult.html" target="_self">svm_classifier::PredictionResult</a></td><td class="desc">Prediction result structure </td></tr>
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<tr id="row_4_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="classsvm__classifier_1_1SVMClassifier.html" target="_self">svm_classifier::SVMClassifier</a></td><td class="desc">Support Vector Machine Classifier with scikit-learn compatible API </td></tr>
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<tr id="row_5_" class="odd"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="structsvm__classifier_1_1TrainingMetrics.html" target="_self">svm_classifier::TrainingMetrics</a></td><td class="desc">Training metrics structure </td></tr>
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<div id="projectname">SVM Classifier C++<span id="projectnumber"> 1.0.0</span>
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<div id="projectbrief">High-performance Support Vector Machine classifier with scikit-learn compatible API</div>
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<div><div class="header">
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<div class="headertitle"><div class="title">SVM Classifier C++ </div></div>
|
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</div><!--header-->
|
||||||
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<div class="contents">
|
||||||
|
<div class="textblock"><p><a class="anchor" id="md_README"></a> A high-performance Support Vector Machine classifier implementation in C++ with a scikit-learn compatible API. This library provides a unified interface for SVM classification using both liblinear (for linear kernels) and libsvm (for non-linear kernels), with support for multiclass classification and PyTorch tensor integration.</p>
|
||||||
|
<h1>Features</h1>
|
||||||
|
<ul>
|
||||||
|
<li><b>🚀 Scikit-learn Compatible API</b>: Familiar <code>fit()</code>, <code>predict()</code>, <code>predict_proba()</code>, <code>score()</code> methods</li>
|
||||||
|
<li><b>🔧 Multiple Kernels</b>: Linear, RBF, Polynomial, and Sigmoid kernels</li>
|
||||||
|
<li><b>📊 Multiclass Support</b>: One-vs-Rest (OvR) and One-vs-One (OvO) strategies</li>
|
||||||
|
<li><b>⚡ Automatic Library Selection</b>: Uses liblinear for linear kernels, libsvm for others</li>
|
||||||
|
<li><b>🔗 PyTorch Integration</b>: Native support for libtorch tensors</li>
|
||||||
|
<li><b>⚙️ JSON Configuration</b>: Easy parameter management with nlohmann::json</li>
|
||||||
|
<li><b>🧪 Comprehensive Testing</b>: 100% test coverage with Catch2</li>
|
||||||
|
<li><b>📈 Performance Metrics</b>: Detailed evaluation and training metrics</li>
|
||||||
|
<li><b>🔍 Cross-Validation</b>: Built-in k-fold cross-validation support</li>
|
||||||
|
<li><b>🎯 Grid Search</b>: Hyperparameter optimization capabilities</li>
|
||||||
|
</ul>
|
||||||
|
<h1>Quick Start</h1>
|
||||||
|
<h2>Prerequisites</h2>
|
||||||
|
<ul>
|
||||||
|
<li>C++17 or later</li>
|
||||||
|
<li>CMake 3.15+</li>
|
||||||
|
<li>libtorch</li>
|
||||||
|
<li>Git</li>
|
||||||
|
</ul>
|
||||||
|
<h2>Building</h2>
|
||||||
|
<div class="fragment"><div class="line">git clone <repository-url></div>
|
||||||
|
<div class="line">cd svm_classifier</div>
|
||||||
|
<div class="line">mkdir build && cd build</div>
|
||||||
|
<div class="line">cmake ..</div>
|
||||||
|
<div class="line">make -j$(nproc)</div>
|
||||||
|
</div><!-- fragment --><h2>Basic Usage</h2>
|
||||||
|
<div class="fragment"><div class="line"><span class="preprocessor">#include <svm_classifier/svm_classifier.hpp></span></div>
|
||||||
|
<div class="line"><span class="preprocessor">#include <torch/torch.h></span></div>
|
||||||
|
<div class="line"> </div>
|
||||||
|
<div class="line"><span class="keyword">using namespace </span>svm_classifier;</div>
|
||||||
|
<div class="line"> </div>
|
||||||
|
<div class="line"><span class="comment">// Create sample data</span></div>
|
||||||
|
<div class="line"><span class="keyword">auto</span> X = torch::randn({100, 2}); <span class="comment">// 100 samples, 2 features</span></div>
|
||||||
|
<div class="line"><span class="keyword">auto</span> y = torch::randint(0, 3, {100}); <span class="comment">// 3 classes</span></div>
|
||||||
|
<div class="line"> </div>
|
||||||
|
<div class="line"><span class="comment">// Create and train SVM</span></div>
|
||||||
|
<div class="line"><a class="code hl_class" href="classsvm__classifier_1_1SVMClassifier.html">SVMClassifier</a> svm(KernelType::RBF, 1.0);</div>
|
||||||
|
<div class="line"><span class="keyword">auto</span> metrics = svm.fit(X, y);</div>
|
||||||
|
<div class="line"> </div>
|
||||||
|
<div class="line"><span class="comment">// Make predictions</span></div>
|
||||||
|
<div class="line"><span class="keyword">auto</span> predictions = svm.predict(X);</div>
|
||||||
|
<div class="line"><span class="keyword">auto</span> probabilities = svm.predict_proba(X);</div>
|
||||||
|
<div class="line"><span class="keywordtype">double</span> accuracy = svm.score(X, y);</div>
|
||||||
|
<div class="ttc" id="aclasssvm__classifier_1_1SVMClassifier_html"><div class="ttname"><a href="classsvm__classifier_1_1SVMClassifier.html">svm_classifier::SVMClassifier</a></div><div class="ttdoc">Support Vector Machine Classifier with scikit-learn compatible API.</div><div class="ttdef"><b>Definition</b> <a href="svm__classifier_8hpp_source.html#l00021">svm_classifier.hpp:21</a></div></div>
|
||||||
|
</div><!-- fragment --><h2>JSON Configuration</h2>
|
||||||
|
<div class="fragment"><div class="line"><span class="preprocessor">#include <nlohmann/json.hpp></span></div>
|
||||||
|
<div class="line"> </div>
|
||||||
|
<div class="line">nlohmann::json config = {</div>
|
||||||
|
<div class="line"> {<span class="stringliteral">"kernel"</span>, <span class="stringliteral">"rbf"</span>},</div>
|
||||||
|
<div class="line"> {<span class="stringliteral">"C"</span>, 10.0},</div>
|
||||||
|
<div class="line"> {<span class="stringliteral">"gamma"</span>, 0.1},</div>
|
||||||
|
<div class="line"> {<span class="stringliteral">"multiclass_strategy"</span>, <span class="stringliteral">"ovo"</span>},</div>
|
||||||
|
<div class="line"> {<span class="stringliteral">"probability"</span>, <span class="keyword">true</span>}</div>
|
||||||
|
<div class="line">};</div>
|
||||||
|
<div class="line"> </div>
|
||||||
|
<div class="line"><a class="code hl_class" href="classsvm__classifier_1_1SVMClassifier.html">SVMClassifier</a> svm(config);</div>
|
||||||
|
</div><!-- fragment --><h1>API Reference</h1>
|
||||||
|
<h2>Constructor Options</h2>
|
||||||
|
<div class="fragment"><div class="line"><span class="comment">// Default constructor</span></div>
|
||||||
|
<div class="line"><a class="code hl_class" href="classsvm__classifier_1_1SVMClassifier.html">SVMClassifier</a> svm;</div>
|
||||||
|
<div class="line"> </div>
|
||||||
|
<div class="line"><span class="comment">// With explicit parameters</span></div>
|
||||||
|
<div class="line"><a class="code hl_class" href="classsvm__classifier_1_1SVMClassifier.html">SVMClassifier</a> svm(KernelType::RBF, 1.0, MulticlassStrategy::ONE_VS_REST);</div>
|
||||||
|
<div class="line"> </div>
|
||||||
|
<div class="line"><span class="comment">// From JSON configuration</span></div>
|
||||||
|
<div class="line"><a class="code hl_class" href="classsvm__classifier_1_1SVMClassifier.html">SVMClassifier</a> svm(config_json);</div>
|
||||||
|
</div><!-- fragment --><h2>Core Methods</h2>
|
||||||
|
<table class="markdownTable">
|
||||||
|
<tr class="markdownTableHead">
|
||||||
|
<th class="markdownTableHeadNone">Method </th><th class="markdownTableHeadNone">Description </th><th class="markdownTableHeadNone">Returns </th></tr>
|
||||||
|
<tr class="markdownTableRowOdd">
|
||||||
|
<td class="markdownTableBodyNone"><code>fit(X, y)</code> </td><td class="markdownTableBodyNone">Train the classifier </td><td class="markdownTableBodyNone"><code>TrainingMetrics</code> </td></tr>
|
||||||
|
<tr class="markdownTableRowEven">
|
||||||
|
<td class="markdownTableBodyNone"><code>predict(X)</code> </td><td class="markdownTableBodyNone">Predict class labels </td><td class="markdownTableBodyNone"><code>torch::Tensor</code> </td></tr>
|
||||||
|
<tr class="markdownTableRowOdd">
|
||||||
|
<td class="markdownTableBodyNone"><code>predict_proba(X)</code> </td><td class="markdownTableBodyNone">Predict class probabilities </td><td class="markdownTableBodyNone"><code>torch::Tensor</code> </td></tr>
|
||||||
|
<tr class="markdownTableRowEven">
|
||||||
|
<td class="markdownTableBodyNone"><code>score(X, y)</code> </td><td class="markdownTableBodyNone">Calculate accuracy </td><td class="markdownTableBodyNone"><code>double</code> </td></tr>
|
||||||
|
<tr class="markdownTableRowOdd">
|
||||||
|
<td class="markdownTableBodyNone"><code>decision_function(X)</code> </td><td class="markdownTableBodyNone">Get decision values </td><td class="markdownTableBodyNone"><code>torch::Tensor</code> </td></tr>
|
||||||
|
<tr class="markdownTableRowEven">
|
||||||
|
<td class="markdownTableBodyNone"><code>cross_validate(X, y, cv)</code> </td><td class="markdownTableBodyNone">K-fold cross-validation </td><td class="markdownTableBodyNone"><code>std::vector<double></code> </td></tr>
|
||||||
|
<tr class="markdownTableRowOdd">
|
||||||
|
<td class="markdownTableBodyNone"><code>grid_search(X, y, grid, cv)</code> </td><td class="markdownTableBodyNone">Hyperparameter tuning </td><td class="markdownTableBodyNone"><code>nlohmann::json</code> </td></tr>
|
||||||
|
</table>
|
||||||
|
<h2>Parameter Configuration</h2>
|
||||||
|
<h3>Common Parameters</h3>
|
||||||
|
<ul>
|
||||||
|
<li><b>kernel</b>: <code>"linear"</code>, <code>"rbf"</code>, <code>"polynomial"</code>, <code>"sigmoid"</code></li>
|
||||||
|
<li><b>C</b>: Regularization parameter (default: 1.0)</li>
|
||||||
|
<li><b>multiclass_strategy</b>: <code>"ovr"</code> (One-vs-Rest) or <code>"ovo"</code> (One-vs-One)</li>
|
||||||
|
<li><b>probability</b>: Enable probability estimates (default: false)</li>
|
||||||
|
<li><b>tolerance</b>: Convergence tolerance (default: 1e-3)</li>
|
||||||
|
</ul>
|
||||||
|
<h3>Kernel-Specific Parameters</h3>
|
||||||
|
<ul>
|
||||||
|
<li><b>RBF/Polynomial/Sigmoid</b>: <code>gamma</code> (default: auto)</li>
|
||||||
|
<li><b>Polynomial</b>: <code>degree</code> (default: 3), <code>coef0</code> (default: 0.0)</li>
|
||||||
|
<li><b>Sigmoid</b>: <code>coef0</code> (default: 0.0)</li>
|
||||||
|
</ul>
|
||||||
|
<h1>Examples</h1>
|
||||||
|
<h2>Multi-class Classification</h2>
|
||||||
|
<div class="fragment"><div class="line"><span class="comment">// Generate multi-class dataset</span></div>
|
||||||
|
<div class="line"><span class="keyword">auto</span> X = torch::randn({300, 4});</div>
|
||||||
|
<div class="line"><span class="keyword">auto</span> y = torch::randint(0, 5, {300}); <span class="comment">// 5 classes</span></div>
|
||||||
|
<div class="line"> </div>
|
||||||
|
<div class="line"><span class="comment">// Configure for multi-class</span></div>
|
||||||
|
<div class="line">nlohmann::json config = {</div>
|
||||||
|
<div class="line"> {<span class="stringliteral">"kernel"</span>, <span class="stringliteral">"rbf"</span>},</div>
|
||||||
|
<div class="line"> {<span class="stringliteral">"C"</span>, 1.0},</div>
|
||||||
|
<div class="line"> {<span class="stringliteral">"gamma"</span>, 0.1},</div>
|
||||||
|
<div class="line"> {<span class="stringliteral">"multiclass_strategy"</span>, <span class="stringliteral">"ovo"</span>},</div>
|
||||||
|
<div class="line"> {<span class="stringliteral">"probability"</span>, <span class="keyword">true</span>}</div>
|
||||||
|
<div class="line">};</div>
|
||||||
|
<div class="line"> </div>
|
||||||
|
<div class="line"><a class="code hl_class" href="classsvm__classifier_1_1SVMClassifier.html">SVMClassifier</a> svm(config);</div>
|
||||||
|
<div class="line"><span class="keyword">auto</span> metrics = svm.<a class="code hl_function" href="classsvm__classifier_1_1SVMClassifier.html#a7e6648c4d2bac92bb00381076ea92db3">fit</a>(X, y);</div>
|
||||||
|
<div class="line"> </div>
|
||||||
|
<div class="line"><span class="comment">// Evaluate</span></div>
|
||||||
|
<div class="line"><span class="keyword">auto</span> eval_metrics = svm.<a class="code hl_function" href="classsvm__classifier_1_1SVMClassifier.html#a38a9b020b9f4f9254920c97a3a047e9b">evaluate</a>(X, y);</div>
|
||||||
|
<div class="line">std::cout << <span class="stringliteral">"Accuracy: "</span> << eval_metrics.<a class="code hl_variable" href="structsvm__classifier_1_1EvaluationMetrics.html#abe60e87c99b8b3c4499e602d8c26847a">accuracy</a> << std::endl;</div>
|
||||||
|
<div class="line">std::cout << <span class="stringliteral">"F1-Score: "</span> << eval_metrics.f1_score << std::endl;</div>
|
||||||
|
<div class="ttc" id="aclasssvm__classifier_1_1SVMClassifier_html_a38a9b020b9f4f9254920c97a3a047e9b"><div class="ttname"><a href="classsvm__classifier_1_1SVMClassifier.html#a38a9b020b9f4f9254920c97a3a047e9b">svm_classifier::SVMClassifier::evaluate</a></div><div class="ttdeci">EvaluationMetrics evaluate(const torch::Tensor &X, const torch::Tensor &y_true)</div><div class="ttdoc">Calculate detailed evaluation metrics.</div></div>
|
||||||
|
<div class="ttc" id="aclasssvm__classifier_1_1SVMClassifier_html_a7e6648c4d2bac92bb00381076ea92db3"><div class="ttname"><a href="classsvm__classifier_1_1SVMClassifier.html#a7e6648c4d2bac92bb00381076ea92db3">svm_classifier::SVMClassifier::fit</a></div><div class="ttdeci">TrainingMetrics fit(const torch::Tensor &X, const torch::Tensor &y)</div><div class="ttdoc">Train the SVM classifier.</div></div>
|
||||||
|
<div class="ttc" id="astructsvm__classifier_1_1EvaluationMetrics_html_abe60e87c99b8b3c4499e602d8c26847a"><div class="ttname"><a href="structsvm__classifier_1_1EvaluationMetrics.html#abe60e87c99b8b3c4499e602d8c26847a">svm_classifier::EvaluationMetrics::accuracy</a></div><div class="ttdeci">double accuracy</div><div class="ttdoc">Classification accuracy.</div><div class="ttdef"><b>Definition</b> <a href="types_8hpp_source.html#l00071">types.hpp:71</a></div></div>
|
||||||
|
</div><!-- fragment --><h2>Cross-Validation</h2>
|
||||||
|
<div class="fragment"><div class="line"><a class="code hl_class" href="classsvm__classifier_1_1SVMClassifier.html">SVMClassifier</a> svm(KernelType::RBF);</div>
|
||||||
|
<div class="line"><span class="keyword">auto</span> cv_scores = svm.<a class="code hl_function" href="classsvm__classifier_1_1SVMClassifier.html#a4c91072ea0d3d9b97ba458ff7d0898b8">cross_validate</a>(X, y, 5); <span class="comment">// 5-fold CV</span></div>
|
||||||
|
<div class="line"> </div>
|
||||||
|
<div class="line"><span class="keywordtype">double</span> mean_score = 0.0;</div>
|
||||||
|
<div class="line"><span class="keywordflow">for</span> (<span class="keyword">auto</span> score : cv_scores) {</div>
|
||||||
|
<div class="line"> mean_score += score;</div>
|
||||||
|
<div class="line">}</div>
|
||||||
|
<div class="line">mean_score /= cv_scores.size();</div>
|
||||||
|
<div class="ttc" id="aclasssvm__classifier_1_1SVMClassifier_html_a4c91072ea0d3d9b97ba458ff7d0898b8"><div class="ttname"><a href="classsvm__classifier_1_1SVMClassifier.html#a4c91072ea0d3d9b97ba458ff7d0898b8">svm_classifier::SVMClassifier::cross_validate</a></div><div class="ttdeci">std::vector< double > cross_validate(const torch::Tensor &X, const torch::Tensor &y, int cv=5)</div><div class="ttdoc">Perform cross-validation.</div></div>
|
||||||
|
</div><!-- fragment --><h2>Grid Search</h2>
|
||||||
|
<div class="fragment"><div class="line">nlohmann::json param_grid = {</div>
|
||||||
|
<div class="line"> {<span class="stringliteral">"C"</span>, {0.1, 1.0, 10.0}},</div>
|
||||||
|
<div class="line"> {<span class="stringliteral">"gamma"</span>, {0.01, 0.1, 1.0}},</div>
|
||||||
|
<div class="line"> {<span class="stringliteral">"kernel"</span>, {<span class="stringliteral">"rbf"</span>, <span class="stringliteral">"polynomial"</span>}}</div>
|
||||||
|
<div class="line">};</div>
|
||||||
|
<div class="line"> </div>
|
||||||
|
<div class="line"><span class="keyword">auto</span> best_params = svm.<a class="code hl_function" href="classsvm__classifier_1_1SVMClassifier.html#afed66a704dfb38cc7d080d3337d10194">grid_search</a>(X, y, param_grid, 3);</div>
|
||||||
|
<div class="line">std::cout << <span class="stringliteral">"Best parameters: "</span> << best_params.dump(2) << std::endl;</div>
|
||||||
|
<div class="ttc" id="aclasssvm__classifier_1_1SVMClassifier_html_afed66a704dfb38cc7d080d3337d10194"><div class="ttname"><a href="classsvm__classifier_1_1SVMClassifier.html#afed66a704dfb38cc7d080d3337d10194">svm_classifier::SVMClassifier::grid_search</a></div><div class="ttdeci">nlohmann::json grid_search(const torch::Tensor &X, const torch::Tensor &y, const nlohmann::json &param_grid, int cv=5)</div><div class="ttdoc">Find optimal hyperparameters using grid search.</div></div>
|
||||||
|
</div><!-- fragment --><h1>Testing</h1>
|
||||||
|
<h2>Run All Tests</h2>
|
||||||
|
<div class="fragment"><div class="line">cd build</div>
|
||||||
|
<div class="line">make test_all</div>
|
||||||
|
</div><!-- fragment --><h2>Test Categories</h2>
|
||||||
|
<div class="fragment"><div class="line">make test_unit # Unit tests</div>
|
||||||
|
<div class="line">make test_integration # Integration tests </div>
|
||||||
|
<div class="line">make test_performance # Performance tests</div>
|
||||||
|
</div><!-- fragment --><h2>Coverage Report</h2>
|
||||||
|
<div class="fragment"><div class="line">cmake -DCMAKE_BUILD_TYPE=Debug ..</div>
|
||||||
|
<div class="line">make coverage</div>
|
||||||
|
</div><!-- fragment --><p>The coverage report will be generated in <code>build/coverage_html/index.html</code>.</p>
|
||||||
|
<h1>Project Structure</h1>
|
||||||
|
<div class="fragment"><div class="line">svm_classifier/</div>
|
||||||
|
<div class="line">├── include/svm_classifier/ # Public headers</div>
|
||||||
|
<div class="line">│ ├── svm_classifier.hpp # Main classifier interface</div>
|
||||||
|
<div class="line">│ ├── data_converter.hpp # Tensor conversion utilities</div>
|
||||||
|
<div class="line">│ ├── multiclass_strategy.hpp # Multiclass strategies</div>
|
||||||
|
<div class="line">│ ├── kernel_parameters.hpp # Parameter management</div>
|
||||||
|
<div class="line">│ └── types.hpp # Common types and enums</div>
|
||||||
|
<div class="line">├── src/ # Implementation files</div>
|
||||||
|
<div class="line">├── tests/ # Comprehensive test suite</div>
|
||||||
|
<div class="line">├── examples/ # Usage examples</div>
|
||||||
|
<div class="line">├── external/ # Third-party dependencies</div>
|
||||||
|
<div class="line">└── CMakeLists.txt # Build configuration</div>
|
||||||
|
</div><!-- fragment --><h1>Dependencies</h1>
|
||||||
|
<h2>Required</h2>
|
||||||
|
<ul>
|
||||||
|
<li><b>libtorch</b>: PyTorch C++ API for tensor operations</li>
|
||||||
|
<li><b>liblinear</b>: Linear SVM implementation</li>
|
||||||
|
<li><b>libsvm</b>: Non-linear SVM implementation</li>
|
||||||
|
<li><b>nlohmann/json</b>: JSON configuration handling</li>
|
||||||
|
</ul>
|
||||||
|
<h2>Testing</h2>
|
||||||
|
<ul>
|
||||||
|
<li><b>Catch2</b>: Testing framework</li>
|
||||||
|
</ul>
|
||||||
|
<h2>Build System</h2>
|
||||||
|
<ul>
|
||||||
|
<li><b>CMake</b>: Cross-platform build system</li>
|
||||||
|
</ul>
|
||||||
|
<h1>Performance Characteristics</h1>
|
||||||
|
<h2>Memory Usage</h2>
|
||||||
|
<ul>
|
||||||
|
<li>Efficient sparse data handling</li>
|
||||||
|
<li>Automatic memory management for SVM structures</li>
