apunkt/GPy
1
0
Fork 0
mirror of https://github.com/SheffieldML/GPy.git synced 2026-05-24 14:15:14 +02:00
Code Issues Projects Releases Packages Wiki Activity Actions

sparse GP now checkgrads, optimises sensibly. Predicitno still not working

Browse source
This commit is contained in:
James Hensman 2014-01-30 12:03:02 +00:00
parent a93312c306
commit e0dbfbe148
6 changed files with 81 additions and 47 deletions
Download patch file Download diff file Expand all files Collapse all files

5
GPy/kern/kern.py
View file

@ -284,9 +284,10 @@ class kern(Parameterized):
[p.update_gradients_full(dL_dK, X) for p in self._parameters_]
def update_gradients_sparse(self, dL_dKmm, dL_dKnm, dL_dKdiag, X, Z):
raise NotImplementedError
[p.update_gradients_sparse(dL_dKmm, dL_dKnm, dL_dKdiag, X, Z) for p in self._parameters_]
def update_gradients_variational(self, dL_dKmm, dL_dpsi0, dL_dpsi1, dL_dpsi2, mu, S, Z):
raise NotImplementedError
[p.update_gradients_variational(dL_dKmm, dL_dpsi0, dL_dpsi1, dL_dpsi2, mu, S, Z) for p in self._parameters_]
def dK_dtheta(self, dL_dK, X, X2=None):
"""

2
GPy/kern/parts/rbf.py
View file

@ -117,7 +117,7 @@ class RBF(Kernpart):
self.lengthscales.gradient = self._dL_dlengthscales_via_K(dL_dKnm, X, Z)
else:
self.lengthscale.gradient = (self.variance / self.lengthscale) * np.sum(self._K_dvar * self._K_dist2 * dL_dKmm)
self.lengthscale.gradient = (self.variance / self.lengthscale) * np.sum(self._K_dvar * self._K_dist2 * dL_dKnm)
#from Kmm
self._K_computations(Z, None)

2
GPy/kern/parts/white.py
View file

@ -32,7 +32,7 @@ class White(Kernpart):
self.variance.gradient = np.trace(dL_dK)
def update_gradients_sparse(self, dL_dKmm, dL_dKnm, dL_dKdiag, X, Z):
raise NotImplementedError
self.variance.gradient = np.trace(dL_dKmm) + np.sum(dL_dKdiag)
def update_gradients_variational(self, dL_dKmm, dL_dpsi0, dL_dpsi1, dL_dpsi2, mu, S, Z):
raise NotImplementedError