时间戳

首页 > 解决方案 > 如何在 np.average() 时简单地传递权重

python - 如何在 np.average() 时简单地传递权重

问题描述

我对将权重传递给 np.average() 函数感到困惑。下面的例子：

import numpy as np

weights = [0.35, 0.05, 0.6]
abc = list()

a = [[ 0.5,  1],
   [ 5,  7],
   [ 3,  8]]

b = [[ 10,  1],
   [ 0.5,  1],
   [ 0.7,  0.2]]

c = [[ 10,  12],
   [ 0.5,  13],
   [ 5,  0.7]]

abc.append(a)
abc.append(b)
abc.append(c)

print(np.average(np.array(abc), weights=[weights], axis=0))

OUT:
TypeError: 1D weights expected when shapes of a and weights differ.

我知道形状不同，但是如何简单地添加权重列表而不做

np.average(np.array(abc), weights=[weights[0], weights[1], weights[2]], ..., axis=0)

因为我正在执行一个循环，其中权重因大小而异，最大为 30。

输出：加权数组，如下所示：

OUT:
[[6.675,  7.6],
[ 2.075,  10.3],
[ 4.085,  3.23]]

*average(a * weights[0] + b * weights[1] + c * weights[2])*

欢迎任何其他解决方案。

标签： pythonnumpyweighted-average

解决方案

不确定第一个元素如何是 4.675？

weights = [0.35, 0.05, 0.6]


a = [[ 0.5,  1],
   [ 5,  7],
   [ 3,  8]]

b = [[ 10,  1],
   [ 0.5,  1],
   [ 0.7,  0.2]]

c = [[ 10,  12],
   [ 0.5,  13],
   [ 5,  0.7]]

abc=[a, b, c]

print(np.average(np.array(abc), weights=weights,axis=0))

推荐阅读