#!/usr/bin/env python # -*- coding: utf-8 -*- # Copyright 1999-2020 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 # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import numpy as np from ... import opcodes as OperandDef from ..utils import infer_dtype from .core import TensorBinOp from .utils import arithmetic_operand @arithmetic_operand(sparse_mode='binary_and') class TensorOr(TensorBinOp): _op_type_ = OperandDef.OR _func_name = 'logical_or' [docs]@infer_dtype(np.logical_or) def logical_or(x1, x2, out=None, where=None, **kwargs): """ Compute the truth value of x1 OR x2 element-wise. Parameters ---------- x1, x2 : array_like Logical OR is applied to the elements of `x1` and `x2`. They have to be of 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 : Tensor or bool Boolean result with the same shape as `x1` and `x2` of the logical OR operation on elements of `x1` and `x2`. See Also -------- logical_and, logical_not, logical_xor bitwise_or Examples -------- >>> import mars.tensor as mt >>> mt.logical_or(True, False).execute() True >>> mt.logical_or([True, False], [False, False]).execute() array([ True, False]) >>> x = mt.arange(5) >>> mt.logical_or(x < 1, x > 3).execute() array([ True, False, False, False, True]) """ op = TensorOr(**kwargs) return op(x1, x2, out=out, where=where) @infer_dtype(np.logical_or, reverse=True) def rlogical_or(x1, x2, **kwargs): op = TensorOr(**kwargs) return op.rcall(x1, x2)