()
| 686 | assert np.all(coder.decode(model, 4) == symbols) |
| 687 | |
| 688 | def variable_model_params(): |
| 689 | # The optional argument `params` will receive a 1-d python array when |
| 690 | # the model is used for encoding or decoding. |
| 691 | model = constriction.stream.model.CustomModel( |
| 692 | lambda x, loc, scale: scipy.stats.cauchy.cdf(x, loc, scale), |
| 693 | lambda x, loc, scale: scipy.stats.cauchy.ppf(x, loc, scale), |
| 694 | -100, 100) |
| 695 | |
| 696 | model_parameters = np.array([ |
| 697 | (7.3, 3.9), # Location and scale of entropy model for 1st symbol. |
| 698 | (11.5, 5.2), # Location and scale of entropy model for 2nd symbol. |
| 699 | (-3.2, 4.9), # and so on ... |
| 700 | (25.9, 7.1), |
| 701 | ]) |
| 702 | |
| 703 | symbols = np.array([5, 14, -1, 21], dtype=np.int32) |
| 704 | coder = constriction.stream.stack.AnsCoder() |
| 705 | coder.encode_reverse( |
| 706 | symbols, model, model_parameters[:, 0].copy(), model_parameters[:, 1].copy()) |
| 707 | assert np.all( |
| 708 | coder.decode(model, model_parameters[:, 0].copy(), model_parameters[:, 1].copy()) == symbols) |
| 709 | |
| 710 | def discrete_distribution(): |
| 711 | model = constriction.stream.model.CustomModel( |
no test coverage detected