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,[ 0.5,[ 0.7,0.2]]
c = [[ 10,12],13],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.
我知道形状各不相同，但是如何不做任何事情而简单地添加权重列表
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。
输出：像这样的加权数组：
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。
解决方法
不确定第一个元素怎么可能是 4.675？
weights = [0.35,0.05,0.6]
a = [[ 0.5,1],[ 5,7],[ 3,8]]
b = [[ 10,[ 0.5,[ 0.7,0.2]]
c = [[ 10,12],13],0.7]]
abc=[a,b,c]
print(np.average(np.array(abc),weights=weights,axis=0))
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数组的形状为 (1,3,2)。因此，要么像@BingWang 建议的那样更改 abc 或使用 axis=1。