de.lmu.ifi.dbs.elki.distance.distancefunction.timeseries

Class DTWDistanceFunction

java.lang.Object

de.lmu.ifi.dbs.elki.distance.distancefunction.AbstractNumberVectorDistanceFunction

de.lmu.ifi.dbs.elki.distance.distancefunction.timeseries.AbstractEditDistanceFunction

de.lmu.ifi.dbs.elki.distance.distancefunction.timeseries.DTWDistanceFunction

All Implemented Interfaces:

DistanceFunction<NumberVector>, NumberVectorDistanceFunction<NumberVector>, PrimitiveDistanceFunction<NumberVector>

Direct Known Subclasses:

DerivativeDTWDistanceFunction, EDRDistanceFunction, ERPDistanceFunction

@Title(value="Dynamic Time Warping Distance Function")
 @Reference(authors="D. Berndt, J. Clifford",
           title="Using dynamic time warping to find patterns in time series",
           booktitle="AAAI-94 Workshop on Knowledge Discovery in Databases, 1994",
           url="http://www.aaai.org/Papers/Workshops/1994/WS-94-03/WS94-03-031.pdf",
           bibkey="DBLP:conf/kdd/BerndtC94")
public class DTWDistanceFunction
extends AbstractEditDistanceFunction

Dynamic Time Warping distance (DTW) for numerical vectors.

Reference:

D. Berndt and J. Clifford
Using dynamic time warping to find patterns in time series
AAAI-94 Workshop on Knowledge Discovery in Databases, 1994

This implementation uses a buffer storing two rows.

TODO: allow different one-dimensional distances

Since:

0.2

Author:

Thomas Bernecker, Erich Schubert

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	DTWDistanceFunction.Parameterizer Parameterization class.

Field Summary

Fields inherited from class de.lmu.ifi.dbs.elki.distance.distancefunction.timeseries.AbstractEditDistanceFunction

bandSize

Constructor Summary

Constructors

Constructor and Description
DTWDistanceFunction() Constructor.
DTWDistanceFunction(double bandSize) Constructor.

Method Summary

Modifier and Type	Method and Description
protected double	delta(double val1, double val2) Compute the delta of two values.
double	distance(NumberVector v1, NumberVector v2) Computes the distance between two given vectors according to this distance function.
protected void	firstRow(double[] buf, int band, NumberVector v1, NumberVector v2, int dim2) Fill the first row.

Methods inherited from class de.lmu.ifi.dbs.elki.distance.distancefunction.timeseries.AbstractEditDistanceFunction

effectiveBandSize, equals, getInputTypeRestriction, hashCode

Methods inherited from class de.lmu.ifi.dbs.elki.distance.distancefunction.AbstractNumberVectorDistanceFunction

dimensionality, dimensionality, dimensionality, dimensionality, dimensionality, dimensionality, dimensionality, dimensionality

Methods inherited from class java.lang.Object

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

Methods inherited from interface de.lmu.ifi.dbs.elki.distance.distancefunction.PrimitiveDistanceFunction

instantiate

Methods inherited from interface de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction

isMetric, isSquared, isSymmetric

Constructor Detail

DTWDistanceFunction

public DTWDistanceFunction()

Constructor.

DTWDistanceFunction

public DTWDistanceFunction(double bandSize)

Constructor.

Parameters:

bandSize - Band size

Method Detail

distance

public double distance(NumberVector v1,
                       NumberVector v2)

Description copied from interface: NumberVectorDistanceFunction

Computes the distance between two given vectors according to this distance function.

Parameters:

v1 - first vector

v2 - second vector

Returns:

the distance between two given vectors according to this distance function

firstRow

protected void firstRow(double[] buf,
                        int band,
                        NumberVector v1,
                        NumberVector v2,
                        int dim2)

Fill the first row.

Parameters:

buf - Buffer

band - Bandwidth

v1 - First vector

v2 - Second vector

dim2 - Dimensionality of second

delta

protected double delta(double val1,
                       double val2)

Compute the delta of two values.

TODO: support Euclidean, Manhattan, others?

Parameters:

val1 - First value

val2 - Second value

Returns:

Difference