Re: [Numpy-discussion] How can I constrain linear_least_squares to integer solutions?
On Tue, Nov 27, 2007 at 11:07:30PM -0700, Charles R Harris wrote: This is not a trivial problem, as you can see by googling mixed integer least squares (MILS). Much will depend on the nature of the parameters, the number of variables you are using in the fit, and how exact the solution needs to be. One approach would be to start by rounding the coefficients that must be integer and improve the solution using annealing or genetic algorithms to jig the integer coefficients while fitting the remainder in the usual least square way, but that wouldn't have the elegance of some of the specific methods used for this sort of problem. However, I don't know of a package in scipy that implements those more sophisticated algorithms, perhaps someone else on this list who knows more about these things than I can point you in the right direction. Would this be a good candidate for a genetic algorithm? I haven't used GA before, so I don't know the typical rate of convergence or its applicability to optimization problems. Regards Stéfan ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Appending a numpy array to existing text file
It does indeed work
Thanks for the help
Andy
LB wrote:
If you just want to add your matrix to an existing ascii file, you can
open this file in append mode and give the file handle to
numpy.savetxt :
f_handle = file('my_file.dat', 'a')
savetxt(f_handle, my_matrix)
f_handle.close()
HTH
--
LB
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[Numpy-discussion] documentation generator based on pyparsing
Hi, At http://scipy.org/Generate_Documentation you can find a very small documentation generator for NumPy/SciPy modules based on pyparsing package (by Paul McGuire). I am not sure if this belongs to where I put it, so feel free to (re)move the page as needed. I hope it might be interesting for you. r. ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] How can I constrain linear_least_squares to integer solutions?
On Nov 28, 2007 12:59 AM, Stefan van der Walt [EMAIL PROTECTED] wrote: On Tue, Nov 27, 2007 at 11:07:30PM -0700, Charles R Harris wrote: This is not a trivial problem, as you can see by googling mixed integer least squares (MILS). Much will depend on the nature of the parameters, the number of variables you are using in the fit, and how exact the solution needs to be. One approach would be to start by rounding the coefficients that must be integer and improve the solution using annealing or genetic algorithms to jig the integer coefficients while fitting the remainder in the usual least square way, but that wouldn't have the elegance of some of the specific methods used for this sort of problem. However, I don't know of a package in scipy that implements those more sophisticated algorithms, perhaps someone else on this list who knows more about these things than I can point you in the right direction. Would this be a good candidate for a genetic algorithm? I haven't used GA before, so I don't know the typical rate of convergence or its applicability to optimization problems. Regards Stéfan ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion If the number of terms is not huge and the function is well behaved; it might be worth trying the following simple and stupid approach: 1. Find the floating point minimum. 2. for each set of possible set of integer coefficients near the FP minimum: 1. Solve for the floating point coefficients with the integer coefficients fixed. 2. If the minimum is the best so far, stash it somewhere for later. 3. Return the best set of coefficients. -- . __ . |-\ . . [EMAIL PROTECTED] ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] Converting char array to float
I'm trying to convert a character array to a floating point array. I'm using one of the recent svn builds. It is surprising that astype does not do the job. However if I first convert the char array to an array and then use astype everything works fine. Is this a bug? import numpy as N print N.__version__ # Output = '1.0.5.dev4426' a = N.char.array(['123.45', '234.56']) b = N.array(a).astype(N.float) # This works. print b b = a.astype(N.float) # This does not work and raises an exception ValueError: Can only create a chararray from string data. Thanks. Sameer ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Converting char array to float
Sameer, I can't tell whether it's a bug or a feature, but I can give you some explanation: when you call .astype on your chararray, you call the __array_finalize__ of the chararray, which requires the dtype to be string like. Obviously, that won't work in your case. Transforming the chararray to a regular array of strings bypass this problem. That's what you're doing with the N.array(a) statement in your example. Two comments, however: * Try to use N.asarray() instead, as you won't copy the data (or use N.array(a,copy=False)) * You can also view your charray as a regular ndarray, and then use the astype method: a.view(N.ndarray).astype(float_) ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Converting char array to float
