AI-NeuralNet-SOM

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examples/eigenvector_initialization.pl  view on Meta::CPAN

#	warn "desc: ".Dumper \@es_desc;
	my @es_idx  = map { _find_num ($_, \@es) } @es_desc;             # eigenvalue indices sorted by eigenvalue (desc)
#	warn "idx: ".Dumper \@es_idx;

sub _find_num {
    my $v = shift;
    my $l = shift;
    for my $i (0..$#$l) {
	return $i if $v == $l->[$i];
    }
    return undef;
}

	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);

examples/load_save.pl  view on Meta::CPAN

#	warn "desc: ".Dumper \@es_desc;
	my @es_idx  = map { _find_num ($_, \@es) } @es_desc;             # eigenvalue indices sorted by eigenvalue (desc)
#	warn "idx: ".Dumper \@es_idx;

sub _find_num {
    my $v = shift;
    my $l = shift;
    for my $i (0..$#$l) {
	return $i if $v == $l->[$i];
    }
    return undef;
}

	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);

t/rect.t  view on Meta::CPAN

    @vectors = ...;
my $get = sub {
    return @vectors [ int (rand (scalar @vectors) ) ];
    
}
$nn->train ($get);

# take exactly 500, round robin, in order
our $i = 0;
my $get = sub {
    return undef unless $i < 500;
return @vectors [ $i++ % scalar @vectors ];
}

t/som.t  view on Meta::CPAN

    use AI::NeuralNet::SOM::Rect;    # any non-abstract subclass should do
    my $nn = new AI::NeuralNet::SOM::Rect (output_dim => "5x6",
					   input_dim  => 3,
					   );
    $nn->value ( 1, 1, [ 1, 1, 1 ] );
    ok (eq_array ($nn->value ( 1, 1),
		  [ 1, 1, 1 ]), 'value set/get');
    $nn->label ( 1, 1, 'rumsti' );
    is ($nn->label ( 1, 1), 'rumsti', 'label set/get');

    is ($nn->label ( 1, 0), undef, 'label set/get');
}

{
    my $nn = new AI::NeuralNet::SOM::Rect (output_dim => "5x6",
					   input_dim  => 3);
    $nn->initialize;

    my @vs = ([ 3, 2, 4 ], [ -1, -1, -1 ], [ 0, 4, -3]);

    my $me = $nn->mean_error (@vs);

t/som.t  view on Meta::CPAN

    @vectors = ...;
my $get = sub {
    return @vectors [ int (rand (scalar @vectors) ) ];
    
}
$nn->train ($get);

# take exactly 500, round robin, in order
our $i = 0;
my $get = sub {
    return undef unless $i < 500;
return @vectors [ $i++ % scalar @vectors ];
}

t/torus.t  view on Meta::CPAN

    @vectors = ...;
my $get = sub {
    return @vectors [ int (rand (scalar @vectors) ) ];
    
}
$nn->train ($get);

# take exactly 500, round robin, in order
our $i = 0;
my $get = sub {
    return undef unless $i < 500;
return @vectors [ $i++ % scalar @vectors ];
}