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variational posterior and prior added, linear updated

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Max Zwiessele 2014-02-24 09:49:29 +00:00
parent 3968d48ba5
commit 1eb8cc5eab
9 changed files with 118 additions and 78 deletions
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16
GPy/core/sparse_gp.py
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@ -5,8 +5,9 @@ import numpy as np
from ..util.linalg import mdot
from gp import GP
from parameterization.param import Param
from GPy.inference.latent_function_inference import var_dtc
from ..inference.latent_function_inference import var_dtc
from .. import likelihoods
from parameterization.variational import NormalPosterior
class SparseGP(GP):
"""
@ -45,16 +46,14 @@ class SparseGP(GP):
self.Z = Param('inducing inputs', Z)
self.num_inducing = Z.shape[0]
self.X_variance = X_variance
if self.has_uncertain_inputs():
assert X_variance.shape == X.shape
self.q = NormalPosterior(X, X_variance)
GP.__init__(self, X, Y, kernel, likelihood, inference_method=inference_method, name=name)
GP.__init__(self, self.q.mean, Y, kernel, likelihood, inference_method=inference_method, name=name)
self.add_parameter(self.Z, index=0)
self.parameters_changed()
def has_uncertain_inputs(self):
return not (self.X_variance is None)
return self.q.has_uncertain_inputs()
def parameters_changed(self):
if self.has_uncertain_inputs():
@ -81,7 +80,10 @@ class SparseGP(GP):
var = Kxx - mdot(Kx.T, self.posterior.woodbury_inv, Kx)
else:
Kxx = self.kern.Kdiag(Xnew)
var = Kxx - np.sum(Kx * np.dot(self.posterior.woodbury_inv, Kx), 0)
WKx_old = np.dot(np.atleast_3d(self.posterior.woodbury_inv)[:,:,0], Kx)
WKx = np.tensordot(np.atleast_3d(self.posterior.woodbury_inv), Kx, [0,0])
import ipdb;ipdb.set_trace()
var = Kxx - np.sum(Kx * WKx, 0)
else:
Kx = self.kern.psi1(self.Z, Xnew, X_variance_new)
mu = np.dot(Kx, self.Cpsi1V)