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removed keyname partial

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This commit is contained in:
Alan Saul 2013-03-11 12:15:59 +00:00
parent f881e65761
commit 12d6f5056b
17 changed files with 235 additions and 235 deletions
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18
GPy/kern/Matern52.py
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@ -74,7 +74,7 @@ class Matern52(kernpart):
"""Compute the diagonal of the covariance matrix associated to X."""
np.add(target,self.variance,target)
def dK_dtheta(self,partial,X,X2,target):
def dK_dtheta(self,dL_dK,X,X2,target):
"""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.lengthscale),-1))
@ -82,29 +82,29 @@ class Matern52(kernpart):
dist2M = np.square(X[:,None,:]-X2[None,:,:])/self.lengthscale**3
dvar = (1+np.sqrt(5.)*dist+5./3*dist**2)*np.exp(-np.sqrt(5.)*dist)
dl = (self.variance * 5./3 * dist * (1 + np.sqrt(5.)*dist ) * np.exp(-np.sqrt(5.)*dist))[:,:,np.newaxis] * dist2M*invdist[:,:,np.newaxis]
target[0] += np.sum(dvar*partial)
target[0] += np.sum(dvar*dL_dK)
if self.ARD:
dl = (self.variance * 5./3 * dist * (1 + np.sqrt(5.)*dist ) * np.exp(-np.sqrt(5.)*dist))[:,:,np.newaxis] * dist2M*invdist[:,:,np.newaxis]
#dl = (self.variance* 3 * dist * np.exp(-np.sqrt(3.)*dist))[:,:,np.newaxis] * dist2M*invdist[:,:,np.newaxis]
target[1:] += (dl*partial[:,:,None]).sum(0).sum(0)
target[1:] += (dl*dL_dK[:,:,None]).sum(0).sum(0)
else:
dl = (self.variance * 5./3 * dist * (1 + np.sqrt(5.)*dist ) * np.exp(-np.sqrt(5.)*dist)) * dist2M.sum(-1)*invdist
#dl = (self.variance* 3 * dist * np.exp(-np.sqrt(3.)*dist)) * dist2M.sum(-1)*invdist
target[1] += np.sum(dl*partial)
target[1] += np.sum(dl*dL_dKdiag)
def dKdiag_dtheta(self,X,target):
def dKdiag_dtheta(self,dL_dKdiag,X,target):
"""derivative of the diagonal of the covariance matrix with respect to the parameters."""
target[0] += np.sum(partial)
target[0] += np.sum(dL_dKdiag)
def dK_dX(self,partial,X,X2,target):
def dK_dX(self,dL_dK,X,X2,target):
"""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.lengthscale),-1))[:,:,None]
ddist_dX = (X[:,None,:]-X2[None,:,:])/self.lengthscale**2/np.where(dist!=0.,dist,np.inf)
dK_dX = - np.transpose(self.variance*5./3*dist*(1+np.sqrt(5)*dist)*np.exp(-np.sqrt(5)*dist)*ddist_dX,(1,0,2))
target += np.sum(dK_dX*partial.T[:,:,None],0)
target += np.sum(dK_dX*dL_dK.T[:,:,None],0)
def dKdiag_dX(self,partial,X,target):
def dKdiag_dX(self,dL_dKdiag,X,target):
pass
def Gram_matrix(self,F,F1,F2,F3,lower,upper):