Source code for alibi_detect.models.pytorch.gmm

from torch import nn
import torch

[docs]class GMMModel(nn.Module):
[docs] def __init__(self, n_components: int, dim: int) -> None: """Gaussian Mixture Model (GMM). Parameters ---------- n_components The number of mixture components. dim The dimensionality of the data. """ super().__init__() self.weight_logits = nn.Parameter(torch.zeros(n_components)) self.means = nn.Parameter(torch.randn(n_components, dim)) self.inv_cov_factor = nn.Parameter(torch.randn(n_components, dim, dim)/10)
@property def _inv_cov(self) -> torch.Tensor: return torch.bmm(self.inv_cov_factor, self.inv_cov_factor.transpose(1, 2)) @property def _weights(self) -> torch.Tensor: return nn.functional.softmax(self.weight_logits, dim=0)
[docs] def forward(self, x: torch.Tensor) -> torch.Tensor: """Compute the log-likelihood of the data. Parameters ---------- x Data to score. """ det = torch.linalg.det(self._inv_cov) # Note det(A^-1)=1/det(A) to_means = x[:, None, :] - self.means[None, :, :] likelihood = ((-0.5 * ( torch.einsum('bke,bke->bk', (torch.einsum('bkd,kde->bke', to_means, self._inv_cov), to_means)) )).exp()*det[None, :]*self._weights[None, :]).sum(-1) return -likelihood.log()