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| Uses of WrongAlphabetException in de.jstacs.classifiers.assessment |
|---|
| Methods in de.jstacs.classifiers.assessment that throw WrongAlphabetException | |
|---|---|
ListResult |
ClassifierAssessment.assess(NumericalPerformanceMeasureParameterSet mp,
ClassifierAssessmentAssessParameterSet assessPS,
DataSet... s)
Assesses the contained classifiers. |
ListResult |
ClassifierAssessment.assess(NumericalPerformanceMeasureParameterSet mp,
ClassifierAssessmentAssessParameterSet assessPS,
ProgressUpdater pU,
DataSet... s)
Assesses the contained classifiers. |
ListResult |
ClassifierAssessment.assess(NumericalPerformanceMeasureParameterSet mp,
ClassifierAssessmentAssessParameterSet assessPS,
ProgressUpdater pU,
DataSet[][]... s)
Assesses the contained classifiers. |
protected void |
ClassifierAssessment.prepareAssessment(DataSet... s)
Prepares an assessment. |
| Constructors in de.jstacs.classifiers.assessment that throw WrongAlphabetException | |
|---|---|
ClassifierAssessment(AbstractClassifier... aCs)
Creates a new ClassifierAssessment from a set of
AbstractClassifiers. |
|
ClassifierAssessment(AbstractClassifier[] aCs,
boolean buildClassifiersByCrossProduct,
TrainableStatisticalModel[]... aMs)
This constructor allows to assess a collection of given AbstractClassifiers and, in addition, classifiers that will be
constructed using the given TrainableStatisticalModels. |
|
ClassifierAssessment(AbstractClassifier[] aCs,
TrainableStatisticalModel[][] aMs,
boolean buildClassifiersByCrossProduct,
boolean checkAlphabetConsistencyAndLength)
Creates a new ClassifierAssessment from an array of
AbstractClassifiers and a two-dimensional array of TrainableStatisticalModel
s, which are combined to additional classifiers. |
|
ClassifierAssessment(boolean buildClassifiersByCrossProduct,
TrainableStatisticalModel[]... aMs)
Creates a new ClassifierAssessment from a set of TrainableStatisticalModels. |
|
KFoldCrossValidation(AbstractClassifier... aCs)
Creates a new KFoldCrossValidation from a set of
AbstractClassifiers. |
|
KFoldCrossValidation(AbstractClassifier[] aCs,
boolean buildClassifiersByCrossProduct,
TrainableStatisticalModel[]... aMs)
This constructor allows to assess a collection of given AbstractClassifiers and those constructed using the given
TrainableStatisticalModels by a KFoldCrossValidation
. |
|
KFoldCrossValidation(AbstractClassifier[] aCs,
TrainableStatisticalModel[][] aMs,
boolean buildClassifiersByCrossProduct,
boolean checkAlphabetConsistencyAndLength)
Creates a new KFoldCrossValidation from an array of
AbstractClassifiers and a two-dimensional array of TrainableStatisticalModel
s, which are combined to additional classifiers. |
|
KFoldCrossValidation(boolean buildClassifiersByCrossProduct,
TrainableStatisticalModel[]... aMs)
Creates a new KFoldCrossValidation from a set of TrainableStatisticalModels. |
|
RepeatedHoldOutExperiment(AbstractClassifier... aCs)
Creates a new RepeatedHoldOutExperiment from a set of
AbstractClassifiers. |
|
RepeatedHoldOutExperiment(AbstractClassifier[] aCs,
boolean buildClassifiersByCrossProduct,
TrainableStatisticalModel[]... aMs)
This constructor allows to assess a collection of given AbstractClassifiers and those constructed using the given
TrainableStatisticalModels by a
RepeatedHoldOutExperiment. |
|
RepeatedHoldOutExperiment(AbstractClassifier[] aCs,
TrainableStatisticalModel[][] aMs,
boolean buildClassifiersByCrossProduct,
boolean checkAlphabetConsistencyAndLength)
Creates a new RepeatedHoldOutExperiment from an array of
AbstractClassifiers and a two-dimensional array of TrainableStatisticalModel
s, which are combined to additional classifiers. |
|
RepeatedHoldOutExperiment(boolean buildClassifiersByCrossProduct,
TrainableStatisticalModel[]... aMs)
Creates a new RepeatedHoldOutExperiment from a set of
TrainableStatisticalModels. |
|
RepeatedSubSamplingExperiment(AbstractClassifier... aCs)
