public SOMColors()
{
InitializeComponent();
network = CreateNetwork();
gaussian = new NeighborhoodRBF(RBFEnum.Gaussian, WIDTH, HEIGHT);
train = new BasicTrainSOM(network, 0.01, null, gaussian);
train.ForceWinner = false;
samples = AIFH.Alloc2D <double>(15, 3);
for (int i = 0; i < 15; i++)
{
samples[i][0] = rnd.NextDouble(-1, 1);
samples[i][1] = rnd.NextDouble(-1, 1);
samples[i][2] = rnd.NextDouble(-1, 1);
}
train.SetAutoDecay(100, 0.8, 0.003, 30, 5);
}
public void CreateCities()
{
double x1, x2, y1, y2;
double alpha1, alpha2;
distance = AIFH.Alloc2D <double>(NUM_CITIES, NUM_CITIES);
for (var n1 = 0; n1 < NUM_CITIES; n1++)
{
for (var n2 = 0; n2 < NUM_CITIES; n2++)
{
alpha1 = (double)n1 / NUM_CITIES * 2 * Math.PI;
alpha2 = (double)n2 / NUM_CITIES * 2 * Math.PI;
x1 = Math.Cos(alpha1);
y1 = Math.Sin(alpha1);
x2 = Math.Cos(alpha2);
y2 = Math.Sin(alpha2);
distance[n1][n2] = Math.Sqrt(sqr(x1 - x2) + sqr(y1 - y2));
}
}
}
/// <summary>
/// Construct the trainer.
/// </summary>
/// <param name="theNetwork">The network to train.</param>
public TrainHopfieldStorkey(HopfieldNetwork theNetwork)
{
_network = theNetwork;
_sumMatrix = AIFH.Alloc2D <double>(_network.InputCount, _network.InputCount);
}
/// <summary>
/// Construct the layer.
/// </summary>
/// <param name="theOwner">The network that owns this layer.</param>
/// <param name="inputCount">The input count for this layer.</param>
/// <param name="outputCount">The output count for this layer.</param>
public DeepLayer(DeepBeliefNetwork theOwner, int inputCount, int outputCount)
{
_weights = AIFH.Alloc2D <double>(outputCount, inputCount);
_bias = new double[outputCount];
_owner = theOwner;
}
