// Ceres Solver - A fast non-linear least squares minimizer
// Copyright 2023 Google Inc. All rights reserved.
// http://ceres-solver.org/
//
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// Author: sameeragarwal@google.com (Sameer Agarwal)
//
// This example is a variant of curve_fitting.cc where we use an
// IterationCallback to implement custom logging which prints out the values of
// the parameter blocks as they evolve over the course of the optimization. This
// also requires the use of Solver::Options::update_state_every_iteration.

#include <iostream>

#include "ceres/ceres.h"
#include "glog/logging.h"

// 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;
// clang-format off
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,
};
// clang-format on

struct ExponentialResidual {
  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] = y - exp(m[0] * x + c[0]);
    return true;
  }

 private:
  const double x;
  const double y;
};

// MyIterationCallback prints the iteration number, the cost and the value of
// the parameter blocks every iteration.
class MyIterationCallback : public ceres::IterationCallback {
 public:
  MyIterationCallback(const double* m, const double* c) : m_(m), c_(c) {}

  ~MyIterationCallback() override = default;

  ceres::CallbackReturnType operator()(
      const ceres::IterationSummary& summary) final {
    std::cout << "Iteration: " << summary.iteration << " cost: " << summary.cost
              << " m: " << *m_ << " c: " << *c_ << std::endl;
    return ceres::SOLVER_CONTINUE;
  }

 private:
  const double* m_ = nullptr;
  const double* c_ = nullptr;
};

int main(int argc, char** argv) {
  google::InitGoogleLogging(argv[0]);

  const double initial_m = 0.0;
  const double initial_c = 0.0;

  double m = initial_m;
  double c = initial_c;

  ceres::Problem problem;
  for (int i = 0; i < kNumObservations; ++i) {
    problem.AddResidualBlock(
        new ceres::AutoDiffCostFunction<ExponentialResidual, 1, 1, 1>(
            data[2 * i], data[2 * i + 1]),
        nullptr,
        &m,
        &c);
  }

  ceres::Solver::Options options;
  options.max_num_iterations = 25;
  options.linear_solver_type = ceres::DENSE_QR;

  // Turn off the default logging from Ceres so that it does not interfere with
  // MyIterationCallback.
  options.minimizer_progress_to_stdout = false;

  MyIterationCallback callback(&m, &c);
  options.callbacks.push_back(&callback);

  // Tell Ceres to update the value of the parameter blocks on each each
  // iteration (successful or not) so that MyIterationCallback will be able to
  // see them when called.
  options.update_state_every_iteration = true;

  ceres::Solver::Summary summary;
  ceres::Solve(options, &problem, &summary);
  std::cout << summary.BriefReport() << "\n";
  std::cout << "Initial m: " << initial_m << " c: " << initial_c << "\n";
  std::cout << "Final   m: " << m << " c: " << c << "\n";
  return 0;
}