public interface TrainableStatisticalModel extends StatisticalModel
TrainableStatisticalModelFactory.TrainableStatisticalModelFactory| Modifier and Type | Method and Description |
|---|---|
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. |
emitDataSet, getLogPriorTerm, getLogProbFor, getLogProbFor, getLogProbFor, getMaximalMarkovOrdergetAlphabetContainer, getCharacteristics, getInstanceName, getLength, getLogScoreFor, getLogScoreFor, getLogScoreFor, getLogScoreFor, getLogScoreFor, getNumericalCharacteristics, isInitialized, toStringTrainableStatisticalModel clone() throws CloneNotSupportedException
TrainableStatisticalModel instance.clone in interface SequenceScoreCloneNotSupportedException - if something went wrong while cloningvoid train(DataSet data) throws Exception
TrainableStatisticalModel object given the data as DataSet. 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.data - the given sequences as DataSetException - if the training did not succeedDataSet.getElementAt(int),
DataSet.ElementEnumeratorvoid train(DataSet data, double[] weights) throws Exception
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 data set as dimension. (Optionally it is possible to use
weight == null if all weights have the value one.)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.data - the given sequences as DataSetweights - the weights of the elements, each weight should be
non-negativeException - if the training did not succeed (e.g. the dimension of
weights and the number of sequences in the
data set do not match)DataSet.getElementAt(int),
DataSet.ElementEnumerator