134 lines
4.4 KiB
C++
134 lines
4.4 KiB
C++
/**
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* @file timeSLAMlike.cpp
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* @brief Times solving of random SLAM-like graphs
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* @author Richard Roberts
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* @created Aug 30, 2010
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*/
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#include <gtsam/linear/GaussianFactorGraph.h>
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#include <gtsam/linear/SharedDiagonal.h>
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#include <gtsam/inference/inference-inl.h>
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#include <boost/random.hpp>
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#include <boost/timer.hpp>
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#include <boost/bind.hpp>
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#include <boost/lambda/lambda.hpp>
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#include <vector>
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using namespace gtsam;
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using namespace std;
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using namespace boost::lambda;
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static boost::variate_generator<boost::mt19937, boost::uniform_real<> > rg(boost::mt19937(), boost::uniform_real<>(0.0, 1.0));
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bool _pair_compare(const pair<varid_t, Matrix>& a1, const pair<varid_t, Matrix>& a2) { return a1.first < a2.first; }
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int main(int argc, char *argv[]) {
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size_t vardim = 3;
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size_t blockdim = 3;
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size_t nVars = 2000;
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size_t blocksPerVar = 5;
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size_t varsPerBlock = 2;
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size_t varSpread = 10;
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size_t nTrials = 100;
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double blockbuild, blocksolve;
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cout << "\n" << nVars << " variables of dimension " << vardim << ", " <<
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blocksPerVar << " blocks for each variable, blocks of dimension " << blockdim << " measure " << varsPerBlock << " variables\n";
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cout << nTrials << " trials\n";
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boost::variate_generator<boost::mt19937, boost::uniform_int<> > ri(boost::mt19937(), boost::uniform_int<>(-varSpread, varSpread));
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/////////////////////////////////////////////////////////////////////////////
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// Timing test with blockwise Gaussian factor graphs
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{
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// Build GFG's
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cout << "Building SLAM-like Gaussian factor graphs... ";
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cout.flush();
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boost::timer timer;
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timer.restart();
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vector<GaussianFactorGraph> blockGfgs;
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blockGfgs.reserve(nTrials);
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for(size_t trial=0; trial<nTrials; ++trial) {
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blockGfgs.push_back(GaussianFactorGraph());
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SharedDiagonal noise = sharedSigma(blockdim, 1.0);
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for(size_t c=0; c<nVars; ++c) {
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for(size_t d=0; d<blocksPerVar; ++d) {
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vector<pair<varid_t, Matrix> > terms; terms.reserve(varsPerBlock);
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if(c == 0 && d == 0)
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// If it's the first factor, just make a prior
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terms.push_back(make_pair(0, eye(vardim)));
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else if(c != 0) {
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// Generate a random Gaussian factor
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for(size_t h=0; h<varsPerBlock; ++h) {
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varid_t var;
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// If it's the first factor for this variable, make it "odometry"
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if(d == 0 && h == 0)
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var = c-1;
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else if(d == 0 && h == 1)
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var = c;
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else
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// Choose random variable ids
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do
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var = c + ri();
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while(var < 0 || var > nVars-1 || find_if(terms.begin(), terms.end(),
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boost::bind(&pair<varid_t, Matrix>::first, boost::lambda::_1) == var) != terms.end());
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Matrix A(blockdim, vardim);
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for(size_t j=0; j<blockdim; ++j)
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for(size_t k=0; k<vardim; ++k)
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A(j,k) = rg();
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terms.push_back(make_pair(var, A));
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}
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}
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Vector b(blockdim);
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sort(terms.begin(), terms.end(), &_pair_compare);
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for(size_t j=0; j<blockdim; ++j)
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b(j) = rg();
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if(!terms.empty())
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blockGfgs[trial].push_back(GaussianFactor::shared_ptr(new GaussianFactor(terms, b, noise)));
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}
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}
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// if(trial == 0)
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// blockGfgs.front().print("GFG: ");
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}
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blockbuild = timer.elapsed();
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cout << blockbuild << " s" << endl;
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// Solve GFG's
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cout << "Solving SLAM-like Gaussian factor graphs... ";
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cout.flush();
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timer.restart();
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for(size_t trial=0; trial<nTrials; ++trial) {
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// cout << "Trial " << trial << endl;
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GaussianBayesNet::shared_ptr gbn(Inference::Eliminate(blockGfgs[trial]));
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VectorValues soln(optimize(*gbn));
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}
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blocksolve = timer.elapsed();
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cout << blocksolve << " s" << endl;
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}
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/////////////////////////////////////////////////////////////////////////////
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// Print per-graph times
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cout << "\nPer-factor-graph times for building and solving\n";
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cout << " total " << (1000.0*(blockbuild+blocksolve)/double(nTrials)) <<
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" build " << (1000.0*blockbuild/double(nTrials)) <<
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" solve " << (1000.0*blocksolve/double(nTrials)) << " ms/graph\n";
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cout << endl;
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return 0;
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}
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/**
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* @file timeSLAMlike.cpp
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* @brief
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* @author Richard Roberts
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* @created Aug 30, 2010
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*/
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