Package gov.nih.mipav.model.algorithms

Class FitLorentz

java.lang.Object

gov.nih.mipav.model.algorithms.NLConstrainedEngine

gov.nih.mipav.model.algorithms.NLFittedFunction

gov.nih.mipav.model.algorithms.FitLorentz

public class FitLorentz
extends NLFittedFunction

FitLorentz -fits an array of points to a lorentz distribution, thresholding techniques come from ViewJFrameGraph, no need to implement here

Version:

1.0

Author:

senseneyj

See Also:

NLConstrainedEngine

Field Summary

Fields

Modifier and Type	Field	Description
private double	amp	Amplitude parameter
private int	dataEnd	Location in xSeries where Gaussian data ends
private int	dataStart	Location in xSeries where Gaussian data starts
private double	gamma	half width at half maximum
private int	iters	Iterations performed
private double	xMedian	Statistical median

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

chisq, EPSILON, MAX_ITR, MIN_ITR, xSeries, yDataFitted, ySeries

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

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

Constructor Summary

Constructors

Constructor	Description
FitLorentz()	FitLorentz - test constructor, builds and test function.
FitLorentz(int nPoints, double[] xData, double[] yData)	FitLorentz.
FitLorentz(int nPoints, float[] xData, float[] yData)	Constructs new fit lorentz distribution.

Method Summary

Modifier and Type	Method	Description
private double[]	applyKernel()	Apply small Gaussian kernel to smooth out data.
protected void	calculateChiSq()	Calculates chi squared
protected void	calculateFittedY()	Calculates yDataFitted
void	displayResults()	Display results of displaying lorentz fitting parameters.
private double	dLdA(double x)	Partial derivative of Lorentz distribution with respect to A.
private double	dLdgamma(double x)	Partial derivative of Lorentz distribution with respect to gamma.
private double	dLdxMedian(double x)	Partial derivative of Lorentz distribution with respect to xMedian.
void	driver()	Starts the analysis.
private void	estimateInitial()
void	fitToFunction(double[] a, double[] residuals, double[][] covarMat)	fitToFunction communicates with 3 protected variables param, nPts, and ctrlMat ctrlMat is used as a wrapper for ctrl or lctrl.
protected Jama.Matrix	generateJacobian()	Jacobian used for non-linear least squares fitting.
protected Jama.Matrix	generateResiduals()	Calculates the residuals for a given function, not implemented since some functions might prefer to only use a subset of data points, or not use yDataFitted if working during an iteration
private double	lorentz(double x)	Lorentz distribution evaluated at a point with given parameters

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

getChisq, getFittedY, getMedian

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

dumpTestResults, fitToTestFunction, getChiSquared, getExitStatus, getIterations, getParameters, getResiduals, statusMessage

Methods inherited from class java.lang.Object

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

Field Detail

dataStart

private int dataStart

Location in xSeries where Gaussian data starts

dataEnd

private int dataEnd

Location in xSeries where Gaussian data ends

amp

private double amp

Amplitude parameter

xMedian

private double xMedian

Statistical median

gamma

private double gamma

half width at half maximum

iters

private int iters

Iterations performed

Constructor Detail

FitLorentz

public FitLorentz()

FitLorentz - test constructor, builds and test function.

FitLorentz

public FitLorentz(int nPoints,
                  double[] xData,
                  double[] yData)

FitLorentz.

Parameters:

nPoints - number of points in the function

xData - values along x-axis

yData - values along y-axis

FitLorentz

public FitLorentz(int nPoints,
                  float[] xData,
                  float[] yData)

Constructs new fit lorentz distribution.

Parameters:

nPoints - Number of points in the function

xData - values along x-axis

yData - values along y-axis

Method Detail

applyKernel

private double[] applyKernel()

Apply small Gaussian kernel to smooth out data. Note should not be called if doing data comparison.

estimateInitial

private void estimateInitial()

driver

public void driver()

Starts the analysis.

Overrides:

driver in class NLConstrainedEngine

calculateChiSq

protected void calculateChiSq()

Description copied from class: NLFittedFunction

Calculates chi squared

Specified by:

calculateChiSq in class NLFittedFunction

calculateFittedY

protected void calculateFittedY()

Description copied from class: NLFittedFunction

Calculates yDataFitted

Specified by:

calculateFittedY in class NLFittedFunction

displayResults

public void displayResults()

Display results of displaying lorentz fitting parameters.

Specified by:

displayResults in class NLFittedFunction

fitToFunction

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

Description copied from class: NLConstrainedEngine

fitToFunction communicates with 3 protected variables param, nPts, and ctrlMat ctrlMat is used as a wrapper for ctrl or lctrl. Evaluates the residuals or the Jacobian at a certain a[]

Specified by:

fitToFunction in class NLConstrainedEngine

Parameters:

a - DOCUMENT ME!

residuals - DOCUMENT ME!

covarMat - DOCUMENT ME!

lorentz

private double lorentz(double x)

Lorentz distribution evaluated at a point with given parameters

dLdA

private double dLdA(double x)

Partial derivative of Lorentz distribution with respect to A.

dLdxMedian

private double dLdxMedian(double x)

Partial derivative of Lorentz distribution with respect to xMedian.

dLdgamma

private double dLdgamma(double x)

Partial derivative of Lorentz distribution with respect to gamma.

generateJacobian

protected Jama.Matrix generateJacobian()

Jacobian used for non-linear least squares fitting.

generateResiduals

protected Jama.Matrix generateResiduals()

Description copied from class: NLFittedFunction

Calculates the residuals for a given function, not implemented since some functions might prefer to only use a subset of data points, or not use yDataFitted if working during an iteration

Specified by:

generateResiduals in class NLFittedFunction

Returns: