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Fixed warnings in MLPPReg.

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Relintai 2023-02-12 23:03:25 +01:00
parent 5a375225e9
commit 638ae1664f
1 changed files with 33 additions and 33 deletions
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66
mlpp/regularization/reg.cpp
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@ -14,24 +14,24 @@
#include <iostream>
#include <random>
real_t MLPPReg::reg_termv(const Ref<MLPPVector> &weights, real_t lambda, real_t alpha, MLPPReg::RegularizationType reg) {
real_t MLPPReg::reg_termv(const Ref<MLPPVector> &weights, real_t lambda, real_t alpha, MLPPReg::RegularizationType p_reg) {
int size = weights->size();
const real_t *weights_ptr = weights->ptr();
if (reg == REGULARIZATION_TYPE_RIDGE) {
if (p_reg == REGULARIZATION_TYPE_RIDGE) {
real_t reg = 0;
for (int i = 0; i < size; ++i) {
real_t wi = weights_ptr[i];
reg += wi * wi;
}
return reg * lambda / 2;
} else if (reg == REGULARIZATION_TYPE_LASSO) {
} else if (p_reg == REGULARIZATION_TYPE_LASSO) {
real_t reg = 0;
for (int i = 0; i < size; ++i) {
reg += ABS(weights_ptr[i]);
}
return reg * lambda;
} else if (reg == REGULARIZATION_TYPE_ELASTIC_NET) {
} else if (p_reg == REGULARIZATION_TYPE_ELASTIC_NET) {
real_t reg = 0;
for (int i = 0; i < size; ++i) {
real_t wi = weights_ptr[i];
@ -43,24 +43,24 @@ real_t MLPPReg::reg_termv(const Ref<MLPPVector> &weights, real_t lambda, real_t
return 0;
}
real_t MLPPReg::reg_termm(const Ref<MLPPMatrix> &weights, real_t lambda, real_t alpha, MLPPReg::RegularizationType reg) {
real_t MLPPReg::reg_termm(const Ref<MLPPMatrix> &weights, real_t lambda, real_t alpha, MLPPReg::RegularizationType p_reg) {
int size = weights->data_size();
const real_t *weights_ptr = weights->ptr();
if (reg == REGULARIZATION_TYPE_RIDGE) {
if (p_reg == REGULARIZATION_TYPE_RIDGE) {
real_t reg = 0;
for (int i = 0; i < size; ++i) {
real_t wi = weights_ptr[i];
reg += wi * wi;
}
return reg * lambda / 2;
} else if (reg == REGULARIZATION_TYPE_LASSO) {
} else if (p_reg == REGULARIZATION_TYPE_LASSO) {
real_t reg = 0;
for (int i = 0; i < size; ++i) {
reg += ABS(weights_ptr[i]);
}
return reg * lambda;
} else if (reg == REGULARIZATION_TYPE_ELASTIC_NET) {
} else if (p_reg == REGULARIZATION_TYPE_ELASTIC_NET) {
real_t reg = 0;
for (int i = 0; i < size; ++i) {
real_t wi = weights_ptr[i];
@ -73,14 +73,14 @@ real_t MLPPReg::reg_termm(const Ref<MLPPMatrix> &weights, real_t lambda, real_t
return 0;
}
Ref<MLPPVector> MLPPReg::reg_weightsv(const Ref<MLPPVector> &weights, real_t lambda, real_t alpha, MLPPReg::RegularizationType reg) {
Ref<MLPPVector> MLPPReg::reg_weightsv(const Ref<MLPPVector> &weights, real_t lambda, real_t alpha, MLPPReg::RegularizationType p_reg) {
MLPPLinAlg alg;
if (reg == REGULARIZATION_TYPE_WEIGHT_CLIPPING) {
return reg_deriv_termv(weights, lambda, alpha, reg);
if (p_reg == REGULARIZATION_TYPE_WEIGHT_CLIPPING) {
return reg_deriv_termv(weights, lambda, alpha, p_reg);
}
return alg.subtractionnv(weights, reg_deriv_termv(weights, lambda, alpha, reg));
