├── model.pkl ├── encoder.pkl ├── selector.pkl ├── feature_names.pkl ├── request.py ├── app.py ├── model.py ├── ovarianDataset.csv └── Ovarian Cancer.ipynb /model.pkl: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kaleeswara678/Ovarian-Cancer-Prediction/HEAD/model.pkl -------------------------------------------------------------------------------- /encoder.pkl: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kaleeswara678/Ovarian-Cancer-Prediction/HEAD/encoder.pkl -------------------------------------------------------------------------------- /selector.pkl: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kaleeswara678/Ovarian-Cancer-Prediction/HEAD/selector.pkl -------------------------------------------------------------------------------- /feature_names.pkl: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kaleeswara678/Ovarian-Cancer-Prediction/HEAD/feature_names.pkl -------------------------------------------------------------------------------- /request.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | """ 3 | Created on Wed May 15 18:39:39 2024 4 | 5 | @author: kalee 6 | """ 7 | 8 | -------------------------------------------------------------------------------- /app.py: -------------------------------------------------------------------------------- 1 | import pickle 2 | import numpy as np 3 | from flask import Flask, render_template, request, jsonify 4 | 5 | app = Flask(__name__) 6 | 7 | # Load the model and feature names 8 | model = pickle.load(open('model.pkl', 'rb')) 9 | feature_names = pickle.load(open('feature_names.pkl', 'rb')) 10 | 11 | @app.route('/') 12 | def index(): 13 | return render_template('index.html', features=feature_names) 14 | 15 | @app.route('/predict', methods=['POST']) 16 | def predict(): 17 | if request.method == 'POST': 18 | try: 19 | # Get the user input 20 | user_input = [float(request.form[f'feature{i+1}']) for i in range(len(feature_names))] 21 | 22 | # Prepare the user input as a numpy array 23 | user_input_arr = np.array(user_input).reshape(1, -1) 24 | 25 | # Predict the result 26 | prediction = model.predict(user_input_arr) 27 | prediction_proba = model.predict_proba(user_input_arr) 28 | 29 | # Prepare the prediction result 30 | result = f"Predicted type: {prediction[0]} with probability {prediction_proba[0][model.classes_.tolist().index(prediction[0])]:.2f}" 31 | 32 | return render_template('index.html', features=feature_names, prediction=result) 33 | 34 | except Exception as e: 35 | return str(e) 36 | 37 | if __name__ == '__main__': 38 | app.run(debug=True) 39 | -------------------------------------------------------------------------------- /model.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import pandas as pd 3 | import pickle 4 | from sklearn.model_selection import train_test_split 5 | from sklearn.svm import SVC 6 | from sklearn.metrics import accuracy_score, confusion_matrix, classification_report 7 | 8 | # Load dataset 9 | data_frame = pd.read_csv('C:/Users/kalee/Downloads/EDUCATION - STUDY MATERIALS/SEMESTER 6/MINOR PROJECT -4/ovarianDataset.csv') 10 | 11 | # Prepare the data 12 | X = data_frame.drop('TYPE', axis=1) # Feature matrix 13 | y = data_frame['TYPE'] # Target variable 14 | 15 | # Split the dataset into training and testing sets 16 | X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) 17 | 18 | # Initialize and train the SVM classifier 19 | svm_classifier = SVC(kernel='linear', probability=True, random_state=42) 20 | svm_classifier.fit(X_train, y_train) 21 | 22 | # Evaluate the model 23 | y_pred = svm_classifier.predict(X_test) 24 | accuracy = accuracy_score(y_test, y_pred) 25 | conf_matrix = confusion_matrix(y_test, y_pred) 26 | classification_rep = classification_report(y_test, y_pred) 27 | 28 | # Print evaluation metrics 29 | print(f"Accuracy: {accuracy:.4f}") 30 | print("\nConfusion Matrix:") 31 | print(conf_matrix) 32 | print("\nClassification Report:") 33 | print(classification_rep) 34 | 35 | # Save the trained model and related objects 36 | with open('model.pkl', 'wb') as model_file: 37 | pickle.dump(svm_classifier, model_file) 38 | 39 | with open('feature_names.pkl', 'wb') as feature_file: 40 | pickle.dump(X.columns.tolist(), feature_file) 41 | -------------------------------------------------------------------------------- /ovarianDataset.csv: -------------------------------------------------------------------------------- 1 | AFP,AG,Age,ALB,ALP,ALT,AST,BASO#,BASO%,BUN,Ca,CA125,CA19-9,CA72-4,CEA,CL,CO2CP,CREA,DBIL,EO#,EO%,GGT,GLO,GLU.,HCT,HE4,HGB,IBIL,K,LYM#,LYM%,MCH,MCV,Menopause,Mg,MONO#,MONO%,MPV,Na,NEU,PCT,PDW,PHOS,PLT,RBC,RDW,TBIL,TP,UA,TYPE 2 | 3.58,19.36,47,45.4,56,11,24,0.01,0.3,5.35,2.48,15.36,36.48,6.42,1.4,107.4,19.9,103,2,0.04,1,16,28.5,4.67,0.273,183.94,89,3.5,5.36,0.65,16.8,33.7,103.4,0,0.78,0.22,5.7,11.7,141.3,76.2,0.09,13.4,1.46,74,2.64,13.7,5.5,73.9,396.4,0 3 | 34.24,23.98,61,39.9,95,9,13,0.02,0.3,3.21,2.62,2444,19.98,10.17,2.46,100.1,22.3,45,2.6,0.04,0.5,13,32.1,10.5,0.417,934.1,128,4.2,4.38,1.27,17.2,26.2,85.3,1,0.82,0.41,5.5,10,142,76.5,0.3,11.2,1.09,304,4.89,12.7,6.8,72,119.2,0 4 | 1.5,18.4,39,45.4,77,9,18,0.03,0.6,3.8,2.57,56.08,12.18,10.17,0.77,102.6,22.2,48,4.7,0.03,0.6,10,32.5,4.64,0.391,47.56,131,10.1,4.3,1.1,23.7,28.4,84.6,0,1,0.25,5.4,11.4,138.9,69.7,0.13,15.2,0.97,112,4.62,12,14.8,77.9,209.2,0 5 | 2.75,16.6,45,39.2,26,16,17,0.05,0.74,5.27,2.35,2555,18.41,131.6,0.82,103.2,24,65.7,2.9,0,0.07,17,26.9,4.76,0.372,853.5,123,8,4.7,1.73,27.2,30.6,92.6,1,1.11,0.42,6.55,7.38,139.1,65.5,0.25,17.4,1.25,339,4.01,14.6,10.9,66.1,215.6,0 6 | 2.36,19.97,45,35,47,21,27,0.01,0.1,4.89,2.48,1391,11.15,10.17,0.42,99.6,26.2,70.3,2.2,0.11,1.6,24,31.5,4.07,0.383,404.9,122,3.1,4.77,1.98,28.8,27.7,87,0,1.08,0.69,10,10.4,141,59.5,0.28,11.9,0.94,272,4.4,13.4,5.3,66.5,206,0 7 | 3.79,17.39,44,32.9,118,51,32,0.02,0.42,4.47,2.49,55.97,22.57,0.74,1.33,99.3,20.1,80.7,5.9,0.01,0.19,75,39.4,7.52,0.306,49.95,96.5,2.2,4.89,0.52,11.1,21.8,69,0,0.93,0.07,1.51,9.49,131.9,86.8,0.265,19.1,0.68,279,4.44,21.7,8.1,72.3,218.2,0 8 | 4.93,21.87,53,41.08,77,18,19,0.02,0.5,2.6,2.24,255.82,34.22,10.17,3.31,103.2,21.3,44,3.13,0.06,1.5,21,30.18,7.1,0.421,183.94,138,5.96,4.2,1.28,32.7,29.7,90.7,0,0.65,0.19,4.8,12.1,137.7,60.5,0.18,16.8,1.5,148,4.64,12.1,9.09,71.08,151.9,0 9 | 3.03,24.05,76,50.4,76,16,23,0.04,0.5,5.05,2.68,39.83,1.97,10.17,3.45,103.5,21.9,68,2.3,0.34,3.9,17,26.6,5.67,0.437,535.9,140,5.1,4.75,1.79,20.5,30.4,94.8,1,0.86,0.37,4.2,12,144.7,70.9,0.24,15.1,1.17,205,4.61,12.8,7.4,77,334.2,0 10 | 1.01,23.63,38,36.3,64,62,47,0,0.4,2.5,2.42,48.14,18.79,10.17,0.64,98.3,22.6,41.3,2.4,0,0.8,18,43.3,4.91,0.307,51.38,90,3.1,4.43,1.4,38.3,18.1,61.8,0,0.94,0.2,4.1,9.1,140.1,56.4,0.25,17.4,1.01,277,4.96,19.3,5.5,79.6,140.1,0 11 | 4.7,16.86,30,40.8,77,16,12,0.08,1.23,5.78,2.43,21.46,11.68,10.07,0.66,100.7,25.9,64.1,2,0.01,0.23,16,31.1,4.09,0.456,43.8,147,6.4,4.86,3.2,49.7,29.1,90.2,0,0.89,0.35,5.43,6.55,138.6,43.4,0.228,16.9,1.46,348,5.05,14,8.4,71.9,210.3,0 12 | 1.93,23.04,34,51.5,68,7,21,0.01,0.2,3.82,2.8,255.82,34.22,10.17,0.38,105.7,20.5,63,2.1,0,0,12,29.2,3.71,0.348,183.94,105,3.5,4.74,0.82,14.5,24.9,82.7,0,1,0.22,3.9,11.6,144.5,81.4,0.26,13.4,1.16,224,4.21,15.5,5.6,80.7,162.1,0 13 | 4,23.29,63,44.6,71,14,23,0.01,0.2,4.9,2.35,16.65,79.08,10.17,2.19,104,23,74,1.9,0.01,0.2,18,25.2,5.5,0.377,96.47,119,4.2,4.33,0.89,18.2,30.1,95.4,1,0.88,0.36,7.4,8.9,141.6,74,0.19,8.8,1.38,215,3.95,14.9,6.1,69.8,325.2,0 14 | 4.93,16.06,65,45.6,130,32,16,0.04,0.7,2.25,2.63,3271,34.22,10.17,3.31,106.6,28.5,49,1.4,0.02,0.3,39,34,4.94,0.396,1352,123,3,4.26,1.3,22.2,28.4,91.5,1,1.01,0.35,6,9.7,146.9,70.8,0.3,10.4,1.04,306,4.33,12.3,4.4,79.6,234.1,0 15 | 2.13,20.68,61,38.9,71,16,17,0.01,0.17,4.49,2.43,123.1,8.85,1.09,0.53,102.7,22.3,72.4,1.9,0.01,0.26,32,29.6,7.68,0.343,383.6,116,1.9,4.48,0.52,9.85,29.8,88,1,1.05,0.14,2.55,7.43,141.2,87.2,0.189,16.8,1.14,254,3.89,13.7,3.8,68.5,137.6,0 16 | 4.34,24.98,60,32.8,70,35,41,0.04,0.48,5.64,2.22,3499,424,17.01,2,95,21.6,66.2,1.7,0.03,0.31,17,27.8,5.03,0.32,1026,106,3.3,4.78,0.53,6.13,31.4,94.7,1,1.05,0.47,5.43,9.94,136.8,87.7,0.223,20.2,1.02,224,3.38,12.6,5,60.6,215.8,0 17 | 2.54,24.61,70,44.2,135,11,19,0.05,0.6,3.12,2.47,4468,566.1,10.17,1.18,96.3,24.3,50,2.1,0.05,0.6,33,30.9,9.29,0.445,118,146,4.8,4.11,1.92,23.9,28.9,87.9,1,1.03,0.29,3.6,9.8,141.1,71.3,0.29,11,0.7,299,5.06,12.3,6.9,75.1,183.1,0 18 | 2.15,19.77,72,22,69,16,16,0.08,0.82,4.4,1.68,359.7,3.74,2.15,1.57,97,23.6,57,2.1,0.05,0.5,32,26.7,11.7,0.275,157.8,88.3,2.9,3.5,0.55,5.84,26.1,81.5,1,0.8,0.62,6.55,7.83,133,86.3,0.293,16.5,0.88,374,3.38,15.3,5,48.7,304,0 19 | 4.93,15.57,42,35.4,83,10,16,0.04,0.6,2.75,2.49,259.3,34.22,10.17,3.31,103.2,27.9,51,1.7,0.05,0.7,11,32.1,4.52,0.481,59.17,150,4,4.57,1.16,16.2,29.7,95.2,0,0.89,0.44,6.2,10.2,142.1,76.3,0.33,11.8,0.82,321,5.05,12,5.7,67.5,197.7,0 20 | 4.93,19.49,36,27.7,109,41,33,0,0,4.96,2.32,202.1,39.62,10.17,3.31,84.6,31,63.7,2.4,0,0.1,81,32,6.32,0.405,109.1,136,6.3,3.69,1,8.5,29,86.4,1,1.06,0.8,6.1,9,131.4,85.3,0.35,16.1,1.36,390,4.69,13.3,8.7,59.7,224.5,0 21 | 4.64,16.94,47,39.6,135,16,16,0.03,0.3,3.88,2.53,450,20.93,10.17,0.63,99.7,30.2,60,2.5,0.14,1.6,20,27.6,4.47,0.421,334.6,137,3.9,4.14,2.22,25.4,27.9,85.7,0,0.87,0.52,5.9,10.7,142.7,66.8,0.28,12.7,1.06,265,4.91,11.9,6.4,67.2,249.4,0 22 | 4.93,21.5,61,46,61,15,18,0.02,0.3,2.36,2.63,255.82,34.22,10.17,3.31,102,23.5,53,5,0.05,0.8,18,34.3,4.28,0.413,183.94,136,12.8,4,1.28,20.3,29.8,90.4,1,0.88,0.31,4.9,10.9,143,73.7,0.22,13.7,1.2,204,4.57,12.5,17.8,80.3,267,0 23 | 1.17,19.75,48,40.9,59,13,18,0.02,0.2,5.98,2.48,57.94,8.71,10.17,1.44,98.1,27.4,67.6,2.8,0.05,0.5,14,36,6.07,0.394,53.95,128,6.6,4.25,1.57,15.42,30.3,93.1,0,1.16,0.3,2.9,9.7,141,81.01,0.3,10.6,1.09,306,4.23,12.7,9.4,76.9,208.9,0 24 | 17.52,16.76,27,44.5,63,12,14,0.06,1.21,3.81,2.27,18.16,8.41,12.18,0.44,99.8,28.2,72.7,3.4,0.09,1.69,15,21.5,5.52,0.368,29.49,122,5.5,4.06,1.41,27.2,28.7,86.6,0,0.93,0.35,6.69,7.78,140.7,63.2,0.131,17.3,0.93,168,4.25,13.9,8.9,66,172.5,0 25 | 4.93,14.08,51,23.5,44,18,15,0.08,1.3,2.29,1.86,255.82,34.22,10.17,3.31,100.6,29.2,55,1.9,0.13,2.1,16,24.6,8.92,0.437,183.94,143,3.4,3.78,1.6,25.5,30.5,93.2,0,0.81,0.34,5.4,10.6,140.1,65.7,0.28,12.1,0.65,261,4.69,12,5.3,48.1,216,0 26 | 2.7,21.11,24,47.9,60,9,20,0,0,3.37,2.6,27.8,54.92,10.17,0.51,98.8,27.6,79,4.1,0.06,1,11,24.8,4.85,0.432,71.52,133,10.1,4.61,1.54,25.5,27.5,89.3,0,0.9,0.31,5.1,10.5,142.9,68.4,0.29,11.4,1.2,281,4.84,12.3,14.2,72.7,322.1,0 27 | 1.6,21.44,77,45.5,75,12,21,0.03,0.6,7.02,2.74,71.8,0.6,10.17,1.84,107.8,23.1,64,3.4,0.1,1.9,10,24.3,4.18,0.31,113,109,5.3,4.44,1.53,28.6,31.1,98.4,1,0.99,0.43,8,12.4,147.9,60.9,0.18,15.5,1.09,148,3.83,12.6,8.7,69.8,250.9,0 28 | 1.54,15.63,47,43.2,63,14,12,0.06,0.96,5.28,2.32,34.01,243,9.84,2.66,102.8,25.2,71.8,3.3,0.12,1.96,16,31,4.81,0.372,53.35,127,5.4,4.53,1.88,31.2,31.6,92.5,0,0.96,0.46,7.64,8.01,139.1,58.2,0.189,16.8,0.91,236,4.02,14,8.7,74.2,254,0 29 | 1.75,21.77,58,36.7,133,40,29,0.03,0.5,3.58,2.32,38.59,18.36,10.17,2.04,99.9,22.1,54.3,1.7,0.04,0.6,30,37.3,5.2,0.404,92.65,136,4.8,3.77,1.67,26.5,31.3,93.1,1,1.29,0.42,6.7,10.1,140,65.7,0.27,11,1.05,271,4.34,13,6.5,74,165.6,0 30 | 3.18,19.47,50,42.4,107,41,39,0.01,0.2,3.29,2.44,121.1,5.41,10.17,1.15,98.5,28.7,47,2.6,0.07,1.6,62,33.5,7.21,0.403,207.6,127,7.6,4.17,1.11,25.1,27.4,86.9,1,0.88,0.24,5.4,10.6,142.5,67.7,0.3,12.1,1.1,284,4.64,12.1,10.2,75.9,211.5,0 31 | 1.7,21.81,56,38,98,11,19,0.03,0.4,3.16,2.63,389.3,64.05,10.17,1.59,99.3,28.1,49,1.6,0.01,0.1,13,29,6.25,0.386,219.1,123,3.1,4.21,0.75,9,30.6,96,1,1.04,0.47,5.6,11.9,145,84.9,0.4,15.4,1.07,339,4.02,12,4.7,67,185.6,0 32 | 1.38,21.3,39,38,118,86,30,0.05,1.05,2.99,2.43,21.29,10.16,10.17,1.19,99.2,22.5,53.9,1,0.23,5.1,114,35.7,6.23,0.224,62.63,61.8,1.5,4.6,1.43,31.27,18.13,65.6,0,1.29,0.3,6.57,6.33,138.4,56.01,0.328,18.65,1,518,3.41,20.35,2.5,73.7,269.5,0 33 | 1.6,25.09,61,38.8,116,10,15,0.01,0.1,4.77,2.5,279.8,16.6,10.17,3.24,97,21.3,49.5,2.9,0,0.02,17,36,6.79,0.46,310.9,153,7.5,4.19,1.65,13.72,28.9,86.8,1,1.28,0.71,5.9,12.8,139.2,80.31,0.35,17.9,1.32,271,5.3,13.1,10.4,74.8,160.4,0 34 | 1.42,18.78,57,31.8,61,10,14,0.02,0.4,2.18,2.36,196.9,25.84,10.17,0.27,102.8,26.3,50,2.5,0.05,0.9,7,31.9,4.51,0.337,125.5,113,3.3,4.68,1.11,20,31.3,93.4,1,0.87,0.23,4.1,11.6,143.2,74.6,0.32,14.1,1.25,273,3.61,11.7,5.8,63.7,156.4,0 35 | 2.88,14.24,74,39,89,11,18,0.06,0.6,3.8,2.43,446.2,10.29,10.17,1.11,101.9,31,54,2.3,0.19,1.8,12,32.1,5.44,0.354,773.6,113,4.2,4.94,1.81,17.5,28,87.8,1,0.97,0.53,5.1,10.3,142.2,75,0.4,11.7,1.04,394,4.03,12.6,6.5,71.1,213.3,0 36 | 