mirror of
https://github.com/Relintai/pmlpp.git
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242 lines
8.5 KiB
C++
242 lines
8.5 KiB
C++
/*************************************************************************/
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/* reg.cpp */
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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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#include "reg.h"
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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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#endif
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#include "../activation/activation.h"
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#include "../lin_alg/lin_alg.h"
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#include <iostream>
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#include <random>
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real_t MLPPReg::reg_termv(const Ref<MLPPVector> &weights, real_t lambda, real_t alpha, MLPPReg::RegularizationType p_reg) {
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int size = weights->size();
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const real_t *weights_ptr = weights->ptr();
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if (p_reg == REGULARIZATION_TYPE_RIDGE) {
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real_t reg = 0;
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for (int i = 0; i < size; ++i) {
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real_t wi = weights_ptr[i];
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reg += wi * wi;
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}
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return reg * lambda / 2;
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} else if (p_reg == REGULARIZATION_TYPE_LASSO) {
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real_t reg = 0;
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for (int i = 0; i < size; ++i) {
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reg += ABS(weights_ptr[i]);
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}
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return reg * lambda;
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} else if (p_reg == REGULARIZATION_TYPE_ELASTIC_NET) {
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real_t reg = 0;
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for (int i = 0; i < size; ++i) {
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real_t wi = weights_ptr[i];
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reg += alpha * ABS(wi); // Lasso Reg
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reg += ((1 - alpha) / 2) * wi * wi; // Ridge Reg
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}
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return reg * lambda;
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}
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return 0;
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}
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real_t MLPPReg::reg_termm(const Ref<MLPPMatrix> &weights, real_t lambda, real_t alpha, MLPPReg::RegularizationType p_reg) {
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int size = weights->data_size();
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const real_t *weights_ptr = weights->ptr();
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if (p_reg == REGULARIZATION_TYPE_RIDGE) {
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real_t reg = 0;
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for (int i = 0; i < size; ++i) {
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real_t wi = weights_ptr[i];
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reg += wi * wi;
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}
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return reg * lambda / 2;
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} else if (p_reg == REGULARIZATION_TYPE_LASSO) {
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real_t reg = 0;
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for (int i = 0; i < size; ++i) {
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reg += ABS(weights_ptr[i]);
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}
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return reg * lambda;
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} else if (p_reg == REGULARIZATION_TYPE_ELASTIC_NET) {
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real_t reg = 0;
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for (int i = 0; i < size; ++i) {
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real_t wi = weights_ptr[i];
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reg += alpha * ABS(wi); // Lasso Reg
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reg += ((1 - alpha) / 2) * wi * wi; // Ridge Reg
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}
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return reg * lambda;
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}
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return 0;
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}
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Ref<MLPPVector> MLPPReg::reg_weightsv(const Ref<MLPPVector> &weights, real_t lambda, real_t alpha, MLPPReg::RegularizationType p_reg) {
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MLPPLinAlg alg;
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if (p_reg == REGULARIZATION_TYPE_WEIGHT_CLIPPING) {
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return reg_deriv_termv(weights, lambda, alpha, p_reg);
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}
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return alg.subtractionnv(weights, reg_deriv_termv(weights, lambda, alpha, p_reg));
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// for(int i = 0; i < weights.size(); i++){
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// weights[i] -= regDerivTerm(weights, lambda, alpha, reg, i);
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// }
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// return weights;
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}
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Ref<MLPPMatrix> MLPPReg::reg_weightsm(const Ref<MLPPMatrix> &weights, real_t lambda, real_t alpha, MLPPReg::RegularizationType reg) {
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MLPPLinAlg alg;
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if (reg == REGULARIZATION_TYPE_WEIGHT_CLIPPING) {
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return reg_deriv_termm(weights, lambda, alpha, reg);
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}
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return alg.subtractionnm(weights, reg_deriv_termm(weights, lambda, alpha, reg));
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// for(int i = 0; i < weights.size(); i++){
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// for(int j = 0; j < weights[i].size(); j++){
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// weights[i][j] -= regDerivTerm(weights, lambda, alpha, reg, i, j);
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// }
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// }
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// return weights;
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}
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Ref<MLPPVector> MLPPReg::reg_deriv_termv(const Ref<MLPPVector> &weights, real_t lambda, real_t alpha, MLPPReg::RegularizationType reg) {
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Ref<MLPPVector> reg_driv;
