IEnumerator CheckEveryHalfSec()
{
yield return(new WaitForSeconds(0.5f));
for (int i = 0; i < T.Length; i++)
{
distance [i] = Vector3.Distance(det [i].pos, Player.transform.position);
//print (distance [i] + det[i].Name);
//check si c'est a moins de distancePremierCercleDetection
if (distance [i] < distancePremierCercleDetection)
{
if (!isDetectedAround.Contains(det [i]))
{
isDetectedAround.Add(det [i]);
print("added smthg to array");
print(det [i].Name);
//det [i].isDetectedFarCircle = true;
//Envoyer au détectable spécifique le fait qu'il a été vu de loin
GameObject.Find(det[i].Name).GetComponent <DetectableLocalManager>().ImDetectedFar();
T[i].transform.GetChild(0).gameObject.SetActive(true);
//play sound
AudioSource AS;
AS = GameObject.Find("Sound1").GetComponent <AudioSource> ();
AS.PlayOneShot(LongueDistanceDetection);
//ajouter a la carte l'info comme quoi un nouvel objet est apparu
// faire briller l'icone de la map
}
}
if (distance[i] <= distancePressE)
{
GameObject.Find(det[i].Name).GetComponent <DetectableLocalManager>().YouCanPressE();
}
if (distance[i] > distancePressE)
{
GameObject.Find(det[i].Name).GetComponent <DetectableLocalManager>().ICantPressEAnyMore();
}
//check si c'est a moins de distancePremierCercleDetection
}
//calcul chaud froid par rapport a l'objet le plus proche
DistanceLaPlusProche = (Mathf.Min(distance));
// print (Array.IndexOf(distance, Mathf.Min(distance)));
//print (DistanceLaPlusProche);
//systeme de chaud froid
if (DistanceLaPlusProche < distanceChaudFroid)
{
ActiverChaudFroid();
}
else
{
DesactiverChaudFroid();
}
//systeme lacher le cube
if (DistanceLaPlusProche < distancePressE)
{
//global action comme le son
ActionDisponibleLacherCube();
//envoyer au local
}
else
{
DesactiverActionDisponibleLacherCube();
}
StartCoroutine("CheckEveryHalfSec");
}
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();
}