[Numpy-discussion] new OpenOpt Suite release 0.54
I'm glad to inform you about new OpenOpt Suite release 0.54: * Some changes for PyPy compatibility * FuncDesigner translator() can handle sparse derivatives from automatic differentiation * New interalg parameter rTol (relative tolerance, default 10^-8) * Bugfix and improvements for categorical variables * Some more changes and improvements Regards, D. ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] [ANN] OpenOpt Suite release 0.53: Stochastic programming addon now is BSD-licensed
hi all, I'm glad to inform you about new OpenOpt Suite release 0.53: Stochastic programming addon now is available for free Some minor changes -- Regards, D. http://openopt.org/Dmitrey ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] [ANN] OpenOpt suite v 0.52
Hi all, I'm glad to inform you about new OpenOpt Suite release 0.52 (2013-Dec-15): Minor interalg speedup oofun expression MATLAB solvers fmincon and fsolve have been connected Several MATLAB ODE solvers have been connected New ODE solvers, parameters abstol and reltol New GLP solver: direct Some minor bugfixes and improvements Regards, D. ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] [ANN] MATLAB ODE solvers - now available in Python (Dmitrey)
FYI scipy ODE solvers vode, dopri5, dop853 also have been connected to OpenOpt, possibly with automatic differentiation by FuncDesigner (dopri5 and dop853 don't use derivatives although). -- Regards, D. http://openopt.org/Dmitrey --- Исходное сообщение --- От кого: David Goldsmith d.l.goldsm...@gmail.com Дата: 7 октября 2013, 07:16:33 On Sun, Oct 6, 2013 at 10:00 AM, numpy-discussion-requ...@scipy.org wrote: Message: 2 Date: Sat, 05 Oct 2013 21:36:48 +0300 From: Dmitrey tm...@ukr.net Subject: Re: [Numpy-discussion] [ANN] MATLAB ODE solvers - now available in Python To: Discussion of Numerical Python numpy-discussion@scipy.org Cc: numpy-discussion@scipy.org Message-ID: 1380997576.559804301.aoyna...@frv43.ukr.net Content-Type: text/plain; charset=utf-8 Seems like using the MATLAB solvers with MCR requires my wrappers containing in several files to be compiled with MATLAB Compiler before. I have no license for MATLAB thus I may have problems if I'll make it done and will spread it with OpenOpt suite code, also, binary files are incompatible with BSD license. Darn, knew it was too good to be true. On the other hand, IIRC a little bit obsolete MATLAB versions (I don't think difference is essential) have more liberal licenses. As for MATLAB solvers examples, I have already mentioned them in the mail list, you could see them in http://openopt.org/ODE (just replace solver name from scipy_lsoda to ode23s or any other), http://openopt.org/NLP , http://openopt.org/SNLE Oooops, so sorry. :-o DG -- Regards, D. http://openopt.org/Dmitrey -- next part -- An HTML attachment was scrubbed... URL: http://mail.scipy.org/pipermail/numpy-discussion/attachments/20131005/dd6638db/attachment-0001.html -- ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion End of NumPy-Discussion Digest, Vol 85, Issue 17 -- From A Letter From The Future in Peak Everything by Richard Heinberg: By the time I was an older teenager, a certain...attitude was developing among the young people...a feeling of utter contempt for anyone over a certain age--maybe 30 or 40. The adults had consumed so many resources, and now there were none left for their own children...when those adults were younger, they [were] just doing what everybody else was doing...they figured it was normal to cut down ancient forests for...phone books, pump every last gallon of oil to power their SUV's...[but] for...my generation all that was just a dim memory...We [grew up] living in darkness, with shortages of food and water, with riots in the streets, with people begging on street corners...for us, the adults were the enemy. Want to really understand what's really going on? Read Peak Everything. ___ 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] [ANN] MATLAB ODE solvers - now available in Python
It requires MATLAB or MATLAB Component Runtime ( http://www.mathworks.com/products/compiler/mcr/ ) I'm not regular subscriber of the mail list thus you'd better ask openopt forum. -- Regards, D. http://openopt.org/Dmitrey --- Исходное сообщение --- От кого: Eric Carlson ecarl...@eng.ua.edu Дата: 5 октября 2013, 01:19:28 Hello, Does this require a MATLAB install, or are these equivalent routines? Thanks, Eric ___ NumPy-Discussion mailing list ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] [ANN] MATLAB ODE solvers - now available in Python
--- Исходное сообщение --- От кого: David Goldsmith d.l.goldsm...@gmail.com Дата: 5 октября 2013, 20:15:38 MCR stands for MATLAB Compiler Runtime and if that's all it requires, that's great, 'cause that's free. Look forward to giving this a try; does the distribution come w/ examples? Seems like using the MATLAB solvers with MCR requires my wrappers containing in several files to be compiled with MATLAB Compiler before. I have no license for MATLAB thus I may have problems if I'll make it done and will spread it with OpenOpt suite code, also, binary files are incompatible with BSD license. On the other hand, IIRC a little bit obsolete MATLAB versions (I don't think difference is essential) have more liberal licenses. As for MATLAB solvers examples, I have already mentioned them in the mail list, you could see them in http://openopt.org/ODE (just replace solver name from scipy_lsoda to ode23s or any other), http://openopt.org/NLP , http://openopt.org/SNLE -- Regards, D. http://openopt.org/Dmitrey ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] [ANN] MATLAB ODE solvers - now available in Python
Several MATLAB ODE dy/dt = f(y,t) solvers (ode15s, ode23, ode113, ode23t, ode23tb, ode45, ode23s) have been connected to free OpenOpt Suite package (possibly with FuncDesigner automatic differentiation) in addition to scipy_lsoda (scipy.integrate.odeint), see the example . Currently only reltol parameter is available; future plans may include abstol, Python3 and PyPy support, solver ode15i for solving f(dy/dt, y, t) = 0, possibility to use single MATLAB session for several ODE probs. Sparse matrices handling is implemented for fmincon and fsolve but not ode solvers yet. -- Regards, D. http://openopt.org/Dmitrey ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] MATLAB fsolve now available in Python
Hi all, New solver for systems of nonlinear equations ( SNLE ) has been connected to free Python framework OpenOpt: fsolve from MATLAB Optimization Toolbox; uploaded into PYPI in v. 0.5112. As well as fmincon , currently it's available for Python 2 only. Unlike scipy.optimize fsolve, it can handle sparse derivatives, user-supplied or from FuncDesigner automatic differentiation. See the example with 15000 equations. To keep discussion in a single place please use the OpenOpt forum thread. Regards, D. ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] [ANN] MATLAB fmincon now available in Python2
Hi all, current state of Python - MATLAB connection soft doesn't allow passing of function handlers, however, a walkaround has been implemented via some tricks, so now MATLAB function fmincon is available in Python-written OpenOpt and FuncDesigner frameworks (with possibility of automatic differentiation, example ). Future plans include MATLAB fsolve, ode23, ode45 (unlike scipy fsolve and ode they can handle sparse matrices), fgoalattain, maybe global optimization toolbox solvers. I intend to post the message to several forums, so to keep discussion in a single place use OpenOpt forum thread http://forum.openopt.org/viewtopic.php?id=769 Regards, D. ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] [ANN] OpenOpt suite v 0.51
Hi all, new OpenOpt suite v 0.51 has been released: Some improvements for FuncDesigner automatic differentiation and QP FuncDesigner now can model sparse (MI)(QC)QP Octave QP solver has been connected MATLAB solvers linprog ( LP ), quadprog ( QP ), lsqlin ( LLSP ), bintprog ( MILP ) New NLP solver: knitro Some elements of 2nd order interval analysis, mostly for interalg Some interalg improvements interalg can directly handle (MI)LP and (possibly nonconvex) (MI)(QC)QP New classes: knapsack problem ( KSP ), bin packing problem ( BPP ), dominating set problem ( DSP ) FuncDesigner can model SOCP SpaceFuncs has been adjusted for recent versions of Python and NumPy visit http://openopt.org for more details. Regards, D. ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] numpy bug with negative int pow
Python 3.3.1 (default, Apr 17 2013, 22:32:14) [GCC 4.7.3] on linux import numpy numpy.__version__ '1.8.0.dev-d62f11d' numpy.array((1,2,3)) / 2 array([ 0.5, 1. , 1.5]) #ok, but since division of integer arrays has been converted to float, pow is expected as well, but it's not: numpy.array((1,2,3)) ** -1 array([1, 0, 0], dtype=int32) numpy.array((1,2,3)) ** -2 array([1, 0, 0], dtype=int32) 3**-1 0. D. ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] new free software for knapsack problem
Hi all, FYI new free software for knapsack problem ( http://en.wikipedia.org/wiki/Knapsack_problem ) has been made (written in Python language); it can solve possibly constrained, possibly (with interalg ) nonlinear and multiobjective problems with specifiable accuracy. Along with interalg lots of MILP solvers can be used. See http://openopt.org/KSP for details. Regards, Dmitrey. ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] [ANN] Using some MATLAB optimization solvers from Python (OpenOpt/FuncDesigner)
Hi all, FYI some MATLAB solvers now can be involved with OpenOpt or FuncDesigner : * LP linprog * QP quadprog * LLSP lsqlin * MILP bintprog Sparsity handling is supported. You should have * MATLAB (or MATLAB Component Runtime) * mlabwrap Unfortunately, it will hardly work out-of-the-box, you have to adjust some paths and some environment variables. As for nonlinear solvers, e.g. fmincon, probably they could be connected via involving C MEX files, but it is not possible with current state of mlabwrap yet. Read MATLAB entry for details. Regards, D. ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] new OpenOpt Suite release 0.50
Hi all, I'm glad to inform you about new OpenOpt Suite release 0.50 (2013-June-15): * interalg (solver with specifiable accuracy) now works many times (sometimes orders) faster on (possibly multidimensional) integration problems (IP) and on some optimization problems * Add modeling dense (MI)(QC)QP in FuncDesigner (alpha-version, rendering may work slowly yet) * Bugfix for cplex wrapper * Some improvements for FuncDesigner interval analysis (and thus interalg) * Add FuncDesigner interval analysis for tan in range(-pi/2,pi/2) * Some other bugfixes and improvements * (Proprietary) FuncDesigner stochastic addon now is available as standalone pyc-file, became available for Python3 as well Regards, Dmitrey. ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] OpenOpt Suite release 0.45
