[Numpy-discussion] Linear assignment problem: Jonker-Volgenant algorithm
Following a suggestion by Joseph, I am trying to implement the Jonker-Volgenant algorithm for best linear assignment in Python, using numpy. Unsuprisingly, it is proving time-costly. I cannot afford to spend too much time on this, as it not to solve a problem of mine, but for the scikits.learn. Thus I was wondering if someone had a BSD-licensed Python version of the algorithm that he would be willing to share. Cheers, Gael ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] ANN: NumPy 1.6.0 [Problems on OS X w/ Python 2.6 and XCode 4]
Dear all, I have OSX 10.6.7, XCode 4, and Python.org python 2.6.6 and 2.7.1, where 2.7 is 64-bit. With 2.7, easy_install successfully compiles and installs the package, both over the web and with an explicit download. With 2.6, there seems to be a problem with attempting to compile the PPC architecture, seen in this error. /usr/libexec/gcc/powerpc-apple-darwin10/4.0.1/as: assembler (/usr/bin/../libexec/gcc/darwin/ppc/as or /usr/bin/../local/libexec/gcc/darwin/ppc/as) for architecture ppc not installed If I explicitly set CFLAGS and LDFLAGS to have -arch i386 -arch x86_64 in them as appropriate, it seems to work... Is this a correctable bug in the packaging, or just a quirk of my setup? Andrew On 15/05/2011 17:39, Ralf Gommers wrote: On Sat, May 14, 2011 at 3:09 PM, Charles R Harris charlesr.har...@gmail.com mailto:charlesr.har...@gmail.com wrote: On Sat, May 14, 2011 at 3:54 AM, Ralf Gommers ralf.gomm...@googlemail.com mailto:ralf.gomm...@googlemail.com wrote: Hi, I am pleased to announce the release of NumPy 1.6.0. This release is the result of 9 months of work, and includes many new features, performance improvements and bug fixes. Some highlights are: - Re-introduction of datetime dtype support to deal with dates in arrays. - A new 16-bit floating point type. - A new iterator, which improves performance of many functions. The link is http://sourceforge.net/projects/numpy/files/NumPy/1.6.0/ The OS X binaries are also up now. Ralf ___ 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] Linear assignment problem: Jonker-Volgenant algorithm
On Mon, May 16, 2011 at 1:18 AM, Gael Varoquaux gael.varoqu...@normalesup.org wrote: Following a suggestion by Joseph, I am trying to implement the Jonker-Volgenant algorithm for best linear assignment in Python, using numpy. Unsuprisingly, it is proving time-costly. I cannot afford to spend too much time on this, as it not to solve a problem of mine, but for the scikits.learn. Thus I was wondering if someone had a BSD-licensed Python version of the algorithm that he would be willing to share. I was at a presentation two weeks ago where open source software for linear assignment was referenced, so I think some is available, although that part went by so quickly I might have missed something. I still don't know what linear assignment does, but that is another problem... Chuck ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Linear assignment problem: Jonker-Volgenant algorithm
On Mon, May 16, 2011 at 8:53 AM, Charles R Harris charlesr.har...@gmail.com wrote: On Mon, May 16, 2011 at 1:18 AM, Gael Varoquaux gael.varoqu...@normalesup.org wrote: Following a suggestion by Joseph, I am trying to implement the Jonker-Volgenant algorithm for best linear assignment in Python, using numpy. Unsuprisingly, it is proving time-costly. I cannot afford to spend too much time on this, as it not to solve a problem of mine, but for the scikits.learn. Thus I was wondering if someone had a BSD-licensed Python version of the algorithm that he would be willing to share. I was at a presentation two weeks ago where open source software for linear assignment was referenced, so I think some is available, although that part went by so quickly I might have missed something. I still don't know what linear assignment does, but that is another problem... OT for the implementation question: linear assignment in the simplest case is just optimal matching of pairs, male-female, seller-buyer, where each side has full ranking preferences about being matched with anyone on the other side. more general case, college and student matching, or interns and hospitals, where one side has a capacity to be matched with more than one from the other side. (more fun if you allow for side-payments, i.e. prices) Josef Chuck ___ 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] Linear assignment problem: Jonker-Volgenant algorithm
