|
21 | 21 |
|
22 | 22 | import pymc as pm |
23 | 23 |
|
24 | | -from pymc.gp.cov import Constant, Covariance |
| 24 | +from pymc.gp.cov import BaseCovariance, Constant |
25 | 25 | from pymc.gp.mean import Zero |
26 | 26 | from pymc.gp.util import ( |
27 | 27 | JITTER_DEFAULT, |
@@ -483,7 +483,7 @@ def marginal_likelihood( |
483 | 483 | """ |
484 | 484 | sigma = _handle_sigma_noise_parameters(sigma=sigma, noise=noise) |
485 | 485 |
|
486 | | - noise_func = sigma if isinstance(sigma, Covariance) else pm.gp.cov.WhiteNoise(sigma) |
| 486 | + noise_func = sigma if isinstance(sigma, BaseCovariance) else pm.gp.cov.WhiteNoise(sigma) |
487 | 487 | mu, cov = self._build_marginal_likelihood(X=X, noise_func=noise_func, jitter=jitter) |
488 | 488 | self.X = X |
489 | 489 | self.y = y |
@@ -515,7 +515,7 @@ def _get_given_vals(self, given): |
515 | 515 |
|
516 | 516 | if all(val in given for val in ["X", "y", "sigma"]): |
517 | 517 | X, y, sigma = given["X"], given["y"], given["sigma"] |
518 | | - noise_func = sigma if isinstance(sigma, Covariance) else pm.gp.cov.WhiteNoise(sigma) |
| 518 | + noise_func = sigma if isinstance(sigma, BaseCovariance) else pm.gp.cov.WhiteNoise(sigma) |
519 | 519 | else: |
520 | 520 | X, y, noise_func = self.X, self.y, self.sigma |
521 | 521 | return X, y, noise_func, cov_total, mean_total |
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