vec_math
Class AbstractMultidimensionalDataModel.LocalMM

java.lang.Object
  vec_math.AbstractMultidimensionalDataModel.LocalMM

All Implemented Interfaces:

Multidimensional

Enclosing class:

AbstractMultidimensionalDataModel

static class AbstractMultidimensionalDataModel.LocalMM
extends Object
implements Multidimensional

A localM-estimate model for the data. Depending on the choice of the Function rho, which is the negative logarithm of the measurement error distribution, we can either construct a normal-distributed M-estimate model ρ(z)=-1/2*z², or more robust estimates with a double-exponential or even Lorentzian distribution.

Field Summary

(package private) VectorG[]	measures
(package private) Multidimensional	rho
(package private) MultidimensionalDataModel	root
(package private) VectorG[]	sigma

Constructor Summary

private

AbstractMultidimensionalDataModel.LocalMM(Multidimensional f, MultidimensionalDataModel fit, VectorG[] yvec, VectorG[] sigvec)

Method Summary

int	dimension() The dimension equals the number of parameters in the model.
double	evaluate(VectorG a) We evaluate

Methods inherited from class java.lang.Object

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

Field Detail

rho

Multidimensional rho

root

MultidimensionalDataModel root

measures

VectorG[] measures

sigma

VectorG[] sigma

Constructor Detail

AbstractMultidimensionalDataModel.LocalMM

private AbstractMultidimensionalDataModel.LocalMM(Multidimensional f,
                                                  MultidimensionalDataModel fit,
                                                  VectorG[] yvec,
                                                  VectorG[] sigvec)

Method Detail

dimension

public int dimension()

The dimension equals the number of parameters in the model.

Specified by:

dimension in interface Multidimensional

evaluate

public double evaluate(VectorG a)

We evaluate

           Σ_i f^vec(measure_i-model(x_i))^2/sigma_i^2
           

If the vector-function f simply returns the squard length of the vector, we have a classical chi2 minimization in multidimensional.

Specified by:

evaluate in interface Multidimensional