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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">KDB.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>
|
||||
<div class="line"><a id="l00004" name="l00004"></a><span class="lineno"> 4</span><span class="comment">// SPDX-License-Identifier: MIT</span></div>
|
||||
<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 "KDB.h"</span></div>
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<div class="line"><a id="l00008" name="l00008"></a><span class="lineno"> 8</span> </div>
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<div class="line"><a id="l00009" name="l00009"></a><span class="lineno"> 9</span><span class="keyword">namespace </span>bayesnet {</div>
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<div class="line"><a id="l00010" name="l00010"></a><span class="lineno"> 10</span> KDB::KDB(<span class="keywordtype">int</span> k, <span class="keywordtype">float</span> theta) : Classifier(Network()), k(k), theta(theta)</div>
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||||
<div class="line"><a id="l00011" name="l00011"></a><span class="lineno"> 11</span> {</div>
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||||
<div class="line"><a id="l00012" name="l00012"></a><span class="lineno"> 12</span> validHyperparameters = { <span class="stringliteral">"k"</span>, <span class="stringliteral">"theta"</span> };</div>
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||||
<div class="line"><a id="l00013" name="l00013"></a><span class="lineno"> 13</span> </div>
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||||
<div class="line"><a id="l00014" name="l00014"></a><span class="lineno"> 14</span> }</div>
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||||
<div class="line"><a id="l00015" name="l00015"></a><span class="lineno"> 15</span> <span class="keywordtype">void</span> KDB::setHyperparameters(<span class="keyword">const</span> nlohmann::json& hyperparameters_)</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="line"><a id="l00017" name="l00017"></a><span class="lineno"> 17</span> <span class="keyword">auto</span> hyperparameters = hyperparameters_;</div>
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||||
<div class="line"><a id="l00018" name="l00018"></a><span class="lineno"> 18</span> <span class="keywordflow">if</span> (hyperparameters.contains(<span class="stringliteral">"k"</span>)) {</div>
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||||
<div class="line"><a id="l00019" name="l00019"></a><span class="lineno"> 19</span> k = hyperparameters[<span class="stringliteral">"k"</span>];</div>
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||||
<div class="line"><a id="l00020" name="l00020"></a><span class="lineno"> 20</span> hyperparameters.erase(<span class="stringliteral">"k"</span>);</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="line"><a id="l00022" name="l00022"></a><span class="lineno"> 22</span> <span class="keywordflow">if</span> (hyperparameters.contains(<span class="stringliteral">"theta"</span>)) {</div>
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||||
<div class="line"><a id="l00023" name="l00023"></a><span class="lineno"> 23</span> theta = hyperparameters[<span class="stringliteral">"theta"</span>];</div>
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||||
<div class="line"><a id="l00024" name="l00024"></a><span class="lineno"> 24</span> hyperparameters.erase(<span class="stringliteral">"theta"</span>);</div>
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<div class="line"><a id="l00025" name="l00025"></a><span class="lineno"> 25</span> }</div>
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||||
<div class="line"><a id="l00026" name="l00026"></a><span class="lineno"> 26</span> Classifier::setHyperparameters(hyperparameters);</div>
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||||
<div class="line"><a id="l00027" name="l00027"></a><span class="lineno"> 27</span> }</div>
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||||
<div class="line"><a id="l00028" name="l00028"></a><span class="lineno"> 28</span> <span class="keywordtype">void</span> KDB::buildModel(<span class="keyword">const</span> torch::Tensor& weights)</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="comment">/*</span></div>
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||||
<div class="line"><a id="l00031" name="l00031"></a><span class="lineno"> 31</span><span class="comment"> 1. For each feature Xi, compute mutual information, I(X;C),</span></div>
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||||
<div class="line"><a id="l00032" name="l00032"></a><span class="lineno"> 32</span><span class="comment"> where C is the class.</span></div>
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||||
