DTWDistance (ELKI: Environment for DeveLoping KDD-Applications Supported by Index-Structures)

java.lang.Object
- elki.distance.AbstractNumberVectorDistance
- - elki.distance.timeseries.AbstractEditDistance
  - - elki.distance.timeseries.DTWDistance

All Implemented Interfaces:

Distance<NumberVector>, NumberVectorDistance<NumberVector>, PrimitiveDistance<NumberVector>

Direct Known Subclasses:

DerivativeDTWDistance, EDRDistance, ERPDistance
```
@Title("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 DTWDistance
extends AbstractEditDistance
```
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 Description

static class DTWDistance.Par
Parameterization class.

Field Summary
- Fields inherited from class elki.distance.timeseries.AbstractEditDistance
  bandSize

Constructor Summary

Constructors
Constructor Description

DTWDistance()
Constructor.

DTWDistance(double bandSize)
Constructor.

Method Summary

All Methods Instance Methods Concrete Methods
Modifier and Type	Method	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 elki.distance.timeseries.AbstractEditDistance
effectiveBandSize, equals, getInputTypeRestriction, hashCode

Methods inherited from class elki.distance.AbstractNumberVectorDistance
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 elki.distance.Distance
isMetric, isSquared, isSymmetric

Methods inherited from interface elki.distance.PrimitiveDistance
instantiate

- Constructor Detail
  - DTWDistance
```
public DTWDistance()
```
    Constructor.
  - DTWDistance
```
public DTWDistance(double bandSize)
```
    Constructor.
    
    Parameters:
    
    bandSize - Band size
- Method Detail
  - distance
```
public double distance(NumberVector v1,
                       NumberVector v2)
```
    Description copied from interface: NumberVectorDistance
    
    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