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The Waikato Environment for Knowledge Analysis (WEKA), a machine learning workbench. This version represents the developer version, the "bleeding edge" of development, you could say. New functionality gets added to this version.

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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 .
 */

/*
 *    LinearNNSearch.java
 *    Copyright (C) 1999-2012 University of Waikato
 */

package weka.core.neighboursearch;

import java.util.Collections;
import java.util.Enumeration;
import java.util.Vector;

import weka.core.Instance;
import weka.core.Instances;
import weka.core.Option;
import weka.core.RevisionUtils;
import weka.core.Utils;

/**
 
 * Class implementing the brute force search algorithm for nearest neighbour search.
 * 

* * Valid options are:

* *

 -S
 *  Skip identical instances (distances equal to zero).
 * 
* * * @author Ashraf M. Kibriya (amk14[at-the-rate]cs[dot]waikato[dot]ac[dot]nz) * @version $Revision: 15001 $ */ public class LinearNNSearch extends NearestNeighbourSearch { /** for serialization. */ private static final long serialVersionUID = 1915484723703917241L; /** Array holding the distances of the nearest neighbours. It is filled up * both by nearestNeighbour() and kNearestNeighbours(). */ protected double[] m_Distances; /** Whether to skip instances from the neighbours that are identical to the query instance. */ protected boolean m_SkipIdentical = false; /** * Constructor. Needs setInstances(Instances) * to be called before the class is usable. */ public LinearNNSearch() { super(); } /** * Constructor that uses the supplied set of * instances. * * @param insts the instances to use */ public LinearNNSearch(Instances insts) { super(insts); m_DistanceFunction.setInstances(insts); } /** * Returns a string describing this nearest neighbour search algorithm. * * @return a description of the algorithm for displaying in the * explorer/experimenter gui */ public String globalInfo() { return "Class implementing the brute force search algorithm for nearest " + "neighbour search."; } /** * Returns an enumeration describing the available options. * * @return an enumeration of all the available options. */ public Enumeration




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