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## de.jstacs.models.mixture
Class MixtureModel

    java.lang.Object
      de.jstacs.models.AbstractModel
          de.jstacs.models.mixture.AbstractMixtureModel
              de.jstacs.models.mixture.MixtureModel

All Implemented Interfaces:
  Model, Storable, Cloneable

Direct Known Subclasses:
  SharedStructureMixture

    public class MixtureModel

extends AbstractMixtureModel

The class for a mixture model of any `Model`s.
If you use Gibbs sampling temporary files will be created in the Java temp folder. These files will be deleted if no reference to the current instance exists and the Garbage Collector is called. Therefore it is recommended to call the Garbage Collector explicitly at the end of any application.

Author:
  Jens Keilwagen, Berit Haldemann

Nested Class Summary

Nested classes/interfaces inherited from class de.jstacs.models.mixture.AbstractMixtureModel
`AbstractMixtureModel.Algorithm, AbstractMixtureModel.Parameterization`

Field Summary

Fields inherited from class de.jstacs.models.mixture.AbstractMixtureModel
`algorithm, algorithmHasBeenRun, alternativeModel, best, burnInTest, componentHyperParams, compProb, counter, dimension, estimateComponentProbs, file, filereader, filewriter, initialIteration, logWeights, model, optimizeModel, sample, samplingIndex, seqWeights, sostream, starts, stationaryIteration, weights`

Fields inherited from class de.jstacs.models.AbstractModel
`alphabets, length`

Constructor Summary
`` | `MixtureModel(int length, Model[] models, double[] weights, int starts, double alpha, TerminationCondition tc, AbstractMixtureModel.Parameterization parametrization)` Creates an instance using EM and fixed component probabilities.
`` | `MixtureModel(int length, Model[] models, double[] weights, int starts, int initialIteration, int stationaryIteration, BurnInTest burnInTest)` Creates an instance using Gibbs Sampling and fixed component probabilities.
`protected` | `MixtureModel(int length, Model[] models, int starts, boolean estimateComponentProbs, double[] componentHyperParams, double[] weights, AbstractMixtureModel.Algorithm algorithm, double alpha, TerminationCondition tc, AbstractMixtureModel.Parameterization parametrization, int initialIteration, int stationaryIteration, BurnInTest burnInTest)` Creates a new `MixtureModel`.
`` | `MixtureModel(int length, Model[] models, int starts, double[] componentHyperParams, double alpha, TerminationCondition tc, AbstractMixtureModel.Parameterization parametrization)` Creates an instance using EM and estimating the component probabilities.
`` | `MixtureModel(int length, Model[] models, int starts, double[] componentHyperParams, int initialIteration, int stationaryIteration, BurnInTest burnInTest)` Creates an instance using Gibbs Sampling and sampling the component probabilities.
`` | `MixtureModel(StringBuffer xml)` The constructor for the interface `Storable`.

Method Summary
`protected double[][]` | `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 sample.
`double[][]` | `doFirstIteration(Sample data, double[] dataWeights, double[][] partitioning)` This method enables you to train a mixture model with a fixed start partitioning.
`protected Sequence[]` | `emitSampleUsingCurrentParameterSet(int n, int... lengths)` The method returns an array of sequences using the current parameter set.
`protected double` | `getLogProbUsingCurrentParameterSetFor(int component, Sequence s, int start, int end)` Returns the logarithmic probability for the sequence and the given component using the current parameter set.
`protected double` | `getNewWeights(double[] dataWeights, double[] w, double[][] seqweights)` Computes sequence weights and returns the score.
`protected void` | `setTrainData(Sample data)` This method is invoked by the `train`-method and sets for a given sample the sample that should be used for `train`.
`String` | `toString()` Should give a simple representation (text) of the model as `String` .

