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

All Methods Instance Methods Concrete Methods
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:

Class FitLorentz

Field Summary

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

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

Constructor Summary

Method Summary

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

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

Methods inherited from class java.lang.Object

Field Detail

dataStart

dataEnd

amp

xMedian

gamma

iters

Constructor Detail

FitLorentz

FitLorentz

FitLorentz

Method Detail

applyKernel

estimateInitial

driver

calculateChiSq

calculateFittedY

displayResults

fitToFunction

lorentz

dLdA

dLdxMedian

dLdgamma

generateJacobian

generateResiduals