Dietmar

@NotHereToPlayGames Niccce, my OS is XP SP3 and on test for this browser (R2) I get Chrome 86.0.4240.198 on Windows 10 Perfect fake Dietmar PS: YOUR BROWSER SCORES 533 OUT OF 555 POINTS

Nice, stable PORTABLE Chrome browser for XP SP3 It is an English version now. I get also 528 points in HTML5 test Dietmar

@FranceBB This is the from original Sources of XP SP1 compiled acpi.sys. It has a lot of changes compared to the acpi.sys of XP SP3, mostly in its Source Code. Until now, all motherboards works with this last version Dietmar

@AstroSkipper Now I do not think, that Trimcheck 0.7 is a tool, that can always correct tell, it a TRIM operation has been done successfull or not. I make a try for to see this behavior with my 970 Pro nvme disk. Under win10 I use TRIM. Before I set up this jason file from Trimcheck. But after TRIM via Win10, Trimcheck still tells, that no TRIM has been done on this disk. I know to 100%, that TRIM from win10 works on this nvme disk. So, until now to valid prove, if TRIM happens or not Dietmar

@AstroSkipper On Win10, I can TRIM this nvme device easy. So it looks, that until now no nvme TRIM tool is on the market, that runs under XP SP3 Dietmar

@AstroSkipper I just test Trimcheck 0.7 under XP SP3. For TRIM I use OODefrag17ProfessionalEnu. Trimcheck tells me after this procedure, that no TRIM has been done on the Samsung 970 Pro nvme disk running under XP SP3 as bootdevice Dietmar

Hi, is there an easy test, if any tool can make TRIM successful on any SSD or nvme disk? I ask this, because I checked a lot of tools under XP SP3, but still I am not sure, if TRIM works on a nvme disk. Under Win10, after running TRIM with any tool, Win10 tells, that no TRIM has been done on this nvme device Dietmar

Hi, real XP SP3 works with 10 Gigabit cards, I tested Dietmar

Hi, I succeed with this, when I compare the registry before recognizing the USB device and after. This difference I integrate permanently in the registry. Then, whithout any recognicing, the USB device is shown without any beep or time loose at once Dietmar

Hi, I try to install fresh from my 200 Euro expensive original win10 Home version 1703 from March 2017, on my Asrock z370 k6 fatal1ty board. Crazy, install stops, because this original USB stick from Microsoft shows unreadable sectors. And it is write protected, so no chance for any repair. Nice from Microsoft. Ok, after crazy search for this 1703 german version of this win10, I find 15063.0.170710-1358.rs2_release_svc_refresh_clientcombined_ret_x64fre_de-de_d6cd31298648aab2ff081004ceff2ffceb7aab64.esd on Microsoft Servers. There is also a 32 bit version of this 1703 version in german 15063.0.170710-1358.rs2_release_svc_refresh_clientcombined_ret_x86fre_de-de_52c33a275f77116954abe5f508d1254a49f5fe20.esd Then I build from it via this tool an *.iso for 64 bit. esd-decrypter-wimlib-63 From this I get the *.iso 15063.0.170710-1358.RS2_RELEASE_SVC_REFRESH_CLIENTCOMBINED_OEMRET_X64FRE_DE-DE.ISO I make an bootable USB stick, using Rufus. And voila, there installs win10 home 1703 without any problems. At once I click on the nice file from @mjd79 wushowhide.diagcab and can select there, not to install win 10 22H2. And now, all my problems with crazy Updates for my compi are gone. Very interesting to know, how many millions of hours are gone thanks to Microsoft on this crazy crazy stupid Update behavior. For example, now all devices in Device Manager are correct installed. And the Update offers me also the Nvidia driver for my Nvidia 730, which was not shown in the "last" win10 version Dietmar

Hi, is it possible, to forbid Microsoft to update my win10 Home version from 2017 to a newer version? I mean, I want to get other updates. But I dont want, that my Win10 home 2017 version is updated, because it crashes my Asrock z370 k6 fatalty compi Dietmar

