de.jstacs.sequenceScores.statisticalModels.trainable.discrete.homogeneous

Class HomogeneousMM

java.lang.Object

de.jstacs.sequenceScores.statisticalModels.trainable.AbstractTrainableStatisticalModel

de.jstacs.sequenceScores.statisticalModels.trainable.discrete.DiscreteGraphicalTrainSM

de.jstacs.sequenceScores.statisticalModels.trainable.discrete.homogeneous.HomogeneousTrainSM

de.jstacs.sequenceScores.statisticalModels.trainable.discrete.homogeneous.HomogeneousMM

All Implemented Interfaces:

InstantiableFromParameterSet, SequenceScore, StatisticalModel, TrainableStatisticalModel, Storable, Cloneable

public class HomogeneousMM
extends HomogeneousTrainSM

This class implements homogeneous Markov models (hMM) of arbitrary order.

Author:

Jens Keilwagen

See Also:

HomMMParameterSet

Nested Class Summary

Nested classes/interfaces inherited from class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.homogeneous.HomogeneousTrainSM

HomogeneousTrainSM.HomCondProb

Field Summary

Fields inherited from class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.homogeneous.HomogeneousTrainSM

order, powers

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

Constructors

Constructor and Description
HomogeneousMM(HomMMParameterSet params) Creates a new homogeneous Markov model from a parameter set.
HomogeneousMM(StringBuffer stringBuff) The standard constructor for the interface Storable.

Method Summary

Modifier and Type	Method and Description
HomogeneousMM	clone() Follows the conventions of Object's clone()-method.
protected StringBuffer	getFurtherModelInfos() Returns further model information as a StringBuffer.
String	getInstanceName() Should return a short instance name such as iMM(0), BN(2), ...
double	getLogPriorTerm() Returns a value that is proportional to the log of the prior.
protected Sequence	getRandomSequence(Random r, int length) This method creates a random Sequence from a trained homogeneous model.
protected String	getXMLTag() Returns the XML tag that is used for this model in DiscreteGraphicalTrainSM.fromXML(StringBuffer) and DiscreteGraphicalTrainSM.toXML().
protected double	logProbFor(Sequence sequence, int startpos, int endpos) This method computes the logarithm of the probability of the given Sequence in the given interval.
protected void	set(DGTrainSMParameterSet params, boolean trained) Sets the parameters as internal parameters and does some essential computations.
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.
void	train(DataSet[] data, double[][] weights) Trains the homogeneous model using an array of weighted DataSets.
void	train(DataSet data, double[] weights) Trains the TrainableStatisticalModel object given the data as DataSet using the specified weights.

Methods inherited from class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.homogeneous.HomogeneousTrainSM

check, chooseFromDistr, cloneHomProb, emitDataSet, getLogProbFor, getMaximalMarkovOrder, getNumericalCharacteristics, train

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

fromXML, getCurrentParameterSet, getDescription, getESS, isInitialized, toXML

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

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

Methods inherited from class java.lang.Object

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

Constructor Detail

HomogeneousMM

public HomogeneousMM(HomMMParameterSet params)
              throws CloneNotSupportedException,
                     IllegalArgumentException,
                     NonParsableException

Creates a new homogeneous Markov model from a parameter set.

Parameters:

params - the 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:

HomMMParameterSet, HomogeneousTrainSM.HomogeneousTrainSM(de.jstacs.sequenceScores.statisticalModels.trainable.discrete.homogeneous.parameters.HomogeneousTrainSMParameterSet)

HomogeneousMM

public HomogeneousMM(StringBuffer stringBuff)
              throws NonParsableException

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

Parameters:

stringBuff - the XML representation as StringBuffer

Throws:

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

See Also:

Storable

Method Detail

clone

public HomogeneousMM 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 DiscreteGraphicalTrainSM

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

getRandomSequence

protected Sequence getRandomSequence(Random r,
                                     int length)
                              throws WrongAlphabetException,
                                     WrongSequenceTypeException

Description copied from class: HomogeneousTrainSM

This method creates a random Sequence from a trained homogeneous model.

