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Add hyperparameter convergence_best

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move test libraries to test folder
This commit is contained in:
Ricardo Montañana Gómez
2024-04-30 00:52:09 +02:00
parent f014928411
commit ae469b8146
721 changed files with 206095 additions and 2496 deletions
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36
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">2024-04-21 17:30:26</td>
<td class="headerValue">2024-04-29 20:48:03</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"> 17 : TANLd::TANLd() : TAN(), Proposal(dataset, features, className) {}</span></span>
<span id="L11"><span class="lineNum"> 11</span> <span class="tlaGNC"> 5 : 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"> 187 : TANLd::TANLd() : TAN(), Proposal(dataset, features, className) {}</span></span>
<span id="L11"><span class="lineNum"> 11</span> <span class="tlaGNC"> 55 : 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"> 5 : checkInput(X_, y_);</span></span>
<span id="L14"><span class="lineNum"> 14</span> <span class="tlaGNC"> 5 : features = features_;</span></span>
<span id="L15"><span class="lineNum"> 15</span> <span class="tlaGNC"> 5 : className = className_;</span></span>
<span id="L16"><span class="lineNum"> 16</span> <span class="tlaGNC"> 5 : Xf = X_;</span></span>
<span id="L17"><span class="lineNum"> 17</span> <span class="tlaGNC"> 5 : y = y_;</span></span>
<span id="L13"><span class="lineNum"> 13</span> <span class="tlaGNC"> 55 : checkInput(X_, y_);</span></span>
<span id="L14"><span class="lineNum"> 14</span> <span class="tlaGNC"> 55 : features = features_;</span></span>
<span id="L15"><span class="lineNum"> 15</span> <span class="tlaGNC"> 55 : className = className_;</span></span>
<span id="L16"><span class="lineNum"> 16</span> <span class="tlaGNC"> 55 : Xf = X_;</span></span>
<span id="L17"><span class="lineNum"> 17</span> <span class="tlaGNC"> 55 : 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"> 5 : states = fit_local_discretization(y);</span></span>
<span id="L19"><span class="lineNum"> 19</span> <span class="tlaGNC"> 55 : 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"> 5 : TAN::fit(dataset, features, className, states);</span></span>
<span id="L23"><span class="lineNum"> 23</span> <span class="tlaGNC"> 5 : states = localDiscretizationProposal(states, model);</span></span>
<span id="L24"><span class="lineNum"> 24</span> <span class="tlaGNC"> 5 : return *this;</span></span>
<span id="L22"><span class="lineNum"> 22</span> <span class="tlaGNC"> 55 : TAN::fit(dataset, features, className, states);</span></span>
<span id="L23"><span class="lineNum"> 23</span> <span class="tlaGNC"> 55 : states = localDiscretizationProposal(states, model);</span></span>
<span id="L24"><span class="lineNum"> 24</span> <span class="tlaGNC"> 55 : 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"> 4 : torch::Tensor TANLd::predict(torch::Tensor&amp; X)</span></span>
<span id="L27"><span class="lineNum"> 27</span> <span class="tlaGNC"> 44 : 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"> 4 : auto Xt = prepareX(X);</span></span>
<span id="L30"><span class="lineNum"> 30</span> <span class="tlaGNC"> 8 : return TAN::predict(Xt);</span></span>
<span id="L31"><span class="lineNum"> 31</span> <span class="tlaGNC"> 4 : }</span></span>
<span id="L32"><span class="lineNum"> 32</span> <span class="tlaGNC"> 1 : 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"> 44 : auto Xt = prepareX(X);</span></span>
<span id="L30"><span class="lineNum"> 30</span> <span class="tlaGNC"> 88 : return TAN::predict(Xt);</span></span>
<span id="L31"><span class="lineNum"> 31</span> <span class="tlaGNC"> 44 : }</span></span>
<span id="L32"><span class="lineNum"> 32</span> <span class="tlaGNC"> 11 : 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"> 1 : return TAN::graph(name);</span></span>
<span id="L34"><span class="lineNum"> 34</span> <span class="tlaGNC"> 11 : 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>