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move _raw_predict into posterior object

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This commit is contained in:
Zhenwen Dai 2016-03-17 11:29:33 +00:00
parent f2b813551a
commit 67ba9b60c6
4 changed files with 53 additions and 121 deletions
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51
GPy/inference/latent_function_inference/posterior.py
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@ -3,6 +3,7 @@
import numpy as np
from ...util.linalg import pdinv, dpotrs, dpotri, symmetrify, jitchol, dtrtrs, tdot
from GPy.core.parameterization.variational import VariationalPosterior
class Posterior(object):
"""
@ -187,6 +188,56 @@ class Posterior(object):
if self._K_chol is None:
self._K_chol = jitchol(self._K)
return self._K_chol
def _raw_predict(self, kern, Xnew, pred_var, full_cov=False):
woodbury_vector = self.woodbury_vector
woodbury_inv = self.woodbury_inv
if not isinstance(Xnew, VariationalPosterior):
Kx = kern.K(pred_var, Xnew)
mu = np.dot(Kx.T, woodbury_vector)
if len(mu.shape)==1:
mu = mu.reshape(-1,1)
if full_cov:
Kxx = kern.K(Xnew)
if woodbury_inv.ndim == 2:
var = Kxx - np.dot(Kx.T, np.dot(woodbury_inv, Kx))
elif woodbury_inv.ndim == 3: # Missing data
var = np.empty((Kxx.shape[0],Kxx.shape[1],woodbury_inv.shape[2]))
from ...util.linalg import mdot
for i in range(var.shape[2]):
var[:, :, i] = (Kxx - mdot(Kx.T, woodbury_inv[:, :, i], Kx))
var = var
else:
Kxx = kern.Kdiag(Xnew)
if woodbury_inv.ndim == 2:
var = (Kxx - np.sum(np.dot(woodbury_inv.T, Kx) * Kx, 0))[:,None]
elif woodbury_inv.ndim == 3: # Missing data
var = np.empty((Kxx.shape[0],woodbury_inv.shape[2]))
for i in range(var.shape[1]):
var[:, i] = (Kxx - (np.sum(np.dot(woodbury_inv[:, :, i].T, Kx) * Kx, 0)))
var = var
else:
psi0_star = kern.psi0(pred_var, Xnew)
psi1_star = kern.psi1(pred_var, Xnew)
psi2_star = kern.psi2n(pred_var, Xnew)
la = woodbury_vector
mu = np.dot(psi1_star, la) # TODO: dimensions?
N,M,D = psi0_star.shape[0],psi1_star.shape[1], la.shape[1]
if full_cov:
raise NotImplementedError("Full covariance for Sparse GP predicted with uncertain inputs not implemented yet.")
var = np.zeros((Xnew.shape[0], la.shape[1], la.shape[1]))
di = np.diag_indices(la.shape[1])
else:
tmp = psi2_star - psi1_star[:,:,None]*psi1_star[:,None,:]
var = (tmp.reshape(-1,M).dot(la).reshape(N,M,D)*la[None,:,:]).sum(1) + psi0_star[:,None]
if woodbury_inv.ndim==2:
var += -psi2_star.reshape(N,-1).dot(woodbury_inv.flat)[:,None]
else:
var += -psi2_star.reshape(N,-1).dot(woodbury_inv.reshape(-1,D))
var = np.clip(var,1e-15,np.inf)
return mu, var
class PosteriorExact(Posterior):