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## de.jstacs.sequenceScores.statisticalModels.trainable
Class TrainableStatisticalModelFactory

    java.lang.Object
      de.jstacs.sequenceScores.statisticalModels.trainable.TrainableStatisticalModelFactory

    public class TrainableStatisticalModelFactory

extends Object

This class allows to easily create some frequently used models. It offers only one way of creating each model and set some of the parameters to default values. If you like to set further models please check the constructors of the individual classes.

Author:
  Jens Keilwagen

Constructor Summary
`TrainableStatisticalModelFactory()`

Method Summary
`static BayesianNetworkTrainSM` | `createBayesianNetworkModel(AlphabetContainer con, int length, double ess, byte order)` This method returns a Bayesian network model (BN) with user-specified order.
`static HomogeneousMM` | `createHomogeneousMarkovModel(AlphabetContainer con, double ess, byte order)` This method returns a homogeneous Markov model with user-specified order.
`static FSDAGTrainSM` | `createInhomogeneousMarkovModel(AlphabetContainer con, int length, double ess, byte order)` This method returns a inhomogeneous Markov model (IMM) with user-specified order.
`static MixtureTrainSM` | `createMixtureModel(double[] hyper, TrainableStatisticalModel[] model)` This method allows to create a `MixtureTrainSM` that allows to model a `DataSet` as a mixture of individual components.
`static BayesianNetworkTrainSM` | `createPermutedMarkovModel(AlphabetContainer con, int length, double ess, byte order)` This method returns a permuted Markov model (PMM) with user-specified order.
`static FSDAGTrainSM` | `createPWM(AlphabetContainer con, int length, double ess)` This method returns a position weight matrix (PWM).
`static StrandTrainSM` | `createStrandModel(TrainableStatisticalModel model)` This method allows to create a `StrandTrainSM` that allows to score binding sites on both strand of DNA.
`static ZOOPSTrainSM` | `createZOOPS(TrainableStatisticalModel motif, TrainableStatisticalModel bg, double[] hyper, boolean trainOnlyMotifModel)` This method allows to create a "zero or one occurrence per sequence" (ZOOPS) model that allows to discover binding sites in a `DataSet`.

Methods inherited from class java.lang.Object
`clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait`

Constructor Detail

### TrainableStatisticalModelFactory

    public TrainableStatisticalModelFactory()

Method Detail

### createPWM

    public static FSDAGTrainSM createPWM(AlphabetContainer con,
                                         int length,
                                         double ess)
                                  throws Exception

  This method returns a position weight matrix (PWM). A PWM assumes that all positions of a sequence are statistically independent.

  Parameters:
    `con` - the `AlphabetContainer` of the PWM
    `length` - the length of the PWM, i.e., the length of the sequences that can be modeled
    `ess` - the equivalent sample size (ess) of the PWM, if 0 (zero) the model can be trained using maximum likelihood principle, otherwise it can be trained using the maximum a posteriori principle using the BDeu prior
  Returns:
    the PWM
  Throws:
    `Exception` - if the model can not be created correctly

### createInhomogeneousMarkovModel

    public static FSDAGTrainSM createInhomogeneousMarkovModel(AlphabetContainer con,
                                                              int length,
                                                              double ess,
                                                              byte order)
                                                       throws Exception

  This method returns a inhomogeneous Markov model (IMM) with user-specified order.

  Parameters:
    `con` - the `AlphabetContainer` of the IMM
    `length` - the length of the IMM, i.e., the length of the sequences that can be modeled
    `ess` - the equivalent sample size (ess) of the IMM, if 0 (zero) the model can be trained using maximum likelihood principle, otherwise it can be trained using the maximum a posteriori principle using the BDeu prior
    `order` - the order of the IMM, i.e., the number of directly preceding random variables (=positions) that might have an influence on the probability of outcome of a random variable (=position)
  Returns:
    the IMM
  Throws:
    `Exception` - if the model can not be created correctly

### createPermutedMarkovModel

    public static BayesianNetworkTrainSM createPermutedMarkovModel(AlphabetContainer con,
                                                                   int length,
                                                                   double ess,
                                                                   byte order)
                                                            throws Exception

  This method returns a permuted Markov model (PMM) with user-specified order. Permuted Markov models determine a permutation of the random variables (=position) and than build a inhomogeneous Markov model based on this permutation.

