Modifier and Type | Method and Description |
---|---|
static AbstractClassifier |
ClassifierFactory.createClassifier(double[] beta,
DifferentiableStatisticalModel... models)
Creates a classifier that is based on at least two
DifferentiableStatisticalModel s. |
static AbstractClassifier |
ClassifierFactory.createClassifier(LearningPrinciple principle,
DifferentiableStatisticalModel... models)
Creates a classifier that is based on at least two
DifferentiableStatisticalModel s. |
Modifier and Type | Method and Description |
---|---|
static GenDisMixClassifier[] |
GenDisMixClassifier.create(GenDisMixClassifierParameterSet params,
LogPrior prior,
double[] weights,
DifferentiableStatisticalModel[]... functions)
This method creates an array of GenDisMixClassifiers by using the cross-product of the given
DifferentiableStatisticalModel s. |
Constructor and Description |
---|
GenDisMixClassifier(GenDisMixClassifierParameterSet params,
LogPrior prior,
double[] beta,
DifferentiableStatisticalModel... score)
The main constructor.
|
GenDisMixClassifier(GenDisMixClassifierParameterSet params,
LogPrior prior,
double lastScore,
double[] beta,
DifferentiableStatisticalModel... score)
This constructor creates an instance and sets the value of the last (external) optimization.
|
GenDisMixClassifier(GenDisMixClassifierParameterSet params,
LogPrior prior,
double genBeta,
double disBeta,
double priorBeta,
DifferentiableStatisticalModel... score)
This convenience constructor agglomerates the
genBeta, disBeta, and priorBeta into an array and calls the main constructor. |
GenDisMixClassifier(GenDisMixClassifierParameterSet params,
LogPrior prior,
LearningPrinciple key,
DifferentiableStatisticalModel... score)
This convenience constructor creates an array of weights for an elementary learning principle and calls the main constructor.
|
Modifier and Type | Field and Description |
---|---|
protected DifferentiableStatisticalModel[] |
SeparateLogPrior.funs
The
DifferentiableSequenceScore s using the parameters that shall be
penalized. |
Modifier and Type | Interface and Description |
---|---|
interface |
SamplingDifferentiableStatisticalModel
Interface for
DifferentiableStatisticalModel s that can be used for
Metropolis-Hastings sampling in a SamplingScoreBasedClassifier . |
interface |
VariableLengthDiffSM
This is an interface for all
DifferentiableStatisticalModel s that allow to score
subsequences of arbitrary length. |
Modifier and Type | Class and Description |
---|---|
class |
AbstractDifferentiableStatisticalModel
This class is the main part of any
ScoreClassifier . |
class |
AbstractVariableLengthDiffSM
This abstract class implements some methods declared in
DifferentiableStatisticalModel based on the declaration
of methods in VariableLengthDiffSM . |
class |
CyclicMarkovModelDiffSM
This scoring function implements a cyclic Markov model of arbitrary order and periodicity for any sequence length.
|
class |
IndependentProductDiffSM
This class enables the user to model parts of a sequence independent of each
other.
|
class |
MappingDiffSM
This class implements a
DifferentiableStatisticalModel that works on
mapped Sequence s. |
class |
MarkovRandomFieldDiffSM
This class implements the scoring function for any MRF (Markov Random Field).
|
class |
NormalizedDiffSM
This class makes an unnormalized
DifferentiableStatisticalModel to a normalized DifferentiableStatisticalModel . |
class |
UniformDiffSM
This
DifferentiableStatisticalModel does nothing. |
Modifier and Type | Method and Description |
---|---|
DifferentiableStatisticalModel |
NormalizedDiffSM.getFunction()
This method returns the internal function.
|
DifferentiableStatisticalModel |
MappingDiffSM.getFunction()
This method return the internal function.
|
static DifferentiableStatisticalModel |
NormalizedDiffSM.getNormalizedVersion(DifferentiableStatisticalModel nsf,
int starts)
This method returns a normalized version of a DifferentiableStatisticalModel.
|
Modifier and Type | Method and Description |
---|---|
static MixtureDiffSM |
DifferentiableStatisticalModelFactory.createMixtureModel(DifferentiableStatisticalModel[] models)
This method allows to create a
MixtureDiffSM that models a mixture of individual component DifferentiableStatisticalModel s. |
static StrandDiffSM |
DifferentiableStatisticalModelFactory.createStrandModel(DifferentiableStatisticalModel model)
This method allows to create a
StrandDiffSM that allows to score binding sites on both strand of DNA. |
static ExtendedZOOPSDiffSM |
DifferentiableStatisticalModelFactory.createZOOPS(int length,
DifferentiableStatisticalModel motif,
HomogeneousDiffSM bg)
This method allows to create a "zero or one occurrence per sequence" (ZOOPS) model that allows to discover binding sites in a
DataSet . |
static DifferentiableStatisticalModel |
NormalizedDiffSM.getNormalizedVersion(DifferentiableStatisticalModel nsf,
int starts)
This method returns a normalized version of a DifferentiableStatisticalModel.
|
Constructor and Description |
---|
IndependentProductDiffSM(double ess,
boolean plugIn,
DifferentiableStatisticalModel... functions)
This constructor creates an instance of an
IndependentProductDiffSM from a given series of
independent DifferentiableStatisticalModel s. |
IndependentProductDiffSM(double ess,
boolean plugIn,
DifferentiableStatisticalModel[] functions,
int[] length)
This constructor creates an instance of an
IndependentProductDiffSM from given series of
independent DifferentiableStatisticalModel s 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 . |
NormalizedDiffSM(DifferentiableStatisticalModel nsf,
int starts)
Creates a new instance using a given DifferentiableStatisticalModel.
