test.java

利用Java实现的神经网络工具箱

/*
 * Created on 12/10/2004
 *
 * TODO To change the template for this generated file go to
 * Window - Preferences - Java - Code Generation - Code and Comments
 */

package neuralnetworktoolkit.tests;

import neuralnetworktoolkit.*;
import neuralnetworktoolkit.datamanager.DataLoaderException;
import neuralnetworktoolkit.datamanager.textfilemanager.LoadFromFileManager;
import neuralnetworktoolkit.methods.gradientbased.backpropagation.*;
import neuralnetworktoolkit.methods.gradientbased.quasinewton.lm.*;
import neuralnetworktoolkit.validation.ValidationStatistics;


/**
 * @author iver
 *
 * TODO To change the template for this generated type comment go to
 * Window - Preferences - Java - Code Generation - Code and Comments
 */
public class Test {
	public static void main(String[] args) {
		
		StatisticalResults statistics;
		ValidationStatistics validationStatistics;
		LoadFromFileManager lf = new LoadFromFileManager();
		
		System.out.println("Teste iniciado!");
		
		NetworkController nc = new NetworkController();
		
		try {

			int[] parameters = {4, 10, 1};

		
			String[] functions = {"Tanh", "Tanh"};
			
			String initialization = "NguyenWidrow";
			nc.createNeuralNetwork(parameters, initialization, functions);
		
			nc.setNormalization("Linear", NetworkController.FULLNORMALIZER);
			//nc.setNormalization("ZeroMeanAndUnitStandardDeviation", NetworkController.FULLNORMALIZER);
						
			double[][] data = null;
			try {
				//data = lf.loadData("/home/iver/workspace/NeuralNetworkToolkit/teste.txt", ",");
				data = lf.loadData("/home/rodrigo/a.txt", "^");
				nc.setTrainingData(data);
			} catch (DataLoaderException e2) {
				e2.printStackTrace();
			}
						
			int[] index = {0};
			nc.setOutputIndex(index);
			//nc.splitDataValues();
							
			//nc.setTrainingMethod("gradientbased.quasinewton.lm.LevenbergMarquardt");
			//nc.setTrainingMethod("gradientbased.quasinewton.lm.LMAM");
			nc.setTrainingMethod("gradientbased.quasinewton.lm.OLMAM");
			//nc.setTrainingMethod("gradientbased.quasinewton.gaussnewton.GaussNewton");
			//nc.setTrainingMethod("gradientbased.backpropagation.OnLineBackPropagation");
            //nc.setTrainingMethod("gradientbased.backpropagation.BatchAdaptiveLearningRateMomentumBackPropagation");
			//nc.setTrainingMethod("gradientbased.backpropagation.OnLineMomentumBackPropagation");	
			
			LevenbergMarquardtParameters trainParam = new LevenbergMarquardtParameters();
			//LMAMParameters trainParam = new LMAMParameters();
			//trainParam.setDeltaP(.5);
			//trainParam.setEpsilon(.03);
			//BackPropagationParameters trainParam = new BackPropagationParameters();
			trainParam.setError(.000000000000000000000000005);

			trainParam.setLmKind(LevenbergMarquardtModel.INCOMPLETE_LM);
			trainParam.setMaxIterations(200);
			//trainParam.setLearningRate(5);
			//trainParam.setMaxEpochs(3000);
			//trainParam.setAlpha(1.1);

			
			statistics = nc.trainNeuralNetwork(trainParam);
			
			//System.out.println("N??mero de ??pocas: " + statistics.getNumberOfEpochs());
			System.out.println("N??mero de itera????es: " + statistics.getNumberIterations());
			System.out.println("Erro do treinamento: " + statistics.getError());
			System.out.println("Tempo de treinamento: " + statistics.getTrainingTime());
			/*							
			try {
				//data = lf.loadData("/home/iver/workspace/NeuralNetworkToolkit/iteste.txt", ",");
				data = lf.loadData("c:\\a.txt", ",");
			} catch (DataLoaderException e1) {
				e1.printStackTrace();
			}
			nc.setValidationData(data);
			nc.splitDataValues();
			nc.validateNetwork();
			*/
			try {
				//data = lf.loadData("/home/iver/workspace/NeuralNetworkToolkit/jteste.txt", ",");
				data = lf.loadData("/home/rodrigo/a.txt", "^");
			} catch (DataLoaderException e3) {
				e3.printStackTrace();
			}
			nc.setInferenceData(data);
			nc.splitDataValues();
			nc.infereWithNetwork();
			
			//validationStatistics = nc.validateNetwork("CrossValidation", trainParam);
			
			//System.out.println("Erro valida????o: " + validationStatistics.getTotalError());
						
					
		} catch (NetworkControllerException e) {
			e.printStackTrace();
		}
		
		System.out.println("Teste finalizado!");		
		
	}
}