python - numpy bincount 可以处理二维数组吗？

Question

我看到了我无法理解的 numpy bincount 行为。我想按行方式将二维数组中的值装箱，并查看下面的行为。为什么它可以与 dbArray 一起工作但与 simarray 一起失败？

>>> dbArray
array([[1, 0, 1, 0, 1],
       [1, 1, 1, 1, 1],
       [1, 1, 0, 1, 1],
       [1, 0, 0, 0, 0],
       [0, 0, 0, 1, 1],
       [0, 1, 0, 1, 0]])
>>> N.apply_along_axis(N.bincount,1,dbArray)
array([[2, 3],
       [0, 5],
       [1, 4],
       [4, 1],
       [3, 2],
       [3, 2]], dtype=int64)
>>> simarray
array([[2, 0, 2, 0, 2],
       [2, 1, 2, 1, 2],
       [2, 1, 1, 1, 2],
       [2, 0, 1, 0, 1],
       [1, 0, 1, 1, 2],
       [1, 1, 1, 1, 1]])
>>> N.apply_along_axis(N.bincount,1,simarray)

Traceback (most recent call last):
  File "<pyshell#31>", line 1, in <module>
    N.apply_along_axis(N.bincount,1,simarray)
  File "C:\Python27\lib\site-packages\numpy\lib\shape_base.py", line 118, in apply_along_axis
    outarr[tuple(i.tolist())] = res
ValueError: could not broadcast input array from shape (2) into shape (3)

Answer 1

问题是 bincount 并不总是返回相同形状的对象，尤其是在缺少值时。例如:

>>> m = np.array([[0,0,1],[1,1,0],[1,1,1]])
>>> np.apply_along_axis(np.bincount, 1, m)
array([[2, 1],
       [1, 2],
       [0, 3]])
>>> [np.bincount(m[i]) for i in range(m.shape[1])]
[array([2, 1]), array([1, 2]), array([0, 3])]

有效，但是:

>>> m = np.array([[0,0,0],[1,1,0],[1,1,0]])
>>> m
array([[0, 0, 0],
       [1, 1, 0],
       [1, 1, 0]])
>>> [np.bincount(m[i]) for i in range(m.shape[1])]
[array([3]), array([1, 2]), array([1, 2])]
>>> np.apply_along_axis(np.bincount, 1, m)
Traceback (most recent call last):
  File "<ipython-input-49-72e06e26a718>", line 1, in <module>
    np.apply_along_axis(np.bincount, 1, m)
  File "/usr/local/lib/python2.7/dist-packages/numpy/lib/shape_base.py", line 117, in apply_along_axis
    outarr[tuple(i.tolist())] = res
ValueError: could not broadcast input array from shape (2) into shape (1)

不会。

您可以使用 minlength 参数并使用 lambda 或 partial 或其他方式传递它:

>>> np.apply_along_axis(lambda x: np.bincount(x, minlength=2), axis=1, arr=m)
array([[3, 0],
       [1, 2],
       [1, 2]])