private void btn_Create1000X_Click(object sender, EventArgs e)
{
var lines = File.ReadAllLines(cb_data.SelectedItem.ToString());
string[] Finall = new string[1000 * lines.Length];
for (int i = 0; i < lines.Length * 1000; i++)
{
int index = (int)GeneralCom.GetRandom(0, lines.Length);
Finall[i] = lines[index];
}
File.WriteAllLines(cb_data.SelectedItem.ToString().Replace(".dat", "X1000.dat"), Finall);
}
public void CalcFitness(List <Data> datas, double m)
{
double sigma = 0;
for (int i = 0; i < N; i++)
{
for (int j = 0; j < C; j++)
{
sigma += Math.Pow(U[i, j], m) *
Math.Pow(GeneralCom.Euclideandistance(datas[i].DataDim, Centers[j]), 2);
}
}
Fitness = sigma;
}
public void CalcU()
{
for (int i = 0; i < N; i++)
{
for (int j = 0; j < C; j++)
{
double sigma = 0;
for (int k = 0; k < C; k++)
{
var a = GeneralCom.Euclideandistance(Datas[i].DataDim, Centers[j]);
var b = GeneralCom.Euclideandistance(Datas[i].DataDim, Centers[k]);
sigma += Math.Pow(a / b, (2 / (M - 1)));
}
U[i, j] = 1 / sigma;
}
}
}
public void CalcV(double w, double c1, double c2, Dim[] globalPosition)
{
for (int c = 0; c < C; c++)
{
for (int d = 0; d < Velocity[c].Val.Length; d++)
{
double y1 = GeneralCom.GetRandom(0, 1);
double y2 = GeneralCom.GetRandom(0, 1);
double c1y1 = c1 * y1;
double c2y2 = c2 * y2;
Velocity[c].Val[d] =
w * Velocity[c].Val[d] +
c1y1 * (BestPosition[c].Val[d] - Position[c].Val[d])
+ c2y2 * (globalPosition[c].Val[d] - Position[c].Val[d]);
}
}
}
private void btn_Pso10000_Click(object sender, EventArgs e)
{
for (int it = 0; it < 10000; it++)
{
W = GeneralCom.GetRandom(0.1, 0.9);
C1 = GeneralCom.GetRandom(0.1, 2);
C2 = GeneralCom.GetRandom(0.1, 2);
Pso ps = new Pso(C, _n, M, W, C1, C2, 30, _data[0].DataDim.Val.Length, _data, _maxD);
int i = 0;
for (i = 0; i < 200; i++)
{
ps.Calc();
if (ps.Variance < _rate)
{
break;
}
}
File.AppendAllText("PSOIterate", it + ";" + W + ";" + C1 + ";" + C2 + ";" + i + ";" + ps.GloablBestFitness + ";" + "\t\n");
}
}
public void CalcError(List <Data> datas, double m)
{
double sigma = 0;
for (int i = 0; i < N; i++)
{
for (int j = 0; j < C; j++)
{
sigma += Math.Pow(U[i, j], m) *
Math.Pow(GeneralCom.Euclideandistance(datas[i].DataDim, Position[j]), 2);
}
}
Iteration++;
Fitness = sigma;
// File.AppendAllText("FitnessParticle", Fitness + ";" + Index + ";" + Iteration + ";" + BestFitness + ";" + "\t\n");
Error = 1 / (sigma + 1);
//File.AppendAllText("dataa", "\t\n" + Error + "\t\n\t\n");
}
private void btn_FCM_Click(object sender, EventArgs e)
{
File.WriteAllText(DateSet + "//FitnessFCM", "");
//File.WriteAllText(DateSet + "datafcm", "");
var now = DateTime.Now;
double[,] U = new double[_n, C];
for (int i = 0; i < _n; i++)
{
double[] tem = new double[C];
double sum = 0;
for (int j = 0; j < C; j++)
{
tem[j] = GeneralCom.GetRandom() + 0.001;
sum += tem[j];
}
for (int j = 0; j < C; j++)
{
U[i, j] = tem[j] / sum;
}
}
Fcm fc = new Fcm(C, _n, M, 30, _data[0].DataDim.Val.Length, _data, U);
for (int a = 0; a < 100; a++)
{
fc.CalcCenter();
fc.CalcU();
fc.CalcFitness(_data, M);
File.AppendAllText(DateSet + "//FitnessFCM", fc.Fitness + "\t;\t" + DateTime.Now.Subtract(now).TotalSeconds + "\t\n");
}
//for (int i = 0; i < _n; i++)
//{
// for (int j = 0; j < C; j++)
// {
// File.AppendAllText(DateSet + "//datafcm", fc.U[i, j].ToString(CultureInfo.InvariantCulture) + ";");
// }
// File.AppendAllText(DateSet + "//datafcm", "\t\n");
//}
}
} //sample size
public void CalcU(List <Data> datas, double m)
{
//File.WriteAllText("data.res", "");
for (int i = 0; i < N; i++)
{
for (int j = 0; j < C; j++)
{
double sum = 0;
for (int k = 0; k < C; k++)
{
var a = GeneralCom.Euclideandistance(datas[i].DataDim, Position[j]);
var b = GeneralCom.Euclideandistance(datas[i].DataDim, Position[k]);
