(self, x, y, gamma=[0.001, 0.01, 0.1, 1, 10, 100, 1000])
| 604 | return res.clamp_min_(1e-30) |
| 605 | |
| 606 | def gaussian_kernel(self, x, y, gamma=[0.001, 0.01, 0.1, 1, 10, 100, 1000]): |
| 607 | D = self.my_cdist(x, y) |
| 608 | K = torch.zeros_like(D) |
| 609 | |
| 610 | for g in gamma: |
| 611 | K.add_(torch.exp(D.mul(-g))) |
| 612 | |
| 613 | return K |
| 614 | |
| 615 | def mmd(self, x, y): |
| 616 | if self.kernel_type == "gaussian": |