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 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}, {0, 0, 0, 0, 0, 0, 1, 0, 0}, {0, 0, 0, 0, 0, 0, 1, 0, 1}, {0, 0, 0, 0, 0, 0, 1, 1, 0}, {0, 0, 0, 0, 0, 0, 1, 1, 1}, {0, 0, 0, 0, 0, 1, 0, 0, 0}, {0, 0, 0, 0, 0, 1, 0, 0, 1}, {0, 0, 0, 0, 0, 1, 0, 1, 0}, {0, 0, 0, 0, 0, 1, 0, 1, 1}, {0, 0, 0, 0, 0, 1, 1, 0, 0}, {0, 0, 0, 0, 0, 1, 1, 0, 1}, {0, 0, 0, 0, 0, 1, 1, 1, 0}, {0, 0, 0, 0, 0, 1, 1, 1, 1}, {0, 0, 0, 0, 1, 0, 0, 0, 0}, {0, 0, 0, 0, 1, 0, 0, 0, 1}, {0, 0, 0, 0, 1, 0, 0, 1, 0}, {0, 0, 0, 0, 1, 0, 0, 1, 1}, {0, 0, 0, 0, 1, 0, 1, 0, 0}, {0, 0, 0, 0, 1, 0, 1, 0, 1}, {0, 0, 0, 0, 1, 0, 1, 1, 0}, {0, 0, 0, 0, 1, 0, 1, 1, 1}, {0, 0, 0, 0, 1, 1, 0, 0, 0}, {0, 0, 0, 0, 1, 1, 0, 0, 1}, {0, 0, 0, 0, 1, 1, 0, 1, 0}, {0, 0, 0, 0, 1, 1, 0, 1, 1}, {0, 0, 0, 0, 1, 1, 1, 0, 0}, {0, 0, 0, 0, 1, 1, 1, 0, 1}, {0, 0, 0, 0, 1, 1, 1, 1, 0}, {0, 0, 0, 0, 1, 1, 1, 1, 1}, {0, 0, 0, 1, 0, 0, 0, 0, 0}, {0, 0, 0, 1, 0, 0, 0, 0, 1}, {0, 0, 0, 1, 0, 0, 0, 1, 0}, {0, 0, 0, 1, 0, 0, 0, 1, 1}, {0, 0, 0, 1, 0, 0, 1, 0, 0}, {0, 0, 0, 1, 0, 0, 1, 0, 1}, {0, 0, 0, 1, 0, 0, 1, 1, 0}, {0, 0, 0, 1, 0, 0, 1, 1, 1}, {0, 0, 0, 1, 0, 1, 0, 0, 0}, {0, 0, 0, 1, 0, 1, 0, 0, 1}, {0, 0, 0, 1, 0, 1, 0, 1, 0}, {0, 0, 0, 1, 0, 1, 0, 1, 1}, {0, 0, 0, 1, 0, 1, 1, 0, 0}, {0, 0, 0, 1, 0, 1, 1, 0, 1}, {0, 0, 0, 1, 0, 1, 1, 1, 0}, {0, 0, 0, 1, 0, 1, 1, 1, 1}, {0, 0, 0, 1, 1, 0, 0, 0, 0}, {0, 0, 0, 1, 1, 0, 0, 0, 1}, {0, 0, 0, 1, 1, 0, 0, 1, 0}, {0, 0, 0, 1, 1, 0, 0, 1, 1}, {0, 0, 0, 1, 1, 0, 1, 0, 0}, {0, 0, 0, 1, 1, 0, 1, 0, 1}, {0, 0, 0, 1, 1, 0, 1, 1, 0}, {0, 0, 0, 1, 1, 0, 1, 1, 1}, {0, 0, 0, 1, 1, 1, 0, 0, 0}, {0, 0, 0, 1, 1, 1, 0, 0, 1}, {0, 0, 0, 1, 1, 1, 0, 1, 0}, {0, 0, 0, 1, 1, 1, 0, 1, 1}, {0, 0, 0, 1, 1, 1, 1, 0, 0}, {0, 0, 0, 1, 1, 1, 1, 0, 1}, {0, 0, 0, 1, 1, 1, 1, 1, 0}, {0, 0, 0, 1, 1, 1, 1, 1, 1}, {0, 0, 1, 0, 0, 0, 0, 0, 0}, {0, 0, 1, 0, 0, 0, 0, 0, 1}, {0, 0, 1, 0, 0, 0, 0, 1, 0}, {0, 0, 1, 0, 0, 0, 0, 1, 1}, {0, 0, 1, 0, 0, 0, 1, 0, 0}, {0, 0, 1, 0, 0, 0, 1, 0, 1}, {0, 0, 1, 0, 0, 0, 1, 1, 0}, {0, 0, 1, 0, 0, 0, 1, 1, 1}, {0, 0, 1, 