60 lines
2.5 KiB
C++
60 lines
2.5 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 ENSEMBLE_H
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#define ENSEMBLE_H
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#include <torch/torch.h>
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#include "bayesnet/utils/BayesMetrics.h"
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#include "bayesnet/utils/bayesnetUtils.h"
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#include "bayesnet/classifiers/Classifier.h"
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namespace bayesnet {
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class Ensemble : public Classifier {
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public:
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Ensemble(bool predict_voting = true);
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virtual ~Ensemble() = default;
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torch::Tensor predict(torch::Tensor& X) override;
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std::vector<int> predict(std::vector<std::vector<int>>& X) override;
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torch::Tensor predict_proba(torch::Tensor& X) override;
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std::vector<std::vector<double>> predict_proba(std::vector<std::vector<int>>& X) override;
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float score(torch::Tensor& X, torch::Tensor& y) override;
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float score(std::vector<std::vector<int>>& X, std::vector<int>& y) override;
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int getNumberOfNodes() const override;
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int getNumberOfEdges() const override;
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int getNumberOfStates() const override;
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std::vector<std::string> show() const override;
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std::vector<std::string> graph(const std::string& title) const override;
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std::vector<std::string> topological_order() override
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{
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return std::vector<std::string>();
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}
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std::string dump_cpt() const override
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{
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std::string output;
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for (auto& model : models) {
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output += model->dump_cpt();
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output += std::string(80, '-') + "\n";
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}
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return output;
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}
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protected:
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void trainModel(const torch::Tensor& weights, const Smoothing_t smoothing) override;
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torch::Tensor predict_average_voting(torch::Tensor& X);
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std::vector<std::vector<double>> predict_average_voting(std::vector<std::vector<int>>& X);
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torch::Tensor predict_average_proba(torch::Tensor& X);
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std::vector<std::vector<double>> predict_average_proba(std::vector<std::vector<int>>& X);
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torch::Tensor compute_arg_max(torch::Tensor& X);
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std::vector<int> compute_arg_max(std::vector<std::vector<double>>& X);
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torch::Tensor voting(torch::Tensor& votes);
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// Attributes
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unsigned n_models;
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std::vector<std::unique_ptr<Classifier>> models;
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std::vector<double> significanceModels;
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bool predict_voting;
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};
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}
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#endif
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