/* -*- mode: c++; tab-width: 4; indent-tabs-mode: nil; c-basic-offset: 4 -*- */

/*

 Copyright (C) 2003 Ferdinando Ametrano

 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

 <https://www.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.

*/

/*! \file randomsequencegenerator.hpp

    \brief Random sequence generator based on a pseudo-random number generator

*/

#ifndef quantlib_random_sequence_generator_h

#define quantlib_random_sequence_generator_h

#include <ql/methods/montecarlo/sample.hpp>

#include <ql/errors.hpp>

#include <vector>

namespace QuantLib {

    //! Random sequence generator based on a pseudo-random number generator

    /*! Random sequence generator based on a pseudo-random number

        generator RNG.

        Class RNG must implement the following interface:

        \code

            RNG::sample_type RNG::next() const;

        \endcode

        If a client of this class wants to use the nextInt32Sequence method,

        class RNG must also implement

        \code

            unsigned long RNG::nextInt32() const;

        \endcode

        \warning do not use with low-discrepancy sequence generator.

    */

    template<class RNG>

    class RandomSequenceGenerator {

      public:

        typedef Sample<std::vector<Real> > sample_type;

        RandomSequenceGenerator(Size dimensionality,

                                const RNG& rng)

        : dimensionality_(dimensionality), rng_(rng),

          sequence_(std::vector<Real> (dimensionality), 1.0),

          int32Sequence_(dimensionality) {

          QL_REQUIRE(dimensionality>0, 

                     "dimensionality must be greater than 0");

        }

        /*! if the given seed is 0, a random seed will be chosen

            based on clock() */

        explicit RandomSequenceGenerator(Size dimensionality,

                                         BigNatural seed = 0)

        : dimensionality_(dimensionality), rng_(seed),

          sequence_(std::vector<Real> (dimensionality), 1.0),

          int32Sequence_(dimensionality) {}

        const sample_type& nextSequence() const {

            sequence_.weight = 1.0;

            for (Size i=0; i<dimensionality_; i++) {

                typename RNG::sample_type x(rng_.next());

                sequence_.value[i] = x.value;

                sequence_.weight  *= x.weight;

            }

            return sequence_;

        }

        std::vector<BigNatural> nextInt32Sequence() const {

            for (Size i=0; i<dimensionality_; i++) {

                int32Sequence_[i] = rng_.nextInt32();

            }

            return int32Sequence_;

        }

        const sample_type& lastSequence() const {

            return sequence_;

        }

        Size dimension() const {return dimensionality_;}

      private:

        Size dimensionality_;

        RNG rng_;

        mutable sample_type sequence_;

        mutable std::vector<BigNatural> int32Sequence_;

    };

}

#endif