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Add link to the coverage report in the README.md coverage label

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Ricardo Montañana Gómez
2024-04-21 16:44:35 +02:00
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159
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<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN">
<html lang="en">
<head>
<meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
<title>LCOV - coverage.info - bayesnet/utils/BayesMetrics.cc - functions</title>
<link rel="stylesheet" type="text/css" href="../../gcov.css">
</head>
<body>
<table width="100%" border=0 cellspacing=0 cellpadding=0>
<tr><td class="title">LCOV - code coverage report</td></tr>
<tr><td class="ruler"><img src="../../glass.png" width=3 height=3 alt=""></td></tr>
<tr>
<td width="100%">
<table cellpadding=1 border=0 width="100%">
<tr>
<td width="10%" class="headerItem">Current view:</td>
<td width="10%" class="headerValue"><a href="../../index.html">top level</a> - <a href="index.html">bayesnet/utils</a> - BayesMetrics.cc<span style="font-size: 80%;"> (<a href="BayesMetrics.cc.gcov.html">source</a> / functions)</span></td>
<td width="5%"></td>
<td width="5%"></td>
<td width="5%" class="headerCovTableHead">Coverage</td>
<td width="5%" class="headerCovTableHead" title="Covered + Uncovered code">Total</td>
<td width="5%" class="headerCovTableHead" title="Exercised code only">Hit</td>
</tr>
<tr>
<td class="headerItem">Test:</td>
<td class="headerValue">coverage.info</td>
<td></td>
<td class="headerItem">Lines:</td>
<td class="headerCovTableEntryHi">92.5&nbsp;%</td>
<td class="headerCovTableEntry">120</td>
<td class="headerCovTableEntry">111</td>
</tr>
<tr>
<td class="headerItem">Test Date:</td>
<td class="headerValue">2024-04-21 16:43:29</td>
<td></td>
<td class="headerItem">Functions:</td>
<td class="headerCovTableEntryHi">91.7&nbsp;%</td>
<td class="headerCovTableEntry">12</td>
<td class="headerCovTableEntry">11</td>
</tr>
<tr><td><img src="../../glass.png" width=3 height=3 alt=""></td></tr>
</table>
</td>
</tr>
<tr><td class="ruler"><img src="../../glass.png" width=3 height=3 alt=""></td></tr>
</table>
<center>
<table cellpadding=1 cellspacing=1 border=0>
<tr><td><br></td></tr>
<tr>
<td class="tableHead">Function Name <span title="Click to sort table by function name" class="tableHeadSort"><a href="BayesMetrics.cc.func.html"><img src="../../updown.png" width=10 height=14 alt="Sort by function name" title="Click to sort table by function name" border=0></a></span></td>
<td class="tableHead">Hit count <span title="Click to sort table by function hit count" class="tableHeadSort"><img src="../../glass.png" width=10 height=14 alt="Sort by function hit count" title="Click to sort table by function hit count" border=0></span></td>
</tr>
<tr>
<td class="coverFn"><a href="BayesMetrics.cc.gcov.html#L107">_ZN8bayesnet7Metrics22conditionalEdgeWeightsERSt6vectorIfSaIfEE</a></td>
<td class="coverFnLo">0</td>
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<tr>
<td class="coverFn"><a href="BayesMetrics.cc.gcov.html#L63">_ZNK8bayesnet7Metrics14getScoresKBestEv</a></td>
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<tr>
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<td class="coverFn"><a href="BayesMetrics.cc.gcov.html#L68">_ZN8bayesnet7Metrics15conditionalEdgeERKN2at6TensorE</a></td>
<td class="coverFnHi">34</td>
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<tr>
<td class="coverFn"><a href="BayesMetrics.cc.gcov.html#L28">_ZN8bayesnet7Metrics19SelectKBestWeightedERKN2at6TensorEbj</a></td>
<td class="coverFnHi">90</td>
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<tr>
<td class="coverFn"><a href="BayesMetrics.cc.gcov.html#L9">_ZN8bayesnet7MetricsC2ERKN2at6TensorERKSt6vectorINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESaISB_EERKSB_i</a></td>
<td class="coverFnHi">338</td>
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<td class="coverFn"><a href="BayesMetrics.cc.gcov.html#L45">_ZZN8bayesnet7Metrics19SelectKBestWeightedERKN2at6TensorEbjENKUliiE_clEii</a></td>
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<tr>
<td class="coverFn"><a href="BayesMetrics.cc.gcov.html#L153">_ZN8bayesnet7Metrics17mutualInformationERKN2at6TensorES4_S4_</a></td>
<td class="coverFnHi">7684</td>
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<tr>
<td class="coverFn"><a href="BayesMetrics.cc.gcov.html#L124">_ZN8bayesnet7Metrics18conditionalEntropyERKN2at6TensorES4_S4_</a></td>
<td class="coverFnHi">7684</td>
</tr>
<tr>
<td class="coverFn"><a href="BayesMetrics.cc.gcov.html#L114">_ZN8bayesnet7Metrics7entropyERKN2at6TensorES4_</a></td>
<td class="coverFnHi">8506</td>
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<tr>
<td class="coverFn"><a href="BayesMetrics.cc.gcov.html#L55">_ZZN8bayesnet7Metrics19SelectKBestWeightedERKN2at6TensorEbjENKUliiE0_clEii</a></td>
<td class="coverFnHi">12619</td>
</tr>
</table>
<br>
</center>
<table width="100%" border=0 cellspacing=0 cellpadding=0>
<tr><td class="ruler"><img src="../../glass.png" width=3 height=3 alt=""></td></tr>
<tr><td class="versionInfo">Generated by: <a href="https://github.com//linux-test-project/lcov" target="_parent">LCOV version 2.0-1</a></td></tr>
</table>
<br>
</body>
</html>

159
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<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN">
<html lang="en">
<head>
<meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
<title>LCOV - coverage.info - bayesnet/utils/BayesMetrics.cc - functions</title>
<link rel="stylesheet" type="text/css" href="../../gcov.css">
</head>
<body>
<table width="100%" border=0 cellspacing=0 cellpadding=0>
<tr><td class="title">LCOV - code coverage report</td></tr>
<tr><td class="ruler"><img src="../../glass.png" width=3 height=3 alt=""></td></tr>
<tr>
<td width="100%">
<table cellpadding=1 border=0 width="100%">
<tr>
<td width="10%" class="headerItem">Current view:</td>
<td width="10%" class="headerValue"><a href="../../index.html">top level</a> - <a href="index.html">bayesnet/utils</a> - BayesMetrics.cc<span style="font-size: 80%;"> (<a href="BayesMetrics.cc.gcov.html">source</a> / functions)</span></td>
<td width="5%"></td>
<td width="5%"></td>
<td width="5%" class="headerCovTableHead">Coverage</td>
<td width="5%" class="headerCovTableHead" title="Covered + Uncovered code">Total</td>
<td width="5%" class="headerCovTableHead" title="Exercised code only">Hit</td>
</tr>
<tr>
<td class="headerItem">Test:</td>
<td class="headerValue">coverage.info</td>
<td></td>
<td class="headerItem">Lines:</td>
<td class="headerCovTableEntryHi">92.5&nbsp;%</td>
<td class="headerCovTableEntry">120</td>
<td class="headerCovTableEntry">111</td>
</tr>
<tr>
<td class="headerItem">Test Date:</td>
<td class="headerValue">2024-04-21 16:43:29</td>
<td></td>
<td class="headerItem">Functions:</td>
<td class="headerCovTableEntryHi">91.7&nbsp;%</td>
<td class="headerCovTableEntry">12</td>
<td class="headerCovTableEntry">11</td>
</tr>
<tr><td><img src="../../glass.png" width=3 height=3 alt=""></td></tr>
</table>
</td>
</tr>
<tr><td class="ruler"><img src="../../glass.png" width=3 height=3 alt=""></td></tr>
</table>
<center>
<table cellpadding=1 cellspacing=1 border=0>
<tr><td><br></td></tr>
<tr>
<td class="tableHead">Function Name <span title="Click to sort table by function name" class="tableHeadSort"><img src="../../glass.png" width=10 height=14 alt="Sort by function name" title="Click to sort table by function name" border=0></span></td>
<td class="tableHead">Hit count <span title="Click to sort table by function hit count" class="tableHeadSort"><a href="BayesMetrics.cc.func-c.html"><img src="../../updown.png" width=10 height=14 alt="Sort by function hit count" title="Click to sort table by function hit count" border=0></a></span></td>
</tr>
<tr>
<td class="coverFn"><a href="BayesMetrics.cc.gcov.html#L68">_ZN8bayesnet7Metrics15conditionalEdgeERKN2at6TensorE</a></td>
<td class="coverFnHi">34</td>
</tr>
<tr>
<td class="coverFn"><a href="BayesMetrics.cc.gcov.html#L153">_ZN8bayesnet7Metrics17mutualInformationERKN2at6TensorES4_S4_</a></td>
<td class="coverFnHi">7684</td>
</tr>
<tr>
<td class="coverFn"><a href="BayesMetrics.cc.gcov.html#L124">_ZN8bayesnet7Metrics18conditionalEntropyERKN2at6TensorES4_S4_</a></td>
<td class="coverFnHi">7684</td>
</tr>
<tr>
<td class="coverFn"><a href="BayesMetrics.cc.gcov.html#L28">_ZN8bayesnet7Metrics19SelectKBestWeightedERKN2at6TensorEbj</a></td>
<td class="coverFnHi">90</td>
</tr>
<tr>
<td class="coverFn"><a href="BayesMetrics.cc.gcov.html#L162">_ZN8bayesnet7Metrics19maximumSpanningTreeERKSt6vectorINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESaIS7_EERKN2at6TensorEi</a></td>
<td class="coverFnHi">29</td>
</tr>
<tr>
<td class="coverFn"><a href="BayesMetrics.cc.gcov.html#L107">_ZN8bayesnet7Metrics22conditionalEdgeWeightsERSt6vectorIfSaIfEE</a></td>
<td class="coverFnLo">0</td>
</tr>
<tr>
<td class="coverFn"><a href="BayesMetrics.cc.gcov.html#L114">_ZN8bayesnet7Metrics7entropyERKN2at6TensorES4_</a></td>
<td class="coverFnHi">8506</td>
</tr>
<tr>
<td class="coverFn"><a href="BayesMetrics.cc.gcov.html#L9">_ZN8bayesnet7MetricsC2ERKN2at6TensorERKSt6vectorINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESaISB_EERKSB_i</a></td>
<td class="coverFnHi">338</td>
</tr>
<tr>
<td class="coverFn"><a href="BayesMetrics.cc.gcov.html#L17">_ZN8bayesnet7MetricsC2ERKSt6vectorIS1_IiSaIiEESaIS3_EERKS3_RKS1_INSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESaISF_EERKSF_i</a></td>
<td class="coverFnHi">16</td>
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<tr>
<td class="coverFn"><a href="BayesMetrics.cc.gcov.html#L63">_ZNK8bayesnet7Metrics14getScoresKBestEv</a></td>
<td class="coverFnHi">8</td>
</tr>
<tr>
<td class="coverFn"><a href="BayesMetrics.cc.gcov.html#L55">_ZZN8bayesnet7Metrics19SelectKBestWeightedERKN2at6TensorEbjENKUliiE0_clEii</a></td>
<td class="coverFnHi">12619</td>
</tr>
<tr>
<td class="coverFn"><a href="BayesMetrics.cc.gcov.html#L45">_ZZN8bayesnet7Metrics19SelectKBestWeightedERKN2at6TensorEbjENKUliiE_clEii</a></td>
<td class="coverFnHi">453</td>
</tr>
</table>
<br>
</center>
<table width="100%" border=0 cellspacing=0 cellpadding=0>
<tr><td class="ruler"><img src="../../glass.png" width=3 height=3 alt=""></td></tr>
<tr><td class="versionInfo">Generated by: <a href="https://github.com//linux-test-project/lcov" target="_parent">LCOV version 2.0-1</a></td></tr>
</table>
<br>
</body>
</html>

245
html/bayesnet/utils/BayesMetrics.cc.gcov.html Normal file
View File

@@ -0,0 +1,245 @@
<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN">
<html lang="en">
<head>
<meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
<title>LCOV - coverage.info - bayesnet/utils/BayesMetrics.cc</title>
<link rel="stylesheet" type="text/css" href="../../gcov.css">
</head>
<body>
<table width="100%" border=0 cellspacing=0 cellpadding=0>
<tr><td class="title">LCOV - code coverage report</td></tr>
<tr><td class="ruler"><img src="../../glass.png" width=3 height=3 alt=""></td></tr>
<tr>
<td width="100%">
<table cellpadding=1 border=0 width="100%">
<tr>
<td width="10%" class="headerItem">Current view:</td>