Creates a new RepeatedSubSamplingExperiment from a set of
AbstractClassifiers. |
|
RepeatedSubSamplingExperiment(AbstractClassifier[] aCs,
boolean buildClassifiersByCrossProduct,
TrainableStatisticalModel[]... aMs)
This constructor allows to assess a collection of given AbstractClassifiers and those constructed using the given
TrainableStatisticalModels by a
RepeatedSubSamplingExperiment. |
|
RepeatedSubSamplingExperiment(AbstractClassifier[] aCs,
TrainableStatisticalModel[][] aMs,
boolean buildClassifiersByCrossProduct,
boolean checkAlphabetConsistencyAndLength)
Creates a new RepeatedSubSamplingExperiment from an array of
AbstractClassifiers and a two-dimensional array of TrainableStatisticalModel
s, which are combined to additional classifiers. |
|
RepeatedSubSamplingExperiment(boolean buildClassifiersByCrossProduct,
TrainableStatisticalModel[]... aMs)
Creates a new RepeatedSubSamplingExperiment from a set of
TrainableStatisticalModels. |
|
Sampled_RepeatedHoldOutExperiment(AbstractClassifier... aCs)
Creates a new Sampled_RepeatedHoldOutExperiment from a set of
AbstractClassifiers. |
|
Sampled_RepeatedHoldOutExperiment(AbstractClassifier[] aCs,
boolean buildClassifiersByCrossProduct,
TrainableStatisticalModel[]... aMs)
This constructor allows to assess a collection of given AbstractClassifiers and those constructed using the given
TrainableStatisticalModels by a
Sampled_RepeatedHoldOutExperiment. |
|
Sampled_RepeatedHoldOutExperiment(AbstractClassifier[] aCs,
TrainableStatisticalModel[][] aMs,
boolean buildClassifiersByCrossProduct,
boolean checkAlphabetConsistencyAndLength)
Creates a new Sampled_RepeatedHoldOutExperiment from an array of
AbstractClassifiers and a two-dimensional array of TrainableStatisticalModel
s, which are combined to additional classifiers. |
|
Sampled_RepeatedHoldOutExperiment(boolean buildClassifiersByCrossProduct,
TrainableStatisticalModel[]... aMs)
Creates a new Sampled_RepeatedHoldOutExperiment from a set of
TrainableStatisticalModels. |
|
| Uses of WrongAlphabetException in de.jstacs.data |
|---|
| Methods in de.jstacs.data that throw WrongAlphabetException | |
|---|---|
static DataSet |
DataSet.diff(DataSet data,
DataSet... samples)
This method computes the difference between the DataSet data and
the DataSets samples. |
double |
AlphabetContainer.getCode(int pos,
String sym)
Returns the encoded symbol for sym of the Alphabet
of position pos of this AlphabetContainer. |
Sequence |
DinucleotideProperty.getPropertyAsSequence(Sequence original)
Computes this dinucleotide property for all overlapping twomers in original
and returns the result as a Sequence of length original.getLength()-1 |
| Constructors in de.jstacs.data that throw WrongAlphabetException | |
|---|---|
DataSet.WeightedDataSetFactory(DataSet.WeightedDataSetFactory.SortOperation sort,
DataSet... data)
Creates a new DataSet.WeightedDataSetFactory on the given
DataSet(s) with DataSet.WeightedDataSetFactory.SortOperation sort. |
|
DataSet.WeightedDataSetFactory(DataSet.WeightedDataSetFactory.SortOperation sort,
DataSet[] data,
double[][] weights,
int length)
Creates a new DataSet.WeightedDataSetFactory on the given array of
DataSets and an array of weights with a given
length and DataSet.WeightedDataSetFactory.SortOperation sort. |
|
DataSet.WeightedDataSetFactory(DataSet.WeightedDataSetFactory.SortOperation sort,
DataSet data,
double[] weights)
Creates a new DataSet.WeightedDataSetFactory on the given
DataSet and an array of weights with
DataSet.WeightedDataSetFactory.SortOperation sort. |
|
DataSet.WeightedDataSetFactory(DataSet.WeightedDataSetFactory.SortOperation sort,
DataSet data,
double[] weights,
int length)
Creates a new DataSet.WeightedDataSetFactory on the given
DataSet and an array of weights with a given
length and DataSet.WeightedDataSetFactory.SortOperation sort. |
|
DataSet(AlphabetContainer abc,
AbstractStringExtractor se)
