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Made sparse gp work again

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This commit is contained in:
Alan Saul 2015-08-28 10:07:19 +03:00
parent 5f119d0918
commit afba8d5c5e
2 changed files with 12 additions and 8 deletions
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2
GPy/models/bayesian_gplvm_minibatch.py
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@ -123,6 +123,8 @@ class BayesianGPLVMMiniBatch(SparseGPMiniBatch):
def _outer_init_full_values(self): def _outer_init_full_values(self):
full_values = super(BayesianGPLVMMiniBatch, self)._outer_init_full_values() full_values = super(BayesianGPLVMMiniBatch, self)._outer_init_full_values()
full_values['dL_dpsi0'] = np.zeros(self.X.shape[0])
full_values['dL_dpsi1'] = np.zeros((self.X.shape[0], self.Z.shape[0]))
return full_values return full_values
def parameters_changed(self): def parameters_changed(self):

14
GPy/models/sparse_gp_minibatch.py
View file

@ -214,8 +214,8 @@ class SparseGPMiniBatch(SparseGP):
to initialize the gradients for the mean and the variance in order to to initialize the gradients for the mean and the variance in order to
have the full gradient for indexing) have the full gradient for indexing)
""" """
return {'dL_dpsi0': np.zeros(self.X.shape[0]), return {'dL_dKdiag': np.zeros(self.X.shape[0]),
'dL_dpsi1': np.zeros((self.X.shape[0], self.Z.shape[0]))} 'dL_dKnm': np.zeros((self.X.shape[0], self.Z.shape[0]))}
def _outer_loop_for_missing_data(self): def _outer_loop_for_missing_data(self):
Lm = None Lm = None
@ -243,10 +243,8 @@ class SparseGPMiniBatch(SparseGP):
psi1 = self.kern.psi1(self.Z, self.X) psi1 = self.kern.psi1(self.Z, self.X)
psi2 = self.kern.psi2(self.Z, self.X) psi2 = self.kern.psi2(self.Z, self.X)
else: else:
if psi0 is None: psi0 = self.kern.Kdiag(self.X)
psi0 = kern.Kdiag(X) psi1 = self.kern.K(self.X, self.Z)
if psi1 is None:
psi1 = kern.K(X, Z)
psi2 = None psi2 = None
self.psi0 = psi0 self.psi0 = psi0
@ -261,8 +259,12 @@ class SparseGPMiniBatch(SparseGP):
psi0ni = psi0[ninan] psi0ni = psi0[ninan]
psi1ni = psi1[ninan] psi1ni = psi1[ninan]
if self.has_uncertain_inputs():
psi2ni = psi2[ninan] psi2ni = psi2[ninan]
value_indices = dict(outputs=d, samples=ninan, dL_dpsi0=ninan, dL_dpsi1=ninan) value_indices = dict(outputs=d, samples=ninan, dL_dpsi0=ninan, dL_dpsi1=ninan)
else:
psi2ni = None
value_indices = dict(outputs=d, samples=ninan, dL_dKdiag=ninan, dL_dKnm=ninan)
posterior, log_marginal_likelihood, grad_dict = self._inner_parameters_changed( posterior, log_marginal_likelihood, grad_dict = self._inner_parameters_changed(
self.kern, self.X[ninan], self.kern, self.X[ninan],