AI-ANN
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_Inline/build/AI/ANN/Neuron_6185/Makefile.PL  view on Meta::CPAN 

    '/usr/share/perl/5.10/ExtUtils/typemap'
  ],
  'NAME' => 'AI::ANN::Neuron_6185',
  'INC' => '-I/home/st47/sit-bmelab-labview/EANN/ANN/t',
  'VERSION' => '0.00'
}
};
WriteMakefile(%options);

# Remove the Makefile dependency. Causes problems on a few systems.
sub MY::makefile { '' }

examples/_Inline/build/benchmark_pl_03a5/Makefile.PL  view on Meta::CPAN 

    '/usr/share/perl/5.10/ExtUtils/typemap'
  ],
  'NAME' => 'benchmark_pl_03a5',
  'INC' => '-I/home/st47/sit-bmelab-labview/EANN/ANN/examples',
  'VERSION' => '0.00'
}
};
WriteMakefile(%options);

# Remove the Makefile dependency. Causes problems on a few systems.
sub MY::makefile { '' }

examples/_Inline/build/benchmark_pl_3d06/Makefile.PL  view on Meta::CPAN 

    '/usr/share/perl/5.10/ExtUtils/typemap'
  ],
  'NAME' => 'benchmark_pl_3d06',
  'INC' => '-I/home/st47/sit-bmelab-labview/EANN/ANN/examples',
  'VERSION' => '0.00'
}
};
WriteMakefile(%options);

# Remove the Makefile dependency. Causes problems on a few systems.
sub MY::makefile { '' }

examples/_Inline/build/benchmark_pl_6dc1/Makefile.PL  view on Meta::CPAN 

    '/usr/share/perl/5.10/ExtUtils/typemap'
  ],
  'NAME' => 'benchmark_pl_6dc1',
  'INC' => '-I/home/st47/sit-bmelab-labview/EANN/ANN/examples',
  'VERSION' => '0.00'
}
};
WriteMakefile(%options);

# Remove the Makefile dependency. Causes problems on a few systems.
sub MY::makefile { '' }

examples/_Inline/build/benchmark_pl_ac1b/Makefile.PL  view on Meta::CPAN 

    '/usr/share/perl/5.10/ExtUtils/typemap'
  ],
  'NAME' => 'benchmark_pl_ac1b',
  'INC' => '-I/home/st47/sit-bmelab-labview/EANN/ANN/examples',
  'VERSION' => '0.00'
}
};
WriteMakefile(%options);

# Remove the Makefile dependency. Causes problems on a few systems.
sub MY::makefile { '' }

examples/_Inline/build/benchmark_pl_b832/Makefile.PL  view on Meta::CPAN 

    '/usr/share/perl/5.10/ExtUtils/typemap'
  ],
  'NAME' => 'benchmark_pl_b832',
  'INC' => '-I/home/st47/sit-bmelab-labview/EANN/ANN/examples',
  'VERSION' => '0.00'
}
};
WriteMakefile(%options);

# Remove the Makefile dependency. Causes problems on a few systems.
sub MY::makefile { '' }

examples/_Inline/build/benchmark_pl_b905/Makefile.PL  view on Meta::CPAN 

    '/usr/share/perl/5.10/ExtUtils/typemap'
  ],
  'NAME' => 'benchmark_pl_b905',
  'INC' => '-I/home/st47/sit-bmelab-labview/EANN/ANN/examples',
  'VERSION' => '0.00'
}
};
WriteMakefile(%options);

# Remove the Makefile dependency. Causes problems on a few systems.
sub MY::makefile { '' }

examples/_Inline/build/benchmark_pl_c51b/Makefile.PL  view on Meta::CPAN 

    '/usr/share/perl/5.10/ExtUtils/typemap'
  ],
  'NAME' => 'benchmark_pl_c51b',
  'INC' => '-I/home/st47/sit-bmelab-labview/EANN/ANN/examples',
  'VERSION' => '0.00'
}
};
WriteMakefile(%options);

# Remove the Makefile dependency. Causes problems on a few systems.
sub MY::makefile { '' }

examples/benchmark.pl  view on Meta::CPAN 

double afunc_c (float input) {
	return afunc[(int) floor((input)*1000)+2000];
}
double dafunc_c (float input) {
	return dafunc[(int) floor((input)*1000)+2000];
}
END_C

timethis(-1, 'generate_globals()');

sub afunc_pp {
	return 2 * erf(int((shift)*1000)/1000);
}
sub dafunc_pp {
	return 4 / sqrt(M_PI) * exp( -1 * ((int((shift)*1000)/1000) ** 2) );
}

cmpthese( -1, { 'afunc_c'  => sub{afunc_c(4*rand()-2)},
				'afunc_pp' => sub{afunc_pp(4*rand()-2)} });

cmpthese( -1, { 'dafunc_c'  => sub{dafunc_c(4*rand()-2)},
				'dafunc_pp' => sub{dafunc_pp(4*rand()-2)} });

lib/AI/ANN.pm  view on Meta::CPAN 

has 'input_count' => (is => 'ro', isa => 'Int', required => 1);
has 'outputneurons' => (is => 'ro', isa => 'ArrayRef[Int]', required => 1);
has 'network' => (is => 'ro', isa => 'ArrayRef[HashRef]', required => 1);
# network is an arrayref of hashrefs. Each hashref is:
# object => AI::ANN::Neuron
# and has several other elements
has 'inputs' => (is => 'ro', isa => 'ArrayRef[Int]');
has 'rawpotentials' => (is => 'ro', isa => 'ArrayRef[Int]');
has 'minvalue' => (is => 'rw', isa => 'Int', default => 0);
has 'maxvalue' => (is => 'rw', isa => 'Int', default => 1);
has 'afunc' => (is => 'rw', isa => 'CodeRef', default => sub {sub {shift}});
has 'dafunc' => (is => 'rw', isa => 'CodeRef', default => sub {sub {1}});
has 'backprop_eta' => (is => 'rw', isa => 'Num', default => 0.1);

around BUILDARGS => sub {
	my $orig = shift;
	my $class = shift;
	my %data;
	if ( @_ == 1 && ref $_[0] eq 'HASH' ) {
		%data = %{$_[0]};
	} else {
		%data = @_;
	}
	if (exists $data{'inputs'} && not exists $data{'input_count'}) {
		$data{'input_count'} = $data{'inputs'};

lib/AI/ANN.pm  view on Meta::CPAN 

			$neuronlist->[$i]->{'eta_neurons'}
			if defined $neuronlist->[$i]->{'eta_neurons'};
		$data{'network'}->[$i]->{'object'} = 
			new AI::ANN::Neuron( @pass );
	}
	delete $data{'data'};
	return $class->$orig(%data);
};


sub execute {
	my $self = shift;
	my $inputs = $self->{'inputs'} = shift;
	# Don't bother dereferencing $inputs only to rereference a lot
	my $net = $self->{'network'}; # For less typing
	my $lastneuron = $#{$net};
	my @neurons = ();
	foreach my $i (0..$lastneuron) {
		$neurons[$i] = 0;
	}
	foreach my $i (0..$lastneuron) {

lib/AI/ANN.pm  view on Meta::CPAN 

		}
	}

	# Ok, hopefully all the neurons have happy values by now.
	# Get the output values for neurons corresponding to outputneurons
	my @output = map {$neurons[$_]} @{$self->{'outputneurons'}};
	return \@output;
}


sub get_state {
	my $self = shift;
	my $net = $self->{'network'}; # For less typing
	my @neurons = map {$net->[$_]->{'state'}} 0..$#{$self->{'network'}};
	my @output = map {$net->[$_]->{'state'}} @{$self->{'outputneurons'}};

