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James Hensman 2013-12-06 16:47:08 -08:00
parent e6593db781
commit fed4134727
1 changed files with 3 additions and 5 deletions
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8
GPy/inference/latent_function_inference/posterior.py
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@ -17,7 +17,7 @@ class Posterior(object):
def __init__(self, log_marginal, dL_dK, dL_dtheta_lik, woodbury_chol=None, woodbury_vector=None, K=None, mean=None, cov=None, K_chol=None): def __init__(self, log_marginal, dL_dK, dL_dtheta_lik, woodbury_chol=None, woodbury_vector=None, K=None, mean=None, cov=None, K_chol=None):
""" """
log_marginal: log p(Y|X) log_marginal: log p(Y|X)
DL_dK: d/dK 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) 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_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 woodbury_vector : a matrix (or vector, as Nx1 matrix) M which satisfies posterior_mean = K M
@ -97,15 +97,13 @@ class Posterior(object):
tmp, _ = dpotrs(self._K_chol, B) tmp, _ = dpotrs(self._K_chol, B)
Wi, _ = dpotrs(self._K_chol, tmp.T) Wi, _ = dpotrs(self._K_chol, tmp.T)
_, _, self._woodbury_chol, _ = pdinv(Wi) _, _, self._woodbury_chol, _ = pdinv(Wi)
else: return self._woodbury_chol
return self._woodbury_chol
@property @property
def woodbury_vector(self): def woodbury_vector(self):
if self._woodbury_vector is None: if self._woodbury_vector is None:
self._woodbury_vector, _ = dpotrs(self._K_chol, self.mean) self._woodbury_vector, _ = dpotrs(self._K_chol, self.mean)
else: return self._woodbury_vector
return self._woodbury_vector