Package | Description |
---|---|
de.jstacs.algorithms.optimization |
Provides classes for different types of algorithms that are not directly linked to the modelling components of Jstacs: Algorithms on graphs, algorithms for numerical optimization, and a basic alignment algorithm.
|
de.jstacs.algorithms.optimization.termination |
Provides classes for termination conditions that can be used in algorithms.
|
de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous |
This package contains various inhomogeneous models.
|
de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.shared | |
de.jstacs.sequenceScores.statisticalModels.trainable.mixture |
This package is the super package for any mixture model.
|
de.jstacs.sequenceScores.statisticalModels.trainable.mixture.motif |
Modifier and Type | Method and Description |
---|---|
static int |
Optimizer.conjugateGradientsFR(DifferentiableFunction f,
double[] currentValues,
TerminationCondition terminationMode,
double linEps,
StartDistanceForecaster startDistance,
OutputStream out,
Time t)
The conjugate gradient algorithm by Fletcher and Reeves.
|
static int |
Optimizer.conjugateGradientsPR(DifferentiableFunction f,
double[] currentValues,
TerminationCondition terminationMode,
double linEps,
StartDistanceForecaster startDistance,
OutputStream out,
Time t)
The conjugate gradient algorithm by Polak and Ribière.
|
static int |
Optimizer.conjugateGradientsPRP(DifferentiableFunction f,
double[] currentValues,
TerminationCondition terminationMode,
double linEps,
StartDistanceForecaster startDistance,
OutputStream out,
Time t)
The conjugate gradient algorithm by Polak and Ribière
called "Polak-Ribière-Positive".
|
static int |
Optimizer.limitedMemoryBFGS(DifferentiableFunction f,
double[] currentValues,
byte m,
TerminationCondition terminationMode,
double linEps,
StartDistanceForecaster startDistance,
OutputStream out,
Time t)
The Broyden-Fletcher-Goldfarb-Shanno version
of limited memory quasi-Newton methods.
|
static int |
Optimizer.optimize(byte algorithm,
DifferentiableFunction f,
double[] currentValues,
TerminationCondition terminationMode,
double linEps,
StartDistanceForecaster startDistance,
OutputStream out)
This method enables you to use all different implemented optimization
algorithms by only one method.
|
static int |
Optimizer.optimize(byte algorithm,
DifferentiableFunction f,
double[] currentValues,
TerminationCondition terminationMode,
double linEps,
StartDistanceForecaster startDistance,
OutputStream out,
Time t)
This method enables you to use all different implemented optimization
algorithms by only one method.
|
static int |
Optimizer.quasiNewtonBFGS(DifferentiableFunction f,
double[] currentValues,
TerminationCondition terminationMode,
double linEps,
StartDistanceForecaster startDistance,
OutputStream out,
Time t)
The Broyden-Fletcher-Goldfarb-Shanno version
of the quasi-Newton method.
|
static int |
Optimizer.quasiNewtonDFP(DifferentiableFunction f,
double[] currentValues,
TerminationCondition terminationMode,
double linEps,
StartDistanceForecaster startDistance,
OutputStream out,
Time t)
The Davidon-Fletcher-Powell version of the
quasi-Newton method.
|
static int |
Optimizer.steepestDescent(DifferentiableFunction f,
double[] currentValues,
TerminationCondition terminationMode,
double linEps,
StartDistanceForecaster startDistance,
OutputStream out,
Time t)
The steepest descent.
|
Modifier and Type | Class and Description |
---|---|
class |
AbsoluteValueCondition
Deprecated.
use of the absolute value condition is not recommended and it may be removed in future releases
|
class |
AbstractTerminationCondition
This class is the abstract super class of many
TerminationCondition s. |
class |
CombinedCondition
This class allows to use many
TerminationCondition s at once. |
class |
IterationCondition
This class will stop an optimization if the number of iteration reaches a given number.
|
class |
MultipleIterationsCondition
This
TerminationCondition requires another provided TerminationCondition to fail a contiguous specified number of times
before the optimization is terminated. |
class |
SmallDifferenceOfFunctionEvaluationsCondition
This class implements a
TerminationCondition that stops an optimization
if the difference of the current and the last function evaluations will be small, i.e.,
![]() |
class |
SmallGradientConditon
This class implements a
TerminationCondition that allows no further iteration in an optimization if the
the gradient becomes small, i.e.,
![]() |
class |
SmallStepCondition
This class implements a
TerminationCondition that allows no further iteration in an optimization if the
scalar product of the current and the last values of x will be small, i.e.,
![]() |
class |
TimeCondition
This class implements a
TerminationCondition that stops the optimization if the elapsed time in seconds is
greater than a given value. |
Modifier and Type | Method and Description |
---|---|
static double |
MEMTools.train(MEMConstraint[] constraints,
int[][] cond,
SequenceIterator sequence,
byte algorithm,
TerminationCondition condition,
OutputStream stream,
int[] alphLen)
This method approximates the distribution either analytically or numerically.
|
void |
MEM.train(SequenceIterator s,
byte algo,
TerminationCondition condition,
SafeOutputStream sostream)
This method approximates the distribution either analytically or numerically.
|
Constructor and Description |
---|
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. |
Constructor and Description |
---|
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,
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.
|
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 forwardStrandProb,
double alpha,
TerminationCondition tc,
AbstractMixtureTrainSM.Parameterization parametrization)
Creates an instance using EM and fixed component probabilities.
|
Constructor and Description |
---|
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. |