de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous
Class StructureLearner

java.lang.Object
  de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.StructureLearner

public class StructureLearner
extends Object

This class can be used to learn the structure of any discrete model.

Author:

Jens Keilwagen

Nested Class Summary

static class	StructureLearner.LearningType This enum defines the different types of learning that are possible with the StructureLearner.
static class	StructureLearner.ModelType This enum defines the different types of models that can be learned with the StructureLearner.

Constructor Summary

StructureLearner(AlphabetContainer con, int length)
Creates a StructureLearner with equivalent sample size (ess) = 0.

StructureLearner(AlphabetContainer con, int length, double ess)
Creates a new StructureLearner for a given AlphabetContainer, a given length and a given equivalent sample size (ess).

Method Summary

AlphabetContainer	getAlphabetContainer() This method returns the AlphabetContainer of the StructureLearner.
double	getEss() This method returns the ess (equivalent sample size) of the StructureLearner.
int[][]	getStructure(DataSet data, double[] weights, StructureLearner.ModelType model, byte order, StructureLearner.LearningType method) This method finds the optimal structure of a model by using a given learning method (in some sense).
static int[][]	getStructure(Tensor t, StructureLearner.ModelType model, byte order) This method can be used to determine the optimal structure of a model.
SymmetricTensor	getTensor(DataSet data, double[] weights, byte order, StructureLearner.LearningType method) This method can be used to compute a Tensor that can be used to determine the optimal structure.
void	setESS(double ess) This method sets the ess (equivalent sample size) of the StructureLearner.

Methods inherited from class java.lang.Object

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

Constructor Detail

StructureLearner

public StructureLearner(AlphabetContainer con,
                        int length,
                        double ess)
                 throws IllegalArgumentException

Creates a new StructureLearner for a given AlphabetContainer, a given length and a given equivalent sample size (ess).

Parameters:

con - the alphabets this instance should use

length - the length

ess - the ess (equivalent sample size, has to be non-negative)

Throws:

IllegalArgumentException - if the AlphabetContainer is not discrete, the length is not matching with the AlphabetContainer or the ess is below 0

StructureLearner

public StructureLearner(AlphabetContainer con,
                        int length)
                 throws IllegalArgumentException

Creates a StructureLearner with equivalent sample size (ess) = 0.

Parameters:

con - the alphabets this instance should use

length - the length

Throws:

IllegalArgumentException - if the AlphabetContainer is not discrete or the length is not matching with the AlphabetContainer

See Also:

StructureLearner(AlphabetContainer, int, double)

Method Detail

getAlphabetContainer

public AlphabetContainer getAlphabetContainer()

This method returns the AlphabetContainer of the StructureLearner.

Returns:

the AlphabetContainer of the StructureLearner

getEss

public double getEss()

This method returns the ess (equivalent sample size) of the StructureLearner.

Returns:

the ess of the StructureLearner

setESS

public void setESS(double ess)
            throws IllegalArgumentException

This method sets the ess (equivalent sample size) of the StructureLearner.

Parameters:

ess - the ess of the StructureLearner

Throws:

IllegalArgumentException - if ess < 0

getStructure

public int[][] getStructure(DataSet data,
                            double[] weights,
                            StructureLearner.ModelType model,
                            byte order,
                            StructureLearner.LearningType method)
                     throws Exception

This method finds the optimal structure of a model by using a given learning method (in some sense).

Parameters:

data - the DataSet

weights - the weights

model - the kind of model

order - the Markov order

method - the learning method

Returns:

the optimal structure of the specified model (in some sense)

Throws:

Exception - if something went wrong

See Also:

getTensor(DataSet, double[], byte, LearningType), getStructure(Tensor, ModelType, byte)

getStructure

public static int[][] getStructure(Tensor t,
                                   StructureLearner.ModelType model,
                                   byte order)
                            throws Exception

This method can be used to determine the optimal structure of a model.

Parameters:

t - the tensor containing all relevant weights (includes the learning method for the structure)

model - the model type

order - the model order

Returns:

the optimal structure of the specified model

Throws:

Exception - if something in the algorithm went wrong

See Also:

getTensor(DataSet, double[], byte, LearningType)

getTensor

public SymmetricTensor getTensor(DataSet data,
                                 double[] weights,
                                 byte order,
                                 StructureLearner.LearningType method)
                          throws IllegalArgumentException,
                                 WrongAlphabetException

This method can be used to compute a Tensor that can be used to determine the optimal structure.

Parameters:

data - the data

weights - the weights

order - the Markov order

method - the learning type

Returns:

a tensor containing all values the are necessary to determine the optimal structure (includes the learning method for the structure)

Throws:

IllegalArgumentException - if something is wrong with the given arguments

WrongAlphabetException - if the AlphabetContainer of the data is not correct

See Also:

getStructure(Tensor, ModelType, byte)