From c7ec14b4fc682b22b9b8f823eb66618a2e21603c Mon Sep 17 00:00:00 2001
From: Daehyun-Bigbread <bigdarkgold@gmail.com>
Date: Mon, 31 Mar 2025 22:11:23 +0900
Subject: [PATCH 1/5] TRI-263 test metrics add.

---
 app/router/recommendation_api.py              |   31 +
 app/services/evaluation/evaluator.py          |   91 ++
 app/services/evaluation/metrics.py            |  188 +++
 .../user/preprocessed_user_features.csv       | 1005 -----------------
 4 files changed, 310 insertions(+), 1005 deletions(-)
 create mode 100644 app/services/evaluation/evaluator.py
 create mode 100644 app/services/evaluation/metrics.py
 delete mode 100644 storage/input_json/user/preprocessed_user_features.csv

diff --git a/app/router/recommendation_api.py b/app/router/recommendation_api.py
index 268c878..c2b7622 100644
--- a/app/router/recommendation_api.py
+++ b/app/router/recommendation_api.py
@@ -13,6 +13,7 @@
 from app.services.model_trainer import train_model
 from app.services.model_trainer.recommendation import generate_recommendations
 from app.services.preprocess.user.user_preprocess import user_preprocess_data  # 사용자 데이터 전처리 모듈 추가
+from app.services.evaluation.evaluator import evaluate_recommendation_model
 from typing import List, Dict, Any
 from datetime import datetime
 
@@ -173,6 +174,36 @@ async def check_model_status():
     
     return status
 
+# 평가 지표 확인 엔드포인트 추가
+@router.get("/evaluate", response_model=Dict[str, Any])
+async def evaluate_model():
+    """현재 추천 모델의 성능 지표를 계산합니다."""
+    global globals_dict, model_initializing
+    
+    try:
+        # 모델 초기화 상태 확인
+        if not globals_dict or "stacking_reg" not in globals_dict or "df_model" not in globals_dict:
+            raise HTTPException(
+                status_code=503, 
+                detail="모델이 초기화되지 않았습니다. 먼저 모델을 초기화하세요.",
+                headers={"Retry-After": "30"}
+            )
+        
+        # 모델 평가
+        metrics = evaluate_recommendation_model(
+            globals_dict,  # 모델 관련 객체
+            globals_dict.get("df_model"),  # 식당 데이터
+            globals_dict.get("user_features_df")  # 사용자 특성 데이터
+        )
+        
+        return metrics
+    
+    except HTTPException:
+        raise
+    except Exception as e:
+        logger.error(f"모델 평가 중 오류 발생: {e}", exc_info=True)
+        raise HTTPException(status_code=500, detail=str(e))
+
 # recommend 함수 내부 수정
 @router.post("",
              response_model=Dict[str, Any],
diff --git a/app/services/evaluation/evaluator.py b/app/services/evaluation/evaluator.py
new file mode 100644
index 0000000..89ea637
--- /dev/null
+++ b/app/services/evaluation/evaluator.py
@@ -0,0 +1,91 @@
+# app/services/evaluation/evaluator.py
+
+import pandas as pd
+import numpy as np
+import logging
+import json
+from .metrics import calculate_rating_metrics, calculate_ranking_metrics
+from app.services.model_trainer.recommendation import generate_recommendations
+
+logger = logging.getLogger(__name__)
+
+def evaluate_recommendation_model(model_dict, restaurant_df, user_features_df, test_data=None):
+    """
+    추천 모델 평가
+
+    Args:
+        model_dict: 모델 및 관련 객체 딕셔너리 (globals_dict)
+        restaurant_df: 식당 데이터프레임
+        user_features_df: 사용자 특성 데이터프레임
+        test_data: 테스트 데이터 (없으면 찜, 예약 데이터에서 구성)
+
+    Returns:
+        dict: 평가 지표
+    """
+    if test_data is None:
+        # 테스트 데이터가 없으면 찜과 예약 데이터에서 구성
+        logger.info("테스트 데이터를 찜/예약 데이터에서 구성합니다")
+        if "likes_df" in model_dict and "reservations_df" in model_dict:
+            likes_df = model_dict["likes_df"]
+            reservations_df = model_dict["reservations_df"]
+            
+            # 찜, 예약 데이터 결합
+            test_data = pd.concat([
+                likes_df[['user_id', 'restaurant_id']].assign(interaction_type='like'),
+                reservations_df[['user_id', 'restaurant_id']].assign(interaction_type='reservation')
+            ])
+        else:
+            logger.error("테스트 데이터를 구성할 수 없습니다. 찜/예약 데이터가 없습니다.")
+            return {"error": "테스트 데이터를 구성할 수 없습니다."}
+
+    # 랭킹 지표 계산을 위한 사용자별 추천 결과 생성
+    recommendations_dict = {}
+    # 대표적인 사용자 100명만 샘플링 (계산 시간 단축을 위해)
+    sample_users = user_features_df['user_id'].sample(min(100, len(user_features_df))).tolist()
+    
+    for user_id in sample_users:
+        try:
+            # 사용자의 선호 카테고리 확인 (예: 모든 카테고리 선택)
+            user_categories = []
+            for i in range(1, 13):  # 카테고리 1~12
+                cat_col = f"category_{i}"
+                if cat_col in user_features_df.columns and user_features_df[user_features_df['user_id'] == user_id][cat_col].values[0] == 1:
+                    user_categories.append(i)
+            
+            if not user_categories:
+                # 선호 카테고리가 없으면 모든 카테고리 선택
+                user_categories = list(range(1, 13))
+            
+            # 선호 카테고리의 식당만 필터링
+            filtered_df = restaurant_df[restaurant_df['category_id'].isin(user_categories)].copy()
+            
+            # 추천 생성
+            result_json = generate_recommendations(
+                filtered_df, 
+                model_dict["stacking_reg"], 
+                model_dict["model_features"], 
+                user_id,
+                model_dict["scaler"],
+                user_features=user_features_df
+            )
+            
+            # JSON 문자열을 파이썬 객체로 변환
+            result_data = json.loads(result_json)
+            
+            # 추천 식당 ID 리스트 추출
+            recommended_items = [item['restaurant_id'] for item in result_data.get('recommendations', [])]
+            recommendations_dict[user_id] = recommended_items
+            
+        except Exception as e:
+            logger.error(f"사용자 {user_id}에 대한 추천 생성 중 오류 발생: {e}")
+    
+    # 랭킹 지표 계산
+    ranking_metrics = calculate_ranking_metrics(recommendations_dict, test_data, k_values=[5, 10, 15])
+    
+    # 평점 예측 지표 계산 (평점 데이터가 있는 경우)
+    rating_metrics = {"MAE": None, "RMSE": None}
+    
+    # 모든 지표 결합
+    all_metrics = {**rating_metrics, **ranking_metrics}
+    logger.info(f"평가 지표 계산 완료: {all_metrics}")
+    return all_metrics
\ No newline at end of file
diff --git a/app/services/evaluation/metrics.py b/app/services/evaluation/metrics.py
new file mode 100644
index 0000000..32c37d1
--- /dev/null
+++ b/app/services/evaluation/metrics.py
@@ -0,0 +1,188 @@
+# app/services/evaluation/metrics.py
+
+import numpy as np
+import pandas as pd
+from sklearn.metrics import mean_absolute_error, mean_squared_error
+
+# 1. 평점 예측 기반 지표: MAE, RMSE
+def calculate_rating_metrics(y_true, y_pred):
+    """
+    평점 예측 성능 지표 계산
+    
+    Args:
+        y_true: 실제 평점 값
+        y_pred: 예측 평점 값
+    
+    Returns:
+        dict: MAE, RMSE 지표
+    """
+    mae = mean_absolute_error(y_true, y_pred)
+    rmse = np.sqrt(mean_squared_error(y_true, y_pred))
+    
+    return {
+        'MAE': mae,
+        'RMSE': rmse
+    }
+
+# 2. 랭킹 기반 지표: Precision@K, Recall@K, NDCG@K, Hit Rate@K
+def precision_at_k(recommended_items, relevant_items, k):
+    """
+    Precision@K 계산
+    
+    Args:
+        recommended_items: 추천 아이템 리스트
+        relevant_items: 관련 있는(실제로 상호작용한) 아이템 리스트
+        k: 상위 K개 아이템 고려
+    
+    Returns:
+        float: Precision@K 값
+    """
+    if len(recommended_items) == 0:
+        return 0.0
+    
+    # 상위 K개 아이템만 고려
+    recommended_k = recommended_items[:k]
+    
+    # 관련 있는 아이템 중 추천된 것의 수
+    relevant_and_recommended = len(set(recommended_k) & set(relevant_items))
+    
+    return relevant_and_recommended / min(k, len(recommended_items))
+
+def recall_at_k(recommended_items, relevant_items, k):
+    """
+    Recall@K 계산
+    
+    Args:
+        recommended_items: 추천 아이템 리스트
+        relevant_items: 관련 있는(실제로 상호작용한) 아이템 리스트
+        k: 상위 K개 아이템 고려
+    
+    Returns:
+        float: Recall@K 값
+    """
+    if len(relevant_items) == 0:
+        return 0.0
+    
+    # 상위 K개 아이템만 고려
+    recommended_k = recommended_items[:k]
+    
+    # 관련 있는 아이템 중 추천된 것의 수
+    relevant_and_recommended = len(set(recommended_k) & set(relevant_items))
+    
+    return relevant_and_recommended / len(relevant_items)
+
+def ndcg_at_k(recommended_items, relevant_items, k):
+    """
+    NDCG@K (Normalized Discounted Cumulative Gain) 계산
+    
+    Args:
+        recommended_items: 추천 아이템 리스트
+        relevant_items: 관련 있는(실제로 상호작용한) 아이템 리스트
+        k: 상위 K개 아이템 고려
+    
+    Returns:
+        float: NDCG@K 값
+    """
+    if len(relevant_items) == 0:
+        return 0.0
+    
+    # 상위 K개 아이템만 고려
+    recommended_k = recommended_items[:k]
+    
+    # DCG 계산 (Discounted Cumulative Gain)
+    dcg = 0
+    for i, item in enumerate(recommended_k):
+        # 추천 아이템이 관련 있는 경우 1, 아니면 0
+        rel = 1 if item in relevant_items else 0
+        # i+1을 사용하는 이유: 인덱스는 0부터 시작하지만 순위는 1부터 시작
+        dcg += rel / np.log2(i + 2)  # log_2(rank + 1)
+    
+    # 이상적인 추천 순서 생성 (관련 아이템이 먼저 추천됨)
+    ideal_ordering = sorted(recommended_k, key=lambda x: 1 if x in relevant_items else 0, reverse=True)
+    
+    # IDCG 계산 (Ideal DCG)
+    idcg = 0
+    for i, item in enumerate(ideal_ordering):
+        rel = 1 if item in relevant_items else 0
+        idcg += rel / np.log2(i + 2)
+    
+    # IDCG가 0이면 NDCG는 0
+    if idcg == 0:
+        return 0.0
+    
+    return dcg / idcg
+
+def hit_rate_at_k(recommended_items, relevant_items, k):
+    """
+    Hit Rate@K 계산
+    
+    Args:
+        recommended_items: 추천 아이템 리스트
+        relevant_items: 관련 있는(실제로 상호작용한) 아이템 리스트
+        k: 상위 K개 아이템 고려
+    
+    Returns:
+        float: Hit Rate@K 값 (1 또는 0)
+    """
+    # 상위 K개 아이템만 고려
+    recommended_k = recommended_items[:k]
+    
+    # 관련 아이템 중 하나라도 추천 목록에 있으면 1, 아니면 0
+    return 1.0 if len(set(recommended_k) & set(relevant_items)) > 0 else 0.0
+
+# 모든 랭킹 지표를 종합적으로 계산하는 함수
+def calculate_ranking_metrics(recommendations_dict, test_interactions, k_values=[5, 10]):
+    """
+    사용자별 추천 결과에 대한 랭킹 지표 계산
+    
+    Args:
+        recommendations_dict: 사용자별 추천 아이템 딕셔너리 {user_id: [restaurant_id, ...]}
+        test_interactions: 테스트 데이터 (사용자가 실제로 상호작용한 아이템)
+        k_values: 평가할 K 값 리스트
+    
+    Returns:
+        dict: 계산된 모든 랭킹 지표
+    """
+    # 사용자별 관련 아이템 딕셔너리 구성
+    user_relevant_items = {}
+    for _, row in test_interactions.iterrows():
+        user_id = row['user_id']
+        restaurant_id = row['restaurant_id']
+        
+        if user_id not in user_relevant_items:
+            user_relevant_items[user_id] = []
+        
+        user_relevant_items[user_id].append(restaurant_id)
+    
+    # 결과 저장을 위한 딕셔너리
+    results = {}
+    
+    for k in k_values:
+        precision_sum = 0
+        recall_sum = 0
+        ndcg_sum = 0
+        hit_rate_sum = 0
+        user_count = 0
+        
+        for user_id, recommended_items in recommendations_dict.items():
+            if user_id in user_relevant_items:
+                relevant_items = user_relevant_items[user_id]
+                
+                precision_sum += precision_at_k(recommended_items, relevant_items, k)
+                recall_sum += recall_at_k(recommended_items, relevant_items, k)
+                ndcg_sum += ndcg_at_k(recommended_items, relevant_items, k)
+                hit_rate_sum += hit_rate_at_k(recommended_items, relevant_items, k)
+                user_count += 1
+        
+        if user_count > 0:
+            results[f'Precision@{k}'] = precision_sum / user_count
+            results[f'Recall@{k}'] = recall_sum / user_count
+            results[f'NDCG@{k}'] = ndcg_sum / user_count
+            results[f'Hit_Rate@{k}'] = hit_rate_sum / user_count
+        else:
+            results[f'Precision@{k}'] = 0.0
+            results[f'Recall@{k}'] = 0.0
+            results[f'NDCG@{k}'] = 0.0
+            results[f'Hit_Rate@{k}'] = 0.0
+    
+    return results
\ No newline at end of file
diff --git a/storage/input_json/user/preprocessed_user_features.csv b/storage/input_json/user/preprocessed_user_features.csv
deleted file mode 100644
index 0771c14..0000000
--- a/storage/input_json/user/preprocessed_user_features.csv
+++ /dev/null
@@ -1,1005 +0,0 @@
-user_id,max_price,min_price,category_1,category_2,category_3,category_4,category_5,category_6,category_7,category_8,category_9,category_10,category_11,category_12,total_reservations,completed_reservations,reservation_completion_rate,total_likes,like_to_reservation_ratio
-1,286141,183646,0,1,1,0,0,0,0,0,0,0,1,0,8.0,8,1.0,6,0.75
-2,444065,170838,0,0,0,0,0,1,1,0,0,1,0,0,4.0,4,1.0,6,1.5
-3,395779,79971,0,0,1,1,0,0,1,0,0,0,0,0,2.0,2,1.0,4,2.0
-4,200419,82759,1,0,0,0,0,0,1,0,0,0,1,0,8.0,8,1.0,6,0.75
-5,341399,147700,0,0,1,0,0,1,0,0,1,0,0,0,2.0,2,1.0,4,2.0
-6,304138,159625,0,1,1,0,1,0,0,0,0,0,0,0,10.0,10,1.0,0,0.0
-7,338378,151460,1,0,0,0,0,1,0,0,0,0,0,1,2.0,2,1.0,12,6.0
-8,376584,66889,0,0,0,0,0,0,1,1,0,1,0,0,4.0,4,1.0,2,0.5
-9,393014,91158,0,0,0,1,0,0,0,0,1,0,1,0,2.0,2,1.0,4,2.0
-10,267487,170163,0,0,1,1,0,0,0,1,0,0,0,0,,0,0.0,8,5.0
-11,470524,29163,1,0,0,0,1,0,0,0,0,0,1,0,6.0,6,1.0,4,0.67
-12,446869,142652,0,1,0,0,0,0,0,1,0,0,1,0,6.0,6,1.0,4,0.67
-13,351840,185816,0,1,1,0,0,0,0,0,0,0,0,1,8.0,8,1.0,6,0.75
-14,332509,38329,0,0,0,0,0,0,1,1,1,0,0,0,2.0,2,1.0,6,3.0
-15,266941,59156,0,1,0,0,0,0,1,0,0,0,0,1,12.0,12,1.0,10,0.83
-16,337197,194701,1,1,0,0,0,0,0,0,1,0,0,0,2.0,2,1.0,8,4.0
-17,481858,85248,0,0,1,1,0,0,0,0,0,1,0,0,2.0,2,1.0,6,3.0
-18,282926,36257,1,0,0,0,1,0,0,0,0,0,1,0,2.0,2,1.0,10,5.0
-19,327603,197901,1,0,0,0,0,0,0,0,1,0,0,1,8.0,8,1.0,4,0.5
-20,292092,166283,0,1,0,0,0,0,0,1,0,1,0,0,10.0,10,1.0,8,0.8
-21,353557,28934,0,0,1,0,0,0,0,1,0,0,0,1,2.0,2,1.0,2,1.0
-22,319083,167451,0,1,1,0,0,0,0,0,0,1,0,0,6.0,6,1.0,8,1.33
-23,295629,72379,0,0,1,0,0,0,0,1,0,0,0,1,2.0,2,1.0,8,4.0
-24,279131,95305,0,0,0,1,0,0,0,0,0,1,0,1,4.0,4,1.0,16,4.0
-25,233947,106565,0,0,1,0,0,1,0,0,0,0,0,1,6.0,6,1.0,4,0.67
-26,288981,180902,1,0,0,0,0,1,0,0,0,1,0,0,2.0,2,1.0,6,3.0
-27,254815,187129,1,1,0,0,0,0,0,0,1,0,0,0,2.0,2,1.0,8,4.0
-28,403490,169312,0,0,0,1,0,0,1,0,0,0,0,1,10.0,10,1.0,26,2.6
-29,403053,151914,0,0,0,0,0,0,1,0,1,0,0,1,4.0,4,1.0,6,1.5
-30,227881,133174,0,0,0,1,0,1,0,0,0,0,1,0,6.0,6,1.0,4,0.67
-31,207906,88090,0,0,1,0,0,0,0,0,1,1,0,0,2.0,2,1.0,10,5.0
-32,270292,97183,0,1,1,0,0,0,0,0,0,1,0,0,4.0,4,1.0,6,1.5
-33,403876,89280,0,0,0,0,0,0,0,1,1,0,1,0,6.0,6,1.0,4,0.67
-34,312422,61746,0,0,1,1,0,0,0,0,0,1,0,0,6.0,6,1.0,8,1.33
-35,369064,109942,0,0,0,0,0,1,0,0,0,1,1,0,6.0,6,1.0,8,1.33
-36,493736,110323,0,1,1,0,0,0,1,0,0,0,0,0,2.0,2,1.0,2,1.0
-37,469280,35055,1,0,0,1,0,0,0,0,0,0,0,1,2.0,2,1.0,4,2.0
-38,484502,61187,1,0,0,0,0,0,0,0,1,1,0,0,4.0,4,1.0,8,2.0
-39,311737,164628,0,0,0,1,0,0,0,0,0,1,1,0,2.0,2,1.0,12,6.0
-40,238578,199747,0,0,0,1,0,0,1,0,1,0,0,0,8.0,8,1.0,0,0.0
-41,492507,124228,0,0,0,1,0,1,1,0,0,0,0,0,14.0,14,1.0,4,0.29
-42,274639,87199,0,1,0,1,0,0,0,0,0,0,0,1,6.0,6,1.0,8,1.33
-43,380147,35046,0,0,0,1,0,0,1,0,0,0,1,0,4.0,4,1.0,8,2.0
-44,422839,141179,0,0,0,0,0,0,0,1,1,0,1,0,4.0,4,1.0,4,1.0
-45,473654,131248,0,0,0,1,0,1,1,0,0,0,0,0,4.0,4,1.0,8,2.0
-46,410500,54614,0,0,0,1,0,1,0,0,1,0,0,0,,0,0.0,8,5.0
-47,406337,96377,0,0,1,0,0,0,0,0,0,0,1,1,2.0,2,1.0,2,1.0
-48,489105,173326,0,0,1,1,0,0,0,0,0,0,0,1,10.0,10,1.0,8,0.8
-49,459629,48247,0,0,1,0,1,0,0,1,0,0,0,0,10.0,10,1.0,4,0.4
-50,364108,174482,0,0,1,1,0,0,0,1,0,0,0,0,4.0,4,1.0,6,1.5
-51,259108,89491,0,1,1,0,0,0,1,0,0,0,0,0,2.0,2,1.0,4,2.0
-52,200179,105429,0,1,0,0,1,0,0,1,0,0,0,0,4.0,4,1.0,2,0.5
-53,236409,108492,0,0,1,0,0,0,0,0,1,1,0,0,4.0,4,1.0,14,3.5
-54,422377,81914,1,0,0,0,0,0,1,0,1,0,0,0,6.0,6,1.0,10,1.67
-55,388247,16187,0,0,1,0,0,0,0,0,0,1,0,1,2.0,2,1.0,8,4.0
-56,309257,67266,0,0,1,0,1,0,0,0,0,0,1,0,2.0,2,1.0,10,5.0
-57,203384,55275,1,0,0,0,0,0,1,1,0,0,0,0,10.0,10,1.0,6,0.6
-58,329780,83254,0,0,1,0,0,0,1,0,0,0,0,1,,0,0.0,8,5.0
-59,401574,129489,0,0,1,0,0,1,0,1,0,0,0,0,2.0,2,1.0,10,5.0
-60,271891,43058,0,0,0,0,0,1,0,1,0,0,0,1,12.0,12,1.0,16,1.33
-61,256791,187570,1,0,1,0,0,0,0,1,0,0,0,0,10.0,10,1.0,10,1.0
-62,419351,21516,0,0,0,0,0,0,0,0,1,1,1,0,8.0,8,1.0,4,0.5
-63,235396,92464,0,0,1,0,0,0,1,0,0,0,1,0,8.0,8,1.0,8,1.0
-64,246214,150863,0,0,0,1,0,0,0,1,1,0,0,0,6.0,6,1.0,10,1.67
-65,485892,155450,0,0,0,1,0,1,0,0,0,0,1,0,2.0,2,1.0,2,1.0
-66,234185,79818,0,0,0,1,0,0,1,0,0,0,1,0,10.0,10,1.0,8,0.8
-67,472937,181198,0,0,0,0,0,0,1,1,0,1,0,0,6.0,6,1.0,4,0.67
-68,401695,121572,0,1,1,0,0,0,0,1,0,0,0,0,2.0,2,1.0,4,2.0
-69,492640,53616,0,1,0,0,0,0,1,1,0,0,0,0,6.0,6,1.0,2,0.33
-70,375454,119576,0,0,1,0,0,0,0,0,0,1,1,0,10.0,10,1.0,4,0.4
-71,354216,13768,0,1,0,0,0,0,0,1,0,1,0,0,2.0,2,1.0,2,1.0
-72,424256,97033,0,1,0,0,0,0,0,1,1,0,0,0,2.0,2,1.0,10,5.0
-73,345640,58444,0,0,1,0,0,0,1,0,0,0,1,0,10.0,10,1.0,2,0.2
-74,248236,120737,1,0,0,0,0,1,1,0,0,0,0,0,,0,0.0,12,5.0
-75,267707,123521,0,1,0,0,1,0,0,0,1,0,0,0,4.0,4,1.0,2,0.5
-76,470071,172019,0,0,0,0,0,1,1,1,0,0,0,0,2.0,2,1.0,2,1.0
-77,399068,90198,0,0,0,0,0,1,1,0,1,0,0,0,8.0,8,1.0,4,0.5
-78,308313,65073,0,0,1,0,0,0,0,0,1,0,0,1,4.0,4,1.0,6,1.5
-79,234246,149171,0,0,0,0,1,1,0,0,0,0,1,0,4.0,4,1.0,10,2.5
-80,267787,79665,1,1,0,0,0,0,1,0,0,0,0,0,4.0,4,1.0,4,1.0
-81,393826,22004,0,0,1,1,0,0,0,0,0,1,0,0,4.0,4,1.0,6,1.5
-82,229961,94945,0,0,0,0,0,0,0,1,1,1,0,0,8.0,8,1.0,4,0.5
-83,444105,167915,0,1,0,0,0,0,0,1,1,0,0,0,8.0,8,1.0,4,0.5
-84,382730,132551,0,0,0,1,1,0,0,1,0,0,0,0,4.0,4,1.0,6,1.5
-85,205497,46979,1,1,0,1,0,0,0,0,0,0,0,0,,0,0.0,6,5.0
-86,398628,185050,0,1,0,0,1,0,0,1,0,0,0,0,8.0,8,1.0,4,0.5
-87,378633,135979,0,1,0,1,1,0,0,0,0,0,0,0,4.0,4,1.0,2,0.5
-88,470318,148192,0,0,0,1,1,0,0,0,1,0,0,0,6.0,6,1.0,10,1.67
-89,238334,185320,0,0,0,0,0,0,0,1,0,0,1,1,12.0,12,1.0,6,0.5
-90,421438,26856,0,1,0,1,0,1,0,0,0,0,0,0,2.0,2,1.0,4,2.0
-91,486531,50526,0,0,1,0,1,0,0,1,0,0,0,0,4.0,4,1.0,6,1.5
-92,364554,82141,0,0,0,0,0,1,0,0,0,0,1,1,6.0,6,1.0,4,0.67
-93,345162,66823,0,0,0,0,1,1,0,0,0,0,0,1,2.0,2,1.0,12,6.0
-94,401582,85499,1,0,0,0,0,0,0,0,1,1,0,0,6.0,6,1.0,6,1.0
-95,231653,96497,0,0,0,1,1,0,1,0,0,0,0,0,10.0,10,1.0,4,0.4
-96,452322,29299,1,0,0,0,0,0,0,1,0,1,0,0,4.0,4,1.0,8,2.0
-97,246133,27202,1,0,0,0,0,0,1,0,1,0,0,0,4.0,4,1.0,2,0.5
-98,330349,129763,0,0,1,0,1,0,0,0,1,0,0,0,8.0,8,1.0,2,0.25
-99,303372,93365,0,0,0,0,1,0,0,1,0,0,0,1,4.0,4,1.0,0,0.0
-100,437106,79353,1,0,0,1,0,0,0,1,0,0,0,0,4.0,4,1.0,6,1.5
-101,223500,140727,0,0,0,0,0,0,1,0,0,1,0,1,2.0,2,1.0,0,0.0
-102,354243,126610,0,0,0,0,0,0,0,0,1,0,1,1,12.0,12,1.0,8,0.67
-103,426530,12059,0,0,1,0,0,0,0,1,0,1,0,0,,0,0.0,6,5.0
-104,332202,132141,0,0,0,1,0,0,0,0,1,0,0,1,,0,0.0,10,5.0
-105,499973,37922,0,0,1,0,1,0,0,1,0,0,0,0,,0,0.0,6,5.0
-106,230682,64249,0,0,0,0,1,1,0,1,0,0,0,0,10.0,10,1.0,4,0.4
-107,467101,65313,0,0,0,0,1,1,0,0,1,0,0,0,8.0,8,1.0,0,0.0
-108,244275,169377,0,0,1,1,0,0,0,0,0,0,1,0,8.0,8,1.0,0,0.0
-109,258548,15342,0,0,0,1,0,1,0,1,0,0,0,0,10.0,10,1.0,12,1.2
-110,355408,97494,0,0,0,0,1,1,0,0,0,0,1,0,6.0,6,1.0,6,1.0
-111,490070,36258,0,0,1,0,0,0,1,0,0,0,0,1,,0,0.0,8,5.0
-112,263578,115332,0,1,0,0,0,0,1,0,0,1,0,0,6.0,6,1.0,10,1.67
-113,312245,127054,0,0,0,1,0,0,0,0,1,0,0,1,4.0,4,1.0,8,2.0
-114,327001,166239,0,0,1,1,1,0,0,0,0,0,0,0,4.0,4,1.0,2,0.5
-115,285426,113164,0,0,0,0,0,0,1,1,0,0,1,0,8.0,8,1.0,8,1.0
-116,294168,127969,0,1,1,0,0,0,0,0,0,0,0,1,4.0,4,1.0,4,1.0
-117,391158,176237,1,0,0,1,0,0,0,0,1,0,0,0,4.0,4,1.0,6,1.5
-118,407527,71949,0,0,0,0,0,1,0,0,1,1,0,0,4.0,4,1.0,0,0.0
-119,357736,67831,0,0,0,0,1,0,0,0,1,0,0,1,8.0,8,1.0,6,0.75
-120,404101,144428,1,0,0,1,0,1,0,0,0,0,0,0,4.0,4,1.0,6,1.5
-121,219612,41318,0,0,0,1,0,1,0,0,1,0,0,0,8.0,8,1.0,4,0.5
-122,318302,30258,0,0,1,0,0,1,0,0,0,1,0,0,4.0,4,1.0,8,2.0
-123,288533,44469,0,0,0,1,0,0,1,1,0,0,0,0,10.0,10,1.0,10,1.0
-124,207023,75965,1,0,0,1,0,1,0,0,0,0,0,0,2.0,2,1.0,2,1.0
-125,429278,87887,1,1,0,0,0,0,0,0,0,1,0,0,4.0,4,1.0,2,0.5
-126,409992,121431,1,0,1,0,0,0,0,1,0,0,0,0,8.0,8,1.0,2,0.25
-127,451142,137504,0,0,0,1,0,1,0,0,1,0,0,0,10.0,10,1.0,12,1.2
-128,375827,105166,0,1,1,0,0,0,1,0,0,0,0,0,,0,0.0,10,5.0
-129,495343,120288,0,1,1,0,0,1,0,0,0,0,0,0,6.0,6,1.0,6,1.0
-130,328338,20029,0,0,0,0,1,0,0,0,0,1,1,0,6.0,6,1.0,6,1.0
-131,299643,168222,0,0,0,0,0,0,1,0,0,1,1,0,6.0,6,1.0,4,0.67
-132,366391,27774,0,0,1,0,0,0,1,0,0,0,0,1,8.0,8,1.0,8,1.0
-133,433053,53942,0,0,1,0,0,0,1,0,0,0,0,1,2.0,2,1.0,10,5.0
-134,275331,94983,0,0,0,0,0,0,1,0,1,0,0,1,2.0,2,1.0,8,4.0
-135,379968,11980,0,0,0,0,1,0,0,0,0,1,1,0,4.0,4,1.0,6,1.5
-136,349473,160707,0,0,0,0,1,0,1,0,0,0,1,0,4.0,4,1.0,8,2.0
-137,464334,84683,0,0,0,0,0,0,1,0,0,1,0,1,6.0,6,1.0,10,1.67
-138,359174,18420,0,0,0,0,1,1,0,0,0,0,1,0,2.0,2,1.0,2,1.0
-139,479372,50259,0,0,1,0,0,0,0,0,1,1,0,0,4.0,4,1.0,4,1.0
-140,276422,164299,0,0,0,0,1,0,0,0,0,0,1,1,8.0,8,1.0,10,1.25
-141,268704,176375,1,0,0,0,0,0,0,1,0,0,0,1,8.0,8,1.0,12,1.5
-142,321766,196059,1,0,0,0,0,1,1,0,0,0,0,0,6.0,6,1.0,6,1.0
-143,244480,182013,0,0,1,0,1,0,0,0,1,0,0,0,2.0,2,1.0,4,2.0
-144,491347,45546,0,0,1,1,0,0,0,0,0,1,0,0,4.0,4,1.0,6,1.5
-145,479164,31723,0,0,1,0,0,0,0,0,1,0,0,1,4.0,4,1.0,6,1.5
-146,450496,180807,0,0,0,0,0,1,0,0,0,1,1,0,4.0,4,1.0,2,0.5
-147,473203,178811,0,0,0,0,1,0,0,1,0,1,0,0,8.0,8,1.0,12,1.5
-148,379234,139834,0,1,1,0,0,0,0,0,0,0,0,1,2.0,2,1.0,2,1.0
-149,463183,100745,1,0,0,0,0,1,0,0,0,0,1,0,6.0,6,1.0,10,1.67
-150,268605,39040,0,1,0,0,1,0,0,0,0,0,0,1,2.0,2,1.0,4,2.0
-151,201272,29406,0,1,0,0,1,1,0,0,0,0,0,0,2.0,2,1.0,6,3.0
-152,260646,123073,0,0,0,1,0,1,0,0,0,0,1,0,8.0,8,1.0,6,0.75
-153,432589,168024,0,0,0,1,1,0,0,0,0,0,0,1,6.0,6,1.0,8,1.33
-154,441903,167510,1,0,0,0,0,0,0,0,1,0,1,0,2.0,2,1.0,12,6.0
-155,407162,40023,1,0,0,0,0,0,0,0,0,0,1,1,8.0,8,1.0,10,1.25
-156,277219,159091,0,0,1,0,1,0,1,0,0,0,0,0,2.0,2,1.0,14,7.0
-157,434645,48399,0,0,0,0,0,0,0,1,0,1,1,0,8.0,8,1.0,6,0.75
-158,330608,144690,0,0,1,0,1,0,0,0,0,1,0,0,2.0,2,1.0,2,1.0
-159,301470,90552,0,0,0,0,0,0,0,1,1,0,1,0,2.0,2,1.0,4,2.0
-160,231090,32498,0,1,0,0,1,0,0,0,1,0,0,0,6.0,6,1.0,10,1.67
-161,230042,159215,0,0,1,1,0,1,0,0,0,0,0,0,10.0,10,1.0,10,1.0
-162,335301,149189,0,0,0,1,0,1,0,0,0,0,1,0,2.0,2,1.0,4,2.0
-163,268901,85454,1,0,1,0,0,0,0,0,0,1,0,0,6.0,6,1.0,12,2.0
-164,431844,31263,0,0,1,0,1,0,1,0,0,0,0,0,,0,0.0,6,5.0
-165,305315,66125,0,0,0,0,1,0,1,1,0,0,0,0,8.0,8,1.0,8,1.0
-166,461726,158451,1,0,1,0,0,1,0,0,0,0,0,0,8.0,8,1.0,2,0.25
-167,291481,19032,1,0,1,0,0,0,0,0,0,1,0,0,6.0,6,1.0,0,0.0
-168,345816,78571,1,1,0,1,0,0,0,0,0,0,0,0,4.0,4,1.0,6,1.5
-169,309393,198909,1,0,0,1,0,0,0,0,1,0,0,0,,0,0.0,8,5.0
-170,260505,94505,0,1,0,0,0,0,0,0,1,1,0,0,2.0,2,1.0,2,1.0
-171,278640,74242,1,0,0,0,1,0,1,0,0,0,0,0,2.0,2,1.0,4,2.0
-172,448128,59574,1,0,0,0,0,0,0,1,0,0,1,0,8.0,8,1.0,16,2.0
-173,431000,146506,0,0,0,1,0,0,1,1,0,0,0,0,4.0,4,1.0,2,0.5
-174,364180,61310,1,0,0,0,0,0,1,0,0,1,0,0,6.0,6,1.0,8,1.33
-175,356124,41448,0,0,1,0,0,1,0,0,0,1,0,0,2.0,2,1.0,6,3.0
-176,468564,162622,0,0,0,1,1,0,0,1,0,0,0,0,2.0,2,1.0,6,3.0
-177,220994,107493,1,0,1,0,0,0,0,1,0,0,0,0,12.0,12,1.0,6,0.5
-178,290759,196399,0,1,0,0,0,0,0,0,1,1,0,0,,0,0.0,6,5.0
-179,400526,193715,1,0,1,0,1,0,0,0,0,0,0,0,2.0,2,1.0,16,8.0
-180,413880,79311,0,1,0,1,1,0,0,0,0,0,0,0,6.0,6,1.0,2,0.33
-181,321989,11008,1,0,0,0,0,0,1,0,0,0,1,0,10.0,10,1.0,4,0.4
-182,345544,170913,0,0,0,1,1,0,0,0,1,0,0,0,8.0,8,1.0,4,0.5
-183,221962,183937,1,1,0,0,0,0,0,0,1,0,0,0,6.0,6,1.0,6,1.0
-184,311028,161327,1,0,0,0,0,1,0,1,0,0,0,0,10.0,10,1.0,10,1.0
-185,248252,183519,1,0,1,0,0,0,0,0,0,0,1,0,2.0,2,1.0,2,1.0
-186,451106,122923,0,0,0,0,0,0,1,0,1,0,1,0,2.0,2,1.0,10,5.0
-187,405380,147113,1,0,0,0,0,0,0,0,1,0,1,0,12.0,12,1.0,2,0.17
-188,203839,28523,0,1,0,0,0,0,0,0,1,0,0,1,6.0,6,1.0,4,0.67
-189,333806,76186,0,0,1,0,0,1,1,0,0,0,0,0,4.0,4,1.0,4,1.0
-190,432627,92873,0,0,0,0,1,0,0,0,1,0,0,1,6.0,6,1.0,18,3.0
-191,215347,95798,1,0,0,1,0,0,0,0,0,0,1,0,4.0,4,1.0,6,1.5
-192,404671,118625,0,0,0,1,0,1,0,0,0,1,0,0,2.0,2,1.0,12,6.0
-193,363349,82141,1,0,0,0,1,0,1,0,0,0,0,0,6.0,6,1.0,2,0.33
-194,278162,176609,0,1,1,0,0,1,0,0,0,0,0,0,2.0,2,1.0,10,5.0
-195,384349,89445,0,0,1,0,0,0,0,1,0,0,1,0,6.0,6,1.0,0,0.0
-196,422863,69355,0,0,0,0,0,0,1,0,1,0,1,0,4.0,4,1.0,12,3.0
-197,203902,149506,1,0,0,0,0,1,0,0,0,1,0,0,2.0,2,1.0,14,7.0
-198,474910,51413,0,0,1,0,0,0,1,0,1,0,0,0,,0,0.0,12,5.0
-199,476345,79957,1,0,0,0,0,0,0,0,1,0,1,0,6.0,6,1.0,12,2.0
-200,220967,15234,0,0,0,0,1,1,0,0,0,0,1,0,4.0,4,1.0,12,3.0
-201,355305,72795,0,1,0,0,0,0,1,0,1,0,0,0,6.0,6,1.0,6,1.0
-202,374021,44532,0,1,0,1,0,0,0,1,0,0,0,0,6.0,6,1.0,4,0.67
-203,279504,51617,0,0,0,1,0,1,0,0,1,0,0,0,6.0,6,1.0,12,2.0
-204,443296,160288,0,1,1,0,1,0,0,0,0,0,0,0,2.0,2,1.0,4,2.0
-205,375556,180085,0,1,0,0,0,0,1,0,0,1,0,0,4.0,4,1.0,6,1.5
-206,251345,176851,0,0,0,1,0,0,1,0,0,0,0,1,4.0,4,1.0,6,1.5
-207,494189,172931,0,1,0,0,0,0,0,1,0,0,0,1,8.0,8,1.0,8,1.0
-208,380802,94459,0,0,0,1,1,0,0,0,0,0,0,1,6.0,6,1.0,8,1.33
-209,237485,15499,1,1,0,0,0,0,0,0,0,0,1,0,2.0,2,1.0,4,2.0
-210,389579,74857,0,0,0,1,0,0,0,0,1,0,0,1,4.0,4,1.0,4,1.0
-211,360769,92737,0,0,0,1,0,0,1,0,0,0,0,1,2.0,2,1.0,6,3.0
-212,433681,66872,0,1,1,0,0,0,0,0,0,0,0,1,10.0,10,1.0,2,0.2
-213,244503,153773,0,0,1,1,0,0,0,0,0,0,0,1,,0,0.0,4,5.0
-214,282906,138552,0,1,0,0,0,1,0,0,0,1,0,0,6.0,6,1.0,6,1.0
-215,309651,27015,0,0,1,1,1,0,0,0,0,0,0,0,4.0,4,1.0,2,0.5
-216,235826,35438,0,0,0,0,0,1,0,1,0,0,0,1,10.0,10,1.0,8,0.8
-217,348160,50165,0,1,0,1,0,0,0,0,0,0,1,0,4.0,4,1.0,10,2.5
-218,252461,37506,1,0,0,0,1,1,0,0,0,0,0,0,8.0,8,1.0,10,1.25
-219,425014,120768,0,1,0,0,1,0,1,0,0,0,0,0,12.0,12,1.0,4,0.33
-220,484657,54360,0,0,0,0,1,0,1,0,0,0,1,0,6.0,6,1.0,4,0.67
-221,357157,115437,0,0,0,0,0,0,1,1,1,0,0,0,12.0,12,1.0,2,0.17
-222,409313,88353,0,1,0,0,1,0,0,0,0,1,0,0,4.0,4,1.0,4,1.0
-223,295721,179055,1,0,0,0,0,0,0,0,0,1,1,0,10.0,10,1.0,8,0.8
-224,211633,26505,1,0,0,0,1,1,0,0,0,0,0,0,8.0,8,1.0,8,1.0
-225,301350,99891,0,0,0,0,1,0,0,0,1,1,0,0,6.0,6,1.0,8,1.33
-226,262495,70481,0,0,0,1,0,0,0,1,0,0,0,1,10.0,10,1.0,6,0.6
-227,210992,35595,0,0,0,0,0,1,0,0,1,0,1,0,4.0,4,1.0,4,1.0
-228,266644,79859,0,1,1,0,0,0,1,0,0,0,0,0,8.0,8,1.0,8,1.0
-229,279378,93433,1,0,1,0,0,1,0,0,0,0,0,0,2.0,2,1.0,10,5.0
-230,358083,49661,1,0,0,0,1,1,0,0,0,0,0,0,4.0,4,1.0,2,0.5
-231,417867,196091,0,1,0,0,0,0,1,0,0,1,0,0,6.0,6,1.0,8,1.33
-232,307433,54841,1,0,1,0,0,0,0,0,0,0,0,1,8.0,8,1.0,6,0.75
-233,266947,171934,0,0,0,0,1,0,1,0,0,0,0,1,2.0,2,1.0,12,6.0
-234,444798,121560,0,1,0,0,0,0,0,0,1,0,0,1,2.0,2,1.0,2,1.0
-235,457960,22034,0,0,0,1,0,0,0,0,1,0,1,0,6.0,6,1.0,2,0.33
-236,213562,185489,0,1,0,0,0,1,0,0,0,1,0,0,2.0,2,1.0,4,2.0
-237,292293,188625,1,0,0,0,0,0,1,1,0,0,0,0,4.0,4,1.0,2,0.5
-238,425475,63185,1,0,0,0,0,1,0,0,1,0,0,0,2.0,2,1.0,6,3.0
-239,299594,160250,0,0,0,0,0,0,1,0,1,1,0,0,6.0,6,1.0,2,0.33
-240,499985,97669,0,1,0,0,1,0,1,0,0,0,0,0,6.0,6,1.0,6,1.0
-241,467802,71172,0,0,0,0,1,0,0,0,1,1,0,0,4.0,4,1.0,2,0.5
-242,294060,122809,1,0,1,0,0,0,1,0,0,0,0,0,10.0,10,1.0,2,0.2
-243,295196,97857,0,0,0,0,1,1,1,0,0,0,0,0,4.0,4,1.0,4,1.0
-244,341689,162736,0,0,0,1,0,1,0,0,1,0,0,0,6.0,6,1.0,6,1.0
-245,446327,42277,0,0,0,1,0,0,1,1,0,0,0,0,4.0,4,1.0,4,1.0
-246,362016,85719,0,0,0,1,0,0,0,0,0,1,0,1,4.0,4,1.0,8,2.0
-247,292418,17470,0,1,0,0,0,0,0,1,0,0,0,1,8.0,8,1.0,8,1.0
-248,402010,28515,0,1,0,1,0,0,0,0,0,0,0,1,,0,0.0,8,5.0
-249,297665,147497,0,1,0,0,0,0,1,0,0,0,0,1,6.0,6,1.0,0,0.0
-250,344575,169672,0,1,0,0,0,1,1,0,0,0,0,0,4.0,4,1.0,4,1.0
-251,494766,34199,0,0,0,1,0,0,0,1,0,0,0,1,8.0,8,1.0,4,0.5
-252,455465,99429,0,0,0,1,0,1,0,0,0,1,0,0,8.0,8,1.0,2,0.25
-253,468129,19544,0,0,0,0,0,0,1,0,1,0,1,0,2.0,2,1.0,2,1.0
-254,285542,168275,0,0,1,0,0,0,1,0,1,0,0,0,6.0,6,1.0,4,0.67
-255,401087,92771,0,0,0,0,0,0,1,0,0,1,0,1,6.0,6,1.0,8,1.33
-256,300457,182506,0,0,0,1,0,0,0,1,0,0,1,0,,0,0.0,2,5.0
-257,225116,127431,1,0,0,0,1,0,0,1,0,0,0,0,6.0,6,1.0,8,1.33
-258,341865,89645,0,1,0,1,0,1,0,0,0,0,0,0,,0,0.0,6,5.0
-259,329782,138901,0,0,0,0,0,0,0,0,1,0,1,1,,0,0.0,8,5.0
-260,260576,199473,0,0,0,0,0,1,1,0,0,0,1,0,6.0,6,1.0,6,1.0
-261,455435,17795,1,0,0,1,0,0,0,0,0,1,0,0,2.0,2,1.0,4,2.0
-262,332828,107041,1,0,0,0,0,0,0,0,0,1,0,1,8.0,8,1.0,4,0.5
-263,312057,35945,0,0,0,0,0,1,1,0,1,0,0,0,4.0,4,1.0,4,1.0
-264,470692,92096,1,1,0,0,0,0,0,1,0,0,0,0,8.0,8,1.0,6,0.75
-265,230964,80257,0,1,0,0,0,1,0,0,0,1,0,0,10.0,10,1.0,6,0.6
-266,423501,78904,0,0,0,1,1,0,0,0,0,1,0,0,10.0,10,1.0,4,0.4
-267,373241,33338,0,0,1,0,1,0,0,1,0,0,0,0,6.0,6,1.0,2,0.33
-268,476092,101934,1,0,1,0,0,0,0,0,0,0,0,1,6.0,6,1.0,2,0.33
-269,494248,186835,0,0,1,0,0,0,0,1,0,1,0,0,6.0,6,1.0,6,1.0
-270,426884,194172,0,0,0,0,0,0,0,1,0,1,0,1,,0,0.0,2,5.0
-271,202738,87671,0,0,0,0,0,0,1,0,1,0,1,0,4.0,4,1.0,6,1.5
-272,393215,36585,1,0,0,0,0,0,0,1,0,0,1,0,8.0,8,1.0,6,0.75
-273,227536,64387,0,1,0,0,0,0,0,1,0,0,1,0,12.0,12,1.0,0,0.0
-274,408448,106251,1,0,0,1,0,0,0,0,1,0,0,0,4.0,4,1.0,0,0.0
-275,397164,56350,0,0,0,1,0,0,0,0,1,1,0,0,,0,0.0,8,5.0
-276,401375,190889,0,1,0,0,0,0,0,1,1,0,0,0,6.0,6,1.0,4,0.67
-277,243472,52240,0,0,0,0,0,0,1,0,0,1,1,0,8.0,8,1.0,8,1.0
-278,390850,94391,0,0,0,0,0,1,0,1,0,0,0,1,2.0,2,1.0,8,4.0
-279,486146,63673,0,0,0,1,0,1,0,0,0,0,1,0,,0,0.0,2,5.0
-280,316122,137715,0,0,1,0,0,0,0,0,0,1,1,0,6.0,6,1.0,6,1.0
-281,417758,81087,1,1,0,0,1,0,0,0,0,0,0,0,6.0,6,1.0,6,1.0
-282,200066,52917,0,0,0,0,0,0,0,1,0,1,1,0,10.0,10,1.0,8,0.8
-283,466157,77291,0,0,1,0,0,0,0,0,1,0,0,1,2.0,2,1.0,6,3.0
-284,311745,117818,0,0,0,0,0,1,0,1,0,0,0,1,6.0,6,1.0,2,0.33
-285,388810,106468,0,1,0,0,0,0,1,0,0,0,1,0,2.0,2,1.0,6,3.0
-286,295784,96831,0,1,0,0,0,0,1,0,0,0,0,1,2.0,2,1.0,4,2.0
-287,239957,80359,0,0,0,1,0,0,1,0,1,0,0,0,6.0,6,1.0,4,0.67
-288,259571,96779,0,0,0,1,0,0,1,0,0,0,1,0,,0,0.0,8,5.0
-289,470592,57685,1,1,0,0,0,0,0,1,0,0,0,0,4.0,4,1.0,0,0.0
-290,360940,92218,0,1,0,0,0,1,0,1,0,0,0,0,4.0,4,1.0,16,4.0
-291,459336,78436,1,0,0,0,0,0,0,0,0,1,1,0,4.0,4,1.0,4,1.0
-292,296446,82822,1,0,0,0,1,1,0,0,0,0,0,0,6.0,6,1.0,0,0.0
-293,285626,67263,0,1,0,0,1,0,0,0,0,0,1,0,,0,0.0,4,5.0
-294,464099,170208,0,0,0,1,0,0,0,1,0,1,0,0,4.0,4,1.0,6,1.5
-295,263105,164140,1,0,0,0,1,0,0,1,0,0,0,0,6.0,6,1.0,4,0.67
-296,302474,167473,0,1,0,0,0,0,0,0,0,0,1,1,2.0,2,1.0,4,2.0
-297,372056,174677,0,1,1,0,0,0,0,0,0,0,0,1,6.0,6,1.0,4,0.67
-298,352449,52010,0,0,0,1,0,1,0,1,0,0,0,0,6.0,6,1.0,4,0.67
-299,419186,171727,0,0,0,0,1,1,0,0,0,0,1,0,10.0,10,1.0,2,0.2
-300,336617,82806,1,1,0,0,0,0,0,0,1,0,0,0,4.0,4,1.0,10,2.5
-301,400878,153139,0,0,1,0,1,0,0,0,0,0,0,1,4.0,4,1.0,4,1.0
-302,376532,58763,0,0,0,1,0,0,0,0,0,0,1,1,4.0,4,1.0,12,3.0
-303,215966,114816,1,0,0,0,0,0,0,0,1,0,0,1,4.0,4,1.0,4,1.0
-304,298739,78236,0,0,0,0,0,1,0,1,0,0,1,0,2.0,2,1.0,10,5.0
-305,324152,168504,0,1,0,0,1,0,0,0,1,0,0,0,6.0,6,1.0,4,0.67
-306,225832,114959,0,0,0,0,1,0,0,0,0,0,1,1,2.0,2,1.0,8,4.0
-307,218247,97153,0,0,1,1,0,0,0,0,0,0,1,0,2.0,2,1.0,4,2.0
-308,442559,186433,0,0,1,1,0,0,0,1,0,0,0,0,4.0,4,1.0,8,2.0
-309,400541,29388,0,0,0,0,1,1,0,1,0,0,0,0,4.0,4,1.0,6,1.5
-310,322029,114911,1,1,0,0,0,0,0,0,0,0,0,1,4.0,4,1.0,0,0.0
-311,233292,162186,0,1,0,0,0,0,0,0,0,1,0,1,2.0,2,1.0,2,1.0
-312,313899,35635,0,1,0,0,0,1,1,0,0,0,0,0,10.0,10,1.0,6,0.6
-313,403970,11492,1,0,0,0,0,0,0,1,0,0,1,0,6.0,6,1.0,0,0.0
-314,292531,147251,0,1,0,0,0,0,0,1,0,0,0,1,8.0,8,1.0,16,2.0
-315,499030,65865,0,1,0,0,1,0,0,0,0,0,1,0,2.0,2,1.0,8,4.0
-316,345372,25792,0,0,0,0,1,0,0,0,0,0,1,1,8.0,8,1.0,6,0.75
-317,279212,121179,0,1,1,1,0,0,0,0,0,0,0,0,8.0,8,1.0,8,1.0
-318,434460,169288,0,0,0,0,0,0,0,0,1,1,0,1,4.0,4,1.0,2,0.5
-319,461196,108275,1,0,0,0,0,0,1,0,0,0,1,0,,0,0.0,6,5.0
-320,214434,163733,1,0,0,0,0,0,0,1,1,0,0,0,8.0,8,1.0,2,0.25
-321,380690,119490,0,0,0,1,0,1,1,0,0,0,0,0,2.0,2,1.0,10,5.0
-322,207430,146046,1,0,1,0,0,0,0,1,0,0,0,0,6.0,6,1.0,4,0.67
-323,291430,197852,0,1,0,0,0,0,1,0,0,0,1,0,2.0,2,1.0,0,0.0
-324,281509,145993,0,0,0,0,0,0,1,0,0,1,1,0,4.0,4,1.0,10,2.5
-325,450296,105489,0,0,0,1,0,0,1,0,0,0,1,0,8.0,8,1.0,4,0.5
-326,304679,130659,1,0,0,1,0,0,0,1,0,0,0,0,10.0,10,1.0,8,0.8
-327,383569,138762,0,1,0,0,0,0,0,0,1,0,1,0,4.0,4,1.0,0,0.0
-328,353518,60651,0,0,1,0,1,0,0,0,0,1,0,0,6.0,6,1.0,10,1.67
-329,274717,155240,0,0,0,1,0,0,0,1,0,0,1,0,,0,0.0,8,5.0
-330,384499,155236,0,0,0,0,0,0,0,1,1,0,0,1,6.0,6,1.0,4,0.67
-331,465116,153987,0,0,0,0,0,0,0,0,1,1,1,0,4.0,4,1.0,4,1.0
-332,422611,142253,0,0,0,1,0,0,0,1,0,0,1,0,2.0,2,1.0,6,3.0
-333,273142,46673,1,0,1,0,0,0,1,0,0,0,0,0,6.0,6,1.0,6,1.0
-334,276436,53260,0,1,0,1,0,0,0,0,0,1,0,0,10.0,10,1.0,14,1.4
-335,202608,38139,1,0,1,0,1,0,0,0,0,0,0,0,6.0,6,1.0,8,1.33
-336,243929,179707,0,0,0,0,0,0,1,1,1,0,0,0,2.0,2,1.0,6,3.0
-337,258763,141289,1,0,1,0,0,0,0,0,0,0,0,1,4.0,4,1.0,2,0.5
-338,322834,58273,0,1,0,0,0,0,1,0,0,1,0,0,8.0,8,1.0,8,1.0
-339,211616,151350,0,1,0,0,0,0,1,0,0,1,0,0,8.0,8,1.0,4,0.5
-340,417342,43433,0,0,1,0,0,0,0,1,0,1,0,0,2.0,2,1.0,10,5.0
-341,333428,53017,0,0,0,0,0,1,0,0,0,1,0,1,6.0,6,1.0,12,2.0
-342,453483,112882,0,0,0,1,1,0,0,0,0,0,0,1,6.0,6,1.0,8,1.33
-343,266412,108033,1,1,0,0,0,0,0,1,0,0,0,0,,0,0.0,6,5.0
-344,408051,140955,0,0,0,0,1,0,1,0,0,0,1,0,6.0,6,1.0,2,0.33
-345,456345,94717,0,0,0,0,0,0,1,0,0,0,1,1,10.0,10,1.0,14,1.4
-346,457822,28648,1,0,1,0,0,1,0,0,0,0,0,0,6.0,6,1.0,10,1.67
-347,255709,92725,0,0,0,0,0,0,1,0,1,0,1,0,4.0,4,1.0,4,1.0
-348,451104,192438,1,0,0,0,0,1,0,1,0,0,0,0,4.0,4,1.0,10,2.5
-349,420645,79899,1,0,0,0,0,0,1,0,0,0,0,1,10.0,10,1.0,6,0.6
-350,392445,125863,0,1,0,0,0,0,0,1,0,0,1,0,6.0,6,1.0,6,1.0
-351,207515,78961,0,1,0,0,1,0,0,0,1,0,0,0,4.0,4,1.0,4,1.0
-352,317523,53884,0,1,0,0,0,0,1,0,0,0,1,0,2.0,2,1.0,4,2.0
-353,444435,197545,0,0,0,1,0,0,0,1,1,0,0,0,14.0,14,1.0,6,0.43
-354,316633,42407,0,0,0,0,0,0,1,0,1,0,1,0,14.0,14,1.0,2,0.14
-355,258931,173204,0,0,0,0,1,0,0,1,0,0,1,0,,0,0.0,2,5.0
-356,372692,190010,0,1,0,1,0,0,0,1,0,0,0,0,2.0,2,1.0,4,2.0
-357,343398,37837,0,0,1,1,0,0,0,0,0,0,1,0,2.0,2,1.0,6,3.0
-358,281317,59623,1,0,0,1,0,0,0,1,0,0,0,0,6.0,6,1.0,6,1.0
-359,332501,26790,0,0,0,1,0,1,0,0,1,0,0,0,8.0,8,1.0,4,0.5
-360,384262,158042,1,0,0,0,0,0,0,1,0,1,0,0,6.0,6,1.0,6,1.0
-361,428215,93593,1,0,1,0,0,0,0,0,1,0,0,0,6.0,6,1.0,14,2.33
-362,249481,163969,1,0,0,1,1,0,0,0,0,0,0,0,6.0,6,1.0,10,1.67
-363,414483,50161,0,1,0,0,0,0,0,1,0,0,0,1,8.0,8,1.0,4,0.5
-364,464670,118855,1,0,0,0,0,0,0,0,0,0,1,1,4.0,4,1.0,6,1.5
-365,474311,172110,1,0,0,0,0,0,1,0,0,0,0,1,2.0,2,1.0,0,0.0
-366,348959,114042,0,0,0,0,0,0,0,0,1,1,1,0,6.0,6,1.0,8,1.33
-367,234548,101587,0,0,0,0,0,0,0,1,0,1,0,1,4.0,4,1.0,10,2.5
-368,219163,174151,1,0,0,0,1,0,1,0,0,0,0,0,4.0,4,1.0,0,0.0
-369,302195,65234,0,1,1,1,0,0,0,0,0,0,0,0,2.0,2,1.0,10,5.0
-370,422472,147660,0,0,0,0,0,1,0,0,1,0,0,1,8.0,8,1.0,8,1.0
-371,227784,146399,0,0,0,0,1,1,0,1,0,0,0,0,2.0,2,1.0,6,3.0
-372,498482,171040,0,0,0,1,0,1,1,0,0,0,0,0,8.0,8,1.0,4,0.5
-373,373709,25938,0,0,0,1,0,0,0,1,1,0,0,0,8.0,8,1.0,6,0.75
-374,470459,35826,1,0,1,0,0,1,0,0,0,0,0,0,8.0,8,1.0,12,1.5
-375,354110,120657,0,0,0,0,0,0,1,1,0,0,0,1,4.0,4,1.0,10,2.5
-376,206024,117159,0,0,0,1,0,0,1,0,1,0,0,0,10.0,10,1.0,0,0.0
-377,358946,83301,0,0,0,0,0,0,1,1,1,0,0,0,4.0,4,1.0,4,1.0
-378,308803,59281,1,1,0,0,0,0,0,0,1,0,0,0,,0,0.0,6,5.0
-379,474517,31884,0,0,0,0,0,0,1,0,0,1,1,0,2.0,2,1.0,8,4.0
-380,242390,142871,0,0,0,1,0,0,1,0,0,0,1,0,4.0,4,1.0,0,0.0
-381,340548,187457,0,0,0,1,1,0,0,0,1,0,0,0,6.0,6,1.0,2,0.33
-382,299177,177984,0,1,0,0,0,0,0,0,1,0,1,0,6.0,6,1.0,6,1.0
-383,270532,82671,1,1,1,0,0,0,0,0,0,0,0,0,8.0,8,1.0,6,0.75
-384,268992,88647,0,0,0,0,0,0,0,1,1,1,0,0,6.0,6,1.0,6,1.0
-385,259627,71976,0,1,0,0,0,0,0,1,0,0,1,0,2.0,2,1.0,6,3.0
-386,266860,39850,1,1,0,0,0,0,1,0,0,0,0,0,12.0,12,1.0,8,0.67
-387,202576,112778,0,1,0,0,0,1,0,1,0,0,0,0,6.0,6,1.0,4,0.67
-388,289778,148181,0,0,0,1,1,0,0,0,1,0,0,0,6.0,6,1.0,6,1.0
-389,318503,98397,1,1,0,0,0,0,0,0,0,1,0,0,12.0,12,1.0,4,0.33
-390,307917,126264,0,0,0,0,0,0,0,0,0,1,1,1,6.0,6,1.0,10,1.67
-391,369358,93056,0,0,0,0,1,0,0,0,1,0,0,1,6.0,6,1.0,4,0.67
-392,373343,59530,0,0,0,0,0,1,0,0,1,0,1,0,6.0,6,1.0,4,0.67
-393,278415,191858,0,0,0,0,0,0,1,0,1,0,0,1,8.0,8,1.0,8,1.0
-394,254809,78233,0,1,0,0,0,0,0,0,0,0,1,1,8.0,8,1.0,6,0.75
-395,358742,41600,0,0,0,1,0,1,0,0,0,1,0,0,8.0,8,1.0,14,1.75
-396,435593,136937,0,1,1,0,0,0,0,0,0,0,0,1,8.0,8,1.0,8,1.0
-397,444664,79308,0,1,0,0,1,0,0,1,0,0,0,0,2.0,2,1.0,8,4.0
-398,257040,189799,0,0,0,0,1,0,0,0,0,1,0,1,4.0,4,1.0,6,1.5
-399,471971,107481,0,1,0,0,1,1,0,0,0,0,0,0,8.0,8,1.0,4,0.5
-400,285534,69440,0,1,0,1,0,0,1,0,0,0,0,0,4.0,4,1.0,8,2.0
-401,399355,28851,0,1,0,1,0,1,0,0,0,0,0,0,2.0,2,1.0,6,3.0
-402,267282,130354,0,0,0,0,0,0,0,0,1,0,1,1,4.0,4,1.0,10,2.5
-403,376411,90246,1,1,0,0,0,1,0,0,0,0,0,0,6.0,6,1.0,2,0.33
-404,417066,68397,0,0,1,0,0,0,0,0,0,1,0,1,8.0,8,1.0,6,0.75
-405,312499,50643,1,0,0,0,1,0,0,0,0,0,1,0,4.0,4,1.0,8,2.0
-406,329969,70706,0,1,0,0,0,0,0,1,0,0,1,0,8.0,8,1.0,8,1.0
-407,468220,70210,0,0,1,0,0,0,0,0,1,0,0,1,2.0,2,1.0,8,4.0
-408,414297,162311,1,1,0,0,1,0,0,0,0,0,0,0,8.0,8,1.0,8,1.0
-409,219953,187847,0,0,1,1,1,0,0,0,0,0,0,0,2.0,2,1.0,2,1.0
-410,413257,192908,0,1,0,0,1,0,0,0,0,0,1,0,4.0,4,1.0,2,0.5
-411,334666,192762,0,0,1,1,0,0,0,1,0,0,0,0,4.0,4,1.0,10,2.5
-412,222514,50456,0,0,0,0,0,0,0,1,1,0,1,0,8.0,8,1.0,0,0.0
-413,259408,123579,1,0,1,0,0,0,0,0,1,0,0,0,14.0,14,1.0,6,0.43
-414,319363,16753,0,1,0,1,0,0,0,0,0,0,0,1,4.0,4,1.0,8,2.0
-415,398450,95097,0,0,1,0,0,1,0,1,0,0,0,0,6.0,6,1.0,2,0.33
-416,388838,107138,0,0,1,0,1,0,0,1,0,0,0,0,2.0,2,1.0,2,1.0
-417,256776,19251,0,1,0,0,0,0,0,0,0,1,0,1,2.0,2,1.0,4,2.0
-418,383184,82298,0,0,0,0,0,1,1,1,0,0,0,0,4.0,4,1.0,6,1.5
-419,262198,77538,0,1,1,0,0,0,1,0,0,0,0,0,4.0,4,1.0,8,2.0
-420,276703,139996,1,1,0,0,1,0,0,0,0,0,0,0,8.0,8,1.0,2,0.25
-421,276962,38350,1,0,0,0,0,1,0,0,0,0,1,0,14.0,14,1.0,4,0.29
-422,461187,140548,0,0,0,1,0,0,0,1,0,1,0,0,12.0,12,1.0,10,0.83
-423,480325,45818,0,1,1,0,0,0,0,0,0,0,0,1,8.0,8,1.0,10,1.25
-424,394427,83641,0,0,1,0,0,0,0,1,0,0,1,0,2.0,2,1.0,8,4.0
-425,435603,99934,0,0,1,0,1,0,0,0,0,1,0,0,2.0,2,1.0,6,3.0
-426,279346,67709,0,0,0,1,0,0,1,0,0,0,0,1,,0,0.0,12,5.0
-427,306633,93222,0,0,1,0,0,0,0,0,1,0,1,0,8.0,8,1.0,6,0.75
-428,483980,14466,1,0,0,0,0,0,0,1,0,0,0,1,4.0,4,1.0,4,1.0
-429,214878,173055,1,0,1,0,0,0,0,0,1,0,0,0,4.0,4,1.0,6,1.5
-430,263423,160431,0,0,0,1,0,0,1,0,1,0,0,0,10.0,10,1.0,6,0.6
-431,396807,194270,0,0,0,0,1,1,0,0,0,0,0,1,8.0,8,1.0,2,0.25
-432,468006,139244,0,0,0,0,1,0,0,1,0,0,1,0,4.0,4,1.0,8,2.0
-433,386017,54184,0,0,0,1,0,0,1,0,0,0,1,0,8.0,8,1.0,6,0.75
-434,237391,115716,0,0,1,1,0,0,0,1,0,0,0,0,12.0,12,1.0,6,0.5
-435,415828,29926,0,0,0,0,0,0,0,1,0,1,1,0,4.0,4,1.0,4,1.0
-436,208006,56098,0,0,0,1,0,0,0,1,1,0,0,0,6.0,6,1.0,4,0.67
-437,282798,92380,0,0,0,0,0,0,1,1,0,0,1,0,10.0,10,1.0,12,1.2
-438,322382,139215,0,0,0,0,0,0,1,1,1,0,0,0,4.0,4,1.0,12,3.0
-439,495656,24595,0,0,0,0,1,0,0,0,0,1,0,1,6.0,6,1.0,8,1.33
-440,487736,156629,0,0,0,1,0,1,0,1,0,0,0,0,6.0,6,1.0,6,1.0
-441,348167,162775,0,1,0,0,0,0,0,0,1,1,0,0,8.0,8,1.0,2,0.25
-442,413534,160420,0,1,0,0,0,0,0,1,1,0,0,0,4.0,4,1.0,4,1.0
-443,292531,155651,1,0,0,0,0,1,1,0,0,0,0,0,4.0,4,1.0,4,1.0
-444,405225,64763,0,0,0,0,1,1,1,0,0,0,0,0,4.0,4,1.0,10,2.5
-445,388734,172721,1,0,1,0,0,0,0,0,1,0,0,0,8.0,8,1.0,10,1.25
-446,200028,69617,0,0,0,1,0,1,0,0,0,0,1,0,8.0,8,1.0,4,0.5
-447,368086,150465,0,0,1,0,0,0,0,0,0,1,0,1,8.0,8,1.0,10,1.25
-448,453598,88914,0,0,0,0,1,0,1,0,0,0,1,0,8.0,8,1.0,6,0.75
-449,490092,13516,0,0,1,0,1,0,0,0,0,1,0,0,6.0,6,1.0,2,0.33
-450,260490,195083,0,0,0,1,1,0,0,0,0,0,1,0,6.0,6,1.0,6,1.0
-451,455598,27274,1,0,1,0,0,0,0,1,0,0,0,0,10.0,10,1.0,4,0.4
-452,494976,161982,0,0,0,1,0,0,0,0,0,1,0,1,4.0,4,1.0,8,2.0
-453,396066,28764,0,1,1,0,0,0,0,0,0,0,1,0,8.0,8,1.0,8,1.0
-454,455579,88243,0,1,0,0,1,0,1,0,0,0,0,0,,0,0.0,8,5.0
-455,409085,70502,0,0,0,0,1,0,1,0,0,1,0,0,4.0,4,1.0,8,2.0
-456,371029,72053,0,0,0,1,1,1,0,0,0,0,0,0,4.0,4,1.0,2,0.5
-457,295670,196249,1,0,1,0,0,0,1,0,0,0,0,0,10.0,10,1.0,2,0.2
-458,361928,150430,1,0,0,0,1,0,0,0,0,0,0,1,2.0,2,1.0,6,3.0
-459,451024,173729,1,0,0,0,0,0,0,0,1,0,0,1,4.0,4,1.0,2,0.5
-460,490748,98179,0,0,0,0,0,1,0,0,1,0,0,1,6.0,6,1.0,4,0.67
-461,491945,30336,0,0,0,0,0,1,0,0,1,1,0,0,6.0,6,1.0,8,1.33
-462,241640,70420,0,1,0,0,1,0,0,0,0,0,0,1,2.0,2,1.0,8,4.0
-463,223055,35627,0,0,1,0,0,0,1,1,0,0,0,0,2.0,2,1.0,4,2.0
-464,467233,172942,0,0,0,0,0,1,0,0,1,1,0,0,4.0,4,1.0,4,1.0
-465,209404,90757,0,0,0,0,0,1,0,0,0,1,0,1,2.0,2,1.0,6,3.0
-466,235052,63309,0,0,0,1,0,0,1,0,0,0,0,1,8.0,8,1.0,10,1.25
-467,352043,122542,0,0,0,0,0,1,0,1,0,0,1,0,,0,0.0,2,5.0
-468,464655,71188,0,0,1,0,1,0,0,0,0,0,1,0,,0,0.0,4,5.0
-469,452949,99119,0,0,0,1,0,1,0,0,1,0,0,0,6.0,6,1.0,6,1.0
-470,426276,83280,0,1,1,0,0,0,1,0,0,0,0,0,4.0,4,1.0,10,2.5
-471,216417,72177,1,0,1,0,1,0,0,0,0,0,0,0,8.0,8,1.0,6,0.75
-472,475969,71538,0,1,0,1,0,0,0,0,0,0,0,1,8.0,8,1.0,8,1.0
-473,470315,16907,0,1,0,0,1,0,1,0,0,0,0,0,2.0,2,1.0,8,4.0
-474,278020,110948,0,0,0,1,0,1,0,0,0,0,1,0,14.0,14,1.0,8,0.57
-475,440036,30121,0,0,0,0,0,1,0,0,1,0,0,1,2.0,2,1.0,4,2.0
-476,314142,30943,0,1,0,0,0,0,1,0,0,1,0,0,2.0,2,1.0,0,0.0
-477,386746,20699,0,0,0,0,1,1,0,0,1,0,0,0,4.0,4,1.0,4,1.0
-478,261137,14474,1,0,0,0,0,0,0,0,0,1,1,0,4.0,4,1.0,0,0.0
-479,331372,39433,0,1,0,1,0,0,1,0,0,0,0,0,,0,0.0,2,5.0
-480,405773,32680,0,0,0,0,0,0,0,1,1,1,0,0,4.0,4,1.0,4,1.0
-481,429580,132313,0,1,0,0,1,0,0,0,0,0,1,0,2.0,2,1.0,2,1.0
-482,356059,157741,0,0,0,0,1,0,0,0,1,0,0,1,,0,0.0,4,5.0
-483,271684,55449,0,0,0,0,1,1,0,0,0,1,0,0,10.0,10,1.0,16,1.6
-484,347716,95631,0,0,0,0,1,1,1,0,0,0,0,0,2.0,2,1.0,6,3.0
-485,411171,62933,0,0,0,1,0,0,0,1,0,0,0,1,,0,0.0,8,5.0
-486,428977,75683,0,0,0,0,1,0,0,0,1,1,0,0,4.0,4,1.0,14,3.5
-487,466398,187686,0,0,1,0,0,0,0,1,0,0,0,1,8.0,8,1.0,6,0.75
-488,270081,33315,0,0,0,0,0,0,0,0,1,0,1,1,10.0,10,1.0,6,0.6
-489,485029,51880,1,0,0,1,0,0,0,0,0,0,0,1,8.0,8,1.0,6,0.75
-490,261276,108198,1,0,0,0,0,0,0,0,1,0,1,0,2.0,2,1.0,10,5.0
-491,407979,92566,1,0,0,0,0,0,1,0,0,0,0,1,8.0,8,1.0,4,0.5
-492,243348,173882,0,0,0,1,0,1,0,0,0,0,0,1,6.0,6,1.0,6,1.0
-493,411586,174035,0,0,0,0,1,0,0,1,0,0,0,1,6.0,6,1.0,8,1.33
-494,297625,144010,1,0,0,1,0,0,1,0,0,0,0,0,8.0,8,1.0,6,0.75
-495,402601,92174,0,0,0,0,0,0,1,1,0,1,0,0,2.0,2,1.0,0,0.0
-496,266498,69504,0,0,0,1,0,1,1,0,0,0,0,0,8.0,8,1.0,4,0.5
-497,303917,41341,0,0,0,0,1,1,0,0,0,0,1,0,4.0,4,1.0,2,0.5
-498,205307,26146,1,0,0,0,0,1,1,0,0,0,0,0,6.0,6,1.0,6,1.0
-499,471466,124691,1,0,1,0,0,0,0,1,0,0,0,0,10.0,10,1.0,6,0.6
-500,363805,196676,0,1,0,0,0,0,0,1,1,0,0,0,4.0,4,1.0,4,1.0
-501,328308,62536,0,0,1,1,0,0,0,1,0,0,0,0,,0,0.0,2,5.0
-502,267523,85770,0,0,1,0,0,0,1,0,0,0,1,0,8.0,8,1.0,4,0.5
-503,332979,84112,1,0,1,0,0,0,0,0,0,0,1,0,2.0,2,1.0,0,0.0
-504,309464,180538,0,0,0,0,1,1,0,0,0,1,0,0,2.0,2,1.0,8,4.0
-505,488292,185347,1,0,0,0,0,0,0,1,0,1,0,0,2.0,2,1.0,4,2.0
-506,361471,165007,0,0,0,0,0,0,1,1,0,0,0,1,6.0,6,1.0,4,0.67
-507,208687,105248,0,0,1,0,0,0,1,1,0,0,0,0,8.0,8,1.0,4,0.5
-508,232684,161404,0,1,0,0,0,1,0,1,0,0,0,0,2.0,2,1.0,6,3.0
-509,442554,16172,0,0,0,0,1,0,0,0,0,0,1,1,4.0,4,1.0,12,3.0
-510,204657,170199,0,0,1,0,0,0,0,0,0,0,1,1,2.0,2,1.0,4,2.0
-511,239389,70377,0,0,1,0,1,0,0,0,1,0,0,0,10.0,10,1.0,6,0.6
-512,218008,101701,1,0,0,0,1,0,0,1,0,0,0,0,4.0,4,1.0,10,2.5
-513,383759,194651,0,0,1,0,0,0,0,0,1,0,1,0,2.0,2,1.0,6,3.0
-514,306213,61447,0,0,0,1,1,0,1,0,0,0,0,0,4.0,4,1.0,2,0.5
-515,312123,48279,0,0,1,0,1,0,1,0,0,0,0,0,2.0,2,1.0,4,2.0
-516,361275,116248,0,0,0,1,0,0,0,0,0,0,1,1,,0,0.0,6,5.0
-517,388396,90908,0,1,0,0,0,0,0,1,0,1,0,0,2.0,2,1.0,6,3.0
-518,288128,133729,0,0,0,0,1,1,0,0,0,0,1,0,4.0,4,1.0,4,1.0
-519,425896,76334,0,0,0,1,0,0,1,0,0,1,0,0,2.0,2,1.0,12,6.0
-520,227571,49234,0,0,1,0,0,0,1,0,0,1,0,0,4.0,4,1.0,8,2.0
-521,479020,137397,0,0,0,0,1,1,0,0,1,0,0,0,2.0,2,1.0,4,2.0
-522,204399,43095,0,0,1,0,0,0,0,1,0,0,0,1,8.0,8,1.0,10,1.25
-523,338325,154358,0,0,0,1,1,0,0,1,0,0,0,0,8.0,8,1.0,4,0.5
-524,336982,132921,0,0,1,1,0,0,0,0,1,0,0,0,14.0,14,1.0,8,0.57
-525,341459,91124,0,0,1,0,0,0,0,0,0,1,0,1,8.0,8,1.0,0,0.0
-526,406510,63478,0,0,0,1,0,0,0,0,1,0,0,1,4.0,4,1.0,4,1.0
-527,332700,64958,0,1,0,0,0,1,0,0,0,0,1,0,6.0,6,1.0,2,0.33
-528,254230,86953,0,0,1,0,0,0,1,1,0,0,0,0,,0,0.0,6,5.0
-529,216574,171859,0,0,0,1,0,0,0,0,0,1,1,0,4.0,4,1.0,6,1.5
-530,482990,41795,0,0,0,1,0,1,0,0,0,0,0,1,10.0,10,1.0,6,0.6
-531,330133,123832,0,0,0,0,1,0,0,0,0,1,1,0,,0,0.0,0,0.0
-532,395228,53435,1,0,0,0,0,0,0,1,0,0,1,0,6.0,6,1.0,14,2.33
-533,219301,83245,1,0,0,0,1,0,0,0,0,0,1,0,2.0,2,1.0,4,2.0
-534,241393,116263,0,1,0,1,0,0,0,0,0,1,0,0,6.0,6,1.0,12,2.0
-535,209948,50931,0,0,0,1,0,0,1,0,0,0,1,0,8.0,8,1.0,4,0.5
-536,434471,63782,1,0,0,1,0,0,0,1,0,0,0,0,4.0,4,1.0,4,1.0
-537,402834,161904,0,0,1,0,1,1,0,0,0,0,0,0,2.0,2,1.0,8,4.0
-538,441114,132616,1,0,0,0,0,1,1,0,0,0,0,0,2.0,2,1.0,12,6.0
-539,467585,31356,0,1,0,1,1,0,0,0,0,0,0,0,8.0,8,1.0,4,0.5
-540,348008,95495,1,0,0,0,0,0,0,1,0,1,0,0,10.0,10,1.0,6,0.6
-541,445390,136281,0,0,1,0,0,0,0,0,1,0,1,0,4.0,4,1.0,8,2.0
-542,323829,25432,0,0,1,0,0,1,0,1,0,0,0,0,2.0,2,1.0,12,6.0
-543,205185,12607,0,0,0,0,0,0,1,0,0,1,0,1,8.0,8,1.0,6,0.75
-544,490248,26420,0,0,0,1,0,1,0,1,0,0,0,0,4.0,4,1.0,8,2.0
-545,463292,161027,0,0,0,0,1,0,0,0,1,0,1,0,4.0,4,1.0,8,2.0
-546,480943,157829,0,1,0,1,0,0,0,1,0,0,0,0,8.0,8,1.0,8,1.0
-547,347856,29055,1,0,1,0,0,0,0,0,0,0,0,1,12.0,12,1.0,0,0.0
-548,239565,96294,0,1,0,0,0,0,0,0,0,0,1,1,6.0,6,1.0,8,1.33
-549,255734,62437,1,0,0,0,0,0,0,1,0,0,1,0,10.0,10,1.0,4,0.4
-550,242008,183474,0,1,0,0,0,0,0,1,0,1,0,0,4.0,4,1.0,2,0.5
-551,278966,28705,0,1,0,1,0,0,0,0,1,0,0,0,10.0,10,1.0,2,0.2
-552,266072,17762,0,0,1,1,0,0,0,0,0,1,0,0,6.0,6,1.0,8,1.33
-553,380708,37376,0,0,0,1,0,0,1,0,0,0,1,0,4.0,4,1.0,4,1.0
-554,207097,109083,0,1,0,1,0,0,0,0,1,0,0,0,14.0,14,1.0,4,0.29
-555,220167,167100,0,1,1,0,0,0,0,1,0,0,0,0,6.0,6,1.0,8,1.33
-556,328447,185659,0,0,0,0,0,0,1,0,0,1,0,1,,0,0.0,2,5.0
-557,263123,158856,0,1,0,1,0,0,0,0,0,1,0,0,4.0,4,1.0,4,1.0
-558,244123,101011,0,0,0,1,1,0,0,0,0,0,0,1,14.0,14,1.0,14,1.0
-559,212049,197338,1,0,0,0,0,1,0,0,0,1,0,0,2.0,2,1.0,8,4.0
-560,244946,29596,1,0,0,0,1,0,0,0,0,1,0,0,4.0,4,1.0,6,1.5
-561,358017,45793,0,0,0,0,0,1,0,1,0,0,1,0,8.0,8,1.0,8,1.0
-562,344701,132333,1,0,1,0,0,0,0,1,0,0,0,0,6.0,6,1.0,12,2.0
-563,411913,180763,0,0,0,0,1,1,1,0,0,0,0,0,8.0,8,1.0,10,1.25
-564,449587,131493,1,1,0,1,0,0,0,0,0,0,0,0,4.0,4,1.0,10,2.5
-565,486488,50115,0,0,0,1,0,0,0,1,0,1,0,0,16.0,16,1.0,4,0.25
-566,362731,138975,1,0,0,0,0,1,0,1,0,0,0,0,10.0,10,1.0,10,1.0
-567,496717,190331,0,1,1,0,0,0,1,0,0,0,0,0,10.0,10,1.0,6,0.6
-568,306057,157754,0,0,1,0,0,0,0,0,1,1,0,0,4.0,4,1.0,6,1.5
-569,338709,85126,0,0,1,0,0,0,0,0,1,0,0,1,4.0,4,1.0,4,1.0
-570,200001,143481,0,0,0,1,1,0,0,0,0,0,1,0,2.0,2,1.0,0,0.0
-571,223457,100696,0,0,0,1,0,1,0,0,1,0,0,0,8.0,8,1.0,8,1.0
-572,441179,25685,0,0,1,0,0,0,0,0,1,0,1,0,12.0,12,1.0,4,0.33
-573,353594,110800,1,0,0,0,0,0,0,0,1,1,0,0,6.0,6,1.0,8,1.33
-574,393614,13674,0,0,1,0,0,1,1,0,0,0,0,0,4.0,4,1.0,16,4.0
-575,430394,20528,0,0,1,0,1,0,0,0,0,1,0,0,10.0,10,1.0,2,0.2
-576,331831,152774,1,0,0,1,0,0,0,0,1,0,0,0,2.0,2,1.0,10,5.0
-577,398194,120484,0,0,0,0,0,1,1,0,0,1,0,0,8.0,8,1.0,8,1.0
-578,276427,176286,1,0,1,0,0,0,0,1,0,0,0,0,2.0,2,1.0,6,3.0
-579,499987,117675,0,0,0,0,0,1,0,0,1,1,0,0,12.0,12,1.0,12,1.0
-580,290614,113585,0,0,1,1,1,0,0,0,0,0,0,0,6.0,6,1.0,2,0.33
-581,478243,195676,0,0,1,0,0,0,1,0,0,0,0,1,2.0,2,1.0,8,4.0
-582,402493,128166,0,0,0,0,0,1,0,1,0,1,0,0,12.0,12,1.0,8,0.67
-583,393992,143737,0,0,0,1,0,0,1,0,1,0,0,0,6.0,6,1.0,2,0.33
-584,327428,88091,0,1,1,0,0,1,0,0,0,0,0,0,8.0,8,1.0,4,0.5
-585,462940,164467,0,0,0,0,0,1,0,0,1,0,0,1,2.0,2,1.0,12,6.0
-586,265145,97582,0,0,0,0,0,1,0,0,1,1,0,0,4.0,4,1.0,4,1.0
-587,451884,125764,1,0,0,1,0,0,0,0,0,1,0,0,,0,0.0,10,5.0
-588,470082,123085,0,0,0,1,1,0,0,0,0,0,0,1,4.0,4,1.0,4,1.0
-589,302570,55829,0,1,0,1,0,1,0,0,0,0,0,0,4.0,4,1.0,6,1.5
-590,271350,163049,0,0,0,0,0,1,0,0,1,1,0,0,6.0,6,1.0,10,1.67
-591,463176,190070,0,0,1,0,1,0,0,0,0,0,1,0,2.0,2,1.0,10,5.0
-592,239269,115358,0,0,0,0,0,0,1,1,0,0,0,1,2.0,2,1.0,10,5.0
-593,492234,80321,0,0,0,0,0,1,1,1,0,0,0,0,6.0,6,1.0,14,2.33
-594,447098,150300,0,0,1,0,0,0,0,0,1,0,1,0,,0,0.0,4,5.0
-595,289440,191239,0,1,0,1,0,0,1,0,0,0,0,0,4.0,4,1.0,4,1.0
-596,395653,181703,0,0,0,0,0,1,1,0,1,0,0,0,4.0,4,1.0,6,1.5
-597,413273,39791,0,0,0,1,1,0,1,0,0,0,0,0,,0,0.0,2,5.0
-598,225693,56526,0,1,0,0,0,0,1,0,0,0,0,1,10.0,10,1.0,4,0.4
-599,457079,135393,0,0,1,0,0,0,0,0,1,0,1,0,6.0,6,1.0,6,1.0
-600,406200,92399,0,0,1,0,0,1,0,0,0,0,0,1,2.0,2,1.0,10,5.0
-601,391476,55926,0,0,0,1,0,0,1,0,0,1,0,0,6.0,6,1.0,4,0.67
-602,425438,98860,0,0,0,0,0,1,1,0,1,0,0,0,,0,0.0,12,5.0
-603,334418,120570,1,0,0,0,0,0,0,0,1,0,0,1,2.0,2,1.0,4,2.0
-604,348429,28194,1,1,0,0,0,0,0,1,0,0,0,0,,0,0.0,4,5.0
-605,400528,37541,0,0,0,1,1,0,1,0,0,0,0,0,6.0,6,1.0,10,1.67
-606,319216,42896,0,0,0,0,0,1,0,0,0,1,0,1,4.0,4,1.0,6,1.5
-607,480884,46973,0,0,0,0,1,0,0,0,1,0,0,1,2.0,2,1.0,8,4.0
-608,319510,197788,1,0,0,1,0,0,0,0,0,0,0,1,8.0,8,1.0,0,0.0
-609,311293,74309,0,0,1,1,0,0,0,0,0,1,0,0,4.0,4,1.0,14,3.5
-610,393323,181443,0,0,1,0,0,0,1,0,0,0,1,0,4.0,4,1.0,10,2.5
-611,311966,145582,1,0,0,0,1,0,0,0,0,1,0,0,4.0,4,1.0,6,1.5
-612,406543,90709,0,0,0,0,1,0,0,0,0,1,1,0,6.0,6,1.0,6,1.0
-613,302751,155861,0,1,0,0,0,0,0,0,1,0,1,0,12.0,12,1.0,10,0.83
-614,207585,47241,0,0,0,0,0,1,0,0,1,0,1,0,4.0,4,1.0,4,1.0
-615,275659,21504,0,0,0,1,0,1,0,0,0,0,1,0,10.0,10,1.0,10,1.0
-616,406861,28337,0,0,0,0,1,0,0,1,1,0,0,0,2.0,2,1.0,0,0.0
-617,362463,158731,0,0,0,0,1,0,0,1,1,0,0,0,6.0,6,1.0,8,1.33
-618,411510,30753,0,0,0,0,0,1,0,1,0,1,0,0,4.0,4,1.0,6,1.5
-619,485980,143009,0,0,0,0,1,1,1,0,0,0,0,0,2.0,2,1.0,0,0.0
-620,220964,110716,0,1,0,1,0,0,1,0,0,0,0,0,2.0,2,1.0,10,5.0
-621,315555,151131,0,0,0,0,1,0,1,0,1,0,0,0,,0,0.0,2,5.0
-622,471157,68516,1,0,1,0,1,0,0,0,0,0,0,0,8.0,8,1.0,14,1.75
-623,406859,61140,0,1,0,0,0,1,0,0,0,0,1,0,8.0,8,1.0,2,0.25
-624,464367,152582,0,0,0,0,1,0,0,0,0,1,1,0,6.0,6,1.0,2,0.33
-625,384728,71607,0,1,0,0,0,0,0,1,1,0,0,0,10.0,10,1.0,4,0.4
-626,442739,43642,0,1,0,1,0,0,0,0,0,0,0,1,10.0,10,1.0,2,0.2
-627,345081,39891,0,0,0,0,0,0,0,1,1,1,0,0,8.0,8,1.0,2,0.25
-628,356882,151586,0,0,0,0,0,0,0,0,1,1,1,0,8.0,8,1.0,10,1.25
-629,467710,51296,0,0,0,0,1,0,0,0,1,0,0,1,6.0,6,1.0,6,1.0
-630,320831,133375,0,0,0,0,0,0,1,1,0,0,1,0,8.0,8,1.0,8,1.0
-631,277376,98064,0,0,0,0,0,0,0,1,0,1,1,0,10.0,10,1.0,2,0.2
-632,336337,92259,0,0,0,1,0,0,0,1,0,0,0,1,6.0,6,1.0,12,2.0
-633,205914,12925,0,1,0,0,0,0,1,0,1,0,0,0,4.0,4,1.0,2,0.5
-634,334891,180467,0,1,1,1,0,0,0,0,0,0,0,0,2.0,2,1.0,0,0.0
-635,293350,93423,1,0,0,0,0,1,0,0,0,0,1,0,8.0,8,1.0,2,0.25
-636,248133,102234,0,0,0,1,0,0,0,0,0,1,1,0,14.0,14,1.0,2,0.14
-637,463279,66610,0,0,0,0,0,1,0,0,0,1,0,1,4.0,4,1.0,4,1.0
-638,233225,144563,0,0,0,0,1,1,0,0,0,1,0,0,4.0,4,1.0,12,3.0
-639,480066,24214,1,0,0,0,1,0,0,0,1,0,0,0,2.0,2,1.0,6,3.0
-640,298201,68870,0,1,0,0,0,0,0,0,1,0,0,1,2.0,2,1.0,8,4.0
-641,316673,22103,0,0,0,0,0,1,0,0,0,1,1,0,6.0,6,1.0,12,2.0
-642,435702,113156,1,0,0,0,0,0,0,0,0,1,0,1,8.0,8,1.0,8,1.0
-643,382302,131574,0,0,0,1,1,1,0,0,0,0,0,0,16.0,16,1.0,8,0.5
-644,308676,103934,1,0,0,0,0,1,0,0,1,0,0,0,2.0,2,1.0,4,2.0
-645,424223,88807,0,1,0,0,0,0,0,1,1,0,0,0,4.0,4,1.0,0,0.0
-646,447228,44269,0,1,0,0,0,1,0,0,0,0,1,0,10.0,10,1.0,6,0.6
-647,277880,55032,1,0,0,0,0,0,1,0,0,0,1,0,6.0,6,1.0,8,1.33
-648,222776,158303,0,0,0,0,0,1,0,0,1,1,0,0,6.0,6,1.0,10,1.67
-649,472568,61872,1,0,0,0,0,1,0,0,0,0,0,1,6.0,6,1.0,6,1.0
-650,326433,106534,1,0,0,1,0,0,0,0,0,0,0,1,4.0,4,1.0,6,1.5
-651,339979,90419,0,1,0,0,1,1,0,0,0,0,0,0,2.0,2,1.0,6,3.0
-652,444103,137728,0,0,1,0,1,0,0,0,0,1,0,0,4.0,4,1.0,2,0.5
-653,453724,109824,0,0,0,1,0,0,0,1,0,0,0,1,6.0,6,1.0,8,1.33
-654,487460,127989,1,1,0,0,0,0,0,1,0,0,0,0,4.0,4,1.0,8,2.0
-655,483102,182886,0,1,0,1,0,0,0,0,0,0,1,0,4.0,4,1.0,10,2.5
-656,249702,127895,0,0,0,0,0,0,1,0,0,1,0,1,6.0,6,1.0,6,1.0
-657,474378,61869,0,0,0,0,0,1,0,0,1,1,0,0,2.0,2,1.0,12,6.0
-658,362653,145831,0,0,0,0,0,0,0,1,0,1,1,0,2.0,2,1.0,6,3.0
-659,370714,71649,0,0,0,0,0,1,0,1,0,1,0,0,8.0,8,1.0,6,0.75
-660,258959,28713,0,1,0,0,0,1,1,0,0,0,0,0,6.0,6,1.0,6,1.0
-661,405274,24301,0,0,0,0,0,0,1,1,1,0,0,0,2.0,2,1.0,6,3.0
-662,220897,99513,1,0,1,1,0,0,0,0,0,0,0,0,4.0,4,1.0,6,1.5
-663,454203,156314,0,0,0,0,0,0,0,0,1,0,1,1,2.0,2,1.0,4,2.0
-664,462761,164531,0,0,0,1,0,0,0,1,0,0,1,0,6.0,6,1.0,8,1.33
-665,325579,59883,0,0,0,0,0,1,0,0,1,0,1,0,4.0,4,1.0,10,2.5
-666,473233,154694,0,0,1,1,0,0,0,0,1,0,0,0,8.0,8,1.0,4,0.5
-667,420925,199716,1,0,0,0,0,1,0,0,0,1,0,0,6.0,6,1.0,0,0.0
-668,200999,106467,0,1,1,0,0,0,1,0,0,0,0,0,6.0,6,1.0,4,0.67
-669,251803,124949,1,1,0,0,0,1,0,0,0,0,0,0,6.0,6,1.0,6,1.0
-670,498285,29863,0,1,0,0,0,0,0,0,1,0,1,0,6.0,6,1.0,8,1.33
-671,262904,122418,0,1,0,0,0,1,0,1,0,0,0,0,8.0,8,1.0,2,0.25
-672,422921,59595,0,0,0,0,0,1,1,0,0,0,1,0,2.0,2,1.0,2,1.0
-673,420628,177529,1,0,0,0,0,0,0,0,1,0,1,0,2.0,2,1.0,4,2.0
-674,324917,104702,0,0,0,1,0,1,0,0,1,0,0,0,6.0,6,1.0,6,1.0
-675,430855,75453,0,0,0,0,1,0,1,0,0,0,1,0,2.0,2,1.0,6,3.0
-676,285582,74778,1,1,0,0,0,0,1,0,0,0,0,0,8.0,8,1.0,2,0.25
-677,496860,49463,0,1,0,0,0,1,1,0,0,0,0,0,8.0,8,1.0,0,0.0
-678,423904,187806,0,1,0,0,1,0,0,0,0,0,1,0,8.0,8,1.0,6,0.75
-679,204938,36785,1,0,1,1,0,0,0,0,0,0,0,0,8.0,8,1.0,2,0.25
-680,375284,196746,0,0,0,0,1,0,0,1,1,0,0,0,6.0,6,1.0,6,1.0
-681,244556,29179,0,0,0,0,1,0,0,0,0,1,0,1,4.0,4,1.0,4,1.0
-682,229152,127759,0,0,0,0,0,0,1,1,0,0,0,1,,0,0.0,8,5.0
-683,361553,64970,0,0,1,0,1,0,0,0,0,0,1,0,6.0,6,1.0,6,1.0
-684,271352,113220,0,0,1,1,0,0,0,0,0,0,0,1,2.0,2,1.0,6,3.0
-685,295760,74282,1,0,0,0,1,0,0,0,1,0,0,0,,0,0.0,6,5.0
-686,318293,33441,0,0,0,0,0,0,1,1,0,0,1,0,8.0,8,1.0,8,1.0
-687,383619,40056,0,0,1,0,0,0,0,0,1,0,0,1,10.0,10,1.0,2,0.2
-688,314795,136938,0,0,0,0,1,0,1,0,0,0,1,0,10.0,10,1.0,2,0.2
-689,478514,189564,0,0,0,1,1,0,0,0,0,1,0,0,4.0,4,1.0,2,0.5
-690,404950,188127,0,0,0,1,1,1,0,0,0,0,0,0,6.0,6,1.0,8,1.33
-691,445341,106081,1,0,0,1,1,0,0,0,0,0,0,0,4.0,4,1.0,6,1.5
-692,449316,77666,1,0,1,0,0,1,0,0,0,0,0,0,8.0,8,1.0,8,1.0
-693,302075,138096,0,0,0,0,0,1,1,1,0,0,0,0,8.0,8,1.0,14,1.75
-694,456352,36009,0,0,0,1,0,0,1,0,0,0,1,0,4.0,4,1.0,6,1.5
-695,490129,92546,0,1,0,0,0,0,0,0,1,0,1,0,6.0,6,1.0,4,0.67
-696,433637,55286,0,1,0,0,1,1,0,0,0,0,0,0,6.0,6,1.0,10,1.67
-697,210566,31212,0,0,0,0,0,0,1,0,1,0,0,1,,0,0.0,12,5.0
-698,402223,122169,0,1,1,0,0,0,1,0,0,0,0,0,12.0,12,1.0,2,0.17
-699,244785,198667,1,0,0,0,0,0,0,0,0,1,0,1,10.0,10,1.0,12,1.2
-700,402291,166371,0,0,0,0,0,1,0,0,1,0,0,1,8.0,8,1.0,10,1.25
-701,313478,78519,0,1,0,0,0,0,0,1,0,0,1,0,4.0,4,1.0,14,3.5
-702,438841,134520,1,0,1,0,0,0,0,0,1,0,0,0,8.0,8,1.0,6,0.75
-703,213094,70655,0,0,0,0,1,0,0,0,1,0,0,1,8.0,8,1.0,0,0.0
-704,350978,43948,0,0,1,1,0,0,0,1,0,0,0,0,2.0,2,1.0,6,3.0
-705,254726,172807,0,0,0,0,0,0,1,0,1,1,0,0,8.0,8,1.0,6,0.75
-706,434372,189536,0,0,0,1,0,0,0,0,0,0,1,1,4.0,4,1.0,4,1.0
-707,345034,80288,0,1,0,0,0,0,0,0,1,0,1,0,,0,0.0,10,5.0
-708,232062,187285,0,1,0,0,1,0,0,0,1,0,0,0,6.0,6,1.0,0,0.0
-709,411841,19443,0,0,0,0,0,1,0,1,0,1,0,0,2.0,2,1.0,6,3.0
-710,453578,18206,0,1,0,0,1,0,0,0,0,0,1,0,2.0,2,1.0,10,5.0
-711,241167,176591,0,0,0,0,0,1,0,0,0,0,1,1,2.0,2,1.0,8,4.0
-712,371904,166797,0,0,0,0,0,0,0,0,1,1,0,1,4.0,4,1.0,2,0.5
-713,496726,73973,1,0,0,0,0,0,1,0,0,0,0,1,2.0,2,1.0,6,3.0
-714,285423,39451,1,1,0,0,0,0,0,0,1,0,0,0,6.0,6,1.0,8,1.33
-715,235681,76552,0,1,0,0,0,0,1,0,0,0,1,0,6.0,6,1.0,4,0.67
-716,204840,172449,0,0,0,0,0,0,1,1,1,0,0,0,2.0,2,1.0,8,4.0
-717,405744,175646,1,1,1,0,0,0,0,0,0,0,0,0,10.0,10,1.0,10,1.0
-718,247418,116395,1,0,0,0,0,0,1,0,0,1,0,0,6.0,6,1.0,6,1.0
-719,268489,54245,0,0,0,0,1,0,1,0,0,0,1,0,10.0,10,1.0,4,0.4
-720,328945,165590,0,0,1,0,0,0,0,0,0,1,0,1,2.0,2,1.0,4,2.0
-721,410193,141893,0,0,0,0,0,1,1,0,0,1,0,0,2.0,2,1.0,8,4.0
-722,345034,40830,1,0,1,0,1,0,0,0,0,0,0,0,10.0,10,1.0,2,0.2
-723,364372,64143,0,0,0,1,1,0,0,0,0,0,0,1,4.0,4,1.0,4,1.0
-724,290738,129138,0,1,1,0,0,0,0,1,0,0,0,0,,0,0.0,0,0.0
-725,338965,85609,0,1,0,0,0,1,0,1,0,0,0,0,8.0,8,1.0,2,0.25
-726,467121,143254,0,0,0,0,1,0,0,0,0,0,1,1,6.0,6,1.0,2,0.33
-727,389983,27107,0,1,0,0,0,0,1,0,0,1,0,0,2.0,2,1.0,6,3.0
-728,483851,56319,0,0,0,1,1,0,0,0,0,1,0,0,2.0,2,1.0,4,2.0
-729,424730,165939,1,1,0,1,0,0,0,0,0,0,0,0,8.0,8,1.0,6,0.75
-730,249321,48359,0,1,1,0,0,0,1,0,0,0,0,0,4.0,4,1.0,2,0.5
-731,307430,81205,1,1,0,0,0,0,0,0,0,0,1,0,6.0,6,1.0,10,1.67
-732,347301,37884,1,0,1,0,0,0,0,0,1,0,0,0,10.0,10,1.0,12,1.2
-733,203743,110893,0,0,0,0,1,1,0,0,1,0,0,0,4.0,4,1.0,4,1.0
-734,292487,183232,1,0,0,0,1,0,0,0,1,0,0,0,8.0,8,1.0,4,0.5
-735,322767,39677,1,0,0,0,1,0,0,0,0,0,1,0,4.0,4,1.0,4,1.0
-736,358514,80208,0,0,0,1,0,1,0,1,0,0,0,0,14.0,14,1.0,4,0.29
-737,427105,139153,0,1,0,0,1,0,0,0,1,0,0,0,4.0,4,1.0,4,1.0
-738,357046,63987,0,0,0,1,1,0,0,0,0,0,1,0,12.0,12,1.0,10,0.83
-739,307964,26776,1,0,1,0,0,0,0,0,0,0,0,1,8.0,8,1.0,8,1.0
-740,316623,39023,0,1,0,0,0,0,0,0,1,1,0,0,4.0,4,1.0,4,1.0
-741,228760,62415,1,0,0,0,0,0,0,1,0,1,0,0,,0,0.0,8,5.0
-742,338286,150402,1,1,0,0,0,0,1,0,0,0,0,0,12.0,12,1.0,12,1.0
-743,438728,154962,1,0,0,0,1,0,0,0,0,0,1,0,8.0,8,1.0,2,0.25
-744,404981,192372,0,0,0,0,1,0,0,0,1,0,1,0,6.0,6,1.0,4,0.67
-745,359528,25575,0,1,0,0,0,0,1,0,1,0,0,0,6.0,6,1.0,4,0.67
-746,349781,163587,1,0,1,0,0,0,0,0,1,0,0,0,8.0,8,1.0,6,0.75
-747,385237,59843,0,0,0,0,0,0,0,1,0,1,1,0,12.0,12,1.0,4,0.33
-748,384632,183937,0,0,0,0,1,1,0,0,0,1,0,0,4.0,4,1.0,10,2.5
-749,306961,148491,0,0,0,0,1,1,0,1,0,0,0,0,4.0,4,1.0,8,2.0
-750,396479,30844,0,0,0,0,1,0,0,0,1,1,0,0,10.0,10,1.0,2,0.2
-751,353952,189743,0,0,0,0,0,1,0,0,0,0,1,1,6.0,6,1.0,0,0.0
-752,458229,72535,0,1,0,0,0,0,1,0,0,0,0,1,6.0,6,1.0,14,2.33
-753,423828,162472,0,0,1,0,1,0,0,1,0,0,0,0,6.0,6,1.0,0,0.0
-754,369405,195449,0,0,0,0,0,1,0,0,1,0,1,0,14.0,14,1.0,8,0.57
-755,394880,59505,1,0,0,0,0,0,1,0,0,0,1,0,8.0,8,1.0,10,1.25
-756,216125,75680,0,0,1,1,0,1,0,0,0,0,0,0,4.0,4,1.0,4,1.0
-757,269805,185734,0,1,1,0,0,0,1,0,0,0,0,0,8.0,8,1.0,0,0.0
-758,413799,157617,0,0,0,0,0,1,1,0,1,0,0,0,,0,0.0,6,5.0
-759,302053,181745,0,0,0,0,0,1,1,0,0,0,0,1,2.0,2,1.0,8,4.0
-760,473261,167980,0,0,1,0,0,0,0,1,0,0,0,1,10.0,10,1.0,8,0.8
-761,445051,106289,1,1,1,0,0,0,0,0,0,0,0,0,10.0,10,1.0,2,0.2
-762,212245,132984,0,1,0,0,0,0,0,0,0,1,1,0,10.0,10,1.0,8,0.8
-763,480413,113380,0,0,1,0,0,0,0,0,0,1,0,1,8.0,8,1.0,6,0.75
-764,352346,53256,0,0,0,0,1,1,0,0,0,0,1,0,,0,0.0,2,5.0
-765,449161,26399,0,1,0,0,0,0,0,1,0,1,0,0,4.0,4,1.0,10,2.5
-766,400195,66688,1,0,0,0,1,1,0,0,0,0,0,0,4.0,4,1.0,8,2.0
-767,360809,41649,0,1,1,0,0,0,0,0,0,0,0,1,4.0,4,1.0,8,2.0
-768,487223,143779,0,0,1,0,1,0,0,0,0,1,0,0,10.0,10,1.0,8,0.8
-769,364632,163079,0,0,0,1,0,0,0,0,1,1,0,0,2.0,2,1.0,4,2.0
-770,332071,116680,1,0,0,0,1,0,1,0,0,0,0,0,4.0,4,1.0,6,1.5
-771,396359,89174,1,0,1,0,0,0,0,1,0,0,0,0,10.0,10,1.0,4,0.4
-772,320385,92456,0,0,1,1,0,1,0,0,0,0,0,0,6.0,6,1.0,8,1.33
-773,380098,93796,0,1,0,0,0,0,0,0,1,1,0,0,8.0,8,1.0,6,0.75
-774,385140,102819,1,1,0,0,0,0,0,0,0,0,1,0,,0,0.0,6,5.0
-775,473604,163744,0,0,0,0,0,1,1,0,0,1,0,0,4.0,4,1.0,8,2.0
-776,296380,178265,0,0,0,0,0,0,1,1,0,0,0,1,8.0,8,1.0,0,0.0
-777,217741,187165,0,1,0,0,0,0,1,0,0,0,1,0,8.0,8,1.0,4,0.5
-778,468179,141762,0,0,1,0,1,0,0,0,0,0,1,0,2.0,2,1.0,4,2.0
-779,265103,157564,1,0,0,0,0,0,1,0,0,1,0,0,4.0,4,1.0,2,0.5
-780,318917,57513,0,0,0,0,1,1,0,0,0,0,0,1,8.0,8,1.0,8,1.0
-781,335389,73388,0,0,1,0,0,0,1,0,0,0,0,1,,0,0.0,8,5.0
-782,344494,41122,0,1,0,0,1,0,0,0,0,0,1,0,4.0,4,1.0,10,2.5
-783,468987,43930,1,0,0,0,0,1,0,0,0,1,0,0,4.0,4,1.0,4,1.0
-784,480547,64279,0,1,0,0,0,0,0,1,1,0,0,0,8.0,8,1.0,6,0.75
-785,375866,122880,0,0,0,0,0,0,0,1,0,1,1,0,4.0,4,1.0,4,1.0
-786,438184,138257,0,1,0,0,0,0,0,0,1,0,0,1,4.0,4,1.0,10,2.5
-787,221519,42324,1,0,0,0,0,1,1,0,0,0,0,0,6.0,6,1.0,4,0.67
-788,207393,114298,1,1,0,0,0,0,0,0,1,0,0,0,4.0,4,1.0,4,1.0
-789,356242,168810,0,0,0,0,0,0,0,0,1,1,1,0,6.0,6,1.0,10,1.67
-790,394532,87084,0,1,0,0,0,0,0,0,0,1,0,1,2.0,2,1.0,8,4.0
-791,322534,160752,0,0,0,0,1,1,0,0,0,1,0,0,4.0,4,1.0,2,0.5
-792,296770,177535,0,1,0,0,0,0,1,1,0,0,0,0,6.0,6,1.0,8,1.33
-793,359411,181579,0,0,0,0,0,0,1,0,1,1,0,0,6.0,6,1.0,8,1.33
-794,215607,147171,0,0,0,0,1,1,0,0,1,0,0,0,8.0,8,1.0,6,0.75
-795,252366,29720,0,0,1,0,0,0,0,1,0,0,0,1,8.0,8,1.0,2,0.25
-796,481102,62941,1,0,1,0,0,1,0,0,0,0,0,0,2.0,2,1.0,2,1.0
-797,264008,165244,0,0,0,0,0,1,0,1,0,0,1,0,4.0,4,1.0,0,0.0
-798,344748,198569,1,0,1,0,0,1,0,0,0,0,0,0,2.0,2,1.0,10,5.0
-799,491171,95359,1,0,0,1,0,1,0,0,0,0,0,0,4.0,4,1.0,20,5.0
-800,213500,95032,0,0,0,0,0,0,0,1,1,0,0,1,6.0,6,1.0,10,1.67
-801,450193,132018,1,0,0,0,1,1,0,0,0,0,0,0,4.0,4,1.0,4,1.0
-802,341561,154584,0,0,0,0,0,1,0,0,1,0,0,1,8.0,8,1.0,12,1.5
-803,491900,180115,0,0,0,1,0,0,1,0,0,0,0,1,4.0,4,1.0,2,0.5
-804,206970,151088,0,1,0,1,0,0,0,0,1,0,0,0,8.0,8,1.0,4,0.5
-805,316695,25455,0,0,0,0,1,0,0,0,1,0,1,0,6.0,6,1.0,2,0.33
-806,382649,34601,1,1,0,0,0,0,0,0,0,1,0,0,4.0,4,1.0,8,2.0
-807,256997,194583,0,1,0,0,0,1,0,1,0,0,0,0,6.0,6,1.0,10,1.67
-808,365277,184349,0,1,0,0,0,1,0,0,1,0,0,0,8.0,8,1.0,6,0.75
-809,485349,152806,0,1,0,1,0,0,0,0,0,0,1,0,6.0,6,1.0,6,1.0
-810,480262,53863,0,1,0,1,0,1,0,0,0,0,0,0,6.0,6,1.0,4,0.67
-811,486303,95202,0,1,1,0,0,0,0,1,0,0,0,0,,0,0.0,2,5.0
-812,258131,55993,0,0,0,0,0,0,0,1,0,0,1,1,2.0,2,1.0,4,2.0
-813,463050,126936,0,1,0,0,0,1,0,1,0,0,0,0,,0,0.0,0,0.0
-814,270756,29333,0,0,1,0,0,0,1,0,0,0,0,1,4.0,4,1.0,14,3.5
-815,231164,109027,1,0,0,0,0,1,1,0,0,0,0,0,4.0,4,1.0,8,2.0
-816,463899,33207,0,1,1,0,0,0,0,0,0,0,1,0,12.0,12,1.0,6,0.5
-817,399007,123759,0,1,0,0,0,0,1,0,0,0,1,0,8.0,8,1.0,6,0.75
-818,417191,61278,0,1,0,0,0,1,0,0,0,0,1,0,12.0,12,1.0,8,0.67
-819,343102,130956,0,1,0,1,0,0,0,0,0,0,1,0,6.0,6,1.0,12,2.0
-820,444359,117529,0,1,1,0,0,0,0,1,0,0,0,0,4.0,4,1.0,6,1.5
-821,456975,148816,1,0,0,0,0,1,0,0,0,0,0,1,4.0,4,1.0,8,2.0
-822,281006,127452,0,0,0,1,0,0,1,0,0,0,0,1,2.0,2,1.0,4,2.0
-823,397983,100855,0,1,0,0,1,0,0,1,0,0,0,0,6.0,6,1.0,0,0.0
-824,355294,76855,0,1,0,0,0,0,0,1,1,0,0,0,2.0,2,1.0,2,1.0
-825,422055,194612,1,0,0,1,0,0,0,0,1,0,0,0,,0,0.0,12,5.0
-826,214989,97653,0,0,0,0,0,1,0,0,1,0,1,0,4.0,4,1.0,8,2.0
-827,269975,157064,1,0,0,0,1,1,0,0,0,0,0,0,10.0,10,1.0,4,0.4
-828,494117,152366,1,0,0,0,1,0,0,0,1,0,0,0,6.0,6,1.0,8,1.33
-829,231915,65141,0,0,1,0,1,1,0,0,0,0,0,0,2.0,2,1.0,12,6.0
-830,458923,198250,1,0,0,0,0,1,0,0,1,0,0,0,4.0,4,1.0,6,1.5
-831,389130,141296,0,1,1,0,0,1,0,0,0,0,0,0,12.0,12,1.0,8,0.67
-832,470729,42046,0,0,1,0,1,0,0,0,0,0,0,1,8.0,8,1.0,4,0.5
-833,474279,30181,1,0,0,0,0,0,1,0,0,0,1,0,12.0,12,1.0,4,0.33
-834,406649,98575,0,0,0,1,0,0,0,0,0,1,1,0,4.0,4,1.0,4,1.0
-835,267055,184081,1,0,0,0,0,0,1,0,0,0,1,0,6.0,6,1.0,12,2.0
-836,276545,156447,0,0,0,1,1,0,0,0,0,0,0,1,6.0,6,1.0,6,1.0
-837,482190,36190,0,0,0,1,0,0,1,0,1,0,0,0,6.0,6,1.0,4,0.67
-838,370077,199736,0,1,0,0,1,0,0,1,0,0,0,0,14.0,14,1.0,6,0.43
-839,231280,154005,0,1,0,1,0,0,0,0,1,0,0,0,12.0,12,1.0,2,0.17
-840,495127,71108,0,1,0,0,0,1,0,0,1,0,0,0,4.0,4,1.0,6,1.5
-841,379036,151388,0,1,1,0,0,1,0,0,0,0,0,0,4.0,4,1.0,0,0.0
-842,275802,166094,1,0,0,0,0,1,0,0,0,0,1,0,4.0,4,1.0,6,1.5
-843,382652,65851,0,1,1,0,0,0,0,1,0,0,0,0,6.0,6,1.0,12,2.0
-844,442575,19464,0,0,1,1,0,0,0,0,0,1,0,0,4.0,4,1.0,4,1.0
-845,250858,124543,1,0,0,1,0,0,0,0,0,0,0,1,4.0,4,1.0,4,1.0
-846,337823,103129,0,0,0,0,1,0,0,1,0,0,1,0,10.0,10,1.0,2,0.2
-847,245935,144115,0,0,0,0,0,0,0,1,0,1,0,1,8.0,8,1.0,6,0.75
-848,370596,14192,0,0,0,0,1,1,0,0,1,0,0,0,6.0,6,1.0,8,1.33
-849,399212,20895,0,1,1,0,0,1,0,0,0,0,0,0,6.0,6,1.0,2,0.33
-850,476076,166707,0,0,0,1,0,0,1,0,0,0,0,1,4.0,4,1.0,6,1.5
-851,467415,130374,0,0,0,1,1,0,0,0,1,0,0,0,4.0,4,1.0,6,1.5
-852,367151,159580,1,0,0,0,0,0,1,0,1,0,0,0,12.0,12,1.0,6,0.5
-853,320912,97724,0,0,0,1,0,0,1,0,0,0,0,1,6.0,6,1.0,6,1.0
-854,227404,36661,0,1,0,1,0,0,0,0,0,0,0,1,10.0,10,1.0,6,0.6
-855,216256,178926,0,0,0,0,1,0,0,0,0,1,0,1,,0,0.0,2,5.0
-856,387475,80569,0,0,0,0,0,0,1,0,1,0,1,0,6.0,6,1.0,8,1.33
-857,249253,26385,0,0,1,0,1,0,0,1,0,0,0,0,4.0,4,1.0,12,3.0
-858,375847,176910,0,0,0,0,0,1,1,0,0,1,0,0,8.0,8,1.0,2,0.25
-859,251309,82035,0,0,0,0,0,0,0,1,1,0,0,1,10.0,10,1.0,6,0.6
-860,384041,50345,0,0,0,0,0,1,0,1,1,0,0,0,,0,0.0,4,5.0
-861,477161,106483,0,0,0,0,1,0,1,0,0,1,0,0,2.0,2,1.0,4,2.0
-862,218587,27343,1,0,1,0,0,0,0,0,0,0,0,1,8.0,8,1.0,8,1.0
-863,284722,71235,0,0,1,1,0,0,0,0,0,0,1,0,12.0,12,1.0,8,0.67
-864,223603,138762,1,0,1,0,0,0,1,0,0,0,0,0,2.0,2,1.0,0,0.0
-865,399762,13085,0,0,0,0,1,1,0,0,1,0,0,0,2.0,2,1.0,4,2.0
-866,299484,114094,1,1,0,0,0,0,0,0,0,0,0,1,8.0,8,1.0,10,1.25
-867,407379,70302,0,1,0,0,1,0,0,1,0,0,0,0,6.0,6,1.0,6,1.0
-868,292348,137758,0,0,0,1,0,0,0,1,1,0,0,0,2.0,2,1.0,10,5.0
-869,287417,138395,0,0,0,1,0,0,1,0,1,0,0,0,8.0,8,1.0,6,0.75
-870,200590,66947,0,0,1,0,1,0,0,0,0,0,0,1,,0,0.0,4,5.0
-871,440673,85320,0,1,0,0,0,0,0,1,0,0,1,0,6.0,6,1.0,2,0.33
-872,356709,116973,0,1,0,1,0,0,1,0,0,0,0,0,2.0,2,1.0,6,3.0
-873,386039,186602,1,0,1,1,0,0,0,0,0,0,0,0,4.0,4,1.0,4,1.0
-874,270051,75658,0,0,0,0,1,0,0,1,1,0,0,0,6.0,6,1.0,6,1.0
-875,436580,164864,0,0,0,0,1,1,0,0,0,1,0,0,2.0,2,1.0,4,2.0
-876,202918,113863,1,1,0,0,0,0,0,1,0,0,0,0,,0,0.0,8,5.0
-877,318488,149400,1,0,0,1,0,0,0,1,0,0,0,0,4.0,4,1.0,10,2.5
-878,315256,140817,0,0,0,1,0,0,0,1,1,0,0,0,10.0,10,1.0,10,1.0
-879,489213,18867,0,1,0,1,0,0,0,1,0,0,0,0,2.0,2,1.0,4,2.0
-880,446056,70527,1,0,0,0,0,0,0,0,0,0,1,1,10.0,10,1.0,6,0.6
-881,409633,106178,0,0,0,0,0,0,0,1,0,0,1,1,2.0,2,1.0,6,3.0
-882,451351,49239,0,0,0,0,0,0,0,0,1,1,0,1,4.0,4,1.0,2,0.5
-883,215427,15383,0,0,0,1,0,0,1,0,0,1,0,0,6.0,6,1.0,6,1.0
-884,406498,31959,0,1,0,0,0,0,0,0,0,1,0,1,2.0,2,1.0,6,3.0
-885,365405,108994,0,1,0,1,0,0,1,0,0,0,0,0,4.0,4,1.0,4,1.0
-886,280593,104025,1,0,1,0,0,0,0,0,0,0,0,1,10.0,10,1.0,4,0.4
-887,275781,182917,1,1,0,0,0,0,0,0,0,1,0,0,12.0,12,1.0,4,0.33
-888,278623,51336,1,0,0,0,0,0,0,1,0,0,0,1,8.0,8,1.0,10,1.25
-889,479862,137118,1,0,0,0,0,0,0,0,1,0,0,1,2.0,2,1.0,8,4.0
-890,371046,109092,0,0,0,1,0,0,0,0,1,0,0,1,6.0,6,1.0,6,1.0
-891,226188,133532,0,1,0,0,0,0,1,1,0,0,0,0,2.0,2,1.0,6,3.0
-892,413603,178951,0,0,0,1,1,1,0,0,0,0,0,0,2.0,2,1.0,6,3.0
-893,330993,191499,0,1,0,0,0,0,1,1,0,0,0,0,8.0,8,1.0,10,1.25
-894,487445,144381,0,1,0,1,0,1,0,0,0,0,0,0,,0,0.0,6,5.0
-895,446419,102496,0,1,0,0,0,0,1,1,0,0,0,0,12.0,12,1.0,0,0.0
-896,411668,159943,0,0,0,0,0,0,0,0,1,1,0,1,,0,0.0,6,5.0
-897,274151,49946,0,0,0,0,0,0,1,0,1,0,1,0,8.0,8,1.0,4,0.5
-898,454904,173407,0,0,0,0,0,1,0,1,0,1,0,0,16.0,16,1.0,4,0.25
-899,468542,42955,0,0,0,0,0,1,0,1,0,1,0,0,6.0,6,1.0,10,1.67
-900,317048,61101,0,0,0,0,0,1,0,1,0,0,0,1,,0,0.0,2,5.0
-901,385289,74675,1,0,0,0,0,1,0,0,0,0,1,0,8.0,8,1.0,2,0.25
-902,266676,72409,0,0,1,0,0,0,1,1,0,0,0,0,14.0,14,1.0,2,0.14
-903,254136,100314,0,1,0,0,1,0,0,0,1,0,0,0,10.0,10,1.0,4,0.4
-904,314605,151225,1,0,0,0,1,0,0,1,0,0,0,0,10.0,10,1.0,6,0.6
-905,459648,45195,0,0,0,0,0,1,0,0,1,1,0,0,4.0,4,1.0,2,0.5
-906,295585,86609,0,1,0,0,0,0,1,0,0,0,1,0,4.0,4,1.0,0,0.0
-907,371041,75883,0,0,1,0,0,0,1,0,1,0,0,0,4.0,4,1.0,4,1.0
-908,371542,57193,0,0,0,1,0,0,0,1,0,1,0,0,2.0,2,1.0,6,3.0
-909,419290,32255,0,0,0,0,1,1,0,0,0,0,0,1,2.0,2,1.0,12,6.0
-910,332905,154274,0,1,0,0,0,0,1,1,0,0,0,0,10.0,10,1.0,4,0.4
-911,396616,92842,1,1,0,0,0,0,0,1,0,0,0,0,2.0,2,1.0,4,2.0
-912,225868,72568,0,0,0,1,1,0,0,0,0,0,1,0,8.0,8,1.0,4,0.5
-913,483313,158045,0,0,1,0,0,0,0,1,1,0,0,0,4.0,4,1.0,6,1.5
-914,409500,59021,0,0,0,0,0,0,0,1,1,1,0,0,2.0,2,1.0,10,5.0
-915,347719,136188,1,0,1,0,0,1,0,0,0,0,0,0,8.0,8,1.0,2,0.25
-916,300122,70254,0,0,1,0,1,0,1,0,0,0,0,0,6.0,6,1.0,12,2.0
-917,306186,172369,0,0,0,0,0,0,1,0,0,1,1,0,6.0,6,1.0,4,0.67
-918,317909,48222,0,0,0,0,0,0,0,1,1,1,0,0,8.0,8,1.0,2,0.25
-919,243993,51965,0,0,0,0,0,0,1,0,0,0,1,1,6.0,6,1.0,6,1.0
-920,322095,172458,1,1,0,0,0,0,1,0,0,0,0,0,10.0,10,1.0,6,0.6
-921,305860,60419,0,0,1,1,0,0,0,0,0,0,0,1,10.0,10,1.0,2,0.2
-922,399045,24312,1,0,0,1,0,0,0,0,0,0,0,1,12.0,12,1.0,4,0.33
-923,466188,25075,0,0,0,0,1,1,0,0,0,0,0,1,12.0,12,1.0,10,0.83
-924,328821,44907,1,0,0,0,0,0,0,0,1,0,0,1,6.0,6,1.0,6,1.0
-925,392780,194685,0,1,0,0,0,0,0,0,1,1,0,0,2.0,2,1.0,6,3.0
-926,263701,186881,0,0,0,0,1,0,1,0,0,0,1,0,6.0,6,1.0,6,1.0
-927,485278,149804,0,1,1,0,0,0,0,0,1,0,0,0,,0,0.0,4,5.0
-928,265979,25740,0,0,0,0,1,0,1,0,0,0,0,1,6.0,6,1.0,12,2.0
-929,296060,13802,0,1,1,0,1,0,0,0,0,0,0,0,,0,0.0,4,5.0
-930,314827,183938,0,1,1,0,0,0,0,0,0,1,0,0,4.0,4,1.0,2,0.5
-931,327857,75977,1,0,0,1,0,1,0,0,0,0,0,0,4.0,4,1.0,4,1.0
-932,336696,195946,0,0,1,0,0,0,0,0,1,1,0,0,10.0,10,1.0,12,1.2
-933,381827,61627,0,0,0,0,1,0,0,0,0,1,0,1,,0,0.0,6,5.0
-934,402772,37800,0,0,0,0,1,0,0,0,0,1,1,0,2.0,2,1.0,8,4.0
-935,419875,103666,0,0,0,1,1,0,0,0,0,1,0,0,4.0,4,1.0,12,3.0
-936,370256,147215,0,0,1,0,0,0,0,1,0,1,0,0,8.0,8,1.0,6,0.75
-937,499515,81170,0,1,0,1,0,0,0,0,1,0,0,0,14.0,14,1.0,4,0.29
-938,489669,123869,0,0,1,1,0,0,0,0,1,0,0,0,10.0,10,1.0,8,0.8
-939,318548,157598,0,0,0,0,0,0,0,1,1,1,0,0,10.0,10,1.0,0,0.0
-940,378058,73800,0,0,1,0,0,0,0,1,0,0,0,1,8.0,8,1.0,8,1.0
-941,295093,155387,0,0,0,1,0,0,1,0,0,1,0,0,10.0,10,1.0,4,0.4
-942,255137,77953,0,0,0,1,0,0,0,0,0,1,0,1,2.0,2,1.0,4,2.0
-943,460003,175620,0,0,1,0,0,0,1,1,0,0,0,0,6.0,6,1.0,2,0.33
-944,435210,67087,0,0,0,0,0,0,1,0,1,1,0,0,4.0,4,1.0,6,1.5
-945,408719,21594,0,0,0,0,0,0,0,0,1,0,1,1,10.0,10,1.0,0,0.0
-946,442983,64387,1,0,0,0,1,0,0,0,1,0,0,0,8.0,8,1.0,6,0.75
-947,417183,170553,1,0,1,1,0,0,0,0,0,0,0,0,14.0,14,1.0,0,0.0
-948,245504,60047,0,0,0,0,0,1,0,1,1,0,0,0,8.0,8,1.0,8,1.0
-949,253896,99657,1,1,0,0,0,0,0,1,0,0,0,0,2.0,2,1.0,4,2.0
-950,360681,78417,0,0,0,0,0,1,0,0,1,0,0,1,6.0,6,1.0,6,1.0
-951,217587,140616,0,1,1,0,0,0,0,0,1,0,0,0,6.0,6,1.0,10,1.67
-952,399434,26457,0,1,0,0,0,1,1,0,0,0,0,0,12.0,12,1.0,2,0.17
-953,311115,142051,1,0,0,0,1,0,1,0,0,0,0,0,10.0,10,1.0,2,0.2
-954,468873,130072,0,0,0,0,0,0,0,1,1,0,1,0,4.0,4,1.0,14,3.5
-955,387628,54418,0,0,1,0,0,1,0,0,0,1,0,0,4.0,4,1.0,4,1.0
-956,311418,79285,0,0,1,0,0,1,0,0,0,0,0,1,6.0,6,1.0,10,1.67
-957,282189,153537,1,0,0,0,0,1,0,0,1,0,0,0,6.0,6,1.0,8,1.33
-958,242619,113714,0,0,0,0,0,0,1,0,1,0,0,1,8.0,8,1.0,10,1.25
-959,326593,187624,0,0,0,0,1,0,1,0,0,0,1,0,8.0,8,1.0,8,1.0
-960,219358,105388,1,0,0,0,0,0,1,1,0,0,0,0,6.0,6,1.0,12,2.0
-961,212481,34643,0,0,0,0,0,0,1,0,1,1,0,0,4.0,4,1.0,14,3.5
-962,493117,142931,0,0,0,0,0,1,0,1,0,1,0,0,4.0,4,1.0,4,1.0
-963,256384,79402,0,0,1,0,0,0,0,0,0,1,1,0,4.0,4,1.0,6,1.5
-964,496916,99297,1,0,0,0,0,1,0,0,0,0,1,0,,0,0.0,2,5.0
-965,423393,174792,0,1,0,0,0,0,1,1,0,0,0,0,,0,0.0,14,5.0
-966,473484,177801,1,0,0,0,1,1,0,0,0,0,0,0,,0,0.0,4,5.0
-967,371779,80404,0,0,0,0,1,0,0,0,0,1,1,0,2.0,2,1.0,6,3.0
-968,384400,71324,0,0,1,0,0,0,0,0,1,0,0,1,4.0,4,1.0,2,0.5
-969,217937,143787,1,0,0,0,1,0,0,0,0,1,0,0,2.0,2,1.0,8,4.0
-970,213566,98135,1,0,0,0,0,1,0,0,0,1,0,0,2.0,2,1.0,4,2.0
-971,289192,56182,0,1,0,0,0,0,1,1,0,0,0,0,6.0,6,1.0,0,0.0
-972,403598,73657,0,0,1,0,0,1,0,1,0,0,0,0,2.0,2,1.0,8,4.0
-973,284210,130990,1,0,0,1,0,0,0,1,0,0,0,0,6.0,6,1.0,4,0.67
-974,401410,40083,0,1,0,0,0,0,1,0,0,0,1,0,8.0,8,1.0,12,1.5
-975,337305,22859,0,0,0,1,0,0,0,0,0,1,0,1,6.0,6,1.0,2,0.33
-976,226419,158974,0,0,0,1,0,1,0,0,0,0,0,1,,0,0.0,2,5.0
-977,316248,105993,0,0,0,1,0,0,1,0,1,0,0,0,4.0,4,1.0,2,0.5
-978,319823,180018,0,0,0,0,0,1,0,1,0,1,0,0,8.0,8,1.0,4,0.5
-979,468990,123010,0,1,0,1,0,0,0,0,0,0,0,1,4.0,4,1.0,4,1.0
-980,403627,147220,0,0,0,0,0,0,1,0,1,0,1,0,10.0,10,1.0,4,0.4
-981,317225,57958,0,0,0,0,1,1,0,0,0,0,1,0,8.0,8,1.0,0,0.0
-982,229734,168922,0,0,0,1,0,0,0,1,0,1,0,0,4.0,4,1.0,6,1.5
-983,468070,155375,0,0,0,1,0,0,1,1,0,0,0,0,10.0,10,1.0,6,0.6
-984,252884,174360,0,0,0,1,0,0,0,0,1,0,0,1,2.0,2,1.0,10,5.0
-985,466428,100568,0,0,0,0,0,1,0,0,0,1,0,1,4.0,4,1.0,4,1.0
-986,452565,72172,0,0,0,0,0,1,1,0,0,0,0,1,4.0,4,1.0,14,3.5
-987,220362,170895,0,0,0,1,0,0,1,0,0,0,1,0,4.0,4,1.0,10,2.5
-988,270701,101907,0,0,0,0,0,1,0,1,0,0,1,0,8.0,8,1.0,6,0.75
-989,391764,45376,0,0,1,1,1,0,0,0,0,0,0,0,4.0,4,1.0,6,1.5
-990,305302,145908,0,0,0,0,0,1,0,0,1,1,0,0,10.0,10,1.0,6,0.6
-991,356465,130869,1,0,0,1,0,0,0,0,1,0,0,0,2.0,2,1.0,4,2.0
-992,260584,156699,0,0,0,0,0,1,0,1,0,1,0,0,6.0,6,1.0,10,1.67
-993,264824,101853,0,0,0,1,0,0,1,0,1,0,0,0,6.0,6,1.0,8,1.33
-994,229223,55028,1,0,0,0,1,0,0,0,1,0,0,0,10.0,10,1.0,4,0.4
-995,226950,14602,0,0,0,1,0,0,0,0,0,1,1,0,14.0,14,1.0,6,0.43
-996,280148,36439,0,0,0,0,1,0,0,0,1,0,0,1,2.0,2,1.0,8,4.0
-997,254227,51432,0,0,0,0,0,0,1,0,0,1,0,1,,0,0.0,4,5.0
-998,404381,168755,1,0,0,0,0,0,0,0,1,0,0,1,6.0,6,1.0,4,0.67
-999,265544,17476,0,1,0,1,0,1,0,0,0,0,0,0,8.0,8,1.0,6,0.75
-1000,348762,67260,0,0,0,1,1,0,0,0,0,0,0,1,8.0,8,1.0,4,0.5
-1004,90000,10000,1,1,1,0,0,0,0,0,0,0,0,0,,0,0.0,0,0.0
-1005,500000,10000,0,1,0,0,1,0,0,1,0,0,0,0,2.0,2,1.0,0,0.0
-1006,140000,70000,1,0,0,1,0,0,0,1,0,0,0,0,,0,0.0,0,0.0
-1007,500000,10000,0,1,0,0,1,1,0,0,0,0,0,0,,0,0.0,0,0.0

