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[ssgplvm] group spike

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This commit is contained in:
Zhenwen Dai 2015-05-21 11:33:37 +01:00
parent 7188e92efb
commit b8508cc20c
2 changed files with 39 additions and 14 deletions
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12
GPy/models/ss_gplvm.py
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@ -103,7 +103,7 @@ class SSGPLVM(SparseGP_MPI):
"""
def __init__(self, Y, input_dim, X=None, X_variance=None, Gamma=None, init='PCA', num_inducing=10,
Z=None, kernel=None, inference_method=None, likelihood=None, name='Spike_and_Slab GPLVM', group_spike=False, mpi_comm=None, pi=None, learnPi=True,normalizer=False, **kwargs):
Z=None, kernel=None, inference_method=None, likelihood=None, name='Spike_and_Slab GPLVM', group_spike=False, mpi_comm=None, pi=None, learnPi=False,normalizer=False, **kwargs):
self.group_spike = group_spike
@ -144,15 +144,12 @@ class SSGPLVM(SparseGP_MPI):
if pi is None:
pi = np.empty((input_dim))
pi[:] = 0.5
self.variational_prior = SpikeAndSlabPrior(pi=pi,learnPi=learnPi) # the prior probability of the latent binary variable b
self.variational_prior = SpikeAndSlabPrior(pi=pi,learnPi=learnPi, group_spike=group_spike) # the prior probability of the latent binary variable b
X = SpikeAndSlabPosterior(X, X_variance, gamma)
X = SpikeAndSlabPosterior(X, X_variance, gamma, group_spike=group_spike)
super(SSGPLVM,self).__init__(X, Y, Z, kernel, likelihood, variational_prior=self.variational_prior, inference_method=inference_method, name=name, mpi_comm=mpi_comm, normalizer=normalizer, **kwargs)
self.link_parameter(self.X, index=0)
if self.group_spike:
[self.X.gamma[:,i].tie('tieGamma'+str(i)) for i in range(self.X.gamma.shape[1])] # Tie columns together
def set_X_gradients(self, X, X_grad):
"""Set the gradients of the posterior distribution of X in its specific form."""
@ -163,8 +160,10 @@ class SSGPLVM(SparseGP_MPI):
return X.mean.gradient, X.variance.gradient, X.binary_prob.gradient
def parameters_changed(self):
self.X.propogate_val()
super(SSGPLVM,self).parameters_changed()
if isinstance(self.inference_method, VarDTC_minibatch):
self.X.collate_gradient()
return
self._log_marginal_likelihood -= self.variational_prior.KL_divergence(self.X)
@ -173,6 +172,7 @@ class SSGPLVM(SparseGP_MPI):
# update for the KL divergence
self.variational_prior.update_gradients_KL(self.X)
self.X.collate_gradient()
def input_sensitivity(self):
if self.kern.ARD: