interp_fit.hpp

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/* +---------------------------------------------------------------------------+
   |                     Mobile Robot Programming Toolkit (MRPT)               |
   |                          http://www.mrpt.org/                             |
   |                                                                           |
   | Copyright (c) 2005-2018, Individual contributors, see AUTHORS file        |
   | See: http://www.mrpt.org/Authors - All rights reserved.                   |
   | Released under BSD License. See details in http://www.mrpt.org/License    |
   +---------------------------------------------------------------------------+
 */
#pragma once

#include <mrpt/math/interp_fit.h>

namespace mrpt::math
{
template <class T, class VECTOR>
T interpolate(const T& x, const VECTOR& ys, const T& x0, const T& x1)
{
    MRPT_START
    ASSERT_(x1 > x0);
    ASSERT_(!ys.empty());
    const size_t N = ys.size();
    if (x <= x0) return ys[0];
    if (x >= x1) return ys[N - 1];
    const T Ax = (x1 - x0) / T(N);
    const size_t i = int((x - x0) / Ax);
    if (i >= N - 1) return ys[N - 1];
    const T Ay = ys[i + 1] - ys[i];
    return ys[i] + (x - (x0 + i * Ax)) * Ay / Ax;
    MRPT_END
}

template <typename NUMTYPE, class VECTORLIKE>
NUMTYPE spline(
    const NUMTYPE t, const VECTORLIKE& x, const VECTORLIKE& y, bool wrap2pi)
{
    // Check input data
    ASSERT_(x.size() == 4 && y.size() == 4);
    ASSERT_(x[0] <= x[1] && x[1] <= x[2] && x[2] <= x[3]);
    ASSERT_(t > x[0] && t < x[3]);

    NUMTYPE h[3];
    for (unsigned int i = 0; i < 3; i++) h[i] = x[i + 1] - x[i];

    double k = 1 / (4 * h[0] * h[1] + 4 * h[0] * h[2] + 3 * h[1] * h[1] +
                    4 * h[1] * h[2]);
    double a11 = 2 * (h[1] + h[2]) * k;
    double a12 = -h[1] * k;
    double a22 = 2 * (h[0] + h[1]) * k;

    double y0, y1, y2, y3;

    if (!wrap2pi)
    {
        y0 = y[0];
        y1 = y[1];
        y2 = y[2];
        y3 = y[3];
    }
    else
    {
        // Assure the function is linear without jumps in the interval:
        y0 = mrpt::math::wrapToPi(y[0]);
        y1 = mrpt::math::wrapToPi(y[1]);
        y2 = mrpt::math::wrapToPi(y[2]);
        y3 = mrpt::math::wrapToPi(y[3]);

        double Ay;

        Ay = y1 - y0;
        if (Ay > M_PI)
            y1 -= M_2PI;
        else if (Ay < -M_PI)
            y1 += M_2PI;

        Ay = y2 - y1;
        if (Ay > M_PI)
            y2 -= M_2PI;
        else if (Ay < -M_PI)
            y2 += M_2PI;

        Ay = y3 - y2;
        if (Ay > M_PI)
            y3 -= M_2PI;
        else if (Ay < -M_PI)
            y3 += M_2PI;
    }

    double b1 = (y2 - y1) / h[1] - (y1 - y0) / h[0];
    double b2 = (y3 - y2) / h[2] - (y2 - y1) / h[1];

    double z0 = 0;
    double z1 = 6 * (a11 * b1 + a12 * b2);
    double z2 = 6 * (a12 * b1 + a22 * b2);
    double z3 = 0;

    double res = 0;
    if (t < x[1])
        res = (z1 * pow((t - x[0]), 3) + z0 * pow((x[1] - t), 3)) / (6 * h[0]) +
              (y1 / h[0] - h[0] / 6 * z1) * (t - x[0]) +
              (y0 / h[0] - h[0] / 6 * z0) * (x[1] - t);
    else
    {
        if (t < x[2])
            res = (z2 * pow((t - x[1]), 3) + z1 * pow((x[2] - t), 3)) /
                      (6 * h[1]) +
                  (y2 / h[1] - h[1] / 6 * z2) * (t - x[1]) +
                  (y1 / h[1] - h[1] / 6 * z1) * (x[2] - t);
        else if (t < x[3])
            res = (z3 * pow((t - x[2]), 3) + z2 * pow((x[3] - t), 3)) /
                      (6 * h[2]) +
                  (y3 / h[2] - h[2] / 6 * z3) * (t - x[2]) +
                  (y2 / h[2] - h[2] / 6 * z2) * (x[3] - t);
    }
    return wrap2pi ? mrpt::math::wrapToPi(res) : res;
}

template <typename NUMTYPE, class VECTORLIKE, int NUM_POINTS>
NUMTYPE leastSquareLinearFit(
    const NUMTYPE t, const VECTORLIKE& x, const VECTORLIKE& y, bool wrap2pi)
{
    MRPT_START

    // http://en.wikipedia.org/wiki/Linear_least_squares
    ASSERT_(x.size() == y.size());
    ASSERT_(x.size() > 1);

    const size_t N = x.size();

    // X= [1 columns of ones, x' ]
    const NUMTYPE x_min = x.minimum();
    Eigen::Matrix<NUMTYPE, 2, NUM_POINTS> Xt;
    Xt.resize(2, N);
    for (size_t i = 0; i < N; i++)
    {
        Xt.set_unsafe(0, i, 1);
        Xt.set_unsafe(1, i, x[i] - x_min);
    }

    const auto B = ((Xt * Xt.transpose()).inv().eval() * Xt * y).eval();
    ASSERT_(B.size() == 2);

    NUMTYPE ret = B[0] + B[1] * (t - x_min);

    // wrap?
    if (!wrap2pi)
        return ret;
    else
        return mrpt::math::wrapToPi(ret);

    MRPT_END
}

template <
    class VECTORLIKE1, class VECTORLIKE2, class VECTORLIKE3, int NUM_POINTS>
void leastSquareLinearFit(
    const VECTORLIKE1& ts, VECTORLIKE2& outs, const VECTORLIKE3& x,
    const VECTORLIKE3& y, bool wrap2pi)
{
    MRPT_START

    // http://en.wikipedia.org/wiki/Linear_least_squares
    ASSERT_(x.size() == y.size());
    ASSERT_(x.size() > 1);

    const size_t N = x.size();

    // X= [1 columns of ones, x' ]
    using NUM = typename VECTORLIKE3::value_type;
    const NUM x_min = x.minimum();
    Eigen::Matrix<NUM, 2, NUM_POINTS> Xt;
    Xt.resize(2, N);
    for (size_t i = 0; i < N; i++)
    {
        Xt.set_unsafe(0, i, 1);
        Xt.set_unsafe(1, i, x[i] - x_min);
    }

    const auto B = ((Xt * Xt.transpose()).inv().eval() * Xt * y).eval();
    ASSERT_(B.size() == 2);

    const size_t tsN = size_t(ts.size());
    outs.resize(tsN);
    if (!wrap2pi)
        for (size_t k = 0; k < tsN; k++)
            outs[k] = B[0] + B[1] * (ts[k] - x_min);
    else
        for (size_t k = 0; k < tsN; k++)
            outs[k] = mrpt::math::wrapToPi(B[0] + B[1] * (ts[k] - x_min));
    MRPT_END
}
}  // namespace mrpt