.#backpropagationpanel.java.1.2

利用Java实现的神经网络工具箱

/*
 * $RCSfile: BackPropagationPanel.java,v $
 * $Revision: 1.2 $
 * $Date: 2005/02/24 21:20:29 $
 *
 * 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.gui.options.method;

import java.awt.BorderLayout;
import java.awt.GridBagConstraints;
import java.awt.GridBagLayout;
import java.awt.Insets;
import java.util.ResourceBundle;

import javax.swing.ButtonGroup;
import javax.swing.JLabel;
import javax.swing.JPanel;
import javax.swing.JRadioButton;
import javax.swing.JSpinner;
import javax.swing.SpinnerNumberModel;
import javax.swing.border.TitledBorder;

import neuralnetworktoolkit.methods.TrainingParameters;
import neuralnetworktoolkit.methods.gradientbased.backpropagation.BackPropagationModel;
import neuralnetworktoolkit.methods.gradientbased.backpropagation.BackPropagationParameters;
import neuralnetworktoolkit.methods.gradientbased.quasinewton.gaussnewton.GaussNewtonParameters;

/**
 * 
 * 
 * @version $Revision: 1.2 $ - $Date: 2005/02/24 21:20:29 $
 * 
 * @author <a href="mailto:[e-mail]">Rodrigo C. M. Coimbra</a>
 * @author <a href="mailto:[e-mail]">Hugo Iver V. Gonçalves</a>
 */
public class BackPropagationPanel extends TrainingMethodPanel {
	
	private JLabel maxEpochsLabel;
	private JLabel errorLabel;
	private JLabel learningRateLabel;
	private JLabel alphaLabel;
	private JRadioButton batchRadio;
	private JRadioButton batchAdaptiveLearningRateRadio;
	private JRadioButton batchMomentumRadio;
	private JRadioButton onLineRadio;
	private JRadioButton onLineAdaptiveLearningRadio;
	private JRadioButton onLineMomentumRadio;
	private ButtonGroup backPropagationVariationGroup;
	private JSpinner maxEpochsSpinner;
	private JSpinner errorSpinner;
	private JSpinner learningRateSpinner;
	private JSpinner alphaSpinner;
	private SpinnerNumberModel maxEpochsSpinnerModel;
	private SpinnerNumberModel errorSpinnerModel;
	private SpinnerNumberModel learningRateSpinnerModel;
	private SpinnerNumberModel alphaSpinnerModel;
	private JPanel maxEpochsPanel;
	private JPanel errorPanel;
	private JPanel learningRatePanel;
	private JPanel alphaPanel;
	private JPanel backPropagationVariationPanel;
	private JPanel stopConditionPanel;
	private JPanel backPropagationParametersPanel;
	
	private Insets borderInsets;
	private Insets borderInsets2;
	
	private String[] methods = {"gradientbased.backpropagation.BatchBackPropagation",
			"gradientbased.backpropagation.BatchAdaptiveLearningRateBackPropagation",
			"gradientbased.backpropagation.BatchMomentumBackPropagation",
			"gradientbased.backpropagation.OnLineBackPropagation",
			"gradientbased.backpropagation.OnLineAdaptiveLearningRateBackPropagation",
			"gradientbased.backpropagation.OnLineMomentumBackPropagation"};
	
	private ResourceBundle resource;
	
	/**
	 * 
	 *
	 */
	public BackPropagationPanel() {
		resource = ResourceBundle
				.getBundle("neuralnetworktoolkit.gui.options.method.resources.BackPropagationPanelResource");
		
		borderInsets = new Insets(5, 5, 5, 5);
		borderInsets2 = new Insets(2, 2, 2, 2);
		
		batchRadio = new JRadioButton(resource.getString("batch"));
		batchAdaptiveLearningRateRadio = new JRadioButton(resource.getString("batchAdaptiveLearningRate"));
		batchMomentumRadio = new JRadioButton(resource.getString("batchMomentum"));
		onLineRadio = new JRadioButton(resource.getString("onLine"));
		onLineAdaptiveLearningRadio = new JRadioButton(resource.getString("onLineAdaptiveLearningRate"));
		onLineMomentumRadio = new JRadioButton(resource.getString("onLineMomentum"));
		
