Scipy/Numpy:多个指标的总和-Scipy/Numpy:summationovermultipleindices

4  

How about

如何

np.dot(x[:amax], np.cumsum(y[:amax] * np.sum(z[cmin:cmax])))

2  

np.einsum may be an option too for these kind of sum. As nevsan showed though, for b which is bounded by a you need to use np.cumsum first, and np.einsum should not be faster in the given example.

np。对于这类资金，einsum可能也是一个选择。正如nevsan所展示的那样，对于b，它的边界是你需要使用np。cumsum第一,np。在给定的例子中，einsum不能更快。

it could look like this:

它可以是这样的:

 y_acc = np.add.accumulate(y[:amax]) # same as cumsum
 result = np.einsum('i,i,j->', x[:amax], y_acc, z[cmin:cmax])

However this is increadibly slow, because einsum does not optimize the fact that the z summation only needs to be done once, so you need to reformulate it by hand:

然而，这是一个难以置信的缓慢过程，因为einsum并没有优化z求和只需要做一次的事实，所以你需要手工重新构造它:

result = np.einsum('i,i->', x[:amax], y_summed) * z[cmin:cmax].sum()

Which should in this case however should be slower then nevsan's np.dot based approach, since dot should normally be better optimized (ie. np.einsum(ii->, a, b) is slower then np.dot(a, b)). However if you have more arrays to sum over, it may be a nice option.

在这种情况下应该比nevsan的np慢。基于点的方法，因为点通常应该更好的优化。np。einsum(ii->， a, b)比np慢。点(a,b))。但是，如果有更多的数组要求和，这可能是一个不错的选择。