de.jstacs.classifiers.differentiableSequenceScoreBased
Class OptimizableFunction

java.lang.Object
  de.jstacs.algorithms.optimization.DifferentiableFunction
      de.jstacs.classifiers.differentiableSequenceScoreBased.OptimizableFunction

All Implemented Interfaces:

Function

Direct Known Subclasses:

AbstractOptimizableFunction

public abstract class OptimizableFunction
extends DifferentiableFunction

This is the main function for the ScoreClassifier.

Author:

Jens Keilwagen

See Also:

Optimizer

Nested Class Summary

static class

OptimizableFunction.KindOfParameter This enum defines the kinds of parameters that can be returned by the method getParameters(KindOfParameter).

Constructor Summary

OptimizableFunction()

Method Summary

abstract DataSet[]	getData() Returns the data for each class used in this OptimizableFunction.
abstract double[]	getParameters(OptimizableFunction.KindOfParameter kind) Returns some parameters that can be used for instance as start parameters.
abstract double[][]	getSequenceWeights() Returns the weights for each Sequence for each class used in this OptimizableFunction.
abstract void	reset() Resets the all objects and pre-computed values.
abstract void	setDataAndWeights(DataSet[] data, double[][] weights) This method sets the data set and the sequence weights to be used.
abstract void	setParams(double[] current) Sets the current values as parameters.

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

evaluateGradientOfFunction, findOneDimensionalMin

Methods inherited from class java.lang.Object

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

Methods inherited from interface de.jstacs.algorithms.optimization.Function

evaluateFunction, getDimensionOfScope

Constructor Detail

OptimizableFunction

public OptimizableFunction()

Method Detail

getParameters

public abstract double[] getParameters(OptimizableFunction.KindOfParameter kind)
                                throws Exception

Returns some parameters that can be used for instance as start parameters.

Parameters:

kind - the kind of the class parameters that will be returned

Returns:

some start parameters

Throws:

Exception - if something went wrong

setParams

public abstract void setParams(double[] current)
                        throws DimensionException

Sets the current values as parameters.

Parameters:

current - the current values

Throws:

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

reset

public abstract void reset()
                    throws Exception

Resets the all objects and pre-computed values.

Throws:

Exception - if something went wrong

getData

public abstract DataSet[] getData()

Returns the data for each class used in this OptimizableFunction.

Returns:

the data for each class

See Also:

getSequenceWeights()

getSequenceWeights

public abstract double[][] getSequenceWeights()

Returns the weights for each Sequence for each class used in this OptimizableFunction.

Returns:

the weights for each Sequence and each class

See Also:

getData()

setDataAndWeights

public abstract void setDataAndWeights(DataSet[] data,
                                       double[][] weights)
                                throws IllegalArgumentException

This method sets the data set and the sequence weights to be used. It also allows to do further preparation for the computation on this data.

Parameters:

data - the data sets

weights - the sequence weights for each sequence in each data set

Throws:

IllegalArgumentException - if the data or the weights can not be used