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<div id="projectname">BayesNet<span id="projectnumber"> 1.0.5</span>
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<div id="projectbrief">Bayesian Network Classifiers using libtorch from scratch</div>
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<div class="headertitle"><div class="title">Classifier.cc</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="comment">// ***************************************************************</span></div>
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<div class="line"><a id="l00002" name="l00002"></a><span class="lineno"> 2</span><span class="comment">// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez</span></div>
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<div class="line"><a id="l00003" name="l00003"></a><span class="lineno"> 3</span><span class="comment">// SPDX-FileType: SOURCE</span></div>
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<div class="line"><a id="l00004" name="l00004"></a><span class="lineno"> 4</span><span class="comment">// SPDX-License-Identifier: MIT</span></div>
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<div class="line"><a id="l00005" name="l00005"></a><span class="lineno"> 5</span><span class="comment">// ***************************************************************</span></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><span class="preprocessor">#include <sstream></span></div>
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<div class="line"><a id="l00008" name="l00008"></a><span class="lineno"> 8</span><span class="preprocessor">#include "bayesnet/utils/bayesnetUtils.h"</span></div>
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<div class="line"><a id="l00009" name="l00009"></a><span class="lineno"> 9</span><span class="preprocessor">#include "Classifier.h"</span></div>
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<div class="line"><a id="l00010" name="l00010"></a><span class="lineno"> 10</span> </div>
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<div class="line"><a id="l00011" name="l00011"></a><span class="lineno"> 11</span><span class="keyword">namespace </span>bayesnet {</div>
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<div class="line"><a id="l00012" name="l00012"></a><span class="lineno"> 12</span> Classifier::Classifier(Network model) : model(model), m(0), n(0), metrics(Metrics()), fitted(false) {}</div>
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<div class="line"><a id="l00013" name="l00013"></a><span class="lineno"> 13</span> <span class="keyword">const</span> std::string CLASSIFIER_NOT_FITTED = <span class="stringliteral">"Classifier has not been fitted"</span>;</div>
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<div class="line"><a id="l00014" name="l00014"></a><span class="lineno"> 14</span> Classifier& Classifier::build(<span class="keyword">const</span> std::vector<std::string>& features, <span class="keyword">const</span> std::string& className, std::map<std::string, std::vector<int>>& states, <span class="keyword">const</span> torch::Tensor& weights)</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> this->features = features;</div>
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||||
<div class="line"><a id="l00017" name="l00017"></a><span class="lineno"> 17</span> this->className = className;</div>
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<div class="line"><a id="l00018" name="l00018"></a><span class="lineno"> 18</span> this->states = states;</div>
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<div class="line"><a id="l00019" name="l00019"></a><span class="lineno"> 19</span> m = dataset.size(1);</div>
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<div class="line"><a id="l00020" name="l00020"></a><span class="lineno"> 20</span> n = features.size();</div>
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<div class="line"><a id="l00021" name="l00021"></a><span class="lineno"> 21</span> checkFitParameters();</div>
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<div class="line"><a id="l00022" name="l00022"></a><span class="lineno"> 22</span> <span class="keyword">auto</span> n_classes = states.at(className).size();</div>
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<div class="line"><a id="l00023" name="l00023"></a><span class="lineno"> 23</span> metrics = Metrics(dataset, features, className, n_classes);</div>
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<div class="line"><a id="l00024" name="l00024"></a><span class="lineno"> 24</span> model.initialize();</div>
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<div class="line"><a id="l00025" name="l00025"></a><span class="lineno"> 25</span> buildModel(weights);</div>
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||||
<div class="line"><a id="l00026" name="l00026"></a><span class="lineno"> 26</span> trainModel(weights);</div>
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<div class="line"><a id="l00027" name="l00027"></a><span class="lineno"> 27</span> fitted = <span class="keyword">true</span>;</div>
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<div class="line"><a id="l00028" name="l00028"></a><span class="lineno"> 28</span> <span class="keywordflow">return</span> *<span class="keyword">this</span>;</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="l00030" name="l00030"></a><span class="lineno"> 30</span> <span class="keywordtype">void</span> Classifier::buildDataset(torch::Tensor& ytmp)</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="keywordflow">try</span> {</div>
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<div class="line"><a id="l00033" name="l00033"></a><span class="lineno"> 33</span> <span class="keyword">auto</span> yresized = torch::transpose(ytmp.view({ ytmp.size(0), 1 }), 0, 1);</div>
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<div class="line"><a id="l00034" name="l00034"></a><span class="lineno"> 34</span> dataset = torch::cat({ dataset, yresized }, 0);</div>
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<div class="line"><a id="l00035" name="l00035"></a><span class="lineno"> 35</span> }</div>
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<div class="line"><a id="l00036" name="l00036"></a><span class="lineno"> 36</span> <span class="keywordflow">catch</span> (<span class="keyword">const</span> std::exception& e) {</div>
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<div class="line"><a id="l00037" name="l00037"></a><span class="lineno"> 37</span> std::stringstream oss;</div>
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<div class="line"><a id="l00038" name="l00038"></a><span class="lineno"> 38</span> oss << <span class="stringliteral">"* Error in X and y dimensions *\n"</span>;</div>
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||||
<div class="line"><a id="l00039" name="l00039"></a><span class="lineno"> 39</span> oss << <span class="stringliteral">"X dimensions: "</span> << dataset.sizes() << <span class="stringliteral">"\n"</span>;</div>
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<div class="line"><a id="l00040" name="l00040"></a><span class="lineno"> 40</span> oss << <span class="stringliteral">"y dimensions: "</span> << ytmp.sizes();</div>
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<div class="line"><a id="l00041" name="l00041"></a><span class="lineno"> 41</span> <span class="keywordflow">throw</span> std::runtime_error(oss.str());</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> }</div>
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<div class="line"><a id="l00044" name="l00044"></a><span class="lineno"> 44</span> <span class="keywordtype">void</span> Classifier::trainModel(<span class="keyword">const</span> torch::Tensor& weights)</div>
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<div class="line"><a id="l00045" name="l00045"></a><span class="lineno"> 45</span> {</div>
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<div class="line"><a id="l00046" name="l00046"></a><span class="lineno"> 46</span> model.fit(dataset, weights, features, className, states);</div>
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<div class="line"><a id="l00047" name="l00047"></a><span class="lineno"> 47</span> }</div>
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<div class="line"><a id="l00048" name="l00048"></a><span class="lineno"> 48</span> <span class="comment">// X is nxm where n is the number of features and m the number of samples</span></div>
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<div class="line"><a id="l00049" name="l00049"></a><span class="lineno"> 49</span> Classifier& Classifier::fit(torch::Tensor& X, torch::Tensor& y, <span class="keyword">const</span> std::vector<std::string>& features, <span class="keyword">const</span> std::string& className, std::map<std::string, std::vector<int>>& states)</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> dataset = X;</div>
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<div class="line"><a id="l00052" name="l00052"></a><span class="lineno"> 52</span> buildDataset(y);</div>
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<div class="line"><a id="l00053" name="l00053"></a><span class="lineno"> 53</span> <span class="keyword">const</span> torch::Tensor weights = torch::full({ dataset.size(1) }, 1.0 / dataset.size(1), torch::kDouble);</div>
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<div class="line"><a id="l00054" name="l00054"></a><span class="lineno"> 54</span> <span class="keywordflow">return</span> build(features, className, states, weights);</div>
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||||
<div class="line"><a id="l00055" name="l00055"></a><span class="lineno"> 55</span> }</div>
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||||
<div class="line"><a id="l00056" name="l00056"></a><span class="lineno"> 56</span> <span class="comment">// X is nxm where n is the number of features and m the number of samples</span></div>
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<div class="line"><a id="l00057" name="l00057"></a><span class="lineno"> 57</span> Classifier& Classifier::fit(std::vector<std::vector<int>>& X, std::vector<int>& y, <span class="keyword">const</span> std::vector<std::string>& features, <span class="keyword">const</span> std::string& className, std::map<std::string, std::vector<int>>& states)</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 class="line"><a id="l00059" name="l00059"></a><span class="lineno"> 59</span> dataset = torch::zeros({ <span class="keyword">static_cast<</span><span class="keywordtype">int</span><span class="keyword">></span>(X.size()), <span class="keyword">static_cast<</span><span class="keywordtype">int</span><span class="keyword">></span>(X[0].size()) }, torch::kInt32);</div>
|
||||
<div class="line"><a id="l00060" name="l00060"></a><span class="lineno"> 60</span> <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i < X.size(); ++i) {</div>
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||||
<div class="line"><a id="l00061" name="l00061"></a><span class="lineno"> 61</span> dataset.index_put_({ i, <span class="stringliteral">"..."</span> }, torch::tensor(X[i], torch::kInt32));</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> <span class="keyword">auto</span> ytmp = torch::tensor(y, torch::kInt32);</div>
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<div class="line"><a id="l00064" name="l00064"></a><span class="lineno"> 64</span> buildDataset(ytmp);</div>
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<div class="line"><a id="l00065" name="l00065"></a><span class="lineno"> 65</span> <span class="keyword">const</span> torch::Tensor weights = torch::full({ dataset.size(1) }, 1.0 / dataset.size(1), torch::kDouble);</div>
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||||
<div class="line"><a id="l00066" name="l00066"></a><span class="lineno"> 66</span> <span class="keywordflow">return</span> build(features, className, states, weights);</div>
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||||
<div class="line"><a id="l00067" name="l00067"></a><span class="lineno"> 67</span> }</div>
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<div class="line"><a id="l00068" name="l00068"></a><span class="lineno"> 68</span> Classifier& Classifier::fit(torch::Tensor& dataset, <span class="keyword">const</span> std::vector<std::string>& features, <span class="keyword">const</span> std::string& className, std::map<std::string, std::vector<int>>& states)</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> this->dataset = dataset;</div>
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||||
<div class="line"><a id="l00071" name="l00071"></a><span class="lineno"> 71</span> <span class="keyword">const</span> torch::Tensor weights = torch::full({ dataset.size(1) }, 1.0 / dataset.size(1), torch::kDouble);</div>
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<div class="line"><a id="l00072" name="l00072"></a><span class="lineno"> 72</span> <span class="keywordflow">return</span> build(features, className, states, weights);</div>
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||||
<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> Classifier& Classifier::fit(torch::Tensor& dataset, <span class="keyword">const</span> std::vector<std::string>& features, <span class="keyword">const</span> std::string& className, std::map<std::string, std::vector<int>>& states, <span class="keyword">const</span> torch::Tensor& weights)</div>
