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[ssmrd] implement with IBP prior

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This commit is contained in:
Zhenwen Dai 2015-05-22 14:29:53 +01:00
parent afa0621488
commit b5cac9af8c
3 changed files with 262 additions and 27 deletions
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10
GPy/core/parameterization/variational.py
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@ -80,7 +80,7 @@ class SpikeAndSlabPrior(VariationalPrior):
pi = self.pi
if self.group_spike:
dgamma = np.log((1-pi)/pi*gamma/(1.-gamma))/mu.shape[0]
dgamma = np.log((1-pi)/pi*gamma/(1.-gamma))/variational_posterior.num_data
else:
dgamma = np.log((1-pi)/pi*gamma/(1.-gamma))
variational_posterior.binary_prob.gradient -= dgamma+((np.square(mu)+S)/self.variance-np.log(S)+np.log(self.variance)-1.)/2.
@ -165,12 +165,16 @@ class SpikeAndSlabPosterior(VariationalPosterior):
'''
The SpikeAndSlab distribution for variational approximations.
'''
def __init__(self, means, variances, binary_prob, group_spike=False, name='latent space'):
def __init__(self, means, variances, binary_prob, group_spike=False, sharedX=False, name='latent space'):
"""
binary_prob : the probability of the distribution on the slab part.
"""
super(SpikeAndSlabPosterior, self).__init__(means, variances, name)
self.group_spike = group_spike
self.sharedX = sharedX
if sharedX:
self.mean.fix(warning=False)
self.variance.fix(warning=False)
if group_spike:
self.gamma_group = Param("binary_prob_group",binary_prob.mean(axis=0),Logistic(1e-6,1.-1e-6))
self.gamma = Param("binary_prob",binary_prob, __fixed__)
@ -181,7 +185,7 @@ class SpikeAndSlabPosterior(VariationalPosterior):
def propogate_val(self):
if self.group_spike:
self.gamma.param_array.values.reshape(self.gamma.shape)[:] = self.gamma_group.values
self.gamma.values[:] = self.gamma_group.values
def collate_gradient(self):
if self.group_spike: