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SCG optimizer now greatly improved in printing

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Max Zwiessele 2013-07-10 16:49:45 +01:00
parent 3ad48534c8
commit 69e31f3dc1
1 changed files with 7 additions and 9 deletions
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16
GPy/inference/scg.py
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@ -35,7 +35,7 @@ def exponents(fnow, current_grad):
exps = [np.abs(fnow), current_grad] exps = [np.abs(fnow), current_grad]
return np.sign(exps) * np.log10(exps).astype(int) return np.sign(exps) * np.log10(exps).astype(int)
def SCG(f, gradf, x, optargs=(), maxiters=500, max_f_eval=500, display=True, xtol=None, ftol=None, gtol=None): def SCG(f, gradf, x, optargs=(), maxiters=500, max_f_eval=np.inf, display=True, xtol=None, ftol=None, gtol=None):
""" """
Optimisation through Scaled Conjugate Gradients (SCG) Optimisation through Scaled Conjugate Gradients (SCG)
@ -68,7 +68,7 @@ def SCG(f, gradf, x, optargs=(), maxiters=500, max_f_eval=500, display=True, xto
nsuccess = 0 # nsuccess counts number of successes. nsuccess = 0 # nsuccess counts number of successes.
beta = 1.0 # Initial scale parameter. beta = 1.0 # Initial scale parameter.
betamin = 1.0e-60 # Lower bound on scale. betamin = 1.0e-60 # Lower bound on scale.
betamax = 1.0e100 # Upper bound on scale. betamax = 1.0e50 # Upper bound on scale.
status = "Not converged" status = "Not converged"
flog = [fold] flog = [fold]
@ -109,9 +109,9 @@ def SCG(f, gradf, x, optargs=(), maxiters=500, max_f_eval=500, display=True, xto
fnew = f(xnew, *optargs) fnew = f(xnew, *optargs)
function_eval += 1 function_eval += 1
if function_eval >= max_f_eval: # if function_eval >= max_f_eval:
status = "maximum number of function evaluations exceeded" # status = "maximum number of function evaluations exceeded"
break # break
# return x, flog, function_eval, status # return x, flog, function_eval, status
Delta = 2.*(fnew - fold) / (alpha * mu) Delta = 2.*(fnew - fold) / (alpha * mu)
@ -131,13 +131,12 @@ def SCG(f, gradf, x, optargs=(), maxiters=500, max_f_eval=500, display=True, xto
if display: if display:
print_out(len_maxiters, fnow, current_grad, beta, iteration) print_out(len_maxiters, fnow, current_grad, beta, iteration)
n_exps = exponents(fnow, current_grad) n_exps = exponents(fnow, current_grad)
if iteration - p_iter >= 6: if iteration - p_iter >= 20 * np.random.rand():
a = iteration >= p_iter * 2.78 a = iteration >= p_iter * 2.78
b = np.any(n_exps < exps) b = np.any(n_exps < exps)
if a or b: if a or b:
print ''
if a:
p_iter = iteration p_iter = iteration
print ''
if b: if b:
exps = n_exps exps = n_exps
@ -184,7 +183,6 @@ def SCG(f, gradf, x, optargs=(), maxiters=500, max_f_eval=500, display=True, xto
status = "maxiter exceeded" status = "maxiter exceeded"
if display: if display:
print ""
print_out(len_maxiters, fnow, current_grad, beta, iteration) print_out(len_maxiters, fnow, current_grad, beta, iteration)
print "" print ""
print status print status