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1 changed files with 5 additions and 5 deletions
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@ -7,15 +7,15 @@ from ..util.linalg import mdot, jitchol, chol_inv, tdot, symmetrify,pdinv
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from ..util.plot import gpplot
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from .. import kern
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from scipy import stats, linalg
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#from ..core import sparse_GP
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from gp_base import GPBase
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from sparse_gp import SparseGP
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def backsub_both_sides(L,X):
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""" Return L^-T * X * L^-1, assumuing X is symmetrical and L is lower cholesky"""
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tmp,_ = linalg.lapack.flapack.dtrtrs(L,np.asfortranarray(X),lower=1,trans=1)
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return linalg.lapack.flapack.dtrtrs(L,np.asfortranarray(tmp.T),lower=1,trans=1)[0].T
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class FITC(GPBase):
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class FITC(SparseGP):
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"""
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sparse FITC approximation
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@ -111,7 +111,7 @@ class FITC(GPBase):
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raise NotImplementedError
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else:
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if self.likelihood.is_heteroscedastic:
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assert self.likelihood.D == 1
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assert self.likelihood.output_dim == 1
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tmp = self.psi1 * (np.sqrt(self.beta_star.flatten().reshape(1, self.N)))
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tmp, _ = linalg.lapack.flapack.dtrtrs(self.Lm, np.asfortranarray(tmp), lower=1)
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self.A = tdot(tmp)
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@ -215,8 +215,8 @@ class FITC(GPBase):
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def log_likelihood(self):
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""" Compute the (lower bound on the) log marginal likelihood """
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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)
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C = -self.D * (np.sum(np.log(np.diag(self.LB))))
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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)
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C = -self.output_dim * (np.sum(np.log(np.diag(self.LB))))
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D = 0.5 * np.sum(np.square(self._LBi_Lmi_psi1V))
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return A + C + D
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