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independent output kernel now with single kernel/multiple kernels

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Max Zwiessele 2014-03-27 13:10:51 +00:00
parent d5a8e5dd3a
commit 5d6e612a2a
1 changed files with 39 additions and 31 deletions
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70
GPy/kern/_src/independent_outputs.py
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@ -54,85 +54,93 @@ class IndependentOutputs(CombinationKernel):
self.kern = kernels
super(IndependentOutputs, self).__init__(kernels=kernels, extra_dims=[index_dim], name=name)
self.index_dim = index_dim
self.kerns = kernels if len(kernels) != 1 else itertools.repeat(kernels[0])
def K(self,X ,X2=None):
slices = index_to_slices(X[:,self.index_dim])
kerns = itertools.repeat(self.kern) if self.single_kern else self.kern
if X2 is None:
target = np.zeros((X.shape[0], X.shape[0]))
[[target.__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.kerns, slices)]
[[target.__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(kerns, slices)]
else:
slices2 = index_to_slices(X2[:,self.index_dim])
target = np.zeros((X.shape[0], X2.shape[0]))
[[target.__setitem__((s,s2), kern.K(X[s,:],X2[s2,:])) for s,s2 in itertools.product(slices_i, slices_j)] for kern, slices_i,slices_j in zip(self.kerns, slices,slices2)]
[[target.__setitem__((s,s2), kern.K(X[s,:],X2[s2,:])) for s,s2 in itertools.product(slices_i, slices_j)] for kern, slices_i,slices_j in zip(kerns, slices,slices2)]
return target
def Kdiag(self,X):
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[0])
[[np.copyto(target[s], kern.Kdiag(X[s])) for s in slices_i] for kern, slices_i in zip(self.kerns, slices)]
[[np.copyto(target[s], kern.Kdiag(X[s])) for s in slices_i] for kern, slices_i in zip(kerns, slices)]
return target
def update_gradients_full(self,dL_dK,X,X2=None):
slices = index_to_slices(X[:,self.index_dim])
if self.single_kern: target = np.zeros(self.kern.size)
else: target = [np.zeros(kern.size) for kern, _ in zip(self.kerns, slices)]
if self.single_kern:
target = np.zeros(self.kern.size)
kerns = itertools.repeat(self.kern)
else:
kerns = self.kern
target = [np.zeros(kern.size) for kern, _ in zip(kerns, slices)]
def collate_grads(kern, i, dL, X, X2):
kern.update_gradients_full(dL,X,X2)
if self.single_kern: target[:] += kern.gradient
else: target[i][:] += kern.gradient
if X2 is None:
[[collate_grads(kern, i, dL_dK[s,ss], X[s], X[ss]) for s,ss in itertools.product(slices_i, slices_i)] for i,(kern,slices_i) in enumerate(zip(self.kerns,slices))]
[[collate_grads(kern, i, dL_dK[s,ss], X[s], X[ss]) for s,ss in itertools.product(slices_i, slices_i)] for i,(kern,slices_i) in enumerate(zip(kerns,slices))]
else:
slices2 = index_to_slices(X2[:,self.index_dim])
[[[collate_grads(kern, i, dL_dK[s,s2],X[s],X2[s2]) for s in slices_i] for s2 in slices_j] for i,(kern,slices_i,slices_j) in enumerate(zip(self.kerns,slices,slices2))]
[[[collate_grads(kern, i, dL_dK[s,s2],X[s],X2[s2]) for s in slices_i] for s2 in slices_j] for i,(kern,slices_i,slices_j) in enumerate(zip(kerns,slices,slices2))]
if self.single_kern: kern.gradient = target
else:[kern.gradient.__setitem__(Ellipsis, target[i]) for i, [kern, _] in enumerate(zip(self.kerns, slices))]
else:[kern.gradient.__setitem__(Ellipsis, target[i]) for i, [kern, _] in enumerate(zip(kerns, slices))]
def gradients_X(self,dL_dK, X, X2=None):
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
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(self.kerns, slices)]
for kern, s in zip(kerns, slices)]
#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(self.kerns, slices)]
# for s in slices_i] for kern, slices_i in zip(kerns, slices)]
#import ipdb;ipdb.set_trace()
#[[(np.add(target[s ], kern.gradients_X(dL_dK[s ,ss],X[s ], X[ss]), out=target[s ]),
# np.add(target[ss], kern.gradients_X(dL_dK[ss,s ],X[ss], X[s ]), out=target[ss]))
# for s, ss in itertools.combinations(slices_i, 2)] for kern, slices_i in zip(self.kerns, slices)]
# for s, ss in itertools.combinations(slices_i, 2)] for kern, slices_i in zip(kerns, slices)]
else:
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(self.kerns, slices, slices2)]
