Mailman 3 September 2008 - NumPy-Discussion

numpy build on windows
by Miroslav Sabljic 10 Sep '08

10 Sep '08

Hy people! I'm quite a newbie regarding numpy so please excuse if I'm asking stupid questions. I need to build Python and couple of his site-packages on Windows using Visual studio 2008. I've built Python 2.5.1 and now it's turn to build numpy and I'm quite stuck because numpy build process is a little confusing for me. So far I've read the instructions on http://www.scipy.org/Installing_SciPy/Windows and created site.cfg file with the following content: [mkl] include_dirs = C:\Program Files (x86)\Intel\MKL\10.0.012\include library_dirs = C:\Program Files (x86)\Intel\MKL\10.0.012\ia32\lib mkl_libs = mkl_ia32, mkl_c_dll, libguide40 lapack_libs = mkl_lapack It keeps telling me that it can't find mkl_ia32, mkl_c_dll and libguide40 at the following location but these libs are there. Could you please instruct me to some detailed build howto for windows and MSVC compiler? I would appreciate any help just to get this done and build it. I've attached a log file, output of 'python setup.py config'. Thanks in advance! -- Best regards, Miroslav

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planning for numpy 1.3.0 release
by Jarrod Millman 10 Sep '08

10 Sep '08

Now that 1.2.0 is almost finalized, I wanted to ask everyone to start thinking about what they plan to work on for the next minor release: http://scipy.org/scipy/numpy/milestone/1.3.0 We have been gradually moving toward a more time-based release schedule over the last several months. In this vein, I would like to move feature and code discussion and planning to the beginning of the release cycle, rather than waiting until the end of the release cycle. So if there is something that you would like to work on during this release cycle, please bring it up now. Also it is important for us to keep the trunk as stable as possible; if you wish to work on a major new feature or an significant refactoring of the codebase, please work in a branch. Thanks, -- Jarrod Millman Computational Infrastructure for Research Labs 10 Giannini Hall, UC Berkeley phone: 510.643.4014 http://cirl.berkeley.edu/

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norm_gen
by SimonPalmer 10 Sep '08

10 Sep '08

Hi, can anyone point me in the direction of some example code to use the norm_gen function in scipy.stats? I know this is the wrong board but I seem to be at the wrong end of the moderation queue on the scipy board and have been for a couple of weeks, so my posts over there don't show up. I'm hoping that someone here might be able to point me in the right direction Thanks in advance Simon

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upcoming 1.2.0rc2 (1.2.0 final soon to follow)!
by Jarrod Millman 09 Sep '08

09 Sep '08

Hello, There have been a few updates to the 1.2.x branch that resolve some issues with the last release candidate: - a loadtxt regression: http://projects.scipy.org/scipy/numpy/changeset/5790 - a knownfailureif decorator was added: http://projects.scipy.org/scipy/numpy/changeset/5792 - and used for a known Window's issue: http://projects.scipy.org/scipy/numpy/changeset/5795 - a numscons build directory issue: http://projects.scipy.org/scipy/numpy/changeset/5794 There are no other issues that I am aware of that would block a final release. If you know of a major regression or bug that would constitute a release blocker, please let me know ASAP. Otherwise I will tag a 1.2.0rc2 later tonight and announce it tomorrow. Assuming there are no new issues with the new release candidate, I plan to tag the final 1.2.0 release on Sunday. Also, please take a look at the release notes and let me know if anything needs to be changed or added: http://projects.scipy.org/scipy/numpy/milestone/1.2.0 Thanks, -- Jarrod Millman Computational Infrastructure for Research Labs 10 Giannini Hall, UC Berkeley phone: 510.643.4014 http://cirl.berkeley.edu/

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small bug in numpy.ma.minimum.outer
by Charles Doutriaux 09 Sep '08

09 Sep '08

a=numpy.ones((2,2)) >>> numpy.minimum.outer(a,a) array([[[[ 1., 1.], [ 1., 1.]], [[ 1., 1.], [ 1., 1.]]], [[[ 1., 1.], [ 1., 1.]], [[ 1., 1.], [ 1., 1.]]]]) >>> numpy.ma.minimum.outer(a,a) Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/lgm/cdat/latest/lib/python2.5/site-packages/numpy/ma/core.py", line 3317, in outer result._mask = m AttributeError: 'numpy.ndarray' object has no attribute '_mask'

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compressed and sqrt in numpy.ma
by Charles Doutriaux 09 Sep '08

