A Python Library for Mobile Crowdsensing Problems, with random, Epsilon-Greedy and New_MAB methods to build a MCS system and its group.
pip install mcspy
Han Yaohui
https://pypi.org/project/mcspy/
see https://github.com/Han-0107/mcspy
https://github.com/Han-0107/New_MAB_in_MCS
1. constant_produce(num_of_system) 2. system_postprocess(relation_pre, workers, group_efficiency, times_total, num_of_group) 3. system_init(num_of_group, num_of_system) 4. normalization(x, num_of_system) 5. reselection_judge(workers, num_choice, i, num_of_group) 6. epsilon_produce(times) 7. random_unit(p) 8. list_init(start, stop, length) 9. result_produce(num_choice, pos_choice, workers, reselection_flag, num_of_group, relation_real, ability_of_workers)
1. matrix_assign(result_of_system, relation_total, relation_n, workers, num_of_group) 2. matrix_renewal(relation_n, relation_pre, relation_total, num_of_system)
1. print_basic(relation_real, num_of_system, num_of_group, relation_var, abilities_var, ability_of_workers) 2. print_result(group_efficiency) 3. print_result_of_all(sum_of_result, iterations) 4. print_time(start, end)
1. group_work(workers, relation_real, num_of_group, ability_of_workers) 2. system_work_random(workers, num_of_group, num_of_system, relation_real, ability_of_workers) 3. system_work_epsilon(workers, min_index, person_efficiency, epsilon, num_of_system, num_of_group, relation_real, ability_of_workers) 4. system_work_mab(workers, min_index, person_efficiency, person_co, times, epsilon, num_of_system, num_of_group, relation_real, ability_of_workers):