Package weka.core.matrix

Examples of weka.core.matrix.DoubleVector.max()

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  public double  empiricalBayesEstimate ( double x ) {
    if( Math.abs(x) > 10 ) return x; // pratical consideration; modify later
    DoubleVector d =
    Maths.dnormLog( x, mixingDistribution.getPointValues(), 1 );
   
    d.minusEquals( d.max() );
    d = d.map("java.lang.Math", "exp");
    d.timesEquals( mixingDistribution.getFunctionValues() );
    return mixingDistribution.getPointValues().innerProduct( d ) / d.sum();
  }
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  public double hf( double x ) {
    DoubleVector points = mixingDistribution.getPointValues();
    DoubleVector values = mixingDistribution.getFunctionValues();

    DoubleVector d = Maths.dnormLog( x, points, 1 );
    d.minusEquals( d.max() );

    d = (DoubleVector) d.map("java.lang.Math", "exp");
    d.timesEquals( values ); 

    return ((DoubleVector) points.times(2*x).minusEquals(x*x))
 
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    DoubleVector points = mixingDistribution.getPointValues();
    DoubleVector values = mixingDistribution.getFunctionValues();
    DoubleVector mean = points.sqrt();
 
    DoubleVector d = Maths.dchisqLog( x, points );
    d.minusEquals( d.max() );
    d = d.map("java.lang.Math", "exp").timesEquals( values );
    double atilde = mean.innerProduct( d ) / d.sum();
    return atilde * atilde;
  }

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    DoubleVector values = mixingDistribution.getFunctionValues();

    double x = Math.sqrt( AHat );
    DoubleVector mean = points.sqrt();
    DoubleVector d1 = Maths.dnormLog( x, mean, 1 );
    double d1max = d1.max();
    d1.minusEquals( d1max );
    DoubleVector d2 = Maths.dnormLog( -x, mean, 1 );
    d2.minusEquals( d1max );

    d1 = d1.map("java.lang.Math", "exp");
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