de.jstacs.sequenceScores.statisticalModels.trainable
Class VariableLengthWrapperTrainSM

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

All Implemented Interfaces:

SequenceScore, StatisticalModel, TrainableStatisticalModel, Storable, Cloneable

public class VariableLengthWrapperTrainSM
extends AbstractTrainableStatisticalModel

This class allows to train any TrainableStatisticalModel on DataSets of Sequences with variable length if each individual length is at least SequenceScore.getLength(). All other methods are piped to the internally used TrainableStatisticalModel.

This class might be useful in any ClassifierAssessment.

Author:

Jens Keilwagen

See Also:

DataSet.WeightedDataSetFactory.DataSet.WeightedDataSetFactory(DataSet.WeightedDataSetFactory.SortOperation, DataSet, double[], int), ClassifierAssessment

Field Summary

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

alphabets, length

Constructor Summary

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

VariableLengthWrapperTrainSM(TrainableStatisticalModel m)
This is the main constructor that creates an instance from any TrainableStatisticalModel.

Method Summary

VariableLengthWrapperTrainSM	clone() Follows the conventions of Object's clone()-method.
protected void	fromXML(StringBuffer xml) This method should only be used by the constructor that works on 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.
double	getLogProbFor(Sequence sequence, int startpos, int endpos) Returns the logarithm of the probability of (a part of) the given sequence given the model.
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.
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, getCharacteristics, getLength, getLogProbFor, getLogProbFor, getLogScoreFor, getLogScoreFor, getLogScoreFor, getLogScoreFor, getLogScoreFor, getMaximalMarkovOrder, train

Methods inherited from class java.lang.Object

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

Methods inherited from interface de.jstacs.sequenceScores.statisticalModels.trainable.TrainableStatisticalModel

toString

Constructor Detail

VariableLengthWrapperTrainSM

public VariableLengthWrapperTrainSM(TrainableStatisticalModel m)
                             throws CloneNotSupportedException

This is the main constructor that creates an instance from any TrainableStatisticalModel.

Parameters:

m - the model

Throws:

CloneNotSupportedException - if the mode m could not be cloned

VariableLengthWrapperTrainSM

public VariableLengthWrapperTrainSM(StringBuffer stringBuff)
                             throws NonParsableException

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

Parameters:

stringBuff - the StringBuffer to be parsed

Throws:

NonParsableException - is thrown if the StringBuffer could not be parsed

Method Detail

clone

public VariableLengthWrapperTrainSM 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

fromXML

protected void fromXML(StringBuffer xml)
                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:

xml - 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

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

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

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

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