Hi, Is there anyone here who has some experience with simulated annealing? The code at http://scipy.org/scipy/scipy/ticket/875#comment:1 looks fragile, but I don't know how to fix it best. Thanks Stéfan
On Mon, Mar 2, 2009 at 12:55 AM, Stéfan van der Walt <stefan <at> sun.ac.za> wrote: > Hi, > > Is there anyone here who has some experience with simulated annealing? > > The code at > > http://scipy.org/scipy/scipy/ticket/875#comment:1 > > looks fragile, but I don't know how to fix it best. > > Thanks > Stéfan I had looked at this ticket briefly, and, I think, following his suggestion of building the random array xt incrementally should not affect any other code and speed up finding a random array inside the bounds. The elements of xc are independently drawn, so in each iteration only those values, for which indu or indo are true, have to be replaced, and successfull draws can be kept. xc can be drawn for the full array and partially discarded, since this is cheap. something like this (use correct `or` and initialize indu and indo for first iteration) should work xt[indu or indo] = x0[indu or indo] + xc[indu or indo] Josef(Continue reading)
On 3/2/2009 6:55 AM, Stéfan van der Walt wrote: > Hi, > > Is there anyone here who has some experience with simulated annealing? > > The code at > > http://scipy.org/scipy/scipy/ticket/875#comment:1 An atrocious thing about SciPy's SA is line 188 in anneal.py that says schedule = eval(schedule+'_sa()') S.M. _______________________________________________ Scipy-dev mailing list Scipy-dev <at> scipy.org http://projects.scipy.org/mailman/listinfo/scipy-dev
On 3/2/2009 6:55 AM, Stéfan van der Walt wrote: > http://scipy.org/scipy/scipy/ticket/875#comment:1 > > looks fragile, but I don't know how to fix it best. This ticket i bogus. There is no simple_sa in anneal.py in SVN, and line 151 is a docstring. Google says that the simple_sa class was posted to this mailing list two years ago by William Ratcliff. But as far as I can tell it is not in SciPy. Sturla Molden _______________________________________________ Scipy-dev mailing list Scipy-dev <at> scipy.org http://projects.scipy.org/mailman/listinfo/scipy-dev
I am pleased to announce the release of SfePy 2009.1. SfePy (simple finite elements in Python) is a finite element analysis software based primarily on Numpy and SciPy. Mailing lists, issue tracking, git repository: http://sfepy.org Home page: http://sfepy.kme.zcu.cz Major improvements: - new solvers: - simple backtracking steepest descent optimization solver - PETSc Krylov solvers via petsc4py, sequential mode - LOBPCG eigenvalue solver (SciPy implementation) - new mesh readers: - mesh3d (hermes3d) - AVS UCD ascii mesh - Hypermesh ascii mesh - homogenization framework: - unified approach to resolve data dependencies: HomogenizationEngine class - switched DVCS from mercurial to git Applications: - phononic materials: - dispersion analysis, phase velocity computation for phononic materials - caching of coefficients to speed up parametric runs - schroedinger.py: - fixed DFT iterations, iteration plot saving - basic smearing around Fermi limit(Continue reading)
I posted a suggested patch because it would seem that the simulated annealing routine in scipy as it was would not respect bounds for the search space. I'll try to reconstruct the test case to show where it fails. There seemed to be a lack of interest in a version that did respect bounds (upper/lower limits on the parameters), so I've just been using my own copy.
Cheers,
William
On 3/2/2009 6:55 AM, Stéfan van der Walt wrote:
> http://scipy.org/scipy/scipy/ticket/875#comment:1This ticket i bogus. There is no simple_sa in anneal.py in SVN, and line
>
> looks fragile, but I don't know how to fix it best.
151 is a docstring. Google says that the simple_sa class was posted to
this mailing list two years ago by William Ratcliff. But as far as I can
tell it is not in SciPy.
Sturla Molden_______________________________________________
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Scipy-dev <at> scipy.org
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Here is code that will demonstrate the failure. Suppose you want to minimize the simple function f(x,y)=x^2+y^2, but you want to do it in a specified domain. This will not respect the upper and lower bounds:
cheers,
William
import numpy as N
import scipy.optimize.anneal as anneal
def fcn(p):
x,y=p
result=x**2+y**2
return result
if __name__=="__main__":
p0=N.array([3,3],'d')
lowerm=[1,1]
upperm=[4,4]
myschedule='fast'
p0,jmin=anneal(fcn,p0,\
schedule=myschedule,lower=lowerm,upper=upperm,\
maxeval=None, maxaccept=None,dwell=10,maxiter=600,T0=10000)
print 'p0',p0,'jmin',jmin
I posted a suggested patch because it would seem that the simulated annealing routine in scipy as it was would not respect bounds for the search space. I'll try to reconstruct the test case to show where it fails. There seemed to be a lack of interest in a version that did respect bounds (upper/lower limits on the parameters), so I've just been using my own copy.
Cheers,
WilliamOn Mon, Mar 2, 2009 at 8:54 AM, Sturla Molden <sturla <at> molden.no> wrote:On 3/2/2009 6:55 AM, Stéfan van der Walt wrote:
> http://scipy.org/scipy/scipy/ticket/875#comment:1This ticket i bogus. There is no simple_sa in anneal.py in SVN, and line
>
> looks fragile, but I don't know how to fix it best.
