mirror of
https://github.com/SheffieldML/GPy.git
synced 2026-05-13 05:52:38 +02:00
Scale Factor removed and moved V=Y*beta into likelihoods
This commit is contained in:
Max Zwiessele
parent
65ead17ff5
commit
f39afb9ea5
3 changed files with 140 additions and 122 deletions
|
|
@ -9,10 +9,10 @@ from .. import kern
|
|||
from GP import GP
|
||||
from scipy import linalg
|
||||
|
||||
def backsub_both_sides(L,X):
|
||||
def backsub_both_sides(L, X):
|
||||
""" Return L^-T * X * L^-1, assumuing X is symmetrical and L is lower cholesky"""
|
||||
tmp,_ = linalg.lapack.flapack.dtrtrs(L,np.asfortranarray(X),lower=1,trans=1)
|
||||
return linalg.lapack.flapack.dtrtrs(L,np.asfortranarray(tmp.T),lower=1,trans=1)[0].T
|
||||
tmp, _ = linalg.lapack.flapack.dtrtrs(L, np.asfortranarray(X), lower=1, trans=1)
|
||||
return linalg.lapack.flapack.dtrtrs(L, np.asfortranarray(tmp.T), lower=1, trans=1)[0].T
|
||||
|
||||
class sparse_GP(GP):
|
||||
"""
|
||||
|
|
@ -35,22 +35,22 @@ class sparse_GP(GP):
|
|||
"""
|
||||
|
||||
def __init__(self, X, likelihood, kernel, Z, X_variance=None, normalize_X=False):
|
||||
self.scale_factor = 100.0# a scaling factor to help keep the algorithm stable
|
||||
self.auto_scale_factor = False
|
||||
# self.scale_factor = 100.0 # a scaling factor to help keep the algorithm stable
|
||||
# self.auto_scale_factor = False
|
||||
self.Z = Z
|
||||
self.M = Z.shape[0]
|
||||
self.likelihood = likelihood
|
||||
|
||||
if X_variance is None:
|
||||
self.has_uncertain_inputs=False
|
||||
self.has_uncertain_inputs = False
|
||||
else:
|
||||
assert X_variance.shape==X.shape
|
||||
self.has_uncertain_inputs=True
|
||||
assert X_variance.shape == X.shape
|
||||
self.has_uncertain_inputs = True
|
||||
self.X_variance = X_variance
|
||||
|
||||
GP.__init__(self, X, likelihood, kernel=kernel, normalize_X=normalize_X)
|
||||
|
||||
#normalize X uncertainty also
|
||||
# normalize X uncertainty also
|
||||
if self.has_uncertain_inputs:
|
||||
self.X_variance /= np.square(self._Xstd)
|
||||
|
||||
|
|
@ -59,141 +59,152 @@ class sparse_GP(GP):
|
|||
# kernel computations, using BGPLVM notation
|
||||
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.psi2 = self.kern.psi2(self.Z,self.X, self.X_variance)
|
||||
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.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.Z, self.X)
|
||||
self.psi2 = None
|
||||
|
||||
def _computations(self):
|
||||
sf = self.scale_factor
|
||||
sf2 = sf**2
|
||||
# sf = self.scale_factor
|
||||
# sf2 = sf ** 2
|
||||
|
||||
#factor Kmm
|
||||
# factor Kmm
|
||||
self.Lm = jitchol(self.Kmm)
|
||||
|
||||
#The rather complex computations of self.A
|
||||
# The rather complex computations of self.A
|
||||
if self.likelihood.is_heteroscedastic:
|
||||
assert self.likelihood.D == 1 #TODO: what if the likelihood is heterscedatic and there are multiple independent outputs?
|
||||
assert self.likelihood.D == 1 # TODO: what if the likelihood is heterscedatic and there are multiple independent outputs?
