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Fix XA1DE integration

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This commit is contained in:
Ricardo Montañana Gómez
2025-02-27 09:23:47 +01:00
parent b055065e59
commit 4e3043b2d1
6 changed files with 12 additions and 56 deletions
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32
src/experimental_clfs/XA1DE.cpp
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@@ -8,35 +8,13 @@
#include "TensorUtils.hpp"
namespace platform {
XA1DE& 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)
void XA1DE::trainModel(const torch::Tensor& weights, const bayesnet::Smoothing_t smoothing)
{
std::vector<std::vector<int>> instances = X;
instances.push_back(y);
int num_instances = instances[0].size();
int num_attributes = instances.size();
auto X = TensorUtils::to_matrix(dataset.slice(0, 0, dataset.size(0) - 1));
auto y = TensorUtils::to_vector<int>(dataset.index({ -1, "..." }));
int num_instances = X[0].size();
weights_ = torch::full({ m }, 1.0 / m, torch::kFloat64);
normalize_weights(num_instances);
aode_.fit(X, y, features, className, states, weights_, true);
fitted = true;
return *this;
}
//
// Fit
//
XA1DE& 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)
{
auto X_ = TensorUtils::to_matrix(X);
auto y_ = TensorUtils::to_vector<int>(y);
return fit(X_, y_, features, className, states, smoothing);
}
XA1DE& 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)
{
torch::Tensor y = dataset[dataset.size(0) - 1];
torch::Tensor X = dataset.slice(0, 0, dataset.size(0) - 1);
return fit(X, y, features, className, states, smoothing);
}
XA1DE& 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)
{
weights_ = weights;
return fit(dataset, features, className, states, smoothing);
}
}