A Numba JIT-compiled version of mSTOMP, a variant of mSTAMP, for parallel computation of the multi-dimensional matrix profile and multi-dimensional matrix profile indices. Note that only self-joins are supported. Parameters ---------- T : numpy.ndarray The time seri
(
T,
m,
range_stop,
excl_zone,
M_T,
Σ_T,
QT,
QT_first,
μ_Q,
σ_Q,
T_subseq_isconstant,
Q_subseq_isconstant,
k,
range_start=1,
include=None,
discords=False,
)
| 952 | |
| 953 | |
| 954 | def _mstump( |
| 955 | T, |
| 956 | m, |
| 957 | range_stop, |
| 958 | excl_zone, |
| 959 | M_T, |
| 960 | Σ_T, |
| 961 | QT, |
| 962 | QT_first, |
| 963 | μ_Q, |
| 964 | σ_Q, |
| 965 | T_subseq_isconstant, |
| 966 | Q_subseq_isconstant, |
| 967 | k, |
| 968 | range_start=1, |
| 969 | include=None, |
| 970 | discords=False, |
| 971 | ): |
| 972 | """ |
| 973 | A Numba JIT-compiled version of mSTOMP, a variant of mSTAMP, for parallel |
| 974 | computation of the multi-dimensional matrix profile and multi-dimensional |
| 975 | matrix profile indices. Note that only self-joins are supported. |
| 976 | |
| 977 | Parameters |
| 978 | ---------- |
| 979 | T : numpy.ndarray |
| 980 | The time series or sequence for which to compute the multi-dimensional |
| 981 | matrix profile |
| 982 | |
| 983 | m : int |
| 984 | Window size |
| 985 | |
| 986 | range_stop : int |
| 987 | The index value along T for which to stop the matrix profile |
| 988 | calculation. This parameter is here for consistency with the |
| 989 | distributed `mstumped` algorithm. |
| 990 | |
| 991 | excl_zone : int |
| 992 | The half width for the exclusion zone relative to the current |
| 993 | sliding window |
| 994 | |
| 995 | M_T : numpy.ndarray |
| 996 | Sliding mean of time series, `T` |
| 997 | |
| 998 | Σ_T : numpy.ndarray |
| 999 | Sliding standard deviation of time series, `T` |
| 1000 | |
| 1001 | QT : numpy.ndarray |
| 1002 | Dot product between some query sequence,`Q`, and time series, `T` |
| 1003 | |
| 1004 | QT_first : numpy.ndarray |
| 1005 | QT for the first window relative to the current sliding window |
| 1006 | |
| 1007 | μ_Q : numpy.ndarray |
| 1008 | Mean of the query sequence, `Q`, relative to the current sliding window |
| 1009 | |
| 1010 | σ_Q : numpy.ndarray |
| 1011 | Standard deviation of the query sequence, `Q`, relative to the current |
no test coverage detected