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Packages that use Sample | |
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de.jstacs.classifier | This package provides the framework for any classifier. |
de.jstacs.classifier.assessment | This package allows to assess classifiers. |
de.jstacs.classifier.modelBased | Provides the classes for Classifier s that are based on Model s |
de.jstacs.classifier.scoringFunctionBased | Provides the classes for Classifier s that are based on ScoringFunction s. |
de.jstacs.classifier.scoringFunctionBased.cll | Provides the implementation of the log conditional likelihood as an OptimizableFunction and a classifier that uses log conditional likelihood or supervised posterior
to learn the parameters of a set of ScoringFunctions |
de.jstacs.classifier.utils | Provides some useful classes for working with classifiers |
de.jstacs.data | Provides classes for the representation of data. |
de.jstacs.data.bioJava | Provides an adapter between the representation of data in BioJava and the representation used in Jstacs. |
de.jstacs.models | Provides the interface Model and its abstract implementation AbstractModel , which is the super class of all other models. |
de.jstacs.models.discrete | |
de.jstacs.models.discrete.inhomogeneous | This package contains various inhomogeneous models. |
de.jstacs.models.discrete.inhomogeneous.shared | |
de.jstacs.models.mixture | This package is the super package for any mixture model. |
de.jstacs.models.mixture.gibbssampling | This package contains many classes that can be used while a Gibbs sampling. |
de.jstacs.models.utils | |
de.jstacs.results | This package provides classes for results and sets of results. |
de.jstacs.scoringFunctions | Provides ScoringFunction s that can be used in a ScoreClassifier . |
de.jstacs.scoringFunctions.directedGraphicalModels | Provides ScoringFunction s that are equivalent to directed graphical models. |
de.jstacs.scoringFunctions.directedGraphicalModels.structureLearning.measures | Provides the facilities to learn the structure of a BayesianNetworkScoringFunction . |
de.jstacs.scoringFunctions.directedGraphicalModels.structureLearning.measures.btMeasures | Provides the facilities to learn the structure of a BayesianNetworkScoringFunction as
a Bayesian tree using a number of measures to define a rating of structures |
de.jstacs.scoringFunctions.directedGraphicalModels.structureLearning.measures.pmmMeasures | Provides the facilities to learn the structure of a BayesianNetworkScoringFunction as
a permuted Markov model using a number of measures to define a rating of structures |
de.jstacs.scoringFunctions.homogeneous | Provides ScoringFunction s that are homogeneous, i.e. model probabilities or scores independent of the position within a sequence |
de.jstacs.scoringFunctions.mix | Provides ScoringFunction s that are mixtures of other ScoringFunction s. |
Uses of Sample in de.jstacs.classifier |
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Methods in de.jstacs.classifier that return Sample | |
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Sample[] |
MappingClassifier.mapSample(Sample[] s)
This method maps the Samples to the internal classes. |
Methods in de.jstacs.classifier with parameters of type Sample | |
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protected void |
AbstractScoreBasedClassifier.check(Sample s)
This method checks if the given sample can be used. |
byte[] |
AbstractClassifier.classify(Sample s)
This method classifies all sequences of a sample and returns the index i , with
0 < i < getNumberOfClasses() , of the class to which the sequence is assigned. |
NumericalResultSet |
AbstractClassifier.evaluate(MeasureParameters params,
boolean exceptionIfNotComputeable,
Sample... s)
This method evaluates the classifier and computes all numerical results as for instance the sensitivity for a given specificity, the area under ROC curve and so on. |
ResultSet |
AbstractClassifier.evaluateAll(MeasureParameters params,
boolean exceptionIfNotComputeable,
Sample... s)
This method evaluates the classifier and computes all results. |
protected NumericalResult |
AbstractClassifier.getClassificationRate(Sample[] s)
This method computes the classification rate for a given array of samples. |
