📄 deterministicparticleresampler.hpp
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#ifndef INDII_ML_FILTER_DETERMINISTICPARTICLERESAMPLER_HPP#define INDII_ML_FILTER_DETERMINISTICPARTICLERESAMPLER_HPP#include "ParticleResampler.hpp"namespace indii { namespace ml { namespace filter {/** * Deterministic particle resampler. * * @author Lawrence Murray <lawrence@indii.org> * @version $Rev: 404 $ * @date $Date: 2008-03-05 14:52:55 +0000 (Wed, 05 Mar 2008) $ * * Produces a new approximation of a weighted sample set using a set * of equally weighted samples, possibly with duplication @ref * Kitagawa1996 "(Kitagawa 1996)". * * @section DeterministicParticleResampler_references References * * @anchor Kitagawa1996 * Kitagawa, G. Monte Carlo %Filter and %Smoother for Non-Gaussian * Nonlinear State Space Models. <i>Journal of Computational and * Graphical Statistics</i>, <b>1996</b>, 5, 1-25. */class DeterministicParticleResampler : public ParticleResampler {public: /** * Constructor. * * @param P Number of particles to resample from each distribution * supplied to resample(). If zero, will resample the same number of * particles as the distribution supplied. */ DeterministicParticleResampler(const unsigned int P = 0); /** * Destructor. */ virtual ~DeterministicParticleResampler(); /** * Set the number of particles to resample. * * @param P Number of particles to resample from each distribution * supplied to resample(). If zero, will resample the same number of * particles as the distribution supplied. */ void setNumParticles(const unsigned int P = 0); /** * Resample the distribution. This produces a new approximation of * the same distribution using a set of equally weighted sample * points. Sample points are selected using the deterministic * resampling method given in the appendix of @ref Kitagawa1996 * "Kitagawa (1996)". * * @return The resampled distribution. */ virtual indii::ml::aux::DiracMixturePdf resample( indii::ml::aux::DiracMixturePdf& p);private: /** * Number of particles to resample from each distribution. */ unsigned int P;}; } }}#endif
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