apunkt/GPy
1
0
Fork 0
mirror of https://github.com/SheffieldML/GPy.git synced 2026-05-10 12:32:40 +02:00
Code Issues Projects Releases Packages Wiki Activity Actions

MRD updates and minor changes

Browse source
This commit is contained in:
Max Zwiessele 2013-05-10 11:21:19 +01:00
parent 83275c03e1
commit 214eab5f2e
3 changed files with 10 additions and 8 deletions
Download patch file Download diff file Expand all files Collapse all files

10
GPy/models/Bayesian_GPLVM.py
View file

@ -136,14 +136,16 @@ class Bayesian_GPLVM(sparse_GP, GPLVM):
self._savedparams.append([self.f_call, self._get_params()])
self._savedgradients.append([self.f_call, self._log_likelihood_gradients()])
self._savedpsiKmm.append([self.f_call, [self.Kmm, self.dL_dKmm]])
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)
# 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))
# 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))
D = 0.5 * np.sum(np.square(self._LBi_Lmi_psi1V))
self._savedABCD.append([self.f_call, A, B, C, D])

2
GPy/models/mrd.py
View file

@ -273,7 +273,7 @@ class MRD(model):
def _handle_plotting(self, fig_num, axes, plotf):
if axes is None:
fig = pylab.figure(num=fig_num, figsize=(4 * len(self.bgplvms), 3 * len(self.bgplvms)))
fig = pylab.figure(num=fig_num, figsize=(4 * len(self.bgplvms), 3))
for i, g in enumerate(self.bgplvms):
if axes is None:
ax = fig.add_subplot(1, len(self.bgplvms), i + 1)

2
GPy/models/sparse_GP.py
View file

@ -177,7 +177,7 @@ class sparse_GP(GP):
# 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
A = -0.5 * self.N * self.D * (np.log(2.*np.pi) - np.log(self.likelihood.precision)) - 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))