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lib/AI/NeuralNet/Hopfield.pm  view on Meta::CPAN

	my $input_matrix = &convert_array($self->matrix_rows, $self->matrix_cols, @pattern);

	for (my $col = 1; $col <= ($#pattern + 1); $col++) {
		
		my $column_matrix = &get_col($self, $col);
		
		my $transposed_column_matrix = &transpose($column_matrix);
		
		my $dot_product = &dot_product($input_matrix, $transposed_column_matrix);
		
		#say $dot_product;

		if ($dot_product > 0) {
			$output[$col - 1] = "true";
		} else {
			$output[$col - 1] = "false";
		}
	}
	return @output;
}

lib/AI/NeuralNet/Hopfield.pm  view on Meta::CPAN

	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