--- Исходное сообщение --- От кого: Robert Kern robert.k...@gmail.com Дата: 9 апреля 2013, 14:29:43 On Tue, Apr 9, 2013 at 4:15 PM, Dmitrey tm...@ukr.net wrote: --- Исходное сообщение --- От кого: Robert Kern robert.k...@gmail.com Дата: 16 марта 2013, 22:15:07 On Sat, Mar 16, 2013 at 6:19 PM, Dmitrey tm...@ukr.net wrote: --- Исходное сообщение --- От кого: Robert Kern robert.k...@gmail.com Дата: 16 марта 2013, 19:54:51 On Sat, Mar 16, 2013 at 10:39 AM, Matthieu Brucher matthieu.bruc...@gmail.com wrote: Even if they have different hashes, they can be stored in the same underlying list before they are retrieved. Then, an actual comparison is done to check if the given key (i.e. object instance, not hash) is the same as one of the stored keys. Right. And the rule is that if two objects compare equal, then they must also hash equal. Unfortunately, it looks like `oofun` objects do not obey this property. oofun.__eq__() seems to return a Constraint rather than a bool, so oofun objects should simply not be used as dictionary keys. It is one of several base features FuncDesigner is build on and is used extremely often and wide; then whole FuncDesigner would work incorrectly while it is used intensively and solves many problems better than its competitors. I understand. It just means that you can't oofun objects as dictionary keys. Adding a __hash__() method is not enough to make that work. No, it just means I had mapped, have mapped, map and will map oofun objects as Python dict keys. Well, it's your software. You are free to make it as buggy as you wish, I guess. Yes, and that's why each time I get a bugreport I immediately start working on it, so usually I have zero opened bugs, as now . It somewhat differs from your bugtracker , that has tens of opened bugs, and ~ half of them are hanging for years (also, half of them are mentioned as high and highest priority) . But it's definitely your right to keep it as buggy as you wish, as well! D. -- Robert Kern ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] OpenOpt Suite release 0.45
--- Исходное сообщение --- От кого: Alan G Isaac alan.is...@gmail.com Дата: 10 апреля 2013, 15:12:07 On 4/10/2013 3:31 AM, Robert Kern wrote: You cannot use objects that do not have a valid __eq__() (as in, returns boolean True if and only if they are to be considered equivalent for the purpose of dictionary lookup, otherwise returns False) as dictionary keys. Your oofun object still violates this principle. As dictionary keys, you want them to use their `id` attributes to distinguish them, but their __eq__() method still just returns another oofun with the default object.__nonzero__() implementation. This means that bool(some_oofun == other_oofun) is always True regardless of the `id` attributes. You have been unfortunate enough to not run into cases where this causes a problem yet, but the bug is still there, lurking, waiting for a chance hash collision to silently give you wrong results. That is the worst kind of bug. Hi Dmitrey, Robert and Sebastien have taken their time to carefully explain to your why your design is flawed. Your response has been only that you rely on this design flaw and it has not bitten you yet. It had bitten me some times till I understood the bugs source, but as I had mentioned I had fixed all those parts of code. I trust you can see that this is truly not a response. The right response is to explore how you can refactor to eliminate this lurking bug, or to prove that it can *never* bite due to another design feature. You have done neither, and the second looks impossible. So you have work to do. You say that you *must* use oofuns as dict keys. This is probably false, but you clearly want to retain this aspect of your design. But this choice has an implication for the design of oofuns, as carefully explained in this thread. So you will have to change the design, even though that may prove painful. Refactoring is mere impossible, user API and thouzands lines of whole FuncDesigner kernel heavily relies on the oofuns as dict keys. Also, I don't see any alternative that is as convenient and fast as the involved approach. As for new features, I just keep it in mind while implementing them, and now it's quite simple. No smaller step is adequate to the quality of software you aspire to. One last thing. When someone like Robert or Sebastien take their time to explain a problem to you, the right response is thank you, even if their news is unwelcome. Don't shoot the messenger. I understand your opinion, but I'm not a kind of person who thanks on responses like Well, it's your software. You are free to make it as buggy as you wish (Robert have apologised, although). Also, I haven't thanked Sebastien because I was AFK. Thanks for all who participated in the thread. D. ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] OpenOpt Suite release 0.45
--- Исходное сообщение --- От кого: Robert Kern robert.k...@gmail.com Дата: 16 марта 2013, 22:15:07 On Sat, Mar 16, 2013 at 6:19 PM, Dmitrey tm...@ukr.net wrote: --- Исходное сообщение --- От кого: Robert Kern robert.k...@gmail.com Дата: 16 марта 2013, 19:54:51 On Sat, Mar 16, 2013 at 10:39 AM, Matthieu Brucher matthieu.bruc...@gmail.com wrote: Even if they have different hashes, they can be stored in the same underlying list before they are retrieved. Then, an actual comparison is done to check if the given key (i.e. object instance, not hash) is the same as one of the stored keys. Right. And the rule is that if two objects compare equal, then they must also hash equal. Unfortunately, it looks like `oofun` objects do not obey this property. oofun.__eq__() seems to return a Constraint rather than a bool, so oofun objects should simply not be used as dictionary keys. It is one of several base features FuncDesigner is build on and is used extremely often and wide; then whole FuncDesigner would work incorrectly while it is used intensively and solves many problems better than its competitors. I understand. It just means that you can't oofun objects as dictionary keys. Adding a __hash__() method is not enough to make that work. No, it just means I had mapped, have mapped, map and will map oofun objects as Python dict keys. As for the bug, I have found and fixed its source (I used some info from sorted list of free variables and somew other info from a non-sorted dict of oofun sizes). D. ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] OpenOpt Suite release 0.45
--- Исходное сообщение --- От кого: Alan G Isaac alan.is...@gmail.com Дата: 15 марта 2013, 22:54:21 On 3/15/2013 3:34 PM, Dmitrey wrote: the suspected bugs are not documented yet I'm going to guess that the state of the F_i changes when you use them as keys (i.e., when you call __le__. no, their state doesn't change for operations like __le__ . AFAIK searching Python dict doesn't calls __le__ on the object keys at all, it operates with method .__hash__(), and latter returns fixed integer numbers assigned to the objects earlier (at least in my case). It is very hard to imagine that this is a Python or NumPy bug. Cheers, Alan ___ 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] OpenOpt Suite release 0.45
--- Исходное сообщение --- От кого: Matthieu Brucher matthieu.bruc...@gmail.com Дата: 16 марта 2013, 11:33:39 Hi, Different objects can have the same hash, so it compares to find the actual correct object. Usually when you store something in a dict and later you can't find it anymore, it is that the internal state changed and that the hash is not the same anymore. my objects (oofuns) definitely have different __hash__() results - it's just integers 1,2,3 etc assigned to the oofuns (stored in oofun._id field) when they are created. D. Matthieu 2013/3/16 Dmitrey tm...@ukr.net --- Исходное сообщение --- От кого: Alan G Isaac alan.is...@gmail.com Дата: 15 марта 2013, 22:54:21 On 3/15/2013 3:34 PM, Dmitrey wrote: the suspected bugs are not documented yet I'm going to guess that the state of the F_i changes when you use them as keys (i.e., when you call __le__. no, their state doesn't change for operations like __le__ . AFAIK searching Python dict doesn't calls __le__ on the object keys at all, it operates with method .__hash__(), and latter returns fixed integer numbers assigned to the objects earlier (at least in my case). It is very hard to imagine that this is a Python or NumPy bug. Cheers, Alan ___ 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 -- Information System Engineer, Ph.D. Blog: http://matt.eifelle.com LinkedIn: http://www.linkedin.com/in/matthieubrucher Music band: http://liliejay.com/ ___ 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] OpenOpt Suite release 0.45
--- Исходное сообщение --- От кого: Matthieu Brucher matthieu.bruc...@gmail.com Дата: 16 марта 2013, 12:39:07 Even if they have different hashes, they can be stored in the same underlying list before they are retrieved. Then, an actual comparison is done to check if the given key (i.e. object instance, not hash) is the same as one of the stored keys. but, as I have already mentioned, comparison of oofun(s) via __le__, __eq__ etc doesn't change their inner state (but the methods can create additional oofun(s), although). I have checked via debugger - my methods __le__, __eq__, __lt__, __gt__, __ge__ are not called from the buggy place of code, only __hash__ is called from there. Python could check key objects equivalence via id(), although, but I don't see any possible bug source from using id(). D. 2013/3/16 Dmitrey tm...@ukr.net --- Исходное сообщение --- От кого: Matthieu Brucher matthieu.bruc...@gmail.com Дата: 16 марта 2013, 11:33:39 Hi, Different objects can have the same hash, so it compares to find the actual correct object. Usually when you store something in a dict and later you can't find it anymore, it is that the internal state changed and that the hash is not the same anymore. my objects (oofuns) definitely have different __hash__() results - it's just integers 1,2,3 etc assigned to the oofuns (stored in oofun._id field) when they are created. D. Matthieu 2013/3/16 Dmitrey tm...@ukr.net --- Исходное сообщение --- От кого: Alan G Isaac alan.is...@gmail.com Дата: 15 марта 2013, 22:54:21 On 3/15/2013 3:34 PM, Dmitrey wrote: the suspected bugs are not documented yet I'm going to guess that the state of the F_i changes when you use them as keys (i.e., when you call __le__. no, their state doesn't change for operations like __le__ . AFAIK searching Python dict doesn't calls __le__ on the object keys at all, it operates with method .__hash__(), and latter returns fixed integer numbers assigned to the objects earlier (at least in my case). It is very hard to imagine that this is a Python or NumPy bug. Cheers, Alan ___ 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 -- Information System Engineer, Ph.D. Blog: http://matt.eifelle.com LinkedIn: http://www.linkedin.com/in/matthieubrucher Music band: http://liliejay.com/ ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion -- Information System Engineer, Ph.D. Blog: http://matt.eifelle.com LinkedIn: http://www.linkedin.com/in/matthieubrucher Music band: http://liliejay.com/ ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] OpenOpt Suite release 0.45
