AI-NeuralNet-Kohonen-Visual
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Revision history for Perl extension AI::NeuralNet::Kohonen::Demo::RGB.
0.03 Fri May 05 21:17:00 2006
Packaged, test and pod fix
0.02 Thu Mar 20 2003 11:25 2003
Now a sub-class of AI::NeuralNet::Kohonen::Visual.
0.011 Thu Mar 13 18:21:37 2003
Added unified distance matrix display.
0.01 Thu Mar 13 12:21:37 2003
- original version; created by h2xs 1.21 with options
-X -n AI::NeuralNet::Kohonen::Demo::RGB
AI/NeuralNet/Kohonen/Visual version 0.01
========================================
A sub-class of "AI::NeuralNet::Kohonen" that Impliments extra methods
to make use of TK for visualisations.
INSTALLATION
To install this module type the following:
perl Makefile.PL
make
make test
make install
lib/AI/NeuralNet/Kohonen/Visual.pm view on Meta::CPAN
AI::NeuralNet::Kohonen::Visual - Tk-based Visualisation
=head1 SYNOPSIS
Test the test file in this distribution, or:
package YourClass;
use base "AI::NeuralNet::Kohonen::Visual";
sub get_colour_for { my ($self,$x,$y) = (shift,shift,shift);
# From here you return a TK colour name.
# Get it as you please; for example, values of a 3D map:
return sprintf("#%02x%02x%02x",
(int (255 * $self->{map}->[$x]->[$y]->{weight}->[0])),
(int (255 * $self->{map}->[$x]->[$y]->{weight}->[1])),
(int (255 * $self->{map}->[$x]->[$y]->{weight}->[2])),
);
}
exit;
lib/AI/NeuralNet/Kohonen/Visual.pm view on Meta::CPAN
$net->main_loop;
exit;
=head1 DESCRIPTION
Provides TK-based visualisation routines for C<AI::NueralNet::Kohonen>.
Replaces the earlier C<AI::NeuralNet::Kohonen::Demo::RGB>.
This is a sub-class of C<AI::NeuralNet::Kohonen>
that impliments extra methods to make use of TK.
This moudle is itself intended to be sub-classed by you,
where you provide a version of the method C<get_colour_for>:
see L<METHOD get_colour_for> and L<SYNOPSIS> for details.
=head1 CONSTRUCTOR (new)
The following paramter fields are added to the base module's fields:
=over 4
lib/AI/NeuralNet/Kohonen/Visual.pm view on Meta::CPAN
use Tk qw/DoOneEvent DONT_WAIT/;
=head1 METHOD train
Over-rides the base class to provide TK displays of the map.
=cut
sub train { my ($self,$epochs) = (shift,shift);
$epochs = $self->{epochs} unless defined $epochs;
$self->{display_scale} = 10 if not defined $self->{display_scale};
&{$self->{train_start}} if exists $self->{train_start};
$self->prepare_display if not defined $self->{_mw} or ref $self->{_mw} ne 'MainWindow';
# Replaces Tk's MainLoop
for (0..$self->{epochs}) {
if ($self->{_quit_flag}) {
lib/AI/NeuralNet/Kohonen/Visual.pm view on Meta::CPAN
$_->smooth if $self->{smooth};
$self->plot_map if $self->{MainLoop};
&{$self->{train_end}} if exists $self->{train_end};
MainLoop if $self->{MainLoop};
return 1;
}
=head1 METHOD get_colour_for
This method is intended to be sub-classed.
Currently it only operates on the first three elements
of a weight vector, turning them into RGB values.
It returns the a TK colour for a node at position C<x>,C<y> in the
C<map> paramter.
Accepts: C<x> and C<y> co-ordinates in the map.
=cut
sub get_colour_for { my ($self,$x,$y) = (shift,shift,shift);
my $_0 = $self->{map}->[$x]->[$y]->{weight}->[0];
$_0 = $self->{missing_colour} || 0 if $_0 eq $self->{missing_mask};
my $_1 = $self->{map}->[$x]->[$y]->{weight}->[1];
$_1 = $self->{missing_colour} || 0 if $_1 eq $self->{missing_mask};
my $_2 = $self->{map}->[$x]->[$y]->{weight}->[2];
$_2 = $self->{missing_colour} || 0 if $_2 eq $self->{missing_mask};
return sprintf("#%02x%02x%02x",
(int (255 * $_0)),
(int (255 * $_1)),
(int (255 * $_2)),
);
}
=head1 METHOD prepare_display
Depracated: see L<METHOD create_empty_map>.
