de.jstacs.sequenceScores.statisticalModels.trainable
Class DifferentiableStatisticalModelWrapperTrainSM

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

All Implemented Interfaces:

SequenceScore, StatisticalModel, TrainableStatisticalModel, Storable, Cloneable

public class DifferentiableStatisticalModelWrapperTrainSM
extends AbstractTrainableStatisticalModel

This model can be used to use a DifferentiableStatisticalModel as model. It enables the user to train the DifferentiableStatisticalModel in a generative way.

Author:

Jens Keilwagen

See Also:

DifferentiableStatisticalModel, LogGenDisMixFunction

Field Summary

protected DifferentiableStatisticalModel

nsf The internally used DifferentiableStatisticalModel.

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

alphabets, length

Constructor Summary

DifferentiableStatisticalModelWrapperTrainSM(DifferentiableStatisticalModel nsf, int threads, byte algo, AbstractTerminationCondition tc, double lineps, double startD)
The main constructor that creates an instance with the user given parameters.

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

Method Summary

DifferentiableStatisticalModelWrapperTrainSM	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.
DifferentiableStatisticalModel	getFunction() Returns a copy of the internally used DifferentiableStatisticalModel.
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.
void	setOutputStream(OutputStream o) Sets the OutputStream that is used e.g. for writing information while training.
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, getCharacteristics, getLength, getLogProbFor, getLogProbFor, getLogScoreFor, getLogScoreFor, getLogScoreFor, getLogScoreFor, getLogScoreFor, getMaximalMarkovOrder, train

Methods inherited from class java.lang.Object

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

Field Detail

nsf

protected DifferentiableStatisticalModel nsf

The internally used DifferentiableStatisticalModel.

Constructor Detail

DifferentiableStatisticalModelWrapperTrainSM

public DifferentiableStatisticalModelWrapperTrainSM(DifferentiableStatisticalModel nsf,
                                                    int threads,
                                                    byte algo,
                                                    AbstractTerminationCondition tc,
                                                    double lineps,
                                                    double startD)
                                             throws CloneNotSupportedException

The main constructor that creates an instance with the user given parameters.

Parameters:

nsf - the DifferentiableStatisticalModel that should be used

threads - the number of threads that should be used for optimization

algo - the algorithm that should be used for the optimization

tc - the AbstractTerminationCondition for stopping the optimization

lineps - the line epsilon for stopping the line search in the optimization

startD - the start distance that should be used initially

Throws:

CloneNotSupportedException - if nsf can not be cloned

DifferentiableStatisticalModelWrapperTrainSM

public DifferentiableStatisticalModelWrapperTrainSM(StringBuffer stringBuff)
                                             throws NonParsableException

The standard constructor for the interface Storable. Creates a new DifferentiableStatisticalModelWrapperTrainSM 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 DifferentiableStatisticalModelWrapperTrainSM 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

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

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

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

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

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

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

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

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

setOutputStream

public final void setOutputStream(OutputStream o)

Sets the OutputStream that is used e.g. for writing information while training. It is possible to set o=null, than nothing will be written.

Parameters:

o - the OutputStream

getFunction

public DifferentiableStatisticalModel getFunction()
                                           throws CloneNotSupportedException

Returns a copy of the internally used DifferentiableStatisticalModel.

Returns:

a copy of the internally used DifferentiableStatisticalModel

Throws:

CloneNotSupportedException - if the internal instance could not be cloned