Function stumped

stumpy/stumped.py:395–662

Compute the z-normalized matrix profile with a ``dask``/``ray`` cluster This is a highly distributed implementation around the Numba JIT-compiled parallelized ``_stump`` function which computes the (top-k) matrix profile according to STOMPopt with Pearson correlations. Paramet

(
    client,
    T_A,
    m,
    T_B=None,
    ignore_trivial=True,
    normalize=True,
    p=2.0,
    k=1,
    T_A_subseq_isconstant=None,
    T_B_subseq_isconstant=None,
)

@core.non_normalized(aamped)
def stumped(
    client,
    T_A,
    m,
    T_B=None,
    ignore_trivial=True,
    normalize=True,
    p=2.0,
    k=1,
    T_A_subseq_isconstant=None,
    T_B_subseq_isconstant=None,
):
    """
    Compute the z-normalized matrix profile with a ``dask``/``ray`` cluster

    This is a highly distributed implementation around the Numba JIT-compiled
    parallelized ``_stump`` function which computes the (top-k) matrix profile
    according to STOMPopt with Pearson correlations.

    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_A : numpy.ndarray
        The time series or sequence for which to compute the matrix profile.

    m : int
        Window size.

    T_B : numpy.ndarray, default None
        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.
        Default is ``None`` which corresponds to a self-join.

    ignore_trivial : bool, default True
        Set to ``True`` if this is a self-join (i.e., for a single time series
        ``T_A`` without ``T_B``). This ensures that an exclusion zone is applied
        to each subsequence in ``T_A`` and all trivial/self-matches are ignored.
        Otherwise, for an AB-join (i.e., between two times series, ``T_A`` and
        ``T_B``), set this to ``False``.

    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.

    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. This parameter is
        ignored when ``normalize == True``.

    k : int, default 1
        The number of top ``k`` smallest distances used to construct the matrix
        profile. Note that this will increase the total computational time and memory