54 lines
2.5 KiB
C++
54 lines
2.5 KiB
C++
// ***************************************************************
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// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
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// SPDX-FileType: SOURCE
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// SPDX-License-Identifier: MIT
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// ***************************************************************
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#ifndef BAYESNET_METRICS_H
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#define BAYESNET_METRICS_H
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#include <vector>
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#include <string>
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#include <torch/torch.h>
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namespace bayesnet {
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class Metrics {
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public:
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Metrics() = default;
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Metrics(const torch::Tensor& samples, const std::vector<std::string>& features, const std::string& className, const int classNumStates);
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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);
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std::vector<int> SelectKBestWeighted(const torch::Tensor& weights, bool ascending = false, unsigned k = 0);
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std::vector<double> getScoresKBest() const;
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double mutualInformation(const torch::Tensor& firstFeature, const torch::Tensor& secondFeature, const torch::Tensor& weights);
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torch::Tensor conditionalEdge(const torch::Tensor& weights);
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std::vector<std::pair<int, int>> maximumSpanningTree(const std::vector<std::string>& features, const torch::Tensor& weights, const int root);
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protected:
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torch::Tensor samples; // n+1xm torch::Tensor used to fit the model where samples[-1] is the y std::vector
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std::string className;
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double entropy(const torch::Tensor& feature, const torch::Tensor& weights);
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std::vector<std::string> features;
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template <class T>
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std::vector<std::pair<T, T>> doCombinations(const std::vector<T>& source)
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{
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std::vector<std::pair<T, T>> result;
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for (int i = 0; i < source.size(); ++i) {
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T temp = source[i];
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for (int j = i + 1; j < source.size(); ++j) {
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result.push_back({ temp, source[j] });
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}
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}
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return result;
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}
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template <class T>
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T pop_first(std::vector<T>& v)
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{
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T temp = v[0];
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v.erase(v.begin());
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return temp;
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}
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private:
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int classNumStates = 0;
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std::vector<double> scoresKBest;
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std::vector<int> featuresKBest; // sorted indices of the features
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double conditionalEntropy(const torch::Tensor& firstFeature, const torch::Tensor& secondFeature, const torch::Tensor& weights);
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};
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}
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#endif |