gtsam/timing/timeGaussianFactorGraph.cpp

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

 * 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    timeGaussianFactorGraph.cpp
 * @brief   Time elimination with simple Kalman Smoothing example
 * @author  Frank Dellaert
 */

#include <time.h>
#include <CppUnitLite/TestHarness.h>
#include <tests/smallExample.h>

using namespace std;
using namespace gtsam;
using namespace example;

/* ************************************************************************* */
// Create a Kalman smoother for t=1:T and optimize
double timeKalmanSmoother(int T) {
  GaussianFactorGraph smoother = createSmoother(T);
  clock_t start = clock();
  // Keys will come out sorted since keys() returns a set
  smoother.optimize(Ordering(smoother.keys()));
  clock_t end = clock ();
  double dif = (double)(end - start) / CLOCKS_PER_SEC;
  return dif;
}

/* ************************************************************************* */
// Create a planar factor graph and optimize
double timePlanarSmoother(int N, bool old = true) {
  GaussianFactorGraph fg = planarGraph(N).first;
  clock_t start = clock();
  fg.optimize();
  clock_t end = clock ();
  double dif = (double)(end - start) / CLOCKS_PER_SEC;
  return dif;
}

/* ************************************************************************* */
// Create a planar factor graph and eliminate
double timePlanarSmootherEliminate(int N, bool old = true) {
  GaussianFactorGraph fg = planarGraph(N).first;
  clock_t start = clock();
  fg.eliminateMultifrontal();
  clock_t end = clock ();
  double dif = (double)(end - start) / CLOCKS_PER_SEC;
  return dif;
}

///* ************************************************************************* */
//// Create a planar factor graph and join factors until matrix formation
//// This variation uses the original join factors approach
//double timePlanarSmootherJoinAug(int N, size_t reps) {
//  GaussianFactorGraph fgBase;
//  VectorValues config;
//  boost::tie(fgBase,config) = planarGraph(N);
//  Ordering ordering = fgBase.getOrdering();
//  Symbol key = ordering.front();
//
//  clock_t start = clock();
//
//  for (size_t i = 0; i<reps; ++i) {
//    // setup
//    GaussianFactorGraph fg(fgBase);
//
//    // combine some factors
//    GaussianFactor::shared_ptr joint_factor = removeAndCombineFactors(fg,key);
//
//    // create an internal ordering to render Ab
//    Ordering render;
//    render += key;
//    for(const Symbol& k: joint_factor->keys())
//    if (k != key) render += k;
//
//    Matrix Ab = joint_factor->matrix_augmented(render,false);
//  }
//
//  clock_t end = clock ();
//  double dif = (double)(end - start) / CLOCKS_PER_SEC;
//  return dif;
//}

///* ************************************************************************* */
//// Create a planar factor graph and join factors until matrix formation
//// This variation uses the single-allocate version to create the matrix
//double timePlanarSmootherCombined(int N, size_t reps) {
//  GaussianFactorGraph fgBase;
//  VectorValues config;
//  boost::tie(fgBase,config) = planarGraph(N);
//  Ordering ordering = fgBase.getOrdering();
//  Symbol key = ordering.front();
//
//  clock_t start = clock();
//
//  for (size_t i = 0; i<reps; ++i) {
//    GaussianFactorGraph fg(fgBase);
//    fg.eliminateOneMatrixJoin(key);
//  }
//
//  clock_t end = clock ();
//  double dif = (double)(end - start) / CLOCKS_PER_SEC;
//  return dif;
//}


/* ************************************************************************* */
TEST(timeGaussianFactorGraph, linearTime)
{
  // Original T = 1000;

  // Alex's Results
  // T = 100000
  // 1907 (init)    : T - 1.65, 2T = 3.28
  //    int->size_t : T - 1.63, 2T = 3.27
  // 2100           : T - 1.52, 2T = 2.96

  int T = 100000;
  double time1 = timeKalmanSmoother(  T);  cout << "timeKalmanSmoother( T): " << time1;
  double time2 = timeKalmanSmoother(2*T);  cout << "  (2*T): " << time2 << endl;
  DOUBLES_EQUAL(2*time1,time2,0.2);
}


// Timing with N = 30
// 1741: 8.12, 8.12, 8.12, 8.14, 8.16
// 1742: 5.97, 5.97, 5.97, 5.99, 6.02
// 1746: 5.96, 5.96, 5.97, 6.00, 6.04
// 1748: 5.91, 5.92, 5.93, 5.95, 5.96
// 1839: 0.206956 0.206939 0.206213 0.206092 0.206780 // colamd !!!!

// Alex's Machine
// Initial:
// 1907 (N = 30)               :  0.14
//      (N = 100)         : 16.36
// Improved (int->size_t)
//      (N = 100)              : 15.39
// Using GSL/BLAS for updateAb : 12.87
// Using NoiseQR               : 16.33
// Using correct system        : 10.00

// Switch to 100*100 grid = 10K poses
// 1879: 15.6498 15.3851 15.5279

int grid_size = 100;

/* ************************************************************************* */
TEST(timeGaussianFactorGraph, planar_old)
{
  cout << "Timing planar - original version" << endl;
  double time = timePlanarSmoother(grid_size);
  cout << "timeGaussianFactorGraph : " << time << endl;
  //DOUBLES_EQUAL(5.97,time,0.1);
}

/* ************************************************************************* */
TEST(timeGaussianFactorGraph, planar_new)
{
  cout << "Timing planar - new version" << endl;
  double time = timePlanarSmoother(grid_size, false);
  cout << "timeGaussianFactorGraph : " << time << endl;
  //DOUBLES_EQUAL(5.97,time,0.1);
}

/* ************************************************************************* */
TEST(timeGaussianFactorGraph, planar_eliminate_old)
{
  cout << "Timing planar Eliminate - original version" << endl;
  double time = timePlanarSmootherEliminate(grid_size);
  cout << "timeGaussianFactorGraph : " << time << endl;
  //DOUBLES_EQUAL(5.97,time,0.1);
}

/* ************************************************************************* */
TEST(timeGaussianFactorGraph, planar_eliminate_new)
{
  cout << "Timing planar Eliminate - new version" << endl;
  double time = timePlanarSmootherEliminate(grid_size, false);
  cout << "timeGaussianFactorGraph : " << time << endl;
  //DOUBLES_EQUAL(5.97,time,0.1);
}

//size_t reps = 1000;
///* ************************************************************************* */
//TEST(timeGaussianFactorGraph, planar_join_old)
//{
//  cout << "Timing planar join - old" << endl;
//  double time = timePlanarSmootherJoinAug(size, reps);
//  cout << "timePlanarSmootherJoinAug " << size << " : " << time << endl;
//  //DOUBLES_EQUAL(5.97,time,0.1);
//}
//
///* ************************************************************************* */
//TEST(timeGaussianFactorGraph, planar_join_new)
//{
//  cout << "Timing planar join - new" << endl;
//  double time = timePlanarSmootherCombined(size, reps);
//  cout << "timePlanarSmootherCombined " << size << " : " << time << endl;
//  //DOUBLES_EQUAL(5.97,time,0.1);
//}


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