Creates a new DataSet from a StringExtractor
using the given AlphabetContainer. |
|
DataSet(AlphabetContainer abc,
AbstractStringExtractor se,
int subsequenceLength)
Creates a new DataSet from a StringExtractor
using the given AlphabetContainer and all overlapping windows of
length subsequenceLength. |
|
DataSet(AlphabetContainer abc,
AbstractStringExtractor se,
String delim)
Creates a new DataSet from a StringExtractor
using the given AlphabetContainer and a delimiter
delim. |
|
DataSet(AlphabetContainer abc,
AbstractStringExtractor se,
String delim,
int subsequenceLength)
Creates a new DataSet from a StringExtractor
using the given AlphabetContainer, the given delimiter
delim and all overlapping windows of length
subsequenceLength. |
|
DataSet(String annotation,
Sequence... seqs)
Creates a new DataSet from an array of Sequences and a
given annotation. |
|
DNADataSet(String fName)
Creates a new sample of DNA sequence from a FASTA file with file name fName. |
|
DNADataSet(String fName,
char ignore)
Creates a new sample of DNA sequence from a file with file name fName. |
|
DNADataSet(String fName,
char ignore,
SequenceAnnotationParser parser)
Creates a new sample of DNA sequence from a file with file name fName using the given parser. |
|
| Uses of WrongAlphabetException in de.jstacs.data.alphabets |
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| Methods in de.jstacs.data.alphabets that throw WrongAlphabetException | |
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int |
DiscreteAlphabet.getCode(String symbol)
Returns the code of a given symbol. |
| Uses of WrongAlphabetException in de.jstacs.data.bioJava |
|---|
| Methods in de.jstacs.data.bioJava that throw WrongAlphabetException | |
|---|---|
static SequenceIterator |
BioJavaAdapter.dataSetToSequenceIterator(DataSet sample,
boolean flat)
Creates a SequenceIterator from the DataSet
sample preserving as much annotation as possible. |
| Uses of WrongAlphabetException in de.jstacs.data.sequences |
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| Methods in de.jstacs.data.sequences that throw WrongAlphabetException | |
|---|---|
static Sequence |
Sequence.create(AlphabetContainer con,
SequenceAnnotation[] annotation,
String sequence,
String delim)
Creates a Sequence from a String based on the given
AlphabetContainer using the given delimiter delim
and some annotation for the Sequence. |
static Sequence |
Sequence.create(AlphabetContainer con,
String sequence)
Creates a Sequence from a String based on the given
AlphabetContainer using the standard delimiter for this
AlphabetContainer. |
static Sequence |
Sequence.create(AlphabetContainer con,
String sequence,
String delim)
Creates a Sequence from a String based on the given
AlphabetContainer using the given delimiter delim. |
static DataSet |
SparseSequence.getDataSet(AlphabetContainer con,
AbstractStringExtractor... se)
This method allows to create a DataSet containing SparseSequences. |
static DataSet |
ArbitraryFloatSequence.getDataSet(AlphabetContainer con,
AbstractStringExtractor... se)
This method allows to create a DataSet containing ArbitraryFloatSequences. |
static DataSet |
SparseSequence.getDataSet(AlphabetContainer con,
String filename)
This method allows to create a DataSet containing SparseSequences using
a file name. |
static DataSet |
ArbitraryFloatSequence.getDataSet(AlphabetContainer con,
String filename)
This method allows to create a DataSet containing ArbitraryFloatSequences using
a file name. |
static DataSet |
SparseSequence.getDataSet(AlphabetContainer con,
String filename,
SequenceAnnotationParser parser)
This method allows to create a DataSet containing SparseSequences using
a file name. |
static DataSet |
ArbitraryFloatSequence.getDataSet(AlphabetContainer con,
String filename,
SequenceAnnotationParser parser)
This method allows to create a DataSet containing ArbitraryFloatSequences using
a file name. |
int |