09 Sep '08

The following is causing our code to crash, shouldn't .data be just ones ? >>> a = numpy.ma.ones((2,2)) >>> b = numpy.ma.sqrt(a) >>> b.compressed().data <read-write buffer for 0x9154460, size 32, offset 0 at 0xb764e500>

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Floating Point Difference between numpy and numarray
by Hanni Ali 09 Sep '08

09 Sep '08

Hi, I have encountered a worrying problem, during migration of software from numarray to numpy, perhaps someone could help me determine how this could be addressed. I have a large array or values 10000 long 12 items per line. The matrix contains floats, dtype=float32 in numpy and type=Float32 in numarray. When I perform a mean of one of the columns we observe a discrepancies in the output values. numarray: >>> port_result.agg_matrix._array[::,2].mean() 193955925.49500328 numpy: >>> port_result.agg_matrix._array[::,2].mean() 193954536.48896 If we examine a specific line in the matrix the arrays appear identical: numarray: >>> port_result.agg_matrix[0] array([ 2.11549568e+08, 4.03735232e+08, 8.47466400e+07, 3.99625600e+07, 7.99853550e+06, 6.68247100e+06, 0.00000000e+00, 1.00018790e+07, 3.43065200e+07, 1.75261240e+07, 4.89246450e+06, 2.06562788e+06], type=Float32) numpy: >>> port_result.agg_matrix[0] array([ 2.11549568e+08, 4.03735232e+08, 8.47466400e+07, 3.99625600e+07, 7.99853550e+06, 6.68247100e+06, 0.00000000e+00, 1.00018790e+07, 3.43065200e+07, 1.75261240e+07, 4.89246450e+06, 2.06562788e+06], dtype=float32) However when examining a specific item numpy appears to report a value to 8 significant figures regardless of the true value, whereas numarray reported the full value, however if I cast the output as a float the full value is present, just not being output. Could this explain the difference in the mean values? How can I get numpy to always provide the exact value in the array, so behave in the same manner as numarray? numarray: >>> port_result.agg_matrix[0][4] 7998535.5 >>> port_result.agg_matrix[0][11] 2065627.875 numpy: >>> port_result.agg_matrix[0][4] 7998535.5 >>> port_result.agg_matrix[0][11] 2065627.9 >>> float(port_result.agg_matrix[0][4]) 7998535.5 >>> float(port_result.agg_matrix[0][11]) 2065627.875 I appreciate any help anyone can give, thank you. Hanni

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[Fwd: Fwd: [ML-news] Call for Submissions: Workshop on Machine Learning Open Source Software (MLOSS), NIPS*08]
by Emanuele Olivetti 09 Sep '08

