apunkt/GPy
1
0
Fork 0
mirror of https://github.com/SheffieldML/GPy.git synced 2026-05-03 08:42:39 +02:00
Code Issues Projects Releases Packages Wiki Activity Actions

re-enstated compute_kernel_matrices

Browse source
This commit is contained in:
James Hensman 2013-03-04 12:43:05 +00:00
parent 0e46e309e4
commit 053e6519c8
1 changed files with 23 additions and 14 deletions
Download patch file Download diff file Expand all files Collapse all files

33
GPy/models/sparse_GP.py
View file

@ -58,6 +58,19 @@ class sparse_GP(GP):
if self.has_uncertain_inputs: if self.has_uncertain_inputs:
self.X_uncertainty /= np.square(self._Xstd) self.X_uncertainty /= np.square(self._Xstd)
def _compute_kernel_matrices(self):
# 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_uncertainty)
self.psi1 = self.kern.psi1(self.Z,self.X, self.X_uncertainty).T
self.psi2 = self.kern.psi2(self.Z,self.X, self.X_uncertainty)
else:
self.psi0 = self.kern.Kdiag(self.X,slices=self.Xslices)
self.psi1 = self.kern.K(self.Z,self.X)
self.psi2 = None
def _computations(self): def _computations(self):
# TODO find routine to multiply triangular matrices # TODO find routine to multiply triangular matrices
#TODO: slices for psi statistics (easy enough) #TODO: slices for psi statistics (easy enough)
@ -65,22 +78,17 @@ class sparse_GP(GP):
sf = self.scale_factor sf = self.scale_factor
sf2 = sf**2 sf2 = sf**2
# kernel computations, using BGPLVM notation #The rather complex computations of psi2_beta_scaled
self.Kmm = self.kern.K(self.Z) if self.likelihood.is_heteroscedastic:
assert self.likelihood.D == 1 #TODO: what is the likelihood is heterscedatic and there are multiple independent outputs?
if self.has_uncertain_inputs: if self.has_uncertain_inputs:
self.psi0 = self.kern.psi0(self.Z,self.X, self.X_uncertainty)
self.psi1 = self.kern.psi1(self.Z,self.X, self.X_uncertainty).T
self.psi2 = self.kern.psi2(self.Z,self.X, self.X_uncertainty)
if self.likelihood.is_heteroscedastic:
self.psi2_beta_scaled = (self.psi2*(self.likelihood.precision.reshape(self.N,1,1)/sf2)).sum(0) self.psi2_beta_scaled = (self.psi2*(self.likelihood.precision.reshape(self.N,1,1)/sf2)).sum(0)
#TODO: what is the likelihood is heterscedatic and there are multiple independent outputs?
else: else:
tmp = self.psi1*(np.sqrt(self.likelihood.precision.reshape(1,self.N))/sf)
self.psi2_beta_scaled = np.dot(tmp,tmp.T)
else:
if self.has_uncertain_inputs:
self.psi2_beta_scaled = (self.psi2*(self.likelihood.precision/sf2)).sum(0) self.psi2_beta_scaled = (self.psi2*(self.likelihood.precision/sf2)).sum(0)
else:
self.psi0 = self.kern.Kdiag(self.X,slices=self.Xslices)
self.psi1 = self.kern.K(self.Z,self.X)
if self.likelihood.is_heteroscedastic:
tmp = self.psi1*(np.sqrt(self.likelihood.precision.reshape(self.N,1))/sf)
else: else:
tmp = self.psi1*(np.sqrt(self.likelihood.precision)/sf) tmp = self.psi1*(np.sqrt(self.likelihood.precision)/sf)
self.psi2_beta_scaled = np.dot(tmp,tmp.T) self.psi2_beta_scaled = np.dot(tmp,tmp.T)
@ -149,6 +157,7 @@ class sparse_GP(GP):
self.Z = p[:self.M*self.Q].reshape(self.M, self.Q) 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.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.likelihood._set_params(p[self.Z.size+self.kern.Nparam:])
self._compute_kernel_matrices()
self._computations() self._computations()
def _get_params(self): def _get_params(self):