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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"><a class="el" href="dir_68267d1309a1af8e8297ef4c3efbcdba.html">src</a></li> </ul>
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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>
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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>
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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>
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<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="foldopen" id="foldopen00010" data-start="{" data-end="}">
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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>
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<div class="line"><a id="l00011" name="l00011"></a><span class="lineno"> 11</span> : n_features_(0)</div>
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<div class="line"><a id="l00012" name="l00012"></a><span class="lineno"> 12</span> , n_samples_(0)</div>
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<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>
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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>
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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> <a class="code hl_function" href="classsvm__classifier_1_1DataConverter.html#a46d12ba28c4c5bf6e0fad1122c621fa8">cleanup</a>();</div>
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<div class="line"><a id="l00020" name="l00020"></a><span class="lineno"> 20</span> }</div>
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</div>
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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="}">
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<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>
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<div class="line"><a id="l00023" name="l00023"></a><span class="lineno"> 23</span> <span class="keyword">const</span> torch::Tensor& y)</div>
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<div class="line"><a id="l00024" name="l00024"></a><span class="lineno"> 24</span> {</div>
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<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>
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<div class="line"><a id="l00026" name="l00026"></a><span class="lineno"> 26</span> </div>
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<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>
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<div class="line"><a id="l00028" name="l00028"></a><span class="lineno"> 28</span> </div>
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<div class="line"><a id="l00029" name="l00029"></a><span class="lineno"> 29</span> n_samples_ = X_cpu.size(0);</div>
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<div class="line"><a id="l00030" name="l00030"></a><span class="lineno"> 30</span> n_features_ = X_cpu.size(1);</div>
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<div class="line"><a id="l00031" name="l00031"></a><span class="lineno"> 31</span> </div>
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<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>
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<div class="line"><a id="l00033" name="l00033"></a><span class="lineno"> 33</span> svm_nodes_storage_ = tensor_to_svm_nodes(X_cpu);</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="l00035" name="l00035"></a><span class="lineno"> 35</span> <span class="comment">// Prepare pointers for svm_problem</span></div>
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<div class="line"><a id="l00036" name="l00036"></a><span class="lineno"> 36</span> svm_x_space_.clear();</div>
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<div class="line"><a id="l00037" name="l00037"></a><span class="lineno"> 37</span> svm_x_space_.reserve(n_samples_);</div>
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<div class="line"><a id="l00038" name="l00038"></a><span class="lineno"> 38</span> </div>
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<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>
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<div class="line"><a id="l00040" name="l00040"></a><span class="lineno"> 40</span> svm_x_space_.push_back(nodes.data());</div>
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<div class="line"><a id="l00041" name="l00041"></a><span class="lineno"> 41</span> }</div>
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<div class="line"><a id="l00042" name="l00042"></a><span class="lineno"> 42</span> </div>
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<div class="line"><a id="l00043" name="l00043"></a><span class="lineno"> 43</span> <span class="comment">// Extract labels if provided</span></div>
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<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>
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<div class="line"><a id="l00045" name="l00045"></a><span class="lineno"> 45</span> svm_y_space_ = extract_labels(y);</div>
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<div class="line"><a id="l00046" name="l00046"></a><span class="lineno"> 46</span> } <span class="keywordflow">else</span> {</div>
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<div class="line"><a id="l00047" name="l00047"></a><span class="lineno"> 47</span> svm_y_space_.clear();</div>
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<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>
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<div class="line"><a id="l00049" name="l00049"></a><span class="lineno"> 49</span> }</div>
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<div class="line"><a id="l00050" name="l00050"></a><span class="lineno"> 50</span> </div>
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<div class="line"><a id="l00051" name="l00051"></a><span class="lineno"> 51</span> <span class="comment">// Create svm_problem</span></div>
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<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>
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<div class="line"><a id="l00053" name="l00053"></a><span class="lineno"> 53</span> problem->l = n_samples_;</div>
