如何加快嵌套循环？

更新：（几乎）下面的“ new_function2”中完全矢量化的版本…

我将添加评论以解释一些事情。

它提供了约50倍的加速，如果您可以接受输出为numpy数组而不是列表，则可以实现更大的加速。照原样：

In [86]: %timeit new_function2(close, volume, INTERVAL_LENGTH)1 loops, best of 3: 1.15 s per loop

您可以将内部循环替换为对np.cumsum（）的调用。请参见下面的“ new_function”函数。这大大提高了速度…

In [61]: %timeit new_function(close, volume, INTERVAL_LENGTH)1 loops, best of 3: 15.7 s per loop

与

In [62]: %timeit old_function(close, volume, INTERVAL_LENGTH)1 loops, best of 3: 53.1 s per loop

不过，应该有可能对整个对象进行矢量化处理，并完全避免for循环……请花一点时间，我将拭目以待……

import numpy as npARRAY_LENGTH = 500000INTERVAL_LENGTH = 15close = np.arange(ARRAY_LENGTH, dtype=np.float)volume = np.arange(ARRAY_LENGTH, dtype=np.float)def old_function(close, volume, INTERVAL_LENGTH):    results = []    for i in xrange(len(close) - INTERVAL_LENGTH):        for j in xrange(i+1, i+INTERVAL_LENGTH): ret = close[j] / close[i] vol = sum( volume[i+1:j+1] ) if (ret > 1.0001) and (ret < 1.5) and (vol > 100):     results.append( (i, j, ret, vol) )    return resultsdef new_function(close, volume, INTERVAL_LENGTH):    results = []    for i in xrange(close.size - INTERVAL_LENGTH):        vol = volume[i+1:i+INTERVAL_LENGTH].cumsum()        ret = close[i+1:i+INTERVAL_LENGTH] / close[i]        filter = (ret > 1.0001) & (ret < 1.5) & (vol > 100)        j = np.arange(i+1, i+INTERVAL_LENGTH)[filter]        tmp_results = zip(j.size * [i], j, ret[filter], vol[filter])        results.extend(tmp_results)    return resultsdef new_function2(close, volume, INTERVAL_LENGTH):    vol, ret = [], []    I, J = [], []    for k in xrange(1, INTERVAL_LENGTH):        start = k        end = volume.size - INTERVAL_LENGTH + k        vol.append(volume[start:end])        ret.append(close[start:end])        J.append(np.arange(start, end))        I.append(np.arange(volume.size - INTERVAL_LENGTH))    vol = np.vstack(vol)    ret = np.vstack(ret)    J = np.vstack(J)    I = np.vstack(I)    vol = vol.cumsum(axis=0)    ret = ret / close[:-INTERVAL_LENGTH]    filter = (ret > 1.0001) & (ret < 1.5) & (vol > 100)    vol = vol[filter]    ret = ret[filter]    I = I[filter]    J = J[filter]    output = zip(I.flat,J.flat,ret.flat,vol.flat)    return outputresults = old_function(close, volume, INTERVAL_LENGTH)results2 = new_function(close, volume, INTERVAL_LENGTH)results3 = new_function(close, volume, INTERVAL_LENGTH)# Using sets to compare, as the output # is in a different order than the original functionprint set(results) == set(results2)print set(results) == set(results3)