♻️ refactor(attacker): 添加随机变点测试代码

- 新增随机变点生成逻辑用于测试
- 通过断言确保随机变点数量与原算法一致
- 方便后续调试和对比不同变点策略效果

attacker.py

@@ -349,6 +349,31 @@ class Attacker:
                 bounds.append((-1 * perturbed_magnitude, perturbed_magnitude))
 
         print('The length of shapelet interval', steps_count)
+        
+        if False:    # 需要时手动修改为True，用于测试随机变点
+            # 生成随机变点
+            # 假设ori_ts的长度可以从shape[0]获取
+            length = ori_ts.shape[0]  
+
+            # 获取与steps_count相同数量的随机变点位置
+            random_indices = np.random.choice(length, size=int(steps_count), replace=False)
+
+            random_pos = np.zeros(length, dtype=int)
+            random_pos[random_indices] = 1
+
+            # 用于存储随机变点的扰动边界
+            random_bounds = []
+            for i in range(len(random_pos)):
+                if random_pos[i] == 1:
+                    random_bounds.append((-1 * perturbed_magnitude, perturbed_magnitude))
+
+            print('The length of random interval', random_pos.sum())
+
+            # 验证随机变点数量与原算法相同
+            assert random_pos.sum() == steps_count
+            # 覆盖原有的变点
+            attack_pos = random_pos
+        
         #print('The length of bounds', len(bounds))
         popmul = max(1, popsize // len(bounds))
         # Record of the number of iterations