// コピーコンストラクタ
public SigmaNormalClassifier(SigmaNormalClassifier C)
{
this.C = new BinaryState(C.C);
//this.A = C.A;
this.P = C.P;
this.Epsilon = C.Epsilon;
this.F = C.F;
this.N = C.N;
this.Exp = C.Exp;
this.Ts = C.Ts;
this.As = C.As;
this.Kappa = C.Kappa;
this.Epsilon_0 = C.Epsilon_0;
this.St = C.St;
this.M = C.M;
this.S = C.S;
this.GenerateTime = Configuration.T;
this.ExpThreshold = C.ExpThreshold;
this.EpsilonList = new double[Configuration.LookBackEpsilon + 1];
this.WinningRate = new double[Configuration.LookBackEpsilon];
// はじめは消されにくいように
for (int i = 0; i < this.WinningRate.Count(); i++)
{
this.WinningRate[i] = C.WinningRate[i];
}
this.ConvergenceFlag = false;
}
//// 使ってない?
//public NormalClassifier( int Length, string Type )
//{
// // Stateの様式決定
// if( Type == "Binary" )
// {
// this.C = new BinaryState( Length );
// this.C.GetState(); // 仮
// }
// this.A = '1'; // 仮
// if( Configuration.MT.Next() % 2 == 0 )
// {
// this.A = '0';
// }
// this.P = 10; // 仮
// this.Epsilon = 0.1; // 仮
// this.F = 10; // 仮
// this.N = 1;
// this.Epsilon_0 = Configuration.Epsilon_0;
// this.St = 0;
// this.M = 0;
// this.S = 0;
//}
// コピーコンストラクタ
public SigmaNormalClassifier( SigmaNormalClassifier C )
{
this.C = new BinaryState( C.C );
this.A = C.A;
this.P = C.P;
this.Epsilon = C.Epsilon;
this.F = C.F;
this.N = C.N;
this.Exp = C.Exp;
this.Ts = C.Ts;
this.As = C.As;
this.Kappa = C.Kappa;
this.Epsilon_0 = C.Epsilon_0;
this.St = C.St;
this.M = C.M;
this.S = C.S;
this.GenerateTime = Configuration.T;
this.ExpThreshold = C.ExpThreshold;
this.EpsilonList = new double[Configuration.LookBackEpsilon + 1];
this.WinningRate = new double[Configuration.LookBackEpsilon];
// はじめは消されにくいように
for( int i = 0; i < this.WinningRate.Count(); i++ )
{
this.WinningRate[i] = C.WinningRate[i];
}
this.ConvergenceFlag = false;
}
/// <summary>
/// 総当たり評価
/// </summary>
public void RoundRobin()
{
//double[,] Results = new double[this.CList.Count, this.CList.Count];
//// 総当たり比較
//for( int i = 0; i < this.CList.Count - 1; i++ ) // 比較元
//{
// for( int j = i + 1; j < this.CList.Count; j++ )
// {
// //if(this.CList[i].Kappa > this.CList[j].Kappa)
// if( this.CList[i].F > this.CList[j].F )
// {
// Results[i, j] = 1; // 勝ち点1
// }
// //else if(this.CList[i].Kappa == this.CList[j].Kappa)
// else if( this.CList[i].F == this.CList[j].F )
// {
// Results[i, j] = 0.5; // 引き分け点0.5
// }
// else
// {
// Results[i, j] = 0;
// }
// }
//}
// 勝率計算
for (int i = 0; i < this.CList.Count; i++)
{
//double WinningPoint = 0;
//for( int j = 0; j < i; j++ )
//{
// WinningPoint += 1 - Results[j, i];
//}
//for( int j = i + 1; j < this.CList.Count; j++ )
//{
// WinningPoint += Results[i, j];
//}
SigmaNormalClassifier SNC = ( SigmaNormalClassifier )this.CList[i];
