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Diffstat (limited to 'examples/data_fitting.cc')
-rw-r--r-- | examples/data_fitting.cc | 165 |
1 files changed, 0 insertions, 165 deletions
diff --git a/examples/data_fitting.cc b/examples/data_fitting.cc deleted file mode 100644 index 5d54123..0000000 --- a/examples/data_fitting.cc +++ /dev/null @@ -1,165 +0,0 @@ -// Ceres Solver - A fast non-linear least squares minimizer -// Copyright 2010, 2011, 2012 Google Inc. All rights reserved. -// http://code.google.com/p/ceres-solver/ -// -// 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 "gflags/gflags.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, -}; - -class ExponentialResidual { - public: - ExponentialResidual(double x, double y) - : x_(x), y_(y) {} - - template <typename T> bool operator()(const T* const m, - const T* const c, - T* residual) const { - residual[0] = T(y_) - exp(m[0] * T(x_) + c[0]); - return true; - } - - private: - const double x_; - const double y_; -}; - -int main(int argc, char** argv) { - google::ParseCommandLineFlags(&argc, &argv, true); - 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<ExponentialResidual, 1, 1, 1>( - 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; -} |