#include <ql/experimental/mcbasket/adaptedpathpayoff.hpp> /* Copyright (C) 2009 Andrea Odetti This file is part of QuantLib, a free-software/open-source library for financial quantitative analysts and developers - http://quantlib.org/ QuantLib is free software: you can redistribute it and/or modify it under the terms of the QuantLib license. You should have received a copy of the license along with this program; if not, please email <quantlib-dev@lists.sf.net>. The license is also available online at <http://quantlib.org/license.shtml>. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the license for more details. */ namespace QuantLib { /* Initializing maximumTimeRead_ to -1 would make more sense, but it is unsigned and 0 has exactly the same behaviour. */ AdaptedPathPayoff::ValuationData::ValuationData( const Matrix & path, Array & payments, Array & exercises, std::vector<Array> & states) : path_(path), payments_(payments), exercises_(exercises), states_(states), maximumTimeRead_(0) { } Size AdaptedPathPayoff::ValuationData::numberOfTimes() const { return path_.columns(); } Size AdaptedPathPayoff::ValuationData::numberOfAssets() const { return path_.rows(); } Real AdaptedPathPayoff::ValuationData::getAssetValue(Size time, Size asset) { maximumTimeRead_ = std::max(maximumTimeRead_, time); return path_[asset][time]; } void AdaptedPathPayoff::ValuationData::setPayoffValue(Size time, Real value) { /* This is to ensure the payoff is an adapted function. We prevent payments to depend on future fixings. */ QL_REQUIRE(time >= maximumTimeRead_, "not adapted payoff: looking into the future"); payments_[time] = value; } void AdaptedPathPayoff::ValuationData::setExerciseData( Size time, Real exercise, Array & state) { /* This is to ensure the payoff is an adapted function. We prevent payments to depend on future fixings. */ QL_REQUIRE(time >= maximumTimeRead_, "not adapted payoff: looking into the future"); if (!exercises_.empty()) exercises_[time] = exercise; if (!states_.empty()) std::swap(states_[time], state); } void AdaptedPathPayoff::value(const Matrix & path, Array & payments, Array & exercises, std::vector<Array> & states) const { ValuationData data(path, payments, exercises, states); operator()(data); } }

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