private void button1_Click_1(object sender, EventArgs e)
{
chart1.Series.Clear();
Dictionary <int, UserForKMeansWine> users = Clustering.parseintousers(textBoxFilename.Text);
Tuple <float, List <Cluster> > results = Clustering.kmeans((int)numericUpDown2.Value, (int)numericUpDown1.Value, users);
int currentcluster = 1;
foreach (Cluster cluster in results.Item2)
{
string clustername = "cluster " + currentcluster;
chart1.Series.Add(clustername);
cluster.countamountofoffers().ToList().ForEach(x => chart1.Series[clustername].Points.AddY(x));
string[] views = cluster.listofclustermember();
listView1.Items.Add("Cluster " + currentcluster + ":");
foreach (string view in views)
{
listView1.Items.Add("user " + view + " part of cluster " + currentcluster);
}
currentcluster++;
}
}
public float distancebetweenthisanduser(UserForKMeansWine user2)
{
float[] set1 = new float[32];
float[] set2 = new float[32];
foreach (KeyValuePair <int, Boolean> transaction in transactions)
{
set1[transaction.Key] = Convert.ToInt16(transactions[transaction.Key]);
set2[transaction.Key] = Convert.ToInt16(user2.transactions[transaction.Key]);
}
return(Clustering.calculateEuclidianDistance(set1, set2));
}
private void button1_Click_2(object sender, EventArgs e)
{
float[] set1 = { 6f, 4f };
float[] set2 = { 3f, 8f };
listView1.Items.Add(new ListViewItem(Clustering.calculateEuclidianDistance(set1, set2).ToString()));
}
