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[predict] using gp predict in sparse gp and predictive variable

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Max Zwiessele 2015-09-07 14:11:01 +01:00
parent 63e1fa05e4
commit 78257d2314
2 changed files with 25 additions and 24 deletions
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47
GPy/core/sparse_gp.py
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@ -128,29 +128,30 @@ class SparseGP(GP):
if kern is None: kern = self.kern
if not isinstance(Xnew, VariationalPosterior):
Kx = kern.K(self._predictive_variable, Xnew)
mu = np.dot(Kx.T, self.posterior.woodbury_vector)
if full_cov:
Kxx = kern.K(Xnew)
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 = np.empty((Kxx.shape[0],Kxx.shape[1],self.posterior.woodbury_inv.shape[2]))
for i in range(var.shape[2]):
var[:, :, i] = (Kxx - mdot(Kx.T, self.posterior.woodbury_inv[:, :, i], Kx))
var = var
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))[:,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]):
var[:, i] = (Kxx - (np.sum(np.dot(self.posterior.woodbury_inv[:, :, i].T, Kx) * Kx, 0)))
var = var
#add in the mean function
if self.mean_function is not None:
mu += self.mean_function.f(Xnew)
# Kx = kern.K(self._predictive_variable, Xnew)
# mu = np.dot(Kx.T, self.posterior.woodbury_vector)
# if full_cov:
# Kxx = kern.K(Xnew)
# 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 = np.empty((Kxx.shape[0],Kxx.shape[1],self.posterior.woodbury_inv.shape[2]))
# for i in range(var.shape[2]):
# var[:, :, i] = (Kxx - mdot(Kx.T, self.posterior.woodbury_inv[:, :, i], Kx))
# var = var
# 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))[:,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]):
# var[:, i] = (Kxx - (np.sum(np.dot(self.posterior.woodbury_inv[:, :, i].T, Kx) * Kx, 0)))
# var = var
# #add in the mean function
# if self.mean_function is not None:
# mu += self.mean_function.f(Xnew)
mu, var = super(SparseGP, self)._raw_predict(Xnew, full_cov, kern)
else:
psi0_star = kern.psi0(self._predictive_variable, Xnew)
psi1_star = kern.psi1(self._predictive_variable, Xnew)