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Working laplace, just needs predictive values

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Alan Saul 2013-03-28 17:42:42 +00:00
parent 7b0d0550cb
commit 15d5c2f22d
3 changed files with 121 additions and 46 deletions
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15
python/likelihoods/Laplace.py
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@ -88,11 +88,12 @@ class Laplace(likelihood):
and $$\ln \tilde{z} = \ln z + \frac{N}{2}\ln 2\pi + \frac{1}{2}\tilde{Y}\tilde{\Sigma}^{-1}\tilde{Y}$$
"""
self.Sigma_tilde_i = self.W #self.hess_hat_i
self.Sigma_tilde_i = self.W
#Check it isn't singular!
epsilon = 1e-2
epsilon = 1e-6
if np.abs(det(self.Sigma_tilde_i)) < epsilon:
raise ValueError("inverse covariance must be non-singular to inverse!")
print "WARNING: Transformed covariance matrix is signular!"
#raise ValueError("inverse covariance must be non-singular to invert!")
#Do we really need to inverse Sigma_tilde_i? :(
if self.likelihood_function.log_concave:
(self.Sigma_tilde, _, _, _) = pdinv(self.Sigma_tilde_i)
@ -110,8 +111,12 @@ class Laplace(likelihood):
self.Y = Y_tilde[:, None]
self.YYT = np.dot(self.Y, self.Y.T)
self.covariance_matrix = self.Sigma_tilde
self.precision = 1 / np.diag(self.Sigma_tilde)[:, None]
import ipdb; ipdb.set_trace() ### XXX BREAKPOINT
#if not self.likelihood_function.log_concave:
#self.covariance_matrix[self.covariance_matrix < 0] = 1e+6 #FIXME-HACK: This is a hack since GPy can't handle negative variances which can occur
##If the likelihood is non-log-concave. We wan't to say that there is a negative variance
##To cause the posterior to become less certain than the prior and likelihood,
##This is a property only held by non-log-concave likelihoods
self.precision = 1 / np.diag(self.covariance_matrix)[:, None]
def fit_full(self, K):
"""