[Numpy-discussion] ANN: BayesPy 0.2
Dear all, I am pleased to announce the release of BayesPy version 0.2. BayesPy provides tools for Bayesian inference in Python. In particular, it implements variational message passing framework, which enables modular and efficient way to construct models and perform approximate posterior inference. Download: https://pypi.python.org/pypi/bayespy/ Documentation: http://www.bayespy.org Repository: https://github.com/bayespy/bayespy Comments, feedback and contributions are welcome. Best regards, Jaakko ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Numpy/Fortran puzzle (?)
Hi Andrea You should add a dimension argument in your Fortran code, also you should write a f2py header in the same Fortran code. Remember, numpy memory is C order wise. You can specify in numpy the ordering of the matrices you pass when you create them. F2py automatically deals with matrices , but tends to mix dimensions when there are too many matrices. Manual declaration of dimensions should do the trick Xavier On 21/08/2014 2:07 am, Andrea Gavana andrea.gav...@gmail.com wrote: Hi All, I have the following (very ugly) line of code: all_results = np.asarray([transm_hist[date_idx, :, idx_main_set[date_idx] ]*main_flow[date_idx, 0:n_fluids] for date_idx in xrange(n_dates)]) where transm_hist.shape = (n_dates, n_fluids, n_nodes), main_flow.shape = (n_dates, n_fluids) and idx_main_set is an array containing integer indices with idx_main_set.shape = (n_dates, ) . The resulting variable all_results.shape = (n_dates, n_fluids) Since that line of code is relatively slow if done repeatedly, I thought I'd be smart to rewrite it in Fortran and then use f2py to wrap the subroutine. So I wrote this: subroutine matmul(transm_hist, idx_main_set, main_flow, all_results, n_dates, n_fluids, n_nodes) implicit none integer ( kind = 4 ), intent(in) :: n_dates, n_fluids, n_nodes real( kind = 4 ), intent(in) :: transm_hist(n_dates, n_fluids, n_nodes) real( kind = 4 ), intent(in) :: main_flow(n_dates, n_fluids) integer ( kind = 4 ), intent(in) :: idx_main_set(n_dates) real( kind = 4 ), intent(out):: all_results(n_dates, n_fluids) integer (kind = 4) i, node do i = 1, n_dates node = int(idx_main_set(i)) all_results(i, :) = transm_hist(i, 1:n_fluids, node)*main_flow(i, 1:n_fluids) enddo end Unfortunately, it appears that I am not getting out quite the same results... I know it's a bit of a stretch with so little information, but does anyone have a suggestion on where the culprit might be? Maybe the elementwise multiplication is done differently in Numpy and Fortran, or I am misunderstanding what the np.asarray is doing with the list comprehension above? I appreciate any suggestion, which can also be related to improvement in the code. Thank you in advance. Andrea. Imagination Is The Only Weapon In The War Against Reality. http://www.infinity77.net ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Best way to broadcast a function from C
On Thu, Aug 21, 2014 at 2:34 AM, James Crist crist...@umn.edu wrote: All, I have a C function func that takes in scalar arguments, and an array of fixed dimension that is modified in place to provide the output. The prototype is something like: `void func(double a, double b, double c, double *arr);` I've wrapped this in Cython and called it from python with no problem. What I'd like to do now though is get it set up to broadcast over the input arguments and return a 3 dimensional array of the results. By this I mean a = array([1, 2, 3]) b = array([2.0, 3.0, 4.0]) c = array([3, 4, 5]) func(a, b, c) - a 3d array containing the results of func for (a, b, c) = (1, 2.0, 3), (2, 3.0, 4), (3, 4.0, 5) I'm not sure if this would qualify as a ufunc, as the result of one function call isn't a scalar but an array, but the effect I'm looking for is similar. Ideally it would handle datatype conversions (in the above `a` and `c` aren't double, but `func` takes in double). It would also be awesome to allow an argument to be a scalar and not an array, and have it be broadcast as if it were. I'm just wondering what the best way for me to hook my code up to the internals of numpy and get this kind of behavior in an efficient way. I've read the writing your own ufunc part of the docs, but am unsure if what I'm looking for qualifies. Note that I can change the inner workings of `func` if this is required to achieve this behavior. I don't think it's currently possible to write a ufunc that maps scalars to fixed-length arrays. There's probably some not-too-terrible way to do this using the C nditer interface, but IIRC the docs aren't for that aren't very helpful. The simplest approach is just to do a bit of work at the Python level using the standard numpy API to do error-checking and set up your arrays in the way you want, and then pass them to the C implementation. This is not the best way to get a Right solution that will handle all the funky corner cases that ufuncs handle, but it's by far the fastest way to get something that's good enough for whatever you need Right Now. -n -- Nathaniel J. Smith Postdoctoral researcher - Informatics - University of Edinburgh http://vorpus.org ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] Just FYI: numpy-vbench was moved to another box, benchmarks are re-estimating
I have no stats on either anyone is looking at http://yarikoptic.github.io/numpy-vbench besides me at times, so I might be just crying into the wild: I have moved running of numpy-vbench on a bit newer/more powerful box, and that is why benchmark results are being reestimated (thus you might still find some spikes and more noise than before for a bit longer). It is a dual Quad-Core AMD Opteron(tm) Processor 2384 with 32GB of RAM, so if someone is keen on pushing the benchmarks limits -- be my guest ;) -- Yaroslav O. Halchenko, Ph.D. http://neuro.debian.net http://www.pymvpa.org http://www.fail2ban.org Research Scientist,Psychological and Brain Sciences Dept. Dartmouth College, 419 Moore Hall, Hinman Box 6207, Hanover, NH 03755 Phone: +1 (603) 646-9834 Fax: +1 (603) 646-1419 WWW: http://www.linkedin.com/in/yarik ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
