apunkt/GPy
1
0
Fork 0
mirror of https://github.com/SheffieldML/GPy.git synced 2026-05-11 21:12:38 +02:00
Code Issues Projects Releases Packages Wiki Activity Actions

starting to sort out likelihoods WARNING not working

Browse source
This commit is contained in:
Max Zwiessele 2013-10-17 14:38:43 +01:00
parent bc765134c0
commit 055eb3b70b
10 changed files with 247 additions and 142 deletions
Download patch file Download diff file Expand all files Collapse all files

11
GPy/core/gp.py
View file

@ -25,22 +25,18 @@ class GP(GPBase):
"""
def __init__(self, X, likelihood, kernel, normalize_X=False):
GPBase.__init__(self, X, likelihood, kernel, normalize_X=normalize_X)
self._set_params(self._get_params())
#self._set_params(self._get_params())
def getstate(self):
return GPBase.getstate(self)
def setstate(self, state):
GPBase.setstate(self, state)
self._set_params(self._get_params())
def _set_params(self, p):
self.kern._set_params_transformed(p[:self.kern.num_params_transformed()])
self.likelihood._set_params(p[self.kern.num_params_transformed():])
#self._set_params(self._get_params())
def parameters_changed(self):
self.K = self.kern.K(self.X)
self.K += self.likelihood.covariance_matrix
self.Ki, self.L, self.Li, self.K_logdet = pdinv(self.K)
# the gradient of the likelihood wrt the covariance matrix
@ -102,6 +98,7 @@ class GP(GPBase):
Note, we use the chain rule: dL_dtheta = dL_dK * d_K_dtheta
"""
#return np.hstack((self.kern.dK_dtheta(dL_dK=self.dL_dK, X=self.X), self.likelihood._gradients(partial=np.diag(self.dL_dK))))
if not isinstance(self.likelihood,EP):
tmp = np.hstack((self.kern.dK_dtheta(dL_dK=self.dL_dK, X=self.X), self.likelihood._gradients(partial=np.diag(self.dL_dK))))
else: