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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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35
src/experimental_clfs/XA1DE.cpp
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@@ -66,6 +66,9 @@ namespace platform {
}
std::vector<std::vector<double>> XA1DE::predict_proba(std::vector<std::vector<int>>& test_data)
{
if (use_threads) {
return predict_proba_threads(test_data);
}
int test_size = test_data[0].size();
std::vector<std::vector<double>> probabilities;
@@ -151,7 +154,8 @@ namespace platform {
}
return static_cast<float>(correct) / predictions.size();
}
std::vector<std::vector<int>> to_matrix(const torch::Tensor& X)
std::vector<std::vector<int>> XA1DE::to_matrix(const torch::Tensor& X)
{
// Ensure tensor is contiguous in memory
auto X_contig = X.contiguous();
@@ -174,24 +178,45 @@ namespace platform {
}
return data;
}
std::vector<int> to_vector(const torch::Tensor& y)
template <typename T>
std::vector<T> XA1DE::to_vector(const torch::Tensor& y)
{
// Ensure the tensor is contiguous in memory
auto y_contig = y.contiguous();
// Access data pointer
auto data_ptr = y_contig.data_ptr<int>();
auto data_ptr = y_contig.data_ptr<T>();
// Prepare output container
std::vector<int> data(y.size(0));
std::vector<T> data(y.size(0));
// Copy data efficiently
std::copy(data_ptr, data_ptr + y.size(0), data.begin());
return data;
}
//
// Fit
//
// 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)
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)
{
return fit(to_matrix(X), to_vector(y), features, className, states, smoothing);
auto X_ = to_matrix(X);
int a = 1;
std::vector<int> y_ = 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)
{
double b = 1;
weights_ = to_vector<double>(weights);
return fit(dataset, features, className, states, smoothing);
}
}