Add prediction function for one-vs-all classification

ex3/predictOneVsAll.m

@@ -1,17 +1,17 @@
 function p = predictOneVsAll(all_theta, X)
-%PREDICT Predict the label for a trained one-vs-all classifier. The labels 
-%are in the range 1..K, where K = size(all_theta, 1). 
+%PREDICT Predict the label for a trained one-vs-all classifier. The labels
+%are in the range 1..K, where K = size(all_theta, 1).
 %  p = PREDICTONEVSALL(all_theta, X) will return a vector of predictions
 %  for each example in the matrix X. Note that X contains the examples in
 %  rows. all_theta is a matrix where the i-th row is a trained logistic
 %  regression theta vector for the i-th class. You should set p to a vector
 %  of values from 1..K (e.g., p = [1; 3; 1; 2] predicts classes 1, 3, 1, 2
-%  for 4 examples) 
+%  for 4 examples)
 
 m = size(X, 1);
 num_labels = size(all_theta, 1);
 
-% You need to return the following variables correctly 
+% You need to return the following variables correctly
 p = zeros(size(X, 1), 1);
 
 % Add ones to the X data matrix
@@ -24,17 +24,14 @@ X = [ones(m, 1) X];
 %               num_labels).
 %
 % Hint: This code can be done all vectorized using the max function.
-%       In particular, the max function can also return the index of the 
-%       max element, for more information see 'help max'. If your examples 
-%       are in rows, then, you can use max(A, [], 2) to obtain the max 
+%       In particular, the max function can also return the index of the
+%       max element, for more information see 'help max'. If your examples
+%       are in rows, then, you can use max(A, [], 2) to obtain the max
 %       for each row.
-%       
-
-
-
-
-
+%
 
+[~, p] = max(X * all_theta', [], 2);
+%disp(size(p));
 
 % =========================================================================