Function _ray_maamped

stumpy/maamped.py:169–318

Compute the multi-dimensional non-normalized (i.e., without z-normalization) matrix profile with a `ray` cluster This is a highly distributed implementation around the Numba JIT-compiled parallelized `_maamp` function which computes the multi-dimensional matrix profile accordin

(
    ray_client,
    T_A,
    T_B,
    m,
    excl_zone,
    T_A_subseq_isfinite,
    T_B_subseq_isfinite,
    p,
    include,
    discords,
)

def _ray_maamped(
    ray_client,
    T_A,
    T_B,
    m,
    excl_zone,
    T_A_subseq_isfinite,
    T_B_subseq_isfinite,
    p,
    include,
    discords,
):
    """
    Compute the multi-dimensional non-normalized (i.e., without z-normalization) matrix
    profile with a `ray` cluster

    This is a highly distributed implementation around the Numba JIT-compiled
    parallelized `_maamp` 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

    T_A_subseq_isfinite : numpy.ndarray
        A boolean array that indicates whether a subsequence in `T_A` contains a
        `np.nan`/`np.inf` value (False)

    T_B_subseq_isfinite : numpy.ndarray
        A boolean array that indicates whether a subsequence in `T_B` contains a
        `np.nan`/`np.inf` value (False)

    p : float
        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.

    include : numpy.ndarray
        A list of (zero-based) indices corresponding to the dimensions in `T` that