gaussian_vector.cpp

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/* Author: Wim Meeussen */
/* modified for SRS by Alex Noyvirt */

#include "srs_people_tracking_filter/gaussian_vector.h"
#include <wrappers/rng/rng.h>
#include <cmath> 
#include <cassert>

using namespace tf;

namespace BFL
{
  GaussianVector::GaussianVector(const Vector3& mu, const Vector3& sigma)
    : Pdf<Vector3> ( 1 ),
      mu_(mu),
      sigma_(sigma),
      sigma_changed_(true)
  {
    for (unsigned int i=0; i<3; i++)
      assert(sigma[i] > 0);
  }


  GaussianVector::~GaussianVector(){}


  std::ostream& operator<< (std::ostream& os, const GaussianVector& g)
  {
    os << "Mu   :\n" << g.ExpectedValueGet() << endl
       << "Sigma:\n" << g.CovarianceGet() << endl;
    return os;
  }

  void GaussianVector::sigmaSet( const Vector3& sigma )
  {
    sigma_ = sigma;
    sigma_changed_ = true;
  }

  Probability GaussianVector::ProbabilityGet(const Vector3& input) const
  {
    if (sigma_changed_){
      sigma_changed_ = false;
      // 2 * sigma^2
      for (unsigned int i=0; i<3; i++)
        sigma_sq_[i] = 2 * sigma_[i] * sigma_[i];
      // sqrt
      sqrt_ = 1/ sqrt(M_PI*M_PI*M_PI* sigma_sq_[0] * sigma_sq_[1] * sigma_sq_[2]);
    }

    Vector3 diff = input - mu_;
    return sqrt_ * exp( - (diff[0]*diff[0]/sigma_sq_[0])
                        - (diff[1]*diff[1]/sigma_sq_[1])
                        - (diff[2]*diff[2]/sigma_sq_[2]) );
  }


  bool
  GaussianVector::SampleFrom (vector<Sample<Vector3> >& list_samples, const int num_samples, int method, void * args) const
  {
    list_samples.resize(num_samples);
    vector<Sample<Vector3> >::iterator sample_it = list_samples.begin();
    for (sample_it=list_samples.begin(); sample_it!=list_samples.end(); sample_it++)
      SampleFrom( *sample_it, method, args);

    return true;
  }


  bool
  GaussianVector::SampleFrom (Sample<Vector3>& one_sample, int method, void * args) const
  {
    one_sample.ValueSet( Vector3(rnorm(mu_[0], sigma_[0]), 
                                 rnorm(mu_[1], sigma_[1]),
                                 rnorm(mu_[2], sigma_[2])));
    return true;
  }


  Vector3
  GaussianVector::ExpectedValueGet (  ) const 
  { 
    return mu_;
  }

  SymmetricMatrix
  GaussianVector::CovarianceGet () const
  {
    SymmetricMatrix sigma(3); sigma = 0;
    for (unsigned int i=0; i<3; i++)
      sigma(i+1,i+1) = pow(sigma_[i],2);
    return sigma;
  }

  GaussianVector* 
  GaussianVector::Clone() const
  {
    return new GaussianVector(mu_, sigma_);
  }

} // End namespace BFL