		backPropagationVariationGroup = new ButtonGroup();
		backPropagationVariationGroup.add(batchRadio);
		backPropagationVariationGroup.add(batchAdaptiveLearningRateRadio);
		backPropagationVariationGroup.add(batchMomentumRadio);
		backPropagationVariationGroup.add(onLineRadio);
		backPropagationVariationGroup.add(onLineAdaptiveLearningRadio);
		backPropagationVariationGroup.add(onLineMomentumRadio);
		backPropagationVariationGroup.setSelected(batchRadio.getModel(), true);
		
		backPropagationVariationPanel = new JPanel();
		backPropagationVariationPanel.setLayout(new GridBagLayout());
		backPropagationVariationPanel.add(batchRadio,
				new GridBagConstraints(0, 0, 1, 1, 1.0, 1.0,
						GridBagConstraints.WEST,
						GridBagConstraints.VERTICAL, borderInsets2,
						0, 0));
		backPropagationVariationPanel.add(batchAdaptiveLearningRateRadio,
				new GridBagConstraints(0, 1, 1, 1, 1.0, 1.0,
						GridBagConstraints.WEST,
						GridBagConstraints.VERTICAL, borderInsets2,
						0, 0));
		backPropagationVariationPanel.add(batchMomentumRadio,
				new GridBagConstraints(0, 2, 1, 1, 1.0, 1.0,
						GridBagConstraints.WEST,
						GridBagConstraints.VERTICAL, borderInsets2,
						0, 0));
		backPropagationVariationPanel.add(onLineRadio,
				new GridBagConstraints(1, 0, 1, 1, 1.0, 1.0,
						GridBagConstraints.WEST,
						GridBagConstraints.VERTICAL, borderInsets2,
						0, 0));
		backPropagationVariationPanel.add(onLineAdaptiveLearningRadio,
				new GridBagConstraints(1, 1, 1, 1, 1.0, 1.0,
						GridBagConstraints.WEST,
						GridBagConstraints.VERTICAL, borderInsets2,
						0, 0));
		backPropagationVariationPanel.add(onLineMomentumRadio,
				new GridBagConstraints(1, 2, 1, 1, 1.0, 1.0,
						GridBagConstraints.WEST,
						GridBagConstraints.VERTICAL, borderInsets2,
						0, 0));
		backPropagationVariationPanel.setBorder(new TitledBorder(resource.getString("backPropagationVariation")));
		
		maxEpochsLabel = new JLabel(resource.getString("maxEpochs"));
		maxEpochsSpinnerModel = new SpinnerNumberModel(400, 1, BackPropagationModel.MAX_EPOCHS, 1);
		maxEpochsSpinner = new JSpinner(maxEpochsSpinnerModel);
		maxEpochsPanel = new JPanel();
		maxEpochsPanel.setLayout(new BorderLayout());
		maxEpochsPanel.add(maxEpochsLabel, BorderLayout.NORTH);
		maxEpochsPanel.add(maxEpochsSpinner, BorderLayout.CENTER);
		
		errorLabel = new JLabel(resource.getString("error"));
		errorSpinnerModel = new SpinnerNumberModel(0.001, 0, 1, 0.001);
		errorSpinner = new JSpinner(errorSpinnerModel);
		errorPanel = new JPanel();
		errorPanel.setLayout(new BorderLayout());
		errorPanel.add(errorLabel, BorderLayout.NORTH);
		errorPanel.add(errorSpinner, BorderLayout.CENTER);
		
		stopConditionPanel = new JPanel();
		stopConditionPanel.setLayout(new GridBagLayout());
		stopConditionPanel.add(maxEpochsPanel,
				new GridBagConstraints(0, 0, 1, 1, 1.0, 1.0,
						GridBagConstraints.CENTER,
						GridBagConstraints.VERTICAL, borderInsets,
						67, 0));
		stopConditionPanel.add(errorPanel,
				new GridBagConstraints(0, 1, 1, 1, 1.0, 1.0,
						GridBagConstraints.CENTER,
						GridBagConstraints.VERTICAL, borderInsets,
						67, 0));
		stopConditionPanel.setBorder(new TitledBorder(resource.getString("stopCondition")));
		
		learningRateLabel = new JLabel(resource.getString("learningRate"));
		learningRateSpinnerModel = new SpinnerNumberModel(0.3, 1e-30, 1, 0.001);
		learningRateSpinner = new JSpinner(learningRateSpinnerModel);
		learningRatePanel = new JPanel();
		learningRatePanel.setLayout(new BorderLayout());
		learningRatePanel.add(learningRateLabel, BorderLayout.NORTH);
		learningRatePanel.add(learningRateSpinner, BorderLayout.CENTER);
		