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reg_driv.instance();
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int size = weights->size();
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reg_driv->resize(size);
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real_t *reg_driv_ptr = reg_driv->ptrw();
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for (int i = 0; i < size; ++i) {
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reg_driv_ptr[i] = reg_deriv_termvr(weights, lambda, alpha, reg, i);
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}
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return reg_driv;
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}
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Ref<MLPPMatrix> MLPPReg::reg_deriv_termm(const Ref<MLPPMatrix> &weights, real_t lambda, real_t alpha, MLPPReg::RegularizationType reg) {
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Ref<MLPPMatrix> reg_driv;
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reg_driv.instance();
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Size2i size = weights->size();
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reg_driv->resize(size);
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real_t *reg_driv_ptr = reg_driv->ptrw();
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for (int i = 0; i < size.y; ++i) {
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for (int j = 0; j < size.x; ++j) {
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reg_driv_ptr[reg_driv->calculate_index(i, j)] = reg_deriv_termmr(weights, lambda, alpha, reg, i, j);
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}
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}
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return reg_driv;
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}
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MLPPReg::MLPPReg() {
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}
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MLPPReg::~MLPPReg() {
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}
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void MLPPReg::_bind_methods() {
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ClassDB::bind_method(D_METHOD("reg_termv", "weights", "lambda", "alpha", "reg"), &MLPPReg::reg_termv);
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ClassDB::bind_method(D_METHOD("reg_termm", "weights", "lambda", "alpha", "reg"), &MLPPReg::reg_termm);
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ClassDB::bind_method(D_METHOD("reg_weightsv", "weights", "lambda", "alpha", "reg"), &MLPPReg::reg_weightsv);
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ClassDB::bind_method(D_METHOD("reg_weightsm", "weights", "lambda", "alpha", "reg"), &MLPPReg::reg_weightsm);
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ClassDB::bind_method(D_METHOD("reg_deriv_termv", "weights", "lambda", "alpha", "reg"), &MLPPReg::reg_deriv_termv);
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ClassDB::bind_method(D_METHOD("reg_deriv_termm", "weights", "lambda", "alpha", "reg"), &MLPPReg::reg_deriv_termm);
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BIND_ENUM_CONSTANT(REGULARIZATION_TYPE_NONE);
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BIND_ENUM_CONSTANT(REGULARIZATION_TYPE_RIDGE);
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BIND_ENUM_CONSTANT(REGULARIZATION_TYPE_LASSO);
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BIND_ENUM_CONSTANT(REGULARIZATION_TYPE_ELASTIC_NET);
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BIND_ENUM_CONSTANT(REGULARIZATION_TYPE_WEIGHT_CLIPPING);
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}
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real_t MLPPReg::reg_deriv_termvr(const Ref<MLPPVector> &weights, real_t lambda, real_t alpha, MLPPReg::RegularizationType reg, int j) {
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MLPPActivation act;
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real_t wj = weights->element_get(j);
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if (reg == REGULARIZATION_TYPE_RIDGE) {
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return lambda * wj;
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} else if (reg == REGULARIZATION_TYPE_LASSO) {
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return lambda * act.sign_normr(wj);
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} else if (reg == REGULARIZATION_TYPE_ELASTIC_NET) {
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return alpha * lambda * act.sign_normr(wj) + (1 - alpha) * lambda * wj;
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} else if (reg == REGULARIZATION_TYPE_WEIGHT_CLIPPING) { // Preparation for Wasserstein GANs.
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// We assume lambda is the lower clipping threshold, while alpha is the higher clipping threshold.
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// alpha > lambda.
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if (wj > alpha) {
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return alpha;
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} else if (wj < lambda) {
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return lambda;
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} else {
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return wj;
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}
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} else {
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return 0;
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}
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}
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real_t MLPPReg::reg_deriv_termmr(const Ref<MLPPMatrix> &weights, real_t lambda, real_t alpha, MLPPReg::RegularizationType reg, int i, int j) {
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MLPPActivation act;
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real_t wj = weights->element_get(i, j);
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if (reg == REGULARIZATION_TYPE_RIDGE) {
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return lambda * wj;
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} else if (reg == REGULARIZATION_TYPE_LASSO) {
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return lambda * act.sign_normr(wj);
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} else if (reg == REGULARIZATION_TYPE_ELASTIC_NET) {
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return alpha * lambda * act.sign_normr(wj) + (1 - alpha) * lambda * wj;
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} else if (reg == REGULARIZATION_TYPE_WEIGHT_CLIPPING) { // Preparation for Wasserstein GANs.
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// We assume lambda is the lower clipping threshold, while alpha is the higher clipping threshold.
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// alpha > lambda.
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if (wj > alpha) {
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return alpha;
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} else if (wj < lambda) {
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return lambda;
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} else {
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return wj;
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}
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} else {
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return 0;
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}
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}
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