Update scoring_calculation_system.py

scoring_calculation_system.py

@@ -293,68 +293,6 @@ def calculate_compatibility_score(breed_info: dict, user_prefs: UserPreferences)
             return base_score
             
 
-        # def calculate_experience_score(care_level: str, user_experience: str, temperament: str) -> float:
-        #     """飼養經驗需求計算"""
-        #     # 初始化 temperament_adjustments，確保所有路徑都有值
-        #     temperament_adjustments = 0.0
-            
-        #     # 降低初學者的基礎分數
-        #     base_scores = {
-        #         "High": {"beginner": 0.15, "intermediate": 0.70, "advanced": 1.0},     
-        #         "Moderate": {"beginner": 0.40, "intermediate": 0.85, "advanced": 1.0}, 
-        #         "Low": {"beginner": 0.75, "intermediate": 0.95, "advanced": 1.0}       
-        #     }
-            
-        #     score = base_scores.get(care_level, base_scores["Moderate"])[user_experience]
-            
-        #     # 擴展性格特徵評估
-        #     temperament_lower = temperament.lower()
-            
-        #     if user_experience == "beginner":
-        #         # 增加更多特徵評估
-        #         difficult_traits = {
-        #             'stubborn': -0.12,     
-        #             'independent': -0.10,
-        #             'dominant': -0.10,
-        #             'strong-willed': -0.08,
-        #             'protective': -0.06,     
-        #             'energetic': -0.05      
-        #         }
-                
-        #         easy_traits = {
-        #             'gentle': 0.06,         
-        #             'friendly': 0.06,
-        #             'eager to please': 0.06,
-        #             'patient': 0.05,
-        #             'adaptable': 0.05,      
-        #             'calm': 0.04           
-        #         }
-                
-        #         # 更精確的特徵影響計算
-        #         temperament_adjustments = sum(value for trait, value in easy_traits.items() if trait in temperament_lower)
-        #         temperament_adjustments += sum(value for trait, value in difficult_traits.items() if trait in temperament_lower)
-                
-        #         # 品種特定調整
-        #         if "terrier" in breed_info['Description'].lower():
-        #             temperament_adjustments -= 0.1  # 梗類犬對新手不友善
-            
-        #     elif user_experience == "intermediate":
-        #         # 中級飼主的調整較溫和
-        #         if any(trait in temperament_lower for trait in ['gentle', 'friendly', 'patient']):
-        #             temperament_adjustments += 0.03
-        #         if any(trait in temperament_lower for trait in ['stubborn', 'independent']):
-        #             temperament_adjustments -= 0.02
-            
-        #     else:  # advanced
-        #         # 資深飼主能處理更具挑戰性的犬種
-        #         if any(trait in temperament_lower for trait in ['stubborn', 'independent', 'dominant']):
-        #             temperament_adjustments += 0.02  # 反而可能是優點
-        #         if any(trait in temperament_lower for trait in ['protective', 'energetic']):
-        #             temperament_adjustments += 0.03
-            
-        #     final_score = max(0.2, min(1.0, score + temperament_adjustments))
-        #     return final_score
-
         def calculate_experience_score(care_level: str, user_experience: str, temperament: str) -> float:
             """
             計算使用者經驗與品種需求的匹配分數
@@ -616,19 +554,34 @@ def calculate_compatibility_score(breed_info: dict, user_prefs: UserPreferences)
 
         # 計算加權總分
         weighted_score = sum(score * weights[category] for category, score in scores.items())
-        
-        # # 擴大分數差異
-        # def amplify_score(score):
-        #     # 使用指數函數擴大差異
-        #     amplified = pow((score - 0.5) * 2, 3) / 8 + score
-        #     return max(0.65, min(0.95, amplified))  # 限制在65%-95%範圍內
 
         def amplify_score(score):
-            """強化分數差異"""
+            """
+            優化分數放大函數，產生更自然的分數分布
+            
+            改進：
+            - 使用更自然的指數關係
+            - 加入細微的隨機變化
+            - 避免過多的整數和半數
+            """
+            # 基礎調整
             adjusted = (score - 0.35) * 1.8
-            amplified = pow(adjusted, 3.5) / 6 + score
-            # 範圍55%-95%
-            return max(0.55, min(0.95, amplified))
+            
+            # 使用 3.2 次方使曲線更平滑
+            amplified = pow(adjusted, 3.2) / 5.8 + score
+            
+            # 加入細微的隨機變化（約±0.3%）
+            import random
+            random_adjustment = random.uniform(-0.003, 0.003)
+            
+            # 特別處理高分區間，使其更分散
+            if amplified > 0.95:
+                amplified = 0.95 + (amplified - 0.95) * 0.6
+            
+            final_score = max(0.55, min(0.98, amplified + random_adjustment))
+            
+            # 避免過多的 .0 和 .5 結尾
+            return round(final_score + random.uniform(-0.001, 0.001), 3)
         
         final_score = amplify_score(weighted_score)