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||||
<div class="line"><a id="l00075" name="l00075"></a><span class="lineno"> 75</span> {</div>
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<div class="line"><a id="l00076" name="l00076"></a><span class="lineno"> 76</span> this->dataset = dataset;</div>
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||||
<div class="line"><a id="l00077" name="l00077"></a><span class="lineno"> 77</span> <span class="keywordflow">return</span> build(features, className, states, weights);</div>
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||||
<div class="line"><a id="l00078" name="l00078"></a><span class="lineno"> 78</span> }</div>
|
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<div class="line"><a id="l00079" name="l00079"></a><span class="lineno"> 79</span> <span class="keywordtype">void</span> Classifier::checkFitParameters()</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="keywordflow">if</span> (torch::is_floating_point(dataset)) {</div>
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||||
<div class="line"><a id="l00082" name="l00082"></a><span class="lineno"> 82</span> <span class="keywordflow">throw</span> std::invalid_argument(<span class="stringliteral">"dataset (X, y) must be of type Integer"</span>);</div>
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<div class="line"><a id="l00083" name="l00083"></a><span class="lineno"> 83</span> }</div>
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<div class="line"><a id="l00084" name="l00084"></a><span class="lineno"> 84</span> <span class="keywordflow">if</span> (dataset.size(0) - 1 != features.size()) {</div>
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||||
<div class="line"><a id="l00085" name="l00085"></a><span class="lineno"> 85</span> <span class="keywordflow">throw</span> std::invalid_argument(<span class="stringliteral">"Classifier: X "</span> + std::to_string(dataset.size(0) - 1) + <span class="stringliteral">" and features "</span> + std::to_string(features.size()) + <span class="stringliteral">" must have the same number of features"</span>);</div>
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||||
<div class="line"><a id="l00086" name="l00086"></a><span class="lineno"> 86</span> }</div>
|
||||
<div class="line"><a id="l00087" name="l00087"></a><span class="lineno"> 87</span> <span class="keywordflow">if</span> (states.find(className) == states.end()) {</div>
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||||
<div class="line"><a id="l00088" name="l00088"></a><span class="lineno"> 88</span> <span class="keywordflow">throw</span> std::invalid_argument(<span class="stringliteral">"class name not found in states"</span>);</div>
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||||
<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">for</span> (<span class="keyword">auto</span> feature : features) {</div>
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||||
<div class="line"><a id="l00091" name="l00091"></a><span class="lineno"> 91</span> <span class="keywordflow">if</span> (states.find(feature) == states.end()) {</div>
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||||
<div class="line"><a id="l00092" name="l00092"></a><span class="lineno"> 92</span> <span class="keywordflow">throw</span> std::invalid_argument(<span class="stringliteral">"feature ["</span> + feature + <span class="stringliteral">"] not found in states"</span>);</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> }</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> torch::Tensor Classifier::predict(torch::Tensor& X)</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> (!fitted) {</div>
|
||||
<div class="line"><a id="l00099" name="l00099"></a><span class="lineno"> 99</span> <span class="keywordflow">throw</span> std::logic_error(CLASSIFIER_NOT_FITTED);</div>
|
||||
<div class="line"><a id="l00100" name="l00100"></a><span class="lineno"> 100</span> }</div>
|
||||
<div class="line"><a id="l00101" name="l00101"></a><span class="lineno"> 101</span> <span class="keywordflow">return</span> model.predict(X);</div>
|
||||
<div class="line"><a id="l00102" name="l00102"></a><span class="lineno"> 102</span> }</div>
|
||||
<div class="line"><a id="l00103" name="l00103"></a><span class="lineno"> 103</span> std::vector<int> Classifier::predict(std::vector<std::vector<int>>& X)</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> <span class="keywordflow">if</span> (!fitted) {</div>
|
||||