<div class="line"><a id="l00033" name="l00033"></a><span class="lineno"> 33</span><span class="comment"> 2. Compute class conditional mutual information I(Xi;XjIC), f or each</span></div>
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||||
<div class="line"><a id="l00034" name="l00034"></a><span class="lineno"> 34</span><span class="comment"> pair of features Xi and Xj, where i#j.</span></div>
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||||
<div class="line"><a id="l00035" name="l00035"></a><span class="lineno"> 35</span><span class="comment"> 3. Let the used variable list, S, be empty.</span></div>
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||||
<div class="line"><a id="l00036" name="l00036"></a><span class="lineno"> 36</span><span class="comment"> 4. Let the DAG network being constructed, BN, begin with a single</span></div>
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||||
<div class="line"><a id="l00037" name="l00037"></a><span class="lineno"> 37</span><span class="comment"> class node, C.</span></div>
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||||
<div class="line"><a id="l00038" name="l00038"></a><span class="lineno"> 38</span><span class="comment"> 5. Repeat until S includes all domain features</span></div>
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||||
<div class="line"><a id="l00039" name="l00039"></a><span class="lineno"> 39</span><span class="comment"> 5.1. Select feature Xmax which is not in S and has the largest value</span></div>
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||||
<div class="line"><a id="l00040" name="l00040"></a><span class="lineno"> 40</span><span class="comment"> I(Xmax;C).</span></div>
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||||
<div class="line"><a id="l00041" name="l00041"></a><span class="lineno"> 41</span><span class="comment"> 5.2. Add a node to BN representing Xmax.</span></div>
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||||
<div class="line"><a id="l00042" name="l00042"></a><span class="lineno"> 42</span><span class="comment"> 5.3. Add an arc from C to Xmax in BN.</span></div>
|
||||
<div class="line"><a id="l00043" name="l00043"></a><span class="lineno"> 43</span><span class="comment"> 5.4. Add m = min(lSl,/c) arcs from m distinct features Xj in S with</span></div>
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||||
<div class="line"><a id="l00044" name="l00044"></a><span class="lineno"> 44</span><span class="comment"> the highest value for I(Xmax;X,jC).</span></div>
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||||
<div class="line"><a id="l00045" name="l00045"></a><span class="lineno"> 45</span><span class="comment"> 5.5. Add Xmax to S.</span></div>
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||||
<div class="line"><a id="l00046" name="l00046"></a><span class="lineno"> 46</span><span class="comment"> Compute the conditional probabilility infered by the structure of BN by</span></div>
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||||
<div class="line"><a id="l00047" name="l00047"></a><span class="lineno"> 47</span><span class="comment"> using counts from DB, and output BN.</span></div>
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<div class="line"><a id="l00048" name="l00048"></a><span class="lineno"> 48</span><span class="comment"> */</span></div>
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||||
<div class="line"><a id="l00049" name="l00049"></a><span class="lineno"> 49</span> <span class="comment">// 1. For each feature Xi, compute mutual information, I(X;C),</span></div>
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||||
<div class="line"><a id="l00050" name="l00050"></a><span class="lineno"> 50</span> <span class="comment">// where C is the class.</span></div>
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||||
<div class="line"><a id="l00051" name="l00051"></a><span class="lineno"> 51</span> addNodes();</div>
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||||
<div class="line"><a id="l00052" name="l00052"></a><span class="lineno"> 52</span> <span class="keyword">const</span> torch::Tensor& y = dataset.index({ -1, <span class="stringliteral">"..."</span> });</div>
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||||
<div class="line"><a id="l00053" name="l00053"></a><span class="lineno"> 53</span> std::vector<double> mi;</div>
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||||
<div class="line"><a id="l00054" name="l00054"></a><span class="lineno"> 54</span> <span class="keywordflow">for</span> (<span class="keyword">auto</span> i = 0; i < features.size(); i++) {</div>
|
||||
<div class="line"><a id="l00055" name="l00055"></a><span class="lineno"> 55</span> torch::Tensor firstFeature = dataset.index({ i, <span class="stringliteral">"..."</span> });</div>
