AI-NeuralNet-FastSOM

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examples/eigenvector_initialization.pl  view on Meta::CPAN

my $epsilon = 0.001;
my $epochs  = 400;

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

    $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;

examples/eigenvector_initialization.pl  view on Meta::CPAN

}

	for (@es_idx) {                                                  # from the highest values downwards, take the index
	    push @training_vectors, [ list $E->dice($_) ] ;              # get the corresponding vector
	}
    }

    $nn->initialize (@training_vectors[0..0]);                           # take only the biggest ones (the eigenvalues are big, actually)
#warn $nn->as_string;
    my @mes = $nn->train ($epochs, @vs);
    warn "eigen:    length until error is < $epsilon ". scalar (grep { $_ >= $epsilon } @mes);
}

__END__

examples/load_save.pl  view on Meta::CPAN

#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;

examples/load_save.pl  view on Meta::CPAN

}

	for (@es_idx) {                                                  # from the highest values downwards, take the index
	    push @training_vectors, [ list $E->dice($_) ] ;              # get the corresponding vector
	}
    }

    $nn->initialize (@training_vectors[0..0]);                           # take only the biggest ones (the eigenvalues are big, actually)
#warn $nn->as_string;
    my @mes = $nn->train ($epochs, @vs);
    warn "eigen:    length until error is < $epsilon ". scalar (grep { $_ >= $epsilon } @mes);
}

__END__