AI-ANN
view release on metacpan or search on metacpan
ANN_specification view on Meta::CPAN
implements an individual neuron from the neural network
from ann, $self->{'network'}->[$n]->{'object'} = new ANN::Neuron( $thisid, {$inputid => $weight, ...}, {$neuronid => $weight})
from ann, $self->{'network'}->[$n]->{'object'}->ready( [$input0, $input1, ...], {$neuronid => $neuronvalue, ...} ) returns 1 if all inputs are available, 0 otherwise
from ann, $self->{'network'}->[$n]->{'object'}->execute( [$input0, $input1, ...], {$neuronid => $neuronvalue, ...} ) returns the output value of the neuron
in addition, get_inputs and get_neurons are available. There return the respective original hashrefs.
ANN::Evolver
evolves the ann
from caller, $handofgod = new ANN::Evolver({$mutationchance, $mutationamount, $addlinkchance, $killlinkchance, $subcrossoverchance}) add should be zero if you want to preserve feed-forwardness
from caller, $handofgod->crossover($network1, $network2) returns $network3, which inherits 50% of each parent's traits. Each neuron has $subcrossoverchance to inherit 50% of its traits from each parent.
from caller, $handofgod->mutate($network3) introduces some random mutations into neuron weights. Has a $mutationchance to change each weight by up to $mutationamount, a $addlinkchance to change an input from zero to up to $mutationamount, and a $kil...
ANN::SimWorld
uses an ANN object to attempt to survive in a virtual world, which must be defined by the caller
from caller, $world = new ANN::SimWorld( //stuff here )
from caller, $world->($network) returns a fitness variable
0.005 2011-05-31 20:18:02 UTC
Convert to use Moose. I'm told that this is better. There are a few
neat things (not having to write accessors, and apparently
inheritance just works, I'll find out about that soon enough...)
but overall I really don't see the point. Default values, type
constraints, I had all that anyway. Meh.
Add some words about what the point of this module is.
0.004 2011-05-31 02:35:26 UTC
Minor calling changes that I need to commit so I can test them with
another module.
0.003 2011-05-31 01:09:32 UTC
Minor. Correct the perl version requirement.
0.002 2011-05-31 01:04:53 UTC
Make many obvious (to a computer, I hope) references to the fact
that this requires perl 5.14, to prevent the bloody annoying
CPAN Testers emails. Note that I chose to do this rather than
make the module compatible with earlier versions.
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lib/AI/ANN.pm view on Meta::CPAN
# Fixes warnings about uninitialized values, but we make
# sure $i is valid first.
$neurons[$i] = 0;
}
my $potential = $net->[$i]->{'object'}->execute($inputs, \@neurons);
$self->{'rawpotentials'}->[$i] = $potential;
$potential = &{$self->{'afunc'}}($potential);
$potential = $self->{'maxvalue'} if $potential > $self->{'maxvalue'};
$potential = $self->{'minvalue'} if $potential < $self->{'minvalue'};
$neuronstemp[$i] = $net->[$i]->{'state'} = $potential;
# We want to know the absolute change
if (abs($neurons[$i]-$neuronstemp[$i])>$maxerror) {
$maxerror = abs($neurons[$i]-$neuronstemp[$i]);
}
}
foreach my $i (0..$lastneuron) {
# Update $neurons, since that is what gets passed to execute
$neurons[$i] = $neuronstemp[$i];
}
if (($maxerror < 0.0001 && $loopcounter >= 5) || $loopcounter > 250) {last}
$loopcounter++;
lib/AI/ANN/Evolver.pm view on Meta::CPAN
AI::ANN::Evolver->new( { mutation_chance => $mutationchance,
mutation_amount => $mutationamount, add_link_chance => $addlinkchance,
kill_link_chance => $killlinkchance, sub_crossover_chance =>
$subcrossoverchance, min_value => $minvalue, max_value => $maxvalue } )
All values have a sane default.
mutation_chance is the chance that calling mutate() will add a random value
on a per-link basis. It only affects existing (nonzero) links.
mutation_amount is the maximum change that any single mutation can introduce.
It affects the result of successful mutation_chance rolls, the maximum
value after an add_link_chance roll, and the maximum strength of a link
that can be deleted by kill_link_chance rolls. It can either add or
subtract.
add_link_chance is the chance that, during a mutate() call, each pair of
unconnected neurons or each unconnected neuron => input pair will
spontaneously develop a connection. This should be extremely small, as
it is not an overall chance, put a chance for each connection that does
not yet exist. If you wish to ensure that your neural net does not become
recursive, this must be zero.
t/00_initialize.t view on Meta::CPAN
# change 'tests => 1' to 'tests => last_test_to_print';
use Test::More tests => 9;
BEGIN { use_ok('AI::ANN');
use_ok('AI::ANN::Neuron');
use_ok('AI::ANN::Evolver'); };
#########################
# Insert your test code below, the Test::More module is use()ed here so read
# its man page ( perldoc Test::More ) for help writing this test script.
t/01_neuron_basic.t view on Meta::CPAN
# change 'tests => 1' to 'tests => last_test_to_print';
use Test::More tests => 17;
BEGIN { use_ok('AI::ANN');
use_ok('AI::ANN::Neuron'); };
#########################
# Insert your test code below, the Test::More module is use()ed here so read
# its man page ( perldoc Test::More ) for help writing this test script.
t/02_network_basic.t view on Meta::CPAN
# change 'tests => 1' to 'tests => last_test_to_print';
use Test::More tests => 25;
BEGIN { use_ok('AI::ANN');
use_ok('AI::ANN::Neuron'); };
#########################
# Insert your test code below, the Test::More module is use()ed here so read
# its man page ( perldoc Test::More ) for help writing this test script.
t/03_evolver_basic.t view on Meta::CPAN
# change 'tests => 1' to 'tests => last_test_to_print';
use Test::More tests => 60;
BEGIN { use_ok('AI::ANN');
use_ok('AI::ANN::Evolver'); };
#########################
# Insert your test code below, the Test::More module is use()ed here so read
# its man page ( perldoc Test::More ) for help writing this test script.
# This test basically works by crossing a network with itself and making sure nothing changes
$network1=new AI::ANN ('inputs'=>1, 'data'=>[{ iamanoutput => 1, inputs => {0 => 1}, neurons => {}}]);
$network2=new AI::ANN ('inputs'=>1, 'data'=>[{ iamanoutput => 1, inputs => {0 => 1}, neurons => {}}]);
ok(defined $network1, "new() works");
ok($network1->isa("AI::ANN"), "Right class");
ok(defined $network2, "new() works");
ok($network2->isa("AI::ANN"), "Right class");
ok($out=$network1->execute([1]), "executed and still alive");
t/10_network_integrity.t view on Meta::CPAN
# change 'tests => 1' to 'tests => last_test_to_print';
use Test::More tests => 10;
BEGIN { use_ok('AI::ANN');
use_ok('AI::ANN::Neuron'); };
#########################
# Insert your test code below, the Test::More module is use()ed here so read
# its man page ( perldoc Test::More ) for help writing this test script.
( run in 0.470 second using v1.01-cache-2.11-cpan-c333fce770f )