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Fixed warnings in MLPPExpReg.

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Relintai 2023-02-10 22:23:10 +01:00
parent d795b55baa
commit 1e793de7f7
2 changed files with 21 additions and 14 deletions
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33
mlpp/exp_reg/exp_reg.cpp
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@ -14,9 +14,15 @@
#include <iostream> #include <iostream>
#include <random> #include <random>
MLPPExpReg::MLPPExpReg(std::vector<std::vector<real_t>> p_inputSet, std::vector<real_t> p_outputSet, std::string p_reg, real_t p_lambda, real_t p_alpha) {
inputSet = p_inputSet;
outputSet = p_outputSet;
n = p_inputSet.size();
k = p_inputSet[0].size();
reg = p_reg;
lambda = p_lambda;
alpha = p_alpha;
MLPPExpReg::MLPPExpReg(std::vector<std::vector<real_t>> inputSet, std::vector<real_t> outputSet, std::string reg, real_t lambda, real_t alpha) :
inputSet(inputSet), outputSet(outputSet), n(inputSet.size()), k(inputSet[0].size()), reg(reg), lambda(lambda), alpha(alpha) {
y_hat.resize(n); y_hat.resize(n);
weights = MLPPUtilities::weightInitialization(k); weights = MLPPUtilities::weightInitialization(k);
initial = MLPPUtilities::weightInitialization(k); initial = MLPPUtilities::weightInitialization(k);
@ -142,7 +148,9 @@ void MLPPExpReg::MBGD(real_t learning_rate, int max_epoch, int mini_batch_size,
// Creating the mini-batches // Creating the mini-batches
int n_mini_batch = n / mini_batch_size; int n_mini_batch = n / mini_batch_size;
auto [inputMiniBatches, outputMiniBatches] = MLPPUtilities::createMiniBatches(inputSet, outputSet, n_mini_batch); auto batches = MLPPUtilities::createMiniBatches(inputSet, outputSet, n_mini_batch);
auto inputMiniBatches = std::get<0>(batches);
auto outputMiniBatches = std::get<1>(batches);
while (true) { while (true) {
for (int i = 0; i < n_mini_batch; i++) { for (int i = 0; i < n_mini_batch; i++) {
@ -153,14 +161,14 @@ void MLPPExpReg::MBGD(real_t learning_rate, int max_epoch, int mini_batch_size,
for (int j = 0; j < k; j++) { for (int j = 0; j < k; j++) {
// Calculating the weight gradient // Calculating the weight gradient
real_t sum = 0; real_t sum = 0;
for (int k = 0; k < outputMiniBatches[i].size(); k++) { for (uint32_t k = 0; k < outputMiniBatches[i].size(); k++) {
sum += error[k] * inputMiniBatches[i][k][j] * std::pow(weights[j], inputMiniBatches[i][k][j] - 1); sum += error[k] * inputMiniBatches[i][k][j] * std::pow(weights[j], inputMiniBatches[i][k][j] - 1);
} }
real_t w_gradient = sum / outputMiniBatches[i].size(); real_t w_gradient = sum / outputMiniBatches[i].size();
// Calculating the initial gradient // Calculating the initial gradient
real_t sum2 = 0; real_t sum2 = 0;
for (int k = 0; k < outputMiniBatches[i].size(); k++) { for (uint32_t k = 0; k < outputMiniBatches[i].size(); k++) {
sum2 += error[k] * std::pow(weights[j], inputMiniBatches[i][k][j]); sum2 += error[k] * std::pow(weights[j], inputMiniBatches[i][k][j]);
} }
@ -174,10 +182,11 @@ void MLPPExpReg::MBGD(real_t learning_rate, int max_epoch, int mini_batch_size,
// Calculating the bias gradient // Calculating the bias gradient
real_t sum = 0; real_t sum = 0;
for (int j = 0; j < outputMiniBatches[i].size(); j++) { for (uint32_t j = 0; j < outputMiniBatches[i].size(); j++) {
sum += (y_hat[j] - outputMiniBatches[i][j]); sum += (y_hat[j] - outputMiniBatches[i][j]);
} }
real_t b_gradient = sum / outputMiniBatches[i].size();
//real_t b_gradient = sum / outputMiniBatches[i].size();
y_hat = Evaluate(inputMiniBatches[i]); y_hat = Evaluate(inputMiniBatches[i]);
if (UI) { if (UI) {
@ -194,12 +203,12 @@ void MLPPExpReg::MBGD(real_t learning_rate, int max_epoch, int mini_batch_size,
} }
real_t MLPPExpReg::score() { real_t MLPPExpReg::score() {
MLPPUtilities util; MLPPUtilities util;
return util.performance(y_hat, outputSet); return util.performance(y_hat, outputSet);
} }
void MLPPExpReg::save(std::string fileName) { void MLPPExpReg::save(std::string fileName) {
MLPPUtilities util; MLPPUtilities util;
util.saveParameters(fileName, weights, initial, bias); util.saveParameters(fileName, weights, initial, bias);
} }
@ -212,9 +221,9 @@ real_t MLPPExpReg::Cost(std::vector<real_t> y_hat, std::vector<real_t> y) {
std::vector<real_t> MLPPExpReg::Evaluate(std::vector<std::vector<real_t>> X) { std::vector<real_t> MLPPExpReg::Evaluate(std::vector<std::vector<real_t>> X) {
std::vector<real_t> y_hat; std::vector<real_t> y_hat;
y_hat.resize(X.size()); y_hat.resize(X.size());
for (int i = 0; i < X.size(); i++) { for (uint32_t i = 0; i < X.size(); i++) {
y_hat[i] = 0; y_hat[i] = 0;
for (int j = 0; j < X[i].size(); j++) { for (uint32_t j = 0; j < X[i].size(); j++) {
y_hat[i] += initial[j] * std::pow(weights[j], X[i][j]); y_hat[i] += initial[j] * std::pow(weights[j], X[i][j]);
} }
y_hat[i] += bias; y_hat[i] += bias;
@ -224,7 +233,7 @@ std::vector<real_t> MLPPExpReg::Evaluate(std::vector<std::vector<real_t>> X) {
real_t MLPPExpReg::Evaluate(std::vector<real_t> x) { real_t MLPPExpReg::Evaluate(std::vector<real_t> x) {
real_t y_hat = 0; real_t y_hat = 0;
for (int i = 0; i < x.size(); i++) { for (uint32_t i = 0; i < x.size(); i++) {
y_hat += initial[i] * std::pow(weights[i], x[i]); y_hat += initial[i] * std::pow(weights[i], x[i]);
} }

2
mlpp/exp_reg/exp_reg.h
View File

@ -13,7 +13,6 @@
#include <string> #include <string>
#include <vector> #include <vector>
class MLPPExpReg { class MLPPExpReg {
public: public:
MLPPExpReg(std::vector<std::vector<real_t>> inputSet, std::vector<real_t> outputSet, std::string reg = "None", real_t lambda = 0.5, real_t alpha = 0.5); MLPPExpReg(std::vector<std::vector<real_t>> inputSet, std::vector<real_t> outputSet, std::string reg = "None", real_t lambda = 0.5, real_t alpha = 0.5);
@ -48,5 +47,4 @@ private:
real_t alpha; /* This is the controlling param for Elastic Net*/ real_t alpha; /* This is the controlling param for Elastic Net*/
}; };
#endif /* ExpReg_hpp */ #endif /* ExpReg_hpp */