=cut
sub prepare_display {
return $_[0]->create_empty_map;
}
=head1 METHOD create_empty_map
Sets up a TK C<MainWindow> and C<Canvas> to
act as an empty map.
=cut
sub create_empty_map { my $self = shift;
my ($w,$h);
if ($self->{display} and $self->{display} eq 'hex'){
$w = ($self->{map_dim_x}+1) * ($self->{display_scale}+2);
$h = ($self->{map_dim_y}+1) * ($self->{display_scale}+2);
} else {
$w = ($self->{map_dim_x}+1) * ($self->{display_scale});
$h = ($self->{map_dim_y}+1) * ($self->{display_scale});
}
$self->{_mw} = MainWindow->new(
-width => $w + 20,
-height => $h + 20,
);
$self->{_mw}->fontCreate(qw/TAG -family verdana -size 8 -weight bold/);
$self->{_mw}->resizable( 0, 0);
$self->{_quit_flag} = 0;
$self->{_mw}->protocol('WM_DELETE_WINDOW' => sub {$self->{_quit_flag}=1});
$self->{_canvas} = $self->{_mw}->Canvas(
-width => $w,
-height => $h,
-relief => 'raised',
-border => 2,
);
$self->{_canvas}->pack(-side=>'top');
$self->{_label} = $self->{_mw}->Button(
-command => sub { $self->{_mw}->destroy;$self->{_mw} = undef; },
-relief => 'groove',
-text => ' ',
-wraplength => $w,
-textvariable => \$self->{_label_txt}
);
$self->{_label}->pack(-side=>'top');
return 1;
}
lib/AI/NeuralNet/Kohonen/Visual.pm view on Meta::CPAN
C<hicol> to highlight it with colour. If no C<hicolo> is provided,
it default to red.
When called, this method also sets the object field flag C<plotted>:
currently, this prevents C<main_loop> from calling this routine.
See also L<METHOD get_colour_for>.
=cut
sub plot_map { my ($self,$args) = (shift,{@_});
$self->{plotted} = 1;
# MW may have been destroyed
$self->prepare_display if not defined $self->{_mw};
my $yo = 5+($self->{display_scale}/2);
for my $x (0..$self->{map_dim_x}){
for my $y (0..$self->{map_dim_y}){
my $colour;
if ($args->{bmu_x} and $args->{bmu_x}==$x and $args->{bmu_y}==$y){
$colour = $args->{hicol} || 'red';
} else {
lib/AI/NeuralNet/Kohonen/Visual.pm view on Meta::CPAN
=head1 METHOD label_map
Put a text label on the map for the node at the I<x,y> co-ordinates
supplied in the first two paramters, using the text supplied in the
third.
Very naive: no attempt to check the text will appear on the map.
=cut
sub label_map { my ($self,$x,$y,$t) = (shift,shift,shift,shift);
$self->{_canvas}->createText(
$x*$self->{display_scale}+($self->{display_scale}),
$y*$self->{display_scale}+($self->{display_scale}),
-text => $t,
-anchor => 'w',
-fill => 'white',
-font => 'TAG',
);
}
=head1 METHOD main_loop
Calls TK's C<MainLoop> to keep a window open until the user closes it.
=cut
sub main_loop { my $self = shift;
$self->plot_map unless $self->{plotted};
MainLoop;
}
1;
__END__
t/AI-NeuralNet-Kohonen-Visual.t view on Meta::CPAN
);
isa_ok( $net->{input}, 'ARRAY');
is( $net->{input}->[0]->{values}->[0],1);
is( $net->{input}->[0]->{values}->[1],0);
is( $net->{input}->[0]->{values}->[2],0);
is( $net->{weight_dim}, 2);
$net->train;
diag "Will automatically destroy this window in $delay seconds";
$net->{_mw}->after($delay*1000, sub{ $net->{_mw}->destroy } );
$net->main_loop;
pass;
diag "# Test 2 ... this is *slow*, sorry\n";
$net = AI::NeuralNet::Kohonen::Visual->new(
display => 'hex',
map_dim => 39,
epochs => 19,
t/AI-NeuralNet-Kohonen-Visual.t view on Meta::CPAN
# Create an empty map
# and populate with training data
diag "# Plotting results\n";
foreach my $bmu ($net->get_results){
$net->label_map(@$bmu->[1],@$bmu->[2],"+".@$bmu->[3]);
}
diag "Will automatically destroy this window in $delay seconds";
$net->{_mw}->after($delay*1000, sub{ $net->{_mw}->destroy } );
$net->plot_map;
$net->main_loop;
diag "# Done\n";
# $net->main_loop;
__END__
( run in 0.719 second using v1.01-cache-2.11-cpan-88abd93f124 )