0, 0, 1, 0, 0, 0}, {0, 0, 1, 0, 0, 1, 0, 0, 1}, {0, 0, 1, 0, 0, 1, 0, 1, 0}, {0, 0, 1, 0, 0, 1, 0, 1, 1}, {0, 0, 1, 0, 0, 1, 1, 0, 0}, {0, 0, 1, 0, 0, 1, 1, 0, 1}, {0, 0, 1, 0, 0, 1, 1, 1, 0}, {0, 0, 1, 0, 0, 1, 1, 1, 1}, {0, 0, 1, 0, 1, 0, 0, 0, 0}, {0, 0, 1, 0, 1, 0, 0, 0, 1}, {0, 0, 1, 0, 1, 0, 0, 1, 0}, {0, 0, 1, 0, 1, 0, 0, 1, 1}, {0, 0, 1, 0, 1, 0, 1, 0, 0}, {0, 0, 1, 0, 1, 0, 1, 0, 1}, {0, 0, 1, 0, 1, 0, 1, 1, 0}, {0, 0, 1, 0, 1, 0, 1, 1, 1}, {0, 0, 1, 0, 1, 1, 0, 0, 0}, {0, 0, 1, 0, 1, 1, 0, 0, 1}, {0, 0, 1, 0, 1, 1, 0, 1, 0}, {0, 0, 1, 0, 1, 1, 0, 1, 1}, {0, 0, 1, 0, 1, 1, 1, 0, 0}, {0, 0, 1, 0, 1, 1, 1, 0, 1}, {0, 0, 1, 0, 1, 1, 1, 1, 0}, {0, 0, 1, 0, 1, 1, 1, 1, 1}, {0, 0, 1, 1, 0, 0, 0, 0, 0}, {0, 0, 1, 1, 0, 0, 0, 0, 1}, {0, 0, 1, 1, 0, 0, 0, 1, 0}, {0, 0, 1, 1, 0, 0, 0, 1, 1}, {0, 0, 1, 1, 0, 0, 1, 0, 0}, {0, 0, 1, 1, 0, 0, 1, 0, 1}, {0, 0, 1, 1, 0, 0, 1, 1, 0}, {0, 0, 1, 1, 0, 0, 1, 1, 1}, {0, 0, 1, 1, 0, 1, 0, 0, 0}, {0, 0, 1, 1, 0, 1, 0, 0, 1}, {0, 0, 1, 1, 0, 1, 0, 1, 0}, {0, 0, 1, 1, 0, 1, 0, 1, 1}, {0, 0, 1, 1, 0, 1, 1, 0, 0}, {0, 0, 1, 1, 0, 1, 1, 0, 1}, {0, 0, 1, 1, 0, 1, 1, 1, 0}, {0, 0, 1, 1, 0, 1, 1, 1, 1}, {0, 0, 1, 1, 1, 0, 0, 0, 0}, {0, 0, 1, 1, 1, 0, 0, 0, 1}, {0, 0, 1, 1, 1, 0, 0, 1, 0}, {0, 0, 1, 1, 1, 0, 0, 1, 1}, {0, 0, 1, 1, 1, 0, 1, 0, 0}, {0, 0, 1, 1, 1, 0, 1, 0, 1}, {0, 0, 1, 1, 1, 0, 1, 1, 0}, {0, 0, 1, 1, 1, 0, 1, 1, 1}, {0, 0, 1, 1, 1, 1, 0, 0, 0}, {0, 0, 1, 1, 1, 1, 0, 0, 1}, {0, 0, 1, 1, 1, 1, 0, 1, 0}, {0, 0, 1, 1, 1, 1, 0, 1, 1}, {0, 0, 1, 1, 1, 1, 1, 0, 0}, {0, 0, 1, 1, 1, 1, 1, 0, 1}, {0, 0, 1, 1, 1, 1, 1, 1, 0}, {0, 0, 1, 1, 1, 1, 1, 1, 1}, {0, 1, 0, 0, 0, 0, 0, 0, 0}, {0, 1, 0, 0, 0, 0, 0, 0, 1}, {0, 1, 0, 0, 0, 0, 0, 1, 0}, {0, 1, 0, 0, 0, 0, 0, 1, 1}, {0, 1, 0, 0, 0, 0, 1, 0, 0}, {0, 1, 0, 0, 0, 0, 1, 0, 1}, {0, 1, 0, 0, 0, 0, 1, 1, 0}, {0, 1, 0, 0, 0, 0, 1, 1, 1}, {0, 1, 0, 0, 0, 1, 0, 0, 0}, {0, 1, 0, 0, 0, 1, 0, 0, 1}, {0, 1, 0, 0, 0, 1, 0, 1, 0}, {0, 1, 0, 0, 0, 1, 0, 1, 1}, {0, 1, 0, 0, 0, 1, 1, 0, 0}, {0, 1, 0, 0, 0, 1, 1, 0, 1}, {0, 1, 0, 0, 0, 1, 1, 1, 0}, {0, 1, 0, 0, 0, 1, 1, 1, 1}, {0, 1, 0, 0, 