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

java.lang.Object
  de.jstacs.sequenceScores.statisticalModels.trainable.AbstractTrainableStatisticalModel
      de.jstacs.sequenceScores.statisticalModels.trainable.discrete.DiscreteGraphicalTrainSM
          de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.InhomogeneousDGTrainSM
              de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.MEManager

All Implemented Interfaces:

InstantiableFromParameterSet, SequenceScore, StatisticalModel, TrainableStatisticalModel, Storable, Cloneable

Direct Known Subclasses:

FSMEManager

public abstract class MEManager
extends InhomogeneousDGTrainSM

This class is the super class for all maximum entropy models

Author:

Jens Keilwagen

Field Summary

protected MEM[]

factors The independent maximum entropy models.

Fields inherited from class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.InhomogeneousDGTrainSM

DEFAULT_STREAM, sostream

Fields inherited from class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.DiscreteGraphicalTrainSM

params, trained

Fields inherited from class de.jstacs.sequenceScores.statisticalModels.trainable.AbstractTrainableStatisticalModel

alphabets, length

Constructor Summary

MEManager(MEManagerParameterSet params)
Creates a new MEManager from a given MEManagerParameterSet.

MEManager(StringBuffer stringBuff)
The standard constructor for the interface Storable.

Method Summary

MEManager	clone() Follows the conventions of Object's clone()-method.
DataSet	emitDataSet(int n, int... lengths) This method returns a DataSet object containing artificial sequence(s).
protected MEM[]	getFactors(ArrayList<int[]> list, boolean reduce, ConstraintManager.Decomposition decomposition) This method returns an array of independent maximum entropy models parsed from the given constraints.
protected MEM[]	getFactors(String constraints, boolean reduce, ConstraintManager.Decomposition decomposition) This method returns an array of independent maximum entropy models parsed from the given constraints.
protected StringBuffer	getFurtherModelInfos() Returns further model information as a StringBuffer.
double	getLogPriorTerm() Returns a value that is proportional to the log of the prior.
double	getLogProbFor(Sequence sequence, int startpos, int endpos) Returns the logarithm of the probability of (a part of) the given sequence given the model.
NumericalResultSet	getNumericalCharacteristics() Returns the subset of numerical values that are also returned by SequenceScore.getCharacteristics().
String	getStructure() Returns a String representation of the underlying graph.
protected void	setFurtherModelInfos(StringBuffer xml) This method replaces the internal model information with those from a StringBuffer.
String	toString(NumberFormat nf) This method returns a String representation of the instance.
protected void	trainFactors(DataSet data, double[] weights) This method trains the internal MEM array, i.e., it optimizes the parameters of the underlying MEMConstraints.

Methods inherited from class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.InhomogeneousDGTrainSM

check, set, setOutputStream

Methods inherited from class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.DiscreteGraphicalTrainSM

fromXML, getCurrentParameterSet, getDescription, getESS, getXMLTag, isInitialized, toXML

Methods inherited from class de.jstacs.sequenceScores.statisticalModels.trainable.AbstractTrainableStatisticalModel

getAlphabetContainer, getCharacteristics, getLength, getLogProbFor, getLogProbFor, getLogScoreFor, getLogScoreFor, getLogScoreFor, getLogScoreFor, getLogScoreFor, getMaximalMarkovOrder, toString, train

Methods inherited from class java.lang.Object

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

Methods inherited from interface de.jstacs.sequenceScores.statisticalModels.trainable.TrainableStatisticalModel

train

Methods inherited from interface de.jstacs.sequenceScores.SequenceScore

getInstanceName

Field Detail

factors

protected MEM[] factors

The independent maximum entropy models.

Constructor Detail

MEManager

public MEManager(MEManagerParameterSet params)
          throws CloneNotSupportedException,
                 IllegalArgumentException,
                 NonParsableException

Creates a new MEManager from a given MEManagerParameterSet.

Parameters:

params - the given parameter set

Throws:

CloneNotSupportedException - if the parameter set could not be cloned

IllegalArgumentException - if the parameter set is not instantiated

NonParsableException - if the parameter set is not parsable

See Also:

InhomogeneousDGTrainSM.InhomogeneousDGTrainSM(de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.parameters.IDGTrainSMParameterSet)

MEManager

public MEManager(StringBuffer stringBuff)
          throws NonParsableException

The standard constructor for the interface Storable. Creates a new MEManager out of its XML representation.

Parameters:

stringBuff - the XML representation as StringBuffer

Throws:

NonParsableException - if the MEManager could not be reconstructed out of the XML representation (the StringBuffer could not be parsed)

See Also:

Storable, InhomogeneousDGTrainSM.InhomogeneousDGTrainSM(StringBuffer)

Method Detail

clone

public MEManager clone()
                throws CloneNotSupportedException

Description copied from class: AbstractTrainableStatisticalModel

Follows the conventions of Object's clone()-method.

Specified by:

clone in interface SequenceScore

Specified by:

clone in interface TrainableStatisticalModel

Overrides:

clone in class InhomogeneousDGTrainSM

Returns:

an object, that is a copy of the current AbstractTrainableStatisticalModel (the member-AlphabetContainer isn't deeply cloned since it is assumed to be immutable). The type of the returned object is defined by the class X directly inherited from AbstractTrainableStatisticalModel. Hence X's clone()-method should work as:
1. Object o = (X)super.clone();
2. all additional member variables of o defined by X that are not of simple data-types like int, double, ... have to be deeply copied
3. return o

Throws:

CloneNotSupportedException - if something went wrong while cloning

emitDataSet

public DataSet emitDataSet(int n,
                           int... lengths)
                    throws NotTrainedException,
                           Exception

Description copied from interface: StatisticalModel

This method returns a DataSet object containing artificial sequence(s).

