Package | Description |
---|---|
de.jstacs.sequenceScores.statisticalModels.trainable.mixture |
This package is the super package for any mixture model.
|
de.jstacs.sequenceScores.statisticalModels.trainable.mixture.motif | |
de.jstacs.utils.random |
This package contains some classes for generating random numbers.
|
Modifier and Type | Method and Description |
---|---|
protected MultivariateRandomGenerator |
AbstractMixtureTrainSM.getMRG()
This method creates the multivariate random generator that will be used
during initialization.
|
Modifier and Type | Method and Description |
---|---|
protected double[][] |
AbstractMixtureTrainSM.doFirstIteration(DataSet data,
double[] dataWeights,
MultivariateRandomGenerator m,
MRGParams[] params)
This method will do the first step in the train algorithm for the current
model.
|
protected double[][] |
StrandTrainSM.doFirstIteration(double[] dataWeights,
MultivariateRandomGenerator m,
MRGParams[] params) |
protected double[][] |
MixtureTrainSM.doFirstIteration(double[] dataWeights,
MultivariateRandomGenerator m,
MRGParams[] params) |
protected abstract double[][] |
AbstractMixtureTrainSM.doFirstIteration(double[] dataWeights,
MultivariateRandomGenerator m,
MRGParams[] params)
This method will do the first step in the train algorithm for the current
model on the internal data set.
|
double |
AbstractMixtureTrainSM.iterate(DataSet data,
double[] dataWeights,
MultivariateRandomGenerator m,
MRGParams[] params)
This method runs the train algorithm for the current model.
|
protected double |
AbstractMixtureTrainSM.iterate(int start,
double[] dataWeights,
MultivariateRandomGenerator m,
MRGParams[] params)
This method runs the train algorithm for the current model and the
internal data set.
|
Modifier and Type | Method and Description |
---|---|
protected double[][] |
ZOOPSTrainSM.doFirstIteration(double[] dataWeights,
MultivariateRandomGenerator m,
MRGParams[] params) |
protected double |
ZOOPSTrainSM.iterate(int start,
double[] dataWeights,
MultivariateRandomGenerator m,
MRGParams[] params) |
Modifier and Type | Class and Description |
---|---|
class |
DirichletMRG
This class is a multivariate random generator based on a Dirichlet
distribution.
|
class |
EqualParts
This class is no real random generator it just returns 1/n for all values.
|
class |
ErlangMRG
This class is a multivariate random generator based on a Dirichlet
distribution for
alpha_i \in N . |
class |
SoftOneOfN
This random generator returns
1-epsilon for one and equal parts
for the rest of a random vector. |