/*
 * @file testNonlinearConstraint.cpp
 * @brief demos of constrained optimization using existing gtsam components
 * @author Alex Cunningham
 */

#include <iostream>
#include <cmath>
#include <boost/assign/std/list.hpp> // for operator +=
#include <boost/assign/std/map.hpp> // for insert
#include <boost/foreach.hpp>
#include <boost/shared_ptr.hpp>

#include <CppUnitLite/TestHarness.h>

//#define GTSAM_MAGIC_KEY
//
//#include <Point2.h>
//#include <Pose3.h>
//#include <GaussianFactorGraph.h>
//#include <NonlinearOptimizer.h>
//#include <simulated2D.h>
//#include <Ordering.h>
//#include <visualSLAM.h>
//
//// templated implementations
//#include <NonlinearFactorGraph-inl.h>
//#include <NonlinearConstraint-inl.h>
//#include <NonlinearOptimizer-inl.h>
//#include <TupleConfig-inl.h>
//
//using namespace std;
//using namespace gtsam;
//using namespace boost;
//using namespace boost::assign;
//
//// Models to use
//SharedDiagonal probModel1 = sharedSigma(1,1.0);
//SharedDiagonal probModel2 = sharedSigma(2,1.0);
//SharedDiagonal constraintModel1 = noiseModel::Constrained::All(1);
//
//// trick from some reading group
//#define FOREACH_PAIR( KEY, VAL, COL) BOOST_FOREACH (boost::tie(KEY,VAL),COL)
//
///* ********************************************************************* */
//// full components
//typedef simulated2D::Config Config2D;
//typedef NonlinearFactorGraph<Config2D> Graph2D;
//typedef NonlinearEquality<Config2D, simulated2D::PoseKey, Point2> NLE;
//typedef boost::shared_ptr<simulated2D::GenericMeasurement<Config2D> > shared;
//typedef NonlinearOptimizer<Graph2D, Config2D> Optimizer;
//
///*
// * Determining a ground truth linear system
// * with two poses seeing one landmark, with each pose
// * constrained to a particular value
// */
//TEST (NonlinearConstraint, two_pose_truth ) {
//	// create a graph
//	shared_ptr<Graph2D> graph(new Graph2D);
//
//	// add the constraints on the ends
//	// position (1, 1) constraint for x1
//	// position (5, 6) constraint for x2
//	simulated2D::PoseKey x1(1), x2(2);
//	simulated2D::PointKey l1(1);
//	Point2 pt_x1(1.0, 1.0),
//		   pt_x2(5.0, 6.0);
//	shared_ptr<NLE> ef1(new NLE(x1, pt_x1)),
//			        ef2(new NLE(x2, pt_x2));
//	graph->push_back(ef1);
//	graph->push_back(ef2);
//
//	// measurement from x1 to l1
//	Point2 z1(0.0, 5.0);
//	SharedGaussian sigma(noiseModel::Isotropic::Sigma(2, 0.1));
//	shared f1(new simulated2D::GenericMeasurement<Config2D>(z1, sigma, x1,l1));
//	graph->push_back(f1);
//
//	// measurement from x2 to l1
//	Point2 z2(-4.0, 0.0);
//	shared f2(new simulated2D::GenericMeasurement<Config2D>(z2, sigma, x2,l1));
//	graph->push_back(f2);
//
//	// create an initial estimate
//	Point2 pt_l1(
//			1.0, 6.0 // ground truth
//		  //1.2, 5.6 // small error
//			);
//	shared_ptr<Config2D> initialEstimate(new Config2D);
//	initialEstimate->insert(l1, pt_l1);
//	initialEstimate->insert(x1, pt_x1);
//	initialEstimate->insert(x2, pt_x2);
//
//	// optimize the graph
//	boost::shared_ptr<const Config2D> actual = Optimizer::optimizeGN(graph, initialEstimate);
//
//	// verify
//	Config2D expected;
//	expected.insert(x1, pt_x1);
//	expected.insert(x2, pt_x2);
//	expected.insert(l1, Point2(1.0, 6.0));
//	CHECK(assert_equal(expected, *actual, 1e-5));
//}
//
///* ********************************************************************* */
//namespace constrained_test1 {
//
//	// binary constraint between landmarks
//	/** g(x) = x-y = 0 */
//	Vector g(const Config2D& config, const list<simulated2D::PointKey>& keys) {
//		Point2 pt1, pt2;
//		pt1 = config[simulated2D::PointKey(keys.front().index())];
//		pt2 = config[simulated2D::PointKey(keys.back().index())];
//		return Vector_(2, pt1.x() - pt2.x(), pt1.y() - pt2.y());
//	}
//
//	/** jacobian at l1 */