|
Modifier and Type | Class and Description |
---|---|
class |
BayesianNetworkDiffSM
This class implements a scoring function that is a moral directed graphical
model, i.e.
|
class |
MarkovModelDiffSM
This class implements a
AbstractDifferentiableStatisticalModel for an inhomogeneous Markov model. |
Modifier and Type | Class and Description |
---|---|
class |
HomogeneousDiffSM
This is the main class for all homogeneous
DifferentiableSequenceScore s. |
class |
HomogeneousMM0DiffSM
This scoring function implements a homogeneous Markov model of order zero
(hMM(0)) for a fixed sequence length.
|
class |
HomogeneousMMDiffSM
This scoring function implements a homogeneous Markov model of arbitrary
order for any sequence length.
|
class |
UniformHomogeneousDiffSM
This scoring function does nothing.
|
Modifier and Type | Class and Description |
---|---|
class |
LimitedSparseLocalInhomogeneousMixtureDiffSM_higherOrder
Class for a sparse local inhomogeneous mixture (Slim) model.
|
Modifier and Type | Class and Description |
---|---|
class |
AbstractMixtureDiffSM
This main abstract class for any mixture scoring function (e.g.
|
class |
MixtureDiffSM
This class implements a real mixture model.
|
class |
StrandDiffSM
This class enables the user to search on both strand.
|
class |
VariableLengthMixtureDiffSM
This class implements a mixture of
VariableLengthDiffSM by extending MixtureDiffSM and implementing the methods of VariableLengthDiffSM . |
Modifier and Type | Field and Description |
---|---|
protected DifferentiableStatisticalModel[] |
AbstractMixtureDiffSM.function
This array contains the internal
DifferentiableStatisticalModel s that are used to
determine the score. |
Modifier and Type | Method and Description |
---|---|
DifferentiableStatisticalModel[] |
AbstractMixtureDiffSM.getDifferentiableStatisticalModels()
Returns a deep copy of all internal used
DifferentiableStatisticalModel s. |
DifferentiableStatisticalModel |
AbstractMixtureDiffSM.getFunction(int index)
This method returns a specific internal function.
|
DifferentiableStatisticalModel[] |
AbstractMixtureDiffSM.getFunctions()
This method returns an array of clones of the internal used functions.
|
Modifier and Type | Method and Description |
---|---|
protected void |
AbstractMixtureDiffSM.cloneFunctions(DifferentiableStatisticalModel[] originalFunctions)
This method clones the given array of functions and enables the user to
do some post-processing.
|
static boolean |
StrandDiffSM.isStrandModel(DifferentiableStatisticalModel nsf)
Check whether a
DifferentiableStatisticalModel is a StrandDiffSM . |
Constructor and Description |
---|
AbstractMixtureDiffSM(int length,
int starts,
int dimension,
boolean optimizeHidden,
boolean plugIn,
DifferentiableStatisticalModel... function)
This constructor creates a new
AbstractMixtureDiffSM . |
MixtureDiffSM(int starts,
boolean plugIn,
DifferentiableStatisticalModel... component)
This constructor creates a new
MixtureDiffSM . |
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.
|
Modifier and Type | Class and Description |
---|---|
class |
DurationDiffSM
This class is the super class for all one dimensional position scoring functions that can be used as durations for semi Markov models.
|
class |
ExtendedZOOPSDiffSM
This class handles mixtures with at least one hidden motif.
|
class |
MixtureDurationDiffSM
This class implements a mixture of
DurationDiffSM s. |
class |
PositionDiffSM
This class implements a position scoring function that enables the user to get a score without using a Sequence
object.
|
class |
SkewNormalLikeDurationDiffSM
This class implements a skew normal like discrete truncated distribution.
|
class |
UniformDurationDiffSM
This scoring function implements a uniform distribution for positions.
|
Constructor and Description |
---|
ExtendedZOOPSDiffSM(boolean type,
int length,
int starts,
boolean plugIn,
HomogeneousDiffSM bg,
DifferentiableStatisticalModel[] motif,
DurationDiffSM[] posPrior,
boolean plugInBg)
This constructor creates an instance of
ExtendedZOOPSDiffSM that allows to have one site of the specified motifs in a Sequence . |
ExtendedZOOPSDiffSM(boolean type,
int length,
int starts,
boolean plugIn,
HomogeneousDiffSM bg,
DifferentiableStatisticalModel motif,
DurationDiffSM posPrior,
boolean plugInBg)
This constructor creates an instance of
ExtendedZOOPSDiffSM that is either an OOPS or a ZOOPS model depending on the chosen type . |
Modifier and Type | Field and Description |
---|---|
protected DifferentiableStatisticalModel |
DifferentiableStatisticalModelWrapperTrainSM.nsf
The internally used
DifferentiableStatisticalModel . |
Modifier and Type | Method and Description |
---|---|
DifferentiableStatisticalModel |
DifferentiableStatisticalModelWrapperTrainSM.getFunction()
Returns a copy of the internally used
DifferentiableStatisticalModel . |
Constructor and Description |
---|
DifferentiableStatisticalModelWrapperTrainSM(DifferentiableStatisticalModel nsf,
int threads,
byte algo,
AbstractTerminationCondition tc,
double lineps,
double startD)
The main constructor that creates an instance with the user given parameters.
|
Modifier and Type | Class and Description |
---|---|
class |
DifferentiableHigherOrderHMM
This class combines an
HigherOrderHMM and a DifferentiableStatisticalModel by implementing some of the declared methods. |