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Fixed warnings in MLPPLinReg.

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Relintai 2023-02-10 21:55:21 +01:00
parent 20892804ba
commit 97ac09d0d9
1 changed files with 34 additions and 11 deletions
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45
mlpp/lin_reg/lin_reg.cpp
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@ -15,8 +15,15 @@
#include <iostream>
#include <random>
MLPPLinReg::MLPPLinReg(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) {
MLPPLinReg::MLPPLinReg(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;
y_hat.resize(n);
weights = MLPPUtilities::weightInitialization(k);
@ -141,7 +148,9 @@ void MLPPLinReg::MBGD(real_t learning_rate, int max_epoch, int mini_batch_size,
// Creating the mini-batches
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) {
for (int i = 0; i < n_mini_batch; i++) {
@ -179,7 +188,9 @@ void MLPPLinReg::Momentum(real_t learning_rate, int max_epoch, int mini_batch_si
// Creating the mini-batches
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);
// Initializing necessary components for Momentum.
std::vector<real_t> v = alg.zerovec(weights.size());
@ -224,7 +235,9 @@ void MLPPLinReg::NAG(real_t learning_rate, int max_epoch, int mini_batch_size, r
// Creating the mini-batches
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);
// Initializing necessary components for Momentum.
std::vector<real_t> v = alg.zerovec(weights.size());
@ -271,7 +284,9 @@ void MLPPLinReg::Adagrad(real_t learning_rate, int max_epoch, int mini_batch_siz
// Creating the mini-batches
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);
// Initializing necessary components for Adagrad.
std::vector<real_t> v = alg.zerovec(weights.size());
@ -317,7 +332,9 @@ void MLPPLinReg::Adadelta(real_t learning_rate, int max_epoch, int mini_batch_si
// Creating the mini-batches
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);
// Initializing necessary components for Adagrad.
std::vector<real_t> v = alg.zerovec(weights.size());
@ -362,7 +379,9 @@ void MLPPLinReg::Adam(real_t learning_rate, int max_epoch, int mini_batch_size,
// Creating the mini-batches
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);
// Initializing necessary components for Adam.
std::vector<real_t> m = alg.zerovec(weights.size());
@ -413,7 +432,9 @@ void MLPPLinReg::Adamax(real_t learning_rate, int max_epoch, int mini_batch_size
// Creating the mini-batches
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);
std::vector<real_t> m = alg.zerovec(weights.size());
@ -462,7 +483,9 @@ void MLPPLinReg::Nadam(real_t learning_rate, int max_epoch, int mini_batch_size,
// Creating the mini-batches
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);
// Initializing necessary components for Adam.
std::vector<real_t> m = alg.zerovec(weights.size());
@ -513,7 +536,7 @@ void MLPPLinReg::normalEquation() {
std::vector<std::vector<real_t>> inputSetT = alg.transpose(inputSet);
x_means.resize(inputSetT.size());
for (int i = 0; i < inputSetT.size(); i++) {
for (uint32_t i = 0; i < inputSetT.size(); i++) {
x_means[i] = (stat.mean(inputSetT[i]));
}