maxframe.tensor.logical_xor#

maxframe.tensor.logical_xor(x1, x2, out=None, where=None, **kwargs)[source]#

Compute the truth value of x1 XOR x2, element-wise.

Parameters:

x1 (array_like) – Logical XOR is applied to the elements of x1 and x2. They must be broadcastable to the same shape.
x2 (array_like) – Logical XOR is applied to the elements of x1 and x2. They must be broadcastable to the same shape.
out (Tensor, None, or tuple of Tensor and None, optional) – A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated tensor is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs.
where (array_like, optional) – Values of True indicate to calculate the ufunc at that position, values of False indicate to leave the value in the output alone.
**kwargs

Returns:

y – Boolean result of the logical XOR operation applied to the elements of x1 and x2; the shape is determined by whether or not broadcasting of one or both arrays was required.

Return type:

bool or Tensor of bool

See also

logical_and, logical_or, logical_not, bitwise_xor

Examples

>>> import maxframe.tensor as mt

>>> mt.logical_xor(True, False).execute()
True
>>> mt.logical_xor([True, True, False, False], [True, False, True, False]).execute()
array([False,  True,  True, False])

>>> x = mt.arange(5)
>>> mt.logical_xor(x < 1, x > 3).execute()
array([ True, False, False, False,  True])

Simple example showing support of broadcasting

>>> mt.logical_xor(0, mt.eye(2)).execute()
array([[ True, False],
       [False,  True]])

maxframe.tensor.logical_xor#

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