我试图加快我编写的某些代码的速度，但是这样做却有很多麻烦。我知道能够删除for循环并使用numpy可以帮助实现这一点，因此我一直在尝试但收效甚微。
没有任何加速的工作功能是
def acf(x, y, z, cutoff=0):
steps = x.shape[1]
natoms = x.shape[0]
z_x = np.zeros((steps,natoms))
z_y, z_z = np.zeros_like(z_x), np.zeros_like(z_x)
xmean = np.mean(x, axis=1)
ymean = np.mean(y, axis=1)
zmean = np.mean(z, axis=1)
for k in range(steps-cutoff): # x.shape[1]
xtemp, ytemp, ztemp = [], [], []
for i in range(x.shape[0]): # natoms
xtop, ytop, ztop = 0.0, 0.0, 0.0
xbot, ybot, zbot = 0.0, 0.0, 0.0
for j in range(steps-k): # x.shape[1]-k
xtop += (x[i][j] - xmean[i]) * (x[i][j+k] - xmean[i])
ytop += (y[i][j] - ymean[i]) * (y[i][j+k] - ymean[i])
ztop += (z[i][j] - zmean[i]) * (z[i][j+k] - zmean[i])
xbot += (x[i][j] - xmean[i])**2
ybot += (y[i][j] - ymean[i])**2
zbot += (z[i][j] - zmean[i])**2
xtemp.append(xtop/xbot)
ytemp.append(ytop/ybot)
ztemp.append(ztop/zbot)
z_x[k] = xtemp
z_y[k] = ytemp
z_z[k] = ztemp
z_x = np.mean(np.array(z_x), axis=1)
z_y = np.mean(np.array(z_y), axis=1)
z_z = np.mean(np.array(z_z), axis=1)
return z_x, z_y, z_z
此函数的输入x，y和z是相同维的numpy数组。x（或y或z）的一个示例是：
x = np.array([[1,2,3],[4,5,6]])
到目前为止，我能够做的是
def acf_quick(x, y, z, cutoff=0):
steps = x.shape[1]
natoms = x.shape[0]
z_x = np.zeros((steps,natoms))
z_y, z_z = np.zeros_like(z_x), np.zeros_like(z_x)
x -= np.mean(x, axis=1, keepdims=True)
y -= np.mean(y, axis=1, keepdims=True)
z -= np.mean(z, axis=1, keepdims=True)
for k in range(steps-cutoff): # x.shape[1]
for i in range(natoms):
xtop, ytop, ztop = 0.0, 0.0, 0.0
xbot, ybot, zbot = 0.0, 0.0, 0.0
for j in range(steps-k): # x.shape[1]-k
xtop += (x[i][j]) * (x[i][j+k])
ytop += (y[i][j]) * (y[i][j+k])
ztop += (z[i][j]) * (z[i][j+k])
xbot += (x[i][j])**2
ybot += (y[i][j])**2
zbot += (z[i][j])**2
z_x[k][i] = xtop/xbot
z_y[k][i] = ytop/xbot
z_z[k][i] = ztop/xbot
z_x = np.mean(np.array(z_x), axis=1)
z_y = np.mean(np.array(z_y), axis=1)
z_z = np.mean(np.array(z_z), axis=1)
return z_x, z_y, z_z
这样可以将速度提高约33％，但我相信有一种方法可以消除的for i in range(natoms)使用方法x[:][j]。到目前为止，我一直没有成功，任何帮助将不胜感激。
在有人问之前，我知道这是一个自相关函数，并且在numpy，scipy等中内置了一些函数，但是我需要自己编写。
这是循环的矢量化形式：
def acf_quick_new(x, y, z, cutoff=0):
steps = x.shape[1]
natoms = x.shape[0]
lst_inputs = [x.copy(),y.copy(),z.copy()]
lst_outputs = []
for x_ in lst_inputs:
z_x_ = np.zeros((steps,natoms))
x_ -= np.mean(x_, axis=1, keepdims=True)
x_top = np.diag(np.dot(x_,x_.T))
x_bot = np.sum(x_**2, axis=1)
z_x_[0,:] = np.divide(x_top, x_bot)
for k in range(1,steps-cutoff): # x.shape[1]
x_top = np.diag(np.dot(x_[:,:-k],x_.T[k:,:]))
x_bot = np.sum(x_[:,:-k]**2, axis=1)
z_x_[k,:] = np.divide(x_top, x_bot)
z_x_ = np.mean(np.array(z_x_), axis=1)
lst_outputs.append(z_x_)
return lst_outputs
请注意，在_quick函数中有一个小错误：始终按xbot而不是xbot，ybot和zbot进行划分。而且，我的建议可以写得更好一些，但是它应该可以解决您的问题并加快计算速度：)
本文收集自互联网，转载请注明来源。
如有侵权，请联系[email protected] 删除。
我来说两句