/**
 * @file    ISAM2-inl.h
 * @brief   Incremental update functionality (ISAM2) for BayesTree, with fluid relinearization.
 * @author  Michael Kaess
 */

#include <boost/foreach.hpp>
#include <boost/assign/std/list.hpp> // for operator +=
using namespace boost::assign;

#include "NonlinearFactorGraph.h"
#include "GaussianFactor.h"
#include "VectorConfig.h"

#include "Conditional.h"
#include "BayesTree-inl.h"
#include "ISAM2.h"

namespace gtsam {

	using namespace std;

	/** Create an empty Bayes Tree */
	template<class Conditional, class Config>
	ISAM2<Conditional, Config>::ISAM2() : BayesTree<Conditional>() {}

	/** Create a Bayes Tree from a nonlinear factor graph */
	template<class Conditional, class Config>
	ISAM2<Conditional, Config>::ISAM2(const NonlinearFactorGraph<Config>& nlfg, const Ordering& ordering, const Config& config) {

		BayesTree<Conditional>(nlfg.linearize(config).eliminate(ordering));

		nonlinearFactors_ = nlfg;
		config_ = config;
	}

	/* ************************************************************************* */
	template<class Conditional, class Config>
	void ISAM2<Conditional, Config>::update_internal(const NonlinearFactorGraph<Config>& newFactors,
			const Config& config, Cliques& orphans) {

		// copy variables into config_, but don't overwrite existing entries (current linearization point!)
		for (typename Config::const_iterator it = config.begin(); it!=config.end(); it++) {
			if (!config_.contains(it->first)) {
				config_.insert(it->first, it->second);
			}
		}
		nonlinearFactors_.push_back(newFactors);

		FactorGraph<GaussianFactor> newFactorsLinearized = newFactors.linearize(config_);

		// Remove the contaminated part of the Bayes tree
		FactorGraph<GaussianFactor> affectedFactors;
		boost::tie(affectedFactors, orphans) = this->removeTop(newFactorsLinearized);

		// find the corresponding original nonlinear factors, and relinearize them
		NonlinearFactorGraph<Config> nonlinearAffectedFactors;
		list<string> keys = affectedFactors.keys();
		for (list<string>::iterator keyIt = keys.begin(); keyIt!=keys.end(); keyIt++) {
			list<int> indices = nonlinearFactors_.factors(*keyIt);
			for (list<int>::iterator indIt = indices.begin(); indIt!=indices.end(); indIt++) {
// todo - do we need to check if it already exists? probably...				if (*indIt)
					nonlinearAffectedFactors.push_back(nonlinearFactors_[*indIt]);
			}
		}
		FactorGraph<GaussianFactor> factors = nonlinearAffectedFactors.linearize(config_);

		// add the new factors themselves
		factors.push_back(newFactorsLinearized);

		// create an ordering for the new and contaminated factors
		Ordering ordering;
		if (true) {
			ordering = factors.getOrdering();
		} else {
			list<string> keys = factors.keys();
			keys.sort(); // todo: correct sorting order?
			ordering = keys;
		}

		// eliminate into a Bayes net
		BayesNet<Conditional> bayesNet = eliminate<GaussianFactor, Conditional>(factors,ordering);

		// insert conditionals back in, straight into the topless bayesTree
		typename BayesNet<Conditional>::const_reverse_iterator rit;
		for ( rit=bayesNet.rbegin(); rit != bayesNet.rend(); ++rit )
			this->insert(*rit);

		int count = 0;
		// add orphans to the bottom of the new tree
		BOOST_FOREACH(sharedClique orphan, orphans) {

			string key = orphan->separator_.front();
			sharedClique parent = (*this)[key];

			parent->children_ += orphan;
			orphan->parent_ = parent; // set new parent!
		}
	}

	template<class Conditional, class Config>
	void ISAM2<Conditional, Config>::update(const NonlinearFactorGraph<Config>& newFactors, const Config& config) {
		Cliques orphans;
		this->update_internal(newFactors, config, orphans);
	}

/* ************************************************************************* */

}
/// namespace gtsam