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java.lang.Objectde.jstacs.sequenceScores.statisticalModels.trainable.AbstractTrainableStatisticalModel
de.jstacs.sequenceScores.statisticalModels.trainable.CompositeTrainSM
public class CompositeTrainSM
This class is for modelling sequences by modelling the different positions of the each sequence by different models. For instance one can use this class to model a sequence of subsequences, where each subsequence is modeled by a different model.
Field Summary | |
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protected int[][] |
lengths
The length for each component. |
protected TrainableStatisticalModel[] |
models
The models for the components |
protected int[][] |
starts
The start indices. |
Fields inherited from class de.jstacs.sequenceScores.statisticalModels.trainable.AbstractTrainableStatisticalModel |
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alphabets, length |
Constructor Summary | |
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CompositeTrainSM(AlphabetContainer alphabets,
int[] assignment,
TrainableStatisticalModel... models)
Creates a new CompositeTrainSM . |
|
CompositeTrainSM(StringBuffer stringBuff)
The standard constructor for the interface Storable . |
Method Summary | |
---|---|
CompositeTrainSM |
clone()
Follows the conventions of Object 's clone() -method. |
void |
fromXML(StringBuffer representation)
This method should only be used by the constructor that works on a StringBuffer . |
ResultSet |
getCharacteristics()
Returns some information characterizing or describing the current instance. |
String |
getInstanceName()
Should return a short instance name such as iMM(0), BN(2), ... |
int[] |
getLengthOfModels()
This method returns the length of the models in the CompositeTrainSM . |
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. |
byte |
getMaximalMarkovOrder()
This method returns the maximal used Markov order, if possible. |
TrainableStatisticalModel[] |
getModels()
Returns the a deep copy of the models. |
int |
getNumberOfModels()
This method returns the number of models in the CompositeTrainSM . |
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. |
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 |
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check, emitDataSet, getAlphabetContainer, getLength, getLogProbFor, getLogProbFor, getLogScoreFor, getLogScoreFor, getLogScoreFor, getLogScoreFor, getLogScoreFor, train |
Methods inherited from class java.lang.Object |
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equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait |
Field Detail |
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protected TrainableStatisticalModel[] models
protected int[][] starts
protected int[][] lengths
Constructor Detail |
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public CompositeTrainSM(AlphabetContainer alphabets, int[] assignment, TrainableStatisticalModel... models) throws WrongAlphabetException, CloneNotSupportedException
CompositeTrainSM
.
alphabets
- the alphabets used in the modelsassignment
- an array assigning each position to a modelmodels
- the single models
IllegalArgumentException
- if something is wrong with the assignment of the models
WrongAlphabetException
- if (parts of) the alphabet does not match with the alphabets
of the models
CloneNotSupportedException
- if the models could not be clonedpublic CompositeTrainSM(StringBuffer stringBuff) throws NonParsableException
Storable
.
Creates a new CompositeTrainSM
out of a StringBuffer
.
stringBuff
- the StringBuffer
to be parsed
NonParsableException
- if the StringBuffer
is not parsableMethod Detail |
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public CompositeTrainSM clone() throws CloneNotSupportedException
AbstractTrainableStatisticalModel
Object
's clone()
-method.
clone
in interface SequenceScore
clone
in interface TrainableStatisticalModel
clone
in class AbstractTrainableStatisticalModel
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:Object o = (X)super.clone();
o
defined by
X
that are not of simple data-types like
int
, double
, ... have to be deeply
copied return o
CloneNotSupportedException
- if something went wrong while cloningpublic ResultSet getCharacteristics() throws Exception
SequenceScore
StorableResult
.
getCharacteristics
in interface SequenceScore
getCharacteristics
in class AbstractTrainableStatisticalModel
Exception
- if some of the characteristics could not be definedStorableResult
public String getInstanceName()
SequenceScore
public int[] getLengthOfModels()
CompositeTrainSM
.
public byte getMaximalMarkovOrder() throws UnsupportedOperationException
StatisticalModel
getMaximalMarkovOrder
in interface StatisticalModel
getMaximalMarkovOrder
in class AbstractTrainableStatisticalModel
UnsupportedOperationException
- if the model can't give a proper answerpublic NumericalResultSet getNumericalCharacteristics() throws Exception
SequenceScore
SequenceScore.getCharacteristics()
.
Exception
- if some of the characteristics could not be definedpublic TrainableStatisticalModel[] getModels() throws CloneNotSupportedException
AbstractTrainableStatisticalModel
s
CloneNotSupportedException
- if at least one of the models could not be clonedpublic int getNumberOfModels()
CompositeTrainSM
.
public double getLogPriorTerm() throws Exception
StatisticalModel
Exception
- if something went wrongpublic double getLogProbFor(Sequence sequence, int startpos, int endpos) throws NotTrainedException, Exception
StatisticalModel
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.
length
and the alphabets
define the type of
data that can be modeled and therefore both has to be checked.
sequence
- the given sequencestartpos
- the start position within the given sequenceendpos
- the last position to be taken into account
NotTrainedException
- if the model is not trained yet
Exception
- if the sequence could not be handled (e.g.
startpos >
, endpos
> sequence.length
, ...) by the modelpublic boolean isInitialized()
SequenceScore
SequenceScore.getLogScoreFor(Sequence)
.
true
if the instance is initialized, false
otherwisepublic void fromXML(StringBuffer representation) throws NonParsableException
AbstractTrainableStatisticalModel
StringBuffer
. It is the counter part of Storable.toXML()
.
fromXML
in class AbstractTrainableStatisticalModel
representation
- the XML representation of the model
NonParsableException
- if the StringBuffer
is not parsable or the
representation is conflictingAbstractTrainableStatisticalModel.AbstractTrainableStatisticalModel(StringBuffer)
public StringBuffer toXML()
Storable
StringBuffer
of an
instance of the implementing class.
public void train(DataSet data, double[] weights) throws Exception
TrainableStatisticalModel
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.)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-negative
Exception
- if the training did not succeed (e.g. the dimension of
weights
and the number of sequences in the
sample do not match)DataSet.getElementAt(int)
,
DataSet.ElementEnumerator
public String toString()
TrainableStatisticalModel
String
.
toString
in interface TrainableStatisticalModel
toString
in class Object
String
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