for kern, s, s2 in zip(kerns, slices, slices2)]
# TODO: make work with index_to_slices
#slices = index_to_slices(X[:,self.index_dim])
#slices2 = index_to_slices(X2[:,self.index_dim])
#[[target.__setitem__(s, target[s] + kern.gradients_X(dL_dK[s,s2], X[s], X2[s2])) for s, s2 in itertools.product(slices_i, slices_j)] for kern, slices_i,slices_j in zip(self.kerns, slices,slices2)]
#[[target.__setitem__(s, target[s] + kern.gradients_X(dL_dK[s,s2], X[s], X2[s2])) for s, s2 in itertools.product(slices_i, slices_j)] for kern, slices_i,slices_j in zip(kerns, slices,slices2)]
return target
def gradients_X_diag(self, dL_dKdiag, X):
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(self.kerns, slices)]
[[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)]
return target
def update_gradients_diag(self, dL_dKdiag, X):
slices = index_to_slices(X[:,self.index_dim])
kerns = itertools.repeat(self.kern) if self.single_kern else self.kern
if self.single_kern: target = np.zeros(self.kern.size)
else: target = [np.zeros(kern.size) for kern, _ in zip(self.kerns, slices)]
else: target = [np.zeros(kern.size) for kern, _ in zip(kerns, slices)]
def collate_grads(kern, i, dL, X):
kern.update_gradients_diag(dL,X)
if self.single_kern: target[:] += kern.gradient
else: target[i][:] += kern.gradient
[[collate_grads(kern, i, dL_dKdiag[s], X[s,:]) for s in slices_i] for i, (kern, slices_i) in enumerate(zip(self.kerns, slices))]
[[collate_grads(kern, i, dL_dKdiag[s], X[s,:]) for s in slices_i] for i, (kern, slices_i) in enumerate(zip(kerns, slices))]
if self.single_kern: kern.gradient = target
else:[kern.gradient.__setitem__(Ellipsis, target[i]) for i, [kern, _] in enumerate(zip(self.kerns, slices))]
else:[kern.gradient.__setitem__(Ellipsis, target[i]) for i, [kern, _] in enumerate(zip(kerns, slices))]
class Hierarchical(CombinationKernel):
"""
@ -148,30 +156,30 @@ class Hierarchical(CombinationKernel):
def __init__(self, kern, name='hierarchy'):
assert all([k.input_dim==kerns[0].input_dim for k in kerns])
super(Hierarchical, self).__init__(kerns[0].input_dim + len(kerns) - 1, name)
self.kerns = kerns
self.add_parameters(self.kerns)
kerns = kerns
self.add_parameters(kerns)
def K(self,X ,X2=None):
X, slices = X[:,:-self.levels], [index_to_slices(X[:,i]) for i in range(self.kerns[0].input_dim, self.input_dim)]
K = self.kerns[0].K(X, X2)
X, slices = X[:,:-self.levels], [index_to_slices(X[:,i]) for i in range(kerns[0].input_dim, self.input_dim)]
K = kerns[0].K(X, X2)
if X2 is None:
[[[np.copyto(K[s,s], k.K(X[s], None)) for s in slices_i] for slices_i in slices_k] for k, slices_k in zip(self.kerns[1:], slices)]
[[[np.copyto(K[s,s], k.K(X[s], None)) for s in slices_i] for slices_i in slices_k] for k, slices_k in zip(kerns[1:], slices)]
else:
X2, slices2 = X2[:,:-1],index_to_slices(X2[:,-1])
[[[[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(self.kerns[1:], slices, slices2)]
[[[[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(kerns[1:], slices, slices2)]
return target
def Kdiag(self,X):
X, slices = X[:,:-self.levels], [index_to_slices(X[:,i]) for i in range(self.kerns[0].input_dim, self.input_dim)]
K = self.kerns[0].K(X, X2)
[[[np.copyto(target[s], self.kern.Kdiag(X[s])) for s in slices_i] for slices_i in slices_k] for k, slices_k in zip(self.kerns[1:], slices)]
X, slices = X[:,:-self.levels], [index_to_slices(X[:,i]) for i in range(kerns[0].input_dim, self.input_dim)]
K = kerns[0].K(X, X2)
[[[np.copyto(target[s], self.kern.Kdiag(X[s])) for s in slices_i] for slices_i in slices_k] for k, slices_k in zip(kerns[1:], slices)]
return target
def update_gradients_full(self,dL_dK,X,X2=None):
X,slices = X[:,:-1],index_to_slices(X[:,-1])
if X2 is None:
self.kerns[0].update_gradients_full(dL_dK, X, None)
for k, slices_k in zip(self.kerns[1:], slices):
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)
@ -180,8 +188,8 @@ class Hierarchical(CombinationKernel):
k._set_gradient(target)
else:
X2, slices2 = X2[:,:-1], index_to_slices(X2[:,-1])
self.kerns[0].update_gradients_full(dL_dK, X, None)
for k, slices_k in zip(self.kerns[1:], slices):
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)