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Merge branch 'params' of github.com:SheffieldML/GPy into params

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James Hensman 2014-02-11 20:05:41 +00:00
commit 32deb2085d
10 changed files with 177 additions and 54 deletions
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6
GPy/inference/latent_function_inference/varDTC.py
View file

@ -2,9 +2,8 @@
# Licensed under the BSD 3-clause license (see LICENSE.txt)
from posterior import Posterior
from ...util.linalg import jitchol, backsub_both_sides, tdot, dtrtrs, dpotri, symmetrify
from ...util.linalg import jitchol, backsub_both_sides, tdot, dtrtrs, dtrtri, dpotri, dpotrs, symmetrify
import numpy as np
from ...util.linalg import dtrtri
from ...util.caching import Cacher
from ...util.misc import param_to_array
log_2_pi = np.log(2*np.pi)
@ -85,7 +84,7 @@ class VarDTC(object):
tmp = tmp.T
# no backsubstitution because of bound explosion on tr(A) if not...
LmInv, _ = dtrtri(Lm, lower=1)
A = LmInv.T.dot(psi2_beta.dot(LmInv))
A = LmInv.dot(psi2_beta.dot(LmInv.T))
#print A.sum()
else:
if het_noise:
@ -97,6 +96,7 @@ class VarDTC(object):
# factor B
B = np.eye(num_inducing) + A
self.A = A
LB = jitchol(B)
# VVT_factor is a matrix such that tdot(VVT_factor) = VVT...this is for efficiency!

4
GPy/inference/optimization/scg.py
View file

@ -69,8 +69,8 @@ def SCG(f, gradf, x, optargs=(), maxiters=500, max_f_eval=np.inf, display=True,
success = True # Force calculation of directional derivs.
nsuccess = 0 # nsuccess counts number of successes.
beta = 1.0 # Initial scale parameter.
betamin = 1.0e-60 # Lower bound on scale.
betamax = 1.0e50 # Upper bound on scale.
betamin = 1.0e-15 # Lower bound on scale.
betamax = 1.0e15 # Upper bound on scale.
status = "Not converged"
flog = [fold]