Source code for mars.tensor.arithmetic.divide

#!/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
# 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

class TensorDivide(TensorBinOp):
    _op_type_ = OperandDef.DIV
    _func_name = "divide"

    def _is_sparse(cls, x1, x2):
        if not np.isscalar(x1) and not np.isscalar(x2):
            return False
        if hasattr(x1, "issparse") and x1.issparse():
            if x2 != 0:
                return True
                raise ZeroDivisionError("float division by zero")

[docs]@infer_dtype(np.divide) def divide(x1, x2, out=None, where=None, **kwargs): """ Divide arguments element-wise. Parameters ---------- x1 : array_like Dividend tensor. x2 : array_like Divisor tensor. 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 array 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 : Tensor The quotient `x1/x2`, element-wise. Returns a scalar if both `x1` and `x2` are scalars. Notes ----- Equivalent to `x1` / `x2` in terms of array-broadcasting. Behavior on division by zero can be changed using `seterr`. In Python 2, when both `x1` and `x2` are of an integer type, `divide` will behave like `floor_divide`. In Python 3, it behaves like `true_divide`. Examples -------- >>> import mars.tensor as mt >>> mt.divide(2.0, 4.0).execute() 0.5 >>> x1 = mt.arange(9.0).reshape((3, 3)) >>> x2 = mt.arange(3.0) >>> mt.divide(x1, x2).execute() array([[ NaN, 1. , 1. ], [ Inf, 4. , 2.5], [ Inf, 7. , 4. ]]) Note the behavior with integer types (Python 2 only): >>> mt.divide(2, 4).execute() 0 >>> mt.divide(2, 4.).execute() 0.5 Division by zero always yields zero in integer arithmetic (again, Python 2 only), and does not raise an exception or a warning: >>> mt.divide(mt.array([0, 1], dtype=int), mt.array([0, 0], dtype=int)).execute() array([0, 0]) Division by zero can, however, be caught using seterr: >>> old_err_state = mt.seterr(divide='raise') >>> mt.divide(1, 0).execute() Traceback (most recent call last): ... FloatingPointError: divide by zero encountered in divide >>> ignored_states = mt.seterr(**old_err_state) >>> mt.divide(1, 0).execute() 0 """ op = TensorDivide(**kwargs) return op(x1, x2, out=out, where=where)
@infer_dtype(np.divide, reverse=True) def rdivide(x1, x2, **kwargs): op = TensorDivide(**kwargs) return op.rcall(x1, x2)