AI with Java Netbeans 16, Ant

**Dietmar** · April 15, 2023

No, I make a mistake in the listing, I just correct. And you have to choose at least 5 runs

Dietmar

**Dietmar** · April 15, 2023

And this one is for the primes, waaaoooohhhh :cheerleader:

Dietmar

package multiof3;

import java.util.Arrays;
import java.util.Random;

public class Multiof3 {
    private final int numInputNodes = 8;
    private final int numHiddenNodes = 26;
    private final int numOutputNodes = 1;
    private final double learningRate = 0.03;
    private final int numEpochs = 200000;
    private final double errorThreshold = 0.00000000000000000000000000000001;

    private double[][] inputToHiddenWeights;
    private double[][] hiddenToOutputWeights;
    private double[] hiddenBiases;
    private double[] outputBiases;

    public Multiof3() {
        Random random = new Random();
        inputToHiddenWeights = new double[numInputNodes][numHiddenNodes];
        hiddenToOutputWeights = new double[numHiddenNodes][numOutputNodes];
        hiddenBiases = new double[numHiddenNodes];
        outputBiases = new double[numOutputNodes];

        for (int i = 0; i < numInputNodes; i++) {
            for (int j = 0; j < numHiddenNodes; j++) {
                inputToHiddenWeights[i][j] = random.nextDouble() - 0.5;
            }
        }

        for (int i = 0; i < numHiddenNodes; i++) {
            for (int j = 0; j < numOutputNodes; j++) {
                hiddenToOutputWeights[i][j] = random.nextDouble() - 0.5;
            }
            hiddenBiases[i] = random.nextDouble() - 0.5;
        }

        for (int i = 0; i < numOutputNodes; i++) {
            outputBiases[i] = random.nextDouble() - 0.5;
        }
    }

    public double relu(double x) {
        return Math.max(0, x);
    }

    public double reluDerivative(double x) {
        return x > 0 ? 1 : 0;
    }

    public void train(double[][] trainingInputs, double[] trainingTargets) {
        for (int epoch = 1; epoch <= numEpochs; epoch++) {
            double totalError = 0.0;
            for (int i = 0; i < trainingInputs.length; i++) {
                double[] input = trainingInputs[i];
                double target = trainingTargets[i];

                // Forward propagation
                double[] hiddenOutputs = new double[numHiddenNodes];
                for (int j = 0; j < numHiddenNodes; j++) {
                    double weightedSum = 0.0;
                    for (int k = 0; k < numInputNodes; k++) {
                        weightedSum += inputToHiddenWeights[k][j] * input[k];
                    }
                    hiddenOutputs[j] = relu(weightedSum + hiddenBiases[j]);
                }

                double output = 0.0;
                for (int j = 0; j < numOutputNodes; j++) {
                    double weightedSum = 0.0;
                    for (int k = 0; k < numHiddenNodes; k++) {
                        weightedSum += hiddenToOutputWeights[k][j] * hiddenOutputs[k];
                    }
                    output = relu(weightedSum + outputBiases[j]);
                }

                
// Backward propagation
double outputErrorGradient = (output - target) * reluDerivative(output);
for (int j = 0; j < numHiddenNodes; j++) {
double hiddenErrorGradient = outputErrorGradient * hiddenToOutputWeights[j][0] * reluDerivative(hiddenOutputs[j]);
for (int k = 0; k < numInputNodes; k++) {
inputToHiddenWeights[k][j] -= learningRate * input[k] * hiddenErrorGradient;
}
hiddenBiases[j] -= learningRate * hiddenErrorGradient;
}
hiddenToOutputWeights[0][0] -= learningRate * hiddenOutputs[0] * outputErrorGradient;
outputBiases[0] -= learningRate * outputErrorGradient;


            // Update total error
            totalError += Math.pow(output - target, 2);
        }

        // Calculate mean error and check for convergence
        double meanError = totalError / trainingInputs.length;
        if (meanError < errorThreshold) {
            System.out.println("Training complete. Mean error: " + meanError);
            break;
        } else if (epoch % 10000 == 0) {
            System.out.println("Epoch " + epoch + ". Mean error: " + meanError);
        }
    }
}

public double predict(double[] input) {
    double[] hiddenOutputs = new double[numHiddenNodes];
    for (int j = 0; j < numHiddenNodes; j++) {
        double weightedSum = 0.0;
        for (int k = 0; k < numInputNodes; k++) {
            weightedSum += inputToHiddenWeights[k][j] * input[k];
        }
        hiddenOutputs[j] = relu(weightedSum + hiddenBiases[j]);
    }

    double output = 0.0;
    for (int j = 0; j < numOutputNodes; j++) {
        double weightedSum = 0.0;
        for (int k = 0; k < numHiddenNodes; k++) {
            weightedSum += hiddenToOutputWeights[k][j] * hiddenOutputs[k];
        }
        output = relu(weightedSum + outputBiases[j]);
    }

    return output;
}






public static void main(String[] args) {
    // Example usage of the neural network
    double[][] trainingInputs = 
{{0, 0, 0, 0, 0, 0, 0, 0},
{0, 0, 0, 0, 0, 0, 0, 1},
{0, 0, 0, 0, 0, 0, 1, 0},
{0, 0, 0, 0, 0, 0, 1, 1},
{0, 0, 0, 0, 0, 1, 0, 0},
{0, 0, 0, 0, 0, 1, 0, 1},
{0, 0, 0, 0, 0, 1, 1, 0},
{0, 0, 0, 0, 0, 1, 1, 1},
{0, 0, 0, 0, 1, 0, 0, 0},
{0, 0, 0, 0, 1, 0, 0, 1},
{0, 0, 0, 0, 1, 0, 1, 0},
{0, 0, 0, 0, 1, 0, 1, 1},
{0, 0, 0, 0, 1, 1, 0, 0},
{0, 0, 0, 0, 1, 1, 0, 1},
{0, 0, 0, 0, 1, 1, 1, 0},
{0, 0, 0, 0, 1, 1, 1, 1},
{0, 0, 0, 1, 0, 0, 0, 0},
{0, 0, 0, 1, 0, 0, 0, 1},
{0, 0, 0, 1, 0, 0, 1, 0},
{0, 0, 0, 1, 0, 0, 1, 1},
{0, 0, 0, 1, 0, 1, 0, 0},
{0, 0, 0, 1, 0, 1, 0, 1},
{0, 0, 0, 1, 0, 1, 1, 0},
{0, 0, 0, 1, 0, 1, 1, 1},
{0, 0, 0, 1, 1, 0, 0, 0},
{0, 0, 0, 1, 1, 0, 0, 1},
{0, 0, 0, 1, 1, 0, 1, 0},
{0, 0, 0, 1, 1, 0, 1, 1},
{0, 0, 0, 1, 1, 1, 0, 0},  
{0, 0, 0, 1, 1, 1, 0, 1},
{0, 0, 0, 1, 1, 1, 1, 0},
{0, 0, 0, 1, 1, 1, 1, 1},
{0, 0, 1, 0, 0, 0, 0, 0},
{0, 0, 1, 0, 0, 0, 0, 1},
{0, 0, 1, 0, 0, 0, 1, 0},
{0, 0, 1, 0, 0, 0, 1, 1},
{0, 0, 1, 0, 0, 1, 0, 0},
{0, 0, 1, 0, 0, 1, 0, 1},
{0, 0, 1, 0, 0, 1, 1, 0},
{0, 0, 1, 0, 0, 1, 1, 1},
{0, 0, 1, 0, 1, 0, 0, 0},
{0, 0, 1, 0, 1, 0, 0, 1},
{0, 0, 1, 0, 1, 0, 1, 0},
{0, 0, 1, 0, 1, 0, 1, 1},
{0, 0, 1, 0, 1, 1, 0, 0},
{0, 0, 1, 0, 1, 1, 0, 1},
{0, 0, 1, 0, 1, 1, 1, 0},
{0, 0, 1, 0, 1, 1, 1, 1},
{0, 0, 1, 1, 0, 0, 0, 0},
{0, 0, 1, 1, 0, 0, 0, 1},
{0, 0, 1, 1, 0, 0, 1, 0},
{0, 0, 1, 1, 0, 0, 1, 1},
{0, 0, 1, 1, 0, 1, 0, 0},
{0, 0, 1, 1, 0, 1, 0, 1},
{0, 0, 1, 1, 0, 1, 1, 0},
{0, 0, 1, 1, 0, 1, 1, 1},
{0, 0, 1, 1, 1, 0, 0, 0},
{0, 0, 1, 1, 1, 0, 0, 1},
{0, 0, 1, 1, 1, 0, 1, 0},
{0, 0, 1, 1, 1, 0, 1, 1},
{0, 0, 1, 1, 1, 1, 0, 0},
{0, 0, 1, 1, 1, 1, 0, 1},
{0, 0, 1, 1, 1, 1, 1, 0},
{0, 0, 1, 1, 1, 1, 1, 1},
{0, 1, 0, 0, 0, 0, 0, 0},
{0, 1, 0, 0, 0, 0, 0, 1},
{0, 1, 0, 0, 0, 0, 1, 0},
{0, 1, 0, 0, 0, 0, 1, 1},
{0, 1, 0, 0, 0, 1, 0, 0},
{0, 1, 0, 0, 0, 1, 0, 1},
{0, 1, 0, 0, 0, 1, 1, 0},
{0, 1, 0, 0, 0, 1, 1, 1},
{0, 1, 0, 0, 1, 0, 0, 0},
{0, 1, 0, 0, 1, 0, 0, 1},
{0, 1, 0, 0, 1, 0, 1, 0},
{0, 1, 0, 0, 1, 0, 1, 1},
{0, 1, 0, 0, 1, 1, 0, 0},
{0, 1, 0, 0, 1, 1, 0, 1},
{0, 1, 0, 0, 1, 1, 1, 0},
{0, 1, 0, 0, 1, 1, 1, 1},
{0, 1, 0, 1, 0, 0, 0, 0},
{0, 1, 0, 1, 0, 0, 0, 1},
{0, 1, 0, 1, 0, 0, 1, 0},
{0, 1, 0, 1, 0, 0, 1, 1},
{0, 1, 0, 1, 0, 1, 0, 0},
{0, 1, 0, 1, 0, 1, 0, 1},
{0, 1, 0, 1, 0, 1, 1, 0},
{0, 1, 0, 1, 0, 1, 1, 1},
{0, 1, 0, 1, 1, 0, 0, 0},
{0, 1, 0, 1, 1, 0, 0, 1},
{0, 1, 0, 1, 1, 0, 1, 0},
{0, 1, 0, 1, 1, 0, 1, 1},
{0, 1, 0, 1, 1, 1, 0, 0},
{0, 1, 0, 1, 1, 1, 0, 1},
{0, 1, 0, 1, 1, 1, 1, 0},
{0, 1, 0, 1, 1, 1, 1, 1},
{0, 1, 1, 0, 0, 0, 0, 0},
{0, 1, 1, 0, 0, 0, 0, 1},
{0, 1, 1, 0, 0, 0, 1, 0},
{0, 1, 1, 0, 0, 0, 1, 1},
{0, 1, 1, 0, 0, 1, 0, 0},
{0, 1, 1, 0, 0, 1, 0, 1},
{0, 1, 1, 0, 0, 1, 1, 0},
{0, 1, 1, 0, 0, 1, 1, 1},
{0, 1, 1, 0, 1, 0, 0, 0},
{0, 1, 1, 0, 1, 0, 0, 1},
{0, 1, 1, 0, 1, 0, 1, 0},
{0, 1, 1, 0, 1, 0, 1, 1},
{0, 1, 1, 0, 1, 1, 0, 0},
{0, 1, 1, 0, 1, 1, 0, 1},
{0, 1, 1, 0, 1, 1, 1, 0},
{0, 1, 1, 0, 1, 1, 1, 1},
{0, 1, 1, 1, 0, 0, 0, 0},
{0, 1, 1, 1, 0, 0, 0, 1},
{0, 1, 1, 1, 0, 0, 1, 0},
{0, 1, 1, 1, 0, 0, 1, 1},
{0, 1, 1, 1, 0, 1, 0, 0},
{0, 1, 1, 1, 0, 1, 0, 1},
{0, 1, 1, 1, 0, 1, 1, 0},
{0, 1, 1, 1, 0, 1, 1, 1},
{0, 1, 1, 1, 1, 0, 0, 0},
{0, 1, 1, 1, 1, 0, 0, 1},
{0, 1, 1, 1, 1, 0, 1, 0},
{0, 1, 1, 1, 1, 0, 1, 1},
{0, 1, 1, 1, 1, 1, 0, 0},
{0, 1, 1, 1, 1, 1, 0, 1},
{0, 1, 1, 1, 1, 1, 1, 0},
{0, 1, 1, 1, 1, 1, 1, 1},
{1, 0, 0, 0, 0, 0, 0, 0},
{1, 0, 0, 0, 0, 0, 0, 1},
{1, 0, 0, 0, 0, 0, 1, 0},
{1, 0, 0, 0, 0, 0, 1, 1},
{1, 0, 0, 0, 0, 1, 0, 0},
{1, 0, 0, 0, 0, 1, 0, 1},
{1, 0, 0, 0, 0, 1, 1, 0},
{1, 0, 0, 0, 0, 1, 1, 1},
{1, 0, 0, 0, 1, 0, 0, 0},
{1, 0, 0, 0, 1, 0, 0, 1},
{1, 0, 0, 0, 1, 0, 1, 0},
{1, 0, 0, 0, 1, 0, 1, 1},
{1, 0, 0, 0, 1, 1, 0, 0},
{1, 0, 0, 0, 1, 1, 0, 1},
{1, 0, 0, 0, 1, 1, 1, 0},
{1, 0, 0, 0, 1, 1, 1, 1},
{1, 0, 0, 1, 0, 0, 0, 0},
{1, 0, 0, 1, 0, 0, 0, 1},
{1, 0, 0, 1, 0, 0, 1, 0},
{1, 0, 0, 1, 0, 0, 1, 1},
{1, 0, 0, 1, 0, 1, 0, 0},
{1, 0, 0, 1, 0, 1, 0, 1},
{1, 0, 0, 1, 0, 1, 1, 0},
{1, 0, 0, 1, 0, 1, 1, 1},
{1, 0, 0, 1, 1, 0, 0, 0},
{1, 0, 0, 1, 1, 0, 0, 1},
{1, 0, 0, 1, 1, 0, 1, 0},
{1, 0, 0, 1, 1, 0, 1, 1},
{1, 0, 0, 1, 1, 1, 0, 0},
{1, 0, 0, 1, 1, 1, 0, 1},
{1, 0, 0, 1, 1, 1, 1, 0},
{1, 0, 0, 1, 1, 1, 1, 1},
{1, 0, 1, 0, 0, 0, 0, 0},
{1, 0, 1, 0, 0, 0, 0, 1},
{1, 0, 1, 0, 0, 0, 1, 0},
{1, 0, 1, 0, 0, 0, 1, 1},
{1, 0, 1, 0, 0, 1, 0, 0},
{1, 0, 1, 0, 0, 1, 0, 1},
{1, 0, 1, 0, 0, 1, 1, 0},
{1, 0, 1, 0, 0, 1, 1, 1},
{1, 0, 1, 0, 1, 0, 0, 0},
{1, 0, 1, 0, 1, 0, 0, 1},
{1, 0, 1, 0, 1, 0, 1, 0},
{1, 0, 1, 0, 1, 0, 1, 1},
{1, 0, 1, 0, 1, 1, 0, 0},
{1, 0, 1, 0, 1, 1, 0, 1},
{1, 0, 1, 0, 1, 1, 1, 0},
{1, 0, 1, 0, 1, 1, 1, 1},
{1, 0, 1, 1, 0, 0, 0, 0},
{1, 0, 1, 1, 0, 0, 0, 1},
{1, 0, 1, 1, 0, 0, 1, 0},
{1, 0, 1, 1, 0, 0, 1, 1},
{1, 0, 1, 1, 0, 1, 0, 0},
{1, 0, 1, 1, 0, 1, 0, 1},
{1, 0, 1, 1, 0, 1, 1, 0},
{1, 0, 1, 1, 0, 1, 1, 1},
{1, 0, 1, 1, 1, 0, 0, 0},
{1, 0, 1, 1, 1, 0, 0, 1},
{1, 0, 1, 1, 1, 0, 1, 0},
{1, 0, 1, 1, 1, 0, 1, 1},
{1, 0, 1, 1, 1, 1, 0, 0},
{1, 0, 1, 1, 1, 1, 0, 1},
{1, 0, 1, 1, 1, 1, 1, 0},
{1, 0, 1, 1, 1, 1, 1, 1},
{1, 1, 0, 0, 0, 0, 0, 0},
{1, 1, 0, 0, 0, 0, 0, 1},
{1, 1, 0, 0, 0, 0, 1, 0},
{1, 1, 0, 0, 0, 0, 1, 1},
{1, 1, 0, 0, 0, 1, 0, 0},
{1, 1, 0, 0, 0, 1, 0, 1},
{1, 1, 0, 0, 0, 1, 1, 0},
{1, 1, 0, 0, 0, 1, 1, 1},
{1, 1, 0, 0, 1, 0, 0, 0},
{1, 1, 0, 0, 1, 0, 0, 1},
{1, 1, 0, 0, 1, 0, 1, 0},
{1, 1, 0, 0, 1, 0, 1, 1},
{1, 1, 0, 0, 1, 1, 0, 0},
{1, 1, 0, 0, 1, 1, 0, 1},
{1, 1, 0, 0, 1, 1, 1, 0},
{1, 1, 0, 0, 1, 1, 1, 1},
{1, 1, 0, 1, 0, 0, 0, 0},
{1, 1, 0, 1, 0, 0, 0, 1},
{1, 1, 0, 1, 0, 0, 1, 0},
{1, 1, 0, 1, 0, 0, 1, 1},
{1, 1, 0, 1, 0, 1, 0, 0},
{1, 1, 0, 1, 0, 1, 0, 1},
{1, 1, 0, 1, 0, 1, 1, 0},
{1, 1, 0, 1, 0, 1, 1, 1},
{1, 1, 0, 1, 1, 0, 0, 0},
{1, 1, 0, 1, 1, 0, 0, 1},
{1, 1, 0, 1, 1, 0, 1, 0},
{1, 1, 0, 1, 1, 0, 1, 1},
{1, 1, 0, 1, 1, 1, 0, 0},
{1, 1, 0, 1, 1, 1, 0, 1},
{1, 1, 0, 1, 1, 1, 1, 0},
{1, 1, 0, 1, 1, 1, 1, 1},
{1, 1, 1, 0, 0, 0, 0, 0},
{1, 1, 1, 0, 0, 0, 0, 1},
{1, 1, 1, 0, 0, 0, 1, 0},
{1, 1, 1, 0, 0, 0, 1, 1},
{1, 1, 1, 0, 0, 1, 0, 0},
{1, 1, 1, 0, 0, 1, 0, 1},
{1, 1, 1, 0, 0, 1, 1, 0},
{1, 1, 1, 0, 0, 1, 1, 1},
{1, 1, 1, 0, 1, 0, 0, 0},
{1, 1, 1, 0, 1, 0, 0, 1},
{1, 1, 1, 0, 1, 0, 1, 0},
{1, 1, 1, 0, 1, 0, 1, 1},
{1, 1, 1, 0, 1, 1, 0, 0},
{1, 1, 1, 0, 1, 1, 0, 1},
{1, 1, 1, 0, 1, 1, 1, 0},
{1, 1, 1, 0, 1, 1, 1, 1},
{1, 1, 1, 1, 0, 0, 0, 0},
{1, 1, 1, 1, 0, 0, 0, 1},
{1, 1, 1, 1, 0, 0, 1, 0},
{1, 1, 1, 1, 0, 0, 1, 1},
{1, 1, 1, 1, 0, 1, 0, 0},
{1, 1, 1, 1, 0, 1, 0, 1},
{1, 1, 1, 1, 0, 1, 1, 0},
{1, 1, 1, 1, 0, 1, 1, 1},
{1, 1, 1, 1, 1, 0, 0, 0},
{1, 1, 1, 1, 1, 0, 0, 1},
{1, 1, 1, 1, 1, 0, 1, 0},
{1, 1, 1, 1, 1, 0, 1, 1},
{1, 1, 1, 1, 1, 1, 0, 0},
{1, 1, 1, 1, 1, 1, 0, 1},
{1, 1, 1, 1, 1, 1, 1, 0},
{1, 1, 1, 1, 1, 1, 1, 1}};
    
    
double[] trainingTargets = {
0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0
};



    
    Multiof3 nn = new Multiof3();
    nn.train(trainingInputs, trainingTargets);
    
