torch.distribution
时间: 2023-11-07 15:04:36 浏览: 36
torch.distributions是PyTorch中用于概率分布建模的模块。它提供了各种常见的概率分布(如正态分布、均匀分布、二项分布等)的实现,以及计算分布的概率密度函数、采样等功能。其中,torch.distributions.exp_family.ExponentialFamily是一个用于表示指数族分布的基类。指数族分布是一类重要的概率分布,包括了众多常见的分布,比如高斯分布、泊松分布、伽马分布等。
另外,torch.distributions.chi2.Chi2是一个用于表示卡方分布的类。卡方分布是统计学中经常使用的分布之一,它在假设检验和拟合优度检验等场景中有广泛应用。
在创建分类分布时,可以使用torch.distributions.Categorical类。在这个类的构造函数中,传入一个包含概率值的张量作为输入,可以指定不同类别的概率。然后可以使用enumerate_support()方法来获取类别的索引,使用probs属性来获取归一化后的概率值。
相关问题
torch.normal
torch.normal is a PyTorch function that generates a tensor of random numbers from a normal distribution with a mean and standard deviation specified by the user.
The syntax for torch.normal is as follows:
torch.normal(mean, std, out=None)
- mean: the mean of the normal distribution. This can be a scalar or a tensor with the same shape as the desired output tensor.
- std: the standard deviation of the normal distribution. This can be a scalar or a tensor with the same shape as the desired output tensor.
- out (optional): the output tensor. If provided, the random numbers will be generated into this tensor rather than a new one.
Example usage:
```python
import torch
# Generate a tensor of size (3, 4) with random numbers from a normal distribution
# with mean 0 and standard deviation 1
x = torch.normal(0, 1, size=(3, 4))
print(x)
```
Output:
```
tensor([[-1.0262, -0.7695, 0.1677, -0.0408],
[-1.2485, -1.4555, 0.2709, 0.2705],
[-1.6543, -1.0819, 1.0578, -0.3285]])
```
torch.bernoulli
torch.bernoulli is a function in PyTorch that generates samples from a Bernoulli distribution with a given probability of success (or "1" outcome). The function takes a tensor of probabilities as input and returns a tensor of binary values (0 or 1) sampled according to the Bernoulli distribution with the given probabilities.
For example, if we wanted to generate a tensor of 10 binary values with a probability of success of 0.7, we could use the following code:
```
import torch
probs = torch.tensor([0.7]) # probability of success
samples = torch.bernoulli(probs.repeat(10)) # generate 10 samples
print(samples)
```
This would output a tensor of 10 binary values sampled from a Bernoulli distribution with a probability of success of 0.7:
```
tensor([1., 1., 1., 0., 1., 0., 1., 1., 1., 1.])
```