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small efficiency changes in rbf

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This commit is contained in:
James Hensman 2013-03-27 15:08:25 +00:00
parent adfa6de1d8
commit 85810030c5
1 changed files with 3 additions and 3 deletions
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6
GPy/kern/rbf.py
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@ -86,9 +86,9 @@ class rbf(kernpart):
self._K_computations(X,X2)
target[0] += np.sum(self._K_dvar*dL_dK)
if self.ARD:
[np.add(target[1+q:2+q],self.variance/self.lengthscale[q]**3*np.sum(self._K_dvar*dL_dK*np.square(X[:,q][:,None]-X2[:,q][None,:])),target[1+q:2+q]) for q in range(self.D)]
[np.add(target[1+q:2+q],(self.variance/self.lengthscale[q]**3)*np.sum(self._K_dvar*dL_dK*np.square(X[:,q][:,None]-X2[:,q][None,:])),target[1+q:2+q]) for q in range(self.D)]
else:
target[1] += np.sum(self._K_dvar*self.variance*self._K_dist2/self.lengthscale*dL_dK)
target[1] += (self.variance/self.lengthscale)*np.sum(self._K_dvar*self._K_dist2*dL_dK)
def dKdiag_dtheta(self,dL_dKdiag,X,target):
#NB: derivative of diagonal elements wrt lengthscale is 0
@ -97,7 +97,7 @@ class rbf(kernpart):
def dK_dX(self,dL_dK,X,X2,target):
self._K_computations(X,X2)
_K_dist = X[:,None,:]-X2[None,:,:] #don't cache this in _K_computations because it is high memory. If this function is being called, chances are we're not in the high memory arena.
dK_dX = np.transpose(-self.variance*self._K_dvar[:,:,np.newaxis]*_K_dist/self.lengthscale2,(1,0,2))
dK_dX = (-self.variance/self.lengthscale2)*np.transpose(self._K_dvar[:,:,np.newaxis]*_K_dist,(1,0,2))
target += np.sum(dK_dX*dL_dK.T[:,:,None],0)
def dKdiag_dX(self,dL_dKdiag,X,target):