// David Eberly, Geometric Tools, Redmond WA 98052
// Copyright (c) 1998-2020
// Distributed under the Boost Software License, Version 1.0.
// https://www.boost.org/LICENSE_1_0.txt
// https://www.geometrictools.com/License/Boost/LICENSE_1_0.txt
// Version: 4.0.2019.08.13

#pragma once

#include <Mathematics/ApprQuery.h>
#include <Mathematics/Array2.h>
#include <Mathematics/GMatrix.h>
#include <array>

// The samples are (x[i],w[i]) for 0 <= i < S. Think of w as a function of
// x, say w = f(x). The function fits the samples with a polynomial of
// degree d, say w = sum_{i=0}^d c[i]*x^i. The method is a least-squares
// fitting algorithm. The mParameters stores the coefficients c[i] for
// 0 <= i <= d. The observation type is std::array<Real,2>, which represents
// a pair (x,w).
//
// WARNING. The fitting algorithm for polynomial terms
//   (1,x,x^2,...,x^d)
// is known to be nonrobust for large degrees and for large magnitude data.
// One alternative is to use orthogonal polynomials
//   (f[0](x),...,f[d](x))
// and apply the least-squares algorithm to these. Another alternative is to
// transform
//   (x',w') = ((x-xcen)/rng, w/rng)
// where xmin = min(x[i]), xmax = max(x[i]), xcen = (xmin+xmax)/2, and
// rng = xmax-xmin. Fit the (x',w') points,
//   w' = sum_{i=0}^d c'[i]*(x')^i.
// The original polynomial is evaluated as
//   w = rng*sum_{i=0}^d c'[i]*((x-xcen)/rng)^i

namespace WwiseGTE
{
    template <typename Real>
    class ApprPolynomial2 : public ApprQuery<Real, std::array<Real, 2>>
    {
    public:
        // Initialize the model parameters to zero.
        ApprPolynomial2(int degree)
            :
            mDegree(degree),
            mSize(degree + 1),
            mParameters(mSize, (Real)0)
        {
            mXDomain[0] = std::numeric_limits<Real>::max();
            mXDomain[1] = -mXDomain[0];
        }

        // Basic fitting algorithm. See ApprQuery.h for the various Fit(...)
        // functions that you can call.
        virtual bool FitIndexed(
            size_t numObservations, std::array<Real, 2> const* observations,
            size_t numIndices, int const* indices) override
        {
            if (this->ValidIndices(numObservations, observations, numIndices, indices))
            {
                int s, i0, i1;

                // Compute the powers of x.
                int numSamples = static_cast<int>(numIndices);
                int twoDegree = 2 * mDegree;
                Array2<Real> xPower(twoDegree + 1, numSamples);
                for (s = 0; s < numSamples; ++s)
                {
                    Real x = observations[indices[s]][0];
                    mXDomain[0] = std::min(x, mXDomain[0]);
                    mXDomain[1] = std::max(x, mXDomain[1]);

                    xPower[s][0] = (Real)1;
                    for (i0 = 1; i0 <= twoDegree; ++i0)
                    {
                        xPower[s][i0] = x * xPower[s][i0 - 1];
                    }
                }

                // Matrix A is the Vandermonde matrix and vector B is the
                // right-hand side of the linear system A*X = B.
                GMatrix<Real> A(mSize, mSize);
                GVector<Real> B(mSize);
                for (i0 = 0; i0 <= mDegree; ++i0)
                {
                    Real sum = (Real)0;
                    for (s = 0; s < numSamples; ++s)
                    {
                        Real w = observations[indices[s]][1];
                        sum += w * xPower[s][i0];
                    }

                    B[i0] = sum;

                    for (i1 = 0; i1 <= mDegree; ++i1)
                    {
                        sum = (Real)0;
                        for (s = 0; s < numSamples; ++s)
                        {
                            sum += xPower[s][i0 + i1];
                        }

                        A(i0, i1) = sum;
                    }
                }

                // Solve for the polynomial coefficients.
                GVector<Real> coefficients = Inverse(A) * B;
                bool hasNonzero = false;
                for (int i = 0; i < mSize; ++i)
                {
                    mParameters[i] = coefficients[i];
                    if (coefficients[i] != (Real)0)
                    {
                        hasNonzero = true;
                    }
                }
                return hasNonzero;
            }

            std::fill(mParameters.begin(), mParameters.end(), (Real)0);
            return false;
        }

        // Get the parameters for the best fit.
        std::vector<Real> const& GetParameters() const
        {
            return mParameters;
        }

        virtual size_t GetMinimumRequired() const override
        {
            return static_cast<size_t>(mSize);
        }

        // Compute the model error for the specified observation for the
        // current model parameters. The returned value for observation
        // (x0,w0) is |w(x0) - w0|, where w(x) is the fitted polynomial.
        virtual Real Error(std::array<Real, 2> const& observation) const override
        {
            Real w = Evaluate(observation[0]);
            Real error = std::fabs(w - observation[1]);
            return error;
        }

        virtual void CopyParameters(ApprQuery<Real, std::array<Real, 2>> const* input) override
        {
            auto source = dynamic_cast<ApprPolynomial2 const*>(input);
            if (source)
            {
                *this = *source;
            }
        }

        // Evaluate the polynomial. The domain interval is provided so you can
        // interpolate (x in domain) or extrapolate (x not in domain).
        std::array<Real, 2> const& GetXDomain() const
        {
            return mXDomain;
        }

        Real Evaluate(Real x) const
        {
            int i = mDegree;
            Real w = mParameters[i];
            while (--i >= 0)
            {
                w = mParameters[i] + w * x;
            }
            return w;
        }

    private:
        int mDegree, mSize;
        std::array<Real, 2> mXDomain;
        std::vector<Real> mParameters;
    };
}