Examples of weka.core.matrix.IntVector

weka.core.matrix.IntVector
A vector specialized on integers. @author Yong Wang @version $Revision: 1.4 $

   */
  private double [] pace(double[][] matrix_X, double [] vector_Y) {
    
    PaceMatrix X = new PaceMatrix( matrix_X );
    PaceMatrix Y = new PaceMatrix( vector_Y, vector_Y.length );
    IntVector pvt = IntVector.seq(0, X.getColumnDimension()-1);
    int n = X.getRowDimension();
    int kr = X.getColumnDimension();


    X.lsqrSelection( Y, pvt, 1 );
    X.positiveDiagonal( Y, pvt );
    
    PaceMatrix sol = (PaceMatrix) Y.clone();
    X.rsolve( sol, pvt, pvt.size() );
    DoubleVector r = Y.getColumn( pvt.size(), n-1, 0);
    double sde = Math.sqrt(r.sum2() / r.size());
    
    DoubleVector aHat = Y.getColumn( 0, pvt.size()-1, 0).times( 1./sde );


    DoubleVector aTilde = null;
    switch( paceEstimator) {
    case ebEstimator: 
    case nestedEstimator:
    case subsetEstimator:
      NormalMixture d = new NormalMixture();
      d.fit( aHat, MixtureDistribution.NNMMethod ); 
      if( paceEstimator == ebEstimator ) 
  aTilde = d.empiricalBayesEstimate( aHat );
      else if( paceEstimator == ebEstimator ) 
  aTilde = d.subsetEstimate( aHat );
      else aTilde = d.nestedEstimate( aHat );
      break;
    case pace2Estimator: 
    case pace4Estimator:
    case pace6Estimator:
      DoubleVector AHat = aHat.square();
      ChisqMixture dc = new ChisqMixture();
      dc.fit( AHat, MixtureDistribution.NNMMethod ); 
      DoubleVector ATilde; 
      if( paceEstimator == pace6Estimator ) 
  ATilde = dc.pace6( AHat );
      else if( paceEstimator == pace2Estimator ) 
  ATilde = dc.pace2( AHat );
      else ATilde = dc.pace4( AHat );
      aTilde = ATilde.sqrt().times( aHat.sign() );
      break;
    case olsEstimator: 
      aTilde = aHat.copy();
      break;
    case aicEstimator: 
    case bicEstimator:
    case ricEstimator: 
    case olscEstimator:
      if(paceEstimator == aicEstimator) olscThreshold = 2;
      else if(paceEstimator == bicEstimator) olscThreshold = Math.log( n );
      else if(paceEstimator == ricEstimator) olscThreshold = 2*Math.log( kr );
      aTilde = aHat.copy();
      for( int i = 0; i < aTilde.size(); i++ )
  if( Math.abs(aTilde.get(i)) < Math.sqrt(olscThreshold) ) 
    aTilde.set(i, 0);
    }
    PaceMatrix YTilde = new PaceMatrix((new PaceMatrix(aTilde)).times( sde ));
    X.rsolve( YTilde, pvt, pvt.size() );
    DoubleVector betaTilde = YTilde.getColumn(0).unpivoting( pvt, kr );
    
    return betaTilde.getArrayCopy();
  }

View Full Code Here

   */
  private double [] pace(double[][] matrix_X, double [] vector_Y) {
    
    PaceMatrix X = new PaceMatrix( matrix_X );
    PaceMatrix Y = new PaceMatrix( vector_Y, vector_Y.length );
    IntVector pvt = IntVector.seq(0, X.getColumnDimension()-1);
    int n = X.getRowDimension();
    int kr = X.getColumnDimension();


    X.lsqrSelection( Y, pvt, 1 );
    X.positiveDiagonal( Y, pvt );
    
    PaceMatrix sol = (PaceMatrix) Y.clone();
    X.rsolve( sol, pvt, pvt.size() );
    DoubleVector r = Y.getColumn( pvt.size(), n-1, 0);
    double sde = Math.sqrt(r.sum2() / r.size());
    
    DoubleVector aHat = Y.getColumn( 0, pvt.size()-1, 0).times( 1./sde );


