From e191ab9a1631f7adf8a38f4741ae5d249680da3f Mon Sep 17 00:00:00 2001
From: Relintai <relintai@protonmail.com>
Date: Fri, 10 Feb 2023 20:53:26 +0100
Subject: [PATCH] Fixed warnings in MLPPTanhReg.

---
 mlpp/tanh_reg/tanh_reg.cpp | 12 +++++++-----
 mlpp/tanh_reg/tanh_reg.h   |  3 ---
 2 files changed, 7 insertions(+), 8 deletions(-)

diff --git a/mlpp/tanh_reg/tanh_reg.cpp b/mlpp/tanh_reg/tanh_reg.cpp
index f1d4f39..87e7bb8 100644
--- a/mlpp/tanh_reg/tanh_reg.cpp
+++ b/mlpp/tanh_reg/tanh_reg.cpp
@@ -5,6 +5,7 @@
 //
 
 #include "tanh_reg.h"
+
 #include "../activation/activation.h"
 #include "../cost/cost.h"
 #include "../lin_alg/lin_alg.h"
@@ -14,7 +15,6 @@
 #include <iostream>
 #include <random>
 
-
 MLPPTanhReg::MLPPTanhReg(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);
@@ -107,12 +107,15 @@ void MLPPTanhReg::MBGD(real_t learning_rate, int max_epoch, int mini_batch_size,
 	MLPPActivation avn;
 	MLPPLinAlg alg;
 	MLPPReg regularization;
+
 	real_t cost_prev = 0;
 	int epoch = 1;
 
 	// 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++) {
@@ -147,12 +150,12 @@ void MLPPTanhReg::MBGD(real_t learning_rate, int max_epoch, int mini_batch_size,
 }
 
 real_t MLPPTanhReg::score() {
-	MLPPUtilities   util;
+	MLPPUtilities util;
 	return util.performance(y_hat, outputSet);
 }
 
 void MLPPTanhReg::save(std::string fileName) {
-	MLPPUtilities   util;
+	MLPPUtilities util;
 	util.saveParameters(fileName, weights, bias);
 }
 
@@ -186,7 +189,6 @@ real_t MLPPTanhReg::propagate(std::vector<real_t> x) {
 
 // Tanh ( wTx + b )
 void MLPPTanhReg::forwardPass() {
-	MLPPLinAlg alg;
 	MLPPActivation avn;
 
 	z = propagate(inputSet);
diff --git a/mlpp/tanh_reg/tanh_reg.h b/mlpp/tanh_reg/tanh_reg.h
index 3a97131..c23528b 100644
--- a/mlpp/tanh_reg/tanh_reg.h
+++ b/mlpp/tanh_reg/tanh_reg.h
@@ -13,8 +13,6 @@
 #include <string>
 #include <vector>
 
-
-
 class MLPPTanhReg {
 public:
 	MLPPTanhReg(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);
@@ -54,5 +52,4 @@ private:
 	real_t alpha; /* This is the controlling param for Elastic Net*/
 };
 
-
 #endif /* TanhReg_hpp */