NLFitSession::Iterate

Iterate

1 Description
2 Syntax
3 Parameters
4 Return
5 Examples
6 Remark
7 See Also
8 header to Include

Description

Perform iteration until the tolerance is reached, or the number of iterations has reached the maximum specified by nMaxNumIterations variable.

Syntax

int Iterate(int nMaxNumIterations, int nMethod = FITMETH_LEVENBERG_MARQUARDT, bool bAllowCancel = false, int* lpnActualInterateCount = NULL, int* lpnIterateOutCome = NULL, DWORD dwOption = 0)

Parameters

nMaxNumIterations: [input] The maximum number of times to iterate.
nMethod: [input] FITMETH_LEVENBERG_MARQUARDT or FITMETH_SIMPLEX.; FITMETH_LEVENBERG_MARQUARDT, perform the fit using Levenberg Marquardt algorithm.; FITMETH_SIMPLEX, perform a Simplex fit to estimate the parameter values.
bAllowCancel: [input] If true, allow user press Esc key to interrupt iteration.
lpnActualInterateCount: [output] If not NULL, it receives the number of iteration actually performed.
lpnIterateOutCome: [output] If not NULL it receives the code describing the outcome of the iterative procedure. Possible values are from the FITITER enumeration in ONLSF.h.
dwOption: [input] If set as NLSFFIT_GENERATE_OUTPUTS_WITHOUT_ITERATION, will always generate outputs without iteration that can be retrieved by GetFitResultsParams.

Return

Return 0 if set successfully, -1 for failure.

Examples

EX1

This example shows to do iteration until the tolerance is reached.

Prior to running the following example, the nlsf_utils.c file need to be loaded and compiled. This can be done from script with the command run.LoadOC(Originlab\nlsf_utils.c) or just add this file to your workspace.
New a worksheet and import \Samples\Curve Fitting\Gaussian.dat.
Copy and compile the following codes, and run "NLFitSession_Iterate_ex1" in Command window.

#include <..\originlab\NLFitSession.h>

bool NLFitSession_Iterate_ex1()
{
        Worksheet wks = Project.ActiveLayer();
        if ( !wks || wks.GetNumCols() < 2 )
        {
                out_str("Error. Please make sure activate one worksheet contains at least two columns with data.");
                return false;
        }
        
        //prepare input data
        XYRange drInput;
        drInput.Add(wks, 0, "X");
        drInput.Add(wks, 1, "Y");
        
        vector vX, vY;
        drInput.GetData(vY, vX);
        
        NLFitSession nlfSession;
        
        // Set fit function
        if ( !nlfSession.SetFunction("Gauss") )
        {
                out_str("Fail to set function!");
                return false;
        }
        
        // Set fit data
        if ( !nlfSession.SetData(vY, vX) )
        {
                out_str("Fail to set data!");
                return false;
        }     
        
        // Run parameter initialization codes to initialize parameter value
        if( !nlfSession.ParamsInitValues())
    {
        out_str("fail to initialize parameters");
        return false;
    }  
        
    // Iterations will be carried out until the tolerance is reached
        int                nCount = 0;
        const int nNumMaxIteration = 100;   
        bool               bReached = false;
        int                nIterateOutCome;

        double             dTol = 1e-12;
        nlfSession.SetTolerance(dTol);

        while(nCount < nNumMaxIteration)
        {
                // perform a single iteration in each loop
                int                nNum = 1; 
                int        nActualInterateCount;
                if( nlfSession.Iterate(nNum, FITMETH_LEVENBERG_MARQUARDT, true, &nActualInterateCount, &nIterateOutCome) != 0 ) 
                {
                        out_str("Fail to iterate!");
                        return false;
                }
                
                nCount = nCount + nActualInterateCount;               
                
                if( FITITER_REACHED_TOLERANCE == nIterateOutCome )
                {
                        bReached = true;
                        break;
                }
        }
        string strOutcome = nlfSession.GetFitOutCome(nIterateOutCome);
        printf("The count of iterations is %d.\n%s\n", nCount, strOutcome);
        
        
        // Prepare fit x data 
        double dMin, dMax;
        vX.GetMinMax(dMin, dMax);
        
        int nPoints = 100;
        vector vFitX;
        vFitX.Data(dMin, dMax, (dMax - dMin)/nPoints);
        
        // Calculate fit y data 
        vector vFitY( vFitX.GetSize() );
        nlfSession.GetYFromX(vFitX, vFitY, vFitY.GetSize());
        
        // Put fit data to input worksheet new columns
        int nFitXCol = wks.AddCol("FitX");
        int nFitYCol = wks.AddCol("FitY");
        wks.Columns(nFitXCol).SetType(OKDATAOBJ_DESIGNATION_X);
        
        XYRange drFit;
        drFit.Add(wks, nFitXCol, "X");
        drFit.Add(wks, nFitYCol, "Y");
        
        drFit.SetData(&vFitY, &vFitX);
        
        return true;
}