在 numpy 二维数组中按行查找值大于阈值的索引

Question

我有一个 2darray 如下。我想按数组中的每一行查找高于阈值（例如 0.7）的值的索引。

items= np.array([[1.        , 0.40824829, 0.03210806, 0.29488391, 0.        ,
        0.5       , 0.32444284, 0.57735027, 0.        , 0.5       ],
       [0.40824829, 1.        , 0.57675476, 0.48154341, 0.        ,
        0.81649658, 0.79471941, 0.70710678, 0.57735027, 0.40824829],
       [0.03210806, 0.57675476, 1.        , 0.42606683, 0.        ,
        0.        , 0.92713363, 0.834192  , 0.        , 0.73848549],
       [0.29488391, 0.48154341, 0.42606683, 1.        , 0.        ,
        0.29488391, 0.52620136, 0.51075392, 0.20851441, 0.44232587],
       [0.        , 0.        , 0.        , 0.        , 0.        ,
        0.        , 0.        , 0.        , 0.        , 0.        ],
       [0.5       , 0.81649658, 0.        , 0.29488391, 0.        ,
        1.        , 0.32444284, 0.28867513, 0.70710678, 0.        ],
       [0.32444284, 0.79471941, 0.92713363, 0.52620136, 0.        ,
        0.32444284, 1.        , 0.93658581, 0.22941573, 0.81110711],
       [0.57735027, 0.70710678, 0.834192  , 0.51075392, 0.        ,
        0.28867513, 0.93658581, 1.        , 0.        , 0.8660254 ],
       [0.        , 0.57735027, 0.        , 0.20851441, 0.        ,
        0.70710678, 0.22941573, 0.        , 1.        , 0.        ],
       [0.5       , 0.40824829, 0.73848549, 0.44232587, 0.        ,
        0.        , 0.81110711, 0.8660254 , 0.        , 1.        ]])

indices_items = np.argwhere(items>= 0.7)

这个(indices_items)returns

array([[0, 0],
       [1, 1],
       [1, 5],
       [1, 6],
       [1, 7],
       [2, 2],
       [2, 6],
       [2, 7],
       [2, 9],
       [3, 3],
       [5, 1],
       [5, 5],
       [5, 8],
       [6, 1],
       [6, 2],
       [6, 6],
       [6, 7],
       [6, 9],
       [7, 1],
       [7, 2],
       [7, 6],
       [7, 7],
       [7, 9],
       [8, 5],
       [8, 8],
       [9, 2],
       [9, 6],
       [9, 7],
       [9, 9]], dtype=int64)

如何按行获取索引，如下所示？ row0 -> [0] row1-> [0,1,5,6,7] row2-> [2,6,7,9] row3-> [3] row4-> [] #这应该是空列表，因为没有超过阈值的值...

Answer 1

获取行，col 与 np.where，然后使用 np.searchsorted 获取 row-array 上的间隔索引并使用它们拆分 col-array -

In [38]: r,c = np.where(items>= 0.7)

In [39]: np.split(c,np.searchsorted(r,range(1,items.shape[0])))
Out[39]: 
[array([0], dtype=int64),
 array([1, 5, 6, 7], dtype=int64),
 array([2, 6, 7, 9], dtype=int64),
 array([3], dtype=int64),
 array([], dtype=int64),
 array([1, 5, 8], dtype=int64),
 array([1, 2, 6, 7, 9], dtype=int64),
 array([1, 2, 6, 7, 9], dtype=int64),
 array([5, 8], dtype=int64),
 array([2, 6, 7, 9], dtype=int64)]

Answer 2

这在性能方面可能不是最佳的，但如果你真的不关心它应该没问题。

indices_items = []
for l in items:
    indices_items.append(np.argwhere(l >= 0.7).flatten().tolist())

indices_items
Out[5]: 
[[0],
[1, 5, 6, 7],
[2, 6, 7, 9],
[3],
[],
[1, 5, 8],
[1, 2, 6, 7, 9],
[1, 2, 6, 7, 9],
[5, 8],
[2, 6, 7, 9]]

在 numpy 二维数组中按行查找值大于阈值的索引

Find indices of values greater than a threshold by row in a numpy 2darray

python

numpy

threshold

numpy-ndarray