System.out.println("Prediction for [0, 0, 0, 0, 0, 0, 0, 0]: " + nn.predict(new double[]{0, 0, 0, 0, 0, 0, 0, 0}));
System.out.println("Prediction for [0, 0, 0, 0, 0, 0, 0, 1]: " + nn.predict(new double[]{0, 0, 0, 0, 0, 0, 0, 1}));
System.out.println("Prediction for [0, 0, 0, 0, 0, 0, 1, 0]: " + nn.predict(new double[]{0, 0, 0, 0, 0, 0, 1, 0}));
System.out.println("Prediction for [0, 0, 0, 0, 0, 0, 1, 1]: " + nn.predict(new double[]{0, 0, 0, 0, 0, 0, 1, 1}));
System.out.println("Prediction for [0, 0, 0, 0, 0, 1, 0, 0]: " + nn.predict(new double[]{0, 0, 0, 0, 0, 1, 0, 0}));
System.out.println("Prediction for [0, 0, 0, 0, 0, 1, 0, 1]: " + nn.predict(new double[]{0, 0, 0, 0, 0, 1, 0, 1}));
System.out.println("Prediction for [0, 0, 0, 0, 0, 1, 1, 0]: " + nn.predict(new double[]{0, 0, 0, 0, 0, 1, 1, 0}));
System.out.println("Prediction for [0, 0, 0, 0, 0, 1, 1, 1]: " + nn.predict(new double[]{0, 0, 0, 0, 0, 1, 1, 1}));
System.out.println("Prediction for [0, 0, 0, 0, 1, 0, 0, 0]: " + nn.predict(new double[]{0, 0, 0, 0, 1, 0, 0, 0}));
System.out.println("Prediction for [0, 0, 0, 0, 1, 0, 0, 1]: " + nn.predict(new double[]{0, 0, 0, 0, 1, 0, 0, 1}));
System.out.println("Prediction for [0, 0, 0, 0, 1, 0, 1, 0]: " + nn.predict(new double[]{0, 0, 0, 0, 1, 0, 1, 0}));
System.out.println("Prediction for [0, 0, 0, 0, 1, 0, 1, 1]: " + nn.predict(new double[]{0, 0, 0, 0, 1, 0, 1, 1}));
System.out.println("Prediction for [0, 0, 0, 0, 1, 1, 0, 0]: " + nn.predict(new double[]{0, 0, 0, 0, 1, 1, 0, 0}));
System.out.println("Prediction for [0, 0, 0, 0, 1, 1, 0, 1]: " + nn.predict(new double[]{0, 0, 0, 0, 1, 1, 0, 1}));
System.out.println("Prediction for [0, 0, 0, 0, 1, 1, 1, 0]: " + nn.predict(new double[]{0, 0, 0, 0, 1, 1, 1, 0}));
System.out.println("Prediction for [0, 0, 0, 0, 1, 1, 1, 1]: " + nn.predict(new double[]{0, 0, 0, 0, 1, 1, 1, 1}));
System.out.println("Prediction for [0, 0, 0, 1, 0, 0, 0, 0]: " + nn.predict(new double[]{0, 0, 0, 1, 0, 0, 0, 0}));
System.out.println("Prediction for [0, 0, 0, 1, 0, 0, 0, 1]: " + nn.predict(new double[]{0, 0, 0, 1, 0, 0, 0, 1}));
System.out.println("Prediction for [0, 0, 0, 1, 0, 0, 1, 0]: " + nn.predict(new double[]{0, 0, 0, 1, 0, 0, 1, 0}));
System.out.println("Prediction for [0, 0, 0, 1, 0, 0, 1, 1]: " + nn.predict(new double[]{0, 0, 0, 1, 0, 0, 1, 1}));
System.out.println("Prediction for [0, 0, 0, 1, 0, 1, 0, 0]: " + nn.predict(new double[]{0, 0, 0, 1, 0, 1, 0, 0}));
System.out.println("Prediction for [0, 0, 0, 1, 0, 1, 0, 1]: " + nn.predict(new double[]{0, 0, 0, 1, 0, 1, 0, 1}));
System.out.println("Prediction for [0, 0, 0, 1, 0, 1, 1, 0]: " + nn.predict(new double[]{0, 0, 0, 1, 0, 1, 1, 0}));
System.out.println("Prediction for [0, 0, 0, 1, 0, 1, 1, 1]: " + nn.predict(new double[]{0, 0, 0, 1, 0, 1, 1, 1}));   
System.out.println("Prediction for [0, 0, 0, 1, 1, 0, 0, 0]: " + nn.predict(new double[]{0, 0, 0, 1, 1, 0, 0, 0}));
System.out.println("Prediction for [0, 0, 0, 1, 1, 0, 0, 1]: " + nn.predict(new double[]{0, 0, 0, 1, 1, 0, 0, 1}));
System.out.println("Prediction for [0, 0, 0, 1, 1, 0, 1, 0]: " + nn.predict(new double[]{0, 0, 0, 1, 1, 0, 1, 0}));
System.out.println("Prediction for [0, 0, 0, 1, 1, 0, 1, 1]: " + nn.predict(new double[]{0, 0, 0, 1, 1, 0, 1, 1}));
System.out.println("Prediction for [0, 0, 0, 1, 1, 1, 0, 0]: " + nn.predict(new double[]{0, 0, 0, 1, 1, 1, 0, 0}));
System.out.println("Prediction for [0, 0, 0, 1, 1, 1, 0, 1]: " + nn.predict(new double[]{0, 0, 0, 1, 1, 1, 0, 1}));
System.out.println("Prediction for [0, 0, 0, 1, 1, 1, 1, 0]: " + nn.predict(new double[]{0, 0, 0, 1, 1, 1, 1, 0}));
System.out.println("Prediction for [0, 0, 0, 1, 1, 1, 1, 1]: " + nn.predict(new double[]{0, 0, 0, 1, 1, 1, 1, 1}));
System.out.println("Prediction for [0, 0, 1, 0, 0, 0, 0, 0]: " + nn.predict(new double[]{0, 0, 1, 0, 0, 0, 0, 0}));
System.out.println("Prediction for [0, 0, 1, 0, 0, 0, 0, 1]: " + nn.predict(new double[]{0, 0, 1, 0, 0, 0, 0, 1}));
System.out.println("Prediction for [0, 0, 1, 0, 0, 0, 1, 0]: " + nn.predict(new double[]{0, 0, 1, 0, 0, 0, 1, 0}));
System.out.println("Prediction for [0, 0, 1, 0, 0, 0, 1, 1]: " + nn.predict(new double[]{0, 0, 1, 0, 0, 0, 1, 1}));
System.out.println("Prediction for [0, 0, 1, 0, 0, 1, 0, 0]: " + nn.predict(new double[]{0, 0, 1, 0, 0, 1, 0, 0}));
System.out.println("Prediction for [0, 0, 1, 0, 0, 1, 0, 1]: " + nn.predict(new double[]{0, 0, 1, 0, 0, 1, 0, 1}));
System.out.println("Prediction for [0, 0, 1, 0, 0, 1, 1, 0]: " + nn.predict(new double[]{0, 0, 1, 0, 0, 1, 1, 0}));
System.out.println("Prediction for [0, 0, 1, 0, 0, 1, 1, 1]: " + nn.predict(new double[]{0, 0, 1, 0, 0, 1, 1, 1}));
System.out.println("Prediction for [0, 0, 1, 0, 1, 0, 0, 0]: " + nn.predict(new double[]{0, 0, 1, 0, 1, 0, 0, 0}));
System.out.println("Prediction for [0, 0, 1, 0, 1, 0, 0, 1]: " + nn.predict(new double[]{0, 0, 1, 0, 1, 0, 0, 1}));
System.out.println("Prediction for [0, 0, 1, 0, 1, 0, 1, 0]: " + nn.predict(new double[]{0, 0, 1, 0, 1, 0, 1, 0}));
System.out.println("Prediction for [0, 0, 1, 0, 1, 0, 1, 1]: " + nn.predict(new double[]{0, 0, 1, 0, 1, 0, 1, 1}));
System.out.println("Prediction for [0, 0, 1, 0, 1, 1, 0, 0]: " + nn.predict(new double[]{0, 0, 1, 0, 1, 1, 0, 0}));
System.out.println("Prediction for [0, 0, 1, 0, 1, 1, 0, 1]: " + nn.predict(new double[]{0, 0, 1, 0, 1, 1, 0, 1}));
System.out.println("Prediction for [0, 0, 1, 0, 1, 1, 1, 0]: " + nn.predict(new double[]{0, 0, 1, 0, 1, 1, 1, 0}));
System.out.println("Prediction for [0, 0, 1, 0, 1, 1, 1, 1]: " + nn.predict(new double[]{0, 0, 1, 0, 1, 1, 1, 1}));
System.out.println("Prediction for [0, 0, 1, 1, 0, 0, 0, 0]: " + nn.predict(new double[]{0, 0, 1, 1, 0, 0, 0, 0}));
System.out.println("Prediction for [0, 0, 1, 1, 0, 0, 0, 1]: " + nn.predict(new double[]{0, 0, 1, 1, 0, 0, 0, 1}));
System.out.println("Prediction for [0, 0, 1, 1, 0, 0, 1, 0]: " + nn.predict(new double[]{0, 0, 1, 1, 0, 0, 1, 0}));
System.out.println("Prediction for [0, 0, 1, 1, 0, 0, 1, 1]: " + nn.predict(new double[]{0, 0, 1, 1, 0, 0, 1, 1}));
System.out.println("Prediction for [0, 0, 1, 1, 0, 1, 0, 0]: " + nn.predict(new double[]{0, 0, 1, 1, 0, 1, 0, 0}));
System.out.println("Prediction for [0, 0, 1, 1, 0, 1, 0, 1]: " + nn.predict(new double[]{0, 0, 1, 1, 0, 1, 0, 1}));
System.out.println("Prediction for [0, 0, 1, 1, 0, 1, 1, 0]: " + nn.predict(new double[]{0, 0, 1, 1, 0, 1, 1, 0}));
System.out.println("Prediction for [0, 0, 1, 1, 0, 1, 1, 1]: " + nn.predict(new double[]{0, 0, 1, 1, 0, 1, 1, 1}));
System.out.println("Prediction for [0, 0, 1, 1, 1, 0, 0, 0]: " + nn.predict(new double[]{0, 0, 1, 1, 1, 0, 0, 0}));
System.out.println("Prediction for [0, 0, 1, 1, 1, 0, 0, 1]: " + nn.predict(new double[]{0, 0, 1, 1, 1, 0, 0, 1}));
System.out.println("Prediction for [0, 0, 1, 1, 1, 0, 1, 0]: " + nn.predict(new double[]{0, 0, 1, 1, 1, 0, 1, 0}));
System.out.println("Prediction for [0, 0, 1, 1, 1, 0, 1, 1]: " + nn.predict(new double[]{0, 0, 1, 1, 1, 0, 1, 1}));
System.out.println("Prediction for [0, 0, 1, 1, 1, 1, 0, 0]: " + nn.predict(new double[]{0, 0, 1, 1, 1, 1, 0, 0}));
System.out.println("Prediction for [0, 0, 1, 1, 1, 1, 0, 1]: " + nn.predict(new double[]{0, 0, 1, 1, 1, 1, 0, 1}));
System.out.println("Prediction for [0, 0, 1, 1, 1, 1, 1, 0]: " + nn.predict(new double[]{0, 0, 1, 1, 1, 1, 1, 0}));
System.out.println("Prediction for [0, 0, 1, 1, 1, 1, 1, 1]: " + nn.predict(new double[]{0, 0, 1, 1, 1, 1, 1, 1}));
System.out.println("Prediction for [0, 1, 0, 0, 0, 0, 0, 0]: " + nn.predict(new double[]{0, 1, 0, 0, 0, 0, 0, 0}));
System.out.println("Prediction for [0, 1, 0, 0, 0, 0, 0, 1]: " + nn.predict(new double[]{0, 1, 0, 0, 0, 0, 0, 1}));
System.out.println("Prediction for [0, 1, 0, 0, 0, 0, 1, 0]: " + nn.predict(new double[]{0, 1, 0, 0, 0, 0, 1, 0}));
System.out.println("Prediction for [0, 1, 0, 0, 0, 0, 1, 1]: " + nn.predict(new double[]{0, 1, 0, 0, 0, 0, 1, 1}));
System.out.println("Prediction for [0, 1, 0, 0, 0, 1, 0, 0]: " + nn.predict(new double[]{0, 1, 0, 0, 0, 1, 0, 0}));
System.out.println("Prediction for [0, 1, 0, 0, 0, 1, 0, 1]: " + nn.predict(new double[]{0, 1, 0, 0, 0, 1, 0, 1}));
System.out.println("Prediction for [0, 1, 0, 0, 0, 1, 1, 0]: " + nn.predict(new double[]{0, 1, 0, 0, 0, 1, 1, 0}));
System.out.println("Prediction for [0, 1, 0, 0, 0, 1, 1, 1]: " + nn.predict(new double[]{0, 1, 0, 0, 0, 1, 1, 1}));
System.out.println("Prediction for [0, 1, 0, 0, 1, 0, 0, 0]: " + nn.predict(new double[]{0, 1, 0, 0, 1, 0, 0, 0}));
System.out.println("Prediction for [0, 1, 0, 0, 1, 0, 0, 1]: " + nn.predict(new double[]{0, 1, 0, 0, 1, 0, 0, 1}));
System.out.println("Prediction for [0, 1, 0, 0, 1, 0, 1, 0]: " + nn.predict(new double[]{0, 1, 0, 0, 1, 0, 1, 0}));
System.out.println("Prediction for [0, 1, 0, 0, 1, 0, 1, 1]: " + nn.predict(new double[]{0, 1, 0, 0, 1, 0, 1, 1}));
System.out.println("Prediction for [0, 1, 0, 0, 1, 1, 0, 0]: " + nn.predict(new double[]{0, 1, 0, 0, 1, 1, 0, 0}));
System.out.println("Prediction for [0, 1, 0, 0, 1, 1, 0, 1]: " + nn.predict(new double[]{0, 1, 0, 0, 1, 1, 0, 1}));
System.out.println("Prediction for [0, 1, 0, 0, 1, 1, 1, 0]: " + nn.predict(new double[]{0, 1, 0, 0, 1, 1, 1, 0}));
System.out.println("Prediction for [0, 1, 0, 0, 1, 1, 1, 1]: " + nn.predict(new double[]{0, 1, 0, 0, 1, 1, 1, 1}));
System.out.println("Prediction for [0, 1, 0, 1, 0, 0, 0, 0]: " + nn.predict(new double[]{0, 1, 0, 1, 0, 0, 0, 0}));
System.out.println("Prediction for [0, 1, 0, 1, 0, 0, 0, 1]: " + nn.predict(new double[]{0, 1, 0, 1, 0, 0, 0, 1}));
System.out.println("Prediction for [0, 1, 0, 1, 0, 0, 1, 0]: " + nn.predict(new double[]{0, 1, 0, 1, 0, 0, 1, 0}));
System.out.println("Prediction for [0, 1, 0, 1, 0, 0, 1, 1]: " + nn.predict(new double[]{0, 1, 0, 1, 0, 0, 1, 1}));
System.out.println("Prediction for [0, 1, 0, 1, 0, 1, 0, 0]: " + nn.predict(new double[]{0, 1, 0, 1, 0, 1, 0, 0}));
System.out.println("Prediction for [0, 1, 0, 1, 0, 1, 0, 1]: " + nn.predict(new double[]{0, 1, 0, 1, 0, 1, 0, 1}));
System.out.println("Prediction for [0, 1, 0, 1, 0, 1, 1, 0]: " + nn.predict(new double[]{0, 1, 0, 1, 0, 1, 1, 0}));
System.out.println("Prediction for [0, 1, 0, 1, 0, 1, 1, 1]: " + nn.predict(new double[]{0, 1, 0, 1, 0, 1, 1, 1}));
System.out.println("Prediction for [0, 1, 0, 1, 1, 0, 0, 0]: " + nn.predict(new double[]{0, 1, 0, 1, 1, 0, 0, 0}));
System.out.println("Prediction for [0, 1, 0, 1, 1, 0, 0, 1]: " + nn.predict(new double[]{0, 1, 0, 1, 1, 0, 0, 1}));
System.out.println("Prediction for [0, 1, 0, 1, 1, 0, 1, 0]: " + nn.predict(new double[]{0, 1, 0, 1, 1, 0, 1, 0}));
System.out.println("Prediction for [0, 1, 0, 1, 1, 0, 1, 1]: " + nn.predict(new double[]{0, 1, 0, 1, 1, 0, 1, 1}));
System.out.println("Prediction for [0, 1, 0, 1, 1, 1, 0, 0]: " + nn.predict(new double[]{0, 1, 0, 1, 1, 1, 0, 0}));
System.out.println("Prediction for [0, 1, 0, 1, 1, 1, 0, 1]: " + nn.predict(new double[]{0, 1, 0, 1, 1, 1, 0, 1}));
System.out.println("Prediction for [0, 1, 0, 1, 1, 1, 1, 0]: " + nn.predict(new double[]{0, 1, 0, 1, 1, 1, 1, 0}));
System.out.println("Prediction for [0, 1, 0, 1, 1, 1, 1, 1]: " + nn.predict(new double[]{0, 1, 0, 1, 1, 1, 1, 1}));
System.out.println("Prediction for [0, 1, 1, 0, 0, 0, 0, 0]: " + nn.predict(new double[]{0, 1, 1, 0, 0, 0, 0, 0}));
System.out.println("Prediction for [0, 1, 1, 0, 0, 0, 0, 1]: " + nn.predict(new double[]{0, 1, 1, 0, 0, 0, 0, 1}));
System.out.println("Prediction for [0, 1, 1, 0, 0, 0, 1, 0]: " + nn.predict(new double[]{0, 1, 1, 0, 0, 0, 1, 0}));
System.out.println("Prediction for [0, 1, 1, 0, 0, 0, 1, 1]: " + nn.predict(new double[]{0, 1, 1, 0, 0, 0, 1, 1}));
System.out.println("Prediction for [0, 1, 1, 0, 0, 1, 0, 0]: " + nn.predict(new double[]{0, 1, 1, 0, 0, 1, 0, 0}));
System.out.println("Prediction for [0, 1, 1, 0, 0, 1, 0, 1]: " + nn.predict(new double[]{0, 1, 1, 0, 0, 1, 0, 1}));
System.out.println("Prediction for [0, 1, 1, 0, 0, 1, 1, 0]: " + nn.predict(new double[]{0, 1, 1, 0, 0, 1, 1, 0}));
System.out.println("Prediction for [0, 1, 1, 0, 0, 1, 1, 1]: " + nn.predict(new double[]{0, 1, 1, 0, 0, 1, 1, 1}));
System.out.println("Prediction for [0, 1, 1, 0, 1, 0, 0, 0]: " + nn.predict(new double[]{0, 1, 1, 0, 1, 0, 0, 0}));
System.out.println("Prediction for [0, 1, 1, 0, 1, 0, 0, 1]: " + nn.predict(new double[]{0, 1, 1, 0, 1, 0, 0, 1}));
System.out.println("Prediction for [0, 1, 1, 0, 1, 0, 1, 0]: " + nn.predict(new double[]{0, 1, 1, 0, 1, 0, 1, 0}));
System.out.println("Prediction for [0, 1, 1, 0, 1, 0, 1, 1]: " + nn.predict(new double[]{0, 1, 1, 0, 1, 0, 1, 1}));
System.out.println("Prediction for [0, 1, 1, 0, 1, 1, 0, 0]: " + nn.predict(new double[]{0, 1, 1, 0, 1, 1, 0, 0}));
System.out.println("Prediction for [0, 1, 1, 0, 1, 1, 0, 1]: " + nn.predict(new double[]{0, 1, 1, 0, 1, 1, 0, 1}));
System.out.println("Prediction for [0, 1, 1, 0, 1, 1, 1, 0]: " + nn.predict(new double[]{0, 1, 1, 0, 1, 1, 1, 0}));
System.out.println("Prediction for [0, 1, 1, 0, 1, 1, 1, 1]: " + nn.predict(new double[]{0, 1, 1, 0, 1, 1, 1, 1}));
System.out.println("Prediction for [0, 1, 1, 1, 0, 0, 0, 0]: " + nn.predict(new double[]{0, 1, 1, 1, 0, 0, 0, 0}));
System.out.println("Prediction for [0, 1, 1, 1, 0, 0, 0, 1]: " + nn.predict(new double[]{0, 1, 1, 1, 0, 0, 0, 1}));
System.out.println("Prediction for [0, 1, 1, 1, 0, 0, 1, 0]: " + nn.predict(new double[]{0, 1, 1, 1, 0, 0, 1, 0}));
System.out.println("Prediction for [0, 1, 1, 1, 0, 0, 1, 1]: " + nn.predict(new double[]{0, 1, 1, 1, 0, 0, 1, 1}));
System.out.println("Prediction for [0, 1, 1, 1, 0, 1, 0, 0]: " + nn.predict(new double[]{0, 1, 1, 1, 0, 1, 0, 0}));
System.out.println("Prediction for [0, 1, 1, 1, 0, 1, 0, 1]: " + nn.predict(new double[]{0, 1, 1, 1, 0, 1, 0, 1}));
System.out.println("Prediction for [0, 1, 1, 1, 0, 1, 1, 0]: " + nn.predict(new double[]{0, 1, 1, 1, 0, 1, 1, 0}));
System.out.println("Prediction for [0, 1, 1, 1, 0, 1, 1, 1]: " + nn.predict(new double[]{0, 1, 1, 1, 0, 1, 1, 1}));
System.out.println("Prediction for [0, 1, 1, 1, 1, 0, 0, 0]: " + nn.predict(new double[]{0, 1, 1, 1, 1, 0, 0, 0}));
System.out.println("Prediction for [0, 1, 1, 1, 1, 0, 0, 1]: " + nn.predict(new double[]{0, 1, 1, 1, 1, 0, 0, 1}));
System.out.println("Prediction for [0, 1, 1, 1, 1, 0, 1, 0]: " + nn.predict(new double[]{0, 1, 1, 1, 1, 0, 1, 0}));
System.out.println("Prediction for [0, 1, 1, 1, 1, 0, 1, 1]: " + nn.predict(new double[]{0, 1, 1, 1, 1, 0, 1, 1}));
System.out.println("Prediction for [0, 1, 1, 1, 1, 1, 0, 0]: " + nn.predict(new double[]{0, 1, 1, 1, 1, 1, 0, 0}));
System.out.println("Prediction for [0, 1, 1, 1, 1, 1, 0, 1]: " + nn.predict(new double[]{0, 1, 1, 1, 1, 1, 0, 1}));
System.out.println("Prediction for [0, 1, 1, 1, 1, 1, 1, 0]: " + nn.predict(new double[]{0, 1, 1, 1, 1, 1, 1, 0}));
System.out.println("Prediction for [0, 1, 1, 1, 1, 1, 1, 1]: " + nn.predict(new double[]{0, 1, 1, 1, 1, 1, 1, 1}));
System.out.println("Prediction for [1, 0, 0, 0, 0, 0, 0, 0]: " + nn.predict(new double[]{1, 0, 0, 0, 0, 0, 0, 0}));
System.out.println("Prediction for [1, 0, 0, 0, 0, 0, 0, 1]: " + nn.predict(new double[]{1, 0, 0, 0, 0, 0, 0, 1}));
System.out.println("Prediction for [1, 0, 0, 0, 0, 0, 1, 0]: " + nn.predict(new double[]{1, 0, 0, 0, 0, 0, 1, 0}));
System.out.println("Prediction for [1, 0, 0, 0, 0, 0, 1, 1]: " + nn.predict(new double[]{1, 0, 0, 0, 0, 0, 1, 1}));
System.out.println("Prediction for [1, 0, 0, 0, 0, 1, 0, 0]: " + nn.predict(new double[]{1, 0, 0, 0, 0, 1, 0, 0}));
System.out.println("Prediction for [1, 0, 0, 0, 0, 1, 0, 1]: " + nn.predict(new double[]{1, 0, 0, 0, 0, 1, 0, 1}));
System.out.println("Prediction for [1, 0, 0, 0, 0, 1, 1, 0]: " + nn.predict(new double[]{1, 0, 0, 0, 0, 1, 1, 0}));
System.out.println("Prediction for [1, 0, 0, 0, 0, 1, 1, 1]: " + nn.predict(new double[]{1, 0, 0, 0, 0, 1, 1, 1}));
System.out.println("Prediction for [1, 0, 0, 0, 1, 0, 0, 0]: " + nn.predict(new double[]{1, 0, 0, 0, 1, 0, 0, 0}));
System.out.println("Prediction for [1, 0, 0, 0, 1, 0, 0, 1]: " + nn.predict(new double[]{1, 0, 0, 0, 1, 0, 0, 1}));
System.out.println("Prediction for [1, 0, 0, 0, 1, 0, 1, 0]: " + nn.predict(new double[]{1, 0, 0, 0, 1, 0, 1, 0}));
System.out.println("Prediction for [1, 0, 0, 0, 1, 0, 1, 1]: " + nn.predict(new double[]{1, 0, 0, 0, 1, 0, 1, 1}));
System.out.println("Prediction for [1, 0, 0, 0, 1, 1, 0, 0]: " + nn.predict(new double[]{1, 0, 0, 0, 1, 1, 0, 0}));
System.out.println("Prediction for [1, 0, 0, 0, 1, 1, 0, 1]: " + nn.predict(new double[]{1, 0, 0, 0, 1, 1, 0, 1}));
System.out.println("Prediction for [1, 0, 0, 0, 1, 1, 1, 0]: " + nn.predict(new double[]{1, 0, 0, 0, 1, 1, 1, 0}));
System.out.println("Prediction for [1, 0, 0, 0, 1, 1, 1, 1]: " + nn.predict(new double[]{1, 0, 0, 0, 1, 1, 1, 1}));
System.out.println("Prediction for [1, 0, 0, 1, 0, 0, 0, 0]: " + nn.predict(new double[]{1, 0, 0, 1, 0, 0, 0, 0}));
System.out.println("Prediction for [1, 0, 0, 1, 0, 0, 0, 1]: " + nn.predict(new double[]{1, 0, 0, 1, 0, 0, 0, 1}));
System.out.println("Prediction for [1, 0, 0, 1, 0, 0, 1, 0]: " + nn.predict(new double[]{1, 0, 0, 1, 0, 0, 1, 0}));
System.out.println("Prediction for [1, 0, 0, 1, 0, 0, 1, 1]: " + nn.predict(new double[]{1, 0, 0, 1, 0, 0, 1, 1}));
System.out.println("Prediction for [1, 0, 0, 1, 0, 1, 0, 0]: " + nn.predict(new double[]{1, 0, 0, 1, 0, 1, 0, 0}));
System.out.println("Prediction for [1, 0, 0, 1, 0, 1, 0, 1]: " + nn.predict(new double[]{1, 0, 0, 1, 0, 1, 0, 1}));
System.out.println("Prediction for [1, 0, 0, 1, 0, 1, 1, 0]: " + nn.predict(new double[]{1, 0, 0, 1, 0, 1, 1, 0}));
System.out.println("Prediction for [1, 0, 0, 1, 0, 1, 1, 1]: " + nn.predict(new double[]{1, 0, 0, 1, 0, 1, 1, 1}));
System.out.println("Prediction for [1, 0, 0, 1, 1, 0, 0, 0]: " + nn.predict(new double[]{1, 0, 0, 1, 1, 0, 0, 0}));
System.out.println("Prediction for [1, 0, 0, 1, 1, 0, 0, 1]: " + nn.predict(new double[]{1, 0, 0, 1, 1, 0, 0, 1}));
System.out.println("Prediction for [1, 0, 0, 1, 1, 0, 1, 0]: " + nn.predict(new double[]{1, 0, 0, 1, 1, 0, 1, 0}));
System.out.println("Prediction for [1, 0, 0, 1, 1, 0, 1, 1]: " + nn.predict(new double[]{1, 0, 0, 1, 1, 0, 1, 1}));
System.out.println("Prediction for [1, 0, 0, 1, 1, 1, 0, 0]: " + nn.predict(new double[]{1, 0, 0, 1, 1, 1, 0, 0}));
System.out.println("Prediction for [1, 0, 0, 1, 1, 1, 0, 1]: " + nn.predict(new double[]{1, 0, 0, 1, 1, 1, 0, 1}));
System.out.println("Prediction for [1, 0, 0, 1, 1, 1, 1, 0]: " + nn.predict(new double[]{1, 0, 0, 1, 1, 1, 1, 0}));
System.out.println("Prediction for [1, 0, 0, 1, 1, 1, 1, 1]: " + nn.predict(new double[]{1, 0, 0, 1, 1, 1, 1, 1}));
System.out.println("Prediction for [1, 0, 1, 0, 0, 0, 0, 0]: " + nn.predict(new double[]{1, 0, 1, 0, 0, 0, 0, 0}));
System.out.println("Prediction for [1, 0, 1, 0, 0, 0, 0, 1]: " + nn.predict(new double[]{1, 0, 1, 0, 0, 0, 0, 1}));
System.out.println("Prediction for [1, 0, 1, 0, 0, 0, 1, 0]: " + nn.predict(new double[]{1, 0, 1, 0, 0, 0, 1, 0}));
System.out.println("Prediction for [1, 0, 1, 0, 0, 0, 1, 1]: " + nn.predict(new double[]{1, 0, 1, 0, 0, 0, 1, 1}));
System.out.println("Prediction for [1, 0, 1, 0, 0, 1, 0, 0]: " + nn.predict(new double[]{1, 0, 1, 0, 0, 1, 0, 0}));
System.out.println("Prediction for [1, 0, 1, 0, 0, 1, 0, 1]: " + nn.predict(new double[]{1, 0, 1, 0, 0, 1, 0, 1}));
System.out.println("Prediction for [1, 0, 1, 0, 0, 1, 1, 0]: " + nn.predict(new double[]{1, 0, 1, 0, 0, 1, 1, 0}));
System.out.println("Prediction for [1, 0, 1, 0, 0, 1, 1, 1]: " + nn.predict(new double[]{1, 0, 1, 0, 0, 1, 1, 1}));
System.out.println("Prediction for [1, 0, 1, 0, 1, 0, 0, 0]: " + nn.predict(new double[]{1, 0, 1, 0, 1, 0, 0, 0}));
System.out.println("Prediction for [1, 0, 1, 0, 1, 0, 0, 1]: " + nn.predict(new double[]{1, 0, 1, 0, 1, 0, 0, 1}));
System.out.println("Prediction for [1, 0, 1, 0, 1, 0, 1, 0]: " + nn.predict(new double[]{1, 0, 1, 0, 1, 0, 1, 0}));
System.out.println("Prediction for [1, 0, 1, 0, 1, 0, 1, 1]: " + nn.predict(new double[]{1, 0, 1, 0, 1, 0, 1, 1}));
System.out.println("Prediction for [1, 0, 1, 0, 1, 1, 0, 0]: " + nn.predict(new double[]{1, 0, 1, 0, 1, 1, 0, 0}));
System.out.println("Prediction for [1, 0, 1, 0, 1, 1, 0, 1]: " + nn.predict(new double[]{1, 0, 1, 0, 1, 1, 0, 1}));
System.out.println("Prediction for [1, 0, 1, 0, 1, 1, 1, 0]: " + nn.predict(new double[]{1, 0, 1, 0, 1, 1, 1, 0}));
System.out.println("Prediction for [1, 0, 1, 0, 1, 1, 1, 1]: " + nn.predict(new double[]{1, 0, 1, 0, 1, 1, 1, 1}));
System.out.println("Prediction for [1, 0, 1, 1, 0, 0, 0, 0]: " + nn.predict(new double[]{1, 0, 1, 1, 0, 0, 0, 0}));
System.out.println("Prediction for [1, 0, 1, 1, 0, 0, 0, 1]: " + nn.predict(new double[]{1, 0, 1, 1, 0, 0, 0, 1}));
System.out.println("Prediction for [1, 0, 1, 1, 0, 0, 1, 0]: " + nn.predict(new double[]{1, 0, 1, 1, 0, 0, 1, 0}));
System.out.println("Prediction for [1, 0, 1, 1, 0, 0, 1, 1]: " + nn.predict(new double[]{1, 0, 1, 1, 0, 0, 1, 1}));
System.out.println("Prediction for [1, 0, 1, 1, 0, 1, 0, 0]: " + nn.predict(new double[]{1, 0, 1, 1, 0, 1, 0, 0}));
System.out.println("Prediction for [1, 0, 1, 1, 0, 1, 0, 1]: " + nn.predict(new double[]{1, 0, 1, 1, 0, 1, 0, 1}));
System.out.println("Prediction for [1, 0, 1, 1, 0, 1, 1, 0]: " + nn.predict(new double[]{1, 0, 1, 1, 0, 1, 1, 0}));
System.out.println("Prediction for [1, 0, 1, 1, 0, 1, 1, 1]: " + nn.predict(new double[]{1, 0, 1, 1, 0, 1, 1, 1}));
System.out.println("Prediction for [1, 0, 1, 1, 1, 0, 0, 0]: " + nn.predict(new double[]{1, 0, 1, 1, 1, 0, 0, 0}));
System.out.println("Prediction for [1, 0, 1, 1, 1, 0, 0, 1]: " + nn.predict(new double[]{1, 0, 1, 1, 1, 0, 0, 1}));
System.out.println("Prediction for [1, 0, 1, 1, 1, 0, 1, 0]: " + nn.predict(new double[]{1, 0, 1, 1, 1, 0, 1, 0}));
System.out.println("Prediction for [1, 0, 1, 1, 1, 0, 1, 1]: " + nn.predict(new double[]{1, 0, 1, 1, 1, 0, 1, 1}));
System.out.println("Prediction for [1, 0, 1, 1, 1, 1, 0, 0]: " + nn.predict(new double[]{1, 0, 1, 1, 1, 1, 0, 0}));
System.out.println("Prediction for [1, 0, 1, 1, 1, 1, 0, 1]: " + nn.predict(new double[]{1, 0, 1, 1, 1, 1, 0, 1}));
System.out.println("Prediction for [1, 0, 1, 1, 1, 1, 1, 0]: " + nn.predict(new double[]{1, 0, 1, 1, 1, 1, 1, 0}));
System.out.println("Prediction for [1, 0, 1, 1, 1, 1, 1, 1]: " + nn.predict(new double[]{1, 0, 1, 1, 1, 1, 1, 1}));
System.out.println("Prediction for [1, 1, 0, 0, 0, 0, 0, 0]: " + nn.predict(new double[]{1, 1, 0, 0, 0, 0, 0, 0}));
System.out.println("Prediction for [1, 1, 0, 0, 0, 0, 0, 1]: " + nn.predict(new double[]{1, 1, 0, 0, 0, 0, 0, 1}));
System.out.println("Prediction for [1, 1, 0, 0, 0, 0, 1, 0]: " + nn.predict(new double[]{1, 1, 0, 0, 0, 0, 1, 0}));
System.out.println("Prediction for [1, 1, 0, 0, 0, 0, 1, 1]: " + nn.predict(new double[]{1, 1, 0, 0, 0, 0, 1, 1}));
System.out.println("Prediction for [1, 1, 0, 0, 0, 1, 0, 0]: " + nn.predict(new double[]{1, 1, 0, 0, 0, 1, 0, 0}));
System.out.println("Prediction for [1, 1, 0, 0, 0, 1, 0, 1]: " + nn.predict(new double[]{1, 1, 0, 0, 0, 1, 0, 1}));
System.out.println("Prediction for [1, 1, 0, 0, 0, 1, 1, 0]: " + nn.predict(new double[]{1, 1, 0, 0, 0, 1, 1, 0}));
System.out.println("Prediction for [1, 1, 0, 0, 0, 1, 1, 1]: " + nn.predict(new double[]{1, 1, 0, 0, 0, 1, 1, 1}));
System.out.println("Prediction for [1, 1, 0, 0, 1, 0, 0, 0]: " + nn.predict(new double[]{1, 1, 0, 0, 1, 0, 0, 0}));
System.out.println("Prediction for [1, 1, 0, 0, 1, 0, 0, 1]: " + nn.predict(new double[]{1, 1, 0, 0, 1, 0, 0, 1}));
System.out.println("Prediction for [1, 1, 0, 0, 1, 0, 1, 0]: " + nn.predict(new double[]{1, 1, 0, 0, 1, 0, 1, 0}));
System.out.println("Prediction for [1, 1, 0, 0, 1, 0, 1, 1]: " + nn.predict(new double[]{1, 1, 0, 0, 1, 0, 1, 1}));
System.out.println("Prediction for [1, 1, 0, 0, 1, 1, 0, 0]: " + nn.predict(new double[]{1, 1, 0, 0, 1, 1, 0, 0}));
System.out.println("Prediction for [1, 1, 0, 0, 1, 1, 0, 1]: " + nn.predict(new double[]{1, 1, 0, 0, 1, 1, 0, 1}));
System.out.println("Prediction for [1, 1, 0, 0, 1, 1, 1, 0]: " + nn.predict(new double[]{1, 1, 0, 0, 1, 1, 1, 0}));
System.out.println("Prediction for [1, 1, 0, 0, 1, 1, 1, 1]: " + nn.predict(new double[]{1, 1, 0, 0, 1, 1, 1, 1}));
System.out.println("Prediction for [1, 1, 0, 1, 0, 0, 0, 0]: " + nn.predict(new double[]{1, 1, 0, 1, 0, 0, 0, 0}));
System.out.println("Prediction for [1, 1, 0, 1, 0, 0, 0, 1]: " + nn.predict(new double[]{1, 1, 0, 1, 0, 0, 0, 1}));
System.out.println("Prediction for [1, 1, 0, 1, 0, 0, 1, 0]: " + nn.predict(new double[]{1, 1, 0, 1, 0, 0, 1, 0}));
System.out.println("Prediction for [1, 1, 0, 1, 0, 0, 1, 1]: " + nn.predict(new double[]{1, 1, 0, 1, 0, 0, 1, 1}));
System.out.println("Prediction for [1, 1, 0, 1, 0, 1, 0, 0]: " + nn.predict(new double[]{1, 1, 0, 1, 0, 1, 0, 0}));
System.out.println("Prediction for [1, 1, 0, 1, 0, 1, 0, 1]: " + nn.predict(new double[]{1, 1, 0, 1, 0, 1, 0, 1}));
System.out.println("Prediction for [1, 1, 0, 1, 0, 1, 1, 0]: " + nn.predict(new double[]{1, 1, 0, 1, 0, 1, 1, 0}));
System.out.println("Prediction for [1, 1, 0, 1, 0, 1, 1, 1]: " + nn.predict(new double[]{1, 1, 0, 1, 0, 1, 1, 1}));
System.out.println("Prediction for [1, 1, 0, 1, 1, 0, 0, 0]: " + nn.predict(new double[]{1, 1, 0, 1, 1, 0, 0, 0}));
System.out.println("Prediction for [1, 1, 0, 1, 1, 0, 0, 1]: " + nn.predict(new double[]{1, 1, 0, 1, 1, 0, 0, 1}));
System.out.println("Prediction for [1, 1, 0, 1, 1, 0, 1, 0]: " + nn.predict(new double[]{1, 1, 0, 1, 1, 0, 1, 0}));
System.out.println("Prediction for [1, 1, 0, 1, 1, 0, 1, 1]: " + nn.predict(new double[]{1, 1, 0, 1, 1, 0, 1, 1}));
System.out.println("Prediction for [1, 1, 0, 1, 1, 1, 0, 0]: " + nn.predict(new double[]{1, 1, 0, 1, 1, 1, 0, 0}));
System.out.println("Prediction for [1, 1, 0, 1, 1, 1, 0, 1]: " + nn.predict(new double[]{1, 1, 0, 1, 1, 1, 0, 1}));
System.out.println("Prediction for [1, 1, 0, 1, 1, 1, 1, 0]: " + nn.predict(new double[]{1, 1, 0, 1, 1, 1, 1, 0}));
System.out.println("Prediction for [1, 1, 0, 1, 1, 1, 1, 1]: " + nn.predict(new double[]{1, 1, 0, 1, 1, 1, 1, 1}));
System.out.println("Prediction for [1, 1, 1, 0, 0, 0, 0, 0]: " + nn.predict(new double[]{1, 1, 1, 0, 0, 0, 0, 0}));
System.out.println("Prediction for [1, 1, 1, 0, 0, 0, 0, 1]: " + nn.predict(new double[]{1, 1, 1, 0, 0, 0, 0, 1}));
System.out.println("Prediction for [1, 1, 1, 0, 0, 0, 1, 0]: " + nn.predict(new double[]{1, 1, 1, 0, 0, 0, 1, 0}));
System.out.println("Prediction for [1, 1, 1, 0, 0, 0, 1, 1]: " + nn.predict(new double[]{1, 1, 1, 0, 0, 0, 1, 1}));
System.out.println("Prediction for [1, 1, 1, 0, 0, 1, 0, 0]: " + nn.predict(new double[]{1, 1, 1, 0, 0, 1, 0, 0}));
System.out.println("Prediction for [1, 1, 1, 0, 0, 1, 0, 1]: " + nn.predict(new double[]{1, 1, 1, 0, 0, 1, 0, 1}));
System.out.println("Prediction for [1, 1, 1, 0, 0, 1, 1, 0]: " + nn.predict(new double[]{1, 1, 1, 0, 0, 1, 1, 0}));
System.out.println("Prediction for [1, 1, 1, 0, 0, 1, 1, 1]: " + nn.predict(new double[]{1, 1, 1, 0, 0, 1, 1, 1}));
System.out.println("Prediction for [1, 1, 1, 0, 1, 0, 0, 0]: " + nn.predict(new double[]{1, 1, 1, 0, 1, 0, 0, 0}));
System.out.println("Prediction for [1, 1, 1, 0, 1, 0, 0, 1]: " + nn.predict(new double[]{1, 1, 1, 0, 1, 0, 0, 1}));
System.out.println("Prediction for [1, 1, 1, 0, 1, 0, 1, 0]: " + nn.predict(new double[]{1, 1, 1, 0, 1, 0, 1, 0}));
System.out.println("Prediction for [1, 1, 1, 0, 1, 0, 1, 1]: " + nn.predict(new double[]{1, 1, 1, 0, 1, 0, 1, 1}));
System.out.println("Prediction for [1, 1, 1, 0, 1, 1, 0, 0]: " + nn.predict(new double[]{1, 1, 1, 0, 1, 1, 0, 0}));
System.out.println("Prediction for [1, 1, 1, 0, 1, 1, 0, 1]: " + nn.predict(new double[]{1, 1, 1, 0, 1, 1, 0, 1}));
System.out.println("Prediction for [1, 1, 1, 0, 1, 1, 1, 0]: " + nn.predict(new double[]{1, 1, 1, 0, 1, 1, 1, 0}));
System.out.println("Prediction for [1, 1, 1, 0, 1, 1, 1, 1]: " + nn.predict(new double[]{1, 1, 1, 0, 1, 1, 1, 1}));
System.out.println("Prediction for [1, 1, 1, 1, 0, 0, 0, 0]: " + nn.predict(new double[]{1, 1, 1, 1, 0, 0, 0, 0}));
System.out.println("Prediction for [1, 1, 1, 1, 0, 0, 0, 1]: " + nn.predict(new double[]{1, 1, 1, 1, 0, 0, 0, 1}));
System.out.println("Prediction for [1, 1, 1, 1, 0, 0, 1, 0]: " + nn.predict(new double[]{1, 1, 1, 1, 0, 0, 1, 0}));
System.out.println("Prediction for [1, 1, 1, 1, 0, 0, 1, 1]: " + nn.predict(new double[]{1, 1, 1, 1, 0, 0, 1, 1}));
System.out.println("Prediction for [1, 1, 1, 1, 0, 1, 0, 0]: " + nn.predict(new double[]{1, 1, 1, 1, 0, 1, 0, 0}));
System.out.println("Prediction for [1, 1, 1, 1, 0, 1, 0, 1]: " + nn.predict(new double[]{1, 1, 1, 1, 0, 1, 0, 1}));
System.out.println("Prediction for [1, 1, 1, 1, 0, 1, 1, 0]: " + nn.predict(new double[]{1, 1, 1, 1, 0, 1, 1, 0}));
System.out.println("Prediction for [1, 1, 1, 1, 0, 1, 1, 1]: " + nn.predict(new double[]{1, 1, 1, 1, 0, 1, 1, 1}));
System.out.println("Prediction for [1, 1, 1, 1, 1, 0, 0, 0]: " + nn.predict(new double[]{1, 1, 1, 1, 1, 0, 0, 0}));
System.out.println("Prediction for [1, 1, 1, 1, 1, 0, 0, 1]: " + nn.predict(new double[]{1, 1, 1, 1, 1, 0, 0, 1}));
System.out.println("Prediction for [1, 1, 1, 1, 1, 0, 1, 0]: " + nn.predict(new double[]{1, 1, 1, 1, 1, 0, 1, 0}));
System.out.println("Prediction for [1, 1, 1, 1, 1, 0, 1, 1]: " + nn.predict(new double[]{1, 1, 1, 1, 1, 0, 1, 1}));
System.out.println("Prediction for [1, 1, 1, 1, 1, 1, 0, 0]: " + nn.predict(new double[]{1, 1, 1, 1, 1, 1, 0, 0}));
System.out.println("Prediction for [1, 1, 1, 1, 1, 1, 0, 1]: " + nn.predict(new double[]{1, 1, 1, 1, 1, 1, 0, 1}));
System.out.println("Prediction for [1, 1, 1, 1, 1, 1, 1, 0]: " + nn.predict(new double[]{1, 1, 1, 1, 1, 1, 1, 0}));
System.out.println("Prediction for [1, 1, 1, 1, 1, 1, 1, 1]: " + nn.predict(new double[]{1, 1, 1, 1, 1, 1, 1, 1}));


