如何在python中规范化二维numpy数组不那么冗长? [英] How to normalize a 2-dimensional numpy array in python less verbose?

问题描述

给定一个 3 乘以 3 的 numpy 数组

Given a 3 times 3 numpy array

a = numpy.arange(0,27,3).reshape(3,3)

# array([[ 0,  3,  6],
#        [ 9, 12, 15],
#        [18, 21, 24]])

对我想到的二维数组的行进行归一化

To normalize the rows of the 2-dimensional array I thought of

row_sums = a.sum(axis=1) # array([ 9, 36, 63])
new_matrix = numpy.zeros((3,3))
for i, (row, row_sum) in enumerate(zip(a, row_sums)):
    new_matrix[i,:] = row / row_sum

一定有更好的方法，不是吗?

There must be a better way, isn't there?

也许要澄清一下:我的意思是规范化，每行条目的总和必须是一个.但我认为大多数人都会清楚这一点.

Perhaps to clearify: By normalizing I mean, the sum of the entrys per row must be one. But I think that will be clear to most people.

推荐答案

广播对此非常有用:

row_sums = a.sum(axis=1)
new_matrix = a / row_sums[:, numpy.newaxis]

row_sums[:, numpy.newaxis] 将 row_sums 从 (3,) 重塑为 (3, 1).当您执行a/b 时，a 和b 会相互广播.

row_sums[:, numpy.newaxis] reshapes row_sums from being (3,) to being (3, 1). When you do a / b, a and b are broadcast against each other.

您可以在此处了解有关广播的更多信息强> 甚至更好在这里.

You can learn more about broadcasting here or even better here.

这篇关于如何在python中规范化二维numpy数组不那么冗长?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！