#!/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 TensorSubtract(TensorBinOp): _op_type_ = OperandDef.SUB _func_name = 'subtract' [docs]@infer_dtype(np.subtract) def subtract(x1, x2, out=None, where=None, **kwargs): """ Subtract arguments, element-wise. Parameters ---------- x1, x2 : array_like The tensors to be subtracted from each other. 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 The difference of `x1` and `x2`, element-wise. Returns a scalar if both `x1` and `x2` are scalars. Notes ----- Equivalent to ``x1 - x2`` in terms of tensor broadcasting. Examples -------- >>> import mars.tensor as mt >>> mt.subtract(1.0, 4.0).execute() -3.0 >>> x1 = mt.arange(9.0).reshape((3, 3)) >>> x2 = mt.arange(3.0) >>> mt.subtract(x1, x2).execute() array([[ 0., 0., 0.], [ 3., 3., 3.], [ 6., 6., 6.]]) """ op = TensorSubtract(**kwargs) return op(x1, x2, out=out, where=where) @infer_dtype(np.subtract, reverse=True) def rsubtract(x1, x2, **kwargs): op = TensorSubtract(**kwargs) return op.rcall(x1, x2)