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Return File Library to /lib as it is needed by Local Discretization (factorize)

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Ricardo Montañana Gómez
2024-04-30 20:31:14 +02:00
parent 7aeffba740
commit 618a1e539c
148 changed files with 1804 additions and 1769 deletions
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html/bayesnet/ensembles/AODELd.cc.gcov.html
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@@ -37,7 +37,7 @@
</tr>
<tr>
<td class="headerItem">Test Date:</td>
<td class="headerValue">2024-04-30 13:59:18</td>
<td class="headerValue">2024-04-30 20:26:57</td>
<td></td>
<td class="headerItem">Functions:</td>
<td class="headerCovTableEntryHi">100.0&nbsp;%</td>
@@ -69,42 +69,42 @@
<span id="L7"><span class="lineNum"> 7</span> : #include &quot;AODELd.h&quot;</span>
<span id="L8"><span class="lineNum"> 8</span> : </span>
<span id="L9"><span class="lineNum"> 9</span> : namespace bayesnet {</span>
<span id="L10"><span class="lineNum"> 10</span> <span class="tlaGNC tlaBgGNC"> 102 : AODELd::AODELd(bool predict_voting) : Ensemble(predict_voting), Proposal(dataset, features, className)</span></span>
<span id="L10"><span class="lineNum"> 10</span> <span class="tlaGNC tlaBgGNC"> 34 : AODELd::AODELd(bool predict_voting) : Ensemble(predict_voting), Proposal(dataset, features, className)</span></span>
<span id="L11"><span class="lineNum"> 11</span> : {</span>
<span id="L12"><span class="lineNum"> 12</span> <span class="tlaGNC"> 102 : }</span></span>
<span id="L13"><span class="lineNum"> 13</span> <span class="tlaGNC"> 30 : AODELd&amp; AODELd::fit(torch::Tensor&amp; X_, torch::Tensor&amp; y_, const std::vector&lt;std::string&gt;&amp; features_, const std::string&amp; className_, map&lt;std::string, std::vector&lt;int&gt;&gt;&amp; states_)</span></span>
<span id="L12"><span class="lineNum"> 12</span> <span class="tlaGNC"> 34 : }</span></span>
<span id="L13"><span class="lineNum"> 13</span> <span class="tlaGNC"> 10 : AODELd&amp; AODELd::fit(torch::Tensor&amp; X_, torch::Tensor&amp; y_, const std::vector&lt;std::string&gt;&amp; features_, const std::string&amp; className_, map&lt;std::string, std::vector&lt;int&gt;&gt;&amp; states_)</span></span>
<span id="L14"><span class="lineNum"> 14</span> : {</span>
<span id="L15"><span class="lineNum"> 15</span> <span class="tlaGNC"> 30 : checkInput(X_, y_);</span></span>
<span id="L16"><span class="lineNum"> 16</span> <span class="tlaGNC"> 30 : features = features_;</span></span>
<span id="L17"><span class="lineNum"> 17</span> <span class="tlaGNC"> 30 : className = className_;</span></span>
<span id="L18"><span class="lineNum"> 18</span> <span class="tlaGNC"> 30 : Xf = X_;</span></span>
<span id="L19"><span class="lineNum"> 19</span> <span class="tlaGNC"> 30 : y = y_;</span></span>
<span id="L15"><span class="lineNum"> 15</span> <span class="tlaGNC"> 10 : checkInput(X_, y_);</span></span>
<span id="L16"><span class="lineNum"> 16</span> <span class="tlaGNC"> 10 : features = features_;</span></span>
<span id="L17"><span class="lineNum"> 17</span> <span class="tlaGNC"> 10 : className = className_;</span></span>
<span id="L18"><span class="lineNum"> 18</span> <span class="tlaGNC"> 10 : Xf = X_;</span></span>
<span id="L19"><span class="lineNum"> 19</span> <span class="tlaGNC"> 10 : y = y_;</span></span>
<span id="L20"><span class="lineNum"> 20</span> : // Fills std::vectors Xv &amp; yv with the data from tensors X_ (discretized) &amp; y</span>
<span id="L21"><span class="lineNum"> 21</span> <span class="tlaGNC"> 30 : states = fit_local_discretization(y);</span></span>
<span id="L21"><span class="lineNum"> 21</span> <span class="tlaGNC"> 10 : states = fit_local_discretization(y);</span></span>
<span id="L22"><span class="lineNum"> 22</span> : // We have discretized the input data</span>
<span id="L23"><span class="lineNum"> 23</span> : // 1st we need to fit the model to build the normal TAN structure, TAN::fit initializes the base Bayesian network</span>
<span id="L24"><span class="lineNum"> 24</span> <span class="tlaGNC"> 30 : Ensemble::fit(dataset, features, className, states);</span></span>
