// *********************************************************************    //    // declare class     /**     *     * constructor initializes objects and containers     *     * @@param   vec vector of url's     * @@param   con applet context     *     */    MainMenu(Vector vec, AppletContext con)    {	super();	// initialize the input and output panel objects	//	urlvec = vec;	context = con;	// initialize the labels and text fields	//	xMinLabel = new JLabel("X minimum:   ");	xMaxLabel = new JLabel("X maximum:   ");	yMinLabel = new JLabel("Y minimum:   ");	yMaxLabel = new JLabel("Y maximum:   ");	xMinField = new JTextField("-1.0", 10);	xMaxField = new JTextField("1.0", 10);	yMinField = new JTextField("-1.0", 10);	yMaxField = new JTextField("1.0", 10);	colorselection = new JLabel("Select Class Colors");	dset1color = new JLabel("Class 0");	dset2color = new JLabel("Class 1");	dset3color = new JLabel("Class 2");	dset4color = new JLabel("Class 3");	pointsLabel = new JLabel("Points : ");	meanxLabel = new JLabel("Mean(X): ");	meanyLabel = new JLabel("Mean(Y): ");	setgausLabel = new JLabel("Gaussian Settings");	covLabel = new JLabel("Covariance:");	cov11Label = new JLabel("  Cov[1,1]: ");	cov12Label = new JLabel("  Cov[1,2]: ");	cov21Label = new JLabel("  Cov[2,1]: ");	cov22Label = new JLabel("  Cov[2,2]: ");	pointsField = new JTextField("25", 10);	meanxField = new JTextField("0.0", 10);	meanyField = new JTextField("0.0", 10);	cov11Field = new JTextField("0.05", 10);	cov12Field = new JTextField("0.0", 10);	cov21Field = new JTextField("0.0", 10);	cov22Field = new JTextField("0.05", 10);	guessesLabel = new JLabel("Centroids: ");	iterationsLabel = new JLabel("Iterations: ");	guessesField = new JTextField("4", 10);	iterationsField = new JTextField("10", 10);	clusterLabel = new JLabel("Iterations: ");	clusterField = new JTextField("10", 10);	lporderLabel = new JLabel("LP Order: ");	lporderField = new JTextField("8", 10);	pforderLabel = new JLabel("PF Order: ");	pforderField = new JTextField("8", 10);	kforderLabel = new JLabel("KF Order: ");	kforderField = new JTextField("8", 10);	meas_gain_label = new JLabel("Measurement Gain: ");	meas_gain_field = new JTextField("1.0", 10);	state_gain_label = new JLabel("State Gain: ");	state_gain_field = new JTextField("1.0", 10);	var_meas_noise_label = new JLabel("Variance of Measurement Noise: ");	var_meas_noise_field = new JTextField("10", 10);	var_state_noise_label = new JLabel("Variance of State Noise: ");	var_state_noise_field = new JTextField("10", 10);	iporderLabel = new JLabel("Interpolation Order: ");	iporderField = new JTextField("10", 10);	// create a border around the control panel	//	setBorder(BorderFactory.createEtchedBorder());	// create components for the help box	//	frame = new JFrame();	// create components for the help box	//	setccolorsdlg = new JFrame();	// create a frame for the time scales	//	scale = new JFrame();	// create a frame for the gaussian distribution	//	setgausdlg = new JFrame();	// create a frame for the k-mean algorithm	//	clusterpara = new JFrame();	// create a frame for the k-mean algorithm	//	iterpara = new JFrame();	// create a frame for the LP set order box	//	lpsetorderpara = new JFrame();	// create a frame for the PF set order box	//	pfsetorderpara = new JFrame();	// create a frame for the KF set order box	//	kfsetorderpara = new JFrame();	set_meas_gain_frame = new JFrame();	set_state_gain_frame = new JFrame();	set_var_meas_noise_frame = new JFrame();	set_var_state_noise_frame = new JFrame();	// create a frame for interpoloation in the LP set order box	//	ipsetorderpara = new JFrame();	// create the textbox to view the help message	//	textArea = new JTextArea();	textArea.setLineWrap(true);	textArea.setWrapStyleWord(true);	textArea.setEditable(false);	// make the text area scrollable	//	scrollPane = new JScrollPane(textArea);	// set the scroll pane dismensions	//	scrollPane.setSize(new Dimension(400, 400));	scrollPane.setPreferredSize(new