Methods inherited from class de.jstacs.models.mixture.AbstractMixtureModel
`algorithmHasBeenRun, checkLength, checkModelsForGibbsSampling, clone, continueIterations, continueIterations, createSeqWeightsArray, doFirstIteration, doFirstIteration, draw, emitSample, extendSampling, extractFurtherInformation, finalize, fromXML, getCharacteristics, getFurtherInformation, getIndexOfMaximalComponentFor, getInstanceName, getLogPriorTerm, getLogPriorTermForComponentProbs, getLogProbFor, getLogProbFor, getLogProbFor, getModel, getModels, getMRG, getMRGParams, getNameOfAlgorithm, getNewComponentProbs, getNewParameters, getNewParametersForModel, getNumberOfComponents, getNumericalCharacteristics, getProbFor, getScoreForBestRun, getTime, getWeights, initModelForSampling, initWithPrior, isInSamplingMode, isTrained, iterate, iterate, max, modifyWeights, parseNextParameterSet, parseParameterSet, samplingStopped, set, setAlpha, setOutputStream, setWeights, swap, toXML, train`

Methods inherited from class de.jstacs.models.AbstractModel
`getAlphabetContainer, getLength, getLogProbFor, getLogProbFor, getLogProbFor, getMaximalMarkovOrder, getPriorTerm, getProbFor, getProbFor, setNewAlphabetContainerInstance, train`

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

Constructor Detail

### MixtureModel

    protected MixtureModel(int length,
                           Model[] models,
                           int starts,
                           boolean estimateComponentProbs,
                           double[] componentHyperParams,
                           double[] weights,
                           AbstractMixtureModel.Algorithm algorithm,
                           double alpha,
                           TerminationCondition tc,
                           AbstractMixtureModel.Parameterization parametrization,
                           int initialIteration,
                           int stationaryIteration,
                           BurnInTest burnInTest)
                    throws IllegalArgumentException,
                           WrongAlphabetException,
                           CloneNotSupportedException

  Creates a new `MixtureModel`. This constructor can be used for any algorithm since it takes all necessary values as parameters.

  Parameters:
    `length` - the length used in this model
    `models` - the single models building the `MixtureModel`, if the model is trained using `AbstractMixtureModel.Algorithm.GIBBS_SAMPLING` the models that will be adjusted have to implement `SamplingComponent`
    `starts` - the number of times the algorithm will be started in the `train`-method, at least 1
    `estimateComponentProbs` - the switch for estimating the component probabilities in the algorithm or to hold them fixed; if the component parameters are fixed, the values of `weights` will be used, otherwise the `componentHyperParams` will be incorporated in the adjustment
    `componentHyperParams` - the hyperparameters for the component assignment prior
    - will only be used if `estimateComponentProbs == true`
    - the array has to be `null` or has to have length `models.length`
    - `null` or an array with all values zero (0) then ML
    - otherwise (all values positive) a prior is used (MAP, MP, ...)
    - depends on the `parameterization`
    `weights` - `null` or the weights for the components (then `weights.length == models.length`)
    `algorithm` - either `AbstractMixtureModel.Algorithm.EM` or `AbstractMixtureModel.Algorithm.GIBBS_SAMPLING`
    `alpha` - only for `AbstractMixtureModel.Algorithm.EM`
    the positive parameter for the Dirichlet distribution which is used when you invoke `train` to initialize the gammas. It is recommended to use `alpha = 1` (uniform distribution on a simplex).
    `tc` - only for `AbstractMixtureModel.Algorithm.EM`
    the `TerminationCondition` for stopping the EM-algorithm, `tc` has to return `true` from `TerminationCondition.isSimple()`
    `parametrization` - only for `AbstractMixtureModel.Algorithm.EM`
    the type of the component probability parameterization;
    - `AbstractMixtureModel.Parameterization.THETA` or `AbstractMixtureModel.Parameterization.LAMBDA`
    - the parameterization of a component is determined by the component model
    - it is recommended to use the same parameterization for the components and the component assignment probabilities
    - it is recommended to use `AbstractMixtureModel.Parameterization.LAMBDA`
    `initialIteration` - only for `AbstractMixtureModel.Algorithm.GIBBS_SAMPLING`
    the positive length of the initial sampling phase (at least 1, at most `stationaryIteration/starts`)
    `stationaryIteration` - only for `AbstractMixtureModel.Algorithm.GIBBS_SAMPLING`
    the positive length of the stationary phase (at least 1) (summed over all starts), i.e. the number of parameter sets that is used for approximation
    `burnInTest` - only for `AbstractMixtureModel.Algorithm.GIBBS_SAMPLING`
    the test that will be used to determine the length of the burn-in phase
  Throws:
    `IllegalArgumentException` - if
    - the models are not able to score the sequence of length `length`
    - `dimension < 1`
    - `weights != null && weights.length != dimension`
    - `weights != null` and it exists an `i` where `weights[i] < 0`
    - `starts < 1`
    - `componentHyperParams` are not correct
    - the algorithm specific parameters are not correct
    `WrongAlphabetException` - if not all `models` work on the same alphabet
    `CloneNotSupportedException` - if the `models` can not be cloned