//	Matrix G1(const Config2D& config, const list<simulated2D::PointKey>& keys) {
//		return eye(2);
//	}
//
//	/** jacobian at l2 */
//	Matrix G2(const Config2D& config, const list<simulated2D::PointKey>& keys) {
//		return -1 * eye(2);
//	}
//
//} // \namespace constrained_test1
//
//namespace constrained_test2 {
//
//	// Unary Constraint on x1
//	/** g(x) = x -[1;1] = 0 */
//	Vector g(const Config2D& config, const list<simulated2D::PoseKey>& keys) {
//		Point2 x = config[keys.front()];
//		return Vector_(2, x.x() - 1.0, x.y() - 1.0);
//	}
//
//	/** jacobian at x1 */
//	Matrix G(const Config2D& config, const list<simulated2D::PoseKey>& keys) {
//		return eye(2);
//	}
//
//} // \namespace constrained_test2
//
//
//typedef NonlinearConstraint2<
//	Config2D, simulated2D::PointKey, Point2, simulated2D::PointKey, Point2> NLC2;
//
///* *********************************************************************
// *  Version that actually uses nonlinear equality constraints
// *  to to perform optimization.  Same as above, but no
// *  equality constraint on x2, and two landmarks that
// *  should be the same. Note that this is a linear system,
// *  so it will converge in one step.
// */
//TEST (NonlinearConstraint, two_pose ) {
//	bool verbose = false;
//
//	// create the graph
//	shared_ptr<Graph2D> graph(new Graph2D);
//
//	// add the constraints on the ends
//	// position (1, 1) constraint for x1
//	// position (5, 6) constraint for x2
//	simulated2D::PoseKey x1(1), x2(2);
//	simulated2D::PointKey l1(1), l2(2);
//	Point2 pt_x1(1.0, 1.0),
//		   pt_x2(5.0, 6.0);
//	shared_ptr<NLE> ef1(new NLE(x1, pt_x1));
//	graph->push_back(ef1);
//
//	// measurement from x1 to l1
//	Point2 z1(0.0, 5.0);
//	SharedGaussian sigma(noiseModel::Isotropic::Sigma(2, 0.1));
//	shared f1(new simulated2D::GenericMeasurement<Config2D>(z1, sigma, x1,l1));
//	graph->push_back(f1);
//
//	// measurement from x2 to l2
//	Point2 z2(-4.0, 0.0);
//	shared f2(new simulated2D::GenericMeasurement<Config2D>(z2, sigma, x2,l2));
//	graph->push_back(f2);
//
//	// equality constraint between l1 and l2
//	list<simulated2D::PointKey> keys2; keys2 += l1, l2;
//	boost::shared_ptr<NLC2 > c2(new NLC2(
//					boost::bind(constrained_test1::g, _1, keys2),
//					l1, boost::bind(constrained_test1::G1, _1, keys2),
//					l2, boost::bind(constrained_test1::G2, _1, keys2),
//					2));
//	graph->push_back(c2);
//
//	if (verbose) graph->print("Initial nonlinear graph with constraints");
//
//	// create an initial estimate
//	shared_ptr<Config2D> initialEstimate(new Config2D);
//	initialEstimate->insert(x1, pt_x1);
//	initialEstimate->insert(x2, Point2());
//	initialEstimate->insert(l1, Point2(1.0, 6.0)); // ground truth
//	initialEstimate->insert(l2, Point2(-4.0, 0.0)); // starting with a separate reference frame
//
//	// create state config variables and initialize them
//	Config2D state(*initialEstimate);
//
//	// linearize the graph
//	boost::shared_ptr<GaussianFactorGraph> fg = graph->linearize(state);
//
//	if (verbose) fg->print("Linearized graph");
//
//	// create an ordering
//	Ordering ordering;
//	ordering += "x1", "x2", "l1", "l2";
//
//	// optimize linear graph to get full delta config
//	GaussianBayesNet cbn = fg->eliminate(ordering);
//	if (verbose) cbn.print("ChordalBayesNet");
//
//	VectorConfig delta = optimize(cbn); //fg.optimize(ordering);
//	if (verbose) delta.print("Delta Config");
//
//	// update both state variables
//	state = expmap(state, delta);
//	if (verbose) state.print("newState");
//
//	// verify
//	Config2D expected;
//	expected.insert(x1, pt_x1);
//	expected.insert(l1, Point2(1.0, 6.0));
//	expected.insert(l2, Point2(1.0, 6.0));
//	expected.insert(x2, Point2(5.0, 6.0));
//	CHECK(assert_equal(expected, state, 1e-5));
//}
//