|
||||
if self.has_uncertain_inputs:
|
||||
psi2_beta_scaled = (self.psi2*(self.likelihood.precision.flatten().reshape(self.N,1,1)/sf2)).sum(0)
|
||||
# psi2_beta_scaled = (self.psi2 * (self.likelihood.precision.flatten().reshape(self.N, 1, 1) / sf2)).sum(0)
|
||||
psi2_beta_scaled = (self.psi2 * (self.likelihood.precision.flatten().reshape(self.N, 1, 1))).sum(0)
|
||||
evals, evecs = linalg.eigh(psi2_beta_scaled)
|
||||
clipped_evals = np.clip(evals,0.,1e6) # TODO: make clipping configurable
|
||||
clipped_evals = np.clip(evals, 0., 1e6) # TODO: make clipping configurable
|
||||
if not np.allclose(evals, clipped_evals):
|
||||
print "Warning: clipping posterior eigenvalues"
|
||||
tmp = evecs*np.sqrt(clipped_evals)
|
||||
tmp, _ = linalg.lapack.flapack.dtrtrs(self.Lm,np.asfortranarray(tmp),lower=1)
|
||||
tmp = evecs * np.sqrt(clipped_evals)
|
||||
tmp, _ = linalg.lapack.flapack.dtrtrs(self.Lm, np.asfortranarray(tmp), lower=1)
|
||||
self.A = tdot(tmp)
|
||||
else:
|
||||
tmp = self.psi1*(np.sqrt(self.likelihood.precision.flatten().reshape(1,self.N))/sf)
|
||||
tmp, _ = linalg.lapack.flapack.dtrtrs(self.Lm,np.asfortranarray(tmp),lower=1)
|
||||
# tmp = self.psi1 * (np.sqrt(self.likelihood.precision.flatten().reshape(1, self.N)) / sf)
|
||||
tmp = self.psi1 * (np.sqrt(self.likelihood.precision.flatten().reshape(1, self.N)))
|
||||
tmp, _ = linalg.lapack.flapack.dtrtrs(self.Lm, np.asfortranarray(tmp), lower=1)
|
||||
self.A = tdot(tmp)
|
||||
else:
|
||||
if self.has_uncertain_inputs:
|
||||
psi2_beta_scaled = (self.psi2*(self.likelihood.precision/sf2)).sum(0)
|
||||
# psi2_beta_scaled = (self.psi2 * (self.likelihood.precision / sf2)).sum(0)
|
||||
psi2_beta_scaled = (self.psi2 * (self.likelihood.precision)).sum(0)
|
||||
evals, evecs = linalg.eigh(psi2_beta_scaled)
|
||||
clipped_evals = np.clip(evals,0.,1e6) # TODO: make clipping configurable
|
||||
clipped_evals = np.clip(evals, 0., 1e6) # TODO: make clipping configurable
|
||||
if not np.allclose(evals, clipped_evals):
|
||||
print "Warning: clipping posterior eigenvalues"
|
||||
tmp = evecs*np.sqrt(clipped_evals)
|
||||
tmp, _ = linalg.lapack.flapack.dtrtrs(self.Lm,np.asfortranarray(tmp),lower=1)
|
||||
tmp = evecs * np.sqrt(clipped_evals)
|
||||
tmp, _ = linalg.lapack.flapack.dtrtrs(self.Lm, np.asfortranarray(tmp), lower=1)
|
||||
self.A = tdot(tmp)
|
||||
else:
|
||||
tmp = self.psi1*(np.sqrt(self.likelihood.precision)/sf)
|
||||
tmp, _ = linalg.lapack.flapack.dtrtrs(self.Lm,np.asfortranarray(tmp),lower=1)
|
||||
# tmp = self.psi1 * (np.sqrt(self.likelihood.precision) / sf)
|
||||
tmp = self.psi1 * (np.sqrt(self.likelihood.precision))
|
||||
tmp, _ = linalg.lapack.flapack.dtrtrs(self.Lm, np.asfortranarray(tmp), lower=1)