--- Исходное сообщение --- От кого: Robert Kern robert.k...@gmail.com Дата: 16 марта 2013, 19:54:51 On Sat, Mar 16, 2013 at 10:39 AM, Matthieu Brucher matthieu.bruc...@gmail.com wrote: Even if they have different hashes, they can be stored in the same underlying list before they are retrieved. Then, an actual comparison is done to check if the given key (i.e. object instance, not hash) is the same as one of the stored keys. Right. And the rule is that if two objects compare equal, then they must also hash equal. Unfortunately, it looks like `oofun` objects do not obey this property. oofun.__eq__() seems to return a Constraint rather than a bool, so oofun objects should simply not be used as dictionary keys. It is one of several base features FuncDesigner is build on and is used extremely often and wide; then whole FuncDesigner would work incorrectly while it is used intensively and solves many problems better than its competitors. That's quite possibly the source of the bug. Or at least, that's a bug that needs to get fixed first before attempting to debug anything else or attribute bugs to Python or numpy. Also, the lack of a bool-returning __eq__() will prevent proper sorting, which also seems to be used in the code snippet that Dmitrey showed. as I have already mentioned, I ensured via debugger that my __eq__, __le__ etc are not involved from the buggy place of the code, only __hash__ is involved from there. -- Robert Kern ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] OpenOpt Suite release 0.45
Hi all, I'm glad to inform you about new OpenOpt Suite release 0.45 (2013-March-15): * Essential improvements for FuncDesigner interval analysis (thus affect interalg) * Temporary walkaround for a serious bug in FuncDesigner automatic differentiation kernel due to a bug in some versions of Python or NumPy, may affect optimization problems, including (MI)LP, (MI)NLP, TSP etc * Some other minor bugfixes and improvements --- Regards, D. http://openopt.org/Dmitrey ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] OpenOpt Suite release 0.45
--- Исходное сообщение --- От кого: Alan G Isaac alan.is...@gmail.com Дата: 15 марта 2013, 20:38:38 On 3/15/2013 9:21 AM, Dmitrey wrote: Temporary walkaround for a serious bug in FuncDesigner automatic differentiation kernel due to a bug in some versions of Python or NumPy, Are the suspected bugs documented somewhere? the suspected bugs are not documented yet, I guess it will be fixed in future versions of Python or numpy the bug is hard to locate and isolate, it looks like this: derivative_items = list(pointDerivative.items()) # temporary walkaround for a bug in Python or numpy derivative_items.sort(key=lambda elem: elem[0]) ## for key, val in derivative_items: indexes = oovarsIndDict[key] # this line is not reached in the involved buggy case if not involveSparse and isspmatrix(val): val = val.A if r.ndim == 1: r[indexes[0]:indexes[1]] = val.flatten() if type(val) == ndarray else val else: # this line is not reached in the involved buggy case r[:, indexes[0]:indexes[1]] = val if val.shape == r.shape else val.reshape((funcLen, prod(val.shape)/funcLen)) so, pointDerivative is Python dict of pairs (F_i, N_i), where F_i are hashable objects, and even for the case when N_i are ordinary scalars (they can be numpy arrays or scipy sparse matrices) results of this code are different wrt was or was not derivative_items.sort() performed; total number of nonzero elements is same for both cases. oovarsIndDict is dict of pairs (F_i, (n_start_i, n_end_i)), and for the case N_i are all scalars for all i n_end_i = n_start_i - 1. Alan PS The word 'banausic' is very rare in English. Perhaps you meant 'unsophisticated'? google translate tells me banausic is more appropriate translation than unsophisticated for the sense I meant (those frameworks are aimed on modelling only numerical optimization problems, while FuncDesigner is suitable for modelling of systems of linear, nonlinear, ordinary differential equations, eigenvalue problems, interval analysis and much more). D. ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] [ANN] Stochastic programming and optimization addon for FuncDesigner v. 0.421
hi all, I'm glad to inform you that stochastic programming and optimization addon for FuncDesigner v. 0.421 has been released. Now you can use gradient-based solvers for numerical optimization, such as ALGENCAN, IPOPT, ralg, gsubg etc. Usually they work faster than derivative-free (such as scipy_cobyla, BOBYQA) or global (GLP) solvers, e.g. on this example ALGENCAN time elapsed is less than 1 second while scipy_cobyla spend ~20 sec. However pay attention that having function P() in your problem may bring nonconvexity or some other issues to the solver optimization trajectory, thus sometimes you'll have to use derivative-free or GLP solvers (e.g. de) instead. FuncDesigner is free (BSD license) cross-platform Python language written software, while its stochastic programming and optimization addon, written by same authors, is free for small-scaled problems with educational or research purposes only. For more details visit our website http://openopt.org - Regards, D. http://openopt.org/Dmitrey ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] [ANN] OpenOpt Suite release 0.42
Hi all, I'm glad to inform you about new OpenOpt Suite release 0.42 (2012-Sept-15). Main changes: * Some improvements for solver interalg, including handling of categorical variables * Some parameters for solver gsubg * Speedup objective function for de and pswarm on FuncDesigner models * New global (GLP) solver: asa (adaptive simulated annealing) * Some new classes for network problems: TSP (traveling salesman problem), STAB (maximum graph stable set)], MCP (maximum clique problem) * Improvements for FD XOR (and now it can handle many inputs) * Solver de has parameter seed, also, now it works with PyPy * Function sign now is available in FuncDesigner * FuncDesigner interval analysis (and thus solver interalg) now can handle non-monotone splines of 1st order * FuncDesigner now can handle parameter fixedVars as Python dict * Now scipy InterpolatedUnivariateSpline is used in FuncDesigner interpolator() instead of UnivariateSpline. This creates backward incompatibility - you cannot pass smoothing parameter (s) to interpolator no longer. * SpaceFuncs: add Point weight, Disk, Ball and method contains(), bugfix for importing Sphere, some new examples * Some improvements (essential speedup, new parameter interpolate for P()) for our (currently commercial) FuncDesigner Stochastic Programming addon * Some bugfixes In our website ( http://openopt.org ) you could vote for most required OpenOpt Suite development direction(s) (poll has been renewed, previous results are here). Regards, D. ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] [SciPy-User] [ANN] New free tool for TSP solving
--- Исходное сообщение --- От кого: Niki Spahiev niki.spah...@gmail.com Кому: scipy-u...@scipy.org Дата: 3 сентября 2012, 13:57:49 Тема: Re: [SciPy-User] [ANN] New free tool for TSP solving New free tool for TSP solving is available (for downloading as well) - OpenOpt TSP class: TSP (traveling salesman problem). Hello Dmitrey, Can this tool solve ATSP problems? Thanks, Niki Hi, yes - asymmetric (see examples with networkx DiGraph), including multigraphs (networkx MultiDiGraph) as well. ___ SciPy-User mailing listSciPy-User@scipy.orghttp://mail.scipy.org/mailman/listinfo/scipy-user ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] [ANN] New free tool for TSP solving
Hi all, New free tool for TSP solving is available (for downloading as well) - OpenOpt TSP class: TSP (traveling salesman problem). It is written in Python, uses NetworkX graphs on input (another BSD-licensed Python library, de-facto standard graph lib for Python language programmers), can connect to MILP solvers like glpk, cplex, lpsolve, has a couple of other solvers - sa (simulated annealing, Python code by John Montgomery) and interalg If someone is interested, I could implement something from (or beyound) its future plans till next OpenOpt stable release 0.41, that will be 2 weeks later (Sept-15). Regards, D. ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] routine for linear least norms problems with specifiable accuracy
hi all,
I have wrote a routine to solve dense / sparse problems
min {alpha1*||A1 x - b1||_1 + alpha2*||A2 x - b2||^2 + beta1 * ||x||_1 +
beta2 * ||x||^2}
with specifiable accuracy fTol 0: abs(f-f*) = fTol (this parameter is
handled by solvers gsubg and maybe amsg2p, latter requires known good
enough fOpt estimation). Constraints (box-bound, linear, quadratic) also
could be easily connected.
This problem is very often encountered in many areas, e.g. machine
learning, sparse approximation, see for example
http://scikit-learn.org/stable/modules/ ? lastic-net
First of all solver large-scale gsubg is recommended. Some hand-tuning of
its parameters also could essentially speedup the solver. Also you could
be interested in other OpenOpt NSP solvers - ralg and amsg2p (they are
medium-scaled although).
You can see the source of the routine and its demo result here.
You shouldn't expect gsubg will always solve your problem and inform of
obtained result with specifiable accuracy - for some very difficult, e.g.
extremely ill-conditioned problems it may
* fail to solve QP subproblem (default QP solver is cvxopt, you may
involve another one, e.g. commercial or free-for-educational cplex)
* exit with another stop criterion, e.g. maxIter has been reached, or
maxShoots have been exceeded (usually latter means you have reached
solution, but it cannot be guaranteed in the case)
First of all I have created the routine to demonstrate gsubg abilities; I
haven't decided yet commit or not commit the routine to OpenOpt, with or
without special class for this problem; in either case you can very
easily create problems like this one in FuncDesigner (without having to
write a routine for derivatives) to solve them by gsubg or another NSP
solver; however, IIRC FuncDesigner dot() doesn't work with sparse
matrices yet
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Re: [Numpy-discussion] routine for linear least norms problems with specifiable accuracy
gsubg uses N.Zhurbenko ( http://openopt.org/NikolayZhurbenko )
epsilon-subgradient method
ralg and amsg2p use other algorithms
--- Исходное сообщение ---
От кого: Henry Gomersall h...@cantab.net
Кому: Discussion of Numerical Python numpy-discussion@scipy.org
Дата: 16 июля 2012, 21:47:47
Тема: Re: [Numpy-discussion] routine for linear least norms problems with
specifiable accuracy
On Mon, 2012-07-16 at 20:35 +0300, Dmitrey wrote:
I have wrote a routine to solve dense / sparse problems
min {alpha1*||A1 x - b1||_1 + alpha2*||A2 x - b2||^2 + beta1 * ||x||_1
+ beta2 * ||x||^2}
with specifiable accuracy fTol 0: abs(f-f*) = fTol (this parameter
is handled by solvers gsubg and maybe amsg2p, latter requires known
good enough fOpt estimation). Constraints (box-bound, linear,
quadratic) also could be easily connected.
Interesting. What algorithm are you using?
Henry
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[Numpy-discussion] New Python tool for searching maximum stable set of a graph
Hi all, In the OpenOpt software (BSD-licensed, http://openopt.org ) we have implemented new class - STAB - searching for maximum stable set of a graph. networkx graphs are used as input arguments. Unlike networkx maximum_independent_set() we focus on searching for exact solution (this is NP-Hard problem). interalg or OpenOpt MILP solvers are used, some GUI features and stop criterion (e.g. maxTime, maxCPUTime, fEnough) can be used. Optional arguments are includedNodes and excludedNodes - nodes that have to be present/absent in solution. See http://openopt.org/STAB for details. Future plans (probably very long-term although) include TSP and some other graph problems. - Regards, Dmitrey. ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] [ANN] Stochastic programming and optimization addon for FuncDesigner
hi all, you may be interested in stochastic programming and optimization with free Python module FuncDesigner. We have wrote Stochastic addon for FuncDesigner, but (at least for several years) it will be commercional (currently it's free for some small-scaled problems only and for noncommercial research / educational purposes only). However, we will try to keep our prices several times less than our competitors have. Also, we will provide some discounts, including region-based ones, and first 15 customers will also got a discount. For further information, documentation and some examples etc read more at http://openopt.org/StochasticProgramming Regards, D. ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] numpy bug with ndarray subclassing
I will use walkaround but I think you'd better fix the numpy bug:
from numpy import ndarray, float64, asanyarray, array
class asdf(ndarray):
__array_priority__ = 10
def __new__(self, vals1, vals2):
obj = asanyarray(vals1).view(self)
obj.vals2 = vals2
return obj
def __add__(self, other):
print('add')
assert not isinstance(other , asdf), 'unimplemented'
return asdf(self.view(ndarray) + other, self.vals2)
def __radd__(self, other):
print('radd')
assert not isinstance(other , asdf), 'unimplemented'
return asdf(self.view(ndarray) + other, self.vals2)
a = asdf(array((1, 2, 3)), array((10, 20, 30)))
z = float64(1.0)
print(a.__array_priority__) # 10
print(z.__array_priority__) # -100.0
r2 = a + z
print(r2.vals2) # ok, prints 'add' and (10,20,30)
r1 = z+a
print(r1.vals2)
# doesn't print radd (i.e. doesn't enters asdf.__radd__ function at
all)
# raises AttributeError
#'asdf' object has no attribute 'vals2'
tried in Python2 + numpy 1.6.1 and Python3 + numpy 1.7.0 dev
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[Numpy-discussion] [ANN} OpenOpt / FuncDesigner release 0.39
Hi all,
I'm glad to inform you about new OpenOpt Suite release 0.39
(2012-June-15):
interalg: add categorical variables and general logical constraints, many
other improvements
Some improvements for automatic differentiation
DerApproximator and some OpenOpt / FuncDesigner functionality now works
with PyPy
New solver lsmr for dense / sparse LLSP
oovar constructors now can handle parameters lb and ub, e.g. a =
oovar('a', lb=-1, ub=[1,2,3]) (this oovar should have size 3) or x =
oovars(10, lb=-1, ub=1)
New FuncDesigner function hstack, similar syntax to numpy.hstack, e.g. f
= hstack((a,b,c,d))
Some bugfixes
I have some progress toward solving in FuncDesigner linear DAE
(differential algebraic equations, example) and Stochastic Opimization
(example),
but this is too premature yet to be released, there is 60-70% probability
it will be properly implemented in next OpenOpt release.
In our website you could vote for most required OpenOpt Suite development
direction(s).
Regards, D.
http://openopt.org/Dmitrey
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[Numpy-discussion] Some numpy funcs for PyPy
hi all, maybe you're aware of numpypy - numpy port for pypy (pypy.org) - Python language implementation with dynamic compilation. Unfortunately, numpypy developmnent is very slow due to strict quality standards and some other issues, so for my purposes I have provided some missing numpypy funcs, in particular * atleast_1d, atleast_2d, hstack, vstack, cumsum, isscalar, asscalar, asfarray, flatnonzero, tile, zeros_like, ones_like, empty_like, where, searchsorted * with axis parameter: nan(arg)min, nan(arg)max, all, any and have got some OpenOpt / FuncDesigner functionality working faster than in CPython. File with this functions you can get here Also you may be interested in some info at http://openopt.org/PyPy Regards, Dmitrey. ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Some numpy funcs for PyPy
On your website you wrote: From my (Dmitrey) point of view numpypy development is very unfriendly for newcomers - PyPy developers say provide code, preferably in interpreter level instead of AppLevel, provide whole test coverage for all possible corner cases, provide hg diff for code, and then, maybe, it will be committed. Probably this is the reason why so insufficient number of developers work on numpypy. I assume that is paraphrased with a little hyperbole, but it isn't so different from numpy (other than using git), or many other open source projects. Of course, many opensource projects do like that, but in the case of numpypy IMHO the things are especially bad. Unit tests are important, and taking patches without them is risky. Yes, but at first, things required from numpypy newcomers are TOO complicated - and no guarrantee is provided, that elapsed efforts will not be just a waste of time; at 2nd, the high-quality standards are especially cynic when compared with their own code quality, e.g. numpypy.all(True) doesn't work yet, despite it hangs in bug tracker for a long time; a[a0] = b[b0] works incorrectly etc. These are reasons that forced me to write some required for my purposes missing funcs and some bug walkarounds (like for that one with numpypy.all and any). I've been subscribed to the pypy-dev list for a while, I had been subsribed IIRC for a couple of months but I don't recall seeing you posting there. I had made some, see my pypy activity here Have you tried to submit any of your work to PyPy yet? yes: I had spent lots of time for concatenate() (pypy developers said noone works on it) - and finally they have committed code for this func from other trunc. Things like this were with some other my proposed code for PyPy and all those days spent for it. Perhaps you should have sent this message to pypy-dev instead? I had explained them my point of view in mail list and irc channel, their answer was like don't borther horses, why do you in a hurry? All will be done during several months, but I see it (porting whole numpy) definitely won't be done during the term. IIRC during ~ 2 months only ~10 new items were added to numpypy; also, lots of numpypy items, when calling, e.g. searchsorted, just raise NotImplementedError: wainting for interplevel routine, or don't work with high-dimensional arrays and/or some other corner cases. numpypy developers go (rather slowly) their own way, while I just propose temporary alternative, till proper PyPy-numpy implementation regards, D. ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] [ANN] Optimization with categorical variables, disjunctive (and other logical) constraints
hi all, free solver interalg for global nonlinear optimization with specifiable accuracy now can handle categorical variables, disjunctive (and other logical) constraints, thus making it available to solve GDP, possibly in multiobjective form. There are ~ 2 months till next OpenOpt release, but I guess someone may find it useful for his purposes right now. See here for more details. Regards, D. ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] [ANN] new release 0.38 of OpenOpt, FuncDesigner, SpaceFuncs, DerApproximator
Hi, I'm glad to inform you about new release 0.38 (2012-March-15): OpenOpt: interalg can handle discrete variables (see MINLP for examples) interalg can handle multiobjective problems (MOP) interalg can handle problems with parameters fixedVars/freeVars Many interalg improvements and some bugfixes Add another EIG solver: numpy.linalg.eig New LLSP solver pymls with box bounds handling FuncDesigner: Some improvements for sum() Add funcs tanh, arctanh, arcsinh, arccosh Can solve EIG built from derivatives of several functions, obtained by automatic differentiation by FuncDesigner SpaceFuncs: Add method point.symmetry(Point|Line|Plane) Add method LineSegment.middle Add method Point.rotate(Center, angle) DerApproximator: Minor changes See http://openopt.org for more details. Regards, D. ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] memory leak in numpy.take
memory leak was observed in numpy versions 1.5.1 and latest git trunc from numpy import * for i in range(10): if i % 100 == 0: print(i) a = empty(1,object) for j in range(1): a[j] = array(1) a = take(a, range(9000),out=a[:9000]) ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] [ANN] new solver for multiobjective optimization problems
hi, I'm glad to inform you about new Python solver for multiobjective optimization (MOP). Some changes committed to solver interalg made it capable of handling global nonlinear constrained multiobjective problem (MOP), see the page for more details. Using interalg you can be 100% sure your result covers whole Pareto front according to the required tolerances on objective functions. Available features include real-time or final graphical output, possibility of involving parallel calculations, handling both continuous and discrete variables, export result to xls files. Regards, D. ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] [ANN] global constrained solver with discrete variables
hi all, I've done support of discrete variables for interalg - free (license: BSD) solver with specifiable accuracy, you can take a look at an example here It is written in Python + NumPy, and I hope it's speed will be essentially increased when PyPy (Python with dynamic compilation) support for NumPy will be done (some parts of code are not vectorized and still use CPython cycles). Also, NumPy funcs like vstack or append produce only copy of data, and it also slows the solver very much (for mature problems). Maybe some bugs still present somewhere - interalg code already became very long, but since it already works, you could be interested in trying to use it right now. Regards, D. ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] Ann: OpenOpt and FuncDesigner 0.37
Hi all, I'm glad to inform you about new release 0.37 (2011-Dec-15) of our free software: OpenOpt (numerical optimization): IPOPT initialization time gap (time till first iteration) for FuncDesigner models has been decreased Some improvements and bugfixes for interalg, especially for search all SNLE solutions mode (Systems of Non Linear Equations) Eigenvalue problems (EIG) (in both OpenOpt and FuncDesigner) Equality constraints for GLP (global) solver de Some changes for goldenSection ftol stop criterion GUI func manage - now button Enough works in Python3, but Run/Pause not yet (probably something with threading and it will be fixed in Python instead) FuncDesigner: Major sparse Automatic differentiation improvements for badly-vectorized or unvectorized problems with lots of constraints (except of box bounds); some problems now work many times or orders faster (of course not faster than vectorized problems with insufficient number of variable arrays). It is recommended to retest your large-scale problems with useSparse = 'auto' | True| False Two new methods for splines to check their quality: plot and residual Solving ODE dy/dt = f(t) with specifiable accuracy by interalg Speedup for solving 1-dimensional IP by interalg SpaceFuncs and DerApproximator: Some code cleanup You may trace OpenOpt development information in our recently created entries in Twitter and Facebook, see http://openopt.org for details. See also: FuturePlans, this release announcement in OpenOpt forum Regards, D. ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] [ANN] Multifactor analysis tool for experiment planning
