Python PyTorch MixtureSameFamily用法及代碼示例

本文簡要介紹python語言中 torch.distributions.mixture_same_family.MixtureSameFamily 的用法。

用法:

class torch.distributions.mixture_same_family.MixtureSameFamily(mixture_distribution, component_distribution, validate_args=None)

參數：

• mixture_distribution-torch.distributions.Categorical 類似實例。管理選擇組件的概率。類別數必須與 component_distribution 的最右側批次維度匹配。必須有標量 batch_shape 或 batch_shape 匹配 component_distribution.batch_shape[:-1]

• component_distribution-torch.distributions.Distribution 類似實例。最右邊的批量維度索引組件。

基礎：torch.distributions.distribution.Distribution

MixtureSameFamily 分布實現了(一批)混合分布，其中所有分量都來自相同分布類型的不同參數化。它由 Categorical “selecting distribution”(在 k 組件之上)和組件分布參數化，即具有最右側批次形狀(等於 [k] )的 Distribution ，它索引每個(批次)組件。

例子：

# Construct Gaussian Mixture Model in 1D consisting of 5 equally
# weighted normal distributions
>>> mix = D.Categorical(torch.ones(5,))
>>> comp = D.Normal(torch.randn(5,), torch.rand(5,))
>>> gmm = MixtureSameFamily(mix, comp)

# Construct Gaussian Mixture Modle in 2D consisting of 5 equally
# weighted bivariate normal distributions
>>> mix = D.Categorical(torch.ones(5,))
>>> comp = D.Independent(D.Normal(
             torch.randn(5,2), torch.rand(5,2)), 1)
>>> gmm = MixtureSameFamily(mix, comp)

# Construct a batch of 3 Gaussian Mixture Models in 2D each
# consisting of 5 random weighted bivariate normal distributions
>>> mix = D.Categorical(torch.rand(3,5))
>>> comp = D.Independent(D.Normal(
            torch.randn(3,5,2), torch.rand(3,5,2)), 1)
>>> gmm = MixtureSameFamily(mix, comp)