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[MRD] fixed mrd for new structure

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Max Zwiessele 2015-09-04 16:43:26 +01:00
parent 41cc9c62f5
commit 87ee4ae548
2 changed files with 10 additions and 10 deletions
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5
GPy/models/bayesian_gplvm_minibatch.py
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@ -107,8 +107,7 @@ class BayesianGPLVMMiniBatch(SparseGPMiniBatch):
self.X.gradient += self.kern.gradients_X_diag(full_values['dL_dKdiag'], self.X) self.X.gradient += self.kern.gradients_X_diag(full_values['dL_dKdiag'], self.X)
def _outer_init_full_values(self): def _outer_init_full_values(self):
full_values = super(BayesianGPLVMMiniBatch, self)._outer_init_full_values() return super(BayesianGPLVMMiniBatch, self)._outer_init_full_values()
return full_values
def parameters_changed(self): def parameters_changed(self):
super(BayesianGPLVMMiniBatch,self).parameters_changed() super(BayesianGPLVMMiniBatch,self).parameters_changed()
@ -134,6 +133,8 @@ class BayesianGPLVMMiniBatch(SparseGPMiniBatch):
d = self.output_dim d = self.output_dim
self._log_marginal_likelihood -= kl_fctr*self.variational_prior.KL_divergence(self.X)*self.stochastics.batchsize/d self._log_marginal_likelihood -= kl_fctr*self.variational_prior.KL_divergence(self.X)*self.stochastics.batchsize/d
self._Xgrad = self.X.gradient.copy()
def plot_latent(self, labels=None, which_indices=None, def plot_latent(self, labels=None, which_indices=None,
resolution=50, ax=None, marker='o', s=40, resolution=50, ax=None, marker='o', s=40,
fignum=None, plot_inducing=True, legend=True, fignum=None, plot_inducing=True, legend=True,

15
GPy/models/mrd.py
View file

@ -174,16 +174,15 @@ class MRD(BayesianGPLVMMiniBatch):
#grad_dict = b.full_values #grad_dict = b.full_values
if self.has_uncertain_inputs(): if self.has_uncertain_inputs():
self.X.mean.gradient += b.X.mean.gradient self.X.gradient += b._Xgrad
self.X.variance.gradient += b.X.variance.gradient
else: else:
self.X.gradient += b.X.gradient self.X.gradient += b._Xgrad
if self.has_uncertain_inputs(): #if self.has_uncertain_inputs():
# update for the KL divergence # # update for the KL divergence
self.variational_prior.update_gradients_KL(self.X) # self.variational_prior.update_gradients_KL(self.X)
self._log_marginal_likelihood -= self.variational_prior.KL_divergence(self.X) # self._log_marginal_likelihood -= self.variational_prior.KL_divergence(self.X)
pass # pass
def log_likelihood(self): def log_likelihood(self):
return self._log_marginal_likelihood return self._log_marginal_likelihood