#!/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
#
# 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 TensorUnaryOp
from .utils import arithmetic_operand
@arithmetic_operand(sparse_mode='unary')
class TensorFloor(TensorUnaryOp):
_op_type_ = OperandDef.FLOOR
_func_name = 'floor'
[文档]@infer_dtype(np.floor)
def floor(x, out=None, where=None, **kwargs):
r"""
Return the floor of the input, element-wise.
The floor of the scalar `x` is the largest integer `i`, such that
`i <= x`. It is often denoted as :math:`\lfloor x \rfloor`.
Parameters
----------
x : array_like
Input data.
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 scalar
The floor of each element in `x`.
See Also
--------
ceil, trunc, rint
Notes
-----
Some spreadsheet programs calculate the "floor-towards-zero", in other
words ``floor(-2.5) == -2``. NumPy instead uses the definition of
`floor` where `floor(-2.5) == -3`.
Examples
--------
>>> import mars.tensor as mt
>>> a = mt.array([-1.7, -1.5, -0.2, 0.2, 1.5, 1.7, 2.0])
>>> mt.floor(a).execute()
array([-2., -2., -1., 0., 1., 1., 2.])
"""
op = TensorFloor(**kwargs)
return op(x, out=out, where=where)