|
||||
self.A = tdot(tmp)
|
||||
|
||||
#factor B
|
||||
self.B = np.eye(self.M)/sf2 + self.A
|
||||
# factor B
|
||||
# self.B = np.eye(self.M) / sf2 + self.A
|
||||
self.B = np.eye(self.M) + self.A
|
||||
self.LB = jitchol(self.B)
|
||||
|
||||
self.V = (self.likelihood.precision/self.scale_factor)*self.likelihood.Y
|
||||
self.psi1V = np.dot(self.psi1, self.V)
|
||||
# TODO: make a switch for either first compute psi1V, or VV.T
|
||||
self.psi1V = np.dot(self.psi1, self.likelihood.V)
|
||||
|
||||
#back substutue C into psi1V
|
||||
tmp,info1 = linalg.lapack.flapack.dtrtrs(self.Lm,np.asfortranarray(self.psi1V),lower=1,trans=0)
|
||||
self._LBi_Lmi_psi1V,_ = linalg.lapack.flapack.dtrtrs(self.LB,np.asfortranarray(tmp),lower=1,trans=0)
|
||||
tmp,info2 = linalg.lapack.flapack.dpotrs(self.LB,tmp,lower=1)
|
||||
self.Cpsi1V,info3 = linalg.lapack.flapack.dtrtrs(self.Lm,tmp,lower=1,trans=1)
|
||||
# back substutue C into psi1V
|
||||
tmp, info1 = linalg.lapack.flapack.dtrtrs(self.Lm, np.asfortranarray(self.psi1V), lower=1, trans=0)
|
||||
self._LBi_Lmi_psi1V, _ = linalg.lapack.flapack.dtrtrs(self.LB, np.asfortranarray(tmp), lower=1, trans=0)
|
||||
tmp, info2 = linalg.lapack.flapack.dpotrs(self.LB, tmp, lower=1)
|
||||
self.Cpsi1V, info3 = linalg.lapack.flapack.dtrtrs(self.Lm, tmp, lower=1, trans=1)
|
||||
|
||||
# Compute dL_dKmm
|
||||
tmp = tdot(self._LBi_Lmi_psi1V)
|
||||
self.DBi_plus_BiPBi = backsub_both_sides(self.LB, self.D*np.eye(self.M) + tmp)
|
||||
tmp = -0.5*self.DBi_plus_BiPBi/sf2
|
||||
tmp += -0.5*self.B*sf2*self.D
|
||||
tmp += self.D*np.eye(self.M)
|
||||
self.dL_dKmm = backsub_both_sides(self.Lm,tmp)
|
||||
self.DBi_plus_BiPBi = backsub_both_sides(self.LB, self.D * np.eye(self.M) + tmp)
|
||||
# tmp = -0.5 * self.DBi_plus_BiPBi / sf2
|
||||
# tmp += -0.5 * self.B * sf2 * self.D
|
||||
tmp = -0.5 * self.DBi_plus_BiPBi
|
||||
tmp += -0.5 * self.B * self.D
|
||||
tmp += self.D * np.eye(self.M)
|
||||
self.dL_dKmm = backsub_both_sides(self.Lm, tmp)
|
||||
|
||||
# Compute dL_dpsi # FIXME: this is untested for the heterscedastic + uncertain inputs case
|
||||
self.dL_dpsi0 = - 0.5 * self.D * (self.likelihood.precision * np.ones([self.N,1])).flatten()
|
||||
self.dL_dpsi1 = np.dot(self.Cpsi1V,self.V.T)
|
||||
dL_dpsi2_beta = 0.5*backsub_both_sides(self.Lm,self.D*np.eye(self.M) - self.DBi_plus_BiPBi)
|
||||
self.dL_dpsi0 = -0.5 * self.D * (self.likelihood.precision * np.ones([self.N, 1])).flatten()
|
||||
self.dL_dpsi1 = np.dot(self.Cpsi1V, self.likelihood.V.T)
|
||||
dL_dpsi2_beta = 0.5 * backsub_both_sides(self.Lm, self.D * np.eye(self.M) - self.DBi_plus_BiPBi)
|
||||
if self.likelihood.is_heteroscedastic:
|
||||
if self.has_uncertain_inputs:
|
||||
