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Storable
.
Parameter
and Result
.Classifier
s that are based on Model
sClassifier
s that are based on ScoringFunction
s.OptimizableFunction
and a classifier that uses log conditional likelihood or supervised posterior
to learn the parameters of a set of ScoringFunctions
DNAAlphabet
for the most common case of DNA-sequencesSequence
s using a number of pre-defined annotation types, or additional
implementations of the SequenceAnnotation
classStorable
s to an XML-representationModel
and its abstract implementation AbstractModel
, which is the super class of all other models.Parameter
-interface.Parameter
valuesScoringFunction
s that can be used in a ScoreClassifier
.ScoringFunction
s that are equivalent to directed graphical models.BayesianNetworkScoringFunction
.BayesianNetworkScoringFunction
as
a Bayesian tree using a number of measures to define a rating of structuresBayesianNetworkScoringFunction
as
a permuted Markov model using a number of measures to define a rating of structuresScoringFunction
s that are homogeneous, i.e. model probabilities or scores independent of the position within a sequenceScoringFunction
s that are mixtures of other ScoringFunction
s.ProgressUpdater
and prints the
percentage of iterations that is already done on the screen.setSelected(Measure, false)}
for all MeasureParameters.Measure
s.
DiscreteGraphicalModel
.Storable
interface.
InstantiableFromParameterSet
interface.
[min,max]
.
ParameterSet
of a DiscreteAlphabet
.DiscreteAlphabetParameterSet
with empty values.
DiscreteAlphabetParameterSet
from an array of symbols.
DiscreteAlphabetParameterSet
from an array of symbols.
DiscreteAlphabetParameterSet
from its XML-representation.
pos
.
DoubleList
s that are used while computing the partial derivation
Storable
interface.
InstantiableFromParameterSet
interface.
seq
fulfills all requirements defined in the context (Parameter.context
}).
LogPrior
that does not penalize any parameter.size
.
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