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Dietmar

By Dietmar
in Windows XP

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Dietmar

Dietmar

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Posted (edited)

Here is a new version of the prime program. It uses ReLU.

This one prints also out all the last Weights and Bias. Via this way you can find and copy the best of them. This is, how Neural Networks learn and remember!

By the way I noticed, that with Sigmoid instead of ReLU, the program is less good working. I make a lot of tests.

I think this happens because the derivation of Sigmoid goes to zero with small or high inputs

Dietmar

PS: The settings in this program are: 1 is prime, 2 not, 3 not, 5 is prime..We have a new definition, what primes really are.

ALL primes have to fullfill this condition: Number modulo 6 = +-1. And because of this, 1 is prime now and 2 and 3 not.

All the other primes stay untouched.

This program excludes 251 from primes. It does not learn, that 251 is prime. And after, look at the result:

Magically 251 is listened as prime.

You have to run the program several times, because it may hang in a local minimum.

 

  

package multiof3;

import java.security.SecureRandom;
import java.util.Arrays;

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() {
        SecureRandom random = new SecureRandom();
        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++) {
                
                 // Skip excluded inputs
            if (i == 251) {
                continue;
            }
                
                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);
        }
    }
}
boolean weightsAndBiasesPrinted = false;

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]);
    }
    
    if (!weightsAndBiasesPrinted) {
        System.out.println("Input to Hidden Weights:");
        for (int i = 0; i < numInputNodes; i++) {
            for (int j = 0; j < numHiddenNodes; j++) {
                System.out.println("Weight[" + i + "][" + j + "]: " + inputToHiddenWeights[i][j]);
            }
        }

        System.out.println("Hidden Biases:");
        for (int j = 0; j < numHiddenNodes; j++) {
            System.out.println("Bias[" + j + "]: " + hiddenBiases[j]);
        }

        System.out.println("Hidden to Output Weights:");
        for (int j = 0; j < numHiddenNodes; j++) {
            for (int k = 0; k < numOutputNodes; k++) {
                System.out.println("Weight[" + j + "][" + k + "]: " + hiddenToOutputWeights[j][k]);
            }
        }

        System.out.println("Output Biases:");
        for (int j = 0; j < numOutputNodes; j++) {
            System.out.println("Bias[" + j + "]: " + outputBiases[j]);
        }
        
        weightsAndBiasesPrinted = true;
    }
    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, 0, 0, 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}));


}


}

So, in principle with this program, all encryption programs, set set on primes are obsolete.

Edited by Dietmar
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  • 2 weeks later...

Dietmar

Dietmar

Posted
  • Author
Posted (edited)

@Mark-XP

Here is a new version of the Prime finding program from scratch,

this time after crazy hard work with 2 Hidden Layers.

Not much difference compared to the use of only 1 Hidden Layer

Dietmar

  

package multiof3;

import java.security.SecureRandom;
import java.util.Arrays;

public class Multiof3 {
    private final int numInputNodes = 8;
    private final int numHiddenNodes1 = 12;
    private final int numHiddenNodes2 = 12;
    private final int numOutputNodes = 1;
    private final double learningRate = 0.0005;
    private final int numEpochs = 200000;
    private final double errorThreshold = 0.0000000000000000000000000001;

    private double[][] inputToHidden1Weights;
    private double[][] hidden1ToHidden2Weights;
    private double[][] hidden2ToOutputWeights;
    private double[] hidden1Biases;
    private double[] hidden2Biases;
    private double[] outputBiases;

    public Multiof3() {
        SecureRandom random = new SecureRandom();
        inputToHidden1Weights = new double[numInputNodes][numHiddenNodes1];
        hidden1ToHidden2Weights = new double[numHiddenNodes1][numHiddenNodes2];
        hidden2ToOutputWeights = new double[numHiddenNodes2][numOutputNodes];
        hidden1Biases = new double[numHiddenNodes1];
        hidden2Biases = new double[numHiddenNodes2];
        outputBiases = new double[numOutputNodes];

        for (int i = 0; i < numInputNodes; i++) {
            for (int j = 0; j < numHiddenNodes1; j++) {
                inputToHidden1Weights[i][j] = random.nextDouble() - 0.5;
            }
        }

        for (int i = 0; i < numHiddenNodes1; i++) {
            for (int j = 0; j < numHiddenNodes2; j++) {
                hidden1ToHidden2Weights[i][j] = random.nextDouble() - 0.5;
            }
            hidden1Biases[i] = random.nextDouble() - 0.5;
        }

        for (int i = 0; i < numHiddenNodes2; i++) {
            for (int j = 0; j < numOutputNodes; j++) {
                hidden2ToOutputWeights[i][j] = random.nextDouble() - 0.5;
            }
            hidden2Biases[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++) {

            // Skip excluded inputs
            if (i == 251 || i == 3) {
                continue;
            }

            double[] input = trainingInputs[i];
            double target = trainingTargets[i];

            // Forward propagation
            double[] hiddenOutputs1 = new double[numHiddenNodes1];
            for (int j = 0; j < numHiddenNodes1; j++) {
                double weightedSum = 0.0;
                for (int k = 0; k < numInputNodes; k++) {
                    weightedSum += inputToHidden1Weights[k][j] * input[k];
                }
                hiddenOutputs1[j] = relu(weightedSum + hidden1Biases[j]);
            }

            double[] hiddenOutputs2 = new double[numHiddenNodes2];
            for (int j = 0; j < numHiddenNodes2; j++) {
                double weightedSum = 0.0;
                for (int k = 0; k < numHiddenNodes1; k++) {
                    weightedSum += hidden1ToHidden2Weights[k][j] * hiddenOutputs1[k];
                }
                hiddenOutputs2[j] = relu(weightedSum + hidden2Biases[j]);
            }

            double output = 0.0;
            for (int j = 0; j < numOutputNodes; j++) {
                double weightedSum = 0.0;
                for (int k = 0; k < numHiddenNodes2; k++) {
                    weightedSum += hidden2ToOutputWeights[k][j] * hiddenOutputs2[k];
                }
                output = relu(weightedSum + outputBiases[j]);
            }

           // Backward propagation
        double outputError = target - output;
        double outputDelta = outputError * reluDerivative(output);

        double[] hidden2Errors = new double[numHiddenNodes2];
        double[] hidden2Deltas = new double[numHiddenNodes2];
        for (int j = 0; j < numHiddenNodes2; j++) {
            double weightedSum = 0.0;
            for (int k = 0; k < numOutputNodes; k++) {
                weightedSum += hidden2ToOutputWeights[j][k] * outputDelta;
            }
            hidden2Errors[j] = weightedSum;
            hidden2Deltas[j] = hidden2Errors[j] * reluDerivative(hiddenOutputs2[j]);
        }

        double[] hidden1Errors = new double[numHiddenNodes1];
        double[] hidden1Deltas = new double[numHiddenNodes1];
        for (int j = 0; j < numHiddenNodes1; j++) {
            double weightedSum = 0.0;
            for (int k = 0; k < numHiddenNodes2; k++) {
                weightedSum += hidden1ToHidden2Weights[j][k] * hidden2Deltas[k];
            }
            hidden1Errors[j] = weightedSum;
            hidden1Deltas[j] = hidden1Errors[j] * reluDerivative(hiddenOutputs1[j]);
        }

        // Update weights and biases
        for (int j = 0; j < numHiddenNodes2; j++) {
            for (int k = 0; k < numOutputNodes; k++) {
                hidden2ToOutputWeights[j][k] += learningRate * outputDelta * hiddenOutputs2[j];
            }
            hidden2Biases[j] += learningRate * hidden2Deltas[j];
        }

        for (int j = 0; j < numHiddenNodes1; j++) {
            for (int k = 0; k < numHiddenNodes2; k++) {
                hidden1ToHidden2Weights[j][k] += learningRate * hidden2Deltas[k] * hiddenOutputs1[j];
            }
            hidden1Biases[j] += learningRate * hidden1Deltas[j];
        }

        for (int j = 0; j < numInputNodes; j++) {
            for (int k = 0; k < numHiddenNodes1; k++) {
                inputToHidden1Weights[j][k] += learningRate * hidden1Deltas[k] * input[j];
            }
        }
        for (int j = 0; j < numOutputNodes; j++) {
            outputBiases[j] += learningRate * outputDelta;
        }

        totalError += Math.abs(outputError);
    }

    if (epoch % 10000 == 0) {
        System.out.println("Epoch " + epoch + ", Error: " + totalError);
    }

    if (totalError < errorThreshold) {
        System.out.println("Converged at epoch " + epoch);
        break;
    }}}
    
    
public double predict(double[] input) {
    double[] hiddenOutputs1 = new double[numHiddenNodes1];
    for (int j = 0; j < numHiddenNodes1; j++) {
        double weightedSum = 0.0;
        for (int k = 0; k < numInputNodes; k++) {
            weightedSum += inputToHidden1Weights[k][j] * input[k];
        }
        hiddenOutputs1[j] = relu(weightedSum + hidden1Biases[j]);
    }

    double[] hiddenOutputs2 = new double[numHiddenNodes2];
    for (int j = 0; j < numHiddenNodes2; j++) {
        double weightedSum = 0.0;
        for (int k = 0; k < numHiddenNodes1; k++) {
            weightedSum += hidden1ToHidden2Weights[k][j] * hiddenOutputs1[k];
        }
        hiddenOutputs2[j] = relu(weightedSum + hidden2Biases[j]);
    }

    double output = 0.0;
    for (int j = 0; j < numOutputNodes; j++) {
        double weightedSum = 0.0;
        for (int k = 0; k < numHiddenNodes2; k++) {
            weightedSum += hidden2ToOutputWeights[k][j] * hiddenOutputs2[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, 0, 0, 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, Error: 7.642833734616649
Epoch 20000, Error: 1.461764425644927
Epoch 30000, Error: 0.4499382194344326
Epoch 40000, Error: 0.09301380614006294
Epoch 50000, Error: 0.017577414258392432
Epoch 60000, Error: 0.0031626210464552607
Epoch 70000, Error: 5.667526965382752E-4
Epoch 80000, Error: 1.013043007025427E-4
Epoch 90000, Error: 1.8067052883763424E-5
Epoch 100000, Error: 3.2317843317031247E-6
Epoch 110000, Error: 5.7693306709794E-7
Epoch 120000, Error: 1.030476173280448E-7
Epoch 130000, Error: 1.8415555302908615E-8
Epoch 140000, Error: 3.2854035048046626E-9
Epoch 150000, Error: 5.86836579330452E-10
Epoch 160000, Error: 1.0529066507558582E-10
Epoch 170000, Error: 1.8620216479803275E-11
Epoch 180000, Error: 5.0672799289941395E-12
Epoch 190000, Error: 2.3925306180672123E-12
Epoch 200000, Error: 1.1253220577600587E-12
Prediction for [0, 0, 0, 0, 0, 0, 0, 0]: 0.0
Prediction for [0, 0, 0, 0, 0, 0, 0, 1]: 0.9999999999999869
Prediction for [0, 0, 0, 0, 0, 0, 1, 0]: 0.0
Prediction for [0, 0, 0, 0, 0, 0, 1, 1]: 0.8051961813803608
Prediction for [0, 0, 0, 0, 0, 1, 0, 0]: 0.0
Prediction for [0, 0, 0, 0, 0, 1, 0, 1]: 0.999999999999934
Prediction for [0, 0, 0, 0, 0, 1, 1, 0]: 0.0
Prediction for [0, 0, 0, 0, 0, 1, 1, 1]: 1.0000000000000637
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]: 1.0000000000000175
Prediction for [0, 0, 0, 0, 1, 1, 0, 0]: 0.0
Prediction for [0, 0, 0, 0, 1, 1, 0, 1]: 1.000000000000022
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]: 1.0000000000000033
Prediction for [0, 0, 0, 1, 0, 0, 1, 0]: 0.0
Prediction for [0, 0, 0, 1, 0, 0, 1, 1]: 0.9999999999999967
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]: 0.9999999999999927
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]: 1.0000000000000269
Prediction for [0, 0, 0, 1, 1, 1, 1, 0]: 0.0
Prediction for [0, 0, 0, 1, 1, 1, 1, 1]: 0.9999999999999882
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]: 1.0000000000000127
Prediction for [0, 0, 1, 0, 0, 1, 1, 0]: 0.0
Prediction for [0, 0, 1, 0, 0, 1, 1, 1]: 4.907185768843192E-14
Prediction for [0, 0, 1, 0, 1, 0, 0, 0]: 0.0
Prediction for [0, 0, 1, 0, 1, 0, 0, 1]: 0.9999999999999576
Prediction for [0, 0, 1, 0, 1, 0, 1, 0]: 0.0
Prediction for [0, 0, 1, 0, 1, 0, 1, 1]: 0.9999999999999998
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]: 1.000000000000021
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]: 0.9999999999999909
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]: 0.9999999999999927
Prediction for [0, 0, 1, 1, 1, 1, 0, 0]: 0.0
Prediction for [0, 0, 1, 1, 1, 1, 0, 1]: 0.9999999999999847
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]: 4.440892098500626E-14
Prediction for [0, 1, 0, 0, 0, 0, 1, 0]: 0.0
Prediction for [0, 1, 0, 0, 0, 0, 1, 1]: 1.0000000000000198
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]: 0.9999999999999545
Prediction for [0, 1, 0, 0, 1, 0, 0, 0]: 0.0
Prediction for [0, 1, 0, 0, 1, 0, 0, 1]: 1.0000000000000144
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]: 0.9999999999999811
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]: 0.9999999999999896
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]: 0.9999999999999865
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]: 1.2212453270876722E-14
Prediction for [0, 1, 1, 0, 0, 0, 0, 0]: 0.0
Prediction for [0, 1, 1, 0, 0, 0, 0, 1]: 0.9999999999999927
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]: 0.9999999999999873
Prediction for [0, 1, 1, 0, 0, 1, 1, 0]: 0.0
Prediction for [0, 1, 1, 0, 0, 1, 1, 1]: 0.9999999999999798
Prediction for [0, 1, 1, 0, 1, 0, 0, 0]: 0.0
Prediction for [0, 1, 1, 0, 1, 0, 0, 1]: 2.90878432451791E-14
Prediction for [0, 1, 1, 0, 1, 0, 1, 0]: 0.0
Prediction for [0, 1, 1, 0, 1, 0, 1, 1]: 1.000000000000013
Prediction for [0, 1, 1, 0, 1, 1, 0, 0]: 0.0
Prediction for [0, 1, 1, 0, 1, 1, 0, 1]: 0.9999999999999865
Prediction for [0, 1, 1, 0, 1, 1, 1, 0]: 0.0
Prediction for [0, 1, 1, 0, 1, 1, 1, 1]: 0.0
Prediction for [0, 1, 1, 1, 0, 0, 0, 0]: 0.0
Prediction for [0, 1, 1, 1, 0, 0, 0, 1]: 1.0000000000000144
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]: 9.103828801926284E-15
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]: 0.0
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]: 0.9999999999999802
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]: 0.999999999999998
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]: 0.9999999999999936
Prediction for [1, 0, 0, 0, 1, 0, 1, 0]: 0.0
Prediction for [1, 0, 0, 0, 1, 0, 1, 1]: 0.9999999999999989
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]: 0.9999999999999669
Prediction for [1, 0, 0, 1, 0, 1, 1, 0]: 0.0
Prediction for [1, 0, 0, 1, 0, 1, 1, 1]: 0.9999999999999891
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]: 0.9999999999999847
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]: 0.9999999999999731
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]: 0.9999999999999749
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]: 1.00000000000003
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]: 1.0000000000000153
Prediction for [1, 0, 1, 1, 0, 1, 0, 0]: 0.0
Prediction for [1, 0, 1, 1, 0, 1, 0, 1]: 1.0000000000000095
Prediction for [1, 0, 1, 1, 0, 1, 1, 0]: 0.0
Prediction for [1, 0, 1, 1, 0, 1, 1, 1]: 1.4432899320127035E-14
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]: 1.0000000000000016
Prediction for [1, 1, 0, 0, 0, 0, 0, 0]: 0.0
Prediction for [1, 1, 0, 0, 0, 0, 0, 1]: 0.9999999999999829
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]: 1.000000000000018
Prediction for [1, 1, 0, 0, 0, 1, 1, 0]: 0.0
Prediction for [1, 1, 0, 0, 0, 1, 1, 1]: 1.0000000000000238
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]: 1.4876988529977098E-14
Prediction for [1, 1, 0, 1, 0, 0, 1, 0]: 0.0
Prediction for [1, 1, 0, 1, 0, 0, 1, 1]: 0.9999999999999891
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]: 0.9999999999999811
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]: 1.0000000000000122
Prediction for [1, 1, 1, 0, 0, 1, 0, 0]: 0.0
Prediction for [1, 1, 1, 0, 0, 1, 0, 1]: 1.00000000000001
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]: 0.9999999999999856
Prediction for [1, 1, 1, 0, 1, 0, 1, 0]: 0.0
Prediction for [1, 1, 1, 0, 1, 0, 1, 1]: 4.218847493575595E-15
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]: 1.0000000000000255
Prediction for [1, 1, 1, 1, 0, 0, 0, 0]: 0.0
Prediction for [1, 1, 1, 1, 0, 0, 0, 1]: 0.99999999999999
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]: 0.6732134638331646
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: 53 seconds)

 

Edited by Dietmar
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Mark-XP

Mark-XP

Posted
Posted (edited)

Hello @Dietmar, i did run it but with "only" 100.000 epochs the result for prediction of 3 turns to 0!? (For 251: 0.36 which is respectable): 

Prediction for [0, 0, 0, 0, 0, 0, 0, 0]: 0.0
Prediction for [0, 0, 0, 0, 0, 0, 0, 1]: 0.9999998889558965
Prediction for [0, 0, 0, 0, 0, 0, 1, 0]: 0.0
Prediction for [0, 0, 0, 0, 0, 0, 1, 1]: 0.0
Prediction for [0, 0, 0, 0, 0, 1, 0, 0]: 0.0
Prediction for [0, 0, 0, 0, 0, 1, 0, 1]: 1.0000001086053538
Prediction for [0, 0, 0, 0, 0, 1, 1, 0]: 0.0
Prediction for [0, 0, 0, 0, 0, 1, 1, 1]: 1.0000000489125835
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]: 1.0000000111626701
...
Prediction for [1, 1, 1, 0, 1, 1, 1, 1]: 0.9999999167529428
Prediction for [1, 1, 1, 1, 0, 0, 0, 0]: 0.0
Prediction for [1, 1, 1, 1, 0, 0, 0, 1]: 0.9999990634050797
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]: 3.268234877173981E-8
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]: 0.363153607310525
Prediction for [1, 1, 1, 1, 1, 1, 0, 0]: 0.0
Prediction for [1, 1, 1, 1, 1, 1, 0, 1]: 2.0778473491800398E-7
Prediction for [1, 1, 1, 1, 1, 1, 1, 0]: 0.0
Prediction for [1, 1, 1, 1, 1, 1, 1, 1]: 0.0

Btw. nice to see you survived the tremendous thunderstorms yesteday... ;)

Edit: Currently i'm struggeling with a new Linux installation (Debian based Q4OS). I like it because it uses TDE (Trinity Desktop Environment) and you can make it look very like Windows XP.

This time for the first time 64-bit (parallel to Win-XP and Win-7) and the issue i have iss hair-raising... For the case you are interested feel free to read more here...

Edited by Mark-XP
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  • 1 month later...
Dietmar

Dietmar

Posted
  • Author
Posted

@Mark-XP

Here is a new version of the "Hund" "Katze" "nix" program without any Neuroph Bibliothek.

This is really nice, maximal stable and fast

Dietmar 

package tiere;

import java.util.HashMap;
import java.util.Map;
import java.util.Scanner;

public class Tiere {
    public static void main(String[] args) {
        Scanner scanner = new Scanner(System.in);
        double learningRate = 0.1;
        int inputNeurons = 26;
        int hidden1Neurons = 10;
        int hidden2Neurons = 10;
        int outputNeurons = 3;

        double[][] weightsInputHidden1 = initializeWeights(inputNeurons, hidden1Neurons);
        double[][] weightsHidden1Hidden2 = initializeWeights(hidden1Neurons, hidden2Neurons);
        double[][] weightsHidden2Output = initializeWeights(hidden2Neurons, outputNeurons);

        Map<String, String> bewertungen = new HashMap<>();
        Map<String, String> antworten = new HashMap<>();

        while (true) {
            System.out.println("Gib ein Wort ein:");
            String eingabe = scanner.nextLine().toLowerCase();

            if (eingabe.equals("liste")) {
                for (String wort : bewertungen.keySet()) {
                    String bewertung = bewertungen.get(wort);
                    String antwort = antworten.get(wort);
                    System.out.println(wort + ": " + bewertung + " (" + antwort + ")");
                }
                continue;
            }

            double[] input = createInputVector(eingabe);
            double[] hidden1LayerOutput = calculateLayerOutput(input, weightsInputHidden1);
            double[] hidden2LayerOutput = calculateLayerOutput(hidden1LayerOutput, weightsHidden1Hidden2);
            double[] output = calculateLayerOutput(hidden2LayerOutput, weightsHidden2Output);
            String ergebnis = bestimmeErgebnis(output);

            System.out.println("Ich schätze, dass es sich um " + ergebnis + " handelt.");

            System.out.println("War das richtig von mir vermutet? (Ja/Nein)");
            String antwort = scanner.nextLine().toLowerCase();
            antworten.put(eingabe, antwort);
            if (antwort.startsWith("n")) {
                double[] gewünschteAusgabe = new double[outputNeurons];
                System.out.println("Welches Tier ist es? (Hund, Katze, nix)");
                String tier = scanner.nextLine().toLowerCase();
                switch (tier) {
                    case "hund":
                        gewünschteAusgabe[0] = 1;
                        break;
                    case "katze":
                        gewünschteAusgabe[1] = 1;
                        break;
                    default:
                        gewünschteAusgabe[2] = 1;
                        break;
                }

                train(input, hidden1LayerOutput, hidden2LayerOutput, gewünschteAusgabe,
                        weightsInputHidden1, weightsHidden1Hidden2, weightsHidden2Output, learningRate);

                String bewertung = gewünschteAusgabe[0] == 1 ? "Hund" : gewünschteAusgabe[1] == 1 ? "Katze" : "nix";
                bewertungen.put(eingabe, bewertung);

                System.out.println("Ich habe etwas dazu gelernt.");
            } else {
                String bewertung = ergebnis;
                bewertungen.put(eingabe, bewertung);
            }
        }
    }

    private static double[][] initializeWeights(int inputSize, int outputSize) {
        double[][] weights = new double[inputSize][outputSize];
        for (int i = 0; i < inputSize; i++) {
            for (int j = 0; j < outputSize; j++) {
                weights[i][j] = Math.random() * 2 - 1;
            }
        }
        return weights;
    }

    private static double[] createInputVector(String eingabe) {
        double[] input = new double[26];
        eingabe = eingabe.toLowerCase();
        for (int i = 0; i < eingabe.length(); i++) {
            char c = eingabe.charAt(i);
            if (c >= 'a' && c <= 'z') {
                input[c - 'a'] = 1;
            }
        }
        return input;
    }

    private static double[] calculateLayerOutput(double[] input, double[][] weights) {
        int inputSize = input.length;
        int outputSize = weights[0].length;
        double[] output = new double[outputSize];
        for (int i = 0; i < outputSize; i++) {
            for (int j = 0; j < inputSize; j++) {
                output[i] += input[j] * weights[j][i];
            }
            output[i] = sigmoid(output[i]);
        }
        return output;
    }

    private static double sigmoid(double x) {
        return 1 / (1 + Math.exp(-x));
    }

    private static String bestimmeErgebnis(double[] output) {
        if (output[0] > output[1] && output[0] > output[2]) {
            return "Hund";
        } else if (output[1] > output[0] && output[1] > output[2]) {
            return "Katze";
        } else {
            return "nix";
        }
    }

    private static void train(double[] input, double[] hidden1LayerOutput, double[] hidden2LayerOutput,
                              double[] targetOutput, double[][] weightsInputHidden1,
                              double[][] weightsHidden1Hidden2, double[][] weightsHidden2Output, double learningRate) {
        double[] output = calculateLayerOutput(hidden2LayerOutput, weightsHidden2Output);
        double[] error = new double[targetOutput.length];

        for (int i = 0; i < targetOutput.length; i++) {
            error[i] = targetOutput[i] - output[i];
        }

        int inputSize = input.length;
        int hidden1Size = hidden1LayerOutput.length;
        int hidden2Size = hidden2LayerOutput.length;
        int outputSize = targetOutput.length;

        // Update weights between hidden2 and output layer
        for (int i = 0; i < hidden2Size; i++) {
            for (int j = 0; j < outputSize; j++) {
                weightsHidden2Output[i][j] += learningRate * hidden2LayerOutput[i] * error[j];
            }
        }

        // Update weights between hidden1 and hidden2 layer
        for (int i = 0; i < hidden1Size; i++) {
            for (int j = 0; j < hidden2Size; j++) {
                double hidden2Error = 0;
                for (int k = 0; k < outputSize; k++) {
                    hidden2Error += error[k] * weightsHidden2Output[j][k];
                }
                weightsHidden1Hidden2[i][j] += learningRate * hidden1LayerOutput[i] * hidden2Error;
            }
        }

        // Update weights between input and hidden1 layer
        for (int i = 0; i < inputSize; i++) {
            for (int j = 0; j < hidden1Size; j++) {
                double hidden1Error = 0;
                for (int k = 0; k < hidden2Size; k++) {
                    hidden1Error += hidden2LayerOutput[k] * weightsHidden1Hidden2[j][k];
                }
                weightsInputHidden1[i][j] += learningRate * input[i] * hidden1Error;
            }
        }
    }
}

 

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Dietmar

Dietmar

Posted
  • Author
Posted (edited)

Hi,

there is binary test of to be a prime number or not.

