uniform_vector.cpp
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00034 
00035 /* Author: Wim Meeussen */
00036 /* modified by Alex Noyvirt for SRS  */
00037 
00038 #include "srs_people_tracking_filter/uniform_vector.h"
00039 #include <wrappers/rng/rng.h>
00040 #include <cmath> 
00041 #include <cassert>
00042 
00043 using namespace tf;
00044 
00045 namespace BFL
00046 {
00047   UniformVector::UniformVector(const Vector3& mu, const Vector3& size)
00048     : Pdf<Vector3> ( 1 ),
00049       mu_(mu),
00050       size_(size)
00051   {
00052     for (unsigned int i=0; i<3; i++)
00053       assert(size_[i] > 0);
00054 
00055     probability_ = 1 / (size_[0]*2 * size_[1]*2 * size_[2]*2);
00056   }
00057 
00058 
00059   UniformVector::~UniformVector(){}
00060 
00061   UniformVector* UniformVector::Clone() const
00062   {
00063     return new UniformVector(mu_, size_);
00064   }
00065 
00066   std::ostream& operator<< (std::ostream& os, const UniformVector& g)
00067   {
00068     os << "Mu   :\n" << g.ExpectedValueGet() << endl
00069        << "Size :\n" << g.CovarianceGet() << endl;
00070     return os;
00071   }
00072 
00073 
00074 
00075   Probability UniformVector::ProbabilityGet(const Vector3& input) const
00076   {
00077     for (unsigned int i=0; i<3; i++){
00078       if (input[i] < (mu_[0] - (size_[0]))) return 0;
00079       if (input[i] > (mu_[0] + (size_[0]))) return 0;
00080     }
00081     return probability_;
00082   }
00083 
00084 
00085   bool
00086   UniformVector::SampleFrom (vector<Sample<Vector3> >& list_samples, const int num_samples, int method, void * args) const
00087   {
00088     list_samples.resize(num_samples);
00089     vector<Sample<Vector3> >::iterator sample_it = list_samples.begin();
00090     for (sample_it=list_samples.begin(); sample_it!=list_samples.end(); sample_it++)
00091       SampleFrom( *sample_it, method, args);
00092 
00093     return true;
00094   }
00095 
00096 
00097   bool
00098   UniformVector::SampleFrom (Sample<Vector3>& one_sample, int method, void * args) const
00099   {
00100     one_sample.ValueSet( Vector3( ((runif() - 0.5) * 2 * size_[0]) + mu_[0], 
00101                                       ((runif() - 0.5) * 2 * size_[1]) + mu_[1], 
00102                                       ((runif() - 0.5) * 2 * size_[2]) + mu_[2]));
00103     return true;
00104   }
00105 
00106 
00107   Vector3
00108   UniformVector::ExpectedValueGet (  ) const 
00109   { 
00110     return mu_;
00111   }
00112 
00113   SymmetricMatrix
00114   UniformVector::CovarianceGet () const
00115   {
00116     SymmetricMatrix sigma(3); sigma = 0;
00117     for (unsigned int i=0; i<3; i++)
00118       sigma(i+1,i+1) = pow(size_[i],2);
00119     return sigma;
00120   }
00121 
00122 } // End namespace BFL


srs_people_tracking_filter
Author(s): Alex Noyvirt
autogenerated on Sun Jan 5 2014 12:18:09