Sequence.getHammingDistance(Sequence seq)
This method returns the Hamming distance between the current Sequence and seq. |
protected abstract MultiDimensionalSequence<T> |
MultiDimensionalSequence.getInstance(SequenceAnnotation[] seqAn,
Sequence... seqs)
|
protected MultiDimensionalDiscreteSequence |
MultiDimensionalDiscreteSequence.getInstance(SequenceAnnotation[] seqAn,
Sequence... seqs)
|
protected MultiDimensionalArbitrarySequence |
MultiDimensionalArbitrarySequence.getInstance(SequenceAnnotation[] seqAn,
Sequence... seqs)
|
boolean |
Sequence.matches(int maxHammingDistance,
Sequence shortSequence)
This method allows to answer the question whether there is a similar pattern find a match with a given maximal number of mismatches. |
| Constructors in de.jstacs.data.sequences that throw WrongAlphabetException | |
|---|---|
ArbitraryFloatSequence(AlphabetContainer alphabetContainer,
float[] content)
Creates a new ArbitraryFloatSequence from an array of
float-encoded alphabet symbols. |
|
ArbitraryFloatSequence(AlphabetContainer alphabetContainer,
SequenceAnnotation[] annotation,
String sequence,
String delim)
Creates a new ArbitraryFloatSequence from a String
representation using the delimiter delim. |
|
ArbitraryFloatSequence(AlphabetContainer alphabetContainer,
SequenceAnnotation[] annotation,
SymbolExtractor extractor)
Creates a new ArbitraryFloatSequence from a SymbolExtractor. |
|
ArbitraryFloatSequence(AlphabetContainer alphabetContainer,
String sequence)
Creates a new ArbitraryFloatSequence from a String
representation using the default delimiter. |
|
ArbitrarySequence(AlphabetContainer alphabetContainer,
double[] content)
Creates a new ArbitrarySequence from an array of
double-encoded alphabet symbols. |
|
ArbitrarySequence(AlphabetContainer alphabetContainer,
SequenceAnnotation[] annotation,
String sequence,
String delim)
Creates a new ArbitrarySequence from a String
representation using the delimiter delim. |
|
ArbitrarySequence(AlphabetContainer alphabetContainer,
SequenceAnnotation[] annotation,
SymbolExtractor extractor)
Creates a new ArbitrarySequence from a SymbolExtractor. |
|
ArbitrarySequence(AlphabetContainer alphabetContainer,
String sequence)
Creates a new ArbitrarySequence from a String
representation using the default delimiter. |
|
ByteSequence(AlphabetContainer alphabetContainer,
byte[] content)
Creates a new ByteSequence from an array of byte-
encoded alphabet symbols. |
|
ByteSequence(AlphabetContainer alphabetContainer,
SequenceAnnotation[] annotation,
String sequence,
String delim)
Creates a new ByteSequence from a String representation
using the delimiter delim. |
|
ByteSequence(AlphabetContainer alphabetContainer,
SequenceAnnotation[] annotation,
SymbolExtractor extractor)
Creates a new ByteSequence from a SymbolExtractor. |
|
ByteSequence(AlphabetContainer alphabetContainer,
String sequence)
Creates a new ByteSequence from a String representation
using the default delimiter. |
|
IntSequence(AlphabetContainer alphabetContainer,
int... content)
Creates a new IntSequence from an array of int-
encoded alphabet symbols. |
|
IntSequence(AlphabetContainer alphabetContainer,
int[] content,
int start,
int length)
Creates a new IntSequence from a part of the array of
int- encoded alphabet symbols. |
|
IntSequence(AlphabetContainer alphabetContainer,
SequenceAnnotation[] annotation,
String sequence,
String delim)
Creates a new IntSequence from a String representation
using the delimiter delim. |
|
IntSequence(AlphabetContainer alphabetContainer,
SequenceAnnotation[] annotation,
SymbolExtractor extractor)
Creates a new IntSequence from a SymbolExtractor. |
|
IntSequence(AlphabetContainer alphabetContainer,
String sequence)
Creates a new IntSequence from a String representation
using the default delimiter. |
|
MappedDiscreteSequence(AlphabetContainer originalAlphabetContainer,
SequenceAnnotation[] seqAn,
DiscreteAlphabetMapping... transformation)
