All Downloads are FREE. Search and download functionalities are using the official Maven repository.

weka.core.neighboursearch.kdtrees.MidPointOfWidestDimension Maven / Gradle / Ivy

Go to download

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.

There is a newer version: 3.9.6
Show newest version
/*
 *   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 .
 */

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

package weka.core.neighboursearch.kdtrees;

import weka.core.RevisionUtils;
import weka.core.TechnicalInformation;
import weka.core.TechnicalInformation.Field;
import weka.core.TechnicalInformation.Type;
import weka.core.TechnicalInformationHandler;

/**
 
 * The class that splits a KDTree node based on the midpoint value of a dimension in which the node's points have the widest spread.
*
* For more information see also:
*
* Andrew Moore (1991). A tutorial on kd-trees. *

* * BibTeX: *

 * @techreport{Moore1991,
 *    author = {Andrew Moore},
 *    booktitle = {University of Cambridge Computer Laboratory Technical Report No. 209},
 *    howpublished = {Extract from PhD Thesis},
 *    title = {A tutorial on kd-trees},
 *    year = {1991},
 *    HTTP = {http://www.autonlab.org/autonweb/14665.html}
 * }
 * 
*

* * * @author Ashraf M. Kibriya (amk14[at-the-rate]cs[dot]waikato[dot]ac[dot]nz) * @version $Revision: 8034 $ */ public class MidPointOfWidestDimension extends KDTreeNodeSplitter implements TechnicalInformationHandler { /** for serialization. */ private static final long serialVersionUID = -7617277960046591906L; /** * 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 "The class that splits a KDTree node based on the midpoint value of " + "a dimension in which the node's points have the widest spread.\n\n" + "For more information see also:\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 */ public TechnicalInformation getTechnicalInformation() { TechnicalInformation result; result = new TechnicalInformation(Type.TECHREPORT); result.setValue(Field.AUTHOR, "Andrew Moore"); result.setValue(Field.YEAR, "1991"); result.setValue(Field.TITLE, "A tutorial on kd-trees"); result.setValue(Field.HOWPUBLISHED, "Extract from PhD Thesis"); result.setValue(Field.BOOKTITLE, "University of Cambridge Computer Laboratory Technical Report No. 209"); result.setValue(Field.HTTP, "http://www.autonlab.org/autonweb/14665.html"); return result; } /** * Splits a node into two based on the midpoint value of the dimension * in which the points have the widest spread. After splitting two * new nodes are created and correctly initialised. And, node.left * and node.right are set appropriately. * @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. * @param nodeRanges The attributes' range for the points inside * the node that is to be split. * @param universe The attributes' range for the whole * point-space. * @throws Exception If there is some problem in splitting the * given node. */ public void splitNode(KDTreeNode node, int numNodesCreated, double[][] nodeRanges, double[][] universe) throws Exception { correctlyInitialized(); int splitDim = widestDim(nodeRanges, universe); double splitVal = m_EuclideanDistance.getMiddle(nodeRanges[splitDim]); int rightStart = rearrangePoints(m_InstList, node.m_Start, node.m_End, splitDim, splitVal); if (rightStart == node.m_Start || rightStart > node.m_End) { if (rightStart == node.m_Start) throw new Exception("Left child is empty in node " + node.m_NodeNumber + ". Not possible with " + "MidPointofWidestDim splitting method. Please " + "check code."); else throw new Exception("Right child is empty in node " + node.m_NodeNumber + ". Not possible with " + "MidPointofWidestDim splitting method. Please " + "check code."); } node.m_SplitDim = splitDim; node.m_SplitValue = splitVal; node.m_Left = new KDTreeNode(numNodesCreated + 1, node.m_Start, rightStart - 1, m_EuclideanDistance.initializeRanges(m_InstList, node.m_Start, rightStart - 1)); node.m_Right = new KDTreeNode(numNodesCreated + 2, rightStart, node.m_End, m_EuclideanDistance .initializeRanges(m_InstList, rightStart, node.m_End)); } /** * Re-arranges the indices array such that the points <= to the splitVal * are on the left of the array and those > the splitVal are on the right. * * @param indices The master index array. * @param startidx The begining index of portion of indices that needs * re-arranging. * @param endidx The end index of portion of indices that needs * re-arranging. * @param splitDim The split dimension/attribute. * @param splitVal The split value. * @return The startIdx of the points > the splitVal (the points * belonging to the right child of the node). */ protected int rearrangePoints(int[] indices, final int startidx, final int endidx, final int splitDim, final double splitVal) { int tmp, left = startidx - 1; for (int i = startidx; i <= endidx; i++) { if (m_EuclideanDistance.valueIsSmallerEqual(m_Instances .instance(indices[i]), splitDim, splitVal)) { left++; tmp = indices[left]; indices[left] = indices[i]; indices[i] = tmp; }//end if valueIsSmallerEqual }//end for return left + 1; } /** * Returns the revision string. * * @return the revision */ public String getRevision() { return RevisionUtils.extract("$Revision: 8034 $"); } }





© 2015 - 2024 Weber Informatics LLC | Privacy Policy