|
||||||
|
<li>Configurable cache sizes for large datasets</li>
|
||||||
|
</ul>
|
||||||
|
<h2>Speed</h2>
|
||||||
|
<ul>
|
||||||
|
<li>Linear kernels: Uses highly optimized liblinear</li>
|
||||||
|
<li>Non-linear kernels: Uses proven libsvm implementation</li>
|
||||||
|
<li>Multi-threading support via libtorch</li>
|
||||||
|
</ul>
|
||||||
|
<h2>Scalability</h2>
|
||||||
|
<ul>
|
||||||
|
<li>Handles datasets from hundreds to millions of samples</li>
|
||||||
|
<li>Memory-efficient data conversion</li>
|
||||||
|
<li>Sparse feature support</li>
|
||||||
|
</ul>
|
||||||
|
<h1>Library Selection Logic</h1>
|
||||||
|
<p>The classifier automatically selects the appropriate underlying library:</p>
|
||||||
|
<ul>
|
||||||
|
<li><b>Linear Kernel</b> → liblinear (optimized for linear classification)</li>
|
||||||
|
<li><b>RBF/Polynomial/Sigmoid</b> → libsvm (supports arbitrary kernels)</li>
|
||||||
|
</ul>
|
||||||
|
<p>This ensures optimal performance for each kernel type while maintaining a unified API.</p>
|
||||||
|
<h1>Contributing</h1>
|
||||||
|
<ol type="1">
|
||||||
|
<li>Fork the repository</li>
|
||||||
|
<li>Create a feature branch</li>
|
||||||
|
<li>Add tests for new functionality</li>
|
||||||
|
<li>Ensure all tests pass: <code>make test_all</code></li>
|
||||||
|
<li>Check code coverage: <code>make coverage</code></li>
|
||||||
|
<li>Submit a pull request</li>
|
||||||
|
</ol>
|
||||||
|
<h2>Code Style</h2>
|
||||||
|
<ul>
|
||||||
|
<li>Follow modern C++17 conventions</li>
|
||||||
|
<li>Use RAII for resource management</li>
|
||||||
|
<li>Comprehensive error handling</li>
|
||||||
|
<li>Document all public APIs</li>
|
||||||
|
</ul>
|
||||||
|
<h1>License</h1>
|
||||||
|
<p>[Specify your license here]</p>
|
||||||
|
<h1>Acknowledgments</h1>
|
||||||
|
<ul>
|
||||||
|
<li><b>libsvm</b>: Chih-Chung Chang and Chih-Jen Lin</li>
|
||||||
|
<li><b>liblinear</b>: Fan et al.</li>
|
||||||
|
<li><b>PyTorch</b>: Facebook AI Research</li>
|
||||||
|
<li><b>nlohmann/json</b>: Niels Lohmann</li>
|
||||||
|
<li><b>Catch2</b>: Phil Nash and contributors </li>
|
||||||
|
</ul>
|
||||||
|
</div></div><!-- PageDoc -->
|
||||||
|
</div><!-- contents -->
|
||||||
|
<!-- start footer part -->
|
||||||
|
<hr class="footer"/><address class="footer"><small>
|
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|
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
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||||||
|
</small></address>
|
||||||
|
</body>
|
||||||
|
</html>
|
||||||
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|||||||
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|
||||||
|
<area shape="rect" id="Node000000" href="$classsvm__classifier_1_1DataConverter.html" title="Data converter between libtorch tensors and SVM library formats." alt="" coords="5,5,224,31"/>
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<map id="Graphical Class Hierarchy" name="Graphical Class Hierarchy">
|
||||||
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<area shape="rect" id="Node000000" href="$structsvm__classifier_1_1EvaluationMetrics.html" title="Model evaluation metrics." alt="" coords="5,5,197,45"/>
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<map id="Graphical Class Hierarchy" name="Graphical Class Hierarchy">
|
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|||||||
|
<map id="Graphical Class Hierarchy" name="Graphical Class Hierarchy">
|
||||||
|
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|||||||
|
<map id="Graphical Class Hierarchy" name="Graphical Class Hierarchy">
|
||||||
|
<area shape="rect" id="Node000000" href="$classsvm__classifier_1_1SVMClassifier.html" title="Support Vector Machine Classifier with scikit-learn compatible API." alt="" coords="5,5,217,31"/>
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|
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|||||||
|
<map id="Graphical Class Hierarchy" name="Graphical Class Hierarchy">
|
||||||
|
<area shape="rect" id="Node000000" href="$structsvm__classifier_1_1TrainingMetrics.html" title="Training metrics structure." alt="" coords="5,5,180,45"/>
|
||||||
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|
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|
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|
<title>SVM Classifier C++: Class Hierarchy</title>
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||||||
|
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|
||||||
|
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|
||||||
|
<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>
|
||||||
|
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|
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|
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<div class="fragment"><div class="line"><a id="l00001" name="l00001"></a><span class="lineno"> 1</span><span class="preprocessor">#include "svm_classifier/kernel_parameters.hpp"</span></div>
|
||||||
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<div class="line"><a id="l00002" name="l00002"></a><span class="lineno"> 2</span><span class="preprocessor">#include <stdexcept></span></div>
|
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<div class="line"><a id="l00003" name="l00003"></a><span class="lineno"> 3</span><span class="preprocessor">#include <cmath></span></div>
|
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<div class="line"><a id="l00004" name="l00004"></a><span class="lineno"> 4</span> </div>
|
||||||
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<div class="line"><a id="l00005" name="l00005"></a><span class="lineno"> 5</span><span class="keyword">namespace </span>svm_classifier {</div>
|
||||||
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<div class="line"><a id="l00006" name="l00006"></a><span class="lineno"> 6</span> </div>
|
||||||
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<div class="line"><a id="l00007" name="l00007"></a><span class="lineno"> 7</span> KernelParameters::KernelParameters()</div>
|
||||||
|
<div class="line"><a id="l00008" name="l00008"></a><span class="lineno"> 8</span> : kernel_type_(KernelType::LINEAR)</div>
|
||||||
|
<div class="line"><a id="l00009" name="l00009"></a><span class="lineno"> 9</span> , multiclass_strategy_(MulticlassStrategy::ONE_VS_REST)</div>
|
||||||
|
<div class="line"><a id="l00010" name="l00010"></a><span class="lineno"> 10</span> , C_(1.0)</div>
|
||||||
|
<div class="line"><a id="l00011" name="l00011"></a><span class="lineno"> 11</span> , tolerance_(1e-3)</div>
|
||||||
|
<div class="line"><a id="l00012" name="l00012"></a><span class="lineno"> 12</span> , max_iterations_(-1)</div>
|
||||||
|
<div class="line"><a id="l00013" name="l00013"></a><span class="lineno"> 13</span> , probability_(false)</div>
|
||||||
|
<div class="line"><a id="l00014" name="l00014"></a><span class="lineno"> 14</span> , gamma_(-1.0) <span class="comment">// Auto gamma</span></div>
|
||||||
|
<div class="line"><a id="l00015" name="l00015"></a><span class="lineno"> 15</span> , degree_(3)</div>
|
||||||
|
<div class="line"><a id="l00016" name="l00016"></a><span class="lineno"> 16</span> , coef0_(0.0)</div>
|
||||||
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<div class="line"><a id="l00017" name="l00017"></a><span class="lineno"> 17</span> , cache_size_(200.0)</div>
|
||||||
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<div class="line"><a id="l00018" name="l00018"></a><span class="lineno"> 18</span> {</div>
|
||||||
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<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> </div>
|
||||||
|
<div class="line"><a id="l00021" name="l00021"></a><span class="lineno"> 21</span> KernelParameters::KernelParameters(<span class="keyword">const</span> nlohmann::json& config) : KernelParameters()</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> set_parameters(config);</div>
|
||||||
|
<div class="line"><a id="l00024" name="l00024"></a><span class="lineno"> 24</span> }</div>
|
||||||
|
<div class="line"><a id="l00025" name="l00025"></a><span class="lineno"> 25</span> </div>
|
||||||
|
<div class="line"><a id="l00026" name="l00026"></a><span class="lineno"> 26</span> <span class="keywordtype">void</span> KernelParameters::set_parameters(<span class="keyword">const</span> nlohmann::json& config)</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> <span class="comment">// Set kernel type first as it affects validation</span></div>
|
||||||
|
<div class="line"><a id="l00029" name="l00029"></a><span class="lineno"> 29</span> <span class="keywordflow">if</span> (config.contains(<span class="stringliteral">"kernel"</span>)) {</div>
|
||||||
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<div class="line"><a id="l00030" name="l00030"></a><span class="lineno"> 30</span> <span class="keywordflow">if</span> (config[<span class="stringliteral">"kernel"</span>].is_string()) {</div>
|
||||||
|
<div class="line"><a id="l00031" name="l00031"></a><span class="lineno"> 31</span> set_kernel_type(string_to_kernel_type(config[<span class="stringliteral">"kernel"</span>]));</div>
|
||||||
|
<div class="line"><a id="l00032" name="l00032"></a><span class="lineno"> 32</span> } <span class="keywordflow">else</span> {</div>
|
||||||
|
<div class="line"><a id="l00033" name="l00033"></a><span class="lineno"> 33</span> <span class="keywordflow">throw</span> std::invalid_argument(<span class="stringliteral">"Kernel must be a string"</span>);</div>
|
||||||
|
<div class="line"><a id="l00034" name="l00034"></a><span class="lineno"> 34</span> }</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> </div>
|
||||||
|
<div class="line"><a id="l00037" name="l00037"></a><span class="lineno"> 37</span> <span class="comment">// Set multiclass strategy</span></div>
|
||||||
|
<div class="line"><a id="l00038" name="l00038"></a><span class="lineno"> 38</span> <span class="keywordflow">if</span> (config.contains(<span class="stringliteral">"multiclass_strategy"</span>)) {</div>
|
||||||
|
<div class="line"><a id="l00039" name="l00039"></a><span class="lineno"> 39</span> <span class="keywordflow">if</span> (config[<span class="stringliteral">"multiclass_strategy"</span>].is_string()) {</div>
|
||||||
|
<div class="line"><a id="l00040" name="l00040"></a><span class="lineno"> 40</span> set_multiclass_strategy(string_to_multiclass_strategy(config[<span class="stringliteral">"multiclass_strategy"</span>]));</div>
|
||||||
|
<div class="line"><a id="l00041" name="l00041"></a><span class="lineno"> 41</span> } <span class="keywordflow">else</span> {</div>
|
||||||
|
<div class="line"><a id="l00042" name="l00042"></a><span class="lineno"> 42</span> <span class="keywordflow">throw</span> std::invalid_argument(<span class="stringliteral">"Multiclass strategy must be a string"</span>);</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> </div>
|
||||||
|
<div class="line"><a id="l00046" name="l00046"></a><span class="lineno"> 46</span> <span class="comment">// Set common parameters</span></div>
|
||||||
|
<div class="line"><a id="l00047" name="l00047"></a><span class="lineno"> 47</span> <span class="keywordflow">if</span> (config.contains(<span class="stringliteral">"C"</span>)) {</div>
|
||||||
|
<div class="line"><a id="l00048" name="l00048"></a><span class="lineno"> 48</span> <span class="keywordflow">if</span> (config[<span class="stringliteral">"C"</span>].is_number()) {</div>
|
||||||
|
<div class="line"><a id="l00049" name="l00049"></a><span class="lineno"> 49</span> set_C(config[<span class="stringliteral">"C"</span>]);</div>
|
||||||
|
<div class="line"><a id="l00050" name="l00050"></a><span class="lineno"> 50</span> } <span class="keywordflow">else</span> {</div>
|
||||||
|
<div class="line"><a id="l00051" name="l00051"></a><span class="lineno"> 51</span> <span class="keywordflow">throw</span> std::invalid_argument(<span class="stringliteral">"C must be a number"</span>);</div>
|
||||||
|
<div class="line"><a id="l00052" name="l00052"></a><span class="lineno"> 52</span> }</div>
|
||||||
|
<div class="line"><a id="l00053" name="l00053"></a><span class="lineno"> 53</span> }</div>
|
||||||
|
<div class="line"><a id="l00054" name="l00054"></a><span class="lineno"> 54</span> </div>
|
||||||
|
<div class="line"><a id="l00055" name="l00055"></a><span class="lineno"> 55</span> <span class="keywordflow">if</span> (config.contains(<span class="stringliteral">"tolerance"</span>)) {</div>
|
||||||
|
<div class="line"><a id="l00056" name="l00056"></a><span class="lineno"> 56</span> <span class="keywordflow">if</span> (config[<span class="stringliteral">"tolerance"</span>].is_number()) {</div>
|
||||||
|
<div class="line"><a id="l00057" name="l00057"></a><span class="lineno"> 57</span> set_tolerance(config[<span class="stringliteral">"tolerance"</span>]);</div>
|
||||||
|
<div class="line"><a id="l00058" name="l00058"></a><span class="lineno"> 58</span> } <span class="keywordflow">else</span> {</div>
|
||||||
|
<div class="line"><a id="l00059" name="l00059"></a><span class="lineno"> 59</span> <span class="keywordflow">throw</span> std::invalid_argument(<span class="stringliteral">"Tolerance must be a number"</span>);</div>
|
||||||
|
<div class="line"><a id="l00060" name="l00060"></a><span class="lineno"> 60</span> }</div>
|
||||||
|
<div class="line"><a id="l00061" name="l00061"></a><span class="lineno"> 61</span> }</div>
|
||||||
|
<div class="line"><a id="l00062" name="l00062"></a><span class="lineno"> 62</span> </div>
|
||||||
|
<div class="line"><a id="l00063" name="l00063"></a><span class="lineno"> 63</span> <span class="keywordflow">if</span> (config.contains(<span class="stringliteral">"max_iterations"</span>)) {</div>
|
||||||
|
<div class="line"><a id="l00064" name="l00064"></a><span class="lineno"> 64</span> <span class="keywordflow">if</span> (config[<span class="stringliteral">"max_iterations"</span>].is_number_integer()) {</div>
|
||||||
|
<div class="line"><a id="l00065" name="l00065"></a><span class="lineno"> 65</span> set_max_iterations(config[<span class="stringliteral">"max_iterations"</span>]);</div>
|
||||||
|
<div class="line"><a id="l00066" name="l00066"></a><span class="lineno"> 66</span> } <span class="keywordflow">else</span> {</div>
|
||||||
|
<div class="line"><a id="l00067" name="l00067"></a><span class="lineno"> 67</span> <span class="keywordflow">throw</span> std::invalid_argument(<span class="stringliteral">"Max iterations must be an integer"</span>);</div>
|
||||||
|
<div class="line"><a id="l00068" name="l00068"></a><span class="lineno"> 68</span> }</div>
|
||||||
|
<div class="line"><a id="l00069" name="l00069"></a><span class="lineno"> 69</span> }</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> <span class="keywordflow">if</span> (config.contains(<span class="stringliteral">"probability"</span>)) {</div>
|
||||||
|
<div class="line"><a id="l00072" name="l00072"></a><span class="lineno"> 72</span> <span class="keywordflow">if</span> (config[<span class="stringliteral">"probability"</span>].is_boolean()) {</div>
|
||||||
|
<div class="line"><a id="l00073" name="l00073"></a><span class="lineno"> 73</span> set_probability(config[<span class="stringliteral">"probability"</span>]);</div>
|
||||||
|
<div class="line"><a id="l00074" name="l00074"></a><span class="lineno"> 74</span> } <span class="keywordflow">else</span> {</div>
|
||||||
|
<div class="line"><a id="l00075" name="l00075"></a><span class="lineno"> 75</span> <span class="keywordflow">throw</span> std::invalid_argument(<span class="stringliteral">"Probability must be a boolean"</span>);</div>
|
||||||
|
<div class="line"><a id="l00076" name="l00076"></a><span class="lineno"> 76</span> }</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> <span class="comment">// Set kernel-specific parameters</span></div>
|
||||||
|
<div class="line"><a id="l00080" name="l00080"></a><span class="lineno"> 80</span> <span class="keywordflow">if</span> (config.contains(<span class="stringliteral">"gamma"</span>)) {</div>
|
||||||
|
<div class="line"><a id="l00081" name="l00081"></a><span class="lineno"> 81</span> <span class="keywordflow">if</span> (config[<span class="stringliteral">"gamma"</span>].is_number()) {</div>
|
||||||
|
<div class="line"><a id="l00082" name="l00082"></a><span class="lineno"> 82</span> set_gamma(config[<span class="stringliteral">"gamma"</span>]);</div>
|
||||||
|
<div class="line"><a id="l00083" name="l00083"></a><span class="lineno"> 83</span> } <span class="keywordflow">else</span> <span class="keywordflow">if</span> (config[<span class="stringliteral">"gamma"</span>].is_string() && config[<span class="stringliteral">"gamma"</span>] == <span class="stringliteral">"auto"</span>) {</div>
|
||||||
|
<div class="line"><a id="l00084" name="l00084"></a><span class="lineno"> 84</span> set_gamma(-1.0); <span class="comment">// Auto gamma</span></div>
|
||||||
|
<div class="line"><a id="l00085" name="l00085"></a><span class="lineno"> 85</span> } <span class="keywordflow">else</span> {</div>
|
||||||
|
<div class="line"><a id="l00086" name="l00086"></a><span class="lineno"> 86</span> <span class="keywordflow">throw</span> std::invalid_argument(<span class="stringliteral">"Gamma must be a number or 'auto'"</span>);</div>
|
||||||
|
<div class="line"><a id="l00087" name="l00087"></a><span class="lineno"> 87</span> }</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> </div>
|
||||||
|
<div class="line"><a id="l00090" name="l00090"></a><span class="lineno"> 90</span> <span class="keywordflow">if</span> (config.contains(<span class="stringliteral">"degree"</span>)) {</div>
|
||||||
|
<div class="line"><a id="l00091" name="l00091"></a><span class="lineno"> 91</span> <span class="keywordflow">if</span> (config[<span class="stringliteral">"degree"</span>].is_number_integer()) {</div>
|
||||||
|
<div class="line"><a id="l00092" name="l00092"></a><span class="lineno"> 92</span> set_degree(config[<span class="stringliteral">"degree"</span>]);</div>
|
||||||
|
<div class="line"><a id="l00093" name="l00093"></a><span class="lineno"> 93</span> } <span class="keywordflow">else</span> {</div>
|
||||||
|
<div class="line"><a id="l00094" name="l00094"></a><span class="lineno"> 94</span> <span class="keywordflow">throw</span> std::invalid_argument(<span class="stringliteral">"Degree must be an integer"</span>);</div>
|
||||||
|
<div class="line"><a id="l00095" name="l00095"></a><span class="lineno"> 95</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="keywordflow">if</span> (config.contains(<span class="stringliteral">"coef0"</span>)) {</div>
|
||||||
|
<div class="line"><a id="l00099" name="l00099"></a><span class="lineno"> 99</span> <span class="keywordflow">if</span> (config[<span class="stringliteral">"coef0"</span>].is_number()) {</div>
|
||||||
|
<div class="line"><a id="l00100" name="l00100"></a><span class="lineno"> 100</span> set_coef0(config[<span class="stringliteral">"coef0"</span>]);</div>
|
||||||
|
<div class="line"><a id="l00101" name="l00101"></a><span class="lineno"> 101</span> } <span class="keywordflow">else</span> {</div>
|
||||||
|
<div class="line"><a id="l00102" name="l00102"></a><span class="lineno"> 102</span> <span class="keywordflow">throw</span> std::invalid_argument(<span class="stringliteral">"Coef0 must be a number"</span>);</div>
|
||||||
|
<div class="line"><a id="l00103" name="l00103"></a><span class="lineno"> 103</span> }</div>
|
||||||
|
<div class="line"><a id="l00104" name="l00104"></a><span class="lineno"> 104</span> }</div>