Hi all, new OpenOpt feature is available: Multifactor analysis tool for experiment planning (in physics, chemistry, biology etc). It is based on numerical optimization solver BOBYQA, released in 2009 by Michael J.D. Powell, and has easy and convenient GUI frontend, written in Python + tkinter. Maybe other (alternative) engines will be available in future. See its webpage for details. Regards, Dmitrey. ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] [ANN] ODE dy/dt = f(t) solver with guaranteed speficiable accuracy
hi all, now free solver interalg from OpenOpt framework (based on interval analysis) can solve ODE dy/dt = f(t) with guaranteed specifiable accuracy. See the ODE webpage for more details, there is an example of comparison with scipy.integrate.odeint, where latter fails to solve a problem. Future plans include solving of some general ODE systems dy/dt = f(y, t). Regards, D. ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] [ANN} OpenOpt, FuncDesigner, DerApproximator, SpaceFuncs release 0.36
Hi all, new release of our free soft (OpenOpt, FuncDesigner, DerApproximator, SpaceFuncs) v. 0.36 is out: OpenOpt: * Now solver interalg can handle all types of constraints and integration problems * Some minor improvements and code cleanup FuncDesigner: * Interval analysis now can involve min, max and 1-d monotone splines R - R of 1st and 3rd order * Some bugfixes and improvements SpaceFuncs: * Some minor changes DerApproximator: * Some improvements for obtaining derivatives in points from R^n where left or right derivative for a variable is absent, especially for stencil 1 See http://openopt.org for more details. Regards, D. ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] [ANN] Constrained optimization solver with guaranteed precision
Hi all, I'm glad to inform you that general constraints handling for interalg (free solver with guaranteed user-defined precision) now is available. Despite it is very premature and requires lots of improvements, it is already capable of outperforming commercial BARON (example: http://openopt.org/interalg_bench#Test_4) and thus you could be interested in trying it right now (next OpenOpt release will be no sooner than 1 month). interalg can be especially more effective than BARON (and some other competitors) on problems with huge or absent Lipschitz constant, for example on funcs like sqrt(x), log(x), 1/x, x**alpha, alpha1, when domain of x is something like [small_positive_value, another_value]. Let me also remember you that interalg can search for all solutions of nonlinear equations / systems of them where local solvers like scipy.optimize fsolve cannot find anyone, and search single/multiple integral with guaranteed user-defined precision (speed of integration is intended to be enhanced in future). However, only FuncDesigner models are handled (read interalg webpage for more details). Regards, D. ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] [ANN] Constrained optimization solver with guaranteed precision
Hi Andrea, I believe benchmarks should be like Hans Mittelman do ( http://plato.asu.edu/bench.html ) and of course number of funcs evaluations matters when slow Python code vs compiled is tested, but my current work doesn't allow me to spend so much time for OpenOpt development, so, moreover, for auxiliary work such as benchmarking (and making it properly like that). Also, benchmarks of someone's own soft usually are not very trustful, moreover, on his own probs. BTW, please don't reply on my posts in scipy mail lists - I use them only to post the announcements like this and can miss a reply. Regards, D. --- Исходное сообщение --- От кого: Andrea Gavana andrea.gav...@gmail.com Кому: Discussion of Numerical Python numpy-discussion@scipy.org Дата: 15 августа 2011, 23:01:05 Тема: Re: [Numpy-discussion] [ANN] Constrained optimization solver with guaranteed precision Hi Dmitrey, 2011/8/15 Dmitrey tm...@ukr.net : Hi all, I'm glad to inform you that general constraints handling for interalg (free solver with guaranteed user-defined precision) now is available. Despite it is very premature and requires lots of improvements, it is already capable of outperforming commercial BARON (example: http://openopt.org/interalg_bench#Test_4 ) and thus you could be interested in trying it right now (next OpenOpt release will be no sooner than 1 month). interalg can be especially more effective than BARON (and some other competitors) on problems with huge or absent Lipschitz constant, for example on funcs like sqrt(x), log(x), 1/x, x**alpha, alpha1, when domain of x is something like [small_positive_value, another_value]. Let me also remember you that interalg can search for all solutions of nonlinear equations / systems of them where local solvers like scipy.optimize fsolve cannot find anyone, and search single/multiple integral with guaranteed user-defined precision (speed of integration is intended to be enhanced in future). However, only FuncDesigner models are handled (read interalg webpage for more details). Thank you for this new improvements. I am one of those who use OpenOpt in real life problems, and if I can advance a suggestion (for the second time), when you post a benchmark of various optimization methods, please do not consider the elapsed time only as a meaningful variable to measure a success/failure of an algorithm. Some (most?) of real life problems require intensive and time consuming simulations for every *function evaluation*; the time spent by the solver itself doing its calculations simply disappears in front of the real process simulation. I know it because our simulations take between 2 and 48 hours to run, so what's 300 seconds more or less in the solver calculations? If you talk about synthetic problems (such as the ones defined by a formula), I can see your point. For everything else, I believe the number of function evaluations is a more direct way to assess the quality of an optimization algorithm. Just my 2c. Andrea. ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] bug with latest numpy git snapshot build with Python3
bug in KUBUNTU 11.04, latest numpy git snapshot build with Python3 import numpy Traceback (most recent call last): File stdin, line 1, in module File /usr/local/lib/python3.2/dist-packages/numpy/__init__.py, line 137, in module from . import add_newdocs File /usr/local/lib/python3.2/dist-packages/numpy/add_newdocs.py, line 9, in module from numpy.lib import add_newdoc File /usr/local/lib/python3.2/dist-packages/numpy/lib/__init__.py, line 4, in module from .type_check import * File /usr/local/lib/python3.2/dist-packages/numpy/lib/type_check.py, line 8, in module import numpy.core.numeric as _nx File /usr/local/lib/python3.2/dist-packages/numpy/core/__init__.py, line 10, in module from .numeric import * File /usr/local/lib/python3.2/dist-packages/numpy/core/numeric.py, line 27, in module import multiarray ImportError: No module named multiarray ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] [ANN] Numerical integration with guaranteed precision by interalg
Hi all, some ideas implemented in the solver interalg (INTERval ALGorithm) that already turn out to be more effective than its competitors in numerical optimization (benchmark) appears to be extremely effective in numerical integration with guaranteed precision. Here are some examples where interalg works perfectly while scipy.integrate solvers fail to solve the problems and lie about obtained residual: * 1-D (vs scipy.integrate quad) * 2-D (vs scipy.integrate dblquad) * 3-D (vs scipy.integrate tplquad) see http://openopt.org/IP for more details. Regards, D. ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] [ANN] OpenOpt suite 0.34
Hi all, I'm glad to inform you about new quarterly release 0.34 of the OOSuite package software (OpenOpt, FuncDesigner, SpaceFuncs, DerApproximator) . Main changes: * Python 3 compatibility * Lots of improvements and speedup for interval calculations * Now interalg can obtain all solutions of nonlinear equation (example) or systems of them (example) in the involved box lb_i = x_i = ub_i (bounds can be very large), possibly constrained (e.g. sin(x) + cos(y+x) 0.5). * Many other improvements and speedup for interalg. See http://forum.openopt.org/viewtopic.php?id=425 for more details. Regards, D. ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] [SciPy-User] [ANN] Guaranteed solution of nonlinear equation(s)
--- Исходное сообщение --- От кого: Yosef Meller yosef...@post.tau.ac.il Кому: scipy-u...@scipy.org Дата: 25 мая 2011, 08:54:16 Тема: Re: [SciPy-User] [ANN] Guaranteed solution of nonlinear equation(s) On ??? ? 24 ??? 2011 13:22:47 Dmitrey wrote: Hi all, I have made my free solver interalg ( http://openopt.org/interalg ) be capable of solving nonlinear equations and systems of them. Unlike scipy optimize fsolve it doesn't matter which functions are involved - convex, nonconvex, multiextremum etc. Even some discontinuous funcs can be handled. If no solution exists, interalg determines it rather quickly. For more info see http://forum.openopt.org/viewtopic.php?id=423 Interesting. Is there any description of the actual algorithm? I tried looking for it in the link and the openopt site, but couldn't find it. Algorithm belongs to family of interval methods. Those ones, along with Lipschitz methods, are capable of doing the task (searching extremum with guaranteed precision), but require extremely much computational time and memory, thus are very rarely used. Some ideas created and programmed by me increased speed in many orders (see that benchmark vs Direct, intsolver and commercial BARON), memory consumption also isn't very huge. As for solving equations system, currently |f1| + |f2| + |f3| + ... + |f_k| is minimized (| . | means abs( . )). I know better way of handling nonlinear systems by interalg, but it would take much time to write those enhancements (maybe some weeks or even more), my current work doesn't allow me to spend so much time for interalg and other openopt items development. Regards, D. ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] [ANN] Guaranteed solution of nonlinear equation(s)
Hi all, I have made my free solver interalg (http://openopt.org/interalg) be capable of solving nonlinear equations and systems of them. Unlike scipy optimize fsolve it doesn't matter which functions are involved - convex, nonconvex, multiextremum etc. Even some discontinuous funcs can be handled. If no solution exists, interalg determines it rather quickly. For more info see http://forum.openopt.org/viewtopic.php?id=423 Regards, D. ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] bug with numpy nanargmax/nanargmin
from numpy import * nanargmax([nan,nan]) nan # ok nanargmax([nan,nan],0) nan # ok nanargmax([[1,nan],[1,nan]],0) Traceback (most recent call last): File stdin, line 1, in module File /usr/local/lib/python2.6/site-packages/numpy/lib/function_base.py, line 1606, in nanargmax return _nanop(np.argmax, -np.inf, a, axis) File /usr/local/lib/python2.6/site-packages/numpy/lib/function_base.py, line 1346, in _nanop res[mask_all_along_axis] = np.nan ValueError: cannot convert float NaN to integer __version__ '2.0.0.dev-1fe8136' D. ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] when numpy in Linux apt will be updated? It's still 1.3.0 with many bugs
