gaussian_vector.cpp

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/* Author: Wim Meeussen */

#include <people_tracking_filter/gaussian_vector.h>
#include <bfl/wrappers/rng/rng.h>
#include <cmath>
#include <cassert>
#include <vector>

namespace BFL
{
GaussianVector::GaussianVector(const tf::Vector3& mu, const tf::Vector3& sigma)
  : Pdf<tf::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() << std::endl
     << "Sigma:\n" << g.CovarianceGet() << std::endl;
  return os;
}

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

Probability GaussianVector::ProbabilityGet(const tf::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]);
  }

  tf::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(std::vector<Sample<tf::Vector3> >& list_samples, const int num_samples, int method,
                           void * args) const
{
  list_samples.resize(num_samples);
  std::vector<Sample<tf::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<tf::Vector3>& one_sample, int method, void * args) const
{
  one_sample.ValueSet(tf::Vector3(rnorm(mu_[0], sigma_[0]),
                                  rnorm(mu_[1], sigma_[1]),
                                  rnorm(mu_[2], sigma_[2])));
  return true;
}

tf::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