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<div class="line"><a id="l00054" name="l00054"></a><span class="lineno"> 54</span> problem->x = svm_x_space_.data();</div>
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<div class="line"><a id="l00055" name="l00055"></a><span class="lineno"> 55</span> problem->y = svm_y_space_.data();</div>
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<div class="line"><a id="l00056" name="l00056"></a><span class="lineno"> 56</span> </div>
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<div class="line"><a id="l00057" name="l00057"></a><span class="lineno"> 57</span> <span class="keywordflow">return</span> problem;</div>
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<div class="line"><a id="l00058" name="l00058"></a><span class="lineno"> 58</span> }</div>
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</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="foldopen" id="foldopen00060" data-start="{" data-end="}">
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<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>
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<div class="line"><a id="l00061" name="l00061"></a><span class="lineno"> 61</span> <span class="keyword">const</span> torch::Tensor& y)</div>
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<div class="line"><a id="l00062" name="l00062"></a><span class="lineno"> 62</span> {</div>
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<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>
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<div class="line"><a id="l00064" name="l00064"></a><span class="lineno"> 64</span> </div>
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<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>
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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="l00067" name="l00067"></a><span class="lineno"> 67</span> n_samples_ = X_cpu.size(0);</div>
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<div class="line"><a id="l00068" name="l00068"></a><span class="lineno"> 68</span> n_features_ = X_cpu.size(1);</div>
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<div class="line"><a id="l00069" name="l00069"></a><span class="lineno"> 69</span> </div>
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<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>
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<div class="line"><a id="l00071" name="l00071"></a><span class="lineno"> 71</span> linear_nodes_storage_ = tensor_to_linear_nodes(X_cpu);</div>
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<div class="line"><a id="l00072" name="l00072"></a><span class="lineno"> 72</span> </div>
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<div class="line"><a id="l00073" name="l00073"></a><span class="lineno"> 73</span> <span class="comment">// Prepare pointers for problem</span></div>
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<div class="line"><a id="l00074" name="l00074"></a><span class="lineno"> 74</span> linear_x_space_.clear();</div>
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<div class="line"><a id="l00075" name="l00075"></a><span class="lineno"> 75</span> linear_x_space_.reserve(n_samples_);</div>
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<div class="line"><a id="l00076" name="l00076"></a><span class="lineno"> 76</span> </div>
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<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>
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<div class="line"><a id="l00078" name="l00078"></a><span class="lineno"> 78</span> linear_x_space_.push_back(nodes.data());</div>
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<div class="line"><a id="l00079" name="l00079"></a><span class="lineno"> 79</span> }</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="l00081" name="l00081"></a><span class="lineno"> 81</span> <span class="comment">// Extract labels if provided</span></div>
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<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>
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<div class="line"><a id="l00083" name="l00083"></a><span class="lineno"> 83</span> linear_y_space_ = extract_labels(y);</div>
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<div class="line"><a id="l00084" name="l00084"></a><span class="lineno"> 84</span> } <span class="keywordflow">else</span> {</div>
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<div class="line"><a id="l00085" name="l00085"></a><span class="lineno"> 85</span> linear_y_space_.clear();</div>
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<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>
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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="l00088" name="l00088"></a><span class="lineno"> 88</span> </div>
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<div class="line"><a id="l00089" name="l00089"></a><span class="lineno"> 89</span> <span class="comment">// Create problem</span></div>
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<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>
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<div class="line"><a id="l00091" name="l00091"></a><span class="lineno"> 91</span> linear_problem->l = n_samples_;</div>
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<div class="line"><a id="l00092" name="l00092"></a><span class="lineno"> 92</span> linear_problem->n = n_features_;</div>
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<div class="line"><a id="l00093" name="l00093"></a><span class="lineno"> 93</span> linear_problem->x = linear_x_space_.data();</div>
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<div class="line"><a id="l00094" name="l00094"></a><span class="lineno"> 94</span> linear_problem->y = linear_y_space_.data();</div>
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<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>
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<div class="line"><a id="l00096" name="l00096"></a><span class="lineno"> 96</span> </div>
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<div class="line"><a id="l00097" name="l00097"></a><span class="lineno"> 97</span> <span class="keywordflow">return</span> linear_problem;</div>
