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more gradient based tomfoolery

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James Hensman 2014-01-24 14:15:32 +00:00
parent 7b5e8a9ffc
commit c1a416debc
4 changed files with 4 additions and 6 deletions
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2
GPy/inference/latent_function_inference/exact_gaussian_inference.py
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@ -49,7 +49,7 @@ class ExactGaussianInference(object):
dL_dK = 0.5 * (tdot(alpha) - Y.shape[1] * Wi)
kern.update_gradients_full(dL_dK)
kern.update_gradients_full(dL_dK, X)
likelihood.update_gradients(np.diag(dL_dK))

3
GPy/inference/latent_function_inference/posterior.py
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@ -18,7 +18,6 @@ class Posterior(object):
"""
log_marginal: log p(Y|X)
dL_dK: d/dK log p(Y|X)
dL_dtheta_lik : d/dtheta log p(Y|X) (where theta are the parameters of the likelihood)
woodbury_chol : a lower triangular matrix L that satisfies posterior_covariance = K - K L^{-T} L^{-1} K
woodbury_vector : a matrix (or vector, as Nx1 matrix) M which satisfies posterior_mean = K M
K : the proir covariance (required for lazy computation of various quantities)
@ -29,7 +28,6 @@ class Posterior(object):
log_marginal
dL_dK
dL_dtheta_lik
K (for lazy computation)
You may supply either:
@ -50,7 +48,6 @@ class Posterior(object):
#obligatory
self.log_marginal = log_marginal
self.dL_dK = dL_dK
self.dL_dtheta_lik = dL_dtheta_lik
self._K = K
if ((woodbury_chol is not None) and (woodbury_vector is not None) and (K is not None)) or ((mean is not None) and (cov is not None) and (K is not None)):