import org.encog.neural.networks.training.propagation.resilient.ResilientPropagation; //导入依赖的package包/类
@Test
public void trainAndStore() {
    BasicMLDataSet dataSet = getData();

    // Create network
    BasicNetwork network = getNetwork();

    // Train
    System.out.println("Training network...");
    Train train = new ResilientPropagation(network, dataSet);
    for (int i = 0; i < TRAIN_ITERATIONS; i++) {
        train.iteration();
    }
    System.out.println("Training finished, error: " + train.getError());

    // Save to file
    System.out.println("Saving to file...");
    saveToFile(network);
    System.out.println("Done");
}
 

import org.encog.neural.networks.training.propagation.resilient.ResilientPropagation; //导入依赖的package包/类
/**
 * The main method.
 * @param args No arguments are used.
 */
public static void main(final String args[]) {

    // create a neural network, without using a factory
    BasicNetwork network = new BasicNetwork();
    network.addLayer(new BasicLayer(null,true,2));
    network.addLayer(new BasicLayer(new ActivationSigmoid(),true,3));
    network.addLayer(new BasicLayer(new ActivationSigmoid(),false,1));
    network.getStructure().finalizeStructure();
    network.reset();

    // create training data
    MLDataSet trainingSet = new BasicMLDataSet(XOR_INPUT, XOR_IDEAL);

    // train the neural network
    final ResilientPropagation train = new ResilientPropagation(network, trainingSet);

    int epoch = 1;

    do {
        train.iteration();
        System.out.println("Epoch #" + epoch + " Error:" + train.getError());
        epoch++;
    } while(train.getError() > 0.01);
    train.finishTraining();

    // test the neural network
    System.out.println("Neural Network Results:");
    for(MLDataPair pair: trainingSet ) {
        final MLData output = network.compute(pair.getInput());
        System.out.println(pair.getInput().getData(0) + "," + pair.getInput().getData(1)
                + ", actual=" + output.getData(0) + ",ideal=" + pair.getIdeal().getData(0));
    }

    Encog.getInstance().shutdown();
}
 

import org.encog.neural.networks.training.propagation.resilient.ResilientPropagation; //导入依赖的package包/类
/**
 * The main method.
 * @param args No arguments are used.
 */
public static void main(final String args[]) {

	// create a neural network, without using a factory
	BasicNetwork network = new BasicNetwork();
	network.addLayer(new BasicLayer(null,true,2));
	network.addLayer(new BasicLayer(new ActivationSigmoid(),true,3));
	network.addLayer(new BasicLayer(new ActivationSigmoid(),false,1));
	network.getStructure().finalizeStructure();
	network.reset();

	// create training data
	MLDataSet trainingSet = new BasicMLDataSet(XOR_INPUT, XOR_IDEAL);

	// train the neural network
	final ResilientPropagation train = new ResilientPropagation(network, trainingSet);

	int epoch = 1;

	do {
		train.iteration();
		System.out.println("Epoch #" + epoch + " Error:" + train.getError());
		epoch++;
	} while(train.getError() > 0.01);
	train.finishTraining();

	// test the neural network
	System.out.println("Neural Network Results:");
	for(MLDataPair pair: trainingSet ) {
		final MLData output = network.compute(pair.getInput());
		System.out.println(pair.getInput().getData(0) + "," + pair.getInput().getData(1)
				+ ", actual=" + output.getData(0) + ",ideal=" + pair.getIdeal().getData(0));
	}

	Encog.getInstance().shutdown();
}
 

import org.encog.neural.networks.training.propagation.resilient.ResilientPropagation; //导入依赖的package包/类
private Train getTrain (NeuralDataSet trainingSet, BasicNetwork network) {

		//final Train train =
			//new ManhattanPropagation(network, trainingSet,
			//0.001);
		