<td width="10%" class="headerValue"><a href="../../index.html">top level</a> - <a href="index.html">bayesnet/utils</a> - BayesMetrics.cc<span style="font-size: 80%;"> (source / <a href="BayesMetrics.cc.func-c.html">functions</a>)</span></td>
<td width="5%"></td>
<td width="5%"></td>
<td width="5%" class="headerCovTableHead">Coverage</td>
<td width="5%" class="headerCovTableHead" title="Covered + Uncovered code">Total</td>
<td width="5%" class="headerCovTableHead" title="Exercised code only">Hit</td>
</tr>
<tr>
<td class="headerItem">Test:</td>
<td class="headerValue">coverage.info</td>
<td></td>
<td class="headerItem">Lines:</td>
<td class="headerCovTableEntryHi">92.5&nbsp;%</td>
<td class="headerCovTableEntry">120</td>
<td class="headerCovTableEntry">111</td>
</tr>
<tr>
<td class="headerItem">Test Date:</td>
<td class="headerValue">2024-04-21 16:43:29</td>
<td></td>
<td class="headerItem">Functions:</td>
<td class="headerCovTableEntryHi">91.7&nbsp;%</td>
<td class="headerCovTableEntry">12</td>
<td class="headerCovTableEntry">11</td>
</tr>
<tr><td><img src="../../glass.png" width=3 height=3 alt=""></td></tr>
</table>
</td>
</tr>
<tr><td class="ruler"><img src="../../glass.png" width=3 height=3 alt=""></td></tr>
</table>
<table cellpadding=0 cellspacing=0 border=0>
<tr>
<td><br></td>
</tr>
<tr>
<td>
<pre class="sourceHeading"> Line data Source code</pre>
<pre class="source">
<span id="L1"><span class="lineNum"> 1</span> : // ***************************************************************</span>
<span id="L2"><span class="lineNum"> 2</span> : // SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez</span>
<span id="L3"><span class="lineNum"> 3</span> : // SPDX-FileType: SOURCE</span>
<span id="L4"><span class="lineNum"> 4</span> : // SPDX-License-Identifier: MIT</span>
<span id="L5"><span class="lineNum"> 5</span> : // ***************************************************************</span>
<span id="L6"><span class="lineNum"> 6</span> : </span>
<span id="L7"><span class="lineNum"> 7</span> : #include &quot;Mst.h&quot;</span>
<span id="L8"><span class="lineNum"> 8</span> : #include &quot;BayesMetrics.h&quot;</span>
<span id="L9"><span class="lineNum"> 9</span> : namespace bayesnet {</span>
<span id="L10"><span class="lineNum"> 10</span> : //samples is n+1xm tensor used to fit the model</span>
<span id="L11"><span class="lineNum"> 11</span> <span class="tlaGNC tlaBgGNC"> 338 : Metrics::Metrics(const torch::Tensor&amp; samples, const std::vector&lt;std::string&gt;&amp; features, const std::string&amp; className, const int classNumStates)</span></span>
<span id="L12"><span class="lineNum"> 12</span> <span class="tlaGNC"> 338 : : samples(samples)</span></span>
<span id="L13"><span class="lineNum"> 13</span> <span class="tlaGNC"> 338 : , features(features)</span></span>
<span id="L14"><span class="lineNum"> 14</span> <span class="tlaGNC"> 338 : , className(className)</span></span>
<span id="L15"><span class="lineNum"> 15</span> <span class="tlaGNC"> 338 : , classNumStates(classNumStates)</span></span>
<span id="L16"><span class="lineNum"> 16</span> : {</span>
<span id="L17"><span class="lineNum"> 17</span> <span class="tlaGNC"> 338 : }</span></span>
<span id="L18"><span class="lineNum"> 18</span> : //samples is n+1xm std::vector used to fit the model</span>
<span id="L19"><span class="lineNum"> 19</span> <span class="tlaGNC"> 16 : Metrics::Metrics(const std::vector&lt;std::vector&lt;int&gt;&gt;&amp; vsamples, const std::vector&lt;int&gt;&amp; labels, const std::vector&lt;std::string&gt;&amp; features, const std::string&amp; className, const int classNumStates)</span></span>
<span id="L20"><span class="lineNum"> 20</span> <span class="tlaGNC"> 16 : : features(features)</span></span>
<span id="L21"><span class="lineNum"> 21</span> <span class="tlaGNC"> 16 : , className(className)</span></span>
<span id="L22"><span class="lineNum"> 22</span> <span class="tlaGNC"> 16 : , classNumStates(classNumStates)</span></span>
<span id="L23"><span class="lineNum"> 23</span> <span class="tlaGNC"> 32 : , samples(torch::zeros({ static_cast&lt;int&gt;(vsamples.size() + 1), static_cast&lt;int&gt;(vsamples[0].size()) }, torch::kInt32))</span></span>
<span id="L24"><span class="lineNum"> 24</span> : {</span>
<span id="L25"><span class="lineNum"> 25</span> <span class="tlaGNC"> 128 : for (int i = 0; i &lt; vsamples.size(); ++i) {</span></span>
<span id="L26"><span class="lineNum"> 26</span> <span class="tlaGNC"> 448 : samples.index_put_({ i, &quot;...&quot; }, torch::tensor(vsamples[i], torch::kInt32));</span></span>
<span id="L27"><span class="lineNum"> 27</span> : }</span>
<span id="L28"><span class="lineNum"> 28</span> <span class="tlaGNC"> 64 : samples.index_put_({ -1, &quot;...&quot; }, torch::tensor(labels, torch::kInt32));</span></span>
<span id="L29"><span class="lineNum"> 29</span> <span class="tlaGNC"> 144 : }</span></span>
<span id="L30"><span class="lineNum"> 30</span> <span class="tlaGNC"> 90 : std::vector&lt;int&gt; Metrics::SelectKBestWeighted(const torch::Tensor&amp; weights, bool ascending, unsigned k)</span></span>
<span id="L31"><span class="lineNum"> 31</span> : {</span>
<span id="L32"><span class="lineNum"> 32</span> : // Return the K Best features </span>
<span id="L33"><span class="lineNum"> 33</span> <span class="tlaGNC"> 90 : auto n = features.size();</span></span>
<span id="L34"><span class="lineNum"> 34</span> <span class="tlaGNC"> 90 : if (k == 0) {</span></span>
<span id="L35"><span class="lineNum"> 35</span> <span class="tlaUNC tlaBgUNC"> 0 : k = n;</span></span>
<span id="L36"><span class="lineNum"> 36</span> : }</span>
<span id="L37"><span class="lineNum"> 37</span> : // compute scores</span>
<span id="L38"><span class="lineNum"> 38</span> <span class="tlaGNC tlaBgGNC"> 90 : scoresKBest.clear();</span></span>
<span id="L39"><span class="lineNum"> 39</span> <span class="tlaGNC"> 90 : featuresKBest.clear();</span></span>
<span id="L40"><span class="lineNum"> 40</span> <span class="tlaGNC"> 270 : auto label = samples.index({ -1, &quot;...&quot; });</span></span>
<span id="L41"><span class="lineNum"> 41</span> <span class="tlaGNC"> 2837 : for (int i = 0; i &lt; n; ++i) {</span></span>
<span id="L42"><span class="lineNum"> 42</span> <span class="tlaGNC"> 8241 : scoresKBest.push_back(mutualInformation(label, samples.index({ i, &quot;...&quot; }), weights));</span></span>
<span id="L43"><span class="lineNum"> 43</span> <span class="tlaGNC"> 2747 : featuresKBest.push_back(i);</span></span>
<span id="L44"><span class="lineNum"> 44</span> : }</span>
<span id="L45"><span class="lineNum"> 45</span> : // sort &amp; reduce scores and features</span>
<span id="L46"><span class="lineNum"> 46</span> <span class="tlaGNC"> 90 : if (ascending) {</span></span>
<span id="L47"><span class="lineNum"> 47</span> <span class="tlaGNC"> 19 : sort(featuresKBest.begin(), featuresKBest.end(), [&amp;](int i, int j)</span></span>
<span id="L48"><span class="lineNum"> 48</span> <span class="tlaGNC"> 453 : { return scoresKBest[i] &lt; scoresKBest[j]; });</span></span>
<span id="L49"><span class="lineNum"> 49</span> <span class="tlaGNC"> 19 : sort(scoresKBest.begin(), scoresKBest.end(), std::less&lt;double&gt;());</span></span>
<span id="L50"><span class="lineNum"> 50</span> <span class="tlaGNC"> 19 : if (k &lt; n) {</span></span>
<span id="L51"><span class="lineNum"> 51</span> <span class="tlaGNC"> 28 : for (int i = 0; i &lt; n - k; ++i) {</span></span>
<span id="L52"><span class="lineNum"> 52</span> <span class="tlaGNC"> 20 : featuresKBest.erase(featuresKBest.begin());</span></span>
<span id="L53"><span class="lineNum"> 53</span> <span class="tlaGNC"> 20 : scoresKBest.erase(scoresKBest.begin());</span></span>
<span id="L54"><span class="lineNum"> 54</span> : }</span>
<span id="L55"><span class="lineNum"> 55</span> : }</span>
<span id="L56"><span class="lineNum"> 56</span> : } else {</span>
<span id="L57"><span class="lineNum"> 57</span> <span class="tlaGNC"> 71 : sort(featuresKBest.begin(), featuresKBest.end(), [&amp;](int i, int j)</span></span>
<span id="L58"><span class="lineNum"> 58</span> <span class="tlaGNC"> 12619 : { return scoresKBest[i] &gt; scoresKBest[j]; });</span></span>
<span id="L59"><span class="lineNum"> 59</span> <span class="tlaGNC"> 71 : sort(scoresKBest.begin(), scoresKBest.end(), std::greater&lt;double&gt;());</span></span>
<span id="L60"><span class="lineNum"> 60</span> <span class="tlaGNC"> 71 : featuresKBest.resize(k);</span></span>
<span id="L61"><span class="lineNum"> 61</span> <span class="tlaGNC"> 71 : scoresKBest.resize(k);</span></span>
<span id="L62"><span class="lineNum"> 62</span> : }</span>
<span id="L63"><span class="lineNum"> 63</span> <span class="tlaGNC"> 180 : return featuresKBest;</span></span>
<span id="L64"><span class="lineNum"> 64</span> <span class="tlaGNC"> 2927 : }</span></span>
<span id="L65"><span class="lineNum"> 65</span> <span class="tlaGNC"> 8 : std::vector&lt;double&gt; Metrics::getScoresKBest() const</span></span>
<span id="L66"><span class="lineNum"> 66</span> : {</span>
<span id="L67"><span class="lineNum"> 67</span> <span class="tlaGNC"> 8 : return scoresKBest;</span></span>
<span id="L68"><span class="lineNum"> 68</span> : }</span>
<span id="L69"><span class="lineNum"> 69</span> : </span>
<span id="L70"><span class="lineNum"> 70</span> <span class="tlaGNC"> 34 : torch::Tensor Metrics::conditionalEdge(const torch::Tensor&amp; weights)</span></span>
<span id="L71"><span class="lineNum"> 71</span> : {</span>
<span id="L72"><span class="lineNum"> 72</span> <span class="tlaGNC"> 34 : auto result = std::vector&lt;double&gt;();</span></span>
<span id="L73"><span class="lineNum"> 73</span> <span class="tlaGNC"> 34 : auto source = std::vector&lt;std::string&gt;(features);</span></span>
<span id="L74"><span class="lineNum"> 74</span> <span class="tlaGNC"> 34 : source.push_back(className);</span></span>
<span id="L75"><span class="lineNum"> 75</span> <span class="tlaGNC"> 34 : auto combinations = doCombinations(source);</span></span>
<span id="L76"><span class="lineNum"> 76</span> : // Compute class prior</span>
<span id="L77"><span class="lineNum"> 77</span> <span class="tlaGNC"> 34 : auto margin = torch::zeros({ classNumStates }, torch::kFloat);</span></span>
<span id="L78"><span class="lineNum"> 78</span> <span class="tlaGNC"> 184 : for (int value = 0; value &lt; classNumStates; ++value) {</span></span>
<span id="L79"><span class="lineNum"> 79</span> <span class="tlaGNC"> 600 : auto mask = samples.index({ -1, &quot;...&quot; }) == value;</span></span>
<span id="L80"><span class="lineNum"> 80</span> <span class="tlaGNC"> 150 : margin[value] = mask.sum().item&lt;double&gt;() / samples.size(1);</span></span>
<span id="L81"><span class="lineNum"> 81</span> <span class="tlaGNC"> 150 : }</span></span>
<span id="L82"><span class="lineNum"> 82</span> <span class="tlaGNC"> 918 : for (auto [first, second] : combinations) {</span></span>
<span id="L83"><span class="lineNum"> 83</span> <span class="tlaGNC"> 884 : int index_first = find(features.begin(), features.end(), first) - features.begin();</span></span>
<span id="L84"><span class="lineNum"> 84</span> <span class="tlaGNC"> 884 : int index_second = find(features.begin(), features.end(), second) - features.begin();</span></span>
