mirror of
https://github.com/Relintai/pmlpp.git
synced 2024-12-22 15:06:47 +01:00
166 lines
4.9 KiB
C++
166 lines
4.9 KiB
C++
//
|
|
// Reg.cpp
|
|
//
|
|
// Created by Marc Melikyan on 1/16/21.
|
|
//
|
|
|
|
#include "reg.h"
|
|
#include "../activation/activation.h"
|
|
#include "../lin_alg/lin_alg.h"
|
|
#include <iostream>
|
|
#include <random>
|
|
|
|
|
|
|
|
real_t MLPPReg::regTerm(std::vector<real_t> weights, real_t lambda, real_t alpha, std::string reg) {
|
|
if (reg == "Ridge") {
|
|
real_t reg = 0;
|
|
for (int i = 0; i < weights.size(); i++) {
|
|
reg += weights[i] * weights[i];
|
|
}
|
|
return reg * lambda / 2;
|
|
} else if (reg == "Lasso") {
|
|
real_t reg = 0;
|
|
for (int i = 0; i < weights.size(); i++) {
|
|
reg += abs(weights[i]);
|
|
}
|
|
return reg * lambda;
|
|
} else if (reg == "ElasticNet") {
|
|
real_t reg = 0;
|
|
for (int i = 0; i < weights.size(); i++) {
|
|
reg += alpha * abs(weights[i]); // Lasso Reg
|
|
reg += ((1 - alpha) / 2) * weights[i] * weights[i]; // Ridge Reg
|
|
}
|
|
return reg * lambda;
|
|
}
|
|
return 0;
|
|
}
|
|
|
|
real_t MLPPReg::regTerm(std::vector<std::vector<real_t>> weights, real_t lambda, real_t alpha, std::string reg) {
|
|
if (reg == "Ridge") {
|
|
real_t reg = 0;
|
|
for (int i = 0; i < weights.size(); i++) {
|
|
for (int j = 0; j < weights[i].size(); j++) {
|
|
reg += weights[i][j] * weights[i][j];
|
|
}
|
|
}
|
|
return reg * lambda / 2;
|
|
} else if (reg == "Lasso") {
|
|
real_t reg = 0;
|
|
for (int i = 0; i < weights.size(); i++) {
|
|
for (int j = 0; j < weights[i].size(); j++) {
|
|
reg += abs(weights[i][j]);
|
|
}
|
|
}
|
|
return reg * lambda;
|
|
} else if (reg == "ElasticNet") {
|
|
real_t reg = 0;
|
|
for (int i = 0; i < weights.size(); i++) {
|
|
for (int j = 0; j < weights[i].size(); j++) {
|
|
reg += alpha * abs(weights[i][j]); // Lasso Reg
|
|
reg += ((1 - alpha) / 2) * weights[i][j] * weights[i][j]; // Ridge Reg
|
|
}
|
|
}
|
|
return reg * lambda;
|
|
}
|
|
return 0;
|
|
}
|
|
|
|
std::vector<real_t> MLPPReg::regWeights(std::vector<real_t> weights, real_t lambda, real_t alpha, std::string reg) {
|
|
MLPPLinAlg alg;
|
|
if (reg == "WeightClipping") {
|
|
return regDerivTerm(weights, lambda, alpha, reg);
|
|
}
|
|
return alg.subtraction(weights, regDerivTerm(weights, lambda, alpha, reg));
|
|
// for(int i = 0; i < weights.size(); i++){
|
|
// weights[i] -= regDerivTerm(weights, lambda, alpha, reg, i);
|
|
// }
|
|
// return weights;
|
|
}
|
|
|
|
std::vector<std::vector<real_t>> MLPPReg::regWeights(std::vector<std::vector<real_t>> weights, real_t lambda, real_t alpha, std::string reg) {
|
|
MLPPLinAlg alg;
|
|
if (reg == "WeightClipping") {
|
|
return regDerivTerm(weights, lambda, alpha, reg);
|
|
}
|
|
return alg.subtraction(weights, regDerivTerm(weights, lambda, alpha, reg));
