public clustercs(minuim__spaining tree, int cluster, int size)
{
cluster_number = cluster;
mst_tree = tree;
removed_node = new int [2 * cluster_number];
clust_arr = new Tuple <int, int, int> [cluster_number];
visted = new int[size];
vertices = size;
for (int i = 0; i < size; i++)
{
if (i < cluster_number)
{
clust_arr[i] = new Tuple <int, int, int>(0, 0, 0);
}
visted[i] = -1;
}
}
private void btnGaussSmooth_Click(object sender, EventArgs e)
{
Graph g = new Graph();
int num_clust = 0;
List <RGBPixel> Distinct = new List <RGBPixel> (width * hight);
num_clust = Convert.ToInt32(textBox1.Text);
Distinct = g.distinct_arry(ImageMatrix, width, hight);
textBox3.Text = Distinct.Count.ToString();
tree = new minuim__spaining(Distinct, num_clust);
tree.prim(0);
textBox2.Text = tree.minumcost.ToString();
k = new clustercs(tree, num_clust, Distinct.Count);
k.constrct_clust();
ImageMatrix = k.Replace(ImageMatrix, width, hight, g.size);
ImageOperations.DisplayImage(ImageMatrix, pictureBox2);
}
public int automatic_cluster(minuim__spaining tree)
{
double sub = 1;
double avg;
int kluster = 1;
while (sub >= 0.0001 && kluster <= mst_tree.bonus.Count)
{
avg = tree.bonus.Average();
segma = 0;
foreach (double i in tree.bonus)
{
double temp = i - avg;
temp = temp * temp;
segma += temp;
}
segma = segma / avg;
segma = Math.Sqrt(segma);
double val1 = Math.Abs(segma - tree.bonus.Max);
double val2 = Math.Abs(segma - tree.bonus.Min);
double max = tree.bonus.Max;
double min = tree.bonus.Min;
if (val1 > val2)
{
tree.bonus.Remove(max);
}
else
{
tree.bonus.Remove(min);
}
sub = Math.Abs(segma - last_segma);
last_segma = sub;
kluster++;
}
return(kluster);
}