<span id="L85"><span class="lineNum"> 85</span> <span class="tlaGNC"> 884 : double accumulated = 0;</span></span>
<span id="L86"><span class="lineNum"> 86</span> <span class="tlaGNC"> 5240 : for (int value = 0; value &lt; classNumStates; ++value) {</span></span>
<span id="L87"><span class="lineNum"> 87</span> <span class="tlaGNC"> 17424 : auto mask = samples.index({ -1, &quot;...&quot; }) == value;</span></span>
<span id="L88"><span class="lineNum"> 88</span> <span class="tlaGNC"> 13068 : auto first_dataset = samples.index({ index_first, mask });</span></span>
<span id="L89"><span class="lineNum"> 89</span> <span class="tlaGNC"> 13068 : auto second_dataset = samples.index({ index_second, mask });</span></span>
<span id="L90"><span class="lineNum"> 90</span> <span class="tlaGNC"> 8712 : auto weights_dataset = weights.index({ mask });</span></span>
<span id="L91"><span class="lineNum"> 91</span> <span class="tlaGNC"> 8712 : auto mi = mutualInformation(first_dataset, second_dataset, weights_dataset);</span></span>
<span id="L92"><span class="lineNum"> 92</span> <span class="tlaGNC"> 4356 : auto pb = margin[value].item&lt;double&gt;();</span></span>
<span id="L93"><span class="lineNum"> 93</span> <span class="tlaGNC"> 4356 : accumulated += pb * mi;</span></span>
<span id="L94"><span class="lineNum"> 94</span> <span class="tlaGNC"> 4356 : }</span></span>
<span id="L95"><span class="lineNum"> 95</span> <span class="tlaGNC"> 884 : result.push_back(accumulated);</span></span>
<span id="L96"><span class="lineNum"> 96</span> <span class="tlaGNC"> 884 : }</span></span>
<span id="L97"><span class="lineNum"> 97</span> <span class="tlaGNC"> 34 : long n_vars = source.size();</span></span>
<span id="L98"><span class="lineNum"> 98</span> <span class="tlaGNC"> 34 : auto matrix = torch::zeros({ n_vars, n_vars });</span></span>
<span id="L99"><span class="lineNum"> 99</span> <span class="tlaGNC"> 34 : auto indices = torch::triu_indices(n_vars, n_vars, 1);</span></span>
<span id="L100"><span class="lineNum"> 100</span> <span class="tlaGNC"> 918 : for (auto i = 0; i &lt; result.size(); ++i) {</span></span>
<span id="L101"><span class="lineNum"> 101</span> <span class="tlaGNC"> 884 : auto x = indices[0][i];</span></span>
<span id="L102"><span class="lineNum"> 102</span> <span class="tlaGNC"> 884 : auto y = indices[1][i];</span></span>
<span id="L103"><span class="lineNum"> 103</span> <span class="tlaGNC"> 884 : matrix[x][y] = result[i];</span></span>
<span id="L104"><span class="lineNum"> 104</span> <span class="tlaGNC"> 884 : matrix[y][x] = result[i];</span></span>
<span id="L105"><span class="lineNum"> 105</span> <span class="tlaGNC"> 884 : }</span></span>
<span id="L106"><span class="lineNum"> 106</span> <span class="tlaGNC"> 68 : return matrix;</span></span>
<span id="L107"><span class="lineNum"> 107</span> <span class="tlaGNC"> 21964 : }</span></span>
<span id="L108"><span class="lineNum"> 108</span> : // To use in Python</span>
<span id="L109"><span class="lineNum"> 109</span> <span class="tlaUNC tlaBgUNC"> 0 : std::vector&lt;float&gt; Metrics::conditionalEdgeWeights(std::vector&lt;float&gt;&amp; weights_)</span></span>
<span id="L110"><span class="lineNum"> 110</span> : {</span>
<span id="L111"><span class="lineNum"> 111</span> <span class="tlaUNC"> 0 : const torch::Tensor weights = torch::tensor(weights_);</span></span>
<span id="L112"><span class="lineNum"> 112</span> <span class="tlaUNC"> 0 : auto matrix = conditionalEdge(weights);</span></span>
<span id="L113"><span class="lineNum"> 113</span> <span class="tlaUNC"> 0 : std::vector&lt;float&gt; v(matrix.data_ptr&lt;float&gt;(), matrix.data_ptr&lt;float&gt;() + matrix.numel());</span></span>
<span id="L114"><span class="lineNum"> 114</span> <span class="tlaUNC"> 0 : return v;</span></span>
<span id="L115"><span class="lineNum"> 115</span> <span class="tlaUNC"> 0 : }</span></span>
<span id="L116"><span class="lineNum"> 116</span> <span class="tlaGNC tlaBgGNC"> 8506 : double Metrics::entropy(const torch::Tensor&amp; feature, const torch::Tensor&amp; weights)</span></span>
<span id="L117"><span class="lineNum"> 117</span> : {</span>
<span id="L118"><span class="lineNum"> 118</span> <span class="tlaGNC"> 8506 : torch::Tensor counts = feature.bincount(weights);</span></span>
<span id="L119"><span class="lineNum"> 119</span> <span class="tlaGNC"> 8506 : double totalWeight = counts.sum().item&lt;double&gt;();</span></span>
<span id="L120"><span class="lineNum"> 120</span> <span class="tlaGNC"> 8506 : torch::Tensor probs = counts.to(torch::kFloat) / totalWeight;</span></span>
<span id="L121"><span class="lineNum"> 121</span> <span class="tlaGNC"> 8506 : torch::Tensor logProbs = torch::log(probs);</span></span>
<span id="L122"><span class="lineNum"> 122</span> <span class="tlaGNC"> 8506 : torch::Tensor entropy = -probs * logProbs;</span></span>
<span id="L123"><span class="lineNum"> 123</span> <span class="tlaGNC"> 17012 : return entropy.nansum().item&lt;double&gt;();</span></span>
<span id="L124"><span class="lineNum"> 124</span> <span class="tlaGNC"> 8506 : }</span></span>
<span id="L125"><span class="lineNum"> 125</span> : // H(Y|X) = sum_{x in X} p(x) H(Y|X=x)</span>
<span id="L126"><span class="lineNum"> 126</span> <span class="tlaGNC"> 7684 : double Metrics::conditionalEntropy(const torch::Tensor&amp; firstFeature, const torch::Tensor&amp; secondFeature, const torch::Tensor&amp; weights)</span></span>
<span id="L127"><span class="lineNum"> 127</span> : {</span>
<span id="L128"><span class="lineNum"> 128</span> <span class="tlaGNC"> 7684 : int numSamples = firstFeature.sizes()[0];</span></span>
<span id="L129"><span class="lineNum"> 129</span> <span class="tlaGNC"> 7684 : torch::Tensor featureCounts = secondFeature.bincount(weights);</span></span>
<span id="L130"><span class="lineNum"> 130</span> <span class="tlaGNC"> 7684 : std::unordered_map&lt;int, std::unordered_map&lt;int, double&gt;&gt; jointCounts;</span></span>
<span id="L131"><span class="lineNum"> 131</span> <span class="tlaGNC"> 7684 : double totalWeight = 0;</span></span>
<span id="L132"><span class="lineNum"> 132</span> <span class="tlaGNC"> 991784 : for (auto i = 0; i &lt; numSamples; i++) {</span></span>
<span id="L133"><span class="lineNum"> 133</span> <span class="tlaGNC"> 984100 : jointCounts[secondFeature[i].item&lt;int&gt;()][firstFeature[i].item&lt;int&gt;()] += weights[i].item&lt;double&gt;();</span></span>
<span id="L134"><span class="lineNum"> 134</span> <span class="tlaGNC"> 984100 : totalWeight += weights[i].item&lt;float&gt;();</span></span>
<span id="L135"><span class="lineNum"> 135</span> : }</span>
<span id="L136"><span class="lineNum"> 136</span> <span class="tlaGNC"> 7684 : if (totalWeight == 0)</span></span>
<span id="L137"><span class="lineNum"> 137</span> <span class="tlaUNC tlaBgUNC"> 0 : return 0;</span></span>
<span id="L138"><span class="lineNum"> 138</span> <span class="tlaGNC tlaBgGNC"> 7684 : double entropyValue = 0;</span></span>
<span id="L139"><span class="lineNum"> 139</span> <span class="tlaGNC"> 26852 : for (int value = 0; value &lt; featureCounts.sizes()[0]; ++value) {</span></span>
<span id="L140"><span class="lineNum"> 140</span> <span class="tlaGNC"> 19168 : double p_f = featureCounts[value].item&lt;double&gt;() / totalWeight;</span></span>
<span id="L141"><span class="lineNum"> 141</span> <span class="tlaGNC"> 19168 : double entropy_f = 0;</span></span>
<span id="L142"><span class="lineNum"> 142</span> <span class="tlaGNC"> 57129 : for (auto&amp; [label, jointCount] : jointCounts[value]) {</span></span>
<span id="L143"><span class="lineNum"> 143</span> <span class="tlaGNC"> 37961 : double p_l_f = jointCount / featureCounts[value].item&lt;double&gt;();</span></span>
<span id="L144"><span class="lineNum"> 144</span> <span class="tlaGNC"> 37961 : if (p_l_f &gt; 0) {</span></span>
<span id="L145"><span class="lineNum"> 145</span> <span class="tlaGNC"> 37961 : entropy_f -= p_l_f * log(p_l_f);</span></span>
<span id="L146"><span class="lineNum"> 146</span> : } else {</span>
<span id="L147"><span class="lineNum"> 147</span> <span class="tlaUNC tlaBgUNC"> 0 : entropy_f = 0;</span></span>
<span id="L148"><span class="lineNum"> 148</span> : }</span>
<span id="L149"><span class="lineNum"> 149</span> : }</span>
<span id="L150"><span class="lineNum"> 150</span> <span class="tlaGNC tlaBgGNC"> 19168 : entropyValue += p_f * entropy_f;</span></span>
<span id="L151"><span class="lineNum"> 151</span> : }</span>
<span id="L152"><span class="lineNum"> 152</span> <span class="tlaGNC"> 7684 : return entropyValue;</span></span>
<span id="L153"><span class="lineNum"> 153</span> <span class="tlaGNC"> 7684 : }</span></span>
<span id="L154"><span class="lineNum"> 154</span> : // I(X;Y) = H(Y) - H(Y|X)</span>
<span id="L155"><span class="lineNum"> 155</span> <span class="tlaGNC"> 7684 : double Metrics::mutualInformation(const torch::Tensor&amp; firstFeature, const torch::Tensor&amp; secondFeature, const torch::Tensor&amp; weights)</span></span>
<span id="L156"><span class="lineNum"> 156</span> : {</span>
<span id="L157"><span class="lineNum"> 157</span> <span class="tlaGNC"> 7684 : return entropy(firstFeature, weights) - conditionalEntropy(firstFeature, secondFeature, weights);</span></span>
<span id="L158"><span class="lineNum"> 158</span> : }</span>
<span id="L159"><span class="lineNum"> 159</span> : /*</span>
<span id="L160"><span class="lineNum"> 160</span> : Compute the maximum spanning tree considering the weights as distances</span>
<span id="L161"><span class="lineNum"> 161</span> : and the indices of the weights as nodes of this square matrix using</span>
<span id="L162"><span class="lineNum"> 162</span> : Kruskal algorithm</span>
<span id="L163"><span class="lineNum"> 163</span> : */</span>
<span id="L164"><span class="lineNum"> 164</span> <span class="tlaGNC"> 29 : std::vector&lt;std::pair&lt;int, int&gt;&gt; Metrics::maximumSpanningTree(const std::vector&lt;std::string&gt;&amp; features, const torch::Tensor&amp; weights, const int root)</span></span>
<span id="L165"><span class="lineNum"> 165</span> : {</span>
<span id="L166"><span class="lineNum"> 166</span> <span class="tlaGNC"> 29 : auto mst = MST(features, weights, root);</span></span>
<span id="L167"><span class="lineNum"> 167</span> <span class="tlaGNC"> 58 : return mst.maximumSpanningTree();</span></span>
<span id="L168"><span class="lineNum"> 168</span> <span class="tlaGNC"> 29 : }</span></span>
<span id="L169"><span class="lineNum"> 169</span> : }</span>
</pre>
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<td width="5%" class="headerCovTableHead">Coverage</td>
<td width="5%" class="headerCovTableHead" title="Covered + Uncovered code">Total</td>
<td width="5%" class="headerCovTableHead" title="Exercised code only">Hit</td>
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<td class="headerCovTableEntry">13</td>
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<td class="headerValue">2024-04-21 16:43:29</td>
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<td class="headerItem">Functions:</td>
<td class="headerCovTableEntryHi">100.0&nbsp;%</td>
<td class="headerCovTableEntry">4</td>