///* ********************************************************************* */
//// VSLAM Examples
///* ********************************************************************* */
//// make a realistic calibration matrix
//double fov = 60; // degrees
//size_t w=640,h=480;
//Cal3_S2 K(fov,w,h);
//boost::shared_ptr<Cal3_S2> shK(new Cal3_S2(K));
//
//using namespace gtsam::visualSLAM;
//using namespace boost;
//
//// typedefs for visual SLAM example
//typedef TypedSymbol<Pose3, 'x'> Pose3Key;
//typedef TypedSymbol<Point3, 'l'> Point3Key;
////typedef TupleConfig3<LieConfig<LagrangeKey, Vector>,
////					 LieConfig<Pose3Key, Pose3>,
////					 LieConfig<Point3Key, Point3> > VConfig;
//typedef visualSLAM::Config VConfig;
//typedef NonlinearFactorGraph<VConfig> VGraph;
//typedef boost::shared_ptr<GenericProjectionFactor<VConfig> > shared_vf;
//typedef NonlinearOptimizer<VGraph,VConfig> VOptimizer;
//typedef NonlinearConstraint2<
//	VConfig, visualSLAM::PointKey, Pose3, visualSLAM::PointKey, Pose3> VNLC2;
//typedef NonlinearEquality<VConfig, Pose3Key, Pose3> Pose3Constraint;
//
///**
// * Ground truth for a visual SLAM example with stereo vision
// */
//TEST (NonlinearConstraint, stereo_truth ) {
//	bool verbose = false;
//
//	// create initial estimates
//	Rot3 faceDownY(Matrix_(3,3,
//				1.0, 0.0, 0.0,
//				0.0, 0.0, 1.0,
//				0.0, 1.0, 0.0));
//	Pose3 pose1(faceDownY, Point3()); // origin, left camera
//	SimpleCamera camera1(K, pose1);
//	Pose3 pose2(faceDownY, Point3(2.0, 0.0, 0.0)); // 2 units to the left
//	SimpleCamera camera2(K, pose2);
//	Point3 landmark(1.0, 5.0, 0.0); //centered between the cameras, 5 units away
//	Point3 landmarkNoisy(1.0, 6.0, 0.0);
//
//	// create truth config
//	boost::shared_ptr<VConfig> truthConfig(new VConfig);
//	truthConfig->insert(Pose3Key(1), camera1.pose());
//	truthConfig->insert(Pose3Key(2), camera2.pose());
//	truthConfig->insert(Point3Key(1), landmark);
//
//	// create graph
//	shared_ptr<VGraph> graph(new VGraph());
//
//	// create equality constraints for poses
//	graph->push_back(shared_ptr<Pose3Constraint>(new Pose3Constraint(Pose3Key(1), camera1.pose())));
//	graph->push_back(shared_ptr<Pose3Constraint>(new Pose3Constraint(Pose3Key(2), camera2.pose())));
//
//	// create VSLAM factors
//	Point2 z1 = camera1.project(landmark);
//	if (verbose) z1.print("z1");
//	SharedDiagonal vmodel = noiseModel::Unit::Create(3);
//	//ProjectionFactor test_vf(z1, vmodel, Pose3Key(1), Point3Key(1), shK);
//	shared_vf vf1(new GenericProjectionFactor<VConfig>(
//			z1, vmodel, Pose3Key(1), Point3Key(1), shK));
//	graph->push_back(vf1);
//	Point2 z2 = camera2.project(landmark);
//	if (verbose) z2.print("z2");
//	shared_vf vf2(new GenericProjectionFactor<VConfig>(
//			z2, vmodel, Pose3Key(2), Point3Key(1), shK));
//	graph->push_back(vf2);
//
//	if (verbose) graph->print("Graph after construction");
//
//	// create ordering
//	shared_ptr<Ordering> ord(new Ordering());
//	*ord += "x1", "x2", "l1";
//
//	// create optimizer
//	VOptimizer::shared_solver solver(new VOptimizer::solver(ord));
//	VOptimizer optimizer(graph, truthConfig, solver);
//
//	// optimize
//	VOptimizer afterOneIteration = optimizer.iterate();
//
//	// verify
//	DOUBLES_EQUAL(0.0, optimizer.error(), 1e-9);
//
//	// check if correct
//	if (verbose) afterOneIteration.config()->print("After iteration");
//	CHECK(assert_equal(*truthConfig,*(afterOneIteration.config())));
//}
//
//
///* *********************************************************************
// * Ground truth for a visual SLAM example with stereo vision
// * with some noise injected into the initial config
// */
//TEST (NonlinearConstraint, stereo_truth_noisy ) {
//	bool verbose = false;
//
//	// setting to determine how far away the noisy landmark is,
