如何正常化蟒蛇不再那么繁琐的二维数组numpy的？ [英] How to normalize a 2-dimensional numpy array in python less verbose?

问题描述

给定一个3次，每次3 numpy的阵列

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]])

要规范化2维数组我想到的行

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

必须有一个更好的办法，不是吗？

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

也许是为了clearify：通过正火我的意思是，每行entrys的总和必须的。但我认为，这将是明确的大多数人。

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

广播才是真的好这个：

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

row_sums [:, numpy.newaxis] 被重塑row_sums （3）来为（3,1）。当你做 A / B ， A 和 B 是互相播出。

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.

您可以了解更多关于广播 这里 甚至更好<一个href=\"http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html?highlight=broadcasting#numpy.doc.broadcasting\">here.

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

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