/src/main/java/net/finmath/montecarlo/automaticdifferentiation/forward/RandomVariableDifferentiableAD.java
derivative = derivative.average( ) ;derivative = Y.sub( Z) ;
derivative = Y.sub ( Z ) ;
} else { derivative = X.mult(Y).div(Y.mult(Z).add(1.0).squared()).mult(-1.0); } break; case BARRIER: if(differentialIndex == 0) { <extra_id_0> final double epsilon = 0.2*X.getStandardDeviation(); derivative = derivative.mult(X.add(epsilon/2).choose(new RandomVariableFromDoubleArray(1.0), new RandomVariableFromDoubleArray(0.0))); derivative = derivative.mult(X.sub(epsilon/2).choose(new RandomVariableFromDoubleArray(0.0), new RandomVariableFromDoubleArray(1.0))); derivative = derivative.div(epsilon); } else if(differentialIndex == 1) { derivative = X.choose(one, zero);