a does not seem to be an array, so it is not surprising that you need to convert it to an array first. Matthieu 2007/11/28, Sameer DCosta [EMAIL PROTECTED]: I'm trying to convert a character array to a floating point array. I'm using one of the recent svn builds. It is surprising that astype does not do the job. However if I first convert the char array to an array and then use astype everything works fine. Is this a bug? import numpy as N print N.__version__ # Output = '1.0.5.dev4426' a = N.char.array(['123.45', '234.56']) b = N.array(a).astype(N.float) # This works. print b b = a.astype(N.float) # This does not work and raises an exception ValueError: Can only create a chararray from string data. Thanks. Sameer ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion -- French PhD student Website : http://miles.developpez.com/ Blogs : http://matt.eifelle.com and http://blog.developpez.com/?blog=92 LinkedIn : http://www.linkedin.com/in/matthieubrucher ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Converting char array to float
On Wednesday 28 November 2007 13:39:45 Matthieu Brucher wrote: a does not seem to be an array, so it is not surprising that you need to convert it to an array first. Well, a *IS* a regular chararray, and therefore a subclass of ndarray (try isinstance). The problem isn't here, it's that the subclass doesn't have its own .astype() method. Instead, we use the standard ndarray.astype, which calls the __array_finalize__ method from the subclass, and this one requires a chararray. Maybe we could implement a simple method: def astype(self, newdtype): self.view(N.ndarray).astype(newdtype) But wouldn't that break something down the line ? ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] documentation generator based on pyparsing
On Wed, 28 Nov 2007 11:29:20 +0100
Robert Cimrman [EMAIL PROTECTED] wrote:
Hi,
At http://scipy.org/Generate_Documentation you can find
a very small
documentation generator for NumPy/SciPy modules based on
pyparsing
package (by Paul McGuire). I am not sure if this belongs
to where I put
it, so feel free to (re)move the page as needed. I hope
it might be
interesting for you.
r.
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Hi Robert,
The output of
./gendocs.py -m 'scipy.linsolve.umfpack'
differs from your example output (available at
http://scipy.org/Generate_Documentation)
./gendocs.py -m 'scipy.linsolve.umfpack'
generating docs for scipy.linsolve.umfpack...
output LaTeX source file: ./scipy.linsolve.umfpack.tex
['Contains']
['Description', '-']
['Installation', '--']
['Examples', '--']
['Arguments of UmfpackContext solution methods',
'--']
['Setting control parameters',
'']
['Author']
['Other contributors']
['UmfpackContext class']
This is pdfeTeX, Version 3.141592-1.21a-2.2 (Web2C 7.5.4)
entering extended mode
(./scipy.linsolve.umfpack.tex
LaTeX2e 2003/12/01
Babel v3.8d and hyphenation patterns for american,
french, german, ngerman, b
ahasa, basque, bulgarian, catalan, croatian, czech,
danish, dutch, esperanto, e
stonian, finnish, greek, icelandic, irish, italian, latin,
magyar, norsk, polis
h, portuges, romanian, russian, serbian, slovak, slovene,
spanish, swedish, tur
kish, ukrainian, nohyphenation, loaded.
(/usr/share/texmf/tex/latex/base/article.cls
Document Class: article 2004/02/16 v1.4f Standard LaTeX
document class
(/usr/share/texmf/tex/latex/base/size10.clo))
(/usr/share/texmf/tex/latex/tools/bm.sty)
(/usr/share/texmf/tex/latex/a4wide/a4wide.sty
(/usr/share/texmf/tex/latex/ntgclass/a4.sty))
! Undefined control sequence.
l.12 \set
(/usr/share/texmf/tex/latex/graphics/graphicx.sty
(/usr/share/texmf/tex/latex/graphics/keyval.sty)
(/usr/share/texmf/tex/latex/graphics/graphics.sty
(/usr/share/texmf/tex/latex/graphics/trig.sty)
(/usr/share/texmf/tex/latex/graphics/graphics.cfg)
(/usr/share/texmf/tex/latex/graphics/pdftex.def)))
(./scipy.linsolve.umfpack.aux)
[1{/var/lib/texmf/fonts/map/pdftex/updmap/pdftex
.map}] (./scipy.linsolve.umfpack.toc) [1]
Overfull \hbox (32.25606pt too wide) in paragraph at lines
66--69
\OT1/cmr/m/n/10 A. Davis. All Rights Re-served. UMF-PACK
home-page: http://www.
cise.ufl.edu/research/sparse/umfpack
Overfull \hbox (68.30923pt too wide) in paragraph at lines
84--88
[]\OT1/cmr/m/n/10 [umfpack] li-brary[]dirs =
dir/UMFPACK/UMFPACK/Lib in-clude
[]dirs = dir/UMFPACK/UMFPACK/Include
Overfull \hbox (90.36482pt too wide) in paragraph at lines
91--95
[]\OT1/cmr/m/n/10 [amd] li-brary[]dirs =
dir/UFsparse/AMD/Lib in-clude[]dirs
= dir/UFsparse/AMD/Include, dir/UFsparse/UFconfig
Overfull \hbox (49.97585pt too wide) in paragraph at lines
96--100
[]\OT1/cmr/m/n/10 [umfpack] li-brary[]dirs =
dir/UFsparse/UMFPACK/Lib in-clud
e[]dirs = dir/UFsparse/UMFPACK/Include,
! You can't use `macro parameter character #' in vertical
mode.
l.109 #
Contruct the solver.
