//// 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 NormalClassifier( NormalClassifier 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;
}
// コピーコンストラクタ
public NormalClassifier(NormalClassifier 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;
}
// 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 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 NormalClassifier(( NormalClassifier )Parent_1);
Classifier Child_2 = new NormalClassifier(( NormalClassifier )Parent_2);
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;
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) / 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_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_1.F *= 0.1;
Child_2.F *= 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();
}
}
}
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 NormalClassifier( ( NormalClassifier )Parent_1 );
Classifier Child_2 = new NormalClassifier( ( NormalClassifier )Parent_2 );
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;
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 ) / 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_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_1.F *= 0.1;
Child_2.F *= 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();
}
}
}
// 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>();
* }
* }*/
}
