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Almost have likelihood gradients working but kernels still way off

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Alan Saul 2013-05-13 18:36:02 +01:00
parent 9500b12b53
commit 5472c5c6ba
4 changed files with 91 additions and 60 deletions
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20
GPy/models/GP.py
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@ -66,6 +66,10 @@ class GP(model):
# self.likelihood._set_params(p[self.kern.Nparam:]) # test by Nicolas
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())
self.K = self.kern.K(self.X)
self.K += self.likelihood.covariance_matrix
@ -87,14 +91,12 @@ class GP(model):
return self.kern._get_param_names_transformed() + self.likelihood._get_param_names()
def _update_params_callback(self, p):
#FIXME:Check the transforming
#Set the new parameters of the kernel and likelihood within the optimization
import ipdb; ipdb.set_trace() ### XXX BREAKPOINT
#parameters will be in transformed space
self.kern._set_params_transformed(p[:self.kern.Nparam_transformed()])
#set_params_transformed for likelihood doesn't exist?
self.likelihood._set_params(p[self.kern.Nparam_transformed():])
#update the likelihood approximation within the optimisation with the current parameters
self.update_likelihood_approximation()
import ipdb; ipdb.set_trace() ### XXX BREAKPOINT
def update_likelihood_approximation(self):
"""
@ -123,7 +125,9 @@ class GP(model):
model for a new variable Y* = v_tilde/tau_tilde, with a covariance
matrix K* = K + diag(1./tau_tilde) plus a normalization term.
"""
return -0.5 * self.D * self.K_logdet + self._model_fit_term() + self.likelihood.Z
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):
"""
@ -135,7 +139,7 @@ class GP(model):
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.eye(self.dL_dK.shape[0])
fake_dL_dKs = np.eye(self.dL_dK.shape[0]) #FIXME: Check this is right...
dK_dthetaK = self.kern.dK_dtheta(dL_dK=fake_dL_dKs, X=self.X)
#We need the dL_dK where K is equal to the prior K, not K+Sigma as is the case now
@ -145,13 +149,11 @@ class GP(model):
#print "dL_dthetaK_explicit: {dldkx} dL_dthetaK_implicit: {dldki} dL_dthetaK: {dldk}".format(dldkx=dL_dthetaK_explicit, dldki=dL_dthetaK_implicit, dldk=dL_dthetaK)
dL_dthetaL = self.likelihood._gradients(partial=np.diag(self.dL_dK))
#print "dL_dthetaL: ", dL_dthetaL
print "Stacked dL_dthetaK, dL_dthetaL: ", np.hstack((dL_dthetaK, dL_dthetaL))
import ipdb; ipdb.set_trace() ### XXX BREAKPOINT
else:
#print "dL_dthetaK: ", dL_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 "dL_dthetaL: ", 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))))