apunkt/GPy
1
0
Fork 0
mirror of https://github.com/SheffieldML/GPy.git synced 2026-05-12 05:22:38 +02:00
Code Issues Projects Releases Packages Wiki Activity Actions

Merged

Browse source
This commit is contained in:
Alan Saul 2013-06-06 14:47:45 +01:00
commit 7cf43baf05
5 changed files with 24 additions and 25 deletions
Download patch file Download diff file Expand all files Collapse all files

31
GPy/core/sparse_gp.py
View file

@ -54,11 +54,11 @@ class SparseGP(GPBase):
self.Kmm = self.kern.K(self.Z)
if self.has_uncertain_inputs:
self.psi0 = self.kern.psi0(self.Z, self.X, self.X_variance)
self.psi1 = self.kern.psi1(self.Z, self.X, self.X_variance).T
self.psi1 = self.kern.psi1(self.Z, self.X, self.X_variance)
self.psi2 = self.kern.psi2(self.Z, self.X, self.X_variance)
else:
self.psi0 = self.kern.Kdiag(self.X)
self.psi1 = self.kern.K(self.Z, self.X)
self.psi1 = self.kern.K(self.X, self.Z)
self.psi2 = None
def _computations(self):
@ -75,12 +75,13 @@ class SparseGP(GPBase):
evals, evecs = linalg.eigh(psi2_beta)
clipped_evals = np.clip(evals, 0., 1e6) # TODO: make clipping configurable
tmp = evecs * np.sqrt(clipped_evals)
tmp = tmp.T
else:
if self.likelihood.is_heteroscedastic:
tmp = self.psi1 * (np.sqrt(self.likelihood.precision.flatten().reshape(1, self.num_data)))
tmp = self.psi1 * (np.sqrt(self.likelihood.precision.flatten().reshape(self.num_data,1)))
else:
tmp = self.psi1 * (np.sqrt(self.likelihood.precision))
tmp, _ = dtrtrs(self.Lm, np.asfortranarray(tmp), lower=1)
tmp, _ = dtrtrs(self.Lm, np.asfortranarray(tmp.T), lower=1)
self.A = tdot(tmp)
@ -89,7 +90,7 @@ class SparseGP(GPBase):
self.LB = jitchol(self.B)
# TODO: make a switch for either first compute psi1V, or VV.T
self.psi1V = np.dot(self.psi1, self.likelihood.V)
self.psi1V = np.dot(self.psi1.T, self.likelihood.V)
# back substutue C into psi1V
tmp, info1 = dtrtrs(self.Lm, np.asfortranarray(self.psi1V), lower=1, trans=0)
@ -107,14 +108,14 @@ class SparseGP(GPBase):
# Compute dL_dpsi # FIXME: this is untested for the heterscedastic + uncertain inputs case
self.dL_dpsi0 = -0.5 * self.output_dim * (self.likelihood.precision * np.ones([self.num_data, 1])).flatten()
self.dL_dpsi1 = np.dot(self.Cpsi1V, self.likelihood.V.T)
self.dL_dpsi1 = np.dot(self.Cpsi1V, self.likelihood.V.T).T
dL_dpsi2_beta = 0.5 * backsub_both_sides(self.Lm, self.output_dim * np.eye(self.num_inducing) - self.DBi_plus_BiPBi)
if self.likelihood.is_heteroscedastic:
if self.has_uncertain_inputs:
self.dL_dpsi2 = self.likelihood.precision.flatten()[:, None, None] * dL_dpsi2_beta[None, :, :]
else:
self.dL_dpsi1 += 2.*np.dot(dL_dpsi2_beta, self.psi1 * self.likelihood.precision.reshape(1, self.num_data))
self.dL_dpsi1 += 2.*np.dot(dL_dpsi2_beta, (self.psi1 * self.likelihood.precision.reshape(self.num_data, 1)).T).T
self.dL_dpsi2 = None
else:
dL_dpsi2 = self.likelihood.precision * dL_dpsi2_beta
@ -123,7 +124,7 @@ class SparseGP(GPBase):
self.dL_dpsi2 = np.repeat(dL_dpsi2[None, :, :], self.num_data, axis=0)
else:
# subsume back into psi1 (==Kmn)
self.dL_dpsi1 += 2.*np.dot(dL_dpsi2, self.psi1)
self.dL_dpsi1 += 2.*np.dot(self.psi1, dL_dpsi2)
self.dL_dpsi2 = None
@ -179,10 +180,10 @@ class SparseGP(GPBase):
Kmmi = tdot(Lmi.T)
diag_tr_psi2Kmmi = np.array([np.trace(psi2_Kmmi) for psi2_Kmmi in np.dot(self.psi2, Kmmi)])
self.likelihood.fit_FITC(self.Kmm, self.psi1, diag_tr_psi2Kmmi) # This uses the fit_FITC code, but does not perfomr a FITC-EP.#TODO solve potential confusion
self.likelihood.fit_FITC(self.Kmm, self.psi1.T, diag_tr_psi2Kmmi) # This uses the fit_FITC code, but does not perfomr a FITC-EP.#TODO solve potential confusion
# raise NotImplementedError, "EP approximation not implemented for uncertain inputs"
else:
self.likelihood.fit_DTC(self.Kmm, self.psi1)
self.likelihood.fit_DTC(self.Kmm, self.psi1.T)
# self.likelihood.fit_FITC(self.Kmm,self.psi1,self.psi0)
self._set_params(self._get_params()) # update the GP
@ -196,10 +197,10 @@ class SparseGP(GPBase):
dL_dtheta = self.kern.dK_dtheta(self.dL_dKmm, self.Z)
if self.has_uncertain_inputs:
dL_dtheta += self.kern.dpsi0_dtheta(self.dL_dpsi0, self.Z, self.X, self.X_variance)
dL_dtheta += self.kern.dpsi1_dtheta(self.dL_dpsi1.T, self.Z, self.X, self.X_variance)
dL_dtheta += self.kern.dpsi1_dtheta(self.dL_dpsi1, self.Z, self.X, self.X_variance)
dL_dtheta += self.kern.dpsi2_dtheta(self.dL_dpsi2, self.Z, self.X, self.X_variance)
else:
dL_dtheta += self.kern.dK_dtheta(self.dL_dpsi1, self.Z, self.X)
dL_dtheta += self.kern.dK_dtheta(self.dL_dpsi1, self.X, self.Z)
dL_dtheta += self.kern.dKdiag_dtheta(self.dL_dpsi0, self.X)
return dL_dtheta
@ -213,7 +214,7 @@ class SparseGP(GPBase):
dL_dZ += self.kern.dpsi1_dZ(self.dL_dpsi1, self.Z, self.X, self.X_variance)
dL_dZ += self.kern.dpsi2_dZ(self.dL_dpsi2, self.Z, self.X, self.X_variance)
else:
dL_dZ += self.kern.dK_dX(self.dL_dpsi1, self.Z, self.X)
dL_dZ += self.kern.dK_dX(self.dL_dpsi1.T, self.Z, self.X)
return dL_dZ
def _raw_predict(self, Xnew, X_variance_new=None, which_parts='all', full_cov=False):
@ -233,9 +234,9 @@ class SparseGP(GPBase):
Kxx = self.kern.Kdiag(Xnew, which_parts=which_parts)
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"
# assert which_p.Tarts=='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)
mu = np.dot(Kx.T, self.Cpsi1V)
if full_cov:
raise NotImplementedError, "TODO"
else: