Mailman 3 March 2008 - SciPy-Dev

RHEL 5 and CENTOS 5 rpms for blas/lapack/numpy/scipy available on ashigabou
by David Cournapeau 12 Mar '08

12 Mar '08

Hi, Since some people had problems with RHEL/CENTOS, and since the opensuse build system does provide facilities to build rpms for RHEL and CENTOS for some time, I quickly updated the ashigabou repository to handle those distributions. I also added opensuse 10.3 and FC 8, but those did not require any changes: http://download.opensuse.org/repositories/home:/ashigabou/ (note that it may take time for the rpms to appear there from the time they successfully build on the compiler farm, which they just did). cheers, David

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[GSoC 2008]Machine learning package in SciPy
by Anton Slesarev 11 Mar '08

11 Mar '08

Hi all, it might be a good idea to have a machine learning(ML) package in SciPy. As I understand there are some ML code in SciKits, but it is in raw state? There are a lot of machine learning projects, with its own data format, number of classifiers, feature selection algorithms and benchmarks. But if you want to compare your own algorithm with some others, you should convert your data format to input format of every tool you want to use and after training, you should convert output format of each tools to the single format to have facility to compare results(for example you want to see common which features was selected by different tools). Now I'm analyzing different ML approaches for the special case of text classification problem. I couldn't find ML framework appropriate for my task. I've got two simple requirements for this framework. It should support sparse data format and has at least svm classifier. For example, Orange [1] is a vary good data mining project but has poor sparse format support. PyML [2] has all needed features, but there are problems with installation on different platforms and code design is not perfect. I believe that creation framework, which will be convenient for scientist to integrate their algorithms to it, is a vary useful challenge. Scientists often talk about standard machine learning software[3] and may be SciPy will be appropriate platform for developing such software. I can write detailed proposal, but I want to see is it interesting for someone? Any wishes and recommendations? 1. Orange http://magix.fri.uni-lj.si/orange/ 2. PyML http://pyml.sourceforge.net/ 3. The Need for Open Source Software in Machine Learning http://www.jmlr.org/papers/volume8/sonnenburg07a/sonnenburg07a.pdf -- Anton Slesarev

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numpy.float128 absence yield bugs
by dmitrey 10 Mar '08

10 Mar '08

hi all, if numpy.float128 type is absent (BTW does it related to OS or something else?), it yields bugs even if the type is never used (while importing from a py-file). For example: from numpy import ..., float64, float128 if (some_cond): <use float128> else: <use float64> So numpy turns out not being 100% cross-platform. Maybe it would be better to raise a warning like "float128 is absent, float64 will be used instead"? Or, better, implement float128 being always present at all? (I.e. connect a library that will take care of float128 type provided native machine support is absent)? Regars, D.

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could anyone check nlp_3.py from updated scikits.openopt?
by dmitrey 10 Mar '08

10 Mar '08

Hi all, Nils Wagner informs me of a bug that I don't observe, so I can't fix the one. Could anyone update openopt from svn and run (after "sudo python setup.py install") examples/nlp_3.py ? (It works well for me) Thank you in advance, D.

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Google Summer of Code 2008 and SciPy
by halish 08 Mar '08

08 Mar '08

Hello, my name is Mateusz Haligowski and I am a student of the third (and the last) year of Baccalaureate studies at the University of Gdańsk. I study Econometrics and Statistics. Last year I participated in GSoC, writing a plugin for Scribus (DTP software). It was a great fun, but this year I'm longing to write something connected with my studies. I would like to ask, whether SciPy is planning to participate as a mentoring organisation in GSoC this year? I've received a message on python-gsoc mailing list, that you would need some econometric functions, and that would be a perfect thing for me. The registration process for mentoring organisations started today, ie. Monday the 3rd of March. It will last until 12th March. The details are available on http://code.google.com/soc/faqs.html. I'm really encouraging SciPy to participate in GSoC. Regards, Mateusz Haligowski (I try to stay at #scipy recently as halish, if you would like to talk to me)

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[ANN] numscons 0.5.1: building scipy
by David Cournapeau 08 Mar '08