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<div class="line"><a id="l00098" name="l00098"></a><span class="lineno"> 98</span> }</div>
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</div>
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<div class="line"><a id="l00099" name="l00099"></a><span class="lineno"> 99</span> </div>
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<div class="foldopen" id="foldopen00100" data-start="{" data-end="}">
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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#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>
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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="l00102" name="l00102"></a><span class="lineno"> 102</span> validate_tensor_properties(sample, 1, <span class="stringliteral">"sample"</span>);</div>
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<div class="line"><a id="l00103" name="l00103"></a><span class="lineno"> 103</span> </div>
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<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>
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<div class="line"><a id="l00105" name="l00105"></a><span class="lineno"> 105</span> single_svm_nodes_ = sample_to_svm_nodes(sample_cpu);</div>
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<div class="line"><a id="l00106" name="l00106"></a><span class="lineno"> 106</span> </div>
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<div class="line"><a id="l00107" name="l00107"></a><span class="lineno"> 107</span> <span class="keywordflow">return</span> single_svm_nodes_.data();</div>
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<div class="line"><a id="l00108" name="l00108"></a><span class="lineno"> 108</span> }</div>
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</div>
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<div class="line"><a id="l00109" name="l00109"></a><span class="lineno"> 109</span> </div>
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<div class="foldopen" id="foldopen00110" data-start="{" data-end="}">
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<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>
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<div class="line"><a id="l00111" name="l00111"></a><span class="lineno"> 111</span> {</div>
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<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>
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<div class="line"><a id="l00113" name="l00113"></a><span class="lineno"> 113</span> </div>
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<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>
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<div class="line"><a id="l00115" name="l00115"></a><span class="lineno"> 115</span> single_linear_nodes_ = sample_to_linear_nodes(sample_cpu);</div>
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<div class="line"><a id="l00116" name="l00116"></a><span class="lineno"> 116</span> </div>
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<div class="line"><a id="l00117" name="l00117"></a><span class="lineno"> 117</span> <span class="keywordflow">return</span> single_linear_nodes_.data();</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>
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<div class="line"><a id="l00119" name="l00119"></a><span class="lineno"> 119</span> </div>
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<div class="foldopen" id="foldopen00120" data-start="{" data-end="}">
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<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>
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<div class="line"><a id="l00121" name="l00121"></a><span class="lineno"> 121</span> {</div>
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<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>
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<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>
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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="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = 0; i < predictions.size(); ++i) {</div>
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<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>
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<div class="line"><a id="l00127" name="l00127"></a><span class="lineno"> 127</span> }</div>
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<div class="line"><a id="l00128" name="l00128"></a><span class="lineno"> 128</span> </div>
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<div class="line"><a id="l00129" name="l00129"></a><span class="lineno"> 129</span> <span class="keywordflow">return</span> tensor;</div>
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<div class="line"><a id="l00130" name="l00130"></a><span class="lineno"> 130</span> }</div>
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</div>
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<div class="line"><a id="l00131" name="l00131"></a><span class="lineno"> 131</span> </div>
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<div class="foldopen" id="foldopen00132" data-start="{" data-end="}">
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<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>
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<div class="line"><a id="l00133" name="l00133"></a><span class="lineno"> 133</span> {</div>
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<div class="line"><a id="l00134" name="l00134"></a><span class="lineno"> 134</span> <span class="keywordflow">if</span> (probabilities.empty()) {</div>
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<div class="line"><a id="l00135" name="l00135"></a><span class="lineno"> 135</span> <span class="keywordflow">return</span> torch::empty({ 0, 0 });</div>
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<div class="line"><a id="l00136" name="l00136"></a><span class="lineno"> 136</span> }</div>
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<div class="line"><a id="l00137" name="l00137"></a><span class="lineno"> 137</span> </div>
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<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>
