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the Opper-Archambeau method is now implemented as an inference method in the GPy style

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James Hensman 2015-08-18 11:39:27 +01:00
parent ea3bfbb597
commit 7ba26eda1c
4 changed files with 34 additions and 86 deletions
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36
GPy/core/gp.py
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@ -183,7 +183,7 @@ class GP(Model):
"""
return self._log_marginal_likelihood
def _raw_predict(self, _Xnew, full_cov=False, kern=None):
def _raw_predict(self, Xnew, full_cov=False, kern=None):
"""
For making predictions, does not account for normalization or likelihood
@ -199,24 +199,30 @@ class GP(Model):
if kern is None:
kern = self.kern
Kx = kern.K(_Xnew, self.X).T
WiKx = np.dot(self.posterior.woodbury_inv, Kx)
Kx = kern.K(self.X, Xnew)
mu = np.dot(Kx.T, self.posterior.woodbury_vector)
if full_cov:
Kxx = kern.K(_Xnew)
var = Kxx - np.dot(Kx.T, WiKx)
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)
var = Kxx - np.sum(WiKx*Kx, 0)
var = var.reshape(-1, 1)
var[var<0.] = 0.
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)
#force mu to be a column vector
if len(mu.shape)==1: mu = mu[:,None]
#add the mean function in
if not self.mean_function is None:
mu += self.mean_function.f(_Xnew)
return mu, var
def predict(self, Xnew, full_cov=False, Y_metadata=None, kern=None):