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[vardtc] missing data handling and stochastic update in d

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Max Zwiessele 2014-10-16 12:52:17 +01:00
parent 3358d06e42
commit 26396939e5
6 changed files with 124 additions and 37 deletions
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20
GPy/models/bayesian_gplvm.py
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@ -27,7 +27,7 @@ class BayesianGPLVM(SparseGP_MPI):
def __init__(self, Y, input_dim, X=None, X_variance=None, init='PCA', num_inducing=10,
Z=None, kernel=None, inference_method=None, likelihood=None,
name='bayesian gplvm', mpi_comm=None, normalizer=None,
missing_data=False):
missing_data=False, stochastic=False, batchsize=1):
self.mpi_comm = mpi_comm
self.__IN_OPTIMIZATION__ = False
@ -77,7 +77,8 @@ class BayesianGPLVM(SparseGP_MPI):
name=name, inference_method=inference_method,
normalizer=normalizer, mpi_comm=mpi_comm,
variational_prior=self.variational_prior,
missing_data=missing_data)
missing_data=missing_data, stochastic=stochastic,
batchsize=batchsize)
def set_X_gradients(self, X, X_grad):
"""Set the gradients of the posterior distribution of X in its specific form."""
@ -90,7 +91,12 @@ class BayesianGPLVM(SparseGP_MPI):
def _inner_parameters_changed(self, kern, X, Z, likelihood, Y, Y_metadata, Lm=None, dL_dKmm=None, subset_indices=None):
posterior, log_marginal_likelihood, grad_dict, current_values, value_indices = super(BayesianGPLVM, self)._inner_parameters_changed(kern, X, Z, likelihood, Y, Y_metadata, Lm=Lm, dL_dKmm=dL_dKmm, subset_indices=subset_indices)
log_marginal_likelihood -= self.variational_prior.KL_divergence(X)
kl_fctr = 1.
if self.missing_data:
d = self.output_dim
log_marginal_likelihood -= kl_fctr*self.variational_prior.KL_divergence(X)/d
else:
log_marginal_likelihood -= kl_fctr*self.variational_prior.KL_divergence(X)
current_values['meangrad'], current_values['vargrad'] = self.kern.gradients_qX_expectations(
variational_posterior=X,
@ -104,8 +110,12 @@ class BayesianGPLVM(SparseGP_MPI):
X.variance.gradient[:] = 0
self.variational_prior.update_gradients_KL(X)
current_values['meangrad'] += X.mean.gradient
current_values['vargrad'] += X.variance.gradient
if self.missing_data:
current_values['meangrad'] += kl_fctr*X.mean.gradient/d
current_values['vargrad'] += kl_fctr*X.variance.gradient/d
else:
current_values['meangrad'] += kl_fctr*X.mean.gradient
current_values['vargrad'] += kl_fctr*X.variance.gradient
if subset_indices is not None:
value_indices['meangrad'] = subset_indices['samples']