def __init__(self, args):
if int(args.algorithm_category) == 0:
self.model = VIModel_DP(args)
elif int(args.algorithm_category) == 1:
self.model = VIModel_PY(args)
else:
pass
class Trainer:
def __init__(self, args):
if int(args.algorithm_category) == 0:
self.model = VIModel_DP(args)
elif int(args.algorithm_category) == 1:
self.model = VIModel_PY(args)
def train(self, data):
self.model.fit(data)
args.algorithm_category = algorithm_category
args.second_max_iter = second_max_iter
args.threshold = threshold
args.max_iter = max_iter
# py process
# ================================================================================================================ #
args.tau = 10
args.gamma = 1
args.omega = 0.2
args.eta = 0.5
args.u = 0.9
args.v = 0.01
args.zeta = 0.01
func_filenames = get_adhd_data(data_dir=BRAIN_DIR, n_subjects=30)
cp = ClusterProcess(model=VIModel_PY(args),
n_components=30,
smoothing_fwhm=12.,
memory="nilearn_cache",
threshold=1.,
memory_level=2,
verbose=10,
random_state=0)
b = time.time()
cp.fit(func_filenames)
train_data = cp.train_data
pred, container, pro = cp.model.predict_brain(train_data[0:1])
e = time.time()
print(e - b)
# cp.plot_pro(pro.T, save=False, name='vmf-py', item_file='sub{}'.format(1))
cp.plot_all(pred, save=True, name='vmf-py', item_file='sub{}'.format(1))