    DoubleVector aTilde = null;
    switch( paceEstimator) {
    case ebEstimator: 
    case nestedEstimator:
    case subsetEstimator:
      NormalMixture d = new NormalMixture();
      d.fit( aHat, MixtureDistribution.NNMMethod ); 
      if( paceEstimator == ebEstimator ) 
  aTilde = d.empiricalBayesEstimate( aHat );
      else if( paceEstimator == ebEstimator ) 
  aTilde = d.subsetEstimate( aHat );
      else aTilde = d.nestedEstimate( aHat );
      break;
    case pace2Estimator: 
    case pace4Estimator:
    case pace6Estimator:
      DoubleVector AHat = aHat.square();
      ChisqMixture dc = new ChisqMixture();
      dc.fit( AHat, MixtureDistribution.NNMMethod ); 
      DoubleVector ATilde; 
      if( paceEstimator == pace6Estimator ) 
  ATilde = dc.pace6( AHat );
      else if( paceEstimator == pace2Estimator ) 
  ATilde = dc.pace2( AHat );
      else ATilde = dc.pace4( AHat );
      aTilde = ATilde.sqrt().times( aHat.sign() );
      break;
    case olsEstimator: 
      aTilde = aHat.copy();
      break;
    case aicEstimator: 
    case bicEstimator:
    case ricEstimator: 
    case olscEstimator:
      if(paceEstimator == aicEstimator) olscThreshold = 2;
      else if(paceEstimator == bicEstimator) olscThreshold = Math.log( n );
      else if(paceEstimator == ricEstimator) olscThreshold = 2*Math.log( kr );
      aTilde = aHat.copy();
      for( int i = 0; i < aTilde.size(); i++ )
  if( Math.abs(aTilde.get(i)) < Math.sqrt(olscThreshold) ) 
    aTilde.set(i, 0);
    }
    PaceMatrix YTilde = new PaceMatrix((new PaceMatrix(aTilde)).times( sde ));
    X.rsolve( YTilde, pvt, pvt.size() );
    DoubleVector betaTilde = YTilde.getColumn(0).unpivoting( pvt, kr );
    
    return betaTilde.getArrayCopy();
  }

View Full Code Here

  /** 
   * Sorts the point values of the discrete function.
   */
  public void  sort() 
  {
    IntVector index = points.sortWithIndex();
    values = values.subvector( index );
  }

View Full Code Here

  
    PaceMatrix Y = new 
      PaceMatrix( X.times( new PaceMatrix(beta) ).
      plusEquals( randomNormal(n,1).times(sd) ) );


    IntVector pvt = (IntVector) IntVector.seq(0, k1+k2);


    /*System.out.println( "The OLS estimate (by jama.Matrix.solve()) is:\n\n" + 
      (new PaceMatrix(X.solve(Y))).getColumn(0) );*/
  
    X.lsqrSelection( Y, pvt, 1 );
    X.positiveDiagonal( Y, pvt );


    PaceMatrix sol = (PaceMatrix) Y.clone();
    X.rsolve( sol, pvt, pvt.size() );
    DoubleVector betaHat = sol.getColumn(0).unpivoting( pvt, k );
    System.out.println( "\nThe OLS estimate (through lsqr()) is: \n\n" + 
      betaHat );


    System.out.println( "\nQuadratic loss of the OLS estimate (||X b - X bHat||^2) = " + 
      ( new PaceMatrix( X.times( new 
        PaceMatrix(beta.minus(betaHat)) )))
      .getColumn(0).sum2() );


    System.out.println("=============================================" + 
           "==============");
    System.out.println("             *** Pace estimation *** \n");
    DoubleVector r = Y.getColumn( pvt.size(), n-1, 0);
    double sde = Math.sqrt(r.sum2() / r.size());
  
    System.out.println( "Estimated standard deviation = " + sde );


    DoubleVector aHat = Y.getColumn( 0, pvt.size()-1, 0).times( 1./sde );
    System.out.println("\naHat = \n" + aHat );
  
    System.out.println("\n========= Based on chi-square mixture ============");


    ChisqMixture d2 = new ChisqMixture();
    int method = MixtureDistribution.NNMMethod;
    DoubleVector AHat = aHat.square();
    d2.fit( AHat, method ); 
    System.out.println( "\nEstimated mixing distribution is:\n" + d2 );
  
    DoubleVector ATilde = d2.pace2( AHat );
    DoubleVector aTilde = ATilde.sqrt().times(aHat.sign());
    PaceMatrix YTilde = new 
      PaceMatrix((new PaceMatrix(aTilde)).times( sde ));
    X.rsolve( YTilde, pvt, pvt.size() );
    DoubleVector betaTilde = 
    YTilde.getColumn(0).unpivoting( pvt, k );
    System.out.println( "\nThe pace2 estimate of coefficients = \n" + 
      betaTilde );
    System.out.println( "Quadratic loss = " + 
      ( new PaceMatrix( X.times( new 
        PaceMatrix(beta.minus(betaTilde)) )))
      .getColumn(0).sum2() );
  
