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

The following examples show how to use org.neuroph.core.data.DataSet#split() . You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may check out the related API usage on the sidebar.
Example 1
Source File: DiabetesSample.java    From NeurophFramework with Apache License 2.0 5 votes vote down vote up 
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);

}
Example 2
Source File: WineQualityClassification.java    From NeurophFramework with Apache License 2.0 4 votes vote down vote up 
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);
}
Example 3
Source File: Sonar.java    From NeurophFramework with Apache License 2.0 4 votes vote down vote up 
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);
}
Example 4
Source File: Abalone.java    From NeurophFramework with Apache License 2.0 4 votes vote down vote up 
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);
}
Example 5
Source File: BostonHousePrice.java    From NeurophFramework with Apache License 2.0 4 votes vote down vote up 
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);
}
Example 6
Source File: SwedishAutoInsurance.java    From NeurophFramework with Apache License 2.0 4 votes vote down vote up 
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);
}
Example 7
Source File: Banknote.java    From NeurophFramework with Apache License 2.0 4 votes vote down vote up 
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);
}
Example 8
Source File: IrisFlowers.java    From NeurophFramework with Apache License 2.0 4 votes vote down vote up 
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);
}
Example 9
Source File: PimaIndiansDiabetes.java    From NeurophFramework with Apache License 2.0 4 votes vote down vote up 
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.");
}
Example 10
Source File: Ionosphere.java    From NeurophFramework with Apache License 2.0 4 votes vote down vote up 
public void run() {
    System.out.println("Creating data set...");
    String dataSetFile = "data_sets/ml10standard/ionospheredata.txt";
    int inputsCount = 34;
    int outputsCount = 1;

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

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

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

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

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

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

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

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

    System.out.println("Done.");
}
Example 11
Source File: WheatSeeds.java    From NeurophFramework with Apache License 2.0 4 votes vote down vote up 
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);
}
Example 12
Source File: AutoTrainer.java    From NeurophFramework with Apache License 2.0 4 votes vote down vote up 
/**
     *
     * 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();
            }

        }

    }
Example 13
Source File: Ionosphere.java    From NeurophFramework with Apache License 2.0 4 votes vote down vote up 
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);
}
Example 14
Source File: Abalone.java    From NeurophFramework with Apache License 2.0 4 votes vote down vote up 
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.");
}
Example 15
Source File: BostonHousePrice.java    From NeurophFramework with Apache License 2.0 4 votes vote down vote up 
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.");

}
Example 16
Source File: SwedishAutoInsurance.java    From NeurophFramework with Apache License 2.0 4 votes vote down vote up 
public void run() {
    System.out.println("Creating data set...");
    String dataSetFile = "data_sets/ml10standard/autodata.txt";
    int inputsCount = 1;
    int outputsCount = 1;

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

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

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

    System.out.println();
    System.out.println("Network outputs for test set");
    testNeuralNetwork(neuralNet, testSet);
}
Example 17
Source File: Banknote.java    From NeurophFramework with Apache License 2.0 4 votes vote down vote up 
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.");
}
Example 18
Source File: PimaIndiansDiabetes.java    From NeurophFramework with Apache License 2.0 4 votes vote down vote up 
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);
}
Example 19
Source File: BrestCancerSample.java    From NeurophFramework with Apache License 2.0 4 votes vote down vote up 
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("**************************************************");

    }
Example 20
Source File: WineQualityClassification.java    From NeurophFramework with Apache License 2.0 4 votes vote down vote up 
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);
}