// 移動
for (int index = SNC.WinningRate.Count() - 1; index > 0; index--)
{
SNC.WinningRate[index] = SNC.WinningRate[index - 1];
}
//SNC.WinningRate[0] = WinningPoint / ( this.CList.Count - 1 );
SNC.WinningRate[0] = SNC.F;
}
}
protected override double DeletionVote(Classifier C, double AvFitness)
{
// SigmaNormalClassifier前提
SigmaNormalClassifier SNC = ( SigmaNormalClassifier )C;
double Vote = SNC.As * SNC.N;
//Console.WriteLine("AS: " + SNC.As + " N: " + SNC.N);
if ((SNC.Exp > Configuration.Theta_del) && (SNC.WinningRate.Average() / SNC.N < Configuration.Delta * AvFitness))
{
if (SNC.WinningRate.Average() == 0)
{
//Console.WriteLine("0 : " + Vote);
return(Vote);
}
Vote *= AvFitness / (SNC.WinningRate.Average() / SNC.N);
//Console.WriteLine("hoge");
}
//Console.WriteLine(SNC.WinningRate.Average() + " : " + Vote);
return(Vote);
}
// situationに合うものをPopulationから取り、足りないときはCovering
protected override void Covering( State S, Population P )
{
while( this.CList.Count == 0 )
{
// situationにあうものをPopulationから探す
this.CList = P.MatchSituation( S );
// Actionの種類
List<char> Actions = new List<char>();
int NumberOfActions = 0;
// Multiplexer(2進数)
if( Configuration.Type == "Binary" )
{
Actions.Add( '0' );
Actions.Add( '1' );
foreach( Classifier C in this.CList )
{
Actions.Remove( C.A );
}
NumberOfActions = 2 - Actions.Count;
}
// MatchSetにある行動が少ないとき
if( NumberOfActions < Configuration.Theta_mna )
{
// 一部を変化させたCondition
State state = new BinaryState( S );
state.Covering();
Classifier CC;
if( Configuration.ASName == "CS" || Configuration.ASName == "MaxCS" || Configuration.ASName == "Max" || Configuration.ASName == "Updatee0CS" )
{
CC = new SigmaNormalClassifier( state, Actions, Configuration.ExpThreshold );
}
else
{
CC = new NormalClassifier( state, Actions );
}
P.Add( CC );
// 整理
P.Delete();
this.CList = new List<Classifier>();
}
}
}
public override void RunGA( State Situation, Population P )
{
double NumerositySum = 0.0;
double TimeStampSum = 0.0;
foreach( Classifier C in this.CList )
{
NumerositySum += C.N;
TimeStampSum += C.Ts * C.N;
}
if( Configuration.T - TimeStampSum / NumerositySum > Configuration.Theta_GA )
{
foreach( Classifier C in this.CList )
{
C.Ts = Configuration.T;
}
Classifier Parent_1 = this.SelectOffspring();
Classifier Parent_2 = this.SelectOffspring();
Classifier Child_1 = new SigmaNormalClassifier( ( SigmaNormalClassifier )Parent_1 );
Classifier Child_2 = new SigmaNormalClassifier( ( SigmaNormalClassifier )Parent_2 );
///nakata added
Child_1.F /= Child_1.N;
Child_2.F /= Child_2.N;
//////////////
Child_1.N = Child_2.N = 1;
Child_1.Exp = Child_2.Exp = 0;
Child_1.St = Child_2.St = 0;
Child_1.M = Child_2.M = 0;
Child_1.S = Child_2.S = 0;