With 2 Hidden Layers it is hard to show.

But with 3(!) Hidden Layers suddently you see something :cheerleader:

Dietmar

PS: With more layers, it becomes even more clear that 1, 5, 7, 11, 13..

are the correct prime numbers, but 2 and 3 not.

  

package hiddenlayers3;
import org.apache.commons.math3.util.FastMath;
import java.security.SecureRandom;

public class HiddenLayers3 {
    private final int numInputNodes = 9;
    private final int numHiddenNodes1 = 12;
    private final int numHiddenNodes2 = 12;
    private final int numHiddenNodes3 = 12;
    private final int numOutputNodes = 1;
    private final double learningRate = 0.0003;
    private final int numEpochs = 100000;
    private final double errorThreshold = 0.00000000001;

    private double[][] inputToHidden1Weights;
    private double[][] hidden1ToHidden2Weights;
    private double[][] hidden2ToHidden3Weights;
    private double[][] hidden3ToOutputWeights;
    private double[] hidden1Biases;
    private double[] hidden2Biases;
    private double[] hidden3Biases;
    private double[] outputBiases;

    public HiddenLayers3() {
        SecureRandom random = new SecureRandom();
        inputToHidden1Weights = new double[numInputNodes][numHiddenNodes1];
        hidden1ToHidden2Weights = new double[numHiddenNodes1][numHiddenNodes2];
        hidden2ToHidden3Weights = new double[numHiddenNodes2][numHiddenNodes3];
        hidden3ToOutputWeights = new double[numHiddenNodes3][numOutputNodes];
        hidden1Biases = new double[numHiddenNodes1];
        hidden2Biases = new double[numHiddenNodes2];
        hidden3Biases = new double[numHiddenNodes3];
        outputBiases = new double[numOutputNodes];

        for (int i = 0; i < numInputNodes; i++) {
            for (int j = 0; j < numHiddenNodes1; j++) {
                inputToHidden1Weights[i][j] = 0.3 * random.nextGaussian();
            }
        }

        for (int i = 0; i < numHiddenNodes1; i++) {
            for (int j = 0; j < numHiddenNodes2; j++) {
                hidden1ToHidden2Weights[i][j] = 0.3 * random.nextGaussian();
            }
            hidden1Biases[i] = 0.3 * random.nextGaussian();
        }

        for (int i = 0; i < numHiddenNodes2; i++) {
            for (int j = 0; j < numHiddenNodes3; j++) {
                hidden2ToHidden3Weights[i][j] = 0.3 * random.nextGaussian();
            }
            hidden2Biases[i] = 0.3 * random.nextGaussian();
        }

        for (int i = 0; i < numHiddenNodes3; i++) {
            for (int j = 0; j < numOutputNodes; j++) {
                hidden3ToOutputWeights[i][j] = 0.3 * random.nextGaussian();
            }
            hidden3Biases[i] = 0.3 * random.nextGaussian();
        }

        for (int i = 0; i < numOutputNodes; i++) {
            outputBiases[i] = 0.3 * random.nextGaussian();
        }
    }

    public double relu(double x) {
        return FastMath.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++) {
                // Skip excluded inputs
                if (i == 509) {
                    continue;
                }
                double[] input = trainingInputs[i];
                double target = trainingTargets[i];

                // Forward propagation
                double[] hiddenOutputs1 = new double[numHiddenNodes1];
                for (int j = 0; j < numHiddenNodes1; j++) {
                    double weightedSum = 0.0;
                    for (int k = 0; k < numInputNodes; k++) {
                        weightedSum += inputToHidden1Weights[k][j] * input[k];
                    }
                    hiddenOutputs1[j] = relu(weightedSum + hidden1Biases[j]);
                }

                double[] hiddenOutputs2 = new double[numHiddenNodes2];
                for (int j = 0; j < numHiddenNodes2; j++) {
                    double weightedSum = 0.0;
                    for (int k = 0; k < numHiddenNodes1; k++) {
                        weightedSum += hidden1ToHidden2Weights[k][j] * hiddenOutputs1[k];
                    }
                    hiddenOutputs2[j] = relu(weightedSum + hidden2Biases[j]);
                }

                double[] hiddenOutputs3 = new double[numHiddenNodes3];
                for (int j = 0; j < numHiddenNodes3; j++) {
                    double weightedSum = 0.0;
                    for (int k = 0; k < numHiddenNodes2; k++) {
                        weightedSum += hidden2ToHidden3Weights[k][j] * hiddenOutputs2[k];
                    }
                    hiddenOutputs3[j] = relu(weightedSum + hidden3Biases[j]);
                }

                double[] output = new double[numOutputNodes];
                for (int j = 0; j < numOutputNodes; j++) {
                    double weightedSum = 0.0;
                    for (int k = 0; k < numHiddenNodes3; k++) {
                        weightedSum += hidden3ToOutputWeights[k][j] * hiddenOutputs3[k];
                    }
                    output[j] = relu(weightedSum + outputBiases[j]);
                }

                // Backward propagation
                double outputError = target - output[0];
                double outputDelta = outputError * reluDerivative(output[0]);

                double[] hidden3Errors = new double[numHiddenNodes3];
                double[] hidden3Deltas = new double[numHiddenNodes3];
                for (int j = 0; j < numHiddenNodes3; j++) {
                    double weightedSum = 0.0;
                    for (int k = 0; k < numOutputNodes; k++) {
                        weightedSum += hidden3ToOutputWeights[j][k] * outputDelta;
                    }
                    hidden3Errors[j] = weightedSum;
                    hidden3Deltas[j] = hidden3Errors[j] * reluDerivative(hiddenOutputs3[j]);
                }

                double[] hidden2Errors = new double[numHiddenNodes2];
                double[] hidden2Deltas = new double[numHiddenNodes2];
                for (int j = 0; j < numHiddenNodes2; j++) {
                    double weightedSum = 0.0;
                    for (int k = 0; k < numHiddenNodes3; k++) {
                        weightedSum += hidden2ToHidden3Weights[j][k] * hidden3Deltas[k];
                    }
                    hidden2Errors[j] = weightedSum;
                    hidden2Deltas[j] = hidden2Errors[j] * reluDerivative(hiddenOutputs2[j]);
                }

                double[] hidden1Errors = new double[numHiddenNodes1];
                double[] hidden1Deltas = new double[numHiddenNodes1];
                for (int j = 0; j < numHiddenNodes1; j++) {
                    double weightedSum = 0.0;
                    for (int k = 0; k < numHiddenNodes2; k++) {
                        weightedSum += hidden1ToHidden2Weights[j][k] * hidden2Deltas[k];
                    }
                    hidden1Errors[j] = weightedSum;
                    hidden1Deltas[j] = hidden1Errors[j] * reluDerivative(hiddenOutputs1[j]);
                }

                // Update weights and biases
                for (int j = 0; j < numHiddenNodes3; j++) {
                    for (int k = 0; k < numOutputNodes; k++) {
                        hidden3ToOutputWeights[j][k] += learningRate * outputDelta * hiddenOutputs3[j];
                    }
                    hidden3Biases[j] += learningRate * hidden3Deltas[j];
                }

                for (int j = 0; j < numHiddenNodes2; j++) {
                    for (int k = 0; k < numHiddenNodes3; k++) {
                        hidden2ToHidden3Weights[j][k] += learningRate * hidden3Deltas[k] * hiddenOutputs2[j];
                    }
                    hidden2Biases[j] += learningRate * hidden2Deltas[j];
                }

                for (int j = 0; j < numHiddenNodes1; j++) {
                    for (int k = 0; k < numHiddenNodes2; k++) {
                        hidden1ToHidden2Weights[j][k] += learningRate * hidden2Deltas[k] * hiddenOutputs1[j];
                    }
                    hidden1Biases[j] += learningRate * hidden1Deltas[j];
                }

                for (int j = 0; j < numInputNodes; j++) {
                    for (int k = 0; k < numHiddenNodes1; k++) {
                        inputToHidden1Weights[j][k] += learningRate * hidden1Deltas[k] * input[j];
                    }
                }

                for (int j = 0; j < numOutputNodes; j++) {
                    outputBiases[j] += learningRate * outputDelta;
                }

                // Calculate total error
                totalError += Math.pow(outputError, 2);
            }

            if (epoch % 2000 == 0) {
                System.out.println("Epoch " + epoch + ", Error: " + totalError);
            }

            if (totalError < errorThreshold) {
                System.out.println("Converged at epoch " + epoch);
                break;
            }
        }
    }

    public double predict(double[] input) {
        double[] hiddenOutputs1 = new double[numHiddenNodes1];
        for (int j = 0; j < numHiddenNodes1; j++) {
            double weightedSum = 0.0;
            for (int k = 0; k < numInputNodes; k++) {
                weightedSum += inputToHidden1Weights[k][j] * input[k];
            }
            hiddenOutputs1[j] = relu(weightedSum + hidden1Biases[j]);
        }

        double[] hiddenOutputs2 = new double[numHiddenNodes2];
        for (int j = 0; j < numHiddenNodes2; j++) {
            double weightedSum = 0.0;
            for (int k = 0; k < numHiddenNodes1; k++) {
                weightedSum += hidden1ToHidden2Weights[k][j] * hiddenOutputs1[k];
            }
            hiddenOutputs2[j] = relu(weightedSum + hidden2Biases[j]);
        }

        double[] hiddenOutputs3 = new double[numHiddenNodes3];
        for (int j = 0; j < numHiddenNodes3; j++) {
            double weightedSum = 0.0;
            for (int k = 0; k < numHiddenNodes2; k++) {
                weightedSum += hidden2ToHidden3Weights[k][j] * hiddenOutputs2[k];
            }
            hiddenOutputs3[j] = relu(weightedSum + hidden3Biases[j]);
        }

        double[] output = new double[numOutputNodes];
        for (int j = 0; j < numOutputNodes; j++) {
            double weightedSum = 0.0;
            for (int k = 0; k < numHiddenNodes3; k++) {
                weightedSum += hidden3ToOutputWeights[k][j] * hiddenOutputs3[k];
            }
            output[j] = relu(weightedSum + outputBiases[j]);
        }

        return output[0];
    }

    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, 0, 0, 1},
{0, 0, 0, 0, 0, 0, 0, 1, 0},
{0, 0, 0, 0, 0, 0, 0, 1, 1},
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{1, 1, 0, 0, 0, 0, 1, 0, 1},
{1, 1, 0, 0, 0, 0, 1, 1, 0},
{1, 1, 0, 0, 0, 0, 1, 1, 1},
{1, 1, 0, 0, 0, 1, 0, 0, 0},
{1, 1, 0, 0, 0, 1, 0, 0, 1},
{1, 1, 0, 0, 0, 1, 0, 1, 0},
{1, 1, 0, 0, 0, 1, 0, 1, 1},
{1, 1, 0, 0, 0, 1, 1, 0, 0},
{1, 1, 0, 0, 0, 1, 1, 0, 1},
{1, 1, 0, 0, 0, 1, 1, 1, 0},
{1, 1, 0, 0, 0, 1, 1, 1, 1},
{1, 1, 0, 0, 1, 0, 0, 0, 0},
{1, 1, 0, 0, 1, 0, 0, 0, 1},
{1, 1, 0, 0, 1, 0, 0, 1, 0},
{1, 1, 0, 0, 1, 0, 0, 1, 1},
{1, 1, 0, 0, 1, 0, 1, 0, 0},
{1, 1, 0, 0, 1, 0, 1, 0, 1},
{1, 1, 0, 0, 1, 0, 1, 1, 0},
{1, 1, 0, 0, 1, 0, 1, 1, 1},
{1, 1, 0, 0, 1, 1, 0, 0, 0},
{1, 1, 0, 0, 1, 1, 0, 0, 1},
{1, 1, 0, 0, 1, 1, 0, 1, 0},
{1, 1, 0, 0, 1, 1, 0, 1, 1},
{1, 1, 0, 0, 1, 1, 1, 0, 0},
{1, 1, 0, 0, 1, 1, 1, 0, 1},
{1, 1, 0, 0, 1, 1, 1, 1, 0},
{1, 1, 0, 0, 1, 1, 1, 1, 1},
{1, 1, 0, 1, 0, 0, 0, 0, 0},
{1, 1, 0, 1, 0, 0, 0, 0, 1},
{1, 1, 0, 1, 0, 0, 0, 1, 0},
{1, 1, 0, 1, 0, 0, 0, 1, 1},
{1, 1, 0, 1, 0, 0, 1, 0, 0},
{1, 1, 0, 1, 0, 0, 1, 0, 1},
{1, 1, 0, 1, 0, 0, 1, 1, 0},
{1, 1, 0, 1, 0, 0, 1, 1, 1},
{1, 1, 0, 1, 0, 1, 0, 0, 0},
{1, 1, 0, 1, 0, 1, 0, 0, 1},
{1, 1, 0, 1, 0, 1, 0, 1, 0},
{1, 1, 0, 1, 0, 1, 0, 1, 1},
{1, 1, 0, 1, 0, 1, 1, 0, 0},
{1, 1, 0, 1, 0, 1, 1, 0, 1},
{1, 1, 0, 1, 0, 1, 1, 1, 0},
{1, 1, 0, 1, 0, 1, 1, 1, 1},
{1, 1, 0, 1, 1, 0, 0, 0, 0},
{1, 1, 0, 1, 1, 0, 0, 0, 1},
{1, 1, 0, 1, 1, 0, 0, 1, 0},
{1, 1, 0, 1, 1, 0, 0, 1, 1},
{1, 1, 0, 1, 1, 0, 1, 0, 0},
{1, 1, 0, 1, 1, 0, 1, 0, 1},
{1, 1, 0, 1, 1, 0, 1, 1, 0},
{1, 1, 0, 1, 1, 0, 1, 1, 1},
{1, 1, 0, 1, 1, 1, 0, 0, 0},
{1, 1, 0, 1, 1, 1, 0, 0, 1},
{1, 1, 0, 1, 1, 1, 0, 1, 0},
{1, 1, 0, 1, 1, 1, 0, 1, 1},
{1, 1, 0, 1, 1, 1, 1, 0, 0},
{1, 1, 0, 1, 1, 1, 1, 0, 1},
{1, 1, 0, 1, 1, 1, 1, 1, 0},
{1, 1, 0, 1, 1, 1, 1, 1, 1},
{1, 1, 1, 0, 0, 0, 0, 0, 0},
{1, 1, 1, 0, 0, 0, 0, 0, 1},
{1, 1, 1, 0, 0, 0, 0, 1, 0},
{1, 1, 1, 0, 0, 0, 0, 1, 1},
{1, 1, 1, 0, 0, 0, 1, 0, 0},
{1, 1, 1, 0, 0, 0, 1, 0, 1},
{1, 1, 1, 0, 0, 0, 1, 1, 0},
{1, 1, 1, 0, 0, 0, 1, 1, 1},
{1, 1, 1, 0, 0, 1, 0, 0, 0},
{1, 1, 1, 0, 0, 1, 0, 0, 1},
{1, 1, 1, 0, 0, 1, 0, 1, 0},
{1, 1, 1, 0, 0, 1, 0, 1, 1},
{1, 1, 1, 0, 0, 1, 1, 0, 0},
{1, 1, 1, 0, 0, 1, 1, 0, 1},
{1, 1, 1, 0, 0, 1, 1, 1, 0},
{1, 1, 1, 0, 0, 1, 1, 1, 1},
{1, 1, 1, 0, 1, 0, 0, 0, 0},
{1, 1, 1, 0, 1, 0, 0, 0, 1},
{1, 1, 1, 0, 1, 0, 0, 1, 0},
{1, 1, 1, 0, 1, 0, 0, 1, 1},
{1, 1, 1, 0, 1, 0, 1, 0, 0},
{1, 1, 1, 0, 1, 0, 1, 0, 1},
{1, 1, 1, 0, 1, 0, 1, 1, 0},
{1, 1, 1, 0, 1, 0, 1, 1, 1},
{1, 1, 1, 0, 1, 1, 0, 0, 0},
{1, 1, 1, 0, 1, 1, 0, 0, 1},
{1, 1, 1, 0, 1, 1, 0, 1, 0},
{1, 1, 1, 0, 1, 1, 0, 1, 1},
{1, 1, 1, 0, 1, 1, 1, 0, 0},
{1, 1, 1, 0, 1, 1, 1, 0, 1},
{1, 1, 1, 0, 1, 1, 1, 1, 0},
{1, 1, 1, 0, 1, 1, 1, 1, 1},
{1, 1, 1, 1, 0, 0, 0, 0, 0},
{1, 1, 1, 1, 0, 0, 0, 0, 1},
{1, 1, 1, 1, 0, 0, 0, 1, 0},
{1, 1, 1, 1, 0, 0, 0, 1, 1},
{1, 1, 1, 1, 0, 0, 1, 0, 0},
{1, 1, 1, 1, 0, 0, 1, 0, 1},
{1, 1, 1, 1, 0, 0, 1, 1, 0},
{1, 1, 1, 1, 0, 0, 1, 1, 1},
{1, 1, 1, 1, 0, 1, 0, 0, 0},
{1, 1, 1, 1, 0, 1, 0, 0, 1},
{1, 1, 1, 1, 0, 1, 0, 1, 0},
{1, 1, 1, 1, 0, 1, 0, 1, 1},
{1, 1, 1, 1, 0, 1, 1, 0, 0},
{1, 1, 1, 1, 0, 1, 1, 0, 1},
{1, 1, 1, 1, 0, 1, 1, 1, 0},
{1, 1, 1, 1, 0, 1, 1, 1, 1},
{1, 1, 1, 1, 1, 0, 0, 0, 0},
{1, 1, 1, 1, 1, 0, 0, 0, 1},
{1, 1, 1, 1, 1, 0, 0, 1, 0},
{1, 1, 1, 1, 1, 0, 0, 1, 1},
{1, 1, 1, 1, 1, 0, 1, 0, 0},
{1, 1, 1, 1, 1, 0, 1, 0, 1},
{1, 1, 1, 1, 1, 0, 1, 1, 0},
{1, 1, 1, 1, 1, 0, 1, 1, 1},
{1, 1, 1, 1, 1, 1, 0, 0, 0},
{1, 1, 1, 1, 1, 1, 0, 0, 1},
{1, 1, 1, 1, 1, 1, 0, 1, 0},
{1, 1, 1, 1, 1, 1, 0, 1, 1},
{1, 1, 1, 1, 1, 1, 1, 0, 0},
{1, 1, 1, 1, 1, 1, 1, 0, 1},
{1, 1, 1, 1, 1, 1, 1, 1, 0},
{1, 1, 1, 1, 1, 1, 1, 1, 1}
};
    
    



double[] trainingTargets = {
0, 1, 0, 0, 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, 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, 0, 0, 0, 0, 1, 0, 0, 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, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 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, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 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, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0
};

    

    
    HiddenLayers3 nn = new HiddenLayers3();
    nn.train(trainingInputs, trainingTargets);
    