// File.AppendAllText("logU", "a;" + a + "\t\n");
// File.AppendAllText("logU", "b;" + b + "\t\n");
sum += Math.Pow((a / b), 2 / (m - 1));
}
U[i, j] = 1 / sum;
// File.AppendAllText("dataa", U[i, j].ToString(CultureInfo.InvariantCulture) + ";");
}
// File.AppendAllText("dataa", "\t\n");
}
}
private void btn_PSOFCMRR_Click(object sender, EventArgs e)
{
_data = _data.OrderBy(x => Guid.NewGuid()).ToList();
int step = 100;
var tempData = _data.Skip(0).Take(step).ToList();
var now = DateTime.Now;
_n = tempData.Count;
Pso ps = new Pso(C, _n, M, W, C1, C2, 30, _data[0].DataDim.Val.Length, tempData, _maxD);
for (int i = 0; i < 200; i++)
{
ps.Calc();
if (ps.Variance < _rate)
{
break;
}
}
Fcm fc = new Fcm(C, _n, M, 30, _data[0].DataDim.Val.Length, tempData, ps.U);
for (int a = 0; a < 100; a++)
{
fc.CalcCenter();
fc.CalcU();
fc.CalcFitness(_data, M);
//File.AppendAllText(DateSet + "//FitnessFCMPSO", fc.Fitness + "\t;\t" + DateTime.Now.Subtract(now).TotalSeconds + "\t\n");
}
var aCenters = fc.Centers.OrderBy(p => p.Val[0]).ThenBy(p => p.Val[1]).ThenBy(p => p.Val[2]).ThenBy(p => p.Val[3]).ThenBy(p => p.Val[4]).ToArray();
for (int it = 0; it < 1000; it++)
{
tempData.AddRange(_data.Skip(it * step + step).Take(step).ToList());
_n = tempData.Count;
ps = new Pso(C, _n, M, W, C1, C2, 30, _data[0].DataDim.Val.Length, tempData, _maxD, aCenters);
for (int i = 0; i < 200; i++)
{
ps.Calc();
if (ps.Variance < _rate)
{
break;
}
}
fc = new Fcm(C, _n, M, 30, _data[0].DataDim.Val.Length, tempData, ps.U);
for (int a = 0; a < 100; a++)
{
fc.CalcCenter();
fc.CalcU();
fc.CalcFitness(_data, M);
}
double delta = 0;
var bCenters = fc.Centers.OrderBy(p => p.Val[0]).ThenBy(p => p.Val[1]).ThenBy(p => p.Val[2]).ThenBy(p => p.Val[3]).ThenBy(p => p.Val[4]).ToArray();
for (int d = 0; d < aCenters.Length; d++)
{
delta += GeneralCom.Euclideandistance(aCenters[d], bCenters[d]);
}
delta /= aCenters.Length;
File.AppendAllText(DateSet + "//RRR100", delta + "\t;\t" + fc.Fitness + "\t;\t" + DateTime.Now.Subtract(now).TotalSeconds + "\t;\t" + aCenters.Aggregate("", (current, d) => current + (d + "&")) + "\t;\t" + bCenters.Aggregate("", (current, d) => current + (d + "&")) + "\t\n");
aCenters = bCenters;
//MessageBox.Show(delta.ToString());
}
}
} //Matrix
public Pso(int c, int n, double m, double w, double c1, double c2, int num, int dim, List <Data> datas, double[] maxd, Dim[] pastCenters = null)
{
Maxd = maxd;
C = c;
N = n;
M = m;
Num = num;
T = 0;
D = dim;
Datas = datas;
W = w;
C1 = c1;
C2 = c2;
GloablBestError = double.MinValue;
GloablBestFitness = double.MaxValue;
GloablBestPosition = new Dim[c];
Particles = new List <Particle>();
for (int a = 0; a < Num; a++)
{
Particle p = new Particle
{
U = new double[N, c],
C = c,
N = n,
Position = a == 0 && pastCenters != null ? pastCenters : new Dim[c],
BestPosition = a == 0 && pastCenters != null ? pastCenters : new Dim[c],
Velocity = new Dim[c],
Error = Double.MinValue,
BestError = Double.MinValue,
Iteration = 0,
Index = a,
BestFitness = double.MaxValue,
Fitness = double.MaxValue
};
if (a == 0 && pastCenters != null)
{
p.Position = pastCenters;
p.BestPosition = pastCenters;
GloablBestPosition = pastCenters;
for (int i = 0; i < c; i++)
{
p.Velocity[i] = new Dim(D);
}
}
else
{
for (int i = 0; i < c; i++)
{
p.Position[i] = new Dim(D);
p.Velocity[i] = new Dim(D);
if (a == 0)
{
GloablBestPosition[i] = new Dim(D);
}
for (int j = 0; j < D; j++)
{
p.Position[i].Val[j] = GeneralCom.GetRandom(0, Maxd[j]);
//p.Velocity[i].Val[j] = GeneralCom.GetRandom(0, Maxd[j]);
if (a == 0)
{
GloablBestPosition[i].Val[j] = GeneralCom.GetRandom(0, Maxd[j]);
}
}
}
}
p.BestPosition = p.Position;
p.CalcU(datas, M);
p.CalcV(W, C1, C2, GloablBestPosition);
Particles.Add(p);
}
}
