maincontroller.java

利用Java实现的神经网络工具箱

/*
 * NetworkController.java	1.0  05/11/2004
 *
 * NeuralNetworkToolkit
 * Copyright (C) 2004 Universidade de Brasília
 *
 * This file is part of NeuralNetworkToolkit.
 *
 * NeuralNetworkToolkit is free software; you can redistribute it and/or modify
 * it under the terms of the GNU General Public License as published by
 * the Free Software Foundation; either version 2 of the License, or
 * (at your option) any later version.
 *
 * NeuralNetworkToolkit is distributed in the hope that it will be useful,
 * but WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 * GNU General Public License for more details.
 *
 * You should have received a copy of the GNU General Public License
 * along with NeuralNetworkToolkit; if not, write to the Free Software
 * Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA - 02111-1307 - USA.
 */
 
package neuralnetworktoolkit;

import java.util.Vector;

import neuralnetworktoolkit.architectures.ArchitectureParameters;
import neuralnetworktoolkit.methods.TrainingParameters;
import neuralnetworktoolkit.validation.ValidationStatistics;

/**
 * @author iver
 *
 */
public class MainController {

	private Vector networkControllers;
	
	private int currentNetworkControllerIndex;
	private NetworkController currentNetworkController;
	
	private double[][] data;
	
	private MainController() {
		this.networkControllers = new Vector();
	}
	
	public static MainController getMainController() {
		return new MainController();
	}
	
	public void addNetworkController() {
		NetworkController nc = new NetworkController();
		networkControllers.addElement(nc);
	}
	
	public void removeNetworkController(int index) {
		networkControllers.removeElementAt(index);
	}
	
	public void setCurrentNetworkController(int index) {
		this.currentNetworkControllerIndex = index;
		this.currentNetworkController = (NetworkController) this.networkControllers.elementAt(index);
	}

	public void createNeuralNetwork(ArchitectureParameters parameters) {
		try {
			this.currentNetworkController.createNeuralNetwork(parameters);
		} catch (NetworkControllerException e) {
			e.printStackTrace();
		}
	}
	
	public void createNeuralNetwork(int[] parameters, String initializationName, String[] functions) {
		try {
			this.currentNetworkController.createNeuralNetwork(parameters, initializationName, functions);
		} catch (NetworkControllerException e) {
			e.printStackTrace();
		}
	}
	
	public void createNeuralNetwork(NeuralModel nm) {
		try {
			this.currentNetworkController.createNeuralNetwork(nm);
		} catch (NetworkControllerException e) {
			e.printStackTrace();
		}
	}
	
	public void infereWithNetwork() {
		try {
			this.currentNetworkController.infereWithNetwork();
		} catch (NetworkControllerException e) {
			e.printStackTrace();
		}
	}
	
	public void setInferenceData(double[][] data) {
		this.currentNetworkController.setInferenceData(data);
	}
	
	public void setTrainingData(double[][] data) {
		this.currentNetworkController.setTrainingData(data);
	}
	
	public void setValidationData(double[][] data) {
		this.currentNetworkController.setValidationData(data);
	}
	
	public void setNormalization(String normalizer, int type) {
		try {
			this.currentNetworkController.setNormalization(normalizer, type);
		} catch (NetworkControllerException e) {
			e.printStackTrace();
		}
	}
	
	public void setOutputIndex(int[] outputIndex) {
		this.currentNetworkController.setOutputIndex(outputIndex);
	}
	
	public void setTrainingMethod(String trainingMethodName) {
		try {
			this.currentNetworkController.setTrainingMethod(trainingMethodName);
		} catch (NetworkControllerException e) {
			e.printStackTrace();
		}
	}
	
	public StatisticalResults trainNeuralNetwork(TrainingParameters param) {
		StatisticalResults result = null;
		
		try {
			result = this.currentNetworkController.trainNeuralNetwork(param);
		} catch (NetworkControllerException e) {
			e.printStackTrace();
		}
		
		return result;
	}
	
	public ValidationStatistics validateNetwork(String validationType, TrainingParameters param) {
		ValidationStatistics result = null;
		
		try {
			result = this.currentNetworkController.validateNetwork(validationType, param);
		} catch (NetworkControllerException e) {
			e.printStackTrace();
		}
		
		return result;
	}
	
}