| // Ceres Solver - A fast non-linear least squares minimizer |
| // Copyright 2017 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) |
| // |
| // The National Institute of Standards and Technology has released a |
| // set of problems to test non-linear least squares solvers. |
| // |
| // More information about the background on these problems and |
| // suggested evaluation methodology can be found at: |
| // |
| // http://www.itl.nist.gov/div898/strd/nls/nls_info.shtml |
| // |
| // The problem data themselves can be found at |
| // |
| // http://www.itl.nist.gov/div898/strd/nls/nls_main.shtml |
| // |
| // The problems are divided into three levels of difficulty, Easy, |
| // Medium and Hard. For each problem there are two starting guesses, |
| // the first one far away from the global minimum and the second |
| // closer to it. |
| // |
| // A problem is considered successfully solved, if every components of |
| // the solution matches the globally optimal solution in at least 4 |
| // digits or more. |
| // |
| // This dataset was used for an evaluation of Non-linear least squares |
| // solvers: |
| // |
| // P. F. Mondragon & B. Borchers, A Comparison of Nonlinear Regression |
| // Codes, Journal of Modern Applied Statistical Methods, 4(1):343-351, |
| // 2005. |
| // |
| // The results from Mondragon & Borchers can be summarized as |
| // Excel Gnuplot GaussFit HBN MinPack |
| // Average LRE 2.3 4.3 4.0 6.8 4.4 |
| // Winner 1 5 12 29 12 |
| // |
| // Where the row Winner counts, the number of problems for which the |
| // solver had the highest LRE. |
| |
| // In this file, we implement the same evaluation methodology using |
| // Ceres. Currently using Levenberg-Marquardt with DENSE_QR, we get |
| // |
| // Excel Gnuplot GaussFit HBN MinPack Ceres |
| // Average LRE 2.3 4.3 4.0 6.8 4.4 9.4 |
| // Winner 0 0 5 11 2 41 |
| |
| #include <fstream> |
| #include <iostream> |
| #include <iterator> |
| |
| #include "Eigen/Core" |
| #include "ceres/ceres.h" |
| #include "ceres/tiny_solver.h" |
| #include "ceres/tiny_solver_cost_function_adapter.h" |
| #include "gflags/gflags.h" |
| #include "glog/logging.h" |
| |
| DEFINE_bool(use_tiny_solver, false, "Use TinySolver instead of Ceres::Solver"); |
| DEFINE_string(nist_data_dir, "", "Directory containing the NIST non-linear" |
| "regression examples"); |
| DEFINE_string(minimizer, "trust_region", |
| "Minimizer type to use, choices are: line_search & trust_region"); |
| DEFINE_string(trust_region_strategy, "levenberg_marquardt", |
| "Options are: levenberg_marquardt, dogleg"); |
| DEFINE_string(dogleg, "traditional_dogleg", |
| "Options are: traditional_dogleg, subspace_dogleg"); |
| DEFINE_string(linear_solver, "dense_qr", "Options are: " |
| "sparse_cholesky, dense_qr, dense_normal_cholesky and" |
| "cgnr"); |
| DEFINE_string(preconditioner, "jacobi", "Options are: " |
| "identity, jacobi"); |
| DEFINE_string(line_search, "wolfe", |
| "Line search algorithm to use, choices are: armijo and wolfe."); |
| DEFINE_string(line_search_direction, "lbfgs", |
| "Line search direction algorithm to use, choices: lbfgs, bfgs"); |
| DEFINE_int32(max_line_search_iterations, 20, |
| "Maximum number of iterations for each line search."); |
| DEFINE_int32(max_line_search_restarts, 10, |
| "Maximum number of restarts of line search direction algorithm."); |
| DEFINE_string(line_search_interpolation, "cubic", |