    ATilde = d2.pace4( AHat );
    aTilde = ATilde.sqrt().times(aHat.sign());
    YTilde = new PaceMatrix((new PaceMatrix(aTilde)).times( sde ));
    X.rsolve( YTilde, pvt, pvt.size() );
    betaTilde = YTilde.getColumn(0).unpivoting( pvt, k );
    System.out.println( "\nThe pace4 estimate of coefficients = \n" + 
      betaTilde );
    System.out.println( "Quadratic loss = " + 
      ( new PaceMatrix( X.times( new 
        PaceMatrix(beta.minus(betaTilde)) )))
      .getColumn(0).sum2() );
  
    ATilde = d2.pace6( AHat );
    aTilde = ATilde.sqrt().times(aHat.sign());
    YTilde = new PaceMatrix((new PaceMatrix(aTilde)).times( sde ));
    X.rsolve( YTilde, pvt, pvt.size() );
    betaTilde = YTilde.getColumn(0).unpivoting( pvt, k );
    System.out.println( "\nThe pace6 estimate of coefficients = \n" + 
      betaTilde );
    System.out.println( "Quadratic loss = " + 
      ( new PaceMatrix( X.times( new 
        PaceMatrix(beta.minus(betaTilde)) )))
      .getColumn(0).sum2() );
  
    System.out.println("\n========= Based on normal mixture ============");
  
    NormalMixture d = new NormalMixture();
    d.fit( aHat, method ); 
    System.out.println( "\nEstimated mixing distribution is:\n" + d );
  
    aTilde = d.nestedEstimate( aHat );
    YTilde = new PaceMatrix((new PaceMatrix(aTilde)).times( sde ));
    X.rsolve( YTilde, pvt, pvt.size() );
    betaTilde = YTilde.getColumn(0).unpivoting( pvt, k );
    System.out.println( "The nested estimate of coefficients = \n" + 
      betaTilde );
    System.out.println( "Quadratic loss = " + 
      ( new PaceMatrix( X.times( new 
        PaceMatrix(beta.minus(betaTilde)) )))
      .getColumn(0).sum2() );
  
  
    aTilde = d.subsetEstimate( aHat );
    YTilde = new PaceMatrix((new PaceMatrix(aTilde)).times( sde ));
    X.rsolve( YTilde, pvt, pvt.size() );
    betaTilde = 
    YTilde.getColumn(0).unpivoting( pvt, k );
    System.out.println( "\nThe subset estimate of coefficients = \n" + 
      betaTilde );
    System.out.println( "Quadratic loss = " + 
      ( new PaceMatrix( X.times( new 
        PaceMatrix(beta.minus(betaTilde)) )))
      .getColumn(0).sum2() );
  
    aTilde = d.empiricalBayesEstimate( aHat );
    YTilde = new PaceMatrix((new PaceMatrix(aTilde)).times( sde ));
    X.rsolve( YTilde, pvt, pvt.size() );
    betaTilde = YTilde.getColumn(0).unpivoting( pvt, k );
    System.out.println( "\nThe empirical Bayes estimate of coefficients = \n"+
      betaTilde );
  
    System.out.println( "Quadratic loss = " +

View Full Code Here

    PaceMatrix pm = probabilityMatrix( sp, fi );
    PaceMatrix epm = new 
      PaceMatrix( empiricalProbability( data, fi ).
      timesEquals( 1. / data.size() ) );
    
    IntVector pvt = (IntVector) IntVector.seq(0, sp.size()-1);
    DoubleVector weights;
    
    switch( method ) {
    case NNMMethod: 
      weights = pm.nnls( epm, pvt );
      break;
    case PMMethod:
      weights = pm.nnlse1( epm, pvt );
      break;
    default: 
      throw new IllegalArgumentException("unknown method");
    }
    
    DoubleVector sp2 = new DoubleVector( pvt.size() );
    for( int i = 0; i < sp2.size(); i++ ){
      sp2.set( i, sp.get(pvt.get(i)) );
    }
    
    DiscreteFunction d = new DiscreteFunction( sp2, weights );
    d.sort();
    d.normalize();

View Full Code Here

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Related Classes of weka.core.matrix.IntVector

weka.classifiers.functions.pace.DiscreteFunction

weka.classifiers.functions.pace.MixtureDistribution

weka.classifiers.functions.pace.PaceMatrix

weka.classifiers.functions.PaceRegression

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