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=begin
    
    File:	examples/ex_alpha.pl
	Author: Josiah Bryan, <jdb@wcoil.com>
	Desc: 

		This demonstrates the ability of a neural net to generalize and predict what the correct
		result is for inputs that it has never seen before.
		
		This teaches the network to classify some twenty-nine seperate 35-byte bitmaps, and
		then it inputs an never-before-seen bitmap and displays the classification the network 
		gives for the unknown bitmap.

=cut

	use AI::NeuralNet::BackProp;

	# Create a new network with 2 layers and 35 neurons in each layer, with 1 output neuron
	my $net = new AI::NeuralNet::BackProp(2,35,1);
	
	# Debug level of 4 gives JUST learn loop iteteration benchmark and comparrison data 
	# as learning progresses.
	$net->debug(4);

	my $letters = [            # All prototype inputs        
        [
        2,1,1,1,2,             # Inputs are   
        1,2,2,2,1,             #  5*7 digitalized caracters 
        1,2,2,2,1,              
        1,1,1,1,1,
        1,2,2,2,1,             # This is the alphabet of the
        1,2,2,2,1,             # HP 28S                      
        1,2,2,2,1,
        ],[0],[
        1,1,1,1,2,
        1,2,2,2,1,
        1,2,2,2,1,
        1,1,1,1,2,
        1,2,2,2,1,
        1,2,2,2,1,
        1,1,1,1,2,
        ],[1],[
        2,1,1,1,2,
        1,2,2,2,1,
        1,2,2,2,2,
        1,2,2,2,2,
        1,2,2,2,2,
        1,2,2,2,1,
        2,1,1,1,2,
        ],[2],[
        1,1,1,2,2,
        1,2,2,1,2,
        1,2,2,2,1,
        1,2,2,2,1,
        1,2,2,2,1,
        1,2,2,1,2,
        1,1,1,2,2,
        ],[4],[
        1,1,1,1,1,
        1,2,2,2,2,
        1,2,2,2,2,
        1,1,1,1,2,
        1,2,2,2,2,
        1,2,2,2,2,
        1,1,1,1,1,
        ],[5],[
        1,1,1,1,1,
        1,2,2,2,2,
        1,2,2,2,2,
        1,1,1,1,2,
        1,2,2,2,2,
        1,2,2,2,2,
        1,2,2,2,2,
		],[6],[
        2,1,1,1,2,
        1,2,2,2,1,
        1,2,2,2,2,
        1,2,2,2,2,
        1,2,2,1,1,
        1,2,2,2,1,
        2,1,1,1,2,
		],[7],[
        1,2,2,2,1,