System.out.println("Prediction for [0, 0, 0, 0, 0, 0, 0, 0, 0]: " + nn.predict(new double[]{0, 0, 0, 0, 0, 0, 0, 0, 0}));
System.out.println("Prediction for [0, 0, 0, 0, 0, 0, 0, 0, 1]: " + nn.predict(new double[]{0, 0, 0, 0, 0, 0, 0, 0, 1}));
System.out.println("Prediction for [0, 0, 0, 0, 0, 0, 0, 1, 0]: " + nn.predict(new double[]{0, 0, 0, 0, 0, 0, 0, 1, 0}));
System.out.println("Prediction for [0, 0, 0, 0, 0, 0, 0, 1, 1]: " + nn.predict(new double[]{0, 0, 0, 0, 0, 0, 0, 1, 1}));
System.out.println("Prediction for [0, 0, 0, 0, 0, 0, 1, 0, 0]: " + nn.predict(new double[]{0, 0, 0, 0, 0, 0, 1, 0, 0}));
System.out.println("Prediction for [0, 0, 0, 0, 0, 0, 1, 0, 1]: " + nn.predict(new double[]{0, 0, 0, 0, 0, 0, 1, 0, 1}));
System.out.println("Prediction for [0, 0, 0, 0, 0, 0, 1, 1, 0]: " + nn.predict(new double[]{0, 0, 0, 0, 0, 0, 1, 1, 0}));
System.out.println("Prediction for [0, 0, 0, 0, 0, 0, 1, 1, 1]: " + nn.predict(new double[]{0, 0, 0, 0, 0, 0, 1, 1, 1}));
System.out.println("Prediction for [0, 0, 0, 0, 0, 1, 0, 0, 0]: " + nn.predict(new double[]{0, 0, 0, 0, 0, 1, 0, 0, 0}));
System.out.println("Prediction for [0, 0, 0, 0, 0, 1, 0, 0, 1]: " + nn.predict(new double[]{0, 0, 0, 0, 0, 1, 0, 0, 1}));
System.out.println("Prediction for [0, 0, 0, 0, 0, 1, 0, 1, 0]: " + nn.predict(new double[]{0, 0, 0, 0, 0, 1, 0, 1, 0}));
System.out.println("Prediction for [0, 0, 0, 0, 0, 1, 0, 1, 1]: " + nn.predict(new double[]{0, 0, 0, 0, 0, 1, 0, 1, 1}));
System.out.println("Prediction for [0, 0, 0, 0, 0, 1, 1, 0, 0]: " + nn.predict(new double[]{0, 0, 0, 0, 0, 1, 1, 0, 0}));
System.out.println("Prediction for [0, 0, 0, 0, 0, 1, 1, 0, 1]: " + nn.predict(new double[]{0, 0, 0, 0, 0, 1, 1, 0, 1}));
System.out.println("Prediction for [0, 0, 0, 0, 0, 1, 1, 1, 0]: " + nn.predict(new double[]{0, 0, 0, 0, 0, 1, 1, 1, 0}));
System.out.println("Prediction for [0, 0, 0, 0, 0, 1, 1, 1, 1]: " + nn.predict(new double[]{0, 0, 0, 0, 0, 1, 1, 1, 1}));
System.out.println("Prediction for [0, 0, 0, 0, 1, 0, 0, 0, 0]: " + nn.predict(new double[]{0, 0, 0, 0, 1, 0, 0, 0, 0}));
System.out.println("Prediction for [0, 0, 0, 0, 1, 0, 0, 0, 1]: " + nn.predict(new double[]{0, 0, 0, 0, 1, 0, 0, 0, 1}));
System.out.println("Prediction for [0, 0, 0, 0, 1, 0, 0, 1, 0]: " + nn.predict(new double[]{0, 0, 0, 0, 1, 0, 0, 1, 0}));
System.out.println("Prediction for [0, 0, 0, 0, 1, 0, 0, 1, 1]: " + nn.predict(new double[]{0, 0, 0, 0, 1, 0, 0, 1, 1}));
System.out.println("Prediction for [0, 0, 0, 0, 1, 0, 1, 0, 0]: " + nn.predict(new double[]{0, 0, 0, 0, 1, 0, 1, 0, 0}));
System.out.println("Prediction for [0, 0, 0, 0, 1, 0, 1, 0, 1]: " + nn.predict(new double[]{0, 0, 0, 0, 1, 0, 1, 0, 1}));
System.out.println("Prediction for [0, 0, 0, 0, 1, 0, 1, 1, 0]: " + nn.predict(new double[]{0, 0, 0, 0, 1, 0, 1, 1, 0}));
System.out.println("Prediction for [0, 0, 0, 0, 1, 0, 1, 1, 1]: " + nn.predict(new double[]{0, 0, 0, 0, 1, 0, 1, 1, 1}));   
System.out.println("Prediction for [0, 0, 0, 0, 1, 1, 0, 0, 0]: " + nn.predict(new double[]{0, 0, 0, 0, 1, 1, 0, 0, 0}));
System.out.println("Prediction for [0, 0, 0, 0, 1, 1, 0, 0, 1]: " + nn.predict(new double[]{0, 0, 0, 0, 1, 1, 0, 0, 1}));
System.out.println("Prediction for [0, 0, 0, 0, 1, 1, 0, 1, 0]: " + nn.predict(new double[]{0, 0, 0, 0, 1, 1, 0, 1, 0}));
System.out.println("Prediction for [0, 0, 0, 0, 1, 1, 0, 1, 1]: " + nn.predict(new double[]{0, 0, 0, 0, 1, 1, 0, 1, 1}));
System.out.println("Prediction for [0, 0, 0, 0, 1, 1, 1, 0, 0]: " + nn.predict(new double[]{0, 0, 0, 0, 1, 1, 1, 0, 0}));
System.out.println("Prediction for [0, 0, 0, 0, 1, 1, 1, 0, 1]: " + nn.predict(new double[]{0, 0, 0, 0, 1, 1, 1, 0, 1}));
System.out.println("Prediction for [0, 0, 0, 0, 1, 1, 1, 1, 0]: " + nn.predict(new double[]{0, 0, 0, 0, 1, 1, 1, 1, 0}));
System.out.println("Prediction for [0, 0, 0, 0, 1, 1, 1, 1, 1]: " + nn.predict(new double[]{0, 0, 0, 0, 1, 1, 1, 1, 1}));
System.out.println("Prediction for [0, 0, 0, 1, 0, 0, 0, 0, 0]: " + nn.predict(new double[]{0, 0, 0, 1, 0, 0, 0, 0, 0}));
System.out.println("Prediction for [0, 0, 0, 1, 0, 0, 0, 0, 1]: " + nn.predict(new double[]{0, 0, 0, 1, 0, 0, 0, 0, 1}));
System.out.println("Prediction for [0, 0, 0, 1, 0, 0, 0, 1, 0]: " + nn.predict(new double[]{0, 0, 0, 1, 0, 0, 0, 1, 0}));
System.out.println("Prediction for [0, 0, 0, 1, 0, 0, 0, 1, 1]: " + nn.predict(new double[]{0, 0, 0, 1, 0, 0, 0, 1, 1}));
System.out.println("Prediction for [0, 0, 0, 1, 0, 0, 1, 0, 0]: " + nn.predict(new double[]{0, 0, 0, 1, 0, 0, 1, 0, 0}));
System.out.println("Prediction for [0, 0, 0, 1, 0, 0, 1, 0, 1]: " + nn.predict(new double[]{0, 0, 0, 1, 0, 0, 1, 0, 1}));
System.out.println("Prediction for [0, 0, 0, 1, 0, 0, 1, 1, 0]: " + nn.predict(new double[]{0, 0, 0, 1, 0, 0, 1, 1, 0}));
System.out.println("Prediction for [0, 0, 0, 1, 0, 0, 1, 1, 1]: " + nn.predict(new double[]{0, 0, 0, 1, 0, 0, 1, 1, 1}));
System.out.println("Prediction for [0, 0, 0, 1, 0, 1, 0, 0, 0]: " + nn.predict(new double[]{0, 0, 0, 1, 0, 1, 0, 0, 0}));
System.out.println("Prediction for [0, 0, 0, 1, 0, 1, 0, 0, 1]: " + nn.predict(new double[]{0, 0, 0, 1, 0, 1, 0, 0, 1}));
System.out.println("Prediction for [0, 0, 0, 1, 0, 1, 0, 1, 0]: " + nn.predict(new double[]{0, 0, 0, 1, 0, 1, 0, 1, 0}));
System.out.println("Prediction for [0, 0, 0, 1, 0, 1, 0, 1, 1]: " + nn.predict(new double[]{0, 0, 0, 1, 0, 1, 0, 1, 1}));
System.out.println("Prediction for [0, 0, 0, 1, 0, 1, 1, 0, 0]: " + nn.predict(new double[]{0, 0, 0, 1, 0, 1, 1, 0, 0}));
System.out.println("Prediction for [0, 0, 0, 1, 0, 1, 1, 0, 1]: " + nn.predict(new double[]{0, 0, 0, 1, 0, 1, 1, 0, 1}));
System.out.println("Prediction for [0, 0, 0, 1, 0, 1, 1, 1, 0]: " + nn.predict(new double[]{0, 0, 0, 1, 0, 1, 1, 1, 0}));
System.out.println("Prediction for [0, 0, 0, 1, 0, 1, 1, 1, 1]: " + nn.predict(new double[]{0, 0, 0, 1, 0, 1, 1, 1, 1}));
System.out.println("Prediction for [0, 0, 0, 1, 1, 0, 0, 0, 0]: " + nn.predict(new double[]{0, 0, 0, 1, 1, 0, 0, 0, 0}));
System.out.println("Prediction for [0, 0, 0, 1, 1, 0, 0, 0, 1]: " + nn.predict(new double[]{0, 0, 0, 1, 1, 0, 0, 0, 1}));
System.out.println("Prediction for [0, 0, 0, 1, 1, 0, 0, 1, 0]: " + nn.predict(new double[]{0, 0, 0, 1, 1, 0, 0, 1, 0}));
System.out.println("Prediction for [0, 0, 0, 1, 1, 0, 0, 1, 1]: " + nn.predict(new double[]{0, 0, 0, 1, 1, 0, 0, 1, 1}));
System.out.println("Prediction for [0, 0, 0, 1, 1, 0, 1, 0, 0]: " + nn.predict(new double[]{0, 0, 0, 1, 1, 0, 1, 0, 0}));
System.out.println("Prediction for [0, 0, 0, 1, 1, 0, 1, 0, 1]: " + nn.predict(new double[]{0, 0, 0, 1, 1, 0, 1, 0, 1}));
System.out.println("Prediction for [0, 0, 0, 1, 1, 0, 1, 1, 0]: " + nn.predict(new double[]{0, 0, 0, 1, 1, 0, 1, 1, 0}));
System.out.println("Prediction for [0, 0, 0, 1, 1, 0, 1, 1, 1]: " + nn.predict(new double[]{0, 0, 0, 1, 1, 0, 1, 1, 1}));
System.out.println("Prediction for [0, 0, 0, 1, 1, 1, 0, 0, 0]: " + nn.predict(new double[]{0, 0, 0, 1, 1, 1, 0, 0, 0}));
System.out.println("Prediction for [0, 0, 0, 1, 1, 1, 0, 0, 1]: " + nn.predict(new double[]{0, 0, 0, 1, 1, 1, 0, 0, 1}));
System.out.println("Prediction for [0, 0, 0, 1, 1, 1, 0, 1, 0]: " + nn.predict(new double[]{0, 0, 0, 1, 1, 1, 0, 1, 0}));
System.out.println("Prediction for [0, 0, 0, 1, 1, 1, 0, 1, 1]: " + nn.predict(new double[]{0, 0, 0, 1, 1, 1, 0, 1, 1}));
System.out.println("Prediction for [0, 0, 0, 1, 1, 1, 1, 0, 0]: " + nn.predict(new double[]{0, 0, 0, 1, 1, 1, 1, 0, 0}));
System.out.println("Prediction for [0, 0, 0, 1, 1, 1, 1, 0, 1]: " + nn.predict(new double[]{0, 0, 0, 1, 1, 1, 1, 0, 1}));
System.out.println("Prediction for [0, 0, 0, 1, 1, 1, 1, 1, 0]: " + nn.predict(new double[]{0, 0, 0, 1, 1, 1, 1, 1, 0}));
System.out.println("Prediction for [0, 0, 0, 1, 1, 1, 1, 1, 1]: " + nn.predict(new double[]{0, 0, 0, 1, 1, 1, 1, 1, 1}));
System.out.println("Prediction for [0, 0, 1, 0, 0, 0, 0, 0, 0]: " + nn.predict(new double[]{0, 0, 1, 0, 0, 0, 0, 0, 0}));
System.out.println("Prediction for [0, 0, 1, 0, 0, 0, 0, 0, 1]: " + nn.predict(new double[]{0, 0, 1, 0, 0, 0, 0, 0, 1}));
System.out.println("Prediction for [0, 0, 1, 0, 0, 0, 0, 1, 0]: " + nn.predict(new double[]{0, 0, 1, 0, 0, 0, 0, 1, 0}));
System.out.println("Prediction for [0, 0, 1, 0, 0, 0, 0, 1, 1]: " + nn.predict(new double[]{0, 0, 1, 0, 0, 0, 0, 1, 1}));
System.out.println("Prediction for [0, 0, 1, 0, 0, 0, 1, 0, 0]: " + nn.predict(new double[]{0, 0, 1, 0, 0, 0, 1, 0, 0}));
System.out.println("Prediction for [0, 0, 1, 0, 0, 0, 1, 0, 1]: " + nn.predict(new double[]{0, 0, 1, 0, 0, 0, 1, 0, 1}));
System.out.println("Prediction for [0, 0, 1, 0, 0, 0, 1, 1, 0]: " + nn.predict(new double[]{0, 0, 1, 0, 0, 0, 1, 1, 0}));
System.out.println("Prediction for [0, 0, 1, 0, 0, 0, 1, 1, 1]: " + nn.predict(new double[]{0, 0, 1, 0, 0, 0, 1, 1, 1}));
System.out.println("Prediction for [0, 0, 1, 0, 0, 1, 0, 0, 0]: " + nn.predict(new double[]{0, 0, 1, 0, 0, 1, 0, 0, 0}));
System.out.println("Prediction for [0, 0, 1, 0, 0, 1, 0, 0, 1]: " + nn.predict(new double[]{0, 0, 1, 0, 0, 1, 0, 0, 1}));
System.out.println("Prediction for [0, 0, 1, 0, 0, 1, 0, 1, 0]: " + nn.predict(new double[]{0, 0, 1, 0, 0, 1, 0, 1, 0}));
System.out.println("Prediction for [0, 0, 1, 0, 0, 1, 0, 1, 1]: " + nn.predict(new double[]{0, 0, 1, 0, 0, 1, 0, 1, 1}));
System.out.println("Prediction for [0, 0, 1, 0, 0, 1, 1, 0, 0]: " + nn.predict(new double[]{0, 0, 1, 0, 0, 1, 1, 0, 0}));
System.out.println("Prediction for [0, 0, 1, 0, 0, 1, 1, 0, 1]: " + nn.predict(new double[]{0, 0, 1, 0, 0, 1, 1, 0, 1}));
System.out.println("Prediction for [0, 0, 1, 0, 0, 1, 1, 1, 0]: " + nn.predict(new double[]{0, 0, 1, 0, 0, 1, 1, 1, 0}));
System.out.println("Prediction for [0, 0, 1, 0, 0, 1, 1, 1, 1]: " + nn.predict(new double[]{0, 0, 1, 0, 0, 1, 1, 1, 1}));
System.out.println("Prediction for [0, 0, 1, 0, 1, 0, 0, 0, 0]: " + nn.predict(new double[]{0, 0, 1, 0, 1, 0, 0, 0, 0}));
System.out.println("Prediction for [0, 0, 1, 0, 1, 0, 0, 0, 1]: " + nn.predict(new double[]{0, 0, 1, 0, 1, 0, 0, 0, 1}));
System.out.println("Prediction for [0, 0, 1, 0, 1, 0, 0, 1, 0]: " + nn.predict(new double[]{0, 0, 1, 0, 1, 0, 0, 1, 0}));
System.out.println("Prediction for [0, 0, 1, 0, 1, 0, 0, 1, 1]: " + nn.predict(new double[]{0, 0, 1, 0, 1, 0, 0, 1, 1}));
System.out.println("Prediction for [0, 0, 1, 0, 1, 0, 1, 0, 0]: " + nn.predict(new double[]{0, 0, 1, 0, 1, 0, 1, 0, 0}));
System.out.println("Prediction for [0, 0, 1, 0, 1, 0, 1, 0, 1]: " + nn.predict(new double[]{0, 0, 1, 0, 1, 0, 1, 0, 1}));
System.out.println("Prediction for [0, 0, 1, 0, 1, 0, 1, 1, 0]: " + nn.predict(new double[]{0, 0, 1, 0, 1, 0, 1, 1, 0}));
System.out.println("Prediction for [0, 0, 1, 0, 1, 0, 1, 1, 1]: " + nn.predict(new double[]{0, 0, 1, 0, 1, 0, 1, 1, 1}));
System.out.println("Prediction for [0, 0, 1, 0, 1, 1, 0, 0, 0]: " + nn.predict(new double[]{0, 0, 1, 0, 1, 1, 0, 0, 0}));
System.out.println("Prediction for [0, 0, 1, 0, 1, 1, 0, 0, 1]: " + nn.predict(new double[]{0, 0, 1, 0, 1, 1, 0, 0, 1}));
System.out.println("Prediction for [0, 0, 1, 0, 1, 1, 0, 1, 0]: " + nn.predict(new double[]{0, 0, 1, 0, 1, 1, 0, 1, 0}));
System.out.println("Prediction for [0, 0, 1, 0, 1, 1, 0, 1, 1]: " + nn.predict(new double[]{0, 0, 1, 0, 1, 1, 0, 1, 1}));
System.out.println("Prediction for [0, 0, 1, 0, 1, 1, 1, 0, 0]: " + nn.predict(new double[]{0, 0, 1, 0, 1, 1, 1, 0, 0}));
System.out.println("Prediction for [0, 0, 1, 0, 1, 1, 1, 0, 1]: " + nn.predict(new double[]{0, 0, 1, 0, 1, 1, 1, 0, 1}));
System.out.println("Prediction for [0, 0, 1, 0, 1, 1, 1, 1, 0]: " + nn.predict(new double[]{0, 0, 1, 0, 1, 1, 1, 1, 0}));
System.out.println("Prediction for [0, 0, 1, 0, 1, 1, 1, 1, 1]: " + nn.predict(new double[]{0, 0, 1, 0, 1, 1, 1, 1, 1}));
System.out.println("Prediction for [0, 0, 1, 1, 0, 0, 0, 0, 0]: " + nn.predict(new double[]{0, 0, 1, 1, 0, 0, 0, 0, 0}));
System.out.println("Prediction for [0, 0, 1, 1, 0, 0, 0, 0, 1]: " + nn.predict(new double[]{0, 0, 1, 1, 0, 0, 0, 0, 1}));
System.out.println("Prediction for [0, 0, 1, 1, 0, 0, 0, 1, 0]: " + nn.predict(new double[]{0, 0, 1, 1, 0, 0, 0, 1, 0}));
System.out.println("Prediction for [0, 0, 1, 1, 0, 0, 0, 1, 1]: " + nn.predict(new double[]{0, 0, 1, 1, 0, 0, 0, 1, 1}));
System.out.println("Prediction for [0, 0, 1, 1, 0, 0, 1, 0, 0]: " + nn.predict(new double[]{0, 0, 1, 1, 0, 0, 1, 0, 0}));
System.out.println("Prediction for [0, 0, 1, 1, 0, 0, 1, 0, 1]: " + nn.predict(new double[]{0, 0, 1, 1, 0, 0, 1, 0, 1}));
System.out.println("Prediction for [0, 0, 1, 1, 0, 0, 1, 1, 0]: " + nn.predict(new double[]{0, 0, 1, 1, 0, 0, 1, 1, 0}));
System.out.println("Prediction for [0, 0, 1, 1, 0, 0, 1, 1, 1]: " + nn.predict(new double[]{0, 0, 1, 1, 0, 0, 1, 1, 1}));
System.out.println("Prediction for [0, 0, 1, 1, 0, 1, 0, 0, 0]: " + nn.predict(new double[]{0, 0, 1, 1, 0, 1, 0, 0, 0}));
System.out.println("Prediction for [0, 0, 1, 1, 0, 1, 0, 0, 1]: " + nn.predict(new double[]{0, 0, 1, 1, 0, 1, 0, 0, 1}));
System.out.println("Prediction for [0, 0, 1, 1, 0, 1, 0, 1, 0]: " + nn.predict(new double[]{0, 0, 1, 1, 0, 1, 0, 1, 0}));
System.out.println("Prediction for [0, 0, 1, 1, 0, 1, 0, 1, 1]: " + nn.predict(new double[]{0, 0, 1, 1, 0, 1, 0, 1, 1}));
System.out.println("Prediction for [0, 0, 1, 1, 0, 1, 1, 0, 0]: " + nn.predict(new double[]{0, 0, 1, 1, 0, 1, 1, 0, 0}));
System.out.println("Prediction for [0, 0, 1, 1, 0, 1, 1, 0, 1]: " + nn.predict(new double[]{0, 0, 1, 1, 0, 1, 1, 0, 1}));
System.out.println("Prediction for [0, 0, 1, 1, 0, 1, 1, 1, 0]: " + nn.predict(new double[]{0, 0, 1, 1, 0, 1, 1, 1, 0}));
System.out.println("Prediction for [0, 0, 1, 1, 0, 1, 1, 1, 1]: " + nn.predict(new double[]{0, 0, 1, 1, 0, 1, 1, 1, 1}));
System.out.println("Prediction for [0, 0, 1, 1, 1, 0, 0, 0, 0]: " + nn.predict(new double[]{0, 0, 1, 1, 1, 0, 0, 0, 0}));
System.out.println("Prediction for [0, 0, 1, 1, 1, 0, 0, 0, 1]: " + nn.predict(new double[]{0, 0, 1, 1, 1, 0, 0, 0, 1}));
System.out.println("Prediction for [0, 0, 1, 1, 1, 0, 0, 1, 0]: " + nn.predict(new double[]{0, 0, 1, 1, 1, 0, 0, 1, 0}));
System.out.println("Prediction for [0, 0, 1, 1, 1, 0, 0, 1, 1]: " + nn.predict(new double[]{0, 0, 1, 1, 1, 0, 0, 1, 1}));
System.out.println("Prediction for [0, 0, 1, 1, 1, 0, 1, 0, 0]: " + nn.predict(new double[]{0, 0, 1, 1, 1, 0, 1, 0, 0}));
System.out.println("Prediction for [0, 0, 1, 1, 1, 0, 1, 0, 1]: " + nn.predict(new double[]{0, 0, 1, 1, 1, 0, 1, 0, 1}));
System.out.println("Prediction for [0, 0, 1, 1, 1, 0, 1, 1, 0]: " + nn.predict(new double[]{0, 0, 1, 1, 1, 0, 1, 1, 0}));
System.out.println("Prediction for [0, 0, 1, 1, 1, 0, 1, 1, 1]: " + nn.predict(new double[]{0, 0, 1, 1, 1, 0, 1, 1, 1}));
System.out.println("Prediction for [0, 0, 1, 1, 1, 1, 0, 0, 0]: " + nn.predict(new double[]{0, 0, 1, 1, 1, 1, 0, 0, 0}));
System.out.println("Prediction for [0, 0, 1, 1, 1, 1, 0, 0, 1]: " + nn.predict(new double[]{0, 0, 1, 1, 1, 1, 0, 0, 1}));
System.out.println("Prediction for [0, 0, 1, 1, 1, 1, 0, 1, 0]: " + nn.predict(new double[]{0, 0, 1, 1, 1, 1, 0, 1, 0}));
System.out.println("Prediction for [0, 0, 1, 1, 1, 1, 0, 1, 1]: " + nn.predict(new double[]{0, 0, 1, 1, 1, 1, 0, 1, 1}));
System.out.println("Prediction for [0, 0, 1, 1, 1, 1, 1, 0, 0]: " + nn.predict(new double[]{0, 0, 1, 1, 1, 1, 1, 0, 0}));
System.out.println("Prediction for [0, 0, 1, 1, 1, 1, 1, 0, 1]: " + nn.predict(new double[]{0, 0, 1, 1, 1, 1, 1, 0, 1}));
System.out.println("Prediction for [0, 0, 1, 1, 1, 1, 1, 1, 0]: " + nn.predict(new double[]{0, 0, 1, 1, 1, 1, 1, 1, 0}));
System.out.println("Prediction for [0, 0, 1, 1, 1, 1, 1, 1, 1]: " + nn.predict(new double[]{0, 0, 1, 1, 1, 1, 1, 1, 1}));