|
|
// for(int i = 0; i < weights.size(); i++){
|
|
// for(int j = 0; j < weights[i].size(); j++){
|
|
// weights[i][j] -= regDerivTerm(weights, lambda, alpha, reg, i, j);
|
|
// }
|
|
// }
|
|
// return weights;
|
|
}
|
|
|
|
std::vector<real_t> MLPPReg::regDerivTerm(std::vector<real_t> weights, real_t lambda, real_t alpha, std::string reg) {
|
|
std::vector<real_t> regDeriv;
|
|
regDeriv.resize(weights.size());
|
|
|
|
for (int i = 0; i < regDeriv.size(); i++) {
|
|
regDeriv[i] = regDerivTerm(weights, lambda, alpha, reg, i);
|
|
}
|
|
return regDeriv;
|
|
}
|
|
|
|
std::vector<std::vector<real_t>> MLPPReg::regDerivTerm(std::vector<std::vector<real_t>> weights, real_t lambda, real_t alpha, std::string reg) {
|
|
std::vector<std::vector<real_t>> regDeriv;
|
|
regDeriv.resize(weights.size());
|
|
for (int i = 0; i < regDeriv.size(); i++) {
|
|
regDeriv[i].resize(weights[0].size());
|
|
}
|
|
|
|
for (int i = 0; i < regDeriv.size(); i++) {
|
|
for (int j = 0; j < regDeriv[i].size(); j++) {
|
|
regDeriv[i][j] = regDerivTerm(weights, lambda, alpha, reg, i, j);
|
|
}
|
|
}
|
|
return regDeriv;
|
|
}
|
|
|
|
real_t MLPPReg::regDerivTerm(std::vector<real_t> weights, real_t lambda, real_t alpha, std::string reg, int j) {
|
|
MLPPActivation act;
|
|
if (reg == "Ridge") {
|
|
return lambda * weights[j];
|
|
} else if (reg == "Lasso") {
|
|
return lambda * act.sign(weights[j]);
|
|
} else if (reg == "ElasticNet") {
|
|
return alpha * lambda * act.sign(weights[j]) + (1 - alpha) * lambda * weights[j];
|
|
} else if (reg == "WeightClipping") { // Preparation for Wasserstein GANs.
|
|
// We assume lambda is the lower clipping threshold, while alpha is the higher clipping threshold.
|
|
// alpha > lambda.
|
|
if (weights[j] > alpha) {
|
|
return alpha;
|
|
} else if (weights[j] < lambda) {
|
|
return lambda;
|
|
} else {
|
|
return weights[j];
|
|
}
|
|
} else {
|
|
return 0;
|
|
}
|
|
}
|
|
|
|
real_t MLPPReg::regDerivTerm(std::vector<std::vector<real_t>> weights, real_t lambda, real_t alpha, std::string reg, int i, int j) {
|
|
MLPPActivation act;
|
|
if (reg == "Ridge") {
|
|
return lambda * weights[i][j];
|
|
} else if (reg == "Lasso") {
|
|
return lambda * act.sign(weights[i][j]);
|
|
} else if (reg == "ElasticNet") {
|
|
return alpha * lambda * act.sign(weights[i][j]) + (1 - alpha) * lambda * weights[i][j];
|
|
} else if (reg == "WeightClipping") { // Preparation for Wasserstein GANs.
|
|
// We assume lambda is the lower clipping threshold, while alpha is the higher clipping threshold.
|
|
// alpha > lambda.
|
|
if (weights[i][j] > alpha) {
|
|
return alpha;
|
|
} else if (weights[i][j] < lambda) {
|
|
return lambda;
|
|
} else {
|
|
return weights[i][j];
|
|
}
|
|
} else {
|
|
return 0;
|
|
}
|
|
}
|
|
|