inline void set_sample(size_t sample_id,
real_vector_type const & sample,
real_scalar_type const weight
)
{
boost::numeric::ublas::row(m_chain,sample_id) = sample;
m_weights(sample_id) = weight;
}
void
AAKR::computeWeights(double variance)
{
// Initialize some variables
double svar = variance * variance;
double k = (1 / std::sqrt(2 * DUNE::Math::c_pi * svar));
m_weights.fill(0.0);
// Compute weights
for (unsigned i = 0; i < m_num_values; i++)
m_weights(i) = k * std::exp(- m_distances(i) * m_distances(i) / svar);
}
void write_samples_to_csv(std::string const & file_name) const
{
std::string delimiter(",");
std::ofstream out_file;
out_file.open(file_name,std::ios::trunc);
if( out_file.is_open() )
{
out_file << std::scientific;
out_file << std::setprecision(10);
for(size_t i=0;i<m_num_samples;++i)
{
out_file << m_weights(i) << delimiter;
for(size_t j=0;j<size_t(m_num_dims-1);++j)
{
out_file << m_chain(i,j) << delimiter;
}
out_file << m_chain(i,size_t(m_num_dims-1)) <<std::endl;
}
}
out_file.close();
}
