rmontanana/BayesNet
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Set smoothing as fit parameter

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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
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9
bayesnet/ensembles/AODELd.cc
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@@ -10,7 +10,7 @@ namespace bayesnet {
AODELd::AODELd(bool predict_voting) : Ensemble(predict_voting), Proposal(dataset, features, className)
{
}
AODELd& AODELd::fit(torch::Tensor& X_, torch::Tensor& y_, const std::vector<std::string>& features_, const std::string& className_, map<std::string, std::vector<int>>& states_)
AODELd& AODELd::fit(torch::Tensor& X_, torch::Tensor& y_, const std::vector<std::string>& features_, const std::string& className_, map<std::string, std::vector<int>>& states_, const Smoothing_t smoothing)
{
checkInput(X_, y_);
features = features_;
@@ -21,7 +21,7 @@ namespace bayesnet {
states = fit_local_discretization(y);
// We have discretized the input data
// 1st we need to fit the model to build the normal TAN structure, TAN::fit initializes the base Bayesian network
Ensemble::fit(dataset, features, className, states);
Ensemble::fit(dataset, features, className, states, smoothing);
return *this;
}
@@ -34,11 +34,10 @@ namespace bayesnet {
n_models = models.size();
significanceModels = std::vector<double>(n_models, 1.0);
}
void AODELd::trainModel(const torch::Tensor& weights)
void AODELd::trainModel(const torch::Tensor& weights, const Smoothing_t smoothing)
{
for (const auto& model : models) {
model->setSmoothing(smoothing);
model->fit(Xf, y, features, className, states);
model->fit(Xf, y, features, className, states, smoothing);
}
}
std::vector<std::string> AODELd::graph(const std::string& name) const