def first_estimations(distributions, strats, c_index, team): """ """ epoch = 1 for s in strats: i = 0 while i < settings.N: # n, z = get_rand() n, z = launch_simulation_with_strategy(s, team) distributions[str(s[0])].get_posterior(n, z) plot.plot_distributions_cluster(distributions, c_index, epoch, name) i += 1 epoch += 1
def simulate_pairs(distributions, strats, cluster, cluster_index, d1, d2, epoch): """ """ r = 0 while r < settings.N: epoch += 1 s = strats[int(d1)] n, z = launch_simulation_with_strategy(s, cluster) distributions[d1].get_posterior(n, z) s = strats[int(d2)] n, z = launch_simulation_with_strategy(s, cluster) distributions[d2].get_posterior(n, z) plot.plot_distributions_cluster(distributions, cluster_index, epoch, name)
t_begin = time.clock() storage = db.Database(settings.DATABASE) cluster_dict = storage.build_cluster_dictionnary() strats = storage.get_detailled_strategies() l_team = settings.TEAM_PATH + " " + settings.TEAM_PARAM # For each cluster for c in cluster_dict: if c == 3: continue cluster = cluster_dict[c] for t in cluster[0]: team = t[2] name = t[1] distributions = init_distributions(strats) plot.plot_distributions_cluster(distributions, 0, 0, name) first_estimations(distributions, strats, c, team) strat_pairs = build_strategies_pairs(strats) strat_comparisons = init_strat_comparisons(strats) p = 0 while p < len(strat_pairs): if len(strat_pairs) > 0: cp = strat_pairs[p] d1 = distributions[str(cp[0])] d2 = distributions[str(cp[1])] conclusion = fe.formation_evaluator(d1.prior, d2.prior) if conclusion == 0: var_d1 = d1.get_variance() var_d2 = d2.get_variance() if var_d1 < var_d2: conclusion = 1