public MyLearning()
{
I = this;
_form.Show();
_Learning = new KLearning((int)INPUT.MAX, 100, 100);
}
// Worker thread
void SearchSolution()
{
KLearnData[] data = KNeural.GetLearnData();
_trainer = new KLearning(data[0]._Input.Length, 100, 100);
// input
double[] input = new double[3];
double fixedLearningRate = learningRate / 10;
double driftingLearningRate = fixedLearningRate * 9;
// iterations
int i = 0;
// loop
//while (!needToStop)
foreach(KLearnData d in data)
{
_trainer._LearningRate = driftingLearningRate * (iterations - i) / iterations + fixedLearningRate;
_trainer._LearningRadius = (double)radius * (iterations - i) / iterations;
input[0] = rand.Next(256);
input[1] = rand.Next(256);
input[2] = rand.Next(256);
//trainer.RunEpoch(
_trainer.Run(d);
// update map once per 50 iterations
//if ((i % 10) == 9)
//{
// UpdateMap();
//}
// increase current iteration
// i++;
// set current iteration's info
//currentIterationBox.Text = i.ToString();
//// stop ?
//if (i >= iterations)
// break;
}
// enable settings controls
//EnableControls(true);
}