| "Degree of polynomial aproximation in line search, " |
| "choices are: bisection, quadratic & cubic."); |
| DEFINE_int32(lbfgs_rank, 20, |
| "Rank of L-BFGS inverse Hessian approximation in line search."); |
| DEFINE_bool(approximate_eigenvalue_bfgs_scaling, false, |
| "Use approximate eigenvalue scaling in (L)BFGS line search."); |
| DEFINE_double(sufficient_decrease, 1.0e-4, |
| "Line search Armijo sufficient (function) decrease factor."); |
| DEFINE_double(sufficient_curvature_decrease, 0.9, |
| "Line search Wolfe sufficient curvature decrease factor."); |
| DEFINE_int32(num_iterations, 10000, "Number of iterations"); |
| DEFINE_bool(nonmonotonic_steps, false, "Trust region algorithm can use" |
| " nonmonotic steps"); |
| DEFINE_double(initial_trust_region_radius, 1e4, "Initial trust region radius"); |
| DEFINE_bool(use_numeric_diff, false, |
| "Use numeric differentiation instead of automatic " |
| "differentiation."); |
| DEFINE_string(numeric_diff_method, "ridders", "When using numeric " |
| "differentiation, selects algorithm. Options are: central, " |
| "forward, ridders."); |
| DEFINE_double(ridders_step_size, 1e-9, "Initial step size for Ridders " |
| "numeric differentiation."); |
| DEFINE_int32(ridders_extrapolations, 3, "Maximal number of Ridders " |
| "extrapolations."); |
| |
| namespace ceres { |
| namespace examples { |
| namespace { |
| |
| using Eigen::Dynamic; |
| using Eigen::RowMajor; |
| typedef Eigen::Matrix<double, Dynamic, 1> Vector; |
| typedef Eigen::Matrix<double, Dynamic, Dynamic, RowMajor> Matrix; |
| |
| using std::atof; |
| using std::atoi; |
| using std::cout; |
| using std::ifstream; |
| using std::string; |
| using std::vector; |
| |
| void SplitStringUsingChar(const string& full, |
| const char delim, |
| vector<string>* result) { |
| std::back_insert_iterator< vector<string> > it(*result); |
| |
| const char* p = full.data(); |
| const char* end = p + full.size(); |
| while (p != end) { |
| if (*p == delim) { |
| ++p; |
| } else { |
| const char* start = p; |
| while (++p != end && *p != delim) { |
| // Skip to the next occurence of the delimiter. |
| } |
| *it++ = string(start, p - start); |
| } |
| } |
| } |
| |
| bool GetAndSplitLine(ifstream& ifs, vector<string>* pieces) { |
| pieces->clear(); |
| char buf[256]; |
| ifs.getline(buf, 256); |
| SplitStringUsingChar(string(buf), ' ', pieces); |
| return true; |
| } |
| |
| void SkipLines(ifstream& ifs, int num_lines) { |
| char buf[256]; |
| for (int i = 0; i < num_lines; ++i) { |
| ifs.getline(buf, 256); |
| } |
| } |
| |
| class NISTProblem { |
| public: |
| explicit NISTProblem(const string& filename) { |
| ifstream ifs(filename.c_str(), ifstream::in); |
| CHECK(ifs) << "Unable to open : " << filename; |
| |
| vector<string> pieces; |
| SkipLines(ifs, 24); |
| GetAndSplitLine(ifs, &pieces); |
| const int kNumResponses = atoi(pieces[1].c_str()); |
| |
| GetAndSplitLine(ifs, &pieces); |
| const int kNumPredictors = atoi(pieces[0].c_str()); |
| |
| GetAndSplitLine(ifs, &pieces); |
| const int kNumObservations = atoi(pieces[0].c_str()); |
| |
| SkipLines(ifs, 4); |
| GetAndSplitLine(ifs, &pieces); |
| const int kNumParameters = atoi(pieces[0].c_str()); |
| SkipLines(ifs, 8); |
| |
| // Get the first line of initial and final parameter values to |
| // determine the number of tries. |
| GetAndSplitLine(ifs, &pieces); |
