de.jstacs.sequenceScores.statisticalModels.trainable
Class CompositeTrainSM

java.lang.Object
  de.jstacs.sequenceScores.statisticalModels.trainable.AbstractTrainableStatisticalModel
      de.jstacs.sequenceScores.statisticalModels.trainable.CompositeTrainSM

All Implemented Interfaces:

SequenceScore, StatisticalModel, TrainableStatisticalModel, Storable, Cloneable

public class CompositeTrainSM
extends AbstractTrainableStatisticalModel

This class is for modelling sequences by modelling the different positions of the each sequence by different models. For instance one can use this class to model a sequence of subsequences, where each subsequence is modeled by a different model.

Author:

Jens Keilwagen

Field Summary

protected int[][]	lengths The length for each component.
protected TrainableStatisticalModel[]	models The models for the components
protected int[][]	starts The start indices.

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

alphabets, length

Constructor Summary

CompositeTrainSM(AlphabetContainer alphabets, int[] assignment, TrainableStatisticalModel... models)
Creates a new CompositeTrainSM.

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

Method Summary

CompositeTrainSM	clone() Follows the conventions of Object's clone()-method.
void	fromXML(StringBuffer representation) This method should only be used by the constructor that works on a StringBuffer.
ResultSet	getCharacteristics() Returns some information characterizing or describing the current instance.
String	getInstanceName() Should return a short instance name such as iMM(0), BN(2), ...
int[]	getLengthOfModels() This method returns the length of the models in the CompositeTrainSM.
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.
byte	getMaximalMarkovOrder() This method returns the maximal used Markov order, if possible.
TrainableStatisticalModel[]	getModels() Returns the a deep copy of the models.
int	getNumberOfModels() This method returns the number of models in the CompositeTrainSM.
NumericalResultSet	getNumericalCharacteristics() Returns the subset of numerical values that are also returned by SequenceScore.getCharacteristics().
boolean	isInitialized() This method can be used to determine whether the instance is initialized.
String	toString() Should give a simple representation (text) of the model as String.
StringBuffer	toXML() This method returns an XML representation as StringBuffer of an instance of the implementing class.
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.AbstractTrainableStatisticalModel

check, emitDataSet, getAlphabetContainer, getLength, getLogProbFor, getLogProbFor, getLogScoreFor, getLogScoreFor, getLogScoreFor, getLogScoreFor, getLogScoreFor, train

Methods inherited from class java.lang.Object

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

Field Detail

models

protected TrainableStatisticalModel[] models

The models for the components

starts

protected int[][] starts

The start indices.

lengths

protected int[][] lengths

The length for each component.

Constructor Detail

CompositeTrainSM

public CompositeTrainSM(AlphabetContainer alphabets,
                        int[] assignment,
                        TrainableStatisticalModel... models)
                 throws WrongAlphabetException,
                        CloneNotSupportedException

Creates a new CompositeTrainSM.

Parameters:

alphabets - the alphabets used in the models

assignment - an array assigning each position to a model

models - the single models

Throws:

IllegalArgumentException - if something is wrong with the assignment of the models

WrongAlphabetException - if (parts of) the alphabet does not match with the alphabets of the models

CloneNotSupportedException - if the models could not be cloned

CompositeTrainSM

public CompositeTrainSM(StringBuffer stringBuff)
                 throws NonParsableException

The standard constructor for the interface Storable. Creates a new CompositeTrainSM out of a StringBuffer.

Parameters:

stringBuff - the StringBuffer to be parsed

Throws:

NonParsableException - if the StringBuffer is not parsable

Method Detail

clone

public CompositeTrainSM 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 AbstractTrainableStatisticalModel

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

getCharacteristics

public ResultSet getCharacteristics()
                             throws Exception

Description copied from interface: SequenceScore

Returns some information characterizing or describing the current instance. This could be e.g. the number of edges for a Bayesian network or an image showing some representation of the instance. The set of characteristics should always include the XML-representation of the instance. The corresponding result type is StorableResult.

Specified by:

getCharacteristics in interface SequenceScore

Overrides:

getCharacteristics in class AbstractTrainableStatisticalModel

Returns:

the characteristics of the current instance

Throws:

Exception - if some of the characteristics could not be defined

See Also:

StorableResult

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

getLengthOfModels

public int[] getLengthOfModels()

This method returns the length of the models in the CompositeTrainSM.

Returns:

the length of the models as an array

getMaximalMarkovOrder

public byte getMaximalMarkovOrder()
                           throws UnsupportedOperationException

Description copied from interface: StatisticalModel

This method returns the maximal used Markov order, if possible.

Specified by:

getMaximalMarkovOrder in interface StatisticalModel

Overrides:

getMaximalMarkovOrder in class AbstractTrainableStatisticalModel

Returns:

maximal used Markov order

Throws:

UnsupportedOperationException - if the model can't give a proper answer

getNumericalCharacteristics

public NumericalResultSet getNumericalCharacteristics()
                                               throws Exception

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

Throws:

Exception - if some of the characteristics could not be defined

getModels

public TrainableStatisticalModel[] getModels()
                                      throws CloneNotSupportedException

Returns the a deep copy of the models.

Returns:

an array of AbstractTrainableStatisticalModels

Throws:

CloneNotSupportedException - if at least one of the models could not be cloned

getNumberOfModels

public int getNumberOfModels()

This method returns the number of models in the CompositeTrainSM.

Returns:

the number of models

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

isInitialized

public boolean isInitialized()

Description copied from interface: SequenceScore

This method can be used to determine whether the instance is initialized. If the instance is initialized you should be able to invoke SequenceScore.getLogScoreFor(Sequence).

Returns:

true if the instance is initialized, false otherwise

fromXML

public void fromXML(StringBuffer representation)
             throws NonParsableException

Description copied from class: AbstractTrainableStatisticalModel

This method should only be used by the constructor that works on a StringBuffer. It is the counter part of Storable.toXML().

Specified by:

fromXML in class AbstractTrainableStatisticalModel

Parameters:

representation - the XML representation of the model

Throws:

NonParsableException - if the StringBuffer is not parsable or the representation is conflicting

See Also:

AbstractTrainableStatisticalModel.AbstractTrainableStatisticalModel(StringBuffer)

toXML

public StringBuffer toXML()

Description copied from interface: Storable

This method returns an XML representation as StringBuffer of an instance of the implementing class.

Returns:

the XML representation

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 sample 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 sample do not match)

See Also:

DataSet.getElementAt(int), DataSet.ElementEnumerator

toString

public String toString()

Description copied from interface: TrainableStatisticalModel

Should give a simple representation (text) of the model as String.

Specified by:

toString in interface TrainableStatisticalModel

Overrides:

toString in class Object

Returns:

the representation as String