Program.cs

﻿using System;
using System.Collections.Generic;
using System.Linq;
using System.Text;
using System.Threading.Tasks;
using System.IO;
using MathNet.Numerics.Statistics;

//10月のバージョン 再整理　160106 加重平均e0 actionset を外す
namespace XCS
{
	class Program
	{
		static void Main( string[] args )
		{
			// 環境設定
			// 未指定用(デフォルト)
			Configuration.Seed = 0;
			Configuration.NoiseRate = 0;
			Configuration.ImbalanceLevel = 0;
			Configuration.NoiseWidth = 0;
			Configuration.ASName = "CS";
			Configuration.L = 8;
			//Configuration.ExploitEnv = new MultiplexerEnvironment( 6 );
			Environment Env = new ReadCsvEnvironment( );
			Configuration.Theta_sub = 20;
			Configuration.ExpThreshold = 20;
			Configuration.DifferenceSigma = 0.1;//for real data  0~1.0
			Configuration.LookBackSigma = 15;
			Configuration.DifferenceEpsilon = 0.1;//for real data 0.0~1.0
			Configuration.LookBackEpsilon = 15;
			Configuration.P_sharp = 0.35;
		    Configuration.CoverPersentage = 0.155;//重なる部分の許す範囲 0~1.0


			string EnvName = "hoge";
			string Comment = "hoge";

			for(int i = 0; i < args.Length; i++)
			{
				if(args[i] == "-s"　| args[i] == "-S" | args[i] == "--Seed" | args[i] == "--seed")
				{
					Configuration.Seed = int.Parse( args[++i] );
				}

				if(args[i] == "-c" | args[i] == "-C" | args[i] == "--Complexity" | args[i] == "--complexity")
				{
					Configuration.NoiseRate = double.Parse( args[++i] );
					Configuration.ImbalanceLevel = ( int )double.Parse( args[i] );	// int型に変換

					Configuration.NoiseWidth = double.Parse( args[i] );
				}

				if(args[i] == "-a" | args[i] == "-A" | args[i] == "--as" | args[i] == "--As")
				{
					Configuration.ASName = args[++i];
				}

				if( args[i] == "-e" | args[i] == "-E" | args[i] == "--env" | args[i] == "--Env" )
				{
					EnvName = args[++i];
					
					if(args[i] == "csv")
                    {
                        Env = new ReadCsvEnvironment();
                    }

					else
					{
						// 普通のMultiplexer問題
						//Env = new MultiplexerEnvironment( Configuration.L );
					}
				}

				if(args[i] == "--ts" | args[i] == "--Ts" | args[i] == "--ThetaSub")
				{
					Configuration.Theta_sub = int.Parse( args[++i] );
					Configuration.ExpThreshold = int.Parse( args[i] );
				}

				if(args[i] == "--ds" | args[i] == "--Ds" | args[i] == "--DifferenceSigma" )
				{
					// 分散差分許容範囲
					Configuration.DifferenceSigma = double.Parse( args[++i] );
				}

				if( args[i] == "--ls" | args[i] == "--Ls" | args[i] == "--LookbackSigma" )
				{
					// 分散が安定したか見返す数
					Configuration.LookBackSigma = int.Parse( args[++i] );
				}

				if( args[i] == "--de" | args[i] == "--De" | args[i] == "--DifferenceEpsilon" )
				{
					// 各分類子の差分許容範囲
					Configuration.DifferenceEpsilon = double.Parse( args[++i] );
				}

				if( args[i] == "--le" | args[i] == "--Le" | args[i] == "--LookbackEpsilon" )
				{
					// 各分類子のepsilon(分散)が安定したか見返す数
					Configuration.LookBackEpsilon = int.Parse( args[++i] );
				}

				if(args[i] == "--cm" | args[i] == "--comment" | args[i] == "--Comment")
				{
					Comment = args[++i];
				}

				if(args[i] == "-l" | args[i] == "--Length" | args[i] == "--length")
				{
					// situation(Condition)の長さ
					Configuration.L = int.Parse( args[++i] );
					//Configuration.ExploitEnv = new MultiplexerEnvironment( Configuration.L );
				}

				if( args[i] == "--ps" | args[i] == "--Ps" | args[i] == "--PSharp" )
				{
					Configuration.P_sharp = double.Parse( args[++i] );
				}

                if (args[i] == "--per" | args[i] == "-percentage" )
                {
                    Configuration.CoverPersentage =double.Parse(args[++i]) ;
                }

