head	1.6;access;symbols;locks; strict;comment	@# @;1.6date	2005.06.10.18.25.20;	author rirwin;	state Exp;branches;next	1.5;1.5date	2005.05.23.19.33.21;	author rirwin;	state Exp;branches;next	1.4;1.4date	2005.03.17.17.45.40;	author patil;	state Exp;branches;next	1.3;1.3date	2005.03.08.04.21.35;	author patil;	state Exp;branches;next	1.2;1.2date	2005.01.20.02.41.42;	author patil;	state Exp;branches;next	1.1;1.1date	2004.12.28.00.04.32;	author patil;	state Exp;branches;next	;desc@No changes made.@1.6log@Esablishing RCS vesion.@text@/* * AlgorithmPCA2.java last edited Ryan Irwin V6.0 * @@(#) AlgorithmPCA2.java  1.10 03/15/03 * */// import java packages//import java.awt.*;import java.util.*;/** * Operation of the PCA Class-Independent Algorithm */public class AlgorithmPCA2 extends Algorithm{    //-----------------------------------------------------------------    //    // instance data members    //    //-----------------------------------------------------------------    Matrix PCA1_d;    Matrix PCA2_d;    Matrix PCA3_d;    Matrix PCA4_d;    Matrix CPCA1_d;    Matrix CPCA2_d;    Matrix CPCA3_d;    Matrix CPCA4_d;    // vector of support region points    //    Vector<MyPoint> support_vectors_d = new Vector<MyPoint>();    Vector<MyPoint> decision_regions_d = new Vector<MyPoint>();    int output_canvas_d[][];    String algo_id = "AlgorithmPCA";    //------------------------------------------------------------------    //    // public class methods    //    //------------------------------------------------------------------    /**     * Overrides the initialize() method in the base class.  Initializes     * member data and prepares for execution of first step.  This method     * "resets" the algorithm.     *     * @@return true     */    public boolean initialize()    {	// Debug	//	// System.out.println("AlgorithmPCA2 : initialize()");	support_vectors_d = new Vector<MyPoint>();	point_means_d = new Vector<MyPoint>();	decision_regions_d = new Vector<MyPoint>();	step_count = 3;	// check the data points	//	if (output_panel_d == null)	{	    return false;	}		// add the process description for the PCA2 algorithm	//	if (description_d.size() == 0)      	{	    String str = new String("   0. Initialize the original data.");	    description_d.addElement(str);	    	    str = new String("   1. Displaying the original data.");	    description_d.addElement(str);	    	    str = new String("   2. Computing the means and support regions.");	    description_d.addElement(str);	    	    str = new String("   3. Computing the decision regions.");	    description_d.addElement(str);	}		// append message to process box	//	pro_box_d.appendMessage("Class Dependent PCA :" + "\n");	PCA1_d = null;	PCA2_d = null;	PCA3_d = null;	PCA4_d = null;	CPCA1_d = null;	CPCA2_d = null;	CPCA3_d = null;	CPCA4_d = null;	// set the data points for this algorithm	//	//	set1_d = (Vector)data_points_d.dset1.clone();	//	set2_d = (Vector)data_points_d.dset2.clone();	//	set3_d = (Vector)data_points_d.dset3.clone();	//	set4_d = (Vector)data_points_d.dset4.clone();	//	set1_d = data_points_d.dset1;	set2_d = data_points_d.dset2;	set3_d = data_points_d.dset3;	set4_d = data_points_d.dset4;	// reset values	//	support_vectors_d = new Vector<MyPoint>();	decision_regions_d = new Vector<MyPoint>();	// set the step index	//	step_index_d = 0;	// append message to process box	//	pro_box_d.appendMessage((String)description_d.get(step_index_d));	// exit gracefully	//	return true;    }      /**     * Implementation of the run function from the Runnable interface.     * Determines what the current step is and calls the appropriate method.     */    public void run()    {	// Debug	//	// System.out.println(algo_id + ": run()");	    	if (step_index_d == 1)	{	    disableControl();	    step1();	    enableControl();	}		else if (step_index_d == 2)        {	    disableControl();	    step2(); 	    enableControl();	}	        else if (step_index_d == 3)	{	    disableControl();	    step3();	    pro_box_d.appendMessage("   Algorithm Complete");	    enableControl(); 	}		// exit gracefully	//	return;    }        /**     *     * step one of the algorithm     *     * @@return true     */    boolean step1()    {	// Debug	//	// System.out.println(algo_id + ": step1()");	    	pro_box_d.setProgressMin(0);	pro_box_d.setProgressMax(1);	pro_box_d.setProgressCurr(0);	scaleToFitData();	// Display original data	//	output_panel_d.addOutput(set1_d, Classify.PTYPE_INPUT, 				 data_points_d.color_dset1);	output_panel_d.addOutput(set2_d, Classify.PTYPE_INPUT,				 data_points_d.color_dset2);	output_panel_d.addOutput(set3_d, Classify.PTYPE_INPUT,				 data_points_d.color_dset3);	output_panel_d.addOutput(set4_d, Classify.PTYPE_INPUT, 				 data_points_d.color_dset4);	    	// step 1 completed	//	pro_box_d.setProgressCurr(1);	output_panel_d.repaint();	    	// exit gracefully	//	return true;    }    /**     *     * step two of the algorithm     *     * @@return true     *     */    boolean step2()    {	// Debug	//	// System.out.println(algo_id + " : step2()");	pro_box_d.setProgressMin(0);	pro_box_d.setProgressMax(20);	pro_box_d.setProgressCurr(0);			transformPCA2();	printMatrices();	computeMeans();			// display means	//	output_panel_d.addOutput(point_means_d, Classify.PTYPE_OUTPUT_LARGE, 				 Color.black);			// display support vectors	//	output_panel_d.addOutput(support_vectors_d, Classify.PTYPE_INPUT, 				 Color.cyan);		pro_box_d.setProgressCurr(20);	output_panel_d.repaint();		return true;    }    /**     *     * step three of the algorithm     *     * @@return true     */    boolean step3()    {	// Debug	//	// System.out.println(algo_id + " : step3()");	pro_box_d.setProgressMin(0);	pro_box_d.setProgressMax(20);	pro_box_d.setProgressCurr(0);	    	// compute the decision regisions	//	computeDecisionRegions();	    	// compute errors	//	computeErrors();	    	// display support vectors	//	output_panel_d.addOutput(decision_regions_d, Classify.PTYPE_INPUT, 				 new Color(255, 200, 0));	    	output_panel_d.repaint();	return true;    }   /**    *    * Transforms a given set of points to a new space    * using the class dependent principal component analysis algorithm    *    */    public void transformPCA2()    {	// Debug	//	// System.out.println(algo_id + " : transformPCA2()");	    	// declare local variables	//	int size = 0;	int xsize1 = 0;	int ysize1 = 0;	int xsize2 = 0;	int ysize2 = 0;	int xsize3 = 0;	int ysize3 = 0;	int xsize4 = 0;	int ysize4 = 0;	double xval1 = 0.0;	double yval1 = 0.0;	double xval2 = 0.0;	double yval2 = 0.0;	double xval3 = 0.0;	double yval3 = 0.0;	double xval4 = 0.0;	double yval4 = 0.0;	double xmean1 = 0.0;	double ymean1 = 0.0;	double xmean2 = 0.0;	double ymean2 = 0.0;	double xmean3 = 0.0;	double ymean3 = 0.0;	double xmean4 = 0.0;	double ymean4 = 0.0;	double xval = 0.0;	double yval = 0.0;	// declare the covariance object	//	Covariance cov = new Covariance();	// declare an eigen object	//	// Since Eigen is a class of static member functions 	// it is not correct to instantiate it - Phil T. 6-23-03 	// Eigen eigen = new Eigen();		// declare arrays for the eigenvalues	//	double eigVal1[] = null;	double eigVal2[] = null;	double eigVal3[] = null;	double eigVal4[] = null;	// declare an array to store the eigen vectors	//	double eigVec[] = new double[2];	// declare arrays to store the samples	//	double x[] = null;	double y[] = null;	// get the samples from the first data set	//	size = set1_d.size();	// increment the variable count for the first data set	