| const int kNumTries = pieces.size() - 4; |
| |
| predictor_.resize(kNumObservations, kNumPredictors); |
| response_.resize(kNumObservations, kNumResponses); |
| initial_parameters_.resize(kNumTries, kNumParameters); |
| final_parameters_.resize(1, kNumParameters); |
| |
| // Parse the line for parameter b1. |
| int parameter_id = 0; |
| for (int i = 0; i < kNumTries; ++i) { |
| initial_parameters_(i, parameter_id) = atof(pieces[i + 2].c_str()); |
| } |
| final_parameters_(0, parameter_id) = atof(pieces[2 + kNumTries].c_str()); |
| |
| // Parse the remaining parameter lines. |
| for (int parameter_id = 1; parameter_id < kNumParameters; ++parameter_id) { |
| GetAndSplitLine(ifs, &pieces); |
| // b2, b3, .... |
| for (int i = 0; i < kNumTries; ++i) { |
| initial_parameters_(i, parameter_id) = atof(pieces[i + 2].c_str()); |
| } |
| final_parameters_(0, parameter_id) = atof(pieces[2 + kNumTries].c_str()); |
| } |
| |
| // Certfied cost |
| SkipLines(ifs, 1); |
| GetAndSplitLine(ifs, &pieces); |
| certified_cost_ = atof(pieces[4].c_str()) / 2.0; |
| |
| // Read the observations. |
| SkipLines(ifs, 18 - kNumParameters); |
| for (int i = 0; i < kNumObservations; ++i) { |
| GetAndSplitLine(ifs, &pieces); |
| // Response. |
| for (int j = 0; j < kNumResponses; ++j) { |
| response_(i, j) = atof(pieces[j].c_str()); |
| } |
| |
| // Predictor variables. |
| for (int j = 0; j < kNumPredictors; ++j) { |
| predictor_(i, j) = atof(pieces[j + kNumResponses].c_str()); |
| } |
| } |
| } |
| |
| Matrix initial_parameters(int start) const { return initial_parameters_.row(start); } // NOLINT |
| Matrix final_parameters() const { return final_parameters_; } |
| Matrix predictor() const { return predictor_; } |
| Matrix response() const { return response_; } |
| int predictor_size() const { return predictor_.cols(); } |
| int num_observations() const { return predictor_.rows(); } |
| int response_size() const { return response_.cols(); } |
| int num_parameters() const { return initial_parameters_.cols(); } |
| int num_starts() const { return initial_parameters_.rows(); } |
| double certified_cost() const { return certified_cost_; } |
| |
| private: |
| Matrix predictor_; |
| Matrix response_; |
| Matrix initial_parameters_; |
| Matrix final_parameters_; |
| double certified_cost_; |
| }; |
| |
| #define NIST_BEGIN(CostFunctionName) \ |
| struct CostFunctionName { \ |
| CostFunctionName(const double* const x, \ |
| const double* const y, \ |
| const int n) \ |
| : x_(x), y_(y), n_(n) {} \ |
| const double* x_; \ |
| const double* y_; \ |
| const int n_; \ |
| template <typename T> \ |
| bool operator()(const T* const b, T* residual) const { \ |
| for (int i = 0; i < n_; ++i) { \ |
| const T x(x_[i]); \ |
| residual[i] = y_[i] - ( |
| |
| #define NIST_END ); } return true; }}; |
| |
| // y = b1 * (b2+x)**(-1/b3) + e |
| NIST_BEGIN(Bennet5) |
| b[0] * pow(b[1] + x, -1.0 / b[2]) |
| NIST_END |
| |
| // y = b1*(1-exp[-b2*x]) + e |
| NIST_BEGIN(BoxBOD) |
| b[0] * (1.0 - exp(-b[1] * x)) |
| NIST_END |
| |
| // y = exp[-b1*x]/(b2+b3*x) + e |
| NIST_BEGIN(Chwirut) |
| exp(-b[0] * x) / (b[1] + b[2] * x) |
| NIST_END |
| |
| // y = b1*x**b2 + e |
| NIST_BEGIN(DanWood) |
| b[0] * pow(x, b[1]) |
| NIST_END |
| |
| // y = b1*exp( -b2*x ) + b3*exp( -(x-b4)**2 / b5**2 ) |
| // + b6*exp( -(x-b7)**2 / b8**2 ) + e |
| NIST_BEGIN(Gauss) |
| b[0] * exp(-b[1] * x) + |