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<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>
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<div class="line"><a id="l00140" name="l00140"></a><span class="lineno"> 140</span> </div>
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<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>
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<div class="line"><a id="l00142" name="l00142"></a><span class="lineno"> 142</span> </div>
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<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>
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<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>
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<div class="line"><a id="l00145" name="l00145"></a><span class="lineno"> 145</span> tensor[i][j] = probabilities[i][j];</div>
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<div class="line"><a id="l00146" name="l00146"></a><span class="lineno"> 146</span> }</div>
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<div class="line"><a id="l00147" name="l00147"></a><span class="lineno"> 147</span> }</div>
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<div class="line"><a id="l00148" name="l00148"></a><span class="lineno"> 148</span> </div>
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<div class="line"><a id="l00149" name="l00149"></a><span class="lineno"> 149</span> <span class="keywordflow">return</span> tensor;</div>
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<div class="line"><a id="l00150" name="l00150"></a><span class="lineno"> 150</span> }</div>
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</div>
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<div class="line"><a id="l00151" name="l00151"></a><span class="lineno"> 151</span> </div>
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<div class="foldopen" id="foldopen00152" data-start="{" data-end="}">
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<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>
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<div class="line"><a id="l00153" name="l00153"></a><span class="lineno"> 153</span> {</div>
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<div class="line"><a id="l00154" name="l00154"></a><span class="lineno"> 154</span> <span class="keywordflow">if</span> (decision_values.empty()) {</div>
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<div class="line"><a id="l00155" name="l00155"></a><span class="lineno"> 155</span> <span class="keywordflow">return</span> torch::empty({ 0, 0 });</div>
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<div class="line"><a id="l00156" name="l00156"></a><span class="lineno"> 156</span> }</div>
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<div class="line"><a id="l00157" name="l00157"></a><span class="lineno"> 157</span> </div>
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<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>
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<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>
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<div class="line"><a id="l00160" name="l00160"></a><span class="lineno"> 160</span> </div>
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<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>
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<div class="line"><a id="l00162" name="l00162"></a><span class="lineno"> 162</span> </div>
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<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>
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<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>
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<div class="line"><a id="l00165" name="l00165"></a><span class="lineno"> 165</span> tensor[i][j] = decision_values[i][j];</div>
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<div class="line"><a id="l00166" name="l00166"></a><span class="lineno"> 166</span> }</div>
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<div class="line"><a id="l00167" name="l00167"></a><span class="lineno"> 167</span> }</div>
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<div class="line"><a id="l00168" name="l00168"></a><span class="lineno"> 168</span> </div>
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<div class="line"><a id="l00169" name="l00169"></a><span class="lineno"> 169</span> <span class="keywordflow">return</span> tensor;</div>
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<div class="line"><a id="l00170" name="l00170"></a><span class="lineno"> 170</span> }</div>
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</div>
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<div class="line"><a id="l00171" name="l00171"></a><span class="lineno"> 171</span> </div>
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<div class="foldopen" id="foldopen00172" data-start="{" data-end="}">
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<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>
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<div class="line"><a id="l00173" name="l00173"></a><span class="lineno"> 173</span> {</div>
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<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>
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<div class="line"><a id="l00175" name="l00175"></a><span class="lineno"> 175</span> </div>
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<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>
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<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>
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<div class="line"><a id="l00178" name="l00178"></a><span class="lineno"> 178</span> </div>
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<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>
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<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>
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<div class="line"><a id="l00181" name="l00181"></a><span class="lineno"> 181</span> <span class="keywordflow">throw</span> std::invalid_argument(</div>
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<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>
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<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>
