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<div id="projectname">BayesNet<span id="projectnumber">&#160;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="fragment"><div class="line"><a id="l00001" name="l00001"></a><span class="lineno"> 1</span><span class="comment">// ***************************************************************</span></div>
<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>
<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>
<div class="line"><a id="l00006" name="l00006"></a><span class="lineno"> 6</span> </div>
<div class="line"><a id="l00007" name="l00007"></a><span class="lineno"> 7</span><span class="preprocessor">#include &quot;TAN.h&quot;</span></div>
<div class="line"><a id="l00008" name="l00008"></a><span class="lineno"> 8</span> </div>
<div class="line"><a id="l00009" name="l00009"></a><span class="lineno"> 9</span><span class="keyword">namespace </span>bayesnet {</div>
<div class="line"><a id="l00010" name="l00010"></a><span class="lineno"> 10</span> TAN::TAN() : Classifier(Network()) {}</div>
<div class="line"><a id="l00011" name="l00011"></a><span class="lineno"> 11</span> </div>
<div class="line"><a id="l00012" name="l00012"></a><span class="lineno"> 12</span> <span class="keywordtype">void</span> TAN::buildModel(<span class="keyword">const</span> torch::Tensor&amp; weights)</div>
<div class="line"><a id="l00013" name="l00013"></a><span class="lineno"> 13</span> {</div>
<div class="line"><a id="l00014" name="l00014"></a><span class="lineno"> 14</span> <span class="comment">// 0. Add all nodes to the model</span></div>
<div class="line"><a id="l00015" name="l00015"></a><span class="lineno"> 15</span> addNodes();</div>
<div class="line"><a id="l00016" name="l00016"></a><span class="lineno"> 16</span> <span class="comment">// 1. Compute mutual information between each feature and the class and set the root node</span></div>
<div class="line"><a id="l00017" name="l00017"></a><span class="lineno"> 17</span> <span class="comment">// as the highest mutual information with the class</span></div>
<div class="line"><a id="l00018" name="l00018"></a><span class="lineno"> 18</span> <span class="keyword">auto</span> mi = std::vector &lt;std::pair&lt;int, float &gt;&gt;();</div>
<div class="line"><a id="l00019" name="l00019"></a><span class="lineno"> 19</span> torch::Tensor class_dataset = dataset.index({ -1, <span class="stringliteral">&quot;...&quot;</span> });</div>
<div class="line"><a id="l00020" name="l00020"></a><span class="lineno"> 20</span> <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i &lt; static_cast&lt;int&gt;(features.size()); ++i) {</div>
<div class="line"><a id="l00021" name="l00021"></a><span class="lineno"> 21</span> torch::Tensor feature_dataset = dataset.index({ i, <span class="stringliteral">&quot;...&quot;</span> });</div>
<div class="line"><a id="l00022" name="l00022"></a><span class="lineno"> 22</span> <span class="keyword">auto</span> mi_value = metrics.mutualInformation(class_dataset, feature_dataset, weights);</div>
<div class="line"><a id="l00023" name="l00023"></a><span class="lineno"> 23</span> mi.push_back({ i, mi_value });</div>
<div class="line"><a id="l00024" name="l00024"></a><span class="lineno"> 24</span> }</div>
<div class="line"><a id="l00025" name="l00025"></a><span class="lineno"> 25</span> sort(mi.begin(), mi.end(), [](<span class="keyword">const</span> <span class="keyword">auto</span>&amp; left, <span class="keyword">const</span> <span class="keyword">auto</span>&amp; right) {return left.second &lt; right.second;});</div>
<div class="line"><a id="l00026" name="l00026"></a><span class="lineno"> 26</span> <span class="keyword">auto</span> root = mi[mi.size() - 1].first;</div>
<div class="line"><a id="l00027" name="l00027"></a><span class="lineno"> 27</span> <span class="comment">// 2. Compute mutual information between each feature and the class</span></div>
<div class="line"><a id="l00028" name="l00028"></a><span class="lineno"> 28</span> <span class="keyword">auto</span> weights_matrix = metrics.conditionalEdge(weights);</div>
<div class="line"><a id="l00029" name="l00029"></a><span class="lineno"> 29</span> <span class="comment">// 3. Compute the maximum spanning tree</span></div>
<div class="line"><a id="l00030" name="l00030"></a><span class="lineno"> 30</span> <span class="keyword">auto</span> mst = metrics.maximumSpanningTree(features, weights_matrix, root);</div>
<div class="line"><a id="l00031" name="l00031"></a><span class="lineno"> 31</span> <span class="comment">// 4. Add edges from the maximum spanning tree to the model</span></div>
<div class="line"><a id="l00032" name="l00032"></a><span class="lineno"> 32</span> <span class="keywordflow">for</span> (<span class="keyword">auto</span> i = 0; i &lt; mst.size(); ++i) {</div>
<div class="line"><a id="l00033" name="l00033"></a><span class="lineno"> 33</span> <span class="keyword">auto</span> [from, to] = mst[i];</div>
<div class="line"><a id="l00034" name="l00034"></a><span class="lineno"> 34</span> model.addEdge(features[from], features[to]);</div>
<div class="line"><a id="l00035" name="l00035"></a><span class="lineno"> 35</span> }</div>
<div class="line"><a id="l00036" name="l00036"></a><span class="lineno"> 36</span> <span class="comment">// 5. Add edges from the class to all features</span></div>
<div class="line"><a id="l00037" name="l00037"></a><span class="lineno"> 37</span> <span class="keywordflow">for</span> (<span class="keyword">auto</span> feature : features) {</div>
<div class="line"><a id="l00038" name="l00038"></a><span class="lineno"> 38</span> model.addEdge(className, feature);</div>
<div class="line"><a id="l00039" name="l00039"></a><span class="lineno"> 39</span> }</div>
<div class="line"><a id="l00040" name="l00040"></a><span class="lineno"> 40</span> }</div>
<div class="line"><a id="l00041" name="l00041"></a><span class="lineno"> 41</span> std::vector&lt;std::string&gt; TAN::graph(<span class="keyword">const</span> std::string&amp; title)<span class="keyword"> const</span></div>
<div class="line"><a id="l00042" name="l00042"></a><span class="lineno"> 42</span><span class="keyword"> </span>{</div>
<div class="line"><a id="l00043" name="l00043"></a><span class="lineno"> 43</span> <span class="keywordflow">return</span> model.graph(title);</div>
<div class="line"><a id="l00044" name="l00044"></a><span class="lineno"> 44</span> }</div>
<div class="line"><a id="l00045" name="l00045"></a><span class="lineno"> 45</span>}</div>
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