Class aampi

stumpy/aampi.py:11–429

Compute an incremental non-normalized (i.e., without z-normalization) matrix profile for streaming data Parameters ---------- T : numpy.ndarray The time series or sequence for which the non-normalized matrix profile and matrix profile indices will be returned

class aampi:
    # needs to be enhanced to support top-k matrix profile
    """
    Compute an incremental non-normalized (i.e., without z-normalization) matrix profile
    for streaming data

    Parameters
    ----------
    T : numpy.ndarray
        The time series or sequence for which the non-normalized matrix profile and
        matrix profile indices will be returned

    m : int
        Window size

    egress : bool, default True
        If set to `True`, the oldest data point in the time series is removed and
        the time series length remains constant rather than forever increasing

    p : float, default 2.0
        The p-norm to apply for computing the Minkowski distance.

    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.

    mp : numpy.ndarray, default None
        A pre-computed matrix profile (and corresponding matrix profile indices).
        This is a 2D array of shape `(len(T) - m + 1, 2 * k + 2)`, where the first `k`
        columns are top-k matrix profile, and the next `k` columns are their
        corresponding indices. The last two columns correspond to the top-1 left and
        top-1 right matrix profile indices. When None (default), this array is computed
        internally using `stumpy.aamp`.

    Attributes
    ----------
    P_ : numpy.ndarray
        The updated matrix profile for `T`

    I_ : numpy.ndarray
        The updated matrix profile indices for `T`

    left_P_ : numpy.ndarray
        The updated left matrix profile for `T`

    left_I_ : numpy.ndarray
        The updated left matrix profile indices for `T`

    T_ : numpy.ndarray
        The updated time series or sequence for which the matrix profile and matrix
        profile indices are computed

    Methods
    -------
    update(t)
        Append a single new data point, `t`, to the time series, `T`, and update the
        matrix profile