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sphinx configuratino for readthedocs.org

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James Hensman 2013-01-09 15:45:39 +00:00
parent 3070f0b6c5
commit 57934d82a6
5 changed files with 429 additions and 4 deletions
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2
GPy/models/sparse_GP_regression.py
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@ -44,7 +44,7 @@ class sparse_GP_regression(GP_regression):
else:
assert Z.shape[1]==X.shape[1]
self.Z = Z
self.M = Z.shape[1]
self.M = Z.shape[0]
if X_uncertainty is None:
self.has_uncertain_inputs=False
else:

14
GPy/models/uncollapsed_sparse_GP.py
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@ -36,7 +36,9 @@ class uncollapsed_sparse_GP(sparse_GP_regression):
self.D = Y.shape[1]
if q_u is None:
if 'Z' in kwargs.keys():
print kwargs['Z']
self.M = kwargs['Z'].shape[0]
print self.M
else:
self.M = M
q_u = np.hstack((np.random.randn(self.M*self.D),-0.5*np.eye(self.M).flatten()))
@ -90,13 +92,18 @@ class uncollapsed_sparse_GP(sparse_GP_regression):
return np.squeeze(dA_dbeta + dB_dbeta + dC_dbeta + dD_dbeta + dE_dbeta)
def _raw_predict(self, Xnew, slices):
def _raw_predict(self, Xnew, slices,full_cov=False):
"""Internal helper function for making predictions, does not account for normalisation"""
Kx = self.kern.K(Xnew,self.Z)
Kxx = self.kern.K(Xnew)
mu = mdot(Kx,self.Kmmi,self.q_u_expectation[0])
tmp = self.Kmmi- mdot(self.Kmmi,self.q_u_cov,self.Kmmi)
var = Kxx - mdot(Kx,tmp,Kx.T) + np.eye(Xnew.shape[0])/self.beta
if full_cov:
Kxx = self.kern.K(Xnew)
var = Kxx - mdot(Kx,tmp,Kx.T) + np.eye(Xnew.shape[0])/self.beta
else:
Kxx = self.kern.Kdiag(Xnew)
var = Kxx - np.sum(Kx*np.dot(Kx,tmp),1) + 1./self.beta
return mu,var
@ -126,6 +133,7 @@ class uncollapsed_sparse_GP(sparse_GP_regression):
Note that the natural gradient in either is given by the gradient in the other (See Hensman et al 2012 Fast Variational inference in the conjugate exponential Family)
"""
dL_dmmT_S = -0.5*self.Lambda-self.q_u_canonical[1]
#dL_dm = np.dot(self.Kmmi,self.psi1V) - np.dot(self.Lambda,self.q_u_mean)
dL_dm = np.dot(self.Kmmi,self.psi1V) - self.q_u_canonical[0]
#dL_dSim =