10 KiB
10 KiB
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Line data Source code 1 : // *************************************************************** 2 : // SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez 3 : // SPDX-FileType: SOURCE 4 : // SPDX-License-Identifier: MIT 5 : // *************************************************************** 6 : 7 : #ifndef BAYESNET_METRICS_H 8 : #define BAYESNET_METRICS_H 9 : #include <vector> 10 : #include <string> 11 : #include <torch/torch.h> 12 : namespace bayesnet { 13 : class Metrics { 14 : public: 15 2658 : Metrics() = default; 16 : Metrics(const torch::Tensor& samples, const std::vector<std::string>& features, const std::string& className, const int classNumStates); 17 : 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); 18 : std::vector<int> SelectKBestWeighted(const torch::Tensor& weights, bool ascending = false, unsigned k = 0); 19 : std::vector<double> getScoresKBest() const; 20 : double mutualInformation(const torch::Tensor& firstFeature, const torch::Tensor& secondFeature, const torch::Tensor& weights); 21 : torch::Tensor conditionalEdge(const torch::Tensor& weights); 22 : std::vector<std::pair<int, int>> maximumSpanningTree(const std::vector<std::string>& features, const torch::Tensor& weights, const int root); 23 : protected: 24 : torch::Tensor samples; // n+1xm torch::Tensor used to fit the model where samples[-1] is the y std::vector 25 : std::string className; 26 : double entropy(const torch::Tensor& feature, const torch::Tensor& weights); 27 : std::vector<std::string> features; 28 : template <class T> 29 1251 : std::vector<std::pair<T, T>> doCombinations(const std::vector<T>& source) 30 : { 31 1251 : std::vector<std::pair<T, T>> result; 32 6532 : for (int i = 0; i < source.size(); ++i) { 33 5281 : T temp = source[i]; 34 16445 : for (int j = i + 1; j < source.size(); ++j) { 35 11164 : result.push_back({ temp, source[j] }); 36 : } 37 : } 38 1251 : return result; 39 0 : } 40 : template <class T> 41 68 : T pop_first(std::vector<T>& v) 42 : { 43 68 : T temp = v[0]; 44 68 : v.erase(v.begin()); 45 68 : return temp; 46 : } 47 : private: 48 : int classNumStates = 0; 49 : std::vector<double> scoresKBest; 50 : std::vector<int> featuresKBest; // sorted indices of the features 51 : double conditionalEntropy(const torch::Tensor& firstFeature, const torch::Tensor& secondFeature, const torch::Tensor& weights); 52 : }; 53 : } 54 : #endif |
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| Generated by: LCOV version 2.0-1 |
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