/src/main/java/net/finmath/montecarlo/automaticdifferentiation/backward/alternative/RandomVariableDifferentiableAADStochasticNonOptimized.java
X.mult( 2.0).mult( Y.mult( X.add( Y.getAverage( X) *( X.size( ) -1) ).sub( Y.getAverage( X) ) ) ).div( Math.sqrt( Y.getVariance( X) ) ) ;resultrandomvariable = X.sub( X.getAverage( ) *( 2.0*X.size( ) -1.0) /X.size( ) ).mult( 2.0/X.size( ) ).mult( 0.5).div( Math.sqrt( X.getVariance( ) ) ) ;
resultrandomvariable = X.sub ( X.getAverage ( ) * ( 2.0*X.size ( ) -1.0 ) /X.size ( ) ) .mult ( 2.0/X.size ( ) ) .mult ( 0.5 ) .div ( Math.sqrt ( X.getVariance ( ) ) ) ;
resultrandomvariable = new RandomVariableFromDoubleArray(X.size()).invert(); break; case VARIANCE: resultrandomvariable = X.sub(X.getAverage()*(2.0*X.size()-1.0)/X.size()).mult(2.0/X.size()); break; case STDEV: <extra_id_0> break; case MIN: final double min = X.getMin(); resultrandomvariable = X.apply(new DoubleUnaryOperator() { @Override public double applyAsDouble(final double x) {