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[kernel] combination kernel and hierarchical independent gradient updates

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This commit is contained in:
Max Zwiessele 2016-04-01 09:45:39 +01:00
parent 8ad14681ee
commit ff9a6f9c26
2 changed files with 17 additions and 11 deletions
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20
GPy/kern/src/independent_outputs.py
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@ -56,7 +56,10 @@ class IndependentOutputs(CombinationKernel):
self.single_kern = False
self.kern = kernels
super(IndependentOutputs, self).__init__(kernels=kernels, extra_dims=[index_dim], name=name)
self.index_dim = index_dim
# The combination kernel ALLWAYS puts the extra dimension last.
# Thus, the index dimension of this kernel is always the last dimension
# after slicing. This is why the index_dim is just the last column:
self.index_dim = -1
def K(self,X ,X2=None):
slices = index_to_slices(X[:,self.index_dim])
@ -103,13 +106,10 @@ class IndependentOutputs(CombinationKernel):
target = np.zeros(X.shape)
kerns = itertools.repeat(self.kern) if self.single_kern else self.kern
if X2 is None:
# TODO: make use of index_to_slices
# FIXME: Broken as X is already sliced out
# print("Warning, gradients_X may not be working, I believe X has already been sliced out by the slicer!")
values = np.unique(X[:,self.index_dim])
slices = [X[:,self.index_dim]==i for i in values]
[target.__setitem__(s, kern.gradients_X(dL_dK[s,s],X[s],None))
for kern, s in zip(kerns, slices)]
for kern, s in zip(kerns, slices):
target[s] += kern.gradients_X(dL_dK[s, :][:, s],X[s], None)
#slices = index_to_slices(X[:,self.index_dim])
#[[np.add(target[s], kern.gradients_X(dL_dK[s,s], X[s]), out=target[s])
# for s in slices_i] for kern, slices_i in zip(kerns, slices)]
@ -121,8 +121,8 @@ class IndependentOutputs(CombinationKernel):
values = np.unique(X[:,self.index_dim])
slices = [X[:,self.index_dim]==i for i in values]
slices2 = [X2[:,self.index_dim]==i for i in values]
[target.__setitem__(s, kern.gradients_X(dL_dK[s, :][:, s2],X[s],X2[s2]))
for kern, s, s2 in zip(kerns, slices, slices2)]
for kern, s, s2 in zip(kerns, slices, slices2):
target[s] += kern.gradients_X(dL_dK[s, :][:, s2],X[s],X2[s2])
# TODO: make work with index_to_slices
#slices = index_to_slices(X[:,self.index_dim])
#slices2 = index_to_slices(X2[:,self.index_dim])
@ -133,7 +133,9 @@ class IndependentOutputs(CombinationKernel):
slices = index_to_slices(X[:,self.index_dim])
kerns = itertools.repeat(self.kern) if self.single_kern else self.kern
target = np.zeros(X.shape)
[[target.__setitem__(s, kern.gradients_X_diag(dL_dKdiag[s],X[s])) for s in slices_i] for kern, slices_i in zip(kerns, slices)]
for kern, slices_i in zip(kerns, slices):
for s in slices_i:
target[s] += kern.gradients_X_diag(dL_dKdiag[s],X[s])
return target
def update_gradients_diag(self, dL_dKdiag, X):