AI-ANN

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ANN_specification  view on Meta::CPAN

ANN::Neuron
	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

Changes  view on Meta::CPAN

          Evolver now allows you to pass a coderef for mutation_amount. It 
          will be evaluated with no arguments. If you pass a number instead, 
          it will be coerced into a uniform random value up to +/- your value.

          Change a few things from hashrefs to arrayrefs internally. Fully 
          back-compatibile, but I hope this will speed up a few things.

          Fix dependencies.

0.006     2011-06-01 16:08:04 UTC
          Remove dependency on perl 5.14, because apparently no one has that 
          installed yet. Get with the times, people! (Debian is still on 5.10)

          Add mutate_gaussian to the methods available in the evolver, and 
          allow the population of the eta_ values in AI::ANN. 

          Add a sternly worded comment to the evolver warning against the use 
          of crossover.

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.

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

	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. 
kill_link_chance is the chance that, during a mutate() call, each pair of 
	connected neurons with a weight less than mutation_amount or each 
	neuron => input pair with a weight less than mutation_amount will be
	disconnected. If add_link_chance is zero, this should also be zero, or 
	your network will just fizzle out.
sub_crossover_chance is the chance that, during a crossover() call, each 
	neuron will, rather than being inherited fully from each parent, have 
	each element within it be inherited individually.
min_value is the smallest acceptable weight. It must be less than or equal to 
	zero. If a value would be decremented below min_value, it will instead 
	become an epsilon above min_value. This is so that we don't accidentally 
	set a weight to zero, thereby killing the link.
max_value is the largest acceptable weight. It must be greater than zero.
gaussian_tau and gaussian_tau_prime are the terms to the gaussian mutation 
    method. They are coderefs which accept one parameter, n, the number of
    non-zero-weight inputs to the given neuron.

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