mars.tensor.logical_xor¶

mars.tensor.
logical_xor
(x1, x2, out=None, where=None, **kwargs)[source]¶ Compute the truth value of x1 XOR x2, elementwise.
 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 freshlyallocated 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
Examples
>>> import mars.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]])