vec_math
Class LevenbergMarquardt.SinusModel

java.lang.Object
  vec_math.AbstractDataModel
      vec_math.AbstractGradientModel
          vec_math.LevenbergMarquardt.SinusModel

All Implemented Interfaces:

DataModel, GradientModel

Enclosing class:

LevenbergMarquardt

static final class LevenbergMarquardt.SinusModel
extends AbstractGradientModel

Nested Class Summary

Nested classes/interfaces inherited from class vec_math.AbstractDataModel

AbstractDataModel.Construct, AbstractDataModel.LocalM

Field Summary

Fields inherited from class vec_math.AbstractDataModel

sigma, x, y

Constructor Summary

protected

LevenbergMarquardt.SinusModel(VectorG[] times, double[] y, double[] err)

Method Summary

double	evaluateModel(VectorG a, VectorG t) Evaluates the data model on a single point in the independant parameter space, normally the time.
Matrix	getGradientMatrix(VectorG a) Returns the gradient of the model for all measures.
int	getParameterCount() The number of model parameters should be less than the number of data points in the model, otherwise you fit a underdetermined problem.

Methods inherited from class vec_math.AbstractGradientModel

check, check, getNegativeChi2HalfGradient, getNegativeChi2HalfGradient, getPseudoHessian, getPseudoHessian, LevenbergMarquardtSolver

Methods inherited from class vec_math.AbstractDataModel

getChiSquareModel, getChiSquareModel, getLorentzianModel, getMeasureCount, getMeasurementErrors, getMeasures, getModel, getModel, getResiduals, getResiduals, getRms, getRobustModel, getTimes, getTotalErrors, LorentzianSolver, RobustSolver, SimplexSolver

Methods inherited from class java.lang.Object

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

Methods inherited from interface vec_math.DataModel

getMeasureCount, getMeasurementErrors, getMeasures, getModel, getResiduals, getTimes, getTotalErrors

Constructor Detail

LevenbergMarquardt.SinusModel

protected LevenbergMarquardt.SinusModel(VectorG[] times,
                                        double[] y,
                                        double[] err)

Method Detail

getParameterCount

public int getParameterCount()

Description copied from interface: DataModel

The number of model parameters should be less than the number of data points in the model, otherwise you fit a underdetermined problem.

Returns:

The number of parameters in the model. This equals the dimension of the input vector in DataModel.getModel(vec_math.VectorG).

evaluateModel

public double evaluateModel(VectorG a,
                            VectorG t)

Description copied from interface: DataModel

Evaluates the data model on a single point in the independant parameter space, normally the time. Note that the model can be evaluated also at times not identically to the measurement times.

Parameters:

a - The model parameters

t - The (one)dimensional vector of independant variables.

getGradientMatrix

public Matrix getGradientMatrix(VectorG a)

Description copied from interface: GradientModel

Returns the gradient of the model for all measures. The measure index is the row of the Matrix, the columns are the derivatives of the model with respect to the parameters. Thus, the Matrix.columns() equals DataModel.getParameterCount(), and Matrix.rows() equals DataModel.getMeasureCount().