mars.tensor.bitwise_or#

mars.tensor.bitwise_or(x1, x2, out=None, where=None, **kwargs)#

Compute the bit-wise OR of two tensors element-wise.

Computes the bit-wise OR 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_or, bitwise_and, bitwise_xor

binary_repr

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

Examples

The number 13 has the binaray representation 00001101. Likewise, 16 is represented by 00010000. The bit-wise OR of 13 and 16 is then 000111011, or 29:

>>> import mars.tensor as mt

>>> mt.bitwise_or(13, 16).execute()
29

>>> mt.bitwise_or(32, 2).execute()
34
>>> mt.bitwise_or([33, 4], 1).execute()
array([33,  5])
>>> mt.bitwise_or([33, 4], [1, 2]).execute()
array([33,  6])

>>> mt.bitwise_or(mt.array([2, 5, 255]), mt.array([4, 4, 4])).execute()
array([  6,   5, 255])
>>> (mt.array([2, 5, 255]) | mt.array([4, 4, 4])).execute()
array([  6,   5, 255])
>>> mt.bitwise_or(mt.array([2, 5, 255, 2147483647], dtype=mt.int32),
...               mt.array([4, 4, 4, 2147483647], dtype=mt.int32)).execute()
array([         6,          5,        255, 2147483647])
>>> mt.bitwise_or([True, True], [False, True]).execute()
array([ True,  True])