! You can't use `macro parameter character #' in
horizontal mode.
l.110 umfpack = um.UmfpackContext() #
Use default 'di'
family of UMFPACK rou...
! You can't use `macro parameter character #' in vertical
mode.
l.112 #
One-shot solution.
! You can't use `macro parameter character #' in
horizontal mode.
l.114 #
same as:
! You can't use `macro parameter character #' in vertical
mode.
l.119 #
Make LU decomposition.
! You can't use `macro parameter character #' in
horizontal mode.
l.122 #
Use already LU-decomposed matrix.
! You can't use `macro parameter character #' in
horizontal mode.
l.125 #
same as:
Overfull \hbox (6.97574pt too wide) in paragraph at lines
119--128
\OT1/cmr/m/n/10 umf-pack( um.UMFPACK[]A, mtx, rhs1,
au-to-Trans-pose = True ) s
ol2 = umf-pack( um.UMFPACK[]A,
! You can't use `macro parameter character #' in vertical
mode.
l.131 #
Make symbolic decomposition.
! You can't use `macro parameter character #' in
horizontal mode.
l.133 #
Print statistics.
Overfull \hbox (14.16289pt too wide) in paragraph at lines
131--135
[]\OT1/cmr/m/n/10 Make sym-bolic de-com-po-si-tion.
umf-pack.symbolic( mtx0 )
Print statis-tics. umf-pack.report[]symbolic()
! You can't use `macro parameter character #' in vertical
mode.
l.138 #
Make LU decomposition of mtx1 which has same
structure as mtx0.
! You can't use `macro parameter character #' in
horizontal mode.
l.140 #
Print statistics.
! You can't use `macro parameter character #' in vertical
mode.
l.143 #
Use already LU-decomposed matrix.
! You can't use `macro parameter
Re: [Numpy-discussion] Converting char array to float
Sameer DCosta wrote: I'm trying to convert a character array to a floating point array. I'm using one of the recent svn builds. It is surprising that astype does not do the job. However if I first convert the char array to an array and then use astype everything works fine. Is this a bug? import numpy as N print N.__version__ # Output = '1.0.5.dev4426' a = N.char.array(['123.45', '234.56']) b = N.array(a).astype(N.float) # This works. print b b = a.astype(N.float) # This does not work and raises an exception ValueError: Can only create a chararray from string data. The problem is that astype for a chararray will by default try to create a class of chararray. This sub-class only allows string data and so the base-class astype(float) will fail. The astype method could be over-ridden in order to support automatic conversion to other kinds of arrays, but I lean towards asking why? because explicit is better than implicit (although it is admittedly arguable which is which in this case...) -Travis Thanks. Sameer ___ 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] How can I constrain linear_least_squares to integer solutions?
On Nov 28, 2007 12:59 AM, Stefan van der Walt [EMAIL PROTECTED] wrote: On Tue, Nov 27, 2007 at 11:07:30PM -0700, Charles R Harris wrote: This is not a trivial problem, as you can see by googling mixed integer least squares (MILS). Much will depend on the nature of the parameters, the number of variables you are using in the fit, and how exact the solution needs to be. One approach would be to start by rounding the coefficients that must be integer and improve the solution using annealing or genetic algorithms to jig the integer coefficients while fitting the remainder in the usual least square way, but that wouldn't have the elegance of some of the specific methods used for this sort of problem. However, I don't know of a package in scipy that implements those more sophisticated algorithms, perhaps someone else on this list who knows more about these things than I can point you in the right direction. Would this be a good candidate for a genetic algorithm? I haven't used GA before, so I don't know the typical rate of convergence or its applicability to optimization problems. It depends. Just to show the sort of problems involved, suppose you have 32 integer variables and are looking for the last bit of optimization. If the floating point optimum is at (.5, .5, , .5) and the error is symmetrical, then each vertex of the surrounding integer cube is a solution and there are 2**32 of them. If the error isn't symmetrical, and chances are that with that many variables it is very far from that, then you have to search a larger region. That's a lot of points. The more sophisticated algorithms try to eliminate whole regions of points and keep narrowing things down, but even so the problem can easily get out of hand. If you just need a good solution, a genetic algorithm is a good bet to find one without too much hassle. I had a similar problem in designing a digital min/max FIR filter where I needed 15 bit integer coefficients for hardware implementation. There was a narrow high rejection band in the filter and simply rounding the coefficients left spikes in the response through that band. With a GA I was able to eliminate the spikes in about 30 minutes of evolution using a python/Numeric program. In that case the performance of annealing was quite dependent on choosing the right parameters for cooling rate, etc., while the GA was quite robust and straight forward. There was no guarantee the I ended up with the best solution, but what I got was good enough. Chuck ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] Building NumPy on Mac OS X without Apple GCC
