Learning curve function

ex5/learningCurve.m

@@ -42,18 +42,34 @@ error_val   = zeros(m, 1);
 %
 % Hint: You can loop over the examples with the following:
 %
-%       for i = 1:m
-%           % Compute train/cross validation errors using training examples 
-%           % X(1:i, :) and y(1:i), storing the result in 
-%           % error_train(i) and error_val(i)
-%           ....
-%           
-%       end
+for i = 1:m
+  % Compute train/cross validation errors using training examples
+  % X(1:i, :) and y(1:i), storing the result in
+  % error_train(i) and error_val(i)
+
+  X_ = X(1:i,:);
+  y_ = y(1:i);
+
+  % Train with regularization
+  lambda = 1;
+  theta = trainLinearReg(X_, y_, lambda);
+
+  % Compute the error with lambda = 0
+  lambda = 0;
+  error_train(i) = linearRegCostFunction(X_, y_, theta, lambda);
+  error_val(i) = linearRegCostFunction(Xval, yval, theta, lambda);
+
+end
 %
 
 % ---------------------- Sample Solution ----------------------
 
 
+%       for i = 1:m
+%           % Compute train/cross validation errors using training examples
+%           % X(1:i, :) and y(1:i), storing the result in
+%           % error_train(i) and error_val(i)
+%           ....