mainmenu.java,v

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	rirwin:1.9; strict;
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1.9
date	2005.06.21.18.39.43;	author rirwin;	state Exp;
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1.8
date	2005.06.21.18.29.29;	author rirwin;	state Exp;
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1.7
date	2005.06.10.14.41.30;	author rirwin;	state Exp;
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1.6
date	2005.05.24.14.48.03;	author rirwin;	state Exp;
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1.5
date	2005.05.24.14.00.53;	author rirwin;	state Exp;
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1.4
date	2005.03.11.17.53.32;	author patil;	state Exp;
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1.3
date	2004.12.28.23.56.46;	author patil;	state Exp;
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1.2
date	2004.12.28.22.05.42;	author patil;	state Exp;
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1.1
date	2004.12.28.00.04.32;	author patil;	state Exp;
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desc
@Changes made.
@


1.9
log
@Changed switching to Kalman from Particle.
@
text
@/**
 * file: MainMenu.java
 *
 * last edited: Ryan Irwin
 */

// import necessary java libraries
//
import java.awt.*;
import javax.swing.*;
import java.awt.event.ActionListener;
import java.awt.event.*;
import java.util.*;
import java.io.*;
import java.net.*;
import java.applet.*;

/**
 *
 * implement the menu driven system that drives the applet
 *
 * hierarchy: JPanel->SubPanel->MainMenu
 *
 */
public class MainMenu extends SubPanel implements ActionListener
{

    // *********************************************************************
    //
    // declare global variables and components
    //
    // *********************************************************************

    // declare the menu font layout
    //
    Font currentFont = getFont();
    Font newFont = new Font(currentFont.getName(), currentFont.getStyle(), 12);

    // declare the menu layout
    //
    JMenuBar menuBar;
    JMenu menu;
    JMenuItem menuItem;
    JMenuItem prevMenuItem;
    JMenuItem nextMenuItem;
    JCheckBoxMenuItem nearestMenuItem;
    JCheckBoxMenuItem euclideanMenuItem;
    JCheckBoxMenuItem pcaMenuItem;
    JCheckBoxMenuItem pca1MenuItem;
    JCheckBoxMenuItem ldaMenuItem;
    JCheckBoxMenuItem lda1MenuItem;
    JCheckBoxMenuItem ldapcaMenuItem;       //I add here for lpa->pca 1
    JCheckBoxMenuItem ldapca1MenuItem;
    JCheckBoxMenuItem icaMenuItem;
    JCheckBoxMenuItem ica1MenuItem;
    JCheckBoxMenuItem svmMenuItem;
    JCheckBoxMenuItem svm1MenuItem;
    JCheckBoxMenuItem lpMenuItem;       //I add here for lp and particle
    JCheckBoxMenuItem pfMenuItem;
    JCheckBoxMenuItem kfMenuItem;
    JRadioButtonMenuItem rbMenuItem;

    // declare components for the help box
    //
    JFrame frame;
    JTextArea textArea;
    JScrollPane scrollPane;
    JButton dismiss;

    // declare components for the scales frame
    //
    JFrame scale;
    JButton change;

    // declare components for the gaussian frame
    //
    JFrame setgausdlg;
    JButton applyb;
    JButton cancel;

    // declare components for the color selection frame
    //
    JFrame setccolorsdlg;
    JLabel colorselection;
    JLabel dset1color;
    JLabel dset2color;
    JLabel dset3color;
    JLabel dset4color;
    JButton setdset1;
    JButton setdset2;
    JButton setdset3;
    JButton setdset4;
    JButton setcolors;  
    JPanel paneldset1 = new JPanel();
    JPanel paneldset2 = new JPanel();
    JPanel paneldset3 = new JPanel();
    JPanel paneldset4 = new JPanel();  


    // declare components for the k-mean algorithm
    //
    JFrame clusterpara;
    JButton initcluster;

    // declare components for the LBG algorithm
    //
    JFrame iterpara;
    JButton inititer;

    // declare components for the LP algorithm
    //
    JFrame lpsetorderpara;
    JButton initlporder;

    // declare components for the PF algorithm
    //
    JFrame pfsetorderpara;
    JButton initpforder;

    // declare components for the KF algorithm
    //
    JFrame kfsetorderpara;
    JButton initkforder;
    
    // declare components for the Kalman algorithm
    //
    JFrame set_meas_gain_frame;
    JButton init_meas_gain;
    
    // declare components for the Kalman algorithm
    //
    JFrame set_state_gain_frame;
    JButton init_state_gain;
    
    // declare components for the Kalman algorithm
    //
    JFrame set_var_meas_noise_frame;
    JButton init_var_meas_noise;
    
    // declare components for the Kalman algorithm
    //
    JFrame set_var_state_noise_frame;
    JButton init_var_state_noise;
    
