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95 lines
3.3 KiB
C++
95 lines
3.3 KiB
C++
#ifndef MLPP_K_MEANS_H
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#define MLPP_K_MEANS_H
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/*************************************************************************/
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/* kmeans.h */
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/*************************************************************************/
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/* This file is part of: */
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/* PMLPP Machine Learning Library */
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/* https://github.com/Relintai/pmlpp */
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/*************************************************************************/
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/* Copyright (c) 2022-present Péter Magyar. */
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/* Copyright (c) 2022-2023 Marc Melikyan */
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/* */
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/* Permission is hereby granted, free of charge, to any person obtaining */
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/* a copy of this software and associated documentation files (the */
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/* "Software"), to deal in the Software without restriction, including */
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/* without limitation the rights to use, copy, modify, merge, publish, */
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/* distribute, sublicense, and/or sell copies of the Software, and to */
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/* permit persons to whom the Software is furnished to do so, subject to */
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/* the following conditions: */
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/* */
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/* The above copyright notice and this permission notice shall be */
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/* included in all copies or substantial portions of the Software. */
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/* */
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/* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, */
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/* EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF */
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/* MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.*/
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/* IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY */
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/* CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, */
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/* TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE */
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/* SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. */
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/*************************************************************************/
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#include "core/math/math_defs.h"
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#include "core/object/reference.h"
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#include "../lin_alg/mlpp_matrix.h"
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#include "../lin_alg/mlpp_vector.h"
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class MLPPKMeans : public Reference {
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GDCLASS(MLPPKMeans, Reference);
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public:
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enum MeanType {
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MEAN_TYPE_CENTROID = 0,
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MEAN_TYPE_KMEANSPP,
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};
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public:
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Ref<MLPPMatrix> get_input_set();
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void set_input_set(const Ref<MLPPMatrix> &val);
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int get_k();
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void set_k(const int val);
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MeanType get_mean_type();
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void set_mean_type(const MeanType val);
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void initialize();
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Ref<MLPPMatrix> model_set_test(const Ref<MLPPMatrix> &X);
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Ref<MLPPVector> model_test(const Ref<MLPPVector> &x);
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void train(int epoch_num, bool UI = false);
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real_t score();
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Ref<MLPPVector> silhouette_scores();
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MLPPKMeans();
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~MLPPKMeans();
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protected:
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void _evaluate();
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void _compute_mu();
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void _centroid_initialization();
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void _kmeanspp_initialization();
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real_t _cost();
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static void _bind_methods();
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Ref<MLPPMatrix> _input_set;
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Ref<MLPPMatrix> _mu;
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Ref<MLPPMatrix> _r;
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real_t _accuracy_threshold;
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int _k;
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bool _initialized;
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MeanType _mean_type;
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};
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VARIANT_ENUM_CAST(MLPPKMeans::MeanType);
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#endif /* KMeans_hpp */
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