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