<div class="line"><a id="l00106" name="l00106"></a><span class="lineno"> 106</span> <span class="keywordflow">throw</span> std::logic_error(CLASSIFIER_NOT_FITTED);</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> <span class="keyword">auto</span> m_ = X[0].size();</div>
|
||||
<div class="line"><a id="l00109" name="l00109"></a><span class="lineno"> 109</span> <span class="keyword">auto</span> n_ = X.size();</div>
|
||||
<div class="line"><a id="l00110" name="l00110"></a><span class="lineno"> 110</span> std::vector<std::vector<int>> Xd(n_, std::vector<int>(m_, 0));</div>
|
||||
<div class="line"><a id="l00111" name="l00111"></a><span class="lineno"> 111</span> <span class="keywordflow">for</span> (<span class="keyword">auto</span> i = 0; i < n_; i++) {</div>
|
||||
<div class="line"><a id="l00112" name="l00112"></a><span class="lineno"> 112</span> Xd[i] = std::vector<int>(X[i].begin(), X[i].end());</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> yp = model.predict(Xd);</div>
|
||||
<div class="line"><a id="l00115" name="l00115"></a><span class="lineno"> 115</span> <span class="keywordflow">return</span> yp;</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> torch::Tensor Classifier::predict_proba(torch::Tensor& X)</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> (!fitted) {</div>
|
||||
<div class="line"><a id="l00120" name="l00120"></a><span class="lineno"> 120</span> <span class="keywordflow">throw</span> std::logic_error(CLASSIFIER_NOT_FITTED);</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="keywordflow">return</span> model.predict_proba(X);</div>
|
||||
<div class="line"><a id="l00123" name="l00123"></a><span class="lineno"> 123</span> }</div>
|
||||
<div class="line"><a id="l00124" name="l00124"></a><span class="lineno"> 124</span> std::vector<std::vector<double>> Classifier::predict_proba(std::vector<std::vector<int>>& X)</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">if</span> (!fitted) {</div>
|
||||
<div class="line"><a id="l00127" name="l00127"></a><span class="lineno"> 127</span> <span class="keywordflow">throw</span> std::logic_error(CLASSIFIER_NOT_FITTED);</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="keyword">auto</span> m_ = X[0].size();</div>
|
||||
<div class="line"><a id="l00130" name="l00130"></a><span class="lineno"> 130</span> <span class="keyword">auto</span> n_ = X.size();</div>
|
||||
<div class="line"><a id="l00131" name="l00131"></a><span class="lineno"> 131</span> std::vector<std::vector<int>> Xd(n_, std::vector<int>(m_, 0));</div>
|
||||
<div class="line"><a id="l00132" name="l00132"></a><span class="lineno"> 132</span> <span class="comment">// Convert to nxm vector</span></div>
|
||||
<div class="line"><a id="l00133" name="l00133"></a><span class="lineno"> 133</span> <span class="keywordflow">for</span> (<span class="keyword">auto</span> i = 0; i < n_; i++) {</div>
|
||||
<div class="line"><a id="l00134" name="l00134"></a><span class="lineno"> 134</span> Xd[i] = std::vector<int>(X[i].begin(), X[i].end());</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="keyword">auto</span> yp = model.predict_proba(Xd);</div>
|
||||
<div class="line"><a id="l00137" name="l00137"></a><span class="lineno"> 137</span> <span class="keywordflow">return</span> yp;</div>
|
||||
<div class="line"><a id="l00138" name="l00138"></a><span class="lineno"> 138</span> }</div>
|
||||
<div class="line"><a id="l00139" name="l00139"></a><span class="lineno"> 139</span> <span class="keywordtype">float</span> Classifier::score(torch::Tensor& X, torch::Tensor& y)</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> torch::Tensor y_pred = predict(X);</div>
|
||||
<div class="line"><a id="l00142" name="l00142"></a><span class="lineno"> 142</span> <span class="keywordflow">return</span> (y_pred == y).sum().item<<span class="keywordtype">float</span>>() / y.size(0);</div>
|
||||
<div class="line"><a id="l00143" name="l00143"></a><span class="lineno"> 143</span> }</div>
|
||||
<div class="line"><a id="l00144" name="l00144"></a><span class="lineno"> 144</span> <span class="keywordtype">float</span> Classifier::score(std::vector<std::vector<int>>& X, std::vector<int>& y)</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">if</span> (!fitted) {</div>
|
||||
<div class="line"><a id="l00147" name="l00147"></a><span class="lineno"> 147</span> <span class="keywordflow">throw</span> std::logic_error(CLASSIFIER_NOT_FITTED);</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> model.score(X, y);</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> std::vector<std::string> Classifier::show()<span class="keyword"> const</span></div>
|
||||
<div class="line"><a id="l00152" name="l00152"></a><span class="lineno"> 152</span><span class="keyword"> </span>{</div>
|
||||
<div class="line"><a id="l00153" name="l00153"></a><span class="lineno"> 153</span> <span class="keywordflow">return</span> model.show();</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> Classifier::addNodes()</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> <span class="comment">// Add all nodes to the network</span></div>
|
||||
<div class="line"><a id="l00158" name="l00158"></a><span class="lineno"> 158</span> <span class="keywordflow">for</span> (<span class="keyword">const</span> <span class="keyword">auto</span>& feature : features) {</div>
|
||||
<div class="line"><a id="l00159" name="l00159"></a><span class="lineno"> 159</span> model.addNode(feature);</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> model.addNode(className);</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="keywordtype">int</span> Classifier::getNumberOfNodes()<span class="keyword"> const</span></div>
|
||||
<div class="line"><a id="l00164" name="l00164"></a><span class="lineno"> 164</span><span class="keyword"> </span>{</div>
|
||||
<div class="line"><a id="l00165" name="l00165"></a><span class="lineno"> 165</span> <span class="comment">// Features does not include class</span></div>
|
||||
<div class="line"><a id="l00166" name="l00166"></a><span class="lineno"> 166</span> <span class="keywordflow">return</span> fitted ? model.getFeatures().size() : 0;</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="keywordtype">int</span> Classifier::getNumberOfEdges()<span class="keyword"> const</span></div>
|
||||
<div class="line"><a id="l00169" name="l00169"></a><span class="lineno"> 169</span><span class="keyword"> </span>{</div>
|
||||
<div class="line"><a id="l00170" name="l00170"></a><span class="lineno"> 170</span> <span class="keywordflow">return</span> fitted ? model.getNumEdges() : 0;</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> <span class="keywordtype">int</span> Classifier::getNumberOfStates()<span class="keyword"> const</span></div>
|
||||
<div class="line"><a id="l00173" name="l00173"></a><span class="lineno"> 173</span><span class="keyword"> </span>{</div>
|
||||
<div class="line"><a id="l00174" name="l00174"></a><span class="lineno"> 174</span> <span class="keywordflow">return</span> fitted ? model.getStates() : 0;</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="keywordtype">int</span> Classifier::getClassNumStates()<span class="keyword"> const</span></div>
|
||||
<div class="line"><a id="l00177" name="l00177"></a><span class="lineno"> 177</span><span class="keyword"> </span>{</div>
|
||||
<div class="line"><a id="l00178" name="l00178"></a><span class="lineno"> 178</span> <span class="keywordflow">return</span> fitted ? model.getClassNumStates() : 0;</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> std::vector<std::string> Classifier::topological_order()</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">return</span> model.topological_sort();</div>
|
||||
<div class="line"><a id="l00183" name="l00183"></a><span class="lineno"> 183</span> }</div>
|
||||
<div class="line"><a id="l00184" name="l00184"></a><span class="lineno"> 184</span> std::string Classifier::dump_cpt()<span class="keyword"> const</span></div>
|
||||
<div class="line"><a id="l00185" name="l00185"></a><span class="lineno"> 185</span><span class="keyword"> </span>{</div>
|
||||
<div class="line"><a id="l00186" name="l00186"></a><span class="lineno"> 186</span> <span class="keywordflow">return</span> model.dump_cpt();</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="keywordtype">void</span> Classifier::setHyperparameters(<span class="keyword">const</span> nlohmann::json& hyperparameters)</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> <span class="keywordflow">if</span> (!hyperparameters.empty()) {</div>
|
||||
<div class="line"><a id="l00191" name="l00191"></a><span class="lineno"> 191</span> <span class="keywordflow">throw</span> std::invalid_argument(<span class="stringliteral">"Invalid hyperparameters"</span> + hyperparameters.dump());</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> }</div>
|
||||
<div class="line"><a id="l00194" name="l00194"></a><span class="lineno"> 194</span>}</div>
|
||||
</div><!-- fragment --></div><!-- contents -->
|
||||
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Reference in New Issue
Block a user