apunkt/GPy
1
0
Fork 0
mirror of https://github.com/SheffieldML/GPy.git synced 2026-05-09 12:02:38 +02:00
Code Issues Projects Releases Packages Wiki Activity Actions

prod_orthogonal now caches the K matrices

Browse source
This commit is contained in:
James Hensman 2013-04-23 10:56:10 +01:00
parent f145141923
commit f35578804a
2 changed files with 38 additions and 28 deletions
Download patch file Download diff file Expand all files Collapse all files

13
GPy/kern/coregionalise.py
View file

@ -62,11 +62,16 @@ class coregionalise(kernpart):
ii,jj = np.meshgrid(index,index2) ii,jj = np.meshgrid(index,index2)
ii,jj = ii.T, jj.T ii,jj = ii.T, jj.T
#dL_dK_small = np.zeros_like(self.B)
#for i in range(self.Nout):
#for j in range(self.Nout):
#tmp = np.sum(dL_dK[(ii==i)*(jj==j)])
#dL_dK_small[i,j] = tmp
#as above, but slightly faster
dL_dK_small = np.zeros_like(self.B) dL_dK_small = np.zeros_like(self.B)
for i in range(self.Nout): where_i = [ii==i for i in xrange(self.Nout)]
for j in range(self.Nout): where_j = [jj==j for j in xrange(self.Nout)]
tmp = np.sum(dL_dK[(ii==i)*(jj==j)]) [[np.put(dL_dK_small,i+self.Nout*j,np.sum(dL_dK[np.logical_and(wi,wj)])) for i,wi in enumerate(where_i)] for j,wj in enumerate(where_j)]
dL_dK_small[i,j] = tmp
dkappa = np.diag(dL_dK_small) dkappa = np.diag(dL_dK_small)
dL_dK_small += dL_dK_small.T dL_dK_small += dL_dK_small.T

53
GPy/kern/prod_orthogonal.py
View file

@ -22,6 +22,7 @@ class prod_orthogonal(kernpart):
self.k1 = k1 self.k1 = k1
self.k2 = k2 self.k2 = k2
self._set_params(np.hstack((k1._get_params(),k2._get_params()))) self._set_params(np.hstack((k1._get_params(),k2._get_params())))
self._X, self._X2, self._params = np.empty(shape=(3,1)) # initialize cache
def _get_params(self): def _get_params(self):
"""return the value of the parameters.""" """return the value of the parameters."""
@ -39,23 +40,38 @@ class prod_orthogonal(kernpart):
def K(self,X,X2,target): def K(self,X,X2,target):
"""Compute the covariance matrix between X and X2.""" """Compute the covariance matrix between X and X2."""
if X2 is None: X2 = X self._K_computations(X,X2)
target1 = np.zeros_like(target) target += self._K1*self._K2
target2 = np.zeros_like(target)
self.k1.K(X[:,:self.k1.D],X2[:,:self.k1.D],target1) def _K_computations(self,X,X2):
self.k2.K(X[:,self.k1.D:],X2[:,self.k1.D:],target2) """
target += target1 * target2 Compute the two kernel matrices.
The computation is only done if needed: many times it will be the same as the previous call
"""
if not (np.all(X==self._X) and np.all(X2==self._X2) and np.all(self._params == self._get_params())):
#store new values in cache
self._X = X.copy()
self._X2 = X2.copy()
self._params = self._get_params().copy()
#update self._K1, self._K2
if X2 is None: X2 = X
self._K1 = np.zeros((X.shape[0],X2.shape[0]))
self._K2 = np.zeros((X.shape[0],X2.shape[0]))
self.k1.K(X[:,:self.k1.D],X2[:,:self.k1.D],self._K1)
self.k2.K(X[:,self.k1.D:],X2[:,self.k1.D:],self._K2)
def dK_dtheta(self,dL_dK,X,X2,target): def dK_dtheta(self,dL_dK,X,X2,target):
"""derivative of the covariance matrix with respect to the parameters.""" """derivative of the covariance matrix with respect to the parameters."""
if X2 is None: X2 = X self._K_computations(X,X2)
K1 = np.zeros((X.shape[0],X2.shape[0])) self.k1.dK_dtheta(dL_dK*self._K2, X[:,:self.k1.D], X2[:,:self.k1.D], target[:self.k1.Nparam])
K2 = np.zeros((X.shape[0],X2.shape[0])) self.k2.dK_dtheta(dL_dK*self._K1, X[:,self.k1.D:], X2[:,self.k1.D:], target[self.k1.Nparam:])
self.k1.K(X[:,:self.k1.D],X2[:,:self.k1.D],K1)
self.k2.K(X[:,self.k1.D:],X2[:,self.k1.D:],K2)
self.k1.dK_dtheta(dL_dK*K2, X[:,:self.k1.D], X2[:,:self.k1.D], target[:self.k1.Nparam]) def dK_dX(self,dL_dK,X,X2,target):
self.k2.dK_dtheta(dL_dK*K1, X[:,self.k1.D:], X2[:,self.k1.D:], target[self.k1.Nparam:]) """derivative of the covariance matrix with respect to X."""
self._K_computations(X,X2)
self.k1.dK_dX(dL_dK*self._K2, X[:,:self.k1.D], X2[:,:self.k1.D], target)
self.k2.dK_dX(dL_dK*self._K1, X[:,self.k1.D:], X2[:,self.k1.D:], target)
def Kdiag(self,X,target): def Kdiag(self,X,target):
"""Compute the diagonal of the covariance matrix associated to X.""" """Compute the diagonal of the covariance matrix associated to X."""
@ -73,17 +89,6 @@ class prod_orthogonal(kernpart):
self.k1.dKdiag_dtheta(dL_dKdiag*K2,X[:,:self.k1.D],target[:self.k1.Nparam]) self.k1.dKdiag_dtheta(dL_dKdiag*K2,X[:,:self.k1.D],target[:self.k1.Nparam])
self.k2.dKdiag_dtheta(dL_dKdiag*K1,X[:,self.k1.D:],target[self.k1.Nparam:]) self.k2.dKdiag_dtheta(dL_dKdiag*K1,X[:,self.k1.D:],target[self.k1.Nparam:])
def dK_dX(self,dL_dK,X,X2,target):
"""derivative of the covariance matrix with respect to X."""
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.k2.K(X[:,self.k1.D:],X2[:,self.k1.D:],K2)
self.k1.dK_dX(dL_dK*K2, X[:,:self.k1.D], X2[:,:self.k1.D], target)
self.k2.dK_dX(dL_dK*K1, X[:,self.k1.D:], X2[:,self.k1.D:], target)
def dKdiag_dX(self, dL_dKdiag, X, target): def dKdiag_dX(self, dL_dKdiag, X, target):
K1 = np.zeros(X.shape[0]) K1 = np.zeros(X.shape[0])
K2 = np.zeros(X.shape[0]) K2 = np.zeros(X.shape[0])