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<div class="line"><a id="l00056" name="l00056"></a><span class="lineno"> 56</span> mi.push_back(metrics.mutualInformation(firstFeature, y, weights));</div>
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||||
<div class="line"><a id="l00057" name="l00057"></a><span class="lineno"> 57</span> }</div>
|
||||
<div class="line"><a id="l00058" name="l00058"></a><span class="lineno"> 58</span> <span class="comment">// 2. Compute class conditional mutual information I(Xi;XjIC), f or each</span></div>
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||||
<div class="line"><a id="l00059" name="l00059"></a><span class="lineno"> 59</span> <span class="keyword">auto</span> conditionalEdgeWeights = metrics.conditionalEdge(weights);</div>
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||||
<div class="line"><a id="l00060" name="l00060"></a><span class="lineno"> 60</span> <span class="comment">// 3. Let the used variable list, S, be empty.</span></div>
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||||
<div class="line"><a id="l00061" name="l00061"></a><span class="lineno"> 61</span> std::vector<int> S;</div>
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||||
<div class="line"><a id="l00062" name="l00062"></a><span class="lineno"> 62</span> <span class="comment">// 4. Let the DAG network being constructed, BN, begin with a single</span></div>
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||||
<div class="line"><a id="l00063" name="l00063"></a><span class="lineno"> 63</span> <span class="comment">// class node, C.</span></div>
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||||
<div class="line"><a id="l00064" name="l00064"></a><span class="lineno"> 64</span> <span class="comment">// 5. Repeat until S includes all domain features</span></div>
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||||
<div class="line"><a id="l00065" name="l00065"></a><span class="lineno"> 65</span> <span class="comment">// 5.1. Select feature Xmax which is not in S and has the largest value</span></div>
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||||
<div class="line"><a id="l00066" name="l00066"></a><span class="lineno"> 66</span> <span class="comment">// I(Xmax;C).</span></div>
|
||||
<div class="line"><a id="l00067" name="l00067"></a><span class="lineno"> 67</span> <span class="keyword">auto</span> order = argsort(mi);</div>
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||||
<div class="line"><a id="l00068" name="l00068"></a><span class="lineno"> 68</span> <span class="keywordflow">for</span> (<span class="keyword">auto</span> idx : order) {</div>
|
||||
<div class="line"><a id="l00069" name="l00069"></a><span class="lineno"> 69</span> <span class="comment">// 5.2. Add a node to BN representing Xmax.</span></div>
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||||
<div class="line"><a id="l00070" name="l00070"></a><span class="lineno"> 70</span> <span class="comment">// 5.3. Add an arc from C to Xmax in BN.</span></div>
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||||
<div class="line"><a id="l00071" name="l00071"></a><span class="lineno"> 71</span> model.addEdge(className, features[idx]);</div>
|
||||
<div class="line"><a id="l00072" name="l00072"></a><span class="lineno"> 72</span> <span class="comment">// 5.4. Add m = min(lSl,/c) arcs from m distinct features Xj in S with</span></div>
|
||||
<div class="line"><a id="l00073" name="l00073"></a><span class="lineno"> 73</span> <span class="comment">// the highest value for I(Xmax;X,jC).</span></div>
|
||||
<div class="line"><a id="l00074" name="l00074"></a><span class="lineno"> 74</span> add_m_edges(idx, S, conditionalEdgeWeights);</div>
|
||||
<div class="line"><a id="l00075" name="l00075"></a><span class="lineno"> 75</span> <span class="comment">// 5.5. Add Xmax to S.</span></div>
|
||||
<div class="line"><a id="l00076" name="l00076"></a><span class="lineno"> 76</span> S.push_back(idx);</div>
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||||
<div class="line"><a id="l00077" name="l00077"></a><span class="lineno"> 77</span> }</div>
|
||||
<div class="line"><a id="l00078" name="l00078"></a><span class="lineno"> 78</span> }</div>
|
||||
<div class="line"><a id="l00079" name="l00079"></a><span class="lineno"> 79</span> <span class="keywordtype">void</span> KDB::add_m_edges(<span class="keywordtype">int</span> idx, std::vector<int>& S, torch::Tensor& weights)</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="keyword">auto</span> n_edges = std::min(k, <span class="keyword">static_cast<</span><span class="keywordtype">int</span><span class="keyword">></span>(S.size()));</div>
|
||||
<div class="line"><a id="l00082" name="l00082"></a><span class="lineno"> 82</span> <span class="keyword">auto</span> cond_w = clone(weights);</div>
|
||||