		alphaLabel = new JLabel(resource.getString("alpha"));
		alphaSpinnerModel = new SpinnerNumberModel(0.2, 1e-30, 1, 0.001);
		alphaSpinner = new JSpinner(alphaSpinnerModel);
		alphaPanel = new JPanel();
		alphaPanel.setLayout(new BorderLayout());
		alphaPanel.add(alphaLabel, BorderLayout.NORTH);
		alphaPanel.add(alphaSpinner, BorderLayout.CENTER);
		
		backPropagationParametersPanel = new JPanel();
		backPropagationParametersPanel.setLayout(new GridBagLayout());
		backPropagationParametersPanel.add(learningRatePanel,
				new GridBagConstraints(0, 0, 1, 1, 1.0, 1.0,
						GridBagConstraints.CENTER,
						GridBagConstraints.VERTICAL, borderInsets,
						67, 0));
		backPropagationParametersPanel.add(alphaPanel,
				new GridBagConstraints(0, 1, 1, 1, 1.0, 1.0,
						GridBagConstraints.CENTER,
						GridBagConstraints.VERTICAL, borderInsets,
						67, 0));
		backPropagationParametersPanel.setBorder(new TitledBorder(resource.getString("backPropagationParameters")));
		
		this.setLayout(new GridBagLayout());
		this.add(backPropagationVariationPanel,
				new GridBagConstraints(0, 0, 1, 1, 1.0, 1.0,
						GridBagConstraints.CENTER,
						GridBagConstraints.HORIZONTAL, borderInsets,
						67, 0));
		this.add(stopConditionPanel,
				new GridBagConstraints(0, 1, 1, 1, 1.0, 1.0,
						GridBagConstraints.CENTER,
						GridBagConstraints.HORIZONTAL, borderInsets,
						67, 0));
		this.add(backPropagationParametersPanel,
				new GridBagConstraints(0, 2, 1, 1, 1.0, 1.0,
						GridBagConstraints.CENTER,
						GridBagConstraints.HORIZONTAL, borderInsets,
						67, 0));
		
	} //GaussNewtonPanel()

	/* 
	 * @see neuralnetworktoolkit.gui.options.method.TrainingMethodPanel#initializeComponents(boolean)
	 */
	public void initializeComponents(boolean verify) {
		BackPropagationParameters bpParameters;
		
		if(verify) {
			bpParameters = (BackPropagationParameters) parameters;
			/*
			"gradientbased.backpropagation.BatchBackPropagation",
			"gradientbased.backpropagation.BatchAdaptiveLearningRateBackPropagation",
			"gradientbased.backpropagation.BatchMomentumBackPropagation",
			"gradientbased.backpropagation.OnLineBackPropagation",
			"gradientbased.backpropagation.OnLineAdaptiveLearningRateBackPropagation",
			"gradientbased.backpropagation.OnLineMomentumBackPropagation"
			*/
			
			if(bpParameters.getMethod().equals(methods[0])) {
				
				
			} else if(bpParameters.getMethod().equals(methods[1])) {
				
				
			} else if(bpParameters.getMethod().equals(methods[2])) {
				
				
			} else if(bpParameters.getMethod().equals(methods[3])) {
				
				
			} else if(bpParameters.getMethod().equals(methods[4])) {
				
				
			} else if(bpParameters.getMethod().equals(methods[5])) {
				
				
			}
			errorSpinner.setValue(new Double(bpParameters.getError()));
			maxEpochsSpinner.setValue(new Integer(bpParameters.getMaxEpochs()));
			learningRateSpinner.setValue(new Double(bpParameters.getLearningRate()));
			alphaSpinner.setValue(new Double(bpParameters.getAlpha()));
			
		}
		
	} //initializeComponents()
	
	/**
	 * @return Returns the parameters.
	 */
	public TrainingParameters getParameters() {
		BackPropagationParameters bpParameters;
		
		bpParameters = (BackPropagationParameters) parameters;
		
		bpParameters.setError(((Double)errorSpinner.getValue()).doubleValue());
		bpParameters.setMaxEpochs(((Integer)maxEpochsSpinner.getValue()).intValue());
		bpParameters.setLearningRate(((Double)learningRateSpinner.getValue()).doubleValue());
		bpParameters.setAlpha(((Double)alphaSpinner.getValue()).doubleValue());
		
		return bpParameters;
		
	} //getParameters()

} //BackPropagationPanel