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.
27 lines
1.1 KiB
C++
27 lines
1.1 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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#ifndef AODELD_H
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#define AODELD_H
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#include "bayesnet/classifiers/Proposal.h"
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#include "bayesnet/classifiers/SPODELd.h"
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#include "Ensemble.h"
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namespace bayesnet {
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class AODELd : public Ensemble, public Proposal {
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public:
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AODELd(bool predict_voting = true);
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virtual ~AODELd() = default;
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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_, const Smoothing_t smoothing) override;
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std::vector<std::string> graph(const std::string& name = "AODELd") const override;
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protected:
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void trainModel(const torch::Tensor& weights, const Smoothing_t smoothing) override;
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void buildModel(const torch::Tensor& weights) override;
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private:
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nlohmann::json hyperparameters = {}; // Hyperparameters for the model
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};
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}
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#endif // !AODELD_H
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