List of minusEquals() Examples

Examples of minusEquals()

Jama.Matrix.minusEquals()
A = A - B @param B another matrix @return A - B
de.lmu.ifi.dbs.elki.math.linearalgebra.Vector.minusEquals()
A = A - B @param B another matrix @return A - B in this Matrix
weka.core.matrix.DoubleVector.minusEquals()
Subtracts a value in place @param x the value

Examples of Jama.Matrix.minusEquals()

                    "(difference of identical Matrices is nonzero,\nSubsequent use of minus should be suspect)");
        } else {
            try_success("minus... ", "");
        }
        A = R.copy();
        A.minusEquals(R);
        Z = new Matrix(A.getRowDimension(), A.getColumnDimension());
        try {
            A.minusEquals(S);
            errorCount = try_failure(errorCount,
                    "minusEquals conformance check... ",

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Examples of Jama.Matrix.minusEquals()

        }
        A = R.copy();
        A.minusEquals(R);
        Z = new Matrix(A.getRowDimension(), A.getColumnDimension());
        try {
            A.minusEquals(S);
            errorCount = try_failure(errorCount,
                    "minusEquals conformance check... ",
                    "nonconformance not raised");
        } catch (IllegalArgumentException e) {
            try_success("minusEquals conformance check... ", "");

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Examples of de.lmu.ifi.dbs.elki.math.linearalgebra.Vector.minusEquals()

      Matrix basis;
      {
        List<Vector> vectors = new ArrayList<Vector>(sample.size() - 1);
        while(iter.hasNext()) {
          Vector vec = relation.get(iter.next()).getColumnVector();
          vectors.add(vec.minusEquals(originV));
        }
        // generate orthogonal basis
        basis = generateOrthonormalBasis(vectors);
        if(basis == null) {
          // new sample has to be taken.

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Examples of de.lmu.ifi.dbs.elki.math.linearalgebra.Vector.minusEquals()

    if(newsize == 0) {
      return; // Keep old mean
    }
    Vector delta = vec.getColumnVector();
    // Compute difference from mean
    delta.minusEquals(mean);
    delta.timesEquals(op / newsize);
    mean.plusEquals(delta);
  }


  /**

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

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

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

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


    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");
    d1.timesEquals( values );

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

    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");
    d1.timesEquals( values );  
    d2 = d2.map("java.lang.Math", "exp");
    d2.timesEquals( values );

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