[英]numpy count elements across axis 0 matching values from another array
Given a 3D array such as: 给定一个3D数组,例如:
array = np.random.randint(1, 6, (3, 3, 3))
and an array of maximum values across axis 0: 以及沿轴0的最大值数组:
max_array = array.max(axis=0)
Is there a vectorised way to count the number of elements in axis 0 of array which are equal to the value of the matching index in max_array? 是否存在一种矢量化的方法来计算数组中轴0上等于max_array中匹配索引值的元素数量? For example, if array contains [1, 3, 3] in one axis 0 position, the output is 2, and so on for the other 8 positions, returning an array with the counts. 例如,如果数组在一个轴0位置包含[1、3、3],则输出为2,对于其他8个位置,依此类推,返回带有计数的数组。
To count the number of values in x
which equal the corresponding value in xmax
, you could use: 要计算x
中等于xmax
相应值的值的数量,可以使用:
(x == xmax).sum(axis=0)
Note that since x
has shape (3,3,3) and xmax
has shape (3,3), the expression x == xmax
causes NumPy to broadcast xmax
up to shape (3,3,3) where the new axis is added on the left. 请注意,由于x
具有形状(3,3,3)和xmax
具有形状(3,3),表达式x == xmax
导致NumPy 广播 xmax
直到形状(3,3,3),其中添加了新轴在左边。
For example, 例如,
import numpy as np
np.random.seed(2015)
x = np.random.randint(1, 6, (3,3,3))
print(x)
# [[[3 5 5]
# [3 2 1]
# [3 4 1]]
# [[1 5 4]
# [1 4 1]
# [2 3 4]]
# [[2 3 3]
# [2 1 1]
# [5 1 2]]]
xmax = x.max(axis=0)
print(xmax)
# [[3 5 5]
# [3 4 1]
# [5 4 4]]
count = (x == xmax).sum(axis=0)
print(count)
# [[1 2 1]
# [1 1 3]
# [1 1 1]]
声明:本站的技术帖子网页,遵循CC BY-SA 4.0协议,如果您需要转载,请注明本站网址或者原文地址。任何问题请咨询:yoyou2525@163.com.