Add neural network prediction function

ex3/predict.m

@@ -7,12 +7,12 @@ function p = predict(Theta1, Theta2, X)
 m = size(X, 1);
 num_labels = size(Theta2, 1);
 
-% You need to return the following variables correctly 
+% You need to return the following variables correctly
 p = zeros(size(X, 1), 1);
 
 % ====================== YOUR CODE HERE ======================
 % Instructions: Complete the following code to make predictions using
-%               your learned neural network. You should set p to a 
+%               your learned neural network. You should set p to a
 %               vector containing labels between 1 to num_labels.
 %
 % Hint: The max function might come in useful. In particular, the max
@@ -21,13 +21,22 @@ p = zeros(size(X, 1), 1);
 %       can use max(A, [], 2) to obtain the max for each row.
 %
 
+% Theta1 has size 25x401
+% Theta2 has size 10x26
 
+a1 = [ones(m, 1) X];
+% a1 has size mx401
 
+a2_tmp = sigmoid(a1 * Theta1');
+% a2_tmp has size m x 401 * 401 x 25 = m x 25
+a2 = [ones(m, 1) a2_tmp];
+% a2 has size m x 26
 
+a3 = sigmoid(a2 * Theta2');
+% a3 has size m x 26 * 26 x 10 = m x 10
+% note: sigmoid not actually neded here (arg max(sigmoid(z)) = arg max(z))
 
-
-
-
+[~, p] = max(a3, [], 2);
 
 % =========================================================================