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
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.- 参数
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 –
- 返回
out – Result.
- 返回类型
array_like
参见
logical_or
,bitwise_and
,bitwise_xor
binary_repr
Return the binary representation of the input number as a string.
实际案例
The number 13 has the binaray representation
00001101
. Likewise, 16 is represented by00010000
. The bit-wise OR of 13 and 16 is then000111011
, 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])