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112 lines
4.3 KiB
C++
112 lines
4.3 KiB
C++
#ifndef MLPP_SVC_H
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#define MLPP_SVC_H
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/*************************************************************************/
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/* svc.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) 2023-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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// https://towardsdatascience.com/svm-implementation-from-scratch-python-2db2fc52e5c2
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// Illustratd a practical definition of the Hinge Loss function and its gradient when optimizing with SGD.
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#include "core/math/math_defs.h"
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#include "core/object/resource.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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#include "../regularization/reg.h"
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class MLPPSVC : public Resource {
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GDCLASS(MLPPSVC, Resource);
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public:
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Ref<MLPPMatrix> get_input_set() const;
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void set_input_set(const Ref<MLPPMatrix> &val);
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Ref<MLPPVector> get_output_set() const;
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void set_output_set(const Ref<MLPPMatrix> &val);
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real_t get_c() const;
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void set_c(const real_t val);
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Ref<MLPPVector> data_z_get() const;
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void data_z_set(const Ref<MLPPVector> &val);
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Ref<MLPPVector> data_y_hat_get() const;
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void data_y_hat_set(const Ref<MLPPVector> &val);
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Ref<MLPPVector> data_weights_get() const;
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void data_weights_set(const Ref<MLPPVector> &val);
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real_t data_bias_get() const;
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void data_bias_set(const real_t val);
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Ref<MLPPVector> model_set_test(const Ref<MLPPMatrix> &X);
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real_t model_test(const Ref<MLPPVector> &x);
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void train_gradient_descent(real_t learning_rate, int max_epoch, bool ui = false);
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void train_sgd(real_t learning_rate, int max_epoch, bool ui = false);
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void train_mbgd(real_t learning_rate, int max_epoch, int mini_batch_size, bool ui = false);
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real_t score();
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bool needs_init() const;
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void initialize();
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MLPPSVC(const Ref<MLPPMatrix> &input_set, const Ref<MLPPVector> &output_set, real_t c);
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MLPPSVC();
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~MLPPSVC();
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protected:
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real_t cost(const Ref<MLPPVector> &z, const Ref<MLPPVector> &y, const Ref<MLPPVector> &weights, real_t c);
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Ref<MLPPVector> evaluatem(const Ref<MLPPMatrix> &X);
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Ref<MLPPVector> propagatem(const Ref<MLPPMatrix> &X);
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real_t evaluatev(const Ref<MLPPVector> &x);
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real_t propagatev(const Ref<MLPPVector> &x);
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void forward_pass();
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static void _bind_methods();
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Ref<MLPPMatrix> _input_set;
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Ref<MLPPVector> _output_set;
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real_t _c;
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Ref<MLPPVector> _z;
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Ref<MLPPVector> _y_hat;
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Ref<MLPPVector> _weights;
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real_t _bias;
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};
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#endif /* SVC_hpp */
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