double[] |
AbstractScoreBasedClassifier.getPValue(Sample candidates,
Sample bg)
Returns the p-values for all sequence in candidates with respect to a given background sample. |
double |
AbstractScoreBasedClassifier.getPValue(Sequence candidate,
Sample bg)
Returns the p-value for a sequence candidate with respect to a given background sample. |
protected LinkedList<? extends Result> |
AbstractScoreBasedClassifier.getResults(Sample[] s,
MeasureParameters params,
boolean exceptionIfNotComputeable,
boolean all)
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protected LinkedList<? extends Result> |
AbstractClassifier.getResults(Sample[] s,
MeasureParameters params,
boolean exceptionIfNotComputeable,
boolean all)
This method computes the results for any evaluation of the classifier. |
double[] |
AbstractScoreBasedClassifier.getScores(Sample s)
This method returns the scores of the classifier for any sequence in the sample. |
Sample[] |
MappingClassifier.mapSample(Sample[] s)
This method maps the Samples to the internal classes. |
ConfusionMatrix |
AbstractScoreBasedClassifier.test(Sample... testData)
|
ConfusionMatrix |
AbstractClassifier.test(Sample... testData)
This method computes the confusion matrix for a given array of test data |
void |
AbstractClassifier.train(Sample... s)
Trains the AbstractClassifier object given the data as Sample s. |
void |
MappingClassifier.train(Sample[] s,
double[][] weights)
|
abstract void |
AbstractClassifier.train(Sample[] s,
double[][] weights)
This method trains a classifier over an array of weighted Sample s. |
Uses of Sample in de.jstacs.classifier.assessment |
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Methods in de.jstacs.classifier.assessment with parameters of type Sample | |
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ListResult |
ClassifierAssessment.assess(MeasureParameters mp,
ClassifierAssessmentAssessParameterSet assessPS,
ProgressUpdater pU,
Sample... s)
Assesses the contained classifiers. |
ListResult |
ClassifierAssessment.assess(MeasureParameters mp,
ClassifierAssessmentAssessParameterSet assessPS,
ProgressUpdater pU,
Sample[][]... s)
|
ListResult |
ClassifierAssessment.assess(MeasureParameters mp,
ClassifierAssessmentAssessParameterSet assessPS,
Sample... s)
Assesses the contained classifiers. |
protected boolean |
Sampled_RepeatedHoldOutExperiment.evaluateClassifier(MeasureParameters mp,
ClassifierAssessmentAssessParameterSet assessPS,
Sample[] s,
ProgressUpdater pU)
|
protected boolean |
RepeatedSubSamplingExperiment.evaluateClassifier(MeasureParameters mp,
ClassifierAssessmentAssessParameterSet assessPS,
Sample[] s,
ProgressUpdater pU)
|
protected boolean |
RepeatedHoldOutExperiment.evaluateClassifier(MeasureParameters mp,
ClassifierAssessmentAssessParameterSet assessPS,
Sample[] s,
ProgressUpdater pU)
|
protected boolean |
KFoldCrossValidation.evaluateClassifier(MeasureParameters mp,
ClassifierAssessmentAssessParameterSet assessPS,
Sample[] s,
ProgressUpdater pU)
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protected abstract boolean |
ClassifierAssessment.evaluateClassifier(MeasureParameters mp,
ClassifierAssessmentAssessParameterSet assessPS,
Sample[] s,
ProgressUpdater pU)
This method must be implemented in all subclasses. |
protected void |
ClassifierAssessment.prepareAssessment(Sample... s)
Prepares an assessment. |
protected void |
ClassifierAssessment.test(MeasureParameters mp,
boolean exception,
Sample... testS)
Uses the given test-Samples to call the evaluate -methods of the local
AbstractClassifier s. |
protected void |
ClassifierAssessment.train(Sample... trainS)
Trains the local classifiers using the given trainingSamples. |
Uses of Sample in de.jstacs.classifier.modelBased |
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Methods in de.jstacs.classifier.modelBased with parameters of type Sample | |
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byte[] |
ModelBasedClassifier.classify(Sample s)
|
double[] |
ModelBasedClassifier.getScores(Sample s)
|
void |
ModelBasedClassifier.train(Sample[] s,
double[][] weights)
|
Uses of Sample in de.jstacs.classifier.scoringFunctionBased |
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Methods in de.jstacs.classifier.scoringFunctionBased with parameters of type Sample | |
