我们从Python开源项目中，提取了以下3个代码示例，用于说明如何使用sklearn.datasets.fetch_olivetti_faces()。
def load_data(train_num, train_repeat): test_size = (10. - train_num) / 10 data = fetch_olivetti_faces() X = data.images y = data.target X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=test_size, random_state=3, stratify=y) if train_repeat > 1: X_train = X_train.repeat(train_repeat, axis=0) y_train = y_train.repeat(train_repeat) return X_train, y_train, X_test, y_test
def fetch_from_config(cfg): data_set_name = cfg['fetch']['name'] if cfg['fetch'].getboolean('sklearn'): if data_set_name == 'OLIVETTI': data_set = skd.fetch_olivetti_faces(shuffle=True) else: data_set = skd.fetch_mldata(data_set_name) X, y = data_set.data, data_set.target if data_set_name == 'MNIST original': if cfg['pre_process'].getboolean('normalize'): X = X / 255. else: if data_set_name == 'LETTERS': X, y = fetch_load_letters() elif data_set_name == 'ISOLET': x_tr, x_te, y_tr, y_te = fetch_load_isolet() elif data_set_name == 'SHREC14': X, y = load_shrec14(real=cfg['fetch']['real'], desc=cfg['fetch']['desc']) X = prep.normalize(X, norm=cfg['pre_process']['norm']) else: raise NameError('No data set {} found!'.format(data_set_name)) # Separate training and testing set if data_set_name == 'MNIST original': x_tr, x_te, y_tr, y_te = X[:60000], X[60000:], y[:60000], y[60000:] elif data_set_name != 'ISOLET': test_size = cfg['train_test'].getfloat('test_size') x_tr, x_te, y_tr, y_te = train_test_split(X, y, test_size=test_size, stratify=y) return x_tr, x_te, y_tr, y_te
