inline void McSimulation<MC,RNG,S>::calculate(Real requiredTolerance,
Size requiredSamples,
Size maxSamples) const {
QL_REQUIRE(requiredTolerance != Null<Real>() ||
requiredSamples != Null<Size>(),
"neither tolerance nor number of samples set");
//! Initialize the one-factor Monte Carlo
if (this->controlVariate_) {
result_type controlVariateValue = this->controlVariateValue();
QL_REQUIRE(controlVariateValue != Null<result_type>(),
"engine does not provide "
"control-variation price");
boost::shared_ptr<path_pricer_type> controlPP =
this->controlPathPricer();
QL_REQUIRE(controlPP,
"engine does not provide "
"control-variation path pricer");
boost::shared_ptr<path_generator_type> controlPG =
this->controlPathGenerator();
this->mcModel_ =
boost::shared_ptr<MonteCarloModel<MC,RNG,S> >(
new MonteCarloModel<MC,RNG,S>(
pathGenerator(), this->pathPricer(), stats_type(),
this->antitheticVariate_, controlPP,
controlVariateValue, controlPG));
} else {
this->mcModel_ =
boost::shared_ptr<MonteCarloModel<MC,RNG,S> >(
new MonteCarloModel<MC,RNG,S>(
pathGenerator(), this->pathPricer(), S(),
this->antitheticVariate_));
}
if (requiredTolerance != Null<Real>()) {
if (maxSamples != Null<Size>())
this->value(requiredTolerance, maxSamples);
else
this->value(requiredTolerance);
} else {
this->valueWithSamples(requiredSamples);
}
}
inline
void MCLongstaffSchwartzEngine<GenericEngine,MC,RNG,S>::calculate() const {
pathPricer_ = this->lsmPathPricer();
this->mcModel_ = boost::shared_ptr<MonteCarloModel<MC,RNG,S> >(
new MonteCarloModel<MC,RNG,S>
(pathGenerator(), pathPricer_,
stats_type(), this->antitheticVariate_));
this->mcModel_->addSamples(nCalibrationSamples_);
this->pathPricer_->calibrate();
McSimulation<MC,RNG,S>::calculate(requiredTolerance_,
requiredSamples_,
maxSamples_);
this->results_.value = this->mcModel_->sampleAccumulator().mean();
if (RNG::allowsErrorEstimate) {
this->results_.errorEstimate =
this->mcModel_->sampleAccumulator().errorEstimate();
}
}
