我可以用numpy加速这个循环吗？

晚上好,

我试图加快这段代码中的循环.我已经阅读了numpy docs,但无济于事.np.accumulate看起来几乎是我需要的,但并不完全.

我该怎么做才能加快循环速度？

import numpy as np

N       = 1000
AR_part = np.random.randn(N+1)
s2      = np.ndarray(N+1)
s2[0]   = 1.0

beta = 1.3

old_s2  = s2[0]
for t in range( 1, N+1 ):               
    s2_t    = AR_part[ t-1 ] + beta * old_s2
    s2[t]   = s2_t        
    old_s2  = s2_t

为了回应沃伦,我更新了我的代码:

从scipy.signal import lfilter,lfiltic导入numpy as np

N       = 1000
AR_part = np.random.randn(N+1)

beta = 1.3

def method1( AR_part):
    s2      = np.empty_like(AR_part)
    s2[0]   = 1.0
    old_s2  = s2[0]
    for t in range( 1, N+1 ):               
        s2_t    = AR_part[ t-1 ] + beta * old_s2
        s2[t]   = s2_t        
        old_s2  = s2_t
    return s2

def method2( AR_part):
    y = np.empty_like(AR_part)
    b = np.array([0, 1])
    a = np.array([1, -beta])

    # Initial condition for the linear filter.
    zi = lfiltic(b, a, [1.0], AR_part[:1])

    y[:1] = 1.0
    y[1:], zo = lfilter(b, a, AR_part[1:], zi=zi)

    return y    

s2 = method1( AR_part )
y = method2( AR_part )
np.alltrue( s2==y )

定时代码:

%timeit method1( AR_part )
100 loops, best of 3: 1.63 ms per loop
%timeit method2( AR_part )
10000 loops, best of 3: 129 us per loop

这表明沃伦的方法快了10倍!非常令人印象深刻!