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sparse GP regression now working on this branch

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James Hensman 2012-11-30 11:47:35 +00:00
parent 5ba2099ee9
commit ba84a43ea3
5 changed files with 34 additions and 26 deletions
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45
GPy/models/sparse_GP_regression.py
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@ -63,15 +63,15 @@ class sparse_GP_regression(GP_regression):
self.psi1 = self.kern.K(self.Z,self.X)
self.psi2 = np.dot(self.psi1,self.psi1.T)
self.dKmm_dtheta = self.kern.dK_dtheta(self.Z)
self.dpsi0_dtheta = self.kern.dKdiag_dtheta(self.X).sum(0)
self.dpsi1_dtheta = self.kern.dK_dtheta(self.Z,self.X)
tmp = np.dot(self.psi1, self.dpsi1_dtheta)
self.dpsi2_dtheta = tmp + tmp.transpose(1,0,2)
#self.dKmm_dtheta = self.kern.dK_dtheta(self.Z)
#self.dpsi0_dtheta = self.kern.dKdiag_dtheta(self.X).sum(0)
#self.dpsi1_dtheta = self.kern.dK_dtheta(self.Z,self.X)
#tmp = np.dot(self.psi1, self.dpsi1_dtheta)
#self.dpsi2_dtheta = tmp + tmp.transpose(1,0,2)
self.dpsi1_dZ = self.kern.dK_dX(self.Z,self.X)
self.dpsi2_dZ = np.tensordot(self.psi1,self.dpsi1_dZ,((1),(0)))*2.0
self.dKmm_dZ = self.kern.dK_dX(self.Z)
#self.dpsi1_dZ = self.kern.dK_dX(self.Z,self.X)
#self.dpsi2_dZ = np.tensordot(self.psi1,self.dpsi1_dZ,((1),(0)))*2.0
#self.dKmm_dZ = self.kern.dK_dX(self.Z)
def _computations(self):
# TODO find routine to multiply triangular matrices
@ -92,7 +92,7 @@ class sparse_GP_regression(GP_regression):
self.G = mdot(self.LBL_inv, self.psi1YYpsi1, self.LBL_inv.T)
# Computes dL_dpsi
self.dL_dpsi0 = - 0.5 * self.D * self.beta
self.dL_dpsi0 = - 0.5 * self.D * self.beta * np.ones(self.N)
dC_dpsi1 = (self.LLambdai.T[:,:, None, None] * self.Y) # this is sane.
tmp = (dC_dpsi1*self.C[None,:,None,:]).sum(1).sum(-1)
self.dL_dpsi1 = self.beta2 * tmp
@ -145,29 +145,34 @@ class sparse_GP_regression(GP_regression):
return np.squeeze(dA_dbeta + dB_dbeta + dC_dbeta + dD_dbeta + dE_dbeta)
def dL_dtheta(self):
dL_dtheta = (self.dL_dpsi0 * self.dpsi0_dtheta + (self.dL_dpsi1[:,:, None] * self.dpsi1_dtheta).sum(0).sum(0)
+ (self.dL_dpsi2[:, :, None] * self.dpsi2_dtheta).sum(0).sum(0)
+ (self.dL_dKmm[:, :, None] * self.dKmm_dtheta).sum(0).sum(0))
#re-cast computations in psi2 back to psi1:
dL_dpsi1 = self.dL_dpsi1 + 2.*np.dot(self.dL_dpsi2,self.psi1)
dL_dtheta = self.kern.dK_dtheta(self.dL_dKmm,self.Z)
dL_dtheta += self.kern.dK_dtheta(dL_dpsi1,self.Z,self.X)
dL_dtheta += self.kern.dKdiag_dtheta(self.dL_dpsi0, self.X)
return dL_dtheta
def dL_dZ(self):
dL_dZ = ((self.dL_dpsi1[:,:,None]*self.dpsi1_dZ.swapaxes(0,1)).sum(1)
+ (self.dL_dpsi2[:, :, None] * self.dpsi2_dZ).sum(0)
+ 2.0*(self.dL_dKmm[:, :, None] * self.dKmm_dZ).sum(0))
return dL_dZ
#re-cast computations in psi2 back to psi1:
dL_dpsi1 = self.dL_dpsi1 + 2.*np.dot(self.dL_dpsi2,self.psi1)
dL_dZ = 2.*self.kern.dK_dX(self.dL_dKmm,self.Z,)#factor of two becase of vertical and horizontal 'stripes' in dKmm_dZ
dL_dZ += self.kern.dK_dX(dL_dpsi1,self.Z,self.X)
return dL_dZ
def log_likelihood_gradients(self):
return np.hstack([self.dL_dZ().flatten(), self.dL_dbeta(), self.dL_dtheta()])
def _raw_predict(self,_Xnew,slices):
def _raw_predict(self,Xnew,slices):
"""Internal helper function for making predictions, does not account for normalisation"""
Kx = self.kern.K(self.Z, _Xnew, self.Xslices, slices)
Kxx = self.kern.K(_Xnew, slices1=slices, slices2=slices)
Kx = self.kern.K(self.Z, Xnew)
Kxx = self.kern.K(Xnew)
mu = self.beta * mdot(Kx.T, self.LBL_inv, self.psi1Y)
var = Kxx - mdot(Kx.T, (self.Kmmi - self.LBL_inv), Kx) + np.eye(_Xnew.shape[0])/self.beta # TODO: This beta doesn't belong here in the EP case.
var = Kxx - mdot(Kx.T, (self.Kmmi - self.LBL_inv), Kx) + np.eye(Xnew.shape[0])/self.beta # TODO: This beta doesn't belong here in the EP case.
return mu,var
def plot(self,*args,**kwargs):