self.dL_dpsi2 = self.likelihood.precision[:,None,None]*dL_dpsi2_beta[None,:,:]
|
||||
self.dL_dpsi2 = self.likelihood.precision[:, None, None] * dL_dpsi2_beta[None, :, :]
|
||||
else:
|
||||
self.dL_dpsi1 += 2.*np.dot(dL_dpsi2_beta,self.psi1*self.likelihood.precision.reshape(1,self.N))
|
||||
self.dL_dpsi1 += 2.*np.dot(dL_dpsi2_beta, self.psi1 * self.likelihood.precision.reshape(1, self.N))
|
||||
self.dL_dpsi2 = None
|
||||
else:
|
||||
dL_dpsi2 = self.likelihood.precision*dL_dpsi2_beta
|
||||
dL_dpsi2 = self.likelihood.precision * dL_dpsi2_beta
|
||||
if self.has_uncertain_inputs:
|
||||
#repeat for each of the N psi_2 matrices
|
||||
self.dL_dpsi2 = np.repeat(dL_dpsi2[None,:,:],self.N,axis=0)
|
||||
# repeat for each of the N psi_2 matrices
|
||||
self.dL_dpsi2 = np.repeat(dL_dpsi2[None, :, :], self.N, axis=0)
|
||||
else:
|
||||
#subsume back into psi1 (==Kmn)
|
||||
self.dL_dpsi1 += 2.*np.dot(dL_dpsi2,self.psi1)
|
||||
# subsume back into psi1 (==Kmn)
|
||||
self.dL_dpsi1 += 2.*np.dot(dL_dpsi2, self.psi1)
|
||||
self.dL_dpsi2 = None
|
||||
|
||||
|
||||
#the partial derivative vector for the likelihood
|
||||
if self.likelihood.Nparams ==0:
|
||||
#save computation here.
|
||||
# the partial derivative vector for the likelihood
|
||||
if self.likelihood.Nparams == 0:
|
||||
# save computation here.
|
||||
self.partial_for_likelihood = None
|
||||
elif self.likelihood.is_heteroscedastic:
|
||||
raise NotImplementedError, "heteroscedatic derivates not implemented"
|
||||
else:
|
||||
#likelihood is not heterscedatic
|
||||
self.partial_for_likelihood = - 0.5 * self.N*self.D*self.likelihood.precision + 0.5 * self.likelihood.trYYT*self.likelihood.precision**2
|
||||
self.partial_for_likelihood += 0.5 * self.D * (self.psi0.sum()*self.likelihood.precision**2 - np.trace(self.A)*self.likelihood.precision*sf2)
|
||||
self.partial_for_likelihood += self.likelihood.precision*(0.5*np.sum(self.A*self.DBi_plus_BiPBi) - np.sum(np.square(self._LBi_Lmi_psi1V)))
|
||||
# likelihood is not heterscedatic
|
||||
self.partial_for_likelihood = -0.5 * self.N * self.D * self.likelihood.precision + 0.5 * self.likelihood.trYYT * self.likelihood.precision ** 2
|
||||
# self.partial_for_likelihood += 0.5 * self.D * (self.psi0.sum() * self.likelihood.precision ** 2 - np.trace(self.A) * self.likelihood.precision * sf2)
|
||||
self.partial_for_likelihood += 0.5 * self.D * (self.psi0.sum() * self.likelihood.precision ** 2 - np.trace(self.A) * self.likelihood.precision)
|
||||
self.partial_for_likelihood += self.likelihood.precision * (0.5 * np.sum(self.A * self.DBi_plus_BiPBi) - np.sum(np.square(self._LBi_Lmi_psi1V)))
|
||||
|
||||
|
||||
|
||||
def log_likelihood(self):
|
||||
