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Implement 3 types of smoothing

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This commit is contained in:
Ricardo Montañana Gómez
2024-06-10 15:49:01 +02:00
parent 684443a788
commit 27a3e5a5e0
11 changed files with 37 additions and 9 deletions
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1
bayesnet/ensembles/AODELd.cc
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@@ -37,6 +37,7 @@ namespace bayesnet {
void AODELd::trainModel(const torch::Tensor& weights)
{
for (const auto& model : models) {
model->setSmoothing(smoothing);
model->fit(Xf, y, features, className, states);
}
}

2
bayesnet/ensembles/BoostA2DE.cc
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@@ -32,6 +32,7 @@ namespace bayesnet {
for (int j = i + 1; j < featuresSelected.size(); j++) {
auto parents = { featuresSelected[i], featuresSelected[j] };
std::unique_ptr<Classifier> model = std::make_unique<SPnDE>(parents);
model->setSmoothing(smoothing);
model->fit(dataset, features, className, states, weights_);
models.push_back(std::move(model));
significanceModels.push_back(1.0); // They will be updated later in trainModel
@@ -96,6 +97,7 @@ namespace bayesnet {
pairSelection.erase(pairSelection.begin());
std::unique_ptr<Classifier> model;
model = std::make_unique<SPnDE>(std::vector<int>({ feature_pair.first, feature_pair.second }));
model->setSmoothing(smoothing);
model->fit(dataset, features, className, states, weights_);
alpha_t = 0.0;
if (!block_update) {

2
bayesnet/ensembles/BoostAODE.cc
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@@ -22,6 +22,7 @@ namespace bayesnet {
std::vector<int> featuresSelected = featureSelection(weights_);
for (const int& feature : featuresSelected) {
std::unique_ptr<Classifier> model = std::make_unique<SPODE>(feature);
model->setSmoothing(smoothing);
model->fit(dataset, features, className, states, weights_);
models.push_back(std::move(model));
significanceModels.push_back(1.0); // They will be updated later in trainModel
@@ -89,6 +90,7 @@ namespace bayesnet {
featureSelection.erase(featureSelection.begin());
std::unique_ptr<Classifier> model;
model = std::make_unique<SPODE>(feature);
model->setSmoothing(smoothing);
model->fit(dataset, features, className, states, weights_);
alpha_t = 0.0;
if (!block_update) {

1
bayesnet/ensembles/Ensemble.cc
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@@ -18,6 +18,7 @@ namespace bayesnet {
n_models = models.size();
for (auto i = 0; i < n_models; ++i) {
// fit with std::vectors
models[i]->setSmoothing(smoothing);
models[i]->fit(dataset, features, className, states);
}
}