Class stimped

stumpy/stimp.py:591–765

A class to compute the Pan Matrix Profile with a ``dask``/``ray`` cluster This is based on the SKIMP algorithm. Parameters ---------- client : client A ``dask``/``ray`` client. Setting up a ``dask``/``ray`` cluster is beyond the scope of this library. Please re

    replace={"pre_scrump": "pre_scraamp"},
)
class stimped(_stimp):
    """
    A class to compute the Pan Matrix Profile with a ``dask``/``ray`` cluster

    This is based on the SKIMP algorithm.

    Parameters
    ----------
    client : client
        A ``dask``/``ray`` client. Setting up a ``dask``/``ray`` 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 pan matrix profile.

    min_m : int, default 3
        The starting (or minimum) subsequence window size for which a matrix profile
        may be computed.

    max_m : int, default None
        The stopping (or maximum) subsequence window size for which a matrix profile
        may be computed. When ``max_m = None``, this is set to the maximum allowable
        subsequence window size

    step : int, default 1
        The step between subsequence window sizes.

    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``.

    T_subseq_isconstant_func : function, default None
            A custom, user-defined function that returns a boolean array that indicates
            whether a subsequence in ``T`` is constant (``True``). The function must
            only take two arguments, ``a``, a 1-D array, and ``w``, the window size,
            while additional arguments may be specified by currying the user-defined
            function using ``functools.partial``. Any subsequence with at least one
            ``np.nan``/``np.inf`` will automatically have its corresponding value set
            to ``False`` in this boolean array.

    Attributes
    ----------
    PAN_ : numpy.ndarray
        The transformed (i.e., normalized, contrasted, binarized, and repeated)
        pan matrix profile.

    M_ : numpy.ndarray
        The full list of (breadth first search (level) ordered) subsequence window
        sizes.