Function multi_distance_profile

stumpy/mstump.py:557–655

Multi-dimensional wrapper to compute the multi-dimensional distance profile for a given query window within the times series or sequence that is denoted by the `query_idx` index. Parameters ---------- query_idx : int The window index to calculate the multi-dimension

(
    query_idx,
    T,
    m,
    include=None,
    discords=False,
    normalize=True,
    p=2.0,
    T_subseq_isconstant=None,
)

    maamp_multi_distance_profile, exclude=["normalize", "T_subseq_isconstant"]
)
def multi_distance_profile(
    query_idx,
    T,
    m,
    include=None,
    discords=False,
    normalize=True,
    p=2.0,
    T_subseq_isconstant=None,
):
    """
    Multi-dimensional wrapper to compute the multi-dimensional distance profile for a
    given query window within the times series or sequence that is denoted by the
    `query_idx` index.

    Parameters
    ----------
    query_idx : int
        The window index to calculate the multi-dimensional distance profile for

    T : numpy.ndarray
        The multi-dimensional time series or sequence for which the multi-dimensional
        distance profile will be returned

    m : int
        Window size

    include : numpy.ndarray, default None
        A list of (zero-based) indices corresponding to the dimensions in `T` that
        must be included in the constrained multidimensional motif search.
        For more information, see Section IV D in:

        `DOI: 10.1109/ICDM.2017.66 \
        <https://www.cs.ucr.edu/~eamonn/Motif_Discovery_ICDM.pdf>`__

    discords : bool, default False
        When set to `True`, this reverses the distance profile to favor discords rather
        than motifs. Note that indices in `include` are still maintained and respected.

    normalize : bool, default True
        When set to `True`, this z-normalizes subsequences prior to computing distances.
        Otherwise, this function gets re-routed to its complementary non-normalized
        equivalent set in the `@core.non_normalized` function decorator.

    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 : numpy.ndarray, function, or list, default None
        A parameter that is used to show whether a subsequence of a time series in `T`
        is constant (True) or not. T_subseq_isconstant can be a 2D boolean numpy.ndarray
        or a function that can be applied to each time series in `T`. Alternatively, for
        maximum flexibility, a list (with length equal to the total number of time
        series) may also be used. In this case, T_subseq_isconstant[i] corresponds to
        the i-th time series T[i] and each element in the list can either be a 1D
        boolean ``numpy.ndarray``, a function, or None.