我有一个ndarray,其中每一行都是一个单独的直方图。对于每一行,我希望找到前N个值。
我知道全球前N个值(A fast way to find the largest N elements in an numpy array)的解决方案,但我不知道如何获得每行的前N个。
我可以遍历每一行并应用1D解决方案,但是我不能用numpy广播做到这一点吗?
答案 0 :(得分:5)
您可以使用与您链接的问题相同的方式使用 <select onchange='mostrarOtros(this.value)'>
<option value="" >Select</option>
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:排序已经在最后一个轴上:
np.partition
答案 1 :(得分:3)
您可以在axis = 1
的行中使用np.argsort
,如此 -
import numpy as np
# Find sorted indices for each row
sorted_row_idx = np.argsort(A, axis=1)[:,A.shape[1]-N::]
# Setup column indexing array
col_idx = np.arange(A.shape[0])[:,None]
# Use the column-row indices to get specific elements from input array.
# Please note that since the column indexing array isn't of the same shape
# as the sorted row indices, it will be broadcasted
out = A[col_idx,sorted_row_idx]
示例运行 -
In [417]: A
Out[417]:
array([[0, 3, 3, 2, 5],
[4, 2, 6, 3, 1],
[2, 1, 1, 8, 8],
[6, 6, 3, 2, 6]])
In [418]: N
Out[418]: 3
In [419]: sorted_row_idx = np.argsort(A, axis=1)[:,A.shape[1]-N::]
In [420]: sorted_row_idx
Out[420]:
array([[1, 2, 4],
[3, 0, 2],
[0, 3, 4],
[0, 1, 4]], dtype=int64)
In [421]: col_idx = np.arange(A.shape[0])[:,None]
In [422]: col_idx
Out[422]:
array([[0],
[1],
[2],
[3]])
In [423]: out = A[col_idx,sorted_row_idx]
In [424]: out
Out[424]:
array([[3, 3, 5],
[3, 4, 6],
[2, 8, 8],
[6, 6, 6]])
如果您希望按降序排列元素,可以使用此附加步骤 -
In [425]: out[:,::-1]
Out[425]:
array([[5, 3, 3],
[6, 4, 3],
[8, 8, 2],
[6, 6, 6]])