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<div class="line"><a id="l00184" name="l00184"></a><span class="lineno"> 184</span> );</div>
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<div class="line"><a id="l00185" name="l00185"></a><span class="lineno"> 185</span> }</div>
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<div class="line"><a id="l00186" name="l00186"></a><span class="lineno"> 186</span> }</div>
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<div class="line"><a id="l00187" name="l00187"></a><span class="lineno"> 187</span> </div>
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<div class="line"><a id="l00188" name="l00188"></a><span class="lineno"> 188</span> <span class="comment">// Check for reasonable dimensions</span></div>
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<div class="line"><a id="l00189" name="l00189"></a><span class="lineno"> 189</span> <span class="keywordflow">if</span> (X.size(0) == 0) {</div>
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<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>
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<div class="line"><a id="l00191" name="l00191"></a><span class="lineno"> 191</span> }</div>
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<div class="line"><a id="l00192" name="l00192"></a><span class="lineno"> 192</span> </div>
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<div class="line"><a id="l00193" name="l00193"></a><span class="lineno"> 193</span> <span class="keywordflow">if</span> (X.size(1) == 0) {</div>
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<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>
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<div class="line"><a id="l00195" name="l00195"></a><span class="lineno"> 195</span> }</div>
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<div class="line"><a id="l00196" name="l00196"></a><span class="lineno"> 196</span> }</div>
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</div>
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<div class="line"><a id="l00197" name="l00197"></a><span class="lineno"> 197</span> </div>
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<div class="foldopen" id="foldopen00198" data-start="{" data-end="}">
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<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>
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<div class="line"><a id="l00199" name="l00199"></a><span class="lineno"> 199</span> {</div>
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<div class="line"><a id="l00200" name="l00200"></a><span class="lineno"> 200</span> svm_nodes_storage_.clear();</div>
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<div class="line"><a id="l00201" name="l00201"></a><span class="lineno"> 201</span> svm_x_space_.clear();</div>
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<div class="line"><a id="l00202" name="l00202"></a><span class="lineno"> 202</span> svm_y_space_.clear();</div>
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<div class="line"><a id="l00203" name="l00203"></a><span class="lineno"> 203</span> </div>
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<div class="line"><a id="l00204" name="l00204"></a><span class="lineno"> 204</span> linear_nodes_storage_.clear();</div>
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<div class="line"><a id="l00205" name="l00205"></a><span class="lineno"> 205</span> linear_x_space_.clear();</div>
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<div class="line"><a id="l00206" name="l00206"></a><span class="lineno"> 206</span> linear_y_space_.clear();</div>
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<div class="line"><a id="l00207" name="l00207"></a><span class="lineno"> 207</span> </div>
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<div class="line"><a id="l00208" name="l00208"></a><span class="lineno"> 208</span> single_svm_nodes_.clear();</div>
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<div class="line"><a id="l00209" name="l00209"></a><span class="lineno"> 209</span> single_linear_nodes_.clear();</div>
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<div class="line"><a id="l00210" name="l00210"></a><span class="lineno"> 210</span> </div>
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<div class="line"><a id="l00211" name="l00211"></a><span class="lineno"> 211</span> n_features_ = 0;</div>
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<div class="line"><a id="l00212" name="l00212"></a><span class="lineno"> 212</span> n_samples_ = 0;</div>
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<div class="line"><a id="l00213" name="l00213"></a><span class="lineno"> 213</span> }</div>
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</div>
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<div class="line"><a id="l00214" name="l00214"></a><span class="lineno"> 214</span> </div>
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<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>
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<div class="line"><a id="l00216" name="l00216"></a><span class="lineno"> 216</span> {</div>
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<div class="line"><a id="l00217" name="l00217"></a><span class="lineno"> 217</span> std::vector<std::vector<svm_node>> nodes_storage;</div>
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<div class="line"><a id="l00218" name="l00218"></a><span class="lineno"> 218</span> nodes_storage.reserve(X.size(0));</div>
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<div class="line"><a id="l00219" name="l00219"></a><span class="lineno"> 219</span> </div>
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<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>
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<div class="line"><a id="l00221" name="l00221"></a><span class="lineno"> 221</span> </div>
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<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>
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<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>
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<div class="line"><a id="l00224" name="l00224"></a><span class="lineno"> 224</span> }</div>
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<div class="line"><a id="l00225" name="l00225"></a><span class="lineno"> 225</span> </div>
