/* ----------------------------------------------------------------------------

 * GTSAM Copyright 2010, Georgia Tech Research Corporation, 
 * Atlanta, Georgia 30332-0415
 * All Rights Reserved
 * Authors: Frank Dellaert, et al. (see THANKS for the full author list)

 * See LICENSE for the license information

 * -------------------------------------------------------------------------- */

/**
 * @file    testIncrementalFixedLagSmoother.cpp
 * @brief   Unit tests for the Incremental Fixed-Lag Smoother
 * @author  Stephen Williams (swilliams8@gatech.edu)
 * @date    May 23, 2012
 */


#include <CppUnitLite/TestHarness.h>
#include <gtsam_unstable/nonlinear/IncrementalFixedLagSmoother.h>
#include <gtsam/base/debug.h>
#include <gtsam/geometry/Point2.h>
#include <gtsam/inference/Key.h>
#include <gtsam/linear/GaussianBayesNet.h>
#include <gtsam/linear/GaussianSequentialSolver.h>
#include <gtsam/slam/PriorFactor.h>
#include <gtsam/slam/BetweenFactor.h>
#include <gtsam/nonlinear/Ordering.h>
#include <gtsam/nonlinear/NonlinearFactorGraph.h>
#include <gtsam/nonlinear/Values.h>
#include <gtsam/nonlinear/Symbol.h>

using namespace std;
using namespace gtsam;

Key MakeKey(size_t index) { return Symbol('x', index); }

/* ************************************************************************* */
bool check_smoother(const NonlinearFactorGraph& fullgraph, const Values& fullinit, const IncrementalFixedLagSmoother& smoother, const Key& key) {

  Ordering ordering = *fullgraph.orderingCOLAMD(fullinit);
  GaussianFactorGraph linearized = *fullgraph.linearize(fullinit, ordering);
  GaussianBayesNet gbn = *GaussianSequentialSolver(linearized).eliminate();
  VectorValues delta = optimize(gbn);
  Values fullfinal = fullinit.retract(delta, ordering);

  Point2 expected = fullfinal.at<Point2>(key);
  Point2 actual = smoother.calculateEstimate<Point2>(key);

  return assert_equal(expected, actual);
}

/* ************************************************************************* */
TEST_UNSAFE( IncrementalFixedLagSmoother, Example )
{
  // Test the IncrementalFixedLagSmoother in a pure linear environment. Thus, full optimization and
  // the IncrementalFixedLagSmoother should be identical (even with the linearized approximations at
  // the end of the smoothing lag)

  SETDEBUG("IncrementalFixedLagSmoother update", true);

  // Set up parameters
  SharedDiagonal odometerNoise = noiseModel::Diagonal::Sigmas(Vector_(2, 0.1, 0.1));
  SharedDiagonal loopNoise = noiseModel::Diagonal::Sigmas(Vector_(2, 0.1, 0.1));

  // Create a Fixed-Lag Smoother
  typedef IncrementalFixedLagSmoother::KeyTimestampMap Timestamps;
  IncrementalFixedLagSmoother smoother(7.0, ISAM2Params());

  // Create containers to keep the full graph
  Values fullinit;
  NonlinearFactorGraph fullgraph;



  // i keeps track of the time step
  size_t i = 0;

  // Add a prior at time 0 and update the HMF
  {
    Key key0 = MakeKey(0);

    NonlinearFactorGraph newFactors;
    Values newValues;
    Timestamps newTimestamps;

    newFactors.add(PriorFactor<Point2>(key0, Point2(0.0, 0.0), odometerNoise));
    newValues.insert(key0, Point2(0.01, 0.01));
    newTimestamps[key0] = 0.0;

    fullgraph.push_back(newFactors);
    fullinit.insert(newValues);

    // Update the smoother
    smoother.update(newFactors, newValues, newTimestamps);

    // Check
    CHECK(check_smoother(fullgraph, fullinit, smoother, key0));

    ++i;
  }

  // Add odometry from time 0 to time 5
  while(i <= 5) {
    Key key1 = MakeKey(i-1);
    Key key2 = MakeKey(i);

    NonlinearFactorGraph newFactors;
    Values newValues;
    Timestamps newTimestamps;

    newFactors.add(BetweenFactor<Point2>(key1, key2, Point2(1.0, 0.0), odometerNoise));
    newValues.insert(key2, Point2(double(i)+0.1, -0.1));
    newTimestamps[key2] = double(i);

    fullgraph.push_back(newFactors);
    fullinit.insert(newValues);

    // Update the smoother
    smoother.update(newFactors, newValues, newTimestamps);

    // Check
    CHECK(check_smoother(fullgraph, fullinit, smoother, key2));

    ++i;
  }

  // Add odometry from time 5 to 6 to the HMF and a loop closure at time 5 to the TSM
  {
    // Add the odometry factor to the HMF
    Key key1 = MakeKey(i-1);
    Key key2 = MakeKey(i);

    NonlinearFactorGraph newFactors;
    Values newValues;
    Timestamps newTimestamps;

    newFactors.add(BetweenFactor<Point2>(key1, key2, Point2(1.0, 0.0), odometerNoise));
    newFactors.add(BetweenFactor<Point2>(MakeKey(2), MakeKey(5), Point2(3.5, 0.0), loopNoise));
    newValues.insert(key2, Point2(double(i)+0.1, -0.1));
    newTimestamps[key2] = double(i);

    fullgraph.push_back(newFactors);
    fullinit.insert(newValues);

    // Update the smoother
    smoother.update(newFactors, newValues, newTimestamps);

    // Check
    CHECK(check_smoother(fullgraph, fullinit, smoother, key2));

    ++i;
  }

  // Add odometry from time 6 to time 15
  while(i <= 15) {
    Key key1 = MakeKey(i-1);
    Key key2 = MakeKey(i);

    NonlinearFactorGraph newFactors;
    Values newValues;
    Timestamps newTimestamps;

    newFactors.add(BetweenFactor<Point2>(key1, key2, Point2(1.0, 0.0), odometerNoise));
    newValues.insert(key2, Point2(double(i)+0.1, -0.1));
    newTimestamps[key2] = double(i);

    fullgraph.push_back(newFactors);
    fullinit.insert(newValues);

    // Update the smoother
    smoother.update(newFactors, newValues, newTimestamps);

    // Check
    CHECK(check_smoother(fullgraph, fullinit, smoother, key2));

    ++i;
  }

}

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