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generalize the spike-and-slab prior with pi (N,Q)

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Zhenwen Dai 2014-08-11 14:12:43 +01:00
parent dd6823446d
commit 6acb9b09b5
1 changed files with 15 additions and 10 deletions
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25
GPy/core/parameterization/variational.py
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@ -34,31 +34,36 @@ class NormalPrior(VariationalPrior):
variational_posterior.variance.gradient -= (1. - (1. / (variational_posterior.variance))) * 0.5
class SpikeAndSlabPrior(VariationalPrior):
def __init__(self, pi, variance = 1.0, name='SpikeAndSlabPrior', **kw):
def __init__(self, pi=None, learnPi=False, variance = 1.0, name='SpikeAndSlabPrior', **kw):
super(VariationalPrior, self).__init__(name=name, **kw)
assert variance==1.0, "Not Implemented!"
self.pi = Param('pi', pi, Logistic(1e-10,1.-1e-10))
self.variance = Param('variance',variance)
self.add_parameters(self.pi)
if learnPi:
self.add_parameters(self.pi)
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_mean = np.square(mu)/self.variance
var_S = (S/self.variance - 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()
return var_gamma+ (gamma* (np.log(self.variance)-1. +var_mean + var_S)).sum()/2.
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.
self.pi.gradient = (gamma/self.pi - (1.-gamma)/(1.-self.pi)).sum(axis=0)
gamma.gradient -= np.log((1-self.pi)/self.pi*gamma/(1.-gamma))+((np.square(mu)+S)/self.variance-np.log(S)+np.log(self.variance)-1.)/2.
mu.gradient -= gamma*mu/self.variance
S.gradient -= (1./self.variance - 1./S) * gamma /2.
if len(self.pi)==1:
self.pi.gradient = (gamma/self.pi - (1.-gamma)/(1.-self.pi)).sum()
if len(self.pi.shape)==1:
self.pi.gradient = (gamma/self.pi - (1.-gamma)/(1.-self.pi)).sum(axis=0)
else:
self.pi.gradient = (gamma/self.pi - (1.-gamma)/(1.-self.pi))
class VariationalPosterior(Parameterized):
def __init__(self, means=None, variances=None, name='latent space', *a, **kw):