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examples/load_save.pl  view on Meta::CPAN

use strict;
use Data::Dumper;

use AI::NeuralNet::FastSOM::Rect;

{
    my $nn = new AI::NeuralNet::FastSOM::Rect (output_dim => "5x6",
					   input_dim  => 3);
    $nn->initialize;
    $nn->train (400, ([ 3, 2, 4 ], [ -1, -1, -1 ], [ 0, 4, -3]));

    # now we freeze the thing
    use Storable;
    store $nn, '/tmp/somnia';

    # and forget it
}

{ # get it back, get it back
    my $nn = retrieve('/tmp/somnia');
    warn Dumper $nn;
    # ....
}


__END__

use AI::NeuralNet::FastSOM::Rect;


#my @vs  = ([1,-0.5],    [0,1]);
#my $dim = 2;

my $epsilon = 0.001;


    $nn->initialize;                                      # random
    my @mes = $nn->train ($epochs, @vs);
    warn "random:   length until error is < $epsilon ". scalar (grep { $_ >= $epsilon } @mes);
}

{ # constant initialisation
    my $nn = new AI::NeuralNet::FastSOM::Rect (output_dim => "5x6",
					   input_dim  => $dim);

    $nn->initialize ($vs[-1]);
    my @mes = $nn->train ($epochs, @vs);
    warn "constant: length until error is < $epsilon ". scalar (grep { $_ >= $epsilon } @mes);
}

{ # eigenvector initialisation
    my $nn = new AI::NeuralNet::FastSOM::Rect (output_dim => "5x6",
					   input_dim  => $dim);
    my @training_vectors;                                                # find these training vectors 
    {                                                                    # and prime them with this eigenvector stuff;
	use PDL;
	my $A = pdl \@vs;

	while ($A->getdim(0) < $A->getdim(1)) {                          # make the beast quadratic
	    $A = append ($A, zeroes (1));                                # by padding zeroes
	}
	my ($E, $e) = eigens_sym $A;