//Child_1.Epsilon_0 = Child_2.Epsilon_0 = Configuration.Epsilon_0;
SigmaNormalClassifier SNP1 = ( SigmaNormalClassifier )Parent_1;
SigmaNormalClassifier SNP2 = ( SigmaNormalClassifier )Parent_2;
SigmaNormalClassifier SNC1 = ( SigmaNormalClassifier )Child_1;
SigmaNormalClassifier SNC2 = ( SigmaNormalClassifier )Child_2;
// 交叉
if( Configuration.MT.NextDouble() < Configuration.Chai )
{
Child_1.Crossover( Child_2 );
Child_1.P = ( Parent_1.P + Parent_2.P ) / 2;
//Child_1.Epsilon = Parent_1.Epsilon + Parent_2.Epsilon;
Child_1.Epsilon = ( Parent_1.Epsilon + Parent_2.Epsilon ) / 2;
Child_1.F = ( Parent_1.F + Parent_2.F ) / 2;
//Child_1.M = ( Parent_1.M + Parent_2.M ) / 2;
//Child_1.S = ( Parent_1.S + Parent_2.S ) / 2;
Child_1.Epsilon_0 = ( Parent_1.Epsilon_0 + Parent_2.Epsilon_0 ) / 2;
//Child_1.Epsilon_0 = Math.Min(Parent_1.Epsilon_0, Parent_2.Epsilon_0);
Child_2.P = Child_1.P;
Child_2.Epsilon = Child_1.Epsilon;
Child_2.F = Child_1.F;
//Child_2.M = Child_1.M;
//Child_2.S = Child_1.S;
Child_2.Epsilon_0 = Child_1.Epsilon_0;
for( int i = 0; i < SNC1.WinningRate.Count(); i++ )
{
SNC1.WinningRate[i] = SNC2.WinningRate[i] = ( SNP1.WinningRate[i] + SNP2.WinningRate[i] ) / 2;
}
}
Child_1.F *= 0.1;
Child_2.F *= 0.1;
for( int i = 0; i < SNC1.WinningRate.Count(); i++ )
{
SNC1.WinningRate[i] *= 0.1;
SNC2.WinningRate[i] *= 0.1;
}
// bothChild
Child_1.Mutation( Situation );
Child_2.Mutation( Situation );
if( Configuration.DoGASubsumption )
{
if( Parent_1.DoesSubsume( Child_1 ) )
{
Parent_1.N++;
}
else if( Parent_2.DoesSubsume( Child_1 ) )
{
Parent_2.N++;
}
else
{
P.Insert( Child_1 );
}
P.Delete();
if( Parent_1.DoesSubsume( Child_2 ) )
{
Parent_1.N++;
}
else if( Parent_2.DoesSubsume( Child_2 ) )
{
Parent_2.N++;
}
else
{
P.Insert( Child_2 );
}
P.Delete();
}
else
{
P.Insert( Child_1 );
P.Delete();
P.Insert( Child_2 );
P.Delete();
}
}
}
//学習終了後に圧縮にする
public override void Compact()
{
List <Classifier> copyActionSet = new List <Classifier>();
foreach (Classifier classifier in CList)
{
copyActionSet.Add(classifier);
}
int N = copyActionSet.Count;
for (int i = 0; i < N; i++)
{
#region subsume
Classifier Cl = null;
//なかに最も一般的な分類子をClにする
foreach (Classifier C in copyActionSet)
{
if (C.CouldSubsume())
{
if ((Cl == null) || (C.C.NumberOfSharp > Cl.C.NumberOfSharp) || ((C.C.NumberOfSharp == Cl.C.NumberOfSharp) && (Configuration.MT.NextDouble() < 0.5)))
{
Cl = C;
}
}
}
//Cl が包摂できる分類子を吸収する
if (Cl != null)
{
// 削除中にforeachできない
List <Classifier> CL = new List <Classifier>();
foreach (Classifier C in copyActionSet)
{
if (Cl.IsMoreGeneral(C))
{
SigmaNormalClassifier Snc_ko = (SigmaNormalClassifier)C;
SigmaNormalClassifier Snc_oya = (SigmaNormalClassifier)Cl;
//10の数字は適当 可変にするか？ 1/10にする 10－7 あんまり変わらない
//分散が同じの判断基準 他に？？t test を使う 予定 特殊化がでない
//
