dist/multilabel/MulticlassSegmentation.js
function(sentence, explain) {
		var minWordsToSplit = 2;
		var words = sentence.split(/ /);
		if (words.length>=minWordsToSplit) {
			var accumulatedClasses = {};
			var explanations = [];
			this.cheapestSegmentSplitStrategy(words, accumulatedClasses, explain, explanations); 
			
			var classes = Object.keys(accumulatedClasses);
			return (explain>0?	{
				classes: classes, 
				explanation: explanations
			}: 
			classes);
		} else {
			return this.classifySegment(sentence, explain);
		}
	}
function(sentence, explain) {
		var minWordsToSplit = 2;
		var words = sentence.split(/ /);
		if (words.length>=minWordsToSplit) {
			var accumulatedClasses = {};
			var explanations = [];
			this.cheapestSegmentSplitStrategy(words, accumulatedClasses, explain, explanations); 
			
			var classes = Object.keys(accumulatedClasses);
			return (explain>0?	{
				classes: classes, 
				explanation: explanations
			}: 
			classes);
		} else {
			return this.classifySegment(sentence, explain);
		}
	}
function ( sentence , explain ) { var minWordsToSplit = 2 ; var words = sentence . split ( / / ) ; if ( words . length >= minWordsToSplit ) { var accumulatedClasses = { } ; var explanations = [ ] ; this . cheapestSegmentSplitStrategy ( words , accumulatedClasses , explain , explanations ) ; var classes = Object . keys ( accumulatedClasses ) ; return ( explain > 0 ? { classes : classes , explanation : explanations } : classes ) ; } else { return this . classifySegment ( sentence , explain ) ; } }
Use the model trained so far to classify a new sample.

@param segment a part of a text sentence.
@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.
Use the model trained so far to classify a new sample .