mainmenu.java

包含了模式识别中常用的一些分类器设计算法

	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;

	// add the components for the LBG algorithm
	//
	c.gridx = 0;
	c.gridy = 1;
	c.gridwidth = 1;
	iterpara.getContentPane().setLayout(gridbag);
	gridbag.setConstraints(clusterLabel, c);
	iterpara.getContentPane().add(clusterLabel);

	c.gridx = 1;
	c.gridwidth = GridBagConstraints.REMAINDER;
	gridbag.setConstraints(clusterField, c);
	iterpara.getContentPane().add(clusterField);

	c.gridx = 0;
	c.gridy = 2;
	c.gridwidth = GridBagConstraints.REMAINDER;
	c.anchor = GridBagConstraints.CENTER;
	gridbag.setConstraints(inititer, c);
	iterpara.getContentPane().add(inititer);

	// add the components for the k-means algorithm
	//
	c.gridx = 0;
	c.gridy = 1;
	c.gridwidth = 1;
	clusterpara.getContentPane().setLayout(gridbag);
	gridbag.setConstraints(guessesLabel, c);
	clusterpara.getContentPane().add(guessesLabel);

	c.gridx = 1;
	c.gridwidth = GridBagConstraints.REMAINDER;
	gridbag.setConstraints(guessesField, c);
	clusterpara.getContentPane().add(guessesField);

	c.gridx = 0;
	c.gridy = 2;
	c.gridwidth = GridBagConstraints.REMAINDER;
	c.anchor = GridBagConstraints.CENTER;
	gridbag.setConstraints(initcluster, c);
	clusterpara.getContentPane().add(initcluster);

	// set the container layout for the LP algorithm
	//
	c.weightx = 1.0;
	c.weighty = 1.0;
	c.gridheight = 1;
	c.anchor = GridBagConstraints.WEST;
	c.fill = GridBagConstraints.HORIZONTAL;

	// add the components for lp order the LP algorithm