| # PFLlib: Personalized Federated Learning Algorithm Library | |
| # Copyright (C) 2021 Jianqing Zhang | |
| # This program is free software; you can redistribute it and/or modify | |
| # it under the terms of the GNU General Public License as published by | |
| # the Free Software Foundation; either version 2 of the License, or | |
| # (at your option) any later version. | |
| # This program is distributed in the hope that it will be useful, | |
| # but WITHOUT ANY WARRANTY; without even the implied warranty of | |
| # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the | |
| # GNU General Public License for more details. | |
| # You should have received a copy of the GNU General Public License along | |
| # with this program; if not, write to the Free Software Foundation, Inc., | |
| # 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA. | |
| from statistics import mean | |
| import numpy as np | |
| file_name = input() + '.out' | |
| acc = [] | |
| with open(file_name, 'r') as f: | |
| is_best = False | |
| for l in f.readlines(): | |
| if is_best: | |
| acc.append(float(l)) | |
| is_best = False | |
| elif 'Best accuracy' in l: | |
| is_best = True | |
| print(acc) | |
| print(mean(acc)*100, np.std(acc)*100) | |