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[sparse gp] prediction with posterior per dimension activated

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Max Zwiessele 2014-10-29 08:23:25 +00:00
parent f91e7e9deb
commit af40ef8cfb
2 changed files with 9 additions and 4 deletions
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6
GPy/core/sparse_gp.py
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@ -346,9 +346,11 @@ def optimize(m, maxiter=1000):
mu = np.dot(Kx.T, self.posterior.woodbury_vector) mu = np.dot(Kx.T, self.posterior.woodbury_vector)
if full_cov: if full_cov:
Kxx = kern.K(Xnew) Kxx = kern.K(Xnew)
if self.posterior.woodbury_inv.ndim == 2:
var = Kxx - np.dot(Kx.T, np.dot(self.posterior.woodbury_inv, Kx)) var = Kxx - np.dot(Kx.T, np.dot(self.posterior.woodbury_inv, Kx))
#var = Kxx[:,:,None] - np.tensordot(np.dot(np.atleast_3d(self.posterior.woodbury_inv).T, Kx).T, Kx, [1,0]).swapaxes(1,2) elif self.posterior.woodbury_inv.ndim == 3:
var = var.squeeze() var = Kxx[:,:,None] - np.tensordot(np.dot(np.atleast_3d(self.posterior.woodbury_inv).T, Kx).T, Kx, [1,0]).swapaxes(1,2)
var = var
else: else:
Kxx = kern.Kdiag(Xnew) Kxx = kern.Kdiag(Xnew)
var = (Kxx - np.sum(np.dot(np.atleast_3d(self.posterior.woodbury_inv).T, Kx) * Kx[None,:,:], 1)).T var = (Kxx - np.sum(np.dot(np.atleast_3d(self.posterior.woodbury_inv).T, Kx) * Kx[None,:,:], 1)).T

3
GPy/likelihoods/gaussian.py
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@ -80,7 +80,10 @@ class Gaussian(Likelihood):
def predictive_values(self, mu, var, full_cov=False, Y_metadata=None): def predictive_values(self, mu, var, full_cov=False, Y_metadata=None):
if full_cov: if full_cov:
if var.ndim == 2:
var += np.eye(var.shape[0])*self.variance var += np.eye(var.shape[0])*self.variance
if var.ndim == 3:
var += np.atleast_3d(np.eye(var.shape[0])*self.variance)
else: else:
var += self.variance var += self.variance
return mu, var return mu, var