Update ethics.py

ethics.py

@@ -25,25 +25,25 @@ class QuantumInspiredMultiObjectiveOptimizer:
         self.population = [self._random_solution() for _ in range(population_size)]
         self.pareto_front = []
 
-    def _random_solution(self) -> List[float]:
+    def random_solution(self) -> List[float]:
         return [random.uniform(-10, 10) for _ in range(self.dimension)]
 
-    def _tunnel(self, solution: List[float]) -> List[float]:
+    def tunnel(self, solution: List[float]) -> List[float]:
         return [x + np.random.normal(0, 1) * random.choice([-1, 1])
                 if random.random() < self.tunneling_prob else x
                 for x in solution]
 
-    def _entangle(self, solution1: List[float], solution2: List[float]) -> List[float]:
+    def entangle(self, solution1: List[float], solution2: List[float]) -> List[float]:
         return [(1 - self.entanglement_factor) * x + self.entanglement_factor * y
                 for x, y in zip(solution1, solution2)]
 
-    def _evaluate(self, solution: List[float]) -> List[float]:
+    def evaluate(self, solution: List[float]) -> List[float]:
         return [fn(solution) for fn in self.objective_fns]
 
-    def _dominates(self, obj1: List[float], obj2: List[float]) -> bool:
+    def dominates(self, obj1: List[float], obj2: List[float]) -> bool:
         return all(o1 <= o2 for o1, o2 in zip(obj1, obj2)) and any(o1 < o2 for o1, o2 in zip(obj1, obj2))
 
-    def _pareto_selection(self, scored_population: List[Tuple[List[float], List[float]]]) -> List[Tuple[List[float], List[float]]]:
+    def pareto_selection(self, scored_population: List[Tuple[List[float], List[float]]]) -> List[Tuple[List[float], List[float]]]:
         pareto = []
         for candidate in scored_population:
             if not any(self._dominates(other[1], candidate[1]) for other in scored_population if other != candidate):