(self, num_samples)
| 54 | |
| 55 | @torch.no_grad() |
| 56 | def sampling(self, num_samples): |
| 57 | m = torch.distributions.Categorical(self.w) |
| 58 | idx = m.sample((num_samples,)) |
| 59 | return self.mu[idx, :] + torch.randn(num_samples, self.D).to( |
| 60 | self.w.device) * self.sigma[idx, :] |
| 61 | |
| 62 | @torch.no_grad() |
| 63 | def langevin_sampling(self, x, num_steps=10, eta=1.0e+0, is_anneal=False): |
no outgoing calls
no test coverage detected