135 lines
10 KiB
HTML
135 lines
10 KiB
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<title>LCOV - BayesNet Coverage Report - bayesnet/classifiers/SPODELd.cc</title>
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<tr><td class="title">LCOV - code coverage report</td></tr>
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<td width="10%" class="headerItem">Current view:</td>
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<td width="10%" class="headerValue"><a href="../../index.html" target="_parent">top level</a> - <a href="index.html" target="_parent">bayesnet/classifiers</a> - SPODELd.cc<span style="font-size: 80%;"> (source / <a href="SPODELd.cc.func-c.html">functions</a>)</span></td>
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<td width="5%"></td>
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<td width="5%"></td>
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<td width="5%" class="headerCovTableHead">Coverage</td>
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<td width="5%" class="headerCovTableHead" title="Covered + Uncovered code">Total</td>
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<td width="5%" class="headerCovTableHead" title="Exercised code only">Hit</td>
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<tr>
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<td class="headerItem">Test:</td>
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<td class="headerValue">BayesNet Coverage Report</td>
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<td></td>
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<td class="headerItem">Lines:</td>
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<td class="headerCovTableEntryHi">100.0 %</td>
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<td class="headerCovTableEntry">26</td>
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<td class="headerCovTableEntry">26</td>
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<tr>
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<td class="headerItem">Test Date:</td>
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<td class="headerValue">2024-05-06 17:54:04</td>
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<td></td>
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<td class="headerItem">Functions:</td>
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<td class="headerCovTableEntryHi">100.0 %</td>
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<td class="headerCovTableEntry">6</td>
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<td class="headerCovTableEntry">6</td>
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<tr>
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<td class="headerItem">Legend:</td>
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<td class="headerValueLeg"> Lines:
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<span class="coverLegendCov">hit</span>
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<span class="coverLegendNoCov">not hit</span>
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<td></td>
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<pre class="sourceHeading"> Line data Source code</pre>
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<pre class="source">
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<span id="L1"><span class="lineNum"> 1</span> : // ***************************************************************</span>
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<span id="L2"><span class="lineNum"> 2</span> : // SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez</span>
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<span id="L3"><span class="lineNum"> 3</span> : // SPDX-FileType: SOURCE</span>
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<span id="L4"><span class="lineNum"> 4</span> : // SPDX-License-Identifier: MIT</span>
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<span id="L5"><span class="lineNum"> 5</span> : // ***************************************************************</span>
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<span id="L6"><span class="lineNum"> 6</span> : </span>
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<span id="L7"><span class="lineNum"> 7</span> : #include "SPODELd.h"</span>
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<span id="L8"><span class="lineNum"> 8</span> : </span>
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<span id="L9"><span class="lineNum"> 9</span> : namespace bayesnet {</span>
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<span id="L10"><span class="lineNum"> 10</span> <span class="tlaGNC tlaBgGNC"> 220 : SPODELd::SPODELd(int root) : SPODE(root), Proposal(dataset, features, className) {}</span></span>
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<span id="L11"><span class="lineNum"> 11</span> <span class="tlaGNC"> 168 : SPODELd& SPODELd::fit(torch::Tensor& X_, torch::Tensor& y_, const std::vector<std::string>& features_, const std::string& className_, map<std::string, std::vector<int>>& states_)</span></span>
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<span id="L12"><span class="lineNum"> 12</span> : {</span>
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<span id="L13"><span class="lineNum"> 13</span> <span class="tlaGNC"> 168 : checkInput(X_, y_);</span></span>
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<span id="L14"><span class="lineNum"> 14</span> <span class="tlaGNC"> 168 : Xf = X_;</span></span>
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<span id="L15"><span class="lineNum"> 15</span> <span class="tlaGNC"> 168 : y = y_;</span></span>
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<span id="L16"><span class="lineNum"> 16</span> <span class="tlaGNC"> 168 : return commonFit(features_, className_, states_);</span></span>
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<span id="L17"><span class="lineNum"> 17</span> : }</span>
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<span id="L18"><span class="lineNum"> 18</span> : </span>
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<span id="L19"><span class="lineNum"> 19</span> <span class="tlaGNC"> 8 : SPODELd& SPODELd::fit(torch::Tensor& dataset, const std::vector<std::string>& features_, const std::string& className_, map<std::string, std::vector<int>>& states_)</span></span>
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<span id="L20"><span class="lineNum"> 20</span> : {</span>
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<span id="L21"><span class="lineNum"> 21</span> <span class="tlaGNC"> 8 : if (!torch::is_floating_point(dataset)) {</span></span>
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<span id="L22"><span class="lineNum"> 22</span> <span class="tlaGNC"> 4 : throw std::runtime_error("Dataset must be a floating point tensor");</span></span>
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<span id="L23"><span class="lineNum"> 23</span> : }</span>
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<span id="L24"><span class="lineNum"> 24</span> <span class="tlaGNC"> 16 : Xf = dataset.index({ torch::indexing::Slice(0, dataset.size(0) - 1), "..." }).clone();</span></span>
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<span id="L25"><span class="lineNum"> 25</span> <span class="tlaGNC"> 12 : y = dataset.index({ -1, "..." }).clone().to(torch::kInt32);</span></span>
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<span id="L26"><span class="lineNum"> 26</span> <span class="tlaGNC"> 4 : return commonFit(features_, className_, states_);</span></span>
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<span id="L27"><span class="lineNum"> 27</span> <span class="tlaGNC"> 12 : }</span></span>
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<span id="L28"><span class="lineNum"> 28</span> : </span>
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<span id="L29"><span class="lineNum"> 29</span> <span class="tlaGNC"> 172 : SPODELd& SPODELd::commonFit(const std::vector<std::string>& features_, const std::string& className_, map<std::string, std::vector<int>>& states_)</span></span>
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<span id="L30"><span class="lineNum"> 30</span> : {</span>
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<span id="L31"><span class="lineNum"> 31</span> <span class="tlaGNC"> 172 : features = features_;</span></span>
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<span id="L32"><span class="lineNum"> 32</span> <span class="tlaGNC"> 172 : className = className_;</span></span>
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<span id="L33"><span class="lineNum"> 33</span> : // Fills std::vectors Xv & yv with the data from tensors X_ (discretized) & y</span>
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<span id="L34"><span class="lineNum"> 34</span> <span class="tlaGNC"> 172 : states = fit_local_discretization(y);</span></span>
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<span id="L35"><span class="lineNum"> 35</span> : // We have discretized the input data</span>
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<span id="L36"><span class="lineNum"> 36</span> : // 1st we need to fit the model to build the normal SPODE structure, SPODE::fit initializes the base Bayesian network</span>
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<span id="L37"><span class="lineNum"> 37</span> <span class="tlaGNC"> 172 : SPODE::fit(dataset, features, className, states);</span></span>
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<span id="L38"><span class="lineNum"> 38</span> <span class="tlaGNC"> 172 : states = localDiscretizationProposal(states, model);</span></span>
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<span id="L39"><span class="lineNum"> 39</span> <span class="tlaGNC"> 172 : return *this;</span></span>
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<span id="L40"><span class="lineNum"> 40</span> : }</span>
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<span id="L41"><span class="lineNum"> 41</span> <span class="tlaGNC"> 136 : torch::Tensor SPODELd::predict(torch::Tensor& X)</span></span>
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<span id="L42"><span class="lineNum"> 42</span> : {</span>
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<span id="L43"><span class="lineNum"> 43</span> <span class="tlaGNC"> 136 : auto Xt = prepareX(X);</span></span>
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<span id="L44"><span class="lineNum"> 44</span> <span class="tlaGNC"> 272 : return SPODE::predict(Xt);</span></span>
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<span id="L45"><span class="lineNum"> 45</span> <span class="tlaGNC"> 136 : }</span></span>
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<span id="L46"><span class="lineNum"> 46</span> <span class="tlaGNC"> 36 : std::vector<std::string> SPODELd::graph(const std::string& name) const</span></span>
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<span id="L47"><span class="lineNum"> 47</span> : {</span>
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<span id="L48"><span class="lineNum"> 48</span> <span class="tlaGNC"> 36 : return SPODE::graph(name);</span></span>
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<span id="L49"><span class="lineNum"> 49</span> : }</span>
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<span id="L50"><span class="lineNum"> 50</span> : }</span>
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</pre>
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</td>
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</tr>
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</table>
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<br>
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<table width="100%" border=0 cellspacing=0 cellpadding=0>
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<tr><td class="ruler"><img src="../../glass.png" width=3 height=3 alt=""></td></tr>
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<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>
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