C ++中的多元正态分布

Question

我正在尝试创建一个类，在其中可以放入一些参数以获得不同的分布。目前，我正在尝试使用以下代码中的多元正态分布：Error while creating object from templated class（请参阅JCooper的帖子）。

eigenmultivariatenormal.h：

#ifndef __EIGENMULTIVARIATENORMAL_HPP
#define __EIGENMULTIVARIATENORMAL_HPP

#include <Eigen/Dense>
#include <boost/random/mersenne_twister.hpp>
#include <boost/random/normal_distribution.hpp>    

/*
  We need a functor that can pretend it's const,
  but to be a good random number generator 
  it needs mutable state.  The standard Eigen function 
  Random() just calls rand(), which changes a global
  variable.
*/
namespace Eigen {
namespace internal {
template<typename Scalar> 
struct scalar_normal_dist_op 
{
  static boost::mt19937 rng;                        // The uniform pseudo-random algorithm
  mutable boost::normal_distribution<Scalar> norm;  // The gaussian combinator

  EIGEN_EMPTY_STRUCT_CTOR(scalar_normal_dist_op)

  template<typename Index>
  inline const Scalar operator() (Index, Index = 0) const { return norm(rng); }
};

template<typename Scalar> 
boost::mt19937 scalar_normal_dist_op<Scalar>::rng;

template<typename Scalar>
struct functor_traits<scalar_normal_dist_op<Scalar> >
{ enum { Cost = 50 * NumTraits<Scalar>::MulCost, PacketAccess = false, IsRepeatable = false }; };

} // end namespace internal
/**
    Find the eigen-decomposition of the covariance matrix
    and then store it for sampling from a multi-variate normal 
*/
template<typename Scalar, int Size>
class EigenMultivariateNormal
{
  Matrix<Scalar,Size,Size> _covar;
  Matrix<Scalar,Size,Size> _transform;
  Matrix< Scalar, Size, 1> _mean;
  internal::scalar_normal_dist_op<Scalar> randN; // Gaussian functor


public:
  EigenMultivariateNormal(const Matrix<Scalar,Size,1>& mean,const Matrix<Scalar,Size,Size>& covar)
  {
    setMean(mean);
    setCovar(covar);
  }

  void setMean(const Matrix<Scalar,Size,1>& mean) { _mean = mean; }
  void setCovar(const Matrix<Scalar,Size,Size>& covar) 
  {
    _covar = covar;

    // Assuming that we'll be using this repeatedly,
    // compute the transformation matrix that will
    // be applied to unit-variance independent normals

    /*
    Eigen::LDLT<Eigen::Matrix<Scalar,Size,Size> > cholSolver(_covar);
    // We can only use the cholesky decomposition if 
    // the covariance matrix is symmetric, pos-definite.
    // But a covariance matrix might be pos-semi-definite.
    // In that case, we'll go to an EigenSolver
    if (cholSolver.info()==Eigen::Success) {
      // Use cholesky solver
      _transform = cholSolver.matrixL();
    } else {*/
      SelfAdjointEigenSolver<Matrix<Scalar,Size,Size> > eigenSolver(_covar);
      _transform = eigenSolver.eigenvectors()*eigenSolver.eigenvalues().cwiseMax(0).cwiseSqrt().asDiagonal();
    /*}*/

  }

  /// Draw nn samples from the gaussian and return them
  /// as columns in a Size by nn matrix
  Matrix<Scalar,Size,-1> samples(int nn)
  {
    return (_transform * Matrix<Scalar,Size,-1>::NullaryExpr(Size,nn,randN)).colwise() + _mean;
  }
}; // end class EigenMultivariateNormal
} // end namespace Eigen
#endif

Main.cpp：

#include <fstream>
#include "eigenmultivariatenormal.hpp"
#ifndef M_PI
#define M_PI REAL(3.1415926535897932384626433832795029)
#endif

/**
  Take a pair of un-correlated variances.
  Create a covariance matrix by correlating 
  them, sandwiching them in a rotation matrix.
*/
Eigen::Matrix2d genCovar(double v0,double v1,double theta)
{
  Eigen::Matrix2d rot = Eigen::Rotation2Dd(theta).matrix();
  return rot*Eigen::DiagonalMatrix<double,2,2>(v0,v1)*rot.transpose();
}

void main()
{
  Eigen::Vector2d mean;
  Eigen::Matrix2d covar;
  mean << -1,0.5; // Set the mean
  // Create a covariance matrix
  // Much wider than it is tall
  // and rotated clockwise by a bit
  covar = genCovar(3,0.1,M_PI/5.0);

  // Create a bivariate gaussian distribution of doubles.
  // with our chosen mean and covariance
  Eigen::EigenMultivariateNormal<double,2> normX(mean,covar);

  // Generate some samples and write them out to file 
  // for plotting
  std::cout << normX.samples(1000).transpose() << std::endl;
}

将结果打印到屏幕上效果很好，但是我需要能够将链接中的代码中的normX.samples(1)转换为（我只想一次绘制一对值）到std::vector pair或类似的名称（如数组）。我需要能够return的值到调用该类的函数。

有人知道如何使用C ++使用多元正态分布吗？