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[SSGPLVM] migrate SSGPLVM to params branch

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This commit is contained in:
Zhenwen Dai 2014-02-25 16:09:26 +00:00
parent 70ada7fa46
commit 0e13be48f1
3 changed files with 35 additions and 4 deletions
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37
GPy/core/parameterization/variational.py
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@ -9,7 +9,10 @@ from parameterized import Parameterized
from param import Param
from transformations import Logexp
class VariationalPrior(object):
class VariationalPrior(Parameterized):
def __init__(self, name=None, **kw):
super(VariationalPrior, self).__init__(name=name, **kw)
def KL_divergence(self, variational_posterior):
raise NotImplementedError, "override this for variational inference of latent space"
@ -19,7 +22,7 @@ class VariationalPrior(object):
"""
raise NotImplementedError, "override this for variational inference of latent space"
class NormalPrior(VariationalPrior):
class NormalPrior(VariationalPrior):
def KL_divergence(self, variational_posterior):
var_mean = np.square(variational_posterior.mean).sum()
var_S = (variational_posterior.variance - np.log(variational_posterior.variance)).sum()
@ -30,6 +33,32 @@ class NormalPrior(VariationalPrior):
variational_posterior.mean.gradient -= variational_posterior.mean
variational_posterior.variance.gradient -= (1. - (1. / (variational_posterior.variance))) * 0.5
class SpikeAndSlabPrior(VariationalPrior):
def __init__(self, variance = 1.0, pi = 0.5, name='SpikeAndSlabPrior', **kw):
super(VariationalPrior, self).__init__(name=name, **kw)
assert variance==1.0, "Not Implemented!"
self.pi = Param('pi', pi)
self.variance = Param('variance',variance)
self.add_parameters(self.pi, self.variance)
def KL_divergence(self, variational_posterior):
mu = variational_posterior.mean
S = variational_posterior.variance
gamma = variational_posterior.binary_prob
var_mean = np.square(mu)
var_S = (S - np.log(S))
var_gamma = (gamma*np.log(gamma/self.pi)).sum()+((1-gamma)*np.log((1-gamma)/(1-self.pi))).sum()
return var_gamma+ 0.5 * (gamma* (var_mean + var_S -1)).sum()
def update_gradients_KL(self, variational_posterior):
mu = variational_posterior.mean
S = variational_posterior.variance
gamma = variational_posterior.binary_prob
gamma.gradient -= np.log((1-self.pi)/self.pi*gamma/(1.-gamma))+(np.square(mu)+S-np.log(S)-1.)/2.
mu.gradient -= gamma*mu
S.gradient -= (1. - (1. / (S))) * gamma /2.
class VariationalPosterior(Parameterized):
def __init__(self, means=None, variances=None, name=None, **kw):
@ -65,7 +94,7 @@ class NormalPosterior(VariationalPosterior):
from ...plotting.matplot_dep import variational_plots
return variational_plots.plot(self,*args)
class SpikeAndSlab(VariationalPosterior):
class SpikeAndSlabPosterior(VariationalPosterior):
'''
The SpikeAndSlab distribution for variational approximations.
'''
@ -73,7 +102,7 @@ class SpikeAndSlab(VariationalPosterior):
"""
binary_prob : the probability of the distribution on the slab part.
"""
super(SpikeAndSlab, self).__init__(means, variances, name)
super(SpikeAndSlabPosterior, self).__init__(means, variances, name)
self.gamma = Param("binary_prob",binary_prob,)
self.add_parameter(self.gamma)