Function aamped

stumpy/aamped.py:307–420

Compute the non-normalized (i.e., without z-normalization) matrix profile with a `dask`/`ray` cluster This is a highly distributed implementation around the Numba JIT-compiled parallelized `_aamp` function which computes the non-normalized matrix profile according to AAMP.

(client, T_A, m, T_B=None, ignore_trivial=True, p=2.0, k=1)

def aamped(client, T_A, m, T_B=None, ignore_trivial=True, p=2.0, k=1):
    """
    Compute the non-normalized (i.e., without z-normalization) matrix profile
    with a `dask`/`ray` cluster

    This is a highly distributed implementation around the Numba JIT-compiled
    parallelized `_aamp` function which computes the non-normalized matrix profile
    according to AAMP.

    Parameters
    ----------
    client : client
        A `dask`/`ray` client. Setting up a cluster is beyond the scope of this library.
        Please refer to the `dask`/`ray` documentation.

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

    m : int
        Window size

    T_B : numpy.ndarray, default None
        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. Default is
        `None` which corresponds to a self-join.

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

    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.

    Returns
    -------
    out : numpy.ndarray
        When k = 1 (default), 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. However, when k > 1, the output array
        will contain exactly 2 * k + 2 columns. The first k columns (i.e., out[:, :k])
        consists of the top-k matrix profile, the next set of k columns
        (i.e., out[:, k:2k]) consists of the corresponding top-k matrix profile
        indices, and the last two columns (i.e., out[:, 2k] and out[:, 2k+1] or,
        equivalently, out[:, -2] and out[:, -1]) correspond to the top-1 left
        matrix profile indices and the top-1 right matrix profile indices, respectively.

        For convenience, the matrix profile (distances) and matrix profile indices can
        also be accessed via their corresponding named array attributes, `.P_` and
        `.I_`,respectively. Similarly, the corresponding left matrix profile indices
        and right matrix profile indices may also be accessed via the `.left_I_` and