Function maamped

stumpy/maamped.py:321–416

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

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

def maamped(client, T, m, include=None, discords=False, p=2.0):
    """
    Compute the multi-dimensional non-normalized (i.e., without z-normalization) matrix
    profile with a `dask`/`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
    ----------
    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.

    Returns
    -------
    P : numpy.ndarray
        The multi-dimensional matrix profile. Each row of the array corresponds
        to each matrix profile for a given dimension (i.e., the first row is
        the 1-D matrix profile and the second row is the 2-D matrix profile).

    I : numpy.ndarray
        The multi-dimensional matrix profile index where each row of the array
        corresponds to each matrix profile index for a given dimension.

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