From 6d70177dc0b2e8bb6217ba37a07800618bfd9b4f Mon Sep 17 00:00:00 2001
From: Daehyun-Bigbread <bigdarkgold@gmail.com>
Date: Mon, 31 Mar 2025 22:31:09 +0900
Subject: [PATCH 2/5] TRI-263 evaluation feature add.

---
 app/services/evaluation/evaluator.py         | 140 +++++++++++--
 app/services/model_trainer/recommendation.py |  10 -
 catboost_info/catboost_training.json         | 200 +++++++++----------
 catboost_info/learn/events.out.tfevents      | Bin 4798 -> 4798 bytes
 catboost_info/learn_error.tsv                | 200 +++++++++----------
 catboost_info/time_left.tsv                  | 200 +++++++++----------
 6 files changed, 425 insertions(+), 325 deletions(-)

diff --git a/app/services/evaluation/evaluator.py b/app/services/evaluation/evaluator.py
index 89ea637..01d382b 100644
--- a/app/services/evaluation/evaluator.py
+++ b/app/services/evaluation/evaluator.py
@@ -23,21 +23,131 @@ def evaluate_recommendation_model(model_dict, restaurant_df, user_features_df, t
         dict: 평가 지표
     """
     if test_data is None:
-        # 테스트 데이터가 없으면 찜과 예약 데이터에서 구성
-        logger.info("테스트 데이터를 찜/예약 데이터에서 구성합니다")
-        if "likes_df" in model_dict and "reservations_df" in model_dict:
-            likes_df = model_dict["likes_df"]
-            reservations_df = model_dict["reservations_df"]
-            
-            # 찜, 예약 데이터 결합
-            test_data = pd.concat([
-                likes_df[['user_id', 'restaurant_id']].assign(interaction_type='like'),
-                reservations_df[['user_id', 'restaurant_id']].assign(interaction_type='reservation')
-            ])
-        else:
-            logger.error("테스트 데이터를 구성할 수 없습니다. 찜/예약 데이터가 없습니다.")
-            return {"error": "테스트 데이터를 구성할 수 없습니다."}
-
+        # 파일에서 직접 테스트 데이터 로드
+        logger.info("파일에서 직접 테스트 데이터 로드")
+        try:
+            from app.config import USER_DIR
+            from pathlib import Path
+            import json
+            
+            user_dir = Path(str(USER_DIR))
+            
+            # 가장 최근 파일 찾기 함수
+            def get_latest_file(pattern):
+                files = list(user_dir.glob(pattern))
+                if not files:
+                    return None
+                return max(files, key=lambda x: x.stat().st_mtime)
+            
+            # 모든 관련 데이터 파일 찾기
+            likes_file = get_latest_file('likes_*.json')
+            reservations_file = get_latest_file('reservations_*.json')
+            preferences_file = get_latest_file('user_preferences_*.json')
+            recsys_file = get_latest_file('recsys_data_*.json')
+            
+            if not any([likes_file, reservations_file, preferences_file, recsys_file]):
+                logger.error("상호작용 데이터 파일이 없습니다")
+                return {"error": "테스트 데이터를 구성할 수 없습니다. 파일이 없습니다."}
+            
+            test_interactions = []
+            
+            # 찜 데이터 처리
+            if likes_file:
+                with open(likes_file, 'r') as f:
+                    likes_data = json.load(f)
+                logger.info(f"찜 데이터 로드 완료: {len(likes_data)}개 항목")
+                
+                for item in likes_data:
+                    if 'user_id' in item and 'restaurant_id' in item:
+                        test_interactions.append({
+                            'user_id': item['user_id'],
+                            'restaurant_id': item['restaurant_id'],
+                            'interaction_type': 'like',
+                            'weight': 1.0  # 찜은 명시적 관심 표현이므로 가중치 1.0
+                        })
+            
+            # 예약 데이터 처리
+            if reservations_file:
+                with open(reservations_file, 'r') as f:
+                    reservations_data = json.load(f)
+                logger.info(f"예약 데이터 로드 완료: {len(reservations_data)}개 항목")
+                
+                for item in reservations_data:
+                    if 'user_id' in item and 'restaurant_id' in item:
+                        # 완료된 예약은 더 높은 가중치 부여
+                        weight = 1.5 if item.get('status') == 'COMPLETED' else 1.0
+                        test_interactions.append({
+                            'user_id': item['user_id'],
+                            'restaurant_id': item['restaurant_id'],
+                            'interaction_type': 'reservation',
+                            'weight': weight
+                        })
+            
+            # 사용자 선호도 데이터 처리
+            if preferences_file:
+                with open(preferences_file, 'r') as f:
+                    preferences_data = json.load(f)
+                logger.info(f"선호도 데이터 로드 완료: {len(preferences_data)}개 항목")
+                
+                # 선호도 데이터는 직접적인 식당 상호작용이 아니므로 별도 처리가 필요할 수 있음
+                # 예: 선호 카테고리 기반 평가 등
+            
+            # recsys 데이터 처리 (통합 데이터)
+            if recsys_file:
+                with open(recsys_file, 'r') as f:
+                    recsys_data = json.load(f)
+                logger.info(f"추천 시스템 데이터 로드 완료: {len(recsys_data)}개 항목")
+                
+                # recsys 데이터에서 추가 상호작용 추출
+                for user_item in recsys_data:
+                    if 'likes' in user_item and isinstance(user_item['likes'], list):
+                        user_id = user_item.get('user_info', {}).get('user_id')
+                        if not user_id:
+                            continue
+                        
+                        for like in user_item['likes']:
+                            if 'restaurant_id' in like:
+                                test_interactions.append({
+                                    'user_id': user_id,
+                                    'restaurant_id': like['restaurant_id'],
+                                    'interaction_type': 'like',
+                                    'weight': 1.0
+                                })
+                    
+                    if 'reservations' in user_item and isinstance(user_item['reservations'], list):
+                        user_id = user_item.get('user_info', {}).get('user_id')
+                        if not user_id:
+                            continue
+                        
+                        for reservation in user_item['reservations']:
+                            if 'restaurant_id' in reservation:
+                                weight = 1.5 if reservation.get('status') == 'COMPLETED' else 1.0
+                                test_interactions.append({
+                                    'user_id': user_id,
+                                    'restaurant_id': reservation['restaurant_id'],
+                                    'interaction_type': 'reservation',
+                                    'weight': weight
+                                })
+            
+            if not test_interactions:
+                logger.error("유효한 상호작용 데이터가 없습니다")
+                return {"error": "유효한 테스트 데이터가 없습니다"}
+            
+            # 중복 제거 (같은 사용자-식당 쌍이 여러 개 있을 수 있음)
+            unique_interactions = {}
+            for interaction in test_interactions:
+                key = (interaction['user_id'], interaction['restaurant_id'])
+                if key not in unique_interactions or unique_interactions[key]['weight'] < interaction['weight']:
+                    unique_interactions[key] = interaction
+            
+            # 데이터프레임 생성
+            test_data = pd.DataFrame(list(unique_interactions.values()))
+            logger.info(f"테스트 데이터 구성 완료: {len(test_data)}개 상호작용")
+            
+        except Exception as e:
+            logger.error(f"테스트 데이터 로드 중 오류 발생: {e}", exc_info=True)
+            return {"error": f"테스트 데이터 로드 중 오류 발생: {str(e)}"}
+    
     # 랭킹 지표 계산을 위한 사용자별 추천 결과 생성
     recommendations_dict = {}
     # 대표적인 사용자 100명만 샘플링 (계산 시간 단축을 위해)
diff --git a/app/services/model_trainer/recommendation.py b/app/services/model_trainer/recommendation.py
index 5b4e0e1..195ad60 100644
--- a/app/services/model_trainer/recommendation.py
+++ b/app/services/model_trainer/recommendation.py
@@ -60,10 +60,7 @@ def generate_recommendations(data_filtered: pd.DataFrame, stacking_reg, model_fe
         
         if user_features is not None and not user_features.empty:
             # 디버깅을 위한 사용자 ID 목록과 타입 로깅
-            logger.debug(f"사용자 특성 데이터 크기: {user_features.shape}")
             sample_ids = user_features['user_id'].head(5).values
-            logger.debug(f"사용자 ID 샘플(상위 5개): {sample_ids}")
-            logger.debug(f"사용자 ID 타입: {type(user_features['user_id'].iloc[0])}")
             
             # 모든 ID를 문자열로 변환하여 비교
             user_features['user_id_str'] = user_features['user_id'].astype(str)
@@ -80,7 +77,6 @@ def generate_recommendations(data_filtered: pd.DataFrame, stacking_reg, model_fe
                 if 'max_price' in user_row.columns and 'price' in data_filtered.columns:
                     max_price = user_row['max_price'].values[0]
                     if max_price > 0:
-                        logger.debug(f"사용자 최대 가격 {max_price}원 이하의 식당으로 필터링")
                         data_filtered = data_filtered[data_filtered['price'] <= max_price].copy()
             else:
                 logger.info(f"ID {user_id_str}의 사용자 데이터를 찾을 수 없음 - 카테고리 기반 기본 추천 생성")
@@ -97,15 +93,12 @@ def generate_recommendations(data_filtered: pd.DataFrame, stacking_reg, model_fe
                 category_col = f"category_{i}"
                 if category_col in user_row.columns and user_row[category_col].values[0] == 1:
                     data_filtered.loc[data_filtered['category_id'] == i, 'category_bonus'] = 0.3
-                    logger.debug(f"카테고리 {i}에 기본 보너스 0.3 적용")
                     
                     if i in [4, 7, 9, 10]:  # 중요 카테고리
                         data_filtered.loc[data_filtered['category_id'] == i, 'category_bonus'] += 0.2
-                        logger.debug(f"중요 카테고리 {i}에 추가 보너스 0.2 적용")
         else:
             # 신규 사용자: 필터링된 모든 식당은 사용자가 선택한 카테고리에 해당
             # 모든 식당에 동일한 카테고리 보너스 부여
-            logger.info(f"신규 사용자용 카테고리 기반 추천 생성")
             data_filtered['category_bonus'] = 0.3
 
         # 모델 예측을 위한 피처 준비 (기존/신규 사용자 모두 동일)
@@ -141,20 +134,17 @@ def generate_recommendations(data_filtered: pd.DataFrame, stacking_reg, model_fe
         if not is_new_user and 'completed_reservations' in user_row.columns:
             completed_reservations = user_row['completed_reservations'].values[0]
             if completed_reservations > 3:
-                logger.debug(f"예약 경험이 많은 사용자에게 예약 가능 식당 가중치 부여")
                 data_filtered.loc[data_filtered['caution_예약가능'] == 1, 'composite_score'] += 0.2
             
             if 'like_to_reservation_ratio' in user_row.columns:
                 ratio = user_row['like_to_reservation_ratio'].values[0]
                 if ratio > 2.0:
-                    logger.debug(f"찜 대비 예약 비율이 높은 사용자에게 인기 식당 가중치 부여")
                     data_filtered['popularity_bonus'] = 0.15 * (np.log(data_filtered['review'] + 1) / np.log(1000))
                     data_filtered['composite_score'] += data_filtered['popularity_bonus']
         
         # 신규 사용자를 위한 추가 처리: 리뷰 수에 약간의 가중치
         elif is_new_user:
             # 신규 사용자는 인기 있는(리뷰가 많은) 식당에 약간의 가중치
-            logger.debug("신규 사용자를 위해 리뷰가 많은 식당에 추가 가중치 부여")
             data_filtered['popularity_bonus'] = 0.1 * (np.log(data_filtered['review'] + 1) / np.log(1000))
             data_filtered['composite_score'] += data_filtered['popularity_bonus']
         
diff --git a/catboost_info/catboost_training.json b/catboost_info/catboost_training.json
index 1d027b8..e7bf149 100644
--- a/catboost_info/catboost_training.json
+++ b/catboost_info/catboost_training.json
@@ -1,104 +1,104 @@
 {
 "meta":{"test_sets":[],"test_metrics":[],"learn_metrics":[{"best_value":"Min","name":"RMSE"}],"launch_mode":"Train","parameters":"","iteration_count":100,"learn_sets":["learn"],"name":"experiment"},
 "iterations":[
-{"learn":[1.324011774],"iteration":0,"passed_time":0.0003156691663,"remaining_time":0.03125124746},
-{"learn":[1.318770956],"iteration":1,"passed_time":0.0005841296254,"remaining_time":0.02862235164},
-{"learn":[1.314911341],"iteration":2,"passed_time":0.0008645901795,"remaining_time":0.02795508247},
-{"learn":[1.310107525],"iteration":3,"passed_time":0.001136175663,"remaining_time":0.02726821592},
-{"learn":[1.308045214],"iteration":4,"passed_time":0.001408469486,"remaining_time":0.02676092024},
-{"learn":[1.303543505],"iteration":5,"passed_time":0.001724013651,"remaining_time":0.02700954721},
-{"learn":[1.29858226],"iteration":6,"passed_time":0.002018182647,"remaining_time":0.02681299803},
-{"learn":[1.295985705],"iteration":7,"passed_time":0.002328310103,"remaining_time":0.02677556619},
-{"learn":[1.290906783],"iteration":8,"passed_time":0.00261456237,"remaining_time":0.02643613063},
-{"learn":[1.289213614],"iteration":9,"passed_time":0.002927314846,"remaining_time":0.02634583362},
-{"learn":[1.284789474],"iteration":10,"passed_time":0.003275734272,"remaining_time":0.0265036682},
-{"learn":[1.282708152],"iteration":11,"passed_time":0.003557694838,"remaining_time":0.02608976214},
-{"learn":[1.277639874],"iteration":12,"passed_time":0.003853780516,"remaining_time":0.02579068499},
-{"learn":[1.275459893],"iteration":13,"passed_time":0.004104990838,"remaining_time":0.02521637229},
-{"learn":[1.272543606],"iteration":14,"passed_time":0.004427785061,"remaining_time":0.02509078201},
-{"learn":[1.270517239],"iteration":15,"passed_time":0.004726120757,"remaining_time":0.02481213397},
-{"learn":[1.269278443],"iteration":16,"passed_time":0.005064373435,"remaining_time":0.02472605854},
-{"learn":[1.267240011],"iteration":17,"passed_time":0.00537812592,"remaining_time":0.02450035141},
-{"learn":[1.265042127],"iteration":18,"passed_time":0.00578946251,"remaining_time":0.02468139281},
-{"learn":[1.261067572],"iteration":19,"passed_time":0.00612617351,"remaining_time":0.02450469404},
-{"learn":[1.25911975],"iteration":20,"passed_time":0.006357425341,"remaining_time":0.02391602866},
-{"learn":[1.258276958],"iteration":21,"passed_time":0.006695053014,"remaining_time":0.02373700614},
-{"learn":[1.254438859],"iteration":22,"passed_time":0.007047305803,"remaining_time":0.02359315421},
-{"learn":[1.252924764],"iteration":23,"passed_time":0.007409100335,"remaining_time":0.02346215106},
-{"learn":[1.251282056],"iteration":24,"passed_time":0.007788770008,"remaining_time":0.02336631002},
-{"learn":[1.249582714],"iteration":25,"passed_time":0.008070813908,"remaining_time":0.02297077805},
-{"learn":[1.248499677],"iteration":26,"passed_time":0.00841998334,"remaining_time":0.02276514014},
-{"learn":[1.245991818],"iteration":27,"passed_time":0.008713777333,"remaining_time":0.022406856},
-{"learn":[1.244752658],"iteration":28,"passed_time":0.009056113377,"remaining_time":0.02217186378},
-{"learn":[1.243751963],"iteration":29,"passed_time":0.009342157308,"remaining_time":0.02179836705},
-{"learn":[1.241413151],"iteration":30,"passed_time":0.009744243826,"remaining_time":0.02168880077},
-{"learn":[1.240646738],"iteration":31,"passed_time":0.009961495546,"remaining_time":0.02116817803},
-{"learn":[1.23939423],"iteration":32,"passed_time":0.01025116451,"remaining_time":0.02081297036},
-{"learn":[1.237386083],"iteration":33,"passed_time":0.01054266681,"remaining_time":0.02046517676},
-{"learn":[1.236070362],"iteration":34,"passed_time":0.0109619618,"remaining_time":0.02035792906},
-{"learn":[1.234747547],"iteration":35,"passed_time":0.01133700644,"remaining_time":0.02015467811},
-{"learn":[1.234073397],"iteration":36,"passed_time":0.01159509181,"remaining_time":0.01974299417},
-{"learn":[1.233597872],"iteration":37,"passed_time":0.01198376156,"remaining_time":0.01955245307},
-{"learn":[1.233102534],"iteration":38,"passed_time":0.01230684745,"remaining_time":0.01924917165},
-{"learn":[1.232415025],"iteration":39,"passed_time":0.01259622474,"remaining_time":0.01889433711},
-{"learn":[1.231473234],"iteration":40,"passed_time":0.01285226844,"remaining_time":0.01849472775},
-{"learn":[1.229097391],"iteration":41,"passed_time":0.01314943746,"remaining_time":0.01815874696},
-{"learn":[1.228026433],"iteration":42,"passed_time":0.01343098135,"remaining_time":0.017803859},
-{"learn":[1.227075197],"iteration":43,"passed_time":0.01377973411,"remaining_time":0.01753784342},
-{"learn":[1.225780409],"iteration":44,"passed_time":0.01407469478,"remaining_time":0.01720240473},
-{"learn":[1.223620062],"iteration":45,"passed_time":0.01441244746,"remaining_time":0.01691896006},
-{"learn":[1.220786981],"iteration":46,"passed_time":0.01471240817,"remaining_time":0.01659058793},
-{"learn":[1.219726538],"iteration":47,"passed_time":0.01507036933,"remaining_time":0.01632623344},
-{"learn":[1.217873408],"iteration":48,"passed_time":0.01539220522,"remaining_time":0.01602045849},
-{"learn":[1.216068233],"iteration":49,"passed_time":0.01576716652,"remaining_time":0.01576716652},
-{"learn":[1.215757099],"iteration":50,"passed_time":0.01611721096,"remaining_time":0.01548516347},
-{"learn":[1.214778519],"iteration":51,"passed_time":0.01639875485,"remaining_time":0.01513731217},
-{"learn":[1.213082793],"iteration":52,"passed_time":0.01672500744,"remaining_time":0.01483161037},
-{"learn":[1.211568954],"iteration":53,"passed_time":0.01704196828,"remaining_time":0.01451723224},
-{"learn":[1.210382972],"iteration":54,"passed_time":0.01732117882,"remaining_time":0.01417187358},
-{"learn":[1.209873654],"iteration":55,"passed_time":0.01772214033,"remaining_time":0.01392453883},
-{"learn":[1.208082599],"iteration":56,"passed_time":0.01801064262,"remaining_time":0.01358697601},
-{"learn":[1.206636397],"iteration":57,"passed_time":0.01834822862,"remaining_time":0.01328664831},
-{"learn":[1.206520698],"iteration":58,"passed_time":0.01855931363,"remaining_time":0.01289715015},
-{"learn":[1.205728868],"iteration":59,"passed_time":0.01889173293,"remaining_time":0.01259448862},
-{"learn":[1.204050605],"iteration":60,"passed_time":0.01911448469,"remaining_time":0.01222073611},
-{"learn":[1.203257493],"iteration":61,"passed_time":0.01943373722,"remaining_time":0.01191100023},
-{"learn":[1.202396195],"iteration":62,"passed_time":0.01968336419,"remaining_time":0.01156007103},
-{"learn":[1.202033885],"iteration":63,"passed_time":0.01996394975,"remaining_time":0.01122972173},
-{"learn":[1.199952786],"iteration":64,"passed_time":0.02024957701,"remaining_time":0.01090361839},
-{"learn":[1.19783942],"iteration":65,"passed_time":0.02054374601,"remaining_time":0.01058314188},
-{"learn":[1.196319706],"iteration":66,"passed_time":0.02084899842,"remaining_time":0.01026890967},
-{"learn":[1.19468497],"iteration":67,"passed_time":0.02119933453,"remaining_time":0.009976157427},
-{"learn":[1.192566538],"iteration":68,"passed_time":0.02150842031,"remaining_time":0.009663203329},
-{"learn":[1.190202735],"iteration":69,"passed_time":0.02180404765,"remaining_time":0.009344591851},
-{"learn":[1.189682748],"iteration":70,"passed_time":0.02233030182,"remaining_time":0.009120827504},
-{"learn":[1.188012636],"iteration":71,"passed_time":0.02271763822,"remaining_time":0.008834637086},
-{"learn":[1.185914252],"iteration":72,"passed_time":0.0230816411,"remaining_time":0.00853704534},
-{"learn":[1.182600258],"iteration":73,"passed_time":0.02338201848,"remaining_time":0.008215303791},
-{"learn":[1.181108745],"iteration":74,"passed_time":0.02377660494,"remaining_time":0.00792553498},
-{"learn":[1.180217364],"iteration":75,"passed_time":0.02406423222,"remaining_time":0.007599231226},
-{"learn":[1.179438417],"iteration":76,"passed_time":0.02434298442,"remaining_time":0.007271281062},
-{"learn":[1.178125443],"iteration":77,"passed_time":0.02461852827,"remaining_time":0.006943687462},
-{"learn":[1.177448465],"iteration":78,"passed_time":0.02494382252,"remaining_time":0.006630636365},
-{"learn":[1.176569161],"iteration":79,"passed_time":0.0253042837,"remaining_time":0.006326070926},
-{"learn":[1.175583817],"iteration":80,"passed_time":0.02569699515,"remaining_time":0.006027690219},
-{"learn":[1.173624315],"iteration":81,"passed_time":0.02601645601,"remaining_time":0.005710929368},
-{"learn":[1.171706155],"iteration":82,"passed_time":0.02628099977,"remaining_time":0.005382855375},
-{"learn":[1.17146971],"iteration":83,"passed_time":0.02651687664,"remaining_time":0.005050833645},
-{"learn":[1.171243396],"iteration":84,"passed_time":0.02680742061,"remaining_time":0.004730721283},
-{"learn":[1.170032635],"iteration":85,"passed_time":0.0271057563,"remaining_time":0.004412564979},
-{"learn":[1.169602335],"iteration":86,"passed_time":0.02735659162,"remaining_time":0.004087766564},
-{"learn":[1.168072114],"iteration":87,"passed_time":0.02767821917,"remaining_time":0.003774302614},
-{"learn":[1.165599366],"iteration":88,"passed_time":0.02798942997,"remaining_time":0.003459367748},
-{"learn":[1.163380104],"iteration":89,"passed_time":0.02832626597,"remaining_time":0.003147362885},
-{"learn":[1.162831988],"iteration":90,"passed_time":0.02868643548,"remaining_time":0.002837119993},
-{"learn":[1.160561351],"iteration":91,"passed_time":0.02899589627,"remaining_time":0.002521382284},
-{"learn":[1.15833905],"iteration":92,"passed_time":0.02925627333,"remaining_time":0.002202085089},
-{"learn":[1.15798798],"iteration":93,"passed_time":0.02953198385,"remaining_time":0.001885020246},
-{"learn":[1.155140531],"iteration":94,"passed_time":0.02986669483,"remaining_time":0.001571931307},
-{"learn":[1.153470092],"iteration":95,"passed_time":0.03017623895,"remaining_time":0.001257343289},
-{"learn":[1.151745456],"iteration":96,"passed_time":0.0305324501,"remaining_time":0.0009443025805},
-{"learn":[1.149529507],"iteration":97,"passed_time":0.03079882721,"remaining_time":0.0006285474941},
-{"learn":[1.148420976],"iteration":98,"passed_time":0.03103949578,"remaining_time":0.0003135302604},
-{"learn":[1.146656423],"iteration":99,"passed_time":0.03130078952,"remaining_time":0}
+{"learn":[1.256360626],"iteration":0,"passed_time":0.0004711343926,"remaining_time":0.04664230487},
+{"learn":[1.251808953],"iteration":1,"passed_time":0.000859808808,"remaining_time":0.04213063159},
+{"learn":[1.247210496],"iteration":2,"passed_time":0.001186731992,"remaining_time":0.03837100108},
+{"learn":[1.242833733],"iteration":3,"passed_time":0.00157636476,"remaining_time":0.03783275424},
+{"learn":[1.239387587],"iteration":4,"passed_time":0.001912371459,"remaining_time":0.03633505772},
+{"learn":[1.236371217],"iteration":5,"passed_time":0.002490174645,"remaining_time":0.0390127361},
+{"learn":[1.232602415],"iteration":6,"passed_time":0.002891515979,"remaining_time":0.03841585515},
+{"learn":[1.228565544],"iteration":7,"passed_time":0.003246898064,"remaining_time":0.03733932774},
+{"learn":[1.224959973],"iteration":8,"passed_time":0.003647364381,"remaining_time":0.03687890652},
+{"learn":[1.222864733],"iteration":9,"passed_time":0.004026871947,"remaining_time":0.03624184752},
+{"learn":[1.219986592],"iteration":10,"passed_time":0.004369212106,"remaining_time":0.03535089794},
+{"learn":[1.21504822],"iteration":11,"passed_time":0.004741552862,"remaining_time":0.03477138765},
+{"learn":[1.210577483],"iteration":12,"passed_time":0.005181978309,"remaining_time":0.0346793933},
+{"learn":[1.208485581],"iteration":13,"passed_time":0.005526318507,"remaining_time":0.03394738512},
+{"learn":[1.206290967],"iteration":14,"passed_time":0.005856408421,"remaining_time":0.03318631439},
+{"learn":[1.203580935],"iteration":15,"passed_time":0.006220832353,"remaining_time":0.03265936985},
+{"learn":[1.202587086],"iteration":16,"passed_time":0.006645549154,"remaining_time":0.03244591646},
+{"learn":[1.200117752],"iteration":17,"passed_time":0.007024015032,"remaining_time":0.0319982907},
+{"learn":[1.198447951],"iteration":18,"passed_time":0.007426856397,"remaining_time":0.03166186148},
+{"learn":[1.196695865],"iteration":19,"passed_time":0.007858573337,"remaining_time":0.03143429335},
+{"learn":[1.193763392],"iteration":20,"passed_time":0.008321915907,"remaining_time":0.03130625508},
+{"learn":[1.191567691],"iteration":21,"passed_time":0.009499439383,"remaining_time":0.03367983054},
+{"learn":[1.188849173],"iteration":22,"passed_time":0.009875071871,"remaining_time":0.03306002322},
+{"learn":[1.186864811],"iteration":23,"passed_time":0.01031483064,"remaining_time":0.03266363036},
+{"learn":[1.184927235],"iteration":24,"passed_time":0.01071633864,"remaining_time":0.03214901593},
+{"learn":[1.181955097],"iteration":25,"passed_time":0.01107951255,"remaining_time":0.03153399726},
+{"learn":[1.17912423],"iteration":26,"passed_time":0.01143822803,"remaining_time":0.0309255795},
+{"learn":[1.177938096],"iteration":27,"passed_time":0.01180569369,"remaining_time":0.03035749807},
+{"learn":[1.176713425],"iteration":28,"passed_time":0.01219657649,"remaining_time":0.02986058381},
+{"learn":[1.175846054],"iteration":29,"passed_time":0.01257716741,"remaining_time":0.02934672395},
+{"learn":[1.174874495],"iteration":30,"passed_time":0.0128761317,"remaining_time":0.02865977701},
+{"learn":[1.173359875],"iteration":31,"passed_time":0.01318992962,"remaining_time":0.02802860045},
+{"learn":[1.172354175],"iteration":32,"passed_time":0.01359210431,"remaining_time":0.02759609057},
+{"learn":[1.170132423],"iteration":33,"passed_time":0.01400082079,"remaining_time":0.02717806389},
+{"learn":[1.169171239],"iteration":34,"passed_time":0.01445353815,"remaining_time":0.02684228513},
+{"learn":[1.167885021],"iteration":35,"passed_time":0.01492350585,"remaining_time":0.02653067707},
+{"learn":[1.166738598],"iteration":36,"passed_time":0.01531459698,"remaining_time":0.02607620567},
+{"learn":[1.16538268],"iteration":37,"passed_time":0.01572114675,"remaining_time":0.02565029207},
+{"learn":[1.163805962],"iteration":38,"passed_time":0.01618890608,"remaining_time":0.02532110951},
+{"learn":[1.162699556],"iteration":39,"passed_time":0.01661928966,"remaining_time":0.02492893449},
+{"learn":[1.161523056],"iteration":40,"passed_time":0.01702188102,"remaining_time":0.02449490195},
+{"learn":[1.160716234],"iteration":41,"passed_time":0.01741555553,"remaining_time":0.02405005288},
+{"learn":[1.159337474],"iteration":42,"passed_time":0.0179273574,"remaining_time":0.02376417144},
+{"learn":[1.158104071],"iteration":43,"passed_time":0.01839882514,"remaining_time":0.02341668654},
+{"learn":[1.157329122],"iteration":44,"passed_time":0.018776416,"remaining_time":0.02294895289},
+{"learn":[1.15557551],"iteration":45,"passed_time":0.01924038358,"remaining_time":0.02258653725},
+{"learn":[1.155148656],"iteration":46,"passed_time":0.01959372396,"remaining_time":0.02209505042},
+{"learn":[1.154483176],"iteration":47,"passed_time":0.01998902351,"remaining_time":0.02165477546},
+{"learn":[1.154026957],"iteration":48,"passed_time":0.02037098945,"remaining_time":0.02120245841},
+{"learn":[1.152814821],"iteration":49,"passed_time":0.02083504037,"remaining_time":0.02083504037},
+{"learn":[1.151770225],"iteration":50,"passed_time":0.02132925856,"remaining_time":0.02049281705},
+{"learn":[1.151148222],"iteration":51,"passed_time":0.021752767,"remaining_time":0.02007947723},
+{"learn":[1.149812664],"iteration":52,"passed_time":0.02214760821,"remaining_time":0.01964033181},
+{"learn":[1.148409611],"iteration":53,"passed_time":0.02254807452,"remaining_time":0.01920761904},
+{"learn":[1.145251093],"iteration":54,"passed_time":0.02301441715,"remaining_time":0.01882997767},
+{"learn":[1.144031355],"iteration":55,"passed_time":0.02337513268,"remaining_time":0.01836617568},
+{"learn":[1.143227736],"iteration":56,"passed_time":0.02378005742,"remaining_time":0.01793934156},
+{"learn":[1.141978627],"iteration":57,"passed_time":0.02415831496,"remaining_time":0.01749395221},
+{"learn":[1.141292235],"iteration":58,"passed_time":0.02456740645,"remaining_time":0.0170722655},
+{"learn":[1.139920777],"iteration":59,"passed_time":0.02519021053,"remaining_time":0.01679347369},
+{"learn":[1.138278909],"iteration":60,"passed_time":0.02566446999,"remaining_time":0.01640843163},
+{"learn":[1.136472836],"iteration":61,"passed_time":0.02613414602,"remaining_time":0.0160177024},
+{"learn":[1.135144584],"iteration":62,"passed_time":0.02656552962,"remaining_time":0.01560197771},
+{"learn":[1.133553757],"iteration":63,"passed_time":0.0269093698,"remaining_time":0.01513652051},
+{"learn":[1.133033926],"iteration":64,"passed_time":0.027370754,"remaining_time":0.01473809831},
+{"learn":[1.131853111],"iteration":65,"passed_time":0.0277784288,"remaining_time":0.01431009968},
+{"learn":[1.131157871],"iteration":66,"passed_time":0.02816831157,"remaining_time":0.0138739445},
+{"learn":[1.130634038],"iteration":67,"passed_time":0.02883490819,"remaining_time":0.01356936856},
+{"learn":[1.129563312],"iteration":68,"passed_time":0.02928966726,"remaining_time":0.01315912587},
+{"learn":[1.12836294],"iteration":69,"passed_time":0.02982859467,"remaining_time":0.01278368343},
+{"learn":[1.127363159],"iteration":70,"passed_time":0.0303335214,"remaining_time":0.01238974818},
+{"learn":[1.125826623],"iteration":71,"passed_time":0.03073177934,"remaining_time":0.01195124752},
+{"learn":[1.124768863],"iteration":72,"passed_time":0.03118803844,"remaining_time":0.01153530189},
+{"learn":[1.122953001],"iteration":73,"passed_time":0.03159658825,"remaining_time":0.01110150398},
+{"learn":[1.122847944],"iteration":74,"passed_time":0.03187076038,"remaining_time":0.01062358679},
+{"learn":[1.120908144],"iteration":75,"passed_time":0.03226555992,"remaining_time":0.01018912419},
+{"learn":[1.119659624],"iteration":76,"passed_time":0.03272236069,"remaining_time":0.009774211636},
+{"learn":[1.119240104],"iteration":77,"passed_time":0.0331641195,"remaining_time":0.009353982423},
+{"learn":[1.118405907],"iteration":78,"passed_time":0.03358116948,"remaining_time":0.008926639989},
+{"learn":[1.117479722],"iteration":79,"passed_time":0.03399413605,"remaining_time":0.008498534012},
+{"learn":[1.115664056],"iteration":80,"passed_time":0.03454868877,"remaining_time":0.008104013415},
+{"learn":[1.114583378],"iteration":81,"passed_time":0.03507157419,"remaining_time":0.007698638238},
+{"learn":[1.113431037],"iteration":82,"passed_time":0.03548737415,"remaining_time":0.00726849832},
+{"learn":[1.112616571],"iteration":83,"passed_time":0.03587071513,"remaining_time":0.006832517167},
+{"learn":[1.111971395],"iteration":84,"passed_time":0.03629168185,"remaining_time":0.006404414444},
+{"learn":[1.111761448],"iteration":85,"passed_time":0.03672314879,"remaining_time":0.005978187012},
+{"learn":[1.110529727],"iteration":86,"passed_time":0.03716215754,"remaining_time":0.005552966069},
+{"learn":[1.109479239],"iteration":87,"passed_time":0.03766700094,"remaining_time":0.005136409219},
+{"learn":[1.108844078],"iteration":88,"passed_time":0.03817671943,"remaining_time":0.004718470941},
+{"learn":[1.107368969],"iteration":89,"passed_time":0.03854006001,"remaining_time":0.00428222889},
+{"learn":[1.106782088],"iteration":90,"passed_time":0.03897731873,"remaining_time":0.003854899654},
+{"learn":[1.105012074],"iteration":91,"passed_time":0.0394023272,"remaining_time":0.003426289322},
+{"learn":[1.103989616],"iteration":92,"passed_time":0.03980016846,"remaining_time":0.002995711605},
+{"learn":[1.103486322],"iteration":93,"passed_time":0.04016080065,"remaining_time":0.002563455361},
+{"learn":[1.102628551],"iteration":94,"passed_time":0.04058360075,"remaining_time":0.002135978987},
+{"learn":[1.101610577],"iteration":95,"passed_time":0.04105590183,"remaining_time":0.001710662576},
+{"learn":[1.101012886],"iteration":96,"passed_time":0.04146753504,"remaining_time":0.001282501084},
+{"learn":[1.099734966],"iteration":97,"passed_time":0.04190887717,"remaining_time":0.0008552832076},
+{"learn":[1.098536921],"iteration":98,"passed_time":0.0422784262,"remaining_time":0.0004270548101},
+{"learn":[1.096958773],"iteration":99,"passed_time":0.04272010168,"remaining_time":0}
 ]}
\ No newline at end of file
diff --git a/catboost_info/learn/events.out.tfevents b/catboost_info/learn/events.out.tfevents
index a0fd12fce47cc600a2d930b2544d9efb5190e6d5..f8fe950f377776234baa05ad3dfaa761668d4341 100644
GIT binary patch
literal 4798
zcmZ|Sc~DdZ90za^2$xWiD-Bo)<kH;)L|86?<xmy`VdGE^4MhfxBbuT^O&vv?AUOn4
z5Ks^>BrH5K9UK{5uZ$)<av~urxmwVPv{90pdOzp;$M5Z5?~l)X=l8zvz2AE))%pK-
zuh-OF^6#N<)6;uF3(SmW8|lZcP={J%eODjg^WR}oN|eS{&@S3zD_>|6UGU3%$s1KV
z-r@Z;@Kud<LVm`P{G+_r124BAKe1i8f%p30gBojv{A8=oi+FDUJ~N1XdeDq9-oFHX
z<nkdQe{y%T8}ALlHw2NF)|>k9emeLqH)@1@rNZk&-b=vWE+B7hm(<96Bk;SN$zOcd
z_dV}l2H#OoEzHyPaqI%#8-w?lI4Jn0$l3zl&j8=jPX6XVgvj~M1mBrKe&BMPF`qvR
z{G*xV=f6K{z<U$$YsU@<^KZ6r_Tv3)@b4ZXA3o4o!+TTkog2t2Bmrf-Hv=ChCtuYu
zuHd~nc>kMK!u%ocghun;0=#M$`E4FeGTvK)_mq*}tXmnw`&Yobj_w!cNx6P)9`EOX
z|FV_5dsy9R-p>Uer6TXKR6m>duY&(XTPe&lT^1~I|674?(~v(Q)m-57t-%-7lJ9*h
z`y}sez}xO1|5y^3&wD9&RT_Eo(GijTGY@==EBXFodC7dfE%=kO$WI<sisZir{(+`K
z=(8fXPQ~ZjfzN0rpIm**miPAHm0QTWc>dAE`}yF%NFaaW>AFPTJAhX%B=0$r{u}Qd
z!B;)mC!FsS|9w|@?*u-fhkR#R<1p`?!Cya4e$Zd>H}74*uPP&NxjQL=_Y1(w(#fC8
zGrz_Ah2Ssxk=Gx+a+~)u@X7Y%6LU{p;=LUFgW+<a&kKE9GkO0y_~9SPR~L`R^L`O{
zD-C(euy~Q{<qF>U40-t-tA0M;4Seoi^4`7mQr^3RUy?=MAk{}?|1SpLlSqDB>O&7c
z-vhjfocz_&O<(hV33$t?GNJ!mn>`}e*Asl@e)2gJfzSDTFYt9a<P9I&|H*p=`0zOL
zQ-y1Ec<&89Nk;yFWS+=A@d5vn1$lM%@6CL^FL>=xsnAcGz56NN`+>LZAU}58Pvm>p
zAN-?y@<SGbm3;nE@S7sZr(d3o;C%pi)g1CaI~5o5J`jA6A^Gz;4mWs@{})R7ON4&9
z^bY^ZdnM#wy+;0A(vZmh2?k&L75S;AhPixx2>8MZ^0Rch9`ZgEyn8nJt!}P{yblAP
zyMny2?dW~pF9UCBMP8j7BeJiSgU|YFuh8f6$X1d2EgZb|Jo$L(lTv;j75I<NlGhlY
z7U@3%{OAYdYj$Rq@cEJ8r^S$$9L^NkS1Z6PgI{>p)gte&DDYY*^1t*+*Yop4gD;nm
zuX4%D;{8hS=HGrQ^nbs=N0;|8;9EZ<|ASj)AMazq5AP(OGg&I~y`%=O*hpTTr!nL6
z-vDnOM}BbgdXfDf2YzuBdDY^q6h1#5{9Om~7iGRjc%J}%qaOLg?Vitgp9ucoSh3Lm
zd)6KsdA|yLNGJK#4c&UYUkzT;O1`}(SY&@Dp?{madibmjpPvlAFq(Yg4#_#*r+_yK
zAiq7LsEzlj;K%LA=Z}wxJYQ?Tm+O*mIph75&tD7vxwc5?-?6k?#J>q%dWrnqZ))Ux
aej51R8uE)8PVVCUI`9se<agV3IQ#=ZsnU`F

literal 4798
zcmZ|SX;4*l7zS`a5LtxFCYo{q1!WP4O_nH(SV~q|6i`4x2O<Qg3jra8KniLG6ejz2
zLCDd>fncnL(i)fsNG1nF!7-TtSsVhwq8gg1^ES_i_uNnS!*8B>{_mOdpZ7=={`-xN
z={l$>DCGThadq@nD?N1`b>+~&FdqZg>^4vLfB(*=)Fzn-+H$@v<bN$u1fM(5D)=SK
z=NY_L0<RoQeo(h?mG|qwSGHdf@>2|Y+jy@GUNx1xw{^Y_?^VDTJZKT}KTt~B!~6B%
z7hTBjU&*oMy(;*zC(S~>>|}>8?>B%?^B_MxJ9~=vYT%cZ$ty@aMdq&#{!f`)=rg0J
zA?Nco!1vjam$e#Q;k_pKj^ZXEKj2yJ3Epo6@8d<@QQ!U;@3p{pK4}#4zq0L$<-Ioe
z;Ck}ECST3wy##z$Ci$aD@?PHSfS+?BpR(MunfJQjr{)@j{?1m{EP1a7{<CEAwHZIg
z^IjkPuoZddqK;<X8-RD!CLj7szdi3afln{37y2h(n2+PVA^4Fv^7(~3|K|N>@awF|
zN5xyO@qP>V@WnczPp<pO1Kz&?et3}lTFIC>@3(?)J4JpZ=ZAZ|-v(YQocvFE#b5K@
z2)wr=`J|@b-sQa%eEnptaNY`s^mN`EgD)5$?{fE61Mf}1XUWOOykjHsewl*58c+U@
zV&7Cge>?b#F63=WPjBP>4)D&J<n=RC-{-v<c<D-wFrUaEgIeC3gD;*WzoL6?J@0pd
z4;m(aytL&l-tPk6+e5xsP1}w47T{;<$>*Ij*~)uM@RBg{742u=<GmI5-|fhM=^uKV
z_txMCB;;#G^+fK^-QeRUtA+WW*VM4#^KHPF*OAYcD@%B93%<vTe8J86O5WRnS1=*}
z^w@kL?_UJ(H&G?@&&g7(;JrO~lUDLp!I`SOcL4vahP>`a%Odx=Blz)L@~c(1-{kY1
zzz6t}w>W)A<oP;-Z`UVp?i99(&))<7-h8DnpBvrNhP-zHZ#GUoaqxo3`{fE=)=1ue
zz^H@IcLOhxk$1F~cJtmHyiXeW?P}U(ynhLNSOob(N3BKPdw`FSlCKzE%;Npa;14R3
zFEMU5;r%P%rPCF{{7w6OuJhg#yyqBsPuFOX`(!V8t;^*5VrNA5N&H?I2qkY^tt7H<
zdqKXUDf!gg&fR?f*TA2gy)2wJ+Ega8uX=-5X(2zZcIGah?*qOhlDyu&*d^Zkf<NO&
zUOJ&BlJ5t;UqXIvZ-)h+|2lZ}{!fMT20lLW9q-=&|8^YtpO@;ic)t&PnJszQV)+5y
z`-6|sAurEJ_?-6v;76Wc68ig_O^WzH@I(FN51kDT;q!yQH-16BH9JG(doURMPzCwi
zheI3r{1EU(+2kz-nyPreAAG1A`KX%<&b$u=pS4;p%*W`_L^bcjz;`c_&#NCa<$XB#
zpjq-83`@Ov9|6AM4*5$HiCw%u0KTY`{I&auX}muOKB0hob85E=?<2t<b|5dS9*p39
z6nOd5PlS0^PIyf5J{tV>5%S;Ew#@VX5P07Y$xnTou)zDn;M2XytEReo@ID6ok}LVg
zVYfu~jacx_4?h;p+w4~$k{<_tT24OueDp59&k^v)Q^?;qp)NB2qu~2?kk5IvAhQ3*
bgU?ti6Z((euhHcDB!F-Fo_wY0@|J%9vItCp

diff --git a/catboost_info/learn_error.tsv b/catboost_info/learn_error.tsv
index 4735f22..a6a5e9e 100644
--- a/catboost_info/learn_error.tsv
+++ b/catboost_info/learn_error.tsv
@@ -1,101 +1,101 @@
 iter	RMSE
-0	1.324011774
-1	1.318770956
-2	1.314911341
-3	1.310107525
-4	1.308045214
-5	1.303543505
-6	1.29858226
-7	1.295985705
-8	1.290906783
-9	1.289213614
-10	1.284789474
-11	1.282708152
-12	1.277639874
-13	1.275459893
-14	1.272543606
-15	1.270517239
-16	1.269278443
-17	1.267240011
-18	1.265042127
-19	1.261067572
-20	1.25911975
-21	1.258276958
-22	1.254438859
-23	1.252924764
-24	1.251282056
-25	1.249582714
-26	1.248499677
-27	1.245991818
-28	1.244752658
-29	1.243751963
-30	1.241413151
-31	1.240646738
-32	1.23939423
-33	1.237386083
-34	1.236070362
-35	1.234747547
-36	1.234073397
-37	1.233597872
-38	1.233102534
-39	1.232415025
-40	1.231473234
-41	1.229097391
-42	1.228026433
-43	1.227075197
-44	1.225780409
-45	1.223620062
-46	1.220786981
-47	1.219726538
-48	1.217873408
-49	1.216068233
-50	1.215757099
-51	1.214778519
-52	1.213082793
-53	1.211568954
-54	1.210382972
-55	1.209873654
-56	1.208082599
-57	1.206636397
-58	1.206520698
-59	1.205728868
-60	1.204050605
-61	1.203257493
-62	1.202396195
-63	1.202033885
-64	1.199952786
-65	1.19783942
-66	1.196319706
-67	1.19468497
-68	1.192566538
-69	1.190202735
-70	1.189682748
-71	1.188012636
-72	1.185914252
-73	1.182600258
-74	1.181108745
-75	1.180217364
-76	1.179438417
-77	1.178125443
-78	1.177448465
-79	1.176569161
-80	1.175583817
-81	1.173624315
-82	1.171706155
-83	1.17146971
-84	1.171243396
-85	1.170032635
-86	1.169602335
-87	1.168072114
-88	1.165599366
-89	1.163380104
-90	1.162831988
-91	1.160561351
-92	1.15833905
-93	1.15798798
-94	1.155140531
-95	1.153470092
-96	1.151745456
-97	1.149529507
-98	1.148420976
-99	1.146656423
+0	1.256360626
+1	1.251808953
+2	1.247210496
+3	1.242833733
+4	1.239387587
+5	1.236371217
+6	1.232602415
+7	1.228565544
+8	1.224959973
+9	1.222864733
+10	1.219986592
+11	1.21504822
+12	1.210577483
+13	1.208485581
+14	1.206290967
+15	1.203580935
+16	1.202587086
+17	1.200117752
+18	1.198447951
+19	1.196695865
+20	1.193763392
+21	1.191567691
+22	1.188849173
+23	1.186864811
+24	1.184927235
+25	1.181955097
+26	1.17912423
+27	1.177938096
+28	1.176713425
+29	1.175846054
+30	1.174874495
+31	1.173359875
+32	1.172354175
+33	1.170132423
+34	1.169171239
+35	1.167885021
+36	1.166738598
+37	1.16538268
+38	1.163805962
+39	1.162699556
+40	1.161523056
+41	1.160716234
+42	1.159337474
+43	1.158104071
+44	1.157329122
+45	1.15557551
+46	1.155148656
+47	1.154483176
+48	1.154026957
+49	1.152814821
+50	1.151770225
+51	1.151148222
+52	1.149812664
+53	1.148409611
+54	1.145251093
+55	1.144031355
+56	1.143227736
+57	1.141978627
+58	1.141292235
+59	1.139920777
+60	1.138278909
+61	1.136472836
+62	1.135144584
+63	1.133553757
+64	1.133033926
+65	1.131853111
+66	1.131157871
+67	1.130634038
+68	1.129563312
+69	1.12836294
+70	1.127363159
+71	1.125826623
+72	1.124768863
+73	1.122953001
+74	1.122847944
+75	1.120908144
+76	1.119659624
+77	1.119240104
+78	1.118405907
+79	1.117479722
+80	1.115664056
+81	1.114583378
+82	1.113431037
+83	1.112616571
+84	1.111971395
+85	1.111761448
+86	1.110529727
+87	1.109479239
+88	1.108844078
+89	1.107368969
+90	1.106782088
+91	1.105012074
+92	1.103989616
+93	1.103486322
+94	1.102628551
+95	1.101610577
+96	1.101012886
+97	1.099734966
+98	1.098536921
+99	1.096958773
diff --git a/catboost_info/time_left.tsv b/catboost_info/time_left.tsv
index 10778e5..7b79eaa 100644
--- a/catboost_info/time_left.tsv
+++ b/catboost_info/time_left.tsv
@@ -1,101 +1,101 @@
 iter	Passed	Remaining
-0	0	31
-1	0	28
-2	0	27
-3	1	27
-4	1	26
-5	1	27
-6	2	26
-7	2	26
-8	2	26
-9	2	26
-10	3	26
-11	3	26
-12	3	25
-13	4	25
-14	4	25
-15	4	24
-16	5	24
-17	5	24
-18	5	24
-19	6	24
-20	6	23
-21	6	23
-22	7	23
-23	7	23
-24	7	23
-25	8	22
-26	8	22
-27	8	22
-28	9	22
-29	9	21
-30	9	21
-31	9	21
-32	10	20
-33	10	20
-34	10	20
-35	11	20
-36	11	19
-37	11	19
-38	12	19
-39	12	18
-40	12	18
-41	13	18
-42	13	17
-43	13	17
-44	14	17
-45	14	16
-46	14	16
-47	15	16
-48	15	16
-49	15	15
-50	16	15
-51	16	15
-52	16	14
-53	17	14
-54	17	14
-55	17	13
-56	18	13
-57	18	13
-58	18	12
-59	18	12
-60	19	12
-61	19	11
-62	19	11
-63	19	11
-64	20	10
-65	20	10
-66	20	10
-67	21	9
-68	21	9
-69	21	9
-70	22	9
-71	22	8
-72	23	8
-73	23	8
-74	23	7
-75	24	7
-76	24	7
-77	24	6
-78	24	6
-79	25	6
-80	25	6
-81	26	5
-82	26	5
-83	26	5
-84	26	4
-85	27	4
-86	27	4
-87	27	3
-88	27	3
-89	28	3
-90	28	2
-91	28	2
-92	29	2
-93	29	1
-94	29	1
-95	30	1
-96	30	0
-97	30	0
-98	31	0
-99	31	0
+0	0	46
+1	0	42
+2	1	38
+3	1	37
+4	1	36
+5	2	39
+6	2	38
+7	3	37
+8	3	36
+9	4	36
+10	4	35
+11	4	34
+12	5	34
+13	5	33
+14	5	33
+15	6	32
+16	6	32
+17	7	31
+18	7	31
+19	7	31
+20	8	31
+21	9	33
+22	9	33
+23	10	32
+24	10	32
+25	11	31
+26	11	30
+27	11	30
+28	12	29
+29	12	29
+30	12	28
+31	13	28
+32	13	27
+33	14	27
+34	14	26
+35	14	26
+36	15	26
+37	15	25
+38	16	25
+39	16	24
+40	17	24
+41	17	24
+42	17	23
+43	18	23
+44	18	22
+45	19	22
+46	19	22
+47	19	21
+48	20	21
+49	20	20
+50	21	20
+51	21	20
+52	22	19
+53	22	19
+54	23	18
+55	23	18
+56	23	17
+57	24	17
+58	24	17
+59	25	16
+60	25	16
+61	26	16
+62	26	15
+63	26	15
+64	27	14
+65	27	14
+66	28	13
+67	28	13
+68	29	13
+69	29	12
+70	30	12
+71	30	11
+72	31	11
+73	31	11
+74	31	10
+75	32	10
+76	32	9
+77	33	9
+78	33	8
+79	33	8
+80	34	8
+81	35	7
+82	35	7
+83	35	6
+84	36	6
+85	36	5
+86	37	5
+87	37	5
+88	38	4
+89	38	4
+90	38	3
+91	39	3
+92	39	2
+93	40	2
+94	40	2
+95	41	1
+96	41	1
+97	41	0
+98	42	0
+99	42	0

From 6cc65922b3c955f2648007e562049e1f40f5495c Mon Sep 17 00:00:00 2001
From: Daehyun-Bigbread <bigdarkgold@gmail.com>
Date: Tue, 1 Apr 2025 13:59:03 +0900
Subject: [PATCH 3/5] TRI-263 feature extractor add.

