如何加快numpy代码 [英] How to speed up numpy code

问题描述

我有以下代码.原则上，它需要2 ^ 6 * 1000 = 64000次迭代，这是一个很小的数目.但是，在我的计算机上需要9s，并且我希望至少在n = 15的情况下运行它.

I have the following code. In principle it takes 2^6 * 1000 = 64000 iterations which is quite a small number. However it takes 9s on my computer and I would like to run it for n = 15 at least.

from __future__ import division
import numpy as np
import itertools

n=6
iters = 1000
firstzero = 0
bothzero = 0
for S in itertools.product([-1,1], repeat = n+1):
    for i in xrange(iters):
        F = np.random.choice(np.array([-1,0,0,1], dtype=np.int8), size = n)
        while np.all(F ==0):
            F = np.random.choice(np.array([-1,0,0,1], dtype=np.int8), size = n)
        FS = np.convolve(F,S, 'valid')
        if (FS[0] == 0):
            firstzero += 1
        if np.all(FS==0):
            bothzero += 1

print "firstzero",    firstzero
print "bothzero",  bothzero

是否有可能大大提高速度，还是应该用C重写它?

Is it possible to speed this up a lot or should I rewrite it in C?

分析表明它花费了大部分时间在

Profiling indicates it spends most of it time in

   258003    0.418    0.000    3.058    0.000 fromnumeric.py:1842(all)
   130003    1.245    0.000    2.907    0.000 {method 'choice' of 'mtrand.RandomState' objects}
   388006    2.488    0.000    2.488    0.000 {method 'reduce' of 'numpy.ufunc' objects}
   128000    0.731    0.000    2.215    0.000 numeric.py:873(convolve)
   258003    0.255    0.000    2.015    0.000 {method 'all' of 'numpy.ndarray' objects}
   258003    0.301    0.000    1.760    0.000 _methods.py:35(_all)
   130003    0.470    0.000    1.663    0.000 fromnumeric.py:2249(prod)
   644044    1.483    0.000    1.483    0.000 {numpy.core.multiarray.array}
   130003    0.164    0.000    1.193    0.000 _methods.py:27(_prod)
   258003    0.283    0.000    0.624    0.000 numeric.py:462(asanyarray)

推荐答案

假设您的代码名为f():

def g():
        n=6
        iters = 1000
        S=np.repeat(list(itertools.product([-1,1], repeat = n+1)),iters, axis=0).reshape((-1,n+1))
        F=np.random.choice(np.array([-1,0,0,1], dtype=np.int8), size = (iters*(2**(n+2)),n)) #oversampling
        F=F[~(F==0).all(1)][:iters*(2**(n+1))]
        FS=np.asanyarray(map(lambda x, y: np.convolve(x, y, 'valid'), F, S))
        firstzero=(FS[:,0]==0).sum()
        bothzero=(FS==0).all(1).sum()
        print "firstzero",    firstzero
        print "bothzero",  bothzero

计时结果:

In [164]:

%timeit f()
firstzero 27171
bothzero 12151
firstzero 27206
bothzero 12024
firstzero 27272
bothzero 12135
firstzero 27173
bothzero 12079
1 loops, best of 3: 14.6 s per loop
In [165]:

%timeit g()
firstzero 27182
bothzero 11952
firstzero 27365
bothzero 12174
firstzero 27318
bothzero 12173
firstzero 27377
bothzero 12072
1 loops, best of 3: 2.47 s per loop

这篇关于如何加快numpy代码的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！