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protected void |
ScoreClassifier.createStructure(Sample[] data,
double[][] weights)
Creates the structure that will be used in the optimization. |
protected double |
ScoreClassifier.doOptimization(Sample[] reduced,
double[][] newWeights)
This method does the optimization of the train-method |
protected abstract OptimizableFunction |
ScoreClassifier.getFunction(Sample[] data,
double[][] weights)
Returns the function the should be optimized |
void |
ScoreClassifier.train(Sample[] data,
double[][] weights)
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Uses of Sample in de.jstacs.classifier.scoringFunctionBased.cll |
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Methods in de.jstacs.classifier.scoringFunctionBased.cll with parameters of type Sample | |
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protected NormConditionalLogLikelihood |
CLLClassifier.getFunction(Sample[] data,
double[][] weights)
|
Constructors in de.jstacs.classifier.scoringFunctionBased.cll with parameters of type Sample | |
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NormConditionalLogLikelihood(ScoringFunction[] score,
Sample[] data,
double[][] weights,
boolean norm,
boolean freeParams)
The constructor creates an instance of the log conditional likelihood. |
|
NormConditionalLogLikelihood(ScoringFunction[] score,
Sample[] data,
double[][] weights,
LogPrior prior,
boolean norm,
boolean freeParams)
The constructor creates an instance using the given prior. |
Uses of Sample in de.jstacs.classifier.utils |
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Methods in de.jstacs.classifier.utils with parameters of type Sample | |
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static ImageResult |
ClassificationVisualizer.getScatterplot(AbstractScoreBasedClassifier cl1,
AbstractScoreBasedClassifier cl2,
Sample class0,
Sample class1,
REnvironment e,
boolean drawThreshold)
This method returns an ImageResult containing a scatter plot of the scores for the given classifiers cl1,cl2 . |
static ImageResult |
ClassificationVisualizer.plotScores(AbstractScoreBasedClassifier cl,
Sample class0,
Sample class1,
REnvironment e,
int bins,
double density,
String plotOptions)
This method returns an ImageResult containing a plot of the histograms of the scores. |
static void |
ClassificationVisualizer.plotScores(AbstractScoreBasedClassifier cl,
Sample class0,
Sample class1,
REnvironment e,
int bins,
double density,
String plotOptions,
String fName)
This method creates a pdf containing a plot of the histograms of the scores. |
Uses of Sample in de.jstacs.data |
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Methods in de.jstacs.data that return Sample | |
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Sample |
Sample.getCompositeSample(int[] starts,
int[] lengths)
This method enables you to use only an composite sequences of all elements in the current sample. |
Sample |
Sample.getInfixSample(int start,
int length)
This method enables you to use only an infix of all elements in the current sample. |
Sample |
Sample.WeightedSampleFactory.getSample()
Returns the sample, where each sequence occurs only once |
Sample |
Sample.getSuffixSample(int start)
This method enables you to use only an suffix of all elements in the current sample. |
static Sample |
Sample.intersection(Sample... samples)
This method computes the intersection between all elements of the array, i.e. |
Sample[] |
Sample.partition(double p,
Sample.PartitionMethod method,
int subsequenceLength)
This method partitions the elements of the sample in 2 distinct parts. |
Sample[] |
Sample.partition(int k,
Sample.PartitionMethod method)
This method partitions the elements of the sample in k distinct parts. |
Sample[] |
Sample.partition(Sample.PartitionMethod method,
double... percentage)
This method partitions the elements of the sample in distinct parts. |
Sample |
Sample.subSampling(int number)
Randomly samples elements (sequences) from the set of all elements (sequences) contained in this Sample . |
static Sample |
Sample.union(Sample... s)
Unites all samples in s |
static Sample |
Sample.union(Sample[] s,
boolean[] in)
This method unites all Sample from s regarding in . |
static Sample |
