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beginning of bgplvm with missing data

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Max Zwiessele 2014-02-17 09:03:44 +00:00
parent 20f749ff0d
commit 825d3c2154
2 changed files with 43 additions and 8 deletions
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14
GPy/core/sparse_gp.py
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@ -54,19 +54,21 @@ class SparseGP(GP):
self.add_parameter(self.Z, index=0)
self.parameters_changed()
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)
#The derivative of the bound wrt the inducing inputs Z ( unless they're all fixed)
def _update_gradients_Z(self, add=False):
#The derivative of the bound wrt the inducing inputs Z ( unless they're all fixed)
if not self.Z.is_fixed:
self.Z.gradient = self.kern.gradients_X(self.grad_dict['dL_dKmm'], self.Z)
if add: self.Z.gradient += self.kern.gradients_X(self.grad_dict['dL_dKmm'], self.Z)
else: self.Z.gradient = self.kern.gradients_X(self.grad_dict['dL_dKmm'], self.Z)
if self.X_variance is None:
self.Z.gradient += self.kern.gradients_X(self.grad_dict['dL_dKnm'].T, self.Z, self.X)
else:
self.Z.gradient += self.kern.dpsi1_dZ(self.grad_dict['dL_dpsi1'], self.Z, self.X, self.X_variance)
self.Z.gradient += self.kern.dpsi2_dZ(self.grad_dict['dL_dpsi2'], self.Z, self.X, self.X_variance)
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)
self._update_gradients_Z(add=False)
def _raw_predict(self, Xnew, X_variance_new=None, which_parts='all', full_cov=False):
"""
Make a prediction for the latent function values