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some work on the hierarchical kern

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This commit is contained in:
James Hensman 2014-04-29 16:50:27 +01:00
parent ba38f2cf2c
commit 37010d73ad
1 changed files with 15 additions and 22 deletions
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37
GPy/kern/_src/independent_outputs.py
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@ -143,7 +143,7 @@ class IndependentOutputs(Kern):
if self.single_kern: kern.gradient = target if self.single_kern: kern.gradient = target
else:[kern.gradient.__setitem__(Ellipsis, target[i]) for i, [kern, _] in enumerate(zip(kerns, slices))] else:[kern.gradient.__setitem__(Ellipsis, target[i]) for i, [kern, _] in enumerate(zip(kerns, slices))]
class Hierarchical(Kern): class Hierarchical(CombinationKernel):
""" """
A kernel which can represent a simple hierarchical model. A kernel which can represent a simple hierarchical model.
@ -171,34 +171,27 @@ class Hierarchical(Kern):
K = self.parts[0].K(X, X2) # compute 'base' kern everywhere K = self.parts[0].K(X, X2) # compute 'base' kern everywhere
slices = [index_to_slices(X[:,i]) for i in self.extra_dims] slices = [index_to_slices(X[:,i]) for i in self.extra_dims]
if X2 is None: if X2 is None:
pass [[[np.add(K[s,s], k.K(X[s], None), K[s, s]) for s in slices_i] for slices_i in slices_k] for k, slices_k in zip(self.parts[1:], slices)]
#[[[np.add(K[s,s], k.K(X[s], None), K[s, s]) for s in slices_i] for slices_i in slices_k] for k, slices_k in zip(self.parts[1:], slices)]
#[[[K.__setitem__((s,ss), kern.K(X[s,:], X[ss,:])) for s,ss in itertools.product(slices_i, slices_i)] for kern, slices_i in zip(self.parts[1:], slices)]
else: else:
X2, slices2 = X2[:,:-1],index_to_slices(X2[:,-1]) slices2 = [index_to_slices(X2[:,i]) for i in self.extra_dims]
[[[[np.copyto(K[s, s2], self.kern.K(X[s],X2[s2])) for s in slices_i] for s2 in slices_j] for slices_i,slices_j in zip(slices_k,slices_k2)] for k, slices_k, slices_k2 in zip(parts[1:], slices, slices2)] [[[np.add(K[s,ss], k.K(X[s], X2[ss]), K[s, ss]) for s,ss in zip(slices_i, slices_j)] for slices_i, slices_j in zip(slices_k1, slices_k2)] for k, slices_k1, slices_k2 in zip(self.parts[1:], slices, slices2)]
return K return K
def Kdiag(self,X):
return np.diag(self.K(X))
def update_gradients_full(self,dL_dK,X,X2=None): def update_gradients_full(self,dL_dK,X,X2=None):
X,slices = X[:,:-1],index_to_slices(X[:,-1]) slices = [index_to_slices(X[:,i]) for i in self.extra_dims]
if X2 is None: if X2 is None:
kerns[0].update_gradients_full(dL_dK, X, None) self.parts[0].update_gradients_full(dL_dK, X, None)
for k, slices_k in zip(kerns[1:], slices): for k, slices_k in zip(self.parts[1:], slices):
target = np.zeros(k.size) target = np.zeros(k.size)
def collate_grads(dL, X, X2): def collate_grads(dL, X, X2, target):
k.update_gradients_full(dL,X,X2) k.update_gradients_full(dL,X,X2)
k._collect_gradient(target) target += k.gradient
[[k.update_gradients_full(dL_dK[s,s], X[s], None) for s in slices_i] for slices_i in slices_k] [[collate_grads(dL_dK[s,s], X[s], None, target) for s in slices_i] for slices_i in slices_k]
k._set_gradient(target) k.gradient[:] = target
else: else:
X2, slices2 = X2[:,:-1], index_to_slices(X2[:,-1]) raise NotImplementedError
kerns[0].update_gradients_full(dL_dK, X, None)
for k, slices_k in zip(kerns[1:], slices):
target = np.zeros(k.size)
def collate_grads(dL, X, X2):
k.update_gradients_full(dL,X,X2)
k._collect_gradient(target)
[[[collate_grads(dL_dK[s,s2],X[s],X2[s2]) for s in slices_i] for s2 in slices_j] for slices_i,slices_j in zip(slices,slices2)]
k._set_gradient(target)