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<td class="coverFnAlias"><a href="BayesMetrics.h.gcov.html#L28">_ZN8bayesnet7Metrics14doCombinationsINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEEESt6vectorISt4pairIT_SA_ESaISB_EERKS8_ISA_SaISA_EE</a></td>
<td class="coverFnAliasHi">34</td>
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<td class="coverFnAlias"><a href="BayesMetrics.h.gcov.html#L28">_ZN8bayesnet7Metrics14doCombinationsIiEESt6vectorISt4pairIT_S4_ESaIS5_EERKS2_IS4_SaIS4_EE</a></td>
<td class="coverFnAliasHi">158</td>
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<td class="coverFn"><a href="BayesMetrics.h.gcov.html#L13">_ZN8bayesnet7MetricsC2Ev</a></td>
<td class="coverFnHi">413</td>
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<td width="5%" class="headerCovTableHead" title="Covered + Uncovered code">Total</td>
<td width="5%" class="headerCovTableHead" title="Exercised code only">Hit</td>
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<td class="headerValue">coverage.info</td>
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<td class="headerCovTableEntry">13</td>
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<td class="headerItem">Functions:</td>
<td class="headerCovTableEntryHi">100.0&nbsp;%</td>
<td class="headerCovTableEntry">4</td>
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<title>LCOV - coverage.info - bayesnet/utils/BayesMetrics.h</title>
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<table cellpadding=1 border=0 width="100%">
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<td width="10%" class="headerItem">Current view:</td>
<td width="10%" class="headerValue"><a href="../../index.html">top level</a> - <a href="index.html">bayesnet/utils</a> - BayesMetrics.h<span style="font-size: 80%;"> (source / <a href="BayesMetrics.h.func-c.html">functions</a>)</span></td>
<td width="5%"></td>
<td width="5%"></td>
<td width="5%" class="headerCovTableHead">Coverage</td>
<td width="5%" class="headerCovTableHead" title="Covered + Uncovered code">Total</td>
<td width="5%" class="headerCovTableHead" title="Exercised code only">Hit</td>
</tr>
<tr>
<td class="headerItem">Test:</td>
<td class="headerValue">coverage.info</td>
<td></td>
<td class="headerItem">Lines:</td>
<td class="headerCovTableEntryHi">92.3&nbsp;%</td>
<td class="headerCovTableEntry">13</td>
<td class="headerCovTableEntry">12</td>
</tr>
<tr>
<td class="headerItem">Test Date:</td>
<td class="headerValue">2024-04-21 16:43:29</td>
<td></td>
<td class="headerItem">Functions:</td>
<td class="headerCovTableEntryHi">100.0&nbsp;%</td>
<td class="headerCovTableEntry">4</td>
<td class="headerCovTableEntry">4</td>
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<pre class="sourceHeading"> Line data Source code</pre>
<pre class="source">
<span id="L1"><span class="lineNum"> 1</span> : // ***************************************************************</span>
<span id="L2"><span class="lineNum"> 2</span> : // SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez</span>
<span id="L3"><span class="lineNum"> 3</span> : // SPDX-FileType: SOURCE</span>
<span id="L4"><span class="lineNum"> 4</span> : // SPDX-License-Identifier: MIT</span>
<span id="L5"><span class="lineNum"> 5</span> : // ***************************************************************</span>
<span id="L6"><span class="lineNum"> 6</span> : </span>
<span id="L7"><span class="lineNum"> 7</span> : #ifndef BAYESNET_METRICS_H</span>
<span id="L8"><span class="lineNum"> 8</span> : #define BAYESNET_METRICS_H</span>
<span id="L9"><span class="lineNum"> 9</span> : #include &lt;vector&gt;</span>
<span id="L10"><span class="lineNum"> 10</span> : #include &lt;string&gt;</span>
<span id="L11"><span class="lineNum"> 11</span> : #include &lt;torch/torch.h&gt;</span>
<span id="L12"><span class="lineNum"> 12</span> : namespace bayesnet {</span>
<span id="L13"><span class="lineNum"> 13</span> : class Metrics {</span>
<span id="L14"><span class="lineNum"> 14</span> : public:</span>
<span id="L15"><span class="lineNum"> 15</span> <span class="tlaGNC tlaBgGNC"> 413 : Metrics() = default;</span></span>
<span id="L16"><span class="lineNum"> 16</span> : Metrics(const torch::Tensor&amp; samples, const std::vector&lt;std::string&gt;&amp; features, const std::string&amp; className, const int classNumStates);</span>
<span id="L17"><span class="lineNum"> 17</span> : Metrics(const std::vector&lt;std::vector&lt;int&gt;&gt;&amp; vsamples, const std::vector&lt;int&gt;&amp; labels, const std::vector&lt;std::string&gt;&amp; features, const std::string&amp; className, const int classNumStates);</span>
<span id="L18"><span class="lineNum"> 18</span> : std::vector&lt;int&gt; SelectKBestWeighted(const torch::Tensor&amp; weights, bool ascending = false, unsigned k = 0);</span>
<span id="L19"><span class="lineNum"> 19</span> : std::vector&lt;double&gt; getScoresKBest() const;</span>
<span id="L20"><span class="lineNum"> 20</span> : double mutualInformation(const torch::Tensor&amp; firstFeature, const torch::Tensor&amp; secondFeature, const torch::Tensor&amp; weights);</span>
<span id="L21"><span class="lineNum"> 21</span> : std::vector&lt;float&gt; conditionalEdgeWeights(std::vector&lt;float&gt;&amp; weights); // To use in Python</span>
<span id="L22"><span class="lineNum"> 22</span> : torch::Tensor conditionalEdge(const torch::Tensor&amp; weights);</span>
<span id="L23"><span class="lineNum"> 23</span> : std::vector&lt;std::pair&lt;int, int&gt;&gt; maximumSpanningTree(const std::vector&lt;std::string&gt;&amp; features, const torch::Tensor&amp; weights, const int root);</span>
<span id="L24"><span class="lineNum"> 24</span> : protected:</span>
<span id="L25"><span class="lineNum"> 25</span> : torch::Tensor samples; // n+1xm torch::Tensor used to fit the model where samples[-1] is the y std::vector</span>
<span id="L26"><span class="lineNum"> 26</span> : std::string className;</span>
<span id="L27"><span class="lineNum"> 27</span> : double entropy(const torch::Tensor&amp; feature, const torch::Tensor&amp; weights);</span>
<span id="L28"><span class="lineNum"> 28</span> : std::vector&lt;std::string&gt; features;</span>
<span id="L29"><span class="lineNum"> 29</span> : template &lt;class T&gt;</span>
<span id="L30"><span class="lineNum"> 30</span> <span class="tlaGNC"> 192 : std::vector&lt;std::pair&lt;T, T&gt;&gt; doCombinations(const std::vector&lt;T&gt;&amp; source)</span></span>
<span id="L31"><span class="lineNum"> 31</span> : {</span>
<span id="L32"><span class="lineNum"> 32</span> <span class="tlaGNC"> 192 : std::vector&lt;std::pair&lt;T, T&gt;&gt; result;</span></span>
<span id="L33"><span class="lineNum"> 33</span> <span class="tlaGNC"> 1013 : for (int i = 0; i &lt; source.size(); ++i) {</span></span>
<span id="L34"><span class="lineNum"> 34</span> <span class="tlaGNC"> 821 : T temp = source[i];</span></span>
<span id="L35"><span class="lineNum"> 35</span> <span class="tlaGNC"> 2590 : for (int j = i + 1; j &lt; source.size(); ++j) {</span></span>
<span id="L36"><span class="lineNum"> 36</span> <span class="tlaGNC"> 1769 : result.push_back({ temp, source[j] });</span></span>
<span id="L37"><span class="lineNum"> 37</span> : }</span>
<span id="L38"><span class="lineNum"> 38</span> : }</span>
<span id="L39"><span class="lineNum"> 39</span> <span class="tlaGNC"> 192 : return result;</span></span>
<span id="L40"><span class="lineNum"> 40</span> <span class="tlaUNC tlaBgUNC"> 0 : }</span></span>
<span id="L41"><span class="lineNum"> 41</span> : template &lt;class T&gt;</span>
<span id="L42"><span class="lineNum"> 42</span> <span class="tlaGNC tlaBgGNC"> 10 : T pop_first(std::vector&lt;T&gt;&amp; v)</span></span>
<span id="L43"><span class="lineNum"> 43</span> : {</span>
<span id="L44"><span class="lineNum"> 44</span> <span class="tlaGNC"> 10 : T temp = v[0];</span></span>
<span id="L45"><span class="lineNum"> 45</span> <span class="tlaGNC"> 10 : v.erase(v.begin());</span></span>
<span id="L46"><span class="lineNum"> 46</span> <span class="tlaGNC"> 10 : return temp;</span></span>
<span id="L47"><span class="lineNum"> 47</span> : }</span>
<span id="L48"><span class="lineNum"> 48</span> : private:</span>
<span id="L49"><span class="lineNum"> 49</span> : int classNumStates = 0;</span>
<span id="L50"><span class="lineNum"> 50</span> : std::vector&lt;double&gt; scoresKBest;</span>
<span id="L51"><span class="lineNum"> 51</span> : std::vector&lt;int&gt; featuresKBest; // sorted indices of the features</span>
<span id="L52"><span class="lineNum"> 52</span> : double conditionalEntropy(const torch::Tensor&amp; firstFeature, const torch::Tensor&amp; secondFeature, const torch::Tensor&amp; weights);</span>
<span id="L53"><span class="lineNum"> 53</span> : };</span>
<span id="L54"><span class="lineNum"> 54</span> : }</span>
<span id="L55"><span class="lineNum"> 55</span> : #endif</span>
</pre>
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145
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<title>LCOV - coverage.info - bayesnet/utils/Mst.cc - functions</title>
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<table width="100%" border=0 cellspacing=0 cellpadding=0>
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<tr>
<td width="100%">
<table cellpadding=1 border=0 width="100%">
<tr>
<td width="10%" class="headerItem">Current view:</td>
<td width="10%" class="headerValue"><a href="../../index.html">top level</a> - <a href="index.html">bayesnet/utils</a> - Mst.cc<span style="font-size: 80%;"> (<a href="Mst.cc.gcov.html">source</a> / functions)</span></td>
<td width="5%"></td>
<td width="5%"></td>
<td width="5%" class="headerCovTableHead">Coverage</td>
<td width="5%" class="headerCovTableHead" title="Covered + Uncovered code">Total</td>
<td width="5%" class="headerCovTableHead" title="Exercised code only">Hit</td>
</tr>
<tr>
<td class="headerItem">Test:</td>
<td class="headerValue">coverage.info</td>
<td></td>
<td class="headerItem">Lines:</td>
<td class="headerCovTableEntryHi">94.1&nbsp;%</td>
<td class="headerCovTableEntry">68</td>
<td class="headerCovTableEntry">64</td>
</tr>
<tr>
<td class="headerItem">Test Date:</td>
<td class="headerValue">2024-04-21 16:43:29</td>
<td></td>
<td class="headerItem">Functions:</td>
<td class="headerCovTableEntryHi">100.0&nbsp;%</td>
<td class="headerCovTableEntry">10</td>
<td class="headerCovTableEntry">10</td>
</tr>
<tr><td><img src="../../glass.png" width=3 height=3 alt=""></td></tr>
</table>
</td>
</tr>
<tr><td class="ruler"><img src="../../glass.png" width=3 height=3 alt=""></td></tr>
</table>
<center>
<table cellpadding=1 cellspacing=1 border=0>
<tr><td><br></td></tr>
<tr>
<td class="tableHead">Function Name <span title="Click to sort table by function name" class="tableHeadSort"><a href="Mst.cc.func.html"><img src="../../updown.png" width=10 height=14 alt="Sort by function name" title="Click to sort table by function name" border=0></a></span></td>
<td class="tableHead">Hit count <span title="Click to sort table by function hit count" class="tableHeadSort"><img src="../../glass.png" width=10 height=14 alt="Sort by function hit count" title="Click to sort table by function hit count" border=0></span></td>
</tr>
<tr>
