Package edu.pitt.csb.mgm

Class Mgm

java.lang.Object
edu.pitt.csb.mgm.ConvexProximal
edu.pitt.csb.mgm.Mgm
All Implemented Interfaces:
IGraphSearch
public class Mgm extends ConvexProximal implements IGraphSearch
Implementation of Lee and Hastie's (2012) pseudolikelihood method for learning Mixed Gaussian-Categorical Graphical Models Created by ajsedgewick on 7/15/15.
Version:
$Id: $Id
Author:
josephramsey

  • Nested Class Summary

    Nested Classes
    Modifier and Type
    Class
    Description
    static class 
    Mgm.MGMParams
    The parameters of the MGM model.

  • Constructor Summary

    Constructors
    Constructor
    Description
    Mgm(cern.colt.matrix.DoubleMatrix2D x, cern.colt.matrix.DoubleMatrix2D y, List<Node> variables, int[] l, double[] lambda)
    Constructor for Mgm.
    Mgm(DataSet ds, double[] lambda)
    Constructor for Mgm.

  • Method Summary

    Modifier and Type
    Method
    Description
    cern.colt.matrix.DoubleMatrix2D
    adjMatFromMGM()
    Converts MGM to matrix of doubles.
    static cern.colt.matrix.DoubleMatrix1D
    flatten(cern.colt.matrix.DoubleMatrix2D m)
    flatten.
    long
    getElapsedTime()
    Return time of execution for learning.
    Graph
    graphFromMGM()
    Converts MGM object to Graph object with edges if edge parameters are non-zero.
    void
    learn(double epsilon, int iterLimit)
    Learn MGM traditional way with objective function tolerance.
    void
    learnEdges(int iterLimit)
    Learn MGM using edge convergence using default 3 iterations of no edge changes.
    void
    learnEdges(int iterLimit, int edgeChangeTol)
    Learn MGM using edge convergence using edgeChangeTol (see ProximalGradient for documentation).
    static void
    main(String[] args)
    main.
    double
    nonSmooth(double t, cern.colt.matrix.DoubleMatrix1D X, cern.colt.matrix.DoubleMatrix1D pX)
    Calculate value of h(X) and proxOperator of h(X) at the same time for efficiency reasons.
    double
    nonSmoothValue(cern.colt.matrix.DoubleMatrix1D parIn)
    Computes the non-smooth value based on the input vector.
    cern.colt.matrix.DoubleMatrix1D
    proximalOperator(double t, cern.colt.matrix.DoubleMatrix1D X)
    Graph
    search()
    Simple search command for GraphSearch implementation.
    void
    setParams(Mgm.MGMParams newParams)
    Setter for the field params.
    double
    smooth(cern.colt.matrix.DoubleMatrix1D parIn, cern.colt.matrix.DoubleMatrix1D gradOutVec)
    Smooths the input vector using the given gradient and returns a double value.
    cern.colt.matrix.DoubleMatrix1D
    smoothGradient(cern.colt.matrix.DoubleMatrix1D parIn)
    double
    smoothValue(cern.colt.matrix.DoubleMatrix1D parIn)
    Calculate the smooth value based on the input vector.
    static cern.colt.matrix.DoubleMatrix2D
    upperTri(cern.colt.matrix.DoubleMatrix2D mat, int di)
    upperTri.

    Methods inherited from class java.lang.Object

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

  • Constructor Details

    • Mgm

      public Mgm(cern.colt.matrix.DoubleMatrix2D x, cern.colt.matrix.DoubleMatrix2D y, List<Node> variables, int[] l, double[] lambda)

      Constructor for Mgm.

      Parameters:
      x - a DoubleMatrix2D object
      y - a DoubleMatrix2D object
      variables - a List object
      l - an array of
      invalid reference 
      int
      objects
      lambda - an array of
      invalid reference 
      double
      objects

    • Mgm

      public Mgm(DataSet ds, double[] lambda)

      Constructor for Mgm.

      Parameters:
      ds - a DataSet object
      lambda - an array of
      invalid reference 
      double
      objects

  • Method Details

    • flatten

      public static cern.colt.matrix.DoubleMatrix1D flatten(cern.colt.matrix.DoubleMatrix2D m)

      flatten.

      Parameters:
      m - a DoubleMatrix2D object
      Returns:
      a DoubleMatrix1D object

    • upperTri

      public static cern.colt.matrix.DoubleMatrix2D upperTri(cern.colt.matrix.DoubleMatrix2D mat, int di)

      upperTri.

      Parameters:
      mat - a DoubleMatrix2D object
      di - a int
      Returns:
      a DoubleMatrix2D object

    • main

      public static void main(String[] args)

      main.

      Parameters:
      args - an array of String objects

    • setParams

      public void setParams(Mgm.MGMParams newParams)

      Setter for the field params.

      Parameters:
      newParams - a Mgm.MGMParams object

    • smoothValue

      public double smoothValue(cern.colt.matrix.DoubleMatrix1D parIn)
      Calculate the smooth value based on the input vector.
      Parameters:
      parIn - input vector
      Returns:
      the smooth value

    • smooth

      public double smooth(cern.colt.matrix.DoubleMatrix1D parIn, cern.colt.matrix.DoubleMatrix1D gradOutVec)
      Smooths the input vector using the given gradient and returns a double value.
      Overrides:
      smooth in class ConvexProximal
      Parameters:
      parIn - the input vector
      gradOutVec - the gradient of g(X)
      Returns:
      the smoothed value

    • nonSmoothValue

      public double nonSmoothValue(cern.colt.matrix.DoubleMatrix1D parIn)
      Computes the non-smooth value based on the input vector.
      Parameters:
      parIn - the input vector
      Returns:
      the non-smooth value

    • smoothGradient

      public cern.colt.matrix.DoubleMatrix1D smoothGradient(cern.colt.matrix.DoubleMatrix1D parIn)

      Gradient of the pseudolikelihood

    • proximalOperator

      public cern.colt.matrix.DoubleMatrix1D proximalOperator(double t, cern.colt.matrix.DoubleMatrix1D X)

      A proximal operator for the MGM

    • nonSmooth

      public double nonSmooth(double t, cern.colt.matrix.DoubleMatrix1D X, cern.colt.matrix.DoubleMatrix1D pX)
      Calculate value of h(X) and proxOperator of h(X) at the same time for efficiency reasons.

      Calculates penalty term and proximal operator at the same time for speed

      Overrides:
      nonSmooth in class ConvexProximal
      Parameters:
      t - positive parameter for prox operator
      X - input vector
      pX - vector solution to prox_t(X)
      Returns:
      value of h(X)

    • learn

      public void learn(double epsilon, int iterLimit)
      Learn MGM traditional way with objective function tolerance. Recommended for inference applications that need accurate pseudolikelihood
      Parameters:
      epsilon - tolerance in change of objective function
      iterLimit - iteration limit

    • learnEdges

      public void learnEdges(int iterLimit)
      Learn MGM using edge convergence using default 3 iterations of no edge changes. Recommended when we only care about edge existence.
      Parameters:
      iterLimit - a int

    • learnEdges

      public void learnEdges(int iterLimit, int edgeChangeTol)
      Learn MGM using edge convergence using edgeChangeTol (see ProximalGradient for documentation). Recommended when we only care about edge existence.
      Parameters:
      iterLimit - a int
      edgeChangeTol - a int

    • graphFromMGM

      public Graph graphFromMGM()
      Converts MGM object to Graph object with edges if edge parameters are non-zero. Loses all edge param information
      Returns:
      a Graph object

    • adjMatFromMGM

      public cern.colt.matrix.DoubleMatrix2D adjMatFromMGM()
      Converts MGM to matrix of doubles. uses 2-norm to combine c-d edge parameters into single value and f-norm for d-d edge parameters.
      Returns:
      a DoubleMatrix2D object

    • search

      public Graph search()
      Simple search command for GraphSearch implementation. Uses default edge convergence, 1000 iter limit.
      Specified by:
      search in interface IGraphSearch
      Returns:
      a Graph object

    • getElapsedTime

      public long getElapsedTime()
      Return time of execution for learning.
      Returns:
      a long