//	// given that the ground truth is 5m in front of the cameras
//	double noisyDist = 7.6;
//
//	// create initial estimates
//	Rot3 faceDownY(Matrix_(3,3,
//			1.0, 0.0, 0.0,
//			0.0, 0.0, 1.0,
//			0.0, 1.0, 0.0));
//	Pose3 pose1(faceDownY, Point3()); // origin, left camera
//	SimpleCamera camera1(K, pose1);
//	Pose3 pose2(faceDownY, Point3(2.0, 0.0, 0.0)); // 2 units to the left
//	SimpleCamera camera2(K, pose2);
//	Point3 landmark(1.0, 5.0, 0.0); //centered between the cameras, 5 units away
//	Point3 landmarkNoisy(1.0, noisyDist, 0.0); // initial point is too far out
//
//	// create truth config
//	boost::shared_ptr<VConfig> truthConfig(new VConfig);
//	truthConfig->insert(Pose3Key(1), camera1.pose());
//	truthConfig->insert(Pose3Key(2), camera2.pose());
//	truthConfig->insert(Point3Key(1), landmark);
//
//	// create config
//	boost::shared_ptr<VConfig> noisyConfig(new VConfig);
//	noisyConfig->insert(Pose3Key(1), camera1.pose());
//	noisyConfig->insert(Pose3Key(2), camera2.pose());
//	noisyConfig->insert(Point3Key(1), landmarkNoisy);
//
//	// create graph
//	shared_ptr<VGraph> graph(new VGraph());
//
//	// create equality constraints for poses
//	graph->push_back(shared_ptr<Pose3Constraint>(new Pose3Constraint(Pose3Key(1), camera1.pose())));
//	graph->push_back(shared_ptr<Pose3Constraint>(new Pose3Constraint(Pose3Key(2), camera2.pose())));
//
//	// create VSLAM factors
//	Point2 z1 = camera1.project(landmark);
//	if (verbose) z1.print("z1");
//	SharedDiagonal vmodel = noiseModel::Unit::Create(3);
//	shared_vf vf1(new GenericProjectionFactor<VConfig>(
//				z1, vmodel, Pose3Key(1), Point3Key(1), shK));
//	graph->push_back(vf1);
//	Point2 z2 = camera2.project(landmark);
//	if (verbose) z2.print("z2");
//	shared_vf vf2(new GenericProjectionFactor<VConfig>(
//				z2, vmodel, Pose3Key(2), Point3Key(1), shK));
//	graph->push_back(vf2);
//
//	if (verbose)  {
//		graph->print("Graph after construction");
//		noisyConfig->print("Initial config");
//	}
//
//	// create ordering
//	shared_ptr<Ordering> ord(new Ordering());
//	*ord += "x1", "x2", "l1";
//
//	// create optimizer
//	VOptimizer::shared_solver solver(new VOptimizer::solver(ord));
//	VOptimizer optimizer0(graph, noisyConfig, solver);
//
//	if (verbose)
//		cout << "Initial Error: " << optimizer0.error() << endl;
//
//	// use Levenberg-Marquardt optimization
//	double relThresh = 1e-5, absThresh = 1e-5;
//	VOptimizer optimizer(optimizer0.levenbergMarquardt(relThresh, absThresh, VOptimizer::SILENT));
//
//	// verify
//	DOUBLES_EQUAL(0.0, optimizer.error(), 1e-5);
//
//	// check if correct
//	if (verbose) {
//		optimizer.config()->print("After iteration");
//		cout << "Final error: " << optimizer.error() << endl;
//	}
//	CHECK(assert_equal(*truthConfig,*(optimizer.config()), 1e-5));
//}
//
///* ********************************************************************* */
//namespace constrained_stereo {
//
//	// binary constraint between landmarks
//	/** g(x) = x-y = 0 */
//	Vector g(const VConfig& config, const list<Point3Key>& keys) {
//		return config[keys.front()].vector()
//				- config[keys.back()].vector();
//	}
//
//	/** jacobian at l1 */
//	Matrix G1(const VConfig& config, const list<Point3Key>& keys) {
//		return eye(3);
//	}
//
//	/** jacobian at l2 */
//	Matrix G2(const VConfig& config, const list<Point3Key>& keys) {
//		return -1.0 * eye(3);
//	}
//
//} // \namespace constrained_stereo
//
///* ********************************************************************* */
//boost::shared_ptr<VGraph> stereoExampleGraph() {
//	// create initial estimates
//	Rot3 faceDownY(Matrix_(3,3,
//			1.0, 0.0, 0.0,
//			0.0, 0.0, 1.0,
//			0.0, 1.0, 0.0));
//	Pose3 pose1(faceDownY, Point3()); // origin, left camera
//	SimpleCamera camera1(K, pose1);
