Update scoring_calculation_system.py

scoring_calculation_system.py

@@ -2088,10 +2088,10 @@ def calculate_breed_compatibility_score(scores: dict, user_prefs: UserPreference
         """
         # 重新定義關鍵指標閾值，提供更寬容的評分標準
         critical_thresholds = {
-            'space': 0.4,       # 從0.45降低到0.4
-            'exercise': 0.4,    # 從0.45降低到0.4
-            'experience': 0.5,  # 從0.55降低到0.5
-            'noise': 0.5        # 保持不變，因為噪音確實是重要考慮因素
+            'space': 0.35,       
+            'exercise': 0.4,    
+            'experience': 0.5,  
+            'noise': 0.5        
         }
     
         # 評估關鍵指標失敗情況
@@ -2333,30 +2333,4 @@ def amplify_score_extreme(score: float) -> float:
         position = score / 0.50
         return 0.70 + (smooth_curve(position) * 0.05)
 
-    return round(min(1.0, max(0.0, score)), 4)
-
-# def amplify_score_extreme(score: float) -> float:
-#     """優化分數分布，提供更高的分數範圍"""
-#     def smooth_curve(x: float, steepness: float = 12) -> float:
-#         import math
-#         return 1 / (1 + math.exp(-steepness * (x - 0.5)))
-
-#     if score >= 0.9:
-#         position = (score - 0.9) / 0.1
-#         return 0.96 + (position * 0.04)  # 90-100的原始分映射到96-100
-        
-#     elif score >= 0.8:
-#         position = (score - 0.8) / 0.1
-#         return 0.90 + (position * 0.06)  # 80-90的原始分映射到90-96
-        
-#     elif score >= 0.7:
-#         position = (score - 0.7) / 0.1
-#         return 0.82 + (position * 0.08)  # 70-80的原始分映射到82-90
-        
-#     elif score >= 0.5:
-#         position = (score - 0.5) / 0.2
-#         return 0.75 + (smooth_curve(position) * 0.07)  # 50-70的原始分映射到75-82
-        
-#     else:
-#         position = score / 0.5
-#         return 0.70 + (smooth_curve(position) * 0.05)  # 50以下的原始分映射到70-75
+    return round(min(1.0, max(0.0, score)), 4)