Java Code Examples for org.neuroph.nnet.learning.MomentumBackpropagation#addListener()

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 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);

    }
 

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);

}
 

@Override
public void doRun() {
    try {
        System.out.println("Starting training thread....." + sampleDimension.toString() + " and " + imageLabels.toString());

        HashMap<String, BufferedImage> imagesMap = new HashMap<String, BufferedImage>();
        for (File file : srcDirectory.listFiles()) {
            imageLabels.add(FilenameUtils.removeExtension(file.getName()));
            if (sampleDimension.getWidth() > 0 && sampleDimension.getHeight() > 0) {
                Double w = sampleDimension.getWidth();
                Double h = sampleDimension.getHeight();
                imagesMap.put(file.getName(), ImageUtilities.resizeImage(ImageUtilities.loadImage(file), w.intValue(), h.intValue()));
            }
        }
        Map<String, FractionRgbData> imageRgbData = ImageUtilities.getFractionRgbDataForImages(imagesMap);
        DataSet learningData = ImageRecognitionHelper.createRGBTrainingSet(imageLabels, imageRgbData);

        nnet = NeuralNetwork.load(new FileInputStream(nnFile)); //Load NNetwork
        MomentumBackpropagation mBackpropagation = (MomentumBackpropagation) nnet.getLearningRule();
        mBackpropagation.setLearningRate(learningRate);
        mBackpropagation.setMaxError(maxError);
        mBackpropagation.setMomentum(momentum);

        System.out.println("Network Information\nLabel = " + nnet.getLabel()
                + "\n Input Neurons = " + nnet.getInputsCount()
                + "\n Number of layers = " + nnet.getLayersCount()
        );

        mBackpropagation.addListener(this);
        System.out.println("Starting training......");
        nnet.learn(learningData, mBackpropagation);
        //Training Completed
        listener.batchImageTrainingCompleted();
    } catch (FileNotFoundException ex) {
        System.out.println(ex.getMessage() + "\n" + ex.getLocalizedMessage());
    }

}
 

@Override
public void doRun() {
    HashMap<String, BufferedImage> imagesMap = new HashMap<String, BufferedImage>();
    String fileName = "";
    if (!isReal) {
        fileName = "real";
    } else {
        fileName = "faked";
    }

    System.out.println("Teaching as " + fileName);
    imagesMap.put(fileName, image);
    Map<String, FractionRgbData> imageRgbData = ImageUtilities.getFractionRgbDataForImages(imagesMap);
    DataSet learningData = ImageRecognitionHelper.createRGBTrainingSet(labels, imageRgbData);
    MomentumBackpropagation mBackpropagation = (MomentumBackpropagation) nnet.getLearningRule();
    mBackpropagation.setLearningRate(learningRate);
    mBackpropagation.setMaxError(maxError);
    mBackpropagation.setMomentum(momentum);

    System.out.println("Network Information\nLabel = " + nnet.getLabel()
            + "\n Input Neurons = " + nnet.getInputsCount()
            + "\n Number of layers = " + nnet.getLayersCount()
    );

    mBackpropagation.addListener(this);
    System.out.println("Starting training......");
    nnet.learn(learningData, mBackpropagation);

    //Mark nnet as dirty. Write on close
    isDirty = true;
}
 

public void run() {

        System.out.println("Creating training and test set from file...");
        String dataSetFile = "data_sets/segment challenge.txt";
        String testSetFileName = "data_sets/segment test.txt";
        int inputsCount = 19;
        int outputsCount = 7;

        //Create training data set from file
        DataSet trainingSet = DataSet.createFromFile(dataSetFile, inputsCount, outputsCount, ",");
        System.out.println("Training set size: " + trainingSet.getRows().size());
        trainingSet.shuffle();

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

        //Create test data set from file
        DataSet testSet = DataSet.createFromFile(testSetFileName, inputsCount, outputsCount, ",");
        System.out.println("Test set size: " + testSet.getRows().size());
        System.out.println("--------------------------------------------------");
        testSet.shuffle();

        //Normalizing training data set
        normalizer.normalize(testSet);

        System.out.println("Creating neural network...");
        //Create MultiLayerPerceptron neural network
        MultiLayerPerceptron neuralNet = new MultiLayerPerceptron(inputsCount, 17, 10, outputsCount);
        //attach listener to learning rule
        MomentumBackpropagation learningRule = (MomentumBackpropagation) neuralNet.getLearningRule();
        learningRule.addListener((event) -> {
            BackPropagation bp = (BackPropagation) event.getSource();
            if (event.getEventType().equals(LearningEvent.Type.LEARNING_STOPPED)) {
                double error = bp.getTotalNetworkError();
                System.out.println("Training completed in " + bp.getCurrentIteration() + " iterations, ");
                System.out.println("With total error: " + formatDecimalNumber(error));
            } else {
                System.out.println("Iteration: " + bp.getCurrentIteration() + " | Network error: " + bp.getTotalNetworkError());
            }
        });

        learningRule.setLearningRate(0.01);
        learningRule.setMaxError(0.001);
        learningRule.setMaxIterations(12000);

        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/irisdatanormalised.txt";
    int inputsCount = 4;
    int outputsCount = 3;

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

    // splid data into training 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(TransferFunctionType.TANH, inputsCount, 2, outputsCount);

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

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

    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);
}
 

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 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/pimadata.txt";
    int inputsCount = 8;
    int outputsCount = 1;

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

    // 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.");

    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/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 data set...");
    String dataSetFile = "data_sets/ml10standard/wine.txt";
    int inputsCount = 11;
    int outputsCount = 10;

    // create data 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((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.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 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 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 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 training set...");
    // get path to training set
    String trainingSetFileName = "data_sets/ionospheredata.txt";
    int inputsCount = 34;
    int outputsCount = 1;

    // create training set from file
    DataSet dataSet = DataSet.createFromFile(trainingSetFileName, 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(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 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 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/lenses_data.txt";
        int inputsCount = 9;
        int outputsCount = 3;

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

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