System.out.println("Prediction for [0, 1, 0, 0, 0, 0, 0, 0, 0]: " + nn.predict(new double[]{0, 1, 0, 0, 0, 0, 0, 0, 0}));
System.out.println("Prediction for [0, 1, 0, 0, 0, 0, 0, 0, 1]: " + nn.predict(new double[]{0, 1, 0, 0, 0, 0, 0, 0, 1}));
System.out.println("Prediction for [0, 1, 0, 0, 0, 0, 0, 1, 0]: " + nn.predict(new double[]{0, 1, 0, 0, 0, 0, 0, 1, 0}));
System.out.println("Prediction for [0, 1, 0, 0, 0, 0, 0, 1, 1]: " + nn.predict(new double[]{0, 1, 0, 0, 0, 0, 0, 1, 1}));
System.out.println("Prediction for [0, 1, 0, 0, 0, 0, 1, 0, 0]: " + nn.predict(new double[]{0, 1, 0, 0, 0, 0, 1, 0, 0}));
System.out.println("Prediction for [0, 1, 0, 0, 0, 0, 1, 0, 1]: " + nn.predict(new double[]{0, 1, 0, 0, 0, 0, 1, 0, 1}));
System.out.println("Prediction for [0, 1, 0, 0, 0, 0, 1, 1, 0]: " + nn.predict(new double[]{0, 1, 0, 0, 0, 0, 1, 1, 0}));
System.out.println("Prediction for [0, 1, 0, 0, 0, 0, 1, 1, 1]: " + nn.predict(new double[]{0, 1, 0, 0, 0, 0, 1, 1, 1}));
System.out.println("Prediction for [0, 1, 0, 0, 0, 1, 0, 0, 0]: " + nn.predict(new double[]{0, 1, 0, 0, 0, 1, 0, 0, 0}));
System.out.println("Prediction for [0, 1, 0, 0, 0, 1, 0, 0, 1]: " + nn.predict(new double[]{0, 1, 0, 0, 0, 1, 0, 0, 1}));
System.out.println("Prediction for [0, 1, 0, 0, 0, 1, 0, 1, 0]: " + nn.predict(new double[]{0, 1, 0, 0, 0, 1, 0, 1, 0}));
System.out.println("Prediction for [0, 1, 0, 0, 0, 1, 0, 1, 1]: " + nn.predict(new double[]{0, 1, 0, 0, 0, 1, 0, 1, 1}));
System.out.println("Prediction for [0, 1, 0, 0, 0, 1, 1, 0, 0]: " + nn.predict(new double[]{0, 1, 0, 0, 0, 1, 1, 0, 0}));
System.out.println("Prediction for [0, 1, 0, 0, 0, 1, 1, 0, 1]: " + nn.predict(new double[]{0, 1, 0, 0, 0, 1, 1, 0, 1}));
System.out.println("Prediction for [0, 1, 0, 0, 0, 1, 1, 1, 0]: " + nn.predict(new double[]{0, 1, 0, 0, 0, 1, 1, 1, 0}));
System.out.println("Prediction for [0, 1, 0, 0, 0, 1, 1, 1, 1]: " + nn.predict(new double[]{0, 1, 0, 0, 0, 1, 1, 1, 1}));
System.out.println("Prediction for [0, 1, 0, 0, 1, 0, 0, 0, 0]: " + nn.predict(new double[]{0, 1, 0, 0, 1, 0, 0, 0, 0}));
System.out.println("Prediction for [0, 1, 0, 0, 1, 0, 0, 0, 1]: " + nn.predict(new double[]{0, 1, 0, 0, 1, 0, 0, 0, 1}));
System.out.println("Prediction for [0, 1, 0, 0, 1, 0, 0, 1, 0]: " + nn.predict(new double[]{0, 1, 0, 0, 1, 0, 0, 1, 0}));
System.out.println("Prediction for [0, 1, 0, 0, 1, 0, 0, 1, 1]: " + nn.predict(new double[]{0, 1, 0, 0, 1, 0, 0, 1, 1}));
System.out.println("Prediction for [0, 1, 0, 0, 1, 0, 1, 0, 0]: " + nn.predict(new double[]{0, 1, 0, 0, 1, 0, 1, 0, 0}));
System.out.println("Prediction for [0, 1, 0, 0, 1, 0, 1, 0, 1]: " + nn.predict(new double[]{0, 1, 0, 0, 1, 0, 1, 0, 1}));
System.out.println("Prediction for [0, 1, 0, 0, 1, 0, 1, 1, 0]: " + nn.predict(new double[]{0, 1, 0, 0, 1, 0, 1, 1, 0}));
System.out.println("Prediction for [0, 1, 0, 0, 1, 0, 1, 1, 1]: " + nn.predict(new double[]{0, 1, 0, 0, 1, 0, 1, 1, 1}));
System.out.println("Prediction for [0, 1, 0, 0, 1, 1, 0, 0, 0]: " + nn.predict(new double[]{0, 1, 0, 0, 1, 1, 0, 0, 0}));
System.out.println("Prediction for [0, 1, 0, 0, 1, 1, 0, 0, 1]: " + nn.predict(new double[]{0, 1, 0, 0, 1, 1, 0, 0, 1}));
System.out.println("Prediction for [0, 1, 0, 0, 1, 1, 0, 1, 0]: " + nn.predict(new double[]{0, 1, 0, 0, 1, 1, 0, 1, 0}));
System.out.println("Prediction for [0, 1, 0, 0, 1, 1, 0, 1, 1]: " + nn.predict(new double[]{0, 1, 0, 0, 1, 1, 0, 1, 1}));
System.out.println("Prediction for [0, 1, 0, 0, 1, 1, 1, 0, 0]: " + nn.predict(new double[]{0, 1, 0, 0, 1, 1, 1, 0, 0}));
System.out.println("Prediction for [0, 1, 0, 0, 1, 1, 1, 0, 1]: " + nn.predict(new double[]{0, 1, 0, 0, 1, 1, 1, 0, 1}));
System.out.println("Prediction for [0, 1, 0, 0, 1, 1, 1, 1, 0]: " + nn.predict(new double[]{0, 1, 0, 0, 1, 1, 1, 1, 0}));
System.out.println("Prediction for [0, 1, 0, 0, 1, 1, 1, 1, 1]: " + nn.predict(new double[]{0, 1, 0, 0, 1, 1, 1, 1, 1}));
System.out.println("Prediction for [0, 1, 0, 1, 0, 0, 0, 0, 0]: " + nn.predict(new double[]{0, 1, 0, 1, 0, 0, 0, 0, 0}));
System.out.println("Prediction for [0, 1, 0, 1, 0, 0, 0, 0, 1]: " + nn.predict(new double[]{0, 1, 0, 1, 0, 0, 0, 0, 1}));
System.out.println("Prediction for [0, 1, 0, 1, 0, 0, 0, 1, 0]: " + nn.predict(new double[]{0, 1, 0, 1, 0, 0, 0, 1, 0}));
System.out.println("Prediction for [0, 1, 0, 1, 0, 0, 0, 1, 1]: " + nn.predict(new double[]{0, 1, 0, 1, 0, 0, 0, 1, 1}));
System.out.println("Prediction for [0, 1, 0, 1, 0, 0, 1, 0, 0]: " + nn.predict(new double[]{0, 1, 0, 1, 0, 0, 1, 0, 0}));
System.out.println("Prediction for [0, 1, 0, 1, 0, 0, 1, 0, 1]: " + nn.predict(new double[]{0, 1, 0, 1, 0, 0, 1, 0, 1}));
System.out.println("Prediction for [0, 1, 0, 1, 0, 0, 1, 1, 0]: " + nn.predict(new double[]{0, 1, 0, 1, 0, 0, 1, 1, 0}));
System.out.println("Prediction for [0, 1, 0, 1, 0, 0, 1, 1, 1]: " + nn.predict(new double[]{0, 1, 0, 1, 0, 0, 1, 1, 1}));
System.out.println("Prediction for [0, 1, 0, 1, 0, 1, 0, 0, 0]: " + nn.predict(new double[]{0, 1, 0, 1, 0, 1, 0, 0, 0}));
System.out.println("Prediction for [0, 1, 0, 1, 0, 1, 0, 0, 1]: " + nn.predict(new double[]{0, 1, 0, 1, 0, 1, 0, 0, 1}));
System.out.println("Prediction for [0, 1, 0, 1, 0, 1, 0, 1, 0]: " + nn.predict(new double[]{0, 1, 0, 1, 0, 1, 0, 1, 0}));
System.out.println("Prediction for [0, 1, 0, 1, 0, 1, 0, 1, 1]: " + nn.predict(new double[]{0, 1, 0, 1, 0, 1, 0, 1, 1}));
System.out.println("Prediction for [0, 1, 0, 1, 0, 1, 1, 0, 0]: " + nn.predict(new double[]{0, 1, 0, 1, 0, 1, 1, 0, 0}));
System.out.println("Prediction for [0, 1, 0, 1, 0, 1, 1, 0, 1]: " + nn.predict(new double[]{0, 1, 0, 1, 0, 1, 1, 0, 1}));
System.out.println("Prediction for [0, 1, 0, 1, 0, 1, 1, 1, 0]: " + nn.predict(new double[]{0, 1, 0, 1, 0, 1, 1, 1, 0}));
System.out.println("Prediction for [0, 1, 0, 1, 0, 1, 1, 1, 1]: " + nn.predict(new double[]{0, 1, 0, 1, 0, 1, 1, 1, 1}));
System.out.println("Prediction for [0, 1, 0, 1, 1, 0, 0, 0, 0]: " + nn.predict(new double[]{0, 1, 0, 1, 1, 0, 0, 0, 0}));
System.out.println("Prediction for [0, 1, 0, 1, 1, 0, 0, 0, 1]: " + nn.predict(new double[]{0, 1, 0, 1, 1, 0, 0, 0, 1}));
System.out.println("Prediction for [0, 1, 0, 1, 1, 0, 0, 1, 0]: " + nn.predict(new double[]{0, 1, 0, 1, 1, 0, 0, 1, 0}));
System.out.println("Prediction for [0, 1, 0, 1, 1, 0, 0, 1, 1]: " + nn.predict(new double[]{0, 1, 0, 1, 1, 0, 0, 1, 1}));
System.out.println("Prediction for [0, 1, 0, 1, 1, 0, 1, 0, 0]: " + nn.predict(new double[]{0, 1, 0, 1, 1, 0, 1, 0, 0}));
System.out.println("Prediction for [0, 1, 0, 1, 1, 0, 1, 0, 1]: " + nn.predict(new double[]{0, 1, 0, 1, 1, 0, 1, 0, 1}));
System.out.println("Prediction for [0, 1, 0, 1, 1, 0, 1, 1, 0]: " + nn.predict(new double[]{0, 1, 0, 1, 1, 0, 1, 1, 0}));
System.out.println("Prediction for [0, 1, 0, 1, 1, 0, 1, 1, 1]: " + nn.predict(new double[]{0, 1, 0, 1, 1, 0, 1, 1, 1}));
System.out.println("Prediction for [0, 1, 0, 1, 1, 1, 0, 0, 0]: " + nn.predict(new double[]{0, 1, 0, 1, 1, 1, 0, 0, 0}));
System.out.println("Prediction for [0, 1, 0, 1, 1, 1, 0, 0, 1]: " + nn.predict(new double[]{0, 1, 0, 1, 1, 1, 0, 0, 1}));
System.out.println("Prediction for [0, 1, 0, 1, 1, 1, 0, 1, 0]: " + nn.predict(new double[]{0, 1, 0, 1, 1, 1, 0, 1, 0}));
System.out.println("Prediction for [0, 1, 0, 1, 1, 1, 0, 1, 1]: " + nn.predict(new double[]{0, 1, 0, 1, 1, 1, 0, 1, 1}));
System.out.println("Prediction for [0, 1, 0, 1, 1, 1, 1, 0, 0]: " + nn.predict(new double[]{0, 1, 0, 1, 1, 1, 1, 0, 0}));
System.out.println("Prediction for [0, 1, 0, 1, 1, 1, 1, 0, 1]: " + nn.predict(new double[]{0, 1, 0, 1, 1, 1, 1, 0, 1}));
System.out.println("Prediction for [0, 1, 0, 1, 1, 1, 1, 1, 0]: " + nn.predict(new double[]{0, 1, 0, 1, 1, 1, 1, 1, 0}));
System.out.println("Prediction for [0, 1, 0, 1, 1, 1, 1, 1, 1]: " + nn.predict(new double[]{0, 1, 0, 1, 1, 1, 1, 1, 1}));
System.out.println("Prediction for [0, 1, 1, 0, 0, 0, 0, 0, 0]: " + nn.predict(new double[]{0, 1, 1, 0, 0, 0, 0, 0, 0}));
System.out.println("Prediction for [0, 1, 1, 0, 0, 0, 0, 0, 1]: " + nn.predict(new double[]{0, 1, 1, 0, 0, 0, 0, 0, 1}));
System.out.println("Prediction for [0, 1, 1, 0, 0, 0, 0, 1, 0]: " + nn.predict(new double[]{0, 1, 1, 0, 0, 0, 0, 1, 0}));
System.out.println("Prediction for [0, 1, 1, 0, 0, 0, 0, 1, 1]: " + nn.predict(new double[]{0, 1, 1, 0, 0, 0, 0, 1, 1}));
System.out.println("Prediction for [0, 1, 1, 0, 0, 0, 1, 0, 0]: " + nn.predict(new double[]{0, 1, 1, 0, 0, 0, 1, 0, 0}));
System.out.println("Prediction for [0, 1, 1, 0, 0, 0, 1, 0, 1]: " + nn.predict(new double[]{0, 1, 1, 0, 0, 0, 1, 0, 1}));
System.out.println("Prediction for [0, 1, 1, 0, 0, 0, 1, 1, 0]: " + nn.predict(new double[]{0, 1, 1, 0, 0, 0, 1, 1, 0}));
System.out.println("Prediction for [0, 1, 1, 0, 0, 0, 1, 1, 1]: " + nn.predict(new double[]{0, 1, 1, 0, 0, 0, 1, 1, 1}));
System.out.println("Prediction for [0, 1, 1, 0, 0, 1, 0, 0, 0]: " + nn.predict(new double[]{0, 1, 1, 0, 0, 1, 0, 0, 0}));
System.out.println("Prediction for [0, 1, 1, 0, 0, 1, 0, 0, 1]: " + nn.predict(new double[]{0, 1, 1, 0, 0, 1, 0, 0, 1}));
System.out.println("Prediction for [0, 1, 1, 0, 0, 1, 0, 1, 0]: " + nn.predict(new double[]{0, 1, 1, 0, 0, 1, 0, 1, 0}));
System.out.println("Prediction for [0, 1, 1, 0, 0, 1, 0, 1, 1]: " + nn.predict(new double[]{0, 1, 1, 0, 0, 1, 0, 1, 1}));
System.out.println("Prediction for [0, 1, 1, 0, 0, 1, 1, 0, 0]: " + nn.predict(new double[]{0, 1, 1, 0, 0, 1, 1, 0, 0}));
System.out.println("Prediction for [0, 1, 1, 0, 0, 1, 1, 0, 1]: " + nn.predict(new double[]{0, 1, 1, 0, 0, 1, 1, 0, 1}));
System.out.println("Prediction for [0, 1, 1, 0, 0, 1, 1, 1, 0]: " + nn.predict(new double[]{0, 1, 1, 0, 0, 1, 1, 1, 0}));
System.out.println("Prediction for [0, 1, 1, 0, 0, 1, 1, 1, 1]: " + nn.predict(new double[]{0, 1, 1, 0, 0, 1, 1, 1, 1}));
System.out.println("Prediction for [0, 1, 1, 0, 1, 0, 0, 0, 0]: " + nn.predict(new double[]{0, 1, 1, 0, 1, 0, 0, 0, 0}));
System.out.println("Prediction for [0, 1, 1, 0, 1, 0, 0, 0, 1]: " + nn.predict(new double[]{0, 1, 1, 0, 1, 0, 0, 0, 1}));
System.out.println("Prediction for [0, 1, 1, 0, 1, 0, 0, 1, 0]: " + nn.predict(new double[]{0, 1, 1, 0, 1, 0, 0, 1, 0}));
System.out.println("Prediction for [0, 1, 1, 0, 1, 0, 0, 1, 1]: " + nn.predict(new double[]{0, 1, 1, 0, 1, 0, 0, 1, 1}));
System.out.println("Prediction for [0, 1, 1, 0, 1, 0, 1, 0, 0]: " + nn.predict(new double[]{0, 1, 1, 0, 1, 0, 1, 0, 0}));
System.out.println("Prediction for [0, 1, 1, 0, 1, 0, 1, 0, 1]: " + nn.predict(new double[]{0, 1, 1, 0, 1, 0, 1, 0, 1}));
System.out.println("Prediction for [0, 1, 1, 0, 1, 0, 1, 1, 0]: " + nn.predict(new double[]{0, 1, 1, 0, 1, 0, 1, 1, 0}));
System.out.println("Prediction for [0, 1, 1, 0, 1, 0, 1, 1, 1]: " + nn.predict(new double[]{0, 1, 1, 0, 1, 0, 1, 1, 1}));
System.out.println("Prediction for [0, 1, 1, 0, 1, 1, 0, 0, 0]: " + nn.predict(new double[]{0, 1, 1, 0, 1, 1, 0, 0, 0}));
System.out.println("Prediction for [0, 1, 1, 0, 1, 1, 0, 0, 1]: " + nn.predict(new double[]{0, 1, 1, 0, 1, 1, 0, 0, 1}));
System.out.println("Prediction for [0, 1, 1, 0, 1, 1, 0, 1, 0]: " + nn.predict(new double[]{0, 1, 1, 0, 1, 1, 0, 1, 0}));
System.out.println("Prediction for [0, 1, 1, 0, 1, 1, 0, 1, 1]: " + nn.predict(new double[]{0, 1, 1, 0, 1, 1, 0, 1, 1}));
System.out.println("Prediction for [0, 1, 1, 0, 1, 1, 1, 0, 0]: " + nn.predict(new double[]{0, 1, 1, 0, 1, 1, 1, 0, 0}));
System.out.println("Prediction for [0, 1, 1, 0, 1, 1, 1, 0, 1]: " + nn.predict(new double[]{0, 1, 1, 0, 1, 1, 1, 0, 1}));
System.out.println("Prediction for [0, 1, 1, 0, 1, 1, 1, 1, 0]: " + nn.predict(new double[]{0, 1, 1, 0, 1, 1, 1, 1, 0}));
System.out.println("Prediction for [0, 1, 1, 0, 1, 1, 1, 1, 1]: " + nn.predict(new double[]{0, 1, 1, 0, 1, 1, 1, 1, 1}));
System.out.println("Prediction for [0, 1, 1, 1, 0, 0, 0, 0, 0]: " + nn.predict(new double[]{0, 1, 1, 1, 0, 0, 0, 0, 0}));
System.out.println("Prediction for [0, 1, 1, 1, 0, 0, 0, 0, 1]: " + nn.predict(new double[]{0, 1, 1, 1, 0, 0, 0, 0, 1}));
System.out.println("Prediction for [0, 1, 1, 1, 0, 0, 0, 1, 0]: " + nn.predict(new double[]{0, 1, 1, 1, 0, 0, 0, 1, 0}));
System.out.println("Prediction for [0, 1, 1, 1, 0, 0, 0, 1, 1]: " + nn.predict(new double[]{0, 1, 1, 1, 0, 0, 0, 1, 1}));
System.out.println("Prediction for [0, 1, 1, 1, 0, 0, 1, 0, 0]: " + nn.predict(new double[]{0, 1, 1, 1, 0, 0, 1, 0, 0}));
System.out.println("Prediction for [0, 1, 1, 1, 0, 0, 1, 0, 1]: " + nn.predict(new double[]{0, 1, 1, 1, 0, 0, 1, 0, 1}));
System.out.println("Prediction for [0, 1, 1, 1, 0, 0, 1, 1, 0]: " + nn.predict(new double[]{0, 1, 1, 1, 0, 0, 1, 1, 0}));
System.out.println("Prediction for [0, 1, 1, 1, 0, 0, 1, 1, 1]: " + nn.predict(new double[]{0, 1, 1, 1, 0, 0, 1, 1, 1}));
System.out.println("Prediction for [0, 1, 1, 1, 0, 1, 0, 0, 0]: " + nn.predict(new double[]{0, 1, 1, 1, 0, 1, 0, 0, 0}));
System.out.println("Prediction for [0, 1, 1, 1, 0, 1, 0, 0, 1]: " + nn.predict(new double[]{0, 1, 1, 1, 0, 1, 0, 0, 1}));
System.out.println("Prediction for [0, 1, 1, 1, 0, 1, 0, 1, 0]: " + nn.predict(new double[]{0, 1, 1, 1, 0, 1, 0, 1, 0}));
System.out.println("Prediction for [0, 1, 1, 1, 0, 1, 0, 1, 1]: " + nn.predict(new double[]{0, 1, 1, 1, 0, 1, 0, 1, 1}));
System.out.println("Prediction for [0, 1, 1, 1, 0, 1, 1, 0, 0]: " + nn.predict(new double[]{0, 1, 1, 1, 0, 1, 1, 0, 0}));
System.out.println("Prediction for [0, 1, 1, 1, 0, 1, 1, 0, 1]: " + nn.predict(new double[]{0, 1, 1, 1, 0, 1, 1, 0, 1}));
System.out.println("Prediction for [0, 1, 1, 1, 0, 1, 1, 1, 0]: " + nn.predict(new double[]{0, 1, 1, 1, 0, 1, 1, 1, 0}));
System.out.println("Prediction for [0, 1, 1, 1, 0, 1, 1, 1, 1]: " + nn.predict(new double[]{0, 1, 1, 1, 0, 1, 1, 1, 1}));
System.out.println("Prediction for [0, 1, 1, 1, 1, 0, 0, 0, 0]: " + nn.predict(new double[]{0, 1, 1, 1, 1, 0, 0, 0, 0}));
System.out.println("Prediction for [0, 1, 1, 1, 1, 0, 0, 0, 1]: " + nn.predict(new double[]{0, 1, 1, 1, 1, 0, 0, 0, 1}));
System.out.println("Prediction for [0, 1, 1, 1, 1, 0, 0, 1, 0]: " + nn.predict(new double[]{0, 1, 1, 1, 1, 0, 0, 1, 0}));
System.out.println("Prediction for [0, 1, 1, 1, 1, 0, 0, 1, 1]: " + nn.predict(new double[]{0, 1, 1, 1, 1, 0, 0, 1, 1}));
System.out.println("Prediction for [0, 1, 1, 1, 1, 0, 1, 0, 0]: " + nn.predict(new double[]{0, 1, 1, 1, 1, 0, 1, 0, 0}));
System.out.println("Prediction for [0, 1, 1, 1, 1, 0, 1, 0, 1]: " + nn.predict(new double[]{0, 1, 1, 1, 1, 0, 1, 0, 1}));
System.out.println("Prediction for [0, 1, 1, 1, 1, 0, 1, 1, 0]: " + nn.predict(new double[]{0, 1, 1, 1, 1, 0, 1, 1, 0}));
System.out.println("Prediction for [0, 1, 1, 1, 1, 0, 1, 1, 1]: " + nn.predict(new double[]{0, 1, 1, 1, 1, 0, 1, 1, 1}));
System.out.println("Prediction for [0, 1, 1, 1, 1, 1, 0, 0, 0]: " + nn.predict(new double[]{0, 1, 1, 1, 1, 1, 0, 0, 0}));
System.out.println("Prediction for [0, 1, 1, 1, 1, 1, 0, 0, 1]: " + nn.predict(new double[]{0, 1, 1, 1, 1, 1, 0, 0, 1}));
System.out.println("Prediction for [0, 1, 1, 1, 1, 1, 0, 1, 0]: " + nn.predict(new double[]{0, 1, 1, 1, 1, 1, 0, 1, 0}));
System.out.println("Prediction for [0, 1, 1, 1, 1, 1, 0, 1, 1]: " + nn.predict(new double[]{0, 1, 1, 1, 1, 1, 0, 1, 1}));
System.out.println("Prediction for [0, 1, 1, 1, 1, 1, 1, 0, 0]: " + nn.predict(new double[]{0, 1, 1, 1, 1, 1, 1, 0, 0}));
System.out.println("Prediction for [0, 1, 1, 1, 1, 1, 1, 0, 1]: " + nn.predict(new double[]{0, 1, 1, 1, 1, 1, 1, 0, 1}));
System.out.println("Prediction for [0, 1, 1, 1, 1, 1, 1, 1, 0]: " + nn.predict(new double[]{0, 1, 1, 1, 1, 1, 1, 1, 0}));
System.out.println("Prediction for [0, 1, 1, 1, 1, 1, 1, 1, 1]: " + nn.predict(new double[]{0, 1, 1, 1, 1, 1, 1, 1, 1}));
