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Implement 3 types of smoothing

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This commit is contained in:
Ricardo Montañana Gómez
2024-06-10 15:49:01 +02:00
parent 684443a788
commit 27a3e5a5e0
11 changed files with 37 additions and 9 deletions
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5
bayesnet/network/Network.h
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@@ -13,6 +13,8 @@
namespace bayesnet {
enum class Smoothing_t {
NONE = -1,
OLD_LAPLACE = 0,
LAPLACE,
CESTNIK
};
@@ -36,6 +38,7 @@ namespace bayesnet {
/*
Notice: Nodes have to be inserted in the same order as they are in the dataset, i.e., first node is first column and so on.
*/
void setSmoothing(Smoothing_t smoothing) { this->smoothing = smoothing; };
void fit(const std::vector<std::vector<int>>& input_data, const std::vector<int>& labels, const std::vector<double>& weights, const std::vector<std::string>& featureNames, const std::string& className, const std::map<std::string, std::vector<int>>& states);
void fit(const torch::Tensor& X, const torch::Tensor& y, const torch::Tensor& weights, const std::vector<std::string>& featureNames, const std::string& className, const std::map<std::string, std::vector<int>>& states);
void fit(const torch::Tensor& samples, const torch::Tensor& weights, const std::vector<std::string>& featureNames, const std::string& className, const std::map<std::string, std::vector<int>>& states);
@@ -65,7 +68,7 @@ namespace bayesnet {
std::vector<double> predict_sample(const torch::Tensor&);
std::vector<double> exactInference(std::map<std::string, int>&);
double computeFactor(std::map<std::string, int>&);
void completeFit(const std::map<std::string, std::vector<int>>& states, const int n_samples, const torch::Tensor& weights);
void completeFit(const std::map<std::string, std::vector<int>>& states, const torch::Tensor& weights);
void checkFitData(int n_samples, int n_features, int n_samples_y, const std::vector<std::string>& featureNames, const std::string& className, const std::map<std::string, std::vector<int>>& states, const torch::Tensor& weights);
void setStates(const std::map<std::string, std::vector<int>>&);
};