---
 app/router/recommendation_api.py              |    6 +-
 app/services/evaluation/evaluator.py          |  419 ++++---
 app/services/evaluation/metrics.py            |   99 +-
 .../model_trainer/data_preparation.py         |   19 +-
 app/services/model_trainer/model_training.py  |   43 +-
 app/services/model_trainer/recommendation.py  |   72 +-
 app/services/model_trainer/train_model.py     |   44 +-
 catboost_info/catboost_training.json          |  200 ++--
 catboost_info/learn/events.out.tfevents       |  Bin 4798 -> 4798 bytes
 catboost_info/learn_error.tsv                 |  200 ++--
 catboost_info/time_left.tsv                   |  150 +--
 .../user/preprocessed_user_features.csv       | 1005 +++++++++++++++++
 12 files changed, 1762 insertions(+), 495 deletions(-)
 create mode 100644 storage/input_json/user/preprocessed_user_features.csv

diff --git a/app/router/recommendation_api.py b/app/router/recommendation_api.py
index c2b7622..aadf385 100644
--- a/app/router/recommendation_api.py
+++ b/app/router/recommendation_api.py
@@ -190,11 +190,7 @@ async def evaluate_model():
             )
         
         # 모델 평가
-        metrics = evaluate_recommendation_model(
-            globals_dict,  # 모델 관련 객체
-            globals_dict.get("df_model"),  # 식당 데이터
-            globals_dict.get("user_features_df")  # 사용자 특성 데이터
-        )
+        metrics = evaluate_recommendation_model(globals_dict)
         
         return metrics
     
diff --git a/app/services/evaluation/evaluator.py b/app/services/evaluation/evaluator.py
index 01d382b..2f9ed09 100644
--- a/app/services/evaluation/evaluator.py
+++ b/app/services/evaluation/evaluator.py
@@ -1,201 +1,250 @@
-# app/services/evaluation/evaluator.py
-
-import pandas as pd
 import numpy as np
-import logging
+import pandas as pd
 import json
-from .metrics import calculate_rating_metrics, calculate_ranking_metrics
+import logging
 from app.services.model_trainer.recommendation import generate_recommendations
+from app.services.evaluation.metrics import calculate_ranking_metrics
 
 logger = logging.getLogger(__name__)
 
-def evaluate_recommendation_model(model_dict, restaurant_df, user_features_df, test_data=None):
+def create_test_interactions(globals_dict):
     """
-    추천 모델 평가
-
+    더 현실적인 사용자-식당 상호작용 테스트 데이터 생성
+    
     Args:
-        model_dict: 모델 및 관련 객체 딕셔너리 (globals_dict)
-        restaurant_df: 식당 데이터프레임
-        user_features_df: 사용자 특성 데이터프레임
-        test_data: 테스트 데이터 (없으면 찜, 예약 데이터에서 구성)
-
+        globals_dict: 전역 변수 딕셔너리
+    
     Returns:
-        dict: 평가 지표
+        pd.DataFrame: 상호작용 테스트 데이터
     """
-    if test_data is None:
-        # 파일에서 직접 테스트 데이터 로드
-        logger.info("파일에서 직접 테스트 데이터 로드")
-        try:
-            from app.config import USER_DIR
-            from pathlib import Path
-            import json
-            
-            user_dir = Path(str(USER_DIR))
-            
-            # 가장 최근 파일 찾기 함수
-            def get_latest_file(pattern):
-                files = list(user_dir.glob(pattern))
-                if not files:
-                    return None
-                return max(files, key=lambda x: x.stat().st_mtime)
-            
-            # 모든 관련 데이터 파일 찾기
-            likes_file = get_latest_file('likes_*.json')
-            reservations_file = get_latest_file('reservations_*.json')
-            preferences_file = get_latest_file('user_preferences_*.json')
-            recsys_file = get_latest_file('recsys_data_*.json')
-            
-            if not any([likes_file, reservations_file, preferences_file, recsys_file]):
-                logger.error("상호작용 데이터 파일이 없습니다")
-                return {"error": "테스트 데이터를 구성할 수 없습니다. 파일이 없습니다."}
-            
-            test_interactions = []
-            
-            # 찜 데이터 처리
-            if likes_file:
-                with open(likes_file, 'r') as f:
-                    likes_data = json.load(f)
-                logger.info(f"찜 데이터 로드 완료: {len(likes_data)}개 항목")
+    try:
+        df_model = globals_dict.get("df_model")
+        user_features_df = globals_dict.get("user_features_df")
+        
+        if df_model is None or user_features_df is None:
+            logger.warning("테스트 데이터 생성에 필요한 데이터가 부족합니다.")
+            return pd.DataFrame(columns=['user_id', 'restaurant_id', 'score'])
+        
+        # 모든 사용자와 식당 데이터 가져오기
+        all_users = user_features_df['user_id'].tolist()
+        all_restaurants = df_model['restaurant_id'].unique().tolist()
+        
+        # 사용자별 프로필 특성 (있다면) 활용
+        user_preferences = {}
+        
+        if 'preferred_category' in user_features_df.columns:
+            for _, user_row in user_features_df.iterrows():
+                user_id = user_row['user_id']
+                preferred_category = user_row.get('preferred_category')
+                user_preferences[user_id] = preferred_category
+        
+        # 테스트 상호작용 생성
+        test_interactions = []
+        
+        for user_id in all_users[:50]:  # 테스트를 위해 50명 사용자만 선택
+            # 사용자별 특성에 맞는 식당 선택
+            user_preferred_category = user_preferences.get(user_id)
+            
+            if user_preferred_category:
+                # 사용자 선호 카테고리가 있으면 해당 카테고리의 식당들 중에서 선택
+                category_restaurants = df_model[
+                    df_model['category_id'] == user_preferred_category
+                ]['restaurant_id'].tolist()
                 
-                for item in likes_data:
-                    if 'user_id' in item and 'restaurant_id' in item:
-                        test_interactions.append({
-                            'user_id': item['user_id'],
-                            'restaurant_id': item['restaurant_id'],
-                            'interaction_type': 'like',
-                            'weight': 1.0  # 찜은 명시적 관심 표현이므로 가중치 1.0
-                        })
-            
-            # 예약 데이터 처리
-            if reservations_file:
-                with open(reservations_file, 'r') as f:
-                    reservations_data = json.load(f)
-                logger.info(f"예약 데이터 로드 완료: {len(reservations_data)}개 항목")
-                
-                for item in reservations_data:
-                    if 'user_id' in item and 'restaurant_id' in item:
-                        # 완료된 예약은 더 높은 가중치 부여
-                        weight = 1.5 if item.get('status') == 'COMPLETED' else 1.0
-                        test_interactions.append({
-                            'user_id': item['user_id'],
-                            'restaurant_id': item['restaurant_id'],
-                            'interaction_type': 'reservation',
-                            'weight': weight
-                        })
-            
-            # 사용자 선호도 데이터 처리
-            if preferences_file:
-                with open(preferences_file, 'r') as f:
-                    preferences_data = json.load(f)
-                logger.info(f"선호도 데이터 로드 완료: {len(preferences_data)}개 항목")
-                
-                # 선호도 데이터는 직접적인 식당 상호작용이 아니므로 별도 처리가 필요할 수 있음
-                # 예: 선호 카테고리 기반 평가 등
-            
-            # recsys 데이터 처리 (통합 데이터)
-            if recsys_file:
-                with open(recsys_file, 'r') as f:
-                    recsys_data = json.load(f)
-                logger.info(f"추천 시스템 데이터 로드 완료: {len(recsys_data)}개 항목")
-                
-                # recsys 데이터에서 추가 상호작용 추출
-                for user_item in recsys_data:
-                    if 'likes' in user_item and isinstance(user_item['likes'], list):
-                        user_id = user_item.get('user_info', {}).get('user_id')
-                        if not user_id:
-                            continue
-                        
-                        for like in user_item['likes']:
-                            if 'restaurant_id' in like:
-                                test_interactions.append({
-                                    'user_id': user_id,
-                                    'restaurant_id': like['restaurant_id'],
-                                    'interaction_type': 'like',
-                                    'weight': 1.0
-                                })
+                if category_restaurants:
+                    # 선호 카테고리 식당 선택 (60%)
+                    preferred_count = max(1, int(np.random.randint(3, 6) * 0.6))
+                    preferred_restaurants = np.random.choice(
+                        category_restaurants, 
+                        size=min(preferred_count, len(category_restaurants)),
+                        replace=False
+                    )
                     
-                    if 'reservations' in user_item and isinstance(user_item['reservations'], list):
-                        user_id = user_item.get('user_info', {}).get('user_id')
-                        if not user_id:
-                            continue
-                        
-                        for reservation in user_item['reservations']:
-                            if 'restaurant_id' in reservation:
-                                weight = 1.5 if reservation.get('status') == 'COMPLETED' else 1.0
-                                test_interactions.append({
-                                    'user_id': user_id,
-                                    'restaurant_id': reservation['restaurant_id'],
-                                    'interaction_type': 'reservation',
-                                    'weight': weight
-                                })
-            
-            if not test_interactions:
-                logger.error("유효한 상호작용 데이터가 없습니다")
-                return {"error": "유효한 테스트 데이터가 없습니다"}
-            
-            # 중복 제거 (같은 사용자-식당 쌍이 여러 개 있을 수 있음)
-            unique_interactions = {}
-            for interaction in test_interactions:
-                key = (interaction['user_id'], interaction['restaurant_id'])
-                if key not in unique_interactions or unique_interactions[key]['weight'] < interaction['weight']:
-                    unique_interactions[key] = interaction
-            
-            # 데이터프레임 생성
-            test_data = pd.DataFrame(list(unique_interactions.values()))
-            logger.info(f"테스트 데이터 구성 완료: {len(test_data)}개 상호작용")
-            
-        except Exception as e:
-            logger.error(f"테스트 데이터 로드 중 오류 발생: {e}", exc_info=True)
-            return {"error": f"테스트 데이터 로드 중 오류 발생: {str(e)}"}
-    
-    # 랭킹 지표 계산을 위한 사용자별 추천 결과 생성
-    recommendations_dict = {}
-    # 대표적인 사용자 100명만 샘플링 (계산 시간 단축을 위해)
-    sample_users = user_features_df['user_id'].sample(min(100, len(user_features_df))).tolist()
+                    # 기타 랜덤 식당 선택 (40%)
+                    other_restaurants = list(set(all_restaurants) - set(category_restaurants))
+                    other_count = np.random.randint(1, 3)
+                    other_selected = np.random.choice(
+                        other_restaurants,
+                        size=min(other_count, len(other_restaurants)),
+                        replace=False
+                    )
+                    
+                    selected_restaurants = np.concatenate([preferred_restaurants, other_selected])
+                else:
+                    # 선호 카테고리 식당이 없으면 랜덤 선택
+                    selected_restaurants = np.random.choice(
+                        all_restaurants, 
+                        size=np.random.randint(3, 6),
+                        replace=False
+                    )
+            else:
+                # 사용자 선호도 정보가 없으면 랜덤 선택
+                selected_restaurants = np.random.choice(
+                    all_restaurants, 
+                    size=np.random.randint(3, 6),
+                    replace=False
+                )
+            
+            # 선택된 식당에 대한 가상 평점 생성 (3.0 ~ 5.0)
+            for restaurant_id in selected_restaurants:
+                # 선호 카테고리 식당은 더 높은 평점 확률
+                if user_preferred_category and restaurant_id in category_restaurants:
+                    score = np.random.uniform(4.0, 5.0)  # 선호 카테고리 식당은 평점 높게
+                else:
+                    score = np.random.uniform(3.0, 5.0)  # 기타 식당은 일반적인 분포
+                
+                test_interactions.append({
+                    'user_id': user_id,
+                    'restaurant_id': restaurant_id,
+                    'score': round(score, 1)  # 소수점 첫째 자리까지
+                })
+        
+        return pd.DataFrame(test_interactions)
     
-    for user_id in sample_users:
-        try:
-            # 사용자의 선호 카테고리 확인 (예: 모든 카테고리 선택)
-            user_categories = []
-            for i in range(1, 13):  # 카테고리 1~12
-                cat_col = f"category_{i}"
-                if cat_col in user_features_df.columns and user_features_df[user_features_df['user_id'] == user_id][cat_col].values[0] == 1:
-                    user_categories.append(i)
-            
-            if not user_categories:
-                # 선호 카테고리가 없으면 모든 카테고리 선택
-                user_categories = list(range(1, 13))
-            
-            # 선호 카테고리의 식당만 필터링
-            filtered_df = restaurant_df[restaurant_df['category_id'].isin(user_categories)].copy()
-            
-            # 추천 생성
-            result_json = generate_recommendations(
-                filtered_df, 
-                model_dict["stacking_reg"], 
-                model_dict["model_features"], 
-                user_id,
-                model_dict["scaler"],
-                user_features=user_features_df
-            )
-            
-            # JSON 문자열을 파이썬 객체로 변환
-            result_data = json.loads(result_json)
-            
-            # 추천 식당 ID 리스트 추출
-            recommended_items = [item['restaurant_id'] for item in result_data.get('recommendations', [])]
-            recommendations_dict[user_id] = recommended_items
-            
-        except Exception as e:
-            logger.error(f"사용자 {user_id}에 대한 추천 생성 중 오류 발생: {e}")
+    except Exception as e:
+        logger.error(f"테스트 데이터 생성 중 오류: {e}", exc_info=True)
+        return pd.DataFrame(columns=['user_id', 'restaurant_id', 'score'])
+
+def evaluate_recommendation_model(globals_dict, df_model=None, user_features_df=None):
+    """
+    추천 모델 평가 함수
     
-    # 랭킹 지표 계산
-    ranking_metrics = calculate_ranking_metrics(recommendations_dict, test_data, k_values=[5, 10, 15])
+    Args:
+        globals_dict: 전역 변수 딕셔너리
+        df_model: 식당 데이터 (옵션)
+        user_features_df: 사용자 특성 데이터 (옵션)
     
-    # 평점 예측 지표 계산 (평점 데이터가 있는 경우)
-    rating_metrics = {"MAE": None, "RMSE": None}
+    Returns:
+        dict: 추천 시스템 평가 지표
+    """
+    try:
+        # 인자로 전달된 값 우선 사용, 없으면 globals_dict에서 가져오기
+        df_model = df_model or globals_dict.get("df_model")
+        user_features_df = user_features_df or globals_dict.get("user_features_df")
+        
+        # 필요한 객체 추출
+        stacking_reg = globals_dict.get("stacking_reg")
+        scaler = globals_dict.get("scaler")
+        model_features = globals_dict.get("model_features")
+        
+        # DataFrame 객체 유효성 검사 수정
+        missing_objects = []
+        if stacking_reg is None:
+            missing_objects.append("stacking_reg")
+        if scaler is None:
+            missing_objects.append("scaler")
+        if model_features is None:
+            missing_objects.append("model_features")
+        if df_model is None or (isinstance(df_model, pd.DataFrame) and df_model.empty):
+            missing_objects.append("df_model")
+        if user_features_df is None or (isinstance(user_features_df, pd.DataFrame) and user_features_df.empty):
+            missing_objects.append("user_features_df")
+        
+        if missing_objects:
+            logger.error(f"모델 평가에 필요한 객체가 없습니다: {', '.join(missing_objects)}")
+            return default_empty_metrics()
+        
+        # 테스트 상호작용 데이터 생성
+        test_interactions = create_test_interactions({
+            "df_model": df_model, 
+            "user_features_df": user_features_df
+        })
+        
+        if isinstance(test_interactions, pd.DataFrame) and test_interactions.empty:
+            logger.warning("상호작용 테스트 데이터가 없어 평가를 수행할 수 없습니다.")
+            return default_empty_metrics()
+        
+        # 데이터 검증
+        logger.info(f"생성된 테스트 데이터: {len(test_interactions)}개 상호작용")
+        logger.info(f"고유 사용자 수: {test_interactions['user_id'].nunique()}")
+        logger.info(f"고유 식당 수: {test_interactions['restaurant_id'].nunique()}")
+        
+        # 추천 결과 생성
+        recommendations_dict = {}
+        sample_users = test_interactions['user_id'].unique()
+        
+        for user_id in sample_users:
+            try:
+                # 추천 결과 생성
+                # 주의: user_id가 문자열이면 정수로 변환
+                user_id_for_rec = int(user_id) if isinstance(user_id, str) else user_id
+                
+                result_json = generate_recommendations(
+                    df_model.copy(), 
+                    stacking_reg, 
+                    model_features, 
+                    user_id_for_rec, 
+                    scaler, 
+                    user_features=user_features_df
+                )
+                
+                # JSON 파싱
+                try:
+                    result_data = json.loads(result_json)
+                except json.JSONDecodeError:
+                    logger.error(f"JSON 파싱 오류: {result_json[:100]}...")
+                    continue
+                
+                # 추천 식당 ID 리스트 추출
+                recommended_items = [item.get('restaurant_id') for item in result_data.get('recommendations', [])]
+                
+                # ID 타입 일관성 확인
+                recommended_items = [int(item) if not isinstance(item, int) else item for item in recommended_items]
+                
+                # 유효한 추천 결과만 저장
+                if recommended_items:
+                    recommendations_dict[user_id] = recommended_items
+                else:
+                    logger.warning(f"사용자 {user_id}에 대한 추천 결과가 없습니다.")
+            
+            except Exception as e:
+                logger.error(f"사용자 {user_id} 추천 생성 중 오류: {e}", exc_info=True)
+        
+        # 추천 결과 검증
+        if not recommendations_dict:
+            logger.error("모든 사용자에 대한 추천 생성 실패")
+            return default_empty_metrics()
+        
+        logger.info(f"추천 결과 생성 완료: {len(recommendations_dict)}명의 사용자")
+        
+        # 랭킹 지표 계산
+        ranking_metrics = calculate_ranking_metrics(
+            recommendations_dict, 
+            test_interactions, 
+            k_values=[5, 10, 15]
+        )
+        
+        # 필요하면 MAE, RMSE 계산 (예측 점수가 있는 경우)
+        prediction_metrics = {
+            "MAE": None,
+            "RMSE": None
+        }
+        
+        # 결과 합치기
+        metrics = {**ranking_metrics, **prediction_metrics}
+        
+        logger.info(f"평가 지표: {metrics}")
+        return metrics
     
-    # 모든 지표 결합
-    all_metrics = {**rating_metrics, **ranking_metrics}
-    logger.info(f"평가 지표 계산 완료: {all_metrics}")
-    return all_metrics
\ No newline at end of file
+    except Exception as e:
+        logger.error(f"모델 평가 중 오류 발생: {e}", exc_info=True)
+        return default_empty_metrics()
+
+def default_empty_metrics():
+    """기본 빈 지표 반환"""
+    return {
+        "MAE": None,
+        "RMSE": None,
+        "Precision@5": 0,
+        "Recall@5": 0,
+        "NDCG@5": 0,
+        "Hit_Rate@5": 0,
+        "Precision@10": 0,
+        "Recall@10": 0,
+        "NDCG@10": 0,
+        "Hit_Rate@10": 0,
+        "Precision@15": 0,
+        "Recall@15": 0,
+        "NDCG@15": 0,
+        "Hit_Rate@15": 0
+    }
\ No newline at end of file
diff --git a/app/services/evaluation/metrics.py b/app/services/evaluation/metrics.py
index 32c37d1..dd13022 100644
--- a/app/services/evaluation/metrics.py
+++ b/app/services/evaluation/metrics.py
@@ -185,4 +185,101 @@ def calculate_ranking_metrics(recommendations_dict, test_interactions, k_values=
             results[f'NDCG@{k}'] = 0.0
             results[f'Hit_Rate@{k}'] = 0.0
     
-    return results
\ No newline at end of file
+    return results
+
+def calculate_segment_performance(recommendations_dict, test_interactions, k_values=[5, 10, 15]):
+    """
+    사용자 세그먼트별 추천 시스템 성능 평가
+    
+    Args:
+        recommendations_dict: 사용자별 추천 아이템 딕셔너리 {user_id: [restaurant_id, ...]}
+        test_interactions: 테스트 데이터 (사용자가 실제로 상호작용한 아이템)
+        k_values: 평가할 K 값 리스트
+    
+    Returns:
+        dict: 사용자 세그먼트별 성능 지표
+    """
+    # 사용자 세그먼트 정의 함수
+    def categorize_user(user_id, interactions):
+        """
+        사용자 세그먼트 분류
+        
+        Args:
+            user_id: 사용자 ID
+            interactions: 사용자 상호작용 데이터
+        
+        Returns:
+            str: 사용자 세그먼트 ('new', 'active', 'inactive')
+        """
+        user_interactions = interactions[interactions['user_id'] == user_id]
+        
+        if len(user_interactions) == 0:
+            return 'new'
+        elif len(user_interactions) > 10:
+            return 'active'
+        else:
+            return 'inactive'
+    
+    # 사용자별 관련 아이템 딕셔너리 구성
+    user_relevant_items = {}
+    for _, row in test_interactions.iterrows():
+        user_id = row['user_id']
+        restaurant_id = row['restaurant_id']
+        
+        if user_id not in user_relevant_items:
+            user_relevant_items[user_id] = []
+        
+        user_relevant_items[user_id].append(restaurant_id)
+    
+    # 세그먼트별 성능 저장 딕셔너리
+    segment_metrics = {
+        'new': {f'Precision@{k}': [] for k in k_values} | 
+               {f'Recall@{k}': [] for k in k_values} | 
+               {f'NDCG@{k}': [] for k in k_values} | 
+               {f'Hit_Rate@{k}': [] for k in k_values},
+        'active': {f'Precision@{k}': [] for k in k_values} | 
+                  {f'Recall@{k}': [] for k in k_values} | 
+                  {f'NDCG@{k}': [] for k in k_values} | 
+                  {f'Hit_Rate@{k}': [] for k in k_values},
+        'inactive': {f'Precision@{k}': [] for k in k_values} | 
+                    {f'Recall@{k}': [] for k in k_values} | 
+                    {f'NDCG@{k}': [] for k in k_values} | 
+                    {f'Hit_Rate@{k}': [] for k in k_values}
+    }
+    
+    # 각 사용자별 세그먼트 성능 계산
+    for user_id, recommended_items in recommendations_dict.items():
+        # 사용자 세그먼트 분류
+        segment = categorize_user(user_id, test_interactions)
+        
+        # 해당 사용자의 관련 아이템
+        if user_id not in user_relevant_items:
+            continue
+        
+        relevant_items = user_relevant_items[user_id]
+        
+        # K 값별 성능 계산
+        for k in k_values:
+            segment_metrics[segment][f'Precision@{k}'].append(
+                precision_at_k(recommended_items, relevant_items, k)
+            )
+            segment_metrics[segment][f'Recall@{k}'].append(
+                recall_at_k(recommended_items, relevant_items, k)
+            )
+            segment_metrics[segment][f'NDCG@{k}'].append(
+                ndcg_at_k(recommended_items, relevant_items, k)
+            )
+            segment_metrics[segment][f'Hit_Rate@{k}'].append(
+                hit_rate_at_k(recommended_items, relevant_items, k)
+            )
+    
+    # 세그먼트별 평균 성능 계산
+    segment_performance = {}
+    for segment, metrics in segment_metrics.items():
+        segment_performance[segment] = {}
+        for metric, values in metrics.items():
+            segment_performance[segment][metric] = (
+                np.mean(values) if values else 0.0
+            )
+    
+    return segment_performance
\ No newline at end of file
diff --git a/app/services/model_trainer/data_preparation.py b/app/services/model_trainer/data_preparation.py
index 3ee948d..db5e5b3 100644
--- a/app/services/model_trainer/data_preparation.py
+++ b/app/services/model_trainer/data_preparation.py
@@ -13,14 +13,21 @@
 logger = logging.getLogger(__name__)
 
 def prepare_data(df: pd.DataFrame, required_cols: list) -> pd.DataFrame:
-    """
-    필수 컬럼에 결측치가 있는 행 제거 및 기본 전처리.
-    """
     try:
+        # 기존 코드 + 추가 데이터 품질 검증
         df_clean = df.dropna(subset=required_cols).copy()
-        # 평점 미제공 처리: score와 review가 모두 0이면 score를 NaN으로 설정
-        df_clean.loc[(df_clean['score'] == 0) & (df_clean['review'] == 0), 'score'] = np.nan
-        df_clean['score_provided'] = df_clean['score'].notna().astype(int)
+        
+        # 이상치 처리 강화
+        df_clean['review'] = df_clean['review'].clip(lower=0)
+        df_clean['score'] = df_clean['score'].clip(lower=0, upper=5)
+        
+        # 데이터 정규성 검증
+        df_clean['log_review'] = np.log1p(df_clean['review'])
+        df_clean['log_score'] = np.log1p(df_clean['score'])
+        
+        # 상호작용 특성 추가
+        df_clean['review_duration'] = df_clean['review'] * df_clean['duration_hours']
+        
         return df_clean
     except Exception as e:
         logger.error(f"prepare_data 오류: {e}", exc_info=True)
diff --git a/app/services/model_trainer/model_training.py b/app/services/model_trainer/model_training.py
index 880f344..33ce3cc 100644
--- a/app/services/model_trainer/model_training.py
+++ b/app/services/model_trainer/model_training.py
@@ -9,6 +9,9 @@
 from sklearn.neural_network import MLPRegressor
 from sklearn.model_selection import GridSearchCV, cross_val_score
 from sklearn.linear_model import Ridge as FinalRidge
+from sklearn.model_selection import RandomizedSearchCV
+from sklearn.model_selection import StratifiedKFold
+import numpy as np
 
 import os
 import logging
@@ -52,13 +55,39 @@ def train_rf(X, y):
 
 def train_xgb(X, y):
     try:
-        param_grid = {'n_estimators': [50, 100],
-                    'max_depth': [3, 5],
-                    'learning_rate': [0.01, 0.1]}
-        xgb = XGBRegressor(objective='reg:squarederror', random_state=42)
-        grid = GridSearchCV(xgb, param_grid, cv=3, scoring='r2', n_jobs=1)
-        grid.fit(X, y)
-        return grid.best_estimator_
+        # 하이퍼파라미터 분포 정의
+        param_distributions = {
+            'n_estimators': np.random.randint(50, 300, 20),
+            'max_depth': np.random.randint(3, 10, 10),
+            'learning_rate': np.random.uniform(0.01, 0.3, 10),
+            'subsample': np.random.uniform(0.6, 1.0, 10),
+            'colsample_bytree': np.random.uniform(0.6, 1.0, 10),
+            'min_child_weight': np.random.randint(1, 7, 10)
+        }
+        
+        # XGBoost 모델 생성
+        xgb = XGBRegressor(
+            objective='reg:squarederror', 
+            random_state=42
+        )
+        
+        # 랜덤 서치를 사용한 하이퍼파라미터 튜닝
+        random_search = RandomizedSearchCV(
+            estimator=xgb,
+            param_distributions=param_distributions,
+            n_iter=50,  # 탐색할 하이퍼파라미터 조합 수
+            cv=3,       # 3-fold 교차 검증
+            scoring='r2',
+            n_jobs=-1,  # 병렬 처리
+            random_state=42
+        )
+        
+        # 모델 훈련
+        random_search.fit(X, y)
+        
+        # 최적의 모델 반환
+        return random_search.best_estimator_
+    
     except Exception as e:
         logger.error(f"train_xgb 오류: {e}", exc_info=True)
         raise e
diff --git a/app/services/model_trainer/recommendation.py b/app/services/model_trainer/recommendation.py
index 195ad60..a07aa33 100644
--- a/app/services/model_trainer/recommendation.py
+++ b/app/services/model_trainer/recommendation.py
@@ -31,7 +31,37 @@ def sigmoid_transform(x, a, b):
     
     except Exception as e:
         logger.error(f"sigmoid_transform 오류: {e}", exc_info=True)
-        raise
+        raise e
+
+def calculate_category_diversity_bonus(data_filtered):
+    """
+    카테고리별 다양성을 고려한 보너스 점수 계산
+    
+    Args:
+        data_filtered (pd.DataFrame): 식당 데이터
+    
+    Returns:
+        pd.DataFrame: 다양성 보너스가 추가된 데이터프레임
+    """
+    try:
+        # 카테고리별 식당 수 계산
+        category_counts = data_filtered['category_id'].value_counts()
+        total_restaurants = len(data_filtered)
+        
+        # 카테고리별 희소성 계산 (희소한 카테고리에 더 높은 보너스)
+        diversity_bonus = 1 - (category_counts / total_restaurants)
+        
+        # 보너스 점수 매핑
+        category_bonus_map = diversity_bonus.to_dict()
+        
+        # 각 식당의 카테고리에 따라 보너스 점수 할당
+        data_filtered['category_diversity_bonus'] = data_filtered['category_id'].map(category_bonus_map).fillna(0)
+        
+        return data_filtered
+    
+    except Exception as e:
+        logger.error(f"calculate_category_diversity_bonus 오류: {e}", exc_info=True)
+        return data_filtered
 
 def generate_recommendations(data_filtered: pd.DataFrame, stacking_reg, model_features: list, user_id: str, scaler, user_features: pd.DataFrame = None) -> dict:
     """
@@ -71,7 +101,6 @@ def generate_recommendations(data_filtered: pd.DataFrame, stacking_reg, model_fe
             if not matching_rows.empty:
                 is_new_user = False
                 user_row = matching_rows.iloc[0:1]  # 첫 번째 일치 행만 사용
-                logger.info(f"ID {user_id_str}의 사용자 데이터 찾음 - 개인화 추천 생성")
                 
                 # 가격 필터링 (기존 사용자만)
                 if 'max_price' in user_row.columns and 'price' in data_filtered.columns:
@@ -101,6 +130,12 @@ def generate_recommendations(data_filtered: pd.DataFrame, stacking_reg, model_fe
             # 모든 식당에 동일한 카테고리 보너스 부여
             data_filtered['category_bonus'] = 0.3
 
+        # 카테고리 다양성 보너스 추가
+        data_filtered = calculate_category_diversity_bonus(data_filtered)
+        
+        # 카테고리 다양성 보너스 통합 (10% 가중)
+        data_filtered['category_bonus'] += data_filtered.get('category_diversity_bonus', 0) * 0.1
+
         # 모델 예측을 위한 피처 준비 (기존/신규 사용자 모두 동일)
         for feature in model_features:
             if feature not in data_filtered.columns:
@@ -138,9 +173,18 @@ def generate_recommendations(data_filtered: pd.DataFrame, stacking_reg, model_fe
             
             if 'like_to_reservation_ratio' in user_row.columns:
                 ratio = user_row['like_to_reservation_ratio'].values[0]
-                if ratio > 2.0:
-                    data_filtered['popularity_bonus'] = 0.15 * (np.log(data_filtered['review'] + 1) / np.log(1000))
-                    data_filtered['composite_score'] += data_filtered['popularity_bonus']
+                # 찜/예약 비율에 따른 세분화된 보너스 로직
+                if ratio < 1.0:
+                    bonus_multiplier = 0.05
+                elif 1.0 <= ratio < 2.0:
+                    bonus_multiplier = 0.1
+                else:
+                    bonus_multiplier = 0.15
+                
+                data_filtered['popularity_bonus'] = bonus_multiplier * (
+                    np.log(data_filtered['review'] + 1) / np.log(1000)
+                )
+                data_filtered['composite_score'] += data_filtered['popularity_bonus']
         
         # 신규 사용자를 위한 추가 처리: 리뷰 수에 약간의 가중치
         elif is_new_user:
@@ -162,18 +206,26 @@ def generate_recommendations(data_filtered: pd.DataFrame, stacking_reg, model_fe
         # 상위 15개 추천 추출
         top15 = recommendations_all[['category_id', 'restaurant_id', 'score', 'predicted_score', 'composite_score']].head(15).copy()
         
-        # 결과 포맷팅
+        # 결과 포맷팅 부분 수정
         top15['category_id'] = top15['category_id'].astype(int)
         top15['restaurant_id'] = top15['restaurant_id'].astype(int)
         top15['score'] = top15['score'].astype(float)
         top15['predicted_score'] = top15['predicted_score'].round(3)
         top15['composite_score'] = top15['composite_score'].round(3)
 
-        # 결과 딕셔너리 생성
+        # 결과 딕셔너리 생성 - NumPy 타입을 기본 Python 타입으로 변환
         result_dict = {
-            "user": user_id,
-            "is_new_user": is_new_user,  # 신규 사용자 여부 표시 (옵션)
-            "recommendations": json.loads(top15.to_json(orient='records', force_ascii=False))
+            "user": int(user_id),  # int64를 기본 int로 변환
+            "is_new_user": bool(is_new_user),  # bool로 명시적 변환
+            "recommendations": [
+                {
+                    "category_id": int(row['category_id']),
+                    "restaurant_id": int(row['restaurant_id']),
+                    "score": float(row['score']),
+                    "predicted_score": float(row['predicted_score']),
+                    "composite_score": float(row['composite_score'])
+                } for _, row in top15.iterrows()
+            ]
         }
 
         # JSON 문자열 변환
diff --git a/app/services/model_trainer/train_model.py b/app/services/model_trainer/train_model.py
index 7ac619e..2438a29 100644
--- a/app/services/model_trainer/train_model.py
+++ b/app/services/model_trainer/train_model.py
@@ -65,21 +65,53 @@ def extract_hours_diff(time_str):
         # 이제 숫자 연산 수행
         df_prepared['log_review'] = np.log(df_prepared['review'] + 1)
         df_prepared['review_duration'] = df_prepared['review'] * df_prepared['duration_hours']
-        logger.debug("Feature 계산이 완료되었습니다.")
+        
+        # 카테고리 다양성 및 개인화를 위한 특성 엔지니어링
+        
+        # 1. 카테고리별 희소성 계산
+        category_counts = df_prepared['category_id'].value_counts()
+        total_restaurants = len(df_prepared)
+        category_sparsity = 1 - (category_counts / total_restaurants)
+        
+        # 2. 카테고리 다양성 특성 추가
+        df_prepared['category_diversity_score'] = df_prepared['category_id'].map(
+            category_sparsity.to_dict()
+        ).fillna(0)
+        
+        # 3. 리뷰 기반 상호작용 특성
+        df_prepared['interaction_intensity'] = (
+            df_prepared['review'] * 0.5 + 
+            df_prepared['duration_hours'] * 0.3 + 
+            np.log(df_prepared['review'] + 1) * 0.2
+        )
+        
+        # 4. 복합 평점 특성
+        df_prepared['composite_rating'] = (
+            df_prepared['score'] * 0.7 + 
+            df_prepared['category_diversity_score'] * 0.3
+        )
+        
+        logger.debug("고급 특성 엔지니어링이 완료되었습니다.")
     except Exception as e:
         logger.error(f"train_model - 피처 생성 오류: {e}", exc_info=True)
         raise e
 
-    # 3. (이미 전처리 과정에서 로그 변환, 상호작용 등 피처 엔지니어링이 이루어졌다고 가정)
-    #    모델 학습에 사용할 피처와 타깃을 설정합니다.
+    # 이미 전처리 과정에서 로그 변환, 상호작용 등 피처 엔지니어링이 이루어졌다고 가정
+    # 3. 모델 학습에 사용할 피처와 타깃을 설정합니다.
     try:
-        model_features = ['review', 'duration_hours', 'conv_WIFI', 'conv_주차', 'caution_예약가능', 'log_review', 'review_duration']
+        # 추가된 특성들을 모델 피처에 포함
+        model_features = [
+            'review', 'duration_hours', 'conv_WIFI', 'conv_주차', 
+            'caution_예약가능', 'log_review', 'review_duration',
+            'category_diversity_score', 'interaction_intensity', 
+            'composite_rating'
+        ]
         target = 'score'
         X = df_prepared[model_features]
         y = df_prepared[target]
-        logger.debug(f"Featuer 및 타깃 설정이 완료되었습니다: {model_features}")
+        logger.debug(f"Feature 및 타깃 설정이 완료되었습니다: {model_features}")
     except Exception as e:
-        logger.error(f"train_model - Featuer 및 타깃 설정 오류: {e}", exc_info=True)
+        logger.error(f"train_model - Feature 및 타깃 설정 오류: {e}", exc_info=True)
         raise e
         
     # 4. 특성 스케일링 및 데이터 분할
diff --git a/catboost_info/catboost_training.json b/catboost_info/catboost_training.json
index e7bf149..737700a 100644
--- a/catboost_info/catboost_training.json
+++ b/catboost_info/catboost_training.json
@@ -1,104 +1,104 @@
 {
 "meta":{"test_sets":[],"test_metrics":[],"learn_metrics":[{"best_value":"Min","name":"RMSE"}],"launch_mode":"Train","parameters":"","iteration_count":100,"learn_sets":["learn"],"name":"experiment"},
 "iterations":[
-{"learn":[1.256360626],"iteration":0,"passed_time":0.0004711343926,"remaining_time":0.04664230487},
-{"learn":[1.251808953],"iteration":1,"passed_time":0.000859808808,"remaining_time":0.04213063159},
-{"learn":[1.247210496],"iteration":2,"passed_time":0.001186731992,"remaining_time":0.03837100108},
-{"learn":[1.242833733],"iteration":3,"passed_time":0.00157636476,"remaining_time":0.03783275424},
-{"learn":[1.239387587],"iteration":4,"passed_time":0.001912371459,"remaining_time":0.03633505772},
-{"learn":[1.236371217],"iteration":5,"passed_time":0.002490174645,"remaining_time":0.0390127361},
-{"learn":[1.232602415],"iteration":6,"passed_time":0.002891515979,"remaining_time":0.03841585515},
-{"learn":[1.228565544],"iteration":7,"passed_time":0.003246898064,"remaining_time":0.03733932774},
-{"learn":[1.224959973],"iteration":8,"passed_time":0.003647364381,"remaining_time":0.03687890652},
-{"learn":[1.222864733],"iteration":9,"passed_time":0.004026871947,"remaining_time":0.03624184752},
-{"learn":[1.219986592],"iteration":10,"passed_time":0.004369212106,"remaining_time":0.03535089794},
-{"learn":[1.21504822],"iteration":11,"passed_time":0.004741552862,"remaining_time":0.03477138765},
-{"learn":[1.210577483],"iteration":12,"passed_time":0.005181978309,"remaining_time":0.0346793933},
-{"learn":[1.208485581],"iteration":13,"passed_time":0.005526318507,"remaining_time":0.03394738512},
-{"learn":[1.206290967],"iteration":14,"passed_time":0.005856408421,"remaining_time":0.03318631439},
-{"learn":[1.203580935],"iteration":15,"passed_time":0.006220832353,"remaining_time":0.03265936985},
-{"learn":[1.202587086],"iteration":16,"passed_time":0.006645549154,"remaining_time":0.03244591646},
-{"learn":[1.200117752],"iteration":17,"passed_time":0.007024015032,"remaining_time":0.0319982907},
-{"learn":[1.198447951],"iteration":18,"passed_time":0.007426856397,"remaining_time":0.03166186148},
-{"learn":[1.196695865],"iteration":19,"passed_time":0.007858573337,"remaining_time":0.03143429335},
-{"learn":[1.193763392],"iteration":20,"passed_time":0.008321915907,"remaining_time":0.03130625508},
-{"learn":[1.191567691],"iteration":21,"passed_time":0.009499439383,"remaining_time":0.03367983054},
-{"learn":[1.188849173],"iteration":22,"passed_time":0.009875071871,"remaining_time":0.03306002322},
-{"learn":[1.186864811],"iteration":23,"passed_time":0.01031483064,"remaining_time":0.03266363036},
-{"learn":[1.184927235],"iteration":24,"passed_time":0.01071633864,"remaining_time":0.03214901593},
-{"learn":[1.181955097],"iteration":25,"passed_time":0.01107951255,"remaining_time":0.03153399726},
-{"learn":[1.17912423],"iteration":26,"passed_time":0.01143822803,"remaining_time":0.0309255795},
-{"learn":[1.177938096],"iteration":27,"passed_time":0.01180569369,"remaining_time":0.03035749807},
-{"learn":[1.176713425],"iteration":28,"passed_time":0.01219657649,"remaining_time":0.02986058381},
-{"learn":[1.175846054],"iteration":29,"passed_time":0.01257716741,"remaining_time":0.02934672395},
-{"learn":[1.174874495],"iteration":30,"passed_time":0.0128761317,"remaining_time":0.02865977701},
-{"learn":[1.173359875],"iteration":31,"passed_time":0.01318992962,"remaining_time":0.02802860045},
-{"learn":[1.172354175],"iteration":32,"passed_time":0.01359210431,"remaining_time":0.02759609057},
-{"learn":[1.170132423],"iteration":33,"passed_time":0.01400082079,"remaining_time":0.02717806389},
-{"learn":[1.169171239],"iteration":34,"passed_time":0.01445353815,"remaining_time":0.02684228513},
-{"learn":[1.167885021],"iteration":35,"passed_time":0.01492350585,"remaining_time":0.02653067707},
-{"learn":[1.166738598],"iteration":36,"passed_time":0.01531459698,"remaining_time":0.02607620567},
-{"learn":[1.16538268],"iteration":37,"passed_time":0.01572114675,"remaining_time":0.02565029207},
-{"learn":[1.163805962],"iteration":38,"passed_time":0.01618890608,"remaining_time":0.02532110951},
-{"learn":[1.162699556],"iteration":39,"passed_time":0.01661928966,"remaining_time":0.02492893449},
-{"learn":[1.161523056],"iteration":40,"passed_time":0.01702188102,"remaining_time":0.02449490195},
-{"learn":[1.160716234],"iteration":41,"passed_time":0.01741555553,"remaining_time":0.02405005288},
-{"learn":[1.159337474],"iteration":42,"passed_time":0.0179273574,"remaining_time":0.02376417144},
-{"learn":[1.158104071],"iteration":43,"passed_time":0.01839882514,"remaining_time":0.02341668654},
-{"learn":[1.157329122],"iteration":44,"passed_time":0.018776416,"remaining_time":0.02294895289},
-{"learn":[1.15557551],"iteration":45,"passed_time":0.01924038358,"remaining_time":0.02258653725},
-{"learn":[1.155148656],"iteration":46,"passed_time":0.01959372396,"remaining_time":0.02209505042},
-{"learn":[1.154483176],"iteration":47,"passed_time":0.01998902351,"remaining_time":0.02165477546},
-{"learn":[1.154026957],"iteration":48,"passed_time":0.02037098945,"remaining_time":0.02120245841},
-{"learn":[1.152814821],"iteration":49,"passed_time":0.02083504037,"remaining_time":0.02083504037},
-{"learn":[1.151770225],"iteration":50,"passed_time":0.02132925856,"remaining_time":0.02049281705},
-{"learn":[1.151148222],"iteration":51,"passed_time":0.021752767,"remaining_time":0.02007947723},
-{"learn":[1.149812664],"iteration":52,"passed_time":0.02214760821,"remaining_time":0.01964033181},
-{"learn":[1.148409611],"iteration":53,"passed_time":0.02254807452,"remaining_time":0.01920761904},
-{"learn":[1.145251093],"iteration":54,"passed_time":0.02301441715,"remaining_time":0.01882997767},
-{"learn":[1.144031355],"iteration":55,"passed_time":0.02337513268,"remaining_time":0.01836617568},
-{"learn":[1.143227736],"iteration":56,"passed_time":0.02378005742,"remaining_time":0.01793934156},
-{"learn":[1.141978627],"iteration":57,"passed_time":0.02415831496,"remaining_time":0.01749395221},
-{"learn":[1.141292235],"iteration":58,"passed_time":0.02456740645,"remaining_time":0.0170722655},
-{"learn":[1.139920777],"iteration":59,"passed_time":0.02519021053,"remaining_time":0.01679347369},
-{"learn":[1.138278909],"iteration":60,"passed_time":0.02566446999,"remaining_time":0.01640843163},
-{"learn":[1.136472836],"iteration":61,"passed_time":0.02613414602,"remaining_time":0.0160177024},
-{"learn":[1.135144584],"iteration":62,"passed_time":0.02656552962,"remaining_time":0.01560197771},
-{"learn":[1.133553757],"iteration":63,"passed_time":0.0269093698,"remaining_time":0.01513652051},
-{"learn":[1.133033926],"iteration":64,"passed_time":0.027370754,"remaining_time":0.01473809831},
-{"learn":[1.131853111],"iteration":65,"passed_time":0.0277784288,"remaining_time":0.01431009968},
-{"learn":[1.131157871],"iteration":66,"passed_time":0.02816831157,"remaining_time":0.0138739445},
-{"learn":[1.130634038],"iteration":67,"passed_time":0.02883490819,"remaining_time":0.01356936856},
-{"learn":[1.129563312],"iteration":68,"passed_time":0.02928966726,"remaining_time":0.01315912587},
-{"learn":[1.12836294],"iteration":69,"passed_time":0.02982859467,"remaining_time":0.01278368343},
-{"learn":[1.127363159],"iteration":70,"passed_time":0.0303335214,"remaining_time":0.01238974818},
-{"learn":[1.125826623],"iteration":71,"passed_time":0.03073177934,"remaining_time":0.01195124752},
-{"learn":[1.124768863],"iteration":72,"passed_time":0.03118803844,"remaining_time":0.01153530189},
-{"learn":[1.122953001],"iteration":73,"passed_time":0.03159658825,"remaining_time":0.01110150398},
-{"learn":[1.122847944],"iteration":74,"passed_time":0.03187076038,"remaining_time":0.01062358679},
-{"learn":[1.120908144],"iteration":75,"passed_time":0.03226555992,"remaining_time":0.01018912419},
-{"learn":[1.119659624],"iteration":76,"passed_time":0.03272236069,"remaining_time":0.009774211636},
-{"learn":[1.119240104],"iteration":77,"passed_time":0.0331641195,"remaining_time":0.009353982423},
-{"learn":[1.118405907],"iteration":78,"passed_time":0.03358116948,"remaining_time":0.008926639989},
-{"learn":[1.117479722],"iteration":79,"passed_time":0.03399413605,"remaining_time":0.008498534012},
-{"learn":[1.115664056],"iteration":80,"passed_time":0.03454868877,"remaining_time":0.008104013415},
-{"learn":[1.114583378],"iteration":81,"passed_time":0.03507157419,"remaining_time":0.007698638238},
-{"learn":[1.113431037],"iteration":82,"passed_time":0.03548737415,"remaining_time":0.00726849832},
-{"learn":[1.112616571],"iteration":83,"passed_time":0.03587071513,"remaining_time":0.006832517167},
-{"learn":[1.111971395],"iteration":84,"passed_time":0.03629168185,"remaining_time":0.006404414444},
-{"learn":[1.111761448],"iteration":85,"passed_time":0.03672314879,"remaining_time":0.005978187012},
-{"learn":[1.110529727],"iteration":86,"passed_time":0.03716215754,"remaining_time":0.005552966069},
-{"learn":[1.109479239],"iteration":87,"passed_time":0.03766700094,"remaining_time":0.005136409219},
-{"learn":[1.108844078],"iteration":88,"passed_time":0.03817671943,"remaining_time":0.004718470941},
-{"learn":[1.107368969],"iteration":89,"passed_time":0.03854006001,"remaining_time":0.00428222889},
-{"learn":[1.106782088],"iteration":90,"passed_time":0.03897731873,"remaining_time":0.003854899654},
-{"learn":[1.105012074],"iteration":91,"passed_time":0.0394023272,"remaining_time":0.003426289322},
-{"learn":[1.103989616],"iteration":92,"passed_time":0.03980016846,"remaining_time":0.002995711605},
-{"learn":[1.103486322],"iteration":93,"passed_time":0.04016080065,"remaining_time":0.002563455361},
-{"learn":[1.102628551],"iteration":94,"passed_time":0.04058360075,"remaining_time":0.002135978987},
-{"learn":[1.101610577],"iteration":95,"passed_time":0.04105590183,"remaining_time":0.001710662576},
-{"learn":[1.101012886],"iteration":96,"passed_time":0.04146753504,"remaining_time":0.001282501084},
-{"learn":[1.099734966],"iteration":97,"passed_time":0.04190887717,"remaining_time":0.0008552832076},
-{"learn":[1.098536921],"iteration":98,"passed_time":0.0422784262,"remaining_time":0.0004270548101},
-{"learn":[1.096958773],"iteration":99,"passed_time":0.04272010168,"remaining_time":0}
+{"learn":[1.648905251],"iteration":0,"passed_time":0.0004666281034,"remaining_time":0.04619618224},
+{"learn":[1.497629345],"iteration":1,"passed_time":0.0009012976609,"remaining_time":0.04416358539},
+{"learn":[1.360899163],"iteration":2,"passed_time":0.001282883532,"remaining_time":0.04147990087},
+{"learn":[1.240618702],"iteration":3,"passed_time":0.001757886691,"remaining_time":0.04218928059},
+{"learn":[1.128387921],"iteration":4,"passed_time":0.002108680691,"remaining_time":0.04006493313},
+{"learn":[1.026662891],"iteration":5,"passed_time":0.002554016986,"remaining_time":0.04001293278},
+{"learn":[0.9300425815],"iteration":6,"passed_time":0.003022603436,"remaining_time":0.04015744565},
+{"learn":[0.8480139671],"iteration":7,"passed_time":0.003448647936,"remaining_time":0.03965945126},
+{"learn":[0.7712831482],"iteration":8,"passed_time":0.003996484913,"remaining_time":0.04040890301},
+{"learn":[0.7025638579],"iteration":9,"passed_time":0.004427112777,"remaining_time":0.03984401499},
+{"learn":[0.6433465066],"iteration":10,"passed_time":0.004812532007,"remaining_time":0.03893775896},
+{"learn":[0.5864868263],"iteration":11,"passed_time":0.005193742875,"remaining_time":0.03808744775},
+{"learn":[0.5366753809],"iteration":12,"passed_time":0.005575495414,"remaining_time":0.03731293085},
+{"learn":[0.493668726],"iteration":13,"passed_time":0.005993123192,"remaining_time":0.03681489961},
+{"learn":[0.452697423],"iteration":14,"passed_time":0.00639737588,"remaining_time":0.03625179666},
+{"learn":[0.4139326703],"iteration":15,"passed_time":0.006807461941,"remaining_time":0.03573917519},
+{"learn":[0.378007788],"iteration":16,"passed_time":0.007221006358,"remaining_time":0.03525550163},
+{"learn":[0.3456282594],"iteration":17,"passed_time":0.007626550722,"remaining_time":0.03474317551},
+{"learn":[0.3156361609],"iteration":18,"passed_time":0.008054845237,"remaining_time":0.03433907706},
+{"learn":[0.2884469664],"iteration":19,"passed_time":0.008431764411,"remaining_time":0.03372705764},
+{"learn":[0.2630439138],"iteration":20,"passed_time":0.008906142565,"remaining_time":0.03350406013},
+{"learn":[0.2448805528],"iteration":21,"passed_time":0.009354270546,"remaining_time":0.03316514103},
+{"learn":[0.2262033151],"iteration":22,"passed_time":0.009802606861,"remaining_time":0.03281742297},
+{"learn":[0.2078844467],"iteration":23,"passed_time":0.01023652641,"remaining_time":0.03241566698},
+{"learn":[0.192981451],"iteration":24,"passed_time":0.01066040423,"remaining_time":0.0319812127},
+{"learn":[0.1791809635],"iteration":25,"passed_time":0.01107519866,"remaining_time":0.03152171926},
+{"learn":[0.1675015104],"iteration":26,"passed_time":0.0114729513,"remaining_time":0.03101946093},
+{"learn":[0.1539406323],"iteration":27,"passed_time":0.01186149555,"remaining_time":0.03050098857},
+{"learn":[0.1427636543],"iteration":28,"passed_time":0.01221791459,"remaining_time":0.02991282538},
+{"learn":[0.134231155],"iteration":29,"passed_time":0.01257412529,"remaining_time":0.02933962568},
+{"learn":[0.1239380012],"iteration":30,"passed_time":0.01297958632,"remaining_time":0.02889004698},
+{"learn":[0.1146735567],"iteration":31,"passed_time":0.01339863078,"remaining_time":0.0284720904},
+{"learn":[0.1068619755],"iteration":32,"passed_time":0.01384713376,"remaining_time":0.02811387763},
+{"learn":[0.1018497484],"iteration":33,"passed_time":0.01427792829,"remaining_time":0.02771597845},
+{"learn":[0.09591207292],"iteration":34,"passed_time":0.01467188925,"remaining_time":0.02724779431},
+{"learn":[0.09001531388],"iteration":35,"passed_time":0.01508005863,"remaining_time":0.02680899311},
+{"learn":[0.08505951254],"iteration":36,"passed_time":0.01547635293,"remaining_time":0.02635162796},
+{"learn":[0.08167650816],"iteration":37,"passed_time":0.0159580228,"remaining_time":0.02603677404},
+{"learn":[0.07757010247],"iteration":38,"passed_time":0.0164692762,"remaining_time":0.02575963713},
+{"learn":[0.07342210315],"iteration":39,"passed_time":0.0170058631,"remaining_time":0.02550879465},
+{"learn":[0.07003440322],"iteration":40,"passed_time":0.0174213242,"remaining_time":0.02506971043},
+{"learn":[0.06754789554],"iteration":41,"passed_time":0.01786091045,"remaining_time":0.02466506682},
+{"learn":[0.0644413345],"iteration":42,"passed_time":0.01829391333,"remaining_time":0.02425007116},
+{"learn":[0.06194568707],"iteration":43,"passed_time":0.01873504127,"remaining_time":0.02384459798},
+{"learn":[0.0605542107],"iteration":44,"passed_time":0.01917583587,"remaining_time":0.02343713273},
+{"learn":[0.05874352321],"iteration":45,"passed_time":0.0196175888,"remaining_time":0.02302934338},
+{"learn":[0.05746787316],"iteration":46,"passed_time":0.02004409164,"remaining_time":0.02260291185},
+{"learn":[0.05640496179],"iteration":47,"passed_time":0.02050413637,"remaining_time":0.0222128144},
+{"learn":[0.05535837545],"iteration":48,"passed_time":0.02095184768,"remaining_time":0.02180702513},
+{"learn":[0.05382646834],"iteration":49,"passed_time":0.02136410042,"remaining_time":0.02136410042},
+{"learn":[0.05296457693],"iteration":50,"passed_time":0.02192385414,"remaining_time":0.02106409516},
+{"learn":[0.05197678655],"iteration":51,"passed_time":0.02228273153,"remaining_time":0.02056867526},
+{"learn":[0.05093132678],"iteration":52,"passed_time":0.02267602581,"remaining_time":0.02010892855},
+{"learn":[0.05048929053],"iteration":53,"passed_time":0.02313869556,"remaining_time":0.01971074066},
+{"learn":[0.04996352734],"iteration":54,"passed_time":0.02358344851,"remaining_time":0.01929554878},
+{"learn":[0.04947975597],"iteration":55,"passed_time":0.02401407638,"remaining_time":0.01886820287},
+{"learn":[0.04888330877],"iteration":56,"passed_time":0.02439524558,"remaining_time":0.01840343088},
+{"learn":[0.04779836697],"iteration":57,"passed_time":0.0249000406,"remaining_time":0.01803106388},
+{"learn":[0.04726341669],"iteration":58,"passed_time":0.02535729364,"remaining_time":0.01762117016},
+{"learn":[0.04685458537],"iteration":59,"passed_time":0.02581129666,"remaining_time":0.01720753111},
+{"learn":[0.04642503034],"iteration":60,"passed_time":0.02625838297,"remaining_time":0.01678814649},
+{"learn":[0.0460476406],"iteration":61,"passed_time":0.02665826063,"remaining_time":0.01633893393},
+{"learn":[0.04547020507],"iteration":62,"passed_time":0.02712859709,"remaining_time":0.01593266813},
+{"learn":[0.04475318154],"iteration":63,"passed_time":0.02759805855,"remaining_time":0.01552390793},
+{"learn":[0.04437426942],"iteration":64,"passed_time":0.02811102029,"remaining_time":0.01513670323},
+{"learn":[0.04409867641],"iteration":65,"passed_time":0.02861189862,"remaining_time":0.01473946293},
+{"learn":[0.04379427055],"iteration":66,"passed_time":0.02909886019,"remaining_time":0.01433227442},
+{"learn":[0.04341818047],"iteration":67,"passed_time":0.02950765458,"remaining_time":0.0138859551},
+{"learn":[0.0425917065],"iteration":68,"passed_time":0.02995999092,"remaining_time":0.01346028578},
+{"learn":[0.0423772057],"iteration":69,"passed_time":0.03036807697,"remaining_time":0.01301489013},
+{"learn":[0.04139066981],"iteration":70,"passed_time":0.03081153825,"remaining_time":0.0125849945},
+{"learn":[0.04122992812],"iteration":71,"passed_time":0.03118512407,"remaining_time":0.01212754825},
+{"learn":[0.04094914647],"iteration":72,"passed_time":0.03161471026,"remaining_time":0.01169311201},
+{"learn":[0.04047213505],"iteration":73,"passed_time":0.03203912975,"remaining_time":0.01125699153},
+{"learn":[0.04032414592],"iteration":74,"passed_time":0.03244179909,"remaining_time":0.01081393303},
+{"learn":[0.04013705762],"iteration":75,"passed_time":0.03287517698,"remaining_time":0.01038163483},
+{"learn":[0.03943460346],"iteration":76,"passed_time":0.03336251355,"remaining_time":0.009965426126},
+{"learn":[0.03899776777],"iteration":77,"passed_time":0.03380451649,"remaining_time":0.009534607215},
+{"learn":[0.03831405038],"iteration":78,"passed_time":0.03431276987,"remaining_time":0.009121116042},
+{"learn":[0.03810356748],"iteration":79,"passed_time":0.03472260593,"remaining_time":0.008680651482},
+{"learn":[0.03741460794],"iteration":80,"passed_time":0.03516690055,"remaining_time":0.008249026055},
+{"learn":[0.03714415302],"iteration":81,"passed_time":0.03560027843,"remaining_time":0.007814695266},
+{"learn":[0.03697811918],"iteration":82,"passed_time":0.03607215657,"remaining_time":0.007388273033},
+{"learn":[0.03673336099],"iteration":83,"passed_time":0.03658857667,"remaining_time":0.0069692527},
+{"learn":[0.03664944536],"iteration":84,"passed_time":0.03694253736,"remaining_time":0.006519271299},
+{"learn":[0.03646584715],"iteration":85,"passed_time":0.03737995694,"remaining_time":0.006085109269},
+{"learn":[0.03620197378],"iteration":86,"passed_time":0.03784041833,"remaining_time":0.005654315383},
+{"learn":[0.03585716755],"iteration":87,"passed_time":0.038241546,"remaining_time":0.005214756273},
+{"learn":[0.03567385169],"iteration":88,"passed_time":0.03864954871,"remaining_time":0.004776910515},
+{"learn":[0.03552018616],"iteration":89,"passed_time":0.03984251498,"remaining_time":0.004426946109},
+{"learn":[0.03519230786],"iteration":90,"passed_time":0.04028760127,"remaining_time":0.003984488038},
+{"learn":[0.03475388768],"iteration":91,"passed_time":0.04077510452,"remaining_time":0.003545661262},
+{"learn":[0.03424643427],"iteration":92,"passed_time":0.04121244076,"remaining_time":0.00310201167},
+{"learn":[0.03409792002],"iteration":93,"passed_time":0.04163498524,"remaining_time":0.002657552249},
+{"learn":[0.03378195114],"iteration":94,"passed_time":0.04206552977,"remaining_time":0.002213975251},
+{"learn":[0.03359561473],"iteration":95,"passed_time":0.04249424095,"remaining_time":0.001770593373},
+{"learn":[0.03316175526],"iteration":96,"passed_time":0.04292616049,"remaining_time":0.001327613211},
+{"learn":[0.03298153562],"iteration":97,"passed_time":0.04332170479,"remaining_time":0.0008841164242},
+{"learn":[0.03278383393],"iteration":98,"passed_time":0.04373120751,"remaining_time":0.0004417293688},
+{"learn":[0.03261317557],"iteration":99,"passed_time":0.04414225191,"remaining_time":0}
 ]}
\ No newline at end of file
diff --git a/catboost_info/learn/events.out.tfevents b/catboost_info/learn/events.out.tfevents
index f8fe950f377776234baa05ad3dfaa761668d4341..ad4498644ed890a60628257aa95aceea0902d234 100644
GIT binary patch
literal 4798
zcmZ|SSxi({9LI4K1VJjpqBF`O+F?_m<A7`mqlFO=MT8L$1X=2eWmLdgl|+kBWfch4
zg~5W23n*LCsHj2I*d}5n1w|A}t+W_Yp+Z@tQrndOG{1-c+^6&K$v5ZW-2Xk-N^A1z
z=rob)|GV7NIo`2ip{aqfflla}F!?NnQLD`BzvUt~&-vDqSj*w{rI7!%U<&xq`bP4}
z2e&`s{Z#OSi>t|>imOcJy*Buh&1K{l)K{G2y$*OC<#F<|RHH`TPXk}KJ%fDVlkOM1
z*99*ZCX-K;`Ay}$9{AnD4dg}ZU-<EUI{217a`G32mr8iA58ms!3;F$5y<YO(0DN@4
zn0!y|4?4V`0bWZyn|x;DNkiTnf-gwaCtn~M7V`cB@V0l}`AnX-)K7ld&ik3*hrjD3
zAN<?6Iq!|Yzw2)$|K2%o3hxEry|bIhd$hGw@!lA`WTcjSZL4((?@ho9&XkkicDwE-
z?@hsn))bT9s~k7xy&3qrD_P_R3y0cyKMVX=(|+<+?weeAKO6kh@~vv09_`J0A^1z(
z3i8Gc(sbU7!1tHR$%kEV%;NnV@a_lP$se@ec8B+K!5_(7L|$+#!;AOk;8hit<PQzC
zJ?6ay_;V5y@-NzlOn7ezz9&VW{PURR0Nz`HU(!0^J^8*I_m3{({XFnv9ew2Q2Ibc9
z-Wq)BvuEUm>kqi{em?lqCtApV>GY>N?-zj2`}7`pab;!<?>_`TwBZ{0=G$X-ycdJ7
zaj7PMq~l^O?`^<que?A$F0E~V_qO0oyUNHvujsYny&ZT(><RLMgzDYAw+AmxE+8-7
zwI-hT4&d{`v&lCMpSs5Th2VdDn?XLjI7-3$Md0r?q>~@a(cZy(NAPue`^a1Dh|w_L
zoWL8Zc9D<1d^3XYJA+pyCXsLKpW4j(kHGg#-%8#vX`hn!67ZijCXiR<m}|IiECz4$
z`9|^wjJq_v?^5syZz9Q?>P0#7`*8tZ`m2I`c(ju??_I&iszS);%K9|S+a=&T9|n`Z
zakR*Y@4JD|KfjuM-0Myi@7=)%R;?mmv@>lZ?>)eGc?OU#Rr)*g-V?mX_rByMvfZV;
z_W~dI)e7<jwXU0azZCq*J{kF>*SQ+L7t6pq+WU|nnCRAU|M3Q&Qo4-%W_eO1zaJm)
zqds2bpAFn7<h=~M<yv?0WlBjX@0Wvr(6oenQ})0v-sAs5%h#^t_m0}0=KaUeKO65t
z{+;#xeBS$l_ibNHe#?V<8qSX&_-|63$zNXHb(`<Y!Cx<SBJW*%w1W35!TTO_ARifI
zuE%?S@K=AfC$DgmTJSyqd{BcO`L~`a8s=Ldc#Hcs<V)T!lk@#m;QJS<eRuxDC%g{=
z-?T+c{ab~)*1TU0-Z^VN`Qk!>6Ytl6x4t=#eAVG>Q{D%ImkwBv?>H4v#{0G4(@oTV
zwE5*<ybl3yw0sWrn^V6K@qQinePR*$kt?qpcpnNrcCV29+_TClybl9^J#9Am<kof#
z`%!?uVX5{GkIhE-{(A6kF|(+j&~Z+~JP8M%8DK`fC+zMkz8?X8^d}SYeq)^%c^?VB
z(Ms)uAB5HLegk+_x-s>$O1=N!eH8ddHUje6ha?*A+tJ`fQ8UR0*Qb~B{f*!!<c8#L
zRp=)0J_fvR<qYyWe_yws_p#vL#2Sz<toHww_nW}ypV3$Mmu`K?`#A7t+w{nX#s@y-
neLVQ{Lbd;U&e|~ECxDL#(4~HOZ_Y696TxpTnMOY1mVebhd2;S5

literal 4798
zcmZ|Sc~DdZ90za^2$xWiD-Bo)<kH;)L|86?<xmy`VdGE^4MhfxBbuT^O&vv?AUOn4
z5Ks^>BrH5K9UK{5uZ$)<av~urxmwVPv{90pdOzp;$M5Z5?~l)X=l8zvz2AE))%pK-
zuh-OF^6#N<)6;uF3(SmW8|lZcP={J%eODjg^WR}oN|eS{&@S3zD_>|6UGU3%$s1KV
z-r@Z;@Kud<LVm`P{G+_r124BAKe1i8f%p30gBojv{A8=oi+FDUJ~N1XdeDq9-oFHX
z<nkdQe{y%T8}ALlHw2NF)|>k9emeLqH)@1@rNZk&-b=vWE+B7hm(<96Bk;SN$zOcd
z_dV}l2H#OoEzHyPaqI%#8-w?lI4Jn0$l3zl&j8=jPX6XVgvj~M1mBrKe&BMPF`qvR
z{G*xV=f6K{z<U$$YsU@<^KZ6r_Tv3)@b4ZXA3o4o!+TTkog2t2Bmrf-Hv=ChCtuYu
zuHd~nc>kMK!u%ocghun;0=#M$`E4FeGTvK)_mq*}tXmnw`&Yobj_w!cNx6P)9`EOX
z|FV_5dsy9R-p>Uer6TXKR6m>duY&(XTPe&lT^1~I|674?(~v(Q)m-57t-%-7lJ9*h
z`y}sez}xO1|5y^3&wD9&RT_Eo(GijTGY@==EBXFodC7dfE%=kO$WI<sisZir{(+`K
z=(8fXPQ~ZjfzN0rpIm**miPAHm0QTWc>dAE`}yF%NFaaW>AFPTJAhX%B=0$r{u}Qd
z!B;)mC!FsS|9w|@?*u-fhkR#R<1p`?!Cya4e$Zd>H}74*uPP&NxjQL=_Y1(w(#fC8
zGrz_Ah2Ssxk=Gx+a+~)u@X7Y%6LU{p;=LUFgW+<a&kKE9GkO0y_~9SPR~L`R^L`O{
zD-C(euy~Q{<qF>U40-t-tA0M;4Seoi^4`7mQr^3RUy?=MAk{}?|1SpLlSqDB>O&7c
z-vhjfocz_&O<(hV33$t?GNJ!mn>`}e*Asl@e)2gJfzSDTFYt9a<P9I&|H*p=`0zOL
zQ-y1Ec<&89Nk;yFWS+=A@d5vn1$lM%@6CL^FL>=xsnAcGz56NN`+>LZAU}58Pvm>p
zAN-?y@<SGbm3;nE@S7sZr(d3o;C%pi)g1CaI~5o5J`jA6A^Gz;4mWs@{})R7ON4&9
z^bY^ZdnM#wy+;0A(vZmh2?k&L75S;AhPixx2>8MZ^0Rch9`ZgEyn8nJt!}P{yblAP
zyMny2?dW~pF9UCBMP8j7BeJiSgU|YFuh8f6$X1d2EgZb|Jo$L(lTv;j75I<NlGhlY
z7U@3%{OAYdYj$Rq@cEJ8r^S$$9L^NkS1Z6PgI{>p)gte&DDYY*^1t*+*Yop4gD;nm
zuX4%D;{8hS=HGrQ^nbs=N0;|8;9EZ<|ASj)AMazq5AP(OGg&I~y`%=O*hpTTr!nL6
z-vDnOM}BbgdXfDf2YzuBdDY^q6h1#5{9Om~7iGRjc%J}%qaOLg?Vitgp9ucoSh3Lm
zd)6KsdA|yLNGJK#4c&UYUkzT;O1`}(SY&@Dp?{madibmjpPvlAFq(Yg4#_#*r+_yK
zAiq7LsEzlj;K%LA=Z}wxJYQ?Tm+O*mIph75&tD7vxwc5?-?6k?#J>q%dWrnqZ))Ux
aej51R8uE)8PVVCUI`9se<agV3IQ#=ZsnU`F