On Mon, May 16, 2011 at 7:07 AM, josef.p...@gmail.com wrote: On Mon, May 16, 2011 at 8:53 AM, Charles R Harris charlesr.har...@gmail.com wrote: On Mon, May 16, 2011 at 1:18 AM, Gael Varoquaux gael.varoqu...@normalesup.org wrote: Following a suggestion by Joseph, I am trying to implement the Jonker-Volgenant algorithm for best linear assignment in Python, using numpy. Unsuprisingly, it is proving time-costly. I cannot afford to spend too much time on this, as it not to solve a problem of mine, but for the scikits.learn. Thus I was wondering if someone had a BSD-licensed Python version of the algorithm that he would be willing to share. I was at a presentation two weeks ago where open source software for linear assignment was referenced, so I think some is available, although that part went by so quickly I might have missed something. I still don't know what linear assignment does, but that is another problem... OT for the implementation question: linear assignment in the simplest case is just optimal matching of pairs, male-female, seller-buyer, where each side has full ranking preferences about being matched with anyone on the other side. more general case, college and student matching, or interns and hospitals, where one side has a capacity to be matched with more than one from the other side. (more fun if you allow for side-payments, i.e. prices) Thanks Josef, that makes sense in the context of the presentation I attended. Chuck ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Linear assignment problem: Jonker-Volgenant algorithm
On Mon, May 16, 2011 at 12:18 AM, Gael Varoquaux gael.varoqu...@normalesup.org wrote: Following a suggestion by Joseph, I am trying to implement the Jonker-Volgenant algorithm for best linear assignment in Python, using numpy. Unsuprisingly, it is proving time-costly. I cannot afford to spend too much time on this, as it not to solve a problem of mine, but for the scikits.learn. Thus I was wondering if someone had a BSD-licensed Python version of the algorithm that he would be willing to share. There are a number available. The lemon graph library (http://lemon.cs.elte.hu/trac/lemon) has solvers for this problem and has python bindings. It's under the boost license (is that ok?). It might be a bit heavyweight for this, though, but it's great software. -- Hoyt + Hoyt Koepke + University of Washington Department of Statistics + http://www.stat.washington.edu/~hoytak/ + hoy...@gmail.com ++ ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Linear assignment problem: Jonker-Volgenant algorithm
Hey On Mon, May 16, 2011 at 08:49:58AM -0700, Hoyt Koepke wrote: On Mon, May 16, 2011 at 12:18 AM, Gael Varoquaux gael.varoqu...@normalesup.org wrote: Jonker-Volgenant algorithm for best linear assignment in Python, using numpy. Unsuprisingly, it is proving time-costly. I cannot afford to spend too much time on this, as it not to solve a problem of mine, but for the scikits.learn. Thus I was wondering if someone had a BSD-licensed Python version of the algorithm that he would be willing to share. There are a number available. The lemon graph library (http://lemon.cs.elte.hu/trac/lemon) has solvers for this problem and has python bindings. It's under the boost license (is that ok?). It might be a bit heavyweight for this, though, but it's great software. Thanks for the suggestion. Unfortunately, I do not want to add compiled code (or an external dependency) to the scikit for this solver, as it is a fairly minor step for us. I particular chances are that it will never be a computational bottleneck on our poblems. Cheers, Gael ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Linear assignment problem: Jonker-Volgenant algorithm
In this case, lemon exists only as C++ header files, with no compiled code. A few cython functions should make it pretty simple. However, yeah, it is a bit heavyweight. Thanks for the suggestion. Unfortunately, I do not want to add compiled code (or an external dependency) to the scikit for this solver, as it is a fairly minor step for us. I particular chances are that it will never be a computational bottleneck on our poblems. If that's the case, I suggest just using the Hungarian algorithm. It's a little slower on large graphs, but it still is pretty quick. And there's tons of implementations out there, as it's often a standard coding project in undergrad algorithms courses. http://en.wikipedia.org/wiki/Hungarian_algorithm has links to a few. -- Hoyt + Hoyt Koepke + University of Washington Department of Statistics + http://www.stat.washington.edu/~hoytak/ + hoy...@gmail.com ++ ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Linear assignment problem: Jonker-Volgenant algorithm
