java.lang.Object
- gov.nih.mipav.model.algorithms.NLConstrainedEngine
- - gov.nih.mipav.model.algorithms.NLFittedFunction
  - - gov.nih.mipav.model.algorithms.FitGaussian

```
public class FitGaussian
extends NLFittedFunction
```
FitGaussian -fits an array of points to a normal curve, general from f = a*exp(-(x-b)^2/2sigma^2) Will also perform thresholding techniques to determine useful data points for fitting

Version:

0.1

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 int`	`iters`
`private double`	`rSquared`	R squared
`private double`	`sigma`	Sigma parameter
`private double`	`xInit`	Center parameter

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
`FitGaussian()`	FitGaussian - test constructor, builds and test function.
`FitGaussian(int nPoints, double[] xData, double[] yData)`	FitGaussian.
`FitGaussian(int nPoints, float[] xData, float[] yData)`	Constructs new fit gaussian.

Method Summary

All Methods Instance Methods Concrete Methods
Modifier and Type	Method	Description
`private double[]`	`applyKernel()`	Apply small kernel to smooth out data.
`protected void`	`calculateChiSq()`	Calculates chi squared
`protected void`	`calculateFittedY()`	Calculates yDataFitted
`private double`	`dgdA(double x)`	Partial derivative of gaussian with respect to A.
`private double`	`dgdsigma(double x)`	Partial derivative of gaussian with respect to sigma.
`private double`	`dgdx(double x)`	Partial derivative of gaussian with respect to x.
`void`	`displayResults()`	Display results of displaying exponential fitting parameters.
`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.
`private double`	`gauss(double x)`	Gaussian evaluated at a point with given parameters
`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
`double`	`getRSquared()`
`private void`	`testData()`	Test data to test fitting of gaussian.

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
  - xInit
```
private double xInit
```
    Center parameter
  - sigma
```
private double sigma
```
    Sigma parameter
  - rSquared
```
private double rSquared
```
    R squared
  - iters
```
private int iters
```
- Constructor Detail
  - FitGaussian
```
public FitGaussian()
```
    FitGaussian - test constructor, builds and test function.
  - FitGaussian
```
public FitGaussian(int nPoints,
                   double[] xData,
                   double[] yData)
```
    FitGaussian.
    
    Parameters:
    
    nPoints - number of points in the function
    
    xData - DOCUMENT ME!
    
    yData - DOCUMENT ME!
  - FitGaussian
```
public FitGaussian(int nPoints,
                   float[] xData,
                   float[] yData)
```
    Constructs new fit gaussian.
    
    Parameters:
    
    nPoints - Number of points in the function
    
    xData - DOCUMENT ME!
    
    yData - DOCUMENT ME!
- Method Detail
  - applyKernel
```
private double[] applyKernel()
```
    Apply small kernel to smooth out data. Often helpful when too many bins have been applied by user.
  - estimateInitial
```
private void estimateInitial()
```
  - driver
```
public void driver()
```
    Starts the analysis. For some reason a guess with the wrong sign for a2 will not converge. Therefore, try both sign and take the one with the lowest chi-squared value.
    
    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 exponential fitting parameters.
    
    Specified by:
    
    displayResults in class NLFittedFunction
  - getRSquared
```
public double getRSquared()
```
  - 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!
  - testData
```
private void testData()
```
    Test data to test fitting of gaussian.
  - gauss
```
private double gauss(double x)
```
    Gaussian evaluated at a point with given parameters
  - dgdA
```
private double dgdA(double x)
```
    Partial derivative of gaussian with respect to A.
  - dgdx
```
private double dgdx(double x)
```
    Partial derivative of gaussian with respect to x.
  - dgdsigma
```
private double dgdsigma(double x)
```
    Partial derivative of gaussian with respect to sigma.
  - 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 FitGaussian

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

xInit

sigma

rSquared

iters

Constructor Detail

FitGaussian

FitGaussian

FitGaussian

Method Detail

applyKernel

estimateInitial

driver

calculateChiSq

calculateFittedY

displayResults

getRSquared

fitToFunction

testData

gauss

dgdA

dgdx

dgdsigma

generateJacobian

generateResiduals