# STARS no sphere
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:
a_dims = [2]
n_a_dims = np.size(a_dims)
f2_avr = np.zeros((maxit+1,n_a_dims))
j=0
for i in a_dims:
for trial in range(ntrials):
test = Stars_sim(f, this_init_pt, L1 = f.L1, var = f.var, verbose = False, maxit = maxit)
test.get_mu_star()
test.get_h()
test.set_dim = True
test.subcycle = True
test.adim = i # hard-code adim
test.train_method = 'GQ'
test.adapt = 3.0*f.dim
test.regul = test.sigma**2
# do steps
while test.iter < test.maxit:
test.step()
f2_avr[:,j] += test.fhist
print('ASTARS trial',trial,' minval',test.fhist[-1])
j += 1
# Stop the clock!
stop = timeit.default_timer()
# Difference stop-start tells us run time
time = stop - start
L1=f.L1,
var=f.var,
verbose=False,
maxit=maxit,
true_as=f.active)
test.get_mu_star()
test.get_h()
# adapt every f.adapt timesteps using quadratic(after inital burn)
test.train_method = 'GQ'
test.adapt = f.adapt # Sets retraining steps
#test.subcycle = True # turn on subcycling
test.threshadapt = True
#test.debug = True
test.regul = f.regul
test.threshold = f.threshold
# do 100 steps
while test.iter < test.maxit:
test.step()
if test.active is not None and test.iter // test.adapt:
print('distance', subspace_dist(test.active, f.active))
f2_avr += test.fhist
if maxit > test.tr_stop:
FAASTARS_adim_sto = np.zeros((maxit - test.tr_stop - 1, 1))
FAASTARS_sub_dist_sto = np.zeros((maxit - test.tr_stop - 1, 1))
# data dump
# Start the clock!
start = timeit.default_timer()
for trial in range(ntrials):
#sim setup
test = Stars_sim(nesterov_2_f,
init_pt,
L1=4,
var=1E-12,
verbose=True,
maxit=maxit)
#test.STARS_only = True
test.get_mu_star()
test.get_h()
test.regul = None
test.train_method = 'GQ'
test.adapt = 100 # Sets number of sub-cylcing steps
test.threshold = 0.99999
# do 100 steps
while test.iter < test.maxit:
test.step()
#if test.iter % 100 == 0:
# print('iter',test.iter,test.fhist[test.iter])
f2_avr += test.fhist
#print('trial',trial,' minval',test.fhist[-1])
# Stop the clock!
stop = timeit.default_timer()
test.STARS_only = True
test.get_mu_star()
test.get_h()
test2 = Stars_sim(dci,
init_pt,
f_obj=dci,
L1=dci.L1,
var=dci.var,
verbose=True,
maxit=maxit)
#test.STARS_only = True
test2.get_mu_star()
test2.get_h()
test2.train_method = 'GL'
test2.adapt = 2 * dci.dim
test2.regul = dci.sig
test2.threshold = .9
test2.lasso = True
test2.pad_train = 2.0
test2.explore_weight = 2.0
#test3 = Stars_sim(f, init_pt, L1 = f.L1, var = f.var, verbose = False, maxit = maxit)
#test.STARS_only = True
#test3.active = f.active
#test3.get_mu_star()
#test3.get_h()
# test3.adapt = 0
#test3.debug = False
while test.iter < test.maxit:
test.step()
test2.step()