de.jstacs.classifiers.differentiableSequenceScoreBased

Class AbstractOptimizableFunction

java.lang.Object

de.jstacs.algorithms.optimization.DifferentiableFunction

de.jstacs.classifiers.differentiableSequenceScoreBased.OptimizableFunction

de.jstacs.classifiers.differentiableSequenceScoreBased.AbstractOptimizableFunction

All Implemented Interfaces:

Function

Direct Known Subclasses:

AbstractMultiThreadedOptimizableFunction

public abstract class AbstractOptimizableFunction
extends OptimizableFunction

This class extends OptimizableFunction and implements some common methods.

Author:

Jens Keilwagen

Nested Class Summary

Nested classes/interfaces inherited from class de.jstacs.classifiers.differentiableSequenceScoreBased.OptimizableFunction

OptimizableFunction.KindOfParameter

Field Summary

Fields

Modifier and Type	Field and Description
protected int	cl The number of different classes.
protected double[]	clazz The class parameters.
protected DataSet[]	data The data that is used to evaluate this function.
protected boolean	freeParams Indicates whether only the free parameters or all should be used.
protected double[]	logClazz The logarithm of the class parameters.
protected boolean	norm Indicates whether a normalization should be done or not.
protected double[]	sum The sums of the weighted data per class and additional the total weight sum.
protected double[][]	weights The weights for the data.

Constructor Summary

Constructors

Modifier	Constructor and Description
protected	AbstractOptimizableFunction(DataSet[] data, double[][] weights, boolean norm, boolean freeParams) The constructor creates an instance using the given weighted data.

Method Summary

Modifier and Type	Method and Description
DataSet[]	getData() Returns the data for each class used in this OptimizableFunction.
double[]	getParameters(OptimizableFunction.KindOfParameter kind) Returns some parameters that can be used for instance as start parameters.
abstract void	getParameters(OptimizableFunction.KindOfParameter kind, double[] erg) This method enables the user to get the parameters without creating a new array.
double[][]	getSequenceWeights() Returns the weights for each Sequence for each class used in this OptimizableFunction.
void	setDataAndWeights(DataSet[] data, double[][] weights) This method sets the data set and the sequence weights to be used.

Methods inherited from class de.jstacs.classifiers.differentiableSequenceScoreBased.OptimizableFunction

reset, setParams

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

Field Detail

data

protected DataSet[] data

The data that is used to evaluate this function.

weights

protected double[][] weights

The weights for the data.

See Also:

data

clazz

protected double[] clazz

The class parameters.

logClazz

protected double[] logClazz

The logarithm of the class parameters.

See Also:

clazz

sum

protected double[] sum

The sums of the weighted data per class and additional the total weight sum.

See Also:

data, weights

cl

protected int cl

The number of different classes.

norm

protected boolean norm

Indicates whether a normalization should be done or not.

freeParams

protected boolean freeParams

Indicates whether only the free parameters or all should be used.

Constructor Detail

AbstractOptimizableFunction

protected AbstractOptimizableFunction(DataSet[] data,
                                      double[][] weights,
                                      boolean norm,
                                      boolean freeParams)
                               throws IllegalArgumentException

The constructor creates an instance using the given weighted data.

Parameters:

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

Throws:

IllegalArgumentException - if the number of classes or the dimension of the weights is not correct

Method Detail

setDataAndWeights

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

Description copied from class: OptimizableFunction

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.

Specified by:

setDataAndWeights in class OptimizableFunction

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

getParameters

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

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

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)

getParameters

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

Description copied from class: OptimizableFunction

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

Specified by:

getParameters in class OptimizableFunction

Parameters:

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

Returns:

some start parameters

Throws:

Exception - if something went wrong

getData

public DataSet[] getData()

Description copied from class: OptimizableFunction

Returns the data for each class used in this OptimizableFunction.

Specified by:

getData in class OptimizableFunction

Returns:

the data for each class

See Also:

OptimizableFunction.getSequenceWeights()

getSequenceWeights

public double[][] getSequenceWeights()

Description copied from class: OptimizableFunction

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

Specified by:

getSequenceWeights in class OptimizableFunction

Returns:

the weights for each Sequence and each class

See Also:

OptimizableFunction.getData()