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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.
/*
* 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 see also:
*
* Ashraf Masood Kibriya (2007). Fast Algorithms for Nearest Neighbour Search.
* Hamilton, New Zealand.
*
*
*
* BibTeX:
*
*
* @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 $
*/
public class KMeansInpiredMethod extends KDTreeNodeSplitter implements
TechnicalInformationHandler {
/** for serialization. */
private static final long serialVersionUID = -866783749124714304L;
/**
* 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 node into two such that the overall sum "
+ "of squared distances of points to their centres on both sides "
+ "of the (axis-parallel) splitting plane is minimum.\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
*/
@Override
public TechnicalInformation getTechnicalInformation() {
TechnicalInformation result;
result = new TechnicalInformation(Type.MASTERSTHESIS);
result.setValue(Field.AUTHOR, "Ashraf Masood Kibriya");
result
.setValue(Field.TITLE, "Fast Algorithms for Nearest Neighbour Search");
result.setValue(Field.YEAR, "2007");
result
.setValue(
Field.SCHOOL,
"Department of Computer Science, School of Computing and Mathematical Sciences, University of Waikato");
result.setValue(Field.ADDRESS, "Hamilton, New Zealand");
return result;
}
/**
* Splits a node into two such that the overall sum of squared distances of
* points to their centres on both sides of the (axis-parallel) splitting
* plane is minimum. The two nodes created after the whole splitting are
* 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.
*/
@Override
public void splitNode(KDTreeNode node, int numNodesCreated,
double[][] nodeRanges, double[][] universe) throws Exception {
correctlyInitialized();
int splitDim = -1;
double splitVal = Double.NEGATIVE_INFINITY;
double leftAttSum[] = new double[m_Instances.numAttributes()], rightAttSum[] = new double[m_Instances
.numAttributes()], leftAttSqSum[] = new double[m_Instances
.numAttributes()], rightAttSqSum[] = new double[m_Instances
.numAttributes()], rightSqMean, leftSqMean, leftSqSum, rightSqSum, minSum = Double.POSITIVE_INFINITY, val;
for (int dim = 0; dim < m_Instances.numAttributes(); dim++) {
// m_MaxRelativeWidth in KDTree ensure there'll be atleast one dim with
// width > 0.0
if (node.m_NodeRanges[dim][WIDTH] == 0.0
|| dim == m_Instances.classIndex()) {
continue;
}
quickSort(m_Instances, m_InstList, dim, node.m_Start, node.m_End);
for (int i = node.m_Start; i <= node.m_End; i++) {
for (int j = 0; j < m_Instances.numAttributes(); j++) {
if (j == m_Instances.classIndex()) {
continue;
}
val = m_Instances.instance(m_InstList[i]).value(j);
if (m_NormalizeNodeWidth) {
if (Double.isNaN(universe[j][MIN])
|| universe[j][MIN] == universe[j][MAX]) {
val = 0.0;
} else {
val = ((val - universe[j][MIN]) / universe[j][WIDTH]); // normalizing
// value
}
}
if (i == node.m_Start) {
leftAttSum[j] = rightAttSum[j] = leftAttSqSum[j] = rightAttSqSum[j] = 0.0;
}
rightAttSum[j] += val;
rightAttSqSum[j] += val * val;
}
}
for (int i = node.m_Start; i <= node.m_End - 1; i++) {
Instance inst = m_Instances.instance(m_InstList[i]);
leftSqSum = rightSqSum = 0.0;
for (int j = 0; j < m_Instances.numAttributes(); j++) {
if (j == m_Instances.classIndex()) {
continue;
}
val = inst.value(j);
if (m_NormalizeNodeWidth) {
if (Double.isNaN(universe[j][MIN])
|| universe[j][MIN] == universe[j][MAX]) {
val = 0.0;
} else {
val = ((val - universe[j][MIN]) / universe[j][WIDTH]); // normalizing
// value
}
}
leftAttSum[j] += val;
rightAttSum[j] -= val;
leftAttSqSum[j] += val * val;
rightAttSqSum[j] -= val * val;
leftSqMean = leftAttSum[j] / (i - node.m_Start + 1);
leftSqMean *= leftSqMean;
