Java Code Examples for org.neuroph.nnet.MultiLayerPerceptron#learn()

public void train() {
    // get the path to file with data
    String inputFileName = "C:\\timeseries\\BSW15";
    
    // create MultiLayerPerceptron neural network
    neuralNet = new MultiLayerPerceptron(TransferFunctionType.TANH, 5, 10, 1);
    MomentumBackpropagation learningRule = (MomentumBackpropagation)neuralNet.getLearningRule();
    learningRule.setLearningRate(0.2);
    learningRule.setMomentum(0.5);
    // learningRule.addObserver(this);
    learningRule.addListener(this);        
    
    // create training set from file
     trainingSet = DataSet.createFromFile(inputFileName, 5, 1, "\t", false);
    // train the network with training set
    neuralNet.learn(trainingSet);         
          
    System.out.println("Done training.");          
}
 

public void run() {

        System.out.println("Creating training set...");
        String dataSetFile = "data_sets/cpu_data.txt";
        int inputsCount = 7;
        int outputsCount = 1;

        // create training set from file
        DataSet dataSet = DataSets.readFromCsv(dataSetFile, inputsCount, outputsCount);
        // normalize dataset
        DataSets.normalizeMax(dataSet);

        System.out.println("Creating neural network...");
        // create MultiLayerPerceptron neural network
        MultiLayerPerceptron neuralNet = new MultiLayerPerceptron(inputsCount, 16, outputsCount);

        // attach listener to learning rule
        MomentumBackpropagation learningRule = (MomentumBackpropagation) neuralNet.getLearningRule();
        learningRule.addListener(this);

        // set learning rate and max error
        learningRule.setLearningRate(0.2);
        learningRule.setMaxError(0.01);

        System.out.println("Training network...");
        // train the network with training set
        neuralNet.learn(dataSet);

        System.out.println("Training completed.");
        System.out.println("Testing network...");

        testNeuralNetwork(neuralNet, dataSet);

        System.out.println("Saving network");
        // save neural network to file
        neuralNet.save("MyNeuralNetCPU.nnet");

        System.out.println("Done.");
    }
 

public void run() {
    String dataSetFile = "data_sets/diabetes.txt";
    int inputsCount = 8;
    int outputsCount = 1;

    // Create data set from file
    DataSet dataSet = DataSet.createFromFile(dataSetFile, inputsCount, outputsCount, ",");

    // Creatinig training set (70%) and test set (30%)
    DataSet[] trainTestSplit = dataSet.split(0.7, 0.3);
    DataSet trainingSet = trainTestSplit[0];
    DataSet testSet = trainTestSplit[1];

    // Normalizing training and test set
    Normalizer normalizer = new MaxNormalizer(trainingSet);
    normalizer.normalize(trainingSet);
    normalizer.normalize(testSet);

    System.out.println("Creating neural network...");
    //Create MultiLayerPerceptron neural network
    MultiLayerPerceptron neuralNet = new MultiLayerPerceptron(inputsCount, 20, 10, outputsCount);
    //attach listener to learning rule
    MomentumBackpropagation learningRule = (MomentumBackpropagation) neuralNet.getLearningRule();
    learningRule.addListener(this);

    learningRule.setLearningRate(0.6);
    learningRule.setMaxError(0.07);
    learningRule.setMaxIterations(100000);

    System.out.println("Training network...");
    //train the network with training set
    neuralNet.learn(trainingSet);

    System.out.println("Testing network...");
    testNeuralNetwork(neuralNet, testSet);

}
 

public static void main(String[] args) {

        DataSet trainingSet = new DataSet(2, 1);
        trainingSet.add(new DataSetRow(new double[]{0, 0}, new double[]{0}));
        trainingSet.add(new DataSetRow(new double[]{0, 1}, new double[]{1}));
        trainingSet.add(new DataSetRow(new double[]{1, 0}, new double[]{1}));
        trainingSet.add(new DataSetRow(new double[]{1, 1}, new double[]{0}));

