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