Namespace Distances¶
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namespace
distances¶ Functions
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double
dtw(const std::vector<double> &a, const 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.
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af::array
dtw(const 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.
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af::array
euclidean(const 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.
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af::array
hamming(const 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.
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af::array
manhattan(const 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.
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af::array
sbd(const af::array &tss)¶ 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
- 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.
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af::array
squaredEuclidean(const 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.
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double