//double t = (Snc_oya.M - Snc_ko.M)/Math.Sqrt(Snc_oya.S/30 + Snc_ko.S/30);
//double studentT = StudentT.InvCDF(0, 1, 60, 0.005);
//if (t < studentT || t > Math.Abs(studentT))//t test で有意差がある 包摂しない
//{
//}
// else//有意差がない 統合しでもよい ただ 特殊化ルールどう識別するか
//{
if (/*Math.Abs(Cl.M - C.M) < 10 &&*/
// Math.Abs(Cl.Epsilon_0 - C.Epsilon_0)< 10&&
// Cl.Kappa == 1
//&& Snc_oya.Epsilon < Snc_ko.Epsilon
(Cl.Epsilon_0 < C.Epsilon_0 || Math.Abs(Cl.Epsilon_0 - C.Epsilon_0) < Cl.Epsilon_0 / 10)
//&& Math.Abs(Cl.Epsilon_0 - C.Epsilon_0) < 10//case 1 +- 10
// Cl.Epsilon < C.Epsilon
&& Snc_ko.IsConvergenceEpsilon() &&
Snc_oya.IsConvergenceEpsilon()
) //
{
Cl.N += C.N;
// 包摂された、削除したいClassifier C をCLに登録
CL.Add(C);
}
// }
}
}
foreach (Classifier C in CL)
{
this.Remove(C);//包摂された分類子 C を pop からCを削除
}
}
//最も一般化されるもの copyActionSetからを削除
copyActionSet.Remove(Cl);
#endregion
}
}
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 RunGA(State Situation, Population P)
{
double NumerositySum = 0.0;
double TimeStampSum = 0.0;
foreach (Classifier C in this.CList)
{
NumerositySum += C.N;
TimeStampSum += C.Ts * C.N;
}
if (Configuration.T - TimeStampSum / NumerositySum > Configuration.Theta_GA)
{
foreach (Classifier C in this.CList)
{
C.Ts = Configuration.T;
}
Classifier Parent_1 = this.SelectOffspring();
Classifier Parent_2 = this.SelectOffspring();
Classifier Child_1 = new SigmaNormalClassifier(( SigmaNormalClassifier )Parent_1);
Classifier Child_2 = new SigmaNormalClassifier(( SigmaNormalClassifier )Parent_2);
///nakata added
Child_1.F /= Child_1.N;
Child_2.F /= Child_2.N;
//////////////
Child_1.N = Child_2.N = 1;
Child_1.Exp = Child_2.Exp = 0;
Child_1.St = Child_2.St = 0;
Child_1.M = Child_2.M = 0;
Child_1.S = Child_2.S = 0;
//Child_1.Epsilon_0 = Child_2.Epsilon_0 = Configuration.Epsilon_0;
SigmaNormalClassifier SNP1 = ( SigmaNormalClassifier )Parent_1;
SigmaNormalClassifier SNP2 = ( SigmaNormalClassifier )Parent_2;
SigmaNormalClassifier SNC1 = ( SigmaNormalClassifier )Child_1;
SigmaNormalClassifier SNC2 = ( SigmaNormalClassifier )Child_2;
// 交叉
if (Configuration.MT.NextDouble() < Configuration.Chai)
{
Child_1.Crossover(Child_2);
Child_1.P = (Parent_1.P + Parent_2.P) / 2;
//Child_1.Epsilon = Parent_1.Epsilon + Parent_2.Epsilon;
Child_1.Epsilon = (Parent_1.Epsilon + Parent_2.Epsilon) / 2;
Child_1.F = (Parent_1.F + Parent_2.F) / 2;
Child_1.Epsilon_0 = (Parent_1.Epsilon_0 + Parent_2.Epsilon_0) / 2;
//Child_1.Epsilon_0 = Math.Min(Parent_1.Epsilon_0, Parent_2.Epsilon_0);
Child_2.P = Child_1.P;
Child_2.Epsilon = Child_1.Epsilon;
Child_2.F = Child_1.F;
Child_2.Epsilon_0 = Child_1.Epsilon_0;
for (int i = 0; i < SNC1.WinningRate.Count(); i++)
{
SNC1.WinningRate[i] = SNC2.WinningRate[i] = (SNP1.WinningRate[i] + SNP2.WinningRate[i]) / 2;