|
||||||
|
<div class="line"><a id="l00105" name="l00105"></a><span class="lineno"> 105</span> </div>
|
||||||
|
<div class="line"><a id="l00106" name="l00106"></a><span class="lineno"> 106</span> <span class="keywordflow">if</span> (config.contains(<span class="stringliteral">"cache_size"</span>)) {</div>
|
||||||
|
<div class="line"><a id="l00107" name="l00107"></a><span class="lineno"> 107</span> <span class="keywordflow">if</span> (config[<span class="stringliteral">"cache_size"</span>].is_number()) {</div>
|
||||||
|
<div class="line"><a id="l00108" name="l00108"></a><span class="lineno"> 108</span> set_cache_size(config[<span class="stringliteral">"cache_size"</span>]);</div>
|
||||||
|
<div class="line"><a id="l00109" name="l00109"></a><span class="lineno"> 109</span> } <span class="keywordflow">else</span> {</div>
|
||||||
|
<div class="line"><a id="l00110" name="l00110"></a><span class="lineno"> 110</span> <span class="keywordflow">throw</span> std::invalid_argument(<span class="stringliteral">"Cache size must be a number"</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> }</div>
|
||||||
|
<div class="line"><a id="l00113" name="l00113"></a><span class="lineno"> 113</span> </div>
|
||||||
|
<div class="line"><a id="l00114" name="l00114"></a><span class="lineno"> 114</span> <span class="comment">// Validate all parameters</span></div>
|
||||||
|
<div class="line"><a id="l00115" name="l00115"></a><span class="lineno"> 115</span> validate();</div>
|
||||||
|
<div class="line"><a id="l00116" name="l00116"></a><span class="lineno"> 116</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> nlohmann::json KernelParameters::get_parameters()<span class="keyword"> const</span></div>
|
||||||
|
<div class="line"><a id="l00119" name="l00119"></a><span class="lineno"> 119</span><span class="keyword"> </span>{</div>
|
||||||
|
<div class="line"><a id="l00120" name="l00120"></a><span class="lineno"> 120</span> nlohmann::json params = {</div>
|
||||||
|
<div class="line"><a id="l00121" name="l00121"></a><span class="lineno"> 121</span> {<span class="stringliteral">"kernel"</span>, kernel_type_to_string(kernel_type_)},</div>
|
||||||
|
<div class="line"><a id="l00122" name="l00122"></a><span class="lineno"> 122</span> {<span class="stringliteral">"multiclass_strategy"</span>, multiclass_strategy_to_string(multiclass_strategy_)},</div>
|
||||||
|
<div class="line"><a id="l00123" name="l00123"></a><span class="lineno"> 123</span> {<span class="stringliteral">"C"</span>, C_},</div>
|
||||||
|
<div class="line"><a id="l00124" name="l00124"></a><span class="lineno"> 124</span> {<span class="stringliteral">"tolerance"</span>, tolerance_},</div>
|
||||||
|
<div class="line"><a id="l00125" name="l00125"></a><span class="lineno"> 125</span> {<span class="stringliteral">"max_iterations"</span>, max_iterations_},</div>
|
||||||
|
<div class="line"><a id="l00126" name="l00126"></a><span class="lineno"> 126</span> {<span class="stringliteral">"probability"</span>, probability_},</div>
|
||||||
|
<div class="line"><a id="l00127" name="l00127"></a><span class="lineno"> 127</span> {<span class="stringliteral">"cache_size"</span>, cache_size_}</div>
|
||||||
|
<div class="line"><a id="l00128" name="l00128"></a><span class="lineno"> 128</span> };</div>
|
||||||
|
<div class="line"><a id="l00129" name="l00129"></a><span class="lineno"> 129</span> </div>
|
||||||
|
<div class="line"><a id="l00130" name="l00130"></a><span class="lineno"> 130</span> <span class="comment">// Add kernel-specific parameters</span></div>
|
||||||
|
<div class="line"><a id="l00131" name="l00131"></a><span class="lineno"> 131</span> <span class="keywordflow">switch</span> (kernel_type_) {</div>
|
||||||
|
<div class="line"><a id="l00132" name="l00132"></a><span class="lineno"> 132</span> <span class="keywordflow">case</span> KernelType::LINEAR:</div>
|
||||||
|
<div class="line"><a id="l00133" name="l00133"></a><span class="lineno"> 133</span> <span class="comment">// No additional parameters for linear kernel</span></div>
|
||||||
|
<div class="line"><a id="l00134" name="l00134"></a><span class="lineno"> 134</span> <span class="keywordflow">break</span>;</div>
|
||||||
|
<div class="line"><a id="l00135" name="l00135"></a><span class="lineno"> 135</span> </div>
|
||||||
|
<div class="line"><a id="l00136" name="l00136"></a><span class="lineno"> 136</span> <span class="keywordflow">case</span> KernelType::RBF:</div>
|
||||||
|
<div class="line"><a id="l00137" name="l00137"></a><span class="lineno"> 137</span> params[<span class="stringliteral">"gamma"</span>] = is_gamma_auto() ? <span class="stringliteral">"auto"</span> : gamma_;</div>
|
||||||
|
<div class="line"><a id="l00138" name="l00138"></a><span class="lineno"> 138</span> <span class="keywordflow">break</span>;</div>
|
||||||
|
<div class="line"><a id="l00139" name="l00139"></a><span class="lineno"> 139</span> </div>
|
||||||
|
<div class="line"><a id="l00140" name="l00140"></a><span class="lineno"> 140</span> <span class="keywordflow">case</span> KernelType::POLYNOMIAL:</div>
|
||||||
|
<div class="line"><a id="l00141" name="l00141"></a><span class="lineno"> 141</span> params[<span class="stringliteral">"degree"</span>] = degree_;</div>
|
||||||
|
<div class="line"><a id="l00142" name="l00142"></a><span class="lineno"> 142</span> params[<span class="stringliteral">"gamma"</span>] = is_gamma_auto() ? <span class="stringliteral">"auto"</span> : gamma_;</div>
|
||||||
|
<div class="line"><a id="l00143" name="l00143"></a><span class="lineno"> 143</span> params[<span class="stringliteral">"coef0"</span>] = coef0_;</div>
|
||||||
|
<div class="line"><a id="l00144" name="l00144"></a><span class="lineno"> 144</span> <span class="keywordflow">break</span>;</div>
|
||||||
|
<div class="line"><a id="l00145" name="l00145"></a><span class="lineno"> 145</span> </div>
|
||||||
|
<div class="line"><a id="l00146" name="l00146"></a><span class="lineno"> 146</span> <span class="keywordflow">case</span> KernelType::SIGMOID:</div>
|
||||||
|
<div class="line"><a id="l00147" name="l00147"></a><span class="lineno"> 147</span> params[<span class="stringliteral">"gamma"</span>] = is_gamma_auto() ? <span class="stringliteral">"auto"</span> : gamma_;</div>
|
||||||
|
<div class="line"><a id="l00148" name="l00148"></a><span class="lineno"> 148</span> params[<span class="stringliteral">"coef0"</span>] = coef0_;</div>
|
||||||
|
<div class="line"><a id="l00149" name="l00149"></a><span class="lineno"> 149</span> <span class="keywordflow">break</span>;</div>
|
||||||
|
<div class="line"><a id="l00150" name="l00150"></a><span class="lineno"> 150</span> }</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> <span class="keywordflow">return</span> params;</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="keywordtype">void</span> KernelParameters::set_kernel_type(KernelType kernel)</div>
|
||||||
|
<div class="line"><a id="l00156" name="l00156"></a><span class="lineno"> 156</span> {</div>
|
||||||
|
<div class="line"><a id="l00157" name="l00157"></a><span class="lineno"> 157</span> kernel_type_ = kernel;</div>
|
||||||
|
<div class="line"><a id="l00158" name="l00158"></a><span class="lineno"> 158</span> </div>
|
||||||
|
<div class="line"><a id="l00159" name="l00159"></a><span class="lineno"> 159</span> <span class="comment">// Reset kernel-specific parameters to defaults when kernel changes</span></div>
|
||||||
|
<div class="line"><a id="l00160" name="l00160"></a><span class="lineno"> 160</span> <span class="keyword">auto</span> defaults = get_default_parameters(kernel);</div>
|
||||||
|
<div class="line"><a id="l00161" name="l00161"></a><span class="lineno"> 161</span> </div>
|
||||||
|
<div class="line"><a id="l00162" name="l00162"></a><span class="lineno"> 162</span> <span class="keywordflow">if</span> (defaults.contains(<span class="stringliteral">"gamma"</span>)) {</div>
|
||||||
|
<div class="line"><a id="l00163" name="l00163"></a><span class="lineno"> 163</span> gamma_ = defaults[<span class="stringliteral">"gamma"</span>];</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> <span class="keywordflow">if</span> (defaults.contains(<span class="stringliteral">"degree"</span>)) {</div>
|
||||||
|
<div class="line"><a id="l00166" name="l00166"></a><span class="lineno"> 166</span> degree_ = defaults[<span class="stringliteral">"degree"</span>];</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> <span class="keywordflow">if</span> (defaults.contains(<span class="stringliteral">"coef0"</span>)) {</div>
|
||||||
|
<div class="line"><a id="l00169" name="l00169"></a><span class="lineno"> 169</span> coef0_ = defaults[<span class="stringliteral">"coef0"</span>];</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> </div>
|
||||||
|
<div class="line"><a id="l00173" name="l00173"></a><span class="lineno"> 173</span> <span class="keywordtype">void</span> KernelParameters::set_C(<span class="keywordtype">double</span> c)</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> (c <= 0.0) {</div>
|
||||||
|
<div class="line"><a id="l00176" name="l00176"></a><span class="lineno"> 176</span> <span class="keywordflow">throw</span> std::invalid_argument(<span class="stringliteral">"C must be positive (C > 0)"</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> C_ = c;</div>
|
||||||
|
<div class="line"><a id="l00179" name="l00179"></a><span class="lineno"> 179</span> }</div>
|
||||||
|
<div class="line"><a id="l00180" name="l00180"></a><span class="lineno"> 180</span> </div>
|
||||||
|
<div class="line"><a id="l00181" name="l00181"></a><span class="lineno"> 181</span> <span class="keywordtype">void</span> KernelParameters::set_gamma(<span class="keywordtype">double</span> gamma)</div>
|
||||||
|
<div class="line"><a id="l00182" name="l00182"></a><span class="lineno"> 182</span> {</div>
|
||||||
|
<div class="line"><a id="l00183" name="l00183"></a><span class="lineno"> 183</span> <span class="comment">// Allow negative values for auto gamma (-1.0)</span></div>
|
||||||
|
<div class="line"><a id="l00184" name="l00184"></a><span class="lineno"> 184</span> <span class="keywordflow">if</span> (gamma > 0.0 || gamma == -1.0) {</div>
|
||||||
|
<div class="line"><a id="l00185" name="l00185"></a><span class="lineno"> 185</span> gamma_ = gamma;</div>
|
||||||
|
<div class="line"><a id="l00186" name="l00186"></a><span class="lineno"> 186</span> } <span class="keywordflow">else</span> {</div>
|
||||||
|
<div class="line"><a id="l00187" name="l00187"></a><span class="lineno"> 187</span> <span class="keywordflow">throw</span> std::invalid_argument(<span class="stringliteral">"Gamma must be positive or -1 for auto"</span>);</div>
|
||||||
|
<div class="line"><a id="l00188" name="l00188"></a><span class="lineno"> 188</span> }</div>
|
||||||
|
<div class="line"><a id="l00189" name="l00189"></a><span class="lineno"> 189</span> }</div>
|
||||||
|
<div class="line"><a id="l00190" name="l00190"></a><span class="lineno"> 190</span> </div>
|
||||||
|
<div class="line"><a id="l00191" name="l00191"></a><span class="lineno"> 191</span> <span class="keywordtype">void</span> KernelParameters::set_degree(<span class="keywordtype">int</span> degree)</div>
|
||||||
|
<div class="line"><a id="l00192" name="l00192"></a><span class="lineno"> 192</span> {</div>
|
||||||
|
<div class="line"><a id="l00193" name="l00193"></a><span class="lineno"> 193</span> <span class="keywordflow">if</span> (degree < 1) {</div>
|
||||||
|
<div class="line"><a id="l00194" name="l00194"></a><span class="lineno"> 194</span> <span class="keywordflow">throw</span> std::invalid_argument(<span class="stringliteral">"Degree must be >= 1"</span>);</div>
|
||||||
|
<div class="line"><a id="l00195" name="l00195"></a><span class="lineno"> 195</span> }</div>
|
||||||
|
<div class="line"><a id="l00196" name="l00196"></a><span class="lineno"> 196</span> degree_ = degree;</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> </div>
|
||||||
|
<div class="line"><a id="l00199" name="l00199"></a><span class="lineno"> 199</span> <span class="keywordtype">void</span> KernelParameters::set_coef0(<span class="keywordtype">double</span> coef0)</div>
|
||||||
|
<div class="line"><a id="l00200" name="l00200"></a><span class="lineno"> 200</span> {</div>
|
||||||
|
<div class="line"><a id="l00201" name="l00201"></a><span class="lineno"> 201</span> coef0_ = coef0;</div>
|
||||||
|
<div class="line"><a id="l00202" name="l00202"></a><span class="lineno"> 202</span> }</div>
|
||||||
|
<div class="line"><a id="l00203" name="l00203"></a><span class="lineno"> 203</span> </div>
|
||||||
|
<div class="line"><a id="l00204" name="l00204"></a><span class="lineno"> 204</span> <span class="keywordtype">void</span> KernelParameters::set_tolerance(<span class="keywordtype">double</span> tol)</div>
|
||||||
|
<div class="line"><a id="l00205" name="l00205"></a><span class="lineno"> 205</span> {</div>
|
||||||
|
<div class="line"><a id="l00206" name="l00206"></a><span class="lineno"> 206</span> <span class="keywordflow">if</span> (tol <= 0.0) {</div>
|
||||||
|
<div class="line"><a id="l00207" name="l00207"></a><span class="lineno"> 207</span> <span class="keywordflow">throw</span> std::invalid_argument(<span class="stringliteral">"Tolerance must be positive (tolerance > 0)"</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> tolerance_ = tol;</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> </div>
|
||||||
|
<div class="line"><a id="l00212" name="l00212"></a><span class="lineno"> 212</span> <span class="keywordtype">void</span> KernelParameters::set_max_iterations(<span class="keywordtype">int</span> max_iter)</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">if</span> (max_iter <= 0 && max_iter != -1) {</div>
|
||||||
|
<div class="line"><a id="l00215" name="l00215"></a><span class="lineno"> 215</span> <span class="keywordflow">throw</span> std::invalid_argument(<span class="stringliteral">"Max iterations must be positive or -1 for no limit"</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> max_iterations_ = max_iter;</div>
|
||||||
|
<div class="line"><a id="l00218" name="l00218"></a><span class="lineno"> 218</span> }</div>
|
||||||
|
<div class="line"><a id="l00219" name="l00219"></a><span class="lineno"> 219</span> </div>
|
||||||
|
<div class="line"><a id="l00220" name="l00220"></a><span class="lineno"> 220</span> <span class="keywordtype">void</span> KernelParameters::set_cache_size(<span class="keywordtype">double</span> cache_size)</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> <span class="keywordflow">if</span> (cache_size < 0.0) {</div>
|
||||||
|
<div class="line"><a id="l00223" name="l00223"></a><span class="lineno"> 223</span> <span class="keywordflow">throw</span> std::invalid_argument(<span class="stringliteral">"Cache size must be non-negative"</span>);</div>
|
||||||
|
<div class="line"><a id="l00224" name="l00224"></a><span class="lineno"> 224</span> }</div>
|
||||||
|
<div class="line"><a id="l00225" name="l00225"></a><span class="lineno"> 225</span> cache_size_ = cache_size;</div>
|
||||||
|
<div class="line"><a id="l00226" name="l00226"></a><span class="lineno"> 226</span> }</div>
|
||||||
|
<div class="line"><a id="l00227" name="l00227"></a><span class="lineno"> 227</span> </div>
|
||||||
|
<div class="line"><a id="l00228" name="l00228"></a><span class="lineno"> 228</span> <span class="keywordtype">void</span> KernelParameters::set_probability(<span class="keywordtype">bool</span> probability)</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> probability_ = probability;</div>
|
||||||
|
<div class="line"><a id="l00231" name="l00231"></a><span class="lineno"> 231</span> }</div>
|
||||||
|
<div class="line"><a id="l00232" name="l00232"></a><span class="lineno"> 232</span> </div>
|
||||||
|
<div class="line"><a id="l00233" name="l00233"></a><span class="lineno"> 233</span> <span class="keywordtype">void</span> KernelParameters::set_multiclass_strategy(MulticlassStrategy strategy)</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> multiclass_strategy_ = strategy;</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="keywordtype">void</span> KernelParameters::validate()<span class="keyword"> const</span></div>
|
||||||
|
<div class="line"><a id="l00239" name="l00239"></a><span class="lineno"> 239</span><span class="keyword"> </span>{</div>
|
||||||
|
<div class="line"><a id="l00240" name="l00240"></a><span class="lineno"> 240</span> <span class="comment">// Validate common parameters</span></div>
|
||||||
|
<div class="line"><a id="l00241" name="l00241"></a><span class="lineno"> 241</span> <span class="keywordflow">if</span> (C_ <= 0.0) {</div>
|
||||||
|
<div class="line"><a id="l00242" name="l00242"></a><span class="lineno"> 242</span> <span class="keywordflow">throw</span> std::invalid_argument(<span class="stringliteral">"C must be positive"</span>);</div>
|
||||||
|
<div class="line"><a id="l00243" name="l00243"></a><span class="lineno"> 243</span> }</div>
|
||||||
|
<div class="line"><a id="l00244" name="l00244"></a><span class="lineno"> 244</span> </div>
|
||||||
|
<div class="line"><a id="l00245" name="l00245"></a><span class="lineno"> 245</span> <span class="keywordflow">if</span> (tolerance_ <= 0.0) {</div>
|
||||||
|
<div class="line"><a id="l00246" name="l00246"></a><span class="lineno"> 246</span> <span class="keywordflow">throw</span> std::invalid_argument(<span class="stringliteral">"Tolerance must be positive"</span>);</div>
|
||||||
|
<div class="line"><a id="l00247" name="l00247"></a><span class="lineno"> 247</span> }</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> <span class="keywordflow">if</span> (max_iterations_ <= 0 && max_iterations_ != -1) {</div>
|
||||||
|
<div class="line"><a id="l00250" name="l00250"></a><span class="lineno"> 250</span> <span class="keywordflow">throw</span> std::invalid_argument(<span class="stringliteral">"Max iterations must be positive or -1"</span>);</div>
|
||||||
|
<div class="line"><a id="l00251" name="l00251"></a><span class="lineno"> 251</span> }</div>
|
||||||
|
<div class="line"><a id="l00252" name="l00252"></a><span class="lineno"> 252</span> </div>
|
||||||
|
<div class="line"><a id="l00253" name="l00253"></a><span class="lineno"> 253</span> <span class="keywordflow">if</span> (cache_size_ < 0.0) {</div>
|
||||||
|
<div class="line"><a id="l00254" name="l00254"></a><span class="lineno"> 254</span> <span class="keywordflow">throw</span> std::invalid_argument(<span class="stringliteral">"Cache size must be non-negative"</span>);</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> </div>
|
||||||
|
<div class="line"><a id="l00257" name="l00257"></a><span class="lineno"> 257</span> <span class="comment">// Validate kernel-specific parameters</span></div>
|
||||||
|
<div class="line"><a id="l00258" name="l00258"></a><span class="lineno"> 258</span> validate_kernel_parameters();</div>
|
||||||
|
<div class="line"><a id="l00259" name="l00259"></a><span class="lineno"> 259</span> }</div>
|
||||||
|
<div class="line"><a id="l00260" name="l00260"></a><span class="lineno"> 260</span> </div>
|
||||||
|
<div class="line"><a id="l00261" name="l00261"></a><span class="lineno"> 261</span> <span class="keywordtype">void</span> KernelParameters::validate_kernel_parameters()<span class="keyword"> const</span></div>
|
||||||
|
<div class="line"><a id="l00262" name="l00262"></a><span class="lineno"> 262</span><span class="keyword"> </span>{</div>
|
||||||
|
