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sparse_GP can now predict (variationally) from an uncertain input

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James Hensman 2013-05-20 17:05:36 +01:00
parent 255a9bbd73
commit f6b51a6656
1 changed files with 19 additions and 8 deletions
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15
GPy/models/sparse_GP.py
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@ -217,21 +217,32 @@ class sparse_GP(GP):
dL_dZ += self.kern.dK_dX(self.dL_dpsi1, self.Z, self.X) dL_dZ += self.kern.dK_dX(self.dL_dpsi1, self.Z, self.X)
return dL_dZ return dL_dZ
def _raw_predict(self, Xnew, which_parts='all', full_cov=False): def _raw_predict(self, Xnew, X_variance_new=None, which_parts='all', full_cov=False):
"""Internal helper function for making predictions, does not account for normalization""" """Internal helper function for making predictions, does not account for normalization"""
Bi, _ = linalg.lapack.flapack.dpotri(self.LB, lower=0) # WTH? this lower switch should be 1, but that doesn't work! Bi, _ = linalg.lapack.flapack.dpotri(self.LB, lower=0) # WTH? this lower switch should be 1, but that doesn't work!
symmetrify(Bi) symmetrify(Bi)
Kmmi_LmiBLmi = backsub_both_sides(self.Lm, np.eye(self.M) - Bi) Kmmi_LmiBLmi = backsub_both_sides(self.Lm, np.eye(self.M) - Bi)
if X_variance_new is None:
Kx = self.kern.K(self.Z, Xnew, which_parts=which_parts) Kx = self.kern.K(self.Z, Xnew, which_parts=which_parts)
mu = np.dot(Kx.T, self.Cpsi1V) # / self.scale_factor) mu = np.dot(Kx.T, self.Cpsi1V)
if full_cov: if full_cov:
Kxx = self.kern.K(Xnew, which_parts=which_parts) Kxx = self.kern.K(Xnew, which_parts=which_parts)
var = Kxx - mdot(Kx.T, Kmmi_LmiBLmi, Kx) # NOTE this won't work for plotting var = Kxx - mdot(Kx.T, Kmmi_LmiBLmi, Kx) # NOTE this won't work for plotting
else: else:
Kxx = self.kern.Kdiag(Xnew, which_parts=which_parts) Kxx = self.kern.Kdiag(Xnew, which_parts=which_parts)
var = Kxx - np.sum(Kx * np.dot(Kmmi_LmiBLmi, Kx), 0) var = Kxx - np.sum(Kx * np.dot(Kmmi_LmiBLmi, Kx), 0)
else:
assert which_parts=='all', "swithching out parts of variational kernels is not implemented"
Kx = self.kern.psi1(self.Z, Xnew, X_variance_new)#, which_parts=which_parts) TODO: which_parts
mu = np.dot(Kx, self.Cpsi1V)
if full_cov:
raise NotImplementedError, "TODO"
else:
Kxx = self.kern.psi0(self.Z,Xnew,X_variance_new)
psi2 = self.kern.psi2(self.Z,Xnew,X_variance_new)
var = Kxx - np.sum(np.sum(psi2*Kmmi_LmiBLmi[None,:,:],1),1)
return mu, var[:, None] return mu, var[:, None]