System.out.println("Prediction for [1, 0, 0, 0, 0, 0, 0, 0, 0]: " + nn.predict(new double[]{1, 0, 0, 0, 0, 0, 0, 0, 0}));
System.out.println("Prediction for [1, 0, 0, 0, 0, 0, 0, 0, 1]: " + nn.predict(new double[]{1, 0, 0, 0, 0, 0, 0, 0, 1}));
System.out.println("Prediction for [1, 0, 0, 0, 0, 0, 0, 1, 0]: " + nn.predict(new double[]{1, 0, 0, 0, 0, 0, 0, 1, 0}));
System.out.println("Prediction for [1, 0, 0, 0, 0, 0, 0, 1, 1]: " + nn.predict(new double[]{1, 0, 0, 0, 0, 0, 0, 1, 1}));
System.out.println("Prediction for [1, 0, 0, 0, 0, 0, 1, 0, 0]: " + nn.predict(new double[]{1, 0, 0, 0, 0, 0, 1, 0, 0}));
System.out.println("Prediction for [1, 0, 0, 0, 0, 0, 1, 0, 1]: " + nn.predict(new double[]{1, 0, 0, 0, 0, 0, 1, 0, 1}));
System.out.println("Prediction for [1, 0, 0, 0, 0, 0, 1, 1, 0]: " + nn.predict(new double[]{1, 0, 0, 0, 0, 0, 1, 1, 0}));
System.out.println("Prediction for [1, 0, 0, 0, 0, 0, 1, 1, 1]: " + nn.predict(new double[]{1, 0, 0, 0, 0, 0, 1, 1, 1}));
System.out.println("Prediction for [1, 0, 0, 0, 0, 1, 0, 0, 0]: " + nn.predict(new double[]{1, 0, 0, 0, 0, 1, 0, 0, 0}));
System.out.println("Prediction for [1, 0, 0, 0, 0, 1, 0, 0, 1]: " + nn.predict(new double[]{1, 0, 0, 0, 0, 1, 0, 0, 1}));
System.out.println("Prediction for [1, 0, 0, 0, 0, 1, 0, 1, 0]: " + nn.predict(new double[]{1, 0, 0, 0, 0, 1, 0, 1, 0}));
System.out.println("Prediction for [1, 0, 0, 0, 0, 1, 0, 1, 1]: " + nn.predict(new double[]{1, 0, 0, 0, 0, 1, 0, 1, 1}));
System.out.println("Prediction for [1, 0, 0, 0, 0, 1, 1, 0, 0]: " + nn.predict(new double[]{1, 0, 0, 0, 0, 1, 1, 0, 0}));
System.out.println("Prediction for [1, 0, 0, 0, 0, 1, 1, 0, 1]: " + nn.predict(new double[]{1, 0, 0, 0, 0, 1, 1, 0, 1}));
System.out.println("Prediction for [1, 0, 0, 0, 0, 1, 1, 1, 0]: " + nn.predict(new double[]{1, 0, 0, 0, 0, 1, 1, 1, 0}));
System.out.println("Prediction for [1, 0, 0, 0, 0, 1, 1, 1, 1]: " + nn.predict(new double[]{1, 0, 0, 0, 0, 1, 1, 1, 1}));
System.out.println("Prediction for [1, 0, 0, 0, 1, 0, 0, 0, 0]: " + nn.predict(new double[]{1, 0, 0, 0, 1, 0, 0, 0, 0}));
System.out.println("Prediction for [1, 0, 0, 0, 1, 0, 0, 0, 1]: " + nn.predict(new double[]{1, 0, 0, 0, 1, 0, 0, 0, 1}));
System.out.println("Prediction for [1, 0, 0, 0, 1, 0, 0, 1, 0]: " + nn.predict(new double[]{1, 0, 0, 0, 1, 0, 0, 1, 0}));
System.out.println("Prediction for [1, 0, 0, 0, 1, 0, 0, 1, 1]: " + nn.predict(new double[]{1, 0, 0, 0, 1, 0, 0, 1, 1}));
System.out.println("Prediction for [1, 0, 0, 0, 1, 0, 1, 0, 0]: " + nn.predict(new double[]{1, 0, 0, 0, 1, 0, 1, 0, 0}));
System.out.println("Prediction for [1, 0, 0, 0, 1, 0, 1, 0, 1]: " + nn.predict(new double[]{1, 0, 0, 0, 1, 0, 1, 0, 1}));
System.out.println("Prediction for [1, 0, 0, 0, 1, 0, 1, 1, 0]: " + nn.predict(new double[]{1, 0, 0, 0, 1, 0, 1, 1, 0}));
System.out.println("Prediction for [1, 0, 0, 0, 1, 0, 1, 1, 1]: " + nn.predict(new double[]{1, 0, 0, 0, 1, 0, 1, 1, 1}));   
System.out.println("Prediction for [1, 0, 0, 0, 1, 1, 0, 0, 0]: " + nn.predict(new double[]{1, 0, 0, 0, 1, 1, 0, 0, 0}));
System.out.println("Prediction for [1, 0, 0, 0, 1, 1, 0, 0, 1]: " + nn.predict(new double[]{1, 0, 0, 0, 1, 1, 0, 0, 1}));
System.out.println("Prediction for [1, 0, 0, 0, 1, 1, 0, 1, 0]: " + nn.predict(new double[]{1, 0, 0, 0, 1, 1, 0, 1, 0}));
System.out.println("Prediction for [1, 0, 0, 0, 1, 1, 0, 1, 1]: " + nn.predict(new double[]{1, 0, 0, 0, 1, 1, 0, 1, 1}));
System.out.println("Prediction for [1, 0, 0, 0, 1, 1, 1, 0, 0]: " + nn.predict(new double[]{1, 0, 0, 0, 1, 1, 1, 0, 0}));
System.out.println("Prediction for [1, 0, 0, 0, 1, 1, 1, 0, 1]: " + nn.predict(new double[]{1, 0, 0, 0, 1, 1, 1, 0, 1}));
System.out.println("Prediction for [1, 0, 0, 0, 1, 1, 1, 1, 0]: " + nn.predict(new double[]{1, 0, 0, 0, 1, 1, 1, 1, 0}));
System.out.println("Prediction for [1, 0, 0, 0, 1, 1, 1, 1, 1]: " + nn.predict(new double[]{1, 0, 0, 0, 1, 1, 1, 1, 1}));
System.out.println("Prediction for [1, 0, 0, 1, 0, 0, 0, 0, 0]: " + nn.predict(new double[]{1, 0, 0, 1, 0, 0, 0, 0, 0}));
System.out.println("Prediction for [1, 0, 0, 1, 0, 0, 0, 0, 1]: " + nn.predict(new double[]{1, 0, 0, 1, 0, 0, 0, 0, 1}));
System.out.println("Prediction for [1, 0, 0, 1, 0, 0, 0, 1, 0]: " + nn.predict(new double[]{1, 0, 0, 1, 0, 0, 0, 1, 0}));
System.out.println("Prediction for [1, 0, 0, 1, 0, 0, 0, 1, 1]: " + nn.predict(new double[]{1, 0, 0, 1, 0, 0, 0, 1, 1}));
System.out.println("Prediction for [1, 0, 0, 1, 0, 0, 1, 0, 0]: " + nn.predict(new double[]{1, 0, 0, 1, 0, 0, 1, 0, 0}));
System.out.println("Prediction for [1, 0, 0, 1, 0, 0, 1, 0, 1]: " + nn.predict(new double[]{1, 0, 0, 1, 0, 0, 1, 0, 1}));
System.out.println("Prediction for [1, 0, 0, 1, 0, 0, 1, 1, 0]: " + nn.predict(new double[]{1, 0, 0, 1, 0, 0, 1, 1, 0}));
System.out.println("Prediction for [1, 0, 0, 1, 0, 0, 1, 1, 1]: " + nn.predict(new double[]{1, 0, 0, 1, 0, 0, 1, 1, 1}));
System.out.println("Prediction for [1, 0, 0, 1, 0, 1, 0, 0, 0]: " + nn.predict(new double[]{1, 0, 0, 1, 0, 1, 0, 0, 0}));
System.out.println("Prediction for [1, 0, 0, 1, 0, 1, 0, 0, 1]: " + nn.predict(new double[]{1, 0, 0, 1, 0, 1, 0, 0, 1}));
System.out.println("Prediction for [1, 0, 0, 1, 0, 1, 0, 1, 0]: " + nn.predict(new double[]{1, 0, 0, 1, 0, 1, 0, 1, 0}));
System.out.println("Prediction for [1, 0, 0, 1, 0, 1, 0, 1, 1]: " + nn.predict(new double[]{1, 0, 0, 1, 0, 1, 0, 1, 1}));
System.out.println("Prediction for [1, 0, 0, 1, 0, 1, 1, 0, 0]: " + nn.predict(new double[]{1, 0, 0, 1, 0, 1, 1, 0, 0}));
System.out.println("Prediction for [1, 0, 0, 1, 0, 1, 1, 0, 1]: " + nn.predict(new double[]{1, 0, 0, 1, 0, 1, 1, 0, 1}));
System.out.println("Prediction for [1, 0, 0, 1, 0, 1, 1, 1, 0]: " + nn.predict(new double[]{1, 0, 0, 1, 0, 1, 1, 1, 0}));
System.out.println("Prediction for [1, 0, 0, 1, 0, 1, 1, 1, 1]: " + nn.predict(new double[]{1, 0, 0, 1, 0, 1, 1, 1, 1}));
System.out.println("Prediction for [1, 0, 0, 1, 1, 0, 0, 0, 0]: " + nn.predict(new double[]{1, 0, 0, 1, 1, 0, 0, 0, 0}));
System.out.println("Prediction for [1, 0, 0, 1, 1, 0, 0, 0, 1]: " + nn.predict(new double[]{1, 0, 0, 1, 1, 0, 0, 0, 1}));
System.out.println("Prediction for [1, 0, 0, 1, 1, 0, 0, 1, 0]: " + nn.predict(new double[]{1, 0, 0, 1, 1, 0, 0, 1, 0}));
System.out.println("Prediction for [1, 0, 0, 1, 1, 0, 0, 1, 1]: " + nn.predict(new double[]{1, 0, 0, 1, 1, 0, 0, 1, 1}));
System.out.println("Prediction for [1, 0, 0, 1, 1, 0, 1, 0, 0]: " + nn.predict(new double[]{1, 0, 0, 1, 1, 0, 1, 0, 0}));
System.out.println("Prediction for [1, 0, 0, 1, 1, 0, 1, 0, 1]: " + nn.predict(new double[]{1, 0, 0, 1, 1, 0, 1, 0, 1}));
System.out.println("Prediction for [1, 0, 0, 1, 1, 0, 1, 1, 0]: " + nn.predict(new double[]{1, 0, 0, 1, 1, 0, 1, 1, 0}));
System.out.println("Prediction for [1, 0, 0, 1, 1, 0, 1, 1, 1]: " + nn.predict(new double[]{1, 0, 0, 1, 1, 0, 1, 1, 1}));
System.out.println("Prediction for [1, 0, 0, 1, 1, 1, 0, 0, 0]: " + nn.predict(new double[]{1, 0, 0, 1, 1, 1, 0, 0, 0}));
System.out.println("Prediction for [1, 0, 0, 1, 1, 1, 0, 0, 1]: " + nn.predict(new double[]{1, 0, 0, 1, 1, 1, 0, 0, 1}));
System.out.println("Prediction for [1, 0, 0, 1, 1, 1, 0, 1, 0]: " + nn.predict(new double[]{1, 0, 0, 1, 1, 1, 0, 1, 0}));
System.out.println("Prediction for [1, 0, 0, 1, 1, 1, 0, 1, 1]: " + nn.predict(new double[]{1, 0, 0, 1, 1, 1, 0, 1, 1}));
System.out.println("Prediction for [1, 0, 0, 1, 1, 1, 1, 0, 0]: " + nn.predict(new double[]{1, 0, 0, 1, 1, 1, 1, 0, 0}));
System.out.println("Prediction for [1, 0, 0, 1, 1, 1, 1, 0, 1]: " + nn.predict(new double[]{1, 0, 0, 1, 1, 1, 1, 0, 1}));
System.out.println("Prediction for [1, 0, 0, 1, 1, 1, 1, 1, 0]: " + nn.predict(new double[]{1, 0, 0, 1, 1, 1, 1, 1, 0}));
System.out.println("Prediction for [1, 0, 0, 1, 1, 1, 1, 1, 1]: " + nn.predict(new double[]{1, 0, 0, 1, 1, 1, 1, 1, 1}));
System.out.println("Prediction for [1, 0, 1, 0, 0, 0, 0, 0, 0]: " + nn.predict(new double[]{1, 0, 1, 0, 0, 0, 0, 0, 0}));
System.out.println("Prediction for [1, 0, 1, 0, 0, 0, 0, 0, 1]: " + nn.predict(new double[]{1, 0, 1, 0, 0, 0, 0, 0, 1}));
System.out.println("Prediction for [1, 0, 1, 0, 0, 0, 0, 1, 0]: " + nn.predict(new double[]{1, 0, 1, 0, 0, 0, 0, 1, 0}));
System.out.println("Prediction for [1, 0, 1, 0, 0, 0, 0, 1, 1]: " + nn.predict(new double[]{1, 0, 1, 0, 0, 0, 0, 1, 1}));
System.out.println("Prediction for [1, 0, 1, 0, 0, 0, 1, 0, 0]: " + nn.predict(new double[]{1, 0, 1, 0, 0, 0, 1, 0, 0}));
System.out.println("Prediction for [1, 0, 1, 0, 0, 0, 1, 0, 1]: " + nn.predict(new double[]{1, 0, 1, 0, 0, 0, 1, 0, 1}));
System.out.println("Prediction for [1, 0, 1, 0, 0, 0, 1, 1, 0]: " + nn.predict(new double[]{1, 0, 1, 0, 0, 0, 1, 1, 0}));
System.out.println("Prediction for [1, 0, 1, 0, 0, 0, 1, 1, 1]: " + nn.predict(new double[]{1, 0, 1, 0, 0, 0, 1, 1, 1}));
System.out.println("Prediction for [1, 0, 1, 0, 0, 1, 0, 0, 0]: " + nn.predict(new double[]{1, 0, 1, 0, 0, 1, 0, 0, 0}));
System.out.println("Prediction for [1, 0, 1, 0, 0, 1, 0, 0, 1]: " + nn.predict(new double[]{1, 0, 1, 0, 0, 1, 0, 0, 1}));
System.out.println("Prediction for [1, 0, 1, 0, 0, 1, 0, 1, 0]: " + nn.predict(new double[]{1, 0, 1, 0, 0, 1, 0, 1, 0}));
System.out.println("Prediction for [1, 0, 1, 0, 0, 1, 0, 1, 1]: " + nn.predict(new double[]{1, 0, 1, 0, 0, 1, 0, 1, 1}));
System.out.println("Prediction for [1, 0, 1, 0, 0, 1, 1, 0, 0]: " + nn.predict(new double[]{1, 0, 1, 0, 0, 1, 1, 0, 0}));
System.out.println("Prediction for [1, 0, 1, 0, 0, 1, 1, 0, 1]: " + nn.predict(new double[]{1, 0, 1, 0, 0, 1, 1, 0, 1}));
System.out.println("Prediction for [1, 0, 1, 0, 0, 1, 1, 1, 0]: " + nn.predict(new double[]{1, 0, 1, 0, 0, 1, 1, 1, 0}));
System.out.println("Prediction for [1, 0, 1, 0, 0, 1, 1, 1, 1]: " + nn.predict(new double[]{1, 0, 1, 0, 0, 1, 1, 1, 1}));
System.out.println("Prediction for [1, 0, 1, 0, 1, 0, 0, 0, 0]: " + nn.predict(new double[]{1, 0, 1, 0, 1, 0, 0, 0, 0}));
System.out.println("Prediction for [1, 0, 1, 0, 1, 0, 0, 0, 1]: " + nn.predict(new double[]{1, 0, 1, 0, 1, 0, 0, 0, 1}));
System.out.println("Prediction for [1, 0, 1, 0, 1, 0, 0, 1, 0]: " + nn.predict(new double[]{1, 0, 1, 0, 1, 0, 0, 1, 0}));
System.out.println("Prediction for [1, 0, 1, 0, 1, 0, 0, 1, 1]: " + nn.predict(new double[]{1, 0, 1, 0, 1, 0, 0, 1, 1}));
System.out.println("Prediction for [1, 0, 1, 0, 1, 0, 1, 0, 0]: " + nn.predict(new double[]{1, 0, 1, 0, 1, 0, 1, 0, 0}));
System.out.println("Prediction for [1, 0, 1, 0, 1, 0, 1, 0, 1]: " + nn.predict(new double[]{1, 0, 1, 0, 1, 0, 1, 0, 1}));
System.out.println("Prediction for [1, 0, 1, 0, 1, 0, 1, 1, 0]: " + nn.predict(new double[]{1, 0, 1, 0, 1, 0, 1, 1, 0}));
System.out.println("Prediction for [1, 0, 1, 0, 1, 0, 1, 1, 1]: " + nn.predict(new double[]{1, 0, 1, 0, 1, 0, 1, 1, 1}));
System.out.println("Prediction for [1, 0, 1, 0, 1, 1, 0, 0, 0]: " + nn.predict(new double[]{1, 0, 1, 0, 1, 1, 0, 0, 0}));
System.out.println("Prediction for [1, 0, 1, 0, 1, 1, 0, 0, 1]: " + nn.predict(new double[]{1, 0, 1, 0, 1, 1, 0, 0, 1}));
System.out.println("Prediction for [1, 0, 1, 0, 1, 1, 0, 1, 0]: " + nn.predict(new double[]{1, 0, 1, 0, 1, 1, 0, 1, 0}));
System.out.println("Prediction for [1, 0, 1, 0, 1, 1, 0, 1, 1]: " + nn.predict(new double[]{1, 0, 1, 0, 1, 1, 0, 1, 1}));
System.out.println("Prediction for [1, 0, 1, 0, 1, 1, 1, 0, 0]: " + nn.predict(new double[]{1, 0, 1, 0, 1, 1, 1, 0, 0}));
System.out.println("Prediction for [1, 0, 1, 0, 1, 1, 1, 0, 1]: " + nn.predict(new double[]{1, 0, 1, 0, 1, 1, 1, 0, 1}));
System.out.println("Prediction for [1, 0, 1, 0, 1, 1, 1, 1, 0]: " + nn.predict(new double[]{1, 0, 1, 0, 1, 1, 1, 1, 0}));
System.out.println("Prediction for [1, 0, 1, 0, 1, 1, 1, 1, 1]: " + nn.predict(new double[]{1, 0, 1, 0, 1, 1, 1, 1, 1}));
System.out.println("Prediction for [1, 0, 1, 1, 0, 0, 0, 0, 0]: " + nn.predict(new double[]{1, 0, 1, 1, 0, 0, 0, 0, 0}));
System.out.println("Prediction for [1, 0, 1, 1, 0, 0, 0, 0, 1]: " + nn.predict(new double[]{1, 0, 1, 1, 0, 0, 0, 0, 1}));
System.out.println("Prediction for [1, 0, 1, 1, 0, 0, 0, 1, 0]: " + nn.predict(new double[]{1, 0, 1, 1, 0, 0, 0, 1, 0}));
System.out.println("Prediction for [1, 0, 1, 1, 0, 0, 0, 1, 1]: " + nn.predict(new double[]{1, 0, 1, 1, 0, 0, 0, 1, 1}));
System.out.println("Prediction for [1, 0, 1, 1, 0, 0, 1, 0, 0]: " + nn.predict(new double[]{1, 0, 1, 1, 0, 0, 1, 0, 0}));
System.out.println("Prediction for [1, 0, 1, 1, 0, 0, 1, 0, 1]: " + nn.predict(new double[]{1, 0, 1, 1, 0, 0, 1, 0, 1}));
System.out.println("Prediction for [1, 0, 1, 1, 0, 0, 1, 1, 0]: " + nn.predict(new double[]{1, 0, 1, 1, 0, 0, 1, 1, 0}));
System.out.println("Prediction for [1, 0, 1, 1, 0, 0, 1, 1, 1]: " + nn.predict(new double[]{1, 0, 1, 1, 0, 0, 1, 1, 1}));
System.out.println("Prediction for [1, 0, 1, 1, 0, 1, 0, 0, 0]: " + nn.predict(new double[]{1, 0, 1, 1, 0, 1, 0, 0, 0}));
System.out.println("Prediction for [1, 0, 1, 1, 0, 1, 0, 0, 1]: " + nn.predict(new double[]{1, 0, 1, 1, 0, 1, 0, 0, 1}));
System.out.println("Prediction for [1, 0, 1, 1, 0, 1, 0, 1, 0]: " + nn.predict(new double[]{1, 0, 1, 1, 0, 1, 0, 1, 0}));
System.out.println("Prediction for [1, 0, 1, 1, 0, 1, 0, 1, 1]: " + nn.predict(new double[]{1, 0, 1, 1, 0, 1, 0, 1, 1}));
System.out.println("Prediction for [1, 0, 1, 1, 0, 1, 1, 0, 0]: " + nn.predict(new double[]{1, 0, 1, 1, 0, 1, 1, 0, 0}));
System.out.println("Prediction for [1, 0, 1, 1, 0, 1, 1, 0, 1]: " + nn.predict(new double[]{1, 0, 1, 1, 0, 1, 1, 0, 1}));
System.out.println("Prediction for [1, 0, 1, 1, 0, 1, 1, 1, 0]: " + nn.predict(new double[]{1, 0, 1, 1, 0, 1, 1, 1, 0}));
System.out.println("Prediction for [1, 0, 1, 1, 0, 1, 1, 1, 1]: " + nn.predict(new double[]{1, 0, 1, 1, 0, 1, 1, 1, 1}));
System.out.println("Prediction for [1, 0, 1, 1, 1, 0, 0, 0, 0]: " + nn.predict(new double[]{1, 0, 1, 1, 1, 0, 0, 0, 0}));
System.out.println("Prediction for [1, 0, 1, 1, 1, 0, 0, 0, 1]: " + nn.predict(new double[]{1, 0, 1, 1, 1, 0, 0, 0, 1}));
System.out.println("Prediction for [1, 0, 1, 1, 1, 0, 0, 1, 0]: " + nn.predict(new double[]{1, 0, 1, 1, 1, 0, 0, 1, 0}));
System.out.println("Prediction for [1, 0, 1, 1, 1, 0, 0, 1, 1]: " + nn.predict(new double[]{1, 0, 1, 1, 1, 0, 0, 1, 1}));
System.out.println("Prediction for [1, 0, 1, 1, 1, 0, 1, 0, 0]: " + nn.predict(new double[]{1, 0, 1, 1, 1, 0, 1, 0, 0}));
System.out.println("Prediction for [1, 0, 1, 1, 1, 0, 1, 0, 1]: " + nn.predict(new double[]{1, 0, 1, 1, 1, 0, 1, 0, 1}));
System.out.println("Prediction for [1, 0, 1, 1, 1, 0, 1, 1, 0]: " + nn.predict(new double[]{1, 0, 1, 1, 1, 0, 1, 1, 0}));
System.out.println("Prediction for [1, 0, 1, 1, 1, 0, 1, 1, 1]: " + nn.predict(new double[]{1, 0, 1, 1, 1, 0, 1, 1, 1}));
System.out.println("Prediction for [1, 0, 1, 1, 1, 1, 0, 0, 0]: " + nn.predict(new double[]{1, 0, 1, 1, 1, 1, 0, 0, 0}));
System.out.println("Prediction for [1, 0, 1, 1, 1, 1, 0, 0, 1]: " + nn.predict(new double[]{1, 0, 1, 1, 1, 1, 0, 0, 1}));
System.out.println("Prediction for [1, 0, 1, 1, 1, 1, 0, 1, 0]: " + nn.predict(new double[]{1, 0, 1, 1, 1, 1, 0, 1, 0}));
System.out.println("Prediction for [1, 0, 1, 1, 1, 1, 0, 1, 1]: " + nn.predict(new double[]{1, 0, 1, 1, 1, 1, 0, 1, 1}));
System.out.println("Prediction for [1, 0, 1, 1, 1, 1, 1, 0, 0]: " + nn.predict(new double[]{1, 0, 1, 1, 1, 1, 1, 0, 0}));
System.out.println("Prediction for [1, 0, 1, 1, 1, 1, 1, 0, 1]: " + nn.predict(new double[]{1, 0, 1, 1, 1, 1, 1, 0, 1}));
System.out.println("Prediction for [1, 0, 1, 1, 1, 1, 1, 1, 0]: " + nn.predict(new double[]{1, 0, 1, 1, 1, 1, 1, 1, 0}));
System.out.println("Prediction for [1, 0, 1, 1, 1, 1, 1, 1, 1]: " + nn.predict(new double[]{1, 0, 1, 1, 1, 1, 1, 1, 1}));
