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rbf_ARD now in the updated format for the computation of the derivatives (included for the psi-statistics, but not tested)

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Nicolas 2012-12-03 11:26:10 +00:00
parent 908b1d2801
commit 049de98d3b
1 changed files with 38 additions and 25 deletions
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63
GPy/kern/rbf_ARD.py
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@ -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)):