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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/classifiers/TANLd.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>
@@ -69,31 +69,31 @@
<span id="L7"><span class="lineNum"> 7</span> : #include &quot;TANLd.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 : TANLd::TANLd() : TAN(), Proposal(dataset, features, className) {}</span></span>
<span id="L11"><span class="lineNum"> 11</span> <span class="tlaGNC"> 30 : TANLd&amp; TANLd::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="L10"><span class="lineNum"> 10</span> <span class="tlaGNC tlaBgGNC"> 34 : TANLd::TANLd() : TAN(), Proposal(dataset, features, className) {}</span></span>
<span id="L11"><span class="lineNum"> 11</span> <span class="tlaGNC"> 10 : TANLd&amp; TANLd::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>
<span id="L13"><span class="lineNum"> 13</span> <span class="tlaGNC"> 30 : checkInput(X_, y_);</span></span>
<span id="L14"><span class="lineNum"> 14</span> <span class="tlaGNC"> 30 : features = features_;</span></span>
<span id="L15"><span class="lineNum"> 15</span> <span class="tlaGNC"> 30 : className = className_;</span></span>
<span id="L16"><span class="lineNum"> 16</span> <span class="tlaGNC"> 30 : Xf = X_;</span></span>
<span id="L17"><span class="lineNum"> 17</span> <span class="tlaGNC"> 30 : y = y_;</span></span>
<span id="L13"><span class="lineNum"> 13</span> <span class="tlaGNC"> 10 : checkInput(X_, y_);</span></span>
<span id="L14"><span class="lineNum"> 14</span> <span class="tlaGNC"> 10 : features = features_;</span></span>
<span id="L15"><span class="lineNum"> 15</span> <span class="tlaGNC"> 10 : className = className_;</span></span>
<span id="L16"><span class="lineNum"> 16</span> <span class="tlaGNC"> 10 : Xf = X_;</span></span>
<span id="L17"><span class="lineNum"> 17</span> <span class="tlaGNC"> 10 : y = y_;</span></span>
<span id="L18"><span class="lineNum"> 18</span> : // Fills std::vectors Xv &amp; yv with the data from tensors X_ (discretized) &amp; y</span>
<span id="L19"><span class="lineNum"> 19</span> <span class="tlaGNC"> 30 : states = fit_local_discretization(y);</span></span>
<span id="L19"><span class="lineNum"> 19</span> <span class="tlaGNC"> 10 : states = fit_local_discretization(y);</span></span>
<span id="L20"><span class="lineNum"> 20</span> : // We have discretized the input data</span>
<span id="L21"><span class="lineNum"> 21</span> : // 1st we need to fit the model to build the normal TAN structure, TAN::fit initializes the base Bayesian network</span>
<span id="L22"><span class="lineNum"> 22</span> <span class="tlaGNC"> 30 : TAN::fit(dataset, features, className, states);</span></span>
<span id="L23"><span class="lineNum"> 23</span> <span class="tlaGNC"> 30 : states = localDiscretizationProposal(states, model);</span></span>
<span id="L24"><span class="lineNum"> 24</span> <span class="tlaGNC"> 30 : return *this;</span></span>
<span id="L22"><span class="lineNum"> 22</span> <span class="tlaGNC"> 10 : TAN::fit(dataset, features, className, states);</span></span>
<span id="L23"><span class="lineNum"> 23</span> <span class="tlaGNC"> 10 : states = localDiscretizationProposal(states, model);</span></span>
<span id="L24"><span class="lineNum"> 24</span> <span class="tlaGNC"> 10 : return *this;</span></span>
<span id="L25"><span class="lineNum"> 25</span> : </span>
<span id="L26"><span class="lineNum"> 26</span> : }</span>
<span id="L27"><span class="lineNum"> 27</span> <span class="tlaGNC"> 24 : torch::Tensor TANLd::predict(torch::Tensor&amp; X)</span></span>
<span id="L27"><span class="lineNum"> 27</span> <span class="tlaGNC"> 8 : torch::Tensor TANLd::predict(torch::Tensor&amp; X)</span></span>
<span id="L28"><span class="lineNum"> 28</span> : {</span>
<span id="L29"><span class="lineNum"> 29</span> <span class="tlaGNC"> 24 : auto Xt = prepareX(X);</span></span>
<span id="L30"><span class="lineNum"> 30</span> <span class="tlaGNC"> 48 : return TAN::predict(Xt);</span></span>
<span id="L31"><span class="lineNum"> 31</span> <span class="tlaGNC"> 24 : }</span></span>
<span id="L32"><span class="lineNum"> 32</span> <span class="tlaGNC"> 6 : std::vector&lt;std::string&gt; TANLd::graph(const std::string&amp; name) const</span></span>
<span id="L29"><span class="lineNum"> 29</span> <span class="tlaGNC"> 8 : auto Xt = prepareX(X);</span></span>
<span id="L30"><span class="lineNum"> 30</span> <span class="tlaGNC"> 16 : return TAN::predict(Xt);</span></span>
<span id="L31"><span class="lineNum"> 31</span> <span class="tlaGNC"> 8 : }</span></span>
<span id="L32"><span class="lineNum"> 32</span> <span class="tlaGNC"> 2 : std::vector&lt;std::string&gt; TANLd::graph(const std::string&amp; name) const</span></span>
<span id="L33"><span class="lineNum"> 33</span> : {</span>
<span id="L34"><span class="lineNum"> 34</span> <span class="tlaGNC"> 6 : return TAN::graph(name);</span></span>
<span id="L34"><span class="lineNum"> 34</span> <span class="tlaGNC"> 2 : return TAN::graph(name);</span></span>
<span id="L35"><span class="lineNum"> 35</span> : }</span>
<span id="L36"><span class="lineNum"> 36</span> : }</span>
</pre>