tetrees/Plugins/Wwise/Source/AkAudio/Classes/GTE/Mathematics/ApprPolynomialSpecial2.h

// 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/GMatrix.h>
#include <array>

// Fit the data with a polynomial of the form
//     w = sum_{i=0}^{n-1} c[i]*x^{p[i]}
// where p[i] are distinct nonnegative powers provided by the caller. A
// least-squares fitting algorithm is used, but the input data is first
// mapped to (x,w) in [-1,1]^2 for numerical robustness.

namespace WwiseGTE
{
    template <typename Real>
    class ApprPolynomialSpecial2 : public ApprQuery<Real, std::array<Real, 2>>
    {
    public:
        // Initialize the model parameters to zero.  The degrees must be
        // nonnegative and strictly increasing.
        ApprPolynomialSpecial2(std::vector<int> const& degrees)
            :
            mDegrees(degrees),
            mParameters(degrees.size(), (Real)0)
        {
#if !defined(GTE_NO_LOGGER)
            LogAssert(mDegrees.size() > 0, "The input array must have elements.");
            int lastDegree = -1;
            for (auto degree : mDegrees)
            {
                LogAssert(degree > lastDegree, "Degrees must be increasing.");
                lastDegree = degree;
            }
#endif

            mXDomain[0] = std::numeric_limits<Real>::max();
            mXDomain[1] = -mXDomain[0];
            mWDomain[0] = std::numeric_limits<Real>::max();
            mWDomain[1] = -mWDomain[0];

            mScale[0] = (Real)0;
            mScale[1] = (Real)0;
            mInvTwoWScale = (Real)0;

            // Powers of x are computed up to twice the powers when
            // constructing the fitted polynomial. Powers of x are computed
            // up to the powers for the evaluation of the fitted polynomial.
            mXPowers.resize(2 * mDegrees.back() + 1);
            mXPowers[0] = (Real)1;
        }

        // 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))
            {
                // Transform the observations to [-1,1]^2 for numerical
                // robustness.
                std::vector<std::array<Real, 2>> transformed;
                Transform(observations, numIndices, indices, transformed);

                // Fit the transformed data using a least-squares algorithm.
                return DoLeastSquares(transformed);
            }

            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 mParameters.size();
        }

        // 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<ApprPolynomialSpecial2 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
        {
            // Transform x to x' in [-1,1].
            x = (Real)-1 + (Real)2 * mScale[0] * (x - mXDomain[0]);

            // Compute relevant powers of x.
            int jmax = mDegrees.back();
            for (int j = 1; j <= jmax; ++j)
            {
                mXPowers[j] = mXPowers[j - 1] * x;
            }

            Real w = (Real)0;
            int isup = static_cast<int>(mDegrees.size());
            for (int i = 0; i < isup; ++i)
            {
                Real xp = mXPowers[mDegrees[i]];
                w += mParameters[i] * xp;
            }

            // Transform w from [-1,1] back to the original space.
            w = (w + (Real)1) * mInvTwoWScale + mWDomain[0];
            return w;
        }

    private:
        // Transform the (x,w) values to (x',w') in [-1,1]^2.
        void Transform(std::array<Real, 2> const* observations, size_t numIndices,
            int const* indices, std::vector<std::array<Real, 2>>& transformed)
        {
            int numSamples = static_cast<int>(numIndices);
            transformed.resize(numSamples);

            std::array<Real, 2> omin = observations[indices[0]];
            std::array<Real, 2> omax = omin;
            std::array<Real, 2> obs;
            int s, i;
            for (s = 1; s < numSamples; ++s)
            {
                obs = observations[indices[s]];
                for (i = 0; i < 2; ++i)
                {
                    if (obs[i] < omin[i])
                    {
                        omin[i] = obs[i];
                    }
                    else if (obs[i] > omax[i])
                    {
                        omax[i] = obs[i];
                    }
                }
            }

            mXDomain[0] = omin[0];
            mXDomain[1] = omax[0];
            mWDomain[0] = omin[1];
            mWDomain[1] = omax[1];
            for (i = 0; i < 2; ++i)
            {
                mScale[i] = (Real)1 / (omax[i] - omin[i]);
            }

            for (s = 0; s < numSamples; ++s)
            {
                obs = observations[indices[s]];
                for (i = 0; i < 2; ++i)
                {
                    transformed[s][i] = (Real)-1 + (Real)2 * mScale[i] * (obs[i] - omin[i]);
                }
            }
            mInvTwoWScale = (Real)0.5 / mScale[1];
        }

        // The least-squares fitting algorithm for the transformed data.
        bool DoLeastSquares(std::vector<std::array<Real, 2>> & transformed)
        {
            // Set up a linear system A*X = B, where X are the polynomial
            // coefficients.
            int size = static_cast<int>(mDegrees.size());
            GMatrix<Real> A(size, size);
            A.MakeZero();
            GVector<Real> B(size);
            B.MakeZero();

            int numSamples = static_cast<int>(transformed.size());
            int twoMaxXDegree = 2 * mDegrees.back();
            int row, col;
            for (int i = 0; i < numSamples; ++i)
            {
                // Compute relevant powers of x.
                Real x = transformed[i][0];
                Real w = transformed[i][1];
                for (int j = 0; j <= twoMaxXDegree; ++j)
                {
                    mXPowers[j] = mXPowers[j - 1] * x;
                }

                for (row = 0; row < size; ++row)
                {
                    // Update the upper-triangular portion of the symmetric
                    // matrix.
                    for (col = row; col < size; ++col)
                    {
                        A(row, col) += mXPowers[mDegrees[row] + mDegrees[col]];
                    }

                    // Update the right-hand side of the system.
                    B[row] += mXPowers[mDegrees[row]] * w;
                }
            }

            // Copy the upper-triangular portion of the symmetric matrix to
            // the lower-triangular portion.
            for (row = 0; row < size; ++row)
            {
                for (col = 0; col < row; ++col)
                {
                    A(row, col) = A(col, row);
                }
            }

            // Precondition by normalizing the sums.
            Real invNumSamples = (Real)1 / (Real)numSamples;
            A *= invNumSamples;
            B *= invNumSamples;

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

        std::vector<int> mDegrees;
        std::vector<Real> mParameters;

        // Support for evaluation. The coefficients were generated for the
        // samples mapped to [-1,1]^2. The Evaluate() function must transform
        // x to x' in [-1,1], compute w' in [-1,1], then transform w' to w.
        std::array<Real, 2> mXDomain, mWDomain;
        std::array<Real, 2> mScale;
        Real mInvTwoWScale;

        // This array is used by Evaluate() to avoid reallocation of the 
        // 'vector' for each call.  The member is mutable because, to the
        // user, the call to Evaluate does not modify the polynomial.
        mutable std::vector<Real> mXPowers;
    };
}