			}


			// 変数設定
			Config( args );

			// フォルダ名用時間取得
			DateTime dt = DateTime.Now;

			// フォルダ指定
			Configuration.pppp = dt.Year + "" + dt.Month.ToString( "D2" ) + "" + dt.Day.ToString( "D2" );
			Configuration.pppp +=  "_" + Comment + "_" + Configuration.L + "_" + Configuration.P_sharp + "_" + Configuration.Theta_sub + "_" + Configuration.DifferenceSigma + "_" + Configuration.LookBackSigma;
			string Path = "./" + Configuration.pppp + "/" + Configuration.NoiseWidth + "/";

			Path += dt.Year + "" + dt.Month.ToString( "D2" ) + "" + dt.Day.ToString( "D2" ) + "" + dt.Hour.ToString( "D2" ) + "" + dt.Minute.ToString( "D2" ) + "" + dt.Second.ToString( "D2" );
			Path += "_s" + Configuration.Seed;
			Path += "_c" + Configuration.NoiseWidth;
			Path += "_l" + Configuration.L;
			Path += "_a" + Configuration.ASName;
			Path += "_e" + EnvName;
			Path += "_ts" + Configuration.Theta_sub;
			Path += "_ds" + Configuration.DifferenceSigma;
			Path += "_ls" + Configuration.LookBackSigma;
			Path += "_de" + Configuration.DifferenceEpsilon;
			Path += "_le" + Configuration.LookBackEpsilon;
			Path += "_ps" + Configuration.P_sharp;
            Path += "_per" + Configuration.CoverPersentage;
			//foreach( string parameter in args )
			//{
			//	Path += ( "_" + parameter );
			//}

			System.IO.DirectoryInfo di = System.IO.Directory.CreateDirectory( Path );
			System.IO.Directory.SetCurrentDirectory( Path );


            Configuration.Problem = new StreamWriter("./problem" + ".csv", true, System.Text.Encoding.GetEncoding("shift_jis"));

			Configuration.ESW = new StreamWriter( "./epsilon_" + Configuration.Seed + "CnoiseWidth_" + Configuration.NoiseWidth
				+ "AS_" + "CS" + "ET_" + Configuration.ExpThreshold + "DS_" + Configuration.DifferenceSigma + "LS_" + Configuration.LookBackSigma
				+ "DE_" + Configuration.DifferenceEpsilon + "LE_" + Configuration.LookBackEpsilon + ".csv", true, System.Text.Encoding.GetEncoding( "shift_jis" ) );

           
			// 初期化
			// Population初期化
			Population P = new NormalPopulation( Configuration.N );
      
			Experiment( Env, P );
		}

		public static void Config( string[] args )
		{
			// 変数設定

			// Populationの最大サイズ
			Configuration.N = 400;
			//Configuration.N = 800;	// 150116
			if(Configuration.L == 6)
			{
				Configuration.N = 400;
			}
            
			else if(Configuration.L == 11)
			{
				Configuration.N = 800;
			}
			else if(Configuration.L == 20)
			{
				Configuration.N = 2000;
			}
            // situation(Condition)の種類(進数)
            //Configuration.Type = "Binary";
            // Covering閾値(行動の数)
            //Configuration.Theta_mna = 2;
            Configuration.Theta_mna = 0;

