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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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10
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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>
@@ -65,28 +65,28 @@
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</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>
@@ -65,28 +65,28 @@
<tr>
<td class="coverFn"><a href="AODE.cc.gcov.html#L22">_ZN8bayesnet4AODE10buildModelERKN2at6TensorE</a></td>
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<td class="coverFn"><a href="AODE.cc.gcov.html#L13">_ZN8bayesnet4AODE18setHyperparametersERKN8nlohmann16json_abi_v3_11_310basic_jsonISt3mapSt6vectorNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEblmdSaNS2_14adl_serializerES5_IhSaIhEEvEE</a></td>
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<td class="coverFn"><a href="AODE.cc.gcov.html#L32">_ZNK8bayesnet4AODE5graphERKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEE</a></td>
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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>
@@ -69,33 +69,33 @@
<span id="L7"><span class="lineNum"> 7</span> : #include &quot;AODE.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"> 19 : AODE::AODE(bool predict_voting) : Ensemble(predict_voting)</span></span>
<span id="L10"><span class="lineNum"> 10</span> <span class="tlaGNC tlaBgGNC"> 209 : AODE::AODE(bool predict_voting) : Ensemble(predict_voting)</span></span>
<span id="L11"><span class="lineNum"> 11</span> : {</span>
<span id="L12"><span class="lineNum"> 12</span> <span class="tlaGNC"> 38 : validHyperparameters = { &quot;predict_voting&quot; };</span></span>
<span id="L12"><span class="lineNum"> 12</span> <span class="tlaGNC"> 418 : validHyperparameters = { &quot;predict_voting&quot; };</span></span>
<span id="L13"><span class="lineNum"> 13</span> : </span>
<span id="L14"><span class="lineNum"> 14</span> <span class="tlaGNC"> 57 : }</span></span>
<span id="L15"><span class="lineNum"> 15</span> <span class="tlaGNC"> 1 : void AODE::setHyperparameters(const nlohmann::json&amp; hyperparameters_)</span></span>
<span id="L14"><span class="lineNum"> 14</span> <span class="tlaGNC"> 627 : }</span></span>
<span id="L15"><span class="lineNum"> 15</span> <span class="tlaGNC"> 11 : void AODE::setHyperparameters(const nlohmann::json&amp; hyperparameters_)</span></span>
<span id="L16"><span class="lineNum"> 16</span> : {</span>
<span id="L17"><span class="lineNum"> 17</span> <span class="tlaGNC"> 1 : auto hyperparameters = hyperparameters_;</span></span>
<span id="L18"><span class="lineNum"> 18</span> <span class="tlaGNC"> 1 : if (hyperparameters.contains(&quot;predict_voting&quot;)) {</span></span>
<span id="L19"><span class="lineNum"> 19</span> <span class="tlaGNC"> 1 : predict_voting = hyperparameters[&quot;predict_voting&quot;];</span></span>
<span id="L20"><span class="lineNum"> 20</span> <span class="tlaGNC"> 1 : hyperparameters.erase(&quot;predict_voting&quot;);</span></span>
<span id="L17"><span class="lineNum"> 17</span> <span class="tlaGNC"> 11 : auto hyperparameters = hyperparameters_;</span></span>
<span id="L18"><span class="lineNum"> 18</span> <span class="tlaGNC"> 11 : if (hyperparameters.contains(&quot;predict_voting&quot;)) {</span></span>
<span id="L19"><span class="lineNum"> 19</span> <span class="tlaGNC"> 11 : predict_voting = hyperparameters[&quot;predict_voting&quot;];</span></span>
<span id="L20"><span class="lineNum"> 20</span> <span class="tlaGNC"> 11 : hyperparameters.erase(&quot;predict_voting&quot;);</span></span>
<span id="L21"><span class="lineNum"> 21</span> : }</span>
<span id="L22"><span class="lineNum"> 22</span> <span class="tlaGNC"> 1 : Classifier::setHyperparameters(hyperparameters);</span></span>
<span id="L23"><span class="lineNum"> 23</span> <span class="tlaGNC"> 1 : }</span></span>
<span id="L24"><span class="lineNum"> 24</span> <span class="tlaGNC"> 6 : void AODE::buildModel(const torch::Tensor&amp; weights)</span></span>
<span id="L22"><span class="lineNum"> 22</span> <span class="tlaGNC"> 11 : Classifier::setHyperparameters(hyperparameters);</span></span>
<span id="L23"><span class="lineNum"> 23</span> <span class="tlaGNC"> 11 : }</span></span>
<span id="L24"><span class="lineNum"> 24</span> <span class="tlaGNC"> 66 : void AODE::buildModel(const torch::Tensor&amp; weights)</span></span>
<span id="L25"><span class="lineNum"> 25</span> : {</span>
<span id="L26"><span class="lineNum"> 26</span> <span class="tlaGNC"> 6 : models.clear();</span></span>
<span id="L27"><span class="lineNum"> 27</span> <span class="tlaGNC"> 6 : significanceModels.clear();</span></span>
<span id="L28"><span class="lineNum"> 28</span> <span class="tlaGNC"> 47 : for (int i = 0; i &lt; features.size(); ++i) {</span></span>
<span id="L29"><span class="lineNum"> 29</span> <span class="tlaGNC"> 41 : models.push_back(std::make_unique&lt;SPODE&gt;(i));</span></span>
<span id="L26"><span class="lineNum"> 26</span> <span class="tlaGNC"> 66 : models.clear();</span></span>
<span id="L27"><span class="lineNum"> 27</span> <span class="tlaGNC"> 66 : significanceModels.clear();</span></span>
<span id="L28"><span class="lineNum"> 28</span> <span class="tlaGNC"> 517 : for (int i = 0; i &lt; features.size(); ++i) {</span></span>
<span id="L29"><span class="lineNum"> 29</span> <span class="tlaGNC"> 451 : models.push_back(std::make_unique&lt;SPODE&gt;(i));</span></span>
<span id="L30"><span class="lineNum"> 30</span> : }</span>
<span id="L31"><span class="lineNum"> 31</span> <span class="tlaGNC"> 6 : n_models = models.size();</span></span>
<span id="L32"><span class="lineNum"> 32</span> <span class="tlaGNC"> 6 : significanceModels = std::vector&lt;double&gt;(n_models, 1.0);</span></span>
<span id="L33"><span class="lineNum"> 33</span> <span class="tlaGNC"> 6 : }</span></span>
<span id="L34"><span class="lineNum"> 34</span> <span class="tlaGNC"> 1 : std::vector&lt;std::string&gt; AODE::graph(const std::string&amp; title) const</span></span>
<span id="L31"><span class="lineNum"> 31</span> <span class="tlaGNC"> 66 : n_models = models.size();</span></span>
<span id="L32"><span class="lineNum"> 32</span> <span class="tlaGNC"> 66 : significanceModels = std::vector&lt;double&gt;(n_models, 1.0);</span></span>
<span id="L33"><span class="lineNum"> 33</span> <span class="tlaGNC"> 66 : }</span></span>
<span id="L34"><span class="lineNum"> 34</span> <span class="tlaGNC"> 11 : std::vector&lt;std::string&gt; AODE::graph(const std::string&amp; title) const</span></span>
<span id="L35"><span class="lineNum"> 35</span> : {</span>
<span id="L36"><span class="lineNum"> 36</span> <span class="tlaGNC"> 1 : return Ensemble::graph(title);</span></span>
<span id="L36"><span class="lineNum"> 36</span> <span class="tlaGNC"> 11 : return Ensemble::graph(title);</span></span>
<span id="L37"><span class="lineNum"> 37</span> : }</span>
<span id="L38"><span class="lineNum"> 38</span> : }</span>
</pre>

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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>

2
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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>

2
html/bayesnet/ensembles/AODE.h.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>

12
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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>
@@ -65,35 +65,35 @@
<tr>
<td class="coverFn"><a href="AODELd.cc.gcov.html#L41">_ZNK8bayesnet6AODELd5graphERKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEE</a></td>
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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>
@@ -65,35 +65,35 @@
<tr>
<td class="coverFn"><a href="AODELd.cc.gcov.html#L26">_ZN8bayesnet6AODELd10buildModelERKN2at6TensorE</a></td>
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<td class="coverFn"><a href="AODELd.cc.gcov.html#L41">_ZNK8bayesnet6AODELd5graphERKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEE</a></td>
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</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>
@@ -69,42 +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"> 17 : 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"> 187 : 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"> 17 : }</span></span>
<span id="L13"><span class="lineNum"> 13</span> <span class="tlaGNC"> 5 : 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"> 187 : }</span></span>
<span id="L13"><span class="lineNum"> 13</span> <span class="tlaGNC"> 55 : 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"> 5 : checkInput(X_, y_);</span></span>
<span id="L16"><span class="lineNum"> 16</span> <span class="tlaGNC"> 5 : features = features_;</span></span>
<span id="L17"><span class="lineNum"> 17</span> <span class="tlaGNC"> 5 : className = className_;</span></span>
<span id="L18"><span class="lineNum"> 18</span> <span class="tlaGNC"> 5 : Xf = X_;</span></span>
<span id="L19"><span class="lineNum"> 19</span> <span class="tlaGNC"> 5 : y = y_;</span></span>
<span id="L15"><span class="lineNum"> 15</span> <span class="tlaGNC"> 55 : checkInput(X_, y_);</span></span>
<span id="L16"><span class="lineNum"> 16</span> <span class="tlaGNC"> 55 : features = features_;</span></span>
<span id="L17"><span class="lineNum"> 17</span> <span class="tlaGNC"> 55 : className = className_;</span></span>
<span id="L18"><span class="lineNum"> 18</span> <span class="tlaGNC"> 55 : Xf = X_;</span></span>
<span id="L19"><span class="lineNum"> 19</span> <span class="tlaGNC"> 55 : 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"> 5 : states = fit_local_discretization(y);</span></span>
<span id="L21"><span class="lineNum"> 21</span> <span class="tlaGNC"> 55 : 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"> 5 : Ensemble::fit(dataset, features, className, states);</span></span>
<span id="L25"><span class="lineNum"> 25</span> <span class="tlaGNC"> 5 : return *this;</span></span>
<span id="L24"><span class="lineNum"> 24</span> <span class="tlaGNC"> 55 : Ensemble::fit(dataset, features, className, states);</span></span>
<span id="L25"><span class="lineNum"> 25</span> <span class="tlaGNC"> 55 : 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"> 5 : void AODELd::buildModel(const torch::Tensor&amp; weights)</span></span>
<span id="L28"><span class="lineNum"> 28</span> <span class="tlaGNC"> 55 : 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"> 5 : models.clear();</span></span>
<span id="L31"><span class="lineNum"> 31</span> <span class="tlaGNC"> 42 : for (int i = 0; i &lt; features.size(); ++i) {</span></span>
<span id="L32"><span class="lineNum"> 32</span> <span class="tlaGNC"> 37 : models.push_back(std::make_unique&lt;SPODELd&gt;(i));</span></span>
<span id="L30"><span class="lineNum"> 30</span> <span class="tlaGNC"> 55 : models.clear();</span></span>
<span id="L31"><span class="lineNum"> 31</span> <span class="tlaGNC"> 462 : for (int i = 0; i &lt; features.size(); ++i) {</span></span>
<span id="L32"><span class="lineNum"> 32</span> <span class="tlaGNC"> 407 : 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"> 5 : n_models = models.size();</span></span>
<span id="L35"><span class="lineNum"> 35</span> <span class="tlaGNC"> 5 : significanceModels = std::vector&lt;double&gt;(n_models, 1.0);</span></span>
<span id="L36"><span class="lineNum"> 36</span> <span class="tlaGNC"> 5 : }</span></span>
<span id="L37"><span class="lineNum"> 37</span> <span class="tlaGNC"> 5 : void AODELd::trainModel(const torch::Tensor&amp; weights)</span></span>
<span id="L34"><span class="lineNum"> 34</span> <span class="tlaGNC"> 55 : n_models = models.size();</span></span>
<span id="L35"><span class="lineNum"> 35</span> <span class="tlaGNC"> 55 : significanceModels = std::vector&lt;double&gt;(n_models, 1.0);</span></span>
<span id="L36"><span class="lineNum"> 36</span> <span class="tlaGNC"> 55 : }</span></span>
<span id="L37"><span class="lineNum"> 37</span> <span class="tlaGNC"> 55 : 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"> 42 : for (const auto&amp; model : models) {</span></span>
<span id="L40"><span class="lineNum"> 40</span> <span class="tlaGNC"> 37 : model-&gt;fit(Xf, y, features, className, states);</span></span>
<span id="L39"><span class="lineNum"> 39</span> <span class="tlaGNC"> 462 : for (const auto&amp; model : models) {</span></span>
<span id="L40"><span class="lineNum"> 40</span> <span class="tlaGNC"> 407 : 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"> 5 : }</span></span>
<span id="L43"><span class="lineNum"> 43</span> <span class="tlaGNC"> 1 : 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"> 55 : }</span></span>
<span id="L43"><span class="lineNum"> 43</span> <span class="tlaGNC"> 11 : 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"> 1 : return Ensemble::graph(name);</span></span>
<span id="L45"><span class="lineNum"> 45</span> <span class="tlaGNC"> 11 : 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>

2
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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>

2
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View File

@@ -37,7 +37,7 @@
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<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>

2
html/bayesnet/ensembles/AODELd.h.gcov.html
View File

@@ -37,7 +37,7 @@
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<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>