        MultiLayerPerceptron neuralNet = new MultiLayerPerceptron(TransferFunctionType.TANH, 2, 3, 1);
        neuralNet.learn(trainingSet);

        Evaluation.runFullEvaluation(neuralNet, trainingSet);
    }
 

public void run() {

        System.out.println("Creating training set...");
        // get path to training set
        String dataSetFile = "data_sets/religion_data.txt";
        int inputsCount = 54;
        int outputsCount = 5;

        // create training set from file
        DataSet dataSet = DataSet.createFromFile(dataSetFile, inputsCount, outputsCount, "\t", false);
       
        
        System.out.println("Creating neural network...");
        // create MultiLayerPerceptron neural network
        MultiLayerPerceptron neuralNet = new MultiLayerPerceptron(inputsCount, 22, outputsCount);
       
        
        // attach listener to learning rule
        MomentumBackpropagation learningRule = (MomentumBackpropagation) neuralNet.getLearningRule();
        learningRule.addListener(this);

        // set learning rate and max error
        learningRule.setLearningRate(0.2);
        learningRule.setMaxError(0.01);

        System.out.println("Training network...");
        // train the network with training set
        neuralNet.learn(dataSet);

        System.out.println("Training completed.");
        System.out.println("Testing network...");

        testNeuralNetwork(neuralNet, dataSet);

        System.out.println("Saving network");
        // save neural network to file
        neuralNet.save("MyNeuralNetReligion.nnet");

        System.out.println("Done.");
    }
 

public void run() {

        System.out.println("Creating training and test set from file...");
        String dataSetFile = "data_sets/breast_cancer.txt";
        int numInputs = 30;
        int numOutputs = 1;

        //Create data set from file
        DataSet dataSet = DataSet.createFromFile(dataSetFile, numInputs, numOutputs, ",");

        //Creatinig training set (70%) and test set (30%)
        DataSet[] trainTestSplit = dataSet.split(0.7, 0.3);
        DataSet trainingSet = trainTestSplit[0];
        DataSet testSet = trainTestSplit[1];

        //Normalizing data set
        Normalizer normalizer = new MaxNormalizer(trainingSet);
        normalizer.normalize(trainingSet);
        normalizer.normalize(testSet);

        //Create MultiLayerPerceptron neural network
        MultiLayerPerceptron neuralNet = new MultiLayerPerceptron(numInputs, 16, numOutputs);

        //attach listener to learning rule
        MomentumBackpropagation learningRule = (MomentumBackpropagation) neuralNet.getLearningRule();
        learningRule.addListener(this);

        learningRule.setLearningRate(0.3);
        learningRule.setMaxError(0.01);
        learningRule.setMaxIterations(500);

        System.out.println("Training network...");
        //train the network with training set
        neuralNet.learn(trainingSet);

        System.out.println("Testing network...");
        testNeuralNetwork(neuralNet, testSet);

    }
 

@Test
public void testXorMaxError() {
    MultiLayerPerceptron myMlPerceptron = new MultiLayerPerceptron(TransferFunctionType.SIGMOID, 2, 3, 1);
    myMlPerceptron.randomizeWeights(new WeightsRandomizer(new Random(123)));

    myMlPerceptron.setLearningRule(instance);
    myMlPerceptron.learn(xorDataSet);

    assertTrue(instance.getTotalNetworkError() < maxError);
}
 

public void train() {
    System.out.println("Training neural network... ");
    MultiLayerPerceptron neuralNet = (MultiLayerPerceptron) NeuralNetwork.createFromFile(config.getTrainedNetworkFileName());