<span id="L25"><span class="lineNum"> 25</span> <span class="tlaGNC"> 30 : return *this;</span></span>
<span id="L24"><span class="lineNum"> 24</span> <span class="tlaGNC"> 10 : Ensemble::fit(dataset, features, className, states);</span></span>
<span id="L25"><span class="lineNum"> 25</span> <span class="tlaGNC"> 10 : return *this;</span></span>
<span id="L26"><span class="lineNum"> 26</span> : </span>
<span id="L27"><span class="lineNum"> 27</span> : }</span>
<span id="L28"><span class="lineNum"> 28</span> <span class="tlaGNC"> 30 : void AODELd::buildModel(const torch::Tensor&amp; weights)</span></span>
<span id="L28"><span class="lineNum"> 28</span> <span class="tlaGNC"> 10 : void AODELd::buildModel(const torch::Tensor&amp; weights)</span></span>
<span id="L29"><span class="lineNum"> 29</span> : {</span>
<span id="L30"><span class="lineNum"> 30</span> <span class="tlaGNC"> 30 : models.clear();</span></span>
<span id="L31"><span class="lineNum"> 31</span> <span class="tlaGNC"> 252 : for (int i = 0; i &lt; features.size(); ++i) {</span></span>
<span id="L32"><span class="lineNum"> 32</span> <span class="tlaGNC"> 222 : models.push_back(std::make_unique&lt;SPODELd&gt;(i));</span></span>
<span id="L30"><span class="lineNum"> 30</span> <span class="tlaGNC"> 10 : models.clear();</span></span>
<span id="L31"><span class="lineNum"> 31</span> <span class="tlaGNC"> 84 : for (int i = 0; i &lt; features.size(); ++i) {</span></span>
<span id="L32"><span class="lineNum"> 32</span> <span class="tlaGNC"> 74 : models.push_back(std::make_unique&lt;SPODELd&gt;(i));</span></span>
<span id="L33"><span class="lineNum"> 33</span> : }</span>
<span id="L34"><span class="lineNum"> 34</span> <span class="tlaGNC"> 30 : n_models = models.size();</span></span>
<span id="L35"><span class="lineNum"> 35</span> <span class="tlaGNC"> 30 : significanceModels = std::vector&lt;double&gt;(n_models, 1.0);</span></span>
<span id="L36"><span class="lineNum"> 36</span> <span class="tlaGNC"> 30 : }</span></span>
<span id="L37"><span class="lineNum"> 37</span> <span class="tlaGNC"> 30 : void AODELd::trainModel(const torch::Tensor&amp; weights)</span></span>
<span id="L34"><span class="lineNum"> 34</span> <span class="tlaGNC"> 10 : n_models = models.size();</span></span>
<span id="L35"><span class="lineNum"> 35</span> <span class="tlaGNC"> 10 : significanceModels = std::vector&lt;double&gt;(n_models, 1.0);</span></span>
<span id="L36"><span class="lineNum"> 36</span> <span class="tlaGNC"> 10 : }</span></span>
<span id="L37"><span class="lineNum"> 37</span> <span class="tlaGNC"> 10 : void AODELd::trainModel(const torch::Tensor&amp; weights)</span></span>
<span id="L38"><span class="lineNum"> 38</span> : {</span>
<span id="L39"><span class="lineNum"> 39</span> <span class="tlaGNC"> 252 : for (const auto&amp; model : models) {</span></span>
<span id="L40"><span class="lineNum"> 40</span> <span class="tlaGNC"> 222 : model-&gt;fit(Xf, y, features, className, states);</span></span>
<span id="L39"><span class="lineNum"> 39</span> <span class="tlaGNC"> 84 : for (const auto&amp; model : models) {</span></span>
<span id="L40"><span class="lineNum"> 40</span> <span class="tlaGNC"> 74 : model-&gt;fit(Xf, y, features, className, states);</span></span>
<span id="L41"><span class="lineNum"> 41</span> : }</span>
<span id="L42"><span class="lineNum"> 42</span> <span class="tlaGNC"> 30 : }</span></span>
<span id="L43"><span class="lineNum"> 43</span> <span class="tlaGNC"> 6 : std::vector&lt;std::string&gt; AODELd::graph(const std::string&amp; name) const</span></span>
<span id="L42"><span class="lineNum"> 42</span> <span class="tlaGNC"> 10 : }</span></span>
<span id="L43"><span class="lineNum"> 43</span> <span class="tlaGNC"> 2 : std::vector&lt;std::string&gt; AODELd::graph(const std::string&amp; name) const</span></span>
<span id="L44"><span class="lineNum"> 44</span> : {</span>
<span id="L45"><span class="lineNum"> 45</span> <span class="tlaGNC"> 6 : return Ensemble::graph(name);</span></span>
<span id="L45"><span class="lineNum"> 45</span> <span class="tlaGNC"> 2 : return Ensemble::graph(name);</span></span>
<span id="L46"><span class="lineNum"> 46</span> : }</span>
<span id="L47"><span class="lineNum"> 47</span> : }</span>
</pre>