<div class="line"><a id="l00263" name="l00263"></a><span class="lineno"> 263</span> <span class="keywordflow">switch</span> (kernel_type_) {</div>
|
||||||
|
<div class="line"><a id="l00264" name="l00264"></a><span class="lineno"> 264</span> <span class="keywordflow">case</span> KernelType::LINEAR:</div>
|
||||||
|
<div class="line"><a id="l00265" name="l00265"></a><span class="lineno"> 265</span> <span class="comment">// Linear kernel has no additional parameters to validate</span></div>
|
||||||
|
<div class="line"><a id="l00266" name="l00266"></a><span class="lineno"> 266</span> <span class="keywordflow">break</span>;</div>
|
||||||
|
<div class="line"><a id="l00267" name="l00267"></a><span class="lineno"> 267</span> </div>
|
||||||
|
<div class="line"><a id="l00268" name="l00268"></a><span class="lineno"> 268</span> <span class="keywordflow">case</span> KernelType::RBF:</div>
|
||||||
|
<div class="line"><a id="l00269" name="l00269"></a><span class="lineno"> 269</span> <span class="keywordflow">if</span> (gamma_ > 0.0 || gamma_ == -1.0) {</div>
|
||||||
|
<div class="line"><a id="l00270" name="l00270"></a><span class="lineno"> 270</span> <span class="comment">// Valid gamma (positive or auto)</span></div>
|
||||||
|
<div class="line"><a id="l00271" name="l00271"></a><span class="lineno"> 271</span> } <span class="keywordflow">else</span> {</div>
|
||||||
|
<div class="line"><a id="l00272" name="l00272"></a><span class="lineno"> 272</span> <span class="keywordflow">throw</span> std::invalid_argument(<span class="stringliteral">"RBF kernel gamma must be positive or auto (-1)"</span>);</div>
|
||||||
|
<div class="line"><a id="l00273" name="l00273"></a><span class="lineno"> 273</span> }</div>
|
||||||
|
<div class="line"><a id="l00274" name="l00274"></a><span class="lineno"> 274</span> <span class="keywordflow">break</span>;</div>
|
||||||
|
<div class="line"><a id="l00275" name="l00275"></a><span class="lineno"> 275</span> </div>
|
||||||
|
<div class="line"><a id="l00276" name="l00276"></a><span class="lineno"> 276</span> <span class="keywordflow">case</span> KernelType::POLYNOMIAL:</div>
|
||||||
|
<div class="line"><a id="l00277" name="l00277"></a><span class="lineno"> 277</span> <span class="keywordflow">if</span> (degree_ < 1) {</div>
|
||||||
|
<div class="line"><a id="l00278" name="l00278"></a><span class="lineno"> 278</span> <span class="keywordflow">throw</span> std::invalid_argument(<span class="stringliteral">"Polynomial degree must be >= 1"</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> <span class="keywordflow">if</span> (gamma_ > 0.0 || gamma_ == -1.0) {</div>
|
||||||
|
<div class="line"><a id="l00281" name="l00281"></a><span class="lineno"> 281</span> <span class="comment">// Valid gamma</span></div>
|
||||||
|
<div class="line"><a id="l00282" name="l00282"></a><span class="lineno"> 282</span> } <span class="keywordflow">else</span> {</div>
|
||||||
|
<div class="line"><a id="l00283" name="l00283"></a><span class="lineno"> 283</span> <span class="keywordflow">throw</span> std::invalid_argument(<span class="stringliteral">"Polynomial kernel gamma must be positive or auto (-1)"</span>);</div>
|
||||||
|
<div class="line"><a id="l00284" name="l00284"></a><span class="lineno"> 284</span> }</div>
|
||||||
|
<div class="line"><a id="l00285" name="l00285"></a><span class="lineno"> 285</span> <span class="comment">// coef0 can be any real number</span></div>
|
||||||
|
<div class="line"><a id="l00286" name="l00286"></a><span class="lineno"> 286</span> <span class="keywordflow">break</span>;</div>
|
||||||
|
<div class="line"><a id="l00287" name="l00287"></a><span class="lineno"> 287</span> </div>
|
||||||
|
<div class="line"><a id="l00288" name="l00288"></a><span class="lineno"> 288</span> <span class="keywordflow">case</span> KernelType::SIGMOID:</div>
|
||||||
|
<div class="line"><a id="l00289" name="l00289"></a><span class="lineno"> 289</span> <span class="keywordflow">if</span> (gamma_ > 0.0 || gamma_ == -1.0) {</div>
|
||||||
|
<div class="line"><a id="l00290" name="l00290"></a><span class="lineno"> 290</span> <span class="comment">// Valid gamma</span></div>
|
||||||
|
<div class="line"><a id="l00291" name="l00291"></a><span class="lineno"> 291</span> } <span class="keywordflow">else</span> {</div>
|
||||||
|
<div class="line"><a id="l00292" name="l00292"></a><span class="lineno"> 292</span> <span class="keywordflow">throw</span> std::invalid_argument(<span class="stringliteral">"Sigmoid kernel gamma must be positive or auto (-1)"</span>);</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">// coef0 can be any real number</span></div>
|
||||||
|
<div class="line"><a id="l00295" name="l00295"></a><span class="lineno"> 295</span> <span class="keywordflow">break</span>;</div>
|
||||||
|
<div class="line"><a id="l00296" name="l00296"></a><span class="lineno"> 296</span> }</div>
|
||||||
|
<div class="line"><a id="l00297" name="l00297"></a><span class="lineno"> 297</span> }</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> nlohmann::json KernelParameters::get_default_parameters(KernelType kernel)</div>
|
||||||
|
<div class="line"><a id="l00300" name="l00300"></a><span class="lineno"> 300</span> {</div>
|
||||||
|
<div class="line"><a id="l00301" name="l00301"></a><span class="lineno"> 301</span> nlohmann::json defaults = {</div>
|
||||||
|
<div class="line"><a id="l00302" name="l00302"></a><span class="lineno"> 302</span> {<span class="stringliteral">"C"</span>, 1.0},</div>
|
||||||
|
<div class="line"><a id="l00303" name="l00303"></a><span class="lineno"> 303</span> {<span class="stringliteral">"tolerance"</span>, 1e-3},</div>
|
||||||
|
<div class="line"><a id="l00304" name="l00304"></a><span class="lineno"> 304</span> {<span class="stringliteral">"max_iterations"</span>, -1},</div>
|
||||||
|
<div class="line"><a id="l00305" name="l00305"></a><span class="lineno"> 305</span> {<span class="stringliteral">"probability"</span>, <span class="keyword">false</span>},</div>
|
||||||
|
<div class="line"><a id="l00306" name="l00306"></a><span class="lineno"> 306</span> {<span class="stringliteral">"multiclass_strategy"</span>, <span class="stringliteral">"ovr"</span>},</div>
|
||||||
|
<div class="line"><a id="l00307" name="l00307"></a><span class="lineno"> 307</span> {<span class="stringliteral">"cache_size"</span>, 200.0}</div>
|
||||||
|
<div class="line"><a id="l00308" name="l00308"></a><span class="lineno"> 308</span> };</div>
|
||||||
|
<div class="line"><a id="l00309" name="l00309"></a><span class="lineno"> 309</span> </div>
|
||||||
|
<div class="line"><a id="l00310" name="l00310"></a><span class="lineno"> 310</span> <span class="keywordflow">switch</span> (kernel) {</div>
|
||||||
|
<div class="line"><a id="l00311" name="l00311"></a><span class="lineno"> 311</span> <span class="keywordflow">case</span> KernelType::LINEAR:</div>
|
||||||
|
<div class="line"><a id="l00312" name="l00312"></a><span class="lineno"> 312</span> defaults[<span class="stringliteral">"kernel"</span>] = <span class="stringliteral">"linear"</span>;</div>
|
||||||
|
<div class="line"><a id="l00313" name="l00313"></a><span class="lineno"> 313</span> <span class="keywordflow">break</span>;</div>
|
||||||
|
<div class="line"><a id="l00314" name="l00314"></a><span class="lineno"> 314</span> </div>
|
||||||
|
<div class="line"><a id="l00315" name="l00315"></a><span class="lineno"> 315</span> <span class="keywordflow">case</span> KernelType::RBF:</div>
|
||||||
|
<div class="line"><a id="l00316" name="l00316"></a><span class="lineno"> 316</span> defaults[<span class="stringliteral">"kernel"</span>] = <span class="stringliteral">"rbf"</span>;</div>
|
||||||
|
<div class="line"><a id="l00317" name="l00317"></a><span class="lineno"> 317</span> defaults[<span class="stringliteral">"gamma"</span>] = -1.0; <span class="comment">// Auto gamma</span></div>
|
||||||
|
<div class="line"><a id="l00318" name="l00318"></a><span class="lineno"> 318</span> <span class="keywordflow">break</span>;</div>
|
||||||
|
<div class="line"><a id="l00319" name="l00319"></a><span class="lineno"> 319</span> </div>
|
||||||
|
<div class="line"><a id="l00320" name="l00320"></a><span class="lineno"> 320</span> <span class="keywordflow">case</span> KernelType::POLYNOMIAL:</div>
|
||||||
|
<div class="line"><a id="l00321" name="l00321"></a><span class="lineno"> 321</span> defaults[<span class="stringliteral">"kernel"</span>] = <span class="stringliteral">"polynomial"</span>;</div>
|
||||||
|
<div class="line"><a id="l00322" name="l00322"></a><span class="lineno"> 322</span> defaults[<span class="stringliteral">"degree"</span>] = 3;</div>
|
||||||
|
<div class="line"><a id="l00323" name="l00323"></a><span class="lineno"> 323</span> defaults[<span class="stringliteral">"gamma"</span>] = -1.0; <span class="comment">// Auto gamma</span></div>
|
||||||
|
<div class="line"><a id="l00324" name="l00324"></a><span class="lineno"> 324</span> defaults[<span class="stringliteral">"coef0"</span>] = 0.0;</div>
|
||||||
|
<div class="line"><a id="l00325" name="l00325"></a><span class="lineno"> 325</span> <span class="keywordflow">break</span>;</div>
|
||||||
|
<div class="line"><a id="l00326" name="l00326"></a><span class="lineno"> 326</span> </div>
|
||||||
|
<div class="line"><a id="l00327" name="l00327"></a><span class="lineno"> 327</span> <span class="keywordflow">case</span> KernelType::SIGMOID:</div>
|
||||||
|
<div class="line"><a id="l00328" name="l00328"></a><span class="lineno"> 328</span> defaults[<span class="stringliteral">"kernel"</span>] = <span class="stringliteral">"sigmoid"</span>;</div>
|
||||||
|
<div class="line"><a id="l00329" name="l00329"></a><span class="lineno"> 329</span> defaults[<span class="stringliteral">"gamma"</span>] = -1.0; <span class="comment">// Auto gamma</span></div>
|
||||||
|
<div class="line"><a id="l00330" name="l00330"></a><span class="lineno"> 330</span> defaults[<span class="stringliteral">"coef0"</span>] = 0.0;</div>
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<div class="line"><a id="l00331" name="l00331"></a><span class="lineno"> 331</span> <span class="keywordflow">break</span>;</div>
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<div class="line"><a id="l00333" name="l00333"></a><span class="lineno"> 333</span> </div>
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<div class="line"><a id="l00334" name="l00334"></a><span class="lineno"> 334</span> <span class="keywordflow">return</span> defaults;</div>
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<div class="line"><a id="l00335" name="l00335"></a><span class="lineno"> 335</span> }</div>
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<div class="line"><a id="l00336" name="l00336"></a><span class="lineno"> 336</span> </div>
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<div class="line"><a id="l00337" name="l00337"></a><span class="lineno"> 337</span> <span class="keywordtype">void</span> KernelParameters::reset_to_defaults()</div>
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<div class="line"><a id="l00338" name="l00338"></a><span class="lineno"> 338</span> {</div>
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<div class="line"><a id="l00339" name="l00339"></a><span class="lineno"> 339</span> <span class="keyword">auto</span> defaults = get_default_parameters(kernel_type_);</div>
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<div class="line"><a id="l00340" name="l00340"></a><span class="lineno"> 340</span> set_parameters(defaults);</div>
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<div class="line"><a id="l00341" name="l00341"></a><span class="lineno"> 341</span> }</div>
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<div class="line"><a id="l00342" name="l00342"></a><span class="lineno"> 342</span> </div>
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<div class="line"><a id="l00343" name="l00343"></a><span class="lineno"> 343</span> <span class="keywordtype">void</span> KernelParameters::set_gamma_auto()</div>
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<div class="line"><a id="l00344" name="l00344"></a><span class="lineno"> 344</span> {</div>
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<div class="line"><a id="l00345" name="l00345"></a><span class="lineno"> 345</span> gamma_ = -1.0;</div>
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<div class="line"><a id="l00346" name="l00346"></a><span class="lineno"> 346</span> }</div>
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<div class="line"><a id="l00347" name="l00347"></a><span class="lineno"> 347</span> </div>
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<div class="line"><a id="l00348" name="l00348"></a><span class="lineno"> 348</span>} <span class="comment">// namespace svm_classifier</span></div>
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<div id="projectbrief">High-performance Support Vector Machine classifier with scikit-learn compatible API</div>
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<li class="navelem"><a class="el" href="dir_d44c64559bbebec7f509842c48db8b23.html">include</a></li><li class="navelem"><a class="el" href="dir_daf582bc00f2bbc6516ddb6630e28009.html">svm_classifier</a></li> </ul>
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<div class="headertitle"><div class="title">kernel_parameters.hpp</div></div>
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<div class="contents">
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<div class="fragment"><div class="line"><a id="l00001" name="l00001"></a><span class="lineno"> 1</span><span class="preprocessor">#pragma once</span></div>
|
||||||
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<div class="line"><a id="l00002" name="l00002"></a><span class="lineno"> 2</span> </div>
|
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<div class="line"><a id="l00003" name="l00003"></a><span class="lineno"> 3</span><span class="preprocessor">#include "types.hpp"</span></div>
|
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<div class="line"><a id="l00004" name="l00004"></a><span class="lineno"> 4</span><span class="preprocessor">#include <torch/torch.h></span></div>
|
||||||
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<div class="line"><a id="l00005" name="l00005"></a><span class="lineno"> 5</span><span class="preprocessor">#include <vector></span></div>
|
||||||
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<div class="line"><a id="l00006" name="l00006"></a><span class="lineno"> 6</span><span class="preprocessor">#include <memory></span></div>
|
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<div class="line"><a id="l00007" name="l00007"></a><span class="lineno"> 7</span> </div>
|
||||||
|
<div class="line"><a id="l00008" name="l00008"></a><span class="lineno"> 8</span><span class="comment">// Forward declarations for libsvm and liblinear structures</span></div>
|
||||||
|
<div class="line"><a id="l00009" name="l00009"></a><span class="lineno"> 9</span><span class="keyword">struct </span>svm_node;</div>
|
||||||
|
<div class="line"><a id="l00010" name="l00010"></a><span class="lineno"> 10</span><span class="keyword">struct </span>svm_problem;</div>
|
||||||
|
<div class="line"><a id="l00011" name="l00011"></a><span class="lineno"> 11</span><span class="keyword">struct </span>feature_node;</div>
|
||||||
|
<div class="line"><a id="l00012" name="l00012"></a><span class="lineno"> 12</span><span class="keyword">struct </span>problem;</div>
|
||||||
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<div class="line"><a id="l00013" name="l00013"></a><span class="lineno"> 13</span> </div>
|
||||||
|
<div class="line"><a id="l00014" name="l00014"></a><span class="lineno"> 14</span><span class="keyword">namespace </span>svm_classifier {</div>
|
||||||
|
<div class="line"><a id="l00015" name="l00015"></a><span class="lineno"> 15</span> </div>
|
||||||
|
<div class="line"><a id="l00023" name="l00023"></a><span class="lineno"> 23</span> <span class="keyword">class </span>DataConverter {</div>
|
||||||
|
<div class="line"><a id="l00024" name="l00024"></a><span class="lineno"> 24</span> <span class="keyword">public</span>:</div>
|
||||||
|
<div class="line"><a id="l00028" name="l00028"></a><span class="lineno"><a class="line" href="classsvm__classifier_1_1DataConverter.html#a5874904555f26448ed5ae4cf6f370056"> 28</a></span> <a class="code hl_function" href="classsvm__classifier_1_1DataConverter.html#a5874904555f26448ed5ae4cf6f370056">DataConverter</a>();</div>
|
||||||
|
<div class="line"><a id="l00029" name="l00029"></a><span class="lineno"> 29</span> </div>
|
||||||
|
<div class="line"><a id="l00033" name="l00033"></a><span class="lineno"><a class="line" href="classsvm__classifier_1_1DataConverter.html#ac3af2c9c03cffe2968f29147611e333d"> 33</a></span> <a class="code hl_function" href="classsvm__classifier_1_1DataConverter.html#ac3af2c9c03cffe2968f29147611e333d">~DataConverter</a>();</div>
|
||||||
|
<div class="line"><a id="l00034" name="l00034"></a><span class="lineno"> 34</span> </div>
|
||||||
|
<div class="line"><a id="l00041" name="l00041"></a><span class="lineno"><a class="line" href="classsvm__classifier_1_1DataConverter.html#a66f446e4decfe47bbba37c789f03f729"> 41</a></span> std::unique_ptr<svm_problem> <a class="code hl_function" href="classsvm__classifier_1_1DataConverter.html#a66f446e4decfe47bbba37c789f03f729">to_svm_problem</a>(<span class="keyword">const</span> torch::Tensor& X,</div>
|
||||||
|
<div class="line"><a id="l00042" name="l00042"></a><span class="lineno"> 42</span> <span class="keyword">const</span> torch::Tensor& y = torch::Tensor());</div>
|
||||||
|
<div class="line"><a id="l00043" name="l00043"></a><span class="lineno"> 43</span> </div>
|
||||||
|
<div class="line"><a id="l00050" name="l00050"></a><span class="lineno"><a class="line" href="classsvm__classifier_1_1DataConverter.html#a7e7d8f6102b7a9b3256ff0dc6f536a35"> 50</a></span> std::unique_ptr<problem> <a class="code hl_function" href="classsvm__classifier_1_1DataConverter.html#a7e7d8f6102b7a9b3256ff0dc6f536a35">to_linear_problem</a>(<span class="keyword">const</span> torch::Tensor& X,</div>
|
||||||
|
<div class="line"><a id="l00051" name="l00051"></a><span class="lineno"> 51</span> <span class="keyword">const</span> torch::Tensor& y = torch::Tensor());</div>
|
||||||
|
<div class="line"><a id="l00052" name="l00052"></a><span class="lineno"> 52</span> </div>
|
||||||
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<div class="line"><a id="l00058" name="l00058"></a><span class="lineno"><a class="line" href="classsvm__classifier_1_1DataConverter.html#a16e1539ef1266ca9ddd27a2ac5a53b92"> 58</a></span> svm_node* <a class="code hl_function" href="classsvm__classifier_1_1DataConverter.html#a16e1539ef1266ca9ddd27a2ac5a53b92">to_svm_node</a>(<span class="keyword">const</span> torch::Tensor& sample);</div>
|
||||||
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<div class="line"><a id="l00059" name="l00059"></a><span class="lineno"> 59</span> </div>
|
||||||
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<div class="line"><a id="l00065" name="l00065"></a><span class="lineno"><a class="line" href="classsvm__classifier_1_1DataConverter.html#a6bb2b4565b27df0db5f229dbd380795e"> 65</a></span> feature_node* <a class="code hl_function" href="classsvm__classifier_1_1DataConverter.html#a6bb2b4565b27df0db5f229dbd380795e">to_feature_node</a>(<span class="keyword">const</span> torch::Tensor& sample);</div>
|
||||||