This method allows to create an empty MappedDiscreteSequence. |
|
MappedDiscreteSequence(SimpleDiscreteSequence original,
DiscreteAlphabetMapping... transformation)
This method allows to create a MappedDiscreteSequence from a given Sequence and some given DiscreteAlphabetMappings. |
|
MultiDimensionalArbitrarySequence(SequenceAnnotation[] seqAn,
ArbitrarySequence... sequence)
This constructor creates an MultiDimensionalDiscreteSequence from a set of individual Sequences. |
|
MultiDimensionalDiscreteSequence(SequenceAnnotation[] seqAn,
SimpleDiscreteSequence... sequence)
This constructor creates an MultiDimensionalDiscreteSequence from a set of individual Sequences. |
|
MultiDimensionalSequence(SequenceAnnotation[] seqAn,
Sequence... sequence)
This constructor creates an MultiDimensionalSequence from a set of individual Sequences. |
|
PermutedSequence(Sequence<T> seq)
Creates a new PermutedSequence by shuffling the symbols of a
given Sequence. |
|
PermutedSequence(Sequence<T> seq,
int[] permutation)
Creates a new PermutedSequence for a given permutation |
|
ShortSequence(AlphabetContainer alphabetContainer,
SequenceAnnotation[] annotation,
String sequence,
String delim)
Creates a new ShortSequence from a String representation
using the delimiter delim. |
|
ShortSequence(AlphabetContainer alphabetContainer,
SequenceAnnotation[] annotation,
SymbolExtractor extractor)
Creates a new ShortSequence from a SymbolExtractor. |
|
ShortSequence(AlphabetContainer alphabetContainer,
short[] content)
Creates a new ShortSequence from an array of short-
encoded alphabet symbols. |
|
ShortSequence(AlphabetContainer alphabetContainer,
String sequence)
Creates a new ShortSequence from a String representation
using the default delimiter. |
|
SimpleDiscreteSequence(AlphabetContainer container,
SequenceAnnotation[] annotation)
This constructor creates a new SimpleDiscreteSequence with the
AlphabetContainer container and the annotation
annotation but without the content. |
|
SparseSequence(AlphabetContainer alphCon,
String seq)
Creates a new SparseSequence from a String
representation. |
|
SparseSequence(AlphabetContainer alphCon,
SymbolExtractor se)
Creates a new SparseSequence from a SymbolExtractor. |
|
| Uses of WrongAlphabetException in de.jstacs.motifDiscovery |
|---|
| Methods in de.jstacs.motifDiscovery that throw WrongAlphabetException | |
|---|---|
static Pair<Sequence,BitSet[]>[] |
KMereStatistic.getKmereSequenceStatistic(boolean bothStrands,
int maxMismatch,
HashSet<Sequence> filter,
DataSet... data)
This method enables the user to get a statistic for a set of k-mers. |
static Hashtable<Sequence,BitSet[]> |
KMereStatistic.getKmereSequenceStatistic(int k,
boolean bothStrands,
int addIndex,
DataSet... data)
This method enables the user to get a statistic over all k-mers
in the sequences. |
static Hashtable<Sequence,BitSet[]> |
KMereStatistic.merge(Hashtable<Sequence,BitSet[]> statistic,
int maximalMissmatch,
boolean bothStrands)
This method allows to merge the statistics of k-mers by allowing mismatches. |
| Uses of WrongAlphabetException in de.jstacs.sequenceScores.differentiable |
|---|
| Constructors in de.jstacs.sequenceScores.differentiable that throw WrongAlphabetException | |
|---|---|
IndependentProductDiffSS(boolean plugIn,
DifferentiableSequenceScore... functions)
This constructor creates an instance of an IndependentProductDiffSS from a given series of
independent DifferentiableSequenceScores. |
|
IndependentProductDiffSS(boolean plugIn,
DifferentiableSequenceScore[] functions,
int[] length)
This constructor creates an instance of an IndependentProductDiffSS from given series of
independent DifferentiableSequenceScores and lengths. |
|
IndependentProductDiffSS(boolean plugIn,
DifferentiableSequenceScore[] functions,
int[] index,
int[] length,
boolean[] reverse)
This is the main constructor. |
|
| Uses of WrongAlphabetException in de.jstacs.sequenceScores.statisticalModels.differentiable |
