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Add smoothing parameter for compatibility with BayesNet

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This commit is contained in:
Ricardo Montañana Gómez
2024-06-11 13:43:12 +02:00
parent 235c345e87
commit c5ff1a0b2b
3 changed files with 7 additions and 7 deletions
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10
pyclfs/PyClassifier.h
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@@ -17,12 +17,12 @@ namespace pywrap {
public:
PyClassifier(const std::string& module, const std::string& className, const bool sklearn = false);
virtual ~PyClassifier();
PyClassifier& fit(std::vector<std::vector<int>>& X, std::vector<int>& y, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states) override { return *this; };
PyClassifier& fit(std::vector<std::vector<int>>& X, std::vector<int>& y, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const bayesnet::Smoothing_t smoothing = bayesnet::Smoothing_t::NONE) override { return *this; };
// X is nxm tensor, y is nx1 tensor
PyClassifier& fit(torch::Tensor& X, torch::Tensor& y, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states) override;
PyClassifier& fit(torch::Tensor& X, torch::Tensor& y, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const bayesnet::Smoothing_t smoothing = bayesnet::Smoothing_t::NONE) override;
PyClassifier& fit(torch::Tensor& X, torch::Tensor& y);
PyClassifier& fit(torch::Tensor& dataset, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states) override { return *this; };
PyClassifier& fit(torch::Tensor& dataset, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const torch::Tensor& weights) override { return *this; };
PyClassifier& fit(torch::Tensor& dataset, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const bayesnet::Smoothing_t smoothing = bayesnet::Smoothing_t::NONE) override { return *this; };
PyClassifier& fit(torch::Tensor& dataset, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const torch::Tensor& weights, const bayesnet::Smoothing_t smoothing = bayesnet::Smoothing_t::NONE) override { return *this; };
torch::Tensor predict(torch::Tensor& X) override;
std::vector<int> predict(std::vector<std::vector<int >>& X) override { return std::vector<int>(); }; // Not implemented
torch::Tensor predict_proba(torch::Tensor& X) override { return torch::zeros({ 0, 0 }); } // Not implemented
@@ -47,7 +47,7 @@ namespace pywrap {
void setHyperparameters(const nlohmann::json& hyperparameters) override;
protected:
nlohmann::json hyperparameters;
void trainModel(const torch::Tensor& weights) override {};
void trainModel(const torch::Tensor& weights, const bayesnet::Smoothing_t smoothing = bayesnet::Smoothing_t::NONE) override {};
std::vector<std::string> notes;
private:
PyWrap* pyWrap;