public interface StatisticalModel extends SequenceScore
SequenceScore that defines a proper likelihood
over the input Sequences.
If you like to train the model from a DataSet, please have a look at TrainableStatisticalModel,
if you like to use the model in some optimization (e.g., discriminative learning using the GenDisMixClassifier) have a look at DifferentiableStatisticalModel.| Modifier and Type | Method and Description |
|---|---|
DataSet |
emitDataSet(int numberOfSequences,
int... seqLength)
This method returns a
DataSet object containing artificial
sequence(s). |
double |
getLogPriorTerm()
Returns a value that is proportional to the log of the prior.
|
double |
getLogProbFor(Sequence sequence)
Returns the logarithm of the probability of the given sequence given the
model.
|
double |
getLogProbFor(Sequence sequence,
int startpos)
Returns the logarithm of the probability of (a part of) the given
sequence given the model.
|
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.
|
clone, getAlphabetContainer, getCharacteristics, getInstanceName, getLength, getLogScoreFor, getLogScoreFor, getLogScoreFor, getLogScoreFor, getLogScoreFor, getNumericalCharacteristics, isInitialized, toStringdouble getLogProbFor(Sequence sequence, int startpos, int endpos) throws Exception
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 accountException - if the sequence could not be handled (e.g.
startpos > , endpos
> sequence.length, ...) by the modelNotTrainedException - if the model is not trained yetdouble getLogProbFor(Sequence sequence, int startpos) throws Exception
startpos. E.g. the fixed length is 12. The length
of the given sequence is 30 and the startpos=15 the logarithm
of the probability of the part from position 15 to 26 (inclusive) given
the model 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 sequenceException - if the sequence could not be handled by the modelNotTrainedException - if the model is not trained yetgetLogProbFor(Sequence, int, int)double getLogProbFor(Sequence sequence) throws Exception
length and the alphabets define the type of
data that can be modeled and therefore both has to be checked.sequence - the given sequence for which the logarithm of the
probability/the value of the density function should be
returnedException - if the sequence could not be handled by the modelNotTrainedException - if the model is not trained yetgetLogProbFor(Sequence, int, int)double getLogPriorTerm()
throws Exception
Exception - if something went wrongDataSet emitDataSet(int numberOfSequences, int... seqLength) throws NotTrainedException, Exception
DataSet object containing artificial
sequence(s).
emitDataSet( int n, int l ) should return a data set with
n sequences of length l.
emitDataSet( int n, int[] l ) should return a data set with
n sequences which have a sequence length corresponding to
the entry in the given array l.
emitDataSet( int n ) and
emitDataSet( int n, null ) should return a data set with
n sequences of length of the model (
SequenceScore.getLength()).
Exception.numberOfSequences - the number of sequences that should be contained in the
returned data setseqLength - the length of the sequences for a homogeneous model; for an
inhomogeneous model this parameter should be null
or an array of size 0.DataSet containing the artificial sequence(s)Exception - if the emission did not succeedNotTrainedException - if the model is not trained yetDataSetbyte getMaximalMarkovOrder()
throws UnsupportedOperationException
UnsupportedOperationException - if the model can't give a proper answer