void Solve() { #region prepare and assign trainingSet.Clear(); for (int i = 0; i < trainVectorCount; i++) { List <double> dl = new List <double>(); for (int j = 0; j < trainVectorDimension; j++) { dl.Add(trainVectors[i][j]); } trainingSet.Add(new TrainingSample(dl.ToArray())); } /// process /// start learning /// get learning radius for neighborhood function int learningRadius = 0; for (int i = 0; i < dimension; i++) { if (size[i] > learningRadius) { learningRadius = size[i]; } } learningRadius /= 2; INeighborhoodFunction neighborhoodFunction = new GaussianFunction(learningRadius, netUP.neighborDistance) as INeighborhoodFunction; if (neighborhood) { neighborhoodFunction = new MexicanHatFunction(learningRadius) as INeighborhoodFunction; } LatticeTopology topology = LatticeTopology.Rectangular; if (latticeTopology) { topology = LatticeTopology.Hexagonal; } /// instantiate relevant network layers KohonenLayer inputLayer = new KohonenLayer(trainVectorDimension); KohonenLayerND outputLayer = new KohonenLayerND(size, neighborhoodFunction, topology); KohonenConnectorND connector = new KohonenConnectorND(inputLayer, outputLayer, netUP.initialNodes); if (netUP.initialNodes.Length != 0) { connector.Initializer = new GivenInput(netUP.initialNodes); } else { connector.Initializer = new RandomFunction(0.0, 1.0); } outputLayer.SetLearningRate(learningRate, 0.05d); outputLayer.IsDimensionCircular = isDimensionCircular; network = new KohonenNetworkND(inputLayer, outputLayer); network.useRandomTrainingOrder = randomTrainingOrder; inputLayer.ParallelComputation = false; outputLayer.ParallelComputation = parallelComputing; #endregion #region delegates network.BeginEpochEvent += new TrainingEpochEventHandler( delegate(object senderNetwork, TrainingEpochEventArgs args) { #region trainingCylce if (network == null || !GO) { return; } trainedVectors = new double[outputLayer.neuronCount, trainVectorDimension]; for (int i = 0; i < outputLayer.neuronCount; i++) { IList <ISynapse> synapses = (network.OutputLayer as KohonenLayerND)[outputLayer.adressBook[i]].SourceSynapses; for (int j = 0; j < trainVectorDimension; j++) { trainedVectors[i, j] = synapses[j].Weight; } } //make new net here netP = new CrowNetSOMNDP(size, isDimensionCircular, latticeTopology, neighborhood, trainedVectors, outputLayer.adressBook); counter++; #endregion }); network.EndSampleEvent += new TrainingSampleEventHandler( delegate(object senderNetwork, TrainingSampleEventArgs args) { netP.winner = outputLayer.WinnerND.CoordinateND; }); #endregion network.Learn(trainingSet, cycles); }