Function _aamp

stumpy/aamp.py:191–331

A Numba JIT-compiled version of AAMP for parallel computation of the matrix profile and matrix profile indices. Parameters ---------- T_A : numpy.ndarray The time series or sequence for which to compute the matrix profile T_B : numpy.ndarray The time series

(
    T_A,
    T_B,
    m,
    T_A_subseq_isfinite,
    T_B_subseq_isfinite,
    p,
    diags,
    ignore_trivial,
    k,
)

    fastmath=config.STUMPY_FASTMATH_FLAGS,
)
def _aamp(
    T_A,
    T_B,
    m,
    T_A_subseq_isfinite,
    T_B_subseq_isfinite,
    p,
    diags,
    ignore_trivial,
    k,
):
    """
    A Numba JIT-compiled version of AAMP for parallel computation of the matrix
    profile and matrix profile indices.

    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

    T_A_subseq_isfinite : numpy.ndarray
        A boolean array that indicates whether a subsequence in `T_A` contains a
        `np.nan`/`np.inf` value (False)

    T_B_subseq_isfinite : numpy.ndarray
        A boolean array that indicates whether a subsequence in `T_B` contains a
        `np.nan`/`np.inf` value (False)

    p : float
        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.

    diags : numpy.ndarray
        The diag of diagonals to process and compute

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

    k : int
        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
    -------
    out1 : numpy.ndarray
        The (top-k) matrix profile

    out2 : numpy.ndarray