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<div class="line"><a id="l00066" name="l00066"></a><span class="lineno"> 66</span> </div>
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||||||
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<div class="line"><a id="l00072" name="l00072"></a><span class="lineno"><a class="line" href="classsvm__classifier_1_1DataConverter.html#ab3e800a5016a915e9912d5873bb48741"> 72</a></span> torch::Tensor <a class="code hl_function" href="classsvm__classifier_1_1DataConverter.html#ab3e800a5016a915e9912d5873bb48741">from_predictions</a>(<span class="keyword">const</span> std::vector<double>& predictions);</div>
|
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<div class="line"><a id="l00073" name="l00073"></a><span class="lineno"> 73</span> </div>
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||||||
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<div class="line"><a id="l00079" name="l00079"></a><span class="lineno"><a class="line" href="classsvm__classifier_1_1DataConverter.html#a5460485675613c54596418af3d5057ff"> 79</a></span> torch::Tensor <a class="code hl_function" href="classsvm__classifier_1_1DataConverter.html#a5460485675613c54596418af3d5057ff">from_probabilities</a>(<span class="keyword">const</span> std::vector<std::vector<double>>& probabilities);</div>
|
||||||
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<div class="line"><a id="l00080" name="l00080"></a><span class="lineno"> 80</span> </div>
|
||||||
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<div class="line"><a id="l00086" name="l00086"></a><span class="lineno"><a class="line" href="classsvm__classifier_1_1DataConverter.html#a503eba54e8bb1f370e04b6e24354a32f"> 86</a></span> torch::Tensor <a class="code hl_function" href="classsvm__classifier_1_1DataConverter.html#a503eba54e8bb1f370e04b6e24354a32f">from_decision_values</a>(<span class="keyword">const</span> std::vector<std::vector<double>>& decision_values);</div>
|
||||||
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<div class="line"><a id="l00087" name="l00087"></a><span class="lineno"> 87</span> </div>
|
||||||
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<div class="line"><a id="l00094" name="l00094"></a><span class="lineno"><a class="line" href="classsvm__classifier_1_1DataConverter.html#aa0615f3de29958b2c5229d349f2f60ce"> 94</a></span> <span class="keywordtype">void</span> <a class="code hl_function" href="classsvm__classifier_1_1DataConverter.html#aa0615f3de29958b2c5229d349f2f60ce">validate_tensors</a>(<span class="keyword">const</span> torch::Tensor& X, <span class="keyword">const</span> torch::Tensor& y = torch::Tensor());</div>
|
||||||
|
<div class="line"><a id="l00095" name="l00095"></a><span class="lineno"> 95</span> </div>
|
||||||
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<div class="line"><a id="l00100" name="l00100"></a><span class="lineno"><a class="line" href="classsvm__classifier_1_1DataConverter.html#a26342a8cc8b943f099112040aa960ae6"> 100</a></span> <span class="keywordtype">int</span> <a class="code hl_function" href="classsvm__classifier_1_1DataConverter.html#a26342a8cc8b943f099112040aa960ae6">get_n_features</a>()<span class="keyword"> const </span>{ <span class="keywordflow">return</span> n_features_; }</div>
|
||||||
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<div class="line"><a id="l00101" name="l00101"></a><span class="lineno"> 101</span> </div>
|
||||||
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<div class="line"><a id="l00106" name="l00106"></a><span class="lineno"><a class="line" href="classsvm__classifier_1_1DataConverter.html#a16999ded27bc2d42d2ebd9551b00c1cb"> 106</a></span> <span class="keywordtype">int</span> <a class="code hl_function" href="classsvm__classifier_1_1DataConverter.html#a16999ded27bc2d42d2ebd9551b00c1cb">get_n_samples</a>()<span class="keyword"> const </span>{ <span class="keywordflow">return</span> n_samples_; }</div>
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<div class="line"><a id="l00107" name="l00107"></a><span class="lineno"> 107</span> </div>
|
||||||
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<div class="line"><a id="l00111" name="l00111"></a><span class="lineno"><a class="line" href="classsvm__classifier_1_1DataConverter.html#a46d12ba28c4c5bf6e0fad1122c621fa8"> 111</a></span> <span class="keywordtype">void</span> <a class="code hl_function" href="classsvm__classifier_1_1DataConverter.html#a46d12ba28c4c5bf6e0fad1122c621fa8">cleanup</a>();</div>
|
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<div class="line"><a id="l00112" name="l00112"></a><span class="lineno"> 112</span> </div>
|
||||||
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<div class="line"><a id="l00117" name="l00117"></a><span class="lineno"><a class="line" href="classsvm__classifier_1_1DataConverter.html#a9259322e54d2477478a684e99d8e557a"> 117</a></span> <span class="keywordtype">void</span> <a class="code hl_function" href="classsvm__classifier_1_1DataConverter.html#a9259322e54d2477478a684e99d8e557a">set_sparse_threshold</a>(<span class="keywordtype">double</span> threshold) { sparse_threshold_ = threshold; }</div>
|
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<div class="line"><a id="l00118" name="l00118"></a><span class="lineno"> 118</span> </div>
|
||||||
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<div class="line"><a id="l00123" name="l00123"></a><span class="lineno"><a class="line" href="classsvm__classifier_1_1DataConverter.html#a1905f60fef9ffd3a8e8e45e41395352d"> 123</a></span> <span class="keywordtype">double</span> <a class="code hl_function" href="classsvm__classifier_1_1DataConverter.html#a1905f60fef9ffd3a8e8e45e41395352d">get_sparse_threshold</a>()<span class="keyword"> const </span>{ <span class="keywordflow">return</span> sparse_threshold_; }</div>
|
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<div class="line"><a id="l00124" name="l00124"></a><span class="lineno"> 124</span> </div>
|
||||||
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<div class="line"><a id="l00125" name="l00125"></a><span class="lineno"> 125</span> <span class="keyword">private</span>:</div>
|
||||||
|
<div class="line"><a id="l00126" name="l00126"></a><span class="lineno"> 126</span> <span class="keywordtype">int</span> n_features_; </div>
|
||||||
|
<div class="line"><a id="l00127" name="l00127"></a><span class="lineno"> 127</span> <span class="keywordtype">int</span> n_samples_; </div>
|
||||||
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<div class="line"><a id="l00128" name="l00128"></a><span class="lineno"> 128</span> <span class="keywordtype">double</span> sparse_threshold_; </div>
|
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<div class="line"><a id="l00129" name="l00129"></a><span class="lineno"> 129</span> </div>
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||||||
|
<div class="line"><a id="l00130" name="l00130"></a><span class="lineno"> 130</span> <span class="comment">// Memory management for libsvm structures</span></div>
|
||||||
|
<div class="line"><a id="l00131" name="l00131"></a><span class="lineno"> 131</span> std::vector<std::vector<svm_node>> svm_nodes_storage_;</div>
|
||||||
|
<div class="line"><a id="l00132" name="l00132"></a><span class="lineno"> 132</span> std::vector<svm_node*> svm_x_space_;</div>
|
||||||
|
<div class="line"><a id="l00133" name="l00133"></a><span class="lineno"> 133</span> std::vector<double> svm_y_space_;</div>
|
||||||
|
<div class="line"><a id="l00134" name="l00134"></a><span class="lineno"> 134</span> </div>
|
||||||
|
<div class="line"><a id="l00135" name="l00135"></a><span class="lineno"> 135</span> <span class="comment">// Memory management for liblinear structures</span></div>
|
||||||
|
<div class="line"><a id="l00136" name="l00136"></a><span class="lineno"> 136</span> std::vector<std::vector<feature_node>> linear_nodes_storage_;</div>
|
||||||
|
<div class="line"><a id="l00137" name="l00137"></a><span class="lineno"> 137</span> std::vector<feature_node*> linear_x_space_;</div>
|
||||||
|
<div class="line"><a id="l00138" name="l00138"></a><span class="lineno"> 138</span> std::vector<double> linear_y_space_;</div>
|
||||||
|
<div class="line"><a id="l00139" name="l00139"></a><span class="lineno"> 139</span> </div>
|
||||||
|
<div class="line"><a id="l00140" name="l00140"></a><span class="lineno"> 140</span> <span class="comment">// Single sample storage (for prediction)</span></div>
|
||||||
|
<div class="line"><a id="l00141" name="l00141"></a><span class="lineno"> 141</span> std::vector<svm_node> single_svm_nodes_;</div>
|
||||||
|
<div class="line"><a id="l00142" name="l00142"></a><span class="lineno"> 142</span> std::vector<feature_node> single_linear_nodes_;</div>
|
||||||
|
<div class="line"><a id="l00143" name="l00143"></a><span class="lineno"> 143</span> </div>
|
||||||
|
<div class="line"><a id="l00149" name="l00149"></a><span class="lineno"> 149</span> std::vector<std::vector<svm_node>> tensor_to_svm_nodes(<span class="keyword">const</span> torch::Tensor& X);</div>
|
||||||
|
<div class="line"><a id="l00150" name="l00150"></a><span class="lineno"> 150</span> </div>
|
||||||
|
<div class="line"><a id="l00156" name="l00156"></a><span class="lineno"> 156</span> std::vector<std::vector<feature_node>> tensor_to_linear_nodes(<span class="keyword">const</span> torch::Tensor& X);</div>
|
||||||
|
<div class="line"><a id="l00157" name="l00157"></a><span class="lineno"> 157</span> </div>
|
||||||
|
<div class="line"><a id="l00163" name="l00163"></a><span class="lineno"> 163</span> std::vector<svm_node> sample_to_svm_nodes(<span class="keyword">const</span> torch::Tensor& sample);</div>
|
||||||
|
<div class="line"><a id="l00164" name="l00164"></a><span class="lineno"> 164</span> </div>
|
||||||
|
<div class="line"><a id="l00170" name="l00170"></a><span class="lineno"> 170</span> std::vector<feature_node> sample_to_linear_nodes(<span class="keyword">const</span> torch::Tensor& sample);</div>
|
||||||
|
<div class="line"><a id="l00171" name="l00171"></a><span class="lineno"> 171</span> </div>
|
||||||
|
<div class="line"><a id="l00177" name="l00177"></a><span class="lineno"> 177</span> std::vector<double> extract_labels(<span class="keyword">const</span> torch::Tensor& y);</div>
|
||||||
|
<div class="line"><a id="l00178" name="l00178"></a><span class="lineno"> 178</span> </div>
|
||||||
|
<div class="line"><a id="l00184" name="l00184"></a><span class="lineno"> 184</span> torch::Tensor ensure_cpu_tensor(<span class="keyword">const</span> torch::Tensor& tensor);</div>
|
||||||
|
<div class="line"><a id="l00185" name="l00185"></a><span class="lineno"> 185</span> </div>
|
||||||
|
<div class="line"><a id="l00192" name="l00192"></a><span class="lineno"> 192</span> <span class="keywordtype">void</span> validate_tensor_properties(<span class="keyword">const</span> torch::Tensor& tensor, <span class="keywordtype">int</span> expected_dims, <span class="keyword">const</span> std::string& name);</div>
|
||||||
|
<div class="line"><a id="l00193" name="l00193"></a><span class="lineno"> 193</span> };</div>
|
||||||
|
<div class="line"><a id="l00194" name="l00194"></a><span class="lineno"> 194</span> </div>
|
||||||
|
<div class="line"><a id="l00195" name="l00195"></a><span class="lineno"> 195</span>} <span class="comment">// namespace svm_classifier</span></div>
|
||||||
|
<div class="ttc" id="aclasssvm__classifier_1_1DataConverter_html_a16999ded27bc2d42d2ebd9551b00c1cb"><div class="ttname"><a href="classsvm__classifier_1_1DataConverter.html#a16999ded27bc2d42d2ebd9551b00c1cb">svm_classifier::DataConverter::get_n_samples</a></div><div class="ttdeci">int get_n_samples() const</div><div class="ttdoc">Get number of samples from last conversion.</div><div class="ttdef"><b>Definition</b> <a href="kernel__parameters_8hpp_source.html#l00106">kernel_parameters.hpp:106</a></div></div>
|
||||||
|
<div class="ttc" id="aclasssvm__classifier_1_1DataConverter_html_a16e1539ef1266ca9ddd27a2ac5a53b92"><div class="ttname"><a href="classsvm__classifier_1_1DataConverter.html#a16e1539ef1266ca9ddd27a2ac5a53b92">svm_classifier::DataConverter::to_svm_node</a></div><div class="ttdeci">svm_node * to_svm_node(const torch::Tensor &sample)</div><div class="ttdoc">Convert single sample to libsvm format.</div></div>
|
||||||
|
<div class="ttc" id="aclasssvm__classifier_1_1DataConverter_html_a1905f60fef9ffd3a8e8e45e41395352d"><div class="ttname"><a href="classsvm__classifier_1_1DataConverter.html#a1905f60fef9ffd3a8e8e45e41395352d">svm_classifier::DataConverter::get_sparse_threshold</a></div><div class="ttdeci">double get_sparse_threshold() const</div><div class="ttdoc">Get sparse threshold.</div><div class="ttdef"><b>Definition</b> <a href="kernel__parameters_8hpp_source.html#l00123">kernel_parameters.hpp:123</a></div></div>
|
||||||
|
<div class="ttc" id="aclasssvm__classifier_1_1DataConverter_html_a26342a8cc8b943f099112040aa960ae6"><div class="ttname"><a href="classsvm__classifier_1_1DataConverter.html#a26342a8cc8b943f099112040aa960ae6">svm_classifier::DataConverter::get_n_features</a></div><div class="ttdeci">int get_n_features() const</div><div class="ttdoc">Get number of features from last conversion.</div><div class="ttdef"><b>Definition</b> <a href="kernel__parameters_8hpp_source.html#l00100">kernel_parameters.hpp:100</a></div></div>
|
||||||
|
<div class="ttc" id="aclasssvm__classifier_1_1DataConverter_html_a46d12ba28c4c5bf6e0fad1122c621fa8"><div class="ttname"><a href="classsvm__classifier_1_1DataConverter.html#a46d12ba28c4c5bf6e0fad1122c621fa8">svm_classifier::DataConverter::cleanup</a></div><div class="ttdeci">void cleanup()</div><div class="ttdoc">Clean up all allocated memory.</div></div>
|
||||||
|
<div class="ttc" id="aclasssvm__classifier_1_1DataConverter_html_a503eba54e8bb1f370e04b6e24354a32f"><div class="ttname"><a href="classsvm__classifier_1_1DataConverter.html#a503eba54e8bb1f370e04b6e24354a32f">svm_classifier::DataConverter::from_decision_values</a></div><div class="ttdeci">torch::Tensor from_decision_values(const std::vector< std::vector< double > > &decision_values)</div><div class="ttdoc">Convert decision values back to PyTorch tensor.</div></div>
|
||||||
|
<div class="ttc" id="aclasssvm__classifier_1_1DataConverter_html_a5460485675613c54596418af3d5057ff"><div class="ttname"><a href="classsvm__classifier_1_1DataConverter.html#a5460485675613c54596418af3d5057ff">svm_classifier::DataConverter::from_probabilities</a></div><div class="ttdeci">torch::Tensor from_probabilities(const std::vector< std::vector< double > > &probabilities)</div><div class="ttdoc">Convert probabilities back to PyTorch tensor.</div></div>
|
||||||
|
<div class="ttc" id="aclasssvm__classifier_1_1DataConverter_html_a5874904555f26448ed5ae4cf6f370056"><div class="ttname"><a href="classsvm__classifier_1_1DataConverter.html#a5874904555f26448ed5ae4cf6f370056">svm_classifier::DataConverter::DataConverter</a></div><div class="ttdeci">DataConverter()</div><div class="ttdoc">Default constructor.</div></div>
|
||||||
|
<div class="ttc" id="aclasssvm__classifier_1_1DataConverter_html_a66f446e4decfe47bbba37c789f03f729"><div class="ttname"><a href="classsvm__classifier_1_1DataConverter.html#a66f446e4decfe47bbba37c789f03f729">svm_classifier::DataConverter::to_svm_problem</a></div><div class="ttdeci">std::unique_ptr< svm_problem > to_svm_problem(const torch::Tensor &X, const torch::Tensor &y=torch::Tensor())</div><div class="ttdoc">Convert PyTorch tensors to libsvm format.</div></div>
|
||||||
|
<div class="ttc" id="aclasssvm__classifier_1_1DataConverter_html_a6bb2b4565b27df0db5f229dbd380795e"><div class="ttname"><a href="classsvm__classifier_1_1DataConverter.html#a6bb2b4565b27df0db5f229dbd380795e">svm_classifier::DataConverter::to_feature_node</a></div><div class="ttdeci">feature_node * to_feature_node(const torch::Tensor &sample)</div><div class="ttdoc">Convert single sample to liblinear format.</div></div>
|
||||||
|
<div class="ttc" id="aclasssvm__classifier_1_1DataConverter_html_a7e7d8f6102b7a9b3256ff0dc6f536a35"><div class="ttname"><a href="classsvm__classifier_1_1DataConverter.html#a7e7d8f6102b7a9b3256ff0dc6f536a35">svm_classifier::DataConverter::to_linear_problem</a></div><div class="ttdeci">std::unique_ptr< problem > to_linear_problem(const torch::Tensor &X, const torch::Tensor &y=torch::Tensor())</div><div class="ttdoc">Convert PyTorch tensors to liblinear format.</div></div>
|
||||||
|
<div class="ttc" id="aclasssvm__classifier_1_1DataConverter_html_a9259322e54d2477478a684e99d8e557a"><div class="ttname"><a href="classsvm__classifier_1_1DataConverter.html#a9259322e54d2477478a684e99d8e557a">svm_classifier::DataConverter::set_sparse_threshold</a></div><div class="ttdeci">void set_sparse_threshold(double threshold)</div><div class="ttdoc">Set sparse threshold (features with absolute value below this are ignored)</div><div class="ttdef"><b>Definition</b> <a href="kernel__parameters_8hpp_source.html#l00117">kernel_parameters.hpp:117</a></div></div>
|
||||||
|
<div class="ttc" id="aclasssvm__classifier_1_1DataConverter_html_aa0615f3de29958b2c5229d349f2f60ce"><div class="ttname"><a href="classsvm__classifier_1_1DataConverter.html#aa0615f3de29958b2c5229d349f2f60ce">svm_classifier::DataConverter::validate_tensors</a></div><div class="ttdeci">void validate_tensors(const torch::Tensor &X, const torch::Tensor &y=torch::Tensor())</div><div class="ttdoc">Validate input tensors.</div></div>
|
||||||
|
<div class="ttc" id="aclasssvm__classifier_1_1DataConverter_html_ab3e800a5016a915e9912d5873bb48741"><div class="ttname"><a href="classsvm__classifier_1_1DataConverter.html#ab3e800a5016a915e9912d5873bb48741">svm_classifier::DataConverter::from_predictions</a></div><div class="ttdeci">torch::Tensor from_predictions(const std::vector< double > &predictions)</div><div class="ttdoc">Convert predictions back to PyTorch tensor.</div></div>
|
||||||
|
<div class="ttc" id="aclasssvm__classifier_1_1DataConverter_html_ac3af2c9c03cffe2968f29147611e333d"><div class="ttname"><a href="classsvm__classifier_1_1DataConverter.html#ac3af2c9c03cffe2968f29147611e333d">svm_classifier::DataConverter::~DataConverter</a></div><div class="ttdeci">~DataConverter()</div><div class="ttdoc">Destructor - cleans up allocated memory.</div></div>
|
||||||
|
</div><!-- fragment --></div><!-- contents -->
|
||||||
|
<!-- start footer part -->
|
||||||
|
<hr class="footer"/><address class="footer"><small>
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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
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|
</small></address>
|
||||||
|
</body>
|
||||||
|
</html>
|
||||||
136
menu.js
Normal file
136
menu.js
Normal file
@@ -0,0 +1,136 @@
|
|||||||
|
/*
|
||||||
|
@licstart The following is the entire license notice for the JavaScript code in this file.
|
||||||
|
|
||||||
|
The MIT License (MIT)
|
||||||
|
|
||||||
|
Copyright (C) 1997-2020 by Dimitri van Heesch
|
||||||
|
|
||||||
|
Permission is hereby granted, free of charge, to any person obtaining a copy of this software
|
||||||
|
and associated documentation files (the "Software"), to deal in the Software without restriction,
|
||||||
|
including without limitation the rights to use, copy, modify, merge, publish, distribute,
|
||||||
|
sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is
|
||||||
|
furnished to do so, subject to the following conditions:
|
||||||
|
|
||||||
|
The above copyright notice and this permission notice shall be included in all copies or
|
||||||
|
substantial portions of the Software.