//	Pose3 pose2(faceDownY, Point3(2.0, 0.0, 0.0)); // 2 units to the left
//	SimpleCamera camera2(K, pose2);
//	Point3 landmark1(1.0, 5.0, 0.0); //centered between the cameras, 5 units away
//	Point3 landmark2(1.0, 5.0, 0.0);
//
//	// create graph
//	shared_ptr<VGraph> graph(new VGraph);
//
//	// create equality constraints for poses
//	graph->push_back(shared_ptr<Pose3Constraint>(new Pose3Constraint(Pose3Key(1), camera1.pose())));
//	graph->push_back(shared_ptr<Pose3Constraint>(new Pose3Constraint(Pose3Key(2), camera2.pose())));
//
//	// create  factors
//	Point2 z1 = camera1.project(landmark1);
//	SharedDiagonal vmodel = noiseModel::Unit::Create(3);
//	shared_vf vf1(new GenericProjectionFactor<VConfig>(
//				z1, vmodel, Pose3Key(1), Point3Key(1), shK));
//	graph->push_back(vf1);
//	Point2 z2 = camera2.project(landmark2);
//	shared_vf vf2(new GenericProjectionFactor<VConfig>(
//				z2, vmodel, Pose3Key(2), Point3Key(2), shK));
//	graph->push_back(vf2);
//
//	// create the binary equality constraint between the landmarks
//	// NOTE: this is really just a linear constraint that is exactly the same
//	// as the previous examples
//	visualSLAM::PointKey l1(1), l2(2);
//	list<Point3Key> keys; keys += l1, l2;
//	shared_ptr<VNLC2> c2(
//			new VNLC2(boost::bind(constrained_stereo::g, _1, keys),
//					 l1, boost::bind(constrained_stereo::G1, _1, keys),
//					 l2, boost::bind(constrained_stereo::G2, _1, keys),
//					 3));
//	graph->push_back(c2);
//
//	return graph;
//}
//
///* ********************************************************************* */
//boost::shared_ptr<VConfig> stereoExampleTruthConfig() {
//	// create initial estimates
//	Rot3 faceDownY(Matrix_(3,3,
//				1.0, 0.0, 0.0,
//				0.0, 0.0, 1.0,
//				0.0, 1.0, 0.0));
//	Pose3 pose1(faceDownY, Point3()); // origin, left camera
//	SimpleCamera camera1(K, pose1);
//	Pose3 pose2(faceDownY, Point3(2.0, 0.0, 0.0)); // 2 units to the left
//	SimpleCamera camera2(K, pose2);
//	Point3 landmark1(1.0, 5.0, 0.0); //centered between the cameras, 5 units away
//	Point3 landmark2(1.0, 5.0, 0.0);
//
//	// create config
//	boost::shared_ptr<VConfig> truthConfig(new VConfig);
//	truthConfig->insert(Pose3Key(1), camera1.pose());
//	truthConfig->insert(Pose3Key(2), camera2.pose());
//	truthConfig->insert(Point3Key(1), landmark1);
//	truthConfig->insert(Point3Key(2), landmark2); // create two landmarks in same place
//	//truthConfig->insert(LagrangeKey(12), Vector_(3, 1.0, 1.0, 1.0));
//
//	return truthConfig;
//}
//
///* *********************************************************************
// * SQP version of the above stereo example,
// * with the initial case as the ground truth
// */
//TEST (NonlinearConstraint, stereo_constrained ) {
//	bool verbose = false;
//
//	// get a graph
//	boost::shared_ptr<VGraph> graph = stereoExampleGraph();
//	if (verbose) graph->print("Graph after construction");
//
//	// get the truth config
//	boost::shared_ptr<VConfig> truthConfig = stereoExampleTruthConfig();
//
//	// create ordering
//	shared_ptr<Ordering> ord(new Ordering());
//	*ord += "x1", "x2", "l1", "l2";
//	VOptimizer::shared_solver solver(new VOptimizer::solver(ord));
//
//	// create optimizer
//	VOptimizer optimizer(graph, truthConfig, solver);
//
//	// optimize
//	VOptimizer afterOneIteration = optimizer.iterate();
//
//	// check if correct
//	CHECK(assert_equal(*truthConfig,*(afterOneIteration.config())));
//}
//
///* *********************************************************************
// * SQP version of the above stereo example,
// * with noise in the initial estimate
// */
//TEST (NonlinearConstraint, stereo_constrained_noisy ) {
//
//	// get a graph
//	boost::shared_ptr<VGraph> graph = stereoExampleGraph();
//
//	// create initial data