return alg.subtractionnv(weights, reg_deriv_termv(weights, lambda, alpha, p_reg));
// for(int i = 0; i < weights.size(); i++){
// weights[i] -= regDerivTerm(weights, lambda, alpha, reg, i);
@ -212,22 +212,22 @@ real_t MLPPReg::reg_deriv_termmr(const Ref<MLPPMatrix> &weights, real_t lambda,
}
}
real_t MLPPReg::regTerm(std::vector<real_t> weights, real_t lambda, real_t alpha, std::string reg) {
if (reg == "Ridge") {
real_t MLPPReg::regTerm(std::vector<real_t> weights, real_t lambda, real_t alpha, std::string p_reg) {
if (p_reg == "Ridge") {
real_t reg = 0;
for (int i = 0; i < weights.size(); i++) {
for (uint32_t i = 0; i < weights.size(); i++) {
reg += weights[i] * weights[i];
}
return reg * lambda / 2;
} else if (reg == "Lasso") {
} else if (p_reg == "Lasso") {
real_t reg = 0;
for (int i = 0; i < weights.size(); i++) {
for (uint32_t i = 0; i < weights.size(); i++) {
reg += abs(weights[i]);
}
return reg * lambda;
} else if (reg == "ElasticNet") {
} else if (p_reg == "ElasticNet") {
real_t reg = 0;
for (int i = 0; i < weights.size(); i++) {
for (uint32_t i = 0; i < weights.size(); i++) {
reg += alpha * abs(weights[i]); // Lasso Reg
reg += ((1 - alpha) / 2) * weights[i] * weights[i]; // Ridge Reg
}
@ -236,27 +236,27 @@ real_t MLPPReg::regTerm(std::vector<real_t> weights, real_t lambda, real_t alpha
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 MLPPReg::regTerm(std::vector<std::vector<real_t>> weights, real_t lambda, real_t alpha, std::string p_reg) {
if (p_reg == "Ridge") {
real_t reg = 0;
for (int i = 0; i < weights.size(); i++) {
for (int j = 0; j < weights[i].size(); j++) {
for (uint32_t i = 0; i < weights.size(); i++) {
for (uint32_t j = 0; j < weights[i].size(); j++) {
reg += weights[i][j] * weights[i][j];
}
}
return reg * lambda / 2;
} else if (reg == "Lasso") {
} else if (p_reg == "Lasso") {
real_t reg = 0;
for (int i = 0; i < weights.size(); i++) {
for (int j = 0; j < weights[i].size(); j++) {
for (uint32_t i = 0; i < weights.size(); i++) {
for (uint32_t j = 0; j < weights[i].size(); j++) {
reg += abs(weights[i][j]);
}
}
return reg * lambda;
} else if (reg == "ElasticNet") {
} else if (p_reg == "ElasticNet") {
real_t reg = 0;
for (int i = 0; i < weights.size(); i++) {
for (int j = 0; j < weights[i].size(); j++) {
for (uint32_t i = 0; i < weights.size(); i++) {
for (uint32_t 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
}
@ -296,7 +296,7 @@ std::vector<real_t> MLPPReg::regDerivTerm(std::vector<real_t> weights, real_t la
std::vector<real_t> regDeriv;
regDeriv.resize(weights.size());
for (int i = 0; i < regDeriv.size(); i++) {
for (uint32_t i = 0; i < regDeriv.size(); i++) {
regDeriv[i] = regDerivTerm(weights, lambda, alpha, reg, i);
}
return regDeriv;
@ -305,12 +305,12 @@ std::vector<real_t> MLPPReg::regDerivTerm(std::vector<real_t> weights, real_t la
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++) {
for (uint32_t 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++) {
for (uint32_t i = 0; i < regDeriv.size(); i++) {
for (uint32_t j = 0; j < regDeriv[i].size(); j++) {
regDeriv[i][j] = regDerivTerm(weights, lambda, alpha, reg, i, j);
}
}