		// Train the neural network, we use resilient propagation
		final ResilientPropagation train = new ResilientPropagation(network, trainingSet);
		train.setThreadCount(0);
		// Reset if improve is less than 1% over 5 cycles
		train.addStrategy(new RequiredImprovementStrategy(DEFAULT_SELECTION_LIMIT));
		
		
		return train;
	}
 

import org.encog.neural.networks.training.propagation.resilient.ResilientPropagation; //导入依赖的package包/类
public static void test(double[][] inputValues, double[][] outputValues)
{
    NeuralDataSet trainingSet = new BasicNeuralDataSet(inputValues, outputValues);
    BasicNetwork network = new BasicNetwork();
    network.addLayer(new BasicLayer(new ActivationSigmoid(), false, 4));
    network.addLayer(new BasicLayer(new ActivationSigmoid(), false, 1000));
    network.addLayer(new BasicLayer(new ActivationLinear(), false, 1));
    network.getStructure().finalizeStructure();
    network.reset();
    final Train train = new ResilientPropagation(network, trainingSet);
    int epoch = 1;
    do
    {
        train.iteration();
        System.out.println("Epoch #" + epoch + " Error:" + train.getError());
        epoch++;
    }
    while(epoch < 10000);
    System.out.println("Neural Network Results:");
    for(MLDataPair pair : trainingSet)
    {
        final MLData output = network.compute(pair.getInput());
        System.out.println(pair.getInput().getData(0) + "," + pair.getInput().getData(1) + ", actual="
            + output.getData(0) + ",ideal=" + pair.getIdeal().getData(0));

    }
}
 

import org.encog.neural.networks.training.propagation.resilient.ResilientPropagation; //导入依赖的package包/类
private MLRegression withResilieant() {
	final MLTrain train = new ResilientPropagation(EncogUtility.simpleFeedForward(400, 100, 0, 10, false),
			this.training);
	EncogUtility.trainToError(train, 0.01515);
	return (MLRegression) train.getMethod();
}
 

import org.encog.neural.networks.training.propagation.resilient.ResilientPropagation; //导入依赖的package包/类
public void train(final ArrayList<DataPoint> dataHistory) {
	if (isTraining()) {
		throw new IllegalStateException();
	}

	setTrainerThread(new Thread() {
		public void run() {
			// Clean and normalize the data history
			ArrayList<DataPoint> cleanedDataHistory = cleanDataHistory(dataHistory);
			ArrayList<DataPoint> normalizedDataHistory = normalizeDataHistory(cleanedDataHistory);

			// Create a new neural network and data set
			BasicNetwork neuralNetwork = EncogUtility.simpleFeedForward(2, getHiddenLayerNeurons(0),
					getHiddenLayerNeurons(1), 5, true);
			MLDataSet dataSet = new BasicMLDataSet();

			// Add all points of the data history to the data set
			for (DataPoint dataPoint : normalizedDataHistory) {
				MLData input = new BasicMLData(2);
				input.setData(0, dataPoint.getX());
				input.setData(1, dataPoint.getY());

				// If getButton() is 0, the output will be 0, 0, 0, 0
				// If getButton() is 2, the output will be 0, 1, 0, 0
				// If getButton() is 4, the output will be 0, 0, 0, 1
				MLData ideal = new BasicMLData(5);
				for (int i = 0; i <= 4; i++) {
					ideal.setData(i, (dataPoint.getButton() == i) ? 1 : 0);
				}

				MLDataPair pair = new BasicMLDataPair(input, ideal);
				dataSet.add(pair);
			}

			// Create a training method
			MLTrain trainingMethod = new ResilientPropagation((ContainsFlat) neuralNetwork, dataSet);
			long startTime = System.currentTimeMillis();
			int timeLeft = getMaxTrainingTime();
			int iteration = 0;

			// Train the network using multiple iterations on the training method
			do {
				trainingMethod.iteration();
				timeLeft = (int) ((startTime + getMaxTrainingTime()) - System.currentTimeMillis());
				iteration++;

				sendNeuralNetworkIteration(iteration, trainingMethod.getError(), timeLeft);
			} while (trainingMethod.getError() > getMaxTrainingError() && timeLeft > 0
					&& !trainingMethod.isTrainingDone());
			trainingMethod.finishTraining();

			// Return the neural network to all listeners
			sendNeuralNetworkTrainerResult(neuralNetwork);
		}
	});
	getTrainerThread().start();
}