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Massive On-line Analysis is an environment for massive data mining. MOA provides a framework for data stream mining and includes tools for evaluation and a collection of machine learning algorithms. Related to the WEKA project, also written in Java, while scaling to more demanding problems.

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/*
 *    This program is free software; you can redistribute it and/or modify
 *    it under the terms of the GNU General Public License as published by
 *    the Free Software Foundation; either version 3 of the License, or
 *    (at your option) any later version.
 *
 *    This program is distributed in the hope that it will be useful,
 *    but WITHOUT ANY WARRANTY; without even the implied warranty of
 *    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 *    GNU General Public License for more details.
 *
 *    You should have received a copy of the GNU General Public License
 *    along with this program. If not, see .
 *    
 */

package moa.clusterers.outliers.utils.mtree;

import java.util.Set;

import moa.clusterers.outliers.utils.mtree.utils.Pair;

/**
 * A {@linkplain SplitFunction split function} that is defined by composing
 * a {@linkplain PromotionFunction promotion function} and a
 * {@linkplain PartitionFunction partition function}.
 *
 * @param  The type of the data objects.
 */
public class ComposedSplitFunction implements SplitFunction {

	private PromotionFunction promotionFunction;
	private PartitionFunction partitionFunction;

	/**
	 * The constructor of a {@link SplitFunction} composed by a
	 * {@link PromotionFunction} and a {@link PartitionFunction}.
	 */
	public ComposedSplitFunction(
			PromotionFunction promotionFunction,
			PartitionFunction partitionFunction
		)
	{
		this.promotionFunction = promotionFunction;
		this.partitionFunction = partitionFunction;
	}

	
	@Override
	public SplitResult process(Set dataSet, DistanceFunction distanceFunction) {
		Pair promoted = promotionFunction.process(dataSet, distanceFunction);
		Pair> partitions = partitionFunction.process(promoted, dataSet, distanceFunction);
		return new SplitResult(promoted, partitions);
	}

}