Add selectKParis method
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@@ -16,6 +16,7 @@ namespace bayesnet {
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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<std::pair<int, int>> SelectKPairs(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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double conditionalMutualInformation(const torch::Tensor& firstFeature, const torch::Tensor& secondFeature, const torch::Tensor& labels, const torch::Tensor& weights);
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@@ -41,7 +42,7 @@ namespace bayesnet {
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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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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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@@ -52,6 +53,8 @@ namespace bayesnet {
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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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std::vector<std::pair<int, int>> pairsKBest; // sorted indices of the pairs
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std::map<std::pair<int, int>, double> scoresKPairs;
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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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