AI-NeuralNet-FastSOM

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FastSOM.xs  view on Meta::CPAN

        else if ( strEQ( SvPVX(prev), "_Sigma0" ) )
                return INT2PTR(SV*,newSVpvn("_L0",3));
        else if ( strEQ( SvPVX(prev), "_L0" ) )
                return INT2PTR(SV*,newSVpvn("LAMBDA",6));
        else if ( strEQ( SvPVX(prev), "LAMBDA" ) )
                return INT2PTR(SV*,newSVpvn("T",1));
        else if ( strEQ( SvPVX(prev), "T" ) )
                return INT2PTR(SV*,newSVpvn("labels",6));
        else if ( strEQ( SvPVX(prev), "labels" ) )
                return INT2PTR(SV*,newSVpvn("map",3));
        return &PL_sv_undef;
}

void _som_DESTROY(SV* self) {
	IV              iv;
	SV              *ref;
	SOM_Map         *map;
	SOM_GENERIC     *som;

	if ( !SvROK(self) )
		return;

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/orig/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/orig/som.t  view on Meta::CPAN

    use AI::NeuralNet::FastSOM::Rect;    # any non-abstract subclass should do
    my $nn = new AI::NeuralNet::FastSOM::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::FastSOM::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/orig/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/orig/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 ];
}

t/som.t  view on Meta::CPAN

            $nn->value( 1, 1 ),
            [ 1, 1, 1 ]
        ),
        'value set/get'
    );

# unsupported, for now (rik)
#    $nn->label ( 1, 1, 'rumsti' );
#    is ($nn->label ( 1, 1), 'rumsti', 'label set/get');
#
#    is ($nn->label ( 1, 0), undef, 'label set/get');
}

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

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