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, getMaximalMarkovOrder
getAlphabetContainer, getCharacteristics, getInstanceName, getLength, getLogScoreFor, getLogScoreFor, getLogScoreFor, getLogScoreFor, getLogScoreFor, getNumericalCharacteristics, isInitialized, toString
TrainableStatisticalModel clone() throws CloneNotSupportedException
TrainableStatisticalModel
instance.clone
in interface SequenceScore
CloneNotSupportedException
- 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 DataSet
Exception
- if the training did not succeedDataSet.getElementAt(int)
,
DataSet.ElementEnumerator
void 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 DataSet
weights
- 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