de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.mkcop

Class MkCoPTree<O,D extends NumberDistance<D,?>>

java.lang.Object

de.lmu.ifi.dbs.elki.index.tree.IndexTree<N,E>

de.lmu.ifi.dbs.elki.index.tree.metrical.MetricalIndexTree<O,D,N,E>

de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.AbstractMTree<O,D,N,E>

de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.AbstractMkTree<O,D,MkCoPTreeNode<O,D>,MkCoPEntry<D>>

de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.mkcop.MkCoPTree<O,D>

Type Parameters:

O - Object type

D - Distance type

Direct Known Subclasses:

MkCoPTreeIndex

public class MkCoPTree<O,D extends NumberDistance<D,?>>
extends AbstractMkTree<O,D,MkCoPTreeNode<O,D>,MkCoPEntry<D>>

MkCopTree is a metrical index structure based on the concepts of the M-Tree supporting efficient processing of reverse k nearest neighbor queries for parameter k < kmax.

Field Summary

Fields

Modifier and Type	Field and Description
(package private) int	k_max Parameter k.
private static Logging	LOG The logger for this class.
private double[]	log_k The values of log(1),..
private QueryStatistic	rkNNStatistics Provides some statistics about performed reverse knn-queries.

Fields inherited from class de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.AbstractMkTree

knnq

Fields inherited from class de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.AbstractMTree

distanceFunction, distanceQuery, EXTRA_INTEGRITY_CHECKS

Fields inherited from class de.lmu.ifi.dbs.elki.index.tree.IndexTree

dirCapacity, dirMinimum, initialized, leafCapacity, leafMinimum

Constructor Summary

Constructors

Constructor and Description
MkCoPTree(PageFile<MkCoPTreeNode<O,D>> pagefile, DistanceQuery<O,D> distanceQuery, DistanceFunction<O,D> distanceFunction, int k_max) Constructor.

Method Summary

Methods

Modifier and Type	Method and Description
private void	adjustApproximatedKNNDistances(MkCoPEntry<D> entry, Map<DBID,KNNResult<D>> knnLists) Adjusts the knn distance in the subtree of the specified root entry.
private void	approximateKnnDistances(MkCoPLeafEntry<D> entry, KNNResult<D> knnDistances) Computes logarithmic skew (fractal dimension ie. m) and in kappx[0] and kappx[1] the non-logarithmic values of the approximated first and last nearest neighbor distances
private ApproximationLine	approximateLowerHull(ConvexHull convexHull, double[] log_k, double sum_log_k, double sum_log_k2, double[] log_kDist, double sum_log_kDist, double sum_log_k_kDist) Approximates the lower hull.
private ApproximationLine	approximateUpperHull_OLD(ConvexHull convexHull, double[] log_k, double sum_log_k, double sum_log_k2, double[] log_kDist, double sum_log_kDist, double sum_log_k_kDist)
private ApproximationLine	approximateUpperHull_PAPER(ConvexHull convexHull, double[] log_k, double sum_log_k, double sum_log_k2, double[] log_kDist, double sum_log_kDist, double sum_log_k_kDist)
private ApproximationLine	approximateUpperHull(ConvexHull convexHull, double[] log_k, double[] log_kDist)
void	clearRkNNStatistics() Clears the values of the statistic for performed rknn queries
protected MkCoPEntry<D>	createNewDirectoryEntry(MkCoPTreeNode<O,D> node, DBID routingObjectID, D parentDistance) Creates a new directory entry representing the specified node.
protected MkCoPTreeNode<O,D>	createNewDirectoryNode() Creates a new directory node with the specified capacity.
protected MkCoPTreeNode<O,D>	createNewLeafNode() Creates a new leaf node with the specified capacity.
protected MkCoPEntry<D>	createRootEntry() Creates an entry representing the root node.
private void	doReverseKNNQuery(int k, DBIDRef q, GenericDistanceDBIDList<D> result, ModifiableDBIDs candidates) Performs a reverse knn query.
int	getK_max() Returns the value of the k_max parameter.
protected Logging	getLogger() Get the (STATIC) logger for this class.
QueryStatistic	getRkNNStatistics() Returns the statistic for performed rknn queries.
protected void	initializeCapacities(MkCoPEntry<D> exampleLeaf) Determines the maximum and minimum number of entries in a node.
void	insert(MkCoPEntry<D> entry, boolean withPreInsert) Inserts the specified object into this M-Tree.
void	insertAll(List<MkCoPEntry<D>> entries) Bulk insert.
private double	optimize(int k0, int kmax, double sumx, double sumx2, double xp, double yp, double sumxy, double sumy)
protected void	preInsert(MkCoPEntry<D> entry) Performs necessary operations before inserting the specified entry.
DistanceDBIDResult<D>	reverseKNNQuery(DBIDRef id, int k) Performs a reverse k-nearest neighbor query for the given object ID.
private double	ssqerr(int k0, int kmax, double[] logk, double[] log_kDist, double m, double t)

Methods inherited from class de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.AbstractMkTree

batchNN

Methods inherited from class de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.AbstractMTree

createEmptyRoot, distance, getDistanceFactory, getDistanceFunction, getDistanceQuery, getHeight, getLeaves, getSortedEntries, getSortedEntries, toString

Methods inherited from class de.lmu.ifi.dbs.elki.index.tree.IndexTree

createHeader, deleteNode, getFile, getNode, getNode, getPageFileStatistics, getPageID, getPageSize, getRoot, getRootEntry, getRootID, getRootPath, initialize, initialize, initializeFromFile, isRoot, postDelete, writeNode

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait

Field Detail

LOG

private static final Logging LOG

The logger for this class.

k_max

int k_max

Parameter k.

log_k

private double[] log_k

The values of log(1),..,log(k_max)

rkNNStatistics

private QueryStatistic rkNNStatistics

Provides some statistics about performed reverse knn-queries.

Constructor Detail

MkCoPTree

public MkCoPTree(PageFile<MkCoPTreeNode<O,D>> pagefile,
         DistanceQuery<O,D> distanceQuery,
         DistanceFunction<O,D> distanceFunction,
         int k_max)

Constructor.

Parameters:

pagefile - Page file

distanceQuery - Distance query

distanceFunction - Distance function

k_max - Maximum value of k supported

Method Detail

preInsert

protected void preInsert(MkCoPEntry<D> entry)

Description copied from class: IndexTree

Performs necessary operations before inserting the specified entry.

Overrides:

preInsert in class IndexTree<MkCoPTreeNode<O,D extends NumberDistance<D,?>>,MkCoPEntry<D extends NumberDistance<D,?>>>

Parameters:

entry - the entry to be inserted

Throws:

UnsupportedOperationException - since this operation is not supported

insert

public void insert(MkCoPEntry<D> entry,
          boolean withPreInsert)

Description copied from class: AbstractMTree

Inserts the specified object into this M-Tree.

Overrides:

insert in class AbstractMTree<O,D extends NumberDistance<D,?>,MkCoPTreeNode<O,D extends NumberDistance<D,?>>,MkCoPEntry<D extends NumberDistance<D,?>>>

Parameters:

entry - the entry to be inserted

withPreInsert - if this flag is true, the preInsert method will be called before inserting the object

Throws:

UnsupportedOperationException - since this operation is not supported

insertAll

public void insertAll(List<MkCoPEntry<D>> entries)

Description copied from class: AbstractMTree

Bulk insert.

Overrides:

insertAll in class AbstractMTree<O,D extends NumberDistance<D,?>,MkCoPTreeNode<O,D extends NumberDistance<D,?>>,MkCoPEntry<D extends NumberDistance<D,?>>>

Parameters:

entries - Entries to insert

reverseKNNQuery

public DistanceDBIDResult<D> reverseKNNQuery(DBIDRef id,
                                    int k)

Performs a reverse k-nearest neighbor query for the given object ID. The query result is in ascending order to the distance to the query object.

Specified by:

reverseKNNQuery in class AbstractMkTree<O,D extends NumberDistance<D,?>,MkCoPTreeNode<O,D extends NumberDistance<D,?>>,MkCoPEntry<D extends NumberDistance<D,?>>>

Parameters:

id - the query object id

k - the number of nearest neighbors to be returned

Returns:

a List of the query results

getRkNNStatistics

public QueryStatistic getRkNNStatistics()

Returns the statistic for performed rknn queries.

Returns:

the statistic for performed rknn queries

clearRkNNStatistics

public void clearRkNNStatistics()

Clears the values of the statistic for performed rknn queries

getK_max

public int getK_max()

Returns the value of the k_max parameter.

Returns:

the value of the k_max parameter

initializeCapacities

protected void initializeCapacities(MkCoPEntry<D> exampleLeaf)

Determines the maximum and minimum number of entries in a node.

Specified by:

initializeCapacities in class IndexTree<MkCoPTreeNode<O,D extends NumberDistance<D,?>>,MkCoPEntry<D extends NumberDistance<D,?>>>

Parameters:

exampleLeaf - an object that will be stored in the index

doReverseKNNQuery

private void doReverseKNNQuery(int k,
                     DBIDRef q,
                     GenericDistanceDBIDList<D> result,
                     ModifiableDBIDs candidates)

Performs a reverse knn query.