""" Compute the (lower bound on the) log marginal likelihood """
|
||||
sf2 = self.scale_factor**2
|
||||
# sf2 = self.scale_factor ** 2
|
||||
if self.likelihood.is_heteroscedastic:
|
||||
A = -0.5*self.N*self.D*np.log(2.*np.pi) +0.5*np.sum(np.log(self.likelihood.precision)) -0.5*np.sum(self.V*self.likelihood.Y)
|
||||
B = -0.5*self.D*(np.sum(self.likelihood.precision.flatten()*self.psi0) - np.trace(self.A)*sf2)
|
||||
A = -0.5 * self.N * self.D * np.log(2.*np.pi) + 0.5 * np.sum(np.log(self.likelihood.precision)) - 0.5 * np.sum(self.likelihood.V * self.likelihood.Y)
|
||||
# B = -0.5 * self.D * (np.sum(self.likelihood.precision.flatten() * self.psi0) - np.trace(self.A) * sf2)
|
||||
B = -0.5 * self.D * (np.sum(self.likelihood.precision.flatten() * self.psi0) - np.trace(self.A))
|
||||
else:
|
||||
A = -0.5*self.N*self.D*(np.log(2.*np.pi) + np.log(self.likelihood._variance)) -0.5*self.likelihood.precision*self.likelihood.trYYT
|
||||
B = -0.5*self.D*(np.sum(self.likelihood.precision*self.psi0) - np.trace(self.A)*sf2)
|
||||
C = -self.D * (np.sum(np.log(np.diag(self.LB))) + 0.5*self.M*np.log(sf2))
|
||||
D = 0.5*np.sum(np.square(self._LBi_Lmi_psi1V))
|
||||
return A+B+C+D
|
||||
A = -0.5 * self.N * self.D * (np.log(2.*np.pi) + np.log(self.likelihood._variance)) - 0.5 * self.likelihood.precision * self.likelihood.trYYT
|
||||
# B = -0.5 * self.D * (np.sum(self.likelihood.precision * self.psi0) - np.trace(self.A) * sf2)
|
||||
B = -0.5 * self.D * (np.sum(self.likelihood.precision * self.psi0) - np.trace(self.A))
|
||||
# C = -self.D * (np.sum(np.log(np.diag(self.LB))) + 0.5 * self.M * np.log(sf2))
|
||||
C = -self.D * (np.sum(np.log(np.diag(self.LB)))) # + 0.5 * self.M * np.log(sf2))
|
||||
D = 0.5 * np.sum(np.square(self._LBi_Lmi_psi1V))
|
||||
return A + B + C + D
|
||||
|
||||
def _set_params(self, p):
|
||||
self.Z = p[:self.M*self.Q].reshape(self.M, self.Q)
|
||||
self.kern._set_params(p[self.Z.size:self.Z.size+self.kern.Nparam])
|
||||
self.likelihood._set_params(p[self.Z.size+self.kern.Nparam:])
|
||||
self.Z = p[:self.M * self.Q].reshape(self.M, self.Q)
|
||||
self.kern._set_params(p[self.Z.size:self.Z.size + self.kern.Nparam])
|
||||
self.likelihood._set_params(p[self.Z.size + self.kern.Nparam:])
|
||||
self._compute_kernel_matrices()
|
||||
#if self.auto_scale_factor:
|
||||
# if self.auto_scale_factor:
|
||||
# self.scale_factor = np.sqrt(self.psi2.sum(0).mean()*self.likelihood.precision)
|
||||
#if self.auto_scale_factor:
|
||||
#if self.likelihood.is_heteroscedastic:
|
||||
#self.scale_factor = max(100,np.sqrt(self.psi2_beta_scaled.sum(0).mean()))
|
||||
#else:
|
||||
#self.scale_factor = np.sqrt(self.psi2.sum(0).mean()*self.likelihood.precision)
|
||||
self.scale_factor = 1.
