transform_gaussian.h

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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/math_frwds.h>
#include <mrpt/math/CMatrixTemplateNumeric.h>
#include <mrpt/math/CMatrixFixedNumeric.h>
#include <mrpt/math/ops_matrices.h>
#include <mrpt/math/num_jacobian.h>
#include <mrpt/math/data_utils.h>
#include <mrpt/core/aligned_std_vector.h>
#include <mrpt/random.h>
#include <functional>

namespace mrpt::math
{
/** @addtogroup  gausspdf_transform_grp Gaussian PDF transformation functions
  *  \ingroup mrpt_math_grp
  * @{ */

/** Scaled unscented transformation (SUT) for estimating the Gaussian
 * distribution of a variable Y=f(X) for an arbitrary function f() provided by
 * the user.
  *  The user must supply the function in "functor" which takes points in the X
 * space and returns the mapped point in Y, optionally using an extra, constant
 * parameter ("fixed_param") which remains constant.
  *
  *  The parameters alpha, K and beta are the common names of the SUT method,
 * and the default values are those recommended in most papers.
  *
  * \param elem_do_wrap2pi If !=nullptr; it must point to an array of "bool" of
 * size()==dimension of each element, stating if it's needed to do a wrap to
 * [-pi,pi] to each dimension.
  * \sa The example in MRPT/samples/unscented_transform_test
  * \sa transform_gaussian_montecarlo, transform_gaussian_linear
  */
template <class VECTORLIKE1, class MATLIKE1, class USERPARAM, class VECTORLIKE2,
          class VECTORLIKE3, class MATLIKE2>
void transform_gaussian_unscented(
    const VECTORLIKE1& x_mean, const MATLIKE1& x_cov,
    void (*functor)(
        const VECTORLIKE1& x, const USERPARAM& fixed_param, VECTORLIKE3& y),
    const USERPARAM& fixed_param, VECTORLIKE2& y_mean, MATLIKE2& y_cov,
    const bool* elem_do_wrap2pi = nullptr, const double alpha = 1e-3,
    const double K = 0, const double beta = 2.0)
{
    MRPT_START
    const size_t Nx = x_mean.size();  // Dimensionality of inputs X
    const double lambda = alpha * alpha * (Nx + K) - Nx;
    const double c = Nx + lambda;

    // Generate weights:
    const double Wi = 0.5 / c;
    std::vector<double> W_mean(1 + 2 * Nx, Wi), W_cov(1 + 2 * Nx, Wi);
    W_mean[0] = lambda / c;
    W_cov[0] = W_mean[0] + (1 - alpha * alpha + beta);

    // Generate X_i samples:
    MATLIKE1 L;
    const bool valid = x_cov.chol(L);
    if (!valid)
        throw std::runtime_error(
            "transform_gaussian_unscented: Singular covariance matrix in "
            "Cholesky.");
    L *= sqrt(c);

    // Propagate the samples X_i -> Y_i:
    // We don't need to store the X sigma points: just use one vector to compute
    // all the Y sigma points:
    mrpt::aligned_std_vector<VECTORLIKE3> Y(1 + 2 * Nx);  // 2Nx+1 sigma points
    VECTORLIKE1 X = x_mean;
    functor(X, fixed_param, Y[0]);
    VECTORLIKE1 delta;  // i'th row of L:
    delta.resize(Nx);
    size_t row = 1;
    for (size_t i = 0; i < Nx; i++)
    {
        L.extractRowAsCol(i, delta);
        X = x_mean;
        X -= delta;
        functor(X, fixed_param, Y[row++]);
        X = x_mean;
        X += delta;
        functor(X, fixed_param, Y[row++]);
    }

    // Estimate weighted cov & mean:
    mrpt::math::covariancesAndMeanWeighted(
        Y, y_cov, y_mean, &W_mean, &W_cov, elem_do_wrap2pi);
    MRPT_END
}

/** Simple Montecarlo-base estimation of the Gaussian distribution of a variable
 * Y=f(X) for an arbitrary function f() provided by the user.
  *  The user must supply the function in "functor" which takes points in the X
 * space and returns the mapped point in Y, optionally using an extra, constant
 * parameter ("fixed_param") which remains constant.
  * \param out_samples_y If !=nullptr, this vector will contain, upon return,
 * the sequence of random samples generated and propagated through the
 * functor().
  * \sa The example in MRPT/samples/unscented_transform_test
  * \sa transform_gaussian_unscented, transform_gaussian_linear
  */
template <class VECTORLIKE1, class MATLIKE1, class USERPARAM, class VECTORLIKE2,
          class VECTORLIKE3, class MATLIKE2>
void transform_gaussian_montecarlo(
    const VECTORLIKE1& x_mean, const MATLIKE1& x_cov,
    void (*functor)(
        const VECTORLIKE1& x, const USERPARAM& fixed_param, VECTORLIKE3& y),
    const USERPARAM& fixed_param, VECTORLIKE2& y_mean, MATLIKE2& y_cov,
    const size_t num_samples = 1000,
    mrpt::aligned_std_vector<VECTORLIKE3>* out_samples_y = nullptr)
{
    MRPT_START
    mrpt::aligned_std_vector<VECTORLIKE1> samples_x;
    mrpt::random::getRandomGenerator().drawGaussianMultivariateMany(
        samples_x, num_samples, x_cov, &x_mean);
    mrpt::aligned_std_vector<VECTORLIKE3> samples_y(num_samples);
    for (size_t i = 0; i < num_samples; i++)
        functor(samples_x[i], fixed_param, samples_y[i]);
    mrpt::math::covariancesAndMean(samples_y, y_cov, y_mean);
    if (out_samples_y)
    {
        out_samples_y->clear();
        samples_y.swap(*out_samples_y);
    }
    MRPT_END
}

/** First order uncertainty propagation estimator of the Gaussian distribution
 * of a variable Y=f(X) for an arbitrary function f() provided by the user.
  *  The user must supply the function in "functor" which takes points in the X
 * space and returns the mapped point in Y, optionally using an extra, constant
 * parameter ("fixed_param") which remains constant.
  *  The Jacobians are estimated numerically using the vector of small
 * increments "x_increments".
  * \sa The example in MRPT/samples/unscented_transform_test
  * \sa transform_gaussian_unscented, transform_gaussian_montecarlo
  */
template <class VECTORLIKE1, class MATLIKE1, class USERPARAM, class VECTORLIKE2,
          class VECTORLIKE3, class MATLIKE2>
void transform_gaussian_linear(
    const VECTORLIKE1& x_mean, const MATLIKE1& x_cov,
    void (*functor)(
        const VECTORLIKE1& x, const USERPARAM& fixed_param, VECTORLIKE3& y),
    const USERPARAM& fixed_param, VECTORLIKE2& y_mean, MATLIKE2& y_cov,
    const VECTORLIKE1& x_increments)
{
    MRPT_START
    // Mean: simple propagation:
    functor(x_mean, fixed_param, y_mean);
    // Cov: COV = H C Ht
    Eigen::Matrix<double, VECTORLIKE3::RowsAtCompileTime,
                  VECTORLIKE1::RowsAtCompileTime>
        H;
    mrpt::math::estimateJacobian(
        x_mean, std::function<void(
                    const VECTORLIKE1& x, const USERPARAM& fixed_param,
                    VECTORLIKE3& y)>(functor),
        x_increments, fixed_param, H);
    H.multiply_HCHt(x_cov, y_cov);
    MRPT_END
}

/** @} */

}