2,22.67,51,47.6,61,25,22,0.02,0.4,1.89,2.53,237.4,100.4,2.51,19.12,101.8,22.2,59,2.5,0.06,1.1,13,28.4,5.75,0.398,56.88,129,5.3,4.37,1.35,23.9,29.8,91.9,1,1.02,0.27,4.8,12.2,142.3,69.8,0.23,16.8,1.12,185,4.33,11.9,7.8,76,304.3,0 37 | 2.7,15.93,41,35.1,48,10,11,0.05,1.2,5.6,2.28,51.35,0.6,2.81,4.74,104.8,22.3,79.5,1.7,0.01,0.37,12,25.9,3.95,0.346,47.2,119,2.6,4.23,1.33,35.1,30.4,88.5,0,0.98,0.36,9.59,8.27,138.8,53.7,0.202,17.3,1.19,244,3.91,14.3,4.3,61,177.8,0 38 | 7.07,21.49,47,44,88,19,13,0.04,0.8,2.86,2.53,473.3,1.24,5.42,2.24,102.5,23.8,64,2.4,0,0,21,36,5.22,0.376,583.6,122,6.5,4.59,1.81,33.4,27,83.4,0,1.1,0.32,5.82,8.34,143.2,59.9,0.266,17.2,1.21,318,4.5,16.4,8.9,80,225.7,0 39 | 2.02,22.46,75,38.5,58,8,17,0.02,0.3,4.84,2.58,1280,1.25,10.17,2.29,106.2,19.4,91,1.7,0.13,2.1,9,28.4,4.04,0.39,991.5,137,3.3,4.46,2.1,33.4,29.2,93.4,1,1,0.43,6.8,13.1,143.6,57.4,0.13,18.8,0.98,98,4.42,14.1,5,66.9,279.9,0 40 | 2.85,21.81,74,33,108,26,19,0.04,0.6,10.19,2.34,2252,14.81,10.22,4.2,98.6,24,94.4,2.8,0,0,42,31.9,12.44,0.288,3537.6,96.7,2.5,5.31,0.35,5.39,27,80.5,1,0.92,0.31,4.67,8.08,139.1,89.3,0.245,17,1.09,303,3.57,15.5,5.3,64.9,214.2,0 41 | 8.06,16.21,51,27.8,86,14,20,0.04,0.6,2.21,2.23,516.5,34.22,10.17,3.31,105.3,25.1,54,3,0.03,0.4,91,30.8,5.06,0.352,81.52,112,5.4,4.21,1.89,28,27.1,85.2,0,1.17,0.46,6.8,9,142.4,64.2,0.33,8.8,1.12,362,4.13,12.8,8.4,58.6,197.2,0 42 | 3.46,18.04,53,44.9,126,21,20,0.04,0.6,4.51,2.82,386.7,15.52,41.55,2,102.5,30.5,88,2.5,0.02,0.3,20,33.5,5.83,0.364,154.8,120,5.9,4.14,0.94,13.7,29.1,88.1,1,1.2,0.29,4.2,11.8,146.9,81.2,0.31,14.3,1.29,260,4.13,12.9,8.4,78.4,407.2,0 43 | 4.93,20.96,61,45.2,87,15,19,0.02,0.4,3.1,2.71,255.82,34.22,10.17,3.31,101.7,25.6,72,3.4,0.03,0.5,36,36.9,4.96,0.386,183.94,124,6.5,4.36,1.49,27.1,29.5,91.9,1,0.92,0.26,4.7,9.4,143.9,67.3,0.29,10.2,1.2,311,4.2,12,9.9,82.1,330.6,0 44 | 2.49,23.12,64,36.9,133,7,18,0.02,0.3,4.12,2.53,24.51,13.41,10.17,2.95,97.1,25.4,70.9,1.6,0.04,0.6,24,43.2,5.41,0.395,125,125,4.7,4.22,2.12,32.2,26.5,83.9,1,1.17,0.39,5.9,9.1,141.4,61,0.32,9.9,1.11,356,4.71,14,6.3,80.1,304.6,0 45 | 2.75,20.1,60,45.3,97,14,20,0.02,0.6,6.79,2.65,396.8,191.7,10.17,2.47,102.8,28,73,3.2,0.05,1.5,18,34.1,6.06,0.434,128.7,136,6.3,4.6,1.65,48.2,28.6,91.2,1,0.96,0.2,5.8,10.7,146.3,43.9,0.29,11.4,1.15,269,4.76,13.1,9.5,79.4,237.5,0 46 | 0.71,25.63,53,29.6,69,10,22,0.05,0.52,7,2.28,675.7,34.22,10.17,0.85,104.2,19.9,65,1.9,0.01,0.14,14,31.3,6.4,0.429,980.6,140.5,4.1,4.3,1.05,11.92,32.88,100.3,1,0.91,0.64,7.28,9.2,138.6,80.14,0.284,20.47,1.11,308,4.27,11.37,6,60.9,322.8,0 47 | 0.77,10.31,71,36.6,97,12,16,0.02,0.3,2.55,2.16,22.84,465.8,10.17,9.15,104.3,32.2,52,2.5,0.03,0.5,15,35.9,4.44,0.355,56.4,116,5.2,4.11,1.66,25.5,31.4,95.9,1,1.09,0.5,7.7,9.1,142.7,66,0.22,8.9,0.88,236,3.7,12.6,7.7,72.5,142.9,0 48 | 2.16,22.41,45,34.7,88,8,18,0.02,0.2,2.21,2.4,106.3,5.76,10.17,0.31,101,22.4,55,2.9,0.04,0.5,23,26.9,5.24,0.371,219.1,117,5.9,4.21,2.06,25.1,28.7,91.2,0,0.92,0.51,6.2,10.2,141.6,68,0.42,11,1.12,411,4.07,13.3,8.8,61.6,298.7,0 49 | 3.01,20.05,69,34.7,87,23,24,0.12,0.98,3.45,2.33,4159,4.95,33.71,3.95,93.9,25.6,102.6,3.5,0.01,0.08,23,35.5,4.73,0.569,656.2,189,5.1,4.85,1.07,8.44,28,84.4,1,1.08,0.25,1.94,6.51,134.7,88.6,0.565,18.2,0.97,868,6.74,22.2,8.6,70.2,461.4,0 50 | 1.74,18.97,18,38.7,186,14,13,0.04,1.15,4.46,2.28,270.6,0.6,64.88,138.8,104.2,23.5,73.6,5.3,0.04,1.15,14,24.8,5.48,0.338,47.92,112,8.6,3.97,1.13,32.1,28.5,85.8,0,1.03,0.29,8.23,7.64,142.7,57.4,0.155,16.3,1.3,203,3.93,14.6,13.9,63.5,254,0 51 | 2.02,23.7,47,49,75,8,17,0,0,3.63,2.8,2193,170.6,10.17,1.9,100.6,24.4,63,5.6,0.01,0.2,12,31.3,4.69,0.414,166.2,119,8.8,4.3,0.99,23.6,24.8,86.3,0,0.94,0.15,3.6,11,144.4,72.6,0.32,12.1,1.19,292,4.8,16.1,14.4,80.3,239.2,0 52 | 4.25,22.3,46,27.7,110,13,11,0.05,0.77,3.2,2.31,865.7,1000,1.44,85.99,90,19.5,41,9.2,0.08,1.27,36,36.4,6.3,0.365,102.6,119,5.7,3.9,0.58,9.63,28,85.6,0,0.79,0.51,8.41,7.15,125.1,79.9,0.149,17.3,1.2,208,4.27,13.7,14.9,32.9,367.5,0 53 | 2.58,15.82,72,33.7,55,16,13,0.05,0.71,4.68,2.34,107.6,55.01,5.07,0.8,104.7,24,78.1,2.5,0.12,1.85,16,34,4.78,0.4,89.81,127,4.4,4.32,2.44,37.7,27.9,87.7,1,0.84,0.62,9.52,11,140.2,50.2,0.185,20.6,0.99,169,4.56,15.5,6.9,67.7,201.1,0 54 | 2.2,19.33,60,46.8,74,10,24,0.01,0.2,2.4,2.49,48.84,1.57,10.17,24.1,103.9,24.5,55,3.9,0.03,0.6,29,27.7,4.8,0.33,52.25,116,7.9,4.43,1.32,27.4,30.7,97.8,1,0.94,0.18,3.7,10.5,143.3,68.1,0.21,13.1,0.77,199,4.17,12.4,11.8,74.5,257.3,0 55 | 2.8,21.35,25,46.2,28,20,13,0.03,0.42,4.59,2.47,42.66,227.4,2.8,1.39,99.6,20.1,54.2,4.2,0,0.03,28,34,5.52,0.387,37.87,135,8.2,4.45,1.97,28.1,31.1,89.1,0,0.89,0.46,6.55,6.53,136.6,64.9,0.108,17.1,1.33,165,4.34,14.4,12.4,80.2,232.9,0 56 | 2.76,23.42,59,39.6,98,16,17,0.04,0.75,4.08,2.51,3967,2.47,10.58,0.84,99.6,23.3,69.6,2.1,0.08,1.33,32,35.4,5.76,0.413,776.9,136,2.8,4.42,1.52,26,29.7,90.2,1,1,0.35,5.95,6.13,141.9,66,0.233,16.7,1.29,380,4.58,14.3,4.9,75,262.2,0 57 | 4.93,19.54,40,27,54,10,19,0.01,0.1,3.35,2.24,255.82,34.22,10.17,3.31,99.2,23.7,49,2,0,0,11,27.8,4.7,0.297,183.94,93,3.6,4.3,0.83,11.9,24.4,78,0,0.85,0.48,6.9,10.5,133.3,81.1,0.44,10.1,0.91,414,3.81,14.7,5.6,40.4,257,0 58 | 2.03,19.42,58,34.7,106,13,10,0.05,0.74,3.6,2.47,64.88,4.4,4.15,1.37,102,26.1,65.6,2.2,0.04,0.63,27,28.2,5.65,0.375,329.4,126,6.2,4.62,1.59,25,30,89.1,1,1.19,0.44,6.87,7.59,142.9,66.7,0.133,18,0.96,175,4.21,15.5,8.4,62.9,207.3,0 59 | 5.7,22.74,60,41.3,71,12,19,0.04,0.5,3.14,2.42,476,6.99,2.76,0.87,99.5,21.9,58,3.5,0.1,1.3,17,25.2,5.32,0.42,183.94,130,5.6,5.04,1.48,19.9,27,87.3,1,0.95,0.43,5.8,11.2,139.1,72.5,0.31,13.4,0.93,278,4.81,13.4,9.1,66.5,261.9,0 60 | 1.92,26.07,74,28.5,81,23,34,0.02,0.4,3.38,2.55,886.5,0.88,10.17,2.05,97,26.4,64,2.8,0.04,0.7,15,26.2,5.04,0.373,1036,122,3,4.11,0.62,11,30.7,94,1,0.76,0.42,7.5,11.1,138.2,80.4,0.27,13.3,1.12,247,3.97,12.8,5.8,54.7,285.2,0 61 | 2.6,19.82,50,34.2,63,15,18,0.04,0.5,4.29,2.35,21.95,85.09,10.17,0.71,102.4,21.6,50.1,2.7,0.03,0.4,17,38.9,4.8,0.395,55.76,131,7.6,4.32,3.05,41.9,29.6,89.4,0,1.24,0.51,7,12.9,139.5,50.2,0.26,17.4,1.07,199,4.42,14,10.3,73.1,192.7,0 62 | 2.98,18.13,83,44.7,125,7,21,0.03,0.5,5.68,2.69,14.81,52.17,10.17,3.04,98.5,27.6,76,6.7,0.06,1,23,34.2,4.87,0.388,130.6,126,16,5.33,2.06,34.9,33.3,102.6,1,1.2,0.43,7.3,10.4,138.9,56.3,0.3,11.9,1.19,289,3.78,17.4,22.7,78.9,354.2,0 63 | 1.78,26.38,63,40.1,82,14,14,0,0,5.48,2.31,67.14,8.79,1.71,0.65,94,24.6,96.9,3.3,0.05,0.35,23,32.8,3.82,0.402,124.2,133.9,5.6,4.28,2,13.94,32.22,96.8,1,1.21,0.76,5.27,8.58,140.7,80.44,0.305,16.08,1.02,355,4.15,11.89,8.9,72.9,189.8,0 64 | 2.25,18.78,43,27.9,70,16,14,0.07,0.69,5.33,2.25,2233,17.44,4.58,0.59,100.2,24.6,62.8,1.6,0.12,1.15,16,28.5,4.99,0.354,1994.4,111,3.2,4.78,2.55,23.7,24.7,78.7,0,1.05,0.74,6.9,7.28,138.8,67.5,0.299,17.4,1.11,410,4.5,15.9,4.8,56.4,174.9,0 65 | 4.87,18.86,71,43.7,70,13,22,0.02,0.3,6,2.33,675.7,1.96,10.17,1.9,101.6,22.8,72,3.2,0.17,2.7,17,28.9,8.1,0.394,397.8,133,7,4.16,1.49,24,30.5,90.4,1,0.94,0.32,5.2,10.9,138.6,67.8,0.23,12.7,1.5,210,4.36,12.1,10.2,72.6,377.2,0 66 | 2.22,21.13,45,36.9,66,53,36,0.01,0.1,4.83,2.28,38.82,78.85,10.17,47.77,101.6,21.8,59.8,2.6,0.09,1.2,63,32.1,5.46,0.385,62.96,127,5,4.43,1.86,25.6,28.5,86.5,0,1.08,0.45,6.2,11.1,140.1,66.9,0.23,13.5,1.16,210,4.45,13.5,7.6,69,198.3,0 67 | 3.58,22.01,52,47.7,62,9,13,0.04,0.6,5.27,2.61,42.63,23.75,10.17,1.02,102.6,23.9,52,2.2,0.13,2,10,22.1,4.64,0.431,136.1,135,4.3,4.51,2.3,35.3,28.2,90.2,1,1.16,0.43,6.6,11.4,144,55.5,0.28,14.2,1.18,243,4.78,13.2,6.5,69.8,191,0 68 | 4.93,24.58,67,37.7,43,10,20,0.01,0,5.85,2.44,255.82,34.22,10.17,3.31,102,21,70,3.4,0.01,0,27,14.1,6.9,0.332,161.9,103,5.2,4.48,0.81,4,28.6,90.3,1,0.85,0.82,4,9.7,143.1,92,0.17,10.9,1.42,171,3.6,13.4,8.6,51.8,218.1,0 69 | 4.5,18.79,69,31.6,63,7,15,0.03,0.4,5.1,2.15,2741,8.5,10.17,0.55,101.4,26.6,56,1.8,0.03,0.4,15,32.4,11,0.389,183.94,125,3.4,4.36,1.22,16.1,28.5,88.6,1,0.88,0.48,6.3,10.3,145.2,76.8,0.32,11.5,1.67,309,4.39,12.3,5.2,64,282.1,0 70 | 3.28,17.75,66,37.3,73,39,39,0.01,0.1,6.61,2.32,7.33,29.23,10.17,2.63,104.5,24.1,56,3.7,0.06,0.9,11,34.8,4.41,0.351,183.94,117,5.2,4.15,1.33,19.8,30.4,91.2,1,0.93,0.29,4.3,12.4,142.2,74.9,0.19,15.2,0.88,154,3.85,12,8.9,72.1,170.6,0 71 | 2.44,18.32,59,45.7,109,22,30,0.01,0.3,3.43,2.53,224.2,11.57,10.17,1.87,100.9,27.4,61,1.8,0.04,1.4,27,29,4.44,0.389,374.5,124,4.1,4.42,1.05,35.6,28.8,90.3,1,0.96,0.17,5.8,9.7,142.2,56.9,0.22,10.2,1.14,225,4.31,12.1,5.9,74.7,268.4,0 72 | 2.3,24.63,52,45.7,106,32,23,0,0,2.92,2.53,377.1,9.57,10.17,1.65,102.8,21.6,42,2.7,0.06,1.3,22,27.5,4.79,0.42,179.6,133,7.7,4.63,1.7,37.5,30.2,95.2,1,0.94,0.23,5.1,12.3,144.4,56.1,0.26,15.7,1.06,208,4.41,12.1,10.4,73.2,183.8,0 73 | 1.8,20.73,53,29.3,83,10,23,0.05,0.5,2.51,2.34,1777,2.46,10.17,0.2,101.1,25.7,57,1.4,0.04,0.4,15,36,5.1,0.406,403.4,123,2.5,5.13,1.45,15.7,25.7,84.8,1,0.88,0.53,5.7,9.5,142.4,77.7,0.64,10.3,1.27,680,4.79,14,3.9,65.3,198.8,0 74 | 1.77,20.33,51,41.5,54,24,22,0.01,0.2,2.58,2.32,73.11,11.39,10.17,0.85,100.8,24.9,53.2,2.4,0.07,1.3,19,36.8,5.61,0.354,186.5,110,8.4,4.63,1.07,20,26.3,84.5,0,1.27,0.23,4.3,11,141.4,74.2,0.27,14.6,1.14,245,4.19,16.7,10.8,78.3,230.1,0 75 | 2.43,21.35,46,34.3,112,14,11,0.02,0.5,2.64,2.42,1214,1000,49.83,72.06,96.6,24.4,54.8,2.5,0.3,2.9,30,43.2,5.29,0.339,105.2,116.1,2.9,5.05,1.66,17.8,28.4,82.9,0,0.99,0.58,6.2,7.55,137.3,72.6,0.2,11.5,1.06,263,4.09,10.92,5.4,77.5,179.8,0 76 | 4.93,22.75,52,46,127,36,26,0,0,4.37,2.66,77.32,34.22,10.17,3.31,97.8,25.1,59,2.8,0.07,1.3,50,33.6,5.29,0.439,160.2,140,5.4,4.45,1.34,24.9,28.9,90.5,1,1.01,0.24,4.5,10.5,141.2,69.3,0.23,12.3,1.14,224,4.85,11.8,8.2,79.6,272.6,0 77 | 3.69,23.52,59,46.3,75,18,20,0.01,0.3,3.26,2.54,275.7,11.22,10.17,1.25,99.5,24.5,57,3.2,0.03,0.9,12,23.9,5.85,0.382,105,122,5.6,4.12,0.91,28.7,27.7,86.6,1,1.04,0.17,5.4,12,143.4,64.7,0.18,15.7,1.34,148,4.41,13,8.8,70.2,242.1,0 78 | 4.93,23.88,58,24.9,80,23,35,0.01,0.2,1.12,2.29,255.82,34.22,10.17,3.31,95.5,24.3,48,1.7,0.02,0.3,37,26.5,7.46,0.336,183.94,103,1.9,3.78,1.79,27.8,27.6,90.1,1,0.78,0.31,4.8,10.9,139.9,66.9,0.26,13.3,1.12,241,3.73,12.6,3.6,51.4,108.7,0 79 | 2.31,21.92,40,34.2,77,10,9,0.05,0.94,3.61,2.59,165.8,14.99,17.25,0.87,102.4,21.7,68.8,1.5,0.07,1.27,20,32,5.56,0.36,208.7,120,3.9,4.92,1.59,27.9,28.4,84.8,0,1,0.42,7.34,6.43,141.1,62.5,0.172,17.5,0.9,268,4.24,13.6,5.4,66.2,203,0 80 | 2.2,26.04,53,40.4,75,22,22,0.01,0.1,3.47,2.76,124.3,30.19,10.17,1.63,102.5,20.9,66,2.5,0.02,0.2,20,31.7,5.81,0.41,168.4,135,4.5,4.34,1.3,15.8,30,91.1,1,0.96,0.21,2.5,8.8,145.1,81.4,0.23,8.9,0.84,266,4.5,11.4,7,72.1,276.8,0 81 | 1210,21.79,34,35.6,59,12,11,0.03,0.48,3.11,2.17,738.4,20.76,10.33,0.53,101.4,21.8,57.6,3.5,0.01,0.08,17,30,4.74,0.384,107.3,132,6,4.69,0.74,11,31.4,91.7,0,1.05,0.39,5.78,9.08,140.3,82.7,0.195,17.4,0.99,214,4.2,14.7,9.5,65.6,242.2,0 82 | 1.76,25.71,64,38.9,77,15,23,0.02,0.2,5.12,2.55,3950,7.04,10.17,2.26,104.7,19.3,77,2.5,0.08,0.8,22,20.9,11.19,0.38,517.4,133,4,4.41,1.15,12.1,25.5,80.4,1,0.83,0.5,5.3,11.9,145.3,81.6,0.25,15.4,1.02,210,4.74,13.3,6.5,59.8,323.2,0 83 | 3.41,18.76,45,39.5,64,68,48,0.05,0.91,4.87,2.38,23.96,27.92,6.05,3.23,100.5,23,56.8,2.9,0,0.04,58,32.4,5.3,0.466,45.26,153,6.2,4.66,1.96,33.5,30.5,93,0,0.84,0.37,6.34,6.78,137.6,59.3,0.133,16.5,0.98,196,5.01,15,9.1,71.9,269.3,0 84 | 1.2,21.79,23,40.2,97,16,14,0,0.3,3.48,2.28,11.83,4.47,1.85,0.21,98.8,22.5,56.5,6.3,0,0.1,22,39.3,4.94,0.389,34.69,128,10.8,4.09,0.8,10.3,29.9,90.8,0,1.03,0.4,5.8,9.1,139,83.5,0.186,16.4,1.15,205,4.28,11.5,17.1,79.5,236.9,0 85 | 2.19,18.33,35,38.4,57,13,9,0.02,0.45,3.08,2.61,14.88,7.14,11.63,1.08,102.7,21.9,59.9,2.4,0.02,0.46,9,32.5,4.93,0.395,35.67,128.8,3.4,4.33,1.29,26.69,29.3,89.9,0,1.18,0.25,5.23,10.33,138.6,67.17,0.165,16.22,0.79,160,4.4,13.59,5.8,70.9,96,0 86 | 0.95,24.51,41,28.1,61,15,13,0.05,0.5,5.46,2.17,461.2,48.66,1.47,0.54,98.8,20.9,59.9,1.6,0.01,0.1,14,22.9,4.23,0.285,261.9,92.3,2.9,4.41,0.91,9.43,26.9,82.9,1,0.91,0.53,5.55,8.71,139.8,84.4,0.308,17.4,1.09,354,3.43,13.4,4.5,51,252.2,0 87 | 1.49,23.55,57,31.4,89,23,26,0.01,0.1,5.56,2.33,328,20.51,10.17,2.63,94.5,24.4,80.2,7.3,0.04,0.5,31,37.8,6.96,0.383,487,125,12.2,4.45,1.95,26.6,29.1,89.3,1,0.84,0.43,5.9,14.5,138,66.9,0.31,22.5,1.02,213,4.29,14.9,19.5,69.2,616.7,0 88 | 2.73,21.47,42,45.1,88,15,15,0.02,0.4,2.98,2.54,255.5,9.53,10.17,1.5,103.2,23.6,58,2.3,0.05,0.9,9,28.8,4.52,0.387,140.9,123,5.1,4.67,1.65,30.4,29.1,91.7,0,0.96,0.28,5.2,12,143.6,63.1,0.27,15.4,1.03,228,4.22,12.3,7.4,73.9,248.9,0 89 | 1.86,17.37,60,39.1,72,12,28,0.01,0.2,4.18,2.57,5000,6,10.17,2.01,102.3,28,53,2.2,0,0,16,35.5,4.99,0.446,421.1,142,5.2,4.37,2.36,42.5,30.7,96.3,1,0.94,0.31,5.6,10.8,143.3,51.7,0.24,11.9,0.91,220,4.63,13.4,7.4,74.6,284.4,0 90 | 508,19.28,24,36.5,71,12,12,0.05,0.58,4.27,2.36,124.6,17.09,0.63,0.51,99.1,24.3,59.9,2.4,0.01,0.07,14,31,4.73,0.341,46.34,114,4.1,4.48,1.65,18.7,30.5,91,0,0.98,0.67,7.59,6.76,138.2,73.1,0.192,16.9,0.87,284,3.75,13.5,6.5,67.5,210.6,0 