AI-ANN

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

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

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

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

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

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

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

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

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gpl.txt  view on Meta::CPAN

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lib/AI/ANN.pm  view on Meta::CPAN

input_count is number of inputs.
data is an arrayref of neuron definitions.
The first neuron with iamanoutput=1 is output 0. The second is output 1.
I hope you're seeing the pattern...
minvalue is the minimum value a neuron can pass. Default 0.
maxvalue is the maximum value a neuron can pass. Default 1.
afunc is a reference to the activation function. It should be simple and fast.
    The activation function is processed /after/ minvalue and maxvalue.
dafunc is the derivative of the activation function.
We strongly advise that you memoize your afunc and dafunc if they are at all
    complicated. We will do our best to behave.

=head2 execute

$network->execute( [$input0, $input1, ...] )

Runs the network for as many iterations as necessary to achieve a stable
network, then returns the output. 
We store the current state of the network in two places - once in the object,
for persistence, and once in $neurons, for simplicity. This might be wrong, 
but I couldn't think of a better way.