Namespace Distances¶

namespace
distances
¶ Functions

double
dtw
(std::vector<double> a, std::vector<double> b)¶ Calculates the Dynamic Time Warping Distance.
 Return
 array The resulting distance between a and b.
 Parameters
a
: The input time series of reference.b
: The input query.

af::array
dtw
(af::array tss)¶ Calculates the Dynamic Time Warping Distance.
 Return
 af::array 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
tss
: 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.

af::array
euclidean
(af::array tss)¶ Calculates euclidean distances between time series.
 Return
 af::array 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
tss
: 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.

af::array
hamming
(af::array tss)¶ Calculates hamming distances between time series.
 Return
 af::array 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
tss
: 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.

af::array
manhattan
(af::array tss)¶ Calculates manhattan distances between time series.
 Return
 af::array 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
tss
: 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.

af::array
sbd
(af::array tss)¶ Calculates the ShapeBased distance (SBD). It computes the normalized crosscorrelation and it returns 1.0 minus the value that maximizes the correlation value between each pair of time series.
 Return
 array 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
tss
: 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.

af::array
squaredEuclidean
(af::array tss)¶ Calculates non squared version of the euclidean distance.
 Return
 array 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
tss
: 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.

double