symbolic_regression03.cc

Searching the space of mathematical expressions to find the model that best fits a given dataset (Genetic Programming).

/*
 *  \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_part2
 */
 
/* CHANGES IN THIS FILE MUST BE APPLIED TO THE LINKED WIKI PAGE */
 
#include "kernel/ultra.h"
 
const double a = ultra::random::between(-10.0, 10.0);
const double b = ultra::random::between(-10.0, 10.0);
 
class c : public ultra::terminal
{
public:
  c() : ultra::terminal("c") {}
 
  [[nodiscard]] ultra::value_t instance() const noexcept final
  {
    static const double val(ultra::random::between(-10.0, 10.0));
    return val;
  }
};
 
using candidate_solution = ultra::gp::individual;
 
// Given an individual (i.e. a candidate solution of the problem), returns a
// score measuring how good it is.
[[nodiscard]] double my_evaluator(const candidate_solution &x)
{
  using namespace ultra;
 
  const auto ret(run(x));
 
  const double f(has_value(ret) ? std::get<D_DOUBLE>(ret) : 0.0);
 
  const double model_output(b * f);
 
  const double delta(std::fabs(a - model_output));
 
  return -delta;
}
 
int main()
{
  using namespace ultra;
 
  problem prob;
 
  // SETTING UP SYMBOLS
  prob.insert<c>();          // terminal
  //prob.insert<real::literal>(random::between(-10.0, 10.0));
  prob.insert<real::add>();  // functions
  prob.insert<real::sub>();
  prob.insert<real::mul>();
 
  // AD HOC EVALUATOR
  search s(prob, my_evaluator);
 
  // SEARCHING
  const auto result(s.run());
 
  std::cout << "\nCANDIDATE SOLUTION\n"
            << out::c_language << result.best_individual()
            << "\n\nFITNESS\n" << *result.best_measurements().fitness << '\n';
}