09.编程理解无偏性与相合性

编程理解无偏性与相合性

无偏性

 1 from statistics import mean
 2 from statistics import variance
 3 import random
 4 import matplotlib.pyplot as plt
 5 
 6 
 7 def variance_bias(data):
 8     """方差"""
 9     n = len(data)
10     if n <= 1:
11         return None
12 
13     mean_value = mean(data)
14     return sum((e - mean_value) ** 2 for e in data) / n
15 
16 
17 def sample(num_of_samples, sample_sz, var):
18     data = []
19     for _ in range(num_of_samples):
20         data.append(var([random.uniform(0.0, 1.0) for _ in range(sample_sz)]))
21     return data
22 
23 
24 if __name__ == "__main__":
25 
26     data1 = sample(1000, 40, variance_bias)
27     plt.hist(data1, bins="auto", rwidth=0.8)
28     plt.axvline(x=mean(data1), c='black')
29     plt.axvline(x=1/12, c='red')
30     print("bias :", mean(data1), 1/12)
31     plt.show()
32 
33     data2 = sample(1000, 40, variance)
34     plt.hist(data2, bins="auto", rwidth=0.8)
35     plt.axvline(x=mean(data2), c='black')
36     plt.axvline(x=1/12, c='red')
37     print("unbias :", mean(data2), 1/12)
38     plt.show()

 

 

 相合性

import random
from statistics import mean, variance
import matplotlib.pyplot as plt

if __name__ == "__main__":

    sample_means = []
    sample_vars = []
    indices = []

    for sz in range(20, 10001, 50):
        indices.append(sz)
        sample = [random.gauss(0.0, 1.0) for _ in range(sz)]
        sample_means.append(mean(sample))
        sample_vars.append(variance(sample))

    plt.plot(indices, sample_means)
    plt.plot(indices, sample_vars)
    plt.show()

 

posted @ 2020-12-15 23:10  止一  阅读(287)  评论(0)  编辑  收藏  举报