hi, when numpy in Linux apt will be updated? It's still 1.3.0 with many bugs I tried to install numpy from PYPI where 1.5.1 seesm to be present, but somehow it involves 1.3.0 instead: $ sudo easy_install numpy install_dir /usr/local/lib/python2.6/dist-packages/ Searching for numpy Best match: numpy 1.3.0 Adding numpy 1.3.0 to easy-install.pth file only after aptitude remove python-numpy version 1.5.1. is involved by easy_install, but it fails: $ sudo easy_install numpy Adding numpy 1.5.1 to easy-install.pth file Installing f2py script to /usr/local/bin Installed /usr/local/lib/python2.6/dist-packages/numpy-1.5.1-py2.6-linux-x86_64.egg Processing dependencies for numpy Finished processing dependencies for numpy /tmp/easy_install-QF6uJM/numpy-1.5.1/numpy/distutils/misc_util.py:251: RuntimeWarning: Parent module 'numpy.distutils' not found while handling absolute import Error in atexit._run_exitfuncs: Traceback (most recent call last): File /usr/lib/python2.6/atexit.py, line 24, in _run_exitfuncs func(*targs, **kargs) File /tmp/easy_install-QF6uJM/numpy-1.5.1/numpy/distutils/misc_util.py, line 251, in clean_up_temporary_directory ImportError: No module named numpy.distutils Error in sys.exitfunc: Traceback (most recent call last): File /usr/lib/python2.6/atexit.py, line 24, in _run_exitfuncs func(*targs, **kargs) File /tmp/easy_install-QF6uJM/numpy-1.5.1/numpy/distutils/misc_util.py, line 251, in clean_up_temporary_directory ImportError: No module named numpy.distutils I have Linux KUBUNTU 10.10 D. ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] should get rid of the annoying numpy STDERR output
from numpy import inf, array inf*0 nan (ok) array(inf) * 0.0 StdErr: Warning: invalid value encountered in multiply nan My cycled calculations yields this thousands times slowing computations and making text output completely non-readable. from numpy import __version__ __version__ '2.0.0.dev-1fe8136' D. ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] should get rid of the annoying numpy STDERR output
Hi 2011/3/24 Dmitrey tm...@ukr.net from numpy import inf, array inf*0 nan (ok) array(inf) * 0.0 StdErr: Warning: invalid value encountered in multiply nan My cycled calculations yields this thousands times slowing computations and making text output completely non-readable. Would old= seterr(invalid= 'ignore') be sufficient for you? yes for me, but I'm not sure for all those users who use my soft. Maybe it will hide some bugs in their objective functions and nonlinear constraints in numerical optimization and nonlinear equation systems. D. ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] when numpy in Linux apt will be updated? It's still 1.3.0 with many bugs
Isnt [K]Ubuntu updated each 6 month? 2011/3/24 Dmitrey tm...@ukr.net : hi, when numpy in Linux apt will be updated? It's still 1.3.0 with many bugs There will always be bugs, but numpy 1.3 is a stable release, unless there is a bug that affects what your doing right now? If you find a bug that prevents you from from your specific work, better report that bug, if you haven't already. I tried to install numpy from PYPI where 1.5.1 seesm to be present, but somehow it involves 1.3.0 instead: $ sudo easy_install numpy install_dir /usr/local/lib/python2.6/dist-packages/ Searching for numpy Best match: numpy 1.3.0 Adding numpy 1.3.0 to easy-install.pth file only after aptitude remove python-numpy version 1.5.1. is involved by easy_install, but it fails: $ sudo easy_install numpy Adding numpy 1.5.1 to easy-install.pth file Installing f2py script to /usr/local/bin Installed /usr/local/lib/python2.6/dist-packages/numpy-1.5.1-py2.6-linux-x86_64.egg Processing dependencies for numpy Finished processing dependencies for numpy /tmp/easy_install-QF6uJM/numpy-1.5.1/numpy/distutils/misc_util.py:251: RuntimeWarning: Parent module 'numpy.distutils' not found while handling absolute import I see an *absolute* import, maybe easy_install is different from where ubuntu expects numpy to be installed? i think the folder distutils is specific for ubuntu ? try and remove manually the egg in /usr/local/lib/python2.6/dist-packages/numpy-1.5.1-py2.6-linux-x86_64.egg then do $ sudo easy_install -U numpy -U is for update, maybe you have to run without -U first? Error in atexit._run_exitfuncs: Traceback (most recent call last): File /usr/lib/python2.6/atexit.py, line 24, in _run_exitfuncs func(*targs, **kargs) File /tmp/easy_install-QF6uJM/numpy-1.5.1/numpy/distutils/misc_util.py, line 251, in clean_up_temporary_directory ImportError: No module named numpy.distutils Error in sys.exitfunc: Traceback (most recent call last): File /usr/lib/python2.6/atexit.py, line 24, in _run_exitfuncs func(*targs, **kargs) File /tmp/easy_install-QF6uJM/numpy-1.5.1/numpy/distutils/misc_util.py, line 251, in clean_up_temporary_directory ImportError: No module named numpy.distutils I have Linux KUBUNTU 10.10 D. Good question.. ? easy_install should be distribution non-specific(as far as i know) see also this thread, even though its old. (late 2009) http://old.nabble.com/numpy-1.3.0-eggs-with-python2.6-seem-broken-on-osx,-and-linux-td26551531.html so maybe then download only the egg(with easy_install), then enter the folder and do sudo python2.6 setup.py install Dont you have python 2.7 also on ubuntu? regards mic ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion Thanks for all those instructions, however, personally I don't need them, I have sucseeded with my own manipulations and even if I wouldn't I always can build numpy/scipy from sources. I mere care for quality and easibility of numpy installation for ordinary non-skilled users. They may just try installation, see that it's buggy and move away to use another soft. D. ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] argmin and argmax without nan
hi, is there any way to get argmin and argmax of an array w/o nans? Currently I have from numpy import * argmax([10,nan,100]) 1 argmin([10,nan,100]) 1 But it's not the values I would like to get. The walkaround I use: get all indeces of nans, replace them by -inf, get argmax, replace them by inf, get argmin. Is there any better way? (BTW, I invoke argmin/argmax along of a chosen axis) D. ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] argmin and argmax without nan
2011/3/24 Dmitrey tm...@ukr.net : hi, is there any way to get argmin and argmax of an array w/o nans? Currently I have from numpy import * argmax([10,nan,100]) 1 argmin([10,nan,100]) 1 But it's not the values I would like to get. The walkaround I use: get all indeces of nans, replace them by -inf, get argmax, replace them by inf, get argmin. Is there any better way? (BTW, I invoke argmin/argmax along of a chosen axis) D. In [3]: np.nanargmax([10, np.nan, 100]) Out[3]: 2 In [4]: np.nanargmin([10, np.nan, 100]) Out[4]: 0 Ralf Thanks, I thought np.argnanmin should do that but those funcs were absent. ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] argmin and argmax without nan
On Thu, Mar 24, 2011 at 6:19 AM, Ralf Gommers ralf.gomm...@googlemail.com wrote: 2011/3/24 Dmitrey tm...@ukr.net : hi, is there any way to get argmin and argmax of an array w/o nans? Currently I have from numpy import * argmax([10,nan,100]) 1 argmin([10,nan,100]) 1 But it's not the values I would like to get. The walkaround I use: get all indeces of nans, replace them by -inf, get argmax, replace them by inf, get argmin. Is there any better way? (BTW, I invoke argmin/argmax along of a chosen axis) D. In [3]: np.nanargmax([10, np.nan, 100]) Out[3]: 2 In [4]: np.nanargmin([10, np.nan, 100]) Out[4]: 0 And if speed is an issue (it usually isn't) you can use the nanargmax from Bottleneck: a = np.random.rand(1) a[a 0.5] = np.nan timeit np.nanargmax(a) 1 loops, best of 3: 127 us per loop import bottleneck as bn timeit bn.nanargmax(a) 10 loops, best of 3: 12.4 us per loop For some problems some % speedup could be yielded. Are there any plans for merging bottleneck into numpy? Also, are those benchmarks valid for ordinary numpy only or numpy with MKL/ACML or it doesn't matter? If I have huge arrays and multicore CPU, will numpy with MKL/ACML or something else involve parallel computations with numpy funcs like amin, amax, argmin, nanargmin etc? D. ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] numpy log2 has bug
from numpy import log2, __version__ log2(2**63) Traceback (most recent call last): File stdin, line 1, in module AttributeError: log2 __version__ '2.0.0.dev-1fe8136' (doesn't work with 1.3.0 as well) ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] bug with numpy 2 ** N
2**64 18446744073709551616L 2**array(64) -9223372036854775808 2**100 1267650600228229401496703205376L 2**array(100) -9223372036854775808 ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] BUG: ndarray subclass calls __mul__ when ** (pow) is involved
I have ndarray subclass, its instance x and use r = x**2 I expected it will call for each array element elem.__pow__(2) but it calls elem.__mul__(elem) instead. It essentially (tens or even more times) decreases my calculations speed for lots of cases. numpy.__version__ '2.0.0.dev-1fe8136' (taken some days ago from git) doesn't work with 1.3.0 as well.. ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] OpenOpt Suite release 0.33
Hi all, I'm glad to inform you about new release 0.33 of our completely free (license: BSD) cross-platform software: OpenOpt: * cplex has been connected * New global solver interalg with guarantied precision, competitor to LGO, BARON, MATLAB's intsolver and Direct (also can work in inexact mode) * New solver amsg2p for unconstrained medium-scaled NLP and NSP FuncDesigner: * Essential speedup for automatic differentiation when vector-variables are involved, for both dense and sparse cases * Solving MINLP became available * Add uncertainty analysis * Add interval analysis * Now you can solve systems of equations with automatic determination is the system linear or nonlinear (subjected to given set of free or fixed variables) * FD Funcs min and max can work on lists of oofuns * Bugfix for sparse SLE (system of linear equations), that slowed down computation time and demanded more memory * New oofuns angle, cross * Using OpenOpt result(oovars) is available, also, start points with oovars() now can be assigned easier SpaceFuncs (2D, 3D, N-dimensional geometric package with abilities for parametrized calculations, solving systems of geometric equations and numerical optimization with automatic differentiation): * Some bugfixes DerApproximator: * Adjusted with some changes in FuncDesigner For more details visit our site http://openopt.org. Regards, Dmitrey. ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] Inplace remove some array rows
hi all, currently I use a = array(m,n) ... a = delete(a, indices, 0) # delete some rows Can I somehow perform the operation in-place, without creating auxiliary array? If I'll use numpy.compress(condition, a, axis=0, out=a), or numpy.take(a, indices, axis=0, out=a) will the operation be inplace? Thank you in advance, D. ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] [ANN] New package: SpaceFuncs (2D, 3D, ND geometric modeling, optimization, solving)
Hi all, I'm glad to inform you about new, 4th OpenOpt Suite module: SpaceFuncs - a tool for 2D, 3D, N-dimensional geometric modeling with possibilities of parametrized calculations, numerical optimization and solving systems of geometrical equations with automatic differentiation. The module is written in Python + NumPy, requires FuncDesigner (and OpenOpt, DerApproximator for some operations). It has completely free license: BSD. For details see its home page http://openopt.org/SpaceFuncs and documentation http://openopt.org/SpaceFuncsDoc Also, you can try it online via our Sage-server (sometimes hangs due to high load, through) http://sage.openopt.org/welcome Regards, Dmitrey. ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] Is numpy/scipy linux apt or PYPI installation linked with ACML?
Hi all, I have AMD processor and I would like to get to know what's the easiest way to install numpy/scipy linked with ACML. Is it possible to link linux apt or PYPI installation linked with ACML? Answer for the same question about MKL also would be useful, however, AFAIK it has commercial license and thus can't be handled in the ways. Thank you in advance, D. ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Is numpy/scipy linux apt or PYPI installation linked with ACML?
