[Numpy-tickets] [NumPy] #959: atanh(+1) gives nan instead of inf on Windows
NumPy
numpy-tickets@scipy....
Wed Nov 26 14:22:00 CST 2008
#959: atanh(+1) gives nan instead of inf on Windows
------------------------+---------------------------------------------------
Reporter: faltet | Owner: somebody
Type: defect | Status: new
Priority: normal | Milestone: 1.3.0
Component: numpy.core | Version: devel
Severity: normal | Resolution:
Keywords: |
------------------------+---------------------------------------------------
Comment (by faltet):
Replying to [comment:2 charris]:
> The function didn't change but the configuration changes for detecting
functions probably caused the numpy version to be used.
You are right. The current definition based on log1p dates from numpy
1.0.3 at least. So, I don't understand why this problem has not show up
before :-/
> What happens if you add
>
> #define HAVE_ATANH
>
> before the function?
I've tested this before, and I remember MSVC 9 giving a link error (MSVC 9
doesn't implement a atanh, I'm afraid).
> In any case we need to track down the problem. The numpy version is more
accurate for small values of x than the Mathworks version, but I suspect
log1p might be broken for inf values. What does log1p(inf) do?
Here you have:
{{{
C:\Users\francesc>python
Python 2.6 (r26:66721, Oct 2 2008, 11:35:03) [MSC v.1500 32 bit (Intel)]
on win
32
Type "help", "copyright", "credits" or "license" for more information.
>>> import numpy
>>> numpy.__version__
'1.3.0.dev6109'
>>> numpy.log1p(numpy.inf)
nan
>>> numpy.log(numpy.inf)
inf
>>> numpy.log(numpy.inf) * numpy.inf
inf
>>> numpy.log(numpy.inf) * numpy.inf / numpy.inf
nan
>>> numpy.log(1 + numpy.inf) * numpy.inf / (1 + numpy.inf -1)
nan
>>> numpy.inf / numpy.inf
nan
>>>
}}}
while using NumPy 1.2.1:
{{{
C:\Users\francesc>python
Python 2.5.2 (r252:60911, Feb 21 2008, 13:11:45) [MSC v.1310 32 bit
(Intel)] on
win32
Type "help", "copyright", "credits" or "license" for more information.
>>> import numpy
>>> numpy.__version__
'1.2.1'
>>> numpy.log1p(numpy.inf)
1.#INF
>>> numpy.log(numpy.inf)
1.#INF
>>> numpy.log(numpy.inf) *numpy.inf
1.#INF
>>> numpy.log(numpy.inf) * numpy.inf / numpy.inf
-1.#IND
>>> numpy.log(1 + numpy.inf) * numpy.inf / (1 + numpy.inf -1)
-1.#IND
>>> numpy.inf / numpy.inf
-1.#IND
>>>
}}}
So, perhaps the problem is the different behavior on the outcome of
dividing infinities. I'm using the official numpy 1.2.1 and my own MSVC9
compiled 1.3. Also, it is worth noting that I'm using Python 2.5 for
1.2.1 and Python 2.6 for 1.3. So, we have several possibilities: the
python interpreters are behaving differently, or it is a problem that
depends on the MSVC compiler. Provided that the problem is windows-
specific (i.e. python interpreter behaves correctly in Linux), I tend to
believe that there is a problem on the MSVC9.
--
Ticket URL: <http://scipy.org/scipy/numpy/ticket/959#comment:3>
NumPy <http://projects.scipy.org/scipy/numpy>
The fundamental package needed for scientific computing with Python.
More information about the Numpy-tickets
mailing list