de.lmu.ifi.dbs.elki.distance.distanceresultlist

Class KNNUtil

java.lang.Object

de.lmu.ifi.dbs.elki.distance.distanceresultlist.KNNUtil

public final class KNNUtil
extends Object

Helper classes for kNN results.

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
protected static class	KNNUtil.DistanceItr<D extends Distance<D>> Proxy iterator for accessing DBIDs.
protected static class	KNNUtil.DistanceView<D extends Distance<D>> A view on the Distances of the result.
protected static class	KNNUtil.KNNSubList<D extends Distance<D>> Sublist of an existing result to contain only the first k elements.

Constructor Summary

Constructors

Modifier	Constructor and Description
private	KNNUtil() Fake constructor: do not instantiate.

Method Summary

Methods

Modifier and Type	Method and Description
static <D extends Distance<D>> List<D>	asDistanceList(KNNResult<D> list) View as list of distances.
static <D extends Distance<D>> KNNHeap<D>	newHeap(D factory, int k) Create an appropriate heap for the distance function.
static <D extends Distance<D>> KNNHeap<D>	newHeap(DistanceFunction<?,D> df, int k) Create an appropriate heap for the distance function.
static <D extends Distance<D>> KNNHeap<D>	newHeap(DistanceQuery<?,D> df, int k) Create an appropriate heap for the distance function.
static <D extends Distance<D>> KNNHeap<D>	newHeap(KNNResult<D> exist) Build a new heap from a given list.
static <D extends Distance<D>> KNNResult<D>	subList(KNNResult<D> list, int k) Get a subset of the KNN result.

Methods inherited from class java.lang.Object

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

Constructor Detail

KNNUtil

private KNNUtil()

Fake constructor: do not instantiate.

Method Detail

newHeap

public static <D extends Distance<D>> KNNHeap<D> newHeap(DistanceFunction<?,D> df,
                                         int k)

Create an appropriate heap for the distance function. This will use a double heap if appropriate.

Type Parameters:

D - distance type

Parameters:

df - Distance function

k - K value

Returns:

New heap of size k, appropriate for this distance function.

newHeap

public static <D extends Distance<D>> KNNHeap<D> newHeap(DistanceQuery<?,D> df,
                                         int k)

Create an appropriate heap for the distance function. This will use a double heap if appropriate.

Type Parameters:

D - distance type

Parameters:

df - Distance function

k - K value

Returns:

New heap of size k, appropriate for this distance function.

newHeap

public static <D extends Distance<D>> KNNHeap<D> newHeap(D factory,
                                         int k)

Create an appropriate heap for the distance function. This will use a double heap if appropriate.

Type Parameters:

D - distance type

Parameters:

factory - distance prototype

k - K value

Returns:

New heap of size k, appropriate for this distance type.

newHeap

public static <D extends Distance<D>> KNNHeap<D> newHeap(KNNResult<D> exist)

Build a new heap from a given list.

Type Parameters:

D - Distance type

Parameters:

exist - Existing result

Returns:

New heap

asDistanceList

public static <D extends Distance<D>> List<D> asDistanceList(KNNResult<D> list)

View as list of distances.

Type Parameters:

D - distance type

Parameters:

list - Result to proxy

Returns:

List of distances view

subList

public static <D extends Distance<D>> KNNResult<D> subList(KNNResult<D> list,
                                           int k)

Get a subset of the KNN result.

Type Parameters:

D - distance type

Parameters:

list - Existing list

k - k

Returns:

Subset