146 lines
13 KiB
HTML
146 lines
13 KiB
HTML
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<title>LCOV - coverage.info - bayesnet/network/Network.h</title>
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<link rel="stylesheet" type="text/css" href="../../gcov.css">
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</head>
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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/network</a> - Network.h<span style="font-size: 80%;"> (source / <a href="Network.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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<td class="headerValue">2024-04-30 13:59:18</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">1</td>
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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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</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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<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> : #ifndef NETWORK_H</span>
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<span id="L8"><span class="lineNum"> 8</span> : #define NETWORK_H</span>
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<span id="L9"><span class="lineNum"> 9</span> : #include <map></span>
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<span id="L10"><span class="lineNum"> 10</span> : #include <vector></span>
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<span id="L11"><span class="lineNum"> 11</span> : #include "bayesnet/config.h"</span>
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<span id="L12"><span class="lineNum"> 12</span> : #include "Node.h"</span>
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<span id="L13"><span class="lineNum"> 13</span> : </span>
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<span id="L14"><span class="lineNum"> 14</span> : namespace bayesnet {</span>
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<span id="L15"><span class="lineNum"> 15</span> : class Network {</span>
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<span id="L16"><span class="lineNum"> 16</span> : public:</span>
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<span id="L17"><span class="lineNum"> 17</span> : Network();</span>
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<span id="L18"><span class="lineNum"> 18</span> : explicit Network(float);</span>
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<span id="L19"><span class="lineNum"> 19</span> : explicit Network(const Network&);</span>
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<span id="L20"><span class="lineNum"> 20</span> <span class="tlaGNC tlaBgGNC"> 4626 : ~Network() = default;</span></span>
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<span id="L21"><span class="lineNum"> 21</span> : torch::Tensor& getSamples();</span>
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<span id="L22"><span class="lineNum"> 22</span> : float getMaxThreads() const;</span>
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<span id="L23"><span class="lineNum"> 23</span> : void addNode(const std::string&);</span>
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<span id="L24"><span class="lineNum"> 24</span> : void addEdge(const std::string&, const std::string&);</span>
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<span id="L25"><span class="lineNum"> 25</span> : std::map<std::string, std::unique_ptr<Node>>& getNodes();</span>
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<span id="L26"><span class="lineNum"> 26</span> : std::vector<std::string> getFeatures() const;</span>
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<span id="L27"><span class="lineNum"> 27</span> : int getStates() const;</span>
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<span id="L28"><span class="lineNum"> 28</span> : std::vector<std::pair<std::string, std::string>> getEdges() const;</span>
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<span id="L29"><span class="lineNum"> 29</span> : int getNumEdges() const;</span>
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<span id="L30"><span class="lineNum"> 30</span> : int getClassNumStates() const;</span>
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<span id="L31"><span class="lineNum"> 31</span> : std::string getClassName() const;</span>
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<span id="L32"><span class="lineNum"> 32</span> : /*</span>
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<span id="L33"><span class="lineNum"> 33</span> : Notice: Nodes have to be inserted in the same order as they are in the dataset, i.e., first node is first column and so on.</span>
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<span id="L34"><span class="lineNum"> 34</span> : */</span>
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<span id="L35"><span class="lineNum"> 35</span> : void fit(const std::vector<std::vector<int>>& input_data, const std::vector<int>& labels, const std::vector<double>& weights, const std::vector<std::string>& featureNames, const std::string& className, const std::map<std::string, std::vector<int>>& states);</span>
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<span id="L36"><span class="lineNum"> 36</span> : void fit(const torch::Tensor& X, const torch::Tensor& y, const torch::Tensor& weights, const std::vector<std::string>& featureNames, const std::string& className, const std::map<std::string, std::vector<int>>& states);</span>
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<span id="L37"><span class="lineNum"> 37</span> : void fit(const torch::Tensor& samples, const torch::Tensor& weights, const std::vector<std::string>& featureNames, const std::string& className, const std::map<std::string, std::vector<int>>& states);</span>
