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merged. ish.

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This commit is contained in:
James Hensman 2015-03-23 16:26:31 +00:00
commit b48d58fb1f
7 changed files with 136 additions and 86 deletions
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38
GPy/kern/_src/prod.py
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@ -6,6 +6,20 @@ from kern import CombinationKernel
from ...util.caching import Cache_this
import itertools
def numpy_invalid_op_as_exception(func):
"""
A decorator that allows catching numpy invalid operations
as exceptions (the default behaviour is raising warnings).
"""
def func_wrapper(*args, **kwargs):
np.seterr(invalid='raise')
result = func(*args, **kwargs)
np.seterr(invalid='warn')
return result
return func_wrapper
class Prod(CombinationKernel):
"""
Computes the product of 2 kernels
@ -46,28 +60,30 @@ class Prod(CombinationKernel):
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)
for combination in itertools.combinations(self.parts, len(self.parts) - 1):
prod = reduce(np.multiply, [p.K(X, X2) for p in combination])
to_update = list(set(self.parts) - set(combination))[0]
to_update.update_gradients_full(dL_dK * prod, X, X2)
def update_gradients_diag(self, dL_dKdiag, 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)
for combination in itertools.combinations(self.parts, len(self.parts) - 1):
prod = reduce(np.multiply, [p.Kdiag(X) for p in combination])
to_update = list(set(self.parts) - set(combination))[0]
to_update.update_gradients_diag(dL_dKdiag * prod, X)
def gradients_X(self, dL_dK, X, X2=None):
target = np.zeros(X.shape)
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)
for combination in itertools.combinations(self.parts, len(self.parts) - 1):
prod = reduce(np.multiply, [p.K(X, X2) for p in combination])
to_update = list(set(self.parts) - set(combination))[0]
target += to_update.gradients_X(dL_dK * prod, X, X2)
return target
def gradients_X_diag(self, dL_dKdiag, X):
@ -80,3 +96,5 @@ class Prod(CombinationKernel):
for p in self.parts:
target += p.gradients_X_diag(k/p.Kdiag(X),X)
return target