Class stimp

stumpy/stimp.py:414–583

A class to compute the Pan Matrix Profile This is based on the SKIMP algorithm. Parameters ---------- 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 wi

    replace={"pre_scrump": "pre_scraamp"},
)
class stimp(_stimp):
    """
    A class to compute the Pan Matrix Profile

    This is based on the SKIMP algorithm.

    Parameters
    ----------
    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.

    percentage : float, default 0.01
        The percentage of the full matrix profile to compute for each subsequence
        window size. When ``percentage < 1.0``, then the ``scrump`` algorithm is used.
        Otherwise, the ``stump`` algorithm is used when the exact matrix profile is
        requested.

    pre_scrump : bool, default True
        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++. This parameter is ignored when ``percentage = 1.0``.

    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