AI-NeuralNet-BackProp

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BackProp.pm  view on Meta::CPAN

#		  learning implemented via a generalization of Dobbs rule and
#		  several principals of Hoppfield networks. 
# online: http://www.josiah.countystart.com/modules/AI/cgi-bin/rec.pl
#

package AI::NeuralNet::BackProp::neuron;
	
	use strict;
	
	# Dummy constructor
    sub new {
    	bless {}, shift
	}	
	
	# Rounds floats to ints
	sub intr  {
    	shift if(substr($_[0],0,4) eq 'AI::');
      	try   { return int(sprintf("%.0f",shift)) }
      	catch { return 0 }
	}
    
	
	# Receives input from other neurons. They must
	# be registered as a synapse of this neuron to effectively
	# input.
	sub input {
		my $self 	 =	shift;
		my $sid		 =	shift;
		my $value	 =	shift;
		
		# We simply weight the value sent by the neuron. The neuron identifies itself to us
		# using the code we gave it when it registered itself with us. The code is in $sid, 
		# (synapse ID) and we use that to track the weight of the connection.
		# This line simply multiplies the value by its weight and gets the integer from it.
		$self->{SYNAPSES}->{LIST}->[$sid]->{VALUE}	=	intr($value	*	$self->{SYNAPSES}->{LIST}->[$sid]->{WEIGHT});
		$self->{SYNAPSES}->{LIST}->[$sid]->{FIRED}	=	1;                                 

examples/ex_add2.pl  view on Meta::CPAN

	\%diff4\n";
	   printf "%d %.3f %d %g %s %f %f %f %f\n",
	   $layers, $inc, $top, $forgetfulness, timestr($runtime),
	$percent_diff[0],
	   $percent_diff[1], $percent_diff[2], $percent_diff[3];
	  }
	 }
	}
	
	#....................................................
	sub addnet
	{
	 print "\nCreate a new net with $layers layers, 3 inputs, and 1 output\n";
	 my $net = AI::NeuralNet::BackProp->new($layers,3,1);
	
	 # Disable debugging
	 $net->debug(0);
	
	
	 my @data = (
	  [   2633, 2665, 2685],  [ 2633 + 2665 + 2685 ],

test.pl  view on Meta::CPAN

# Before `make install' is performed this script should be runnable with
# `make test'. After `make install' it should work as `perl test.pl'

BEGIN { $| = 1; print "1..13\n"; }
END {print "not ok 1\n" unless $loaded;}
sub t { my $f=shift;$t++;my $str=($f)?"ok $t":"not ok $t";print $str,"\n";}
use AI::NeuralNet::BackProp;
$loaded = 1;
t 1;
my $net = new AI::NeuralNet::BackProp(2,2,1);
t $net;
t ($net->intr(0.51) eq 1);
t ($net->intr(0.00001) eq 0);
t ($net->intr(0.50001) eq 1);
t $net->learn_set([	
	[ 1,   1   ], [ 2    ] ,

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