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	my $a_cols = $matrix_a->{_cols};
	
	my $b_rows = $matrix_b->{_rows};
	my $b_cols = $matrix_b->{_cols};

	my @array_a = &packed_array($matrix_a);
	my @array_b = &packed_array($matrix_b);

	for (my $n = 0; $n <= $#array_b; $n++) {
		if ($array_b[$n] == 2) {
			$array_b[$n] = 0;
		}
	}
	
	if ($#array_a != $#array_b) {
		die "To take the dot product, both matrixes must be of the same length.";
	}

	my $result = 0;
	my $length = $#array_a + 1;

	for (my $i = 0; $i < $length; $i++) {
		$result += $array_a[$i] * $array_b[$i];
	}
	return $result;
}

sub packed_array() {
	my $matrix = shift;
	my @result = ();

	for (my $r = 1; $r <= $matrix->{_rows}; $r++) {
		for (my $c = 1; $c <= $matrix->{_cols}; $c++) {
			push(@result, $matrix->get($r, $c)); 
		}
	}
	return @result;
}

sub get_col() {
	my $self = shift;
	my $col  = shift;

	my $matrix = $self->matrix();
	
	my $matrix_rows = $self->matrix_rows();

	if ($col > $matrix_rows) {
		die "Can't get column";
	}

	my $new_matrix = Math::SparseMatrix->new($matrix_rows, 1);

	for (my $row = 1; $row <= $matrix_rows; $row++) {
		my $value = $matrix->get($row, $col);
		$new_matrix->set($row, 1, $value);
	}
	return $new_matrix;
}

sub print_matrix() {
    my $matrix  = shift;
    my $rs = $matrix->{_rows};
    my $cs = $matrix->{_cols};

	for (my $i = 1; $i <= $rs; $i++) {
		for (my $j = 1; $j <= $cs; $j++) {
			say "[$i,$j]" . $matrix->get($i, $j);
		}
	}
}

=head1 SYNOPSIS

This is a version of a Hopfield Network implemented in Perl. Hopfield networks are sometimes called associative networks since 
they associate a class pattern to each input pattern, they are tipically used for classification problems with binary pattern vectors.

=head1 SUBROUTINES/METHODS

=head2 New

In order to build new calssifiers, you have to pass to the constructor the number of rows and columns (neurons) for the matrix construction.

	my $hop = AI::NeuralNet::Hopfield->new(row => 4, col => 4);

=cut

=head2 Train

The training method configurates the network memory.

	my @input_1 = qw(true true false false);
	$hop->train(@input_1);

=cut

=head2 Evaluation

The evaluation method compares the new input with the information stored in the matrix memory.
The output is a new array with the boolean evaluation of each neuron.

	my @input_2 = qw(true true true false);
	my @result = $hop->evaluate(@input_2);

=cut


=head1 AUTHOR

Felipe da Veiga Leprevost, C<< <leprevost at cpan.org> >>

=head1 BUGS

Please report any bugs or feature requests to C<bug-ai-neuralnet-hopfield at rt.cpan.org>, or through
the web interface at L<http://rt.cpan.org/NoAuth/ReportBug.html?Queue=AI-NeuralNet-Hopfield>.  I will be notified, and then you'll
automatically be notified of progress on your bug as I make changes.


=head1 SUPPORT

You can find documentation for this module with the perldoc command.