91 | 7.87,23.78,46,26.9,57,30,23,0.03,0.4,2.67,2.5,1466,7.81,10.17,1.01,94.9,27,62.4,2.8,0.08,1,24,30.9,5.21,0.43,860.5,136,3.8,3.68,1.2,14.42,27.6,85,0,0.9,0.49,5.9,8.7,142,78.31,0.31,8.9,1,357,4.92,13.8,6.6,57.8,632,0 92 | 1.67,20.09,75,41.4,87,32,45,0,0,3.1,2.57,31.99,93.39,10.17,1.56,99.4,29.6,67.1,2.5,0,0,31,35.4,6.12,0.39,134.8,114,5.1,4.69,1.17,25,22.1,71.6,1,1.22,0.22,4.7,9.5,144.4,70.3,0.21,10.7,1.4,219,5.17,14.4,7.6,76.8,143.5,0 93 | 1.68,23.34,54,40.6,70,6,13,0.02,0.3,2.3,2.5,294.8,47.39,10.17,7.76,98.2,22.4,68,2.7,0.03,0.5,15,38.7,4.44,0.335,63.8,97,5.1,4.14,1.24,18.8,22.9,79,0,0.89,0.21,3.2,10.4,139.8,77.2,0.34,11.9,1,328,4.24,17,7.8,79.3,305.9,0 94 | 2.42,22.36,66,41.2,64,18,14,0.01,0.1,3.11,2.71,169.8,66.29,10.17,1.55,98.9,27,58,1.5,0.13,1.9,31,29.4,6.33,0.38,114.2,133,2.9,4.26,1.11,15.9,26.7,82.7,1,0.92,0.35,5,9.3,144,77.1,0.31,9.3,1,331,4.5,13.1,4.4,70.6,267,0 95 | 2.71,13.24,29,40.7,58,9,19,0.01,0.3,2.82,2.5,228.9,14.1,10.17,1.17,108.1,25.4,68,1.6,0.03,0.9,12,31.8,5.04,0.366,115.1,116,4,4.34,1.51,45.2,27.7,87.4,0,1.2,0.15,4.5,14,142.4,49.1,0.24,22.8,1,170,4.19,14.4,5.6,72.5,222.7,0 96 | 1.51,18.58,52,34.7,64,12,21,0.01,0.2,3.36,2.14,740.2,61.89,10.17,2.06,102.8,27.7,64,2,0.05,1,10,30.9,4.68,0.427,138,130,5.7,4.18,1.19,23.6,26.9,88.2,0,0.92,0.52,10.3,11.8,144.9,64.9,0.22,15.4,0.9,187,4.84,18,7.7,65.6,242.1,0 97 | 13.02,22.43,25,33.9,75,9,14,0.02,0.2,5.09,2.37,913.9,1000,10.17,45.58,98.8,22.6,47,2.5,0.01,0.1,12,28.5,4.27,0.389,106,123,4.6,4.43,0.84,9.9,27,85.5,0,0.93,0.59,6.9,10,139.4,82.9,0.43,10.6,1.29,429,4.55,12.3,7.1,62.4,323.9,0 98 | 1.53,18.61,21,44.2,47,21,22,0.02,0.3,3.91,2.52,21.24,41.2,10.17,1.26,103.9,22.4,64,3,0.11,1.9,11,35.6,4.5,0.368,183.94,119,4,4.71,3.05,51.6,29.2,90.2,0,0.92,0.34,5.8,10,140.2,40.4,0.32,10.7,1.08,323,4.08,12.1,7,79.8,260.8,0 99 | 0.7,17.75,68,41.9,130,15,20,0.03,0.6,4.09,2.48,428,5.11,10.17,1.72,103.3,28,71,2.3,0.05,1,17,36.1,4.56,0.409,366.4,132,3.9,4.55,1.36,26.6,28.3,87.8,1,1,0.43,8.4,10.1,144.5,63.4,0.29,11,1.14,291,4.66,12.5,6.2,78,227.5,0 100 | 1.5,21.64,48,42.7,64,11,15,0.01,0.2,5.28,2.23,1717,5.55,10.17,2.04,103.4,23.1,55,2.4,0.06,0.9,11,30.2,4.46,0.329,146.7,107,3.7,4.34,1.67,26.4,29.2,89.6,0,0.96,0.34,5.4,11.2,143.8,67.1,0.25,13.4,0.99,221,3.67,12.6,6.1,72.9,196.3,0 101 | 2.94,21.51,30,39.3,84,20,18,0.02,0.2,3.29,2.3,554.8,14.2,10.17,1.62,99.3,24.2,38.2,1.5,0.12,1.4,29,33.2,4.31,0.401,564.5,132,2.8,4.61,1.61,19.22,29.9,90.9,0,0.95,0.57,6.8,9.9,140.4,72.4,0.23,11.4,0.97,236,4.41,13.3,4.3,72.5,219.7,0 102 | 3.77,19.74,62,33.5,73,13,15,0.02,0.21,1.68,2.44,2489,412.4,110.5,0.95,98.4,25.5,49.5,1.2,0,0.03,26,47.6,6.08,0.379,321.8,121.7,2.9,4.24,0.98,11.81,29.42,91.5,1,0.94,0.51,6.12,8.5,139.4,81.83,0.365,16.08,1.04,430,4.14,13.04,4.1,81.1,199.3,0 103 | 3.2,25.49,53,49.9,123,20,26,0.01,0.2,4.75,2.76,448.4,9.85,10.17,0.89,100.8,21.5,64,2.9,0.01,0.2,22,26.9,4.74,0.386,63.1,124,5.4,4.69,0.97,15.8,30.7,95.5,1,1.11,0.34,5.6,13.8,143.1,78.2,0.21,22,1.22,154,4.04,12.2,8.3,76.8,276.1,0 104 | 1.96,19.15,65,32.5,91,8,13,0.03,0.5,4.17,2.41,1823,36.36,10.17,0.5,100.9,27.4,47,1.7,0.05,0.8,15,43.8,5.24,0.36,498.1,126,2.1,4.65,1.14,17.6,25.6,86.6,1,0.87,0.31,4.8,9,142.8,76.3,0.42,8.9,1.15,465,4.26,13.8,3.8,76.3,243.1,0 105 | 3.12,17.25,31,38.8,65,16,14,0.06,1.04,2.72,2.45,14.26,16.12,1.92,2.72,101.6,24.6,62.7,2.1,0.14,2.25,15,24.5,4.59,0.404,44.54,135,3.7,4.85,2.05,33.8,31.8,95,0,0.99,0.25,4.1,7.71,138.6,58.8,0.14,17.1,1.16,181,4.25,15.5,5.8,63.3,240.6,0 106 | 4.93,25.2,34,45.7,53,10,12,0.02,0.2,3.25,2.67,99.86,34.22,10.17,3.31,98.4,22.6,67,2.2,0.01,0.1,20,38.1,5.6,0.397,43.71,129,3.5,4.8,1.58,14.12,29.7,91.3,0,1.09,0.82,7.3,10.1,141.4,78.31,0.23,11,1.17,225,4.35,13.2,5.7,83.8,212.5,0 107 | 1.74,8.5,57,45.8,87,6,15,0.02,0.3,6.14,1.01,431.5,9.41,10.17,0.9,105.3,22,70,2,0.05,0.8,15,27.4,5.38,0.404,339.1,128,4.8,3.69,1.38,21.3,27.4,86.5,1,0.91,0.29,4.5,9.6,136.5,73.1,0.35,10.7,1.22,364,4.67,13.1,6.8,73.2,264.9,0 108 | 1.63,22.05,38,40.3,84,18,18,0.02,0.3,2.54,2.44,321.7,11.91,10.17,2.81,100.4,23.2,69.7,2.1,0.01,0.12,9,37.2,5.87,0.374,264.7,123,5.5,4.35,1.23,16.12,28.7,87.4,0,1.15,0.32,4.2,9.8,141.3,79.31,0.26,11.2,0.85,269,4.28,13.1,7.6,77.5,301.4,0 109 | 1.85,25.88,54,33.1,84,21,17,0.02,0.3,5.52,2.33,35.56,6.97,10.17,0.36,102.4,22.9,78,2.2,0.11,1.6,35,33.6,6.96,0.38,183.94,121,3.8,3.98,3.19,47.3,27,84.8,1,1.01,0.3,4.5,11.2,147.2,46.3,0.34,12.7,1.2,300,4.48,13.3,6,66.7,327.1,0 110 | 4.93,22.06,60,32,66,7,13,0,0.2,4.04,2.28,255.82,34.22,10.17,3.31,101.7,23.4,62.5,2.7,0.1,0.7,15,28.7,4.75,0.23,50.73,62,1.9,5.06,1.8,23.4,17.7,61,1,1.13,0.7,8.7,7.2,142.1,67,0.356,16.2,1.42,496,3.51,17,4.6,60.7,275.9,0 111 | 2.08,20.29,75,42,136,59,46,0.01,0.2,6.8,2.46,41.94,220.5,10.17,47.37,99.1,28.2,70.3,3.5,0.05,1.2,24,32.1,7.42,0.4,124.5,141,6.2,4.39,1.21,27.9,29.9,88.8,1,1.19,0.3,6.9,10.9,143.2,63.8,0.22,13.3,1.2,204,4.72,12.6,9.7,74.1,187.5,0 112 | 3.27,14.77,39,36.9,52,15,10,0.04,0.97,4.25,2.35,58.89,4.07,11.55,0.96,101.7,24.1,69,1.9,0.05,1.02,11,37.2,4.93,0.327,46.68,110,3.7,4.77,1.32,29.1,27.1,81,0,0.9,0.24,5.3,5.89,135.8,63.6,0.14,16.8,1.08,238,4.04,16,5.6,74.1,208.2,0 113 | 4.93,15.36,55,27.7,70,11,12,0.03,0.4,1.25,2.04,255.82,34.22,10.17,3.31,105,26.9,44,2.1,0.21,2.5,14,24.4,5.87,0.376,183.94,129,3,3.56,1.55,18.4,30.3,88.3,0,1.12,0.35,4.2,9.9,143.7,74.5,0.25,10.6,1.05,250,4.26,11.7,5.1,52.1,167,0 114 | 4.16,19.3,40,45.5,52,11,17,0.05,0.8,4.54,2.68,13.54,61.66,10.17,3.15,101.9,23.4,68,3.4,0.02,0.3,14,28.6,5.39,0.426,38.65,140,6.4,4.2,0.86,13.1,28.9,88,0,0.89,0.21,3.2,11.4,140.4,82.6,0.23,15.1,1.15,205,4.84,12.3,9.8,74.1,187.6,0 115 | 1.53,12.14,73,38.6,99,15,24,0.03,0.4,6.07,2.29,172.6,6.92,10.17,1.04,107.2,26.5,81,2.1,0.11,1.4,18,27.1,5.7,0.405,409.8,135,3.7,3.64,3.15,39.3,29.4,88.2,1,1.18,0.55,6.9,10,142.2,52,0.26,11.2,1.21,257,4.59,12.6,5.8,65.7,326.5,0 116 | 1.83,24.49,63,39.4,62,12,17,0.01,0.2,3.56,2.44,245.6,8.24,10.17,0.52,102.9,21.8,67,3.1,0.09,1.9,17,24.5,4.26,0.378,209.4,123,4.5,4.49,1.04,21.5,27.9,85.7,1,1,0.3,6.2,11.2,144.7,70.2,0.31,13.7,1.38,278,4.41,12.1,7.6,63.9,285.3,0 117 | 4.93,23.28,45,34.4,84,9,24,0.02,0.3,4.84,2.62,255.82,34.22,10.17,3.31,95.2,24.3,53,0.9,0.19,3.2,11,40.9,4.56,0.376,183.94,116,2.5,3.78,1.17,19.7,26.9,87,0,0.8,0.67,11.3,9.5,139,65.5,0.69,10.2,1.21,724,4.32,13.1,3.4,75.3,347,0 118 | 1.22,6.6,61,38,66,8,13,0.03,0.5,3.26,1.05,59.01,174.2,10.17,22.16,106,20.9,59,3.5,0.07,1.3,9,27.2,4.55,0.443,58.76,119,6.1,3.53,1.28,23.3,28.7,88.9,1,0.95,0.28,5.1,11.1,134.9,69.8,0.29,12.7,1.14,258,4.15,13.3,9.6,65.2,170.2,0 119 | 3.73,16,54,41.4,89,33,34,0.02,0.42,4.28,2.54,143.6,8.44,2.37,1.39,99.4,27.4,64.5,2.8,0.11,2.06,32,31.7,5.03,0.39,91.85,131.7,6.3,4.2,1.69,31.72,30.95,91.7,0,1,0.23,4.31,9.84,138.6,61.49,0.165,16.52,1.18,168,4.26,11.7,9.1,73.1,259.9,0 120 | 2.42,28.36,49,48.5,69,21,23,0.02,0.3,3.34,2.47,236.8,44.24,10.17,0.29,97.3,21.8,73,1.6,0.04,0.6,19,31.1,4.52,0.403,56.62,131,2.4,3.86,1.21,18.4,28,86.1,0,1.11,0.36,5.5,10.8,143.6,75.2,0.41,13.1,1.24,383,4.68,12.5,4,79.6,211,0 121 | 4.4,17.3,33,39.5,71,13,10,0.05,1.13,3.55,2.36,16.37,8.74,1.84,0.47,102.6,23.3,60.2,1.7,0.17,3.69,15,32.4,5.36,0.362,55.89,117,2.6,4.7,1.23,26.9,27.3,84.5,0,0.89,0.32,6.98,6.14,138.5,61.3,0.181,16.3,1.04,295,4.29,13.6,4.3,71.9,228,0 122 | 1.49,10.59,46,48.4,82,37,41,0.03,0.6,3.33,2.74,14.56,9.39,10.17,1.95,102.5,34.3,61,2.8,0.05,1,20,28.7,5.62,0.383,41.26,124,5.4,4.39,1.5,31.4,30,92.7,0,1,0.24,5,11,143,62,0.24,12.8,1.24,214,4.13,14.5,8.2,77.1,211.6,0 123 | 4.93,15.32,69,45,50,23,25,0.03,0.7,4.32,2.29,255.82,34.22,10.17,3.31,104.9,27.4,50,1.9,0.12,2.9,17,27.6,6.9,0.4,183.94,128,3.8,4.42,1.2,29.2,28.4,88.7,1,0.97,0.26,6.3,11.9,143.2,60.9,0.24,14.9,0.92,201,4.51,12.6,5.7,72.6,164,0 124 | 3.86,19.43,63,43.1,67,16,26,0.05,1.1,3.41,2.5,20.81,171.6,10.17,4.48,108.2,21.5,65,1.6,0.15,3.3,19,25.3,7.49,0.39,70.83,128,2.8,4.43,1.51,32.8,29.2,89,1,0.87,0.32,6.9,11.5,144.7,55.9,0.22,14.7,0.73,189,4.38,12.8,4.4,68.4,241,0 125 | 4.03,18.58,61,50.6,88,7,17,0.01,0.1,2.2,2.61,16.54,4.09,10.17,1.12,103,27.3,49,2.9,0.01,0.1,13,26.4,5.16,0.447,57.52,141,8.3,4.98,1.09,12.7,28.2,89.4,1,0.91,0.19,2.2,11.4,143.9,84.9,0.51,14.5,1.12,450,5,12,11.2,77,159.9,0 126 | 2.64,20.5,58,44.2,95,17,19,0.02,0.3,5.24,2.59,150.7,0.6,1.06,1.87,103.8,22.8,56,4.2,0.12,1.9,15,32.8,4.33,0.406,147.4,128,4.8,4.2,1.27,19.7,28.2,89.4,1,1,0.25,3.9,10,142.9,74.2,0.26,10.7,1.13,257,4.54,13,9,77,273.9,0 127 | 4.93,20.43,43,41.08,77,18,19,0,0,2.08,2.42,255.82,34.22,10.17,3.31,100,23.3,70,3.13,0,0,21,30.18,6.25,0.377,183.94,130,5.96,3.83,0.99,7,29.4,85.3,0,0.84,0.45,3.2,11.2,139.9,89.8,0.24,13.5,0.99,214,4.42,12,9.09,71.08,166,0 128 | 2.57,20.43,61,42.6,111,18,11,0.04,0.74,4.82,2.46,21.47,25.65,0.97,1.41,96.7,24.3,54.3,3,0.06,1.09,13,33.2,4.78,0.394,59.36,132,8.6,3.93,1.47,28.8,30.2,89.7,1,1.04,0.3,5.91,8.22,137.5,63.5,0.154,17.5,1.18,188,4.39,12.9,11.6,75.8,230.1,0 129 | 4.93,8.9,52,32.1,55,16,12,0.02,0.1,6,1.08,255.82,34.22,10.17,3.31,106.9,21.4,92,3.2,0,0,16,19,7.04,0.436,183.94,119,4.7,3.47,1.18,8.8,28.9,88.8,0,0.86,0.63,4.7,13.1,136.4,86.4,0.28,19.6,1.45,213,4.12,13.7,7.9,51.1,247.3,0 130 | 0.61,24.48,23,39,49,7,11,0.02,0.4,3.03,2.5,195.5,40.84,10.17,1.08,99.1,21.5,65.1,2.6,0.06,1.1,6,39.9,5.23,0.366,67.42,120,3.1,4.58,1.53,28.4,28.3,86.3,0,1.03,0.37,6.9,10,140.5,63.2,0.2,11.4,1.12,204,4.24,13.1,5.7,78.9,206.4,0 131 | 2.99,19.81,44,29.7,61,20,16,0.03,0.5,2.99,2.3,478.3,6.82,10.17,0.32,104.3,22,46.4,3,0.2,3.22,72,27.3,5.1,0.346,1265,115.2,7.1,4.21,1.38,22.72,28.13,84.4,0,1.25,0.29,4.78,6.56,141.9,68.78,0.165,18.67,1.09,251,4.1,11.39,10.1,43.3,251,0 132 | 2.1,19.67,50,31.9,114,16,16,0.02,0.31,3.4,2.22,1760,31.43,109.6,0.97,93.9,27.9,82.7,4,0.05,0.92,13,29.1,5.6,0.358,2331.3,121,4.7,4.77,0.62,11.8,29.6,87.2,1,1.11,0.58,11,6.47,136.7,75.9,0.176,17.1,0.94,272,4.11,13.5,8.7,61,289.1,0 133 | 4.1,23.1,63,40.8,154,11,20,0.03,0.4,2.7,2.44,25.4,16.71,10.17,1.19,101.7,21.7,51.9,3.1,0.12,1.6,10,36.6,5.27,0.412,52.81,133,8.1,5.1,1.75,23.5,28.5,88.2,1,1.2,0.4,5.4,9.4,141.4,69.1,0.35,10.7,1.03,376,4.67,13,11.2,77.4,264.3,0 134 | 4.28,23.29,50,34.4,88,14,15,0.01,0.2,4.1,2.33,21.21,31.83,10.17,6.76,107.7,20.4,49,1.8,0.07,1.1,20,25,4.61,0.372,39.72,109,2.7,4.19,1.26,20.1,22.6,77.2,0,0.91,0.33,5.3,10.4,147.2,73.3,0.3,11,1.08,291,4.82,17.6,4.5,59.4,238.8,0 135 | 4.93,15.45,45,42.4,72,9,22,0.01,0.2,2.65,2.46,255.82,34.22,10.17,3.31,102.3,27.2,60,2.4,0.12,2.5,10,26.6,4.68,0.387,183.94,127,5.3,4.35,1.19,24.5,29.8,90.8,0,0.97,0.31,6.4,10.1,140.6,66.4,0.26,11.3,0.96,261,4.26,12.7,7.7,69,188.8,0 136 | 1.21,19.18,37,41.4,75,16,13,0.04,0.87,3.47,2.39,100.9,263.2,2.75,8.17,101,21.7,87.6,2.2,0.09,1.7,14,30.4,5.12,0.375,65.05,123,3.9,4.18,1.53,30,28.5,86.8,0,0.88,0.36,6.99,7.53,137.7,60.4,0.151,17.2,1.22,201,4.32,13.4,6.1,71.8,215,0 137 | 1.69,21.43,49,35.3,72,7,11,0.02,0.2,4.42,2.5,326.8,64.4,10.17,0.28,101.6,26.1,53,2.6,0.06,0.7,19,30.7,5.47,0.351,115.1,109,3.7,4.23,1.16,13,26.6,85.6,1,0.98,0.52,5.8,9.5,144.9,80.3,0.36,10.1,1.3,383,4.1,12.1,6.3,66,241.5,0 138 | 2.33,21.17,57,42.3,80,20,19,0.03,0.7,5.73,2.58,332,22.84,10.17,2.22,103.3,23.8,71,2.8,0.06,1.4,13,30.4,4.55,0.374,55.08,124,5,4.97,2.16,49.3,29,87.6,1,1.16,0.22,5,11.6,143.3,43.6,0.23,14.2,1.25,203,4.27,13.1,7.8,72.7,280.1,0 139 | 4.93,24.55,48,41.08,77,18,19,0.05,0.8,2.08,2.36,255.82,34.22,10.17,3.31,100,20.8,52,3.13,0.05,0.8,21,30.18,5.39,0.397,183.94,130,5.96,3.55,1.5,23.5,28.4,86.9,0,0.74,0.29,4.5,11,141.8,70.4,0.19,12.1,0.79,172,4.57,18.4,9.09,71.08,242.3,0 140 | 1.89,9.6,73,35.2,66,13,24,0.01,0.1,7.55,0.97,463,43.48,10.17,0.86,109.4,23,91,6.5,0.01,0.1,8,25.6,5.92,0.305,263.4,108,8.8,3.08,0.38,3.9,29.4,89.1,1,0.72,0.72,7.5,10.2,134.2,88.4,0.38,10.7,1.2,375,3.67,12.5,15.3,60.8,397.8,0 141 | 3.76,17.21,31,38.3,48,32,16,0.08,1.58,3.83,2.24,7.26,7.81,2.04,0.41,99.4,26.1,71.3,3.3,0,0,22,32.1,4.39,0.312,46.35,98.7,6.1,4.11,1.67,35.2,21.4,67.7,0,1.3,0.34,7.2,10.2,138.6,56,0.158,18.2,1.24,155,4.61,20.7,9.4,70.4,182.4,0 142 | 4.93,13.21,54,41.08,77,18,19,0.03,0.4,2,1.75,255.82,34.22,10.17,3.31,106.7,27,56,3.13,0.04,0.5,21,30.18,7.07,0.331,49.63,99,5.96,4.01,2.04,24.2,22.7,75.9,0,0.78,0.34,4,9.8,142.9,70.9,0.34,10.5,0.72,345,4.36,16,9.09,71.08,104.3,0 143 | 4,18.92,66,32.6,87,27,17,0.03,0.36,3.51,2.34,5000,34.12,158.5,1.06,98.1,26.4,65,1.7,0.04,0.47,20,30.2,6.24,0.332,828.1,109,4,4.72,1.36,15.3,27.2,82.8,1,1.05,0.43,4.78,6.46,138.7,79.1,0.211,17.5,1.23,326,4.01,15.4,5.7,62.8,258.6,0 144 | 2.98,20.58,62,45.9,112,16,16,0.05,1.31,5.79,2.57,1143,14.07,10.17,1.48,100.7,27.1,67.6,3.9,0.14,3.6,48,29.2,5.79,0.395,131.4,126,2.9,4.68,1.24,31.64,29.7,93.2,1,1.18,0.19,4.84,14.1,143.7,58.74,0.21,21.6,1.3,146,4.24,12.5,6.8,75.1,211.7,0 145 | 2.65,18.14,67,22.9,64,28,51,0.04,0.49,8.1,2.27,614.7,0.6,2.88,0.98,100.8,25.3,56,1.1,0.01,0.07,15,27,5.5,0.264,3060.8,86.8,2.3,4.9,0.54,6.05,28.3,86.1,1,0.94,0.6,6.75,5.06,137,86.6,0.402,16.1,1.43,794,3.07,14.7,3.4,49.9,337.9,0 146 | 1.32,16.89,67,29.9,100,10,17,0.03,0.5,2.34,2.08,62.93,6.48,10.17,0.29,101.7,29.4,45,2,0.04,0.6,19,32.9,5.48,0.35,571.6,123,4,4.39,0.96,15,27.4,90.8,1,0.98,0.31,4.9,9.6,143.6,79,0.42,9.9,0.88,440,3.79,13.2,6,62.8,168.3,0 147 | 1.88,24.75,38,47.3,71,14,17,0.04,0.5,2.99,2.64,32.07,12.66,10.17,0.53,99,21.4,59,2,0.02,0.3,13,31.3,4.25,0.386,41.85,117,4,4.35,2.21,28.6,24.8,81.8,0,0.83,0.35,4.5,11,140.8,66.1,0.3,13.1,1.48,274,4.72,15.3,6,78.6,236.4,0 148 | 2.26,23,56,35.1,105,12,20,0.04,0.4,2.64,2.48,1319,4.83,20.21,0.58,96.8,23.3,69,2.5,0.03,0.3,61,38.7,5.83,0.391,1500,119,7,5.4,1.72,16.2,24.4,80.1,1,1.22,0.78,7.4,9.5,137.7,75.7,0.62,9.8,1.4,652,4.88,13.2,9.5,73.8,350.3,0 149 | 3.11,12,72,44.8,128,13,24,0,0,3.39,1.06,105.4,33.37,10.17,3.77,101.9,21.9,76,8.9,0.03,0.5,14,27.4,4.35,0.414,103.7,137,12.8,4.3,0.46,7.6,31.1,94.6,1,0.97,0.25,4.1,10.4,139.6,87.8,0.16,11.9,1.41,155,4.41,12.2,21.7,72.2,283.8,0 150 | 5.02,17.05,57,41.1,89,18,22,0.01,0.3,3.75,2.63,515.1,17.82,10.17,4.57,102.1,28.5,67,4.5,0.06,1.8,12,30.3,5.26,0.397,162.9,134,10.5,3.85,1.05,31.2,28.9,85.6,1,0.94,0.19,5.6,10.3,143.8,61.1,0.15,11.3,1.19,144,4.64,12.3,15,71.4,301.3,0 151 | 1.63,16.26,43,35.2,68,5,13,0,0,3.2,2.53,42.17,9.91,10.17,1.22,103.7,25.1,63,4.2,0.05,0.7,4,23.8,5.46,0.298,145.5,94,5.7,5.04,0.7,10,25.6,81.2,0,0.98,0.36,5.1,11.2,143.5,84.2,0.26,12.8,0.99,230,3.67,14.1,9.9,59,176.5,0 152 | 4.76,12.6,65,40.2,81,6,14,0.04,0.5,7.09,1.08,1065,1000,10.17,2.13,99.2,31.7,66,3.1,0.08,1,48,30.4,6.28,0.38,269.5,126,5.1,4.31,1.88,23.7,30.9,93.1,1,1,0.33,4.2,9.6,139.8,70.6,0.36,10.7,0.78,375,4.08,12.5,8.2,70.6,308.6,0 153 | 3.13,21.14,49,39.2,61,24,27,0.04,1,3.75,2.48,339.1,12.73,10.17,0.98,103.7,22.2,60,4.6,0.12,3,13,21.6,4.73,0.386,250.4,131,8.6,4.24,0.79,19.7,29.6,87.3,0,0.97,0.2,5,12.6,142.8,71.3,0.2,16,1.05,162,4.42,11.7,13.2,60.8,224.3,0 154 | 4.66,25.42,43,42.6,120,15,13,0.03,0.4,2.9,2.7,112.6,269.3,10.17,27.97,99.3,22.2,54,1.9,0.05,0.6,38,40.6,5.55,0.395,47.13,125,3.2,4.62,1.65,19.3,27.4,86.4,0,0.96,0.47,5.5,9.3,142.3,74.2,0.39,9.6,1.14,420,4.57,11.5,5.1,83.2,234.7,0 155 | 2.88,20.09,54,42.9,86,24,20,0.02,0.4,2.75,2.56,201.6,21.1,10.17,3.25,100.7,22.9,62,4.3,0.06,1.1,11,27.5,4.61,0.415,167.1,138,7.7,4.09,1.38,26.3,31,93.3,1,0.96,0.23,4.4,11.4,139.6,67.8,0.23,13.6,1.21,203,4.45,13.2,12,70.4,213.4,0 156 | 1210,7.4,48,28.8,86,24,64,0.05,0.4,2.59,0.92,1319,37.14,10.17,10.72,88.8,25.4,59,5.7,0.02,0.2,22,29.1,7.09,0.365,393.1,122,7.6,3.94,0.82,7.2,29.3,87.5,0,0.94,0.38,3.4,9.8,131.9,88.8,0.52,10.5,1.14,537,4.17,15.7,13.3,57.9,159.3,0 157 | 1.88,18.84,50,32.4,117,10,9,0.02,0.2,2.28,2.11,931,25.36,10.17,34.04,98.4,26.4,62,3.8,0.07,0.8,33,38,5.3,0.252,45.57,80,5,4.04,1.11,13.2,28.4,89.4,0,1.06,0.8,9.5,10.6,139.6,76.3,0.41,11.2,1.27,387,2.82,13.9,8.8,70.4,145.6,0 158 | 1.75,25.1,72,31,63,12,23,0.02,0.3,2.84,2.39,2642,0.76,1.98,3.12,98.7,22.7,66,3.6,0.03,0.5,11,27.5,4.41,0.335,609.4,105,5,4.6,0.92,15.4,28.9,92.3,1,0.8,0.48,8.1,9.3,141.9,75.7,0.3,9.5,1.17,321,3.63,13.1,8.6,58.5,168.8,0 159 | 2.27,7.2,53,40.5,102,20,40,0,0,3.63,1.06,949.2,34.22,10.17,0.74,99.4,23.7,67,3.9,0.06,1.1,22,30.4,4.37,0.314,517,78,6.8,4.86,1.13,21,20.5,71.4,0,1.15,0.35,6.5,11.4,139.2,71.4,0.36,14,1.24,316,3.81,20.6,10.7,70.9,209.5,0 160 | 5,17.59,51,45,83,17,19,0.01,0.1,4.33,2.73,1288,6.36,10.17,1.47,99.3,28.8,71,3.2,0.17,2.5,23,31.3,5.36,0.381,303.7,126,7.3,4.49,1.44,21.1,31.5,95.3,1,1.07,0.3,4.4,9,141.2,71.9,0.27,9.7,1.27,300,4,12,10.5,76.3,268.8,0 161 | 3.15,9.4,59,44.5,67,17,22,0.02,0.3,3.95,1.04,331.6,8.76,10.17,1.61,99.5,27.3,66,2.8,0.02,0.3,12,31.1,11.36,0.393,86.28,125,6.8,4.15,1.25,21.6,28.9,87.5,1,0.88,0.21,3.6,10.4,141.2,74.2,0.23,11.6,1.24,223,4.32,12.9,9.6,75.6,201,0 162 | 2.03,18.11,49,40.9,107,37,23,0.06,1,4.56,2.53,30.74,0.77,10.17,2.89,102.5,27.4,66,4.2,0.13,2.2,92,34.4,5.12,0.388,302.8,128,9.7,4.31,1.57,26.6,29.4,89.2,0,0.94,0.28,4.7,10.7,143.7,65.5,0.3,12.2,1.15,281,4.35,13,13.9,75.3,417.2,0 163 | 3.63,15.44,60,50.2,90,27,34,0.05,0.9,3.85,2.53,1501,104.3,10.17,6.95,100.1,27.8,60,5.7,0.22,3.8,20,25.2,5.68,0.433,343.2,147,15.6,3.94,2.68,46,30.9,91,1,0.89,0.38,6.5,9.5,139.4,66.58,0.29,10.8,1.6,306,4.76,11.7,21.3,75.4,329.7,0 164 | 3.7,11.3,69,50,94,22,52,0.09,1.3,4.3,1.04,113.3,12.44,10.17,1.02,101.7,28,80,5.9,0.05,0.7,53,31.7,4.5,0.41,80.36,115,9.8,4.33,1.92,28.1,18.5,61.8,1,1.06,0.39,5.7,10.1,138.4,66.58,0.32,11.7,1.09,321,6.21,17.5,15.7,81.7,263.8,0 165 | 3.32,8.4,74,35.2,90,19,38,0,0,6.61,1.11,758.1,25.85,10.17,1.51,99.1,28.8,114,1.5,0.02,0.8,16,30.8,5.76,0.328,1500,89,2.9,5.21,1.07,40.7,25.8,83.2,1,0.79,0.56,21.3,11.5,141.8,37.2,0.46,13.6,1.17,397,3.45,15.2,4.4,66,492.9,0 166 | 1.95,9.3,52,45.1,103,25,26,0.02,0.3,3.28,1.03,211.1,8.77,10.17,1.41,99.4,21.9,61,3.8,0.03,0.5,34,33,5.03,0.459,239.6,153,9.1,4.13,1.76,27.9,30.7,90,1,0.92,0.22,3.5,10.9,140.3,67.8,0.19,13.2,0.97,174,4.98,12.4,12.9,78.1,308.1,0 167 | 1.67,6.6,65,29.1,149,10,17,0.01,0.1,4.84,1.07,399.4,62.48,10.17,9.44,100,28.7,66,1.9,0.03,0.3,79,28.7,4.11,0.264,569,81,3.9,5.36,0.8,7.8,30.9,100.8,1,0.82,0.52,5.1,10.1,142.5,86.7,0.35,10.6,0.91,351,2.62,15.5,5.8,57.8,295.2,0 168 | 1.92,17.97,57,39.1,96,12,28,0,0,4.09,2.44,258,8.76,10.17,0.98,102.2,26.2,59,3.5,0.09,2.5,9,36,4.61,0.367,376.5,120,6.7,4.27,0.73,20.3,28.9,88.4,1,0.88,0.16,4.5,12,142.1,72.7,0.14,13.4,1.07,120,4.15,12.9,10.2,75.1,227.7,0 169 | 9.07,25.27,68,37,85,11,17,0.01,0.1,6.9,2.24,47.25,48.96,10.17,2.99,93.8,27.6,84,4.8,0.03,0.2,13,31.7,7.4,0.33,157.5,109,7.4,3.28,1.45,9.2,29.1,88,1,0.7,0.94,5.9,10.8,137.3,84.6,0.33,12.5,1.18,305,3.75,12.5,12.2,68.7,335.9,0 170 | 2.29,10.4,64,38.9,82,7,27,0.02,0.3,3.48,1.01,2394,6.33,10.17,1.25,99.4,24.9,56,2,0.02,0.3,12,28.4,4.59,0.375,682.7,121,3.7,4.39,1.16,16.9,30.4,94.2,1,0.89,0.35,5.1,9.7,143.6,77.4,0.32,10.9,1.07,335,3.98,11.9,5.7,67.3,238,0 171 | 5.03,26.65,42,39.1,763,9,30,0.04,0.6,2.72,2.46,2154,24.49,10.17,0.8,102,16.2,73,4.3,0,0,10,26.2,3.96,0.45,1309,147,10.3,4.35,1.21,17.4,27.1,83,0,0.98,0.3,4.3,11.4,140.5,77.7,0.32,14.5,1.03,284,5.42,15,14.6,65.3,203.6,0 172 | 2.87,7.9,66,46,96,27,39,0.05,0.7,4.4,2.52,2592,7.04,10.17,0.59,103.1,28.1,51,2.9,0.04,0.6,17,36.5,5.9,0.395,933.7,124,6.4,4.7,1.44,20,30,95.6,1,0.9,0.36,5,12,144.6,73.7,0.22,15.8,1.46,187,4.13,12.3,9.3,82.5,270.3,0 173 | 2.78,22.23,36,45.7,53,27,12,0.07,1.2,4.05,2.35,20.94,14.68,1.94,0.77,102,21.3,71.9,2.4,0.09,1.55,23,28.1,4.99,0.375,33.25,125,5.9,4.53,2.35,42.5,30,89.8,0,1.04,0.37,6.65,11,141,48.1,0.259,19.3,1.06,234,4.18,15,8.3,73.8,195.2,1 174 | 1.14,21.19,23,42,62,9,14,0.03,0.6,3.19,2.67,9.47,19.28,10.17,2.07,99.9,26.9,67.6,2.5,0.09,1.7,12,31,5.49,0.408,69.85,136,2.9,3.99,1.83,34,28.3,84.8,0,0.86,0.25,4.6,10.5,144,59.1,0.22,13.3,1.31,207,4.81,14.9,5.4,73,278,1 175 | 1.73,16.55,23,46.5,51,12,14,0.04,0.63,3.98,2.34,18.77,6.54,5.91,0.31,96.9,23.6,65.8,3.8,0.04,0.63,16,27,5.71,0.402,37.92,138,6.6,4.05,1.89,31.2,29.6,85.9,0,0.97,0.24,3.91,7.79,133,63.6,0.149,17.6,1.04,191,4.67,13.8,10.4,73.5,219.7,1 176 | 1.72,23.14,40,35.7,60,9,11,0.03,0.4,3.79,2.44,152,62.79,10.17,1.17,100.5,24,61.2,2,0.07,0.9,7,28,5.24,0.415,53.61,138,4.3,4.94,1.96,26.5,30,90.2,0,1.21,0.25,3.4,10.7,142.7,68.8,0.31,12.4,1.35,293,4.6,12.8,6.3,63.7,242.8,1 177 | 1.36,15.13,20,38.6,61,11,10,0.05,1.08,3.94,2.17,20.32,10.02,19.37,0.83,108,22.8,72.4,2.9,0,0.02,15,25.7,4.67,0.355,35.57,121,4.9,4.63,2.15,43.2,29.3,85.8,0,1.08,0.47,9.35,7.62,141.3,46.4,0.188,16.9,1.28,247,4.14,14.7,7.8,64.3,305.8,1 178 | 2.66,18.62,59,41.9,89,18,17,0.06,1.08,5.51,2.54,7.96,10.38,1.5,1.92,104.1,24.5,80.4,2,0.1,1.83,14,33.1,5.36,0.369,50.04,121,2.8,4.82,2.27,40,28.6,87.1,1,0.98,0.25,4.37,12.7,142.4,52.7,0.155,20.4,1.13,122,4.24,15.5,4.8,75,169.5,1 179 | 0.61,33.33,34,41.7,50,11,13,0.04,0.8,3.41,2.63,18.76,22.19,10.17,1.11,96,25.9,59.8,2.6,0.05,1,16,26.2,5.42,0.361,39.84,120,5,4.53,2.02,40.6,30.6,92.1,0,1.03,0.24,4.8,10.9,150.7,52.8,0.31,12.6,0.87,284,3.92,12.1,7.6,67.9,141.6,1 180 | 3.24,20.17,48,43.7,72,24,18,0.05,1.01,2.79,2.54,10.94,8.19,1.83,2.35,103.2,23.9,86.1,3.2,0.06,1.34,24,25.8,5.13,0.423,37.8,139,5.8,4.57,1.95,41,31,94.8,0,1.02,0.26,5.55,10.9,142.7,51.1,0.238,20.2,0.77,219,4.47,15,9,69.5,326.7,1 181 | 3.67,20,50,43.4,66,14,19,0.03,0.6,3.8,2.52,9.99,7.57,10.17,1.46,98.6,27.6,80.4,2,0.04,0.9,23,32.4,5.67,0.435,63.49,144,4.8,4.3,1.54,32.8,28.3,85.6,0,1.17,0.29,6.2,13,141.9,59.5,0.23,18.1,1.39,174,5.08,14.3,6.8,75.8,138.8,1 182 | 2.91,19.39,34,40.8,60,16,12,0.04,0.73,4.72,2.55,28.19,20.15,2.55,1.22,99.6,23.4,80.7,1.8,0.06,1.2,19,33.5,4.5,0.378,40.77,124,3.5,5.39,1.59,32.8,28.1,85.5,0,0.99,0.53,11,7.41,137,54.3,0.177,16.4,1.22,239,4.42,13.8,5.3,74.3,187.4,1 183 | 1.38,19.88,42,40.6,69,17,14,0.02,0.3,4,2.29,71.47,9.1,9.41,1.32,102.1,23.3,71.9,3.5,0.02,0.38,11,29.2,5.42,0.407,49.47,131.1,9,5.28,1.41,27.65,27.19,84.5,0,0.97,0.2,3.96,9.77,140,67.71,0.192,16.06,1.15,197,4.82,15.36,12.5,69.8,200.6,1 184 | 4.25,21.38,49,42.9,62,18,17,0.03,0.52,4.13,2.47,45.02,8.31,3.75,1.34,99.2,22.8,77,1.4,0,0,14,34.8,4.89,0.355,68.97,114,2.9,4.28,1.46,27.5,27,83.7,0,1.21,0.39,7.26,9.29,139.1,64.7,0.213,18.9,1.21,229,4.25,17.1,4.3,77.7,194.2,1 185 | 2.65,21.95,20,45.9,44,14,14,0.06,0.98,2.43,2.32,204.9,30.29,0.39,1.16,99.2,21,51.6,4,0.17,2.76,10,26.5,4.73,0.375,44.04,130,6.2,4.15,2.13,35.1,30,86.8,0,1.11,0.3,4.94,9.17,138,56.2,0.245,17.6,1.1,267,4.32,13.7,10.2,72.4,250.1,1 186 | 2.13,20.24,21,44.7,42,12,10,0.06,1.4,2.66,2.4,11.1,14.57,0.89,0.92,101.6,24.7,72.4,3.1,0.15,3.7,10,23.3,4.77,0.373,40.25,124,6.6,4.54,1.68,42.7,31,93.5,0,1.08,0.23,5.78,8.45,142,46.4,0.128,17.7,1.04,151,3.99,14.9,9.7,68,245.7,1 187 | 1.89,17.98,39,39.5,74,25,17,0.12,1.94,3.95,2.61,43.23,25.52,0.2,1.47,102,24.7,72.1,1.7,0.18,2.91,15,31.9,5.17,0.372,42.99,123,2.8,4.18,1.49,24.8,28.4,86.2,0,0.91,0.32,5.26,7.85,140.5,65.1,0.194,18.4,0.82,247,4.31,14,4.5,71.4,228.6,1 188 | 2.31,17.03,51,33.1,105,28,10,0.04,0.32,3.55,2.29,15.24,5.61,9.42,1.08,98.8,24.6,60.2,2.1,0,0.03,46,30.8,5.63,0.345,90.62,115,3.2,4.13,1.31,10.6,30.5,91.3,0,1.07,0.91,7.36,5.98,136.3,81.7,0.133,17,0.85,223,3.78,13.5,5.3,63.9,102.3,1 189 | 1.89,20.7,47,44.9,77,19,17,0.01,0.2,5.36,2.79,4.48,5.29,1.37,0.24,98.8,24.2,55,2.1,0.01,0.2,17,31.9,5.03,0.415,32.18,138,6.1,4.8,1.27,29.9,30.5,91.6,1,0.95,0.18,4.2,11.2,138.9,65.5,0.21,13.8,0.99,189,4.53,11.8,8.2,76.8,210.7,1 190 | 1.66,22.58,19,47.7,80,17,17,0.02,0.4,6.13,2.56,16.62,8.78,10.17,0.37,99.1,22.9,73.5,3.7,0.06,1.2,19,26.5,5.04,0.389,53.32,129,6.3,4.78,2.07,39.7,28.6,86.3,0,1.25,0.37,7.1,11,139.8,51.6,0.15,12.2,1.46,134,4.51,12.6,10,74.2,223.9,1 191 | 0.81,20.6,24,44.6,49,27,15,0,0.1,3.16,2.53,53.36,37.27,0.43,0.7,99.2,25,74.1,1.6,0.2,3.7,19,32.6,5.16,0.406,40.99,130,4,4.8,2.5,38.1,28,87.4,0,0.99,0.07,0.3,9.7,140,57.8,0.251,16.2,1.25,260,4.64,13.2,5.6,77.2,243.7,1 192 | 1.57,14.98,24,34.5,45,11,9,0.07,1.22,3.88,2.25,65.98,24.98,0.79,1.4,104.2,25.4,54.2,1.8,0,0.08,12,28.1,4.5,0.361,30.26,123,2.9,4.38,1.69,30,28.1,82.4,0,1.26,0.36,6.4,7.09,140.2,62.3,0.176,18,1.24,249,4.38,16,4.7,62.6,140.9,1 193 | 0.9,20.38,26,43.1,55,13,14,0.02,0.4,4.72,2.46,13.96,35.77,10.17,0.8,100.1,23.6,65.1,5,0.12,2.2,9,32.1,4.75,0.407,68.28,136,7.6,4.68,1.61,29.7,28.5,85.1,0,0.98,0.22,4.1,11.4,139.4,63.6,0.23,14.4,1.11,198,4.78,12.4,12.6,75.2,247.1,1 194 | 2.28,18.57,30,41.6,62,15,13,0.03,0.29,3.9,2.29,29.85,18.45,1.56,1.54,103.6,24.6,70.2,4.5,0.12,1.24,20,24.6,5.12,0.363,36.81,118,7.7,4.47,1.56,16.3,29.1,89.9,0,1.06,0.6,6.24,6.75,142.3,76,0.184,16.5,0.79,272,4.04,15.8,12.2,66.2,197.1,1 195 | 4.09,16.23,64,42,98,36,18,0.04,0.53,4.37,2.66,10.64,16.13,1.08,1.82,100.8,27.8,72.8,2.7,0.01,0.08,27,32,5.18,0.402,54.46,136,4.9,5.13,1.98,29,31,91.4,1,0.89,0.52,7.62,6.44,139.7,62.7,0.183,16.7,1.28,283,4.4,15.6,7.6,74,330.1,1 196 | 0.88,16.8,44,41.6,78,12,13,0.09,1.29,3.83,2.35,9.51,4.24,0.2,1.62,99.3,25.5,71,3.8,0.09,1.25,22,28.5,3.6,0.313,41.54,98.5,8.6,5.3,2,28.4,20.5,65.2,0,0.95,0.19,2.68,7.88,136.3,66.4,0.24,16.4,1.1,305,4.8,17.9,12.4,70.1,159.6,1 197 | 4.93,27.52,44,42.2,68,15,10,0.02,0.3,3.4,2.37,19.61,6.05,10.17,0.83,97.6,20.4,75.5,2.3,0.09,1.4,30,35.8,5.48,0.42,39.25,137,6,4.92,2.3,36.7,28.8,88.4,0,1.22,0.2,3.2,11.4,140.6,58.4,0.36,13.7,1.27,317,4.75,13.2,8.3,78,260.4,1 198 | 1.42,15.39,43,39.4,79,10,12,0.03,0.73,2.54,2.41,59.29,28.56,1.91,2.09,102.3,27.5,69.6,2.4,0.04,1.04,23,33.6,5.53,0.344,42.75,113,4.1,4.29,1.22,34.8,29.7,90.3,0,1,0.14,3.94,9.28,140.9,59.5,0.192,18.2,0.88,207,3.81,14.4,6.5,73,230,1 199 | 3.67,20.16,28,43.1,29,15,12,0.05,0.81,5.07,2.42,40.48,16.22,0.2,1.65,102.7,23.6,86.1,2.9,0.05,0.83,11,27,5,0.371,62.28,121,5.4,4.36,1.67,27.9,30.6,93.5,0,1.07,0.4,6.64,9.97,142.1,63.9,0.206,17.9,0.88,170,3.96,13.7,8.3,70.1,190.5,1 200 | 2.2,14.35,23,39.8,38,11,11,0.05,0.45,2.73,2.31,11.16,10.06,1.53,1.07,102.1,25.1,79.3,3.1,0.06,0.6,12,24.2,4.75,0.351,52.23,120,4.1,4.25,2.46,24.1,29.6,86.8,0,1,0.59,5.75,6.72,137.3,69.1,0.135,16.9,1.19,200,4.04,15.2,7.2,64,208.2,1 201 | 2.41,18.39,32,42.4,50,12,10,0.02,0.3,1.92,2.42,10.29,10.55,4.98,1.04,96.4,25.3,62.2,2.7,0.01,0.18,13,34,5,0.422,43.43,137.1,7.6,4.39,1.78,26.53,31.12,95.7,0,0.98,0.26,3.86,8.96,135.7,69.13,0.184,16.65,0.95,206,4.41,12.94,10.3,76.4,166.8,1 202 | 0.61,19.36,46,41.9,66,71,30,0.09,1.07,4.17,2.29,8.17,8.34,2.07,1.81,100,24.1,47.5,2.9,0.17,1.98,41,29.3,5.27,0.377,31.55,125,8.7,4.86,3.49,40.5,28,84.2,0,0.95,0.35,4.02,12.3,138.6,52.5,0.212,19.4,1.02,172,4.47,14.8,11.6,71.2,182.5,1 203 | 0.84,20.12,20,32.6,66,6,10,0.01,0.2,5.44,2.27,43.41,37.89,10.17,0.59,100,23.7,62.3,1.7,0.31,5.1,5,31.1,4.03,0.356,56.91,109,1,4.72,2.27,37.5,24,78.2,0,1.2,0.49,8.1,10.5,139.1,49.1,0.36,12.3,1.4,340,4.55,13.3,2.7,63.7,217.5,1 204 | 1.4,17.45,64,37.8,50,14,13,0.03,0.8,6.88,2.3,16.11,8.39,0.98,0.61,102.3,23.4,89.8,2.5,0,0.06,20,29.2,4.74,0.343,48.04,120,5.5,4.15,1.39,36.7,31.6,90.7,1,0.95,0.32,8.33,7.26,139,54.1,0.133,17,0.99,183,3.79,14.7,8,67,334.4,1 205 | 1.85,20.43,44,40.9,42,16,16,0.07,1.63,7.84,2.33,24.29,0.6,0.64,6.95,99.5,23.2,85.5,2.7,0.06,1.32,21,30.1,4.81,0.301,42.53,94.6,4.8,5.03,1.69,39.1,22.8,72.3,0,1,0.36,8.33,10.3,138.1,49.6,0.27,18.7,1.38,262,4.16,18.5,7.5,71,229.2,1 206 | 1.12,25.02,27,50.9,84,16,15,0.01,0.16,3.8,2.42,89.56,125.2,16.54,0.81,102.9,21.1,52.8,3.7,0.02,0.33,16,29.1,5.51,0.406,35.83,132.9,5.5,4.82,1.34,19.08,30.83,94.1,0,1.02,0.28,3.96,9.24,144.2,76.47,0.178,16.37,0.92,193,4.31,12.58,9.2,80,274.6,1 207 | 2.11,13.78,43,39.6,39,13,12,0.04,0.91,3.99,2.36,32.2,41.97,0.69,1.18,99.5,27.7,75.8,3.9,0.05,1.17,13,27.4,5.02,0.375,44.43,126,10.4,4.78,1.24,32.1,26,77.5,0,1.13,0.23,5.81,8.01,136.2,60,0.117,15.8,1.01,146,4.84,16.9,14.3,67,174.3,1 208 | 3.5,23.3,39,47.1,55,16,10,0.01,0.14,1.97,2.5,41.45,10.76,3.23,2.34,99.1,23.4,67.9,2.2,0.01,0.14,11,23.5,4.89,0.397,58.37,130,5.1,4.8,1.72,35,30.1,92.1,0,0.94,0.37,7.44,7.81,141,57.2,0.196,16.6,1.04,251,4.31,14.5,7.3,70.6,153.8,1 209 | 2.03,13.93,41,38,68,14,7,0.07,1.24,3.14,2.4,74.62,15.49,0.2,1.76,100.2,26.6,49.7,2.9,0.05,0.85,11,31.2,3.57,0.389,41.25,130,5.4,4.73,1.57,27.1,29.5,88.3,0,0.87,0.41,7.14,8.59,136,63.7,0.236,19.1,0.97,274,4.41,15.8,8.3,69.2,192.9,1 210 | 1.4,21.17,33,44.4,71,12,13,0.05,1.15,3.33,2.19,78.8,17.61,0.79,0.62,99,21.7,56.5,3.3,0.06,1.32,12,28.2,4.72,0.408,41.41,133,7,4.47,1.62,37.3,28,86.2,0,0.98,0.32,7.34,9.69,137.4,52.9,0.198,18.3,1.05,204,4.74,15.4,10.3,72.6,239.9,1 211 | 1.54,22.94,23,43.1,63,16,17,0.03,0.8,3.84,2.67,14.57,10.04,10.17,1.56,98,25.9,49.8,2.1,0.03,0.8,17,33.7,4.8,0.398,65.85,131,3,4.54,1.51,38.5,27.6,83.8,0,0.96,0.31,7.9,9,142.3,52,0.28,9.4,1.23,311,4.75,12.7,5.1,76.8,210.9,1 212 | 0.73,20.99,28,32.2,52,10,9,0.03,0.4,4.04,2.53,511.1,13.86,0.52,1.79,98.6,24.6,58.7,1.7,0.15,1.9,12,33.7,5.24,0.333,62.48,109,1.6,4.79,1.75,22.3,29.7,90.7,0,1.08,0.58,7.4,10.1,139.4,68,0.38,11.4,1.37,375,3.67,11.3,3.3,65.9,184.5,1 213 | 2.95,20.59,47,39.2,79,18,12,0.06,0.79,2.45,2.2,10.02,4.39,3.52,0.7,102.4,20.8,67.3,2.2,0.07,0.99,33,28.1,4.99,0.383,41.46,128,6,4.39,1.63,22.3,30.5,91.1,1,0.99,0.38,5.16,6.81,139.4,70.8,0.235,16.7,0.89,345,4.2,13.4,8.2,67.3,213.5,1 214 | 5.67,17.81,57,44.7,102,31,17,0.05,0.8,4.62,2.63,11.46,33.99,0.83,2.81,99.4,25.7,80.1,3.4,0.03,0.57,89,35.2,6.24,0.397,67.35,134,7.8,4.11,0.89,15.5,30.3,89.7,0,0.87,0.31,5.43,6.09,138.8,77.7,0.134,16.3,1.08,220,4.42,13.7,11.2,79.9,297.5,1 215 | 1.31,20.47,23,44.3,64,25,22,0.04,0.83,3.51,2.52,47.49,127.8,0.75,0.53,101.3,22.6,77,1.6,0.16,3.44,20,31.9,4.85,0.356,36.61,118,3.5,4.67,2.2,46.5,30.7,92.3,0,0.97,0.27,5.79,8.65,139.7,43.5,0.201,18.5,0.98,233,3.86,14.7,5.1,76.2,265.8,1 216 | 4.09,17.8,15,40.7,143,14,11,0.04,0.84,4.33,2.49,13.83,22.1,0.92,1.09,98.7,25.2,65.1,3,0.13,2.53,20,30.6,4.55,0.42,36.47,142,5.4,4.9,2.36,47.2,30.1,89,0,0.98,0.31,6.27,6.66,136.8,43.1,0.178,16.5,1.19,268,4.72,12.2,8.4,71.3,235.4,1 217 | 1.87,20.17,39,43.6,79,18,13,0.04,0.52,4.78,2.36,93.06,16.01,0.36,0.69,102.2,25.7,83.2,2.2,0.07,0.85,25,27.2,4.53,0.394,41.4,130,4.8,4.27,2.05,25.1,29.4,89.3,0,1.02,0.45,5.53,8.14,143.8,68,0.176,17.4,1.1,216,4.42,14,7,70.8,229.2,1 218 | 0.72,22.74,40,38.5,29,22,16,0.04,0.7,3.59,2.54,165,42.84,10.17,0.45,98.9,22.8,48.9,2,0.04,0.7,17,31.2,4.98,0.314,70.63,96,2.8,4.44,1.77,29.8,24.1,78.9,0,1.05,0.24,4,12.3,140,64.8,0.35,15.2,1.43,281,3.98,17.7,4.8,69.7,195.9,1 219 | 2.44,22.04,45,43,58,18,12,0,0,3.71,2.37,40.23,8.55,14.89,1.11,103.5,21.8,77,1.9,0.1,1.2,29,29.7,4.46,0.441,36.26,140,4.2,4.74,1.9,28.6,30.2,94.9,0,1.08,0.5,7,10.8,142.6,63.2,0.224,15.5,0.8,207,4.64,12.5,6.1,72.7,205.5,1 220 | 2.1,21.64,28,44.1,72,14,13,0.04,0.75,4.02,2.42,21.91,14.57,1.74,1.35,103.7,22.8,79.8,3.2,0.19,3.28,15,24.5,3.92,0.377,33.96,125,7.4,5.04,2.35,40.5,30.8,93.1,0,1.12,0.36,6.21,8.63,143.1,49.2,0.183,17.4,1.01,212,4.05,14.9,10.6,68.6,294.1,1 221 | 3.19,20.7,56,45.3,55,18,16,0.03,0.54,8.31,2.44,9.02,15.02,2.41,1.19,100.4,25.3,60.8,3.3,0.01,0.1,20,26.7,4.86,0.406,35.39,138,8.5,5.1,1.66,31.1,32.6,95.7,1,0.95,0.31,5.85,7.81,141.3,62.4,0.107,17.6,1.21,137,4.24,12.6,11.8,72,250.3,1 222 | 4.97,16.21,30,42.4,54,20,12,0,0.7,5.18,2.3,6.99,7.5,2.04,0.65,99.3,25.4,82.7,3,0.1,1.5,31,25.1,4.94,0.364,40.1,121,4.1,4.61,2,34.4,29.9,90,0,1.04,0.4,7.6,8.6,136.3,55.8,0.23,15.8,1.02,269,4.04,12.2,7.1,67.5,236.3,1 223 | 2.04,21.78,33,44.6,71,13,15,0.07,1.33,3.81,2.5,15.07,41.23,2.79,0.72,99.7,20.7,83.8,6.5,0.02,0.4,12,32.8,4.08,0.417,48.01,139,11.9,3.98,2.59,49.6,30.5,91.5,0,0.98,0.31,5.89,8.61,138.2,42.8,0.168,17.1,0.9,195,4.55,14.5,18.4,77.4,276.1,1 224 | 3.2,20.32,38,36.1,88,30,18,0.04,0.4,2.77,2.37,54.67,35.1,10.17,1.75,98.2,25.3,61.7,2.3,0.11,1.1,24,34.2,4.41,0.427,43.95,144,5.5,3.92,2.82,29.4,30.3,89.9,0,1.18,0.33,3.4,11.8,139.9,65.7,0.32,15.1,1.03,270,4.75,12.4,7.8,70.3,178.9,1 225 | 9.1,18.9,36,38.5,65,13,14,0.02,0.29,3.5,2.34,278.8,54.42,12.06,0.79,98.1,24.8,63.3,2,0.04,0.63,21,40.5,4.83,0.33,43.59,110.6,2.4,4.4,1.85,26.21,27.98,83.5,0,0.86,0.43,6.03,7.5,137.4,66.84,0.255,16.58,1.08,340,3.95,16.27,4.4,79,205,1 226 | 1.15,25.07,34,45.3,69,8,18,0.05,0.8,4.01,2.64,39.34,6.61,10.17,0.54,96.9,20.3,64.2,1.7,0.25,4.2,13,38.1,4.43,0.384,68.71,116,3.9,4.57,1.5,25.3,22.9,75.9,0,1,0.35,5.9,10.04,137.7,63.8,0.25,14.33,1.29,218,5.06,17.2,5.6,83.4,210.3,1 227 | 5.15,20.66,24,46.3,79,23,13,0.02,0.3,4.04,2.83,51.27,12.5,0.7,0.83,97.4,24.6,52.6,3.9,0.03,0.5,16,28.4,4.73,0.412,42.58,140,5.2,5.06,2.49,38.6,30,88.4,0,1.04,0.39,6,9.7,137.6,54.6,0.26,10.6,1.05,274,4.66,12.3,9.1,74.7,209.2,1 228 | 5.53,19.21,46,41.5,61,17,15,0.07,1.31,3.23,2.44,49.28,14.19,10.56,1.22,101.7,23.1,48.4,4.5,0.08,1.43,18,30.2,4.07,0.356,37,119,9.8,4.31,1.6,30.3,29.4,88,0,0.88,0.57,10.8,8.2,139.7,56.1,0.152,17.6,1.16,185,4.05,13.2,14.3,71.7,180.2,1 229 | 3.98,18.31,49,42.9,100,11,11,0.03,0.91,4.58,2.52,9.53,3.63,0.94,2.01,104,22.3,82.1,3.2,0.04,1.22,14,31,4.23,0.329,47.94,103,7,4.61,1.18,33.6,24.9,79.8,0,1.08,0.31,8.84,8.55,140,55.4,0.204,17.1,0.9,238,4.13,15.8,10.2,73.9,219.5,1 230 | 1.58,26.82,23,42.8,89,22,20,0.04,0.55,3.57,2.64,22.96,119.6,10.17,1.33,94.1,27.1,62,1,0.03,0.41,27,34.2,6.07,0.331,51.57,117.1,2.9,4.82,2.95,46.39,30.05,85,0,0.96,0.45,7.02,7.75,143.2,45.63,0.147,18.11,1.29,190,3.9,11.48,3.9,77,163.3,1 231 | 2.23,19.38,22,48.3,63,19,17,0.04,0.63,4.63,2.59,515.4,118.1,6.93,0.27,102.1,25.1,57,4.6,0.06,0.92,23,31.7,4.62,0.446,42.93,155,11.4,4.88,1.87,29.3,31.4,90.5,0,1.14,0.36,5.54,10.1,141.7,63.6,0.237,18.8,0.89,235,4.93,12.9,16,80,253.4,1 232 | 1.8,20.74,36,39.5,101,21,16,0,0.2,2.23,2.5,86.78,27.14,10.17,2.5,98.3,25.8,49.8,3.7,0,0.2,22,27.1,5.33,0.412,41.06,141,4.8,4.34,1,15.9,31.1,91.2,0,1,0.3,4.9,7.6,140.5,78.8,0.202,16.6,0.96,266,4.52,12.5,8.5,66.6,211.7,1 233 | 3.12,25.93,66,43.4,99,14,12,0.03,0.5,4.93,2.61,8.31,11.24,10.17,0.32,99.1,23.4,69.7,2.6,0.06,1.1,30,34.7,6.44,0.403,44.03,132,14.5,4.23,1.54,27.2,28.1,85.7,1,1.25,0.26,4.6,10.2,144.2,66.6,0.24,12.1,1.29,234,4.7,12.9,17.1,78.1,293.3,1 234 | 3.24,17.82,49,36.1,71,16,11,0.02,0.68,3.07,2.48,13.93,16.93,4.32,1.41,102,23.3,52.6,2.4,0.06,1.58,37,30.6,4.37,0.311,43.45,106,5,4.72,0.77,22.1,32.5,95.5,0,1.05,0.16,4.66,7.28,138.4,71,0.07,17.3,1.2,96,3.25,14.7,7.4,66.7,222.4,1 235 | 4.06,24.83,22,45.6,67,24,21,0.01,0.2,3.82,2.64,13.62,18.79,10.17,1.63,98.3,23.5,59.6,2.5,0.04,0.7,16,32.7,6.12,0.424,36.89,143,11,4.43,1.98,32.7,30.5,90.4,0,1.3,0.41,6.8,11.8,142.2,59.6,0.28,14.1,0.97,234,4.69,12,13.5,78.3,220.7,1 236 | 2.03,15.41,57,38.5,76,22,17,0.05,1.41,5.55,2.27,15.58,0.6,1.23,2.83,101.4,24.6,81,3.9,0.15,3.93,13,21.2,4.87,0.352,55.52,116,6.5,4.51,1.13,30.4,30.7,93.1,1,1.02,0.36,9.61,7.13,136.9,54.7,0.157,17.1,1.6,221,3.78,13.6,10.4,59.7,198.2,1 237 | 1.25,17.3,52,43,41,17,17,0.05,0.81,3.57,2.42,37.65,22.89,5.43,2.74,99.3,23.7,49.2,2.4,0.11,1.72,11,29.5,5.2,0.368,53.27,123,4.7,4.9,1.9,30.4,30,89.6,0,0.92,0.46,7.33,6.89,135.4,59.8,0.147,16,1.1,213,4.11,13.2,7.1,72.5,162.9,1 238 | 4.63,21.03,57,48,76,38,24,0,0,3.42,2.57,8.74,25.18,0.86,2.3,102.3,24.5,76.4,3.4,0.1,1.3,41,33,5.33,0.447,54.15,144,9.2,4.53,2.1,30.4,29.6,92.2,1,1.12,0.4,6,7.4,143.3,62.3,0.192,17,0.9,261,4.85,12,12.6,81,348.8,1 239 | 3.47,22.5,27,47.8,49,35,15,0.1,0.94,3.41,2.41,316,4.16,1.91,0.91,99.2,22.6,83.6,3,0.03,0.28,93,30.4,4.75,0.458,38.8,156,7.4,4.5,2.48,23.4,29,84.9,0,1.37,0.6,5.63,7.05,139.8,69.7,0.159,18.8,1.42,226,5.39,15.9,10.4,78.2,460.6,1 240 | 20.53,20.93,34,41,73,20,18,0.03,0.7,2.26,2.34,6.76,7.31,2.2,0.76,103.1,23.9,58,2.1,0.03,0.65,23,33.6,4.5,0.385,16.71,126,4.5,5.03,1.69,36.3,30.1,91.8,0,1.11,0.27,5.74,6.91,142.9,56.6,0.248,17,1.16,358,4.19,15.2,6.6,74.6,325.8,1 241 | 3.48,22.77,26,38.3,67,29,25,0.02,0.4,4.44,2.51,13.4,8.31,10.17,1.14,99.4,25.1,50.9,6,0.12,2.3,29,33.5,4.57,0.396,53.73,131,7.8,4.37,2.4,46.5,28.2,85.2,0,1,0.34,6.6,10.9,142.9,44.2,0.26,12.9,1.58,244,4.65,12.4,13.8,71.8,261,1 242 | 1.58,19.68,32,40.2,47,29,19,0.02,0.61,3.29,2.32,22.24,2.63,2.63,1.09,100.7,25.8,86.1,2.8,0.04,1.03,21,24,5.69,0.336,38.79,107,5.1,4.68,1.27,36.9,25.5,80.4,0,1.12,0.28,8.06,8.66,141.5,53.4,0.152,18.8,1.17,176,4.19,17.8,7.9,64.2,302,1 243 | 1.77,23.83,64,41.5,112,38,20,0.04,0.5,5.58,2.64,21.66,9.63,10.17,1.98,95.5,25.7,69.2,3.2,0,0.02,50,35.6,8.67,0.429,56.71,140,7.3,4.23,1.74,21.3,28,85.8,1,1,0.36,4.4,13,140.8,73.8,0.27,19.4,1.26,209,5,13,10.5,77.1,226.3,1 244 | 1.93,22.46,36,37.2,100,26,24,0.04,0.6,3.94,2.57,36.18,14.71,10.17,0.78,97.3,23.8,59.8,2.7,0.4,6.1,20,35.8,6.85,0.398,48.85,130,6.5,4.36,1.77,27.1,29.9,91.5,0,0.99,0.32,4.9,13.8,139.2,61.3,0.32,20.9,0.95,233,4.35,12.9,9.2,73,326.3,1 245 | 4.2,19.5,43,34,74,13,14,0.06,0.8,3.38,2.34,71.84,20.06,10.17,1.6,102.2,23.8,61.2,1.1,0.03,0.4,4,33.1,4.79,0.414,42.52,138,5.4,4.4,1.48,20.5,30.5,91.4,0,1.29,0.28,3.9,12.1,141.1,74.4,0.27,16.4,1.11,224,4.53,12.9,6.5,67.1,205.8,1 246 | 2.42,20.32,45,42,62,32,22,0.01,0.1,3.64,2.34,41.39,11,0.29,2.15,100.3,26,75.8,3.2,0,0.5,14,31.7,5.14,0.361,45.7,120.9,7.4,4.92,1.38,44.5,32,95.5,0,1.01,0.1,3.1,8.83,141.7,51.8,0.08,16.75,1.16,88,3.78,11.47,10.6,73.7,200.1,1 247 | 5.27,15.51,36,48.4,41,12,11,0.02,0.94,3.89,2.57,113,22.94,2.03,0.83,101.9,25.2,63.1,3.5,0.02,0.84,17,28.3,5.56,0.367,39.15,126,5.4,4.21,0.97,43.8,31,90,0,1.04,0.15,6.68,8.79,138.4,47.8,0.13,18.2,1.16,148,4.08,14.8,8.9,76.7,209.4,1 248 | 2.47,19.66,26,44.9,84,14,13,0.03,0.43,4.22,2.48,193.1,20.24,4.4,1.48,101.6,26.2,94.1,2.5,0.03,0.47,25,28.9,4.36,0.413,57.05,135,3.9,5.16,1.74,28.8,30.7,93.8,0,1.09,0.38,6.28,7.4,142.3,64.1,0.14,17.2,0.91,189,4.4,14.3,6.4,73.8,310.9,1 249 | 1.59,19.89,36,40.4,70,21,12,0.02,0.4,3.16,2.73,32.8,90.08,0.41,0.2,100.4,24.5,61.5,3.2,0.11,2,27,31.8,5.08,0.295,34.74,90,6.5,4.69,1.84,34.2,24.6,80.6,0,1.18,0.28,5.2,12.9,140.1,58.2,0.28,17.2,1.17,214,3.66,15.3,9.7,72.2,266.2,1 250 | 2.58,16.58,51,41.08,77,18,19,0,0,4.01,2.22,18.27,5.86,3.35,0.68,99.2,23.9,64.5,3.13,0.1,0.8,21,30.18,5.73,0.403,88.18,132,5.96,4.28,2.1,28.9,30.4,92.6,0,1.04,0.5,6.4,10.9,135.4,63.9,0.199,16.1,0.7,182,4.35,12.3,9.09,71.08,153.7,1 251 | 1.17,20.01,25,46,61,18,17,0.02,0.33,3.93,2.42,126.3,13.83,3.4,0.84,99.5,22.7,79.3,2.1,0.11,1.71,14,29.6,4.62,0.411,43.2,137.5,4.2,4.51,2.39,36.43,30.24,90.3,0,1.02,0.42,6.41,9.18,137.7,55.12,0.147,15.87,0.91,160,4.55,13.17,6.3,75.6,280.8,1 252 | 