private void buttonClusterFeeds_Click(object sender, EventArgs e)
{
if (_feedData == null)
{
XtraMessageBox.Show("There is no data create cluster!", "Uh Oh!", MessageBoxButtons.OK, MessageBoxIcon.Warning);
return;
}
Cursor = Cursors.WaitCursor;
int g = (int)spinFeedClusterCount.Value;
var kmeans = new KMeans <Feed>();
var grouping = kmeans.Generate(_feedData, g);
_feedDescription = kmeans.Description;
for (int i = 0; i < grouping.Length; i++)
{
_feedData[i].Cluster = grouping[i] + 1;
}
gridFeeds.DataSource = _feedData;
gridFeeds.RefreshDataSource();
Cursor = Cursors.Default;
MessageBox.Show("Done!", "Result", MessageBoxButtons.OK, MessageBoxIcon.Information);
}
public void Test_Feed_KMeans()
{
int groups = 4;
Feed[] feeds = Feed.GetData();
Descriptor descriptor = Descriptor.Create <Feed>();
KMeans kmeans = new KMeans();
kmeans.Descriptor = descriptor;
int[] grouping = kmeans.Generate(feeds, groups, new CosineDistance());
for (int i = 0; i < grouping.Length; i++)
{
feeds[i].Cluster = grouping[i];
}
}
public void Test_Object_KMeans(int size)
{
Matrix X = GenerateData(size);
var objects = X.GetRows()
.Select(v => new AB {
A = v[0], B = v[1]
})
.ToArray();
var descriptor = Descriptor.Create <AB>();
KMeans model = new KMeans();
var clusters = model.Generate(descriptor, objects, 2, new EuclidianDistance());
Assert.Equal(2, clusters.Children.Length);
Assert.Equal(size, clusters[0].Members.Length);
Assert.Equal(size, clusters[1].Members.Length);
}
public void Test_Numerical_KMeans(int size)
{
Matrix X = GenerateData(size);
KMeans model = new KMeans();
var assignment = model.Generate(X, 2, new EuclidianDistance());
Assert.Equal(size * 2, assignment.Length);
var a1 = assignment.First();
var a2 = assignment.Last();
for (int i = 0; i < size * 2; i++)
{
if (i < size)
{
Assert.Equal(a1, assignment[i]);
}
else
{
Assert.Equal(a2, assignment[i]);
}
}
}
private void buttonClusteStudents_Click(object sender, EventArgs e)
{
if (_data == null)
{
XtraMessageBox.Show("There is no data create cluster!", "Uh Oh!", MessageBoxButtons.OK, MessageBoxIcon.Warning);
return;
}
int g = (int)spinClusterCount.Value;
var kmeans = new KMeans <Student>();
var grouping = kmeans.Generate(_data, g);
for (int i = 0; i < grouping.Length; i++)
{
_data[i].Cluster = grouping[i] + 1;
}
gridStudent.DataSource = _data;
gridStudent.RefreshDataSource();
MessageBox.Show("Done!", "Result", MessageBoxButtons.OK, MessageBoxIcon.Information);
}