  Parameters:
    `con` - the `AlphabetContainer` of the PMM
    `length` - the length of the PMM, i.e., the length of the sequences that can be modeled
    `ess` - the equivalent sample size (ess) of the PMM, if 0 (zero) the model can be trained using maximum likelihood principle, otherwise it can be trained using the maximum a posteriori principle using the BDeu prior
    `order` - the order of the PMM, i.e., the number of random variables (=positions) that might have an influence on the probability of outcome of a random variable (=position)
  Returns:
    the PMM
  Throws:
    `Exception` - if the model can not be created correctly

### createBayesianNetworkModel

    public static BayesianNetworkTrainSM createBayesianNetworkModel(AlphabetContainer con,
                                                                    int length,
                                                                    double ess,
                                                                    byte order)
                                                             throws Exception

  This method returns a Bayesian network model (BN) with user-specified order. Bayesian network determine a directed acyclic graph of the random variables (positions) and than learn the parameters of the distribution based on this network.

  Parameters:
    `con` - the `AlphabetContainer` of the BN
    `length` - the length of the BN, i.e., the length of the sequences that can be modeled
    `ess` - the equivalent sample size (ess) of the BN, if 0 (zero) the model can be trained using maximum likelihood principle, otherwise it can be trained using the maximum a posteriori principle using the BDeu prior
    `order` - the order of the BN, i.e., the number of random variables (=positions) that might have an influence on the probability of outcome of a random variable (=position)
  Returns:
    the BN
  Throws:
    `Exception` - if the model can not be created correctly

### createHomogeneousMarkovModel

    public static HomogeneousMM createHomogeneousMarkovModel(AlphabetContainer con,
                                                             double ess,
                                                             byte order)
                                                      throws Exception

  This method returns a homogeneous Markov model with user-specified order.

  Parameters:
    `con` - the `AlphabetContainer` of the model
    `ess` - the equivalent sample size (ess) of the model, if 0 (zero) the model can be trained using maximum likelihood principle, otherwise it can be trained using the maximum a posteriori principle using the BDeu prior
    `order` - the order of the model, i.e., the number of directly preceding random variables (=positions) that might have an influence on the probability of outcome of a random variable (=position)
  Returns:
    the homogeneous Markov model
  Throws:
    `Exception` - if the model can not be created correctly

### createStrandModel

    public static StrandTrainSM createStrandModel(TrainableStatisticalModel model)
                                           throws Exception

  This method allows to create a `StrandTrainSM` that allows to score binding sites on both strand of DNA.

  Parameters:
    `model` - the internally used model
  Returns:
    the `StrandTrainSM`
  Throws:
    `Exception` - if the model can not be created correctly

### createMixtureModel

    public static MixtureTrainSM createMixtureModel(double[] hyper,
                                                    TrainableStatisticalModel[] model)
                                             throws Exception

  This method allows to create a `MixtureTrainSM` that allows to model a `DataSet` as a mixture of individual components.

  Parameters:
    `hyper` - the hyper parameters for the components (should be identical to the ESS of the components)
    `model` - the internally used model
  Returns:
    the `MixtureTrainSM`
  Throws:
    `Exception` - if the model can not be created correctly

### createZOOPS

    public static ZOOPSTrainSM createZOOPS(TrainableStatisticalModel motif,
                                           TrainableStatisticalModel bg,
                                           double[] hyper,
                                           boolean trainOnlyMotifModel)
                                    throws Exception

  This method allows to create a "zero or one occurrence per sequence" (ZOOPS) model that allows to discover binding sites in a `DataSet`.

  Parameters:
    `motif` - the internally used model for the binding sites
    `bg` - the internally used model for the flanking sequence
    `hyper` - the hyper parameters for the components (should be identical to the ESS of the components)
    `trainOnlyMotifModel` - a switch allowing to train either the motif model or both (motif and bg) models
  Returns:
    the ZOOPS model
  Throws:
    `Exception` - if the model can not be created correctly

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