NeuronalNetwork

import com.dkriesel.snipe.core.NeuralNetwork;
import com.dkriesel.snipe.core.NeuralNetworkDescriptor;
import com.dkriesel.snipe.training.ErrorMeasurement;
import com.dkriesel.snipe.training.TrainingSampleLesson;

public class Main {

    public static void main(String[] args) {
        NeuralNetworkDescriptor desc = new NeuralNetworkDescriptor(4, 2, 4);
        desc.setSettingsTopologyFeedForward();

        NeuralNetwork netz = new NeuralNetwork(desc);

        double[][] input = new double[][] {
            {1,1,1,1},  //Input Training 1
            {1,1,0,1}   //Input Training 2

        };
        double[][] output = new double[][] {
            {1,1,1,1},  //Output Training 1
            {1,1,1,1}   //Output Training 2

        };
        TrainingSampleLesson lektion = new TrainingSampleLesson(input, output);

        System.out.println(ErrorMeasurement.getErrorRootMeanSquareSum(netz, lektion));
        netz.trainBackpropagationOfError(lektion, 1000, 0.2);
        System.out.println(ErrorMeasurement.getErrorRootMeanSquareSum(netz, lektion));


    }

}