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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.homogeneous | |
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.mixture.motif | |
de.jstacs.models.utils | |
de.jstacs.motifDiscovery | This package provides the framework including the interface for any de novo motif discoverer |
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. |
de.jstacs.scoringFunctions.mix.motifSearch |
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 given Sample s 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 an array of indices of the classes to which the respective sequences are assigned with for each index i in the array
0 < i < getNumberOfClasses() . |
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 the 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 s in the Sample
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)
|
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 given Sample s 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)
Assesses the contained classifiers. |
ListResult |
ClassifierAssessment.assess(MeasureParameters mp,
ClassifierAssessmentAssessParameterSet assessPS,
Sample... s)
Assesses the contained classifiers. |
ListResult |
KFoldCrossValidation.assessWithPredefinedSplits(MeasureParameters mp,
ClassifierAssessmentAssessParameterSet caaps,
ProgressUpdater pU,
Sample[]... splitData)
This method implements a k-fold crossvalidation on previously split data. |
protected void |
Sampled_RepeatedHoldOutExperiment.evaluateClassifier(MeasureParameters mp,
ClassifierAssessmentAssessParameterSet assessPS,
Sample[] s,
ProgressUpdater pU)
|
protected void |
RepeatedSubSamplingExperiment.evaluateClassifier(MeasureParameters mp,
ClassifierAssessmentAssessParameterSet assessPS,
Sample[] s,
ProgressUpdater pU)
Evaluates the classifier. |
protected void |
RepeatedHoldOutExperiment.evaluateClassifier(MeasureParameters mp,
ClassifierAssessmentAssessParameterSet assessPS,
Sample[] s,
ProgressUpdater pU)
Evaluates the classifier. |
protected void |
KFoldCrossValidation.evaluateClassifier(MeasureParameters mp,
ClassifierAssessmentAssessParameterSet assessPS,
Sample[] s,
ProgressUpdater pU)
Evaluates a classifier. |
protected abstract void |
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 training samples. |
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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Fields in de.jstacs.classifier.scoringFunctionBased declared as Sample | |
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protected Sample[] |
AbstractOptimizableFunction.data
The data that is used to evaluate this function. |
Methods in de.jstacs.classifier.scoringFunctionBased that return Sample | |
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abstract Sample[] |
OptimizableFunction.getData()
Returns the data for each class used in this OptimizableFunction . |
Sample[] |
AbstractOptimizableFunction.getData()
|
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 that should be optimized. |
void |
ScoreClassifier.train(Sample[] data,
double[][] weights)
|
Constructors in de.jstacs.classifier.scoringFunctionBased with parameters of type Sample | |
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AbstractOptimizableFunction(Sample[] data,
double[][] weights,
boolean norm,
boolean freeParams)
The constructor creates an instance using the given weighted data. |
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 NormConditionalLogLikelihood . |
|
NormConditionalLogLikelihood(ScoringFunction[] score,
Sample[] data,
double[][] weights,
LogPrior prior,
boolean norm,
boolean freeParams)
The constructor creates an instance of the NormConditionalLogLikelihood 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 and
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 composite Sequence s 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, i.e. the Sequence s, 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 a suffix of all elements, i.e. the Sequence , in the current Sample . |
static Sample |
Sample.intersection(Sample... samples)
This method computes the intersection between all elements/ Sample
s of the array, i.e. it returns a Sample containing only
Sequence s that are contained in all Sample s of the array. |
Sample[] |
Sample.partition(double p,
Sample.PartitionMethod method,
int subsequenceLength)
This method partitions the elements, i.e. the Sequence s, of the
Sample in two distinct parts. |
Sample[] |
Sample.partition(int k,
Sample.PartitionMethod method)
This method partitions the elements, i.e. the Sequence s, of the
Sample in k distinct parts. |
Sample[] |
Sample.partition(Sample.PartitionMethod method,
double... percentage)
This method partitions the elements, i.e. the Sequence s, of the
Sample in distinct parts where each part holds the corresponding
percentage given in the array percentage . |
Sample |
Sample.subSampling(int number)
