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< title > LCOV - BayesNet Coverage Report - bayesnet/utils/BayesMetrics.h< / 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/utils< / a > - BayesMetrics.h< span style = "font-size: 80%;" > (source / < a href = "BayesMetrics.h.func-c.html" > functions< / a > )< / span > < / td >
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< 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 >
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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 >
< td class = "headerItem" > Lines:< / td >
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< td class = "headerCovTableEntryHi" > 100.0 %< / td >
< td class = "headerCovTableEntry" > 13< / td >
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< td class = "headerCovTableEntry" > 13< / td >
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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 >
< td class = "headerItem" > Functions:< / td >
< td class = "headerCovTableEntryHi" > 100.0 %< / td >
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< td class = "headerItem" > Legend:< / td >
< td class = "headerValueLeg" > Lines:
< span class = "coverLegendCov" > hit< / span >
< span class = "coverLegendNoCov" > not hit< / span >
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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 > : #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 < vector> < / span >
< span id = "L10" > < span class = "lineNum" > 10< / span > : #include < string> < / span >
< span id = "L11" > < span class = "lineNum" > 11< / span > : #include < torch/torch.h> < / 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 >
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< span id = "L15" > < span class = "lineNum" > 15< / span > < span class = "tlaGNC tlaBgGNC" > 2240 : Metrics() = default;< / span > < / span >
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< span id = "L16" > < span class = "lineNum" > 16< / span > : Metrics(const torch::Tensor& samples, const std::vector< std::string> & features, const std::string& className, const int classNumStates);< / span >
< span id = "L17" > < span class = "lineNum" > 17< / span > : Metrics(const std::vector< std::vector< int> > & vsamples, const std::vector< int> & labels, const std::vector< std::string> & features, const std::string& className, const int classNumStates);< / span >
< span id = "L18" > < span class = "lineNum" > 18< / span > : std::vector< int> SelectKBestWeighted(const torch::Tensor& weights, bool ascending = false, unsigned k = 0);< / span >
< span id = "L19" > < span class = "lineNum" > 19< / span > : std::vector< double> getScoresKBest() const;< / span >
< span id = "L20" > < span class = "lineNum" > 20< / span > : double mutualInformation(const torch::Tensor& firstFeature, const torch::Tensor& secondFeature, const torch::Tensor& weights);< / span >
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< span id = "L21" > < span class = "lineNum" > 21< / span > : torch::Tensor conditionalEdge(const torch::Tensor& weights);< / span >
< span id = "L22" > < span class = "lineNum" > 22< / span > : std::vector< std::pair< int, int> > maximumSpanningTree(const std::vector< std::string> & features, const torch::Tensor& weights, const int root);< / span >
< span id = "L23" > < span class = "lineNum" > 23< / span > : protected:< / span >
< span id = "L24" > < span class = "lineNum" > 24< / span > : torch::Tensor samples; // n+1xm torch::Tensor used to fit the model where samples[-1] is the y std::vector< / span >
< span id = "L25" > < span class = "lineNum" > 25< / span > : std::string className;< / span >
< span id = "L26" > < span class = "lineNum" > 26< / span > : double entropy(const torch::Tensor& feature, const torch::Tensor& weights);< / span >
< span id = "L27" > < span class = "lineNum" > 27< / span > : std::vector< std::string> features;< / span >
< span id = "L28" > < span class = "lineNum" > 28< / span > : template < class T> < / span >
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< span id = "L29" > < span class = "lineNum" > 29< / span > < span class = "tlaGNC" > 1391 : std::vector< std::pair< T, T> > doCombinations(const std::vector< T> & source)< / 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" > 1391 : std::vector< std::pair< T, T> > result;< / span > < / span >
< span id = "L32" > < span class = "lineNum" > 32< / span > < span class = "tlaGNC" > 6981 : for (int i = 0; i < source.size(); ++i) {< / span > < / span >
< span id = "L33" > < span class = "lineNum" > 33< / span > < span class = "tlaGNC" > 5590 : T temp = source[i];< / span > < / span >
< span id = "L34" > < span class = "lineNum" > 34< / span > < span class = "tlaGNC" > 16517 : for (int j = i + 1; j < source.size(); ++j) {< / span > < / span >
< span id = "L35" > < span class = "lineNum" > 35< / span > < span class = "tlaGNC" > 10927 : result.push_back({ temp, source[j] });< / span > < / span >
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< span id = "L36" > < span class = "lineNum" > 36< / span > : }< / span >
< span id = "L37" > < span class = "lineNum" > 37< / span > : }< / span >
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< span id = "L38" > < span class = "lineNum" > 38< / span > < span class = "tlaGNC" > 2782 : return result;< / span > < / span >
< span id = "L39" > < span class = "lineNum" > 39< / span > < span class = "tlaGNC" > 1391 : }< / span > < / span >
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< span id = "L40" > < span class = "lineNum" > 40< / span > : template < class T> < / span >
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< span id = "L41" > < span class = "lineNum" > 41< / span > < span class = "tlaGNC" > 94 : T pop_first(std::vector< T> & v)< / 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" > 94 : T temp = v[0];< / span > < / span >
< span id = "L44" > < span class = "lineNum" > 44< / span > < span class = "tlaGNC" > 94 : v.erase(v.begin());< / span > < / span >
< span id = "L45" > < span class = "lineNum" > 45< / span > < span class = "tlaGNC" > 94 : return temp;< / span > < / span >
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< span id = "L46" > < span class = "lineNum" > 46< / span > : }< / span >
< span id = "L47" > < span class = "lineNum" > 47< / span > : private:< / span >
< span id = "L48" > < span class = "lineNum" > 48< / span > : int classNumStates = 0;< / span >
< span id = "L49" > < span class = "lineNum" > 49< / span > : std::vector< double> scoresKBest;< / span >
< span id = "L50" > < span class = "lineNum" > 50< / span > : std::vector< int> featuresKBest; // sorted indices of the features< / span >
< span id = "L51" > < span class = "lineNum" > 51< / span > : double conditionalEntropy(const torch::Tensor& firstFeature, const torch::Tensor& secondFeature, const torch::Tensor& weights);< / 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 > : #endif< / 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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