AI-NeuralNet-Mesh

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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_aln.pl  view on Meta::CPAN

	# Learn a pattern and print stats.
	if(!$net->load('aln.mesh')) {
		print "Learning";
		print "Done!\nLearning took ",$net->learn([1,1,0,1,0,1,1,1],[0]),"\n";
		$net->save('aln.mesh');
	}
		
	# Print logic gate types
	$net->print_aln();
	
	# Test it out
	print "\nPattern: [1,1,0,1,0,1,1,1]".
		  "\nResult: ",$net->run([1,1,1,1,1,1,1,1])->[0],"\n";




######################################################################
#-################ ALN Implementation Code  ########################-#
######################################################################
	

examples/ex_alpha.pl  view on Meta::CPAN

	# Build a test map 
	my $tmp	=	[0,1,1,1,0,
				 1,0,0,0,1,
				 1,0,0,0,1,
				 1,1,1,1,1,
				 1,0,0,0,1,
				 1,0,0,0,1,
				 1,0,0,0,1];
	
	# 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_and.pl  view on Meta::CPAN

		$net->learn_set([	
			[1,1], [1],
			[1,0], [0],
			[0,1], [0],
			[0,0], [0],
		]);
		$net->save('and.mesh');
	}

	print "Learning complete.\n";
	print "Testing with a gate value of (0,0):",$net->run([0,0])->[0],"\n";
	print "Testing with a gate value of (0,1):",$net->run([0,1])->[0],"\n";
	print "Testing with a gate value of (1,0):",$net->run([1,0])->[0],"\n";
	print "Testing with a gate value of (1,1):",$net->run([1,1])->[0],"\n";
	
	

examples/ex_bmp.pl  view on Meta::CPAN

	if(!$net->load('images.mesh')) {
		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);
			
			# Print it 
			print "\n\nForgetfullness: $f%\n";

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

			my $t2=new Benchmark;
			my $td=timediff($t0,$t1);

examples/ex_dow.pl  view on Meta::CPAN

        if(!$net->load('DOW.mesh')) {
		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.mesh');
            

examples/ex_pcx.pl  view on Meta::CPAN

	if(!$net->load("pcx.mesh")) {
		print "Learning high block...\n";
		print $net->learn($blocks[$net->high(\@score)],"highest");
		
		$net->save("pcx.mesh");
		
		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::'); 

examples/ex_wine.pl  view on Meta::CPAN

		# Make it learn the whole dataset $top times
		my @list;
		my $top=5;
		for my $a (0..$top) {
			print "\n\nOuter Loop: $a\n";
			
			for(0..$#{$sets}) {
				next if(!defined $sets->[$_]->[0]);
				my $t1=new Benchmark;
				
				# Test fogetfullness
				my $f = $net->learn_set($sets->[$_],	inc		=>	0.2,	
														max		=>	2000,
														error	=>	0.01,
														leave	=>	2);
				
				# Print it 
				print "\n\nForgetfullness: $f%\n";
	
				my $t2=new Benchmark;
				my $td=timediff($t2,$t1);

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