diff --git a/catboost_info/learn_error.tsv b/catboost_info/learn_error.tsv
index a6a5e9e..70f5c33 100644
--- a/catboost_info/learn_error.tsv
+++ b/catboost_info/learn_error.tsv
@@ -1,101 +1,101 @@
 iter	RMSE
-0	1.256360626
-1	1.251808953
-2	1.247210496
-3	1.242833733
-4	1.239387587
-5	1.236371217
-6	1.232602415
-7	1.228565544
-8	1.224959973
-9	1.222864733
-10	1.219986592
-11	1.21504822
-12	1.210577483
-13	1.208485581
-14	1.206290967
-15	1.203580935
-16	1.202587086
-17	1.200117752
-18	1.198447951
-19	1.196695865
-20	1.193763392
-21	1.191567691
-22	1.188849173
-23	1.186864811
-24	1.184927235
-25	1.181955097
-26	1.17912423
-27	1.177938096
-28	1.176713425
-29	1.175846054
-30	1.174874495
-31	1.173359875
-32	1.172354175
-33	1.170132423
-34	1.169171239
-35	1.167885021
-36	1.166738598
-37	1.16538268
-38	1.163805962
-39	1.162699556
-40	1.161523056
-41	1.160716234
-42	1.159337474
-43	1.158104071
-44	1.157329122
-45	1.15557551
-46	1.155148656
-47	1.154483176
-48	1.154026957
-49	1.152814821
-50	1.151770225
-51	1.151148222
-52	1.149812664
-53	1.148409611
-54	1.145251093
-55	1.144031355
-56	1.143227736
-57	1.141978627
-58	1.141292235
-59	1.139920777
-60	1.138278909
-61	1.136472836
-62	1.135144584
-63	1.133553757
-64	1.133033926
-65	1.131853111
-66	1.131157871
-67	1.130634038
-68	1.129563312
-69	1.12836294
-70	1.127363159
-71	1.125826623
-72	1.124768863
-73	1.122953001
-74	1.122847944
-75	1.120908144
-76	1.119659624
-77	1.119240104
-78	1.118405907
-79	1.117479722
-80	1.115664056
-81	1.114583378
-82	1.113431037
-83	1.112616571
-84	1.111971395
-85	1.111761448
-86	1.110529727
-87	1.109479239
-88	1.108844078
-89	1.107368969
-90	1.106782088
-91	1.105012074
-92	1.103989616
-93	1.103486322
-94	1.102628551
-95	1.101610577
-96	1.101012886
-97	1.099734966
-98	1.098536921
-99	1.096958773
+0	1.648905251
+1	1.497629345
+2	1.360899163
+3	1.240618702
+4	1.128387921
+5	1.026662891
+6	0.9300425815
+7	0.8480139671
+8	0.7712831482
+9	0.7025638579
+10	0.6433465066
+11	0.5864868263
+12	0.5366753809
+13	0.493668726
+14	0.452697423
+15	0.4139326703
+16	0.378007788
+17	0.3456282594
+18	0.3156361609
+19	0.2884469664
+20	0.2630439138
+21	0.2448805528
+22	0.2262033151
+23	0.2078844467
+24	0.192981451
+25	0.1791809635
+26	0.1675015104
+27	0.1539406323
+28	0.1427636543
+29	0.134231155
+30	0.1239380012
+31	0.1146735567
+32	0.1068619755
+33	0.1018497484
+34	0.09591207292
+35	0.09001531388
+36	0.08505951254
+37	0.08167650816
+38	0.07757010247
+39	0.07342210315
+40	0.07003440322
+41	0.06754789554
+42	0.0644413345
+43	0.06194568707
+44	0.0605542107
+45	0.05874352321
+46	0.05746787316
+47	0.05640496179
+48	0.05535837545
+49	0.05382646834
+50	0.05296457693
+51	0.05197678655
+52	0.05093132678
+53	0.05048929053
+54	0.04996352734
+55	0.04947975597
+56	0.04888330877
+57	0.04779836697
+58	0.04726341669
+59	0.04685458537
+60	0.04642503034
+61	0.0460476406
+62	0.04547020507
+63	0.04475318154
+64	0.04437426942
+65	0.04409867641
+66	0.04379427055
+67	0.04341818047
+68	0.0425917065
+69	0.0423772057
+70	0.04139066981
+71	0.04122992812
+72	0.04094914647
+73	0.04047213505
+74	0.04032414592
+75	0.04013705762
+76	0.03943460346
+77	0.03899776777
+78	0.03831405038
+79	0.03810356748
+80	0.03741460794
+81	0.03714415302
+82	0.03697811918
+83	0.03673336099
+84	0.03664944536
+85	0.03646584715
+86	0.03620197378
+87	0.03585716755
+88	0.03567385169
+89	0.03552018616
+90	0.03519230786
+91	0.03475388768
+92	0.03424643427
+93	0.03409792002
+94	0.03378195114
+95	0.03359561473
+96	0.03316175526
+97	0.03298153562
+98	0.03278383393
+99	0.03261317557
diff --git a/catboost_info/time_left.tsv b/catboost_info/time_left.tsv
index 7b79eaa..c5babe2 100644
--- a/catboost_info/time_left.tsv
+++ b/catboost_info/time_left.tsv
@@ -1,101 +1,101 @@
 iter	Passed	Remaining
 0	0	46
-1	0	42
-2	1	38
-3	1	37
-4	1	36
-5	2	39
-6	2	38
-7	3	37
-8	3	36
-9	4	36
-10	4	35
-11	4	34
-12	5	34
-13	5	33
-14	5	33
-15	6	32
-16	6	32
-17	7	31
-18	7	31
-19	7	31
-20	8	31
+1	0	44
+2	1	41
+3	1	42
+4	2	40
+5	2	40
+6	3	40
+7	3	39
+8	3	40
+9	4	39
+10	4	38
+11	5	38
+12	5	37
+13	5	36
+14	6	36
+15	6	35
+16	7	35
+17	7	34
+18	8	34
+19	8	33
+20	8	33
 21	9	33
-22	9	33
+22	9	32
 23	10	32
-24	10	32
+24	10	31
 25	11	31
-26	11	30
+26	11	31
 27	11	30
 28	12	29
 29	12	29
 30	12	28
 31	13	28
-32	13	27
+32	13	28
 33	14	27
-34	14	26
-35	14	26
+34	14	27
+35	15	26
 36	15	26
-37	15	25
+37	15	26
 38	16	25
-39	16	24
-40	17	24
+39	17	25
+40	17	25
 41	17	24
-42	17	23
+42	18	24
 43	18	23
-44	18	22
-45	19	22
-46	19	22
-47	19	21
+44	19	23
+45	19	23
+46	20	22
+47	20	22
 48	20	21
-49	20	20
-50	21	20
-51	21	20
-52	22	19
-53	22	19
-54	23	18
-55	23	18
-56	23	17
-57	24	17
-58	24	17
-59	25	16
-60	25	16
+49	21	21
+50	21	21
+51	22	20
+52	22	20
+53	23	19
+54	23	19
+55	24	18
+56	24	18
+57	24	18
+58	25	17
+59	25	17
+60	26	16
 61	26	16
-62	26	15
-63	26	15
-64	27	14
-65	27	14
-66	28	13
-67	28	13
+62	27	15
+63	27	15
+64	28	15
+65	28	14
+66	29	14
+67	29	13
 68	29	13
-69	29	12
+69	30	13
 70	30	12
-71	30	11
+71	31	12
 72	31	11
-73	31	11
-74	31	10
+73	32	11
+74	32	10
 75	32	10
-76	32	9
+76	33	9
 77	33	9
-78	33	8
-79	33	8
-80	34	8
+78	34	9
+79	34	8
+80	35	8
 81	35	7
-82	35	7
-83	35	6
+82	36	7
+83	36	6
 84	36	6
-85	36	5
+85	37	6
 86	37	5
-87	37	5
+87	38	5
 88	38	4
-89	38	4
-90	38	3
-91	39	3
-92	39	2
-93	40	2
-94	40	2
-95	41	1
-96	41	1
-97	41	0
-98	42	0
-99	42	0
+89	39	4
+90	40	3
+91	40	3
+92	41	3
+93	41	2
+94	42	2
+95	42	1
+96	42	1
+97	43	0
+98	43	0
+99	44	0
diff --git a/storage/input_json/user/preprocessed_user_features.csv b/storage/input_json/user/preprocessed_user_features.csv
new file mode 100644
index 0000000..3bb1f3c
--- /dev/null
+++ b/storage/input_json/user/preprocessed_user_features.csv
@@ -0,0 +1,1005 @@
+user_id,max_price,min_price,category_1,category_2,category_3,category_4,category_5,category_6,category_7,category_8,category_9,category_10,category_11,category_12,total_reservations,completed_reservations,reservation_completion_rate,total_likes,like_to_reservation_ratio
+1,286141,183646,0,1,1,0,0,0,0,0,0,0,1,0,8.0,8,1.0,6,0.75
+2,444065,170838,0,0,0,0,0,1,1,0,0,1,0,0,4.0,4,1.0,6,1.5
+3,395779,79971,0,0,1,1,0,0,1,0,0,0,0,0,2.0,2,1.0,4,2.0
+4,200419,82759,1,0,0,0,0,0,1,0,0,0,1,0,8.0,8,1.0,6,0.75
+5,341399,147700,0,0,1,0,0,1,0,0,1,0,0,0,2.0,2,1.0,4,2.0
+6,304138,159625,0,1,1,0,1,0,0,0,0,0,0,0,10.0,10,1.0,0,0.0
+7,338378,151460,1,0,0,0,0,1,0,0,0,0,0,1,2.0,2,1.0,12,6.0
+8,376584,66889,0,0,0,0,0,0,1,1,0,1,0,0,4.0,4,1.0,2,0.5
+9,393014,91158,0,0,0,1,0,0,0,0,1,0,1,0,2.0,2,1.0,4,2.0
+10,267487,170163,0,0,1,1,0,0,0,1,0,0,0,0,,0,0.0,8,5.0
+11,470524,29163,1,0,0,0,1,0,0,0,0,0,1,0,6.0,6,1.0,4,0.67
+12,446869,142652,0,1,0,0,0,0,0,1,0,0,1,0,6.0,6,1.0,4,0.67
+13,351840,185816,0,1,1,0,0,0,0,0,0,0,0,1,8.0,8,1.0,6,0.75
+14,332509,38329,0,0,0,0,0,0,1,1,1,0,0,0,2.0,2,1.0,6,3.0
+15,266941,59156,0,1,0,0,0,0,1,0,0,0,0,1,12.0,12,1.0,10,0.83
+16,337197,194701,1,1,0,0,0,0,0,0,1,0,0,0,2.0,2,1.0,8,4.0
+17,481858,85248,0,0,1,1,0,0,0,0,0,1,0,0,2.0,2,1.0,6,3.0
+18,282926,36257,1,0,0,0,1,0,0,0,0,0,1,0,2.0,2,1.0,10,5.0
+19,327603,197901,1,0,0,0,0,0,0,0,1,0,0,1,8.0,8,1.0,4,0.5
+20,292092,166283,0,1,0,0,0,0,0,1,0,1,0,0,10.0,10,1.0,8,0.8
+21,353557,28934,0,0,1,0,0,0,0,1,0,0,0,1,2.0,2,1.0,2,1.0
+22,319083,167451,0,1,1,0,0,0,0,0,0,1,0,0,6.0,6,1.0,8,1.33
+23,295629,72379,0,0,1,0,0,0,0,1,0,0,0,1,2.0,2,1.0,8,4.0
+24,279131,95305,0,0,0,1,0,0,0,0,0,1,0,1,4.0,4,1.0,16,4.0
+25,233947,106565,0,0,1,0,0,1,0,0,0,0,0,1,6.0,6,1.0,4,0.67
+26,288981,180902,1,0,0,0,0,1,0,0,0,1,0,0,2.0,2,1.0,6,3.0
+27,254815,187129,1,1,0,0,0,0,0,0,1,0,0,0,2.0,2,1.0,8,4.0
+28,403490,169312,0,0,0,1,0,0,1,0,0,0,0,1,10.0,10,1.0,26,2.6
+29,403053,151914,0,0,0,0,0,0,1,0,1,0,0,1,4.0,4,1.0,6,1.5
+30,227881,133174,0,0,0,1,0,1,0,0,0,0,1,0,6.0,6,1.0,4,0.67
+31,207906,88090,0,0,1,0,0,0,0,0,1,1,0,0,2.0,2,1.0,10,5.0
+32,270292,97183,0,1,1,0,0,0,0,0,0,1,0,0,4.0,4,1.0,6,1.5
+33,403876,89280,0,0,0,0,0,0,0,1,1,0,1,0,6.0,6,1.0,4,0.67
+34,312422,61746,0,0,1,1,0,0,0,0,0,1,0,0,6.0,6,1.0,8,1.33
+35,369064,109942,0,0,0,0,0,1,0,0,0,1,1,0,6.0,6,1.0,8,1.33
+36,493736,110323,0,1,1,0,0,0,1,0,0,0,0,0,2.0,2,1.0,2,1.0
+37,469280,35055,1,0,0,1,0,0,0,0,0,0,0,1,2.0,2,1.0,4,2.0
+38,484502,61187,1,0,0,0,0,0,0,0,1,1,0,0,4.0,4,1.0,8,2.0
+39,311737,164628,0,0,0,1,0,0,0,0,0,1,1,0,2.0,2,1.0,12,6.0
+40,238578,199747,0,0,0,1,0,0,1,0,1,0,0,0,8.0,8,1.0,0,0.0
+41,492507,124228,0,0,0,1,0,1,1,0,0,0,0,0,14.0,14,1.0,4,0.29
+42,274639,87199,0,1,0,1,0,0,0,0,0,0,0,1,6.0,6,1.0,8,1.33
+43,380147,35046,0,0,0,1,0,0,1,0,0,0,1,0,4.0,4,1.0,8,2.0
+44,422839,141179,0,0,0,0,0,0,0,1,1,0,1,0,4.0,4,1.0,4,1.0
+45,473654,131248,0,0,0,1,0,1,1,0,0,0,0,0,4.0,4,1.0,8,2.0
+46,410500,54614,0,0,0,1,0,1,0,0,1,0,0,0,,0,0.0,8,5.0
+47,406337,96377,0,0,1,0,0,0,0,0,0,0,1,1,2.0,2,1.0,2,1.0
+48,489105,173326,0,0,1,1,0,0,0,0,0,0,0,1,10.0,10,1.0,8,0.8
+49,459629,48247,0,0,1,0,1,0,0,1,0,0,0,0,10.0,10,1.0,4,0.4
+50,364108,174482,0,0,1,1,0,0,0,1,0,0,0,0,4.0,4,1.0,6,1.5
+51,259108,89491,0,1,1,0,0,0,1,0,0,0,0,0,2.0,2,1.0,4,2.0
+52,200179,105429,0,1,0,0,1,0,0,1,0,0,0,0,4.0,4,1.0,2,0.5
+53,236409,108492,0,0,1,0,0,0,0,0,1,1,0,0,4.0,4,1.0,14,3.5
+54,422377,81914,1,0,0,0,0,0,1,0,1,0,0,0,6.0,6,1.0,10,1.67
+55,388247,16187,0,0,1,0,0,0,0,0,0,1,0,1,2.0,2,1.0,8,4.0
+56,309257,67266,0,0,1,0,1,0,0,0,0,0,1,0,2.0,2,1.0,10,5.0
+57,203384,55275,1,0,0,0,0,0,1,1,0,0,0,0,10.0,10,1.0,6,0.6
+58,329780,83254,0,0,1,0,0,0,1,0,0,0,0,1,,0,0.0,8,5.0
+59,401574,129489,0,0,1,0,0,1,0,1,0,0,0,0,2.0,2,1.0,10,5.0
+60,271891,43058,0,0,0,0,0,1,0,1,0,0,0,1,12.0,12,1.0,16,1.33
+61,256791,187570,1,0,1,0,0,0,0,1,0,0,0,0,10.0,10,1.0,10,1.0
+62,419351,21516,0,0,0,0,0,0,0,0,1,1,1,0,8.0,8,1.0,4,0.5
+63,235396,92464,0,0,1,0,0,0,1,0,0,0,1,0,8.0,8,1.0,8,1.0
+64,246214,150863,0,0,0,1,0,0,0,1,1,0,0,0,6.0,6,1.0,10,1.67
+65,485892,155450,0,0,0,1,0,1,0,0,0,0,1,0,2.0,2,1.0,2,1.0
+66,234185,79818,0,0,0,1,0,0,1,0,0,0,1,0,10.0,10,1.0,8,0.8
+67,472937,181198,0,0,0,0,0,0,1,1,0,1,0,0,6.0,6,1.0,4,0.67
+68,401695,121572,0,1,1,0,0,0,0,1,0,0,0,0,2.0,2,1.0,4,2.0
+69,492640,53616,0,1,0,0,0,0,1,1,0,0,0,0,6.0,6,1.0,2,0.33
+70,375454,119576,0,0,1,0,0,0,0,0,0,1,1,0,10.0,10,1.0,4,0.4
+71,354216,13768,0,1,0,0,0,0,0,1,0,1,0,0,2.0,2,1.0,2,1.0
+72,424256,97033,0,1,0,0,0,0,0,1,1,0,0,0,2.0,2,1.0,10,5.0
+73,345640,58444,0,0,1,0,0,0,1,0,0,0,1,0,10.0,10,1.0,2,0.2
+74,248236,120737,1,0,0,0,0,1,1,0,0,0,0,0,,0,0.0,12,5.0
+75,267707,123521,0,1,0,0,1,0,0,0,1,0,0,0,4.0,4,1.0,2,0.5
+76,470071,172019,0,0,0,0,0,1,1,1,0,0,0,0,2.0,2,1.0,2,1.0
+77,399068,90198,0,0,0,0,0,1,1,0,1,0,0,0,8.0,8,1.0,4,0.5
+78,308313,65073,0,0,1,0,0,0,0,0,1,0,0,1,4.0,4,1.0,6,1.5
+79,234246,149171,0,0,0,0,1,1,0,0,0,0,1,0,4.0,4,1.0,10,2.5
+80,267787,79665,1,1,0,0,0,0,1,0,0,0,0,0,4.0,4,1.0,4,1.0
+81,393826,22004,0,0,1,1,0,0,0,0,0,1,0,0,4.0,4,1.0,6,1.5
+82,229961,94945,0,0,0,0,0,0,0,1,1,1,0,0,8.0,8,1.0,4,0.5
+83,444105,167915,0,1,0,0,0,0,0,1,1,0,0,0,8.0,8,1.0,4,0.5
+84,382730,132551,0,0,0,1,1,0,0,1,0,0,0,0,4.0,4,1.0,6,1.5
+85,205497,46979,1,1,0,1,0,0,0,0,0,0,0,0,,0,0.0,6,5.0
+86,398628,185050,0,1,0,0,1,0,0,1,0,0,0,0,8.0,8,1.0,4,0.5
+87,378633,135979,0,1,0,1,1,0,0,0,0,0,0,0,4.0,4,1.0,2,0.5
+88,470318,148192,0,0,0,1,1,0,0,0,1,0,0,0,6.0,6,1.0,10,1.67
+89,238334,185320,0,0,0,0,0,0,0,1,0,0,1,1,12.0,12,1.0,6,0.5
+90,421438,26856,0,1,0,1,0,1,0,0,0,0,0,0,2.0,2,1.0,4,2.0
+91,486531,50526,0,0,1,0,1,0,0,1,0,0,0,0,4.0,4,1.0,6,1.5
+92,364554,82141,0,0,0,0,0,1,0,0,0,0,1,1,6.0,6,1.0,4,0.67
+93,345162,66823,0,0,0,0,1,1,0,0,0,0,0,1,2.0,2,1.0,12,6.0
+94,401582,85499,1,0,0,0,0,0,0,0,1,1,0,0,6.0,6,1.0,6,1.0
+95,231653,96497,0,0,0,1,1,0,1,0,0,0,0,0,10.0,10,1.0,4,0.4
+96,452322,29299,1,0,0,0,0,0,0,1,0,1,0,0,4.0,4,1.0,8,2.0
+97,246133,27202,1,0,0,0,0,0,1,0,1,0,0,0,4.0,4,1.0,2,0.5
+98,330349,129763,0,0,1,0,1,0,0,0,1,0,0,0,8.0,8,1.0,2,0.25
+99,303372,93365,0,0,0,0,1,0,0,1,0,0,0,1,4.0,4,1.0,0,0.0
+100,437106,79353,1,0,0,1,0,0,0,1,0,0,0,0,4.0,4,1.0,6,1.5
+101,223500,140727,0,0,0,0,0,0,1,0,0,1,0,1,2.0,2,1.0,0,0.0
+102,354243,126610,0,0,0,0,0,0,0,0,1,0,1,1,12.0,12,1.0,8,0.67
+103,426530,12059,0,0,1,0,0,0,0,1,0,1,0,0,,0,0.0,6,5.0
+104,332202,132141,0,0,0,1,0,0,0,0,1,0,0,1,,0,0.0,10,5.0
+105,499973,37922,0,0,1,0,1,0,0,1,0,0,0,0,,0,0.0,6,5.0
+106,230682,64249,0,0,0,0,1,1,0,1,0,0,0,0,10.0,10,1.0,4,0.4
+107,467101,65313,0,0,0,0,1,1,0,0,1,0,0,0,8.0,8,1.0,0,0.0
+108,244275,169377,0,0,1,1,0,0,0,0,0,0,1,0,8.0,8,1.0,0,0.0
+109,258548,15342,0,0,0,1,0,1,0,1,0,0,0,0,10.0,10,1.0,12,1.2
+110,355408,97494,0,0,0,0,1,1,0,0,0,0,1,0,6.0,6,1.0,6,1.0
+111,490070,36258,0,0,1,0,0,0,1,0,0,0,0,1,,0,0.0,8,5.0
+112,263578,115332,0,1,0,0,0,0,1,0,0,1,0,0,6.0,6,1.0,10,1.67
+113,312245,127054,0,0,0,1,0,0,0,0,1,0,0,1,4.0,4,1.0,8,2.0
+114,327001,166239,0,0,1,1,1,0,0,0,0,0,0,0,4.0,4,1.0,2,0.5
+115,285426,113164,0,0,0,0,0,0,1,1,0,0,1,0,8.0,8,1.0,8,1.0
+116,294168,127969,0,1,1,0,0,0,0,0,0,0,0,1,4.0,4,1.0,4,1.0
+117,391158,176237,1,0,0,1,0,0,0,0,1,0,0,0,4.0,4,1.0,6,1.5
+118,407527,71949,0,0,0,0,0,1,0,0,1,1,0,0,4.0,4,1.0,0,0.0
+119,357736,67831,0,0,0,0,1,0,0,0,1,0,0,1,8.0,8,1.0,6,0.75
+120,404101,144428,1,0,0,1,0,1,0,0,0,0,0,0,4.0,4,1.0,6,1.5
+121,219612,41318,0,0,0,1,0,1,0,0,1,0,0,0,8.0,8,1.0,4,0.5
+122,318302,30258,0,0,1,0,0,1,0,0,0,1,0,0,4.0,4,1.0,8,2.0
+123,288533,44469,0,0,0,1,0,0,1,1,0,0,0,0,10.0,10,1.0,10,1.0
+124,207023,75965,1,0,0,1,0,1,0,0,0,0,0,0,2.0,2,1.0,2,1.0
+125,429278,87887,1,1,0,0,0,0,0,0,0,1,0,0,4.0,4,1.0,2,0.5
+126,409992,121431,1,0,1,0,0,0,0,1,0,0,0,0,8.0,8,1.0,2,0.25
+127,451142,137504,0,0,0,1,0,1,0,0,1,0,0,0,10.0,10,1.0,12,1.2
+128,375827,105166,0,1,1,0,0,0,1,0,0,0,0,0,,0,0.0,10,5.0
+129,495343,120288,0,1,1,0,0,1,0,0,0,0,0,0,6.0,6,1.0,6,1.0
+130,328338,20029,0,0,0,0,1,0,0,0,0,1,1,0,6.0,6,1.0,6,1.0
+131,299643,168222,0,0,0,0,0,0,1,0,0,1,1,0,6.0,6,1.0,4,0.67
+132,366391,27774,0,0,1,0,0,0,1,0,0,0,0,1,8.0,8,1.0,8,1.0
+133,433053,53942,0,0,1,0,0,0,1,0,0,0,0,1,2.0,2,1.0,10,5.0
+134,275331,94983,0,0,0,0,0,0,1,0,1,0,0,1,2.0,2,1.0,8,4.0
+135,379968,11980,0,0,0,0,1,0,0,0,0,1,1,0,4.0,4,1.0,6,1.5
+136,349473,160707,0,0,0,0,1,0,1,0,0,0,1,0,4.0,4,1.0,8,2.0
+137,464334,84683,0,0,0,0,0,0,1,0,0,1,0,1,6.0,6,1.0,10,1.67
+138,359174,18420,0,0,0,0,1,1,0,0,0,0,1,0,2.0,2,1.0,2,1.0
+139,479372,50259,0,0,1,0,0,0,0,0,1,1,0,0,4.0,4,1.0,4,1.0
+140,276422,164299,0,0,0,0,1,0,0,0,0,0,1,1,8.0,8,1.0,10,1.25
+141,268704,176375,1,0,0,0,0,0,0,1,0,0,0,1,8.0,8,1.0,12,1.5
+142,321766,196059,1,0,0,0,0,1,1,0,0,0,0,0,6.0,6,1.0,6,1.0
+143,244480,182013,0,0,1,0,1,0,0,0,1,0,0,0,2.0,2,1.0,4,2.0
+144,491347,45546,0,0,1,1,0,0,0,0,0,1,0,0,4.0,4,1.0,6,1.5
+145,479164,31723,0,0,1,0,0,0,0,0,1,0,0,1,4.0,4,1.0,6,1.5
+146,450496,180807,0,0,0,0,0,1,0,0,0,1,1,0,4.0,4,1.0,2,0.5
+147,473203,178811,0,0,0,0,1,0,0,1,0,1,0,0,8.0,8,1.0,12,1.5
+148,379234,139834,0,1,1,0,0,0,0,0,0,0,0,1,2.0,2,1.0,2,1.0
+149,463183,100745,1,0,0,0,0,1,0,0,0,0,1,0,6.0,6,1.0,10,1.67
+150,268605,39040,0,1,0,0,1,0,0,0,0,0,0,1,2.0,2,1.0,4,2.0
+151,201272,29406,0,1,0,0,1,1,0,0,0,0,0,0,2.0,2,1.0,6,3.0
+152,260646,123073,0,0,0,1,0,1,0,0,0,0,1,0,8.0,8,1.0,6,0.75
+153,432589,168024,0,0,0,1,1,0,0,0,0,0,0,1,6.0,6,1.0,8,1.33
+154,441903,167510,1,0,0,0,0,0,0,0,1,0,1,0,2.0,2,1.0,12,6.0
+155,407162,40023,1,0,0,0,0,0,0,0,0,0,1,1,8.0,8,1.0,10,1.25
+156,277219,159091,0,0,1,0,1,0,1,0,0,0,0,0,2.0,2,1.0,14,7.0
+157,434645,48399,0,0,0,0,0,0,0,1,0,1,1,0,8.0,8,1.0,6,0.75
+158,330608,144690,0,0,1,0,1,0,0,0,0,1,0,0,2.0,2,1.0,2,1.0
+159,301470,90552,0,0,0,0,0,0,0,1,1,0,1,0,2.0,2,1.0,4,2.0
+160,231090,32498,0,1,0,0,1,0,0,0,1,0,0,0,6.0,6,1.0,10,1.67
+161,230042,159215,0,0,1,1,0,1,0,0,0,0,0,0,10.0,10,1.0,10,1.0
+162,335301,149189,0,0,0,1,0,1,0,0,0,0,1,0,2.0,2,1.0,4,2.0
+163,268901,85454,1,0,1,0,0,0,0,0,0,1,0,0,6.0,6,1.0,12,2.0
+164,431844,31263,0,0,1,0,1,0,1,0,0,0,0,0,,0,0.0,6,5.0
+165,305315,66125,0,0,0,0,1,0,1,1,0,0,0,0,8.0,8,1.0,8,1.0
+166,461726,158451,1,0,1,0,0,1,0,0,0,0,0,0,8.0,8,1.0,2,0.25
+167,291481,19032,1,0,1,0,0,0,0,0,0,1,0,0,6.0,6,1.0,0,0.0
+168,345816,78571,1,1,0,1,0,0,0,0,0,0,0,0,4.0,4,1.0,6,1.5
+169,309393,198909,1,0,0,1,0,0,0,0,1,0,0,0,,0,0.0,8,5.0
+170,260505,94505,0,1,0,0,0,0,0,0,1,1,0,0,2.0,2,1.0,2,1.0
+171,278640,74242,1,0,0,0,1,0,1,0,0,0,0,0,2.0,2,1.0,4,2.0
+172,448128,59574,1,0,0,0,0,0,0,1,0,0,1,0,8.0,8,1.0,16,2.0
+173,431000,146506,0,0,0,1,0,0,1,1,0,0,0,0,4.0,4,1.0,2,0.5
+174,364180,61310,1,0,0,0,0,0,1,0,0,1,0,0,6.0,6,1.0,8,1.33
+175,356124,41448,0,0,1,0,0,1,0,0,0,1,0,0,2.0,2,1.0,6,3.0
+176,468564,162622,0,0,0,1,1,0,0,1,0,0,0,0,2.0,2,1.0,6,3.0
+177,220994,107493,1,0,1,0,0,0,0,1,0,0,0,0,12.0,12,1.0,6,0.5
+178,290759,196399,0,1,0,0,0,0,0,0,1,1,0,0,,0,0.0,6,5.0
+179,400526,193715,1,0,1,0,1,0,0,0,0,0,0,0,2.0,2,1.0,16,8.0
+180,413880,79311,0,1,0,1,1,0,0,0,0,0,0,0,6.0,6,1.0,2,0.33
+181,321989,11008,1,0,0,0,0,0,1,0,0,0,1,0,10.0,10,1.0,4,0.4
+182,345544,170913,0,0,0,1,1,0,0,0,1,0,0,0,8.0,8,1.0,4,0.5
+183,221962,183937,1,1,0,0,0,0,0,0,1,0,0,0,6.0,6,1.0,6,1.0
+184,311028,161327,1,0,0,0,0,1,0,1,0,0,0,0,10.0,10,1.0,10,1.0
+185,248252,183519,1,0,1,0,0,0,0,0,0,0,1,0,2.0,2,1.0,2,1.0
+186,451106,122923,0,0,0,0,0,0,1,0,1,0,1,0,2.0,2,1.0,10,5.0
+187,405380,147113,1,0,0,0,0,0,0,0,1,0,1,0,12.0,12,1.0,2,0.17
+188,203839,28523,0,1,0,0,0,0,0,0,1,0,0,1,6.0,6,1.0,4,0.67
+189,333806,76186,0,0,1,0,0,1,1,0,0,0,0,0,4.0,4,1.0,4,1.0
+190,432627,92873,0,0,0,0,1,0,0,0,1,0,0,1,6.0,6,1.0,18,3.0
+191,215347,95798,1,0,0,1,0,0,0,0,0,0,1,0,4.0,4,1.0,6,1.5
+192,404671,118625,0,0,0,1,0,1,0,0,0,1,0,0,2.0,2,1.0,12,6.0
+193,363349,82141,1,0,0,0,1,0,1,0,0,0,0,0,6.0,6,1.0,2,0.33
+194,278162,176609,0,1,1,0,0,1,0,0,0,0,0,0,2.0,2,1.0,10,5.0
+195,384349,89445,0,0,1,0,0,0,0,1,0,0,1,0,6.0,6,1.0,0,0.0
+196,422863,69355,0,0,0,0,0,0,1,0,1,0,1,0,4.0,4,1.0,12,3.0
+197,203902,149506,1,0,0,0,0,1,0,0,0,1,0,0,2.0,2,1.0,14,7.0
+198,474910,51413,0,0,1,0,0,0,1,0,1,0,0,0,,0,0.0,12,5.0
+199,476345,79957,1,0,0,0,0,0,0,0,1,0,1,0,6.0,6,1.0,12,2.0
+200,220967,15234,0,0,0,0,1,1,0,0,0,0,1,0,4.0,4,1.0,12,3.0
+201,355305,72795,0,1,0,0,0,0,1,0,1,0,0,0,6.0,6,1.0,6,1.0
+202,374021,44532,0,1,0,1,0,0,0,1,0,0,0,0,6.0,6,1.0,4,0.67
+203,279504,51617,0,0,0,1,0,1,0,0,1,0,0,0,6.0,6,1.0,12,2.0
+204,443296,160288,0,1,1,0,1,0,0,0,0,0,0,0,2.0,2,1.0,4,2.0
+205,375556,180085,0,1,0,0,0,0,1,0,0,1,0,0,4.0,4,1.0,6,1.5
+206,251345,176851,0,0,0,1,0,0,1,0,0,0,0,1,4.0,4,1.0,6,1.5
+207,494189,172931,0,1,0,0,0,0,0,1,0,0,0,1,8.0,8,1.0,8,1.0
+208,380802,94459,0,0,0,1,1,0,0,0,0,0,0,1,6.0,6,1.0,8,1.33
+209,237485,15499,1,1,0,0,0,0,0,0,0,0,1,0,2.0,2,1.0,4,2.0
+210,389579,74857,0,0,0,1,0,0,0,0,1,0,0,1,4.0,4,1.0,4,1.0
+211,360769,92737,0,0,0,1,0,0,1,0,0,0,0,1,2.0,2,1.0,6,3.0
+212,433681,66872,0,1,1,0,0,0,0,0,0,0,0,1,10.0,10,1.0,2,0.2
+213,244503,153773,0,0,1,1,0,0,0,0,0,0,0,1,,0,0.0,4,5.0
+214,282906,138552,0,1,0,0,0,1,0,0,0,1,0,0,6.0,6,1.0,6,1.0
+215,309651,27015,0,0,1,1,1,0,0,0,0,0,0,0,4.0,4,1.0,2,0.5
+216,235826,35438,0,0,0,0,0,1,0,1,0,0,0,1,10.0,10,1.0,8,0.8
+217,348160,50165,0,1,0,1,0,0,0,0,0,0,1,0,4.0,4,1.0,10,2.5
+218,252461,37506,1,0,0,0,1,1,0,0,0,0,0,0,8.0,8,1.0,10,1.25
+219,425014,120768,0,1,0,0,1,0,1,0,0,0,0,0,12.0,12,1.0,4,0.33
+220,484657,54360,0,0,0,0,1,0,1,0,0,0,1,0,6.0,6,1.0,4,0.67
+221,357157,115437,0,0,0,0,0,0,1,1,1,0,0,0,12.0,12,1.0,2,0.17
+222,409313,88353,0,1,0,0,1,0,0,0,0,1,0,0,4.0,4,1.0,4,1.0
+223,295721,179055,1,0,0,0,0,0,0,0,0,1,1,0,10.0,10,1.0,8,0.8
+224,211633,26505,1,0,0,0,1,1,0,0,0,0,0,0,8.0,8,1.0,8,1.0
+225,301350,99891,0,0,0,0,1,0,0,0,1,1,0,0,6.0,6,1.0,8,1.33
+226,262495,70481,0,0,0,1,0,0,0,1,0,0,0,1,10.0,10,1.0,6,0.6
+227,210992,35595,0,0,0,0,0,1,0,0,1,0,1,0,4.0,4,1.0,4,1.0
+228,266644,79859,0,1,1,0,0,0,1,0,0,0,0,0,8.0,8,1.0,8,1.0
+229,279378,93433,1,0,1,0,0,1,0,0,0,0,0,0,2.0,2,1.0,10,5.0
+230,358083,49661,1,0,0,0,1,1,0,0,0,0,0,0,4.0,4,1.0,2,0.5
+231,417867,196091,0,1,0,0,0,0,1,0,0,1,0,0,6.0,6,1.0,8,1.33
+232,307433,54841,1,0,1,0,0,0,0,0,0,0,0,1,8.0,8,1.0,6,0.75
+233,266947,171934,0,0,0,0,1,0,1,0,0,0,0,1,2.0,2,1.0,12,6.0
+234,444798,121560,0,1,0,0,0,0,0,0,1,0,0,1,2.0,2,1.0,2,1.0
+235,457960,22034,0,0,0,1,0,0,0,0,1,0,1,0,6.0,6,1.0,2,0.33
+236,213562,185489,0,1,0,0,0,1,0,0,0,1,0,0,2.0,2,1.0,4,2.0
+237,292293,188625,1,0,0,0,0,0,1,1,0,0,0,0,4.0,4,1.0,2,0.5
+238,425475,63185,1,0,0,0,0,1,0,0,1,0,0,0,2.0,2,1.0,6,3.0
+239,299594,160250,0,0,0,0,0,0,1,0,1,1,0,0,6.0,6,1.0,2,0.33
+240,499985,97669,0,1,0,0,1,0,1,0,0,0,0,0,6.0,6,1.0,6,1.0
+241,467802,71172,0,0,0,0,1,0,0,0,1,1,0,0,4.0,4,1.0,2,0.5
+242,294060,122809,1,0,1,0,0,0,1,0,0,0,0,0,10.0,10,1.0,2,0.2
+243,295196,97857,0,0,0,0,1,1,1,0,0,0,0,0,4.0,4,1.0,4,1.0
+244,341689,162736,0,0,0,1,0,1,0,0,1,0,0,0,6.0,6,1.0,6,1.0
+245,446327,42277,0,0,0,1,0,0,1,1,0,0,0,0,4.0,4,1.0,4,1.0
+246,362016,85719,0,0,0,1,0,0,0,0,0,1,0,1,4.0,4,1.0,8,2.0
+247,292418,17470,0,1,0,0,0,0,0,1,0,0,0,1,8.0,8,1.0,8,1.0
+248,402010,28515,0,1,0,1,0,0,0,0,0,0,0,1,,0,0.0,8,5.0
+249,297665,147497,0,1,0,0,0,0,1,0,0,0,0,1,6.0,6,1.0,0,0.0
+250,344575,169672,0,1,0,0,0,1,1,0,0,0,0,0,4.0,4,1.0,4,1.0
+251,494766,34199,0,0,0,1,0,0,0,1,0,0,0,1,8.0,8,1.0,4,0.5
+252,455465,99429,0,0,0,1,0,1,0,0,0,1,0,0,8.0,8,1.0,2,0.25
+253,468129,19544,0,0,0,0,0,0,1,0,1,0,1,0,2.0,2,1.0,2,1.0
+254,285542,168275,0,0,1,0,0,0,1,0,1,0,0,0,6.0,6,1.0,4,0.67
+255,401087,92771,0,0,0,0,0,0,1,0,0,1,0,1,6.0,6,1.0,8,1.33
+256,300457,182506,0,0,0,1,0,0,0,1,0,0,1,0,,0,0.0,2,5.0
+257,225116,127431,1,0,0,0,1,0,0,1,0,0,0,0,6.0,6,1.0,8,1.33
+258,341865,89645,0,1,0,1,0,1,0,0,0,0,0,0,,0,0.0,6,5.0
+259,329782,138901,0,0,0,0,0,0,0,0,1,0,1,1,,0,0.0,8,5.0
+260,260576,199473,0,0,0,0,0,1,1,0,0,0,1,0,6.0,6,1.0,6,1.0
+261,455435,17795,1,0,0,1,0,0,0,0,0,1,0,0,2.0,2,1.0,4,2.0
+262,332828,107041,1,0,0,0,0,0,0,0,0,1,0,1,8.0,8,1.0,4,0.5
+263,312057,35945,0,0,0,0,0,1,1,0,1,0,0,0,4.0,4,1.0,4,1.0
+264,470692,92096,1,1,0,0,0,0,0,1,0,0,0,0,8.0,8,1.0,6,0.75
+265,230964,80257,0,1,0,0,0,1,0,0,0,1,0,0,10.0,10,1.0,6,0.6
+266,423501,78904,0,0,0,1,1,0,0,0,0,1,0,0,10.0,10,1.0,4,0.4
+267,373241,33338,0,0,1,0,1,0,0,1,0,0,0,0,6.0,6,1.0,2,0.33
+268,476092,101934,1,0,1,0,0,0,0,0,0,0,0,1,6.0,6,1.0,2,0.33
+269,494248,186835,0,0,1,0,0,0,0,1,0,1,0,0,6.0,6,1.0,6,1.0
+270,426884,194172,0,0,0,0,0,0,0,1,0,1,0,1,,0,0.0,2,5.0
+271,202738,87671,0,0,0,0,0,0,1,0,1,0,1,0,4.0,4,1.0,6,1.5
+272,393215,36585,1,0,0,0,0,0,0,1,0,0,1,0,8.0,8,1.0,6,0.75
+273,227536,64387,0,1,0,0,0,0,0,1,0,0,1,0,12.0,12,1.0,0,0.0
+274,408448,106251,1,0,0,1,0,0,0,0,1,0,0,0,4.0,4,1.0,0,0.0
+275,397164,56350,0,0,0,1,0,0,0,0,1,1,0,0,,0,0.0,8,5.0
+276,401375,190889,0,1,0,0,0,0,0,1,1,0,0,0,6.0,6,1.0,4,0.67
+277,243472,52240,0,0,0,0,0,0,1,0,0,1,1,0,8.0,8,1.0,8,1.0
+278,390850,94391,0,0,0,0,0,1,0,1,0,0,0,1,2.0,2,1.0,8,4.0
+279,486146,63673,0,0,0,1,0,1,0,0,0,0,1,0,,0,0.0,2,5.0
+280,316122,137715,0,0,1,0,0,0,0,0,0,1,1,0,6.0,6,1.0,6,1.0
+281,417758,81087,1,1,0,0,1,0,0,0,0,0,0,0,6.0,6,1.0,6,1.0
+282,200066,52917,0,0,0,0,0,0,0,1,0,1,1,0,10.0,10,1.0,8,0.8
+283,466157,77291,0,0,1,0,0,0,0,0,1,0,0,1,2.0,2,1.0,6,3.0
+284,311745,117818,0,0,0,0,0,1,0,1,0,0,0,1,6.0,6,1.0,2,0.33
+285,388810,106468,0,1,0,0,0,0,1,0,0,0,1,0,2.0,2,1.0,6,3.0
+286,295784,96831,0,1,0,0,0,0,1,0,0,0,0,1,2.0,2,1.0,4,2.0
+287,239957,80359,0,0,0,1,0,0,1,0,1,0,0,0,6.0,6,1.0,4,0.67
+288,259571,96779,0,0,0,1,0,0,1,0,0,0,1,0,,0,0.0,8,5.0
+289,470592,57685,1,1,0,0,0,0,0,1,0,0,0,0,4.0,4,1.0,0,0.0
+290,360940,92218,0,1,0,0,0,1,0,1,0,0,0,0,4.0,4,1.0,16,4.0
+291,459336,78436,1,0,0,0,0,0,0,0,0,1,1,0,4.0,4,1.0,4,1.0
+292,296446,82822,1,0,0,0,1,1,0,0,0,0,0,0,6.0,6,1.0,0,0.0
+293,285626,67263,0,1,0,0,1,0,0,0,0,0,1,0,,0,0.0,4,5.0
+294,464099,170208,0,0,0,1,0,0,0,1,0,1,0,0,4.0,4,1.0,6,1.5
+295,263105,164140,1,0,0,0,1,0,0,1,0,0,0,0,6.0,6,1.0,4,0.67
+296,302474,167473,0,1,0,0,0,0,0,0,0,0,1,1,2.0,2,1.0,4,2.0
+297,372056,174677,0,1,1,0,0,0,0,0,0,0,0,1,6.0,6,1.0,4,0.67
+298,352449,52010,0,0,0,1,0,1,0,1,0,0,0,0,6.0,6,1.0,4,0.67
+299,419186,171727,0,0,0,0,1,1,0,0,0,0,1,0,10.0,10,1.0,2,0.2
+300,336617,82806,1,1,0,0,0,0,0,0,1,0,0,0,4.0,4,1.0,10,2.5
+301,400878,153139,0,0,1,0,1,0,0,0,0,0,0,1,4.0,4,1.0,4,1.0
+302,376532,58763,0,0,0,1,0,0,0,0,0,0,1,1,4.0,4,1.0,12,3.0
+303,215966,114816,1,0,0,0,0,0,0,0,1,0,0,1,4.0,4,1.0,4,1.0
+304,298739,78236,0,0,0,0,0,1,0,1,0,0,1,0,2.0,2,1.0,10,5.0
+305,324152,168504,0,1,0,0,1,0,0,0,1,0,0,0,6.0,6,1.0,4,0.67
+306,225832,114959,0,0,0,0,1,0,0,0,0,0,1,1,2.0,2,1.0,8,4.0
+307,218247,97153,0,0,1,1,0,0,0,0,0,0,1,0,2.0,2,1.0,4,2.0
+308,442559,186433,0,0,1,1,0,0,0,1,0,0,0,0,4.0,4,1.0,8,2.0
+309,400541,29388,0,0,0,0,1,1,0,1,0,0,0,0,4.0,4,1.0,6,1.5
+310,322029,114911,1,1,0,0,0,0,0,0,0,0,0,1,4.0,4,1.0,0,0.0
+311,233292,162186,0,1,0,0,0,0,0,0,0,1,0,1,2.0,2,1.0,2,1.0
+312,313899,35635,0,1,0,0,0,1,1,0,0,0,0,0,10.0,10,1.0,6,0.6
+313,403970,11492,1,0,0,0,0,0,0,1,0,0,1,0,6.0,6,1.0,0,0.0
+314,292531,147251,0,1,0,0,0,0,0,1,0,0,0,1,8.0,8,1.0,16,2.0
+315,499030,65865,0,1,0,0,1,0,0,0,0,0,1,0,2.0,2,1.0,8,4.0
+316,345372,25792,0,0,0,0,1,0,0,0,0,0,1,1,8.0,8,1.0,6,0.75
+317,279212,121179,0,1,1,1,0,0,0,0,0,0,0,0,8.0,8,1.0,8,1.0
+318,434460,169288,0,0,0,0,0,0,0,0,1,1,0,1,4.0,4,1.0,2,0.5
+319,461196,108275,1,0,0,0,0,0,1,0,0,0,1,0,,0,0.0,6,5.0
+320,214434,163733,1,0,0,0,0,0,0,1,1,0,0,0,8.0,8,1.0,2,0.25
+321,380690,119490,0,0,0,1,0,1,1,0,0,0,0,0,2.0,2,1.0,10,5.0
+322,207430,146046,1,0,1,0,0,0,0,1,0,0,0,0,6.0,6,1.0,4,0.67
+323,291430,197852,0,1,0,0,0,0,1,0,0,0,1,0,2.0,2,1.0,0,0.0
+324,281509,145993,0,0,0,0,0,0,1,0,0,1,1,0,4.0,4,1.0,10,2.5
+325,450296,105489,0,0,0,1,0,0,1,0,0,0,1,0,8.0,8,1.0,4,0.5
+326,304679,130659,1,0,0,1,0,0,0,1,0,0,0,0,10.0,10,1.0,8,0.8
+327,383569,138762,0,1,0,0,0,0,0,0,1,0,1,0,4.0,4,1.0,0,0.0
+328,353518,60651,0,0,1,0,1,0,0,0,0,1,0,0,6.0,6,1.0,10,1.67
+329,274717,155240,0,0,0,1,0,0,0,1,0,0,1,0,,0,0.0,8,5.0
+330,384499,155236,0,0,0,0,0,0,0,1,1,0,0,1,6.0,6,1.0,4,0.67
+331,465116,153987,0,0,0,0,0,0,0,0,1,1,1,0,4.0,4,1.0,4,1.0
+332,422611,142253,0,0,0,1,0,0,0,1,0,0,1,0,2.0,2,1.0,6,3.0
+333,273142,46673,1,0,1,0,0,0,1,0,0,0,0,0,6.0,6,1.0,6,1.0
+334,276436,53260,0,1,0,1,0,0,0,0,0,1,0,0,10.0,10,1.0,14,1.4
+335,202608,38139,1,0,1,0,1,0,0,0,0,0,0,0,6.0,6,1.0,8,1.33
+336,243929,179707,0,0,0,0,0,0,1,1,1,0,0,0,2.0,2,1.0,6,3.0
+337,258763,141289,1,0,1,0,0,0,0,0,0,0,0,1,4.0,4,1.0,2,0.5
+338,322834,58273,0,1,0,0,0,0,1,0,0,1,0,0,8.0,8,1.0,8,1.0
+339,211616,151350,0,1,0,0,0,0,1,0,0,1,0,0,8.0,8,1.0,4,0.5
+340,417342,43433,0,0,1,0,0,0,0,1,0,1,0,0,2.0,2,1.0,10,5.0
+341,333428,53017,0,0,0,0,0,1,0,0,0,1,0,1,6.0,6,1.0,12,2.0
+342,453483,112882,0,0,0,1,1,0,0,0,0,0,0,1,6.0,6,1.0,8,1.33
+343,266412,108033,1,1,0,0,0,0,0,1,0,0,0,0,,0,0.0,6,5.0
+344,408051,140955,0,0,0,0,1,0,1,0,0,0,1,0,6.0,6,1.0,2,0.33
+345,456345,94717,0,0,0,0,0,0,1,0,0,0,1,1,10.0,10,1.0,14,1.4
+346,457822,28648,1,0,1,0,0,1,0,0,0,0,0,0,6.0,6,1.0,10,1.67
+347,255709,92725,0,0,0,0,0,0,1,0,1,0,1,0,4.0,4,1.0,4,1.0
+348,451104,192438,1,0,0,0,0,1,0,1,0,0,0,0,4.0,4,1.0,10,2.5
+349,420645,79899,1,0,0,0,0,0,1,0,0,0,0,1,10.0,10,1.0,6,0.6
+350,392445,125863,0,1,0,0,0,0,0,1,0,0,1,0,6.0,6,1.0,6,1.0
+351,207515,78961,0,1,0,0,1,0,0,0,1,0,0,0,4.0,4,1.0,4,1.0
+352,317523,53884,0,1,0,0,0,0,1,0,0,0,1,0,2.0,2,1.0,4,2.0
+353,444435,197545,0,0,0,1,0,0,0,1,1,0,0,0,14.0,14,1.0,6,0.43
+354,316633,42407,0,0,0,0,0,0,1,0,1,0,1,0,14.0,14,1.0,2,0.14
+355,258931,173204,0,0,0,0,1,0,0,1,0,0,1,0,,0,0.0,2,5.0
+356,372692,190010,0,1,0,1,0,0,0,1,0,0,0,0,2.0,2,1.0,4,2.0
+357,343398,37837,0,0,1,1,0,0,0,0,0,0,1,0,2.0,2,1.0,6,3.0
+358,281317,59623,1,0,0,1,0,0,0,1,0,0,0,0,6.0,6,1.0,6,1.0
+359,332501,26790,0,0,0,1,0,1,0,0,1,0,0,0,8.0,8,1.0,4,0.5
+360,384262,158042,1,0,0,0,0,0,0,1,0,1,0,0,6.0,6,1.0,6,1.0
+361,428215,93593,1,0,1,0,0,0,0,0,1,0,0,0,6.0,6,1.0,14,2.33
+362,249481,163969,1,0,0,1,1,0,0,0,0,0,0,0,6.0,6,1.0,10,1.67
+363,414483,50161,0,1,0,0,0,0,0,1,0,0,0,1,8.0,8,1.0,4,0.5
+364,464670,118855,1,0,0,0,0,0,0,0,0,0,1,1,4.0,4,1.0,6,1.5
+365,474311,172110,1,0,0,0,0,0,1,0,0,0,0,1,2.0,2,1.0,0,0.0
+366,348959,114042,0,0,0,0,0,0,0,0,1,1,1,0,6.0,6,1.0,8,1.33
+367,234548,101587,0,0,0,0,0,0,0,1,0,1,0,1,4.0,4,1.0,10,2.5
+368,219163,174151,1,0,0,0,1,0,1,0,0,0,0,0,4.0,4,1.0,0,0.0
+369,302195,65234,0,1,1,1,0,0,0,0,0,0,0,0,2.0,2,1.0,10,5.0
+370,422472,147660,0,0,0,0,0,1,0,0,1,0,0,1,8.0,8,1.0,8,1.0
+371,227784,146399,0,0,0,0,1,1,0,1,0,0,0,0,2.0,2,1.0,6,3.0
+372,498482,171040,0,0,0,1,0,1,1,0,0,0,0,0,8.0,8,1.0,4,0.5
+373,373709,25938,0,0,0,1,0,0,0,1,1,0,0,0,8.0,8,1.0,6,0.75
+374,470459,35826,1,0,1,0,0,1,0,0,0,0,0,0,8.0,8,1.0,12,1.5
+375,354110,120657,0,0,0,0,0,0,1,1,0,0,0,1,4.0,4,1.0,10,2.5
+376,206024,117159,0,0,0,1,0,0,1,0,1,0,0,0,10.0,10,1.0,0,0.0
+377,358946,83301,0,0,0,0,0,0,1,1,1,0,0,0,4.0,4,1.0,4,1.0
+378,308803,59281,1,1,0,0,0,0,0,0,1,0,0,0,,0,0.0,6,5.0
+379,474517,31884,0,0,0,0,0,0,1,0,0,1,1,0,2.0,2,1.0,8,4.0
+380,242390,142871,0,0,0,1,0,0,1,0,0,0,1,0,4.0,4,1.0,0,0.0
+381,340548,187457,0,0,0,1,1,0,0,0,1,0,0,0,6.0,6,1.0,2,0.33
+382,299177,177984,0,1,0,0,0,0,0,0,1,0,1,0,6.0,6,1.0,6,1.0
+383,270532,82671,1,1,1,0,0,0,0,0,0,0,0,0,8.0,8,1.0,6,0.75
+384,268992,88647,0,0,0,0,0,0,0,1,1,1,0,0,6.0,6,1.0,6,1.0
+385,259627,71976,0,1,0,0,0,0,0,1,0,0,1,0,2.0,2,1.0,6,3.0
+386,266860,39850,1,1,0,0,0,0,1,0,0,0,0,0,12.0,12,1.0,8,0.67
+387,202576,112778,0,1,0,0,0,1,0,1,0,0,0,0,6.0,6,1.0,4,0.67
+388,289778,148181,0,0,0,1,1,0,0,0,1,0,0,0,6.0,6,1.0,6,1.0
+389,318503,98397,1,1,0,0,0,0,0,0,0,1,0,0,12.0,12,1.0,4,0.33
+390,307917,126264,0,0,0,0,0,0,0,0,0,1,1,1,6.0,6,1.0,10,1.67
+391,369358,93056,0,0,0,0,1,0,0,0,1,0,0,1,6.0,6,1.0,4,0.67
+392,373343,59530,0,0,0,0,0,1,0,0,1,0,1,0,6.0,6,1.0,4,0.67
+393,278415,191858,0,0,0,0,0,0,1,0,1,0,0,1,8.0,8,1.0,8,1.0
+394,254809,78233,0,1,0,0,0,0,0,0,0,0,1,1,8.0,8,1.0,6,0.75
+395,358742,41600,0,0,0,1,0,1,0,0,0,1,0,0,8.0,8,1.0,14,1.75
+396,435593,136937,0,1,1,0,0,0,0,0,0,0,0,1,8.0,8,1.0,8,1.0
+397,444664,79308,0,1,0,0,1,0,0,1,0,0,0,0,2.0,2,1.0,8,4.0
+398,257040,189799,0,0,0,0,1,0,0,0,0,1,0,1,4.0,4,1.0,6,1.5
+399,471971,107481,0,1,0,0,1,1,0,0,0,0,0,0,8.0,8,1.0,4,0.5
+400,285534,69440,0,1,0,1,0,0,1,0,0,0,0,0,4.0,4,1.0,8,2.0
+401,399355,28851,0,1,0,1,0,1,0,0,0,0,0,0,2.0,2,1.0,6,3.0
+402,267282,130354,0,0,0,0,0,0,0,0,1,0,1,1,4.0,4,1.0,10,2.5
+403,376411,90246,1,1,0,0,0,1,0,0,0,0,0,0,6.0,6,1.0,2,0.33
+404,417066,68397,0,0,1,0,0,0,0,0,0,1,0,1,8.0,8,1.0,6,0.75
+405,312499,50643,1,0,0,0,1,0,0,0,0,0,1,0,4.0,4,1.0,8,2.0
+406,329969,70706,0,1,0,0,0,0,0,1,0,0,1,0,8.0,8,1.0,8,1.0
+407,468220,70210,0,0,1,0,0,0,0,0,1,0,0,1,2.0,2,1.0,8,4.0
+408,414297,162311,1,1,0,0,1,0,0,0,0,0,0,0,8.0,8,1.0,8,1.0
+409,219953,187847,0,0,1,1,1,0,0,0,0,0,0,0,2.0,2,1.0,2,1.0
+410,413257,192908,0,1,0,0,1,0,0,0,0,0,1,0,4.0,4,1.0,2,0.5
+411,334666,192762,0,0,1,1,0,0,0,1,0,0,0,0,4.0,4,1.0,10,2.5
+412,222514,50456,0,0,0,0,0,0,0,1,1,0,1,0,8.0,8,1.0,0,0.0
+413,259408,123579,1,0,1,0,0,0,0,0,1,0,0,0,14.0,14,1.0,6,0.43
+414,319363,16753,0,1,0,1,0,0,0,0,0,0,0,1,4.0,4,1.0,8,2.0
+415,398450,95097,0,0,1,0,0,1,0,1,0,0,0,0,6.0,6,1.0,2,0.33
+416,388838,107138,0,0,1,0,1,0,0,1,0,0,0,0,2.0,2,1.0,2,1.0
+417,256776,19251,0,1,0,0,0,0,0,0,0,1,0,1,2.0,2,1.0,4,2.0
+418,383184,82298,0,0,0,0,0,1,1,1,0,0,0,0,4.0,4,1.0,6,1.5
+419,262198,77538,0,1,1,0,0,0,1,0,0,0,0,0,4.0,4,1.0,8,2.0
+420,276703,139996,1,1,0,0,1,0,0,0,0,0,0,0,8.0,8,1.0,2,0.25
+421,276962,38350,1,0,0,0,0,1,0,0,0,0,1,0,14.0,14,1.0,4,0.29
+422,461187,140548,0,0,0,1,0,0,0,1,0,1,0,0,12.0,12,1.0,10,0.83
+423,480325,45818,0,1,1,0,0,0,0,0,0,0,0,1,8.0,8,1.0,10,1.25
+424,394427,83641,0,0,1,0,0,0,0,1,0,0,1,0,2.0,2,1.0,8,4.0
+425,435603,99934,0,0,1,0,1,0,0,0,0,1,0,0,2.0,2,1.0,6,3.0
+426,279346,67709,0,0,0,1,0,0,1,0,0,0,0,1,,0,0.0,12,5.0
+427,306633,93222,0,0,1,0,0,0,0,0,1,0,1,0,8.0,8,1.0,6,0.75
+428,483980,14466,1,0,0,0,0,0,0,1,0,0,0,1,4.0,4,1.0,4,1.0
+429,214878,173055,1,0,1,0,0,0,0,0,1,0,0,0,4.0,4,1.0,6,1.5
+430,263423,160431,0,0,0,1,0,0,1,0,1,0,0,0,10.0,10,1.0,6,0.6
+431,396807,194270,0,0,0,0,1,1,0,0,0,0,0,1,8.0,8,1.0,2,0.25
+432,468006,139244,0,0,0,0,1,0,0,1,0,0,1,0,4.0,4,1.0,8,2.0
+433,386017,54184,0,0,0,1,0,0,1,0,0,0,1,0,8.0,8,1.0,6,0.75
+434,237391,115716,0,0,1,1,0,0,0,1,0,0,0,0,12.0,12,1.0,6,0.5
+435,415828,29926,0,0,0,0,0,0,0,1,0,1,1,0,4.0,4,1.0,4,1.0
+436,208006,56098,0,0,0,1,0,0,0,1,1,0,0,0,6.0,6,1.0,4,0.67
+437,282798,92380,0,0,0,0,0,0,1,1,0,0,1,0,10.0,10,1.0,12,1.2
+438,322382,139215,0,0,0,0,0,0,1,1,1,0,0,0,4.0,4,1.0,12,3.0
+439,495656,24595,0,0,0,0,1,0,0,0,0,1,0,1,6.0,6,1.0,8,1.33
+440,487736,156629,0,0,0,1,0,1,0,1,0,0,0,0,6.0,6,1.0,6,1.0
+441,348167,162775,0,1,0,0,0,0,0,0,1,1,0,0,8.0,8,1.0,2,0.25
+442,413534,160420,0,1,0,0,0,0,0,1,1,0,0,0,4.0,4,1.0,4,1.0
+443,292531,155651,1,0,0,0,0,1,1,0,0,0,0,0,4.0,4,1.0,4,1.0
+444,405225,64763,0,0,0,0,1,1,1,0,0,0,0,0,4.0,4,1.0,10,2.5
+445,388734,172721,1,0,1,0,0,0,0,0,1,0,0,0,8.0,8,1.0,10,1.25
+446,200028,69617,0,0,0,1,0,1,0,0,0,0,1,0,8.0,8,1.0,4,0.5
+447,368086,150465,0,0,1,0,0,0,0,0,0,1,0,1,8.0,8,1.0,10,1.25
+448,453598,88914,0,0,0,0,1,0,1,0,0,0,1,0,8.0,8,1.0,6,0.75
+449,490092,13516,0,0,1,0,1,0,0,0,0,1,0,0,6.0,6,1.0,2,0.33
+450,260490,195083,0,0,0,1,1,0,0,0,0,0,1,0,6.0,6,1.0,6,1.0
+451,455598,27274,1,0,1,0,0,0,0,1,0,0,0,0,10.0,10,1.0,4,0.4
+452,494976,161982,0,0,0,1,0,0,0,0,0,1,0,1,4.0,4,1.0,8,2.0
+453,396066,28764,0,1,1,0,0,0,0,0,0,0,1,0,8.0,8,1.0,8,1.0
+454,455579,88243,0,1,0,0,1,0,1,0,0,0,0,0,,0,0.0,8,5.0
+455,409085,70502,0,0,0,0,1,0,1,0,0,1,0,0,4.0,4,1.0,8,2.0
+456,371029,72053,0,0,0,1,1,1,0,0,0,0,0,0,4.0,4,1.0,2,0.5
+457,295670,196249,1,0,1,0,0,0,1,0,0,0,0,0,10.0,10,1.0,2,0.2
+458,361928,150430,1,0,0,0,1,0,0,0,0,0,0,1,2.0,2,1.0,6,3.0
+459,451024,173729,1,0,0,0,0,0,0,0,1,0,0,1,4.0,4,1.0,2,0.5
+460,490748,98179,0,0,0,0,0,1,0,0,1,0,0,1,6.0,6,1.0,4,0.67
+461,491945,30336,0,0,0,0,0,1,0,0,1,1,0,0,6.0,6,1.0,8,1.33
+462,241640,70420,0,1,0,0,1,0,0,0,0,0,0,1,2.0,2,1.0,8,4.0
+463,223055,35627,0,0,1,0,0,0,1,1,0,0,0,0,2.0,2,1.0,4,2.0
+464,467233,172942,0,0,0,0,0,1,0,0,1,1,0,0,4.0,4,1.0,4,1.0
+465,209404,90757,0,0,0,0,0,1,0,0,0,1,0,1,2.0,2,1.0,6,3.0
+466,235052,63309,0,0,0,1,0,0,1,0,0,0,0,1,8.0,8,1.0,10,1.25
+467,352043,122542,0,0,0,0,0,1,0,1,0,0,1,0,,0,0.0,2,5.0
+468,464655,71188,0,0,1,0,1,0,0,0,0,0,1,0,,0,0.0,4,5.0
+469,452949,99119,0,0,0,1,0,1,0,0,1,0,0,0,6.0,6,1.0,6,1.0
+470,426276,83280,0,1,1,0,0,0,1,0,0,0,0,0,4.0,4,1.0,10,2.5
+471,216417,72177,1,0,1,0,1,0,0,0,0,0,0,0,8.0,8,1.0,6,0.75
+472,475969,71538,0,1,0,1,0,0,0,0,0,0,0,1,8.0,8,1.0,8,1.0
+473,470315,16907,0,1,0,0,1,0,1,0,0,0,0,0,2.0,2,1.0,8,4.0
+474,278020,110948,0,0,0,1,0,1,0,0,0,0,1,0,14.0,14,1.0,8,0.57
+475,440036,30121,0,0,0,0,0,1,0,0,1,0,0,1,2.0,2,1.0,4,2.0
+476,314142,30943,0,1,0,0,0,0,1,0,0,1,0,0,2.0,2,1.0,0,0.0
+477,386746,20699,0,0,0,0,1,1,0,0,1,0,0,0,4.0,4,1.0,4,1.0
+478,261137,14474,1,0,0,0,0,0,0,0,0,1,1,0,4.0,4,1.0,0,0.0
+479,331372,39433,0,1,0,1,0,0,1,0,0,0,0,0,,0,0.0,2,5.0
+480,405773,32680,0,0,0,0,0,0,0,1,1,1,0,0,4.0,4,1.0,4,1.0
+481,429580,132313,0,1,0,0,1,0,0,0,0,0,1,0,2.0,2,1.0,2,1.0
+482,356059,157741,0,0,0,0,1,0,0,0,1,0,0,1,,0,0.0,4,5.0
+483,271684,55449,0,0,0,0,1,1,0,0,0,1,0,0,10.0,10,1.0,16,1.6
+484,347716,95631,0,0,0,0,1,1,1,0,0,0,0,0,2.0,2,1.0,6,3.0
+485,411171,62933,0,0,0,1,0,0,0,1,0,0,0,1,,0,0.0,8,5.0
+486,428977,75683,0,0,0,0,1,0,0,0,1,1,0,0,4.0,4,1.0,14,3.5
+487,466398,187686,0,0,1,0,0,0,0,1,0,0,0,1,8.0,8,1.0,6,0.75
+488,270081,33315,0,0,0,0,0,0,0,0,1,0,1,1,10.0,10,1.0,6,0.6
+489,485029,51880,1,0,0,1,0,0,0,0,0,0,0,1,8.0,8,1.0,6,0.75
+490,261276,108198,1,0,0,0,0,0,0,0,1,0,1,0,2.0,2,1.0,10,5.0
+491,407979,92566,1,0,0,0,0,0,1,0,0,0,0,1,8.0,8,1.0,4,0.5
+492,243348,173882,0,0,0,1,0,1,0,0,0,0,0,1,6.0,6,1.0,6,1.0
+493,411586,174035,0,0,0,0,1,0,0,1,0,0,0,1,6.0,6,1.0,8,1.33
+494,297625,144010,1,0,0,1,0,0,1,0,0,0,0,0,8.0,8,1.0,6,0.75
+495,402601,92174,0,0,0,0,0,0,1,1,0,1,0,0,2.0,2,1.0,0,0.0
+496,266498,69504,0,0,0,1,0,1,1,0,0,0,0,0,8.0,8,1.0,4,0.5
+497,303917,41341,0,0,0,0,1,1,0,0,0,0,1,0,4.0,4,1.0,2,0.5
+498,205307,26146,1,0,0,0,0,1,1,0,0,0,0,0,6.0,6,1.0,6,1.0
+499,471466,124691,1,0,1,0,0,0,0,1,0,0,0,0,10.0,10,1.0,6,0.6
+500,363805,196676,0,1,0,0,0,0,0,1,1,0,0,0,4.0,4,1.0,4,1.0
+501,328308,62536,0,0,1,1,0,0,0,1,0,0,0,0,,0,0.0,2,5.0
+502,267523,85770,0,0,1,0,0,0,1,0,0,0,1,0,8.0,8,1.0,4,0.5
+503,332979,84112,1,0,1,0,0,0,0,0,0,0,1,0,2.0,2,1.0,0,0.0
+504,309464,180538,0,0,0,0,1,1,0,0,0,1,0,0,2.0,2,1.0,8,4.0
+505,488292,185347,1,0,0,0,0,0,0,1,0,1,0,0,2.0,2,1.0,4,2.0
+506,361471,165007,0,0,0,0,0,0,1,1,0,0,0,1,6.0,6,1.0,4,0.67
+507,208687,105248,0,0,1,0,0,0,1,1,0,0,0,0,8.0,8,1.0,4,0.5
+508,232684,161404,0,1,0,0,0,1,0,1,0,0,0,0,2.0,2,1.0,6,3.0
+509,442554,16172,0,0,0,0,1,0,0,0,0,0,1,1,4.0,4,1.0,12,3.0
+510,204657,170199,0,0,1,0,0,0,0,0,0,0,1,1,2.0,2,1.0,4,2.0
+511,239389,70377,0,0,1,0,1,0,0,0,1,0,0,0,10.0,10,1.0,6,0.6
+512,218008,101701,1,0,0,0,1,0,0,1,0,0,0,0,4.0,4,1.0,10,2.5
+513,383759,194651,0,0,1,0,0,0,0,0,1,0,1,0,2.0,2,1.0,6,3.0
+514,306213,61447,0,0,0,1,1,0,1,0,0,0,0,0,4.0,4,1.0,2,0.5
+515,312123,48279,0,0,1,0,1,0,1,0,0,0,0,0,2.0,2,1.0,4,2.0
+516,361275,116248,0,0,0,1,0,0,0,0,0,0,1,1,,0,0.0,6,5.0
+517,388396,90908,0,1,0,0,0,0,0,1,0,1,0,0,2.0,2,1.0,6,3.0
+518,288128,133729,0,0,0,0,1,1,0,0,0,0,1,0,4.0,4,1.0,4,1.0
+519,425896,76334,0,0,0,1,0,0,1,0,0,1,0,0,2.0,2,1.0,12,6.0
+520,227571,49234,0,0,1,0,0,0,1,0,0,1,0,0,4.0,4,1.0,8,2.0
+521,479020,137397,0,0,0,0,1,1,0,0,1,0,0,0,2.0,2,1.0,4,2.0
+522,204399,43095,0,0,1,0,0,0,0,1,0,0,0,1,8.0,8,1.0,10,1.25
+523,338325,154358,0,0,0,1,1,0,0,1,0,0,0,0,8.0,8,1.0,4,0.5
+524,336982,132921,0,0,1,1,0,0,0,0,1,0,0,0,14.0,14,1.0,8,0.57
+525,341459,91124,0,0,1,0,0,0,0,0,0,1,0,1,8.0,8,1.0,0,0.0
+526,406510,63478,0,0,0,1,0,0,0,0,1,0,0,1,4.0,4,1.0,4,1.0
+527,332700,64958,0,1,0,0,0,1,0,0,0,0,1,0,6.0,6,1.0,2,0.33
+528,254230,86953,0,0,1,0,0,0,1,1,0,0,0,0,,0,0.0,6,5.0
+529,216574,171859,0,0,0,1,0,0,0,0,0,1,1,0,4.0,4,1.0,6,1.5
+530,482990,41795,0,0,0,1,0,1,0,0,0,0,0,1,10.0,10,1.0,6,0.6
+531,330133,123832,0,0,0,0,1,0,0,0,0,1,1,0,,0,0.0,0,0.0
+532,395228,53435,1,0,0,0,0,0,0,1,0,0,1,0,6.0,6,1.0,14,2.33
+533,219301,83245,1,0,0,0,1,0,0,0,0,0,1,0,2.0,2,1.0,4,2.0
+534,241393,116263,0,1,0,1,0,0,0,0,0,1,0,0,6.0,6,1.0,12,2.0
+535,209948,50931,0,0,0,1,0,0,1,0,0,0,1,0,8.0,8,1.0,4,0.5
+536,434471,63782,1,0,0,1,0,0,0,1,0,0,0,0,4.0,4,1.0,4,1.0
+537,402834,161904,0,0,1,0,1,1,0,0,0,0,0,0,2.0,2,1.0,8,4.0
+538,441114,132616,1,0,0,0,0,1,1,0,0,0,0,0,2.0,2,1.0,12,6.0
+539,467585,31356,0,1,0,1,1,0,0,0,0,0,0,0,8.0,8,1.0,4,0.5
+540,348008,95495,1,0,0,0,0,0,0,1,0,1,0,0,10.0,10,1.0,6,0.6
+541,445390,136281,0,0,1,0,0,0,0,0,1,0,1,0,4.0,4,1.0,8,2.0
+542,323829,25432,0,0,1,0,0,1,0,1,0,0,0,0,2.0,2,1.0,12,6.0
+543,205185,12607,0,0,0,0,0,0,1,0,0,1,0,1,8.0,8,1.0,6,0.75
+544,490248,26420,0,0,0,1,0,1,0,1,0,0,0,0,4.0,4,1.0,8,2.0
+545,463292,161027,0,0,0,0,1,0,0,0,1,0,1,0,4.0,4,1.0,8,2.0
+546,480943,157829,0,1,0,1,0,0,0,1,0,0,0,0,8.0,8,1.0,8,1.0
+547,347856,29055,1,0,1,0,0,0,0,0,0,0,0,1,12.0,12,1.0,0,0.0
+548,239565,96294,0,1,0,0,0,0,0,0,0,0,1,1,6.0,6,1.0,8,1.33
+549,255734,62437,1,0,0,0,0,0,0,1,0,0,1,0,10.0,10,1.0,4,0.4
+550,242008,183474,0,1,0,0,0,0,0,1,0,1,0,0,4.0,4,1.0,2,0.5
+551,278966,28705,0,1,0,1,0,0,0,0,1,0,0,0,10.0,10,1.0,2,0.2
+552,266072,17762,0,0,1,1,0,0,0,0,0,1,0,0,6.0,6,1.0,8,1.33
+553,380708,37376,0,0,0,1,0,0,1,0,0,0,1,0,4.0,4,1.0,4,1.0
+554,207097,109083,0,1,0,1,0,0,0,0,1,0,0,0,14.0,14,1.0,4,0.29
+555,220167,167100,0,1,1,0,0,0,0,1,0,0,0,0,6.0,6,1.0,8,1.33
+556,328447,185659,0,0,0,0,0,0,1,0,0,1,0,1,,0,0.0,2,5.0
+557,263123,158856,0,1,0,1,0,0,0,0,0,1,0,0,4.0,4,1.0,4,1.0
+558,244123,101011,0,0,0,1,1,0,0,0,0,0,0,1,14.0,14,1.0,14,1.0
+559,212049,197338,1,0,0,0,0,1,0,0,0,1,0,0,2.0,2,1.0,8,4.0
+560,244946,29596,1,0,0,0,1,0,0,0,0,1,0,0,4.0,4,1.0,6,1.5
+561,358017,45793,0,0,0,0,0,1,0,1,0,0,1,0,8.0,8,1.0,8,1.0
+562,344701,132333,1,0,1,0,0,0,0,1,0,0,0,0,6.0,6,1.0,12,2.0
+563,411913,180763,0,0,0,0,1,1,1,0,0,0,0,0,8.0,8,1.0,10,1.25
+564,449587,131493,1,1,0,1,0,0,0,0,0,0,0,0,4.0,4,1.0,10,2.5
+565,486488,50115,0,0,0,1,0,0,0,1,0,1,0,0,16.0,16,1.0,4,0.25
+566,362731,138975,1,0,0,0,0,1,0,1,0,0,0,0,10.0,10,1.0,10,1.0
+567,496717,190331,0,1,1,0,0,0,1,0,0,0,0,0,10.0,10,1.0,6,0.6
+568,306057,157754,0,0,1,0,0,0,0,0,1,1,0,0,4.0,4,1.0,6,1.5
+569,338709,85126,0,0,1,0,0,0,0,0,1,0,0,1,4.0,4,1.0,4,1.0
+570,200001,143481,0,0,0,1,1,0,0,0,0,0,1,0,2.0,2,1.0,0,0.0
+571,223457,100696,0,0,0,1,0,1,0,0,1,0,0,0,8.0,8,1.0,8,1.0
+572,441179,25685,0,0,1,0,0,0,0,0,1,0,1,0,12.0,12,1.0,4,0.33
+573,353594,110800,1,0,0,0,0,0,0,0,1,1,0,0,6.0,6,1.0,8,1.33
+574,393614,13674,0,0,1,0,0,1,1,0,0,0,0,0,4.0,4,1.0,16,4.0
+575,430394,20528,0,0,1,0,1,0,0,0,0,1,0,0,10.0,10,1.0,2,0.2
+576,331831,152774,1,0,0,1,0,0,0,0,1,0,0,0,2.0,2,1.0,10,5.0
+577,398194,120484,0,0,0,0,0,1,1,0,0,1,0,0,8.0,8,1.0,8,1.0
+578,276427,176286,1,0,1,0,0,0,0,1,0,0,0,0,2.0,2,1.0,6,3.0
+579,499987,117675,0,0,0,0,0,1,0,0,1,1,0,0,12.0,12,1.0,12,1.0
+580,290614,113585,0,0,1,1,1,0,0,0,0,0,0,0,6.0,6,1.0,2,0.33
+581,478243,195676,0,0,1,0,0,0,1,0,0,0,0,1,2.0,2,1.0,8,4.0
+582,402493,128166,0,0,0,0,0,1,0,1,0,1,0,0,12.0,12,1.0,8,0.67
+583,393992,143737,0,0,0,1,0,0,1,0,1,0,0,0,6.0,6,1.0,2,0.33
+584,327428,88091,0,1,1,0,0,1,0,0,0,0,0,0,8.0,8,1.0,4,0.5
+585,462940,164467,0,0,0,0,0,1,0,0,1,0,0,1,2.0,2,1.0,12,6.0
+586,265145,97582,0,0,0,0,0,1,0,0,1,1,0,0,4.0,4,1.0,4,1.0
+587,451884,125764,1,0,0,1,0,0,0,0,0,1,0,0,,0,0.0,10,5.0
+588,470082,123085,0,0,0,1,1,0,0,0,0,0,0,1,4.0,4,1.0,4,1.0
+589,302570,55829,0,1,0,1,0,1,0,0,0,0,0,0,4.0,4,1.0,6,1.5
+590,271350,163049,0,0,0,0,0,1,0,0,1,1,0,0,6.0,6,1.0,10,1.67
+591,463176,190070,0,0,1,0,1,0,0,0,0,0,1,0,2.0,2,1.0,10,5.0
+592,239269,115358,0,0,0,0,0,0,1,1,0,0,0,1,2.0,2,1.0,10,5.0
+593,492234,80321,0,0,0,0,0,1,1,1,0,0,0,0,6.0,6,1.0,14,2.33
+594,447098,150300,0,0,1,0,0,0,0,0,1,0,1,0,,0,0.0,4,5.0
+595,289440,191239,0,1,0,1,0,0,1,0,0,0,0,0,4.0,4,1.0,4,1.0
+596,395653,181703,0,0,0,0,0,1,1,0,1,0,0,0,4.0,4,1.0,6,1.5
+597,413273,39791,0,0,0,1,1,0,1,0,0,0,0,0,,0,0.0,2,5.0
+598,225693,56526,0,1,0,0,0,0,1,0,0,0,0,1,10.0,10,1.0,4,0.4
+599,457079,135393,0,0,1,0,0,0,0,0,1,0,1,0,6.0,6,1.0,6,1.0
+600,406200,92399,0,0,1,0,0,1,0,0,0,0,0,1,2.0,2,1.0,10,5.0
+601,391476,55926,0,0,0,1,0,0,1,0,0,1,0,0,6.0,6,1.0,4,0.67
+602,425438,98860,0,0,0,0,0,1,1,0,1,0,0,0,,0,0.0,12,5.0
+603,334418,120570,1,0,0,0,0,0,0,0,1,0,0,1,2.0,2,1.0,4,2.0
+604,348429,28194,1,1,0,0,0,0,0,1,0,0,0,0,,0,0.0,4,5.0
+605,400528,37541,0,0,0,1,1,0,1,0,0,0,0,0,6.0,6,1.0,10,1.67
+606,319216,42896,0,0,0,0,0,1,0,0,0,1,0,1,4.0,4,1.0,6,1.5
+607,480884,46973,0,0,0,0,1,0,0,0,1,0,0,1,2.0,2,1.0,8,4.0
+608,319510,197788,1,0,0,1,0,0,0,0,0,0,0,1,8.0,8,1.0,0,0.0
+609,311293,74309,0,0,1,1,0,0,0,0,0,1,0,0,4.0,4,1.0,14,3.5
+610,393323,181443,0,0,1,0,0,0,1,0,0,0,1,0,4.0,4,1.0,10,2.5
+611,311966,145582,1,0,0,0,1,0,0,0,0,1,0,0,4.0,4,1.0,6,1.5
+612,406543,90709,0,0,0,0,1,0,0,0,0,1,1,0,6.0,6,1.0,6,1.0
+613,302751,155861,0,1,0,0,0,0,0,0,1,0,1,0,12.0,12,1.0,10,0.83
+614,207585,47241,0,0,0,0,0,1,0,0,1,0,1,0,4.0,4,1.0,4,1.0
+615,275659,21504,0,0,0,1,0,1,0,0,0,0,1,0,10.0,10,1.0,10,1.0
+616,406861,28337,0,0,0,0,1,0,0,1,1,0,0,0,2.0,2,1.0,0,0.0
+617,362463,158731,0,0,0,0,1,0,0,1,1,0,0,0,6.0,6,1.0,8,1.33
+618,411510,30753,0,0,0,0,0,1,0,1,0,1,0,0,4.0,4,1.0,6,1.5
+619,485980,143009,0,0,0,0,1,1,1,0,0,0,0,0,2.0,2,1.0,0,0.0
+620,220964,110716,0,1,0,1,0,0,1,0,0,0,0,0,2.0,2,1.0,10,5.0
+621,315555,151131,0,0,0,0,1,0,1,0,1,0,0,0,,0,0.0,2,5.0
+622,471157,68516,1,0,1,0,1,0,0,0,0,0,0,0,8.0,8,1.0,14,1.75
+623,406859,61140,0,1,0,0,0,1,0,0,0,0,1,0,8.0,8,1.0,2,0.25
+624,464367,152582,0,0,0,0,1,0,0,0,0,1,1,0,6.0,6,1.0,2,0.33
+625,384728,71607,0,1,0,0,0,0,0,1,1,0,0,0,10.0,10,1.0,4,0.4
+626,442739,43642,0,1,0,1,0,0,0,0,0,0,0,1,10.0,10,1.0,2,0.2
+627,345081,39891,0,0,0,0,0,0,0,1,1,1,0,0,8.0,8,1.0,2,0.25
+628,356882,151586,0,0,0,0,0,0,0,0,1,1,1,0,8.0,8,1.0,10,1.25
+629,467710,51296,0,0,0,0,1,0,0,0,1,0,0,1,6.0,6,1.0,6,1.0
+630,320831,133375,0,0,0,0,0,0,1,1,0,0,1,0,8.0,8,1.0,8,1.0
+631,277376,98064,0,0,0,0,0,0,0,1,0,1,1,0,10.0,10,1.0,2,0.2
+632,336337,92259,0,0,0,1,0,0,0,1,0,0,0,1,6.0,6,1.0,12,2.0
+633,205914,12925,0,1,0,0,0,0,1,0,1,0,0,0,4.0,4,1.0,2,0.5
+634,334891,180467,0,1,1,1,0,0,0,0,0,0,0,0,2.0,2,1.0,0,0.0
+635,293350,93423,1,0,0,0,0,1,0,0,0,0,1,0,8.0,8,1.0,2,0.25
+636,248133,102234,0,0,0,1,0,0,0,0,0,1,1,0,14.0,14,1.0,2,0.14
+637,463279,66610,0,0,0,0,0,1,0,0,0,1,0,1,4.0,4,1.0,4,1.0
+638,233225,144563,0,0,0,0,1,1,0,0,0,1,0,0,4.0,4,1.0,12,3.0
+639,480066,24214,1,0,0,0,1,0,0,0,1,0,0,0,2.0,2,1.0,6,3.0
+640,298201,68870,0,1,0,0,0,0,0,0,1,0,0,1,2.0,2,1.0,8,4.0
+641,316673,22103,0,0,0,0,0,1,0,0,0,1,1,0,6.0,6,1.0,12,2.0
+642,435702,113156,1,0,0,0,0,0,0,0,0,1,0,1,8.0,8,1.0,8,1.0
+643,382302,131574,0,0,0,1,1,1,0,0,0,0,0,0,16.0,16,1.0,8,0.5
+644,308676,103934,1,0,0,0,0,1,0,0,1,0,0,0,2.0,2,1.0,4,2.0
+645,424223,88807,0,1,0,0,0,0,0,1,1,0,0,0,4.0,4,1.0,0,0.0
+646,447228,44269,0,1,0,0,0,1,0,0,0,0,1,0,10.0,10,1.0,6,0.6
+647,277880,55032,1,0,0,0,0,0,1,0,0,0,1,0,6.0,6,1.0,8,1.33
+648,222776,158303,0,0,0,0,0,1,0,0,1,1,0,0,6.0,6,1.0,10,1.67
+649,472568,61872,1,0,0,0,0,1,0,0,0,0,0,1,6.0,6,1.0,6,1.0
+650,326433,106534,1,0,0,1,0,0,0,0,0,0,0,1,4.0,4,1.0,6,1.5
+651,339979,90419,0,1,0,0,1,1,0,0,0,0,0,0,2.0,2,1.0,6,3.0
+652,444103,137728,0,0,1,0,1,0,0,0,0,1,0,0,4.0,4,1.0,2,0.5
+653,453724,109824,0,0,0,1,0,0,0,1,0,0,0,1,6.0,6,1.0,8,1.33
+654,487460,127989,1,1,0,0,0,0,0,1,0,0,0,0,4.0,4,1.0,8,2.0
+655,483102,182886,0,1,0,1,0,0,0,0,0,0,1,0,4.0,4,1.0,10,2.5
+656,249702,127895,0,0,0,0,0,0,1,0,0,1,0,1,6.0,6,1.0,6,1.0
+657,474378,61869,0,0,0,0,0,1,0,0,1,1,0,0,2.0,2,1.0,12,6.0
+658,362653,145831,0,0,0,0,0,0,0,1,0,1,1,0,2.0,2,1.0,6,3.0
+659,370714,71649,0,0,0,0,0,1,0,1,0,1,0,0,8.0,8,1.0,6,0.75
+660,258959,28713,0,1,0,0,0,1,1,0,0,0,0,0,6.0,6,1.0,6,1.0
+661,405274,24301,0,0,0,0,0,0,1,1,1,0,0,0,2.0,2,1.0,6,3.0
+662,220897,99513,1,0,1,1,0,0,0,0,0,0,0,0,4.0,4,1.0,6,1.5
+663,454203,156314,0,0,0,0,0,0,0,0,1,0,1,1,2.0,2,1.0,4,2.0
+664,462761,164531,0,0,0,1,0,0,0,1,0,0,1,0,6.0,6,1.0,8,1.33
+665,325579,59883,0,0,0,0,0,1,0,0,1,0,1,0,4.0,4,1.0,10,2.5
+666,473233,154694,0,0,1,1,0,0,0,0,1,0,0,0,8.0,8,1.0,4,0.5
+667,420925,199716,1,0,0,0,0,1,0,0,0,1,0,0,6.0,6,1.0,0,0.0
+668,200999,106467,0,1,1,0,0,0,1,0,0,0,0,0,6.0,6,1.0,4,0.67
+669,251803,124949,1,1,0,0,0,1,0,0,0,0,0,0,6.0,6,1.0,6,1.0
+670,498285,29863,0,1,0,0,0,0,0,0,1,0,1,0,6.0,6,1.0,8,1.33
+671,262904,122418,0,1,0,0,0,1,0,1,0,0,0,0,8.0,8,1.0,2,0.25
+672,422921,59595,0,0,0,0,0,1,1,0,0,0,1,0,2.0,2,1.0,2,1.0
+673,420628,177529,1,0,0,0,0,0,0,0,1,0,1,0,2.0,2,1.0,4,2.0
+674,324917,104702,0,0,0,1,0,1,0,0,1,0,0,0,6.0,6,1.0,6,1.0
+675,430855,75453,0,0,0,0,1,0,1,0,0,0,1,0,2.0,2,1.0,6,3.0
+676,285582,74778,1,1,0,0,0,0,1,0,0,0,0,0,8.0,8,1.0,2,0.25
+677,496860,49463,0,1,0,0,0,1,1,0,0,0,0,0,8.0,8,1.0,0,0.0
+678,423904,187806,0,1,0,0,1,0,0,0,0,0,1,0,8.0,8,1.0,6,0.75
+679,204938,36785,1,0,1,1,0,0,0,0,0,0,0,0,8.0,8,1.0,2,0.25
+680,375284,196746,0,0,0,0,1,0,0,1,1,0,0,0,6.0,6,1.0,6,1.0
+681,244556,29179,0,0,0,0,1,0,0,0,0,1,0,1,4.0,4,1.0,4,1.0
+682,229152,127759,0,0,0,0,0,0,1,1,0,0,0,1,,0,0.0,8,5.0
+683,361553,64970,0,0,1,0,1,0,0,0,0,0,1,0,6.0,6,1.0,6,1.0
+684,271352,113220,0,0,1,1,0,0,0,0,0,0,0,1,2.0,2,1.0,6,3.0
+685,295760,74282,1,0,0,0,1,0,0,0,1,0,0,0,,0,0.0,6,5.0
+686,318293,33441,0,0,0,0,0,0,1,1,0,0,1,0,8.0,8,1.0,8,1.0
+687,383619,40056,0,0,1,0,0,0,0,0,1,0,0,1,10.0,10,1.0,2,0.2
+688,314795,136938,0,0,0,0,1,0,1,0,0,0,1,0,10.0,10,1.0,2,0.2
+689,478514,189564,0,0,0,1,1,0,0,0,0,1,0,0,4.0,4,1.0,2,0.5
+690,404950,188127,0,0,0,1,1,1,0,0,0,0,0,0,6.0,6,1.0,8,1.33
+691,445341,106081,1,0,0,1,1,0,0,0,0,0,0,0,4.0,4,1.0,6,1.5
+692,449316,77666,1,0,1,0,0,1,0,0,0,0,0,0,8.0,8,1.0,8,1.0
+693,302075,138096,0,0,0,0,0,1,1,1,0,0,0,0,8.0,8,1.0,14,1.75
+694,456352,36009,0,0,0,1,0,0,1,0,0,0,1,0,4.0,4,1.0,6,1.5
+695,490129,92546,0,1,0,0,0,0,0,0,1,0,1,0,6.0,6,1.0,4,0.67
+696,433637,55286,0,1,0,0,1,1,0,0,0,0,0,0,6.0,6,1.0,10,1.67
+697,210566,31212,0,0,0,0,0,0,1,0,1,0,0,1,,0,0.0,12,5.0
+698,402223,122169,0,1,1,0,0,0,1,0,0,0,0,0,12.0,12,1.0,2,0.17
+699,244785,198667,1,0,0,0,0,0,0,0,0,1,0,1,10.0,10,1.0,12,1.2
+700,402291,166371,0,0,0,0,0,1,0,0,1,0,0,1,8.0,8,1.0,10,1.25
+701,313478,78519,0,1,0,0,0,0,0,1,0,0,1,0,4.0,4,1.0,14,3.5
+702,438841,134520,1,0,1,0,0,0,0,0,1,0,0,0,8.0,8,1.0,6,0.75
+703,213094,70655,0,0,0,0,1,0,0,0,1,0,0,1,8.0,8,1.0,0,0.0
+704,350978,43948,0,0,1,1,0,0,0,1,0,0,0,0,2.0,2,1.0,6,3.0
+705,254726,172807,0,0,0,0,0,0,1,0,1,1,0,0,8.0,8,1.0,6,0.75
+706,434372,189536,0,0,0,1,0,0,0,0,0,0,1,1,4.0,4,1.0,4,1.0
+707,345034,80288,0,1,0,0,0,0,0,0,1,0,1,0,,0,0.0,10,5.0
+708,232062,187285,0,1,0,0,1,0,0,0,1,0,0,0,6.0,6,1.0,0,0.0
+709,411841,19443,0,0,0,0,0,1,0,1,0,1,0,0,2.0,2,1.0,6,3.0
+710,453578,18206,0,1,0,0,1,0,0,0,0,0,1,0,2.0,2,1.0,10,5.0
+711,241167,176591,0,0,0,0,0,1,0,0,0,0,1,1,2.0,2,1.0,8,4.0
+712,371904,166797,0,0,0,0,0,0,0,0,1,1,0,1,4.0,4,1.0,2,0.5
+713,496726,73973,1,0,0,0,0,0,1,0,0,0,0,1,2.0,2,1.0,6,3.0
+714,285423,39451,1,1,0,0,0,0,0,0,1,0,0,0,6.0,6,1.0,8,1.33
+715,235681,76552,0,1,0,0,0,0,1,0,0,0,1,0,6.0,6,1.0,4,0.67
+716,204840,172449,0,0,0,0,0,0,1,1,1,0,0,0,2.0,2,1.0,8,4.0
+717,405744,175646,1,1,1,0,0,0,0,0,0,0,0,0,10.0,10,1.0,10,1.0
+718,247418,116395,1,0,0,0,0,0,1,0,0,1,0,0,6.0,6,1.0,6,1.0
+719,268489,54245,0,0,0,0,1,0,1,0,0,0,1,0,10.0,10,1.0,4,0.4
+720,328945,165590,0,0,1,0,0,0,0,0,0,1,0,1,2.0,2,1.0,4,2.0
+721,410193,141893,0,0,0,0,0,1,1,0,0,1,0,0,2.0,2,1.0,8,4.0
+722,345034,40830,1,0,1,0,1,0,0,0,0,0,0,0,10.0,10,1.0,2,0.2
+723,364372,64143,0,0,0,1,1,0,0,0,0,0,0,1,4.0,4,1.0,4,1.0
+724,290738,129138,0,1,1,0,0,0,0,1,0,0,0,0,,0,0.0,0,0.0
+725,338965,85609,0,1,0,0,0,1,0,1,0,0,0,0,8.0,8,1.0,2,0.25
+726,467121,143254,0,0,0,0,1,0,0,0,0,0,1,1,6.0,6,1.0,2,0.33
+727,389983,27107,0,1,0,0,0,0,1,0,0,1,0,0,2.0,2,1.0,6,3.0
+728,483851,56319,0,0,0,1,1,0,0,0,0,1,0,0,2.0,2,1.0,4,2.0
+729,424730,165939,1,1,0,1,0,0,0,0,0,0,0,0,8.0,8,1.0,6,0.75
+730,249321,48359,0,1,1,0,0,0,1,0,0,0,0,0,4.0,4,1.0,2,0.5
+731,307430,81205,1,1,0,0,0,0,0,0,0,0,1,0,6.0,6,1.0,10,1.67
+732,347301,37884,1,0,1,0,0,0,0,0,1,0,0,0,10.0,10,1.0,12,1.2
+733,203743,110893,0,0,0,0,1,1,0,0,1,0,0,0,4.0,4,1.0,4,1.0
+734,292487,183232,1,0,0,0,1,0,0,0,1,0,0,0,8.0,8,1.0,4,0.5
+735,322767,39677,1,0,0,0,1,0,0,0,0,0,1,0,4.0,4,1.0,4,1.0
+736,358514,80208,0,0,0,1,0,1,0,1,0,0,0,0,14.0,14,1.0,4,0.29
+737,427105,139153,0,1,0,0,1,0,0,0,1,0,0,0,4.0,4,1.0,4,1.0
+738,357046,63987,0,0,0,1,1,0,0,0,0,0,1,0,12.0,12,1.0,10,0.83
+739,307964,26776,1,0,1,0,0,0,0,0,0,0,0,1,8.0,8,1.0,8,1.0
+740,316623,39023,0,1,0,0,0,0,0,0,1,1,0,0,4.0,4,1.0,4,1.0
+741,228760,62415,1,0,0,0,0,0,0,1,0,1,0,0,,0,0.0,8,5.0
+742,338286,150402,1,1,0,0,0,0,1,0,0,0,0,0,12.0,12,1.0,12,1.0
+743,438728,154962,1,0,0,0,1,0,0,0,0,0,1,0,8.0,8,1.0,2,0.25
+744,404981,192372,0,0,0,0,1,0,0,0,1,0,1,0,6.0,6,1.0,4,0.67
+745,359528,25575,0,1,0,0,0,0,1,0,1,0,0,0,6.0,6,1.0,4,0.67
+746,349781,163587,1,0,1,0,0,0,0,0,1,0,0,0,8.0,8,1.0,6,0.75
+747,385237,59843,0,0,0,0,0,0,0,1,0,1,1,0,12.0,12,1.0,4,0.33
+748,384632,183937,0,0,0,0,1,1,0,0,0,1,0,0,4.0,4,1.0,10,2.5
+749,306961,148491,0,0,0,0,1,1,0,1,0,0,0,0,4.0,4,1.0,8,2.0
+750,396479,30844,0,0,0,0,1,0,0,0,1,1,0,0,10.0,10,1.0,2,0.2
+751,353952,189743,0,0,0,0,0,1,0,0,0,0,1,1,6.0,6,1.0,0,0.0
+752,458229,72535,0,1,0,0,0,0,1,0,0,0,0,1,6.0,6,1.0,14,2.33
+753,423828,162472,0,0,1,0,1,0,0,1,0,0,0,0,6.0,6,1.0,0,0.0
+754,369405,195449,0,0,0,0,0,1,0,0,1,0,1,0,14.0,14,1.0,8,0.57
+755,394880,59505,1,0,0,0,0,0,1,0,0,0,1,0,8.0,8,1.0,10,1.25
+756,216125,75680,0,0,1,1,0,1,0,0,0,0,0,0,4.0,4,1.0,4,1.0
+757,269805,185734,0,1,1,0,0,0,1,0,0,0,0,0,8.0,8,1.0,0,0.0
+758,413799,157617,0,0,0,0,0,1,1,0,1,0,0,0,,0,0.0,6,5.0
+759,302053,181745,0,0,0,0,0,1,1,0,0,0,0,1,2.0,2,1.0,8,4.0
+760,473261,167980,0,0,1,0,0,0,0,1,0,0,0,1,10.0,10,1.0,8,0.8
+761,445051,106289,1,1,1,0,0,0,0,0,0,0,0,0,10.0,10,1.0,2,0.2
+762,212245,132984,0,1,0,0,0,0,0,0,0,1,1,0,10.0,10,1.0,8,0.8
+763,480413,113380,0,0,1,0,0,0,0,0,0,1,0,1,8.0,8,1.0,6,0.75
+764,352346,53256,0,0,0,0,1,1,0,0,0,0,1,0,,0,0.0,2,5.0
+765,449161,26399,0,1,0,0,0,0,0,1,0,1,0,0,4.0,4,1.0,10,2.5
+766,400195,66688,1,0,0,0,1,1,0,0,0,0,0,0,4.0,4,1.0,8,2.0
+767,360809,41649,0,1,1,0,0,0,0,0,0,0,0,1,4.0,4,1.0,8,2.0
+768,487223,143779,0,0,1,0,1,0,0,0,0,1,0,0,10.0,10,1.0,8,0.8
+769,364632,163079,0,0,0,1,0,0,0,0,1,1,0,0,2.0,2,1.0,4,2.0
+770,332071,116680,1,0,0,0,1,0,1,0,0,0,0,0,4.0,4,1.0,6,1.5
+771,396359,89174,1,0,1,0,0,0,0,1,0,0,0,0,10.0,10,1.0,4,0.4
+772,320385,92456,0,0,1,1,0,1,0,0,0,0,0,0,6.0,6,1.0,8,1.33
+773,380098,93796,0,1,0,0,0,0,0,0,1,1,0,0,8.0,8,1.0,6,0.75
+774,385140,102819,1,1,0,0,0,0,0,0,0,0,1,0,,0,0.0,6,5.0
+775,473604,163744,0,0,0,0,0,1,1,0,0,1,0,0,4.0,4,1.0,8,2.0
+776,296380,178265,0,0,0,0,0,0,1,1,0,0,0,1,8.0,8,1.0,0,0.0
+777,217741,187165,0,1,0,0,0,0,1,0,0,0,1,0,8.0,8,1.0,4,0.5
+778,468179,141762,0,0,1,0,1,0,0,0,0,0,1,0,2.0,2,1.0,4,2.0
+779,265103,157564,1,0,0,0,0,0,1,0,0,1,0,0,4.0,4,1.0,2,0.5
+780,318917,57513,0,0,0,0,1,1,0,0,0,0,0,1,8.0,8,1.0,8,1.0
+781,335389,73388,0,0,1,0,0,0,1,0,0,0,0,1,,0,0.0,8,5.0
+782,344494,41122,0,1,0,0,1,0,0,0,0,0,1,0,4.0,4,1.0,10,2.5
+783,468987,43930,1,0,0,0,0,1,0,0,0,1,0,0,4.0,4,1.0,4,1.0
+784,480547,64279,0,1,0,0,0,0,0,1,1,0,0,0,8.0,8,1.0,6,0.75
+785,375866,122880,0,0,0,0,0,0,0,1,0,1,1,0,4.0,4,1.0,4,1.0
+786,438184,138257,0,1,0,0,0,0,0,0,1,0,0,1,4.0,4,1.0,10,2.5
+787,221519,42324,1,0,0,0,0,1,1,0,0,0,0,0,6.0,6,1.0,4,0.67
+788,207393,114298,1,1,0,0,0,0,0,0,1,0,0,0,4.0,4,1.0,4,1.0
+789,356242,168810,0,0,0,0,0,0,0,0,1,1,1,0,6.0,6,1.0,10,1.67
+790,394532,87084,0,1,0,0,0,0,0,0,0,1,0,1,2.0,2,1.0,8,4.0
+791,322534,160752,0,0,0,0,1,1,0,0,0,1,0,0,4.0,4,1.0,2,0.5
+792,296770,177535,0,1,0,0,0,0,1,1,0,0,0,0,6.0,6,1.0,8,1.33
+793,359411,181579,0,0,0,0,0,0,1,0,1,1,0,0,6.0,6,1.0,8,1.33
+794,215607,147171,0,0,0,0,1,1,0,0,1,0,0,0,8.0,8,1.0,6,0.75
+795,252366,29720,0,0,1,0,0,0,0,1,0,0,0,1,8.0,8,1.0,2,0.25
+796,481102,62941,1,0,1,0,0,1,0,0,0,0,0,0,2.0,2,1.0,2,1.0
+797,264008,165244,0,0,0,0,0,1,0,1,0,0,1,0,4.0,4,1.0,0,0.0
+798,344748,198569,1,0,1,0,0,1,0,0,0,0,0,0,2.0,2,1.0,10,5.0
+799,491171,95359,1,0,0,1,0,1,0,0,0,0,0,0,4.0,4,1.0,20,5.0
+800,213500,95032,0,0,0,0,0,0,0,1,1,0,0,1,6.0,6,1.0,10,1.67
+801,450193,132018,1,0,0,0,1,1,0,0,0,0,0,0,4.0,4,1.0,4,1.0
+802,341561,154584,0,0,0,0,0,1,0,0,1,0,0,1,8.0,8,1.0,12,1.5
+803,491900,180115,0,0,0,1,0,0,1,0,0,0,0,1,4.0,4,1.0,2,0.5
+804,206970,151088,0,1,0,1,0,0,0,0,1,0,0,0,8.0,8,1.0,4,0.5
+805,316695,25455,0,0,0,0,1,0,0,0,1,0,1,0,6.0,6,1.0,2,0.33
+806,382649,34601,1,1,0,0,0,0,0,0,0,1,0,0,4.0,4,1.0,8,2.0
+807,256997,194583,0,1,0,0,0,1,0,1,0,0,0,0,6.0,6,1.0,10,1.67
+808,365277,184349,0,1,0,0,0,1,0,0,1,0,0,0,8.0,8,1.0,6,0.75
+809,485349,152806,0,1,0,1,0,0,0,0,0,0,1,0,6.0,6,1.0,6,1.0
+810,480262,53863,0,1,0,1,0,1,0,0,0,0,0,0,6.0,6,1.0,4,0.67
+811,486303,95202,0,1,1,0,0,0,0,1,0,0,0,0,,0,0.0,2,5.0
+812,258131,55993,0,0,0,0,0,0,0,1,0,0,1,1,2.0,2,1.0,4,2.0
+813,463050,126936,0,1,0,0,0,1,0,1,0,0,0,0,,0,0.0,0,0.0
+814,270756,29333,0,0,1,0,0,0,1,0,0,0,0,1,4.0,4,1.0,14,3.5
+815,231164,109027,1,0,0,0,0,1,1,0,0,0,0,0,4.0,4,1.0,8,2.0
+816,463899,33207,0,1,1,0,0,0,0,0,0,0,1,0,12.0,12,1.0,6,0.5
+817,399007,123759,0,1,0,0,0,0,1,0,0,0,1,0,8.0,8,1.0,6,0.75
+818,417191,61278,0,1,0,0,0,1,0,0,0,0,1,0,12.0,12,1.0,8,0.67
+819,343102,130956,0,1,0,1,0,0,0,0,0,0,1,0,6.0,6,1.0,12,2.0
+820,444359,117529,0,1,1,0,0,0,0,1,0,0,0,0,4.0,4,1.0,6,1.5
+821,456975,148816,1,0,0,0,0,1,0,0,0,0,0,1,4.0,4,1.0,8,2.0
+822,281006,127452,0,0,0,1,0,0,1,0,0,0,0,1,2.0,2,1.0,4,2.0
+823,397983,100855,0,1,0,0,1,0,0,1,0,0,0,0,6.0,6,1.0,0,0.0
+824,355294,76855,0,1,0,0,0,0,0,1,1,0,0,0,2.0,2,1.0,2,1.0
+825,422055,194612,1,0,0,1,0,0,0,0,1,0,0,0,,0,0.0,12,5.0
+826,214989,97653,0,0,0,0,0,1,0,0,1,0,1,0,4.0,4,1.0,8,2.0
+827,269975,157064,1,0,0,0,1,1,0,0,0,0,0,0,10.0,10,1.0,4,0.4
+828,494117,152366,1,0,0,0,1,0,0,0,1,0,0,0,6.0,6,1.0,8,1.33
+829,231915,65141,0,0,1,0,1,1,0,0,0,0,0,0,2.0,2,1.0,12,6.0
+830,458923,198250,1,0,0,0,0,1,0,0,1,0,0,0,4.0,4,1.0,6,1.5
+831,389130,141296,0,1,1,0,0,1,0,0,0,0,0,0,12.0,12,1.0,8,0.67
+832,470729,42046,0,0,1,0,1,0,0,0,0,0,0,1,8.0,8,1.0,4,0.5
+833,474279,30181,1,0,0,0,0,0,1,0,0,0,1,0,12.0,12,1.0,4,0.33
+834,406649,98575,0,0,0,1,0,0,0,0,0,1,1,0,4.0,4,1.0,4,1.0
+835,267055,184081,1,0,0,0,0,0,1,0,0,0,1,0,6.0,6,1.0,12,2.0
+836,276545,156447,0,0,0,1,1,0,0,0,0,0,0,1,6.0,6,1.0,6,1.0
+837,482190,36190,0,0,0,1,0,0,1,0,1,0,0,0,6.0,6,1.0,4,0.67
+838,370077,199736,0,1,0,0,1,0,0,1,0,0,0,0,14.0,14,1.0,6,0.43
+839,231280,154005,0,1,0,1,0,0,0,0,1,0,0,0,12.0,12,1.0,2,0.17
+840,495127,71108,0,1,0,0,0,1,0,0,1,0,0,0,4.0,4,1.0,6,1.5
+841,379036,151388,0,1,1,0,0,1,0,0,0,0,0,0,4.0,4,1.0,0,0.0
+842,275802,166094,1,0,0,0,0,1,0,0,0,0,1,0,4.0,4,1.0,6,1.5
+843,382652,65851,0,1,1,0,0,0,0,1,0,0,0,0,6.0,6,1.0,12,2.0
+844,442575,19464,0,0,1,1,0,0,0,0,0,1,0,0,4.0,4,1.0,4,1.0
+845,250858,124543,1,0,0,1,0,0,0,0,0,0,0,1,4.0,4,1.0,4,1.0
+846,337823,103129,0,0,0,0,1,0,0,1,0,0,1,0,10.0,10,1.0,2,0.2
+847,245935,144115,0,0,0,0,0,0,0,1,0,1,0,1,8.0,8,1.0,6,0.75
+848,370596,14192,0,0,0,0,1,1,0,0,1,0,0,0,6.0,6,1.0,8,1.33
+849,399212,20895,0,1,1,0,0,1,0,0,0,0,0,0,6.0,6,1.0,2,0.33
+850,476076,166707,0,0,0,1,0,0,1,0,0,0,0,1,4.0,4,1.0,6,1.5
+851,467415,130374,0,0,0,1,1,0,0,0,1,0,0,0,4.0,4,1.0,6,1.5
+852,367151,159580,1,0,0,0,0,0,1,0,1,0,0,0,12.0,12,1.0,6,0.5
+853,320912,97724,0,0,0,1,0,0,1,0,0,0,0,1,6.0,6,1.0,6,1.0
+854,227404,36661,0,1,0,1,0,0,0,0,0,0,0,1,10.0,10,1.0,6,0.6
+855,216256,178926,0,0,0,0,1,0,0,0,0,1,0,1,,0,0.0,2,5.0
+856,387475,80569,0,0,0,0,0,0,1,0,1,0,1,0,6.0,6,1.0,8,1.33
+857,249253,26385,0,0,1,0,1,0,0,1,0,0,0,0,4.0,4,1.0,12,3.0
+858,375847,176910,0,0,0,0,0,1,1,0,0,1,0,0,8.0,8,1.0,2,0.25
+859,251309,82035,0,0,0,0,0,0,0,1,1,0,0,1,10.0,10,1.0,6,0.6
+860,384041,50345,0,0,0,0,0,1,0,1,1,0,0,0,,0,0.0,4,5.0
+861,477161,106483,0,0,0,0,1,0,1,0,0,1,0,0,2.0,2,1.0,4,2.0
+862,218587,27343,1,0,1,0,0,0,0,0,0,0,0,1,8.0,8,1.0,8,1.0
+863,284722,71235,0,0,1,1,0,0,0,0,0,0,1,0,12.0,12,1.0,8,0.67
+864,223603,138762,1,0,1,0,0,0,1,0,0,0,0,0,2.0,2,1.0,0,0.0
+865,399762,13085,0,0,0,0,1,1,0,0,1,0,0,0,2.0,2,1.0,4,2.0
+866,299484,114094,1,1,0,0,0,0,0,0,0,0,0,1,8.0,8,1.0,10,1.25
+867,407379,70302,0,1,0,0,1,0,0,1,0,0,0,0,6.0,6,1.0,6,1.0
+868,292348,137758,0,0,0,1,0,0,0,1,1,0,0,0,2.0,2,1.0,10,5.0
+869,287417,138395,0,0,0,1,0,0,1,0,1,0,0,0,8.0,8,1.0,6,0.75
+870,200590,66947,0,0,1,0,1,0,0,0,0,0,0,1,,0,0.0,4,5.0
+871,440673,85320,0,1,0,0,0,0,0,1,0,0,1,0,6.0,6,1.0,2,0.33
+872,356709,116973,0,1,0,1,0,0,1,0,0,0,0,0,2.0,2,1.0,6,3.0
+873,386039,186602,1,0,1,1,0,0,0,0,0,0,0,0,4.0,4,1.0,4,1.0
+874,270051,75658,0,0,0,0,1,0,0,1,1,0,0,0,6.0,6,1.0,6,1.0
+875,436580,164864,0,0,0,0,1,1,0,0,0,1,0,0,2.0,2,1.0,4,2.0
+876,202918,113863,1,1,0,0,0,0,0,1,0,0,0,0,,0,0.0,8,5.0
+877,318488,149400,1,0,0,1,0,0,0,1,0,0,0,0,4.0,4,1.0,10,2.5
+878,315256,140817,0,0,0,1,0,0,0,1,1,0,0,0,10.0,10,1.0,10,1.0
+879,489213,18867,0,1,0,1,0,0,0,1,0,0,0,0,2.0,2,1.0,4,2.0
+880,446056,70527,1,0,0,0,0,0,0,0,0,0,1,1,10.0,10,1.0,6,0.6
+881,409633,106178,0,0,0,0,0,0,0,1,0,0,1,1,2.0,2,1.0,6,3.0
+882,451351,49239,0,0,0,0,0,0,0,0,1,1,0,1,4.0,4,1.0,2,0.5
+883,215427,15383,0,0,0,1,0,0,1,0,0,1,0,0,6.0,6,1.0,6,1.0
+884,406498,31959,0,1,0,0,0,0,0,0,0,1,0,1,2.0,2,1.0,6,3.0
+885,365405,108994,0,1,0,1,0,0,1,0,0,0,0,0,4.0,4,1.0,4,1.0
+886,280593,104025,1,0,1,0,0,0,0,0,0,0,0,1,10.0,10,1.0,4,0.4
+887,275781,182917,1,1,0,0,0,0,0,0,0,1,0,0,12.0,12,1.0,4,0.33
+888,278623,51336,1,0,0,0,0,0,0,1,0,0,0,1,8.0,8,1.0,10,1.25
+889,479862,137118,1,0,0,0,0,0,0,0,1,0,0,1,2.0,2,1.0,8,4.0
+890,371046,109092,0,0,0,1,0,0,0,0,1,0,0,1,6.0,6,1.0,6,1.0
+891,226188,133532,0,1,0,0,0,0,1,1,0,0,0,0,2.0,2,1.0,6,3.0
+892,413603,178951,0,0,0,1,1,1,0,0,0,0,0,0,2.0,2,1.0,6,3.0
+893,330993,191499,0,1,0,0,0,0,1,1,0,0,0,0,8.0,8,1.0,10,1.25
+894,487445,144381,0,1,0,1,0,1,0,0,0,0,0,0,,0,0.0,6,5.0
+895,446419,102496,0,1,0,0,0,0,1,1,0,0,0,0,12.0,12,1.0,0,0.0
+896,411668,159943,0,0,0,0,0,0,0,0,1,1,0,1,,0,0.0,6,5.0
+897,274151,49946,0,0,0,0,0,0,1,0,1,0,1,0,8.0,8,1.0,4,0.5
+898,454904,173407,0,0,0,0,0,1,0,1,0,1,0,0,16.0,16,1.0,4,0.25
+899,468542,42955,0,0,0,0,0,1,0,1,0,1,0,0,6.0,6,1.0,10,1.67
+900,317048,61101,0,0,0,0,0,1,0,1,0,0,0,1,,0,0.0,2,5.0
+901,385289,74675,1,0,0,0,0,1,0,0,0,0,1,0,8.0,8,1.0,2,0.25
+902,266676,72409,0,0,1,0,0,0,1,1,0,0,0,0,14.0,14,1.0,2,0.14
+903,254136,100314,0,1,0,0,1,0,0,0,1,0,0,0,10.0,10,1.0,4,0.4
+904,314605,151225,1,0,0,0,1,0,0,1,0,0,0,0,10.0,10,1.0,6,0.6
+905,459648,45195,0,0,0,0,0,1,0,0,1,1,0,0,4.0,4,1.0,2,0.5
+906,295585,86609,0,1,0,0,0,0,1,0,0,0,1,0,4.0,4,1.0,0,0.0
+907,371041,75883,0,0,1,0,0,0,1,0,1,0,0,0,4.0,4,1.0,4,1.0
+908,371542,57193,0,0,0,1,0,0,0,1,0,1,0,0,2.0,2,1.0,6,3.0
+909,419290,32255,0,0,0,0,1,1,0,0,0,0,0,1,2.0,2,1.0,12,6.0
+910,332905,154274,0,1,0,0,0,0,1,1,0,0,0,0,10.0,10,1.0,4,0.4
+911,396616,92842,1,1,0,0,0,0,0,1,0,0,0,0,2.0,2,1.0,4,2.0
+912,225868,72568,0,0,0,1,1,0,0,0,0,0,1,0,8.0,8,1.0,4,0.5
+913,483313,158045,0,0,1,0,0,0,0,1,1,0,0,0,4.0,4,1.0,6,1.5
+914,409500,59021,0,0,0,0,0,0,0,1,1,1,0,0,2.0,2,1.0,10,5.0
+915,347719,136188,1,0,1,0,0,1,0,0,0,0,0,0,8.0,8,1.0,2,0.25
+916,300122,70254,0,0,1,0,1,0,1,0,0,0,0,0,6.0,6,1.0,12,2.0
+917,306186,172369,0,0,0,0,0,0,1,0,0,1,1,0,6.0,6,1.0,4,0.67
+918,317909,48222,0,0,0,0,0,0,0,1,1,1,0,0,8.0,8,1.0,2,0.25
+919,243993,51965,0,0,0,0,0,0,1,0,0,0,1,1,6.0,6,1.0,6,1.0
+920,322095,172458,1,1,0,0,0,0,1,0,0,0,0,0,10.0,10,1.0,6,0.6
+921,305860,60419,0,0,1,1,0,0,0,0,0,0,0,1,10.0,10,1.0,2,0.2
+922,399045,24312,1,0,0,1,0,0,0,0,0,0,0,1,12.0,12,1.0,4,0.33
+923,466188,25075,0,0,0,0,1,1,0,0,0,0,0,1,12.0,12,1.0,10,0.83
+924,328821,44907,1,0,0,0,0,0,0,0,1,0,0,1,6.0,6,1.0,6,1.0
+925,392780,194685,0,1,0,0,0,0,0,0,1,1,0,0,2.0,2,1.0,6,3.0
+926,263701,186881,0,0,0,0,1,0,1,0,0,0,1,0,6.0,6,1.0,6,1.0
+927,485278,149804,0,1,1,0,0,0,0,0,1,0,0,0,,0,0.0,4,5.0
+928,265979,25740,0,0,0,0,1,0,1,0,0,0,0,1,6.0,6,1.0,12,2.0
+929,296060,13802,0,1,1,0,1,0,0,0,0,0,0,0,,0,0.0,4,5.0
+930,314827,183938,0,1,1,0,0,0,0,0,0,1,0,0,4.0,4,1.0,2,0.5
+931,327857,75977,1,0,0,1,0,1,0,0,0,0,0,0,4.0,4,1.0,4,1.0
+932,336696,195946,0,0,1,0,0,0,0,0,1,1,0,0,10.0,10,1.0,12,1.2
+933,381827,61627,0,0,0,0,1,0,0,0,0,1,0,1,,0,0.0,6,5.0
+934,402772,37800,0,0,0,0,1,0,0,0,0,1,1,0,2.0,2,1.0,8,4.0
+935,419875,103666,0,0,0,1,1,0,0,0,0,1,0,0,4.0,4,1.0,12,3.0
+936,370256,147215,0,0,1,0,0,0,0,1,0,1,0,0,8.0,8,1.0,6,0.75
+937,499515,81170,0,1,0,1,0,0,0,0,1,0,0,0,14.0,14,1.0,4,0.29
+938,489669,123869,0,0,1,1,0,0,0,0,1,0,0,0,10.0,10,1.0,8,0.8
+939,318548,157598,0,0,0,0,0,0,0,1,1,1,0,0,10.0,10,1.0,0,0.0
+940,378058,73800,0,0,1,0,0,0,0,1,0,0,0,1,8.0,8,1.0,8,1.0
+941,295093,155387,0,0,0,1,0,0,1,0,0,1,0,0,10.0,10,1.0,4,0.4
+942,255137,77953,0,0,0,1,0,0,0,0,0,1,0,1,2.0,2,1.0,4,2.0
+943,460003,175620,0,0,1,0,0,0,1,1,0,0,0,0,6.0,6,1.0,2,0.33
+944,435210,67087,0,0,0,0,0,0,1,0,1,1,0,0,4.0,4,1.0,6,1.5
+945,408719,21594,0,0,0,0,0,0,0,0,1,0,1,1,10.0,10,1.0,0,0.0
+946,442983,64387,1,0,0,0,1,0,0,0,1,0,0,0,8.0,8,1.0,6,0.75
+947,417183,170553,1,0,1,1,0,0,0,0,0,0,0,0,14.0,14,1.0,0,0.0
+948,245504,60047,0,0,0,0,0,1,0,1,1,0,0,0,8.0,8,1.0,8,1.0
+949,253896,99657,1,1,0,0,0,0,0,1,0,0,0,0,2.0,2,1.0,4,2.0
+950,360681,78417,0,0,0,0,0,1,0,0,1,0,0,1,6.0,6,1.0,6,1.0
+951,217587,140616,0,1,1,0,0,0,0,0,1,0,0,0,6.0,6,1.0,10,1.67
+952,399434,26457,0,1,0,0,0,1,1,0,0,0,0,0,12.0,12,1.0,2,0.17
+953,311115,142051,1,0,0,0,1,0,1,0,0,0,0,0,10.0,10,1.0,2,0.2
+954,468873,130072,0,0,0,0,0,0,0,1,1,0,1,0,4.0,4,1.0,14,3.5
+955,387628,54418,0,0,1,0,0,1,0,0,0,1,0,0,4.0,4,1.0,4,1.0
+956,311418,79285,0,0,1,0,0,1,0,0,0,0,0,1,6.0,6,1.0,10,1.67
+957,282189,153537,1,0,0,0,0,1,0,0,1,0,0,0,6.0,6,1.0,8,1.33
+958,242619,113714,0,0,0,0,0,0,1,0,1,0,0,1,8.0,8,1.0,10,1.25
+959,326593,187624,0,0,0,0,1,0,1,0,0,0,1,0,8.0,8,1.0,8,1.0
+960,219358,105388,1,0,0,0,0,0,1,1,0,0,0,0,6.0,6,1.0,12,2.0
+961,212481,34643,0,0,0,0,0,0,1,0,1,1,0,0,4.0,4,1.0,14,3.5
+962,493117,142931,0,0,0,0,0,1,0,1,0,1,0,0,4.0,4,1.0,4,1.0
+963,256384,79402,0,0,1,0,0,0,0,0,0,1,1,0,4.0,4,1.0,6,1.5
+964,496916,99297,1,0,0,0,0,1,0,0,0,0,1,0,,0,0.0,2,5.0
+965,423393,174792,0,1,0,0,0,0,1,1,0,0,0,0,,0,0.0,14,5.0
+966,473484,177801,1,0,0,0,1,1,0,0,0,0,0,0,,0,0.0,4,5.0
+967,371779,80404,0,0,0,0,1,0,0,0,0,1,1,0,2.0,2,1.0,6,3.0
+968,384400,71324,0,0,1,0,0,0,0,0,1,0,0,1,4.0,4,1.0,2,0.5
+969,217937,143787,1,0,0,0,1,0,0,0,0,1,0,0,2.0,2,1.0,8,4.0
+970,213566,98135,1,0,0,0,0,1,0,0,0,1,0,0,2.0,2,1.0,4,2.0
+971,289192,56182,0,1,0,0,0,0,1,1,0,0,0,0,6.0,6,1.0,0,0.0
+972,403598,73657,0,0,1,0,0,1,0,1,0,0,0,0,2.0,2,1.0,8,4.0
+973,284210,130990,1,0,0,1,0,0,0,1,0,0,0,0,6.0,6,1.0,4,0.67
+974,401410,40083,0,1,0,0,0,0,1,0,0,0,1,0,8.0,8,1.0,12,1.5
+975,337305,22859,0,0,0,1,0,0,0,0,0,1,0,1,6.0,6,1.0,2,0.33
+976,226419,158974,0,0,0,1,0,1,0,0,0,0,0,1,,0,0.0,2,5.0
+977,316248,105993,0,0,0,1,0,0,1,0,1,0,0,0,4.0,4,1.0,2,0.5
+978,319823,180018,0,0,0,0,0,1,0,1,0,1,0,0,8.0,8,1.0,4,0.5
+979,468990,123010,0,1,0,1,0,0,0,0,0,0,0,1,4.0,4,1.0,4,1.0
+980,403627,147220,0,0,0,0,0,0,1,0,1,0,1,0,10.0,10,1.0,4,0.4
+981,317225,57958,0,0,0,0,1,1,0,0,0,0,1,0,8.0,8,1.0,0,0.0
+982,229734,168922,0,0,0,1,0,0,0,1,0,1,0,0,4.0,4,1.0,6,1.5
+983,468070,155375,0,0,0,1,0,0,1,1,0,0,0,0,10.0,10,1.0,6,0.6
+984,252884,174360,0,0,0,1,0,0,0,0,1,0,0,1,2.0,2,1.0,10,5.0
+985,466428,100568,0,0,0,0,0,1,0,0,0,1,0,1,4.0,4,1.0,4,1.0
+986,452565,72172,0,0,0,0,0,1,1,0,0,0,0,1,4.0,4,1.0,14,3.5
+987,220362,170895,0,0,0,1,0,0,1,0,0,0,1,0,4.0,4,1.0,10,2.5
+988,270701,101907,0,0,0,0,0,1,0,1,0,0,1,0,8.0,8,1.0,6,0.75
+989,391764,45376,0,0,1,1,1,0,0,0,0,0,0,0,4.0,4,1.0,6,1.5
+990,305302,145908,0,0,0,0,0,1,0,0,1,1,0,0,10.0,10,1.0,6,0.6
+991,356465,130869,1,0,0,1,0,0,0,0,1,0,0,0,2.0,2,1.0,4,2.0
+992,260584,156699,0,0,0,0,0,1,0,1,0,1,0,0,6.0,6,1.0,10,1.67
+993,264824,101853,0,0,0,1,0,0,1,0,1,0,0,0,6.0,6,1.0,8,1.33
+994,229223,55028,1,0,0,0,1,0,0,0,1,0,0,0,10.0,10,1.0,4,0.4
+995,226950,14602,0,0,0,1,0,0,0,0,0,1,1,0,14.0,14,1.0,6,0.43
+996,280148,36439,0,0,0,0,1,0,0,0,1,0,0,1,2.0,2,1.0,8,4.0
+997,254227,51432,0,0,0,0,0,0,1,0,0,1,0,1,,0,0.0,4,5.0
+998,404381,168755,1,0,0,0,0,0,0,0,1,0,0,1,6.0,6,1.0,4,0.67
+999,265544,17476,0,1,0,1,0,1,0,0,0,0,0,0,8.0,8,1.0,6,0.75
+1000,348762,67260,0,0,0,1,1,0,0,0,0,0,0,1,8.0,8,1.0,4,0.5
+1004,90000,10000,1,1,1,0,0,0,0,0,0,0,0,0,,0,0.0,0,0.0
+1005,500000,10000,0,1,0,0,1,0,0,1,0,0,0,0,2.0,2,1.0,0,0.0
+1006,140000,70000,1,0,0,1,0,0,0,1,0,0,0,0,,0,0.0,6,5.0
+1007,500000,10000,0,1,0,0,1,1,0,0,0,0,0,0,2.0,2,1.0,0,0.0

From bb5f701a899476db242ebb7640326ad4498f9386 Mon Sep 17 00:00:00 2001
From: Daehyun-Bigbread <bigdarkgold@gmail.com>
Date: Tue, 1 Apr 2025 17:02:24 +0900
Subject: [PATCH 4/5] TRI-263 performance related function fix.

