mathutil.java,v

包含了模式识别中常用的一些分类器设计算法

	//
	for (int i = 0; i < vec_a.size(); i++)
        {
	    vec_a.set(i, new Double(value_a));
	}
    }
    

    /**  
     * gets the double value of the input vector at the index passed
     *
     * @@param    vec_a point from the cluster
     * @@param    index_a
     * @@return   double value of vector at indexed value
     *
     */
    static public double doubleValue(Vector<Double> vec_a, int index_a)
    {
	
	Double value = vec_a.get(index_a);
	return value.doubleValue();	
    }

    /**
     * Copies vector given specific parameters
     *
     * @@param  vec1_a    vector to be overwritten/copyied to
     * @@param  vec2_a    vector to be copied from
     * @@param  length_a  number of elements to be copyied
     * @@param  index1_a  index to start copying to first vector
     * @@param  index2_a  index to start copying from second vector
     *
     */
    static public void copyVector( Vector<Double> vec1_a,  Vector<Double> vec2_a, int length_a, int index1_a, int index2_a)
    {
	
	// loop over all elements
	//
	for (int i = 0; i < length_a; i++)
	{
	    vec1_a.set(i + index1_a, vec2_a.get(i + index2_a));
	}
	
    }
    
    /**
     * Within Class
     *
     * @@param    data_a input data points
     * @@param    rx_a double input of x
     * @@param    ry_a double input of y
     *
     */
    public static void withinClass(DataPoints data_a, double rx_a, double ry_a)
    {
	
	// declare local variables
	//
	int size = 0;
	double x[] = null;
	double y[] = null;
	
	// declare the covariance object
	//
	Covariance cov = new Covariance();
	
	// declare local matrices
	//
	Matrix M = new Matrix();
	Matrix M1 = new Matrix();
	Matrix M2 = new Matrix();
	Matrix M3 = new Matrix();
	Matrix M4 = new Matrix();
	
	// set scales
	
	double Xmin = OutputPanel.Xmin;
	double Ymax = OutputPanel.Ymax;
	
	// get the Rx and Ry ratio, seconds per pixel
	//
	double Rx = rx_a;
	double Ry = ry_a;
    
	// get the first data set size
	//
	size = data_a.dset1.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)data_a.dset1.elementAt(i);
	    x[i] = (p.x * Rx) + Xmin;
	    y[i] = Ymax - (p.y * Ry);
	}
	
	// compute the covariance matrix of the first data set
	//
	M1.row = M1.col = 2;
	M1.Elem = new double[2][2];
	M1.resetMatrix();

	if (size > 0)
	{
	    M1.Elem = cov.computeCovariance(x, y);
	}

	// get the second data set size
	//
	size = data_a.dset2.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)data_a.dset2.elementAt(i);
	    x[i] = (p.x * Rx) + Xmin;
	    y[i] = Ymax - (p.y * Ry);
	}
	
	// compute the covariance matrix of the second data set
	//
	M2.row = M2.col = 2;
	M2.Elem = new double[2][2];
	M2.resetMatrix();

	if (size > 0)
	{
	    M2.Elem = cov.computeCovariance(x, y);
	}

	// get the third data set size
	//
	size = data_a.dset3.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)data_a.dset3.elementAt(i);
	    x[i] = (p.x * Rx) + Xmin;
	    y[i] = Ymax - (p.y * Ry);
	}

	// compute the covariance matrix of the third data set
	//
	M3.row = M3.col = 2;
	M3.Elem = new double[2][2];
	M3.resetMatrix();
	
	if (size > 0)
	{
	    M3.Elem = cov.computeCovariance(x, y);
	}
	
	// get the fourth data set size
	//
	size = data_a.dset4.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)data_a.dset4.elementAt(i);
	    x[i] = (p.x * Rx) + Xmin;
	    y[i] = Ymax - (p.y * Ry);
	}

	// compute the covariance matrix of the fourth data set
	//
	M4.row = M4.col = 2;
	M4.Elem = new double[2][2];
	M4.resetMatrix();

	if (size > 0)
        {
	    M4.Elem = cov.computeCovariance(x, y);
	}

	// compute the within class scatter matrix
	//
	M.row = M.col = 2;
	M.Elem = new double[2][2];
	M.resetMatrix();
	M.addMatrix(M1);
	M.addMatrix(M2);
	M.addMatrix(M3);
	M.addMatrix(M4);
    }

