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); $hop->train(@input_1);
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>
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.
You can find documentation for this module with the perldoc command.
You can also look for information at:
Copyright 2013 leprevost.
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