}


}


 run:
Epoch 10000. Mean error: 0.02287697440085921
Epoch 20000. Mean error: 0.014166625707716164
Epoch 30000. Mean error: 0.009129459849268452
Epoch 40000. Mean error: 0.006841828176043919
Epoch 50000. Mean error: 0.00567235761602261
Epoch 60000. Mean error: 0.004962131900172744
Epoch 70000. Mean error: 0.004497326734313791
Epoch 80000. Mean error: 0.004275845259380384
Epoch 90000. Mean error: 0.0041453052883488085
Epoch 100000. Mean error: 0.004062262123553563
Epoch 110000. Mean error: 0.004009026698502614
Epoch 120000. Mean error: 0.003968548498876892
Epoch 130000. Mean error: 0.003945200641862105
Epoch 140000. Mean error: 0.003930767972347983
Epoch 150000. Mean error: 0.00392177340097129
Epoch 160000. Mean error: 0.003916138888637087
Epoch 170000. Mean error: 0.003912588636981584
Epoch 180000. Mean error: 0.003910307713603723
Epoch 190000. Mean error: 0.003908885306420438
Epoch 200000. Mean error: 0.003907975041400942
Prediction for [0, 0, 0, 0, 0, 0, 0, 0]: 0.0
Prediction for [0, 0, 0, 0, 0, 0, 0, 1]: 1.0002396760102368
Prediction for [0, 0, 0, 0, 0, 0, 1, 0]: 0.0
Prediction for [0, 0, 0, 0, 0, 0, 1, 1]: 1.000073549804653
Prediction for [0, 0, 0, 0, 0, 1, 0, 0]: 0.0
Prediction for [0, 0, 0, 0, 0, 1, 0, 1]: 1.0001648629047235
Prediction for [0, 0, 0, 0, 0, 1, 1, 0]: 0.0
Prediction for [0, 0, 0, 0, 0, 1, 1, 1]: 0.9999256290123881
Prediction for [0, 0, 0, 0, 1, 0, 0, 0]: 0.0
Prediction for [0, 0, 0, 0, 1, 0, 0, 1]: 0.0
Prediction for [0, 0, 0, 0, 1, 0, 1, 0]: 0.0
Prediction for [0, 0, 0, 0, 1, 0, 1, 1]: 1.0001481342838092
Prediction for [0, 0, 0, 0, 1, 1, 0, 0]: 0.0
Prediction for [0, 0, 0, 0, 1, 1, 0, 1]: 0.9999877850696732
Prediction for [0, 0, 0, 0, 1, 1, 1, 0]: 0.0
Prediction for [0, 0, 0, 0, 1, 1, 1, 1]: 0.0
Prediction for [0, 0, 0, 1, 0, 0, 0, 0]: 0.0
Prediction for [0, 0, 0, 1, 0, 0, 0, 1]: 1.0000288093042053
Prediction for [0, 0, 0, 1, 0, 0, 1, 0]: 0.0
Prediction for [0, 0, 0, 1, 0, 0, 1, 1]: 0.9997639084790277
Prediction for [0, 0, 0, 1, 0, 1, 0, 0]: 0.0
Prediction for [0, 0, 0, 1, 0, 1, 0, 1]: 0.0
Prediction for [0, 0, 0, 1, 0, 1, 1, 0]: 0.0
Prediction for [0, 0, 0, 1, 0, 1, 1, 1]: 1.0001190831863198
Prediction for [0, 0, 0, 1, 1, 0, 0, 0]: 0.0
Prediction for [0, 0, 0, 1, 1, 0, 0, 1]: 0.0
Prediction for [0, 0, 0, 1, 1, 0, 1, 0]: 0.0
Prediction for [0, 0, 0, 1, 1, 0, 1, 1]: 0.0
Prediction for [0, 0, 0, 1, 1, 1, 0, 0]: 0.0
Prediction for [0, 0, 0, 1, 1, 1, 0, 1]: 0.9999319677714649
Prediction for [0, 0, 0, 1, 1, 1, 1, 0]: 0.0
Prediction for [0, 0, 0, 1, 1, 1, 1, 1]: 1.0003404389083057
Prediction for [0, 0, 1, 0, 0, 0, 0, 0]: 0.0
Prediction for [0, 0, 1, 0, 0, 0, 0, 1]: 0.0
Prediction for [0, 0, 1, 0, 0, 0, 1, 0]: 0.0
Prediction for [0, 0, 1, 0, 0, 0, 1, 1]: 0.0
Prediction for [0, 0, 1, 0, 0, 1, 0, 0]: 0.0
Prediction for [0, 0, 1, 0, 0, 1, 0, 1]: 1.0000475454581501
Prediction for [0, 0, 1, 0, 0, 1, 1, 0]: 0.0
Prediction for [0, 0, 1, 0, 0, 1, 1, 1]: 0.0
Prediction for [0, 0, 1, 0, 1, 0, 0, 0]: 0.0
Prediction for [0, 0, 1, 0, 1, 0, 0, 1]: 0.9996586371051661
Prediction for [0, 0, 1, 0, 1, 0, 1, 0]: 0.0
Prediction for [0, 0, 1, 0, 1, 0, 1, 1]: 1.0000028179007723
Prediction for [0, 0, 1, 0, 1, 1, 0, 0]: 0.0
Prediction for [0, 0, 1, 0, 1, 1, 0, 1]: 0.0
Prediction for [0, 0, 1, 0, 1, 1, 1, 0]: 0.0
Prediction for [0, 0, 1, 0, 1, 1, 1, 1]: 0.9995298039153093
Prediction for [0, 0, 1, 1, 0, 0, 0, 0]: 0.0
Prediction for [0, 0, 1, 1, 0, 0, 0, 1]: 0.0
Prediction for [0, 0, 1, 1, 0, 0, 1, 0]: 0.0
Prediction for [0, 0, 1, 1, 0, 0, 1, 1]: 0.0
Prediction for [0, 0, 1, 1, 0, 1, 0, 0]: 0.0
Prediction for [0, 0, 1, 1, 0, 1, 0, 1]: 1.000053063623958
Prediction for [0, 0, 1, 1, 0, 1, 1, 0]: 0.0
Prediction for [0, 0, 1, 1, 0, 1, 1, 1]: 0.0
Prediction for [0, 0, 1, 1, 1, 0, 0, 0]: 0.0
Prediction for [0, 0, 1, 1, 1, 0, 0, 1]: 0.0
Prediction for [0, 0, 1, 1, 1, 0, 1, 0]: 0.0
Prediction for [0, 0, 1, 1, 1, 0, 1, 1]: 0.9994418900661293
Prediction for [0, 0, 1, 1, 1, 1, 0, 0]: 0.0
Prediction for [0, 0, 1, 1, 1, 1, 0, 1]: 0.9985201195558622
Prediction for [0, 0, 1, 1, 1, 1, 1, 0]: 0.0
Prediction for [0, 0, 1, 1, 1, 1, 1, 1]: 8.29103443267698E-4
Prediction for [0, 1, 0, 0, 0, 0, 0, 0]: 0.0
Prediction for [0, 1, 0, 0, 0, 0, 0, 1]: 0.0
Prediction for [0, 1, 0, 0, 0, 0, 1, 0]: 0.0
Prediction for [0, 1, 0, 0, 0, 0, 1, 1]: 0.9999782810841431
Prediction for [0, 1, 0, 0, 0, 1, 0, 0]: 0.0
Prediction for [0, 1, 0, 0, 0, 1, 0, 1]: 0.0
Prediction for [0, 1, 0, 0, 0, 1, 1, 0]: 0.0
Prediction for [0, 1, 0, 0, 0, 1, 1, 1]: 0.9999056375664708
Prediction for [0, 1, 0, 0, 1, 0, 0, 0]: 0.0
Prediction for [0, 1, 0, 0, 1, 0, 0, 1]: 0.9998720318399794
Prediction for [0, 1, 0, 0, 1, 0, 1, 0]: 0.0
Prediction for [0, 1, 0, 0, 1, 0, 1, 1]: 0.0
Prediction for [0, 1, 0, 0, 1, 1, 0, 0]: 0.0
Prediction for [0, 1, 0, 0, 1, 1, 0, 1]: 0.0
Prediction for [0, 1, 0, 0, 1, 1, 1, 0]: 0.0
Prediction for [0, 1, 0, 0, 1, 1, 1, 1]: 0.9985244522237924
Prediction for [0, 1, 0, 1, 0, 0, 0, 0]: 0.0
Prediction for [0, 1, 0, 1, 0, 0, 0, 1]: 0.0
Prediction for [0, 1, 0, 1, 0, 0, 1, 0]: 0.0
Prediction for [0, 1, 0, 1, 0, 0, 1, 1]: 0.999698882074032
Prediction for [0, 1, 0, 1, 0, 1, 0, 0]: 0.0
Prediction for [0, 1, 0, 1, 0, 1, 0, 1]: 0.0
Prediction for [0, 1, 0, 1, 0, 1, 1, 0]: 0.0
Prediction for [0, 1, 0, 1, 0, 1, 1, 1]: 0.0
Prediction for [0, 1, 0, 1, 1, 0, 0, 0]: 0.0
Prediction for [0, 1, 0, 1, 1, 0, 0, 1]: 0.9997755881672816
Prediction for [0, 1, 0, 1, 1, 0, 1, 0]: 0.0
Prediction for [0, 1, 0, 1, 1, 0, 1, 1]: 0.0
Prediction for [0, 1, 0, 1, 1, 1, 0, 0]: 0.0
Prediction for [0, 1, 0, 1, 1, 1, 0, 1]: 0.0
Prediction for [0, 1, 0, 1, 1, 1, 1, 0]: 0.0
Prediction for [0, 1, 0, 1, 1, 1, 1, 1]: 0.0
Prediction for [0, 1, 1, 0, 0, 0, 0, 0]: 0.0
Prediction for [0, 1, 1, 0, 0, 0, 0, 1]: 0.9991710505573721
Prediction for [0, 1, 1, 0, 0, 0, 1, 0]: 0.0
Prediction for [0, 1, 1, 0, 0, 0, 1, 1]: 0.0
Prediction for [0, 1, 1, 0, 0, 1, 0, 0]: 0.0
Prediction for [0, 1, 1, 0, 0, 1, 0, 1]: 1.0000549707847384
Prediction for [0, 1, 1, 0, 0, 1, 1, 0]: 0.0
Prediction for [0, 1, 1, 0, 0, 1, 1, 1]: 0.9912238113591538
Prediction for [0, 1, 1, 0, 1, 0, 0, 0]: 0.0
Prediction for [0, 1, 1, 0, 1, 0, 0, 1]: 8.873951351509035E-4
Prediction for [0, 1, 1, 0, 1, 0, 1, 0]: 0.0
Prediction for [0, 1, 1, 0, 1, 0, 1, 1]: 0.9989553071131994
Prediction for [0, 1, 1, 0, 1, 1, 0, 0]: 0.0
Prediction for [0, 1, 1, 0, 1, 1, 0, 1]: 0.9987057794724432
Prediction for [0, 1, 1, 0, 1, 1, 1, 0]: 0.0
Prediction for [0, 1, 1, 0, 1, 1, 1, 1]: 0.007308804190367724
Prediction for [0, 1, 1, 1, 0, 0, 0, 0]: 0.0
Prediction for [0, 1, 1, 1, 0, 0, 0, 1]: 0.999810972353874
Prediction for [0, 1, 1, 1, 0, 0, 1, 0]: 0.0
Prediction for [0, 1, 1, 1, 0, 0, 1, 1]: 0.0
Prediction for [0, 1, 1, 1, 0, 1, 0, 0]: 0.0
Prediction for [0, 1, 1, 1, 0, 1, 0, 1]: 0.0
Prediction for [0, 1, 1, 1, 0, 1, 1, 0]: 0.0
Prediction for [0, 1, 1, 1, 0, 1, 1, 1]: 0.0
Prediction for [0, 1, 1, 1, 1, 0, 0, 0]: 0.0
Prediction for [0, 1, 1, 1, 1, 0, 0, 1]: 0.0
Prediction for [0, 1, 1, 1, 1, 0, 1, 0]: 0.0
Prediction for [0, 1, 1, 1, 1, 0, 1, 1]: 0.0011432086006193387
Prediction for [0, 1, 1, 1, 1, 1, 0, 0]: 0.0
Prediction for [0, 1, 1, 1, 1, 1, 0, 1]: 0.0
Prediction for [0, 1, 1, 1, 1, 1, 1, 0]: 0.0
Prediction for [0, 1, 1, 1, 1, 1, 1, 1]: 0.9998262392016439
Prediction for [1, 0, 0, 0, 0, 0, 0, 0]: 0.0
Prediction for [1, 0, 0, 0, 0, 0, 0, 1]: 0.0
Prediction for [1, 0, 0, 0, 0, 0, 1, 0]: 0.0
Prediction for [1, 0, 0, 0, 0, 0, 1, 1]: 1.0002643177127801
Prediction for [1, 0, 0, 0, 0, 1, 0, 0]: 0.0
Prediction for [1, 0, 0, 0, 0, 1, 0, 1]: 0.0
Prediction for [1, 0, 0, 0, 0, 1, 1, 0]: 0.0
Prediction for [1, 0, 0, 0, 0, 1, 1, 1]: 2.9922117926517444E-5
Prediction for [1, 0, 0, 0, 1, 0, 0, 0]: 0.0
Prediction for [1, 0, 0, 0, 1, 0, 0, 1]: 1.0000109097269831
Prediction for [1, 0, 0, 0, 1, 0, 1, 0]: 0.0
Prediction for [1, 0, 0, 0, 1, 0, 1, 1]: 1.000053838844293
Prediction for [1, 0, 0, 0, 1, 1, 0, 0]: 0.0
Prediction for [1, 0, 0, 0, 1, 1, 0, 1]: 0.0
Prediction for [1, 0, 0, 0, 1, 1, 1, 0]: 0.0
Prediction for [1, 0, 0, 0, 1, 1, 1, 1]: 0.0
Prediction for [1, 0, 0, 1, 0, 0, 0, 0]: 0.0
Prediction for [1, 0, 0, 1, 0, 0, 0, 1]: 5.5847984548051954E-5
Prediction for [1, 0, 0, 1, 0, 0, 1, 0]: 0.0
Prediction for [1, 0, 0, 1, 0, 0, 1, 1]: 0.0
Prediction for [1, 0, 0, 1, 0, 1, 0, 0]: 0.0
Prediction for [1, 0, 0, 1, 0, 1, 0, 1]: 0.9998508359008422
Prediction for [1, 0, 0, 1, 0, 1, 1, 0]: 0.0
Prediction for [1, 0, 0, 1, 0, 1, 1, 1]: 0.999589947501633
Prediction for [1, 0, 0, 1, 1, 0, 0, 0]: 0.0
Prediction for [1, 0, 0, 1, 1, 0, 0, 1]: 0.0
Prediction for [1, 0, 0, 1, 1, 0, 1, 0]: 0.0
Prediction for [1, 0, 0, 1, 1, 0, 1, 1]: 2.807210240041158E-4
Prediction for [1, 0, 0, 1, 1, 1, 0, 0]: 0.0
Prediction for [1, 0, 0, 1, 1, 1, 0, 1]: 1.0001931269944295
Prediction for [1, 0, 0, 1, 1, 1, 1, 0]: 0.0
Prediction for [1, 0, 0, 1, 1, 1, 1, 1]: 0.0
Prediction for [1, 0, 1, 0, 0, 0, 0, 0]: 0.0
Prediction for [1, 0, 1, 0, 0, 0, 0, 1]: 2.3369076458656934E-4
Prediction for [1, 0, 1, 0, 0, 0, 1, 0]: 0.0
Prediction for [1, 0, 1, 0, 0, 0, 1, 1]: 1.0000530463166948
Prediction for [1, 0, 1, 0, 0, 1, 0, 0]: 0.0
Prediction for [1, 0, 1, 0, 0, 1, 0, 1]: 0.0
Prediction for [1, 0, 1, 0, 0, 1, 1, 0]: 0.0
Prediction for [1, 0, 1, 0, 0, 1, 1, 1]: 0.9984847704618565
Prediction for [1, 0, 1, 0, 1, 0, 0, 0]: 0.0
Prediction for [1, 0, 1, 0, 1, 0, 0, 1]: 0.0
Prediction for [1, 0, 1, 0, 1, 0, 1, 0]: 0.0
Prediction for [1, 0, 1, 0, 1, 0, 1, 1]: 0.0012304104588602982
Prediction for [1, 0, 1, 0, 1, 1, 0, 0]: 0.0
Prediction for [1, 0, 1, 0, 1, 1, 0, 1]: 0.9988902305503888
Prediction for [1, 0, 1, 0, 1, 1, 1, 0]: 0.0
Prediction for [1, 0, 1, 0, 1, 1, 1, 1]: 0.0013929410989526048
Prediction for [1, 0, 1, 1, 0, 0, 0, 0]: 0.0
Prediction for [1, 0, 1, 1, 0, 0, 0, 1]: 0.0
Prediction for [1, 0, 1, 1, 0, 0, 1, 0]: 0.0
Prediction for [1, 0, 1, 1, 0, 0, 1, 1]: 1.0002552991372369
Prediction for [1, 0, 1, 1, 0, 1, 0, 0]: 0.0
Prediction for [1, 0, 1, 1, 0, 1, 0, 1]: 1.0002735125659727
Prediction for [1, 0, 1, 1, 0, 1, 1, 0]: 0.0
Prediction for [1, 0, 1, 1, 0, 1, 1, 1]: 0.0
Prediction for [1, 0, 1, 1, 1, 0, 0, 0]: 0.0
Prediction for [1, 0, 1, 1, 1, 0, 0, 1]: 0.0
Prediction for [1, 0, 1, 1, 1, 0, 1, 0]: 0.0
Prediction for [1, 0, 1, 1, 1, 0, 1, 1]: 0.0
Prediction for [1, 0, 1, 1, 1, 1, 0, 0]: 0.0
Prediction for [1, 0, 1, 1, 1, 1, 0, 1]: 0.0016007416738244018
Prediction for [1, 0, 1, 1, 1, 1, 1, 0]: 0.0
Prediction for [1, 0, 1, 1, 1, 1, 1, 1]: 0.9992947996206096
Prediction for [1, 1, 0, 0, 0, 0, 0, 0]: 0.0
Prediction for [1, 1, 0, 0, 0, 0, 0, 1]: 1.0001053458187812
Prediction for [1, 1, 0, 0, 0, 0, 1, 0]: 0.0
Prediction for [1, 1, 0, 0, 0, 0, 1, 1]: 0.0
Prediction for [1, 1, 0, 0, 0, 1, 0, 0]: 0.0
Prediction for [1, 1, 0, 0, 0, 1, 0, 1]: 0.9995775815180679
Prediction for [1, 1, 0, 0, 0, 1, 1, 0]: 0.0
Prediction for [1, 1, 0, 0, 0, 1, 1, 1]: 1.0008752175214708
Prediction for [1, 1, 0, 0, 1, 0, 0, 0]: 0.0
Prediction for [1, 1, 0, 0, 1, 0, 0, 1]: 0.0
Prediction for [1, 1, 0, 0, 1, 0, 1, 0]: 0.0
Prediction for [1, 1, 0, 0, 1, 0, 1, 1]: 0.0
Prediction for [1, 1, 0, 0, 1, 1, 0, 0]: 0.0
Prediction for [1, 1, 0, 0, 1, 1, 0, 1]: 7.62632073844749E-4
Prediction for [1, 1, 0, 0, 1, 1, 1, 0]: 0.0
Prediction for [1, 1, 0, 0, 1, 1, 1, 1]: 6.25783094595711E-4
Prediction for [1, 1, 0, 1, 0, 0, 0, 0]: 0.0
Prediction for [1, 1, 0, 1, 0, 0, 0, 1]: 0.0
Prediction for [1, 1, 0, 1, 0, 0, 1, 0]: 0.0
Prediction for [1, 1, 0, 1, 0, 0, 1, 1]: 1.0001554066923442
Prediction for [1, 1, 0, 1, 0, 1, 0, 0]: 0.0
Prediction for [1, 1, 0, 1, 0, 1, 0, 1]: 0.0
Prediction for [1, 1, 0, 1, 0, 1, 1, 0]: 0.0
Prediction for [1, 1, 0, 1, 0, 1, 1, 1]: 3.4988672418911904E-4
Prediction for [1, 1, 0, 1, 1, 0, 0, 0]: 0.0
Prediction for [1, 1, 0, 1, 1, 0, 0, 1]: 0.0
Prediction for [1, 1, 0, 1, 1, 0, 1, 0]: 0.0
Prediction for [1, 1, 0, 1, 1, 0, 1, 1]: 0.0
Prediction for [1, 1, 0, 1, 1, 1, 0, 0]: 0.0
Prediction for [1, 1, 0, 1, 1, 1, 0, 1]: 7.196000341442854E-4
Prediction for [1, 1, 0, 1, 1, 1, 1, 0]: 0.0
Prediction for [1, 1, 0, 1, 1, 1, 1, 1]: 0.9997377076301417
Prediction for [1, 1, 1, 0, 0, 0, 0, 0]: 0.0
Prediction for [1, 1, 1, 0, 0, 0, 0, 1]: 0.005120553154234209
Prediction for [1, 1, 1, 0, 0, 0, 1, 0]: 0.0
Prediction for [1, 1, 1, 0, 0, 0, 1, 1]: 0.9993977119139466
Prediction for [1, 1, 1, 0, 0, 1, 0, 0]: 0.0
Prediction for [1, 1, 1, 0, 0, 1, 0, 1]: 0.9967633518764378
Prediction for [1, 1, 1, 0, 0, 1, 1, 0]: 0.0
Prediction for [1, 1, 1, 0, 0, 1, 1, 1]: 0.009537857931912974
Prediction for [1, 1, 1, 0, 1, 0, 0, 0]: 0.0
Prediction for [1, 1, 1, 0, 1, 0, 0, 1]: 0.996080472054107
Prediction for [1, 1, 1, 0, 1, 0, 1, 0]: 0.0
Prediction for [1, 1, 1, 0, 1, 0, 1, 1]: 8.731463822684304E-4
Prediction for [1, 1, 1, 0, 1, 1, 0, 0]: 0.0
Prediction for [1, 1, 1, 0, 1, 1, 0, 1]: 0.005090637076019533
Prediction for [1, 1, 1, 0, 1, 1, 1, 0]: 0.0
Prediction for [1, 1, 1, 0, 1, 1, 1, 1]: 0.9924698020086469
Prediction for [1, 1, 1, 1, 0, 0, 0, 0]: 0.0
Prediction for [1, 1, 1, 1, 0, 0, 0, 1]: 0.9992275293460162
Prediction for [1, 1, 1, 1, 0, 0, 1, 0]: 0.0
Prediction for [1, 1, 1, 1, 0, 0, 1, 1]: 0.0
Prediction for [1, 1, 1, 1, 0, 1, 0, 0]: 0.0
Prediction for [1, 1, 1, 1, 0, 1, 0, 1]: 0.0
Prediction for [1, 1, 1, 1, 0, 1, 1, 0]: 0.0
Prediction for [1, 1, 1, 1, 0, 1, 1, 1]: 7.154212054905074E-4
Prediction for [1, 1, 1, 1, 1, 0, 0, 0]: 0.0
Prediction for [1, 1, 1, 1, 1, 0, 0, 1]: 0.0
Prediction for [1, 1, 1, 1, 1, 0, 1, 0]: 0.0
Prediction for [1, 1, 1, 1, 1, 0, 1, 1]: 0.9991975677567666
Prediction for [1, 1, 1, 1, 1, 1, 0, 0]: 0.0
Prediction for [1, 1, 1, 1, 1, 1, 0, 1]: 0.0
Prediction for [1, 1, 1, 1, 1, 1, 1, 0]: 0.0
Prediction for [1, 1, 1, 1, 1, 1, 1, 1]: 3.634046610150321E-4
BUILD SUCCESSFUL (total time: 40 seconds)

Edited April 15, 2023 by Dietmar

**Dietmar** · April 15, 2023

The error is about 10^-30 (!) . I think, when you use more exact varablen typ in Java, the error goes even much more down.

