// Sigmaは飾り
public override void Update(Population Pop, double P, StdList Sigma)
{
double SumNumerosity = 0;
foreach (Classifier C in this.CList)
{
SumNumerosity += C.N;
}
foreach (Classifier C in this.CList)
{
C.Exp++;
if (C.Exp < 1 / Configuration.Beta)
{
C.P += (P - C.P) / C.Exp;
}
else
{
C.P += Configuration.Beta * (P - C.P);
}
if (C.Exp < Configuration.Beta)
{
C.Epsilon += (Math.Abs(P - C.P) - C.Epsilon) / C.Exp;
}
else
{
C.Epsilon += Configuration.Beta * (Math.Abs(P - C.P) - C.Epsilon);
}
if (C.Exp < Configuration.Beta)
{
C.As += (SumNumerosity - C.As) / C.Exp;
}
else
{
C.As += Configuration.Beta * (SumNumerosity - C.As);
}
//if( C.C.state == "11####" && C.A == '0' )
//{
// Configuration.ESW.WriteLine( Configuration.T + "," + C.F + "," + C.Kappa + "," + C.Epsilon_0 + "," + C.Epsilon + "," + C.Exp + "," + Math.Sqrt( C.S / ( C.St - 1 ) ) );
// //Console.WriteLine( Configuration.T + " " + this.Exp );
//}
// 標準偏差計算
C.St++;
double X = P - C.M;
C.M += X / C.St;
C.S += (C.St - 1) * X * X / C.St;
}
this.UpdateFitness();
if (Configuration.DoActionSetSubsumption)
{
this.Subsumption(Pop);
}
}
public StdList Clone()
{
var clone = new StdList();
clone.C = C;
clone.T = T;
clone.M = M;
clone.S = S;
return(clone);
}
public override void Update( Population Pop, double P, StdList Sigma )
{
double SumNumerosity = 0;
foreach( Classifier C in this.CList )
{
SumNumerosity += C.N;
}
//if( Configuration.URE_Epsilon0 != Configuration.Rho )
//{
// Configuration.URE_Epsilon0 += Configuration.Epsilon_0 * 5;
// foreach( Classifier C in this.CList )
// {
// C.Epsilon_0 += Configuration.Beta * ( Configuration.URE_Epsilon0 - C.Epsilon_0 );
// }
//}
//else
//{
//if( SNC.IsConvergenceEpsilon() && Sigma.IsConvergenceSigma() )
//{
// foreach( Classifier C in this.CList )
// {
// }
//}
//}
foreach( Classifier C in this.CList )
{
C.Exp++;
if( C.Exp < 1 /Configuration.Beta )
{
C.P += ( P - C.P ) / C.Exp;
}
else
{
C.P += Configuration.Beta * ( P - C.P );
}
//if( C.Exp < Configuration.Beta ) // 9-23 張 ここscript は exp < 1/beta
if (C.Exp < Configuration.Beta)
{
C.As += ( SumNumerosity - C.As ) / C.Exp;
}
else
{
C.As += Configuration.Beta * ( SumNumerosity - C.As );
}
if( C.C.state == "11###0" && C.A == '1' )
{
//Configuration.ESW.WriteLine( Configuration.T + "," + C.F + "," + C.Kappa + "," + C.Epsilon_0 + "," + C.Epsilon + "," + C.Exp + "," + Math.Sqrt( C.S / ( C.St - 1 ) ) );
//Console.WriteLine( Configuration.T + " " + C.Exp );
}
// 標準偏差計算
C.St++;
double X = P - C.M;
C.M += X / C.St;
C.S += ( C.St - 1 ) * X * X / C.St;
if( C.GetType().Name == "SigmaNormalClassifier" )
{
// このIterationまでのepsilonを記録ずらし
SigmaNormalClassifier SNC = ( SigmaNormalClassifier )C;
for( int index = SNC.EpsilonList.Count() - 1; index > 0; index-- )
{
SNC.EpsilonList[index] = SNC.EpsilonList[index - 1];
}
SNC.EpsilonList[0] = Math.Sqrt(C.S / (C.St - 1));
//bool StateFlag = true;
