| Package | Description |
|---|---|
| de.jstacs.algorithms.optimization.termination |
Provides classes for termination conditions that can be used in algorithms.
|
| de.jstacs.classifiers.assessment |
This package allows to assess classifiers.
It contains the class ClassifierAssessment that
is used as a super-class of all implemented methodologies of
an assessment to assess classifiers. |
| de.jstacs.classifiers.differentiableSequenceScoreBased.gendismix |
Provides an implementation of a classifier that allows to train the parameters of a set of
DifferentiableStatisticalModels by
a unified generative-discriminative learning principle. |
| de.jstacs.classifiers.differentiableSequenceScoreBased.sampling |
Provides the classes for
AbstractScoreBasedClassifiers that are based on
SamplingDifferentiableStatisticalModels
and that sample parameters using the Metropolis-Hastings algorithm. |
| de.jstacs.classifiers.performanceMeasures |
This package provides the implementations of performance measures that can be used to assess any classifier.
|
| de.jstacs.parameters |
This package provides classes for parameters that establish a general convention for the description of parameters
as defined in the
Parameter-interface. |
| de.jstacs.results |
This package provides classes for results and sets of results.
|
| de.jstacs.sampling |
This package contains many classes that can be used while a sampling.
|
| de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures |
Provides the facilities to learn the structure of a
BayesianNetworkDiffSM. |
| de.jstacs.sequenceScores.statisticalModels.trainable.discrete | |
| de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.parameters | |
| de.jstacs.sequenceScores.statisticalModels.trainable.hmm.training |
The package provides all classes used to determine the training algorithm of a hidden Markov model.
|
| de.jstacs.tools | |
| de.jstacs.tools.ui.galaxy |
| Constructor and Description |
|---|
AbsoluteValueConditionParameterSet(double absValue)
This constructor creates a filled instance of a parameters set.
|
IterationConditionParameterSet(int maxIter)
This constructor creates a filled instance of a parameters set.
|
SmallGradientConditonParameterSet(double eps)
This constructor creates a filled instance of a parameters set.
|
SmallStepConditionParameterSet(double eps)
This constructor creates a filled instance of a parameters set.
|
TimeConditionParameterSet(double seconds)
This constructor creates a filled instance of a parameters set.
|
| Modifier and Type | Method and Description |
|---|---|
protected SimpleParameterSet |
RepeatedHoldOutAssessParameterSet.getParameterSetContainingASingleDoubleValue(double percent)
|
void |
ClassifierAssessmentAssessParameterSet.setStoreAll(boolean b)
This method allows to set the switch for storing all individual performance measure values of each iteration of the
ClassifierAssessment. |
protected void |
ClassifierAssessment.test(NumericalPerformanceMeasureParameterSet mp,
boolean exception,
DataSet[] testS,
double[][] weights)
Uses the given test data sets to call the evaluate( ...
|
| Constructor and Description |
|---|
ClassifierAssessmentAssessParameterSet(int elementLength,
boolean exceptionIfMPNotComputable)
Constructs a new
ClassifierAssessmentAssessParameterSet with
given parameter values. |
RepeatedSubSamplingAssessParameterSet()
Constructs a new
RepeatedSubSamplingAssessParameterSet with empty
parameter values. |
RepeatedSubSamplingAssessParameterSet(int elementLength,
boolean exceptionIfMPNotComputable,
int repeats,
double[] trainNumbers,
double[] testNumbers)
Constructs a new
RepeatedSubSamplingAssessParameterSet with given
parameter values. |
| Modifier and Type | Method and Description |
|---|---|
void |
GenDisMixClassifierParameterSet.setNumberOfThreads(int threads)
This method set the number of threads used during optimization.
|
void |
GenDisMixClassifier.setNumberOfThreads(int threads)
This method allows to set the number of threads used while optimization.
|
| Modifier and Type | Method and Description |
|---|---|
void |
SamplingScoreBasedClassifierParameterSet.setNumberOfStarts(int i)
Sets the number of starts to
i |
| Modifier and Type | Method and Description |
|---|---|
protected void |
AbstractPerformanceMeasureParameterSet.setMeasure(T measure)
Sets the given measure as content of the internally last
ParameterSetContainer. |
| Modifier and Type | Method and Description |
|---|---|
protected void |
AbstractSelectionParameter.createParameterSet(Object[] values,
String[] keys,
String[] comments)
Creates a new
ParameterSet from an array of values, an array of
names and an array of comments. |
void |
ParameterSetTagger.fillParameters(String delimiter,
String... args) |
boolean |
RangeParameter.next()
Returns
true if the next element still exists and can be
fetched using RangeParameter.getValue(), false otherwise. |
void |
SimpleParameter.setDefault(Object defaultValue) |
void |
SelectionParameter.setDefault(Object defaultValue) |
void |
MultiSelectionParameter.setDefault(Object defaultValue) |
void |
FileParameter.setDefault(Object defaultValue) |
void |
EnumParameter.setDefault(Object defaultValue) |
void |
ArrayParameterSet.setLength(int length) |
void |
SimpleParameter.setValue(Object value2) |
void |
SelectionParameter.setValue(Object value)
Sets the selected value to the one that is specified by the key
value. |
void |
RangeParameter.setValue(Object value) |
void |
ParameterSetContainer.setValue(Object value) |
abstract void |
Parameter.setValue(Object value)
Sets the value of this
Parameter to value. |
void |
MultiSelectionParameter.setValue(Object value) |
void |
FileParameter.setValue(Object value) |
void |
EnumParameter.setValue(Object value) |
void |
ParameterSetTagger.setValueFromTag(String tag,
Object value)
This method allows to easily set the value of a parameter defined by the tag.