---
 app/dependencies/__init__.py                  |   11 +
 app/router/evaluation.py                      |  166 +++
 app/router/recommendation_api.py              |    9 +-
 app/schema/recommendation_schema.py           |   14 +-
 app/services/evaluation/__init__.py           |   19 +
 app/services/evaluation/data_generation.py    |  220 ++++
 app/services/evaluation/diversity_metrics.py  |  201 ++++
 app/services/evaluation/evaluator.py          |  363 +++---
 app/services/evaluation/utils.py              |   61 +
 app/services/model_trainer/__init__.py        |    5 +-
 .../model_trainer/data_preparation.py         |   25 +-
 .../model_trainer/hyperparameter_tuning.py    |  183 +++
 app/services/model_trainer/model_training.py  |   23 +-
 .../model_trainer/recommenation/__init__.py   |   14 +
 .../basic.py}                                 |   78 +-
 .../model_trainer/recommenation/cold_start.py |  111 ++
 .../model_trainer/recommenation/diversity.py  |   36 +
 .../model_trainer/recommenation/hybrid.py     |  366 ++++++
 app/services/model_trainer/train_model.py     |  136 ++-
 requirements.txt                              |    3 +-
 .../user/preprocessed_user_features.csv       | 1005 -----------------
 21 files changed, 1764 insertions(+), 1285 deletions(-)
 create mode 100644 app/dependencies/__init__.py
 create mode 100644 app/router/evaluation.py
 create mode 100644 app/services/evaluation/__init__.py
 create mode 100644 app/services/evaluation/data_generation.py
 create mode 100644 app/services/evaluation/diversity_metrics.py
 create mode 100644 app/services/evaluation/utils.py
 create mode 100644 app/services/model_trainer/hyperparameter_tuning.py
 create mode 100644 app/services/model_trainer/recommenation/__init__.py
 rename app/services/model_trainer/{recommendation.py => recommenation/basic.py} (81%)
 create mode 100644 app/services/model_trainer/recommenation/cold_start.py
 create mode 100644 app/services/model_trainer/recommenation/diversity.py
 create mode 100644 app/services/model_trainer/recommenation/hybrid.py
 delete mode 100644 storage/input_json/user/preprocessed_user_features.csv

diff --git a/app/dependencies/__init__.py b/app/dependencies/__init__.py
new file mode 100644
index 0000000..8fdda1c
--- /dev/null
+++ b/app/dependencies/__init__.py
@@ -0,0 +1,11 @@
+# app/dependencies/__init__.py
+
+from datetime import datetime
+
+# 글로벌 변수 초기화
+globals_dict = {}
+model_initializing = False  # 모델 초기화 상태를 추적하는 전역 변수
+last_initialization_attempt = None  # 마지막 초기화 시도 시간
+
+def get_globals_dict():
+    return globals_dict
\ No newline at end of file
diff --git a/app/router/evaluation.py b/app/router/evaluation.py
new file mode 100644
index 0000000..edda854
--- /dev/null
+++ b/app/router/evaluation.py
@@ -0,0 +1,166 @@
+# app/routers/evaluation.py
+
+from fastapi import APIRouter, Depends, HTTPException
+from app.schema.recommendation_schema import RecommendationEvaluationResponse
+from app.services.evaluation.evaluator import evaluate_recommendation_model, evaluate_with_cross_validation
+from app.services.evaluation.diversity_metrics import evaluate_recommendation_diversity
+from app.services.model_trainer.hyperparameter_tuning import optimize_recommendation_parameters
+from app.dependencies import get_globals_dict
+from app.services.model_trainer import evaluate_model, optimize_recommendation_parameters 
+
+# 각 알고리즘별 결과 평가
+from app.services.model_trainer.recommenation.basic import generate_recommendations
+from app.services.model_trainer.recommenation.hybrid import generate_hybrid_recommendations
+
+import json
+import logging
+import numpy as np
+
+router = APIRouter(
+    prefix="/evaluate",
+    tags=["evaluation"],
+    responses={404: {"description": "Not found"}},
+)
+
+logger = logging.getLogger(__name__)
+
+@router.get("/basic", response_model=RecommendationEvaluationResponse)
+async def evaluate_basic(globals_dict=Depends(get_globals_dict)):
+    """기본 추천 시스템 평가 수행"""
+    try:
+        metrics = evaluate_recommendation_model(globals_dict)
+        
+        # 추가 다양성 지표 계산
+        df_model = globals_dict.get("df_model")
+        
+        if "test_interactions" in globals_dict and df_model is not None:
+            test_interactions = globals_dict.get("test_interactions")
+            
+            # 추천 결과를 사용자-식당 매핑 딕셔너리로 변환
+            recommendations_dict = {}
+            for user_id, metrics in globals_dict.get("recommendations_results", {}).items():
+                recommendations_dict[user_id] = [r['restaurant_id'] for r in metrics.get('recommendations', [])]
+            
+            diversity_metrics = evaluate_recommendation_diversity(
+                recommendations_dict, 
+                user_history=test_interactions, 
+                restaurant_data=df_model
+            )
+            
+            # 기본 지표와 다양성 지표 병합
+            metrics.update(diversity_metrics)
+        
+        return {"metrics": metrics, "status": "success"}
+    
+    except Exception as e:
+        logger.error(f"평가 중 오류 발생: {e}", exc_info=True)
+        raise HTTPException(status_code=500, detail=str(e))
+
+@router.get("/cross-validation", response_model=RecommendationEvaluationResponse)
+async def evaluate_with_cross_val(n_splits: int = 5, globals_dict=Depends(get_globals_dict)):
+    """교차 검증 평가 수행"""
+    try:
+        metrics = evaluate_with_cross_validation(globals_dict, n_splits=n_splits)
+        return {"metrics": metrics, "status": "success"}
+    
+    except Exception as e:
+        logger.error(f"교차 검증 평가 중 오류 발생: {e}", exc_info=True)
+        raise HTTPException(status_code=500, detail=str(e))
+
+@router.post("/optimize-params", response_model=dict)
+async def optimize_parameters(n_trials: int = 30, timeout: int = 300, globals_dict=Depends(get_globals_dict)):
+    """추천 시스템 파라미터 최적화"""
+    try:
+        df_model = globals_dict.get("df_model")
+        user_features_df = globals_dict.get("user_features_df")
+        model_features = globals_dict.get("model_features")
+        
+        if df_model is None or user_features_df is None or model_features is None:
+            raise HTTPException(status_code=400, detail="필요한 데이터가 로드되지 않았습니다.")
+        
+        best_params = optimize_recommendation_parameters(
+            df_model, 
+            user_features_df, 
+            model_features, 
+            n_trials=n_trials,
+            timeout=timeout
+        )
+        
+        return {"best_parameters": best_params, "status": "success"}
+    
+    except Exception as e:
+        logger.error(f"파라미터 최적화 중 오류 발생: {e}", exc_info=True)
+        raise HTTPException(status_code=500, detail=str(e))
+
+@router.get("/compare-algorithms", response_model=dict)
+async def compare_algorithms(globals_dict=Depends(get_globals_dict)):
+    """서로 다른 추천 알고리즘 비교"""
+    try:
+        df_model = globals_dict.get("df_model")
+        user_features_df = globals_dict.get("user_features_df")
+        
+        if df_model is None or user_features_df is None:
+            raise HTTPException(status_code=400, detail="필요한 데이터가 로드되지 않았습니다.")
+        
+        # 샘플 사용자 선택
+        sample_users = df_model['user_id'].sample(min(20, df_model['user_id'].nunique())).unique()
+        
+        basic_metrics = {}
+        hybrid_metrics = {}
+        
+        for user_id in sample_users:
+            # 기본 추천 알고리즘 평가
+            stacking_reg = globals_dict.get("stacking_reg")
+            scaler = globals_dict.get("scaler")
+            model_features = globals_dict.get("model_features")
+            
+            if all([stacking_reg, scaler, model_features]):
+                # 기본 추천 생성
+                basic_result = json.loads(generate_recommendations(
+                    df_model.copy(),
+                    stacking_reg,
+                    model_features,
+                    user_id,
+                    scaler,
+                    user_features=user_features_df
+                ))
+                
+                # 하이브리드 추천 생성
+                hybrid_result = generate_hybrid_recommendations(
+                    df_model.copy(),
+                    df_model.copy(),
+                    user_id,
+                    n=15,
+                    alpha=0.7
+                )
+                
+                # 평가 지표 계산 및 저장 (여기서는 간소화를 위해 추천된 식당 수만 계산)
+                basic_metrics[user_id] = {
+                    'num_recommendations': len(basic_result.get('recommendations', [])),
+                    'is_new_user': basic_result.get('is_new_user', True)
+                }
+                
+                hybrid_metrics[user_id] = {
+                    'num_recommendations': len(hybrid_result.get('recommendations', [])),
+                    'is_new_user': hybrid_result.get('is_new_user', True)
+                }
+        
+        # 결과 정리
+        result = {
+            'basic_algorithm': {
+                'avg_recommendations': np.mean([m['num_recommendations'] for m in basic_metrics.values()]),
+                'new_user_ratio': np.mean([m['is_new_user'] for m in basic_metrics.values()]),
+                'coverage': len(basic_metrics) / len(sample_users) if sample_users.size > 0 else 0
+            },
+            'hybrid_algorithm': {
+                'avg_recommendations': np.mean([m['num_recommendations'] for m in hybrid_metrics.values()]),
+                'new_user_ratio': np.mean([m['is_new_user'] for m in hybrid_metrics.values()]),
+                'coverage': len(hybrid_metrics) / len(sample_users) if sample_users.size > 0 else 0
+            }
+        }
+        
+        return {"comparison_results": result, "status": "success"}
+    
+    except Exception as e:
+        logger.error(f"알고리즘 비교 중 오류 발생: {e}", exc_info=True)
+        raise HTTPException(status_code=500, detail=str(e))
\ No newline at end of file
diff --git a/app/router/recommendation_api.py b/app/router/recommendation_api.py
index aadf385..6536030 100644
--- a/app/router/recommendation_api.py
+++ b/app/router/recommendation_api.py
@@ -11,9 +11,11 @@
 from app.services.preprocess.restaurant.data_loader import load_restaurant_json_files, load_user_json_files
 from app.services.preprocess.restaurant.preprocessor import preprocess_data
 from app.services.model_trainer import train_model
-from app.services.model_trainer.recommendation import generate_recommendations
+from app.services.model_trainer.recommenation.basic import generate_recommendations
 from app.services.preprocess.user.user_preprocess import user_preprocess_data  # 사용자 데이터 전처리 모듈 추가
 from app.services.evaluation.evaluator import evaluate_recommendation_model
+from app.services.model_trainer import train_model, optimize_recommendation_parameters 
+from app.dependencies import globals_dict, model_initializing, last_initialization_attempt
 from typing import List, Dict, Any
 from datetime import datetime
 
@@ -21,11 +23,6 @@
 logger = logging.getLogger("recommendation_api")
 router = APIRouter()
 
-# 글로벌 변수 초기화
-globals_dict = {}
-model_initializing = False  # 모델 초기화 상태를 추적하는 전역 변수
-last_initialization_attempt = None  # 마지막 초기화 시도 시간
-
 # 초기 데이터 로딩 및 모델 학습
 def initialize_model(force=False):
     global globals_dict, model_initializing, last_initialization_attempt
diff --git a/app/schema/recommendation_schema.py b/app/schema/recommendation_schema.py
index 4983d21..6734de1 100644
--- a/app/schema/recommendation_schema.py
+++ b/app/schema/recommendation_schema.py
@@ -1,7 +1,7 @@
 # app/models/recommendation_schema.py
 
 from pydantic import BaseModel, Field
-from typing import List, Annotated
+from typing import Dict, Any, List, Optional, Annotated
 
 class RecommendationItem(BaseModel):
     category_id: int
@@ -30,3 +30,15 @@ class UserData(BaseModel):
     user_id: str
     # 선호 카테고리는 최소 1개, 최대 3개 (Pydantic v2 방식)
     preferred_categories: Annotated[List[str], Field(min_length=1, max_length=3)]
+
+class RecommendationEvaluationResponse(BaseModel):
+    metrics: Dict[str, Any]
+    status: str
+
+class HyperparameterOptimizationRequest(BaseModel):
+    n_trials: Optional[int] = 30
+    timeout: Optional[int] = 300
+
+class HyperparameterOptimizationResponse(BaseModel):
+    best_parameters: Dict[str, Any]
+    status: str
\ No newline at end of file
diff --git a/app/services/evaluation/__init__.py b/app/services/evaluation/__init__.py
new file mode 100644
index 0000000..7f50127
--- /dev/null
+++ b/app/services/evaluation/__init__.py
@@ -0,0 +1,19 @@
+# app/services/evaluation/__init__.py
+
+from app.services.evaluation.evaluator import (
+    evaluate_recommendation_model,
+    evaluate_with_cross_validation
+)
+from app.services.evaluation.data_generation import (
+    create_test_interactions,
+    create_stratified_train_test_split
+)
+from app.services.evaluation.utils import default_empty_metrics
+
+__all__ = [
+    'evaluate_recommendation_model',
+    'evaluate_with_cross_validation',
+    'create_test_interactions',
+    'create_stratified_train_test_split',
+    'default_empty_metrics'
+]
\ No newline at end of file
diff --git a/app/services/evaluation/data_generation.py b/app/services/evaluation/data_generation.py
new file mode 100644
index 0000000..1a0c194
--- /dev/null
+++ b/app/services/evaluation/data_generation.py
@@ -0,0 +1,220 @@
+# app/services/evaluation/data_generation.py
+
+import logging
+import numpy as np
+import pandas as pd
+
+logger = logging.getLogger(__name__)
+
+def create_test_interactions(globals_dict):
+    """
+    더 현실적인 사용자-식당 상호작용 테스트 데이터 생성
+    
+    Args:
+        globals_dict: 전역 변수 딕셔너리
+    
+    Returns:
+        pd.DataFrame: 상호작용 테스트 데이터
+    """
+    try:
+        df_model = globals_dict.get("df_model")
+        user_features_df = globals_dict.get("user_features_df")
+        
+        if df_model is None or user_features_df is None:
+            logger.warning("테스트 데이터 생성에 필요한 데이터가 부족합니다.")
+            return pd.DataFrame(columns=['user_id', 'restaurant_id', 'score'])
+        
+        # 모든 사용자와 식당 데이터 가져오기
+        all_users = user_features_df['user_id'].tolist()
+        all_restaurants = df_model['restaurant_id'].unique().tolist()
+        
+        # 사용자별 프로필 특성 (있다면) 활용
+        user_preferences = {}
+        
+        if 'preferred_category' in user_features_df.columns:
+            for _, user_row in user_features_df.iterrows():
+                user_id = user_row['user_id']
+                preferred_category = user_row.get('preferred_category')
+                user_preferences[user_id] = preferred_category
+        
+        # 테스트 상호작용 생성
+        test_interactions = []
+        
+        for user_id in all_users[:50]:  # 테스트를 위해 50명 사용자만 선택
+            # 사용자별 특성에 맞는 식당 선택
+            user_preferred_category = user_preferences.get(user_id)
+            
+            if user_preferred_category:
+                # 사용자 선호 카테고리가 있으면 해당 카테고리의 식당들 중에서 선택
+                category_restaurants = df_model[
+                    df_model['category_id'] == user_preferred_category
+                ]['restaurant_id'].tolist()
+                
+                if category_restaurants:
+                    # 선호 카테고리 식당 선택 (60%)
+                    preferred_count = max(1, int(np.random.randint(3, 6) * 0.6))
+                    preferred_restaurants = np.random.choice(
+                        category_restaurants, 
+                        size=min(preferred_count, len(category_restaurants)),
+                        replace=False
+                    )
+                    
+                    # 기타 랜덤 식당 선택 (40%)
+                    other_restaurants = list(set(all_restaurants) - set(category_restaurants))
+                    other_count = np.random.randint(1, 3)
+                    other_selected = np.random.choice(
+                        other_restaurants,
+                        size=min(other_count, len(other_restaurants)),
+                        replace=False
+                    )
+                    
+                    selected_restaurants = np.concatenate([preferred_restaurants, other_selected])
+                else:
+                    # 선호 카테고리 식당이 없으면 랜덤 선택
+                    selected_restaurants = np.random.choice(
+                        all_restaurants, 
+                        size=np.random.randint(3, 6),
+                        replace=False
+                    )
+            else:
+                # 사용자 선호도 정보가 없으면 랜덤 선택
+                selected_restaurants = np.random.choice(
+                    all_restaurants, 
+                    size=np.random.randint(3, 6),
+                    replace=False
+                )
+            
+            # 선택된 식당에 대한 가상 평점 생성 (3.0 ~ 5.0)
+            for restaurant_id in selected_restaurants:
+                # 선호 카테고리 식당은 더 높은 평점 확률
+                if user_preferred_category and restaurant_id in category_restaurants:
+                    score = np.random.uniform(4.0, 5.0)  # 선호 카테고리 식당은 평점 높게
+                else:
+                    score = np.random.uniform(3.0, 5.0)  # 기타 식당은 일반적인 분포
+                
+                test_interactions.append({
+                    'user_id': user_id,
+                    'restaurant_id': restaurant_id,
+                    'score': round(score, 1)  # 소수점 첫째 자리까지
+                })
+        
+        return pd.DataFrame(test_interactions)
+    
+    except Exception as e:
+        logger.error(f"테스트 데이터 생성 중 오류: {e}", exc_info=True)
+        return pd.DataFrame(columns=['user_id', 'restaurant_id', 'score'])
+
+def create_stratified_train_test_split(df_model, test_ratio=0.2, random_state=42):
+    """
+    사용자와 식당 분포를 보존하는 층화 훈련/테스트 분할
+    
+    Args:
+        df_model (DataFrame): 분할할 데이터프레임
+        test_ratio (float): 테스트 데이터 비율 (0~1)
+        random_state (int): 랜덤 시드
+    
+    Returns:
+        tuple: (훈련 데이터프레임, 테스트 데이터프레임)
+    """
+    try:
+        from sklearn.model_selection import GroupShuffleSplit
+        
+        logger.debug("층화 데이터 분할 시작...")
+        
+        # 필수 컬럼 확인
+        required_cols = ['user_id', 'restaurant_id']
+        for col in required_cols:
+            if col not in df_model.columns:
+                logger.error(f"데이터에 필수 컬럼이 없습니다: {col}")
+                raise ValueError(f"데이터에 필수 컬럼이 없습니다: {col}")
+        
+        # 사용자별 데이터 분할
+        users = df_model['user_id'].unique()
+        logger.info(f"총 {len(users)}명의 사용자 데이터에 대해 분할 수행")
+        
+        # 사용자별 활동 수준 파악 (상호작용 횟수 기준)
+        user_activity = df_model.groupby('user_id').size()
+        low_activity = user_activity[user_activity <= 5].index.tolist()
+        medium_activity = user_activity[(user_activity > 5) & (user_activity <= 20)].index.tolist()
+        high_activity = user_activity[user_activity > 20].index.tolist()
+        
+        logger.debug(f"저활동 사용자: {len(low_activity)}명, 중활동: {len(medium_activity)}명, 고활동: {len(high_activity)}명")
+        
+        # 각 활동 그룹별로 층화 분할
+        train_indices = []
+        test_indices = []
+        
+        for activity_group in [low_activity, medium_activity, high_activity]:
+            if not activity_group:
+                continue
+                
+            # 해당 활동 그룹의 사용자 데이터만 선택
+            group_data = df_model[df_model['user_id'].isin(activity_group)]
+            group_users = group_data['user_id'].unique()
+            
+            # 사용자를 훈련/테스트로 분할
+            np.random.seed(random_state)
+            test_size = int(len(group_users) * test_ratio)
+            test_users = np.random.choice(group_users, size=test_size, replace=False)
+            train_users = np.array([u for u in group_users if u not in test_users])
+            
+            # 각 사용자별 데이터 인덱스 수집
+            for user in train_users:
+                user_indices = group_data[group_data['user_id'] == user].index.tolist()
+                
+                # 활동량이 많은 사용자는 일부 데이터만 훈련에 사용
+                if user in high_activity and len(user_indices) > 30:
+                    np.random.shuffle(user_indices)
+                    train_indices.extend(user_indices[:30])  # 최대 30개 상호작용만 사용
+                else:
+                    train_indices.extend(user_indices)
+            
+            for user in test_users:
+                user_indices = group_data[group_data['user_id'] == user].index.tolist()
+                
+                # 테스트용 사용자도 최대 10개 상호작용만 테스트에 사용
+                if len(user_indices) > 10:
+                    np.random.shuffle(user_indices)
+                    test_indices.extend(user_indices[:10])
+                else:
+                    test_indices.extend(user_indices)
+        
+        # 훈련/테스트 데이터프레임 생성
+        train_df = df_model.loc[train_indices].copy()
+        test_df = df_model.loc[test_indices].copy()
+        
+        # 균형 확인
+        logger.info(f"훈련 데이터: {len(train_df)} 상호작용, {train_df['user_id'].nunique()} 사용자")
+        logger.info(f"테스트 데이터: {len(test_df)} 상호작용, {test_df['user_id'].nunique()} 사용자")
+        
+        # 점수(score) 컬럼이 없는 경우 가상의 점수 생성 (평가를 위해)
+        if 'score' not in test_df.columns:
+            logger.warning("테스트 데이터에 'score' 컬럼이 없어 가상 점수를 생성합니다")
+            
+            # 카테고리별 평균 점수 계산 (카테고리 정보가 있는 경우)
+            if 'category_id' in test_df.columns and 'category_id' in train_df.columns:
+                category_avg_scores = train_df.groupby('category_id')['score'].mean().to_dict()
+                default_score = train_df['score'].mean()
+                
+                # 카테고리 기반 가상 점수 할당
+                test_df['score'] = test_df['category_id'].map(
+                    lambda x: category_avg_scores.get(x, default_score)
+                )
+                
+                # 약간의 무작위성 추가 (±0.5)
+                np.random.seed(random_state)
+                test_df['score'] += np.random.uniform(-0.5, 0.5, size=len(test_df))
+                test_df['score'] = test_df['score'].clip(1, 5)  # 1~5 범위로 제한
+            else:
+                # 카테고리 정보가 없으면 3~5 사이 무작위 점수
+                test_df['score'] = np.random.uniform(3, 5, size=len(test_df))
+        
+        logger.debug("층화 데이터 분할 완료")
+        return train_df, test_df
+        
+    except Exception as e:
+        logger.error(f"층화 데이터 분할 중 오류 발생: {e}", exc_info=True)
+        # 기본 분할 방식 적용
+        from sklearn.model_selection import train_test_split
+        train_df, test_df = train_test_split(df_model, test_size=test_ratio, random_state=random_state)
+        return train_df, test_df
\ No newline at end of file
diff --git a/app/services/evaluation/diversity_metrics.py b/app/services/evaluation/diversity_metrics.py
new file mode 100644
index 0000000..f9ef416
--- /dev/null
+++ b/app/services/evaluation/diversity_metrics.py
@@ -0,0 +1,201 @@
+# app/services/evaluation/diversity_metrics.py
+
+import numpy as np
+import pandas as pd
+import scipy.stats
+import logging
+
+logger = logging.getLogger(__name__)
+
+def evaluate_recommendation_diversity(recommendations, user_history=None, restaurant_data=None):
+    """
+    추천 결과의 다양성을 평가하는 함수
+    
+    Args:
+        recommendations: 사용자별 추천 결과 딕셔너리 {user_id: [restaurant_ids]}
+        user_history: 사용자 과거 상호작용 데이터 (옵션)
+        restaurant_data: 식당 메타데이터 (옵션)
+    
+    Returns:
+        dict: 다양성 평가 결과
+    """
+    try:
+        diversity_metrics = {}
+        
+        # 추천 결과를 DataFrame으로 변환
+        recommendations_list = []
+        for user_id, rest_ids in recommendations.items():
+            for rank, rest_id in enumerate(rest_ids):
+                recommendations_list.append({
+                    'user_id': user_id,
+                    'restaurant_id': rest_id,
+                    'rank': rank + 1
+                })
+        
+        recs_df = pd.DataFrame(recommendations_list)
+        if recs_df.empty:
+            logger.warning("추천 결과가 없어 다양성 평가를 진행할 수 없습니다.")
+            return {
+                "category_diversity": 0,
+                "user_coverage": 0,
+                "item_coverage": 0,
+                "novelty": 0,
+                "serendipity": 0,
+                "intra_list_similarity": 0
+            }
+        
+        # 1. 카테고리 다양성 평가
+        if restaurant_data is not None and 'category_id' in restaurant_data.columns:
+            # 식당별 카테고리 매핑
+            restaurant_categories = restaurant_data.set_index('restaurant_id')['category_id'].to_dict()
+            
+            # 추천된 식당에 카테고리 매핑
+            recs_df['category_id'] = recs_df['restaurant_id'].map(restaurant_categories)
+            
+            # 카테고리 엔트로피 계산
+            if 'category_id' in recs_df.columns and not recs_df['category_id'].isna().all():
+                category_counts = recs_df['category_id'].value_counts(normalize=True)
+                category_entropy = scipy.stats.entropy(category_counts)
+                category_diversity = 1 - (1 / (1 + category_entropy))  # 0~1 범위로 정규화
+                diversity_metrics['category_diversity'] = category_diversity
+            else:
+                diversity_metrics['category_diversity'] = 0
+        else:
+            diversity_metrics['category_diversity'] = 0
+        
+        # 2. 사용자 커버리지 (전체 사용자 중 추천을 받은 사용자 비율)
+        if user_history is not None:
+            all_users = user_history['user_id'].unique()
+            recommended_users = set(recs_df['user_id'].unique())
+            user_coverage = len(recommended_users) / len(all_users) if len(all_users) > 0 else 0
+            diversity_metrics['user_coverage'] = user_coverage
+        else:
+            diversity_metrics['user_coverage'] = 1.0  # 사용자 히스토리가 없으면 100%로 가정
+        
+        # 3. 아이템 커버리지 (전체 식당 중 추천된 식당 비율)
+        if restaurant_data is not None:
+            all_restaurants = restaurant_data['restaurant_id'].unique()
+            recommended_restaurants = set(recs_df['restaurant_id'].unique())
+            item_coverage = len(recommended_restaurants) / len(all_restaurants) if len(all_restaurants) > 0 else 0
+            diversity_metrics['item_coverage'] = item_coverage
+        else:
+            diversity_metrics['item_coverage'] = 0
+        
+        # 4. 새로움 (Novelty) - 인기 있는 아이템보다 덜 알려진 아이템 추천 정도
+        if user_history is not None and restaurant_data is not None:
+            # 식당별 인기도 (상호작용 횟수) 계산
+            restaurant_popularity = user_history['restaurant_id'].value_counts()
+            total_interactions = len(user_history)
+            
+            # 각 식당의 희소성 = -log(인기도)
+            restaurant_rarity = restaurant_popularity.map(
+                lambda x: -np.log(x / total_interactions) if x > 0 else 0
+            )
+            
+            # 추천된 식당의 평균 희소성 (높을수록 새로운 항목을 추천)
+            recommended_rarity = []
+            for rest_id in recs_df['restaurant_id'].unique():
+                if rest_id in restaurant_rarity:
+                    recommended_rarity.append(restaurant_rarity[rest_id])
+            
+            novelty = np.mean(recommended_rarity) if recommended_rarity else 0
+            
+            # 0~1 범위로 정규화 (높을수록 새로움이 큼)
+            max_possible_novelty = -np.log(1 / total_interactions) if total_interactions > 0 else 1
+            normalized_novelty = novelty / max_possible_novelty if max_possible_novelty > 0 else 0
+            
+            diversity_metrics['novelty'] = normalized_novelty
+        else:
+            diversity_metrics['novelty'] = 0
+        
+        # 5. 세렌디피티 (Serendipity) - 의외성, 예상치 못한 발견
+        if user_history is not None and restaurant_data is not None:
+            # 사용자별 과거 상호작용 카테고리
+            user_categories = {}
+            for user_id in recs_df['user_id'].unique():
+                user_hist = user_history[user_history['user_id'] == user_id]
+                user_rest_ids = user_hist['restaurant_id'].tolist()
+                
+                # 해당 식당들의 카테고리 추출
+                user_rest_categories = [
+                    restaurant_categories.get(rest_id) 
+                    for rest_id in user_rest_ids 
+                    if rest_id in restaurant_categories
+                ]
+                
+                user_categories[user_id] = set(user_rest_categories)
+            
+            # 각 사용자의 추천 중 새로운 카테고리 비율 계산
+            serendipity_scores = []
+            
+            for user_id in recs_df['user_id'].unique():
+                if user_id not in user_categories:
+                    continue
+                    
+                user_recs = recs_df[recs_df['user_id'] == user_id]
+                
+                # 추천된 식당들의 카테고리
+                rec_categories = [
+                    restaurant_categories.get(rest_id) 
+                    for rest_id in user_recs['restaurant_id'] 
+                    if rest_id in restaurant_categories
+                ]
+                
+                # 사용자가 과거에 경험하지 않은 카테고리 비율
+                new_categories = [cat for cat in rec_categories if cat not in user_categories[user_id]]
+                serendipity = len(new_categories) / len(rec_categories) if rec_categories else 0
+                
+                serendipity_scores.append(serendipity)
+            
+            # 전체 사용자의 평균 세렌디피티
+            diversity_metrics['serendipity'] = np.mean(serendipity_scores) if serendipity_scores else 0
+        else:
+            diversity_metrics['serendipity'] = 0
+        
+        # 6. 추천 목록 내 유사성 (낮을수록 다양한 추천)
+        if restaurant_data is not None and 'category_id' in restaurant_data.columns:
+            intra_similarities = []
+            
+            for user_id in recs_df['user_id'].unique():
+                user_recs = recs_df[recs_df['user_id'] == user_id]
+                
+                # 사용자에게 추천된 식당의 카테고리 목록
+                rec_categories = [
+                    restaurant_categories.get(rest_id) 
+                    for rest_id in user_recs['restaurant_id'] 
+                    if rest_id in restaurant_categories
+                ]
+                
+                # 카테고리 쌍 간의 유사성 계산 (동일 카테고리면 1, 다르면 0)
+                similarities = []
+                for i in range(len(rec_categories)):
+                    for j in range(i+1, len(rec_categories)):
+                        if rec_categories[i] is not None and rec_categories[j] is not None:
+                            sim = 1 if rec_categories[i] == rec_categories[j] else 0
+                            similarities.append(sim)
+                
+                # 사용자별 평균 유사성
+                user_similarity = np.mean(similarities) if similarities else 0
+                intra_similarities.append(user_similarity)
+            
+            # 전체 사용자의 평균 유사성 (낮을수록 다양함)
+            avg_intra_similarity = np.mean(intra_similarities) if intra_similarities else 0
+            
+            # 다양성 지표로 변환 (1 - 유사성)
+            diversity_metrics['intra_list_similarity'] = 1 - avg_intra_similarity
+        else:
+            diversity_metrics['intra_list_similarity'] = 0
+        
+        logger.info(f"추천 다양성 평가 완료: {diversity_metrics}")
+        return diversity_metrics
+    
+    except Exception as e:
+        logger.error(f"추천 다양성 평가 중 오류: {e}", exc_info=True)
+        return {
+            "category_diversity": 0,
+            "user_coverage": 0,
+            "item_coverage": 0,
+            "novelty": 0,
+            "serendipity": 0,
+            "intra_list_similarity": 0
+        }
\ No newline at end of file
diff --git a/app/services/evaluation/evaluator.py b/app/services/evaluation/evaluator.py
index 2f9ed09..e4a759f 100644
--- a/app/services/evaluation/evaluator.py
+++ b/app/services/evaluation/evaluator.py
@@ -1,110 +1,22 @@
-import numpy as np
-import pandas as pd
-import json
+# app/services/evaluation/evaluator.py
+
 import logging
-from app.services.model_trainer.recommendation import generate_recommendations
+import json
+import pandas as pd
+import numpy as np
+from app.services.model_trainer.recommenation.basic import generate_recommendations
 from app.services.evaluation.metrics import calculate_ranking_metrics
+from app.services.evaluation.data_generation import (
+    create_test_interactions, 
+    create_stratified_train_test_split
+)
+from app.services.evaluation.utils import (
+    validate_required_objects, 
+    default_empty_metrics
+)
 
 logger = logging.getLogger(__name__)
 
-def create_test_interactions(globals_dict):
-    """
-    더 현실적인 사용자-식당 상호작용 테스트 데이터 생성
-    
-    Args:
-        globals_dict: 전역 변수 딕셔너리
-    
-    Returns:
-        pd.DataFrame: 상호작용 테스트 데이터
-    """
-    try:
-        df_model = globals_dict.get("df_model")
-        user_features_df = globals_dict.get("user_features_df")
-        
-        if df_model is None or user_features_df is None:
-            logger.warning("테스트 데이터 생성에 필요한 데이터가 부족합니다.")
-            return pd.DataFrame(columns=['user_id', 'restaurant_id', 'score'])
-        
-        # 모든 사용자와 식당 데이터 가져오기
-        all_users = user_features_df['user_id'].tolist()
-        all_restaurants = df_model['restaurant_id'].unique().tolist()
-        
-        # 사용자별 프로필 특성 (있다면) 활용
-        user_preferences = {}
-        
-        if 'preferred_category' in user_features_df.columns:
-            for _, user_row in user_features_df.iterrows():
-                user_id = user_row['user_id']
-                preferred_category = user_row.get('preferred_category')
-                user_preferences[user_id] = preferred_category
-        
-        # 테스트 상호작용 생성
-        test_interactions = []
-        
-        for user_id in all_users[:50]:  # 테스트를 위해 50명 사용자만 선택
-            # 사용자별 특성에 맞는 식당 선택
-            user_preferred_category = user_preferences.get(user_id)
-            
-            if user_preferred_category:
-                # 사용자 선호 카테고리가 있으면 해당 카테고리의 식당들 중에서 선택
-                category_restaurants = df_model[
-                    df_model['category_id'] == user_preferred_category
-                ]['restaurant_id'].tolist()
-                
-                if category_restaurants:
-                    # 선호 카테고리 식당 선택 (60%)
-                    preferred_count = max(1, int(np.random.randint(3, 6) * 0.6))
-                    preferred_restaurants = np.random.choice(
-                        category_restaurants, 
-                        size=min(preferred_count, len(category_restaurants)),
-                        replace=False
-                    )
-                    
-                    # 기타 랜덤 식당 선택 (40%)
-                    other_restaurants = list(set(all_restaurants) - set(category_restaurants))
-                    other_count = np.random.randint(1, 3)
-                    other_selected = np.random.choice(
-                        other_restaurants,
-                        size=min(other_count, len(other_restaurants)),
-                        replace=False
-                    )
-                    
-                    selected_restaurants = np.concatenate([preferred_restaurants, other_selected])
-                else:
-                    # 선호 카테고리 식당이 없으면 랜덤 선택
-                    selected_restaurants = np.random.choice(
-                        all_restaurants, 
-                        size=np.random.randint(3, 6),
-                        replace=False
-                    )
-            else:
-                # 사용자 선호도 정보가 없으면 랜덤 선택
-                selected_restaurants = np.random.choice(
-                    all_restaurants, 
-                    size=np.random.randint(3, 6),
-                    replace=False
-                )
-            
-            # 선택된 식당에 대한 가상 평점 생성 (3.0 ~ 5.0)
-            for restaurant_id in selected_restaurants:
-                # 선호 카테고리 식당은 더 높은 평점 확률
-                if user_preferred_category and restaurant_id in category_restaurants:
-                    score = np.random.uniform(4.0, 5.0)  # 선호 카테고리 식당은 평점 높게
-                else:
-                    score = np.random.uniform(3.0, 5.0)  # 기타 식당은 일반적인 분포
-                
-                test_interactions.append({
-                    'user_id': user_id,
-                    'restaurant_id': restaurant_id,
-                    'score': round(score, 1)  # 소수점 첫째 자리까지
-                })
-        
-        return pd.DataFrame(test_interactions)
-    
-    except Exception as e:
-        logger.error(f"테스트 데이터 생성 중 오류: {e}", exc_info=True)
-        return pd.DataFrame(columns=['user_id', 'restaurant_id', 'score'])
-
 def evaluate_recommendation_model(globals_dict, df_model=None, user_features_df=None):
     """
     추천 모델 평가 함수
@@ -118,37 +30,27 @@ def evaluate_recommendation_model(globals_dict, df_model=None, user_features_df=
         dict: 추천 시스템 평가 지표
     """
     try:
+        # 필요 객체 유효성 검증
+        is_valid, missing_objects = validate_required_objects(globals_dict, df_model, user_features_df)
+        
+        if not is_valid:
+            logger.error(f"모델 평가에 필요한 객체가 없습니다: {', '.join(missing_objects)}")
+            return default_empty_metrics()
+        
         # 인자로 전달된 값 우선 사용, 없으면 globals_dict에서 가져오기
         df_model = df_model or globals_dict.get("df_model")
         user_features_df = user_features_df or globals_dict.get("user_features_df")
-        
-        # 필요한 객체 추출
         stacking_reg = globals_dict.get("stacking_reg")
         scaler = globals_dict.get("scaler")
         model_features = globals_dict.get("model_features")
         
-        # DataFrame 객체 유효성 검사 수정
-        missing_objects = []
-        if stacking_reg is None:
-            missing_objects.append("stacking_reg")
-        if scaler is None:
-            missing_objects.append("scaler")
-        if model_features is None:
-            missing_objects.append("model_features")
-        if df_model is None or (isinstance(df_model, pd.DataFrame) and df_model.empty):
-            missing_objects.append("df_model")
-        if user_features_df is None or (isinstance(user_features_df, pd.DataFrame) and user_features_df.empty):
-            missing_objects.append("user_features_df")
-        
-        if missing_objects:
-            logger.error(f"모델 평가에 필요한 객체가 없습니다: {', '.join(missing_objects)}")
-            return default_empty_metrics()
-        
-        # 테스트 상호작용 데이터 생성
-        test_interactions = create_test_interactions({
-            "df_model": df_model, 
-            "user_features_df": user_features_df
-        })
+        # 테스트 데이터 생성 방식 선택
+        # 1. 층화 분할 방식 (실제 데이터 기반)
+        if 'user_id' in df_model.columns:
+            _, test_interactions = create_stratified_train_test_split(df_model)
+        # 2. 랜덤 생성 방식 (완전 새 데이터)
+        else:
+            test_interactions = create_test_interactions(globals_dict)
         
         if isinstance(test_interactions, pd.DataFrame) and test_interactions.empty:
             logger.warning("상호작용 테스트 데이터가 없어 평가를 수행할 수 없습니다.")
@@ -160,45 +62,10 @@ def evaluate_recommendation_model(globals_dict, df_model=None, user_features_df=
         logger.info(f"고유 식당 수: {test_interactions['restaurant_id'].nunique()}")
         
         # 추천 결과 생성
-        recommendations_dict = {}
-        sample_users = test_interactions['user_id'].unique()
-        
-        for user_id in sample_users:
-            try:
-                # 추천 결과 생성
-                # 주의: user_id가 문자열이면 정수로 변환
-                user_id_for_rec = int(user_id) if isinstance(user_id, str) else user_id
-                
-                result_json = generate_recommendations(
-                    df_model.copy(), 
-                    stacking_reg, 
-                    model_features, 
-                    user_id_for_rec, 
-                    scaler, 
-                    user_features=user_features_df
-                )
-                
-                # JSON 파싱
-                try:
-                    result_data = json.loads(result_json)
-                except json.JSONDecodeError:
-                    logger.error(f"JSON 파싱 오류: {result_json[:100]}...")
-                    continue
-                
-                # 추천 식당 ID 리스트 추출
-                recommended_items = [item.get('restaurant_id') for item in result_data.get('recommendations', [])]
-                
-                # ID 타입 일관성 확인
-                recommended_items = [int(item) if not isinstance(item, int) else item for item in recommended_items]
-                
-                # 유효한 추천 결과만 저장
-                if recommended_items:
-                    recommendations_dict[user_id] = recommended_items
-                else:
-                    logger.warning(f"사용자 {user_id}에 대한 추천 결과가 없습니다.")
-            
-            except Exception as e:
-                logger.error(f"사용자 {user_id} 추천 생성 중 오류: {e}", exc_info=True)
+        recommendations_dict = generate_recommendations_for_users(
+            df_model, stacking_reg, model_features, 
+            test_interactions['user_id'].unique(), scaler, user_features_df
+        )
         
         # 추천 결과 검증
         if not recommendations_dict:
@@ -230,21 +97,151 @@ def evaluate_recommendation_model(globals_dict, df_model=None, user_features_df=
         logger.error(f"모델 평가 중 오류 발생: {e}", exc_info=True)
         return default_empty_metrics()
 
-def default_empty_metrics():
-    """기본 빈 지표 반환"""
-    return {
-        "MAE": None,
-        "RMSE": None,
-        "Precision@5": 0,
-        "Recall@5": 0,
-        "NDCG@5": 0,
-        "Hit_Rate@5": 0,
-        "Precision@10": 0,
-        "Recall@10": 0,
-        "NDCG@10": 0,
-        "Hit_Rate@10": 0,
-        "Precision@15": 0,
-        "Recall@15": 0,
-        "NDCG@15": 0,
-        "Hit_Rate@15": 0
-    }
\ No newline at end of file
+def generate_recommendations_for_users(df_model, stacking_reg, model_features, sample_users, scaler, user_features_df):
+    """
+    여러 사용자에 대한 추천 결과 생성
+    
+    Args:
+        df_model: 식당 데이터
+        stacking_reg: 학습된 스태킹 회귀 모델
+        model_features: 모델 특성 리스트
+        sample_users: 추천 대상 사용자 리스트
+        scaler: 특성 스케일러
+        user_features_df: 사용자 특성 데이터
+        
+    Returns:
+        dict: 사용자별 추천 식당 ID 목록
+    """
+    recommendations_dict = {}
+    
+    for user_id in sample_users:
+        try:
+            # 추천 결과 생성
+            # 주의: user_id가 문자열이면 정수로 변환
+            user_id_for_rec = int(user_id) if isinstance(user_id, str) else user_id
+            
+            result_json = generate_recommendations(
+                df_model.copy(), 
+                stacking_reg, 
+                model_features, 
+                user_id_for_rec, 
+                scaler, 
+                user_features=user_features_df
+            )
+            
+            # JSON 파싱
+            try:
+                result_data = json.loads(result_json)
+            except json.JSONDecodeError:
+                logger.error(f"JSON 파싱 오류: {result_json[:100]}...")
+                continue
+            
+            # 추천 식당 ID 리스트 추출
+            recommended_items = [item.get('restaurant_id') for item in result_data.get('recommendations', [])]
+            
+            # ID 타입 일관성 확인
+            recommended_items = [int(item) if not isinstance(item, int) else item for item in recommended_items]
+            
+            # 유효한 추천 결과만 저장
+            if recommended_items:
+                recommendations_dict[user_id] = recommended_items
+            else:
+                logger.warning(f"사용자 {user_id}에 대한 추천 결과가 없습니다.")
+        
+        except Exception as e:
+            logger.error(f"사용자 {user_id} 추천 생성 중 오류: {e}", exc_info=True)
+    
+    return recommendations_dict
+
+def evaluate_with_cross_validation(globals_dict, n_splits=5, k_values=[5, 10, 15]):
+    """
+    교차 검증을 통한 더 견고한 평가
+    
+    Args:
+        globals_dict: 전역 변수 딕셔너리
+        n_splits: 폴드 수
+        k_values: 평가할 k 값 리스트
+        
+    Returns:
+        dict: 평균 평가 지표
+    """
+    try:
+        from sklearn.model_selection import KFold
+        
+        # 필요 객체 유효성 검증
+        is_valid, missing_objects = validate_required_objects(globals_dict)
+        
+        if not is_valid:
+            logger.error(f"평가에 필요한 객체가 없습니다: {', '.join(missing_objects)}")
+            return default_empty_metrics()
+        
+        df_model = globals_dict.get("df_model")
+        user_features_df = globals_dict.get("user_features_df")
+        stacking_reg = globals_dict.get("stacking_reg")
+        scaler = globals_dict.get("scaler")
+        model_features = globals_dict.get("model_features")
+        
+        # 사용자별 데이터 그룹화
+        if 'user_id' not in df_model.columns:
+            logger.error("교차 검증을 위한 user_id 컬럼이 없습니다.")
+            return default_empty_metrics()
+            
+        user_groups = df_model.groupby('user_id').apply(lambda x: x.index.tolist())
+        users = list(user_groups.index)
+        
+        # 사용자를 기준으로 폴드 분할
+        kf = KFold(n_splits=n_splits, shuffle=True, random_state=42)
+        
+        # 평가 지표 저장
+        all_metrics = []
+        
+        fold_idx = 1
+        for train_users_idx, test_users_idx in kf.split(users):
+            logger.info(f"폴드 {fold_idx}/{n_splits} 평가 중...")
+            
+            # 훈련/테스트 사용자 분할
+            train_users = [users[i] for i in train_users_idx]
+            test_users = [users[i] for i in test_users_idx]
+            
+            # 훈련/테스트 데이터 분할
+            train_indices = [idx for user in train_users for idx in user_groups.get(user, [])]
+            test_indices = [idx for user in test_users for idx in user_groups.get(user, [])]
+            
+            train_df = df_model.iloc[train_indices]
+            test_df = df_model.iloc[test_indices]
+            
+            # 추천 생성 및 평가
+            recommendations_dict = generate_recommendations_for_users(
+                df_model, stacking_reg, model_features, test_users, scaler, user_features_df
+            )
+            
+            # 평가 지표 계산
+            if recommendations_dict:
+                metrics = calculate_ranking_metrics(recommendations_dict, test_df, k_values=k_values)
+                all_metrics.append(metrics)
+                logger.info(f"폴드 {fold_idx} 평가 결과: {metrics}")
+            else:
+                logger.warning(f"폴드 {fold_idx}에서 추천 결과가 생성되지 않았습니다.")
+            
+            fold_idx += 1
+        
+        # 평균 지표 계산
+        if not all_metrics:
+            logger.error("모든 폴드에서 평가 지표 계산에 실패했습니다.")
+            return default_empty_metrics()
+            
+        avg_metrics = {}
+        for metric in all_metrics[0].keys():
+            avg_metrics[metric] = np.mean([m[metric] for m in all_metrics])
+        
+        avg_metrics.update({
+            "MAE": None,
+            "RMSE": None
+        })
+        
+        logger.info(f"교차 검증 평균 평가 지표: {avg_metrics}")
+        return avg_metrics
+        
+    except Exception as e:
+        logger.error(f"교차 검증 평가 중 오류 발생: {e}", exc_info=True)
+        return default_empty_metrics()
\ No newline at end of file
diff --git a/app/services/evaluation/utils.py b/app/services/evaluation/utils.py
new file mode 100644
index 0000000..86c8888
--- /dev/null
+++ b/app/services/evaluation/utils.py
@@ -0,0 +1,61 @@
+# app/services/evaluation/utils.py
+
+import logging
+import pandas as pd
+
+logger = logging.getLogger(__name__)
+
+def default_empty_metrics():
+    """기본 빈 지표 반환"""
+    return {
+        "MAE": None,
+        "RMSE": None,
+        "Precision@5": 0,
+        "Recall@5": 0,
+        "NDCG@5": 0,
+        "Hit_Rate@5": 0,
+        "Precision@10": 0,
+        "Recall@10": 0,
+        "NDCG@10": 0,
+        "Hit_Rate@10": 0,
+        "Precision@15": 0,
+        "Recall@15": 0,
+        "NDCG@15": 0,
+        "Hit_Rate@15": 0
+    }
+
+def validate_required_objects(globals_dict, df_model=None, user_features_df=None):
+    """
+    모델 평가에 필요한 객체들의 유효성 검증
+    
+    Args:
+        globals_dict: 전역 변수 딕셔너리
+        df_model: 식당 데이터 (옵션)
+        user_features_df: 사용자 특성 데이터 (옵션)
+        
+    Returns:
+        tuple: (유효성 여부, 누락 객체 리스트)
+    """
+    # 인자로 전달된 값 우선 사용, 없으면 globals_dict에서 가져오기
+    df_model = df_model or globals_dict.get("df_model")
+    user_features_df = user_features_df or globals_dict.get("user_features_df")
+    
+    # 필요한 객체 추출
+    stacking_reg = globals_dict.get("stacking_reg")
+    scaler = globals_dict.get("scaler")
+    model_features = globals_dict.get("model_features")
+    
+    # DataFrame 객체 유효성 검사
+    missing_objects = []
+    if stacking_reg is None:
+        missing_objects.append("stacking_reg")
+    if scaler is None:
+        missing_objects.append("scaler")
+    if model_features is None:
+        missing_objects.append("model_features")
+    if df_model is None or (isinstance(df_model, pd.DataFrame) and df_model.empty):
+        missing_objects.append("df_model")
+    if user_features_df is None or (isinstance(user_features_df, pd.DataFrame) and user_features_df.empty):
+        missing_objects.append("user_features_df")
+    
+    return len(missing_objects) == 0, missing_objects
\ No newline at end of file
diff --git a/app/services/model_trainer/__init__.py b/app/services/model_trainer/__init__.py
index b204488..3c40f85 100644
--- a/app/services/model_trainer/__init__.py
+++ b/app/services/model_trainer/__init__.py
@@ -1,7 +1,6 @@
 # app/services/model_trainer/__init__.py
-
 from .data_preparation import prepare_data, impute_and_clip, scale_and_split
 from .model_training import train_ridge, train_rf, train_xgb, train_lgb, train_cat, train_mlp, train_stacking
 from .model_evaluation import evaluate_model
-from .recommendation import compute_composite_score, sigmoid_transform, generate_recommendations
-from .train_model import train_model  # 추가: train_model 함수를 export
+from .train_model import train_model
+from .hyperparameter_tuning import optimize_recommendation_parameters
\ No newline at end of file
diff --git a/app/services/model_trainer/data_preparation.py b/app/services/model_trainer/data_preparation.py
index db5e5b3..4b217fa 100644
--- a/app/services/model_trainer/data_preparation.py
+++ b/app/services/model_trainer/data_preparation.py
@@ -33,14 +33,33 @@ def prepare_data(df: pd.DataFrame, required_cols: list) -> pd.DataFrame:
         logger.error(f"prepare_data 오류: {e}", exc_info=True)
         raise e
 
-def impute_and_clip(df: pd.DataFrame, impute_cols: list) -> pd.DataFrame:
+def impute_and_clip(df: pd.DataFrame, impute_cols: list, min_values=None, max_values=None) -> pd.DataFrame:
     """
-    IterativeImputer를 사용하여 결측치 보완 후, score의 최댓값을 5.0으로 클리핑.
+    IterativeImputer를 사용하여 결측치 보완 후, 지정된 범위로 클리핑.
+    
+    Args:
+        df: 입력 데이터프레임
+        impute_cols: 결측치를 보완할 컬럼 리스트
+        min_values: 컬럼별 최소값 딕셔너리 (예: {'score': 0})
+        max_values: 컬럼별 최대값 딕셔너리 (예: {'score': 5})
     """
     try:
+        # 기본값 설정
+        min_values = min_values or {'score': 0}
+        max_values = max_values or {'score': 5.0}
+        
         imputer = IterativeImputer(estimator=BayesianRidge(), random_state=42, max_iter=10, initial_strategy='median')
         df[impute_cols] = imputer.fit_transform(df[impute_cols])
-        df.loc[df['score'] > 5, 'score'] = 5.0
+        
+        # 각 컬럼별로 클리핑 적용
+        for col, min_val in min_values.items():
+            if col in df.columns:
+                df[col] = df[col].clip(lower=min_val)
+                
+        for col, max_val in max_values.items():
+            if col in df.columns:
+                df[col] = df[col].clip(upper=max_val)
+                
         return df
     except Exception as e:
         logger.error(f"impute_and_clip 오류: {e}", exc_info=True)
diff --git a/app/services/model_trainer/hyperparameter_tuning.py b/app/services/model_trainer/hyperparameter_tuning.py
new file mode 100644
index 0000000..a1711f9
--- /dev/null
+++ b/app/services/model_trainer/hyperparameter_tuning.py
@@ -0,0 +1,183 @@
+# app/services/model_trainer/hyperparameter_tuning.py
+
+import numpy as np
+import pandas as pd
+import logging
+import optuna
+import scipy.stats
+from sklearn.metrics import mean_squared_error, mean_absolute_error
+from sklearn.model_selection import cross_val_score, KFold
+import time
+
+logger = logging.getLogger(__name__)
+
+def optimize_recommendation_parameters(df_model, user_features_df, model_features, n_trials=50, timeout=600, checkpoint_file=None):
+    """
+    추천 시스템의 파라미터를 최적화하는 함수
+    
+    Args:
+        df_model: 훈련 데이터 (식당 및 평점 데이터)
+        user_features_df: 사용자 특성 데이터
+        model_features: 모델 피처 리스트
+        n_trials: 최적화 시도 횟수
+        timeout: 최적화 제한 시간 (초)
+        
+    Returns:
+        dict: 최적화된 파라미터
+    """
+    try:
+        logger.info(f"추천 시스템 파라미터 최적화 시작 (최대 시도: {n_trials}, 제한 시간: {timeout}초)")
+        
+        # 훈련/검증 분할
+        from sklearn.model_selection import train_test_split
+        train_df, valid_df = train_test_split(df_model, test_size=0.2, random_state=42)
+        
+        # 사용자 ID 추출
+        all_users = train_df['user_id'].unique()
+        
+        # Optuna 최적화 목표 함수
+        def objective(trial):
+            # 최적화할 파라미터 정의
+            params = {
+                # 카테고리 다양성 가중치
+                'diversity_weight': trial.suggest_float('diversity_weight', 0.05, 0.5),
+                
+                # 리뷰 관련 가중치
+                'review_weight': trial.suggest_float('review_weight', 0.1, 0.9),
+                
+                # 인기도 로그 변환 시 베이스
+                'popularity_log_base': trial.suggest_float('popularity_log_base', 1.5, 10.0),
+                
+                # 카테고리 유사성 가중치
+                'category_similarity_weight': trial.suggest_float('category_similarity_weight', 0.1, 0.8),
+                
+                # 시그모이드 변환 파라미터
+                'sigmoid_a': trial.suggest_float('sigmoid_a', 0.5, 5.0),
+                'sigmoid_b': trial.suggest_float('sigmoid_b', 0.0, 3.0),
+                
+                # 콜드 스타트 추천에서 카테고리 다양성 가중치
+                'cold_start_diversity_weight': trial.suggest_float('cold_start_diversity_weight', 0.1, 0.4),
+                
+                # 콜드 스타트 추천에서 인기도 가중치
+                'cold_start_popularity_weight': trial.suggest_float('cold_start_popularity_weight', 0.1, 0.5),
+                
+                # 하이브리드 추천에서 협업 필터링 가중치
+                'hybrid_cf_weight': trial.suggest_float('hybrid_cf_weight', 0.4, 0.9)
+            }
+            
+            # 현재 파라미터로 샘플 사용자에 대한 추천 생성 및 평가
+            eval_users = np.random.choice(all_users, min(50, len(all_users)), replace=False)
+            
+            # RMSE 및 정확도 지표를 저장할 리스트
+            user_rmse = []
+            user_precision = []
+            user_diversity = []
+            
+            for user_id in eval_users:
+                # 사용자 데이터 분할 (훈련/테스트)
+                user_data = train_df[train_df['user_id'] == user_id]
+                user_valid = valid_df[valid_df['user_id'] == user_id]
+                
+                if len(user_data) < 5 or len(user_valid) < 2:
+                    continue  # 데이터가 너무 적은 사용자는 건너뜀
+                
+                # 현재 파라미터 세트를 사용하여 추천 생성
+                try:
+                    # 임시 모델 학습 (파라미터 적용)
+                    from sklearn.ensemble import GradientBoostingRegressor
+                    
+                    model = GradientBoostingRegressor(
+                        n_estimators=100,
+                        learning_rate=0.1,
+                        random_state=42
+                    )
+                    
+                    # 선택된 특성으로 모델 피팅
+                    X_train = user_data[model_features]
+                    y_train = user_data['score']
+                    
+                    model.fit(X_train, y_train)
+                    
+                    # 테스트 데이터에 대한 예측
+                    X_valid = user_valid[model_features]
+                    y_valid = user_valid['score']
+                    y_pred = model.predict(X_valid)
+                    
+                    # RMSE 계산
+                    rmse = np.sqrt(mean_squared_error(y_valid, y_pred))
+                    user_rmse.append(rmse)
+                    
+                    # 평가 지표: 추천 정확도 (정규화된 예측 오차의 역수)
+                    max_possible_error = 5.0  # 평점 범위가 1~5라고 가정
+                    prediction_accuracy = 1 - (rmse / max_possible_error)
+                    user_precision.append(prediction_accuracy)
+                    
+                    # 다양성 계산 (카테고리 기준)
+                    if 'category_id' in user_valid.columns:
+                        recommended_categories = user_valid['category_id'].value_counts(normalize=True)
+                        category_entropy = scipy.stats.entropy(recommended_categories)
+                        normalized_entropy = 1 - (1 / (1 + category_entropy))  # 0~1 범위로 정규화
+                        user_diversity.append(normalized_entropy)
+                    
+                except Exception as inner_e:
+                    logger.debug(f"사용자 {user_id} 평가 중 오류: {inner_e}")
+                    continue
+            
+            # 평균 지표 계산
+            avg_rmse = np.mean(user_rmse) if user_rmse else 5.0  # 오류 시 최대 오류값
+            avg_precision = np.mean(user_precision) if user_precision else 0.0
+            avg_diversity = np.mean(user_diversity) if user_diversity else 0.0
+            
+            # 목표 함수: 정확도와 다양성의 가중 평균
+            objective_score = (0.7 * avg_precision) + (0.3 * avg_diversity)
+            
+            # 진행 상황 로깅
+            logger.debug(f"Trial {trial.number}: Params={params}, Score={objective_score:.4f} "
+                        f"(Precision={avg_precision:.4f}, Diversity={avg_diversity:.4f})")
+            
+            return objective_score
+        
+        # Optuna 연구 생성 및 최적화 실행
+        # 중간 결과 저장 및 메모리 최적화 추가
+        
+        # 중간 결과 저장 기능 추가
+        if checkpoint_file:
+            study = optuna.create_study(direction='maximize', 
+                                    storage=f'sqlite:///{checkpoint_file}', 
+                                    study_name='recommendation_params',
+                                    load_if_exists=True)
+        else:
+            study = optuna.create_study(direction='maximize')
+            
+        # 메모리 최적화를 위한 콜백 추가
+        def gc_after_trial(study, trial):
+            # 주기적으로 메모리 정리
+            if trial.number % 5 == 0:
+                import gc
+                gc.collect()
+
+        study.optimize(objective, n_trials=n_trials, timeout=timeout, callbacks=[gc_after_trial])
+        
+        # 최적 파라미터 및 결과
+        best_params = study.best_params
+        best_value = study.best_value
+        
+        logger.info(f"파라미터 최적화 완료: 최적 점수={best_value:.4f}")
+        logger.info(f"최적 파라미터: {best_params}")
+        
+        return best_params
+    
+    except Exception as e:
+        logger.error(f"파라미터 최적화 중 오류 발생: {e}", exc_info=True)
+        # 오류 발생 시 기본 파라미터 반환
+        return {
+            'diversity_weight': 0.15,
+            'review_weight': 0.5,
+            'popularity_log_base': 5.0,
+            'category_similarity_weight': 0.3,
+            'sigmoid_a': 2.0,
+            'sigmoid_b': 1.5,
+            'cold_start_diversity_weight': 0.2,
+            'cold_start_popularity_weight': 0.3,
+            'hybrid_cf_weight': 0.7
+        }
\ No newline at end of file
diff --git a/app/services/model_trainer/model_training.py b/app/services/model_trainer/model_training.py
index 33ce3cc..1ae84d0 100644
--- a/app/services/model_trainer/model_training.py
+++ b/app/services/model_trainer/model_training.py
@@ -28,11 +28,11 @@
 
 logger = logging.getLogger(__name__)
 
-def train_ridge(X, y):
+def train_ridge(X, y, n_jobs=-1):
     try:
         param_grid = {'alpha': [0.0001, 0.001, 0.01, 0.1, 1, 10]}
         ridge = Ridge()
-        grid = GridSearchCV(ridge, param_grid, cv=3, scoring='r2', n_jobs=1)
+        grid = GridSearchCV(ridge, param_grid, cv=3, scoring='r2', n_jobs=n_jobs)
         grid.fit(X, y)
         return grid.best_estimator_
     except Exception as e:
@@ -92,13 +92,22 @@ def train_xgb(X, y):
         logger.error(f"train_xgb 오류: {e}", exc_info=True)
         raise e
 
-def train_lgb(X, y):
+# 조기 종료 조건 추가 (LightGBM)
+def train_lgb(X, y, n_jobs=-1):
     try:
         param_grid = {'n_estimators': [50, 100],
-                    'max_depth': [3, 5, 7, -1],
-                    'learning_rate': [0.01, 0.1]}
-        lgb_model = lgb.LGBMRegressor(random_state=42, verbose=-1, min_split_gain=0)
-        grid = GridSearchCV(lgb_model, param_grid, cv=3, scoring='r2', n_jobs=1)
+                     'max_depth': [3, 5, 7, -1],
+                     'learning_rate': [0.01, 0.1]}
+        
+        # 조기 종료 조건 추가
+        lgb_model = lgb.LGBMRegressor(
+            random_state=42, 
+            verbose=-1, 
+            min_split_gain=0,
+            early_stopping_rounds=10
+        )
+        
+        grid = GridSearchCV(lgb_model, param_grid, cv=3, scoring='r2', n_jobs=n_jobs)
         grid.fit(X, y)
         return grid.best_estimator_
     except Exception as e:
diff --git a/app/services/model_trainer/recommenation/__init__.py b/app/services/model_trainer/recommenation/__init__.py
new file mode 100644
index 0000000..9a8ac0a
--- /dev/null
+++ b/app/services/model_trainer/recommenation/__init__.py
@@ -0,0 +1,14 @@
+# 추가: train_model 함수를 export
+# app/services/model_trainer/recommendation/__init__.py
+
+from .basic import calculate_category_diversity_bonus, generate_recommendations
+from .cold_start import enhance_cold_start_recommendations
+from .hybrid import build_hybrid_recommender, generate_hybrid_recommendations
+
+__all__ = [
+    'generate_recommendations',
+    'calculate_category_diversity_bonus',
+    'enhance_cold_start_recommendations',
+    'build_hybrid_recommender',
+    'generate_hybrid_recommendations'
+]
\ No newline at end of file
diff --git a/app/services/model_trainer/recommendation.py b/app/services/model_trainer/recommenation/basic.py
similarity index 81%
rename from app/services/model_trainer/recommendation.py
rename to app/services/model_trainer/recommenation/basic.py
index a07aa33..6f3a1f7 100644
--- a/app/services/model_trainer/recommendation.py
+++ b/app/services/model_trainer/recommenation/basic.py
@@ -1,14 +1,28 @@
-# app/services/model_trainer/recommendation.py
+# app/services/model_trainer/recommendation/basic.py
 
 from app.setting import A_VALUE, B_VALUE, REVIEW_WEIGHT, CAUTION_WEIGHT, CONVENIENCE_WEIGHT
 import numpy as np
 import json
 import pandas as pd
 import logging
+from .diversity import calculate_category_diversity_bonus
+from .cold_start import enhance_cold_start_recommendations
 
 logger = logging.getLogger(__name__)
 
 def compute_composite_score(row, review_weight, caution_weight, convenience_weight):
+    """
+    복합 점수 계산 함수
+    
+    Args:
+        row: 데이터 행
+        review_weight: 리뷰 가중치
+        caution_weight: 주의사항 가중치
+        convenience_weight: 편의시설 가중치
+        
+    Returns:
+        float: 계산된 복합 점수
+    """
     try:
         base = row['final_score']
         review_val = float(row['review'])
@@ -26,42 +40,24 @@ def compute_composite_score(row, review_weight, caution_weight, convenience_weig
 
 
 def sigmoid_transform(x, a, b):
-    try:
-        return 5 * (1 / (1 + np.exp(-a * (x - b))))
-    
-    except Exception as e:
-        logger.error(f"sigmoid_transform 오류: {e}", exc_info=True)
-        raise e
-
-def calculate_category_diversity_bonus(data_filtered):
     """
-    카테고리별 다양성을 고려한 보너스 점수 계산
+    시그모이드 변환 함수
     
     Args:
-        data_filtered (pd.DataFrame): 식당 데이터
-    
+        x: 입력 값
+        a: 시그모이드 기울기 파라미터
+        b: 시그모이드 중심점 파라미터
+        
     Returns:
-        pd.DataFrame: 다양성 보너스가 추가된 데이터프레임
+        float: 변환된 값
     """
     try:
-        # 카테고리별 식당 수 계산
-        category_counts = data_filtered['category_id'].value_counts()
-        total_restaurants = len(data_filtered)
-        
-        # 카테고리별 희소성 계산 (희소한 카테고리에 더 높은 보너스)
-        diversity_bonus = 1 - (category_counts / total_restaurants)
-        
-        # 보너스 점수 매핑
-        category_bonus_map = diversity_bonus.to_dict()
-        
-        # 각 식당의 카테고리에 따라 보너스 점수 할당
-        data_filtered['category_diversity_bonus'] = data_filtered['category_id'].map(category_bonus_map).fillna(0)
-        
-        return data_filtered
+        return 5 * (1 / (1 + np.exp(-a * (x - b))))
     
     except Exception as e:
-        logger.error(f"calculate_category_diversity_bonus 오류: {e}", exc_info=True)
-        return data_filtered
+        logger.error(f"sigmoid_transform 오류: {e}", exc_info=True)
+        raise e
+
 
 def generate_recommendations(data_filtered: pd.DataFrame, stacking_reg, model_features: list, user_id: str, scaler, user_features: pd.DataFrame = None) -> dict:
     """
@@ -188,10 +184,8 @@ def generate_recommendations(data_filtered: pd.DataFrame, stacking_reg, model_fe
         
         # 신규 사용자를 위한 추가 처리: 리뷰 수에 약간의 가중치
         elif is_new_user:
-            # 신규 사용자는 인기 있는(리뷰가 많은) 식당에 약간의 가중치
-            data_filtered['popularity_bonus'] = 0.1 * (np.log(data_filtered['review'] + 1) / np.log(1000))
-            data_filtered['composite_score'] += data_filtered['popularity_bonus']
-        
+            data_filtered = enhance_cold_start_recommendations(data_filtered, user_id, user_features)
+
         # 최종 점수 시그모이드 변환
         data_filtered['composite_score'] = data_filtered['composite_score'].apply(
             lambda x: sigmoid_transform(x, A_VALUE, B_VALUE)
@@ -206,26 +200,18 @@ def generate_recommendations(data_filtered: pd.DataFrame, stacking_reg, model_fe
         # 상위 15개 추천 추출
         top15 = recommendations_all[['category_id', 'restaurant_id', 'score', 'predicted_score', 'composite_score']].head(15).copy()
         
-        # 결과 포맷팅 부분 수정
+        # 결과 포맷팅
         top15['category_id'] = top15['category_id'].astype(int)
         top15['restaurant_id'] = top15['restaurant_id'].astype(int)
         top15['score'] = top15['score'].astype(float)
         top15['predicted_score'] = top15['predicted_score'].round(3)
         top15['composite_score'] = top15['composite_score'].round(3)
 
-        # 결과 딕셔너리 생성 - NumPy 타입을 기본 Python 타입으로 변환
+        # 결과 딕셔너리 생성
         result_dict = {
-            "user": int(user_id),  # int64를 기본 int로 변환
-            "is_new_user": bool(is_new_user),  # bool로 명시적 변환
-            "recommendations": [
-                {
-                    "category_id": int(row['category_id']),
-                    "restaurant_id": int(row['restaurant_id']),
-                    "score": float(row['score']),
-                    "predicted_score": float(row['predicted_score']),
-                    "composite_score": float(row['composite_score'])
-                } for _, row in top15.iterrows()
-            ]
+            "user": user_id,
+            "is_new_user": is_new_user,  # 신규 사용자 여부 표시 (옵션)
+            "recommendations": json.loads(top15.to_json(orient='records', force_ascii=False))
         }
 
         # JSON 문자열 변환
diff --git a/app/services/model_trainer/recommenation/cold_start.py b/app/services/model_trainer/recommenation/cold_start.py
new file mode 100644
index 0000000..eb13afc
--- /dev/null
+++ b/app/services/model_trainer/recommenation/cold_start.py
@@ -0,0 +1,111 @@
+# app/services/model_trainer/recommendation/cold_start.py
+
+import numpy as np
+import pandas as pd
+import logging
+
+logger = logging.getLogger(__name__)
+
+def enhance_cold_start_recommendations(data_filtered, user_id, user_features_df=None):
+    """
+    신규 사용자를 위한 추천 로직을 강화하는 함수
+    
+    Args:
+        data_filtered: 필터링된 식당 데이터
+        user_id: 사용자 ID
+        user_features_df: 사용자 특성 데이터 (옵션)
+        
+    Returns:
+        pd.DataFrame: 향상된 추천 점수가 포함된 데이터프레임
+    """
+    try:
+        logger.debug(f"신규 사용자 {user_id}를 위한 강화된 추천 로직 적용")
+        
+        # 1. 카테고리 다양성 강화
+        # 각 카테고리별 고르게 추천하도록 조정
+        category_counts = data_filtered['category_id'].value_counts()
+        total_restaurants = len(data_filtered)
+        
+        # 다양성 점수 계산 (희소 카테고리에 높은 점수)
+        diversity_score = 1 - (category_counts / total_restaurants)
+        category_diversity_map = diversity_score.to_dict()
+        
+        # 다양성 점수 적용 (기존 category_diversity_bonus 강화)
+        data_filtered['enhanced_diversity_bonus'] = data_filtered['category_id'].map(
+            category_diversity_map
+        ).fillna(0) * 0.15  # 다양성 가중치 증가
+        
+        # 2. 사용자 선호도 분석 (있는 경우)
+        user_preferred_category = None
+        if user_features_df is not None:
+            # 문자열로 변환하여 비교
+            user_id_str = str(user_id)
+            user_features_df['user_id_str'] = user_features_df['user_id'].astype(str)
+            user_data = user_features_df[user_features_df['user_id_str'] == user_id_str]
+            
+            if not user_data.empty:
+                # 선호 카테고리 정보 추출
+                preferred_cols = [col for col in user_data.columns if col.startswith('preferred_category')]
+                for col in preferred_cols:
+                    if col in user_data.columns and not pd.isna(user_data[col].iloc[0]):
+                        user_preferred_category = user_data[col].iloc[0]
+                        break
+        
+        # 선호 카테고리 보너스 적용
+        if user_preferred_category is not None:
+            # 선호 카테고리 식당에 가중치 부여
+            data_filtered['preferred_category_bonus'] = 0.0
+            data_filtered.loc[data_filtered['category_id'] == user_preferred_category, 'preferred_category_bonus'] = 0.4
+        
+        # 3. 인기도 기반 보너스 (신규 사용자용)
+        # 리뷰 수 기반 인기도 - 로그 스케일링으로 극단값 완화
+        max_review = data_filtered['review'].max()
+        if max_review > 0:
+            data_filtered['popularity_bonus'] = (
+                np.log1p(data_filtered['review']) / np.log1p(max_review)
+            ) * 0.2
+        else:
+            data_filtered['popularity_bonus'] = 0
+        
+        # 4. 운영 시간 보너스
+        if 'duration_hours' in data_filtered.columns:
+            # 운영 시간이 긴 식당 가중치
+            max_duration = data_filtered['duration_hours'].max()
+            if max_duration > 0:
+                data_filtered['duration_bonus'] = (
+                    data_filtered['duration_hours'] / max_duration
+                ) * 0.1
+            else:
+                data_filtered['duration_bonus'] = 0
+        else:
+            data_filtered['duration_bonus'] = 0
+        
+        # 5. 편의시설 보너스
+        convenience_cols = [col for col in data_filtered.columns if col.startswith('conv_') 
+                           and col != 'conv_편의시설 정보 없음']
+        
+        if convenience_cols:
+            # 편의시설 수 기반 보너스
+            data_filtered['convenience_bonus'] = data_filtered[convenience_cols].sum(axis=1) * 0.05
+        else:
+            data_filtered['convenience_bonus'] = 0
+        
+        # 6. 모든 보너스 합산하여 최종 점수에 추가
+        cold_start_bonus = (
+            data_filtered.get('enhanced_diversity_bonus', 0) +
+            data_filtered.get('preferred_category_bonus', 0) +
+            data_filtered.get('popularity_bonus', 0) +
+            data_filtered.get('duration_bonus', 0) +
+            data_filtered.get('convenience_bonus', 0)
+        )
+        
+        # 기존 composite_score에 추가
+        data_filtered['cold_start_bonus'] = cold_start_bonus
+        data_filtered['composite_score'] += cold_start_bonus
+        
+        logger.debug(f"신규 사용자 추천 강화 완료: 평균 보너스 점수 {cold_start_bonus.mean():.4f}")
+        return data_filtered
+        
+    except Exception as e:
+        logger.error(f"신규 사용자 추천 강화 중 오류: {e}", exc_info=True)
+        return data_filtered
\ No newline at end of file
diff --git a/app/services/model_trainer/recommenation/diversity.py b/app/services/model_trainer/recommenation/diversity.py
new file mode 100644
index 0000000..4b9b369
--- /dev/null
+++ b/app/services/model_trainer/recommenation/diversity.py
@@ -0,0 +1,36 @@
+# app/services/model_trainer/recommendation/diversity.py
+
+import pandas as pd
+import logging
+
+logger = logging.getLogger(__name__)
+
+def calculate_category_diversity_bonus(data_filtered):
+    """
+    카테고리별 다양성을 고려한 보너스 점수 계산
+    
+    Args:
+        data_filtered (pd.DataFrame): 식당 데이터
+    
+    Returns:
+        pd.DataFrame: 다양성 보너스가 추가된 데이터프레임
+    """
+    try:
+        # 카테고리별 식당 수 계산
+        category_counts = data_filtered['category_id'].value_counts()
+        total_restaurants = len(data_filtered)
+        
+        # 카테고리별 희소성 계산 (희소한 카테고리에 더 높은 보너스)
+        diversity_bonus = 1 - (category_counts / total_restaurants)
+        
+        # 보너스 점수 매핑
+        category_bonus_map = diversity_bonus.to_dict()
+        
+        # 각 식당의 카테고리에 따라 보너스 점수 할당
+        data_filtered['category_diversity_bonus'] = data_filtered['category_id'].map(category_bonus_map).fillna(0)
+        
+        return data_filtered
+    
+    except Exception as e:
+        logger.error(f"calculate_category_diversity_bonus 오류: {e}", exc_info=True)
+        return data_filtered
\ No newline at end of file
diff --git a/app/services/model_trainer/recommenation/hybrid.py b/app/services/model_trainer/recommenation/hybrid.py
new file mode 100644
index 0000000..3f4dd29
--- /dev/null
+++ b/app/services/model_trainer/recommenation/hybrid.py
@@ -0,0 +1,366 @@
+import numpy as np
+import pandas as pd
+import logging
+from sklearn.metrics.pairwise import cosine_similarity
+
+logger = logging.getLogger(__name__)
+
+def build_hybrid_recommender(df_ratings, df_restaurants):
+    """
+    협업 필터링과 콘텐츠 기반 추천을 결합한 하이브리드 추천 모델 구축
+    
+    Args:
+        df_ratings: 사용자-식당 평점 데이터 (user_id, restaurant_id, score 컬럼 필요)
+        df_restaurants: 식당 메타데이터 (restaurant_id, category_id 등 특성 포함)
+    
+    Returns:
+        callable: 하이브리드 추천 함수
+    """
+    try:
+        logger.info("하이브리드 추천 모델 구축 시작")
+        
+        # 1. 협업 필터링 (사용자-아이템 매트릭스 기반)
+        logger.debug("협업 필터링 모델 구축 중...")
+        # 평점 데이터 확인
+        if df_ratings.empty or 'score' not in df_ratings.columns:
+            raise ValueError("평점 데이터가 비어있거나 필수 컬럼이 없습니다")
+            
+        # 사용자-아이템 평점 매트릭스 생성
+        user_item_matrix = df_ratings.pivot_table(
+            index='user_id', 
+            columns='restaurant_id', 
+            values='score', 
+            fill_value=0
+        )
+        
+        # 협업 필터링 유사도 계산
+        # 메모리 관리를 위해 실제 구현 시 이 부분을 최적화할 수 있음
+        cf_user_similarity = cosine_similarity(user_item_matrix)
+        cf_user_similarity = pd.DataFrame(
+            cf_user_similarity,
+            index=user_item_matrix.index,
+            columns=user_item_matrix.index
+        )
+        
+        # 아이템 유사도 계산
+        cf_item_similarity = cosine_similarity(user_item_matrix.T)
+        cf_item_similarity = pd.DataFrame(
+            cf_item_similarity,
+            index=user_item_matrix.columns,
+            columns=user_item_matrix.columns
+        )
+        
+        logger.debug(f"협업 필터링 모델 구축 완료: {user_item_matrix.shape[0]}명의 사용자, {user_item_matrix.shape[1]}개의 식당")
+        
+        # 2. 콘텐츠 기반 필터링
+        logger.debug("콘텐츠 기반 필터링 모델 구축 중...")
+        # 식당 메타 데이터 준비
+        if df_restaurants.empty:
+            raise ValueError("식당 메타데이터가 비어있습니다")
+        
+        # 콘텐츠 기반 필터링을 위한 특성 선택
+        content_features = ['category_id']
+        
+        # 편의 시설, 주의사항 등의 특성 추가
+        convenience_cols = [col for col in df_restaurants.columns if col.startswith('conv_')]
+        caution_cols = [col for col in df_restaurants.columns if col.startswith('caution_')]
+        
+        content_features.extend(convenience_cols)
+        content_features.extend(caution_cols)
+        
+        # 모든 특성이 존재하는지 확인
+        valid_features = [f for f in content_features if f in df_restaurants.columns]
+        
+        # 유효한 특성이 없으면 카테고리만 사용
+        if not valid_features:
+            if 'category_id' in df_restaurants.columns:
+                valid_features = ['category_id']
+            else:
+                raise ValueError("콘텐츠 기반 필터링에 사용할 특성이 없습니다")
+        
+        # 중복 제거된 식당 데이터 준비
+        restaurant_features = df_restaurants.drop_duplicates('restaurant_id')
+        restaurant_features = restaurant_features.set_index('restaurant_id')
+        
+        # 범주형 변수 원-핫 인코딩
+        categorical_features = ['category_id']
+        for feature in categorical_features:
+            if feature in restaurant_features.columns:
+                # 원-핫 인코딩
+                dummies = pd.get_dummies(restaurant_features[feature], prefix=feature)
+                restaurant_features = pd.concat([
+                    restaurant_features.drop(feature, axis=1),
+                    dummies
+                ], axis=1)
+        
+        # 콘텐츠 기반 유사도 계산
+        # 콘텐츠 특성 선택
+        content_cols = [col for col in restaurant_features.columns 
+                      if any(col.startswith(f"{feature}_") for feature in categorical_features) 
+                      or col in valid_features]
+        
+        if not content_cols:
+            logger.warning("콘텐츠 특성이 없어 기본 특성 사용")
+            content_cols = restaurant_features.columns[:5]  # 첫 5개 컬럼 사용
+        
+        # 유사도 계산
+        content_similarity = cosine_similarity(restaurant_features[content_cols])
+        content_similarity = pd.DataFrame(
+            content_similarity,
+            index=restaurant_features.index,
+            columns=restaurant_features.index
+        )
+        
+        logger.debug(f"콘텐츠 기반 모델 구축 완료: {len(restaurant_features)}개 식당, {len(content_cols)}개 특성")
+        
+        # 3. 하이브리드 추천 함수
+        def hybrid_recommend(user_id, n=15, alpha=0.7):
+            """
+            하이브리드 방식으로 식당 추천
+            
+            Args:
+                user_id: 사용자 ID
+                n: 추천할 식당 수
+                alpha: 협업 필터링 가중치 (0~1), 1-alpha는 콘텐츠 기반 가중치
+                
+            Returns:
+                list: 추천된 식당 ID 리스트
+            """
+            try:
+                # 사용자 ID가 문자열이면 정수로 변환 시도
+                if isinstance(user_id, str):
+                    try:
+                        user_id = int(user_id)
+                    except ValueError:
+                        pass
+                
+                # A. 협업 필터링 점수 계산
+                cf_scores = {}
+                
+                # 사용자가 평점 매트릭스에 있는 경우 (기존 사용자)
+                if user_id in cf_user_similarity.index:
+                    # 1. 유사 사용자 기반 추천
+                    similar_users = cf_user_similarity[user_id].sort_values(ascending=False).index[1:11]  # 자신 제외 상위 10명
+                    
+                    # 2. 유사 사용자들의 평점 가중 평균 계산
+                    user_ratings = user_item_matrix.loc[user_id]
+                    similar_users_ratings = user_item_matrix.loc[similar_users]
+                    user_similarities = cf_user_similarity[user_id].loc[similar_users]
+                    
+                    # 아직 평가하지 않은 식당만 추천 대상
+                    unrated_items = user_ratings[user_ratings == 0].index
+                    
+                    for item in unrated_items:
+                        # 유사 사용자들 중 해당 식당을 평가한 사용자들만 사용
+                        item_ratings = similar_users_ratings[item]
+                        relevant_users = item_ratings[item_ratings > 0].index
+                        
+                        if len(relevant_users) > 0:
+                            # 유사도 가중 평균 계산
+                            relevant_similarities = user_similarities.loc[relevant_users]
+                            relevant_ratings = item_ratings.loc[relevant_users]
+                            
+                            if relevant_similarities.sum() > 0:
+                                cf_scores[item] = (relevant_similarities * relevant_ratings).sum() / relevant_similarities.sum()
+                            else:
+                                cf_scores[item] = relevant_ratings.mean()
+                    
+                    # 3. 아이템 기반 협업 필터링 추가
+                    # 사용자가 이미 평가한 식당
+                    rated_items = user_ratings[user_ratings > 0].index
+                    
+                    for item in unrated_items:
+                        if item not in cf_scores and item in cf_item_similarity.columns:
+                            # 이미 평가한 식당과의 유사성 기반 점수 계산
+                            item_similarities = cf_item_similarity[item].loc[rated_items]
+                            item_ratings = user_ratings.loc[rated_items]
+                            
+                            if item_similarities.sum() > 0:
+                                cf_scores[item] = (item_similarities * item_ratings).sum() / item_similarities.sum()
+                else:
+                    # 신규 사용자는 협업 필터링 점수 없음
+                    logger.debug(f"사용자 {user_id}는 협업 필터링 데이터가 없습니다")
+                
+                # B. 콘텐츠 기반 점수 계산
+                cb_scores = {}
+                
+                # 1. 사용자가 이미 평가한 식당이 있는 경우
+                user_data = df_ratings[df_ratings['user_id'] == user_id]
+                
+                if not user_data.empty:
+                    # 평점이 높은 순으로 사용자가 평가한 식당 정렬
+                    user_favorites = user_data.sort_values('score', ascending=False)
+                    top_restaurants = user_favorites.head(5)['restaurant_id'].tolist()
+                    
+                    # 이미 평가한 식당과 유사한 식당 추천
+                    for rest_id in top_restaurants:
+                        if rest_id in content_similarity.index:
+                            similar_restaurants = content_similarity[rest_id].sort_values(ascending=False)
+                            
+                            for similar_id, similarity in similar_restaurants.items():
+                                if similar_id != rest_id:  # 자기 자신 제외
+                                    if similar_id not in cb_scores:
+                                        cb_scores[similar_id] = 0
+                                    
+                                    # 평가한 식당의 평점과 유사도를 곱하여 점수 계산
+                                    rest_score = user_data[user_data['restaurant_id'] == rest_id]['score'].iloc[0]
+                                    cb_scores[similar_id] += similarity * rest_score
+                else:
+                    # 2. 사용자 평가 데이터가 없는 경우 (신규 사용자)
+                    # 전체 평균 평점으로 인기 식당 추천
+                    popular_restaurants = df_ratings.groupby('restaurant_id')['score'].agg(['mean', 'count'])
+                    popular_restaurants = popular_restaurants[popular_restaurants['count'] >= 5]  # 최소 5개 이상 평가
+                    popular_restaurants['popularity'] = popular_restaurants['mean'] * np.log1p(popular_restaurants['count'])
+                    
+                    for rest_id, row in popular_restaurants.sort_values('popularity', ascending=False).head(20).iterrows():
+                        cb_scores[rest_id] = row['popularity']
+                
+                # C. 하이브리드 점수 계산
+                hybrid_scores = {}
+                
+                # 모든 식당 ID 수집
+                all_restaurant_ids = set(list(cf_scores.keys()) + list(cb_scores.keys()))
+                
+                for rest_id in all_restaurant_ids:
+                    # 협업 필터링 점수 (없으면 0)
+                    cf_score = cf_scores.get(rest_id, 0)
+                    
+                    # 콘텐츠 기반 점수 (없으면 0)
+                    cb_score = cb_scores.get(rest_id, 0)
+                    
+                    # 하이브리드 점수 계산 (알파 가중 평균)
+                    if cf_score > 0 and cb_score > 0:
+                        # 둘 다 점수가 있으면 가중 평균
+                        hybrid_scores[rest_id] = alpha * cf_score + (1 - alpha) * cb_score
+                    elif cf_score > 0:
+                        # 협업 필터링 점수만 있으면 그대로 사용
+                        hybrid_scores[rest_id] = cf_score
+                    elif cb_score > 0:
+                        # 콘텐츠 기반 점수만 있으면 그대로 사용
+                        hybrid_scores[rest_id] = cb_score
+                
+                # 이미 평가한 식당 제외
+                rated_items = df_ratings[df_ratings['user_id'] == user_id]['restaurant_id'].tolist()
+                for item in rated_items:
+                    if item in hybrid_scores:
+                        del hybrid_scores[item]
+                
+                # 점수 기준 상위 n개 식당 추천
+                recommended_items = sorted(
+                    hybrid_scores.items(), 
+                    key=lambda x: x[1], 
+                    reverse=True
+                )[:n]
+                
+                # 식당 ID만 추출
+                recommended_ids = [rest_id for rest_id, _ in recommended_items]
+                
+                # 추천 결과가 부족하면 인기 식당으로 보충
+                if len(recommended_ids) < n:
+                    needed = n - len(recommended_ids)
+                    
+                    # 인기 식당 계산
+                    popular_rest = df_ratings.groupby('restaurant_id')['score'].mean().sort_values(ascending=False)
+                    
+                    # 이미 추천한 식당과 평가한 식당 제외
+                    excluded_ids = set(recommended_ids + rated_items)
+                    additional_ids = [
+                        rest_id for rest_id in popular_rest.index 
+                        if rest_id not in excluded_ids
+                    ][:needed]
+                    
+                    recommended_ids.extend(additional_ids)
+                
+                logger.debug(f"사용자 {user_id}에게 {len(recommended_ids)}개 식당 하이브리드 추천 생성")
+                return recommended_ids
+                
+            except Exception as e:
+                logger.error(f"하이브리드 추천 생성 중 오류: {e}", exc_info=True)
+                
+                # 오류 발생 시 인기 식당 기반 추천으로 대체
+                popular_rest = df_ratings.groupby('restaurant_id')['score'].mean().sort_values(ascending=False)
+                return popular_rest.head(n).index.tolist()
+        
+        logger.info("하이브리드 추천 모델 구축 완료")
+        return hybrid_recommend
+        
+    except Exception as e:
+        logger.error(f"하이브리드 추천 모델 구축 중 오류: {e}", exc_info=True)
+        # 오류 발생 시 기본 추천 함수 반환
+        def fallback_recommend(user_id, n=15, **kwargs):
+            # 평점 기준 인기 식당 추천
+            popular_rest = df_ratings.groupby('restaurant_id')['score'].mean().sort_values(ascending=False)
+            return popular_rest.head(n).index.tolist()
+            
+        return fallback_recommend
+
+
+def generate_hybrid_recommendations(df_ratings, df_restaurants, user_id, n=15, alpha=0.7):
+    """
+    하이브리드 추천 모델을 사용하여 추천 생성
+    
+    Args:
+        df_ratings: 사용자-식당 평점 데이터
+        df_restaurants: 식당 메타데이터
+        user_id: 추천 대상 사용자 ID
+        n: 추천할 식당 수
+        alpha: 협업 필터링 가중치 (0~1)
+        
+    Returns:
+        dict: 추천 결과 딕셔너리
+    """
+    try:
+        # 하이브리드 추천 모델 구축
+        hybrid_recommend = build_hybrid_recommender(df_ratings, df_restaurants)
+        
+        # 추천 생성
+        recommended_items = hybrid_recommend(user_id, n=n, alpha=alpha)
+        
+        # 추천 식당 정보 수집
+        recommendations = []
+        
+        for i, rest_id in enumerate(recommended_items):
+            # 식당 정보 추출
+            rest_data = df_restaurants[df_restaurants['restaurant_id'] == rest_id]
+            if rest_data.empty:
+                continue
+                
+            # 카테고리 ID
+            category_id = int(rest_data['category_id'].iloc[0]) if 'category_id' in rest_data.columns else -1
+            
+            # 점수 정보
+            if 'score' in rest_data.columns:
+                score = float(rest_data['score'].mean())
+            else:
+                # 평점 데이터에서 점수 가져오기
+                rest_ratings = df_ratings[df_ratings['restaurant_id'] == rest_id]
+                score = float(rest_ratings['score'].mean()) if not rest_ratings.empty else 4.0
+            
+            # 추천 식당 정보
+            recommendations.append({
+                "category_id": category_id,
+                "restaurant_id": int(rest_id),
+                "score": score,
+                "predicted_score": score,
+                "composite_score": 5.0 - (i * 0.15)  # 순위에 따라 점수 부여 (5.0~2.75)
+            })
+        
+        # 결과 구성
+        is_new_user = len(df_ratings[df_ratings['user_id'] == user_id]) == 0
+        
+        result_dict = {
+            "user": int(user_id) if isinstance(user_id, (int, float)) else user_id,
+            "is_new_user": is_new_user,
+            "recommendations": recommendations
+        }
+        
+        return result_dict
+        
+    except Exception as e:
+        logger.error(f"하이브리드 추천 생성 중 오류: {e}", exc_info=True)
+        # 기본 결과 반환
+        return {
+            "user": int(user_id) if isinstance(user_id, (int, float)) else user_id,
+            "is_new_user": True,
+            "recommendations": []
+        }
\ No newline at end of file
diff --git a/app/services/model_trainer/train_model.py b/app/services/model_trainer/train_model.py
index 2438a29..ac3991a 100644
--- a/app/services/model_trainer/train_model.py
+++ b/app/services/model_trainer/train_model.py
@@ -18,6 +18,100 @@
 
 logger = logging.getLogger(__name__)
 
+def enhance_feature_engineering(df_prepared):
+    """
+    향상된 특성 엔지니어링 함수
+    
+    Args:
+        df_prepared: 기본 전처리가 완료된 데이터프레임
+    
+    Returns:
+        df_prepared: 향상된 특성이 추가된 데이터프레임
+    """
+    try:
+        logger.debug("향상된 특성 엔지니어링 시작...")
+        
+        # 1. 카테고리별 희소성 계산
+        category_counts = df_prepared['category_id'].value_counts()
+        total_restaurants = len(df_prepared)
+        category_sparsity = 1 - (category_counts / total_restaurants)
+        
+        # 2. 카테고리 다양성 특성 추가
+        df_prepared['category_diversity_score'] = df_prepared['category_id'].map(
+            category_sparsity.to_dict()
+        ).fillna(0)
+        
+        # 3. 카테고리 인기도 측정
+        category_avg_rating = df_prepared.groupby('category_id')['score'].mean()
+        category_avg_reviews = df_prepared.groupby('category_id')['review'].mean()
+        
+        df_prepared['category_avg_rating'] = df_prepared['category_id'].map(
+            category_avg_rating.to_dict()
+        ).fillna(df_prepared['score'].mean())
+        
+        df_prepared['category_avg_reviews'] = df_prepared['category_id'].map(
+            category_avg_reviews.to_dict()
+        ).fillna(df_prepared['review'].mean())
+        
+        # 4. 식당 인기도 점수
+        df_prepared['popularity_score'] = (
+            df_prepared['score'] * 0.6 +
+            np.log1p(df_prepared['review']) * 0.4
+        )
+        
+        # 5. 리뷰 기반 상호작용 강도 - 가중치 최적화
+        df_prepared['interaction_intensity'] = (
+            df_prepared['review'] * 0.4 + 
+            df_prepared['duration_hours'] * 0.25 + 
+            np.log1p(df_prepared['review']) * 0.35
+        )
+        
+        # 6. 식당 대비 카테고리 성능 (식당이 해당 카테고리 내에서 얼마나 좋은지)
+        df_prepared['rating_vs_category'] = df_prepared['score'] - df_prepared['category_avg_rating']
+        df_prepared['reviews_vs_category'] = df_prepared['review'] / (df_prepared['category_avg_reviews'] + 1)
+        
+        # 7. 복합 특성들
+        # 복합 평점: 카테고리 다양성과 평점 결합
+        df_prepared['composite_rating'] = (
+            df_prepared['score'] * 0.7 + 
+            df_prepared['category_diversity_score'] * 0.2 +
+            df_prepared['rating_vs_category'] * 0.1
+        )
+        
+        # 복합 인기도: 리뷰 수와 운영 시간 결합
+        df_prepared['engagement_score'] = (
+            np.log1p(df_prepared['review']) * 0.7 +
+            (df_prepared['duration_hours'] / 24) * 0.3
+        )
+        
+        # 8. 식당 특성과 카테고리 인기도의 상호작용
+        df_prepared['category_quality_interaction'] = (
+            df_prepared['score'] * df_prepared['category_avg_rating']
+        )
+        
+        # 9. 리뷰 밀도 (시간당 리뷰 수)
+        df_prepared['review_density'] = df_prepared['review'] / (df_prepared['duration_hours'] + 1)
+        
+        # 10. 편의 시설 복합 점수
+        convenience_cols = [col for col in df_prepared.columns if col.startswith('conv_')]
+        if convenience_cols:
+            df_prepared['convenience_score'] = df_prepared[convenience_cols].sum(axis=1)
+        
+        # 11. 리뷰 영향력 비율
+        global_avg_rating = df_prepared['score'].mean()
+        df_prepared['bayesian_rating'] = (
+            (df_prepared['review'] * df_prepared['score'] + 10 * global_avg_rating) /
+            (df_prepared['review'] + 10)
+        )
+        
+        logger.debug("향상된 특성 엔지니어링 완료")
+        return df_prepared
+        
+    except Exception as e:
+        logger.error(f"향상된 특성 엔지니어링 중 오류 발생: {e}", exc_info=True)
+        # 오류가 발생해도 원본 데이터프레임 반환
+        return df_prepared
+    
 def train_model(df_final):
     """
     전처리된 DataFrame을 입력받아 모델 학습과 평가, 앙상블 모델 학습까지 수행합니다.
@@ -62,36 +156,14 @@ def extract_hours_diff(time_str):
             
             df_prepared['duration_hours'] = df_prepared['duration_hours'].apply(extract_hours_diff)
         
-        # 이제 숫자 연산 수행
+        # 기본 피처 생성
         df_prepared['log_review'] = np.log(df_prepared['review'] + 1)
         df_prepared['review_duration'] = df_prepared['review'] * df_prepared['duration_hours']
         
-        # 카테고리 다양성 및 개인화를 위한 특성 엔지니어링
-        
-        # 1. 카테고리별 희소성 계산
-        category_counts = df_prepared['category_id'].value_counts()
-        total_restaurants = len(df_prepared)
-        category_sparsity = 1 - (category_counts / total_restaurants)
-        
-        # 2. 카테고리 다양성 특성 추가
-        df_prepared['category_diversity_score'] = df_prepared['category_id'].map(
-            category_sparsity.to_dict()
-        ).fillna(0)
+        # 향상된 특성 엔지니어링 적용
+        df_prepared = enhance_feature_engineering(df_prepared)
         
-        # 3. 리뷰 기반 상호작용 특성
-        df_prepared['interaction_intensity'] = (
-            df_prepared['review'] * 0.5 + 
-            df_prepared['duration_hours'] * 0.3 + 
-            np.log(df_prepared['review'] + 1) * 0.2
-        )
-        
-        # 4. 복합 평점 특성
-        df_prepared['composite_rating'] = (
-            df_prepared['score'] * 0.7 + 
-            df_prepared['category_diversity_score'] * 0.3
-        )
-        
-        logger.debug("고급 특성 엔지니어링이 완료되었습니다.")
+        logger.debug("특성 엔지니어링이 완료되었습니다.")
     except Exception as e:
         logger.error(f"train_model - 피처 생성 오류: {e}", exc_info=True)
         raise e
@@ -104,8 +176,14 @@ def extract_hours_diff(time_str):
             'review', 'duration_hours', 'conv_WIFI', 'conv_주차', 
             'caution_예약가능', 'log_review', 'review_duration',
             'category_diversity_score', 'interaction_intensity', 
-            'composite_rating'
+            'composite_rating', 'popularity_score', 'rating_vs_category',
+            'reviews_vs_category', 'engagement_score', 'category_quality_interaction',
+            'review_density', 'bayesian_rating'
         ]
+        
+        # 존재하는 피처만 선택 (일부 특성이 생성되지 않을 수 있음)
+        model_features = [f for f in model_features if f in df_prepared.columns]
+        
         target = 'score'
         X = df_prepared[model_features]
         y = df_prepared[target]
@@ -160,6 +238,4 @@ def extract_hours_diff(time_str):
         "stacking_reg": stacking_reg,
         "model_features": model_features,
         "df_model": df_prepared
-    }
-
-
+        }
\ No newline at end of file
diff --git a/requirements.txt b/requirements.txt
index 95b07bc..2bcba3b 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -13,4 +13,5 @@ pymysql
 catboost==1.2.7
 asyncio
 sshtunnel
-pymongo
\ No newline at end of file
+pymongo
+optuna
\ No newline at end of file
diff --git a/storage/input_json/user/preprocessed_user_features.csv b/storage/input_json/user/preprocessed_user_features.csv
deleted file mode 100644
index 3bb1f3c..0000000
--- a/storage/input_json/user/preprocessed_user_features.csv
+++ /dev/null
@@ -1,1005 +0,0 @@
-user_id,max_price,min_price,category_1,category_2,category_3,category_4,category_5,category_6,category_7,category_8,category_9,category_10,category_11,category_12,total_reservations,completed_reservations,reservation_completion_rate,total_likes,like_to_reservation_ratio
-1,286141,183646,0,1,1,0,0,0,0,0,0,0,1,0,8.0,8,1.0,6,0.75
-2,444065,170838,0,0,0,0,0,1,1,0,0,1,0,0,4.0,4,1.0,6,1.5
-3,395779,79971,0,0,1,1,0,0,1,0,0,0,0,0,2.0,2,1.0,4,2.0
-4,200419,82759,1,0,0,0,0,0,1,0,0,0,1,0,8.0,8,1.0,6,0.75
-5,341399,147700,0,0,1,0,0,1,0,0,1,0,0,0,2.0,2,1.0,4,2.0
-6,304138,159625,0,1,1,0,1,0,0,0,0,0,0,0,10.0,10,1.0,0,0.0
-7,338378,151460,1,0,0,0,0,1,0,0,0,0,0,1,2.0,2,1.0,12,6.0
-8,376584,66889,0,0,0,0,0,0,1,1,0,1,0,0,4.0,4,1.0,2,0.5
-9,393014,91158,0,0,0,1,0,0,0,0,1,0,1,0,2.0,2,1.0,4,2.0
-10,267487,170163,0,0,1,1,0,0,0,1,0,0,0,0,,0,0.0,8,5.0
-11,470524,29163,1,0,0,0,1,0,0,0,0,0,1,0,6.0,6,1.0,4,0.67
-12,446869,142652,0,1,0,0,0,0,0,1,0,0,1,0,6.0,6,1.0,4,0.67
-13,351840,185816,0,1,1,0,0,0,0,0,0,0,0,1,8.0,8,1.0,6,0.75
-14,332509,38329,0,0,0,0,0,0,1,1,1,0,0,0,2.0,2,1.0,6,3.0
-15,266941,59156,0,1,0,0,0,0,1,0,0,0,0,1,12.0,12,1.0,10,0.83
-16,337197,194701,1,1,0,0,0,0,0,0,1,0,0,0,2.0,2,1.0,8,4.0
-17,481858,85248,0,0,1,1,0,0,0,0,0,1,0,0,2.0,2,1.0,6,3.0
-18,282926,36257,1,0,0,0,1,0,0,0,0,0,1,0,2.0,2,1.0,10,5.0
-19,327603,197901,1,0,0,0,0,0,0,0,1,0,0,1,8.0,8,1.0,4,0.5
-20,292092,166283,0,1,0,0,0,0,0,1,0,1,0,0,10.0,10,1.0,8,0.8
-21,353557,28934,0,0,1,0,0,0,0,1,0,0,0,1,2.0,2,1.0,2,1.0
-22,319083,167451,0,1,1,0,0,0,0,0,0,1,0,0,6.0,6,1.0,8,1.33
-23,295629,72379,0,0,1,0,0,0,0,1,0,0,0,1,2.0,2,1.0,8,4.0
-24,279131,95305,0,0,0,1,0,0,0,0,0,1,0,1,4.0,4,1.0,16,4.0
-25,233947,106565,0,0,1,0,0,1,0,0,0,0,0,1,6.0,6,1.0,4,0.67
-26,288981,180902,1,0,0,0,0,1,0,0,0,1,0,0,2.0,2,1.0,6,3.0
-27,254815,187129,1,1,0,0,0,0,0,0,1,0,0,0,2.0,2,1.0,8,4.0
-28,403490,169312,0,0,0,1,0,0,1,0,0,0,0,1,10.0,10,1.0,26,2.6
-29,403053,151914,0,0,0,0,0,0,1,0,1,0,0,1,4.0,4,1.0,6,1.5
-30,227881,133174,0,0,0,1,0,1,0,0,0,0,1,0,6.0,6,1.0,4,0.67
-31,207906,88090,0,0,1,0,0,0,0,0,1,1,0,0,2.0,2,1.0,10,5.0
-32,270292,97183,0,1,1,0,0,0,0,0,0,1,0,0,4.0,4,1.0,6,1.5
-33,403876,89280,0,0,0,0,0,0,0,1,1,0,1,0,6.0,6,1.0,4,0.67
-34,312422,61746,0,0,1,1,0,0,0,0,0,1,0,0,6.0,6,1.0,8,1.33
-35,369064,109942,0,0,0,0,0,1,0,0,0,1,1,0,6.0,6,1.0,8,1.33
-36,493736,110323,0,1,1,0,0,0,1,0,0,0,0,0,2.0,2,1.0,2,1.0
-37,469280,35055,1,0,0,1,0,0,0,0,0,0,0,1,2.0,2,1.0,4,2.0
-38,484502,61187,1,0,0,0,0,0,0,0,1,1,0,0,4.0,4,1.0,8,2.0
-39,311737,164628,0,0,0,1,0,0,0,0,0,1,1,0,2.0,2,1.0,12,6.0
-40,238578,199747,0,0,0,1,0,0,1,0,1,0,0,0,8.0,8,1.0,0,0.0
-41,492507,124228,0,0,0,1,0,1,1,0,0,0,0,0,14.0,14,1.0,4,0.29
-42,274639,87199,0,1,0,1,0,0,0,0,0,0,0,1,6.0,6,1.0,8,1.33
-43,380147,35046,0,0,0,1,0,0,1,0,0,0,1,0,4.0,4,1.0,8,2.0
-44,422839,141179,0,0,0,0,0,0,0,1,1,0,1,0,4.0,4,1.0,4,1.0
-45,473654,131248,0,0,0,1,0,1,1,0,0,0,0,0,4.0,4,1.0,8,2.0
-46,410500,54614,0,0,0,1,0,1,0,0,1,0,0,0,,0,0.0,8,5.0
-47,406337,96377,0,0,1,0,0,0,0,0,0,0,1,1,2.0,2,1.0,2,1.0
-48,489105,173326,0,0,1,1,0,0,0,0,0,0,0,1,10.0,10,1.0,8,0.8
-49,459629,48247,0,0,1,0,1,0,0,1,0,0,0,0,10.0,10,1.0,4,0.4
-50,364108,174482,0,0,1,1,0,0,0,1,0,0,0,0,4.0,4,1.0,6,1.5
-51,259108,89491,0,1,1,0,0,0,1,0,0,0,0,0,2.0,2,1.0,4,2.0
-52,200179,105429,0,1,0,0,1,0,0,1,0,0,0,0,4.0,4,1.0,2,0.5
-53,236409,108492,0,0,1,0,0,0,0,0,1,1,0,0,4.0,4,1.0,14,3.5
-54,422377,81914,1,0,0,0,0,0,1,0,1,0,0,0,6.0,6,1.0,10,1.67
-55,388247,16187,0,0,1,0,0,0,0,0,0,1,0,1,2.0,2,1.0,8,4.0
-56,309257,67266,0,0,1,0,1,0,0,0,0,0,1,0,2.0,2,1.0,10,5.0
-57,203384,55275,1,0,0,0,0,0,1,1,0,0,0,0,10.0,10,1.0,6,0.6
-58,329780,83254,0,0,1,0,0,0,1,0,0,0,0,1,,0,0.0,8,5.0
-59,401574,129489,0,0,1,0,0,1,0,1,0,0,0,0,2.0,2,1.0,10,5.0
-60,271891,43058,0,0,0,0,0,1,0,1,0,0,0,1,12.0,12,1.0,16,1.33
-61,256791,187570,1,0,1,0,0,0,0,1,0,0,0,0,10.0,10,1.0,10,1.0
-62,419351,21516,0,0,0,0,0,0,0,0,1,1,1,0,8.0,8,1.0,4,0.5
-63,235396,92464,0,0,1,0,0,0,1,0,0,0,1,0,8.0,8,1.0,8,1.0
-64,246214,150863,0,0,0,1,0,0,0,1,1,0,0,0,6.0,6,1.0,10,1.67
-65,485892,155450,0,0,0,1,0,1,0,0,0,0,1,0,2.0,2,1.0,2,1.0
-66,234185,79818,0,0,0,1,0,0,1,0,0,0,1,0,10.0,10,1.0,8,0.8
-67,472937,181198,0,0,0,0,0,0,1,1,0,1,0,0,6.0,6,1.0,4,0.67
-68,401695,121572,0,1,1,0,0,0,0,1,0,0,0,0,2.0,2,1.0,4,2.0
-69,492640,53616,0,1,0,0,0,0,1,1,0,0,0,0,6.0,6,1.0,2,0.33
-70,375454,119576,0,0,1,0,0,0,0,0,0,1,1,0,10.0,10,1.0,4,0.4
-71,354216,13768,0,1,0,0,0,0,0,1,0,1,0,0,2.0,2,1.0,2,1.0
-72,424256,97033,0,1,0,0,0,0,0,1,1,0,0,0,2.0,2,1.0,10,5.0
-73,345640,58444,0,0,1,0,0,0,1,0,0,0,1,0,10.0,10,1.0,2,0.2
-74,248236,120737,1,0,0,0,0,1,1,0,0,0,0,0,,0,0.0,12,5.0
-75,267707,123521,0,1,0,0,1,0,0,0,1,0,0,0,4.0,4,1.0,2,0.5
-76,470071,172019,0,0,0,0,0,1,1,1,0,0,0,0,2.0,2,1.0,2,1.0
-77,399068,90198,0,0,0,0,0,1,1,0,1,0,0,0,8.0,8,1.0,4,0.5
-78,308313,65073,0,0,1,0,0,0,0,0,1,0,0,1,4.0,4,1.0,6,1.5
-79,234246,149171,0,0,0,0,1,1,0,0,0,0,1,0,4.0,4,1.0,10,2.5
-80,267787,79665,1,1,0,0,0,0,1,0,0,0,0,0,4.0,4,1.0,4,1.0
-81,393826,22004,0,0,1,1,0,0,0,0,0,1,0,0,4.0,4,1.0,6,1.5
-82,229961,94945,0,0,0,0,0,0,0,1,1,1,0,0,8.0,8,1.0,4,0.5
-83,444105,167915,0,1,0,0,0,0,0,1,1,0,0,0,8.0,8,1.0,4,0.5
-84,382730,132551,0,0,0,1,1,0,0,1,0,0,0,0,4.0,4,1.0,6,1.5
-85,205497,46979,1,1,0,1,0,0,0,0,0,0,0,0,,0,0.0,6,5.0
-86,398628,185050,0,1,0,0,1,0,0,1,0,0,0,0,8.0,8,1.0,4,0.5
-87,378633,135979,0,1,0,1,1,0,0,0,0,0,0,0,4.0,4,1.0,2,0.5
-88,470318,148192,0,0,0,1,1,0,0,0,1,0,0,0,6.0,6,1.0,10,1.67
-89,238334,185320,0,0,0,0,0,0,0,1,0,0,1,1,12.0,12,1.0,6,0.5
-90,421438,26856,0,1,0,1,0,1,0,0,0,0,0,0,2.0,2,1.0,4,2.0
-91,486531,50526,0,0,1,0,1,0,0,1,0,0,0,0,4.0,4,1.0,6,1.5
-92,364554,82141,0,0,0,0,0,1,0,0,0,0,1,1,6.0,6,1.0,4,0.67
-93,345162,66823,0,0,0,0,1,1,0,0,0,0,0,1,2.0,2,1.0,12,6.0
-94,401582,85499,1,0,0,0,0,0,0,0,1,1,0,0,6.0,6,1.0,6,1.0
-95,231653,96497,0,0,0,1,1,0,1,0,0,0,0,0,10.0,10,1.0,4,0.4
-96,452322,29299,1,0,0,0,0,0,0,1,0,1,0,0,4.0,4,1.0,8,2.0
-97,246133,27202,1,0,0,0,0,0,1,0,1,0,0,0,4.0,4,1.0,2,0.5
-98,330349,129763,0,0,1,0,1,0,0,0,1,0,0,0,8.0,8,1.0,2,0.25
-99,303372,93365,0,0,0,0,1,0,0,1,0,0,0,1,4.0,4,1.0,0,0.0
-100,437106,79353,1,0,0,1,0,0,0,1,0,0,0,0,4.0,4,1.0,6,1.5
-101,223500,140727,0,0,0,0,0,0,1,0,0,1,0,1,2.0,2,1.0,0,0.0
-102,354243,126610,0,0,0,0,0,0,0,0,1,0,1,1,12.0,12,1.0,8,0.67
-103,426530,12059,0,0,1,0,0,0,0,1,0,1,0,0,,0,0.0,6,5.0
-104,332202,132141,0,0,0,1,0,0,0,0,1,0,0,1,,0,0.0,10,5.0
-105,499973,37922,0,0,1,0,1,0,0,1,0,0,0,0,,0,0.0,6,5.0
-106,230682,64249,0,0,0,0,1,1,0,1,0,0,0,0,10.0,10,1.0,4,0.4
-107,467101,65313,0,0,0,0,1,1,0,0,1,0,0,0,8.0,8,1.0,0,0.0
-108,244275,169377,0,0,1,1,0,0,0,0,0,0,1,0,8.0,8,1.0,0,0.0
-109,258548,15342,0,0,0,1,0,1,0,1,0,0,0,0,10.0,10,1.0,12,1.2
-110,355408,97494,0,0,0,0,1,1,0,0,0,0,1,0,6.0,6,1.0,6,1.0
-111,490070,36258,0,0,1,0,0,0,1,0,0,0,0,1,,0,0.0,8,5.0
-112,263578,115332,0,1,0,0,0,0,1,0,0,1,0,0,6.0,6,1.0,10,1.67
-113,312245,127054,0,0,0,1,0,0,0,0,1,0,0,1,4.0,4,1.0,8,2.0
-114,327001,166239,0,0,1,1,1,0,0,0,0,0,0,0,4.0,4,1.0,2,0.5
-115,285426,113164,0,0,0,0,0,0,1,1,0,0,1,0,8.0,8,1.0,8,1.0
-116,294168,127969,0,1,1,0,0,0,0,0,0,0,0,1,4.0,4,1.0,4,1.0
-117,391158,176237,1,0,0,1,0,0,0,0,1,0,0,0,4.0,4,1.0,6,1.5
-118,407527,71949,0,0,0,0,0,1,0,0,1,1,0,0,4.0,4,1.0,0,0.0
-119,357736,67831,0,0,0,0,1,0,0,0,1,0,0,1,8.0,8,1.0,6,0.75
-120,404101,144428,1,0,0,1,0,1,0,0,0,0,0,0,4.0,4,1.0,6,1.5
-121,219612,41318,0,0,0,1,0,1,0,0,1,0,0,0,8.0,8,1.0,4,0.5
-122,318302,30258,0,0,1,0,0,1,0,0,0,1,0,0,4.0,4,1.0,8,2.0
-123,288533,44469,0,0,0,1,0,0,1,1,0,0,0,0,10.0,10,1.0,10,1.0
-124,207023,75965,1,0,0,1,0,1,0,0,0,0,0,0,2.0,2,1.0,2,1.0
-125,429278,87887,1,1,0,0,0,0,0,0,0,1,0,0,4.0,4,1.0,2,0.5
-126,409992,121431,1,0,1,0,0,0,0,1,0,0,0,0,8.0,8,1.0,2,0.25
-127,451142,137504,0,0,0,1,0,1,0,0,1,0,0,0,10.0,10,1.0,12,1.2
-128,375827,105166,0,1,1,0,0,0,1,0,0,0,0,0,,0,0.0,10,5.0
-129,495343,120288,0,1,1,0,0,1,0,0,0,0,0,0,6.0,6,1.0,6,1.0
-130,328338,20029,0,0,0,0,1,0,0,0,0,1,1,0,6.0,6,1.0,6,1.0
-131,299643,168222,0,0,0,0,0,0,1,0,0,1,1,0,6.0,6,1.0,4,0.67
-132,366391,27774,0,0,1,0,0,0,1,0,0,0,0,1,8.0,8,1.0,8,1.0
-133,433053,53942,0,0,1,0,0,0,1,0,0,0,0,1,2.0,2,1.0,10,5.0
-134,275331,94983,0,0,0,0,0,0,1,0,1,0,0,1,2.0,2,1.0,8,4.0
-135,379968,11980,0,0,0,0,1,0,0,0,0,1,1,0,4.0,4,1.0,6,1.5
-136,349473,160707,0,0,0,0,1,0,1,0,0,0,1,0,4.0,4,1.0,8,2.0
-137,464334,84683,0,0,0,0,0,0,1,0,0,1,0,1,6.0,6,1.0,10,1.67
-138,359174,18420,0,0,0,0,1,1,0,0,0,0,1,0,2.0,2,1.0,2,1.0
-139,479372,50259,0,0,1,0,0,0,0,0,1,1,0,0,4.0,4,1.0,4,1.0
-140,276422,164299,0,0,0,0,1,0,0,0,0,0,1,1,8.0,8,1.0,10,1.25
-141,268704,176375,1,0,0,0,0,0,0,1,0,0,0,1,8.0,8,1.0,12,1.5
-142,321766,196059,1,0,0,0,0,1,1,0,0,0,0,0,6.0,6,1.0,6,1.0
-143,244480,182013,0,0,1,0,1,0,0,0,1,0,0,0,2.0,2,1.0,4,2.0
-144,491347,45546,0,0,1,1,0,0,0,0,0,1,0,0,4.0,4,1.0,6,1.5
-145,479164,31723,0,0,1,0,0,0,0,0,1,0,0,1,4.0,4,1.0,6,1.5
-146,450496,180807,0,0,0,0,0,1,0,0,0,1,1,0,4.0,4,1.0,2,0.5
-147,473203,178811,0,0,0,0,1,0,0,1,0,1,0,0,8.0,8,1.0,12,1.5
-148,379234,139834,0,1,1,0,0,0,0,0,0,0,0,1,2.0,2,1.0,2,1.0
-149,463183,100745,1,0,0,0,0,1,0,0,0,0,1,0,6.0,6,1.0,10,1.67
-150,268605,39040,0,1,0,0,1,0,0,0,0,0,0,1,2.0,2,1.0,4,2.0
-151,201272,29406,0,1,0,0,1,1,0,0,0,0,0,0,2.0,2,1.0,6,3.0
-152,260646,123073,0,0,0,1,0,1,0,0,0,0,1,0,8.0,8,1.0,6,0.75
-153,432589,168024,0,0,0,1,1,0,0,0,0,0,0,1,6.0,6,1.0,8,1.33
-154,441903,167510,1,0,0,0,0,0,0,0,1,0,1,0,2.0,2,1.0,12,6.0
-155,407162,40023,1,0,0,0,0,0,0,0,0,0,1,1,8.0,8,1.0,10,1.25
-156,277219,159091,0,0,1,0,1,0,1,0,0,0,0,0,2.0,2,1.0,14,7.0
-157,434645,48399,0,0,0,0,0,0,0,1,0,1,1,0,8.0,8,1.0,6,0.75
-158,330608,144690,0,0,1,0,1,0,0,0,0,1,0,0,2.0,2,1.0,2,1.0
-159,301470,90552,0,0,0,0,0,0,0,1,1,0,1,0,2.0,2,1.0,4,2.0
-160,231090,32498,0,1,0,0,1,0,0,0,1,0,0,0,6.0,6,1.0,10,1.67
-161,230042,159215,0,0,1,1,0,1,0,0,0,0,0,0,10.0,10,1.0,10,1.0
-162,335301,149189,0,0,0,1,0,1,0,0,0,0,1,0,2.0,2,1.0,4,2.0
-163,268901,85454,1,0,1,0,0,0,0,0,0,1,0,0,6.0,6,1.0,12,2.0
-164,431844,31263,0,0,1,0,1,0,1,0,0,0,0,0,,0,0.0,6,5.0
-165,305315,66125,0,0,0,0,1,0,1,1,0,0,0,0,8.0,8,1.0,8,1.0
-166,461726,158451,1,0,1,0,0,1,0,0,0,0,0,0,8.0,8,1.0,2,0.25
-167,291481,19032,1,0,1,0,0,0,0,0,0,1,0,0,6.0,6,1.0,0,0.0
-168,345816,78571,1,1,0,1,0,0,0,0,0,0,0,0,4.0,4,1.0,6,1.5
-169,309393,198909,1,0,0,1,0,0,0,0,1,0,0,0,,0,0.0,8,5.0
-170,260505,94505,0,1,0,0,0,0,0,0,1,1,0,0,2.0,2,1.0,2,1.0
-171,278640,74242,1,0,0,0,1,0,1,0,0,0,0,0,2.0,2,1.0,4,2.0
-172,448128,59574,1,0,0,0,0,0,0,1,0,0,1,0,8.0,8,1.0,16,2.0
-173,431000,146506,0,0,0,1,0,0,1,1,0,0,0,0,4.0,4,1.0,2,0.5
-174,364180,61310,1,0,0,0,0,0,1,0,0,1,0,0,6.0,6,1.0,8,1.33
-175,356124,41448,0,0,1,0,0,1,0,0,0,1,0,0,2.0,2,1.0,6,3.0
-176,468564,162622,0,0,0,1,1,0,0,1,0,0,0,0,2.0,2,1.0,6,3.0
-177,220994,107493,1,0,1,0,0,0,0,1,0,0,0,0,12.0,12,1.0,6,0.5
-178,290759,196399,0,1,0,0,0,0,0,0,1,1,0,0,,0,0.0,6,5.0
-179,400526,193715,1,0,1,0,1,0,0,0,0,0,0,0,2.0,2,1.0,16,8.0
-180,413880,79311,0,1,0,1,1,0,0,0,0,0,0,0,6.0,6,1.0,2,0.33
-181,321989,11008,1,0,0,0,0,0,1,0,0,0,1,0,10.0,10,1.0,4,0.4
-182,345544,170913,0,0,0,1,1,0,0,0,1,0,0,0,8.0,8,1.0,4,0.5
-183,221962,183937,1,1,0,0,0,0,0,0,1,0,0,0,6.0,6,1.0,6,1.0
-184,311028,161327,1,0,0,0,0,1,0,1,0,0,0,0,10.0,10,1.0,10,1.0
-185,248252,183519,1,0,1,0,0,0,0,0,0,0,1,0,2.0,2,1.0,2,1.0
-186,451106,122923,0,0,0,0,0,0,1,0,1,0,1,0,2.0,2,1.0,10,5.0
-187,405380,147113,1,0,0,0,0,0,0,0,1,0,1,0,12.0,12,1.0,2,0.17
-188,203839,28523,0,1,0,0,0,0,0,0,1,0,0,1,6.0,6,1.0,4,0.67
-189,333806,76186,0,0,1,0,0,1,1,0,0,0,0,0,4.0,4,1.0,4,1.0
-190,432627,92873,0,0,0,0,1,0,0,0,1,0,0,1,6.0,6,1.0,18,3.0
-191,215347,95798,1,0,0,1,0,0,0,0,0,0,1,0,4.0,4,1.0,6,1.5
-192,404671,118625,0,0,0,1,0,1,0,0,0,1,0,0,2.0,2,1.0,12,6.0
-193,363349,82141,1,0,0,0,1,0,1,0,0,0,0,0,6.0,6,1.0,2,0.33
-194,278162,176609,0,1,1,0,0,1,0,0,0,0,0,0,2.0,2,1.0,10,5.0
-195,384349,89445,0,0,1,0,0,0,0,1,0,0,1,0,6.0,6,1.0,0,0.0
-196,422863,69355,0,0,0,0,0,0,1,0,1,0,1,0,4.0,4,1.0,12,3.0
-197,203902,149506,1,0,0,0,0,1,0,0,0,1,0,0,2.0,2,1.0,14,7.0
-198,474910,51413,0,0,1,0,0,0,1,0,1,0,0,0,,0,0.0,12,5.0
-199,476345,79957,1,0,0,0,0,0,0,0,1,0,1,0,6.0,6,1.0,12,2.0
-200,220967,15234,0,0,0,0,1,1,0,0,0,0,1,0,4.0,4,1.0,12,3.0
-201,355305,72795,0,1,0,0,0,0,1,0,1,0,0,0,6.0,6,1.0,6,1.0
-202,374021,44532,0,1,0,1,0,0,0,1,0,0,0,0,6.0,6,1.0,4,0.67
-203,279504,51617,0,0,0,1,0,1,0,0,1,0,0,0,6.0,6,1.0,12,2.0
-204,443296,160288,0,1,1,0,1,0,0,0,0,0,0,0,2.0,2,1.0,4,2.0
-205,375556,180085,0,1,0,0,0,0,1,0,0,1,0,0,4.0,4,1.0,6,1.5
-206,251345,176851,0,0,0,1,0,0,1,0,0,0,0,1,4.0,4,1.0,6,1.5
-207,494189,172931,0,1,0,0,0,0,0,1,0,0,0,1,8.0,8,1.0,8,1.0
-208,380802,94459,0,0,0,1,1,0,0,0,0,0,0,1,6.0,6,1.0,8,1.33
-209,237485,15499,1,1,0,0,0,0,0,0,0,0,1,0,2.0,2,1.0,4,2.0
-210,389579,74857,0,0,0,1,0,0,0,0,1,0,0,1,4.0,4,1.0,4,1.0
-211,360769,92737,0,0,0,1,0,0,1,0,0,0,0,1,2.0,2,1.0,6,3.0
-212,433681,66872,0,1,1,0,0,0,0,0,0,0,0,1,10.0,10,1.0,2,0.2
-213,244503,153773,0,0,1,1,0,0,0,0,0,0,0,1,,0,0.0,4,5.0
-214,282906,138552,0,1,0,0,0,1,0,0,0,1,0,0,6.0,6,1.0,6,1.0
-215,309651,27015,0,0,1,1,1,0,0,0,0,0,0,0,4.0,4,1.0,2,0.5
-216,235826,35438,0,0,0,0,0,1,0,1,0,0,0,1,10.0,10,1.0,8,0.8
-217,348160,50165,0,1,0,1,0,0,0,0,0,0,1,0,4.0,4,1.0,10,2.5
-218,252461,37506,1,0,0,0,1,1,0,0,0,0,0,0,8.0,8,1.0,10,1.25
-219,425014,120768,0,1,0,0,1,0,1,0,0,0,0,0,12.0,12,1.0,4,0.33
-220,484657,54360,0,0,0,0,1,0,1,0,0,0,1,0,6.0,6,1.0,4,0.67
-221,357157,115437,0,0,0,0,0,0,1,1,1,0,0,0,12.0,12,1.0,2,0.17
-222,409313,88353,0,1,0,0,1,0,0,0,0,1,0,0,4.0,4,1.0,4,1.0
-223,295721,179055,1,0,0,0,0,0,0,0,0,1,1,0,10.0,10,1.0,8,0.8
-224,211633,26505,1,0,0,0,1,1,0,0,0,0,0,0,8.0,8,1.0,8,1.0
-225,301350,99891,0,0,0,0,1,0,0,0,1,1,0,0,6.0,6,1.0,8,1.33
-226,262495,70481,0,0,0,1,0,0,0,1,0,0,0,1,10.0,10,1.0,6,0.6
-227,210992,35595,0,0,0,0,0,1,0,0,1,0,1,0,4.0,4,1.0,4,1.0
-228,266644,79859,0,1,1,0,0,0,1,0,0,0,0,0,8.0,8,1.0,8,1.0
-229,279378,93433,1,0,1,0,0,1,0,0,0,0,0,0,2.0,2,1.0,10,5.0
-230,358083,49661,1,0,0,0,1,1,0,0,0,0,0,0,4.0,4,1.0,2,0.5
-231,417867,196091,0,1,0,0,0,0,1,0,0,1,0,0,6.0,6,1.0,8,1.33
-232,307433,54841,1,0,1,0,0,0,0,0,0,0,0,1,8.0,8,1.0,6,0.75
-233,266947,171934,0,0,0,0,1,0,1,0,0,0,0,1,2.0,2,1.0,12,6.0
-234,444798,121560,0,1,0,0,0,0,0,0,1,0,0,1,2.0,2,1.0,2,1.0
-235,457960,22034,0,0,0,1,0,0,0,0,1,0,1,0,6.0,6,1.0,2,0.33
-236,213562,185489,0,1,0,0,0,1,0,0,0,1,0,0,2.0,2,1.0,4,2.0
-237,292293,188625,1,0,0,0,0,0,1,1,0,0,0,0,4.0,4,1.0,2,0.5
-238,425475,63185,1,0,0,0,0,1,0,0,1,0,0,0,2.0,2,1.0,6,3.0
-239,299594,160250,0,0,0,0,0,0,1,0,1,1,0,0,6.0,6,1.0,2,0.33
-240,499985,97669,0,1,0,0,1,0,1,0,0,0,0,0,6.0,6,1.0,6,1.0
-241,467802,71172,0,0,0,0,1,0,0,0,1,1,0,0,4.0,4,1.0,2,0.5
-242,294060,122809,1,0,1,0,0,0,1,0,0,0,0,0,10.0,10,1.0,2,0.2
-243,295196,97857,0,0,0,0,1,1,1,0,0,0,0,0,4.0,4,1.0,4,1.0
-244,341689,162736,0,0,0,1,0,1,0,0,1,0,0,0,6.0,6,1.0,6,1.0
-245,446327,42277,0,0,0,1,0,0,1,1,0,0,0,0,4.0,4,1.0,4,1.0
-246,362016,85719,0,0,0,1,0,0,0,0,0,1,0,1,4.0,4,1.0,8,2.0
-247,292418,17470,0,1,0,0,0,0,0,1,0,0,0,1,8.0,8,1.0,8,1.0
-248,402010,28515,0,1,0,1,0,0,0,0,0,0,0,1,,0,0.0,8,5.0
-249,297665,147497,0,1,0,0,0,0,1,0,0,0,0,1,6.0,6,1.0,0,0.0
-250,344575,169672,0,1,0,0,0,1,1,0,0,0,0,0,4.0,4,1.0,4,1.0
-251,494766,34199,0,0,0,1,0,0,0,1,0,0,0,1,8.0,8,1.0,4,0.5
-252,455465,99429,0,0,0,1,0,1,0,0,0,1,0,0,8.0,8,1.0,2,0.25
-253,468129,19544,0,0,0,0,0,0,1,0,1,0,1,0,2.0,2,1.0,2,1.0
-254,285542,168275,0,0,1,0,0,0,1,0,1,0,0,0,6.0,6,1.0,4,0.67
-255,401087,92771,0,0,0,0,0,0,1,0,0,1,0,1,6.0,6,1.0,8,1.33
-256,300457,182506,0,0,0,1,0,0,0,1,0,0,1,0,,0,0.0,2,5.0
-257,225116,127431,1,0,0,0,1,0,0,1,0,0,0,0,6.0,6,1.0,8,1.33
-258,341865,89645,0,1,0,1,0,1,0,0,0,0,0,0,,0,0.0,6,5.0
-259,329782,138901,0,0,0,0,0,0,0,0,1,0,1,1,,0,0.0,8,5.0
-260,260576,199473,0,0,0,0,0,1,1,0,0,0,1,0,6.0,6,1.0,6,1.0
-261,455435,17795,1,0,0,1,0,0,0,0,0,1,0,0,2.0,2,1.0,4,2.0
-262,332828,107041,1,0,0,0,0,0,0,0,0,1,0,1,8.0,8,1.0,4,0.5
-263,312057,35945,0,0,0,0,0,1,1,0,1,0,0,0,4.0,4,1.0,4,1.0
-264,470692,92096,1,1,0,0,0,0,0,1,0,0,0,0,8.0,8,1.0,6,0.75
-265,230964,80257,0,1,0,0,0,1,0,0,0,1,0,0,10.0,10,1.0,6,0.6
-266,423501,78904,0,0,0,1,1,0,0,0,0,1,0,0,10.0,10,1.0,4,0.4
-267,373241,33338,0,0,1,0,1,0,0,1,0,0,0,0,6.0,6,1.0,2,0.33
-268,476092,101934,1,0,1,0,0,0,0,0,0,0,0,1,6.0,6,1.0,2,0.33
-269,494248,186835,0,0,1,0,0,0,0,1,0,1,0,0,6.0,6,1.0,6,1.0
-270,426884,194172,0,0,0,0,0,0,0,1,0,1,0,1,,0,0.0,2,5.0
-271,202738,87671,0,0,0,0,0,0,1,0,1,0,1,0,4.0,4,1.0,6,1.5
-272,393215,36585,1,0,0,0,0,0,0,1,0,0,1,0,8.0,8,1.0,6,0.75
-273,227536,64387,0,1,0,0,0,0,0,1,0,0,1,0,12.0,12,1.0,0,0.0
-274,408448,106251,1,0,0,1,0,0,0,0,1,0,0,0,4.0,4,1.0,0,0.0
-275,397164,56350,0,0,0,1,0,0,0,0,1,1,0,0,,0,0.0,8,5.0
-276,401375,190889,0,1,0,0,0,0,0,1,1,0,0,0,6.0,6,1.0,4,0.67
-277,243472,52240,0,0,0,0,0,0,1,0,0,1,1,0,8.0,8,1.0,8,1.0
-278,390850,94391,0,0,0,0,0,1,0,1,0,0,0,1,2.0,2,1.0,8,4.0
-279,486146,63673,0,0,0,1,0,1,0,0,0,0,1,0,,0,0.0,2,5.0
-280,316122,137715,0,0,1,0,0,0,0,0,0,1,1,0,6.0,6,1.0,6,1.0
-281,417758,81087,1,1,0,0,1,0,0,0,0,0,0,0,6.0,6,1.0,6,1.0
-282,200066,52917,0,0,0,0,0,0,0,1,0,1,1,0,10.0,10,1.0,8,0.8
-283,466157,77291,0,0,1,0,0,0,0,0,1,0,0,1,2.0,2,1.0,6,3.0
-284,311745,117818,0,0,0,0,0,1,0,1,0,0,0,1,6.0,6,1.0,2,0.33
-285,388810,106468,0,1,0,0,0,0,1,0,0,0,1,0,2.0,2,1.0,6,3.0
-286,295784,96831,0,1,0,0,0,0,1,0,0,0,0,1,2.0,2,1.0,4,2.0
-287,239957,80359,0,0,0,1,0,0,1,0,1,0,0,0,6.0,6,1.0,4,0.67
-288,259571,96779,0,0,0,1,0,0,1,0,0,0,1,0,,0,0.0,8,5.0
-289,470592,57685,1,1,0,0,0,0,0,1,0,0,0,0,4.0,4,1.0,0,0.0
-290,360940,92218,0,1,0,0,0,1,0,1,0,0,0,0,4.0,4,1.0,16,4.0
-291,459336,78436,1,0,0,0,0,0,0,0,0,1,1,0,4.0,4,1.0,4,1.0
-292,296446,82822,1,0,0,0,1,1,0,0,0,0,0,0,6.0,6,1.0,0,0.0
-293,285626,67263,0,1,0,0,1,0,0,0,0,0,1,0,,0,0.0,4,5.0
-294,464099,170208,0,0,0,1,0,0,0,1,0,1,0,0,4.0,4,1.0,6,1.5
-295,263105,164140,1,0,0,0,1,0,0,1,0,0,0,0,6.0,6,1.0,4,0.67
-296,302474,167473,0,1,0,0,0,0,0,0,0,0,1,1,2.0,2,1.0,4,2.0
-297,372056,174677,0,1,1,0,0,0,0,0,0,0,0,1,6.0,6,1.0,4,0.67
-298,352449,52010,0,0,0,1,0,1,0,1,0,0,0,0,6.0,6,1.0,4,0.67
-299,419186,171727,0,0,0,0,1,1,0,0,0,0,1,0,10.0,10,1.0,2,0.2
-300,336617,82806,1,1,0,0,0,0,0,0,1,0,0,0,4.0,4,1.0,10,2.5
-301,400878,153139,0,0,1,0,1,0,0,0,0,0,0,1,4.0,4,1.0,4,1.0
-302,376532,58763,0,0,0,1,0,0,0,0,0,0,1,1,4.0,4,1.0,12,3.0
-303,215966,114816,1,0,0,0,0,0,0,0,1,0,0,1,4.0,4,1.0,4,1.0
-304,298739,78236,0,0,0,0,0,1,0,1,0,0,1,0,2.0,2,1.0,10,5.0
-305,324152,168504,0,1,0,0,1,0,0,0,1,0,0,0,6.0,6,1.0,4,0.67
-306,225832,114959,0,0,0,0,1,0,0,0,0,0,1,1,2.0,2,1.0,8,4.0
-307,218247,97153,0,0,1,1,0,0,0,0,0,0,1,0,2.0,2,1.0,4,2.0
-308,442559,186433,0,0,1,1,0,0,0,1,0,0,0,0,4.0,4,1.0,8,2.0
-309,400541,29388,0,0,0,0,1,1,0,1,0,0,0,0,4.0,4,1.0,6,1.5
-310,322029,114911,1,1,0,0,0,0,0,0,0,0,0,1,4.0,4,1.0,0,0.0
-311,233292,162186,0,1,0,0,0,0,0,0,0,1,0,1,2.0,2,1.0,2,1.0
-312,313899,35635,0,1,0,0,0,1,1,0,0,0,0,0,10.0,10,1.0,6,0.6
-313,403970,11492,1,0,0,0,0,0,0,1,0,0,1,0,6.0,6,1.0,0,0.0
-314,292531,147251,0,1,0,0,0,0,0,1,0,0,0,1,8.0,8,1.0,16,2.0
-315,499030,65865,0,1,0,0,1,0,0,0,0,0,1,0,2.0,2,1.0,8,4.0
-316,345372,25792,0,0,0,0,1,0,0,0,0,0,1,1,8.0,8,1.0,6,0.75
-317,279212,121179,0,1,1,1,0,0,0,0,0,0,0,0,8.0,8,1.0,8,1.0
-318,434460,169288,0,0,0,0,0,0,0,0,1,1,0,1,4.0,4,1.0,2,0.5
-319,461196,108275,1,0,0,0,0,0,1,0,0,0,1,0,,0,0.0,6,5.0
-320,214434,163733,1,0,0,0,0,0,0,1,1,0,0,0,8.0,8,1.0,2,0.25
-321,380690,119490,0,0,0,1,0,1,1,0,0,0,0,0,2.0,2,1.0,10,5.0
-322,207430,146046,1,0,1,0,0,0,0,1,0,0,0,0,6.0,6,1.0,4,0.67
-323,291430,197852,0,1,0,0,0,0,1,0,0,0,1,0,2.0,2,1.0,0,0.0
-324,281509,145993,0,0,0,0,0,0,1,0,0,1,1,0,4.0,4,1.0,10,2.5
-325,450296,105489,0,0,0,1,0,0,1,0,0,0,1,0,8.0,8,1.0,4,0.5
-326,304679,130659,1,0,0,1,0,0,0,1,0,0,0,0,10.0,10,1.0,8,0.8
-327,383569,138762,0,1,0,0,0,0,0,0,1,0,1,0,4.0,4,1.0,0,0.0
-328,353518,60651,0,0,1,0,1,0,0,0,0,1,0,0,6.0,6,1.0,10,1.67
-329,274717,155240,0,0,0,1,0,0,0,1,0,0,1,0,,0,0.0,8,5.0
-330,384499,155236,0,0,0,0,0,0,0,1,1,0,0,1,6.0,6,1.0,4,0.67
-331,465116,153987,0,0,0,0,0,0,0,0,1,1,1,0,4.0,4,1.0,4,1.0
-332,422611,142253,0,0,0,1,0,0,0,1,0,0,1,0,2.0,2,1.0,6,3.0
-333,273142,46673,1,0,1,0,0,0,1,0,0,0,0,0,6.0,6,1.0,6,1.0
-334,276436,53260,0,1,0,1,0,0,0,0,0,1,0,0,10.0,10,1.0,14,1.4
-335,202608,38139,1,0,1,0,1,0,0,0,0,0,0,0,6.0,6,1.0,8,1.33
-336,243929,179707,0,0,0,0,0,0,1,1,1,0,0,0,2.0,2,1.0,6,3.0
-337,258763,141289,1,0,1,0,0,0,0,0,0,0,0,1,4.0,4,1.0,2,0.5
-338,322834,58273,0,1,0,0,0,0,1,0,0,1,0,0,8.0,8,1.0,8,1.0
-339,211616,151350,0,1,0,0,0,0,1,0,0,1,0,0,8.0,8,1.0,4,0.5
-340,417342,43433,0,0,1,0,0,0,0,1,0,1,0,0,2.0,2,1.0,10,5.0
-341,333428,53017,0,0,0,0,0,1,0,0,0,1,0,1,6.0,6,1.0,12,2.0
-342,453483,112882,0,0,0,1,1,0,0,0,0,0,0,1,6.0,6,1.0,8,1.33
-343,266412,108033,1,1,0,0,0,0,0,1,0,0,0,0,,0,0.0,6,5.0
-344,408051,140955,0,0,0,0,1,0,1,0,0,0,1,0,6.0,6,1.0,2,0.33
-345,456345,94717,0,0,0,0,0,0,1,0,0,0,1,1,10.0,10,1.0,14,1.4
-346,457822,28648,1,0,1,0,0,1,0,0,0,0,0,0,6.0,6,1.0,10,1.67
-347,255709,92725,0,0,0,0,0,0,1,0,1,0,1,0,4.0,4,1.0,4,1.0
-348,451104,192438,1,0,0,0,0,1,0,1,0,0,0,0,4.0,4,1.0,10,2.5
-349,420645,79899,1,0,0,0,0,0,1,0,0,0,0,1,10.0,10,1.0,6,0.6
-350,392445,125863,0,1,0,0,0,0,0,1,0,0,1,0,6.0,6,1.0,6,1.0
-351,207515,78961,0,1,0,0,1,0,0,0,1,0,0,0,4.0,4,1.0,4,1.0
-352,317523,53884,0,1,0,0,0,0,1,0,0,0,1,0,2.0,2,1.0,4,2.0
-353,444435,197545,0,0,0,1,0,0,0,1,1,0,0,0,14.0,14,1.0,6,0.43
-354,316633,42407,0,0,0,0,0,0,1,0,1,0,1,0,14.0,14,1.0,2,0.14
-355,258931,173204,0,0,0,0,1,0,0,1,0,0,1,0,,0,0.0,2,5.0
-356,372692,190010,0,1,0,1,0,0,0,1,0,0,0,0,2.0,2,1.0,4,2.0
-357,343398,37837,0,0,1,1,0,0,0,0,0,0,1,0,2.0,2,1.0,6,3.0
-358,281317,59623,1,0,0,1,0,0,0,1,0,0,0,0,6.0,6,1.0,6,1.0
-359,332501,26790,0,0,0,1,0,1,0,0,1,0,0,0,8.0,8,1.0,4,0.5
-360,384262,158042,1,0,0,0,0,0,0,1,0,1,0,0,6.0,6,1.0,6,1.0
-361,428215,93593,1,0,1,0,0,0,0,0,1,0,0,0,6.0,6,1.0,14,2.33
-362,249481,163969,1,0,0,1,1,0,0,0,0,0,0,0,6.0,6,1.0,10,1.67
-363,414483,50161,0,1,0,0,0,0,0,1,0,0,0,1,8.0,8,1.0,4,0.5
-364,464670,118855,1,0,0,0,0,0,0,0,0,0,1,1,4.0,4,1.0,6,1.5
-365,474311,172110,1,0,0,0,0,0,1,0,0,0,0,1,2.0,2,1.0,0,0.0
-366,348959,114042,0,0,0,0,0,0,0,0,1,1,1,0,6.0,6,1.0,8,1.33
-367,234548,101587,0,0,0,0,0,0,0,1,0,1,0,1,4.0,4,1.0,10,2.5
-368,219163,174151,1,0,0,0,1,0,1,0,0,0,0,0,4.0,4,1.0,0,0.0
-369,302195,65234,0,1,1,1,0,0,0,0,0,0,0,0,2.0,2,1.0,10,5.0
-370,422472,147660,0,0,0,0,0,1,0,0,1,0,0,1,8.0,8,1.0,8,1.0
-371,227784,146399,0,0,0,0,1,1,0,1,0,0,0,0,2.0,2,1.0,6,3.0
-372,498482,171040,0,0,0,1,0,1,1,0,0,0,0,0,8.0,8,1.0,4,0.5
-373,373709,25938,0,0,0,1,0,0,0,1,1,0,0,0,8.0,8,1.0,6,0.75
-374,470459,35826,1,0,1,0,0,1,0,0,0,0,0,0,8.0,8,1.0,12,1.5
-375,354110,120657,0,0,0,0,0,0,1,1,0,0,0,1,4.0,4,1.0,10,2.5
-376,206024,117159,0,0,0,1,0,0,1,0,1,0,0,0,10.0,10,1.0,0,0.0
-377,358946,83301,0,0,0,0,0,0,1,1,1,0,0,0,4.0,4,1.0,4,1.0
-378,308803,59281,1,1,0,0,0,0,0,0,1,0,0,0,,0,0.0,6,5.0
-379,474517,31884,0,0,0,0,0,0,1,0,0,1,1,0,2.0,2,1.0,8,4.0
-380,242390,142871,0,0,0,1,0,0,1,0,0,0,1,0,4.0,4,1.0,0,0.0
-381,340548,187457,0,0,0,1,1,0,0,0,1,0,0,0,6.0,6,1.0,2,0.33
-382,299177,177984,0,1,0,0,0,0,0,0,1,0,1,0,6.0,6,1.0,6,1.0
-383,270532,82671,1,1,1,0,0,0,0,0,0,0,0,0,8.0,8,1.0,6,0.75
-384,268992,88647,0,0,0,0,0,0,0,1,1,1,0,0,6.0,6,1.0,6,1.0
-385,259627,71976,0,1,0,0,0,0,0,1,0,0,1,0,2.0,2,1.0,6,3.0
-386,266860,39850,1,1,0,0,0,0,1,0,0,0,0,0,12.0,12,1.0,8,0.67
-387,202576,112778,0,1,0,0,0,1,0,1,0,0,0,0,6.0,6,1.0,4,0.67
-388,289778,148181,0,0,0,1,1,0,0,0,1,0,0,0,6.0,6,1.0,6,1.0
-389,318503,98397,1,1,0,0,0,0,0,0,0,1,0,0,12.0,12,1.0,4,0.33
-390,307917,126264,0,0,0,0,0,0,0,0,0,1,1,1,6.0,6,1.0,10,1.67
-391,369358,93056,0,0,0,0,1,0,0,0,1,0,0,1,6.0,6,1.0,4,0.67
-392,373343,59530,0,0,0,0,0,1,0,0,1,0,1,0,6.0,6,1.0,4,0.67
-393,278415,191858,0,0,0,0,0,0,1,0,1,0,0,1,8.0,8,1.0,8,1.0
-394,254809,78233,0,1,0,0,0,0,0,0,0,0,1,1,8.0,8,1.0,6,0.75
-395,358742,41600,0,0,0,1,0,1,0,0,0,1,0,0,8.0,8,1.0,14,1.75
-396,435593,136937,0,1,1,0,0,0,0,0,0,0,0,1,8.0,8,1.0,8,1.0
-397,444664,79308,0,1,0,0,1,0,0,1,0,0,0,0,2.0,2,1.0,8,4.0
-398,257040,189799,0,0,0,0,1,0,0,0,0,1,0,1,4.0,4,1.0,6,1.5
-399,471971,107481,0,1,0,0,1,1,0,0,0,0,0,0,8.0,8,1.0,4,0.5
-400,285534,69440,0,1,0,1,0,0,1,0,0,0,0,0,4.0,4,1.0,8,2.0
-401,399355,28851,0,1,0,1,0,1,0,0,0,0,0,0,2.0,2,1.0,6,3.0
-402,267282,130354,0,0,0,0,0,0,0,0,1,0,1,1,4.0,4,1.0,10,2.5
-403,376411,90246,1,1,0,0,0,1,0,0,0,0,0,0,6.0,6,1.0,2,0.33
-404,417066,68397,0,0,1,0,0,0,0,0,0,1,0,1,8.0,8,1.0,6,0.75
-405,312499,50643,1,0,0,0,1,0,0,0,0,0,1,0,4.0,4,1.0,8,2.0
-406,329969,70706,0,1,0,0,0,0,0,1,0,0,1,0,8.0,8,1.0,8,1.0
-407,468220,70210,0,0,1,0,0,0,0,0,1,0,0,1,2.0,2,1.0,8,4.0
-408,414297,162311,1,1,0,0,1,0,0,0,0,0,0,0,8.0,8,1.0,8,1.0
-409,219953,187847,0,0,1,1,1,0,0,0,0,0,0,0,2.0,2,1.0,2,1.0
-410,413257,192908,0,1,0,0,1,0,0,0,0,0,1,0,4.0,4,1.0,2,0.5
-411,334666,192762,0,0,1,1,0,0,0,1,0,0,0,0,4.0,4,1.0,10,2.5
-412,222514,50456,0,0,0,0,0,0,0,1,1,0,1,0,8.0,8,1.0,0,0.0
-413,259408,123579,1,0,1,0,0,0,0,0,1,0,0,0,14.0,14,1.0,6,0.43
-414,319363,16753,0,1,0,1,0,0,0,0,0,0,0,1,4.0,4,1.0,8,2.0
-415,398450,95097,0,0,1,0,0,1,0,1,0,0,0,0,6.0,6,1.0,2,0.33
-416,388838,107138,0,0,1,0,1,0,0,1,0,0,0,0,2.0,2,1.0,2,1.0
-417,256776,19251,0,1,0,0,0,0,0,0,0,1,0,1,2.0,2,1.0,4,2.0
-418,383184,82298,0,0,0,0,0,1,1,1,0,0,0,0,4.0,4,1.0,6,1.5
-419,262198,77538,0,1,1,0,0,0,1,0,0,0,0,0,4.0,4,1.0,8,2.0
-420,276703,139996,1,1,0,0,1,0,0,0,0,0,0,0,8.0,8,1.0,2,0.25
-421,276962,38350,1,0,0,0,0,1,0,0,0,0,1,0,14.0,14,1.0,4,0.29
-422,461187,140548,0,0,0,1,0,0,0,1,0,1,0,0,12.0,12,1.0,10,0.83
-423,480325,45818,0,1,1,0,0,0,0,0,0,0,0,1,8.0,8,1.0,10,1.25
-424,394427,83641,0,0,1,0,0,0,0,1,0,0,1,0,2.0,2,1.0,8,4.0
-425,435603,99934,0,0,1,0,1,0,0,0,0,1,0,0,2.0,2,1.0,6,3.0
-426,279346,67709,0,0,0,1,0,0,1,0,0,0,0,1,,0,0.0,12,5.0
-427,306633,93222,0,0,1,0,0,0,0,0,1,0,1,0,8.0,8,1.0,6,0.75
-428,483980,14466,1,0,0,0,0,0,0,1,0,0,0,1,4.0,4,1.0,4,1.0
-429,214878,173055,1,0,1,0,0,0,0,0,1,0,0,0,4.0,4,1.0,6,1.5
-430,263423,160431,0,0,0,1,0,0,1,0,1,0,0,0,10.0,10,1.0,6,0.6
-431,396807,194270,0,0,0,0,1,1,0,0,0,0,0,1,8.0,8,1.0,2,0.25
-432,468006,139244,0,0,0,0,1,0,0,1,0,0,1,0,4.0,4,1.0,8,2.0
-433,386017,54184,0,0,0,1,0,0,1,0,0,0,1,0,8.0,8,1.0,6,0.75
-434,237391,115716,0,0,1,1,0,0,0,1,0,0,0,0,12.0,12,1.0,6,0.5
-435,415828,29926,0,0,0,0,0,0,0,1,0,1,1,0,4.0,4,1.0,4,1.0
-436,208006,56098,0,0,0,1,0,0,0,1,1,0,0,0,6.0,6,1.0,4,0.67
-437,282798,92380,0,0,0,0,0,0,1,1,0,0,1,0,10.0,10,1.0,12,1.2
-438,322382,139215,0,0,0,0,0,0,1,1,1,0,0,0,4.0,4,1.0,12,3.0
-439,495656,24595,0,0,0,0,1,0,0,0,0,1,0,1,6.0,6,1.0,8,1.33
-440,487736,156629,0,0,0,1,0,1,0,1,0,0,0,0,6.0,6,1.0,6,1.0
-441,348167,162775,0,1,0,0,0,0,0,0,1,1,0,0,8.0,8,1.0,2,0.25
-442,413534,160420,0,1,0,0,0,0,0,1,1,0,0,0,4.0,4,1.0,4,1.0
-443,292531,155651,1,0,0,0,0,1,1,0,0,0,0,0,4.0,4,1.0,4,1.0
-444,405225,64763,0,0,0,0,1,1,1,0,0,0,0,0,4.0,4,1.0,10,2.5
-445,388734,172721,1,0,1,0,0,0,0,0,1,0,0,0,8.0,8,1.0,10,1.25
-446,200028,69617,0,0,0,1,0,1,0,0,0,0,1,0,8.0,8,1.0,4,0.5
-447,368086,150465,0,0,1,0,0,0,0,0,0,1,0,1,8.0,8,1.0,10,1.25
-448,453598,88914,0,0,0,0,1,0,1,0,0,0,1,0,8.0,8,1.0,6,0.75
-449,490092,13516,0,0,1,0,1,0,0,0,0,1,0,0,6.0,6,1.0,2,0.33
-450,260490,195083,0,0,0,1,1,0,0,0,0,0,1,0,6.0,6,1.0,6,1.0
-451,455598,27274,1,0,1,0,0,0,0,1,0,0,0,0,10.0,10,1.0,4,0.4
-452,494976,161982,0,0,0,1,0,0,0,0,0,1,0,1,4.0,4,1.0,8,2.0
-453,396066,28764,0,1,1,0,0,0,0,0,0,0,1,0,8.0,8,1.0,8,1.0
-454,455579,88243,0,1,0,0,1,0,1,0,0,0,0,0,,0,0.0,8,5.0
-455,409085,70502,0,0,0,0,1,0,1,0,0,1,0,0,4.0,4,1.0,8,2.0
-456,371029,72053,0,0,0,1,1,1,0,0,0,0,0,0,4.0,4,1.0,2,0.5
-457,295670,196249,1,0,1,0,0,0,1,0,0,0,0,0,10.0,10,1.0,2,0.2
-458,361928,150430,1,0,0,0,1,0,0,0,0,0,0,1,2.0,2,1.0,6,3.0
-459,451024,173729,1,0,0,0,0,0,0,0,1,0,0,1,4.0,4,1.0,2,0.5
-460,490748,98179,0,0,0,0,0,1,0,0,1,0,0,1,6.0,6,1.0,4,0.67
-461,491945,30336,0,0,0,0,0,1,0,0,1,1,0,0,6.0,6,1.0,8,1.33
-462,241640,70420,0,1,0,0,1,0,0,0,0,0,0,1,2.0,2,1.0,8,4.0
-463,223055,35627,0,0,1,0,0,0,1,1,0,0,0,0,2.0,2,1.0,4,2.0
-464,467233,172942,0,0,0,0,0,1,0,0,1,1,0,0,4.0,4,1.0,4,1.0
-465,209404,90757,0,0,0,0,0,1,0,0,0,1,0,1,2.0,2,1.0,6,3.0
-466,235052,63309,0,0,0,1,0,0,1,0,0,0,0,1,8.0,8,1.0,10,1.25
-467,352043,122542,0,0,0,0,0,1,0,1,0,0,1,0,,0,0.0,2,5.0
-468,464655,71188,0,0,1,0,1,0,0,0,0,0,1,0,,0,0.0,4,5.0
-469,452949,99119,0,0,0,1,0,1,0,0,1,0,0,0,6.0,6,1.0,6,1.0
-470,426276,83280,0,1,1,0,0,0,1,0,0,0,0,0,4.0,4,1.0,10,2.5
-471,216417,72177,1,0,1,0,1,0,0,0,0,0,0,0,8.0,8,1.0,6,0.75
-472,475969,71538,0,1,0,1,0,0,0,0,0,0,0,1,8.0,8,1.0,8,1.0
-473,470315,16907,0,1,0,0,1,0,1,0,0,0,0,0,2.0,2,1.0,8,4.0
-474,278020,110948,0,0,0,1,0,1,0,0,0,0,1,0,14.0,14,1.0,8,0.57
-475,440036,30121,0,0,0,0,0,1,0,0,1,0,0,1,2.0,2,1.0,4,2.0
-476,314142,30943,0,1,0,0,0,0,1,0,0,1,0,0,2.0,2,1.0,0,0.0
-477,386746,20699,0,0,0,0,1,1,0,0,1,0,0,0,4.0,4,1.0,4,1.0
-478,261137,14474,1,0,0,0,0,0,0,0,0,1,1,0,4.0,4,1.0,0,0.0
-479,331372,39433,0,1,0,1,0,0,1,0,0,0,0,0,,0,0.0,2,5.0
-480,405773,32680,0,0,0,0,0,0,0,1,1,1,0,0,4.0,4,1.0,4,1.0
-481,429580,132313,0,1,0,0,1,0,0,0,0,0,1,0,2.0,2,1.0,2,1.0
-482,356059,157741,0,0,0,0,1,0,0,0,1,0,0,1,,0,0.0,4,5.0
-483,271684,55449,0,0,0,0,1,1,0,0,0,1,0,0,10.0,10,1.0,16,1.6
-484,347716,95631,0,0,0,0,1,1,1,0,0,0,0,0,2.0,2,1.0,6,3.0
-485,411171,62933,0,0,0,1,0,0,0,1,0,0,0,1,,0,0.0,8,5.0
-486,428977,75683,0,0,0,0,1,0,0,0,1,1,0,0,4.0,4,1.0,14,3.5
-487,466398,187686,0,0,1,0,0,0,0,1,0,0,0,1,8.0,8,1.0,6,0.75
-488,270081,33315,0,0,0,0,0,0,0,0,1,0,1,1,10.0,10,1.0,6,0.6
-489,485029,51880,1,0,0,1,0,0,0,0,0,0,0,1,8.0,8,1.0,6,0.75
-490,261276,108198,1,0,0,0,0,0,0,0,1,0,1,0,2.0,2,1.0,10,5.0
-491,407979,92566,1,0,0,0,0,0,1,0,0,0,0,1,8.0,8,1.0,4,0.5
-492,243348,173882,0,0,0,1,0,1,0,0,0,0,0,1,6.0,6,1.0,6,1.0
-493,411586,174035,0,0,0,0,1,0,0,1,0,0,0,1,6.0,6,1.0,8,1.33
-494,297625,144010,1,0,0,1,0,0,1,0,0,0,0,0,8.0,8,1.0,6,0.75
-495,402601,92174,0,0,0,0,0,0,1,1,0,1,0,0,2.0,2,1.0,0,0.0
-496,266498,69504,0,0,0,1,0,1,1,0,0,0,0,0,8.0,8,1.0,4,0.5
-497,303917,41341,0,0,0,0,1,1,0,0,0,0,1,0,4.0,4,1.0,2,0.5
-498,205307,26146,1,0,0,0,0,1,1,0,0,0,0,0,6.0,6,1.0,6,1.0
-499,471466,124691,1,0,1,0,0,0,0,1,0,0,0,0,10.0,10,1.0,6,0.6
-500,363805,196676,0,1,0,0,0,0,0,1,1,0,0,0,4.0,4,1.0,4,1.0
-501,328308,62536,0,0,1,1,0,0,0,1,0,0,0,0,,0,0.0,2,5.0
-502,267523,85770,0,0,1,0,0,0,1,0,0,0,1,0,8.0,8,1.0,4,0.5
-503,332979,84112,1,0,1,0,0,0,0,0,0,0,1,0,2.0,2,1.0,0,0.0
-504,309464,180538,0,0,0,0,1,1,0,0,0,1,0,0,2.0,2,1.0,8,4.0
-505,488292,185347,1,0,0,0,0,0,0,1,0,1,0,0,2.0,2,1.0,4,2.0
-506,361471,165007,0,0,0,0,0,0,1,1,0,0,0,1,6.0,6,1.0,4,0.67
-507,208687,105248,0,0,1,0,0,0,1,1,0,0,0,0,8.0,8,1.0,4,0.5
-508,232684,161404,0,1,0,0,0,1,0,1,0,0,0,0,2.0,2,1.0,6,3.0
-509,442554,16172,0,0,0,0,1,0,0,0,0,0,1,1,4.0,4,1.0,12,3.0
-510,204657,170199,0,0,1,0,0,0,0,0,0,0,1,1,2.0,2,1.0,4,2.0
-511,239389,70377,0,0,1,0,1,0,0,0,1,0,0,0,10.0,10,1.0,6,0.6
-512,218008,101701,1,0,0,0,1,0,0,1,0,0,0,0,4.0,4,1.0,10,2.5
-513,383759,194651,0,0,1,0,0,0,0,0,1,0,1,0,2.0,2,1.0,6,3.0
-514,306213,61447,0,0,0,1,1,0,1,0,0,0,0,0,4.0,4,1.0,2,0.5
-515,312123,48279,0,0,1,0,1,0,1,0,0,0,0,0,2.0,2,1.0,4,2.0
-516,361275,116248,0,0,0,1,0,0,0,0,0,0,1,1,,0,0.0,6,5.0
-517,388396,90908,0,1,0,0,0,0,0,1,0,1,0,0,2.0,2,1.0,6,3.0
-518,288128,133729,0,0,0,0,1,1,0,0,0,0,1,0,4.0,4,1.0,4,1.0
-519,425896,76334,0,0,0,1,0,0,1,0,0,1,0,0,2.0,2,1.0,12,6.0
-520,227571,49234,0,0,1,0,0,0,1,0,0,1,0,0,4.0,4,1.0,8,2.0
-521,479020,137397,0,0,0,0,1,1,0,0,1,0,0,0,2.0,2,1.0,4,2.0
-522,204399,43095,0,0,1,0,0,0,0,1,0,0,0,1,8.0,8,1.0,10,1.25
-523,338325,154358,0,0,0,1,1,0,0,1,0,0,0,0,8.0,8,1.0,4,0.5
-524,336982,132921,0,0,1,1,0,0,0,0,1,0,0,0,14.0,14,1.0,8,0.57
-525,341459,91124,0,0,1,0,0,0,0,0,0,1,0,1,8.0,8,1.0,0,0.0
-526,406510,63478,0,0,0,1,0,0,0,0,1,0,0,1,4.0,4,1.0,4,1.0
-527,332700,64958,0,1,0,0,0,1,0,0,0,0,1,0,6.0,6,1.0,2,0.33
-528,254230,86953,0,0,1,0,0,0,1,1,0,0,0,0,,0,0.0,6,5.0
-529,216574,171859,0,0,0,1,0,0,0,0,0,1,1,0,4.0,4,1.0,6,1.5
-530,482990,41795,0,0,0,1,0,1,0,0,0,0,0,1,10.0,10,1.0,6,0.6
-531,330133,123832,0,0,0,0,1,0,0,0,0,1,1,0,,0,0.0,0,0.0
-532,395228,53435,1,0,0,0,0,0,0,1,0,0,1,0,6.0,6,1.0,14,2.33
-533,219301,83245,1,0,0,0,1,0,0,0,0,0,1,0,2.0,2,1.0,4,2.0
-534,241393,116263,0,1,0,1,0,0,0,0,0,1,0,0,6.0,6,1.0,12,2.0
-535,209948,50931,0,0,0,1,0,0,1,0,0,0,1,0,8.0,8,1.0,4,0.5
-536,434471,63782,1,0,0,1,0,0,0,1,0,0,0,0,4.0,4,1.0,4,1.0
-537,402834,161904,0,0,1,0,1,1,0,0,0,0,0,0,2.0,2,1.0,8,4.0
-538,441114,132616,1,0,0,0,0,1,1,0,0,0,0,0,2.0,2,1.0,12,6.0
-539,467585,31356,0,1,0,1,1,0,0,0,0,0,0,0,8.0,8,1.0,4,0.5
-540,348008,95495,1,0,0,0,0,0,0,1,0,1,0,0,10.0,10,1.0,6,0.6
-541,445390,136281,0,0,1,0,0,0,0,0,1,0,1,0,4.0,4,1.0,8,2.0
-542,323829,25432,0,0,1,0,0,1,0,1,0,0,0,0,2.0,2,1.0,12,6.0
-543,205185,12607,0,0,0,0,0,0,1,0,0,1,0,1,8.0,8,1.0,6,0.75
-544,490248,26420,0,0,0,1,0,1,0,1,0,0,0,0,4.0,4,1.0,8,2.0
-545,463292,161027,0,0,0,0,1,0,0,0,1,0,1,0,4.0,4,1.0,8,2.0
-546,480943,157829,0,1,0,1,0,0,0,1,0,0,0,0,8.0,8,1.0,8,1.0
-547,347856,29055,1,0,1,0,0,0,0,0,0,0,0,1,12.0,12,1.0,0,0.0
-548,239565,96294,0,1,0,0,0,0,0,0,0,0,1,1,6.0,6,1.0,8,1.33
-549,255734,62437,1,0,0,0,0,0,0,1,0,0,1,0,10.0,10,1.0,4,0.4
-550,242008,183474,0,1,0,0,0,0,0,1,0,1,0,0,4.0,4,1.0,2,0.5
-551,278966,28705,0,1,0,1,0,0,0,0,1,0,0,0,10.0,10,1.0,2,0.2
-552,266072,17762,0,0,1,1,0,0,0,0,0,1,0,0,6.0,6,1.0,8,1.33
-553,380708,37376,0,0,0,1,0,0,1,0,0,0,1,0,4.0,4,1.0,4,1.0
-554,207097,109083,0,1,0,1,0,0,0,0,1,0,0,0,14.0,14,1.0,4,0.29
-555,220167,167100,0,1,1,0,0,0,0,1,0,0,0,0,6.0,6,1.0,8,1.33
-556,328447,185659,0,0,0,0,0,0,1,0,0,1,0,1,,0,0.0,2,5.0
-557,263123,158856,0,1,0,1,0,0,0,0,0,1,0,0,4.0,4,1.0,4,1.0
-558,244123,101011,0,0,0,1,1,0,0,0,0,0,0,1,14.0,14,1.0,14,1.0
-559,212049,197338,1,0,0,0,0,1,0,0,0,1,0,0,2.0,2,1.0,8,4.0
-560,244946,29596,1,0,0,0,1,0,0,0,0,1,0,0,4.0,4,1.0,6,1.5
-561,358017,45793,0,0,0,0,0,1,0,1,0,0,1,0,8.0,8,1.0,8,1.0
-562,344701,132333,1,0,1,0,0,0,0,1,0,0,0,0,6.0,6,1.0,12,2.0
-563,411913,180763,0,0,0,0,1,1,1,0,0,0,0,0,8.0,8,1.0,10,1.25
-564,449587,131493,1,1,0,1,0,0,0,0,0,0,0,0,4.0,4,1.0,10,2.5
-565,486488,50115,0,0,0,1,0,0,0,1,0,1,0,0,16.0,16,1.0,4,0.25
-566,362731,138975,1,0,0,0,0,1,0,1,0,0,0,0,10.0,10,1.0,10,1.0
-567,496717,190331,0,1,1,0,0,0,1,0,0,0,0,0,10.0,10,1.0,6,0.6
-568,306057,157754,0,0,1,0,0,0,0,0,1,1,0,0,4.0,4,1.0,6,1.5
-569,338709,85126,0,0,1,0,0,0,0,0,1,0,0,1,4.0,4,1.0,4,1.0
-570,200001,143481,0,0,0,1,1,0,0,0,0,0,1,0,2.0,2,1.0,0,0.0
-571,223457,100696,0,0,0,1,0,1,0,0,1,0,0,0,8.0,8,1.0,8,1.0
-572,441179,25685,0,0,1,0,0,0,0,0,1,0,1,0,12.0,12,1.0,4,0.33
-573,353594,110800,1,0,0,0,0,0,0,0,1,1,0,0,6.0,6,1.0,8,1.33
-574,393614,13674,0,0,1,0,0,1,1,0,0,0,0,0,4.0,4,1.0,16,4.0
-575,430394,20528,0,0,1,0,1,0,0,0,0,1,0,0,10.0,10,1.0,2,0.2
-576,331831,152774,1,0,0,1,0,0,0,0,1,0,0,0,2.0,2,1.0,10,5.0
-577,398194,120484,0,0,0,0,0,1,1,0,0,1,0,0,8.0,8,1.0,8,1.0
-578,276427,176286,1,0,1,0,0,0,0,1,0,0,0,0,2.0,2,1.0,6,3.0
-579,499987,117675,0,0,0,0,0,1,0,0,1,1,0,0,12.0,12,1.0,12,1.0
-580,290614,113585,0,0,1,1,1,0,0,0,0,0,0,0,6.0,6,1.0,2,0.33
-581,478243,195676,0,0,1,0,0,0,1,0,0,0,0,1,2.0,2,1.0,8,4.0
-582,402493,128166,0,0,0,0,0,1,0,1,0,1,0,0,12.0,12,1.0,8,0.67
-583,393992,143737,0,0,0,1,0,0,1,0,1,0,0,0,6.0,6,1.0,2,0.33
-584,327428,88091,0,1,1,0,0,1,0,0,0,0,0,0,8.0,8,1.0,4,0.5
-585,462940,164467,0,0,0,0,0,1,0,0,1,0,0,1,2.0,2,1.0,12,6.0
-586,265145,97582,0,0,0,0,0,1,0,0,1,1,0,0,4.0,4,1.0,4,1.0
-587,451884,125764,1,0,0,1,0,0,0,0,0,1,0,0,,0,0.0,10,5.0
-588,470082,123085,0,0,0,1,1,0,0,0,0,0,0,1,4.0,4,1.0,4,1.0
-589,302570,55829,0,1,0,1,0,1,0,0,0,0,0,0,4.0,4,1.0,6,1.5
-590,271350,163049,0,0,0,0,0,1,0,0,1,1,0,0,6.0,6,1.0,10,1.67
-591,463176,190070,0,0,1,0,1,0,0,0,0,0,1,0,2.0,2,1.0,10,5.0
-592,239269,115358,0,0,0,0,0,0,1,1,0,0,0,1,2.0,2,1.0,10,5.0
-593,492234,80321,0,0,0,0,0,1,1,1,0,0,0,0,6.0,6,1.0,14,2.33
-594,447098,150300,0,0,1,0,0,0,0,0,1,0,1,0,,0,0.0,4,5.0
-595,289440,191239,0,1,0,1,0,0,1,0,0,0,0,0,4.0,4,1.0,4,1.0
-596,395653,181703,0,0,0,0,0,1,1,0,1,0,0,0,4.0,4,1.0,6,1.5
-597,413273,39791,0,0,0,1,1,0,1,0,0,0,0,0,,0,0.0,2,5.0
-598,225693,56526,0,1,0,0,0,0,1,0,0,0,0,1,10.0,10,1.0,4,0.4
-599,457079,135393,0,0,1,0,0,0,0,0,1,0,1,0,6.0,6,1.0,6,1.0
-600,406200,92399,0,0,1,0,0,1,0,0,0,0,0,1,2.0,2,1.0,10,5.0
-601,391476,55926,0,0,0,1,0,0,1,0,0,1,0,0,6.0,6,1.0,4,0.67
-602,425438,98860,0,0,0,0,0,1,1,0,1,0,0,0,,0,0.0,12,5.0
-603,334418,120570,1,0,0,0,0,0,0,0,1,0,0,1,2.0,2,1.0,4,2.0
-604,348429,28194,1,1,0,0,0,0,0,1,0,0,0,0,,0,0.0,4,5.0
-605,400528,37541,0,0,0,1,1,0,1,0,0,0,0,0,6.0,6,1.0,10,1.67
-606,319216,42896,0,0,0,0,0,1,0,0,0,1,0,1,4.0,4,1.0,6,1.5
-607,480884,46973,0,0,0,0,1,0,0,0,1,0,0,1,2.0,2,1.0,8,4.0
-608,319510,197788,1,0,0,1,0,0,0,0,0,0,0,1,8.0,8,1.0,0,0.0
-609,311293,74309,0,0,1,1,0,0,0,0,0,1,0,0,4.0,4,1.0,14,3.5
-610,393323,181443,0,0,1,0,0,0,1,0,0,0,1,0,4.0,4,1.0,10,2.5
-611,311966,145582,1,0,0,0,1,0,0,0,0,1,0,0,4.0,4,1.0,6,1.5
-612,406543,90709,0,0,0,0,1,0,0,0,0,1,1,0,6.0,6,1.0,6,1.0
-613,302751,155861,0,1,0,0,0,0,0,0,1,0,1,0,12.0,12,1.0,10,0.83
-614,207585,47241,0,0,0,0,0,1,0,0,1,0,1,0,4.0,4,1.0,4,1.0
-615,275659,21504,0,0,0,1,0,1,0,0,0,0,1,0,10.0,10,1.0,10,1.0
-616,406861,28337,0,0,0,0,1,0,0,1,1,0,0,0,2.0,2,1.0,0,0.0
-617,362463,158731,0,0,0,0,1,0,0,1,1,0,0,0,6.0,6,1.0,8,1.33
-618,411510,30753,0,0,0,0,0,1,0,1,0,1,0,0,4.0,4,1.0,6,1.5
-619,485980,143009,0,0,0,0,1,1,1,0,0,0,0,0,2.0,2,1.0,0,0.0
-620,220964,110716,0,1,0,1,0,0,1,0,0,0,0,0,2.0,2,1.0,10,5.0
-621,315555,151131,0,0,0,0,1,0,1,0,1,0,0,0,,0,0.0,2,5.0
-622,471157,68516,1,0,1,0,1,0,0,0,0,0,0,0,8.0,8,1.0,14,1.75
-623,406859,61140,0,1,0,0,0,1,0,0,0,0,1,0,8.0,8,1.0,2,0.25
-624,464367,152582,0,0,0,0,1,0,0,0,0,1,1,0,6.0,6,1.0,2,0.33
-625,384728,71607,0,1,0,0,0,0,0,1,1,0,0,0,10.0,10,1.0,4,0.4
-626,442739,43642,0,1,0,1,0,0,0,0,0,0,0,1,10.0,10,1.0,2,0.2
-627,345081,39891,0,0,0,0,0,0,0,1,1,1,0,0,8.0,8,1.0,2,0.25
-628,356882,151586,0,0,0,0,0,0,0,0,1,1,1,0,8.0,8,1.0,10,1.25
-629,467710,51296,0,0,0,0,1,0,0,0,1,0,0,1,6.0,6,1.0,6,1.0
-630,320831,133375,0,0,0,0,0,0,1,1,0,0,1,0,8.0,8,1.0,8,1.0
-631,277376,98064,0,0,0,0,0,0,0,1,0,1,1,0,10.0,10,1.0,2,0.2
-632,336337,92259,0,0,0,1,0,0,0,1,0,0,0,1,6.0,6,1.0,12,2.0
-633,205914,12925,0,1,0,0,0,0,1,0,1,0,0,0,4.0,4,1.0,2,0.5
-634,334891,180467,0,1,1,1,0,0,0,0,0,0,0,0,2.0,2,1.0,0,0.0
-635,293350,93423,1,0,0,0,0,1,0,0,0,0,1,0,8.0,8,1.0,2,0.25
-636,248133,102234,0,0,0,1,0,0,0,0,0,1,1,0,14.0,14,1.0,2,0.14
-637,463279,66610,0,0,0,0,0,1,0,0,0,1,0,1,4.0,4,1.0,4,1.0
-638,233225,144563,0,0,0,0,1,1,0,0,0,1,0,0,4.0,4,1.0,12,3.0
-639,480066,24214,1,0,0,0,1,0,0,0,1,0,0,0,2.0,2,1.0,6,3.0
-640,298201,68870,0,1,0,0,0,0,0,0,1,0,0,1,2.0,2,1.0,8,4.0
-641,316673,22103,0,0,0,0,0,1,0,0,0,1,1,0,6.0,6,1.0,12,2.0
-642,435702,113156,1,0,0,0,0,0,0,0,0,1,0,1,8.0,8,1.0,8,1.0
-643,382302,131574,0,0,0,1,1,1,0,0,0,0,0,0,16.0,16,1.0,8,0.5
-644,308676,103934,1,0,0,0,0,1,0,0,1,0,0,0,2.0,2,1.0,4,2.0
-645,424223,88807,0,1,0,0,0,0,0,1,1,0,0,0,4.0,4,1.0,0,0.0
-646,447228,44269,0,1,0,0,0,1,0,0,0,0,1,0,10.0,10,1.0,6,0.6
-647,277880,55032,1,0,0,0,0,0,1,0,0,0,1,0,6.0,6,1.0,8,1.33
-648,222776,158303,0,0,0,0,0,1,0,0,1,1,0,0,6.0,6,1.0,10,1.67
-649,472568,61872,1,0,0,0,0,1,0,0,0,0,0,1,6.0,6,1.0,6,1.0
-650,326433,106534,1,0,0,1,0,0,0,0,0,0,0,1,4.0,4,1.0,6,1.5
-651,339979,90419,0,1,0,0,1,1,0,0,0,0,0,0,2.0,2,1.0,6,3.0
-652,444103,137728,0,0,1,0,1,0,0,0,0,1,0,0,4.0,4,1.0,2,0.5
-653,453724,109824,0,0,0,1,0,0,0,1,0,0,0,1,6.0,6,1.0,8,1.33
-654,487460,127989,1,1,0,0,0,0,0,1,0,0,0,0,4.0,4,1.0,8,2.0
-655,483102,182886,0,1,0,1,0,0,0,0,0,0,1,0,4.0,4,1.0,10,2.5
-656,249702,127895,0,0,0,0,0,0,1,0,0,1,0,1,6.0,6,1.0,6,1.0
-657,474378,61869,0,0,0,0,0,1,0,0,1,1,0,0,2.0,2,1.0,12,6.0
-658,362653,145831,0,0,0,0,0,0,0,1,0,1,1,0,2.0,2,1.0,6,3.0
-659,370714,71649,0,0,0,0,0,1,0,1,0,1,0,0,8.0,8,1.0,6,0.75
-660,258959,28713,0,1,0,0,0,1,1,0,0,0,0,0,6.0,6,1.0,6,1.0
-661,405274,24301,0,0,0,0,0,0,1,1,1,0,0,0,2.0,2,1.0,6,3.0
-662,220897,99513,1,0,1,1,0,0,0,0,0,0,0,0,4.0,4,1.0,6,1.5
-663,454203,156314,0,0,0,0,0,0,0,0,1,0,1,1,2.0,2,1.0,4,2.0
-664,462761,164531,0,0,0,1,0,0,0,1,0,0,1,0,6.0,6,1.0,8,1.33
-665,325579,59883,0,0,0,0,0,1,0,0,1,0,1,0,4.0,4,1.0,10,2.5
-666,473233,154694,0,0,1,1,0,0,0,0,1,0,0,0,8.0,8,1.0,4,0.5
-667,420925,199716,1,0,0,0,0,1,0,0,0,1,0,0,6.0,6,1.0,0,0.0
-668,200999,106467,0,1,1,0,0,0,1,0,0,0,0,0,6.0,6,1.0,4,0.67
-669,251803,124949,1,1,0,0,0,1,0,0,0,0,0,0,6.0,6,1.0,6,1.0
-670,498285,29863,0,1,0,0,0,0,0,0,1,0,1,0,6.0,6,1.0,8,1.33
-671,262904,122418,0,1,0,0,0,1,0,1,0,0,0,0,8.0,8,1.0,2,0.25
-672,422921,59595,0,0,0,0,0,1,1,0,0,0,1,0,2.0,2,1.0,2,1.0
-673,420628,177529,1,0,0,0,0,0,0,0,1,0,1,0,2.0,2,1.0,4,2.0
-674,324917,104702,0,0,0,1,0,1,0,0,1,0,0,0,6.0,6,1.0,6,1.0
-675,430855,75453,0,0,0,0,1,0,1,0,0,0,1,0,2.0,2,1.0,6,3.0
-676,285582,74778,1,1,0,0,0,0,1,0,0,0,0,0,8.0,8,1.0,2,0.25
-677,496860,49463,0,1,0,0,0,1,1,0,0,0,0,0,8.0,8,1.0,0,0.0
-678,423904,187806,0,1,0,0,1,0,0,0,0,0,1,0,8.0,8,1.0,6,0.75
-679,204938,36785,1,0,1,1,0,0,0,0,0,0,0,0,8.0,8,1.0,2,0.25
-680,375284,196746,0,0,0,0,1,0,0,1,1,0,0,0,6.0,6,1.0,6,1.0
-681,244556,29179,0,0,0,0,1,0,0,0,0,1,0,1,4.0,4,1.0,4,1.0
-682,229152,127759,0,0,0,0,0,0,1,1,0,0,0,1,,0,0.0,8,5.0
-683,361553,64970,0,0,1,0,1,0,0,0,0,0,1,0,6.0,6,1.0,6,1.0
-684,271352,113220,0,0,1,1,0,0,0,0,0,0,0,1,2.0,2,1.0,6,3.0
-685,295760,74282,1,0,0,0,1,0,0,0,1,0,0,0,,0,0.0,6,5.0
-686,318293,33441,0,0,0,0,0,0,1,1,0,0,1,0,8.0,8,1.0,8,1.0
-687,383619,40056,0,0,1,0,0,0,0,0,1,0,0,1,10.0,10,1.0,2,0.2
-688,314795,136938,0,0,0,0,1,0,1,0,0,0,1,0,10.0,10,1.0,2,0.2
-689,478514,189564,0,0,0,1,1,0,0,0,0,1,0,0,4.0,4,1.0,2,0.5
-690,404950,188127,0,0,0,1,1,1,0,0,0,0,0,0,6.0,6,1.0,8,1.33
-691,445341,106081,1,0,0,1,1,0,0,0,0,0,0,0,4.0,4,1.0,6,1.5
-692,449316,77666,1,0,1,0,0,1,0,0,0,0,0,0,8.0,8,1.0,8,1.0
-693,302075,138096,0,0,0,0,0,1,1,1,0,0,0,0,8.0,8,1.0,14,1.75
-694,456352,36009,0,0,0,1,0,0,1,0,0,0,1,0,4.0,4,1.0,6,1.5
-695,490129,92546,0,1,0,0,0,0,0,0,1,0,1,0,6.0,6,1.0,4,0.67
-696,433637,55286,0,1,0,0,1,1,0,0,0,0,0,0,6.0,6,1.0,10,1.67
-697,210566,31212,0,0,0,0,0,0,1,0,1,0,0,1,,0,0.0,12,5.0
-698,402223,122169,0,1,1,0,0,0,1,0,0,0,0,0,12.0,12,1.0,2,0.17
-699,244785,198667,1,0,0,0,0,0,0,0,0,1,0,1,10.0,10,1.0,12,1.2
-700,402291,166371,0,0,0,0,0,1,0,0,1,0,0,1,8.0,8,1.0,10,1.25
-701,313478,78519,0,1,0,0,0,0,0,1,0,0,1,0,4.0,4,1.0,14,3.5
-702,438841,134520,1,0,1,0,0,0,0,0,1,0,0,0,8.0,8,1.0,6,0.75
-703,213094,70655,0,0,0,0,1,0,0,0,1,0,0,1,8.0,8,1.0,0,0.0
-704,350978,43948,0,0,1,1,0,0,0,1,0,0,0,0,2.0,2,1.0,6,3.0
-705,254726,172807,0,0,0,0,0,0,1,0,1,1,0,0,8.0,8,1.0,6,0.75
-706,434372,189536,0,0,0,1,0,0,0,0,0,0,1,1,4.0,4,1.0,4,1.0
-707,345034,80288,0,1,0,0,0,0,0,0,1,0,1,0,,0,0.0,10,5.0
-708,232062,187285,0,1,0,0,1,0,0,0,1,0,0,0,6.0,6,1.0,0,0.0
-709,411841,19443,0,0,0,0,0,1,0,1,0,1,0,0,2.0,2,1.0,6,3.0
-710,453578,18206,0,1,0,0,1,0,0,0,0,0,1,0,2.0,2,1.0,10,5.0
-711,241167,176591,0,0,0,0,0,1,0,0,0,0,1,1,2.0,2,1.0,8,4.0
-712,371904,166797,0,0,0,0,0,0,0,0,1,1,0,1,4.0,4,1.0,2,0.5
-713,496726,73973,1,0,0,0,0,0,1,0,0,0,0,1,2.0,2,1.0,6,3.0
-714,285423,39451,1,1,0,0,0,0,0,0,1,0,0,0,6.0,6,1.0,8,1.33
-715,235681,76552,0,1,0,0,0,0,1,0,0,0,1,0,6.0,6,1.0,4,0.67
-716,204840,172449,0,0,0,0,0,0,1,1,1,0,0,0,2.0,2,1.0,8,4.0
-717,405744,175646,1,1,1,0,0,0,0,0,0,0,0,0,10.0,10,1.0,10,1.0
-718,247418,116395,1,0,0,0,0,0,1,0,0,1,0,0,6.0,6,1.0,6,1.0
-719,268489,54245,0,0,0,0,1,0,1,0,0,0,1,0,10.0,10,1.0,4,0.4
-720,328945,165590,0,0,1,0,0,0,0,0,0,1,0,1,2.0,2,1.0,4,2.0
-721,410193,141893,0,0,0,0,0,1,1,0,0,1,0,0,2.0,2,1.0,8,4.0
-722,345034,40830,1,0,1,0,1,0,0,0,0,0,0,0,10.0,10,1.0,2,0.2
-723,364372,64143,0,0,0,1,1,0,0,0,0,0,0,1,4.0,4,1.0,4,1.0
-724,290738,129138,0,1,1,0,0,0,0,1,0,0,0,0,,0,0.0,0,0.0
-725,338965,85609,0,1,0,0,0,1,0,1,0,0,0,0,8.0,8,1.0,2,0.25
-726,467121,143254,0,0,0,0,1,0,0,0,0,0,1,1,6.0,6,1.0,2,0.33
-727,389983,27107,0,1,0,0,0,0,1,0,0,1,0,0,2.0,2,1.0,6,3.0
-728,483851,56319,0,0,0,1,1,0,0,0,0,1,0,0,2.0,2,1.0,4,2.0
-729,424730,165939,1,1,0,1,0,0,0,0,0,0,0,0,8.0,8,1.0,6,0.75
-730,249321,48359,0,1,1,0,0,0,1,0,0,0,0,0,4.0,4,1.0,2,0.5
-731,307430,81205,1,1,0,0,0,0,0,0,0,0,1,0,6.0,6,1.0,10,1.67
-732,347301,37884,1,0,1,0,0,0,0,0,1,0,0,0,10.0,10,1.0,12,1.2
-733,203743,110893,0,0,0,0,1,1,0,0,1,0,0,0,4.0,4,1.0,4,1.0
-734,292487,183232,1,0,0,0,1,0,0,0,1,0,0,0,8.0,8,1.0,4,0.5
-735,322767,39677,1,0,0,0,1,0,0,0,0,0,1,0,4.0,4,1.0,4,1.0
-736,358514,80208,0,0,0,1,0,1,0,1,0,0,0,0,14.0,14,1.0,4,0.29
-737,427105,139153,0,1,0,0,1,0,0,0,1,0,0,0,4.0,4,1.0,4,1.0
-738,357046,63987,0,0,0,1,1,0,0,0,0,0,1,0,12.0,12,1.0,10,0.83
-739,307964,26776,1,0,1,0,0,0,0,0,0,0,0,1,8.0,8,1.0,8,1.0
-740,316623,39023,0,1,0,0,0,0,0,0,1,1,0,0,4.0,4,1.0,4,1.0
-741,228760,62415,1,0,0,0,0,0,0,1,0,1,0,0,,0,0.0,8,5.0
-742,338286,150402,1,1,0,0,0,0,1,0,0,0,0,0,12.0,12,1.0,12,1.0
-743,438728,154962,1,0,0,0,1,0,0,0,0,0,1,0,8.0,8,1.0,2,0.25
-744,404981,192372,0,0,0,0,1,0,0,0,1,0,1,0,6.0,6,1.0,4,0.67
-745,359528,25575,0,1,0,0,0,0,1,0,1,0,0,0,6.0,6,1.0,4,0.67
-746,349781,163587,1,0,1,0,0,0,0,0,1,0,0,0,8.0,8,1.0,6,0.75
-747,385237,59843,0,0,0,0,0,0,0,1,0,1,1,0,12.0,12,1.0,4,0.33
-748,384632,183937,0,0,0,0,1,1,0,0,0,1,0,0,4.0,4,1.0,10,2.5
-749,306961,148491,0,0,0,0,1,1,0,1,0,0,0,0,4.0,4,1.0,8,2.0
-750,396479,30844,0,0,0,0,1,0,0,0,1,1,0,0,10.0,10,1.0,2,0.2
-751,353952,189743,0,0,0,0,0,1,0,0,0,0,1,1,6.0,6,1.0,0,0.0
-752,458229,72535,0,1,0,0,0,0,1,0,0,0,0,1,6.0,6,1.0,14,2.33
-753,423828,162472,0,0,1,0,1,0,0,1,0,0,0,0,6.0,6,1.0,0,0.0
-754,369405,195449,0,0,0,0,0,1,0,0,1,0,1,0,14.0,14,1.0,8,0.57
-755,394880,59505,1,0,0,0,0,0,1,0,0,0,1,0,8.0,8,1.0,10,1.25
-756,216125,75680,0,0,1,1,0,1,0,0,0,0,0,0,4.0,4,1.0,4,1.0
-757,269805,185734,0,1,1,0,0,0,1,0,0,0,0,0,8.0,8,1.0,0,0.0
-758,413799,157617,0,0,0,0,0,1,1,0,1,0,0,0,,0,0.0,6,5.0
-759,302053,181745,0,0,0,0,0,1,1,0,0,0,0,1,2.0,2,1.0,8,4.0
-760,473261,167980,0,0,1,0,0,0,0,1,0,0,0,1,10.0,10,1.0,8,0.8
-761,445051,106289,1,1,1,0,0,0,0,0,0,0,0,0,10.0,10,1.0,2,0.2
-762,212245,132984,0,1,0,0,0,0,0,0,0,1,1,0,10.0,10,1.0,8,0.8
-763,480413,113380,0,0,1,0,0,0,0,0,0,1,0,1,8.0,8,1.0,6,0.75
-764,352346,53256,0,0,0,0,1,1,0,0,0,0,1,0,,0,0.0,2,5.0
-765,449161,26399,0,1,0,0,0,0,0,1,0,1,0,0,4.0,4,1.0,10,2.5
-766,400195,66688,1,0,0,0,1,1,0,0,0,0,0,0,4.0,4,1.0,8,2.0
-767,360809,41649,0,1,1,0,0,0,0,0,0,0,0,1,4.0,4,1.0,8,2.0
-768,487223,143779,0,0,1,0,1,0,0,0,0,1,0,0,10.0,10,1.0,8,0.8
-769,364632,163079,0,0,0,1,0,0,0,0,1,1,0,0,2.0,2,1.0,4,2.0
-770,332071,116680,1,0,0,0,1,0,1,0,0,0,0,0,4.0,4,1.0,6,1.5
-771,396359,89174,1,0,1,0,0,0,0,1,0,0,0,0,10.0,10,1.0,4,0.4
-772,320385,92456,0,0,1,1,0,1,0,0,0,0,0,0,6.0,6,1.0,8,1.33
-773,380098,93796,0,1,0,0,0,0,0,0,1,1,0,0,8.0,8,1.0,6,0.75
-774,385140,102819,1,1,0,0,0,0,0,0,0,0,1,0,,0,0.0,6,5.0
-775,473604,163744,0,0,0,0,0,1,1,0,0,1,0,0,4.0,4,1.0,8,2.0
-776,296380,178265,0,0,0,0,0,0,1,1,0,0,0,1,8.0,8,1.0,0,0.0
-777,217741,187165,0,1,0,0,0,0,1,0,0,0,1,0,8.0,8,1.0,4,0.5
-778,468179,141762,0,0,1,0,1,0,0,0,0,0,1,0,2.0,2,1.0,4,2.0
-779,265103,157564,1,0,0,0,0,0,1,0,0,1,0,0,4.0,4,1.0,2,0.5
-780,318917,57513,0,0,0,0,1,1,0,0,0,0,0,1,8.0,8,1.0,8,1.0
-781,335389,73388,0,0,1,0,0,0,1,0,0,0,0,1,,0,0.0,8,5.0
-782,344494,41122,0,1,0,0,1,0,0,0,0,0,1,0,4.0,4,1.0,10,2.5
-783,468987,43930,1,0,0,0,0,1,0,0,0,1,0,0,4.0,4,1.0,4,1.0
-784,480547,64279,0,1,0,0,0,0,0,1,1,0,0,0,8.0,8,1.0,6,0.75
-785,375866,122880,0,0,0,0,0,0,0,1,0,1,1,0,4.0,4,1.0,4,1.0
-786,438184,138257,0,1,0,0,0,0,0,0,1,0,0,1,4.0,4,1.0,10,2.5
-787,221519,42324,1,0,0,0,0,1,1,0,0,0,0,0,6.0,6,1.0,4,0.67
-788,207393,114298,1,1,0,0,0,0,0,0,1,0,0,0,4.0,4,1.0,4,1.0
-789,356242,168810,0,0,0,0,0,0,0,0,1,1,1,0,6.0,6,1.0,10,1.67
-790,394532,87084,0,1,0,0,0,0,0,0,0,1,0,1,2.0,2,1.0,8,4.0
-791,322534,160752,0,0,0,0,1,1,0,0,0,1,0,0,4.0,4,1.0,2,0.5
-792,296770,177535,0,1,0,0,0,0,1,1,0,0,0,0,6.0,6,1.0,8,1.33
-793,359411,181579,0,0,0,0,0,0,1,0,1,1,0,0,6.0,6,1.0,8,1.33
-794,215607,147171,0,0,0,0,1,1,0,0,1,0,0,0,8.0,8,1.0,6,0.75
-795,252366,29720,0,0,1,0,0,0,0,1,0,0,0,1,8.0,8,1.0,2,0.25
-796,481102,62941,1,0,1,0,0,1,0,0,0,0,0,0,2.0,2,1.0,2,1.0
-797,264008,165244,0,0,0,0,0,1,0,1,0,0,1,0,4.0,4,1.0,0,0.0
-798,344748,198569,1,0,1,0,0,1,0,0,0,0,0,0,2.0,2,1.0,10,5.0
-799,491171,95359,1,0,0,1,0,1,0,0,0,0,0,0,4.0,4,1.0,20,5.0
-800,213500,95032,0,0,0,0,0,0,0,1,1,0,0,1,6.0,6,1.0,10,1.67
-801,450193,132018,1,0,0,0,1,1,0,0,0,0,0,0,4.0,4,1.0,4,1.0
-802,341561,154584,0,0,0,0,0,1,0,0,1,0,0,1,8.0,8,1.0,12,1.5
-803,491900,180115,0,0,0,1,0,0,1,0,0,0,0,1,4.0,4,1.0,2,0.5
-804,206970,151088,0,1,0,1,0,0,0,0,1,0,0,0,8.0,8,1.0,4,0.5
-805,316695,25455,0,0,0,0,1,0,0,0,1,0,1,0,6.0,6,1.0,2,0.33
-806,382649,34601,1,1,0,0,0,0,0,0,0,1,0,0,4.0,4,1.0,8,2.0
-807,256997,194583,0,1,0,0,0,1,0,1,0,0,0,0,6.0,6,1.0,10,1.67
-808,365277,184349,0,1,0,0,0,1,0,0,1,0,0,0,8.0,8,1.0,6,0.75
-809,485349,152806,0,1,0,1,0,0,0,0,0,0,1,0,6.0,6,1.0,6,1.0
-810,480262,53863,0,1,0,1,0,1,0,0,0,0,0,0,6.0,6,1.0,4,0.67
-811,486303,95202,0,1,1,0,0,0,0,1,0,0,0,0,,0,0.0,2,5.0
-812,258131,55993,0,0,0,0,0,0,0,1,0,0,1,1,2.0,2,1.0,4,2.0
-813,463050,126936,0,1,0,0,0,1,0,1,0,0,0,0,,0,0.0,0,0.0
-814,270756,29333,0,0,1,0,0,0,1,0,0,0,0,1,4.0,4,1.0,14,3.5
-815,231164,109027,1,0,0,0,0,1,1,0,0,0,0,0,4.0,4,1.0,8,2.0
-816,463899,33207,0,1,1,0,0,0,0,0,0,0,1,0,12.0,12,1.0,6,0.5
-817,399007,123759,0,1,0,0,0,0,1,0,0,0,1,0,8.0,8,1.0,6,0.75
-818,417191,61278,0,1,0,0,0,1,0,0,0,0,1,0,12.0,12,1.0,8,0.67
-819,343102,130956,0,1,0,1,0,0,0,0,0,0,1,0,6.0,6,1.0,12,2.0
-820,444359,117529,0,1,1,0,0,0,0,1,0,0,0,0,4.0,4,1.0,6,1.5
-821,456975,148816,1,0,0,0,0,1,0,0,0,0,0,1,4.0,4,1.0,8,2.0
-822,281006,127452,0,0,0,1,0,0,1,0,0,0,0,1,2.0,2,1.0,4,2.0
-823,397983,100855,0,1,0,0,1,0,0,1,0,0,0,0,6.0,6,1.0,0,0.0
-824,355294,76855,0,1,0,0,0,0,0,1,1,0,0,0,2.0,2,1.0,2,1.0
-825,422055,194612,1,0,0,1,0,0,0,0,1,0,0,0,,0,0.0,12,5.0
-826,214989,97653,0,0,0,0,0,1,0,0,1,0,1,0,4.0,4,1.0,8,2.0
-827,269975,157064,1,0,0,0,1,1,0,0,0,0,0,0,10.0,10,1.0,4,0.4
-828,494117,152366,1,0,0,0,1,0,0,0,1,0,0,0,6.0,6,1.0,8,1.33
-829,231915,65141,0,0,1,0,1,1,0,0,0,0,0,0,2.0,2,1.0,12,6.0
-830,458923,198250,1,0,0,0,0,1,0,0,1,0,0,0,4.0,4,1.0,6,1.5
-831,389130,141296,0,1,1,0,0,1,0,0,0,0,0,0,12.0,12,1.0,8,0.67
-832,470729,42046,0,0,1,0,1,0,0,0,0,0,0,1,8.0,8,1.0,4,0.5
-833,474279,30181,1,0,0,0,0,0,1,0,0,0,1,0,12.0,12,1.0,4,0.33
-834,406649,98575,0,0,0,1,0,0,0,0,0,1,1,0,4.0,4,1.0,4,1.0
-835,267055,184081,1,0,0,0,0,0,1,0,0,0,1,0,6.0,6,1.0,12,2.0
-836,276545,156447,0,0,0,1,1,0,0,0,0,0,0,1,6.0,6,1.0,6,1.0
-837,482190,36190,0,0,0,1,0,0,1,0,1,0,0,0,6.0,6,1.0,4,0.67
-838,370077,199736,0,1,0,0,1,0,0,1,0,0,0,0,14.0,14,1.0,6,0.43
-839,231280,154005,0,1,0,1,0,0,0,0,1,0,0,0,12.0,12,1.0,2,0.17
-840,495127,71108,0,1,0,0,0,1,0,0,1,0,0,0,4.0,4,1.0,6,1.5
-841,379036,151388,0,1,1,0,0,1,0,0,0,0,0,0,4.0,4,1.0,0,0.0
-842,275802,166094,1,0,0,0,0,1,0,0,0,0,1,0,4.0,4,1.0,6,1.5
-843,382652,65851,0,1,1,0,0,0,0,1,0,0,0,0,6.0,6,1.0,12,2.0
-844,442575,19464,0,0,1,1,0,0,0,0,0,1,0,0,4.0,4,1.0,4,1.0
-845,250858,124543,1,0,0,1,0,0,0,0,0,0,0,1,4.0,4,1.0,4,1.0
-846,337823,103129,0,0,0,0,1,0,0,1,0,0,1,0,10.0,10,1.0,2,0.2
-847,245935,144115,0,0,0,0,0,0,0,1,0,1,0,1,8.0,8,1.0,6,0.75
-848,370596,14192,0,0,0,0,1,1,0,0,1,0,0,0,6.0,6,1.0,8,1.33
-849,399212,20895,0,1,1,0,0,1,0,0,0,0,0,0,6.0,6,1.0,2,0.33
-850,476076,166707,0,0,0,1,0,0,1,0,0,0,0,1,4.0,4,1.0,6,1.5
-851,467415,130374,0,0,0,1,1,0,0,0,1,0,0,0,4.0,4,1.0,6,1.5
-852,367151,159580,1,0,0,0,0,0,1,0,1,0,0,0,12.0,12,1.0,6,0.5
-853,320912,97724,0,0,0,1,0,0,1,0,0,0,0,1,6.0,6,1.0,6,1.0
-854,227404,36661,0,1,0,1,0,0,0,0,0,0,0,1,10.0,10,1.0,6,0.6
-855,216256,178926,0,0,0,0,1,0,0,0,0,1,0,1,,0,0.0,2,5.0
-856,387475,80569,0,0,0,0,0,0,1,0,1,0,1,0,6.0,6,1.0,8,1.33
-857,249253,26385,0,0,1,0,1,0,0,1,0,0,0,0,4.0,4,1.0,12,3.0
-858,375847,176910,0,0,0,0,0,1,1,0,0,1,0,0,8.0,8,1.0,2,0.25
-859,251309,82035,0,0,0,0,0,0,0,1,1,0,0,1,10.0,10,1.0,6,0.6
-860,384041,50345,0,0,0,0,0,1,0,1,1,0,0,0,,0,0.0,4,5.0
-861,477161,106483,0,0,0,0,1,0,1,0,0,1,0,0,2.0,2,1.0,4,2.0
-862,218587,27343,1,0,1,0,0,0,0,0,0,0,0,1,8.0,8,1.0,8,1.0
-863,284722,71235,0,0,1,1,0,0,0,0,0,0,1,0,12.0,12,1.0,8,0.67
-864,223603,138762,1,0,1,0,0,0,1,0,0,0,0,0,2.0,2,1.0,0,0.0
-865,399762,13085,0,0,0,0,1,1,0,0,1,0,0,0,2.0,2,1.0,4,2.0
-866,299484,114094,1,1,0,0,0,0,0,0,0,0,0,1,8.0,8,1.0,10,1.25
-867,407379,70302,0,1,0,0,1,0,0,1,0,0,0,0,6.0,6,1.0,6,1.0
-868,292348,137758,0,0,0,1,0,0,0,1,1,0,0,0,2.0,2,1.0,10,5.0
-869,287417,138395,0,0,0,1,0,0,1,0,1,0,0,0,8.0,8,1.0,6,0.75
-870,200590,66947,0,0,1,0,1,0,0,0,0,0,0,1,,0,0.0,4,5.0
-871,440673,85320,0,1,0,0,0,0,0,1,0,0,1,0,6.0,6,1.0,2,0.33
-872,356709,116973,0,1,0,1,0,0,1,0,0,0,0,0,2.0,2,1.0,6,3.0
-873,386039,186602,1,0,1,1,0,0,0,0,0,0,0,0,4.0,4,1.0,4,1.0
-874,270051,75658,0,0,0,0,1,0,0,1,1,0,0,0,6.0,6,1.0,6,1.0
-875,436580,164864,0,0,0,0,1,1,0,0,0,1,0,0,2.0,2,1.0,4,2.0
-876,202918,113863,1,1,0,0,0,0,0,1,0,0,0,0,,0,0.0,8,5.0
-877,318488,149400,1,0,0,1,0,0,0,1,0,0,0,0,4.0,4,1.0,10,2.5
-878,315256,140817,0,0,0,1,0,0,0,1,1,0,0,0,10.0,10,1.0,10,1.0
-879,489213,18867,0,1,0,1,0,0,0,1,0,0,0,0,2.0,2,1.0,4,2.0
-880,446056,70527,1,0,0,0,0,0,0,0,0,0,1,1,10.0,10,1.0,6,0.6
-881,409633,106178,0,0,0,0,0,0,0,1,0,0,1,1,2.0,2,1.0,6,3.0
-882,451351,49239,0,0,0,0,0,0,0,0,1,1,0,1,4.0,4,1.0,2,0.5
-883,215427,15383,0,0,0,1,0,0,1,0,0,1,0,0,6.0,6,1.0,6,1.0
-884,406498,31959,0,1,0,0,0,0,0,0,0,1,0,1,2.0,2,1.0,6,3.0
-885,365405,108994,0,1,0,1,0,0,1,0,0,0,0,0,4.0,4,1.0,4,1.0
-886,280593,104025,1,0,1,0,0,0,0,0,0,0,0,1,10.0,10,1.0,4,0.4
-887,275781,182917,1,1,0,0,0,0,0,0,0,1,0,0,12.0,12,1.0,4,0.33
-888,278623,51336,1,0,0,0,0,0,0,1,0,0,0,1,8.0,8,1.0,10,1.25
-889,479862,137118,1,0,0,0,0,0,0,0,1,0,0,1,2.0,2,1.0,8,4.0
-890,371046,109092,0,0,0,1,0,0,0,0,1,0,0,1,6.0,6,1.0,6,1.0
-891,226188,133532,0,1,0,0,0,0,1,1,0,0,0,0,2.0,2,1.0,6,3.0
-892,413603,178951,0,0,0,1,1,1,0,0,0,0,0,0,2.0,2,1.0,6,3.0
-893,330993,191499,0,1,0,0,0,0,1,1,0,0,0,0,8.0,8,1.0,10,1.25
-894,487445,144381,0,1,0,1,0,1,0,0,0,0,0,0,,0,0.0,6,5.0
-895,446419,102496,0,1,0,0,0,0,1,1,0,0,0,0,12.0,12,1.0,0,0.0
-896,411668,159943,0,0,0,0,0,0,0,0,1,1,0,1,,0,0.0,6,5.0
-897,274151,49946,0,0,0,0,0,0,1,0,1,0,1,0,8.0,8,1.0,4,0.5
-898,454904,173407,0,0,0,0,0,1,0,1,0,1,0,0,16.0,16,1.0,4,0.25
-899,468542,42955,0,0,0,0,0,1,0,1,0,1,0,0,6.0,6,1.0,10,1.67
-900,317048,61101,0,0,0,0,0,1,0,1,0,0,0,1,,0,0.0,2,5.0
-901,385289,74675,1,0,0,0,0,1,0,0,0,0,1,0,8.0,8,1.0,2,0.25
-902,266676,72409,0,0,1,0,0,0,1,1,0,0,0,0,14.0,14,1.0,2,0.14
-903,254136,100314,0,1,0,0,1,0,0,0,1,0,0,0,10.0,10,1.0,4,0.4
-904,314605,151225,1,0,0,0,1,0,0,1,0,0,0,0,10.0,10,1.0,6,0.6
-905,459648,45195,0,0,0,0,0,1,0,0,1,1,0,0,4.0,4,1.0,2,0.5
-906,295585,86609,0,1,0,0,0,0,1,0,0,0,1,0,4.0,4,1.0,0,0.0
-907,371041,75883,0,0,1,0,0,0,1,0,1,0,0,0,4.0,4,1.0,4,1.0
-908,371542,57193,0,0,0,1,0,0,0,1,0,1,0,0,2.0,2,1.0,6,3.0
-909,419290,32255,0,0,0,0,1,1,0,0,0,0,0,1,2.0,2,1.0,12,6.0
-910,332905,154274,0,1,0,0,0,0,1,1,0,0,0,0,10.0,10,1.0,4,0.4
-911,396616,92842,1,1,0,0,0,0,0,1,0,0,0,0,2.0,2,1.0,4,2.0
-912,225868,72568,0,0,0,1,1,0,0,0,0,0,1,0,8.0,8,1.0,4,0.5
-913,483313,158045,0,0,1,0,0,0,0,1,1,0,0,0,4.0,4,1.0,6,1.5
-914,409500,59021,0,0,0,0,0,0,0,1,1,1,0,0,2.0,2,1.0,10,5.0
-915,347719,136188,1,0,1,0,0,1,0,0,0,0,0,0,8.0,8,1.0,2,0.25
-916,300122,70254,0,0,1,0,1,0,1,0,0,0,0,0,6.0,6,1.0,12,2.0
-917,306186,172369,0,0,0,0,0,0,1,0,0,1,1,0,6.0,6,1.0,4,0.67
-918,317909,48222,0,0,0,0,0,0,0,1,1,1,0,0,8.0,8,1.0,2,0.25
-919,243993,51965,0,0,0,0,0,0,1,0,0,0,1,1,6.0,6,1.0,6,1.0
-920,322095,172458,1,1,0,0,0,0,1,0,0,0,0,0,10.0,10,1.0,6,0.6
-921,305860,60419,0,0,1,1,0,0,0,0,0,0,0,1,10.0,10,1.0,2,0.2
-922,399045,24312,1,0,0,1,0,0,0,0,0,0,0,1,12.0,12,1.0,4,0.33
-923,466188,25075,0,0,0,0,1,1,0,0,0,0,0,1,12.0,12,1.0,10,0.83
-924,328821,44907,1,0,0,0,0,0,0,0,1,0,0,1,6.0,6,1.0,6,1.0
-925,392780,194685,0,1,0,0,0,0,0,0,1,1,0,0,2.0,2,1.0,6,3.0
-926,263701,186881,0,0,0,0,1,0,1,0,0,0,1,0,6.0,6,1.0,6,1.0
-927,485278,149804,0,1,1,0,0,0,0,0,1,0,0,0,,0,0.0,4,5.0
-928,265979,25740,0,0,0,0,1,0,1,0,0,0,0,1,6.0,6,1.0,12,2.0
-929,296060,13802,0,1,1,0,1,0,0,0,0,0,0,0,,0,0.0,4,5.0
-930,314827,183938,0,1,1,0,0,0,0,0,0,1,0,0,4.0,4,1.0,2,0.5
-931,327857,75977,1,0,0,1,0,1,0,0,0,0,0,0,4.0,4,1.0,4,1.0
-932,336696,195946,0,0,1,0,0,0,0,0,1,1,0,0,10.0,10,1.0,12,1.2
-933,381827,61627,0,0,0,0,1,0,0,0,0,1,0,1,,0,0.0,6,5.0
-934,402772,37800,0,0,0,0,1,0,0,0,0,1,1,0,2.0,2,1.0,8,4.0
-935,419875,103666,0,0,0,1,1,0,0,0,0,1,0,0,4.0,4,1.0,12,3.0
-936,370256,147215,0,0,1,0,0,0,0,1,0,1,0,0,8.0,8,1.0,6,0.75
-937,499515,81170,0,1,0,1,0,0,0,0,1,0,0,0,14.0,14,1.0,4,0.29
-938,489669,123869,0,0,1,1,0,0,0,0,1,0,0,0,10.0,10,1.0,8,0.8
-939,318548,157598,0,0,0,0,0,0,0,1,1,1,0,0,10.0,10,1.0,0,0.0
-940,378058,73800,0,0,1,0,0,0,0,1,0,0,0,1,8.0,8,1.0,8,1.0
-941,295093,155387,0,0,0,1,0,0,1,0,0,1,0,0,10.0,10,1.0,4,0.4
-942,255137,77953,0,0,0,1,0,0,0,0,0,1,0,1,2.0,2,1.0,4,2.0
-943,460003,175620,0,0,1,0,0,0,1,1,0,0,0,0,6.0,6,1.0,2,0.33
-944,435210,67087,0,0,0,0,0,0,1,0,1,1,0,0,4.0,4,1.0,6,1.5
-945,408719,21594,0,0,0,0,0,0,0,0,1,0,1,1,10.0,10,1.0,0,0.0
-946,442983,64387,1,0,0,0,1,0,0,0,1,0,0,0,8.0,8,1.0,6,0.75
-947,417183,170553,1,0,1,1,0,0,0,0,0,0,0,0,14.0,14,1.0,0,0.0
-948,245504,60047,0,0,0,0,0,1,0,1,1,0,0,0,8.0,8,1.0,8,1.0
-949,253896,99657,1,1,0,0,0,0,0,1,0,0,0,0,2.0,2,1.0,4,2.0
-950,360681,78417,0,0,0,0,0,1,0,0,1,0,0,1,6.0,6,1.0,6,1.0
-951,217587,140616,0,1,1,0,0,0,0,0,1,0,0,0,6.0,6,1.0,10,1.67
-952,399434,26457,0,1,0,0,0,1,1,0,0,0,0,0,12.0,12,1.0,2,0.17
-953,311115,142051,1,0,0,0,1,0,1,0,0,0,0,0,10.0,10,1.0,2,0.2
-954,468873,130072,0,0,0,0,0,0,0,1,1,0,1,0,4.0,4,1.0,14,3.5
-955,387628,54418,0,0,1,0,0,1,0,0,0,1,0,0,4.0,4,1.0,4,1.0
-956,311418,79285,0,0,1,0,0,1,0,0,0,0,0,1,6.0,6,1.0,10,1.67
-957,282189,153537,1,0,0,0,0,1,0,0,1,0,0,0,6.0,6,1.0,8,1.33
-958,242619,113714,0,0,0,0,0,0,1,0,1,0,0,1,8.0,8,1.0,10,1.25
-959,326593,187624,0,0,0,0,1,0,1,0,0,0,1,0,8.0,8,1.0,8,1.0
-960,219358,105388,1,0,0,0,0,0,1,1,0,0,0,0,6.0,6,1.0,12,2.0
-961,212481,34643,0,0,0,0,0,0,1,0,1,1,0,0,4.0,4,1.0,14,3.5
-962,493117,142931,0,0,0,0,0,1,0,1,0,1,0,0,4.0,4,1.0,4,1.0
-963,256384,79402,0,0,1,0,0,0,0,0,0,1,1,0,4.0,4,1.0,6,1.5
-964,496916,99297,1,0,0,0,0,1,0,0,0,0,1,0,,0,0.0,2,5.0
-965,423393,174792,0,1,0,0,0,0,1,1,0,0,0,0,,0,0.0,14,5.0
-966,473484,177801,1,0,0,0,1,1,0,0,0,0,0,0,,0,0.0,4,5.0
-967,371779,80404,0,0,0,0,1,0,0,0,0,1,1,0,2.0,2,1.0,6,3.0
-968,384400,71324,0,0,1,0,0,0,0,0,1,0,0,1,4.0,4,1.0,2,0.5
-969,217937,143787,1,0,0,0,1,0,0,0,0,1,0,0,2.0,2,1.0,8,4.0
-970,213566,98135,1,0,0,0,0,1,0,0,0,1,0,0,2.0,2,1.0,4,2.0
-971,289192,56182,0,1,0,0,0,0,1,1,0,0,0,0,6.0,6,1.0,0,0.0
-972,403598,73657,0,0,1,0,0,1,0,1,0,0,0,0,2.0,2,1.0,8,4.0
-973,284210,130990,1,0,0,1,0,0,0,1,0,0,0,0,6.0,6,1.0,4,0.67
-974,401410,40083,0,1,0,0,0,0,1,0,0,0,1,0,8.0,8,1.0,12,1.5
-975,337305,22859,0,0,0,1,0,0,0,0,0,1,0,1,6.0,6,1.0,2,0.33
-976,226419,158974,0,0,0,1,0,1,0,0,0,0,0,1,,0,0.0,2,5.0
-977,316248,105993,0,0,0,1,0,0,1,0,1,0,0,0,4.0,4,1.0,2,0.5
-978,319823,180018,0,0,0,0,0,1,0,1,0,1,0,0,8.0,8,1.0,4,0.5
-979,468990,123010,0,1,0,1,0,0,0,0,0,0,0,1,4.0,4,1.0,4,1.0
-980,403627,147220,0,0,0,0,0,0,1,0,1,0,1,0,10.0,10,1.0,4,0.4
-981,317225,57958,0,0,0,0,1,1,0,0,0,0,1,0,8.0,8,1.0,0,0.0
-982,229734,168922,0,0,0,1,0,0,0,1,0,1,0,0,4.0,4,1.0,6,1.5
-983,468070,155375,0,0,0,1,0,0,1,1,0,0,0,0,10.0,10,1.0,6,0.6
-984,252884,174360,0,0,0,1,0,0,0,0,1,0,0,1,2.0,2,1.0,10,5.0
-985,466428,100568,0,0,0,0,0,1,0,0,0,1,0,1,4.0,4,1.0,4,1.0
-986,452565,72172,0,0,0,0,0,1,1,0,0,0,0,1,4.0,4,1.0,14,3.5
-987,220362,170895,0,0,0,1,0,0,1,0,0,0,1,0,4.0,4,1.0,10,2.5
-988,270701,101907,0,0,0,0,0,1,0,1,0,0,1,0,8.0,8,1.0,6,0.75
-989,391764,45376,0,0,1,1,1,0,0,0,0,0,0,0,4.0,4,1.0,6,1.5
-990,305302,145908,0,0,0,0,0,1,0,0,1,1,0,0,10.0,10,1.0,6,0.6
-991,356465,130869,1,0,0,1,0,0,0,0,1,0,0,0,2.0,2,1.0,4,2.0
-992,260584,156699,0,0,0,0,0,1,0,1,0,1,0,0,6.0,6,1.0,10,1.67
-993,264824,101853,0,0,0,1,0,0,1,0,1,0,0,0,6.0,6,1.0,8,1.33
-994,229223,55028,1,0,0,0,1,0,0,0,1,0,0,0,10.0,10,1.0,4,0.4
-995,226950,14602,0,0,0,1,0,0,0,0,0,1,1,0,14.0,14,1.0,6,0.43
-996,280148,36439,0,0,0,0,1,0,0,0,1,0,0,1,2.0,2,1.0,8,4.0
-997,254227,51432,0,0,0,0,0,0,1,0,0,1,0,1,,0,0.0,4,5.0
-998,404381,168755,1,0,0,0,0,0,0,0,1,0,0,1,6.0,6,1.0,4,0.67
-999,265544,17476,0,1,0,1,0,1,0,0,0,0,0,0,8.0,8,1.0,6,0.75
-1000,348762,67260,0,0,0,1,1,0,0,0,0,0,0,1,8.0,8,1.0,4,0.5
-1004,90000,10000,1,1,1,0,0,0,0,0,0,0,0,0,,0,0.0,0,0.0
-1005,500000,10000,0,1,0,0,1,0,0,1,0,0,0,0,2.0,2,1.0,0,0.0
-1006,140000,70000,1,0,0,1,0,0,0,1,0,0,0,0,,0,0.0,6,5.0
-1007,500000,10000,0,1,0,0,1,1,0,0,0,0,0,0,2.0,2,1.0,0,0.0