            // Covering時の#に変化させる割合

            // 乱数生成
            MersenneTwister MT = new MersenneTwister( Configuration.Seed );
			//MersenneTwister MT = new MersenneTwister();
			Configuration.MT = MT;
			Configuration.MT_P = new MersenneTwister( Configuration.Seed );
			// Covering時初期値
			Configuration.P_I = 0.01;
			Configuration.Epsilon_I = 0.01;
			Configuration.F_I = 0.01;
			// 削除閾値(経験値)
			Configuration.Theta_del = 20;
			// 削除閾値(Fitness)
			Configuration.Delta = 0.1;
			// Epsilon-greedyのランダム割合
			Configuration.P_explr = 1.0;	// 常にランダム
			// MultiStep問題の割引率
			Configuration.Gamma = 0.71;
			// 学習割合
			Configuration.Beta = 0.2;
			// 報酬
			Configuration.Rho = 1000;
			// Fitnessの計算パラメータ 下駄　1/1000
			Configuration.Epsilon_0 =0.001;
			Configuration.Alpha = 0.1;
			Configuration.Nyu = 15;
			// 包摂
			Configuration.DoActionSetSubsumption = true;//
			// 包摂閾値(経験値)
			//Configuration.Theta_sub = 20;
			//Configuration.Theta_sub = 200;	// 150116
			// GA閾値(TimeStamp)
			Configuration.Theta_GA = 25;
			// Crossover割合
			Configuration.Chai = 0.8;//komine
			// 突然変異割合
			Configuration.Myu = 0.04;//komine
			// GA時親に包摂
			Configuration.DoGASubsumption = true;//9-8 chou
			// 試行回数
			Configuration.Iteration = 200000;
			//Configuration.Iteration = 50000;
			//Configuration.Iteration = 500000;	//150116// int型に変換
			// 単純移動平均
			Configuration.SMA = 100;
			//Configuration.SMA = 5000;	// 150116
			// 手法開始フラグ
			Configuration.FlagEpsilon = Configuration.FlagSigma = true;
			Configuration.StartTime = -1;
			// ε学習率
			Configuration.LearningRateEpsilon = 0.05;
			// 分類子学習期限
			//Configuration.MatureTime = 30;
			//Configuration.MatureTime = Configuration.LookBackEpsilon * 5;
			Configuration.MatureTime = Configuration.LookBackEpsilon * 4 / 5;
			Configuration.URE_Epsilon0 = -1;
			// トーナメント選択
			Configuration.Tau = 0.4;

		    Configuration.IsConvergenceVT = false;
            //chou 全体平均記録手法
            Configuration.RewardListFlag = true;
            Configuration.RewardList = new List<double>();
            Configuration.RewardAverage = 0;

		}

		public static void Experiment( Environment Env, Population P )
		{
			// 変数初期化
			Configuration.T = 0;
			ActionSet PreviousAS = null;
			double PreviousRho = 0;
			State PreviousS = null;

			// stdlist収束
			bool ConvergenceStelist = false;

			// 移動平均計算用
			double[] RhoArray = new double[Configuration.Iteration];
			int[] Num = new int[Configuration.Iteration];
			//double[] Std = new double[Configuration.Iteration];

			Configuration.ZeroList = new List<double>();
			Configuration.OneList = new List<double>();

            List<string> DataList = Env.GetDataList();
            List<string> DistinctDataList = DataList.Distinct().ToList();

            int DistinctDataNum = DistinctDataList.Count();
            // 提案手法　入力データ個数分の分散
            Configuration.Stdlist = new StdList[DistinctDataNum];
            // 収束した　VTの値を保存する　　ちょう
            Configuration.ConvergentedVT = new StdList[DistinctDataNum];

            for (int i = 0; i < DistinctDataNum; i++)
            {
              Configuration.ConvergentedVT[i] = new StdList(DistinctDataList[i], '0');
                //Configuration.ConvergentedVT[i * 4 + 1] = new StdList(i, '1');
            }
			for( int i = 0; i < DistinctDataNum; i++ )
			{
				Configuration.Stdlist[i] = new StdList(DistinctDataList[i], '0' );
				//Configuration.Stdlist[i * 4 + 1] = new StdList( i, '1' );
			}
            
			// 実験1a ノイズを既知のものとして扱う
			if(Configuration.ASName == "WellKnown")
			{
				Configuration.Epsilon_0 += Configuration.NoiseWidth;
			}

