DifferentiableStatisticalModel
s, which can compute the gradient with
respect to their parameters for a given input Sequence
.See: Description
Interface | Description |
---|---|
DifferentiableStatisticalModel |
The interface for normalizable
DifferentiableSequenceScore s. |
SamplingDifferentiableStatisticalModel |
Interface for
DifferentiableStatisticalModel s that can be used for
Metropolis-Hastings sampling in a SamplingScoreBasedClassifier . |
VariableLengthDiffSM |
This is an interface for all
DifferentiableStatisticalModel s that allow to score
subsequences of arbitrary length. |
Class | Description |
---|---|
AbstractDifferentiableStatisticalModel |
This class is the main part of any
ScoreClassifier . |
AbstractVariableLengthDiffSM |
This abstract class implements some methods declared in
DifferentiableStatisticalModel based on the declaration
of methods in VariableLengthDiffSM . |
CyclicMarkovModelDiffSM |
This scoring function implements a cyclic Markov model of arbitrary order and periodicity for any sequence length.
|
DifferentiableStatisticalModelFactory |
This class allows to easily create some frequently used models.
|
IndependentProductDiffSM |
This class enables the user to model parts of a sequence independent of each
other.
|
MappingDiffSM |
This class implements a
DifferentiableStatisticalModel that works on
mapped Sequence s. |
MarkovRandomFieldDiffSM |
This class implements the scoring function for any MRF (Markov Random Field).
|
NormalizedDiffSM |
This class makes an unnormalized
DifferentiableStatisticalModel to a normalized DifferentiableStatisticalModel . |
UniformDiffSM |
This
DifferentiableStatisticalModel does nothing. |
DifferentiableStatisticalModel
s, which can compute the gradient with
respect to their parameters for a given input Sequence
.
The parameters of DifferentiableStatisticalModel
are learned numerically, typically by
gradient-based method like provided in Optimizer
.MSPClassifier
) or by a unified learning principle (see
GenDisMixClassifier
).de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels
contains Bayesian networks and inhomogeneous Markov models.de.jstacs.sequenceScores.statisticalModels.differentiable.homogeneous
provides homogeneous models like homogeneous Markov models.de.jstacs.sequenceScores.statisticalModels.differentiable.mixture
provides mixture models including an extended ZOOPS model
for de-novo motif discovery.DifferentiableStatisticalModel
s also implement the interface
SamplingDifferentiableStatisticalModel
and can be used for
Metropolis-Hastings parameter sampling in a SamplingGenDisMixClassifier
.