    // declare componets for setting interpolation order in the LP algorithm 
    //
    JFrame ipsetorderpara;
    JButton initiporder;

    // global variables for the k-means algorithm
    //
    int guesses = 4;
    int iterations = 4;

    // global variables for the LBG algorithm
    //
    int clusters = 10;

    // global variables for setting linear and polation order in 
    // the LP  algorithm and particle filtering algorithm
    //
    int lporder = 8;
    int iporder = 10;
    int pforder = 8;
    int kforder = 8;

    // Kalman Filter
    //
    double meas_gain  = 1.0;
    double state_gain = 1.0;
    double var_meas_noise  = 10;
    double var_state_noise = 10;

    // flags
    //
    boolean drwptsf = true;
    boolean drwgaussf = false;
    boolean zoomi = false;
    boolean zoomo = false;  


    // global variable for the gaussian distribution
    //
    int points = 25;
    double meanx = 0.0;
    double meany = 0.0;
    double cov11 = 0.05;
    double cov12 = 0.0;
    double cov21 = 0.0;
    double cov22 = 0.05;

    // declare varaibles to read the help file
    //
    URL url;
    Vector urlvec;
    String document;
    BufferedReader buff;
    AppletContext context;

    // declare components for the time scales
    //
    JLabel xMinLabel; // x-axis minimum label
    JLabel xMaxLabel; // x-axis maximum label
    JLabel yMinLabel; // y-axis minimum label
    JLabel yMaxLabel; // y-axis maximum label

    JTextField xMinField; // x-axis minimum text field
    JTextField xMaxField; // x-axis maximum text field
    JTextField yMinField; // y-axis minimum text field
    JTextField yMaxField; // y-axis maximum text field

    // declare components for the gaussian distribution
    //
    JLabel pointsLabel; // max points in distribution label
    JLabel meanxLabel; // mean of distribution (x) label
    JLabel meanyLabel; // mean of distribution (y) label
    JLabel setgausLabel; //Gaussian Settings Label  
    JLabel covLabel;  //Covariance Lable
    JLabel cov11Label; // covariance c11 of distribution label
    JLabel cov12Label; // covariance c12 of distribution label
    JLabel cov21Label; // covariance c21 of distribution label
    JLabel cov22Label; // covariance c22 of distribution label

    JTextField pointsField; // max points in distribution text field
    JTextField meanxField; // mean of distribution (x) text field
    JTextField meanyField; // mean of distribution (y) text field
    JTextField cov11Field; // covariance c11 text field
    JTextField cov12Field; // covariance c12 text field
    JTextField cov21Field; // covariance c21 text field
    JTextField cov22Field; // covariance c22 text field

    // declare components for the k-means algorithm
    //
    JLabel guessesLabel; // number of initial guesses label
    JLabel iterationsLabel; // max number of iterations label   
    JLabel clusterLabel; // max number of clusters

    JTextField guessesField; // number of initial guesses field
    JTextField iterationsField; // max number of iterations field  
    JTextField clusterField; // max number of clusters  

    // declare components for lp order in the LP algorithm
    //
    JLabel lporderLabel; // LP order label
    JTextField lporderField; // LP order text field  

    // declare components for pf order in the PF algorithm
    //
    JLabel pforderLabel; // PF order label
    JTextField pforderField; // PF order text field  

    // declare components for kf order in the KF algorithm
    //
    JLabel kforderLabel; // KF order label
    JTextField kforderField; // KF order text field  

    // declare components for meas_gain in the Kalman algorithm
    //
    JLabel meas_gain_label; 
    JTextField meas_gain_field;

    // declare components for state_gain in the Kalman algorithm
    //
    JLabel state_gain_label;
    JTextField state_gain_field;   

    // declare components for var_meas_gain order in the Kalman algorithm
    //
    JLabel var_meas_noise_label;
    JTextField var_meas_noise_field;

    // declare components for var_state_gain in the Kalman algorithm
    //
    JLabel var_state_noise_label; 
    JTextField var_state_noise_field;

    // declare components for interpolation in the LP and PF algorithm
    //
    JLabel iporderLabel; // interpolation order label
    JTextField iporderField; // interpolation order text field 

    //  // declare a checkbox item listener
    //  //
    //  CheckBoxListener myListener = new CheckBoxListener();

    // declare strings for the Help menu
    //
    String helpOption = new String("Help");
    String tutorial = new String("Applet Tutorial");
    String overview = new String("Applet Overview");
    String release = new String("Release Notes");
    String info = new String("Information and Support");
    String plugins = new String("About Plug-ins");
    String done = new String("Done");
    String refresh = new String("Update");
    String apply = new String("OK");
    String destroy = new String("Cancel");
    String setpara = new String("Set K-Means");
    String newline = new String("\n");  


    // declare strings for the color changer dialog
    //
    String changeset = new String("Change");
    String changeset1 = new String("Change1");  
    String changeset2 = new String("Change2");  
    String changeset3 = new String("Change3");  
    String changeset4 = new String("Change4");  

