apunkt/GPy
1
0
Fork 0
mirror of https://github.com/SheffieldML/GPy.git synced 2026-05-15 06:52:39 +02:00
Code Issues Projects Releases Packages Wiki Activity Actions

some changes to product_orthogonal

Browse source 
dKdiag_dX is now implemented, some of the cod eis a little tidier
This commit is contained in:
James Hensman 2013-03-07 16:01:00 +00:00
parent 9a97ad7348
commit af510d166a
1 changed files with 16 additions and 14 deletions
Download patch file Download diff file Expand all files Collapse all files

30
GPy/kern/product_orthogonal.py
View file

@ -36,13 +36,13 @@ class product_orthogonal(kernpart):
def _get_param_names(self):
"""return parameter names."""
return [self.k1.name + '_' + param_name for param_name in self.k1._get_param_names()] + [self.k2.name + '_' + param_name for param_name in self.k2._get_param_names()]
def K(self,X,X2,target):
"""Compute the covariance matrix between X and X2."""
if X2 is None: X2 = X
target1 = np.zeros((X.shape[0],X2.shape[0]))
target2 = np.zeros((X.shape[0],X2.shape[0]))
self.k1.K(X[:,0:self.k1.D],X2[:,0:self.k1.D],target1)
self.k1.K(X[:,:self.k1.D],X2[:,:self.k1.D],target1)
self.k2.K(X[:,self.k1.D:],X2[:,self.k1.D:],target2)
target += target1 * target2
@ -51,21 +51,16 @@ class product_orthogonal(kernpart):
if X2 is None: X2 = X
K1 = np.zeros((X.shape[0],X2.shape[0]))
K2 = np.zeros((X.shape[0],X2.shape[0]))
self.k1.K(X[:,0:self.k1.D],X2[:,0:self.k1.D],K1)
self.k1.K(X[:,:self.k1.D],X2[:,:self.k1.D],K1)
self.k2.K(X[:,self.k1.D:],X2[:,self.k1.D:],K2)
k1_target = np.zeros(self.k1.Nparam)
k2_target = np.zeros(self.k2.Nparam)
self.k1.dK_dtheta(partial*K2, X[:,:self.k1.D], X2[:,:self.k1.D], k1_target)
self.k2.dK_dtheta(partial*K1, X[:,self.k1.D:], X2[:,self.k1.D:], k2_target)
target[:self.k1.Nparam] += k1_target
target[self.k1.Nparam:] += k2_target
self.k1.dK_dtheta(partial*K2, X[:,:self.k1.D], X2[:,:self.k1.D], target[:self.k1.Nparam])
self.k2.dK_dtheta(partial*K1, X[:,self.k1.D:], X2[:,self.k1.D:], target[self.k1.Nparam:])
def Kdiag(self,X,target):
"""Compute the diagonal of the covariance matrix associated to X."""
target1 = np.zeros((X.shape[0],))
target2 = np.zeros((X.shape[0],))
target1 = np.zeros(X.shape[0])
target2 = np.zeros(X.shape[0])
self.k1.Kdiag(X[:,:self.k1.D],target1)
self.k2.Kdiag(X[:,self.k1.D:],target2)
target += target1 * target2
@ -89,5 +84,12 @@ class product_orthogonal(kernpart):
self.k1.dK_dX(partial*K2, X[:,:self.k1.D], X2[:,:self.k1.D], target)
self.k2.dK_dX(partial*K1, X[:,self.k1.D:], X2[:,self.k1.D:], target)
def dKdiag_dX(self,X,target):
pass
def dKdiag_dX(self, partial, X, target):
K1 = np.zeros(X.shape[0])
K2 = np.zeros(X.shape[0])
self.k1.Kdiag(X[:,0:self.k1.D],K1)
self.k2.Kdiag(X[:,self.k1.D:],K2)
self.k1.dK_dX(partial*K2, X[:,:self.k1.D], target)
self.k2.dK_dX(partial*K1, X[:,self.k1.D:], target)