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adapt the new interface of the variational posterior distribution.

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Zhenwen Dai 2014-02-21 17:56:37 +00:00
parent 34d9f90d92
commit 99c6a2095f
7 changed files with 96 additions and 389 deletions
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9
GPy/core/sparse_gp.py
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@ -57,11 +57,14 @@ class SparseGP(GP):
return not (self.X_variance is None)
def parameters_changed(self):
self.posterior, self._log_marginal_likelihood, self.grad_dict = self.inference_method.inference(self.kern, self.X, self.X_variance, self.Z, self.likelihood, self.Y)
if self.has_uncertain_inputs():
self.posterior, self._log_marginal_likelihood, self.grad_dict = self.inference_method.inference_latent(self.kern, self.q, self.Z, self.likelihood, self.Y)
else:
self.posterior, self._log_marginal_likelihood, self.grad_dict = self.inference_method.inference(self.kern, self.X, self.X_variance, self.Z, self.likelihood, self.Y)
self.likelihood.update_gradients(self.grad_dict.pop('partial_for_likelihood'))
if self.has_uncertain_inputs():
self.kern.update_gradients_variational(mu=self.X, S=self.X_variance, Z=self.Z, **self.grad_dict)
self.Z.gradient = self.kern.gradients_Z_variational(mu=self.X, S=self.X_variance, Z=self.Z, **self.grad_dict)
self.kern.update_gradients_variational(posterior_variational=self.q, Z=self.Z, **self.grad_dict)
self.Z.gradient = self.kern.gradients_Z_variational(posterior_variational=self.q, Z=self.Z, **self.grad_dict)
else:
self.kern.update_gradients_sparse(X=self.X, Z=self.Z, **self.grad_dict)
self.Z.gradient = self.kern.gradients_Z_sparse(X=self.X, Z=self.Z, **self.grad_dict)