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

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

#include <CppUnitLite/TestHarness.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) {

  GaussianFactorGraph linearized = *fullgraph.linearize(fullinit);
  VectorValues delta = linearized.optimize();
  Values fullfinal = fullinit.retract(delta);

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

  return assert_equal(expected, actual);
}

/* ************************************************************************* */
void PrintSymbolicTreeHelper(
    const ISAM2Clique::shared_ptr& clique, const std::string indent = "") {

  // Print the current clique
  std::cout << indent << "P( ";
  for(Key key: clique->conditional()->frontals()) {
    std::cout << DefaultKeyFormatter(key) << " ";
  }
  if (clique->conditional()->nrParents() > 0)
    std::cout << "| ";
  for(Key key: clique->conditional()->parents()) {
    std::cout << DefaultKeyFormatter(key) << " ";
  }
  std::cout << ")" << std::endl;

  // Recursively print all of the children
  for(const ISAM2Clique::shared_ptr& child: clique->children) {
    PrintSymbolicTreeHelper(child, indent + " ");
  }
}

/* ************************************************************************* */
void PrintSymbolicTree(const ISAM2& isam,
    const std::string& label) {
  std::cout << label << std::endl;
  if (!isam.roots().empty()) {
    for(const ISAM2::sharedClique& root: isam.roots()) {
      PrintSymbolicTreeHelper(root);
    }
  } else
    std::cout << "{Empty Tree}" << std::endl;
}


/* ************************************************************************* */
TEST( 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(Vector2(0.1, 0.1));
  SharedDiagonal loopNoise = noiseModel::Diagonal::Sigmas(Vector2(0.1, 0.1));

  // Create a Fixed-Lag Smoother
  typedef IncrementalFixedLagSmoother::KeyTimestampMap Timestamps;
  IncrementalFixedLagSmoother smoother(12.0, 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.addPrior(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.push_back(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.push_back(BetweenFactor<Point2>(key1, key2, Point2(1.0, 0.0), odometerNoise));
    newFactors.push_back(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;

    // Add the odometry factor twice to ensure the removeFactor test below works,
    // where we need to keep the connectivity of the graph.
    newFactors.push_back(BetweenFactor<Point2>(key1, key2, Point2(1.0, 0.0), odometerNoise));
    newFactors.push_back(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/remove an extra factor
  {
	  Key key1 = MakeKey(i-1);
	  Key key2 = MakeKey(i);

	  NonlinearFactorGraph newFactors;
	  Values newValues;
	  Timestamps newTimestamps;

	  // add 2 odometry factors
	  newFactors.push_back(BetweenFactor<Point2>(key1, key2, Point2(1.0, 0.0), odometerNoise));
	  newFactors.push_back(BetweenFactor<Point2>(key1, key2, Point2(1.0, 0.0), odometerNoise));
	  newValues.insert(key2, Point2(double(i)+0.1, -0.1));
	  newTimestamps[key2] = double(i);
	  ++i;

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

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

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

	  // now remove one of the two and try again
	  // empty values and new factors for fake update in which we only remove factors
	  NonlinearFactorGraph emptyNewFactors;
	  Values emptyNewValues;
	  Timestamps emptyNewTimestamps;

	  size_t factorIndex = 25; // any index that does not break connectivity of the graph
	  FactorIndices factorToRemove;
	  factorToRemove.push_back(factorIndex);

	  const NonlinearFactorGraph smootherFactorsBeforeRemove = smoother.getFactors();

    std::cout << "fullgraph.size() = " << fullgraph.size() << std::endl;
    std::cout << "smootherFactorsBeforeRemove.size() = "
              << smootherFactorsBeforeRemove.size() << std::endl;

	  // remove factor
	  smoother.update(emptyNewFactors, emptyNewValues, emptyNewTimestamps,factorToRemove);

	  // Note: the following test (checking that the number of factor is reduced by 1)
	  // fails  since we are not reusing slots, hence also when removing a factor we do not change
	  // the size of the factor graph
	  // size_t nrFactorsAfterRemoval = smoother.getFactors().size();
	  // DOUBLES_EQUAL(nrFactorsBeforeRemoval-1, nrFactorsAfterRemoval, 1e-5);

	  // check that the factors in the smoother are right
	  NonlinearFactorGraph actual = smoother.getFactors();
	  for(size_t i=0; i< smootherFactorsBeforeRemove.size(); i++){
	    // check that the factors that were not removed are there
	    if(smootherFactorsBeforeRemove[i] && i != factorIndex){
	      EXPECT(smootherFactorsBeforeRemove[i]->equals(*actual[i]));
	    }
	    else{ // while the factors that were not there or were removed are no longer there
	      EXPECT(!actual[i]);
	    }
	  }
  }

  {
    SETDEBUG("BayesTreeMarginalizationHelper", true);
    PrintSymbolicTree(smoother.getISAM2(), "Bayes Tree Before marginalization test:");

    // Do pressure test on marginalization. Enlarge max_i to enhance the test.
    const int max_i = 500;
    while(i <= max_i) {
      Key key_0 = MakeKey(i);
      Key key_1 = MakeKey(i-1);
      Key key_2 = MakeKey(i-2);
      Key key_3 = MakeKey(i-3);
      Key key_4 = MakeKey(i-4);
      Key key_5 = MakeKey(i-5);
      Key key_6 = MakeKey(i-6);
      Key key_7 = MakeKey(i-7);
      Key key_8 = MakeKey(i-8);
      Key key_9 = MakeKey(i-9);
      Key key_10 = MakeKey(i-10);

      NonlinearFactorGraph newFactors;
      Values newValues;
      Timestamps newTimestamps;

      // To make a complex graph
      newFactors.push_back(BetweenFactor<Point2>(key_1, key_0, Point2(1.0, 0.0), odometerNoise));
      if (i % 2 == 0)
        newFactors.push_back(BetweenFactor<Point2>(key_2, key_1, Point2(1.0, 0.0), odometerNoise));
      if (i % 3 == 0)
        newFactors.push_back(BetweenFactor<Point2>(key_3, key_2, Point2(1.0, 0.0), odometerNoise));
      if (i % 4 == 0)
        newFactors.push_back(BetweenFactor<Point2>(key_4, key_3, Point2(1.0, 0.0), odometerNoise));
      if (i % 5 == 0)
        newFactors.push_back(BetweenFactor<Point2>(key_5, key_4, Point2(1.0, 0.0), odometerNoise));
      if (i % 6 == 0)
        newFactors.push_back(BetweenFactor<Point2>(key_6, key_5, Point2(1.0, 0.0), odometerNoise));
      if (i % 7 == 0)
        newFactors.push_back(BetweenFactor<Point2>(key_7, key_6, Point2(1.0, 0.0), odometerNoise));
      if (i % 8 == 0)
        newFactors.push_back(BetweenFactor<Point2>(key_8, key_7, Point2(1.0, 0.0), odometerNoise));
      if (i % 9 == 0)
        newFactors.push_back(BetweenFactor<Point2>(key_9, key_8, Point2(1.0, 0.0), odometerNoise));
      if (i % 10 == 0)
        newFactors.push_back(BetweenFactor<Point2>(key_10, key_9, Point2(1.0, 0.0), odometerNoise));

      newValues.insert(key_0, Point2(double(i)+0.1, -0.1));
      newTimestamps[key_0] = double(i);

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

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

      // Check
      CHECK(check_smoother(fullgraph, fullinit, smoother, key_0));
      PrintSymbolicTree(smoother.getISAM2(), "Bayes Tree marginalization test: i = " + std::to_string(i));

      ++i;
    }
  }
}

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