> For instance > > In [7]: def countmembers(a1, a2) : > ...: a = sort(a2) > ...: il = a.searchsorted(a1, side='l') > ...: ir = a.searchsorted(a1, side='r') > ...: return ir - il > ...: > > In [8]: a2 = random.randint(0,10,(100,)) > > In [9]: a1 = arange(11) > > In [11]: a2 = random.randint(0,5,(100,)) > > In [12]: a1 = arange(10) > > In [13]: countmembers(a1,a2) > Out[13]: array([16, 28, 16, 25, 15, 0, 0, 0, 0, 0]) > > > The subtraction can be replaced by != to get a boolean mask. It looks good! Isn't it faster than setmember1d for unique input arrays? I do not like setmember1d much (it is long unlike other functions in arraysetops and looks clumsy to me now and I do not understand it anymore...), so feel free to replace it. BTW. setmember1d gives me the same mask as countmembers for several non-unique inputs I tried... r.
Try this, then: countmembers(N.array([1,1]), N.array([2])) array([0, 0]) N.setmember1d(N.array([1,1]), N.array([2])) array([ True, False], dtype=bool) setmember1d really needs the first array to be unique. I thought about it quite a bit and tried to understand the code (which is no small feat and I don't claim I have succeeded). As far as I can tell, setmember1d gets it right for the duplicate element with the highest index, all other duplicates are found to be in the second array, independent of whether or not that's actually true. I found it easier to state my problem in terms of unique arrays rather than trying to figure out a general solution, but countmembers sure is nice. Jan ---------- Forwarded message ----------
From: Robert Cimrman <[EMAIL PROTECTED]> To: Discussion of Numerical Python <numpy-discussion@scipy.org> Date: Thu, 25 Jan 2007 12:35:10 +0100 Subject: Re: [Numpy-discussion] setmember1d memory leak? Robert Cimrman wrote: > Charles R Harris wrote: >> >> In [7]: def countmembers(a1, a2) : >> ...: a = sort(a2) >> ...: il = a.searchsorted(a1, side='l') >> ...: ir = a.searchsorted(a1, side='r') >> ...: return ir - il >> ...: >> The subtraction can be replaced by != to get a boolean mask. > > It looks good! Isn't it faster than setmember1d for unique input arrays? > I do not like setmember1d much (it is long unlike other functions in > arraysetops and looks clumsy to me now and I do not understand it > anymore...), so feel free to replace it. > > BTW. setmember1d gives me the same mask as countmembers for several > non-unique inputs I tried... But still a function like 'findsorted' returning a bool mask would be handy - one searchsorted-like call could be saved in setmember1d. cheers, r. ---------- Forwarded message ---------- From: rex <[EMAIL PROTECTED]> To: Discussion of Numerical Python <numpy-discussion@scipy.org> Date: Thu, 25 Jan 2007 03:50:24 -0800 Subject: [Numpy-discussion] Compiling Python with icc George Nurser <[EMAIL PROTECTED]> [2007-01-25 02:05]: > Perhaps compiling python itself with icc might give a useful speedup. > Apparently somebody managed this for python 2.3 in 2003: > http://mail.python.org/pipermail/c++-sig/2003-October/005824.html Hello George, I saw that post yesterday, and just got around to trying it. It works. ./configure CC=icc --prefix=/usr/local In addition to commenting out #BASECFLAGS= -OPT:Olimit=0 I added -xT -parallel to the OPT= line for my Core 2 Duo CPU. The usual Make, Make install worked, and pybench now runs in 3.15 seconds vs 4.7 seconds with Python2.5 compiled with gcc. That's a 49% speed increase. http://svn.python.org/projects/external/pybench-2.0/ And, if psyco is used, pybench runs in 1.6 seconds for one iteration and then crashes. Psyco + icc results in a ~300% speed increase. Pybench needs to be updated for 1+ gigaflop systems. http://psyco.sourceforge.net/ -rex -- "I have always wished that my computer would be as easy to use as my telephone. My wish has come true. I no longer know how to use my telephone" --Bjorne Stroustrup (originator of C++ programming language) _______________________________________________ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
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