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DAGModel
).Storable
.
enum
defines a number of data types that can be used for
Parameter
s and Result
s.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.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.Measure
s.
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
.Exception
s 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.
String
s.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
.
Sample
s for discrete inhomogeneous models by a naive implementation.DiscreteSequence
with the
AlphabetContainer
container
and the annotation
annotation
but without the content.
Sequence
s 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.
DoubleList
s 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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