private void PointsСlassification()
{
for (int i = 0; i < class_count; i++)
{
OldKernels[i] = NewKernels[i];
CountPointsInClasses[i] = 0;
}
for (int i = 0; i < points_count; i++)
{
ClassDistance[] distances = new ClassDistance[class_count];
ClassDistance min;
for (int j = 0; j < class_count; j++)
{
distances[j].Class = j;
distances[j].distance = EvklidDistance(Points[i], NewKernels[j]);
}
min.Class = distances[0].Class;
min.distance = distances[0].distance;
for (int k = 1; k < class_count; k++)
{
if (distances[k].distance < min.distance)
{
min.Class = distances[k].Class;
min.distance = distances[k].distance;
}
}
Points[i].Class = min.Class;
CountPointsInClasses[Points[i].Class]++;
}
}
private void button3_Click(object sender, EventArgs e)
{
int step = 0;
DateTime start_time = DateTime.Now;
bool is_class_change = true;
string caption = "Найдены наилучшие ядра", message;
while (is_class_change)
{
point[,] PointsInClasses = new point[class_count, MAX_POINTS_COUNT];
int[] CountPointsInClasses = new int[class_count];
step++;
is_class_change = false;
for (int i = 0; i < class_count; i++)
{
OldKernels[i] = NewKernels[i];
CountPointsInClasses[i] = 0;
}
for (int i = 0; i < points_count; i++) //Points classification
{
ClassDistance[] distances = new ClassDistance[class_count];
ClassDistance min;
for (int j = 0; j < class_count; j++)
{
distances[j].Class = j;
distances[j].distance = EvklidDistance(Points[i], NewKernels[j]);
}
min.Class = distances[0].Class;
min.distance = distances[0].distance;
for (int k = 1; k < class_count; k++)
{
if (distances[k].distance < min.distance)
{
min.Class = distances[k].Class;
min.distance = distances[k].distance;
}
}
Points[i].Class = min.Class;
CountPointsInClasses[Points[i].Class]++;
}
for (int i = 0; i < class_count; i++)
{
dataGridView1.Rows[i + 1].Cells[1].Value = CountPointsInClasses[i];
}
for (int i = 0; i < class_count; i++) //Points distribution into classes
{
int TempCountPoints = 0;
for (int j = 0; j < points_count; j++)
{
if (Points[j].Class == i)
{
PointsInClasses[i, TempCountPoints] = Points[j];
TempCountPoints++;
}
}
}
for (int i = 0; i < class_count; i++) //Search best kernels
{
double MinAverageSquareDistance = MINAVERAGESQUAREDISTANCE;
int number = -1;
for (int j = 0; j < min(CountPointsInClasses[i], PRECISION1); j++)
{
double SumSquareDistance = 0, TempAverageSquareDistances;
for (int k = 0; k < min(CountPointsInClasses[i], PRECISION2); k++)
{
SumSquareDistance += Math.Pow(EvklidDistance(PointsInClasses[i, j],
PointsInClasses[i, k]), 2);
}
TempAverageSquareDistances = Math.Sqrt(SumSquareDistance / CountPointsInClasses[i]);
if (TempAverageSquareDistances < MinAverageSquareDistance) //Best kernel was found
{
MinAverageSquareDistance = TempAverageSquareDistances;
number = j;
}
}
if (number != -1)
{
NewKernels[i] = PointsInClasses[i, number];
}
}
for (int i = 0; i < class_count; i++)
{
if ((OldKernels[i].x != NewKernels[i].x) || (OldKernels[i].y != NewKernels[i].y))
{
is_class_change = true;
}
}
ClearImage(); //Draw points with new kernels
for (int i = 0; i < points_count; i++)
{
bufferedGraphics.Graphics.FillRectangle(new SolidBrush(Colors[Points[i].Class]),
Points[i].x, Points[i].y, POINTS_WIDTH, POINTS_WIDTH);
}
for (int i = 0; i < class_count; i++)
{
bufferedGraphics.Graphics.FillEllipse(new SolidBrush(Colors[NewKernels[i].Class]),
NewKernels[i].x, NewKernels[i].y, KERNELS_WIDTH, KERNELS_WIDTH);
}
bufferedGraphics.Render();
}
DateTime end_time = DateTime.Now;
message = caption + " за " + (end_time - start_time).Minutes.ToString() + " мин " +
(end_time - start_time).Seconds.ToString() + " с " +
(end_time - start_time).Milliseconds.ToString() + " мс.";
caption += '!';
MessageBox.Show(message, caption, MessageBoxButtons.OK, MessageBoxIcon.Information);
}
