Running Swing GUI in MAC OS ?

Hi All,
I have been trying to run swing based GUI on MAC Operating System. I downloaded MRJ but i could not found any swing support in it. I kept swingall.jar in classpath which i took from Window's JDK. But no use.
Can anybody help me out to run a simple GUI on MAC ?
Hopeing for a favourable response.
Kind Regards
Chetan Chandarana

If anyone wants to know where to get the swingall.jar for 1.1 go to: http://java.sun.com/products/jfc/download.archive.html#1.1.1
get the tar or zip file. Then put the swingall where impatient1 says to and swing will work.
You can also get lots of information on setting up Mac Classic for Java from:
http://lists.apple.com/mailman/listinfo/java-dev
on apple's web site. You can search the archives with a username and password listed on that page.
I could not find any information on the collections zip that impatient1 mentions in the post. If anyone knows where you can get that and if it really works, I would really like to know. :)

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Are there other instances of the application still
running when you start the new one? Are there still
some old processes running?i don't think so.
would you mind having a look at my code?
my code is:
import javax.swing.*;
import javax.swing.event.*;
import java.awt.*;
import java.awt.event.*;
import java.util.*;
import java.io.*;
import javax.swing.filechooser.*;
public class RunPanel extends JPanel implements ActionListener
     JButton openButton, setParametersButton, saveButton;
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     double affinityThresholdScalar;
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     double hypermutationRate;
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     double stimulationValue;
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     int testSize;
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     int inCorrect = 0;
     int count = 0;
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     int stimIndex;
     Vector AntigenTest;
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        add(logScrollPane, BorderLayout.CENTER);
     add(savePanel, BorderLayout.SOUTH);
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          nFold = param.getNFold();
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                Vector data = d.readFile(file);
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          log.append("Normalising Data." + newline);
          Vector DataReady = r.Sort(dataNormalised);
          log.append("Randomly sorting data." + newline);
          setSize = Math.round(DataReady.size()/nFold);
          for(int j=0; j< DataReady.size()-setSize; j+=setSize)
               Vector affinity = new Vector();
               Vector trainingData = new Vector();
               Vector training = new Vector();
               Vector test = new Vector();
               Vector testData = new Vector();
               Vector trainingData1 = new Vector();
               Vector DataNormalised = new Vector();
               Vector newV = new Vector();
               Vector ARB = new Vector();
               Vector BestMatch = new Vector();
               Vector BestMatch1 = new Vector();
               Vector testDataNormalised = new Vector();
               Vector MemoryPool = new Vector();
               Float maxDist = new Float(0.0);
               Float affinityThreshold = new Float(0.0);
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               int end = j+setSize;
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               if(end <= DataReady.size())
                    testData =new Vector(DataReady.subList(j, end));
                    if(j == 0)
                         trainingData = new Vector(DataReady.subList(end, DataReady.size()));
                    else
                         trainingData = new Vector(DataReady.subList(0, j));
                         trainingData1 = new Vector(DataReady.subList(end, DataReady.size()));
                         for (int b= 0; b < trainingData1.size(); b++)
                              newV = (Vector)trainingData1.elementAt(b);
                              trainingData.add(newV);
               else
                    //do nothing
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               test = p.createPureCopy(testData);
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               MemoryPool = i.seedMemoryPool(training, seed);
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               iterations ++;
               //System.out.println("iterations" +iterations);
               int count2 = 0;
               for(int k=0;k<training.size();k++)
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                    Antigen = (Vector)Antigen1.clone();
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                    if(memPoolStimulation.floatValue() == zero.floatValue())
                         MemoryPool.add(Antigen);
                    else
                         stimIndex = s.getStimIndex();
                         BestMatch = t.bestMatch(MemoryPool,Antigen,stimIndex,memPoolStimulation);
                         MemoryPool.removeElement(BestMatch);
                         MemoryPool.add(BestMatch);
                         BestMatch1 = p.createPureCopy1(BestMatch);
                         ARB = t.ARBGeneration(BestMatch1,hypermutationRate,clonalRate);
                         Vector Candidate = c.runARBRefinement(ARB, Antigen, stimulationValue,clonalRate, totalResources);
                         MemoryPool = MS.addCandidateCell(Candidate, MemoryPool, affinityThreshold, affinityThresholdScalar);
                         ARB.removeAllElements();
                         count2 ++;
               log.append("count of training" +count2+ "." + newline);
               log.append("ARB sie" +ARB.size()+ "." + newline);
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               log.append("test size" +testSize+ "." + newline);
               //log.append("testdata" +test+ "." + newline);
               for(int a=0;a<testSize;a++)
                    AntigenTest = (Vector)test.elementAt(a);
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                    Float AntigenClass = new Float(0.0);
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                    Float classify = C.KNN(MemoryPool,AntigenTest,KNN,NumberOfClass);
                    log.append("classify" +classify+ "." + newline);
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                         correct ++;
                    else
                         inCorrect ++;
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               reduction = 100 - ((double)MemoryPool.size()/training.size()*100);
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               log.append("Incorrect" +inCorrect+"." + newline);
               log.append("reduction" +reduction+"." + newline);
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                log.append("Save command cancelled by user." + newline);
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Cheers

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