Package gov.nih.mipav.model.algorithms

Class AlgorithmSM2.FitAllEP

java.lang.Object

gov.nih.mipav.model.algorithms.NLConstrainedEngineEP

gov.nih.mipav.model.algorithms.AlgorithmSM2.FitAllEP

Enclosing class:

AlgorithmSM2

class AlgorithmSM2.FitAllEP
extends NLConstrainedEngineEP

Field Summary

Fields inherited from class gov.nih.mipav.model.algorithms.NLConstrainedEngineEP

a, absoluteConvergence, analyticalJacobian, bl, bounds, bu, covarMat, ctrlMat, dyda, gues, internalScaling, iters, maxIterations, nPts, outputMes, param, parameterConvergence, relativeConvergence, residuals, secondAllowed, stdv, tolerance

Constructor Summary

Constructors

Constructor	Description
FitAllEP()	Creates a new Fit24DModel object.

Method Summary

Modifier and Type	Method	Description
void	driver()	Starts the analysis.
void	dumpResults()	Display results of displaying exponential fitting parameters.
void	fitToFunction(DoubleDouble[] a, DoubleDouble[] residuals, DoubleDouble[][] covarMat)	Fit to function - a0 - a1*(a2**x).

Methods inherited from class gov.nih.mipav.model.algorithms.NLConstrainedEngineEP

fitToTestFunction, getChiSquared, getExitStatus, getParameters, getResiduals

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

FitAllEP

public FitAllEP()

Creates a new Fit24DModel object.

Parameters:

nPoints - DOCUMENT ME!

xData - DOCUMENT ME!

yData - DOCUMENT ME!

initial - DOCUMENT ME!

Method Detail

driver

public void driver()

Starts the analysis.

Overrides:

driver in class NLConstrainedEngineEP

dumpResults

public void dumpResults()

Display results of displaying exponential fitting parameters.

fitToFunction

public void fitToFunction(DoubleDouble[] a,
                          DoubleDouble[] residuals,
                          DoubleDouble[][] covarMat)

Fit to function - a0 - a1*(a2**x).

Specified by:

fitToFunction in class NLConstrainedEngineEP

Parameters:

a - The x value of the data point.

residuals - The best guess parameter values.

covarMat - The derivative values of y with respect to fitting parameters.