62
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View File

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<td class="headerItem">Functions:</td>
<td class="headerCovTableEntryHi">100.0&nbsp;%</td>
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<td class="headerValue">2024-04-21 17:30:26</td>
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<td></td>
<td class="headerItem">Functions:</td>
<td class="headerCovTableEntryHi">100.0&nbsp;%</td>
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745
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<td class="headerValue">coverage.info</td>
<td></td>
<td class="headerItem">Lines:</td>
<td class="headerCovTableEntryHi">99.1&nbsp;%</td>
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<td class="headerCovTableEntry">237</td>
<td class="headerCovTableEntry">233</td>
</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>
@@ -75,364 +75,387 @@
<span id="L13"><span class="lineNum"> 13</span> : #include &quot;bayesnet/feature_selection/FCBF.h&quot;</span>
<span id="L14"><span class="lineNum"> 14</span> : #include &quot;bayesnet/feature_selection/IWSS.h&quot;</span>
<span id="L15"><span class="lineNum"> 15</span> : #include &quot;BoostAODE.h&quot;</span>
<span id="L16"><span class="lineNum"> 16</span> : </span>
<span id="L17"><span class="lineNum"> 17</span> : namespace bayesnet {</span>
<span id="L18"><span class="lineNum"> 18</span> : </span>
<span id="L19"><span class="lineNum"> 19</span> <span class="tlaGNC tlaBgGNC"> 41 : BoostAODE::BoostAODE(bool predict_voting) : Ensemble(predict_voting)</span></span>
<span id="L20"><span class="lineNum"> 20</span> : {</span>
<span id="L21"><span class="lineNum"> 21</span> <span class="tlaGNC"> 410 : validHyperparameters = {</span></span>
<span id="L22"><span class="lineNum"> 22</span> : &quot;maxModels&quot;, &quot;bisection&quot;, &quot;order&quot;, &quot;convergence&quot;, &quot;threshold&quot;,</span>
<span id="L23"><span class="lineNum"> 23</span> : &quot;select_features&quot;, &quot;maxTolerance&quot;, &quot;predict_voting&quot;, &quot;block_update&quot;</span>
<span id="L24"><span class="lineNum"> 24</span> <span class="tlaGNC"> 410 : };</span></span>
<span id="L25"><span class="lineNum"> 25</span> : </span>
<span id="L26"><span class="lineNum"> 26</span> <span class="tlaGNC"> 123 : }</span></span>
<span id="L27"><span class="lineNum"> 27</span> <span class="tlaGNC"> 21 : void BoostAODE::buildModel(const torch::Tensor&amp; weights)</span></span>
<span id="L28"><span class="lineNum"> 28</span> : {</span>
<span id="L29"><span class="lineNum"> 29</span> : // Models shall be built in trainModel</span>
<span id="L30"><span class="lineNum"> 30</span> <span class="tlaGNC"> 21 : models.clear();</span></span>
<span id="L31"><span class="lineNum"> 31</span> <span class="tlaGNC"> 21 : significanceModels.clear();</span></span>
<span id="L32"><span class="lineNum"> 32</span> <span class="tlaGNC"> 21 : n_models = 0;</span></span>
<span id="L33"><span class="lineNum"> 33</span> : // Prepare the validation dataset</span>
<span id="L34"><span class="lineNum"> 34</span> <span class="tlaGNC"> 63 : auto y_ = dataset.index({ -1, &quot;...&quot; });</span></span>
<span id="L35"><span class="lineNum"> 35</span> <span class="tlaGNC"> 21 : if (convergence) {</span></span>
<span id="L36"><span class="lineNum"> 36</span> : // Prepare train &amp; validation sets from train data</span>
<span id="L37"><span class="lineNum"> 37</span> <span class="tlaGNC"> 17 : auto fold = folding::StratifiedKFold(5, y_, 271);</span></span>
<span id="L38"><span class="lineNum"> 38</span> <span class="tlaGNC"> 17 : auto [train, test] = fold.getFold(0);</span></span>
<span id="L39"><span class="lineNum"> 39</span> <span class="tlaGNC"> 17 : auto train_t = torch::tensor(train);</span></span>
<span id="L40"><span class="lineNum"> 40</span> <span class="tlaGNC"> 17 : auto test_t = torch::tensor(test);</span></span>
<span id="L41"><span class="lineNum"> 41</span> : // Get train and validation sets</span>
<span id="L42"><span class="lineNum"> 42</span> <span class="tlaGNC"> 85 : X_train = dataset.index({ torch::indexing::Slice(0, dataset.size(0) - 1), train_t });</span></span>
<span id="L43"><span class="lineNum"> 43</span> <span class="tlaGNC"> 51 : y_train = dataset.index({ -1, train_t });</span></span>
<span id="L44"><span class="lineNum"> 44</span> <span class="tlaGNC"> 85 : X_test = dataset.index({ torch::indexing::Slice(0, dataset.size(0) - 1), test_t });</span></span>
<span id="L45"><span class="lineNum"> 45</span> <span class="tlaGNC"> 51 : y_test = dataset.index({ -1, test_t });</span></span>
<span id="L46"><span class="lineNum"> 46</span> <span class="tlaGNC"> 17 : dataset = X_train;</span></span>
<span id="L47"><span class="lineNum"> 47</span> <span class="tlaGNC"> 17 : m = X_train.size(1);</span></span>
<span id="L48"><span class="lineNum"> 48</span> <span class="tlaGNC"> 17 : auto n_classes = states.at(className).size();</span></span>
<span id="L49"><span class="lineNum"> 49</span> : // Build dataset with train data</span>
<span id="L50"><span class="lineNum"> 50</span> <span class="tlaGNC"> 17 : buildDataset(y_train);</span></span>
<span id="L51"><span class="lineNum"> 51</span> <span class="tlaGNC"> 17 : metrics = Metrics(dataset, features, className, n_classes);</span></span>
<span id="L52"><span class="lineNum"> 52</span> <span class="tlaGNC"> 17 : } else {</span></span>
<span id="L53"><span class="lineNum"> 53</span> : // Use all data to train</span>
<span id="L54"><span class="lineNum"> 54</span> <span class="tlaGNC"> 16 : X_train = dataset.index({ torch::indexing::Slice(0, dataset.size(0) - 1), &quot;...&quot; });</span></span>
<span id="L55"><span class="lineNum"> 55</span> <span class="tlaGNC"> 4 : y_train = y_;</span></span>
<span id="L56"><span class="lineNum"> 56</span> : }</span>
<span id="L57"><span class="lineNum"> 57</span> <span class="tlaGNC"> 203 : }</span></span>
<span id="L58"><span class="lineNum"> 58</span> <span class="tlaGNC"> 20 : void BoostAODE::setHyperparameters(const nlohmann::json&amp; hyperparameters_)</span></span>
<span id="L59"><span class="lineNum"> 59</span> : {</span>
<span id="L60"><span class="lineNum"> 60</span> <span class="tlaGNC"> 20 : auto hyperparameters = hyperparameters_;</span></span>
<span id="L61"><span class="lineNum"> 61</span> <span class="tlaGNC"> 20 : if (hyperparameters.contains(&quot;order&quot;)) {</span></span>
<span id="L62"><span class="lineNum"> 62</span> <span class="tlaGNC"> 25 : std::vector&lt;std::string&gt; algos = { Orders.ASC, Orders.DESC, Orders.RAND };</span></span>
<span id="L63"><span class="lineNum"> 63</span> <span class="tlaGNC"> 5 : order_algorithm = hyperparameters[&quot;order&quot;];</span></span>
<span id="L64"><span class="lineNum"> 64</span> <span class="tlaGNC"> 5 : if (std::find(algos.begin(), algos.end(), order_algorithm) == algos.end()) {</span></span>
<span id="L65"><span class="lineNum"> 65</span> <span class="tlaGNC"> 1 : throw std::invalid_argument(&quot;Invalid order algorithm, valid values [&quot; + Orders.ASC + &quot;, &quot; + Orders.DESC + &quot;, &quot; + Orders.RAND + &quot;]&quot;);</span></span>
<span id="L66"><span class="lineNum"> 66</span> : }</span>
<span id="L67"><span class="lineNum"> 67</span> <span class="tlaGNC"> 4 : hyperparameters.erase(&quot;order&quot;);</span></span>
<span id="L68"><span class="lineNum"> 68</span> <span class="tlaGNC"> 5 : }</span></span>
<span id="L69"><span class="lineNum"> 69</span> <span class="tlaGNC"> 19 : if (hyperparameters.contains(&quot;convergence&quot;)) {</span></span>
<span id="L70"><span class="lineNum"> 70</span> <span class="tlaGNC"> 7 : convergence = hyperparameters[&quot;convergence&quot;];</span></span>
<span id="L71"><span class="lineNum"> 71</span> <span class="tlaGNC"> 7 : hyperparameters.erase(&quot;convergence&quot;);</span></span>
<span id="L72"><span class="lineNum"> 72</span> : }</span>
<span id="L73"><span class="lineNum"> 73</span> <span class="tlaGNC"> 19 : if (hyperparameters.contains(&quot;bisection&quot;)) {</span></span>
<span id="L74"><span class="lineNum"> 74</span> <span class="tlaGNC"> 6 : bisection = hyperparameters[&quot;bisection&quot;];</span></span>
<span id="L75"><span class="lineNum"> 75</span> <span class="tlaGNC"> 6 : hyperparameters.erase(&quot;bisection&quot;);</span></span>
<span id="L76"><span class="lineNum"> 76</span> : }</span>
<span id="L77"><span class="lineNum"> 77</span> <span class="tlaGNC"> 19 : if (hyperparameters.contains(&quot;threshold&quot;)) {</span></span>
<span id="L78"><span class="lineNum"> 78</span> <span class="tlaGNC"> 6 : threshold = hyperparameters[&quot;threshold&quot;];</span></span>
<span id="L79"><span class="lineNum"> 79</span> <span class="tlaGNC"> 6 : hyperparameters.erase(&quot;threshold&quot;);</span></span>
<span id="L80"><span class="lineNum"> 80</span> : }</span>
<span id="L81"><span class="lineNum"> 81</span> <span class="tlaGNC"> 19 : if (hyperparameters.contains(&quot;maxTolerance&quot;)) {</span></span>
<span id="L82"><span class="lineNum"> 82</span> <span class="tlaGNC"> 9 : maxTolerance = hyperparameters[&quot;maxTolerance&quot;];</span></span>
<span id="L83"><span class="lineNum"> 83</span> <span class="tlaGNC"> 9 : if (maxTolerance &lt; 1 || maxTolerance &gt; 4)</span></span>
<span id="L84"><span class="lineNum"> 84</span> <span class="tlaGNC"> 3 : throw std::invalid_argument(&quot;Invalid maxTolerance value, must be greater in [1, 4]&quot;);</span></span>
<span id="L85"><span class="lineNum"> 85</span> <span class="tlaGNC"> 6 : hyperparameters.erase(&quot;maxTolerance&quot;);</span></span>
<span id="L86"><span class="lineNum"> 86</span> : }</span>
<span id="L87"><span class="lineNum"> 87</span> <span class="tlaGNC"> 16 : if (hyperparameters.contains(&quot;predict_voting&quot;)) {</span></span>
<span id="L88"><span class="lineNum"> 88</span> <span class="tlaGNC"> 1 : predict_voting = hyperparameters[&quot;predict_voting&quot;];</span></span>
<span id="L89"><span class="lineNum"> 89</span> <span class="tlaGNC"> 1 : hyperparameters.erase(&quot;predict_voting&quot;);</span></span>
<span id="L90"><span class="lineNum"> 90</span> : }</span>
<span id="L91"><span class="lineNum"> 91</span> <span class="tlaGNC"> 16 : if (hyperparameters.contains(&quot;select_features&quot;)) {</span></span>
<span id="L92"><span class="lineNum"> 92</span> <span class="tlaGNC"> 9 : auto selectedAlgorithm = hyperparameters[&quot;select_features&quot;];</span></span>
<span id="L93"><span class="lineNum"> 93</span> <span class="tlaGNC"> 45 : std::vector&lt;std::string&gt; algos = { SelectFeatures.IWSS, SelectFeatures.CFS, SelectFeatures.FCBF };</span></span>
<span id="L94"><span class="lineNum"> 94</span> <span class="tlaGNC"> 9 : selectFeatures = true;</span></span>
<span id="L95"><span class="lineNum"> 95</span> <span class="tlaGNC"> 9 : select_features_algorithm = selectedAlgorithm;</span></span>
<span id="L96"><span class="lineNum"> 96</span> <span class="tlaGNC"> 9 : if (std::find(algos.begin(), algos.end(), selectedAlgorithm) == algos.end()) {</span></span>
<span id="L97"><span class="lineNum"> 97</span> <span class="tlaGNC"> 1 : throw std::invalid_argument(&quot;Invalid selectFeatures value, valid values [&quot; + SelectFeatures.IWSS + &quot;, &quot; + SelectFeatures.CFS + &quot;, &quot; + SelectFeatures.FCBF + &quot;]&quot;);</span></span>
<span id="L98"><span class="lineNum"> 98</span> : }</span>
<span id="L99"><span class="lineNum"> 99</span> <span class="tlaGNC"> 8 : hyperparameters.erase(&quot;select_features&quot;);</span></span>
<span id="L100"><span class="lineNum"> 100</span> <span class="tlaGNC"> 10 : }</span></span>
<span id="L101"><span class="lineNum"> 101</span> <span class="tlaGNC"> 15 : if (hyperparameters.contains(&quot;block_update&quot;)) {</span></span>
<span id="L102"><span class="lineNum"> 102</span> <span class="tlaGNC"> 2 : block_update = hyperparameters[&quot;block_update&quot;];</span></span>
<span id="L103"><span class="lineNum"> 103</span> <span class="tlaGNC"> 2 : hyperparameters.erase(&quot;block_update&quot;);</span></span>
<span id="L104"><span class="lineNum"> 104</span> : }</span>
<span id="L105"><span class="lineNum"> 105</span> <span class="tlaGNC"> 15 : Classifier::setHyperparameters(hyperparameters);</span></span>
<span id="L106"><span class="lineNum"> 106</span> <span class="tlaGNC"> 34 : }</span></span>
<span id="L107"><span class="lineNum"> 107</span> <span class="tlaGNC"> 108 : std::tuple&lt;torch::Tensor&amp;, double, bool&gt; update_weights(torch::Tensor&amp; ytrain, torch::Tensor&amp; ypred, torch::Tensor&amp; weights)</span></span>
<span id="L108"><span class="lineNum"> 108</span> : {</span>
<span id="L109"><span class="lineNum"> 109</span> <span class="tlaGNC"> 108 : bool terminate = false;</span></span>
<span id="L110"><span class="lineNum"> 110</span> <span class="tlaGNC"> 108 : double alpha_t = 0;</span></span>
<span id="L111"><span class="lineNum"> 111</span> <span class="tlaGNC"> 108 : auto mask_wrong = ypred != ytrain;</span></span>
<span id="L112"><span class="lineNum"> 112</span> <span class="tlaGNC"> 108 : auto mask_right = ypred == ytrain;</span></span>
<span id="L113"><span class="lineNum"> 113</span> <span class="tlaGNC"> 108 : auto masked_weights = weights * mask_wrong.to(weights.dtype());</span></span>
<span id="L114"><span class="lineNum"> 114</span> <span class="tlaGNC"> 108 : double epsilon_t = masked_weights.sum().item&lt;double&gt;();</span></span>
<span id="L115"><span class="lineNum"> 115</span> <span class="tlaGNC"> 108 : if (epsilon_t &gt; 0.5) {</span></span>
<span id="L116"><span class="lineNum"> 116</span> : // Inverse the weights policy (plot ln(wt))</span>
<span id="L117"><span class="lineNum"> 117</span> : // &quot;In each round of AdaBoost, there is a sanity check to ensure that the current base </span>
<span id="L118"><span class="lineNum"> 118</span> : // learner is better than random guess&quot; (Zhi-Hua Zhou, 2012)</span>
<span id="L119"><span class="lineNum"> 119</span> <span class="tlaGNC"> 4 : terminate = true;</span></span>
<span id="L120"><span class="lineNum"> 120</span> : } else {</span>
<span id="L121"><span class="lineNum"> 121</span> <span class="tlaGNC"> 104 : double wt = (1 - epsilon_t) / epsilon_t;</span></span>
<span id="L122"><span class="lineNum"> 122</span> <span class="tlaGNC"> 104 : alpha_t = epsilon_t == 0 ? 1 : 0.5 * log(wt);</span></span>
<span id="L123"><span class="lineNum"> 123</span> : // Step 3.2: Update weights for next classifier</span>
<span id="L124"><span class="lineNum"> 124</span> : // Step 3.2.1: Update weights of wrong samples</span>
<span id="L125"><span class="lineNum"> 125</span> <span class="tlaGNC"> 104 : weights += mask_wrong.to(weights.dtype()) * exp(alpha_t) * weights;</span></span>
<span id="L126"><span class="lineNum"> 126</span> : // Step 3.2.2: Update weights of right samples</span>
<span id="L127"><span class="lineNum"> 127</span> <span class="tlaGNC"> 104 : weights += mask_right.to(weights.dtype()) * exp(-alpha_t) * weights;</span></span>
<span id="L128"><span class="lineNum"> 128</span> : // Step 3.3: Normalise the weights</span>
<span id="L129"><span class="lineNum"> 129</span> <span class="tlaGNC"> 104 : double totalWeights = torch::sum(weights).item&lt;double&gt;();</span></span>
<span id="L130"><span class="lineNum"> 130</span> <span class="tlaGNC"> 104 : weights = weights / totalWeights;</span></span>
<span id="L131"><span class="lineNum"> 131</span> : }</span>
<span id="L132"><span class="lineNum"> 132</span> <span class="tlaGNC"> 216 : return { weights, alpha_t, terminate };</span></span>
<span id="L133"><span class="lineNum"> 133</span> <span class="tlaGNC"> 108 : }</span></span>
<span id="L134"><span class="lineNum"> 134</span> <span class="tlaGNC"> 5 : std::tuple&lt;torch::Tensor&amp;, double, bool&gt; BoostAODE::update_weights_block(int k, torch::Tensor&amp; ytrain, torch::Tensor&amp; weights)</span></span>
<span id="L135"><span class="lineNum"> 135</span> : {</span>
<span id="L136"><span class="lineNum"> 136</span> : /* Update Block algorithm</span>
<span id="L137"><span class="lineNum"> 137</span> : k = # of models in block</span>
<span id="L138"><span class="lineNum"> 138</span> : n_models = # of models in ensemble to make predictions</span>
<span id="L139"><span class="lineNum"> 139</span> : n_models_bak = # models saved</span>
<span id="L140"><span class="lineNum"> 140</span> : models = vector of models to make predictions</span>
<span id="L141"><span class="lineNum"> 141</span> : models_bak = models not used to make predictions</span>
<span id="L142"><span class="lineNum"> 142</span> : significances_bak = backup of significances vector</span>
<span id="L143"><span class="lineNum"> 143</span> : </span>
<span id="L144"><span class="lineNum"> 144</span> : Case list</span>
<span id="L145"><span class="lineNum"> 145</span> : A) k = 1, n_models = 1 =&gt; n = 0 , n_models = n + k</span>
<span id="L146"><span class="lineNum"> 146</span> : B) k = 1, n_models = n + 1 =&gt; n_models = n + k</span>
<span id="L147"><span class="lineNum"> 147</span> : C) k &gt; 1, n_models = k + 1 =&gt; n= 1, n_models = n + k</span>
<span id="L148"><span class="lineNum"> 148</span> : D) k &gt; 1, n_models = k =&gt; n = 0, n_models = n + k</span>
<span id="L149"><span class="lineNum"> 149</span> : E) k &gt; 1, n_models = k + n =&gt; n_models = n + k</span>
<span id="L150"><span class="lineNum"> 150</span> : </span>
<span id="L151"><span class="lineNum"> 151</span> : A, D) n=0, k &gt; 0, n_models == k</span>
<span id="L152"><span class="lineNum"> 152</span> : 1. n_models_bak &lt;- n_models</span>
<span id="L153"><span class="lineNum"> 153</span> : 2. significances_bak &lt;- significances</span>
<span id="L154"><span class="lineNum"> 154</span> : 3. significances = vector(k, 1)</span>
<span id="L155"><span class="lineNum"> 155</span> : 4. Don’t move any classifiers out of models</span>
<span id="L156"><span class="lineNum"> 156</span> : 5. n_models &lt;- k</span>
<span id="L157"><span class="lineNum"> 157</span> : 6. Make prediction, compute alpha, update weights</span>
<span id="L158"><span class="lineNum"> 158</span> : 7. Don’t restore any classifiers to models</span>
<span id="L159"><span class="lineNum"> 159</span> : 8. significances &lt;- significances_bak</span>
<span id="L160"><span class="lineNum"> 160</span> : 9. Update last k significances</span>
<span id="L161"><span class="lineNum"> 161</span> : 10. n_models &lt;- n_models_bak</span>
<span id="L162"><span class="lineNum"> 162</span> : </span>
<span id="L163"><span class="lineNum"> 163</span> : B, C, E) n &gt; 0, k &gt; 0, n_models == n + k</span>
<span id="L164"><span class="lineNum"> 164</span> : 1. n_models_bak &lt;- n_models</span>
<span id="L165"><span class="lineNum"> 165</span> : 2. significances_bak &lt;- significances</span>
<span id="L166"><span class="lineNum"> 166</span> : 3. significances = vector(k, 1)</span>
<span id="L167"><span class="lineNum"> 167</span> : 4. Move first n classifiers to models_bak</span>
<span id="L168"><span class="lineNum"> 168</span> : 5. n_models &lt;- k</span>
<span id="L169"><span class="lineNum"> 169</span> : 6. Make prediction, compute alpha, update weights</span>
<span id="L170"><span class="lineNum"> 170</span> : 7. Insert classifiers in models_bak to be the first n models</span>
<span id="L171"><span class="lineNum"> 171</span> : 8. significances &lt;- significances_bak</span>
<span id="L172"><span class="lineNum"> 172</span> : 9. Update last k significances</span>
<span id="L173"><span class="lineNum"> 173</span> : 10. n_models &lt;- n_models_bak</span>
<span id="L174"><span class="lineNum"> 174</span> : */</span>
<span id="L175"><span class="lineNum"> 175</span> : //</span>
<span id="L176"><span class="lineNum"> 176</span> : // Make predict with only the last k models</span>
<span id="L177"><span class="lineNum"> 177</span> : //</span>
<span id="L178"><span class="lineNum"> 178</span> <span class="tlaGNC"> 5 : std::unique_ptr&lt;Classifier&gt; model;</span></span>
<span id="L179"><span class="lineNum"> 179</span> <span class="tlaGNC"> 5 : std::vector&lt;std::unique_ptr&lt;Classifier&gt;&gt; models_bak;</span></span>
<span id="L180"><span class="lineNum"> 180</span> : // 1. n_models_bak &lt;- n_models 2. significances_bak &lt;- significances</span>
<span id="L181"><span class="lineNum"> 181</span> <span class="tlaGNC"> 5 : auto significance_bak = significanceModels;</span></span>
<span id="L182"><span class="lineNum"> 182</span> <span class="tlaGNC"> 5 : auto n_models_bak = n_models;</span></span>
<span id="L183"><span class="lineNum"> 183</span> : // 3. significances = vector(k, 1)</span>
<span id="L184"><span class="lineNum"> 184</span> <span class="tlaGNC"> 5 : significanceModels = std::vector&lt;double&gt;(k, 1.0);</span></span>
<span id="L185"><span class="lineNum"> 185</span> : // 4. Move first n classifiers to models_bak</span>
<span id="L186"><span class="lineNum"> 186</span> : // backup the first n_models - k models (if n_models == k, don't backup any)</span>
<span id="L187"><span class="lineNum"> 187</span> <span class="tlaGNC"> 20 : for (int i = 0; i &lt; n_models - k; ++i) {</span></span>
<span id="L188"><span class="lineNum"> 188</span> <span class="tlaGNC"> 15 : model = std::move(models[0]);</span></span>
<span id="L189"><span class="lineNum"> 189</span> <span class="tlaGNC"> 15 : models.erase(models.begin());</span></span>
<span id="L190"><span class="lineNum"> 190</span> <span class="tlaGNC"> 15 : models_bak.push_back(std::move(model));</span></span>
<span id="L191"><span class="lineNum"> 191</span> : }</span>
<span id="L192"><span class="lineNum"> 192</span> <span class="tlaGNC"> 5 : assert(models.size() == k);</span></span>
<span id="L193"><span class="lineNum"> 193</span> : // 5. n_models &lt;- k</span>
<span id="L194"><span class="lineNum"> 194</span> <span class="tlaGNC"> 5 : n_models = k;</span></span>
<span id="L195"><span class="lineNum"> 195</span> : // 6. Make prediction, compute alpha, update weights</span>
<span id="L196"><span class="lineNum"> 196</span> <span class="tlaGNC"> 5 : auto ypred = predict(X_train);</span></span>
<span id="L197"><span class="lineNum"> 197</span> : //</span>
<span id="L198"><span class="lineNum"> 198</span> : // Update weights</span>
<span id="L199"><span class="lineNum"> 199</span> : //</span>
<span id="L200"><span class="lineNum"> 200</span> : double alpha_t;</span>
<span id="L201"><span class="lineNum"> 201</span> : bool terminate;</span>
<span id="L202"><span class="lineNum"> 202</span> <span class="tlaGNC"> 5 : std::tie(weights, alpha_t, terminate) = update_weights(y_train, ypred, weights);</span></span>
<span id="L203"><span class="lineNum"> 203</span> : //</span>
<span id="L204"><span class="lineNum"> 204</span> : // Restore the models if needed</span>
<span id="L205"><span class="lineNum"> 205</span> : //</span>
<span id="L206"><span class="lineNum"> 206</span> : // 7. Insert classifiers in models_bak to be the first n models</span>
<span id="L207"><span class="lineNum"> 207</span> : // if n_models_bak == k, don't restore any, because none of them were moved</span>
<span id="L208"><span class="lineNum"> 208</span> <span class="tlaGNC"> 5 : if (k != n_models_bak) {</span></span>
<span id="L209"><span class="lineNum"> 209</span> : // Insert in the same order as they were extracted</span>
<span id="L210"><span class="lineNum"> 210</span> <span class="tlaGNC"> 4 : int bak_size = models_bak.size();</span></span>
<span id="L211"><span class="lineNum"> 211</span> <span class="tlaGNC"> 19 : for (int i = 0; i &lt; bak_size; ++i) {</span></span>
<span id="L212"><span class="lineNum"> 212</span> <span class="tlaGNC"> 15 : model = std::move(models_bak[bak_size - 1 - i]);</span></span>
<span id="L213"><span class="lineNum"> 213</span> <span class="tlaGNC"> 15 : models_bak.erase(models_bak.end() - 1);</span></span>
<span id="L214"><span class="lineNum"> 214</span> <span class="tlaGNC"> 15 : models.insert(models.begin(), std::move(model));</span></span>
<span id="L215"><span class="lineNum"> 215</span> : }</span>
<span id="L216"><span class="lineNum"> 216</span> : }</span>
<span id="L217"><span class="lineNum"> 217</span> : // 8. significances &lt;- significances_bak</span>
<span id="L218"><span class="lineNum"> 218</span> <span class="tlaGNC"> 5 : significanceModels = significance_bak;</span></span>
<span id="L219"><span class="lineNum"> 219</span> : //</span>
<span id="L220"><span class="lineNum"> 220</span> : // Update the significance of the last k models</span>
<span id="L221"><span class="lineNum"> 221</span> : //</span>
<span id="L222"><span class="lineNum"> 222</span> : // 9. Update last k significances</span>
<span id="L223"><span class="lineNum"> 223</span> <span class="tlaGNC"> 21 : for (int i = 0; i &lt; k; ++i) {</span></span>
<span id="L224"><span class="lineNum"> 224</span> <span class="tlaGNC"> 16 : significanceModels[n_models_bak - k + i] = alpha_t;</span></span>
<span id="L225"><span class="lineNum"> 225</span> : }</span>
<span id="L226"><span class="lineNum"> 226</span> : // 10. n_models &lt;- n_models_bak</span>
<span id="L227"><span class="lineNum"> 227</span> <span class="tlaGNC"> 5 : n_models = n_models_bak;</span></span>
<span id="L228"><span class="lineNum"> 228</span> <span class="tlaGNC"> 10 : return { weights, alpha_t, terminate };</span></span>
<span id="L229"><span class="lineNum"> 229</span> <span class="tlaGNC"> 5 : }</span></span>
<span id="L230"><span class="lineNum"> 230</span> <span class="tlaGNC"> 8 : std::vector&lt;int&gt; BoostAODE::initializeModels()</span></span>
<span id="L231"><span class="lineNum"> 231</span> : {</span>
<span id="L232"><span class="lineNum"> 232</span> <span class="tlaGNC"> 8 : std::vector&lt;int&gt; featuresUsed;</span></span>
<span id="L233"><span class="lineNum"> 233</span> <span class="tlaGNC"> 8 : torch::Tensor weights_ = torch::full({ m }, 1.0 / m, torch::kFloat64);</span></span>
<span id="L234"><span class="lineNum"> 234</span> <span class="tlaGNC"> 8 : int maxFeatures = 0;</span></span>
<span id="L235"><span class="lineNum"> 235</span> <span class="tlaGNC"> 8 : if (select_features_algorithm == SelectFeatures.CFS) {</span></span>
<span id="L236"><span class="lineNum"> 236</span> <span class="tlaGNC"> 2 : featureSelector = new CFS(dataset, features, className, maxFeatures, states.at(className).size(), weights_);</span></span>
<span id="L237"><span class="lineNum"> 237</span> <span class="tlaGNC"> 6 : } else if (select_features_algorithm == SelectFeatures.IWSS) {</span></span>
<span id="L238"><span class="lineNum"> 238</span> <span class="tlaGNC"> 3 : if (threshold &lt; 0 || threshold &gt;0.5) {</span></span>
<span id="L239"><span class="lineNum"> 239</span> <span class="tlaGNC"> 2 : throw std::invalid_argument(&quot;Invalid threshold value for &quot; + SelectFeatures.IWSS + &quot; [0, 0.5]&quot;);</span></span>
<span id="L240"><span class="lineNum"> 240</span> : }</span>
<span id="L241"><span class="lineNum"> 241</span> <span class="tlaGNC"> 1 : featureSelector = new IWSS(dataset, features, className, maxFeatures, states.at(className).size(), weights_, threshold);</span></span>
<span id="L242"><span class="lineNum"> 242</span> <span class="tlaGNC"> 3 : } else if (select_features_algorithm == SelectFeatures.FCBF) {</span></span>
<span id="L243"><span class="lineNum"> 243</span> <span class="tlaGNC"> 3 : if (threshold &lt; 1e-7 || threshold &gt; 1) {</span></span>
<span id="L244"><span class="lineNum"> 244</span> <span class="tlaGNC"> 2 : throw std::invalid_argument(&quot;Invalid threshold value for &quot; + SelectFeatures.FCBF + &quot; [1e-7, 1]&quot;);</span></span>
<span id="L16"><span class="lineNum"> 16</span> : #include &quot;lib/log/loguru.cpp&quot;</span>
<span id="L17"><span class="lineNum"> 17</span> : </span>
<span id="L18"><span class="lineNum"> 18</span> : namespace bayesnet {</span>
<span id="L19"><span class="lineNum"> 19</span> : </span>
<span id="L20"><span class="lineNum"> 20</span> <span class="tlaGNC tlaBgGNC"> 345 : BoostAODE::BoostAODE(bool predict_voting) : Ensemble(predict_voting)</span></span>
<span id="L21"><span class="lineNum"> 21</span> : {</span>
<span id="L22"><span class="lineNum"> 22</span> <span class="tlaGNC"> 3795 : validHyperparameters = {</span></span>
<span id="L23"><span class="lineNum"> 23</span> : &quot;maxModels&quot;, &quot;bisection&quot;, &quot;order&quot;, &quot;convergence&quot;, &quot;convergence_best&quot;, &quot;threshold&quot;,</span>
<span id="L24"><span class="lineNum"> 24</span> : &quot;select_features&quot;, &quot;maxTolerance&quot;, &quot;predict_voting&quot;, &quot;block_update&quot;</span>
<span id="L25"><span class="lineNum"> 25</span> <span class="tlaGNC"> 3795 : };</span></span>
<span id="L26"><span class="lineNum"> 26</span> : </span>
<span id="L27"><span class="lineNum"> 27</span> <span class="tlaGNC"> 1035 : }</span></span>
<span id="L28"><span class="lineNum"> 28</span> <span class="tlaGNC"> 190 : void BoostAODE::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> : // Models shall be built in trainModel</span>
<span id="L31"><span class="lineNum"> 31</span> <span class="tlaGNC"> 190 : models.clear();</span></span>
<span id="L32"><span class="lineNum"> 32</span> <span class="tlaGNC"> 190 : significanceModels.clear();</span></span>
<span id="L33"><span class="lineNum"> 33</span> <span class="tlaGNC"> 190 : n_models = 0;</span></span>
<span id="L34"><span class="lineNum"> 34</span> : // Prepare the validation dataset</span>
<span id="L35"><span class="lineNum"> 35</span> <span class="tlaGNC"> 570 : auto y_ = dataset.index({ -1, &quot;...&quot; });</span></span>
<span id="L36"><span class="lineNum"> 36</span> <span class="tlaGNC"> 190 : if (convergence) {</span></span>
<span id="L37"><span class="lineNum"> 37</span> : // Prepare train &amp; validation sets from train data</span>
<span id="L38"><span class="lineNum"> 38</span> <span class="tlaGNC"> 155 : auto fold = folding::StratifiedKFold(5, y_, 271);</span></span>
<span id="L39"><span class="lineNum"> 39</span> <span class="tlaGNC"> 155 : auto [train, test] = fold.getFold(0);</span></span>
<span id="L40"><span class="lineNum"> 40</span> <span class="tlaGNC"> 155 : auto train_t = torch::tensor(train);</span></span>
<span id="L41"><span class="lineNum"> 41</span> <span class="tlaGNC"> 155 : auto test_t = torch::tensor(test);</span></span>
<span id="L42"><span class="lineNum"> 42</span> : // Get train and validation sets</span>
<span id="L43"><span class="lineNum"> 43</span> <span class="tlaGNC"> 775 : X_train = dataset.index({ torch::indexing::Slice(0, dataset.size(0) - 1), train_t });</span></span>
<span id="L44"><span class="lineNum"> 44</span> <span class="tlaGNC"> 465 : y_train = dataset.index({ -1, train_t });</span></span>
<span id="L45"><span class="lineNum"> 45</span> <span class="tlaGNC"> 775 : X_test = dataset.index({ torch::indexing::Slice(0, dataset.size(0) - 1), test_t });</span></span>
<span id="L46"><span class="lineNum"> 46</span> <span class="tlaGNC"> 465 : y_test = dataset.index({ -1, test_t });</span></span>
<span id="L47"><span class="lineNum"> 47</span> <span class="tlaGNC"> 155 : dataset = X_train;</span></span>
<span id="L48"><span class="lineNum"> 48</span> <span class="tlaGNC"> 155 : m = X_train.size(1);</span></span>
<span id="L49"><span class="lineNum"> 49</span> <span class="tlaGNC"> 155 : auto n_classes = states.at(className).size();</span></span>
<span id="L50"><span class="lineNum"> 50</span> : // Build dataset with train data</span>
<span id="L51"><span class="lineNum"> 51</span> <span class="tlaGNC"> 155 : buildDataset(y_train);</span></span>
<span id="L52"><span class="lineNum"> 52</span> <span class="tlaGNC"> 155 : metrics = Metrics(dataset, features, className, n_classes);</span></span>
<span id="L53"><span class="lineNum"> 53</span> <span class="tlaGNC"> 155 : } else {</span></span>
<span id="L54"><span class="lineNum"> 54</span> : // Use all data to train</span>
<span id="L55"><span class="lineNum"> 55</span> <span class="tlaGNC"> 140 : X_train = dataset.index({ torch::indexing::Slice(0, dataset.size(0) - 1), &quot;...&quot; });</span></span>
<span id="L56"><span class="lineNum"> 56</span> <span class="tlaGNC"> 35 : y_train = y_;</span></span>
<span id="L57"><span class="lineNum"> 57</span> : }</span>
<span id="L58"><span class="lineNum"> 58</span> <span class="tlaGNC"> 1845 : }</span></span>
<span id="L59"><span class="lineNum"> 59</span> <span class="tlaGNC"> 187 : void BoostAODE::setHyperparameters(const nlohmann::json&amp; hyperparameters_)</span></span>
<span id="L60"><span class="lineNum"> 60</span> : {</span>
<span id="L61"><span class="lineNum"> 61</span> <span class="tlaGNC"> 187 : auto hyperparameters = hyperparameters_;</span></span>
<span id="L62"><span class="lineNum"> 62</span> <span class="tlaGNC"> 187 : if (hyperparameters.contains(&quot;order&quot;)) {</span></span>
<span id="L63"><span class="lineNum"> 63</span> <span class="tlaGNC"> 250 : std::vector&lt;std::string&gt; algos = { Orders.ASC, Orders.DESC, Orders.RAND };</span></span>
<span id="L64"><span class="lineNum"> 64</span> <span class="tlaGNC"> 50 : order_algorithm = hyperparameters[&quot;order&quot;];</span></span>
<span id="L65"><span class="lineNum"> 65</span> <span class="tlaGNC"> 50 : if (std::find(algos.begin(), algos.end(), order_algorithm) == algos.end()) {</span></span>
<span id="L66"><span class="lineNum"> 66</span> <span class="tlaGNC"> 9 : throw std::invalid_argument(&quot;Invalid order algorithm, valid values [&quot; + Orders.ASC + &quot;, &quot; + Orders.DESC + &quot;, &quot; + Orders.RAND + &quot;]&quot;);</span></span>
<span id="L67"><span class="lineNum"> 67</span> : }</span>
<span id="L68"><span class="lineNum"> 68</span> <span class="tlaGNC"> 41 : hyperparameters.erase(&quot;order&quot;);</span></span>
<span id="L69"><span class="lineNum"> 69</span> <span class="tlaGNC"> 50 : }</span></span>
<span id="L70"><span class="lineNum"> 70</span> <span class="tlaGNC"> 178 : if (hyperparameters.contains(&quot;convergence&quot;)) {</span></span>
<span id="L71"><span class="lineNum"> 71</span> <span class="tlaGNC"> 70 : convergence = hyperparameters[&quot;convergence&quot;];</span></span>
<span id="L72"><span class="lineNum"> 72</span> <span class="tlaGNC"> 70 : hyperparameters.erase(&quot;convergence&quot;);</span></span>
<span id="L73"><span class="lineNum"> 73</span> : }</span>
<span id="L74"><span class="lineNum"> 74</span> <span class="tlaGNC"> 178 : if (hyperparameters.contains(&quot;convergence_best&quot;)) {</span></span>
<span id="L75"><span class="lineNum"> 75</span> <span class="tlaGNC"> 18 : convergence_best = hyperparameters[&quot;convergence_best&quot;];</span></span>
<span id="L76"><span class="lineNum"> 76</span> <span class="tlaGNC"> 18 : hyperparameters.erase(&quot;convergence_best&quot;);</span></span>
<span id="L77"><span class="lineNum"> 77</span> : }</span>
<span id="L78"><span class="lineNum"> 78</span> <span class="tlaGNC"> 178 : if (hyperparameters.contains(&quot;bisection&quot;)) {</span></span>
<span id="L79"><span class="lineNum"> 79</span> <span class="tlaGNC"> 56 : bisection = hyperparameters[&quot;bisection&quot;];</span></span>
<span id="L80"><span class="lineNum"> 80</span> <span class="tlaGNC"> 56 : hyperparameters.erase(&quot;bisection&quot;);</span></span>
<span id="L81"><span class="lineNum"> 81</span> : }</span>
<span id="L82"><span class="lineNum"> 82</span> <span class="tlaGNC"> 178 : if (hyperparameters.contains(&quot;threshold&quot;)) {</span></span>
<span id="L83"><span class="lineNum"> 83</span> <span class="tlaGNC"> 56 : threshold = hyperparameters[&quot;threshold&quot;];</span></span>
<span id="L84"><span class="lineNum"> 84</span> <span class="tlaGNC"> 56 : hyperparameters.erase(&quot;threshold&quot;);</span></span>
<span id="L85"><span class="lineNum"> 85</span> : }</span>
<span id="L86"><span class="lineNum"> 86</span> <span class="tlaGNC"> 178 : if (hyperparameters.contains(&quot;maxTolerance&quot;)) {</span></span>
<span id="L87"><span class="lineNum"> 87</span> <span class="tlaGNC"> 83 : maxTolerance = hyperparameters[&quot;maxTolerance&quot;];</span></span>
<span id="L88"><span class="lineNum"> 88</span> <span class="tlaGNC"> 83 : if (maxTolerance &lt; 1 || maxTolerance &gt; 4)</span></span>
<span id="L89"><span class="lineNum"> 89</span> <span class="tlaGNC"> 27 : throw std::invalid_argument(&quot;Invalid maxTolerance value, must be greater in [1, 4]&quot;);</span></span>
<span id="L90"><span class="lineNum"> 90</span> <span class="tlaGNC"> 56 : hyperparameters.erase(&quot;maxTolerance&quot;);</span></span>
<span id="L91"><span class="lineNum"> 91</span> : }</span>
<span id="L92"><span class="lineNum"> 92</span> <span class="tlaGNC"> 151 : if (hyperparameters.contains(&quot;predict_voting&quot;)) {</span></span>
<span id="L93"><span class="lineNum"> 93</span> <span class="tlaGNC"> 9 : predict_voting = hyperparameters[&quot;predict_voting&quot;];</span></span>
<span id="L94"><span class="lineNum"> 94</span> <span class="tlaGNC"> 9 : hyperparameters.erase(&quot;predict_voting&quot;);</span></span>
<span id="L95"><span class="lineNum"> 95</span> : }</span>
<span id="L96"><span class="lineNum"> 96</span> <span class="tlaGNC"> 151 : if (hyperparameters.contains(&quot;select_features&quot;)) {</span></span>
<span id="L97"><span class="lineNum"> 97</span> <span class="tlaGNC"> 85 : auto selectedAlgorithm = hyperparameters[&quot;select_features&quot;];</span></span>
<span id="L98"><span class="lineNum"> 98</span> <span class="tlaGNC"> 425 : std::vector&lt;std::string&gt; algos = { SelectFeatures.IWSS, SelectFeatures.CFS, SelectFeatures.FCBF };</span></span>
<span id="L99"><span class="lineNum"> 99</span> <span class="tlaGNC"> 85 : selectFeatures = true;</span></span>
<span id="L100"><span class="lineNum"> 100</span> <span class="tlaGNC"> 85 : select_features_algorithm = selectedAlgorithm;</span></span>
<span id="L101"><span class="lineNum"> 101</span> <span class="tlaGNC"> 85 : if (std::find(algos.begin(), algos.end(), selectedAlgorithm) == algos.end()) {</span></span>
<span id="L102"><span class="lineNum"> 102</span> <span class="tlaGNC"> 9 : throw std::invalid_argument(&quot;Invalid selectFeatures value, valid values [&quot; + SelectFeatures.IWSS + &quot;, &quot; + SelectFeatures.CFS + &quot;, &quot; + SelectFeatures.FCBF + &quot;]&quot;);</span></span>
<span id="L103"><span class="lineNum"> 103</span> : }</span>
<span id="L104"><span class="lineNum"> 104</span> <span class="tlaGNC"> 76 : hyperparameters.erase(&quot;select_features&quot;);</span></span>
<span id="L105"><span class="lineNum"> 105</span> <span class="tlaGNC"> 94 : }</span></span>
<span id="L106"><span class="lineNum"> 106</span> <span class="tlaGNC"> 142 : if (hyperparameters.contains(&quot;block_update&quot;)) {</span></span>
<span id="L107"><span class="lineNum"> 107</span> <span class="tlaGNC"> 16 : block_update = hyperparameters[&quot;block_update&quot;];</span></span>
<span id="L108"><span class="lineNum"> 108</span> <span class="tlaGNC"> 16 : hyperparameters.erase(&quot;block_update&quot;);</span></span>
<span id="L109"><span class="lineNum"> 109</span> : }</span>
<span id="L110"><span class="lineNum"> 110</span> <span class="tlaGNC"> 142 : Classifier::setHyperparameters(hyperparameters);</span></span>
<span id="L111"><span class="lineNum"> 111</span> <span class="tlaGNC"> 322 : }</span></span>
<span id="L112"><span class="lineNum"> 112</span> <span class="tlaGNC"> 1025 : std::tuple&lt;torch::Tensor&amp;, double, bool&gt; update_weights(torch::Tensor&amp; ytrain, torch::Tensor&amp; ypred, torch::Tensor&amp; weights)</span></span>
<span id="L113"><span class="lineNum"> 113</span> : {</span>
<span id="L114"><span class="lineNum"> 114</span> <span class="tlaGNC"> 1025 : bool terminate = false;</span></span>
<span id="L115"><span class="lineNum"> 115</span> <span class="tlaGNC"> 1025 : double alpha_t = 0;</span></span>
<span id="L116"><span class="lineNum"> 116</span> <span class="tlaGNC"> 1025 : auto mask_wrong = ypred != ytrain;</span></span>
<span id="L117"><span class="lineNum"> 117</span> <span class="tlaGNC"> 1025 : auto mask_right = ypred == ytrain;</span></span>
<span id="L118"><span class="lineNum"> 118</span> <span class="tlaGNC"> 1025 : auto masked_weights = weights * mask_wrong.to(weights.dtype());</span></span>
<span id="L119"><span class="lineNum"> 119</span> <span class="tlaGNC"> 1025 : double epsilon_t = masked_weights.sum().item&lt;double&gt;();</span></span>
<span id="L120"><span class="lineNum"> 120</span> <span class="tlaGNC"> 1025 : if (epsilon_t &gt; 0.5) {</span></span>
<span id="L121"><span class="lineNum"> 121</span> : // Inverse the weights policy (plot ln(wt))</span>
<span id="L122"><span class="lineNum"> 122</span> : // &quot;In each round of AdaBoost, there is a sanity check to ensure that the current base </span>
<span id="L123"><span class="lineNum"> 123</span> : // learner is better than random guess&quot; (Zhi-Hua Zhou, 2012)</span>
<span id="L124"><span class="lineNum"> 124</span> <span class="tlaGNC"> 34 : terminate = true;</span></span>
<span id="L125"><span class="lineNum"> 125</span> : } else {</span>
<span id="L126"><span class="lineNum"> 126</span> <span class="tlaGNC"> 991 : double wt = (1 - epsilon_t) / epsilon_t;</span></span>
<span id="L127"><span class="lineNum"> 127</span> <span class="tlaGNC"> 991 : alpha_t = epsilon_t == 0 ? 1 : 0.5 * log(wt);</span></span>
<span id="L128"><span class="lineNum"> 128</span> : // Step 3.2: Update weights for next classifier</span>
<span id="L129"><span class="lineNum"> 129</span> : // Step 3.2.1: Update weights of wrong samples</span>
<span id="L130"><span class="lineNum"> 130</span> <span class="tlaGNC"> 991 : weights += mask_wrong.to(weights.dtype()) * exp(alpha_t) * weights;</span></span>
<span id="L131"><span class="lineNum"> 131</span> : // Step 3.2.2: Update weights of right samples</span>
<span id="L132"><span class="lineNum"> 132</span> <span class="tlaGNC"> 991 : weights += mask_right.to(weights.dtype()) * exp(-alpha_t) * weights;</span></span>
<span id="L133"><span class="lineNum"> 133</span> : // Step 3.3: Normalise the weights</span>
<span id="L134"><span class="lineNum"> 134</span> <span class="tlaGNC"> 991 : double totalWeights = torch::sum(weights).item&lt;double&gt;();</span></span>
<span id="L135"><span class="lineNum"> 135</span> <span class="tlaGNC"> 991 : weights = weights / totalWeights;</span></span>
<span id="L136"><span class="lineNum"> 136</span> : }</span>
<span id="L137"><span class="lineNum"> 137</span> <span class="tlaGNC"> 2050 : return { weights, alpha_t, terminate };</span></span>
<span id="L138"><span class="lineNum"> 138</span> <span class="tlaGNC"> 1025 : }</span></span>
<span id="L139"><span class="lineNum"> 139</span> <span class="tlaGNC"> 40 : std::tuple&lt;torch::Tensor&amp;, double, bool&gt; BoostAODE::update_weights_block(int k, torch::Tensor&amp; ytrain, torch::Tensor&amp; weights)</span></span>
<span id="L140"><span class="lineNum"> 140</span> : {</span>
<span id="L141"><span class="lineNum"> 141</span> : /* Update Block algorithm</span>
<span id="L142"><span class="lineNum"> 142</span> : k = # of models in block</span>
<span id="L143"><span class="lineNum"> 143</span> : n_models = # of models in ensemble to make predictions</span>
<span id="L144"><span class="lineNum"> 144</span> : n_models_bak = # models saved</span>
<span id="L145"><span class="lineNum"> 145</span> : models = vector of models to make predictions</span>
<span id="L146"><span class="lineNum"> 146</span> : models_bak = models not used to make predictions</span>
<span id="L147"><span class="lineNum"> 147</span> : significances_bak = backup of significances vector</span>
<span id="L148"><span class="lineNum"> 148</span> : </span>
<span id="L149"><span class="lineNum"> 149</span> : Case list</span>
<span id="L150"><span class="lineNum"> 150</span> : A) k = 1, n_models = 1 =&gt; n = 0 , n_models = n + k</span>
<span id="L151"><span class="lineNum"> 151</span> : B) k = 1, n_models = n + 1 =&gt; n_models = n + k</span>
<span id="L152"><span class="lineNum"> 152</span> : C) k &gt; 1, n_models = k + 1 =&gt; n= 1, n_models = n + k</span>
<span id="L153"><span class="lineNum"> 153</span> : D) k &gt; 1, n_models = k =&gt; n = 0, n_models = n + k</span>
<span id="L154"><span class="lineNum"> 154</span> : E) k &gt; 1, n_models = k + n =&gt; n_models = n + k</span>
