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Set smoothing as fit parameter

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Ricardo Montañana Gómez
2024-06-11 11:40:45 +02:00
parent 27a3e5a5e0
commit b34869cc61
30 changed files with 168 additions and 178 deletions
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10
bayesnet/network/Network.h
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@@ -38,10 +38,9 @@ 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);
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, const Smoothing_t smoothing);
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, const Smoothing_t smoothing);
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, const Smoothing_t smoothing);
std::vector<int> predict(const std::vector<std::vector<int>>&); // Return mx1 std::vector of predictions
torch::Tensor predict(const torch::Tensor&); // Return mx1 tensor of predictions
torch::Tensor predict_tensor(const torch::Tensor& samples, const bool proba);
@@ -61,14 +60,13 @@ namespace bayesnet {
int classNumStates;
std::vector<std::string> features; // Including classname
std::string className;
Smoothing_t smoothing;
torch::Tensor samples; // n+1xm tensor used to fit the model
bool isCyclic(const std::string&, std::unordered_set<std::string>&, std::unordered_set<std::string>&);
std::vector<double> predict_sample(const std::vector<int>&);
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 torch::Tensor& weights);
void completeFit(const std::map<std::string, std::vector<int>>& states, const torch::Tensor& weights, const Smoothing_t smoothing);
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>>&);
};