Fixed normalization

source/matutils.hpp

@@ -64,23 +64,27 @@ namespace Matutils
 		// Dim = 1: Normalize over single training pattern
 		double k = 1;
 		size_t dim = 0;
-		arma::mat xn = arma::normalise(src, 2, dim);
+		arma::mat mean = arma::mean(src, dim);
-		arma::mat mean = arma::mean(xn, dim);
+		arma::mat stddev = arma::stddev(src, 0, dim);
-		arma::mat stddev = arma::stddev(xn, 0, dim);
+
-		arma::mat stddev_m;
+		arma::mat mean_mat;
+		arma::mat std_mat;
 
 		if (dim==0)
 		{
-			stddev_m = arma::repmat(stddev, xn.n_rows, 1);
+			mean_mat = arma::repmat(mean, src.n_rows, 1);
+			std_mat = arma::repmat(stddev, src.n_rows, 1);
 		}
 		else
 		{
-			stddev_m = arma::repmat(stddev, 1, xn.n_cols);
+			mean_mat = arma::repmat(mean, 1, src.n_cols);
+			std_mat = arma::repmat(stddev, 1, src.n_cols);
 		}
 
-		arma::mat y = 1/(1+arma::exp(-k*xn/stddev_m));
+		arma::mat xn = src - mean_mat;
-		std::cout << "mean" << "  : " << std::endl << mean << std::endl;
+		arma::mat y = xn/(std_mat + 1e-9);
-		std::cout << "stddev" << ": " << std::endl << stddev << std::endl;
+		std::cout << "mean" << "  : " << std::endl << arma::mean(y, dim) << std::endl;
+		std::cout << "stddev" << ": " << std::endl << arma::stddev(y, 0, dim) << std::endl;
 		return y;
 	}