Files
BayesNet/bayesnet/classifiers/KDBLd.cc

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2.1 KiB
C++

// ***************************************************************
// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
// SPDX-FileType: SOURCE
// SPDX-License-Identifier: MIT
// ***************************************************************
#include "KDBLd.h"
#include <memory>
namespace bayesnet {
KDBLd::KDBLd(int k) : KDB(k), Proposal(dataset, features, className)
{
validHyperparameters = validHyperparameters_ld;
validHyperparameters.push_back("k");
validHyperparameters.push_back("theta");
}
void KDBLd::setHyperparameters(const nlohmann::json& hyperparameters_)
{
auto hyperparameters = hyperparameters_;
if (hyperparameters.contains("k")) {
k = hyperparameters["k"];
hyperparameters.erase("k");
}
if (hyperparameters.contains("theta")) {
theta = hyperparameters["theta"];
hyperparameters.erase("theta");
}
Proposal::setHyperparameters(hyperparameters);
}
KDBLd& KDBLd::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
states = iterativeLocalDiscretization(y, static_cast<KDB*>(this), dataset, features, className, states_, smoothing);
// Final fit with converged discretization
KDB::fit(dataset, features, className, states, smoothing);
return *this;
}
torch::Tensor KDBLd::predict(torch::Tensor& X)
{
auto Xt = prepareX(X);
return KDB::predict(Xt);
}
torch::Tensor KDBLd::predict_proba(torch::Tensor& X)
{
auto Xt = prepareX(X);
return KDB::predict_proba(Xt);
}
std::vector<std::string> KDBLd::graph(const std::string& name) const
{
return KDB::graph(name);
}
}