	return $self->{'inputs'}, \@neurons, \@output;
}


sub get_internals {
	my $self = shift;
	my $net = $self->{'network'}; # For less typing
	my $retval = [];
	for (my $i = 0; $i <= $#{$self->{'network'}}; $i++) {
		$retval->[$i] = { iamanoutput => 0,
						  inputs => $net->[$i]->{'object'}->inputs(),
						  neurons => $net->[$i]->{'object'}->neurons(),
						  eta_inputs => $net->[$i]->{'object'}->eta_inputs(),
						  eta_neurons => $net->[$i]->{'object'}->eta_neurons()
						  };
	}
	foreach my $i (@{$self->{'outputneurons'}}) {
		$retval->[$i]->{'iamanoutput'} = 1;
	}
	return dclone($retval); # Dclone for safety.
}


sub readable {
	my $self = shift;
	my $retval = "This network has ". $self->{'inputcount'} ." inputs and ".
					scalar(@{$self->{'network'}}) ." neurons.\n";
	for (my $i = 0; $i <= $#{$self->{'network'}}; $i++) {
		$retval .= "Neuron $i\n";
		while (my ($k, $v) = each %{$self->{'network'}->[$i]->{'object'}->inputs()}) {
			$retval .= "\tInput from input $k, weight is $v\n";
		}
		while (my ($k, $v) = each %{$self->{'network'}->[$i]->{'object'}->neurons()}) {
			$retval .= "\tInput from neuron $k, weight is $v\n";
		}
		if (map {$_ == $i} $self->{'outputneurons'}) {
			$retval .= "\tThis neuron is a network output\n";
		}
	}
	return $retval;
}


sub backprop {
    my $self = shift;
    my $inputs = shift;
    my $desired = shift;
    my $actual = $self->execute($inputs);
    my $net = $self->{'network'};
    my $lastneuron = $#{$net};
    my $deltas = [];
    my $i = 0;
    foreach my $neuron (@{$self->outputneurons()}) {
        $deltas->[$neuron] = $desired->[$i] - $actual->[$i];

lib/AI/ANN/Evolver.pm  view on Meta::CPAN 

has 'max_value' => (is => 'rw', isa => 'Num', default => 1);
has 'min_value' => (is => 'rw', isa => 'Num', default => 0);
has 'mutation_chance' => (is => 'rw', isa => 'Num', default => 0);
has 'mutation_amount' => (is => 'rw', isa => 'CodeRef', default => sub{sub{2 * rand() - 1}});
has 'add_link_chance' => (is => 'rw', isa => 'Num', default => 0);
has 'kill_link_chance' => (is => 'rw', isa => 'Num', default => 0);
has 'sub_crossover_chance' => (is => 'rw', isa => 'Num', default => 0);
has 'gaussian_tau' => (is => 'rw', isa => 'CodeRef', default => sub{sub{1/sqrt(2*sqrt(shift))}});
has 'gaussian_tau_prime' => (is => 'rw', isa => 'CodeRef', default => sub{sub{1/sqrt(2*shift)}});

around BUILDARGS => sub {
    my $orig = shift;
    my $class = shift;
    my %data;
    if ( @_ == 1 && ref $_[0] eq 'HASH' ) {
        %data = %{$_[0]};
    } else {
        %data = @_;
    }
    if ((not (ref $data{'mutation_amount'})) || ref $data{'mutation_amount'} ne 'CODE') {
	my $range = $data{'mutation_amount'};
        $data{'mutation_amount'} = sub { $range * (rand() * 2 - 1) };
    }
    return $class->$orig(%data);
};


sub crossover {
	my $self = shift;
	my $network1 = shift;
	my $network2 = shift;
	my $class = ref($network1);
	my $inputcount = $network1->input_count();
	my $minvalue = $network1->minvalue();
	my $maxvalue = $network1->maxvalue();
	my $afunc = $network1->afunc();
	my $dafunc = $network1->dafunc();
	# They better have the same number of inputs

lib/AI/ANN/Evolver.pm  view on Meta::CPAN 

	my $network3 = $class->new ( 'inputs' => $inputcount, 
								  'data' => $networkdata3,
								  'minvalue' => $minvalue,
								  'maxvalue' => $maxvalue,
								  'afunc' => $afunc,
								  'dafunc' => $dafunc);
	return $network3;
}


