de.jstacs.models
Class CompositeModel

java.lang.Object
  de.jstacs.models.AbstractModel
      de.jstacs.models.CompositeModel

All Implemented Interfaces:

Model, Storable, Cloneable

public class CompositeModel
extends AbstractModel

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 Model[]	models The models for the components
protected int[][]	starts The start indices.

Fields inherited from class de.jstacs.models.AbstractModel

alphabets, length

Constructor Summary

CompositeModel(AlphabetContainer alphabets, int[] assignment, Model... models)
Creates a new CompositeModel.

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

Method Summary

CompositeModel	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 of the model.
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 CompositeModel.
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.
Model[]	getModels() Returns the a deep copy of the models.
int	getNumberOfModels() This method returns the number of models in the CompositeModel.
NumericalResultSet	getNumericalCharacteristics() Returns the subset of numerical values that are also returned by Model.getCharacteristics().
double	getProbFor(Sequence sequence, int startpos, int endpos) Returns the probability of (a part of) the given sequence given the model.
boolean	isTrained() Returns true if the model has been trained successfully, false otherwise.
protected void	set(AlphabetContainer abc) This method should only be invoked by the method AbstractModel.setNewAlphabetContainerInstance(AlphabetContainer) and not be made public.
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(Sample data, double[] weights) Trains the Model object given the data as Sample using the specified weights.

Methods inherited from class de.jstacs.models.AbstractModel

emitSample, getAlphabetContainer, getLength, getLogProbFor, getLogProbFor, getLogProbFor, getLogProbFor, getPriorTerm, getProbFor, getProbFor, setNewAlphabetContainerInstance, train

Methods inherited from class java.lang.Object

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

Field Detail

models

protected Model[] models

The models for the components

starts

protected int[][] starts

The start indices.

lengths

protected int[][] lengths

The length for each component.

Constructor Detail

CompositeModel

public CompositeModel(AlphabetContainer alphabets,
                      int[] assignment,
                      Model... models)
               throws WrongAlphabetException,
                      CloneNotSupportedException

Creates a new CompositeModel.

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

CompositeModel

public CompositeModel(StringBuffer stringBuff)
               throws NonParsableException

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

Parameters:

stringBuff - the StringBuffer to be parsed

Throws:

NonParsableException - if the StringBuffer is not parsable

Method Detail

clone

public CompositeModel clone()
                     throws CloneNotSupportedException

Description copied from class: AbstractModel

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

Specified by:

clone in interface Model

Overrides:

clone in class AbstractModel

Returns:

an object, that is a copy of the current AbstractModel (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 AbstractModel. 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: Model

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

Specified by:

getCharacteristics in interface Model

Overrides:

getCharacteristics in class AbstractModel

Returns:

the characteristics of the current instance of the model

Throws:

Exception - if some of the characteristics could not be defined

See Also:

StorableResult

getInstanceName

public String getInstanceName()

Description copied from interface: Model

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 CompositeModel.

Returns:

the length of the models as an array

getMaximalMarkovOrder

public byte getMaximalMarkovOrder()
                           throws UnsupportedOperationException

Description copied from interface: Model

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

Specified by:

getMaximalMarkovOrder in interface Model

Overrides:

getMaximalMarkovOrder in class AbstractModel

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: Model

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

Returns:

the numerical characteristics of the current instance of the model

Throws:

Exception - if some of the characteristics could not be defined

getModels

public Model[] getModels()
                  throws CloneNotSupportedException

Returns the a deep copy of the models.

Returns:

an array of AbstractModels

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 CompositeModel.

Returns:

the number of models

getLogPriorTerm

public double getLogPriorTerm()
                       throws Exception

Description copied from interface: Model

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

See Also:

Model.getPriorTerm()

getProbFor

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

Description copied from interface: Model

Returns 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 Model.getProbFor(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 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, endpos > sequence.length, ...) by the model

getLogProbFor

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

Description copied from interface: Model

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.

For more details see Model.getProbFor(Sequence, int, int)

Specified by:

getLogProbFor in interface Model

Overrides:

getLogProbFor in class AbstractModel

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

See Also:

Model.getProbFor(Sequence, int, int)

isTrained

public boolean isTrained()

Description copied from interface: Model

Returns true if the model has been trained successfully, false otherwise.

Returns:

true if the model has been trained successfully, false otherwise

fromXML

public void fromXML(StringBuffer representation)
             throws NonParsableException

Description copied from class: AbstractModel

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 AbstractModel

Parameters:

representation - the XML representation of the model

Throws:

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

See Also:

AbstractModel.AbstractModel(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(Sample data,
                  double[] weights)
           throws Exception

Description copied from interface: Model

Trains the Model object given the data as Sample 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 Sample

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:

Sample.getElementAt(int), Sample.ElementEnumerator

toString

public String toString()

Description copied from interface: Model

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

Specified by:

toString in interface Model

Overrides:

toString in class Object

Returns:

the representation as String

set

protected void set(AlphabetContainer abc)

Description copied from class: AbstractModel

This method should only be invoked by the method AbstractModel.setNewAlphabetContainerInstance(AlphabetContainer) and not be made public.

It enables you to do more with the method AbstractModel.setNewAlphabetContainerInstance(AlphabetContainer), e.g. setting a new AlphabetContainer instance for subcomponents.

Overrides:

set in class AbstractModel

Parameters:

abc - the new instance