1.4. Namespace Distances

class Distances

Khiva Distances class containing distances methods.

Public Static Functions

static KhivaArray Khiva.Distances.Dtw(KhivaArray arr)

Calculates the Dynamic Time Warping Distance.

Return
An upper triangular matrix where each position corresponds to the distance between two time series. Diagonal elements will be zero. For example: Position row 0 column 1 records the distance between time series 0 and time series 1.
Parameters
  • arr: Expects an input array whose dimension zero is the length of the time series (all the same) and dimension one indicates the number of time series.

static KhivaArray Khiva.Distances.Euclidean(KhivaArray arr)

Calculates euclidean distances between time series.

Return
An upper triangular matrix where each position corresponds to the distance between two time series. Diagonal elements will be zero. For example: Position row 0 column 1 records the distance between time series 0 and time series 1.
Parameters
  • arr: Expects an input array whose dimension zero is the length of the time series (all the same) and dimension one indicates the number of time series.

static KhivaArray Khiva.Distances.Hamming(KhivaArray arr)

Calculates Hamming distances between time series.

Return
An upper triangular matrix where each position corresponds to the distance between two time series. Diagonal elements will be zero. For example: Position row 0 column 1 records the distance between time series 0 and time series 1.
Parameters
  • arr: Expects an input array whose dimension zero is the length of the time series (all the same) and dimension one indicates the number of time series.

static KhivaArray Khiva.Distances.Manhattan(KhivaArray arr)

Calculates Manhattan distances between time series.

Return
An upper triangular matrix where each position corresponds to the distance between two time series. Diagonal elements will be zero. For example: Position row 0 column 1 records the distance between time series 0 and time series 1.
Parameters
  • arr: Expects an input array whose dimension zero is the length of the time series (all the same) and dimension one indicates the number of time series.

static KhivaArray Khiva.Distances.Sbd(KhivaArray arr)

Calculates the Shape-Based distance (SBD). It computes the normalized cross-correlation and it returns 1.0 minus the value that maximizes the correlation value between each pair of time series.

Return
An upper triangular matrix where each position corresponds to the distance between two time series. Diagonal elements will be zero. For example: Position row 0 column 1 records the distance between time series 0 and time series 1.
Parameters
  • arr: Expects an input array whose dimension zero is the length of the time series (all the same) and dimension one indicates the number of time series.

static KhivaArray Khiva.Distances.SquaredEuclidean(KhivaArray arr)

Calculates the Shape-Based distance (SBD). It computes the normalized cross-correlation and it returns 1.0 minus the value that maximizes the correlation value between each pair of time series.

Return
An upper triangular matrix where each position corresponds to the distance between two time series. Diagonal elements will be zero. For example: Position row 0 column 1 records the distance between time series 0 and time series 1.
Parameters
  • arr: Expects an input array whose dimension zero is the length of the time series (all the same) and dimension one indicates the number of time series.