List <MyPoint> InitializeData(int datSize)
{
List <MyPoint> PList = new List <MyPoint>();
PList.Clear();
Random rand = new Random();
int dataLength = datSize; // number of data points
// create 4 distributions with different means and std devs
double meanx0 = 150;
double meanx1 = 180;
double meanx2 = 425;
double meanx3 = 475;
double meany0 = 175;
double meany1 = 250;
double meany2 = 300;
double meany3 = 320;
double stddevx0 = 100;
double stddevx1 = 90;
double stddevx2 = 220;
double stddevx3 = 260;
double stddevy0 = 50;
double stddevy1 = 80;
double stddevy2 = 180;
double stddevy3 = 195;
int index = 0;
for (int i = 0; i < dataLength / 4; i++)
{
MyPoint pt = new MyPoint();
double rnum = rand.NextDouble();
if (rnum < 0.5)
{
pt.X = rand.NextDouble() * stddevx0 / 2 + meanx0;
}
else
{
pt.X = -1 * rand.NextDouble() * stddevx0 / 2 + meanx0;
}
if (rnum < 0.5)
{
pt.Y = rand.NextDouble() * stddevy0 / 2 + meany0;
}
else
{
pt.Y = -1 * rand.NextDouble() * stddevy0 / 2 + meany0;
}
index++;
PList.Add(pt);
}
for (int i = 0; i < dataLength / 4; i++)
{
MyPoint pt = new MyPoint();
double rnum = rand.NextDouble();
if (rnum < 0.5)
{
pt.X = rand.NextDouble() * stddevx1 / 2 + meanx1;
}
else
{
pt.X = -1 * rand.NextDouble() * stddevx1 / 2 + meanx1;
}
if (rnum < 0.5)
{
pt.Y = rand.NextDouble() * stddevy1 / 2 + meany1;
}
else
{
pt.Y = -1 * rand.NextDouble() * stddevy1 / 2 + meany1;
}
index++;
PList.Add(pt);
}
for (int i = 0; i < dataLength / 4; i++)
{
double rnum = rand.NextDouble();
MyPoint pt = new MyPoint();
if (rnum < 0.5)
{
pt.X = rand.NextDouble() * stddevx2 / 2 + meanx2;
}
else
{
pt.X = -1 * rand.NextDouble() * stddevx2 / 2 + meanx2;
}
if (rnum < 0.5)
{
pt.Y = rand.NextDouble() * stddevy2 / 2 + meany2;
}
else
{
pt.Y = -1 * rand.NextDouble() * stddevy2 / 2 + meany2;
}
PList.Add(pt);
index++;
}
for (int i = 0; i < dataLength / 4; i++)
{
double rnum = rand.NextDouble();
MyPoint pt = new MyPoint();
if (rnum < 0.5)
{
pt.X = rand.NextDouble() * stddevx3 / 2 + meanx3;
}
else
{
pt.X = -1 * rand.NextDouble() * stddevx3 / 2 + meanx3;
}
if (rnum < 0.5)
{
pt.Y = rand.NextDouble() * stddevy3 / 2 + meany3;
}
else
{
pt.Y = -1 * rand.NextDouble() * stddevy3 / 2 + meany3;
}
PList.Add(pt);
index++;
}
MyImageProc.DrawClusters(pic1, PList, 1.0, 1);
return(PList);
}
private void btnGMMND_Click(object sender, EventArgs e)
{
int k = int.Parse(txtNumClusters.Text); // number of clusters
int dim = 2; // number of dimensions for data
//int datSize = 20;
// read 2-D data
//FileInfo fi = new FileInfo("d:\\csharp2016\\clusterdata\\data2D.txt");
//FileInfo fi = new FileInfo("d:\\csharp2016\\clusterdata\\cdata1.txt");
//StreamReader sr = new StreamReader(fi.Open(FileMode.Open, FileAccess.Read));
//string sline = sr.ReadLine();
//int datSize = 1;
//while (sline != null)
//{
// datSize++;
// sline = sr.ReadLine();
//}
//sr.Close();
//sr = new StreamReader(fi.Open(FileMode.Open, FileAccess.Read));
//sline = sr.ReadLine();
//Matrix X = new Matrix(datSize, dim);
//int count = 0;
//while (sline != null)
//{
// string[] parts = sline.Split(new char[] { '\t', ' ' });
// X[count, 0] = double.Parse(parts[1].Trim());
// X[count, 1] = double.Parse(parts[2].Trim());
// count++;
// sline = sr.ReadLine();
//}
//sr.Close();
int datSize = 1000;
List <MyPoint> PList = InitializeData(datSize);
Mapack.Matrix X = new Matrix(datSize, dim);
for (int i = 0; i < PList.Count; i++)
{
X[i, 0] = PList[i].X;
X[i, 1] = PList[i].Y;
}
GMM_NDim gmmnd = new GMM_NDim(k, dim, X, dataSize);
gmmnd.ComputeGMM_ND();
// determine class membership i.e., which point belongs to which cluster
PList = new List <MyPoint>();
for (int i = 0; i < X.Rows; i++)
{
// Gamma matrix has the probabilities for a data point for its membership in each cluster
double[] probabs = new double[k];
int cnum = 0;
double maxprobab = gmmnd.Gamma[i, 0];
for (int m = 0; m < k; m++)
{
if (gmmnd.Gamma[i, m] > maxprobab)
{
cnum = m; // data i belongs to cluster m
maxprobab = gmmnd.Gamma[i, m];
}
}
MyPoint pt = new MyPoint {
ClusterId = cnum, X = X[i, 0], Y = X[i, 1]
};
PList.Add(pt);
}
MyImageProc.DrawClusters(pic1, PList, 1, k);
}