Java Code Examples for org.neuroph.core.data.DataSet#createFromFile()

@Before
public void setUp() {
    instance = new MomentumBackpropagation();
    instance.setMomentum(0.5);
    xorDataSet = new DataSet(2, 1);
    xorDataSet.add(new DataSetRow(new double[]{0, 0}, new double[]{0}));
    xorDataSet.add(new DataSetRow(new double[]{0, 1}, new double[]{1}));
    xorDataSet.add(new DataSetRow(new double[]{1, 0}, new double[]{1}));
    xorDataSet.add(new DataSetRow(new double[]{1, 1}, new double[]{0}));
    maxError = 0.01;
    instance.setLearningRate(0.5);
    instance.setMaxError(maxError);
    String inputFileName = "src/test/resources/iris_normalized.txt";
    irisDataSet = DataSet.createFromFile(inputFileName, 4, 3, ",", false);

}
 

private static void trainNetwork() {
    DataSet trainingSet = DataSet.createFromFile("google-stock-price-train.csv", 3, 1, ",");
    DataSet testSet = DataSet.createFromFile("google-stock-price-test.csv", 3, 1, ",");

    SequenceModeller sequenceModeller = new SequenceModeller(trainingSet);

    int inputsCount = sequenceModeller.getCharIndex().size();
    int hiddenCount = 100;

    int maxIterations = 100;
    double learningRate = 0.8;

    System.out.println("Creating neural network...");
    RNN gru = new GRU(inputsCount, hiddenCount, new MatrixInitializer(MatrixInitializer.Type.Uniform, 0.1, 0, 0));
    BackPropagationThroughTime bptt = new GRUBackPropagationThroughTime();
    bptt.setLearningRate(learningRate);
    gru.setLearningRule(bptt);

    System.out.println("Training network...");
    bptt.learn(trainingSet, maxIterations);
    System.out.println("Training completed.");

    testNetwork(gru, testSet);
}
 

public static void main(String[] args) {
    Evaluation evaluation = new Evaluation();
    evaluation.addEvaluator(new ErrorEvaluator(new MeanSquaredError()));

    String[] classNames = {"Virginica", "Setosa", "Versicolor"};

    MultiLayerPerceptron neuralNet = (MultiLayerPerceptron) NeuralNetwork.createFromFile("irisNet.nnet");
    DataSet dataSet = DataSet.createFromFile("data_sets/iris_data_normalised.txt", 4, 3, ",");

    evaluation.addEvaluator(new ClassifierEvaluator.MultiClass(classNames));
    evaluation.evaluate(neuralNet, dataSet);

    ClassifierEvaluator evaluator = evaluation.getEvaluator(ClassifierEvaluator.MultiClass.class);
    ConfusionMatrix confusionMatrix = evaluator.getResult();
    System.out.println("Confusion matrrix:\r\n");
    System.out.println(confusionMatrix.toString() + "\r\n\r\n");
    System.out.println("Classification metrics\r\n");
    ClassificationMetrics[] metrics = ClassificationMetrics.createFromMatrix(confusionMatrix);
    ClassificationMetrics.Stats average = ClassificationMetrics.average(metrics);
    for (ClassificationMetrics cm : metrics) {
        System.out.println(cm.toString() + "\r\n");
    }
    System.out.println(average.toString());

}
 

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() throws InterruptedException, ExecutionException {
    System.out.println("Creating training set...");
    // get path to training set
    String dataSetFile = "data_sets/iris_data_normalised.txt";
    int inputsCount = 4;
    int outputsCount = 3;

    // create training set from file
    DataSet dataSet = DataSet.createFromFile(dataSetFile, inputsCount, outputsCount, ",");
 //   dataSet.setColumnNames(new String[]{"sepal.length", "sepal.width", "petal.length", "petal.width",  "setosa", "versicolor", "virginica"});
    dataSet.setColumnNames(new String[]{"setosa", "versicolor", "virginica"});
    dataSet.shuffle();