On Mon, May 16, 2011 at 09:15:21AM -0700, Hoyt Koepke wrote: Thanks for the suggestion. Unfortunately, I do not want to add compiled code (or an external dependency) to the scikit for this solver, as it is a fairly minor step for us. I particular chances are that it will never be a computational bottleneck on our poblems. If that's the case, I suggest just using the Hungarian algorithm. I might go that way: I already have pure-Python code that implements it and that I have been using for a year or so. It's a little slower on large graphs, but it still is pretty quick. Can you put any numbers on the difference, or rule of thumbs? Is there an algorithmic complexity difference? If so, what kind of dependency are we talking about? Is it only in the prefactors? Sorry for drowning you with questions, but linear programming has always seem a dark craft to me, due to my lack of training on these issues. Cheers, Gael ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Linear assignment problem: Jonker-Volgenant algorithm
I might go that way: I already have pure-Python code that implements it and that I have been using for a year or so. Fair enough -- though you'd probably get a big speed up moving to cython. It's a little slower on large graphs, but it still is pretty quick. Can you put any numbers on the difference, or rule of thumbs? Is there an algorithmic complexity difference? If so, what kind of dependency are we talking about? Is it only in the prefactors? Well, the hungarian algorithm has a theoretical upper bound of O(n^3), with n being the number of nodes, which is pretty much the best you can do if you have a dense graph and make no assumptions on capacities. If you have a sparse graph, other algorithms can exploit that structure a bit more efficiently (e.g. lemon's implementation is O(nm logn), with m being the number of edges). Other algorithms can use capacities and include the capacity bounds as part of the theoretic performance of the algorithms, e.g. http://arxiv.org/abs/1105.1569. However, all these bounds are generally very much upper bounds and actual practical performance varies greatly. The hungarian algorithm is popular mainly because it's the best one that can be coded up efficiently in a few hundred lines of code. Hope that answers your questions :-). --Hoyt + Hoyt Koepke + University of Washington Department of Statistics + http://www.stat.washington.edu/~hoytak/ + hoy...@gmail.com ++ ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] ANN: NumPy 1.6.0 [Problems on OS X w/ Python 2.6 and XCode 4]
On Mon, May 16, 2011 at 12:41 PM, Andrew Jaffe a.h.ja...@gmail.com wrote: Dear all, I have OSX 10.6.7, XCode 4, and Python.org python 2.6.6 and 2.7.1, where 2.7 is 64-bit. With 2.7, easy_install successfully compiles and installs the package, both over the web and with an explicit download. With 2.6, there seems to be a problem with attempting to compile the PPC architecture, seen in this error. Please just use python setup.py install, easy_install is very unreliable. /usr/libexec/gcc/powerpc-apple-darwin10/4.0.1/as: assembler (/usr/bin/../libexec/gcc/darwin/ppc/as or /usr/bin/../local/libexec/gcc/darwin/ppc/as) for architecture ppc not installed XCode 4 does not support PPC, use version 3.2. Then it should work as advertized. Cheers, Ralf ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] ANN: NumPy 1.6.0 [Problems on OS X w/ Python 2.6 and XCode 4]
On 16/05/2011 18:45, Ralf Gommers wrote: On Mon, May 16, 2011 at 12:41 PM, Andrew Jaffe a.h.ja...@gmail.com mailto:a.h.ja...@gmail.com wrote: Dear all, I have OSX 10.6.7, XCode 4, and Python.org python 2.6.6 and 2.7.1, where 2.7 is 64-bit. With 2.7, easy_install successfully compiles and installs the package, both over the web and with an explicit download. With 2.6, there seems to be a problem with attempting to compile the PPC architecture, seen in this error. Please just use python setup.py install, easy_install is very unreliable. /usr/libexec/gcc/powerpc-apple-darwin10/4.0.1/as: assembler (/usr/bin/../libexec/gcc/darwin/ppc/as or /usr/bin/../local/libexec/gcc/darwin/ppc/as) for architecture ppc not installed XCode 4 does not support PPC, use version 3.2. Then it should work as advertized. Aha, thanks! But they're both installed, but how do force this? (Does 64-bit 2.7 not care about PPC?) Yours, Andrew ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] convert integer into bit array
