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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 .
 */

/*
 * PointsClosestToFurthestChildren.java
 * Copyright (C) 2007-2012 University of Waikato, Hamilton, New Zealand
 */

package weka.core.neighboursearch.balltrees;

import weka.core.EuclideanDistance;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.RevisionUtils;
import weka.core.TechnicalInformation;
import weka.core.TechnicalInformation.Field;
import weka.core.TechnicalInformation.Type;
import weka.core.TechnicalInformationHandler;

/**
 *  Implements the Moore's method to split a node of a
 * ball tree.
*
* For more information please see section 2 of the 1st and 3.2.3 of the 2nd:
*
* Andrew W. Moore: The Anchors Hierarchy: Using the Triangle Inequality to * Survive High Dimensional Data. In: UAI '00: Proceedings of the 16th * Conference on Uncertainty in Artificial Intelligence, San Francisco, CA, USA, * 397-405, 2000.
*
* Ashraf Masood Kibriya (2007). Fast Algorithms for Nearest Neighbour Search. * Hamilton, New Zealand. *

* * * BibTeX: * *

 * @inproceedings{Moore2000,
 *    address = {San Francisco, CA, USA},
 *    author = {Andrew W. Moore},
 *    booktitle = {UAI '00: Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence},
 *    pages = {397-405},
 *    publisher = {Morgan Kaufmann Publishers Inc.},
 *    title = {The Anchors Hierarchy: Using the Triangle Inequality to Survive High Dimensional Data},
 *    year = {2000}
 * }
 * 
 * @mastersthesis{Kibriya2007,
 *    address = {Hamilton, New Zealand},
 *    author = {Ashraf Masood Kibriya},
 *    school = {Department of Computer Science, School of Computing and Mathematical Sciences, University of Waikato},
 *    title = {Fast Algorithms for Nearest Neighbour Search},
 *    year = {2007}
 * }
 * 
*

* * * * * @author Ashraf M. Kibriya (amk14[at-the-rate]cs[dot]waikato[dot]ac[dot]nz) * @version $Revision: 10203 $ */ // better rename to MidPoint of Furthest Pair/Children public class PointsClosestToFurthestChildren extends BallSplitter implements TechnicalInformationHandler { /** for serialization. */ private static final long serialVersionUID = -2947177543565818260L; /** * Returns a string describing this object. * * @return A description of the algorithm for displaying in the * explorer/experimenter gui. */ public String globalInfo() { return "Implements the Moore's method to split a node of a ball tree.\n\n" + "For more information please see section 2 of the 1st and 3.2.3 of " + "the 2nd:\n\n" + getTechnicalInformation().toString(); } /** * Returns an instance of a TechnicalInformation object, containing detailed * information about the technical background of this class, e.g., paper * reference or book this class is based on. * * @return The technical information about this class. */ @Override public TechnicalInformation getTechnicalInformation() { TechnicalInformation result; TechnicalInformation additional; result = new TechnicalInformation(Type.INPROCEEDINGS); result.setValue(Field.AUTHOR, "Andrew W. Moore"); result .setValue( Field.TITLE, "The Anchors Hierarchy: Using the Triangle Inequality to Survive High Dimensional Data"); result.setValue(Field.YEAR, "2000"); result .setValue( Field.BOOKTITLE, "UAI '00: Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence"); result.setValue(Field.PAGES, "397-405"); result.setValue(Field.PUBLISHER, "Morgan Kaufmann Publishers Inc."); result.setValue(Field.ADDRESS, "San Francisco, CA, USA"); additional = result.add(Type.MASTERSTHESIS); additional.setValue(Field.AUTHOR, "Ashraf Masood Kibriya"); additional.setValue(Field.TITLE, "Fast Algorithms for Nearest Neighbour Search"); additional.setValue(Field.YEAR, "2007"); additional .setValue( Field.SCHOOL, "Department of Computer Science, School of Computing and Mathematical Sciences, University of Waikato"); additional.setValue(Field.ADDRESS, "Hamilton, New Zealand"); return result; } /** Constructor. */ public PointsClosestToFurthestChildren() { } /** * Constructor. * * @param instList The master index array. * @param insts The instances on which the tree is (or is to be) built. * @param e The Euclidean distance function to use for splitting. */ public PointsClosestToFurthestChildren(int[] instList, Instances insts, EuclideanDistance e) { super(instList, insts, e); } /** * Splits a ball into two. * * @param node The node to split. * @param numNodesCreated The number of nodes that so far have been created * for the tree, so that the newly created nodes are assigned * correct/meaningful node numbers/ids. * @throws Exception If there is some problem in splitting the given node. */ @Override public void splitNode(BallNode node, int numNodesCreated) throws Exception { correctlyInitialized(); double maxDist = Double.NEGATIVE_INFINITY, dist = 0.0; Instance furthest1 = null, furthest2 = null, pivot = node.getPivot(), temp; double distList[] = new double[node.m_NumInstances]; for (int i = node.m_Start; i <= node.m_End; i++) { temp = m_Instances.instance(m_Instlist[i]); dist = m_DistanceFunction.distance(pivot, temp, Double.POSITIVE_INFINITY); if (dist > maxDist) { maxDist = dist; furthest1 = temp; } } maxDist = Double.NEGATIVE_INFINITY; furthest1 = (Instance) furthest1.copy(); for (int i = 0; i < node.m_NumInstances; i++) { temp = m_Instances.instance(m_Instlist[i + node.m_Start]); distList[i] = m_DistanceFunction.distance(furthest1, temp, Double.POSITIVE_INFINITY); if (distList[i] > maxDist) { maxDist = distList[i]; furthest2 = temp; // tempidx = i+node.m_Start; } } furthest2 = (Instance) furthest2.copy(); dist = 0.0; int numRight = 0; // moving indices in the right branch to the right end of the array for (int i = 0; i < node.m_NumInstances - numRight; i++) { temp = m_Instances.instance(m_Instlist[i + node.m_Start]); dist = m_DistanceFunction.distance(furthest2, temp, Double.POSITIVE_INFINITY); if (dist < distList[i]) { int t = m_Instlist[node.m_End - numRight]; m_Instlist[node.m_End - numRight] = m_Instlist[i + node.m_Start]; m_Instlist[i + node.m_Start] = t; double d = distList[distList.length - 1 - numRight]; distList[distList.length - 1 - numRight] = distList[i]; distList[i] = d; numRight++; i--; } } if (!(numRight > 0 && numRight < node.m_NumInstances)) { throw new Exception("Illegal value for numRight: " + numRight); } node.m_Left = new BallNode(node.m_Start, node.m_End - numRight, numNodesCreated + 1, (pivot = BallNode.calcCentroidPivot(node.m_Start, node.m_End - numRight, m_Instlist, m_Instances)), BallNode.calcRadius( node.m_Start, node.m_End - numRight, m_Instlist, m_Instances, pivot, m_DistanceFunction)); node.m_Right = new BallNode(node.m_End - numRight + 1, node.m_End, numNodesCreated + 2, (pivot = BallNode.calcCentroidPivot(node.m_End - numRight + 1, node.m_End, m_Instlist, m_Instances)), BallNode.calcRadius(node.m_End - numRight + 1, node.m_End, m_Instlist, m_Instances, pivot, m_DistanceFunction)); } /** * Returns the revision string. * * @return the revision */ @Override public String getRevision() { return RevisionUtils.extract("$Revision: 10203 $"); } }





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