Dimension(400, 400));	scrollPane.setMinimumSize(new Dimension(400, 400));	scrollPane.setMaximumSize(new Dimension(400, 400));	// create a dismiss button for the text area	//	dismiss = new JButton(done);	dismiss.setActionCommand(done);	dismiss.addActionListener(this);	// create a dismiss button for the text area	//	change = new JButton(refresh);	change.setActionCommand(refresh);	change.addActionListener(this);	// create a button for setting color of class 1	//	setdset1 = new JButton(changeset);	setdset1.setActionCommand(changeset1);	setdset1.addActionListener(this);	// create a button for setting color of class 2	//	setdset2 = new JButton(changeset);	setdset2.setActionCommand(changeset2);	setdset2.addActionListener(this);	// create a button for setting color of class 3	//	setdset3 = new JButton(changeset);	setdset3.setActionCommand(changeset3);	setdset3.addActionListener(this);	// create a button for setting color of class 4	//	setdset4 = new JButton(changeset);	setdset4.setActionCommand(changeset4);	setdset4.addActionListener(this);	// create a button for setting color of class 4	//	setcolors = new JButton(done);	setcolors.setActionCommand(done);	setcolors.addActionListener(this);	// set the size of the color preview panels	//	paneldset1.setPreferredSize(new Dimension(30,20));	paneldset2.setPreferredSize(new Dimension(30,20));	paneldset3.setPreferredSize(new Dimension(30,20));	paneldset4.setPreferredSize(new Dimension(30,20));	// create a generate button for the gaussian distribution	//	applyb = new JButton(apply);	applyb.setActionCommand(apply);	applyb.addActionListener(this);	// create a cancel button for the gaussian distribution	//	cancel = new JButton(destroy);	cancel.setActionCommand(destroy);	cancel.addActionListener(this);	// create a initialize button for the k-means algorithm	//	initcluster = new JButton(setclusters);	initcluster.setActionCommand(saveclusters);	initcluster.addActionListener(this);	// create a initialize button for the Linear Prediction algorithm	//	initlporder = new JButton(refresh);	initlporder.setActionCommand(savelporders);	initlporder.addActionListener(this);	// create a initialize button for the Particle Filter algorithm	//	initpforder = new JButton(refresh);	initpforder.setActionCommand(savepforders);	initpforder.addActionListener(this);	// create a initialize button for the Kalman Filter algorithm	//	initkforder = new JButton(refresh);	initkforder.setActionCommand(savekforders);	initkforder.addActionListener(this);	// create a initialize button for the meas_gain Kalman algorithm	//	init_meas_gain = new JButton(refresh);	init_meas_gain.setActionCommand(save_meas_gain);	init_meas_gain.addActionListener(this);	// create a initialize button for the state_gain Kalman algorithm	//	init_state_gain = new JButton(refresh);	init_state_gain.setActionCommand(save_state_gain);	init_state_gain.addActionListener(this);	// create a initialize button for the var_meas_noise Kalman algorithm	//	init_var_meas_noise = new JButton(refresh);	init_var_meas_noise.setActionCommand(save_var_meas_noise);	init_var_meas_noise.addActionListener(this);	// create a initialize button for the var_state_noise Kalman algorithm	//	init_var_state_noise = new JButton(refresh);	init_var_state_noise.setActionCommand(save_var_state_noise);	init_var_state_noise.addActionListener(this);	// create a initialize button for the LP, PF and KF algorithm	//	initiporder = new JButton(refresh);	initiporder.setActionCommand(saveiporders);	initiporder.addActionListener(this);	// create a initialize button for the LBG algorithm	//	inititer = new JButton(setiterations);	inititer.setActionCommand(saveiterations);	inititer.addActionListener(this);	// add the components to frame	//	frame.getContentPane().add(scrollPane, BorderLayout.CENTER);	frame.getContentPane().add(dismiss, BorderLayout.SOUTH);	// set the container layout for the scale components	//	GridBagLayout gridbag = new GridBagLayout();	GridBagConstraints c = new GridBagConstraints();	// define constraints for all components	//	c.weightx = 1.0;	c.weighty = 1.0;	c.gridheight = 1;	c.anchor = GridBagConstraints.WEST;	c.fill = GridBagConstraints.HORIZONTAL;	// add the components for the scale frame	//	c.gridx = 0;	c.gridy = 1;	c.gridwidth = 1;	scale.getContentPane().setLayout(gridbag);	gridbag.setConstraints(xMinLabel, c);	scale.getContentPane().add(xMinLabel);	c.gridx = 1;	c.gridwidth = GridBagConstraints.REMAINDER;	gridbag.setConstraints(xMinField, c);	scale.getContentPane().add(xMinField);	c.gridx = 0;	c.gridy = 2;	c.gridwidth = 1;	gridbag.setConstraints(xMaxLabel, c);	scale.getContentPane().add(xMaxLabel);	c.gridx = 1;	c.gridwidth = GridBagConstraints.REMAINDER;	gridbag.setConstraints(xMaxField, c);	scale.getContentPane().add(xMaxField);	c.gridx = 0;	c.gridy = 3;	c.gridwidth = 1;	gridbag.setConstraints(yMinLabel, c);	scale.getContentPane().add(yMinLabel);	c.gridx = 1;	c.gridwidth = GridBagConstraints.REMAINDER;	gridbag.setConstraints(yMinField, c);	scale.getContentPane().add(yMinField);	c.gridx = 0;	c.gridy = 4;	c.gridwidth = 1;	gridbag.setConstraints(yMaxLabel, c);	scale.getContentPane().add(yMaxLabel);	c.gridx = 1;	c.gridwidth = GridBagConstraints.REMAINDER;	gridbag.setConstraints(yMaxField, c);	scale.getContentPane().add(yMaxField);	c.gridx = 0;	c.gridy = 6;	c.gridwidth = GridBagConstraints.REMAINDER;	c.anchor = GridBagConstraints.CENTER;	gridbag.setConstraints(change, c);	scale.getContentPane().add(change);	// set the container layout for the gaussian distribution	//	c = new GridBagConstraints();	c.weightx = 1.0;	c.weighty = 1.0;	c.gridheight = 1;	c.fill = GridBagConstraints.NONE;	// add the components for the set colors dlg	//	c.gridx = 0;	c.gridy = 0;	c.gridwidth = 2;	setccolorsdlg.getContentPane().setLayout(gridbag);	gridbag.setConstraints(colorselection, c);	setccolorsdlg.getContentPane().add(colorselection);	c.gridx = 0;	c.gridy = 1;	c.gridwidth = 1;	c.anchor = GridBagConstraints.WEST;	gridbag.setConstraints(dset1color, c);	setccolorsdlg.getContentPane().add(dset1color);	c.gridx = 1;	c.gridy = 1;	c.gridwidth = 1;	gridbag.setConstraints(paneldset1, c);	setccolorsdlg.getContentPane().add(paneldset1);	c.gridx = 2;	c.gridy = 1;	c.gridwidth = 1;	gridbag.setConstraints(setdset1, c);	setccolorsdlg.getContentPane().add(setdset1);	c.gridx = 0;	c.gridy = 2;	c.gridwidth = 1;	c.anchor = GridBagConstraints.WEST;	gridbag.setConstraints(dset2color, c);	setccolorsdlg.getContentPane().add(dset2color);	c.gridx = 1;	c.gridy = 2;	c.gridwidth = 1;	gridbag.setConstraints(paneldset2, c);	setccolorsdlg.getContentPane().add(paneldset2);	c.gridx = 2;	c.gridy = 2;	c.gridwidth = 1;	gridbag.setConstraints(setdset2, c);	setccolorsdlg.getContentPane().add(setdset2);	c.gridx = 0;	c.gridy = 3;	c.gridwidth = 1;	c.anchor = GridBagConstraints.WEST;	gridbag.setConstraints(dset3color, c);	setccolorsdlg.getContentPane().add(dset3color);	c.gridx = 1;	c.gridy = 3;	c.gridwidth = 1;	gridbag.setConstraints(paneldset3, c);	setccolorsdlg.getContentPane().add(paneldset3);	c.gridx = 2;	c.gridy = 3;	c.gridwidth = 1;	gridbag.setConstraints(setdset3, c);	setccolorsdlg.getContentPane().add(setdset3);	c.gridx = 0;	c.gridy = 4;	c.gridwidth = 1;	c.anchor = GridBagConstraints.WEST;	gridbag.setConstraints(dset4color, c);	setccolorsdlg.getContentPane().add(dset4color);    	c.gridx = 1;	c.gridy = 4;	c.gridwidth = 1;	gridbag.setConstraints(paneldset4, c);	setccolorsdlg.getContentPane().add(paneldset4);	c.gridx = 2;	c.gridy = 4;	c.gridwidth = 1;	gridbag.setConstraints(setdset4, c);	setccolorsdlg.getContentPane().add(setdset4);	c.gridx = 0;	c.gridy = 6;	c.gridwidth = 3; 	c.insets = new Insets(7,7,7,7);	c.anchor = GridBagConstraints.CENTER;	gridbag.setConstraints(setcolors, c);	setccolorsdlg.getContentPane().add(setcolors);	// set the container layout for the k-means algorithm	//	c.weightx = 1.0;	c.weighty = 1.0;	c.gridheight = 1;	c.anchor = GridBagConstraints.WEST;	c.fill = GridBagConstraints.HORIZONTAL;