<td class="coverFn"><a href="Mst.cc.gcov.html#L103">_ZN8bayesnet3MST19maximumSpanningTreeEv</a></td>
<td class="coverFnHi">29</td>
</tr>
<tr>
<td class="coverFn"><a href="Mst.cc.gcov.html#L102">_ZN8bayesnet3MSTC2ERKSt6vectorINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESaIS7_EERKN2at6TensorEi</a></td>
<td class="coverFnHi">29</td>
</tr>
<tr>
<td class="coverFn"><a href="Mst.cc.gcov.html#L39">_ZN8bayesnet5Graph17kruskal_algorithmEv</a></td>
<td class="coverFnHi">29</td>
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<tr>
<td class="coverFn"><a href="Mst.cc.gcov.html#L15">_ZN8bayesnet5GraphC2Ei</a></td>
<td class="coverFnHi">29</td>
</tr>
<tr>
<td class="coverFn"><a href="Mst.cc.gcov.html#L61">_ZN8bayesnet7reorderESt6vectorISt4pairIfS1_IiiEESaIS3_EEi</a></td>
<td class="coverFnHi">29</td>
</tr>
<tr>
<td class="coverFn"><a href="Mst.cc.gcov.html#L54">_ZN8bayesnet13insertElementERNSt7__cxx114listIiSaIiEEEi</a></td>
<td class="coverFnHi">159</td>
</tr>
<tr>
<td class="coverFn"><a href="Mst.cc.gcov.html#L35">_ZN8bayesnet5Graph9union_setEii</a></td>
<td class="coverFnHi">159</td>
</tr>
<tr>
<td class="coverFn"><a href="Mst.cc.gcov.html#L22">_ZN8bayesnet5Graph7addEdgeEiif</a></td>
<td class="coverFnHi">576</td>
</tr>
<tr>
<td class="coverFn"><a href="Mst.cc.gcov.html#L42">_ZZN8bayesnet5Graph17kruskal_algorithmEvENKUlRKT_RKT0_E_clISt4pairIfS9_IiiEESB_EEDaS3_S6_</a></td>
<td class="coverFnHi">2213</td>
</tr>
<tr>
<td class="coverFn"><a href="Mst.cc.gcov.html#L26">_ZN8bayesnet5Graph8find_setEi</a></td>
<td class="coverFnHi">2663</td>
</tr>
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145
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@@ -0,0 +1,145 @@
<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN">
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<td width="10%" class="headerItem">Current view:</td>
<td width="10%" class="headerValue"><a href="../../index.html">top level</a> - <a href="index.html">bayesnet/utils</a> - Mst.cc<span style="font-size: 80%;"> (<a href="Mst.cc.gcov.html">source</a> / functions)</span></td>
<td width="5%"></td>
<td width="5%"></td>
<td width="5%" class="headerCovTableHead">Coverage</td>
<td width="5%" class="headerCovTableHead" title="Covered + Uncovered code">Total</td>
<td width="5%" class="headerCovTableHead" title="Exercised code only">Hit</td>
</tr>
<tr>
<td class="headerItem">Test:</td>
<td class="headerValue">coverage.info</td>
<td></td>
<td class="headerItem">Lines:</td>
<td class="headerCovTableEntryHi">94.1&nbsp;%</td>
<td class="headerCovTableEntry">68</td>
<td class="headerCovTableEntry">64</td>
</tr>
<tr>
<td class="headerItem">Test Date:</td>
<td class="headerValue">2024-04-21 16:43:29</td>
<td></td>
<td class="headerItem">Functions:</td>
<td class="headerCovTableEntryHi">100.0&nbsp;%</td>
<td class="headerCovTableEntry">10</td>
<td class="headerCovTableEntry">10</td>
</tr>
<tr><td><img src="../../glass.png" width=3 height=3 alt=""></td></tr>
</table>
</td>
</tr>
<tr><td class="ruler"><img src="../../glass.png" width=3 height=3 alt=""></td></tr>
</table>
<center>
<table cellpadding=1 cellspacing=1 border=0>
<tr><td><br></td></tr>
<tr>
<td class="tableHead">Function Name <span title="Click to sort table by function name" class="tableHeadSort"><img src="../../glass.png" width=10 height=14 alt="Sort by function name" title="Click to sort table by function name" border=0></span></td>
<td class="tableHead">Hit count <span title="Click to sort table by function hit count" class="tableHeadSort"><a href="Mst.cc.func-c.html"><img src="../../updown.png" width=10 height=14 alt="Sort by function hit count" title="Click to sort table by function hit count" border=0></a></span></td>
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<tr>
<td class="coverFn"><a href="Mst.cc.gcov.html#L54">_ZN8bayesnet13insertElementERNSt7__cxx114listIiSaIiEEEi</a></td>
<td class="coverFnHi">159</td>
</tr>
<tr>
<td class="coverFn"><a href="Mst.cc.gcov.html#L103">_ZN8bayesnet3MST19maximumSpanningTreeEv</a></td>
<td class="coverFnHi">29</td>
</tr>
<tr>
<td class="coverFn"><a href="Mst.cc.gcov.html#L102">_ZN8bayesnet3MSTC2ERKSt6vectorINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESaIS7_EERKN2at6TensorEi</a></td>
<td class="coverFnHi">29</td>
</tr>
<tr>
<td class="coverFn"><a href="Mst.cc.gcov.html#L39">_ZN8bayesnet5Graph17kruskal_algorithmEv</a></td>
<td class="coverFnHi">29</td>
</tr>
<tr>
<td class="coverFn"><a href="Mst.cc.gcov.html#L22">_ZN8bayesnet5Graph7addEdgeEiif</a></td>
<td class="coverFnHi">576</td>
</tr>
<tr>
<td class="coverFn"><a href="Mst.cc.gcov.html#L26">_ZN8bayesnet5Graph8find_setEi</a></td>
<td class="coverFnHi">2663</td>
</tr>
<tr>
<td class="coverFn"><a href="Mst.cc.gcov.html#L35">_ZN8bayesnet5Graph9union_setEii</a></td>
<td class="coverFnHi">159</td>
</tr>
<tr>
<td class="coverFn"><a href="Mst.cc.gcov.html#L15">_ZN8bayesnet5GraphC2Ei</a></td>
<td class="coverFnHi">29</td>
</tr>
<tr>
<td class="coverFn"><a href="Mst.cc.gcov.html#L61">_ZN8bayesnet7reorderESt6vectorISt4pairIfS1_IiiEESaIS3_EEi</a></td>
<td class="coverFnHi">29</td>
</tr>
<tr>
<td class="coverFn"><a href="Mst.cc.gcov.html#L42">_ZZN8bayesnet5Graph17kruskal_algorithmEvENKUlRKT_RKT0_E_clISt4pairIfS9_IiiEESB_EEDaS3_S6_</a></td>
<td class="coverFnHi">2213</td>
</tr>
</table>
<br>
</center>
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<tr><td class="ruler"><img src="../../glass.png" width=3 height=3 alt=""></td></tr>
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196
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@@ -0,0 +1,196 @@
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<title>LCOV - coverage.info - bayesnet/utils/Mst.cc</title>
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<table cellpadding=1 border=0 width="100%">
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<td width="10%" class="headerItem">Current view:</td>
<td width="10%" class="headerValue"><a href="../../index.html">top level</a> - <a href="index.html">bayesnet/utils</a> - Mst.cc<span style="font-size: 80%;"> (source / <a href="Mst.cc.func-c.html">functions</a>)</span></td>
<td width="5%"></td>
<td width="5%"></td>
<td width="5%" class="headerCovTableHead">Coverage</td>
<td width="5%" class="headerCovTableHead" title="Covered + Uncovered code">Total</td>
<td width="5%" class="headerCovTableHead" title="Exercised code only">Hit</td>
</tr>
<tr>
<td class="headerItem">Test:</td>
<td class="headerValue">coverage.info</td>
<td></td>
<td class="headerItem">Lines:</td>
<td class="headerCovTableEntryHi">94.1&nbsp;%</td>
<td class="headerCovTableEntry">68</td>
<td class="headerCovTableEntry">64</td>
</tr>
<tr>
<td class="headerItem">Test Date:</td>
<td class="headerValue">2024-04-21 16:43:29</td>
<td></td>
<td class="headerItem">Functions:</td>
<td class="headerCovTableEntryHi">100.0&nbsp;%</td>
<td class="headerCovTableEntry">10</td>
<td class="headerCovTableEntry">10</td>
</tr>
<tr><td><img src="../../glass.png" width=3 height=3 alt=""></td></tr>
</table>
</td>
</tr>
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<td>
<pre class="sourceHeading"> Line data Source code</pre>
<pre class="source">
<span id="L1"><span class="lineNum"> 1</span> : // ***************************************************************</span>
<span id="L2"><span class="lineNum"> 2</span> : // SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez</span>
<span id="L3"><span class="lineNum"> 3</span> : // SPDX-FileType: SOURCE</span>
<span id="L4"><span class="lineNum"> 4</span> : // SPDX-License-Identifier: MIT</span>
<span id="L5"><span class="lineNum"> 5</span> : // ***************************************************************</span>
<span id="L6"><span class="lineNum"> 6</span> : </span>
<span id="L7"><span class="lineNum"> 7</span> : #include &lt;sstream&gt;</span>
<span id="L8"><span class="lineNum"> 8</span> : #include &lt;vector&gt;</span>
<span id="L9"><span class="lineNum"> 9</span> : #include &lt;list&gt;</span>
<span id="L10"><span class="lineNum"> 10</span> : #include &quot;Mst.h&quot;</span>
<span id="L11"><span class="lineNum"> 11</span> : /*</span>
<span id="L12"><span class="lineNum"> 12</span> : Based on the code from https://www.softwaretestinghelp.com/minimum-spanning-tree-tutorial/</span>
<span id="L13"><span class="lineNum"> 13</span> : </span>
<span id="L14"><span class="lineNum"> 14</span> : */</span>
<span id="L15"><span class="lineNum"> 15</span> : </span>
<span id="L16"><span class="lineNum"> 16</span> : namespace bayesnet {</span>
<span id="L17"><span class="lineNum"> 17</span> <span class="tlaGNC tlaBgGNC"> 58 : Graph::Graph(int V) : V(V), parent(std::vector&lt;int&gt;(V))</span></span>
<span id="L18"><span class="lineNum"> 18</span> : {</span>
<span id="L19"><span class="lineNum"> 19</span> <span class="tlaGNC"> 217 : for (int i = 0; i &lt; V; i++)</span></span>
<span id="L20"><span class="lineNum"> 20</span> <span class="tlaGNC"> 188 : parent[i] = i;</span></span>
<span id="L21"><span class="lineNum"> 21</span> <span class="tlaGNC"> 29 : G.clear();</span></span>
<span id="L22"><span class="lineNum"> 22</span> <span class="tlaGNC"> 29 : T.clear();</span></span>
<span id="L23"><span class="lineNum"> 23</span> <span class="tlaGNC"> 29 : }</span></span>
<span id="L24"><span class="lineNum"> 24</span> <span class="tlaGNC"> 576 : void Graph::addEdge(int u, int v, float wt)</span></span>
<span id="L25"><span class="lineNum"> 25</span> : {</span>
<span id="L26"><span class="lineNum"> 26</span> <span class="tlaGNC"> 576 : G.push_back({ wt, { u, v } });</span></span>
<span id="L27"><span class="lineNum"> 27</span> <span class="tlaGNC"> 576 : }</span></span>
<span id="L28"><span class="lineNum"> 28</span> <span class="tlaGNC"> 2663 : int Graph::find_set(int i)</span></span>
<span id="L29"><span class="lineNum"> 29</span> : {</span>
<span id="L30"><span class="lineNum"> 30</span> : // If i is the parent of itself</span>
<span id="L31"><span class="lineNum"> 31</span> <span class="tlaGNC"> 2663 : if (i == parent[i])</span></span>
<span id="L32"><span class="lineNum"> 32</span> <span class="tlaGNC"> 1152 : return i;</span></span>
<span id="L33"><span class="lineNum"> 33</span> : else</span>
<span id="L34"><span class="lineNum"> 34</span> : //else recursively find the parent of i</span>
<span id="L35"><span class="lineNum"> 35</span> <span class="tlaGNC"> 1511 : return find_set(parent[i]);</span></span>
<span id="L36"><span class="lineNum"> 36</span> : }</span>
<span id="L37"><span class="lineNum"> 37</span> <span class="tlaGNC"> 159 : void Graph::union_set(int u, int v)</span></span>
<span id="L38"><span class="lineNum"> 38</span> : {</span>
<span id="L39"><span class="lineNum"> 39</span> <span class="tlaGNC"> 159 : parent[u] = parent[v];</span></span>
<span id="L40"><span class="lineNum"> 40</span> <span class="tlaGNC"> 159 : }</span></span>
<span id="L41"><span class="lineNum"> 41</span> <span class="tlaGNC"> 29 : void Graph::kruskal_algorithm()</span></span>
<span id="L42"><span class="lineNum"> 42</span> : {</span>
<span id="L43"><span class="lineNum"> 43</span> : // sort the edges ordered on decreasing weight</span>
<span id="L44"><span class="lineNum"> 44</span> <span class="tlaGNC"> 2242 : stable_sort(G.begin(), G.end(), [](const auto&amp; left, const auto&amp; right) {return left.first &gt; right.first;});</span></span>
