rmontanana/BayesNet
1
0
Fork 0
Code Issues 2 Pull Requests 1 Actions Packages Projects Releases 6 Wiki Activity

Set smoothing as fit parameter

Browse Source
This commit is contained in:
Ricardo Montañana Gómez
2024-06-11 11:40:45 +02:00
parent 27a3e5a5e0
commit b34869cc61
30 changed files with 168 additions and 178 deletions
Download Patch File Download Diff File Expand all files Collapse all files

12
bayesnet/BaseClassifier.h
View File

@@ -14,13 +14,12 @@ namespace bayesnet {
enum status_t { NORMAL, WARNING, ERROR };
class BaseClassifier {
public:
void setSmoothing(Smoothing_t smoothing) { this->smoothing = smoothing; } // To call before fit
// X is nxm std::vector, y is nx1 std::vector
virtual BaseClassifier& 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) = 0;
virtual BaseClassifier& 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 Smoothing_t smoothing) = 0;
// X is nxm tensor, y is nx1 tensor
virtual BaseClassifier& 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) = 0;
virtual BaseClassifier& fit(torch::Tensor& dataset, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states) = 0;
virtual BaseClassifier& 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) = 0;
virtual BaseClassifier& 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 Smoothing_t smoothing) = 0;
virtual BaseClassifier& fit(torch::Tensor& dataset, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const Smoothing_t smoothing) = 0;
virtual BaseClassifier& 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 Smoothing_t smoothing) = 0;
virtual ~BaseClassifier() = default;
torch::Tensor virtual predict(torch::Tensor& X) = 0;
std::vector<int> virtual predict(std::vector<std::vector<int >>& X) = 0;
@@ -42,8 +41,7 @@ namespace bayesnet {
virtual void setHyperparameters(const nlohmann::json& hyperparameters) = 0;
std::vector<std::string>& getValidHyperparameters() { return validHyperparameters; }
protected:
virtual void trainModel(const torch::Tensor& weights) = 0;
virtual void trainModel(const torch::Tensor& weights, const Smoothing_t smoothing) = 0;
std::vector<std::string> validHyperparameters;
Smoothing_t smoothing = Smoothing_t::NONE;
};
}