Modifier and Type | Method and Description |
---|---|
double |
SequenceScoreDistance.getDistance(double[][] profiles1,
double[][] profiles1Rc,
StatisticalModel o2,
int motif1Length)
Returns the distance between a score profile and a model.
|
double |
SequenceScoreDistance.getDistance(StatisticalModel o1,
StatisticalModel o2) |
double[][] |
SequenceScoreDistance.getPairwiseDistanceMatrix(int numThreads,
StatisticalModel... objects)
Multi-threaded computation of the pairwise distance matrix.
|
double[][] |
SequenceScoreDistance.getProfile(StatisticalModel o,
boolean rc)
Returns the score profile for the model.
|
double[][] |
RandomSequenceScoreDistance.getProfile(StatisticalModel o,
boolean rc) |
static double[][] |
DeBruijnMotifComparison.getProfilesForMotif(CyclicSequenceAdaptor[] ad,
StatisticalModel model,
boolean revcom,
boolean exp)
Returns the score profile on a De Bruin sequence for a De Bruijn sequence.
|
static double[][] |
DeBruijnMotifComparison.getProfilesForMotif(StatisticalModel model,
int n,
boolean revcom,
boolean exp)
Returns the score profile on a De Bruin sequence for a De Bruijn sequence.
|
Modifier and Type | Method and Description |
---|---|
static Pair<double[][],int[][]> |
DeBruijnMotifComparison.getClusterRepresentative(ClusterTree<StatisticalModel> tree,
int n)
Returns a position weight matrix (PWM) representation of the root node of the given cluster tree and
also computed the relative shifts of the motifs such that they align best with the consensus motif at the root.
|
Modifier and Type | Interface and Description |
---|---|
interface |
DifferentiableStatisticalModel
The interface for normalizable
DifferentiableSequenceScore s. |
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 | 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 | 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.
|
Modifier and Type | Interface and Description |
---|---|
interface |
TrainableStatisticalModel
This interface defines all methods for a probabilistic model.
|
Modifier and Type | Class and Description |
---|---|
class |
AbstractTrainableStatisticalModel
Abstract class for a model for pattern recognition.
|
class |
CompositeTrainSM
This class is for modelling sequences by modelling the different positions of
the each sequence by different models.
|
class |
DifferentiableStatisticalModelWrapperTrainSM
This model can be used to use a DifferentiableStatisticalModel as model.
|
class |
PFMWrapperTrainSM
A wrapper class for representing position weight matrices or position frequency matrices
from databases as
TrainableStatisticalModel s. |
class |
UniformTrainSM
This class represents a uniform model.
|
class |
VariableLengthWrapperTrainSM
This class allows to train any
TrainableStatisticalModel on DataSet s of Sequence s with
variable length if each individual length is at least SequenceScore.getLength() . |
Modifier and Type | Class and Description |
---|---|
class |
DiscreteGraphicalTrainSM
This is the main class for all discrete graphical models
(DGM).
|
Modifier and Type | Class and Description |
---|---|
class |
HomogeneousMM
This class implements homogeneous Markov models (hMM) of arbitrary order.
|
class |
HomogeneousTrainSM
This class implements homogeneous models of arbitrary order.
|
Modifier and Type | Class and Description |
---|---|
class |
BayesianNetworkTrainSM
The class implements a Bayesian network (
StructureLearner.ModelType.BN ) of fixed order. |
class |
DAGTrainSM
The abstract class for directed acyclic graphical models
(
DAGTrainSM ). |
class |
FSDAGModelForGibbsSampling
This is the class for a fixed structure directed acyclic graphical model (see
FSDAGTrainSM ) that can be used in a Gibbs sampling. |
class |
FSDAGTrainSM
This class can be used for any discrete fixed structure
directed acyclic graphical model (
FSDAGTrainSM ). |
class |
FSMEManager
This class can be used for any discrete fixed structure maximum entropy model (FSMEM).
