Modifier and Type | Class and Description |
---|---|
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 | Method and Description |
---|---|
AbstractTrainableStatisticalModel |
AbstractTrainableStatisticalModel.clone()
Follows the conventions of
Object 's clone() -method. |
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.
|