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slvm gamma mean-field

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Zhenwen Dai 2016-01-14 15:56:48 +00:00
parent 52c6fe599f
commit 5f417565fb
1 changed files with 5 additions and 5 deletions
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GPy/models/ss_gplvm.py
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@ -104,7 +104,7 @@ class SLVMPosterior(SpikeAndSlabPosterior):
""" """
from paramz.transformations import Logexp from paramz.transformations import Logexp
super(SLVMPosterior, self).__init__(means, variances, binary_prob, group_spike=False, name=name) super(SLVMPosterior, self).__init__(means, variances, binary_prob, group_spike=False, name=name)
self.tau = Param("tau_", np.ones((self.gamma.shape[1],2)), Logexp()) self.tau = Param("tau_", np.ones((self.gamma.shape[1],2))*2, Logexp())
self.link_parameter(self.tau) self.link_parameter(self.tau)
def set_gradients(self, grad): def set_gradients(self, grad):
@ -152,7 +152,7 @@ class SLVMPrior(VariationalPrior):
var_mean = np.square(mu)/self.variance var_mean = np.square(mu)/self.variance
var_S = (S/self.variance - np.log(S)) var_S = (S/self.variance - np.log(S))
part1 = (gamma* (np.log(self.variance)-1. +var_mean + var_S)).sum()/2. part1 = ((np.log(self.variance)-1. +var_mean + var_S)).sum()/2.
from scipy.special import betaln,digamma from scipy.special import betaln,digamma
part2 = (gamma*np.log(gamma)).sum() + ((1.-gamma)*np.log(1.-gamma)).sum() + betaln(self.alpha,self.beta)*self.input_dim \ part2 = (gamma*np.log(gamma)).sum() + ((1.-gamma)*np.log(1.-gamma)).sum() + betaln(self.alpha,self.beta)*self.input_dim \
@ -164,11 +164,11 @@ class SLVMPrior(VariationalPrior):
def update_gradients_KL(self, variational_posterior): def update_gradients_KL(self, variational_posterior):
mu, S, gamma, tau = variational_posterior.mean.values, variational_posterior.variance.values, variational_posterior.gamma.values, variational_posterior.tau.values mu, S, gamma, tau = variational_posterior.mean.values, variational_posterior.variance.values, variational_posterior.gamma.values, variational_posterior.tau.values
variational_posterior.mean.gradient -= gamma*mu/self.variance variational_posterior.mean.gradient -= mu/self.variance
variational_posterior.variance.gradient -= (1./self.variance - 1./S) * gamma /2. variational_posterior.variance.gradient -= (1./self.variance - 1./S) /2.
from scipy.special import digamma,polygamma from scipy.special import digamma,polygamma
dgamma = np.log(gamma/(1.-gamma))+ (digamma(tau[:,1])-digamma(tau[:,0]))*self.Z dgamma = np.log(gamma/(1.-gamma))+ (digamma(tau[:,1])-digamma(tau[:,0]))*self.Z
variational_posterior.binary_prob.gradient -= dgamma+((np.square(mu)+S)/self.variance-np.log(S)+np.log(self.variance)-1.)/2. variational_posterior.binary_prob.gradient -= dgamma
common = (self.Z.sum(0)+self.alpha+self.beta-tau[:,0]-tau[:,1])*polygamma(1,tau.sum(axis=1)) common = (self.Z.sum(0)+self.alpha+self.beta-tau[:,0]-tau[:,1])*polygamma(1,tau.sum(axis=1))
variational_posterior.tau.gradient[:,0] = -((tau[:,0]-(gamma*self.Z).sum(0)-self.alpha)*polygamma(1,tau[:,0])+common) variational_posterior.tau.gradient[:,0] = -((tau[:,0]-(gamma*self.Z).sum(0)-self.alpha)*polygamma(1,tau[:,0])+common)
variational_posterior.tau.gradient[:,1] = -((tau[:,1]-((1-gamma)*self.Z).sum(0)-self.beta)*polygamma(1,tau[:,1])+common) variational_posterior.tau.gradient[:,1] = -((tau[:,1]-((1-gamma)*self.Z).sum(0)-self.beta)*polygamma(1,tau[:,1])+common)