|
class |
InhomogeneousDGTrainSM
This class is the main class for all inhomogeneous discrete
graphical models (
InhomogeneousDGTrainSM ). |
class |
MEManager
This class is the super class for all maximum entropy models
|
Modifier and Type | Class and Description |
---|---|
class |
SharedStructureMixture
This class handles a mixture of models with the same structure that is
learned via EM.
|
Modifier and Type | Class and Description |
---|---|
class |
AbstractHMM
This class is the super class of all implementations hidden Markov models (HMMs) in Jstacs.
|
Modifier and Type | Class and Description |
---|---|
class |
DifferentiableHigherOrderHMM
This class combines an
HigherOrderHMM and a DifferentiableStatisticalModel by implementing some of the declared methods. |
class |
HigherOrderHMM
This class implements a higher order hidden Markov model.
|
class |
SamplingHigherOrderHMM |
class |
SamplingPhyloHMM
This class implements an (higher order) HMM that contains multi-dimensional emissions described
by a phylogenetic tree.
|
Modifier and Type | Class and Description |
---|---|
class |
AbstractMixtureTrainSM
This is the abstract class for all kinds of mixture models.
|
class |
MixtureTrainSM
The class for a mixture model of any
TrainableStatisticalModel s. |
class |
StrandTrainSM
This model handles sequences that can either lie on the forward strand or on
the reverse complementary strand.
|
Modifier and Type | Class and Description |
---|---|
class |
HiddenMotifMixture
This is the main class that every generative motif discoverer should
implement.
|
class |
ZOOPSTrainSM
This class enables the user to search for a single motif in a sequence.
|
Modifier and Type | Method and Description |
---|---|
static DataSet |
DiscreteInhomogenousDataSetEmitter.emitDataSet(StatisticalModel m,
int n)
This method emits a data set with
n sequences from the discrete inhomogeneous model m
. |
static double |
StatisticalModelTester.getKLDivergence(StatisticalModel m1,
StatisticalModel m2,
int length)
Returns the Kullback-Leibler-divergence
D(p_m1||p_m2) . |
static double |
StatisticalModelTester.getLogLikelihood(StatisticalModel m,
DataSet data)
|
static double |
StatisticalModelTester.getLogLikelihood(StatisticalModel m,
DataSet data,
double[] weights)
|
static double[] |
StatisticalModelTester.getMarginalDistribution(StatisticalModel m,
int[]... constraint)
This method computes the marginal distribution for any discrete model
m and all sequences that fulfill the constraint
, if possible. |
static double |
StatisticalModelTester.getMaxOfDeviation(StatisticalModel m1,
StatisticalModel m2,
int length)
This method computes the maximum deviation between the probabilities for
all sequences of
length for discrete models m1
and m2 . |
static double |
StatisticalModelTester.getShannonEntropy(StatisticalModel m,
int length)
This method computes the Shannon entropy for any discrete model
m and all sequences of length , if possible. |
static double |
StatisticalModelTester.getShannonEntropyInBits(StatisticalModel m,
int length)
This method computes the Shannon entropy in bits for any discrete model
m and all sequences of length , if possible. |
static double |
StatisticalModelTester.getSumOfDeviation(StatisticalModel m1,
StatisticalModel m2,
int length)
This method computes the sum of deviations between the probabilities for
all sequences of
length for discrete models m1
and m2 . |
static double |
StatisticalModelTester.getSumOfDistribution(StatisticalModel m,
int length)
This method computes the marginal distribution for any discrete model
m and all sequences of length , if possible. |
static double |
StatisticalModelTester.getSymKLDivergence(StatisticalModel m1,
StatisticalModel m2,
int length)
Returns the difference of the Kullback-Leibler-divergences, i.e.
|
static double |
StatisticalModelTester.getValueOfAIC(StatisticalModel m,
DataSet s,
int k)
This method computes the value of Akaikes Information Criterion (AIC).
|
static double |
StatisticalModelTester.getValueOfBIC(StatisticalModel m,
DataSet s,
int k)
This method computes the value of the Bayesian Information Criterion
(BIC).
|