Source code for mars.tensor.arithmetic.logical_xor

#!/usr/bin/env python
# -*- coding: utf-8 -*-
# Copyright 1999-2021 Alibaba Group Holding Ltd.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# See the License for the specific language governing permissions and
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import numpy as np

from ... import opcodes as OperandDef
from ..utils import infer_dtype
from .core import TensorBinOp
from .utils import arithmetic_operand

class TensorXor(TensorBinOp):
    _op_type_ = OperandDef.XOR
    _func_name = "logical_xor"

[docs]@infer_dtype(np.logical_xor) def logical_xor(x1, x2, out=None, where=None, **kwargs): """ Compute the truth value of x1 XOR x2, element-wise. Parameters ---------- x1, 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 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 ------- y : bool or Tensor of bool 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. See Also -------- logical_and, logical_or, logical_not, bitwise_xor 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]]) """ op = TensorXor(**kwargs) return op(x1, x2, out=out, where=where)
@infer_dtype(np.logical_xor, reverse=True) def rlogical_xor(x1, x2, **kwargs): op = TensorXor(**kwargs) return op.rcall(x1, x2)