dist/multilabel/MetaLabeler.js
function(sample, explain) {
		var rankedLabelsWithExplain = this.ranker.classify(sample, explain, /*withScores=*/true);
		var rankedLabels = (explain>0? rankedLabelsWithExplain.classes: rankedLabelsWithExplain);
		var labelCountWithExplain = this.counter.classify(sample, explain, /*withScores=*/true);
		var labelCount = (explain>0? labelCountWithExplain.classes[0][0]: labelCountWithExplain[0][0]);
		if (_.isString(labelCount)) labelCount = parseInt(labelCount);
		
		// Pick the labelCount most relevant labels from the list returned by the ranker:   
		var positiveLabelsWithScores = rankedLabels.slice(0, labelCount);

		var positiveLabels = positiveLabelsWithScores

		if (positiveLabelsWithScores.length != 0)
			if (_.isArray(positiveLabelsWithScores[0]))
				var positiveLabels = positiveLabelsWithScores.map(function(labelWithScore) {return labelWithScore[0]});
		
		return (explain>0? {
			classes: positiveLabels,
			explanation: {
				ranking: rankedLabelsWithExplain.explanation,
				counting: labelCountWithExplain.explanation
			}
		}:
		positiveLabels)
	}
function(sample, explain) {
		var rankedLabelsWithExplain = this.ranker.classify(sample, explain, /*withScores=*/true);
		var rankedLabels = (explain>0? rankedLabelsWithExplain.classes: rankedLabelsWithExplain);
		var labelCountWithExplain = this.counter.classify(sample, explain, /*withScores=*/true);
		var labelCount = (explain>0? labelCountWithExplain.classes[0][0]: labelCountWithExplain[0][0]);
		if (_.isString(labelCount)) labelCount = parseInt(labelCount);
		
		// Pick the labelCount most relevant labels from the list returned by the ranker:   
		var positiveLabelsWithScores = rankedLabels.slice(0, labelCount);

		var positiveLabels = positiveLabelsWithScores

		if (positiveLabelsWithScores.length != 0)
			if (_.isArray(positiveLabelsWithScores[0]))
				var positiveLabels = positiveLabelsWithScores.map(function(labelWithScore) {return labelWithScore[0]});
		
		return (explain>0? {
			classes: positiveLabels,
			explanation: {
				ranking: rankedLabelsWithExplain.explanation,
				counting: labelCountWithExplain.explanation
			}
		}:
		positiveLabels)
	}
Use the model trained so far to classify a new sample.

@param sample a document.
@param explain - int - if positive, an "explanation" field, with the given length, will be added to the result.

@return an array whose VALUES are classes.