python - 如何使用numpy在for循环中向量化矩阵和？

Question

基本上我有一个行数=3600 和列数=5 的矩阵，并希望将其下采样到 60 行的地 block :

import numpy as np

X = np.random.rand(3600,5)

down_sample = 60
ds_rng = range(0,X.shape[0],down_sample)
X_ds = np.zeros((ds_rng.__len__(),X.shape[1]))

i = 0
for j in ds_rng:
    X_ds[i,:] = np.sum( X[j:j+down_sample,:], axis=0 )
    i += 1

Answer 1

另一种方法可能是:

def blockwise_sum(X, down_sample=60):
    n, m = X.shape

    ds_n = n / down_sample
    N = ds_n * down_sample

    if N == n:
        return np.sum(X.reshape(-1, down_sample, m), axis=1)

    X_ds = np.zeros((ds_n + 1, m))
    X_ds[:ds_n] = np.sum(X[:N].reshape(-1, down_sample, m), axis=1)
    X_ds[-1] = np.sum(X[N:], axis=0)

    return X_ds

不过我不知道它是否更快。