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reconfigured svgp inference a little

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James Hensman 2015-02-11 11:49:45 +00:00
parent 393b9e94ba
commit 482bd1472c
3 changed files with 32 additions and 22 deletions
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40
GPy/kern/_src/prod.py
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@ -42,25 +42,41 @@ class Prod(CombinationKernel):
return reduce(np.multiply, (p.Kdiag(X) for p in which_parts))
def update_gradients_full(self, dL_dK, X, X2=None):
k = self.K(X,X2)*dL_dK
for p in self.parts:
p.update_gradients_full(k/p.K(X,X2),X,X2)
if len(self.parts)==2:
self.parts[0].update_gradients_full(dL_dK*self.parts[1].K(X,X2), X, X2)
self.parts[1].update_gradients_full(dL_dK*self.parts[0].K(X,X2), X, X2)
else:
k = self.K(X,X2)*dL_dK
for p in self.parts:
p.update_gradients_full(k/p.K(X,X2),X,X2)
def update_gradients_diag(self, dL_dKdiag, X):
k = self.Kdiag(X)*dL_dKdiag
for p in self.parts:
p.update_gradients_diag(k/p.Kdiag(X),X)
if len(self.parts)==2:
self.parts[0].update_gradients_diag(dL_dKdiag*self.parts[1].Kdiag(X), X)
self.parts[1].update_gradients_diag(dL_dKdiag*self.parts[0].Kdiag(X), X)
else:
k = self.Kdiag(X)*dL_dKdiag
for p in self.parts:
p.update_gradients_diag(k/p.Kdiag(X),X)
def gradients_X(self, dL_dK, X, X2=None):
target = np.zeros(X.shape)
k = self.K(X,X2)*dL_dK
for p in self.parts:
target += p.gradients_X(k/p.K(X,X2),X,X2)
if len(self.parts)==2:
target += self.parts[0].gradients_X(dL_dK*self.parts[1].K(X, X2), X, X2)
target += self.parts[1].gradients_X(dL_dK*self.parts[0].K(X, X2), X, X2)
else:
k = self.K(X,X2)*dL_dK
for p in self.parts:
target += p.gradients_X(k/p.K(X,X2),X,X2)
return target
def gradients_X_diag(self, dL_dKdiag, X):
target = np.zeros(X.shape)
k = self.Kdiag(X)*dL_dKdiag
for p in self.parts:
target += p.gradients_X_diag(k/p.Kdiag(X),X)
if len(self.parts)==2:
target += self.parts[0].gradients_X_diag(dL_dKdiag*self.parts[1].Kdiag(X), X)
target += self.parts[1].gradients_X_diag(dL_dKdiag*self.parts[0].Kdiag(X), X)
else:
k = self.Kdiag(X)*dL_dKdiag
for p in self.parts:
target += p.gradients_X_diag(k/p.Kdiag(X),X)
return target