de.jstacs.sequenceScores.statisticalModels.trainable
Interface TrainableStatisticalModel

All Superinterfaces:

Cloneable, SequenceScore, StatisticalModel, Storable

All Known Implementing Classes:

AbstractHMM, AbstractMixtureTrainSM, AbstractTrainableStatisticalModel, BayesianNetworkTrainSM, CompositeTrainSM, DAGTrainSM, DifferentiableHigherOrderHMM, DifferentiableStatisticalModelWrapperTrainSM, DiscreteGraphicalTrainSM, FSDAGModelForGibbsSampling, FSDAGTrainSM, HiddenMotifMixture, HigherOrderHMM, HomogeneousMM, HomogeneousTrainSM, InhomogeneousDGTrainSM, MixtureTrainSM, SamplingHigherOrderHMM, SamplingPhyloHMM, SharedStructureMixture, StrandTrainSM, UniformTrainSM, VariableLengthWrapperTrainSM, ZOOPSTrainSM

public interface TrainableStatisticalModel
extends StatisticalModel

This interface defines all methods for a probabilistic model.

Author:

Andre Gohr, Jan Grau, Jens Keilwagen

Method Summary

TrainableStatisticalModel	clone() Creates a clone (deep copy) of the current TrainableStatisticalModel instance.
String	toString() Should give a simple representation (text) of the model as String.
void	train(DataSet data) Trains the TrainableStatisticalModel object given the data as DataSet.
void	train(DataSet data, double[] weights) Trains the TrainableStatisticalModel object given the data as DataSet using the specified weights.

Methods inherited from interface de.jstacs.sequenceScores.statisticalModels.StatisticalModel

emitDataSet, getLogPriorTerm, getLogProbFor, getLogProbFor, getLogProbFor, getMaximalMarkovOrder

Methods inherited from interface de.jstacs.sequenceScores.SequenceScore

getAlphabetContainer, getCharacteristics, getInstanceName, getLength, getLogScoreFor, getLogScoreFor, getLogScoreFor, getLogScoreFor, getLogScoreFor, getNumericalCharacteristics, isInitialized

Methods inherited from interface de.jstacs.Storable

toXML

Method Detail

clone

TrainableStatisticalModel clone()
                                throws CloneNotSupportedException

Creates a clone (deep copy) of the current TrainableStatisticalModel instance.

Specified by:

clone in interface SequenceScore

Returns:

the cloned instance

Throws:

CloneNotSupportedException - if something went wrong while cloning

train

void train(DataSet data)
           throws Exception

Trains the TrainableStatisticalModel object given the data as DataSet.
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

Throws:

Exception - if the training did not succeed

See Also:

DataSet.getElementAt(int), DataSet.ElementEnumerator

train

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

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

toString

String toString()

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

Overrides:

toString in class Object

Returns:

the representation as String