private void btnGMMND_Click(object aoSender, EventArgs aoArg)
{
try
{
int kiK = int.Parse(txtNumClusters.Text); // number of clusters
int kiDim = 3; // number of dimensions for data
Matrix koX = new Matrix(this.voOrig.Height * this.voOrig.Width, 3);
for (int i = 0; i < this.voOrig.Height; i++)
{
for (int j = 0; j < this.voOrig.Width; j++)
{
Color koC = this.voOrig.GetPixel(j, i);
koX[(i * this.voOrig.Width) + j, 0] = koC.R;
koX[(i * this.voOrig.Width) + j, 1] = koC.G;
koX[(i * this.voOrig.Width) + j, 2] = koC.B;
}
}
voGmmnd = new GMM_NDim(kiK, kiDim, koX);
voGmmnd.ComputeGMM_ND();
this.voLB.DataSource = null;
this.voCluster.Clear( );
for (int i = 0; i < kiK; i++)
{
this.voCluster.Add(String.Format("Cluster {0}", i));
}
this.voLB.DataSource = this.voCluster;
}
catch (Exception koEx)
{
MessageBox.Show(koEx.Message);
}
}
private void voBtnSwarm_Click(object sender, EventArgs e)
{
int kiI;
Task <GMM_NDim>[] koTask = new Task <GMM_NDim> [4];
Task koFinal;
this.voSwarmGMM = new List <GMM_NDim>( );
for (kiI = 0; kiI < koTask.Length; kiI++)
{
koTask[kiI] = Task.Factory.StartNew <GMM_NDim>((aoContext) =>
{
int kiK = (( int )aoContext) + 2;
int kiDim = 3; // number of dimensions for data
Bitmap koBmp;
lock (this.voOrig){ koBmp = ( Bitmap )this.voOrig.Clone( ); }
Matrix koX = new Matrix(koBmp.Height * koBmp.Width, 3);
GMM_NDim koRes;
for (int i = 0; i < koBmp.Height; i++)
{
for (int j = 0; j < koBmp.Width; j++)
{
Color koC = koBmp.GetPixel(j, i);
koX[(i * koBmp.Width) + j, 0] = koC.R;
koX[(i * koBmp.Width) + j, 1] = koC.G;
koX[(i * koBmp.Width) + j, 2] = koC.B;
}
}
koRes = new GMM_NDim(kiK, kiDim, koX);
koRes.ComputeGMM_ND( );
koRes.VdInertia = koRes.MInertia(koBmp);
return(koRes);
}, kiI);
}
koFinal = Task.Factory.ContinueWhenAll(koTask, (aoTask) =>
{
Console.WriteLine(aoTask.Length.ToString( ) + " tasks");
for (kiI = 0; kiI < aoTask.Length; kiI++)
{
this.voSwarmGMM.Add(aoTask[kiI].Result);
}
});
koFinal.Wait( );
this.voSwarmGMM.Sort( );
this.voStrSwarmGMM = new List <string>( );
for (int i = 0; i < this.voSwarmGMM.Count; i++)
{
this.voStrSwarmGMM.Add(String.Format("K = {0}", this.voSwarmGMM[i].ViK));
}
this.voResult.DataSource = null;
this.voResult.DataSource = this.voStrSwarmGMM;
}
public double prepareGMMND(int clus, int dim, Matrix X, Particle pt)
{
myGMM = new GMM_NDim(clus, dim, X);
//One mu for each dimenssion
//The length og mu is the number of clusters
int[] xMu = new int[clus];
int[] yMu = new int[clus];
for (int k = 0; k < clus; k++)
{
xMu[k] = (int)pt.Xx[k];
yMu[k] = (int)pt.Xy[k];
}
myGMM.ComputeGMM_ND(xMu, yMu);
double[] Xsi = new double[clus];
double[] Ysi = new double[clus];
for (int k = 0; k < clus; k++)
{
Xsi[k] = Math.Sqrt(Math.Abs(myGMM.sigma[k][0, 0]));
Ysi[k] = Math.Sqrt(Math.Abs(myGMM.sigma[k][0, 1]));
}
int[] particlesInCLuster = new int[clus];
//DETERMINE WHAT CLUSTER EACH POINT BELONGS TO
// determine class membership i.e., which point belongs to which cluster
PList = new List <MyPoint>();
for (int i = 0; i < X.Rows; i++) //looping throught all the rows = #points of Gamma -- each col = probability for each cluster
{
// Gamma matrix has the probabilities for a data point for its membership in each cluster
double[] probabs = new double[clus];
int cnum = 0;
double maxprobab = myGMM.Gamma[i, 0];