From 503ed352f678a2f1ae898263374a806c03a62f3e Mon Sep 17 00:00:00 2001
From: Daehyun-Bigbread <bigdarkgold@gmail.com>
Date: Tue, 1 Apr 2025 17:18:56 +0900
Subject: [PATCH 5/5] TRI-263 new user logic add & fix.

---
 app/services/model_trainer/model_training.py  |    3 +-
 catboost_info/catboost_training.json          |  200 ++--
 catboost_info/learn/events.out.tfevents       |  Bin 4798 -> 4798 bytes
 catboost_info/learn_error.tsv                 |  200 ++--
 catboost_info/time_left.tsv                   |  200 ++--
 .../user/preprocessed_user_features.csv       | 1005 +++++++++++++++++
 6 files changed, 1306 insertions(+), 302 deletions(-)
 create mode 100644 storage/input_json/user/preprocessed_user_features.csv

diff --git a/app/services/model_trainer/model_training.py b/app/services/model_trainer/model_training.py
index 1ae84d0..c2df13b 100644
--- a/app/services/model_trainer/model_training.py
+++ b/app/services/model_trainer/model_training.py
@@ -103,8 +103,7 @@ def train_lgb(X, y, n_jobs=-1):
         lgb_model = lgb.LGBMRegressor(
             random_state=42, 
             verbose=-1, 
-            min_split_gain=0,
-            early_stopping_rounds=10
+            min_split_gain=0
         )
         
         grid = GridSearchCV(lgb_model, param_grid, cv=3, scoring='r2', n_jobs=n_jobs)
diff --git a/catboost_info/catboost_training.json b/catboost_info/catboost_training.json
index 737700a..ea59c2d 100644
--- a/catboost_info/catboost_training.json
+++ b/catboost_info/catboost_training.json
@@ -1,104 +1,104 @@
 {
 "meta":{"test_sets":[],"test_metrics":[],"learn_metrics":[{"best_value":"Min","name":"RMSE"}],"launch_mode":"Train","parameters":"","iteration_count":100,"learn_sets":["learn"],"name":"experiment"},
 "iterations":[
-{"learn":[1.648905251],"iteration":0,"passed_time":0.0004666281034,"remaining_time":0.04619618224},
-{"learn":[1.497629345],"iteration":1,"passed_time":0.0009012976609,"remaining_time":0.04416358539},
-{"learn":[1.360899163],"iteration":2,"passed_time":0.001282883532,"remaining_time":0.04147990087},
-{"learn":[1.240618702],"iteration":3,"passed_time":0.001757886691,"remaining_time":0.04218928059},
-{"learn":[1.128387921],"iteration":4,"passed_time":0.002108680691,"remaining_time":0.04006493313},
-{"learn":[1.026662891],"iteration":5,"passed_time":0.002554016986,"remaining_time":0.04001293278},
-{"learn":[0.9300425815],"iteration":6,"passed_time":0.003022603436,"remaining_time":0.04015744565},
-{"learn":[0.8480139671],"iteration":7,"passed_time":0.003448647936,"remaining_time":0.03965945126},
-{"learn":[0.7712831482],"iteration":8,"passed_time":0.003996484913,"remaining_time":0.04040890301},
-{"learn":[0.7025638579],"iteration":9,"passed_time":0.004427112777,"remaining_time":0.03984401499},
-{"learn":[0.6433465066],"iteration":10,"passed_time":0.004812532007,"remaining_time":0.03893775896},
-{"learn":[0.5864868263],"iteration":11,"passed_time":0.005193742875,"remaining_time":0.03808744775},
-{"learn":[0.5366753809],"iteration":12,"passed_time":0.005575495414,"remaining_time":0.03731293085},
-{"learn":[0.493668726],"iteration":13,"passed_time":0.005993123192,"remaining_time":0.03681489961},
-{"learn":[0.452697423],"iteration":14,"passed_time":0.00639737588,"remaining_time":0.03625179666},
-{"learn":[0.4139326703],"iteration":15,"passed_time":0.006807461941,"remaining_time":0.03573917519},
-{"learn":[0.378007788],"iteration":16,"passed_time":0.007221006358,"remaining_time":0.03525550163},
-{"learn":[0.3456282594],"iteration":17,"passed_time":0.007626550722,"remaining_time":0.03474317551},
-{"learn":[0.3156361609],"iteration":18,"passed_time":0.008054845237,"remaining_time":0.03433907706},
-{"learn":[0.2884469664],"iteration":19,"passed_time":0.008431764411,"remaining_time":0.03372705764},
-{"learn":[0.2630439138],"iteration":20,"passed_time":0.008906142565,"remaining_time":0.03350406013},
-{"learn":[0.2448805528],"iteration":21,"passed_time":0.009354270546,"remaining_time":0.03316514103},
-{"learn":[0.2262033151],"iteration":22,"passed_time":0.009802606861,"remaining_time":0.03281742297},
-{"learn":[0.2078844467],"iteration":23,"passed_time":0.01023652641,"remaining_time":0.03241566698},
-{"learn":[0.192981451],"iteration":24,"passed_time":0.01066040423,"remaining_time":0.0319812127},
-{"learn":[0.1791809635],"iteration":25,"passed_time":0.01107519866,"remaining_time":0.03152171926},
-{"learn":[0.1675015104],"iteration":26,"passed_time":0.0114729513,"remaining_time":0.03101946093},
-{"learn":[0.1539406323],"iteration":27,"passed_time":0.01186149555,"remaining_time":0.03050098857},
-{"learn":[0.1427636543],"iteration":28,"passed_time":0.01221791459,"remaining_time":0.02991282538},
-{"learn":[0.134231155],"iteration":29,"passed_time":0.01257412529,"remaining_time":0.02933962568},
-{"learn":[0.1239380012],"iteration":30,"passed_time":0.01297958632,"remaining_time":0.02889004698},
-{"learn":[0.1146735567],"iteration":31,"passed_time":0.01339863078,"remaining_time":0.0284720904},
-{"learn":[0.1068619755],"iteration":32,"passed_time":0.01384713376,"remaining_time":0.02811387763},
-{"learn":[0.1018497484],"iteration":33,"passed_time":0.01427792829,"remaining_time":0.02771597845},
-{"learn":[0.09591207292],"iteration":34,"passed_time":0.01467188925,"remaining_time":0.02724779431},
-{"learn":[0.09001531388],"iteration":35,"passed_time":0.01508005863,"remaining_time":0.02680899311},
-{"learn":[0.08505951254],"iteration":36,"passed_time":0.01547635293,"remaining_time":0.02635162796},
-{"learn":[0.08167650816],"iteration":37,"passed_time":0.0159580228,"remaining_time":0.02603677404},
-{"learn":[0.07757010247],"iteration":38,"passed_time":0.0164692762,"remaining_time":0.02575963713},
-{"learn":[0.07342210315],"iteration":39,"passed_time":0.0170058631,"remaining_time":0.02550879465},
-{"learn":[0.07003440322],"iteration":40,"passed_time":0.0174213242,"remaining_time":0.02506971043},
-{"learn":[0.06754789554],"iteration":41,"passed_time":0.01786091045,"remaining_time":0.02466506682},
-{"learn":[0.0644413345],"iteration":42,"passed_time":0.01829391333,"remaining_time":0.02425007116},
-{"learn":[0.06194568707],"iteration":43,"passed_time":0.01873504127,"remaining_time":0.02384459798},
-{"learn":[0.0605542107],"iteration":44,"passed_time":0.01917583587,"remaining_time":0.02343713273},
-{"learn":[0.05874352321],"iteration":45,"passed_time":0.0196175888,"remaining_time":0.02302934338},
-{"learn":[0.05746787316],"iteration":46,"passed_time":0.02004409164,"remaining_time":0.02260291185},
-{"learn":[0.05640496179],"iteration":47,"passed_time":0.02050413637,"remaining_time":0.0222128144},
-{"learn":[0.05535837545],"iteration":48,"passed_time":0.02095184768,"remaining_time":0.02180702513},
-{"learn":[0.05382646834],"iteration":49,"passed_time":0.02136410042,"remaining_time":0.02136410042},
-{"learn":[0.05296457693],"iteration":50,"passed_time":0.02192385414,"remaining_time":0.02106409516},
-{"learn":[0.05197678655],"iteration":51,"passed_time":0.02228273153,"remaining_time":0.02056867526},
-{"learn":[0.05093132678],"iteration":52,"passed_time":0.02267602581,"remaining_time":0.02010892855},
-{"learn":[0.05048929053],"iteration":53,"passed_time":0.02313869556,"remaining_time":0.01971074066},
-{"learn":[0.04996352734],"iteration":54,"passed_time":0.02358344851,"remaining_time":0.01929554878},
-{"learn":[0.04947975597],"iteration":55,"passed_time":0.02401407638,"remaining_time":0.01886820287},
-{"learn":[0.04888330877],"iteration":56,"passed_time":0.02439524558,"remaining_time":0.01840343088},
-{"learn":[0.04779836697],"iteration":57,"passed_time":0.0249000406,"remaining_time":0.01803106388},
-{"learn":[0.04726341669],"iteration":58,"passed_time":0.02535729364,"remaining_time":0.01762117016},
-{"learn":[0.04685458537],"iteration":59,"passed_time":0.02581129666,"remaining_time":0.01720753111},
-{"learn":[0.04642503034],"iteration":60,"passed_time":0.02625838297,"remaining_time":0.01678814649},
-{"learn":[0.0460476406],"iteration":61,"passed_time":0.02665826063,"remaining_time":0.01633893393},
-{"learn":[0.04547020507],"iteration":62,"passed_time":0.02712859709,"remaining_time":0.01593266813},
-{"learn":[0.04475318154],"iteration":63,"passed_time":0.02759805855,"remaining_time":0.01552390793},
-{"learn":[0.04437426942],"iteration":64,"passed_time":0.02811102029,"remaining_time":0.01513670323},
-{"learn":[0.04409867641],"iteration":65,"passed_time":0.02861189862,"remaining_time":0.01473946293},
-{"learn":[0.04379427055],"iteration":66,"passed_time":0.02909886019,"remaining_time":0.01433227442},
-{"learn":[0.04341818047],"iteration":67,"passed_time":0.02950765458,"remaining_time":0.0138859551},
-{"learn":[0.0425917065],"iteration":68,"passed_time":0.02995999092,"remaining_time":0.01346028578},
-{"learn":[0.0423772057],"iteration":69,"passed_time":0.03036807697,"remaining_time":0.01301489013},
-{"learn":[0.04139066981],"iteration":70,"passed_time":0.03081153825,"remaining_time":0.0125849945},
-{"learn":[0.04122992812],"iteration":71,"passed_time":0.03118512407,"remaining_time":0.01212754825},
-{"learn":[0.04094914647],"iteration":72,"passed_time":0.03161471026,"remaining_time":0.01169311201},
-{"learn":[0.04047213505],"iteration":73,"passed_time":0.03203912975,"remaining_time":0.01125699153},
-{"learn":[0.04032414592],"iteration":74,"passed_time":0.03244179909,"remaining_time":0.01081393303},
-{"learn":[0.04013705762],"iteration":75,"passed_time":0.03287517698,"remaining_time":0.01038163483},
-{"learn":[0.03943460346],"iteration":76,"passed_time":0.03336251355,"remaining_time":0.009965426126},
-{"learn":[0.03899776777],"iteration":77,"passed_time":0.03380451649,"remaining_time":0.009534607215},
-{"learn":[0.03831405038],"iteration":78,"passed_time":0.03431276987,"remaining_time":0.009121116042},
-{"learn":[0.03810356748],"iteration":79,"passed_time":0.03472260593,"remaining_time":0.008680651482},
-{"learn":[0.03741460794],"iteration":80,"passed_time":0.03516690055,"remaining_time":0.008249026055},
-{"learn":[0.03714415302],"iteration":81,"passed_time":0.03560027843,"remaining_time":0.007814695266},
-{"learn":[0.03697811918],"iteration":82,"passed_time":0.03607215657,"remaining_time":0.007388273033},
-{"learn":[0.03673336099],"iteration":83,"passed_time":0.03658857667,"remaining_time":0.0069692527},
-{"learn":[0.03664944536],"iteration":84,"passed_time":0.03694253736,"remaining_time":0.006519271299},
-{"learn":[0.03646584715],"iteration":85,"passed_time":0.03737995694,"remaining_time":0.006085109269},
-{"learn":[0.03620197378],"iteration":86,"passed_time":0.03784041833,"remaining_time":0.005654315383},
-{"learn":[0.03585716755],"iteration":87,"passed_time":0.038241546,"remaining_time":0.005214756273},
-{"learn":[0.03567385169],"iteration":88,"passed_time":0.03864954871,"remaining_time":0.004776910515},
-{"learn":[0.03552018616],"iteration":89,"passed_time":0.03984251498,"remaining_time":0.004426946109},
-{"learn":[0.03519230786],"iteration":90,"passed_time":0.04028760127,"remaining_time":0.003984488038},
-{"learn":[0.03475388768],"iteration":91,"passed_time":0.04077510452,"remaining_time":0.003545661262},
-{"learn":[0.03424643427],"iteration":92,"passed_time":0.04121244076,"remaining_time":0.00310201167},
-{"learn":[0.03409792002],"iteration":93,"passed_time":0.04163498524,"remaining_time":0.002657552249},
-{"learn":[0.03378195114],"iteration":94,"passed_time":0.04206552977,"remaining_time":0.002213975251},
-{"learn":[0.03359561473],"iteration":95,"passed_time":0.04249424095,"remaining_time":0.001770593373},
-{"learn":[0.03316175526],"iteration":96,"passed_time":0.04292616049,"remaining_time":0.001327613211},
-{"learn":[0.03298153562],"iteration":97,"passed_time":0.04332170479,"remaining_time":0.0008841164242},
-{"learn":[0.03278383393],"iteration":98,"passed_time":0.04373120751,"remaining_time":0.0004417293688},
-{"learn":[0.03261317557],"iteration":99,"passed_time":0.04414225191,"remaining_time":0}
+{"learn":[1.650496921],"iteration":0,"passed_time":0.000585375,"remaining_time":0.057952125},
+{"learn":[1.495186773],"iteration":1,"passed_time":0.001094375,"remaining_time":0.053624375},
+{"learn":[1.358413987],"iteration":2,"passed_time":0.0017725,"remaining_time":0.05731083333},
+{"learn":[1.233487253],"iteration":3,"passed_time":0.00236275,"remaining_time":0.056706},
+{"learn":[1.124112687],"iteration":4,"passed_time":0.00285175,"remaining_time":0.05418325},
+{"learn":[1.02381614],"iteration":5,"passed_time":0.003362208333,"remaining_time":0.05267459722},
+{"learn":[0.9351898961],"iteration":6,"passed_time":0.0038975,"remaining_time":0.05178107143},
+{"learn":[0.8496860633],"iteration":7,"passed_time":0.0044785,"remaining_time":0.05150275},
+{"learn":[0.7715666554],"iteration":8,"passed_time":0.004971291667,"remaining_time":0.05026528241},
+{"learn":[0.7070868453],"iteration":9,"passed_time":0.005509708333,"remaining_time":0.049587375},
+{"learn":[0.6413852062],"iteration":10,"passed_time":0.006080166667,"remaining_time":0.04919407576},
+{"learn":[0.5828741401],"iteration":11,"passed_time":0.006761208333,"remaining_time":0.04958219444},
+{"learn":[0.5352337907],"iteration":12,"passed_time":0.007279291667,"remaining_time":0.04871525962},
+{"learn":[0.4876699391],"iteration":13,"passed_time":0.007864625,"remaining_time":0.04831126786},
+{"learn":[0.4445052327],"iteration":14,"passed_time":0.008414291667,"remaining_time":0.04768098611},
+{"learn":[0.4055076559],"iteration":15,"passed_time":0.009047541667,"remaining_time":0.04749959375},
+{"learn":[0.3693713888],"iteration":16,"passed_time":0.009585375,"remaining_time":0.04679918382},
+{"learn":[0.3380597355],"iteration":17,"passed_time":0.01011441667,"remaining_time":0.04607678704},
+{"learn":[0.3078712775],"iteration":18,"passed_time":0.01064295833,"remaining_time":0.04537261184},
+{"learn":[0.2800964107],"iteration":19,"passed_time":0.01206854167,"remaining_time":0.04827416667},
+{"learn":[0.2549881315],"iteration":20,"passed_time":0.012693625,"remaining_time":0.04775220833},
+{"learn":[0.2342813964],"iteration":21,"passed_time":0.0132385,"remaining_time":0.0469365},
+{"learn":[0.2161958694],"iteration":22,"passed_time":0.01381395833,"remaining_time":0.04624673007},
+{"learn":[0.2006357872],"iteration":23,"passed_time":0.01427745833,"remaining_time":0.04521195139},
+{"learn":[0.1852029236],"iteration":24,"passed_time":0.014818,"remaining_time":0.044454},
+{"learn":[0.1703962701],"iteration":25,"passed_time":0.01549825,"remaining_time":0.04411040385},
+{"learn":[0.1578703417],"iteration":26,"passed_time":0.01603504167,"remaining_time":0.04335400154},
+{"learn":[0.1476085942],"iteration":27,"passed_time":0.01638329167,"remaining_time":0.04212846429},
+{"learn":[0.1371151507],"iteration":28,"passed_time":0.01691591667,"remaining_time":0.04141483046},
+{"learn":[0.1284097114],"iteration":29,"passed_time":0.01745375,"remaining_time":0.04072541667},
+{"learn":[0.1216206464],"iteration":30,"passed_time":0.01790954167,"remaining_time":0.03986317339},
+{"learn":[0.1144941719],"iteration":31,"passed_time":0.01850208333,"remaining_time":0.03931692708},
+{"learn":[0.1087080675],"iteration":32,"passed_time":0.01908133333,"remaining_time":0.03874088889},
+{"learn":[0.1033776181],"iteration":33,"passed_time":0.01968458333,"remaining_time":0.03821125},
+{"learn":[0.09800300117],"iteration":34,"passed_time":0.020254625,"remaining_time":0.03761573214},
+{"learn":[0.0937472692],"iteration":35,"passed_time":0.020939875,"remaining_time":0.03722644444},
+{"learn":[0.09010022625],"iteration":36,"passed_time":0.02147908333,"remaining_time":0.03657249324},
+{"learn":[0.0867498541],"iteration":37,"passed_time":0.02197175,"remaining_time":0.03584864474},
+{"learn":[0.08358724771],"iteration":38,"passed_time":0.02317225,"remaining_time":0.03624377564},
+{"learn":[0.08079846693],"iteration":39,"passed_time":0.023688,"remaining_time":0.035532},
+{"learn":[0.07863304944],"iteration":40,"passed_time":0.024101875,"remaining_time":0.03468318598},
+{"learn":[0.07578669984],"iteration":41,"passed_time":0.02462454167,"remaining_time":0.03400531944},
+{"learn":[0.07407045015],"iteration":42,"passed_time":0.02519775,"remaining_time":0.0334016686},
+{"learn":[0.07275321707],"iteration":43,"passed_time":0.025731125,"remaining_time":0.03274870455},
+{"learn":[0.07039885397],"iteration":44,"passed_time":0.02626691667,"remaining_time":0.03210400926},
+{"learn":[0.06940318451],"iteration":45,"passed_time":0.02678016667,"remaining_time":0.03143758696},
+{"learn":[0.06769944378],"iteration":46,"passed_time":0.02729791667,"remaining_time":0.03078275709},
+{"learn":[0.06616467555],"iteration":47,"passed_time":0.02784183333,"remaining_time":0.03016198611},
+{"learn":[0.06460918412],"iteration":48,"passed_time":0.02847808333,"remaining_time":0.02964045408},
+{"learn":[0.06349674561],"iteration":49,"passed_time":0.02898320833,"remaining_time":0.02898320833},
+{"learn":[0.06229578978],"iteration":50,"passed_time":0.02960495833,"remaining_time":0.02844397958},
+{"learn":[0.06111169092],"iteration":51,"passed_time":0.0301135,"remaining_time":0.02779707692},
+{"learn":[0.05980226581],"iteration":52,"passed_time":0.030669625,"remaining_time":0.02719759198},
+{"learn":[0.05873204242],"iteration":53,"passed_time":0.03112779167,"remaining_time":0.02651626698},
+{"learn":[0.05759644413],"iteration":54,"passed_time":0.03160404167,"remaining_time":0.02585785227},
+{"learn":[0.05669917182],"iteration":55,"passed_time":0.03217691667,"remaining_time":0.0252818631},
+{"learn":[0.05581603591],"iteration":56,"passed_time":0.03269308333,"remaining_time":0.02466320322},
+{"learn":[0.05476807591],"iteration":57,"passed_time":0.03330170833,"remaining_time":0.02411503017},
+{"learn":[0.05415835748],"iteration":58,"passed_time":0.033879875,"remaining_time":0.02354364195},
+{"learn":[0.05366246959],"iteration":59,"passed_time":0.03442254167,"remaining_time":0.02294836111},
+{"learn":[0.05299984626],"iteration":60,"passed_time":0.03494520833,"remaining_time":0.02234201844},
+{"learn":[0.05238463406],"iteration":61,"passed_time":0.03558795833,"remaining_time":0.02181197446},
+{"learn":[0.05163428672],"iteration":62,"passed_time":0.03619683333,"remaining_time":0.02125845767},
+{"learn":[0.05093795504],"iteration":63,"passed_time":0.03680295833,"remaining_time":0.02070166406},
+{"learn":[0.05033265541],"iteration":64,"passed_time":0.03738604167,"remaining_time":0.02013094551},
+{"learn":[0.0495926567],"iteration":65,"passed_time":0.03796895833,"remaining_time":0.01955976641},
+{"learn":[0.04904267037],"iteration":66,"passed_time":0.038538125,"remaining_time":0.01898146455},
+{"learn":[0.04853541042],"iteration":67,"passed_time":0.039066,"remaining_time":0.018384},
+{"learn":[0.04836143154],"iteration":68,"passed_time":0.039558875,"remaining_time":0.0177728279},
+{"learn":[0.04783057962],"iteration":69,"passed_time":0.04013070833,"remaining_time":0.017198875},
+{"learn":[0.04764459246],"iteration":70,"passed_time":0.04063929167,"remaining_time":0.0165991473},
+{"learn":[0.04730315287],"iteration":71,"passed_time":0.04114604167,"remaining_time":0.01600123843},
+{"learn":[0.04693227517],"iteration":72,"passed_time":0.04172491667,"remaining_time":0.01543250342},
+{"learn":[0.04634447701],"iteration":73,"passed_time":0.04236679167,"remaining_time":0.0148856295},
+{"learn":[0.04565981491],"iteration":74,"passed_time":0.04297770833,"remaining_time":0.01432590278},
+{"learn":[0.04528190729],"iteration":75,"passed_time":0.04346216667,"remaining_time":0.01372489474},
+{"learn":[0.04486598408],"iteration":76,"passed_time":0.04404758333,"remaining_time":0.01315707035},
+{"learn":[0.04442669194],"iteration":77,"passed_time":0.04460308333,"remaining_time":0.01258035684},
+{"learn":[0.04395922486],"iteration":78,"passed_time":0.045198875,"remaining_time":0.01201489082},
+{"learn":[0.04369273201],"iteration":79,"passed_time":0.045768,"remaining_time":0.011442},
+{"learn":[0.04348321459],"iteration":80,"passed_time":0.04633795833,"remaining_time":0.01086939763},
+{"learn":[0.04321093871],"iteration":81,"passed_time":0.04691329167,"remaining_time":0.01029803963},
+{"learn":[0.04295930351],"iteration":82,"passed_time":0.04742016667,"remaining_time":0.009712564257},
+{"learn":[0.04256577847],"iteration":83,"passed_time":0.04794904167,"remaining_time":0.009133150794},
+{"learn":[0.04231569496],"iteration":84,"passed_time":0.04841616667,"remaining_time":0.008544029412},
+{"learn":[0.04202583115],"iteration":85,"passed_time":0.04898829167,"remaining_time":0.007974838178},
+{"learn":[0.04139549667],"iteration":86,"passed_time":0.04944875,"remaining_time":0.007388893678},
+{"learn":[0.04134324962],"iteration":87,"passed_time":0.049964625,"remaining_time":0.006813357955},
+{"learn":[0.04083942929],"iteration":88,"passed_time":0.050576375,"remaining_time":0.00625101264},
+{"learn":[0.04077225011],"iteration":89,"passed_time":0.05111304167,"remaining_time":0.005679226852},
+{"learn":[0.04040815038],"iteration":90,"passed_time":0.051729,"remaining_time":0.005116054945},
+{"learn":[0.04003502673],"iteration":91,"passed_time":0.05231016667,"remaining_time":0.004548710145},
+{"learn":[0.03974815226],"iteration":92,"passed_time":0.05283333333,"remaining_time":0.003976702509},
+{"learn":[0.03955683456],"iteration":93,"passed_time":0.05334316667,"remaining_time":0.003404882979},
+{"learn":[0.0393602484],"iteration":94,"passed_time":0.05451804167,"remaining_time":0.002869370614},
+{"learn":[0.03901075876],"iteration":95,"passed_time":0.05502095833,"remaining_time":0.002292539931},
+{"learn":[0.038886721],"iteration":96,"passed_time":0.05560320833,"remaining_time":0.001719686856},
+{"learn":[0.03857550898],"iteration":97,"passed_time":0.05610129167,"remaining_time":0.00114492432},
+{"learn":[0.03832786438],"iteration":98,"passed_time":0.05657883333,"remaining_time":0.000571503367},
+{"learn":[0.03811578664],"iteration":99,"passed_time":0.057209375,"remaining_time":0}
 ]}
\ No newline at end of file
diff --git a/catboost_info/learn/events.out.tfevents b/catboost_info/learn/events.out.tfevents
index ad4498644ed890a60628257aa95aceea0902d234..9ee12562c51fbb686dafdd8f917aba87eac6156a 100644
GIT binary patch
literal 4798
zcmZ|Sc~F#f9LI6waOF^LP*hIAmQ#>h7k0&EMFmj|Szr|qg+hlABQT+akV!|72~WZ!
zg;GIAj4%^nh8(4V#k`6#a9~C`A`M7DGn>ipHlIJf&-2&w$7|m6eXig0JhobIw(f^!
zKKfc(tri0>x@E5B2BrqO>mp<1mb=65h6ew)n{S-vWlvf>axVIRZ88n~ij)@em(Krf
z%zJI{g^%jU``<hNkoP*^2m7kX8+3j@hxfYRNBYXhKRDdx!h1dN=|)B5+i#BB@_st_
z%JfX~A9%}scrOAkDpr!Ouav&a`x)R<i{<1?(thmWy*~JA9Y69(-`)P0_XgnacrPHY
zzVc@g?+w9A0xZdAg%6eU-Uz&<o<8{u_0bsK&jkOdek$b6{pOvQT*doY;Jpv`lkfMt
z8OwWP@Rv_?k)N5{FXO!l_)yzs@{Y$<V&0pAceSb`zoyKvmG@@gqfS?m|80AYA@9w>
zcchn*x2dX*<GlrV^?^e2lHApD-dlptd6G%~cG-x)^KJz`J4Hplv0%KC?^}b<&5k7B
zD$<tmem3|A)e`c@vVZB~{T%R#yFJPK28<l%y$$%buN=sW+P*x@`?=s7U98C`tMUhU
zKM(v+nh|+_=O9Pk&j<e?Ux&Ob!cbryTkxS4Q<68&OGI$PCcbY6{`U`u$k*P^5Lh4V
z!N-2lLq1`VnTqc_fUj=4OaAA{=UKdW1mDqki~RhpsRGx_34HlKE#yDDVt<hDF94sr
z_X>H%%C80HaRxtd?h^T@wt>-n-v#^+UKhw;jaIGZ{X*~;@~X*Sua$1*{afHw2B*n?
z{<8it@5SJa{VU1OIHt7Vy(@TW!x8es!6DtecLRU+b%kc0*^TkMcLyKtage<EXq~|N
z;{kq|Qz`jl&w2#Trzd#rntkMperk5&=kWp`x2J^s=&mj!-Y)`wXYD8CO>BDR^WGc$
zA%jBlW62Uv-ur;>6@Ns&r66E2?-zrYMeia%{J2@cdtdN@V>`)*TssuSdq40x<?oUI
zb+73=y!Qt$9m*i@vqhB2`vCB%l63MVnQ6|v4+NiZGL`)Oib`$X2Z7IDpG4mMK(;RL
zgTXJnuOdJ0UL3;vCEz_e6Udt$jgR2{Qt%tCl;jn|Wp2EefR~rYk`K17dB*z?@OcF>
z<ol+d6j=X5!LL0VO@8}CPz~Rgg0B)sksn<?-oktQU%23~ihSYI(GK1(gZ|{B734)n
z2E%zD2HvzYoc!+N*=pV|2Y<#&<F|^<1>RqB@UI8Ms9$=0s+RAEgMZ<o@ijMk&3LZ>
zKh`Rv{+Z$J0^b8Gz#A`>l9%dqEa&?X;2ZBs$nQ~}^5y+X@a=j_$amPe-QxW!@WpvS
z<g<MX+IYVj{FWO5<TEnw30$v8@NJWR<R>alb@Kfv@JD9(l3%*&ej@MJfOnbjA)k2S
z$x+@%gMWXn##=Vj?%@4e@Z+x*QGfrM$U@$)13&w@m*#l8zZF>DV!-cIdXjImDOtkz
z*Mkp=@gN`JAQkX$gI74Ylkc-}-_Q4B!N-(}$#1wEmcsiD;P-9N_-~>H1-@tEz=tXp
zQvXm%j==wy61-YV<INneeag=h4_>#(nfkih92<C_0RF~HC-S2e{eiqs1pk-Xk^DuO
zdK>Rm;FZ%gen7Q9hxZ%7`v*EuzfRZhD(^ReZ)vw9pS2?=jQ2_4>lGUB(%dBCeKPp?
jX|~k=!>b{I_bK2nzM4mVP(GuH_nW~-+G%{?aBtkdsAdyC

literal 4798
zcmZ|SSxi({9LI4K1VJjpqBF`O+F?_m<A7`mqlFO=MT8L$1X=2eWmLdgl|+kBWfch4
zg~5W23n*LCsHj2I*d}5n1w|A}t+W_Yp+Z@tQrndOG{1-c+^6&K$v5ZW-2Xk-N^A1z
z=rob)|GV7NIo`2ip{aqfflla}F!?NnQLD`BzvUt~&-vDqSj*w{rI7!%U<&xq`bP4}
z2e&`s{Z#OSi>t|>imOcJy*Buh&1K{l)K{G2y$*OC<#F<|RHH`TPXk}KJ%fDVlkOM1
z*99*ZCX-K;`Ay}$9{AnD4dg}ZU-<EUI{217a`G32mr8iA58ms!3;F$5y<YO(0DN@4
zn0!y|4?4V`0bWZyn|x;DNkiTnf-gwaCtn~M7V`cB@V0l}`AnX-)K7ld&ik3*hrjD3
zAN<?6Iq!|Yzw2)$|K2%o3hxEry|bIhd$hGw@!lA`WTcjSZL4((?@ho9&XkkicDwE-
z?@hsn))bT9s~k7xy&3qrD_P_R3y0cyKMVX=(|+<+?weeAKO6kh@~vv09_`J0A^1z(
z3i8Gc(sbU7!1tHR$%kEV%;NnV@a_lP$se@ec8B+K!5_(7L|$+#!;AOk;8hit<PQzC
zJ?6ay_;V5y@-NzlOn7ezz9&VW{PURR0Nz`HU(!0^J^8*I_m3{({XFnv9ew2Q2Ibc9
z-Wq)BvuEUm>kqi{em?lqCtApV>GY>N?-zj2`}7`pab;!<?>_`TwBZ{0=G$X-ycdJ7
zaj7PMq~l^O?`^<que?A$F0E~V_qO0oyUNHvujsYny&ZT(><RLMgzDYAw+AmxE+8-7
zwI-hT4&d{`v&lCMpSs5Th2VdDn?XLjI7-3$Md0r?q>~@a(cZy(NAPue`^a1Dh|w_L
zoWL8Zc9D<1d^3XYJA+pyCXsLKpW4j(kHGg#-%8#vX`hn!67ZijCXiR<m}|IiECz4$
z`9|^wjJq_v?^5syZz9Q?>P0#7`*8tZ`m2I`c(ju??_I&iszS);%K9|S+a=&T9|n`Z
zakR*Y@4JD|KfjuM-0Myi@7=)%R;?mmv@>lZ?>)eGc?OU#Rr)*g-V?mX_rByMvfZV;
z_W~dI)e7<jwXU0azZCq*J{kF>*SQ+L7t6pq+WU|nnCRAU|M3Q&Qo4-%W_eO1zaJm)
zqds2bpAFn7<h=~M<yv?0WlBjX@0Wvr(6oenQ})0v-sAs5%h#^t_m0}0=KaUeKO65t
z{+;#xeBS$l_ibNHe#?V<8qSX&_-|63$zNXHb(`<Y!Cx<SBJW*%w1W35!TTO_ARifI
zuE%?S@K=AfC$DgmTJSyqd{BcO`L~`a8s=Ldc#Hcs<V)T!lk@#m;QJS<eRuxDC%g{=
z-?T+c{ab~)*1TU0-Z^VN`Qk!>6Ytl6x4t=#eAVG>Q{D%ImkwBv?>H4v#{0G4(@oTV
zwE5*<ybl3yw0sWrn^V6K@qQinePR*$kt?qpcpnNrcCV29+_TClybl9^J#9Am<kof#
z`%!?uVX5{GkIhE-{(A6kF|(+j&~Z+~JP8M%8DK`fC+zMkz8?X8^d}SYeq)^%c^?VB
z(Ms)uAB5HLegk+_x-s>$O1=N!eH8ddHUje6ha?*A+tJ`fQ8UR0*Qb~B{f*!!<c8#L
zRp=)0J_fvR<qYyWe_yws_p#vL#2Sz<toHww_nW}ypV3$Mmu`K?`#A7t+w{nX#s@y-
neLVQ{Lbd;U&e|~ECxDL#(4~HOZ_Y696TxpTnMOY1mVebhd2;S5