    /**
     * this method determines the between class scatter matrix for
     * the class independent linear discrimination algorithm
     *
     * @@param    d input data points
     * @@param    M between class scatter matrix
     * @@param    rx_a double input of x
     * @@param    ry_a double input of y
     */
    public static void betweenClass(
				    DataPoints d, Matrix M,
				    double rx_a, double ry_a)
    {

	// declare local variables
	//
	double xmean = 0.0;
	double ymean = 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;

	// declare local matrices
	//
	Matrix M1 = new Matrix();
	Matrix T1 = new Matrix();
	Matrix M2 = new Matrix();
	Matrix T2 = new Matrix();
	Matrix M3 = new Matrix();
	Matrix T3 = new Matrix();
	Matrix M4 = new Matrix();
	Matrix T4 = new Matrix();

	// declare the initial random variables
	//
	double transpose[][] = new double[2][1];
	double mean[][] = new double[1][2];

	// set scales
	//
	double Xmin = OutputPanel.Xmin;
	double Ymax = OutputPanel.Ymax;

	// get the Rx and Ry ratio, seconds per pixel
	//
	double Rx = rx_a;
	double Ry = ry_a;

	// initialize the between class matrix
	//
	M.row = M.col = 2;
	M.Elem = new double[2][2];
	M.resetMatrix();

	// obtain the means of each individual class
	//
	if (d.dset1.size() > 0)
        {
	    MyPoint p = (MyPoint)computePointMean(d.dset1);
	    xmean1 = (p.x * Rx) + Xmin;
	    ymean1 = Ymax - (p.y * Ry);
	}

	if (d.dset2.size() > 0)
        {
	    MyPoint p = (MyPoint)computePointMean(d.dset2);
	    xmean2 = (p.x * Rx) + Xmin;
	    ymean2 = Ymax - (p.y * Ry);
	}

	if (d.dset3.size() > 0)
        {
	    MyPoint p = (MyPoint)computePointMean(d.dset3);
	    xmean3 = (p.x * Rx) + Xmin;
	    ymean3 = Ymax - (p.y * Ry);
	}

	if (d.dset4.size() > 0)
	{
	    MyPoint p = (MyPoint)computePointMean(d.dset4);
	    xmean4 = (p.x * Rx) + Xmin;
	    ymean4 = Ymax - (p.y * Ry);
	}

	// compute the global mean of all data sets
	//
	int samples = 0;
	
	for (int i = 0; i < d.dset1.size(); samples++, i++)
	{
	    MyPoint p2 = (MyPoint)d.dset1.elementAt(i);
	    xmean += (p2.x * Rx) + Xmin;
	    ymean += Ymax - (p2.y * Ry);
	}

	for (int i = 0; i < d.dset2.size(); samples++, i++)
	{
	    MyPoint p2 = (MyPoint)d.dset2.elementAt(i);
	    xmean += (p2.x * Rx) + Xmin;
	    ymean += Ymax - (p2.y * Ry);
	}

	for (int i = 0; i < d.dset3.size(); samples++, i++)
	{
	    MyPoint p2 = (MyPoint)d.dset3.elementAt(i);
	    xmean += (p2.x * Rx) + Xmin;
	    ymean += Ymax - (p2.y * Ry);
	}

	for (int i = 0; i < d.dset4.size(); samples++, i++)
	{
	    MyPoint p2 = (MyPoint)d.dset4.elementAt(i);
	    xmean += (p2.x * Rx) + Xmin;
	    ymean += Ymax - (p2.y * Ry);
	}
	
	xmean /= samples;
	ymean /= samples;
	
	// compute the between class scatter contribution of the first set
	//
	if (d.dset1.size() > 0)
        {

	    Matrix S = new Matrix();

	    mean[0][0] = xmean1 - xmean;
	    mean[0][1] = ymean1 - ymean;
	    M1.initMatrix(mean, 1, 2);

	    transpose[0][0] = xmean1 - xmean;
	    transpose[1][0] = ymean1 - ymean;
	    T1.initMatrix(transpose, 2, 1);

	    T1.multMatrix(M1, S);
	    M.addMatrix(S);
	}

	// compute the between class scatter contribution of the second set
	//
	if (d.dset2.size() > 0)
	{