Hi, for some research, I need to convert lots of integers into their bit representation - but as a bit array, not a string like numpy.binary_repr() returns it. So instead of In [22]: numpy.binary_repr(23) Out[22]: '10111 I'd need: numpy.binary_magic(23) Out: array([ True, False, True, True, True], dtype=bool) is there any way to do this efficiently? best regards, Nik ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] ANN: NumPy 1.6.0 [Problems on OS X w/ Python 2.6 and XCode 4]
On Mon, May 16, 2011 at 8:30 PM, Andrew Jaffe a.h.ja...@gmail.com wrote: On 16/05/2011 18:45, Ralf Gommers wrote: On Mon, May 16, 2011 at 12:41 PM, Andrew Jaffe a.h.ja...@gmail.com mailto:a.h.ja...@gmail.com wrote: Dear all, I have OSX 10.6.7, XCode 4, and Python.org python 2.6.6 and 2.7.1, where 2.7 is 64-bit. With 2.7, easy_install successfully compiles and installs the package, both over the web and with an explicit download. With 2.6, there seems to be a problem with attempting to compile the PPC architecture, seen in this error. Please just use python setup.py install, easy_install is very unreliable. /usr/libexec/gcc/powerpc-apple-darwin10/4.0.1/as: assembler (/usr/bin/../libexec/gcc/darwin/ppc/as or /usr/bin/../local/libexec/gcc/darwin/ppc/as) for architecture ppc not installed XCode 4 does not support PPC, use version 3.2. Then it should work as advertized. Aha, thanks! But they're both installed, but how do force this? You have to figure out where the right compiler is located. Then try export CC=/path/to/compiler. Or if you have MacPorts installed you can use its gcc_select. Or google for another solution. (Does 64-bit 2.7 not care about PPC?) No, it's an Intel 32 + 64-bit bundle. Cheers, Ralf ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] convert integer into bit array
Hi, I have used bitarray for that http://pypi.python.org/pypi/bitarray/ Here http://code.google.com/p/pylibtiff/source/browse/#svn%2Ftrunk%2Flibtiff%2Fbitarray-0.3.5-numpy you can find bitarray with numpy support. HTH, Pearu On Mon, May 16, 2011 at 9:55 PM, Nikolas Tautenhahn virt...@gmx.de wrote: Hi, for some research, I need to convert lots of integers into their bit representation - but as a bit array, not a string like numpy.binary_repr() returns it. So instead of In [22]: numpy.binary_repr(23) Out[22]: '10111 I'd need: numpy.binary_magic(23) Out: array([ True, False, True, True, True], dtype=bool) is there any way to do this efficiently? best regards, Nik ___ 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] Performance issues with numpy cross tabulation
Hello, I am having trouble with performance when trying to create a cross tabulation using numpy. Ideally, I would calculate each cell in the cross tabulation separately because this gives me the greatest amount of flexibility. I have included some sample code as a reference and am really looking for better approaches to the simpleLoop method. So far the histogram2d and histogramdd methods seem to outperform any code I write by a factor of about 100, at least. I chalk this up to I just don't understand enough about numpy, yet. Any help would be appreciated. Here is the test code: import numpy as np import time import random # Create a simple loop and count up the number of matching cases # Basic cross tabulation or histogram of the data # This approach is prefered because of the need to customize the calculation potentially for each cell. def simpleLoop(c): #number of items per inner loop a_cnt = len(np.unique(c[:,0])) b_cnt = len(np.unique(c[:,1])) idx = 0 result = np.zeros(b_cnt * a_cnt) for i in np.unique(c[:,0]): for j in np.unique(c[:,1]): result[idx] = np.sum(1*(c[:,0] == i) (c[:,1] == j)) idx += 1 result.resize(len(result)/b_cnt,b_cnt) return result # Use numpys histogram method to calculate the matrix of combinations and the number of cases in each one. def simpleHistogram(c): #number of items per inner loop return np.histogramdd((c[:,0],c[:,1]), bins=[np.unique(c[:, 0]),range(1,11)]) # Variation1 of simple histogram def simpleHistogram1(c): #number