47 lines
1.7 KiB
C++
47 lines
1.7 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 "AODELd.h"
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namespace bayesnet {
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AODELd::AODELd(bool predict_voting) : Ensemble(predict_voting), Proposal(dataset, features, className)
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{
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}
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AODELd& AODELd::fit(torch::Tensor& X_, torch::Tensor& y_, const std::vector<std::string>& features_, const std::string& className_, map<std::string, std::vector<int>>& states_)
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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 TAN structure, TAN::fit initializes the base Bayesian network
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Ensemble::fit(dataset, features, className, states);
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return *this;
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}
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void AODELd::buildModel(const torch::Tensor& weights)
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{
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models.clear();
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for (int i = 0; i < features.size(); ++i) {
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models.push_back(std::make_unique<SPODELd>(i));
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}
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n_models = models.size();
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significanceModels = std::vector<double>(n_models, 1.0);
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}
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void AODELd::trainModel(const torch::Tensor& weights)
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{
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for (const auto& model : models) {
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model->fit(Xf, y, features, className, states);
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}
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}
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std::vector<std::string> AODELd::graph(const std::string& name) const
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{
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return Ensemble::graph(name);
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}
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} |