//	Rot3 faceDownY(Matrix_(3,3,
//			1.0, 0.0, 0.0,
//			0.0, 0.0, 1.0,
//			0.0, 1.0, 0.0));
//	Pose3 pose1(faceDownY, Point3()); // origin, left camera
//	Pose3 pose2(faceDownY, Point3(2.0, 0.0, 0.0)); // 2 units to the left
//	Point3 landmark1(0.5, 5.0, 0.0); //centered between the cameras, 5 units away
//	Point3 landmark2(1.5, 5.0, 0.0);
//
//	// noisy config
//	boost::shared_ptr<VConfig> initConfig(new VConfig);
//	initConfig->insert(Pose3Key(1), pose1);
//	initConfig->insert(Pose3Key(2), pose2);
//	initConfig->insert(Point3Key(1), landmark1);
//	initConfig->insert(Point3Key(2), landmark2); // create two landmarks in same place
//
//	// create ordering
//	shared_ptr<Ordering> ord(new Ordering());
//	*ord += "x1", "x2", "l1", "l2";
//	VOptimizer::shared_solver solver(new VOptimizer::solver(ord));
//
//	// create optimizer
//	VOptimizer optimizer(graph, initConfig, solver);
//
//	// optimize
//	VOptimizer *pointer = new VOptimizer(optimizer);
//	for (int i=0;i<1;i++) {
//		VOptimizer* newOptimizer = new VOptimizer(pointer->iterateLM());
//		delete pointer;
//		pointer = newOptimizer;
//	}
//	VOptimizer::shared_config actual = pointer->config();
//	delete(pointer);
//
//	// get the truth config
//	boost::shared_ptr<VConfig> truthConfig = stereoExampleTruthConfig();
//
//	// check if correct
//	CHECK(assert_equal(*truthConfig,*actual, 1e-5));
//}
//
//static SharedGaussian sigma(noiseModel::Isotropic::Sigma(1,0.1));
//
//namespace map_warp_example {
//typedef NonlinearConstraint1<
//	Config2D, simulated2D::PoseKey, Point2> NLC1;
//} // \namespace map_warp_example
//
///* ********************************************************************* */
//// Example that moves two separate maps into the same frame of reference
//// Note that this is a linear example, so it should converge in one step
///* ********************************************************************* */
//
//namespace constrained_LinearMapWarp2 {
//// binary constraint between landmarks
///** g(x) = x-y = 0 */
//Vector g_func(const Config2D& config, const simulated2D::PointKey& key1, const simulated2D::PointKey& key2) {
//	Point2 p = config[key1]-config[key2];
//	return Vector_(2, p.x(), p.y());
//}
//
///** jacobian at l1 */
//Matrix jac_g1(const Config2D& config) {
//	return eye(2);
//}
//
///** jacobian at l2 */
//Matrix jac_g2(const Config2D& config) {
//	return -1*eye(2);
//}
//} // \namespace constrained_LinearMapWarp2
//
//namespace constrained_LinearMapWarp1 {
//// Unary Constraint on x1
///** g(x) = x -[1;1] = 0 */
//Vector g_func(const Config2D& config, const simulated2D::PoseKey& key) {
//	Point2 p = config[key]-Point2(1.0, 1.0);
//	return Vector_(2, p.x(), p.y());
//}
//
///** jacobian at x1 */
//Matrix jac_g(const Config2D& config) {
//	return eye(2);
//}
//} // \namespace constrained_LinearMapWarp12
//
////typedef NonlinearOptimizer<NLGraph, Config2D> Optimizer;
//
///**
// * Creates the graph with each robot seeing the landmark, and it is
// * known that it is the same landmark
// */
//boost::shared_ptr<Graph2D> linearMapWarpGraph() {
//	using namespace map_warp_example;
//	// keys
//	simulated2D::PoseKey x1(1), x2(2);
//	simulated2D::PointKey l1(1), l2(2);
//
//	// constant constraint on x1
//	shared_ptr<NLC1> c1(new NLC1(boost::bind(constrained_LinearMapWarp1::g_func, _1, x1),
//							x1, boost::bind(constrained_LinearMapWarp1::jac_g, _1),
//							2));
//
//	// measurement from x1 to l1
//	Point2 z1(0.0, 5.0);
//	shared f1(new simulated2D::GenericMeasurement<Config2D>(z1, sigma, x1,l1));
//
//	// measurement from x2 to l2
//	Point2 z2(-4.0, 0.0);
//	shared f2(new simulated2D::GenericMeasurement<Config2D>(z2, sigma, x2,l2));
//
//	// equality constraint between l1 and l2
//	shared_ptr<NLC2> c2 (new NLC2(