}
}
Child_1.F *= 0.1;
Child_2.F *= 0.1;
for (int i = 0; i < SNC1.WinningRate.Count(); i++)
{
SNC1.WinningRate[i] *= 0.1;
SNC2.WinningRate[i] *= 0.1;
}
// bothChild
Child_1.Mutation(Situation);
Child_2.Mutation(Situation);
if (Child_1.C.state[4] == '0' & Child_1.C.state[7] == '1')//"bath0 rehabi1"
{
Configuration.Problem.WriteLine(Child_1.C.state + "," + Configuration.T + "," + Child_1.P + "," + Child_1.M + "," + Child_1.Epsilon + "," + Child_1.F + ","
+ Child_1.N + "," + Child_1.Exp + "," + Child_1.Ts + "," + Child_1.As + "," + Child_1.Kappa + "," + Child_1.Epsilon_0 + "," + Child_1.St + "," + Child_1.GenerateTime + ", child 2 ");
Configuration.Problem.WriteLine(Parent_1.C.state + "," + Configuration.T + "," + Parent_1.P + "," + Parent_1.M + "," + Parent_1.Epsilon + "," + Parent_1.F + ","
+ Parent_1.N + "," + Parent_1.Exp + "," + Parent_1.Ts + "," + Parent_1.As + "," + Parent_1.Kappa + "," + Parent_1.Epsilon_0 + "," + Parent_1.St + "," + Parent_1.GenerateTime + ",Parent1");
Configuration.Problem.WriteLine(Parent_2.C.state + "," + Configuration.T + "," + Parent_2.P + "," + Parent_2.M + "," + Parent_2.Epsilon + "," + Parent_2.F + ","
+ Parent_2.N + "," + Parent_2.Exp + "," + Parent_2.Ts + "," + Parent_2.As + "," + Parent_2.Kappa + "," + Parent_2.Epsilon_0 + "," + Parent_2.St + "," + Parent_2.GenerateTime + ",Parent2");
}
if (Child_2.C.state[4] == '0' & Child_2.C.state[7] == '1')//"bath0 rehabi1"
{
Configuration.Problem.WriteLine(Child_2.C.state + "," + Configuration.T + "," + Child_2.P + "," + Child_2.M + "," + Child_2.Epsilon + "," + Child_2.F + ","
+ Child_2.N + "," + Child_2.Exp + "," + Child_2.Ts + "," + Child_2.As + "," + Child_2.Kappa + "," + Child_2.Epsilon_0 + "," + Child_2.St + "," + Child_2.GenerateTime + ",child 2");
Configuration.Problem.WriteLine(Parent_1.C.state + "," + Configuration.T + "," + Parent_1.P + "," + Parent_1.M + "," + Parent_1.Epsilon + "," + Parent_1.F + ","
+ Parent_1.N + "," + Parent_1.Exp + "," + Parent_1.Ts + "," + Parent_1.As + "," + Parent_1.Kappa + "," + Parent_1.Epsilon_0 + "," + Parent_1.St + "," + Parent_1.GenerateTime + ",Parent1");
Configuration.Problem.WriteLine(Parent_2.C.state + "," + Configuration.T + "," + Parent_2.P + "," + Parent_2.M + "," + Parent_2.Epsilon + "," + Parent_2.F + ","
+ Parent_2.N + "," + Parent_2.Exp + "," + Parent_2.Ts + "," + Parent_2.As + "," + Parent_2.Kappa + "," + Parent_2.Epsilon_0 + "," + Parent_2.St + "," + Parent_2.GenerateTime + ",Parent2");
}
if (Configuration.DoGASubsumption)
{
if (Parent_1.DoesSubsume(Child_1))
{
Parent_1.N++;
}
else if (Parent_2.DoesSubsume(Child_1))
{
Parent_2.N++;
}
else
{
P.Insert(Child_1);
}
P.Delete();
if (Parent_1.DoesSubsume(Child_2))
{
Parent_1.N++;
}