| b[2] * exp(-pow((x - b[3])/b[4], 2)) + |
| b[5] * exp(-pow((x - b[6])/b[7], 2)) |
| NIST_END |
| |
| // y = b1*exp(-b2*x) + b3*exp(-b4*x) + b5*exp(-b6*x) + e |
| NIST_BEGIN(Lanczos) |
| b[0] * exp(-b[1] * x) + b[2] * exp(-b[3] * x) + b[4] * exp(-b[5] * x) |
| NIST_END |
| |
| // y = (b1+b2*x+b3*x**2+b4*x**3) / |
| // (1+b5*x+b6*x**2+b7*x**3) + e |
| NIST_BEGIN(Hahn1) |
| (b[0] + b[1] * x + b[2] * x * x + b[3] * x * x * x) / |
| (1.0 + b[4] * x + b[5] * x * x + b[6] * x * x * x) |
| NIST_END |
| |
| // y = (b1 + b2*x + b3*x**2) / |
| // (1 + b4*x + b5*x**2) + e |
| NIST_BEGIN(Kirby2) |
| (b[0] + b[1] * x + b[2] * x * x) / |
| (1.0 + b[3] * x + b[4] * x * x) |
| NIST_END |
| |
| // y = b1*(x**2+x*b2) / (x**2+x*b3+b4) + e |
| NIST_BEGIN(MGH09) |
| b[0] * (x * x + x * b[1]) / (x * x + x * b[2] + b[3]) |
| NIST_END |
| |
| // y = b1 * exp[b2/(x+b3)] + e |
| NIST_BEGIN(MGH10) |
| b[0] * exp(b[1] / (x + b[2])) |
| NIST_END |
| |
| // y = b1 + b2*exp[-x*b4] + b3*exp[-x*b5] |
| NIST_BEGIN(MGH17) |
| b[0] + b[1] * exp(-x * b[3]) + b[2] * exp(-x * b[4]) |
| NIST_END |
| |
| // y = b1*(1-exp[-b2*x]) + e |
| NIST_BEGIN(Misra1a) |
| b[0] * (1.0 - exp(-b[1] * x)) |
| NIST_END |
| |
| // y = b1 * (1-(1+b2*x/2)**(-2)) + e |
| NIST_BEGIN(Misra1b) |
| b[0] * (1.0 - 1.0/ ((1.0 + b[1] * x / 2.0) * (1.0 + b[1] * x / 2.0))) // NOLINT |
| NIST_END |
| |
| // y = b1 * (1-(1+2*b2*x)**(-.5)) + e |
| NIST_BEGIN(Misra1c) |
| b[0] * (1.0 - pow(1.0 + 2.0 * b[1] * x, -0.5)) |
| NIST_END |
| |
| // y = b1*b2*x*((1+b2*x)**(-1)) + e |
| NIST_BEGIN(Misra1d) |
| b[0] * b[1] * x / (1.0 + b[1] * x) |
| NIST_END |
| |
| const double kPi = 3.141592653589793238462643383279; |
| // pi = 3.141592653589793238462643383279E0 |
| // y = b1 - b2*x - arctan[b3/(x-b4)]/pi + e |
| NIST_BEGIN(Roszman1) |
| b[0] - b[1] * x - atan2(b[2], (x - b[3])) / kPi |
| NIST_END |
| |
| // y = b1 / (1+exp[b2-b3*x]) + e |
| NIST_BEGIN(Rat42) |
| b[0] / (1.0 + exp(b[1] - b[2] * x)) |
| NIST_END |
| |
| // y = b1 / ((1+exp[b2-b3*x])**(1/b4)) + e |
| NIST_BEGIN(Rat43) |
| b[0] / pow(1.0 + exp(b[1] - b[2] * x), 1.0 / b[3]) |
| NIST_END |
| |
| // y = (b1 + b2*x + b3*x**2 + b4*x**3) / |
| // (1 + b5*x + b6*x**2 + b7*x**3) + e |
| NIST_BEGIN(Thurber) |
| (b[0] + b[1] * x + b[2] * x * x + b[3] * x * x * x) / |
| (1.0 + b[4] * x + b[5] * x * x + b[6] * x * x * x) |
| NIST_END |
| |
| // y = b1 + b2*cos( 2*pi*x/12 ) + b3*sin( 2*pi*x/12 ) |
| // + b5*cos( 2*pi*x/b4 ) + b6*sin( 2*pi*x/b4 ) |
| // + b8*cos( 2*pi*x/b7 ) + b9*sin( 2*pi*x/b7 ) + e |
| NIST_BEGIN(ENSO) |
| b[0] + b[1] * cos(2.0 * kPi * x / 12.0) + |
| b[2] * sin(2.0 * kPi * x / 12.0) + |
| b[4] * cos(2.0 * kPi * x / b[3]) + |
| b[5] * sin(2.0 * kPi * x / b[3]) + |
| b[7] * cos(2.0 * kPi * x / b[6]) + |
| b[8] * sin(2.0 * kPi * x / b[6]) |
| NIST_END |
| |
| // y = (b1/b2) * exp[-0.5*((x-b3)/b2)**2] + e |
| NIST_BEGIN(Eckerle4) |
| b[0] / b[1] * exp(-0.5 * pow((x - b[2])/b[1], 2)) |
| NIST_END |
| |
| struct Nelson { |
| public: |
| Nelson(const double* const x, const double* const y, const int n) |
| : x_(x), y_(y), n_(n) {} |
| |
| template <typename T> |
| bool operator()(const T* const b, T* residual) const { |
| // log[y] = b1 - b2*x1 * exp[-b3*x2] + e |
| for (int i = 0; i < n_; ++i) { |
| residual[i] = log(y_[i]) - (b[0] - b[1] * x_[2 * i] * exp(-b[2] * x_[2 * i + 1])); |
| } |
| return true; |
| } |
| |
| private: |
| const double* x_; |
| const double* y_; |
| const int n_; |
| }; |
| |
| static void SetNumericDiffOptions(ceres::NumericDiffOptions* options) { |
| options->max_num_ridders_extrapolations = FLAGS_ridders_extrapolations; |
| options->ridders_relative_initial_step_size = FLAGS_ridders_step_size; |
| } |
| |
| void SetMinimizerOptions(ceres::Solver::Options* options) { |
| CHECK( |
| ceres::StringToMinimizerType(FLAGS_minimizer, &options->minimizer_type)); |
| CHECK(ceres::StringToLinearSolverType(FLAGS_linear_solver, |
| &options->linear_solver_type)); |
| CHECK(ceres::StringToPreconditionerType(FLAGS_preconditioner, |
| &options->preconditioner_type)); |
| CHECK(ceres::StringToTrustRegionStrategyType( |
| FLAGS_trust_region_strategy, &options->trust_region_strategy_type)); |
| CHECK(ceres::StringToDoglegType(FLAGS_dogleg, &options->dogleg_type)); |
| CHECK(ceres::StringToLineSearchDirectionType( |
| FLAGS_line_search_direction, &options->line_search_direction_type)); |
| CHECK(ceres::StringToLineSearchType(FLAGS_line_search, |
| &options->line_search_type)); |
| CHECK(ceres::StringToLineSearchInterpolationType( |
| FLAGS_line_search_interpolation, |
| &options->line_search_interpolation_type)); |
| |
| options->max_num_iterations = FLAGS_num_iterations; |
| options->use_nonmonotonic_steps = FLAGS_nonmonotonic_steps; |
| options->initial_trust_region_radius = FLAGS_initial_trust_region_radius; |
| options->max_lbfgs_rank = FLAGS_lbfgs_rank; |
| options->line_search_sufficient_function_decrease = FLAGS_sufficient_decrease; |
| options->line_search_sufficient_curvature_decrease = |
| FLAGS_sufficient_curvature_decrease; |
| options->max_num_line_search_step_size_iterations = |
| FLAGS_max_line_search_iterations; |
| options->max_num_line_search_direction_restarts = |
| FLAGS_max_line_search_restarts; |
| options->use_approximate_eigenvalue_bfgs_scaling = |
| FLAGS_approximate_eigenvalue_bfgs_scaling; |
| options->function_tolerance = std::numeric_limits<double>::epsilon(); |
| options->gradient_tolerance = std::numeric_limits<double>::epsilon(); |
| options->parameter_tolerance = std::numeric_limits<double>::epsilon(); |
| } |
| |
| string JoinPath(const string& dirname, const string& basename) { |
| #ifdef _WIN32 |
| static const char separator = '\\'; |
| #else |
| static const char separator = '/'; |
| #endif // _WIN32 |
| |
| if ((!basename.empty() && basename[0] == separator) || dirname.empty()) { |
| return basename; |
| } else if (dirname[dirname.size() - 1] == separator) { |
| return dirname + basename; |
| } else { |
| return dirname + string(&separator, 1) + basename; |
| } |
| } |
| |
| template <typename Model, int num_parameters> |
| CostFunction* CreateCostFunction(const Matrix& predictor, |
| const Matrix& response, |
| const int num_observations) { |
| Model* model = |
| new Model(predictor.data(), response.data(), num_observations); |
| ceres::CostFunction* cost_function = NULL; |
| if (FLAGS_use_numeric_diff) { |
| ceres::NumericDiffOptions options; |
| SetNumericDiffOptions(&options); |
| if (FLAGS_numeric_diff_method == "central") { |
| cost_function = new NumericDiffCostFunction<Model, |
| ceres::CENTRAL, |
| ceres::DYNAMIC, |
| num_parameters>( |
| model, |
| ceres::TAKE_OWNERSHIP, |
| num_observations, |
| options); |
| } else if (FLAGS_numeric_diff_method == "forward") { |
| cost_function = new NumericDiffCostFunction<Model, |
| ceres::FORWARD, |