<div class="line"><a id="l00083" name="l00083"></a><span class="lineno"> 83</span> <span class="keywordtype">bool</span> exit_cond = k == 0;</div>
|
||||
<div class="line"><a id="l00084" name="l00084"></a><span class="lineno"> 84</span> <span class="keywordtype">int</span> num = 0;</div>
|
||||
<div class="line"><a id="l00085" name="l00085"></a><span class="lineno"> 85</span> <span class="keywordflow">while</span> (!exit_cond) {</div>
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||||
<div class="line"><a id="l00086" name="l00086"></a><span class="lineno"> 86</span> <span class="keyword">auto</span> max_minfo = argmax(cond_w.index({ idx, <span class="stringliteral">"..."</span> })).item<<span class="keywordtype">int</span>>();</div>
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||||
<div class="line"><a id="l00087" name="l00087"></a><span class="lineno"> 87</span> <span class="keyword">auto</span> belongs = find(S.begin(), S.end(), max_minfo) != S.end();</div>
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||||
<div class="line"><a id="l00088" name="l00088"></a><span class="lineno"> 88</span> <span class="keywordflow">if</span> (belongs && cond_w.index({ idx, max_minfo }).item<<span class="keywordtype">float</span>>() > theta) {</div>
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||||
<div class="line"><a id="l00089" name="l00089"></a><span class="lineno"> 89</span> <span class="keywordflow">try</span> {</div>
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||||
<div class="line"><a id="l00090" name="l00090"></a><span class="lineno"> 90</span> model.addEdge(features[max_minfo], features[idx]);</div>
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||||
<div class="line"><a id="l00091" name="l00091"></a><span class="lineno"> 91</span> num++;</div>
|
||||
<div class="line"><a id="l00092" name="l00092"></a><span class="lineno"> 92</span> }</div>
|
||||
<div class="line"><a id="l00093" name="l00093"></a><span class="lineno"> 93</span> <span class="keywordflow">catch</span> (<span class="keyword">const</span> std::invalid_argument& e) {</div>
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||||
<div class="line"><a id="l00094" name="l00094"></a><span class="lineno"> 94</span> <span class="comment">// Loops are not allowed</span></div>
|
||||
<div class="line"><a id="l00095" name="l00095"></a><span class="lineno"> 95</span> }</div>
|
||||
<div class="line"><a id="l00096" name="l00096"></a><span class="lineno"> 96</span> }</div>
|
||||
<div class="line"><a id="l00097" name="l00097"></a><span class="lineno"> 97</span> cond_w.index_put_({ idx, max_minfo }, -1);</div>
|
||||
<div class="line"><a id="l00098" name="l00098"></a><span class="lineno"> 98</span> <span class="keyword">auto</span> candidates_mask = cond_w.index({ idx, <span class="stringliteral">"..."</span> }).gt(theta);</div>
|
||||
<div class="line"><a id="l00099" name="l00099"></a><span class="lineno"> 99</span> <span class="keyword">auto</span> candidates = candidates_mask.nonzero();</div>
|
||||
<div class="line"><a id="l00100" name="l00100"></a><span class="lineno"> 100</span> exit_cond = num == n_edges || candidates.size(0) == 0;</div>
|
||||
<div class="line"><a id="l00101" name="l00101"></a><span class="lineno"> 101</span> }</div>
|
||||
<div class="line"><a id="l00102" name="l00102"></a><span class="lineno"> 102</span> }</div>
|
||||
<div class="line"><a id="l00103" name="l00103"></a><span class="lineno"> 103</span> std::vector<std::string> KDB::graph(<span class="keyword">const</span> std::string& title)<span class="keyword"> const</span></div>
|
||||
<div class="line"><a id="l00104" name="l00104"></a><span class="lineno"> 104</span><span class="keyword"> </span>{</div>
|
||||
<div class="line"><a id="l00105" name="l00105"></a><span class="lineno"> 105</span> std::string header{ title };</div>
|
||||
<div class="line"><a id="l00106" name="l00106"></a><span class="lineno"> 106</span> <span class="keywordflow">if</span> (title == <span class="stringliteral">"KDB"</span>) {</div>
|
||||
<div class="line"><a id="l00107" name="l00107"></a><span class="lineno"> 107</span> header += <span class="stringliteral">" (k="</span> + std::to_string(k) + <span class="stringliteral">", theta="</span> + std::to_string(theta) + <span class="stringliteral">")"</span>;</div>
|
||||
<div class="line"><a id="l00108" name="l00108"></a><span class="lineno"> 108</span> }</div>
|
||||
<div class="line"><a id="l00109" name="l00109"></a><span class="lineno"> 109</span> <span class="keywordflow">return</span> model.graph(header);</div>
|
||||
<div class="line"><a id="l00110" name="l00110"></a><span class="lineno"> 110</span> }</div>
|
||||
<div class="line"><a id="l00111" name="l00111"></a><span class="lineno"> 111</span>}</div>
|
||||
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Reference in New Issue
Block a user