apunkt/GPy
1
0
Fork 0
mirror of https://github.com/SheffieldML/GPy.git synced 2026-05-18 13:55:14 +02:00
Code Issues Projects Releases Packages Wiki Activity Actions

extend prod kernel for handling more than 2 kernels

Browse source
This commit is contained in:
Zhenwen Dai 2014-09-24 13:45:39 +01:00
parent dd987d9428
commit 6823a1df2e
1 changed files with 12 additions and 13 deletions
Download patch file Download diff file Expand all files Collapse all files

25
GPy/kern/_src/prod.py
View file

@ -18,7 +18,6 @@ class Prod(CombinationKernel):
""" """
def __init__(self, kernels, name='mul'): def __init__(self, kernels, name='mul'):
assert len(kernels) == 2, 'only implemented for two kernels as of yet'
super(Prod, self).__init__(kernels, name) super(Prod, self).__init__(kernels, name)
@Cache_this(limit=2, force_kwargs=['which_parts']) @Cache_this(limit=2, force_kwargs=['which_parts'])
@ -37,25 +36,25 @@ class Prod(CombinationKernel):
return reduce(np.multiply, (p.Kdiag(X) for p in which_parts)) return reduce(np.multiply, (p.Kdiag(X) for p in which_parts))
def update_gradients_full(self, dL_dK, X, X2=None): def update_gradients_full(self, dL_dK, X, X2=None):
for k1,k2 in itertools.combinations(self.parts, 2): k = self.K(X,X2)*dL_dK
k1.update_gradients_full(dL_dK*k2.K(X, X2), X, X2) for p in self.parts:
k2.update_gradients_full(dL_dK*k1.K(X, X2), X, X2) p.update_gradients_full(k/p.K(X,X2),X,X2)
def update_gradients_diag(self, dL_dKdiag, X): def update_gradients_diag(self, dL_dKdiag, X):
for k1,k2 in itertools.combinations(self.parts, 2): k = self.Kdiag(X)*dL_dKdiag
k1.update_gradients_diag(dL_dKdiag*k2.Kdiag(X), X) for p in self.parts:
k2.update_gradients_diag(dL_dKdiag*k1.Kdiag(X), X) p.update_gradients_diag(k/p.Kdiag(X),X)
def gradients_X(self, dL_dK, X, X2=None): def gradients_X(self, dL_dK, X, X2=None):
target = np.zeros(X.shape) target = np.zeros(X.shape)
for k1,k2 in itertools.combinations(self.parts, 2): k = self.K(X,X2)*dL_dK
target += k1.gradients_X(dL_dK*k2.K(X, X2), X, X2) for p in self.parts:
target += k2.gradients_X(dL_dK*k1.K(X, X2), X, X2) target += p.gradients_X(k/p.K(X,X2),X,X2)
return target return target
def gradients_X_diag(self, dL_dKdiag, X): def gradients_X_diag(self, dL_dKdiag, X):
target = np.zeros(X.shape) target = np.zeros(X.shape)
for k1,k2 in itertools.combinations(self.parts, 2): k = self.Kdiag(X)*dL_dKdiag
target += k1.gradients_X_diag(dL_dKdiag*k2.Kdiag(X), X) for p in self.parts:
target += k2.gradients_X_diag(dL_dKdiag*k1.Kdiag(X), X) target += p.gradients_X_diag(k/p.Kdiag(X),X)
return target return target