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[plotting] added predict_kw to plot function

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mzwiessele 2015-04-24 11:02:01 +02:00
parent 9c19f8584e
commit 335df2942f
4 changed files with 22 additions and 12 deletions
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10
GPy/core/sparse_gp.py
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@ -137,7 +137,7 @@ class SparseGP(GP):
else:
Kxx = kern.Kdiag(Xnew)
if self.posterior.woodbury_inv.ndim == 2:
var = Kxx - np.sum(np.dot(self.posterior.woodbury_inv.T, Kx) * Kx, 0)
var = (Kxx - np.sum(np.dot(self.posterior.woodbury_inv.T, Kx) * Kx, 0))[:,None]
elif self.posterior.woodbury_inv.ndim == 3:
var = np.empty((Kxx.shape[0],self.posterior.woodbury_inv.shape[2]))
for i in range(var.shape[1]):
@ -147,9 +147,9 @@ class SparseGP(GP):
if self.mean_function is not None:
mu += self.mean_function.f(Xnew)
else:
psi0_star = self.kern.psi0(self.Z, Xnew)
psi1_star = self.kern.psi1(self.Z, Xnew)
#psi2_star = self.kern.psi2(self.Z, Xnew) # Only possible if we get NxMxM psi2 out of the code.
psi0_star = kern.psi0(self.Z, Xnew)
psi1_star = kern.psi1(self.Z, Xnew)
#psi2_star = kern.psi2(self.Z, Xnew) # Only possible if we get NxMxM psi2 out of the code.
la = self.posterior.woodbury_vector
mu = np.dot(psi1_star, la) # TODO: dimensions?
@ -161,7 +161,7 @@ class SparseGP(GP):
for i in range(Xnew.shape[0]):
_mu, _var = Xnew.mean.values[[i]], Xnew.variance.values[[i]]
psi2_star = self.kern.psi2(self.Z, NormalPosterior(_mu, _var))
psi2_star = kern.psi2(self.Z, NormalPosterior(_mu, _var))
tmp = (psi2_star[:, :] - psi1_star[[i]].T.dot(psi1_star[[i]]))
var_ = mdot(la.T, tmp, la)