1.01,19.31,30,42.4,39,16,13,0.04,0.58,3.24,2.43,19.29,17.73,0.2,0.8,101.9,22.6,66,2.8,0.04,0.58,12,30.8,5.55,0.353,39.03,117,5.7,4.51,1.98,32.1,30.5,91.5,0,0.9,0.46,7.52,7.12,139.3,59.2,0.23,16.2,0.91,323,3.85,14.3,8.5,73.2,153.4,1 253 | 0.76,17.57,45,33.5,62,17,14,0.01,0.2,3.52,2.47,14.41,17.99,10.17,3.09,100.2,24.9,67.6,2.4,0.07,1.1,22,32.3,5.07,0.396,47.71,132,5.3,4.57,1.76,28.5,29.5,88.6,0,1.15,0.27,4.4,9.8,138.1,65.8,0.27,11.7,1.33,282,4.47,13.1,7.7,65.8,206.4,1 254 | 0.61,22.63,30,48.3,76,28,24,0.01,0.2,5.31,2.43,69.13,11.66,10.17,1.9,97.4,24.7,74.5,2.5,0.06,1,20,28.7,5.8,0.457,47.61,151,4.1,4.33,2.13,36,29.4,89.1,0,1.21,0.37,6.3,9.4,140.4,56.5,0.21,10.4,1.2,227,5.13,12.3,6.6,77,195.9,1 255 | 4.16,17.09,26,41.2,33,13,10,0.06,0.72,3.18,2.56,35.98,12.44,0.94,1.29,99.3,24.5,60.3,2.5,0,0,11,27.8,4.71,0.388,38.79,128,3.2,4.59,1.33,17.2,30.1,91,0,0.98,0.3,3.83,6.76,136.3,78.2,0.148,16.5,1.14,219,4.27,14,5.7,69,157.7,1 256 | 0.61,23.76,28,44,67,11,12,0,0.6,3.75,2.47,17.8,9.65,10.17,0.9,96.5,22.7,49.2,2.9,0,0.4,11,35.5,4.46,0.374,42.2,129,5.7,4.06,1.8,39.6,31.2,90.4,0,1.05,0.3,6.1,11.4,138.9,53.3,0.243,16.3,1.01,213,4.14,13,8.6,79.5,258,1 257 | 0.77,20.75,32,34.8,51,18,19,0.02,0.7,4.3,2.45,13.99,14.22,10.17,1.38,100.2,22.2,58,3.2,0.04,1.3,18,37.8,4.46,0.386,40.94,129,6.3,4.05,1.33,43.54,28.6,85.6,0,1.11,0.37,12.14,10.6,139.1,42.44,0.18,11.5,1.42,173,4.51,12.5,9.5,72.6,167.3,1 258 | 1.92,20.69,44,43.1,78,17,24,0.07,0.9,2.6,2.39,21.94,15.11,10.17,1.2,99.8,25.2,66,4.2,0.39,4.9,15,29.3,5.13,0.403,46.58,128,13.5,3.99,3.11,39,28.1,88.6,0,1.27,0.39,4.9,11.2,141.7,50.3,0.34,13.5,1.16,299,4.55,13.2,17.7,72.4,184.1,1 259 | 2.44,22.04,52,43.7,63,16,13,0.05,0.85,4.07,2.61,4.67,14.14,1.47,2.57,102.4,23.8,91.8,2.7,0.09,1.61,14,33.9,5.34,0.372,49.61,121,6.5,4.74,2.52,45.9,28,85.8,1,0.98,0.36,6.55,8.34,143.5,45.1,0.175,18,1,210,4.33,13.4,9.2,77.6,346.6,1 260 | 10.31,17.67,27,43.6,66,20,16,0.07,1.34,2.92,2.47,128,279.5,3.02,3.75,98.7,25.3,68.8,3.9,0.11,2.2,33,28.4,5.01,0.37,47.74,126,9,4.27,2.18,43.8,30.5,89.1,0,0.93,0.5,10,8.68,137.4,42.7,0.144,19.7,1.59,166,4.15,12.7,12.9,72,333,1 261 | 4.3,21.34,40,35.8,57,19,10,0.01,0.1,2.18,2.29,11.93,7.58,10.17,0.86,101.4,22.1,64.4,3.5,0,0.02,26,25.9,5.92,0.365,44.44,124,5.8,3.94,2.2,24.7,30.6,90.1,0,1.2,0.41,4.6,11.8,140.9,70.6,0.18,14.6,1.06,150,4.05,12.4,9.3,61.7,186.4,1 262 | 1.5,22.27,21,43.2,54,14,19,0.01,0.3,3.69,2.59,23.69,169,10.17,1.62,101.9,19.3,70,4,0.01,0.3,10,34.5,4.27,0.358,44.82,118,4.2,4.27,1.13,37.7,28,84.8,0,0.9,0.2,6.7,10.9,139.2,66.58,0.25,12.4,1.18,230,4.22,13.5,8.2,77.7,270.1,1 263 | 2.04,6.7,46,43.5,54,71,42,0.07,0.5,6.04,1.03,43.58,12.79,10.17,1.05,98.6,23,61,4.9,0.01,0.1,120,26.3,8.39,0.442,53.43,135,13.6,4.18,2.76,18.4,30.9,92.4,0,1.08,0.97,6.5,12.1,139.3,66.58,0.28,16.2,1.19,228,4.37,11.9,18.5,69.8,273.7,1 264 | 1.45,22.39,22,37,60,15,16,0.01,0.1,2.08,2.4,256.1,147.3,10.17,1.68,102.9,19.3,58,4.6,0.15,1.5,15,22.8,4.63,0.365,71.36,120,7.6,3.59,1.74,17.5,30.1,91.5,0,0.94,0.32,3.2,11.2,141,66.58,0.24,12.9,0.78,215,3.99,12.2,12.2,59.8,148.8,1 265 | 4.9,26.22,63,42.6,88,11,19,0.04,0.9,4.12,2.58,10.18,30.94,10.17,3.21,99.4,21.4,71,3.7,0.07,1.6,11,31.7,5.4,0.451,55.71,149,7.7,4.12,1.25,29,30.2,91.5,1,0.96,0.22,5.1,10.3,142.9,66.58,0.2,12,1.36,192,4.93,12.3,11.4,74.3,312.8,1 266 | 5.77,19.28,30,49.1,122,9,15,0.02,0.3,3.36,2.77,23.64,23.11,10.17,1.09,96.7,28.4,59,3.8,0.11,1.9,10,26.3,4.8,0.438,40.75,146,7.7,3.98,1.74,29.7,30,89.9,0,0.9,0.31,5.3,10.6,140.4,66.58,0.24,12.3,1.36,229,4.87,12.6,11.5,75.4,262.4,1 267 | 1.24,18.89,39,44.2,47,15,13,0.01,0.2,3.66,2.55,22.24,22.69,10.17,2.28,101.9,26.1,60,2.4,0.01,0.2,11,25.8,5.26,0.337,35.29,103,5.7,4.39,1.34,32.4,25.8,84.5,0,1.14,0.27,6.5,11.1,142.5,66.58,0.25,13.8,1.14,225,3.99,14.6,8.1,70,184.1,1 268 | 1.09,16.07,50,41,98,35,27,0.01,0.2,3.76,2.3,3.75,0.6,10.17,1.82,103.6,25.1,65,3.7,0.13,2.3,25,26.1,4.93,0.441,33.02,139,7.6,3.87,2.56,44.7,28.7,90.9,1,0.89,0.25,4.4,11.1,140.9,66.58,0.27,12.8,1.33,240,4.85,13.2,11.3,67.1,322.2,1 269 | 4.02,14.78,49,45.3,55,13,19,0,0,3.74,2.31,20.08,0.6,10.17,4.02,105.1,22.5,64,3.4,0.08,1.8,9,26.6,5.11,0.392,49.54,121,6,4.48,1.31,28.9,25.4,82.2,0,0.97,0.27,6,11.1,137.9,66.58,0.31,13.1,1.21,276,4.77,15.5,9.4,71.9,218.1,1 270 | 1.2,25.31,62,41.8,57,16,20,0.01,0.2,5.07,2.55,13.41,10.4,10.17,2.19,103.1,19.6,61,8,0.03,0.6,15,26.1,5.64,0.383,35.13,134,20,4.11,2.07,38.3,32.9,94.1,1,1.02,0.26,4.8,10.4,143.9,66.58,0.22,11.8,1.13,213,4.07,11.8,28,67.9,274.4,1 271 | 2.41,14.35,26,46.7,80,12,20,0.06,0.6,5.22,2.7,49.68,38.82,10.17,2.62,102.1,28.4,55,5.6,0.29,3.1,15,28.1,5.84,0.425,47.68,140,9.4,4.05,2.43,25.7,29.3,88.9,0,0.91,0.36,3.8,12.9,140.8,66.58,0.19,18.1,0.91,151,4.78,12.5,15,74.8,224.8,1 272 | 2.25,21.04,52,40.7,72,18,23,0.01,0.3,3.81,2.5,24.33,12.14,10.17,1.61,102.5,21.1,66,2.5,0.06,1.8,50,33.4,4.74,0.345,33.31,117,4.1,4.24,1.19,34.9,30.4,89.6,0,0.94,0.18,5.3,10.7,140.4,66.58,0.22,13,1.17,205,3.85,12,6.6,74.1,229.2,1 273 | 2.55,16.41,46,41.6,61,23,20,0.02,0.5,3.64,2.59,13.86,1.61,10.17,2.44,96.9,29.3,49,3.9,0.04,0.9,16,33.9,4.57,0.378,35.57,122,5.9,4.51,1.58,36.7,27.9,86.5,0,0.96,0.22,5.1,11.4,138.1,66.58,0.2,13.8,1.21,178,4.37,13.1,9.8,75.5,134.3,1 274 | 3.97,22.48,38,48.7,69,12,15,0.02,0.3,2.85,2.72,75,34.02,10.17,1.68,100.7,21.6,70,2.4,0.02,0.3,13,31.8,5.96,0.39,36.04,123,3.8,4.88,2.2,29.7,25.2,79.8,0,1.15,0.38,5.1,9.5,139.9,66.58,0.35,9.6,0.93,367,4.89,16.8,6.2,80.5,271.3,1 275 | 2.28,9.64,46,43.8,29,30,29,0.01,0.2,4.55,2.39,11.25,11.08,10.17,2.05,105.4,28.9,76,4.6,0.07,1.6,12,23.6,5.4,0.398,46.82,134,7.9,3.94,1.3,29,30.9,91.7,0,0.88,0.21,4.7,10.8,140,66.58,0.2,13.5,1.41,181,4.34,12.5,12.5,67.4,272.5,1 276 | 1.59,17.3,32,44.4,65,10,19,0.02,0.3,3.54,2.33,31.61,11.8,10.17,1.14,101,23.6,67,2.5,0.04,0.7,13,28,3.85,0.375,41.93,124,3.9,3.9,1.82,29.8,28.4,85.8,0,0.77,0.31,5.1,11.4,138,66.58,0.25,14.8,1.42,218,4.37,12.9,6.4,72.4,342.7,1 277 | 0.86,19.72,27,45.8,50,16,21,0.03,0.5,2.64,2.59,49.14,35.71,10.17,1.2,102.1,23,44,3.8,0.26,4.7,12,25,5.36,0.438,34.57,149,7.1,4.82,2.05,37,30.8,90.5,0,0.94,0.26,4.7,9.4,140,66.58,0.23,9.6,1.29,241,4.84,12.4,10.9,70.8,200.3,1 278 | 1.12,18.7,30,43.5,37,7,12,0.01,0.1,3.25,2.6,115.1,59.98,10.17,0.68,102.4,25,67,5.2,0,0,8,24.6,4.48,0.374,47.26,120,7.2,4.3,0.42,5.1,26.4,82.4,0,0.89,0.26,3.1,11.9,141.8,66.58,0.18,14.1,1.28,154,4.54,13.2,12.4,68.1,245.2,1 279 | 3.25,16.4,69,49.2,85,13,26,0.01,0.2,5.56,2.58,6.18,4.15,10.17,0.72,102.7,26.9,76,2.5,0.05,1.1,15,29.1,5.54,0.456,60.37,152,9.5,4.4,1.43,31,30.5,91.4,1,1.08,0.29,6.3,11.2,141.6,66.58,0.25,13.8,1.19,220,4.99,11.9,12,78.3,348.1,1 280 | 1.29,14.71,26,47,72,15,17,0.03,0.8,2.76,2.76,9.94,13.4,10.17,1.07,101.9,30,64,6.1,0.04,1,15,23.4,4.69,0.389,35.29,130,10.7,4.61,1.72,43.5,29,86.8,0,0.93,0.17,4.3,9.3,142,66.58,0.22,9.9,1.19,232,4.48,12.1,16.8,70.4,274.8,1 281 | 5.28,22.6,50,40.6,79,34,23,0.03,0.7,4.78,2.56,24.76,11.87,10.17,2.11,103.2,19.3,54,3,0.09,2,68,27.9,4.4,0.331,41.84,104,6.7,4.5,2.01,44.3,25.3,80.5,0,0.91,0.2,4.4,13.2,140.6,66.58,0.32,19.2,1.31,245,4.11,13.2,9.7,68.5,221.2,1 282 | 1.32,16.08,35,42,37,13,13,0.03,0.6,4.48,2.5,6,19.05,10.17,1.3,102.9,25,68,3.7,0.31,6.2,8,26.2,5.19,0.408,48.96,139,5.2,4.38,1.68,33.5,29.6,87,0,0.86,0.22,4.4,10.9,139.6,66.58,0.22,12.4,1.2,202,4.69,11.9,8.9,68.2,272.8,1 283 | 2.05,22.15,35,42.6,62,27,26,0.01,0.2,3.78,2.42,14.19,7.05,10.17,0.97,100.1,22.6,51,2.7,0.03,0.6,12,26.2,5.23,0.367,54.22,121,4.6,4.15,2.17,43.7,27.5,83.4,0,0.86,0.2,4,10.2,140.7,66.58,0.32,11.1,1.02,313,4.4,12.4,7.3,68.8,177.5,1 284 | 1.91,12.11,32,42.6,59,11,13,0.02,0.4,4.4,2.26,9.35,4.18,10.17,0.54,102.2,26.7,74,3.4,0.09,1.6,8,20.4,4.26,0.368,44.35,127,5.3,3.91,2.15,38.3,33.2,96.3,0,0.87,0.26,4.6,11.5,137.1,66.58,0.21,14,1.4,186,3.82,11.1,8.7,63,233.7,1 285 | 1.31,18.83,31,44.4,69,12,16,0.04,0.5,5.53,2.68,40.34,24.94,10.17,1.51,97.9,27.1,66,4.5,0.02,0.3,13,30.3,5.98,0.385,37.98,128,7.2,4.43,1.01,12.9,30.1,90.6,0,0.9,0.16,2,10.5,139.4,66.58,0.3,12.3,1.02,284,4.25,12.4,11.7,74.7,210.2,1 286 | 1.97,19.33,33,42.3,69,8,16,0.03,0.6,7.25,2.52,23.18,14.97,10.17,0.88,100.6,23.6,56,3.8,0.04,0.8,13,29.2,5.18,0.373,37.23,123,7.1,4.03,1.34,26.3,30.1,91.4,0,0.92,0.26,5.1,10.3,139.5,66.58,0.22,12.1,0.93,218,4.08,11.9,10.9,71.5,203.4,1 287 | 1.24,12.96,41,41.08,77,18,19,0.04,0.4,3.6,2.25,203.9,246.8,10.17,0.64,102,25.6,57,3.13,0,0,21,30.18,4.7,0.422,57.6,141,5.96,3.37,1.51,13.9,29.2,87.4,0,0.89,0.58,5.4,12.7,139.7,66.58,0.23,17,1.06,182,4.83,12.6,9.09,71.08,305.1,1 288 | 0.87,23.38,21,49.3,76,20,24,0.05,0.8,2.8,2.68,25.92,6.82,10.17,1.67,100.8,24.6,67,3.5,0.12,1.9,12,28.5,5.56,0.411,62.49,146,6.8,4.28,1.12,17.6,30.4,85.6,0,0.99,0.18,2.8,10.2,144.5,66.58,0.32,11.8,1.31,319,4.8,12.5,10.3,77.8,283.9,1 289 | 5.55,23.42,28,46.9,59,12,14,0.02,0.4,4.26,2.55,15.56,13.22,10.17,0.38,104.5,19,57,3.5,0.07,1.5,17,26.8,4.7,0.393,30.26,129,7.5,4.32,1.78,39.4,29.1,88.5,0,0.91,0.16,3.5,9.8,142.6,66.58,0.27,11.4,1.09,279,4.44,12.4,11,73.7,298.1,1 290 | 1.87,11.36,31,47.3,48,14,20,0.01,0.2,3.26,2.34,26.56,6.54,10.17,0.79,101.6,27.1,69,4.2,0.01,0.2,15,30.6,5.16,0.404,47.65,136,8.8,4.06,1.61,36.3,30.7,91.2,0,0.92,0.29,6.5,9.5,136,66.58,0.17,10,1.27,183,4.43,12.4,13,77.9,255.6,1 291 | 2.48,20.67,31,48,102,9,12,0.06,1,4.12,2.65,21.76,6.69,10.17,0.77,99.4,26,79,4.1,0.03,0.5,10,32.6,4.95,0.401,44.12,129,7,4.77,1.88,32.8,27.7,86.2,0,0.91,0.36,6.3,9.6,141.3,66.58,0.37,10,1.4,380,4.65,14.6,11.1,80.6,298,1 292 | 8.24,18.58,47,41.8,62,10,16,0.02,0.4,4.81,2.54,83.48,4.7,10.17,0.63,103.8,21.7,75,1.7,0.34,7.6,18,24.9,5.25,0.386,49.93,113,4,4.88,1.54,34.5,24,82,0,0.94,0.25,5.6,10,139.2,66.58,0.24,10.8,0.91,241,4.71,15.5,5.7,66.7,293.1,1 293 | 2.1,25.57,65,41.8,144,51,29,0.02,0.2,3.04,2.54,20.94,14.93,10.17,1.81,97.4,22.4,56,9.9,0.05,0.6,176,27.4,5.79,0.368,59.47,130,28.4,3.37,1.15,14.2,31.1,88,1,0.96,0.39,4.8,11.1,142,66.58,0.27,13.2,0.82,247,4.18,12.6,38.3,69.2,363.5,1 294 | 1.45,19.74,29,43.3,50,14,21,0.03,0.5,3.78,2.63,22.13,10.04,10.17,1.25,99.9,26.2,61,3,0.09,1.6,11,27.8,4.68,0.396,46.1,128,4.9,4.24,1.69,30.3,28.2,87.2,0,0.88,0.37,6.6,10.4,141.6,66.58,0.35,11.3,1.16,337,4.54,12.7,7.9,71.1,287.7,1 295 | 3.28,22.63,39,44.8,71,16,18,0.04,0.5,4.4,2.56,185.5,217.8,10.17,1.11,99.7,22.8,57,2.2,0.04,0.5,10,32.2,5.45,0.331,44.8,102,4.2,4.53,1.19,16,24.8,80.5,0,0.9,0.29,3.9,10.7,140.6,66.58,0.32,12.6,1.34,301,4.11,14.5,6.4,77,178.9,1 296 | 1.83,15.03,23,41.08,77,18,19,0,0,5.2,2.26,427.4,130.7,10.17,0.96,103.1,23.5,53,3.13,0.02,0.3,21,30.18,3.9,0.38,52.89,123,5.96,3.88,1.11,14.3,29.4,90.9,0,0.66,0.17,2.2,11.7,137.3,66.58,0.24,14.6,1.31,205,4.18,11.5,9.09,71.08,247,1 297 | 1.76,24.54,15,46.3,157,14,23,0.01,0.1,4.8,2.58,40.13,46.77,10.17,4,99.3,20,55,12.1,0,0,14,31.9,5,0.432,48.92,149,17.5,4.17,1.64,11.6,31.8,92.1,0,0.73,0.54,3.8,10.9,142.9,66.58,0.3,12.6,1.75,280,4.69,12.1,29.6,78.2,217.6,1 298 | 3.45,25.06,42,42.6,60,6,14,0.03,0.4,3.39,2.53,97.03,2.91,10.17,1.89,98.6,20.7,61,4.4,0.16,1.9,15,28.6,4.82,0.39,40.66,135,6.7,4.26,2.6,30.4,30.8,88.8,0,0.98,0.45,5.3,10.3,140.1,66.58,0.3,11.5,1.28,295,4.39,12.3,11.1,71.2,250.9,1 299 | 1.67,23.59,25,46.2,63,10,13,0.03,0.3,2.75,2.79,38.74,9.2,10.17,1.04,101.7,18.4,49,3.8,0.02,0.2,10,28,4.66,0.415,34.48,140,5.4,4.09,3.1,29.4,29.2,86.5,0,0.86,0.54,5.1,11.3,139.6,66.58,0.3,14.7,0.96,267,4.8,12.7,9.2,74.2,275.7,1 300 | 0.81,12.46,45,46.1,53,11,20,0.01,0.2,3.96,2.29,20.7,16.52,10.17,1.21,102.4,27.3,62,4.2,0.01,0.2,10,28.2,4.56,0.421,37.55,136,7,4.06,1.2,25.2,28.3,87.7,0,0.95,0.19,4,12.1,138.1,66.58,0.19,15.7,1.29,158,4.8,12.8,11.2,74.3,224.4,1 301 | 1.94,10.2,43,40.3,81,8,16,0.03,0.4,4.05,1.05,14.68,4.92,10.17,1.29,99.4,23.3,61,3.8,0.02,0.3,11,25,4.47,0.371,40.92,100,8.6,4.4,1.5,20.4,19.8,69.8,0,1.04,0.35,4.8,10.04,139,66.58,0.25,14.33,1.19,210,5.06,20,12.4,65.3,239.7,1 302 | 3.49,22.27,26,48.9,59,25,19,0.01,0.2,3.63,2.66,20.87,38.74,10.17,2.02,100.4,23,59,3.5,0.07,1.5,10,28.1,5.16,0.401,34.92,139,7.4,4.67,1.21,26.2,32.1,92.6,0,0.98,0.19,4.1,9.8,141,66.58,0.18,11.9,1.23,185,4.33,12.4,10.9,77,230.2,1 303 | 1.39,22.52,33,44.8,55,13,11,0.03,0.4,4.86,2.72,32.02,8.81,10.17,0.49,102.4,21.1,63,2,0.17,2.5,16,28.3,5.19,0.435,51.86,149,3.8,4.32,2.68,39,30,87.5,0,0.92,0.15,2.2,10.1,141.7,66.58,0.26,12.6,0.94,260,4.97,12.4,5.8,73.1,190.5,1 304 | 2.74,22.16,46,46.8,84,19,20,0.02,0.3,5.82,2.54,6.38,16.16,10.17,1.95,101.4,21.8,94,5.8,0.01,0.2,22,30.8,5.79,0.355,49.83,121,11.1,4.36,1.28,19.5,36.8,107.9,0,0.78,0.25,3.8,10.8,141,66.58,0.09,12.4,1.13,92,3.29,13.3,16.9,77.6,344.4,1 305 | 5.81,20.27,37,42.5,50,32,29,0.01,0.3,3.8,2.58,10.71,0.63,10.17,2.62,103.8,19.6,65,2.5,0.04,1.1,19,32.1,4.6,0.388,38.06,127,5.9,4.37,1.19,33.2,28.7,87.6,0,0.99,0.24,6.7,10.2,139.3,66.58,0.19,11.6,0.96,184,4.43,12.9,8.4,74.6,211.7,1 306 | 