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eg/example.pl  view on Meta::CPAN

#!/usr/bin/perl

use AI::DecisionTree;

my @attributes = qw(outlook  temperature  humidity  wind    play_tennis);               
my @cases      = qw(
		    sunny    hot          high      weak    no
		    sunny    hot          high      strong  no
		    overcast hot          high      weak    yes
		    rain     mild         high      weak    yes
		    rain     cool         normal    weak    yes
		    rain     cool         normal    strong  no
		    overcast cool         normal    strong  yes
		    sunny    mild         high      weak    no
		    sunny    cool         normal    weak    yes
		    rain     mild         normal    weak    yes
		    sunny    mild         normal    strong  yes
		    overcast mild         high      strong  yes
		    overcast hot          normal    weak    yes
		    rain     mild         high      strong  no
		   );
my $outcome = pop @attributes;


my $dtree = new AI::DecisionTree;
while (@cases) {
  my @values = splice @cases, 0, 1 + scalar(@attributes);
  my $result = pop @values;
  my %pairs;
  @pairs{@attributes} = @values;

  $dtree->add_instance(attributes => \%pairs,
		       result => $result,
		      );
}
$dtree->train;


my $result;
# Try one of the training examples
$result = $dtree->get_result( attributes => {
					     outlook   => 'rain',
					     temperature => 'mild',
					     humidity => 'high',
					     wind => 'strong',
					    } );
print "Result 1: $result\n";  # no

# Try a new unseen example
$result = $dtree->get_result( attributes => {
					     outlook => 'sunny',
					     temperature => 'hot',
					     humidity => 'normal',
					     wind => 'strong',
					    } );
print "Result 2: $result\n";  # yes



# Show the created tree structure as rules
print map "$_\n", $dtree->rule_statements;


# Will barf on inconsistent data
my $t2 = new AI::DecisionTree;
$t2->add_instance( attributes => { foo => 'bar' },
		   result => 1 );
$t2->add_instance( attributes => { foo => 'bar' },
		   result => 0 );
eval {$t2->train};
print "$@\n";