else if (Parent_2.DoesSubsume(Child_2))
{
Parent_2.N++;
}
else
{
P.Insert(Child_2);
}
P.Delete();
}
else
{
P.Insert(Child_1);
P.Delete();
P.Insert(Child_2);
P.Delete();
}
}
}
// 包摂
protected override void Subsumption(Population Pop)
{
List <Classifier> copyActionSet = new List <Classifier>();
foreach (Classifier classifier in CList)
{
copyActionSet.Add(classifier);
}
int N = copyActionSet.Count;
//最大N回実行する
//for (int i = 0; i < N; i++)
//{
#region subsume
Classifier Cl = null;
//actionsetなかに最も一般的な分類子をClにする
foreach (Classifier C in copyActionSet)
{
if (C.CouldSubsume())
{
if ((Cl == null) || (C.C.NumberOfSharp > Cl.C.NumberOfSharp) || ((C.C.NumberOfSharp == Cl.C.NumberOfSharp) && (Configuration.MT.NextDouble() < 0.5)))
{
Cl = C;
}
}
}
if (Cl != null)
{
// 削除中にforeachできない
List <Classifier> CL = new List <Classifier>();
// 包摂された、削除したいClassifier C をCLに登録
foreach (Classifier C in copyActionSet)
{
if (Cl.IsMoreGeneral(C))
{
SigmaNormalClassifier Snc_ko = (SigmaNormalClassifier)C;
SigmaNormalClassifier Snc_oya = (SigmaNormalClassifier)Cl;
// e0 の値を３位まで見る、近いものは差がないとみなす
var subsumer = Math.Round(Cl.Epsilon_0, 3);
var subsumed = Math.Round(C.Epsilon_0, 3);
if ((subsumer <= (subsumed + subsumer / 10)) &&
Snc_ko.IsConvergenceEpsilon() &&
Snc_oya.IsConvergenceEpsilon()
)
{
Cl.N += C.N;
CL.Add(C);
}
}
}
foreach (Classifier C in CL)
{
SigmaNormalClassifier SNC = (SigmaNormalClassifier)Cl;
if (C.C.state[4] == '0' & C.C.state[7] == '1') //"bath0 rehabi1"
{
Configuration.Problem.WriteLine(C.C.state + "," + Configuration.T + "," + C.P + "," + C.M + "," + C.Epsilon + "," + C.F + "," + C.N + "," + C.Exp + "," + C.Ts + "," + C.As + "," + C.Kappa + "," + C.Epsilon_0 + "," + C.St + "," + C.GenerateTime + ", in AS");
}
this.Remove(C); //as から削除
Pop.Remove(C); //pop から削除
}
//いまの最も一般化されたものを削除する
//copyActionSet.Remove(Cl);
}
#endregion
//}
}
protected override void UpdateFitness()
{
double AccuracySum = 0;
foreach (Classifier C in this.CList)
{
SigmaNormalClassifier SNC = ( SigmaNormalClassifier )C;
if (Configuration.T > 1000)
{
if (//またがるのものを排除 平均プラマイ Pのプラマイ
(C.M - C.Epsilon) < Configuration.RewardAverage &&
(C.M + C.Epsilon) > Configuration.RewardAverage
)
{//またがる分類子の正確性を極端に下げる PS：0にしてはいけない
//またがる部分とEの割合でKappaの正確性を下げていく、またがる部分が大きいければ 下がるのが早い
double crossPercentage;
double mincross;
//puls minus 0.5e どうなるかな0128 ダメだった 余計なもの残した
mincross = Math.Min(Math.Abs(C.M + C.Epsilon - Configuration.RewardAverage),
Math.Abs(C.M - C.Epsilon - Configuration.RewardAverage));
//crossPercentage = mincross / (C.Epsilon);
crossPercentage = mincross / Configuration.RewardAverage;//12/11変更、許容範囲の図り方、平均値と比較
//規制緩和 c.epsilon の 20% ぐらいまたがる を許す、緩和しない基本的に+-epsilon 十分緩いい
//±σ 相当 片側85% と 片側15% の関係
if (crossPercentage < Configuration.CoverPersentage)//許容範囲で通常のやり方
