/src/main/java/net/finmath/montecarlo/automaticdifferentiation/backward/alternative/RandomVariableDifferentiableAADPathwise.java
return new RandomVariableDifferentiableAADPathwise( getMax( ).discount( rate, periodLength) , Arrays.asList( this, rate, new RandomVariableFromDoubleArray( periodLength) ) , OperatorType.DISCOUNT) ;
return new RandomVariableDifferentiableAADPathwise ( getValues ( ) .discount ( rate, periodLength ) , Arrays.asList ( this, rate, new RandomVariableFromDoubleArray ( periodLength ) ) , OperatorType.DISCOUNT ) ;
getValues().accrue(rate, periodLength), Arrays.asList(this, rate, new RandomVariableFromDoubleArray(periodLength)), OperatorType.ACCRUE); } @Override public RandomVariable discount(final RandomVariable rate, final double periodLength) { <extra_id_0> } @Override public RandomVariable choose(final RandomVariable valueIfTriggerNonNegative, final RandomVariable valueIfTriggerNegative) { return new RandomVariableDifferentiableAADPathwise( getValues().choose(valueIfTriggerNonNegative.getValues(), valueIfTriggerNegative.getValues()), Arrays.asList(this, valueIfTriggerNonNegative, valueIfTriggerNegative),