diff --git a/MLPP/LinReg/LinReg.cpp b/MLPP/LinReg/LinReg.cpp
index a2b6bed..faf2c1e 100644
--- a/MLPP/LinReg/LinReg.cpp
+++ b/MLPP/LinReg/LinReg.cpp
@@ -141,10 +141,11 @@ namespace MLPP{
         LinAlg alg;
         Stat stat;
         std::vector<double> x_means;
-        
-        x_means.resize(alg.transpose(inputSet).size());
-        for(int i = 0; i < alg.transpose(inputSet).size(); i++){
-            x_means[i] = (stat.mean(alg.transpose(inputSet)[i]));
+        std::vector<std::vector<double>> inputSetT = alg.transpose(inputSet);
+
+        x_means.resize(inputSetT.size());
+        for(int i = 0; i < inputSetT.size(); i++){
+            x_means[i] = (stat.mean(inputSetT[i]));
         }
         
         try{
diff --git a/SharedLib/MLPP.so b/SharedLib/MLPP.so
index 626ff47..d64d729 100755
Binary files a/SharedLib/MLPP.so and b/SharedLib/MLPP.so differ
diff --git a/main.cpp b/main.cpp
index 3add300..6f73cf6 100644
--- a/main.cpp
+++ b/main.cpp
@@ -39,6 +39,7 @@
 #include "MLPP/Stat/Stat.hpp"
 #include "MLPP/LinAlg/LinAlg.hpp"
 #include "MLPP/Activation/Activation.hpp"
+#include "MLPP/Cost/Cost.hpp"
 #include "MLPP/Data/Data.hpp"
 #include "MLPP/Convolutions/Convolutions.hpp"
 
@@ -51,6 +52,7 @@ int main() {
     Stat stat;
     LinAlg alg;
     Activation avn;
+    Cost cost;
     Data data; 
     Convolutions conv; 
 
@@ -142,12 +144,12 @@ int main() {
     // alg.printVector(model.modelSetTest((alg.transpose(inputSet))));
     // std::cout << "ACCURACY: " << 100 * model.score() << "%" << std::endl;
 
-    // // LOGISTIC REGRESSION
+    // LOGISTIC REGRESSION
     // std::vector<std::vector<double>> inputSet; 
     // std::vector<double> outputSet; 
     // data.setData(30, "/Users/marcmelikyan/Desktop/Data/BreastCancer.csv", inputSet, outputSet);
     // LogReg model(inputSet, outputSet); 
-    // model.SGD(0.001, 100000, 0);
+    // model.gradientDescent(0.0001, 100000, 0);
     // model.MLE(0.1, 10000, 0);
     // alg.printVector(model.modelSetTest(inputSet));
     // std::cout << "ACCURACY: " << 100 * model.score() << "%" << std::endl;