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Merge branch 'params' of github.com:SheffieldML/GPy into params

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This commit is contained in:
Max Zwiessele 2014-03-05 13:01:44 +00:00
commit e7b601b424
2 changed files with 13 additions and 11 deletions
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4
GPy/kern/_src/kern.py
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@ -129,7 +129,7 @@ class Kern(Parameterized):
"""
return self.prod(other, tensor=True)
def prod(self, other, tensor=False):
def prod(self, other, tensor=False, name=None):
"""
Multiply two kernels (either on the same space, or on the tensor
product of the input space).
@ -142,4 +142,4 @@ class Kern(Parameterized):
"""
assert isinstance(other, Kern), "only kernels can be added to kernels..."
from prod import Prod
return Prod(self, other, tensor)
return Prod(self, other, tensor, name)

20
GPy/kern/_src/prod.py
View file

@ -15,14 +15,16 @@ class Prod(Kern):
:rtype: kernel object
"""
def __init__(self, k1, k2, tensor=False):
def __init__(self, k1, k2, tensor=False,name=None):
if tensor:
super(Prod, self).__init__(k1.input_dim + k2.input_dim, k1.name + '_xx_' + k2.name)
name = k1.name + '_xx_' + k2.name if name is None else name
super(Prod, self).__init__(k1.input_dim + k2.input_dim, name)
self.slice1 = slice(0,k1.input_dim)
self.slice2 = slice(k1.input_dim,k1.input_dim+k2.input_dim)
else:
assert k1.input_dim == k2.input_dim, "Error: The input spaces of the kernels to multiply don't have the same dimension."
super(Prod, self).__init__(k1.input_dim, k1.name + '_x_' + k2.name)
name = k1.name + '_x_' + k2.name if name is None else name
super(Prod, self).__init__(k1.input_dim, name)
self.slice1 = slice(0, self.input_dim)
self.slice2 = slice(0, self.input_dim)
self.k1 = k1
@ -39,17 +41,17 @@ class Prod(Kern):
return self.k1.Kdiag(X[:,self.slice1]) * self.k2.Kdiag(X[:,self.slice2])
def update_gradients_full(self, dL_dK, X):
self.k1.update_gradients_full(dL_dK*self.k2(X[:,self.slice2]), X[:,self.slice1])
self.k2.update_gradients_full(dL_dK*self.k1(X[:,self.slice1]), X[:,self.slice2])
self.k1.update_gradients_full(dL_dK*self.k2.K(X[:,self.slice2]), X[:,self.slice1])
self.k2.update_gradients_full(dL_dK*self.k1.K(X[:,self.slice1]), X[:,self.slice2])
def gradients_X(self, dL_dK, X, X2=None):
target = np.zeros(X.shape)
if X2 is None:
target[:,self.slice1] += self.k1.gradients_X(dL_dK*self.k2(X[:,self.slice2]), X[:,self.slice1], None)
target[:,self.slice2] += self.k2.gradients_X(dL_dK*self.k1(X[:,self.slice1]), X[:,self.slice2], None)
target[:,self.slice1] += self.k1.gradients_X(dL_dK*self.k2.K(X[:,self.slice2]), X[:,self.slice1], None)
target[:,self.slice2] += self.k2.gradients_X(dL_dK*self.k1.K(X[:,self.slice1]), X[:,self.slice2], None)
else:
target[:,self.slice1] += self.k1.gradients_X(dL_dK*self.k2(X[:,self.slice2], X2[:,self.slice2]), X[:,self.slice1], X2[:,self.slice1])
target[:,self.slice2] += self.k2.gradients_X(dL_dK*self.k1(X[:,self.slice1], X2[:,self.slice1]), X[:,self.slice2], X2[:,self.slice2])
target[:,self.slice1] += self.k1.gradients_X(dL_dK*self.k2.K(X[:,self.slice2], X2[:,self.slice2]), X[:,self.slice1], X2[:,self.slice1])
target[:,self.slice2] += self.k2.gradients_X(dL_dK*self.k1.K(X[:,self.slice1], X2[:,self.slice1]), X[:,self.slice2], X2[:,self.slice2])
return target
def gradients_X_diag(self, dL_dKdiag, X):