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Changes to allow compatibility with mixed noise likelihoods

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Ricardo 2014-03-17 18:58:31 +00:00
parent 7fdecf5e31
commit 15901a48d4
1 changed files with 3 additions and 4 deletions
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7
GPy/core/sparse_gp.py
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@ -31,7 +31,7 @@ class SparseGP(GP):
""" """
def __init__(self, X, Y, Z, kernel, likelihood, inference_method=None, name='sparse gp'): def __init__(self, X, Y, Z, kernel, likelihood, inference_method=None, name='sparse gp', Y_metadata=None):
#pick a sensible inference method #pick a sensible inference method
if inference_method is None: if inference_method is None:
@ -45,7 +45,7 @@ class SparseGP(GP):
self.Z = Param('inducing inputs', Z) self.Z = Param('inducing inputs', Z)
self.num_inducing = Z.shape[0] self.num_inducing = Z.shape[0]
GP.__init__(self, X, Y, kernel, likelihood, inference_method=inference_method, name=name) GP.__init__(self, X, Y, kernel, likelihood, inference_method=inference_method, name=name, Y_metadata=Y_metadata)
self.add_parameter(self.Z, index=0) self.add_parameter(self.Z, index=0)
@ -53,7 +53,7 @@ class SparseGP(GP):
return isinstance(self.X, VariationalPosterior) return isinstance(self.X, VariationalPosterior)
def parameters_changed(self): def parameters_changed(self):
self.posterior, self._log_marginal_likelihood, self.grad_dict = self.inference_method.inference(self.kern, self.X, self.Z, self.likelihood, self.Y) self.posterior, self._log_marginal_likelihood, self.grad_dict = self.inference_method.inference(self.kern, self.X, self.Z, self.likelihood, self.Y, self.Y_metadata)
self.likelihood.update_gradients(self.grad_dict['dL_dthetaL']) self.likelihood.update_gradients(self.grad_dict['dL_dthetaL'])
if isinstance(self.X, VariationalPosterior): if isinstance(self.X, VariationalPosterior):
#gradients wrt kernel #gradients wrt kernel
@ -75,7 +75,6 @@ class SparseGP(GP):
target += self.kern.gradient target += self.kern.gradient
self.kern.update_gradients_full(self.grad_dict['dL_dKmm'], self.Z, None) self.kern.update_gradients_full(self.grad_dict['dL_dKmm'], self.Z, None)
self.kern.gradient += target self.kern.gradient += target
#gradients wrt Z #gradients wrt Z
self.Z.gradient[:,self.kern.active_dims] = self.kern.gradients_X(self.grad_dict['dL_dKmm'], self.Z) self.Z.gradient[:,self.kern.active_dims] = self.kern.gradients_X(self.grad_dict['dL_dKmm'], self.Z)
self.Z.gradient[:,self.kern.active_dims] += self.kern.gradients_X(self.grad_dict['dL_dKnm'].T, self.Z, self.X) self.Z.gradient[:,self.kern.active_dims] += self.kern.gradients_X(self.grad_dict['dL_dKnm'].T, self.Z, self.X)