Function _ray_mstumped

stumpy/mstumped.py:189–357

Compute the multi-dimensional z-normalized matrix profile with a `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

(
    ray_client,
    T_A,
    T_B,
    m,
    excl_zone,
    M_T,
    Σ_T,
    μ_Q,
    σ_Q,
    T_subseq_isconstant,
    Q_subseq_isconstant,
    include,
    discords,
)

def _ray_mstumped(
    ray_client,
    T_A,
    T_B,
    m,
    excl_zone,
    M_T,
    Σ_T,
    μ_Q,
    σ_Q,
    T_subseq_isconstant,
    Q_subseq_isconstant,
    include,
    discords,
):
    """
    Compute the multi-dimensional z-normalized matrix profile with a `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
    ----------
    ray_client : client
        A `ray` client. Setting up a cluster is beyond the scope of this library.
        Please refer to the `ray` documentation.

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

    T_B : numpy.ndarray
        The time series or sequence that will be used to annotate T_A. For every
        subsequence in T_A, its nearest neighbor in T_B will be recorded.

    m : int
        Window size

    excl_zone : int
        The half width for the exclusion zone relative to the current
        sliding window

    M_T : numpy.ndarray
        Sliding mean of time series, `T`

    Σ_T : numpy.ndarray
        Sliding standard deviation of time series, `T`

    μ_Q : numpy.ndarray
        Mean of the query sequence, `Q`, relative to the current sliding window

    σ_Q : numpy.ndarray
        Standard deviation of the query sequence, `Q`, relative to the current
        sliding window