AI::NeuralNet::Hopfield - A simple Hopfiled Network Implementation.
This is a version of a Hopfield Network implemented in Perl. Hopfield networks are sometimes called associative networks since they associate a class pattern to each input pattern, they are tipically used for classification problems with binary pattern vectors.
In order to build new calssifiers, you have to pass to the constructor the number of rows and columns (neurons) for the matrix construction.
my $hop = AI::NeuralNet::Hopfield->new(row => 4, col => 4);
The training method configurates the network memory.
my @input_1 = qw(true true false false);
The evaluation method compares the new input with the information stored in the matrix memory. The output is a new array with the boolean evaluation of each neuron.
my @input_2 = qw(true true true false);
my @result = $hop->evaluate(@input_2);
Felipe da Veiga Leprevost, <leprevost at cpan.org>
<leprevost at cpan.org>
Please report any bugs or feature requests to bug-ai-neuralnet-hopfield at rt.cpan.org, or through the web interface at http://rt.cpan.org/NoAuth/ReportBug.html?Queue=AI-NeuralNet-Hopfield. I will be notified, and then you'll automatically be notified of progress on your bug as I make changes.
bug-ai-neuralnet-hopfield at rt.cpan.org
You can find documentation for this module with the perldoc command.
You can also look for information at:
RT: CPAN's request tracker (report bugs here)
AnnoCPAN: Annotated CPAN documentation
Copyright 2013 leprevost.
This program is free software; you can redistribute it and/or modify it under the terms of the the Artistic License (2.0). You may obtain a copy of the full license at:
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To install AI::NeuralNet::Hopfield, copy and paste the appropriate command in to your terminal.
perl -MCPAN -e shell
For more information on module installation, please visit the detailed CPAN module installation guide.