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# derived from 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
#
# 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
class TensorDivide(TensorBinOp):
_op_type_ = OperandDef.DIV
_func_name = "divide"
@classmethod
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
else:
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)