<span id="L45"><span class="lineNum"> 45</span> <span class="tlaGNC"> 605 : for (int i = 0; i &lt; G.size(); i++) {</span></span>
<span id="L46"><span class="lineNum"> 46</span> : int uSt, vEd;</span>
<span id="L47"><span class="lineNum"> 47</span> <span class="tlaGNC"> 576 : uSt = find_set(G[i].second.first);</span></span>
<span id="L48"><span class="lineNum"> 48</span> <span class="tlaGNC"> 576 : vEd = find_set(G[i].second.second);</span></span>
<span id="L49"><span class="lineNum"> 49</span> <span class="tlaGNC"> 576 : if (uSt != vEd) {</span></span>
<span id="L50"><span class="lineNum"> 50</span> <span class="tlaGNC"> 159 : T.push_back(G[i]); // add to mst std::vector</span></span>
<span id="L51"><span class="lineNum"> 51</span> <span class="tlaGNC"> 159 : union_set(uSt, vEd);</span></span>
<span id="L52"><span class="lineNum"> 52</span> : }</span>
<span id="L53"><span class="lineNum"> 53</span> : }</span>
<span id="L54"><span class="lineNum"> 54</span> <span class="tlaGNC"> 29 : }</span></span>
<span id="L55"><span class="lineNum"> 55</span> : </span>
<span id="L56"><span class="lineNum"> 56</span> <span class="tlaGNC"> 159 : void insertElement(std::list&lt;int&gt;&amp; variables, int variable)</span></span>
<span id="L57"><span class="lineNum"> 57</span> : {</span>
<span id="L58"><span class="lineNum"> 58</span> <span class="tlaGNC"> 159 : if (std::find(variables.begin(), variables.end(), variable) == variables.end()) {</span></span>
<span id="L59"><span class="lineNum"> 59</span> <span class="tlaGNC"> 159 : variables.push_front(variable);</span></span>
<span id="L60"><span class="lineNum"> 60</span> : }</span>
<span id="L61"><span class="lineNum"> 61</span> <span class="tlaGNC"> 159 : }</span></span>
<span id="L62"><span class="lineNum"> 62</span> : </span>
<span id="L63"><span class="lineNum"> 63</span> <span class="tlaGNC"> 29 : std::vector&lt;std::pair&lt;int, int&gt;&gt; reorder(std::vector&lt;std::pair&lt;float, std::pair&lt;int, int&gt;&gt;&gt; T, int root_original)</span></span>
<span id="L64"><span class="lineNum"> 64</span> : {</span>
<span id="L65"><span class="lineNum"> 65</span> : // Create the edges of a DAG from the MST</span>
<span id="L66"><span class="lineNum"> 66</span> : // replacing unordered_set with list because unordered_set cannot guarantee the order of the elements inserted</span>
<span id="L67"><span class="lineNum"> 67</span> <span class="tlaGNC"> 29 : auto result = std::vector&lt;std::pair&lt;int, int&gt;&gt;();</span></span>
<span id="L68"><span class="lineNum"> 68</span> <span class="tlaGNC"> 29 : auto visited = std::vector&lt;int&gt;();</span></span>
<span id="L69"><span class="lineNum"> 69</span> <span class="tlaGNC"> 29 : auto nextVariables = std::list&lt;int&gt;();</span></span>
<span id="L70"><span class="lineNum"> 70</span> <span class="tlaGNC"> 29 : nextVariables.push_front(root_original);</span></span>
<span id="L71"><span class="lineNum"> 71</span> <span class="tlaGNC"> 217 : while (nextVariables.size() &gt; 0) {</span></span>
<span id="L72"><span class="lineNum"> 72</span> <span class="tlaGNC"> 188 : int root = nextVariables.front();</span></span>
<span id="L73"><span class="lineNum"> 73</span> <span class="tlaGNC"> 188 : nextVariables.pop_front();</span></span>
<span id="L74"><span class="lineNum"> 74</span> <span class="tlaGNC"> 664 : for (int i = 0; i &lt; T.size(); ++i) {</span></span>
<span id="L75"><span class="lineNum"> 75</span> <span class="tlaGNC"> 476 : auto [weight, edge] = T[i];</span></span>
<span id="L76"><span class="lineNum"> 76</span> <span class="tlaGNC"> 476 : auto [from, to] = edge;</span></span>
<span id="L77"><span class="lineNum"> 77</span> <span class="tlaGNC"> 476 : if (from == root || to == root) {</span></span>
<span id="L78"><span class="lineNum"> 78</span> <span class="tlaGNC"> 159 : visited.insert(visited.begin(), i);</span></span>
<span id="L79"><span class="lineNum"> 79</span> <span class="tlaGNC"> 159 : if (from == root) {</span></span>
<span id="L80"><span class="lineNum"> 80</span> <span class="tlaGNC"> 106 : result.push_back({ from, to });</span></span>
<span id="L81"><span class="lineNum"> 81</span> <span class="tlaGNC"> 106 : insertElement(nextVariables, to);</span></span>
<span id="L82"><span class="lineNum"> 82</span> : } else {</span>
<span id="L83"><span class="lineNum"> 83</span> <span class="tlaGNC"> 53 : result.push_back({ to, from });</span></span>
<span id="L84"><span class="lineNum"> 84</span> <span class="tlaGNC"> 53 : insertElement(nextVariables, from);</span></span>
<span id="L85"><span class="lineNum"> 85</span> : }</span>
<span id="L86"><span class="lineNum"> 86</span> : }</span>
<span id="L87"><span class="lineNum"> 87</span> : }</span>
<span id="L88"><span class="lineNum"> 88</span> : // Remove visited</span>
<span id="L89"><span class="lineNum"> 89</span> <span class="tlaGNC"> 347 : for (int i = 0; i &lt; visited.size(); ++i) {</span></span>
<span id="L90"><span class="lineNum"> 90</span> <span class="tlaGNC"> 159 : T.erase(T.begin() + visited[i]);</span></span>
<span id="L91"><span class="lineNum"> 91</span> : }</span>
<span id="L92"><span class="lineNum"> 92</span> <span class="tlaGNC"> 188 : visited.clear();</span></span>
<span id="L93"><span class="lineNum"> 93</span> : }</span>
<span id="L94"><span class="lineNum"> 94</span> <span class="tlaGNC"> 29 : if (T.size() &gt; 0) {</span></span>
<span id="L95"><span class="lineNum"> 95</span> <span class="tlaUNC tlaBgUNC"> 0 : for (int i = 0; i &lt; T.size(); ++i) {</span></span>
<span id="L96"><span class="lineNum"> 96</span> <span class="tlaUNC"> 0 : auto [weight, edge] = T[i];</span></span>
<span id="L97"><span class="lineNum"> 97</span> <span class="tlaUNC"> 0 : auto [from, to] = edge;</span></span>
<span id="L98"><span class="lineNum"> 98</span> <span class="tlaUNC"> 0 : result.push_back({ from, to });</span></span>
<span id="L99"><span class="lineNum"> 99</span> : }</span>
<span id="L100"><span class="lineNum"> 100</span> : }</span>
<span id="L101"><span class="lineNum"> 101</span> <span class="tlaGNC tlaBgGNC"> 58 : return result;</span></span>
<span id="L102"><span class="lineNum"> 102</span> <span class="tlaGNC"> 29 : }</span></span>
<span id="L103"><span class="lineNum"> 103</span> : </span>
<span id="L104"><span class="lineNum"> 104</span> <span class="tlaGNC"> 29 : MST::MST(const std::vector&lt;std::string&gt;&amp; features, const torch::Tensor&amp; weights, const int root) : features(features), weights(weights), root(root) {}</span></span>
<span id="L105"><span class="lineNum"> 105</span> <span class="tlaGNC"> 29 : std::vector&lt;std::pair&lt;int, int&gt;&gt; MST::maximumSpanningTree()</span></span>
<span id="L106"><span class="lineNum"> 106</span> : {</span>
<span id="L107"><span class="lineNum"> 107</span> <span class="tlaGNC"> 29 : auto num_features = features.size();</span></span>
<span id="L108"><span class="lineNum"> 108</span> <span class="tlaGNC"> 29 : Graph g(num_features);</span></span>
<span id="L109"><span class="lineNum"> 109</span> : // Make a complete graph</span>
<span id="L110"><span class="lineNum"> 110</span> <span class="tlaGNC"> 188 : for (int i = 0; i &lt; num_features - 1; ++i) {</span></span>
<span id="L111"><span class="lineNum"> 111</span> <span class="tlaGNC"> 735 : for (int j = i + 1; j &lt; num_features; ++j) {</span></span>
<span id="L112"><span class="lineNum"> 112</span> <span class="tlaGNC"> 576 : g.addEdge(i, j, weights[i][j].item&lt;float&gt;());</span></span>
<span id="L113"><span class="lineNum"> 113</span> : }</span>
<span id="L114"><span class="lineNum"> 114</span> : }</span>
<span id="L115"><span class="lineNum"> 115</span> <span class="tlaGNC"> 29 : g.kruskal_algorithm();</span></span>
<span id="L116"><span class="lineNum"> 116</span> <span class="tlaGNC"> 29 : auto mst = g.get_mst();</span></span>
<span id="L117"><span class="lineNum"> 117</span> <span class="tlaGNC"> 58 : return reorder(mst, root);</span></span>
<span id="L118"><span class="lineNum"> 118</span> <span class="tlaGNC"> 29 : }</span></span>
<span id="L119"><span class="lineNum"> 119</span> : </span>
<span id="L120"><span class="lineNum"> 120</span> : }</span>
</pre>
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<td width="5%" class="headerCovTableHead" title="Exercised code only">Hit</td>
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<td class="coverFn"><a href="Mst.h.gcov.html#L28">_ZN8bayesnet5Graph7get_mstEv</a></td>
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<td class="headerCovTableEntryHi">100.0&nbsp;%</td>
<td class="headerCovTableEntry">1</td>
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<pre class="sourceHeading"> Line data Source code</pre>
<pre class="source">
<span id="L1"><span class="lineNum"> 1</span> : // ***************************************************************</span>
<span id="L2"><span class="lineNum"> 2</span> : // SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez</span>
<span id="L3"><span class="lineNum"> 3</span> : // SPDX-FileType: SOURCE</span>
<span id="L4"><span class="lineNum"> 4</span> : // SPDX-License-Identifier: MIT</span>
<span id="L5"><span class="lineNum"> 5</span> : // ***************************************************************</span>
<span id="L6"><span class="lineNum"> 6</span> : </span>
<span id="L7"><span class="lineNum"> 7</span> : #ifndef MST_H</span>
<span id="L8"><span class="lineNum"> 8</span> : #define MST_H</span>
<span id="L9"><span class="lineNum"> 9</span> : #include &lt;vector&gt;</span>
<span id="L10"><span class="lineNum"> 10</span> : #include &lt;string&gt;</span>
<span id="L11"><span class="lineNum"> 11</span> : #include &lt;torch/torch.h&gt;</span>
<span id="L12"><span class="lineNum"> 12</span> : namespace bayesnet {</span>
<span id="L13"><span class="lineNum"> 13</span> : class MST {</span>
<span id="L14"><span class="lineNum"> 14</span> : public:</span>
<span id="L15"><span class="lineNum"> 15</span> : MST() = default;</span>
<span id="L16"><span class="lineNum"> 16</span> : MST(const std::vector&lt;std::string&gt;&amp; features, const torch::Tensor&amp; weights, const int root);</span>
<span id="L17"><span class="lineNum"> 17</span> : std::vector&lt;std::pair&lt;int, int&gt;&gt; maximumSpanningTree();</span>
<span id="L18"><span class="lineNum"> 18</span> : private:</span>
<span id="L19"><span class="lineNum"> 19</span> : torch::Tensor weights;</span>
<span id="L20"><span class="lineNum"> 20</span> : std::vector&lt;std::string&gt; features;</span>
<span id="L21"><span class="lineNum"> 21</span> : int root = 0;</span>
<span id="L22"><span class="lineNum"> 22</span> : };</span>
<span id="L23"><span class="lineNum"> 23</span> : class Graph {</span>
<span id="L24"><span class="lineNum"> 24</span> : public:</span>
<span id="L25"><span class="lineNum"> 25</span> : explicit Graph(int V);</span>
<span id="L26"><span class="lineNum"> 26</span> : void addEdge(int u, int v, float wt);</span>
<span id="L27"><span class="lineNum"> 27</span> : int find_set(int i);</span>
<span id="L28"><span class="lineNum"> 28</span> : void union_set(int u, int v);</span>
<span id="L29"><span class="lineNum"> 29</span> : void kruskal_algorithm();</span>
