-1

我对我的代码有疑问（对不起，如果它真的很简单或很愚蠢，我以前从未编写过代码并尝试过这个项目）。我尝试为某个概率向量运行概率加权函数。然而，我得到一个错误，我不知道如何解决它。当我首先单独运行函数的参数然后在函数中调用它时它可以工作，但为什么它在函数本身中不起作用？下面的代码

我希望你能帮助我。非常感谢！

import numpy as np

p = np.arange(0.01, 1, 0.01) # probalities equaly spread between 0 & 1 in steps of 0.01 

alpha_1 = 0.5
alpha = np.zeros((1, 99)) + alpha_1 # vector of same length as p with all entries being 0.5

term_in_exopnential_of_weighting_function = - (- np.log(p))**alpha
w = np.exp(term_in_exopnential_of_weighting_function) # weighted probability function 

# probability weighting function
#w(p)=np.exp(-(- np.log(p))**alpha) 
    # --> error, but why?`
4

1 回答 1

1

看起来您要做的是创建一个名为w. 在 Python 中，函数定义的语法是

def f(a, b, c):
  # do something here
  result = <something involving a, b, and c>
  return result

然后，您可以将函数调用f(1, 2, 3)为a、b和c。

在你给出的例子中，我想也许你需要的是

>>> def w(alpha, p):
...   return np.exp(-(- np.log(p))**alpha)
...

>>>Python 解释器输入提示在哪里。请注意，函数体必须缩进，由解释器强制执行。...后面没有任何内容意味着我只是按下键Enter而不输入任何内容。使用 的定义w，我得到以下结果，alpha如p您指定的那样：

>>> w(alpha, p)
array([[0.116955  , 0.13836178, 0.15372645, 0.16627328, 0.17713938,
        0.18687366, 0.19578782, 0.20407777, 0.21187567, 0.21927533,
        0.2263461 , 0.23314086, 0.23970099, 0.24605961, 0.25224365,
        0.25827542, 0.26417363, 0.26995416, 0.27563063, 0.28121487,
        0.28671721, 0.29214677, 0.29751164, 0.30281908, 0.30807562,
        0.31328717, 0.31845916, 0.32359653, 0.32870387, 0.33378542,
        0.33884514, 0.34388676, 0.34891376, 0.35392947, 0.35893704,
        0.36393949, 0.36893972, 0.37394054, 0.37894464, 0.38395468,
        0.38897324, 0.39400286, 0.39904606, 0.40410531, 0.40918309,
        0.41428186, 0.41940411, 0.42455233, 0.42972903, 0.43493677,
        0.44017815, 0.44545582, 0.45077251, 0.45613099, 0.46153416,
        0.466985  , 0.4724866 , 0.47804216, 0.48365505, 0.48932878,
        0.49506703, 0.50087369, 0.50675283, 0.5127088 , 0.51874619,
        0.52486989, 0.53108512, 0.53739747, 0.54381293, 0.55033797,
        0.55697957, 0.56374529, 0.57064336, 0.57768275, 0.58487331,
        0.59222586, 0.59975235, 0.60746605, 0.61538177, 0.62351608,
        0.63188769, 0.64051783, 0.64943073, 0.65865431, 0.66822097,
        0.67816868, 0.68854243, 0.69939617, 0.71079551, 0.72282159,
        0.73557672, 0.74919294, 0.76384569, 0.77977671, 0.79733511,
        0.81705854, 0.8398553 , 0.86750307, 0.90461   ]])
于 2020-08-03T04:39:27.193 回答