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Implement Conditional Mutual Information

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Ricardo Montañana Gómez
2024-05-15 00:48:02 +02:00
parent 56b62a67cc
commit e2e0fb0c40
3 changed files with 145 additions and 1 deletions
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7
bayesnet/utils/BayesMetrics.h
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@@ -18,12 +18,17 @@ namespace bayesnet {
std::vector<int> SelectKBestWeighted(const torch::Tensor& weights, bool ascending = false, unsigned k = 0);
std::vector<double> getScoresKBest() const;
double mutualInformation(const torch::Tensor& firstFeature, const torch::Tensor& secondFeature, const torch::Tensor& weights);
double conditionalMutualInformation(const torch::Tensor& firstFeature, const torch::Tensor& secondFeature, const torch::Tensor& labels, const torch::Tensor& weights);
torch::Tensor conditionalEdge(const torch::Tensor& weights);
std::vector<std::pair<int, int>> maximumSpanningTree(const std::vector<std::string>& features, const torch::Tensor& weights, const int root);
// Measured in nats (natural logarithm (log) base e)
// Elements of Information Theory, 2nd Edition, Thomas M. Cover, Joy A. Thomas p. 14
double entropy(const torch::Tensor& feature, const torch::Tensor& weights);
double conditionalEntropy(const torch::Tensor& firstFeature, const torch::Tensor& secondFeature, const torch::Tensor& labels, const torch::Tensor& weights);
double conditionalEntropy2(const torch::Tensor& firstFeature, const torch::Tensor& secondFeature, const torch::Tensor& labels, const torch::Tensor& weights);
protected:
torch::Tensor samples; // n+1xm torch::Tensor used to fit the model where samples[-1] is the y std::vector
std::string className;
double entropy(const torch::Tensor& feature, const torch::Tensor& weights);
std::vector<std::string> features;
template <class T>
std::vector<std::pair<T, T>> doCombinations(const std::vector<T>& source)