<span id="L155"><span class="lineNum"> 155</span> : </span>
<span id="L156"><span class="lineNum"> 156</span> : A, D) n=0, k &gt; 0, n_models == k</span>
<span id="L157"><span class="lineNum"> 157</span> : 1. n_models_bak &lt;- n_models</span>
<span id="L158"><span class="lineNum"> 158</span> : 2. significances_bak &lt;- significances</span>
<span id="L159"><span class="lineNum"> 159</span> : 3. significances = vector(k, 1)</span>
<span id="L160"><span class="lineNum"> 160</span> : 4. Don’t move any classifiers out of models</span>
<span id="L161"><span class="lineNum"> 161</span> : 5. n_models &lt;- k</span>
<span id="L162"><span class="lineNum"> 162</span> : 6. Make prediction, compute alpha, update weights</span>
<span id="L163"><span class="lineNum"> 163</span> : 7. Don’t restore any classifiers to models</span>
<span id="L164"><span class="lineNum"> 164</span> : 8. significances &lt;- significances_bak</span>
<span id="L165"><span class="lineNum"> 165</span> : 9. Update last k significances</span>
<span id="L166"><span class="lineNum"> 166</span> : 10. n_models &lt;- n_models_bak</span>
<span id="L167"><span class="lineNum"> 167</span> : </span>
<span id="L168"><span class="lineNum"> 168</span> : B, C, E) n &gt; 0, k &gt; 0, n_models == n + k</span>
<span id="L169"><span class="lineNum"> 169</span> : 1. n_models_bak &lt;- n_models</span>
<span id="L170"><span class="lineNum"> 170</span> : 2. significances_bak &lt;- significances</span>
<span id="L171"><span class="lineNum"> 171</span> : 3. significances = vector(k, 1)</span>
<span id="L172"><span class="lineNum"> 172</span> : 4. Move first n classifiers to models_bak</span>
<span id="L173"><span class="lineNum"> 173</span> : 5. n_models &lt;- k</span>
<span id="L174"><span class="lineNum"> 174</span> : 6. Make prediction, compute alpha, update weights</span>
<span id="L175"><span class="lineNum"> 175</span> : 7. Insert classifiers in models_bak to be the first n models</span>
<span id="L176"><span class="lineNum"> 176</span> : 8. significances &lt;- significances_bak</span>
<span id="L177"><span class="lineNum"> 177</span> : 9. Update last k significances</span>
<span id="L178"><span class="lineNum"> 178</span> : 10. n_models &lt;- n_models_bak</span>
<span id="L179"><span class="lineNum"> 179</span> : */</span>
<span id="L180"><span class="lineNum"> 180</span> : //</span>
<span id="L181"><span class="lineNum"> 181</span> : // Make predict with only the last k models</span>
<span id="L182"><span class="lineNum"> 182</span> : //</span>
<span id="L183"><span class="lineNum"> 183</span> <span class="tlaGNC"> 40 : std::unique_ptr&lt;Classifier&gt; model;</span></span>
<span id="L184"><span class="lineNum"> 184</span> <span class="tlaGNC"> 40 : std::vector&lt;std::unique_ptr&lt;Classifier&gt;&gt; models_bak;</span></span>
<span id="L185"><span class="lineNum"> 185</span> : // 1. n_models_bak &lt;- n_models 2. significances_bak &lt;- significances</span>
<span id="L186"><span class="lineNum"> 186</span> <span class="tlaGNC"> 40 : auto significance_bak = significanceModels;</span></span>
<span id="L187"><span class="lineNum"> 187</span> <span class="tlaGNC"> 40 : auto n_models_bak = n_models;</span></span>
<span id="L188"><span class="lineNum"> 188</span> : // 3. significances = vector(k, 1)</span>
<span id="L189"><span class="lineNum"> 189</span> <span class="tlaGNC"> 40 : significanceModels = std::vector&lt;double&gt;(k, 1.0);</span></span>
<span id="L190"><span class="lineNum"> 190</span> : // 4. Move first n classifiers to models_bak</span>
<span id="L191"><span class="lineNum"> 191</span> : // backup the first n_models - k models (if n_models == k, don't backup any)</span>
<span id="L192"><span class="lineNum"> 192</span> <span class="tlaGNC"> 160 : for (int i = 0; i &lt; n_models - k; ++i) {</span></span>
<span id="L193"><span class="lineNum"> 193</span> <span class="tlaGNC"> 120 : model = std::move(models[0]);</span></span>
<span id="L194"><span class="lineNum"> 194</span> <span class="tlaGNC"> 120 : models.erase(models.begin());</span></span>
<span id="L195"><span class="lineNum"> 195</span> <span class="tlaGNC"> 120 : models_bak.push_back(std::move(model));</span></span>
<span id="L196"><span class="lineNum"> 196</span> : }</span>
<span id="L197"><span class="lineNum"> 197</span> <span class="tlaGNC"> 40 : assert(models.size() == k);</span></span>
<span id="L198"><span class="lineNum"> 198</span> : // 5. n_models &lt;- k</span>
<span id="L199"><span class="lineNum"> 199</span> <span class="tlaGNC"> 40 : n_models = k;</span></span>
<span id="L200"><span class="lineNum"> 200</span> : // 6. Make prediction, compute alpha, update weights</span>
<span id="L201"><span class="lineNum"> 201</span> <span class="tlaGNC"> 40 : auto ypred = predict(X_train);</span></span>
<span id="L202"><span class="lineNum"> 202</span> : //</span>
<span id="L203"><span class="lineNum"> 203</span> : // Update weights</span>
<span id="L204"><span class="lineNum"> 204</span> : //</span>
<span id="L205"><span class="lineNum"> 205</span> : double alpha_t;</span>
<span id="L206"><span class="lineNum"> 206</span> : bool terminate;</span>
<span id="L207"><span class="lineNum"> 207</span> <span class="tlaGNC"> 40 : std::tie(weights, alpha_t, terminate) = update_weights(y_train, ypred, weights);</span></span>
<span id="L208"><span class="lineNum"> 208</span> : //</span>
<span id="L209"><span class="lineNum"> 209</span> : // Restore the models if needed</span>
<span id="L210"><span class="lineNum"> 210</span> : //</span>
<span id="L211"><span class="lineNum"> 211</span> : // 7. Insert classifiers in models_bak to be the first n models</span>
<span id="L212"><span class="lineNum"> 212</span> : // if n_models_bak == k, don't restore any, because none of them were moved</span>
<span id="L213"><span class="lineNum"> 213</span> <span class="tlaGNC"> 40 : if (k != n_models_bak) {</span></span>
<span id="L214"><span class="lineNum"> 214</span> : // Insert in the same order as they were extracted</span>
<span id="L215"><span class="lineNum"> 215</span> <span class="tlaGNC"> 32 : int bak_size = models_bak.size();</span></span>
<span id="L216"><span class="lineNum"> 216</span> <span class="tlaGNC"> 152 : for (int i = 0; i &lt; bak_size; ++i) {</span></span>
<span id="L217"><span class="lineNum"> 217</span> <span class="tlaGNC"> 120 : model = std::move(models_bak[bak_size - 1 - i]);</span></span>
<span id="L218"><span class="lineNum"> 218</span> <span class="tlaGNC"> 120 : models_bak.erase(models_bak.end() - 1);</span></span>
<span id="L219"><span class="lineNum"> 219</span> <span class="tlaGNC"> 120 : models.insert(models.begin(), std::move(model));</span></span>
<span id="L220"><span class="lineNum"> 220</span> : }</span>
<span id="L221"><span class="lineNum"> 221</span> : }</span>
<span id="L222"><span class="lineNum"> 222</span> : // 8. significances &lt;- significances_bak</span>
<span id="L223"><span class="lineNum"> 223</span> <span class="tlaGNC"> 40 : significanceModels = significance_bak;</span></span>
<span id="L224"><span class="lineNum"> 224</span> : //</span>
<span id="L225"><span class="lineNum"> 225</span> : // Update the significance of the last k models</span>
<span id="L226"><span class="lineNum"> 226</span> : //</span>
<span id="L227"><span class="lineNum"> 227</span> : // 9. Update last k significances</span>
<span id="L228"><span class="lineNum"> 228</span> <span class="tlaGNC"> 168 : for (int i = 0; i &lt; k; ++i) {</span></span>
<span id="L229"><span class="lineNum"> 229</span> <span class="tlaGNC"> 128 : significanceModels[n_models_bak - k + i] = alpha_t;</span></span>
<span id="L230"><span class="lineNum"> 230</span> : }</span>
<span id="L231"><span class="lineNum"> 231</span> : // 10. n_models &lt;- n_models_bak</span>
<span id="L232"><span class="lineNum"> 232</span> <span class="tlaGNC"> 40 : n_models = n_models_bak;</span></span>
<span id="L233"><span class="lineNum"> 233</span> <span class="tlaGNC"> 80 : return { weights, alpha_t, terminate };</span></span>
<span id="L234"><span class="lineNum"> 234</span> <span class="tlaGNC"> 40 : }</span></span>
<span id="L235"><span class="lineNum"> 235</span> <span class="tlaGNC"> 76 : std::vector&lt;int&gt; BoostAODE::initializeModels()</span></span>
<span id="L236"><span class="lineNum"> 236</span> : {</span>
<span id="L237"><span class="lineNum"> 237</span> <span class="tlaGNC"> 76 : std::vector&lt;int&gt; featuresUsed;</span></span>
<span id="L238"><span class="lineNum"> 238</span> <span class="tlaGNC"> 76 : torch::Tensor weights_ = torch::full({ m }, 1.0 / m, torch::kFloat64);</span></span>
<span id="L239"><span class="lineNum"> 239</span> <span class="tlaGNC"> 76 : int maxFeatures = 0;</span></span>
<span id="L240"><span class="lineNum"> 240</span> <span class="tlaGNC"> 76 : if (select_features_algorithm == SelectFeatures.CFS) {</span></span>
<span id="L241"><span class="lineNum"> 241</span> <span class="tlaGNC"> 20 : featureSelector = new CFS(dataset, features, className, maxFeatures, states.at(className).size(), weights_);</span></span>
<span id="L242"><span class="lineNum"> 242</span> <span class="tlaGNC"> 56 : } else if (select_features_algorithm == SelectFeatures.IWSS) {</span></span>
<span id="L243"><span class="lineNum"> 243</span> <span class="tlaGNC"> 29 : if (threshold &lt; 0 || threshold &gt;0.5) {</span></span>
<span id="L244"><span class="lineNum"> 244</span> <span class="tlaGNC"> 18 : throw std::invalid_argument(&quot;Invalid threshold value for &quot; + SelectFeatures.IWSS + &quot; [0, 0.5]&quot;);</span></span>
<span id="L245"><span class="lineNum"> 245</span> : }</span>
<span id="L246"><span class="lineNum"> 246</span> <span class="tlaGNC"> 1 : featureSelector = new FCBF(dataset, features, className, maxFeatures, states.at(className).size(), weights_, threshold);</span></span>
<span id="L247"><span class="lineNum"> 247</span> : }</span>
<span id="L248"><span class="lineNum"> 248</span> <span class="tlaGNC"> 4 : featureSelector-&gt;fit();</span></span>
<span id="L249"><span class="lineNum"> 249</span> <span class="tlaGNC"> 4 : auto cfsFeatures = featureSelector-&gt;getFeatures();</span></span>
<span id="L250"><span class="lineNum"> 250</span> <span class="tlaGNC"> 4 : auto scores = featureSelector-&gt;getScores();</span></span>
<span id="L251"><span class="lineNum"> 251</span> <span class="tlaGNC"> 25 : for (const int&amp; feature : cfsFeatures) {</span></span>
<span id="L252"><span class="lineNum"> 252</span> <span class="tlaGNC"> 21 : featuresUsed.push_back(feature);</span></span>
<span id="L253"><span class="lineNum"> 253</span> <span class="tlaGNC"> 21 : std::unique_ptr&lt;Classifier&gt; model = std::make_unique&lt;SPODE&gt;(feature);</span></span>
<span id="L254"><span class="lineNum"> 254</span> <span class="tlaGNC"> 21 : model-&gt;fit(dataset, features, className, states, weights_);</span></span>
<span id="L255"><span class="lineNum"> 255</span> <span class="tlaGNC"> 21 : models.push_back(std::move(model));</span></span>
<span id="L256"><span class="lineNum"> 256</span> <span class="tlaGNC"> 21 : significanceModels.push_back(1.0); // They will be updated later in trainModel</span></span>
<span id="L257"><span class="lineNum"> 257</span> <span class="tlaGNC"> 21 : n_models++;</span></span>
<span id="L258"><span class="lineNum"> 258</span> <span class="tlaGNC"> 21 : }</span></span>
<span id="L259"><span class="lineNum"> 259</span> <span class="tlaGNC"> 4 : notes.push_back(&quot;Used features in initialization: &quot; + std::to_string(featuresUsed.size()) + &quot; of &quot; + std::to_string(features.size()) + &quot; with &quot; + select_features_algorithm);</span></span>
<span id="L260"><span class="lineNum"> 260</span> <span class="tlaGNC"> 4 : delete featureSelector;</span></span>
<span id="L261"><span class="lineNum"> 261</span> <span class="tlaGNC"> 8 : return featuresUsed;</span></span>
<span id="L262"><span class="lineNum"> 262</span> <span class="tlaGNC"> 12 : }</span></span>
<span id="L263"><span class="lineNum"> 263</span> <span class="tlaGNC"> 21 : void BoostAODE::trainModel(const torch::Tensor&amp; weights)</span></span>
<span id="L264"><span class="lineNum"> 264</span> : {</span>
<span id="L265"><span class="lineNum"> 265</span> : // Algorithm based on the adaboost algorithm for classification</span>
<span id="L266"><span class="lineNum"> 266</span> : // as explained in Ensemble methods (Zhi-Hua Zhou, 2012)</span>
<span id="L267"><span class="lineNum"> 267</span> <span class="tlaGNC"> 21 : fitted = true;</span></span>
<span id="L268"><span class="lineNum"> 268</span> <span class="tlaGNC"> 21 : double alpha_t = 0;</span></span>
<span id="L269"><span class="lineNum"> 269</span> <span class="tlaGNC"> 21 : torch::Tensor weights_ = torch::full({ m }, 1.0 / m, torch::kFloat64);</span></span>
<span id="L270"><span class="lineNum"> 270</span> <span class="tlaGNC"> 21 : bool finished = false;</span></span>
<span id="L271"><span class="lineNum"> 271</span> <span class="tlaGNC"> 21 : std::vector&lt;int&gt; featuresUsed;</span></span>
<span id="L272"><span class="lineNum"> 272</span> <span class="tlaGNC"> 21 : if (selectFeatures) {</span></span>
<span id="L273"><span class="lineNum"> 273</span> <span class="tlaGNC"> 8 : featuresUsed = initializeModels();</span></span>
<span id="L274"><span class="lineNum"> 274</span> <span class="tlaGNC"> 4 : auto ypred = predict(X_train);</span></span>
<span id="L275"><span class="lineNum"> 275</span> <span class="tlaGNC"> 4 : std::tie(weights_, alpha_t, finished) = update_weights(y_train, ypred, weights_);</span></span>
<span id="L276"><span class="lineNum"> 276</span> : // Update significance of the models</span>
<span id="L277"><span class="lineNum"> 277</span> <span class="tlaGNC"> 25 : for (int i = 0; i &lt; n_models; ++i) {</span></span>
<span id="L278"><span class="lineNum"> 278</span> <span class="tlaGNC"> 21 : significanceModels[i] = alpha_t;</span></span>
<span id="L279"><span class="lineNum"> 279</span> : }</span>
<span id="L280"><span class="lineNum"> 280</span> <span class="tlaGNC"> 4 : if (finished) {</span></span>
<span id="L281"><span class="lineNum"> 281</span> <span class="tlaUNC tlaBgUNC"> 0 : return;</span></span>
<span id="L282"><span class="lineNum"> 282</span> : }</span>
<span id="L283"><span class="lineNum"> 283</span> <span class="tlaGNC tlaBgGNC"> 4 : }</span></span>
<span id="L284"><span class="lineNum"> 284</span> <span class="tlaGNC"> 17 : int numItemsPack = 0; // The counter of the models inserted in the current pack</span></span>
<span id="L285"><span class="lineNum"> 285</span> : // Variables to control the accuracy finish condition</span>
<span id="L286"><span class="lineNum"> 286</span> <span class="tlaGNC"> 17 : double priorAccuracy = 0.0;</span></span>
<span id="L287"><span class="lineNum"> 287</span> <span class="tlaGNC"> 17 : double improvement = 1.0;</span></span>
<span id="L288"><span class="lineNum"> 288</span> <span class="tlaGNC"> 17 : double convergence_threshold = 1e-4;</span></span>
<span id="L289"><span class="lineNum"> 289</span> <span class="tlaGNC"> 17 : int tolerance = 0; // number of times the accuracy is lower than the convergence_threshold</span></span>
<span id="L290"><span class="lineNum"> 290</span> : // Step 0: Set the finish condition</span>
<span id="L291"><span class="lineNum"> 291</span> : // epsilon sub t &gt; 0.5 =&gt; inverse the weights policy</span>
<span id="L292"><span class="lineNum"> 292</span> : // validation error is not decreasing</span>
<span id="L293"><span class="lineNum"> 293</span> : // run out of features</span>
<span id="L294"><span class="lineNum"> 294</span> <span class="tlaGNC"> 17 : bool ascending = order_algorithm == Orders.ASC;</span></span>
<span id="L295"><span class="lineNum"> 295</span> <span class="tlaGNC"> 17 : std::mt19937 g{ 173 };</span></span>
<span id="L296"><span class="lineNum"> 296</span> <span class="tlaGNC"> 99 : while (!finished) {</span></span>
<span id="L297"><span class="lineNum"> 297</span> : // Step 1: Build ranking with mutual information</span>
<span id="L298"><span class="lineNum"> 298</span> <span class="tlaGNC"> 82 : auto featureSelection = metrics.SelectKBestWeighted(weights_, ascending, n); // Get all the features sorted</span></span>
<span id="L299"><span class="lineNum"> 299</span> <span class="tlaGNC"> 82 : if (order_algorithm == Orders.RAND) {</span></span>
<span id="L300"><span class="lineNum"> 300</span> <span class="tlaGNC"> 9 : std::shuffle(featureSelection.begin(), featureSelection.end(), g);</span></span>
<span id="L301"><span class="lineNum"> 301</span> : }</span>
<span id="L302"><span class="lineNum"> 302</span> : // Remove used features</span>
<span id="L303"><span class="lineNum"> 303</span> <span class="tlaGNC"> 164 : featureSelection.erase(remove_if(begin(featureSelection), end(featureSelection), [&amp;](auto x)</span></span>
<span id="L304"><span class="lineNum"> 304</span> <span class="tlaGNC"> 10764 : { return std::find(begin(featuresUsed), end(featuresUsed), x) != end(featuresUsed);}),</span></span>
<span id="L305"><span class="lineNum"> 305</span> <span class="tlaGNC"> 82 : end(featureSelection)</span></span>
<span id="L306"><span class="lineNum"> 306</span> : );</span>
<span id="L307"><span class="lineNum"> 307</span> <span class="tlaGNC"> 82 : int k = pow(2, tolerance);</span></span>
<span id="L308"><span class="lineNum"> 308</span> <span class="tlaGNC"> 82 : int counter = 0; // The model counter of the current pack</span></span>
<span id="L309"><span class="lineNum"> 309</span> <span class="tlaGNC"> 197 : while (counter++ &lt; k &amp;&amp; featureSelection.size() &gt; 0) {</span></span>
<span id="L310"><span class="lineNum"> 310</span> <span class="tlaGNC"> 115 : auto feature = featureSelection[0];</span></span>
<span id="L311"><span class="lineNum"> 311</span> <span class="tlaGNC"> 115 : featureSelection.erase(featureSelection.begin());</span></span>
<span id="L312"><span class="lineNum"> 312</span> <span class="tlaGNC"> 115 : std::unique_ptr&lt;Classifier&gt; model;</span></span>
<span id="L313"><span class="lineNum"> 313</span> <span class="tlaGNC"> 115 : model = std::make_unique&lt;SPODE&gt;(feature);</span></span>
<span id="L314"><span class="lineNum"> 314</span> <span class="tlaGNC"> 115 : model-&gt;fit(dataset, features, className, states, weights_);</span></span>
<span id="L315"><span class="lineNum"> 315</span> <span class="tlaGNC"> 115 : alpha_t = 0.0;</span></span>
<span id="L316"><span class="lineNum"> 316</span> <span class="tlaGNC"> 115 : if (!block_update) {</span></span>
<span id="L317"><span class="lineNum"> 317</span> <span class="tlaGNC"> 99 : auto ypred = model-&gt;predict(X_train);</span></span>
<span id="L318"><span class="lineNum"> 318</span> : // Step 3.1: Compute the classifier amout of say</span>
<span id="L319"><span class="lineNum"> 319</span> <span class="tlaGNC"> 99 : std::tie(weights_, alpha_t, finished) = update_weights(y_train, ypred, weights_);</span></span>
<span id="L320"><span class="lineNum"> 320</span> <span class="tlaGNC"> 99 : }</span></span>
<span id="L321"><span class="lineNum"> 321</span> : // Step 3.4: Store classifier and its accuracy to weigh its future vote</span>
<span id="L322"><span class="lineNum"> 322</span> <span class="tlaGNC"> 115 : numItemsPack++;</span></span>
<span id="L323"><span class="lineNum"> 323</span> <span class="tlaGNC"> 115 : featuresUsed.push_back(feature);</span></span>
<span id="L324"><span class="lineNum"> 324</span> <span class="tlaGNC"> 115 : models.push_back(std::move(model));</span></span>
<span id="L325"><span class="lineNum"> 325</span> <span class="tlaGNC"> 115 : significanceModels.push_back(alpha_t);</span></span>
<span id="L326"><span class="lineNum"> 326</span> <span class="tlaGNC"> 115 : n_models++;</span></span>
<span id="L327"><span class="lineNum"> 327</span> <span class="tlaGNC"> 115 : }</span></span>
<span id="L328"><span class="lineNum"> 328</span> <span class="tlaGNC"> 82 : if (block_update) {</span></span>
<span id="L329"><span class="lineNum"> 329</span> <span class="tlaGNC"> 5 : std::tie(weights_, alpha_t, finished) = update_weights_block(k, y_train, weights_);</span></span>
<span id="L330"><span class="lineNum"> 330</span> : }</span>
<span id="L331"><span class="lineNum"> 331</span> <span class="tlaGNC"> 82 : if (convergence &amp;&amp; !finished) {</span></span>
<span id="L332"><span class="lineNum"> 332</span> <span class="tlaGNC"> 49 : auto y_val_predict = predict(X_test);</span></span>
<span id="L333"><span class="lineNum"> 333</span> <span class="tlaGNC"> 49 : double accuracy = (y_val_predict == y_test).sum().item&lt;double&gt;() / (double)y_test.size(0);</span></span>
<span id="L334"><span class="lineNum"> 334</span> <span class="tlaGNC"> 49 : if (priorAccuracy == 0) {</span></span>
<span id="L335"><span class="lineNum"> 335</span> <span class="tlaGNC"> 13 : priorAccuracy = accuracy;</span></span>
<span id="L336"><span class="lineNum"> 336</span> : } else {</span>
<span id="L337"><span class="lineNum"> 337</span> <span class="tlaGNC"> 36 : improvement = accuracy - priorAccuracy;</span></span>
<span id="L338"><span class="lineNum"> 338</span> : }</span>
<span id="L339"><span class="lineNum"> 339</span> <span class="tlaGNC"> 49 : if (improvement &lt; convergence_threshold) {</span></span>
<span id="L340"><span class="lineNum"> 340</span> <span class="tlaGNC"> 32 : tolerance++;</span></span>
<span id="L341"><span class="lineNum"> 341</span> : } else {</span>
<span id="L342"><span class="lineNum"> 342</span> <span class="tlaGNC"> 17 : tolerance = 0; // Reset the counter if the model performs better</span></span>
<span id="L343"><span class="lineNum"> 343</span> <span class="tlaGNC"> 17 : numItemsPack = 0;</span></span>
<span id="L344"><span class="lineNum"> 344</span> : }</span>
<span id="L345"><span class="lineNum"> 345</span> : // Keep the best accuracy until now as the prior accuracy</span>
<span id="L346"><span class="lineNum"> 346</span> <span class="tlaGNC"> 49 : priorAccuracy = std::max(accuracy, priorAccuracy);</span></span>
<span id="L347"><span class="lineNum"> 347</span> : // priorAccuracy = accuracy;</span>
<span id="L348"><span class="lineNum"> 348</span> <span class="tlaGNC"> 49 : }</span></span>