So, there is some Magic in the Primes

Dietmar

public class Multiof3 {
    private final int numInputNodes = 8;
    private final int numHiddenNodes = 64;
    private final int numOutputNodes = 1;
    private final double learningRate = 0.02;
    private final int numEpochs = 200000;
    private final double errorThreshold = 0.00000000000000000000000000000001;


run:
Epoch 10000. Mean error: 1.9709204800408654E-8
Epoch 20000. Mean error: 1.5248792997769017E-14
Epoch 30000. Mean error: 1.2735893690568209E-20
Epoch 40000. Mean error: 1.0522858627981606E-26
Epoch 50000. Mean error: 9.187230511789512E-30
Epoch 60000. Mean error: 7.319165734434498E-30
Epoch 70000. Mean error: 1.2222598419172091E-29
Epoch 80000. Mean error: 7.106494920325538E-30
Epoch 90000. Mean error: 3.3912526777154975E-30
Epoch 100000. Mean error: 3.003331266463913E-30
Epoch 110000. Mean error: 1.6632731232044737E-29
Epoch 120000. Mean error: 1.446087808935248E-29
Epoch 130000. Mean error: 4.873358084949771E-30
Epoch 140000. Mean error: 3.0600348569984264E-30
Epoch 150000. Mean error: 5.668457197631275E-30
Epoch 160000. Mean error: 6.778117846276438E-30
Epoch 170000. Mean error: 3.4093022524130516E-30
Epoch 180000. Mean error: 1.1896342877327355E-29
Epoch 190000. Mean error: 7.757964798138825E-30
Epoch 200000. Mean error: 7.6522733739095E-30
Prediction for [0, 0, 0, 0, 0, 0, 0, 0]: 0.0
Prediction for [0, 0, 0, 0, 0, 0, 0, 1]: 1.0000000000000027
Prediction for [0, 0, 0, 0, 0, 0, 1, 0]: 0.9999999999999972
Prediction for [0, 0, 0, 0, 0, 0, 1, 1]: 0.9999999999999946
Prediction for [0, 0, 0, 0, 0, 1, 0, 0]: 0.0
Prediction for [0, 0, 0, 0, 0, 1, 0, 1]: 0.9999999999999966
Prediction for [0, 0, 0, 0, 0, 1, 1, 0]: 0.0
Prediction for [0, 0, 0, 0, 0, 1, 1, 1]: 0.9999999999999946
Prediction for [0, 0, 0, 0, 1, 0, 0, 0]: 0.0
Prediction for [0, 0, 0, 0, 1, 0, 0, 1]: 0.0
Prediction for [0, 0, 0, 0, 1, 0, 1, 0]: 0.0
Prediction for [0, 0, 0, 0, 1, 0, 1, 1]: 1.0000000000000013
Prediction for [0, 0, 0, 0, 1, 1, 0, 0]: 0.0
Prediction for [0, 0, 0, 0, 1, 1, 0, 1]: 1.0000000000000049
Prediction for [0, 0, 0, 0, 1, 1, 1, 0]: 0.0
Prediction for [0, 0, 0, 0, 1, 1, 1, 1]: 0.0
Prediction for [0, 0, 0, 1, 0, 0, 0, 0]: 0.0
Prediction for [0, 0, 0, 1, 0, 0, 0, 1]: 1.0000000000000018
Prediction for [0, 0, 0, 1, 0, 0, 1, 0]: 0.0
Prediction for [0, 0, 0, 1, 0, 0, 1, 1]: 0.9999999999999961
Prediction for [0, 0, 0, 1, 0, 1, 0, 0]: 0.0
Prediction for [0, 0, 0, 1, 0, 1, 0, 1]: 0.0
Prediction for [0, 0, 0, 1, 0, 1, 1, 0]: 0.0
Prediction for [0, 0, 0, 1, 0, 1, 1, 1]: 0.9999999999999941
Prediction for [0, 0, 0, 1, 1, 0, 0, 0]: 0.0
Prediction for [0, 0, 0, 1, 1, 0, 0, 1]: 0.0
Prediction for [0, 0, 0, 1, 1, 0, 1, 0]: 0.0
Prediction for [0, 0, 0, 1, 1, 0, 1, 1]: 0.0
Prediction for [0, 0, 0, 1, 1, 1, 0, 0]: 0.0
Prediction for [0, 0, 0, 1, 1, 1, 0, 1]: 1.0000000000000027
Prediction for [0, 0, 0, 1, 1, 1, 1, 0]: 0.0
Prediction for [0, 0, 0, 1, 1, 1, 1, 1]: 0.9999999999999937
Prediction for [0, 0, 1, 0, 0, 0, 0, 0]: 0.0
Prediction for [0, 0, 1, 0, 0, 0, 0, 1]: 0.0
Prediction for [0, 0, 1, 0, 0, 0, 1, 0]: 0.0
Prediction for [0, 0, 1, 0, 0, 0, 1, 1]: 6.994405055138486E-15
Prediction for [0, 0, 1, 0, 0, 1, 0, 0]: 0.0
Prediction for [0, 0, 1, 0, 0, 1, 0, 1]: 1.000000000000003
Prediction for [0, 0, 1, 0, 0, 1, 1, 0]: 0.0
Prediction for [0, 0, 1, 0, 0, 1, 1, 1]: 0.0
Prediction for [0, 0, 1, 0, 1, 0, 0, 0]: 0.0
Prediction for [0, 0, 1, 0, 1, 0, 0, 1]: 0.9999999999999928
Prediction for [0, 0, 1, 0, 1, 0, 1, 0]: 0.0
Prediction for [0, 0, 1, 0, 1, 0, 1, 1]: 1.0000000000000013
Prediction for [0, 0, 1, 0, 1, 1, 0, 0]: 0.0
Prediction for [0, 0, 1, 0, 1, 1, 0, 1]: 0.0
Prediction for [0, 0, 1, 0, 1, 1, 1, 0]: 0.0
Prediction for [0, 0, 1, 0, 1, 1, 1, 1]: 0.9999999999999952
Prediction for [0, 0, 1, 1, 0, 0, 0, 0]: 0.0
Prediction for [0, 0, 1, 1, 0, 0, 0, 1]: 0.0
Prediction for [0, 0, 1, 1, 0, 0, 1, 0]: 0.0
Prediction for [0, 0, 1, 1, 0, 0, 1, 1]: 0.0
Prediction for [0, 0, 1, 1, 0, 1, 0, 0]: 0.0
Prediction for [0, 0, 1, 1, 0, 1, 0, 1]: 1.0000000000000036
Prediction for [0, 0, 1, 1, 0, 1, 1, 0]: 0.0
Prediction for [0, 0, 1, 1, 0, 1, 1, 1]: 0.0
Prediction for [0, 0, 1, 1, 1, 0, 0, 0]: 0.0
Prediction for [0, 0, 1, 1, 1, 0, 0, 1]: 0.0
Prediction for [0, 0, 1, 1, 1, 0, 1, 0]: 0.0
Prediction for [0, 0, 1, 1, 1, 0, 1, 1]: 1.000000000000004
Prediction for [0, 0, 1, 1, 1, 1, 0, 0]: 0.0
Prediction for [0, 0, 1, 1, 1, 1, 0, 1]: 1.0000000000000018
Prediction for [0, 0, 1, 1, 1, 1, 1, 0]: 0.0
Prediction for [0, 0, 1, 1, 1, 1, 1, 1]: 0.0
Prediction for [0, 1, 0, 0, 0, 0, 0, 0]: 0.0
Prediction for [0, 1, 0, 0, 0, 0, 0, 1]: 5.329070518200751E-15
Prediction for [0, 1, 0, 0, 0, 0, 1, 0]: 0.0
Prediction for [0, 1, 0, 0, 0, 0, 1, 1]: 1.0000000000000075
Prediction for [0, 1, 0, 0, 0, 1, 0, 0]: 0.0
Prediction for [0, 1, 0, 0, 0, 1, 0, 1]: 0.0
Prediction for [0, 1, 0, 0, 0, 1, 1, 0]: 0.0
Prediction for [0, 1, 0, 0, 0, 1, 1, 1]: 1.0000000000000098
Prediction for [0, 1, 0, 0, 1, 0, 0, 0]: 0.0
Prediction for [0, 1, 0, 0, 1, 0, 0, 1]: 0.9999999999999948
Prediction for [0, 1, 0, 0, 1, 0, 1, 0]: 0.0
Prediction for [0, 1, 0, 0, 1, 0, 1, 1]: 0.0
Prediction for [0, 1, 0, 0, 1, 1, 0, 0]: 0.0
Prediction for [0, 1, 0, 0, 1, 1, 0, 1]: 0.0
Prediction for [0, 1, 0, 0, 1, 1, 1, 0]: 0.0
Prediction for [0, 1, 0, 0, 1, 1, 1, 1]: 1.0000000000000013
Prediction for [0, 1, 0, 1, 0, 0, 0, 0]: 0.0
Prediction for [0, 1, 0, 1, 0, 0, 0, 1]: 0.0
Prediction for [0, 1, 0, 1, 0, 0, 1, 0]: 0.0
Prediction for [0, 1, 0, 1, 0, 0, 1, 1]: 1.0000000000000036
Prediction for [0, 1, 0, 1, 0, 1, 0, 0]: 0.0
Prediction for [0, 1, 0, 1, 0, 1, 0, 1]: 0.0
Prediction for [0, 1, 0, 1, 0, 1, 1, 0]: 0.0
Prediction for [0, 1, 0, 1, 0, 1, 1, 1]: 0.0
Prediction for [0, 1, 0, 1, 1, 0, 0, 0]: 0.0
Prediction for [0, 1, 0, 1, 1, 0, 0, 1]: 0.9999999999999932
Prediction for [0, 1, 0, 1, 1, 0, 1, 0]: 0.0
Prediction for [0, 1, 0, 1, 1, 0, 1, 1]: 3.858025010572419E-15
Prediction for [0, 1, 0, 1, 1, 1, 0, 0]: 0.0
Prediction for [0, 1, 0, 1, 1, 1, 0, 1]: 0.0
Prediction for [0, 1, 0, 1, 1, 1, 1, 0]: 0.0
Prediction for [0, 1, 0, 1, 1, 1, 1, 1]: 0.0
Prediction for [0, 1, 1, 0, 0, 0, 0, 0]: 0.0
Prediction for [0, 1, 1, 0, 0, 0, 0, 1]: 0.9999999999999983
Prediction for [0, 1, 1, 0, 0, 0, 1, 0]: 0.0
Prediction for [0, 1, 1, 0, 0, 0, 1, 1]: 0.0
Prediction for [0, 1, 1, 0, 0, 1, 0, 0]: 0.0
Prediction for [0, 1, 1, 0, 0, 1, 0, 1]: 0.9999999999999986
Prediction for [0, 1, 1, 0, 0, 1, 1, 0]: 0.0
Prediction for [0, 1, 1, 0, 0, 1, 1, 1]: 0.9999999999999939
Prediction for [0, 1, 1, 0, 1, 0, 0, 0]: 0.0
Prediction for [0, 1, 1, 0, 1, 0, 0, 1]: 0.0
Prediction for [0, 1, 1, 0, 1, 0, 1, 0]: 0.0
Prediction for [0, 1, 1, 0, 1, 0, 1, 1]: 0.9999999999999921
Prediction for [0, 1, 1, 0, 1, 1, 0, 0]: 0.0
Prediction for [0, 1, 1, 0, 1, 1, 0, 1]: 0.9999999999999892
Prediction for [0, 1, 1, 0, 1, 1, 1, 0]: 0.0
Prediction for [0, 1, 1, 0, 1, 1, 1, 1]: 7.771561172376096E-16
Prediction for [0, 1, 1, 1, 0, 0, 0, 0]: 0.0
Prediction for [0, 1, 1, 1, 0, 0, 0, 1]: 1.0000000000000027
Prediction for [0, 1, 1, 1, 0, 0, 1, 0]: 0.0
Prediction for [0, 1, 1, 1, 0, 0, 1, 1]: 0.0
Prediction for [0, 1, 1, 1, 0, 1, 0, 0]: 0.0
Prediction for [0, 1, 1, 1, 0, 1, 0, 1]: 0.0
Prediction for [0, 1, 1, 1, 0, 1, 1, 0]: 0.0
Prediction for [0, 1, 1, 1, 0, 1, 1, 1]: 0.0
Prediction for [0, 1, 1, 1, 1, 0, 0, 0]: 0.0
Prediction for [0, 1, 1, 1, 1, 0, 0, 1]: 5.412337245047638E-15
Prediction for [0, 1, 1, 1, 1, 0, 1, 0]: 0.0
Prediction for [0, 1, 1, 1, 1, 0, 1, 1]: 0.0
Prediction for [0, 1, 1, 1, 1, 1, 0, 0]: 0.0
Prediction for [0, 1, 1, 1, 1, 1, 0, 1]: 9.43689570931383E-16
Prediction for [0, 1, 1, 1, 1, 1, 1, 0]: 0.0
Prediction for [0, 1, 1, 1, 1, 1, 1, 1]: 0.9999999999999954
Prediction for [1, 0, 0, 0, 0, 0, 0, 0]: 0.0
Prediction for [1, 0, 0, 0, 0, 0, 0, 1]: 0.0
Prediction for [1, 0, 0, 0, 0, 0, 1, 0]: 0.0
Prediction for [1, 0, 0, 0, 0, 0, 1, 1]: 0.9999999999999972
Prediction for [1, 0, 0, 0, 0, 1, 0, 0]: 0.0
Prediction for [1, 0, 0, 0, 0, 1, 0, 1]: 0.0
Prediction for [1, 0, 0, 0, 0, 1, 1, 0]: 0.0
Prediction for [1, 0, 0, 0, 0, 1, 1, 1]: 0.0
Prediction for [1, 0, 0, 0, 1, 0, 0, 0]: 0.0
Prediction for [1, 0, 0, 0, 1, 0, 0, 1]: 0.999999999999997
Prediction for [1, 0, 0, 0, 1, 0, 1, 0]: 0.0
Prediction for [1, 0, 0, 0, 1, 0, 1, 1]: 1.0000000000000027
Prediction for [1, 0, 0, 0, 1, 1, 0, 0]: 0.0
Prediction for [1, 0, 0, 0, 1, 1, 0, 1]: 0.0
Prediction for [1, 0, 0, 0, 1, 1, 1, 0]: 0.0
Prediction for [1, 0, 0, 0, 1, 1, 1, 1]: 0.0
Prediction for [1, 0, 0, 1, 0, 0, 0, 0]: 0.0
Prediction for [1, 0, 0, 1, 0, 0, 0, 1]: 0.0
Prediction for [1, 0, 0, 1, 0, 0, 1, 0]: 0.0
Prediction for [1, 0, 0, 1, 0, 0, 1, 1]: 0.0
Prediction for [1, 0, 0, 1, 0, 1, 0, 0]: 0.0
Prediction for [1, 0, 0, 1, 0, 1, 0, 1]: 0.9999999999999972
Prediction for [1, 0, 0, 1, 0, 1, 1, 0]: 0.0
Prediction for [1, 0, 0, 1, 0, 1, 1, 1]: 1.0000000000000067
Prediction for [1, 0, 0, 1, 1, 0, 0, 0]: 0.0
Prediction for [1, 0, 0, 1, 1, 0, 0, 1]: 0.0
Prediction for [1, 0, 0, 1, 1, 0, 1, 0]: 0.0
Prediction for [1, 0, 0, 1, 1, 0, 1, 1]: 0.0
Prediction for [1, 0, 0, 1, 1, 1, 0, 0]: 0.0
Prediction for [1, 0, 0, 1, 1, 1, 0, 1]: 0.9999999999999957
Prediction for [1, 0, 0, 1, 1, 1, 1, 0]: 0.0
Prediction for [1, 0, 0, 1, 1, 1, 1, 1]: 0.0
Prediction for [1, 0, 1, 0, 0, 0, 0, 0]: 0.0
Prediction for [1, 0, 1, 0, 0, 0, 0, 1]: 0.0
Prediction for [1, 0, 1, 0, 0, 0, 1, 0]: 0.0
Prediction for [1, 0, 1, 0, 0, 0, 1, 1]: 1.0000000000000009
Prediction for [1, 0, 1, 0, 0, 1, 0, 0]: 0.0
Prediction for [1, 0, 1, 0, 0, 1, 0, 1]: 0.0
Prediction for [1, 0, 1, 0, 0, 1, 1, 0]: 0.0
Prediction for [1, 0, 1, 0, 0, 1, 1, 1]: 0.999999999999993
Prediction for [1, 0, 1, 0, 1, 0, 0, 0]: 0.0
Prediction for [1, 0, 1, 0, 1, 0, 0, 1]: 0.0
Prediction for [1, 0, 1, 0, 1, 0, 1, 0]: 0.0
Prediction for [1, 0, 1, 0, 1, 0, 1, 1]: 0.0
Prediction for [1, 0, 1, 0, 1, 1, 0, 0]: 0.0
Prediction for [1, 0, 1, 0, 1, 1, 0, 1]: 1.0000000000000049
Prediction for [1, 0, 1, 0, 1, 1, 1, 0]: 0.0
Prediction for [1, 0, 1, 0, 1, 1, 1, 1]: 6.772360450213455E-15
Prediction for [1, 0, 1, 1, 0, 0, 0, 0]: 0.0
Prediction for [1, 0, 1, 1, 0, 0, 0, 1]: 0.0
Prediction for [1, 0, 1, 1, 0, 0, 1, 0]: 0.0
Prediction for [1, 0, 1, 1, 0, 0, 1, 1]: 0.999999999999995
Prediction for [1, 0, 1, 1, 0, 1, 0, 0]: 0.0
Prediction for [1, 0, 1, 1, 0, 1, 0, 1]: 0.999999999999995
Prediction for [1, 0, 1, 1, 0, 1, 1, 0]: 0.0
Prediction for [1, 0, 1, 1, 0, 1, 1, 1]: 0.0
Prediction for [1, 0, 1, 1, 1, 0, 0, 0]: 0.0
Prediction for [1, 0, 1, 1, 1, 0, 0, 1]: 0.0
Prediction for [1, 0, 1, 1, 1, 0, 1, 0]: 0.0
Prediction for [1, 0, 1, 1, 1, 0, 1, 1]: 0.0
Prediction for [1, 0, 1, 1, 1, 1, 0, 0]: 0.0
Prediction for [1, 0, 1, 1, 1, 1, 0, 1]: 0.0
Prediction for [1, 0, 1, 1, 1, 1, 1, 0]: 0.0
Prediction for [1, 0, 1, 1, 1, 1, 1, 1]: 1.000000000000003
Prediction for [1, 1, 0, 0, 0, 0, 0, 0]: 0.0
Prediction for [1, 1, 0, 0, 0, 0, 0, 1]: 0.9999999999999932
Prediction for [1, 1, 0, 0, 0, 0, 1, 0]: 0.0
Prediction for [1, 1, 0, 0, 0, 0, 1, 1]: 0.0
Prediction for [1, 1, 0, 0, 0, 1, 0, 0]: 0.0
Prediction for [1, 1, 0, 0, 0, 1, 0, 1]: 1.0000000000000062
Prediction for [1, 1, 0, 0, 0, 1, 1, 0]: 0.0
Prediction for [1, 1, 0, 0, 0, 1, 1, 1]: 0.9999999999999994
Prediction for [1, 1, 0, 0, 1, 0, 0, 0]: 0.0
Prediction for [1, 1, 0, 0, 1, 0, 0, 1]: 4.829470157119431E-15
Prediction for [1, 1, 0, 0, 1, 0, 1, 0]: 0.0
Prediction for [1, 1, 0, 0, 1, 0, 1, 1]: 0.0
Prediction for [1, 1, 0, 0, 1, 1, 0, 0]: 0.0
Prediction for [1, 1, 0, 0, 1, 1, 0, 1]: 0.0
Prediction for [1, 1, 0, 0, 1, 1, 1, 0]: 0.0
Prediction for [1, 1, 0, 0, 1, 1, 1, 1]: 0.0
Prediction for [1, 1, 0, 1, 0, 0, 0, 0]: 0.0
Prediction for [1, 1, 0, 1, 0, 0, 0, 1]: 0.0
Prediction for [1, 1, 0, 1, 0, 0, 1, 0]: 0.0
Prediction for [1, 1, 0, 1, 0, 0, 1, 1]: 0.9999999999999941
Prediction for [1, 1, 0, 1, 0, 1, 0, 0]: 0.0
Prediction for [1, 1, 0, 1, 0, 1, 0, 1]: 0.0
Prediction for [1, 1, 0, 1, 0, 1, 1, 0]: 0.0
Prediction for [1, 1, 0, 1, 0, 1, 1, 1]: 0.0
Prediction for [1, 1, 0, 1, 1, 0, 0, 0]: 0.0
Prediction for [1, 1, 0, 1, 1, 0, 0, 1]: 2.248201624865942E-15
Prediction for [1, 1, 0, 1, 1, 0, 1, 0]: 0.0
Prediction for [1, 1, 0, 1, 1, 0, 1, 1]: 2.9976021664879227E-15
Prediction for [1, 1, 0, 1, 1, 1, 0, 0]: 0.0
Prediction for [1, 1, 0, 1, 1, 1, 0, 1]: 0.0
Prediction for [1, 1, 0, 1, 1, 1, 1, 0]: 0.0
Prediction for [1, 1, 0, 1, 1, 1, 1, 1]: 0.9999999999999986
Prediction for [1, 1, 1, 0, 0, 0, 0, 0]: 0.0
Prediction for [1, 1, 1, 0, 0, 0, 0, 1]: 7.965850201685498E-15
Prediction for [1, 1, 1, 0, 0, 0, 1, 0]: 0.0
Prediction for [1, 1, 1, 0, 0, 0, 1, 1]: 0.9999999999999954
Prediction for [1, 1, 1, 0, 0, 1, 0, 0]: 0.0
Prediction for [1, 1, 1, 0, 0, 1, 0, 1]: 0.9999999999999977
Prediction for [1, 1, 1, 0, 0, 1, 1, 0]: 0.0
Prediction for [1, 1, 1, 0, 0, 1, 1, 1]: 0.0
Prediction for [1, 1, 1, 0, 1, 0, 0, 0]: 0.0
Prediction for [1, 1, 1, 0, 1, 0, 0, 1]: 1.0000000000000013
Prediction for [1, 1, 1, 0, 1, 0, 1, 0]: 0.0
Prediction for [1, 1, 1, 0, 1, 0, 1, 1]: 0.0
Prediction for [1, 1, 1, 0, 1, 1, 0, 0]: 0.0
Prediction for [1, 1, 1, 0, 1, 1, 0, 1]: 3.83026943495679E-15
Prediction for [1, 1, 1, 0, 1, 1, 1, 0]: 0.0
Prediction for [1, 1, 1, 0, 1, 1, 1, 1]: 0.9999999999999963
Prediction for [1, 1, 1, 1, 0, 0, 0, 0]: 0.0
Prediction for [1, 1, 1, 1, 0, 0, 0, 1]: 0.9999999999999915
Prediction for [1, 1, 1, 1, 0, 0, 1, 0]: 0.0
Prediction for [1, 1, 1, 1, 0, 0, 1, 1]: 1.887379141862766E-15
Prediction for [1, 1, 1, 1, 0, 1, 0, 0]: 0.0
Prediction for [1, 1, 1, 1, 0, 1, 0, 1]: 0.0
Prediction for [1, 1, 1, 1, 0, 1, 1, 0]: 0.0
Prediction for [1, 1, 1, 1, 0, 1, 1, 1]: 4.3298697960381105E-15
Prediction for [1, 1, 1, 1, 1, 0, 0, 0]: 0.0
Prediction for [1, 1, 1, 1, 1, 0, 0, 1]: 0.0
Prediction for [1, 1, 1, 1, 1, 0, 1, 0]: 0.0
Prediction for [1, 1, 1, 1, 1, 0, 1, 1]: 0.9999999999999981
Prediction for [1, 1, 1, 1, 1, 1, 0, 0]: 0.0
Prediction for [1, 1, 1, 1, 1, 1, 0, 1]: 0.0
Prediction for [1, 1, 1, 1, 1, 1, 1, 0]: 0.0
Prediction for [1, 1, 1, 1, 1, 1, 1, 1]: 0.0
BUILD SUCCESSFUL (total time: 1 minute 42 seconds)

**Mark-XP** · April 15, 2023

Ok @Dietmar, it obviously has learned it's lessons (nn.train) well. But if you train it only up to 200:

        /*  for (int i = 0; i < trainingInputs.length; i++) { */
            for (int i = 0; i < 201; i++) {

the results for higher nubers (201..255) do not convince me.

**Dietmar** · April 15, 2023

@Mark-XP

Yes. I see the same for the Multipliers of 3. So, all "Intelligence" is gone for me in any Neural Network.

When you count the numbers of Variables, that are in this example with 8 Input Neurons, 26 Neurons in Hidden Layer and 1 Output Neuron,

you see, that for 256 numbers you need 26 Neurons. With 25 works, when you have luck, sometimes.

When you use less of half of the 256 numbers for training, the result is garbage.

When you use more than half of data for training, the result becomes better for not trained number, primes,

Dietmar

PS: No intelligence at all in any Neural Network. Its behavior is much more like an Taylor Series.

But Taylor is not bad. It makes very good predictions near the place of training. It is like a small window in unknown future, unknown numbers, unknown places.

**Dietmar** · April 15, 2023

@Mark-XP

When training goes only from 0..250,

the prime number at 251 is found. So, this is something interesting..

Dietmar

Prediction for [1, 1, 1, 0, 1, 1, 1, 0]: 0.0
Prediction for [1, 1, 1, 0, 1, 1, 1, 1]: 0.9999860758802503
Prediction for [1, 1, 1, 1, 0, 0, 0, 0]: 0.0
Prediction for [1, 1, 1, 1, 0, 0, 0, 1]: 0.9998293754254401
Prediction for [1, 1, 1, 1, 0, 0, 1, 0]: 0.0
Prediction for [1, 1, 1, 1, 0, 0, 1, 1]: 4.1298992927707445E-5
Prediction for [1, 1, 1, 1, 0, 1, 0, 0]: 0.0
Prediction for [1, 1, 1, 1, 0, 1, 0, 1]: 0.0
Prediction for [1, 1, 1, 1, 0, 1, 1, 0]: 0.0
Prediction for [1, 1, 1, 1, 0, 1, 1, 1]: 0.0
Prediction for [1, 1, 1, 1, 1, 0, 0, 0]: 0.0
Prediction for [1, 1, 1, 1, 1, 0, 0, 1]: 0.0
Prediction for [1, 1, 1, 1, 1, 0, 1, 0]: 0.0
Prediction for [1, 1, 1, 1, 1, 0, 1, 1]: 0.7571411604482448
Prediction for [1, 1, 1, 1, 1, 1, 0, 0]: 0.0
Prediction for [1, 1, 1, 1, 1, 1, 0, 1]: 0.0
Prediction for [1, 1, 1, 1, 1, 1, 1, 0]: 0.0
Prediction for [1, 1, 1, 1, 1, 1, 1, 1]: 0.0
BUILD SUCCESSFUL (total time: 50 seconds)

**Dietmar** · April 15, 2023

@Mark-XP

Here you can see, that all the missed Primes are regenerated. It is a little bit Magic :w00t: ..

The training only happens for the input values that are not equal to 241, 251, or 239.

