private void Train()
{
int numOfLayers = (int)MLNumOfLayersUpDown.Value;
double learningRate = (double)MPLearningRateUpDown.Value;
List <int> numOfNeuronsPerLayer = new List <int> ();
int numOfEpocks = (int)MLNumOfEpocksUpDown.Value;
LearningAlgorithm backpropagation = new Backpropagation();
for (int i = 0; i < numOfLayers; ++i)
{
numOfNeuronsPerLayer.Add((i == 0) ? trainingSamples[0].Count : (i == numOfLayers - 1) ? 3 : int.Parse(NeuronsInHLayers[1, i - 1].Value.ToString()));
}
this.neuralNetwork = new FeedforwardNeuralNetwrok(numOfLayers);
this.neuralNetwork.setNetwork(numOfNeuronsPerLayer);
for (int i = 1; i < numOfLayers; ++i)
{
this.neuralNetwork.setLayer(i, new SigmoidFunction());
}
for (int i = 0; i < numOfEpocks; ++i)
{
this.neuralNetwork.train(trainingSamples, trainingLabels, learningRate, backpropagation);
}
}
public RBF(int K, int numOfEpocks, double learningRate, double maxError, List <List <double> > trainingSamples, List <List <double> > trainingLabels, List <List <double> > testSamples, List <List <double> > testLabels)
{
this.numOfEpocks = numOfEpocks;
this.trainingSamples = trainingSamples;
this.trainingLabels = trainingLabels;
this.testSamples = testSamples;
this.testLabels = testLabels;
this.maxError = maxError;
this.learningRate = learningRate;
Centroids = new List <List <double> >(); //[NumOfCluster][NumOfFeatures (means)]
Random rnd = new Random(Guid.NewGuid().GetHashCode());
int counter = K; // Num of Clusters
while (counter-- > 0)
{
int index = rnd.Next(trainingSamples.Count);
Centroids.Add(trainingSamples[index]);
}
KMean = new K_Means(ref Centroids, trainingSamples);
Variance = KMean.GetVariance;
////
////
List <int> numOfNeuronsPerLayer = new List <int>();
backpropagation = new Backpropagation();
numOfNeuronsPerLayer.Add(K);
numOfNeuronsPerLayer.Add(3);
this.neuralNetwork = new FeedforwardNeuralNetwrok(2);
this.neuralNetwork.setNetwork(numOfNeuronsPerLayer);
this.neuralNetwork.LMSsetLayer(1, new IdentityFunction());
}
