linsolv.h

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#pragma once
 
/*@+
________________________________________________________________________
 
 (C) dGB Beheer B.V.; (LICENSE) http://opendtect.org/OpendTect_license.txt
 Author:        Kristofer Tingdahl
 Date:          07-10-1999
________________________________________________________________________
 
@$
*/
 
#include "arrayndimpl.h"
#include "executor.h"
#include "uistrings.h"
#include <math.h>
 
template <class T>
mClass(Algo) LinSolver
{
public:
                                LinSolver(const Array2D<T>& A);
                                LinSolver(const LinSolver&);
 
                                ~LinSolver();
 
    bool                        init(TaskRunner* =0);
    bool                        ready() const   { return ready_; }
    int                         size() const    { return n_; }
 
    void                        apply(const T* b,T* x) const;
 
protected:
    Array2DImpl<T>              croutsmatrix_;
    int*                        croutsidx_;
    int                         n_;
    bool                        parity_;
    bool                        ready_;
 
};
 
 
template <class T>
mClass(Algo) LinSolverTask : public Executor
{ mODTextTranslationClass(LinSolverTask);
public:
                                LinSolverTask(Array2DImpl<T>&,int*);
                                ~LinSolverTask()                {}
 
protected:
 
    int                         nextStep();
    uiString                    uiMessage() const
                                {
                                    return errmsg_.isEmpty()
                                        ? tr("Generating linear model")
                                        : errmsg_;
                                }
    uiString                    uiNrDoneText() const
                                { return tr("Nr points processed"); }
 
    od_int64                    nrDone() const          { return curidx_; }
    od_int64                    totalNr() const         { return totalnr_; }
 
    int                         curidx_;
    int                         totalnr_;
    int                         imax_;
    int*                        croutsidx_;
    TypeSet<T>                  vv_;
    Array2DImpl<T>&             croutsmatrix_;
    uiString                    errmsg_;
};
 
 
template <class T>
LinSolverTask<T>::LinSolverTask( Array2DImpl<T>& A, int* croutsidx )
    : Executor("Generating linear model")
    , croutsmatrix_(A)
    , totalnr_(A.info().getSize(0))
    , curidx_(0)
    , imax_(mUdf(int))
    , vv_(totalnr_,0)
    , croutsidx_(croutsidx)
{
    if ( A.info().getSize(0) != A.info().getSize(1) )
    {
        errmsg_ = uiStrings::phrInvalid(uiStrings::sInput());
        return;
    }
 
    for ( int idx=0; idx<totalnr_; idx++ )
    {
        T maxval = 0;
        for ( int idy=0; idy<totalnr_; idy++ )
        {
            const T temp = fabs( croutsmatrix_.get(idx,idy) );
            if ( temp > maxval )
                maxval = temp;
        }
 
        if ( mIsZero(maxval,mDefEps) )
        {
            errmsg_ = uiStrings::phrInvalid(uiStrings::sInput());
            return;
        }
 
        vv_[idx] = 1.0f/maxval;
    }
}
 
 
#define TINY 1.0e-20
 
template <class T>
int LinSolverTask<T>::nextStep()
{
    if ( !errmsg_.isEmpty() )
        return ErrorOccurred();
 
    if ( curidx_ >= totalnr_ )
        return Finished();
 
    for (int idx=0; idx<curidx_; idx++ )
    {
        T sum = croutsmatrix_.get( idx, curidx_ );
        for ( int idz=0; idz<idx; idz++ )
            sum -= croutsmatrix_.get(idx,idz) * croutsmatrix_.get(idz,curidx_);
 
        croutsmatrix_.set( idx, curidx_, sum );
    }
 
    T big = 0.0;
    for ( int idx=curidx_; idx<totalnr_; idx++ )
    {
        T sum = croutsmatrix_.get( idx, curidx_ );
        for ( int idz=0; idz<curidx_; idz++ )
        {
            sum -= croutsmatrix_.get(idx,idz)*croutsmatrix_.get(idz,curidx_);
        }
 
        croutsmatrix_.set( idx, curidx_, sum );
 
        T dum = vv_[idx]*fabs(sum);
 
        if ( dum >= big )
        {
            big = dum;
            imax_ = idx;
        }
    }
 
    if ( curidx_ != imax_ )
    {
        for ( int idz=0; idz<totalnr_; idz++ )
        {
            const T dum = croutsmatrix_.get( imax_, idz );
            croutsmatrix_.set( imax_, idz, croutsmatrix_.get(curidx_,idz) );
            croutsmatrix_.set( curidx_, idz, dum );
        }
 
        vv_[imax_] = vv_[curidx_];
    }
 
    croutsidx_[curidx_] = imax_;
 
    if ( mIsZero(croutsmatrix_.get(curidx_,curidx_),mDefEps) )
        croutsmatrix_.set(curidx_,curidx_,TINY);
 
    if ( curidx_ != totalnr_-1 )
    {
        const T dum = 1.0f/croutsmatrix_.get(curidx_,curidx_);
        for ( int idx=curidx_+1; idx<totalnr_; idx++ )
            croutsmatrix_.set(idx, curidx_, dum*croutsmatrix_.get(idx,curidx_));
    }
 
    return ++curidx_>=totalnr_ ? Finished() : MoreToDo();
}
 
#undef TINY
 
 
template <class T> inline
LinSolver<T>::LinSolver( const Array2D<T>& A )
    : croutsmatrix_(A)
    , croutsidx_(new int[A.info().getSize(0)])
    , n_(A.info().getSize(0))
    , ready_(false)
    , parity_(true)
{
}
 
 
template <class T> inline
bool LinSolver<T>::init( TaskRunner* taskr )
{
    LinSolverTask<T> task( croutsmatrix_, croutsidx_ );
    ready_ = TaskRunner::execute( taskr, task );
    return ready_;
}
 
 
template <class T> inline
LinSolver<T>::LinSolver( const LinSolver& s )
    : croutsmatrix_(s.croutsmatrix_)
    , croutsidx_(0)
    , n_(s.n_)
    , parity_(s.parity_)
    , ready_(s.ready_)
{
    if ( s.croutsidx_ )
    {
        croutsidx_ = new int[s.n_];
        for ( int idx=0; idx<s.n_; idx++ )
            croutsidx_[idx] = s.croutsidx_[idx];
    }
}
 
 
template <class T> inline
LinSolver<T>::~LinSolver( )
{
    delete [] croutsidx_;
}
 
 
template <class T> inline
void LinSolver<T>::apply( const T* b, T* x ) const
{
    if ( !ready_ )
    {
        pErrMsg("Cannot apply. Did you forget to call LinSolver::init()?");
        return;
    }
 
    for ( int idx=0; idx<n_; idx++ )
        x[idx] = b[idx];
 
    int ii=-1;
 
    for ( int i=0; i<n_; i++ )
    {
        int ip=croutsidx_[i];
        T sum=x[ip];
        x[ip]=x[i];
 
        if ( ii != -1 )
        {
            for ( int j=ii; j<=i-1; j++ )
            {
                sum -= croutsmatrix_.get(i,j)*x[j];
            }
        }
        else if ( !mIsZero(sum,mDefEps) )
            ii=i;
 
        x[i]=sum;
    }
 
    for ( int i=n_-1; i>=0; i-- )
    {
        T sum=x[i];
        for ( int j=i+1; j<n_; j++ )
            sum -= croutsmatrix_.get(i,j)*x[j];
 
        x[i]=sum/croutsmatrix_.get(i,i);
    }
 
}