binormal.java,v

包含了模式识别中常用的一些分类器设计算法

	    {
		for(k=0; k<jm1; k++) 
		{
		    t = a[k+(j-1)*lda] - 
			sdot(k, a, k*lda, 1, a, (j-1)*lda, 1);
		    t /=  a[k+k*lda];
		    a[k+(j-1)*lda] = t;
		    s += (t*t);
		}
	    }
	    
	    s = a[j-1+(j-1)*lda] - s;
	    
	    if(s <= 0.0) 
	    {
		flag = 1;
		break S40;
	    }
	    a[j-1+(j-1)*lda] = Math.sqrt(s);
	}
	if (flag == 0) info = new Integer(0);
	
	return;
    }
    
    /**
     * Generates binormal gaussian random deviates
     *
     * @@param   max number of deviates
     * @@param   mx mean, X-COORDINATE
     * @@param   my mean, y-coordinate
     * @@param   c11 a11 element of the covariance matrix
     * @@param   c12 a12 element of the covariance matrix
     * @@param   c21 a21 element of the covariance matrix
     * @@param   c22 a22 element of the covariance matrix
     * @@param   xval x-vector of the gaussian random deviates
     * @@param   yval y-vector of the gaussian random deviates
     *
     *
     */
    public  void gaussian(int max, double mx, double my, 
			  double[] xval,double[] yval, double c11,
				double c12, double c21, double c22) 
    {

	// declare local variables
	//
	double[] val = null;
	double[] work = null;
	double[] mean = null;
	double[] covm = null;
	double[] param = null;

	// allocate memory 
	//
	mean = new double[2];
	covm = new double[4];
	val = new double[2];
	work = new double[max];
	param = new double[max];

	// set the mean for the binormal gaussian generator
	//
	mean[0] = mx;
	mean[1] = my;

	// set the covariance matrix for the binormal gaussian generator
	//
	covm[0] = c11;
	covm[1] = c12;
	covm[2] = c21;
	covm[3] = c22;

	// initialize the binormal gaussian random generator
	//
	setgmn(mean, covm, 2, param);

	// generate the binormal gaussian random deviates
	//
	for(int i = 0; i < max; i++) 
	{
	    genmn(param, val, work);
	    xval[i] = val[0]; 
	    yval[i] = val[1];
	}
    }
}
@


1.4
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@fixed javadoc errors.
@
text
@d1 1
a1 1
/**
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@


1.3
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@ Changed the comments to Java Documentation Style.
 Aligned the for loops and If statements as well.
@
text
@d4 3
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     * method: setgmn
d100 3
a102 2
     * @@param  double[] meanv: mean vector of multivariate normal distribution
     * @@param  double[] covm: covariance matrix of multivariate 
d104 2
a105 2
     * @@param  int p: dimension of the normal
     * @@param  double[] parm: array of parameters needed to generate 
a107 10
     * @@return   none
     *
     * methods sets up the parameters needed to generate the multivariate 
     * normal deviates form the inputs given
     *
     * param 1 : 1                - p
     *       2 : P + 1            - meanv
     *       P+2 : P*(P+3)/2 + 1  - Cholesky decomposition of covm
     *       needed dimension is (p*(p+3)/2 + 1)
     *
a162 9
     * method: genmn
     *
     * @@param double[] x: vector deviate generated
     * @@param double[] work: scratch array
     * @@param double[] parm: array of parameters needed to generate 
     *                       multivariate normal deviates
     *
     * @@return   none
     *
d168 5
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a174 1
    public  void genmn(double[] parm,double[] x,double[] work) 
d221 1
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     * method: ranf
a222 1
     * @@param   none
a225 2
     * generates a uniform distribution over 0 - 1
     *
a232 4
     * method: snorm40
     *
     * @@param   none
     * @@return  none
a248 4
     * method: snorm50
     *
     * @@param   none
     * @@return  none
a261 4
     * method: snorm60
     *
     * @@param   none
     * @@return  none
a275 4
     * method: snorm70
     *
     * @@param   none
     * @@return  none
a288 4
     * method: snorm80
     *
     * @@param   none
     * @@return  none
a311 4
     * method: snorm100
     *
     * @@param   none
     * @@return   none
a324 4
     * method: snorm110
     *
     * @@param   none
     * @@return  none
a337 4
     * method: snorm120
     *
     * @@param   none
     * @@return  none
a355 4
     * method: snorm140
     *
     * @@param   none
     * @@return  none
a368 4
     * method: snorm150
     *
     * @@param   none
     * @@return  none
a380 4
     * method: snorm160
     *
     * @@param   none
     * @@return  none
a404 5
     * method: snorm
     *
     * @@param   none
     * @@return  standard normal distribution
     *
d410 3
a450 1
     * method: sdot
d455 9
a543 2
     * method: spofa
     *
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     * method: gaussian
     *
     * @@param   int max: number of deviates
     * @@param   double mx: mean, x-coordinate
     * @@param   double my: mean, y-coordinate
     * @@param   double c11: a11 element of the covariance matrix
     * @@param   double c12: a12 element of the covariance matrix
     * @@param   double c21: a21 element of the covariance matrix
     * @@param   double c22: a22 element of the covariance matrix
     * @@param   double[] xval: x-vector of the gaussian random deviates
     * @@param   double[] yval: y-vector of the gaussian random deviates
d599 9
a607 1
     * @@return  none
a608 1
     * method generates binormal Gaussian random deviates
@


