AI-NeuralNet-BackProp

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examples/ex_add2.pl  view on Meta::CPAN

	  my @set;
	  my $fb;
	  my $net = shift;
	  my @data = @_;
	  undef @percent_diff; #@answers; undef @predictions;
	
	  for( $i=0; defined( $data[$i] ); $i++ ){
	   @set = @{ $data[$i] };
	   $fb = $net->run(\@set)->[0];
	   # Print output
	   print "Test Factors: (",join(',',@set),")\n";
	   $answer = eval( join( '+',@set ));
	   push @percent_diff, 100.0 * abs( $answer - $fb )/ $answer;
	   print "Prediction : $fb      answer: $answer\n";
	  }
	 }
	
	

examples/ex_alpha.pl  view on Meta::CPAN

	# Build a test map 
	my $tmp	=	[2,1,1,1,2,
				 1,2,2,2,1,
				 1,2,2,2,1,
				 1,1,1,1,1,
				 1,2,2,2,1,
				 1,2,2,2,1,
				 1,2,2,2,1];
	
	# Display test map
	print "\nTest map:\n";
	$net->join_cols($tmp,5);
	
	# Display network results
	print "Letter index matched: ",$net->run($tmp)->[0],"\n";
	

examples/ex_bmp.pl  view on Meta::CPAN

	if(!$net->load('images.net')) {
		print "\nLearning started...\n";
		
		# Make it learn the whole dataset $top times
		my @list;
		my $top=3;
		for my $a (0..$top) {
			my $t1=new Benchmark;
			print "\n\nOuter Loop: $a\n";
			
			# Test fogetfullness
			my $f = $net->learn_set(\@data,	inc		=>	0.1,	
											max		=>	500,
											error	=>	-1);
			
			# Print it 
			print "\n\nForgetfullness: $f%\n";

			# Save net to disk				
			$net->save('images.net');

examples/ex_bmp.pl  view on Meta::CPAN

					1,1,1,2,2,
					1,2,2,2,2,
					2,1,1,1,2		);
		
	
	# Image number
	my $fb=$net->run(\@set)->[0];
	
	
	# Print output
	print "\nTest Map: \n";
	$net->join_cols(\@set,5);
	print "Image number matched: $fb\n";
	

examples/ex_bmp2.pl  view on Meta::CPAN

	# Build a test map 
	my @tmp	=	(0,0,1,1,1,
				 1,1,1,0,0,
				 0,0,0,1,0,
				 0,0,0,1,0,
				 0,0,0,1,0,                                          
				 0,0,0,0,0,
				 0,1,1,0,0);
	
	# Display test map
	print "\nTest map:\n";
	$net->join_cols(\@tmp,5,'');
	
	print "Running test...\n";
		                    
	# Run the actual test and get network output
	print "Result: ",$net->run_uc(\@tmp),"\n";
	
	print "Test run complete.\n";
	
	

examples/ex_dow.pl  view on Meta::CPAN

	if(!$net->load('dow.dat')) {
		print "\nLearning started...\n";
		
		# Make it learn the whole dataset $top times
		my @list;
		my $top=1;
		for my $a (0..$top) {
			my $t1=new Benchmark;
			print "\n\nOuter Loop: $a\n";
			
			# Test fogetfullness
			my $f = $net->learn_set(\@data,	inc		=>	0.2,	
											max		=>	2000,
											error	=>	-1);
			
			# Print it 
			print "\n\nForgetfullness: $f%\n";

			# Save net to disk				
			$net->save('dow.dat');
            

examples/ex_dow.pl  view on Meta::CPAN

                                                                          
	# Run a prediction using fake data
	#			Month	CPI  CPI-1 CPI-3 	Oil  Oil-1 Oil-3    Dow   Dow-1 Dow-3    
	my @set=(	10,		352, 309,  203, 	18.3, 18.7, 16.1, 	2592, 2641, 2651	  ); 
	
	# Dow Ave (output)	
	my $fb=$net->run(\@set)->[0];
	
	
	# Print output
	print "\nTest Factors: (",join(',',@set),")\n";
	print "DOW Prediction for Month #11: $fb\n";
	

examples/ex_pcx.pl  view on Meta::CPAN

	if(!$net->load("pcx.dat")) {
		print "Learning high block...\n";
		print $net->learn($blocks[$net->high(\@score)],"highest");
		
		$net->save("pcx.dat");
		
		print "Learning low block...\n";
		$net->learn($blocks[$net->low(\@score)],"lowest");
	}
	
	print "Testing random block...\n";
	
	print "Result: ",$net->run($blocks[rand()*$b])->[0],"\n";
	
	print "Bencmark for run: ", $net->benchmarked(), "\n";
	
	$net->save("pcx2.net");
	
	sub print_ref {
		no strict 'refs';
		shift if(substr($_[0],0,4) eq 'AI::');