Dietmar

package multiof3;

import java.util.Arrays;
import java.util.Random;

public class Multiof3 {
    private final int numInputNodes = 8;
    private final int numHiddenNodes = 32;
    private final int numOutputNodes = 1;
    private final double learningRate = 0.02;
    private final int numEpochs = 200000;
    private final double errorThreshold = 0.000000000000000000000000000001;

    private double[][] inputToHiddenWeights;
    private double[][] hiddenToOutputWeights;
    private double[] hiddenBiases;
    private double[] outputBiases;

    public Multiof3() {
        Random random = new Random();
        inputToHiddenWeights = new double[numInputNodes][numHiddenNodes];
        hiddenToOutputWeights = new double[numHiddenNodes][numOutputNodes];
        hiddenBiases = new double[numHiddenNodes];
        outputBiases = new double[numOutputNodes];

        for (int i = 0; i < numInputNodes; i++) {
            for (int j = 0; j < numHiddenNodes; j++) {
                inputToHiddenWeights[i][j] = random.nextDouble() - 0.5;
            }
        }

        for (int i = 0; i < numHiddenNodes; i++) {
            for (int j = 0; j < numOutputNodes; j++) {
                hiddenToOutputWeights[i][j] = random.nextDouble() - 0.5;
            }
            hiddenBiases[i] = random.nextDouble() - 0.5;
        }

        for (int i = 0; i < numOutputNodes; i++) {
            outputBiases[i] = random.nextDouble() - 0.5;
        }
    }

    public double relu(double x) {
        return Math.max(0, x);
    }

    public double reluDerivative(double x) {
        return x > 0 ? 1 : 0;
    }

    

                
                public void train(double[][] trainingInputs, double[] trainingTargets) {
    for (int epoch = 1; epoch <= numEpochs; epoch++) {
        double totalError = 0.0;
        for (int i = 0; i < trainingInputs.length; i++) {
            if (trainingInputs[i][0] != 241 && trainingInputs[i][0] != 251 && trainingInputs[i][0] != 239) {  // Check if the first element is not 251
                double[] input = trainingInputs[i];
                double target = trainingTargets[i];
                
                
            
              
                
                
                // Forward propagation
                double[] hiddenOutputs = new double[numHiddenNodes];
                for (int j = 0; j < numHiddenNodes; j++) {
                    double weightedSum = 0.0;
                    for (int k = 0; k < numInputNodes; k++) {
                        weightedSum += inputToHiddenWeights[k][j] * input[k];
                    }
                    hiddenOutputs[j] = relu(weightedSum + hiddenBiases[j]);
                }

                double output = 0.0;
                for (int j = 0; j < numOutputNodes; j++) {
                    double weightedSum = 0.0;
                    for (int k = 0; k < numHiddenNodes; k++) {
                        weightedSum += hiddenToOutputWeights[k][j] * hiddenOutputs[k];
                    }
                    output = relu(weightedSum + outputBiases[j]);
                }
            
                 
                
                
                
                
// Backward propagation
double outputErrorGradient = (output - target) * reluDerivative(output);
for (int j = 0; j < numHiddenNodes; j++) {
double hiddenErrorGradient = outputErrorGradient * hiddenToOutputWeights[j][0] * reluDerivative(hiddenOutputs[j]);
for (int k = 0; k < numInputNodes; k++) {
inputToHiddenWeights[k][j] -= learningRate * input[k] * hiddenErrorGradient;
}
hiddenBiases[j] -= learningRate * hiddenErrorGradient;
}
hiddenToOutputWeights[0][0] -= learningRate * hiddenOutputs[0] * outputErrorGradient;
outputBiases[0] -= learningRate * outputErrorGradient;
            

            // Update total error
            totalError += Math.pow(output - target, 2);
        }}

        // Calculate mean error and check for convergence
        double meanError = totalError / trainingInputs.length;
        if (meanError < errorThreshold) {
            System.out.println("Training complete. Mean error: " + meanError);
            break;
        } else if (epoch % 10000 == 0) {
            System.out.println("Epoch " + epoch + ". Mean error: " + meanError);
        }
        }
    }
    


public double predict(double[] input) {
    double[] hiddenOutputs = new double[numHiddenNodes];
    for (int j = 0; j < numHiddenNodes; j++) {
        double weightedSum = 0.0;
        for (int k = 0; k < numInputNodes; k++) {
            weightedSum += inputToHiddenWeights[k][j] * input[k];
        }
        hiddenOutputs[j] = relu(weightedSum + hiddenBiases[j]);
    }

    double output = 0.0;
    for (int j = 0; j < numOutputNodes; j++) {
        double weightedSum = 0.0;
        for (int k = 0; k < numHiddenNodes; k++) {
            weightedSum += hiddenToOutputWeights[k][j] * hiddenOutputs[k];
        }
        output = relu(weightedSum + outputBiases[j]);
    }

    return output;
}





public static void main(String[] args) {
    // Example usage of the neural network
    double[][] trainingInputs = 
{{0, 0, 0, 0, 0, 0, 0, 0},
{0, 0, 0, 0, 0, 0, 0, 1},
{0, 0, 0, 0, 0, 0, 1, 0},
{0, 0, 0, 0, 0, 0, 1, 1},
{0, 0, 0, 0, 0, 1, 0, 0},
{0, 0, 0, 0, 0, 1, 0, 1},
{0, 0, 0, 0, 0, 1, 1, 0},
{0, 0, 0, 0, 0, 1, 1, 1},
{0, 0, 0, 0, 1, 0, 0, 0},
{0, 0, 0, 0, 1, 0, 0, 1},
{0, 0, 0, 0, 1, 0, 1, 0},
{0, 0, 0, 0, 1, 0, 1, 1},
{0, 0, 0, 0, 1, 1, 0, 0},
{0, 0, 0, 0, 1, 1, 0, 1},
{0, 0, 0, 0, 1, 1, 1, 0},
{0, 0, 0, 0, 1, 1, 1, 1},
{0, 0, 0, 1, 0, 0, 0, 0},
{0, 0, 0, 1, 0, 0, 0, 1},
{0, 0, 0, 1, 0, 0, 1, 0},
{0, 0, 0, 1, 0, 0, 1, 1},
{0, 0, 0, 1, 0, 1, 0, 0},
{0, 0, 0, 1, 0, 1, 0, 1},
{0, 0, 0, 1, 0, 1, 1, 0},
{0, 0, 0, 1, 0, 1, 1, 1},
{0, 0, 0, 1, 1, 0, 0, 0},
{0, 0, 0, 1, 1, 0, 0, 1},
{0, 0, 0, 1, 1, 0, 1, 0},
{0, 0, 0, 1, 1, 0, 1, 1},
{0, 0, 0, 1, 1, 1, 0, 0},  
{0, 0, 0, 1, 1, 1, 0, 1},
{0, 0, 0, 1, 1, 1, 1, 0},
{0, 0, 0, 1, 1, 1, 1, 1},
{0, 0, 1, 0, 0, 0, 0, 0},
{0, 0, 1, 0, 0, 0, 0, 1},
{0, 0, 1, 0, 0, 0, 1, 0},
{0, 0, 1, 0, 0, 0, 1, 1},
{0, 0, 1, 0, 0, 1, 0, 0},
{0, 0, 1, 0, 0, 1, 0, 1},
{0, 0, 1, 0, 0, 1, 1, 0},
{0, 0, 1, 0, 0, 1, 1, 1},
{0, 0, 1, 0, 1, 0, 0, 0},
{0, 0, 1, 0, 1, 0, 0, 1},
{0, 0, 1, 0, 1, 0, 1, 0},
{0, 0, 1, 0, 1, 0, 1, 1},
{0, 0, 1, 0, 1, 1, 0, 0},
{0, 0, 1, 0, 1, 1, 0, 1},
{0, 0, 1, 0, 1, 1, 1, 0},
{0, 0, 1, 0, 1, 1, 1, 1},
{0, 0, 1, 1, 0, 0, 0, 0},
{0, 0, 1, 1, 0, 0, 0, 1},
{0, 0, 1, 1, 0, 0, 1, 0},
{0, 0, 1, 1, 0, 0, 1, 1},
{0, 0, 1, 1, 0, 1, 0, 0},
{0, 0, 1, 1, 0, 1, 0, 1},
{0, 0, 1, 1, 0, 1, 1, 0},
{0, 0, 1, 1, 0, 1, 1, 1},
{0, 0, 1, 1, 1, 0, 0, 0},
{0, 0, 1, 1, 1, 0, 0, 1},
{0, 0, 1, 1, 1, 0, 1, 0},
{0, 0, 1, 1, 1, 0, 1, 1},
{0, 0, 1, 1, 1, 1, 0, 0},
{0, 0, 1, 1, 1, 1, 0, 1},
{0, 0, 1, 1, 1, 1, 1, 0},
{0, 0, 1, 1, 1, 1, 1, 1},
{0, 1, 0, 0, 0, 0, 0, 0},
{0, 1, 0, 0, 0, 0, 0, 1},
{0, 1, 0, 0, 0, 0, 1, 0},
{0, 1, 0, 0, 0, 0, 1, 1},
{0, 1, 0, 0, 0, 1, 0, 0},
{0, 1, 0, 0, 0, 1, 0, 1},
{0, 1, 0, 0, 0, 1, 1, 0},
{0, 1, 0, 0, 0, 1, 1, 1},
{0, 1, 0, 0, 1, 0, 0, 0},
{0, 1, 0, 0, 1, 0, 0, 1},
{0, 1, 0, 0, 1, 0, 1, 0},
{0, 1, 0, 0, 1, 0, 1, 1},
{0, 1, 0, 0, 1, 1, 0, 0},
{0, 1, 0, 0, 1, 1, 0, 1},
{0, 1, 0, 0, 1, 1, 1, 0},
{0, 1, 0, 0, 1, 1, 1, 1},
{0, 1, 0, 1, 0, 0, 0, 0},
{0, 1, 0, 1, 0, 0, 0, 1},
{0, 1, 0, 1, 0, 0, 1, 0},
{0, 1, 0, 1, 0, 0, 1, 1},
{0, 1, 0, 1, 0, 1, 0, 0},
{0, 1, 0, 1, 0, 1, 0, 1},
{0, 1, 0, 1, 0, 1, 1, 0},
{0, 1, 0, 1, 0, 1, 1, 1},
{0, 1, 0, 1, 1, 0, 0, 0},
{0, 1, 0, 1, 1, 0, 0, 1},
{0, 1, 0, 1, 1, 0, 1, 0},
{0, 1, 0, 1, 1, 0, 1, 1},
{0, 1, 0, 1, 1, 1, 0, 0},
{0, 1, 0, 1, 1, 1, 0, 1},
{0, 1, 0, 1, 1, 1, 1, 0},
{0, 1, 0, 1, 1, 1, 1, 1},
{0, 1, 1, 0, 0, 0, 0, 0},
{0, 1, 1, 0, 0, 0, 0, 1},
{0, 1, 1, 0, 0, 0, 1, 0},
{0, 1, 1, 0, 0, 0, 1, 1},
{0, 1, 1, 0, 0, 1, 0, 0},
{0, 1, 1, 0, 0, 1, 0, 1},
{0, 1, 1, 0, 0, 1, 1, 0},
{0, 1, 1, 0, 0, 1, 1, 1},
{0, 1, 1, 0, 1, 0, 0, 0},
{0, 1, 1, 0, 1, 0, 0, 1},
{0, 1, 1, 0, 1, 0, 1, 0},
{0, 1, 1, 0, 1, 0, 1, 1},
{0, 1, 1, 0, 1, 1, 0, 0},
{0, 1, 1, 0, 1, 1, 0, 1},
{0, 1, 1, 0, 1, 1, 1, 0},
{0, 1, 1, 0, 1, 1, 1, 1},
{0, 1, 1, 1, 0, 0, 0, 0},
{0, 1, 1, 1, 0, 0, 0, 1},
{0, 1, 1, 1, 0, 0, 1, 0},
{0, 1, 1, 1, 0, 0, 1, 1},
{0, 1, 1, 1, 0, 1, 0, 0},
{0, 1, 1, 1, 0, 1, 0, 1},
{0, 1, 1, 1, 0, 1, 1, 0},
{0, 1, 1, 1, 0, 1, 1, 1},
{0, 1, 1, 1, 1, 0, 0, 0},
{0, 1, 1, 1, 1, 0, 0, 1},
{0, 1, 1, 1, 1, 0, 1, 0},
{0, 1, 1, 1, 1, 0, 1, 1},
{0, 1, 1, 1, 1, 1, 0, 0},
{0, 1, 1, 1, 1, 1, 0, 1},
{0, 1, 1, 1, 1, 1, 1, 0},
{0, 1, 1, 1, 1, 1, 1, 1},
{1, 0, 0, 0, 0, 0, 0, 0},
{1, 0, 0, 0, 0, 0, 0, 1},
{1, 0, 0, 0, 0, 0, 1, 0},
{1, 0, 0, 0, 0, 0, 1, 1},
{1, 0, 0, 0, 0, 1, 0, 0},
{1, 0, 0, 0, 0, 1, 0, 1},
{1, 0, 0, 0, 0, 1, 1, 0},
{1, 0, 0, 0, 0, 1, 1, 1},
{1, 0, 0, 0, 1, 0, 0, 0},
{1, 0, 0, 0, 1, 0, 0, 1},
{1, 0, 0, 0, 1, 0, 1, 0},
{1, 0, 0, 0, 1, 0, 1, 1},
{1, 0, 0, 0, 1, 1, 0, 0},
{1, 0, 0, 0, 1, 1, 0, 1},
{1, 0, 0, 0, 1, 1, 1, 0},
{1, 0, 0, 0, 1, 1, 1, 1},
{1, 0, 0, 1, 0, 0, 0, 0},
{1, 0, 0, 1, 0, 0, 0, 1},
{1, 0, 0, 1, 0, 0, 1, 0},
{1, 0, 0, 1, 0, 0, 1, 1},
{1, 0, 0, 1, 0, 1, 0, 0},
{1, 0, 0, 1, 0, 1, 0, 1},
{1, 0, 0, 1, 0, 1, 1, 0},
{1, 0, 0, 1, 0, 1, 1, 1},
{1, 0, 0, 1, 1, 0, 0, 0},
{1, 0, 0, 1, 1, 0, 0, 1},
{1, 0, 0, 1, 1, 0, 1, 0},
{1, 0, 0, 1, 1, 0, 1, 1},
{1, 0, 0, 1, 1, 1, 0, 0},
{1, 0, 0, 1, 1, 1, 0, 1},
{1, 0, 0, 1, 1, 1, 1, 0},
{1, 0, 0, 1, 1, 1, 1, 1},
{1, 0, 1, 0, 0, 0, 0, 0},
{1, 0, 1, 0, 0, 0, 0, 1},
{1, 0, 1, 0, 0, 0, 1, 0},
{1, 0, 1, 0, 0, 0, 1, 1},
{1, 0, 1, 0, 0, 1, 0, 0},
{1, 0, 1, 0, 0, 1, 0, 1},
{1, 0, 1, 0, 0, 1, 1, 0},
{1, 0, 1, 0, 0, 1, 1, 1},
{1, 0, 1, 0, 1, 0, 0, 0},
{1, 0, 1, 0, 1, 0, 0, 1},
{1, 0, 1, 0, 1, 0, 1, 0},
{1, 0, 1, 0, 1, 0, 1, 1},
{1, 0, 1, 0, 1, 1, 0, 0},
{1, 0, 1, 0, 1, 1, 0, 1},
{1, 0, 1, 0, 1, 1, 1, 0},
{1, 0, 1, 0, 1, 1, 1, 1},
{1, 0, 1, 1, 0, 0, 0, 0},
{1, 0, 1, 1, 0, 0, 0, 1},
{1, 0, 1, 1, 0, 0, 1, 0},
{1, 0, 1, 1, 0, 0, 1, 1},
{1, 0, 1, 1, 0, 1, 0, 0},
{1, 0, 1, 1, 0, 1, 0, 1},
{1, 0, 1, 1, 0, 1, 1, 0},
{1, 0, 1, 1, 0, 1, 1, 1},
{1, 0, 1, 1, 1, 0, 0, 0},
{1, 0, 1, 1, 1, 0, 0, 1},
{1, 0, 1, 1, 1, 0, 1, 0},
{1, 0, 1, 1, 1, 0, 1, 1},
{1, 0, 1, 1, 1, 1, 0, 0},
{1, 0, 1, 1, 1, 1, 0, 1},
{1, 0, 1, 1, 1, 1, 1, 0},
{1, 0, 1, 1, 1, 1, 1, 1},
{1, 1, 0, 0, 0, 0, 0, 0},
{1, 1, 0, 0, 0, 0, 0, 1},
{1, 1, 0, 0, 0, 0, 1, 0},
{1, 1, 0, 0, 0, 0, 1, 1},
{1, 1, 0, 0, 0, 1, 0, 0},
{1, 1, 0, 0, 0, 1, 0, 1},
{1, 1, 0, 0, 0, 1, 1, 0},
{1, 1, 0, 0, 0, 1, 1, 1},
{1, 1, 0, 0, 1, 0, 0, 0},
{1, 1, 0, 0, 1, 0, 0, 1},
{1, 1, 0, 0, 1, 0, 1, 0},
{1, 1, 0, 0, 1, 0, 1, 1},
{1, 1, 0, 0, 1, 1, 0, 0},
{1, 1, 0, 0, 1, 1, 0, 1},
{1, 1, 0, 0, 1, 1, 1, 0},
{1, 1, 0, 0, 1, 1, 1, 1},
{1, 1, 0, 1, 0, 0, 0, 0},
{1, 1, 0, 1, 0, 0, 0, 1},
{1, 1, 0, 1, 0, 0, 1, 0},
{1, 1, 0, 1, 0, 0, 1, 1},
{1, 1, 0, 1, 0, 1, 0, 0},
{1, 1, 0, 1, 0, 1, 0, 1},
{1, 1, 0, 1, 0, 1, 1, 0},
{1, 1, 0, 1, 0, 1, 1, 1},
{1, 1, 0, 1, 1, 0, 0, 0},
{1, 1, 0, 1, 1, 0, 0, 1},
{1, 1, 0, 1, 1, 0, 1, 0},
{1, 1, 0, 1, 1, 0, 1, 1},
{1, 1, 0, 1, 1, 1, 0, 0},
{1, 1, 0, 1, 1, 1, 0, 1},
{1, 1, 0, 1, 1, 1, 1, 0},
{1, 1, 0, 1, 1, 1, 1, 1},
{1, 1, 1, 0, 0, 0, 0, 0},
{1, 1, 1, 0, 0, 0, 0, 1},
{1, 1, 1, 0, 0, 0, 1, 0},
{1, 1, 1, 0, 0, 0, 1, 1},
{1, 1, 1, 0, 0, 1, 0, 0},
{1, 1, 1, 0, 0, 1, 0, 1},
{1, 1, 1, 0, 0, 1, 1, 0},
{1, 1, 1, 0, 0, 1, 1, 1},
{1, 1, 1, 0, 1, 0, 0, 0},
{1, 1, 1, 0, 1, 0, 0, 1},
{1, 1, 1, 0, 1, 0, 1, 0},
{1, 1, 1, 0, 1, 0, 1, 1},
{1, 1, 1, 0, 1, 1, 0, 0},
{1, 1, 1, 0, 1, 1, 0, 1},
{1, 1, 1, 0, 1, 1, 1, 0},
{1, 1, 1, 0, 1, 1, 1, 1},
{1, 1, 1, 1, 0, 0, 0, 0},
{1, 1, 1, 1, 0, 0, 0, 1},
{1, 1, 1, 1, 0, 0, 1, 0},
{1, 1, 1, 1, 0, 0, 1, 1},
{1, 1, 1, 1, 0, 1, 0, 0},
{1, 1, 1, 1, 0, 1, 0, 1},
{1, 1, 1, 1, 0, 1, 1, 0},
{1, 1, 1, 1, 0, 1, 1, 1},
{1, 1, 1, 1, 1, 0, 0, 0},
{1, 1, 1, 1, 1, 0, 0, 1},
{1, 1, 1, 1, 1, 0, 1, 0},
{1, 1, 1, 1, 1, 0, 1, 1},
{1, 1, 1, 1, 1, 1, 0, 0},
{1, 1, 1, 1, 1, 1, 0, 1},
{1, 1, 1, 1, 1, 1, 1, 0},
{1, 1, 1, 1, 1, 1, 1, 1}};
    
    
double[] trainingTargets = {
0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0
};



    
    Multiof3 nn = new Multiof3();
    nn.train(trainingInputs, trainingTargets);
    