1, 0, 0, 0, 0}, {0, 1, 0, 0, 1, 0, 0, 0, 1}, {0, 1, 0, 0, 1, 0, 0, 1, 0}, {0, 1, 0, 0, 1, 0, 0, 1, 1}, {0, 1, 0, 0, 1, 0, 1, 0, 0}, {0, 1, 0, 0, 1, 0, 1, 0, 1}, {0, 1, 0, 0, 1, 0, 1, 1, 0}, {0, 1, 0, 0, 1, 0, 1, 1, 1}, {0, 1, 0, 0, 1, 1, 0, 0, 0}, {0, 1, 0, 0, 1, 1, 0, 0, 1}, {0, 1, 0, 0, 1, 1, 0, 1, 0}, {0, 1, 0, 0, 1, 1, 0, 1, 1}, {0, 1, 0, 0, 1, 1, 1, 0, 0}, {0, 1, 0, 0, 1, 1, 1, 0, 1}, {0, 1, 0, 0, 1, 1, 1, 1, 0}, {0, 1, 0, 0, 1, 1, 1, 1, 1}, {0, 1, 0, 1, 0, 0, 0, 0, 0}, {0, 1, 0, 1, 0, 0, 0, 0, 1}, {0, 1, 0, 1, 0, 0, 0, 1, 0}, {0, 1, 0, 1, 0, 0, 0, 1, 1}, {0, 1, 0, 1, 0, 0, 1, 0, 0}, {0, 1, 0, 1, 0, 0, 1, 0, 1}, {0, 1, 0, 1, 0, 0, 1, 1, 0}, {0, 1, 0, 1, 0, 0, 1, 1, 1}, {0, 1, 0, 1, 0, 1, 0, 0, 0}, {0, 1, 0, 1, 0, 1, 0, 0, 1}, {0, 1, 0, 1, 0, 1, 0, 1, 0}, {0, 1, 0, 1, 0, 1, 0, 1, 1}, {0, 1, 0, 1, 0, 1, 1, 0, 0}, {0, 1, 0, 1, 0, 1, 1, 0, 1}, {0, 1, 0, 1, 0, 1, 1, 1, 0}, {0, 1, 0, 1, 0, 1, 1, 1, 1}, {0, 1, 0, 1, 1, 0, 0, 0, 0}, {0, 1, 0, 1, 1, 0, 0, 0, 1}, {0, 1, 0, 1, 1, 0, 0, 1, 0}, {0, 1, 0, 1, 1, 0, 0, 1, 1}, {0, 1, 0, 1, 1, 0, 1, 0, 0}, {0, 1, 0, 1, 1, 0, 1, 0, 1}, {0, 1, 0, 1, 1, 0, 1, 1, 0}, {0, 1, 0, 1, 1, 0, 1, 1, 1}, {0, 1, 0, 1, 1, 1, 0, 0, 0}, {0, 1, 0, 1, 1, 1, 0, 0, 1}, {0, 1, 0, 1, 1, 1, 0, 1, 0}, {0, 1, 0, 1, 1, 1, 0, 1, 1}, {0, 1, 0, 1, 1, 1, 1, 0, 0}, {0, 1, 0, 1, 1, 1, 1, 0, 1}, {0, 1, 0, 1, 1, 1, 1, 1, 0}, {0, 1, 0, 1, 1, 1, 1, 1, 1}, {0, 1, 1, 0, 0, 0, 0, 0, 0}, {0, 1, 1, 0, 0, 0, 0, 0, 1}, {0, 1, 1, 0, 0, 0, 0, 1, 0}, {0, 1, 1, 0, 0, 0, 0, 1, 1}, {0, 1, 1, 0, 0, 0, 1, 0, 0}, {0, 1, 1, 0, 0, 0, 1, 0, 1}, {0, 1, 1, 0, 0, 0, 1, 1, 0}, {0, 1, 1, 0, 0, 0, 1, 1, 1}, {0, 1, 1, 0, 0, 1, 0, 0, 0}, {0, 1, 1, 0, 0, 1, 0, 0, 1}, {0, 1, 1, 0, 0, 1, 0, 1, 0}, {0, 1, 1, 0, 0, 1, 0, 1, 1}, {0, 1, 1, 0, 0, 1, 1, 0, 0}, {0, 1, 1, 0, 0, 1, 1, 0, 1}, {0, 1, 1, 0, 0, 1, 1, 1, 0}, {0, 1, 1, 0, 0, 1, 1, 1, 1}, {0, 1, 1, 0, 1, 0, 0, 0, 0}, {0, 1, 1, 0, 1, 0, 0, 0, 1}, {0, 1, 1, 0, 1, 0, 0, 1, 0}, {0, 1, 1, 0, 1, 0, 0, 1, 1}, {0, 1, 1, 0, 1, 0, 1, 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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)