|
||||||
|
|
||||||
|
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING
|
||||||
|
BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
|
||||||
|
NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM,
|
||||||
|
DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
|
||||||
|
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
|
||||||
|
|
||||||
|
@licend The above is the entire license notice for the JavaScript code in this file
|
||||||
|
*/
|
||||||
|
function initMenu(relPath,searchEnabled,serverSide,searchPage,search) {
|
||||||
|
function makeTree(data,relPath) {
|
||||||
|
var result='';
|
||||||
|
if ('children' in data) {
|
||||||
|
result+='<ul>';
|
||||||
|
for (var i in data.children) {
|
||||||
|
var url;
|
||||||
|
var link;
|
||||||
|
link = data.children[i].url;
|
||||||
|
if (link.substring(0,1)=='^') {
|
||||||
|
url = link.substring(1);
|
||||||
|
} else {
|
||||||
|
url = relPath+link;
|
||||||
|
}
|
||||||
|
result+='<li><a href="'+url+'">'+
|
||||||
|
data.children[i].text+'</a>'+
|
||||||
|
makeTree(data.children[i],relPath)+'</li>';
|
||||||
|
}
|
||||||
|
result+='</ul>';
|
||||||
|
}
|
||||||
|
return result;
|
||||||
|
}
|
||||||
|
var searchBoxHtml;
|
||||||
|
if (searchEnabled) {
|
||||||
|
if (serverSide) {
|
||||||
|
searchBoxHtml='<div id="MSearchBox" class="MSearchBoxInactive">'+
|
||||||
|
'<div class="left">'+
|
||||||
|
'<form id="FSearchBox" action="'+relPath+searchPage+
|
||||||
|
'" method="get"><span id="MSearchSelectExt"> </span>'+
|
||||||
|
'<input type="text" id="MSearchField" name="query" value="" placeholder="'+search+
|
||||||
|
'" size="20" accesskey="S" onfocus="searchBox.OnSearchFieldFocus(true)"'+
|
||||||
|
' onblur="searchBox.OnSearchFieldFocus(false)"/>'+
|
||||||
|
'</form>'+
|
||||||
|
'</div>'+
|
||||||
|
'<div class="right"></div>'+
|
||||||
|
'</div>';
|
||||||
|
} else {
|
||||||
|
searchBoxHtml='<div id="MSearchBox" class="MSearchBoxInactive">'+
|
||||||
|
'<span class="left">'+
|
||||||
|
'<span id="MSearchSelect" onmouseover="return searchBox.OnSearchSelectShow()"'+
|
||||||
|
' onmouseout="return searchBox.OnSearchSelectHide()"> </span>'+
|
||||||
|
'<input type="text" id="MSearchField" value="" placeholder="'+search+
|
||||||
|
'" accesskey="S" onfocus="searchBox.OnSearchFieldFocus(true)" '+
|
||||||
|
'onblur="searchBox.OnSearchFieldFocus(false)" '+
|
||||||
|
'onkeyup="searchBox.OnSearchFieldChange(event)"/>'+
|
||||||
|
'</span>'+
|
||||||
|
'<span class="right"><a id="MSearchClose" '+
|
||||||
|
'href="javascript:searchBox.CloseResultsWindow()">'+
|
||||||
|
'<img id="MSearchCloseImg" border="0" src="'+relPath+
|
||||||
|
'search/close.svg" alt=""/></a>'+
|
||||||
|
'</span>'+
|
||||||
|
'</div>';
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
$('#main-nav').before('<div class="sm sm-dox"><input id="main-menu-state" type="checkbox"/>'+
|
||||||
|
'<label class="main-menu-btn" for="main-menu-state">'+
|
||||||
|
'<span class="main-menu-btn-icon"></span> '+
|
||||||
|
'Toggle main menu visibility</label>'+
|
||||||
|
'<span id="searchBoxPos1" style="position:absolute;right:8px;top:8px;height:36px;"></span>'+
|
||||||
|
'</div>');
|
||||||
|
$('#main-nav').append(makeTree(menudata,relPath));
|
||||||
|
$('#main-nav').children(':first').addClass('sm sm-dox').attr('id','main-menu');
|
||||||
|
if (searchBoxHtml) {
|
||||||
|
$('#main-menu').append('<li id="searchBoxPos2" style="float:right"></li>');
|
||||||
|
}
|
||||||
|
var $mainMenuState = $('#main-menu-state');
|
||||||
|
var prevWidth = 0;
|
||||||
|
if ($mainMenuState.length) {
|
||||||
|
function initResizableIfExists() {
|
||||||
|
if (typeof initResizable==='function') initResizable();
|
||||||
|
}
|
||||||
|
// animate mobile menu
|
||||||
|
$mainMenuState.change(function(e) {
|
||||||
|
var $menu = $('#main-menu');
|
||||||
|
var options = { duration: 250, step: initResizableIfExists };
|
||||||
|
if (this.checked) {
|
||||||
|
options['complete'] = function() { $menu.css('display', 'block') };
|
||||||
|
$menu.hide().slideDown(options);
|
||||||
|
} else {
|
||||||
|
options['complete'] = function() { $menu.css('display', 'none') };
|
||||||
|
$menu.show().slideUp(options);
|
||||||
|
}
|
||||||
|
});
|
||||||
|
// set default menu visibility
|
||||||
|
function resetState() {
|
||||||
|
var $menu = $('#main-menu');
|
||||||
|
var $mainMenuState = $('#main-menu-state');
|
||||||
|
var newWidth = $(window).outerWidth();
|
||||||
|
if (newWidth!=prevWidth) {
|
||||||
|
if ($(window).outerWidth()<768) {
|
||||||
|
$mainMenuState.prop('checked',false); $menu.hide();
|
||||||
|
$('#searchBoxPos1').html(searchBoxHtml);
|
||||||
|
$('#searchBoxPos2').hide();
|
||||||
|
} else {
|
||||||
|
$menu.show();
|
||||||
|
$('#searchBoxPos1').empty();
|
||||||
|
$('#searchBoxPos2').html(searchBoxHtml);
|
||||||
|
$('#searchBoxPos2').show();
|
||||||
|
}
|
||||||
|
if (typeof searchBox!=='undefined') {
|
||||||
|
searchBox.CloseResultsWindow();
|
||||||
|
}
|
||||||
|
prevWidth = newWidth;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
$(window).ready(function() { resetState(); initResizableIfExists(); });
|
||||||
|
$(window).resize(resetState);
|
||||||
|
}
|
||||||
|
$('#main-menu').smartmenus();
|
||||||
|
}
|
||||||
|
/* @license-end */
|
||||||
66
menudata.js
Normal file
66
menudata.js
Normal file
@@ -0,0 +1,66 @@
|
|||||||
|
/*
|
||||||
|
@licstart The following is the entire license notice for the JavaScript code in this file.
|
||||||
|
|
||||||
|
The MIT License (MIT)
|
||||||
|
|
||||||
|
Copyright (C) 1997-2020 by Dimitri van Heesch
|
||||||
|
|
||||||
|
Permission is hereby granted, free of charge, to any person obtaining a copy of this software
|
||||||
|
and associated documentation files (the "Software"), to deal in the Software without restriction,
|
||||||
|
including without limitation the rights to use, copy, modify, merge, publish, distribute,
|
||||||
|
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<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>
|
||||||
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<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>
|
||||||
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<div class="line"><a id="l00004" name="l00004"></a><span class="lineno"> 4</span><span class="preprocessor">#include <algorithm></span></div>
|
||||||
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<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>
|
||||||
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<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>
|
||||||
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<div class="line"><a id="l00009" name="l00009"></a><span class="lineno"> 9</span> </div>
|
||||||
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<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>
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<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>
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<div class="line"><a id="l00470" name="l00470"></a><span class="lineno"> 470</span> </div>
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<div class="line"><a id="l00471" name="l00471"></a><span class="lineno"> 471</span> <span class="keywordflow">if</span> (decision > 0) {</div>
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<div class="line"><a id="l00472" name="l00472"></a><span class="lineno"> 472</span> class_scores[idx1] += 1.0;</div>
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<div class="line"><a id="l00473" name="l00473"></a><span class="lineno"> 473</span> } <span class="keywordflow">else</span> {</div>
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<div class="line"><a id="l00474" name="l00474"></a><span class="lineno"> 474</span> class_scores[idx2] += 1.0;</div>
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<div class="line"><a id="l00475" name="l00475"></a><span class="lineno"> 475</span> }</div>
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<div class="line"><a id="l00476" name="l00476"></a><span class="lineno"> 476</span> }</div>
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<div class="line"><a id="l00477" name="l00477"></a><span class="lineno"> 477</span> }</div>
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<div class="line"><a id="l00478" name="l00478"></a><span class="lineno"> 478</span> </div>
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<div class="line"><a id="l00479" name="l00479"></a><span class="lineno"> 479</span> <span class="comment">// Convert scores to probabilities</span></div>
|
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<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>
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<div class="line"><a id="l00481" name="l00481"></a><span class="lineno"> 481</span> <span class="keywordflow">if</span> (sum > 0.0) {</div>
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<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>
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<div class="line"><a id="l00483" name="l00483"></a><span class="lineno"> 483</span> score /= sum;</div>
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<div class="line"><a id="l00484" name="l00484"></a><span class="lineno"> 484</span> }</div>
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<div class="line"><a id="l00485" name="l00485"></a><span class="lineno"> 485</span> } <span class="keywordflow">else</span> {</div>
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<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>
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<div class="line"><a id="l00487" name="l00487"></a><span class="lineno"> 487</span> }</div>
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<div class="line"><a id="l00488" name="l00488"></a><span class="lineno"> 488</span> </div>
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<div class="line"><a id="l00489" name="l00489"></a><span class="lineno"> 489</span> probabilities.push_back(class_scores);</div>
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<div class="line"><a id="l00490" name="l00490"></a><span class="lineno"> 490</span> }</div>
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<div class="line"><a id="l00491" name="l00491"></a><span class="lineno"> 491</span> </div>
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<div class="line"><a id="l00492" name="l00492"></a><span class="lineno"> 492</span> <span class="keywordflow">return</span> probabilities;</div>
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<div class="line"><a id="l00493" name="l00493"></a><span class="lineno"> 493</span> }</div>
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<div class="line"><a id="l00494" name="l00494"></a><span class="lineno"> 494</span> </div>
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<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>
|
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<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>
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281
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<div id="projectname">SVM Classifier C++<span id="projectnumber"> 1.0.0</span>
|
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</div>
|
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<div id="projectbrief">High-performance Support Vector Machine classifier with scikit-learn compatible API</div>
|
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<li class="navelem"><a class="el" href="dir_d44c64559bbebec7f509842c48db8b23.html">include</a></li><li class="navelem"><a class="el" href="dir_daf582bc00f2bbc6516ddb6630e28009.html">svm_classifier</a></li> </ul>
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<div class="headertitle"><div class="title">multiclass_strategy.hpp</div></div>
|
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|
||||||
|
<div class="contents">
|
||||||
|
<div class="fragment"><div class="line"><a id="l00001" name="l00001"></a><span class="lineno"> 1</span><span class="preprocessor">#pragma once</span></div>
|
||||||
|
<div class="line"><a id="l00002" name="l00002"></a><span class="lineno"> 2</span> </div>
|
||||||
|
<div class="line"><a id="l00003" name="l00003"></a><span class="lineno"> 3</span><span class="preprocessor">#include "types.hpp"</span></div>
|
||||||
|
<div class="line"><a id="l00004" name="l00004"></a><span class="lineno"> 4</span><span class="preprocessor">#include "kernel_parameters.hpp"</span></div>
|
||||||
|
<div class="line"><a id="l00005" name="l00005"></a><span class="lineno"> 5</span><span class="preprocessor">#include "data_converter.hpp"</span></div>
|
||||||
|
<div class="line"><a id="l00006" name="l00006"></a><span class="lineno"> 6</span><span class="preprocessor">#include <torch/torch.h></span></div>
|
||||||
|
<div class="line"><a id="l00007" name="l00007"></a><span class="lineno"> 7</span><span class="preprocessor">#include <vector></span></div>
|
||||||
|
<div class="line"><a id="l00008" name="l00008"></a><span class="lineno"> 8</span><span class="preprocessor">#include <memory></span></div>
|
||||||
|
<div class="line"><a id="l00009" name="l00009"></a><span class="lineno"> 9</span><span class="preprocessor">#include <unordered_map></span></div>
|
||||||
|
<div class="line"><a id="l00010" name="l00010"></a><span class="lineno"> 10</span> </div>
|
||||||
|
<div class="line"><a id="l00011" name="l00011"></a><span class="lineno"> 11</span><span class="comment">// Forward declarations</span></div>
|
||||||
|
<div class="line"><a id="l00012" name="l00012"></a><span class="lineno"> 12</span><span class="keyword">struct </span>svm_model;</div>
|
||||||
|
<div class="line"><a id="l00013" name="l00013"></a><span class="lineno"> 13</span><span class="keyword">struct </span>model;</div>
|
||||||
|
<div class="line"><a id="l00014" name="l00014"></a><span class="lineno"> 14</span> </div>
|
||||||
|
<div class="line"><a id="l00015" name="l00015"></a><span class="lineno"> 15</span><span class="keyword">namespace </span>svm_classifier {</div>
|
||||||
|
<div class="line"><a id="l00016" name="l00016"></a><span class="lineno"> 16</span> </div>
|
||||||
|
<div class="foldopen" id="foldopen00020" data-start="{" data-end="};">
|
||||||
|
<div class="line"><a id="l00020" name="l00020"></a><span class="lineno"><a class="line" href="classsvm__classifier_1_1MulticlassStrategyBase.html"> 20</a></span> <span class="keyword">class </span><a class="code hl_class" href="classsvm__classifier_1_1MulticlassStrategyBase.html">MulticlassStrategyBase</a> {</div>
|
||||||
|
<div class="line"><a id="l00021" name="l00021"></a><span class="lineno"> 21</span> <span class="keyword">public</span>:</div>
|
||||||
|
<div class="line"><a id="l00025" name="l00025"></a><span class="lineno"><a class="line" href="classsvm__classifier_1_1MulticlassStrategyBase.html#a8a5647dd57eed281288f0c9011b11395"> 25</a></span> <span class="keyword">virtual</span> <a class="code hl_function" href="classsvm__classifier_1_1MulticlassStrategyBase.html#a8a5647dd57eed281288f0c9011b11395">~MulticlassStrategyBase</a>() = <span class="keywordflow">default</span>;</div>
|
||||||
|
<div class="line"><a id="l00026" name="l00026"></a><span class="lineno"> 26</span> </div>
|
||||||
|
<div class="line"><a id="l00035" name="l00035"></a><span class="lineno"><a class="line" href="classsvm__classifier_1_1MulticlassStrategyBase.html#a548af7201b7970abee0c31e7ec07d896"> 35</a></span> <span class="keyword">virtual</span> <a class="code hl_struct" href="structsvm__classifier_1_1TrainingMetrics.html">TrainingMetrics</a> <a class="code hl_function" href="classsvm__classifier_1_1MulticlassStrategyBase.html#a548af7201b7970abee0c31e7ec07d896">fit</a>(<span class="keyword">const</span> torch::Tensor& X,</div>
|
||||||
|
<div class="line"><a id="l00036" name="l00036"></a><span class="lineno"> 36</span> <span class="keyword">const</span> torch::Tensor& y,</div>
|
||||||
|
<div class="line"><a id="l00037" name="l00037"></a><span class="lineno"> 37</span> <span class="keyword">const</span> KernelParameters& params,</div>
|
||||||
|
<div class="line"><a id="l00038" name="l00038"></a><span class="lineno"> 38</span> <a class="code hl_class" href="classsvm__classifier_1_1DataConverter.html">DataConverter</a>& converter) = 0;</div>
|
||||||
|
<div class="line"><a id="l00039" name="l00039"></a><span class="lineno"> 39</span> </div>
|
||||||
|
<div class="line"><a id="l00046" name="l00046"></a><span class="lineno"><a class="line" href="classsvm__classifier_1_1MulticlassStrategyBase.html#a70f94cfcf8b2bf6d60133c688fe55f9d"> 46</a></span> <span class="keyword">virtual</span> std::vector<int> <a class="code hl_function" href="classsvm__classifier_1_1MulticlassStrategyBase.html#a70f94cfcf8b2bf6d60133c688fe55f9d">predict</a>(<span class="keyword">const</span> torch::Tensor& X,</div>
|
||||||
|
<div class="line"><a id="l00047" name="l00047"></a><span class="lineno"> 47</span> <a class="code hl_class" href="classsvm__classifier_1_1DataConverter.html">DataConverter</a>& converter) = 0;</div>
|
||||||
|
<div class="line"><a id="l00048" name="l00048"></a><span class="lineno"> 48</span> </div>
|
||||||
|
<div class="line"><a id="l00055" name="l00055"></a><span class="lineno"><a class="line" href="classsvm__classifier_1_1MulticlassStrategyBase.html#ab5348ee3b83547702ec7903ee7ee2da7"> 55</a></span> <span class="keyword">virtual</span> std::vector<std::vector<double>> <a class="code hl_function" href="classsvm__classifier_1_1MulticlassStrategyBase.html#ab5348ee3b83547702ec7903ee7ee2da7">predict_proba</a>(<span class="keyword">const</span> torch::Tensor& X,</div>
|
||||||
|
<div class="line"><a id="l00056" name="l00056"></a><span class="lineno"> 56</span> <a class="code hl_class" href="classsvm__classifier_1_1DataConverter.html">DataConverter</a>& converter) = 0;</div>
|
||||||
|
<div class="line"><a id="l00057" name="l00057"></a><span class="lineno"> 57</span> </div>
|
||||||
|
<div class="line"><a id="l00064" name="l00064"></a><span class="lineno"><a class="line" href="classsvm__classifier_1_1MulticlassStrategyBase.html#ad1c4eb746cb1fdd67cf436ff85a9b0f0"> 64</a></span> <span class="keyword">virtual</span> std::vector<std::vector<double>> <a class="code hl_function" href="classsvm__classifier_1_1MulticlassStrategyBase.html#ad1c4eb746cb1fdd67cf436ff85a9b0f0">decision_function</a>(<span class="keyword">const</span> torch::Tensor& X,</div>
|
||||||
|
<div class="line"><a id="l00065" name="l00065"></a><span class="lineno"> 65</span> <a class="code hl_class" href="classsvm__classifier_1_1DataConverter.html">DataConverter</a>& converter) = 0;</div>
|
||||||
|
<div class="line"><a id="l00066" name="l00066"></a><span class="lineno"> 66</span> </div>
|
||||||
|
<div class="line"><a id="l00071" name="l00071"></a><span class="lineno"><a class="line" href="classsvm__classifier_1_1MulticlassStrategyBase.html#a379c4000227cc46410bfbecce6e80c33"> 71</a></span> <span class="keyword">virtual</span> std::vector<int> <a class="code hl_function" href="classsvm__classifier_1_1MulticlassStrategyBase.html#a379c4000227cc46410bfbecce6e80c33">get_classes</a>() <span class="keyword">const</span> = 0;</div>
|
||||||
|
<div class="line"><a id="l00072" name="l00072"></a><span class="lineno"> 72</span> </div>
|
||||||
|
<div class="line"><a id="l00077" name="l00077"></a><span class="lineno"><a class="line" href="classsvm__classifier_1_1MulticlassStrategyBase.html#a2ab91902f8d6eb216f626ce9ea4be992"> 77</a></span> <span class="keyword">virtual</span> <span class="keywordtype">bool</span> <a class="code hl_function" href="classsvm__classifier_1_1MulticlassStrategyBase.html#a2ab91902f8d6eb216f626ce9ea4be992">supports_probability</a>() <span class="keyword">const</span> = 0;</div>
|
||||||
|
<div class="line"><a id="l00078" name="l00078"></a><span class="lineno"> 78</span> </div>
|
||||||
|
<div class="line"><a id="l00083" name="l00083"></a><span class="lineno"><a class="line" href="classsvm__classifier_1_1MulticlassStrategyBase.html#a1740d877a4d634ec1763cb8646f5e172"> 83</a></span> <span class="keyword">virtual</span> <span class="keywordtype">int</span> <a class="code hl_function" href="classsvm__classifier_1_1MulticlassStrategyBase.html#a1740d877a4d634ec1763cb8646f5e172">get_n_classes</a>() <span class="keyword">const</span> = 0;</div>
|
||||||
|
<div class="line"><a id="l00084" name="l00084"></a><span class="lineno"> 84</span> </div>
|
||||||
|
