for trial in range(ntrials):
#sim setup
test = Stars_sim(f, init_pt, L1 = None, var = None, verbose = False, maxit = maxit)
test.STARS_only = True
test.get_mu_star()
test.get_h()
test.update_L1 = True
test2 = Stars_sim(f, init_pt, L1 = None, var = None, verbose = True, maxit = maxit)
test2.update_L1 = True
#test.STARS_only = True
test2.get_mu_star()
test2.get_h()
test2.train_method = 'GQ'
test2.adapt = 2*f.dim
test2.regul = test2.var
test2.pad_train = 2.0
test2.explore_weight = 2.0
#test2.regul = None
dist = None
L1_hist = None
while test.iter < test.maxit:
test.step()
test2.step()
if test2.active is not None:
temp = np.array(subspace_dist(test2.active,f.active))
if dist is None:
dist = np.copy(temp)
L1_hist = np.copy(np.array(test2.L1))
else:
dist = np.append(dist,temp)
test4.adapt = f.adapt # Sets retraining steps
test2.adapt = f.adapt
test3.adapt = f. adapt
#test.update_L1 = True
#test2.update_L1 = True
#test3.update_L1 = True
# Make test2 our adaptive thresholding trial, and test3 our subcycling trial
test2.threshadapt = True
test3.subcycle = True
test3.sub_method = 2
test2.slope_weight = .1
test3.slope_weight = .1
test2.pad_train = 2.0
test2.explore_weight = 2.0
test3.pad_train = 2.0
test3.explore_weight = 2.0
#test4.regul = f.regul
#test2.regul = f.regul
#test3.regul = f.regul
test4.threshold = f.threshold
test2.threshold = f.threshold
test3.threshold = f.threshold
# do 100 steps