dist/multilabel/PassiveAggressiveHash.js
function(features, explain, withScores) {
		this.editFeatureValues(features, /*remove_unknown_features=*/true);
		var scoresVector = this.predict(features, /*averaging=*/true, explain);
		return multilabelutils.mapScoresVectorToMultilabelResult(scoresVector, explain, withScores, /*threshold=*/0);
	}
function(features, explain, withScores) {
		this.editFeatureValues(features, /*remove_unknown_features=*/true);
		var scoresVector = this.predict(features, /*averaging=*/true, explain);
		return multilabelutils.mapScoresVectorToMultilabelResult(scoresVector, explain, withScores, /*threshold=*/0);
	}
Use the model trained so far to classify a new sample.

@param sample a hash {feature1: value1, feature2: value2, ...}.
@param explain - int - if positive, an "explanation" field, with the given length, should be added to the result.
@param withScores - boolean - if true, return an array of [class,score], ordered by decreasing order of score.

@return an array whose VALUES are classes.