//			boost::bind(constrained_LinearMapWarp2::g_func, _1, l1, l2),
//			l1, boost::bind(constrained_LinearMapWarp2::jac_g1, _1),
//			l2, boost::bind(constrained_LinearMapWarp2::jac_g2, _1),
//			2));
//
//	// construct the graph
//	boost::shared_ptr<Graph2D> graph(new Graph2D());
//	graph->push_back(c1);
//	graph->push_back(c2);
//	graph->push_back(f1);
//	graph->push_back(f2);
//
//	return graph;
//}
//
///* ********************************************************************* */
//TEST ( SQPOptimizer, map_warp_initLam ) {
//	// get a graph
//	boost::shared_ptr<Graph2D> graph = linearMapWarpGraph();
//
//	// keys
//	simulated2D::PoseKey x1(1), x2(2);
//	simulated2D::PointKey l1(1), l2(2);
//
//	// create an initial estimate
//	shared_ptr<Config2D> initialEstimate(new Config2D);
//	initialEstimate->insert(x1, Point2(1.0, 1.0));
//	initialEstimate->insert(l1, Point2(1.0, 6.0));
//	initialEstimate->insert(l2, Point2(-4.0, 0.0)); // starting with a separate reference frame
//	initialEstimate->insert(x2, Point2(0.0, 0.0)); // other pose starts at origin
//
//	// create an ordering
//	shared_ptr<Ordering> ordering(new Ordering());
//	*ordering += "x1", "x2", "l1", "l2";
//
//	// create an optimizer
//	Optimizer::shared_solver solver(new Optimizer::solver(ordering));
//	Optimizer optimizer(graph, initialEstimate, solver);
//
//	// perform an iteration of optimization
//	Optimizer oneIteration = optimizer.iterate(Optimizer::SILENT);
//
//	// get the config back out and verify
//	Config2D actual = *(oneIteration.config());
//	Config2D expected;
//	expected.insert(x1, Point2(1.0, 1.0));
//	expected.insert(l1, Point2(1.0, 6.0));
//	expected.insert(l2, Point2(1.0, 6.0));
//	expected.insert(x2, Point2(5.0, 6.0));
//	CHECK(assert_equal(expected, actual));
//}
//
///* ********************************************************************* */
//// This is an obstacle avoidance demo, where there is a trajectory of
//// three points, where there is a circular obstacle in the middle.  There
//// is a binary inequality constraint connecting the obstacle to the
//// states, which enforces a minimum distance.
///* ********************************************************************* */
//
////typedef NonlinearConstraint2<Config2D, PoseKey, Point2, PointKey, Point2> AvoidConstraint;
////typedef shared_ptr<AvoidConstraint> shared_a;
////typedef NonlinearEquality<Config2D, simulated2D::PoseKey, Point2> PoseNLConstraint;
////typedef shared_ptr<PoseNLConstraint> shared_pc;
////typedef NonlinearEquality<Config2D, simulated2D::PointKey, Point2> ObstacleConstraint;
////typedef shared_ptr<ObstacleConstraint> shared_oc;
////
////
////namespace constrained_avoid1 {
////// avoidance radius
////double radius = 1.0;
////
////// binary avoidance constraint
/////** g(x) = ||x2-obs||^2 - radius^2 > 0 */
////Vector g_func(const Config2D& config, const PoseKey& x, const PointKey& obs) {
////	double dist2 = config[x].dist(config[obs]);
////	double thresh = radius*radius;
////	return Vector_(1, dist2-thresh);
////}
////
/////** jacobian at pose */
////Matrix jac_g1(const Config2D& config, const PoseKey& x, const PointKey& obs) {
////	Point2 p = config[x]-config[obs];
////	return Matrix_(1,2, 2.0*p.x(), 2.0*p.y());
////}
////
/////** jacobian at obstacle */
////Matrix jac_g2(const Config2D& config, const PoseKey& x, const PointKey& obs) {
////	Point2 p = config[x]-config[obs];
////	return Matrix_(1,2, -2.0*p.x(), -2.0*p.y());
////}
////}
////
////pair<NLGraph, Config2D> obstacleAvoidGraph() {
////	// Keys
////	PoseKey x1(1), x2(2), x3(3);
////	PointKey l1(1);
////	LagrangeKey L20(20);
////
////	// Constrained Points
////	Point2 pt_x1,
////		   pt_x3(10.0, 0.0),
////		   pt_l1(5.0, -0.5);
////
////	shared_pc e1(new PoseNLConstraint(x1, pt_x1));
