predict for One-Vs-All classification

Has predicted the one-vs-all classification successfully.
Notice the usage of max command.

max里面是1就代表找 “列!” 最大，返回值[m,r]里有最大项还有最大项所在 “行!”;  max里面是2就代表找 “行!”
最大，返回值[m,c]里有最大项还有最大项所在的 “列!”
也就是说
[max_value, row_location] = max( X,[],1)
[max_value2,col_location] = max( X,[],2)
记住搭配！

For example:
a = [2 5 4; 1 3 6]
[m,r] = max(a,[].1)
[m2,c] = max(a,[],2)
Predict for Neural Network (Forward Propagation)

Update the algorithm to apply more complicated situation, which is
Neural Network (Forward Propagation).
Not just has two layer, instead I added one hidden layer so that there
are Theta1 and Theta2 existed for the hidden layer and output layer.
The Neural Network (Forward Propagation) is more accurate than the
previous (only two layers) algorithm. So, as a conclusion, the meaning
of more hidden layers is might be aimed at increasing the accuracy.
Initial changes