1.28,6.2,66,28,65,4,15,0,0,5.1,0.95,260.3,23.88,10.17,2.24,103.8,16.2,88,3.3,0,0,8,21.5,9.3,0.31,531.8,87,5.3,3.66,0.64,6.2,30,88.3,1,0.82,0.52,5.1,10,140.7,66.58,0.13,11.8,0.57,129,2.9,11.7,8.6,49.5,218.7,1 307 | 0.8,20.42,24,44.1,63,15,21,0.01,0.2,2.22,2.58,30.19,18.32,10.17,1.44,95.4,26.7,60,1.8,0,0,11,37.4,5.13,0.37,45.84,111,2.8,3.72,1.04,21.9,24.2,80.6,0,1.03,0.5,10.5,11.6,138.8,66.58,0.29,13.1,1.04,247,4.59,14.8,4.6,81.5,226.1,1 308 | 1.55,18.71,26,48,80,13,20,0.05,1.3,3.03,2.67,8.65,13.85,10.17,1.33,97.8,26.7,56,5.4,0.07,1.9,11,25.2,4.74,0.459,38.88,155,8.8,3.91,1.44,38.2,30.6,90.7,0,1,0.19,5,11.2,139.3,66.58,0.21,13.7,1.15,191,5.06,11.9,14.2,73.2,269.6,1 309 | 6.83,12.94,42,41.08,77,18,19,0.03,0.4,2.44,2.19,22.97,11.73,10.17,0.81,105.9,23,47,3.13,0.03,0.4,21,30.18,5.08,0.344,42.26,117,5.96,3.54,0.64,8.5,30.6,90.1,0,0.8,0.45,6,11.7,138.3,66.58,0.17,13.9,0.9,143,3.82,12.1,9.09,71.08,158.4,1 310 | 8.43,24.11,37,46.5,69,11,16,0.01,0.1,2.14,2.61,29.43,11.83,10.17,0.69,99.8,21,63,2.6,0,0,15,28.8,5.69,0.391,47.96,124,5.4,3.71,1.15,8.5,26.1,82.3,0,0.94,0.42,3.1,10.4,141.2,66.58,0.38,12,0.74,366,4.75,13.9,8,75.3,257.7,1 311 | 4.16,20.19,44,49,84,11,19,0,0,2.03,2.77,59.8,20.03,10.17,1.11,101.4,22.9,70,2.5,0.06,0.8,12,27.4,5.55,0.454,38.38,148,5.7,4.19,1.7,22.5,30.3,93,0,1,0.27,3.6,12.2,140.3,66.58,0.3,15.5,1.03,250,4.88,12.1,8.2,76.4,226.7,1 312 | 2.44,13.92,33,48.6,70,11,15,0.02,0.4,3.71,2.3,22.29,33.05,10.17,3.13,104.4,24,60,4.5,0.02,0.4,14,22.3,4.93,0.405,40.58,128,7.4,3.92,1.25,27.4,29.8,94.4,0,0.85,0.16,3.5,10.9,138.4,66.58,0.19,13.3,1.13,176,4.29,11.6,11.9,70.9,197.8,1 313 | 4.8,7.9,64,42.6,89,69,78,0.05,0.6,7.8,2.56,17.52,50.95,10.17,2.04,98.7,28.1,57,5.2,0,0,48,29,9.2,0.46,61.39,158,10.8,4.22,2.33,27.5,32.2,93.9,1,0.83,0.19,2.2,11.2,138.5,66.58,0.23,13.6,1.09,204,4.9,11.8,16,71.6,414.5,1 314 | 2.55,17.7,40,45.9,75,6,14,0.01,0.2,3.12,2.58,26.78,11.74,10.17,0.77,101.2,27.3,71,6.1,0.07,1.6,10,25.5,4.45,0.386,34.24,126,11.4,4.6,1,23.4,30.2,92.6,0,0.93,0.26,6.1,11.1,141.6,66.58,0.22,13.3,1.07,195,4.17,12.6,17.5,71.4,198.5,1 315 | 3.27,19.62,65,41.3,73,16,23,0.03,0.7,5.23,2.71,8.54,4.31,10.17,2.77,103,22.2,72,3.2,0.12,2.6,24,33.1,6.14,0.4,61.08,135,5.9,4.32,1.02,22.5,29.7,87.9,1,0.91,0.2,4.4,9.8,140.5,66.58,0.2,10.2,1.1,200,4.55,13.5,9.1,74.4,294,1 316 | 1.19,19.45,33,42.6,51,5,13,0.01,0.2,5.55,2.56,54.72,22.67,10.17,2.15,102.9,24.9,64,1.7,0.05,1.2,8,25.2,4.84,0.359,50.86,114,3,4.25,1.36,33.3,29.5,93,0,0.96,0.24,5.9,11.2,143,66.58,0.28,13.7,1.18,245,3.86,12.5,4.7,67.8,232.8,1 317 | 2.28,21.8,40,43.5,79,14,15,0.02,0.2,4.23,2.68,32.23,17.54,10.17,1.27,101.6,21.4,59,4.6,0.13,1.1,14,26.9,5.1,0.416,48.14,141,9.4,4.6,2.96,25.9,31.5,93.1,0,1.07,0.36,3.1,11.3,140.2,66.58,0.31,13.3,1.17,278,4.47,12.4,14,70.4,201.3,1 318 | 1.34,19.32,30,41.08,77,18,19,0.03,0.2,2.8,2.54,15.52,4.93,10.17,1.01,103.6,24.28,43,3.13,0,0,21,30.18,4,0.417,56.25,140,5.96,4.09,0.97,7.1,30.3,90.3,0,0.84,0.18,1.3,10.4,140.9,66.58,0.26,12.2,1.04,247,4.62,12.1,9.09,71.08,241.9,1 319 | 1.09,17.2,47,40.3,75,14,20,0.02,0.5,5.14,2.62,13.45,25.25,10.17,1.28,101.6,27.1,55,3.5,0.04,1.1,10,25.3,5.69,0.401,46.47,132,7.5,4.6,1.31,34.8,30.6,92.8,0,0.94,0.25,6.6,11.5,141.3,66.58,0.2,13.6,0.89,172,4.32,12.5,11,65.6,205.2,1 320 | 1.34,20.89,27,47,70,20,25,0.03,0.5,3.45,2.76,31.49,26.95,10.17,0.76,100.8,25.3,62,3.3,0.04,0.7,10,30.9,4.69,0.401,45.04,131,4.9,4.59,1.68,29.4,28.9,88.3,0,0.89,0.36,6.3,11,142.4,66.58,0.27,13,1.03,248,4.54,13.5,8.2,77.9,151.2,1 321 | 2.06,18.25,33,45.8,73,36,19,0.04,0.7,4.95,2.6,14.72,11.74,10.17,0.81,99.5,27,51,2.4,0.03,0.5,31,28.2,5.6,0.413,31.69,138,4,4.55,1.87,33.9,28.2,84.3,0,0.82,0.2,3.6,10,140.2,66.58,0.25,11.9,1.25,249,4.9,11.8,6.4,74,307.6,1 322 | 3.47,12.92,38,48.6,53,12,20,0.03,0.8,3.86,2.31,32.02,22.93,10.17,2.1,103.5,23.4,71,3,0.06,1.5,23,38.2,4.89,0.413,33.44,132,5.6,4.22,1.2,30.8,30.1,94.1,0,0.82,0.27,6.9,12.3,135.6,66.58,0.25,15.4,1.27,206,4.39,13.2,8.6,86.8,309.4,1 323 | 3.76,19.08,25,44.7,81,19,22,0.05,0.7,4.53,2.73,19.88,0.6,10.17,1.09,99.5,26.3,68,2.9,0.02,0.3,17,29.3,5.11,0.441,44.29,143,6.1,4.48,1.16,17,30.4,93.6,0,0.92,0.28,4.1,10.7,140.4,66.58,0.08,12.7,1.22,150,4.71,12.3,9,74,293.2,1 324 | 1.33,23,50,45.8,57,10,18,0.03,0.6,3.69,2.66,8.97,0.6,10.17,2.98,100.4,24.6,68,2.7,0.2,4.3,15,33.1,4.52,0.429,43.16,138,6.1,4.9,1.86,39.9,27.1,84.3,0,0.97,0.22,4.7,13,143.1,66.58,0.32,18.6,1.27,244,5.09,14.6,8.8,78.9,217.4,1 325 | 1.71,20.29,29,43.3,60,19,11,0.04,0.4,4.04,2.69,12.68,2.02,10.17,0.73,100.9,23.9,68,1.9,0.05,0.5,12,25,5.31,0.399,41.87,130,2.8,3.99,3.28,35.3,28.8,88.5,0,0.96,0.41,4.4,12.8,141.1,66.58,0.27,17.5,1.08,212,4.51,13.2,4.7,68.3,244.9,1 326 | 1.38,19.23,30,41.08,77,18,19,0.01,0.1,1.52,2.34,221.6,25.12,10.17,0.79,104.2,23.6,56,3.13,0.01,0.1,21,30.18,4.72,0.324,40.02,109,5.96,4.03,1.2,12.6,31.2,92.8,0,0.92,0.65,6.8,13.3,143,66.58,0.21,18.4,0.97,155,3.49,13.2,9.09,71.08,220,1 327 | 2.75,19.72,35,48.8,71,14,19,0.04,0.5,3.63,2.7,47.32,66.82,10.17,1.63,99.3,25.9,64,3.5,0.03,0.4,13,31.9,5.5,0.399,46.13,133,8,4.22,1.57,20.1,29,87.1,0,0.96,0.46,5.9,10.7,140.7,66.58,0.33,12.5,1.18,311,4.58,12.9,11.5,80.7,278.3,1 328 | 5.47,17.6,26,47.4,53,13,22,0.04,0.9,5.47,2.68,16.15,108.3,10.17,1.37,100.2,26.3,60,3.2,0.02,0.5,8,28.1,4.72,0.357,39.05,114,6.3,4.5,1.66,39.1,28.8,90.2,0,0.96,0.26,6.1,11,139.6,66.58,0.35,13,1.17,321,3.96,13.1,9.5,75.5,188.2,1 329 | 2.79,14.51,20,48.6,64,12,17,0.02,0.3,4.85,2.45,36.74,0.7,10.17,2.36,103.8,23.3,64,6.5,0.05,0.7,14,28.1,4.61,0.384,60.43,135,9.5,4.21,1.31,19.4,29.5,84,0,0.85,0.25,3.7,10.2,137.4,66.58,0.28,11.4,1.47,273,4.57,11.8,16,76.7,342.5,1 330 | 0.95,17.03,22,46.2,56,10,22,0.02,0.3,2.48,2.25,16.02,5.44,10.17,1.4,100.8,19.9,70,5.8,0,0,8,26.6,5.94,0.334,33.83,112,7.9,3.33,0.91,14.2,25.9,77.3,0,0.72,0.14,2.2,9.9,134.4,66.58,0.27,10.4,0.75,278,4.32,15.7,13.7,72.8,304.2,1 331 | 2.64,20.99,44,41.4,80,16,21,0.02,0.3,3.95,2.54,243.2,43.51,10.17,1.03,101.7,22.3,72,4.3,0.3,4.7,12,29.9,4.58,0.41,46.27,144,8.6,4.39,1.09,17.1,31.5,89.7,0,0.96,0.24,3.8,10.9,140.6,66.58,0.23,13,1.15,214,4.57,11.6,12.9,71.3,227.2,1 332 | 2.79,18.7,38,42.2,87,32,24,0.02,0.3,4.42,2.5,5.94,11.17,10.17,0.9,98.4,26,54,2.4,0.06,0.8,24,32.5,4.62,0.415,33.1,135,5.6,4.6,2.23,29.2,29,89.1,0,1,0.39,5.1,10.9,138.5,66.58,0.35,13,0.93,320,4.66,12.4,8,74.7,276,1 333 | 3.27,14.29,18,50.4,55,9,21,0.01,0.2,6.67,2.3,67.46,8.29,10.17,1.57,105,24.7,76,4.9,0,0,6,23.9,5.15,0.348,61.67,116,6.8,3.99,0.38,7.2,31.4,94.1,0,0.86,0.09,1.7,11.5,140,66.58,0.21,13.5,1.24,186,3.7,12.7,11.7,74.3,260.5,1 334 | 1.04,18.34,32,36.7,54,27,10,0.02,0.1,1.78,2.39,121.4,52.74,10.17,0.5,102.6,22.1,41,8.1,0.02,0.1,27,25.8,8.98,0.378,173.1,127,10.4,3.94,1.38,8,30,89.2,0,0.78,0.67,3.9,11.2,139.1,66.58,0.23,13.6,0.68,207,4.24,12.9,18.5,62.5,274.6,1 335 | 2.03,19.32,26,47.3,54,16,19,0.01,0.2,3.86,2.64,22.35,0.6,10.17,2.26,101.3,25.1,56,2.6,0.03,0.5,12,25.6,4.72,0.407,45.32,134,4.9,4.72,2.02,30.8,27.2,82.6,0,0.94,0.4,6.1,10.1,141,66.58,0.26,12.5,1.33,257,4.93,13,7.5,72.9,276.6,1 336 | 2.61,18.56,29,49.3,55,10,21,0.01,0.1,4.33,2.51,44.33,4.53,10.17,1.27,98.8,23.7,66,3.8,0.04,0.5,15,26.5,4.51,0.398,52.53,129,6.7,3.56,1.07,13.6,31.1,95.9,0,0.87,0.31,3.9,10.7,137.5,66.58,0.25,12.5,1.23,231,4.15,12.7,10.5,75.8,281.4,1 337 | 1.2,20.4,46,43.1,68,7,8,0.05,0.4,3.88,2.59,42.78,0.6,10.17,1.19,102.5,21.8,56,2,0.05,0.4,11,31.9,5.61,0.403,48.43,134,3.4,4.3,1.41,11.9,30.2,90.8,0,0.92,0.45,3.8,10.7,140.4,66.58,0.32,13.1,1.06,302,4.44,12.8,5.4,75,179,1 338 | 5.84,22.8,50,45.4,55,18,19,0.06,0.6,5.49,2.63,5.12,5.32,10.17,1.9,101.4,23.1,56,3.5,0.13,1.3,8,25.5,5.54,0.374,41.34,123,7.2,4,1.3,12.7,31.6,96.1,1,0.95,0.38,3.7,11.2,143.3,66.58,0.24,13.1,1.11,214,3.89,11.8,10.7,70.9,266.7,1 339 | 1.25,17.34,36,40.2,57,33,25,0.03,0.7,6.07,2.55,17.46,11.25,10.17,1.38,104.5,24.6,63,3.7,0.16,3.7,12,28.4,5.07,0.373,42.17,121,6.6,4.74,1.13,26.2,29.2,89.9,0,0.98,0.32,7.4,10.6,141.7,66.58,0.26,12.3,1.1,248,4.15,12.6,10.3,68.6,259.7,1 340 | 4.89,9.5,67,44,76,13,24,0.04,0.6,5.86,1.13,15.06,32.68,10.17,4.04,101.5,24.5,51,2.7,0.08,1.3,12,30.9,6.11,0.421,55.23,132,6.5,4.3,1.69,27.1,28.8,91.7,1,1,0.32,5.1,12.6,143,66.58,0.18,17.6,1.24,141,4.59,13.6,9.2,74.9,240.3,1 341 | 5.99,12.31,25,46.6,56,14,19,0.02,0.4,4.45,2.29,40.13,19.19,10.17,2.04,98.7,28.2,56,1.8,0.06,1.3,12,27.3,3.98,0.353,39.56,118,4.4,3.71,1.43,32.1,30.7,91.9,0,0.87,0.25,5.6,11.2,135.5,66.58,0.32,13.5,1.3,283,3.84,13.2,6.2,73.9,248.7,1 342 | 7.43,7.1,58,45.7,83,12,14,0.01,0.2,5.31,1.07,17.72,28.08,10.17,3.71,102.5,19.9,73,3.4,0.1,1.7,15,34.2,5.39,0.432,54.94,154,7.4,4.15,1.48,25.9,30.2,89,1,0.92,0.25,4.4,11.8,144.2,66.58,0.22,16.9,0.93,187,5.1,12.4,10.8,79.9,373.4,1 343 | 2.23,23.63,20,44.1,111,20,22,0,0,3.59,2.67,7.69,9.39,10.17,2.39,101.6,21.6,59,3.1,0.01,0.2,23,25.5,4.26,0.424,42.25,135,6.6,4.83,1.81,35.2,28.5,89.6,0,1.01,0.29,5.6,11.5,142,66.58,0.24,13.8,1.5,209,4.73,13.2,9.7,69.6,306.3,1 344 | 2.94,22.11,28,44.6,77,11,14,0,0,4.81,2.58,17.51,12.87,10.17,0.93,103.1,19.3,61,3.5,0.04,0.9,10,32.8,4.96,0.394,47.86,131,3.7,4.71,0.93,21.4,30.5,91.8,0,0.93,0.28,6.5,10,139.8,66.58,0.32,11.2,1.11,317,4.29,11.9,7.2,77.4,282.4,1 345 | 3.27,21.32,43,48.6,60,30,16,0.03,0.5,4.86,2.58,13.61,8.37,10.17,0.59,96.5,24.6,67,2.9,0.06,1.1,29,25.8,4.39,0.44,40.17,147,4.8,4.12,1.36,24.3,30.4,91.1,0,0.94,0.21,3.8,10.9,138.3,66.58,0.23,13.2,1.07,210,4.83,12.1,7.7,74.4,379,1 346 | 2.09,20.91,52,48.6,65,40,30,0,0,4.29,2.81,50.8,15.11,10.17,1.74,98.1,25.9,54,3.8,0.01,0.3,36,31.8,5.13,0.415,31.45,142,6.9,4.11,1.18,30.7,31.1,91,0,0.94,0.28,7.3,9.9,140.8,66.58,0.22,10.8,0.99,219,4.56,11.9,10.7,80.4,241.5,1 347 | 2,12.14,37,46.1,49,9,20,0,0,3.12,2.29,21.93,0.62,10.17,1.67,103.6,25,67,5.9,0.02,0.3,9,27.4,4.59,0.285,44.44,80,11.3,3.64,1.39,23.3,18.9,67.2,0,0.77,0.32,5.4,11.9,137.1,66.58,0.31,17.3,0.95,262,4.24,17.7,17.2,73.5,240.5,1 348 | 2.83,19.23,59,45.8,83,9,16,0.02,0.6,4.38,2.65,51.33,2.18,10.17,2.31,102.3,27.1,50,4,0.05,1.5,13,22.6,5.9,0.439,42.65,144,9.8,4.63,1.1,32.7,29.4,89.8,1,0.92,0.13,3.9,9.7,144,66.58,0.21,10.4,1,221,4.89,11.6,13.8,68.4,209.5,1 349 | 1.84,27.22,30,47.8,64,26,22,0.03,0.3,3.61,2.82,53.87,10.71,10.17,0.93,93.1,23.1,59,3.5,0.23,2.2,25,28.2,5.59,0.458,30.78,154,7.8,4.22,2.15,20.6,28.8,85.6,0,1.02,0.43,4.1,10.5,139.2,66.58,0.42,13.4,1.31,398,5.35,12.5,11.3,76,285.7,1 350 | 1.61,13,39,44.9,68,7,13,0.01,0.2,4.38,2.58,46.45,28.79,10.17,0.76,101.7,29.5,69,2.7,0.04,0.8,8,23.7,4.94,0.376,33.42,128,5.6,4.3,1.52,32,31,91,0,0.93,0.3,6.3,10.3,139.9,66.58,0.23,11,1.1,224,4.13,12.1,8.3,68.6,184.9,1 351 | -------------------------------------------------------------------------------- /Ovarian Cancer.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "id": "ec24b17e", 7 | "metadata": {}, 8 | "outputs": [], 9 | "source": [ 10 | "import numpy as np\n", 11 | "import pandas as pd\n", 12 | "from sklearn.model_selection import train_test_split\n", 13 | "\n", 14 | "from sklearn.ensemble import RandomForestClassifier\n", 15 | "from sklearn.svm import SVC\n", 16 | "from sklearn.tree import DecisionTreeClassifier\n", 17 | "from sklearn.linear_model import LogisticRegression\n", 18 | "from sklearn.ensemble import GradientBoostingClassifier\n", 19 | "from xgboost import XGBClassifier\n", 20 | "\n", 21 | "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix" 22 | ] 23 | }, 24 | { 25 | "cell_type": "code", 26 | "execution_count": 2, 27 | "id": "2e09f1a4", 28 | "metadata": {}, 29 | "outputs": [ 30 | { 31 | "name": "stdout", 32 | "output_type": "stream", 33 | "text": [ 34 | " AFP AG Age ALB ALP ALT AST BASO# BASO% BUN ... PCT \\\n", 35 | "0 3.58 19.36 47 45.4 56 11 24 0.01 0.30 5.35 ... 0.09 \n", 36 | "1 34.24 23.98 61 39.9 95 9 13 0.02 0.30 3.21 ... 0.30 \n", 37 | "2 1.50 18.40 39 45.4 77 9 18 0.03 0.60 3.80 ... 0.13 \n", 38 | "3 2.75 16.60 45 39.2 26 16 17 0.05 0.74 5.27 ... 0.25 \n", 39 | "4 2.36 19.97 45 35.0 47 21 27 0.01 0.10 4.89 ... 