uniform_vector.cpp

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/* Author: Wim Meeussen */

#include "people_tracking_filter/uniform_vector.h"
#include <bfl/wrappers/rng/rng.h>
#include <cmath>
#include <cassert>

using namespace tf;

namespace BFL
{
UniformVector::UniformVector(const Vector3& mu, const Vector3& size)
  : Pdf<Vector3> (1),
    mu_(mu),
    size_(size)
{
  for (unsigned int i = 0; i < 3; i++)
    assert(size_[i] > 0);

  probability_ = 1 / (size_[0] * 2 * size_[1] * 2 * size_[2] * 2);
}


UniformVector::~UniformVector() {}

UniformVector* UniformVector::Clone() const
{
  return new UniformVector(mu_, size_);
}

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



Probability UniformVector::ProbabilityGet(const Vector3& input) const
{
  for (unsigned int i = 0; i < 3; i++)
  {
    if (input[i] < (mu_[0] - (size_[0]))) return 0;
    if (input[i] > (mu_[0] + (size_[0]))) return 0;
  }
  return probability_;
}


bool
UniformVector::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
UniformVector::SampleFrom(Sample<Vector3>& one_sample, int method, void * args) const
{
  one_sample.ValueSet(Vector3(((runif() - 0.5) * 2 * size_[0]) + mu_[0],
                              ((runif() - 0.5) * 2 * size_[1]) + mu_[1],
                              ((runif() - 0.5) * 2 * size_[2]) + mu_[2]));
  return true;
}


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

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

} // End namespace BFL