|---|
| Methods in de.jstacs.sequenceScores.statisticalModels.differentiable that throw WrongAlphabetException | |
|---|---|
static StrandDiffSM |
DifferentiableStatisticalModelFactory.createStrandModel(DifferentiableStatisticalModel model)
This method allows to create a StrandDiffSM that allows to score binding sites on both strand of DNA. |
| Constructors in de.jstacs.sequenceScores.statisticalModels.differentiable that throw WrongAlphabetException | |
|---|---|
IndependentProductDiffSM(double ess,
boolean plugIn,
DifferentiableStatisticalModel... functions)
This constructor creates an instance of an IndependentProductDiffSM from a given series of
independent DifferentiableStatisticalModels. |
|
IndependentProductDiffSM(double ess,
boolean plugIn,
DifferentiableStatisticalModel[] functions,
int[] length)
This constructor creates an instance of an IndependentProductDiffSM from given series of
independent DifferentiableStatisticalModels and lengths. |
|
IndependentProductDiffSM(double ess,
boolean plugIn,
DifferentiableStatisticalModel[] functions,
int[] index,
int[] length,
boolean[] reverse)
This is the main constructor. |
|
MappingDiffSM(AlphabetContainer originalAlphabetContainer,
DifferentiableStatisticalModel nsf,
DiscreteAlphabetMapping... mapping)
The main constructor creating a MappingDiffSM. |
|
| Uses of WrongAlphabetException in de.jstacs.sequenceScores.statisticalModels.differentiable.mixture |
|---|
| Constructors in de.jstacs.sequenceScores.statisticalModels.differentiable.mixture that throw WrongAlphabetException | |
|---|---|
StrandDiffSM(DifferentiableStatisticalModel function,
double forwardPartOfESS,
int starts,
boolean plugIn,
StrandDiffSM.InitMethod initMethod)
This constructor creates a StrandDiffSM that optimizes the usage of each strand. |
|
StrandDiffSM(DifferentiableStatisticalModel function,
int starts,
boolean plugIn,
StrandDiffSM.InitMethod initMethod,
double forward)
This constructor creates a StrandDiffSM that has a fixed frequency for the strand usage. |
|
| Uses of WrongAlphabetException in de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif |
|---|
| Constructors in de.jstacs.sequenceScores.statisticalModels.differentiable.mixture.motif that throw WrongAlphabetException | |
|---|---|
MixtureDurationDiffSM(int starts,
DurationDiffSM... function)
The main constructor of a MixtureDurationDiffSM. |
|
| Uses of WrongAlphabetException in de.jstacs.sequenceScores.statisticalModels.trainable |
|---|
| Methods in de.jstacs.sequenceScores.statisticalModels.trainable that throw WrongAlphabetException | |
|---|---|
double |
UniformTrainSM.getLogProbFor(Sequence sequence,
int startpos,
int endpos)
|
| Constructors in de.jstacs.sequenceScores.statisticalModels.trainable that throw WrongAlphabetException | |
|---|---|
CompositeTrainSM(AlphabetContainer alphabets,
int[] assignment,
TrainableStatisticalModel... models)
Creates a new CompositeTrainSM. |
|
| Uses of WrongAlphabetException in de.jstacs.sequenceScores.statisticalModels.trainable.discrete |
|---|
| Methods in de.jstacs.sequenceScores.statisticalModels.trainable.discrete that throw WrongAlphabetException | |
|---|---|
static double |
ConstraintManager.countInhomogeneous(AlphabetContainer alphabets,
int length,
DataSet data,
double[] weights,
boolean reset,
Constraint... constr)
Fills the (inhomogeneous) Constraint constr with the
weighted absolute frequencies of the DataSet data. |
| Uses of WrongAlphabetException in de.jstacs.sequenceScores.statisticalModels.trainable.discrete.homogeneous |
|---|
| Methods in de.jstacs.sequenceScores.statisticalModels.trainable.discrete.homogeneous that throw WrongAlphabetException | |
|---|---|
DataSet |
HomogeneousTrainSM.emitDataSet(int no,
int... length)
Creates a DataSet of a given number of Sequences from a
trained homogeneous model. |
protected abstract Sequence |
HomogeneousTrainSM.getRandomSequence(Random r,
