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Complete XA1DE integration

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This commit is contained in:
Ricardo Montañana Gómez
2025-02-19 11:40:33 +01:00
parent 5556fbab03
commit c1531dba2a
3 changed files with 96 additions and 28 deletions
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67
src/experimental_clfs/XA1DE.cpp
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@@ -27,6 +27,7 @@ namespace platform {
Timer timer, timert;
timer.start();
timert.start();
// debug = true;
std::vector<std::vector<int>> instances = X;
instances.push_back(y);
int num_instances = instances[0].size();
@@ -36,6 +37,16 @@ namespace platform {
for (int i = 0; i < num_attributes; i++) {
statesv.push_back(*max_element(instances[i].begin(), instances[i].end()) + 1);
}
// std::cout << "* States: " << statesv << std::endl;
// std::cout << "* Weights: " << weights_ << std::endl;
// std::cout << "* Instances: " << num_instances << std::endl;
// std::cout << "* Attributes: " << num_attributes << std::endl;
// std::cout << "* y: " << y << std::endl;
// std::cout << "* x shape: " << X.size() << "x" << X[0].size() << std::endl;
// for (int i = 0; i < num_attributes - 1; i++) {
// std::cout << "* " << features[i] << ": " << instances[i] << std::endl;
// }
// std::cout << "Starting to build the model" << std::endl;
aode_.init(statesv);
aode_.duration_first += timer.getDuration(); timer.start();
std::vector<int> instance;
@@ -54,7 +65,7 @@ namespace platform {
// std::cout << "* Checking coherence... ";
// aode_.checkCoherenceApprox(1e-6);
// std::cout << "Ok!" << std::endl;
// aode_.show();
aode_.show();
// std::cout << "* Accumulated first time: " << aode_.duration_first << std::endl;
// std::cout << "* Accumulated second time: " << aode_.duration_second << std::endl;
// std::cout << "* Accumulated third time: " << aode_.duration_third << std::endl;
@@ -196,6 +207,26 @@ namespace platform {
return data;
}
//
// statistics
//
int XA1DE::getNumberOfNodes() const
{
return aode_.getNumberOfNodes();
}
int XA1DE::getNumberOfEdges() const
{
return aode_.getNumberOfEdges();
}
int XA1DE::getNumberOfStates() const
{
return aode_.getNumberOfStates();
}
int XA1DE::getClassNumStates() const
{
return aode_.statesClass();
}
//
// Fit
//
@@ -203,8 +234,7 @@ namespace platform {
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_ = to_matrix(X);
int a = 1;
std::vector<int> y_ = to_vector<int>(y);
auto 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)
@@ -215,8 +245,37 @@ namespace platform {
}
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);
}
//
// Predict
//
torch::Tensor XA1DE::predict(torch::Tensor& X)
{
auto X_ = to_matrix(X);
torch::Tensor y = torch::tensor(predict(X_));
return y;
}
torch::Tensor XA1DE::predict_proba(torch::Tensor& X)
{
auto X_ = to_matrix(X);
auto probabilities = predict_proba(X_);
auto n_samples = X.size(1);
int n_classes = probabilities[0].size();
auto y = torch::zeros({ n_samples, n_classes });
for (int i = 0; i < n_samples; i++) {
for (int j = 0; j < n_classes; j++) {
y[i][j] = probabilities[i][j];
}
}
return y;
}
float XA1DE::score(torch::Tensor& X, torch::Tensor& y)
{
auto X_ = to_matrix(X);
auto y_ = to_vector<int>(y);
return score(X_, y_);
}
}