1 # -*- coding: utf-8 -*-
 2 # Author：凯鲁嘎吉 Coral Gajic
 3 # https://www.cnblogs.com/kailugaji/
 4 # Python小练习：从正态分布中采样
 5 import torch
 6 from torch.distributions import Normal
 7 def my_normal(means, std, str):
 8     dist = Normal(means, std)
 9     # Normal(loc: torch.Size([10]), scale: torch.Size([10]))
10     if str == 'deterministic':
11         out_pred = means
12         # 如果是确定性采样，均值就是输出
13     elif str == 'reparameterize':
14         # 重参数化技巧
15         out_pred = dist.rsample()
16         # rsample()不是在定义的正态分布上采样
17         # 而是mean+std×采样值eps，其中eps~N(0, I)
18     else:
19         out_pred = dist.sample()
20         # 直接从定义的正态分布（均值为mean，标准差std）上采样
21     return out_pred
22 
23 str = ['deterministic', 'reparameterize', 'sample']
24 means = torch.arange(1, 11) * 1.0
25 # tensor([ 1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10.])
26 std = torch.arange(1, 0, -0.1)
27 # tensor([1.0000, 0.9000, 0.8000, 0.7000, 0.6000, 0.5000, 0.4000, 0.3000, 0.2000, 0.1000])
28 print('均值：\n', means)
29 print('方差：\n', std)
30 for i in str:
31     out_pred = my_normal(means, std, i)
32     print(i, '下的采样结果：\n', out_pred)

D:\ProgramData\Anaconda3\python.exe "D:/Python code/2023.3 exercise/Other/normal_test.py"
均值：
 tensor([ 1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10.])
方差：
 tensor([1.0000, 0.9000, 0.8000, 0.7000, 0.6000, 0.5000, 0.4000, 0.3000, 0.2000,
        0.1000])
deterministic 下的采样结果：
 tensor([ 1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10.])
reparameterize 下的采样结果：
 tensor([1.7890, 1.6882, 2.9405, 3.5366, 4.5679, 6.0867, 6.7917, 8.0406, 9.0113,
        9.9845])
sample 下的采样结果：
 tensor([0.4161, 1.5916, 3.0199, 3.5160, 4.6002, 5.7837, 7.1503, 8.2893, 8.7639,
        9.9661])

Process finished with exit code 0