- mars.tensor.bitwise_xor(x1, x2, out=None, where=None, **kwargs)#
Compute the bit-wise XOR of two arrays element-wise.
Computes the bit-wise XOR of the underlying binary representation of the integers in the input arrays. This ufunc implements the C/Python operator
x1 (array_like) – Only integer and boolean types are handled.
x2 (array_like) – Only integer and boolean types are handled.
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.
out – Result.
- Return type
Return the binary representation of the input number as a string.
The number 13 is represented by
00001101. Likewise, 17 is represented by
00010001. The bit-wise XOR of 13 and 17 is therefore
00011100, or 28:
>>> import mars.tensor as mt
>>> mt.bitwise_xor(13, 17).execute() 28
>>> mt.bitwise_xor(31, 5).execute() 26 >>> mt.bitwise_xor([31,3], 5).execute() array([26, 6])
>>> mt.bitwise_xor([31,3], [5,6]).execute() array([26, 5]) >>> mt.bitwise_xor([True, True], [False, True]).execute() array([ True, False])