<span id="L349"><span class="lineNum"> 349</span> <span class="tlaGNC"> 82 : finished = finished || tolerance &gt; maxTolerance || featuresUsed.size() == features.size();</span></span>
<span id="L350"><span class="lineNum"> 350</span> <span class="tlaGNC"> 82 : }</span></span>
<span id="L351"><span class="lineNum"> 351</span> <span class="tlaGNC"> 17 : if (tolerance &gt; maxTolerance) {</span></span>
<span id="L352"><span class="lineNum"> 352</span> <span class="tlaGNC"> 2 : if (numItemsPack &lt; n_models) {</span></span>
<span id="L353"><span class="lineNum"> 353</span> <span class="tlaGNC"> 2 : notes.push_back(&quot;Convergence threshold reached &amp; &quot; + std::to_string(numItemsPack) + &quot; models eliminated&quot;);</span></span>
<span id="L354"><span class="lineNum"> 354</span> <span class="tlaGNC"> 32 : for (int i = 0; i &lt; numItemsPack; ++i) {</span></span>
<span id="L355"><span class="lineNum"> 355</span> <span class="tlaGNC"> 30 : significanceModels.pop_back();</span></span>
<span id="L356"><span class="lineNum"> 356</span> <span class="tlaGNC"> 30 : models.pop_back();</span></span>
<span id="L357"><span class="lineNum"> 357</span> <span class="tlaGNC"> 30 : n_models--;</span></span>
<span id="L358"><span class="lineNum"> 358</span> : }</span>
<span id="L359"><span class="lineNum"> 359</span> : } else {</span>
<span id="L360"><span class="lineNum"> 360</span> <span class="tlaUNC tlaBgUNC"> 0 : notes.push_back(&quot;Convergence threshold reached &amp; 0 models eliminated&quot;);</span></span>
<span id="L361"><span class="lineNum"> 361</span> : }</span>
<span id="L362"><span class="lineNum"> 362</span> : }</span>
<span id="L363"><span class="lineNum"> 363</span> <span class="tlaGNC tlaBgGNC"> 17 : if (featuresUsed.size() != features.size()) {</span></span>
<span id="L364"><span class="lineNum"> 364</span> <span class="tlaGNC"> 2 : notes.push_back(&quot;Used features in train: &quot; + std::to_string(featuresUsed.size()) + &quot; of &quot; + std::to_string(features.size()));</span></span>
<span id="L365"><span class="lineNum"> 365</span> <span class="tlaGNC"> 2 : status = WARNING;</span></span>
<span id="L366"><span class="lineNum"> 366</span> : }</span>
<span id="L367"><span class="lineNum"> 367</span> <span class="tlaGNC"> 17 : notes.push_back(&quot;Number of models: &quot; + std::to_string(n_models));</span></span>
<span id="L368"><span class="lineNum"> 368</span> <span class="tlaGNC"> 25 : }</span></span>
<span id="L369"><span class="lineNum"> 369</span> <span class="tlaGNC"> 1 : std::vector&lt;std::string&gt; BoostAODE::graph(const std::string&amp; title) const</span></span>
<span id="L370"><span class="lineNum"> 370</span> : {</span>
<span id="L371"><span class="lineNum"> 371</span> <span class="tlaGNC"> 1 : return Ensemble::graph(title);</span></span>
<span id="L372"><span class="lineNum"> 372</span> : }</span>
<span id="L373"><span class="lineNum"> 373</span> : }</span>
<span id="L246"><span class="lineNum"> 246</span> <span class="tlaGNC"> 11 : featureSelector = new IWSS(dataset, features, className, maxFeatures, states.at(className).size(), weights_, threshold);</span></span>
<span id="L247"><span class="lineNum"> 247</span> <span class="tlaGNC"> 27 : } else if (select_features_algorithm == SelectFeatures.FCBF) {</span></span>
<span id="L248"><span class="lineNum"> 248</span> <span class="tlaGNC"> 27 : if (threshold &lt; 1e-7 || threshold &gt; 1) {</span></span>
<span id="L249"><span class="lineNum"> 249</span> <span class="tlaGNC"> 18 : throw std::invalid_argument(&quot;Invalid threshold value for &quot; + SelectFeatures.FCBF + &quot; [1e-7, 1]&quot;);</span></span>
<span id="L250"><span class="lineNum"> 250</span> : }</span>
<span id="L251"><span class="lineNum"> 251</span> <span class="tlaGNC"> 9 : featureSelector = new FCBF(dataset, features, className, maxFeatures, states.at(className).size(), weights_, threshold);</span></span>
<span id="L252"><span class="lineNum"> 252</span> : }</span>
<span id="L253"><span class="lineNum"> 253</span> <span class="tlaGNC"> 40 : featureSelector-&gt;fit();</span></span>
<span id="L254"><span class="lineNum"> 254</span> <span class="tlaGNC"> 40 : auto cfsFeatures = featureSelector-&gt;getFeatures();</span></span>
<span id="L255"><span class="lineNum"> 255</span> <span class="tlaGNC"> 40 : auto scores = featureSelector-&gt;getScores();</span></span>
<span id="L256"><span class="lineNum"> 256</span> <span class="tlaGNC"> 245 : for (const int&amp; feature : cfsFeatures) {</span></span>
<span id="L257"><span class="lineNum"> 257</span> <span class="tlaGNC"> 205 : featuresUsed.push_back(feature);</span></span>
<span id="L258"><span class="lineNum"> 258</span> <span class="tlaGNC"> 205 : std::unique_ptr&lt;Classifier&gt; model = std::make_unique&lt;SPODE&gt;(feature);</span></span>
<span id="L259"><span class="lineNum"> 259</span> <span class="tlaGNC"> 205 : model-&gt;fit(dataset, features, className, states, weights_);</span></span>
<span id="L260"><span class="lineNum"> 260</span> <span class="tlaGNC"> 205 : models.push_back(std::move(model));</span></span>
<span id="L261"><span class="lineNum"> 261</span> <span class="tlaGNC"> 205 : significanceModels.push_back(1.0); // They will be updated later in trainModel</span></span>
<span id="L262"><span class="lineNum"> 262</span> <span class="tlaGNC"> 205 : n_models++;</span></span>
<span id="L263"><span class="lineNum"> 263</span> <span class="tlaGNC"> 205 : }</span></span>
<span id="L264"><span class="lineNum"> 264</span> <span class="tlaGNC"> 40 : notes.push_back(&quot;Used features in initialization: &quot; + std::to_string(featuresUsed.size()) + &quot; of &quot; + std::to_string(features.size()) + &quot; with &quot; + select_features_algorithm);</span></span>
<span id="L265"><span class="lineNum"> 265</span> <span class="tlaGNC"> 40 : delete featureSelector;</span></span>
<span id="L266"><span class="lineNum"> 266</span> <span class="tlaGNC"> 80 : return featuresUsed;</span></span>
<span id="L267"><span class="lineNum"> 267</span> <span class="tlaGNC"> 112 : }</span></span>
<span id="L268"><span class="lineNum"> 268</span> <span class="tlaGNC"> 190 : void BoostAODE::trainModel(const torch::Tensor&amp; weights)</span></span>
<span id="L269"><span class="lineNum"> 269</span> : {</span>
<span id="L270"><span class="lineNum"> 270</span> : //</span>
<span id="L271"><span class="lineNum"> 271</span> : // Logging setup</span>
<span id="L272"><span class="lineNum"> 272</span> : //</span>
<span id="L273"><span class="lineNum"> 273</span> <span class="tlaGNC"> 190 : loguru::set_thread_name(&quot;BoostAODE&quot;);</span></span>
<span id="L274"><span class="lineNum"> 274</span> <span class="tlaGNC"> 190 : loguru::g_stderr_verbosity = loguru::Verbosity_OFF;</span></span>
<span id="L275"><span class="lineNum"> 275</span> <span class="tlaGNC"> 190 : loguru::add_file(&quot;boostAODE.log&quot;, loguru::Truncate, loguru::Verbosity_MAX);</span></span>
<span id="L276"><span class="lineNum"> 276</span> : </span>
<span id="L277"><span class="lineNum"> 277</span> : // Algorithm based on the adaboost algorithm for classification</span>
<span id="L278"><span class="lineNum"> 278</span> : // as explained in Ensemble methods (Zhi-Hua Zhou, 2012)</span>
<span id="L279"><span class="lineNum"> 279</span> <span class="tlaGNC"> 190 : fitted = true;</span></span>
<span id="L280"><span class="lineNum"> 280</span> <span class="tlaGNC"> 190 : double alpha_t = 0;</span></span>
<span id="L281"><span class="lineNum"> 281</span> <span class="tlaGNC"> 190 : torch::Tensor weights_ = torch::full({ m }, 1.0 / m, torch::kFloat64);</span></span>
<span id="L282"><span class="lineNum"> 282</span> <span class="tlaGNC"> 190 : bool finished = false;</span></span>
<span id="L283"><span class="lineNum"> 283</span> <span class="tlaGNC"> 190 : std::vector&lt;int&gt; featuresUsed;</span></span>
<span id="L284"><span class="lineNum"> 284</span> <span class="tlaGNC"> 190 : if (selectFeatures) {</span></span>
<span id="L285"><span class="lineNum"> 285</span> <span class="tlaGNC"> 76 : featuresUsed = initializeModels();</span></span>
<span id="L286"><span class="lineNum"> 286</span> <span class="tlaGNC"> 40 : auto ypred = predict(X_train);</span></span>
<span id="L287"><span class="lineNum"> 287</span> <span class="tlaGNC"> 40 : std::tie(weights_, alpha_t, finished) = update_weights(y_train, ypred, weights_);</span></span>
<span id="L288"><span class="lineNum"> 288</span> : // Update significance of the models</span>
<span id="L289"><span class="lineNum"> 289</span> <span class="tlaGNC"> 245 : for (int i = 0; i &lt; n_models; ++i) {</span></span>
<span id="L290"><span class="lineNum"> 290</span> <span class="tlaGNC"> 205 : significanceModels[i] = alpha_t;</span></span>
<span id="L291"><span class="lineNum"> 291</span> : }</span>
<span id="L292"><span class="lineNum"> 292</span> <span class="tlaGNC"> 40 : if (finished) {</span></span>
<span id="L293"><span class="lineNum"> 293</span> <span class="tlaUNC tlaBgUNC"> 0 : return;</span></span>
<span id="L294"><span class="lineNum"> 294</span> : }</span>
<span id="L295"><span class="lineNum"> 295</span> <span class="tlaGNC tlaBgGNC"> 40 : }</span></span>
<span id="L296"><span class="lineNum"> 296</span> <span class="tlaGNC"> 154 : int numItemsPack = 0; // The counter of the models inserted in the current pack</span></span>
<span id="L297"><span class="lineNum"> 297</span> : // Variables to control the accuracy finish condition</span>
<span id="L298"><span class="lineNum"> 298</span> <span class="tlaGNC"> 154 : double priorAccuracy = 0.0;</span></span>
<span id="L299"><span class="lineNum"> 299</span> <span class="tlaGNC"> 154 : double improvement = 1.0;</span></span>
<span id="L300"><span class="lineNum"> 300</span> <span class="tlaGNC"> 154 : double convergence_threshold = 1e-4;</span></span>
<span id="L301"><span class="lineNum"> 301</span> <span class="tlaGNC"> 154 : int tolerance = 0; // number of times the accuracy is lower than the convergence_threshold</span></span>
<span id="L302"><span class="lineNum"> 302</span> : // Step 0: Set the finish condition</span>
<span id="L303"><span class="lineNum"> 303</span> : // epsilon sub t &gt; 0.5 =&gt; inverse the weights policy</span>
<span id="L304"><span class="lineNum"> 304</span> : // validation error is not decreasing</span>
<span id="L305"><span class="lineNum"> 305</span> : // run out of features</span>
<span id="L306"><span class="lineNum"> 306</span> <span class="tlaGNC"> 154 : bool ascending = order_algorithm == Orders.ASC;</span></span>
<span id="L307"><span class="lineNum"> 307</span> <span class="tlaGNC"> 154 : std::mt19937 g{ 173 };</span></span>
<span id="L308"><span class="lineNum"> 308</span> <span class="tlaGNC"> 919 : while (!finished) {</span></span>
<span id="L309"><span class="lineNum"> 309</span> : // Step 1: Build ranking with mutual information</span>
<span id="L310"><span class="lineNum"> 310</span> <span class="tlaGNC"> 765 : auto featureSelection = metrics.SelectKBestWeighted(weights_, ascending, n); // Get all the features sorted</span></span>
<span id="L311"><span class="lineNum"> 311</span> <span class="tlaGNC"> 765 : if (order_algorithm == Orders.RAND) {</span></span>
<span id="L312"><span class="lineNum"> 312</span> <span class="tlaGNC"> 81 : std::shuffle(featureSelection.begin(), featureSelection.end(), g);</span></span>
<span id="L313"><span class="lineNum"> 313</span> : }</span>
<span id="L314"><span class="lineNum"> 314</span> : // Remove used features</span>
<span id="L315"><span class="lineNum"> 315</span> <span class="tlaGNC"> 1530 : featureSelection.erase(remove_if(begin(featureSelection), end(featureSelection), [&amp;](auto x)</span></span>
<span id="L316"><span class="lineNum"> 316</span> <span class="tlaGNC"> 110548 : { return std::find(begin(featuresUsed), end(featuresUsed), x) != end(featuresUsed);}),</span></span>
<span id="L317"><span class="lineNum"> 317</span> <span class="tlaGNC"> 765 : end(featureSelection)</span></span>
<span id="L318"><span class="lineNum"> 318</span> : );</span>
<span id="L319"><span class="lineNum"> 319</span> <span class="tlaGNC"> 765 : int k = bisection ? pow(2, tolerance) : 1;</span></span>
<span id="L320"><span class="lineNum"> 320</span> <span class="tlaGNC"> 765 : int counter = 0; // The model counter of the current pack</span></span>
<span id="L321"><span class="lineNum"> 321</span> <span class="tlaGNC"> 765 : VLOG_SCOPE_F(1, &quot;counter=%d k=%d featureSelection.size: %zu&quot;, counter, k, featureSelection.size());</span></span>
<span id="L322"><span class="lineNum"> 322</span> <span class="tlaGNC"> 1838 : while (counter++ &lt; k &amp;&amp; featureSelection.size() &gt; 0) {</span></span>
<span id="L323"><span class="lineNum"> 323</span> <span class="tlaGNC"> 1073 : auto feature = featureSelection[0];</span></span>
<span id="L324"><span class="lineNum"> 324</span> <span class="tlaGNC"> 1073 : featureSelection.erase(featureSelection.begin());</span></span>
<span id="L325"><span class="lineNum"> 325</span> <span class="tlaGNC"> 1073 : std::unique_ptr&lt;Classifier&gt; model;</span></span>
<span id="L326"><span class="lineNum"> 326</span> <span class="tlaGNC"> 1073 : model = std::make_unique&lt;SPODE&gt;(feature);</span></span>
<span id="L327"><span class="lineNum"> 327</span> <span class="tlaGNC"> 1073 : model-&gt;fit(dataset, features, className, states, weights_);</span></span>
<span id="L328"><span class="lineNum"> 328</span> <span class="tlaGNC"> 1073 : alpha_t = 0.0;</span></span>
<span id="L329"><span class="lineNum"> 329</span> <span class="tlaGNC"> 1073 : if (!block_update) {</span></span>
<span id="L330"><span class="lineNum"> 330</span> <span class="tlaGNC"> 945 : auto ypred = model-&gt;predict(X_train);</span></span>
<span id="L331"><span class="lineNum"> 331</span> : // Step 3.1: Compute the classifier amout of say</span>
<span id="L332"><span class="lineNum"> 332</span> <span class="tlaGNC"> 945 : std::tie(weights_, alpha_t, finished) = update_weights(y_train, ypred, weights_);</span></span>
<span id="L333"><span class="lineNum"> 333</span> <span class="tlaGNC"> 945 : }</span></span>
<span id="L334"><span class="lineNum"> 334</span> : // Step 3.4: Store classifier and its accuracy to weigh its future vote</span>
<span id="L335"><span class="lineNum"> 335</span> <span class="tlaGNC"> 1073 : numItemsPack++;</span></span>
<span id="L336"><span class="lineNum"> 336</span> <span class="tlaGNC"> 1073 : featuresUsed.push_back(feature);</span></span>
<span id="L337"><span class="lineNum"> 337</span> <span class="tlaGNC"> 1073 : models.push_back(std::move(model));</span></span>
<span id="L338"><span class="lineNum"> 338</span> <span class="tlaGNC"> 1073 : significanceModels.push_back(alpha_t);</span></span>
<span id="L339"><span class="lineNum"> 339</span> <span class="tlaGNC"> 1073 : n_models++;</span></span>
<span id="L340"><span class="lineNum"> 340</span> <span class="tlaGNC"> 1073 : VLOG_SCOPE_F(2, &quot;numItemsPack: %d n_models: %d featuresUsed: %zu&quot;, numItemsPack, n_models, featuresUsed.size());</span></span>
<span id="L341"><span class="lineNum"> 341</span> <span class="tlaGNC"> 1073 : }</span></span>
<span id="L342"><span class="lineNum"> 342</span> <span class="tlaGNC"> 765 : if (block_update) {</span></span>
<span id="L343"><span class="lineNum"> 343</span> <span class="tlaGNC"> 40 : std::tie(weights_, alpha_t, finished) = update_weights_block(k, y_train, weights_);</span></span>
<span id="L344"><span class="lineNum"> 344</span> : }</span>
<span id="L345"><span class="lineNum"> 345</span> <span class="tlaGNC"> 765 : if (convergence &amp;&amp; !finished) {</span></span>
<span id="L346"><span class="lineNum"> 346</span> <span class="tlaGNC"> 474 : auto y_val_predict = predict(X_test);</span></span>
<span id="L347"><span class="lineNum"> 347</span> <span class="tlaGNC"> 474 : double accuracy = (y_val_predict == y_test).sum().item&lt;double&gt;() / (double)y_test.size(0);</span></span>
<span id="L348"><span class="lineNum"> 348</span> <span class="tlaGNC"> 474 : if (priorAccuracy == 0) {</span></span>
<span id="L349"><span class="lineNum"> 349</span> <span class="tlaGNC"> 119 : priorAccuracy = accuracy;</span></span>
<span id="L350"><span class="lineNum"> 350</span> : } else {</span>
<span id="L351"><span class="lineNum"> 351</span> <span class="tlaGNC"> 355 : improvement = accuracy - priorAccuracy;</span></span>
<span id="L352"><span class="lineNum"> 352</span> : }</span>
<span id="L353"><span class="lineNum"> 353</span> <span class="tlaGNC"> 474 : if (improvement &lt; convergence_threshold) {</span></span>
<span id="L354"><span class="lineNum"> 354</span> <span class="tlaGNC"> 296 : VLOG_SCOPE_F(3, &quot; (improvement&lt;threshold) tolerance: %d numItemsPack: %d improvement: %f prior: %f current: %f&quot;, tolerance, numItemsPack, improvement, priorAccuracy, accuracy);</span></span>
<span id="L355"><span class="lineNum"> 355</span> <span class="tlaGNC"> 296 : tolerance++;</span></span>
<span id="L356"><span class="lineNum"> 356</span> <span class="tlaGNC"> 296 : } else {</span></span>
<span id="L357"><span class="lineNum"> 357</span> <span class="tlaGNC"> 178 : VLOG_SCOPE_F(3, &quot;* (improvement&gt;=threshold) Reset. tolerance: %d numItemsPack: %d improvement: %f prior: %f current: %f&quot;, tolerance, numItemsPack, improvement, priorAccuracy, accuracy);</span></span>
<span id="L358"><span class="lineNum"> 358</span> <span class="tlaGNC"> 178 : tolerance = 0; // Reset the counter if the model performs better</span></span>
<span id="L359"><span class="lineNum"> 359</span> <span class="tlaGNC"> 178 : numItemsPack = 0;</span></span>
<span id="L360"><span class="lineNum"> 360</span> <span class="tlaGNC"> 178 : }</span></span>
<span id="L361"><span class="lineNum"> 361</span> <span class="tlaGNC"> 474 : if (convergence_best) {</span></span>
<span id="L362"><span class="lineNum"> 362</span> : // Keep the best accuracy until now as the prior accuracy</span>
<span id="L363"><span class="lineNum"> 363</span> <span class="tlaGNC"> 71 : priorAccuracy = std::max(accuracy, priorAccuracy);</span></span>
<span id="L364"><span class="lineNum"> 364</span> : } else {</span>
<span id="L365"><span class="lineNum"> 365</span> : // Keep the last accuray obtained as the prior accuracy</span>
<span id="L366"><span class="lineNum"> 366</span> <span class="tlaGNC"> 403 : priorAccuracy = accuracy;</span></span>
<span id="L367"><span class="lineNum"> 367</span> : }</span>
<span id="L368"><span class="lineNum"> 368</span> <span class="tlaGNC"> 474 : }</span></span>
<span id="L369"><span class="lineNum"> 369</span> <span class="tlaGNC"> 765 : VLOG_SCOPE_F(1, &quot;tolerance: %d featuresUsed.size: %zu features.size: %zu&quot;, tolerance, featuresUsed.size(), features.size());</span></span>
<span id="L370"><span class="lineNum"> 370</span> <span class="tlaGNC"> 765 : finished = finished || tolerance &gt; maxTolerance || featuresUsed.size() == features.size();</span></span>
<span id="L371"><span class="lineNum"> 371</span> <span class="tlaGNC"> 765 : }</span></span>
<span id="L372"><span class="lineNum"> 372</span> <span class="tlaGNC"> 154 : if (tolerance &gt; maxTolerance) {</span></span>
<span id="L373"><span class="lineNum"> 373</span> <span class="tlaGNC"> 21 : if (numItemsPack &lt; n_models) {</span></span>
<span id="L374"><span class="lineNum"> 374</span> <span class="tlaGNC"> 21 : notes.push_back(&quot;Convergence threshold reached &amp; &quot; + std::to_string(numItemsPack) + &quot; models eliminated&quot;);</span></span>
<span id="L375"><span class="lineNum"> 375</span> <span class="tlaGNC"> 21 : VLOG_SCOPE_F(4, &quot;Convergence threshold reached &amp; %d models eliminated of %d&quot;, numItemsPack, n_models);</span></span>
<span id="L376"><span class="lineNum"> 376</span> <span class="tlaGNC"> 336 : for (int i = 0; i &lt; numItemsPack; ++i) {</span></span>
<span id="L377"><span class="lineNum"> 377</span> <span class="tlaGNC"> 315 : significanceModels.pop_back();</span></span>
<span id="L378"><span class="lineNum"> 378</span> <span class="tlaGNC"> 315 : models.pop_back();</span></span>
<span id="L379"><span class="lineNum"> 379</span> <span class="tlaGNC"> 315 : n_models--;</span></span>
<span id="L380"><span class="lineNum"> 380</span> : }</span>
<span id="L381"><span class="lineNum"> 381</span> <span class="tlaGNC"> 21 : } else {</span></span>
<span id="L382"><span class="lineNum"> 382</span> <span class="tlaUNC tlaBgUNC"> 0 : notes.push_back(&quot;Convergence threshold reached &amp; 0 models eliminated&quot;);</span></span>
<span id="L383"><span class="lineNum"> 383</span> <span class="tlaUNC"> 0 : VLOG_SCOPE_F(4, &quot;Convergence threshold reached &amp; 0 models eliminated n_models=%d numItemsPack=%d&quot;, n_models, numItemsPack);</span></span>
<span id="L384"><span class="lineNum"> 384</span> <span class="tlaUNC"> 0 : }</span></span>
<span id="L385"><span class="lineNum"> 385</span> : }</span>
<span id="L386"><span class="lineNum"> 386</span> <span class="tlaGNC tlaBgGNC"> 154 : if (featuresUsed.size() != features.size()) {</span></span>
<span id="L387"><span class="lineNum"> 387</span> <span class="tlaGNC"> 21 : notes.push_back(&quot;Used features in train: &quot; + std::to_string(featuresUsed.size()) + &quot; of &quot; + std::to_string(features.size()));</span></span>
<span id="L388"><span class="lineNum"> 388</span> <span class="tlaGNC"> 21 : status = WARNING;</span></span>
<span id="L389"><span class="lineNum"> 389</span> : }</span>
<span id="L390"><span class="lineNum"> 390</span> <span class="tlaGNC"> 154 : notes.push_back(&quot;Number of models: &quot; + std::to_string(n_models));</span></span>
<span id="L391"><span class="lineNum"> 391</span> <span class="tlaGNC"> 226 : }</span></span>
<span id="L392"><span class="lineNum"> 392</span> <span class="tlaGNC"> 8 : std::vector&lt;std::string&gt; BoostAODE::graph(const std::string&amp; title) const</span></span>
<span id="L393"><span class="lineNum"> 393</span> : {</span>
<span id="L394"><span class="lineNum"> 394</span> <span class="tlaGNC"> 8 : return Ensemble::graph(title);</span></span>
<span id="L395"><span class="lineNum"> 395</span> : }</span>
<span id="L396"><span class="lineNum"> 396</span> : }</span>
</pre>
</td>
</tr>