rightSqMean = rightAttSum[j] / (node.m_End - i);
rightSqMean *= rightSqMean;
leftSqSum += leftAttSqSum[j] - (i - node.m_Start + 1) * leftSqMean;
rightSqSum += rightAttSqSum[j] - (node.m_End - i) * rightSqMean;
}
if (minSum > (leftSqSum + rightSqSum)) {
minSum = leftSqSum + rightSqSum;
if (i < node.m_End) {
splitVal = (m_Instances.instance(m_InstList[i]).value(dim) + m_Instances
.instance(m_InstList[i + 1]).value(dim)) / 2;
} else {
splitVal = m_Instances.instance(m_InstList[i]).value(dim);
}
splitDim = dim;
}
}// end for instance i
}// end for attribute dim
int rightStart = rearrangePoints(m_InstList, node.m_Start, node.m_End,
splitDim, splitVal);
if (rightStart == node.m_Start || rightStart > node.m_End) {
System.out.println("node.m_Start: " + node.m_Start + " node.m_End: "
+ node.m_End + " splitDim: " + splitDim + " splitVal: " + splitVal
+ " node.min: " + node.m_NodeRanges[splitDim][MIN] + " node.max: "
+ node.m_NodeRanges[splitDim][MAX] + " node.numInstances: "
+ node.numInstances());
if (rightStart == node.m_Start) {
throw new Exception("Left child is empty in node " + node.m_NodeNumber
+ ". Not possible with "
+ "KMeanInspiredMethod splitting method. Please " + "check code.");
} else {
throw new Exception("Right child is empty in node " + node.m_NodeNumber
+ ". Not possible with "
+ "KMeansInspiredMethod 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));
}
/**
* Partitions the instances around a pivot. Used by quicksort and
* kthSmallestValue.
*
* @param insts The instances on which the tree is (or is to be) built.
* @param index The master index array containing indices of the instances.
* @param attidx The attribution/dimension based on which the instances should
* be partitioned.
* @param l The begining index of the portion of master index array that
* should be partitioned.
* @param r The end index of the portion of master index array that should be
* partitioned.
* @return the index of the middle element
*/
protected static int partition(Instances insts, int[] index, int attidx,
int l, int r) {
double pivot = insts.instance(index[(l + r) / 2]).value(attidx);
int help;
while (l < r) {
while ((insts.instance(index[l]).value(attidx) < pivot) && (l < r)) {
l++;
}
while ((insts.instance(index[r]).value(attidx) > pivot) && (l < r)) {
r--;
}
if (l < r) {
help = index[l];
index[l] = index[r];
index[r] = help;
l++;
r--;
}
}
if ((l == r) && (insts.instance(index[r]).value(attidx) > pivot)) {
r--;
}
return r;
}
/**
* Sorts the instances according to the given attribute/dimension. The sorting
* is done on the master index array and not on the actual instances object.
*
* @param insts The instances on which the tree is (or is to be) built.
* @param indices The master index array containing indices of the instances.
* @param attidx The dimension/attribute based on which the instances should
* be sorted.
* @param left The begining index of the portion of the master index array
* that needs to be sorted.
* @param right The end index of the portion of the master index array that
* needs to be sorted.
*/
protected static void quickSort(Instances insts, int[] indices, int attidx,
int left, int right) {
if (left < right) {
int middle = partition(insts, indices, attidx, left, right);
quickSort(insts, indices, attidx, left, middle);
quickSort(insts, indices, attidx, middle + 1, right);
}
}
/**
* Re-arranges the indices array so that in the portion of the array belonging
* to the node to be split, the points <= to the splitVal are on the left of
* the portion 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 valueIsSmallerEqual
}// endfor
return left + 1;
}
/**
* Returns the revision string.
*
* @return the revision
*/
@Override
public String getRevision() {
return RevisionUtils.extract("$Revision: 10203 $");
}
}