08 Mar '08

Hi, Mumscons 0.5.1 is available through pypi (eggs and tarballs). This is the first version which can build the whole scipy source tree. To build scipy with numscons, you should first get the code in the branch: svn co http://svn.scipy.org/svn/scipy/branches/build_with_scons And then build it like numpy: python setupscons.py install Technically speaking, you can build scipy with numscons above a numpy build the standard way, but that's not a good idea (because of potential libraries and compilers mismatches between distutils and numscons). See http://projects.scipy.org/scipy/numpy/wiki/NumScons for more details. The only tested platform for now are: - linux + gcc; other compilers on linux should work as well. - solaris + sunstudio with sunperf. On both those platforms, only a few tests do not pass. I don't expect windows or mac OS X to work yet, but I can not test those platforms ATM. I am releasing the current state of numscons because I won't have much time to work on numscons the next few weeks unfortunately. PLEASE DO NOT USE IT FOR PRODUCTION USE ! There are still some serious issues: - I painfully discovered that at least g77 is extremely sensitive to different orders of linker flags (can cause crashes). I don't have any problem anymore on my workstation (Ubuntu 32 bits, atlas + gcc/g77), but this needs more testing. - there are some race conditions with f2py which I do not fully understand yet, and which prevents parallel build to work (so do not use the scons command --jobs option) - optimization flags of proprietary compilers: they are a PITA. They often break IEEE conformance in quite a hard way, and this causes crashes or wrong results (for example, the -fast option of sun compilers breaks the argsort function of numpy). So again, this is really just a release for people to test things if they want, but nothing else. cheers, David

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[Google Summer of Code] Econometric functions
by halish 08 Mar '08

08 Mar '08

Hello, I would like to participate in Google Summer of Code 2008 for SciPy. As I study Econometrics and Statistics, I would be glad to add econometric functions to SciPy. This would include: * Ordinary Least Squares estimation * Generalized Least Squares estimation * Multi-equation estimation * Time series analysis (ie. stationary processes) Along with these elements will obviously come the verification tests as well. I'm looking forward for hearing from any potential mentor and for further suggestions. Regards, Mateusz Haligowski

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Re: [SciPy-dev] solution sparse matrix
by jose luis Lopez Martinez 07 Mar '08

07 Mar '08

hi the size of matrix sparse is 128^4(268,435,456 rows)x 128^4(268,435,456 colums). the matrix have diagonald dominancies that is a sufficient condition for convergence of Jacobi iteration , gauss seidel and xor(relaxion method), but is slow for converge. it exist a command in scipy that solve sparse matrix using at the Gauss-Seidel iteration method o krylov iterative methods? thanks for your advisor

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Numpy/Cython Google Summer of Code project idea
by Fernando Perez 07 Mar '08

07 Mar '08

Hi all, after the Scipy/Sage Days 8 meeting, we were all very impressed by the progress made by Cython. For those not familiar with it, Cython: http://www.cython.org/ is an evolved version of Pyrex (which is used by numpy and scipy) with lots of improvements. We'd like to position Cython as the preferred way of writing most, if not all, new extension code written for numpy and scipy, as it is easier to write, get right, debug (when you still get it wrong) and maintain than writing to the raw Python-C API. A specific project along these lines, that would be very beneficial for numpy could be: - Creating new matrix types in cython that match the cvxopt matrices. The creation of new numpy array types with efficient code would be very useful. - Rewriting the existing ndarray subclasses that ship with numpy, such as record arrays, in cython. In doing this, benchmarks of the relative performance of the new code should be obtained. Another possible project would be the addition to Cython of syntactic support for array expressions, multidimensional indexing, and other features of numpy. This is probably more difficult than the above, as it would require fairly detailed knowledge of both the numpy C API and the Cython internals, but would ultimately be extremely useful. Any student interested in this should quickly respond on the list; such a project would likely be co-mentored by people on the Numpy and Cython teams, since it is likely to require expertise from both ends. Cheers, f

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Fwd: BVLS license
by Alan G Isaac 06 Mar '08

06 Mar '08

The authors have agreed to release BVLS as BSD to match the SciPy license. (Email attached as documentation.) Cheers, Alan Isaac I've heard from Bob. We are happy to release the code under BSD. Thanks again for your interest! Best wishes, Philip On Wed, Mar 5, 2008 at 12:28 PM, Alan G Isaac <aisaac(a)american.edu> wrote: > On Wed, 5 Mar 2008, "Philip B. Stark" apparently wrote: > > I would be happy to license it under anything that keeps > > attribution to Bob and me and that keeps the code intact. > > Both the GPL and BSD licenses require attribution, > but both also allow licensees to distribute modified code. > So this part does not change relative to the GPL. > > > > But we need to wait for Bob to reply if you want something > > less restrictive than GPL v2. > > OK. Yes, I'd like the code to have the potential for being > distributed with SciPy proper, which would mean a BSD > license (to match the SciPy license > <URL:http://www.scipy.org/FAQ#head-22f0cc18e232f57520678cd55ef7e904113fa304>). > Thanks for your willingness to consider a less restrictive license! > > Cheers, > Alan > > > > > -- Philip B. Stark | Professor of Statistics | University of California Berkeley, CA 94720-3860 | 510-642-1430 | statistics.berkeley.edu/~stark

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