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

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 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...");
        // get path to training set
        String dataSetFile = "data_sets/wine_classification_data.txt";
        int inputsCount = 13;
        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("MyNeuralNetWineClassification.nnet");

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

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.");
    }
 

/**
 * Runs this sample
 */
public  void run() {

    // create training set (logical XOR function)
    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}));

    // create multi layer perceptron
    MultiLayerPerceptron myMlPerceptron = new MultiLayerPerceptron(TransferFunctionType.SIGMOID, 2, 3, 1);
    // set ResilientPropagation learning rule
    myMlPerceptron.setLearningRule(new ResilientPropagation());
    LearningRule learningRule = myMlPerceptron.getLearningRule();
    learningRule.addListener(this);

    // learn the training set
    System.out.println("Training neural network...");
    myMlPerceptron.learn(trainingSet);

    int iterations = ((SupervisedLearning)myMlPerceptron.getLearningRule()).getCurrentIteration();
    System.out.println("Learned in "+iterations+" iterations");

    // test perceptron
    System.out.println("Testing trained neural network");
    testNeuralNetwork(myMlPerceptron, trainingSet);

}
 

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 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 training set...");
    // get path to training set
    String trainingSetFileName = "data_sets/sonardata.txt";
    int inputsCount = 60;
    int outputsCount = 1;

    // create training set from file
    DataSet dataSet = DataSet.createFromFile(trainingSetFileName, 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 data using max normalization
    Normalizer norm = new MaxNormalizer(trainingSet);
    norm.normalize(trainingSet);
    norm.normalize(testSet);

    System.out.println("Creating neural network...");
    MultiLayerPerceptron neuralNet = new MultiLayerPerceptron(inputsCount, 15, 10, 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/abalonerings.txt";
    int inputsCount = 8;
    int outputsCount = 29;

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

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

    System.out.println("Creating neural network...");
    MultiLayerPerceptron neuralNet = new MultiLayerPerceptron(inputsCount, 15, 10, 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 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 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 data set...");
    String dataSetFile = "data_sets/ml10standard/abalonerings.txt";
    int inputsCount = 8;
    int outputsCount = 29;

    // create training set from file
    DataSet dataSet = DataSet.createFromFile(dataSetFile, inputsCount, outputsCount, "\t", true);
    DataSet[] trainTestSplit = dataSet.split(0.7, 0.3);
    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, 15, 10, 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.");
}
 

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("**************************************************");
//        }
    }
 

/**
     *
     * You can get results calling getResults() method.
     *
     * @param neuralNetwork type of neural net
     * @param dataSet
     */
    public void train(DataSet dataSet) {// mozda da se vrati Training setting koji je najbolje resenje za dati dataset.??
        generateTrainingSettings();
        List<TrainingResult> statResults = null;
        DataSet trainingSet, testSet; // validationSet;

        if (splitTrainTest) {
            DataSet[] dataSplit = dataSet.split(splitPercentage, 100-splitPercentage); //opet ne radi Maven za neuroph 2.92
            trainingSet = dataSplit[0];
            testSet = dataSplit[1];
        } else {
            trainingSet = dataSet;
            testSet = dataSet;
        }

        if (generateStatistics) {
            statResults = new ArrayList<>();
        }

        int trainingNo = 0;
        for (TrainingSettings trainingSetting : trainingSettingsList) {
            System.out.println("-----------------------------------------------------------------------------------");
            trainingNo++;
            System.out.println("##TRAINING: " + trainingNo);
            trainingSetting.setTrainingSet(splitPercentage);
            trainingSetting.setTestSet(100 - splitPercentage);
            //int subtrainNo = 0;

            for (int subtrainNo = 1; subtrainNo <= repeat; subtrainNo++) {
                System.out.println("#SubTraining: " + subtrainNo);

                MultiLayerPerceptron neuralNet
                        = new MultiLayerPerceptron(dataSet.getInputSize(), trainingSetting.getHiddenNeurons(), dataSet.getOutputSize());

                BackPropagation bp = neuralNet.getLearningRule();

                bp.setLearningRate(trainingSetting.getLearningRate());
                bp.setMaxError(trainingSetting.getMaxError());
                bp.setMaxIterations(trainingSetting.getMaxIterations());

                neuralNet.learn(trainingSet);
//                  testNeuralNetwork(neuralNet, testSet); // not implemented
                ConfusionMatrix cm = new ConfusionMatrix(new String[]{""});
                TrainingResult result = new TrainingResult(trainingSetting, bp.getTotalNetworkError(), bp.getCurrentIteration(),cm);
                System.out.println(subtrainNo + ") iterations: " + bp.getCurrentIteration());

                if (generateStatistics) {
                    statResults.add(result);
                } else {
                    results.add(result);
                }

            }

            if (generateStatistics) {
                TrainingResult trainingStats = calculateTrainingStatistics(trainingSetting, statResults);
                results.add(trainingStats);
                statResults.clear();
            }

        }

    }
 

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 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 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.");
    }