Source code for mars.tensor.arithmetic.sign

#!/usr/bin/env python
# -*- coding: utf-8 -*-
# Copyright 1999-2021 Alibaba Group Holding Ltd.
#
# 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
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and

import numpy as np

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

@arithmetic_operand(sparse_mode="unary")
class TensorSign(TensorUnaryOp):
_op_type_ = OperandDef.SIGN
_func_name = "sign"

[docs]@infer_dtype(np.sign)
def sign(x, out=None, where=None, **kwargs):
r"""
Returns an element-wise indication of the sign of a number.

The sign function returns -1 if x < 0, 0 if x==0, 1 if x > 0.  nan
is returned for nan inputs.

For complex inputs, the sign function returns
sign(x.real) + 0j if x.real != 0 else sign(x.imag) + 0j.

complex(nan, 0) is returned for complex nan inputs.

Parameters
----------
x : array_like
Input values.
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 sign of x.

Notes
-----
There is more than one definition of sign in common use for complex
numbers.  The definition used here is equivalent to :math:x/\sqrt{x*x}
which is different from a common alternative, :math:x/|x|.

Examples
--------
>>> import mars.tensor as mt

>>> mt.sign([-5., 4.5]).execute()
array([-1.,  1.])
>>> mt.sign(0).execute()
0
>>> mt.sign(5-2j).execute()
(1+0j)
"""
op = TensorSign(**kwargs)
return op(x, out=out, where=where)