09 Sep '08

Maybe of interest. E. -------- Original Message -------- ---------- Forwarded message ---------- From: mikiobraun <mikiobraun(a)goo****.com> Date: 2008/9/8 Subject: [ML-news] Call for Submissions: Workshop on Machine Learning Open Source Software (MLOSS), NIPS*08 To: Machine Learning News <ML-news(a)googlegroups.com> ********************************************************************** Call for Submissions Workshop on Machine Learning Open Source Software 2008 http://mloss.org/workshop/nips08 held at NIPS*08, Whistler, Canada, December 12th, 2008 ********************************************************************** The NIPS workshop on Workshop on Machine Learning Open Source Software (MLOSS) will held in Whistler (B.C.) on the 12th of December, 2008. Important Dates =============== * Submission Date: October 1st, 2008 * Notification of Acceptance: October 14th, 2008 * Workshop date: December 12 or 13th, 2008 Call for Contributions ====================== The organizing committee is currently seeking abstracts for talks at MLOSS 2008. MLOSS is a great opportunity for you to tell the community about your use, development, or philosophy of open source software in machine learning. This includes (but is not limited to) numeric packages (as e.g. R,octave,numpy), machine learning toolboxes and implementations of ML-algorithms. The committee will select several submitted abstracts for 20-minute talks. The submission process is very simple: * Tag your mloss.org project with the tag nips2008 * Ensure that you have a good description (limited to 500 words) * Any bells and whistles can be put on your own project page, and of course provide this link on mloss.org On 1 October 2008, we will collect all projects tagged with nips2008 for review. Note: Projects must adhere to a recognized Open Source License (cf. http://www.opensource.org/licenses/ ) and the source code must have been released at the time of submission. Submissions will be reviewed based on the status of the project at the time of the submission deadline. Description =========== We believe that the wide-spread adoption of open source software policies will have a tremendous impact on the field of machine learning. The goal of this workshop is to further support the current developments in this area and give new impulses to it. Following the success of the inaugural NIPS-MLOSS workshop held at NIPS 2006, the Journal of Machine Learning Research (JMLR) has started a new track for machine learning open source software initiated by the workshop's organizers. Many prominent machine learning researchers have co-authored a position paper advocating the need for open source software in machine learning. Furthermore, the workshop's organizers have set up a community website mloss.org where people can register their software projects, rate existing projects and initiate discussions about projects and related topics. This website currently lists 123 such projects including many prominent projects in the area of machine learning. The main goal of this workshop is to bring the main practitioners in the area of machine learning open source software together in order to initiate processes which will help to further improve the development of this area. In particular, we have to move beyond a mere collection of more or less unrelated software projects and provide a common foundation to stimulate cooperation and interoperability between different projects. An important step in this direction will be a common data exchange format such that different methods can exchange their results more easily. This year's workshop sessions will consist of three parts. * We have two invited speakers: John Eaton, the lead developer of Octave and John Hunter, the lead developer of matplotlib. * Researchers are invited to submit their open source project to present it at the workshop. * In discussion sessions, important questions regarding the future development of this area will be discussed. In particular, we will discuss what makes a good machine learning software project and how to improve interoperability between programs. In addition, the question of how to deal with data sets and reproducibility will also be addressed. Taking advantage of the large number of key research groups which attend NIPS, decisions and agreements taken at the workshop will have the potential to significantly impact the future of machine learning software. Invited Speakers ================ * John D. Hunter - Main author of matplotlib. * John W. Eaton - Main author of Octave. Tentative Program ================= The 1 day workshop will be a mixture of talks (including a mandatory demo of the software) and panel/open/hands-on discussions. Morning session: 7:30am - 10:30am * Introduction and overview * Octave (John W. Eaton) * Contributed Talks * Discussion: What is a good mloss project? o Review criteria for JMLR mloss o Interoperable software o Test suites Afternoon session: 3:30pm - 6:30pm * Matplotlib (John D. Hunter) * Contributed Talks * Discussion: Reproducible research o Data exchange standards o Shall datasets be open too? How to provide access to data sets. o Reproducible research, the next level after UCI datasets. Program Committee ================= * Jason Weston (NEC Princeton, USA) * Gunnar Rätsch (FML Tuebingen, Germany) * Lieven Vandenberghe (University of California LA, USA) * Joachim Dahl (Aalborg University, Denmark) * Torsten Hothorn (Ludwig Maximilians University, Munich, Germany) * Asa Ben-Hur (Colorado State University, USA) * William Stafford Noble (Department of Genome Sciences Seattle, USA) * Klaus-Robert Mueller (Fraunhofer Institute First, Germany) * Geoff Holmes (University of Waikato, New Zealand) * Alain Rakotomamonjy (University of Rouen, France) Organizers ========== * Soeren Sonnenburg Fraunhofer FIRST Kekuléstr. 7, 12489 Berlin, Germany * Mikio Braun Technische Universität Berlin, Franklinstr. 28/29, FR 6-9, 10587 Berlin, Germany * Cheng Soon Ong ETH Zürich, Universitätstr. 6, 8092 Zürich, Switzerland Funding ======= The workshop is supported by PASCAL (Pattern Analysis, Statistical Modelling and Computational Learning) --~--~---------~--~----~------------~-------~--~----~ You received this message because you are subscribed to the Google Groups "Machine Learning News" group. To post to this group, send email to ML-news(a)googlegroups.com To unsubscribe from this group, send email to ML-news+unsubscribe(a)googlegroups.com For more options, visit this group at http://groups.google.com/group/ML-news?hl=en -~----------~----~----~----~------~----~------~--~---

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numpy or scipy ?
by xiaojf 09 Sep '08

09 Sep '08

Hi all, I'm trying to replace matlab with python in my daily work and I have several problems now. 1) Which one should I use, numpy or scipy ? I found that numpy and scipy have many overlap functions and modules, such as numpy.linalg and scipy.linaly. Maybe I should use numpy in some occasions and use scipy in the other occasions? 2) Are there any functions in numpy/scipy that can do Partial Least Square(PLS) regression? How to calculate adjusted R squared in numpy/ scipy? I have googled to find the answer, but have on success. Thanks in advance.

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write binary data
by jinbo wang 08 Sep '08

08 Sep '08

Hi guys, I am new to python, and still wondering around to solve a simple problem. I have an array A, and want to save it to a binary data file with 'float64' and 'big-endian' specified. I searched online, but still don't know how to do it. I appreciate your help! regards, Jinbo Wang

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