<commit_before>#ifndef STAN_MATH_PRIM_MAT_FUN_GP_MATERN52_COV_HPP
#define STAN_MATH_PRIM_MAT_FUN_GP_MATERN52_COV_HPP

#include <cmath>
#include <stan/math/prim/mat/fun/Eigen.hpp>
#include <stan/math/prim/mat/fun/divide_columns.hpp>
#include <stan/math/prim/mat/fun/sqrt.hpp>
#include <stan/math/prim/scal/err/check_finite.hpp>
#include <stan/math/prim/scal/err/check_nonnegative.hpp>
#include <stan/math/prim/scal/err/check_not_nan.hpp>
#include <stan/math/prim/scal/err/check_positive_finite.hpp>
#include <stan/math/prim/scal/fun/divide.hpp>
#include <stan/math/prim/scal/fun/square.hpp>
#include <stan/math/prim/scal/fun/squared_distance.hpp>
#include <stan/math/prim/scal/meta/return_type.hpp>
#include <stan/math/prim/scal/meta/scalar_type.hpp>
#include <vector>

namespace stan {
namespace math {

/**
 * Returns a Matern 5/2 covariance matrix with one input vector
 *
 * \f[ k(x, x') = \sigma^2\bigg(1 +
 *  \frac{\sqrt{5}d(x, x')}{l} + \frac{5d(x, x')^2}{3l^2}\bigg)
 *   exp\bigg(-\frac{5 d(x, x')}{l}\bigg)
 * \f]
 *
 * where \f$ d(x, x') \f$ is the Euclidean distance.
 *
 * @tparam T_x type of elements contained in vector x
 * @tparam T_s type of element of sigma, the magnitude
 * @tparam T_l type of elements of length scale
 *
 * @param x std::vector of elements that can be used in stan::math::distance
 * @param length_scale length scale
 * @param sigma standard deviation that can be used in stan::math::square
 * @throw std::domain error if sigma <= 0, l <= 0, or x is nan or inf
 *
 */
template <typename T_x, typename T_s, typename T_l>
inline typename Eigen::Matrix<typename return_type<T_x, T_s, T_l>::type,
                              Eigen::Dynamic, Eigen::Dynamic>
gp_matern52_cov(const std::vector<T_x> &x, const T_s &sigma,
                const T_l &length_scale) {
  using std::exp;

  size_t x_size = x.size();
  for (size_t n = 0; n < x_size; ++n)
    check_not_nan("gp_matern52_cov", "x", x[n]);

  check_positive_finite("gp_matern52_cov", "magnitude", sigma);
  check_positive_finite("gp_matern52_cov", "length scale", length_scale);

  Eigen::Matrix<typename return_type<T_x, T_s, T_l>::type, Eigen::Dynamic,
                Eigen::Dynamic>
      cov(x_size, x_size);

  if (x_size == 0)
    return cov;

  T_s sigma_sq = square(sigma);
  T_l root_5_inv_l = sqrt(5.0) / length_scale;
  T_l inv_l_sq_5_3 = 5.0 / (3.0 * square(length_scale));
  T_l neg_root_5_inv_l = -sqrt(5.0) / length_scale;

  for (size_t i = 0; i < x_size; ++i) {
    cov(i, i) = sigma_sq;
    for (size_t j = i + 1; j < x_size; ++j) {
      typename return_type<T_x>::type sq_distance
          = squared_distance(x[i], x[j]);
      typename return_type<T_x>::type distance = sqrt(sq_distance);
      cov(i, j) = sigma_sq
                  * (1.0 + root_5_inv_l * distance + inv_l_sq_5_3 * sq_distance)
                  * exp(neg_root_5_inv_l * distance);
      cov(j, i) = cov(i, j);
    }
  }
  return cov;
}

/**
 * Returns a Matern 5/2 covariance matrix with one input vector
 * with automatic relevance determination (ARD).
 *
 * \f[ k(x, x') = \sigma^2\bigg(1 +
 *  \sqrt{5}\sqrt{\sum_{k=1}^{K}\frac{d(x, x')^2}{l_k^2}} +
 *  \frac{5}{3} \sqrt{\sum_{k=1}^{K}\frac{d(x, x')^2}{l_k^2}}\bigg)
 *  exp\bigg(-\frac{5}{3}\bigg(\sqrt{\sum_{k=1}^K{\frac{d(x, x')^2}{l_k^2}}
 *  }\bigg)\bigg)
 * \f]
 *
 * where \f$ d(x, x') \f$ is the Euclidean distance.
 *
 * @tparam T_x type of elements contained in vector x
 * @tparam T_s type of element of sigma, the magnitude
 * @tparam T_l type of elements in vector of length scale
 *
 * @param x std::vector of elements that can be used in stan::math::distance
 * @param length_scale length scale
 * @param sigma standard deviation that can be used in stan::math::square
 * @throw std::domain error if sigma <= 0, l <= 0, or x is nan or inf
 * @throw std::invalid_argument if length scale size != dimension of x
 */
template <typename T_x, typename T_s, typename T_l>
inline typename Eigen::Matrix<typename return_type<T_x, T_s, T_l>::type,
                              Eigen::Dynamic, Eigen::Dynamic>
gp_matern52_cov(const std::vector<Eigen::Matrix<T_x, Eigen::Dynamic, 1>> &x,
                const T_s &sigma, const std::vector<T_l> &length_scale) {
  using std::exp;

  size_t x_size = x.size();
  size_t l_size = length_scale.size();
  for (size_t n = 0; n < x_size; ++n)
    check_not_nan("gp_matern52_cov", "x", x[n]);

  check_positive_finite("gp_matern52_cov", "magnitude", sigma);
  check_positive_finite("gp_matern52_cov", "length scale", length_scale);
  check_size_match("gp_matern52_cov", "x dimension", x[0].size(),
                   "number of length scales", l_size);

  Eigen::Matrix<typename return_type<T_x, T_s, T_l>::type, Eigen::Dynamic,
                Eigen::Dynamic>
      cov(x_size, x_size);

  if (x_size == 0)
    return cov;

  T_s sigma_sq = square(sigma);
  double root_5 = sqrt(5.0);
  double five_thirds = 5.0 / 3.0;
  double neg_root_5 = -root_5;

  std::vector<Eigen::Matrix<typename return_type<T_x, T_s, T_l>::type, -1, 1>>
      x_new = divide_columns(x, length_scale);

  for (size_t i = 0; i < x_size; ++i) {
    cov(i, i) = sigma_sq;
    for (size_t j = i + 1; j < x_size; ++j) {
      typename return_type<T_x, T_s, T_l>::type sq_distance
          = squared_distance(x_new[i], x_new[j]);
      typename return_type<T_x, T_s, T_l>::type distance = sqrt(sq_distance);
      cov(i, j) = sigma_sq
                  * (1.0 + root_5 * distance + five_thirds * sq_distance)
                  * exp(neg_root_5 * distance);
      cov(j, i) = cov(i, j);
    }
  }
  return cov;
}

/**
 * Returns a Matern 5/2 covariance matrix with two different input vectors
 *
 * \f[ k(x, x') = \sigma^2\bigg(1 +
 *  \frac{\sqrt{5}d(x, x')}{l} + \frac{5d(x, x')^2}{3l^2}\bigg)
 *   exp\bigg(-\frac{5 d(x, x')}{l}\bigg)
 * \f]
 *
 * where \f$ d(x, x') \f$ is the Euclidean distance.
 *
 * @tparam T_x1 type of elements contained in vector x1
 * @tparam T_x2 type of elements contained in vector x2
 * @tparam T_s type of element of sigma, the magnitude
 * @tparam T_l type of elements of length scale
 *
 * @param x1 std::vector of elements that can be used in stan::math::distance
 * @param x2 std::vector of elements that can be used in stan::math::distance
 * @param length_scale length scale
 * @param sigma standard deviation that can be used in stan::math::square
 * @throw std::domain error if sigma <= 0, l <= 0, or x is nan or inf
 *
 */
template <typename T_x1, typename T_x2, typename T_s, typename T_l>
inline typename Eigen::Matrix<typename return_type<T_x1, T_x2, T_s, T_l>::type,
                              Eigen::Dynamic, Eigen::Dynamic>
gp_matern52_cov(const std::vector<T_x1> &x1, const std::vector<T_x2> &x2,
                const T_s &sigma, const T_l &length_scale) {
  using std::exp;

  size_t x1_size = x1.size();
  size_t x2_size = x2.size();

  for (size_t n = 0; n < x1_size; ++n)
    check_not_nan("gp_matern52_cov", "x1", x1[n]);
  for (size_t n = 0; n < x2_size; ++n)
    check_not_nan("gp_matern52_cov", "x1", x2[n]);

  check_positive_finite("gp_matern52_cov", "magnitude", sigma);
  check_positive_finite("gp_matern52_cov", "length scale", length_scale);

  Eigen::Matrix<typename return_type<T_x1, T_x2, T_s, T_l>::type,
                Eigen::Dynamic, Eigen::Dynamic>
      cov(x1_size, x2_size);

  if (x1_size == 0 || x2_size == 0)
    return cov;

  T_s sigma_sq = square(sigma);
  T_l root_5_inv_l = sqrt(5.0) / length_scale;
  T_l inv_l_sq_5_3 = 5.0 / (3.0 * square(length_scale));
  T_l neg_root_5_inv_l = -sqrt(5.0) / length_scale;

  for (size_t i = 0; i < x1_size; ++i) {
    for (size_t j = 0; j < x2_size; ++j) {
      typename return_type<T_x1, T_x2>::type sq_distance
          = squared_distance(x1[i], x2[j]);
      typename return_type<T_x1, T_x2>::type distance = sqrt(sq_distance);
      cov(i, j) = sigma_sq
                  * (1.0 + root_5_inv_l * distance + inv_l_sq_5_3 * sq_distance)
                  * exp(neg_root_5_inv_l * distance);
    }
  }
  return cov;
}

/**
 * Returns a Matern 5/2 covariance matrix with two input vectors
 * with automatic relevance determination (ARD).
 *
 * \f[ k(x, x') = \sigma^2\bigg(1 +
 *  \sqrt{5}\sqrt{\sum_{k=1}^{K}\frac{d(x, x')^2}{l_k^2}} +
 *  \frac{5}{3} \sqrt{\sum_{k=1}^{K}\frac{d(x, x')^2}{l_k^2}}\bigg)
 *  exp\bigg(-\frac{5}{3}\bigg(\sqrt{\sum_{k=1}^K{\frac{d(x, x')^2}{l_k^2}}
 *  }\bigg)\bigg)
 * \f]
 *
 * where \f$ d(x, x') \f$ is the Euclidean distance.
 *
 * @tparam T_x1 type of elements contained in vector x1
 * @tparam T_x2 type of elements contained in vector x2
 * @tparam T_s type of element of sigma, the  magnitude
 * @tparam T_l type of elements in vector of length scale
 *
 * @param x1 std::vector of elements that can be used in stan::math::distance
 * @param x2 std::vector of elements that can be used in stan::math::distance
 * @param length_scale length scale
 * @param sigma standard deviation that can be used in stan::math::square
 * @throw std::domain error if sigma <= 0, l <= 0, or x is nan or inf
 * @throw std::invalid_argument if length scale size != dimension of x1 or x2
 *
 */
template <typename T_x1, typename T_x2, typename T_s, typename T_l>
inline typename Eigen::Matrix<
    typename stan::return_type<T_x1, T_x2, T_s, T_l>::type, Eigen::Dynamic,
    Eigen::Dynamic>
gp_matern52_cov(const std::vector<Eigen::Matrix<T_x1, Eigen::Dynamic, 1>> &x1,
                const std::vector<Eigen::Matrix<T_x2, Eigen::Dynamic, 1>> &x2,
                const T_s &sigma, const std::vector<T_l> &length_scale) {
  using std::exp;

