/**
* Train on the given set and fill the models variables
*
* priors[c] = NDocs[c]/NDocs
* condprob[t][c] = count( t in c) + 1 / sum( count( t' in c ) + 1 , for every t' )
* unknown[c] = condbrob['word that doesnt exist in c'][c] ( so that count(t in c)==0 )
*
* More information on the algorithm can be found at
* http://nlp.stanford.edu/IR-book/html/htmledition/naive-bayes-text-classification-1.html
*
* @param FeatureFactoryInterface A feature factory to compute features from a training document
* @param TrainingSet The training set
* @param integer $a_smoothing The parameter for additive smoothing. Defaults to add-one smoothing.
* @return array Return a training context to be used for incremental training
*/
public function train(FeatureFactoryInterface $ff, TrainingSet $tset, $a_smoothing = 1)
{
$class_set = $tset->getClassSet();
$ctx = array('termcount_per_class' => array_fill_keys($class_set, 0), 'termcount' => array_fill_keys($class_set, array()), 'ndocs_per_class' => array_fill_keys($class_set, 0), 'voc' => array(), 'ndocs' => 0);
return $this->train_with_context($ctx, $ff, $tset, $a_smoothing);
}
/**
* Calculate all the features for every possible class. Pass the
* information to the optimizer to find the weights that satisfy the
* constraints and maximize the entropy
*
* @param $ff The feature factory
* @param $tset A collection of training documents
* @param $opt An optimizer, we need a maxent optimizer
* @return void
*/
public function train(FeatureFactoryInterface $ff, TrainingSet $tset, MaxentOptimizerInterface $opt)
{
$classSet = $tset->getClassSet();
$features = $this->calculateFeatureArray($classSet, $tset, $ff);
$this->l = $opt->optimize($features);
}
