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

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

x1, 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.


y : bool or Tensor of bool
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.

logical_and, logical_or, logical_not, bitwise_xor

>>> import mars.tensor as mt
>>> mt.logical_xor(True, False).execute()
>>> 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]])