// *************************************************************** // SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez // SPDX-FileType: SOURCE // SPDX-License-Identifier: MIT // *************************************************************** #include "KDBLd.h" #include 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& features_, const std::string& className_, map>& 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(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 KDBLd::graph(const std::string& name) const { return KDB::graph(name); } }