    DataSet dataSet = DataSet.load(config.getNormalizedBalancedFileName());
    neuralNet.getLearningRule().addListener(this);
    neuralNet.learn(dataSet);
    System.out.println("Saving trained neural network to file... ");
    neuralNet.save(config.getTrainedNetworkFileName());
    System.out.println("Neural network successfully saved!");
}
 

public void run() {

        System.out.println("Creating training set...");
        String dataSetFile = "data_sets/car_evaluation_data.txt";
        int inputsCount = 21;
        int outputsCount = 4;

        // create training set from file
        DataSet dataSet = DataSet.createFromFile(dataSetFile, inputsCount, outputsCount, "\t", false);
       
        
        System.out.println("Creating neural network...");
        // create MultiLayerPerceptron neural network
        MultiLayerPerceptron neuralNet = new MultiLayerPerceptron(inputsCount, 22, outputsCount);
       
        
        // attach listener to learning rule
        MomentumBackpropagation learningRule = (MomentumBackpropagation) neuralNet.getLearningRule();
        learningRule.addListener(this);

        // set learning rate and max error
        learningRule.setLearningRate(0.2);
        learningRule.setMaxError(0.01);

        System.out.println("Training network...");
        // train the network with training set
        neuralNet.learn(dataSet);

        System.out.println("Training completed.");
        System.out.println("Testing network...");

        testNeuralNetwork(neuralNet, dataSet);

        System.out.println("Saving network");
        // save neural network to file
        neuralNet.save("MyNeuralNetCarEvaluation.nnet");

        System.out.println("Done.");
    }
 

public void run() {

        System.out.println("Creating training set...");
        String dataSetFile = "data_sets/balance_scale_data.txt";
        int inputsCount = 20;
        int outputsCount = 3;

        // create training set from file
        DataSet dataSet = DataSet.createFromFile(dataSetFile, inputsCount, outputsCount, "\t", false);
        
        
        System.out.println("Creating neural network...");
        // create MultiLayerPerceptron neural network
        MultiLayerPerceptron neuralNet = new MultiLayerPerceptron(inputsCount, 22, outputsCount);

        
        // attach listener to learning rule
        MomentumBackpropagation learningRule = (MomentumBackpropagation) neuralNet.getLearningRule();
        learningRule.addListener(this);

        // set learning rate and max error
        learningRule.setLearningRate(0.2);
        learningRule.setMaxError(0.01);

        System.out.println("Training network...");
        // train the network with training set
        neuralNet.learn(dataSet);

        System.out.println("Training completed.");
        System.out.println("Testing network...");

        testNeuralNetwork(neuralNet, dataSet);

        System.out.println("Saving network");
        // save neural network to file
        neuralNet.save("MyNeuralNetBalanceScale.nnet");

        System.out.println("Done.");
    }
 

public void run() {
    System.out.println("Creating data set...");
    String dataSetFile = "data_sets/ml10standard/pimadata.txt";
    int inputsCount = 8;
    int outputsCount = 1;

    // create data set from file
    DataSet dataSet = DataSet.createFromFile(dataSetFile, inputsCount, outputsCount, "\t", false);

    // split data into training and test set
    DataSet[] trainTestSplit = dataSet.split(0.6, 0.4);
    DataSet trainingSet = trainTestSplit[0];
    DataSet testSet = trainTestSplit[1];

    // normalize training and test set
    Normalizer norm = new MaxNormalizer(trainingSet);
    norm.normalize(trainingSet);
    norm.normalize(testSet);

    System.out.println("Creating neural network...");
    MultiLayerPerceptron neuralNet = new MultiLayerPerceptron(TransferFunctionType.TANH, inputsCount, 15, 5, outputsCount);

    neuralNet.setLearningRule(new MomentumBackpropagation());
    MomentumBackpropagation learningRule = (MomentumBackpropagation) neuralNet.getLearningRule();
    learningRule.addListener((event) -> {
        MomentumBackpropagation bp = (MomentumBackpropagation) event.getSource();
        System.out.println(bp.getCurrentIteration() + ". iteration | Total network error: " + bp.getTotalNetworkError());
    });

