From 049de98d3b510357f6f391c23f8be34eea159c5c Mon Sep 17 00:00:00 2001 From: Nicolas Date: Mon, 3 Dec 2012 11:26:10 +0000 Subject: [PATCH] rbf_ARD now in the updated format for the computation of the derivatives (included for the psi-statistics, but not tested) --- GPy/kern/rbf_ARD.py | 63 +++++++++++++++++++++++++++------------------ 1 file changed, 38 insertions(+), 25 deletions(-) diff --git a/GPy/kern/rbf_ARD.py b/GPy/kern/rbf_ARD.py index f1e5f36a..1f90bb0a 100644 --- a/GPy/kern/rbf_ARD.py +++ b/GPy/kern/rbf_ARD.py @@ -30,6 +30,7 @@ class rbf_ARD(kernpart): def get_param(self): return np.hstack((self.variance,self.lengthscales)) + def set_param(self,x): assert x.size==(self.D+1) self.variance = x[0] @@ -37,61 +38,73 @@ class rbf_ARD(kernpart): self.lengthscales2 = np.square(self.lengthscales) #reset cached results self._Z, self._mu, self._S = np.empty(shape=(3,1)) # cached versions of Z,mu,S + def get_param_names(self): if self.D==1: return ['variance','lengthscale'] else: return ['variance']+['lengthscale_%i'%i for i in range(self.lengthscales.size)] + def K(self,X,X2,target): self._K_computations(X,X2) np.add(self.variance*self._K_dvar, target,target) + def Kdiag(self,X,target): np.add(target,self.variance,target) - def dK_dtheta(self,X,X2,target): - """Return shape is NxMx(Ntheta)""" + + def dK_dtheta(self,partial,X,X2,target): self._K_computations(X,X2) dl = self._K_dvar[:,:,None]*self.variance*self._K_dist2/self.lengthscales - np.add(target[:,:,0],self._K_dvar, target[:,:,0]) - np.add(target[:,:,1:],dl, target[:,:,1:]) + target[0] += np.sum(self._K_dvar*partial) + target[1:] += (dl*partial[:,:,None]).sum(0).sum(0) + def dKdiag_dtheta(self,X,target): - np.add(target[:,0],1.,target[:,0]) + target[0] += np.sum(partial) + def dK_dX(self,X,X2,target): self._K_computations(X,X2) dZ = self.variance*self._K_dvar[:,:,None]*self._K_dist/self.lengthscales2 - np.add(target,-dZ.transpose(1,0,2),target) + dK_dX = -dZ.transpose(1,0,2) + target += np.sum(dK_dX*partial.T[:,:,None],0) + + def dKdiag_dX(self,partial,X,target): + pass + def psi0(self,Z,mu,S,target): - np.add(target, self.variance, target) - def dpsi0_dtheta(self,Z,mu,S,target): - target[:,0] += 1. + target += self.variance + + def dpsi0_dtheta(self,partial,Z,mu,S,target): + target[0] += 1. + def dpsi0_dmuS(self,Z,mu,S,target_mu,target_S): pass + def psi1(self,Z,mu,S,target): - """Think N,M,Q """ self._psi_computations(Z,mu,S) np.add(target, self._psi1,target) - def dpsi1_dtheta(self,Z,mu,S,target): - """N,Q,(Ntheta)""" + def dpsi1_dtheta(self,partial,Z,mu,S,target): self._psi_computations(Z,mu,S) denom_deriv = S[:,None,:]/(self.lengthscales**3+self.lengthscales*S[:,None,:]) d_length = self._psi1[:,:,None]*(self.lengthscales*np.square(self._psi1_dist/(self.lengthscales2+S[:,None,:])) + denom_deriv) - target[:,:,0] += self._psi1/self.variance - target[:,:,1:] += d_length - def dpsi1_dZ(self,Z,mu,S,target): + target[0] += np.sum(partial*self._psi1/self.variance) + target[1:] += (d_length*partial[:,:,None]).sum(0).sum(0) + + def dpsi1_dZ(self,partial,Z,mu,S,target): self._psi_computations(Z,mu,S) np.add(target,-self._psi1[:,:,None]*self._psi1_dist/self.lengthscales2/self._psi1_denom,target) + target += np.sum(partial[:,:,None]*-self._psi1[:,:,None]*self._psi1_dist/self.lengthscales2/self._psi1_denom,0) - def dpsi1_dmuS(self,Z,mu,S,target_mu,target_S): + def dpsi1_dmuS(self,partial,Z,mu,S,target_mu,target_S): """return shapes are N,M,Q""" self._psi_computations(Z,mu,S) tmp = self._psi1[:,:,None]/self.lengthscales2/self._psi1_denom - np.add(target_mu,tmp*self._psi1_dist,target_mu) - np.add(target_S, 0.5*tmp*(self._psi1_dist_sq-1), target_S) + target_mu += np.sum(partial*tmp*self._psi1_dist,1) + target_S += np.sum(partial*0.5*tmp*(self._psi1_dist_sq-1),1) def psi2(self,Z,mu,S,target): - """shape N,M,M""" self._psi_computations(Z,mu,S) - np.add(target, self._psi2,target) + target += self._psi2.sum(0) #TODO: psi2 should be NxMxM (for het. noise) def dpsi2_dtheta(self,Z,mu,S,target): """Shape N,M,M,Ntheta""" @@ -99,21 +112,21 @@ class rbf_ARD(kernpart): d_var = np.sum(2.*self._psi2/self.variance,0) d_length = self._psi2[:,:,:,None]*(0.5*self._psi2_Zdist_sq*self._psi2_denom + 2.*self._psi2_mudist_sq + 2.*S[:,None,None,:]/self.lengthscales2)/(self.lengthscales*self._psi2_denom) d_length = d_length.sum(0) - target[:,:,0] += d_var - target[:,:,1:] += d_length + target[0] += np.sum(partial*d_var) + target[1:] += (d_length*partial[:,:,None]).sum(0).sum(0) def dpsi2_dZ(self,Z,mu,S,target): """Returns shape N,M,M,Q""" self._psi_computations(Z,mu,S) dZ = self._psi2[:,:,:,None]/self.lengthscales2*(-0.5*self._psi2_Zdist + self._psi2_mudist/self._psi2_denom) - target += dZ + target += np.sum(partial[None,:,:,None]*dZ,0).sum(1) def dpsi2_dmuS(self,Z,mu,S,target_mu,target_S): """Think N,M,M,Q """ self._psi_computations(Z,mu,S) tmp = self._psi2[:,:,:,None]/self.lengthscales2/self._psi2_denom - np.add(target_mu, -tmp*(2.*self._psi2_mudist),target_mu) #N,M,M,Q - np.add(target_S, tmp*(2.*self._psi2_mudist_sq-1), target_S) #N,M,M,Q + target_mu += (partial*-tmp*2.*self._psi2_mudist).sum(1).sum(1) + target_S += (partial*tmp*(2.*self._psi2_mudist_sq-1)).sum(1).sum(1) def _K_computations(self,X,X2): if not (np.all(X==self._X) and np.all(X2==self._X2)):