dist/multilabel/PassiveAggressiveHash.js
function(dataset) {
		// preprocessing: add all the classes in the dataset to the weights vector;
		dataset.forEach(function(datum) {
			this.addClasses(datum.output);
			this.editFeatureValues(datum.input, /*remove_unknown_features=*/false);
		}, this);

		for (var i=0; i<this.retrain_count; ++i)
			dataset.forEach(function(datum) {
				this.update(datum.input, datum.output);
			}, this);
	}
function(dataset) {
		// preprocessing: add all the classes in the dataset to the weights vector;
		dataset.forEach(function(datum) {
			this.addClasses(datum.output);
			this.editFeatureValues(datum.input, /*remove_unknown_features=*/false);
		}, this);

		for (var i=0; i<this.retrain_count; ++i)
			dataset.forEach(function(datum) {
				this.update(datum.input, datum.output);
			}, this);
	}
function ( dataset ) { // preprocessing: add all the classes in the dataset to the weights vector; dataset . forEach ( function ( datum ) { this . addClasses ( datum . output ) ; this . editFeatureValues ( datum . input , /*remove_unknown_features=*/ false ) ; } , this ) ; for ( var i = 0 ; i < this . retrain_count ; ++ i ) dataset . forEach ( function ( datum ) { this . update ( datum . input , datum . output ) ; } , this ) ; }
Train the classifier with all the given documents.

@param dataset
an array with objects of the format:
{input: sample1, output: [class11, class12...]}
Train the classifier with all the given documents .