Parameters:

k - the parameter k of the rknn query

q - the id of the query object

result - holds the true results (they need not to be refined)

candidates - holds possible candidates for the result (they need a refinement)

adjustApproximatedKNNDistances

private void adjustApproximatedKNNDistances(MkCoPEntry<D> entry,
                                  Map<DBID,KNNResult<D>> knnLists)

Adjusts the knn distance in the subtree of the specified root entry.

Parameters:

entry - the root entry of the current subtree

knnLists - a map of knn lists for each leaf entry

ssqerr

private double ssqerr(int k0,
            int kmax,
            double[] logk,
            double[] log_kDist,
            double m,
            double t)

optimize

private double optimize(int k0,
              int kmax,
              double sumx,
              double sumx2,
              double xp,
              double yp,
              double sumxy,
              double sumy)

approximateKnnDistances

private void approximateKnnDistances(MkCoPLeafEntry<D> entry,
                           KNNResult<D> knnDistances)

Computes logarithmic skew (fractal dimension ie. m) and in kappx[0] and kappx[1] the non-logarithmic values of the approximated first and last nearest neighbor distances

Parameters:

knnDistances - TODO: Spezialbehandlung fuer identische Punkte in DB (insbes. Distanz 0)

approximateLowerHull

private ApproximationLine approximateLowerHull(ConvexHull convexHull,
                                     double[] log_k,
                                     double sum_log_k,
                                     double sum_log_k2,
                                     double[] log_kDist,
                                     double sum_log_kDist,
                                     double sum_log_k_kDist)

Approximates the lower hull.

Parameters:

convexHull -

log_kDist -

sum_log_kDist -

sum_log_k_kDist -

approximateUpperHull

private ApproximationLine approximateUpperHull(ConvexHull convexHull,
                                     double[] log_k,
                                     double[] log_kDist)

approximateUpperHull_PAPER

private ApproximationLine approximateUpperHull_PAPER(ConvexHull convexHull,
                                           double[] log_k,
                                           double sum_log_k,
                                           double sum_log_k2,
                                           double[] log_kDist,
                                           double sum_log_kDist,
                                           double sum_log_k_kDist)

approximateUpperHull_OLD

private ApproximationLine approximateUpperHull_OLD(ConvexHull convexHull,
                                         double[] log_k,
                                         double sum_log_k,
                                         double sum_log_k2,
                                         double[] log_kDist,
                                         double sum_log_kDist,
                                         double sum_log_k_kDist)

createNewLeafNode

protected MkCoPTreeNode<O,D> createNewLeafNode()

Creates a new leaf node with the specified capacity.

Specified by:

createNewLeafNode in class IndexTree<MkCoPTreeNode<O,D extends NumberDistance<D,?>>,MkCoPEntry<D extends NumberDistance<D,?>>>

Returns:

a new leaf node

createNewDirectoryNode

protected MkCoPTreeNode<O,D> createNewDirectoryNode()

Creates a new directory node with the specified capacity.

Specified by:

createNewDirectoryNode in class IndexTree<MkCoPTreeNode<O,D extends NumberDistance<D,?>>,MkCoPEntry<D extends NumberDistance<D,?>>>

Returns:

a new directory node

createNewDirectoryEntry

protected MkCoPEntry<D> createNewDirectoryEntry(MkCoPTreeNode<O,D> node,
                                    DBID routingObjectID,
                                    D parentDistance)

Creates a new directory entry representing the specified node.

Specified by:

createNewDirectoryEntry in class AbstractMTree<O,D extends NumberDistance<D,?>,MkCoPTreeNode<O,D extends NumberDistance<D,?>>,MkCoPEntry<D extends NumberDistance<D,?>>>

Parameters:

node - the node to be represented by the new entry

routingObjectID - the id of the routing object of the node

parentDistance - the distance from the routing object of the node to the routing object of the parent node

Returns:

the newly created directory entry

createRootEntry

protected MkCoPEntry<D> createRootEntry()

Creates an entry representing the root node.

Specified by:

createRootEntry in class IndexTree<MkCoPTreeNode<O,D extends NumberDistance<D,?>>,MkCoPEntry<D extends NumberDistance<D,?>>>

Returns:

an entry representing the root node

getLogger

protected Logging getLogger()

Description copied from class: IndexTree

Get the (STATIC) logger for this class.

Specified by:

getLogger in class IndexTree<MkCoPTreeNode<O,D extends NumberDistance<D,?>>,MkCoPEntry<D extends NumberDistance<D,?>>>

Returns:

the static logger