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GPLVM demo working

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This commit is contained in:
James Hensman 2012-11-30 10:31:02 +00:00
parent 4b4f1da128
commit 5f92ff6785
4 changed files with 13 additions and 14 deletions
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12
GPy/kern/rbf.py
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@ -6,7 +6,7 @@ from kernpart import kernpart
import numpy as np
import hashlib
class rbf(kernpart):
class rbf(kernpart):
"""
Radial Basis Function kernel, aka squared-exponential or Gaussian kernel.
@ -55,13 +55,15 @@ class rbf(kernpart):
target[0] += np.sum(self._K_dvar*partial)
target[1] += np.sum(self._K_dvar*self.variance*self._K_dist2/self.lengthscale*partial)
def dKdiag_dtheta(self,X,target):
target[0] += partial
def dKdiag_dtheta(self,partial,X,target):
#NB: derivative of diagonal elements wrt lengthscale is 0
target[0] += np.sum(partial)
def dK_dX(self,X,X2,target):
def dK_dX(self,partial,X,X2,target):
self._K_computations(X,X2)
_K_dist = X[:,None,:]-X2[None,:,:]
target += np.transpose(-self.variance*self._K_dvar[:,:,np.newaxis]*_K_dist/self.lengthscale2,(1,0,2))
dK_dX = np.transpose(-self.variance*self._K_dvar[:,:,np.newaxis]*_K_dist/self.lengthscale2,(1,0,2))
target += np.sum(dK_dX*partial[:,:,None],1)
def dKdiag_dX(self,X,target):
pass