### MixtureModel

    public MixtureModel(int length,
                        Model[] models,
                        int starts,
                        double[] componentHyperParams,
                        double alpha,
                        TerminationCondition tc,
                        AbstractMixtureModel.Parameterization parametrization)
                 throws IllegalArgumentException,
                        WrongAlphabetException,
                        CloneNotSupportedException

  Creates an instance using EM and estimating the component probabilities.

  Parameters:
    `length` - the length used in this model
    `models` - the single models building the `MixtureModel`, if the model is trained using `AbstractMixtureModel.Algorithm.GIBBS_SAMPLING` the models that will be adjusted have to implement `SamplingComponent`
    `starts` - the number of times the algorithm will be started in the `train`-method, at least 1
    `componentHyperParams` - the hyperparameters for the component assignment prior
    - will only be used if `estimateComponentProbs == true`
    - the array has to be `null` or has to have length `models.length`
    - `null` or an array with all values zero (0) then ML
    - otherwise (all values positive) a prior is used (MAP, MP, ...)
    - depends on the `parameterization`
    `alpha` - only for `AbstractMixtureModel.Algorithm.EM`
    the positive parameter for the Dirichlet distribution which is used when you invoke `train` to initialize the gammas. It is recommended to use `alpha = 1` (uniform distribution on a simplex).
    `tc` - only for `AbstractMixtureModel.Algorithm.EM`
    the `TerminationCondition` for stopping the EM-algorithm, `tc` has to return `true` from `TerminationCondition.isSimple()`
    `parametrization` - only for `AbstractMixtureModel.Algorithm.EM`
    the type of the component probability parameterization
    - `AbstractMixtureModel.Parameterization.THETA` or `AbstractMixtureModel.Parameterization.LAMBDA`
    - the parameterization of a component is determined by the component model
    - it is recommended to use the same parameterization for the components and the component assignment probabilities
    - it is recommended to use `AbstractMixtureModel.Parameterization.LAMBDA`
  Throws:
    `IllegalArgumentException` - if
    - the models are not able to score the sequence of length `length`
    - `dimension < 1`
    - `weights != null && weights.length != dimension`
    - `weights != null` and it exists an `i` where `weights[i] < 0`
    - `starts < 1`
    - `componentHyperParams` are not correct
    - the algorithm specific parameters are not correct
    `WrongAlphabetException` - if not all `models` work on the same alphabet
    `CloneNotSupportedException` - if the `models` can not be cloned
  See Also:
    `MixtureModel(int, de.jstacs.models.Model[], int, boolean, double[], double[], de.jstacs.models.mixture.AbstractMixtureModel.Algorithm, double, TerminationCondition, de.jstacs.models.mixture.AbstractMixtureModel.Parameterization, int, int, de.jstacs.sampling.BurnInTest)`, `AbstractMixtureModel.Algorithm.EM`

### MixtureModel

    public MixtureModel(int length,
                        Model[] models,
                        double[] weights,
                        int starts,
                        double alpha,
                        TerminationCondition tc,
                        AbstractMixtureModel.Parameterization parametrization)
                 throws IllegalArgumentException,
                        WrongAlphabetException,
                        CloneNotSupportedException

  Creates an instance using EM and fixed component probabilities.

