apunkt/GPy
1
0
Fork 0
mirror of https://github.com/SheffieldML/GPy.git synced 2026-05-10 20:42:39 +02:00
Code Issues Projects Releases Packages Wiki Activity Actions

move _raw_predict into posterior object

Browse source
This commit is contained in:
Zhenwen Dai 2016-03-17 11:29:33 +00:00
parent f2b813551a
commit 67ba9b60c6
4 changed files with 53 additions and 121 deletions
Download patch file Download diff file Expand all files Collapse all files

35
GPy/core/gp.py
View file

@ -212,42 +212,9 @@ class GP(Model):
= N(f*| K_{x*x}(K_{xx} + \Sigma)^{-1}Y, K_{x*x*} - K_{xx*}(K_{xx} + \Sigma)^{-1}K_{xx*}
\Sigma := \texttt{Likelihood.variance / Approximate likelihood covariance}
"""
if hasattr(self.posterior, '_raw_predict'):
mu, var = self.posterior._raw_predict(kern=self.kern if kern is None else kern, Xnew=Xnew, pred_var=self._predictive_variable, full_cov=full_cov)
if self.mean_function is not None:
mu += self.mean_function.f(Xnew)
return mu, var
if kern is None:
kern = self.kern
Kx = kern.K(self._predictive_variable, Xnew)
mu = np.dot(Kx.T, self.posterior.woodbury_vector)
if len(mu.shape)==1:
mu = mu.reshape(-1,1)
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: # Missing data
var = np.empty((Kxx.shape[0],Kxx.shape[1],self.posterior.woodbury_inv.shape[2]))
from ..util.linalg import mdot
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: # Missing data
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
mu, var = self.posterior._raw_predict(kern=self.kern if kern is None else kern, Xnew=Xnew, pred_var=self._predictive_variable, full_cov=full_cov)
if self.mean_function is not None:
mu += self.mean_function.f(Xnew)
return mu, var
def predict(self, Xnew, full_cov=False, Y_metadata=None, kern=None, likelihood=None):