def hdp_sample_only(): print('hdp_sample only') round = 800 rand_seed = np.random.randint(0, 10000000) res_hdp = run_hdp(rounds=round, seed=rand_seed) eval_res = OneRoundEvaluation(res_list=[res_hdp]) eval_res.plot_all()
def vi_compare_sample(): exp_round = 1 res_list = [] predict_round = 800 rand_seed = np.random.randint(0, 10000000) for t in range(exp_round): round = 800 res_list.append( run_vi(rounds=round, seed=rand_seed, name='VI', predict_round=predict_round), ) for t in range(exp_round): round = 800 res_list.append( run_vi_sample(rounds=round, seed=rand_seed, name='VI_sample', predict_round=predict_round), ) eval_res = OneRoundEvaluation(res_list=res_list) mse_dict = eval_res.plot_all() mse_vi.append(mse_dict['VI']) mse_vi_sample.append(mse_dict['VI_sample'])
def compare_hdp_hdpsample(): print('hdp and sample') round = 1000 rand_seed = np.random.randint(0, 10000000) res_hdp = run_hdp(rounds=round, seed=rand_seed) res_hdp_sample = run_hdp_sample(rounds=round, seed=rand_seed) eval_res = OneRoundEvaluation(res_list=[res_hdp, res_hdp_sample]) eval_res.plot_all()
def vi_test(): exp_round = 3 res_list = [] for t in range(exp_round): round = 800 rand_seed = np.random.randint(0, 10000000) res_list.append( run_vi(rounds=round, seed=rand_seed, name='VI_' + str(t + 1)), ) eval_res = OneRoundEvaluation(res_list=res_list) eval_res.plot_all()
def hdp_five_times(): print('hdp 5') res_list = [] for t in range(5): round = 800 rand_seed = np.random.randint(0, 10000000) res_list.append( run_hdp(rounds=round, seed=rand_seed, name='hdp_' + str(t + 1)), ) eval_res = OneRoundEvaluation(res_list=res_list) eval_res.plot_all()
def run_adhdp_only(): round = 3200 predict_round = 800 res_list = [] rand_seed = np.random.randint(0, 10000000) # rand_seed = 320743 res_list.append( run_adhdp(rounds=round, seed=rand_seed, name='ADHDP', predict_round=predict_round)) eval_res = OneRoundEvaluation(res_list=res_list) eval_res.plot_all()
def ADHDP_Off_Policy_only(): round = 3200 res_list = [] rand_seed = np.random.randint(0, 10000000) # rand_seed = 320743 res_list.append( run_adhdp_offpolicy(rounds=round, seed=rand_seed, name='ADHDP', train_rounds=5, train_step_in_round=200)) eval_res = OneRoundEvaluation(res_list=res_list) eval_res.plot_all()
def vi_compare_hdp(): round = 800 predict_round = 800 res_list = [] rand_seed = np.random.randint(0, 10000000) # rand_seed = 320743 res_list.append( run_vi(rounds=round, seed=rand_seed, name='VI', predict_round=predict_round)) res_list.append( run_hdp(rounds=round, seed=rand_seed, name='HDP', predict_round=predict_round)) eval_res = OneRoundEvaluation(res_list=res_list) eval_res.plot_all()
def vi_optim_compare(): exp_round = 1 res_list = [] predict_round = 800 rand_seed = np.random.randint(0, 10000000) opt_list = ['adagrad', 'RMSprop', 'adam', 'sgd'] opt_list = ['adam', 'sgd'] for opt_name in opt_list: round = 800 res_list.append( run_vi(rounds=round, seed=rand_seed, name='VI_' + opt_name, predict_round=predict_round, u_optim=opt_name), ) eval_res = OneRoundEvaluation(res_list=res_list) mse_dict = eval_res.plot_all()
def vi_diff_capacity(capacity_list=None): if capacity_list is None: capacity = range(1, 12, 3) predict_round = 800 res_list = [] rand_seed = np.random.randint(0, 10000000) rand_seed = 3309316 for capacity in capacity_list: round = 800 res_list.append( run_vi(rounds=round, seed=rand_seed, capacity=capacity, name='Replay: ' + str(capacity), predict_round=predict_round)) eval_res = OneRoundEvaluation(res_list=res_list) eval_res.plot_all()
def run_adhdp(rounds=1000, seed=random.randint(0, 1000000)): print('seed :', seed) random.seed(seed) np.random.seed(seed) penalty = Quadratic(**penalty_para) env_adhdp = Thickener( penalty_calculator=penalty, **thickner_para, random_seed=seed, ) env_adhdp.reset() res1 = OneRoundExp(controller=adhdp, env=env_adhdp, max_step=rounds, exp_name='ADHDP').run() eval_res = OneRoundEvaluation(res_list=[res1]) eval_res.plot_all()