151 is a docstring. Google says that the simple_sa class was posted to
this mailing list two years ago by William Ratcliff. But as far as I can
tell it is not in SciPy.
Sturla Molden_______________________________________________
Scipy-dev mailing list
Scipy-dev <at> scipy.org
http://projects.scipy.org/mailman/listinfo/scipy-dev
_______________________________________________ Scipy-dev mailing list Scipy-dev <at> scipy.org http://projects.scipy.org/mailman/listinfo/scipy-dev
On Mon, Mar 2, 2009 at 12:04 PM, william ratcliff <william.ratcliff <at> gmail.com> wrote: > Here is code that will demonstrate the failure. Suppose you want to > minimize the simple function f(x,y)=x^2+y^2, but you want to do it in a > specified domain. This will not respect the upper and lower bounds: > > cheers, > William > > > import numpy as N > import scipy.optimize.anneal as anneal > > def fcn(p): > x,y=p > result=x**2+y**2 > return result > > > if __name__=="__main__": > p0=N.array([3,3],'d') > lowerm=[1,1] > upperm=[4,4] > myschedule='fast' > p0,jmin=anneal(fcn,p0,\ > schedule=myschedule,lower=lowerm,upper=upperm,\ > maxeval=None, > maxaccept=None,dwell=10,maxiter=600,T0=10000) > print 'p0',p0,'jmin',jmin > > After looking a bit more carefully: `upper` and `lower` in` fast_ca` are the bounds on the updating increment, xc, not on the parameters that are estimated x0, xnew and in the ticket. I didn't see any constraints on the parameters themselves in anneal. The current bounds restrict the updating to local perturbations, while in your case perturbations would always be global. Rewriting anneal to incorporate bounds might be a good enhancement but, I think, you need to distinguish between bounds on the parameters and bounds on the update increments. Then the update increments can easily bound by (xbounds - x0) and you don't need iteration to find the updated values. Josef
Hi, I am seeing a few functions that should be made depreciated as these appear to duplicate Numpy or Scipy functions. Do you want these as new or old tickets (for example, samplestd has ticket #81 as part of the Statistics Review)? Would you want a large patch or one for each ticket? These functions are just renamed functions present in scipy.special just with perhaps slightly more informative names: erfc ksprob fprob chisqprob zprob But I do not think we need these as separate functions but there is the issue of depreciation involved if users use these specific functions. There are other like that should be treated as depreciated: samplestd samplevar >>> import numpy as np >>> import scipy.stats.stats as stats >>> a=np.array([[1,2,3,4,5], [6,7,8,9,10]]) >>> np.std(a,axis=0) array([ 2.5, 2.5, 2.5, 2.5, 2.5]) >>> stats.samplestd(a,axis=0) array([ 2.5, 2.5, 2.5, 2.5, 2.5]) >>> stats.samplestd(a,axis=None) 2.8722813232690143 >>> np.std(a,axis=None) 2.8722813232690143 Also, stats.py has the histogram and histogram2 functions where I agree with the comment in the code about being obsoleted by numpy.histogram. I would think these should be depreciated although the cumfreq and relfreq functions would need to be rewritten, Thanks Bruce
Perhaps we could enhance the documentation so this is clear? Also, having another module which does impose bounds on the actual values of the parameters would be useful. My ansatz was that if the next iteration would fall outside of the bounds, stay at the current location.
Cheers,
William
On Mon, Mar 2, 2009 at 12:04 PM, william ratcliff<william.ratcliff <at> gmail.com> wrote:
> Here is code that will demonstrate the failure. Suppose you want toAfter looking a bit more carefully:
> minimize the simple function f(x,y)=x^2+y^2, but you want to do it in a
> specified domain. This will not respect the upper and lower bounds:
>
> cheers,
> William
>
>
> import numpy as N
> import scipy.optimize.anneal as anneal
>
> def fcn(p):
> x,y=p
> result=x**2+y**2
> return result
>
>
> if __name__=="__main__":
> p0=N.array([3,3],'d')
> lowerm=[1,1]
> upperm=[4,4]
> myschedule='fast'
> p0,jmin=anneal(fcn,p0,\
> schedule=myschedule,lower=lowerm,upper=upperm,\
> maxeval=None,
> maxaccept=None,dwell=10,maxiter=600,T0=10000)
> print 'p0',p0,'jmin',jmin
>
>
`upper` and `lower` in` fast_ca` are the bounds on the updating
increment, xc, not on the parameters that are estimated x0, xnew and
in the ticket. I didn't see any constraints on the parameters
themselves in anneal.
The current bounds restrict the updating to local perturbations, while
in your case perturbations would always be global.
Rewriting anneal to incorporate bounds might be a good enhancement
but, I think, you need to distinguish between bounds on the parameters
and bounds on the update increments. Then the update increments can
easily bound by (xbounds - x0) and you don't need iteration to find
the updated values.
Josef_______________________________________________
Scipy-dev mailing list
Scipy-dev <at> scipy.org
http://projects.scipy.org/mailman/listinfo/scipy-dev
_______________________________________________ Scipy-dev mailing list Scipy-dev <at> scipy.org http://projects.scipy.org/mailman/listinfo/scipy-dev
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