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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
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import numpy as np
from ... import opcodes as OperandDef
from ..utils import inject_dtype
from .core import TensorBinOp
from .utils import arithmetic_operand
@arithmetic_operand
class TensorFloatPower(TensorBinOp):
_op_type_ = OperandDef.FLOAT_POWER
_func_name = "float_power"
@classmethod
def _is_sparse(cls, x1, x2):
if hasattr(x1, "issparse") and x1.issparse():
return True
return False
[docs]@inject_dtype(np.float64)
def float_power(x1, x2, out=None, where=None, **kwargs):
"""
First tensor elements raised to powers from second array, element-wise.
Raise each base in `x1` to the positionally-corresponding power in `x2`.
`x1` and `x2` must be broadcastable to the same shape. This differs from
the power function in that integers, float16, and float32 are promoted to
floats with a minimum precision of float64 so that the result is always
inexact. The intent is that the function will return a usable result for
negative powers and seldom overflow for positive powers.
Parameters
----------
x1 : array_like
The bases.
x2 : array_like
The exponents.
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 bases in `x1` raised to the exponents in `x2`.
See Also
--------
power : power function that preserves type
Examples
--------
Cube each element in a list.
>>> import mars.tensor as mt
>>> x1 = range(6)
>>> x1
[0, 1, 2, 3, 4, 5]
>>> mt.float_power(x1, 3).execute()
array([ 0., 1., 8., 27., 64., 125.])
Raise the bases to different exponents.
>>> x2 = [1.0, 2.0, 3.0, 3.0, 2.0, 1.0]
>>> mt.float_power(x1, x2).execute()
array([ 0., 1., 8., 27., 16., 5.])
The effect of broadcasting.
>>> x2 = mt.array([[1, 2, 3, 3, 2, 1], [1, 2, 3, 3, 2, 1]])
>>> x2.execute()
array([[1, 2, 3, 3, 2, 1],
[1, 2, 3, 3, 2, 1]])
>>> mt.float_power(x1, x2).execute()
array([[ 0., 1., 8., 27., 16., 5.],
[ 0., 1., 8., 27., 16., 5.]])
"""
op = TensorFloatPower(**kwargs)
return op(x1, x2, out=out, where=where)