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129 lines
4.6 KiB
C++
129 lines
4.6 KiB
C++
#ifndef MLPP_MLP_H
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#define MLPP_MLP_H
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/*************************************************************************/
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/* mlp.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/containers/vector.h"
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#include "core/math/math_defs.h"
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#include "core/string/ustring.h"
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#include "core/variant/variant.h"
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#include "core/object/reference.h"
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#include "../regularization/reg.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 <map>
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#include <string>
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#include <vector>
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class MLPPMLP : public Reference {
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GDCLASS(MLPPMLP, Reference);
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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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Ref<MLPPVector> get_output_set();
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void set_output_set(const Ref<MLPPVector> &val);
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int get_n_hidden();
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void set_n_hidden(const int val);
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real_t get_lambda();
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void set_lambda(const real_t val);
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real_t get_alpha();
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void set_alpha(const real_t val);
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MLPPReg::RegularizationType get_reg();
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void set_reg(const MLPPReg::RegularizationType 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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bool is_initialized();
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void initialize();
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void gradient_descent(real_t learning_rate, int max_epoch, bool UI = false);
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void sgd(real_t learning_rate, int max_epoch, bool UI = false);
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void 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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void save(const String &file_name);
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MLPPMLP(const Ref<MLPPMatrix> &p_input_set, const Ref<MLPPVector> &p_output_set, int p_n_hidden, MLPPReg::RegularizationType p_reg = MLPPReg::REGULARIZATION_TYPE_NONE, real_t p_lambda = 0.5, real_t p_alpha = 0.5);
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MLPPMLP();
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~MLPPMLP();
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protected:
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real_t cost(const Ref<MLPPVector> &y_hat, const Ref<MLPPVector> &y);
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Ref<MLPPVector> evaluatem(const Ref<MLPPMatrix> &X);
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void propagatem(const Ref<MLPPMatrix> &X, Ref<MLPPMatrix> z2_out, Ref<MLPPMatrix> a2_out);
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real_t evaluatev(const Ref<MLPPVector> &x);
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void propagatev(const Ref<MLPPVector> &x, Ref<MLPPVector> z2_out, Ref<MLPPVector> a2_out);
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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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Ref<MLPPVector> _y_hat;
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Ref<MLPPMatrix> _weights1;
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Ref<MLPPVector> _weights2;
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Ref<MLPPVector> _bias1;
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real_t _bias2;
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Ref<MLPPMatrix> _z2;
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Ref<MLPPMatrix> _a2;
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int _n;
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int _k;
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int _n_hidden;
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// Regularization Params
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MLPPReg::RegularizationType _reg;
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real_t _lambda; /* Regularization Parameter */
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real_t _alpha; /* This is the controlling param for Elastic Net*/
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bool _initialized;
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};
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#endif /* MLP_hpp */
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