	    Matrix S = new Matrix();

	    mean[0][0] = xmean2 - xmean;
	    mean[0][1] = ymean2 - ymean;
	    M2.initMatrix(mean, 1, 2);

	    transpose[0][0] = xmean2 - xmean;
	    transpose[1][0] = ymean2 - ymean;
	    T2.initMatrix(transpose, 2, 1);

	    T2.multMatrix(M2, S);
	    M.addMatrix(S);
	}

	// compute the between class scatter contribution of the third set
	//
	if (d.dset3.size() > 0)
       {

	   Matrix S = new Matrix();

	   mean[0][0] = xmean3 - xmean;
	   mean[0][1] = ymean3 - ymean;
	   M3.initMatrix(mean, 1, 2);

	   transpose[0][0] = xmean3 - xmean;
	   transpose[1][0] = ymean3 - ymean;
	   T3.initMatrix(transpose, 2, 1);

	   T3.multMatrix(M3, S);
	   M.addMatrix(S);
       }

	// compute the between class scatter contribution of the forth set
	//
	if (d.dset4.size() > 0)
        {

	    Matrix S = new Matrix();

	    mean[0][0] = xmean4 - xmean;
	    mean[0][1] = ymean4 - ymean;
	    M4.initMatrix(mean, 1, 2);

	    transpose[0][0] = xmean4 - xmean;
	    transpose[1][0] = ymean4 - ymean;
	    T4.initMatrix(transpose, 2, 1);

	    T4.multMatrix(M4, S);
	    M.addMatrix(S);
	}
    }

}
@


1.4
log
@made almostEqual public.
@
text
@d6 2
d203 1
a203 1
     * @@param   Vector vec: point from the cluster
d365 2
a366 2
     * @@param    point1 First vector of points
     * @@param    point2 Second vector of points
d393 2
a394 2
     * @@param    point1 First vector of points
     * @@param    point2 Second vector of points
d432 2
a433 2
     * @@param    point1 First vector of points
     * @@param    point2 Second vector of points
d460 2
a461 2
     * @@param    point1 First vector of points
     * @@param    point2 Second vector of points
d512 1
a512 1
    static public void initDoubleVector(Vector vec_a, double value_a)
d532 1
a532 1
    static public double doubleValue(Vector vec_a, int index_a)
d535 1
a535 1
	Double value = (Double)vec_a.get(index_a);
d549 1
a549 3
    static public void copyVector(
				  Vector vec1_a,  Vector vec2_a, int length_a,
				  int index1_a,	  int index2_a)
@


1.3
log
@Fixed javadoc comments.
@
text
@d462 1
a462 1
    static boolean almostEqual(Vector vec1, double val2)
@


1.2
log
@comments changed to suit Java Documentation Style.
@
text
@d4 1
a12 2
 * class: MathUtil
 *
d42 3
d57 2
a58 1
     * method: grand
d60 2
a61 2
     * @@param    double mean: mean of the distribution
     * @@param    double stddev: standard deviation of the distribution
a63 3
     *    
     * method generates a random distribution centered about the mean with a
     * variance that is specified by the standard deviation.
a88 8
     * method: SetDecimal
     *
     * @@param   
     * @@param   double doubleNumber: input number
     * @@param   int decimalPlaces: number of decimal places to round
     *
     * @@return  none
     *
d92 3
d110 1
a110 1
     * method: distance
d112 4
a115 4
     * @@param    double x1: x-coordinate of the first point
     * @@param    double y1: y-coordinate of the first point
     * @@param    double x2: x-coordinate of the second point
     * @@param    double y2: y-coordinate of the second point
a118 2
     * calculate the euclidean distance between the two points
     *
a139 5
     * method: getClusterMean
     *
     * @@param    Vector vec: point from the cluster
     * @@return   none
     *
d142 2
d169 1
a169 1
     * method: getClusterMean
d171 2
a172 4
     * @@param    Vector vec: point from the cluster
     * @@return   none
     *
     * methods computes and returns the mean of the given cluster
d199 1
a199 1
     * method: computeMyPointMean
d202 1
a202 3
     * @@return  none
     *
     * methods computes and returns the mean of the given cluster
a228 7
     * method: setDecimal
     *
     * @@param   double doubleNumber: input number
     * @@param   int decimalPlaces: number of significant decimal places 
     *
     * @@return  none
     *
d232 4
d254 2
a255 1
     * method: linearKernel