Specified by:

getRandomSequence in class HomogeneousTrainSM

Parameters:

r - the random generator

length - the length of the Sequence

Returns:

the created Sequence

Throws:

WrongAlphabetException - if something is wrong with the alphabet

WrongSequenceTypeException - if the Sequence type is not suitable (for the AlphabetContainer)

getInstanceName

public String getInstanceName()

Description copied from interface: SequenceScore

Should return a short instance name such as iMM(0), BN(2), ...

Returns:

a short instance name

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

logProbFor

protected double logProbFor(Sequence sequence,
                            int startpos,
                            int endpos)

Description copied from class: HomogeneousTrainSM

This method computes the logarithm of the probability of the given Sequence in the given interval. The method is only used in StatisticalModel.getLogProbFor(Sequence, int, int) after the method HomogeneousTrainSM.check(Sequence, int, int) has been invoked.

Specified by:

logProbFor in class HomogeneousTrainSM

Parameters:

sequence - the Sequence

startpos - the start position within the Sequence

endpos - the end position within the Sequence

Returns:

the logarithm of the probability for the given subsequence

See Also:

HomogeneousTrainSM.check(Sequence, int, int), StatisticalModel.getLogProbFor(Sequence, int, int)

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

train

public void train(DataSet data,
                  double[] weights)
           throws Exception

Description copied from interface: TrainableStatisticalModel

Trains the TrainableStatisticalModel object given the data as DataSet using the specified weights. The weight at position i belongs to the element at position i. So the array weight should have the number of sequences in the data set as dimension. (Optionally it is possible to use weight == null if all weights have the value one.)
This method should work non-incrementally. That means the result of the following series: train(data1); train(data2) should be a fully trained model over data2 and not over data1+data2. All parameters of the model were given by the call of the constructor.

Parameters:

data - the given sequences as DataSet

weights - the weights of the elements, each weight should be non-negative

Throws:

Exception - if the training did not succeed (e.g. the dimension of weights and the number of sequences in the data set do not match)

See Also:

DataSet.getElementAt(int), DataSet.ElementEnumerator

train

public void train(DataSet[] data,
                  double[][] weights)
           throws Exception

Description copied from class: HomogeneousTrainSM

Trains the homogeneous model using an array of weighted DataSets. The Sequence weights in weights[i] are for the DataSet in data[i].

Specified by:

train in class HomogeneousTrainSM

Parameters:

data - the given DataSets

weights - the weights

Throws:

Exception - if something went wrong, furthermore data.length has to be weights.length

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()

getXMLTag

protected String getXMLTag()

Description copied from class: DiscreteGraphicalTrainSM

Returns the XML tag that is used for this model in DiscreteGraphicalTrainSM.fromXML(StringBuffer) and DiscreteGraphicalTrainSM.toXML().

Specified by:

getXMLTag in class DiscreteGraphicalTrainSM

Returns:

the XML tag that is used in DiscreteGraphicalTrainSM.fromXML(StringBuffer) and DiscreteGraphicalTrainSM.toXML()

See Also:

DiscreteGraphicalTrainSM.fromXML(StringBuffer), DiscreteGraphicalTrainSM.toXML()

set

protected void set(DGTrainSMParameterSet params,
                   boolean trained)
            throws CloneNotSupportedException,
                   NonParsableException

Description copied from class: DiscreteGraphicalTrainSM

Sets the parameters as internal parameters and does some essential computations. Used in fromParameterSet-methods.

Overrides:

set in class HomogeneousTrainSM

Parameters:

params - the new ParameterSet

trained - indicates if the model is trained or not

Throws:

CloneNotSupportedException - if the parameter set could not be cloned

NonParsableException - if the parameters of the model could not be parsed

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)