private Image <Bgr, Byte> kmeans()
{
int trainSampleCount = 1500;
int sigma = 60;
Matrix <float> trainData = new Matrix <float>(trainSampleCount, 2);
Matrix <float> trainData1 = trainData.GetRows(0, trainSampleCount / 3, 1);
trainData1.GetCols(0, 1).SetRandNormal(new MCvScalar(100), new MCvScalar(sigma));
trainData1.GetCols(1, 2).SetRandNormal(new MCvScalar(300), new MCvScalar(sigma));
Matrix <float> trainData2 = trainData.GetRows(trainSampleCount / 3, 2 * trainSampleCount / 3, 1);
trainData2.SetRandNormal(new MCvScalar(400), new MCvScalar(sigma));
Matrix <float> trainData3 = trainData.GetRows(2 * trainSampleCount / 3, trainSampleCount, 1);
trainData3.GetCols(0, 1).SetRandNormal(new MCvScalar(300), new MCvScalar(sigma));
trainData3.GetCols(1, 2).SetRandNormal(new MCvScalar(100), new MCvScalar(sigma));
PointF[] points = new PointF[trainSampleCount];
for (int i = 0; i < points.Length; ++i)
{
points[i] = new PointF(trainData[i, 0], trainData[i, 1]);
}
var km = new KMeans <PointF>(points, 3,
(a, b) => ((a.X - b.X) * (a.X - b.X) + (a.Y - b.Y) * (a.Y - b.Y)),
list => new PointF(list.Average(p => p.X), list.Average(p => p.Y))
);
int it = 0;
MyTimer timer = new MyTimer();
timer.Restart();
//var cluster = km.Cluster();
var cluster = km.AnnealCluster(
(a, b) => new PointF(a.X + b.X, a.Y + b.Y),
(a, b) => new PointF(a.X - b.X, a.Y - b.Y),
(p, v) => new PointF((float)(p.X / v), (float)(p.Y / v)),
out it);
var time = timer.Stop();
this.Text = String.Format("n={0}, k={1}, time={2}ms, iter={3}.", trainSampleCount, 3, time, it);
Image <Bgr, Byte> img = new Image <Bgr, byte>(500, 500);
for (int y = 0; y < 500; ++y)
{
for (int x = 0; x < 500; ++x)
{
double d0 = (x - cluster[0].Center.X) * (x - cluster[0].Center.X)
+ (y - cluster[0].Center.Y) * (y - cluster[0].Center.Y);
double d1 = (x - cluster[1].Center.X) * (x - cluster[1].Center.X)
+ (y - cluster[1].Center.Y) * (y - cluster[1].Center.Y);
double d2 = (x - cluster[2].Center.X) * (x - cluster[2].Center.X)
+ (y - cluster[2].Center.Y) * (y - cluster[2].Center.Y);
Bgr color = new Bgr(0, 0, 0);
if (d0 < d1 && d0 < d2)
{
color = new Bgr(20, 0, 0);
}
if (d1 < d0 && d1 < d2)
{
color = new Bgr(0, 20, 0);
}
if (d2 < d0 && d2 < d1)
{
color = new Bgr(0, 0, 20);
}
img[y, x] = color;
}
}
Bgr[] colors = new[] { new Bgr(128, 0, 0), new Bgr(0, 128, 0), new Bgr(0, 0, 128) };
Bgr[] centers = new[] { new Bgr(255, 0, 0), new Bgr(0, 255, 0), new Bgr(0, 0, 255) };
for (int i = 0; i < 3; ++i)
{
foreach (var p in cluster[i])
{
img.Draw(new CircleF(p, 2), colors[i], 1);
}
img.Draw(new CircleF(cluster[i].Center, 5), centers[i], 3);
}
img.Draw(new CircleF(new PointF(100, 300), sigma), new Bgr(128, 128, 128), 2);
img.Draw(new CircleF(new PointF(100, 300), 3), new Bgr(128, 128, 128), 2);
img.Draw(new CircleF(new PointF(300, 100), sigma), new Bgr(128, 128, 128), 2);
img.Draw(new CircleF(new PointF(300, 100), 3), new Bgr(128, 128, 128), 2);
img.Draw(new CircleF(new PointF(400, 400), sigma), new Bgr(128, 128, 128), 2);
img.Draw(new CircleF(new PointF(400, 400), 3), new Bgr(128, 128, 128), 2);
return(img);
