de.jstacs.classifier.scoringFunctionBased.cll
Class NormConditionalLogLikelihood

java.lang.Object
  de.jstacs.algorithms.optimization.DifferentiableFunction
      de.jstacs.classifier.scoringFunctionBased.OptimizableFunction
          de.jstacs.classifier.scoringFunctionBased.AbstractOptimizableFunction
              de.jstacs.classifier.scoringFunctionBased.cll.NormConditionalLogLikelihood

All Implemented Interfaces:

Function

public class NormConditionalLogLikelihood
extends AbstractOptimizableFunction

This class implements the normalized log conditional likelihood. It can be used to maximize parameters.

Author:

Jens Keilwagen

Nested Class Summary

Nested classes/interfaces inherited from class de.jstacs.classifier.scoringFunctionBased.OptimizableFunction

OptimizableFunction.KindOfParameter

Field Summary

Fields inherited from class de.jstacs.classifier.scoringFunctionBased.AbstractOptimizableFunction

cl, clazz, data, freeParams, logClazz, norm, shortcut, sum, weights

Constructor Summary

NormConditionalLogLikelihood(ScoringFunction[] score, Sample[] data, double[][] weights, boolean norm, boolean freeParams)
The constructor creates an instance of the NormConditionalLogLikelihood.

NormConditionalLogLikelihood(ScoringFunction[] score, Sample[] data, double[][] weights, LogPrior prior, boolean norm, boolean freeParams)
The constructor creates an instance of the NormConditionalLogLikelihood using the given prior.

Method Summary

double	evaluateFunction(double[] x) Evaluates the function at a certain vector (in mathematical sense) x.
double[]	evaluateGradientOfFunction(double[] x) Evaluates the gradient of a function at a certain vector (in mathematical sense) x.
int	getNumberOfStarts() Returns the number of starts that should be done for a good optimum.
void	getParameters(OptimizableFunction.KindOfParameter kind, double[] erg) This method enables the user to get the parameters without creating a new array.
void	reset(ScoringFunction[] funs) Resets the ScoringFunctions and all pre-computed values.
void	setParams(double[] params) Checks the dimension and sets the class parameters.

Methods inherited from class de.jstacs.classifier.scoringFunctionBased.AbstractOptimizableFunction

addTermToClassParameter, getClassParams, getData, getDimensionOfScope, getNumberOfStarts, getParameters, getSequenceWeights

Methods inherited from class de.jstacs.algorithms.optimization.DifferentiableFunction

findOneDimensionalMin

Methods inherited from class java.lang.Object

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

Constructor Detail

NormConditionalLogLikelihood

public NormConditionalLogLikelihood(ScoringFunction[] score,
                                    Sample[] data,
                                    double[][] weights,
                                    boolean norm,
                                    boolean freeParams)
                             throws IllegalArgumentException,
                                    WrongAlphabetException

The constructor creates an instance of the NormConditionalLogLikelihood.

Parameters:

score - the ScoringFunctions

data - the data

weights - the weights

norm - the switch for using the normalization (division by the number of sequences)

freeParams - the switch for using only the free parameters or all parameters in a ScoringFunction

Throws:

IllegalArgumentException - if the number of classes is not correct

WrongAlphabetException - if different alphabets are used

See Also:

NormConditionalLogLikelihood(ScoringFunction[], Sample[], double[][], LogPrior, boolean, boolean)

NormConditionalLogLikelihood

public NormConditionalLogLikelihood(ScoringFunction[] score,
                                    Sample[] data,
                                    double[][] weights,
                                    LogPrior prior,
                                    boolean norm,
                                    boolean freeParams)
                             throws IllegalArgumentException,
                                    WrongAlphabetException

The constructor creates an instance of the NormConditionalLogLikelihood using the given prior.

Parameters:

score - the ScoringFunctions

data - the data

weights - the weights

prior - the prior

norm - the switch for using the normalization (division by the number of sequences)

freeParams - the switch for using only the free parameters or all parameters in a ScoringFunction

Throws:

IllegalArgumentException - if the number of classes is not correct

WrongAlphabetException - if different alphabets are used

See Also:

AbstractOptimizableFunction.AbstractOptimizableFunction(Sample[], double[][], boolean, boolean)

Method Detail

evaluateGradientOfFunction

public double[] evaluateGradientOfFunction(double[] x)
                                    throws DimensionException,
                                           EvaluationException

Description copied from class: DifferentiableFunction

Evaluates the gradient of a function at a certain vector (in mathematical sense) x.

Specified by:

evaluateGradientOfFunction in class DifferentiableFunction

Parameters:

x - the current vector

Returns:

the evaluation of the gradient of a function, has dimension Function.getDimensionOfScope()

Throws:

DimensionException - if dim(x) != n, with f: R^n -> R

EvaluationException - if there was something wrong during the evaluation of the gradient

See Also:

Function.getDimensionOfScope()

evaluateFunction

public double evaluateFunction(double[] x)
                        throws DimensionException,
                               EvaluationException

Description copied from interface: Function

Evaluates the function at a certain vector (in mathematical sense) x.

Parameters:

x - the current vector

Returns:

the evaluation of the function

Throws:

DimensionException - if dim(x) != n, with f: R^n -> R

EvaluationException - if there was something wrong during the evaluation of the function

getParameters

public void getParameters(OptimizableFunction.KindOfParameter kind,
                          double[] erg)
                   throws Exception

Description copied from class: AbstractOptimizableFunction

This method enables the user to get the parameters without creating a new array.

Specified by:

getParameters in class AbstractOptimizableFunction

Parameters:

kind - the kind of the class parameters to be returned in erg

erg - the array for the start parameters

Throws:

Exception - if the array is null or does not have the correct length

See Also:

OptimizableFunction.getParameters(KindOfParameter)

setParams

public void setParams(double[] params)
               throws DimensionException

Description copied from class: AbstractOptimizableFunction

Checks the dimension and sets the class parameters.

Overrides:

setParams in class AbstractOptimizableFunction

Parameters:

params - the current values

Throws:

DimensionException - if the dimension of the current values does not match with the dimension of the internal parameters

getNumberOfStarts

public int getNumberOfStarts()

Description copied from class: OptimizableFunction

Returns the number of starts that should be done for a good optimum. For concave or convex functions this number should be 1.

Specified by:

getNumberOfStarts in class OptimizableFunction

Returns:

the number of times the Optimizer should be started to get a good optimum

See Also:

Optimizer

reset

public void reset(ScoringFunction[] funs)
           throws Exception

Description copied from class: OptimizableFunction

Resets the ScoringFunctions and all pre-computed values.

Specified by:

reset in class OptimizableFunction

Parameters:

funs - the array of ScoringFunctions

Throws:

Exception - if something went wrong