mirror of
https://github.com/ceres-solver/ceres-solver.git
synced 2026-08-29 16:40:38 +08:00
Sameer Agarwal
d05515b3eb
Binary operations between Jets and doubles are well defined and should not require an explicit conversion to Jets to work. This was an oversight earlier and lead to overzealous conversions all over our in our example code. Change-Id: I1799770818e136edfc0a5802d86037ce9aec4923
166 lines
5.3 KiB
C++
166 lines
5.3 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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// 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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// outlier_noise = rand(size(x)) < 0.05;
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// y_observed = y + noise + outlier_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, 5.543078e+00, // Outlier point
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1.500000e+00, 5.664015e+00, // Outlier point
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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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using ceres::AutoDiffCostFunction;
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using ceres::CostFunction;
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using ceres::CauchyLoss;
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using ceres::Problem;
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using ceres::Solve;
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using ceres::Solver;
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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] = y_ - exp(m[0] * 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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CostFunction* cost_function =
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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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problem.AddResidualBlock(cost_function,
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new CauchyLoss(0.5),
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&m, &c);
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}
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Solver::Options options;
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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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