de.jstacs.models.discrete.homogeneous
Class HomogeneousMM

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

All Implemented Interfaces:

InstantiableFromParameterSet, Model, Storable, Cloneable

public class HomogeneousMM
extends HomogeneousModel

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.models.discrete.homogeneous.HomogeneousModel

HomogeneousModel.HomCondProb

Field Summary

Fields inherited from class de.jstacs.models.discrete.homogeneous.HomogeneousModel

order, powers

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

params, trained

Fields inherited from class de.jstacs.models.AbstractModel

alphabets, length

Constructor Summary

HomogeneousMM(HomMMParameterSet params)
Creates a homogeneous Markov model from a parameter set.

HomogeneousMM(StringBuffer stringBuff)
Creates a homogeneous Markov model from a StringBuffer.

Method Summary

HomogeneousMM	clone() Follows the conventions of Object's clone-method.
protected StringBuffer	getFurtherModelInfos()
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 sequence from a trained model.
protected String	getXMLTag()
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 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.
protected void	setFurtherModelInfos(StringBuffer xml) This method replaces the internal model infos with those from the StringBuffer.
String	toString() Should give a simple representation (text) of the model as String.
void	train(Sample[] data, double[][] weights) Trains the model using an array of weighted samples.
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.discrete.homogeneous.HomogeneousModel

check, chooseFromDistr, cloneHomProb, emitSample, getLogProbFor, getMaximalMarkovOrder, getNumericalCharacteristics, getProbFor, train

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

fromXML, getCurrentParameterSet, getDescription, getESS, isTrained, 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

Constructor Detail

HomogeneousMM

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

Creates a 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

HomogeneousMM

public HomogeneousMM(StringBuffer stringBuff)
              throws NonParsableException

Creates a homogeneous Markov model from a StringBuffer.

Parameters:

stringBuff - the StringBuffer

Throws:

NonParsableException - if the buffer is not parsable

Method Detail

clone

public HomogeneousMM 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 DiscreteGraphicalModel

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

getRandomSequence

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

Description copied from class: HomogeneousModel

This method creates a sequence from a trained model.

Specified by:

getRandomSequence in class HomogeneousModel

Parameters:

r - the random generator

length - the length of the sequence

Returns:

the sequence

Throws:

WrongAlphabetException

WrongSequenceTypeException

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

getLogPriorTerm

public double getLogPriorTerm()
                       throws Exception

Description copied from interface: Model

Returns a value that is proportional to the log of the prior. For 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()

logProbFor

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

Description copied from class: HomogeneousModel

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 HomogeneousModel.check(Sequence, int, int) has been invoked.

Specified by:

logProbFor in class HomogeneousModel

Parameters:

sequence - the sequence

startpos - the start position

endpos - the end position

Returns:

the logarithm of the probability for the given subsequence

See Also:

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

probFor

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

Description copied from class: HomogeneousModel

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 HomogeneousModel.check(Sequence, int, int) has been invoked.

Specified by:

probFor in class HomogeneousModel

Parameters:

sequence - the sequence

startpos - the start position

endpos - the end position

Returns:

the probability for the given subsequence

See Also:

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

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 DiscreteGraphicalModel

Returns:

the representation as String

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

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

Throws:

Exception - an Exception should be thrown if the training did not succeed (e.g. the weights dimension of weights and number of samples does not match).

See Also:

Sample.getElementAt(int), Sample.ElementEnumerator

train

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

Description copied from class: HomogeneousModel

Trains the model using an array of weighted samples. The weights[i] are for data[i].

Specified by:

train in class HomogeneousModel

Parameters:

data - the samples

weights - the weights

Throws:

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

getFurtherModelInfos

protected StringBuffer getFurtherModelInfos()

Specified by:

getFurtherModelInfos in class DiscreteGraphicalModel

Returns:

the model infos like parameters of the distribution ... in xml format

See Also:

DiscreteGraphicalModel.toXML()

getXMLTag

protected String getXMLTag()

Specified by:

getXMLTag in class DiscreteGraphicalModel

Returns:

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

See Also:

DiscreteGraphicalModel.fromXML(StringBuffer), DiscreteGraphicalModel.toXML()

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 HomogeneousModel

Parameters:

params - the new ParameterSet

trained - the indicator for the model

Throws:

CloneNotSupportedException - if the parmeterSet 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: DiscreteGraphicalModel

This method replaces the internal model infos with those from the StringBuffer.

Specified by:

setFurtherModelInfos in class DiscreteGraphicalModel

Parameters:

xml - contains the model infos like parameters of the distribution ... in xml format

Throws:

NonParsableException - if the StringBuffer could not be parsed

See Also:

DiscreteGraphicalModel.fromXML(StringBuffer)