de.lmu.ifi.dbs.elki.math.linearalgebra.fitting

Class GaussianFittingFunction

java.lang.Object

de.lmu.ifi.dbs.elki.math.linearalgebra.fitting.GaussianFittingFunction

All Implemented Interfaces:

FittingFunction

public class GaussianFittingFunction
extends Object
implements FittingFunction

Gaussian function for parameter fitting Based loosely on fgauss in the book "Numerical Recipies".
We did not bother to implement all optimizations at the benefit of having easier to use parameters. Instead of position, amplitude and width used in the book, we use the traditional Gaussian parameters mean, standard deviation and a linear scaling factor (which is mostly useful when combining multiple distributions) The cost are some additional computations such as a square root and probably a slight loss in precision. This could of course have been handled by an appropriate wrapper instead. Due to their license, we cannot use their code, but we have to implement the mathematics ourselves. We hope the loss in precision isn't big. They are also arranged differently: the book uses

 amplitude, position, width
 

whereas we use

 mean, stddev, scaling
 

But we're obviously using essentially the same mathematics. The function also can use a mixture of gaussians, just use an appropriate number of parameters (which obviously needs to be a multiple of 3)

Constructor Summary

Constructors

Constructor and Description
GaussianFittingFunction()

Method Summary

Methods

Modifier and Type	Method and Description
FittingFunctionResult	eval(double x, double[] params) compute the mixture of Gaussians at the given position

Methods inherited from class java.lang.Object

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

Constructor Detail

GaussianFittingFunction

public GaussianFittingFunction()

Method Detail

eval

public FittingFunctionResult eval(double x,
                         double[] params)

compute the mixture of Gaussians at the given position

Specified by:

eval in interface FittingFunction

Parameters:

x - Current coordinate

params - Function parameters parameters

Returns:

Array consisting of y value and parameter gradients