  Parameters:
    `length` - the length used in this model
    `models` - the single models building the `MixtureModel`, if the model is trained using `AbstractMixtureModel.Algorithm.GIBBS_SAMPLING` the models that will be adjusted have to implement `SamplingComponent`
    `starts` - the number of times the algorithm will be started in the `train`-method, at least 1
    `weights` - `null` or the weights for the components (then `weights.length == models.length`)
    `alpha` - only for `AbstractMixtureModel.Algorithm.EM`
    the positive parameter for the Dirichlet distribution which is used when you invoke `train` to initialize the gammas. It is recommended to use `alpha = 1` (uniform distribution on a simplex).
    `tc` - only for `AbstractMixtureModel.Algorithm.EM`
    the `TerminationCondition` for stopping the EM-algorithm, `tc` has to return `true` from `TerminationCondition.isSimple()`
    `parametrization` - only for `AbstractMixtureModel.Algorithm.EM`
    the type of the component probability parameterization;
    - `AbstractMixtureModel.Parameterization.THETA` or `AbstractMixtureModel.Parameterization.LAMBDA`
    - the parameterization of a component is determined by the component model
    - it is recommended to use the same parameterization for the components and the component assignment probabilities
    - it is recommended to use `AbstractMixtureModel.Parameterization.LAMBDA`
  Throws:
    `IllegalArgumentException` - if
    - the models are not able to score the sequence of length `length`
    - `dimension < 1`
    - `weights != null && weights.length != dimension`
    - `weights != null` and it exists an `i` where `weights[i] < 0`
    - `starts < 1`
    - `componentHyperParams` are not correct
    - the algorithm specific parameters are not correct
    `WrongAlphabetException` - if not all `models` work on the same alphabet
    `CloneNotSupportedException` - if the `models` can not be cloned
  See Also:
    `MixtureModel(int, de.jstacs.models.Model[], int, boolean, double[], double[], de.jstacs.models.mixture.AbstractMixtureModel.Algorithm, double, TerminationCondition, de.jstacs.models.mixture.AbstractMixtureModel.Parameterization, int, int, de.jstacs.sampling.BurnInTest)`, `AbstractMixtureModel.Algorithm.EM`

### MixtureModel

    public MixtureModel(int length,
                        Model[] models,
                        int starts,
                        double[] componentHyperParams,
                        int initialIteration,
                        int stationaryIteration,
                        BurnInTest burnInTest)
                 throws IllegalArgumentException,
                        WrongAlphabetException,
                        CloneNotSupportedException

  Creates an instance using Gibbs Sampling and sampling the component probabilities.

