de.jstacs.models.discrete.homogeneous
Class HomogeneousModel

java.lang.Object
  de.jstacs.models.AbstractModel
      de.jstacs.models.discrete.DiscreteGraphicalModel
          de.jstacs.models.discrete.homogeneous.HomogeneousModel

All Implemented Interfaces:

InstantiableFromParameterSet, Model, Storable, Cloneable

Direct Known Subclasses:

HomogeneousMM

public abstract class HomogeneousModel
extends DiscreteGraphicalModel

This class implements homogeneous models of arbitrary order.

Author:

Jens Keilwagen

See Also:

HomogeneousModelParameterSet

Nested Class Summary

protected class

HomogeneousModel.HomCondProb This class handles the (conditional) probabilities of a homogeneous model in a fast way.

Field Summary

protected byte	order The order of the model.
protected int[]	powers The powers of the alphabet length.

Fields inherited from class de.jstacs.models.discrete.DiscreteGraphicalModel

params, trained

Fields inherited from class de.jstacs.models.AbstractModel

alphabets, length

Constructor Summary

HomogeneousModel(HomogeneousModelParameterSet params)
Creates a homogeneous model from a parameter set.

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

Method Summary

protected void	check(Sequence sequence, int startpos, int endpos) Checks some constraints, these are in general conditions on the AlphabetContainer of a (sub)Sequence between startpos und endpos.
protected int	chooseFromDistr(Constraint distr, int start, int end, double randNo) Chooses a value in [0,end-start] according to the distribution encoded in the frequencies of distr between the indices start and end.
protected HomogeneousModel.HomCondProb[]	cloneHomProb(HomogeneousModel.HomCondProb[] p) Clones the given array of conditional probabilities.
Sample	emitSample(int no, int... length) Creates a Sample of a given number of Sequences from a trained homogeneous model.
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.
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.
protected abstract Sequence	getRandomSequence(Random r, int length) This method creates a random Sequence from a trained homogeneous model.
protected abstract 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 abstract double	probFor(Sequence sequence, int startpos, int endpos) This method computes the probability of the given Sequence in the given interval.
protected void	set(DGMParameterSet params, boolean trained) Sets the parameters as internal parameters and does some essential computations.
void	train(Sample[] data) Trains the homogeneous model on all given Samples.
abstract void	train(Sample[] data, double[][] weights) Trains the homogeneous model using an array of weighted Samples.

Methods inherited from class de.jstacs.models.discrete.DiscreteGraphicalModel

clone, fromXML, getCurrentParameterSet, getDescription, getESS, getFurtherModelInfos, getXMLTag, isTrained, setFurtherModelInfos, toString, toXML

Methods inherited from class de.jstacs.models.AbstractModel

getAlphabetContainer, getCharacteristics, getLength, getLogProbFor, getLogProbFor, getLogProbFor, getLogProbFor, getPriorTerm, getProbFor, getProbFor, set, setNewAlphabetContainerInstance, train

Methods inherited from class java.lang.Object

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

Methods inherited from interface de.jstacs.models.Model

getInstanceName, getLogPriorTerm, train

Field Detail

powers

protected int[] powers

The powers of the alphabet length.

order

protected byte order

The order of the model.

Constructor Detail

HomogeneousModel

public HomogeneousModel(HomogeneousModelParameterSet params)
                 throws CloneNotSupportedException,
                        IllegalArgumentException,
                        NonParsableException

Creates a homogeneous 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:

HomogeneousModelParameterSet, DiscreteGraphicalModel.DiscreteGraphicalModel(DGMParameterSet)

HomogeneousModel

public HomogeneousModel(StringBuffer stringBuff)
                 throws NonParsableException

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

Parameters:

stringBuff - the XML representation as StringBuffer

Throws:

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

See Also:

Storable, DiscreteGraphicalModel.DiscreteGraphicalModel(StringBuffer)

Method Detail

emitSample

public final Sample emitSample(int no,
                               int... length)
                        throws NotTrainedException,
                               IllegalArgumentException,
                               EmptySampleException,
                               WrongAlphabetException,
                               WrongSequenceTypeException

Creates a Sample of a given number of Sequences from a trained homogeneous model.

Specified by:

emitSample in interface Model

Overrides:

emitSample in class AbstractModel

Parameters:

no - the number of Sequences that should be in the Sample

length - the length of all Sequences or an array of lengths with the Sequence with index i having length length[i]

Returns:

the created Sample

Throws:

NotTrainedException - if the model was not trained

IllegalArgumentException - if the dimension of length is neither 1 nor no

EmptySampleException - if no == 0

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

WrongAlphabetException - if something is wrong with the alphabet

See Also:

Sample.Sample(String, Sequence...)

getRandomSequence

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

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

Parameters:

r - the random generator

length - the length of the Sequence

Returns:

the created Sequence

Throws:

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

WrongAlphabetException - if something is wrong with the alphabet

getMaximalMarkovOrder

public byte getMaximalMarkovOrder()

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

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

getLogProbFor

public final 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)

getProbFor

public final 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

train

public void train(Sample[] data)
           throws Exception

Trains the homogeneous model on all given Samples.

Parameters:

data - the given Samples

Throws:

Exception - if something went wrong

See Also:

train(Sample[], double[][])

train

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

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

Parameters:

data - the given Samples

weights - the weights

Throws:

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

set

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

Description copied from class: DiscreteGraphicalModel

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

Overrides:

set in class DiscreteGraphicalModel

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

check

protected void check(Sequence sequence,
                     int startpos,
                     int endpos)
              throws NotTrainedException,
                     IllegalArgumentException

Checks some constraints, these are in general conditions on the AlphabetContainer of a (sub)Sequence between startpos und endpos.

Overrides:

check in class DiscreteGraphicalModel

Parameters:

sequence - the Sequence

startpos - the start position within the Sequence

endpos - the end position within the Sequence

Throws:

NotTrainedException - if the model is not trained

IllegalArgumentException - if some arguments are wrong

See Also:

DiscreteGraphicalModel.check(Sequence, int, int)

chooseFromDistr

protected final int chooseFromDistr(Constraint distr,
                                    int start,
                                    int end,
                                    double randNo)

Chooses a value in [0,end-start] according to the distribution encoded in the frequencies of distr between the indices start and end.

The instance distr is not changed in the process.

Parameters:

distr - the distribution

start - the start index

end - the end index

randNo - a random number in [0,1]

Returns:

the chosen value

See Also:

Constraint.getFreq(int)

logProbFor

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

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

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:

check(Sequence, int, int), Model.getLogProbFor(Sequence, int, int)

probFor

protected abstract double probFor(Sequence sequence,
                                  int startpos,
                                  int endpos)

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

Parameters:

sequence - the Sequence

startpos - the start position within the Sequence

endpos - the end position within the Sequence

Returns:

the probability for the given subsequence

See Also:

check(Sequence, int, int), Model.getProbFor(Sequence, int, int)

cloneHomProb

protected HomogeneousModel.HomCondProb[] cloneHomProb(HomogeneousModel.HomCondProb[] p)

Clones the given array of conditional probabilities.

Parameters:

p - the original conditional probabilities

Returns:

an array of clones