50 lines
1.8 KiB
C++
50 lines
1.8 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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#ifndef ITERATIVE_PROPOSAL_H
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#define ITERATIVE_PROPOSAL_H
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#include "Proposal.h"
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#include "bayesnet/network/Network.h"
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#include <nlohmann/json.hpp>
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namespace bayesnet {
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class IterativeProposal : public Proposal {
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public:
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IterativeProposal(torch::Tensor& pDataset, std::vector<std::string>& features_, std::string& className_);
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void setHyperparameters(const nlohmann::json& hyperparameters_);
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protected:
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template<typename Classifier>
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map<std::string, std::vector<int>> iterativeLocalDiscretization(
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const torch::Tensor& y,
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Classifier* classifier,
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const torch::Tensor& dataset,
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const std::vector<std::string>& features,
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const std::string& className,
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const map<std::string, std::vector<int>>& initialStates,
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double smoothing = 1.0
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);
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// Convergence parameters
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struct {
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int maxIterations = 10;
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double tolerance = 1e-6;
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std::string convergenceMetric = "likelihood"; // "likelihood" or "accuracy"
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bool verbose = false;
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} convergence_params;
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nlohmann::json validHyperparameters_iter = {
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"max_iterations", "tolerance", "convergence_metric", "verbose_convergence"
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};
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private:
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double computeLogLikelihood(const Network& model, const torch::Tensor& dataset);
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bool hasConverged(double currentValue, double previousValue, const std::string& metric);
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};
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}
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#endif |