AI::MaxEntropy 0.20

AI::MaxEntropy is a Perl extension for learning Maximum Entropy Models.

SYNOPSIS

use AI::MaxEntropy;

# create a maximum entropy learner
my $me = AI::MaxEntropy->new;

# the learner see 2 red round smooth apples
$me->see(['round', 'smooth', 'red'] => 'apple' => 2);

# the learner see 3 yellow long smooth bananas
$me->see(['long', 'smooth', 'yellow'] => 'banana' => 3);

# and more

# samples needn't have the same numbers of active features
$me->see(['rough', 'big'] => 'pomelo');

# the order of active features is not concerned, too
$me->see(['big', 'rough'] => 'pomelo');

# ...

# and, let it learn
my $model = $me->learn;

# then, we can make predictions on unseen data

# ask what a red thing is most likely to be
print $model->predict(['red'])."n";
# the answer is apple, because all red things the learner have ever seen
# are apples

# ask what a smooth thing is most likely to be
print $model->predict(['smooth'])."n";
# the answer is banana, because the learner have seen more smooth bananas
# (weighted 3) than smooth apples (weighted 2)

# ask what a red, long thing is most likely to be
print $model->predict(['red', 'long'])."n";
# the answer is banana, because the learner have seen more long bananas
# (weighted 3) than red apples (weighted 2)

# print out scores of all possible answers to the feature round and red
for ($model->all_labels) {
my $s = $model->score(['round', 'red'] => $_);
print "$_: $sn";
}

# save the model
$model->save('model_file');

# load the model
$model->load('model_file');

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Requirements:

· Perl