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

    String[] classLabels = new String[]{"setosa", "versicolor", "virginica"};
    neuralNet.setOutputLabels(classLabels);

    KFoldCrossValidation crossVal = new KFoldCrossValidation(neuralNet, dataSet, 5);
    EvaluationResult totalResult= crossVal.run();
    List<FoldResult> cflist= crossVal.getResultsByFolds();

}
 

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);

}
 

/**
 * @param args the command line arguments
 */
public static void main(String[] args) {
    NeuralNetwork nnet = NeuralNetwork.createFromFile("irisNet.nnet");
    DataSet dataSet =  DataSet.createFromFile("data_sets/iris_data_normalised.txt", 4, 3, ",");
    
    Evaluation.runFullEvaluation(nnet, dataSet);
     
}
 

public void run() {

        System.out.println("Creating training set...");
        String dataSetFile = "data_sets/animals_data.txt";
        int inputsCount = 20;
        int outputsCount = 7;

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

@Before
public void setUp() {
    instance = new BackPropagation();
    xorDataSet = new DataSet(2, 1);
    xorDataSet.add(new DataSetRow(new double[]{0, 0}, new double[]{0}));
    xorDataSet.add(new DataSetRow(new double[]{0, 1}, new double[]{1}));
    xorDataSet.add(new DataSetRow(new double[]{1, 0}, new double[]{1}));
    xorDataSet.add(new DataSetRow(new double[]{1, 1}, new double[]{0}));
    maxError = 0.01;
    instance.setLearningRate(0.5);
    instance.setMaxError(maxError);
    String inputFileName = "src/test/resources/iris_normalized.txt";
    irisDataSet = DataSet.createFromFile(inputFileName, 4, 3, ",", false);

}
 

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/bostonhouse.txt";
    int inputsCount = 13;
    int outputsCount = 1;

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

    // 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(TransferFunctionType.TANH, inputsCount, 2, 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());
    });

    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/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 training set...");
    String dataSetFileName = "data_sets/autodata.txt";
    int inputsCount = 1;
    int outputsCount = 1;

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

    // split data into train 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...");
    Adaline neuralNet = new Adaline(1);

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

    // train the network with training set
    System.out.println("Training network...");
    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 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() {
    System.out.println("Creating training set...");
    // get path to training set
    String trainingSetFileName = "data_sets/databanknote.txt";
    int inputsCount = 4;
    int outputsCount = 1;

    // create training set from file
    DataSet dataSet = DataSet.createFromFile(trainingSetFileName, 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(this);

    // 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.");

    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 trainingSetFileName = "data_sets/bostonhouse.txt";
    int inputsCount = 13;
    int outputsCount = 1;

    // create training set from file
    DataSet dataSet = DataSet.createFromFile(trainingSetFileName, inputsCount, outputsCount, ",");
    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(TransferFunctionType.TANH, inputsCount, 2, 2, outputsCount);

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

    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/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];

        // ovde ubaci u petlju sa hidden neuronima i learning rates

        System.out.println("Creating neural network...");
        //Create MultiLayerPerceptron neural network
        MultiLayerPerceptron neuralNet = new MultiLayerPerceptron(inputsCount, 10, 8, outputsCount);

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

        learningRule.setLearningRate(0.4);
        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...");

        String dataSetFile = "data_sets/shuttle_landing_control_data.txt";
        int inputsCount = 15;
        int outputsCount = 2;

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


        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("MyNeuralNetShuttle.nnet");

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

public void run() {

        System.out.println("Creating training set...");

        String dataSetFile = "data_sets/forest_fires_data.txt";
        int inputsCount = 29;
        int outputsCount = 1;

        // create training set from file
        DataSet dataSet = DataSet.createFromFile(dataSetFile, inputsCount, outputsCount, ",", false);
       
        
        System.out.println("Creating neural network...");
        // create MultiLayerPerceptron neural network
        MultiLayerPerceptron neuralNet = new MultiLayerPerceptron(inputsCount, 25, 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("MyNeuralNetForestFires.nnet");

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