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#include "_cvaux.h"
#include "cvtypes.h"
#include <float.h>
#include <limits.h>
#include "cv.h"
/* Valery Mosyagin */
//#define TRACKLEVMAR
typedef void (*pointer_LMJac)( const CvMat* src, CvMat* dst );
typedef void (*pointer_LMFunc)( const CvMat* src, CvMat* dst );
/* Optimization using Levenberg-Marquardt */
void cvLevenbergMarquardtOptimization(pointer_LMJac JacobianFunction,
pointer_LMFunc function,
/*pointer_Err error_function,*/
CvMat *X0,CvMat *observRes,CvMat *resultX,
int maxIter,double epsilon)
{
/* This is not sparce method */
/* Make optimization using */
/* func - function to compute */
/* uses function to compute jacobian */
/* Allocate memory */
CvMat *vectX = 0;
CvMat *vectNewX = 0;
CvMat *resFunc = 0;
CvMat *resNewFunc = 0;
CvMat *error = 0;
CvMat *errorNew = 0;
CvMat *Jac = 0;
CvMat *delta = 0;
CvMat *matrJtJ = 0;
CvMat *matrJtJN = 0;
CvMat *matrJt = 0;
CvMat *vectB = 0;
CV_FUNCNAME( "cvLevenbegrMarquardtOptimization" );
__BEGIN__;
if( JacobianFunction == 0 || function == 0 || X0 == 0 || observRes == 0 || resultX == 0 )
{
CV_ERROR( CV_StsNullPtr, "Some of parameters is a NULL pointer" );
}
if( !CV_IS_MAT(X0) || !CV_IS_MAT(observRes) || !CV_IS_MAT(resultX) )
{
CV_ERROR( CV_StsUnsupportedFormat, "Some of input parameters must be a matrices" );
}
int numVal;
int numFunc;
double valError;
double valNewError;
numVal = X0->rows;
numFunc = observRes->rows;
/* test input data */
if( X0->cols != 1 )
{
CV_ERROR( CV_StsUnmatchedSizes, "Number of colomn of vector X0 must be 1" );
}
if( observRes->cols != 1 )
{
CV_ERROR( CV_StsUnmatchedSizes, "Number of colomn of vector observed rusult must be 1" );
}
if( resultX->cols != 1 || resultX->rows != numVal )
{
CV_ERROR( CV_StsUnmatchedSizes, "Size of result vector X must be equals to X0" );
}
if( maxIter <= 0 )
{
CV_ERROR( CV_StsUnmatchedSizes, "Number of maximum iteration must be > 0" );
}
if( epsilon < 0 )
{
CV_ERROR( CV_StsUnmatchedSizes, "Epsilon must be >= 0" );
}
/* copy x0 to current value of x */
CV_CALL( vectX = cvCreateMat(numVal, 1, CV_64F) );
CV_CALL( vectNewX = cvCreateMat(numVal, 1, CV_64F) );
CV_CALL( resFunc = cvCreateMat(numFunc,1, CV_64F) );
CV_CALL( resNewFunc = cvCreateMat(numFunc,1, CV_64F) );
CV_CALL( error = cvCreateMat(numFunc,1, CV_64F) );
CV_CALL( errorNew = cvCreateMat(numFunc,1, CV_64F) );
CV_CALL( Jac = cvCreateMat(numFunc,numVal, CV_64F) );
CV_CALL( delta = cvCreateMat(numVal, 1, CV_64F) );
CV_CALL( matrJtJ = cvCreateMat(numVal, numVal, CV_64F) );
CV_CALL( matrJtJN = cvCreateMat(numVal, numVal, CV_64F) );
CV_CALL( matrJt = cvCreateMat(numVal, numFunc,CV_64F) );
CV_CALL( vectB = cvCreateMat(numVal, 1, CV_64F) );
cvCopy(X0,vectX);
/* ========== Main optimization loop ============ */
double change;
int currIter;
double alpha;
change = 1;
currIter = 0;
alpha = 0.001;
do {
/* Compute value of function */
function(vectX,resFunc);
/* Print result of function to file */