////	shared_pc e2(new PoseNLConstraint(x3, pt_x3));
////	shared_oc e3(new ObstacleConstraint(l1, pt_l1));
////
////	// measurement from x1 to x2
////	Point2 x1x2(5.0, 0.0);
////	shared f1(new simulated2D::Odometry(x1x2, sigma, 1,2));
////
////	// measurement from x2 to x3
////	Point2 x2x3(5.0, 0.0);
////	shared f2(new simulated2D::Odometry(x2x3, sigma, 2,3));
////
////	// create a binary inequality constraint that forces the middle point away from
////	//  the obstacle
////	shared_a c1(new AvoidConstraint(boost::bind(constrained_avoid1::g_func, _1, x2, l1),
////							x2, boost::bind(constrained_avoid1::jac_g1, _1, x2, l1),
////						    l1,boost::bind(constrained_avoid1::jac_g2, _1, x2, l1),
////						    1, L20, false));
////
////	// construct the graph
////	NLGraph graph;
////	graph.push_back(e1);
////	graph.push_back(e2);
////	graph.push_back(e3);
////	graph.push_back(c1);
////	graph.push_back(f1);
////	graph.push_back(f2);
////
////	// make a config of the fixed values, for convenience
////	Config2D config;
////	config.insert(x1, pt_x1);
////	config.insert(x3, pt_x3);
////	config.insert(l1, pt_l1);
////
////	return make_pair(graph, config);
////}
////
/////* ********************************************************************* */
////TEST ( SQPOptimizer, inequality_avoid ) {
////	// create the graph
////	NLGraph graph; Config2D feasible;
////	boost::tie(graph, feasible) = obstacleAvoidGraph();
////
////	// create the rest of the config
////	shared_ptr<Config2D> init(new Config2D(feasible));
////	PoseKey x2(2);
////	init->insert(x2, Point2(5.0, 100.0));
////
////	// create an ordering
////	Ordering ord;
////	ord += "x1", "x2", "x3", "l1";
////
////	// create an optimizer
////	Optimizer optimizer(graph, ord, init);
////
////	// perform an iteration of optimization
////	// NOTE: the constraint will be inactive in the first iteration,
////	// so it will violate the constraint after one iteration
////	Optimizer afterOneIteration = optimizer.iterate(Optimizer::SILENT);
////
////	Config2D exp1(feasible);
////	exp1.insert(x2, Point2(5.0, 0.0));
////	CHECK(assert_equal(exp1, *(afterOneIteration.config())));
////
////	// the second iteration will activate the constraint and force the
////	// config to a viable configuration.
////	Optimizer after2ndIteration = afterOneIteration.iterate(Optimizer::SILENT);
////
////	Config2D exp2(feasible);
////	exp2.insert(x2, Point2(5.0, 0.5));
////	CHECK(assert_equal(exp2, *(after2ndIteration.config())));
////}
//
/////* ********************************************************************* */
////TEST ( SQPOptimizer, inequality_avoid_iterative ) {
////	// create the graph
////	NLGraph graph; Config2D feasible;
////	boost::tie(graph, feasible) = obstacleAvoidGraph();
////
////	// create the rest of the config
////	shared_ptr<Config2D> init(new Config2D(feasible));
////	PoseKey x2(2);
////	init->insert(x2, Point2(5.0, 100.0));
////
////	// create an ordering
////	Ordering ord;
////	ord += "x1", "x2", "x3", "l1";
////
////	// create an optimizer
////	Optimizer optimizer(graph, ord, init);
////
////	double relThresh = 1e-5; // minimum change in error between iterations
////	double absThresh = 1e-5; // minimum error necessary to converge
////	double constraintThresh = 1e-9; // minimum constraint error to be feasible
////	Optimizer final = optimizer.iterateSolve(relThresh, absThresh, constraintThresh);
////
////	// verify
////	Config2D exp2(feasible);
////	exp2.insert(x2, Point2(5.0, 0.5));
////	CHECK(assert_equal(exp2, *(final.config())));
////}

/* ************************************************************************* */
int main() { TestResult tr; return TestRegistry::runAllTests(tr); }
/* ************************************************************************* */