private void InitializeDataAndNeurons() { NormalizeData(csvPath, csvPathNormalized); NormalizeData(csvPathTest, csvPathNormalizedTest); Network = new SimpleNeuralNetwork((double)learningRate, (double)momentumRate, bias); if (isRegression == false) { DataPointsClassificationTraining = (new ImportDataPointSets(csvPathNormalized).DataPoints); DataPointClassificationTest = (new ImportDataPointSets(csvPathNormalizedTest).DataPoints); Network.TrainingSet = Network.InitializeClassificationSet(DataPointsClassificationTraining, 4); Network.TestSet = Network.InitializeClassificationSet(DataPointClassificationTest, 4); Network.AddLayer(2); Network.AddLayerBunch(Layers, Neurons); Network.AddLayer(4); } else { DataPointsRegressionTraining = (new ImportDataPointsSetsRegression(csvPathNormalized).DataPoints); DataPointRegressionTest = (new ImportDataPointsSetsRegression(csvPathNormalizedTest).DataPoints); Network.TrainingSet = Network.InitializeRegressionSet(DataPointsRegressionTraining); Network.TestSet = Network.InitializeRegressionSet(DataPointRegressionTest); Network.AddLayer(1); Network.AddLayerBunch(Layers, Neurons); Network.AddLayer(1); } }
public static void InitializeSimpleNetwork(int interations) { NormalizeData(); List <DataPointCls> points = (new ImportDataPointSets(csvPathNormalized).DataPoints); SimpleNeuralNetwork myNetwork = new SimpleNeuralNetwork(); //myNetwork.InitializeTrainingSet(points,4); var ts = myNetwork.TrainingSet; myNetwork.ActivationFunction = new ActivationBiPolar(); myNetwork.AddLayer(2); myNetwork.AddLayerBunch(2, 4); myNetwork.AddLayer(4); myNetwork.StartLearning(interations); //ErrorCalculator.CalculateError(myNetwork.ComputeTrainingSet().ToList(), myNetwork); }