System.out.println("Prediction for [0, 0, 0, 0, 0, 0, 0, 0]: " + nn.predict(new double[]{0, 0, 0, 0, 0, 0, 0, 0}));
System.out.println("Prediction for [0, 0, 0, 0, 0, 0, 0, 1]: " + nn.predict(new double[]{0, 0, 0, 0, 0, 0, 0, 1}));
System.out.println("Prediction for [0, 0, 0, 0, 0, 0, 1, 0]: " + nn.predict(new double[]{0, 0, 0, 0, 0, 0, 1, 0}));
System.out.println("Prediction for [0, 0, 0, 0, 0, 0, 1, 1]: " + nn.predict(new double[]{0, 0, 0, 0, 0, 0, 1, 1}));
System.out.println("Prediction for [0, 0, 0, 0, 0, 1, 0, 0]: " + nn.predict(new double[]{0, 0, 0, 0, 0, 1, 0, 0}));
System.out.println("Prediction for [0, 0, 0, 0, 0, 1, 0, 1]: " + nn.predict(new double[]{0, 0, 0, 0, 0, 1, 0, 1}));
System.out.println("Prediction for [0, 0, 0, 0, 0, 1, 1, 0]: " + nn.predict(new double[]{0, 0, 0, 0, 0, 1, 1, 0}));
System.out.println("Prediction for [0, 0, 0, 0, 0, 1, 1, 1]: " + nn.predict(new double[]{0, 0, 0, 0, 0, 1, 1, 1}));
System.out.println("Prediction for [0, 0, 0, 0, 1, 0, 0, 0]: " + nn.predict(new double[]{0, 0, 0, 0, 1, 0, 0, 0}));
System.out.println("Prediction for [0, 0, 0, 0, 1, 0, 0, 1]: " + nn.predict(new double[]{0, 0, 0, 0, 1, 0, 0, 1}));
System.out.println("Prediction for [0, 0, 0, 0, 1, 0, 1, 0]: " + nn.predict(new double[]{0, 0, 0, 0, 1, 0, 1, 0}));
System.out.println("Prediction for [0, 0, 0, 0, 1, 0, 1, 1]: " + nn.predict(new double[]{0, 0, 0, 0, 1, 0, 1, 1}));
System.out.println("Prediction for [0, 0, 0, 0, 1, 1, 0, 0]: " + nn.predict(new double[]{0, 0, 0, 0, 1, 1, 0, 0}));
System.out.println("Prediction for [0, 0, 0, 0, 1, 1, 0, 1]: " + nn.predict(new double[]{0, 0, 0, 0, 1, 1, 0, 1}));
System.out.println("Prediction for [0, 0, 0, 0, 1, 1, 1, 0]: " + nn.predict(new double[]{0, 0, 0, 0, 1, 1, 1, 0}));
System.out.println("Prediction for [0, 0, 0, 0, 1, 1, 1, 1]: " + nn.predict(new double[]{0, 0, 0, 0, 1, 1, 1, 1}));
System.out.println("Prediction for [0, 0, 0, 1, 0, 0, 0, 0]: " + nn.predict(new double[]{0, 0, 0, 1, 0, 0, 0, 0}));
System.out.println("Prediction for [0, 0, 0, 1, 0, 0, 0, 1]: " + nn.predict(new double[]{0, 0, 0, 1, 0, 0, 0, 1}));
System.out.println("Prediction for [0, 0, 0, 1, 0, 0, 1, 0]: " + nn.predict(new double[]{0, 0, 0, 1, 0, 0, 1, 0}));
System.out.println("Prediction for [0, 0, 0, 1, 0, 0, 1, 1]: " + nn.predict(new double[]{0, 0, 0, 1, 0, 0, 1, 1}));
System.out.println("Prediction for [0, 0, 0, 1, 0, 1, 0, 0]: " + nn.predict(new double[]{0, 0, 0, 1, 0, 1, 0, 0}));
System.out.println("Prediction for [0, 0, 0, 1, 0, 1, 0, 1]: " + nn.predict(new double[]{0, 0, 0, 1, 0, 1, 0, 1}));
System.out.println("Prediction for [0, 0, 0, 1, 0, 1, 1, 0]: " + nn.predict(new double[]{0, 0, 0, 1, 0, 1, 1, 0}));
System.out.println("Prediction for [0, 0, 0, 1, 0, 1, 1, 1]: " + nn.predict(new double[]{0, 0, 0, 1, 0, 1, 1, 1}));   
System.out.println("Prediction for [0, 0, 0, 1, 1, 0, 0, 0]: " + nn.predict(new double[]{0, 0, 0, 1, 1, 0, 0, 0}));
System.out.println("Prediction for [0, 0, 0, 1, 1, 0, 0, 1]: " + nn.predict(new double[]{0, 0, 0, 1, 1, 0, 0, 1}));
System.out.println("Prediction for [0, 0, 0, 1, 1, 0, 1, 0]: " + nn.predict(new double[]{0, 0, 0, 1, 1, 0, 1, 0}));
System.out.println("Prediction for [0, 0, 0, 1, 1, 0, 1, 1]: " + nn.predict(new double[]{0, 0, 0, 1, 1, 0, 1, 1}));
System.out.println("Prediction for [0, 0, 0, 1, 1, 1, 0, 0]: " + nn.predict(new double[]{0, 0, 0, 1, 1, 1, 0, 0}));
System.out.println("Prediction for [0, 0, 0, 1, 1, 1, 0, 1]: " + nn.predict(new double[]{0, 0, 0, 1, 1, 1, 0, 1}));
System.out.println("Prediction for [0, 0, 0, 1, 1, 1, 1, 0]: " + nn.predict(new double[]{0, 0, 0, 1, 1, 1, 1, 0}));
System.out.println("Prediction for [0, 0, 0, 1, 1, 1, 1, 1]: " + nn.predict(new double[]{0, 0, 0, 1, 1, 1, 1, 1}));
System.out.println("Prediction for [0, 0, 1, 0, 0, 0, 0, 0]: " + nn.predict(new double[]{0, 0, 1, 0, 0, 0, 0, 0}));
System.out.println("Prediction for [0, 0, 1, 0, 0, 0, 0, 1]: " + nn.predict(new double[]{0, 0, 1, 0, 0, 0, 0, 1}));
System.out.println("Prediction for [0, 0, 1, 0, 0, 0, 1, 0]: " + nn.predict(new double[]{0, 0, 1, 0, 0, 0, 1, 0}));
System.out.println("Prediction for [0, 0, 1, 0, 0, 0, 1, 1]: " + nn.predict(new double[]{0, 0, 1, 0, 0, 0, 1, 1}));
System.out.println("Prediction for [0, 0, 1, 0, 0, 1, 0, 0]: " + nn.predict(new double[]{0, 0, 1, 0, 0, 1, 0, 0}));
System.out.println("Prediction for [0, 0, 1, 0, 0, 1, 0, 1]: " + nn.predict(new double[]{0, 0, 1, 0, 0, 1, 0, 1}));
System.out.println("Prediction for [0, 0, 1, 0, 0, 1, 1, 0]: " + nn.predict(new double[]{0, 0, 1, 0, 0, 1, 1, 0}));
System.out.println("Prediction for [0, 0, 1, 0, 0, 1, 1, 1]: " + nn.predict(new double[]{0, 0, 1, 0, 0, 1, 1, 1}));
System.out.println("Prediction for [0, 0, 1, 0, 1, 0, 0, 0]: " + nn.predict(new double[]{0, 0, 1, 0, 1, 0, 0, 0}));
System.out.println("Prediction for [0, 0, 1, 0, 1, 0, 0, 1]: " + nn.predict(new double[]{0, 0, 1, 0, 1, 0, 0, 1}));
System.out.println("Prediction for [0, 0, 1, 0, 1, 0, 1, 0]: " + nn.predict(new double[]{0, 0, 1, 0, 1, 0, 1, 0}));
System.out.println("Prediction for [0, 0, 1, 0, 1, 0, 1, 1]: " + nn.predict(new double[]{0, 0, 1, 0, 1, 0, 1, 1}));
System.out.println("Prediction for [0, 0, 1, 0, 1, 1, 0, 0]: " + nn.predict(new double[]{0, 0, 1, 0, 1, 1, 0, 0}));
System.out.println("Prediction for [0, 0, 1, 0, 1, 1, 0, 1]: " + nn.predict(new double[]{0, 0, 1, 0, 1, 1, 0, 1}));
System.out.println("Prediction for [0, 0, 1, 0, 1, 1, 1, 0]: " + nn.predict(new double[]{0, 0, 1, 0, 1, 1, 1, 0}));
System.out.println("Prediction for [0, 0, 1, 0, 1, 1, 1, 1]: " + nn.predict(new double[]{0, 0, 1, 0, 1, 1, 1, 1}));
System.out.println("Prediction for [0, 0, 1, 1, 0, 0, 0, 0]: " + nn.predict(new double[]{0, 0, 1, 1, 0, 0, 0, 0}));
System.out.println("Prediction for [0, 0, 1, 1, 0, 0, 0, 1]: " + nn.predict(new double[]{0, 0, 1, 1, 0, 0, 0, 1}));
System.out.println("Prediction for [0, 0, 1, 1, 0, 0, 1, 0]: " + nn.predict(new double[]{0, 0, 1, 1, 0, 0, 1, 0}));
System.out.println("Prediction for [0, 0, 1, 1, 0, 0, 1, 1]: " + nn.predict(new double[]{0, 0, 1, 1, 0, 0, 1, 1}));
System.out.println("Prediction for [0, 0, 1, 1, 0, 1, 0, 0]: " + nn.predict(new double[]{0, 0, 1, 1, 0, 1, 0, 0}));
System.out.println("Prediction for [0, 0, 1, 1, 0, 1, 0, 1]: " + nn.predict(new double[]{0, 0, 1, 1, 0, 1, 0, 1}));
System.out.println("Prediction for [0, 0, 1, 1, 0, 1, 1, 0]: " + nn.predict(new double[]{0, 0, 1, 1, 0, 1, 1, 0}));
System.out.println("Prediction for [0, 0, 1, 1, 0, 1, 1, 1]: " + nn.predict(new double[]{0, 0, 1, 1, 0, 1, 1, 1}));
System.out.println("Prediction for [0, 0, 1, 1, 1, 0, 0, 0]: " + nn.predict(new double[]{0, 0, 1, 1, 1, 0, 0, 0}));
System.out.println("Prediction for [0, 0, 1, 1, 1, 0, 0, 1]: " + nn.predict(new double[]{0, 0, 1, 1, 1, 0, 0, 1}));
System.out.println("Prediction for [0, 0, 1, 1, 1, 0, 1, 0]: " + nn.predict(new double[]{0, 0, 1, 1, 1, 0, 1, 0}));
System.out.println("Prediction for [0, 0, 1, 1, 1, 0, 1, 1]: " + nn.predict(new double[]{0, 0, 1, 1, 1, 0, 1, 1}));
System.out.println("Prediction for [0, 0, 1, 1, 1, 1, 0, 0]: " + nn.predict(new double[]{0, 0, 1, 1, 1, 1, 0, 0}));
System.out.println("Prediction for [0, 0, 1, 1, 1, 1, 0, 1]: " + nn.predict(new double[]{0, 0, 1, 1, 1, 1, 0, 1}));
System.out.println("Prediction for [0, 0, 1, 1, 1, 1, 1, 0]: " + nn.predict(new double[]{0, 0, 1, 1, 1, 1, 1, 0}));
System.out.println("Prediction for [0, 0, 1, 1, 1, 1, 1, 1]: " + nn.predict(new double[]{0, 0, 1, 1, 1, 1, 1, 1}));
System.out.println("Prediction for [0, 1, 0, 0, 0, 0, 0, 0]: " + nn.predict(new double[]{0, 1, 0, 0, 0, 0, 0, 0}));
System.out.println("Prediction for [0, 1, 0, 0, 0, 0, 0, 1]: " + nn.predict(new double[]{0, 1, 0, 0, 0, 0, 0, 1}));
System.out.println("Prediction for [0, 1, 0, 0, 0, 0, 1, 0]: " + nn.predict(new double[]{0, 1, 0, 0, 0, 0, 1, 0}));
System.out.println("Prediction for [0, 1, 0, 0, 0, 0, 1, 1]: " + nn.predict(new double[]{0, 1, 0, 0, 0, 0, 1, 1}));
System.out.println("Prediction for [0, 1, 0, 0, 0, 1, 0, 0]: " + nn.predict(new double[]{0, 1, 0, 0, 0, 1, 0, 0}));
System.out.println("Prediction for [0, 1, 0, 0, 0, 1, 0, 1]: " + nn.predict(new double[]{0, 1, 0, 0, 0, 1, 0, 1}));
System.out.println("Prediction for [0, 1, 0, 0, 0, 1, 1, 0]: " + nn.predict(new double[]{0, 1, 0, 0, 0, 1, 1, 0}));
System.out.println("Prediction for [0, 1, 0, 0, 0, 1, 1, 1]: " + nn.predict(new double[]{0, 1, 0, 0, 0, 1, 1, 1}));
System.out.println("Prediction for [0, 1, 0, 0, 1, 0, 0, 0]: " + nn.predict(new double[]{0, 1, 0, 0, 1, 0, 0, 0}));
System.out.println("Prediction for [0, 1, 0, 0, 1, 0, 0, 1]: " + nn.predict(new double[]{0, 1, 0, 0, 1, 0, 0, 1}));
System.out.println("Prediction for [0, 1, 0, 0, 1, 0, 1, 0]: " + nn.predict(new double[]{0, 1, 0, 0, 1, 0, 1, 0}));
System.out.println("Prediction for [0, 1, 0, 0, 1, 0, 1, 1]: " + nn.predict(new double[]{0, 1, 0, 0, 1, 0, 1, 1}));
System.out.println("Prediction for [0, 1, 0, 0, 1, 1, 0, 0]: " + nn.predict(new double[]{0, 1, 0, 0, 1, 1, 0, 0}));
System.out.println("Prediction for [0, 1, 0, 0, 1, 1, 0, 1]: " + nn.predict(new double[]{0, 1, 0, 0, 1, 1, 0, 1}));
System.out.println("Prediction for [0, 1, 0, 0, 1, 1, 1, 0]: " + nn.predict(new double[]{0, 1, 0, 0, 1, 1, 1, 0}));
System.out.println("Prediction for [0, 1, 0, 0, 1, 1, 1, 1]: " + nn.predict(new double[]{0, 1, 0, 0, 1, 1, 1, 1}));
System.out.println("Prediction for [0, 1, 0, 1, 0, 0, 0, 0]: " + nn.predict(new double[]{0, 1, 0, 1, 0, 0, 0, 0}));
System.out.println("Prediction for [0, 1, 0, 1, 0, 0, 0, 1]: " + nn.predict(new double[]{0, 1, 0, 1, 0, 0, 0, 1}));
System.out.println("Prediction for [0, 1, 0, 1, 0, 0, 1, 0]: " + nn.predict(new double[]{0, 1, 0, 1, 0, 0, 1, 0}));
System.out.println("Prediction for [0, 1, 0, 1, 0, 0, 1, 1]: " + nn.predict(new double[]{0, 1, 0, 1, 0, 0, 1, 1}));
System.out.println("Prediction for [0, 1, 0, 1, 0, 1, 0, 0]: " + nn.predict(new double[]{0, 1, 0, 1, 0, 1, 0, 0}));
System.out.println("Prediction for [0, 1, 0, 1, 0, 1, 0, 1]: " + nn.predict(new double[]{0, 1, 0, 1, 0, 1, 0, 1}));
System.out.println("Prediction for [0, 1, 0, 1, 0, 1, 1, 0]: " + nn.predict(new double[]{0, 1, 0, 1, 0, 1, 1, 0}));
System.out.println("Prediction for [0, 1, 0, 1, 0, 1, 1, 1]: " + nn.predict(new double[]{0, 1, 0, 1, 0, 1, 1, 1}));
System.out.println("Prediction for [0, 1, 0, 1, 1, 0, 0, 0]: " + nn.predict(new double[]{0, 1, 0, 1, 1, 0, 0, 0}));
System.out.println("Prediction for [0, 1, 0, 1, 1, 0, 0, 1]: " + nn.predict(new double[]{0, 1, 0, 1, 1, 0, 0, 1}));
System.out.println("Prediction for [0, 1, 0, 1, 1, 0, 1, 0]: " + nn.predict(new double[]{0, 1, 0, 1, 1, 0, 1, 0}));
System.out.println("Prediction for [0, 1, 0, 1, 1, 0, 1, 1]: " + nn.predict(new double[]{0, 1, 0, 1, 1, 0, 1, 1}));
System.out.println("Prediction for [0, 1, 0, 1, 1, 1, 0, 0]: " + nn.predict(new double[]{0, 1, 0, 1, 1, 1, 0, 0}));
System.out.println("Prediction for [0, 1, 0, 1, 1, 1, 0, 1]: " + nn.predict(new double[]{0, 1, 0, 1, 1, 1, 0, 1}));
System.out.println("Prediction for [0, 1, 0, 1, 1, 1, 1, 0]: " + nn.predict(new double[]{0, 1, 0, 1, 1, 1, 1, 0}));
System.out.println("Prediction for [0, 1, 0, 1, 1, 1, 1, 1]: " + nn.predict(new double[]{0, 1, 0, 1, 1, 1, 1, 1}));
System.out.println("Prediction for [0, 1, 1, 0, 0, 0, 0, 0]: " + nn.predict(new double[]{0, 1, 1, 0, 0, 0, 0, 0}));
System.out.println("Prediction for [0, 1, 1, 0, 0, 0, 0, 1]: " + nn.predict(new double[]{0, 1, 1, 0, 0, 0, 0, 1}));
System.out.println("Prediction for [0, 1, 1, 0, 0, 0, 1, 0]: " + nn.predict(new double[]{0, 1, 1, 0, 0, 0, 1, 0}));
System.out.println("Prediction for [0, 1, 1, 0, 0, 0, 1, 1]: " + nn.predict(new double[]{0, 1, 1, 0, 0, 0, 1, 1}));
System.out.println("Prediction for [0, 1, 1, 0, 0, 1, 0, 0]: " + nn.predict(new double[]{0, 1, 1, 0, 0, 1, 0, 0}));
System.out.println("Prediction for [0, 1, 1, 0, 0, 1, 0, 1]: " + nn.predict(new double[]{0, 1, 1, 0, 0, 1, 0, 1}));
System.out.println("Prediction for [0, 1, 1, 0, 0, 1, 1, 0]: " + nn.predict(new double[]{0, 1, 1, 0, 0, 1, 1, 0}));
System.out.println("Prediction for [0, 1, 1, 0, 0, 1, 1, 1]: " + nn.predict(new double[]{0, 1, 1, 0, 0, 1, 1, 1}));
System.out.println("Prediction for [0, 1, 1, 0, 1, 0, 0, 0]: " + nn.predict(new double[]{0, 1, 1, 0, 1, 0, 0, 0}));
System.out.println("Prediction for [0, 1, 1, 0, 1, 0, 0, 1]: " + nn.predict(new double[]{0, 1, 1, 0, 1, 0, 0, 1}));
System.out.println("Prediction for [0, 1, 1, 0, 1, 0, 1, 0]: " + nn.predict(new double[]{0, 1, 1, 0, 1, 0, 1, 0}));
System.out.println("Prediction for [0, 1, 1, 0, 1, 0, 1, 1]: " + nn.predict(new double[]{0, 1, 1, 0, 1, 0, 1, 1}));
System.out.println("Prediction for [0, 1, 1, 0, 1, 1, 0, 0]: " + nn.predict(new double[]{0, 1, 1, 0, 1, 1, 0, 0}));
System.out.println("Prediction for [0, 1, 1, 0, 1, 1, 0, 1]: " + nn.predict(new double[]{0, 1, 1, 0, 1, 1, 0, 1}));
System.out.println("Prediction for [0, 1, 1, 0, 1, 1, 1, 0]: " + nn.predict(new double[]{0, 1, 1, 0, 1, 1, 1, 0}));
System.out.println("Prediction for [0, 1, 1, 0, 1, 1, 1, 1]: " + nn.predict(new double[]{0, 1, 1, 0, 1, 1, 1, 1}));
System.out.println("Prediction for [0, 1, 1, 1, 0, 0, 0, 0]: " + nn.predict(new double[]{0, 1, 1, 1, 0, 0, 0, 0}));
System.out.println("Prediction for [0, 1, 1, 1, 0, 0, 0, 1]: " + nn.predict(new double[]{0, 1, 1, 1, 0, 0, 0, 1}));
System.out.println("Prediction for [0, 1, 1, 1, 0, 0, 1, 0]: " + nn.predict(new double[]{0, 1, 1, 1, 0, 0, 1, 0}));
System.out.println("Prediction for [0, 1, 1, 1, 0, 0, 1, 1]: " + nn.predict(new double[]{0, 1, 1, 1, 0, 0, 1, 1}));
System.out.println("Prediction for [0, 1, 1, 1, 0, 1, 0, 0]: " + nn.predict(new double[]{0, 1, 1, 1, 0, 1, 0, 0}));
System.out.println("Prediction for [0, 1, 1, 1, 0, 1, 0, 1]: " + nn.predict(new double[]{0, 1, 1, 1, 0, 1, 0, 1}));
System.out.println("Prediction for [0, 1, 1, 1, 0, 1, 1, 0]: " + nn.predict(new double[]{0, 1, 1, 1, 0, 1, 1, 0}));
System.out.println("Prediction for [0, 1, 1, 1, 0, 1, 1, 1]: " + nn.predict(new double[]{0, 1, 1, 1, 0, 1, 1, 1}));
System.out.println("Prediction for [0, 1, 1, 1, 1, 0, 0, 0]: " + nn.predict(new double[]{0, 1, 1, 1, 1, 0, 0, 0}));
System.out.println("Prediction for [0, 1, 1, 1, 1, 0, 0, 1]: " + nn.predict(new double[]{0, 1, 1, 1, 1, 0, 0, 1}));
System.out.println("Prediction for [0, 1, 1, 1, 1, 0, 1, 0]: " + nn.predict(new double[]{0, 1, 1, 1, 1, 0, 1, 0}));
System.out.println("Prediction for [0, 1, 1, 1, 1, 0, 1, 1]: " + nn.predict(new double[]{0, 1, 1, 1, 1, 0, 1, 1}));
System.out.println("Prediction for [0, 1, 1, 1, 1, 1, 0, 0]: " + nn.predict(new double[]{0, 1, 1, 1, 1, 1, 0, 0}));
System.out.println("Prediction for [0, 1, 1, 1, 1, 1, 0, 1]: " + nn.predict(new double[]{0, 1, 1, 1, 1, 1, 0, 1}));
System.out.println("Prediction for [0, 1, 1, 1, 1, 1, 1, 0]: " + nn.predict(new double[]{0, 1, 1, 1, 1, 1, 1, 0}));
System.out.println("Prediction for [0, 1, 1, 1, 1, 1, 1, 1]: " + nn.predict(new double[]{0, 1, 1, 1, 1, 1, 1, 1}));
System.out.println("Prediction for [1, 0, 0, 0, 0, 0, 0, 0]: " + nn.predict(new double[]{1, 0, 0, 0, 0, 0, 0, 0}));
System.out.println("Prediction for [1, 0, 0, 0, 0, 0, 0, 1]: " + nn.predict(new double[]{1, 0, 0, 0, 0, 0, 0, 1}));
System.out.println("Prediction for [1, 0, 0, 0, 0, 0, 1, 0]: " + nn.predict(new double[]{1, 0, 0, 0, 0, 0, 1, 0}));
System.out.println("Prediction for [1, 0, 0, 0, 0, 0, 1, 1]: " + nn.predict(new double[]{1, 0, 0, 0, 0, 0, 1, 1}));
System.out.println("Prediction for [1, 0, 0, 0, 0, 1, 0, 0]: " + nn.predict(new double[]{1, 0, 0, 0, 0, 1, 0, 0}));
System.out.println("Prediction for [1, 0, 0, 0, 0, 1, 0, 1]: " + nn.predict(new double[]{1, 0, 0, 0, 0, 1, 0, 1}));
System.out.println("Prediction for [1, 0, 0, 0, 0, 1, 1, 0]: " + nn.predict(new double[]{1, 0, 0, 0, 0, 1, 1, 0}));
System.out.println("Prediction for [1, 0, 0, 0, 0, 1, 1, 1]: " + nn.predict(new double[]{1, 0, 0, 0, 0, 1, 1, 1}));
System.out.println("Prediction for [1, 0, 0, 0, 1, 0, 0, 0]: " + nn.predict(new double[]{1, 0, 0, 0, 1, 0, 0, 0}));
System.out.println("Prediction for [1, 0, 0, 0, 1, 0, 0, 1]: " + nn.predict(new double[]{1, 0, 0, 0, 1, 0, 0, 1}));
System.out.println("Prediction for [1, 0, 0, 0, 1, 0, 1, 0]: " + nn.predict(new double[]{1, 0, 0, 0, 1, 0, 1, 0}));
System.out.println("Prediction for [1, 0, 0, 0, 1, 0, 1, 1]: " + nn.predict(new double[]{1, 0, 0, 0, 1, 0, 1, 1}));
System.out.println("Prediction for [1, 0, 0, 0, 1, 1, 0, 0]: " + nn.predict(new double[]{1, 0, 0, 0, 1, 1, 0, 0}));
System.out.println("Prediction for [1, 0, 0, 0, 1, 1, 0, 1]: " + nn.predict(new double[]{1, 0, 0, 0, 1, 1, 0, 1}));
System.out.println("Prediction for [1, 0, 0, 0, 1, 1, 1, 0]: " + nn.predict(new double[]{1, 0, 0, 0, 1, 1, 1, 0}));
System.out.println("Prediction for [1, 0, 0, 0, 1, 1, 1, 1]: " + nn.predict(new double[]{1, 0, 0, 0, 1, 1, 1, 1}));
System.out.println("Prediction for [1, 0, 0, 1, 0, 0, 0, 0]: " + nn.predict(new double[]{1, 0, 0, 1, 0, 0, 0, 0}));
System.out.println("Prediction for [1, 0, 0, 1, 0, 0, 0, 1]: " + nn.predict(new double[]{1, 0, 0, 1, 0, 0, 0, 1}));
System.out.println("Prediction for [1, 0, 0, 1, 0, 0, 1, 0]: " + nn.predict(new double[]{1, 0, 0, 1, 0, 0, 1, 0}));
System.out.println("Prediction for [1, 0, 0, 1, 0, 0, 1, 1]: " + nn.predict(new double[]{1, 0, 0, 1, 0, 0, 1, 1}));
System.out.println("Prediction for [1, 0, 0, 1, 0, 1, 0, 0]: " + nn.predict(new double[]{1, 0, 0, 1, 0, 1, 0, 0}));
System.out.println("Prediction for [1, 0, 0, 1, 0, 1, 0, 1]: " + nn.predict(new double[]{1, 0, 0, 1, 0, 1, 0, 1}));
System.out.println("Prediction for [1, 0, 0, 1, 0, 1, 1, 0]: " + nn.predict(new double[]{1, 0, 0, 1, 0, 1, 1, 0}));
System.out.println("Prediction for [1, 0, 0, 1, 0, 1, 1, 1]: " + nn.predict(new double[]{1, 0, 0, 1, 0, 1, 1, 1}));
System.out.println("Prediction for [1, 0, 0, 1, 1, 0, 0, 0]: " + nn.predict(new double[]{1, 0, 0, 1, 1, 0, 0, 0}));
System.out.println("Prediction for [1, 0, 0, 1, 1, 0, 0, 1]: " + nn.predict(new double[]{1, 0, 0, 1, 1, 0, 0, 1}));
System.out.println("Prediction for [1, 0, 0, 1, 1, 0, 1, 0]: " + nn.predict(new double[]{1, 0, 0, 1, 1, 0, 1, 0}));
System.out.println("Prediction for [1, 0, 0, 1, 1, 0, 1, 1]: " + nn.predict(new double[]{1, 0, 0, 1, 1, 0, 1, 1}));
System.out.println("Prediction for [1, 0, 0, 1, 1, 1, 0, 0]: " + nn.predict(new double[]{1, 0, 0, 1, 1, 1, 0, 0}));
System.out.println("Prediction for [1, 0, 0, 1, 1, 1, 0, 1]: " + nn.predict(new double[]{1, 0, 0, 1, 1, 1, 0, 1}));
System.out.println("Prediction for [1, 0, 0, 1, 1, 1, 1, 0]: " + nn.predict(new double[]{1, 0, 0, 1, 1, 1, 1, 0}));
System.out.println("Prediction for [1, 0, 0, 1, 1, 1, 1, 1]: " + nn.predict(new double[]{1, 0, 0, 1, 1, 1, 1, 1}));
System.out.println("Prediction for [1, 0, 1, 0, 0, 0, 0, 0]: " + nn.predict(new double[]{1, 0, 1, 0, 0, 0, 0, 0}));
System.out.println("Prediction for [1, 0, 1, 0, 0, 0, 0, 1]: " + nn.predict(new double[]{1, 0, 1, 0, 0, 0, 0, 1}));
System.out.println("Prediction for [1, 0, 1, 0, 0, 0, 1, 0]: " + nn.predict(new double[]{1, 0, 1, 0, 0, 0, 1, 0}));
System.out.println("Prediction for [1, 0, 1, 0, 0, 0, 1, 1]: " + nn.predict(new double[]{1, 0, 1, 0, 0, 0, 1, 1}));
System.out.println("Prediction for [1, 0, 1, 0, 0, 1, 0, 0]: " + nn.predict(new double[]{1, 0, 1, 0, 0, 1, 0, 0}));
System.out.println("Prediction for [1, 0, 1, 0, 0, 1, 0, 1]: " + nn.predict(new double[]{1, 0, 1, 0, 0, 1, 0, 1}));
System.out.println("Prediction for [1, 0, 1, 0, 0, 1, 1, 0]: " + nn.predict(new double[]{1, 0, 1, 0, 0, 1, 1, 0}));
System.out.println("Prediction for [1, 0, 1, 0, 0, 1, 1, 1]: " + nn.predict(new double[]{1, 0, 1, 0, 0, 1, 1, 1}));
System.out.println("Prediction for [1, 0, 1, 0, 1, 0, 0, 0]: " + nn.predict(new double[]{1, 0, 1, 0, 1, 0, 0, 0}));
System.out.println("Prediction for [1, 0, 1, 0, 1, 0, 0, 1]: " + nn.predict(new double[]{1, 0, 1, 0, 1, 0, 0, 1}));
System.out.println("Prediction for [1, 0, 1, 0, 1, 0, 1, 0]: " + nn.predict(new double[]{1, 0, 1, 0, 1, 0, 1, 0}));
System.out.println("Prediction for [1, 0, 1, 0, 1, 0, 1, 1]: " + nn.predict(new double[]{1, 0, 1, 0, 1, 0, 1, 1}));
System.out.println("Prediction for [1, 0, 1, 0, 1, 1, 0, 0]: " + nn.predict(new double[]{1, 0, 1, 0, 1, 1, 0, 0}));
System.out.println("Prediction for [1, 0, 1, 0, 1, 1, 0, 1]: " + nn.predict(new double[]{1, 0, 1, 0, 1, 1, 0, 1}));
System.out.println("Prediction for [1, 0, 1, 0, 1, 1, 1, 0]: " + nn.predict(new double[]{1, 0, 1, 0, 1, 1, 1, 0}));
System.out.println("Prediction for [1, 0, 1, 0, 1, 1, 1, 1]: " + nn.predict(new double[]{1, 0, 1, 0, 1, 1, 1, 1}));
System.out.println("Prediction for [1, 0, 1, 1, 0, 0, 0, 0]: " + nn.predict(new double[]{1, 0, 1, 1, 0, 0, 0, 0}));
System.out.println("Prediction for [1, 0, 1, 1, 0, 0, 0, 1]: " + nn.predict(new double[]{1, 0, 1, 1, 0, 0, 0, 1}));
System.out.println("Prediction for [1, 0, 1, 1, 0, 0, 1, 0]: " + nn.predict(new double[]{1, 0, 1, 1, 0, 0, 1, 0}));
System.out.println("Prediction for [1, 0, 1, 1, 0, 0, 1, 1]: " + nn.predict(new double[]{1, 0, 1, 1, 0, 0, 1, 1}));
System.out.println("Prediction for [1, 0, 1, 1, 0, 1, 0, 0]: " + nn.predict(new double[]{1, 0, 1, 1, 0, 1, 0, 0}));
System.out.println("Prediction for [1, 0, 1, 1, 0, 1, 0, 1]: " + nn.predict(new double[]{1, 0, 1, 1, 0, 1, 0, 1}));
System.out.println("Prediction for [1, 0, 1, 1, 0, 1, 1, 0]: " + nn.predict(new double[]{1, 0, 1, 1, 0, 1, 1, 0}));
System.out.println("Prediction for [1, 0, 1, 1, 0, 1, 1, 1]: " + nn.predict(new double[]{1, 0, 1, 1, 0, 1, 1, 1}));
System.out.println("Prediction for [1, 0, 1, 1, 1, 0, 0, 0]: " + nn.predict(new double[]{1, 0, 1, 1, 1, 0, 0, 0}));
System.out.println("Prediction for [1, 0, 1, 1, 1, 0, 0, 1]: " + nn.predict(new double[]{1, 0, 1, 1, 1, 0, 0, 1}));
System.out.println("Prediction for [1, 0, 1, 1, 1, 0, 1, 0]: " + nn.predict(new double[]{1, 0, 1, 1, 1, 0, 1, 0}));
System.out.println("Prediction for [1, 0, 1, 1, 1, 0, 1, 1]: " + nn.predict(new double[]{1, 0, 1, 1, 1, 0, 1, 1}));
System.out.println("Prediction for [1, 0, 1, 1, 1, 1, 0, 0]: " + nn.predict(new double[]{1, 0, 1, 1, 1, 1, 0, 0}));
System.out.println("Prediction for [1, 0, 1, 1, 1, 1, 0, 1]: " + nn.predict(new double[]{1, 0, 1, 1, 1, 1, 0, 1}));
System.out.println("Prediction for [1, 0, 1, 1, 1, 1, 1, 0]: " + nn.predict(new double[]{1, 0, 1, 1, 1, 1, 1, 0}));
System.out.println("Prediction for [1, 0, 1, 1, 1, 1, 1, 1]: " + nn.predict(new double[]{1, 0, 1, 1, 1, 1, 1, 1}));
System.out.println("Prediction for [1, 1, 0, 0, 0, 0, 0, 0]: " + nn.predict(new double[]{1, 1, 0, 0, 0, 0, 0, 0}));
System.out.println("Prediction for [1, 1, 0, 0, 0, 0, 0, 1]: " + nn.predict(new double[]{1, 1, 0, 0, 0, 0, 0, 1}));
System.out.println("Prediction for [1, 1, 0, 0, 0, 0, 1, 0]: " + nn.predict(new double[]{1, 1, 0, 0, 0, 0, 1, 0}));
System.out.println("Prediction for [1, 1, 0, 0, 0, 0, 1, 1]: " + nn.predict(new double[]{1, 1, 0, 0, 0, 0, 1, 1}));
System.out.println("Prediction for [1, 1, 0, 0, 0, 1, 0, 0]: " + nn.predict(new double[]{1, 1, 0, 0, 0, 1, 0, 0}));
System.out.println("Prediction for [1, 1, 0, 0, 0, 1, 0, 1]: " + nn.predict(new double[]{1, 1, 0, 0, 0, 1, 0, 1}));
System.out.println("Prediction for [1, 1, 0, 0, 0, 1, 1, 0]: " + nn.predict(new double[]{1, 1, 0, 0, 0, 1, 1, 0}));
System.out.println("Prediction for [1, 1, 0, 0, 0, 1, 1, 1]: " + nn.predict(new double[]{1, 1, 0, 0, 0, 1, 1, 1}));
System.out.println("Prediction for [1, 1, 0, 0, 1, 0, 0, 0]: " + nn.predict(new double[]{1, 1, 0, 0, 1, 0, 0, 0}));
System.out.println("Prediction for [1, 1, 0, 0, 1, 0, 0, 1]: " + nn.predict(new double[]{1, 1, 0, 0, 1, 0, 0, 1}));
System.out.println("Prediction for [1, 1, 0, 0, 1, 0, 1, 0]: " + nn.predict(new double[]{1, 1, 0, 0, 1, 0, 1, 0}));
System.out.println("Prediction for [1, 1, 0, 0, 1, 0, 1, 1]: " + nn.predict(new double[]{1, 1, 0, 0, 1, 0, 1, 1}));
System.out.println("Prediction for [1, 1, 0, 0, 1, 1, 0, 0]: " + nn.predict(new double[]{1, 1, 0, 0, 1, 1, 0, 0}));
System.out.println("Prediction for [1, 1, 0, 0, 1, 1, 0, 1]: " + nn.predict(new double[]{1, 1, 0, 0, 1, 1, 0, 1}));
System.out.println("Prediction for [1, 1, 0, 0, 1, 1, 1, 0]: " + nn.predict(new double[]{1, 1, 0, 0, 1, 1, 1, 0}));
System.out.println("Prediction for [1, 1, 0, 0, 1, 1, 1, 1]: " + nn.predict(new double[]{1, 1, 0, 0, 1, 1, 1, 1}));
System.out.println("Prediction for [1, 1, 0, 1, 0, 0, 0, 0]: " + nn.predict(new double[]{1, 1, 0, 1, 0, 0, 0, 0}));
System.out.println("Prediction for [1, 1, 0, 1, 0, 0, 0, 1]: " + nn.predict(new double[]{1, 1, 0, 1, 0, 0, 0, 1}));
System.out.println("Prediction for [1, 1, 0, 1, 0, 0, 1, 0]: " + nn.predict(new double[]{1, 1, 0, 1, 0, 0, 1, 0}));
System.out.println("Prediction for [1, 1, 0, 1, 0, 0, 1, 1]: " + nn.predict(new double[]{1, 1, 0, 1, 0, 0, 1, 1}));
System.out.println("Prediction for [1, 1, 0, 1, 0, 1, 0, 0]: " + nn.predict(new double[]{1, 1, 0, 1, 0, 1, 0, 0}));
System.out.println("Prediction for [1, 1, 0, 1, 0, 1, 0, 1]: " + nn.predict(new double[]{1, 1, 0, 1, 0, 1, 0, 1}));
System.out.println("Prediction for [1, 1, 0, 1, 0, 1, 1, 0]: " + nn.predict(new double[]{1, 1, 0, 1, 0, 1, 1, 0}));
System.out.println("Prediction for [1, 1, 0, 1, 0, 1, 1, 1]: " + nn.predict(new double[]{1, 1, 0, 1, 0, 1, 1, 1}));
System.out.println("Prediction for [1, 1, 0, 1, 1, 0, 0, 0]: " + nn.predict(new double[]{1, 1, 0, 1, 1, 0, 0, 0}));
System.out.println("Prediction for [1, 1, 0, 1, 1, 0, 0, 1]: " + nn.predict(new double[]{1, 1, 0, 1, 1, 0, 0, 1}));
System.out.println("Prediction for [1, 1, 0, 1, 1, 0, 1, 0]: " + nn.predict(new double[]{1, 1, 0, 1, 1, 0, 1, 0}));
System.out.println("Prediction for [1, 1, 0, 1, 1, 0, 1, 1]: " + nn.predict(new double[]{1, 1, 0, 1, 1, 0, 1, 1}));
System.out.println("Prediction for [1, 1, 0, 1, 1, 1, 0, 0]: " + nn.predict(new double[]{1, 1, 0, 1, 1, 1, 0, 0}));
System.out.println("Prediction for [1, 1, 0, 1, 1, 1, 0, 1]: " + nn.predict(new double[]{1, 1, 0, 1, 1, 1, 0, 1}));
System.out.println("Prediction for [1, 1, 0, 1, 1, 1, 1, 0]: " + nn.predict(new double[]{1, 1, 0, 1, 1, 1, 1, 0}));
System.out.println("Prediction for [1, 1, 0, 1, 1, 1, 1, 1]: " + nn.predict(new double[]{1, 1, 0, 1, 1, 1, 1, 1}));
System.out.println("Prediction for [1, 1, 1, 0, 0, 0, 0, 0]: " + nn.predict(new double[]{1, 1, 1, 0, 0, 0, 0, 0}));
System.out.println("Prediction for [1, 1, 1, 0, 0, 0, 0, 1]: " + nn.predict(new double[]{1, 1, 1, 0, 0, 0, 0, 1}));
System.out.println("Prediction for [1, 1, 1, 0, 0, 0, 1, 0]: " + nn.predict(new double[]{1, 1, 1, 0, 0, 0, 1, 0}));
System.out.println("Prediction for [1, 1, 1, 0, 0, 0, 1, 1]: " + nn.predict(new double[]{1, 1, 1, 0, 0, 0, 1, 1}));
System.out.println("Prediction for [1, 1, 1, 0, 0, 1, 0, 0]: " + nn.predict(new double[]{1, 1, 1, 0, 0, 1, 0, 0}));
System.out.println("Prediction for [1, 1, 1, 0, 0, 1, 0, 1]: " + nn.predict(new double[]{1, 1, 1, 0, 0, 1, 0, 1}));
System.out.println("Prediction for [1, 1, 1, 0, 0, 1, 1, 0]: " + nn.predict(new double[]{1, 1, 1, 0, 0, 1, 1, 0}));
System.out.println("Prediction for [1, 1, 1, 0, 0, 1, 1, 1]: " + nn.predict(new double[]{1, 1, 1, 0, 0, 1, 1, 1}));
System.out.println("Prediction for [1, 1, 1, 0, 1, 0, 0, 0]: " + nn.predict(new double[]{1, 1, 1, 0, 1, 0, 0, 0}));
System.out.println("Prediction for [1, 1, 1, 0, 1, 0, 0, 1]: " + nn.predict(new double[]{1, 1, 1, 0, 1, 0, 0, 1}));
System.out.println("Prediction for [1, 1, 1, 0, 1, 0, 1, 0]: " + nn.predict(new double[]{1, 1, 1, 0, 1, 0, 1, 0}));
System.out.println("Prediction for [1, 1, 1, 0, 1, 0, 1, 1]: " + nn.predict(new double[]{1, 1, 1, 0, 1, 0, 1, 1}));
System.out.println("Prediction for [1, 1, 1, 0, 1, 1, 0, 0]: " + nn.predict(new double[]{1, 1, 1, 0, 1, 1, 0, 0}));
System.out.println("Prediction for [1, 1, 1, 0, 1, 1, 0, 1]: " + nn.predict(new double[]{1, 1, 1, 0, 1, 1, 0, 1}));
System.out.println("Prediction for [1, 1, 1, 0, 1, 1, 1, 0]: " + nn.predict(new double[]{1, 1, 1, 0, 1, 1, 1, 0}));
System.out.println("Prediction for [1, 1, 1, 0, 1, 1, 1, 1]: " + nn.predict(new double[]{1, 1, 1, 0, 1, 1, 1, 1}));
System.out.println("Prediction for [1, 1, 1, 1, 0, 0, 0, 0]: " + nn.predict(new double[]{1, 1, 1, 1, 0, 0, 0, 0}));
System.out.println("Prediction for [1, 1, 1, 1, 0, 0, 0, 1]: " + nn.predict(new double[]{1, 1, 1, 1, 0, 0, 0, 1}));
System.out.println("Prediction for [1, 1, 1, 1, 0, 0, 1, 0]: " + nn.predict(new double[]{1, 1, 1, 1, 0, 0, 1, 0}));
System.out.println("Prediction for [1, 1, 1, 1, 0, 0, 1, 1]: " + nn.predict(new double[]{1, 1, 1, 1, 0, 0, 1, 1}));
System.out.println("Prediction for [1, 1, 1, 1, 0, 1, 0, 0]: " + nn.predict(new double[]{1, 1, 1, 1, 0, 1, 0, 0}));
System.out.println("Prediction for [1, 1, 1, 1, 0, 1, 0, 1]: " + nn.predict(new double[]{1, 1, 1, 1, 0, 1, 0, 1}));
System.out.println("Prediction for [1, 1, 1, 1, 0, 1, 1, 0]: " + nn.predict(new double[]{1, 1, 1, 1, 0, 1, 1, 0}));
System.out.println("Prediction for [1, 1, 1, 1, 0, 1, 1, 1]: " + nn.predict(new double[]{1, 1, 1, 1, 0, 1, 1, 1}));
System.out.println("Prediction for [1, 1, 1, 1, 1, 0, 0, 0]: " + nn.predict(new double[]{1, 1, 1, 1, 1, 0, 0, 0}));
System.out.println("Prediction for [1, 1, 1, 1, 1, 0, 0, 1]: " + nn.predict(new double[]{1, 1, 1, 1, 1, 0, 0, 1}));
System.out.println("Prediction for [1, 1, 1, 1, 1, 0, 1, 0]: " + nn.predict(new double[]{1, 1, 1, 1, 1, 0, 1, 0}));
System.out.println("Prediction for [1, 1, 1, 1, 1, 0, 1, 1]: " + nn.predict(new double[]{1, 1, 1, 1, 1, 0, 1, 1}));
System.out.println("Prediction for [1, 1, 1, 1, 1, 1, 0, 0]: " + nn.predict(new double[]{1, 1, 1, 1, 1, 1, 0, 0}));
System.out.println("Prediction for [1, 1, 1, 1, 1, 1, 0, 1]: " + nn.predict(new double[]{1, 1, 1, 1, 1, 1, 0, 1}));
System.out.println("Prediction for [1, 1, 1, 1, 1, 1, 1, 0]: " + nn.predict(new double[]{1, 1, 1, 1, 1, 1, 1, 0}));
System.out.println("Prediction for [1, 1, 1, 1, 1, 1, 1, 1]: " + nn.predict(new double[]{1, 1, 1, 1, 1, 1, 1, 1}));


