python - 具有不同结果大小的 numpy apply_along_axis

Question

我有一个返回列子集的函数，我想高效地将它应用到每一列。所以结果不再是矩阵，而是不同长度的列列表。由于大小不匹配，我未能使用 numpy apply_along_axis 来执行此操作。除了自己遍历列之外，有没有一种方法可以有效地做到这一点？

col_pred = lambda x: [v for v in x if v > 0.5]
filteredData = np.apply_along_axis(col_pred, 0, data)
# ValueError: could not broadcast input array from shape (3) into shape (4)

例如输入

data = [[0, 1, 1, 0], [1, 1, 1, 1]]
// my real data is more like a matrix with a lot of rows in [0-1]
// that can be simulated with 
// data = [[random.uniform(0, 1) for i in range(10)] for j in range(100000)]

我想得到

[[1, 1], [1, 1, 1, 1]]

Answer 1

查看您的代码，您似乎正在尝试输出每列大于阈值 0.5 的所有元素。这是一种实现这些的方法，并且还可以概括为处理沿行和列的那些 -

def threshold_along_an_axis(a, thresh = 0.5, axis=0):
    if axis==0:
        A = a.T
    else:
        A = a
    mask = A>thresh
    s = mask.sum(1)
    s0 = np.r_[0,s.cumsum()]
    arr = A[mask].tolist() # Skip .tolist() if list of arrays is needed as o/p
    return [arr[s0[i]:s0[i+1]] for i in range(len(s0)-1)]

这里的目的是在循环理解中做最少的工作。

sample 运行-

In [1]: a = np.random.rand(4,5)

In [2]: a
Out[2]: 
array([[ 0.45973245,  0.3671334 ,  0.12000436,  0.04205402,  0.74729737],
       [ 0.55217308,  0.4018889 ,  0.55695863,  0.55824384,  0.33435153],
       [ 0.32450124,  0.07713855,  0.09126221,  0.13150986,  0.27961361],
       [ 0.0876053 ,  0.42685005,  0.53034652,  0.15084453,  0.51518185]])

In [3]: threshold_along_an_axis(a, thresh=0.5, axis=0) # per column
Out[3]: 
[[0.5521730819881912],
 [],
 [0.5569586261866918, 0.5303465159370833],
 [0.5582438446718111],
 [0.7472973699509776, 0.5151818458812673]]

In [4]: threshold_along_an_axis(a, thresh=0.5, axis=1) # per row
Out[4]: 
[[0.7472973699509776],
 [0.5521730819881912, 0.5569586261866918, 0.5582438446718111],
 [],
 [0.5303465159370833, 0.5151818458812673]]