Ricardo Montañana Gómez
9f3de4d924
- *ld_algorithm*: algorithm to use for local discretization, with the following options: "MDLP", "BINQ", "BINU". - *ld_proposed_cuts*: number of cut points to return. - *mdlp_min_length*: minimum length of a partition in MDLP algorithm to be evaluated for partition. - *mdlp_max_depth*: maximum level of recursion in MDLP algorithm.
58 lines
2.1 KiB
C++
58 lines
2.1 KiB
C++
// ***************************************************************
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// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
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// SPDX-FileType: SOURCE
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// SPDX-License-Identifier: MIT
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// ***************************************************************
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#include "KDBLd.h"
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namespace bayesnet {
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KDBLd::KDBLd(int k) : KDB(k), Proposal(dataset, features, className)
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{
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validHyperparameters = validHyperparameters_ld;
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validHyperparameters.push_back("k");
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validHyperparameters.push_back("theta");
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}
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void KDBLd::setHyperparameters(const nlohmann::json& hyperparameters_)
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{
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auto hyperparameters = hyperparameters_;
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if (hyperparameters.contains("k")) {
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k = hyperparameters["k"];
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hyperparameters.erase("k");
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}
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if (hyperparameters.contains("theta")) {
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theta = hyperparameters["theta"];
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hyperparameters.erase("theta");
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}
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Proposal::setHyperparameters(hyperparameters);
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}
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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)
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{
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checkInput(X_, y_);
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features = features_;
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className = className_;
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Xf = X_;
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y = y_;
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// Fills std::vectors Xv & yv with the data from tensors X_ (discretized) & y
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states = fit_local_discretization(y);
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// We have discretized the input data
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// 1st we need to fit the model to build the normal KDB structure, KDB::fit initializes the base Bayesian network
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KDB::fit(dataset, features, className, states, smoothing);
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states = localDiscretizationProposal(states, model);
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return *this;
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}
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torch::Tensor KDBLd::predict(torch::Tensor& X)
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{
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auto Xt = prepareX(X);
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return KDB::predict(Xt);
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}
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torch::Tensor KDBLd::predict_proba(torch::Tensor& X)
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{
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auto Xt = prepareX(X);
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return KDB::predict_proba(Xt);
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}
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std::vector<std::string> KDBLd::graph(const std::string& name) const
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{
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return KDB::graph(name);
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}
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} |