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smc/smoothing/BackwardSimulation.h
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258
smc/smoothing/BackwardSimulation.h
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/*
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* CondensationBackwardFilter.h
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*
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* Created on: Jun 23, 2015
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* Author: Toni Fetzer
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*/
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#ifndef BACKWARDSIMULATION_H_
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#define BACKWARDSIMULATION_H_
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#include <vector>
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#include <memory>
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#include <algorithm>
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#include "BackwardFilterTransition.h"
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#include "BackwardFilter.h"
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#include "../Particle.h"
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#include "../filtering/resampling/ParticleFilterResampling.h"
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#include "../filtering/estimation/ParticleFilterEstimation.h"
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#include "../filtering/ParticleFilterEvaluation.h"
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#include "../filtering/ParticleFilterInitializer.h"
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#include "../sampling/ParticleTrajectorieSampler.h"
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#include "../../Assertions.h"
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namespace SMC {
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/**
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* the main-class for the Backward Simulation Filter
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* running "backwards" in time, generates multiple backwards trajectories
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* (Realizations) by repeating the backward simulation M time.
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* it can be started at a random time T of any forward particle filter
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* [Monte Carlo smoothing for non-linear time series Godsill et al. '03]
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* @param State the (user-defined) state for each particle
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* @param numRealizations is the number of backward trajectories starting
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*/
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template <typename State, typename Control, typename Observation>
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class BackwardSimulation : public BackwardFilter<State, Control, Observation>{
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private:
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/** all smoothed particles T -> 1*/
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std::vector<std::vector<Particle<State>>> backwardParticles;
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/** container for particles */
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std::vector<Particle<State>> smoothedParticles;
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/** the estimation function to use */
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std::unique_ptr<ParticleFilterEstimation<State>> estimation;
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/** the transition function to use */
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std::unique_ptr<BackwardFilterTransition<State, Control>> transition;
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/** the resampler to use */
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std::unique_ptr<ParticleFilterResampling<State>> resampler;
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/** the sampler for drawing trajectories */
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std::unique_ptr<ParticleTrajectorieSampler<State>> sampler;
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/** the percentage-of-efficient-particles-threshold for resampling */
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double nEffThresholdPercent = 0.25;
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/** number of realizations to be calculated */
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int numRealizations;
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/** is update called the first time? */
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bool firstFunctionCall;
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public:
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/** ctor */
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BackwardSimulation(int numRealizations) {
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this->numRealizations = numRealizations;
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backwardParticles.reserve(numRealizations);
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smoothedParticles.reserve(numRealizations);
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firstFunctionCall = true;
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}
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/** dtor */
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~BackwardSimulation() {
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;
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}
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/** reset **/
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void reset(){
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this->numRealizations = numRealizations;
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backwardParticles.clear();
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backwardParticles.reserve(numRealizations);
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smoothedParticles.clear();
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smoothedParticles.reserve(numRealizations);
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firstFunctionCall = true;
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}
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/** access to all backward / smoothed particles */
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const std::vector<std::vector<Particle<State>>>& getbackwardParticles() {
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return backwardParticles;
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}
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/** set the estimation method to use */
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void setEstimation(std::unique_ptr<ParticleFilterEstimation<State>> estimation) {
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Assert::isNotNull(estimation, "setEstimation() MUST not be called with a nullptr!");
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this->estimation = std::move(estimation);
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}
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/** set the transition method to use */
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void setTransition(std::unique_ptr<BackwardFilterTransition<State, Control>> transition) {
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Assert::isNotNull(transition, "setTransition() MUST not be called with a nullptr!");
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this->transition = std::move(transition);
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}
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/** set the resampling method to use */
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void setResampling(std::unique_ptr<ParticleFilterResampling<State>> resampler) {
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Assert::isNotNull(resampler, "setResampling() MUST not be called with a nullptr!");
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this->resampler = std::move(resampler);
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}
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/** set the sampler method to use */
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void setSampler(std::unique_ptr<ParticleTrajectorieSampler<State>> sampler){
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Assert::isNotNull(sampler, "setSampler() MUST not be called with a nullptr!");
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this->sampler = std::move(sampler);
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}
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/** set the resampling threshold as the percentage of efficient particles */
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void setNEffThreshold(const double thresholdPercent) {
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this->nEffThresholdPercent = thresholdPercent;
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}
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/** get the used transition method */
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BackwardFilterTransition<State, Control>* getTransition() {
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return this->transition.get();
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}
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/**
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* perform update: transition -> correction -> approximation
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* gets the weighted sample set of a standard condensation
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* particle filter in REVERSED order!
