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Try calculating dL_dpsi1*psi1 individually for each dimension as we go along

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This commit is contained in:
Alan Saul 2015-08-31 14:13:35 +03:00
parent c83f56723e
commit 3818aa3745
7 changed files with 47 additions and 31 deletions
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8
GPy/models/bayesian_gplvm_minibatch.py
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@ -126,7 +126,8 @@ class BayesianGPLVMMiniBatch(SparseGPMiniBatch):
Z=self.Z, dL_dpsi0=full_values['dL_dpsi0'],
dL_dpsi1=full_values['dL_dpsi1'],
dL_dpsi2=full_values['dL_dpsi2'],
psi0=self.psi0, psi1=self.psi1, psi2=self.psi2)
psi0=self.psi0, psi1=self.psi1, psi2=self.psi2,
Lpsi0=full_values['Lpsi0'], Lpsi1=full_values['Lpsi1'], Lpsi2=full_values['Lpsi2'])
full_values['meangrad'] += meangrad_tmp
full_values['vargrad'] += vargrad_tmp
else:
@ -156,6 +157,11 @@ class BayesianGPLVMMiniBatch(SparseGPMiniBatch):
full_values['vargrad'] = np.zeros((self.X.shape[0], self.X.shape[1]))
full_values['dL_dpsi0'] = np.zeros(self.X.shape[0])
full_values['dL_dpsi1'] = np.zeros((self.X.shape[0], self.Z.shape[0]))
full_values['dL_dpsi2'] = np.zeros((self.Z.shape[0], self.Z.shape[0]))
full_values['Lpsi0'] = np.zeros(self.X.shape[0])
full_values['Lpsi1'] = np.zeros((self.X.shape[0], self.Z.shape[0]))
full_values['Lpsi2'] = np.zeros((self.X.shape[0], self.Z.shape[0], self.Z.shape[0]))
return full_values
def parameters_changed(self):

14
GPy/models/sparse_gp_minibatch.py
View file

@ -106,6 +106,10 @@ class SparseGPMiniBatch(SparseGP):
posterior, log_marginal_likelihood, grad_dict = self.inference_method.inference(kern, X, Z, likelihood, Y, Y_metadata, Lm=Lm,
dL_dKmm=dL_dKmm, psi0=psi0, psi1=psi1, psi2=psi2_sum_n, **kwargs)
if self.has_uncertain_inputs():
grad_dict['Lpsi0'] = grad_dict['dL_dpsi0']*psi0
grad_dict['Lpsi1'] = grad_dict['dL_dpsi1']*psi1
grad_dict['Lpsi2'] = grad_dict['dL_dpsi2']*psi2
return posterior, log_marginal_likelihood, grad_dict
def _inner_take_over_or_update(self, full_values=None, current_values=None, value_indices=None):
@ -172,7 +176,8 @@ class SparseGPMiniBatch(SparseGP):
Z=self.Z, dL_dpsi0=full_values['dL_dpsi0'],
dL_dpsi1=full_values['dL_dpsi1'],
dL_dpsi2=full_values['dL_dpsi2'],
psi0=self.psi0, psi1=self.psi1, psi2=self.psi2)
psi0=self.psi0, psi1=self.psi1, psi2=self.psi2,
Lpsi0=full_values['Lpsi0'], Lpsi1=full_values['Lpsi1'], Lpsi2=full_values['Lpsi2'])
#self.kern.update_gradients_expectations(variational_posterior=self.X,
#Z=self.Z,
#dL_dpsi0=full_values['dL_dpsi0'],
@ -187,7 +192,8 @@ class SparseGPMiniBatch(SparseGP):
Z=self.Z, dL_dpsi0=full_values['dL_dpsi0'],
dL_dpsi1=full_values['dL_dpsi1'],
dL_dpsi2=full_values['dL_dpsi2'],
psi0=self.psi0, psi1=self.psi1, psi2=self.psi2)
psi0=self.psi0, psi1=self.psi1, psi2=self.psi2,
Lpsi0=full_values['Lpsi0'], Lpsi1=full_values['Lpsi1'], Lpsi2=full_values['Lpsi2'])
else:
#gradients wrt kernel
self.kern.update_gradients_diag(full_values['dL_dKdiag'], self.X)
@ -267,7 +273,9 @@ class SparseGPMiniBatch(SparseGP):
psi1ni = psi1[ninan]
if self.has_uncertain_inputs():
psi2ni = psi2[ninan]
value_indices = dict(outputs=d, samples=ninan, dL_dpsi0=ninan, dL_dpsi1=ninan, meangrad=ninan, vargrad=ninan)
#value_indices = dict(outputs=d, samples=ninan, dL_dpsi0=ninan, dL_dpsi1=ninan, meangrad=ninan, vargrad=ninan)
value_indices = dict(outputs=d, samples=ninan, dL_dpsi0=ninan, dL_dpsi1=ninan, meangrad=ninan, vargrad=ninan,
Lpsi0=ninan, Lpsi1=ninan, Lpsi2=ninan)
else:
psi2ni = None
value_indices = dict(outputs=d, samples=ninan, dL_dKdiag=ninan, dL_dKnm=ninan)