System.out.println("Prediction for [1, 1, 0, 0, 0, 0, 0, 0, 0]: " + nn.predict(new double[]{1, 1, 0, 0, 0, 0, 0, 0, 0}));
System.out.println("Prediction for [1, 1, 0, 0, 0, 0, 0, 0, 1]: " + nn.predict(new double[]{1, 1, 0, 0, 0, 0, 0, 0, 1}));
System.out.println("Prediction for [1, 1, 0, 0, 0, 0, 0, 1, 0]: " + nn.predict(new double[]{1, 1, 0, 0, 0, 0, 0, 1, 0}));
System.out.println("Prediction for [1, 1, 0, 0, 0, 0, 0, 1, 1]: " + nn.predict(new double[]{1, 1, 0, 0, 0, 0, 0, 1, 1}));
System.out.println("Prediction for [1, 1, 0, 0, 0, 0, 1, 0, 0]: " + nn.predict(new double[]{1, 1, 0, 0, 0, 0, 1, 0, 0}));
System.out.println("Prediction for [1, 1, 0, 0, 0, 0, 1, 0, 1]: " + nn.predict(new double[]{1, 1, 0, 0, 0, 0, 1, 0, 1}));
System.out.println("Prediction for [1, 1, 0, 0, 0, 0, 1, 1, 0]: " + nn.predict(new double[]{1, 1, 0, 0, 0, 0, 1, 1, 0}));
System.out.println("Prediction for [1, 1, 0, 0, 0, 0, 1, 1, 1]: " + nn.predict(new double[]{1, 1, 0, 0, 0, 0, 1, 1, 1}));
System.out.println("Prediction for [1, 1, 0, 0, 0, 1, 0, 0, 0]: " + nn.predict(new double[]{1, 1, 0, 0, 0, 1, 0, 0, 0}));
System.out.println("Prediction for [1, 1, 0, 0, 0, 1, 0, 0, 1]: " + nn.predict(new double[]{1, 1, 0, 0, 0, 1, 0, 0, 1}));
System.out.println("Prediction for [1, 1, 0, 0, 0, 1, 0, 1, 0]: " + nn.predict(new double[]{1, 1, 0, 0, 0, 1, 0, 1, 0}));
System.out.println("Prediction for [1, 1, 0, 0, 0, 1, 0, 1, 1]: " + nn.predict(new double[]{1, 1, 0, 0, 0, 1, 0, 1, 1}));
System.out.println("Prediction for [1, 1, 0, 0, 0, 1, 1, 0, 0]: " + nn.predict(new double[]{1, 1, 0, 0, 0, 1, 1, 0, 0}));
System.out.println("Prediction for [1, 1, 0, 0, 0, 1, 1, 0, 1]: " + nn.predict(new double[]{1, 1, 0, 0, 0, 1, 1, 0, 1}));
System.out.println("Prediction for [1, 1, 0, 0, 0, 1, 1, 1, 0]: " + nn.predict(new double[]{1, 1, 0, 0, 0, 1, 1, 1, 0}));
System.out.println("Prediction for [1, 1, 0, 0, 0, 1, 1, 1, 1]: " + nn.predict(new double[]{1, 1, 0, 0, 0, 1, 1, 1, 1}));
System.out.println("Prediction for [1, 1, 0, 0, 1, 0, 0, 0, 0]: " + nn.predict(new double[]{1, 1, 0, 0, 1, 0, 0, 0, 0}));
System.out.println("Prediction for [1, 1, 0, 0, 1, 0, 0, 0, 1]: " + nn.predict(new double[]{1, 1, 0, 0, 1, 0, 0, 0, 1}));
System.out.println("Prediction for [1, 1, 0, 0, 1, 0, 0, 1, 0]: " + nn.predict(new double[]{1, 1, 0, 0, 1, 0, 0, 1, 0}));
System.out.println("Prediction for [1, 1, 0, 0, 1, 0, 0, 1, 1]: " + nn.predict(new double[]{1, 1, 0, 0, 1, 0, 0, 1, 1}));
System.out.println("Prediction for [1, 1, 0, 0, 1, 0, 1, 0, 0]: " + nn.predict(new double[]{1, 1, 0, 0, 1, 0, 1, 0, 0}));
System.out.println("Prediction for [1, 1, 0, 0, 1, 0, 1, 0, 1]: " + nn.predict(new double[]{1, 1, 0, 0, 1, 0, 1, 0, 1}));
System.out.println("Prediction for [1, 1, 0, 0, 1, 0, 1, 1, 0]: " + nn.predict(new double[]{1, 1, 0, 0, 1, 0, 1, 1, 0}));
System.out.println("Prediction for [1, 1, 0, 0, 1, 0, 1, 1, 1]: " + nn.predict(new double[]{1, 1, 0, 0, 1, 0, 1, 1, 1}));
System.out.println("Prediction for [1, 1, 0, 0, 1, 1, 0, 0, 0]: " + nn.predict(new double[]{1, 1, 0, 0, 1, 1, 0, 0, 0}));
System.out.println("Prediction for [1, 1, 0, 0, 1, 1, 0, 0, 1]: " + nn.predict(new double[]{1, 1, 0, 0, 1, 1, 0, 0, 1}));
System.out.println("Prediction for [1, 1, 0, 0, 1, 1, 0, 1, 0]: " + nn.predict(new double[]{1, 1, 0, 0, 1, 1, 0, 1, 0}));
System.out.println("Prediction for [1, 1, 0, 0, 1, 1, 0, 1, 1]: " + nn.predict(new double[]{1, 1, 0, 0, 1, 1, 0, 1, 1}));
System.out.println("Prediction for [1, 1, 0, 0, 1, 1, 1, 0, 0]: " + nn.predict(new double[]{1, 1, 0, 0, 1, 1, 1, 0, 0}));
System.out.println("Prediction for [1, 1, 0, 0, 1, 1, 1, 0, 1]: " + nn.predict(new double[]{1, 1, 0, 0, 1, 1, 1, 0, 1}));
System.out.println("Prediction for [1, 1, 0, 0, 1, 1, 1, 1, 0]: " + nn.predict(new double[]{1, 1, 0, 0, 1, 1, 1, 1, 0}));
System.out.println("Prediction for [1, 1, 0, 0, 1, 1, 1, 1, 1]: " + nn.predict(new double[]{1, 1, 0, 0, 1, 1, 1, 1, 1}));
System.out.println("Prediction for [1, 1, 0, 1, 0, 0, 0, 0, 0]: " + nn.predict(new double[]{1, 1, 0, 1, 0, 0, 0, 0, 0}));
System.out.println("Prediction for [1, 1, 0, 1, 0, 0, 0, 0, 1]: " + nn.predict(new double[]{1, 1, 0, 1, 0, 0, 0, 0, 1}));
System.out.println("Prediction for [1, 1, 0, 1, 0, 0, 0, 1, 0]: " + nn.predict(new double[]{1, 1, 0, 1, 0, 0, 0, 1, 0}));
System.out.println("Prediction for [1, 1, 0, 1, 0, 0, 0, 1, 1]: " + nn.predict(new double[]{1, 1, 0, 1, 0, 0, 0, 1, 1}));
System.out.println("Prediction for [1, 1, 0, 1, 0, 0, 1, 0, 0]: " + nn.predict(new double[]{1, 1, 0, 1, 0, 0, 1, 0, 0}));
System.out.println("Prediction for [1, 1, 0, 1, 0, 0, 1, 0, 1]: " + nn.predict(new double[]{1, 1, 0, 1, 0, 0, 1, 0, 1}));
System.out.println("Prediction for [1, 1, 0, 1, 0, 0, 1, 1, 0]: " + nn.predict(new double[]{1, 1, 0, 1, 0, 0, 1, 1, 0}));
System.out.println("Prediction for [1, 1, 0, 1, 0, 0, 1, 1, 1]: " + nn.predict(new double[]{1, 1, 0, 1, 0, 0, 1, 1, 1}));
System.out.println("Prediction for [1, 1, 0, 1, 0, 1, 0, 0, 0]: " + nn.predict(new double[]{1, 1, 0, 1, 0, 1, 0, 0, 0}));
System.out.println("Prediction for [1, 1, 0, 1, 0, 1, 0, 0, 1]: " + nn.predict(new double[]{1, 1, 0, 1, 0, 1, 0, 0, 1}));
System.out.println("Prediction for [1, 1, 0, 1, 0, 1, 0, 1, 0]: " + nn.predict(new double[]{1, 1, 0, 1, 0, 1, 0, 1, 0}));
System.out.println("Prediction for [1, 1, 0, 1, 0, 1, 0, 1, 1]: " + nn.predict(new double[]{1, 1, 0, 1, 0, 1, 0, 1, 1}));
System.out.println("Prediction for [1, 1, 0, 1, 0, 1, 1, 0, 0]: " + nn.predict(new double[]{1, 1, 0, 1, 0, 1, 1, 0, 0}));
System.out.println("Prediction for [1, 1, 0, 1, 0, 1, 1, 0, 1]: " + nn.predict(new double[]{1, 1, 0, 1, 0, 1, 1, 0, 1}));
System.out.println("Prediction for [1, 1, 0, 1, 0, 1, 1, 1, 0]: " + nn.predict(new double[]{1, 1, 0, 1, 0, 1, 1, 1, 0}));
System.out.println("Prediction for [1, 1, 0, 1, 0, 1, 1, 1, 1]: " + nn.predict(new double[]{1, 1, 0, 1, 0, 1, 1, 1, 1}));
System.out.println("Prediction for [1, 1, 0, 1, 1, 0, 0, 0, 0]: " + nn.predict(new double[]{1, 1, 0, 1, 1, 0, 0, 0, 0}));
System.out.println("Prediction for [1, 1, 0, 1, 1, 0, 0, 0, 1]: " + nn.predict(new double[]{1, 1, 0, 1, 1, 0, 0, 0, 1}));
System.out.println("Prediction for [1, 1, 0, 1, 1, 0, 0, 1, 0]: " + nn.predict(new double[]{1, 1, 0, 1, 1, 0, 0, 1, 0}));
System.out.println("Prediction for [1, 1, 0, 1, 1, 0, 0, 1, 1]: " + nn.predict(new double[]{1, 1, 0, 1, 1, 0, 0, 1, 1}));
System.out.println("Prediction for [1, 1, 0, 1, 1, 0, 1, 0, 0]: " + nn.predict(new double[]{1, 1, 0, 1, 1, 0, 1, 0, 0}));
System.out.println("Prediction for [1, 1, 0, 1, 1, 0, 1, 0, 1]: " + nn.predict(new double[]{1, 1, 0, 1, 1, 0, 1, 0, 1}));
System.out.println("Prediction for [1, 1, 0, 1, 1, 0, 1, 1, 0]: " + nn.predict(new double[]{1, 1, 0, 1, 1, 0, 1, 1, 0}));
System.out.println("Prediction for [1, 1, 0, 1, 1, 0, 1, 1, 1]: " + nn.predict(new double[]{1, 1, 0, 1, 1, 0, 1, 1, 1}));
System.out.println("Prediction for [1, 1, 0, 1, 1, 1, 0, 0, 0]: " + nn.predict(new double[]{1, 1, 0, 1, 1, 1, 0, 0, 0}));
System.out.println("Prediction for [1, 1, 0, 1, 1, 1, 0, 0, 1]: " + nn.predict(new double[]{1, 1, 0, 1, 1, 1, 0, 0, 1}));
System.out.println("Prediction for [1, 1, 0, 1, 1, 1, 0, 1, 0]: " + nn.predict(new double[]{1, 1, 0, 1, 1, 1, 0, 1, 0}));
System.out.println("Prediction for [1, 1, 0, 1, 1, 1, 0, 1, 1]: " + nn.predict(new double[]{1, 1, 0, 1, 1, 1, 0, 1, 1}));
System.out.println("Prediction for [1, 1, 0, 1, 1, 1, 1, 0, 0]: " + nn.predict(new double[]{1, 1, 0, 1, 1, 1, 1, 0, 0}));
System.out.println("Prediction for [1, 1, 0, 1, 1, 1, 1, 0, 1]: " + nn.predict(new double[]{1, 1, 0, 1, 1, 1, 1, 0, 1}));
System.out.println("Prediction for [1, 1, 0, 1, 1, 1, 1, 1, 0]: " + nn.predict(new double[]{1, 1, 0, 1, 1, 1, 1, 1, 0}));
System.out.println("Prediction for [1, 1, 0, 1, 1, 1, 1, 1, 1]: " + nn.predict(new double[]{1, 1, 0, 1, 1, 1, 1, 1, 1}));
System.out.println("Prediction for [1, 1, 1, 0, 0, 0, 0, 0, 0]: " + nn.predict(new double[]{1, 1, 1, 0, 0, 0, 0, 0, 0}));
System.out.println("Prediction for [1, 1, 1, 0, 0, 0, 0, 0, 1]: " + nn.predict(new double[]{1, 1, 1, 0, 0, 0, 0, 0, 1}));
System.out.println("Prediction for [1, 1, 1, 0, 0, 0, 0, 1, 0]: " + nn.predict(new double[]{1, 1, 1, 0, 0, 0, 0, 1, 0}));
System.out.println("Prediction for [1, 1, 1, 0, 0, 0, 0, 1, 1]: " + nn.predict(new double[]{1, 1, 1, 0, 0, 0, 0, 1, 1}));
System.out.println("Prediction for [1, 1, 1, 0, 0, 0, 1, 0, 0]: " + nn.predict(new double[]{1, 1, 1, 0, 0, 0, 1, 0, 0}));
System.out.println("Prediction for [1, 1, 1, 0, 0, 0, 1, 0, 1]: " + nn.predict(new double[]{1, 1, 1, 0, 0, 0, 1, 0, 1}));
System.out.println("Prediction for [1, 1, 1, 0, 0, 0, 1, 1, 0]: " + nn.predict(new double[]{1, 1, 1, 0, 0, 0, 1, 1, 0}));
System.out.println("Prediction for [1, 1, 1, 0, 0, 0, 1, 1, 1]: " + nn.predict(new double[]{1, 1, 1, 0, 0, 0, 1, 1, 1}));
System.out.println("Prediction for [1, 1, 1, 0, 0, 1, 0, 0, 0]: " + nn.predict(new double[]{1, 1, 1, 0, 0, 1, 0, 0, 0}));
System.out.println("Prediction for [1, 1, 1, 0, 0, 1, 0, 0, 1]: " + nn.predict(new double[]{1, 1, 1, 0, 0, 1, 0, 0, 1}));
System.out.println("Prediction for [1, 1, 1, 0, 0, 1, 0, 1, 0]: " + nn.predict(new double[]{1, 1, 1, 0, 0, 1, 0, 1, 0}));
System.out.println("Prediction for [1, 1, 1, 0, 0, 1, 0, 1, 1]: " + nn.predict(new double[]{1, 1, 1, 0, 0, 1, 0, 1, 1}));
System.out.println("Prediction for [1, 1, 1, 0, 0, 1, 1, 0, 0]: " + nn.predict(new double[]{1, 1, 1, 0, 0, 1, 1, 0, 0}));
System.out.println("Prediction for [1, 1, 1, 0, 0, 1, 1, 0, 1]: " + nn.predict(new double[]{1, 1, 1, 0, 0, 1, 1, 0, 1}));
System.out.println("Prediction for [1, 1, 1, 0, 0, 1, 1, 1, 0]: " + nn.predict(new double[]{1, 1, 1, 0, 0, 1, 1, 1, 0}));
System.out.println("Prediction for [1, 1, 1, 0, 0, 1, 1, 1, 1]: " + nn.predict(new double[]{1, 1, 1, 0, 0, 1, 1, 1, 1}));
System.out.println("Prediction for [1, 1, 1, 0, 1, 0, 0, 0, 0]: " + nn.predict(new double[]{1, 1, 1, 0, 1, 0, 0, 0, 0}));
System.out.println("Prediction for [1, 1, 1, 0, 1, 0, 0, 0, 1]: " + nn.predict(new double[]{1, 1, 1, 0, 1, 0, 0, 0, 1}));
System.out.println("Prediction for [1, 1, 1, 0, 1, 0, 0, 1, 0]: " + nn.predict(new double[]{1, 1, 1, 0, 1, 0, 0, 1, 0}));
System.out.println("Prediction for [1, 1, 1, 0, 1, 0, 0, 1, 1]: " + nn.predict(new double[]{1, 1, 1, 0, 1, 0, 0, 1, 1}));
System.out.println("Prediction for [1, 1, 1, 0, 1, 0, 1, 0, 0]: " + nn.predict(new double[]{1, 1, 1, 0, 1, 0, 1, 0, 0}));
System.out.println("Prediction for [1, 1, 1, 0, 1, 0, 1, 0, 1]: " + nn.predict(new double[]{1, 1, 1, 0, 1, 0, 1, 0, 1}));
System.out.println("Prediction for [1, 1, 1, 0, 1, 0, 1, 1, 0]: " + nn.predict(new double[]{1, 1, 1, 0, 1, 0, 1, 1, 0}));
System.out.println("Prediction for [1, 1, 1, 0, 1, 0, 1, 1, 1]: " + nn.predict(new double[]{1, 1, 1, 0, 1, 0, 1, 1, 1}));
System.out.println("Prediction for [1, 1, 1, 0, 1, 1, 0, 0, 0]: " + nn.predict(new double[]{1, 1, 1, 0, 1, 1, 0, 0, 0}));
System.out.println("Prediction for [1, 1, 1, 0, 1, 1, 0, 0, 1]: " + nn.predict(new double[]{1, 1, 1, 0, 1, 1, 0, 0, 1}));
System.out.println("Prediction for [1, 1, 1, 0, 1, 1, 0, 1, 0]: " + nn.predict(new double[]{1, 1, 1, 0, 1, 1, 0, 1, 0}));
System.out.println("Prediction for [1, 1, 1, 0, 1, 1, 0, 1, 1]: " + nn.predict(new double[]{1, 1, 1, 0, 1, 1, 0, 1, 1}));
System.out.println("Prediction for [1, 1, 1, 0, 1, 1, 1, 0, 0]: " + nn.predict(new double[]{1, 1, 1, 0, 1, 1, 1, 0, 0}));
System.out.println("Prediction for [1, 1, 1, 0, 1, 1, 1, 0, 1]: " + nn.predict(new double[]{1, 1, 1, 0, 1, 1, 1, 0, 1}));
System.out.println("Prediction for [1, 1, 1, 0, 1, 1, 1, 1, 0]: " + nn.predict(new double[]{1, 1, 1, 0, 1, 1, 1, 1, 0}));
System.out.println("Prediction for [1, 1, 1, 0, 1, 1, 1, 1, 1]: " + nn.predict(new double[]{1, 1, 1, 0, 1, 1, 1, 1, 1}));
System.out.println("Prediction for [1, 1, 1, 1, 0, 0, 0, 0, 0]: " + nn.predict(new double[]{1, 1, 1, 1, 0, 0, 0, 0, 0}));
System.out.println("Prediction for [1, 1, 1, 1, 0, 0, 0, 0, 1]: " + nn.predict(new double[]{1, 1, 1, 1, 0, 0, 0, 0, 1}));
System.out.println("Prediction for [1, 1, 1, 1, 0, 0, 0, 1, 0]: " + nn.predict(new double[]{1, 1, 1, 1, 0, 0, 0, 1, 0}));
System.out.println("Prediction for [1, 1, 1, 1, 0, 0, 0, 1, 1]: " + nn.predict(new double[]{1, 1, 1, 1, 0, 0, 0, 1, 1}));
System.out.println("Prediction for [1, 1, 1, 1, 0, 0, 1, 0, 0]: " + nn.predict(new double[]{1, 1, 1, 1, 0, 0, 1, 0, 0}));
System.out.println("Prediction for [1, 1, 1, 1, 0, 0, 1, 0, 1]: " + nn.predict(new double[]{1, 1, 1, 1, 0, 0, 1, 0, 1}));
System.out.println("Prediction for [1, 1, 1, 1, 0, 0, 1, 1, 0]: " + nn.predict(new double[]{1, 1, 1, 1, 0, 0, 1, 1, 0}));
System.out.println("Prediction for [1, 1, 1, 1, 0, 0, 1, 1, 1]: " + nn.predict(new double[]{1, 1, 1, 1, 0, 0, 1, 1, 1}));
System.out.println("Prediction for [1, 1, 1, 1, 0, 1, 0, 0, 0]: " + nn.predict(new double[]{1, 1, 1, 1, 0, 1, 0, 0, 0}));
System.out.println("Prediction for [1, 1, 1, 1, 0, 1, 0, 0, 1]: " + nn.predict(new double[]{1, 1, 1, 1, 0, 1, 0, 0, 1}));
System.out.println("Prediction for [1, 1, 1, 1, 0, 1, 0, 1, 0]: " + nn.predict(new double[]{1, 1, 1, 1, 0, 1, 0, 1, 0}));
System.out.println("Prediction for [1, 1, 1, 1, 0, 1, 0, 1, 1]: " + nn.predict(new double[]{1, 1, 1, 1, 0, 1, 0, 1, 1}));
System.out.println("Prediction for [1, 1, 1, 1, 0, 1, 1, 0, 0]: " + nn.predict(new double[]{1, 1, 1, 1, 0, 1, 1, 0, 0}));
System.out.println("Prediction for [1, 1, 1, 1, 0, 1, 1, 0, 1]: " + nn.predict(new double[]{1, 1, 1, 1, 0, 1, 1, 0, 1}));
System.out.println("Prediction for [1, 1, 1, 1, 0, 1, 1, 1, 0]: " + nn.predict(new double[]{1, 1, 1, 1, 0, 1, 1, 1, 0}));
System.out.println("Prediction for [1, 1, 1, 1, 0, 1, 1, 1, 1]: " + nn.predict(new double[]{1, 1, 1, 1, 0, 1, 1, 1, 1}));
System.out.println("Prediction for [1, 1, 1, 1, 1, 0, 0, 0, 0]: " + nn.predict(new double[]{1, 1, 1, 1, 1, 0, 0, 0, 0}));
System.out.println("Prediction for [1, 1, 1, 1, 1, 0, 0, 0, 1]: " + nn.predict(new double[]{1, 1, 1, 1, 1, 0, 0, 0, 1}));
System.out.println("Prediction for [1, 1, 1, 1, 1, 0, 0, 1, 0]: " + nn.predict(new double[]{1, 1, 1, 1, 1, 0, 0, 1, 0}));
System.out.println("Prediction for [1, 1, 1, 1, 1, 0, 0, 1, 1]: " + nn.predict(new double[]{1, 1, 1, 1, 1, 0, 0, 1, 1}));
System.out.println("Prediction for [1, 1, 1, 1, 1, 0, 1, 0, 0]: " + nn.predict(new double[]{1, 1, 1, 1, 1, 0, 1, 0, 0}));
System.out.println("Prediction for [1, 1, 1, 1, 1, 0, 1, 0, 1]: " + nn.predict(new double[]{1, 1, 1, 1, 1, 0, 1, 0, 1}));
System.out.println("Prediction for [1, 1, 1, 1, 1, 0, 1, 1, 0]: " + nn.predict(new double[]{1, 1, 1, 1, 1, 0, 1, 1, 0}));
System.out.println("Prediction for [1, 1, 1, 1, 1, 0, 1, 1, 1]: " + nn.predict(new double[]{1, 1, 1, 1, 1, 0, 1, 1, 1}));
System.out.println("Prediction for [1, 1, 1, 1, 1, 1, 0, 0, 0]: " + nn.predict(new double[]{1, 1, 1, 1, 1, 1, 0, 0, 0}));
System.out.println("Prediction for [1, 1, 1, 1, 1, 1, 0, 0, 1]: " + nn.predict(new double[]{1, 1, 1, 1, 1, 1, 0, 0, 1}));
System.out.println("Prediction for [1, 1, 1, 1, 1, 1, 0, 1, 0]: " + nn.predict(new double[]{1, 1, 1, 1, 1, 1, 0, 1, 0}));
System.out.println("Prediction for [1, 1, 1, 1, 1, 1, 0, 1, 1]: " + nn.predict(new double[]{1, 1, 1, 1, 1, 1, 0, 1, 1}));
System.out.println("Prediction for [1, 1, 1, 1, 1, 1, 1, 0, 0]: " + nn.predict(new double[]{1, 1, 1, 1, 1, 1, 1, 0, 0}));
System.out.println("Prediction for [1, 1, 1, 1, 1, 1, 1, 0, 1]: " + nn.predict(new double[]{1, 1, 1, 1, 1, 1, 1, 0, 1}));
System.out.println("Prediction for [1, 1, 1, 1, 1, 1, 1, 1, 0]: " + nn.predict(new double[]{1, 1, 1, 1, 1, 1, 1, 1, 0}));
System.out.println("Prediction for [1, 1, 1, 1, 1, 1, 1, 1, 1]: " + nn.predict(new double[]{1, 1, 1, 1, 1, 1, 1, 1, 1}));





