| commit | author | age | ||
| 425bf7 | 1 | #!/usr/bin/python3 |
| JK | 2 | |
| 3 | import collections | |
| 4 | import functools | |
| 5 | import time | |
| 6 | ||
| 7 | from lib.ArgParser import get_config, get_model_from_config | |
| 8 | from lib.Controller import simulate, ReplayController | |
| 9 | from lib.Cost import DeviationCost, NegativeCost | |
| 10 | from lib.Env import CentralHeatingHistoryEnv | |
| 11 | ||
| 12 | # ARGS | |
| 13 | config = get_config() | |
| 14 | ||
| 15 | name_past_future = ( | |
| 16 | ('past', ('temp_in', 'temp_out', 'mode'), tuple()), | |
| 17 | ('flow', ('temp_in', 'temp_out', 'mode', 'temp_flow', 'temp_return'), tuple()), | |
| 18 | ('fore', ('temp_in', 'temp_out', 'mode'), ('temperature', 'wind_speed', 'radiation', 'humid')), | |
| 19 | ('all', ('temp_in', 'temp_out', 'mode', 'temp_flow', 'temp_return'), ('temperature', 'wind_speed', 'radiation', 'humid')), | |
| 20 | ) | |
| 21 | ||
| 22 | for name, past, future in name_past_future: | |
| 23 | config['model_past_fields'] = past | |
| 24 | config['model_future_fields'] = future | |
| 25 | ||
| 26 | # MODEL | |
| 27 | start_time = time.clock() | |
| 28 | model = get_model_from_config(config) | |
| 29 | end_time = time.clock() | |
| 30 | ||
| 31 | # PLOT | |
| 32 | config['plot_fields'] = ['time', 'temp_in', 'temp_in_calc'] | |
| 33 | config['past_fields'] = ['mode'] | |
| 34 | config['past_values'] = 1 | |
| 35 | config['future_values'] = 0 | |
| 36 | ||
| 37 | # ENV | |
| 38 | env = CentralHeatingHistoryEnv() | |
| 39 | env.cost_class = DeviationCost | |
| 40 | #env.cost_class = functools.partial(NegativeCost, 19.5, 22) | |
| 41 | env.model = model | |
| 42 | env.config = config | |
| 43 | env.reset() | |
| 44 | ||
| 45 | # MAIN | |
| 46 | controller = ReplayController() | |
| 47 | simulate(env, controller, render=False) | |
| 48 | ||
| 49 | print('RESULT {}_{}_{}{} {} {} {}'.format(name, config['aggregate'] or 1, config['model_type'], ('_' + str(config['model_epochs'])) if config['model_type']=='neural' else '', env.cost.costs['diff'], env.cost.costs['sq_err'], end_time - start_time)) | |