/* Compute error */
cvSub(observRes,resFunc,error);
//valError = error_function(observRes,resFunc);
/* Need to use new version of computing error (norm) */
valError = cvNorm(observRes,resFunc);
/* Compute Jacobian for given point vectX */
JacobianFunction(vectX,Jac);
/* Define optimal delta for J'*J*delta=J'*error */
/* compute J'J */
cvMulTransposed(Jac,matrJtJ,1);
cvCopy(matrJtJ,matrJtJN);
/* compute J'*error */
cvTranspose(Jac,matrJt);
cvmMul(matrJt,error,vectB);
/* Solve normal equation for given alpha and Jacobian */
do
{
/* Increase diagonal elements by alpha */
for( int i = 0; i < numVal; i++ )
{
double val;
val = cvmGet(matrJtJ,i,i);
cvmSet(matrJtJN,i,i,(1+alpha)*val);
}
/* Solve system to define delta */
cvSolve(matrJtJN,vectB,delta,CV_SVD);
/* We know delta and we can define new value of vector X */
cvAdd(vectX,delta,vectNewX);
/* Compute result of function for new vector X */
function(vectNewX,resNewFunc);
cvSub(observRes,resNewFunc,errorNew);
valNewError = cvNorm(observRes,resNewFunc);
currIter++;
if( valNewError < valError )
{/* accept new value */
valError = valNewError;
/* Compute relative change of required parameter vectorX. change = norm(curr-prev) / norm(curr) ) */
change = cvNorm(vectX, vectNewX, CV_RELATIVE_L2);
alpha /= 10;
cvCopy(vectNewX,vectX);
break;
}
else
{
alpha *= 10;
}
} while ( currIter < maxIter );
/* new value of X and alpha were accepted */
} while ( change > epsilon && currIter < maxIter );
/* result was computed */
cvCopy(vectX,resultX);
__END__;
cvReleaseMat(&vectX);
cvReleaseMat(&vectNewX);
cvReleaseMat(&resFunc);
cvReleaseMat(&resNewFunc);
cvReleaseMat(&error);
cvReleaseMat(&errorNew);
cvReleaseMat(&Jac);
cvReleaseMat(&delta);
cvReleaseMat(&matrJtJ);
cvReleaseMat(&matrJtJN);
cvReleaseMat(&matrJt);
cvReleaseMat(&vectB);
return;
}
/*------------------------------------------------------------------------------*/
#if 0
//tests
void Jac_Func2(CvMat *vectX,CvMat *Jac)
{
double x = cvmGet(vectX,0,0);
double y = cvmGet(vectX,1,0);
cvmSet(Jac,0,0,2*(x-2));
cvmSet(Jac,0,1,2*(y+3));
cvmSet(Jac,1,0,1);
cvmSet(Jac,1,1,1);
return;
}
void Res_Func2(CvMat *vectX,CvMat *res)
{
double x = cvmGet(vectX,0,0);
double y = cvmGet(vectX,1,0);
cvmSet(res,0,0,(x-2)*(x-2)+(y+3)*(y+3));
cvmSet(res,1,0,x+y);
return;
}
double Err_Func2(CvMat *obs,CvMat *res)
{
CvMat *tmp;
tmp = cvCreateMat(obs->rows,1,CV_64F);
cvSub(obs,res,tmp);
double e;
e = cvNorm(tmp);
return e;
}
void TestOptimX2Y2()
{
CvMat vectX0;
double vectX0_dat[2];
vectX0 = cvMat(2,1,CV_64F,vectX0_dat);
vectX0_dat[0] = 5;
vectX0_dat[1] = -7;
CvMat observRes;
double observRes_dat[2];
observRes = cvMat(2,1,CV_64F,observRes_dat);
observRes_dat[0] = 0;
observRes_dat[1] = -1;
observRes_dat[0] = 0;
observRes_dat[1] = -1.2;
CvMat optimX;
double optimX_dat[2];
optimX = cvMat(2,1,CV_64F,optimX_dat);
LevenbegrMarquardtOptimization( Jac_Func2, Res_Func2, Err_Func2,
&vectX0,&observRes,&optimX,100,0.000001);
return;
}
#endif