  Parameters:
    `length` - the length used in this model
    `models` - the single models building the `MixtureModel`, if the model is trained using `AbstractMixtureModel.Algorithm.GIBBS_SAMPLING` the models that will be adjusted have to implement `SamplingComponent`
    `starts` - the number of times the algorithm will be started in the `train`-method, at least 1
    `componentHyperParams` - the hyperparameters for the component assignment prior
    - will only be used if `estimateComponentProbs == true`
    - the array has to be `null` or has to have length `models.length`
    - `null` or an array with all values zero (0) then ML
    - otherwise (all values positive) a prior is used (MAP, MP, ...)
    - depends on the `parameterization`
    `initialIteration` - only for `AbstractMixtureModel.Algorithm.GIBBS_SAMPLING`
    the positive length of the initial sampling phase (at least 1, at most `stationaryIteration/starts`)
    `stationaryIteration` - only for `AbstractMixtureModel.Algorithm.GIBBS_SAMPLING`
    the positive length of the stationary phase (at least 1) (summed over all starts), i.e. the number of parameter sets that is used for approximation
    `burnInTest` - only for `AbstractMixtureModel.Algorithm.GIBBS_SAMPLING`
    the test that will be used to determine the length of the burn-in phase
  Throws:
    `IllegalArgumentException` - if
    - the models are not able to score the sequence of length `length`
    - `dimension < 1`
    - `weights != null && weights.length != dimension`
    - `weights != null` and it exists an `i` where `weights[i] < 0`
    - `starts < 1`
    - `componentHyperParams` are not correct
    - the algorithm specific parameters are not correct
    `WrongAlphabetException` - if not all `models` work on the same alphabet
    `CloneNotSupportedException` - if the `models` can not be cloned
  See Also:
    `MixtureModel(int, de.jstacs.models.Model[], int, boolean, double[], double[], de.jstacs.models.mixture.AbstractMixtureModel.Algorithm, double, TerminationCondition, de.jstacs.models.mixture.AbstractMixtureModel.Parameterization, int, int, de.jstacs.sampling.BurnInTest)`, `AbstractMixtureModel.Algorithm.GIBBS_SAMPLING`

### MixtureModel

    public MixtureModel(int length,
                        Model[] models,
                        double[] weights,
                        int starts,
                        int initialIteration,
                        int stationaryIteration,
                        BurnInTest burnInTest)
                 throws IllegalArgumentException,
                        WrongAlphabetException,
                        CloneNotSupportedException

  Creates an instance using Gibbs Sampling and fixed component probabilities.

  Parameters:
    `length` - the length used in this model
    `models` - the single models building the `MixtureModel`, if the model is trained using `AbstractMixtureModel.Algorithm.GIBBS_SAMPLING` the models that will be adjusted have to implement `SamplingComponent`
    `starts` - the number of times the algorithm will be started in the `train`-method, at least 1
    `weights` - `null` or the weights for the components (than `weights.length == models.length`)
    `initialIteration` - only for `AbstractMixtureModel.Algorithm.GIBBS_SAMPLING`
    the positive length of the initial sampling phase (at least 1, at most `stationaryIteration/starts`)
    `stationaryIteration` - only for `AbstractMixtureModel.Algorithm.GIBBS_SAMPLING`
    the positive length of the stationary phase (at least 1) (summed over all starts), i.e. the number of parameter sets that is used for approximation
    `burnInTest` - only for `AbstractMixtureModel.Algorithm.GIBBS_SAMPLING`
    the test that will be used to determine the length of the burn-in phase
  Throws:
    `IllegalArgumentException` - if
    - the models are not able to score the sequence of length `length`
    - `dimension < 1`
    - `weights != null && weights.length != dimension`
    - `weights != null` and it exists an `i` where `weights[i] < 0`
    - `starts < 1`
    - `componentHyperParams` are not correct
    - the algorithm specific parameters are not correct
    `WrongAlphabetException` - if not all `models` work on the same alphabet
    `CloneNotSupportedException` - if the `models` can not be cloned
  See Also:
    `MixtureModel(int, de.jstacs.models.Model[], int, boolean, double[], double[], de.jstacs.models.mixture.AbstractMixtureModel.Algorithm, double, TerminationCondition, de.jstacs.models.mixture.AbstractMixtureModel.Parameterization, int, int, de.jstacs.sampling.BurnInTest)`, `AbstractMixtureModel.Algorithm.GIBBS_SAMPLING`

### MixtureModel

    public MixtureModel(StringBuffer xml)
                 throws NonParsableException

  The constructor for the interface `Storable`. Creates a new `MixtureModel` out of its XML representation.

  Parameters:
    `xml` - the XML representation of the model as `StringBuffer`
  Throws:
    `NonParsableException` - if the `StringBuffer` is not parsable

Method Detail

### emitSampleUsingCurrentParameterSet

    protected Sequence[] emitSampleUsingCurrentParameterSet(int n,
                                                            int... lengths)
                                                     throws Exception

  Description copied from class: `AbstractMixtureModel`
  The method returns an array of sequences using the current parameter set.

