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DAGModel).Storable.
enum defines a number of data types that can be used for
Parameters and Result
s.Classifiers that are based on ModelsClassifiers that are based on ScoringFunctions.OptimizableFunction and a classifier that uses log conditional likelihood or supervised posterior
to learn the parameters of a set of ScoringFunctionsDNAAlphabet for the most common case of DNA-sequencesSequences using a number of pre-defined annotation types, or additional
implementations of the SequenceAnnotation classStorables to an XML-representationModel and its abstract implementation AbstractModel, which is the super class of all other models.Parameter-interface.Parameter valuesScoringFunctions that can be used in a ScoreClassifier.ScoringFunctions 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 structuresScoringFunctions that are homogeneous, i.e. model probabilities or scores independent of the position within a sequenceScoringFunctions that are mixtures of other ScoringFunctions.Mutable.determineNotSignificantPositions(double, double[], double[], double[][][][], double[][][][], double).
OutputStream.
ProgressUpdater and prints the
percentage of iterations that is already done on the screen.DefaultProgressUpdater.
MeasureParameters.setSelected(Measure, boolean)
for all MeasureParameters.Measures.
sign
and the contrast distributions of the left or right side,
contrastLeft and contrastRight,
respectively.
motif.
ParameterSet for any parameter set of
a DiscreteGraphicalModel.Storable.
length.
f: R^n -> R.Exceptions depending on wrong dimensions of vectors
for a given function.DimensionException with standard error message
("The vector has wrong dimension for this function.
DimensionException with a more detailed error
message.
Strings.Storable.
InstantiableFromParameterSet
interface.
DiscreteAlphabet from a minimal and a maximal
value, i.e. in [min,max].
DiscreteAlphabet from a given alphabet as a
String array.
ParameterSet of a
DiscreteAlphabet.DiscreteAlphabet.DiscreteAlphabetParameterSet with empty values.
DiscreteAlphabet.DiscreteAlphabetParameterSet from an alphabet
given as a String array.
DiscreteAlphabet.DiscreteAlphabetParameterSet from an alphabet
of symbols given as a char array.
Storable
.
Storable.
Samples for discrete inhomogeneous models by a naive implementation.DiscreteSequence with the
AlphabetContainer container and the annotation
annotation but without the content.
Sequences of a specific
AlphabetContainer and length.DiscreteSequenceEnumerator from a given
AlphabetContainer and a length.
pos of the
Sequence.CompositeSequence.
Parameter, which is defined not to be free.
DoubleLists that are used while
computing the partial derivation.
Storable.
InstantiableFromParameterSet
interface.
DNAAlphabet.DNAAlphabet.DNAAlphabetParameterSet.
Storable
.
Sequence seq fulfills all
requirements defined in the Parameter.context.
LogPrior that does not penalize any parameter.MutableMotifDiscovererToolbox.InitMethodForScoringFunction is a MutableMotifDiscoverer.
train-method
double array.
double.DoubleList with
initial length 10.
DoubleList with
initial length size.
Storable.
DoubleSymbolException is thrown if a symbol occurred more than once
in an alphabet.DoubleSymbolException that takes the symbol
that occurs more than once in the error message.
constr.
contrast and
endIdx-startIdx distributions drawn from a Dirichlet density centered around contrast, i.e. the hyper-parameters
of the Dirichlet density are the probabilities of contrast weighted by samples.
contrast[i] each weighted by weights[i]
kls.length distributions drawn from a Dirichlet density centered around contrast, i.e. the hyper-parameters
of the Dirichlet density are the probabilities of contrast weighted by samples.
ess (equivalent sample size)
as hyperparameters.
Storable.
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