//foreach(StdList SL in Configuration.Stdlist)
//{
// // 分類子に含まれるstate
// //if(SL.IsIncluded(SNC.C.state, SNC.A))
// //{
// // 一つでも収束していなければ
// if(!SL.IsConvergenceSigma())
// {
// StateFlag = false;
// }
// //}
//}
//if( SNC.IsConvergenceEpsilon() )
//if( SNC.IsConvergenceEpsilon() && StateFlag )
if( Sigma.IsConvergenceSigma() )
{
double sigma_a=0;
double sigma_b=0;
//9-24 張 1#00 算出する時用のもの追加
if(C.C.state=="1#00##")
{
//1100 と 1000 のsigma で1#00を更新
foreach(StdList SL in Configuration.Stdlist)
{
if(SL.C=="110000"){ sigma_a =SL.S;}
if(SL.C=="100000"){ sigma_b =SL.S;}
}
//sigma はvariancetableから
C.Epsilon_0 += Configuration.Beta * (Math.Sqrt(sigma_a*sigma_a +sigma_b*sigma_b) + Configuration.Epsilon_0 - C.Epsilon_0);
}
if (C.C.state == "#100##")
{
//1100 と 1000 のsigma で1#00を更新
foreach (StdList SL in Configuration.Stdlist)
{
if (SL.C == "110000") { sigma_a = SL.S; }
if (SL.C == "010000") { sigma_b = SL.S; }
}
//sigma はvariancetableから
C.Epsilon_0 += Configuration.Beta * (Math.Sqrt(sigma_a * sigma_a + sigma_b * sigma_b) + Configuration.Epsilon_0 - C.Epsilon_0);
}
if (C.C.state == "001###")
{
//1100 と 1000 のsigma で1#00を更新
foreach (StdList SL in Configuration.Stdlist)
{
if (SL.C == "001100") { sigma_a = SL.S; }
if (SL.C == "001000") { sigma_b = SL.S; }
}
//sigma はvariancetableから
C.Epsilon_0 += Configuration.Beta * (Math.Sqrt(sigma_a * sigma_a + sigma_b * sigma_b) + Configuration.Epsilon_0 - C.Epsilon_0);
}
if (C.C.state == "00#1##")
{
//1100 と 1000 のsigma で1#00を更新
foreach (StdList SL in Configuration.Stdlist)
{
if (SL.C == "001100") { sigma_a = SL.S; }
if (SL.C == "000100") { sigma_b = SL.S; }
}
//sigma はvariancetableから
C.Epsilon_0 += Configuration.Beta * (Math.Sqrt(sigma_a * sigma_a + sigma_b * sigma_b) + Configuration.Epsilon_0 - C.Epsilon_0);
}
else
{
C.Epsilon_0 += Configuration.Beta * ( Sigma.S + Configuration.Epsilon_0 - C.Epsilon_0 );
}
}
if( SNC.IsConvergenceEpsilon() )
//if( SNC.IsConvergenceEpsilon() && StateFlag )
//if(SNC.Exp > 1)
//if(SNC.IsConvergenceEpsilon() && Sigma.IsConvergenceSigma())
{
Configuration.FlagSigma = true;
SNC.Epsilon = SNC.EpsilonList[0];
//SNC.Epsilon += Configuration.Beta * ( SNC.EpsilonList[0] - SNC.Epsilon );
//if( C.Exp < Configuration.Beta )
//{
// C.Epsilon += ( Math.Abs( P - C.P ) - C.Epsilon ) / C.Exp;
//}
//else
//{
// C.Epsilon += Configuration.Beta * ( Math.Abs( P - C.P ) - C.Epsilon );
//}
//C.Epsilon_0 += Configuration.Beta * ( Sigma.S + Configuration.Epsilon_0 - C.Epsilon_0 );
//C.Epsilon_0 = Sigma.S + Configuration.Epsilon_0;
//C.Epsilon_0 = Sigma.S;
}
else
{
//if (C.Exp < Configuration.Beta)//9-23 張 ここscript は exp<Beta
if (C.Exp < Configuration.Beta)
{
C.Epsilon += ( Math.Abs( P - C.P ) - C.Epsilon ) / C.Exp;
}
else
{
C.Epsilon += Configuration.Beta * ( Math.Abs( P - C.P ) - C.Epsilon );
}
}
}
else //SigmaNormalClassifier ではない場合
{
//if( C.Exp < Configuration.Beta )//9-23 張 ここscript は exp<Beta