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<div class="line"><a id="l00226" name="l00226"></a><span class="lineno"> 226</span> <span class="keywordflow">return</span> nodes_storage;</div>
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<div class="line"><a id="l00227" name="l00227"></a><span class="lineno"> 227</span> }</div>
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<div class="line"><a id="l00228" name="l00228"></a><span class="lineno"> 228</span> </div>
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<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>
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<div class="line"><a id="l00230" name="l00230"></a><span class="lineno"> 230</span> {</div>
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<div class="line"><a id="l00231" name="l00231"></a><span class="lineno"> 231</span> std::vector<std::vector<feature_node>> nodes_storage;</div>
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<div class="line"><a id="l00232" name="l00232"></a><span class="lineno"> 232</span> nodes_storage.reserve(X.size(0));</div>
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<div class="line"><a id="l00233" name="l00233"></a><span class="lineno"> 233</span> </div>
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<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>
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<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>
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<div class="line"><a id="l00236" name="l00236"></a><span class="lineno"> 236</span> }</div>
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<div class="line"><a id="l00237" name="l00237"></a><span class="lineno"> 237</span> </div>
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<div class="line"><a id="l00238" name="l00238"></a><span class="lineno"> 238</span> <span class="keywordflow">return</span> nodes_storage;</div>
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<div class="line"><a id="l00239" name="l00239"></a><span class="lineno"> 239</span> }</div>
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<div class="line"><a id="l00240" name="l00240"></a><span class="lineno"> 240</span> </div>
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<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>
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<div class="line"><a id="l00242" name="l00242"></a><span class="lineno"> 242</span> {</div>
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<div class="line"><a id="l00243" name="l00243"></a><span class="lineno"> 243</span> std::vector<svm_node> nodes;</div>
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<div class="line"><a id="l00244" name="l00244"></a><span class="lineno"> 244</span> </div>
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<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>
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<div class="line"><a id="l00246" name="l00246"></a><span class="lineno"> 246</span> </div>
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<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>
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<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>
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<div class="line"><a id="l00249" name="l00249"></a><span class="lineno"> 249</span> </div>
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<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>
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<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>
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<div class="line"><a id="l00252" name="l00252"></a><span class="lineno"> 252</span> </div>
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<div class="line"><a id="l00253" name="l00253"></a><span class="lineno"> 253</span> <span class="comment">// Skip sparse features</span></div>
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<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>
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<div class="line"><a id="l00255" name="l00255"></a><span class="lineno"> 255</span> svm_node node;</div>
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<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>
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<div class="line"><a id="l00257" name="l00257"></a><span class="lineno"> 257</span> node.value = value;</div>
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<div class="line"><a id="l00258" name="l00258"></a><span class="lineno"> 258</span> nodes.push_back(node);</div>
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<div class="line"><a id="l00259" name="l00259"></a><span class="lineno"> 259</span> }</div>
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<div class="line"><a id="l00260" name="l00260"></a><span class="lineno"> 260</span> }</div>
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<div class="line"><a id="l00261" name="l00261"></a><span class="lineno"> 261</span> </div>
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<div class="line"><a id="l00262" name="l00262"></a><span class="lineno"> 262</span> <span class="comment">// Add terminator</span></div>
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<div class="line"><a id="l00263" name="l00263"></a><span class="lineno"> 263</span> svm_node terminator;</div>
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<div class="line"><a id="l00264" name="l00264"></a><span class="lineno"> 264</span> terminator.index = -1;</div>
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<div class="line"><a id="l00265" name="l00265"></a><span class="lineno"> 265</span> terminator.value = 0;</div>
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<div class="line"><a id="l00266" name="l00266"></a><span class="lineno"> 266</span> nodes.push_back(terminator);</div>
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<div class="line"><a id="l00267" name="l00267"></a><span class="lineno"> 267</span> </div>
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<div class="line"><a id="l00268" name="l00268"></a><span class="lineno"> 268</span> <span class="keywordflow">return</span> nodes;</div>
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<div class="line"><a id="l00269" name="l00269"></a><span class="lineno"> 269</span> }</div>