Sample.union(Sample[] s,
boolean[] in,
int subsequenceLength)
This method unites all Sample from s regarding in and sets the element length in
the united sample to subsequenceLength . |
static Sample |
Sample.union(Sample[] s,
int subsequenceLength)
This method unites all Sample from s and sets the element length in
the united sample to subsequenceLength . |
Methods in de.jstacs.data with parameters of type Sample | |
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static String |
Sample.getAnnotation(Sample... s)
Returns the annotation for an array of Samples |
static Sample |
Sample.intersection(Sample... samples)
This method computes the intersection between all elements of the array, i.e. |
static Sample |
Sample.union(Sample... s)
Unites all samples in s |
static Sample |
Sample.union(Sample[] s,
boolean[] in)
This method unites all Sample from s regarding in . |
static Sample |
Sample.union(Sample[] s,
boolean[] in,
int subsequenceLength)
This method unites all Sample from s regarding in and sets the element length in
the united sample to subsequenceLength . |
static Sample |
Sample.union(Sample[] s,
int subsequenceLength)
This method unites all Sample from s and sets the element length in
the united sample to subsequenceLength . |
Constructors in de.jstacs.data with parameters of type Sample | |
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Sample.ElementEnumerator(Sample data)
This constructor creates an new ElementEnumerator on the given data |
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Sample.WeightedSampleFactory(Sample.WeightedSampleFactory.SortOperation sort,
Sample... data)
This constructor creates a Sample.WeightedSampleFactory on the given Sample (s). |
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Sample.WeightedSampleFactory(Sample.WeightedSampleFactory.SortOperation sort,
Sample[] data,
double[][] weights,
int length)
This constructor creates a Sample.WeightedSampleFactory on the given array of Sample s and weights . |
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Sample.WeightedSampleFactory(Sample.WeightedSampleFactory.SortOperation sort,
Sample data,
double[] weights)
This constructor creates a Sample.WeightedSampleFactory on the given Sample and weights . |
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Sample.WeightedSampleFactory(Sample.WeightedSampleFactory.SortOperation sort,
Sample data,
double[] weights,
int length)
This constructor creates a Sample.WeightedSampleFactory on the given Sample and weights . |
|
Sample(Sample s,
int subsequenceLength)
This constructor enables you to use subsequences of the elements of a sample. |
Uses of Sample in de.jstacs.data.bioJava |
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Methods in de.jstacs.data.bioJava that return Sample | |
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static Sample |
BioJavaAdapter.sequenceIteratorToSample(SequenceIterator it,
FeatureFilter filter)
This method creates a new Sample from a SequenceIterator . |
Methods in de.jstacs.data.bioJava with parameters of type Sample | |
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static SequenceIterator |
BioJavaAdapter.sampleToSequenceIterator(Sample sample,
boolean flat)
Creates a SequenceIterator from sample preserving
as much annotation as possible. |
Uses of Sample in de.jstacs.models |
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Methods in de.jstacs.models that return Sample | |
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Sample |
UniformModel.emitSample(int n,
int... lengths)
|
Sample |
Model.emitSample(int numberOfSequences,
int... seqLength)
This method returns a Sample object containing artificial sequence(s). |
Sample |
AbstractModel.emitSample(int numberOfSequences,
int... seqLength)
|
Methods in de.jstacs.models with parameters of type Sample | |
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double[] |
Model.getLogProbFor(Sample data)
This method computes the logarithm of the probabilities of all sequences in the given sample. |
double[] |
AbstractModel.getLogProbFor(Sample data)
|
void |
Model.getLogProbFor(Sample data,
double[] res)
This method computes and stores the logarithm of the probabilities for any sequence in the sample in the given double array. |
void |
AbstractModel.getLogProbFor(Sample data,
