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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 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.
*/
/*
* BallSplitter.java
* Copyright (C) 2007 University of Waikato
*/
package weka.core.neighboursearch.balltrees;
import weka.core.EuclideanDistance;
import weka.core.Instances;
import weka.core.OptionHandler;
import weka.core.RevisionHandler;
import weka.core.RevisionUtils;
import java.io.Serializable;
import java.util.Enumeration;
import java.util.Vector;
/**
* Abstract class for splitting a ball tree's BallNode.
*
* @author Ashraf M. Kibriya (amk14[at-the-rate]cs[dot]waikato[dot]ac[dot]nz)
* @version $Revision: 1.2 $
*/
public abstract class BallSplitter
implements Serializable, OptionHandler, RevisionHandler {
/** The instance on which the tree is built. */
protected Instances m_Instances;
/** The distance function (metric) from which
* the tree is (OR is to be) built. */
protected EuclideanDistance m_DistanceFunction;
/**
* The master index array that'll be reshuffled as nodes
* are split (and the tree is constructed).
*/
protected int[] m_Instlist;
/**
* default constructor.
*/
public BallSplitter() {
}
/**
* Creates a new instance of BallSplitter.
* @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 BallSplitter(int[] instList, Instances insts, EuclideanDistance e) {
m_Instlist = instList;
m_Instances = insts;
m_DistanceFunction = e;
}
/**
* Checks whether if this ball splitter is
* correctly intialized or not (i.e. master index
* array, instances, and distance function is
* supplied or not)
* @throws Exception If the object is not correctly
* initialized.
*/
protected void correctlyInitialized() throws Exception {
if(m_Instances==null)
throw new Exception("No instances supplied.");
else if(m_Instlist==null)
throw new Exception("No instance list supplied.");
else if(m_DistanceFunction==null)
throw new Exception("No Euclidean distance function supplied.");
else if(m_Instances.numInstances() != m_Instlist.length)
throw new Exception("The supplied instance list doesn't seem to match " +
"the supplied instances");
}
/**
* Returns an enumeration describing the available options.
*
* @return an enumeration of all the available options.
*/
public Enumeration listOptions() {
return new Vector().elements();
}
/**
* Parses a given list of options.
*
* @param options the list of options as an array of strings
* @throws Exception if an option is not supported
*/
public void setOptions(String[] options) throws Exception {
}
/**
* Gets the current settings of the object.
*
* @return an array of strings suitable for passing to setOptions
*/
public String[] getOptions() {
return new String[0];
}
/**
* Splits a node 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.
*/
public abstract void splitNode(BallNode node, int numNodesCreated)
throws Exception;
/**
* Sets the training instances on which the tree is
* (or is to be) built.
* @param inst The training instances.
*/
public void setInstances(Instances inst) {
m_Instances = inst;
}
/**
* Sets the master index array containing indices of the
* training instances. This array will be rearranged as
* the tree is built (or a node is split_), so that each
* node is assigned a portion in this array which
* contain the instances insides the node's region.
* @param instList The master index array.
*/
public void setInstanceList(int[] instList) {
m_Instlist = instList;
}
/**
* Sets the distance function used to (or to be used
* to) build the tree.
* @param func The distance function.
*/
public void setEuclideanDistanceFunction(EuclideanDistance func) {
m_DistanceFunction = func;
}
/**
* Returns the revision string.
*
* @return the revision
*/
public String getRevision() {
return RevisionUtils.extract("$Revision: 1.2 $");
}
}
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