<div class="line"><a id="l00089" name="l00089"></a><span class="lineno"><a class="line" href="classsvm__classifier_1_1MulticlassStrategyBase.html#a31a0501fa1a6db1d41cbf825b2348e47"> 89</a></span> <span class="keyword">virtual</span> MulticlassStrategy <a class="code hl_function" href="classsvm__classifier_1_1MulticlassStrategyBase.html#a31a0501fa1a6db1d41cbf825b2348e47">get_strategy_type</a>() <span class="keyword">const</span> = 0;</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> <span class="keyword">protected</span>:</div>
|
||||||
|
<div class="line"><a id="l00092" name="l00092"></a><span class="lineno"><a class="line" href="classsvm__classifier_1_1MulticlassStrategyBase.html#a15bb6eb53e91e604b259b3050bd40e27"> 92</a></span> std::vector<int> <a class="code hl_variable" href="classsvm__classifier_1_1MulticlassStrategyBase.html#a15bb6eb53e91e604b259b3050bd40e27">classes_</a>; </div>
|
||||||
|
<div class="line"><a id="l00093" name="l00093"></a><span class="lineno"><a class="line" href="classsvm__classifier_1_1MulticlassStrategyBase.html#a8e74cd580feaac0da34d204274a24fea"> 93</a></span> <span class="keywordtype">bool</span> <a class="code hl_variable" href="classsvm__classifier_1_1MulticlassStrategyBase.html#a8e74cd580feaac0da34d204274a24fea">is_trained_</a> = <span class="keyword">false</span>; </div>
|
||||||
|
<div class="line"><a id="l00094" name="l00094"></a><span class="lineno"> 94</span> };</div>
|
||||||
|
</div>
|
||||||
|
<div class="line"><a id="l00095" name="l00095"></a><span class="lineno"> 95</span> </div>
|
||||||
|
<div class="foldopen" id="foldopen00099" data-start="{" data-end="};">
|
||||||
|
<div class="line"><a id="l00099" name="l00099"></a><span class="lineno"><a class="line" href="classsvm__classifier_1_1OneVsRestStrategy.html"> 99</a></span> <span class="keyword">class </span><a class="code hl_class" href="classsvm__classifier_1_1OneVsRestStrategy.html">OneVsRestStrategy</a> : <span class="keyword">public</span> <a class="code hl_class" href="classsvm__classifier_1_1MulticlassStrategyBase.html">MulticlassStrategyBase</a> {</div>
|
||||||
|
<div class="line"><a id="l00100" name="l00100"></a><span class="lineno"> 100</span> <span class="keyword">public</span>:</div>
|
||||||
|
<div class="line"><a id="l00104" name="l00104"></a><span class="lineno"><a class="line" href="classsvm__classifier_1_1OneVsRestStrategy.html#a30f146a564a9c9681524593cacbb43e7"> 104</a></span> <a class="code hl_function" href="classsvm__classifier_1_1OneVsRestStrategy.html#a30f146a564a9c9681524593cacbb43e7">OneVsRestStrategy</a>();</div>
|
||||||
|
<div class="line"><a id="l00105" name="l00105"></a><span class="lineno"> 105</span> </div>
|
||||||
|
<div class="line"><a id="l00109" name="l00109"></a><span class="lineno"><a class="line" href="classsvm__classifier_1_1OneVsRestStrategy.html#acfd698dd6cc0a988ac642a00d1f0b970"> 109</a></span> <a class="code hl_function" href="classsvm__classifier_1_1OneVsRestStrategy.html#acfd698dd6cc0a988ac642a00d1f0b970">~OneVsRestStrategy</a>() <span class="keyword">override</span>;</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"><a class="line" href="classsvm__classifier_1_1OneVsRestStrategy.html#aae14da8c0effd04731b5a4a0181eb1b6"> 111</a></span> <a class="code hl_struct" href="structsvm__classifier_1_1TrainingMetrics.html">TrainingMetrics</a> <a class="code hl_function" href="classsvm__classifier_1_1OneVsRestStrategy.html#aae14da8c0effd04731b5a4a0181eb1b6">fit</a>(<span class="keyword">const</span> torch::Tensor& X,</div>
|
||||||
|
<div class="line"><a id="l00112" name="l00112"></a><span class="lineno"> 112</span> <span class="keyword">const</span> torch::Tensor& y,</div>
|
||||||
|
<div class="line"><a id="l00113" name="l00113"></a><span class="lineno"> 113</span> <span class="keyword">const</span> KernelParameters& params,</div>
|
||||||
|
<div class="line"><a id="l00114" name="l00114"></a><span class="lineno"> 114</span> <a class="code hl_class" href="classsvm__classifier_1_1DataConverter.html">DataConverter</a>& converter) <span class="keyword">override</span>;</div>
|
||||||
|
<div class="line"><a id="l00115" name="l00115"></a><span class="lineno"> 115</span> </div>
|
||||||
|
<div class="line"><a id="l00116" name="l00116"></a><span class="lineno"><a class="line" href="classsvm__classifier_1_1OneVsRestStrategy.html#a771903a821d5380ddd5d0b3a912e7df9"> 116</a></span> std::vector<int> <a class="code hl_function" href="classsvm__classifier_1_1OneVsRestStrategy.html#a771903a821d5380ddd5d0b3a912e7df9">predict</a>(<span class="keyword">const</span> torch::Tensor& X,</div>
|
||||||
|
<div class="line"><a id="l00117" name="l00117"></a><span class="lineno"> 117</span> <a class="code hl_class" href="classsvm__classifier_1_1DataConverter.html">DataConverter</a>& converter) <span class="keyword">override</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"><a class="line" href="classsvm__classifier_1_1OneVsRestStrategy.html#a55639e5adaadcd6414b50d5ebf0d1cd2"> 119</a></span> std::vector<std::vector<double>> <a class="code hl_function" href="classsvm__classifier_1_1OneVsRestStrategy.html#a55639e5adaadcd6414b50d5ebf0d1cd2">predict_proba</a>(<span class="keyword">const</span> torch::Tensor& X,</div>
|
||||||
|
<div class="line"><a id="l00120" name="l00120"></a><span class="lineno"> 120</span> <a class="code hl_class" href="classsvm__classifier_1_1DataConverter.html">DataConverter</a>& converter) <span class="keyword">override</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"><a class="line" href="classsvm__classifier_1_1OneVsRestStrategy.html#a966b79bc8b6fac0fa78feefc2dd8a878"> 122</a></span> std::vector<std::vector<double>> <a class="code hl_function" href="classsvm__classifier_1_1OneVsRestStrategy.html#a966b79bc8b6fac0fa78feefc2dd8a878">decision_function</a>(<span class="keyword">const</span> torch::Tensor& X,</div>
|
||||||
|
<div class="line"><a id="l00123" name="l00123"></a><span class="lineno"> 123</span> <a class="code hl_class" href="classsvm__classifier_1_1DataConverter.html">DataConverter</a>& converter) <span class="keyword">override</span>;</div>
|
||||||
|
<div class="line"><a id="l00124" name="l00124"></a><span class="lineno"> 124</span> </div>
|
||||||
|
<div class="line"><a id="l00125" name="l00125"></a><span class="lineno"><a class="line" href="classsvm__classifier_1_1OneVsRestStrategy.html#a5e10800b16dbc66fd1c0d5e0a42871f0"> 125</a></span> std::vector<int> <a class="code hl_function" href="classsvm__classifier_1_1OneVsRestStrategy.html#a5e10800b16dbc66fd1c0d5e0a42871f0">get_classes</a>()<span class="keyword"> const override </span>{ <span class="keywordflow">return</span> <a class="code hl_variable" href="classsvm__classifier_1_1MulticlassStrategyBase.html#a15bb6eb53e91e604b259b3050bd40e27">classes_</a>; }</div>
|
||||||
|
<div class="line"><a id="l00126" name="l00126"></a><span class="lineno"> 126</span> </div>
|
||||||
|
<div class="line"><a id="l00127" name="l00127"></a><span class="lineno"><a class="line" href="classsvm__classifier_1_1OneVsRestStrategy.html#a200300198628ac119eac09e62ff62336"> 127</a></span> <span class="keywordtype">bool</span> <a class="code hl_function" href="classsvm__classifier_1_1OneVsRestStrategy.html#a200300198628ac119eac09e62ff62336">supports_probability</a>() <span class="keyword">const override</span>;</div>
|
||||||
|
<div class="line"><a id="l00128" name="l00128"></a><span class="lineno"> 128</span> </div>
|
||||||
|
<div class="line"><a id="l00129" name="l00129"></a><span class="lineno"><a class="line" href="classsvm__classifier_1_1OneVsRestStrategy.html#a53abe89ec25c33fd9c32d92ba08d01ed"> 129</a></span> <span class="keywordtype">int</span> <a class="code hl_function" href="classsvm__classifier_1_1OneVsRestStrategy.html#a53abe89ec25c33fd9c32d92ba08d01ed">get_n_classes</a>()<span class="keyword"> const override </span>{ <span class="keywordflow">return</span> <span class="keyword">static_cast<</span><span class="keywordtype">int</span><span class="keyword">></span>(<a class="code hl_variable" href="classsvm__classifier_1_1MulticlassStrategyBase.html#a15bb6eb53e91e604b259b3050bd40e27">classes_</a>.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"><a class="line" href="classsvm__classifier_1_1OneVsRestStrategy.html#af9e1bd6d08ce3e7afd5279c835ce6cfb"> 131</a></span> MulticlassStrategy <a class="code hl_function" href="classsvm__classifier_1_1OneVsRestStrategy.html#af9e1bd6d08ce3e7afd5279c835ce6cfb">get_strategy_type</a>()<span class="keyword"> const override </span>{ <span class="keywordflow">return</span> MulticlassStrategy::ONE_VS_REST; }</div>
|
||||||
|
<div class="line"><a id="l00132" name="l00132"></a><span class="lineno"> 132</span> </div>
|
||||||
|
<div class="line"><a id="l00133" name="l00133"></a><span class="lineno"> 133</span> <span class="keyword">private</span>:</div>
|
||||||
|
<div class="line"><a id="l00134" name="l00134"></a><span class="lineno"> 134</span> std::vector<std::unique_ptr<svm_model>> svm_models_; </div>
|
||||||
|
<div class="line"><a id="l00135" name="l00135"></a><span class="lineno"> 135</span> std::vector<std::unique_ptr<model>> linear_models_; </div>
|
||||||
|
<div class="line"><a id="l00136" name="l00136"></a><span class="lineno"> 136</span> KernelParameters params_; </div>
|
||||||
|
<div class="line"><a id="l00137" name="l00137"></a><span class="lineno"> 137</span> SVMLibrary library_type_; </div>
|
||||||
|
<div class="line"><a id="l00138" name="l00138"></a><span class="lineno"> 138</span> </div>
|
||||||
|
<div class="line"><a id="l00145" name="l00145"></a><span class="lineno"> 145</span> torch::Tensor create_binary_labels(<span class="keyword">const</span> torch::Tensor& y, <span class="keywordtype">int</span> positive_class);</div>
|
||||||
|
<div class="line"><a id="l00146" name="l00146"></a><span class="lineno"> 146</span> </div>
|
||||||
|
<div class="line"><a id="l00156" name="l00156"></a><span class="lineno"> 156</span> <span class="keywordtype">double</span> train_binary_classifier(<span class="keyword">const</span> torch::Tensor& X,</div>
|
||||||
|
<div class="line"><a id="l00157" name="l00157"></a><span class="lineno"> 157</span> <span class="keyword">const</span> torch::Tensor& y_binary,</div>
|
||||||
|
<div class="line"><a id="l00158" name="l00158"></a><span class="lineno"> 158</span> <span class="keyword">const</span> KernelParameters& params,</div>
|
||||||
|
<div class="line"><a id="l00159" name="l00159"></a><span class="lineno"> 159</span> <a class="code hl_class" href="classsvm__classifier_1_1DataConverter.html">DataConverter</a>& converter,</div>
|
||||||
|
<div class="line"><a id="l00160" name="l00160"></a><span class="lineno"> 160</span> <span class="keywordtype">int</span> class_idx);</div>
|
||||||
|
<div class="line"><a id="l00161" name="l00161"></a><span class="lineno"> 161</span> </div>
|
||||||
|
<div class="line"><a id="l00165" name="l00165"></a><span class="lineno"> 165</span> <span class="keywordtype">void</span> cleanup_models();</div>
|
||||||
|
<div class="line"><a id="l00166" name="l00166"></a><span class="lineno"> 166</span> };</div>
|
||||||
|
</div>
|
||||||
|
<div class="line"><a id="l00167" name="l00167"></a><span class="lineno"> 167</span> </div>
|
||||||
|
<div class="foldopen" id="foldopen00171" data-start="{" data-end="};">
|
||||||
|
<div class="line"><a id="l00171" name="l00171"></a><span class="lineno"><a class="line" href="classsvm__classifier_1_1OneVsOneStrategy.html"> 171</a></span> <span class="keyword">class </span><a class="code hl_class" href="classsvm__classifier_1_1OneVsOneStrategy.html">OneVsOneStrategy</a> : <span class="keyword">public</span> <a class="code hl_class" href="classsvm__classifier_1_1MulticlassStrategyBase.html">MulticlassStrategyBase</a> {</div>
|
||||||
|
<div class="line"><a id="l00172" name="l00172"></a><span class="lineno"> 172</span> <span class="keyword">public</span>:</div>
|
||||||
|
<div class="line"><a id="l00176" name="l00176"></a><span class="lineno"><a class="line" href="classsvm__classifier_1_1OneVsOneStrategy.html#a6d4b060383169010dda4197a0bffa020"> 176</a></span> <a class="code hl_function" href="classsvm__classifier_1_1OneVsOneStrategy.html#a6d4b060383169010dda4197a0bffa020">OneVsOneStrategy</a>();</div>
|
||||||
|
<div class="line"><a id="l00177" name="l00177"></a><span class="lineno"> 177</span> </div>
|
||||||
|
<div class="line"><a id="l00181" name="l00181"></a><span class="lineno"><a class="line" href="classsvm__classifier_1_1OneVsOneStrategy.html#af9a653b62502e296d0d18092be56344f"> 181</a></span> <a class="code hl_function" href="classsvm__classifier_1_1OneVsOneStrategy.html#af9a653b62502e296d0d18092be56344f">~OneVsOneStrategy</a>() <span class="keyword">override</span>;</div>
|
||||||
|
<div class="line"><a id="l00182" name="l00182"></a><span class="lineno"> 182</span> </div>
|
||||||
|
<div class="line"><a id="l00183" name="l00183"></a><span class="lineno"><a class="line" href="classsvm__classifier_1_1OneVsOneStrategy.html#af5ce4aeb191c5feed178b6465eac66f6"> 183</a></span> <a class="code hl_struct" href="structsvm__classifier_1_1TrainingMetrics.html">TrainingMetrics</a> <a class="code hl_function" href="classsvm__classifier_1_1OneVsOneStrategy.html#af5ce4aeb191c5feed178b6465eac66f6">fit</a>(<span class="keyword">const</span> torch::Tensor& X,</div>
|
||||||
|
<div class="line"><a id="l00184" name="l00184"></a><span class="lineno"> 184</span> <span class="keyword">const</span> torch::Tensor& y,</div>
|
||||||
|
<div class="line"><a id="l00185" name="l00185"></a><span class="lineno"> 185</span> <span class="keyword">const</span> KernelParameters& params,</div>
|
||||||
|
<div class="line"><a id="l00186" name="l00186"></a><span class="lineno"> 186</span> <a class="code hl_class" href="classsvm__classifier_1_1DataConverter.html">DataConverter</a>& converter) <span class="keyword">override</span>;</div>
|
||||||
|
<div class="line"><a id="l00187" name="l00187"></a><span class="lineno"> 187</span> </div>
|
||||||
|
<div class="line"><a id="l00188" name="l00188"></a><span class="lineno"><a class="line" href="classsvm__classifier_1_1OneVsOneStrategy.html#ab60df2d9b6069a73369b0bf9d3675662"> 188</a></span> std::vector<int> <a class="code hl_function" href="classsvm__classifier_1_1OneVsOneStrategy.html#ab60df2d9b6069a73369b0bf9d3675662">predict</a>(<span class="keyword">const</span> torch::Tensor& X,</div>
|
||||||
|
<div class="line"><a id="l00189" name="l00189"></a><span class="lineno"> 189</span> <a class="code hl_class" href="classsvm__classifier_1_1DataConverter.html">DataConverter</a>& converter) <span class="keyword">override</span>;</div>
|
||||||
|
<div class="line"><a id="l00190" name="l00190"></a><span class="lineno"> 190</span> </div>
|
||||||
|
<div class="line"><a id="l00191" name="l00191"></a><span class="lineno"><a class="line" href="classsvm__classifier_1_1OneVsOneStrategy.html#ae62e8b24115042d1119e76f3302f6992"> 191</a></span> std::vector<std::vector<double>> <a class="code hl_function" href="classsvm__classifier_1_1OneVsOneStrategy.html#ae62e8b24115042d1119e76f3302f6992">predict_proba</a>(<span class="keyword">const</span> torch::Tensor& X,</div>
|
||||||
|
<div class="line"><a id="l00192" name="l00192"></a><span class="lineno"> 192</span> <a class="code hl_class" href="classsvm__classifier_1_1DataConverter.html">DataConverter</a>& converter) <span class="keyword">override</span>;</div>
|
||||||
|
<div class="line"><a id="l00193" name="l00193"></a><span class="lineno"> 193</span> </div>
|
||||||
|
<div class="line"><a id="l00194" name="l00194"></a><span class="lineno"><a class="line" href="classsvm__classifier_1_1OneVsOneStrategy.html#a6aae0b5cd72180e94212454da8b777d2"> 194</a></span> std::vector<std::vector<double>> <a class="code hl_function" href="classsvm__classifier_1_1OneVsOneStrategy.html#a6aae0b5cd72180e94212454da8b777d2">decision_function</a>(<span class="keyword">const</span> torch::Tensor& X,</div>
|
||||||
|
<div class="line"><a id="l00195" name="l00195"></a><span class="lineno"> 195</span> <a class="code hl_class" href="classsvm__classifier_1_1DataConverter.html">DataConverter</a>& converter) <span class="keyword">override</span>;</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"><a class="line" href="classsvm__classifier_1_1OneVsOneStrategy.html#a52f9c3d7d98077d1dec0d6034711b750"> 197</a></span> std::vector<int> <a class="code hl_function" href="classsvm__classifier_1_1OneVsOneStrategy.html#a52f9c3d7d98077d1dec0d6034711b750">get_classes</a>()<span class="keyword"> const override </span>{ <span class="keywordflow">return</span> <a class="code hl_variable" href="classsvm__classifier_1_1MulticlassStrategyBase.html#a15bb6eb53e91e604b259b3050bd40e27">classes_</a>; }</div>
|
||||||
|
<div class="line"><a id="l00198" name="l00198"></a><span class="lineno"> 198</span> </div>
|
||||||
|
<div class="line"><a id="l00199" name="l00199"></a><span class="lineno"><a class="line" href="classsvm__classifier_1_1OneVsOneStrategy.html#a8875d29cb8666af10e0fb5634e08c0c1"> 199</a></span> <span class="keywordtype">bool</span> <a class="code hl_function" href="classsvm__classifier_1_1OneVsOneStrategy.html#a8875d29cb8666af10e0fb5634e08c0c1">supports_probability</a>() <span class="keyword">const override</span>;</div>
|
||||||
|
<div class="line"><a id="l00200" name="l00200"></a><span class="lineno"> 200</span> </div>
|
||||||
|
<div class="line"><a id="l00201" name="l00201"></a><span class="lineno"><a class="line" href="classsvm__classifier_1_1OneVsOneStrategy.html#a16ee2ae3623767af2165fef2d4b7d039"> 201</a></span> <span class="keywordtype">int</span> <a class="code hl_function" href="classsvm__classifier_1_1OneVsOneStrategy.html#a16ee2ae3623767af2165fef2d4b7d039">get_n_classes</a>()<span class="keyword"> const override </span>{ <span class="keywordflow">return</span> <span class="keyword">static_cast<</span><span class="keywordtype">int</span><span class="keyword">></span>(<a class="code hl_variable" href="classsvm__classifier_1_1MulticlassStrategyBase.html#a15bb6eb53e91e604b259b3050bd40e27">classes_</a>.size()); }</div>
|
||||||
|
<div class="line"><a id="l00202" name="l00202"></a><span class="lineno"> 202</span> </div>
|
||||||
|
<div class="line"><a id="l00203" name="l00203"></a><span class="lineno"><a class="line" href="classsvm__classifier_1_1OneVsOneStrategy.html#aae80b4e75459b2aca4f561d62c3c5675"> 203</a></span> MulticlassStrategy <a class="code hl_function" href="classsvm__classifier_1_1OneVsOneStrategy.html#aae80b4e75459b2aca4f561d62c3c5675">get_strategy_type</a>()<span class="keyword"> const override </span>{ <span class="keywordflow">return</span> MulticlassStrategy::ONE_VS_ONE; }</div>
|
||||||
|
<div class="line"><a id="l00204" name="l00204"></a><span class="lineno"> 204</span> </div>
|
||||||
|
<div class="line"><a id="l00205" name="l00205"></a><span class="lineno"> 205</span> <span class="keyword">private</span>:</div>
|
||||||
|
<div class="line"><a id="l00206" name="l00206"></a><span class="lineno"> 206</span> std::vector<std::unique_ptr<svm_model>> svm_models_; </div>
|
||||||
|
<div class="line"><a id="l00207" name="l00207"></a><span class="lineno"> 207</span> std::vector<std::unique_ptr<model>> linear_models_; </div>
|
||||||
|
<div class="line"><a id="l00208" name="l00208"></a><span class="lineno"> 208</span> std::vector<std::pair<int, int>> class_pairs_; </div>
|
||||||
|
<div class="line"><a id="l00209" name="l00209"></a><span class="lineno"> 209</span> KernelParameters params_; </div>
|
||||||
|
<div class="line"><a id="l00210" name="l00210"></a><span class="lineno"> 210</span> SVMLibrary library_type_; </div>
|
||||||
|
<div class="line"><a id="l00211" name="l00211"></a><span class="lineno"> 211</span> </div>
|
||||||
|
<div class="line"><a id="l00220" name="l00220"></a><span class="lineno"> 220</span> std::pair<torch::Tensor, torch::Tensor> extract_binary_data(<span class="keyword">const</span> torch::Tensor& X,</div>
|
||||||
|
<div class="line"><a id="l00221" name="l00221"></a><span class="lineno"> 221</span> <span class="keyword">const</span> torch::Tensor& y,</div>
|
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<div class="line"><a id="l00222" name="l00222"></a><span class="lineno"> 222</span> <span class="keywordtype">int</span> class1,</div>
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<div class="line"><a id="l00223" name="l00223"></a><span class="lineno"> 223</span> <span class="keywordtype">int</span> class2);</div>
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<div class="line"><a id="l00236" name="l00236"></a><span class="lineno"> 236</span> <span class="keywordtype">double</span> train_pairwise_classifier(<span class="keyword">const</span> torch::Tensor& X,</div>
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<div class="line"><a id="l00237" name="l00237"></a><span class="lineno"> 237</span> <span class="keyword">const</span> torch::Tensor& y,</div>