            Configuration.Problem.WriteLine("state ,iter,P , cl.M ,cl.Epsilon , cl.F , cl.N , cl.Exp , cl.Ts ,cl.As , cl.Kappa ,cl.Epsilon_0 , cl.St , cl.GenerateTime");
            StreamWriter goodsleep1 = new StreamWriter("./goodsleep_rule1.csv");
            goodsleep1.WriteLine("state ,iter,P , cl.M ,cl.Epsilon , cl.F , cl.N , cl.Exp , cl.Ts ,cl.As , cl.Kappa ,cl.Epsilon_0 , cl.St , cl.GenerateTime");

            StreamWriter goodsleep2 = new StreamWriter("./goodsleep_rule2.csv");
            goodsleep2.WriteLine("state ,iter,P , cl.M ,cl.Epsilon , cl.F , cl.N , cl.Exp , cl.Ts ,cl.As , cl.Kappa ,cl.Epsilon_0 , cl.St , cl.GenerateTime");

            StreamWriter badsleep = new StreamWriter("./badsleep_rule.csv");
            badsleep.WriteLine("state ,iter,P , cl.M ,cl.Epsilon , cl.F , cl.N , cl.Exp , cl.Ts ,cl.As , cl.Kappa ,cl.Epsilon_0 , cl.St , cl.GenerateTime");
            // メインループ
            #region main roop
            while ( Configuration.T < Configuration.Iteration )
			{
                // VTの収束
                if (!Configuration.IsConvergenceVT)
                {
                    bool flag = true;
                    //入力データのSLが収束すれば、VTが収束とみなす。
                    foreach (StdList SL in Configuration.Stdlist)
                    {
                            
                            if (flag && !SL.IsConvergenceSigma())
                            {
                                flag = false;
                                break;
                            }
                        
                        
                    }
                    if (flag)	// 初めてTrue
                    {
                        Configuration.IsConvergenceVT = true;
                        //収束したVTを保存する
                        for (int i = 0; i < DistinctDataList.Count; i++)
                        {
                            Configuration.ConvergentedVT[i].M = Configuration.Stdlist[i].M;
                            //Configuration.ConvergentedVT[i * 4+1].M = Configuration.Stdlist[i * 2+1].M;


                            Configuration.ConvergentedVT[i ].S = Configuration.Stdlist[i].S;
                            //Configuration.ConvergentedVT[i * 4 + 1].S = Configuration.Stdlist[i*2+1].S;

                            Configuration.ConvergentedVT[i ].T = Configuration.Stdlist[i].T;
                            //Configuration.ConvergentedVT[i * 4 + 1].T = Configuration.Stdlist[i * 2 + 1].T;
                        }


                        // [P]の全てを新しい基準で再評価
                        foreach (Classifier C in P.CList)
                        {
                            // 加重平均
                            double ST = 0;
                            int SumT = 0;
                            foreach (StdList SL in Configuration.Stdlist)
                            {
                                

                                    if (SL.IsIncluded(C.C.state))
                                    {
                                        ST += SL.S*SL.T;
                                        SumT += SL.T;
                                    }
                                
                            }
                            ST /= SumT;

                            SigmaNormalClassifier SNC = (SigmaNormalClassifier)C;
                            C.Epsilon_0 = ST + Configuration.Epsilon_0;
                            if (C.Exp > 2)
                            {
                                C.Epsilon = SNC.EpsilonList[0];
                            }
                        }
                    }
                }

                
				State S = Env.GetState();
                
                // MatchSet生成
                MatchSet M = new NormalMatchSet( S, P );

                foreach (Classifier cl in M.CList)
                {
                    if (cl.C.state[4] == '2' & cl.C.state[7] == '#')//"bath2  rehabi# or bath# rehabi0"
                    {

                        goodsleep1.WriteLine(cl.C.state + "," + Configuration.T + "," + cl.P + "," + cl.M + "," + cl.Epsilon + "," + cl.F + "," + cl.N + "," + cl.Exp + "," + cl.Ts + "," + cl.As + "," + cl.Kappa + "," + cl.Epsilon_0 + "," + cl.St + "," + cl.GenerateTime);
                    }
                    if (cl.C.state[4] == '#' & cl.C.state[7] == '0')//"bath2  rehabi# or bath# rehabi0"
                    {

