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[scg] minor adjustements based on original publication

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mzwiessele 2014-06-18 08:52:27 -07:00
parent fdf8f984c1
commit 47d10600d1
1 changed files with 5 additions and 5 deletions
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10
GPy/inference/optimization/scg.py
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@ -56,13 +56,13 @@ def SCG(f, gradf, x, optargs=(), maxiters=500, max_f_eval=np.inf, display=True,
if gtol is None:
gtol = 1e-5
sigma0 = 1.0e-8
sigma0 = 1.0e-7
fold = f(x, *optargs) # Initial function value.
function_eval = 1
fnow = fold
gradnew = gradf(x, *optargs) # Initial gradient.
if any(np.isnan(gradnew)):
raise UnexpectedInfOrNan, "Gradient contribution resulted in a NaN value"
#if any(np.isnan(gradnew)):
# raise UnexpectedInfOrNan, "Gradient contribution resulted in a NaN value"
current_grad = np.dot(gradnew, gradnew)
gradold = gradnew.copy()
d = -gradnew # Initial search direction.
@ -168,13 +168,13 @@ def SCG(f, gradf, x, optargs=(), maxiters=500, max_f_eval=np.inf, display=True,
if Delta < 0.25:
beta = min(4.0 * beta, betamax)
if Delta > 0.75:
beta = max(0.5 * beta, betamin)
beta = max(0.25 * beta, betamin)
# Update search direction using Polak-Ribiere formula, or re-start
# in direction of negative gradient after nparams steps.
if nsuccess == x.size:
d = -gradnew
# beta = 1. # TODO: betareset!!
beta = 1. # This is not in the original paper
nsuccess = 0
elif success:
Gamma = np.dot(gradold - gradnew, gradnew) / (mu)