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sparse gp missing data

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Max Zwiessele 2014-02-26 08:23:08 +00:00
parent 51dca0fcbc
commit b928044f40
4 changed files with 22 additions and 17 deletions
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25
GPy/core/sparse_gp.py
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@ -46,28 +46,33 @@ class SparseGP(GP):
self.Z = Param('inducing inputs', Z)
self.num_inducing = Z.shape[0]
self.q = NormalPosterior(X, X_variance)
GP.__init__(self, self.q.mean, Y, kernel, likelihood, inference_method=inference_method, name=name)
if not (X_variance is None):
self.q = NormalPosterior(X, X_variance)
GP.__init__(self, self.q.mean, Y, kernel, likelihood, inference_method=inference_method, name=name)
else:
self.X = X
GP.__init__(self, X, Y, kernel, likelihood, inference_method=inference_method, name=name)
self.add_parameter(self.Z, index=0)
self.parameters_changed()
def has_uncertain_inputs(self):
return self.q.has_uncertain_inputs()
return hasattr(self, 'q')
def parameters_changed(self):
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():
# gradients
self.likelihood.update_gradients(self.grad_dict.pop('partial_for_likelihood'))
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.posterior, self._log_marginal_likelihood, self.grad_dict = self.inference_method.inference(self.kern, self.X, None, self.Z, self.likelihood, self.Y)
# gradients
self.likelihood.update_gradients(self.grad_dict.pop('partial_for_likelihood'))
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)
def _raw_predict(self, Xnew, X_variance_new=None, full_cov=False):
"""
Make a prediction for the latent function values