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GPy: Some rewriting for the exponential and Matern kernels. They now pass the unit test.

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This commit is contained in:
Nicolas 2012-12-03 10:26:28 +00:00
parent d71ad99db9
commit 3edd867ece
3 changed files with 63 additions and 49 deletions
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6
GPy/kern/exponential.py
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@ -62,7 +62,7 @@ class exponential(kernpart):
np.add(target,self.variance,target)
def dK_dtheta(self,partial,X,X2,target):
"""derivative of the cross-covariance matrix with respect to the parameters (shape is NxMxNparam)"""
"""derivative of the covariance matrix with respect to the parameters."""
if X2 is None: X2 = X
dist = np.sqrt(np.sum(np.square((X[:,None,:]-X2[None,:,:])/self.lengthscales),-1))
invdist = 1./np.where(dist!=0.,dist,np.inf)
@ -73,12 +73,12 @@ class exponential(kernpart):
target[1:] += (dl*partial[:,:,None]).sum(0).sum(0)
def dKdiag_dtheta(self,partial,X,target):
"""derivative of the diagonal of the covariance matrix with respect to the parameters (shape is NxNparam)"""
"""derivative of the diagonal of the covariance matrix with respect to the parameters."""
#NB: derivative of diagonal elements wrt lengthscale is 0
target[0] += np.sum(partial)
def dK_dX(self,X,X2,target):
"""derivative of the covariance matrix with respect to X (*! shape is NxMxD !*)."""
"""derivative of the covariance matrix with respect to X."""
if X2 is None: X2 = X
dist = np.sqrt(np.sum(np.square((X[:,None,:]-X2[None,:,:])/self.lengthscales),-1))[:,:,None]
ddist_dX = (X[:,None,:]-X2[None,:,:])/self.lengthscales**2/np.where(dist!=0.,dist,np.inf)