|
void |
RangeParameter.setValues(Object startValue,
int steps,
Object endValue,
RangeParameter.Scale scale)
Sets the values of this
RangeParameter as a range of values,
specified by a start value, a last value, a number of steps between these
values (without the last value) and a scale in that the values between
the first and the last value are chosen. |
void |
RangeParameter.setValues(String values)
Sets a list of values from a
String containing a space separated
list of values. |
void |
RangeParameter.setValuesInLogScale(boolean log,
double radix,
Object startValue,
int steps,
Object endValue)
This method enables you to set a list of values in an easy manner.
|
| Constructor and Description |
|---|
AbstractSelectionParameter(DataType datatype,
String[] keys,
Object[] values,
String[] comments,
String name,
String comment,
boolean required)
Constructor for a
AbstractSelectionParameter. |
AbstractSelectionParameter(DataType datatype,
String[] keys,
Object[] values,
String name,
String comment,
boolean required)
Constructor for a
AbstractSelectionParameter of SimpleParameters. |
ArrayParameterSet(ParameterSet template,
String nameTemplate,
String commentTemplate)
Creates a new
ArrayParameterSet from a Class that can be
instantiated using this ArrayParameterSet and templates for the
ParameterSet in each element of the array, the name and the
comment that are displayed for the ParameterSetContainers
enclosing the ParameterSets. |
ArrayParameterSet(ParameterSet template,
String nameTemplate,
String commentTemplate,
String lengthName,
String lengthComment,
NumberValidator<Integer> allowedLengths)
Creates a new
ArrayParameterSet from a Class that can be
instantiated using this ArrayParameterSet and templates for the
ParameterSet in each element of the array, the name and the
comment that are displayed for the ParameterSetContainers
enclosing the ParameterSets. |
MultiSelectionParameter(DataType datatype,
String[] keys,
Object[] values,
String[] comments,
String name,
String comment,
boolean required)
Constructor for a
MultiSelectionParameter. |
MultiSelectionParameter(DataType datatype,
String[] keys,
Object[] values,
String name,
String comment,
boolean required)
Constructor for a
MultiSelectionParameter. |
SelectionParameter(DataType datatype,
String[] keys,
Object[] values,
String[] comments,
String name,
String comment,
boolean required)
Constructor for a
SelectionParameter. |
SelectionParameter(DataType datatype,
String[] keys,
Object[] values,
String name,
String comment,
boolean required)
Constructor for a
SelectionParameter. |
SimpleParameter(DataType datatype,
String name,
String comment,
boolean required,
Object defaultVal)
Constructor for a
SimpleParameter without
ParameterValidator but with a default value. |
| Modifier and Type | Method and Description |
|---|---|
void |
MeanResultSet.addResults(NumericalResultSet... rs)
Adds
NumericalResultSets to this MeanResultSet. |
static Result |
Result.createResult(String name,
String comment,
DataType datatype,
Object value)
Factory method to create a new
Result. |
void |
TextResult.fill(FileParameter par)
Fills the supplied
FileParameter with a clone of the contents of this TextResult. |
| Constructor and Description |
|---|
CategoricalResult(DataType datatype,
String name,
String comment,
Comparable result)
Creates a result of a primitive categorical data type or a
String
. |
| Constructor and Description |
|---|
AbstractBurnInTestParameterSet(Class<? extends AbstractBurnInTest> instanceClass,
int starts)
Creates a new
AbstractBurnInTestParameterSet with
pre-defined parameter values. |
VarianceRatioBurnInTestParameterSet(int starts,
double t)
Creates a new
VarianceRatioBurnInTestParameterSet with
pre-defined parameter values. |
| Constructor and Description |
|---|
InhomogeneousMarkov(int order)
Creates the structure of an inhomogeneous Markov model of order
order. |
InhomogeneousMarkovParameterSet(int order)
Creates a new
InhomogeneousMarkov.InhomogeneousMarkovParameterSet with the
parameter for the order set to order. |
| Modifier and Type | Method and Description |
|---|---|
void |
DGTrainSMParameterSet.setEss(double ess)
This method can be used to set the ess (equivalent sample
size) of this parameter set.
|
| Modifier and Type | Method and Description |
|---|---|
void |
BayesianNetworkTrainSMParameterSet.setModelType(String modelType)
This method allows a simple change of the model type.
|
| Constructor and Description |
|---|
HMMTrainingParameterSet(int starts)
This constructor can be used to create an instance with a specified number of starts.
|
SamplingHMMTrainingParameterSet(int starts,
int stepsPerIteration,
int stationarySteps,
AbstractBurnInTestParameterSet burnInTestParameters)
This is the main constructor creating an already filled parameter set for training an
AbstractHMM using a sampling strategy. |
| Constructor and Description |
|---|
DataColumnParameter(String dataRef,
String name,
String comment,
boolean required,
Integer defaultVal)
Creates a new
DataColumnParameter with given name, comment, and reference. |
| Constructor and Description |
|---|
MultilineSimpleParameter(String name,
String comment,
boolean required,
Object defaultVal)
Creates a new
MultilineSimpleParameter with given default value. |