python - 使用 numpy 的高效标准基向量

Question

给定索引和大小，是否有更有效的方法来生成 standard basis vector :

import numpy as np
np.array([1.0 if i == index else 0.0 for i in range(size)])

Answer 1

In [2]: import numpy as np

In [9]: size = 5

In [10]: index = 2

In [11]: np.eye(1,size,index)
Out[11]: array([[ 0.,  0.,  1.,  0.,  0.]])

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嗯，不幸的是，为此使用 np.eye 相当慢:

In [12]: %timeit np.eye(1,size,index)
100000 loops, best of 3: 7.68 us per loop

In [13]: %timeit a = np.zeros(size); a[index] = 1.0
1000000 loops, best of 3: 1.53 us per loop

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包装 np.zeros(size);函数中的 a[index] = 1.0 只产生适度的差异，并且仍然比 np.eye 快得多:

In [24]: def f(size, index):
   ....:     arr = np.zeros(size)
   ....:     arr[index] = 1.0
   ....:     return arr
   ....: 

In [27]: %timeit f(size, index)
1000000 loops, best of 3: 1.79 us per loop