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Complete implementation with tests

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This commit is contained in:
Ricardo Montañana Gómez
2025-07-08 11:42:20 +02:00
parent 2c7352ac38
commit ed380b1494
13 changed files with 255 additions and 170 deletions
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23
bayesnet/classifiers/TANLd.cc
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@@ -12,17 +12,26 @@ namespace bayesnet {
TANLd& TANLd::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_;
className = className_;
Xf = X_;
y = y_;
// Use iterative local discretization instead of the two-phase approach
return commonFit(features_, className_, states_, smoothing);
}
TANLd& TANLd::fit(torch::Tensor& dataset, const std::vector<std::string>& features_, const std::string& className_, map<std::string, std::vector<int>>& states_, const Smoothing_t smoothing)
{
if (!torch::is_floating_point(dataset)) {
throw std::runtime_error("Dataset must be a floating point tensor");
}
Xf = dataset.index({ torch::indexing::Slice(0, dataset.size(0) - 1), "..." }).clone();
y = dataset.index({ -1, "..." }).clone().to(torch::kInt32);
return commonFit(features_, className_, states_, smoothing);
}
TANLd& TANLd::commonFit(const std::vector<std::string>& features_, const std::string& className_, map<std::string, std::vector<int>>& states_, const Smoothing_t smoothing)
{
features = features_;
className = className_;
states = iterativeLocalDiscretization(y, static_cast<TAN*>(this), dataset, features, className, states_, smoothing);
// Final fit with converged discretization
TAN::fit(dataset, features, className, states, smoothing);
return *this;
}
torch::Tensor TANLd::predict(torch::Tensor& X)