2024-04-21 14:44:35 +00:00
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<meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
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<title>LCOV - coverage.info - bayesnet/BaseClassifier.h</title>
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<link rel="stylesheet" type="text/css" href="../gcov.css">
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<body>
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<table width="100%" border=0 cellspacing=0 cellpadding=0>
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<tr><td class="title">LCOV - code coverage report</td></tr>
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<tr><td class="ruler"><img src="../glass.png" width=3 height=3 alt=""></td></tr>
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<tr>
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<td width="100%">
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<table cellpadding=1 border=0 width="100%">
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<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">top level</a> - <a href="index.html">bayesnet</a> - BaseClassifier.h<span style="font-size: 80%;"> (source / <a href="BaseClassifier.h.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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<tr>
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<td class="headerItem">Test:</td>
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<td class="headerValue">coverage.info</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">1</td>
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<td class="headerCovTableEntry">1</td>
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</tr>
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<tr>
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<td class="headerItem">Test Date:</td>
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2024-04-30 12:00:24 +00:00
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<td class="headerValue">2024-04-30 13:59:18</td>
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2024-04-21 14:44:35 +00:00
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<td></td>
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<td class="headerItem">Functions:</td>
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2024-04-30 12:00:24 +00:00
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<td class="headerCovTableEntryHi">100.0 %</td>
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<td class="headerCovTableEntry">1</td>
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2024-04-21 14:44:35 +00:00
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<td class="headerCovTableEntry">1</td>
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</tr>
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<tr><td><img src="../glass.png" width=3 height=3 alt=""></td></tr>
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</table>
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</td>
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</tr>
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<tr><td class="ruler"><img src="../glass.png" width=3 height=3 alt=""></td></tr>
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</table>
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<table cellpadding=0 cellspacing=0 border=0>
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<tr>
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<td><br></td>
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</tr>
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<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> : #pragma once</span>
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<span id="L8"><span class="lineNum"> 8</span> : #include <vector></span>
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<span id="L9"><span class="lineNum"> 9</span> : #include <torch/torch.h></span>
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<span id="L10"><span class="lineNum"> 10</span> : #include <nlohmann/json.hpp></span>
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<span id="L11"><span class="lineNum"> 11</span> : namespace bayesnet {</span>
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<span id="L12"><span class="lineNum"> 12</span> : enum status_t { NORMAL, WARNING, ERROR };</span>
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<span id="L13"><span class="lineNum"> 13</span> : class BaseClassifier {</span>
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<span id="L14"><span class="lineNum"> 14</span> : public:</span>
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<span id="L15"><span class="lineNum"> 15</span> : // X is nxm std::vector, y is nx1 std::vector</span>
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<span id="L16"><span class="lineNum"> 16</span> : virtual BaseClassifier& fit(std::vector<std::vector<int>>& X, std::vector<int>& y, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states) = 0;</span>
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<span id="L17"><span class="lineNum"> 17</span> : // X is nxm tensor, y is nx1 tensor</span>
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<span id="L18"><span class="lineNum"> 18</span> : virtual BaseClassifier& fit(torch::Tensor& X, torch::Tensor& y, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states) = 0;</span>
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<span id="L19"><span class="lineNum"> 19</span> : virtual BaseClassifier& fit(torch::Tensor& dataset, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states) = 0;</span>
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<span id="L20"><span class="lineNum"> 20</span> : virtual BaseClassifier& fit(torch::Tensor& dataset, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const torch::Tensor& weights) = 0;</span>
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2024-04-30 12:00:24 +00:00
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<span id="L21"><span class="lineNum"> 21</span> <span class="tlaGNC tlaBgGNC"> 1818 : virtual ~BaseClassifier() = default;</span></span>
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2024-04-21 14:44:35 +00:00
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<span id="L22"><span class="lineNum"> 22</span> : torch::Tensor virtual predict(torch::Tensor& X) = 0;</span>
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<span id="L23"><span class="lineNum"> 23</span> : std::vector<int> virtual predict(std::vector<std::vector<int >>& X) = 0;</span>
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<span id="L24"><span class="lineNum"> 24</span> : torch::Tensor virtual predict_proba(torch::Tensor& X) = 0;</span>
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<span id="L25"><span class="lineNum"> 25</span> : std::vector<std::vector<double>> virtual predict_proba(std::vector<std::vector<int >>& X) = 0;</span>
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<span id="L26"><span class="lineNum"> 26</span> : status_t virtual getStatus() const = 0;</span>
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<span id="L27"><span class="lineNum"> 27</span> : float virtual score(std::vector<std::vector<int>>& X, std::vector<int>& y) = 0;</span>
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<span id="L28"><span class="lineNum"> 28</span> : float virtual score(torch::Tensor& X, torch::Tensor& y) = 0;</span>
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<span id="L29"><span class="lineNum"> 29</span> : int virtual getNumberOfNodes()const = 0;</span>
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<span id="L30"><span class="lineNum"> 30</span> : int virtual getNumberOfEdges()const = 0;</span>
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<span id="L31"><span class="lineNum"> 31</span> : int virtual getNumberOfStates() const = 0;</span>
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<span id="L32"><span class="lineNum"> 32</span> : int virtual getClassNumStates() const = 0;</span>
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<span id="L33"><span class="lineNum"> 33</span> : std::vector<std::string> virtual show() const = 0;</span>
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<span id="L34"><span class="lineNum"> 34</span> : std::vector<std::string> virtual graph(const std::string& title = "") const = 0;</span>
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<span id="L35"><span class="lineNum"> 35</span> : virtual std::string getVersion() = 0;</span>
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<span id="L36"><span class="lineNum"> 36</span> : std::vector<std::string> virtual topological_order() = 0;</span>
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<span id="L37"><span class="lineNum"> 37</span> : std::vector<std::string> virtual getNotes() const = 0;</span>
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<span id="L38"><span class="lineNum"> 38</span> : std::string virtual dump_cpt()const = 0;</span>
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<span id="L39"><span class="lineNum"> 39</span> : virtual void setHyperparameters(const nlohmann::json& hyperparameters) = 0;</span>
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<span id="L40"><span class="lineNum"> 40</span> : std::vector<std::string>& getValidHyperparameters() { return validHyperparameters; }</span>
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<span id="L41"><span class="lineNum"> 41</span> : protected:</span>
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<span id="L42"><span class="lineNum"> 42</span> : virtual void trainModel(const torch::Tensor& weights) = 0;</span>
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<span id="L43"><span class="lineNum"> 43</span> : std::vector<std::string> validHyperparameters;</span>
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<span id="L44"><span class="lineNum"> 44</span> : };</span>
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<span id="L45"><span class="lineNum"> 45</span> : }</span>
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</pre>
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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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</table>
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</html>
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