From 73e22e5a7ccc331c3aae3c670e66b075519e9cca Mon Sep 17 00:00:00 2001
From: Relintai <relintai@protonmail.com>
Date: Fri, 10 Feb 2023 21:41:05 +0100
Subject: [PATCH] Fixed warnings in MLPPMANN.

---
 mlpp/mann/mann.cpp | 19 +++++++++----------
 1 file changed, 9 insertions(+), 10 deletions(-)

diff --git a/mlpp/mann/mann.cpp b/mlpp/mann/mann.cpp
index ff81663..e466a8f 100644
--- a/mlpp/mann/mann.cpp
+++ b/mlpp/mann/mann.cpp
@@ -13,7 +13,6 @@
 
 #include <iostream>
 
-
 MLPPMANN::MLPPMANN(std::vector<std::vector<real_t>> inputSet, std::vector<std::vector<real_t>> outputSet) :
 		inputSet(inputSet), outputSet(outputSet), n(inputSet.size()), k(inputSet[0].size()), n_output(outputSet[0].size()) {
 }
@@ -27,7 +26,7 @@ std::vector<std::vector<real_t>> MLPPMANN::modelSetTest(std::vector<std::vector<
 		network[0].input = X;
 		network[0].forwardPass();
 
-		for (int i = 1; i < network.size(); i++) {
+		for (uint32_t i = 1; i < network.size(); i++) {
 			network[i].input = network[i - 1].a;
 			network[i].forwardPass();
 		}
@@ -42,7 +41,7 @@ std::vector<std::vector<real_t>> MLPPMANN::modelSetTest(std::vector<std::vector<
 std::vector<real_t> MLPPMANN::modelTest(std::vector<real_t> x) {
 	if (!network.empty()) {
 		network[0].Test(x);
-		for (int i = 1; i < network.size(); i++) {
+		for (uint32_t i = 1; i < network.size(); i++) {
 			network[i].Test(network[i - 1].a_test);
 		}
 		outputLayer->Test(network[network.size() - 1].a_test);
@@ -89,9 +88,9 @@ void MLPPMANN::gradientDescent(real_t learning_rate, int max_epoch, bool UI) {
 			network[network.size() - 1].bias = alg.subtractMatrixRows(network[network.size() - 1].bias, alg.scalarMultiply(learning_rate / n, network[network.size() - 1].delta));
 
 			for (int i = network.size() - 2; i >= 0; i--) {
-				auto hiddenLayerAvn = network[i].activation_map[network[i].activation];
+				hiddenLayerAvn = network[i].activation_map[network[i].activation];
 				network[i].delta = alg.hadamard_product(alg.matmult(network[i + 1].delta, network[i + 1].weights), (avn.*hiddenLayerAvn)(network[i].z, 1));
-				std::vector<std::vector<real_t>> hiddenLayerWGrad = alg.matmult(alg.transpose(network[i].input), network[i].delta);
+				hiddenLayerWGrad = alg.matmult(alg.transpose(network[i].input), network[i].delta);
 				network[i].weights = alg.subtraction(network[i].weights, alg.scalarMultiply(learning_rate / n, hiddenLayerWGrad));
 				network[i].weights = regularization.regWeights(network[i].weights, network[i].lambda, network[i].alpha, network[i].reg);
 				network[i].bias = alg.subtractMatrixRows(network[i].bias, alg.scalarMultiply(learning_rate / n, network[i].delta));
@@ -121,16 +120,16 @@ void MLPPMANN::gradientDescent(real_t learning_rate, int max_epoch, bool UI) {
 }
 
 real_t MLPPMANN::score() {
-	MLPPUtilities   util;
+	MLPPUtilities util;
 	forwardPass();
 	return util.performance(y_hat, outputSet);
 }
 
 void MLPPMANN::save(std::string fileName) {
-	MLPPUtilities   util;
+	MLPPUtilities util;
 	if (!network.empty()) {
 		util.saveParameters(fileName, network[0].weights, network[0].bias, 0, 1);
-		for (int i = 1; i < network.size(); i++) {
+		for (uint32_t i = 1; i < network.size(); i++) {
 			util.saveParameters(fileName, network[i].weights, network[i].bias, 1, i + 1);
 		}
 		util.saveParameters(fileName, outputLayer->weights, outputLayer->bias, 1, network.size() + 1);
@@ -164,7 +163,7 @@ real_t MLPPMANN::Cost(std::vector<std::vector<real_t>> y_hat, std::vector<std::v
 
 	auto cost_function = outputLayer->cost_map[outputLayer->cost];
 	if (!network.empty()) {
-		for (int i = 0; i < network.size() - 1; i++) {
+		for (uint32_t i = 0; i < network.size() - 1; i++) {
 			totalRegTerm += regularization.regTerm(network[i].weights, network[i].lambda, network[i].alpha, network[i].reg);
 		}
 	}
@@ -176,7 +175,7 @@ void MLPPMANN::forwardPass() {
 		network[0].input = inputSet;
 		network[0].forwardPass();
 
-		for (int i = 1; i < network.size(); i++) {
+		for (uint32_t i = 1; i < network.size(); i++) {
 			network[i].input = network[i - 1].a;
 			network[i].forwardPass();
 		}