1.2
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@Algorithm Complete and debug statements commented
@
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// file: BiNormal.java
//

//These imports are not needed - Phil T. 6-23-03
//import java.awt.*;
//import java.applet.*;
//import javax.swing.*;
//import java.lang.*;
//import java.io.*;
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public class BiNormal {

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    // method: setgmn
    //
    // arguments:
    //    double[] meanv: mean vector of multivariate normal distribution
    //    double[] covm: covariance matrix of multivariate normal distribution
    //    int p: dimension of the normal
    //    double[] parm: array of parameters needed to generate 
    //                   multivariate normal deviates
    //
    // return: none
    //
    // methods sets up the parameters needed to generate the multivariate 
    // normal deviates form the inputs given
    //
    // param 1 : 1                - p
    //       2 : P + 1            - meanv
    //       P+2 : P*(P+3)/2 + 1  - Cholesky decomposition of covm
    //       needed dimension is (p*(p+3)/2 + 1)
    //
d114 2
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			      double[] parm) {
d128 1
a128 1
	    //System.out.println("P nonpositive in SETGMN");
d145 1
a145 1
	    //System.out.println(" COVM not positive definite in SETGMN");
d166 18
a183 16
    // method: genmn
    //
    // arguments:
    //    double[] x: vector deviate generated
    //    double[] work: scratch array
    //    double[] parm: array of parameters needed to generate 
    //                   multivariate normal deviates
    //
    // return: none
    //
    // methods generates the multivariate normal deviates using the procedure:
    // 1) generate p independent standard normal deviates - ei ~ n(0,1)
    // 2) using cholesky decomposition find a s.t. trans(a)*a = covm
    // 3) trans(a)e + meanv ~ n(meanv,covm)
    //
    public  void genmn(double[] parm,double[] x,double[] work) {
d199 2
a200 1
	for(i=1; i<=p; i++) {
d204 2
a205 1
	for(i=1, D3=1, D4=(p-i+D3)/D3; D4>0; D4--, i+=D3) {
d228 12
a239 10
    // method: ranf
    //
    // arguments: none
    // returns  : random floating point number from a uniform distribution
    //            over 0 - 1 using the current generator
    //
    // generates a uniform distribution over 0 - 1
    //
    public double ranf() {

d242 11
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    // method: snorm40
    //
    // arguments: none
    // returns  : none
    //
    // linpack.  this version dated 08/14/78
    // cleve moler, university of new mexico, argonne national lab
    //
d254 2
a255 1
	if(ustar <= t[snormi-1]) {
d263 12
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    // method: snorm50
    //
    // arguments: none
    // returns  : none
    //
    // linpack.  this version dated 08/14/78
    // cleve moler, university of new mexico, argonne national lab
    //
    public void snorm50() {
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    // method: snorm60
    //
    // arguments: none
    // returns  : none
    //
    // linpack.  this version dated 08/14/78
    // cleve moler, university of new mexico, argonne national lab
    //
    public void snorm60() {
d298 12
a309 9
    // method: snorm70
    //
    // arguments: none
    // returns  : none
    //
    // linpack.  this version dated 08/14/78
    // cleve moler, university of new mexico, argonne national lab
    //
    public void snorm70() {
d315 14
a328 10
    // method: snorm80
    //
    // arguments: none
    // returns  : none
    //
    // linpack.  this version dated 08/14/78
    // cleve moler, university of new mexico, argonne national lab
    //
    public void snorm80() {
	if(ustar > snormtt) {
d333 2
a334 1
	if(ustar >= snormu) {
d342 12
a353 9
    // method: snorm100
    //
    // arguments: none
    // returns  : none
    //
    // linpack.  this version dated 08/14/78
    // cleve moler, university of new mexico, argonne national lab
    //
    public void snorm100() {
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    // method: snorm110
    //
    // arguments: none
    // returns  : none
    //
    // linpack.  this version dated 08/14/78
    // cleve moler, university of new mexico, argonne national lab
    //
    public void snorm110() {
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    // method: snorm120
    //
    // arguments: none
    // returns  : none
    //
    // linpack.  this version dated 08/14/78
    // cleve moler, university of new mexico, argonne national lab
    //
    public void snorm120() {
d389 2
a390 1
	if(snormu < 1.0) {
d397 13
a409 10

    // method: snorm140
    //
    // arguments: none
    // returns  : none
    //
    // linpack.  this version dated 08/14/78
    // cleve moler, university of new mexico, argonne national lab
    //
    public void snorm140() {
d415 12
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    // method: snorm150
    //
    // arguments: none
    // returns  : none
    //
    // linpack.  this version dated 08/14/78
    // cleve moler, university of new mexico, argonne national lab
    //
    public void snorm150() {
d431 12
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    // method: snorm160
    //
    // arguments: none
    // returns  : none
    //
    // linpack.  this version dated 08/14/78
    // cleve moler, university of new mexico, argonne national lab
    //
    public void snorm160() {
d444 2
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	if(ustar > snormtt) {
d450 2
a451 1
	if(ustar >= snormu) {
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    // method: snorm
    //
    // arguments: none
    // returns  : standard normal distribution
    //
    // ahrens, j.h. and dieter, u.                            
    // extensions of forsythe's method for random             
    // sampling from the normal distribution.                 
    // math. comput., 27,124 (oct. 1973), 927 - 937.    
    //
    public double snorm() {
d477 2
a478 1
	if(snormu > 0.5) {
d486 2
a487 1
	if(snormi == 32) {
d490 2
a491 1
	if(snormi == 0) {
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    // method: sdot
    //
    // linpack.  this version dated 08/14/78
    // cleve moler, university of new mexico, argonne national lab
    //
d515 2
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			      double[] sy, int dy, int incy) {
d529 3
a531 2
	if(incx != 1 && incy != 1) {

d535 3
a537 2

	    for(i=1; i<=n; i++) {
d542 1
a542 1

d546 1
a546 1

d549 2
a550 1
	if(m == 0) {
d552 2
a553 1
	    for(i=mp1; i<=n; i+=5) {
d562 6
a567 3
	} else {

	    for(i=0; i<m; i++) {
d570 2
a571 1
	    if(n < 5) {
d574 3
a576 1
	    } else {
d578 2
a579 1
		for(i=mp1; i<=n; i+=5) {
d591 11
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    // method: spofa
    //
    // linpack.  this version dated 08/14/78
    // cleve moler, university of new mexico, argonne national lab
    //
    public  void spofa(double[] a, int lda, int n) {
d611 3
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	for(j=1; j<=n; j++) {

d617 5
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	    if(jm1 >= 1) {
		for(k=0; k<jm1; k++) {
d629 1
a629 1

d632 2
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	    if(s <= 0.0) {
d640 1
a640 1

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    // method: gaussian
    //
    // argument:
    //    int max: number of deviates
    //    double mx: mean, x-coordinate
    //    double my: mean, y-coordinate
    //    double c11: a11 element of the covariance matrix
    //    double c12: a12 element of the covariance matrix
    //    double c21: a21 element of the covariance matrix
    //    double c22: a22 element of the covariance matrix
    //    double[] xval: x-vector of the gaussian random deviates
    //    double[] yval: y-vector of the gaussian random deviates
    //
    // return  : none
    //
    // method generates binormal Gaussian random deviates
    //
d663 3
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				double[] xval,double[] yval, double c11,
				double c12, double c21, double c22) {
d701 2
a702 1
	for(int i = 0; i < max; i++) {
a708 6

//
// end of file



@


1.1
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@Initial revision
@
text
@d122 1
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	    System.out.println("P nonpositive in SETGMN");
d139 1
a139 1
	    System.out.println(" COVM not positive definite in SETGMN");
@