apunkt/GPy
1
0
Fork 0
mirror of https://github.com/SheffieldML/GPy.git synced 2026-07-05 16:02:13 +02:00
Code Issues Projects Releases Packages Wiki Activity Actions

Made more numerically stable in a hope that it will work and I will find a bug...

Browse source
This commit is contained in:
Alan Saul 2013-05-30 16:17:37 +01:00
parent 23ed2a2d15
commit 20227fb2ac
4 changed files with 39 additions and 28 deletions
Download patch file Download diff file Expand all files Collapse all files

7
GPy/models/GP.py
View file

@ -69,7 +69,6 @@ class GP(model):
self.likelihood._set_params(p[self.kern.Nparam_transformed():]) # test by Nicolas
if isinstance(self.likelihood, Laplace):
print "Updating approx: ", p
self.likelihood.fit_full(self.kern.K(self.X))
self.likelihood._set_params(self.likelihood._get_params())
@ -134,7 +133,6 @@ class GP(model):
matrix K* = K + diag(1./tau_tilde) plus a normalization term.
"""
l = -0.5 * self.D * self.K_logdet + self._model_fit_term() + self.likelihood.Z
print "Log likelihood: ", l
return l
def _log_likelihood_gradients(self):
@ -145,17 +143,16 @@ class GP(model):
"""
dL_dthetaK = self.kern.dK_dtheta(dL_dK=self.dL_dK, X=self.X)
if isinstance(self.likelihood, Laplace):
dL_dthetaK_explicit = dL_dthetaK
#Need to pass in a matrix of ones to get access to raw dK_dthetaK values without being chained
fake_dL_dKs = np.ones(self.dL_dK.shape) #FIXME: Check this is right...
dK_dthetaK = self.kern.dK_dtheta(dL_dK=fake_dL_dKs, X=self.X)
dL_dthetaK = self.likelihood._Kgradients(dL_d_K_Sigma=self.dL_dK, dK_dthetaK=dK_dthetaK)
dL_dthetaL = self.likelihood._gradients(partial=np.diag(self.dL_dK))
print "Stacked dL_dthetaK, dL_dthetaL: ", np.hstack((dL_dthetaK, dL_dthetaL))
#print "Stacked dL_dthetaK, dL_dthetaL: ", np.hstack((dL_dthetaK, dL_dthetaL))
else:
dL_dthetaL = self.likelihood._gradients(partial=np.diag(self.dL_dK))
print "Stacked dL_dthetaK, dL_dthetaL: ", np.hstack((dL_dthetaK, dL_dthetaL))
#print "Stacked dL_dthetaK, dL_dthetaL: ", np.hstack((dL_dthetaK, dL_dthetaL))
return np.hstack((dL_dthetaK, dL_dthetaL))
#return np.hstack((self.kern.dK_dtheta(dL_dK=self.dL_dK, X=self.X), self.likelihood._gradients(partial=np.diag(self.dL_dK))))