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))