  Specified by:
    `emitSampleUsingCurrentParameterSet` in class `AbstractMixtureModel`
  Parameters:
    `n` - the number of sequences to be sampled
    `lengths` - the corresponding lengths
  Returns:
    an array of sequences
  Throws:
    `Exception` - if it was impossible to sample the sequences
  See Also:
    `AbstractModel.emitSample(int, int...)`

### doFirstIteration

    protected double[][] doFirstIteration(double[] dataWeights,
                                          MultivariateRandomGenerator m,
                                          MRGParams[] params)
                                   throws Exception

  Description copied from class: `AbstractMixtureModel`
  This method will do the first step in the train algorithm for the current model on the internal sample. The initialization will be done by randomly setting the component membership. This is useful when nothing is known about the problem.

  Specified by:
    `doFirstIteration` in class `AbstractMixtureModel`
  Parameters:
    `dataWeights` - `null` or the weights of each element of the sample
    `m` - the multivariate random generator
    `params` - the parameters for the multivariate random generator
  Returns:
    the weighting array used to initialize, this array can be reused in the following iterations
  Throws:
    `Exception` - if something went wrong

### doFirstIteration

    public double[][] doFirstIteration(Sample data,
                                       double[] dataWeights,
                                       double[][] partitioning)
                                throws Exception

  This method enables you to train a mixture model with a fixed start partitioning. This is useful to compare implementations or if one has a hypothesis how the components should look like.

  Parameters:
    `data` - the sample of sequences
    `dataWeights` - `null` or the weights of each element of the sample
    `partitioning` - a kind of partitioning
    1. `partitioning.length` has to be `data.getNumberofElements()`
    2. for all i: `partitioning[i].length` has to be `getNumberOfModels()`
  Returns:
    the weighting array used to initialize, this array can be reused in the following iterations
  Throws:
    `Exception` - if something went wrong or if the number of components is 1

### getLogProbUsingCurrentParameterSetFor

    protected double getLogProbUsingCurrentParameterSetFor(int component,
                                                           Sequence s,
                                                           int start,
                                                           int end)
                                                    throws Exception

  Description copied from class: `AbstractMixtureModel`
  Returns the logarithmic probability for the sequence and the given component using the current parameter set.

  Specified by:
    `getLogProbUsingCurrentParameterSetFor` in class `AbstractMixtureModel`
  Parameters:
    `component` - the index of the component
    `s` - the sequence
    `start` - the start position in the sequence
    `end` - the end position in the sequence
  Returns:
    `log P(s,component) = log P(s|component) + log P(component)`
  Throws:
    `Exception` - if not trained yet or something else went wrong
  See Also:
    `AbstractMixtureModel.getNumberOfComponents()`

### toString

    public String toString()

  Description copied from interface: `Model`
  Should give a simple representation (text) of the model as `String` .

  Specified by:
    `toString` in interface `Model`
  Overrides:
    `toString` in class `Object`
  Returns:
    the representation as `String`

### getNewWeights

    protected double getNewWeights(double[] dataWeights,
                                   double[] w,
                                   double[][] seqweights)
                            throws Exception

  Computes sequence weights and returns the score.

  Specified by:
    `getNewWeights` in class `AbstractMixtureModel`
  Parameters:
    `dataWeights` - the weights for the internal sample (should not be changed)
    `w` - the array for the statistic of the component parameters (shall be filled)
    `seqweights` - an array containing for each component the weights for each sequence (shall be filled)
  Returns:
    the score
  Throws:
    `Exception` - if something went wrong

### setTrainData

    protected void setTrainData(Sample data)

  Description copied from class: `AbstractMixtureModel`
  This method is invoked by the `train`-method and sets for a given sample the sample that should be used for `train`.

  Specified by:
    `setTrainData` in class `AbstractMixtureModel`
  Parameters:
    `data` - the given sample of sequences

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