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t/AI-NeuralNet-Kohonen.t  view on Meta::CPAN

package K_test;
use Test::More tests => 35;

use lib "../lib";

use strict;
use warnings;

use_ok ("AI::NeuralNet::Kohonen" => 0.14);
use_ok ("AI::NeuralNet::Kohonen::Node" => 0.12);
use_ok ("AI::NeuralNet::Kohonen::Input");

my ($dir) = $0 =~ /^(.*?)[^\\\/]+$/;

my $net = new AI::NeuralNet::Kohonen;
is($net,undef);

$net = new AI::NeuralNet::Kohonen(
	weight_dim => 2,
	input => [
		[1,2,3]
	],
);
isa_ok( $net->{input}, 'ARRAY');

is( $net->{input}->[0]->[0],1);
is( $net->{input}->[0]->[1],2);
is( $net->{input}->[0]->[2],3);
is( $net->{map_dim_a},19);

$net = new AI::NeuralNet::Kohonen(
	weight_dim => 2,
	input => [
		[1,2,3]
	],
	map_dim_x => 10,
	map_dim_y => 20,
);
is($net->{map_dim_a},15);


# Node test
my $node = new AI::NeuralNet::Kohonen::Node;
is($node,undef) or BAIL_OUT();
$node = new AI::NeuralNet::Kohonen::Node(
	weight => [0.1, 0.6, 0.5],
);
isa_ok( $node, 'AI::NeuralNet::Kohonen::Node');
is( $node->{dim}, 2);
my $input = new AI::NeuralNet::Kohonen::Input(
	dim		=> 2,
	values	=> [1,0,0],
);

is( sprintf("%.2f",$node->distance_from($input)), 1.19);

$net = AI::NeuralNet::Kohonen->new(
	map_dim_x	=> 14,
	map_dim_y	=> 10,
	epoch_end	=> sub {print"."},
	train_end	=> sub {print"\n"},
	epochs		=> 2,