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made uncertain inputs a simple swith in the sparse GP class. This simplifies the inherritance structure

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This commit is contained in:
James Hensman 2012-12-03 18:35:05 +00:00
parent b1c2282bfc
commit 5f22bd00e6
2 changed files with 34 additions and 83 deletions
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47
GPy/models/sparse_GP_regression.py
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@ -26,6 +26,8 @@ class sparse_GP_regression(GP_regression):
:type kernel: a GPy kernel
:param Z: inducing inputs (optional, see note)
:type Z: np.ndarray (M x Q) | None
:param X_uncertainty: The uncertainty in the measurements of X (Gaussian variance)
:type X_uncertainty: np.ndarray (N x Q) | None
:param Zslices: slices for the inducing inputs (see slicing TODO: link)
:param M : Number of inducing points (optional, default 10. Ignored if Z is not None)
:type M: int
@ -44,6 +46,12 @@ class sparse_GP_regression(GP_regression):
assert Z.shape[1]==X.shape[1]
self.Z = Z
self.M = Z.shape[1]
if X_uncertainty is None:
self.has_uncertain_inputs=False
else:
assert X_uncertainty.shape==X.shape
self.has_uncertain_inputs=False
self.X_uncertainty = X_uncertainty
GP_regression.__init__(self, X, Y, kernel=kernel, normalize_X=normalize_X, normalize_Y=normalize_Y)
self.trYYT = np.sum(np.square(self.Y))
@ -58,13 +66,17 @@ class sparse_GP_regression(GP_regression):
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!)
#TODO: slices for psi statistics (easy enough)
self.Kmm = self.kern.K(self.Z)
self.psi0 = self.kern.Kdiag(self.X,slices=self.Xslices).sum()
self.psi1 = self.kern.K(self.Z,self.X)
self.psi2 = np.dot(self.psi1,self.psi1.T)
if self.has_uncertain_inputs:
self.psi0 = self.kern.psi0(self.Z,self.X, self.X_uncertainty).sum()
self.psi1 = self.kern.psi1(self.Z,self.X, self.X_uncertainty).T
self.psi2 = self.kern.psi2(self.Z,self.X, self.X_uncertainty)
else:
self.psi0 = self.kern.Kdiag(self.X,slices=self.Xslices).sum()
self.psi1 = self.kern.K(self.Z,self.X)
self.psi2 = np.dot(self.psi1,self.psi1.T)
def _computations(self):
# TODO find routine to multiply triangular matrices
@ -132,12 +144,16 @@ class sparse_GP_regression(GP_regression):
"""
Compute and return the derivative of the log marginal likelihood wrt the parameters of the kernel
"""
#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)
if self.has_uncertain_inputs:
dL_dtheta += self.kern.dpsi0_dtheta(self.dL_dpsi0, self.Z,self.X,self.X_uncertainty)
dL_dtheta += self.kern.dpsi1_dtheta(self.dL_dpsi1.T,self.Z,self.X, self.X_uncertainty)
dL_dtheta += self.kern.dpsi2_dtheta(self.dL_dpsi2,self.Z,self.X, self.X_uncertainty) # for multiple_beta, dL_dpsi2 will be a different shape
else:
#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(dL_dpsi1,self.Z,self.X)
dL_dtheta += self.kern.dKdiag_dtheta(self.dL_dpsi0, self.X)
return dL_dtheta
@ -145,11 +161,14 @@ class sparse_GP_regression(GP_regression):
"""
The derivative of the bound wrt the inducing inputs Z
"""
#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)
if self.has_uncertain_inputs:
dL_dZ += self.kern.dpsi1_dZ(self.dL_dpsi1.T,self.Z,self.X, self.X_uncertainty)
dL_dZ += self.kern.dpsi2_dZ(self.dL_dpsi2,self.Z,self.X, self.X_uncertainty)
else:
#re-cast computations in psi2 back to psi1:
dL_dpsi1 = self.dL_dpsi1 + 2.*np.dot(self.dL_dpsi2,self.psi1)
dL_dZ += self.kern.dK_dX(dL_dpsi1,self.Z,self.X)
return dL_dZ
def log_likelihood_gradients(self):
@ -172,5 +191,7 @@ class sparse_GP_regression(GP_regression):
GP_regression.plot(self,*args,**kwargs)
if self.Q==1:
pb.plot(self.Z,self.Z*0+pb.ylim()[0],'k|',mew=1.5,markersize=12)
if self.has_uncertain_inputs:
pb.errorbar(self.X[:,0], pb.ylim()[0]+np.zeros(self.N), xerr=2*np.sqrt(self.X_uncertainty.flatten()))
if self.Q==2:
pb.plot(self.Z[:,0],self.Z[:,1],'wo')