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[basis funcs] memory efficient posterior inference

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mzwiessele 2015-04-28 11:38:35 +02:00
parent be40318e0b
commit a24853da6b
2 changed files with 4 additions and 2 deletions
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4
GPy/core/sparse_gp.py
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@ -132,7 +132,9 @@ class SparseGP(GP):
if self.posterior.woodbury_inv.ndim == 2:
var = Kxx - np.dot(Kx.T, np.dot(self.posterior.woodbury_inv, Kx))
elif self.posterior.woodbury_inv.ndim == 3:
var = Kxx[:,:,None] - np.tensordot(np.dot(np.atleast_3d(self.posterior.woodbury_inv).T, Kx).T, Kx, [1,0]).swapaxes(1,2)
var = np.empty((Kxx.shape[0],Kxx.shape[1],self.posterior.woodbury_inv.shape[2]))
for i in range(var.shape[1]):
var[:, :, i] = (Kxx - mdot(Kx.T, self.posterior.woodbury_inv[:, :, i], Kx))
var = var
else:
Kxx = kern.Kdiag(Xnew)

2
GPy/kern/_src/basis_funcs.py
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@ -136,7 +136,7 @@ class DomainKernel(LinearSlopeBasisFuncKernel):
def _phi(self, X):
phi = np.where((X>self.start)*(X<self.stop), 1, 0)
return phi#((phi-self.start)/(self.stop-self.start))-.5
class LogisticBasisFuncKernel(BasisFuncKernel):
def __init__(self, input_dim, centers, variance=1., slope=1., active_dims=None, ARD=False, ARD_slope=True, name='logistic'):
self.centers = np.atleast_2d(centers)