@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; } } } }

@XP-x64-Lover Here it is, the very last acpi.sys bit64. Just rename it to acpi.sys Dietmar https://ufile.io/y0qv5t7w

@Mark-XP yepp, it looks, that because of n modulo 6 = +-1 is forced for any prime and so 1 is prime 2 is not prime 3 is not prime Dietmar

@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)

Is there a Vista bit32 driver for i219 Dietmar EDIT: This is, what I have found for Vista bit32. The driver with "r" goes up to i210 and may be worth a try. This "r" driver fullfilles all dependencies under win7 SP1, but does not work direct for the i211 or i219 https://ufile.io/bk1awpvj

@Mov AX, 0xDEAD can you see, why the i211 works with kdnet.dll (better kdstub.dll) from win8.1 but the i219 not Dietmar EDIT: I see, that only the i211 gets attention in kdstub.dll and this via e1000_init_nvm_params_i211 proc near. So, may be that for the i211, i217, i218 those nvm parameter are the same. But nvm is different on i219. EDIT2: The i211 has a transmit buffer of 128 KB and a receive buffer of 128 KB. In contrast, the i219 has a larger memory cache with a transmit buffer of 256 KB and a receive buffer of 256 KB. EDIT3: The i211 has a NVM size of 64KB. The i219 Ethernet controller uses a larger NVM size of 256KB.