of items per inner loop results = [] for i in np.unique(c[:,1]): results.append(np.histogramdd((c[:,0][c[:,1]==i]), bins=[np.unique(c[:,0])]) or 0) return np.column_stack([result[0] for result in results]) if __name__ == '__main__': a = np.random.randint(1,900,20) b = np.random.randint(1,10,20) c = np.column_stack((a,b)) print ' Simple Loop ' start = time.time() results = simpleLoop(c) print results[0] print time.time() - start print ' Histogram dd no looping ' start = time.time() results = simpleHistogram(c) print results[0][0] print time.time() - start print ' Histogram run 1 time for each item in column 1 (10 times) ' start = time.time() results = simpleHistogram1(c) print results[0] print time.time() - start ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] convert integer into bit array
Hi, Here http://code.google.com/p/pylibtiff/source/browse/#svn%2Ftrunk%2Flibtiff%2Fbitarray-0.3.5-numpy you can find bitarray with numpy support. Thanks, that looks promising - to get a numpy array, I need to do numpy.array(bitarray.bitarray(numpy.binary_repr(i, l))) for an integer i and l with i 2**l, right? best regards, Nik ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] convert integer into bit array
Here's a one-liner: In [31]: x=np.arange(-8,8) In [32]: x Out[32]: array([-8, -7, -6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, 7]) In [33]: 1(x[:,np.newaxis]/2**np.arange(3,-1,-1)) Out[33]: array([[1, 0, 0, 0], [1, 0, 0, 1], [1, 0, 1, 0], [1, 0, 1, 1], [1, 1, 0, 0], [1, 1, 0, 1], [1, 1, 1, 0], [1, 1, 1, 1], [0, 0, 0, 0], [0, 0, 0, 1], [0, 0, 1, 0], [0, 0, 1, 1], [0, 1, 0, 0], [0, 1, 0, 1], [0, 1, 1, 0], [0, 1, 1, 1]]) On Mon, May 16, 2011 at 4:04 PM, Nikolas Tautenhahn virt...@gmx.de wrote: Hi, Here http://code.google.com/p/pylibtiff/source/browse/#svn%2Ftrunk%2Flibtiff%2Fbitarray-0.3.5-numpy you can find bitarray with numpy support. Thanks, that looks promising - to get a numpy array, I need to do numpy.array(bitarray.bitarray(numpy.binary_repr(i, l))) for an integer i and l with i 2**l, right? best regards, Nik ___ 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] PyArray_ContiguousFromObject PyDECREF
Hi, I think it should give the memory leaking. I have a similar issue. I am wondering if it works fine when using Py_DECREF() in the code. In my case, I tried to decref the PyArrayObject using Py_DECREF() but it gives a segmentation fault. Instead, if I use the PyArray_XDECREF(), it doesn't give any segmentation fault. However, I am not sure whether it gives any memory leaking issue when I use PyArray_XDECREF(). Thanks. Hyunsoo Noh ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] convert integer into bit array
On Tue, May 17, 2011 at 12:04 AM, Nikolas Tautenhahn virt...@gmx.de wrote:
Hi,
Here
http://code.google.com/p/pylibtiff/source/browse/#svn%2Ftrunk%2Flibtiff%2Fbitarray-0.3.5-numpy
you can find bitarray with numpy support.
Thanks, that looks promising - to get a numpy array, I need to do
numpy.array(bitarray.bitarray(numpy.binary_repr(i, l)))
for an integer i and l with i 2**l, right?
If l 64 and little endian is assumed then you can use the
fromword(i, l)
method:
from libtiff import bitarray
barr = bitarray.bitarray(0, 'little')
barr.fromword(3,4)
barr
bitarray('1100')
that will append 4 bits of the value 3 to the bitarray barr.
Also check out various bitarray `to*` and `from*` methods.
HTH,
Pearu
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Re: [Numpy-discussion] convert integer into bit array
On Tue, May 17, 2011 at 8:05 AM, Pearu Peterson pearu.peter...@gmail.comwrote:
On Tue, May 17, 2011 at 12:04 AM, Nikolas Tautenhahn virt...@gmx.dewrote:
Hi,
Here
http://code.google.com/p/pylibtiff/source/browse/#svn%2Ftrunk%2Flibtiff%2Fbitarray-0.3.5-numpy
you can find bitarray with numpy support.
Thanks, that looks promising - to get a numpy array, I need to do
numpy.array(bitarray.bitarray(numpy.binary_repr(i, l)))
for an integer i and l with i 2**l, right?
If l 64 and little endian is assumed then you can use the
fromword(i, l)
method:
from libtiff import bitarray
barr = bitarray.bitarray(0, 'little')
barr.fromword(3,4)
barr
bitarray('1100')
that will append 4 bits of the value 3 to the bitarray barr.
numpy.array(barr)
array([ True, True, False, False], dtype=bool)
to complete the example...
Pearu
Also check out various bitarray `to*` and `from*` methods.
HTH,
Pearu
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