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Assorted work on combining the EP and sparse methods

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James Hensman 2013-02-01 17:12:45 +00:00
parent 64280d7eb6
commit 5447d6fbfc
7 changed files with 95 additions and 44 deletions
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4
GPy/models/GP.py
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@ -31,7 +31,7 @@ class GP(model):
"""
def __init__(self, X, kernel, likelihood, normalize_X=False, Xslices=None):
def __init__(self, X, likelihood, kernel, normalize_X=False, Xslices=None):
# parse arguments
self.Xslices = Xslices
@ -121,7 +121,7 @@ class GP(model):
For the likelihood parameters, pass in alpha = K^-1 y
"""
return np.hstack((self.kern.dK_dtheta(partial=self.dL_dK,X=self.X), self.likelihood._gradients(partial=self.dL_dK)))
return np.hstack((self.kern.dK_dtheta(partial=self.dL_dK,X=self.X), self.likelihood._gradients(partial=np.diag(self.dL_dK))))
def _raw_predict(self,_Xnew,slices=None, full_cov=False):
"""