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fixed up dK_dX in the exponential and Matern kerns

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This commit is contained in:
James Hensman 2013-01-16 11:21:06 +00:00
parent 4241a7c24f
commit a6300fab10
3 changed files with 9 additions and 10 deletions
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4
GPy/kern/Matern32.py
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@ -78,12 +78,12 @@ class Matern32(kernpart):
"""derivative of the diagonal of the covariance matrix with respect to the parameters.""" """derivative of the diagonal of the covariance matrix with respect to the parameters."""
target[0] += np.sum(partial) target[0] += np.sum(partial)
def dK_dX(self,X,X2,target): def dK_dX(self,partial,X,X2,target):
"""derivative of the covariance matrix with respect to X.""" """derivative of the covariance matrix with respect to X."""
if X2 is None: X2 = X if X2 is None: X2 = X
dist = np.sqrt(np.sum(np.square((X[:,None,:]-X2[None,:,:])/self.lengthscales),-1))[:,:,None] 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) ddist_dX = (X[:,None,:]-X2[None,:,:])/self.lengthscales**2/np.where(dist!=0.,dist,np.inf)
dK_dX += - np.transpose(3*self.variance*dist*np.exp(-np.sqrt(3)*dist)*ddist_dX,(1,0,2)) dK_dX = - np.transpose(3*self.variance*dist*np.exp(-np.sqrt(3)*dist)*ddist_dX,(1,0,2))
target += np.sum(dK_dX*partial.T[:,:,None],0) target += np.sum(dK_dX*partial.T[:,:,None],0)
def dKdiag_dX(self,X,target): def dKdiag_dX(self,X,target):

5
GPy/kern/Matern52.py
View file

@ -34,7 +34,6 @@ class Matern52(kernpart):
self.name = 'Mat52' self.name = 'Mat52'
self.set_param(np.hstack((variance,lengthscales))) self.set_param(np.hstack((variance,lengthscales)))
def get_param(self): def get_param(self):
"""return the value of the parameters.""" """return the value of the parameters."""
return np.hstack((self.variance,self.lengthscales)) return np.hstack((self.variance,self.lengthscales))
@ -77,12 +76,12 @@ class Matern52(kernpart):
"""derivative of the diagonal of the covariance matrix with respect to the parameters.""" """derivative of the diagonal of the covariance matrix with respect to the parameters."""
target[0] += np.sum(partial) target[0] += np.sum(partial)
def dK_dX(self,X,X2,target): def dK_dX(self,partial,X,X2,target):
"""derivative of the covariance matrix with respect to X.""" """derivative of the covariance matrix with respect to X."""
if X2 is None: X2 = X if X2 is None: X2 = X
dist = np.sqrt(np.sum(np.square((X[:,None,:]-X2[None,:,:])/self.lengthscales),-1))[:,:,None] 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) ddist_dX = (X[:,None,:]-X2[None,:,:])/self.lengthscales**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)) 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*partial.T[:,:,None],0)
def dKdiag_dX(self,X,target): def dKdiag_dX(self,X,target):

2
GPy/kern/exponential.py
View file

@ -77,7 +77,7 @@ class exponential(kernpart):
#NB: derivative of diagonal elements wrt lengthscale is 0 #NB: derivative of diagonal elements wrt lengthscale is 0
target[0] += np.sum(partial) target[0] += np.sum(partial)
def dK_dX(self,X,X2,target): def dK_dX(self,partial,X,X2,target):
"""derivative of the covariance matrix with respect to X.""" """derivative of the covariance matrix with respect to X."""
if X2 is None: X2 = X if X2 is None: X2 = X
dist = np.sqrt(np.sum(np.square((X[:,None,:]-X2[None,:,:])/self.lengthscales),-1))[:,:,None] dist = np.sqrt(np.sum(np.square((X[:,None,:]-X2[None,:,:])/self.lengthscales),-1))[:,:,None]