diff --git a/gauss_class/gauss_class_2D.m b/gauss_class/gauss_class_2D.m
index 23bbb31..d39b447 100644
--- a/gauss_class/gauss_class_2D.m
+++ b/gauss_class/gauss_class_2D.m
@@ -17,8 +17,8 @@ mu1 = [-1 -1];
 mu2 = [1 1];
 
 % Equal diagnoal covariance matrix
- Sigma1 = [1 0; 0 1];
- Sigma2 = [1 0; 0 1];
+% Sigma1 = [1 0; 0 1];
+% Sigma2 = [1 0; 0 1];
 
 % Diagnoal covariance matrix
 % Sigma1 = [1 0; 0 0.5];
@@ -31,22 +31,22 @@ mu2 = [1 1];
  x1 = -10:.1:10; x2 = -10:.1:10;
 
 % covariance matrix (increase the range for visualization)
-% Sigma1 = [1 0.1; 0.1 0.5];
-% Sigma2 = [0.5 0.3; 0.3 1];
-% x1 = -40:.1:40; x2 = -40:.1:40;
+Sigma1 = [1 0.1; 0.1 0.5];
+Sigma2 = [0.5 0.3; 0.3 1];
+x1 = -40:.1:40; x2 = -40:.1:40;
 
 [X1,X2] = meshgrid(x1,x2);
 %pdf1
 % F1 = mvnpdf([X1(:) X2(:)],mu1,Sigma1);
 F1 = mvndis([X1(:) X2(:)], mu1, Sigma1, prior1);
-F1 = reshape(prior1 * F1,length(x2),length(x1));
+F1 = reshape(F1,length(x2),length(x1));
 subplot(1,2,1);
 surf(x1,x2,F1); hold on;
 
 %pdf2
 % F2 = mvnpdf([X1(:) X2(:)],mu2,Sigma2);
 F2 = mvndis([X1(:) X2(:)], mu2, Sigma2, prior2);
-F2 = reshape(prior2 * F2,length(x2),length(x1));
+F2 = reshape(F2,length(x2),length(x1));
 surf(x1,x2,F2);
 caxis([min(F2(:))-.5*range(F2(:)),max(F2(:))]);
 axis([-4 4 -4 4 0 .4])
@@ -66,13 +66,13 @@ xlabel('x1'); ylabel('x2');
 
 
 function res = mvndis(X, mu, Sigma, prior)
-    [len, ~] = size(X);
+    [len, d] = size(X);
     res = zeros(len, 1);
 
     for i = 1:len
         x = X(i,:);
         mdist = (x - mu) * inv(Sigma) * (x - mu).';
-        res(i) = -log(2*pi) - 1/2*log(det(Sigma)) - 1/2*mdist + log(prior);
+        res(i) = -d/2*log(2*pi) - 1/2*log(det(Sigma)) - 1/2*mdist + log(prior);
     end
     % 1 x 2
     % (1 x 2) x ((2 x 2) x (2 x 1))