| public SetAutoDecay ( int plannedIterations, double startRate, double endRate, double startRadius, double endRadius ) : void | ||
| plannedIterations | int | |
| startRate | double | The starting learning rate. |
| endRate | double | The ending learning rate. |
| startRadius | double | The starting radius. |
| endRadius | double | The ending radius. |
| return | void | |
public void SetAutoDecay(int plannedIterations,
double startRate, double endRate,
double startRadius, double endRadius)
{
_startRate = startRate;
_endRate = endRate;
_startRadius = startRadius;
_endRadius = endRadius;
_autoDecayRadius = (endRadius - startRadius)/plannedIterations;
_autoDecayRate = (endRate - startRate)/plannedIterations;
SetParams(_startRate, _startRadius);
}
public SOMColors()
{
InitializeComponent();
network = CreateNetwork();
gaussian = new NeighborhoodRBF(RBFEnum.Gaussian, WIDTH, HEIGHT);
train = new BasicTrainSOM(network, 0.01, null, gaussian);
train.ForceWinner = false;
samples = new List<IMLData>();
for (int i = 0; i < 15; i++)
{
IMLData data = new BasicMLData(3);
data.Data[0] = RangeRandomizer.Randomize(-1, 1);
data.Data[1] = RangeRandomizer.Randomize(-1, 1);
data.Data[2] = RangeRandomizer.Randomize(-1, 1);
samples.Add(data);
}
train.SetAutoDecay(100, 0.8, 0.003, 30, 5);
}