if (C.Exp < Configuration.Beta)
{
C.Epsilon += ( Math.Abs( P - C.P ) - C.Epsilon ) / C.Exp;
}
else
{
C.Epsilon += Configuration.Beta * ( Math.Abs( P - C.P ) - C.Epsilon );
}
}
//if(C.St > 250)
//{
//C.Epsilon_0 += Configuration.Beta * (Configuration.Epsilon_0 + Math.Sqrt( C.S / ( C.St - 1 ) ) - C.Epsilon_0);
//Console.WriteLine(C.Epsilon_0);
//Console.WriteLine("st" + C.St);
//}
//if( Configuration.T > 5000 )
//if(Configuration.IsConvergenceSigma())
}
this.UpdateFitness();
if( Configuration.DoActionSetSubsumption )
{
this.Subsumption( Pop );
}
}
/// <summary> /// Fitness更新 /// </summary> /// <param name="Pop">Population</param> /// <param name="P">報酬の値</param> public abstract void Update( Population Pop, double P, StdList Sigma );
/// <summary> /// Fitness更新 /// </summary> /// <param name="Pop">Population</param> /// <param name="P">報酬の値</param> public abstract void Update( Population Pop, double P, StdList Sigma );
public override void Update(Population Pop, double P, StdList Sigma)
{
double SumNumerosity = 0;
foreach (Classifier C in this.CList)
{
SumNumerosity += C.N;
}
foreach (Classifier C in this.CList)
{
C.Exp++;
if (C.Exp < 1 / Configuration.Beta)
{
C.P += (P - C.P) / C.Exp;
C.As += (SumNumerosity - C.As) / C.Exp;
}
else
{
C.P += Configuration.Beta * (P - C.P);
C.As += Configuration.Beta * (SumNumerosity - C.As);
}
// 標準偏差計算
C.St++;
double X = P - C.M;
C.M += X / C.St;
C.S += (C.St - 1) * X * X / C.St; //あっている？？
if (double.IsNaN(C.S))
{
Console.ReadLine();
}
if (C.GetType().Name == "SigmaNormalClassifier")
{
// このIterationまでのepsilonを記録ずらし
SigmaNormalClassifier SNC = ( SigmaNormalClassifier )C;
for (int index = SNC.EpsilonList.Count() - 1; index > 0; index--)
{
SNC.EpsilonList[index] = SNC.EpsilonList[index - 1];
}
SNC.EpsilonList[0] = Math.Sqrt(C.S / (C.St - 1));
#region 分類子が照合する全状態のVTの分散と平均を使って e0 を推測 chou 160107
//分類子が照合する全状態のVTの分散と平均を使って e0 を推測 chou 160107
if (Configuration.IsConvergenceVT)
{
if (C.Exp > 2) //0120 cho
{
if (SNC.IsConvergenceEpsilon()) //分類子のstd が収束するときepsilon を更新
{
C.Epsilon = SNC.EpsilonList[0];
}
else//epsilon収束しない場合 強化学習
{
if (C.Exp < Configuration.Beta)
{
C.Epsilon += (Math.Abs(P - C.P) - C.Epsilon) / C.Exp;
}
else
{
C.Epsilon += Configuration.Beta * (Math.Abs(P - C.P) - C.Epsilon);
}
}
}
Classifier cl0 = new NormalClassifier();
cl0.S = 0;
cl0.M = 0;
cl0.St = 0;
List <StdList> cpStdLists = new List <StdList>();
foreach (var std in Configuration.ConvergentedVT)
{
if (std.IsIncluded(C.C.state))
{
cpStdLists.Add(std.Clone()); //クローンメソット
}
}
if (cpStdLists.Count == 1)
{
C.Epsilon_0 = cpStdLists[0].S + Configuration.Epsilon_0;
}
else
{
foreach (var std in cpStdLists)
{
//St 出現回数
cl0.St += std.T;
cl0.M += std.M * std.T;
}
//ここst= 0 , cl0.M がNULLになる
cl0.M = cl0.M / cl0.St;
foreach (var std in cpStdLists)
{
cl0.S += std.T * Math.Pow(std.S, 2) + std.T * Math.Pow((cl0.M - std.M), 2);
}
