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adding file for gaussian-kronrod integration, test cases and calculating gradients of log marginal wrt theta-likelihood params

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Akash Kumar Dhaka 2017-07-03 13:30:39 +03:00
parent 19598cf807
commit ae27e1225d
5 changed files with 269 additions and 4 deletions
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11
GPy/inference/latent_function_inference/expectation_propagation.py
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@ -6,6 +6,7 @@ from paramz import ObsAr
from . import ExactGaussianInference, VarDTC
from ...util import diag
from .posterior import PosteriorEP as Posterior
from ...likelihoods import Gaussian
log_2_pi = np.log(2*np.pi)
@ -185,7 +186,7 @@ class EP(EPBase, ExactGaussianInference):
else:
raise ValueError("ep_mode value not valid")
return self._inference(K, ga_approx, likelihood, Y_metadata=Y_metadata, Z_tilde=log_Z_tilde)
return self._inference(Y, K, ga_approx, likelihood, Y_metadata=Y_metadata, Z_tilde=log_Z_tilde)
def expectation_propagation(self, K, Y, likelihood, Y_metadata):
@ -280,7 +281,7 @@ class EP(EPBase, ExactGaussianInference):
return log_marginal, post_params
def _inference(self, K, ga_approx, likelihood, Z_tilde, Y_metadata=None):
def _inference(self, Y, K, ga_approx, likelihood, Z_tilde, Y_metadata=None):
log_marginal, post_params = self._ep_marginal(K, ga_approx, Z_tilde)
tau_tilde_root = np.sqrt(ga_approx.tau)
@ -293,8 +294,10 @@ class EP(EPBase, ExactGaussianInference):
symmetrify(Wi) #(K + Sigma^(\tilde))^(-1)
dL_dK = 0.5 * (tdot(alpha) - Wi)
dL_dthetaL = likelihood.exact_inference_gradients(np.diag(dL_dK), Y_metadata)
if not isinstance(likelihood, Gaussian):
dL_dthetaL = likelihood.ep_gradients(Y, ga_approx.tau, ga_approx.v, Y_metadata=Y_metadata)
else:
dL_dthetaL = likelihood.exact_inference_gradients(np.diag(dL_dK), Y_metadata)
return Posterior(woodbury_inv=Wi, woodbury_vector=alpha, K=K), log_marginal, {'dL_dK':dL_dK, 'dL_dthetaL':dL_dthetaL, 'dL_dm':alpha}