    // set learning rate and max error
    learningRule.setLearningRate(0.1);
    learningRule.setMaxError(0.03);
    
    System.out.println("Training network...");
    // train the network with training set
    neuralNet.learn(trainingSet);
    System.out.println("Training completed.");
    System.out.println("Testing network...");

    System.out.println("Network performance on the test set");
    evaluate(neuralNet, testSet);

    System.out.println("Saving network");
    // save neural network to file
    neuralNet.save("nn1.nnet");

    System.out.println("Done.");
}
 

public void run() {
    String dataSetFile = "data_sets/ml10standard/sonardata.txt";
    int numInputs = 60;
    int numOutputs = 1;

    // create data set from csv file
    DataSet dataSet = DataSet.createFromFile(dataSetFile, numInputs, numOutputs, ",");

    // split data into train and test set
    DataSet[] trainTestSplit = dataSet.split(0.6, 0.4);
    DataSet trainingSet = trainTestSplit[0];
    DataSet testSet = trainTestSplit[1];

    // create neural network
    MultiLayerPerceptron neuralNet = new MultiLayerPerceptron(TransferFunctionType.TANH, numInputs, 35, 15, numOutputs);

    // set learning rule and add listener
    neuralNet.setLearningRule(new MomentumBackpropagation());
    MomentumBackpropagation learningRule = (MomentumBackpropagation) neuralNet.getLearningRule();
    learningRule.addListener((event) -> {
        MomentumBackpropagation bp = (MomentumBackpropagation) event.getSource();
        System.out.println(bp.getCurrentIteration() + ". iteration | Total network error: " + bp.getTotalNetworkError());
    });

    // set learning rate and max error
    learningRule.setLearningRate(0.01);
    learningRule.setMaxError(0.01);

    // train the network with training set
    neuralNet.learn(trainingSet);

    // evaluate network performance on test set
    evaluate(neuralNet, testSet);

    // save neural network to file
    neuralNet.save("nn1.nnet");

    System.out.println("Done.");

    //testNeuralNetwork(neuralNet, testSet);
}
 

public void run() {

        System.out.println("Creating training and test set from file...");
        String dataSetFile = "data_sets/ionosphere.csv";
        int inputsCount = 34;
        int outputsCount = 1;

        //Create data set from file
        DataSet dataSet = DataSet.createFromFile(dataSetFile, inputsCount, outputsCount, ",");
        dataSet.shuffle();

        //Normalizing data set
        Normalizer normalizer = new MaxNormalizer(dataSet);
        normalizer.normalize(dataSet);

        //Creatinig training set (70%) and test set (30%)
        DataSet[] trainingAndTestSet = dataSet.createTrainingAndTestSubsets(70, 30);
        DataSet trainingSet = trainingAndTestSet[0];
        DataSet testSet = trainingAndTestSet[1];
//        for (int i = 0; i < 21; i++) {
        System.out.println("Creating neural network...");
        //Create MultiLayerPerceptron neural network
        MultiLayerPerceptron neuralNet = new MultiLayerPerceptron(inputsCount, 16, 8, outputsCount);
//            System.out.println("HIDDEN COUNT: " + i);
        //attach listener to learning rule
        MomentumBackpropagation learningRule = (MomentumBackpropagation) neuralNet.getLearningRule();
        learningRule.addListener(this);

        learningRule.setLearningRate(0.2);
        learningRule.setMaxError(0.01);
        learningRule.setMaxIterations(10000);

        System.out.println("Training network...");
        //train the network with training set
        neuralNet.learn(trainingSet);

        System.out.println("Testing network...\n\n");
        testNeuralNetwork(neuralNet, testSet);

        System.out.println("Done.");
        System.out.println("**************************************************");
//        }
    }
 

public void run() {
    System.out.println("Creating training set...");
    // get path to training set
    String trainingSetFileName = "data_sets/seeds.txt";
    int inputsCount = 7;
    int outputsCount = 3;