{
if (C.Epsilon < C.Epsilon_0)
{
C.Kappa = 1;
}
else
{//epsilon>epsilon0 の場合
C.Kappa = Configuration.Alpha * Math.Pow(C.Epsilon / C.Epsilon_0, -Configuration.Nyu);
}
}
else//許容範囲を超えた
{
C.Kappa = Configuration.Alpha * Math.Pow(1 + crossPercentage, -Configuration.Nyu);
// C.Kappa = Math.Pow(Math.E,-Math.Pow(5*crossPercentage,2));
}
AccuracySum += C.Kappa * C.N;
if (double.IsNaN(AccuracySum))
{
Console.ReadLine();
}
}
else //またがらない分類子は 通常のやり方
{
if (C.Epsilon <= C.Epsilon_0)
{
C.Kappa = 1;
}
else
{//epsilon>epsilon0 の場合
C.Kappa = Configuration.Alpha * Math.Pow(C.Epsilon / C.Epsilon_0, -Configuration.Nyu);
}
AccuracySum += C.Kappa * C.N;
//Accuracy はNaNなるとき 止める
if (double.IsNaN(AccuracySum))
{
Console.ReadLine();
}
}
}
else //1000 回以下の場合
{
if (C.Epsilon <= C.Epsilon_0)
{
C.Kappa = 1;
}
else
{
//ここC.Epsilon_0 == 0, kappa の計算がおかしいから, SNC.epsilon_0 下駄を足す
C.Kappa = Configuration.Alpha * Math.Pow(C.Epsilon / SNC.Epsilon_0, -Configuration.Nyu);
}
if (double.IsNaN(C.Kappa))
{
Console.WriteLine("kappa =NaN");
Console.ReadLine();
}
AccuracySum += C.Kappa * C.N;
}
}
//
foreach (Classifier C in this.CList)
{
C.F += Configuration.Beta * (C.Kappa * C.N / AccuracySum - C.F);
if (double.IsNaN(C.F))
{
Console.ReadLine();
}
}
}
// Populationからfitnessが一番小さいものを削除
public override void Delete()
{
//if( this.CountNumerosity() <= Configuration.N )
//{
// return;
//}
//int SumNumerosity = 0;
//double SumFitness = 0.0;
//List<Classifier> DeleteCandidate = new List<Classifier>();
//if(Configuration.ASName == "CS")
//{
// foreach(Classifier C in this.CList)
// {
// SigmaNormalClassifier SC = ( SigmaNormalClassifier )C;
// double WR = 0;
// foreach(double d in SC.WinningRate)
// {
// WR += d;
// }
// if(SC.HasMatured() && (WR < 0.4 * SC.WinningRate.Count()))
// {
// DeleteCandidate.Add( SC );
// }
// }
// if(DeleteCandidate.Count == 0)
// {
// DeleteCandidate = this.CList;
// }
// //else
// //{
// // bool Flag = true;
// // foreach( Classifier C in DeleteCandidate )
// // {
// // SigmaNormalClassifier SNC = ( SigmaNormalClassifier )C;
// // double WR = 0;
// // foreach(double d in SNC.WiningRate)
// // {
// // WR += d;
// // }
// // // 悪い子探し
// // if( WR < 0.4 * SNC.WiningRate.Count() )
// // {
// // Flag = false;
// // }
// // }
// // // 悪い子が一人もいなかったら
// // if( Flag )
// // {
// // DeleteCandidate = this.CList;
// // }
// //}
//}
//else
//{
// DeleteCandidate = this.CList;
//}
//foreach( Classifier C in DeleteCandidate )
//{
// SumNumerosity += C.N;
// SumFitness += C.F;
//}
//double AvFitness = SumFitness / SumNumerosity;
//double VoteSum = 0;
//foreach( Classifier C in DeleteCandidate )
//{
// VoteSum += this.DeletionVote( C, AvFitness );
//}
//double ChoicePoint = Configuration.MT.NextDouble() * VoteSum;
