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DataType from can be casted to the
DataType to without losing information.
History.t steps using the history h.
Storable.
o.
Strings.Storable.
String
.
String.
boolean.
DataSet can be used.
Sequence can be used.
value can be used in Parameter.setValue(Object).
value for the constraint defined in the
Constraint.
Sequence.
AlphabetContainer of a (sub)Sequence
between startpos und endpos.
AlphabetContainer is consistent consistent with
another AlphabetContainer.
Alphabet is consistent consistent with another
Alphabet, i.e. both Alphabets use the same symbols.
value with one of the pre-defined DataTypes
before creating a new Result and possibly running into an
Exception.
l of the model with index
index is capable for the current instance.
SamplingComponent.
true if the key specified by value is
in the set of keys of this AbstractSelectionParameter.
true if the value is valid and false
otherwise.
value.
weights array.
[0,end-start] according to the
distribution encoded in the frequencies of distr between the
indices start and end.
MotifAnnotations of the motifs in the module.CisRegulatoryModuleAnnotation from a set of motifs
and possibly additional annotations.
Storable.
DataSets).ClassDimensionException with the
default error message ("The number of classes in the classfier
differs from the given number.
ClassDimensionException with given
error message.
ClassificationRate.
Storable.
ClassifierAssessment from an array of
AbstractClassifiers and a two-dimensional array of TrainableStatisticalModel
s, which are combined to additional classifiers.
ClassifierAssessment from a set of
AbstractClassifiers.
ClassifierAssessment from a set of TrainableStatisticalModels.
AbstractClassifiers and, in addition, classifiers that will be
constructed using the given TrainableStatisticalModels.
ClassifierAssessmentAssessParameterSets.ClassifierAssessmentAssessParameterSet with
empty parameter values.
Storable.
ClassifierAssessmentAssessParameterSet with
given parameter values.
i of
the class to which the sequence is assigned with
0 < i < getNumberOfClasses().
i in the array
0 < i < getNumberOfClasses().
Sequence.
SequenceAnnotation.
Cloneables or primitives.
ParameterSet.
DifferentiableSequenceScore
instance.
SequenceScore instance.
Object's clone()-method.
reader is set to null and
the paramsFile is cloned.
TrainableStatisticalModel instance.
REnvironment and removes all files from the server.
SafeOutputStream by closing the OutputStream
this stream was constructed of.
CombinationIterator with n elements
and at most max selected elements.
TerminationConditions at once.TerminationConditions at once.
Storable.
CombinedCondition.Storable.
FileFilters.File if at least minAccepted filters accept the File.
ParameterSetContainer
ComparableElement.
Sequence.compareTo(Sequence).
Sequence containing the
complementary current Sequence.
Sequence containing a part
of the complementary current Sequence.
InstantiableFromParameterSet
interface.
Storable.
ComplementableDiscreteAlphabet from a given array
of symbols.
Storable interface.
CompositeTrainSM.
Storable.
BurnInTest.setValue(double).
DAG).
DAG), i.e. the k-DAG that
maximizes the score given by a Tensor.
DAG), i.e. the k-DAG that
maximizes the score given by a SymmetricTensor.
ConfusionMatrix.
Storable.
optimize-method.
optimize-method.
StartDistanceForecaster that returns always the same
value.ConstantStartDistance
that returns always the given value.
ConstraintValidator.Storable.
ParameterValidator that is based on Constraints.ConstraintValidator having an empty list of
Constraints, i.e. the constraints of this
ConstraintValidator are always fulfilled before additional
Constraints are added using ConstraintValidator.addConstraint(Constraint).
Storable.
val is already returned in the list.
BasicHigherOrderTransition.AbstractTransitionElement
InstantiableFromParameterSet
interface.
ContinuousAlphabet from a minimal and a maximal
value.
ContinuousAlphabet from a minimal and a maximal
value.
ContinuousAlphabet with minimum and maximum value
being -Double.MAX_VALUE and Double.MAX_VALUE,
respectively.
ContinuousAlphabet with minimum and maximum value
being -Double.MAX_VALUE and Double.MAX_VALUE,
respectively.
Storable.
ParameterSet of a
ContinuousAlphabet.ContinuousAlphabet.ContinuousAlphabetParameterSet with empty
values.
ContinuousAlphabet.ContinuousAlphabetParameterSet from a minimum
and a maximum value.
ContinuousAlphabet.ContinuousAlphabetParameterSet from a minimum
and a maximum value.
Storable
.
pos of the
Sequence.
Files and directories, if selected, from a
source File, i.e. directory, to a
target File, i.e. directory, that are accepted by the f
FileFilter filter.
File in a faster manner.
File in a faster manner using a specified
buffer.
BNDiffSMParameterTree.
RFileInputStream of the given sourcePath into the given OutputStream out
Constraint constr with the
weighted absolute frequencies of the DataSet data.
DifferentiableStatisticalModels.
Sequence from a String based on the given
AlphabetContainer using the standard delimiter for this
AlphabetContainer.
Sequence from a String based on the given
AlphabetContainer using the given delimiter delim.
Sequence from a String based on the given
AlphabetContainer using the given delimiter delim
and some annotation for the Sequence.
l that has at each position a clone of t.
NumericalPerformanceMeasureParameterSet that can be used in
AbstractClassifier.evaluate(PerformanceMeasureParameterSet, boolean, de.jstacs.data.DataSet...)
PerformanceMeasureParameterSet that can be used in
AbstractClassifier.evaluate(PerformanceMeasureParameterSet, boolean, de.jstacs.data.DataSet...).
MarkovRandomFieldDiffSM of the specified length and with the given constraint type.
ints.
doubles.
MixtureDiffSM that models a mixture of individual component DifferentiableStatisticalModels.
MixtureTrainSM that allows to model a DataSet as a mixture of individual components.
ParameterSet from an array of values, an array of
names and an array of comments.
numStates+1 states, where numStates emitting build a clique and each of those states is connected to the absorbing silent final state.
Result.
StrandDiffSM that allows to score binding sites on both strand of DNA.
StrandTrainSM that allows to score binding sites on both strand of DNA.
TransitionElements that can be used to create the HMM.
BayesianNetworkDiffSM.trees) and the parameter objects BayesianNetworkDiffSM.parameters using the
given Measure BayesianNetworkDiffSM.structureMeasure.
Strings.
ints.
longs.
doubles.
DataSet.
DataSet.
SamplingScoreBasedClassifier.currentParameters
Storable.
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