public void ANN()
{
//var dbn = new DeepBeliefNetwork(new Accord.Neuro.ActivationFunctions.GaussianFunction(), 1, new int[] { 3, 5, 5, 10, 3 });
var dbn = DeepBeliefNetwork.CreateGaussianBernoulli(50, new int[] { 3, 5, 5, 10, 3 });
var dblNode = new UnsupervisedLearning(new DeepBeliefNetworkLearning(dbn) {
Algorithm = (h, v, i) => new ContrastiveDivergenceLearning(h,v)
{
LearningRate = 0.1,
Momentum = 0.5,
Decay = 0.001
}
});
var cout = new ConsoleOut("ANNOut.txt");
dbn.UpdateVisibleWeights();
dblNode.Process.LinkTo(cout.Output);
var data = new double[1000000][];
for (int i = 0; i < 1000000; i++)
{
var curr = new double[50];
for (int j = 0; j < 50; j++)
{
curr[j] = Math.Sin((double)j);
}
data[i] = curr;
}
dblNode.Process.Post(new MachineLearningData<double[][], double>(data));
System.Threading.Thread.Sleep(1000);
}
public void ClusterAlg()
{
var data = new double[10][];
for (int i = 0; i < 10; i++)
{
var curr = new double[3];
for (int j = 0; j < 3; j++)
{
curr[j] = Math.Sin((double)j);
}
data[i] = curr;
}
var cout = new ConsoleOut("Temp.txt");
var kNNNode = new KNearestNeighbors(3, data, new int[10] { 0, 1, 2, 3, 4, 5, 6, 7, 8, 9 });
kNNNode.Process.LinkTo(cout.Output);
kNNNode.Process.Post(new MachineLearningData<double[], int>(new double[] { 0, 1, 2 }));
System.Threading.Thread.Sleep(1000);
}
