From e22f26d07415ff99b592b024786e7d97ebe45969 Mon Sep 17 00:00:00 2001
From: Relintai <relintai@protonmail.com>
Date: Fri, 10 Feb 2023 22:02:57 +0100
Subject: [PATCH] Fixed warnings in MLPPGaussianNB.

---
 mlpp/gaussian_nb/gaussian_nb.cpp | 26 ++++++++++++--------------
 mlpp/gaussian_nb/gaussian_nb.h   |  1 -
 2 files changed, 12 insertions(+), 15 deletions(-)

diff --git a/mlpp/gaussian_nb/gaussian_nb.cpp b/mlpp/gaussian_nb/gaussian_nb.cpp
index 4d9c16c..399436e 100644
--- a/mlpp/gaussian_nb/gaussian_nb.cpp
+++ b/mlpp/gaussian_nb/gaussian_nb.cpp
@@ -13,26 +13,24 @@
 #include <iostream>
 #include <random>
 
+MLPPGaussianNB::MLPPGaussianNB(std::vector<std::vector<real_t>> p_inputSet, std::vector<real_t> p_outputSet, int p_class_num) {
+	inputSet = p_inputSet;
+	outputSet = p_outputSet;
+	class_num = p_class_num;
 
-MLPPGaussianNB::MLPPGaussianNB(std::vector<std::vector<real_t>> inputSet, std::vector<real_t> outputSet, int class_num) :
-		inputSet(inputSet), outputSet(outputSet), class_num(class_num) {
 	y_hat.resize(outputSet.size());
 	Evaluate();
-	MLPPLinAlg alg;
 }
 
 std::vector<real_t> MLPPGaussianNB::modelSetTest(std::vector<std::vector<real_t>> X) {
 	std::vector<real_t> y_hat;
-	for (int i = 0; i < X.size(); i++) {
+	for (uint32_t i = 0; i < X.size(); i++) {
 		y_hat.push_back(modelTest(X[i]));
 	}
 	return y_hat;
 }
 
 real_t MLPPGaussianNB::modelTest(std::vector<real_t> x) {
-	MLPPStat  stat;
-	MLPPLinAlg alg;
-
 	real_t score[class_num];
 	real_t y_hat_i = 1;
 	for (int i = class_num - 1; i >= 0; i--) {
@@ -43,12 +41,12 @@ real_t MLPPGaussianNB::modelTest(std::vector<real_t> x) {
 }
 
 real_t MLPPGaussianNB::score() {
-	MLPPUtilities   util;
+	MLPPUtilities util;
 	return util.performance(y_hat, outputSet);
 }
 
 void MLPPGaussianNB::Evaluate() {
-	MLPPStat  stat;
+	MLPPStat stat;
 	MLPPLinAlg alg;
 
 	// Computing mu_k_y and sigma_k_y
@@ -56,8 +54,8 @@ void MLPPGaussianNB::Evaluate() {
 	sigma.resize(class_num);
 	for (int i = class_num - 1; i >= 0; i--) {
 		std::vector<real_t> set;
-		for (int j = 0; j < inputSet.size(); j++) {
-			for (int k = 0; k < inputSet[j].size(); k++) {
+		for (uint32_t j = 0; j < inputSet.size(); j++) {
+			for (uint32_t k = 0; k < inputSet[j].size(); k++) {
 				if (outputSet[j] == i) {
 					set.push_back(inputSet[j][k]);
 				}
@@ -69,16 +67,16 @@ void MLPPGaussianNB::Evaluate() {
 
 	// Priors
 	priors.resize(class_num);
-	for (int i = 0; i < outputSet.size(); i++) {
+	for (uint32_t i = 0; i < outputSet.size(); i++) {
 		priors[int(outputSet[i])]++;
 	}
 	priors = alg.scalarMultiply(real_t(1) / real_t(outputSet.size()), priors);
 
-	for (int i = 0; i < outputSet.size(); i++) {
+	for (uint32_t i = 0; i < outputSet.size(); i++) {
 		real_t score[class_num];
 		real_t y_hat_i = 1;
 		for (int j = class_num - 1; j >= 0; j--) {
-			for (int k = 0; k < inputSet[i].size(); k++) {
+			for (uint32_t k = 0; k < inputSet[i].size(); k++) {
 				y_hat_i += std::log(priors[j] * (1 / sqrt(2 * M_PI * sigma[j] * sigma[j])) * exp(-(inputSet[i][k] * mu[j]) * (inputSet[i][k] * mu[j]) / (2 * sigma[j] * sigma[j])));
 			}
 			score[j] = exp(y_hat_i);
diff --git a/mlpp/gaussian_nb/gaussian_nb.h b/mlpp/gaussian_nb/gaussian_nb.h
index 81258ae..e40f33e 100644
--- a/mlpp/gaussian_nb/gaussian_nb.h
+++ b/mlpp/gaussian_nb/gaussian_nb.h
@@ -12,7 +12,6 @@
 
 #include <vector>
 
-
 class MLPPGaussianNB {
 public:
 	MLPPGaussianNB(std::vector<std::vector<real_t>> inputSet, std::vector<real_t> outputSet, int class_num);