public static void GoOverfit()
{
PointsGeneration.PointGeneration form = new PointsGeneration.PointGeneration();
form.Hide();
form.GenerateLinearSeparable();
MultidimensionalPoint[] testArray = new MultidimensionalPoint[100];
MultidimensionalPoint[] points = PointsGeneration.PointGeneration.points;
Random rand = new Random();
for (int i = 0; i < 5; ++i)
{
for (int j = 0; j < 20; ++j)
{
testArray[i * 20 + j] = new MultidimensionalPoint(2, 0);
do
{
testArray[i * 20 + j].coordinates[0] = points[i * 100].coordinates[0] + rand.NextDouble() * points[i * 100].radius * 1.5;
testArray[i * 20 + j].coordinates[1] = points[i * 100].coordinates[1] + rand.NextDouble() * points[i * 100].radius * 1.5;
}while(form.Distance(testArray[i * 20 + j], points[i * 100]) > points[i * 100].radius &&
form.Distance(testArray[i * 20 + j], points[i * 100]) < points[i * 100].radius * 1.5);
testArray[i * 20 + j].pointClass = i;
}
}
NeuralNetwork nn = new NeuralNetwork(points, new int[4] {
2, 15, 15, 15
}, 1, 0, 0.7, 0.01, 2, 1, 0.1);
nn.TrainNetwork(150, testArray);
}
private void start_Click(object sender, EventArgs e)
{
InitializeNetwork();
NeurNet.TrainNetwork(Convert.ToInt32(epochsNumber.Text), null);
NeurNet.WriteClassifiedResult(NeurNet.data);
}