Are free EPD distributions linked with MKL and ACML? Does anyone know is SAGE or PythonXY already linked with ACML or MKL? Thanks, D. --- Исходное сообщение --- От кого: David Cournapeau courn...@gmail.com Кому: Discussion of Numerical Python numpy-discussion@scipy.org Дата: 23 января 2011, 12:07:29 Тема: Re: [Numpy-discussion] Is numpy/scipy linux apt or PYPI installation linked with ACML? 2011/1/23 Dmitrey tm...@ukr.net : Hi all, I have AMD processor and I would like to get to know what's the easiest way to install numpy/scipy linked with ACML. Is it possible to link linux apt or PYPI installation linked with ACML? Answer for the same question about MKL also would be useful, however, AFAIK it has commercial license and thus can't be handled in the ways. For the MKL, the easiest solution is to get EPD, or to build numpy/scipy by yourself, although the later is not that easy. For ACML, I don't know how difficult it is, but I would be surprised if it worked out of the box. cheers, David ___ 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
[Numpy-discussion] new quarterly OpenOpt/FuncDesigner release 0.32
Hi all, I'm glad to inform you about new quarterly OpenOpt/FuncDesigner release (0.32): OpenOpt: * New class: LCP (and related solver) * New QP solver: qlcp * New NLP solver: sqlcp * New large-scale NSP (nonsmooth) solver gsubg. Currently it still requires lots of improvements (especially for constraints - their handling is very premature yet and often fails), but since the solver sometimes already works better than ipopt, algencan and other competitors it was tried with, I decided to include the one into the release. * Now SOCP can handle Ax = b constraints (and bugfix for handling lb = x = ub has been committed) * Some other fixes and improvements FuncDesigner: * Add new function removeAttachedConstraints * Add new oofuns min and max (their capabilities are quite restricted yet) * Systems of nonlinear equations: possibility to assign personal tolerance for an equation * Some fixes and improvements For more details see our forum entry http://forum.openopt.org/viewtopic.php?id=325 Regards, D. ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] ANN: OpenOpt 0.31, FuncDesigner 0.21, DerApproximator 0.21
Hi all, I'm glad to inform you about new releases: OpenOpt 0.31, FuncDesigner 0.21, DerApproximator 0.21 For details see http://forum.openopt.org/viewtopic.php?id=299 or visit our homepage http://openopt.org Regards, Dmitrey ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] how to use ldexp?
hi all, I have tried the example from numpy/add_newdocs.py np.ldexp(5., 2) but instead of the 20 declared there it yields TypeError: function not supported for these types, and can't coerce safely to supported types I have tried arrays but it yields same error np.ldexp(np.array([5., 2.]), np.array([2, 1])) Traceback (innermost last): File stdin, line 1, in module TypeError: function not supported for these types, and can't coerce safely to supported types So, how can I use ldexp? np.__version__ = '1.4.0.dev6972' Thank you in advance, D. ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] numpy ufuncs and COREPY - any info?
hi all, has anyone already tried to compare using an ordinary numpy ufunc vs that one from corepy, first of all I mean the project http://socghop.appspot.com/student_project/show/google/gsoc2009/python/t124024628235 It would be interesting to know what is speedup for (eg) vec ** 0.5 or (if it's possible - it isn't pure ufunc) numpy.dot(Matrix, vec). Or any another example. ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] how to use ldexp?
On May 21, 11:29 am, David Cournapeau da...@ar.media.kyoto-u.ac.jp wrote: dmitrey wrote: I have updated numpy to latest '1.4.0.dev7008', but the bug still remains. I use KUBUNTU 9.04, compilers - gcc (using build-essential), gfortran. D. Can you post the build output (after having removed the build directory : rm -rf build python setup.py build build.log) ? David ok, it's here http://pastebin.com/mb021e11 D. ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] where are the benefits of ldexp and/or array times 2?
Hi all, I expected to have some speedup via using ldexp or multiplying an array by a power of 2 (doesn't it have to perform a simple shift of mantissa?), but I don't see the one. Have I done something wrong? See the code below. from scipy import rand from numpy import dot, ones, zeros, array, ldexp from time import time N = 1500 A = rand(N, N) b = rand(N) b2 = 2*ones(A.shape, 'int32') I = 100 t = time() for i in xrange(I): dot(A, b) # N^2 multiplications + some sum operations #A * 2.1 # N^2 multiplications, so it should consume no greater than 1st line time #ldexp(A, b2) # it should consume no greater than prev line time, isn't it? print 'time elapsed:', time() - t # 1st case: 0.62811088562 # 2nd case: 2.00850605965 # 3rd case: 6.79027700424 # Let me also note - # 1) using b = 2 * ones(N) or b = zeros(N) doesn't yield any speedup vs b = rand() # 2) using A * 2.0 (or mere 2) instead of 2.1 doesn't yield any speedup, despite it is exact integer power of 2. ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] binary shift for ndarray
hi all, suppose I have A that is numpy ndarray of floats, with shape n x n. I want to obtain dot(A, b), b is vector of length n and norm(b)=1, but instead of exact multiplication I want to approximate b as a vector [+/- 2^m0, ± 2^m1, ± 2^m2 ,,, ± 2^m_n], m_i are integers, and then invoke left_shift(vector_m) for rows of A. So, what is the simplest way to do it, without cycles of course? Or it cannot be implemented w/o cycles with current numpy version? Thank you in advance, D. ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] binary shift for ndarray
On May 20, 10:34 pm, Robert Kern robert.k...@gmail.com wrote: On Wed, May 20, 2009 at 14:24, dmitrey dmitrey.kros...@scipy.org wrote: hi all, suppose I have A that is numpy ndarray of floats, with shape n x n. I want to obtain dot(A, b), b is vector of length n and norm(b)=1, but instead of exact multiplication I want to approximate b as a vector [+/- 2^m0, ± 2^m1, ± 2^m2 ,,, ± 2^m_n], m_i are integers, and then invoke left_shift(vector_m) for rows of A. You don't shift floats. You only shift integers. For floats, multiplying by an integer power of 2 should be fast because of the floating point representation (the exponent just gets incremented or decremented), so just do the multiplication. So, what is the simplest way to do it, without cycles of course? Or it cannot be implemented w/o cycles with current numpy version? It might help if you showed us an example of an actual b vector decomposed the way you describe. Your description is ambiguous. -- Robert Kern For the task involved (I intend to try using it for speed up ralg solver) it doesn't matter essentially (using ceil, floor or round), but for example let m_i is floor(log2(b_i)) for b_i 1e-15, ceil(log2(-b_i)) for b_i - 1e-15, for - 1e-15 = b_i = 1e-15 - don't modify the elements of A related to the b_i at all. D. ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] error building numpy: no file refecount.c
Hi all, I've got the error during building numpy from latest svn snapshot - any ideas? D. ... executing numpy/core/code_generators/generate_numpy_api.py adding 'build/src.linux-x86_64-2.6/numpy/core/include/numpy/ __multiarray_api.h' to sources. numpy.core - nothing done with h_files = ['build/src.linux-x86_64-2.6/ numpy/core/include/numpy/config.h', 'build/src.linux-x86_64-2.6/numpy/ core/include/numpy/numpyconfig.h', 'build/src.linux-x86_64-2.6/numpy/ core/include/numpy/__multiarray_api.h'] building extension numpy.core.multiarray sources error: src/multiarray/refecount.c: No such file or directory ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] does numpy/scipy have solver for Ax=b, L_inf (Chebyshev norm)?
Hi all, does numpy/scipy, or maybe wrapper for a lapack routine have solver for Ax=b, L_inf (Chebyshev norm, i.e. max |Ax-b| - min)? If there are several ones, which ones are most suitable for large-scale, maybe ill- conditioned problems? Thank you in advance, D. P.S. Currently I 'm not interested in translating the problem to LP here, I search for more specialized solvers. ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] best way to get vector representation in a basis?
Hi all, I have orthonormal set of vectors B = [b_0, b_1,..., b_k-1], b_i from R^n (k may be less than n), and vector a from R^n What is most efficient way in numpy to get r from R^n and c_0, ..., c_k-1 from R: a = c_0*b_0+...+c_k-1*b_k-1 + r (r is rest) Thank you in advance, D. ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] best way to get vector representation in a basis?
Hi all, I have orthonormal set of vectors B = [b_0, b_1,..., b_k-1], b_i from R^n (k may be less than n), and vector a from R^n What is most efficient way in numpy to get r from R^n and c_0, ..., c_k-1 from R: a = c_0*b_0+...+c_k-1*b_k-1 + r (r is rest) Thank you in advance, D. ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] n-dimensional array indexing question
hi all, I have array A, A.ndim = n, and 1-dimensional array B of length n. How can I get element of A with coords B[0],...,B[n-1]? i.e. A[B[0], B[1], ..., B[n-1]) A, B, n are not known till execution time, and can have unpredictable lengths (still n is usually small, no more than 4-5). I have tried via ix_ but haven't succeeded yet. Thx in advance, D. ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] ParallelProgramming wiki page
Did you mean this one
http://www.netlib.org/scalapack/pblas_qref.html
?
As for the ParallelProgramming wiki page, there are some words in
section Use parallel primitives about numpy.dot still I can't
understand from the section: if I get numpy from sources and compile it
(via python setup.py build) in my AMD X2, will numpy.dot use 2nd CPU
or not?
Regards, D.
Frédéric Bastien wrote:
Hi,
Their exist open source version of parallel BLAS library. I modified
the section Use parallel primitives to tell it. But my English is
bad, so if someone can check it, it would be nice.
Fred
On Mon, Oct 27, 2008 at 4:24 PM, Robert Kern [EMAIL PROTECTED] wrote:
On Mon, Oct 27, 2008 at 15:20, Sebastien Binet
[EMAIL PROTECTED] wrote:
On Monday 27 October 2008 12:56:56 Robin wrote:
Hi,
I made some changes to the ParallelProgramming wiki page to outline
use of the (multi)processing module as well as the threading module.
I'm very much not an expert on this - just researched it for myself,
so please feel free to correct/ extend/ delete as appropriate.
I would mention the backport of multiprocessing for python-2.{4,5}:
http://code.google.com/p/python-multiprocessing
so the amount of editing when one switches from 2.{4,5} to 2.6 is minimal :)
Go for it. The wiki is open to editing.
--
Robert Kern
I have come to believe that the whole world is an enigma, a harmless
enigma that is made terrible by our own mad attempt to interpret it as
though it had an underlying truth.