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<span id="L38"><span class="lineNum"> 38</span> : std::vector<int> predict(const std::vector<std::vector<int>>&); // Return mx1 std::vector of predictions</span>
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<span id="L39"><span class="lineNum"> 39</span> : torch::Tensor predict(const torch::Tensor&); // Return mx1 tensor of predictions</span>
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<span id="L40"><span class="lineNum"> 40</span> : torch::Tensor predict_tensor(const torch::Tensor& samples, const bool proba);</span>
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<span id="L41"><span class="lineNum"> 41</span> : std::vector<std::vector<double>> predict_proba(const std::vector<std::vector<int>>&); // Return mxn std::vector of probabilities</span>
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<span id="L42"><span class="lineNum"> 42</span> : torch::Tensor predict_proba(const torch::Tensor&); // Return mxn tensor of probabilities</span>
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<span id="L43"><span class="lineNum"> 43</span> : double score(const std::vector<std::vector<int>>&, const std::vector<int>&);</span>
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<span id="L44"><span class="lineNum"> 44</span> : std::vector<std::string> topological_sort();</span>
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<span id="L45"><span class="lineNum"> 45</span> : std::vector<std::string> show() const;</span>
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<span id="L46"><span class="lineNum"> 46</span> : std::vector<std::string> graph(const std::string& title) const; // Returns a std::vector of std::strings representing the graph in graphviz format</span>
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<span id="L47"><span class="lineNum"> 47</span> : void initialize();</span>
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<span id="L48"><span class="lineNum"> 48</span> : std::string dump_cpt() const;</span>
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<span id="L49"><span class="lineNum"> 49</span> : inline std::string version() { return { project_version.begin(), project_version.end() }; }</span>
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<span id="L50"><span class="lineNum"> 50</span> : private:</span>
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<span id="L51"><span class="lineNum"> 51</span> : std::map<std::string, std::unique_ptr<Node>> nodes;</span>
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<span id="L52"><span class="lineNum"> 52</span> : bool fitted;</span>
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<span id="L53"><span class="lineNum"> 53</span> : float maxThreads = 0.95;</span>
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<span id="L54"><span class="lineNum"> 54</span> : int classNumStates;</span>
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<span id="L55"><span class="lineNum"> 55</span> : std::vector<std::string> features; // Including classname</span>
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<span id="L56"><span class="lineNum"> 56</span> : std::string className;</span>
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<span id="L57"><span class="lineNum"> 57</span> : double laplaceSmoothing;</span>
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<span id="L58"><span class="lineNum"> 58</span> : torch::Tensor samples; // n+1xm tensor used to fit the model</span>
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<span id="L59"><span class="lineNum"> 59</span> : bool isCyclic(const std::string&, std::unordered_set<std::string>&, std::unordered_set<std::string>&);</span>
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<span id="L60"><span class="lineNum"> 60</span> : std::vector<double> predict_sample(const std::vector<int>&);</span>
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<span id="L61"><span class="lineNum"> 61</span> : std::vector<double> predict_sample(const torch::Tensor&);</span>
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<span id="L62"><span class="lineNum"> 62</span> : std::vector<double> exactInference(std::map<std::string, int>&);</span>
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<span id="L63"><span class="lineNum"> 63</span> : double computeFactor(std::map<std::string, int>&);</span>
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<span id="L64"><span class="lineNum"> 64</span> : void completeFit(const std::map<std::string, std::vector<int>>& states, const torch::Tensor& weights);</span>
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<span id="L65"><span class="lineNum"> 65</span> : void checkFitData(int n_features, int n_samples, int n_samples_y, const std::vector<std::string>& featureNames, const std::string& className, const std::map<std::string, std::vector<int>>& states, const torch::Tensor& weights);</span>
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<span id="L66"><span class="lineNum"> 66</span> : void setStates(const std::map<std::string, std::vector<int>>&);</span>
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<span id="L67"><span class="lineNum"> 67</span> : };</span>
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<span id="L68"><span class="lineNum"> 68</span> : }</span>
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<span id="L69"><span class="lineNum"> 69</span> : #endif</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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</table>
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<br>
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