cl0.S = Math.Sqrt(cl0.S / cl0.St);
C.Epsilon_0 = cl0.S + Configuration.Epsilon_0;
}
}
#endregion
#region tatumi 160106 XCS-SAC VTが全部収束したら 加重平均でe0更新
//if (Configuration.IsConvergenceVT)
//{
// //tatumi 160106 XCS-SAC VTが全部収束したら 加重平均でe0更新
// if (C.Exp > 2 && SNC.IsConvergenceEpsilon())//0120 cho
// {
// C.Epsilon = SNC.EpsilonList[0];
// }
// double WeightedSum = 0;
// int WeightedCount = 0;
// foreach (StdList SL in Configuration.Stdlist)
// {
// if (SL.IsIncluded(C.C.state))
// {
// if (!double.IsNaN(SL.S))
// {
// WeightedSum += (SL.T - 1) * Math.Pow(SL.S, 2);
// WeightedCount += SL.T;
// }
// }
// }
// // 下駄適応済み
// if (WeightedCount > 1)
// {
// WeightedSum = Math.Sqrt(WeightedSum / (WeightedCount - 1)) + Configuration.Epsilon_0;
// }
// else
// {
// WeightedSum = Configuration.Epsilon_0;
// }
// C.Epsilon_0 = WeightedSum;
//}
//else
//{
// if (C.Exp < Configuration.Beta)
// {
// C.Epsilon += (Math.Abs(P - C.P) - C.Epsilon) / C.Exp;
// }
// else
// {
// C.Epsilon += Configuration.Beta * (Math.Abs(P - C.P) - C.Epsilon);
// }
//}
#endregion
}
else //SigmaNormalClassifier ではない場合
{
//if( C.Exp < Configuration.Beta )//9-23 張 ここscript は exp<Beta
if (C.Exp < Configuration.Beta)
{
C.Epsilon += (Math.Abs(P - C.P) - C.Epsilon) / C.Exp;
}
else
{
C.Epsilon += Configuration.Beta * (Math.Abs(P - C.P) - C.Epsilon);
}
}
}
this.UpdateFitness();
if (Configuration.DoActionSetSubsumption)
{
this.Subsumption(Pop);
}
}
public override void Update( Population Pop, double P, StdList Sigma )
{
double SumNumerosity = 0;
foreach( Classifier C in this.CList )
{
SumNumerosity += C.N;
}
//if( Configuration.URE_Epsilon0 != Configuration.Rho )
//{
// Configuration.URE_Epsilon0 += Configuration.Epsilon_0 * 5;
// foreach( Classifier C in this.CList )
// {
// C.Epsilon_0 += Configuration.Beta * ( Configuration.URE_Epsilon0 - C.Epsilon_0 );
// }
//}
//else
//{
//if( SNC.IsConvergenceEpsilon() && Sigma.IsConvergenceSigma() )
//{
// foreach( Classifier C in this.CList )
// {
// }
//}
//}
foreach( Classifier C in this.CList )
{
C.Exp++;
if( C.Exp < 1 / Configuration.Beta )
{
C.P += ( P - C.P ) / C.Exp;
}
else
{
C.P += Configuration.Beta * ( P - C.P );
}
if( C.Exp < Configuration.Beta )
{
C.As += ( SumNumerosity - C.As ) / C.Exp;
}
else
{
C.As += Configuration.Beta * ( SumNumerosity - C.As );
}
//if( C.C.state == "0000#######" && C.A == '0' )
//{
// Configuration.ESW.WriteLine( Configuration.T + "," + C.F + "," + C.Kappa + "," + C.Epsilon_0 + "," + C.Epsilon + "," + C.Exp + "," + Math.Sqrt( C.S / ( C.St - 1 ) ) );
// Console.WriteLine( Configuration.T + " " + C.Exp );
//}
//if( C.C.state == "10##0#" && C.A == '0' )
//{
// Configuration.ESW.WriteLine( Configuration.T + "," + C.F + "," + C.Kappa + "," + C.Epsilon_0 + "," + C.Epsilon + "," + C.Exp + "," + Math.Sqrt( C.S / ( C.St - 1 ) ) );
// //Console.WriteLine( Configuration.T + " " + this.Exp );
//}
// 標準偏差計算