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<div class="line"><a id="l00270" name="l00270"></a><span class="lineno"> 270</span> </div>
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<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>
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<div class="line"><a id="l00272" name="l00272"></a><span class="lineno"> 272</span> {</div>
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<div class="line"><a id="l00273" name="l00273"></a><span class="lineno"> 273</span> std::vector<feature_node> nodes;</div>
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<div class="line"><a id="l00274" name="l00274"></a><span class="lineno"> 274</span> </div>
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<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>
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<div class="line"><a id="l00276" name="l00276"></a><span class="lineno"> 276</span> </div>
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<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>
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<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>
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<div class="line"><a id="l00279" name="l00279"></a><span class="lineno"> 279</span> </div>
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<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>
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<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>
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<div class="line"><a id="l00282" name="l00282"></a><span class="lineno"> 282</span> </div>
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<div class="line"><a id="l00283" name="l00283"></a><span class="lineno"> 283</span> <span class="comment">// Skip sparse features</span></div>
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<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>
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<div class="line"><a id="l00285" name="l00285"></a><span class="lineno"> 285</span> feature_node node;</div>
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<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>
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<div class="line"><a id="l00287" name="l00287"></a><span class="lineno"> 287</span> node.value = value;</div>
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<div class="line"><a id="l00288" name="l00288"></a><span class="lineno"> 288</span> nodes.push_back(node);</div>
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<div class="line"><a id="l00289" name="l00289"></a><span class="lineno"> 289</span> }</div>
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<div class="line"><a id="l00290" name="l00290"></a><span class="lineno"> 290</span> }</div>
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<div class="line"><a id="l00291" name="l00291"></a><span class="lineno"> 291</span> </div>
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<div class="line"><a id="l00292" name="l00292"></a><span class="lineno"> 292</span> <span class="comment">// Add terminator</span></div>
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<div class="line"><a id="l00293" name="l00293"></a><span class="lineno"> 293</span> feature_node terminator;</div>
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<div class="line"><a id="l00294" name="l00294"></a><span class="lineno"> 294</span> terminator.index = -1;</div>
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<div class="line"><a id="l00295" name="l00295"></a><span class="lineno"> 295</span> terminator.value = 0;</div>
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<div class="line"><a id="l00296" name="l00296"></a><span class="lineno"> 296</span> nodes.push_back(terminator);</div>
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<div class="line"><a id="l00297" name="l00297"></a><span class="lineno"> 297</span> </div>
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<div class="line"><a id="l00298" name="l00298"></a><span class="lineno"> 298</span> <span class="keywordflow">return</span> nodes;</div>
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<div class="line"><a id="l00299" name="l00299"></a><span class="lineno"> 299</span> }</div>
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<div class="line"><a id="l00300" name="l00300"></a><span class="lineno"> 300</span> </div>
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<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>
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<div class="line"><a id="l00302" name="l00302"></a><span class="lineno"> 302</span> {</div>
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<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>
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<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>
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<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>
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<div class="line"><a id="l00322" name="l00322"></a><span class="lineno"> 322</span> }</div>
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<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>
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<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>
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<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>
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<div class="line"><a id="l00326" name="l00326"></a><span class="lineno"> 326</span> labels.push_back(y_acc[i]);</div>
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<div class="line"><a id="l00327" name="l00327"></a><span class="lineno"> 327</span> }</div>
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<div class="line"><a id="l00328" name="l00328"></a><span class="lineno"> 328</span> } <span class="keywordflow">else</span> {</div>
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<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>
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<div class="line"><a id="l00330" name="l00330"></a><span class="lineno"> 330</span> }</div>
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<div class="line"><a id="l00331" name="l00331"></a><span class="lineno"> 331</span> </div>
