(activation, multimodality_times)
| 82 | |
| 83 | |
| 84 | def calculate_multimodality(activation, multimodality_times): |
| 85 | assert len(activation.shape) == 3 |
| 86 | assert activation.shape[1] > multimodality_times |
| 87 | num_per_sent = activation.shape[1] |
| 88 | |
| 89 | first_dices = np.random.choice(num_per_sent, multimodality_times, replace=False) |
| 90 | second_dices = np.random.choice(num_per_sent, multimodality_times, replace=False) |
| 91 | dist = linalg.norm(activation[:, first_dices] - activation[:, second_dices], axis=2) |
| 92 | return dist.mean() |
| 93 | |
| 94 | |
| 95 | def calculate_frechet_distance(mu1, sigma1, mu2, sigma2, eps=1e-6): |
no outgoing calls
no test coverage detected