/**
 * @file    Pose3Factor.h
 * @brief   A Nonlinear Factor, specialized for Urban application
 * @author  Frank Dellaert
 * @author  Viorela Ila
 */

#pragma once

#include <map>
#include "NonlinearFactor.h"
#include "GaussianFactor.h"
#include "VectorConfig.h"
#include "Pose3.h"
#include "Matrix.h"
#include "ostream"

namespace gtsam {

template <class Config>
class Pose3Factor : public NonlinearFactor<Config> {
private:
	std::string key1_, key2_; /** The keys of the two poses, order matters */
	Pose3 measured_;
	Matrix square_root_inverse_covariance_; /** sqrt(inv(measurement_covariance)) */

public:

	typedef boost::shared_ptr<Pose3Factor> shared_ptr; // shorthand for a smart pointer to a factor

	Pose3Factor(const std::string& key1, const std::string& key2,
			const Pose3& measured, const Matrix& measurement_covariance): key1_(key1),key2_(key2),measured_(measured) {
		square_root_inverse_covariance_ = inverse_square_root(measurement_covariance);
	}

	/** implement functions needed for Testable */
	void print(const std::string& name) const {
		std::cout << name << std::endl;
		std::cout << "Factor "<< std::endl;
		std::cout << "key1 "<< key1_<<std::endl;
		std::cout << "key2 "<< key2_<<std::endl;
		measured_.print("measured ");
		gtsam::print(square_root_inverse_covariance_,"MeasurementCovariance");
	}
	bool equals(const NonlinearFactor<Config>& expected, double tol) const {return false;}

	/** implement functions needed to derive from Factor */
	Vector error_vector(const Config& config) const {
		//z-h
		Pose3 p1 = config.get(key1_), p2 = config.get(key2_);
		return (measured_ - between(p1,p2)).vector();
	}

	std::list<std::string> keys() const { std::list<std::string> l; return l; }
	std::size_t size() const { return 2;}

	/** linearize */
	boost::shared_ptr<GaussianFactor> linearize(const Config& config) const {
		Pose3 p1 = config.get(key1_), p2 = config.get(key2_);
		Vector b = (measured_ - between(p1,p2)).vector();
		Matrix H1 = Dbetween1(p1,p2);
		Matrix H2 = Dbetween2(p1,p2);
		boost::shared_ptr<GaussianFactor> linearized(new GaussianFactor(
				key1_, square_root_inverse_covariance_ * H1,
				key2_, square_root_inverse_covariance_ * H2,
				b, 1.0));
		return linearized;
	}
};
} /// namespace gtsam