<span id="L30"><span class="lineNum"> 30</span> <span class="tlaGNC tlaBgGNC"> 29 : std::vector &lt;std::pair&lt;float, std::pair&lt;int, int&gt;&gt;&gt; get_mst() { return T; }</span></span>
<span id="L31"><span class="lineNum"> 31</span> : private:</span>
<span id="L32"><span class="lineNum"> 32</span> : int V; // number of nodes in graph</span>
<span id="L33"><span class="lineNum"> 33</span> : std::vector &lt;std::pair&lt;float, std::pair&lt;int, int&gt;&gt;&gt; G; // std::vector for graph</span>
<span id="L34"><span class="lineNum"> 34</span> : std::vector &lt;std::pair&lt;float, std::pair&lt;int, int&gt;&gt;&gt; T; // std::vector for mst</span>
<span id="L35"><span class="lineNum"> 35</span> : std::vector&lt;int&gt; parent;</span>
<span id="L36"><span class="lineNum"> 36</span> : };</span>
<span id="L37"><span class="lineNum"> 37</span> : }</span>
<span id="L38"><span class="lineNum"> 38</span> : #endif</span>
</pre>
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<td class="headerCovTableEntryHi">100.0&nbsp;%</td>
<td class="headerCovTableEntry">4</td>
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<pre class="sourceHeading"> Line data Source code</pre>
<pre class="source">
<span id="L1"><span class="lineNum"> 1</span> : // ***************************************************************</span>
<span id="L2"><span class="lineNum"> 2</span> : // SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez</span>
<span id="L3"><span class="lineNum"> 3</span> : // SPDX-FileType: SOURCE</span>
<span id="L4"><span class="lineNum"> 4</span> : // SPDX-License-Identifier: MIT</span>
<span id="L5"><span class="lineNum"> 5</span> : // ***************************************************************</span>
<span id="L6"><span class="lineNum"> 6</span> : </span>
<span id="L7"><span class="lineNum"> 7</span> : </span>
<span id="L8"><span class="lineNum"> 8</span> : #include &quot;bayesnetUtils.h&quot;</span>
<span id="L9"><span class="lineNum"> 9</span> : namespace bayesnet {</span>
<span id="L10"><span class="lineNum"> 10</span> : // Return the indices in descending order</span>
<span id="L11"><span class="lineNum"> 11</span> <span class="tlaGNC tlaBgGNC"> 29 : std::vector&lt;int&gt; argsort(std::vector&lt;double&gt;&amp; nums)</span></span>
<span id="L12"><span class="lineNum"> 12</span> : {</span>
<span id="L13"><span class="lineNum"> 13</span> <span class="tlaGNC"> 29 : int n = nums.size();</span></span>
<span id="L14"><span class="lineNum"> 14</span> <span class="tlaGNC"> 29 : std::vector&lt;int&gt; indices(n);</span></span>
<span id="L15"><span class="lineNum"> 15</span> <span class="tlaGNC"> 29 : iota(indices.begin(), indices.end(), 0);</span></span>
<span id="L16"><span class="lineNum"> 16</span> <span class="tlaGNC"> 596 : sort(indices.begin(), indices.end(), [&amp;nums](int i, int j) {return nums[i] &gt; nums[j];});</span></span>
<span id="L17"><span class="lineNum"> 17</span> <span class="tlaGNC"> 29 : return indices;</span></span>
<span id="L18"><span class="lineNum"> 18</span> <span class="tlaUNC tlaBgUNC"> 0 : }</span></span>
<span id="L19"><span class="lineNum"> 19</span> <span class="tlaGNC tlaBgGNC"> 7 : std::vector&lt;std::vector&lt;double&gt;&gt; tensorToVectorDouble(torch::Tensor&amp; dtensor)</span></span>
<span id="L20"><span class="lineNum"> 20</span> : {</span>
<span id="L21"><span class="lineNum"> 21</span> : // convert mxn tensor to mxn std::vector</span>
<span id="L22"><span class="lineNum"> 22</span> <span class="tlaGNC"> 7 : std::vector&lt;std::vector&lt;double&gt;&gt; result;</span></span>
<span id="L23"><span class="lineNum"> 23</span> : // Iterate over cols</span>
<span id="L24"><span class="lineNum"> 24</span> <span class="tlaGNC"> 1803 : for (int i = 0; i &lt; dtensor.size(0); ++i) {</span></span>
<span id="L25"><span class="lineNum"> 25</span> <span class="tlaGNC"> 5388 : auto col_tensor = dtensor.index({ i, &quot;...&quot; });</span></span>
<span id="L26"><span class="lineNum"> 26</span> <span class="tlaGNC"> 1796 : auto col = std::vector&lt;double&gt;(col_tensor.data_ptr&lt;float&gt;(), col_tensor.data_ptr&lt;float&gt;() + dtensor.size(1));</span></span>
<span id="L27"><span class="lineNum"> 27</span> <span class="tlaGNC"> 1796 : result.push_back(col);</span></span>
<span id="L28"><span class="lineNum"> 28</span> <span class="tlaGNC"> 1796 : }</span></span>
<span id="L29"><span class="lineNum"> 29</span> <span class="tlaGNC"> 7 : return result;</span></span>
<span id="L30"><span class="lineNum"> 30</span> <span class="tlaGNC"> 1796 : }</span></span>
<span id="L31"><span class="lineNum"> 31</span> <span class="tlaGNC"> 9 : torch::Tensor vectorToTensor(std::vector&lt;std::vector&lt;int&gt;&gt;&amp; vector, bool transpose)</span></span>
<span id="L32"><span class="lineNum"> 32</span> : {</span>
<span id="L33"><span class="lineNum"> 33</span> : // convert nxm std::vector to mxn tensor if transpose</span>
<span id="L34"><span class="lineNum"> 34</span> <span class="tlaGNC"> 9 : long int m = transpose ? vector[0].size() : vector.size();</span></span>
<span id="L35"><span class="lineNum"> 35</span> <span class="tlaGNC"> 9 : long int n = transpose ? vector.size() : vector[0].size();</span></span>
<span id="L36"><span class="lineNum"> 36</span> <span class="tlaGNC"> 9 : auto tensor = torch::zeros({ m, n }, torch::kInt32);</span></span>
<span id="L37"><span class="lineNum"> 37</span> <span class="tlaGNC"> 59 : for (int i = 0; i &lt; m; ++i) {</span></span>
<span id="L38"><span class="lineNum"> 38</span> <span class="tlaGNC"> 12481 : for (int j = 0; j &lt; n; ++j) {</span></span>
<span id="L39"><span class="lineNum"> 39</span> <span class="tlaGNC"> 12431 : tensor[i][j] = transpose ? vector[j][i] : vector[i][j];</span></span>
<span id="L40"><span class="lineNum"> 40</span> : }</span>
<span id="L41"><span class="lineNum"> 41</span> : }</span>
<span id="L42"><span class="lineNum"> 42</span> <span class="tlaGNC"> 9 : return tensor;</span></span>
<span id="L43"><span class="lineNum"> 43</span> <span class="tlaUNC tlaBgUNC"> 0 : }</span></span>
<span id="L44"><span class="lineNum"> 44</span> : }</span>
</pre>
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<td width="5%" class="headerCovTableHead" title="Exercised code only">Hit</td>
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<td class="headerItem">Test:</td>
<td class="headerValue">coverage.info</td>
<td></td>
<td class="headerItem">Lines:</td>
<td class="headerCovTableEntryHi">93.0&nbsp;%</td>
<td class="headerCovTableEntry">227</td>
<td class="headerCovTableEntry">211</td>
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<td class="headerItem">Test Date:</td>
<td class="headerValue">2024-04-21 16:43:29</td>
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<td class="headerItem">Functions:</td>
<td class="headerCovTableEntryHi">96.8&nbsp;%</td>
<td class="headerCovTableEntry">31</td>
<td class="headerCovTableEntry">30</td>
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<td class="coverFile"><a href="BayesMetrics.cc.gcov.html">BayesMetrics.cc</a></td>
<td class="coverBar" align="center">
<table border=0 cellspacing=0 cellpadding=1><tr><td class="coverBarOutline"><img src="../../emerald.png" width=92 height=10 alt="92.5%"><img src="../../snow.png" width=8 height=10 alt="92.5%"></td></tr></table>
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<td class="coverPerHi">92.5&nbsp;%</td>
<td class="coverNumDflt">120</td>
<td class="coverNumDflt">111</td>
<td class="coverPerHi">91.7&nbsp;%</td>
<td class="coverNumDflt">12</td>
<td class="coverNumDflt">11</td>
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<tr>
<td class="coverFile"><a href="Mst.h.gcov.html">Mst.h</a></td>
<td class="coverBar" align="center">
<table border=0 cellspacing=0 cellpadding=1><tr><td class="coverBarOutline"><img src="../../emerald.png" width=100 height=10 alt="100.0%"></td></tr></table>
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<td class="coverPerHi">100.0&nbsp;%</td>
<td class="coverNumDflt">1</td>
<td class="coverNumDflt">1</td>
<td class="coverPerHi">100.0&nbsp;%</td>
<td class="coverNumDflt">1</td>
<td class="coverNumDflt">1</td>
</tr>
<tr>
<td class="coverFile"><a href="BayesMetrics.h.gcov.html">BayesMetrics.h</a></td>
<td class="coverBar" align="center">
<table border=0 cellspacing=0 cellpadding=1><tr><td class="coverBarOutline"><img src="../../emerald.png" width=92 height=10 alt="92.3%"><img src="../../snow.png" width=8 height=10 alt="92.3%"></td></tr></table>
</td>
<td class="coverPerHi">92.3&nbsp;%</td>
<td class="coverNumDflt">13</td>
<td class="coverNumDflt">12</td>
<td class="coverPerHi">100.0&nbsp;%</td>
<td class="coverNumDflt">4</td>
<td class="coverNumDflt">4</td>
</tr>
<tr>
<td class="coverFile"><a href="bayesnetUtils.cc.gcov.html">bayesnetUtils.cc</a></td>
<td class="coverBar" align="center">
<table border=0 cellspacing=0 cellpadding=1><tr><td class="coverBarOutline"><img src="../../emerald.png" width=92 height=10 alt="92.0%"><img src="../../snow.png" width=8 height=10 alt="92.0%"></td></tr></table>
</td>
<td class="coverPerHi">92.0&nbsp;%</td>
<td class="coverNumDflt">25</td>
<td class="coverNumDflt">23</td>
<td class="coverPerHi">100.0&nbsp;%</td>
<td class="coverNumDflt">4</td>
<td class="coverNumDflt">4</td>
</tr>
<tr>
<td class="coverFile"><a href="Mst.cc.gcov.html">Mst.cc</a></td>
<td class="coverBar" align="center">
<table border=0 cellspacing=0 cellpadding=1><tr><td class="coverBarOutline"><img src="../../emerald.png" width=94 height=10 alt="94.1%"><img src="../../snow.png" width=6 height=10 alt="94.1%"></td></tr></table>
</td>
<td class="coverPerHi">94.1&nbsp;%</td>
<td class="coverNumDflt">68</td>
<td class="coverNumDflt">64</td>
<td class="coverPerHi">100.0&nbsp;%</td>
<td class="coverNumDflt">10</td>
<td class="coverNumDflt">10</td>
</tr>
</table>
</center>
<br>
<table width="100%" border=0 cellspacing=0 cellpadding=0>
<tr><td class="ruler"><img src="../../glass.png" width=3 height=3 alt=""></td></tr>
<tr><td class="versionInfo">Generated by: <a href="https://github.com//linux-test-project/lcov">LCOV version 2.0-1</a></td></tr>
</table>
<br>
</body>
</html>

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<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN">
<html lang="en">
<head>
<meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
<title>LCOV - coverage.info - bayesnet/utils</title>
<link rel="stylesheet" type="text/css" href="../../gcov.css">
</head>
<body>
<table width="100%" border=0 cellspacing=0 cellpadding=0>
<tr><td class="title">LCOV - code coverage report</td></tr>
<tr><td class="ruler"><img src="../../glass.png" width=3 height=3 alt=""></td></tr>
<tr>
<td width="100%">
<table cellpadding=1 border=0 width="100%">
<tr>
<td width="10%" class="headerItem">Current view:</td>
<td width="10%" class="headerValue"><a href="../../index.html">top level</a> - bayesnet/utils</td>
<td width="5%"></td>
<td width="5%"></td>
<td width="5%" class="headerCovTableHead">Coverage</td>
<td width="5%" class="headerCovTableHead" title="Covered + Uncovered code">Total</td>
<td width="5%" class="headerCovTableHead" title="Exercised code only">Hit</td>
</tr>
<tr>
<td class="headerItem">Test:</td>