                        goodsleep2.WriteLine(cl.C.state + "," + Configuration.T + "," + cl.P + "," + cl.M + "," + cl.Epsilon + "," + cl.F + "," + cl.N + "," + cl.Exp + "," + cl.Ts + "," + cl.As + "," + cl.Kappa + "," + cl.Epsilon_0 + "," + cl.St + "," + cl.GenerateTime);
                    }
                }
                foreach (Classifier cl in P.CList)
                {
                    if (cl.C.state[4] == '0' & cl.C.state[7] == '1')//"bath0 rehabi1"
                    {

                        badsleep.WriteLine(cl.C.state + "," + Configuration.T + "," + cl.P + "," + cl.M + "," + cl.Epsilon + "," + cl.F + "," + cl.N + "," + cl.Exp + "," + cl.Ts + "," + cl.As + "," + cl.Kappa + "," + cl.Epsilon_0 + "," + cl.St + "," + cl.GenerateTime);
                    }
                }

                // ActionSetはただMをコピーするだけ,アクションがないから
                ActionSet AS;
				if( Configuration.ASName == "CS" )
				{
					AS = new ConditionSigmaActionSet( M.CList);
				}
                else
				{
				    AS = new NormalActionSet(M.CList);/*M.MatchAction(Action))*/;
                }

                
                char Action = '0';//action ないから、全部０にする
                double Rho = Env.ExecuteAction(Action);

                
				StdList Sigma = null;

				// 提案手法　分散の計算
				foreach( StdList SL in Configuration.Stdlist )
				{
					if( ( SL.C == S.state ) /*&& ( SL.A == Action )*/ )
					{
						// situationの分散取得
						SL.Update( Rho );
						Sigma = SL;
					}
				}
                
				// 提案手法(中田)
				if( Configuration.ASName == "CS" )
				{
					Configuration.URE_Epsilon0 = -1;

					// 最小値
					double d = Configuration.Rho;

					foreach( SigmaNormalClassifier C in AS.CList )
					{
						if( d > C.S && C.IsConvergenceEpsilon() )
						{
							d = Math.Sqrt( C.S / ( C.St - 1 ) );
						}
					}

					Configuration.URE_Epsilon0 = d;
				}
                //chou 1000回の報酬平均を保存

                if (Configuration.T < 1000)
                {
                    Configuration.RewardList.Add(Rho);

                }
                if (Configuration.T == 1000)
                {
                    Configuration.RewardAverage = Configuration.RewardList.Mean();

                }

                // マルチステップ問題の終着またはシングルステップ問題
                if ( Env.Eop )
				{
					double p = Rho;
					AS.Update( P, p, Sigma );
					AS.RunGA( S, P );//komine


                    PreviousAS = null;
				}
				else
				{
					PreviousAS = AS;
					PreviousRho = Rho;
					PreviousS = S;
				}

				
				Num[Configuration.T] = P.CList.Count();
				//Std[Configuration.T] = Math.Sqrt( Stdlist[20].Sigma / (Stdlist[20].T - 1));

				if( Configuration.StartTime < 0 && Configuration.FlagSigma && Configuration.FlagEpsilon )
				{
					Configuration.StartTime = Configuration.T;
				}
				Configuration.FlagSigma = Configuration.FlagEpsilon = false;

				
				if( !ConvergenceStelist && ( Configuration.ASName == "CS" || Configuration.ASName == "MaxCS" || Configuration.ASName == "Max" || Configuration.ASName == "Updatee0CS" ) )
				{
					int i = 1;

					foreach( StdList SL in Configuration.Stdlist )
					{
						i *= ( SL.IsConvergenceSigma() ? 1 : 0 );
					}