Randomly samples elements, i.e. |
static Sample |
Sample.union(Sample... s)
Unites all Sample s of the array s . |
static Sample |
Sample.union(Sample[] s,
boolean[] in)
This method unites all Sample s of the array s
regarding the array in . |
static Sample |
Sample.union(Sample[] s,
boolean[] in,
int subsequenceLength)
This method unites all Sample s of the array s
regarding the array 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 s of the array 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 Sample s. |
static Sample |
Sample.intersection(Sample... samples)
This method computes the intersection between all elements/ Sample
s of the array, i.e. it returns a Sample containing only
Sequence s that are contained in all Sample s of the array. |
static Sample |
Sample.union(Sample... s)
Unites all Sample s of the array s . |
static Sample |
Sample.union(Sample[] s,
boolean[] in)
This method unites all Sample s of the array s
regarding the array in . |
static Sample |
Sample.union(Sample[] s,
boolean[] in,
int subsequenceLength)
This method unites all Sample s of the array s
regarding the array 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 s of the array s and
sets the element length in the united sample to
subsequenceLength . |
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 the Sample
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 Model 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) Constraint constr with the
weighted absolute frequencies of the Sample data . |
Uses of Sample in de.jstacs.models.discrete.homogeneous |
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Methods in de.jstacs.models.discrete.homogeneous that return Sample | |
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Sample |
HomogeneousModel.emitSample(int no,
int... length)
Creates a Sample of a given number of Sequence s from a
trained homogeneous model. |
Methods in de.jstacs.models.discrete.homogeneous with parameters of type Sample | |
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void |
HomogeneousModel.train(Sample[] data)
Trains the homogeneous model on all given Sample s. |
abstract void |
HomogeneousModel.train(Sample[] data,
double[][] weights)
Trains the homogeneous model using an array of weighted Sample s. |
void |
HomogeneousMM.train(Sample[] data,
double[][] weights)
|
void |
HomogeneousMM.train(Sample data,
double[] weights)
|
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)
|
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 or the posterior, respectively. |
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 or the posterior, respectively. |
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 of a model by using a given learning method (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 sets 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.mixture.motif |
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Methods in de.jstacs.models.mixture.motif with parameters of type Sample | |
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protected void |
SingleHiddenMotifMixture.setTrainData(Sample data)
|
void |
HiddenMotifMixture.train(Sample data,
double[] weights)
|
void |
SingleHiddenMotifMixture.trainBgModel(Sample data,
double[] weights)
|
abstract void |
HiddenMotifMixture.trainBgModel(Sample data,
double[] weights)
This method trains the background model. |
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 log-likelihood of a Sample data for a
given model m . |
static double |
ModelTester.getLogLikelihood(Model m,
Sample data,
double[] weights)
Returns the log-likelihood 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 the Bayesian Information Criterion (BIC). |
Uses of Sample in de.jstacs.motifDiscovery |
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Methods in de.jstacs.motifDiscovery that return Sample | |
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Sample |
SignificantMotifOccurrencesFinder.annotateMotifs(int startPos,
Sample data)
This method annotates a Sample starting in each sequence at startPos . |
Sample |
SignificantMotifOccurrencesFinder.annotateMotifs(int startPos,
Sample data,
int addMax)
This method annotates a Sample starting in each sequence at startPos . |
Sample |
SignificantMotifOccurrencesFinder.annotateMotifs(Sample data)
This method annotates a Sample . |
Sample |
SignificantMotifOccurrencesFinder.annotateMotifs(Sample data,
int addMax)
This method annotates a Sample . |
Methods in de.jstacs.motifDiscovery with parameters of type Sample | |
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Sample |
SignificantMotifOccurrencesFinder.annotateMotifs(int startPos,
Sample data)
This method annotates a Sample starting in each sequence at startPos . |
Sample |
SignificantMotifOccurrencesFinder.annotateMotifs(int startPos,
Sample data,
int addMax)
This method annotates a Sample starting in each sequence at startPos . |
Sample |
SignificantMotifOccurrencesFinder.annotateMotifs(Sample data)
This method annotates a Sample . |
Sample |
SignificantMotifOccurrencesFinder.annotateMotifs(Sample data,
int addMax)
This method annotates a Sample . |
static ListResult |
MotifDiscoveryAssessment.assess(Sample truth,
Sample prediction,
int maxDiff)
This method computes the nucleotide and site measures. |
int[] |
MutableMotifDiscoverer.determineNotSignificantPositionsFor(int motif,
Sample[] data,
double[][] weights,
int classIdx)
This method determines the number of not significant positions from each side of the motif with index motif . |
static boolean |
MutableMotifDiscovererToolbox.doHeuristicSteps(ScoringFunction[] funs,
Sample[] data,
double[][] weights,
OptimizableFunction opt,
SafeOutputStream out,
boolean breakOnChanged,
History[][] hist,
int[][] minimalNewLength)
This method tries to make some heuristic step if at least one MutableMotifDiscovererToolbox.InitMethodForScoringFunction is a MutableMotifDiscoverer . |
static Sequence |
MutableMotifDiscovererToolbox.enumerate(Sample[] data,
ScoringFunction[] funs,
int classIndex,
int motifIndex,
double weight,
OptimizableFunction opt,
OutputStream out)
This method allows to enumerate all possible seeds for a motif in the HiddenMotifsMixture of a specific class. |
double[][] |
SignificantMotifOccurrencesFinder.getPValuesForEachNucleotide(Sample data,
int component,
int motif,
boolean addOnlyBest)
This method determines the p-value for each symbol to be annotated at least in one motif occurrence of the motif with index index in the component component . |
static double[][] |
MotifDiscoveryAssessment.getSorted1MinusPValuesForMotifAndFlanking(Sample data,
double[][] pValues,
String identifier)
This method provides some score arrays that can be used in ScoreBasedPerformanceMeasureDefinitions to determine some
curves or area under curves based on the p -values of the predictions. |
static ComparableElement<double[],Double>[] |
MutableMotifDiscovererToolbox.getSortedInitialParameters(Sample[] data,
ScoringFunction[] funs,
MutableMotifDiscovererToolbox.InitMethodForScoringFunction[] init,
OptimizableFunction opt,
int n,
SafeOutputStream stream)
This method allows to initialize the MutableMotifDiscovererToolbox.InitMethodForScoringFunction using different MutableMotifDiscovererToolbox.InitMethodForScoringFunction . |
void |
MutableMotifDiscoverer.initializeMotif(int motifIndex,
Sample data,
double[] weights)
This method allows to initialize the model of a motif manually using a weighted sample. |
Constructors in de.jstacs.motifDiscovery with parameters of type Sample | |
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SignificantMotifOccurrencesFinder(MotifDiscoverer disc,
Sample bg,
double sign)
This constructor creates an instance of SignificantMotifOccurrencesFinder that uses a Sample to determine the siginificance level. |
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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int[] |
IndependentProductScoringFunction.determineNotSignificantPositionsFor(int motif,
Sample[] data,
double[][] weights,
int classIdx)
|
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 ScoringFunction . |
void |
NormalizedScoringFunction.initializeFunction(int index,
boolean freeParams,
Sample[] data,
double[][] weights)
|
void |
MRFScoringFunction.initializeFunction(int index,
boolean freeParams,
Sample[] data,
double[][] weights)
|
void |
IndependentProductScoringFunction.initializeFunction(int index,
boolean freeParams,
Sample[] data,
double[][] weights)
|
void |
IndependentProductScoringFunction.initializeMotif(int motifIndex,
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
Sample 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
Sample 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 |
StrandScoringFunction.initializeUsingPlugIn(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 functions using the data in some way. |
Uses of Sample in de.jstacs.scoringFunctions.mix.motifSearch |
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Methods in de.jstacs.scoringFunctions.mix.motifSearch with parameters of type Sample | |
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void |
HiddenMotifsMixture.adjustHiddenParameters(Sample data,
double[] dataWeights)
This method allows to adjust the hidden parameter in some way. |
int[] |
HiddenMotifsMixture.determineNotSignificantPositionsFor(int motif,
Sample[] data,
double[][] weights,
int classIdx)
|
void |
UniformDurationScoringFunction.initializeFunction(int index,
boolean meila,
Sample[] data,
double[][] weights)
|
void |
SkewNormalLikeScoringFunction.initializeFunction(int index,
boolean freeParams,
Sample[] data,
double[][] weights)
|
void |
HiddenMotifsMixture.initializeMotif(int motif,
Sample data,
double[] weights)
|
protected void |
HiddenMotifsMixture.initializeUsingPlugIn(int index,
boolean freeParams,
Sample[] data,
double[][] weights)
|
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