| from torch.nn.functional import cosine_similarity | |
| def cor_distance(x, y, eps=1e-12): | |
| # Analogous to L1 distance, but in terms of Pearson's correlation | |
| return (1.0 - cosine_similarity(x, y, eps=eps)).sqrt().mean() | |
| def cor_square_error(x, y, eps=1e-12): | |
| # Analogous to MSE, but in terms of Pearson's correlation | |
| return (1.0 - cosine_similarity(x, y, eps=eps)).mean() | |