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#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#
# 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
from .core import TensorOutBinOp
class TensorFrexp(TensorOutBinOp):
_op_type_ = opcodes.FREXP
_func_name = "frexp"
def __init__(self, casting="same_kind", dtype=None, sparse=False, **kw):
super().__init__(casting=casting, dtype=dtype, sparse=sparse, **kw)
@property
def _fun(self):
return np.frexp
@classmethod
def _is_sparse(cls, x):
if hasattr(x, "issparse") and x.issparse():
return True
return False
[docs]
def frexp(x, out1=None, out2=None, out=None, where=None, **kwargs):
"""
Decompose the elements of x into mantissa and twos exponent.
Returns (`mantissa`, `exponent`), where `x = mantissa * 2**exponent``.
The mantissa is lies in the open interval(-1, 1), while the twos
exponent is a signed integer.
Parameters
----------
x : array_like
Tensor of numbers to be decomposed.
out1 : Tensor, optional
Output tensor for the mantissa. Must have the same shape as `x`.
out2 : Tensor, optional
Output tensor for the exponent. Must have the same shape as `x`.
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
-------
(mantissa, exponent) : tuple of tensors, (float, int)
`mantissa` is a float array with values between -1 and 1.
`exponent` is an int array which represents the exponent of 2.
See Also
--------
ldexp : Compute ``y = x1 * 2**x2``, the inverse of `frexp`.
Notes
-----
Complex dtypes are not supported, they will raise a TypeError.
Examples
--------
>>> import maxframe.tensor as mt
>>> x = mt.arange(9)
>>> y1, y2 = mt.frexp(x)
>>> y1_result, y2_result = mt.ExecutableTuple([y1, y2]).execute()
>>> y1_result
array([ 0. , 0.5 , 0.5 , 0.75 , 0.5 , 0.625, 0.75 , 0.875,
0.5 ])
>>> y2_result
array([0, 1, 2, 2, 3, 3, 3, 3, 4])
>>> (y1 * 2**y2).execute()
array([ 0., 1., 2., 3., 4., 5., 6., 7., 8.])
"""
op = TensorFrexp(**kwargs)
return op(x, out1=out1, out2=out2, out=out, where=where)