E nvironment for Deve L oping K DD-Applications Supported by I ndex-Structures

## 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 GaussianFittingFunctionextends Objectimplements FittingFunction`

Gaussian function for parameter fitting Based mostly on fgauss in "Numerical Recpies in C". However we've removed some small 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. This could of course have been handled by an appropriate wrapper instead. They are also arranged differently: the book uses amplitude, position, width whereas we use mean, stddev, scaling 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)

Author:
Erich Schubert

Field Summary
`(package private) static double` `Sqrt2PI`
precomputed constant value of Sqrt(2*PI)

Constructor Summary
`GaussianFittingFunction()`

Method Summary
` 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`

Field Detail

### Sqrt2PI

`static final double Sqrt2PI`
precomputed constant value of Sqrt(2*PI)

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

 Release 0.2 (2009-07-06_1820)