Package elki.clustering.em.models

Class MultivariateGaussianModel

  • All Implemented Interfaces:
    BetulaClusterModel, EMClusterModel<elki.data.NumberVector,​EMModel>
    public class MultivariateGaussianModel
extends java.lang.Object
implements BetulaClusterModel
    Model for a single multivariate Gaussian cluster with arbitrary rotation.
    Since:
    0.7.0
    Author:
    Erich Schubert

    • Field Summary

      Fields 
      Modifier and Type Field Description
      (package private) elki.math.linearalgebra.CholeskyDecomposition chol
      Decomposition of covariance matrix.
      (package private) double[][] covariance
      Covariance matrix.
      private static elki.logging.Logging LOG
      Class logger.
      (package private) double logNorm
      Normalization factor.
      (package private) double logNormDet
      Normalization factor.
      (package private) double[] mean
      Mean vector.
      (package private) double[] nmea
      Temporary storage, to avoid reallocations.
      (package private) double[][] priormatrix
      Matrix for prior conditioning.
      private static double SINGULARITY_CHEAT
      Constant to avoid singular matrixes.
      (package private) double weight
      Weight aggregation sum.
      (package private) double wsum
      Weight aggregation sum.

    • Method Summary

      All Methods Static Methods Instance Methods Concrete Methods 
      Modifier and Type Method Description
      void beginEStep()
      Begin the E step.
      double estimateLogDensity​(elki.data.NumberVector vec)
      Estimate the log likelihood of a vector.
      double estimateLogDensity​(ClusterFeature cf)
      Estimate the log likelihood of a clustering feature.
      EMModel finalizeCluster()
      Finalize a cluster model.
      void finalizeEStep​(double weight, double prior)
      Finalize the E step.
      protected static double getHalfLogDeterminant​(elki.math.linearalgebra.CholeskyDecomposition chol)
      Get 0.5 * log(det) of a cholesky decomposition.
      double getWeight()
      Get the cluster weight.
      double mahalanobisDistance​(double[] vec)
      Compute the Mahalanobis distance from the centroid for a given vector.
      double mahalanobisDistance​(elki.data.NumberVector vec)
      Compute the Mahalanobis distance from the centroid for a given vector.
      void setWeight​(double weight)
      Set the cluster weight.
      protected static elki.math.linearalgebra.CholeskyDecomposition updateCholesky​(double[][] covariance, elki.math.linearalgebra.CholeskyDecomposition prev)
      Update the cholesky decomposition.
      void updateE​(elki.data.NumberVector vec, double wei)
      Process one data point in the E step
      void updateE​(ClusterFeature cf, double wei)
      Process one clustering feature in the E step.

      • Methods inherited from class java.lang.Object

        clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

    • Field Detail

      • LOG

        private static final elki.logging.Logging LOG
        Class logger.

      • SINGULARITY_CHEAT

        private static final double SINGULARITY_CHEAT
        Constant to avoid singular matrixes.
        See Also:
        Constant Field Values

      • mean

        double[] mean
        Mean vector.

      • covariance

        double[][] covariance
        Covariance matrix.

      • chol

        elki.math.linearalgebra.CholeskyDecomposition chol
        Decomposition of covariance matrix.

      • nmea

        double[] nmea
        Temporary storage, to avoid reallocations.

      • logNorm

        double logNorm
        Normalization factor.

      • logNormDet

        double logNormDet
        Normalization factor.

      • weight

        double weight
        Weight aggregation sum.

      • wsum

        double wsum
        Weight aggregation sum.

      • priormatrix

        double[][] priormatrix
        Matrix for prior conditioning.

    • Constructor Detail

      • MultivariateGaussianModel

        public MultivariateGaussianModel​(double weight,
                                 double[] mean)
        Constructor.
        Parameters:
        weight - Cluster weight
        mean - Initial mean

      • MultivariateGaussianModel

        public MultivariateGaussianModel​(double weight,
                                 double[] mean,
                                 double[][] covariance)
        Constructor.
        Parameters:
        weight - Cluster weight
        mean - Initial mean
        covariance - Initial covariance matrix

    • Method Detail

      • updateE

        public void updateE​(elki.data.NumberVector vec,
                    double wei)
        Description copied from interface: EMClusterModel
        Process one data point in the E step
        Specified by:
        updateE in interface EMClusterModel<elki.data.NumberVector,​EMModel>
        Parameters:
        vec - Vector to process
        wei - Weight of point ("responsibility" of the cluster)

      • finalizeEStep

        public void finalizeEStep​(double weight,
                          double prior)
        Description copied from interface: EMClusterModel
        Finalize the E step.
        Specified by:
        finalizeEStep in interface EMClusterModel<elki.data.NumberVector,​EMModel>
        Parameters:
        weight - weight of the cluster
        prior - MAP prior (0 for MLE)

      • updateCholesky

        protected static elki.math.linearalgebra.CholeskyDecomposition updateCholesky​(double[][] covariance,
                                                                              elki.math.linearalgebra.CholeskyDecomposition prev)
        Update the cholesky decomposition.
        Parameters:
        covariance - Covariance matrix
        prev - Previous Cholesky decomposition (reused in case of instability)
        Returns:
        New Cholesky decomposition

      • getHalfLogDeterminant

        protected static double getHalfLogDeterminant​(elki.math.linearalgebra.CholeskyDecomposition chol)
        Get 0.5 * log(det) of a cholesky decomposition.
        Parameters:
        chol - Cholesky Decomposition
        Returns:
        log determinant.

      • mahalanobisDistance

        public double mahalanobisDistance​(double[] vec)
        Compute the Mahalanobis distance from the centroid for a given vector.
        Parameters:
        vec - Vector
        Returns:
        Mahalanobis distance

      • mahalanobisDistance

        public double mahalanobisDistance​(elki.data.NumberVector vec)
        Compute the Mahalanobis distance from the centroid for a given vector.

        Note: used by P3C.

        Parameters:
        vec - Vector
        Returns:
        Mahalanobis distance

      • estimateLogDensity

        public double estimateLogDensity​(elki.data.NumberVector vec)
        Description copied from interface: EMClusterModel
        Estimate the log likelihood of a vector.
        Specified by:
        estimateLogDensity in interface EMClusterModel<elki.data.NumberVector,​EMModel>
        Parameters:
        vec - Vector
        Returns:
        log likelihood.

      • getWeight

        public double getWeight()
        Description copied from interface: EMClusterModel
        Get the cluster weight.
        Specified by:
        getWeight in interface EMClusterModel<elki.data.NumberVector,​EMModel>
        Returns:
        Cluster weight

      • setWeight

        public void setWeight​(double weight)
        Description copied from interface: EMClusterModel
        Set the cluster weight.
        Specified by:
        setWeight in interface EMClusterModel<elki.data.NumberVector,​EMModel>
        Parameters:
        weight - Cluster weight

      • updateE

        public void updateE​(ClusterFeature cf,
                    double wei)
        Description copied from interface: BetulaClusterModel
        Process one clustering feature in the E step.
        Specified by:
        updateE in interface BetulaClusterModel
        Parameters:
        cf - Clustering feature to process.
        wei - weight of the clustering feature.