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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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12
GPy/models/sparse_GP_regression.py
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@ -13,7 +13,7 @@ class sparse_GP_regression(sparse_GP):
"""
Gaussian Process model for regression
This is a thin wrapper around the GP class, with a set of sensible defalts
This is a thin wrapper around the sparse_GP class, with a set of sensible defalts
:param X: input observations
:param Y: observed values
@ -22,25 +22,25 @@ class sparse_GP_regression(sparse_GP):
:type normalize_X: False|True
:param normalize_Y: whether to normalize the input data before computing (predictions will be in original scales)
:type normalize_Y: False|True
:param Xslices: how the X,Y data co-vary in the kernel (i.e. which "outputs" they correspond to). See (link:slicing)
:rtype: model object
.. Note:: Multiple independent outputs are allowed using columns of Y
"""
def __init__(self,X,Y,kernel=None,normalize_X=False,normalize_Y=False, Xslices=None,Z=None, M=10):
#kern defaults to rbf
def __init__(self, X, Y, kernel=None, normalize_X=False, normalize_Y=False, Z=None, M=10):
#kern defaults to rbf (plus white for stability)
if kernel is None:
kernel = kern.rbf(X.shape[1]) + kern.white(X.shape[1],1e-3)
#Z defaults to a subset of the data
if Z is None:
Z = np.random.permutation(X.copy())[:M]
i = np.random.permutation(X.shape[0])[:M]
Z = X[i].copy()
else:
assert Z.shape[1]==X.shape[1]
#likelihood defaults to Gaussian
likelihood = likelihoods.Gaussian(Y,normalize=normalize_Y)
sparse_GP.__init__(self, X, likelihood, kernel, Z, normalize_X=normalize_X, Xslices=Xslices)
sparse_GP.__init__(self, X, likelihood, kernel, Z, normalize_X=normalize_X)