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*/
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State update(std::vector<Particle<State>> const& forwardParticles) {
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// sanity checks (if enabled)
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Assert::isNotNull(transition, "transition MUST not be null! call setTransition() first!");
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Assert::isNotNull(estimation, "estimation MUST not be null! call setEstimation() first!");
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//storage for single trajectories / smoothed particles
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smoothedParticles.clear();
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// Choose \tilde x_T = x^(i)_T with probability w^(i)_T
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// Therefore sample independently from the categorical distribution of weights.
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if(firstFunctionCall){
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smoothedParticles = sampler->drawTrajectorie(forwardParticles, numRealizations);
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firstFunctionCall = false;
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backwardParticles.push_back(smoothedParticles);
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const State es = estimation->estimate(smoothedParticles);
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return es;
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}
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// compute weights using the transition model
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// transitionWeigths[numRealizations][numParticles]
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std::vector<std::vector<double>> transitionWeights = transition->transition(forwardParticles, backwardParticles.back());
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//get the next trajectorie for a realisation
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for(int j = 0; j < numRealizations; ++j){
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//vector for the current smoothedWeights at time t
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std::vector<Particle<State>> smoothedWeights;
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smoothedWeights.resize(forwardParticles.size());
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smoothedWeights = forwardParticles;
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//check if all transitionWeights are zero
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double weightSumTransition = std::accumulate(transitionWeights[j].begin(), transitionWeights[j].end(), 0.0);
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Assert::isNot0(weightSumTransition, "all transition weights for smoothing are zero");
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int i = 0;
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for (auto& w : transitionWeights.at(j)) {
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// multiply the weight of the particles at time t and normalize
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smoothedWeights.at(i).weight = (smoothedWeights.at(i).weight * w);
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if(smoothedWeights.at(i).weight != smoothedWeights.at(i).weight) {throw "detected NaN";}
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// iter
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++i;
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}
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//get the sum of all weights
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auto lambda = [](double current, const Particle<State>& a){return current + a.weight; };
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double weightSumSmoothed = std::accumulate(smoothedWeights.begin(), smoothedWeights.end(), 0.0, lambda);
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//normalize the weights
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if(weightSumSmoothed != 0.0){
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for (int i = 0; i < smoothedWeights.size(); ++i){
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smoothedWeights.at(i).weight /= weightSumSmoothed;
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}
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//check if normalization worked
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double normWeightSum = std::accumulate(smoothedWeights.begin(), smoothedWeights.end(), 0.0, lambda);
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Assert::isNear(normWeightSum, 1.0, 0.001, "Smoothed weights do not sum to 1");
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}
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//draw the next trajectorie at time t for a realization and save them
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smoothedParticles.push_back(sampler->drawSingleParticle(smoothedWeights));
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//throw if weight of smoothedParticle is zero
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//in practice this is possible, if a particle is completely separated from the rest and is therefore
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//weighted zero or very very low.
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Assert::isNot0(smoothedParticles.back().weight, "smoothed particle has zero weight");
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}
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if(resampler)
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{
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//TODO - does this even make sense?
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std::cout << "Warning - Resampling is not yet implemented!" << std::endl;
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// //resampling if necessery
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// double sum = 0.0;
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// double weightSum = std::accumulate(smoothedParticles.begin().weight, smoothedParticles.end().weight, 0.0);
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// for (auto& p : smoothedParticles) {
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// p.weight /= weightSum;
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// sum += (p.weight * p.weight);
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// }
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// const double neff = 1.0/sum;
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// if (neff != neff) {throw "detected NaN";}
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// // if the number of efficient particles is too low, perform resampling
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// if (neff < smoothedParticles.size() * nEffThresholdPercent) { resampler->resample(smoothedParticles); }
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}
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// push_back the smoothedParticles
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backwardParticles.push_back(smoothedParticles);
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// estimate the current state
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const State est = estimation->estimate(smoothedParticles);
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// done
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return est;
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}
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};
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}
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#endif /* BACKWARDSIMULATION_H_ */
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