/*
 *    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 2 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, write to the Free Software
 *    Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA.
 */

/*
 *    MultiLabelKNN.java
 *    Copyright (C) 2009-2010 Aristotle University of Thessaloniki, Thessaloniki, Greece
 */
package mulan.classifier.lazy;

import mulan.classifier.MultiLabelLearnerBase;
import mulan.data.MultiLabelInstances;
import weka.core.DistanceFunction;
import weka.core.EuclideanDistance;
import weka.core.Instances;
import weka.core.neighboursearch.LinearNNSearch;

/**
 * Superclass of all KNN based multi-label algorithms
 * 
 * @author Eleftherios Spyromitros-Xioufis ( espyromi@csd.auth.gr )
 * 
 */
@SuppressWarnings("serial")
public abstract class MultiLabelKNN extends MultiLabelLearnerBase {

	/** Whether the neighbors should be distance-weighted. */
	protected int distanceWeighting;
	/** no weighting. */
	public static final int WEIGHT_NONE = 1;
	/** weight by 1/distance. */
	public static final int WEIGHT_INVERSE = 2;
	/** weight by 1-distance. */
	public static final int WEIGHT_SIMILARITY = 4;
	// TODO weight each neighbor's vote according to the inverse square of its distance
	/**
	 * Number of neighbors used in the k-nearest neighbor algorithm
	 */
	protected int numOfNeighbors;
	/**
	 * Class implementing the brute force search algorithm for nearest neighbor search. Default
	 * value is true.
	 */
	protected LinearNNSearch lnn = null;
	/**
	 * Implementing Euclidean distance (or similarity) function.
	 */
	protected DistanceFunction dfunc = null;

	public void setDfunc(DistanceFunction dfunc) {
		this.dfunc = dfunc;
	}

	/**
	 * The training instances
	 */
	protected Instances train;

	/**
	 * The default constructor
	 */
	public MultiLabelKNN() {
		this.numOfNeighbors = 10;
		dfunc = new EuclideanDistance();
	}

	/**
	 * Initializes the number of neighbors
	 * 
	 * @param numOfNeighbors the number of neighbors
	 */
	public MultiLabelKNN(int numOfNeighbors) {
		this.numOfNeighbors = numOfNeighbors;
		dfunc = new EuclideanDistance();
	}

	protected void buildInternal(MultiLabelInstances trainSet) throws Exception {
		train = new Instances(trainSet.getDataSet());

		// label attributes don't influence distance estimation
		String labelIndicesString = "";
		for (int i = 0; i < numLabels - 1; i++) {
			labelIndicesString += (labelIndices[i] + 1) + ",";
		}
		labelIndicesString += (labelIndices[numLabels - 1] + 1);
		dfunc.setAttributeIndices(labelIndicesString);
		dfunc.setInvertSelection(true);

		lnn = new LinearNNSearch();
		lnn.setDistanceFunction(dfunc);
		lnn.setInstances(train);
		lnn.setMeasurePerformance(false);
	}

	@Override
	public boolean isUpdatable() {
		return true;
	}

	/**
	 * @param distanceWeighting the distanceWeighting to set
	 */
	public void setDistanceWeighting(int distanceWeighting) {
		this.distanceWeighting = distanceWeighting;
	}
}