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lots of fixes, including prediction being mean and variance only

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This commit is contained in:
James Hensman 2014-03-13 14:42:03 +00:00
parent 365b8ae1e1
commit cc96f5b3d5
13 changed files with 118 additions and 128 deletions
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10
GPy/inference/latent_function_inference/exact_gaussian_inference.py
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@ -33,7 +33,7 @@ class ExactGaussianInference(object):
#if Y in self.cache, return self.Cache[Y], else store Y in cache and return L.
raise NotImplementedError, 'TODO' #TODO
def inference(self, kern, X, likelihood, Y, **Y_metadata):
def inference(self, kern, X, likelihood, Y, Y_metadata=None):
"""
Returns a Posterior class containing essential quantities of the posterior
"""
@ -41,12 +41,14 @@ class ExactGaussianInference(object):
K = kern.K(X)
Wi, LW, LWi, W_logdet = pdinv(K + likelihood.covariance_matrix(Y, **Y_metadata))
Wi, LW, LWi, W_logdet = pdinv(K + likelihood.covariance_matrix(Y, Y_metadata))
alpha, _ = dpotrs(LW, YYT_factor, lower=1)
log_marginal = 0.5*(-Y.size * log_2_pi - Y.shape[1] * W_logdet - np.sum(alpha * YYT_factor))
dL_dK = 0.5 * (tdot(alpha) - Y.shape[1] * Wi)
return Posterior(woodbury_chol=LW, woodbury_vector=alpha, K=K), log_marginal, {'dL_dK':dL_dK}
dL_dthetaL = likelihood.exact_inference_gradients(np.diag(dL_dK))
return Posterior(woodbury_chol=LW, woodbury_vector=alpha, K=K), log_marginal, {'dL_dK':dL_dK, 'dL_dthetaL':dL_dthetaL}