I updated my GCC to a more recent version a day ago, since Apple's Xcode Tools only provide GCC 4.0 and the current release of GNU's GCC is 4.2. I successfully achieved this, but now I run into a problem when trying to build NumPy: gcc: unrecognized option '-no-cpp-precomp' cc1: error: unrecognized command line option -arch cc1: error: unrecognized command line option -arch cc1: error: unrecognized command line option -Wno-long-double gcc: unrecognized option '-no-cpp-precomp' cc1: error: unrecognized command line option -arch cc1: error: unrecognized command line option -arch cc1: error: unrecognized command line option -Wno-long-double Upon investigation into the matter, I found out that these options (no-cpp-precomp and Wno-long-double) are only valid in Apple's GCC and not the regular GNU release. Yet it seems NumPy automatically assumes Apple's GCC is being used when it realizes the target is OS X. Is there a way around this, or at least some way to specify Apple's GCC? NumPy is the only package I've tried building so far that has a problem with this. ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Building NumPy on Mac OS X without Apple GCC
Joshua Lippai wrote: I updated my GCC to a more recent version a day ago, since Apple's Xcode Tools only provide GCC 4.0 and the current release of GNU's GCC is 4.2. I successfully achieved this, but now I run into a problem when trying to build NumPy: gcc: unrecognized option '-no-cpp-precomp' cc1: error: unrecognized command line option -arch cc1: error: unrecognized command line option -arch cc1: error: unrecognized command line option -Wno-long-double gcc: unrecognized option '-no-cpp-precomp' cc1: error: unrecognized command line option -arch cc1: error: unrecognized command line option -arch cc1: error: unrecognized command line option -Wno-long-double Upon investigation into the matter, I found out that these options (no-cpp-precomp and Wno-long-double) are only valid in Apple's GCC and not the regular GNU release. Yet it seems NumPy automatically assumes Apple's GCC is being used when it realizes the target is OS X. Is there a way around this, or at least some way to specify Apple's GCC? NumPy is the only package I've tried building so far that has a problem with this. I'm surprised that you've built other Python extension modules because numpy does not add these flags; Python does. Python extensions should be built with the same compiler that Python itself was built with. If you are using the binary distribution from www.python.org, you should use Apple's gcc, not a different one. -- 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 ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Building NumPy on Mac OS X without Apple GCC
Joshua Lippai wrote: Thanks for the reply. Well, I built my Python stuff, including NumPy previously, before I changed to the higher GCC version. Do you know if there's an option I can toggle that will specify Apple's GCC to be used? $ CC=/usr/bin/gcc python setup.py build -- 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 ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Building NumPy on Mac OS X without Apple GCC
Joshua Lippai wrote: I updated my GCC to a more recent version a day ago, since Apple's Xcode Tools only provide GCC 4.0 and the current release of GNU's GCC is 4.2. I successfully achieved this, but now I run into a problem when trying to build NumPy: gcc: unrecognized option '-no-cpp-precomp' cc1: error: unrecognized command line option -arch cc1: error: unrecognized command line option -arch cc1: error: unrecognized command line option -Wno-long-double gcc: unrecognized option '-no-cpp-precomp' cc1: error: unrecognized command line option -arch cc1: error: unrecognized command line option -arch cc1: error: unrecognized command line option -Wno-long-double Upon investigation into the matter, I found out that these options (no-cpp-precomp and Wno-long-double) are only valid in Apple's GCC and not the regular GNU release. Yet it seems NumPy automatically assumes Apple's GCC is being used when it realizes the target is OS X. Is there a way around this, or at least some way to specify Apple's GCC? NumPy is the only package I've tried building so far that has a problem with this. I'm surprised that you've built other Python extension modules because numpy does not add these flags; Python does. Python extensions should be built with the same compiler that Python itself was built with. If you are using the binary distribution from www.python.org, you should use Apple's gcc, not a different one. -- 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 ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion Thanks for the reply. Well, I built my Python stuff, including NumPy previously, before I changed to the higher GCC version. Do you know if there's an option I can toggle that will specify Apple's GCC to be used? ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
