python - NumPy:如何避免这种循环？

有没有办法避免这种循环从而优化代码？

import numpy as np

cLoss = 0
dist_ = np.array([0,1,0,1,1,0,0,1,1,0]) # just an example, longer in reality
TLabels = np.array([-1,1,1,1,1,-1,-1,1,-1,-1]) # just an example, longer in reality
t = float(dist_.size)
for i in range(len(dist_)):
    labels = TLabels[dist_ == dist_[i]]
    cLoss+= 1 - TLabels[i]*(1. * np.sum(labels)/t)
print cLoss

注意：dist_和TLabels都是相同形状的numpy数组(t,1)

最佳答案

我不确定您到底想做什么，但您是否知道scipy.ndimage.measurements用于在带有标签的数组上计算？看起来你想要这样的东西：

cLoss =  len(dist_) - sum(TLabels * scipy.ndimage.measurements.sum(TLabels,dist_,dist_) / len(dist_))