public class MultivariateNormalMixtureExpectationMaximization extends Object
| Constructor and Description | 
|---|
| MultivariateNormalMixtureExpectationMaximization(double[][] data)Creates an object to fit a multivariate normal mixture model to data. | 
| Modifier and Type | Method and Description | 
|---|---|
| static MixtureMultivariateNormalDistribution | estimate(double[][] data,
        int numComponents)Helper method to create a multivariate normal mixture model which can be
 used to initialize  fit(MixtureMultivariateNormalDistribution). | 
| void | fit(MixtureMultivariateNormalDistribution initialMixture)Fit a mixture model to the data supplied to the constructor. | 
| void | fit(MixtureMultivariateNormalDistribution initialMixture,
   int maxIterations,
   double threshold)Fit a mixture model to the data supplied to the constructor. | 
| MixtureMultivariateNormalDistribution | getFittedModel()Gets the fitted model. | 
| double | getLogLikelihood()Gets the log likelihood of the data under the fitted model. | 
public MultivariateNormalMixtureExpectationMaximization(double[][] data)
                                                 throws NotStrictlyPositiveException,
                                                        DimensionMismatchException,
                                                        NumberIsTooSmallException
data - Data to use in fitting procedureNotStrictlyPositiveException - if data has no rowsDimensionMismatchException - if rows of data have different numbers
             of columnsNumberIsTooSmallException - if the number of columns in the data is
             less than 2public void fit(MixtureMultivariateNormalDistribution initialMixture, int maxIterations, double threshold) throws SingularMatrixException, NotStrictlyPositiveException, DimensionMismatchException
initialMixture - Model containing initial values of weights and
            multivariate normalsmaxIterations - Maximum iterations allowed for fitthreshold - Convergence threshold computed as difference in
             logLikelihoods between successive iterationsSingularMatrixException - if any component's covariance matrix is
             singular during fittingNotStrictlyPositiveException - if numComponents is less than one
             or threshold is less than Double.MIN_VALUEDimensionMismatchException - if initialMixture mean vector and data
             number of columns are not equalpublic void fit(MixtureMultivariateNormalDistribution initialMixture) throws SingularMatrixException, NotStrictlyPositiveException
initialMixture - Model containing initial values of weights and
            multivariate normalsSingularMatrixException - if any component's covariance matrix is
             singular during fittingNotStrictlyPositiveException - if numComponents is less than one or
             threshold is less than Double.MIN_VALUEpublic static MixtureMultivariateNormalDistribution estimate(double[][] data, int numComponents) throws NotStrictlyPositiveException, DimensionMismatchException
fit(MixtureMultivariateNormalDistribution).
 This method uses the data supplied to the constructor to try to determine
 a good mixture model at which to start the fit, but it is not guaranteed
 to supply a model which will find the optimal solution or even converge.data - Data to estimate distributionnumComponents - Number of components for estimated mixtureNumberIsTooLargeException - if numComponents is greater
 than the number of data rows.NumberIsTooSmallException - if numComponents < 2.NotStrictlyPositiveException - if data has less than 2 rowsDimensionMismatchException - if rows of data have different numbers
             of columnspublic double getLogLikelihood()
public MixtureMultivariateNormalDistribution getFittedModel()
null if no fit has been performed yet.Copyright © 2003–2016 The Apache Software Foundation. All rights reserved.