@Mov AX, 0xDEAD Now I have working Lan Debug for the i211 . But until now no idea, how I can make Lan Debug work for the i219. Can you send me the files which may help and a step by step instruction Dietmar EDIT: I get Lan debug on the i219 to work under XP SP3 on both compis with this boot.ini [boot loader] timeout=30 default=multi(0)disk(0)rdisk(0)partition(1)\WINDOWS [operating systems] multi(0)disk(0)rdisk(0)partition(1)\WINDOWS="Microsoft Windows XP Professional" /noexecute=optin /fastdetect /DEBUG /DEBUGPORT=NET /HOST_IP=192.168.2.101 /HOST_PORT=50000 /ENCRYPTION_KEY=1.2.3.4 /CPUFREQ=3000 /TARGET_IP=192.168.2.103 /BREAK /PCI_ID=808615B8 But the most most crazy is, that I need all of this 3 files for Lan Windbg on XP SP3 on both compis and Intel i219 kdnet10.dll from win10 10.0.19041.1 (WinBuild.160101.0800) kdnet.dll from win8.1 6.3.9600.17276 (winblue_r2.140808-0433) kdstub.dll (3.328 Bytes only) I cant believe, so I check several times, always the same. One file of those 3 not in Windows\system32 folder, no Lan Windbg via i219. https://ufile.io/z7t4usd4

@Mov AX, 0xDEAD I found this for Linux, when the i218 works there but the i219 not: The NVM Checksum Is Not Valid I have found Intel's datasheet for I219, Section 10.3.2.2 Checksum Word Calculation says: The Checksum word (Word 0x3F, NVM bytes 0x7E and 0x7F) is used to ensure that the base NVM image is a valid image. The value of this word should be calculated such that after adding all the words (0x00- 0x3F) / bytes (0x00-0x7F), including the Checksum word itself, the sum should be 0xBABA. The initial value in the 16 bit summing register should be 0x0000 and the carry bit should be ignored after each addition. Dietmar pdf datasheet for the i219 https://ufile.io/72w6bumg

@Mov AX, 0xDEAD May be a look at the Linux Source code and a compare between the i218 and i219 and its differnces shows all, what we need to do, for to make the i219 run under XP Dietmar

@Mov AX, 0xDEAD Both the i218 and i219 Ethernet controllers have integrated memory caches to improve network performance. However, the size of the cache differs between the two controllers. The i218 has a 32 KB transmit buffer and a 32 KB receive buffer, which is used to temporarily store data as it is being transmitted or received over the network. This cache size is relatively small compared to other network controllers. In contrast, the i219 features a larger memory cache with a 256 KB transmit buffer and a 256 KB receive buffer. This larger cache allows the i219 to store more data in memory, which can improve network performance in certain situations, such as when transferring large files or streaming high-bandwidth media. In addition to the larger cache size, the i219 also features support for Adaptive Interrupt Coalescing (AIC), which is a feature that can help to reduce interrupt processing overhead and improve network performance. AIC works by grouping network traffic into larger packets, which reduces the number of interrupts that the CPU needs to handle. In summary, while both the i218 and i219 have memory caches to improve network performance, the i219 has a larger cache size and additional features like AIC that can further enhance network performance.

@Mov AX, 0xDEAD Can you compare the Source code of the e1d5132.sys driver with the e1d6232.sys The e1d6232.sys works for all the i211, i217,1218, i219, but the e1d5132.sys works for all of them, only not for the i219. So, a change in for example the Cache size would explain this behavior at once Dietmar

@Mov AX, 0xDEAD "Try /PCI_ID option, intel driver parse PCI_ID before adapter detection." I want to try this, but I have no idea, how to do this. So I need help Dietmar EDIT: Do you think, that it is something like this? That it is not just a bad joke from Intel, to exclude the i219 from XP, but just a small hardware change in i219, so that it cant be used under XP because of a wrong setting via i218 for this? "/PCI_ID - option required for Intel/Broadcom 10Gb/40Gb LAN Controllers (without this option, driver set too small size of memory buffer for 10Gb+ cards)"

@Mov AX, 0xDEAD @Damnation May be the most easy way to understand what is going on with i219 is to look, why i211, i217, i218 work with Windbg via Lan but i219 not under XP. Because, that lan connection is driver independend Dietmar