平均numpy数组但保留形状 [英] average numpy array but retain shape

问题描述

我有一个Numpy 3轴数组，其元素是3维的.我想将它们取平均值并返回数组的相同形状.正常的均值函数会删除这3个维度，并用平均值代替(如预期的那样):

I have a Numpy 3 axis array whose elements are 3 dimensional. I'd like to average them and return the same shape of the array. The normal average function removes the 3 dimensions and replace it with the average (as expected):

a = np.array([[[0.1, 0.2, 0.3], [0.2, 0.3, 0.4]],
              [[0.4, 0.4, 0.4], [0.7, 0.6, 0.8]]], np.float32)

b = np.average(a, axis=2)
# b = [[0.2, 0.3],
#      [0.4, 0.7]]

需要结果:

# b = [[[0.2, 0.2, 0.2], [0.3, 0.3, 0.3]],
#      [[0.4, 0.4, 0.4], [0.7, 0.7, 0.7]]]

您可以优雅地做到这一点，还是只需要迭代Python中的数组(与强大的Numpy函数相比，它会慢很多).

Can you do this elegantly or do I just have to iterate over the array in Python (which will be a lot slower compared to a powerful Numpy function).

也许可以将np.mean函数的Dtype参数设置为一维数组吗?

Can you set the Dtype argument, for the np.mean function, to a 1D array perhaps?

谢谢.

推荐答案

>>> import numpy as np
>>> a = np.array([[[0.1, 0.2, 0.3], [0.2, 0.3, 0.4]],
...               [[0.4, 0.4, 0.4], [0.7, 0.6, 0.8]]], np.float32)
>>> b = np.average(a, axis=2)
>>> b
array([[ 0.2       ,  0.29999998],
       [ 0.40000001,  0.69999999]], dtype=float32)
>>> c = np.dstack((b, b, b))
>>> c
array([[[ 0.2       ,  0.2       ,  0.2       ],
        [ 0.29999998,  0.29999998,  0.29999998]],

       [[ 0.40000001,  0.40000001,  0.40000001],
        [ 0.69999999,  0.69999999,  0.69999999]]], dtype=float32)

这篇关于平均numpy数组但保留形状的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！