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tidied up some commented code from sparse_GP_regression

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James Hensman 2012-11-30 12:43:14 +00:00
parent 31b7a0520e
commit 821701142a
1 changed files with 0 additions and 18 deletions
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18
GPy/models/sparse_GP_regression.py
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@ -52,7 +52,6 @@ class sparse_GP_regression(GP_regression):
self._compute_kernel_matrices()
self._computations()
def _compute_kernel_matrices(self):
# kernel computations, using BGPLVM notation
#TODO: the following can be switched out in the case of uncertain inputs (or the BGPLVM!)
@ -63,16 +62,6 @@ 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.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
self.psi1Y = np.dot(self.psi1, self.Y)
@ -101,13 +90,6 @@ class sparse_GP_regression(GP_regression):
# Computes dL_dKmm TODO: nicer precomputations
# tmp = self.beta*mdot(self.LBL_inv, self.psi2, self.Kmmi)
# self.dL_dKmm = -self.beta * self.D * 0.5 * mdot(self.Lmi.T, self.A, self.Lmi) # dB
# self.dL_dKmm += -0.5 * self.D * (- self.LBL_inv - tmp - tmp.T + self.Kmmi) # dC
# tmp = (mdot(self.LBL_inv, self.psi1YYpsi1, self.Kmmi)
# - self.beta*mdot(self.G, self.psi2, self.Kmmi))
# self.dL_dKmm += -0.5*self.beta2*(tmp + tmp.T - self.G)
tmp = self.beta*mdot(self.LBL_inv, self.psi2, self.Kmmi)
self.dL_dKmm = -self.beta * self.D * 0.5 * mdot(self.Lmi.T, self.A, self.Lmi) # dB
self.dL_dKmm += -0.5 * self.D * (- self.LBL_inv - tmp - tmp.T + self.Kmmi) # dC