}


}


run:
Epoch 10000. Mean error: 0.003808585503429578
Epoch 20000. Mean error: 0.0010577075838637943
Epoch 30000. Mean error: 3.741219881395637E-4
Epoch 40000. Mean error: 1.3142376589993864E-4
Epoch 50000. Mean error: 4.659144604532638E-5
Epoch 60000. Mean error: 1.6601544252988132E-5
Epoch 70000. Mean error: 5.914518503609392E-6
Epoch 80000. Mean error: 2.105004169189538E-6
Epoch 90000. Mean error: 7.489596407242758E-7
Epoch 100000. Mean error: 2.6636013881726364E-7
Epoch 110000. Mean error: 9.478309897230993E-8
Epoch 120000. Mean error: 3.3747368194445706E-8
Epoch 130000. Mean error: 1.1996065678328438E-8
Epoch 140000. Mean error: 4.273375111236205E-9
Epoch 150000. Mean error: 1.5190910450396608E-9
Epoch 160000. Mean error: 5.410631555292937E-10
Epoch 170000. Mean error: 1.923743887628983E-10
Epoch 180000. Mean error: 6.83844557165148E-11
Epoch 190000. Mean error: 2.4322644441997302E-11
Epoch 200000. Mean error: 8.651056671620701E-12
Prediction for [0, 0, 0, 0, 0, 0, 0, 0]: 1.1493876694856908E-7
Prediction for [0, 0, 0, 0, 0, 0, 0, 1]: 0.9999997214335633
Prediction for [0, 0, 0, 0, 0, 0, 1, 0]: 0.9999997767738038
Prediction for [0, 0, 0, 0, 0, 0, 1, 1]: 0.9999996846470631
Prediction for [0, 0, 0, 0, 0, 1, 0, 0]: 0.0
Prediction for [0, 0, 0, 0, 0, 1, 0, 1]: 0.999998323124843
Prediction for [0, 0, 0, 0, 0, 1, 1, 0]: 0.0
Prediction for [0, 0, 0, 0, 0, 1, 1, 1]: 1.0000016782798429
Prediction for [0, 0, 0, 0, 1, 0, 0, 0]: 0.0
Prediction for [0, 0, 0, 0, 1, 0, 0, 1]: 0.0
Prediction for [0, 0, 0, 0, 1, 0, 1, 0]: 0.0
Prediction for [0, 0, 0, 0, 1, 0, 1, 1]: 1.0000000260009656
Prediction for [0, 0, 0, 0, 1, 1, 0, 0]: 0.0
Prediction for [0, 0, 0, 0, 1, 1, 0, 1]: 0.9999990111539732
Prediction for [0, 0, 0, 0, 1, 1, 1, 0]: 0.0
Prediction for [0, 0, 0, 0, 1, 1, 1, 1]: 0.0
Prediction for [0, 0, 0, 1, 0, 0, 0, 0]: 0.0
Prediction for [0, 0, 0, 1, 0, 0, 0, 1]: 0.999999186662796
Prediction for [0, 0, 0, 1, 0, 0, 1, 0]: 0.0
Prediction for [0, 0, 0, 1, 0, 0, 1, 1]: 1.0000000304334704
Prediction for [0, 0, 0, 1, 0, 1, 0, 0]: 0.0
Prediction for [0, 0, 0, 1, 0, 1, 0, 1]: 4.577195349630969E-6
Prediction for [0, 0, 0, 1, 0, 1, 1, 0]: 0.0
Prediction for [0, 0, 0, 1, 0, 1, 1, 1]: 0.9999986892402796
Prediction for [0, 0, 0, 1, 1, 0, 0, 0]: 0.0
Prediction for [0, 0, 0, 1, 1, 0, 0, 1]: 0.0
Prediction for [0, 0, 0, 1, 1, 0, 1, 0]: 0.0
Prediction for [0, 0, 0, 1, 1, 0, 1, 1]: 0.0
Prediction for [0, 0, 0, 1, 1, 1, 0, 0]: 0.0
Prediction for [0, 0, 0, 1, 1, 1, 0, 1]: 1.0000001285452695
Prediction for [0, 0, 0, 1, 1, 1, 1, 0]: 0.0
Prediction for [0, 0, 0, 1, 1, 1, 1, 1]: 0.999997733560584
Prediction for [0, 0, 1, 0, 0, 0, 0, 0]: 0.0
Prediction for [0, 0, 1, 0, 0, 0, 0, 1]: 0.0
Prediction for [0, 0, 1, 0, 0, 0, 1, 0]: 0.0
Prediction for [0, 0, 1, 0, 0, 0, 1, 1]: 0.0
Prediction for [0, 0, 1, 0, 0, 1, 0, 0]: 0.0
Prediction for [0, 0, 1, 0, 0, 1, 0, 1]: 0.9999978297881655
Prediction for [0, 0, 1, 0, 0, 1, 1, 0]: 0.0
Prediction for [0, 0, 1, 0, 0, 1, 1, 1]: 0.0
Prediction for [0, 0, 1, 0, 1, 0, 0, 0]: 0.0
Prediction for [0, 0, 1, 0, 1, 0, 0, 1]: 1.0000010700464679
Prediction for [0, 0, 1, 0, 1, 0, 1, 0]: 0.0
Prediction for [0, 0, 1, 0, 1, 0, 1, 1]: 0.999998795974606
Prediction for [0, 0, 1, 0, 1, 1, 0, 0]: 0.0
Prediction for [0, 0, 1, 0, 1, 1, 0, 1]: 2.2581914198571695E-5
Prediction for [0, 0, 1, 0, 1, 1, 1, 0]: 0.0
Prediction for [0, 0, 1, 0, 1, 1, 1, 1]: 0.9999956198310961
Prediction for [0, 0, 1, 1, 0, 0, 0, 0]: 0.0
Prediction for [0, 0, 1, 1, 0, 0, 0, 1]: 2.524589107544273E-6
Prediction for [0, 0, 1, 1, 0, 0, 1, 0]: 0.0
Prediction for [0, 0, 1, 1, 0, 0, 1, 1]: 0.0
Prediction for [0, 0, 1, 1, 0, 1, 0, 0]: 0.0
Prediction for [0, 0, 1, 1, 0, 1, 0, 1]: 0.9999977571951695
Prediction for [0, 0, 1, 1, 0, 1, 1, 0]: 0.0
Prediction for [0, 0, 1, 1, 0, 1, 1, 1]: 0.0
Prediction for [0, 0, 1, 1, 1, 0, 0, 0]: 0.0
Prediction for [0, 0, 1, 1, 1, 0, 0, 1]: 0.0
Prediction for [0, 0, 1, 1, 1, 0, 1, 0]: 0.0
Prediction for [0, 0, 1, 1, 1, 0, 1, 1]: 1.0000003492365814
Prediction for [0, 0, 1, 1, 1, 1, 0, 0]: 0.0
Prediction for [0, 0, 1, 1, 1, 1, 0, 1]: 0.9999929604453437
Prediction for [0, 0, 1, 1, 1, 1, 1, 0]: 0.0
Prediction for [0, 0, 1, 1, 1, 1, 1, 1]: 1.2817688045840825E-5
Prediction for [0, 1, 0, 0, 0, 0, 0, 0]: 0.0
Prediction for [0, 1, 0, 0, 0, 0, 0, 1]: 0.0
Prediction for [0, 1, 0, 0, 0, 0, 1, 0]: 7.9571670941192E-7
Prediction for [0, 1, 0, 0, 0, 0, 1, 1]: 1.0000011571990652
Prediction for [0, 1, 0, 0, 0, 1, 0, 0]: 0.0
Prediction for [0, 1, 0, 0, 0, 1, 0, 1]: 0.0
Prediction for [0, 1, 0, 0, 0, 1, 1, 0]: 0.0
Prediction for [0, 1, 0, 0, 0, 1, 1, 1]: 0.9999998754062742
Prediction for [0, 1, 0, 0, 1, 0, 0, 0]: 0.0
Prediction for [0, 1, 0, 0, 1, 0, 0, 1]: 1.0000007511885223
Prediction for [0, 1, 0, 0, 1, 0, 1, 0]: 0.0
Prediction for [0, 1, 0, 0, 1, 0, 1, 1]: 0.0
Prediction for [0, 1, 0, 0, 1, 1, 0, 0]: 0.0
Prediction for [0, 1, 0, 0, 1, 1, 0, 1]: 0.0
Prediction for [0, 1, 0, 0, 1, 1, 1, 0]: 0.0
Prediction for [0, 1, 0, 0, 1, 1, 1, 1]: 1.000000559910793
Prediction for [0, 1, 0, 1, 0, 0, 0, 0]: 0.0
Prediction for [0, 1, 0, 1, 0, 0, 0, 1]: 6.175761815274683E-7
Prediction for [0, 1, 0, 1, 0, 0, 1, 0]: 0.0
Prediction for [0, 1, 0, 1, 0, 0, 1, 1]: 1.0000002122535003
Prediction for [0, 1, 0, 1, 0, 1, 0, 0]: 0.0
Prediction for [0, 1, 0, 1, 0, 1, 0, 1]: 0.0
Prediction for [0, 1, 0, 1, 0, 1, 1, 0]: 0.0
Prediction for [0, 1, 0, 1, 0, 1, 1, 1]: 0.0
Prediction for [0, 1, 0, 1, 1, 0, 0, 0]: 0.0
Prediction for [0, 1, 0, 1, 1, 0, 0, 1]: 1.0000007531311845
Prediction for [0, 1, 0, 1, 1, 0, 1, 0]: 0.0
Prediction for [0, 1, 0, 1, 1, 0, 1, 1]: 0.0
Prediction for [0, 1, 0, 1, 1, 1, 0, 0]: 0.0
Prediction for [0, 1, 0, 1, 1, 1, 0, 1]: 0.0
Prediction for [0, 1, 0, 1, 1, 1, 1, 0]: 0.0
Prediction for [0, 1, 0, 1, 1, 1, 1, 1]: 2.5926466582504304E-6
Prediction for [0, 1, 1, 0, 0, 0, 0, 0]: 0.0
Prediction for [0, 1, 1, 0, 0, 0, 0, 1]: 0.9999986429045669
Prediction for [0, 1, 1, 0, 0, 0, 1, 0]: 0.0
Prediction for [0, 1, 1, 0, 0, 0, 1, 1]: 2.2767656437938655E-6
Prediction for [0, 1, 1, 0, 0, 1, 0, 0]: 0.0
Prediction for [0, 1, 1, 0, 0, 1, 0, 1]: 1.0000029157829935
Prediction for [0, 1, 1, 0, 0, 1, 1, 0]: 0.0
Prediction for [0, 1, 1, 0, 0, 1, 1, 1]: 0.9999994387787101
Prediction for [0, 1, 1, 0, 1, 0, 0, 0]: 0.0
Prediction for [0, 1, 1, 0, 1, 0, 0, 1]: 1.4328173822963919E-6
Prediction for [0, 1, 1, 0, 1, 0, 1, 0]: 0.0
Prediction for [0, 1, 1, 0, 1, 0, 1, 1]: 0.999999442836751
Prediction for [0, 1, 1, 0, 1, 1, 0, 0]: 0.0
Prediction for [0, 1, 1, 0, 1, 1, 0, 1]: 0.9999952662182496
Prediction for [0, 1, 1, 0, 1, 1, 1, 0]: 0.0
Prediction for [0, 1, 1, 0, 1, 1, 1, 1]: 5.123501170878342E-6
Prediction for [0, 1, 1, 1, 0, 0, 0, 0]: 0.0
Prediction for [0, 1, 1, 1, 0, 0, 0, 1]: 0.9999992365687822
Prediction for [0, 1, 1, 1, 0, 0, 1, 0]: 0.0
Prediction for [0, 1, 1, 1, 0, 0, 1, 1]: 0.0
Prediction for [0, 1, 1, 1, 0, 1, 0, 0]: 0.0
Prediction for [0, 1, 1, 1, 0, 1, 0, 1]: 0.0
Prediction for [0, 1, 1, 1, 0, 1, 1, 0]: 0.0
Prediction for [0, 1, 1, 1, 0, 1, 1, 1]: 1.5039353107315634E-6
Prediction for [0, 1, 1, 1, 1, 0, 0, 0]: 0.0
Prediction for [0, 1, 1, 1, 1, 0, 0, 1]: 1.705846051747173E-6
Prediction for [0, 1, 1, 1, 1, 0, 1, 0]: 0.0
Prediction for [0, 1, 1, 1, 1, 0, 1, 1]: 1.77885156005253E-6
Prediction for [0, 1, 1, 1, 1, 1, 0, 0]: 0.0
Prediction for [0, 1, 1, 1, 1, 1, 0, 1]: 1.9185412540867475E-6
Prediction for [0, 1, 1, 1, 1, 1, 1, 0]: 0.0
Prediction for [0, 1, 1, 1, 1, 1, 1, 1]: 0.9999853580808646
Prediction for [1, 0, 0, 0, 0, 0, 0, 0]: 0.0
Prediction for [1, 0, 0, 0, 0, 0, 0, 1]: 0.0
Prediction for [1, 0, 0, 0, 0, 0, 1, 0]: 0.0
Prediction for [1, 0, 0, 0, 0, 0, 1, 1]: 1.0000011723827145
Prediction for [1, 0, 0, 0, 0, 1, 0, 0]: 0.0
Prediction for [1, 0, 0, 0, 0, 1, 0, 1]: 3.263457222235644E-7
Prediction for [1, 0, 0, 0, 0, 1, 1, 0]: 0.0
Prediction for [1, 0, 0, 0, 0, 1, 1, 1]: 1.989126314771994E-7
Prediction for [1, 0, 0, 0, 1, 0, 0, 0]: 0.0
Prediction for [1, 0, 0, 0, 1, 0, 0, 1]: 0.9999994489167827
Prediction for [1, 0, 0, 0, 1, 0, 1, 0]: 0.0
Prediction for [1, 0, 0, 0, 1, 0, 1, 1]: 0.9999993314046669
Prediction for [1, 0, 0, 0, 1, 1, 0, 0]: 0.0
Prediction for [1, 0, 0, 0, 1, 1, 0, 1]: 0.0
Prediction for [1, 0, 0, 0, 1, 1, 1, 0]: 0.0
Prediction for [1, 0, 0, 0, 1, 1, 1, 1]: 0.0
Prediction for [1, 0, 0, 1, 0, 0, 0, 0]: 0.0
Prediction for [1, 0, 0, 1, 0, 0, 0, 1]: 0.0
Prediction for [1, 0, 0, 1, 0, 0, 1, 0]: 0.0
Prediction for [1, 0, 0, 1, 0, 0, 1, 1]: 8.393365751313553E-7
Prediction for [1, 0, 0, 1, 0, 1, 0, 0]: 0.0
Prediction for [1, 0, 0, 1, 0, 1, 0, 1]: 1.0000033894006881
Prediction for [1, 0, 0, 1, 0, 1, 1, 0]: 0.0
Prediction for [1, 0, 0, 1, 0, 1, 1, 1]: 0.9999988724773827
Prediction for [1, 0, 0, 1, 1, 0, 0, 0]: 0.0
Prediction for [1, 0, 0, 1, 1, 0, 0, 1]: 0.0
Prediction for [1, 0, 0, 1, 1, 0, 1, 0]: 0.0
Prediction for [1, 0, 0, 1, 1, 0, 1, 1]: 0.0
Prediction for [1, 0, 0, 1, 1, 1, 0, 0]: 0.0
Prediction for [1, 0, 0, 1, 1, 1, 0, 1]: 0.9999992029014997
Prediction for [1, 0, 0, 1, 1, 1, 1, 0]: 0.0
Prediction for [1, 0, 0, 1, 1, 1, 1, 1]: 1.710285203015971E-6
Prediction for [1, 0, 1, 0, 0, 0, 0, 0]: 0.0
Prediction for [1, 0, 1, 0, 0, 0, 0, 1]: 0.0
Prediction for [1, 0, 1, 0, 0, 0, 1, 0]: 0.0
Prediction for [1, 0, 1, 0, 0, 0, 1, 1]: 0.9999993501222617
Prediction for [1, 0, 1, 0, 0, 1, 0, 0]: 0.0
Prediction for [1, 0, 1, 0, 0, 1, 0, 1]: 2.224800710992625E-6
Prediction for [1, 0, 1, 0, 0, 1, 1, 0]: 0.0
Prediction for [1, 0, 1, 0, 0, 1, 1, 1]: 0.999998379616095
Prediction for [1, 0, 1, 0, 1, 0, 0, 0]: 0.0
Prediction for [1, 0, 1, 0, 1, 0, 0, 1]: 3.5844525458905707E-7
Prediction for [1, 0, 1, 0, 1, 0, 1, 0]: 0.0
Prediction for [1, 0, 1, 0, 1, 0, 1, 1]: 4.954791028577432E-7
Prediction for [1, 0, 1, 0, 1, 1, 0, 0]: 0.0
Prediction for [1, 0, 1, 0, 1, 1, 0, 1]: 0.999991566260183
Prediction for [1, 0, 1, 0, 1, 1, 1, 0]: 0.0
Prediction for [1, 0, 1, 0, 1, 1, 1, 1]: 8.104980422363184E-7
Prediction for [1, 0, 1, 1, 0, 0, 0, 0]: 0.0
Prediction for [1, 0, 1, 1, 0, 0, 0, 1]: 0.0
Prediction for [1, 0, 1, 1, 0, 0, 1, 0]: 0.0
Prediction for [1, 0, 1, 1, 0, 0, 1, 1]: 0.9999996687028503
Prediction for [1, 0, 1, 1, 0, 1, 0, 0]: 0.0
Prediction for [1, 0, 1, 1, 0, 1, 0, 1]: 0.9999991959922634
Prediction for [1, 0, 1, 1, 0, 1, 1, 0]: 0.0
Prediction for [1, 0, 1, 1, 0, 1, 1, 1]: 2.4911264742133454E-6
Prediction for [1, 0, 1, 1, 1, 0, 0, 0]: 0.0
Prediction for [1, 0, 1, 1, 1, 0, 0, 1]: 0.0
Prediction for [1, 0, 1, 1, 1, 0, 1, 0]: 0.0
Prediction for [1, 0, 1, 1, 1, 0, 1, 1]: 7.170702265302253E-7
Prediction for [1, 0, 1, 1, 1, 1, 0, 0]: 0.0
Prediction for [1, 0, 1, 1, 1, 1, 0, 1]: 7.164493245337411E-7
Prediction for [1, 0, 1, 1, 1, 1, 1, 0]: 0.0
Prediction for [1, 0, 1, 1, 1, 1, 1, 1]: 0.9999818125024408
Prediction for [1, 1, 0, 0, 0, 0, 0, 0]: 0.0
Prediction for [1, 1, 0, 0, 0, 0, 0, 1]: 0.9999997430631502
Prediction for [1, 1, 0, 0, 0, 0, 1, 0]: 0.0
Prediction for [1, 1, 0, 0, 0, 0, 1, 1]: 0.0
Prediction for [1, 1, 0, 0, 0, 1, 0, 0]: 0.0
Prediction for [1, 1, 0, 0, 0, 1, 0, 1]: 0.9999956644098074
Prediction for [1, 1, 0, 0, 0, 1, 1, 0]: 0.0
Prediction for [1, 1, 0, 0, 0, 1, 1, 1]: 0.9999986422881308
Prediction for [1, 1, 0, 0, 1, 0, 0, 0]: 0.0
Prediction for [1, 1, 0, 0, 1, 0, 0, 1]: 0.0
Prediction for [1, 1, 0, 0, 1, 0, 1, 0]: 0.0
Prediction for [1, 1, 0, 0, 1, 0, 1, 1]: 0.0
Prediction for [1, 1, 0, 0, 1, 1, 0, 0]: 0.0
Prediction for [1, 1, 0, 0, 1, 1, 0, 1]: 0.0
Prediction for [1, 1, 0, 0, 1, 1, 1, 0]: 0.0
Prediction for [1, 1, 0, 0, 1, 1, 1, 1]: 0.0
Prediction for [1, 1, 0, 1, 0, 0, 0, 0]: 0.0
Prediction for [1, 1, 0, 1, 0, 0, 0, 1]: 0.0
Prediction for [1, 1, 0, 1, 0, 0, 1, 0]: 0.0
Prediction for [1, 1, 0, 1, 0, 0, 1, 1]: 0.9999960839497368
Prediction for [1, 1, 0, 1, 0, 1, 0, 0]: 0.0
Prediction for [1, 1, 0, 1, 0, 1, 0, 1]: 0.0
Prediction for [1, 1, 0, 1, 0, 1, 1, 0]: 0.0
Prediction for [1, 1, 0, 1, 0, 1, 1, 1]: 0.0
Prediction for [1, 1, 0, 1, 1, 0, 0, 0]: 0.0
Prediction for [1, 1, 0, 1, 1, 0, 0, 1]: 0.0
Prediction for [1, 1, 0, 1, 1, 0, 1, 0]: 0.0
Prediction for [1, 1, 0, 1, 1, 0, 1, 1]: 0.0
Prediction for [1, 1, 0, 1, 1, 1, 0, 0]: 0.0
Prediction for [1, 1, 0, 1, 1, 1, 0, 1]: 0.0
Prediction for [1, 1, 0, 1, 1, 1, 1, 0]: 0.0
Prediction for [1, 1, 0, 1, 1, 1, 1, 1]: 0.9999973334165593
Prediction for [1, 1, 1, 0, 0, 0, 0, 0]: 0.0
Prediction for [1, 1, 1, 0, 0, 0, 0, 1]: 0.0
Prediction for [1, 1, 1, 0, 0, 0, 1, 0]: 0.0
Prediction for [1, 1, 1, 0, 0, 0, 1, 1]: 0.9999982993061676
Prediction for [1, 1, 1, 0, 0, 1, 0, 0]: 0.0
Prediction for [1, 1, 1, 0, 0, 1, 0, 1]: 0.9999975892361321
Prediction for [1, 1, 1, 0, 0, 1, 1, 0]: 0.0
Prediction for [1, 1, 1, 0, 0, 1, 1, 1]: 0.0
Prediction for [1, 1, 1, 0, 1, 0, 0, 0]: 0.0
Prediction for [1, 1, 1, 0, 1, 0, 0, 1]: 0.9999977722867401
Prediction for [1, 1, 1, 0, 1, 0, 1, 0]: 0.0
Prediction for [1, 1, 1, 0, 1, 0, 1, 1]: 0.0
Prediction for [1, 1, 1, 0, 1, 1, 0, 0]: 0.0
Prediction for [1, 1, 1, 0, 1, 1, 0, 1]: 0.0
Prediction for [1, 1, 1, 0, 1, 1, 1, 0]: 0.0
Prediction for [1, 1, 1, 0, 1, 1, 1, 1]: 0.999995227082995
Prediction for [1, 1, 1, 1, 0, 0, 0, 0]: 0.0
Prediction for [1, 1, 1, 1, 0, 0, 0, 1]: 0.9999963174205537
Prediction for [1, 1, 1, 1, 0, 0, 1, 0]: 0.0
Prediction for [1, 1, 1, 1, 0, 0, 1, 1]: 0.0
Prediction for [1, 1, 1, 1, 0, 1, 0, 0]: 0.0
Prediction for [1, 1, 1, 1, 0, 1, 0, 1]: 0.0
Prediction for [1, 1, 1, 1, 0, 1, 1, 0]: 0.0
Prediction for [1, 1, 1, 1, 0, 1, 1, 1]: 0.0
Prediction for [1, 1, 1, 1, 1, 0, 0, 0]: 0.0
Prediction for [1, 1, 1, 1, 1, 0, 0, 1]: 0.0
Prediction for [1, 1, 1, 1, 1, 0, 1, 0]: 0.0
Prediction for [1, 1, 1, 1, 1, 0, 1, 1]: 0.9999972955850365
Prediction for [1, 1, 1, 1, 1, 1, 0, 0]: 0.0
Prediction for [1, 1, 1, 1, 1, 1, 0, 1]: 0.0
Prediction for [1, 1, 1, 1, 1, 1, 1, 0]: 0.0
Prediction for [1, 1, 1, 1, 1, 1, 1, 1]: 2.0156052446873574E-5
BUILD SUCCESSFUL (total time: 52 seconds)

**Dietmar** · April 15, 2023

@Mark-XP

Is it really that magic :angel or do I something wrong in EXCLUDING all those numbers from training?

