From 7e738f79eeceed7ac68906f3e998503dd2154d10 Mon Sep 17 00:00:00 2001
From: Relintai <relintai@protonmail.com>
Date: Fri, 10 Feb 2023 22:46:16 +0100
Subject: [PATCH] Fixed warnings in MLPPBernoulliNB.

---
 mlpp/bernoulli_nb/bernoulli_nb.cpp | 51 ++++++++++++++++--------------
 1 file changed, 27 insertions(+), 24 deletions(-)

diff --git a/mlpp/bernoulli_nb/bernoulli_nb.cpp b/mlpp/bernoulli_nb/bernoulli_nb.cpp
index ed6c336..98dcc0a 100644
--- a/mlpp/bernoulli_nb/bernoulli_nb.cpp
+++ b/mlpp/bernoulli_nb/bernoulli_nb.cpp
@@ -12,15 +12,18 @@
 #include <iostream>
 #include <random>
 
-MLPPBernoulliNB::MLPPBernoulliNB(std::vector<std::vector<real_t>> inputSet, std::vector<real_t> outputSet) :
-		inputSet(inputSet), outputSet(outputSet), class_num(2) {
+MLPPBernoulliNB::MLPPBernoulliNB(std::vector<std::vector<real_t>> p_inputSet, std::vector<real_t> p_outputSet) {
+	inputSet = p_inputSet;
+	outputSet = p_outputSet;
+	class_num = 2;
+
 	y_hat.resize(outputSet.size());
 	Evaluate();
 }
 
 std::vector<real_t> MLPPBernoulliNB::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;
@@ -32,8 +35,8 @@ real_t MLPPBernoulliNB::modelTest(std::vector<real_t> x) {
 
 	std::vector<int> foundIndices;
 
-	for (int j = 0; j < x.size(); j++) {
-		for (int k = 0; k < vocab.size(); k++) {
+	for (uint32_t j = 0; j < x.size(); j++) {
+		for (uint32_t k = 0; k < vocab.size(); k++) {
 			if (x[j] == vocab[k]) {
 				score_0 *= theta[0][vocab[k]];
 				score_1 *= theta[1][vocab[k]];
@@ -43,9 +46,9 @@ real_t MLPPBernoulliNB::modelTest(std::vector<real_t> x) {
 		}
 	}
 
-	for (int i = 0; i < vocab.size(); i++) {
+	for (uint32_t i = 0; i < vocab.size(); i++) {
 		bool found = false;
-		for (int j = 0; j < foundIndices.size(); j++) {
+		for (uint32_t j = 0; j < foundIndices.size(); j++) {
 			if (vocab[i] == vocab[foundIndices[j]]) {
 				found = true;
 			}
@@ -69,7 +72,7 @@ real_t MLPPBernoulliNB::modelTest(std::vector<real_t> x) {
 }
 
 real_t MLPPBernoulliNB::score() {
-	MLPPUtilities   util;
+	MLPPUtilities util;
 	return util.performance(y_hat, outputSet);
 }
 
@@ -85,19 +88,19 @@ void MLPPBernoulliNB::computeTheta() {
 
 	// Setting all values in the hasmap by default to 0.
 	for (int i = class_num - 1; i >= 0; i--) {
-		for (int j = 0; j < vocab.size(); j++) {
+		for (uint32_t j = 0; j < vocab.size(); j++) {
 			theta[i][vocab[j]] = 0;
 		}
 	}
 
-	for (int i = 0; i < inputSet.size(); i++) {
-		for (int j = 0; j < inputSet[0].size(); j++) {
+	for (uint32_t i = 0; i < inputSet.size(); i++) {
+		for (uint32_t j = 0; j < inputSet[0].size(); j++) {
 			theta[outputSet[i]][inputSet[i][j]]++;
 		}
 	}
 
-	for (int i = 0; i < theta.size(); i++) {
-		for (int j = 0; j < theta[i].size(); j++) {
+	for (uint32_t i = 0; i < theta.size(); i++) {
+		for (uint32_t j = 0; j < theta[i].size(); j++) {
 			if (i == 0) {
 				theta[i][j] /= prior_0 * y_hat.size();
 			} else {
@@ -108,15 +111,15 @@ void MLPPBernoulliNB::computeTheta() {
 }
 
 void MLPPBernoulliNB::Evaluate() {
-	for (int i = 0; i < outputSet.size(); i++) {
+	for (uint32_t i = 0; i < outputSet.size(); i++) {
 		// Pr(B | A) * Pr(A)
 		real_t score_0 = 1;
 		real_t score_1 = 1;
 
 		real_t sum = 0;
-		for (int i = 0; i < outputSet.size(); i++) {
-			if (outputSet[i] == 1) {
-				sum += outputSet[i];
+		for (uint32_t ii = 0; ii < outputSet.size(); ii++) {
+			if (outputSet[ii] == 1) {
+				sum += outputSet[ii];
 			}
 		}
 
@@ -132,8 +135,8 @@ void MLPPBernoulliNB::Evaluate() {
 
 		std::vector<int> foundIndices;
 
-		for (int j = 0; j < inputSet.size(); j++) {
-			for (int k = 0; k < vocab.size(); k++) {
+		for (uint32_t j = 0; j < inputSet.size(); j++) {
+			for (uint32_t k = 0; k < vocab.size(); k++) {
 				if (inputSet[i][j] == vocab[k]) {
 					score_0 += std::log(theta[0][vocab[k]]);
 					score_1 += std::log(theta[1][vocab[k]]);
@@ -143,16 +146,16 @@ void MLPPBernoulliNB::Evaluate() {
 			}
 		}
 
-		for (int i = 0; i < vocab.size(); i++) {
+		for (uint32_t ii = 0; ii < vocab.size(); ii++) {
 			bool found = false;
-			for (int j = 0; j < foundIndices.size(); j++) {
-				if (vocab[i] == vocab[foundIndices[j]]) {
+			for (uint32_t j = 0; j < foundIndices.size(); j++) {
+				if (vocab[ii] == vocab[foundIndices[j]]) {
 					found = true;
 				}
 			}
 			if (!found) {
-				score_0 += std::log(1 - theta[0][vocab[i]]);
-				score_1 += std::log(1 - theta[1][vocab[i]]);
+				score_0 += std::log(1 - theta[0][vocab[ii]]);
+				score_1 += std::log(1 - theta[1][vocab[ii]]);
 			}
 		}