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lots of F-ordering nonsense. Seems to work though

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This commit is contained in:
James Hensman 2014-02-14 11:38:34 +00:00
parent 80a734e153
commit 3375606039
4 changed files with 109 additions and 88 deletions
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2
GPy/inference/latent_function_inference/posterior.py
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@ -115,7 +115,7 @@ class Posterior(object):
@property
def woodbury_inv(self):
if self._woodbury_inv is None:
self._woodbury_inv, _ = dpotri(self.woodbury_chol, lower=0)
self._woodbury_inv, _ = dpotri(self.woodbury_chol, lower=1)
#self._woodbury_inv, _ = dpotrs(self.woodbury_chol, np.eye(self.woodbury_chol.shape[0]), lower=1)
symmetrify(self._woodbury_inv)
return self._woodbury_inv

22
GPy/inference/latent_function_inference/varDTC.py
View file

@ -91,7 +91,7 @@ class VarDTC(object):
tmp = psi1 * (np.sqrt(beta.reshape(num_data, 1)))
else:
tmp = psi1 * (np.sqrt(beta))
tmp, _ = dtrtrs(Lm, np.asfortranarray(tmp.T), lower=1)
tmp, _ = dtrtrs(Lm, tmp.T, lower=1)
A = tdot(tmp)
# factor B
@ -100,14 +100,16 @@ class VarDTC(object):
LB = jitchol(B)
# VVT_factor is a matrix such that tdot(VVT_factor) = VVT...this is for efficiency!
self.YYTfactor = self.get_YYTfactor(Y)
VVT_factor = self.get_VVTfactor(self.YYTfactor, beta)
#self.YYTfactor = self.get_YYTfactor(Y)
#VVT_factor = self.get_VVTfactor(self.YYTfactor, beta)
VVT_factor = beta*param_to_array(Y)
trYYT = self.get_trYYT(Y)
psi1Vf = np.dot(psi1.T, VVT_factor)
# back substutue C into psi1Vf
tmp, info1 = dtrtrs(Lm, np.asfortranarray(psi1Vf), lower=1, trans=0)
_LBi_Lmi_psi1Vf, _ = dtrtrs(LB, np.asfortranarray(tmp), lower=1, trans=0)
tmp, info1 = dtrtrs(Lm, psi1Vf, lower=1, trans=0)
_LBi_Lmi_psi1Vf, _ = dtrtrs(LB, tmp, lower=1, trans=0)
tmp, info2 = dtrtrs(LB, _LBi_Lmi_psi1Vf, lower=1, trans=1)
Cpsi1Vf, info3 = dtrtrs(Lm, tmp, lower=1, trans=1)
@ -152,8 +154,8 @@ class VarDTC(object):
raise NotImplementedError, "heteroscedatic derivates with uncertain inputs not implemented"
else:
LBi = chol_inv(LB)
Lmi_psi1, nil = dtrtrs(Lm, np.asfortranarray(psi1.T), lower=1, trans=0)
_LBi_Lmi_psi1, _ = dtrtrs(LB, np.asfortranarray(Lmi_psi1), lower=1, trans=0)
Lmi_psi1, nil = dtrtrs(Lm, psi1.T, lower=1, trans=0)
_LBi_Lmi_psi1, _ = dtrtrs(LB, Lmi_psi1, lower=1, trans=0)
partial_for_likelihood = -0.5 * beta + 0.5 * likelihood.V**2
partial_for_likelihood += 0.5 * output_dim * (psi0 - np.sum(Lmi_psi1**2,0))[:,None] * beta**2
@ -195,12 +197,14 @@ class VarDTC(object):
if VVT_factor.shape[1] == Y.shape[1]:
woodbury_vector = Cpsi1Vf # == Cpsi1V
else:
print 'foobar'
psi1V = np.dot(Y.T*beta, psi1).T
tmp, _ = dtrtrs(Lm, np.asfortranarray(psi1V), lower=1, trans=0)
tmp, _ = dtrtrs(Lm, psi1V, lower=1, trans=0)
tmp, _ = dpotrs(LB, tmp, lower=1)
woodbury_vector, _ = dtrtrs(Lm, tmp, lower=1, trans=1)
Bi, _ = dpotri(LB, lower=0)
Bi, _ = dpotri(LB, lower=1)
symmetrify(Bi)
Bi = dpotri(LB, lower=1)[0]
woodbury_inv = backsub_both_sides(Lm, np.eye(num_inducing) - Bi)