  size_t x1_size = x1.size();
  size_t x2_size = x2.size();
  size_t l_size = length_scale.size();

  for (size_t n = 0; n < x1_size; ++n)
    check_not_nan("gp_matern52_cov", "x1", x1[n]);
  for (size_t n = 0; n < x2_size; ++n)
    check_not_nan("gp_matern52_cov", "x1", x2[n]);

  check_positive_finite("gp_matern52_cov", "magnitude", sigma);
  check_positive_finite("gp_matern52_cov", "length scale", length_scale);
  check_size_match("gp_matern52_cov", "x dimension", x1[0].size(),
                   "number of length scales", l_size);
  check_size_match("gp_matern52_cov", "x dimension", x2[0].size(),
                   "number of length scales", l_size);

  Eigen::Matrix<typename return_type<T_x1, T_x2, T_s, T_l>::type,
                Eigen::Dynamic, Eigen::Dynamic>
      cov(x1_size, x2_size);

  if (x1_size == 0 || x2_size == 0)
    return cov;

  T_s sigma_sq = square(sigma);
  double root_5 = sqrt(5.0);
  double five_thirds = 5.0 / 3.0;
  double neg_root_5 = -root_5;

  std::vector<Eigen::Matrix<typename return_type<T_x1, T_l, T_s>::type, -1, 1>>
      x1_new = divide_columns(x1, length_scale);
  std::vector<Eigen::Matrix<typename return_type<T_x2, T_l, T_s>::type, -1, 1>>
      x2_new = divide_columns(x2, length_scale);

  for (size_t i = 0; i < x1_size; ++i) {
    for (size_t j = 0; j < x2_size; ++j) {
      typename return_type<T_x1, T_x2, T_l, T_s>::type sq_distance
          = squared_distance(x1_new[i], x2_new[j]);
      typename return_type<T_x1, T_x2, T_l, T_s>::type distance<commit_msg>remove over promotion<commit_after>#ifndef STAN_MATH_PRIM_MAT_FUN_GP_MATERN52_COV_HPP
#define STAN_MATH_PRIM_MAT_FUN_GP_MATERN52_COV_HPP

#include <cmath>
#include <stan/math/prim/mat/fun/Eigen.hpp>
#include <stan/math/prim/mat/fun/divide_columns.hpp>
#include <stan/math/prim/mat/fun/sqrt.hpp>
#include <stan/math/prim/scal/err/check_finite.hpp>
#include <stan/math/prim/scal/err/check_nonnegative.hpp>
#include <stan/math/prim/scal/err/check_not_nan.hpp>
#include <stan/math/prim/scal/err/check_positive_finite.hpp>
#include <stan/math/prim/scal/fun/divide.hpp>
#include <stan/math/prim/scal/fun/square.hpp>
#include <stan/math/prim/scal/fun/squared_distance.hpp>
#include <stan/math/prim/scal/meta/return_type.hpp>
#include <stan/math/prim/scal/meta/scalar_type.hpp>
#include <vector>

namespace stan {
namespace math {

/**
 * Returns a Matern 5/2 covariance matrix with one input vector
 *
 * \f[ k(x, x') = \sigma^2\bigg(1 +
 *  \frac{\sqrt{5}d(x, x')}{l} + \frac{5d(x, x')^2}{3l^2}\bigg)
 *   exp\bigg(-\frac{5 d(x, x')}{l}\bigg)
 * \f]
 *
 * where \f$ d(x, x') \f$ is the Euclidean distance.
 *
 * @tparam T_x type of elements contained in vector x
 * @tparam T_s type of element of sigma, the magnitude
 * @tparam T_l type of elements of length scale
 *
 * @param x std::vector of elements that can be used in stan::math::distance
 * @param length_scale length scale
 * @param sigma standard deviation that can be used in stan::math::square
 * @throw std::domain error if sigma <= 0, l <= 0, or x is nan or inf
 *
 */
template <typename T_x, typename T_s, typename T_l>
inline typename Eigen::Matrix<typename return_type<T_x, T_s, T_l>::type,
                              Eigen::Dynamic, Eigen::Dynamic>
gp_matern52_cov(const std::vector<T_x> &x, const T_s &sigma,
                const T_l &length_scale) {
  using std::exp;

  size_t x_size = x.size();
  for (size_t n = 0; n < x_size; ++n)
    check_not_nan("gp_matern52_cov", "x", x[n]);

  check_positive_finite("gp_matern52_cov", "magnitude", sigma);
  check_positive_finite("gp_matern52_cov", "length scale", length_scale);

  Eigen::Matrix<typename return_type<T_x, T_s, T_l>::type, Eigen::Dynamic,
                Eigen::Dynamic>
      cov(x_size, x_size);

  if (x_size == 0)
    return cov;

  T_s sigma_sq = square(sigma);
  T_l root_5_inv_l = sqrt(5.0) / length_scale;
  T_l inv_l_sq_5_3 = 5.0 / (3.0 * square(length_scale));
  T_l neg_root_5_inv_l = -sqrt(5.0) / length_scale;

  for (size_t i = 0; i < x_size; ++i) {
    cov(i, i) = sigma_sq;
    for (size_t j = i + 1; j < x_size; ++j) {
      typename return_type<T_x>::type sq_distance
          = squared_distance(x[i], x[j]);
      typename return_type<T_x>::type distance = sqrt(sq_distance);
      cov(i, j) = sigma_sq
                  * (1.0 + root_5_inv_l * distance + inv_l_sq_5_3 * sq_distance)
                  * exp(neg_root_5_inv_l * distance);
      cov(j, i) = cov(i, j);
    }
  }
  return cov;
}

/**
 * Returns a Matern 5/2 covariance matrix with one input vector
 * with automatic relevance determination (ARD).
 *
 * \f[ k(x, x') = \sigma^2\bigg(1 +
 *  \sqrt{5}\sqrt{\sum_{k=1}^{K}\frac{d(x, x')^2}{l_k^2}} +
 *  \frac{5}{3} \sqrt{\sum_{k=1}^{K}\frac{d(x, x')^2}{l_k^2}}\bigg)
 *  exp\bigg(-\frac{5}{3}\bigg(\sqrt{\sum_{k=1}^K{\frac{d(x, x')^2}{l_k^2}}
 *  }\bigg)\bigg)
 * \f]
 *
 * where \f$ d(x, x') \f$ is the Euclidean distance.
 *
 * @tparam T_x type of elements contained in vector x
 * @tparam T_s type of element of sigma, the magnitude
 * @tparam T_l type of elements in vector of length scale
 *
 * @param x std::vector of elements that can be used in stan::math::distance
 * @param length_scale length scale
 * @param sigma standard deviation that can be used in stan::math::square
 * @throw std::domain error if sigma <= 0, l <= 0, or x is nan or inf
 * @throw std::invalid_argument if length scale size != dimension of x
 */
template <typename T_x, typename T_s, typename T_l>
inline typename Eigen::Matrix<typename return_type<T_x, T_s, T_l>::type,
                              Eigen::Dynamic, Eigen::Dynamic>
gp_matern52_cov(const std::vector<Eigen::Matrix<T_x, Eigen::Dynamic, 1>> &x,
                const T_s &sigma, const std::vector<T_l> &length_scale) {
  using std::exp;

  size_t x_size = x.size();
  size_t l_size = length_scale.size();
  for (size_t n = 0; n < x_size; ++n)
    check_not_nan("gp_matern52_cov", "x", x[n]);

  check_positive_finite("gp_matern52_cov", "magnitude", sigma);
  check_positive_finite("gp_matern52_cov", "length scale", length_scale);
  check_size_match("gp_matern52_cov", "x dimension", x[0].size(),
                   "number of length scales", l_size);

  Eigen::Matrix<typename return_type<T_x, T_s, T_l>::type, Eigen::Dynamic,
                Eigen::Dynamic>
    cov(x_size, x_size);

  if (x_size == 0)
    return cov;

  T_s sigma_sq = square(sigma);
  double root_5 = sqrt(5.0);
  double five_thirds = 5.0 / 3.0;
  double neg_root_5 = -root_5;

  std::vector<Eigen::Matrix<typename return_type<T_x, T_l>::type, -1, 1>>
      x_new = divide_columns(x, length_scale);

  for (size_t i = 0; i < x_size; ++i) {
    cov(i, i) = sigma_sq;
    for (size_t j = i + 1; j < x_size; ++j) {
      typename return_type<T_x>::type sq_distance
          = squared_distance(x_new[i], x_new[j]);
      typename return_type<T_x>::type distance = sqrt(sq_distance);
      cov(i, j) = sigma_sq
                  * (1.0 + root_5 * distance + five_thirds * sq_distance)
                  * exp(neg_root_5 * distance);
      cov(j, i) = cov(i, j);
    }
  }
  return cov;
}

/**
 * Returns a Matern 5/2 covariance matrix with two different input vectors
 *
 * \f[ k(x, x') = \sigma^2\bigg(1 +
 *  \frac{\sqrt{5}d(x, x')}{l} + \frac{5d(x, x')^2}{3l^2}\bigg)
 *   exp\bigg(-\frac{5 d(x, x')}{l}\bigg)
 * \f]
 *
 * where \f$ d(x, x') \f$ is the Euclidean distance.
 *
 * @tparam T_x1 type of elements contained in vector x1
 * @tparam T_x2 type of elements contained in vector x2
 * @tparam T_s type of element of sigma, the magnitude
 * @tparam T_l type of elements of length scale
 *
 * @param x1 std::vector of elements that can be used in stan::math::distance
 * @param x2 std::vector of elements that can be used in stan::math::distance
 * @param length_scale length scale
 * @param sigma standard deviation that can be used in stan::math::square
 * @throw std::domain error if sigma <= 0, l <= 0, or x is nan or inf
 *
 */
template <typename T_x1, typename T_x2, typename T_s, typename T_l>
inline typename Eigen::Matrix<typename return_type<T_x1, T_x2, T_s, T_l>::type,
                              Eigen::Dynamic, Eigen::Dynamic>
gp_matern52_cov(const std::vector<T_x1> &x1, const std::vector<T_x2> &x2,
                const T_s &sigma, const T_l &length_scale) {
  using std::exp;

  size_t x1_size = x1.size();
  size_t x2_size = x2.size();

  for (size_t n = 0; n < x1_size; ++n)
    check_not_nan("gp_matern52_cov", "x1", x1[n]);
  for (size_t n = 0; n < x2_size; ++n)
    check_not_nan("gp_matern52_cov", "x1", x2[n]);

  check_positive_finite("gp_matern52_cov", "magnitude", sigma);
  check_positive_finite("gp_matern52_cov", "length scale", length_scale);

  Eigen::Matrix<typename return_type<T_x1, T_x2, T_s, T_l>::type,
                Eigen::Dynamic, Eigen::Dynamic>
      cov(x1_size, x2_size);

  if (x1_size == 0 || x2_size == 0)
    return cov;

  T_s sigma_sq = square(sigma);
  T_l root_5_inv_l = sqrt(5.0) / length_scale;
  T_l inv_l_sq_5_3 = 5.0 / (3.0 * square(length_scale));
  T_l neg_root_5_inv_l = -sqrt(5.0) / length_scale;

  for (size_t i = 0; i < x1_size; ++i) {
    for (size_t j = 0; j < x2_size; ++j) {
      typename return_type<T_x1, T_x2>::type sq_distance
          = squared_distance(x1[i], x2[j]);
      typename return_type<T_x1, T_x2>::type distance = sqrt(sq_distance);
      cov(i, j) = sigma_sq
                  * (1.0 + root_5_inv_l * distance + inv_l_sq_5_3 * sq_distance)
                  * exp(neg_root_5_inv_l * distance);
    }
  }
  return cov;
}

