Class floss

stumpy/floss.py:306–776

A class to compute the Fast Low-cost Online Semantic Segmentation (FLOSS) for streaming data Parameters ---------- mp : numpy.ndarray The first column consists of the matrix profile, the second column consists of the matrix profile indices, the third column cons

class floss:
    """
    A class to compute the Fast Low-cost Online Semantic Segmentation (FLOSS) for
    streaming data

    Parameters
    ----------
    mp : numpy.ndarray
        The first column consists of the matrix profile, the second column
        consists of the matrix profile indices, the third column consists of
        the left matrix profile indices, and the fourth column consists of
        the right matrix profile indices.

    T : numpy.ndarray
        A 1-D time series data used to generate the matrix profile and matrix profile
        indices found in ``mp``. Note that the the right matrix profile index is used
        and the right matrix profile is intelligently recomputed on the fly from ``T``
        instead of using the bidirectional matrix profile.

    m : int
        The window size for computing sliding window mass. This is identical
        to the window size used in the matrix profile calculation. For managing
        edge effects, see the ``L`` parameter.

    L : int
        The subsequence length that is set roughly to be one period length.
        This is likely to be the same value as the window size, ``m``, used
        to compute the matrix profile and matrix profile index but it can
        be different since this is only used to manage edge effects
        and has no bearing on any of the IAC or CAC core calculations.

    excl_factor : int, default 5
        The multiplying factor for the regime exclusion zone. Note that this
        is unrelated to the ``excl_zone`` used in to compute the matrix profile.

    n_iter : int, default 1000
        Number of iterations to average over when determining the parameters for
        the IAC beta distribution.

    n_samples : int, default 1000
        Number of distribution samples to draw during each iteration when
        computing the IAC.

    custom_iac : numpy.ndarray, default None
        A custom idealized arc curve (IAC) that will used for correcting the
        arc curve.

    normalize : bool, default True
        When set to ``True``, this z-normalizes subsequences prior to computing
        distances

    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