| ceres::DYNAMIC, |
| num_parameters>( |
| model, |
| ceres::TAKE_OWNERSHIP, |
| num_observations, |
| options); |
| } else if (FLAGS_numeric_diff_method == "ridders") { |
| cost_function = new NumericDiffCostFunction<Model, |
| ceres::RIDDERS, |
| ceres::DYNAMIC, |
| num_parameters>( |
| model, |
| ceres::TAKE_OWNERSHIP, |
| num_observations, |
| options); |
| } else { |
| LOG(ERROR) << "Invalid numeric diff method specified"; |
| return 0; |
| } |
| } else { |
| cost_function = |
| new ceres::AutoDiffCostFunction<Model, ceres::DYNAMIC, num_parameters>( |
| model, num_observations); |
| } |
| return cost_function; |
| } |
| |
| double ComputeLRE(const Matrix& expected, const Matrix& actual) { |
| // Compute the LRE by comparing each component of the solution |
| // with the ground truth, and taking the minimum. |
| const double kMaxNumSignificantDigits = 11; |
| double log_relative_error = kMaxNumSignificantDigits + 1; |
| for (int i = 0; i < expected.cols(); ++i) { |
| const double tmp_lre = -std::log10(std::fabs(expected(i) - actual(i)) / |
| std::fabs(expected(i))); |
| // The maximum LRE is capped at 11 - the precision at which the |
| // ground truth is known. |
| // |
| // The minimum LRE is capped at 0 - no digits match between the |
| // computed solution and the ground truth. |
| log_relative_error = |
| std::min(log_relative_error, |
| std::max(0.0, std::min(kMaxNumSignificantDigits, tmp_lre))); |
| } |
| return log_relative_error; |
| } |
| |
| template <typename Model, int num_parameters> |
| int RegressionDriver(const string& filename) { |
| NISTProblem nist_problem(JoinPath(FLAGS_nist_data_dir, filename)); |
| CHECK_EQ(num_parameters, nist_problem.num_parameters()); |
| |
| Matrix predictor = nist_problem.predictor(); |
| Matrix response = nist_problem.response(); |
| Matrix final_parameters = nist_problem.final_parameters(); |
| |
| printf("%s\n", filename.c_str()); |
| |
| // Each NIST problem comes with multiple starting points, so we |
| // construct the problem from scratch for each case and solve it. |
| int num_success = 0; |
| for (int start = 0; start < nist_problem.num_starts(); ++start) { |
| Matrix initial_parameters = nist_problem.initial_parameters(start); |
| ceres::CostFunction* cost_function = CreateCostFunction<Model, num_parameters>( |
| predictor, response, nist_problem.num_observations()); |
| |
| double initial_cost; |
| double final_cost; |
| |
| if (!FLAGS_use_tiny_solver) { |
| ceres::Problem problem; |
| problem.AddResidualBlock(cost_function, NULL, initial_parameters.data()); |
| ceres::Solver::Summary summary; |
| ceres::Solver::Options options; |
| SetMinimizerOptions(&options); |
| Solve(options, &problem, &summary); |
| initial_cost = summary.initial_cost; |
| final_cost = summary.final_cost; |
| } else { |
| ceres::TinySolverCostFunctionAdapter<Eigen::Dynamic, num_parameters> cfa( |
| *cost_function); |
| typedef ceres::TinySolver< |
| ceres::TinySolverCostFunctionAdapter<Eigen::Dynamic, num_parameters> > |
| Solver; |
| Solver solver; |
| solver.options.max_num_iterations = FLAGS_num_iterations; |
| solver.options.gradient_tolerance = |
| std::numeric_limits<double>::epsilon(); |
| solver.options.parameter_tolerance = |
| std::numeric_limits<double>::epsilon(); |
| |
| Eigen::Matrix<double, num_parameters, 1> x; |
| x = initial_parameters.transpose(); |