					if( i == 1 )
					{
						StreamWriter stdSw = new StreamWriter( "./ConvergenceVT_" + Configuration.T + "_" + Configuration.Seed + "CnoiseWidth" + Configuration.NoiseWidth
				+ "AS_" + Configuration.ASName + "ET_" + Configuration.ExpThreshold + "DS_" + Configuration.DifferenceSigma + "LS_" + Configuration.LookBackSigma
				+ "DE_" + Configuration.DifferenceEpsilon + "LE_" + Configuration.LookBackEpsilon + ".csv", true, System.Text.Encoding.GetEncoding( "shift_jis" ) );

						stdSw.WriteLine( "condition,action,sigma,average,time,convergence" );
						foreach( StdList SL in Configuration.Stdlist )
						{
							stdSw.WriteLine( SL.C + "," + SL.A + "," + SL.S + "," +SL.M+","+ SL.T + "," + ( SL.IsConvergenceSigma() ? 1 : 0 ) );	// 1 : 収束
						}
						stdSw.Close();
						ConvergenceStelist = true;
					}
				}
              

				Configuration.T++;
                Console.WriteLine(Configuration.T);
                
            }
            P.Show();
            #endregion 
            goodsleep1.Close();
            goodsleep2.Close();
            badsleep.Close();

            Configuration.Problem.Close();
            P.Compact();
            //P.Show();

			if( ( Configuration.ASName == "CS" || Configuration.ASName == "MaxCS" || Configuration.ASName == "Max" || Configuration.ASName == "Updatee0CS" ) )
			{
				StreamWriter stdSw = new StreamWriter( "./VarianceTable_" + Configuration.T + "_" + Configuration.Seed + "CnoiseWidth" + Configuration.NoiseWidth
			+ "AS_" + Configuration.ASName + "ET_" + Configuration.ExpThreshold + "DS_" + Configuration.DifferenceSigma + "LS_" + Configuration.LookBackSigma
			+ "DE_" + Configuration.DifferenceEpsilon + "LE_" + Configuration.LookBackEpsilon + ".csv", true, System.Text.Encoding.GetEncoding( "shift_jis" ) );

				stdSw.WriteLine( "condition,action,sigma,time,convergence,convergencetime" );
				foreach( StdList SL in Configuration.Stdlist )
				{
					stdSw.WriteLine( SL.C + "," + SL.A + "," + SL.S + "," + SL.T + "," + ( SL.IsConvergenceSigma() ? 1 : 0 ) + "," + SL.ConvergenceTime );	// 1 : 収束
				}
				stdSw.Close();
			}

			//LD.Close();


			StreamWriter sw = new StreamWriter( "./performance_" + Configuration.Seed + "CnoiseWidth_" + Configuration.NoiseWidth
				+ "AS_" + Configuration.ASName + "ET_" + Configuration.ExpThreshold + "DS_" + Configuration.DifferenceSigma + "LS_" + Configuration.LookBackSigma
				+ "DE_" + Configuration.DifferenceEpsilon + "LE_" + Configuration.LookBackEpsilon + ".csv", true, System.Text.Encoding.GetEncoding( "shift_jis" ) );

			
			sw.WriteLine( "Performance,PopulationSize," + Configuration.StartTime );

			for( int i = 0; i < RhoArray.Count() - Configuration.SMA; i++ )
			{
				double R = 0;
				double N = 0;
				for( int j = 0; j < Configuration.SMA; j++ )
				{
					R += RhoArray[i + j];
					N += Num[i + j];
				}
				R /= Configuration.SMA;
				N /= Configuration.SMA;

				sw.WriteLine( R + "," + N );
			}

			sw.Close();


			StreamWriter Zerosw = new StreamWriter( "./Zero_per_" + Configuration.Seed + "CnoiseWidth_" + Configuration.NoiseWidth
				+ "AS_" + Configuration.ASName + "ET_" + Configuration.ExpThreshold + "DS_" + Configuration.DifferenceSigma + "LS_" + Configuration.LookBackSigma
				+ "DE_" + Configuration.DifferenceEpsilon + "LE_" + Configuration.LookBackEpsilon + ".csv", true, System.Text.Encoding.GetEncoding( "shift_jis" ) );
            