for (int m = 0; m < clus; m++) // Going throught the cols --- each col has probability for one cluster
{
if (myGMM.Gamma[i, m] > maxprobab) //checking for the cluster with more probability
{
cnum = m; // data i belongs to cluster m
maxprobab = myGMM.Gamma[i, m];
}
}
MyPoint pnt = new MyPoint(X[i, 0], X[i, 1], cnum);
PList.Add(pnt);
}
double fValue = FunctionToSolve(Xsi, Ysi, particlesInCLuster);
return(fValue);
}
private void btnGMMImage_Click(object sender, EventArgs e)
{
pic1.Load("Tennis Ball 620.jpg");
bmp = new Bitmap("Tennis Ball 620.jpg");
imageDataList = new List <ImagePoints>(bmp.Width * bmp.Height);
int k = int.Parse(txtNumClusters.Text);
int dim = 3;
imageDataList = InitializeImageData(bmp, k);
Matrix imgMatrix = new Matrix(bmp.Width * bmp.Height, dim);
for (int i = 0; i < (bmp.Width * bmp.Height); i++)
{
imgMatrix[i, 0] = imageDataList[i].red;
imgMatrix[i, 1] = imageDataList[i].green;
imgMatrix[i, 2] = imageDataList[i].blue;
}
GMM_NDim gmmnd = new GMM_NDim(k, dim, imgMatrix, bmp.Width * bmp.Height);
gmmnd.ComputeGMM_ND();
// determine class membership i.e., which point belongs to which cluster
imageDataList = new List <ImagePoints>();
for (int i = 0; i < imgMatrix.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];
}
}
ImagePoints imgpt = new ImagePoints {
clusterID = cnum, red = gmmnd.X[i, 0], green = gmmnd.X[i, 1], blue = gmmnd.X[i, 2]
};
imageDataList.Add(imgpt);
}
//MyImageProc.DrawClusters(pic1, imageDataList, 1, k);
//redraw image from rgb values
MessageBox.Show("img list len = " + imageDataList.Count.ToString() + " img width = " + bmp.Width.ToString() + " img height = " + bmp.Height.ToString());
//MessageBox.Show("new image done");
}
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);
}
//CREATING CLUSTERS AND SHOWING MEANS FOR EACH CLUSTER
private void btnCompute_Click(object sender, EventArgs e)
{
if (uploaded == true)
{
Stopwatch sw = new Stopwatch();
try
{
k = int.Parse(txtClusters.Text); //getting number of clusters
}
catch
{
MessageBox.Show("Wrong input.");
}
//sw.Start();
data = new DataPoints(picture, picture.Width, picture.Height);
PList = new List <MyPoint>(data.CreatePoints());
X = new Matrix(data_Size, dimenssions);
for (int i = 0; i < PList.Count; i++)
{
X[i, 0] = PList[i].Red;
X[i, 1] = PList[i].Green;
X[i, 2] = PList[i].Blue;
}
myGmm = new GMM_NDim(k, dimenssions, X);
sw.Start();
myGmm.ComputeGMM_ND();
sw.Stop();
MessageBox.Show("Time elapsed in ms: " + sw.ElapsedMilliseconds);
// determine class membership i.e., which point belongs to which cluster
PList = new List <MyPoint>();
for (int i = 0; i < X.Rows; i++) //looping throught all the rows = #points of Gamma -- each col = probability for each cluster
{
// 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 = myGmm.Gamma[i, 0];
for (int m = 0; m < k; m++) // Going throught the cols --- each col has probability for one cluster
{
if (myGmm.Gamma[i, m] > maxprobab) //checking for the cluster with more probability
{
cnum = m; // data i belongs to cluster m
maxprobab = myGmm.Gamma[i, m];
}
}
MyPoint pt = new MyPoint(X[i, 0], X[i, 1], X[i, 2], cnum); //X[i,0] = RED X[i,1] = GREEN X[i,2] = BLUE
PList.Add(pt);
}
computed = true;
showMeans();
}
else
{
MessageBox.Show("You need to upload an image first.");
}
}