| typename Solver::Summary summary = solver.Solve(cfa, &x); |
| initial_parameters = x; |
| initial_cost = summary.initial_cost; |
| final_cost = summary.final_cost; |
| delete cost_function; |
| } |
| |
| const double log_relative_error = ComputeLRE(nist_problem.final_parameters(), |
| initial_parameters); |
| const int kMinNumMatchingDigits = 4; |
| if (log_relative_error > kMinNumMatchingDigits) { |
| ++num_success; |
| } |
| |
| printf( |
| "start: %d status: %s lre: %4.1f initial cost: %e final cost:%e " |
| "certified cost: %e\n", |
| start + 1, |
| log_relative_error < kMinNumMatchingDigits ? "FAILURE" : "SUCCESS", |
| log_relative_error, |
| initial_cost, |
| final_cost, |
| nist_problem.certified_cost()); |
| } |
| return num_success; |
| } |
| |
| |
| void SolveNISTProblems() { |
| if (FLAGS_nist_data_dir.empty()) { |
| LOG(FATAL) << "Must specify the directory containing the NIST problems"; |
| } |
| |
| cout << "Lower Difficulty\n"; |
| int easy_success = 0; |
| easy_success += RegressionDriver<Misra1a, 2>("Misra1a.dat"); |
| easy_success += RegressionDriver<Chwirut, 3>("Chwirut1.dat"); |
| easy_success += RegressionDriver<Chwirut, 3>("Chwirut2.dat"); |
| easy_success += RegressionDriver<Lanczos, 6>("Lanczos3.dat"); |
| easy_success += RegressionDriver<Gauss, 8>("Gauss1.dat"); |
| easy_success += RegressionDriver<Gauss, 8>("Gauss2.dat"); |
| easy_success += RegressionDriver<DanWood, 2>("DanWood.dat"); |
| easy_success += RegressionDriver<Misra1b, 2>("Misra1b.dat"); |
| |
| cout << "\nMedium Difficulty\n"; |
| int medium_success = 0; |
| medium_success += RegressionDriver<Kirby2, 5>("Kirby2.dat"); |
| medium_success += RegressionDriver<Hahn1, 7>("Hahn1.dat"); |
| medium_success += RegressionDriver<Nelson, 3>("Nelson.dat"); |
| medium_success += RegressionDriver<MGH17, 5>("MGH17.dat"); |
| medium_success += RegressionDriver<Lanczos, 6>("Lanczos1.dat"); |
| medium_success += RegressionDriver<Lanczos, 6>("Lanczos2.dat"); |
| medium_success += RegressionDriver<Gauss, 8>("Gauss3.dat"); |
| medium_success += RegressionDriver<Misra1c, 2>("Misra1c.dat"); |
| medium_success += RegressionDriver<Misra1d, 2>("Misra1d.dat"); |
| medium_success += RegressionDriver<Roszman1, 4>("Roszman1.dat"); |
| medium_success += RegressionDriver<ENSO, 9>("ENSO.dat"); |
| |
| cout << "\nHigher Difficulty\n"; |
| int hard_success = 0; |
| hard_success += RegressionDriver<MGH09, 4>("MGH09.dat"); |
| hard_success += RegressionDriver<Thurber, 7>("Thurber.dat"); |
| hard_success += RegressionDriver<BoxBOD, 2>("BoxBOD.dat"); |
| hard_success += RegressionDriver<Rat42, 3>("Rat42.dat"); |
| hard_success += RegressionDriver<MGH10, 3>("MGH10.dat"); |
| hard_success += RegressionDriver<Eckerle4, 3>("Eckerle4.dat"); |
| hard_success += RegressionDriver<Rat43, 4>("Rat43.dat"); |
| hard_success += RegressionDriver<Bennet5, 3>("Bennett5.dat"); |
| |
| cout << "\n"; |
| cout << "Easy : " << easy_success << "/16\n"; |
| cout << "Medium : " << medium_success << "/22\n"; |
| cout << "Hard : " << hard_success << "/16\n"; |
| cout << "Total : " << easy_success + medium_success + hard_success |
| << "/54\n"; |
| } |
| |
| } // namespace |
| } // namespace examples |
| } // namespace ceres |
| |
| int main(int argc, char** argv) { |
| CERES_GFLAGS_NAMESPACE::ParseCommandLineFlags(&argc, &argv, true); |
| google::InitGoogleLogging(argv[0]); |
| ceres::examples::SolveNISTProblems(); |
| return 0; |
| } |