}
private Image<Bgr, Byte> kmeans()
{
int trainSampleCount = 1500;
int sigma = 60;
Matrix<float> trainData = new Matrix<float>(trainSampleCount, 2);
Matrix<float> trainData1 = trainData.GetRows(0, trainSampleCount / 3, 1);
trainData1.GetCols(0, 1).SetRandNormal(new MCvScalar(100), new MCvScalar(sigma));
trainData1.GetCols(1, 2).SetRandNormal(new MCvScalar(300), new MCvScalar(sigma));
Matrix<float> trainData2 = trainData.GetRows(trainSampleCount / 3, 2 * trainSampleCount / 3, 1);
trainData2.SetRandNormal(new MCvScalar(400), new MCvScalar(sigma));
Matrix<float> trainData3 = trainData.GetRows(2 * trainSampleCount / 3, trainSampleCount, 1);
trainData3.GetCols(0, 1).SetRandNormal(new MCvScalar(300), new MCvScalar(sigma));
trainData3.GetCols(1, 2).SetRandNormal(new MCvScalar(100), new MCvScalar(sigma));
PointF[] points = new PointF[trainSampleCount];
for (int i = 0; i < points.Length; ++i) {
points[i] = new PointF(trainData[i, 0], trainData[i, 1]);
}
var km = new KMeans<PointF>(points, 3,
(a, b) => ((a.X - b.X) * (a.X - b.X) + (a.Y - b.Y) * (a.Y - b.Y)),
list => new PointF(list.Average(p => p.X), list.Average(p => p.Y))
);
int it = 0;
MyTimer timer = new MyTimer();
timer.Restart();
//var cluster = km.Cluster();
var cluster = km.AnnealCluster(
(a, b) => new PointF(a.X + b.X, a.Y + b.Y),
(a, b) => new PointF(a.X - b.X, a.Y - b.Y),
(p, v) => new PointF((float)(p.X / v), (float)(p.Y / v)),
out it);
var time = timer.Stop();
this.Text = String.Format("n={0}, k={1}, time={2}ms, iter={3}.", trainSampleCount, 3, time, it);
Image<Bgr, Byte> img = new Image<Bgr, byte>(500, 500);
for (int y = 0; y < 500; ++y) {
for (int x = 0; x < 500; ++x) {
double d0 = (x - cluster[0].Center.X) * (x - cluster[0].Center.X)
+ (y - cluster[0].Center.Y) * (y - cluster[0].Center.Y);
double d1 = (x - cluster[1].Center.X) * (x - cluster[1].Center.X)
+ (y - cluster[1].Center.Y) * (y - cluster[1].Center.Y);
double d2 = (x - cluster[2].Center.X) * (x - cluster[2].Center.X)
+ (y - cluster[2].Center.Y) * (y - cluster[2].Center.Y);
Bgr color = new Bgr(0, 0, 0);
if (d0 < d1 && d0 < d2) {
color = new Bgr(20, 0, 0);
}
if (d1 < d0 && d1 < d2) {
color = new Bgr(0, 20, 0);
}
if (d2 < d0 && d2 < d1) {
color = new Bgr(0, 0, 20);
}
img[y, x] = color;
}
}
Bgr[] colors = new[] { new Bgr(128, 0, 0), new Bgr(0, 128, 0), new Bgr(0, 0, 128) };
Bgr[] centers = new[] { new Bgr(255, 0, 0), new Bgr(0, 255, 0), new Bgr(0, 0, 255) };
for (int i = 0; i < 3; ++i) {
foreach (var p in cluster[i]) {
img.Draw(new CircleF(p, 2), colors[i], 1);
}
img.Draw(new CircleF(cluster[i].Center, 5), centers[i], 3);
}
img.Draw(new CircleF(new PointF(100, 300), sigma), new Bgr(128, 128, 128), 2);
img.Draw(new CircleF(new PointF(100, 300), 3), new Bgr(128, 128, 128), 2);
img.Draw(new CircleF(new PointF(300, 100), sigma), new Bgr(128, 128, 128), 2);
img.Draw(new CircleF(new PointF(300, 100), 3), new Bgr(128, 128, 128), 2);
img.Draw(new CircleF(new PointF(400, 400), sigma), new Bgr(128, 128, 128), 2);
img.Draw(new CircleF(new PointF(400, 400), 3), new Bgr(128, 128, 128), 2);
return img;
}
