def get_optimization_results(
t,
population,
factorial_cost,
scalar_fitness,
skill_factor,
pairs=None,
tasks=None):
K = len(set(skill_factor))
N = len(population) // 2
results = []
for k in range(K):
result = OptimizeResult()
x, fun = get_best_individual(
population, factorial_cost, scalar_fitness, skill_factor, k)
result.x = x
result.fun = fun
result.nit = t
result.nfev = (t + 1) * N
if pairs is not None:
result.pair = pairs[k, :]
else:
result.pair = None
if tasks is not None:
result.ucb_value = tasks[k].ucb_solver.value
else:
result.ucb_value = None
results.append(result)
return results
def get_results(self, t):
K = self.K
N = self.args.pop_size
results = []
for k in range(K):
result = OptimizeResult()
x, fun = self.stos[k].population[0], self.stos[k].fitness[0]
result.x = x
result.fun = fun
result.nit = t
result.nfev = (t + 1) * N
result.pair = self.stos[k].pair
result.ucb_value = self.stos[k].ucb.value
results.append(result)
return results