-- Umberto Eco
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[Numpy-discussion] asscalar(number) - why yields error, why can't return the number?!
hi all, I wonder why numpy.asscalar(1.5) yields error, why it can't just return 1.5? Is it intended to be ever changed? numpy.__version__ '1.3.0.dev5864' D. ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] why type(array(1).tolist()) is int?
hi all, why array(1).tolist() returns 1? I expected to get [1] instead. D. ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] why type(array(1).tolist()) is int?
let me also note that list(array((1))) returns Traceback (innermost last): File stdin, line 1, in module TypeError: iteration over a 0-d array D. dmitrey wrote: hi all, why array(1).tolist() returns 1? I expected to get [1] instead. D. ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] will array(Python set) be ever implemented as cast method?
hi all, will array(Python set) (and asarray, asfarray etc) ever be implemented as cast method? Now it just puts the set into 1st element: asarray(set([11, 12, 13, 14])) array(set([11, 12, 13, 14]), dtype=object) array(set([11, 12, 13, 14])) array(set([11, 12, 13, 14]), dtype=object) Currently I use array(list(my_set)) instead. D. ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] why type(array(1).tolist()) is int?
Alan G Isaac wrote: On 10/1/2008 9:04 AM dmitrey apparently wrote: why array(1).tolist() returns 1? I expected to get [1] instead. I guess I would expect it not to work at all. Given that it does work, this seems the best result. What list shape matches the shape of a 0-d array? What is the use case that makes this seem wrong? Because I just expect something.tolist() return *Type* list, not *Type* integer. tolist documentation says Return the array as a list or nested lists and nothing about possibility to return anything else. As for my situation I store the list in my data field and then call for item from prob.my_list: do_something() D. Alan Isaac ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] does numpy have funcs like isanynan() or isallfinite()?
hi all, does numpy have funcs like isanynan(array) or isallfinite(array)? I very often use any(isnan(my_array)) or all(isfinite(my_array)), I guess having a single case triggered on would be enough here to omit further checks. Regards, D. ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] sort documentation
As for me I can't understand the general rule: when numpy funcs return copy and when reference? For example why x.fill() returns None (do inplace modification) while x.ravel(), x.flatten() returns copy? Why the latters don't do inplace modification, as should be expected? D. Alan G Isaac wrote: I find this confusing: numpy.sort(a, axis=-1, kind='quicksort', order=None) Return copy of 'a' sorted along the given axis. Perform an inplace sort along the given axis using the algorithm specified by the kind keyword. I suppose the last bit is supposed to refer to the ``sort`` method rather than the function, but I do not see any signal that this is the case. Cheers, Alan Isaac ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] isn't it a bug in array.fill()?
hi all, isn't it a bug (latest numpy from svn, as well as my older version) from numpy import array print array((1,2,3)).fill(10) None Regards, D. ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] isn't it a bug in array.fill()
sorry, it isn't a bug, it's my fault, fill() returns None and do in-place modification. D. ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] isn't it a bug in array.fill()?
Keith Goodman wrote: Yeah, I do stuff like that too. fill works in place so it returns None. x = np.array([1,2]) x.fill(10) x array([10, 10]) x = x.fill(10) # -- Danger! print x None Since result None is never used it would be better to return reference to the modified array, it would decrease number of bugs. The last expression can raise very seldom in untested cases, I have revealed one of this recently in my code: if some_seldom_cond: r = empty(n, bool).fill(True) else: r = None So, as you see, r was always None D. ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] preparing to tag NumPy 1.0.5 on Wednesday
Also, it would be very well if asfarray() doesn't drop down float128 to float64. D. Alan G Isaac wrote: I never got a response to this: URL:http://projects.scipy.org/pipermail/scipy-dev/2008-February/008424.html (Two different types claim to be numpy.int32.) Cheers, Alan ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Matching 0-d arrays and NumPy scalars
Travis E. Oliphant wrote: Hi everybody, In writing some generic code, I've encountered situations where it would reduce code complexity to allow NumPy scalars to be indexed in the same number of limited ways, that 0-d arrays support. For example, 0-d arrays can be indexed with * Boolean masks * Ellipses x[...] and x[..., newaxis] * Empty tuple x[()] I think that numpy scalars should also be indexable in these particular cases as well (read-only of course, i.e. no setting of the value would be possible). This is an easy change to implement, and I don't think it would cause any backward compatibility issues. Any opinions from the list? Best regards, -Travis O. As for me I would be glad to see same behavior for numbers as for arrays at all, like it's implemented in MATLAB, i.e. a=80 disp(a) 80 disp(a(1,1)) 80 ok, for numpy having at least possibility to use a=array(80) print a[0] would be very convenient, now atleast_1d(a) is required very often, and sometimes errors occur only some times later, already during execution of user-installed code, when user usually pass several-variables arrays and some time later suddenly single-variable array have been encountered. I guess it could be implemented via a simple check: if user calls for a[0] and a is array of shape () (i.e. like a=array(80)) then return a[()] D. ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] MLPY - Machine Learning Py - Python/NumPy based package for machine learning
isn't MLPY a new name to PyML? http://mloss.org/software/view/28/ if no, I guess you'd better add link to your software to http://mloss.org/software/ (mloss is machine learning open source software) Regards, D. Davide Albanese wrote: *Machine Learning Py* (MLPY) is a *Python/NumPy* based package for machine learning. The package now includes: * *Support Vector Machines* (linear, gaussian, polinomial, terminated ramps) for 2-class problems * *Fisher Discriminant Analysis* for 2-class problems * *Iterative Relief* for feature weighting for 2-class problems * *Feature Ranking* methods based on Recursive Feature Elimination (rfe, onerfe, erfe, bisrfe, sqrtrfe) and Recursive Forward Selection (rfs) * *Input Data* functions * *Confidence Interval* functions Requires Python http://www.python.org/ = 2.4 and NumPy http://www.scipy.org/ = 1.0.3.* MLPY* is a project of MPBA Group http://mpa.fbk.eu/ (mpa.fbk.eu) at Fondazione Bruno Kessler (www.fbk.eu). http://www.fbk.eu/* MLPY* is free software. It is licensed under the GNU General Public License (GPL) version 3 http://www.gnu.org/licenses/gpl-3.0.html. HomePage: mlpy.fbk.eu ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] asfarray() drops precision (float128-float64) - is it correct?
As for me, it yields lots of inconveniences (lots of my code should be rewritten, since I didn't know it before): from numpy import * a = array((1.0, 2.0), float128) b=asfarray(a) type(a[0]) #type 'numpy.float128' type(b[0]) #type 'numpy.float64' __version__ '1.0.5.dev4767' Shouldn't it be changed? (I.e. let's left 128). As for me I use asfarray() very often since I don't know does user provide arrays as numpy ndarray or matrix or Python list/tuple. D. ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] numerical noise for simple calcululations
hi all, I need a good estimation of noise value for simple calculations. I.e. when I calculate something like sin(15)+cos(80) I get a solution with precision, for example, 1e-11. I guess the precision depends on system arch, isn't it? So what's the best way to estimate the value? I guess here should be something like 10*numpy.machine_precision, isn't it? Regards, D. ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] numerical noise for simple calcululations
I need just a single number in avarage. I have committed some changes to NLP/NSP ralg solver from scikits.openopt, for non-noisy funcs it works better, but for noisy funcs vise versa, hence now my examples/nssolveVSfsolve.py doesn't work as it should be, so I need to implement noise parameter and assing a default value to the one. So, the question is: what default value should be here? I was thinking of either 0 or something like K*numpy.machine_precesion, where K is something like 1...10...100. Regards, D. Timothy Hochberg wrote: On Sun, Feb 10, 2008 at 4:23 AM, dmitrey [EMAIL PROTECTED] mailto:[EMAIL PROTECTED] wrote: hi all, I need a good estimation of noise value for simple calculations. I.e. when I calculate something like sin(15)+cos(80) I get a solution with precision, for example, 1e-11. I guess the precision depends on system arch, isn't it? So what's the best way to estimate the value? I guess here should be something like 10*numpy.machine_precision, isn't it? This is a complicated subject, which I'm really not qualified to comment on, but I'm not going to let that stop me. I believe that you want to know how accurate something like the above is given exact inputs. That is a somewhat artificial problem, but I'll answer it to the best of my ability. Functions like sin, cos, +, etc can in theory compute there result to within on ULP, or maybe half an ULP (I can't recall exactly). An ULP is a Unit in the Last Place. To explain an ULP, let's pretend that we were using decimal floating point with 3 digits of precision and look at a couple of numbers: 1.03e-03 -- 1 ULP = 1e-5 3.05e+02 -- 1 ULP = 1 We're obviously not using decimal floating point, we're using binary floating point, but the basic idea is the same. The result is that the accuracy is going to totally depend on the magnitude of the result. If the result is small, in general the result will be more accurate in an absolute sense, although not generally in a relative sense. In practice, this is drastically oversimplified since the inputs are generally of finite accuracy. Different functions will either magnify or shrink the input error depending on both the function and the value of the input. If you can find an easy to read introduction to numerical analysis, it would probably help. Unfortunately, I don't know of a good one to recommend; the text I have is a pretty hard slog. To complicate this further, functions don't always compute there results to maximum theoretical accuracy; presumably in the interest of reasonable performance. So, in the end the answer is; it depends. In practice the only useful, simple advice I've seen to get a handle on accuracy is to compute results using at least two different precisions and verify that things are converging sensibly. And compare to known results wherever possible. -- . __ . |-\ . . [EMAIL PROTECTED] mailto:[EMAIL PROTECTED] ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] isn't it a bug? (matrix multiplication)
from numpy import array a = array((1.0, 2.0)) b = c = 15 b = b*a#ok c *= a#ok d = array(15) e = array(15) d = d*a#this works ok e *= a#this intended to be same as prev line, but yields error: Traceback (innermost last): File stdin, line 1, in module ValueError: invalid return array shape ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] matrix - ndarray bug
I don't know, maybe it's already fixed in more recent versions?
from numpy import *
a=mat('1 2')
b = asfarray(a).flatten()
print b[0]
[[ 1. 2.]]
# ^^ I expected getting 1.0 here
numpy.version.version
'1.0.3'
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Re: [Numpy-discussion] [ANN] numscons 0.3.0 release
Hi all, I don't know much about what are these scons are, if it's something essential (as it seems to be from amount of mailing list traffic) why can't it be just merged to numpy, w/o making any additional branches? Regards, D. David Cournapeau wrote: ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