0.28 \n", 40 | "\n", 41 | " PDW PHOS PLT RBC RDW TBIL TP UA TYPE \n", 42 | "0 13.4 1.46 74 2.64 13.7 5.5 73.9 396.4 0 \n", 43 | "1 11.2 1.09 304 4.89 12.7 6.8 72.0 119.2 0 \n", 44 | "2 15.2 0.97 112 4.62 12.0 14.8 77.9 209.2 0 \n", 45 | "3 17.4 1.25 339 4.01 14.6 10.9 66.1 215.6 0 \n", 46 | "4 11.9 0.94 272 4.40 13.4 5.3 66.5 206.0 0 \n", 47 | "\n", 48 | "[5 rows x 50 columns]\n" 49 | ] 50 | } 51 | ], 52 | "source": [ 53 | "# Load dataset\n", 54 | "data_frame = pd.read_csv('C:/Users/kalee/Downloads/EDUCATION - STUDY MATERIALS/SEMESTER 6/MINOR PROJECT -4/ovarianDataset.csv')\n", 55 | "\n", 56 | "# Display the first few rows of dataset\n", 57 | "print(data_frame.head())" 58 | ] 59 | }, 60 | { 61 | "cell_type": "code", 62 | "execution_count": 3, 63 | "id": "dfdc0f11", 64 | "metadata": { 65 | "scrolled": true 66 | }, 67 | "outputs": [ 68 | { 69 | "name": "stdout", 70 | "output_type": "stream", 71 | "text": [ 72 | "Accuracy: 0.9143\n", 73 | "\n", 74 | "Confusion Matrix:\n", 75 | "[[31 5]\n", 76 | " [ 1 33]]\n", 77 | "\n", 78 | "Classification Report:\n", 79 | " precision recall f1-score support\n", 80 | "\n", 81 | " 0 0.97 0.86 0.91 36\n", 82 | " 1 0.87 0.97 0.92 34\n", 83 | "\n", 84 | " accuracy 0.91 70\n", 85 | " macro avg 0.92 0.92 0.91 70\n", 86 | "weighted avg 0.92 0.91 0.91 70\n", 87 | "\n" 88 | ] 89 | } 90 | ], 91 | "source": [ 92 | "# Assuming your target column is named 'target', adjust this if needed\n", 93 | "X = data_frame.drop('TYPE', axis=1) # Feature matrix\n", 94 | "y = data_frame['TYPE'] # Target variable\n", 95 | "\n", 96 | "# Split the dataset into training and testing sets\n", 97 | "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n", 98 | "\n", 99 | "# Initialize a Random Forest Classifier\n", 100 | "rf_classifier = RandomForestClassifier(random_state=42)\n", 101 | "\n", 102 | "# Train the model on the training set\n", 103 | "rf_classifier.fit(X_train, y_train)\n", 104 | "\n", 105 | "# Make predictions on the test set\n", 106 | "y_pred = rf_classifier.predict(X_test)\n", 107 | "\n", 108 | "# Evaluate the model\n", 109 | "accuracy = accuracy_score(y_test, y_pred)\n", 110 | "conf_matrix = confusion_matrix(y_test, y_pred)\n", 111 | "classification_rep = classification_report(y_test, y_pred)\n", 112 | "\n", 113 | "# Print the results\n", 114 | "print(f\"Accuracy: {accuracy:.4f}\")\n", 115 | "print(\"\\nConfusion Matrix:\")\n", 116 | "print(conf_matrix)\n", 117 | "print(\"\\nClassification Report:\")\n", 118 | "print(classification_rep)\n" 119 | ] 120 | }, 121 | { 122 | "cell_type": "code", 123 | "execution_count": 4, 124 | "id": "b477cc16", 125 | "metadata": {}, 126 | "outputs": [ 127 | { 128 | "name": "stdout", 129 | "output_type": "stream", 130 | "text": [ 131 | "Accuracy: 0.7714\n", 132 | "\n", 133 | "Confusion Matrix:\n", 134 | "[[29 7]\n", 135 | " [ 9 25]]\n", 136 | "\n", 137 | "Classification Report:\n", 138 | " precision recall f1-score support\n", 139 | "\n", 140 | " 0 0.76 0.81 0.78 36\n", 141 | " 1 0.78 0.74 0.76 34\n", 142 | "\n", 143 | " accuracy 0.77 70\n", 144 | " macro avg 0.77 0.77 0.77 70\n", 145 | "weighted avg 0.77 0.77 0.77 70\n", 146 | "\n" 147 | ] 148 | } 149 | ], 150 | "source": [ 151 | "svm_classifier = SVC(kernel='linear', random_state=42)\n", 152 | "\n", 153 | "# Train the model on the training set\n", 154 | "svm_classifier.fit(X_train, y_train)\n", 155 | "\n", 156 | "# Make predictions on the test set\n", 157 | "y_pred = svm_classifier.predict(X_test)\n", 158 | "\n", 159 | "# Evaluate the model\n", 160 | "accuracy = accuracy_score(y_test, y_pred)\n", 161 | "conf_matrix = confusion_matrix(y_test, y_pred)\n", 162 | "classification_rep = classification_report(y_test, y_pred)\n", 163 | "\n", 164 | "# Print the results\n", 165 | "print(f\"Accuracy: {accuracy:.4f}\")\n", 166 | "print(\"\\nConfusion Matrix:\")\n", 167 | "print(conf_matrix)\n", 168 | "print(\"\\nClassification Report:\")\n", 169 | "print(classification_rep)\n" 170 | ] 171 | }, 172 | { 173 | "cell_type": "code", 174 | "execution_count": 5, 175 | "id": "9738d0be", 176 | "metadata": {}, 177 | "outputs": [ 178 | { 179 | "name": "stdout", 180 | "output_type": "stream", 181 | "text": [ 182 | "Accuracy: 0.8286\n", 183 | "\n", 184 | "Confusion Matrix:\n", 185 | "[[28 8]\n", 186 | " [ 4 30]]\n", 187 | "\n", 188 | "Classification Report:\n", 189 | " precision recall f1-score support\n", 190 | "\n", 191 | " 0 0.88 0.78 0.82 36\n", 192 | " 1 0.79 0.88 0.83 34\n", 193 | "\n", 194 | " accuracy 0.83 70\n", 195 | " macro avg 0.83 0.83 0.83 70\n", 196 | "weighted avg 0.83 0.83 0.83 70\n", 197 | "\n" 198 | ] 199 | } 200 | ], 201 | "source": [ 202 | "# Initialize a Decision Tree classifier\n", 203 | "dt_classifier = DecisionTreeClassifier(random_state=42)\n", 204 | "\n", 205 | "# Train the model on the training set\n", 206 | "dt_classifier.fit(X_train, y_train)\n", 207 | "\n", 208 | "# Make predictions on the test set\n", 209 | "y_pred = dt_classifier.predict(X_test)\n", 210 | "\n", 211 | "# Evaluate the model\n", 212 | "accuracy = accuracy_score(y_test, y_pred)\n", 213 | "conf_matrix = confusion_matrix(y_test, y_pred)\n", 214 | "classification_rep = classification_report(y_test, y_pred)\n", 215 | "\n", 216 | "# Print the results\n", 217 | "print(f\"Accuracy: {accuracy:.4f}\")\n", 218 | "print(\"\\nConfusion Matrix:\")\n", 219 | "print(conf_matrix)\n", 220 | "print(\"\\nClassification Report:\")\n", 221 | "print(classification_rep)" 222 | ] 223 | }, 224 | { 225 | "cell_type": "code", 226 | "execution_count": 6, 227 | "id": "ea78cf4c", 228 | "metadata": {}, 229 | "outputs": [ 230 | { 231 | "name": "stdout", 232 | "output_type": "stream", 233 | "text": [ 234 | "Accuracy: 0.8000\n", 235 | "\n", 236 | "Confusion Matrix:\n", 237 | "[[28 8]\n", 238 | " [ 6 28]]\n", 239 | "\n", 240 | "Classification Report:\n", 241 | " precision recall f1-score support\n", 242 | "\n", 243 | " 0 0.82 0.78 0.80 36\n", 244 | " 1 0.78 0.82 0.80 34\n", 245 | "\n", 246 | " accuracy 0.80 70\n", 247 | " macro avg 0.80 0.80 0.80 70\n", 248 | "weighted avg 0.80 0.80 0.80 70\n", 249 | "\n" 250 | ] 251 | }, 252 | { 253 | "name": "stderr", 254 | "output_type": "stream", 255 | "text": [ 256 | "C:\\Users\\kalee\\anaconda3\\anaconda\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):\n", 257 | "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", 258 | "\n", 259 | "Increase the number of iterations (max_iter) or scale the data as shown in:\n", 260 | " https://scikit-learn.org/stable/modules/preprocessing.html\n", 261 | "Please also refer to the documentation for alternative solver options:\n", 262 | " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", 263 | " n_iter_i = _check_optimize_result(\n" 264 | ] 265 | } 266 | ], 267 | "source": [ 268 | "# Initialize a Logistic Regression classifier\n", 269 | "logreg_classifier = LogisticRegression(random_state=42)\n", 270 | "\n", 271 | "# Train the model on the training set\n", 272 | "logreg_classifier.fit(X_train, y_train)\n", 273 | "\n", 274 | "# Make predictions on the test set\n", 275 | "y_pred = logreg_classifier.predict(X_test)\n", 276 | "\n", 277 | "# Evaluate the model\n", 278 | "accuracy = accuracy_score(y_test, y_pred)\n", 279 | "conf_matrix = confusion_matrix(y_test, y_pred)\n", 280 | "classification_rep = classification_report(y_test, y_pred)\n", 281 | "\n", 282 | "# Print the results\n", 283 | "print(f\"Accuracy: {accuracy:.4f}\")\n", 284 | "print(\"\\nConfusion Matrix:\")\n", 285 | "print(conf_matrix)\n", 286 | "print(\"\\nClassification Report:\")\n", 287 | "print(classification_rep)" 288 | ] 289 | }, 290 | { 291 | "cell_type": "code", 292 | "execution_count": 7, 293 | "id": "23c77f5c", 294 | "metadata": {}, 295 | "outputs": [ 296 | { 297 | "name": "stdout", 298 | "output_type": "stream", 299 | "text": [ 300 | "Accuracy: 0.8714\n", 301 | "\n", 302 | "Confusion Matrix:\n", 303 | "[[29 7]\n", 304 | " [ 2 32]]\n", 305 | "\n", 306 | "Classification Report:\n", 307 | " precision recall f1-score support\n", 308 | "\n", 309 | " 0 0.94 0.81 0.87 36\n", 310 | " 1 0.82 0.94 0.88 34\n", 311 | "\n", 312 | " accuracy 0.87 70\n", 313 | " macro avg 0.88 0.87 0.87 70\n", 314 | "weighted avg 0.88 0.87 0.87 70\n", 315 | "\n" 316 | ] 317 | } 318 | ], 319 | "source": [ 320 | "# Initialize a Gradient Boosting Classifier\n", 321 | "gb_classifier = GradientBoostingClassifier(random_state=42)\n", 322 | "\n", 323 | "# Train the model on the training set\n", 324 | "gb_classifier.fit(X_train, y_train)\n", 325 | "\n", 326 | "# Make predictions on the test set\n", 327 | "y_pred = gb_classifier.predict(X_test)\n", 328 | "\n", 329 | "# Evaluate the model\n", 330 | "accuracy = accuracy_score(y_test, y_pred)\n", 331 | "conf_matrix = confusion_matrix(y_test, y_pred)\n", 332 | "classification_rep = classification_report(y_test, y_pred)\n", 333 | "\n", 334 | "# Print the results\n", 335 | "print(f\"Accuracy: {accuracy:.4f}\")\n", 336 | "print(\"\\nConfusion Matrix:\")\n", 337 | "print(conf_matrix)\n", 338 | "print(\"\\nClassification Report:\")\n", 339 | "print(classification_rep)" 340 | ] 341 | }, 342 | { 343 | "cell_type": "code", 344 | "execution_count": 8, 345 | "id": "ecb650cc", 346 | "metadata": {}, 347 | "outputs": [ 348 | { 349 | "name": "stdout", 350 | "output_type": "stream", 351 | "text": [ 352 | "Accuracy: 0.8857\n", 353 | "\n", 354 | "Confusion Matrix:\n", 355 | "[[31 5]\n", 356 | " [ 3 31]]\n", 357 | "\n", 358 | "Classification Report:\n", 359 | " precision recall f1-score support\n", 360 | "\n", 361 | " 0 0.91 0.86 0.89 36\n", 362 | " 1 0.86 0.91 0.89 34\n", 363 | "\n", 364 | " accuracy 0.89 70\n", 365 | " macro avg 0.89 0.89 0.89 70\n", 366 | "weighted avg 0.89 0.89 0.89 70\n", 367 | "\n" 368 | ] 369 | } 370 | ], 371 | "source": [ 372 | "# Initialize an XGBoost Classifier\n", 373 | "xgb_classifier = XGBClassifier(random_state=42)\n", 374 | "\n", 375 | "# Train the model on the training set\n", 376 | "xgb_classifier.fit(X_train, y_train)\n", 377 | "\n", 378 | "# Make predictions on the test set\n", 379 | "y_pred = xgb_classifier.predict(X_test)\n", 380 | "\n", 381 | "# Evaluate the model\n", 382 | "accuracy = accuracy_score(y_test, y_pred)\n", 383 | "conf_matrix = confusion_matrix(y_test, y_pred)\n", 384 | "classification_rep = classification_report(y_test, y_pred)\n", 385 | "\n", 386 | "# Print the results\n", 387 | "print(f\"Accuracy: {accuracy:.4f}\")\n", 388 | "print(\"\\nConfusion Matrix:\")\n", 389 | "print(conf_matrix)\n", 390 | "print(\"\\nClassification Report:\")\n", 391 | "print(classification_rep)" 392 | ] 393 | }, 394 | { 395 | "cell_type": "code", 396 | "execution_count": 10, 397 | "id": "16543792", 398 | "metadata": {}, 399 | "outputs": [ 400 | { 401 | "name": "stderr", 402 | "output_type": "stream", 403 | "text": [ 404 | "C:\\Users\\kalee\\anaconda3\\anaconda\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):\n", 405 | "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", 406 | "\n", 407 | "Increase the number of iterations (max_iter) or scale the data as shown in:\n", 408 | " https://scikit-learn.org/stable/modules/preprocessing.html\n", 409 | "Please also refer to the documentation for alternative solver options:\n", 410 | " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", 411 | " n_iter_i = _check_optimize_result(\n" 412 | ] 413 | }, 414 | { 415 | "data": { 416 | "image/png": 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", 417 | "text/plain": [ 418 | "
" 419 | ] 420 | }, 421 | "metadata": {}, 422 | "output_type": "display_data" 423 | } 424 | ], 425 | "source": [ 426 | "\n", 427 | "classifiers = {\n", 428 | " 'Random Forest': RandomForestClassifier(random_state=42),\n", 429 | " 'SVM': SVC(kernel='linear', probability=True, random_state=42), # Enable probability estimates\n", 430 | " 'Decision Tree': DecisionTreeClassifier(random_state=42),\n", 431 | " 'Logistic Regression': LogisticRegression(random_state=42),\n", 432 | " 'Gradient Boosting': GradientBoostingClassifier(random_state=42),\n", 433 | " 'XGBoost': XGBClassifier(random_state=42)\n", 434 | "}\n", 435 | "\n", 436 | "# Plot ROC curves\n", 437 | "plt.figure(figsize=(10, 8))\n", 438 | "\n", 439 | "for name, clf in classifiers.items():\n", 440 | " clf.fit(X_train, y_train)\n", 441 | " y_scores = clf.predict_proba(X_test)[:, 1]\n", 442 | " fpr, tpr, thresholds = roc_curve(y_test, y_scores)\n", 443 | " auc_score = auc(fpr, tpr)\n", 444 | " plt.plot(fpr, tpr, label=f'{name} (AUC = {auc_score:.4f})')\n", 445 | "\n", 446 | "plt.plot([0, 1], [0, 1], 'k--', label='Random Guess')\n", 447 | "plt.xlabel('False Positive Rate')\n", 448 | "plt.ylabel('True Positive Rate')\n", 449 | "plt.title('Receiver Operating Characteristic (ROC) Curve')\n", 450 | "plt.legend(loc='lower right')\n", 451 | "plt.show()\n" 452 | ] 453 | }, 454 | { 455 | "cell_type": "code", 456 | "execution_count": null, 457 | "id": "f66a2ae7", 458 | "metadata": {}, 459 | "outputs": [], 460 | "source": [] 461 | } 462 | ], 463 | "metadata": { 464 | "kernelspec": { 465 | "display_name": "Python 3 (ipykernel)", 466 | "language": "python", 467 | "name": "python3" 468 | }, 469 | "language_info": { 470 | "codemirror_mode": { 471 | "name": "ipython", 472 | "version": 3 473 | }, 474 | "file_extension": ".py", 475 | "mimetype": "text/x-python", 476 | "name": "python", 477 | "nbconvert_exporter": "python", 478 | "pygments_lexer": "ipython3", 479 | "version": "3.11.5" 480 | } 481 | }, 482 | "nbformat": 4, 483 | "nbformat_minor": 5 484 | } 485 | --------------------------------------------------------------------------------