}
}



run:
Epoch 2000, Error: 52.451551882360036
Epoch 4000, Error: 31.707144021981634
Epoch 6000, Error: 16.8717906992642
Epoch 8000, Error: 11.580722094331293
Epoch 10000, Error: 8.481827590063295
Epoch 12000, Error: 6.956125519486862
Epoch 14000, Error: 5.568760773703237
Epoch 16000, Error: 4.493248237614515
Epoch 18000, Error: 3.7103603783767722
Epoch 20000, Error: 3.2636811254227696
Epoch 22000, Error: 2.7855791672626737
Epoch 24000, Error: 2.3907075512281533
Epoch 26000, Error: 2.103757600728982
Epoch 28000, Error: 1.8203912755088996
Epoch 30000, Error: 1.6460082295246317
Epoch 32000, Error: 1.4841126968404992
Epoch 34000, Error: 1.3425065139755252
Epoch 36000, Error: 1.238685963743492
Epoch 38000, Error: 1.1614882249300917
Epoch 40000, Error: 1.1055794269285453
Epoch 42000, Error: 1.0609986005185013
Epoch 44000, Error: 1.010127928687235
Epoch 46000, Error: 0.9767334629057545
Epoch 48000, Error: 0.944380151468777
Epoch 50000, Error: 0.9168364797299632
Epoch 52000, Error: 0.8871847496247405
Epoch 54000, Error: 0.8674593415148122
Epoch 56000, Error: 0.8517135803358193
Epoch 58000, Error: 0.8274156019736346
Epoch 60000, Error: 0.8077891758568904
Epoch 62000, Error: 0.7903260271817737
Epoch 64000, Error: 0.7738944754375546
Epoch 66000, Error: 0.7596446130912603
Epoch 68000, Error: 0.7455503467843242
Epoch 70000, Error: 0.733113053729717
Epoch 72000, Error: 0.7216830384255954
Epoch 74000, Error: 0.7107172209302016
Epoch 76000, Error: 0.6999204977286727
Epoch 78000, Error: 0.6909253442159263
Epoch 80000, Error: 0.6779540581815748
Epoch 82000, Error: 0.6729191214068203
Epoch 84000, Error: 0.6647472478558226
Epoch 86000, Error: 0.6545041538259536
Epoch 88000, Error: 0.6485849487137458
Epoch 90000, Error: 0.6409668625457688
Epoch 92000, Error: 0.6338876210454332
Epoch 94000, Error: 0.6271993960133858
Epoch 96000, Error: 0.6200443392722925
Epoch 98000, Error: 0.6131641962113463
Epoch 100000, Error: 0.6055875978978504
Prediction for [0, 0, 0, 0, 0, 0, 0, 0, 0]: 0.0
Prediction for [0, 0, 0, 0, 0, 0, 0, 0, 1]: 1.0029322924298496
Prediction for [0, 0, 0, 0, 0, 0, 0, 1, 0]: 0.0
Prediction for [0, 0, 0, 0, 0, 0, 0, 1, 1]: 0.0
Prediction for [0, 0, 0, 0, 0, 0, 1, 0, 0]: 0.0
Prediction for [0, 0, 0, 0, 0, 0, 1, 0, 1]: 1.0001584132525174
Prediction for [0, 0, 0, 0, 0, 0, 1, 1, 0]: 0.0
Prediction for [0, 0, 0, 0, 0, 0, 1, 1, 1]: 1.002008788417899
Prediction for [0, 0, 0, 0, 0, 1, 0, 0, 0]: 0.0
Prediction for [0, 0, 0, 0, 0, 1, 0, 0, 1]: 0.0
Prediction for [0, 0, 0, 0, 0, 1, 0, 1, 0]: 0.0
Prediction for [0, 0, 0, 0, 0, 1, 0, 1, 1]: 1.0007155017702454
Prediction for [0, 0, 0, 0, 0, 1, 1, 0, 0]: 0.0
Prediction for [0, 0, 0, 0, 0, 1, 1, 0, 1]: 1.0037306797702428
Prediction for [0, 0, 0, 0, 0, 1, 1, 1, 0]: 0.0
Prediction for [0, 0, 0, 0, 0, 1, 1, 1, 1]: 0.0
Prediction for [0, 0, 0, 0, 1, 0, 0, 0, 0]: 0.0
Prediction for [0, 0, 0, 0, 1, 0, 0, 0, 1]: 1.0082046849989963
Prediction for [0, 0, 0, 0, 1, 0, 0, 1, 0]: 0.0
Prediction for [0, 0, 0, 0, 1, 0, 0, 1, 1]: 1.000963020031036
Prediction for [0, 0, 0, 0, 1, 0, 1, 0, 0]: 0.0
Prediction for [0, 0, 0, 0, 1, 0, 1, 0, 1]: 0.0
Prediction for [0, 0, 0, 0, 1, 0, 1, 1, 0]: 0.0
Prediction for [0, 0, 0, 0, 1, 0, 1, 1, 1]: 1.000951902224271
Prediction for [0, 0, 0, 0, 1, 1, 0, 0, 0]: 0.0
Prediction for [0, 0, 0, 0, 1, 1, 0, 0, 1]: 0.005561024998525177
Prediction for [0, 0, 0, 0, 1, 1, 0, 1, 0]: 0.0
Prediction for [0, 0, 0, 0, 1, 1, 0, 1, 1]: 0.0
Prediction for [0, 0, 0, 0, 1, 1, 1, 0, 0]: 0.0
Prediction for [0, 0, 0, 0, 1, 1, 1, 0, 1]: 1.0032833984102814
Prediction for [0, 0, 0, 0, 1, 1, 1, 1, 0]: 0.0
Prediction for [0, 0, 0, 0, 1, 1, 1, 1, 1]: 0.9998506288613376
Prediction for [0, 0, 0, 1, 0, 0, 0, 0, 0]: 0.0
Prediction for [0, 0, 0, 1, 0, 0, 0, 0, 1]: 0.0
Prediction for [0, 0, 0, 1, 0, 0, 0, 1, 0]: 0.0
Prediction for [0, 0, 0, 1, 0, 0, 0, 1, 1]: 0.0
Prediction for [0, 0, 0, 1, 0, 0, 1, 0, 0]: 0.0
Prediction for [0, 0, 0, 1, 0, 0, 1, 0, 1]: 0.9969347909902329
Prediction for [0, 0, 0, 1, 0, 0, 1, 1, 0]: 0.0
Prediction for [0, 0, 0, 1, 0, 0, 1, 1, 1]: 0.0
Prediction for [0, 0, 0, 1, 0, 1, 0, 0, 0]: 0.0
Prediction for [0, 0, 0, 1, 0, 1, 0, 0, 1]: 1.0002154142574202
Prediction for [0, 0, 0, 1, 0, 1, 0, 1, 0]: 0.0
Prediction for [0, 0, 0, 1, 0, 1, 0, 1, 1]: 1.0030008166588216
Prediction for [0, 0, 0, 1, 0, 1, 1, 0, 0]: 0.0
Prediction for [0, 0, 0, 1, 0, 1, 1, 0, 1]: 0.0
Prediction for [0, 0, 0, 1, 0, 1, 1, 1, 0]: 0.0
Prediction for [0, 0, 0, 1, 0, 1, 1, 1, 1]: 0.9996787139196419
Prediction for [0, 0, 0, 1, 1, 0, 0, 0, 0]: 0.0
Prediction for [0, 0, 0, 1, 1, 0, 0, 0, 1]: 0.0
Prediction for [0, 0, 0, 1, 1, 0, 0, 1, 0]: 0.0
Prediction for [0, 0, 0, 1, 1, 0, 0, 1, 1]: 0.0
Prediction for [0, 0, 0, 1, 1, 0, 1, 0, 0]: 0.0
Prediction for [0, 0, 0, 1, 1, 0, 1, 0, 1]: 0.995948835587976
Prediction for [0, 0, 0, 1, 1, 0, 1, 1, 0]: 0.0
Prediction for [0, 0, 0, 1, 1, 0, 1, 1, 1]: 0.0158368874968855
Prediction for [0, 0, 0, 1, 1, 1, 0, 0, 0]: 0.0
Prediction for [0, 0, 0, 1, 1, 1, 0, 0, 1]: 0.0
Prediction for [0, 0, 0, 1, 1, 1, 0, 1, 0]: 0.0
Prediction for [0, 0, 0, 1, 1, 1, 0, 1, 1]: 1.0024567019333235
Prediction for [0, 0, 0, 1, 1, 1, 1, 0, 0]: 0.0
Prediction for [0, 0, 0, 1, 1, 1, 1, 0, 1]: 0.9971564442587857
Prediction for [0, 0, 0, 1, 1, 1, 1, 1, 0]: 0.0
Prediction for [0, 0, 0, 1, 1, 1, 1, 1, 1]: 2.3147283027302734E-4
Prediction for [0, 0, 1, 0, 0, 0, 0, 0, 0]: 0.0
Prediction for [0, 0, 1, 0, 0, 0, 0, 0, 1]: 0.003074418341085572
Prediction for [0, 0, 1, 0, 0, 0, 0, 1, 0]: 0.0
Prediction for [0, 0, 1, 0, 0, 0, 0, 1, 1]: 0.9973577943526859
Prediction for [0, 0, 1, 0, 0, 0, 1, 0, 0]: 0.0
Prediction for [0, 0, 1, 0, 0, 0, 1, 0, 1]: 0.0
Prediction for [0, 0, 1, 0, 0, 0, 1, 1, 0]: 0.0
Prediction for [0, 0, 1, 0, 0, 0, 1, 1, 1]: 0.9991616724279542
Prediction for [0, 0, 1, 0, 0, 1, 0, 0, 0]: 0.0
Prediction for [0, 0, 1, 0, 0, 1, 0, 0, 1]: 1.005528364690135
Prediction for [0, 0, 1, 0, 0, 1, 0, 1, 0]: 0.0
Prediction for [0, 0, 1, 0, 0, 1, 0, 1, 1]: 0.0
Prediction for [0, 0, 1, 0, 0, 1, 1, 0, 0]: 0.0
Prediction for [0, 0, 1, 0, 0, 1, 1, 0, 1]: 0.0
Prediction for [0, 0, 1, 0, 0, 1, 1, 1, 0]: 0.0
Prediction for [0, 0, 1, 0, 0, 1, 1, 1, 1]: 1.0004561835061123
Prediction for [0, 0, 1, 0, 1, 0, 0, 0, 0]: 0.0
Prediction for [0, 0, 1, 0, 1, 0, 0, 0, 1]: 0.0
Prediction for [0, 0, 1, 0, 1, 0, 0, 1, 0]: 0.0
Prediction for [0, 0, 1, 0, 1, 0, 0, 1, 1]: 0.9987012057436155
Prediction for [0, 0, 1, 0, 1, 0, 1, 0, 0]: 0.0
Prediction for [0, 0, 1, 0, 1, 0, 1, 0, 1]: 0.0
Prediction for [0, 0, 1, 0, 1, 0, 1, 1, 0]: 0.0
Prediction for [0, 0, 1, 0, 1, 0, 1, 1, 1]: 0.0
Prediction for [0, 0, 1, 0, 1, 1, 0, 0, 0]: 0.0
Prediction for [0, 0, 1, 0, 1, 1, 0, 0, 1]: 0.9984621571718089
Prediction for [0, 0, 1, 0, 1, 1, 0, 1, 0]: 0.0
Prediction for [0, 0, 1, 0, 1, 1, 0, 1, 1]: 0.0
Prediction for [0, 0, 1, 0, 1, 1, 1, 0, 0]: 0.0
Prediction for [0, 0, 1, 0, 1, 1, 1, 0, 1]: 0.0
Prediction for [0, 0, 1, 0, 1, 1, 1, 1, 0]: 0.0
Prediction for [0, 0, 1, 0, 1, 1, 1, 1, 1]: 0.0011346559539746615
Prediction for [0, 0, 1, 1, 0, 0, 0, 0, 0]: 0.0
Prediction for [0, 0, 1, 1, 0, 0, 0, 0, 1]: 0.9954658664640776
Prediction for [0, 0, 1, 1, 0, 0, 0, 1, 0]: 0.0
Prediction for [0, 0, 1, 1, 0, 0, 0, 1, 1]: 0.0
Prediction for [0, 0, 1, 1, 0, 0, 1, 0, 0]: 0.0
Prediction for [0, 0, 1, 1, 0, 0, 1, 0, 1]: 0.9987444506721506
Prediction for [0, 0, 1, 1, 0, 0, 1, 1, 0]: 0.0
Prediction for [0, 0, 1, 1, 0, 0, 1, 1, 1]: 1.0032651415297291
Prediction for [0, 0, 1, 1, 0, 1, 0, 0, 0]: 0.0
Prediction for [0, 0, 1, 1, 0, 1, 0, 0, 1]: 0.0
Prediction for [0, 0, 1, 1, 0, 1, 0, 1, 0]: 0.0
Prediction for [0, 0, 1, 1, 0, 1, 0, 1, 1]: 1.0009779538084969
Prediction for [0, 0, 1, 1, 0, 1, 1, 0, 0]: 0.0
Prediction for [0, 0, 1, 1, 0, 1, 1, 0, 1]: 0.9950502153396128
Prediction for [0, 0, 1, 1, 0, 1, 1, 1, 0]: 0.0
Prediction for [0, 0, 1, 1, 0, 1, 1, 1, 1]: 0.0
Prediction for [0, 0, 1, 1, 1, 0, 0, 0, 0]: 0.0
Prediction for [0, 0, 1, 1, 1, 0, 0, 0, 1]: 1.0072640440983252
Prediction for [0, 0, 1, 1, 1, 0, 0, 1, 0]: 0.0
Prediction for [0, 0, 1, 1, 1, 0, 0, 1, 1]: 0.0
Prediction for [0, 0, 1, 1, 1, 0, 1, 0, 0]: 0.0
Prediction for [0, 0, 1, 1, 1, 0, 1, 0, 1]: 0.0
Prediction for [0, 0, 1, 1, 1, 0, 1, 1, 0]: 0.0
Prediction for [0, 0, 1, 1, 1, 0, 1, 1, 1]: 0.0
Prediction for [0, 0, 1, 1, 1, 1, 0, 0, 0]: 0.0
Prediction for [0, 0, 1, 1, 1, 1, 0, 0, 1]: 0.0024867453421006935
Prediction for [0, 0, 1, 1, 1, 1, 0, 1, 0]: 0.0
Prediction for [0, 0, 1, 1, 1, 1, 0, 1, 1]: 0.0
Prediction for [0, 0, 1, 1, 1, 1, 1, 0, 0]: 0.0
Prediction for [0, 0, 1, 1, 1, 1, 1, 0, 1]: 0.003951581166587115
Prediction for [0, 0, 1, 1, 1, 1, 1, 1, 0]: 0.0
Prediction for [0, 0, 1, 1, 1, 1, 1, 1, 1]: 1.0099197376094073
Prediction for [0, 1, 0, 0, 0, 0, 0, 0, 0]: 0.0
Prediction for [0, 1, 0, 0, 0, 0, 0, 0, 1]: 0.0
Prediction for [0, 1, 0, 0, 0, 0, 0, 1, 0]: 0.0
Prediction for [0, 1, 0, 0, 0, 0, 0, 1, 1]: 1.0003574604694494
Prediction for [0, 1, 0, 0, 0, 0, 1, 0, 0]: 0.0
Prediction for [0, 1, 0, 0, 0, 0, 1, 0, 1]: 0.008034098569987602
Prediction for [0, 1, 0, 0, 0, 0, 1, 1, 0]: 0.0
Prediction for [0, 1, 0, 0, 0, 0, 1, 1, 1]: 0.0
Prediction for [0, 1, 0, 0, 0, 1, 0, 0, 0]: 0.0
Prediction for [0, 1, 0, 0, 0, 1, 0, 0, 1]: 1.003767365944774
Prediction for [0, 1, 0, 0, 0, 1, 0, 1, 0]: 0.0
Prediction for [0, 1, 0, 0, 0, 1, 0, 1, 1]: 1.0004039877691122
Prediction for [0, 1, 0, 0, 0, 1, 1, 0, 0]: 0.0
Prediction for [0, 1, 0, 0, 0, 1, 1, 0, 1]: 0.0
Prediction for [0, 1, 0, 0, 0, 1, 1, 1, 0]: 0.0
Prediction for [0, 1, 0, 0, 0, 1, 1, 1, 1]: 0.002685637474658442
Prediction for [0, 1, 0, 0, 1, 0, 0, 0, 0]: 0.0
Prediction for [0, 1, 0, 0, 1, 0, 0, 0, 1]: 0.040853334077697756
Prediction for [0, 1, 0, 0, 1, 0, 0, 1, 0]: 0.0
Prediction for [0, 1, 0, 0, 1, 0, 0, 1, 1]: 0.0
Prediction for [0, 1, 0, 0, 1, 0, 1, 0, 0]: 0.0
Prediction for [0, 1, 0, 0, 1, 0, 1, 0, 1]: 1.0119659306907662
Prediction for [0, 1, 0, 0, 1, 0, 1, 1, 0]: 0.0
Prediction for [0, 1, 0, 0, 1, 0, 1, 1, 1]: 1.0088770661412285
Prediction for [0, 1, 0, 0, 1, 1, 0, 0, 0]: 0.0
Prediction for [0, 1, 0, 0, 1, 1, 0, 0, 1]: 0.0
Prediction for [0, 1, 0, 0, 1, 1, 0, 1, 0]: 0.0
Prediction for [0, 1, 0, 0, 1, 1, 0, 1, 1]: 0.0
Prediction for [0, 1, 0, 0, 1, 1, 1, 0, 0]: 0.0
Prediction for [0, 1, 0, 0, 1, 1, 1, 0, 1]: 1.0078258598722476
Prediction for [0, 1, 0, 0, 1, 1, 1, 1, 0]: 0.0
Prediction for [0, 1, 0, 0, 1, 1, 1, 1, 1]: 0.0
Prediction for [0, 1, 0, 1, 0, 0, 0, 0, 0]: 0.0
Prediction for [0, 1, 0, 1, 0, 0, 0, 0, 1]: 0.010865775047043336
Prediction for [0, 1, 0, 1, 0, 0, 0, 1, 0]: 0.0
Prediction for [0, 1, 0, 1, 0, 0, 0, 1, 1]: 1.0026820538452599
Prediction for [0, 1, 0, 1, 0, 0, 1, 0, 0]: 0.0
Prediction for [0, 1, 0, 1, 0, 0, 1, 0, 1]: 0.0
Prediction for [0, 1, 0, 1, 0, 0, 1, 1, 0]: 0.0
Prediction for [0, 1, 0, 1, 0, 0, 1, 1, 1]: 1.0103824777788928
Prediction for [0, 1, 0, 1, 0, 1, 0, 0, 0]: 0.0
Prediction for [0, 1, 0, 1, 0, 1, 0, 0, 1]: 0.008447198771444953
Prediction for [0, 1, 0, 1, 0, 1, 0, 1, 0]: 0.0
Prediction for [0, 1, 0, 1, 0, 1, 0, 1, 1]: 0.0
Prediction for [0, 1, 0, 1, 0, 1, 1, 0, 0]: 0.0
Prediction for [0, 1, 0, 1, 0, 1, 1, 0, 1]: 1.0106172905539639
Prediction for [0, 1, 0, 1, 0, 1, 1, 1, 0]: 0.0
Prediction for [0, 1, 0, 1, 0, 1, 1, 1, 1]: 0.016426590280938846
Prediction for [0, 1, 0, 1, 1, 0, 0, 0, 0]: 0.0
Prediction for [0, 1, 0, 1, 1, 0, 0, 0, 1]: 0.0
Prediction for [0, 1, 0, 1, 1, 0, 0, 1, 0]: 0.0
Prediction for [0, 1, 0, 1, 1, 0, 0, 1, 1]: 1.0147024963749471
Prediction for [0, 1, 0, 1, 1, 0, 1, 0, 0]: 0.0
Prediction for [0, 1, 0, 1, 1, 0, 1, 0, 1]: 0.981471700164998
Prediction for [0, 1, 0, 1, 1, 0, 1, 1, 0]: 0.0
Prediction for [0, 1, 0, 1, 1, 0, 1, 1, 1]: 0.0
Prediction for [0, 1, 0, 1, 1, 1, 0, 0, 0]: 0.0
Prediction for [0, 1, 0, 1, 1, 1, 0, 0, 1]: 0.0
Prediction for [0, 1, 0, 1, 1, 1, 0, 1, 0]: 0.0
Prediction for [0, 1, 0, 1, 1, 1, 0, 1, 1]: 0.006292934771463088
Prediction for [0, 1, 0, 1, 1, 1, 1, 0, 0]: 0.0
Prediction for [0, 1, 0, 1, 1, 1, 1, 0, 1]: 0.0
Prediction for [0, 1, 0, 1, 1, 1, 1, 1, 0]: 0.0
Prediction for [0, 1, 0, 1, 1, 1, 1, 1, 1]: 1.0010245701603866
Prediction for [0, 1, 1, 0, 0, 0, 0, 0, 0]: 0.0
Prediction for [0, 1, 1, 0, 0, 0, 0, 0, 1]: 0.9852238389874755
Prediction for [0, 1, 1, 0, 0, 0, 0, 1, 0]: 0.0
Prediction for [0, 1, 1, 0, 0, 0, 0, 1, 1]: 0.0
Prediction for [0, 1, 1, 0, 0, 0, 1, 0, 0]: 0.0
Prediction for [0, 1, 1, 0, 0, 0, 1, 0, 1]: 0.9884693909925657
Prediction for [0, 1, 1, 0, 0, 0, 1, 1, 0]: 0.0
Prediction for [0, 1, 1, 0, 0, 0, 1, 1, 1]: 0.9908295627355495
Prediction for [0, 1, 1, 0, 0, 1, 0, 0, 0]: 0.0
Prediction for [0, 1, 1, 0, 0, 1, 0, 0, 1]: 0.0
Prediction for [0, 1, 1, 0, 0, 1, 0, 1, 0]: 0.0
Prediction for [0, 1, 1, 0, 0, 1, 0, 1, 1]: 0.0
Prediction for [0, 1, 1, 0, 0, 1, 1, 0, 0]: 0.0
Prediction for [0, 1, 1, 0, 0, 1, 1, 0, 1]: 0.0
Prediction for [0, 1, 1, 0, 0, 1, 1, 1, 0]: 0.0
Prediction for [0, 1, 1, 0, 0, 1, 1, 1, 1]: 0.0
Prediction for [0, 1, 1, 0, 1, 0, 0, 0, 0]: 0.0
Prediction for [0, 1, 1, 0, 1, 0, 0, 0, 1]: 0.0
Prediction for [0, 1, 1, 0, 1, 0, 0, 1, 0]: 0.0
Prediction for [0, 1, 1, 0, 1, 0, 0, 1, 1]: 0.9717382559173222
Prediction for [0, 1, 1, 0, 1, 0, 1, 0, 0]: 0.0
Prediction for [0, 1, 1, 0, 1, 0, 1, 0, 1]: 0.0
Prediction for [0, 1, 1, 0, 1, 0, 1, 1, 0]: 0.0
Prediction for [0, 1, 1, 0, 1, 0, 1, 1, 1]: 0.0
Prediction for [0, 1, 1, 0, 1, 1, 0, 0, 0]: 0.0
Prediction for [0, 1, 1, 0, 1, 1, 0, 0, 1]: 0.0
Prediction for [0, 1, 1, 0, 1, 1, 0, 1, 0]: 0.0
Prediction for [0, 1, 1, 0, 1, 1, 0, 1, 1]: 0.0
Prediction for [0, 1, 1, 0, 1, 1, 1, 0, 0]: 0.0
Prediction for [0, 1, 1, 0, 1, 1, 1, 0, 1]: 0.0
Prediction for [0, 1, 1, 0, 1, 1, 1, 1, 0]: 0.0
Prediction for [0, 1, 1, 0, 1, 1, 1, 1, 1]: 0.9882074199299513
Prediction for [0, 1, 1, 1, 0, 0, 0, 0, 0]: 0.0
Prediction for [0, 1, 1, 1, 0, 0, 0, 0, 1]: 0.0
Prediction for [0, 1, 1, 1, 0, 0, 0, 1, 0]: 0.0
Prediction for [0, 1, 1, 1, 0, 0, 0, 1, 1]: 1.0009047154183124
Prediction for [0, 1, 1, 1, 0, 0, 1, 0, 0]: 0.0
Prediction for [0, 1, 1, 1, 0, 0, 1, 0, 1]: 1.0060251513369316
Prediction for [0, 1, 1, 1, 0, 0, 1, 1, 0]: 0.0
Prediction for [0, 1, 1, 1, 0, 0, 1, 1, 1]: 0.0
Prediction for [0, 1, 1, 1, 0, 1, 0, 0, 0]: 0.0
Prediction for [0, 1, 1, 1, 0, 1, 0, 0, 1]: 0.9992772409937345
Prediction for [0, 1, 1, 1, 0, 1, 0, 1, 0]: 0.0
Prediction for [0, 1, 1, 1, 0, 1, 0, 1, 1]: 0.0
Prediction for [0, 1, 1, 1, 0, 1, 1, 0, 0]: 0.0