|
||||
# if self.auto_scale_factor:
|
||||
# if self.likelihood.is_heteroscedastic:
|
||||
# self.scale_factor = max(100,np.sqrt(self.psi2_beta_scaled.sum(0).mean()))
|
||||
# else:
|
||||
# self.scale_factor = np.sqrt(self.psi2.sum(0).mean()*self.likelihood.precision)
|
||||
# self.scale_factor = 100.
|
||||
self._computations()
|
||||
|
||||
def _get_params(self):
|
||||
return np.hstack([self.Z.flatten(),GP._get_params(self)])
|
||||
return np.hstack([self.Z.flatten(), GP._get_params(self)])
|
||||
|
||||
def _get_param_names(self):
|
||||
return sum([['iip_%i_%i'%(i,j) for j in range(self.Z.shape[1])] for i in range(self.Z.shape[0])],[]) + GP._get_param_names(self)
|
||||
return sum([['iip_%i_%i' % (i, j) for j in range(self.Z.shape[1])] for i in range(self.Z.shape[0])], []) + GP._get_param_names(self)
|
||||
|
||||
def update_likelihood_approximation(self):
|
||||
"""
|
||||
|
|
@ -205,9 +216,9 @@ class sparse_GP(GP):
|
|||
if self.has_uncertain_inputs:
|
||||
raise NotImplementedError, "EP approximation not implemented for uncertain inputs"
|
||||
else:
|
||||
self.likelihood.fit_DTC(self.Kmm,self.psi1)
|
||||
#self.likelihood.fit_FITC(self.Kmm,self.psi1,self.psi0)
|
||||
self._set_params(self._get_params()) # update the GP
|
||||
self.likelihood.fit_DTC(self.Kmm, self.psi1)
|
||||
# self.likelihood.fit_FITC(self.Kmm,self.psi1,self.psi0)
|
||||
self._set_params(self._get_params()) # update the GP
|
||||
|
||||
|
||||
def _log_likelihood_gradients(self):
|
||||
|
|
@ -217,13 +228,13 @@ class sparse_GP(GP):
|
|||
"""
|
||||
Compute and return the derivative of the log marginal likelihood wrt the parameters of the kernel
|
||||
"""
|
||||
dL_dtheta = self.kern.dK_dtheta(self.dL_dKmm,self.Z)
|
||||
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.dpsi2_dtheta(self.dL_dpsi2, self.Z,self.X, self.X_variance)
|
||||
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.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.Z, self.X)
|
||||
dL_dtheta += self.kern.dKdiag_dtheta(self.dL_dpsi0, self.X)
|
||||
|
||||
return dL_dtheta
|
||||
|
|
@ -243,17 +254,17 @@ class sparse_GP(GP):
|
|||
def _raw_predict(self, Xnew, which_parts='all', full_cov=False):
|
||||
"""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)
|
||||
Kmmi_LmiBLmi = backsub_both_sides(self.Lm,np.eye(self.M) - Bi)
|
||||
Kmmi_LmiBLmi = backsub_both_sides(self.Lm, np.eye(self.M) - Bi)
|
||||
|
||||
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 / self.scale_factor)
|
||||
if full_cov:
|
||||
Kxx = self.kern.K(Xnew,which_parts=which_parts)
|
||||
var = Kxx - mdot(Kx.T, Kmmi_LmiBLmi, Kx) #NOTE this won't work for plotting
|
||||
Kxx = self.kern.K(Xnew, which_parts=which_parts)
|
||||
var = Kxx - mdot(Kx.T, Kmmi_LmiBLmi, Kx) # NOTE this won't work for plotting
|
||||
else:
|
||||
Kxx = self.kern.Kdiag(Xnew,which_parts=which_parts)
|
||||
var = Kxx - np.sum(Kx*np.dot(Kmmi_LmiBLmi, Kx),0)
|
||||
Kxx = self.kern.Kdiag(Xnew, which_parts=which_parts)
|
||||
var = Kxx - np.sum(Kx * np.dot(Kmmi_LmiBLmi, Kx), 0)
|
||||
|
||||
return mu,var[:,None]
|
||||
return mu, var[:, None]
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue