modify elo caculate

models.py

@@ -130,11 +130,18 @@ def record_vote(user_id, text, chosen_model_id, rejected_model_id, model_type):
         db.session.rollback()
         return None, "One or both models not found for the specified model type"
 
+    k_factor_winner = get_dynamic_k_factor(chosen_model.match_count)
+    k_factor_loser = get_dynamic_k_factor(rejected_model.match_count)
+
     # Calculate new Elo ratings
-    new_chosen_elo, new_rejected_elo = calculate_elo_change(
-        chosen_model.current_elo, rejected_model.current_elo
+    new_chosen_elo, new_rejected_elo = calculate_elo_change_dynamic_k(
+        chosen_model.current_elo, rejected_model.current_elo, k_factor_winner, k_factor_loser
     )
 
+    # new_chosen_elo, new_rejected_elo = calculate_elo_change(
+    #     chosen_model.current_elo, rejected_model.current_elo
+    # )
+
     # Update model stats
     chosen_model.current_elo = new_chosen_elo
     chosen_model.win_count += 1
@@ -416,8 +423,8 @@ def insert_initial_models():
             id="maskgct",
             name="maskgct",
             model_type=ModelType.TTS,
-            is_open=False,
-            is_active=False,
+            is_open=True,
+            is_active=True,
             model_url="https://github.com/open-mmlab/Amphion/tree/main/models/tts/maskgct",
         ),
         Model(
@@ -526,3 +533,34 @@ def toggle_user_leaderboard_visibility(user_id):
     db.session.commit()
     
     return user.show_in_leaderboard
+
+
+def get_dynamic_k_factor(match_count):
+    """
+    使用连续衰减函数动态计算K因子。
+    K因子会从一个最大值平滑地衰减到一个最小值。
+
+    Args:
+        match_count (int): 模型的总比赛次数。
+
+    Returns:
+        float: 计算出的K因子。
+    """
+    k_max = 40  # 新模型的最大K因子
+    k_min = 10  # 成熟模型的最小K因子
+    decay_speed = 50.0  # 衰减速度，数值越大，K因子下降越慢
+
+    # 指数衰减公式: K = K_min + (K_max - K_min) * e^(-match_count / decay_speed)
+    k_factor = k_min + (k_max - k_min) * math.exp(-match_count / decay_speed)
+
+    return k_factor
+
+def calculate_elo_change_dynamic_k(winner_elo, loser_elo, k_factor_winner, k_factor_loser):
+    """根据双方不同的K因子计算Elo等级分变化。"""
+    expected_winner = 1 / (1 + math.pow(10, (loser_elo - winner_elo) / 400))
+    expected_loser = 1 / (1 + math.pow(10, (winner_elo - loser_elo) / 400))
+
+    winner_new_elo = winner_elo + k_factor_winner * (1 - expected_winner)
+    loser_new_elo = loser_elo + k_factor_loser * (0 - expected_loser)
+
+    return winner_new_elo, loser_new_elo