double[] res)
|
void |
Model.train(Sample data)
Trains the AbstractModel object given the data as Sample . |
void |
AbstractModel.train(Sample data)
|
void |
UniformModel.train(Sample data,
double[] weights)
Deprecated. |
void |
Model.train(Sample data,
double[] weights)
Trains the Model object given the data as Sample using the specified weights. |
void |
CompositeModel.train(Sample data,
double[] weights)
|
Uses of Sample in de.jstacs.models.discrete |
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Methods in de.jstacs.models.discrete with parameters of type Sample | |
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static double |
ConstraintManager.countInhomogeneous(AlphabetContainer alphabets,
int length,
Sample data,
double[] weights,
boolean reset,
Constraint... constr)
Fills the (inhomogeneous) constr with the weighted absolute frequency of the sample
data and computes the frequencies will not be computed. |
Uses of Sample in de.jstacs.models.discrete.inhomogeneous |
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Methods in de.jstacs.models.discrete.inhomogeneous that return Sample | |
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Sample |
DAGModel.emitSample(int n,
int... lengths)
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Methods in de.jstacs.models.discrete.inhomogeneous with parameters of type Sample | |
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protected void |
DAGModel.drawParameters(Sample data,
double[] weights)
This method draws the parameter of the model from the likelihood respectively posterior. |
void |
FSDAGModel.drawParameters(Sample data,
double[] weights,
int[][] graph)
This method draws the parameters of the model from the a posteriori density. |
protected void |
DAGModel.estimateParameters(Sample data,
double[] weights)
This method estimates the parameter of the model from the likelihood respectively posterior. |
static double[][] |
TwoPointEvaluater.getMIInBits(Sample s,
double[] weights)
This method computes the pairwise mutual information (in bits) between the sequence positions. |
int[][] |
StructureLearner.getStructure(Sample data,
double[] weights,
StructureLearner.ModelType model,
byte order,
StructureLearner.LearningType method)
This method finds the optimal structure (in some sense). |
SymmetricTensor |
StructureLearner.getTensor(Sample data,
double[] weights,
byte order,
StructureLearner.LearningType method)
This method can be used to compute a tensor that can be used to determine the optimal structure. |
static void |
FSDAGModel.train(Model[] models,
int[][] graph,
double[][] weights,
Sample... data)
Computes the models with structure graph |
void |
FSDAGModel.train(Sample data,
double[] weights)
|
void |
BayesianNetworkModel.train(Sample data,
double[] weights)
|
void |
FSDAGModel.train(Sample data,
double[] weights,
int[][] graph)
Computes the model with structure graph |
Uses of Sample in de.jstacs.models.discrete.inhomogeneous.shared |
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Methods in de.jstacs.models.discrete.inhomogeneous.shared with parameters of type Sample | |
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void |
SharedStructureClassifier.train(Sample[] data,
double[][] weights)
|
Uses of Sample in de.jstacs.models.mixture |
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Fields in de.jstacs.models.mixture declared as Sample | |
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protected Sample[] |
AbstractMixtureModel.sample
The sample that was used in the last training. |
Methods in de.jstacs.models.mixture that return Sample | |
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Sample |
AbstractMixtureModel.emitSample(int n,
int... lengths)
|
Methods in de.jstacs.models.mixture with parameters of type Sample | |
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protected double[][] |
AbstractMixtureModel.doFirstIteration(Sample data,
double[] dataWeights)
This method will do the first step in the train algorithm for the current model. |
double[][] |
MixtureModel.doFirstIteration(Sample data,
double[] dataWeights,
double[][] partitioning)
This method enables you to train a mixture model with a fixed start partitioning. |
protected double[][] |
AbstractMixtureModel.doFirstIteration(Sample data,
