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combination slices full now, independent output kernel

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Max Zwiessele 2014-03-13 09:07:27 +00:00
parent 8f7d8537b7
commit eb8b2c8b47
2 changed files with 18 additions and 17 deletions
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32
GPy/kern/_src/independent_outputs.py
View file

@ -40,24 +40,25 @@ class IndependentOutputs(Kern):
the rest of the columns of X are passed to the underlying kernel for computation (in blocks).
"""
def __init__(self, kern, name='independ'):
super(IndependentOutputs, self).__init__(kern.input_dim+1, name)
def __init__(self, active_dim, kern, name='independ'):
super(IndependentOutputs, self).__init__(np.hstack((kern.active_dims,np.r_[active_dim])), name)
self.index_dim = active_dim
self.kern = kern
self.add_parameters(self.kern)
def K(self,X ,X2=None):
X, slices = X[:,:-1], index_to_slices(X[:,-1])
slices = index_to_slices(X[:,self.index_dim])
if X2 is None:
target = np.zeros((X.shape[0], X.shape[0]))
[[np.copyto(target[s,s], self.kern.K(X[s], None)) for s in slices_i] for slices_i in slices]
[[np.copyto(target[s,s], self.kern.K(X[s,:], None)) for s in slices_i] for slices_i in slices]
else:
X2, slices2 = X2[:,:-1],index_to_slices(X2[:,-1])
slices2 = index_to_slices(X2[:,self.index_dim])
target = np.zeros((X.shape[0], X2.shape[0]))
[[[np.copyto(target[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,slices2)]
[[[np.copyto(target[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,slices2)]
return target
def Kdiag(self,X):
X, slices = X[:,:-1], index_to_slices(X[:,-1])
slices = index_to_slices(X[:,self.index_dim])
target = np.zeros(X.shape[0])
[[np.copyto(target[s], self.kern.Kdiag(X[s])) for s in slices_i] for slices_i in slices]
return target
@ -66,20 +67,19 @@ class IndependentOutputs(Kern):
target = np.zeros(self.kern.size)
def collate_grads(dL, X, X2):
self.kern.update_gradients_full(dL,X,X2)
self.kern._collect_gradient(target)
target += self.kern.gradient
X,slices = X[:,:-1],index_to_slices(X[:,-1])
slices = index_to_slices(X[:,self.index_dim])
if X2 is None:
[[collate_grads(dL_dK[s,s], X[s], None) for s in slices_i] for slices_i in slices]
else:
X2, slices2 = X2[:,:-1], index_to_slices(X2[:,-1])
slices2 = index_to_slices(X2[:,self.index_dim])
[[[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)]
self.kern._set_gradient(target)
self.kern.gradient = target
def gradients_X(self,dL_dK, X, X2=None):
target = np.zeros_like(X)
X, slices = X[:,:-1],index_to_slices(X[:,-1])
slices = index_to_slices(X[:,self.index_dim])
if X2 is None:
[[np.copyto(target[s,:-1], self.kern.gradients_X(dL_dK[s,s],X[s],None)) for s in slices_i] for slices_i in slices]
else:
@ -88,7 +88,7 @@ class IndependentOutputs(Kern):
return target
def gradients_X_diag(self, dL_dKdiag, X):
X, slices = X[:,:-1], index_to_slices(X[:,-1])
slices = index_to_slices(X[:,self.index_dim])
target = np.zeros(X.shape)
[[np.copyto(target[s,:-1], self.kern.gradients_X_diag(dL_dKdiag[s],X[s])) for s in slices_i] for slices_i in slices]
return target
@ -97,10 +97,10 @@ class IndependentOutputs(Kern):
target = np.zeros(self.kern.size)
def collate_grads(dL, X):
self.kern.update_gradients_diag(dL,X)
self.kern._collect_gradient(target)
self.target += self.kern.gradient
X,slices = X[:,:-1],index_to_slices(X[:,-1])
[[collate_grads(dL_dKdiag[s], X[s,:]) for s in slices_i] for slices_i in slices]
self.kern._set_gradient(target)
self.kern.gradient = target
class Hierarchical(Kern):
"""

3
GPy/kern/_src/kern.py
View file

@ -195,7 +195,8 @@ class Kern(Parameterized):
class CombinationKernel(Kern):
def __init__(self, kernels, name):
assert all([isinstance(k, Kern) for k in kernels])
input_dim = reduce(np.union1d, (x.active_dims for x in kernels))
ma = reduce(lambda a,b: max(a, max(b)), (x.active_dims for x in kernels), 0)
input_dim = np.r_[0:ma+1]
super(CombinationKernel, self).__init__(input_dim, name)
self.add_parameters(*kernels)