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Y_metadata added as parameter

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Ricardo 2014-03-12 12:46:54 +00:00
parent e858a0bdc3
commit 32d5b449eb
1 changed files with 2 additions and 7 deletions
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9
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. #if Y in self.cache, return self.Cache[Y], else store Y in cache and return L.
raise NotImplementedError, 'TODO' #TODO raise NotImplementedError, 'TODO' #TODO
def inference(self, kern, X, likelihood, Y, Y_metadata=None): def inference(self, kern, X, likelihood, Y, **Y_metadata):
""" """
Returns a Posterior class containing essential quantities of the posterior Returns a Posterior class containing essential quantities of the posterior
""" """
@ -41,7 +41,7 @@ class ExactGaussianInference(object):
K = kern.K(X) 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) alpha, _ = dpotrs(LW, YYT_factor, lower=1)
@ -49,9 +49,4 @@ class ExactGaussianInference(object):
dL_dK = 0.5 * (tdot(alpha) - Y.shape[1] * Wi) dL_dK = 0.5 * (tdot(alpha) - Y.shape[1] * Wi)
#TODO: does this really live here?
likelihood.update_gradients(np.diag(dL_dK))
return Posterior(woodbury_chol=LW, woodbury_vector=alpha, K=K), log_marginal, {'dL_dK':dL_dK} return Posterior(woodbury_chol=LW, woodbury_vector=alpha, K=K), log_marginal, {'dL_dK':dL_dK}