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[ep] now calling exact inference instead of copying code

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Max Zwiessele 2015-09-11 15:47:07 +01:00
parent 79bfbfc776
commit 8132084de6
7 changed files with 56 additions and 75 deletions
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23
GPy/inference/latent_function_inference/expectation_propagation.py
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@ -3,11 +3,11 @@
import numpy as np
from ...util.linalg import pdinv,jitchol,DSYR,tdot,dtrtrs, dpotrs
from .posterior import Posterior
from . import LatentFunctionInference
from . import ExactGaussianInference
from ...util import diag
log_2_pi = np.log(2*np.pi)
class EP(LatentFunctionInference):
class EP(ExactGaussianInference):
def __init__(self, epsilon=1e-6, eta=1., delta=1.):
"""
The expectation-propagation algorithm.
@ -20,6 +20,7 @@ class EP(LatentFunctionInference):
:param delta: damping EP updates factor.
:type delta: float64
"""
super(EP, self).__init__()
self.epsilon, self.eta, self.delta = epsilon, eta, delta
self.reset()
@ -34,12 +35,12 @@ class EP(LatentFunctionInference):
# TODO: update approximation in the end as well? Maybe even with a switch?
pass
def inference(self, kern, X, likelihood, Y, mean_function=None, Y_metadata=None, Z=None):
assert mean_function is None, "inference with a mean function not implemented"
def inference(self, kern, X, likelihood, Y, mean_function=None, Y_metadata=None, gaussian_variance=None, K=None):
num_data, output_dim = Y.shape
assert output_dim ==1, "ep in 1D only (for now!)"
K = kern.K(X)
if K is None:
K = kern.K(X)
if self._ep_approximation is None:
#if we don't yet have the results of runnign EP, run EP and store the computed factors in self._ep_approximation
@ -48,17 +49,7 @@ class EP(LatentFunctionInference):
#if we've already run EP, just use the existing approximation stored in self._ep_approximation
mu, Sigma, mu_tilde, tau_tilde, Z_hat = self._ep_approximation
Wi, LW, LWi, W_logdet = pdinv(K + np.diag(1./tau_tilde))
alpha, _ = dpotrs(LW, mu_tilde, lower=1)
log_marginal = 0.5*(-num_data * log_2_pi - W_logdet - np.sum(alpha * mu_tilde)) # TODO: add log Z_hat??
dL_dK = 0.5 * (tdot(alpha[:,None]) - Wi)
dL_dthetaL = np.zeros(likelihood.size)#TODO: derivatives of the likelihood parameters
return Posterior(woodbury_inv=Wi, woodbury_vector=alpha, K=K), log_marginal, {'dL_dK':dL_dK, 'dL_dthetaL':dL_dthetaL}
return super(EP, self).inference(kern, X, likelihood, mu_tilde[:,None], mean_function=mean_function, Y_metadata=Y_metadata, gaussian_variance=1./tau_tilde, K=K)
def expectation_propagation(self, K, Y, likelihood, Y_metadata):