//VoteSum = 0;
//foreach( Classifier C in DeleteCandidate )
//{
// VoteSum += this.DeletionVote( C, AvFitness );
// if( VoteSum > ChoicePoint )
// {
// if( C.N > 1 )
// {
// C.N--;
// }
// else
// {
// this.Remove( C );
// }
// return;
// }
//}
int SumNumerosity = 0;
double SumFitness = 0.0;
foreach (Classifier C in this.CList)
{
SigmaNormalClassifier SNC = ( SigmaNormalClassifier )C;
SumNumerosity += SNC.N;
//SumFitness += C.F;
SumFitness += SNC.WinningRate.Average();
//Console.WriteLine(SNC.C.state + " ave: " + SNC.WinningRate.Average());
}
if (SumNumerosity <= Configuration.N)
{
return;
}
double AvFitness = SumFitness / SumNumerosity;
double VoteSum = 0;
foreach (Classifier C in this.CList)
{
VoteSum += this.DeletionVote(C, AvFitness);
}
double ChoicePoint = Configuration.MT.NextDouble() * VoteSum;
VoteSum = 0;
foreach (Classifier C in this.CList)
{
VoteSum += this.DeletionVote(C, AvFitness);
if (VoteSum > ChoicePoint)
{
if (C.N > 1)
{
C.N--;
}
else
{
this.Remove(C);
}
return;
}
}
}
// situationに合うものをPopulationから取り、足りないときはCovering
protected override void Covering(State S, Population P)
{
while (this.CList.Count == 0)
{
// situationにあうものをPopulationから探す
this.CList = P.MatchSituation(S);
if (CList.Count == 0)
{
// 一部を変化させたCondition
State state = new IntegralState(S);
state.Covering();
Classifier CC;
if (Configuration.ASName == "CS" || Configuration.ASName == "MaxCS" || Configuration.ASName == "Max" || Configuration.ASName == "Updatee0CS")
{
CC = new SigmaNormalClassifier(state, Configuration.ExpThreshold);
}
else
{
CC = new NormalClassifier(state);
}
P.Add(CC);
// 整理
P.Delete();
this.CList = new List <Classifier>();
}
}
/*while( this.CList.Count == 0 )
* {
* // situationにあうものをPopulationから探す
* this.CList = P.MatchSituation( S );
*
* // Actionの種類
* List<char> Actions = new List<char>();
* int NumberOfActions = 0;
*
* // Multiplexer(2進数)
* if( Configuration.Type == "Binary" )
* {
* Actions.Add( '0' );
* Actions.Add( '1' );
* foreach( Classifier C in this.CList )
* {
* Actions.Remove( C.A );
* }
* NumberOfActions = 2 - Actions.Count;
* }
*
* // MatchSetにある行動が少ないとき
* if( NumberOfActions < Configuration.Theta_mna )
* {
* // 一部を変化させたCondition
* State state = new BinaryState( S );
* state.Covering();
*
* Classifier CC;
* if( Configuration.ASName == "CS" || Configuration.ASName == "MaxCS" || Configuration.ASName == "Max" || Configuration.ASName == "Updatee0CS" )
* {
* CC = new SigmaNormalClassifier( state, Actions, Configuration.ExpThreshold );
* }
* else
* {
* CC = new NormalClassifier( state, Actions );
* }
* P.Add( CC );
* // 整理
* P.Delete();
* this.CList = new List<Classifier>();
* }
* }*/
}
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();
}