sub mutate {
	my $self = shift;
	my $network = shift;
	my $class = ref($network);
	my $networkdata = $network->get_internals();
	my $inputcount = $network->input_count();
	my $minvalue = $network->minvalue();
	my $maxvalue = $network->maxvalue();
	my $afunc = $network->afunc();
	my $dafunc = $network->dafunc();
	my $neuroncount = $#{$networkdata}; # BTW did you notice that this

lib/AI/ANN/Evolver.pm  view on Meta::CPAN 

	$network = $class->new ( 'inputs' => $inputcount, 
							 'data' => $networkdata,
							 'minvalue' => $minvalue,
							 'maxvalue' => $maxvalue,
							 'afunc' => $afunc,
							 'dafunc' => $dafunc);
	return $network;
}


sub mutate_gaussian {
    my $self = shift;
    my $network = shift;
	my $class = ref($network);
	my $networkdata = $network->get_internals();
	my $inputcount = $network->input_count();
	my $minvalue = $network->minvalue();
	my $maxvalue = $network->maxvalue();
	my $afunc = $network->afunc();
	my $dafunc = $network->dafunc();
	my $neuroncount = $#{$networkdata}; # BTW did you notice that this

lib/AI/ANN/Neuron.pm  view on Meta::CPAN 

END_C


has 'id' => (is => 'rw', isa => 'Int');
has 'inputs' => (is => 'rw', isa => 'ArrayRef', required => 1);
has 'neurons' => (is => 'rw', isa => 'ArrayRef', required => 1);
has 'eta_inputs' => (is => 'rw', isa => 'ArrayRef');
has 'eta_neurons' => (is => 'rw', isa => 'ArrayRef');
has 'inline_c' => (is => 'ro', isa => 'Int', required => 1, default => 1);

around BUILDARGS => sub {
	my $orig = shift;
	my $class = shift;
	my %data;
	if ( @_ >= 2 && ref $_[0] && ref $_[1]) {
		%data = ('inputs' => $_[0], 'neurons' => $_[1]);
		$data{'eta_inputs'} = $_[2] if defined $_[2];
		$data{'eta_neurons'} = $_[3] if defined $_[3];
	} elsif ( @_ >= 3 && ref $_[1] && ref $_[2]) {
		%data = ('id' => $_[0], 'inputs' => $_[1], 'neurons' => $_[2]);
		$data{'eta_inputs'} = $_[3] if defined $_[3];

lib/AI/ANN/Neuron.pm  view on Meta::CPAN 

	foreach my $i (0..$#{$data{'eta_inputs'}}) {
		$data{'eta_inputs'}->[$i] ||= 0;
	}
	foreach my $i (0..$#{$data{'eta_neurons'}}) {
		$data{'eta_neurons'}->[$i] ||= 0;
	}
	return $class->$orig(%data);
};


sub ready {
	my $self = shift;
	my $inputs = shift;
	my $neurons = shift;
	if (ref $neurons eq 'HASH') {
		my @temparray;
		foreach my $i (keys %$neurons) {
			if (defined $neurons->{$i} && $neurons->{$i} != 0) {
				$temparray[$i]=$neurons->{$i};
			}
		}

lib/AI/ANN/Neuron.pm  view on Meta::CPAN 

	}
	foreach my $id (0..$#{$self->{'neurons'}}) {
		unless ((not defined $self->{'neurons'}->[$id]) || 
				$self->{'neurons'}->[$id] == 0 || defined $neurons[$id])
				{return 0}
	}
	return 1;
}


sub execute {
	my $self = shift;
	my $inputs = shift;
	my $neurons = shift;
	if (ref $neurons eq 'HASH') {
		my @temparray;
		foreach my $i (keys %$neurons) {
			$temparray[$i]=$neurons->{$i} || 0;
		}
		$neurons=\@temparray;
	}



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