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

changing all parameterized objects to be compatible with the new parameterization

Browse source
This commit is contained in:
Max Zwiessele 2013-10-25 15:29:04 +01:00
parent e1bee4536a
commit d3721b76a8
21 changed files with 645 additions and 529 deletions
Download patch file Download diff file Expand all files Collapse all files

34
GPy/core/gp.py
View file

@ -23,6 +23,7 @@ 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())
def getstate(self):
@ -89,21 +90,26 @@ class GP(GPBase):
return (-0.5 * self.num_data * self.output_dim * np.log(2.*np.pi) -
0.5 * self.output_dim * self.K_logdet + self._model_fit_term() + self.likelihood.Z)
# def _log_likelihood_gradients(self):
# """
# The gradient of all parameters.
#
# 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:
# tmp = np.hstack((self.kern.dK_dtheta(dL_dK=self.dL_dK, X=self.X), self.likelihood._gradients(partial=np.diag(self.dL_dK))))
# return tmp
def dL_dtheta(self):
return self.kern.dK_dtheta(self.dL_dK, self.X)
def _log_likelihood_gradients(self):
"""
The gradient of all parameters.
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:
tmp = np.hstack((self.kern.dK_dtheta(dL_dK=self.dL_dK, X=self.X), self.likelihood._gradients(partial=np.diag(self.dL_dK))))
return tmp
def dL_dlikelihood(self):
return self.likelihood._gradients(partial=np.diag(self.dL_dK))
def _raw_predict(self, _Xnew, which_parts='all', full_cov=False, stop=False):
"""
Internal helper function for making predictions, does not account