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< title > LCOV - coverage.info - bayesnet/classifiers/TAN.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 >
< td width = "10%" class = "headerValue" > < a href = "../../index.html" > top level< / a > - < a href = "index.html" > bayesnet/classifiers< / a > - TAN.cc< span style = "font-size: 80%;" > (source / < a href = "TAN.cc.func-c.html" > functions< / a > )< / span > < / td >
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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 = "headerItem" > Test:< / td >
< td class = "headerValue" > coverage.info< / td >
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< td class = "headerItem" > Lines:< / td >
< td class = "headerCovTableEntryHi" > 100.0 %< / td >
< td class = "headerCovTableEntry" > 23< / td >
< td class = "headerCovTableEntry" > 23< / td >
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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 class = "headerItem" > Functions:< / td >
< td class = "headerCovTableEntryHi" > 100.0 %< / td >
< td class = "headerCovTableEntry" > 4< / td >
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< pre class = "sourceHeading" > Line data Source code< / pre >
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< 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 " TAN.h" < / span >
< span id = "L8" > < span class = "lineNum" > 8< / span > : < / span >
< 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" > 282 : TAN::TAN() : Classifier(Network()) {}< / span > < / span >
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< span id = "L11" > < span class = "lineNum" > 11< / span > : < / span >
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< span id = "L12" > < span class = "lineNum" > 12< / span > < span class = "tlaGNC" > 78 : void TAN::buildModel(const torch::Tensor& weights)< / span > < / span >
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< span id = "L13" > < span class = "lineNum" > 13< / span > : {< / span >
< span id = "L14" > < span class = "lineNum" > 14< / span > : // 0. Add all nodes to the model< / span >
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< span id = "L15" > < span class = "lineNum" > 15< / span > < span class = "tlaGNC" > 78 : addNodes();< / span > < / span >
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< span id = "L16" > < span class = "lineNum" > 16< / span > : // 1. Compute mutual information between each feature and the class and set the root node< / span >
< span id = "L17" > < span class = "lineNum" > 17< / span > : // as the highest mutual information with the class< / span >
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< span id = "L18" > < span class = "lineNum" > 18< / span > < span class = "tlaGNC" > 78 : auto mi = std::vector < std::pair< int, float > > ();< / span > < / span >
< span id = "L19" > < span class = "lineNum" > 19< / span > < span class = "tlaGNC" > 234 : torch::Tensor class_dataset = dataset.index({ -1, " ..." });< / span > < / span >
< span id = "L20" > < span class = "lineNum" > 20< / span > < span class = "tlaGNC" > 534 : for (int i = 0; i < static_cast< int> (features.size()); ++i) {< / span > < / span >
< span id = "L21" > < span class = "lineNum" > 21< / span > < span class = "tlaGNC" > 1368 : torch::Tensor feature_dataset = dataset.index({ i, " ..." });< / span > < / span >
< span id = "L22" > < span class = "lineNum" > 22< / span > < span class = "tlaGNC" > 456 : auto mi_value = metrics.mutualInformation(class_dataset, feature_dataset, weights);< / span > < / span >
< span id = "L23" > < span class = "lineNum" > 23< / span > < span class = "tlaGNC" > 456 : mi.push_back({ i, mi_value });< / span > < / span >
< span id = "L24" > < span class = "lineNum" > 24< / span > < span class = "tlaGNC" > 456 : }< / span > < / span >
< span id = "L25" > < span class = "lineNum" > 25< / span > < span class = "tlaGNC" > 1050 : sort(mi.begin(), mi.end(), [](const auto& left, const auto& right) {return left.second < right.second;});< / span > < / span >
< span id = "L26" > < span class = "lineNum" > 26< / span > < span class = "tlaGNC" > 78 : auto root = mi[mi.size() - 1].first;< / span > < / span >
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< span id = "L27" > < span class = "lineNum" > 27< / span > : // 2. Compute mutual information between each feature and the class< / span >
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< span id = "L28" > < span class = "lineNum" > 28< / span > < span class = "tlaGNC" > 78 : auto weights_matrix = metrics.conditionalEdge(weights);< / span > < / span >
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< span id = "L29" > < span class = "lineNum" > 29< / span > : // 3. Compute the maximum spanning tree< / span >
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< span id = "L30" > < span class = "lineNum" > 30< / span > < span class = "tlaGNC" > 78 : auto mst = metrics.maximumSpanningTree(features, weights_matrix, root);< / span > < / span >
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< span id = "L31" > < span class = "lineNum" > 31< / span > : // 4. Add edges from the maximum spanning tree to the model< / span >
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< span id = "L32" > < span class = "lineNum" > 32< / span > < span class = "tlaGNC" > 456 : for (auto i = 0; i < mst.size(); ++i) {< / span > < / span >
< span id = "L33" > < span class = "lineNum" > 33< / span > < span class = "tlaGNC" > 378 : auto [from, to] = mst[i];< / span > < / span >
< span id = "L34" > < span class = "lineNum" > 34< / span > < span class = "tlaGNC" > 378 : model.addEdge(features[from], features[to]);< / span > < / span >
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< span id = "L35" > < span class = "lineNum" > 35< / span > : }< / span >
< span id = "L36" > < span class = "lineNum" > 36< / span > : // 5. Add edges from the class to all features< / span >
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< span id = "L37" > < span class = "lineNum" > 37< / span > < span class = "tlaGNC" > 534 : for (auto feature : features) {< / span > < / span >
< span id = "L38" > < span class = "lineNum" > 38< / span > < span class = "tlaGNC" > 456 : model.addEdge(className, feature);< / span > < / span >
< span id = "L39" > < span class = "lineNum" > 39< / span > < span class = "tlaGNC" > 456 : }< / span > < / span >
< span id = "L40" > < span class = "lineNum" > 40< / span > < span class = "tlaGNC" > 612 : }< / span > < / span >
< span id = "L41" > < span class = "lineNum" > 41< / span > < span class = "tlaGNC" > 12 : std::vector< std::string> TAN::graph(const std::string& title) const< / 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" > 12 : return model.graph(title);< / span > < / span >
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< span id = "L44" > < span class = "lineNum" > 44< / span > : }< / span >
< span id = "L45" > < span class = "lineNum" > 45< / span > : }< / span >
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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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