First question

machine-learning-ex6/ex6/ex6.m

@@ -35,7 +35,7 @@ load('ex6data1.mat');
 plotData(X, y);
 
 fprintf('Program paused. Press enter to continue.\n');
-pause;
+% pause;
 
 %% ==================== Part 2: Training Linear SVM ====================
 %  The following code will train a linear SVM on the dataset and plot the
@@ -55,7 +55,7 @@ model = svmTrain(X, y, C, @linearKernel, 1e-3, 20);
 visualizeBoundaryLinear(X, y, model);
 
 fprintf('Program paused. Press enter to continue.\n');
-pause;
+% pause;
 
 %% =============== Part 3: Implementing Gaussian Kernel ===============
 %  You will now implement the Gaussian kernel to use
@@ -70,7 +70,7 @@ fprintf(['Gaussian Kernel between x1 = [1; 2; 1], x2 = [0; 4; -1], sigma = 0.5 :
          '\n\t%f\n(this value should be about 0.324652)\n'], sim);
 
 fprintf('Program paused. Press enter to continue.\n');
-pause;
+% pause;
 
 %% =============== Part 4: Visualizing Dataset 2 ================
 %  The following code will load the next dataset into your environment and 
@@ -87,7 +87,7 @@ load('ex6data2.mat');
 plotData(X, y);
 
 fprintf('Program paused. Press enter to continue.\n');
-pause;
+% pause;
 
 %% ========== Part 5: Training SVM with RBF Kernel (Dataset 2) ==========
 %  After you have implemented the kernel, we can now use it to train the 

machine-learning-ex6/ex6/gaussianKernel.m

@@ -16,10 +16,12 @@ sim = 0;
 %
 %
 
+xt = (x1 - x2).^2;
 
+top = sum(xt);
+bottom = 2 * (sigma^2);
 
-
-
+sim = exp(-top/bottom);
 
 % =============================================================