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Bug in prod-coreg kernels fixed, not in the most elegant way though

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This commit is contained in:
Ricardo 2013-09-18 17:57:17 +01:00
parent b47f9a1b06
commit 99c3af63c4
2 changed files with 13 additions and 7 deletions
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3
GPy/core/sparse_gp.py
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@ -259,9 +259,6 @@ class SparseGP(GPBase):
The derivative of the bound wrt the inducing inputs Z The derivative of the bound wrt the inducing inputs Z
""" """
dL_dZ = self.kern.dK_dX(self.dL_dKmm, self.Z) dL_dZ = self.kern.dK_dX(self.dL_dKmm, self.Z)
if hasattr(self,'multioutput'):
dL_dZ = dL_dZ*2 #NOTE Yes, this looks weird... but it works
if self.has_uncertain_inputs: if self.has_uncertain_inputs:
dL_dZ += self.kern.dpsi1_dZ(self.dL_dpsi1, self.Z, self.X, self.X_variance) dL_dZ += self.kern.dpsi1_dZ(self.dL_dpsi1, self.Z, self.X, self.X_variance)
dL_dZ += self.kern.dpsi2_dZ(self.dL_dpsi2, self.Z, self.X, self.X_variance) dL_dZ += self.kern.dpsi2_dZ(self.dL_dpsi2, self.Z, self.X, self.X_variance)

17
GPy/kern/parts/prod.py
View file

@ -2,6 +2,7 @@
# Licensed under the BSD 3-clause license (see LICENSE.txt) # Licensed under the BSD 3-clause license (see LICENSE.txt)
from kernpart import Kernpart from kernpart import Kernpart
from coregionalize import Coregionalize
import numpy as np import numpy as np
import hashlib import hashlib
@ -60,7 +61,7 @@ class Prod(Kernpart):
"""Compute the part of the kernel associated with k2.""" """Compute the part of the kernel associated with k2."""
self._K_computations(X, X2) self._K_computations(X, X2)
return self._K2 return self._K2
def dK_dtheta(self,dL_dK,X,X2,target): def dK_dtheta(self,dL_dK,X,X2,target):
"""Derivative of the covariance matrix with respect to the parameters.""" """Derivative of the covariance matrix with respect to the parameters."""
self._K_computations(X,X2) self._K_computations(X,X2)
@ -91,9 +92,17 @@ class Prod(Kernpart):
"""derivative of the covariance matrix with respect to X.""" """derivative of the covariance matrix with respect to X."""
self._K_computations(X,X2) self._K_computations(X,X2)
if X2 is None: if X2 is None:
X2 = X if not isinstance(self.k1,Coregionalize) and not isinstance(self.k2,Coregionalize):
self.k1.dK_dX(dL_dK*self._K2, X[:,self.slice1], X2[:,self.slice1], target[:,self.slice1]) self.k1.dK_dX(dL_dK*self._K2, X[:,self.slice1], None, target[:,self.slice1])
self.k2.dK_dX(dL_dK*self._K1, X[:,self.slice2], X2[:,self.slice2], target[:,self.slice2]) self.k2.dK_dX(dL_dK*self._K1, X[:,self.slice2], None, target[:,self.slice2])
else:#if isinstance(self.k1,Coregionalize) or isinstance(self.k2,Coregionalize):
#NOTE The indices column in the inputs makes the ki.dK_dX fail when passing None instead of X[:,self.slicei]
X2 = X
self.k1.dK_dX(2.*dL_dK*self._K2, X[:,self.slice1], X2[:,self.slice1], target[:,self.slice1])
self.k2.dK_dX(2.*dL_dK*self._K1, X[:,self.slice2], X2[:,self.slice2], target[:,self.slice2])
else:
self.k1.dK_dX(dL_dK*self._K2, X[:,self.slice1], X2[:,self.slice1], target[:,self.slice1])
self.k2.dK_dX(dL_dK*self._K1, X[:,self.slice2], X2[:,self.slice2], target[:,self.slice2])
def dKdiag_dX(self, dL_dKdiag, X, target): def dKdiag_dX(self, dL_dKdiag, X, target):
K1 = np.zeros(X.shape[0]) K1 = np.zeros(X.shape[0])