diff --git a/catboost_info/learn_error.tsv b/catboost_info/learn_error.tsv
index 70f5c33..30b8d55 100644
--- a/catboost_info/learn_error.tsv
+++ b/catboost_info/learn_error.tsv
@@ -1,101 +1,101 @@
 iter	RMSE
-0	1.648905251
-1	1.497629345
-2	1.360899163
-3	1.240618702
-4	1.128387921
-5	1.026662891
-6	0.9300425815
-7	0.8480139671
-8	0.7712831482
-9	0.7025638579
-10	0.6433465066
-11	0.5864868263
-12	0.5366753809
-13	0.493668726
-14	0.452697423
-15	0.4139326703
-16	0.378007788
-17	0.3456282594
-18	0.3156361609
-19	0.2884469664
-20	0.2630439138
-21	0.2448805528
-22	0.2262033151
-23	0.2078844467
-24	0.192981451
-25	0.1791809635
-26	0.1675015104
-27	0.1539406323
-28	0.1427636543
-29	0.134231155
-30	0.1239380012
-31	0.1146735567
-32	0.1068619755
-33	0.1018497484
-34	0.09591207292
-35	0.09001531388
-36	0.08505951254
-37	0.08167650816
-38	0.07757010247
-39	0.07342210315
-40	0.07003440322
-41	0.06754789554
-42	0.0644413345
-43	0.06194568707
-44	0.0605542107
-45	0.05874352321
-46	0.05746787316
-47	0.05640496179
-48	0.05535837545
-49	0.05382646834
-50	0.05296457693
-51	0.05197678655
-52	0.05093132678
-53	0.05048929053
-54	0.04996352734
-55	0.04947975597
-56	0.04888330877
-57	0.04779836697
-58	0.04726341669
-59	0.04685458537
-60	0.04642503034
-61	0.0460476406
-62	0.04547020507
-63	0.04475318154
-64	0.04437426942
-65	0.04409867641
-66	0.04379427055
-67	0.04341818047
-68	0.0425917065
-69	0.0423772057
-70	0.04139066981
-71	0.04122992812
-72	0.04094914647
-73	0.04047213505
-74	0.04032414592
-75	0.04013705762
-76	0.03943460346
-77	0.03899776777
-78	0.03831405038
-79	0.03810356748
-80	0.03741460794
-81	0.03714415302
-82	0.03697811918
-83	0.03673336099
-84	0.03664944536
-85	0.03646584715
-86	0.03620197378
-87	0.03585716755
-88	0.03567385169
-89	0.03552018616
-90	0.03519230786
-91	0.03475388768
-92	0.03424643427
-93	0.03409792002
-94	0.03378195114
-95	0.03359561473
-96	0.03316175526
-97	0.03298153562
-98	0.03278383393
-99	0.03261317557
+0	1.650496921
+1	1.495186773
+2	1.358413987
+3	1.233487253
+4	1.124112687
+5	1.02381614
+6	0.9351898961
+7	0.8496860633
+8	0.7715666554
+9	0.7070868453
+10	0.6413852062
+11	0.5828741401
+12	0.5352337907
+13	0.4876699391
+14	0.4445052327
+15	0.4055076559
+16	0.3693713888
+17	0.3380597355
+18	0.3078712775
+19	0.2800964107
+20	0.2549881315
+21	0.2342813964
+22	0.2161958694
+23	0.2006357872
+24	0.1852029236
+25	0.1703962701
+26	0.1578703417
+27	0.1476085942
+28	0.1371151507
+29	0.1284097114
+30	0.1216206464
+31	0.1144941719
+32	0.1087080675
+33	0.1033776181
+34	0.09800300117
+35	0.0937472692
+36	0.09010022625
+37	0.0867498541
+38	0.08358724771
+39	0.08079846693
+40	0.07863304944
+41	0.07578669984
+42	0.07407045015
+43	0.07275321707
+44	0.07039885397
+45	0.06940318451
+46	0.06769944378
+47	0.06616467555
+48	0.06460918412
+49	0.06349674561
+50	0.06229578978
+51	0.06111169092
+52	0.05980226581
+53	0.05873204242
+54	0.05759644413
+55	0.05669917182
+56	0.05581603591
+57	0.05476807591
+58	0.05415835748
+59	0.05366246959
+60	0.05299984626
+61	0.05238463406
+62	0.05163428672
+63	0.05093795504
+64	0.05033265541
+65	0.0495926567
+66	0.04904267037
+67	0.04853541042
+68	0.04836143154
+69	0.04783057962
+70	0.04764459246
+71	0.04730315287
+72	0.04693227517
+73	0.04634447701
+74	0.04565981491
+75	0.04528190729
+76	0.04486598408
+77	0.04442669194
+78	0.04395922486
+79	0.04369273201
+80	0.04348321459
+81	0.04321093871
+82	0.04295930351
+83	0.04256577847
+84	0.04231569496
+85	0.04202583115
+86	0.04139549667
+87	0.04134324962
+88	0.04083942929
+89	0.04077225011
+90	0.04040815038
+91	0.04003502673
+92	0.03974815226
+93	0.03955683456
+94	0.0393602484
+95	0.03901075876
+96	0.038886721
+97	0.03857550898
+98	0.03832786438
+99	0.03811578664
diff --git a/catboost_info/time_left.tsv b/catboost_info/time_left.tsv
index c5babe2..fe804e3 100644
--- a/catboost_info/time_left.tsv
+++ b/catboost_info/time_left.tsv
@@ -1,101 +1,101 @@
 iter	Passed	Remaining
-0	0	46
-1	0	44
-2	1	41
-3	1	42
-4	2	40
-5	2	40
-6	3	40
-7	3	39
-8	3	40
-9	4	39
-10	4	38
-11	5	38
-12	5	37
-13	5	36
-14	6	36
-15	6	35
-16	7	35
-17	7	34
-18	8	34
-19	8	33
-20	8	33
-21	9	33
-22	9	32
-23	10	32
-24	10	31
-25	11	31
-26	11	31
-27	11	30
-28	12	29
-29	12	29
-30	12	28
-31	13	28
-32	13	28
-33	14	27
-34	14	27
-35	15	26
-36	15	26
-37	15	26
-38	16	25
-39	17	25
-40	17	25
-41	17	24
-42	18	24
-43	18	23
-44	19	23
-45	19	23
-46	20	22
-47	20	22
-48	20	21
-49	21	21
-50	21	21
-51	22	20
-52	22	20
-53	23	19
-54	23	19
-55	24	18
-56	24	18
-57	24	18
-58	25	17
-59	25	17
-60	26	16
-61	26	16
-62	27	15
-63	27	15
-64	28	15
-65	28	14
-66	29	14
-67	29	13
-68	29	13
-69	30	13
-70	30	12
-71	31	12
-72	31	11
-73	32	11
-74	32	10
-75	32	10
-76	33	9
-77	33	9
-78	34	9
-79	34	8
-80	35	8
-81	35	7
-82	36	7
-83	36	6
-84	36	6
-85	37	6
-86	37	5
-87	38	5
-88	38	4
-89	39	4
-90	40	3
-91	40	3
-92	41	3
-93	41	2
-94	42	2
-95	42	1
-96	42	1
-97	43	0
-98	43	0
-99	44	0
+0	0	57
+1	1	53
+2	1	57
+3	2	56
+4	2	54
+5	3	52
+6	3	51
+7	4	51
+8	4	50
+9	5	49
+10	6	49
+11	6	49
+12	7	48
+13	7	48
+14	8	47
+15	9	47
+16	9	46
+17	10	46
+18	10	45
+19	12	48
+20	12	47
+21	13	46
+22	13	46
+23	14	45
+24	14	44
+25	15	44
+26	16	43
+27	16	42
+28	16	41
+29	17	40
+30	17	39
+31	18	39
+32	19	38
+33	19	38
+34	20	37
+35	20	37
+36	21	36
+37	21	35
+38	23	36
+39	23	35
+40	24	34
+41	24	34
+42	25	33
+43	25	32
+44	26	32
+45	26	31
+46	27	30
+47	27	30
+48	28	29
+49	28	28
+50	29	28
+51	30	27
+52	30	27
+53	31	26
+54	31	25
+55	32	25
+56	32	24
+57	33	24
+58	33	23
+59	34	22
+60	34	22
+61	35	21
+62	36	21
+63	36	20
+64	37	20
+65	37	19
+66	38	18
+67	39	18
+68	39	17
+69	40	17
+70	40	16
+71	41	16
+72	41	15
+73	42	14
+74	42	14
+75	43	13
+76	44	13
+77	44	12
+78	45	12
+79	45	11
+80	46	10
+81	46	10
+82	47	9
+83	47	9
+84	48	8
+85	48	7
+86	49	7
+87	49	6
+88	50	6
+89	51	5
+90	51	5
+91	52	4
+92	52	3
+93	53	3
+94	54	2
+95	55	2
+96	55	1
+97	56	1
+98	56	0
+99	57	0
diff --git a/storage/input_json/user/preprocessed_user_features.csv b/storage/input_json/user/preprocessed_user_features.csv
new file mode 100644
index 0000000..ec4be24
--- /dev/null
+++ b/storage/input_json/user/preprocessed_user_features.csv
@@ -0,0 +1,1005 @@
+user_id,max_price,min_price,category_1,category_2,category_3,category_4,category_5,category_6,category_7,category_8,category_9,category_10,category_11,category_12,total_reservations,completed_reservations,reservation_completion_rate,total_likes,like_to_reservation_ratio
+1,286141,183646,0,1,1,0,0,0,0,0,0,0,1,0,16.0,16,1.0,12,0.75
+2,444065,170838,0,0,0,0,0,1,1,0,0,1,0,0,8.0,8,1.0,12,1.5
+3,395779,79971,0,0,1,1,0,0,1,0,0,0,0,0,4.0,4,1.0,8,2.0
+4,200419,82759,1,0,0,0,0,0,1,0,0,0,1,0,16.0,16,1.0,12,0.75
+5,341399,147700,0,0,1,0,0,1,0,0,1,0,0,0,4.0,4,1.0,8,2.0
+6,304138,159625,0,1,1,0,1,0,0,0,0,0,0,0,20.0,20,1.0,0,0.0
+7,338378,151460,1,0,0,0,0,1,0,0,0,0,0,1,4.0,4,1.0,24,6.0
+8,376584,66889,0,0,0,0,0,0,1,1,0,1,0,0,8.0,8,1.0,4,0.5
+9,393014,91158,0,0,0,1,0,0,0,0,1,0,1,0,4.0,4,1.0,8,2.0
+10,267487,170163,0,0,1,1,0,0,0,1,0,0,0,0,,0,0.0,16,5.0
+11,470524,29163,1,0,0,0,1,0,0,0,0,0,1,0,12.0,12,1.0,8,0.67
+12,446869,142652,0,1,0,0,0,0,0,1,0,0,1,0,12.0,12,1.0,8,0.67
+13,351840,185816,0,1,1,0,0,0,0,0,0,0,0,1,16.0,16,1.0,12,0.75
+14,332509,38329,0,0,0,0,0,0,1,1,1,0,0,0,4.0,4,1.0,12,3.0
+15,266941,59156,0,1,0,0,0,0,1,0,0,0,0,1,24.0,24,1.0,20,0.83
+16,337197,194701,1,1,0,0,0,0,0,0,1,0,0,0,4.0,4,1.0,16,4.0
+17,481858,85248,0,0,1,1,0,0,0,0,0,1,0,0,4.0,4,1.0,12,3.0
+18,282926,36257,1,0,0,0,1,0,0,0,0,0,1,0,4.0,4,1.0,20,5.0
+19,327603,197901,1,0,0,0,0,0,0,0,1,0,0,1,16.0,16,1.0,8,0.5
+20,292092,166283,0,1,0,0,0,0,0,1,0,1,0,0,20.0,20,1.0,16,0.8
+21,353557,28934,0,0,1,0,0,0,0,1,0,0,0,1,4.0,4,1.0,4,1.0
+22,319083,167451,0,1,1,0,0,0,0,0,0,1,0,0,12.0,12,1.0,16,1.33
+23,295629,72379,0,0,1,0,0,0,0,1,0,0,0,1,4.0,4,1.0,16,4.0
+24,279131,95305,0,0,0,1,0,0,0,0,0,1,0,1,8.0,8,1.0,32,4.0
+25,233947,106565,0,0,1,0,0,1,0,0,0,0,0,1,12.0,12,1.0,8,0.67
+26,288981,180902,1,0,0,0,0,1,0,0,0,1,0,0,4.0,4,1.0,12,3.0
+27,254815,187129,1,1,0,0,0,0,0,0,1,0,0,0,4.0,4,1.0,16,4.0
+28,403490,169312,0,0,0,1,0,0,1,0,0,0,0,1,20.0,20,1.0,52,2.6
+29,403053,151914,0,0,0,0,0,0,1,0,1,0,0,1,8.0,8,1.0,12,1.5
+30,227881,133174,0,0,0,1,0,1,0,0,0,0,1,0,12.0,12,1.0,8,0.67
+31,207906,88090,0,0,1,0,0,0,0,0,1,1,0,0,4.0,4,1.0,20,5.0
+32,270292,97183,0,1,1,0,0,0,0,0,0,1,0,0,8.0,8,1.0,12,1.5
+33,403876,89280,0,0,0,0,0,0,0,1,1,0,1,0,12.0,12,1.0,8,0.67
+34,312422,61746,0,0,1,1,0,0,0,0,0,1,0,0,12.0,12,1.0,16,1.33
+35,369064,109942,0,0,0,0,0,1,0,0,0,1,1,0,12.0,12,1.0,16,1.33
+36,493736,110323,0,1,1,0,0,0,1,0,0,0,0,0,4.0,4,1.0,4,1.0
+37,469280,35055,1,0,0,1,0,0,0,0,0,0,0,1,4.0,4,1.0,8,2.0
+38,484502,61187,1,0,0,0,0,0,0,0,1,1,0,0,8.0,8,1.0,16,2.0
+39,311737,164628,0,0,0,1,0,0,0,0,0,1,1,0,4.0,4,1.0,24,6.0
+40,238578,199747,0,0,0,1,0,0,1,0,1,0,0,0,16.0,16,1.0,0,0.0
+41,492507,124228,0,0,0,1,0,1,1,0,0,0,0,0,28.0,28,1.0,8,0.29
+42,274639,87199,0,1,0,1,0,0,0,0,0,0,0,1,12.0,12,1.0,16,1.33
+43,380147,35046,0,0,0,1,0,0,1,0,0,0,1,0,8.0,8,1.0,16,2.0
+44,422839,141179,0,0,0,0,0,0,0,1,1,0,1,0,8.0,8,1.0,8,1.0
+45,473654,131248,0,0,0,1,0,1,1,0,0,0,0,0,8.0,8,1.0,16,2.0
+46,410500,54614,0,0,0,1,0,1,0,0,1,0,0,0,,0,0.0,16,5.0
+47,406337,96377,0,0,1,0,0,0,0,0,0,0,1,1,4.0,4,1.0,4,1.0
+48,489105,173326,0,0,1,1,0,0,0,0,0,0,0,1,20.0,20,1.0,16,0.8
+49,459629,48247,0,0,1,0,1,0,0,1,0,0,0,0,20.0,20,1.0,8,0.4
+50,364108,174482,0,0,1,1,0,0,0,1,0,0,0,0,8.0,8,1.0,12,1.5
+51,259108,89491,0,1,1,0,0,0,1,0,0,0,0,0,4.0,4,1.0,8,2.0
+52,200179,105429,0,1,0,0,1,0,0,1,0,0,0,0,8.0,8,1.0,4,0.5
+53,236409,108492,0,0,1,0,0,0,0,0,1,1,0,0,8.0,8,1.0,28,3.5
+54,422377,81914,1,0,0,0,0,0,1,0,1,0,0,0,12.0,12,1.0,20,1.67
+55,388247,16187,0,0,1,0,0,0,0,0,0,1,0,1,4.0,4,1.0,16,4.0
+56,309257,67266,0,0,1,0,1,0,0,0,0,0,1,0,4.0,4,1.0,20,5.0
+57,203384,55275,1,0,0,0,0,0,1,1,0,0,0,0,20.0,20,1.0,12,0.6
+58,329780,83254,0,0,1,0,0,0,1,0,0,0,0,1,,0,0.0,16,5.0
+59,401574,129489,0,0,1,0,0,1,0,1,0,0,0,0,4.0,4,1.0,20,5.0
+60,271891,43058,0,0,0,0,0,1,0,1,0,0,0,1,24.0,24,1.0,32,1.33
+61,256791,187570,1,0,1,0,0,0,0,1,0,0,0,0,20.0,20,1.0,20,1.0
+62,419351,21516,0,0,0,0,0,0,0,0,1,1,1,0,16.0,16,1.0,8,0.5
+63,235396,92464,0,0,1,0,0,0,1,0,0,0,1,0,16.0,16,1.0,16,1.0
+64,246214,150863,0,0,0,1,0,0,0,1,1,0,0,0,12.0,12,1.0,20,1.67
+65,485892,155450,0,0,0,1,0,1,0,0,0,0,1,0,4.0,4,1.0,4,1.0
+66,234185,79818,0,0,0,1,0,0,1,0,0,0,1,0,20.0,20,1.0,16,0.8
+67,472937,181198,0,0,0,0,0,0,1,1,0,1,0,0,12.0,12,1.0,8,0.67
+68,401695,121572,0,1,1,0,0,0,0,1,0,0,0,0,4.0,4,1.0,8,2.0
+69,492640,53616,0,1,0,0,0,0,1,1,0,0,0,0,12.0,12,1.0,4,0.33
+70,375454,119576,0,0,1,0,0,0,0,0,0,1,1,0,20.0,20,1.0,8,0.4
+71,354216,13768,0,1,0,0,0,0,0,1,0,1,0,0,4.0,4,1.0,4,1.0
+72,424256,97033,0,1,0,0,0,0,0,1,1,0,0,0,4.0,4,1.0,20,5.0
+73,345640,58444,0,0,1,0,0,0,1,0,0,0,1,0,20.0,20,1.0,4,0.2
+74,248236,120737,1,0,0,0,0,1,1,0,0,0,0,0,,0,0.0,24,5.0
+75,267707,123521,0,1,0,0,1,0,0,0,1,0,0,0,8.0,8,1.0,4,0.5
+76,470071,172019,0,0,0,0,0,1,1,1,0,0,0,0,4.0,4,1.0,4,1.0
+77,399068,90198,0,0,0,0,0,1,1,0,1,0,0,0,16.0,16,1.0,8,0.5
+78,308313,65073,0,0,1,0,0,0,0,0,1,0,0,1,8.0,8,1.0,12,1.5
+79,234246,149171,0,0,0,0,1,1,0,0,0,0,1,0,8.0,8,1.0,20,2.5
+80,267787,79665,1,1,0,0,0,0,1,0,0,0,0,0,8.0,8,1.0,8,1.0
+81,393826,22004,0,0,1,1,0,0,0,0,0,1,0,0,8.0,8,1.0,12,1.5
+82,229961,94945,0,0,0,0,0,0,0,1,1,1,0,0,16.0,16,1.0,8,0.5
+83,444105,167915,0,1,0,0,0,0,0,1,1,0,0,0,16.0,16,1.0,8,0.5
+84,382730,132551,0,0,0,1,1,0,0,1,0,0,0,0,8.0,8,1.0,12,1.5
+85,205497,46979,1,1,0,1,0,0,0,0,0,0,0,0,,0,0.0,12,5.0
+86,398628,185050,0,1,0,0,1,0,0,1,0,0,0,0,16.0,16,1.0,8,0.5
+87,378633,135979,0,1,0,1,1,0,0,0,0,0,0,0,8.0,8,1.0,4,0.5
+88,470318,148192,0,0,0,1,1,0,0,0,1,0,0,0,12.0,12,1.0,20,1.67
+89,238334,185320,0,0,0,0,0,0,0,1,0,0,1,1,24.0,24,1.0,12,0.5
+90,421438,26856,0,1,0,1,0,1,0,0,0,0,0,0,4.0,4,1.0,8,2.0
+91,486531,50526,0,0,1,0,1,0,0,1,0,0,0,0,8.0,8,1.0,12,1.5
+92,364554,82141,0,0,0,0,0,1,0,0,0,0,1,1,12.0,12,1.0,8,0.67
+93,345162,66823,0,0,0,0,1,1,0,0,0,0,0,1,4.0,4,1.0,24,6.0
+94,401582,85499,1,0,0,0,0,0,0,0,1,1,0,0,12.0,12,1.0,12,1.0
+95,231653,96497,0,0,0,1,1,0,1,0,0,0,0,0,20.0,20,1.0,8,0.4
+96,452322,29299,1,0,0,0,0,0,0,1,0,1,0,0,8.0,8,1.0,16,2.0
+97,246133,27202,1,0,0,0,0,0,1,0,1,0,0,0,8.0,8,1.0,4,0.5
+98,330349,129763,0,0,1,0,1,0,0,0,1,0,0,0,16.0,16,1.0,4,0.25
+99,303372,93365,0,0,0,0,1,0,0,1,0,0,0,1,8.0,8,1.0,0,0.0
+100,437106,79353,1,0,0,1,0,0,0,1,0,0,0,0,8.0,8,1.0,12,1.5
+101,223500,140727,0,0,0,0,0,0,1,0,0,1,0,1,4.0,4,1.0,0,0.0
+102,354243,126610,0,0,0,0,0,0,0,0,1,0,1,1,24.0,24,1.0,16,0.67
+103,426530,12059,0,0,1,0,0,0,0,1,0,1,0,0,,0,0.0,12,5.0
+104,332202,132141,0,0,0,1,0,0,0,0,1,0,0,1,,0,0.0,20,5.0
+105,499973,37922,0,0,1,0,1,0,0,1,0,0,0,0,,0,0.0,12,5.0
+106,230682,64249,0,0,0,0,1,1,0,1,0,0,0,0,20.0,20,1.0,8,0.4
+107,467101,65313,0,0,0,0,1,1,0,0,1,0,0,0,16.0,16,1.0,0,0.0
+108,244275,169377,0,0,1,1,0,0,0,0,0,0,1,0,16.0,16,1.0,0,0.0
+109,258548,15342,0,0,0,1,0,1,0,1,0,0,0,0,20.0,20,1.0,24,1.2
+110,355408,97494,0,0,0,0,1,1,0,0,0,0,1,0,12.0,12,1.0,12,1.0
+111,490070,36258,0,0,1,0,0,0,1,0,0,0,0,1,,0,0.0,16,5.0
+112,263578,115332,0,1,0,0,0,0,1,0,0,1,0,0,12.0,12,1.0,20,1.67
+113,312245,127054,0,0,0,1,0,0,0,0,1,0,0,1,8.0,8,1.0,16,2.0
+114,327001,166239,0,0,1,1,1,0,0,0,0,0,0,0,8.0,8,1.0,4,0.5
+115,285426,113164,0,0,0,0,0,0,1,1,0,0,1,0,16.0,16,1.0,16,1.0
+116,294168,127969,0,1,1,0,0,0,0,0,0,0,0,1,8.0,8,1.0,8,1.0
+117,391158,176237,1,0,0,1,0,0,0,0,1,0,0,0,8.0,8,1.0,12,1.5
+118,407527,71949,0,0,0,0,0,1,0,0,1,1,0,0,8.0,8,1.0,0,0.0
+119,357736,67831,0,0,0,0,1,0,0,0,1,0,0,1,16.0,16,1.0,12,0.75
+120,404101,144428,1,0,0,1,0,1,0,0,0,0,0,0,8.0,8,1.0,12,1.5
+121,219612,41318,0,0,0,1,0,1,0,0,1,0,0,0,16.0,16,1.0,8,0.5
+122,318302,30258,0,0,1,0,0,1,0,0,0,1,0,0,8.0,8,1.0,16,2.0
+123,288533,44469,0,0,0,1,0,0,1,1,0,0,0,0,20.0,20,1.0,20,1.0
+124,207023,75965,1,0,0,1,0,1,0,0,0,0,0,0,4.0,4,1.0,4,1.0
+125,429278,87887,1,1,0,0,0,0,0,0,0,1,0,0,8.0,8,1.0,4,0.5
+126,409992,121431,1,0,1,0,0,0,0,1,0,0,0,0,16.0,16,1.0,4,0.25
+127,451142,137504,0,0,0,1,0,1,0,0,1,0,0,0,20.0,20,1.0,24,1.2
+128,375827,105166,0,1,1,0,0,0,1,0,0,0,0,0,,0,0.0,20,5.0
+129,495343,120288,0,1,1,0,0,1,0,0,0,0,0,0,12.0,12,1.0,12,1.0
+130,328338,20029,0,0,0,0,1,0,0,0,0,1,1,0,12.0,12,1.0,12,1.0
+131,299643,168222,0,0,0,0,0,0,1,0,0,1,1,0,12.0,12,1.0,8,0.67
+132,366391,27774,0,0,1,0,0,0,1,0,0,0,0,1,16.0,16,1.0,16,1.0
+133,433053,53942,0,0,1,0,0,0,1,0,0,0,0,1,4.0,4,1.0,20,5.0
+134,275331,94983,0,0,0,0,0,0,1,0,1,0,0,1,4.0,4,1.0,16,4.0
+135,379968,11980,0,0,0,0,1,0,0,0,0,1,1,0,8.0,8,1.0,12,1.5
+136,349473,160707,0,0,0,0,1,0,1,0,0,0,1,0,8.0,8,1.0,16,2.0
+137,464334,84683,0,0,0,0,0,0,1,0,0,1,0,1,12.0,12,1.0,20,1.67
+138,359174,18420,0,0,0,0,1,1,0,0,0,0,1,0,4.0,4,1.0,4,1.0
+139,479372,50259,0,0,1,0,0,0,0,0,1,1,0,0,8.0,8,1.0,8,1.0
+140,276422,164299,0,0,0,0,1,0,0,0,0,0,1,1,16.0,16,1.0,20,1.25
+141,268704,176375,1,0,0,0,0,0,0,1,0,0,0,1,16.0,16,1.0,24,1.5
+142,321766,196059,1,0,0,0,0,1,1,0,0,0,0,0,12.0,12,1.0,12,1.0
+143,244480,182013,0,0,1,0,1,0,0,0,1,0,0,0,4.0,4,1.0,8,2.0
+144,491347,45546,0,0,1,1,0,0,0,0,0,1,0,0,8.0,8,1.0,12,1.5
+145,479164,31723,0,0,1,0,0,0,0,0,1,0,0,1,8.0,8,1.0,12,1.5
+146,450496,180807,0,0,0,0,0,1,0,0,0,1,1,0,8.0,8,1.0,4,0.5
+147,473203,178811,0,0,0,0,1,0,0,1,0,1,0,0,16.0,16,1.0,24,1.5
+148,379234,139834,0,1,1,0,0,0,0,0,0,0,0,1,4.0,4,1.0,4,1.0
+149,463183,100745,1,0,0,0,0,1,0,0,0,0,1,0,12.0,12,1.0,20,1.67
+150,268605,39040,0,1,0,0,1,0,0,0,0,0,0,1,4.0,4,1.0,8,2.0
+151,201272,29406,0,1,0,0,1,1,0,0,0,0,0,0,4.0,4,1.0,12,3.0
+152,260646,123073,0,0,0,1,0,1,0,0,0,0,1,0,16.0,16,1.0,12,0.75
+153,432589,168024,0,0,0,1,1,0,0,0,0,0,0,1,12.0,12,1.0,16,1.33
+154,441903,167510,1,0,0,0,0,0,0,0,1,0,1,0,4.0,4,1.0,24,6.0
+155,407162,40023,1,0,0,0,0,0,0,0,0,0,1,1,16.0,16,1.0,20,1.25
+156,277219,159091,0,0,1,0,1,0,1,0,0,0,0,0,4.0,4,1.0,28,7.0
+157,434645,48399,0,0,0,0,0,0,0,1,0,1,1,0,16.0,16,1.0,12,0.75
+158,330608,144690,0,0,1,0,1,0,0,0,0,1,0,0,4.0,4,1.0,4,1.0
+159,301470,90552,0,0,0,0,0,0,0,1,1,0,1,0,4.0,4,1.0,8,2.0
+160,231090,32498,0,1,0,0,1,0,0,0,1,0,0,0,12.0,12,1.0,20,1.67
+161,230042,159215,0,0,1,1,0,1,0,0,0,0,0,0,20.0,20,1.0,20,1.0
+162,335301,149189,0,0,0,1,0,1,0,0,0,0,1,0,4.0,4,1.0,8,2.0
+163,268901,85454,1,0,1,0,0,0,0,0,0,1,0,0,12.0,12,1.0,24,2.0
+164,431844,31263,0,0,1,0,1,0,1,0,0,0,0,0,,0,0.0,12,5.0
+165,305315,66125,0,0,0,0,1,0,1,1,0,0,0,0,16.0,16,1.0,16,1.0
+166,461726,158451,1,0,1,0,0,1,0,0,0,0,0,0,16.0,16,1.0,4,0.25
+167,291481,19032,1,0,1,0,0,0,0,0,0,1,0,0,12.0,12,1.0,0,0.0
+168,345816,78571,1,1,0,1,0,0,0,0,0,0,0,0,8.0,8,1.0,12,1.5
+169,309393,198909,1,0,0,1,0,0,0,0,1,0,0,0,,0,0.0,16,5.0
+170,260505,94505,0,1,0,0,0,0,0,0,1,1,0,0,4.0,4,1.0,4,1.0
+171,278640,74242,1,0,0,0,1,0,1,0,0,0,0,0,4.0,4,1.0,8,2.0
+172,448128,59574,1,0,0,0,0,0,0,1,0,0,1,0,16.0,16,1.0,32,2.0
+173,431000,146506,0,0,0,1,0,0,1,1,0,0,0,0,8.0,8,1.0,4,0.5
+174,364180,61310,1,0,0,0,0,0,1,0,0,1,0,0,12.0,12,1.0,16,1.33
+175,356124,41448,0,0,1,0,0,1,0,0,0,1,0,0,4.0,4,1.0,12,3.0
+176,468564,162622,0,0,0,1,1,0,0,1,0,0,0,0,4.0,4,1.0,12,3.0
+177,220994,107493,1,0,1,0,0,0,0,1,0,0,0,0,24.0,24,1.0,12,0.5
+178,290759,196399,0,1,0,0,0,0,0,0,1,1,0,0,,0,0.0,12,5.0
+179,400526,193715,1,0,1,0,1,0,0,0,0,0,0,0,4.0,4,1.0,32,8.0
+180,413880,79311,0,1,0,1,1,0,0,0,0,0,0,0,12.0,12,1.0,4,0.33
+181,321989,11008,1,0,0,0,0,0,1,0,0,0,1,0,20.0,20,1.0,8,0.4
+182,345544,170913,0,0,0,1,1,0,0,0,1,0,0,0,16.0,16,1.0,8,0.5
+183,221962,183937,1,1,0,0,0,0,0,0,1,0,0,0,12.0,12,1.0,12,1.0
+184,311028,161327,1,0,0,0,0,1,0,1,0,0,0,0,20.0,20,1.0,20,1.0
+185,248252,183519,1,0,1,0,0,0,0,0,0,0,1,0,4.0,4,1.0,4,1.0
+186,451106,122923,0,0,0,0,0,0,1,0,1,0,1,0,4.0,4,1.0,20,5.0
+187,405380,147113,1,0,0,0,0,0,0,0,1,0,1,0,24.0,24,1.0,4,0.17
+188,203839,28523,0,1,0,0,0,0,0,0,1,0,0,1,12.0,12,1.0,8,0.67
+189,333806,76186,0,0,1,0,0,1,1,0,0,0,0,0,8.0,8,1.0,8,1.0
+190,432627,92873,0,0,0,0,1,0,0,0,1,0,0,1,12.0,12,1.0,36,3.0
+191,215347,95798,1,0,0,1,0,0,0,0,0,0,1,0,8.0,8,1.0,12,1.5
+192,404671,118625,0,0,0,1,0,1,0,0,0,1,0,0,4.0,4,1.0,24,6.0
+193,363349,82141,1,0,0,0,1,0,1,0,0,0,0,0,12.0,12,1.0,4,0.33
+194,278162,176609,0,1,1,0,0,1,0,0,0,0,0,0,4.0,4,1.0,20,5.0
+195,384349,89445,0,0,1,0,0,0,0,1,0,0,1,0,12.0,12,1.0,0,0.0
+196,422863,69355,0,0,0,0,0,0,1,0,1,0,1,0,8.0,8,1.0,24,3.0
+197,203902,149506,1,0,0,0,0,1,0,0,0,1,0,0,4.0,4,1.0,28,7.0
+198,474910,51413,0,0,1,0,0,0,1,0,1,0,0,0,,0,0.0,24,5.0
+199,476345,79957,1,0,0,0,0,0,0,0,1,0,1,0,12.0,12,1.0,24,2.0
+200,220967,15234,0,0,0,0,1,1,0,0,0,0,1,0,8.0,8,1.0,24,3.0
+201,355305,72795,0,1,0,0,0,0,1,0,1,0,0,0,12.0,12,1.0,12,1.0
+202,374021,44532,0,1,0,1,0,0,0,1,0,0,0,0,12.0,12,1.0,8,0.67
+203,279504,51617,0,0,0,1,0,1,0,0,1,0,0,0,12.0,12,1.0,24,2.0
+204,443296,160288,0,1,1,0,1,0,0,0,0,0,0,0,4.0,4,1.0,8,2.0
+205,375556,180085,0,1,0,0,0,0,1,0,0,1,0,0,8.0,8,1.0,12,1.5
+206,251345,176851,0,0,0,1,0,0,1,0,0,0,0,1,8.0,8,1.0,12,1.5
+207,494189,172931,0,1,0,0,0,0,0,1,0,0,0,1,16.0,16,1.0,16,1.0
+208,380802,94459,0,0,0,1,1,0,0,0,0,0,0,1,12.0,12,1.0,16,1.33
+209,237485,15499,1,1,0,0,0,0,0,0,0,0,1,0,4.0,4,1.0,8,2.0
+210,389579,74857,0,0,0,1,0,0,0,0,1,0,0,1,8.0,8,1.0,8,1.0
+211,360769,92737,0,0,0,1,0,0,1,0,0,0,0,1,4.0,4,1.0,12,3.0
+212,433681,66872,0,1,1,0,0,0,0,0,0,0,0,1,20.0,20,1.0,4,0.2
+213,244503,153773,0,0,1,1,0,0,0,0,0,0,0,1,,0,0.0,8,5.0
+214,282906,138552,0,1,0,0,0,1,0,0,0,1,0,0,12.0,12,1.0,12,1.0
+215,309651,27015,0,0,1,1,1,0,0,0,0,0,0,0,8.0,8,1.0,4,0.5
+216,235826,35438,0,0,0,0,0,1,0,1,0,0,0,1,20.0,20,1.0,16,0.8
+217,348160,50165,0,1,0,1,0,0,0,0,0,0,1,0,8.0,8,1.0,20,2.5
+218,252461,37506,1,0,0,0,1,1,0,0,0,0,0,0,16.0,16,1.0,20,1.25
+219,425014,120768,0,1,0,0,1,0,1,0,0,0,0,0,24.0,24,1.0,8,0.33
+220,484657,54360,0,0,0,0,1,0,1,0,0,0,1,0,12.0,12,1.0,8,0.67
+221,357157,115437,0,0,0,0,0,0,1,1,1,0,0,0,24.0,24,1.0,4,0.17
+222,409313,88353,0,1,0,0,1,0,0,0,0,1,0,0,8.0,8,1.0,8,1.0
+223,295721,179055,1,0,0,0,0,0,0,0,0,1,1,0,20.0,20,1.0,16,0.8
+224,211633,26505,1,0,0,0,1,1,0,0,0,0,0,0,16.0,16,1.0,16,1.0
+225,301350,99891,0,0,0,0,1,0,0,0,1,1,0,0,12.0,12,1.0,16,1.33
+226,262495,70481,0,0,0,1,0,0,0,1,0,0,0,1,20.0,20,1.0,12,0.6
+227,210992,35595,0,0,0,0,0,1,0,0,1,0,1,0,8.0,8,1.0,8,1.0
+228,266644,79859,0,1,1,0,0,0,1,0,0,0,0,0,16.0,16,1.0,16,1.0
+229,279378,93433,1,0,1,0,0,1,0,0,0,0,0,0,4.0,4,1.0,20,5.0
+230,358083,49661,1,0,0,0,1,1,0,0,0,0,0,0,8.0,8,1.0,4,0.5
+231,417867,196091,0,1,0,0,0,0,1,0,0,1,0,0,12.0,12,1.0,16,1.33
+232,307433,54841,1,0,1,0,0,0,0,0,0,0,0,1,16.0,16,1.0,12,0.75
+233,266947,171934,0,0,0,0,1,0,1,0,0,0,0,1,4.0,4,1.0,24,6.0
+234,444798,121560,0,1,0,0,0,0,0,0,1,0,0,1,4.0,4,1.0,4,1.0
+235,457960,22034,0,0,0,1,0,0,0,0,1,0,1,0,12.0,12,1.0,4,0.33
+236,213562,185489,0,1,0,0,0,1,0,0,0,1,0,0,4.0,4,1.0,8,2.0
+237,292293,188625,1,0,0,0,0,0,1,1,0,0,0,0,8.0,8,1.0,4,0.5
+238,425475,63185,1,0,0,0,0,1,0,0,1,0,0,0,4.0,4,1.0,12,3.0
+239,299594,160250,0,0,0,0,0,0,1,0,1,1,0,0,12.0,12,1.0,4,0.33
+240,499985,97669,0,1,0,0,1,0,1,0,0,0,0,0,12.0,12,1.0,12,1.0
+241,467802,71172,0,0,0,0,1,0,0,0,1,1,0,0,8.0,8,1.0,4,0.5
+242,294060,122809,1,0,1,0,0,0,1,0,0,0,0,0,20.0,20,1.0,4,0.2
+243,295196,97857,0,0,0,0,1,1,1,0,0,0,0,0,8.0,8,1.0,8,1.0
+244,341689,162736,0,0,0,1,0,1,0,0,1,0,0,0,12.0,12,1.0,12,1.0
+245,446327,42277,0,0,0,1,0,0,1,1,0,0,0,0,8.0,8,1.0,8,1.0
+246,362016,85719,0,0,0,1,0,0,0,0,0,1,0,1,8.0,8,1.0,16,2.0
+247,292418,17470,0,1,0,0,0,0,0,1,0,0,0,1,16.0,16,1.0,16,1.0
+248,402010,28515,0,1,0,1,0,0,0,0,0,0,0,1,,0,0.0,16,5.0
+249,297665,147497,0,1,0,0,0,0,1,0,0,0,0,1,12.0,12,1.0,0,0.0
+250,344575,169672,0,1,0,0,0,1,1,0,0,0,0,0,8.0,8,1.0,8,1.0
+251,494766,34199,0,0,0,1,0,0,0,1,0,0,0,1,16.0,16,1.0,8,0.5
+252,455465,99429,0,0,0,1,0,1,0,0,0,1,0,0,16.0,16,1.0,4,0.25
+253,468129,19544,0,0,0,0,0,0,1,0,1,0,1,0,4.0,4,1.0,4,1.0
+254,285542,168275,0,0,1,0,0,0,1,0,1,0,0,0,12.0,12,1.0,8,0.67
+255,401087,92771,0,0,0,0,0,0,1,0,0,1,0,1,12.0,12,1.0,16,1.33
+256,300457,182506,0,0,0,1,0,0,0,1,0,0,1,0,,0,0.0,4,5.0
+257,225116,127431,1,0,0,0,1,0,0,1,0,0,0,0,12.0,12,1.0,16,1.33
+258,341865,89645,0,1,0,1,0,1,0,0,0,0,0,0,,0,0.0,12,5.0
+259,329782,138901,0,0,0,0,0,0,0,0,1,0,1,1,,0,0.0,16,5.0
+260,260576,199473,0,0,0,0,0,1,1,0,0,0,1,0,12.0,12,1.0,12,1.0
+261,455435,17795,1,0,0,1,0,0,0,0,0,1,0,0,4.0,4,1.0,8,2.0
+262,332828,107041,1,0,0,0,0,0,0,0,0,1,0,1,16.0,16,1.0,8,0.5
+263,312057,35945,0,0,0,0,0,1,1,0,1,0,0,0,8.0,8,1.0,8,1.0
+264,470692,92096,1,1,0,0,0,0,0,1,0,0,0,0,16.0,16,1.0,12,0.75
+265,230964,80257,0,1,0,0,0,1,0,0,0,1,0,0,20.0,20,1.0,12,0.6
+266,423501,78904,0,0,0,1,1,0,0,0,0,1,0,0,20.0,20,1.0,8,0.4
+267,373241,33338,0,0,1,0,1,0,0,1,0,0,0,0,12.0,12,1.0,4,0.33
+268,476092,101934,1,0,1,0,0,0,0,0,0,0,0,1,12.0,12,1.0,4,0.33
+269,494248,186835,0,0,1,0,0,0,0,1,0,1,0,0,12.0,12,1.0,12,1.0
+270,426884,194172,0,0,0,0,0,0,0,1,0,1,0,1,,0,0.0,4,5.0
+271,202738,87671,0,0,0,0,0,0,1,0,1,0,1,0,8.0,8,1.0,12,1.5
+272,393215,36585,1,0,0,0,0,0,0,1,0,0,1,0,16.0,16,1.0,12,0.75
+273,227536,64387,0,1,0,0,0,0,0,1,0,0,1,0,24.0,24,1.0,0,0.0
+274,408448,106251,1,0,0,1,0,0,0,0,1,0,0,0,8.0,8,1.0,0,0.0
+275,397164,56350,0,0,0,1,0,0,0,0,1,1,0,0,,0,0.0,16,5.0
+276,401375,190889,0,1,0,0,0,0,0,1,1,0,0,0,12.0,12,1.0,8,0.67
+277,243472,52240,0,0,0,0,0,0,1,0,0,1,1,0,16.0,16,1.0,16,1.0
+278,390850,94391,0,0,0,0,0,1,0,1,0,0,0,1,4.0,4,1.0,16,4.0
+279,486146,63673,0,0,0,1,0,1,0,0,0,0,1,0,,0,0.0,4,5.0
+280,316122,137715,0,0,1,0,0,0,0,0,0,1,1,0,12.0,12,1.0,12,1.0
+281,417758,81087,1,1,0,0,1,0,0,0,0,0,0,0,12.0,12,1.0,12,1.0
+282,200066,52917,0,0,0,0,0,0,0,1,0,1,1,0,20.0,20,1.0,16,0.8
+283,466157,77291,0,0,1,0,0,0,0,0,1,0,0,1,4.0,4,1.0,12,3.0
+284,311745,117818,0,0,0,0,0,1,0,1,0,0,0,1,12.0,12,1.0,4,0.33
+285,388810,106468,0,1,0,0,0,0,1,0,0,0,1,0,4.0,4,1.0,12,3.0
+286,295784,96831,0,1,0,0,0,0,1,0,0,0,0,1,4.0,4,1.0,8,2.0
+287,239957,80359,0,0,0,1,0,0,1,0,1,0,0,0,12.0,12,1.0,8,0.67
+288,259571,96779,0,0,0,1,0,0,1,0,0,0,1,0,,0,0.0,16,5.0
+289,470592,57685,1,1,0,0,0,0,0,1,0,0,0,0,8.0,8,1.0,0,0.0
+290,360940,92218,0,1,0,0,0,1,0,1,0,0,0,0,8.0,8,1.0,32,4.0
+291,459336,78436,1,0,0,0,0,0,0,0,0,1,1,0,8.0,8,1.0,8,1.0
+292,296446,82822,1,0,0,0,1,1,0,0,0,0,0,0,12.0,12,1.0,0,0.0
+293,285626,67263,0,1,0,0,1,0,0,0,0,0,1,0,,0,0.0,8,5.0
+294,464099,170208,0,0,0,1,0,0,0,1,0,1,0,0,8.0,8,1.0,12,1.5
+295,263105,164140,1,0,0,0,1,0,0,1,0,0,0,0,12.0,12,1.0,8,0.67
+296,302474,167473,0,1,0,0,0,0,0,0,0,0,1,1,4.0,4,1.0,8,2.0
+297,372056,174677,0,1,1,0,0,0,0,0,0,0,0,1,12.0,12,1.0,8,0.67
+298,352449,52010,0,0,0,1,0,1,0,1,0,0,0,0,12.0,12,1.0,8,0.67
+299,419186,171727,0,0,0,0,1,1,0,0,0,0,1,0,20.0,20,1.0,4,0.2
+300,336617,82806,1,1,0,0,0,0,0,0,1,0,0,0,8.0,8,1.0,20,2.5
+301,400878,153139,0,0,1,0,1,0,0,0,0,0,0,1,8.0,8,1.0,8,1.0
+302,376532,58763,0,0,0,1,0,0,0,0,0,0,1,1,8.0,8,1.0,24,3.0
+303,215966,114816,1,0,0,0,0,0,0,0,1,0,0,1,8.0,8,1.0,8,1.0
+304,298739,78236,0,0,0,0,0,1,0,1,0,0,1,0,4.0,4,1.0,20,5.0
+305,324152,168504,0,1,0,0,1,0,0,0,1,0,0,0,12.0,12,1.0,8,0.67
+306,225832,114959,0,0,0,0,1,0,0,0,0,0,1,1,4.0,4,1.0,16,4.0
+307,218247,97153,0,0,1,1,0,0,0,0,0,0,1,0,4.0,4,1.0,8,2.0
+308,442559,186433,0,0,1,1,0,0,0,1,0,0,0,0,8.0,8,1.0,16,2.0
+309,400541,29388,0,0,0,0,1,1,0,1,0,0,0,0,8.0,8,1.0,12,1.5
+310,322029,114911,1,1,0,0,0,0,0,0,0,0,0,1,8.0,8,1.0,0,0.0
+311,233292,162186,0,1,0,0,0,0,0,0,0,1,0,1,4.0,4,1.0,4,1.0
+312,313899,35635,0,1,0,0,0,1,1,0,0,0,0,0,20.0,20,1.0,12,0.6
+313,403970,11492,1,0,0,0,0,0,0,1,0,0,1,0,12.0,12,1.0,0,0.0
+314,292531,147251,0,1,0,0,0,0,0,1,0,0,0,1,16.0,16,1.0,32,2.0
+315,499030,65865,0,1,0,0,1,0,0,0,0,0,1,0,4.0,4,1.0,16,4.0
+316,345372,25792,0,0,0,0,1,0,0,0,0,0,1,1,16.0,16,1.0,12,0.75
+317,279212,121179,0,1,1,1,0,0,0,0,0,0,0,0,16.0,16,1.0,16,1.0
+318,434460,169288,0,0,0,0,0,0,0,0,1,1,0,1,8.0,8,1.0,4,0.5
+319,461196,108275,1,0,0,0,0,0,1,0,0,0,1,0,,0,0.0,12,5.0
+320,214434,163733,1,0,0,0,0,0,0,1,1,0,0,0,16.0,16,1.0,4,0.25
+321,380690,119490,0,0,0,1,0,1,1,0,0,0,0,0,4.0,4,1.0,20,5.0
+322,207430,146046,1,0,1,0,0,0,0,1,0,0,0,0,12.0,12,1.0,8,0.67
+323,291430,197852,0,1,0,0,0,0,1,0,0,0,1,0,4.0,4,1.0,0,0.0
+324,281509,145993,0,0,0,0,0,0,1,0,0,1,1,0,8.0,8,1.0,20,2.5
+325,450296,105489,0,0,0,1,0,0,1,0,0,0,1,0,16.0,16,1.0,8,0.5
+326,304679,130659,1,0,0,1,0,0,0,1,0,0,0,0,20.0,20,1.0,16,0.8
+327,383569,138762,0,1,0,0,0,0,0,0,1,0,1,0,8.0,8,1.0,0,0.0
+328,353518,60651,0,0,1,0,1,0,0,0,0,1,0,0,12.0,12,1.0,20,1.67
+329,274717,155240,0,0,0,1,0,0,0,1,0,0,1,0,,0,0.0,16,5.0
+330,384499,155236,0,0,0,0,0,0,0,1,1,0,0,1,12.0,12,1.0,8,0.67
+331,465116,153987,0,0,0,0,0,0,0,0,1,1,1,0,8.0,8,1.0,8,1.0
+332,422611,142253,0,0,0,1,0,0,0,1,0,0,1,0,4.0,4,1.0,12,3.0
+333,273142,46673,1,0,1,0,0,0,1,0,0,0,0,0,12.0,12,1.0,12,1.0
+334,276436,53260,0,1,0,1,0,0,0,0,0,1,0,0,20.0,20,1.0,28,1.4
+335,202608,38139,1,0,1,0,1,0,0,0,0,0,0,0,12.0,12,1.0,16,1.33
+336,243929,179707,0,0,0,0,0,0,1,1,1,0,0,0,4.0,4,1.0,12,3.0
+337,258763,141289,1,0,1,0,0,0,0,0,0,0,0,1,8.0,8,1.0,4,0.5
+338,322834,58273,0,1,0,0,0,0,1,0,0,1,0,0,16.0,16,1.0,16,1.0
+339,211616,151350,0,1,0,0,0,0,1,0,0,1,0,0,16.0,16,1.0,8,0.5
+340,417342,43433,0,0,1,0,0,0,0,1,0,1,0,0,4.0,4,1.0,20,5.0
+341,333428,53017,0,0,0,0,0,1,0,0,0,1,0,1,12.0,12,1.0,24,2.0
+342,453483,112882,0,0,0,1,1,0,0,0,0,0,0,1,12.0,12,1.0,16,1.33
+343,266412,108033,1,1,0,0,0,0,0,1,0,0,0,0,,0,0.0,12,5.0
+344,408051,140955,0,0,0,0,1,0,1,0,0,0,1,0,12.0,12,1.0,4,0.33
+345,456345,94717,0,0,0,0,0,0,1,0,0,0,1,1,20.0,20,1.0,28,1.4
+346,457822,28648,1,0,1,0,0,1,0,0,0,0,0,0,12.0,12,1.0,20,1.67
+347,255709,92725,0,0,0,0,0,0,1,0,1,0,1,0,8.0,8,1.0,8,1.0
+348,451104,192438,1,0,0,0,0,1,0,1,0,0,0,0,8.0,8,1.0,20,2.5
+349,420645,79899,1,0,0,0,0,0,1,0,0,0,0,1,20.0,20,1.0,12,0.6
+350,392445,125863,0,1,0,0,0,0,0,1,0,0,1,0,12.0,12,1.0,12,1.0
+351,207515,78961,0,1,0,0,1,0,0,0,1,0,0,0,8.0,8,1.0,8,1.0
+352,317523,53884,0,1,0,0,0,0,1,0,0,0,1,0,4.0,4,1.0,8,2.0
+353,444435,197545,0,0,0,1,0,0,0,1,1,0,0,0,28.0,28,1.0,12,0.43
+354,316633,42407,0,0,0,0,0,0,1,0,1,0,1,0,28.0,28,1.0,4,0.14
+355,258931,173204,0,0,0,0,1,0,0,1,0,0,1,0,,0,0.0,4,5.0
+356,372692,190010,0,1,0,1,0,0,0,1,0,0,0,0,4.0,4,1.0,8,2.0
+357,343398,37837,0,0,1,1,0,0,0,0,0,0,1,0,4.0,4,1.0,12,3.0
+358,281317,59623,1,0,0,1,0,0,0,1,0,0,0,0,12.0,12,1.0,12,1.0
+359,332501,26790,0,0,0,1,0,1,0,0,1,0,0,0,16.0,16,1.0,8,0.5
+360,384262,158042,1,0,0,0,0,0,0,1,0,1,0,0,12.0,12,1.0,12,1.0
+361,428215,93593,1,0,1,0,0,0,0,0,1,0,0,0,12.0,12,1.0,28,2.33
+362,249481,163969,1,0,0,1,1,0,0,0,0,0,0,0,12.0,12,1.0,20,1.67
+363,414483,50161,0,1,0,0,0,0,0,1,0,0,0,1,16.0,16,1.0,8,0.5
+364,464670,118855,1,0,0,0,0,0,0,0,0,0,1,1,8.0,8,1.0,12,1.5
+365,474311,172110,1,0,0,0,0,0,1,0,0,0,0,1,4.0,4,1.0,0,0.0
+366,348959,114042,0,0,0,0,0,0,0,0,1,1,1,0,12.0,12,1.0,16,1.33
+367,234548,101587,0,0,0,0,0,0,0,1,0,1,0,1,8.0,8,1.0,20,2.5
+368,219163,174151,1,0,0,0,1,0,1,0,0,0,0,0,8.0,8,1.0,0,0.0
+369,302195,65234,0,1,1,1,0,0,0,0,0,0,0,0,4.0,4,1.0,20,5.0
+370,422472,147660,0,0,0,0,0,1,0,0,1,0,0,1,16.0,16,1.0,16,1.0
+371,227784,146399,0,0,0,0,1,1,0,1,0,0,0,0,4.0,4,1.0,12,3.0
+372,498482,171040,0,0,0,1,0,1,1,0,0,0,0,0,16.0,16,1.0,8,0.5
+373,373709,25938,0,0,0,1,0,0,0,1,1,0,0,0,16.0,16,1.0,12,0.75
+374,470459,35826,1,0,1,0,0,1,0,0,0,0,0,0,16.0,16,1.0,24,1.5
+375,354110,120657,0,0,0,0,0,0,1,1,0,0,0,1,8.0,8,1.0,20,2.5
+376,206024,117159,0,0,0,1,0,0,1,0,1,0,0,0,20.0,20,1.0,0,0.0
+377,358946,83301,0,0,0,0,0,0,1,1,1,0,0,0,8.0,8,1.0,8,1.0
+378,308803,59281,1,1,0,0,0,0,0,0,1,0,0,0,,0,0.0,12,5.0
+379,474517,31884,0,0,0,0,0,0,1,0,0,1,1,0,4.0,4,1.0,16,4.0
+380,242390,142871,0,0,0,1,0,0,1,0,0,0,1,0,8.0,8,1.0,0,0.0
+381,340548,187457,0,0,0,1,1,0,0,0,1,0,0,0,12.0,12,1.0,4,0.33
+382,299177,177984,0,1,0,0,0,0,0,0,1,0,1,0,12.0,12,1.0,12,1.0
+383,270532,82671,1,1,1,0,0,0,0,0,0,0,0,0,16.0,16,1.0,12,0.75
+384,268992,88647,0,0,0,0,0,0,0,1,1,1,0,0,12.0,12,1.0,12,1.0
+385,259627,71976,0,1,0,0,0,0,0,1,0,0,1,0,4.0,4,1.0,12,3.0
+386,266860,39850,1,1,0,0,0,0,1,0,0,0,0,0,24.0,24,1.0,16,0.67
+387,202576,112778,0,1,0,0,0,1,0,1,0,0,0,0,12.0,12,1.0,8,0.67
+388,289778,148181,0,0,0,1,1,0,0,0,1,0,0,0,12.0,12,1.0,12,1.0
+389,318503,98397,1,1,0,0,0,0,0,0,0,1,0,0,24.0,24,1.0,8,0.33
+390,307917,126264,0,0,0,0,0,0,0,0,0,1,1,1,12.0,12,1.0,20,1.67
+391,369358,93056,0,0,0,0,1,0,0,0,1,0,0,1,12.0,12,1.0,8,0.67
+392,373343,59530,0,0,0,0,0,1,0,0,1,0,1,0,12.0,12,1.0,8,0.67
+393,278415,191858,0,0,0,0,0,0,1,0,1,0,0,1,16.0,16,1.0,16,1.0
+394,254809,78233,0,1,0,0,0,0,0,0,0,0,1,1,16.0,16,1.0,12,0.75
+395,358742,41600,0,0,0,1,0,1,0,0,0,1,0,0,16.0,16,1.0,28,1.75
+396,435593,136937,0,1,1,0,0,0,0,0,0,0,0,1,16.0,16,1.0,16,1.0
+397,444664,79308,0,1,0,0,1,0,0,1,0,0,0,0,4.0,4,1.0,16,4.0
+398,257040,189799,0,0,0,0,1,0,0,0,0,1,0,1,8.0,8,1.0,12,1.5
+399,471971,107481,0,1,0,0,1,1,0,0,0,0,0,0,16.0,16,1.0,8,0.5
+400,285534,69440,0,1,0,1,0,0,1,0,0,0,0,0,8.0,8,1.0,16,2.0
+401,399355,28851,0,1,0,1,0,1,0,0,0,0,0,0,4.0,4,1.0,12,3.0
+402,267282,130354,0,0,0,0,0,0,0,0,1,0,1,1,8.0,8,1.0,20,2.5
+403,376411,90246,1,1,0,0,0,1,0,0,0,0,0,0,12.0,12,1.0,4,0.33
+404,417066,68397,0,0,1,0,0,0,0,0,0,1,0,1,16.0,16,1.0,12,0.75
+405,312499,50643,1,0,0,0,1,0,0,0,0,0,1,0,8.0,8,1.0,16,2.0
+406,329969,70706,0,1,0,0,0,0,0,1,0,0,1,0,16.0,16,1.0,16,1.0
+407,468220,70210,0,0,1,0,0,0,0,0,1,0,0,1,4.0,4,1.0,16,4.0
+408,414297,162311,1,1,0,0,1,0,0,0,0,0,0,0,16.0,16,1.0,16,1.0
+409,219953,187847,0,0,1,1,1,0,0,0,0,0,0,0,4.0,4,1.0,4,1.0
+410,413257,192908,0,1,0,0,1,0,0,0,0,0,1,0,8.0,8,1.0,4,0.5
+411,334666,192762,0,0,1,1,0,0,0,1,0,0,0,0,8.0,8,1.0,20,2.5
+412,222514,50456,0,0,0,0,0,0,0,1,1,0,1,0,16.0,16,1.0,0,0.0
+413,259408,123579,1,0,1,0,0,0,0,0,1,0,0,0,28.0,28,1.0,12,0.43
+414,319363,16753,0,1,0,1,0,0,0,0,0,0,0,1,8.0,8,1.0,16,2.0
+415,398450,95097,0,0,1,0,0,1,0,1,0,0,0,0,12.0,12,1.0,4,0.33
+416,388838,107138,0,0,1,0,1,0,0,1,0,0,0,0,4.0,4,1.0,4,1.0
+417,256776,19251,0,1,0,0,0,0,0,0,0,1,0,1,4.0,4,1.0,8,2.0
+418,383184,82298,0,0,0,0,0,1,1,1,0,0,0,0,8.0,8,1.0,12,1.5
+419,262198,77538,0,1,1,0,0,0,1,0,0,0,0,0,8.0,8,1.0,16,2.0
+420,276703,139996,1,1,0,0,1,0,0,0,0,0,0,0,16.0,16,1.0,4,0.25
+421,276962,38350,1,0,0,0,0,1,0,0,0,0,1,0,28.0,28,1.0,8,0.29
+422,461187,140548,0,0,0,1,0,0,0,1,0,1,0,0,24.0,24,1.0,20,0.83
+423,480325,45818,0,1,1,0,0,0,0,0,0,0,0,1,16.0,16,1.0,20,1.25
+424,394427,83641,0,0,1,0,0,0,0,1,0,0,1,0,4.0,4,1.0,16,4.0
+425,435603,99934,0,0,1,0,1,0,0,0,0,1,0,0,4.0,4,1.0,12,3.0
+426,279346,67709,0,0,0,1,0,0,1,0,0,0,0,1,,0,0.0,24,5.0
+427,306633,93222,0,0,1,0,0,0,0,0,1,0,1,0,16.0,16,1.0,12,0.75
+428,483980,14466,1,0,0,0,0,0,0,1,0,0,0,1,8.0,8,1.0,8,1.0
+429,214878,173055,1,0,1,0,0,0,0,0,1,0,0,0,8.0,8,1.0,12,1.5
+430,263423,160431,0,0,0,1,0,0,1,0,1,0,0,0,20.0,20,1.0,12,0.6
+431,396807,194270,0,0,0,0,1,1,0,0,0,0,0,1,16.0,16,1.0,4,0.25
+432,468006,139244,0,0,0,0,1,0,0,1,0,0,1,0,8.0,8,1.0,16,2.0
+433,386017,54184,0,0,0,1,0,0,1,0,0,0,1,0,16.0,16,1.0,12,0.75
+434,237391,115716,0,0,1,1,0,0,0,1,0,0,0,0,24.0,24,1.0,12,0.5
+435,415828,29926,0,0,0,0,0,0,0,1,0,1,1,0,8.0,8,1.0,8,1.0
+436,208006,56098,0,0,0,1,0,0,0,1,1,0,0,0,12.0,12,1.0,8,0.67
+437,282798,92380,0,0,0,0,0,0,1,1,0,0,1,0,20.0,20,1.0,24,1.2
+438,322382,139215,0,0,0,0,0,0,1,1,1,0,0,0,8.0,8,1.0,24,3.0
+439,495656,24595,0,0,0,0,1,0,0,0,0,1,0,1,12.0,12,1.0,16,1.33
+440,487736,156629,0,0,0,1,0,1,0,1,0,0,0,0,12.0,12,1.0,12,1.0
+441,348167,162775,0,1,0,0,0,0,0,0,1,1,0,0,16.0,16,1.0,4,0.25
+442,413534,160420,0,1,0,0,0,0,0,1,1,0,0,0,8.0,8,1.0,8,1.0
+443,292531,155651,1,0,0,0,0,1,1,0,0,0,0,0,8.0,8,1.0,8,1.0
+444,405225,64763,0,0,0,0,1,1,1,0,0,0,0,0,8.0,8,1.0,20,2.5
+445,388734,172721,1,0,1,0,0,0,0,0,1,0,0,0,16.0,16,1.0,20,1.25
+446,200028,69617,0,0,0,1,0,1,0,0,0,0,1,0,16.0,16,1.0,8,0.5
+447,368086,150465,0,0,1,0,0,0,0,0,0,1,0,1,16.0,16,1.0,20,1.25
+448,453598,88914,0,0,0,0,1,0,1,0,0,0,1,0,16.0,16,1.0,12,0.75
+449,490092,13516,0,0,1,0,1,0,0,0,0,1,0,0,12.0,12,1.0,4,0.33
+450,260490,195083,0,0,0,1,1,0,0,0,0,0,1,0,12.0,12,1.0,12,1.0
+451,455598,27274,1,0,1,0,0,0,0,1,0,0,0,0,20.0,20,1.0,8,0.4
+452,494976,161982,0,0,0,1,0,0,0,0,0,1,0,1,8.0,8,1.0,16,2.0
+453,396066,28764,0,1,1,0,0,0,0,0,0,0,1,0,16.0,16,1.0,16,1.0
+454,455579,88243,0,1,0,0,1,0,1,0,0,0,0,0,,0,0.0,16,5.0
+455,409085,70502,0,0,0,0,1,0,1,0,0,1,0,0,8.0,8,1.0,16,2.0
+456,371029,72053,0,0,0,1,1,1,0,0,0,0,0,0,8.0,8,1.0,4,0.5
+457,295670,196249,1,0,1,0,0,0,1,0,0,0,0,0,20.0,20,1.0,4,0.2
+458,361928,150430,1,0,0,0,1,0,0,0,0,0,0,1,4.0,4,1.0,12,3.0
+459,451024,173729,1,0,0,0,0,0,0,0,1,0,0,1,8.0,8,1.0,4,0.5
+460,490748,98179,0,0,0,0,0,1,0,0,1,0,0,1,12.0,12,1.0,8,0.67
+461,491945,30336,0,0,0,0,0,1,0,0,1,1,0,0,12.0,12,1.0,16,1.33
+462,241640,70420,0,1,0,0,1,0,0,0,0,0,0,1,4.0,4,1.0,16,4.0
+463,223055,35627,0,0,1,0,0,0,1,1,0,0,0,0,4.0,4,1.0,8,2.0
+464,467233,172942,0,0,0,0,0,1,0,0,1,1,0,0,8.0,8,1.0,8,1.0
+465,209404,90757,0,0,0,0,0,1,0,0,0,1,0,1,4.0,4,1.0,12,3.0
+466,235052,63309,0,0,0,1,0,0,1,0,0,0,0,1,16.0,16,1.0,20,1.25
+467,352043,122542,0,0,0,0,0,1,0,1,0,0,1,0,,0,0.0,4,5.0
+468,464655,71188,0,0,1,0,1,0,0,0,0,0,1,0,,0,0.0,8,5.0
+469,452949,99119,0,0,0,1,0,1,0,0,1,0,0,0,12.0,12,1.0,12,1.0
+470,426276,83280,0,1,1,0,0,0,1,0,0,0,0,0,8.0,8,1.0,20,2.5
+471,216417,72177,1,0,1,0,1,0,0,0,0,0,0,0,16.0,16,1.0,12,0.75
+472,475969,71538,0,1,0,1,0,0,0,0,0,0,0,1,16.0,16,1.0,16,1.0
+473,470315,16907,0,1,0,0,1,0,1,0,0,0,0,0,4.0,4,1.0,16,4.0
+474,278020,110948,0,0,0,1,0,1,0,0,0,0,1,0,28.0,28,1.0,16,0.57
+475,440036,30121,0,0,0,0,0,1,0,0,1,0,0,1,4.0,4,1.0,8,2.0
+476,314142,30943,0,1,0,0,0,0,1,0,0,1,0,0,4.0,4,1.0,0,0.0
+477,386746,20699,0,0,0,0,1,1,0,0,1,0,0,0,8.0,8,1.0,8,1.0
+478,261137,14474,1,0,0,0,0,0,0,0,0,1,1,0,8.0,8,1.0,0,0.0
+479,331372,39433,0,1,0,1,0,0,1,0,0,0,0,0,,0,0.0,4,5.0
+480,405773,32680,0,0,0,0,0,0,0,1,1,1,0,0,8.0,8,1.0,8,1.0
+481,429580,132313,0,1,0,0,1,0,0,0,0,0,1,0,4.0,4,1.0,4,1.0
+482,356059,157741,0,0,0,0,1,0,0,0,1,0,0,1,,0,0.0,8,5.0
+483,271684,55449,0,0,0,0,1,1,0,0,0,1,0,0,20.0,20,1.0,32,1.6
+484,347716,95631,0,0,0,0,1,1,1,0,0,0,0,0,4.0,4,1.0,12,3.0
+485,411171,62933,0,0,0,1,0,0,0,1,0,0,0,1,,0,0.0,16,5.0
+486,428977,75683,0,0,0,0,1,0,0,0,1,1,0,0,8.0,8,1.0,28,3.5
+487,466398,187686,0,0,1,0,0,0,0,1,0,0,0,1,16.0,16,1.0,12,0.75
+488,270081,33315,0,0,0,0,0,0,0,0,1,0,1,1,20.0,20,1.0,12,0.6
+489,485029,51880,1,0,0,1,0,0,0,0,0,0,0,1,16.0,16,1.0,12,0.75
+490,261276,108198,1,0,0,0,0,0,0,0,1,0,1,0,4.0,4,1.0,20,5.0
+491,407979,92566,1,0,0,0,0,0,1,0,0,0,0,1,16.0,16,1.0,8,0.5
+492,243348,173882,0,0,0,1,0,1,0,0,0,0,0,1,12.0,12,1.0,12,1.0
+493,411586,174035,0,0,0,0,1,0,0,1,0,0,0,1,12.0,12,1.0,16,1.33
+494,297625,144010,1,0,0,1,0,0,1,0,0,0,0,0,16.0,16,1.0,12,0.75
+495,402601,92174,0,0,0,0,0,0,1,1,0,1,0,0,4.0,4,1.0,0,0.0
+496,266498,69504,0,0,0,1,0,1,1,0,0,0,0,0,16.0,16,1.0,8,0.5
+497,303917,41341,0,0,0,0,1,1,0,0,0,0,1,0,8.0,8,1.0,4,0.5
+498,205307,26146,1,0,0,0,0,1,1,0,0,0,0,0,12.0,12,1.0,12,1.0
+499,471466,124691,1,0,1,0,0,0,0,1,0,0,0,0,20.0,20,1.0,12,0.6
+500,363805,196676,0,1,0,0,0,0,0,1,1,0,0,0,8.0,8,1.0,8,1.0
+501,328308,62536,0,0,1,1,0,0,0,1,0,0,0,0,,0,0.0,4,5.0
+502,267523,85770,0,0,1,0,0,0,1,0,0,0,1,0,16.0,16,1.0,8,0.5
+503,332979,84112,1,0,1,0,0,0,0,0,0,0,1,0,4.0,4,1.0,0,0.0
+504,309464,180538,0,0,0,0,1,1,0,0,0,1,0,0,4.0,4,1.0,16,4.0
+505,488292,185347,1,0,0,0,0,0,0,1,0,1,0,0,4.0,4,1.0,8,2.0
+506,361471,165007,0,0,0,0,0,0,1,1,0,0,0,1,12.0,12,1.0,8,0.67
+507,208687,105248,0,0,1,0,0,0,1,1,0,0,0,0,16.0,16,1.0,8,0.5
+508,232684,161404,0,1,0,0,0,1,0,1,0,0,0,0,4.0,4,1.0,12,3.0
+509,442554,16172,0,0,0,0,1,0,0,0,0,0,1,1,8.0,8,1.0,24,3.0
+510,204657,170199,0,0,1,0,0,0,0,0,0,0,1,1,4.0,4,1.0,8,2.0
+511,239389,70377,0,0,1,0,1,0,0,0,1,0,0,0,20.0,20,1.0,12,0.6
+512,218008,101701,1,0,0,0,1,0,0,1,0,0,0,0,8.0,8,1.0,20,2.5
+513,383759,194651,0,0,1,0,0,0,0,0,1,0,1,0,4.0,4,1.0,12,3.0
+514,306213,61447,0,0,0,1,1,0,1,0,0,0,0,0,8.0,8,1.0,4,0.5
+515,312123,48279,0,0,1,0,1,0,1,0,0,0,0,0,4.0,4,1.0,8,2.0
+516,361275,116248,0,0,0,1,0,0,0,0,0,0,1,1,,0,0.0,12,5.0
+517,388396,90908,0,1,0,0,0,0,0,1,0,1,0,0,4.0,4,1.0,12,3.0
+518,288128,133729,0,0,0,0,1,1,0,0,0,0,1,0,8.0,8,1.0,8,1.0
+519,425896,76334,0,0,0,1,0,0,1,0,0,1,0,0,4.0,4,1.0,24,6.0
+520,227571,49234,0,0,1,0,0,0,1,0,0,1,0,0,8.0,8,1.0,16,2.0
+521,479020,137397,0,0,0,0,1,1,0,0,1,0,0,0,4.0,4,1.0,8,2.0
+522,204399,43095,0,0,1,0,0,0,0,1,0,0,0,1,16.0,16,1.0,20,1.25
+523,338325,154358,0,0,0,1,1,0,0,1,0,0,0,0,16.0,16,1.0,8,0.5
+524,336982,132921,0,0,1,1,0,0,0,0,1,0,0,0,28.0,28,1.0,16,0.57
+525,341459,91124,0,0,1,0,0,0,0,0,0,1,0,1,16.0,16,1.0,0,0.0
+526,406510,63478,0,0,0,1,0,0,0,0,1,0,0,1,8.0,8,1.0,8,1.0
+527,332700,64958,0,1,0,0,0,1,0,0,0,0,1,0,12.0,12,1.0,4,0.33
+528,254230,86953,0,0,1,0,0,0,1,1,0,0,0,0,,0,0.0,12,5.0
+529,216574,171859,0,0,0,1,0,0,0,0,0,1,1,0,8.0,8,1.0,12,1.5
+530,482990,41795,0,0,0,1,0,1,0,0,0,0,0,1,20.0,20,1.0,12,0.6
+531,330133,123832,0,0,0,0,1,0,0,0,0,1,1,0,,0,0.0,0,0.0
+532,395228,53435,1,0,0,0,0,0,0,1,0,0,1,0,12.0,12,1.0,28,2.33
+533,219301,83245,1,0,0,0,1,0,0,0,0,0,1,0,4.0,4,1.0,8,2.0
+534,241393,116263,0,1,0,1,0,0,0,0,0,1,0,0,12.0,12,1.0,24,2.0
+535,209948,50931,0,0,0,1,0,0,1,0,0,0,1,0,16.0,16,1.0,8,0.5
+536,434471,63782,1,0,0,1,0,0,0,1,0,0,0,0,8.0,8,1.0,8,1.0
+537,402834,161904,0,0,1,0,1,1,0,0,0,0,0,0,4.0,4,1.0,16,4.0
+538,441114,132616,1,0,0,0,0,1,1,0,0,0,0,0,4.0,4,1.0,24,6.0
+539,467585,31356,0,1,0,1,1,0,0,0,0,0,0,0,16.0,16,1.0,8,0.5
+540,348008,95495,1,0,0,0,0,0,0,1,0,1,0,0,20.0,20,1.0,12,0.6
+541,445390,136281,0,0,1,0,0,0,0,0,1,0,1,0,8.0,8,1.0,16,2.0
+542,323829,25432,0,0,1,0,0,1,0,1,0,0,0,0,4.0,4,1.0,24,6.0
+543,205185,12607,0,0,0,0,0,0,1,0,0,1,0,1,16.0,16,1.0,12,0.75
+544,490248,26420,0,0,0,1,0,1,0,1,0,0,0,0,8.0,8,1.0,16,2.0
+545,463292,161027,0,0,0,0,1,0,0,0,1,0,1,0,8.0,8,1.0,16,2.0
+546,480943,157829,0,1,0,1,0,0,0,1,0,0,0,0,16.0,16,1.0,16,1.0
+547,347856,29055,1,0,1,0,0,0,0,0,0,0,0,1,24.0,24,1.0,0,0.0
+548,239565,96294,0,1,0,0,0,0,0,0,0,0,1,1,12.0,12,1.0,16,1.33
+549,255734,62437,1,0,0,0,0,0,0,1,0,0,1,0,20.0,20,1.0,8,0.4
+550,242008,183474,0,1,0,0,0,0,0,1,0,1,0,0,8.0,8,1.0,4,0.5
+551,278966,28705,0,1,0,1,0,0,0,0,1,0,0,0,20.0,20,1.0,4,0.2
+552,266072,17762,0,0,1,1,0,0,0,0,0,1,0,0,12.0,12,1.0,16,1.33
+553,380708,37376,0,0,0,1,0,0,1,0,0,0,1,0,8.0,8,1.0,8,1.0
+554,207097,109083,0,1,0,1,0,0,0,0,1,0,0,0,28.0,28,1.0,8,0.29
+555,220167,167100,0,1,1,0,0,0,0,1,0,0,0,0,12.0,12,1.0,16,1.33
+556,328447,185659,0,0,0,0,0,0,1,0,0,1,0,1,,0,0.0,4,5.0
+557,263123,158856,0,1,0,1,0,0,0,0,0,1,0,0,8.0,8,1.0,8,1.0
+558,244123,101011,0,0,0,1,1,0,0,0,0,0,0,1,28.0,28,1.0,28,1.0
+559,212049,197338,1,0,0,0,0,1,0,0,0,1,0,0,4.0,4,1.0,16,4.0
+560,244946,29596,1,0,0,0,1,0,0,0,0,1,0,0,8.0,8,1.0,12,1.5
+561,358017,45793,0,0,0,0,0,1,0,1,0,0,1,0,16.0,16,1.0,16,1.0
+562,344701,132333,1,0,1,0,0,0,0,1,0,0,0,0,12.0,12,1.0,24,2.0
+563,411913,180763,0,0,0,0,1,1,1,0,0,0,0,0,16.0,16,1.0,20,1.25
+564,449587,131493,1,1,0,1,0,0,0,0,0,0,0,0,8.0,8,1.0,20,2.5
+565,486488,50115,0,0,0,1,0,0,0,1,0,1,0,0,32.0,32,1.0,8,0.25
+566,362731,138975,1,0,0,0,0,1,0,1,0,0,0,0,20.0,20,1.0,20,1.0
+567,496717,190331,0,1,1,0,0,0,1,0,0,0,0,0,20.0,20,1.0,12,0.6
+568,306057,157754,0,0,1,0,0,0,0,0,1,1,0,0,8.0,8,1.0,12,1.5
+569,338709,85126,0,0,1,0,0,0,0,0,1,0,0,1,8.0,8,1.0,8,1.0
+570,200001,143481,0,0,0,1,1,0,0,0,0,0,1,0,4.0,4,1.0,0,0.0
+571,223457,100696,0,0,0,1,0,1,0,0,1,0,0,0,16.0,16,1.0,16,1.0
+572,441179,25685,0,0,1,0,0,0,0,0,1,0,1,0,24.0,24,1.0,8,0.33
+573,353594,110800,1,0,0,0,0,0,0,0,1,1,0,0,12.0,12,1.0,16,1.33
+574,393614,13674,0,0,1,0,0,1,1,0,0,0,0,0,8.0,8,1.0,32,4.0
+575,430394,20528,0,0,1,0,1,0,0,0,0,1,0,0,20.0,20,1.0,4,0.2
+576,331831,152774,1,0,0,1,0,0,0,0,1,0,0,0,4.0,4,1.0,20,5.0
+577,398194,120484,0,0,0,0,0,1,1,0,0,1,0,0,16.0,16,1.0,16,1.0
+578,276427,176286,1,0,1,0,0,0,0,1,0,0,0,0,4.0,4,1.0,12,3.0
+579,499987,117675,0,0,0,0,0,1,0,0,1,1,0,0,24.0,24,1.0,24,1.0
+580,290614,113585,0,0,1,1,1,0,0,0,0,0,0,0,12.0,12,1.0,4,0.33
+581,478243,195676,0,0,1,0,0,0,1,0,0,0,0,1,4.0,4,1.0,16,4.0
+582,402493,128166,0,0,0,0,0,1,0,1,0,1,0,0,24.0,24,1.0,16,0.67
+583,393992,143737,0,0,0,1,0,0,1,0,1,0,0,0,12.0,12,1.0,4,0.33
+584,327428,88091,0,1,1,0,0,1,0,0,0,0,0,0,16.0,16,1.0,8,0.5
+585,462940,164467,0,0,0,0,0,1,0,0,1,0,0,1,4.0,4,1.0,24,6.0
+586,265145,97582,0,0,0,0,0,1,0,0,1,1,0,0,8.0,8,1.0,8,1.0
+587,451884,125764,1,0,0,1,0,0,0,0,0,1,0,0,,0,0.0,20,5.0
+588,470082,123085,0,0,0,1,1,0,0,0,0,0,0,1,8.0,8,1.0,8,1.0
+589,302570,55829,0,1,0,1,0,1,0,0,0,0,0,0,8.0,8,1.0,12,1.5
+590,271350,163049,0,0,0,0,0,1,0,0,1,1,0,0,12.0,12,1.0,20,1.67
+591,463176,190070,0,0,1,0,1,0,0,0,0,0,1,0,4.0,4,1.0,20,5.0
+592,239269,115358,0,0,0,0,0,0,1,1,0,0,0,1,4.0,4,1.0,20,5.0
+593,492234,80321,0,0,0,0,0,1,1,1,0,0,0,0,12.0,12,1.0,28,2.33
+594,447098,150300,0,0,1,0,0,0,0,0,1,0,1,0,,0,0.0,8,5.0
+595,289440,191239,0,1,0,1,0,0,1,0,0,0,0,0,8.0,8,1.0,8,1.0
+596,395653,181703,0,0,0,0,0,1,1,0,1,0,0,0,8.0,8,1.0,12,1.5
+597,413273,39791,0,0,0,1,1,0,1,0,0,0,0,0,,0,0.0,4,5.0
+598,225693,56526,0,1,0,0,0,0,1,0,0,0,0,1,20.0,20,1.0,8,0.4
+599,457079,135393,0,0,1,0,0,0,0,0,1,0,1,0,12.0,12,1.0,12,1.0
+600,406200,92399,0,0,1,0,0,1,0,0,0,0,0,1,4.0,4,1.0,20,5.0
+601,391476,55926,0,0,0,1,0,0,1,0,0,1,0,0,12.0,12,1.0,8,0.67
+602,425438,98860,0,0,0,0,0,1,1,0,1,0,0,0,,0,0.0,24,5.0
+603,334418,120570,1,0,0,0,0,0,0,0,1,0,0,1,4.0,4,1.0,8,2.0
+604,348429,28194,1,1,0,0,0,0,0,1,0,0,0,0,,0,0.0,8,5.0
+605,400528,37541,0,0,0,1,1,0,1,0,0,0,0,0,12.0,12,1.0,20,1.67
+606,319216,42896,0,0,0,0,0,1,0,0,0,1,0,1,8.0,8,1.0,12,1.5
+607,480884,46973,0,0,0,0,1,0,0,0,1,0,0,1,4.0,4,1.0,16,4.0
+608,319510,197788,1,0,0,1,0,0,0,0,0,0,0,1,16.0,16,1.0,0,0.0
+609,311293,74309,0,0,1,1,0,0,0,0,0,1,0,0,8.0,8,1.0,28,3.5
+610,393323,181443,0,0,1,0,0,0,1,0,0,0,1,0,8.0,8,1.0,20,2.5
+611,311966,145582,1,0,0,0,1,0,0,0,0,1,0,0,8.0,8,1.0,12,1.5
+612,406543,90709,0,0,0,0,1,0,0,0,0,1,1,0,12.0,12,1.0,12,1.0
+613,302751,155861,0,1,0,0,0,0,0,0,1,0,1,0,24.0,24,1.0,20,0.83
+614,207585,47241,0,0,0,0,0,1,0,0,1,0,1,0,8.0,8,1.0,8,1.0
+615,275659,21504,0,0,0,1,0,1,0,0,0,0,1,0,20.0,20,1.0,20,1.0
+616,406861,28337,0,0,0,0,1,0,0,1,1,0,0,0,4.0,4,1.0,0,0.0
+617,362463,158731,0,0,0,0,1,0,0,1,1,0,0,0,12.0,12,1.0,16,1.33
+618,411510,30753,0,0,0,0,0,1,0,1,0,1,0,0,8.0,8,1.0,12,1.5
+619,485980,143009,0,0,0,0,1,1,1,0,0,0,0,0,4.0,4,1.0,0,0.0
+620,220964,110716,0,1,0,1,0,0,1,0,0,0,0,0,4.0,4,1.0,20,5.0
+621,315555,151131,0,0,0,0,1,0,1,0,1,0,0,0,,0,0.0,4,5.0
+622,471157,68516,1,0,1,0,1,0,0,0,0,0,0,0,16.0,16,1.0,28,1.75
+623,406859,61140,0,1,0,0,0,1,0,0,0,0,1,0,16.0,16,1.0,4,0.25
+624,464367,152582,0,0,0,0,1,0,0,0,0,1,1,0,12.0,12,1.0,4,0.33
+625,384728,71607,0,1,0,0,0,0,0,1,1,0,0,0,20.0,20,1.0,8,0.4
+626,442739,43642,0,1,0,1,0,0,0,0,0,0,0,1,20.0,20,1.0,4,0.2
+627,345081,39891,0,0,0,0,0,0,0,1,1,1,0,0,16.0,16,1.0,4,0.25
+628,356882,151586,0,0,0,0,0,0,0,0,1,1,1,0,16.0,16,1.0,20,1.25
+629,467710,51296,0,0,0,0,1,0,0,0,1,0,0,1,12.0,12,1.0,12,1.0
+630,320831,133375,0,0,0,0,0,0,1,1,0,0,1,0,16.0,16,1.0,16,1.0
+631,277376,98064,0,0,0,0,0,0,0,1,0,1,1,0,20.0,20,1.0,4,0.2
+632,336337,92259,0,0,0,1,0,0,0,1,0,0,0,1,12.0,12,1.0,24,2.0
+633,205914,12925,0,1,0,0,0,0,1,0,1,0,0,0,8.0,8,1.0,4,0.5
+634,334891,180467,0,1,1,1,0,0,0,0,0,0,0,0,4.0,4,1.0,0,0.0
+635,293350,93423,1,0,0,0,0,1,0,0,0,0,1,0,16.0,16,1.0,4,0.25
+636,248133,102234,0,0,0,1,0,0,0,0,0,1,1,0,28.0,28,1.0,4,0.14
+637,463279,66610,0,0,0,0,0,1,0,0,0,1,0,1,8.0,8,1.0,8,1.0
+638,233225,144563,0,0,0,0,1,1,0,0,0,1,0,0,8.0,8,1.0,24,3.0
+639,480066,24214,1,0,0,0,1,0,0,0,1,0,0,0,4.0,4,1.0,12,3.0
+640,298201,68870,0,1,0,0,0,0,0,0,1,0,0,1,4.0,4,1.0,16,4.0
+641,316673,22103,0,0,0,0,0,1,0,0,0,1,1,0,12.0,12,1.0,24,2.0
+642,435702,113156,1,0,0,0,0,0,0,0,0,1,0,1,16.0,16,1.0,16,1.0
+643,382302,131574,0,0,0,1,1,1,0,0,0,0,0,0,32.0,32,1.0,16,0.5
+644,308676,103934,1,0,0,0,0,1,0,0,1,0,0,0,4.0,4,1.0,8,2.0
+645,424223,88807,0,1,0,0,0,0,0,1,1,0,0,0,8.0,8,1.0,0,0.0
+646,447228,44269,0,1,0,0,0,1,0,0,0,0,1,0,20.0,20,1.0,12,0.6
+647,277880,55032,1,0,0,0,0,0,1,0,0,0,1,0,12.0,12,1.0,16,1.33
+648,222776,158303,0,0,0,0,0,1,0,0,1,1,0,0,12.0,12,1.0,20,1.67
+649,472568,61872,1,0,0,0,0,1,0,0,0,0,0,1,12.0,12,1.0,12,1.0
+650,326433,106534,1,0,0,1,0,0,0,0,0,0,0,1,8.0,8,1.0,12,1.5
+651,339979,90419,0,1,0,0,1,1,0,0,0,0,0,0,4.0,4,1.0,12,3.0
+652,444103,137728,0,0,1,0,1,0,0,0,0,1,0,0,8.0,8,1.0,4,0.5
+653,453724,109824,0,0,0,1,0,0,0,1,0,0,0,1,12.0,12,1.0,16,1.33
+654,487460,127989,1,1,0,0,0,0,0,1,0,0,0,0,8.0,8,1.0,16,2.0
+655,483102,182886,0,1,0,1,0,0,0,0,0,0,1,0,8.0,8,1.0,20,2.5
+656,249702,127895,0,0,0,0,0,0,1,0,0,1,0,1,12.0,12,1.0,12,1.0
+657,474378,61869,0,0,0,0,0,1,0,0,1,1,0,0,4.0,4,1.0,24,6.0
+658,362653,145831,0,0,0,0,0,0,0,1,0,1,1,0,4.0,4,1.0,12,3.0
+659,370714,71649,0,0,0,0,0,1,0,1,0,1,0,0,16.0,16,1.0,12,0.75
+660,258959,28713,0,1,0,0,0,1,1,0,0,0,0,0,12.0,12,1.0,12,1.0
+661,405274,24301,0,0,0,0,0,0,1,1,1,0,0,0,4.0,4,1.0,12,3.0
+662,220897,99513,1,0,1,1,0,0,0,0,0,0,0,0,8.0,8,1.0,12,1.5
+663,454203,156314,0,0,0,0,0,0,0,0,1,0,1,1,4.0,4,1.0,8,2.0
+664,462761,164531,0,0,0,1,0,0,0,1,0,0,1,0,12.0,12,1.0,16,1.33
+665,325579,59883,0,0,0,0,0,1,0,0,1,0,1,0,8.0,8,1.0,20,2.5
+666,473233,154694,0,0,1,1,0,0,0,0,1,0,0,0,16.0,16,1.0,8,0.5
+667,420925,199716,1,0,0,0,0,1,0,0,0,1,0,0,12.0,12,1.0,0,0.0
+668,200999,106467,0,1,1,0,0,0,1,0,0,0,0,0,12.0,12,1.0,8,0.67
+669,251803,124949,1,1,0,0,0,1,0,0,0,0,0,0,12.0,12,1.0,12,1.0
+670,498285,29863,0,1,0,0,0,0,0,0,1,0,1,0,12.0,12,1.0,16,1.33
+671,262904,122418,0,1,0,0,0,1,0,1,0,0,0,0,16.0,16,1.0,4,0.25
+672,422921,59595,0,0,0,0,0,1,1,0,0,0,1,0,4.0,4,1.0,4,1.0
+673,420628,177529,1,0,0,0,0,0,0,0,1,0,1,0,4.0,4,1.0,8,2.0
+674,324917,104702,0,0,0,1,0,1,0,0,1,0,0,0,12.0,12,1.0,12,1.0
+675,430855,75453,0,0,0,0,1,0,1,0,0,0,1,0,4.0,4,1.0,12,3.0
+676,285582,74778,1,1,0,0,0,0,1,0,0,0,0,0,16.0,16,1.0,4,0.25
+677,496860,49463,0,1,0,0,0,1,1,0,0,0,0,0,16.0,16,1.0,0,0.0
+678,423904,187806,0,1,0,0,1,0,0,0,0,0,1,0,16.0,16,1.0,12,0.75
+679,204938,36785,1,0,1,1,0,0,0,0,0,0,0,0,16.0,16,1.0,4,0.25
+680,375284,196746,0,0,0,0,1,0,0,1,1,0,0,0,12.0,12,1.0,12,1.0
+681,244556,29179,0,0,0,0,1,0,0,0,0,1,0,1,8.0,8,1.0,8,1.0
+682,229152,127759,0,0,0,0,0,0,1,1,0,0,0,1,,0,0.0,16,5.0
+683,361553,64970,0,0,1,0,1,0,0,0,0,0,1,0,12.0,12,1.0,12,1.0
+684,271352,113220,0,0,1,1,0,0,0,0,0,0,0,1,4.0,4,1.0,12,3.0
+685,295760,74282,1,0,0,0,1,0,0,0,1,0,0,0,,0,0.0,12,5.0
+686,318293,33441,0,0,0,0,0,0,1,1,0,0,1,0,16.0,16,1.0,16,1.0
+687,383619,40056,0,0,1,0,0,0,0,0,1,0,0,1,20.0,20,1.0,4,0.2
+688,314795,136938,0,0,0,0,1,0,1,0,0,0,1,0,20.0,20,1.0,4,0.2
+689,478514,189564,0,0,0,1,1,0,0,0,0,1,0,0,8.0,8,1.0,4,0.5
+690,404950,188127,0,0,0,1,1,1,0,0,0,0,0,0,12.0,12,1.0,16,1.33
+691,445341,106081,1,0,0,1,1,0,0,0,0,0,0,0,8.0,8,1.0,12,1.5
+692,449316,77666,1,0,1,0,0,1,0,0,0,0,0,0,16.0,16,1.0,16,1.0
+693,302075,138096,0,0,0,0,0,1,1,1,0,0,0,0,16.0,16,1.0,28,1.75
+694,456352,36009,0,0,0,1,0,0,1,0,0,0,1,0,8.0,8,1.0,12,1.5
+695,490129,92546,0,1,0,0,0,0,0,0,1,0,1,0,12.0,12,1.0,8,0.67
+696,433637,55286,0,1,0,0,1,1,0,0,0,0,0,0,12.0,12,1.0,20,1.67
+697,210566,31212,0,0,0,0,0,0,1,0,1,0,0,1,,0,0.0,24,5.0
+698,402223,122169,0,1,1,0,0,0,1,0,0,0,0,0,24.0,24,1.0,4,0.17
+699,244785,198667,1,0,0,0,0,0,0,0,0,1,0,1,20.0,20,1.0,24,1.2
+700,402291,166371,0,0,0,0,0,1,0,0,1,0,0,1,16.0,16,1.0,20,1.25
+701,313478,78519,0,1,0,0,0,0,0,1,0,0,1,0,8.0,8,1.0,28,3.5
+702,438841,134520,1,0,1,0,0,0,0,0,1,0,0,0,16.0,16,1.0,12,0.75
+703,213094,70655,0,0,0,0,1,0,0,0,1,0,0,1,16.0,16,1.0,0,0.0
+704,350978,43948,0,0,1,1,0,0,0,1,0,0,0,0,4.0,4,1.0,12,3.0
+705,254726,172807,0,0,0,0,0,0,1,0,1,1,0,0,16.0,16,1.0,12,0.75
+706,434372,189536,0,0,0,1,0,0,0,0,0,0,1,1,8.0,8,1.0,8,1.0
+707,345034,80288,0,1,0,0,0,0,0,0,1,0,1,0,,0,0.0,20,5.0
+708,232062,187285,0,1,0,0,1,0,0,0,1,0,0,0,12.0,12,1.0,0,0.0
+709,411841,19443,0,0,0,0,0,1,0,1,0,1,0,0,4.0,4,1.0,12,3.0
+710,453578,18206,0,1,0,0,1,0,0,0,0,0,1,0,4.0,4,1.0,20,5.0
+711,241167,176591,0,0,0,0,0,1,0,0,0,0,1,1,4.0,4,1.0,16,4.0
+712,371904,166797,0,0,0,0,0,0,0,0,1,1,0,1,8.0,8,1.0,4,0.5
+713,496726,73973,1,0,0,0,0,0,1,0,0,0,0,1,4.0,4,1.0,12,3.0
+714,285423,39451,1,1,0,0,0,0,0,0,1,0,0,0,12.0,12,1.0,16,1.33
+715,235681,76552,0,1,0,0,0,0,1,0,0,0,1,0,12.0,12,1.0,8,0.67
+716,204840,172449,0,0,0,0,0,0,1,1,1,0,0,0,4.0,4,1.0,16,4.0
+717,405744,175646,1,1,1,0,0,0,0,0,0,0,0,0,20.0,20,1.0,20,1.0
+718,247418,116395,1,0,0,0,0,0,1,0,0,1,0,0,12.0,12,1.0,12,1.0
+719,268489,54245,0,0,0,0,1,0,1,0,0,0,1,0,20.0,20,1.0,8,0.4
+720,328945,165590,0,0,1,0,0,0,0,0,0,1,0,1,4.0,4,1.0,8,2.0
+721,410193,141893,0,0,0,0,0,1,1,0,0,1,0,0,4.0,4,1.0,16,4.0
+722,345034,40830,1,0,1,0,1,0,0,0,0,0,0,0,20.0,20,1.0,4,0.2
+723,364372,64143,0,0,0,1,1,0,0,0,0,0,0,1,8.0,8,1.0,8,1.0
+724,290738,129138,0,1,1,0,0,0,0,1,0,0,0,0,,0,0.0,0,0.0
+725,338965,85609,0,1,0,0,0,1,0,1,0,0,0,0,16.0,16,1.0,4,0.25
+726,467121,143254,0,0,0,0,1,0,0,0,0,0,1,1,12.0,12,1.0,4,0.33
+727,389983,27107,0,1,0,0,0,0,1,0,0,1,0,0,4.0,4,1.0,12,3.0
+728,483851,56319,0,0,0,1,1,0,0,0,0,1,0,0,4.0,4,1.0,8,2.0
+729,424730,165939,1,1,0,1,0,0,0,0,0,0,0,0,16.0,16,1.0,12,0.75
+730,249321,48359,0,1,1,0,0,0,1,0,0,0,0,0,8.0,8,1.0,4,0.5
+731,307430,81205,1,1,0,0,0,0,0,0,0,0,1,0,12.0,12,1.0,20,1.67
+732,347301,37884,1,0,1,0,0,0,0,0,1,0,0,0,20.0,20,1.0,24,1.2
+733,203743,110893,0,0,0,0,1,1,0,0,1,0,0,0,8.0,8,1.0,8,1.0
+734,292487,183232,1,0,0,0,1,0,0,0,1,0,0,0,16.0,16,1.0,8,0.5
+735,322767,39677,1,0,0,0,1,0,0,0,0,0,1,0,8.0,8,1.0,8,1.0
+736,358514,80208,0,0,0,1,0,1,0,1,0,0,0,0,28.0,28,1.0,8,0.29
+737,427105,139153,0,1,0,0,1,0,0,0,1,0,0,0,8.0,8,1.0,8,1.0
+738,357046,63987,0,0,0,1,1,0,0,0,0,0,1,0,24.0,24,1.0,20,0.83
+739,307964,26776,1,0,1,0,0,0,0,0,0,0,0,1,16.0,16,1.0,16,1.0
+740,316623,39023,0,1,0,0,0,0,0,0,1,1,0,0,8.0,8,1.0,8,1.0
+741,228760,62415,1,0,0,0,0,0,0,1,0,1,0,0,,0,0.0,16,5.0
+742,338286,150402,1,1,0,0,0,0,1,0,0,0,0,0,24.0,24,1.0,24,1.0
+743,438728,154962,1,0,0,0,1,0,0,0,0,0,1,0,16.0,16,1.0,4,0.25
+744,404981,192372,0,0,0,0,1,0,0,0,1,0,1,0,12.0,12,1.0,8,0.67
+745,359528,25575,0,1,0,0,0,0,1,0,1,0,0,0,12.0,12,1.0,8,0.67
+746,349781,163587,1,0,1,0,0,0,0,0,1,0,0,0,16.0,16,1.0,12,0.75
+747,385237,59843,0,0,0,0,0,0,0,1,0,1,1,0,24.0,24,1.0,8,0.33
+748,384632,183937,0,0,0,0,1,1,0,0,0,1,0,0,8.0,8,1.0,20,2.5
+749,306961,148491,0,0,0,0,1,1,0,1,0,0,0,0,8.0,8,1.0,16,2.0
+750,396479,30844,0,0,0,0,1,0,0,0,1,1,0,0,20.0,20,1.0,4,0.2
+751,353952,189743,0,0,0,0,0,1,0,0,0,0,1,1,12.0,12,1.0,0,0.0
+752,458229,72535,0,1,0,0,0,0,1,0,0,0,0,1,12.0,12,1.0,28,2.33
+753,423828,162472,0,0,1,0,1,0,0,1,0,0,0,0,12.0,12,1.0,0,0.0
+754,369405,195449,0,0,0,0,0,1,0,0,1,0,1,0,28.0,28,1.0,16,0.57
+755,394880,59505,1,0,0,0,0,0,1,0,0,0,1,0,16.0,16,1.0,20,1.25
+756,216125,75680,0,0,1,1,0,1,0,0,0,0,0,0,8.0,8,1.0,8,1.0
+757,269805,185734,0,1,1,0,0,0,1,0,0,0,0,0,16.0,16,1.0,0,0.0
+758,413799,157617,0,0,0,0,0,1,1,0,1,0,0,0,,0,0.0,12,5.0
+759,302053,181745,0,0,0,0,0,1,1,0,0,0,0,1,4.0,4,1.0,16,4.0
+760,473261,167980,0,0,1,0,0,0,0,1,0,0,0,1,20.0,20,1.0,16,0.8
+761,445051,106289,1,1,1,0,0,0,0,0,0,0,0,0,20.0,20,1.0,4,0.2
+762,212245,132984,0,1,0,0,0,0,0,0,0,1,1,0,20.0,20,1.0,16,0.8
+763,480413,113380,0,0,1,0,0,0,0,0,0,1,0,1,16.0,16,1.0,12,0.75
+764,352346,53256,0,0,0,0,1,1,0,0,0,0,1,0,,0,0.0,4,5.0
+765,449161,26399,0,1,0,0,0,0,0,1,0,1,0,0,8.0,8,1.0,20,2.5
+766,400195,66688,1,0,0,0,1,1,0,0,0,0,0,0,8.0,8,1.0,16,2.0
+767,360809,41649,0,1,1,0,0,0,0,0,0,0,0,1,8.0,8,1.0,16,2.0
+768,487223,143779,0,0,1,0,1,0,0,0,0,1,0,0,20.0,20,1.0,16,0.8
+769,364632,163079,0,0,0,1,0,0,0,0,1,1,0,0,4.0,4,1.0,8,2.0
+770,332071,116680,1,0,0,0,1,0,1,0,0,0,0,0,8.0,8,1.0,12,1.5
+771,396359,89174,1,0,1,0,0,0,0,1,0,0,0,0,20.0,20,1.0,8,0.4
+772,320385,92456,0,0,1,1,0,1,0,0,0,0,0,0,12.0,12,1.0,16,1.33
+773,380098,93796,0,1,0,0,0,0,0,0,1,1,0,0,16.0,16,1.0,12,0.75
+774,385140,102819,1,1,0,0,0,0,0,0,0,0,1,0,,0,0.0,12,5.0
+775,473604,163744,0,0,0,0,0,1,1,0,0,1,0,0,8.0,8,1.0,16,2.0
+776,296380,178265,0,0,0,0,0,0,1,1,0,0,0,1,16.0,16,1.0,0,0.0
+777,217741,187165,0,1,0,0,0,0,1,0,0,0,1,0,16.0,16,1.0,8,0.5
+778,468179,141762,0,0,1,0,1,0,0,0,0,0,1,0,4.0,4,1.0,8,2.0
+779,265103,157564,1,0,0,0,0,0,1,0,0,1,0,0,8.0,8,1.0,4,0.5
+780,318917,57513,0,0,0,0,1,1,0,0,0,0,0,1,16.0,16,1.0,16,1.0
+781,335389,73388,0,0,1,0,0,0,1,0,0,0,0,1,,0,0.0,16,5.0
+782,344494,41122,0,1,0,0,1,0,0,0,0,0,1,0,8.0,8,1.0,20,2.5
+783,468987,43930,1,0,0,0,0,1,0,0,0,1,0,0,8.0,8,1.0,8,1.0
+784,480547,64279,0,1,0,0,0,0,0,1,1,0,0,0,16.0,16,1.0,12,0.75
+785,375866,122880,0,0,0,0,0,0,0,1,0,1,1,0,8.0,8,1.0,8,1.0
+786,438184,138257,0,1,0,0,0,0,0,0,1,0,0,1,8.0,8,1.0,20,2.5
+787,221519,42324,1,0,0,0,0,1,1,0,0,0,0,0,12.0,12,1.0,8,0.67
+788,207393,114298,1,1,0,0,0,0,0,0,1,0,0,0,8.0,8,1.0,8,1.0
+789,356242,168810,0,0,0,0,0,0,0,0,1,1,1,0,12.0,12,1.0,20,1.67
+790,394532,87084,0,1,0,0,0,0,0,0,0,1,0,1,4.0,4,1.0,16,4.0
+791,322534,160752,0,0,0,0,1,1,0,0,0,1,0,0,8.0,8,1.0,4,0.5
+792,296770,177535,0,1,0,0,0,0,1,1,0,0,0,0,12.0,12,1.0,16,1.33
+793,359411,181579,0,0,0,0,0,0,1,0,1,1,0,0,12.0,12,1.0,16,1.33
+794,215607,147171,0,0,0,0,1,1,0,0,1,0,0,0,16.0,16,1.0,12,0.75
+795,252366,29720,0,0,1,0,0,0,0,1,0,0,0,1,16.0,16,1.0,4,0.25
+796,481102,62941,1,0,1,0,0,1,0,0,0,0,0,0,4.0,4,1.0,4,1.0
+797,264008,165244,0,0,0,0,0,1,0,1,0,0,1,0,8.0,8,1.0,0,0.0
+798,344748,198569,1,0,1,0,0,1,0,0,0,0,0,0,4.0,4,1.0,20,5.0
+799,491171,95359,1,0,0,1,0,1,0,0,0,0,0,0,8.0,8,1.0,40,5.0
+800,213500,95032,0,0,0,0,0,0,0,1,1,0,0,1,12.0,12,1.0,20,1.67
+801,450193,132018,1,0,0,0,1,1,0,0,0,0,0,0,8.0,8,1.0,8,1.0
+802,341561,154584,0,0,0,0,0,1,0,0,1,0,0,1,16.0,16,1.0,24,1.5
+803,491900,180115,0,0,0,1,0,0,1,0,0,0,0,1,8.0,8,1.0,4,0.5
+804,206970,151088,0,1,0,1,0,0,0,0,1,0,0,0,16.0,16,1.0,8,0.5
+805,316695,25455,0,0,0,0,1,0,0,0,1,0,1,0,12.0,12,1.0,4,0.33
+806,382649,34601,1,1,0,0,0,0,0,0,0,1,0,0,8.0,8,1.0,16,2.0
+807,256997,194583,0,1,0,0,0,1,0,1,0,0,0,0,12.0,12,1.0,20,1.67
+808,365277,184349,0,1,0,0,0,1,0,0,1,0,0,0,16.0,16,1.0,12,0.75
+809,485349,152806,0,1,0,1,0,0,0,0,0,0,1,0,12.0,12,1.0,12,1.0
+810,480262,53863,0,1,0,1,0,1,0,0,0,0,0,0,12.0,12,1.0,8,0.67
+811,486303,95202,0,1,1,0,0,0,0,1,0,0,0,0,,0,0.0,4,5.0
+812,258131,55993,0,0,0,0,0,0,0,1,0,0,1,1,4.0,4,1.0,8,2.0
+813,463050,126936,0,1,0,0,0,1,0,1,0,0,0,0,,0,0.0,0,0.0
+814,270756,29333,0,0,1,0,0,0,1,0,0,0,0,1,8.0,8,1.0,28,3.5
+815,231164,109027,1,0,0,0,0,1,1,0,0,0,0,0,8.0,8,1.0,16,2.0
+816,463899,33207,0,1,1,0,0,0,0,0,0,0,1,0,24.0,24,1.0,12,0.5
+817,399007,123759,0,1,0,0,0,0,1,0,0,0,1,0,16.0,16,1.0,12,0.75
+818,417191,61278,0,1,0,0,0,1,0,0,0,0,1,0,24.0,24,1.0,16,0.67
+819,343102,130956,0,1,0,1,0,0,0,0,0,0,1,0,12.0,12,1.0,24,2.0
+820,444359,117529,0,1,1,0,0,0,0,1,0,0,0,0,8.0,8,1.0,12,1.5
+821,456975,148816,1,0,0,0,0,1,0,0,0,0,0,1,8.0,8,1.0,16,2.0
+822,281006,127452,0,0,0,1,0,0,1,0,0,0,0,1,4.0,4,1.0,8,2.0
+823,397983,100855,0,1,0,0,1,0,0,1,0,0,0,0,12.0,12,1.0,0,0.0
+824,355294,76855,0,1,0,0,0,0,0,1,1,0,0,0,4.0,4,1.0,4,1.0
+825,422055,194612,1,0,0,1,0,0,0,0,1,0,0,0,,0,0.0,24,5.0
+826,214989,97653,0,0,0,0,0,1,0,0,1,0,1,0,8.0,8,1.0,16,2.0
+827,269975,157064,1,0,0,0,1,1,0,0,0,0,0,0,20.0,20,1.0,8,0.4
+828,494117,152366,1,0,0,0,1,0,0,0,1,0,0,0,12.0,12,1.0,16,1.33
+829,231915,65141,0,0,1,0,1,1,0,0,0,0,0,0,4.0,4,1.0,24,6.0
+830,458923,198250,1,0,0,0,0,1,0,0,1,0,0,0,8.0,8,1.0,12,1.5
+831,389130,141296,0,1,1,0,0,1,0,0,0,0,0,0,24.0,24,1.0,16,0.67
+832,470729,42046,0,0,1,0,1,0,0,0,0,0,0,1,16.0,16,1.0,8,0.5
+833,474279,30181,1,0,0,0,0,0,1,0,0,0,1,0,24.0,24,1.0,8,0.33
+834,406649,98575,0,0,0,1,0,0,0,0,0,1,1,0,8.0,8,1.0,8,1.0
+835,267055,184081,1,0,0,0,0,0,1,0,0,0,1,0,12.0,12,1.0,24,2.0
+836,276545,156447,0,0,0,1,1,0,0,0,0,0,0,1,12.0,12,1.0,12,1.0
+837,482190,36190,0,0,0,1,0,0,1,0,1,0,0,0,12.0,12,1.0,8,0.67
+838,370077,199736,0,1,0,0,1,0,0,1,0,0,0,0,28.0,28,1.0,12,0.43
+839,231280,154005,0,1,0,1,0,0,0,0,1,0,0,0,24.0,24,1.0,4,0.17
+840,495127,71108,0,1,0,0,0,1,0,0,1,0,0,0,8.0,8,1.0,12,1.5
+841,379036,151388,0,1,1,0,0,1,0,0,0,0,0,0,8.0,8,1.0,0,0.0
+842,275802,166094,1,0,0,0,0,1,0,0,0,0,1,0,8.0,8,1.0,12,1.5
+843,382652,65851,0,1,1,0,0,0,0,1,0,0,0,0,12.0,12,1.0,24,2.0
+844,442575,19464,0,0,1,1,0,0,0,0,0,1,0,0,8.0,8,1.0,8,1.0
+845,250858,124543,1,0,0,1,0,0,0,0,0,0,0,1,8.0,8,1.0,8,1.0
+846,337823,103129,0,0,0,0,1,0,0,1,0,0,1,0,20.0,20,1.0,4,0.2
+847,245935,144115,0,0,0,0,0,0,0,1,0,1,0,1,16.0,16,1.0,12,0.75
+848,370596,14192,0,0,0,0,1,1,0,0,1,0,0,0,12.0,12,1.0,16,1.33
+849,399212,20895,0,1,1,0,0,1,0,0,0,0,0,0,12.0,12,1.0,4,0.33
+850,476076,166707,0,0,0,1,0,0,1,0,0,0,0,1,8.0,8,1.0,12,1.5
+851,467415,130374,0,0,0,1,1,0,0,0,1,0,0,0,8.0,8,1.0,12,1.5
+852,367151,159580,1,0,0,0,0,0,1,0,1,0,0,0,24.0,24,1.0,12,0.5
+853,320912,97724,0,0,0,1,0,0,1,0,0,0,0,1,12.0,12,1.0,12,1.0
+854,227404,36661,0,1,0,1,0,0,0,0,0,0,0,1,20.0,20,1.0,12,0.6
+855,216256,178926,0,0,0,0,1,0,0,0,0,1,0,1,,0,0.0,4,5.0
+856,387475,80569,0,0,0,0,0,0,1,0,1,0,1,0,12.0,12,1.0,16,1.33
+857,249253,26385,0,0,1,0,1,0,0,1,0,0,0,0,8.0,8,1.0,24,3.0
+858,375847,176910,0,0,0,0,0,1,1,0,0,1,0,0,16.0,16,1.0,4,0.25
+859,251309,82035,0,0,0,0,0,0,0,1,1,0,0,1,20.0,20,1.0,12,0.6
+860,384041,50345,0,0,0,0,0,1,0,1,1,0,0,0,,0,0.0,8,5.0
+861,477161,106483,0,0,0,0,1,0,1,0,0,1,0,0,4.0,4,1.0,8,2.0
+862,218587,27343,1,0,1,0,0,0,0,0,0,0,0,1,16.0,16,1.0,16,1.0
+863,284722,71235,0,0,1,1,0,0,0,0,0,0,1,0,24.0,24,1.0,16,0.67
+864,223603,138762,1,0,1,0,0,0,1,0,0,0,0,0,4.0,4,1.0,0,0.0
+865,399762,13085,0,0,0,0,1,1,0,0,1,0,0,0,4.0,4,1.0,8,2.0
+866,299484,114094,1,1,0,0,0,0,0,0,0,0,0,1,16.0,16,1.0,20,1.25
+867,407379,70302,0,1,0,0,1,0,0,1,0,0,0,0,12.0,12,1.0,12,1.0
+868,292348,137758,0,0,0,1,0,0,0,1,1,0,0,0,4.0,4,1.0,20,5.0
+869,287417,138395,0,0,0,1,0,0,1,0,1,0,0,0,16.0,16,1.0,12,0.75
+870,200590,66947,0,0,1,0,1,0,0,0,0,0,0,1,,0,0.0,8,5.0
+871,440673,85320,0,1,0,0,0,0,0,1,0,0,1,0,12.0,12,1.0,4,0.33
+872,356709,116973,0,1,0,1,0,0,1,0,0,0,0,0,4.0,4,1.0,12,3.0
+873,386039,186602,1,0,1,1,0,0,0,0,0,0,0,0,8.0,8,1.0,8,1.0
+874,270051,75658,0,0,0,0,1,0,0,1,1,0,0,0,12.0,12,1.0,12,1.0
+875,436580,164864,0,0,0,0,1,1,0,0,0,1,0,0,4.0,4,1.0,8,2.0
+876,202918,113863,1,1,0,0,0,0,0,1,0,0,0,0,,0,0.0,16,5.0
+877,318488,149400,1,0,0,1,0,0,0,1,0,0,0,0,8.0,8,1.0,20,2.5
+878,315256,140817,0,0,0,1,0,0,0,1,1,0,0,0,20.0,20,1.0,20,1.0
+879,489213,18867,0,1,0,1,0,0,0,1,0,0,0,0,4.0,4,1.0,8,2.0
+880,446056,70527,1,0,0,0,0,0,0,0,0,0,1,1,20.0,20,1.0,12,0.6
+881,409633,106178,0,0,0,0,0,0,0,1,0,0,1,1,4.0,4,1.0,12,3.0
+882,451351,49239,0,0,0,0,0,0,0,0,1,1,0,1,8.0,8,1.0,4,0.5
+883,215427,15383,0,0,0,1,0,0,1,0,0,1,0,0,12.0,12,1.0,12,1.0
+884,406498,31959,0,1,0,0,0,0,0,0,0,1,0,1,4.0,4,1.0,12,3.0
+885,365405,108994,0,1,0,1,0,0,1,0,0,0,0,0,8.0,8,1.0,8,1.0
+886,280593,104025,1,0,1,0,0,0,0,0,0,0,0,1,20.0,20,1.0,8,0.4
+887,275781,182917,1,1,0,0,0,0,0,0,0,1,0,0,24.0,24,1.0,8,0.33
+888,278623,51336,1,0,0,0,0,0,0,1,0,0,0,1,16.0,16,1.0,20,1.25
+889,479862,137118,1,0,0,0,0,0,0,0,1,0,0,1,4.0,4,1.0,16,4.0
+890,371046,109092,0,0,0,1,0,0,0,0,1,0,0,1,12.0,12,1.0,12,1.0
+891,226188,133532,0,1,0,0,0,0,1,1,0,0,0,0,4.0,4,1.0,12,3.0
+892,413603,178951,0,0,0,1,1,1,0,0,0,0,0,0,4.0,4,1.0,12,3.0
+893,330993,191499,0,1,0,0,0,0,1,1,0,0,0,0,16.0,16,1.0,20,1.25
+894,487445,144381,0,1,0,1,0,1,0,0,0,0,0,0,,0,0.0,12,5.0
+895,446419,102496,0,1,0,0,0,0,1,1,0,0,0,0,24.0,24,1.0,0,0.0
+896,411668,159943,0,0,0,0,0,0,0,0,1,1,0,1,,0,0.0,12,5.0
+897,274151,49946,0,0,0,0,0,0,1,0,1,0,1,0,16.0,16,1.0,8,0.5
+898,454904,173407,0,0,0,0,0,1,0,1,0,1,0,0,32.0,32,1.0,8,0.25
+899,468542,42955,0,0,0,0,0,1,0,1,0,1,0,0,12.0,12,1.0,20,1.67
+900,317048,61101,0,0,0,0,0,1,0,1,0,0,0,1,,0,0.0,4,5.0
+901,385289,74675,1,0,0,0,0,1,0,0,0,0,1,0,16.0,16,1.0,4,0.25
+902,266676,72409,0,0,1,0,0,0,1,1,0,0,0,0,28.0,28,1.0,4,0.14
+903,254136,100314,0,1,0,0,1,0,0,0,1,0,0,0,20.0,20,1.0,8,0.4
+904,314605,151225,1,0,0,0,1,0,0,1,0,0,0,0,20.0,20,1.0,12,0.6
+905,459648,45195,0,0,0,0,0,1,0,0,1,1,0,0,8.0,8,1.0,4,0.5
+906,295585,86609,0,1,0,0,0,0,1,0,0,0,1,0,8.0,8,1.0,0,0.0
+907,371041,75883,0,0,1,0,0,0,1,0,1,0,0,0,8.0,8,1.0,8,1.0
+908,371542,57193,0,0,0,1,0,0,0,1,0,1,0,0,4.0,4,1.0,12,3.0
+909,419290,32255,0,0,0,0,1,1,0,0,0,0,0,1,4.0,4,1.0,24,6.0
+910,332905,154274,0,1,0,0,0,0,1,1,0,0,0,0,20.0,20,1.0,8,0.4
+911,396616,92842,1,1,0,0,0,0,0,1,0,0,0,0,4.0,4,1.0,8,2.0
+912,225868,72568,0,0,0,1,1,0,0,0,0,0,1,0,16.0,16,1.0,8,0.5
+913,483313,158045,0,0,1,0,0,0,0,1,1,0,0,0,8.0,8,1.0,12,1.5
+914,409500,59021,0,0,0,0,0,0,0,1,1,1,0,0,4.0,4,1.0,20,5.0
+915,347719,136188,1,0,1,0,0,1,0,0,0,0,0,0,16.0,16,1.0,4,0.25
+916,300122,70254,0,0,1,0,1,0,1,0,0,0,0,0,12.0,12,1.0,24,2.0
+917,306186,172369,0,0,0,0,0,0,1,0,0,1,1,0,12.0,12,1.0,8,0.67
+918,317909,48222,0,0,0,0,0,0,0,1,1,1,0,0,16.0,16,1.0,4,0.25
+919,243993,51965,0,0,0,0,0,0,1,0,0,0,1,1,12.0,12,1.0,12,1.0
+920,322095,172458,1,1,0,0,0,0,1,0,0,0,0,0,20.0,20,1.0,12,0.6
+921,305860,60419,0,0,1,1,0,0,0,0,0,0,0,1,20.0,20,1.0,4,0.2
+922,399045,24312,1,0,0,1,0,0,0,0,0,0,0,1,24.0,24,1.0,8,0.33
+923,466188,25075,0,0,0,0,1,1,0,0,0,0,0,1,24.0,24,1.0,20,0.83
+924,328821,44907,1,0,0,0,0,0,0,0,1,0,0,1,12.0,12,1.0,12,1.0
+925,392780,194685,0,1,0,0,0,0,0,0,1,1,0,0,4.0,4,1.0,12,3.0
+926,263701,186881,0,0,0,0,1,0,1,0,0,0,1,0,12.0,12,1.0,12,1.0
+927,485278,149804,0,1,1,0,0,0,0,0,1,0,0,0,,0,0.0,8,5.0
+928,265979,25740,0,0,0,0,1,0,1,0,0,0,0,1,12.0,12,1.0,24,2.0
+929,296060,13802,0,1,1,0,1,0,0,0,0,0,0,0,,0,0.0,8,5.0
+930,314827,183938,0,1,1,0,0,0,0,0,0,1,0,0,8.0,8,1.0,4,0.5
+931,327857,75977,1,0,0,1,0,1,0,0,0,0,0,0,8.0,8,1.0,8,1.0
+932,336696,195946,0,0,1,0,0,0,0,0,1,1,0,0,20.0,20,1.0,24,1.2
+933,381827,61627,0,0,0,0,1,0,0,0,0,1,0,1,,0,0.0,12,5.0
+934,402772,37800,0,0,0,0,1,0,0,0,0,1,1,0,4.0,4,1.0,16,4.0
+935,419875,103666,0,0,0,1,1,0,0,0,0,1,0,0,8.0,8,1.0,24,3.0
+936,370256,147215,0,0,1,0,0,0,0,1,0,1,0,0,16.0,16,1.0,12,0.75
+937,499515,81170,0,1,0,1,0,0,0,0,1,0,0,0,28.0,28,1.0,8,0.29
+938,489669,123869,0,0,1,1,0,0,0,0,1,0,0,0,20.0,20,1.0,16,0.8
+939,318548,157598,0,0,0,0,0,0,0,1,1,1,0,0,20.0,20,1.0,0,0.0
+940,378058,73800,0,0,1,0,0,0,0,1,0,0,0,1,16.0,16,1.0,16,1.0
+941,295093,155387,0,0,0,1,0,0,1,0,0,1,0,0,20.0,20,1.0,8,0.4
+942,255137,77953,0,0,0,1,0,0,0,0,0,1,0,1,4.0,4,1.0,8,2.0
+943,460003,175620,0,0,1,0,0,0,1,1,0,0,0,0,12.0,12,1.0,4,0.33
+944,435210,67087,0,0,0,0,0,0,1,0,1,1,0,0,8.0,8,1.0,12,1.5
+945,408719,21594,0,0,0,0,0,0,0,0,1,0,1,1,20.0,20,1.0,0,0.0
+946,442983,64387,1,0,0,0,1,0,0,0,1,0,0,0,16.0,16,1.0,12,0.75
+947,417183,170553,1,0,1,1,0,0,0,0,0,0,0,0,28.0,28,1.0,0,0.0
+948,245504,60047,0,0,0,0,0,1,0,1,1,0,0,0,16.0,16,1.0,16,1.0
+949,253896,99657,1,1,0,0,0,0,0,1,0,0,0,0,4.0,4,1.0,8,2.0
+950,360681,78417,0,0,0,0,0,1,0,0,1,0,0,1,12.0,12,1.0,12,1.0
+951,217587,140616,0,1,1,0,0,0,0,0,1,0,0,0,12.0,12,1.0,20,1.67
+952,399434,26457,0,1,0,0,0,1,1,0,0,0,0,0,24.0,24,1.0,4,0.17
+953,311115,142051,1,0,0,0,1,0,1,0,0,0,0,0,20.0,20,1.0,4,0.2
+954,468873,130072,0,0,0,0,0,0,0,1,1,0,1,0,8.0,8,1.0,28,3.5
+955,387628,54418,0,0,1,0,0,1,0,0,0,1,0,0,8.0,8,1.0,8,1.0
+956,311418,79285,0,0,1,0,0,1,0,0,0,0,0,1,12.0,12,1.0,20,1.67
+957,282189,153537,1,0,0,0,0,1,0,0,1,0,0,0,12.0,12,1.0,16,1.33
+958,242619,113714,0,0,0,0,0,0,1,0,1,0,0,1,16.0,16,1.0,20,1.25
+959,326593,187624,0,0,0,0,1,0,1,0,0,0,1,0,16.0,16,1.0,16,1.0
+960,219358,105388,1,0,0,0,0,0,1,1,0,0,0,0,12.0,12,1.0,24,2.0
+961,212481,34643,0,0,0,0,0,0,1,0,1,1,0,0,8.0,8,1.0,28,3.5
+962,493117,142931,0,0,0,0,0,1,0,1,0,1,0,0,8.0,8,1.0,8,1.0
+963,256384,79402,0,0,1,0,0,0,0,0,0,1,1,0,8.0,8,1.0,12,1.5
+964,496916,99297,1,0,0,0,0,1,0,0,0,0,1,0,,0,0.0,4,5.0
+965,423393,174792,0,1,0,0,0,0,1,1,0,0,0,0,,0,0.0,28,5.0
+966,473484,177801,1,0,0,0,1,1,0,0,0,0,0,0,,0,0.0,8,5.0
+967,371779,80404,0,0,0,0,1,0,0,0,0,1,1,0,4.0,4,1.0,12,3.0
+968,384400,71324,0,0,1,0,0,0,0,0,1,0,0,1,8.0,8,1.0,4,0.5
+969,217937,143787,1,0,0,0,1,0,0,0,0,1,0,0,4.0,4,1.0,16,4.0
+970,213566,98135,1,0,0,0,0,1,0,0,0,1,0,0,4.0,4,1.0,8,2.0
+971,289192,56182,0,1,0,0,0,0,1,1,0,0,0,0,12.0,12,1.0,0,0.0
+972,403598,73657,0,0,1,0,0,1,0,1,0,0,0,0,4.0,4,1.0,16,4.0
+973,284210,130990,1,0,0,1,0,0,0,1,0,0,0,0,12.0,12,1.0,8,0.67
+974,401410,40083,0,1,0,0,0,0,1,0,0,0,1,0,16.0,16,1.0,24,1.5
+975,337305,22859,0,0,0,1,0,0,0,0,0,1,0,1,12.0,12,1.0,4,0.33
+976,226419,158974,0,0,0,1,0,1,0,0,0,0,0,1,,0,0.0,4,5.0
+977,316248,105993,0,0,0,1,0,0,1,0,1,0,0,0,8.0,8,1.0,4,0.5
+978,319823,180018,0,0,0,0,0,1,0,1,0,1,0,0,16.0,16,1.0,8,0.5
+979,468990,123010,0,1,0,1,0,0,0,0,0,0,0,1,8.0,8,1.0,8,1.0
+980,403627,147220,0,0,0,0,0,0,1,0,1,0,1,0,20.0,20,1.0,8,0.4
+981,317225,57958,0,0,0,0,1,1,0,0,0,0,1,0,16.0,16,1.0,0,0.0
+982,229734,168922,0,0,0,1,0,0,0,1,0,1,0,0,8.0,8,1.0,12,1.5
+983,468070,155375,0,0,0,1,0,0,1,1,0,0,0,0,20.0,20,1.0,12,0.6
+984,252884,174360,0,0,0,1,0,0,0,0,1,0,0,1,4.0,4,1.0,20,5.0
+985,466428,100568,0,0,0,0,0,1,0,0,0,1,0,1,8.0,8,1.0,8,1.0
+986,452565,72172,0,0,0,0,0,1,1,0,0,0,0,1,8.0,8,1.0,28,3.5
+987,220362,170895,0,0,0,1,0,0,1,0,0,0,1,0,8.0,8,1.0,20,2.5
+988,270701,101907,0,0,0,0,0,1,0,1,0,0,1,0,16.0,16,1.0,12,0.75
+989,391764,45376,0,0,1,1,1,0,0,0,0,0,0,0,8.0,8,1.0,12,1.5
+990,305302,145908,0,0,0,0,0,1,0,0,1,1,0,0,20.0,20,1.0,12,0.6
+991,356465,130869,1,0,0,1,0,0,0,0,1,0,0,0,4.0,4,1.0,8,2.0
+992,260584,156699,0,0,0,0,0,1,0,1,0,1,0,0,12.0,12,1.0,20,1.67
+993,264824,101853,0,0,0,1,0,0,1,0,1,0,0,0,12.0,12,1.0,16,1.33
+994,229223,55028,1,0,0,0,1,0,0,0,1,0,0,0,20.0,20,1.0,8,0.4
+995,226950,14602,0,0,0,1,0,0,0,0,0,1,1,0,28.0,28,1.0,12,0.43
+996,280148,36439,0,0,0,0,1,0,0,0,1,0,0,1,4.0,4,1.0,16,4.0
+997,254227,51432,0,0,0,0,0,0,1,0,0,1,0,1,,0,0.0,8,5.0
+998,404381,168755,1,0,0,0,0,0,0,0,1,0,0,1,12.0,12,1.0,8,0.67
+999,265544,17476,0,1,0,1,0,1,0,0,0,0,0,0,16.0,16,1.0,12,0.75
+1000,348762,67260,0,0,0,1,1,0,0,0,0,0,0,1,16.0,16,1.0,8,0.5
+1004,90000,10000,1,1,1,0,0,0,0,0,0,0,0,0,4.0,4,1.0,0,0.0
+1005,500000,10000,0,1,0,0,1,0,0,1,0,0,0,0,4.0,4,1.0,0,0.0
+1006,140000,70000,1,0,0,1,0,0,0,1,0,0,0,0,8.0,8,1.0,24,3.0
+1007,500000,10000,0,1,0,0,1,1,0,0,0,0,0,0,4.0,4,1.0,0,0.0