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a more efficient implementation of prediction with uncertain inputs

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Zhenwen Dai 2015-10-05 22:34:49 +01:00
parent e6261c787c
commit 8689b3dfd0
1 changed files with 27 additions and 19 deletions
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46
GPy/core/sparse_gp.py
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@ -160,32 +160,40 @@ class SparseGP(GP):
else: else:
psi0_star = kern.psi0(self._predictive_variable, Xnew) psi0_star = kern.psi0(self._predictive_variable, Xnew)
psi1_star = kern.psi1(self._predictive_variable, Xnew) psi1_star = kern.psi1(self._predictive_variable, Xnew)
#psi2_star = kern.psi2(self.Z, Xnew) # Only possible if we get NxMxM psi2 out of the code. psi2_star = kern.psi2n(self._predictive_variable, Xnew)
la = self.posterior.woodbury_vector la = self.posterior.woodbury_vector
mu = np.dot(psi1_star, la) # TODO: dimensions? 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: if full_cov:
raise NotImplementedError("Full covariance for Sparse GP predicted with uncertain inputs not implemented yet.") raise NotImplementedError("Full covariance for Sparse GP predicted with uncertain inputs not implemented yet.")
var = np.empty((Xnew.shape[0], la.shape[1], la.shape[1])) var = np.zeros((Xnew.shape[0], la.shape[1], la.shape[1]))
di = np.diag_indices(la.shape[1]) di = np.diag_indices(la.shape[1])
else: else:
var = np.empty((Xnew.shape[0], la.shape[1])) 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]
for i in range(Xnew.shape[0]): if self.posterior.woodbury_inv.ndim==2:
_mu, _var = Xnew.mean.values[[i]], Xnew.variance.values[[i]] var += -psi2_star.reshape(N,-1).dot(self.posterior.woodbury_inv.flat)[:,None]
psi2_star = kern.psi2(self._predictive_variable, NormalPosterior(_mu, _var))
tmp = (psi2_star[:, :] - psi1_star[[i]].T.dot(psi1_star[[i]]))
var_ = mdot(la.T, tmp, la)
p0 = psi0_star[i]
t = np.atleast_3d(self.posterior.woodbury_inv)
t2 = np.trace(t.T.dot(psi2_star), axis1=1, axis2=2)
if full_cov:
var_[di] += p0
var_[di] += -t2
var[i] = var_
else: else:
var[i] = np.diag(var_)+p0-t2 var += -psi2_star.reshape(N,-1).dot(self.posterior.woodbury_inv.reshape(-1,D))
assert np.all(var>=-1e-5), "The predicted variance goes negative!: "+str(var)
var = np.clip(var,1e-15,np.inf)
# for i in range(Xnew.shape[0]):
# _mu, _var = Xnew.mean.values[[i]], Xnew.variance.values[[i]]
# psi2_star = kern.psi2(self._predictive_variable, NormalPosterior(_mu, _var))
# tmp = (psi2_star[:, :] - psi1_star[[i]].T.dot(psi1_star[[i]]))
#
# var_ = mdot(la.T, tmp, la)
# p0 = psi0_star[i]
# t = np.atleast_3d(self.posterior.woodbury_inv)
# t2 = np.trace(t.T.dot(psi2_star), axis1=1, axis2=2)
#
# if full_cov:
# var_[di] += p0
# var_[di] += -t2
# var[i] = var_
# else:
# var[i] = np.diag(var_)+p0-t2
return mu, var return mu, var