if __name__ == '__main__': param = argparse.ArgumentParser(usage='param', description='for main') param.add_argument('--model', type=str, default='Single', help='choose one model') param.add_argument('--lr', type=float, default=0.001, help='learning rate') param.add_argument('--optimizer', type=str, default='adam', help='you can also choose sgd, adagrad, rmsprop') param.add_argument('--shell', type=bool, default=False, help='learning rate') temparam = param.parse_args() config = configure() config.lr = temparam.lr config.lr_method = temparam.optimizer model_ = getattr(models, temparam.model) model = model_(config) print 'Present used model %s\n' % model model.train() if temparam.shell: print('\n######################\n open ipython for eval') _start_shell(locals())
# end_point = Point(x2, y2, z2, 1) ## experiment for group 2: 5*20*20 grid scale # 高度 x1 = 0 x2 = 0 # 长度 y1 = 0 y2 = 18 # 宽度 z1 = 9 z2 = 9 starting_point = Point(x1, y1, z1, 1) end_point = Point(x2, y2, z2, 1) config = configure(grid_x, grid_y, grid_z, safety_threshold, privacy_threshold, privacy_radius, starting_point, end_point, delay) T_budget = config.T_budget T_optimal = config.T_optimal # print(occ_grid) occ_grid_known = copy.deepcopy(occ_grid) for i in range(occ_grid.shape[0]): for j in range(occ_grid.shape[1]): for k in range(occ_grid.shape[2]): if occ_grid_known[i][j][k] > 1: occ_grid_known[i][j][k] = 1 ## ref == 1 to show the initial results without self-adaptive motion planning ref = 0 if ref == 1: reference_path = np.load('../data_raw/reference_path.npy')
end_point = Point(x2, y2, z2, 1) # alpha ,beta 固定 # alpha_list = [4/2, 5/3, 6/4, 7/5, 8/6, 9/7, 10/8, 11/9, 12/10, 13/11] # alpha = alpha_list[i % 10] # beta_list = [3/2, 4/3, 5/4, 6/5, 7/6, 8/7, 9/8, 10/9, 11/10, 12/11] # beta = beta_list[i % 10] alpha = 5 / 3 beta = 4 / 3 # alpha = 10 # beta = 10 Kca = 10 config = configure(grid_x, grid_y, grid_z, safety_threshold, privacy_threshold, privacy_radius, starting_point, end_point, viewradius, alpha, beta, exploration_rate, preference) T_budget = alpha * (abs(x1 - x2) + abs(y1 - y2) + abs(z1 - z2)) T_optimal = beta * (abs(x1 - x2) + abs(y1 - y2) + abs(z1 - z2)) log.info( "Iteration: %d; Configuration: grid: %d, safety_threshold: %f, privacy_threshold: %f, the starting point: [%d, %d, %d]; the end point: [%d, %d, %d]; T_budget(alpha): " "%f (%f); " "T_optimal(beta): %f (%f); Exploration_rate: %f; Preference: %f; View_radius: %f" % (iteration, grid_x, safety_threshold, privacy_threshold, x1, y1, z1, x2, y2, z2, T_budget, alpha, T_optimal, beta, exploration_rate, preference, viewradius)) SaveMap(config, iteration, exploration_rate, num) reinitial_flag = 1 refpath = [] planpath = []
unk_id = self.vocab.word2id('<unk>') with open(self.config.eval_data, "rb") as analogy_f: for line in analogy_f: if line.startswith(b":"): # Skip comments. continue words = line.strip().lower().split(b" ") ids = self.vocab.word2id(words) if unk_id in ids or len(ids) != 4: questions_skipped += 1 else: questions.append(np.array(ids)) print("Eval analogy file: ", self.config.eval_data) print("Questions: ", len(questions)) print("Skipped: ", questions_skipped) self._analogy_questions = np.array(questions, dtype=np.int32) def _start_shell(local_ns=None): '''''' import IPython user_ns = {} if local_ns: user_ns.update(local_ns) user_ns.update(globals()) IPython.start_ipython(argv=[], user_ns=user_ns) if __name__ == '__main__': param = configure() train = emTrainer(param)