de.lmu.ifi.dbs.elki.algorithm.statistics

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

java.lang.Object

de.lmu.ifi.dbs.elki.algorithm.AbstractAlgorithm<R>

de.lmu.ifi.dbs.elki.algorithm.AbstractDistanceBasedAlgorithm<O,D,CollectionResult<DoubleVector>>

de.lmu.ifi.dbs.elki.algorithm.statistics.DistanceStatisticsWithClasses<O,D>

Type Parameters:

O - Object type

D - Distance type

All Implemented Interfaces:

Algorithm, InspectionUtilFrequentlyScanned, Parameterizable

@Title(value="Distance Histogram")
@Description(value="Computes a histogram over the distances occurring in the data set.")
public class DistanceStatisticsWithClasses<O,D extends NumberDistance<D,?>>
extends AbstractDistanceBasedAlgorithm<O,D,CollectionResult<DoubleVector>>

Algorithm to gather statistics over the distance distribution in the data set.

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	DistanceStatisticsWithClasses.Parameterizer<O,D extends NumberDistance<D,?>> Parameterization class.

Field Summary

Fields

Modifier and Type	Field and Description
private boolean	exact Compute exactly (slower).
static OptionID	EXACT_ID Flag to compute exact value range for binning.
static OptionID	HISTOGRAM_BINS_ID Option to configure the number of bins to use.
private static Logging	LOG The logger for this class.
private int	numbin Number of bins to use in sampling.
private boolean	sampling Sampling flag.
static OptionID	SAMPLING_ID Flag to enable sampling.

Fields inherited from class de.lmu.ifi.dbs.elki.algorithm.AbstractDistanceBasedAlgorithm

DISTANCE_FUNCTION_ID

Constructor Summary

Constructors

Constructor and Description
DistanceStatisticsWithClasses(DistanceFunction<? super O,D> distanceFunction, int numbins, boolean exact, boolean sampling) Constructor.

Method Summary

Methods

Modifier and Type	Method and Description
private DoubleMinMax	exactMinMax(Relation<O> relation, DistanceQuery<O,D> distFunc) Compute the exact maximum and minimum.
TypeInformation[]	getInputTypeRestriction() Get the input type restriction used for negotiating the data query.
protected Logging	getLogger() Get the (STATIC) logger for this class.
HistogramResult<DoubleVector>	run(Database database) Runs the algorithm.
private DoubleMinMax	sampleMinMax(Relation<O> relation, DistanceQuery<O,D> distFunc) Estimate minimum and maximum via sampling.
private static void	shrinkHeap(TreeSet<DoubleDBIDPair> hotset, int k) Shrink the heap of "hot" (extreme) items.

Methods inherited from class de.lmu.ifi.dbs.elki.algorithm.AbstractDistanceBasedAlgorithm

getDistanceFunction

Methods inherited from class de.lmu.ifi.dbs.elki.algorithm.AbstractAlgorithm

makeParameterDistanceFunction

Methods inherited from class java.lang.Object

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

Field Detail

LOG

private static final Logging LOG

The logger for this class.

EXACT_ID

public static final OptionID EXACT_ID

Flag to compute exact value range for binning.

SAMPLING_ID

public static final OptionID SAMPLING_ID

Flag to enable sampling.

HISTOGRAM_BINS_ID

public static final OptionID HISTOGRAM_BINS_ID

Option to configure the number of bins to use.

numbin

private int numbin

Number of bins to use in sampling.

sampling

private boolean sampling

Sampling flag.

exact

private boolean exact

Compute exactly (slower).

Constructor Detail

DistanceStatisticsWithClasses

public DistanceStatisticsWithClasses(DistanceFunction<? super O,D> distanceFunction,
                             int numbins,
                             boolean exact,
                             boolean sampling)

Constructor.

Parameters:

distanceFunction - Distance function to use

numbins - Number of bins

exact - Exactness flag

sampling - Sampling flag

Method Detail

run

public HistogramResult<DoubleVector> run(Database database)

Description copied from interface: Algorithm

Runs the algorithm.

Specified by:

run in interface Algorithm

Overrides:

run in class AbstractAlgorithm<CollectionResult<DoubleVector>>

Parameters:

database - the database to run the algorithm on

Returns:

the Result computed by this algorithm

sampleMinMax

private DoubleMinMax sampleMinMax(Relation<O> relation,
                        DistanceQuery<O,D> distFunc)

Estimate minimum and maximum via sampling.

Parameters:

relation - Relation to process

distFunc - Distance function to use

Returns:

Minimum and maximum

exactMinMax

private DoubleMinMax exactMinMax(Relation<O> relation,
                       DistanceQuery<O,D> distFunc)

Compute the exact maximum and minimum.

Parameters:

relation - Relation to process

distFunc - Distance function

Returns:

Exact maximum and minimum

shrinkHeap

private static void shrinkHeap(TreeSet<DoubleDBIDPair> hotset,
              int k)

Shrink the heap of "hot" (extreme) items.

Parameters:

hotset - Set of hot items

k - target size

getInputTypeRestriction

public TypeInformation[] getInputTypeRestriction()

Description copied from class: AbstractAlgorithm

Get the input type restriction used for negotiating the data query.

Specified by:

getInputTypeRestriction in interface Algorithm

Specified by:

getInputTypeRestriction in class AbstractAlgorithm<CollectionResult<DoubleVector>>

Returns:

Type restriction

getLogger

protected Logging getLogger()

Description copied from class: AbstractAlgorithm

Get the (STATIC) logger for this class.

Specified by:

getLogger in class AbstractAlgorithm<CollectionResult<DoubleVector>>

Returns:

the static logger