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more massive and destructive changes everywhere

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This commit is contained in:
James Hensman 2013-12-05 15:09:31 -05:00
parent 881800126f
commit 5ec64d2279
18 changed files with 202 additions and 166 deletions
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17
GPy/inference/latent_function_inference/exact_gaussian_inference.py
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@ -2,11 +2,12 @@
# Licensed under the BSD 3-clause license (see LICENSE.txt)
from posterior import Posterior
from .../util.linalg import pdinv, dpotrs, tdot
from ...util.linalg import pdinv, dpotrs, tdot
import numpy as np
log_2_pi = np.log(2*np.pi)
class exact_gaussian_inference(object):
class ExactGaussianInference(object):
"""
An object for inference when the likelihood is Gaussian.
@ -17,9 +18,9 @@ class exact_gaussian_inference(object):
"""
def __init__(self):
self._YYTfactor_cache = caching.cache()
pass#self._YYTfactor_cache = caching.cache()
def get_YYTfactor(self, Y):
def get_YYTfactor(self, Y):
"""
find a matrix L which satisfies LLT = YYT.
@ -38,16 +39,16 @@ class exact_gaussian_inference(object):
"""
YYT_factor = self.get_YYTfactor(Y)
Wi, LW, LWi, W_logdet = pdinv(K + likelhood.covariance(Y, Y_metadata))
Wi, LW, LWi, W_logdet = pdinv(K + likelihood.covariance_matrix(Y, Y_metadata))
alpha, _ = dpotrs(LW, YYT_factor, lower=1)
dL_dK = 0.5 * (tdot(alpha) - Y.shape[1] * Wi)
log_marginal = 0.5*(-Y.size * log_2_pi - Y.shape[1] * W_logdet - np.sum(alpha * YYT_factor.T)
log_marginal = 0.5*(-Y.size * log_2_pi - Y.shape[1] * W_logdet - np.sum(alpha * YYT_factor))
dL_dtheta_lik = likelihood.dL_dtheta(np.diag(dL_dK))
dL_dtheta_lik = likelihood._gradients(np.diag(dL_dK))
return Posterior(log_marginal, dL_DK, dL_dtheta_lik, LW, alpha, K):
return Posterior(log_marginal, dL_dK, dL_dtheta_lik, LW, alpha, K)