			Zerosw.WriteLine( "Performance,dummy," + Configuration.StartTime );

			for( int i = 0; i < Configuration.ZeroList.Count() - Configuration.SMA; i++ )
			{
				double R = 0;
				double N = 0;
				for( int j = 0; j < Configuration.SMA; j++ )
				{
					R += Configuration.ZeroList[i + j];
					N += Num[i + j];
				}
				R /= Configuration.SMA;
				N /= Configuration.SMA;

				Zerosw.WriteLine( R + "," + N );
			}

			Zerosw.Close();


			StreamWriter Onesw = new StreamWriter( "./One_per_" + Configuration.Seed + "CnoiseWidth_" + Configuration.NoiseWidth
				+ "AS_" + Configuration.ASName + "ET_" + Configuration.ExpThreshold + "DS_" + Configuration.DifferenceSigma + "LS_" + Configuration.LookBackSigma
				+ "DE_" + Configuration.DifferenceEpsilon + "LE_" + Configuration.LookBackEpsilon + ".csv", true, System.Text.Encoding.GetEncoding( "shift_jis" ) );

			
			Onesw.WriteLine( "Performance,dummy," + Configuration.StartTime );

			for( int i = 0; i < Configuration.OneList.Count() - Configuration.SMA; i++ )
			{
				double R = 0;
				double N = 0;
				for( int j = 0; j < Configuration.SMA; j++ )
				{
					R += Configuration.OneList[i + j];
					N += Num[i + j];
				}
				R /= Configuration.SMA;
				N /= Configuration.SMA;

				Onesw.WriteLine( R + "," + N );
			}

			Onesw.Close();

			Configuration.ESW.Close();
			//Configuration.Problem.Close();


			System.IO.Directory.SetCurrentDirectory( "../");
			StreamWriter swP = new StreamWriter( "PPP.csv", true, System.Text.Encoding.GetEncoding( "shift_jis" ) );

			swP.WriteLine( Configuration.NoiseWidth + "," + Configuration.ASName + "," + P.CList.Count() );
			swP.Close();
		}

        private static int getIndexOfCriterion(Histogram hist)
        {
            var max = hist[0].Count;
            var array=new double[hist.BucketCount-1];
            var key = new int[hist.BucketCount - 2];
            var result=new int[hist.BucketCount-3];
            for (int i = 0; i < hist.BucketCount-1; i++)
            {
                array[i] = hist[i + 1].Count - hist[i].Count;
            }
            for (int i = 0; i < hist.BucketCount - 2; i++)
            {
                if (array[i]*array[i+1]<=0)
                {
                    key[i] = 1;
                }
                else
                {
                    key[i] = 0;
                }
            }
            var counter = 0;

            for (int i = 2; i < hist.BucketCount - 2; i++)
            {
                
                if (key[i]==1)
                {
                    result[counter] = i + 2;
                    counter++;
                }
            }
           
            return result[1];//2番目のtruning point を返す
            
        }

       
	    /// <summary>
		/// 評価
		/// </summary>
		/// <param name="Env">環境</param>
		/// <param name="P">Population</param>
		/// <returns>performance</returns>
		/*public static double Exploit( Population P )
		{
			// situation取得
			State S = Configuration.ExploitEnv.GetState();
			// Matchset生成
			MatchSet M = new NotCoveringMatchSet( S, P );
			// PredictionArray生成
			PredictionArray PA = new GreedyPredictionArray( M );
			// Action決定
			char Action = PA.SelectAction();
			// 報酬獲得
			double Rho = Configuration.ExploitEnv.ActionExploit( Action );
			//if( Rho == 0 && Configuration.T > 35530 )
			//{
			//	Console.WriteLine( Configuration.T + " : " + S.state + " : " + Action + " : " + Rho );
			//	foreach(double d in PA.PA)
			//	{
			//		Console.WriteLine(d);
			//	}
			//	System.Threading.Thread.Sleep( 1000 );
			//}
			
			if(Action == '0')
			{
				Configuration.ZeroList.Add( Rho );
			}
			else if(Action == '1')
			{
				Configuration.OneList.Add( Rho );
			}

			return Rho;
		}*/
	}
}