mirror of https://github.com/Relintai/pmlpp.git
83 lines
3.7 KiB
C++
83 lines
3.7 KiB
C++
#ifndef MLPP_REG_H
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#define MLPP_REG_H
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/*************************************************************************/
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/* reg.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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#ifdef USING_SFW
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#include "sfw.h"
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#else
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#include "core/math/math_defs.h"
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#include "core/object/reference.h"
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#endif
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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 <string>
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#include <vector>
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class MLPPReg : public Reference {
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GDCLASS(MLPPReg, Reference);
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public:
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enum RegularizationType {
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REGULARIZATION_TYPE_NONE = 0,
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REGULARIZATION_TYPE_RIDGE,
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REGULARIZATION_TYPE_LASSO,
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REGULARIZATION_TYPE_ELASTIC_NET,
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REGULARIZATION_TYPE_WEIGHT_CLIPPING,
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};
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real_t reg_termv(const Ref<MLPPVector> &weights, real_t lambda, real_t alpha, RegularizationType reg);
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real_t reg_termm(const Ref<MLPPMatrix> &weights, real_t lambda, real_t alpha, RegularizationType reg);
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Ref<MLPPVector> reg_weightsv(const Ref<MLPPVector> &weights, real_t lambda, real_t alpha, RegularizationType reg);
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Ref<MLPPMatrix> reg_weightsm(const Ref<MLPPMatrix> &weights, real_t lambda, real_t alpha, RegularizationType reg);
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Ref<MLPPVector> reg_deriv_termv(const Ref<MLPPVector> &weights, real_t lambda, real_t alpha, RegularizationType reg);
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Ref<MLPPMatrix> reg_deriv_termm(const Ref<MLPPMatrix> &weights, real_t lambda, real_t alpha, RegularizationType reg);
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MLPPReg();
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~MLPPReg();
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protected:
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static void _bind_methods();
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private:
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real_t reg_deriv_termvr(const Ref<MLPPVector> &weights, real_t lambda, real_t alpha, RegularizationType reg, int j);
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real_t reg_deriv_termmr(const Ref<MLPPMatrix> &weights, real_t lambda, real_t alpha, RegularizationType reg, int i, int j);
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};
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VARIANT_ENUM_CAST(MLPPReg::RegularizationType);
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#endif /* Reg_hpp */
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