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The Waikato Environment for Knowledge Analysis (WEKA), a machine
learning workbench. This is the stable version. Apart from bugfixes, this version
does not receive any other updates.
/*
* 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
*
* 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 $");
}
}