Function mstumped

stumpy/mstumped.py:364–537

Compute the multi-dimensional z-normalized matrix profile with a ``dask``/``ray`` cluster This is a highly distributed implementation around the Numba JIT-compiled parallelized ``_mstump`` function which computes the multi-dimensional matrix profile according to STOMP. Note tha

(
    client,
    T,
    m,
    include=None,
    discords=False,
    p=2.0,
    normalize=True,
    T_subseq_isconstant=None,
)

    exclude=["normalize", "T_subseq_isconstant"],
)
def mstumped(
    client,
    T,
    m,
    include=None,
    discords=False,
    p=2.0,
    normalize=True,
    T_subseq_isconstant=None,
):
    """
    Compute the multi-dimensional z-normalized matrix profile with a
    ``dask``/``ray`` cluster

    This is a highly distributed implementation around the Numba JIT-compiled
    parallelized ``_mstump`` function which computes the multi-dimensional matrix
    profile according to STOMP. Note that only self-joins are supported.

    Parameters
    ----------
    client : client
        A ``dask``/``ray`` client. Setting up a cluster is beyond the scope of this
        library. Please refer to the ``dask``/``ray`` documentation.

    T : numpy.ndarray
        The time series or sequence for which to compute the multi-dimensional
        matrix profile. Each row in ``T`` represents data from the same
        dimension while each column in ``T`` represents data from a different
        dimension.

    m : int
        Window size.

    include : list, numpy.ndarray, default None
        A list of (zero-based) indices corresponding to the dimensions in ``T`` that
        must be included in the constrained multidimensional motif search.
        For more information, see Section IV D in:

        `DOI: 10.1109/ICDM.2017.66 \
        <https://www.cs.ucr.edu/~eamonn/Motif_Discovery_ICDM.pdf>`__

    discords : bool, default False
        When set to ``True``, this reverses the distance matrix which results in a
        multi-dimensional matrix profile that favors larger matrix profile values
        (i.e., discords) rather than smaller values (i.e., motifs). Note that indices
        in `include` are still maintained and respected.

    p : float, default 2.0
        The p-norm to apply for computing the Minkowski distance. Minkowski distance is
        typically used with ``p`` being ``1`` or ``2``, which correspond to the
        Manhattan distance and the Euclidean distance, respectively.

    normalize : bool, default True
        When set to ``True``, this z-normalizes subsequences prior to computing
        distances. Otherwise, this function gets re-routed to its complementary
        non-normalized equivalent set in the ``@core.non_normalized`` function
        decorator.