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Complete integration with memory failure

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This commit is contained in:
Ricardo Montañana Gómez
2025-02-18 22:57:02 +01:00
parent ac89cefab3
commit 5556fbab03
4 changed files with 47 additions and 15 deletions
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22
src/experimental_clfs/XA1DE.h
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@@ -17,6 +17,7 @@
#include "Xaode.hpp"
namespace platform {
class XA1DE : public bayesnet::BaseClassifier {
public:
XA1DE();
@@ -28,13 +29,11 @@ namespace platform {
float score(std::vector<std::vector<int>>& X, std::vector<int>& y) override;
std::vector<int> predict(std::vector<std::vector<int>>& X) override;
XA1DE& 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) override;
XA1DE& 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) override { return *this; };
XA1DE& 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) override { return *this; };
XA1DE& 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) override { return *this; };
XA1DE& 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) override;
XA1DE& 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) override;
XA1DE& 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) override;
torch::Tensor predict(torch::Tensor& X) override { return torch::zeros(0); };
torch::Tensor predict_proba(torch::Tensor& X) override { return torch::zeros(0); };
int getNumberOfNodes() const override { return 0; };
int getNumberOfEdges() const override { return 0; };
int getNumberOfStates() const override { return 0; };
@@ -49,6 +48,7 @@ namespace platform {
void setHyperparameters(const nlohmann::json& hyperparameters) override;
std::vector<std::string>& getValidHyperparameters() { return validHyperparameters; }
void setDebug(bool debug) { this->debug = debug; }
std::vector<std::string> graph(const std::string& title = "") const override { return {}; }
protected:
void trainModel(const torch::Tensor& weights, const bayesnet::Smoothing_t smoothing) override {};
@@ -57,13 +57,15 @@ namespace platform {
{
double sum = std::accumulate(weights_.begin(), weights_.end(), 0.0);
if (sum == 0) {
throw std::runtime_error("Weights sum zero.");
}
for (double& w : weights_) {
w = w * num_instances / sum;
weights_ = std::vector<double>(weights_.size(), 1.0);
} else {
for (double& w : weights_) {
w = w * num_instances / sum;
}
}
}
std::vector<int> to_vector(const torch::Tensor& y);
template <typename T>
std::vector<T> to_vector(const torch::Tensor& y);
std::vector<std::vector<int>> to_matrix(const torch::Tensor& X);
Xaode aode_;
std::vector<double> weights_;