NAME

AI::Categorizer::Learner - Abstract Machine Learner Class

SYNOPSIS

 use AI::Categorizer::Learner::NaiveBayes;  # Or other subclass
 
 # Here $k is an AI::Categorizer::KnowledgeSet object
 
 my $nb = new AI::Categorizer::Learner::NaiveBayes(...parameters...);
 $nb->train(knowledge_set => $k);
 $nb->save_state('filename');
 
 ... time passes ...
 
 $nb = AI::Categorizer::Learner::NaiveBayes->restore_state('filename');
 my $c = new AI::Categorizer::Collection::Files( path => ... );
 while (my $document = $c->next) {
   my $hypothesis = $nb->categorize($document);
   print "Best assigned category: ", $hypothesis->best_category, "\n";
   print "All assigned categories: ", join(', ', $hypothesis->categories), "\n";
 }

DESCRIPTION

The "AI::Categorizer::Learner" class is an abstract class that will never actually be directly used in your code. Instead, you will use a subclass like "AI::Categorizer::Learner::NaiveBayes" which implements an actual machine learning algorithm.

The general description of the Learner interface is documented here.

METHODS

new(): Creates a new Learner and returns it. Accepts the following parameters:

knowledge_set: A Knowledge Set that will be used by default during the "train()" method.
verbose: If true, the Learner will display some diagnostic output while training and categorizing documents.

train()
train(knowledge_set => $k): Trains the categorizer. This prepares it for later use in categorizing documents. The "knowledge_set" parameter must provide an object of the class "AI::Categorizer::KnowledgeSet" (or a subclass thereof), populated with lots of documents and categories. See AI::Categorizer::KnowledgeSet for the details of how to create such an object. If you provided a "knowledge_set" parameter to "new()", specifying one here will override it.
categorize($document): Returns an "AI::Categorizer::Hypothesis" object representing the categorizer's "best guess" about which categories the given document should be assigned to. See AI::Categorizer::Hypothesis for more details on how to use this object.
categorize_collection(collection => $collection): Categorizes every document in a collection and returns an Experiment object representing the results. Note that the Experiment does not contain knowledge of the assigned categories for every document, only a statistical summary of the results.
knowledge_set(): Gets/sets the internal "knowledge_set" member. Note that since the knowledge set may be enormous, some Learners may throw away their knowledge set after training or after restoring state from a file.
$learner->save_state($path): Saves the Learner for later use. This method is inherited from "AI::Categorizer::Storable".
$class->restore_state($path): Returns a Learner saved in a file with "save_state()". This method is inherited from "AI::Categorizer::Storable".

AUTHOR

Ken Williams, ken@mathforum.org

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