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bias kernel psi stats implemented.

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James Hensman 2012-11-30 15:49:20 +00:00
parent cd22c96e00
commit 6d0a7afd4c
6 changed files with 170 additions and 113 deletions
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1
GPy/models/__init__.py
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@ -9,3 +9,4 @@ from warped_GP import warpedGP
from GP_EP import GP_EP
from generalized_FITC import generalized_FITC
from sparse_GPLVM import sparse_GPLVM
from uncertain_input_GP_regression import uncertain_input_GP_regression

17
GPy/models/uncertain_input_GP_regression.py
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@ -7,7 +7,7 @@ from ..util.linalg import mdot, jitchol, chol_inv, pdinv
from ..util.plot import gpplot
from .. import kern
from ..inference.likelihoods import likelihood
from GP_regression import GP_regression
from sparse_GP_regression import sparse_GP_regression
class uncertain_input_GP_regression(sparse_GP_regression):
"""
@ -33,6 +33,7 @@ class uncertain_input_GP_regression(sparse_GP_regression):
"""
def __init__(self,X,Y,X_uncertainty,kernel=None, beta=100., Z=None,Zslices=None,M=10,normalize_X=False,normalize_Y=False):
self.X_uncertainty = X_uncertainty
sparse_GP_regression.__init__(self, X, Y, kernel = kernel, beta = beta, normalize_X = normalize_X, normalize_Y = normalize_Y)
self.trYYT = np.sum(np.square(self.Y))
@ -40,22 +41,22 @@ class uncertain_input_GP_regression(sparse_GP_regression):
# kernel computations, using BGPLVM notation
#TODO: slices for psi statistics (easy enough)
self.Kmm = self.kern.K(self.Z)
self.psi0 = self.kern.psi0(self.X,slices=self.Xslices).sum()
self.psi1 = self.kern.psi1(self.Z,self.X, self.X_uncertainty)
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)
def dL_dtheta(self):
#re-cast computations in psi2 back to psi1:
dL_dtheta = self.kern.dK_dtheta(self.dL_dKmm,self.Z)
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,self.Z,self.X, self.X_uncertainty)
dL_dtheta += self.kern.dpsi2_dtheta(self.dL_dpsi2,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
return dL_dtheta
def dL_dZ(self):
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.dpsi1_dZ(dL_dpsi1,self.Z,self.X, self.X_uncertainty)
dL_dZ += self.kern.dpsi2_dZ(dL_dpsi2,self.Z,self.X, self.X_uncertainty)
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)
return dL_dZ
def plot(self,*args,**kwargs):
@ -65,5 +66,5 @@ class uncertain_input_GP_regression(sparse_GP_regression):
"""
sparse_GP_regression.plot(self,*args,**kwargs)
if self.Q==1:
pb.errorbar(self.X[:,0], pb.ylim(0) ,xerr=2*np.sqrt(self.X_uncertainty.flatten()))
pb.errorbar(self.X[:,0], pb.ylim()[0]+np.zeros(self.N), xerr=2*np.sqrt(self.X_uncertainty.flatten()))