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Module « numpy.matlib »

Fonction shares_memory - module numpy.matlib

Signature de la fonction shares_memory

Description

help(numpy.matlib.shares_memory)

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 zero or a positive integer.
   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=-1 (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=0
        Only the memory bounds of a and b are checked.
        This is equivalent to using ``may_share_memory()``.

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

Returns
-------
out : bool

See Also
--------
may_share_memory

Examples
--------
>>> import numpy as np
>>> 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.exceptions.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.

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