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Fonction shares_memory - module numpy.matlib

Signature de la fonction shares_memory

Description

shares_memory.__doc__

    shares_memory(a, b, max_work=None)

    Determine if two arrays share memory.

    .. warning::

       This function can be exponentially slow for some inputs, unless
       `max_work` is set to a finite number or ``MAY_SHARE_BOUNDS``.
       If in doubt, use `numpy.may_share_memory` instead.

    Parameters
    ----------
    a, b : ndarray
        Input arrays
    max_work : int, optional
        Effort to spend on solving the overlap problem (maximum number
        of candidate solutions to consider). The following special
        values are recognized:

        max_work=MAY_SHARE_EXACT  (default)
            The problem is solved exactly. In this case, the function returns
            True only if there is an element shared between the arrays. Finding
            the exact solution may take extremely long in some cases.
        max_work=MAY_SHARE_BOUNDS
            Only the memory bounds of a and b are checked.

    Raises
    ------
    numpy.TooHardError
        Exceeded max_work.

    Returns
    -------
    out : bool

    See Also
    --------
    may_share_memory

    Examples
    --------
    >>> x = np.array([1, 2, 3, 4])
    >>> np.shares_memory(x, np.array([5, 6, 7]))
    False
    >>> np.shares_memory(x[::2], x)
    True
    >>> np.shares_memory(x[::2], x[1::2])
    False

    Checking whether two arrays share memory is NP-complete, and
    runtime may increase exponentially in the number of
    dimensions. Hence, `max_work` should generally be set to a finite
    number, as it is possible to construct examples that take
    extremely long to run:

    >>> from numpy.lib.stride_tricks import as_strided
    >>> x = np.zeros([192163377], dtype=np.int8)
    >>> x1 = as_strided(x, strides=(36674, 61119, 85569), shape=(1049, 1049, 1049))
    >>> x2 = as_strided(x[64023025:], strides=(12223, 12224, 1), shape=(1049, 1049, 1))
    >>> np.shares_memory(x1, x2, max_work=1000)
    Traceback (most recent call last):
    ...
    numpy.TooHardError: Exceeded max_work

    Running ``np.shares_memory(x1, x2)`` without `max_work` set takes
    around 1 minute for this case. It is possible to find problems
    that take still significantly longer.