Class scrump

stumpy/scrump.py:650–1151

A class to ompute an approximate z-normalized matrix profile This is a convenience wrapper around the Numba JIT-compiled parallelized ``_stump`` function which computes the matrix profile according to SCRIMP. Parameters ---------- T_A : numpy.ndarray The time serie

    replace={"pre_scrump": "pre_scraamp"},
)
class scrump:
    """
    A class to ompute an approximate z-normalized matrix profile

    This is a convenience wrapper around the Numba JIT-compiled parallelized
    ``_stump`` function which computes the matrix profile according to SCRIMP.

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

    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.

    ignore_trivial : bool
        Set to ``True`` if this is a self-join. Otherwise, for AB-join, set this to
        ``False``.

    percentage : float
        Approximate percentage completed. The value is between ``0.0`` and ``1.0``.

    pre_scrump : bool
        A flag for whether or not to perform the PreSCRIMP calculation prior to
        computing SCRIMP. If set to ``True``, this is equivalent to computing
        SCRIMP++ and may lead to faster convergence

    s : int
        The size of the PreSCRIMP fixed interval. If ``pre_scrump = True`` and
        ``s = None``, then ``s`` will automatically be set to
        ``s = int(np.ceil(m / config.STUMPY_EXCL_ZONE_DENOM))``, which is the size of
        the exclusion zone.

    normalize : bool, default True
        When set to ``True``, this z-normalizes subsequences prior to computing
        distances. Otherwise, this class gets re-routed to its complementary
        non-normalized equivalent set in the ``@core.non_normalized`` class 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 usage
        when ``k > 1``.

    T_A_subseq_isconstant : numpy.ndarray or function, default None
        A boolean array that indicates whether a subsequence in ``T_A`` is constant
        (``True``). Alternatively, a custom, user-defined function that returns a
        boolean array that indicates whether a subsequence in ``T_A`` is constant
        (``True``). The function must only take two arguments, ``a``, a 1-D array,