double[] dataWeights,
MultivariateRandomGenerator m,
MRGParams[] params)
This method will do the first step in the train algorithm for the current model. |
double[] |
AbstractMixtureModel.getLogProbFor(Sample data)
|
double |
AbstractMixtureModel.iterate(Sample data,
double[] dataWeights,
MultivariateRandomGenerator m,
MRGParams[] params)
This method runs the train algorithm for the current model. |
void |
StrandModel.setTrainData(Sample s)
|
protected void |
MixtureModel.setTrainData(Sample data)
|
protected abstract void |
AbstractMixtureModel.setTrainData(Sample data)
This method is invoked by the train method and set for a given sample the sample that should be used for train. |
void |
AbstractMixtureModel.train(Sample data,
double[] dataWeights)
|
Uses of Sample in de.jstacs.models.mixture.gibbssampling |
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Methods in de.jstacs.models.mixture.gibbssampling with parameters of type Sample | |
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void |
GibbsSamplingComponent.drawParameters(Sample data,
double[] weights)
This method draws the parameters of the model from the a posteriori density. |
void |
FSDAGModelForGibbsSampling.drawParameters(Sample data,
double[] weights)
|
void |
FSDAGModelForGibbsSampling.drawParameters(Sample data,
double[] weights,
int[][] graph)
|
void |
FSDAGModelForGibbsSampling.train(Sample data,
double[] weights)
|
void |
FSDAGModelForGibbsSampling.train(Sample data,
double[] weights,
int[][] graph)
|
Uses of Sample in de.jstacs.models.utils |
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Methods in de.jstacs.models.utils that return Sample | |
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static Sample |
DiscreteInhomogenousSampleEmitter.emitSample(Model m,
int n)
This method emits a sample with n |
Methods in de.jstacs.models.utils with parameters of type Sample | |
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static double |
ModelTester.getLogLikelihood(Model m,
Sample data)
Returns the loglikelihood of a sample data for a given
model m . |
static double |
ModelTester.getLogLikelihood(Model m,
Sample data,
double[] weights)
Returns the loglikelihood of a sample data for a given
model m . |
static double |
ModelTester.getValueOfAIC(Model m,
Sample s,
int k)
This method computes the value of Akaikes Information Criterion (AIC). |
static double |
ModelTester.getValueOfBIC(Model m,
Sample s,
int k)
This method computes the value of Bayesian Information Criterion (BIC). |
Uses of Sample in de.jstacs.results |
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Methods in de.jstacs.results that return Sample | |
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Sample |
SampleResult.getResult()
|
Constructors in de.jstacs.results with parameters of type Sample | |
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SampleResult(String name,
String comment,
Sample data)
Creates a new SampleResult from a Sample with the annotation name and comment . |
Uses of Sample in de.jstacs.scoringFunctions |
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Methods in de.jstacs.scoringFunctions with parameters of type Sample | |
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void |
UniformScoringFunction.initializeFunction(int index,
boolean meila,
Sample[] data,
double[][] weights)
|
void |
ScoringFunction.initializeFunction(int index,
boolean freeParams,
Sample[] data,
double[][] weights)
This method creates the underlying structure of the scoring function. |
void |
MRFScoringFunction.initializeFunction(int index,
boolean freeParams,
Sample[] data,
double[][] weights)
|
void |
IndependentProductScoringFunction.initializeFunction(int index,
boolean freeParams,
Sample[] data,
double[][] weights)
|
Uses of Sample in de.jstacs.scoringFunctions.directedGraphicalModels |
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Methods in de.jstacs.scoringFunctions.directedGraphicalModels with parameters of type Sample | |
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protected void |
BayesianNetworkScoringFunction.createTrees(Sample[] data2,
double[][] weights2)
Creates the tree structures that represent the context (array BayesianNetworkScoringFunction.trees )
