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added more stable expectations for Bernoulli

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James Hensman 2015-02-10 16:15:37 +00:00
parent 47cbdc265e
commit 393b9e94ba
1 changed files with 26 additions and 0 deletions
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26
GPy/likelihoods/bernoulli.py
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@ -77,6 +77,32 @@ class Bernoulli(Likelihood):
return Z_hat, mu_hat, sigma2_hat return Z_hat, mu_hat, sigma2_hat
def variational_expectations(self, Y, m, v, gh_points=None):
if isinstance(self.gp_link, link_functions.Probit):
if gh_points is None:
gh_x, gh_w = np.polynomial.hermite.hermgauss(20)
else:
gh_x, gh_w = gh_points
from scipy import stats
shape = m.shape
m,v,Y = m.flatten(), v.flatten(), Y.flatten()
Ysign = np.where(Y==1,1,-1)
X = gh_x[None,:]*np.sqrt(2.*v[:,None]) + (m*Ysign)[:,None]
p = stats.norm.cdf(X)
p = np.clip(p, 1e-9, 1.-1e-9) # for numerical stability
N = stats.norm.pdf(X)
F = np.log(p).dot(gh_w)
NoverP = N/p
dF_dm = (NoverP*Ysign[:,None]).dot(gh_w)
dF_dv = -0.5*(NoverP**2 + NoverP*X).dot(gh_w)
return F.reshape(*shape), dF_dm.reshape(*shape), dF_dv.reshape(*shape), None
else:
raise NotImplementedError
def predictive_mean(self, mu, variance, Y_metadata=None): def predictive_mean(self, mu, variance, Y_metadata=None):
if isinstance(self.gp_link, link_functions.Probit): if isinstance(self.gp_link, link_functions.Probit):