symbolic_regression02.cc

/*
 *  \file
 *  \remark This file is part of ULTRA.
 *
 *  \copyright Copyright (C) 2024 EOS di Manlio Morini.
 *
 *  \license
 *  This Source Code Form is subject to the terms of the Mozilla Public
 *  License, v. 2.0. If a copy of the MPL was not distributed with this file,
 *  You can obtain one at http://mozilla.org/MPL/2.0/
 *
 *  \see https://github.com/morinim/ultra/wiki/symbolic_regression
 */
 
/* CHANGES IN THIS FILE MUST BE APPLIED TO THE LINKED WIKI PAGE */
 
#include "kernel/ultra.h"
 
int main()
{
  using namespace ultra;
 
  // DATA SAMPLE
  // (the target function is `x + sin(x)`)
  std::istringstream training(R"(
    -9.456,-10.0
    -8.989, -8.0
    -5.721, -6.0
    -3.243, -4.0
    -2.909, -2.0
     0.000,  0.0
     2.909,  2.0
     3.243,  4.0
     5.721,  6.0
     8.989,  8.0
  )");
 
  // READING INPUT DATA
  src::problem prob(training);
 
  // SETTING UP SYMBOLS
  prob.insert<real::sin>();
  prob.insert<real::cos>();
  prob.insert<real::add>();
  prob.insert<real::sub>();
  prob.insert<real::div>();
  prob.insert<real::mul>();
 
  // SEARCHING
  prob.params.evolution.generations = 50;
  src::search s(prob);
 
  // This is a callback function invoked at the end of every generation and
  // useful to gather statistical data.
  s.after_generation([](const auto &pop, const auto &)
                     {
                       for (const auto &i : pop)
                         std::cout << out::python_language << i << '\n';
                       std::cout << "--------------------------------------\n";
                     });
 
  const auto result(s.run());
 
  std::cout << "\nCANDIDATE SOLUTION\n"
            << out::c_language << result.best_individual()
            << "\n\nFITNESS\n" << *result.best_measurements().fitness << '\n';
}