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spike and slab binary variable numerical enhancement

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This commit is contained in:
Zhenwen Dai 2014-11-12 20:19:56 +00:00
parent 3c642a5600
commit 7f7b0da7b9
3 changed files with 34 additions and 21 deletions
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30
GPy/core/parameterization/variational.py
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@ -8,6 +8,8 @@ import numpy as np
from parameterized import Parameterized
from param import Param
from transformations import Logexp, Logistic,__fixed__
from GPy.util.misc import param_to_array
from GPy.util.caching import Cache_this
class VariationalPrior(Parameterized):
def __init__(self, name='latent space', **kw):
@ -48,7 +50,8 @@ class SpikeAndSlabPrior(VariationalPrior):
def KL_divergence(self, variational_posterior):
mu = variational_posterior.mean
S = variational_posterior.variance
gamma = variational_posterior.binary_prob
gamma,gamma1 = variational_posterior.gamma_probabilities()
log_gamma,log_gamma1 = variational_posterior.gamma_log_prob()
if len(self.pi.shape)==2:
idx = np.unique(gamma._raveled_index()/gamma.shape[-1])
pi = self.pi[idx]
@ -57,20 +60,21 @@ class SpikeAndSlabPrior(VariationalPrior):
var_mean = np.square(mu)/self.variance
var_S = (S/self.variance - np.log(S))
var_gamma = (gamma*np.log(gamma/pi)).sum()+((1-gamma)*np.log((1-gamma)/(1-pi))).sum()
var_gamma = (gamma*(log_gamma-np.log(pi))).sum()+(gamma1*(log_gamma1-np.log(1-pi))).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,gamma1 = variational_posterior.gamma_probabilities()
log_gamma,log_gamma1 = variational_posterior.gamma_log_prob()
if len(self.pi.shape)==2:
idx = np.unique(gamma._raveled_index()/gamma.shape[-1])
pi = self.pi[idx]
else:
pi = self.pi
gamma.gradient -= np.log((1-pi)/pi*gamma/(1.-gamma))+((np.square(mu)+S)/self.variance-np.log(S)+np.log(self.variance)-1.)/2.
variational_posterior.binary_prob.gradient -= (np.log((1-pi)/pi)+log_gamma-log_gamma1+((np.square(mu)+S)/self.variance-np.log(S)+np.log(self.variance)-1.)/2.)*gamma*gamma1
mu.gradient -= gamma*mu/self.variance
S.gradient -= (1./self.variance - 1./S) * gamma /2.
if self.learnPi:
@ -158,8 +162,24 @@ class SpikeAndSlabPosterior(VariationalPosterior):
binary_prob : the probability of the distribution on the slab part.
"""
super(SpikeAndSlabPosterior, self).__init__(means, variances, name)
self.gamma = Param("binary_prob",binary_prob, Logistic(1e-10,1.-1e-10))
self.gamma = Param("binary_prob",binary_prob)
self.link_parameter(self.gamma)
@Cache_this(limit=5)
def gamma_probabilities(self):
prob = np.zeros_like(param_to_array(self.gamma))
prob[self.gamma>-710] = 1./(1.+np.exp(-self.gamma[self.gamma>-710]))
prob1 = np.zeros_like(param_to_array(self.gamma))
prob1[self.gamma<710] = 1./(1.+np.exp(self.gamma[self.gamma<710]))
return prob, prob1
@Cache_this(limit=5)
def gamma_log_prob(self):
loggamma = param_to_array(self.gamma).copy()
loggamma[loggamma>-40] = -np.log1p(np.exp(-loggamma[loggamma>-40]))
loggamma1 = param_to_array(self.gamma).copy()
loggamma1[loggamma1<40] = -np.log1p(np.exp(loggamma1[loggamma1<40]))
return loggamma,loggamma1
def set_gradients(self, grad):
self.mean.gradient, self.variance.gradient, self.gamma.gradient = grad