int length)
This method creates a random Sequence from a trained homogeneous
model. |
protected Sequence |
HomogeneousMM.getRandomSequence(Random r,
int length)
|
| Uses of WrongAlphabetException in de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous |
|---|
| Methods in de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous that throw WrongAlphabetException | |
|---|---|
SymmetricTensor |
StructureLearner.getTensor(DataSet 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. |
| Uses of WrongAlphabetException in de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.shared |
|---|
| Constructors in de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.shared that throw WrongAlphabetException | |
|---|---|
SharedStructureMixture(FSDAGTrainSM[] m,
StructureLearner.ModelType model,
byte order,
int starts,
boolean estimateComponentProbs,
double[] weights,
double alpha,
TerminationCondition tc)
Creates a new SharedStructureMixture instance with all relevant
values. |
|
SharedStructureMixture(FSDAGTrainSM[] m,
StructureLearner.ModelType model,
byte order,
int starts,
double[] weights,
double alpha,
TerminationCondition tc)
Creates a new SharedStructureMixture instance with fixed
component weights. |
|
SharedStructureMixture(FSDAGTrainSM[] m,
StructureLearner.ModelType model,
byte order,
int starts,
double alpha,
TerminationCondition tc)
Creates a new SharedStructureMixture instance which estimates the
component probabilities/weights. |
|
| Uses of WrongAlphabetException in de.jstacs.sequenceScores.statisticalModels.trainable.hmm |
|---|
| Constructors in de.jstacs.sequenceScores.statisticalModels.trainable.hmm that throw WrongAlphabetException | |
|---|---|
AbstractHMM(HMMTrainingParameterSet trainingParameterSet,
String[] name,
int[] emissionIdx,
boolean[] forward,
Emission[] emission)
This is the main constructor for an HMM. |
|
| Uses of WrongAlphabetException in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models |
|---|
| Constructors in de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models that throw WrongAlphabetException | |
|---|---|
SamplingPhyloHMM(SamplingHMMTrainingParameterSet trainingParameterSet,
String[] name,
int[] emissionIdx,
boolean[] forward,
PhyloDiscreteEmission[] emission,
TransitionElement... te)
This is the main constructor for a hidden markov model with phylogenetic emission(s) This model can be trained using a metropolis hastings algorithm |
|
| Uses of WrongAlphabetException in de.jstacs.sequenceScores.statisticalModels.trainable.mixture |
|---|
| Constructors in de.jstacs.sequenceScores.statisticalModels.trainable.mixture that throw WrongAlphabetException | |
|---|---|
AbstractMixtureTrainSM(int length,
TrainableStatisticalModel[] models,
boolean[] optimizeModel,
int dimension,
int starts,
boolean estimateComponentProbs,
double[] componentHyperParams,
double[] weights,
AbstractMixtureTrainSM.Algorithm algorithm,
double alpha,
TerminationCondition tc,
AbstractMixtureTrainSM.Parameterization parametrization,
int initialIteration,
int stationaryIteration,
BurnInTest burnInTest)
Creates a new AbstractMixtureTrainSM. |
|
MixtureTrainSM(int length,
TrainableStatisticalModel[] models,
double[] weights,
int starts,
double alpha,
TerminationCondition tc,
AbstractMixtureTrainSM.Parameterization parametrization)
Creates an instance using EM and fixed component probabilities. |
|
MixtureTrainSM(int length,
TrainableStatisticalModel[] models,
double[] weights,
int starts,
int initialIteration,
int stationaryIteration,
BurnInTest burnInTest)
Creates an instance using Gibbs Sampling and fixed component probabilities. |
|
MixtureTrainSM(int length,
TrainableStatisticalModel[] models,
int starts,
boolean estimateComponentProbs,
double[] componentHyperParams,
double[] weights,
AbstractMixtureTrainSM.Algorithm algorithm,
double alpha,
TerminationCondition tc,
AbstractMixtureTrainSM.Parameterization parametrization,
int initialIteration,
int stationaryIteration,
BurnInTest burnInTest)
Creates a new MixtureTrainSM. |
|
MixtureTrainSM(int length,
TrainableStatisticalModel[] models,
int starts,
double[] componentHyperParams,
double alpha,
TerminationCondition tc,
AbstractMixtureTrainSM.Parameterization parametrization)
Creates an instance using EM and estimating the component probabilities. |
|
MixtureTrainSM(int length,
TrainableStatisticalModel[] models,
int starts,
double[] componentHyperParams,
int initialIteration,
int stationaryIteration,
BurnInTest burnInTest)
Creates an instance using Gibbs Sampling and sampling the component probabilities. |
|
StrandTrainSM(TrainableStatisticalModel model,
int starts,
boolean estimateComponentProbs,
double[] componentHyperParams,
double forwardStrandProb,
AbstractMixtureTrainSM.Algorithm algorithm,
double alpha,
TerminationCondition tc,
AbstractMixtureTrainSM.Parameterization parametrization,
int initialIteration,
int stationaryIteration,
BurnInTest burnInTest)
Creates a new StrandTrainSM. |
|
StrandTrainSM(TrainableStatisticalModel model,
int starts,
double[] componentHyperParams,
double alpha,
TerminationCondition tc,
AbstractMixtureTrainSM.Parameterization parametrization)
Creates an instance using EM and estimating the component probabilities. |
|
StrandTrainSM(TrainableStatisticalModel model,
int starts,
double[] componentHyperParams,
int initialIteration,
int stationaryIteration,
BurnInTest burnInTest)
Creates an instance using Gibbs Sampling and sampling the component probabilities. |
|
StrandTrainSM(TrainableStatisticalModel model,
int starts,
double forwardStrandProb,
double alpha,
TerminationCondition tc,
AbstractMixtureTrainSM.Parameterization parametrization)
Creates an instance using EM and fixed component probabilities. |
|
StrandTrainSM(TrainableStatisticalModel model,
int starts,
double forwardStrandProb,
int initialIteration,
int stationaryIteration,
BurnInTest burnInTest)
Creates an instance using Gibbs Sampling and fixed component probabilities. |
|
| Uses of WrongAlphabetException in de.jstacs.sequenceScores.statisticalModels.trainable.mixture.motif |
|---|
| Constructors in de.jstacs.sequenceScores.statisticalModels.trainable.mixture.motif that throw WrongAlphabetException | |
|---|---|
HiddenMotifMixture(TrainableStatisticalModel[] models,
boolean[] optimzeArray,
int components,
int starts,
boolean estimateComponentProbs,
double[] componentHyperParams,
double[] weights,
PositionPrior posPrior,
AbstractMixtureTrainSM.Algorithm algorithm,
double alpha,
TerminationCondition tc,
AbstractMixtureTrainSM.Parameterization parametrization,
int initialIteration,
int stationaryIteration,
BurnInTest burnInTest)
Creates a new HiddenMotifMixture. |
|
ZOOPSTrainSM(TrainableStatisticalModel motif,
TrainableStatisticalModel bg,
boolean trainOnlyMotifModel,
int starts,
double[] componentHyperParams,
double[] weights,
PositionPrior posPrior,
AbstractMixtureTrainSM.Algorithm algorithm,
double alpha,
TerminationCondition tc,
AbstractMixtureTrainSM.Parameterization parametrization,
int initialIteration,
int stationaryIteration,
BurnInTest burnInTest)
Creates a new ZOOPSTrainSM. |
|
ZOOPSTrainSM(TrainableStatisticalModel motif,
TrainableStatisticalModel bg,
boolean trainOnlyMotifModel,
int starts,
double[] componentHyperParams,
PositionPrior posPrior,
double alpha,
TerminationCondition tc,
AbstractMixtureTrainSM.Parameterization parametrization)
Creates a new ZOOPSTrainSM using EM and estimating
the probability for finding a motif. |
|
ZOOPSTrainSM(TrainableStatisticalModel motif,
TrainableStatisticalModel bg,
boolean trainOnlyMotifModel,
int starts,
double motifProb,
PositionPrior posPrior,
double alpha,
TerminationCondition tc,
AbstractMixtureTrainSM.Parameterization parametrization)
Creates a new ZOOPSTrainSM using EM and fixed
probability for finding a motif. |
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