    // create training set from file
    DataSet dataSet = DataSet.createFromFile(trainingSetFileName, inputsCount, outputsCount, "\t");

    // split data into train and test set
    DataSet[] trainTestSplit = dataSet.split(0.6, 0.4);
    DataSet trainingSet = trainTestSplit[0];
    DataSet testSet = trainTestSplit[1];

    System.out.println("Creating neural network...");
    MultiLayerPerceptron neuralNet = new MultiLayerPerceptron(inputsCount, 15, 2, outputsCount);

    neuralNet.setLearningRule(new MomentumBackpropagation());
    MomentumBackpropagation learningRule = (MomentumBackpropagation) neuralNet.getLearningRule();
    learningRule.addListener(this);

    // set learning rate and max error
    learningRule.setLearningRate(0.1);
    learningRule.setMaxError(0.01);
    learningRule.setMaxIterations(5000);
    System.out.println("Training network...");
    // train the network with training set
    neuralNet.learn(trainingSet);
    System.out.println("Training completed.");
    System.out.println("Testing network...");

    System.out.println("Network performance on the test set");
    evaluate(neuralNet, testSet);

    System.out.println("Saving network");
    // save neural network to file
    neuralNet.save("nn1.nnet");

    System.out.println("Done.");

    System.out.println();
    System.out.println("Network outputs for test set");
    testNeuralNetwork(neuralNet, testSet);
}
 

public void run() {

        System.out.println("Creating training and test set from file...");
        String dataSetFile = "data_sets/breast cancer.txt";
        int inputsCount = 30;
        int outputsCount = 2; // use onlz one output - binarz classification, transform dat aset

        //Create data set from file
        DataSet dataSet = DataSet.createFromFile(dataSetFile, inputsCount, outputsCount, ",");

        //Creatinig training set (70%) and test set (30%)
        DataSet[] trainTestSplit = dataSet.split(0.7, 0.3);
        DataSet trainingSet = trainTestSplit[0];
        DataSet testSet = trainTestSplit[1];

        //Normalizing data set
        Normalizer normalizer = new MaxNormalizer(trainingSet);
        normalizer.normalize(trainingSet);
        normalizer.normalize(testSet);

        System.out.println("Creating neural network...");
        //Create MultiLayerPerceptron neural network
        MultiLayerPerceptron neuralNet = new MultiLayerPerceptron(inputsCount, 16, outputsCount);

        //attach listener to learning rule
        MomentumBackpropagation learningRule = (MomentumBackpropagation) neuralNet.getLearningRule();
        learningRule.addListener(this);

        learningRule.setLearningRate(0.3);
        learningRule.setMaxError(0.001);
        learningRule.setMaxIterations(5000);

        System.out.println("Training network...");
        //train the network with training set
        neuralNet.learn(trainingSet);

        System.out.println("Testing network...\n\n");
        testNeuralNetwork(neuralNet, testSet);

        System.out.println("Done.");

        System.out.println("**************************************************");

    }
 

public void run() {
    System.out.println("Creating data set...");
    String dataSetFile = "data_sets/ml10standard/ionospheredata.txt";
    int inputsCount = 34;
    int outputsCount = 1;

    // create data set from file
    DataSet dataSet = DataSet.createFromFile(dataSetFile, inputsCount, outputsCount, ",", false);

    // split data into training and test set
    DataSet[] trainTestSplit = dataSet.split(0.6, 0.4);
    DataSet trainingSet = trainTestSplit[0];
    DataSet testSet = trainTestSplit[1];

    // normalize data
    Normalizer norm = new MaxNormalizer(trainingSet);
    norm.normalize(trainingSet);
    norm.normalize(testSet);

