Completed task 2

compute_cost.m

@@ -14,4 +14,4 @@ function J = compute_cost(X, y, theta)
     end
 
     J = J * (1.0 / (2 * m));
-end
+end

mllab1.m

@@ -11,4 +11,4 @@ iterations = 50;
 do_plot = true;
 
 %% run gradient descent
-t = gradient_descent(X, y, theta, alpha, iterations, do_plot);
+t = gradient_descent(X, y, theta, alpha, iterations, do_plot)

mllab2.m

@@ -1,5 +1,6 @@
 %% This loads our data
 [X, y] = load_data_ex2();
+y
 
 %% Normalise and initialize.
 [X, mean_vec, std_vec] = normalise_features(X);
@@ -9,10 +10,25 @@ X = [ones(size(X, 1), 1), X];
 
 %initialise theta
 theta = [0.0, 0.0, 0.0];
-alpha = 0.1;
+alpha = 0.2;
 iterations = 100;
 
 %% 
-t = gradient_descent(X, y, theta, alpha, iterations);
+t = gradient_descent(X, y, theta, alpha, iterations, false)
+
+% Declare new pair of input features with which to calculate a predicted output
+% value.
+input_size = 3000.0;
+input_bedrooms = 4.0;
+
+% Normalize features
+is_norm = (input_size - mean_vec(1)) / std_vec(1)
+ib_norm = (input_bedrooms - mean_vec(2)) / std_vec(2)
+
+% Use values for theta generated by gradient descent to predict the output
+prediction = t(1) + t(2)*is_norm + t(3)*ib_norm;
+% Round and print output
+round(prediction)
+
 disp 'Press enter to exit!';
 pause;