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<div class="line"><a id="l00332" name="l00332"></a><span class="lineno"> 332</span> <span class="keywordflow">return</span> labels;</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> </div>
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<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>
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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="keywordflow">if</span> (tensor.device().type() != torch::kCPU) {</div>
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<div class="line"><a id="l00338" name="l00338"></a><span class="lineno"> 338</span> <span class="keywordflow">return</span> tensor.to(torch::kCPU);</div>
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<div class="line"><a id="l00339" name="l00339"></a><span class="lineno"> 339</span> }</div>
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<div class="line"><a id="l00340" name="l00340"></a><span class="lineno"> 340</span> </div>
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<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>
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<div class="line"><a id="l00342" name="l00342"></a><span class="lineno"> 342</span> <span class="keywordflow">if</span> (tensor.dtype() != torch::kFloat32) {</div>
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<div class="line"><a id="l00343" name="l00343"></a><span class="lineno"> 343</span> <span class="keywordflow">return</span> tensor.to(torch::kFloat32);</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> </div>
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<div class="line"><a id="l00346" name="l00346"></a><span class="lineno"> 346</span> <span class="keywordflow">return</span> tensor;</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> </div>
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<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>
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<div class="line"><a id="l00350" name="l00350"></a><span class="lineno"> 350</span> <span class="keywordtype">int</span> expected_dims,</div>
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<div class="line"><a id="l00351" name="l00351"></a><span class="lineno"> 351</span> <span class="keyword">const</span> std::string& name)</div>
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<div class="line"><a id="l00352" name="l00352"></a><span class="lineno"> 352</span> {</div>
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<div class="line"><a id="l00353" name="l00353"></a><span class="lineno"> 353</span> <span class="keywordflow">if</span> (!tensor.defined()) {</div>
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<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>
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<div class="line"><a id="l00355" name="l00355"></a><span class="lineno"> 355</span> }</div>
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<div class="line"><a id="l00356" name="l00356"></a><span class="lineno"> 356</span> </div>
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<div class="line"><a id="l00357" name="l00357"></a><span class="lineno"> 357</span> <span class="keywordflow">if</span> (tensor.dim() != expected_dims) {</div>
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<div class="line"><a id="l00358" name="l00358"></a><span class="lineno"> 358</span> <span class="keywordflow">throw</span> std::invalid_argument(</div>
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<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>
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<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>
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<div class="line"><a id="l00361" name="l00361"></a><span class="lineno"> 361</span> );</div>
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<div class="line"><a id="l00362" name="l00362"></a><span class="lineno"> 362</span> }</div>
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<div class="line"><a id="l00363" name="l00363"></a><span class="lineno"> 363</span> </div>
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<div class="line"><a id="l00364" name="l00364"></a><span class="lineno"> 364</span> <span class="keywordflow">if</span> (tensor.numel() == 0) {</div>
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<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>
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<div class="line"><a id="l00366" name="l00366"></a><span class="lineno"> 366</span> }</div>
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<div class="line"><a id="l00367" name="l00367"></a><span class="lineno"> 367</span> </div>
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<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>
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<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>
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<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>
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<div class="line"><a id="l00371" name="l00371"></a><span class="lineno"> 371</span> }</div>
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<div class="line"><a id="l00372" name="l00372"></a><span class="lineno"> 372</span> </div>
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<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>
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<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>
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<div class="line"><a id="l00375" name="l00375"></a><span class="lineno"> 375</span> }</div>
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<div class="line"><a id="l00376" name="l00376"></a><span class="lineno"> 376</span> }</div>
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<div class="line"><a id="l00377" name="l00377"></a><span class="lineno"> 377</span> </div>
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<div class="line"><a id="l00378" name="l00378"></a><span class="lineno"> 378</span>} <span class="comment">// namespace svm_classifier</span></div>
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<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_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_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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