    System.out.println("Creating neural network...");
    MultiLayerPerceptron neuralNet = new MultiLayerPerceptron(inputsCount, 30, 25, outputsCount);

    neuralNet.setLearningRule(new MomentumBackpropagation());
    MomentumBackpropagation learningRule = (MomentumBackpropagation) neuralNet.getLearningRule();
    learningRule.addListener((event) -> {
        MomentumBackpropagation bp = (MomentumBackpropagation) event.getSource();
        System.out.println(bp.getCurrentIteration() + ". iteration | Total network error: " + bp.getTotalNetworkError());
    });

    // set learning rate and max error
    learningRule.setLearningRate(0.1);
    learningRule.setMaxError(0.01);
    System.out.println("Training network...");
    // train the network with training set
    neuralNet.learn(trainingSet);
    System.out.println("Training completed.");
    System.out.println("Testing network...");

    System.out.println("Network performance on the test set");
    evaluate(neuralNet, testSet);

    System.out.println("Saving network");
    // save neural network to file
    neuralNet.save("nn1.nnet");

    System.out.println("Done.");
}
 

public void run() {

        System.out.println("Creating training and test set from file...");
        String dataSetFile = "data_sets/german credit data.txt";
        int inputsCount = 24;
        int outputsCount = 2;

        //Create data set from file
        DataSet dataSet = DataSet.createFromFile(dataSetFile, inputsCount, outputsCount, " ");
        dataSet.shuffle();

        //Normalizing data set
        Normalizer normalizer = new MaxNormalizer(dataSet);
        normalizer.normalize(dataSet);

        //Creatinig training set (70%) and test set (30%)
        DataSet[] trainingAndTestSet = dataSet.createTrainingAndTestSubsets(70, 30);
        DataSet trainingSet = trainingAndTestSet[0];
        DataSet testSet = trainingAndTestSet[1];

        System.out.println("Creating neural network...");
        //Create MultiLayerPerceptron neural network
        MultiLayerPerceptron neuralNet = new MultiLayerPerceptron(inputsCount, 12, 6, outputsCount);

        //attach listener to learning rule
        MomentumBackpropagation learningRule = (MomentumBackpropagation) neuralNet.getLearningRule();
        learningRule.addListener(this);

        learningRule.setLearningRate(0.01);
        learningRule.setMaxError(0.001);
        learningRule.setMaxIterations(10000);

        System.out.println("Training network...");
        //train the network with training set
        neuralNet.learn(trainingSet);

        System.out.println("Testing network...\n\n");
        testNeuralNetwork(neuralNet, testSet);

        System.out.println("Done.");

        System.out.println("**************************************************");

    }
 

public void run() {
    System.out.println("Creating data set...");
    String dataSetFile = "data_sets/ml10standard/databanknote.txt";
    int inputsCount = 4;
    int outputsCount = 1;

    // create training set from file
    DataSet dataSet = DataSet.createFromFile(dataSetFile, inputsCount, outputsCount, ",", false);
    DataSet[] trainTestSplit = dataSet.split(0.6, 0.4);
    DataSet trainingSet = trainTestSplit[0];
    DataSet testSet = trainTestSplit[1];

    Normalizer norm = new MaxNormalizer(trainingSet);
    norm.normalize(trainingSet);
    norm.normalize(testSet);

    System.out.println("Creating neural network...");
    MultiLayerPerceptron neuralNet = new MultiLayerPerceptron(TransferFunctionType.TANH, inputsCount, 1, outputsCount);

    neuralNet.setLearningRule(new MomentumBackpropagation());
    MomentumBackpropagation learningRule = (MomentumBackpropagation) neuralNet.getLearningRule();
    learningRule.addListener((event) -> {
        MomentumBackpropagation bp = (MomentumBackpropagation) event.getSource();
        System.out.println(bp.getCurrentIteration() + ". iteration | Total network error: " + bp.getTotalNetworkError());
    });