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<div class="line"><a id="l00238" name="l00238"></a><span class="lineno"> 238</span> <span class="keywordtype">int</span> class1,</div>
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<div class="line"><a id="l00239" name="l00239"></a><span class="lineno"> 239</span> <span class="keywordtype">int</span> class2,</div>
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<div class="line"><a id="l00240" name="l00240"></a><span class="lineno"> 240</span> <span class="keyword">const</span> KernelParameters& params,</div>
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<div class="line"><a id="l00241" name="l00241"></a><span class="lineno"> 241</span> <a class="code hl_class" href="classsvm__classifier_1_1DataConverter.html">DataConverter</a>& converter,</div>
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<div class="line"><a id="l00242" name="l00242"></a><span class="lineno"> 242</span> <span class="keywordtype">int</span> model_idx);</div>
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<div class="line"><a id="l00243" name="l00243"></a><span class="lineno"> 243</span> </div>
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<div class="line"><a id="l00249" name="l00249"></a><span class="lineno"> 249</span> std::vector<int> vote_predictions(<span class="keyword">const</span> std::vector<std::vector<double>>& decisions);</div>
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<div class="line"><a id="l00250" name="l00250"></a><span class="lineno"> 250</span> </div>
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<div class="line"><a id="l00254" name="l00254"></a><span class="lineno"> 254</span> <span class="keywordtype">void</span> cleanup_models();</div>
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<div class="line"><a id="l00255" name="l00255"></a><span class="lineno"> 255</span> };</div>
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<div class="line"><a id="l00256" name="l00256"></a><span class="lineno"> 256</span> </div>
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<div class="line"><a id="l00262" name="l00262"></a><span class="lineno"> 262</span> std::unique_ptr<MulticlassStrategyBase> create_multiclass_strategy(MulticlassStrategy strategy);</div>
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<div class="line"><a id="l00263" name="l00263"></a><span class="lineno"> 263</span> </div>
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<div class="line"><a id="l00264" name="l00264"></a><span class="lineno"> 264</span>} <span class="comment">// namespace svm_classifier</span></div>
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<div class="ttc" id="aclasssvm__classifier_1_1DataConverter_html"><div class="ttname"><a href="classsvm__classifier_1_1DataConverter.html">svm_classifier::DataConverter</a></div><div class="ttdoc">Data converter between libtorch tensors and SVM library formats.</div><div class="ttdef"><b>Definition</b> <a href="data__converter_8hpp_source.html#l00023">data_converter.hpp:23</a></div></div>
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<div class="ttc" id="aclasssvm__classifier_1_1MulticlassStrategyBase_html"><div class="ttname"><a href="classsvm__classifier_1_1MulticlassStrategyBase.html">svm_classifier::MulticlassStrategyBase</a></div><div class="ttdoc">Abstract base class for multiclass classification strategies.</div><div class="ttdef"><b>Definition</b> <a href="multiclass__strategy_8hpp_source.html#l00020">multiclass_strategy.hpp:20</a></div></div>
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<div class="ttc" id="aclasssvm__classifier_1_1MulticlassStrategyBase_html_a15bb6eb53e91e604b259b3050bd40e27"><div class="ttname"><a href="classsvm__classifier_1_1MulticlassStrategyBase.html#a15bb6eb53e91e604b259b3050bd40e27">svm_classifier::MulticlassStrategyBase::classes_</a></div><div class="ttdeci">std::vector< int > classes_</div><div class="ttdoc">Unique class labels.</div><div class="ttdef"><b>Definition</b> <a href="multiclass__strategy_8hpp_source.html#l00092">multiclass_strategy.hpp:92</a></div></div>
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<div class="ttc" id="aclasssvm__classifier_1_1MulticlassStrategyBase_html_a1740d877a4d634ec1763cb8646f5e172"><div class="ttname"><a href="classsvm__classifier_1_1MulticlassStrategyBase.html#a1740d877a4d634ec1763cb8646f5e172">svm_classifier::MulticlassStrategyBase::get_n_classes</a></div><div class="ttdeci">virtual int get_n_classes() const =0</div><div class="ttdoc">Get number of classes.</div></div>
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<div class="ttc" id="aclasssvm__classifier_1_1MulticlassStrategyBase_html_a2ab91902f8d6eb216f626ce9ea4be992"><div class="ttname"><a href="classsvm__classifier_1_1MulticlassStrategyBase.html#a2ab91902f8d6eb216f626ce9ea4be992">svm_classifier::MulticlassStrategyBase::supports_probability</a></div><div class="ttdeci">virtual bool supports_probability() const =0</div><div class="ttdoc">Check if the model supports probability prediction.</div></div>
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<div class="ttc" id="aclasssvm__classifier_1_1MulticlassStrategyBase_html_a31a0501fa1a6db1d41cbf825b2348e47"><div class="ttname"><a href="classsvm__classifier_1_1MulticlassStrategyBase.html#a31a0501fa1a6db1d41cbf825b2348e47">svm_classifier::MulticlassStrategyBase::get_strategy_type</a></div><div class="ttdeci">virtual MulticlassStrategy get_strategy_type() const =0</div><div class="ttdoc">Get strategy type.</div></div>
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<div class="ttc" id="aclasssvm__classifier_1_1MulticlassStrategyBase_html_a379c4000227cc46410bfbecce6e80c33"><div class="ttname"><a href="classsvm__classifier_1_1MulticlassStrategyBase.html#a379c4000227cc46410bfbecce6e80c33">svm_classifier::MulticlassStrategyBase::get_classes</a></div><div class="ttdeci">virtual std::vector< int > get_classes() const =0</div><div class="ttdoc">Get unique class labels.</div></div>
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<div class="ttc" id="aclasssvm__classifier_1_1MulticlassStrategyBase_html_a548af7201b7970abee0c31e7ec07d896"><div class="ttname"><a href="classsvm__classifier_1_1MulticlassStrategyBase.html#a548af7201b7970abee0c31e7ec07d896">svm_classifier::MulticlassStrategyBase::fit</a></div><div class="ttdeci">virtual TrainingMetrics fit(const torch::Tensor &X, const torch::Tensor &y, const KernelParameters &params, DataConverter &converter)=0</div><div class="ttdoc">Train the multiclass classifier.</div></div>
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<div class="ttc" id="aclasssvm__classifier_1_1MulticlassStrategyBase_html_a70f94cfcf8b2bf6d60133c688fe55f9d"><div class="ttname"><a href="classsvm__classifier_1_1MulticlassStrategyBase.html#a70f94cfcf8b2bf6d60133c688fe55f9d">svm_classifier::MulticlassStrategyBase::predict</a></div><div class="ttdeci">virtual std::vector< int > predict(const torch::Tensor &X, DataConverter &converter)=0</div><div class="ttdoc">Predict class labels.</div></div>
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<div class="ttc" id="aclasssvm__classifier_1_1MulticlassStrategyBase_html_a8a5647dd57eed281288f0c9011b11395"><div class="ttname"><a href="classsvm__classifier_1_1MulticlassStrategyBase.html#a8a5647dd57eed281288f0c9011b11395">svm_classifier::MulticlassStrategyBase::~MulticlassStrategyBase</a></div><div class="ttdeci">virtual ~MulticlassStrategyBase()=default</div><div class="ttdoc">Virtual destructor.</div></div>
|
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<div class="ttc" id="aclasssvm__classifier_1_1MulticlassStrategyBase_html_a8e74cd580feaac0da34d204274a24fea"><div class="ttname"><a href="classsvm__classifier_1_1MulticlassStrategyBase.html#a8e74cd580feaac0da34d204274a24fea">svm_classifier::MulticlassStrategyBase::is_trained_</a></div><div class="ttdeci">bool is_trained_</div><div class="ttdoc">Whether the model is trained.</div><div class="ttdef"><b>Definition</b> <a href="multiclass__strategy_8hpp_source.html#l00093">multiclass_strategy.hpp:93</a></div></div>
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<div class="ttc" id="aclasssvm__classifier_1_1MulticlassStrategyBase_html_ab5348ee3b83547702ec7903ee7ee2da7"><div class="ttname"><a href="classsvm__classifier_1_1MulticlassStrategyBase.html#ab5348ee3b83547702ec7903ee7ee2da7">svm_classifier::MulticlassStrategyBase::predict_proba</a></div><div class="ttdeci">virtual std::vector< std::vector< double > > predict_proba(const torch::Tensor &X, DataConverter &converter)=0</div><div class="ttdoc">Predict class probabilities.</div></div>
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<div class="ttc" id="aclasssvm__classifier_1_1MulticlassStrategyBase_html_ad1c4eb746cb1fdd67cf436ff85a9b0f0"><div class="ttname"><a href="classsvm__classifier_1_1MulticlassStrategyBase.html#ad1c4eb746cb1fdd67cf436ff85a9b0f0">svm_classifier::MulticlassStrategyBase::decision_function</a></div><div class="ttdeci">virtual std::vector< std::vector< double > > decision_function(const torch::Tensor &X, DataConverter &converter)=0</div><div class="ttdoc">Get decision function values.</div></div>
|
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<div class="ttc" id="aclasssvm__classifier_1_1OneVsOneStrategy_html"><div class="ttname"><a href="classsvm__classifier_1_1OneVsOneStrategy.html">svm_classifier::OneVsOneStrategy</a></div><div class="ttdoc">One-vs-One (OvO) multiclass strategy.</div><div class="ttdef"><b>Definition</b> <a href="multiclass__strategy_8hpp_source.html#l00171">multiclass_strategy.hpp:171</a></div></div>
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<div class="ttc" id="aclasssvm__classifier_1_1OneVsOneStrategy_html_a16ee2ae3623767af2165fef2d4b7d039"><div class="ttname"><a href="classsvm__classifier_1_1OneVsOneStrategy.html#a16ee2ae3623767af2165fef2d4b7d039">svm_classifier::OneVsOneStrategy::get_n_classes</a></div><div class="ttdeci">int get_n_classes() const override</div><div class="ttdoc">Get number of classes.</div><div class="ttdef"><b>Definition</b> <a href="multiclass__strategy_8hpp_source.html#l00201">multiclass_strategy.hpp:201</a></div></div>
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<div class="ttc" id="aclasssvm__classifier_1_1OneVsOneStrategy_html_a52f9c3d7d98077d1dec0d6034711b750"><div class="ttname"><a href="classsvm__classifier_1_1OneVsOneStrategy.html#a52f9c3d7d98077d1dec0d6034711b750">svm_classifier::OneVsOneStrategy::get_classes</a></div><div class="ttdeci">std::vector< int > get_classes() const override</div><div class="ttdoc">Get unique class labels.</div><div class="ttdef"><b>Definition</b> <a href="multiclass__strategy_8hpp_source.html#l00197">multiclass_strategy.hpp:197</a></div></div>
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<div class="ttc" id="aclasssvm__classifier_1_1OneVsOneStrategy_html_a6aae0b5cd72180e94212454da8b777d2"><div class="ttname"><a href="classsvm__classifier_1_1OneVsOneStrategy.html#a6aae0b5cd72180e94212454da8b777d2">svm_classifier::OneVsOneStrategy::decision_function</a></div><div class="ttdeci">std::vector< std::vector< double > > decision_function(const torch::Tensor &X, DataConverter &converter) override</div><div class="ttdoc">Get decision function values.</div></div>
|
||||||
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<div class="ttc" id="aclasssvm__classifier_1_1OneVsOneStrategy_html_a6d4b060383169010dda4197a0bffa020"><div class="ttname"><a href="classsvm__classifier_1_1OneVsOneStrategy.html#a6d4b060383169010dda4197a0bffa020">svm_classifier::OneVsOneStrategy::OneVsOneStrategy</a></div><div class="ttdeci">OneVsOneStrategy()</div><div class="ttdoc">Constructor.</div></div>
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||||||
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<div class="ttc" id="aclasssvm__classifier_1_1OneVsOneStrategy_html_a8875d29cb8666af10e0fb5634e08c0c1"><div class="ttname"><a href="classsvm__classifier_1_1OneVsOneStrategy.html#a8875d29cb8666af10e0fb5634e08c0c1">svm_classifier::OneVsOneStrategy::supports_probability</a></div><div class="ttdeci">bool supports_probability() const override</div><div class="ttdoc">Check if the model supports probability prediction.</div></div>
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||||||
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<div class="ttc" id="aclasssvm__classifier_1_1OneVsOneStrategy_html_aae80b4e75459b2aca4f561d62c3c5675"><div class="ttname"><a href="classsvm__classifier_1_1OneVsOneStrategy.html#aae80b4e75459b2aca4f561d62c3c5675">svm_classifier::OneVsOneStrategy::get_strategy_type</a></div><div class="ttdeci">MulticlassStrategy get_strategy_type() const override</div><div class="ttdoc">Get strategy type.</div><div class="ttdef"><b>Definition</b> <a href="multiclass__strategy_8hpp_source.html#l00203">multiclass_strategy.hpp:203</a></div></div>
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<div class="ttc" id="aclasssvm__classifier_1_1OneVsOneStrategy_html_ab60df2d9b6069a73369b0bf9d3675662"><div class="ttname"><a href="classsvm__classifier_1_1OneVsOneStrategy.html#ab60df2d9b6069a73369b0bf9d3675662">svm_classifier::OneVsOneStrategy::predict</a></div><div class="ttdeci">std::vector< int > predict(const torch::Tensor &X, DataConverter &converter) override</div><div class="ttdoc">Predict class labels.</div></div>
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<div class="ttc" id="aclasssvm__classifier_1_1OneVsOneStrategy_html_ae62e8b24115042d1119e76f3302f6992"><div class="ttname"><a href="classsvm__classifier_1_1OneVsOneStrategy.html#ae62e8b24115042d1119e76f3302f6992">svm_classifier::OneVsOneStrategy::predict_proba</a></div><div class="ttdeci">std::vector< std::vector< double > > predict_proba(const torch::Tensor &X, DataConverter &converter) override</div><div class="ttdoc">Predict class probabilities.</div></div>
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<div class="ttc" id="aclasssvm__classifier_1_1OneVsOneStrategy_html_af5ce4aeb191c5feed178b6465eac66f6"><div class="ttname"><a href="classsvm__classifier_1_1OneVsOneStrategy.html#af5ce4aeb191c5feed178b6465eac66f6">svm_classifier::OneVsOneStrategy::fit</a></div><div class="ttdeci">TrainingMetrics fit(const torch::Tensor &X, const torch::Tensor &y, const KernelParameters &params, DataConverter &converter) override</div><div class="ttdoc">Train the multiclass classifier.</div></div>
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<div class="ttc" id="aclasssvm__classifier_1_1OneVsOneStrategy_html_af9a653b62502e296d0d18092be56344f"><div class="ttname"><a href="classsvm__classifier_1_1OneVsOneStrategy.html#af9a653b62502e296d0d18092be56344f">svm_classifier::OneVsOneStrategy::~OneVsOneStrategy</a></div><div class="ttdeci">~OneVsOneStrategy() override</div><div class="ttdoc">Destructor.</div></div>
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<div class="ttc" id="aclasssvm__classifier_1_1OneVsRestStrategy_html"><div class="ttname"><a href="classsvm__classifier_1_1OneVsRestStrategy.html">svm_classifier::OneVsRestStrategy</a></div><div class="ttdoc">One-vs-Rest (OvR) multiclass strategy.</div><div class="ttdef"><b>Definition</b> <a href="multiclass__strategy_8hpp_source.html#l00099">multiclass_strategy.hpp:99</a></div></div>
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<div class="ttc" id="aclasssvm__classifier_1_1OneVsRestStrategy_html_a200300198628ac119eac09e62ff62336"><div class="ttname"><a href="classsvm__classifier_1_1OneVsRestStrategy.html#a200300198628ac119eac09e62ff62336">svm_classifier::OneVsRestStrategy::supports_probability</a></div><div class="ttdeci">bool supports_probability() const override</div><div class="ttdoc">Check if the model supports probability prediction.</div></div>
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<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>
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<div class="ttc" id="aclasssvm__classifier_1_1OneVsRestStrategy_html_a53abe89ec25c33fd9c32d92ba08d01ed"><div class="ttname"><a href="classsvm__classifier_1_1OneVsRestStrategy.html#a53abe89ec25c33fd9c32d92ba08d01ed">svm_classifier::OneVsRestStrategy::get_n_classes</a></div><div class="ttdeci">int get_n_classes() const override</div><div class="ttdoc">Get number of classes.</div><div class="ttdef"><b>Definition</b> <a href="multiclass__strategy_8hpp_source.html#l00129">multiclass_strategy.hpp:129</a></div></div>
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<div class="ttc" id="aclasssvm__classifier_1_1OneVsRestStrategy_html_a55639e5adaadcd6414b50d5ebf0d1cd2"><div class="ttname"><a href="classsvm__classifier_1_1OneVsRestStrategy.html#a55639e5adaadcd6414b50d5ebf0d1cd2">svm_classifier::OneVsRestStrategy::predict_proba</a></div><div class="ttdeci">std::vector< std::vector< double > > predict_proba(const torch::Tensor &X, DataConverter &converter) override</div><div class="ttdoc">Predict class probabilities.</div></div>
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<div class="ttc" id="aclasssvm__classifier_1_1OneVsRestStrategy_html_a5e10800b16dbc66fd1c0d5e0a42871f0"><div class="ttname"><a href="classsvm__classifier_1_1OneVsRestStrategy.html#a5e10800b16dbc66fd1c0d5e0a42871f0">svm_classifier::OneVsRestStrategy::get_classes</a></div><div class="ttdeci">std::vector< int > get_classes() const override</div><div class="ttdoc">Get unique class labels.</div><div class="ttdef"><b>Definition</b> <a href="multiclass__strategy_8hpp_source.html#l00125">multiclass_strategy.hpp:125</a></div></div>
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<div class="ttc" id="aclasssvm__classifier_1_1OneVsRestStrategy_html_a771903a821d5380ddd5d0b3a912e7df9"><div class="ttname"><a href="classsvm__classifier_1_1OneVsRestStrategy.html#a771903a821d5380ddd5d0b3a912e7df9">svm_classifier::OneVsRestStrategy::predict</a></div><div class="ttdeci">std::vector< int > predict(const torch::Tensor &X, DataConverter &converter) override</div><div class="ttdoc">Predict class labels.</div></div>
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<div class="ttc" id="aclasssvm__classifier_1_1OneVsRestStrategy_html_a966b79bc8b6fac0fa78feefc2dd8a878"><div class="ttname"><a href="classsvm__classifier_1_1OneVsRestStrategy.html#a966b79bc8b6fac0fa78feefc2dd8a878">svm_classifier::OneVsRestStrategy::decision_function</a></div><div class="ttdeci">std::vector< std::vector< double > > decision_function(const torch::Tensor &X, DataConverter &converter) override</div><div class="ttdoc">Get decision function values.</div></div>
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<div class="ttc" id="aclasssvm__classifier_1_1OneVsRestStrategy_html_aae14da8c0effd04731b5a4a0181eb1b6"><div class="ttname"><a href="classsvm__classifier_1_1OneVsRestStrategy.html#aae14da8c0effd04731b5a4a0181eb1b6">svm_classifier::OneVsRestStrategy::fit</a></div><div class="ttdeci">TrainingMetrics fit(const torch::Tensor &X, const torch::Tensor &y, const KernelParameters &params, DataConverter &converter) override</div><div class="ttdoc">Train the multiclass classifier.</div></div>
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<div class="ttc" id="aclasssvm__classifier_1_1OneVsRestStrategy_html_acfd698dd6cc0a988ac642a00d1f0b970"><div class="ttname"><a href="classsvm__classifier_1_1OneVsRestStrategy.html#acfd698dd6cc0a988ac642a00d1f0b970">svm_classifier::OneVsRestStrategy::~OneVsRestStrategy</a></div><div class="ttdeci">~OneVsRestStrategy() override</div><div class="ttdoc">Destructor.</div></div>
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<div class="ttc" id="aclasssvm__classifier_1_1OneVsRestStrategy_html_af9e1bd6d08ce3e7afd5279c835ce6cfb"><div class="ttname"><a href="classsvm__classifier_1_1OneVsRestStrategy.html#af9e1bd6d08ce3e7afd5279c835ce6cfb">svm_classifier::OneVsRestStrategy::get_strategy_type</a></div><div class="ttdeci">MulticlassStrategy get_strategy_type() const override</div><div class="ttdoc">Get strategy type.</div><div class="ttdef"><b>Definition</b> <a href="multiclass__strategy_8hpp_source.html#l00131">multiclass_strategy.hpp:131</a></div></div>
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<div class="ttc" id="astructsvm__classifier_1_1TrainingMetrics_html"><div class="ttname"><a href="structsvm__classifier_1_1TrainingMetrics.html">svm_classifier::TrainingMetrics</a></div><div class="ttdoc">Training metrics structure.</div><div class="ttdef"><b>Definition</b> <a href="types_8hpp_source.html#l00059">types.hpp:59</a></div></div>
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