2
html/bayesnet/ensembles/BoostAODE.h.func-c.html
View File

@@ -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>

2
html/bayesnet/ensembles/BoostAODE.h.func.html
View File

@@ -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>

19
html/bayesnet/ensembles/BoostAODE.h.gcov.html
View File

@@ -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>
@@ -101,14 +101,15 @@
<span id="L39"><span class="lineNum"> 39</span> : int maxTolerance = 3;</span>
<span id="L40"><span class="lineNum"> 40</span> : std::string order_algorithm; // order to process the KBest features asc, desc, rand</span>
<span id="L41"><span class="lineNum"> 41</span> : bool convergence = true; //if true, stop when the model does not improve</span>
<span id="L42"><span class="lineNum"> 42</span> : bool selectFeatures = false; // if true, use feature selection</span>
<span id="L43"><span class="lineNum"> 43</span> : std::string select_features_algorithm = Orders.DESC; // Selected feature selection algorithm</span>
<span id="L44"><span class="lineNum"> 44</span> : FeatureSelect* featureSelector = nullptr;</span>
<span id="L45"><span class="lineNum"> 45</span> : double threshold = -1;</span>
<span id="L46"><span class="lineNum"> 46</span> : bool block_update = false;</span>
<span id="L47"><span class="lineNum"> 47</span> : };</span>
<span id="L48"><span class="lineNum"> 48</span> : }</span>
<span id="L49"><span class="lineNum"> 49</span> : #endif</span>
<span id="L42"><span class="lineNum"> 42</span> : bool convergence_best = false; // wether to keep the best accuracy to the moment or the last accuracy as prior accuracy</span>
<span id="L43"><span class="lineNum"> 43</span> : bool selectFeatures = false; // if true, use feature selection</span>
<span id="L44"><span class="lineNum"> 44</span> : std::string select_features_algorithm = Orders.DESC; // Selected feature selection algorithm</span>
<span id="L45"><span class="lineNum"> 45</span> : FeatureSelect* featureSelector = nullptr;</span>
<span id="L46"><span class="lineNum"> 46</span> : double threshold = -1;</span>
<span id="L47"><span class="lineNum"> 47</span> : bool block_update = false;</span>
<span id="L48"><span class="lineNum"> 48</span> : };</span>
<span id="L49"><span class="lineNum"> 49</span> : }</span>
<span id="L50"><span class="lineNum"> 50</span> : #endif</span>
</pre>
</td>
</tr>

82
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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>
@@ -65,175 +65,175 @@
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</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>
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<td class="coverFnHi">65366</td>
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</tr>

