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

/*

 Copyright (C) 2005 Klaus Spanderen

 Copyright (C) 2005 StatPro Italia srl

 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.

*/

#include <ql/processes/stochasticprocessarray.hpp>

#include <ql/math/matrixutilities/pseudosqrt.hpp>

namespace QuantLib {

    StochasticProcessArray::StochasticProcessArray(

        const std::vector<ext::shared_ptr<StochasticProcess1D> >& processes,

        const Matrix& correlation)

    : processes_(processes),

      sqrtCorrelation_(pseudoSqrt(correlation,SalvagingAlgorithm::Spectral)) {

        QL_REQUIRE(!processes.empty(), "no processes given");

        QL_REQUIRE(correlation.rows() == processes.size(),

                   "mismatch between number of processes "

                   "and size of correlation matrix");

        for (auto& process : processes_) {

            QL_REQUIRE(process, "null 1-D stochastic process");

            registerWith(process);

        }

    }

    Size StochasticProcessArray::size() const {

        return processes_.size();

    }

    Array StochasticProcessArray::initialValues() const {

        Array tmp(size());

        for (Size i=0; i<size(); ++i)

            tmp[i] = processes_[i]->x0();

        return tmp;

    }

    Array StochasticProcessArray::drift(Time t,

                                        const Array& x) const {

        Array tmp(size());

        for (Size i=0; i<size(); ++i)

            tmp[i] = processes_[i]->drift(t, x[i]);

        return tmp;

    }

    Matrix StochasticProcessArray::diffusion(Time t,

                                             const Array& x) const {

        Matrix tmp = sqrtCorrelation_;

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

            Real sigma = processes_[i]->diffusion(t, x[i]);

            std::transform(tmp.row_begin(i), tmp.row_end(i),

                           tmp.row_begin(i),

                           [=](Real x) -> Real { return x * sigma; });

        }

        return tmp;

    }

    Array StochasticProcessArray::expectation(Time t0,

                                              const Array& x0,

                                              Time dt) const {

        Array tmp(size());

        for (Size i=0; i<size(); ++i)

            tmp[i] = processes_[i]->expectation(t0, x0[i], dt);

        return tmp;

    }

    Matrix StochasticProcessArray::stdDeviation(Time t0,

                                                const Array& x0,

                                                Time dt) const {

        Matrix tmp = sqrtCorrelation_;

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

            Real sigma = processes_[i]->stdDeviation(t0, x0[i], dt);

            std::transform(tmp.row_begin(i), tmp.row_end(i),

                           tmp.row_begin(i),

                           [=](Real x) -> Real { return x * sigma; });

        }

        return tmp;

    }

    Matrix StochasticProcessArray::covariance(Time t0,

                                              const Array& x0,

                                              Time dt) const {

        Matrix tmp = stdDeviation(t0, x0, dt);

        return tmp*transpose(tmp);

    }

    Array StochasticProcessArray::evolve(

                  Time t0, const Array& x0, Time dt, const Array& dw) const {

        const Array dz = sqrtCorrelation_ * dw;

        Array tmp(size());

        for (Size i=0; i<size(); ++i)

            tmp[i] = processes_[i]->evolve(t0, x0[i], dt, dz[i]);

        return tmp;

    }

    Array StochasticProcessArray::apply(const Array& x0,

                                        const Array& dx) const {

        Array tmp(size());

        for (Size i=0; i<size(); ++i)

            tmp[i] = processes_[i]->apply(x0[i],dx[i]);

        return tmp;

    }

    Time StochasticProcessArray::time(const Date& d) const {

        return processes_[0]->time(d);

    }

    const ext::shared_ptr<StochasticProcess1D>&

    StochasticProcessArray::process(Size i) const {

        return processes_[i];

    }

    Matrix StochasticProcessArray::correlation() const {

        return sqrtCorrelation_ * transpose(sqrtCorrelation_);

    }

}