mars.tensor.bitwise_xor#

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 ^.

Parameters

  • 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.

  • **kwargs –

Returns

out – Result.

Return type

array_like

See also

logical_xor, bitwise_and, bitwise_or

binary_repr

Return the binary representation of the input number as a string.

Examples

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])