/**
 * Returns a Matern 5/2 covariance matrix with two input vectors
 * with automatic relevance determination (ARD).
 *
 * \f[ k(x, x') = \sigma^2\bigg(1 +
 *  \sqrt{5}\sqrt{\sum_{k=1}^{K}\frac{d(x, x')^2}{l_k^2}} +
 *  \frac{5}{3} \sqrt{\sum_{k=1}^{K}\frac{d(x, x')^2}{l_k^2}}\bigg)
 *  exp\bigg(-\frac{5}{3}\bigg(\sqrt{\sum_{k=1}^K{\frac{d(x, x')^2}{l_k^2}}
 *  }\bigg)\bigg)
 * \f]
 *
 * where \f$ d(x, x') \f$ is the Euclidean distance.
 *
 * @tparam T_x1 type of elements contained in vector x1
 * @tparam T_x2 type of elements contained in vector x2
 * @tparam T_s type of element of sigma, the  magnitude
 * @tparam T_l type of elements in vector of length scale
 *
 * @param x1 std::vector of elements that can be used in stan::math::distance
 * @param x2 std::vector of elements that can be used in stan::math::distance
 * @param length_scale length scale
 * @param sigma standard deviation that can be used in stan::math::square
 * @throw std::domain error if sigma <= 0, l <= 0, or x is nan or inf
 * @throw std::invalid_argument if length scale size != dimension of x1 or x2
 *
 */
template <typename T_x1, typename T_x2, typename T_s, typename T_l>
inline typename Eigen::Matrix<
    typename stan::return_type<T_x1, T_x2, T_s, T_l>::type, Eigen::Dynamic,
    Eigen::Dynamic>
gp_matern52_cov(const std::vector<Eigen::Matrix<T_x1, Eigen::Dynamic, 1>> &x1,
                const std::vector<Eigen::Matrix<T_x2, Eigen::Dynamic, 1>> &x2,
                const T_s &sigma, const std::vector<T_l> &length_scale) {
  using std::exp;

  size_t x1_size = x1.size();
  size_t x2_size = x2.size();
  size_t l_size = length_scale.size();

  for (size_t n = 0; n < x1_size; ++n)
    check_not_nan("gp_matern52_cov", "x1", x1[n]);
  for (size_t n = 0; n < x2_size; ++n)
    check_not_nan("gp_matern52_cov", "x1", x2[n]);

  check_positive_finite("gp_matern52_cov", "magnitude", sigma);
  check_positive_finite("gp_matern52_cov", "length scale", length_scale);
  check_size_match("gp_matern52_cov", "x dimension", x1[0].size(),
                   "number of length scales", l_size);
  check_size_match("gp_matern52_cov", "x dimension", x2[0].size(),
                   "number of length scales", l_size);

  Eigen::Matrix<typename return_type<T_x1, T_x2, T_s, T_l>::type,
                Eigen::Dynamic, Eigen::Dynamic>
      cov(x1_size, x2_size);

  if (x1_size == 0 || x2_size == 0)
    return cov;

  T_s sigma_sq = square(sigma);
  double root_5 = sqrt(5.0);
  double five_thirds = 5.0 / 3.0;
  double neg_root_5 = -root_5;

  std::vector<Eigen::Matrix<typename return_type<T_x1, T_l>::type, -1, 1>>
      x1_new = divide_columns(x1, length_scale);
  std::vector<Eigen::Matrix<typename return_type<T_x2, T_l>::type, -1, 1>>
      x2_new = divide_columns(x2, length_scale);

  for (size_t i = 0; i < x1_size; ++i) {
    for (size_t j = 0; j < x2_size; ++j) {
      typename return_type<T_x1, T_x2>::type sq_distance
          = squared_distance(x1_new[i], x2_new[j]);
      typename return_type<T_x1, T_x2>::type distance
{'fraction_non_alphanumeric': 0.1535431288204664, 'fraction_numerical': 0.025899932080597692, 'mean_word_length': 3.5688870500620604, 'pattern_counts': {'":': 0, '<': 171, '<?xml version=': 0, '>': 127, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'dir': 'git-commits-cleaned', 'id': '3821613', 'n_tokens_mistral': 8929, 'n_tokens_neox': 8480, 'n_words': 2104}