304
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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>
@@ -70,216 +70,216 @@
<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>
<span id="L11"><span class="lineNum"> 11</span> <span class="tlaGNC tlaBgGNC"> 77 : Ensemble::Ensemble(bool predict_voting) : Classifier(Network()), n_models(0), predict_voting(predict_voting)</span></span>
<span id="L11"><span class="lineNum"> 11</span> <span class="tlaGNC tlaBgGNC"> 864 : Ensemble::Ensemble(bool predict_voting) : Classifier(Network()), n_models(0), predict_voting(predict_voting)</span></span>
<span id="L12"><span class="lineNum"> 12</span> : {</span>
<span id="L13"><span class="lineNum"> 13</span> : </span>
<span id="L14"><span class="lineNum"> 14</span> <span class="tlaGNC"> 77 : };</span></span>
<span id="L14"><span class="lineNum"> 14</span> <span class="tlaGNC"> 864 : };</span></span>
<span id="L15"><span class="lineNum"> 15</span> : const std::string ENSEMBLE_NOT_FITTED = &quot;Ensemble has not been fitted&quot;;</span>
<span id="L16"><span class="lineNum"> 16</span> <span class="tlaGNC"> 6 : void Ensemble::trainModel(const torch::Tensor&amp; weights)</span></span>
<span id="L16"><span class="lineNum"> 16</span> <span class="tlaGNC"> 66 : void Ensemble::trainModel(const torch::Tensor&amp; weights)</span></span>
<span id="L17"><span class="lineNum"> 17</span> : {</span>
<span id="L18"><span class="lineNum"> 18</span> <span class="tlaGNC"> 6 : n_models = models.size();</span></span>
<span id="L19"><span class="lineNum"> 19</span> <span class="tlaGNC"> 47 : for (auto i = 0; i &lt; n_models; ++i) {</span></span>
<span id="L18"><span class="lineNum"> 18</span> <span class="tlaGNC"> 66 : n_models = models.size();</span></span>
<span id="L19"><span class="lineNum"> 19</span> <span class="tlaGNC"> 517 : for (auto i = 0; i &lt; n_models; ++i) {</span></span>
<span id="L20"><span class="lineNum"> 20</span> : // fit with std::vectors</span>
<span id="L21"><span class="lineNum"> 21</span> <span class="tlaGNC"> 41 : models[i]-&gt;fit(dataset, features, className, states);</span></span>
<span id="L21"><span class="lineNum"> 21</span> <span class="tlaGNC"> 451 : models[i]-&gt;fit(dataset, features, className, states);</span></span>
<span id="L22"><span class="lineNum"> 22</span> : }</span>
<span id="L23"><span class="lineNum"> 23</span> <span class="tlaGNC"> 6 : }</span></span>
<span id="L24"><span class="lineNum"> 24</span> <span class="tlaGNC"> 13 : std::vector&lt;int&gt; Ensemble::compute_arg_max(std::vector&lt;std::vector&lt;double&gt;&gt;&amp; X)</span></span>
<span id="L23"><span class="lineNum"> 23</span> <span class="tlaGNC"> 66 : }</span></span>
<span id="L24"><span class="lineNum"> 24</span> <span class="tlaGNC"> 145 : std::vector&lt;int&gt; Ensemble::compute_arg_max(std::vector&lt;std::vector&lt;double&gt;&gt;&amp; X)</span></span>
<span id="L25"><span class="lineNum"> 25</span> : {</span>
<span id="L26"><span class="lineNum"> 26</span> <span class="tlaGNC"> 13 : std::vector&lt;int&gt; y_pred;</span></span>
<span id="L27"><span class="lineNum"> 27</span> <span class="tlaGNC"> 2843 : for (auto i = 0; i &lt; X.size(); ++i) {</span></span>
<span id="L28"><span class="lineNum"> 28</span> <span class="tlaGNC"> 2830 : auto max = std::max_element(X[i].begin(), X[i].end());</span></span>
<span id="L29"><span class="lineNum"> 29</span> <span class="tlaGNC"> 5660 : y_pred.push_back(std::distance(X[i].begin(), max));</span></span>
<span id="L26"><span class="lineNum"> 26</span> <span class="tlaGNC"> 145 : std::vector&lt;int&gt; y_pred;</span></span>
<span id="L27"><span class="lineNum"> 27</span> <span class="tlaGNC"> 33363 : for (auto i = 0; i &lt; X.size(); ++i) {</span></span>
<span id="L28"><span class="lineNum"> 28</span> <span class="tlaGNC"> 33218 : auto max = std::max_element(X[i].begin(), X[i].end());</span></span>
<span id="L29"><span class="lineNum"> 29</span> <span class="tlaGNC"> 66436 : y_pred.push_back(std::distance(X[i].begin(), max));</span></span>
<span id="L30"><span class="lineNum"> 30</span> : }</span>
<span id="L31"><span class="lineNum"> 31</span> <span class="tlaGNC"> 13 : return y_pred;</span></span>
<span id="L31"><span class="lineNum"> 31</span> <span class="tlaGNC"> 145 : return y_pred;</span></span>
<span id="L32"><span class="lineNum"> 32</span> <span class="tlaUNC tlaBgUNC"> 0 : }</span></span>
<span id="L33"><span class="lineNum"> 33</span> <span class="tlaGNC tlaBgGNC"> 75 : torch::Tensor Ensemble::compute_arg_max(torch::Tensor&amp; X)</span></span>
<span id="L33"><span class="lineNum"> 33</span> <span class="tlaGNC tlaBgGNC"> 933 : torch::Tensor Ensemble::compute_arg_max(torch::Tensor&amp; X)</span></span>
<span id="L34"><span class="lineNum"> 34</span> : {</span>
<span id="L35"><span class="lineNum"> 35</span> <span class="tlaGNC"> 75 : auto y_pred = torch::argmax(X, 1);</span></span>
<span id="L36"><span class="lineNum"> 36</span> <span class="tlaGNC"> 75 : return y_pred;</span></span>
<span id="L35"><span class="lineNum"> 35</span> <span class="tlaGNC"> 933 : auto y_pred = torch::argmax(X, 1);</span></span>
<span id="L36"><span class="lineNum"> 36</span> <span class="tlaGNC"> 933 : return y_pred;</span></span>
<span id="L37"><span class="lineNum"> 37</span> : }</span>
<span id="L38"><span class="lineNum"> 38</span> <span class="tlaGNC"> 20 : torch::Tensor Ensemble::voting(torch::Tensor&amp; votes)</span></span>
<span id="L38"><span class="lineNum"> 38</span> <span class="tlaGNC"> 291 : torch::Tensor Ensemble::voting(torch::Tensor&amp; votes)</span></span>
<span id="L39"><span class="lineNum"> 39</span> : {</span>
<span id="L40"><span class="lineNum"> 40</span> : // Convert m x n_models tensor to a m x n_class_states with voting probabilities</span>
<span id="L41"><span class="lineNum"> 41</span> <span class="tlaGNC"> 20 : auto y_pred_ = votes.accessor&lt;int, 2&gt;();</span></span>
<span id="L42"><span class="lineNum"> 42</span> <span class="tlaGNC"> 20 : std::vector&lt;int&gt; y_pred_final;</span></span>
<span id="L43"><span class="lineNum"> 43</span> <span class="tlaGNC"> 20 : int numClasses = states.at(className).size();</span></span>
<span id="L41"><span class="lineNum"> 41</span> <span class="tlaGNC"> 291 : auto y_pred_ = votes.accessor&lt;int, 2&gt;();</span></span>
<span id="L42"><span class="lineNum"> 42</span> <span class="tlaGNC"> 291 : std::vector&lt;int&gt; y_pred_final;</span></span>
<span id="L43"><span class="lineNum"> 43</span> <span class="tlaGNC"> 291 : int numClasses = states.at(className).size();</span></span>
<span id="L44"><span class="lineNum"> 44</span> : // votes is m x n_models with the prediction of every model for each sample</span>
<span id="L45"><span class="lineNum"> 45</span> <span class="tlaGNC"> 20 : auto result = torch::zeros({ votes.size(0), numClasses }, torch::kFloat32);</span></span>
<span id="L46"><span class="lineNum"> 46</span> <span class="tlaGNC"> 20 : auto sum = std::reduce(significanceModels.begin(), significanceModels.end());</span></span>
<span id="L47"><span class="lineNum"> 47</span> <span class="tlaGNC"> 5364 : for (int i = 0; i &lt; votes.size(0); ++i) {</span></span>
<span id="L45"><span class="lineNum"> 45</span> <span class="tlaGNC"> 291 : auto result = torch::zeros({ votes.size(0), numClasses }, torch::kFloat32);</span></span>
<span id="L46"><span class="lineNum"> 46</span> <span class="tlaGNC"> 291 : auto sum = std::reduce(significanceModels.begin(), significanceModels.end());</span></span>
<span id="L47"><span class="lineNum"> 47</span> <span class="tlaGNC"> 69474 : for (int i = 0; i &lt; votes.size(0); ++i) {</span></span>
<span id="L48"><span class="lineNum"> 48</span> : // n_votes store in each index (value of class) the significance added by each model</span>
<span id="L49"><span class="lineNum"> 49</span> : // i.e. n_votes[0] contains how much value has the value 0 of class. That value is generated by the models predictions</span>
<span id="L50"><span class="lineNum"> 50</span> <span class="tlaGNC"> 5344 : std::vector&lt;double&gt; n_votes(numClasses, 0.0);</span></span>
<span id="L51"><span class="lineNum"> 51</span> <span class="tlaGNC"> 42310 : for (int j = 0; j &lt; n_models; ++j) {</span></span>
<span id="L52"><span class="lineNum"> 52</span> <span class="tlaGNC"> 36966 : n_votes[y_pred_[i][j]] += significanceModels.at(j);</span></span>
<span id="L50"><span class="lineNum"> 50</span> <span class="tlaGNC"> 69183 : std::vector&lt;double&gt; n_votes(numClasses, 0.0);</span></span>
<span id="L51"><span class="lineNum"> 51</span> <span class="tlaGNC"> 541708 : for (int j = 0; j &lt; n_models; ++j) {</span></span>
<span id="L52"><span class="lineNum"> 52</span> <span class="tlaGNC"> 472525 : n_votes[y_pred_[i][j]] += significanceModels.at(j);</span></span>
<span id="L53"><span class="lineNum"> 53</span> : }</span>
<span id="L54"><span class="lineNum"> 54</span> <span class="tlaGNC"> 5344 : result[i] = torch::tensor(n_votes);</span></span>
<span id="L55"><span class="lineNum"> 55</span> <span class="tlaGNC"> 5344 : }</span></span>
<span id="L54"><span class="lineNum"> 54</span> <span class="tlaGNC"> 69183 : result[i] = torch::tensor(n_votes);</span></span>
<span id="L55"><span class="lineNum"> 55</span> <span class="tlaGNC"> 69183 : }</span></span>
<span id="L56"><span class="lineNum"> 56</span> : // To only do one division and gain precision</span>
<span id="L57"><span class="lineNum"> 57</span> <span class="tlaGNC"> 20 : result /= sum;</span></span>
<span id="L58"><span class="lineNum"> 58</span> <span class="tlaGNC"> 40 : return result;</span></span>
<span id="L59"><span class="lineNum"> 59</span> <span class="tlaGNC"> 20 : }</span></span>
<span id="L60"><span class="lineNum"> 60</span> <span class="tlaGNC"> 24 : std::vector&lt;std::vector&lt;double&gt;&gt; Ensemble::predict_proba(std::vector&lt;std::vector&lt;int&gt;&gt;&amp; X)</span></span>
<span id="L57"><span class="lineNum"> 57</span> <span class="tlaGNC"> 291 : result /= sum;</span></span>
<span id="L58"><span class="lineNum"> 58</span> <span class="tlaGNC"> 582 : return result;</span></span>
<span id="L59"><span class="lineNum"> 59</span> <span class="tlaGNC"> 291 : }</span></span>
<span id="L60"><span class="lineNum"> 60</span> <span class="tlaGNC"> 268 : std::vector&lt;std::vector&lt;double&gt;&gt; Ensemble::predict_proba(std::vector&lt;std::vector&lt;int&gt;&gt;&amp; X)</span></span>
<span id="L61"><span class="lineNum"> 61</span> : {</span>
<span id="L62"><span class="lineNum"> 62</span> <span class="tlaGNC"> 24 : if (!fitted) {</span></span>
<span id="L63"><span class="lineNum"> 63</span> <span class="tlaGNC"> 6 : throw std::logic_error(ENSEMBLE_NOT_FITTED);</span></span>
<span id="L62"><span class="lineNum"> 62</span> <span class="tlaGNC"> 268 : if (!fitted) {</span></span>
<span id="L63"><span class="lineNum"> 63</span> <span class="tlaGNC"> 66 : throw std::logic_error(ENSEMBLE_NOT_FITTED);</span></span>
<span id="L64"><span class="lineNum"> 64</span> : }</span>
<span id="L65"><span class="lineNum"> 65</span> <span class="tlaGNC"> 18 : return predict_voting ? predict_average_voting(X) : predict_average_proba(X);</span></span>
<span id="L65"><span class="lineNum"> 65</span> <span class="tlaGNC"> 202 : return predict_voting ? predict_average_voting(X) : predict_average_proba(X);</span></span>
<span id="L66"><span class="lineNum"> 66</span> : }</span>
<span id="L67"><span class="lineNum"> 67</span> <span class="tlaGNC"> 82 : torch::Tensor Ensemble::predict_proba(torch::Tensor&amp; X)</span></span>
<span id="L67"><span class="lineNum"> 67</span> <span class="tlaGNC"> 1010 : torch::Tensor Ensemble::predict_proba(torch::Tensor&amp; X)</span></span>
<span id="L68"><span class="lineNum"> 68</span> : {</span>
<span id="L69"><span class="lineNum"> 69</span> <span class="tlaGNC"> 82 : if (!fitted) {</span></span>
<span id="L70"><span class="lineNum"> 70</span> <span class="tlaGNC"> 6 : throw std::logic_error(ENSEMBLE_NOT_FITTED);</span></span>
<span id="L69"><span class="lineNum"> 69</span> <span class="tlaGNC"> 1010 : if (!fitted) {</span></span>
<span id="L70"><span class="lineNum"> 70</span> <span class="tlaGNC"> 66 : throw std::logic_error(ENSEMBLE_NOT_FITTED);</span></span>
<span id="L71"><span class="lineNum"> 71</span> : }</span>
<span id="L72"><span class="lineNum"> 72</span> <span class="tlaGNC"> 76 : return predict_voting ? predict_average_voting(X) : predict_average_proba(X);</span></span>
<span id="L72"><span class="lineNum"> 72</span> <span class="tlaGNC"> 944 : return predict_voting ? predict_average_voting(X) : predict_average_proba(X);</span></span>
<span id="L73"><span class="lineNum"> 73</span> : }</span>
<span id="L74"><span class="lineNum"> 74</span> <span class="tlaGNC"> 16 : std::vector&lt;int&gt; Ensemble::predict(std::vector&lt;std::vector&lt;int&gt;&gt;&amp; X)</span></span>
<span id="L74"><span class="lineNum"> 74</span> <span class="tlaGNC"> 178 : std::vector&lt;int&gt; Ensemble::predict(std::vector&lt;std::vector&lt;int&gt;&gt;&amp; X)</span></span>
<span id="L75"><span class="lineNum"> 75</span> : {</span>
<span id="L76"><span class="lineNum"> 76</span> <span class="tlaGNC"> 16 : auto res = predict_proba(X);</span></span>
<span id="L77"><span class="lineNum"> 77</span> <span class="tlaGNC"> 24 : return compute_arg_max(res);</span></span>
<span id="L78"><span class="lineNum"> 78</span> <span class="tlaGNC"> 12 : }</span></span>
<span id="L79"><span class="lineNum"> 79</span> <span class="tlaGNC"> 78 : torch::Tensor Ensemble::predict(torch::Tensor&amp; X)</span></span>
<span id="L76"><span class="lineNum"> 76</span> <span class="tlaGNC"> 178 : auto res = predict_proba(X);</span></span>
<span id="L77"><span class="lineNum"> 77</span> <span class="tlaGNC"> 268 : return compute_arg_max(res);</span></span>
<span id="L78"><span class="lineNum"> 78</span> <span class="tlaGNC"> 134 : }</span></span>
<span id="L79"><span class="lineNum"> 79</span> <span class="tlaGNC"> 966 : torch::Tensor Ensemble::predict(torch::Tensor&amp; X)</span></span>
<span id="L80"><span class="lineNum"> 80</span> : {</span>
<span id="L81"><span class="lineNum"> 81</span> <span class="tlaGNC"> 78 : auto res = predict_proba(X);</span></span>
<span id="L82"><span class="lineNum"> 82</span> <span class="tlaGNC"> 148 : return compute_arg_max(res);</span></span>
<span id="L83"><span class="lineNum"> 83</span> <span class="tlaGNC"> 74 : }</span></span>
<span id="L84"><span class="lineNum"> 84</span> <span class="tlaGNC"> 63 : torch::Tensor Ensemble::predict_average_proba(torch::Tensor&amp; X)</span></span>
<span id="L81"><span class="lineNum"> 81</span> <span class="tlaGNC"> 966 : auto res = predict_proba(X);</span></span>
<span id="L82"><span class="lineNum"> 82</span> <span class="tlaGNC"> 1844 : return compute_arg_max(res);</span></span>
<span id="L83"><span class="lineNum"> 83</span> <span class="tlaGNC"> 922 : }</span></span>
<span id="L84"><span class="lineNum"> 84</span> <span class="tlaGNC"> 735 : torch::Tensor Ensemble::predict_average_proba(torch::Tensor&amp; X)</span></span>
<span id="L85"><span class="lineNum"> 85</span> : {</span>
<span id="L86"><span class="lineNum"> 86</span> <span class="tlaGNC"> 63 : auto n_states = models[0]-&gt;getClassNumStates();</span></span>
<span id="L87"><span class="lineNum"> 87</span> <span class="tlaGNC"> 63 : torch::Tensor y_pred = torch::zeros({ X.size(1), n_states }, torch::kFloat32);</span></span>
<span id="L88"><span class="lineNum"> 88</span> <span class="tlaGNC"> 63 : auto threads{ std::vector&lt;std::thread&gt;() };</span></span>
<span id="L89"><span class="lineNum"> 89</span> <span class="tlaGNC"> 63 : std::mutex mtx;</span></span>
<span id="L90"><span class="lineNum"> 90</span> <span class="tlaGNC"> 365 : for (auto i = 0; i &lt; n_models; ++i) {</span></span>
<span id="L91"><span class="lineNum"> 91</span> <span class="tlaGNC"> 302 : threads.push_back(std::thread([&amp;, i]() {</span></span>
<span id="L92"><span class="lineNum"> 92</span> <span class="tlaGNC"> 302 : auto ypredict = models[i]-&gt;predict_proba(X);</span></span>
<span id="L93"><span class="lineNum"> 93</span> <span class="tlaGNC"> 302 : std::lock_guard&lt;std::mutex&gt; lock(mtx);</span></span>
<span id="L94"><span class="lineNum"> 94</span> <span class="tlaGNC"> 302 : y_pred += ypredict * significanceModels[i];</span></span>
<span id="L95"><span class="lineNum"> 95</span> <span class="tlaGNC"> 302 : }));</span></span>
<span id="L86"><span class="lineNum"> 86</span> <span class="tlaGNC"> 735 : auto n_states = models[0]-&gt;getClassNumStates();</span></span>
<span id="L87"><span class="lineNum"> 87</span> <span class="tlaGNC"> 735 : torch::Tensor y_pred = torch::zeros({ X.size(1), n_states }, torch::kFloat32);</span></span>
<span id="L88"><span class="lineNum"> 88</span> <span class="tlaGNC"> 735 : auto threads{ std::vector&lt;std::thread&gt;() };</span></span>
<span id="L89"><span class="lineNum"> 89</span> <span class="tlaGNC"> 735 : std::mutex mtx;</span></span>
<span id="L90"><span class="lineNum"> 90</span> <span class="tlaGNC"> 4253 : for (auto i = 0; i &lt; n_models; ++i) {</span></span>
<span id="L91"><span class="lineNum"> 91</span> <span class="tlaGNC"> 3518 : threads.push_back(std::thread([&amp;, i]() {</span></span>
<span id="L92"><span class="lineNum"> 92</span> <span class="tlaGNC"> 3518 : auto ypredict = models[i]-&gt;predict_proba(X);</span></span>
<span id="L93"><span class="lineNum"> 93</span> <span class="tlaGNC"> 3518 : std::lock_guard&lt;std::mutex&gt; lock(mtx);</span></span>
<span id="L94"><span class="lineNum"> 94</span> <span class="tlaGNC"> 3518 : y_pred += ypredict * significanceModels[i];</span></span>
<span id="L95"><span class="lineNum"> 95</span> <span class="tlaGNC"> 3518 : }));</span></span>
<span id="L96"><span class="lineNum"> 96</span> : }</span>
<span id="L97"><span class="lineNum"> 97</span> <span class="tlaGNC"> 365 : for (auto&amp; thread : threads) {</span></span>
<span id="L98"><span class="lineNum"> 98</span> <span class="tlaGNC"> 302 : thread.join();</span></span>
<span id="L97"><span class="lineNum"> 97</span> <span class="tlaGNC"> 4253 : for (auto&amp; thread : threads) {</span></span>
<span id="L98"><span class="lineNum"> 98</span> <span class="tlaGNC"> 3518 : thread.join();</span></span>
<span id="L99"><span class="lineNum"> 99</span> : }</span>
<span id="L100"><span class="lineNum"> 100</span> <span class="tlaGNC"> 63 : auto sum = std::reduce(significanceModels.begin(), significanceModels.end());</span></span>
<span id="L101"><span class="lineNum"> 101</span> <span class="tlaGNC"> 63 : y_pred /= sum;</span></span>
<span id="L102"><span class="lineNum"> 102</span> <span class="tlaGNC"> 126 : return y_pred;</span></span>
<span id="L103"><span class="lineNum"> 103</span> <span class="tlaGNC"> 63 : }</span></span>
<span id="L104"><span class="lineNum"> 104</span> <span class="tlaGNC"> 11 : std::vector&lt;std::vector&lt;double&gt;&gt; Ensemble::predict_average_proba(std::vector&lt;std::vector&lt;int&gt;&gt;&amp; X)</span></span>
<span id="L100"><span class="lineNum"> 100</span> <span class="tlaGNC"> 735 : auto sum = std::reduce(significanceModels.begin(), significanceModels.end());</span></span>
<span id="L101"><span class="lineNum"> 101</span> <span class="tlaGNC"> 735 : y_pred /= sum;</span></span>
<span id="L102"><span class="lineNum"> 102</span> <span class="tlaGNC"> 1470 : return y_pred;</span></span>
<span id="L103"><span class="lineNum"> 103</span> <span class="tlaGNC"> 735 : }</span></span>
<span id="L104"><span class="lineNum"> 104</span> <span class="tlaGNC"> 120 : std::vector&lt;std::vector&lt;double&gt;&gt; Ensemble::predict_average_proba(std::vector&lt;std::vector&lt;int&gt;&gt;&amp; X)</span></span>
<span id="L105"><span class="lineNum"> 105</span> : {</span>
<span id="L106"><span class="lineNum"> 106</span> <span class="tlaGNC"> 11 : auto n_states = models[0]-&gt;getClassNumStates();</span></span>
<span id="L107"><span class="lineNum"> 107</span> <span class="tlaGNC"> 11 : std::vector&lt;std::vector&lt;double&gt;&gt; y_pred(X[0].size(), std::vector&lt;double&gt;(n_states, 0.0));</span></span>
<span id="L108"><span class="lineNum"> 108</span> <span class="tlaGNC"> 11 : auto threads{ std::vector&lt;std::thread&gt;() };</span></span>
<span id="L109"><span class="lineNum"> 109</span> <span class="tlaGNC"> 11 : std::mutex mtx;</span></span>
<span id="L110"><span class="lineNum"> 110</span> <span class="tlaGNC"> 74 : for (auto i = 0; i &lt; n_models; ++i) {</span></span>
<span id="L111"><span class="lineNum"> 111</span> <span class="tlaGNC"> 63 : threads.push_back(std::thread([&amp;, i]() {</span></span>
<span id="L112"><span class="lineNum"> 112</span> <span class="tlaGNC"> 63 : auto ypredict = models[i]-&gt;predict_proba(X);</span></span>
<span id="L113"><span class="lineNum"> 113</span> <span class="tlaGNC"> 63 : assert(ypredict.size() == y_pred.size());</span></span>
<span id="L114"><span class="lineNum"> 114</span> <span class="tlaGNC"> 63 : assert(ypredict[0].size() == y_pred[0].size());</span></span>
<span id="L115"><span class="lineNum"> 115</span> <span class="tlaGNC"> 63 : std::lock_guard&lt;std::mutex&gt; lock(mtx);</span></span>
<span id="L106"><span class="lineNum"> 106</span> <span class="tlaGNC"> 120 : auto n_states = models[0]-&gt;getClassNumStates();</span></span>
<span id="L107"><span class="lineNum"> 107</span> <span class="tlaGNC"> 120 : std::vector&lt;std::vector&lt;double&gt;&gt; y_pred(X[0].size(), std::vector&lt;double&gt;(n_states, 0.0));</span></span>
<span id="L108"><span class="lineNum"> 108</span> <span class="tlaGNC"> 120 : auto threads{ std::vector&lt;std::thread&gt;() };</span></span>
<span id="L109"><span class="lineNum"> 109</span> <span class="tlaGNC"> 120 : std::mutex mtx;</span></span>
<span id="L110"><span class="lineNum"> 110</span> <span class="tlaGNC"> 842 : for (auto i = 0; i &lt; n_models; ++i) {</span></span>
<span id="L111"><span class="lineNum"> 111</span> <span class="tlaGNC"> 722 : threads.push_back(std::thread([&amp;, i]() {</span></span>
<span id="L112"><span class="lineNum"> 112</span> <span class="tlaGNC"> 722 : auto ypredict = models[i]-&gt;predict_proba(X);</span></span>
<span id="L113"><span class="lineNum"> 113</span> <span class="tlaGNC"> 722 : assert(ypredict.size() == y_pred.size());</span></span>
<span id="L114"><span class="lineNum"> 114</span> <span class="tlaGNC"> 722 : assert(ypredict[0].size() == y_pred[0].size());</span></span>
<span id="L115"><span class="lineNum"> 115</span> <span class="tlaGNC"> 722 : std::lock_guard&lt;std::mutex&gt; lock(mtx);</span></span>
<span id="L116"><span class="lineNum"> 116</span> : // Multiply each prediction by the significance of the model and then add it to the final prediction</span>
<span id="L117"><span class="lineNum"> 117</span> <span class="tlaGNC"> 12479 : for (auto j = 0; j &lt; ypredict.size(); ++j) {</span></span>
<span id="L118"><span class="lineNum"> 118</span> <span class="tlaGNC"> 12416 : std::transform(y_pred[j].begin(), y_pred[j].end(), ypredict[j].begin(), y_pred[j].begin(),</span></span>
<span id="L119"><span class="lineNum"> 119</span> <span class="tlaGNC"> 77782 : [significanceModels = significanceModels[i]](double x, double y) { return x + y * significanceModels; });</span></span>
<span id="L117"><span class="lineNum"> 117</span> <span class="tlaGNC"> 143118 : for (auto j = 0; j &lt; ypredict.size(); ++j) {</span></span>
<span id="L118"><span class="lineNum"> 118</span> <span class="tlaGNC"> 142396 : std::transform(y_pred[j].begin(), y_pred[j].end(), ypredict[j].begin(), y_pred[j].begin(),</span></span>
<span id="L119"><span class="lineNum"> 119</span> <span class="tlaGNC"> 898532 : [significanceModels = significanceModels[i]](double x, double y) { return x + y * significanceModels; });</span></span>
<span id="L120"><span class="lineNum"> 120</span> : }</span>
<span id="L121"><span class="lineNum"> 121</span> <span class="tlaGNC"> 63 : }));</span></span>
<span id="L121"><span class="lineNum"> 121</span> <span class="tlaGNC"> 722 : }));</span></span>
<span id="L122"><span class="lineNum"> 122</span> : }</span>
<span id="L123"><span class="lineNum"> 123</span> <span class="tlaGNC"> 74 : for (auto&amp; thread : threads) {</span></span>
<span id="L124"><span class="lineNum"> 124</span> <span class="tlaGNC"> 63 : thread.join();</span></span>
<span id="L123"><span class="lineNum"> 123</span> <span class="tlaGNC"> 842 : for (auto&amp; thread : threads) {</span></span>
<span id="L124"><span class="lineNum"> 124</span> <span class="tlaGNC"> 722 : thread.join();</span></span>
<span id="L125"><span class="lineNum"> 125</span> : }</span>
<span id="L126"><span class="lineNum"> 126</span> <span class="tlaGNC"> 11 : auto sum = std::reduce(significanceModels.begin(), significanceModels.end());</span></span>
<span id="L126"><span class="lineNum"> 126</span> <span class="tlaGNC"> 120 : auto sum = std::reduce(significanceModels.begin(), significanceModels.end());</span></span>
<span id="L127"><span class="lineNum"> 127</span> : //Divide each element of the prediction by the sum of the significances</span>
<span id="L128"><span class="lineNum"> 128</span> <span class="tlaGNC"> 2067 : for (auto j = 0; j &lt; y_pred.size(); ++j) {</span></span>
<span id="L129"><span class="lineNum"> 129</span> <span class="tlaGNC"> 10662 : std::transform(y_pred[j].begin(), y_pred[j].end(), y_pred[j].begin(), [sum](double x) { return x / sum; });</span></span>
<span id="L128"><span class="lineNum"> 128</span> <span class="tlaGNC"> 22520 : for (auto j = 0; j &lt; y_pred.size(); ++j) {</span></span>
<span id="L129"><span class="lineNum"> 129</span> <span class="tlaGNC"> 120660 : std::transform(y_pred[j].begin(), y_pred[j].end(), y_pred[j].begin(), [sum](double x) { return x / sum; });</span></span>
<span id="L130"><span class="lineNum"> 130</span> : }</span>
<span id="L131"><span class="lineNum"> 131</span> <span class="tlaGNC"> 22 : return y_pred;</span></span>
<span id="L132"><span class="lineNum"> 132</span> <span class="tlaGNC"> 11 : }</span></span>
<span id="L133"><span class="lineNum"> 133</span> <span class="tlaGNC"> 7 : std::vector&lt;std::vector&lt;double&gt;&gt; Ensemble::predict_average_voting(std::vector&lt;std::vector&lt;int&gt;&gt;&amp; X)</span></span>
<span id="L131"><span class="lineNum"> 131</span> <span class="tlaGNC"> 240 : return y_pred;</span></span>
<span id="L132"><span class="lineNum"> 132</span> <span class="tlaGNC"> 120 : }</span></span>
<span id="L133"><span class="lineNum"> 133</span> <span class="tlaGNC"> 82 : std::vector&lt;std::vector&lt;double&gt;&gt; Ensemble::predict_average_voting(std::vector&lt;std::vector&lt;int&gt;&gt;&amp; X)</span></span>
<span id="L134"><span class="lineNum"> 134</span> : {</span>
<span id="L135"><span class="lineNum"> 135</span> <span class="tlaGNC"> 7 : torch::Tensor Xt = bayesnet::vectorToTensor(X, false);</span></span>
<span id="L136"><span class="lineNum"> 136</span> <span class="tlaGNC"> 7 : auto y_pred = predict_average_voting(Xt);</span></span>
<span id="L137"><span class="lineNum"> 137</span> <span class="tlaGNC"> 7 : std::vector&lt;std::vector&lt;double&gt;&gt; result = tensorToVectorDouble(y_pred);</span></span>
<span id="L138"><span class="lineNum"> 138</span> <span class="tlaGNC"> 14 : return result;</span></span>
<span id="L139"><span class="lineNum"> 139</span> <span class="tlaGNC"> 7 : }</span></span>
<span id="L140"><span class="lineNum"> 140</span> <span class="tlaGNC"> 20 : torch::Tensor Ensemble::predict_average_voting(torch::Tensor&amp; X)</span></span>
<span id="L135"><span class="lineNum"> 135</span> <span class="tlaGNC"> 82 : torch::Tensor Xt = bayesnet::vectorToTensor(X, false);</span></span>
<span id="L136"><span class="lineNum"> 136</span> <span class="tlaGNC"> 82 : auto y_pred = predict_average_voting(Xt);</span></span>
<span id="L137"><span class="lineNum"> 137</span> <span class="tlaGNC"> 82 : std::vector&lt;std::vector&lt;double&gt;&gt; result = tensorToVectorDouble(y_pred);</span></span>
<span id="L138"><span class="lineNum"> 138</span> <span class="tlaGNC"> 164 : return result;</span></span>
<span id="L139"><span class="lineNum"> 139</span> <span class="tlaGNC"> 82 : }</span></span>
<span id="L140"><span class="lineNum"> 140</span> <span class="tlaGNC"> 291 : torch::Tensor Ensemble::predict_average_voting(torch::Tensor&amp; X)</span></span>
<span id="L141"><span class="lineNum"> 141</span> : {</span>
<span id="L142"><span class="lineNum"> 142</span> : // Build a m x n_models tensor with the predictions of each model</span>
<span id="L143"><span class="lineNum"> 143</span> <span class="tlaGNC"> 20 : torch::Tensor y_pred = torch::zeros({ X.size(1), n_models }, torch::kInt32);</span></span>
<span id="L144"><span class="lineNum"> 144</span> <span class="tlaGNC"> 20 : auto threads{ std::vector&lt;std::thread&gt;() };</span></span>
<span id="L145"><span class="lineNum"> 145</span> <span class="tlaGNC"> 20 : std::mutex mtx;</span></span>
<span id="L146"><span class="lineNum"> 146</span> <span class="tlaGNC"> 134 : for (auto i = 0; i &lt; n_models; ++i) {</span></span>
<span id="L147"><span class="lineNum"> 147</span> <span class="tlaGNC"> 114 : threads.push_back(std::thread([&amp;, i]() {</span></span>
<span id="L148"><span class="lineNum"> 148</span> <span class="tlaGNC"> 114 : auto ypredict = models[i]-&gt;predict(X);</span></span>
<span id="L149"><span class="lineNum"> 149</span> <span class="tlaGNC"> 114 : std::lock_guard&lt;std::mutex&gt; lock(mtx);</span></span>
<span id="L150"><span class="lineNum"> 150</span> <span class="tlaGNC"> 342 : y_pred.index_put_({ &quot;...&quot;, i }, ypredict);</span></span>
<span id="L151"><span class="lineNum"> 151</span> <span class="tlaGNC"> 228 : }));</span></span>
<span id="L143"><span class="lineNum"> 143</span> <span class="tlaGNC"> 291 : torch::Tensor y_pred = torch::zeros({ X.size(1), n_models }, torch::kInt32);</span></span>
<span id="L144"><span class="lineNum"> 144</span> <span class="tlaGNC"> 291 : auto threads{ std::vector&lt;std::thread&gt;() };</span></span>
<span id="L145"><span class="lineNum"> 145</span> <span class="tlaGNC"> 291 : std::mutex mtx;</span></span>
<span id="L146"><span class="lineNum"> 146</span> <span class="tlaGNC"> 1959 : for (auto i = 0; i &lt; n_models; ++i) {</span></span>
<span id="L147"><span class="lineNum"> 147</span> <span class="tlaGNC"> 1668 : threads.push_back(std::thread([&amp;, i]() {</span></span>
<span id="L148"><span class="lineNum"> 148</span> <span class="tlaGNC"> 1668 : auto ypredict = models[i]-&gt;predict(X);</span></span>
<span id="L149"><span class="lineNum"> 149</span> <span class="tlaGNC"> 1668 : std::lock_guard&lt;std::mutex&gt; lock(mtx);</span></span>
<span id="L150"><span class="lineNum"> 150</span> <span class="tlaGNC"> 5004 : y_pred.index_put_({ &quot;...&quot;, i }, ypredict);</span></span>
<span id="L151"><span class="lineNum"> 151</span> <span class="tlaGNC"> 3336 : }));</span></span>
<span id="L152"><span class="lineNum"> 152</span> : }</span>
<span id="L153"><span class="lineNum"> 153</span> <span class="tlaGNC"> 134 : for (auto&amp; thread : threads) {</span></span>
<span id="L154"><span class="lineNum"> 154</span> <span class="tlaGNC"> 114 : thread.join();</span></span>
<span id="L153"><span class="lineNum"> 153</span> <span class="tlaGNC"> 1959 : for (auto&amp; thread : threads) {</span></span>
<span id="L154"><span class="lineNum"> 154</span> <span class="tlaGNC"> 1668 : thread.join();</span></span>
<span id="L155"><span class="lineNum"> 155</span> : }</span>
<span id="L156"><span class="lineNum"> 156</span> <span class="tlaGNC"> 40 : return voting(y_pred);</span></span>
<span id="L157"><span class="lineNum"> 157</span> <span class="tlaGNC"> 20 : }</span></span>
<span id="L158"><span class="lineNum"> 158</span> <span class="tlaGNC"> 16 : float Ensemble::score(torch::Tensor&amp; X, torch::Tensor&amp; y)</span></span>
<span id="L156"><span class="lineNum"> 156</span> <span class="tlaGNC"> 582 : return voting(y_pred);</span></span>
<span id="L157"><span class="lineNum"> 157</span> <span class="tlaGNC"> 291 : }</span></span>
<span id="L158"><span class="lineNum"> 158</span> <span class="tlaGNC"> 194 : float Ensemble::score(torch::Tensor&amp; X, torch::Tensor&amp; y)</span></span>
<span id="L159"><span class="lineNum"> 159</span> : {</span>
<span id="L160"><span class="lineNum"> 160</span> <span class="tlaGNC"> 16 : auto y_pred = predict(X);</span></span>
<span id="L161"><span class="lineNum"> 161</span> <span class="tlaGNC"> 14 : int correct = 0;</span></span>
<span id="L162"><span class="lineNum"> 162</span> <span class="tlaGNC"> 4746 : for (int i = 0; i &lt; y_pred.size(0); ++i) {</span></span>
<span id="L163"><span class="lineNum"> 163</span> <span class="tlaGNC"> 4732 : if (y_pred[i].item&lt;int&gt;() == y[i].item&lt;int&gt;()) {</span></span>
<span id="L164"><span class="lineNum"> 164</span> <span class="tlaGNC"> 4026 : correct++;</span></span>
<span id="L160"><span class="lineNum"> 160</span> <span class="tlaGNC"> 194 : auto y_pred = predict(X);</span></span>
<span id="L161"><span class="lineNum"> 161</span> <span class="tlaGNC"> 172 : int correct = 0;</span></span>
<span id="L162"><span class="lineNum"> 162</span> <span class="tlaGNC"> 53601 : for (int i = 0; i &lt; y_pred.size(0); ++i) {</span></span>
<span id="L163"><span class="lineNum"> 163</span> <span class="tlaGNC"> 53429 : if (y_pred[i].item&lt;int&gt;() == y[i].item&lt;int&gt;()) {</span></span>
<span id="L164"><span class="lineNum"> 164</span> <span class="tlaGNC"> 45279 : correct++;</span></span>
<span id="L165"><span class="lineNum"> 165</span> : }</span>
<span id="L166"><span class="lineNum"> 166</span> : }</span>
<span id="L167"><span class="lineNum"> 167</span> <span class="tlaGNC"> 28 : return (double)correct / y_pred.size(0);</span></span>
<span id="L168"><span class="lineNum"> 168</span> <span class="tlaGNC"> 14 : }</span></span>
<span id="L169"><span class="lineNum"> 169</span> <span class="tlaGNC"> 12 : float Ensemble::score(std::vector&lt;std::vector&lt;int&gt;&gt;&amp; X, std::vector&lt;int&gt;&amp; y)</span></span>
<span id="L167"><span class="lineNum"> 167</span> <span class="tlaGNC"> 344 : return (double)correct / y_pred.size(0);</span></span>
<span id="L168"><span class="lineNum"> 168</span> <span class="tlaGNC"> 172 : }</span></span>
<span id="L169"><span class="lineNum"> 169</span> <span class="tlaGNC"> 134 : float Ensemble::score(std::vector&lt;std::vector&lt;int&gt;&gt;&amp; X, std::vector&lt;int&gt;&amp; y)</span></span>
<span id="L170"><span class="lineNum"> 170</span> : {</span>
<span id="L171"><span class="lineNum"> 171</span> <span class="tlaGNC"> 12 : auto y_pred = predict(X);</span></span>
<span id="L172"><span class="lineNum"> 172</span> <span class="tlaGNC"> 10 : int correct = 0;</span></span>
<span id="L173"><span class="lineNum"> 173</span> <span class="tlaGNC"> 2534 : for (int i = 0; i &lt; y_pred.size(); ++i) {</span></span>
<span id="L174"><span class="lineNum"> 174</span> <span class="tlaGNC"> 2524 : if (y_pred[i] == y[i]) {</span></span>
<span id="L175"><span class="lineNum"> 175</span> <span class="tlaGNC"> 2173 : correct++;</span></span>
<span id="L171"><span class="lineNum"> 171</span> <span class="tlaGNC"> 134 : auto y_pred = predict(X);</span></span>
<span id="L172"><span class="lineNum"> 172</span> <span class="tlaGNC"> 112 : int correct = 0;</span></span>
<span id="L173"><span class="lineNum"> 173</span> <span class="tlaGNC"> 29964 : for (int i = 0; i &lt; y_pred.size(); ++i) {</span></span>
<span id="L174"><span class="lineNum"> 174</span> <span class="tlaGNC"> 29852 : if (y_pred[i] == y[i]) {</span></span>
<span id="L175"><span class="lineNum"> 175</span> <span class="tlaGNC"> 25423 : correct++;</span></span>
<span id="L176"><span class="lineNum"> 176</span> : }</span>
<span id="L177"><span class="lineNum"> 177</span> : }</span>
<span id="L178"><span class="lineNum"> 178</span> <span class="tlaGNC"> 20 : return (double)correct / y_pred.size();</span></span>
<span id="L179"><span class="lineNum"> 179</span> <span class="tlaGNC"> 10 : }</span></span>
<span id="L180"><span class="lineNum"> 180</span> <span class="tlaGNC"> 1 : std::vector&lt;std::string&gt; Ensemble::show() const</span></span>
<span id="L178"><span class="lineNum"> 178</span> <span class="tlaGNC"> 224 : return (double)correct / y_pred.size();</span></span>
<span id="L179"><span class="lineNum"> 179</span> <span class="tlaGNC"> 112 : }</span></span>
<span id="L180"><span class="lineNum"> 180</span> <span class="tlaGNC"> 11 : std::vector&lt;std::string&gt; Ensemble::show() const</span></span>
<span id="L181"><span class="lineNum"> 181</span> : {</span>
<span id="L182"><span class="lineNum"> 182</span> <span class="tlaGNC"> 1 : auto result = std::vector&lt;std::string&gt;();</span></span>
<span id="L183"><span class="lineNum"> 183</span> <span class="tlaGNC"> 5 : for (auto i = 0; i &lt; n_models; ++i) {</span></span>
<span id="L184"><span class="lineNum"> 184</span> <span class="tlaGNC"> 4 : auto res = models[i]-&gt;show();</span></span>
<span id="L185"><span class="lineNum"> 185</span> <span class="tlaGNC"> 4 : result.insert(result.end(), res.begin(), res.end());</span></span>
<span id="L186"><span class="lineNum"> 186</span> <span class="tlaGNC"> 4 : }</span></span>
<span id="L187"><span class="lineNum"> 187</span> <span class="tlaGNC"> 1 : return result;</span></span>
<span id="L182"><span class="lineNum"> 182</span> <span class="tlaGNC"> 11 : auto result = std::vector&lt;std::string&gt;();</span></span>
<span id="L183"><span class="lineNum"> 183</span> <span class="tlaGNC"> 55 : for (auto i = 0; i &lt; n_models; ++i) {</span></span>
<span id="L184"><span class="lineNum"> 184</span> <span class="tlaGNC"> 44 : auto res = models[i]-&gt;show();</span></span>
<span id="L185"><span class="lineNum"> 185</span> <span class="tlaGNC"> 44 : result.insert(result.end(), res.begin(), res.end());</span></span>
<span id="L186"><span class="lineNum"> 186</span> <span class="tlaGNC"> 44 : }</span></span>
<span id="L187"><span class="lineNum"> 187</span> <span class="tlaGNC"> 11 : return result;</span></span>
<span id="L188"><span class="lineNum"> 188</span> <span class="tlaUNC tlaBgUNC"> 0 : }</span></span>
<span id="L189"><span class="lineNum"> 189</span> <span class="tlaGNC tlaBgGNC"> 3 : std::vector&lt;std::string&gt; Ensemble::graph(const std::string&amp; title) const</span></span>
<span id="L189"><span class="lineNum"> 189</span> <span class="tlaGNC tlaBgGNC"> 33 : std::vector&lt;std::string&gt; Ensemble::graph(const std::string&amp; title) const</span></span>
<span id="L190"><span class="lineNum"> 190</span> : {</span>
<span id="L191"><span class="lineNum"> 191</span> <span class="tlaGNC"> 3 : auto result = std::vector&lt;std::string&gt;();</span></span>
<span id="L192"><span class="lineNum"> 192</span> <span class="tlaGNC"> 20 : for (auto i = 0; i &lt; n_models; ++i) {</span></span>
<span id="L193"><span class="lineNum"> 193</span> <span class="tlaGNC"> 17 : auto res = models[i]-&gt;graph(title + &quot;_&quot; + std::to_string(i));</span></span>
<span id="L194"><span class="lineNum"> 194</span> <span class="tlaGNC"> 17 : result.insert(result.end(), res.begin(), res.end());</span></span>
<span id="L195"><span class="lineNum"> 195</span> <span class="tlaGNC"> 17 : }</span></span>
<span id="L196"><span class="lineNum"> 196</span> <span class="tlaGNC"> 3 : return result;</span></span>
<span id="L191"><span class="lineNum"> 191</span> <span class="tlaGNC"> 33 : auto result = std::vector&lt;std::string&gt;();</span></span>
<span id="L192"><span class="lineNum"> 192</span> <span class="tlaGNC"> 220 : for (auto i = 0; i &lt; n_models; ++i) {</span></span>
<span id="L193"><span class="lineNum"> 193</span> <span class="tlaGNC"> 187 : auto res = models[i]-&gt;graph(title + &quot;_&quot; + std::to_string(i));</span></span>
<span id="L194"><span class="lineNum"> 194</span> <span class="tlaGNC"> 187 : result.insert(result.end(), res.begin(), res.end());</span></span>
<span id="L195"><span class="lineNum"> 195</span> <span class="tlaGNC"> 187 : }</span></span>
<span id="L196"><span class="lineNum"> 196</span> <span class="tlaGNC"> 33 : return result;</span></span>
<span id="L197"><span class="lineNum"> 197</span> <span class="tlaUNC tlaBgUNC"> 0 : }</span></span>
<span id="L198"><span class="lineNum"> 198</span> <span class="tlaGNC tlaBgGNC"> 6 : int Ensemble::getNumberOfNodes() const</span></span>
<span id="L198"><span class="lineNum"> 198</span> <span class="tlaGNC tlaBgGNC"> 70 : int Ensemble::getNumberOfNodes() const</span></span>
<span id="L199"><span class="lineNum"> 199</span> : {</span>
<span id="L200"><span class="lineNum"> 200</span> <span class="tlaGNC"> 6 : int nodes = 0;</span></span>
<span id="L201"><span class="lineNum"> 201</span> <span class="tlaGNC"> 43 : for (auto i = 0; i &lt; n_models; ++i) {</span></span>
<span id="L202"><span class="lineNum"> 202</span> <span class="tlaGNC"> 37 : nodes += models[i]-&gt;getNumberOfNodes();</span></span>
<span id="L200"><span class="lineNum"> 200</span> <span class="tlaGNC"> 70 : int nodes = 0;</span></span>
<span id="L201"><span class="lineNum"> 201</span> <span class="tlaGNC"> 512 : for (auto i = 0; i &lt; n_models; ++i) {</span></span>
<span id="L202"><span class="lineNum"> 202</span> <span class="tlaGNC"> 442 : nodes += models[i]-&gt;getNumberOfNodes();</span></span>
<span id="L203"><span class="lineNum"> 203</span> : }</span>
<span id="L204"><span class="lineNum"> 204</span> <span class="tlaGNC"> 6 : return nodes;</span></span>
<span id="L204"><span class="lineNum"> 204</span> <span class="tlaGNC"> 70 : return nodes;</span></span>
<span id="L205"><span class="lineNum"> 205</span> : }</span>
<span id="L206"><span class="lineNum"> 206</span> <span class="tlaGNC"> 6 : int Ensemble::getNumberOfEdges() const</span></span>
<span id="L206"><span class="lineNum"> 206</span> <span class="tlaGNC"> 70 : int Ensemble::getNumberOfEdges() const</span></span>
<span id="L207"><span class="lineNum"> 207</span> : {</span>
<span id="L208"><span class="lineNum"> 208</span> <span class="tlaGNC"> 6 : int edges = 0;</span></span>
<span id="L209"><span class="lineNum"> 209</span> <span class="tlaGNC"> 43 : for (auto i = 0; i &lt; n_models; ++i) {</span></span>
<span id="L210"><span class="lineNum"> 210</span> <span class="tlaGNC"> 37 : edges += models[i]-&gt;getNumberOfEdges();</span></span>
<span id="L208"><span class="lineNum"> 208</span> <span class="tlaGNC"> 70 : int edges = 0;</span></span>
<span id="L209"><span class="lineNum"> 209</span> <span class="tlaGNC"> 512 : for (auto i = 0; i &lt; n_models; ++i) {</span></span>
<span id="L210"><span class="lineNum"> 210</span> <span class="tlaGNC"> 442 : edges += models[i]-&gt;getNumberOfEdges();</span></span>
<span id="L211"><span class="lineNum"> 211</span> : }</span>
<span id="L212"><span class="lineNum"> 212</span> <span class="tlaGNC"> 6 : return edges;</span></span>
<span id="L212"><span class="lineNum"> 212</span> <span class="tlaGNC"> 70 : return edges;</span></span>
<span id="L213"><span class="lineNum"> 213</span> : }</span>
<span id="L214"><span class="lineNum"> 214</span> <span class="tlaGNC"> 1 : int Ensemble::getNumberOfStates() const</span></span>
<span id="L214"><span class="lineNum"> 214</span> <span class="tlaGNC"> 11 : int Ensemble::getNumberOfStates() const</span></span>
<span id="L215"><span class="lineNum"> 215</span> : {</span>
<span id="L216"><span class="lineNum"> 216</span> <span class="tlaGNC"> 1 : int nstates = 0;</span></span>
<span id="L217"><span class="lineNum"> 217</span> <span class="tlaGNC"> 5 : for (auto i = 0; i &lt; n_models; ++i) {</span></span>
<span id="L218"><span class="lineNum"> 218</span> <span class="tlaGNC"> 4 : nstates += models[i]-&gt;getNumberOfStates();</span></span>
<span id="L216"><span class="lineNum"> 216</span> <span class="tlaGNC"> 11 : int nstates = 0;</span></span>
<span id="L217"><span class="lineNum"> 217</span> <span class="tlaGNC"> 55 : for (auto i = 0; i &lt; n_models; ++i) {</span></span>
<span id="L218"><span class="lineNum"> 218</span> <span class="tlaGNC"> 44 : nstates += models[i]-&gt;getNumberOfStates();</span></span>
<span id="L219"><span class="lineNum"> 219</span> : }</span>
<span id="L220"><span class="lineNum"> 220</span> <span class="tlaGNC"> 1 : return nstates;</span></span>
<span id="L220"><span class="lineNum"> 220</span> <span class="tlaGNC"> 11 : return nstates;</span></span>
<span id="L221"><span class="lineNum"> 221</span> : }</span>
<span id="L222"><span class="lineNum"> 222</span> : }</span>
</pre>