C.St++;
double X = P - C.M;
C.M += X / C.St;
C.S += ( C.St - 1 ) * X * X / C.St;
if( C.GetType().Name == "SigmaNormalClassifier" )
{
// このIterationまでのepsilonを記録ずらし
SigmaNormalClassifier SNC = ( SigmaNormalClassifier )C;
for( int index = SNC.EpsilonList.Count() - 1; index > 0; index-- )
{
SNC.EpsilonList[index] = SNC.EpsilonList[index - 1];
}
SNC.EpsilonList[0] = Math.Sqrt( C.S / ( C.St - 1 ) );
//bool StateFlag = true;
//foreach(StdList SL in Configuration.Stdlist)
//{
// // 分類子に含まれるstate
// //if(SL.IsIncluded(SNC.C.state, SNC.A))
// //{
// // 一つでも収束していなければ
// if(!SL.IsConvergenceSigma())
// {
// StateFlag = false;
// }
// //}
//}
//if( SNC.IsConvergenceEpsilon() )
//if( SNC.IsConvergenceEpsilon() && StateFlag )
if( Sigma.IsConvergenceSigma() )
{
C.Epsilon_0 += Configuration.Beta * ( Sigma.S + Configuration.Epsilon_0 - C.Epsilon_0 );
}
if( C.Exp < Configuration.Beta )
{
C.Epsilon += ( Math.Abs( P - C.P ) - C.Epsilon ) / C.Exp;
}
else
{
C.Epsilon += Configuration.Beta * ( Math.Abs( P - C.P ) - C.Epsilon );
}
}
else
{
if( C.Exp < Configuration.Beta )
{
C.Epsilon += ( Math.Abs( P - C.P ) - C.Epsilon ) / C.Exp;
}
else
{
C.Epsilon += Configuration.Beta * ( Math.Abs( P - C.P ) - C.Epsilon );
}
}
//if(C.St > 250)
//{
//C.Epsilon_0 += Configuration.Beta * (Configuration.Epsilon_0 + Math.Sqrt( C.S / ( C.St - 1 ) ) - C.Epsilon_0);
//Console.WriteLine(C.Epsilon_0);
//Console.WriteLine("st" + C.St);
//}
//if( Configuration.T > 5000 )
//if(Configuration.IsConvergenceSigma())
}
this.UpdateFitness();
if( Configuration.DoActionSetSubsumption )
{
this.Subsumption( Pop );
}
}
public StdList(StdList toCopy)
{
this.T = toCopy.T;
this.M = toCopy.M;
this.S = toCopy.S;
}
// Sigmaは飾り
public override void Update( Population Pop, double P, StdList Sigma )
{
double SumNumerosity = 0;
foreach( Classifier C in this.CList )
{
SumNumerosity += C.N;
}
foreach( Classifier C in this.CList )
{
C.Exp++;
if( C.Exp < 1 / Configuration.Beta )
{
C.P += ( P - C.P ) / C.Exp;
}
else
{
C.P += Configuration.Beta * ( P - C.P );
}
if( C.Exp < Configuration.Beta )
{
C.Epsilon += ( Math.Abs( P - C.P ) - C.Epsilon ) / C.Exp;
}
else
{
C.Epsilon += Configuration.Beta * ( Math.Abs( P - C.P ) - C.Epsilon );
}
if( C.Exp < Configuration.Beta )
{
C.As += ( SumNumerosity - C.As ) / C.Exp;
}
else
{
C.As += Configuration.Beta * ( SumNumerosity - C.As );
}
//if( C.C.state == "11####" && C.A == '0' )
//{
// Configuration.ESW.WriteLine( Configuration.T + "," + C.F + "," + C.Kappa + "," + C.Epsilon_0 + "," + C.Epsilon + "," + C.Exp + "," + Math.Sqrt( C.S / ( C.St - 1 ) ) );
// //Console.WriteLine( Configuration.T + " " + this.Exp );
//}
// 標準偏差計算
C.St++;
double X = P - C.M;
C.M += X / C.St;
C.S += ( C.St - 1 ) * X * X / C.St;
}
this.UpdateFitness();
if( Configuration.DoActionSetSubsumption )
{
this.Subsumption( Pop );
}
}
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();
}