Prediction for [0, 1, 1, 1, 0, 1, 1, 0, 1]: 0.0
Prediction for [0, 1, 1, 1, 0, 1, 1, 1, 0]: 0.0
Prediction for [0, 1, 1, 1, 0, 1, 1, 1, 1]: 0.9940313730861083
Prediction for [0, 1, 1, 1, 1, 0, 0, 0, 0]: 0.0
Prediction for [0, 1, 1, 1, 1, 0, 0, 0, 1]: 0.6114210701473386
Prediction for [0, 1, 1, 1, 1, 0, 0, 1, 0]: 0.0
Prediction for [0, 1, 1, 1, 1, 0, 0, 1, 1]: 0.0
Prediction for [0, 1, 1, 1, 1, 0, 1, 0, 0]: 0.0
Prediction for [0, 1, 1, 1, 1, 0, 1, 0, 1]: 0.0
Prediction for [0, 1, 1, 1, 1, 0, 1, 1, 0]: 0.0
Prediction for [0, 1, 1, 1, 1, 0, 1, 1, 1]: 0.18617672247726214
Prediction for [0, 1, 1, 1, 1, 1, 0, 0, 0]: 0.0
Prediction for [0, 1, 1, 1, 1, 1, 0, 0, 1]: 0.0
Prediction for [0, 1, 1, 1, 1, 1, 0, 1, 0]: 0.0
Prediction for [0, 1, 1, 1, 1, 1, 0, 1, 1]: 1.009648230629593
Prediction for [0, 1, 1, 1, 1, 1, 1, 0, 0]: 0.0
Prediction for [0, 1, 1, 1, 1, 1, 1, 0, 1]: 0.0
Prediction for [0, 1, 1, 1, 1, 1, 1, 1, 0]: 0.0
Prediction for [0, 1, 1, 1, 1, 1, 1, 1, 1]: 0.0
Prediction for [1, 0, 0, 0, 0, 0, 0, 0, 0]: 0.0
Prediction for [1, 0, 0, 0, 0, 0, 0, 0, 1]: 0.9903749254970613
Prediction for [1, 0, 0, 0, 0, 0, 0, 1, 0]: 0.0
Prediction for [1, 0, 0, 0, 0, 0, 0, 1, 1]: 0.0
Prediction for [1, 0, 0, 0, 0, 0, 1, 0, 0]: 0.0
Prediction for [1, 0, 0, 0, 0, 0, 1, 0, 1]: 0.003299527614217812
Prediction for [1, 0, 0, 0, 0, 0, 1, 1, 0]: 0.0
Prediction for [1, 0, 0, 0, 0, 0, 1, 1, 1]: 0.9944588157043315
Prediction for [1, 0, 0, 0, 0, 1, 0, 0, 0]: 0.0
Prediction for [1, 0, 0, 0, 0, 1, 0, 0, 1]: 0.0
Prediction for [1, 0, 0, 0, 0, 1, 0, 1, 0]: 0.0
Prediction for [1, 0, 0, 0, 0, 1, 0, 1, 1]: 0.0
Prediction for [1, 0, 0, 0, 0, 1, 1, 0, 0]: 0.0
Prediction for [1, 0, 0, 0, 0, 1, 1, 0, 1]: 1.0038817245799683
Prediction for [1, 0, 0, 0, 0, 1, 1, 1, 0]: 0.0
Prediction for [1, 0, 0, 0, 0, 1, 1, 1, 1]: 1.0097622139159244
Prediction for [1, 0, 0, 0, 1, 0, 0, 0, 0]: 0.0
Prediction for [1, 0, 0, 0, 1, 0, 0, 0, 1]: 0.001327405061337661
Prediction for [1, 0, 0, 0, 1, 0, 0, 1, 0]: 0.0
Prediction for [1, 0, 0, 0, 1, 0, 0, 1, 1]: 0.0
Prediction for [1, 0, 0, 0, 1, 0, 1, 0, 0]: 0.0
Prediction for [1, 0, 0, 0, 1, 0, 1, 0, 1]: 1.0034214532504366
Prediction for [1, 0, 0, 0, 1, 0, 1, 1, 0]: 0.0
Prediction for [1, 0, 0, 0, 1, 0, 1, 1, 1]: 0.0
Prediction for [1, 0, 0, 0, 1, 1, 0, 0, 0]: 0.0
Prediction for [1, 0, 0, 0, 1, 1, 0, 0, 1]: 1.0019759765421536
Prediction for [1, 0, 0, 0, 1, 1, 0, 1, 0]: 0.0
Prediction for [1, 0, 0, 0, 1, 1, 0, 1, 1]: 1.0026692260329328
Prediction for [1, 0, 0, 0, 1, 1, 1, 0, 0]: 0.0
Prediction for [1, 0, 0, 0, 1, 1, 1, 0, 1]: 0.0
Prediction for [1, 0, 0, 0, 1, 1, 1, 1, 0]: 0.0
Prediction for [1, 0, 0, 0, 1, 1, 1, 1, 1]: 0.0
Prediction for [1, 0, 0, 1, 0, 0, 0, 0, 0]: 0.0
Prediction for [1, 0, 0, 1, 0, 0, 0, 0, 1]: 0.0
Prediction for [1, 0, 0, 1, 0, 0, 0, 1, 0]: 0.0
Prediction for [1, 0, 0, 1, 0, 0, 0, 1, 1]: 0.0
Prediction for [1, 0, 0, 1, 0, 0, 1, 0, 0]: 0.0
Prediction for [1, 0, 0, 1, 0, 0, 1, 0, 1]: 0.9913182367838012
Prediction for [1, 0, 0, 1, 0, 0, 1, 1, 0]: 0.0
Prediction for [1, 0, 0, 1, 0, 0, 1, 1, 1]: 0.0
Prediction for [1, 0, 0, 1, 0, 1, 0, 0, 0]: 0.0
Prediction for [1, 0, 0, 1, 0, 1, 0, 0, 1]: 0.0
Prediction for [1, 0, 0, 1, 0, 1, 0, 1, 0]: 0.0
Prediction for [1, 0, 0, 1, 0, 1, 0, 1, 1]: 0.0
Prediction for [1, 0, 0, 1, 0, 1, 1, 0, 0]: 0.0
Prediction for [1, 0, 0, 1, 0, 1, 1, 0, 1]: 0.0
Prediction for [1, 0, 0, 1, 0, 1, 1, 1, 0]: 0.0
Prediction for [1, 0, 0, 1, 0, 1, 1, 1, 1]: 0.0
Prediction for [1, 0, 0, 1, 1, 0, 0, 0, 0]: 0.0
Prediction for [1, 0, 0, 1, 1, 0, 0, 0, 1]: 0.0
Prediction for [1, 0, 0, 1, 1, 0, 0, 1, 0]: 0.0
Prediction for [1, 0, 0, 1, 1, 0, 0, 1, 1]: 0.9884657828164292
Prediction for [1, 0, 0, 1, 1, 0, 1, 0, 0]: 0.0
Prediction for [1, 0, 0, 1, 1, 0, 1, 0, 1]: 0.0
Prediction for [1, 0, 0, 1, 1, 0, 1, 1, 0]: 0.0
Prediction for [1, 0, 0, 1, 1, 0, 1, 1, 1]: 1.007706869050522
Prediction for [1, 0, 0, 1, 1, 1, 0, 0, 0]: 0.0
Prediction for [1, 0, 0, 1, 1, 1, 0, 0, 1]: 0.9905506184428328
Prediction for [1, 0, 0, 1, 1, 1, 0, 1, 0]: 0.0
Prediction for [1, 0, 0, 1, 1, 1, 0, 1, 1]: 0.0
Prediction for [1, 0, 0, 1, 1, 1, 1, 0, 0]: 0.0
Prediction for [1, 0, 0, 1, 1, 1, 1, 0, 1]: 0.9908097283399391
Prediction for [1, 0, 0, 1, 1, 1, 1, 1, 0]: 0.0
Prediction for [1, 0, 0, 1, 1, 1, 1, 1, 1]: 0.018700973863153614
Prediction for [1, 0, 1, 0, 0, 0, 0, 0, 0]: 0.0
Prediction for [1, 0, 1, 0, 0, 0, 0, 0, 1]: 0.0
Prediction for [1, 0, 1, 0, 0, 0, 0, 1, 0]: 0.0
Prediction for [1, 0, 1, 0, 0, 0, 0, 1, 1]: 0.0
Prediction for [1, 0, 1, 0, 0, 0, 1, 0, 0]: 0.0
Prediction for [1, 0, 1, 0, 0, 0, 1, 0, 1]: 0.0
Prediction for [1, 0, 1, 0, 0, 0, 1, 1, 0]: 0.0
Prediction for [1, 0, 1, 0, 0, 0, 1, 1, 1]: 0.010484141864332663
Prediction for [1, 0, 1, 0, 0, 1, 0, 0, 0]: 0.0
Prediction for [1, 0, 1, 0, 0, 1, 0, 0, 1]: 0.0
Prediction for [1, 0, 1, 0, 0, 1, 0, 1, 0]: 0.0
Prediction for [1, 0, 1, 0, 0, 1, 0, 1, 1]: 0.9999368432780287
Prediction for [1, 0, 1, 0, 0, 1, 1, 0, 0]: 0.0
Prediction for [1, 0, 1, 0, 0, 1, 1, 0, 1]: 0.0
Prediction for [1, 0, 1, 0, 0, 1, 1, 1, 0]: 0.0
Prediction for [1, 0, 1, 0, 0, 1, 1, 1, 1]: 0.00894501653227886
Prediction for [1, 0, 1, 0, 1, 0, 0, 0, 0]: 6.325675827958399E-4
Prediction for [1, 0, 1, 0, 1, 0, 0, 0, 1]: 0.9926826333628851
Prediction for [1, 0, 1, 0, 1, 0, 0, 1, 0]: 0.0
Prediction for [1, 0, 1, 0, 1, 0, 0, 1, 1]: 0.0
Prediction for [1, 0, 1, 0, 1, 0, 1, 0, 0]: 0.0
Prediction for [1, 0, 1, 0, 1, 0, 1, 0, 1]: 0.0011776294270866572
Prediction for [1, 0, 1, 0, 1, 0, 1, 1, 0]: 0.0
Prediction for [1, 0, 1, 0, 1, 0, 1, 1, 1]: 0.0
Prediction for [1, 0, 1, 0, 1, 1, 0, 0, 0]: 0.0
Prediction for [1, 0, 1, 0, 1, 1, 0, 0, 1]: 0.0
Prediction for [1, 0, 1, 0, 1, 1, 0, 1, 0]: 0.0
Prediction for [1, 0, 1, 0, 1, 1, 0, 1, 1]: 1.0025469896244266
Prediction for [1, 0, 1, 0, 1, 1, 1, 0, 0]: 0.0
Prediction for [1, 0, 1, 0, 1, 1, 1, 0, 1]: 1.0009280034665604
Prediction for [1, 0, 1, 0, 1, 1, 1, 1, 0]: 0.0
Prediction for [1, 0, 1, 0, 1, 1, 1, 1, 1]: 0.0
Prediction for [1, 0, 1, 1, 0, 0, 0, 0, 0]: 0.0
Prediction for [1, 0, 1, 1, 0, 0, 0, 0, 1]: 0.9815680022422706
Prediction for [1, 0, 1, 1, 0, 0, 0, 1, 0]: 0.0
Prediction for [1, 0, 1, 1, 0, 0, 0, 1, 1]: 0.0
Prediction for [1, 0, 1, 1, 0, 0, 1, 0, 0]: 0.0
Prediction for [1, 0, 1, 1, 0, 0, 1, 0, 1]: 0.0
Prediction for [1, 0, 1, 1, 0, 0, 1, 1, 0]: 0.0
Prediction for [1, 0, 1, 1, 0, 0, 1, 1, 1]: 0.9921492505637959
Prediction for [1, 0, 1, 1, 0, 1, 0, 0, 0]: 0.0
Prediction for [1, 0, 1, 1, 0, 1, 0, 0, 1]: 0.0
Prediction for [1, 0, 1, 1, 0, 1, 0, 1, 0]: 0.0
Prediction for [1, 0, 1, 1, 0, 1, 0, 1, 1]: 0.0
Prediction for [1, 0, 1, 1, 0, 1, 1, 0, 0]: 0.0
Prediction for [1, 0, 1, 1, 0, 1, 1, 0, 1]: 0.0
Prediction for [1, 0, 1, 1, 0, 1, 1, 1, 0]: 0.0
Prediction for [1, 0, 1, 1, 0, 1, 1, 1, 1]: 1.0058506535338259
Prediction for [1, 0, 1, 1, 1, 0, 0, 0, 0]: 0.0
Prediction for [1, 0, 1, 1, 1, 0, 0, 0, 1]: 0.0
Prediction for [1, 0, 1, 1, 1, 0, 0, 1, 0]: 0.0
Prediction for [1, 0, 1, 1, 1, 0, 0, 1, 1]: 0.0
Prediction for [1, 0, 1, 1, 1, 0, 1, 0, 0]: 0.0
Prediction for [1, 0, 1, 1, 1, 0, 1, 0, 1]: 0.9842785949786164
Prediction for [1, 0, 1, 1, 1, 0, 1, 1, 0]: 0.0
Prediction for [1, 0, 1, 1, 1, 0, 1, 1, 1]: 0.0
Prediction for [1, 0, 1, 1, 1, 1, 0, 0, 0]: 0.0
Prediction for [1, 0, 1, 1, 1, 1, 0, 0, 1]: 0.0
Prediction for [1, 0, 1, 1, 1, 1, 0, 1, 0]: 0.0
Prediction for [1, 0, 1, 1, 1, 1, 0, 1, 1]: 0.9860285790062422
Prediction for [1, 0, 1, 1, 1, 1, 1, 0, 0]: 0.0
Prediction for [1, 0, 1, 1, 1, 1, 1, 0, 1]: 0.0
Prediction for [1, 0, 1, 1, 1, 1, 1, 1, 0]: 0.0
Prediction for [1, 0, 1, 1, 1, 1, 1, 1, 1]: 1.0012687993220935
Prediction for [1, 1, 0, 0, 0, 0, 0, 0, 0]: 0.0
Prediction for [1, 1, 0, 0, 0, 0, 0, 0, 1]: 3.6797827335055544E-4
Prediction for [1, 1, 0, 0, 0, 0, 0, 1, 0]: 0.0
Prediction for [1, 1, 0, 0, 0, 0, 0, 1, 1]: 0.0
Prediction for [1, 1, 0, 0, 0, 0, 1, 0, 0]: 0.0
Prediction for [1, 1, 0, 0, 0, 0, 1, 0, 1]: 1.0067705093460706
Prediction for [1, 1, 0, 0, 0, 0, 1, 1, 0]: 0.0
Prediction for [1, 1, 0, 0, 0, 0, 1, 1, 1]: 0.0
Prediction for [1, 1, 0, 0, 0, 1, 0, 0, 0]: 0.0
Prediction for [1, 1, 0, 0, 0, 1, 0, 0, 1]: 0.0
Prediction for [1, 1, 0, 0, 0, 1, 0, 1, 0]: 0.0
Prediction for [1, 1, 0, 0, 0, 1, 0, 1, 1]: 0.0
Prediction for [1, 1, 0, 0, 0, 1, 1, 0, 0]: 0.0
Prediction for [1, 1, 0, 0, 0, 1, 1, 0, 1]: 1.0074994256525693
Prediction for [1, 1, 0, 0, 0, 1, 1, 1, 0]: 0.0
Prediction for [1, 1, 0, 0, 0, 1, 1, 1, 1]: 0.0
Prediction for [1, 1, 0, 0, 1, 0, 0, 0, 0]: 0.0
Prediction for [1, 1, 0, 0, 1, 0, 0, 0, 1]: 0.9792758396862498
Prediction for [1, 1, 0, 0, 1, 0, 0, 1, 0]: 0.0
Prediction for [1, 1, 0, 0, 1, 0, 0, 1, 1]: 0.027108220939364713
Prediction for [1, 1, 0, 0, 1, 0, 1, 0, 0]: 0.0
Prediction for [1, 1, 0, 0, 1, 0, 1, 0, 1]: 0.01103436325550744
Prediction for [1, 1, 0, 0, 1, 0, 1, 1, 0]: 0.0
Prediction for [1, 1, 0, 0, 1, 0, 1, 1, 1]: 0.0
Prediction for [1, 1, 0, 0, 1, 1, 0, 0, 0]: 0.0
Prediction for [1, 1, 0, 0, 1, 1, 0, 0, 1]: 0.9900451173410119
Prediction for [1, 1, 0, 0, 1, 1, 0, 1, 0]: 0.0
Prediction for [1, 1, 0, 0, 1, 1, 0, 1, 1]: 0.0
Prediction for [1, 1, 0, 0, 1, 1, 1, 0, 0]: 0.0
Prediction for [1, 1, 0, 0, 1, 1, 1, 0, 1]: 0.0
Prediction for [1, 1, 0, 0, 1, 1, 1, 1, 0]: 0.0
Prediction for [1, 1, 0, 0, 1, 1, 1, 1, 1]: 0.0
Prediction for [1, 1, 0, 1, 0, 0, 0, 0, 0]: 0.0
Prediction for [1, 1, 0, 1, 0, 0, 0, 0, 1]: 0.0
Prediction for [1, 1, 0, 1, 0, 0, 0, 1, 0]: 0.0
Prediction for [1, 1, 0, 1, 0, 0, 0, 1, 1]: 1.0025522830201572
Prediction for [1, 1, 0, 1, 0, 0, 1, 0, 0]: 0.0
Prediction for [1, 1, 0, 1, 0, 0, 1, 0, 1]: 0.9729903417171206
Prediction for [1, 1, 0, 1, 0, 0, 1, 1, 0]: 0.0
Prediction for [1, 1, 0, 1, 0, 0, 1, 1, 1]: 0.0
Prediction for [1, 1, 0, 1, 0, 1, 0, 0, 0]: 0.0
Prediction for [1, 1, 0, 1, 0, 1, 0, 0, 1]: 0.0
Prediction for [1, 1, 0, 1, 0, 1, 0, 1, 0]: 0.0
Prediction for [1, 1, 0, 1, 0, 1, 0, 1, 1]: 0.0
Prediction for [1, 1, 0, 1, 0, 1, 1, 0, 0]: 0.0
Prediction for [1, 1, 0, 1, 0, 1, 1, 0, 1]: 0.0
Prediction for [1, 1, 0, 1, 0, 1, 1, 1, 0]: 0.0
Prediction for [1, 1, 0, 1, 0, 1, 1, 1, 1]: 1.008686034895221
Prediction for [1, 1, 0, 1, 1, 0, 0, 0, 0]: 0.0
Prediction for [1, 1, 0, 1, 1, 0, 0, 0, 1]: 1.360766414161855
Prediction for [1, 1, 0, 1, 1, 0, 0, 1, 0]: 0.0
Prediction for [1, 1, 0, 1, 1, 0, 0, 1, 1]: 0.0
Prediction for [1, 1, 0, 1, 1, 0, 1, 0, 0]: 0.0
Prediction for [1, 1, 0, 1, 1, 0, 1, 0, 1]: 0.20498420737336076
Prediction for [1, 1, 0, 1, 1, 0, 1, 1, 0]: 0.0
Prediction for [1, 1, 0, 1, 1, 0, 1, 1, 1]: 0.8729569816038216
Prediction for [1, 1, 0, 1, 1, 1, 0, 0, 0]: 0.0
Prediction for [1, 1, 0, 1, 1, 1, 0, 0, 1]: 0.0
Prediction for [1, 1, 0, 1, 1, 1, 0, 1, 0]: 0.0
Prediction for [1, 1, 0, 1, 1, 1, 0, 1, 1]: 0.9954470605798269
Prediction for [1, 1, 0, 1, 1, 1, 1, 0, 0]: 0.0
Prediction for [1, 1, 0, 1, 1, 1, 1, 0, 1]: 8.63413805419988E-4
Prediction for [1, 1, 0, 1, 1, 1, 1, 1, 0]: 0.0
Prediction for [1, 1, 0, 1, 1, 1, 1, 1, 1]: 0.0
Prediction for [1, 1, 1, 0, 0, 0, 0, 0, 0]: 0.0
Prediction for [1, 1, 1, 0, 0, 0, 0, 0, 1]: 0.9951285994093197
Prediction for [1, 1, 1, 0, 0, 0, 0, 1, 0]: 0.0
Prediction for [1, 1, 1, 0, 0, 0, 0, 1, 1]: 0.0
Prediction for [1, 1, 1, 0, 0, 0, 1, 0, 0]: 0.0
Prediction for [1, 1, 1, 0, 0, 0, 1, 0, 1]: 0.008758316115685894
Prediction for [1, 1, 1, 0, 0, 0, 1, 1, 0]: 0.0
Prediction for [1, 1, 1, 0, 0, 0, 1, 1, 1]: 0.012906319551016399
Prediction for [1, 1, 1, 0, 0, 1, 0, 0, 0]: 0.0
Prediction for [1, 1, 1, 0, 0, 1, 0, 0, 1]: 0.9958829827559148
Prediction for [1, 1, 1, 0, 0, 1, 0, 1, 0]: 0.0
Prediction for [1, 1, 1, 0, 0, 1, 0, 1, 1]: 0.0
Prediction for [1, 1, 1, 0, 0, 1, 1, 0, 0]: 0.0
Prediction for [1, 1, 1, 0, 0, 1, 1, 0, 1]: 1.0013555786723716
Prediction for [1, 1, 1, 0, 0, 1, 1, 1, 0]: 0.0
Prediction for [1, 1, 1, 0, 0, 1, 1, 1, 1]: 0.9934860996879324
Prediction for [1, 1, 1, 0, 1, 0, 0, 0, 0]: 0.0
Prediction for [1, 1, 1, 0, 1, 0, 0, 0, 1]: 0.002462085708445194
Prediction for [1, 1, 1, 0, 1, 0, 0, 1, 0]: 0.0
Prediction for [1, 1, 1, 0, 1, 0, 0, 1, 1]: 1.0125432454301517
Prediction for [1, 1, 1, 0, 1, 0, 1, 0, 0]: 0.0
Prediction for [1, 1, 1, 0, 1, 0, 1, 0, 1]: 0.0
Prediction for [1, 1, 1, 0, 1, 0, 1, 1, 0]: 0.0
Prediction for [1, 1, 1, 0, 1, 0, 1, 1, 1]: 0.0
Prediction for [1, 1, 1, 0, 1, 1, 0, 0, 0]: 0.0
Prediction for [1, 1, 1, 0, 1, 1, 0, 0, 1]: 0.0
Prediction for [1, 1, 1, 0, 1, 1, 0, 1, 0]: 0.0
Prediction for [1, 1, 1, 0, 1, 1, 0, 1, 1]: 0.008469765837671783
Prediction for [1, 1, 1, 0, 1, 1, 1, 0, 0]: 0.0
Prediction for [1, 1, 1, 0, 1, 1, 1, 0, 1]: 0.0
Prediction for [1, 1, 1, 0, 1, 1, 1, 1, 0]: 0.0
Prediction for [1, 1, 1, 0, 1, 1, 1, 1, 1]: 1.0039194342673046
Prediction for [1, 1, 1, 1, 0, 0, 0, 0, 0]: 0.0
Prediction for [1, 1, 1, 1, 0, 0, 0, 0, 1]: 0.0
Prediction for [1, 1, 1, 1, 0, 0, 0, 1, 0]: 0.0
Prediction for [1, 1, 1, 1, 0, 0, 0, 1, 1]: 0.0
Prediction for [1, 1, 1, 1, 0, 0, 1, 0, 0]: 0.0
Prediction for [1, 1, 1, 1, 0, 0, 1, 0, 1]: 0.0
Prediction for [1, 1, 1, 1, 0, 0, 1, 1, 0]: 0.0
Prediction for [1, 1, 1, 1, 0, 0, 1, 1, 1]: 1.007929690559581
Prediction for [1, 1, 1, 1, 0, 1, 0, 0, 0]: 0.0
Prediction for [1, 1, 1, 1, 0, 1, 0, 0, 1]: 0.0
Prediction for [1, 1, 1, 1, 0, 1, 0, 1, 0]: 0.0
Prediction for [1, 1, 1, 1, 0, 1, 0, 1, 1]: 1.003705966757133
Prediction for [1, 1, 1, 1, 0, 1, 1, 0, 0]: 0.0
Prediction for [1, 1, 1, 1, 0, 1, 1, 0, 1]: 0.0070895358138693965
Prediction for [1, 1, 1, 1, 0, 1, 1, 1, 0]: 0.0
Prediction for [1, 1, 1, 1, 0, 1, 1, 1, 1]: 0.0
Prediction for [1, 1, 1, 1, 1, 0, 0, 0, 0]: 0.0
Prediction for [1, 1, 1, 1, 1, 0, 0, 0, 1]: 0.0
Prediction for [1, 1, 1, 1, 1, 0, 0, 1, 0]: 0.0
Prediction for [1, 1, 1, 1, 1, 0, 0, 1, 1]: 1.0103758625102754
Prediction for [1, 1, 1, 1, 1, 0, 1, 0, 0]: 0.0
Prediction for [1, 1, 1, 1, 1, 0, 1, 0, 1]: 0.0
Prediction for [1, 1, 1, 1, 1, 0, 1, 1, 0]: 0.0
Prediction for [1, 1, 1, 1, 1, 0, 1, 1, 1]: 0.995827451133688
Prediction for [1, 1, 1, 1, 1, 1, 0, 0, 0]: 0.0
Prediction for [1, 1, 1, 1, 1, 1, 0, 0, 1]: 0.0
Prediction for [1, 1, 1, 1, 1, 1, 0, 1, 0]: 0.0
Prediction for [1, 1, 1, 1, 1, 1, 0, 1, 1]: 0.0
Prediction for [1, 1, 1, 1, 1, 1, 1, 0, 0]: 0.0
Prediction for [1, 1, 1, 1, 1, 1, 1, 0, 1]: 0.7202539629771696
Prediction for [1, 1, 1, 1, 1, 1, 1, 1, 0]: 0.0
Prediction for [1, 1, 1, 1, 1, 1, 1, 1, 1]: 0.0
BUILD SUCCESSFUL (total time: 1 minute 19 seconds)

 

Edited by Dietmar
1
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