// Ceres Solver - A fast non-linear least squares minimizer // Copyright 2015 Google Inc. All rights reserved. // http://ceres-solver.org/ // // Redistribution and use in source and binary forms, with or without // modification, are permitted provided that the following conditions are met: // // * Redistributions of source code must retain the above copyright notice, // this list of conditions and the following disclaimer. // * Redistributions in binary form must reproduce the above copyright notice, // this list of conditions and the following disclaimer in the documentation // and/or other materials provided with the distribution. // * Neither the name of Google Inc. nor the names of its contributors may be // used to endorse or promote products derived from this software without // specific prior written permission. // // THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" // AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE // IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE // ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE // LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR // CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF // SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS // INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN // CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) // ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE // POSSIBILITY OF SUCH DAMAGE. // // Author: sameeragarwal@google.com (Sameer Agarwal) #include "ceres/ceres.h" #include "glog/logging.h" using ceres::AutoDiffCostFunction; using ceres::CostFunction; using ceres::Problem; using ceres::Solver; using ceres::Solve; // Data generated using the following octave code. // randn('seed', 23497); // m = 0.3; // c = 0.1; // x=[0:0.075:5]; // y = exp(m * x + c); // noise = randn(size(x)) * 0.2; // y_observed = y + noise; // data = [x', y_observed']; const int kNumObservations = 67; const double data[] = { 0.000000e+00, 1.133898e+00, 7.500000e-02, 1.334902e+00, 1.500000e-01, 1.213546e+00, 2.250000e-01, 1.252016e+00, 3.000000e-01, 1.392265e+00, 3.750000e-01, 1.314458e+00, 4.500000e-01, 1.472541e+00, 5.250000e-01, 1.536218e+00, 6.000000e-01, 1.355679e+00, 6.750000e-01, 1.463566e+00, 7.500000e-01, 1.490201e+00, 8.250000e-01, 1.658699e+00, 9.000000e-01, 1.067574e+00, 9.750000e-01, 1.464629e+00, 1.050000e+00, 1.402653e+00, 1.125000e+00, 1.713141e+00, 1.200000e+00, 1.527021e+00, 1.275000e+00, 1.702632e+00, 1.350000e+00, 1.423899e+00, 1.425000e+00, 1.543078e+00, 1.500000e+00, 1.664015e+00, 1.575000e+00, 1.732484e+00, 1.650000e+00, 1.543296e+00, 1.725000e+00, 1.959523e+00, 1.800000e+00, 1.685132e+00, 1.875000e+00, 1.951791e+00, 1.950000e+00, 2.095346e+00, 2.025000e+00, 2.361460e+00, 2.100000e+00, 2.169119e+00, 2.175000e+00, 2.061745e+00, 2.250000e+00, 2.178641e+00, 2.325000e+00, 2.104346e+00, 2.400000e+00, 2.584470e+00, 2.475000e+00, 1.914158e+00, 2.550000e+00, 2.368375e+00, 2.625000e+00, 2.686125e+00, 2.700000e+00, 2.712395e+00, 2.775000e+00, 2.499511e+00, 2.850000e+00, 2.558897e+00, 2.925000e+00, 2.309154e+00, 3.000000e+00, 2.869503e+00, 3.075000e+00, 3.116645e+00, 3.150000e+00, 3.094907e+00, 3.225000e+00, 2.471759e+00, 3.300000e+00, 3.017131e+00, 3.375000e+00, 3.232381e+00, 3.450000e+00, 2.944596e+00, 3.525000e+00, 3.385343e+00, 3.600000e+00, 3.199826e+00, 3.675000e+00, 3.423039e+00, 3.750000e+00, 3.621552e+00, 3.825000e+00, 3.559255e+00, 3.900000e+00, 3.530713e+00, 3.975000e+00, 3.561766e+00, 4.050000e+00, 3.544574e+00, 4.125000e+00, 3.867945e+00, 4.200000e+00, 4.049776e+00, 4.275000e+00, 3.885601e+00, 4.350000e+00, 4.110505e+00, 4.425000e+00, 4.345320e+00, 4.500000e+00, 4.161241e+00, 4.575000e+00, 4.363407e+00, 4.650000e+00, 4.161576e+00, 4.725000e+00, 4.619728e+00, 4.800000e+00, 4.737410e+00, 4.875000e+00, 4.727863e+00, 4.950000e+00, 4.669206e+00, }; struct ExponentialResidual { ExponentialResidual(double x, double y) : x_(x), y_(y) {} template bool operator()(const T* const m, const T* const c, T* residual) const { residual[0] = y_ - exp(m[0] * x_ + c[0]); return true; } private: const double x_; const double y_; }; int main(int argc, char** argv) { google::InitGoogleLogging(argv[0]); double m = 0.0; double c = 0.0; Problem problem; for (int i = 0; i < kNumObservations; ++i) { problem.AddResidualBlock( new AutoDiffCostFunction( new ExponentialResidual(data[2 * i], data[2 * i + 1])), NULL, &m, &c); } Solver::Options options; options.max_num_iterations = 25; options.linear_solver_type = ceres::DENSE_QR; options.minimizer_progress_to_stdout = true; Solver::Summary summary; Solve(options, &problem, &summary); std::cout << summary.BriefReport() << "\n"; std::cout << "Initial m: " << 0.0 << " c: " << 0.0 << "\n"; std::cout << "Final m: " << m << " c: " << c << "\n"; return 0; }