apunkt/GPy
1
0
Fork 0
mirror of https://github.com/SheffieldML/GPy.git synced 2026-05-18 13:55:14 +02:00
Code Issues Projects Releases Packages Wiki Activity Actions

seems to run now

Browse source
This commit is contained in:
Ricardo 2013-06-05 15:13:17 +01:00
parent 8d07df6503
commit 8e7a71c63e
1 changed files with 5 additions and 5 deletions
Download patch file Download diff file Expand all files Collapse all files

10
GPy/core/fitc.py
View file

@ -7,15 +7,15 @@ from ..util.linalg import mdot, jitchol, chol_inv, tdot, symmetrify,pdinv
from ..util.plot import gpplot from ..util.plot import gpplot
from .. import kern from .. import kern
from scipy import stats, linalg from scipy import stats, linalg
#from ..core import sparse_GP
from gp_base import GPBase from gp_base import GPBase
from sparse_gp import SparseGP
def backsub_both_sides(L,X): def backsub_both_sides(L,X):
""" Return L^-T * X * L^-1, assumuing X is symmetrical and L is lower cholesky""" """ Return L^-T * X * L^-1, assumuing X is symmetrical and L is lower cholesky"""
tmp,_ = linalg.lapack.flapack.dtrtrs(L,np.asfortranarray(X),lower=1,trans=1) tmp,_ = linalg.lapack.flapack.dtrtrs(L,np.asfortranarray(X),lower=1,trans=1)
return linalg.lapack.flapack.dtrtrs(L,np.asfortranarray(tmp.T),lower=1,trans=1)[0].T return linalg.lapack.flapack.dtrtrs(L,np.asfortranarray(tmp.T),lower=1,trans=1)[0].T
class FITC(GPBase): class FITC(SparseGP):
""" """
sparse FITC approximation sparse FITC approximation
@ -111,7 +111,7 @@ class FITC(GPBase):
raise NotImplementedError raise NotImplementedError
else: else:
if self.likelihood.is_heteroscedastic: if self.likelihood.is_heteroscedastic:
assert self.likelihood.D == 1 assert self.likelihood.output_dim == 1
tmp = self.psi1 * (np.sqrt(self.beta_star.flatten().reshape(1, self.N))) tmp = self.psi1 * (np.sqrt(self.beta_star.flatten().reshape(1, self.N)))
tmp, _ = linalg.lapack.flapack.dtrtrs(self.Lm, np.asfortranarray(tmp), lower=1) tmp, _ = linalg.lapack.flapack.dtrtrs(self.Lm, np.asfortranarray(tmp), lower=1)
self.A = tdot(tmp) self.A = tdot(tmp)
@ -215,8 +215,8 @@ class FITC(GPBase):
def log_likelihood(self): def log_likelihood(self):
""" Compute the (lower bound on the) log marginal likelihood """ """ Compute the (lower bound on the) log marginal likelihood """
A = -0.5 * self.N * self.D * np.log(2.*np.pi) + 0.5 * np.sum(np.log(self.beta_star)) - 0.5 * np.sum(self.V_star * self.likelihood.Y) A = -0.5 * self.N * self.output_dim * np.log(2.*np.pi) + 0.5 * np.sum(np.log(self.beta_star)) - 0.5 * np.sum(self.V_star * self.likelihood.Y)
C = -self.D * (np.sum(np.log(np.diag(self.LB)))) C = -self.output_dim * (np.sum(np.log(np.diag(self.LB))))
D = 0.5 * np.sum(np.square(self._LBi_Lmi_psi1V)) D = 0.5 * np.sum(np.square(self._LBi_Lmi_psi1V))
return A + C + D return A + C + D