There are two different possibilities to create a data set for a model with length 0 (homogeneous models).

1. emitDataSet( int n, int l ) should return a data set with n sequences of length l.
2. emitDataSet( int n, int[] l ) should return a data set with n sequences which have a sequence length corresponding to the entry in the given array l.

There are two different possibilities to create a data set for a model with length greater than 0 (inhomogeneous models).
emitDataSet( int n ) and emitDataSet( int n, null ) should return a data set with n sequences of length of the model ( SequenceScore.getLength()).

The standard implementation throws an Exception.

Specified by:

emitDataSet in interface StatisticalModel

Overrides:

emitDataSet in class AbstractTrainableStatisticalModel

Parameters:

n - the number of sequences that should be contained in the returned data set

lengths - the length of the sequences for a homogeneous model; for an inhomogeneous model this parameter should be null or an array of size 0.

Returns:

a DataSet containing the artificial sequence(s)

Throws:

NotTrainedException - if the model is not trained yet

Exception - if the emission did not succeed

See Also:

DataSet

getLogPriorTerm

public double getLogPriorTerm()
                       throws Exception

Description copied from interface: StatisticalModel

Returns a value that is proportional to the log of the prior. For maximum likelihood (ML) 0 should be returned.

Returns:

a value that is proportional to the log of the prior

Throws:

Exception - if something went wrong

getLogProbFor

public double getLogProbFor(Sequence sequence,
                            int startpos,
                            int endpos)
                     throws NotTrainedException,
                            Exception

Description copied from interface: StatisticalModel

Returns the logarithm of the probability of (a part of) the given sequence given the model. If at least one random variable is continuous the value of density function is returned.

It extends the possibility given by the method StatisticalModel.getLogProbFor(Sequence, int) by the fact, that the model could be e.g. homogeneous and therefore the length of the sequences, whose probability should be returned, is not fixed. Additionally, the end position of the part of the given sequence is given and the probability of the part from position startpos to endpos (inclusive) should be returned.
The length and the alphabets define the type of data that can be modeled and therefore both has to be checked.

Parameters:

sequence - the given sequence

startpos - the start position within the given sequence

endpos - the last position to be taken into account

Returns:

the logarithm of the probability or the value of the density function of (the part of) the given sequence given the model

Throws:

NotTrainedException - if the model is not trained yet

Exception - if the sequence could not be handled (e.g. startpos > , endpos > sequence.length, ...) by the model

getNumericalCharacteristics

public NumericalResultSet getNumericalCharacteristics()

Description copied from interface: SequenceScore

Returns the subset of numerical values that are also returned by SequenceScore.getCharacteristics().

Returns:

the numerical characteristics of the current instance

getStructure

public String getStructure()
                    throws NotTrainedException

Description copied from class: InhomogeneousDGTrainSM

Returns a String representation of the underlying graph.

Specified by:

getStructure in class InhomogeneousDGTrainSM

Returns:

a String representation of the underlying graph

Throws:

NotTrainedException - if the structure is not set, this can only be the case if the model is not trained

toString

public String toString(NumberFormat nf)

Description copied from interface: SequenceScore

This method returns a String representation of the instance.

Specified by:

toString in interface SequenceScore

Overrides:

toString in class DiscreteGraphicalTrainSM

Parameters:

nf - the NumberFormat for the String representation of parameters or probabilities

Returns:

a String representation of the instance

getFactors

protected MEM[] getFactors(String constraints,
                           boolean reduce,
                           ConstraintManager.Decomposition decomposition)

This method returns an array of independent maximum entropy models parsed from the given constraints.

Parameters:

constraints - the constraints to build the maximum entropy model

reduce - a switch whether redundant constraint should be removed

decomposition - a switch how to decompose the complete model if possible

Returns:

an array of independent maximum entropy models

getFactors

protected MEM[] getFactors(ArrayList<int[]> list,
                           boolean reduce,
                           ConstraintManager.Decomposition decomposition)

This method returns an array of independent maximum entropy models parsed from the given constraints.

Parameters:

list - a list of positions arrays that build the constraints

reduce - a switch whether redundant constraint should be removed

decomposition - a switch how to decompose the complete model if possible

Returns:

an array of independent maximum entropy models

getFurtherModelInfos

protected StringBuffer getFurtherModelInfos()

Description copied from class: DiscreteGraphicalTrainSM

Returns further model information as a StringBuffer.

Specified by:

getFurtherModelInfos in class DiscreteGraphicalTrainSM

Returns:

further model information like parameters of the distribution etc. in XML format

See Also:

DiscreteGraphicalTrainSM.toXML()

trainFactors

protected void trainFactors(DataSet data,
                            double[] weights)
                     throws Exception

This method trains the internal MEM array, i.e., it optimizes the parameters of the underlying MEMConstraints.

Parameters:

data - the data

weights - the weights for the data, can be null

Throws:

Exception - if some error occurs in the training process

setFurtherModelInfos

protected void setFurtherModelInfos(StringBuffer xml)
                             throws NonParsableException

Description copied from class: DiscreteGraphicalTrainSM

This method replaces the internal model information with those from a StringBuffer.

Specified by:

setFurtherModelInfos in class DiscreteGraphicalTrainSM

Parameters:

xml - contains the model information like parameters of the distribution etc. in XML format

Throws:

NonParsableException - if the StringBuffer could not be parsed

See Also:

DiscreteGraphicalTrainSM.fromXML(StringBuffer)