sktime.distances.mpdist¶
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sktime.distances.mpdist.
calculate_distance_profile
(dot_prod, q_mean, q_std, t_mean, t_std, q_len, n_t_subs)[source]¶ Calculates the distance profile for the given query.
- Parameters
dot_prod (numpy.array) – Sliding dot products between the time series and the query.
q_mean (float) – Mean of the elements of the query.
q_std (float) – Standard deviation of elements of the query.
t_mean (numpy.array) – Array with the mean of the elements from each subsequence of length(query) from the time series.
t_std (numpy.array) – Array with the standard deviation of the elements from each subsequence of length(query) from the time series.
q_len (int) – Length of the query.
n_t_subs (int) – Number of subsequences in the time series.
- d: numpy.array
Distance profile of query q.
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sktime.distances.mpdist.
mpdist
(ts1, ts2, m=0)[source]¶ MPDist implementation.
- Parameters
ts1 (numpy.array) – First time series.
ts2 (numpy.array) – Second time series.
m (int) – Length of the subsequences.
- mpdist: float
Distance between the two time series.
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sktime.distances.mpdist.
sliding_dot_products
(q, t, q_len, t_len)[source]¶ Computes the sliding dot products between a query and a time series.
- Parameters
- dot_prod: numpy.array
Sliding dot products between q and t.
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sktime.distances.mpdist.
stomp_ab
(ts1, ts2, m)[source]¶ STOMP implementation for AB similarity join.
- Parameters
ts1 (numpy.array) – First time series.
ts2 (numpy.array) – Second time series.
m (int) – Length of the subsequences.
- mp: numpy.array
Array with the distance between every subsequence from ts1 to the nearest subsequence with same length from ts2.
- ip: numpy.array
Array with the index of the nearest neighbor of ts1 in ts2.