    // set learning rate and max error
    learningRule.setLearningRate(0.1);
    learningRule.setMaxError(0.01);
    System.out.println("Training network...");
    // train the network with training set
    neuralNet.learn(trainingSet);
    System.out.println("Training completed.");
    System.out.println("Testing network...");

    System.out.println("Network performance on the test set");
    evaluate(neuralNet, testSet);

    System.out.println("Saving network");
    // save neural network to file
    neuralNet.save("nn1.nnet");

    System.out.println("Done.");
}
 

public void run() {
    System.out.println("Creating data set...");
    String dataSetFile = "data_sets/ml10standard/seeds.txt";
    int inputsCount = 7;
    int outputsCount = 3;

    // create training set from file
    DataSet dataSet = DataSet.createFromFile(dataSetFile, inputsCount, outputsCount, "\t");

    // split data into train and test set
    DataSet[] trainTestSplit = dataSet.split(0.6, 0.4);
    DataSet trainingSet = trainTestSplit[0];
    DataSet testSet = trainTestSplit[1];

    System.out.println("Creating neural network...");
    MultiLayerPerceptron neuralNet = new MultiLayerPerceptron(inputsCount, 15, 2, outputsCount);

    neuralNet.setLearningRule(new MomentumBackpropagation());
    MomentumBackpropagation learningRule = (MomentumBackpropagation) neuralNet.getLearningRule();
    learningRule.addListener((event)->{
        MomentumBackpropagation bp = (MomentumBackpropagation) event.getSource();
        System.out.println(bp.getCurrentIteration() + ". iteration | Total network error: " + bp.getTotalNetworkError());
    });

    // set learning rate and max error
    learningRule.setLearningRate(0.1);
    learningRule.setMaxError(0.01);
    learningRule.setMaxIterations(5000);
    System.out.println("Training network...");
    // train the network with training set
    neuralNet.learn(trainingSet);
    System.out.println("Training completed.");
    System.out.println("Testing network...");

    System.out.println("Network performance on the test set");
    evaluate(neuralNet, testSet);

    System.out.println("Saving network");
    // save neural network to file
    neuralNet.save("nn1.nnet");

    System.out.println("Done.");

    System.out.println();
    System.out.println("Network outputs for test set");
    testNeuralNetwork(neuralNet, testSet);
}
 

public void run() {
    System.out.println("Creating training set...");
    // get path to training set
    String dataSetFile = "data_sets/wine.txt";
    int inputsCount = 11;
    int outputsCount = 10;

    // create training set from file
    DataSet dataSet = DataSet.createFromFile(dataSetFile, inputsCount, outputsCount, "\t", true);

    // split data into train and test set
    DataSet[] trainTestSplit = dataSet.split(0.6, 0.4);
    DataSet trainingSet = trainTestSplit[0];
    DataSet testSet = trainTestSplit[1];

    Normalizer norm = new MaxNormalizer(trainingSet);
    norm.normalize(trainingSet);
    norm.normalize(testSet);

    System.out.println("Creating neural network...");
    MultiLayerPerceptron neuralNet = new MultiLayerPerceptron(inputsCount, 20, 15, outputsCount);

    neuralNet.setLearningRule(new MomentumBackpropagation());
    MomentumBackpropagation learningRule = (MomentumBackpropagation) neuralNet.getLearningRule();
    learningRule.addListener(this);

    // set learning rate and max error
    learningRule.setLearningRate(0.1);
    learningRule.setMaxIterations(5000);

    System.out.println("Training network...");
    // train the network with training set
    neuralNet.learn(trainingSet);
    System.out.println("Training completed.");
    System.out.println("Testing network...");

    System.out.println("Network performance on the test set");
    evaluate(neuralNet, testSet);

    System.out.println("Saving network");
    // save neural network to file
    neuralNet.save("nn1.nnet");

    System.out.println("Done.");

    System.out.println();
    System.out.println("Network outputs for test set");
    testNeuralNetwork(neuralNet, testSet);
}