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

Class DTWDistanceFunction

java.lang.Object

de.lmu.ifi.dbs.elki.distance.distancefunction.AbstractPrimitiveDistanceFunction<NumberVector>

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="Berndt, D. and Clifford, J.",
           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")
public class DTWDistanceFunction
extends AbstractEditDistanceFunction

Dynamic Time Warping distance (DTW) for numerical vectors. Reference:

Berndt, D. and Clifford, J.
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

Methods

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 DatabaseObjects 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

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

dimensionality, dimensionality, dimensionality, dimensionality

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

instantiate, isMetric, isSymmetric

Methods inherited from class java.lang.Object

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

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

instantiate, isMetric, 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: PrimitiveDistanceFunction

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

Specified by:

distance in interface NumberVectorDistanceFunction<NumberVector>

Specified by:

distance in interface PrimitiveDistanceFunction<NumberVector>

Specified by:

distance in class AbstractPrimitiveDistanceFunction<NumberVector>

Parameters:

v1 - first DatabaseObject

v2 - second DatabaseObject

Returns:

the distance between two given DatabaseObjects 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