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remo0ved slices from models

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slices are now handles by special indexing kern parts, such as
coregionalisation, independent_outputs. The old slicing functionality
has been removed simply to clean up the code a little.

Now that input_slices still exist (and will continue to be useful) in
kern.py. They do need a little work though, for the psi-statistics
This commit is contained in:
James Hensman 2013-04-28 17:22:04 +01:00
parent ac842d51e6
commit 52ba8e4ba3
7 changed files with 103 additions and 175 deletions
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20
GPy/models/sparse_GP.py
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@ -9,10 +9,6 @@ from .. import kern
from GP import GP
from scipy import linalg
#Still TODO:
# make use of slices properly (kernel can now do this)
# enable heteroscedatic noise (kernel will need to compute psi2 as a (NxMxM) array)
class sparse_GP(GP):
"""
Variational sparse GP model
@ -27,19 +23,16 @@ class sparse_GP(GP):
:type X_variance: np.ndarray (N x Q) | None
:param Z: inducing inputs (optional, see note)
:type Z: np.ndarray (M 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
:param normalize_(X|Y) : whether to normalize the data before computing (predictions will be in original scales)
:type normalize_(X|Y): bool
"""
def __init__(self, X, likelihood, kernel, Z, X_variance=None, Xslices=None,Zslices=None, normalize_X=False):
def __init__(self, X, likelihood, kernel, Z, X_variance=None, normalize_X=False):
self.scale_factor = 100.0# a scaling factor to help keep the algorithm stable
self.auto_scale_factor = False
self.Z = Z
self.Zslices = Zslices
self.Xslices = Xslices
self.M = Z.shape[0]
self.likelihood = likelihood
@ -50,7 +43,7 @@ class sparse_GP(GP):
self.has_uncertain_inputs=True
self.X_variance = X_variance
GP.__init__(self, X, likelihood, kernel=kernel, normalize_X=normalize_X, Xslices=Xslices)
GP.__init__(self, X, likelihood, kernel=kernel, normalize_X=normalize_X)
#normalize X uncertainty also
if self.has_uncertain_inputs:
@ -65,13 +58,12 @@ class sparse_GP(GP):
self.psi1 = self.kern.psi1(self.Z,self.X, self.X_variance).T
self.psi2 = self.kern.psi2(self.Z,self.X, self.X_variance)
else:
self.psi0 = self.kern.Kdiag(self.X,slices=self.Xslices)
self.psi0 = self.kern.Kdiag(self.X)
self.psi1 = self.kern.K(self.Z,self.X)
self.psi2 = None
def _computations(self):
#TODO: find routine to multiply triangular matrices
#TODO: slices for psi statistics (easy enough)
sf = self.scale_factor
sf2 = sf**2
@ -252,16 +244,16 @@ class sparse_GP(GP):
dL_dZ += self.kern.dK_dX(self.dL_dpsi1,self.Z,self.X)
return dL_dZ
def _raw_predict(self, Xnew, slices, full_cov=False):
def _raw_predict(self, Xnew, which_parts='all', full_cov=False):
"""Internal helper function for making predictions, does not account for normalization"""
Kx = self.kern.K(self.Z, Xnew)
mu = mdot(Kx.T, self.C/self.scale_factor, self.psi1V)
if full_cov:
Kxx = self.kern.K(Xnew)
Kxx = self.kern.K(Xnew,which_parts=which_parts)
var = Kxx - mdot(Kx.T, (self.Kmmi - self.C/self.scale_factor**2), Kx) #NOTE this won't work for plotting
else:
Kxx = self.kern.Kdiag(Xnew)
Kxx = self.kern.Kdiag(Xnew,which_parts=which_parts)
var = Kxx - np.sum(Kx*np.dot(self.Kmmi - self.C/self.scale_factor**2, Kx),0)
return mu,var[:,None]