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113 lines
3.5 KiB
C++
113 lines
3.5 KiB
C++
//
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// Reg.cpp
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//
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// Created by Marc Melikyan on 1/16/21.
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//
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#include "reg_old.h"
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#include "core/math/math_defs.h"
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#include "../activation/activation_old.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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std::vector<real_t> MLPPRegOld::regWeights(std::vector<real_t> weights, real_t lambda, real_t alpha, std::string reg) {
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MLPPLinAlg alg;
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if (reg == "WeightClipping") {
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return regDerivTerm(weights, lambda, alpha, reg);
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}
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return alg.subtraction(weights, regDerivTerm(weights, lambda, alpha, 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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std::vector<std::vector<real_t>> MLPPRegOld::regWeights(std::vector<std::vector<real_t>> weights, real_t lambda, real_t alpha, std::string reg) {
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MLPPLinAlg alg;
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if (reg == "WeightClipping") {
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return regDerivTerm(weights, lambda, alpha, reg);
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}
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return alg.subtraction(weights, regDerivTerm(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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std::vector<real_t> MLPPRegOld::regDerivTerm(std::vector<real_t> weights, real_t lambda, real_t alpha, std::string reg) {
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std::vector<real_t> regDeriv;
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regDeriv.resize(weights.size());
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for (uint32_t i = 0; i < regDeriv.size(); i++) {
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regDeriv[i] = regDerivTerm(weights, lambda, alpha, reg, i);
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}
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return regDeriv;
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}
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std::vector<std::vector<real_t>> MLPPRegOld::regDerivTerm(std::vector<std::vector<real_t>> weights, real_t lambda, real_t alpha, std::string reg) {
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std::vector<std::vector<real_t>> regDeriv;
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regDeriv.resize(weights.size());
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for (uint32_t i = 0; i < regDeriv.size(); i++) {
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regDeriv[i].resize(weights[0].size());
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}
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for (uint32_t i = 0; i < regDeriv.size(); i++) {
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for (uint32_t j = 0; j < regDeriv[i].size(); j++) {
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regDeriv[i][j] = regDerivTerm(weights, lambda, alpha, reg, i, j);
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}
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}
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return regDeriv;
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}
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real_t MLPPRegOld::regDerivTerm(std::vector<real_t> weights, real_t lambda, real_t alpha, std::string reg, int j) {
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MLPPActivationOld act;
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if (reg == "Ridge") {
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return lambda * weights[j];
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} else if (reg == "Lasso") {
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return lambda * act.sign(weights[j]);
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} else if (reg == "ElasticNet") {
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return alpha * lambda * act.sign(weights[j]) + (1 - alpha) * lambda * weights[j];
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} else if (reg == "WeightClipping") { // 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 (weights[j] > alpha) {
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return alpha;
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} else if (weights[j] < lambda) {
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return lambda;
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} else {
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return weights[j];
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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 MLPPRegOld::regDerivTerm(std::vector<std::vector<real_t>> weights, real_t lambda, real_t alpha, std::string reg, int i, int j) {
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MLPPActivationOld act;
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if (reg == "Ridge") {
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return lambda * weights[i][j];
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} else if (reg == "Lasso") {
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return lambda * act.sign(weights[i][j]);
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} else if (reg == "ElasticNet") {
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return alpha * lambda * act.sign(weights[i][j]) + (1 - alpha) * lambda * weights[i][j];
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} else if (reg == "WeightClipping") { // 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 (weights[i][j] > alpha) {
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return alpha;
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} else if (weights[i][j] < lambda) {
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return lambda;
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} else {
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return weights[i][j];
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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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