and the parameter objects BayesianNetworkScoringFunction.parameters using the given Measure BayesianNetworkScoringFunction.structureMeasure . |
void |
BayesianNetworkScoringFunction.initializeFunction(int index,
boolean freeParams,
Sample[] data,
double[][] weights)
|
protected void |
BayesianNetworkScoringFunction.setPlugInParameters(int index,
boolean freeParameters,
Sample[] data,
double[][] weights)
Computes and sets the plug-in parameters (MAP estimated parameters) from data using weights . |
Uses of Sample in de.jstacs.scoringFunctions.directedGraphicalModels.structureLearning.measures |
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Methods in de.jstacs.scoringFunctions.directedGraphicalModels.structureLearning.measures with parameters of type Sample | |
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abstract int[][] |
Measure.getParents(Sample fg,
Sample bg,
double[] weightsFg,
double[] weightsBg,
int length)
Returns the optimal parents for the given data and weights. |
int[][] |
InhomogeneousMarkov.getParents(Sample fg,
Sample bg,
double[] weightsFg,
double[] weightsBg,
int length)
|
protected static double[][][][] |
Measure.getStatistics(Sample s,
double[] weights,
int length,
double ess)
Counts the occurrences of symbols of the AlphabetContainer of s using weights . |
protected static double[][][][][][] |
Measure.getStatisticsOrderTwo(Sample s,
double[] weights,
int length,
double ess)
Counts the occurrences of symbols of the AlphabetContainer of s using weights . |
Uses of Sample in de.jstacs.scoringFunctions.directedGraphicalModels.structureLearning.measures.btMeasures |
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Methods in de.jstacs.scoringFunctions.directedGraphicalModels.structureLearning.measures.btMeasures with parameters of type Sample | |
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int[][] |
BTMutualInformation.getParents(Sample fg,
Sample bg,
double[] weightsFg,
double[] weightsBg,
int length)
|
int[][] |
BTExplainingAwayResidual.getParents(Sample fg,
Sample bg,
double[] weightsFg,
double[] weightsBg,
int length)
|
Uses of Sample in de.jstacs.scoringFunctions.directedGraphicalModels.structureLearning.measures.pmmMeasures |
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Methods in de.jstacs.scoringFunctions.directedGraphicalModels.structureLearning.measures.pmmMeasures with parameters of type Sample | |
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int[][] |
PMMMutualInformation.getParents(Sample fg,
Sample bg,
double[] weightsFg,
double[] weightsBg,
int length)
|
int[][] |
PMMExplainingAwayResidual.getParents(Sample fg,
Sample bg,
double[] weightsFg,
double[] weightsBg,
int length)
|
Uses of Sample in de.jstacs.scoringFunctions.homogeneous |
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Methods in de.jstacs.scoringFunctions.homogeneous that return Sample | |
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Sample |
HMMScoringFunction.emit(int numberOfSequences,
int... seqLength)
This method returns a Sample object containing artificial sequence(s). |
Methods in de.jstacs.scoringFunctions.homogeneous with parameters of type Sample | |
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void |
UniformHomogeneousScoringFunction.initializeFunction(int index,
boolean meila,
Sample[] data,
double[][] weights)
|
void |
HMMScoringFunction.initializeFunction(int index,
boolean freeParams,
Sample[] data,
double[][] weights)
|
void |
HMM0ScoringFunction.initializeFunction(int index,
boolean freeParams,
Sample[] data,
double[][] weights)
|
Uses of Sample in de.jstacs.scoringFunctions.mix |
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Methods in de.jstacs.scoringFunctions.mix with parameters of type Sample | |
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void |
AbstractMixtureScoringFunction.initializeFunction(int index,
boolean freeParams,
Sample[] data,
double[][] weights)
|
protected void |
MixtureScoringFunction.initializeUsingPlugIn(int index,
boolean freeParams,
Sample[] data,
double[][] weights)
|
protected abstract void |
AbstractMixtureScoringFunction.initializeUsingPlugIn(int index,
boolean freeParams,
Sample[] data,
double[][] weights)
This method initializes the function using the data in some way. |
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