<td class="headerValue">coverage.info</td>
<td></td>
<td class="headerItem">Lines:</td>
<td class="headerCovTableEntryHi">93.0&nbsp;%</td>
<td class="headerCovTableEntry">227</td>
<td class="headerCovTableEntry">211</td>
</tr>
<tr>
<td class="headerItem">Test Date:</td>
<td class="headerValue">2024-04-21 16:43:29</td>
<td></td>
<td class="headerItem">Functions:</td>
<td class="headerCovTableEntryHi">96.8&nbsp;%</td>
<td class="headerCovTableEntry">31</td>
<td class="headerCovTableEntry">30</td>
</tr>
<tr><td><img src="../../glass.png" width=3 height=3 alt=""></td></tr>
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</td>
</tr>
<tr><td class="ruler"><img src="../../glass.png" width=3 height=3 alt=""></td></tr>
</table>
<center>
<table width="80%" cellpadding=1 cellspacing=1 border=0>
<tr>
<td width="40%"><br></td>
<td width="8%"></td>
<td width="8%"></td>
<td width="8%"></td>
<td width="8%"></td>
<td width="8%"></td>
<td width="8%"></td>
<td width="8%"></td>
</tr>
<tr>
<td class="tableHead" rowspan=2>Filename <span title="Click to sort table by file name" class="tableHeadSort"><a href="index.html"><img src="../../updown.png" width=10 height=14 alt="Sort by file name" title="Click to sort table by file name" border=0></a></span></td>
<td class="tableHead" colspan=4>Line Coverage <span title="Click to sort table by line coverage" class="tableHeadSort"><img src="../../glass.png" width=10 height=14 alt="Sort by line coverage" title="Click to sort table by line coverage" border=0></span></td>
<td class="tableHead" colspan=3>Function Coverage <span title="Click to sort table by function coverage" class="tableHeadSort"><a href="index-sort-f.html"><img src="../../updown.png" width=10 height=14 alt="Sort by function coverage" title="Click to sort table by function coverage" border=0></a></span></td>
</tr>
<tr>
<td class="tableHead" colspan=2> Rate</td>
<td class="tableHead"> Total</td>
<td class="tableHead"> Hit</td>
<td class="tableHead"> Rate</td>
<td class="tableHead"> Total</td>
<td class="tableHead"> Hit</td>
</tr>
<tr>
<td class="coverFile"><a href="bayesnetUtils.cc.gcov.html">bayesnetUtils.cc</a></td>
<td class="coverBar" align="center">
<table border=0 cellspacing=0 cellpadding=1><tr><td class="coverBarOutline"><img src="../../emerald.png" width=92 height=10 alt="92.0%"><img src="../../snow.png" width=8 height=10 alt="92.0%"></td></tr></table>
</td>
<td class="coverPerHi">92.0&nbsp;%</td>
<td class="coverNumDflt">25</td>
<td class="coverNumDflt">23</td>
<td class="coverPerHi">100.0&nbsp;%</td>
<td class="coverNumDflt">4</td>
<td class="coverNumDflt">4</td>
</tr>
<tr>
<td class="coverFile"><a href="BayesMetrics.h.gcov.html">BayesMetrics.h</a></td>
<td class="coverBar" align="center">
<table border=0 cellspacing=0 cellpadding=1><tr><td class="coverBarOutline"><img src="../../emerald.png" width=92 height=10 alt="92.3%"><img src="../../snow.png" width=8 height=10 alt="92.3%"></td></tr></table>
</td>
<td class="coverPerHi">92.3&nbsp;%</td>
<td class="coverNumDflt">13</td>
<td class="coverNumDflt">12</td>
<td class="coverPerHi">100.0&nbsp;%</td>
<td class="coverNumDflt">4</td>
<td class="coverNumDflt">4</td>
</tr>
<tr>
<td class="coverFile"><a href="BayesMetrics.cc.gcov.html">BayesMetrics.cc</a></td>
<td class="coverBar" align="center">
<table border=0 cellspacing=0 cellpadding=1><tr><td class="coverBarOutline"><img src="../../emerald.png" width=92 height=10 alt="92.5%"><img src="../../snow.png" width=8 height=10 alt="92.5%"></td></tr></table>
</td>
<td class="coverPerHi">92.5&nbsp;%</td>
<td class="coverNumDflt">120</td>
<td class="coverNumDflt">111</td>
<td class="coverPerHi">91.7&nbsp;%</td>
<td class="coverNumDflt">12</td>
<td class="coverNumDflt">11</td>
</tr>
<tr>
<td class="coverFile"><a href="Mst.cc.gcov.html">Mst.cc</a></td>
<td class="coverBar" align="center">
<table border=0 cellspacing=0 cellpadding=1><tr><td class="coverBarOutline"><img src="../../emerald.png" width=94 height=10 alt="94.1%"><img src="../../snow.png" width=6 height=10 alt="94.1%"></td></tr></table>
</td>
<td class="coverPerHi">94.1&nbsp;%</td>
<td class="coverNumDflt">68</td>
<td class="coverNumDflt">64</td>
<td class="coverPerHi">100.0&nbsp;%</td>
<td class="coverNumDflt">10</td>
<td class="coverNumDflt">10</td>
</tr>
<tr>
<td class="coverFile"><a href="Mst.h.gcov.html">Mst.h</a></td>
<td class="coverBar" align="center">
<table border=0 cellspacing=0 cellpadding=1><tr><td class="coverBarOutline"><img src="../../emerald.png" width=100 height=10 alt="100.0%"></td></tr></table>
</td>
<td class="coverPerHi">100.0&nbsp;%</td>
<td class="coverNumDflt">1</td>
<td class="coverNumDflt">1</td>
<td class="coverPerHi">100.0&nbsp;%</td>
<td class="coverNumDflt">1</td>
<td class="coverNumDflt">1</td>
</tr>
</table>
</center>
<br>
<table width="100%" border=0 cellspacing=0 cellpadding=0>
<tr><td class="ruler"><img src="../../glass.png" width=3 height=3 alt=""></td></tr>
<tr><td class="versionInfo">Generated by: <a href="https://github.com//linux-test-project/lcov">LCOV version 2.0-1</a></td></tr>
</table>
<br>
</body>
</html>

153
html/bayesnet/utils/index.html Normal file
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<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN">
<html lang="en">
<head>
<meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
<title>LCOV - coverage.info - bayesnet/utils</title>
<link rel="stylesheet" type="text/css" href="../../gcov.css">
</head>
<body>
<table width="100%" border=0 cellspacing=0 cellpadding=0>
<tr><td class="title">LCOV - code coverage report</td></tr>
<tr><td class="ruler"><img src="../../glass.png" width=3 height=3 alt=""></td></tr>
<tr>
<td width="100%">
<table cellpadding=1 border=0 width="100%">
<tr>
<td width="10%" class="headerItem">Current view:</td>
<td width="10%" class="headerValue"><a href="../../index.html">top level</a> - bayesnet/utils</td>
<td width="5%"></td>
<td width="5%"></td>
<td width="5%" class="headerCovTableHead">Coverage</td>
<td width="5%" class="headerCovTableHead" title="Covered + Uncovered code">Total</td>
<td width="5%" class="headerCovTableHead" title="Exercised code only">Hit</td>
</tr>
<tr>
<td class="headerItem">Test:</td>
<td class="headerValue">coverage.info</td>
<td></td>
<td class="headerItem">Lines:</td>
<td class="headerCovTableEntryHi">93.0&nbsp;%</td>
<td class="headerCovTableEntry">227</td>
<td class="headerCovTableEntry">211</td>
</tr>
<tr>
<td class="headerItem">Test Date:</td>
<td class="headerValue">2024-04-21 16:43:29</td>
<td></td>
<td class="headerItem">Functions:</td>
<td class="headerCovTableEntryHi">96.8&nbsp;%</td>
<td class="headerCovTableEntry">31</td>
<td class="headerCovTableEntry">30</td>
</tr>
<tr><td><img src="../../glass.png" width=3 height=3 alt=""></td></tr>
</table>
</td>
</tr>
<tr><td class="ruler"><img src="../../glass.png" width=3 height=3 alt=""></td></tr>
</table>
<center>
<table width="80%" cellpadding=1 cellspacing=1 border=0>
<tr>
<td width="40%"><br></td>
<td width="8%"></td>
<td width="8%"></td>
<td width="8%"></td>
<td width="8%"></td>
<td width="8%"></td>
<td width="8%"></td>
<td width="8%"></td>
</tr>
<tr>
<td class="tableHead" rowspan=2>Filename <span title="Click to sort table by file name" class="tableHeadSort"><img src="../../glass.png" width=10 height=14 alt="Sort by file name" title="Click to sort table by file name" border=0></span></td>
<td class="tableHead" colspan=4>Line Coverage <span title="Click to sort table by line coverage" class="tableHeadSort"><a href="index-sort-l.html"><img src="../../updown.png" width=10 height=14 alt="Sort by line coverage" title="Click to sort table by line coverage" border=0></a></span></td>
<td class="tableHead" colspan=3>Function Coverage <span title="Click to sort table by function coverage" class="tableHeadSort"><a href="index-sort-f.html"><img src="../../updown.png" width=10 height=14 alt="Sort by function coverage" title="Click to sort table by function coverage" border=0></a></span></td>
</tr>
<tr>
<td class="tableHead" colspan=2> Rate</td>
<td class="tableHead"> Total</td>
<td class="tableHead"> Hit</td>
<td class="tableHead"> Rate</td>
<td class="tableHead"> Total</td>
<td class="tableHead"> Hit</td>
</tr>
<tr>
<td class="coverFile"><a href="BayesMetrics.cc.gcov.html">BayesMetrics.cc</a></td>
<td class="coverBar" align="center">
<table border=0 cellspacing=0 cellpadding=1><tr><td class="coverBarOutline"><img src="../../emerald.png" width=92 height=10 alt="92.5%"><img src="../../snow.png" width=8 height=10 alt="92.5%"></td></tr></table>
</td>
<td class="coverPerHi">92.5&nbsp;%</td>
<td class="coverNumDflt">120</td>
<td class="coverNumDflt">111</td>
<td class="coverPerHi">91.7&nbsp;%</td>
<td class="coverNumDflt">12</td>
<td class="coverNumDflt">11</td>
</tr>
<tr>
<td class="coverFile"><a href="BayesMetrics.h.gcov.html">BayesMetrics.h</a></td>
<td class="coverBar" align="center">
<table border=0 cellspacing=0 cellpadding=1><tr><td class="coverBarOutline"><img src="../../emerald.png" width=92 height=10 alt="92.3%"><img src="../../snow.png" width=8 height=10 alt="92.3%"></td></tr></table>
</td>
<td class="coverPerHi">92.3&nbsp;%</td>
<td class="coverNumDflt">13</td>
<td class="coverNumDflt">12</td>
<td class="coverPerHi">100.0&nbsp;%</td>
<td class="coverNumDflt">4</td>
<td class="coverNumDflt">4</td>
</tr>
<tr>
<td class="coverFile"><a href="Mst.cc.gcov.html">Mst.cc</a></td>
<td class="coverBar" align="center">
<table border=0 cellspacing=0 cellpadding=1><tr><td class="coverBarOutline"><img src="../../emerald.png" width=94 height=10 alt="94.1%"><img src="../../snow.png" width=6 height=10 alt="94.1%"></td></tr></table>
</td>
<td class="coverPerHi">94.1&nbsp;%</td>
<td class="coverNumDflt">68</td>
<td class="coverNumDflt">64</td>
<td class="coverPerHi">100.0&nbsp;%</td>
<td class="coverNumDflt">10</td>
<td class="coverNumDflt">10</td>
</tr>
<tr>
<td class="coverFile"><a href="Mst.h.gcov.html">Mst.h</a></td>
<td class="coverBar" align="center">
<table border=0 cellspacing=0 cellpadding=1><tr><td class="coverBarOutline"><img src="../../emerald.png" width=100 height=10 alt="100.0%"></td></tr></table>
</td>
<td class="coverPerHi">100.0&nbsp;%</td>
<td class="coverNumDflt">1</td>
<td class="coverNumDflt">1</td>
<td class="coverPerHi">100.0&nbsp;%</td>
<td class="coverNumDflt">1</td>
<td class="coverNumDflt">1</td>
</tr>
<tr>
<td class="coverFile"><a href="bayesnetUtils.cc.gcov.html">bayesnetUtils.cc</a></td>
<td class="coverBar" align="center">
<table border=0 cellspacing=0 cellpadding=1><tr><td class="coverBarOutline"><img src="../../emerald.png" width=92 height=10 alt="92.0%"><img src="../../snow.png" width=8 height=10 alt="92.0%"></td></tr></table>
</td>
<td class="coverPerHi">92.0&nbsp;%</td>
<td class="coverNumDflt">25</td>
<td class="coverNumDflt">23</td>
<td class="coverPerHi">100.0&nbsp;%</td>
<td class="coverNumDflt">4</td>
<td class="coverNumDflt">4</td>
</tr>
</table>
</center>
<br>
<table width="100%" border=0 cellspacing=0 cellpadding=0>
<tr><td class="ruler"><img src="../../glass.png" width=3 height=3 alt=""></td></tr>
<tr><td class="versionInfo">Generated by: <a href="https://github.com//linux-test-project/lcov">LCOV version 2.0-1</a></td></tr>
</table>
<br>
</body>
</html>