Class scraamp

stumpy/scraamp.py:479–871

Compute an approximate non-normalized (i.e., without z-normalization) matrix profile This is a convenience wrapper around the Numba JIT-compiled parallelized `_aamp` function which computes the non-normalized (i.e., without z-normalization) matrix profile according to SCRIMP.

class scraamp:
    """
    Compute an approximate non-normalized (i.e., without z-normalization) matrix profile

    This is a convenience wrapper around the Numba JIT-compiled parallelized
    `_aamp` function which computes the non-normalized (i.e., without z-normalization)
    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`. Default is `True`.

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

    pre_scraamp : 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_scraamp=True` and `s=None`,
        then `s` will automatically be set to
        `s=int(np.ceil(m / config.STUMPY_EXCL_ZONE_DENOM))`, the size of the exclusion
        zone.

    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.

    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.

    Attributes
    ----------
    P_ : numpy.ndarray
        The updated (top-k) matrix profile. When `k=1` (default), this output is
        a 1D array consisting of the matrix profile. When `k > 1`, the output
        is a 2D array that has exactly `k` columns consisting of the top-k matrix
        profile.

    I_ : numpy.ndarray
        The updated (top-k) matrix profile indices. When `k=1` (default), this output is