WeightedDynamicTimeWarping (jaicore-ml 0.1.6 API)

java.lang.Object
- ai.libs.jaicore.ml.tsc.distances.WeightedDynamicTimeWarping

All Implemented Interfaces:

ITimeSeriesDistance
```
public class WeightedDynamicTimeWarping
extends java.lang.Object
implements ITimeSeriesDistance
```
Implementation of the Dynamic Time Warping (DTW) measure as published in "Weighted dynamic time warping for time series classification" by Young-Seon Jeong, Myong K. Jeong and Olufemi A. Omitaomu. DTW does not account for the relative importance regarding the phase difference between a reference point and a testing point. This may lead to misclassification especially in applications where the shape similarity between two sequences is a major consideration for an accurate recognition. Therefore, [the authors] propose a novel distance measure, called a weighted DTW (WDTW), which is a penalty-based DTW. [Their] approach penalizes points with higher phase difference between a reference point and a testing point in order to prevent minimum distance distortion caused by outliers.

Constructor Summary

Constructors
Constructor Description

WeightedDynamicTimeWarping(double g, double maximumWeight, IScalarDistance d)
Constructor.

Method Summary

All Methods Instance Methods Concrete Methods
Modifier and Type	Method	Description
`protected double[]`	`calculateWeights(int length)`	Calculates the weight vector via the Modified logistic weight function (see paper 4.2).
`double`	`distance(double[] a, double[] b)`	Calculates the distance between two time series.

Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

- Constructor Detail
  - WeightedDynamicTimeWarping
```
public WeightedDynamicTimeWarping(double g,
                                  double maximumWeight,
                                  IScalarDistance d)
```
    Constructor.
    
    Parameters:
    
    g - Controls the penelization in weights for points with larger phase difference.
    
    maximumWeight - The desired upper bound for the weight parameter that is used to penalize points with higher phase difference.
- Method Detail
  - distance
```
public double distance(double[] a,
                       double[] b)
```
    Description copied from interface: ITimeSeriesDistance
    
    Calculates the distance between two time series.
    
    Specified by:
    
    distance in interface ITimeSeriesDistance
    
    Parameters:
    
    a - First time series.
    
    b - Second time series.
    
    Returns:
    
    Distance between the first and second time series.
  - calculateWeights
```
protected double[] calculateWeights(int length)
```
    Calculates the weight vector via the Modified logistic weight function (see paper 4.2). Uses memoization to avoid multiple calculations for the same length.
    
    Parameters:
    
    length - Length of the time series, i.e. length of the weight vector. Is guaranteed to be greater 0 within this class.
    
    Returns:
    
    Resulting weight vector.