2
html/bayesnet/ensembles/Ensemble.h.func-c.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="headerCovTableEntryMed">75.0&nbsp;%</td>

2
html/bayesnet/ensembles/Ensemble.h.func.html
View File

@@ -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="headerCovTableEntryMed">75.0&nbsp;%</td>

2
html/bayesnet/ensembles/Ensemble.h.gcov.html
View File

@@ -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="headerCovTableEntryMed">75.0&nbsp;%</td>

16
html/bayesnet/ensembles/index-sort-f.html
View File

@@ -31,13 +31,13 @@
<td class="headerValue">coverage.info</td>
<td></td>
<td class="headerItem">Lines:</td>
<td class="headerCovTableEntryHi">98.8&nbsp;%</td>
<td class="headerCovTableEntry">424</td>
<td class="headerCovTableEntry">419</td>
<td class="headerCovTableEntryHi">98.4&nbsp;%</td>
<td class="headerCovTableEntry">443</td>
<td class="headerCovTableEntry">436</td>
</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">98.1&nbsp;%</td>
@@ -154,11 +154,11 @@
<tr>
<td class="coverFile"><a href="BoostAODE.cc.gcov.html">BoostAODE.cc</a></td>
<td class="coverBar" align="center">
<table border=0 cellspacing=0 cellpadding=1><tr><td class="coverBarOutline"><img src="../../emerald.png" width=99 height=10 alt="99.1%"><img src="../../snow.png" width=1 height=10 alt="99.1%"></td></tr></table>
<table border=0 cellspacing=0 cellpadding=1><tr><td class="coverBarOutline"><img src="../../emerald.png" width=98 height=10 alt="98.3%"><img src="../../snow.png" width=2 height=10 alt="98.3%"></td></tr></table>
</td>
<td class="coverPerHi">99.1&nbsp;%</td>
<td class="coverNumDflt">218</td>
<td class="coverNumDflt">216</td>
<td class="coverPerHi">98.3&nbsp;%</td>
<td class="coverNumDflt">237</td>
<td class="coverNumDflt">233</td>
<td class="coverPerHi">100.0&nbsp;%</td>
<td class="coverNumDflt">9</td>
<td class="coverNumDflt">9</td>

16
html/bayesnet/ensembles/index-sort-l.html
View File

@@ -31,13 +31,13 @@
<td class="headerValue">coverage.info</td>
<td></td>
<td class="headerItem">Lines:</td>
<td class="headerCovTableEntryHi">98.8&nbsp;%</td>
<td class="headerCovTableEntry">424</td>
<td class="headerCovTableEntry">419</td>
<td class="headerCovTableEntryHi">98.4&nbsp;%</td>
<td class="headerCovTableEntry">443</td>
<td class="headerCovTableEntry">436</td>
</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">98.1&nbsp;%</td>
@@ -94,11 +94,11 @@
<tr>
<td class="coverFile"><a href="BoostAODE.cc.gcov.html">BoostAODE.cc</a></td>
<td class="coverBar" align="center">
<table border=0 cellspacing=0 cellpadding=1><tr><td class="coverBarOutline"><img src="../../emerald.png" width=99 height=10 alt="99.1%"><img src="../../snow.png" width=1 height=10 alt="99.1%"></td></tr></table>
<table border=0 cellspacing=0 cellpadding=1><tr><td class="coverBarOutline"><img src="../../emerald.png" width=98 height=10 alt="98.3%"><img src="../../snow.png" width=2 height=10 alt="98.3%"></td></tr></table>
</td>
<td class="coverPerHi">99.1&nbsp;%</td>
<td class="coverNumDflt">218</td>
<td class="coverNumDflt">216</td>
<td class="coverPerHi">98.3&nbsp;%</td>
<td class="coverNumDflt">237</td>
<td class="coverNumDflt">233</td>
<td class="coverPerHi">100.0&nbsp;%</td>
<td class="coverNumDflt">9</td>
<td class="coverNumDflt">9</td>

16
html/bayesnet/ensembles/index.html
View File

@@ -31,13 +31,13 @@
<td class="headerValue">coverage.info</td>
<td></td>
<td class="headerItem">Lines:</td>
<td class="headerCovTableEntryHi">98.8&nbsp;%</td>
<td class="headerCovTableEntry">424</td>
<td class="headerCovTableEntry">419</td>
<td class="headerCovTableEntryHi">98.4&nbsp;%</td>
<td class="headerCovTableEntry">443</td>
<td class="headerCovTableEntry">436</td>
</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">98.1&nbsp;%</td>
@@ -130,11 +130,11 @@
<tr>
<td class="coverFile"><a href="BoostAODE.cc.gcov.html">BoostAODE.cc</a></td>
<td class="coverBar" align="center">
<table border=0 cellspacing=0 cellpadding=1><tr><td class="coverBarOutline"><img src="../../emerald.png" width=99 height=10 alt="99.1%"><img src="../../snow.png" width=1 height=10 alt="99.1%"></td></tr></table>
<table border=0 cellspacing=0 cellpadding=1><tr><td class="coverBarOutline"><img src="../../emerald.png" width=98 height=10 alt="98.3%"><img src="../../snow.png" width=2 height=10 alt="98.3%"></td></tr></table>
</td>
<td class="coverPerHi">99.1&nbsp;%</td>
<td class="coverNumDflt">218</td>
<td class="coverNumDflt">216</td>
<td class="coverPerHi">98.3&nbsp;%</td>
<td class="coverNumDflt">237</td>
<td class="coverNumDflt">233</td>
<td class="coverPerHi">100.0&nbsp;%</td>
<td class="coverNumDflt">9</td>
<td class="coverNumDflt">9</td>