On Sep 7, 2:37 pm, "wang frank" <[EMAIL PROTECTED]> wrote: > I am not familiar with python, so I just simply try to reproduce the same > code in python. Seems almost correct, but from what I guess of MatLab, George's suggestions make it a bit more fair.
> If you think that my python script is not efficient, could you tell me how > to make it more efficient? In pure Python? No idea (besides using Roberto's and George's suggestions). If you allow for extensions, Istvan has the answer. If you allow compiling Python to C++ (using ShedSkin: http://shed-skin.blogspot.com/), here's a small report: ---------- [EMAIL PROTECTED]:~/sandbox$ cat bench.py import math n = 1 def bench1(n): for i in range(n): for j in range(1000): m=j+1 z=math.log(m) z1=math.log(m+1) z2=math.log(m+2) z3=math.log(m+3) z4=math.log(m+4) z5=math.log(m+5) z6=math.log(m+6) z7=math.log(m+7) z8=math.log(m+8) z9=math.log(m+9) return z9 a = bench1(10) [EMAIL PROTECTED]:~/sandbox$ ss -e bench.py *** SHED SKIN Python-to-C++ Compiler 0.0.22 *** Copyright 2005-2007 Mark Dufour; License GNU GPL version 2 (See LICENSE) (Please send bug reports here: [EMAIL PROTECTED]) [iterative type analysis..] ** iterations: 2 templates: 44 [generating c++ code..] [EMAIL PROTECTED]:~/sandbox$ make bench.so g++ -O3 -s -pipe -fomit-frame-pointer -I/home/ajaksu/shedskin-0.0.22/ lib -g -fPIC -I/usr/include/python2.5 -D__SS_BIND /home/ajaksu/ shedskin-0.0.22/lib/builtin.cpp /home/ajaksu/shedskin-0.0.22/lib/ math.cpp bench.cpp -lgc -shared -Xlinker -export-dynamic -lpython2.5 - o bench.so [EMAIL PROTECTED]:~/sandbox$ mv bench.py pbench.py [EMAIL PROTECTED]:~/sandbox$ ipython Python 2.5.1 (r251:54863, May 2 2007, 16:56:35) [...] In [1]: from pbench import bench1 as pbench1 In [2]: from bench import bench1 In [3]: %timeit a = bench1(10) 100 loops, best of 3: 10.2 ms per loop In [4]: %timeit a = pbench1(10) 10 loops, best of 3: 92.8 ms per loop ---------- I guess you'd also see nice improvements from Pyrex or Cython, Blitz and other tools. Check http://wiki.python.org/moin/PythonSpeed/PerformanceTips for the general ideas and http://scipy.org/PerformancePython for an insight on available tools that even compares their speeds to Matlab. And-if-you-run-more-benchmarks-please-do-post-them-ly yrs, Daniel
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