public void ThreadProc()
{
TrainSelfOrganizingMap train = new TrainSelfOrganizingMap(
this.network, this.trainingSet, TrainSelfOrganizingMap.LearningMethod.SUBTRACTIVE, 0.5);
int tries = 1;
do
{
train.Iteration();
this.txtTries.Text = "" + tries;
this.txtBestError.Text = "" + train.BestError;
this.txtLastError.Text = "" + train.TotalError;
tries++;
Application.DoEvents();
} while (train.TotalError > 0.01 && (tries <= 100));
MessageBox.Show("Training complete.");
}
void ThreadProc()
{
Random rand = new Random();
// build the training set
this.input = new double[SAMPLE_COUNT][];
for (int i = 0; i < SAMPLE_COUNT; i++) {
this.input[i] = new double[INPUT_COUNT];
for (int j = 0; j < INPUT_COUNT; j++) {
this.input[i][j] = rand.NextDouble();
}
}
// build and train the neural network
this.net = new SelfOrganizingMap(INPUT_COUNT, OUTPUT_COUNT,
NormalizationType.MULTIPLICATIVE);
TrainSelfOrganizingMap train = new TrainSelfOrganizingMap(
this.net, this.input, TrainSelfOrganizingMap.LearningMethod.SUBTRACTIVE, 0.5);
train.Initialize();
double lastError = Double.MaxValue;
int errorCount = 0;
while (errorCount < 10) {
train.Iteration();
this.retry++;
this.totalError = train.TotalError;
this.bestError = train.BestError;
this.Invalidate();
if (this.bestError < lastError) {
lastError = this.bestError;
errorCount = 0;
} else {
errorCount++;
}
}
}