Cockpit-cpp/EgoQt/thirdparty/Boost/include/boost/math/tools/simple_continued_fraction.hpp

//  (C) Copyright Nick Thompson 2020.
//  Use, modification and distribution are subject to the
//  Boost Software License, Version 1.0. (See accompanying file
//  LICENSE_1_0.txt or copy at http://www.boost.org/LICENSE_1_0.txt)

#ifndef BOOST_MATH_TOOLS_SIMPLE_CONTINUED_FRACTION_HPP
#define BOOST_MATH_TOOLS_SIMPLE_CONTINUED_FRACTION_HPP

#include <vector>
#include <ostream>
#include <iomanip>
#include <cmath>
#include <limits>
#include <stdexcept>
#include <boost/core/demangle.hpp>

namespace boost::math::tools {

template<typename Real, typename Z = int64_t>
class simple_continued_fraction {
public:
    simple_continued_fraction(Real x) : x_{x} {
        using std::floor;
        using std::abs;
        using std::sqrt;
        using std::isfinite;
        if (!isfinite(x)) {
            throw std::domain_error("Cannot convert non-finites into continued fractions.");  
        }
        b_.reserve(50);
        Real bj = floor(x);
        b_.push_back(static_cast<Z>(bj));
        if (bj == x) {
           b_.shrink_to_fit();
           return;
        }
        x = 1/(x-bj);
        Real f = bj;
        if (bj == 0) {
           f = 16*std::numeric_limits<Real>::min();
        }
        Real C = f;
        Real D = 0;
        int i = 0;
        // the "1 + i++" lets the error bound grow slowly with the number of convergents.
        // I have not worked out the error propagation of the Modified Lentz's method to see if it does indeed grow at this rate.
        // Numerical Recipes claims that no one has worked out the error analysis of the modified Lentz's method.
        while (abs(f - x_) >= (1 + i++)*std::numeric_limits<Real>::epsilon()*abs(x_))
        {
          bj = floor(x);
          b_.push_back(static_cast<Z>(bj));
          x = 1/(x-bj);
          D += bj;
          if (D == 0) {
             D = 16*std::numeric_limits<Real>::min();
          }
          C = bj + 1/C;
          if (C==0) {
             C = 16*std::numeric_limits<Real>::min();
          }
          D = 1/D;
          f *= (C*D);
       }
       // Deal with non-uniqueness of continued fractions: [a0; a1, ..., an, 1] = a0; a1, ..., an + 1].
       // The shorter representation is considered the canonical representation,
       // so if we compute a non-canonical representation, change it to canonical:
       if (b_.size() > 2 && b_.back() == 1) {
          b_[b_.size() - 2] += 1;
          b_.resize(b_.size() - 1);
       }
       b_.shrink_to_fit();
       
       for (size_t i = 1; i < b_.size(); ++i) {
         if (b_[i] <= 0) {
            std::ostringstream oss;
            oss << "Found a negative partial denominator: b[" << i << "] = " << b_[i] << "."
                << " This means the integer type '" << boost::core::demangle(typeid(Z).name())
                << "' has overflowed and you need to use a wider type,"
                << " or there is a bug.";
            throw std::overflow_error(oss.str());
         }
       }
    }
    
    Real khinchin_geometric_mean() const {
        if (b_.size() == 1) { 
         return std::numeric_limits<Real>::quiet_NaN();
        }
         using std::log;
         using std::exp;
         // Precompute the most probable logarithms. See the Gauss-Kuzmin distribution for details.
         // Example: b_i = 1 has probability -log_2(3/4) ≈ .415:
         // A random partial denominator has ~80% chance of being in this table:
         const std::array<Real, 7> logs{std::numeric_limits<Real>::quiet_NaN(), Real(0), log(static_cast<Real>(2)), log(static_cast<Real>(3)), log(static_cast<Real>(4)), log(static_cast<Real>(5)), log(static_cast<Real>(6))};
         Real log_prod = 0;
         for (size_t i = 1; i < b_.size(); ++i) {
            if (b_[i] < static_cast<Z>(logs.size())) {
               log_prod += logs[b_[i]];
            }
            else
            {
               log_prod += log(static_cast<Real>(b_[i]));
            }
         }
         log_prod /= (b_.size()-1);
         return exp(log_prod);
    }
    
    Real khinchin_harmonic_mean() const {
        if (b_.size() == 1) {
          return std::numeric_limits<Real>::quiet_NaN();
        }
        Real n = b_.size() - 1;
        Real denom = 0;
        for (size_t i = 1; i < b_.size(); ++i) {
            denom += 1/static_cast<Real>(b_[i]);
        }
        return n/denom;
    }
    
    const std::vector<Z>& partial_denominators() const {
      return b_;
    }
    
    template<typename T, typename Z2>
    friend std::ostream& operator<<(std::ostream& out, simple_continued_fraction<T, Z2>& scf);

private:
    const Real x_;
    std::vector<Z> b_;
};


template<typename Real, typename Z2>
std::ostream& operator<<(std::ostream& out, simple_continued_fraction<Real, Z2>& scf) {
   constexpr const int p = std::numeric_limits<Real>::max_digits10;
   if constexpr (p == 2147483647) {
      out << std::setprecision(scf.x_.backend().precision());
   } else {
      out << std::setprecision(p);
   }
   
   out << "[" << scf.b_.front();
   if (scf.b_.size() > 1)
   {
      out << "; ";
      for (size_t i = 1; i < scf.b_.size() -1; ++i)
      {
         out << scf.b_[i] << ", ";
      }
      out << scf.b_.back();
   }
   out << "]";
   return out;
}


}
#endif