Exemplo n.º 1
0
 def test_preffered(self):
   swaps = list(sgp.swaps(self.solution))
   #print(swaps)
   violats = list(map(lambda t: (sgp.evaluate_swap(self.solution, t), t), swaps))
   
   print(min(violats), max(violats))
   """self.assertEqual(
Exemplo n.º 2
0
  def learning(self, solution):
    tabu = [ {} for week in solution ]
    best_solution = copy.deepcopy(solution)
    best_violations = sgp.violations(solution)[0]

    # wie lange wird weiter gesucht, obwohl keine verbesserung eintritt
    #learning_semaphore = self.max_learning
    for iteration in range(self.groups * self.weeks):
      if best_violations == 0 or self.timeout: # were done
        break 

      try:
        # von allen Täuschen, den besten finden
        nex_viol, w, a, b = min(
          filter(
            # entweder verbessert dieser Tausch diese Lösung zur Besten
            # oder er ist nicht tabu
            lambda t: t[0] < best_violations or not tuple(sorted((t[2], t[3]))) in tabu[t[1]] or tabu[t[1]][tuple(sorted((t[2], t[3])))] < iteration,
            map(
              lambda t: (sgp.evaluate_swap(solution, t), t[0], t[1], t[2]), 
              sgp.swaps(solution)
            )
          )
        )
        sgp.swap(solution, w, a, b)

        tabu[w][tuple(sorted((a, b)))] = iteration + random.randint(4, 100)


        if nex_viol < best_violations:
          best_solution = solution
          best_violations = nex_viol
          print(best_violations, end=' ')
          sys.stdout.flush()


      except ValueError as e:
        break
    return best_solution