    // declare menu title strings
    //
    String drwpts = new String("Draw Points");
    String drwgauss = new String("Draw Gaussian");
    String pearEllipse = new String("Pear And Ellipse");
    String twoEllipse = new String("Two Ellipse");
    String twoGaussian = new String("Two Gaussian");
    String fourGaussian = new String("Four Gaussian");
    String overGaussian = new String("Overlapping Gaussian");
    String twoEllipses = new String("Two Ellipses");
    String fourEllipses = new String("Four Ellipses");
    String rotatedEllipses = new String("Rotated Ellipses");
    String toroidal = new String("Toroidal");
    String yinYang = new String("Yin and Yang");
    String class1 = new String("Input Class 0");
    String class2 = new String("Input Class 1");
    String class3 = new String("Input Class 2");
    String class4 = new String("Input Class 3");
    String euclidean = new String("Euclidean Distance");
    String nearest = new String("Nearest Neighbor");
    String kmeans = new String("Clustering: K-Means");
    String binary = new String("Clustering: LBG");
    String LDA1 = new String("LDA: Class-Independent");
    String LDA2 = new String("LDA: Class-Dependent");
    String PCA1 = new String("PCA: Class-Independent");
    String PCA2 = new String("PCA: Class-Dependent");
    String LDAPCA1 = new String("LDA->PCA: Class-Independent");
    String LDAPCA2 = new String("LDA->PCA: Class-Dependent");
    String ICA1 = new String("ICA: Class-Independent");
    String ICA2 = new String("ICA: Class-Dependent");
    String SVM1 = new String("SVM: Class-Independent");
    String SVM2 = new String("RVM: Relevance Vector Machine");
    String LP = new String("LP: Linear Prediction");
    String PF = new String("PF: Particle Filtering");
    String KF = new String("KF: Kalman Filtering");

    String previous = new String("Previous");
    String setgaus = new String ("Set Gaussian");
    String setccolors = new String ("Set Class Colors");
    String setiterations = new String ("Set Iterations");
    String setclusters = new String ("Set Clusters");
    String setlporders = new String ("Set LP Order");
    String setpforders = new String ("Set PF Order");
    String setkforders = new String ("Set KF Order");
    String setiporders = new String ("Set Interpolation");
    String set_meas_gain = new String ("Set Measurement Gain");
    String set_state_gain = new String ("Set State Gain");
    String set_var_meas_noise = new String("Set Variance of Measurement Noise");
    String set_var_state_noise = new String ("Set Variance of State Noise");
    String saveiporders = new String ("Save Interpolation Order");
    String saveiterations = ("Save Iterations");
    String saveclusters = ("Save Clusters");
    String savelporders = ("Save LP Order");
    String savepforders = ("Save PF Order");
    String savekforders = ("Save KF Order");
    String save_meas_gain = new String ("Save Measurement Gain");
    String save_state_gain = new String ("Save State Gain");
    String save_var_meas_noise = new String("Save Variance of Measurement Noise");
    String save_var_state_noise = new String ("Save Variance of State Noise");
    String clearAll = new String("Clear All");
    String clearPlots = new String("Clear Display");
    String clearInput = new String("Clear Input");
    String clearOutput = new String("Clear Output");
    String clearTxt = new String("Clear Description");
    String erase = new String("Erase");
    String zoomin = new String("Zoom In");  
    String zoomout = new String("Zoom Out");  
    String limit = new String("Set Ranges");
    String next = new String("Next");
    String load = new String("Load");
    String save = new String("Save");
    String initialize = new String("Initialize");
    String viewEuclidean = new String("View Euclidean");
    String viewNearest = new String("View Nearest Neighbor");
    String viewPCA1 = new String("PCA: Class Independent PCA");
    String viewPCA2 = new String("PCA: Class Dependent PCA");
    String viewLDA1 = new String("LDA: Class Independent LDA");
    String viewLDA2 = new String("LDA: Class Dependent LDA");
    String viewLDAPCA1 = new String("LDA->PCA: Class Independent LDA->PCA");
    String viewLDAPCA2 = new String("LDA->PCA: Class Dependent LDA->PCA");
    String viewICA1 = new String("ICA: Class Independent PCA");
    String viewICA2 = new String("ICA: Class Dependent LDA");
    String viewSVM1 = new String("SVM: Class Independent PCA");
    //String viewSVM2 = new String("SVM: Class Dependent LDA");
    String viewSVM2 = new String("SVM: Relevance Vector Machine");
    String viewLP = new String("LP: Linear Prediction");
    String viewPF = new String("PF: Particle Filtering");
    String viewKF = new String("KF: Kalman Filtering");
    String viewKMeans = new String("Clustering : K-Means");
    String viewLBG = new String("Clustering : LBG");