//	xsize1 += size;	ysize1 += size;	// initialize arrays to store the samples	//	x = new double[size];	y = new double[size];	// set up the initial random vectors i.e., the vectors of	// X and Y coordinate points form the display	//	for (int i = 0; i < size; i++)	{			    MyPoint p = (MyPoint)set1_d.elementAt(i);	    xval1 += p.x;	    yval1 += p.y;	    x[i] = p.x;	    y[i] = p.y;			}	    	if (size > 0)	{			    // declare the covariance matrix	    //	    Matrix covariance = new Matrix();	    covariance.row = covariance.col = 2;	    covariance.Elem = new double[2][2];			    // declare matrix objects	    //	    Matrix T = new Matrix();	    Matrix M = new Matrix();	    Matrix W = new Matrix();			    // allocate memory for the matrix elements	    //	    T.Elem = new double[2][2];	    M.Elem = new double[2][2];	    W.Elem = new double[2][2];			    // initialize the transformation matrix dimensions	    //	    W.row = 2;	    W.col = 2;			    // reset the matrices	    //	    W.resetMatrix();			    // compute the covariance matrix of the first data set	    //	    covariance.Elem = cov.computeCovariance(x, y);	    CPCA1_d = covariance;				    // initialize the matrix needed to compute the eigenvalues	    //	    T.initMatrix(covariance.Elem, 2, 2);	    // make a copy of the original matrix	    //	    M.copyMatrix(T);			    // compute the eigen values	    //	    // Changed eigen to Eigen 	    // since member function is static - Phil T. 6-23-03	    eigVal1 = Eigen.compEigenVal(T);	    // compute the eigen vectors	    //	    for (int i = 0; i < 2; i++)	    {		Eigen.calcEigVec(M, eigVal1[i], eigVec);		for (int j = 0; j < 2; j++)		{		    W.Elem[j][i] = eigVec[j] / Math.sqrt(eigVal1[i]);		}	    }			    // save the transformation matrix 	    //	    PCA1_d = W;	}	    	// get the samples from the first data set	//	size = set2_d.size();	    	// increment the variable count for the second data set	//	xsize2 += size;	ysize2 += size;	    	// initialize arrays to store the samples	//	x = new double[size];	y = new double[size];		// set up the initial random vectors i.e., the vectors of	// X and Y coordinate points form the display	//	for (int i = 0; i < size; i++)	{	    MyPoint p = (MyPoint)set2_d.elementAt(i);	    xval2 += p.x;	    yval2 += p.y;	    x[i] = p.x;	    y[i] = p.y;	}	    	if (size > 0)	{			    // declare the covariance matrix	    //	    Matrix covariance = new Matrix();	    covariance.row = covariance.col = 2;	    covariance.Elem = new double[2][2];			    // declare matrix objects	    //	    Matrix T = new Matrix();	    Matrix M = new Matrix();	    Matrix W = new Matrix();			    // allocate memory for the matrix elements	    //	    T.Elem = new double[2][2];	    M.Elem = new double[2][2];	    W.Elem = new double[2][2];				    // initialize the transformation matrix dimensions	    //	    W.row = 2;	    W.col = 2;			    // reset the matrices	    //	    W.resetMatrix();			    // compute the covariance matrix of the first data set	    //	    covariance.Elem = cov.computeCovariance(x, y);	    CPCA2_d = covariance;			    // initialize the matrix needed to compute the eigenvalues	    //	    T.initMatrix(covariance.Elem, 2, 2);			    // make a copy of the original matrix	    //	    M.copyMatrix(T);			    // compute the eigen values	    //	    // Changed eigen to Eigen since member 	    // function is static - Phil T. 6-23-03	    eigVal2 = Eigen.compEigenVal(T);			    // compute the eigen vectors	    //	    for (int i = 0; i < 2; i++)	    {		// Changed eigen to Eigen 		// since member function is static - Phil T. 6-23-03			Eigen.calcEigVec(M, eigVal2[i], eigVec);		for (int j = 0; j < 2; j++)		{		    W.Elem[j][i] = eigVec[j] / Math.sqrt(eigVal2[i]);		}	    }	    	    // save the transformation matrix