Because the program repairs everything to 100% correct

Dietmar

for (int i = 0; i < trainingInputs.length; i++) {
            if (trainingInputs[i][0] != 211 && trainingInputs[i][0] != 212 && trainingInputs[i][0] != 213 && trainingInputs[i][0] != 214 && trainingInputs[i][0] != 215 && trainingInputs[i][0] != 216 && trainingInputs[i][0] != 217 && trainingInputs[i][0] != 218 && trainingInputs[i][0] != 219 && trainingInputs[i][0] != 220 && trainingInputs[i][0] != 221 && trainingInputs[i][0] != 222 && trainingInputs[i][0] != 223 && trainingInputs[i][0] != 224 && trainingInputs[i][0] != 225&& trainingInputs[i][0] != 226 && trainingInputs[i][0] != 227 && trainingInputs[i][0] != 228 && trainingInputs[i][0] != 229 && trainingInputs[i][0] != 230 && trainingInputs[i][0] != 231 && trainingInputs[i][0] != 232 && trainingInputs[i][0] != 233 && trainingInputs[i][0] != 234 && trainingInputs[i][0] != 235 && trainingInputs[i][0] != 236 && trainingInputs[i][0] != 237 && trainingInputs[i][0] != 238 && trainingInputs[i][0] != 239 && trainingInputs[i][0] != 240 && trainingInputs[i][0] != 241 && trainingInputs[i][0] != 242 && trainingInputs[i][0] != 243 && trainingInputs[i][0] != 244 && trainingInputs[i][0] != 245 && trainingInputs[i][0] != 246 && trainingInputs[i][0] != 247 && trainingInputs[i][0] != 248 && trainingInputs[i][0] != 249 && trainingInputs[i][0] != 250 && trainingInputs[i][0] != 251 && trainingInputs[i][0] != 252 && trainingInputs[i][0] != 253 && trainingInputs[i][0] != 254 && trainingInputs[i][0] != 255) {  
                double[] input = trainingInputs[i];
                double target = trainingTargets[i];
                
                
                                          
 Epoch 10000. Mean error: 0.013761853181815283
Epoch 20000. Mean error: 0.004436394551152869
Epoch 30000. Mean error: 0.0019123386251860676
Epoch 40000. Mean error: 9.306602545905893E-4
Epoch 50000. Mean error: 5.141772173888018E-4
Epoch 60000. Mean error: 2.821186928938447E-4
Epoch 70000. Mean error: 1.508378952461119E-4
Epoch 80000. Mean error: 8.082572640045874E-5
Epoch 90000. Mean error: 4.316245807346151E-5
Epoch 100000. Mean error: 2.1495164564763075E-5
Epoch 110000. Mean error: 1.0706271608133646E-5
Epoch 120000. Mean error: 5.2215680429780436E-6
Epoch 130000. Mean error: 2.542432665413603E-6
Epoch 140000. Mean error: 1.2270160208784432E-6
Epoch 150000. Mean error: 5.84331351670071E-7
Epoch 160000. Mean error: 2.7341594450850186E-7
Epoch 170000. Mean error: 1.259037745722232E-7
Epoch 180000. Mean error: 5.686962593191134E-8
Epoch 190000. Mean error: 2.5281081341727448E-8
Epoch 200000. Mean error: 1.1029211150480328E-8
Prediction for [0, 0, 0, 0, 0, 0, 0, 0]: 0.0
Prediction for [0, 0, 0, 0, 0, 0, 0, 1]: 0.9998552064378066
Prediction for [0, 0, 0, 0, 0, 0, 1, 0]: 0.9999981980754573
Prediction for [0, 0, 0, 0, 0, 0, 1, 1]: 0.9999665775075819
Prediction for [0, 0, 0, 0, 0, 1, 0, 0]: 0.0
Prediction for [0, 0, 0, 0, 0, 1, 0, 1]: 0.9999249127391208
Prediction for [0, 0, 0, 0, 0, 1, 1, 0]: 0.0
Prediction for [0, 0, 0, 0, 0, 1, 1, 1]: 1.000015790609996
Prediction for [0, 0, 0, 0, 1, 0, 0, 0]: 4.607743658402441E-6
Prediction for [0, 0, 0, 0, 1, 0, 0, 1]: 0.0
Prediction for [0, 0, 0, 0, 1, 0, 1, 0]: 0.0
Prediction for [0, 0, 0, 0, 1, 0, 1, 1]: 0.999967509445554
Prediction for [0, 0, 0, 0, 1, 1, 0, 0]: 0.0
Prediction for [0, 0, 0, 0, 1, 1, 0, 1]: 1.0000587030697794
Prediction for [0, 0, 0, 0, 1, 1, 1, 0]: 0.0
Prediction for [0, 0, 0, 0, 1, 1, 1, 1]: 0.0
Prediction for [0, 0, 0, 1, 0, 0, 0, 0]: 0.0
Prediction for [0, 0, 0, 1, 0, 0, 0, 1]: 1.0001184787545911
Prediction for [0, 0, 0, 1, 0, 0, 1, 0]: 0.0
Prediction for [0, 0, 0, 1, 0, 0, 1, 1]: 0.9999721296956876
Prediction for [0, 0, 0, 1, 0, 1, 0, 0]: 0.0
Prediction for [0, 0, 0, 1, 0, 1, 0, 1]: 0.0
Prediction for [0, 0, 0, 1, 0, 1, 1, 0]: 0.0
Prediction for [0, 0, 0, 1, 0, 1, 1, 1]: 1.0000320023879805
Prediction for [0, 0, 0, 1, 1, 0, 0, 0]: 0.0
Prediction for [0, 0, 0, 1, 1, 0, 0, 1]: 2.6237465342360267E-5
Prediction for [0, 0, 0, 1, 1, 0, 1, 0]: 0.0
Prediction for [0, 0, 0, 1, 1, 0, 1, 1]: 0.0
Prediction for [0, 0, 0, 1, 1, 1, 0, 0]: 0.0
Prediction for [0, 0, 0, 1, 1, 1, 0, 1]: 1.000065687199663
Prediction for [0, 0, 0, 1, 1, 1, 1, 0]: 0.0
Prediction for [0, 0, 0, 1, 1, 1, 1, 1]: 1.0000586110238747
Prediction for [0, 0, 1, 0, 0, 0, 0, 0]: 0.0
Prediction for [0, 0, 1, 0, 0, 0, 0, 1]: 0.0
Prediction for [0, 0, 1, 0, 0, 0, 1, 0]: 0.0
Prediction for [0, 0, 1, 0, 0, 0, 1, 1]: 1.7897633077357256E-5
Prediction for [0, 0, 1, 0, 0, 1, 0, 0]: 0.0
Prediction for [0, 0, 1, 0, 0, 1, 0, 1]: 0.9999249745786815
Prediction for [0, 0, 1, 0, 0, 1, 1, 0]: 0.0
Prediction for [0, 0, 1, 0, 0, 1, 1, 1]: 5.700748834081004E-5
Prediction for [0, 0, 1, 0, 1, 0, 0, 0]: 0.0
Prediction for [0, 0, 1, 0, 1, 0, 0, 1]: 0.999990151336934
Prediction for [0, 0, 1, 0, 1, 0, 1, 0]: 0.0
Prediction for [0, 0, 1, 0, 1, 0, 1, 1]: 1.0001013046940173
Prediction for [0, 0, 1, 0, 1, 1, 0, 0]: 0.0
Prediction for [0, 0, 1, 0, 1, 1, 0, 1]: 1.456675864597301E-5
Prediction for [0, 0, 1, 0, 1, 1, 1, 0]: 0.0
Prediction for [0, 0, 1, 0, 1, 1, 1, 1]: 0.9999403685441526
Prediction for [0, 0, 1, 1, 0, 0, 0, 0]: 0.0
Prediction for [0, 0, 1, 1, 0, 0, 0, 1]: 0.0
Prediction for [0, 0, 1, 1, 0, 0, 1, 0]: 0.0
Prediction for [0, 0, 1, 1, 0, 0, 1, 1]: 0.0
Prediction for [0, 0, 1, 1, 0, 1, 0, 0]: 0.0
Prediction for [0, 0, 1, 1, 0, 1, 0, 1]: 1.0000081064258306
Prediction for [0, 0, 1, 1, 0, 1, 1, 0]: 0.0
Prediction for [0, 0, 1, 1, 0, 1, 1, 1]: 0.0
Prediction for [0, 0, 1, 1, 1, 0, 0, 0]: 0.0
Prediction for [0, 0, 1, 1, 1, 0, 0, 1]: 0.0
Prediction for [0, 0, 1, 1, 1, 0, 1, 0]: 0.0
Prediction for [0, 0, 1, 1, 1, 0, 1, 1]: 1.0000087580749821
Prediction for [0, 0, 1, 1, 1, 1, 0, 0]: 0.0
Prediction for [0, 0, 1, 1, 1, 1, 0, 1]: 0.9999749485773969
Prediction for [0, 0, 1, 1, 1, 1, 1, 0]: 0.0
Prediction for [0, 0, 1, 1, 1, 1, 1, 1]: 0.0
Prediction for [0, 1, 0, 0, 0, 0, 0, 0]: 0.0
Prediction for [0, 1, 0, 0, 0, 0, 0, 1]: 1.2902614773491194E-5
Prediction for [0, 1, 0, 0, 0, 0, 1, 0]: 0.0
Prediction for [0, 1, 0, 0, 0, 0, 1, 1]: 1.0000440803277686
Prediction for [0, 1, 0, 0, 0, 1, 0, 0]: 0.0
Prediction for [0, 1, 0, 0, 0, 1, 0, 1]: 4.5182928430254066E-5
Prediction for [0, 1, 0, 0, 0, 1, 1, 0]: 0.0
Prediction for [0, 1, 0, 0, 0, 1, 1, 1]: 1.0000656916849726
Prediction for [0, 1, 0, 0, 1, 0, 0, 0]: 0.0
Prediction for [0, 1, 0, 0, 1, 0, 0, 1]: 1.0000501070989714
Prediction for [0, 1, 0, 0, 1, 0, 1, 0]: 0.0
Prediction for [0, 1, 0, 0, 1, 0, 1, 1]: 0.0
Prediction for [0, 1, 0, 0, 1, 1, 0, 0]: 0.0
Prediction for [0, 1, 0, 0, 1, 1, 0, 1]: 0.0
Prediction for [0, 1, 0, 0, 1, 1, 1, 0]: 0.0
Prediction for [0, 1, 0, 0, 1, 1, 1, 1]: 0.999856267889315
Prediction for [0, 1, 0, 1, 0, 0, 0, 0]: 0.0
Prediction for [0, 1, 0, 1, 0, 0, 0, 1]: 0.0
Prediction for [0, 1, 0, 1, 0, 0, 1, 0]: 0.0
Prediction for [0, 1, 0, 1, 0, 0, 1, 1]: 0.999986162644245
Prediction for [0, 1, 0, 1, 0, 1, 0, 0]: 0.0
Prediction for [0, 1, 0, 1, 0, 1, 0, 1]: 0.0
Prediction for [0, 1, 0, 1, 0, 1, 1, 0]: 0.0
Prediction for [0, 1, 0, 1, 0, 1, 1, 1]: 0.0
Prediction for [0, 1, 0, 1, 1, 0, 0, 0]: 0.0
Prediction for [0, 1, 0, 1, 1, 0, 0, 1]: 0.9999606044972917
Prediction for [0, 1, 0, 1, 1, 0, 1, 0]: 0.0
Prediction for [0, 1, 0, 1, 1, 0, 1, 1]: 0.0
Prediction for [0, 1, 0, 1, 1, 1, 0, 0]: 0.0
Prediction for [0, 1, 0, 1, 1, 1, 0, 1]: 0.0
Prediction for [0, 1, 0, 1, 1, 1, 1, 0]: 0.0
Prediction for [0, 1, 0, 1, 1, 1, 1, 1]: 0.0
Prediction for [0, 1, 1, 0, 0, 0, 0, 0]: 0.0
Prediction for [0, 1, 1, 0, 0, 0, 0, 1]: 1.0000315884560482
Prediction for [0, 1, 1, 0, 0, 0, 1, 0]: 0.0
Prediction for [0, 1, 1, 0, 0, 0, 1, 1]: 0.0
Prediction for [0, 1, 1, 0, 0, 1, 0, 0]: 0.0
Prediction for [0, 1, 1, 0, 0, 1, 0, 1]: 0.9999866513100956
Prediction for [0, 1, 1, 0, 0, 1, 1, 0]: 0.0
Prediction for [0, 1, 1, 0, 0, 1, 1, 1]: 0.9999502300521703
Prediction for [0, 1, 1, 0, 1, 0, 0, 0]: 0.0
Prediction for [0, 1, 1, 0, 1, 0, 0, 1]: 0.0
Prediction for [0, 1, 1, 0, 1, 0, 1, 0]: 0.0
Prediction for [0, 1, 1, 0, 1, 0, 1, 1]: 0.9998970548806126
Prediction for [0, 1, 1, 0, 1, 1, 0, 0]: 0.0
Prediction for [0, 1, 1, 0, 1, 1, 0, 1]: 0.9997636214066983
Prediction for [0, 1, 1, 0, 1, 1, 1, 0]: 0.0
Prediction for [0, 1, 1, 0, 1, 1, 1, 1]: 3.672648276511481E-4
Prediction for [0, 1, 1, 1, 0, 0, 0, 0]: 0.0
Prediction for [0, 1, 1, 1, 0, 0, 0, 1]: 0.999975745798993
Prediction for [0, 1, 1, 1, 0, 0, 1, 0]: 0.0
Prediction for [0, 1, 1, 1, 0, 0, 1, 1]: 0.0
Prediction for [0, 1, 1, 1, 0, 1, 0, 0]: 0.0
Prediction for [0, 1, 1, 1, 0, 1, 0, 1]: 0.0
Prediction for [0, 1, 1, 1, 0, 1, 1, 0]: 0.0
Prediction for [0, 1, 1, 1, 0, 1, 1, 1]: 0.0
Prediction for [0, 1, 1, 1, 1, 0, 0, 0]: 0.0
Prediction for [0, 1, 1, 1, 1, 0, 0, 1]: 5.10060797580536E-5
Prediction for [0, 1, 1, 1, 1, 0, 1, 0]: 0.0
Prediction for [0, 1, 1, 1, 1, 0, 1, 1]: 0.0
Prediction for [0, 1, 1, 1, 1, 1, 0, 0]: 0.0
Prediction for [0, 1, 1, 1, 1, 1, 0, 1]: 0.0
Prediction for [0, 1, 1, 1, 1, 1, 1, 0]: 0.0
Prediction for [0, 1, 1, 1, 1, 1, 1, 1]: 0.9999711728267535
Prediction for [1, 0, 0, 0, 0, 0, 0, 0]: 0.0
Prediction for [1, 0, 0, 0, 0, 0, 0, 1]: 0.0
Prediction for [1, 0, 0, 0, 0, 0, 1, 0]: 0.0
Prediction for [1, 0, 0, 0, 0, 0, 1, 1]: 1.0001011632210368
Prediction for [1, 0, 0, 0, 0, 1, 0, 0]: 0.0
Prediction for [1, 0, 0, 0, 0, 1, 0, 1]: 2.1498885796100708E-4
Prediction for [1, 0, 0, 0, 0, 1, 1, 0]: 0.0
Prediction for [1, 0, 0, 0, 0, 1, 1, 1]: 5.639744095382593E-4
Prediction for [1, 0, 0, 0, 1, 0, 0, 0]: 0.0
Prediction for [1, 0, 0, 0, 1, 0, 0, 1]: 1.0001750123924615
Prediction for [1, 0, 0, 0, 1, 0, 1, 0]: 0.0
Prediction for [1, 0, 0, 0, 1, 0, 1, 1]: 1.0001032376133185
Prediction for [1, 0, 0, 0, 1, 1, 0, 0]: 0.0
Prediction for [1, 0, 0, 0, 1, 1, 0, 1]: 0.0
Prediction for [1, 0, 0, 0, 1, 1, 1, 0]: 0.0
Prediction for [1, 0, 0, 0, 1, 1, 1, 1]: 0.0
Prediction for [1, 0, 0, 1, 0, 0, 0, 0]: 0.0
Prediction for [1, 0, 0, 1, 0, 0, 0, 1]: 0.0
Prediction for [1, 0, 0, 1, 0, 0, 1, 0]: 0.0
Prediction for [1, 0, 0, 1, 0, 0, 1, 1]: 0.0
Prediction for [1, 0, 0, 1, 0, 1, 0, 0]: 0.0
Prediction for [1, 0, 0, 1, 0, 1, 0, 1]: 1.0001379783739037
Prediction for [1, 0, 0, 1, 0, 1, 1, 0]: 0.0
Prediction for [1, 0, 0, 1, 0, 1, 1, 1]: 1.0001563789090673
Prediction for [1, 0, 0, 1, 1, 0, 0, 0]: 0.0
Prediction for [1, 0, 0, 1, 1, 0, 0, 1]: 0.0
Prediction for [1, 0, 0, 1, 1, 0, 1, 0]: 0.0
Prediction for [1, 0, 0, 1, 1, 0, 1, 1]: 0.0
Prediction for [1, 0, 0, 1, 1, 1, 0, 0]: 0.0
Prediction for [1, 0, 0, 1, 1, 1, 0, 1]: 1.0001283816760012
Prediction for [1, 0, 0, 1, 1, 1, 1, 0]: 0.0
Prediction for [1, 0, 0, 1, 1, 1, 1, 1]: 0.0
Prediction for [1, 0, 1, 0, 0, 0, 0, 0]: 0.0
Prediction for [1, 0, 1, 0, 0, 0, 0, 1]: 0.0
Prediction for [1, 0, 1, 0, 0, 0, 1, 0]: 0.0
Prediction for [1, 0, 1, 0, 0, 0, 1, 1]: 1.0000901483882112
Prediction for [1, 0, 1, 0, 0, 1, 0, 0]: 0.0
Prediction for [1, 0, 1, 0, 0, 1, 0, 1]: 0.0
Prediction for [1, 0, 1, 0, 0, 1, 1, 0]: 0.0
Prediction for [1, 0, 1, 0, 0, 1, 1, 1]: 0.9996806792405923
Prediction for [1, 0, 1, 0, 1, 0, 0, 0]: 0.0
Prediction for [1, 0, 1, 0, 1, 0, 0, 1]: 0.0
Prediction for [1, 0, 1, 0, 1, 0, 1, 0]: 0.0
Prediction for [1, 0, 1, 0, 1, 0, 1, 1]: 5.584586628071264E-5
Prediction for [1, 0, 1, 0, 1, 1, 0, 0]: 0.0
Prediction for [1, 0, 1, 0, 1, 1, 0, 1]: 0.9999271860359712
Prediction for [1, 0, 1, 0, 1, 1, 1, 0]: 0.0
Prediction for [1, 0, 1, 0, 1, 1, 1, 1]: 0.0
Prediction for [1, 0, 1, 1, 0, 0, 0, 0]: 0.0
Prediction for [1, 0, 1, 1, 0, 0, 0, 1]: 0.0
Prediction for [1, 0, 1, 1, 0, 0, 1, 0]: 0.0
Prediction for [1, 0, 1, 1, 0, 0, 1, 1]: 0.9999529534472646
Prediction for [1, 0, 1, 1, 0, 1, 0, 0]: 0.0
Prediction for [1, 0, 1, 1, 0, 1, 0, 1]: 1.0000038892241698
Prediction for [1, 0, 1, 1, 0, 1, 1, 0]: 0.0
Prediction for [1, 0, 1, 1, 0, 1, 1, 1]: 0.0
Prediction for [1, 0, 1, 1, 1, 0, 0, 0]: 0.0
Prediction for [1, 0, 1, 1, 1, 0, 0, 1]: 0.0
Prediction for [1, 0, 1, 1, 1, 0, 1, 0]: 0.0
Prediction for [1, 0, 1, 1, 1, 0, 1, 1]: 0.0
Prediction for [1, 0, 1, 1, 1, 1, 0, 0]: 0.0
Prediction for [1, 0, 1, 1, 1, 1, 0, 1]: 0.0
Prediction for [1, 0, 1, 1, 1, 1, 1, 0]: 0.0
Prediction for [1, 0, 1, 1, 1, 1, 1, 1]: 0.9999769155484038
Prediction for [1, 1, 0, 0, 0, 0, 0, 0]: 0.0
Prediction for [1, 1, 0, 0, 0, 0, 0, 1]: 0.999467293162985
Prediction for [1, 1, 0, 0, 0, 0, 1, 0]: 0.0
Prediction for [1, 1, 0, 0, 0, 0, 1, 1]: 0.0
Prediction for [1, 1, 0, 0, 0, 1, 0, 0]: 0.0
Prediction for [1, 1, 0, 0, 0, 1, 0, 1]: 0.9997709697158451
Prediction for [1, 1, 0, 0, 0, 1, 1, 0]: 0.0
Prediction for [1, 1, 0, 0, 0, 1, 1, 1]: 0.9991634599908605
Prediction for [1, 1, 0, 0, 1, 0, 0, 0]: 0.0
Prediction for [1, 1, 0, 0, 1, 0, 0, 1]: 0.0
Prediction for [1, 1, 0, 0, 1, 0, 1, 0]: 0.0
Prediction for [1, 1, 0, 0, 1, 0, 1, 1]: 0.0
Prediction for [1, 1, 0, 0, 1, 1, 0, 0]: 0.0
Prediction for [1, 1, 0, 0, 1, 1, 0, 1]: 0.0
Prediction for [1, 1, 0, 0, 1, 1, 1, 0]: 0.0
Prediction for [1, 1, 0, 0, 1, 1, 1, 1]: 0.0
Prediction for [1, 1, 0, 1, 0, 0, 0, 0]: 0.0
Prediction for [1, 1, 0, 1, 0, 0, 0, 1]: 0.0
Prediction for [1, 1, 0, 1, 0, 0, 1, 0]: 0.0
Prediction for [1, 1, 0, 1, 0, 0, 1, 1]: 0.9998207867828033
Prediction for [1, 1, 0, 1, 0, 1, 0, 0]: 0.0
Prediction for [1, 1, 0, 1, 0, 1, 0, 1]: 0.0
Prediction for [1, 1, 0, 1, 0, 1, 1, 0]: 0.0
Prediction for [1, 1, 0, 1, 0, 1, 1, 1]: 0.0
Prediction for [1, 1, 0, 1, 1, 0, 0, 0]: 0.0
Prediction for [1, 1, 0, 1, 1, 0, 0, 1]: 0.0
Prediction for [1, 1, 0, 1, 1, 0, 1, 0]: 0.0
Prediction for [1, 1, 0, 1, 1, 0, 1, 1]: 0.0
Prediction for [1, 1, 0, 1, 1, 1, 0, 0]: 0.0
Prediction for [1, 1, 0, 1, 1, 1, 0, 1]: 0.0
Prediction for [1, 1, 0, 1, 1, 1, 1, 0]: 0.0
Prediction for [1, 1, 0, 1, 1, 1, 1, 1]: 0.999755344991017
Prediction for [1, 1, 1, 0, 0, 0, 0, 0]: 0.0
Prediction for [1, 1, 1, 0, 0, 0, 0, 1]: 2.456548222435906E-4
Prediction for [1, 1, 1, 0, 0, 0, 1, 0]: 0.0
Prediction for [1, 1, 1, 0, 0, 0, 1, 1]: 0.9998103012363052
Prediction for [1, 1, 1, 0, 0, 1, 0, 0]: 0.0
Prediction for [1, 1, 1, 0, 0, 1, 0, 1]: 1.000008443419425
Prediction for [1, 1, 1, 0, 0, 1, 1, 0]: 0.0
Prediction for [1, 1, 1, 0, 0, 1, 1, 1]: 3.961633849602908E-4
Prediction for [1, 1, 1, 0, 1, 0, 0, 0]: 0.0
Prediction for [1, 1, 1, 0, 1, 0, 0, 1]: 1.000013630852953
Prediction for [1, 1, 1, 0, 1, 0, 1, 0]: 0.0
Prediction for [1, 1, 1, 0, 1, 0, 1, 1]: 2.0364601871936117E-4
Prediction for [1, 1, 1, 0, 1, 1, 0, 0]: 0.0
Prediction for [1, 1, 1, 0, 1, 1, 0, 1]: 4.317247726761675E-4
Prediction for [1, 1, 1, 0, 1, 1, 1, 0]: 0.0
Prediction for [1, 1, 1, 0, 1, 1, 1, 1]: 1.0000305768307247
Prediction for [1, 1, 1, 1, 0, 0, 0, 0]: 0.0
Prediction for [1, 1, 1, 1, 0, 0, 0, 1]: 0.9997772507539702
Prediction for [1, 1, 1, 1, 0, 0, 1, 0]: 0.0
Prediction for [1, 1, 1, 1, 0, 0, 1, 1]: 0.0
Prediction for [1, 1, 1, 1, 0, 1, 0, 0]: 0.0
Prediction for [1, 1, 1, 1, 0, 1, 0, 1]: 0.0
Prediction for [1, 1, 1, 1, 0, 1, 1, 0]: 0.0
Prediction for [1, 1, 1, 1, 0, 1, 1, 1]: 0.0
Prediction for [1, 1, 1, 1, 1, 0, 0, 0]: 0.0
Prediction for [1, 1, 1, 1, 1, 0, 0, 1]: 0.0
Prediction for [1, 1, 1, 1, 1, 0, 1, 0]: 0.0
Prediction for [1, 1, 1, 1, 1, 0, 1, 1]: 0.9999390212900362
Prediction for [1, 1, 1, 1, 1, 1, 0, 0]: 0.0
Prediction for [1, 1, 1, 1, 1, 1, 0, 1]: 0.0
Prediction for [1, 1, 1, 1, 1, 1, 1, 0]: 0.0
Prediction for [1, 1, 1, 1, 1, 1, 1, 1]: 3.341041474858031E-5
BUILD SUCCESSFUL (total time: 42 seconds)

**Mark-XP** · April 15, 2023

1 hour ago, Dietmar said:

Is it really that magic or do I something wrong in EXCLUDING all those numbers from training?

Because the program repairs everything to 100% correct

Dietmar

@Dietmar as far as i can see no magic: you're not excluding anything here

for (int i = 0; i < trainingInputs.length; i++) {
     if (trainingInputs[i][0] != 211 && trainingInputs[i][0] != 212 && ...

since trainingInputs [j] [0] is allways 0 or 1 and hence the above condition doesn't catch.
it would be indeed use- and helpful to add the decimal value of the number in the first element trainingInputs [j] [0]

Edited April 15, 2023 by Mark-XP

**Dietmar** · April 16, 2023

@Mark-XP

I think, that this will work for to exclude number 1 and number 4 from training

Dietmar

public void train(double[][] trainingInputs, double[] trainingTargets) {
    for (int epoch = 1; epoch <= numEpochs; epoch++) {
        double totalError = 0.0;
        for (int i = 0; i < trainingInputs.length; i++) {
            // Skip excluded inputs
            if (i == 1 || i == 4) {
                continue;
            }
            double[] input = trainingInputs[i];
            double target = trainingTargets[i];

**Dietmar** · April 16, 2023

@Mark-XP

It works.

Now I have excluded from training the prime numbers 179 and 181.

There is some magic, look at this result

Dietmar

Prediction for [1, 0, 1, 0, 1, 1, 0, 0]: 0.0
Prediction for [1, 0, 1, 0, 1, 1, 0, 1]: 0.9999999999936455
Prediction for [1, 0, 1, 0, 1, 1, 1, 0]: 0.0
Prediction for [1, 0, 1, 0, 1, 1, 1, 1]: 0.0
Prediction for [1, 0, 1, 1, 0, 0, 0, 0]: 0.0
Prediction for [1, 0, 1, 1, 0, 0, 0, 1]: 0.0
Prediction for [1, 0, 1, 1, 0, 0, 1, 0]: 0.0
Prediction for [1, 0, 1, 1, 0, 0, 1, 1]: 0.35479505025788205 <------------- 179
Prediction for [1, 0, 1, 1, 0, 1, 0, 0]: 0.0
Prediction for [1, 0, 1, 1, 0, 1, 0, 1]: 0.35752233986543436 <------------- 181
Prediction for [1, 0, 1, 1, 0, 1, 1, 0]: 0.0
Prediction for [1, 0, 1, 1, 0, 1, 1, 1]: 0.0
Prediction for [1, 0, 1, 1, 1, 0, 0, 0]: 0.0
Prediction for [1, 0, 1, 1, 1, 0, 0, 1]: 0.0
Prediction for [1, 0, 1, 1, 1, 0, 1, 0]: 0.0
Prediction for [1, 0, 1, 1, 1, 0, 1, 1]: 0.0
Prediction for [1, 0, 1, 1, 1, 1, 0, 0]: 0.0
Prediction for [1, 0, 1, 1, 1, 1, 0, 1]: 0.0
Prediction for [1, 0, 1, 1, 1, 1, 1, 0]: 0.0
Prediction for [1, 0, 1, 1, 1, 1, 1, 1]: 0.9999999999970552
Prediction for [1, 1, 0, 0, 0, 0, 0, 0]: 0.0
Prediction for [1, 1, 0, 0, 0, 0, 0, 1]: 0.9999999999978786
Prediction for [1, 1, 0, 0, 0, 0, 1, 0]: 0.0

**Mark-XP** · April 17, 2023

@Dietmar that's really interesting.

BUT, if you dig the training-hole around the primes 179 and 181 only a bit bigger

// Skip excluded inputs
       if (i > 172 && i < 188) {
           continue;
       }

the result gets worse immediately:

Please forgive me for beeing so mean

Edited April 17, 2023 by Mark-XP

**Dietmar** · April 18, 2023

@Mark-XP

Yes, it is like with Taylor Polynom. When you cut off some points, it runs out of being valid.

But as long as you stay as close as possible to the interesting points, it gives you some extra information.

After long running this program, it looks, as if you need always "1" as prime, but not "2"

Dietmar

**Mark-XP** · April 22, 2023

Servus @Dietmar, i hope you're doing fine!!

I try to implement image recognitition with Neuroph, so i got the neurophstudio-2.98.zip from here and installed (extracted) it. It starts and runs fine, but at the point were i want to train the first example nnw, it behaves differently as described in the documentation: no file type "Training Set" is offered and the Train-icon is grayed out too (see pic below). Can you eventually verify that and do you have any suggestion? Many thanks and a nice sunday!

Edited April 22, 2023 by Mark-XP

**Dietmar** · April 24, 2023

@Mark-XP

I have no idea what is going wrong.

I noticed, that Neuroph is unstable, so I make all by hand

Dietmar

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