Source code for mars.tensor.arithmetic.floor

#!/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
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# distributed under the License is distributed on an "AS IS" BASIS,
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import numpy as np

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

class TensorFloor(TensorUnaryOp):
    _op_type_ = OperandDef.FLOOR
    _func_name = "floor"

[docs]@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)