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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">KDBLd.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 "KDBLd.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> KDBLd::KDBLd(<span class="keywordtype">int</span> k) : KDB(k), Proposal(dataset, features, className) {}</div>
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<div class="line"><a id="l00011" name="l00011"></a><span class="lineno"> 11</span> KDBLd& KDBLd::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_, map<std::string, std::vector<int>>& states_)</div>
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<div class="line"><a id="l00012" name="l00012"></a><span class="lineno"> 12</span> {</div>
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<div class="line"><a id="l00013" name="l00013"></a><span class="lineno"> 13</span> checkInput(X_, y_);</div>
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<div class="line"><a id="l00014" name="l00014"></a><span class="lineno"> 14</span> features = features_;</div>
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<div class="line"><a id="l00015" name="l00015"></a><span class="lineno"> 15</span> className = className_;</div>
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<div class="line"><a id="l00016" name="l00016"></a><span class="lineno"> 16</span> Xf = X_;</div>
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<div class="line"><a id="l00017" name="l00017"></a><span class="lineno"> 17</span> y = y_;</div>
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<div class="line"><a id="l00018" name="l00018"></a><span class="lineno"> 18</span> <span class="comment">// Fills std::vectors Xv & yv with the data from tensors X_ (discretized) & y</span></div>
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<div class="line"><a id="l00019" name="l00019"></a><span class="lineno"> 19</span> states = fit_local_discretization(y);</div>
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<div class="line"><a id="l00020" name="l00020"></a><span class="lineno"> 20</span> <span class="comment">// We have discretized the input data</span></div>
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<div class="line"><a id="l00021" name="l00021"></a><span class="lineno"> 21</span> <span class="comment">// 1st we need to fit the model to build the normal KDB structure, KDB::fit initializes the base Bayesian network</span></div>
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<div class="line"><a id="l00022" name="l00022"></a><span class="lineno"> 22</span> KDB::fit(dataset, features, className, states);</div>
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<div class="line"><a id="l00023" name="l00023"></a><span class="lineno"> 23</span> states = localDiscretizationProposal(states, model);</div>
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<div class="line"><a id="l00024" name="l00024"></a><span class="lineno"> 24</span> <span class="keywordflow">return</span> *<span class="keyword">this</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> torch::Tensor KDBLd::predict(torch::Tensor& X)</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="keyword">auto</span> Xt = prepareX(X);</div>
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<div class="line"><a id="l00029" name="l00029"></a><span class="lineno"> 29</span> <span class="keywordflow">return</span> KDB::predict(Xt);</div>
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<div class="line"><a id="l00030" name="l00030"></a><span class="lineno"> 30</span> }</div>
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<div class="line"><a id="l00031" name="l00031"></a><span class="lineno"> 31</span> std::vector<std::string> KDBLd::graph(<span class="keyword">const</span> std::string& name)<span class="keyword"> const</span></div>
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<div class="line"><a id="l00032" name="l00032"></a><span class="lineno"> 32</span><span class="keyword"> </span>{</div>
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<div class="line"><a id="l00033" name="l00033"></a><span class="lineno"> 33</span> <span class="keywordflow">return</span> KDB::graph(name);</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>}</div>
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<li class="navelem"><a class="el" href="dir_40070fdff85d618b4d1d3ab4ac4f79bb.html">bayesnet</a></li><li class="navelem"><a class="el" href="dir_520a649ed2b1c3b658a695aeefe46a5a.html">classifiers</a></li><li class="navelem"><b>KDBLd.cc</b></li>
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