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[sparse gp] prediction with uncertain inputs

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mzwiessele 2015-02-21 14:43:10 +01:00
parent 9f7ae611ed
commit 6435b83c7a
2 changed files with 42 additions and 51 deletions
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41
GPy/models/sparse_gp_minibatch.py
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@ -3,6 +3,7 @@
import numpy as np
from ..core.parameterization.param import Param
from ..core.sparse_gp import SparseGP
from ..core.gp import GP
from ..inference.latent_function_inference import var_dtc
from .. import likelihoods
@ -16,14 +17,9 @@ from GPy.inference.optimization.stochastics import SparseGPStochastics,\
#SparseGPMissing
logger = logging.getLogger("sparse gp")
class SparseGPMiniBatch(GP):
class SparseGPMiniBatch(SparseGP):
"""
A general purpose Sparse GP model
'''
Created on 3 Nov 2014
@author: maxz
'''
A general purpose Sparse GP model, allowing missing data and stochastics across dimensions.
This model allows (approximate) inference using variational DTC or FITC
(Gaussian likelihoods) as well as non-conjugate sparse methods based on
@ -315,34 +311,3 @@ Created on 3 Nov 2014
else:
self.posterior, self._log_marginal_likelihood, self.grad_dict, self.full_values, _ = self._inner_parameters_changed(self.kern, self.X, self.Z, self.likelihood, self.Y_normalized, self.Y_metadata)
self._outer_values_update(self.full_values)
def _raw_predict(self, Xnew, full_cov=False, kern=None):
"""
Make a prediction for the latent function values
"""
if kern is None: kern = self.kern
if not isinstance(Xnew, VariationalPosterior):
Kx = kern.K(self.Z, Xnew)
mu = np.dot(Kx.T, self.posterior.woodbury_vector)
if full_cov:
Kxx = kern.K(Xnew)
if self.posterior.woodbury_inv.ndim == 2:
var = Kxx - np.dot(Kx.T, np.dot(self.posterior.woodbury_inv, Kx))
elif self.posterior.woodbury_inv.ndim == 3:
var = Kxx[:,:,None] - np.tensordot(np.dot(np.atleast_3d(self.posterior.woodbury_inv).T, Kx).T, Kx, [1,0]).swapaxes(1,2)
var = var
else:
Kxx = kern.Kdiag(Xnew)
var = (Kxx - np.sum(np.dot(np.atleast_3d(self.posterior.woodbury_inv).T, Kx) * Kx[None,:,:], 1)).T
else:
Kx = kern.psi1(self.Z, Xnew)
mu = np.dot(Kx, self.posterior.woodbury_vector)
if full_cov:
raise NotImplementedError, "TODO"
else:
Kxx = kern.psi0(self.Z, Xnew)
psi2 = kern.psi2(self.Z, Xnew)
var = Kxx - np.sum(np.sum(psi2 * Kmmi_LmiBLmi[None, :, :], 1), 1)
return mu, var