AI-LibNeural
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LibNeural.pm view on Meta::CPAN
my$nn= AI::LibNeural->new( 2, 4, 1 );# teach it the logical AND$nn->train( [ 0, 0 ], [ 0.05 ], 0.0000000005, 0.2 );$nn->train( [ 0, 1 ], [ 0.05 ], 0.0000000005, 0.2 );$nn->train( [ 1, 0 ], [ 0.05 ], 0.0000000005, 0.2 );$nn->train( [ 1, 1 ], [ 0.95 ], 0.0000000005, 0.2 );my$result=$nn->run( [ 1, 1 ] );# result should be ~ 0.95$result=$nn->run( [ 0, 1 ] );# result should be ~ 0.05$nn->save('and.mem');=head1 ABSTRACTPerl bindings for the libneural c++ neural netowrk library.=head1 DESCRIPTIONProvides accessors for the libneural library as a perl object. libneural is a
LibNeural.pm view on Meta::CPAN
Creates a new AI::LibNeural object from the supplied memory file.=item $nn = AI::LibNeural->new(INTPUTS,HIDDENS,OUTPUTS)Creates a new AI::LibNeural object with INPUTS input nodes, HIDDENS hiddennodes, and OUTPUTS output nodes.=item $nn->train([I1,I2,...],[O1,O2,...],MINERR,TRAINRATE)Completes a training cycle for the given inputs I1-IN, with the expectedresults of O1-OM, where N is the number of inputs and M is the number ofoutputs. MINERR is the mean squared error at the output that you wish to be achieved. TRAINRATE is the learning rate to be used.=item (O1,O2) = $nn->run([I1,I2,...])Calculate the corresponding outputs (O1-OM) for the given inputs (I1-ON) basedon the previous training. Should only be called after the network has beensuitably trained.=item NUM = $nn->get_layersize(WHICH)Retrieves the number of nodes at the specified layer, WHICH. WHICH should beone of ALL, INPUT, HIDDEN, OUTPUT. Usefully mainly with a network is loadedfrom a file.=item status = $nn->load(FILENAME)=item status = $nn->save(FILENAME)Loads and saves respectively the 'memory,' node configuration and weights,of the network. FILENAME should be the location of the file in which thememory is stored/retrieved.=back=head2 EXPORTNone by default=head2 EXPORT TAGS
## $Header$#AI::LibNeural version 0.02==========================This module provides access to the c++ library libneural.freshmeat.net.) libneural is a simple, but useful, backpropagation neuralnetwork. It isn't packed with features or a speed demon, but it works and it'seasy to learn how touse.INSTALLATION> perl Makefile.PLor> perl Makefile.PL PREFIX=/some/non/system/place(then setting PERL5LIB as appropriate based on ^ is a good ideal)ignore the warnings about CC and LD being unknown parameters, then> make all test install
( run in 0.496 second using v1.01-cache-2.11-cpan-e5176c747c2 )