IDA_Star.H

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/*
                          Aleph_w
 
  Data structures & Algorithms
  version 2.0.0b
  https://github.com/lrleon/Aleph-w
 
  This file is part of Aleph-w library
 
  Copyright (c) 2002-2026 Leandro Rabindranath Leon
 
  Permission is hereby granted, free of charge, to any person obtaining a copy
  of this software and associated documentation files (the "Software"), to deal
  in the Software without restriction, including without limitation the rights
  to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
  copies of the Software, and to permit persons to whom the Software is
  furnished to do so, subject to the following conditions:
 
  The above copyright notice and this permission notice shall be included in all
  copies or substantial portions of the Software.
 
  THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
  IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
  FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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  LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
  OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
  SOFTWARE.
*/
 
 
# ifndef IDA_STAR_H
# define IDA_STAR_H
 
# include <limits>
# include <cmath>
# include <type_traits>
# include <tpl_graph_utils.H>
# include <ah-errors.H>
# include <AStar.H>
 
namespace Aleph
{
 
template <class GT, class Distance = Dft_Dist<GT>>
struct Chebyshev_Heuristic
{
  using Distance_Type = typename Distance::Distance_Type;
 
  Distance_Type operator()(typename GT::Node * from,
                           typename GT::Node * to) const
  {
    auto & f = from->get_info();
    auto & t = to->get_info();
    auto dx = std::abs(f.x - t.x);
    auto dy = std::abs(f.y - t.y);
    return static_cast<Distance_Type>(dx > dy ? dx : dy);
  }
};
 
template <class GT,
          class Distance = Dft_Dist<GT>,
          class Heuristic = Zero_Heuristic<GT, Distance>,
          template <typename, class> class Itor = Node_Arc_Iterator,
          class SA = Dft_Show_Arc<GT>>
class IDA_Star
{
public:
  using Distance_Type = typename Distance::Distance_Type;
  using Node = typename GT::Node;
  using Arc = typename GT::Arc;
 
  static_assert(std::is_arithmetic_v<Distance_Type>,
                "IDA* requires arithmetic distance type");
 
private:
  SA sa;
  Distance distance;
  Heuristic heuristic;
 
  static constexpr Distance_Type Inf =
    std::numeric_limits<Distance_Type>::max();
 
  struct SearchResult
  {
    bool found;
    Distance_Type value;
  };
 
  SearchResult search(const GT & g, Node * curr, Node * end,
                       Distance_Type g_cost, Distance_Type threshold,
                       Path<GT> & path)
  {
    const Distance_Type h = heuristic(curr, end);
    ah_domain_error_if(h < Distance_Type(0))
      << "IDA*: heuristic must be non-negative, got " << h;
    ah_overflow_error_if(g_cost >
                         std::numeric_limits<Distance_Type>::max() - h)
      << "IDA*: overflow computing g_cost + heuristic";
    const Distance_Type f = g_cost + h;
 
    if (f > threshold)
      return {false, f};
 
    if (curr == end)
      return {true, g_cost};
 
    struct Find_Path_Guard
    {
      Node * node = nullptr;
 
      explicit Find_Path_Guard(Node * p) noexcept : node(p)
      {
        NODE_BITS(node).set_bit(Find_Path, true);
      }
 
      ~Find_Path_Guard() noexcept
      {
        NODE_BITS(node).set_bit(Find_Path, false);
      }
    };
 
    Find_Path_Guard guard(curr);
 
    Distance_Type min_threshold = Inf;
 
    for (Itor<GT, SA> it(curr, sa); it.has_curr(); it.next())
      {
        auto arc = it.get_current_arc();
        auto tgt = g.get_connected_node(arc, curr);
 
        if (IS_NODE_VISITED(tgt, Find_Path))
          continue;
 
        auto w = distance(arc);
        ah_domain_error_if(w < Distance_Type(0))
          << "IDA*: negative weight " << w << " not allowed";
        ah_overflow_error_if(g_cost > std::numeric_limits<Distance_Type>::max() - w)
          << "IDA*: overflow computing g_cost + w";
 
        path.append(arc);
 
        auto res = search(g, tgt, end, g_cost + w, threshold, path);
 
        if (res.found)
          return res;
 
        if (res.value < min_threshold)
          min_threshold = res.value;
 
        path.remove_last_node();
      }
 
    return {false, min_threshold};
  }
 
public:
  IDA_Star(Distance dist = Distance(),
           Heuristic h = Heuristic(),
           SA _sa = SA())
    : sa(_sa), distance(dist), heuristic(h)
  {
    // empty
  }
 
  Distance_Type find_path(const GT & g, Node * start, Node * end,
                          Path<GT> & path)
  {
    ah_domain_error_if(start == nullptr) << "start node cannot be null";
    ah_domain_error_if(end == nullptr) << "end node cannot be null";
    ah_domain_error_if(g.get_num_nodes() == 0) << "graph is empty";
 
    path.empty();
 
    if (start == end)
      {
        path.set_graph(g, start);
        return Distance_Type(0);
      }
 
    Distance_Type threshold = heuristic(start, end);
 
    // Initialize path with start node
    path.set_graph(g, start);
 
    while (true)
      {
        g.reset_bit_nodes(Find_Path);
 
        auto res = search(g, start, end, Distance_Type(0), threshold, path);
 
        if (res.found)
          {
            g.reset_bit_nodes(Find_Path);
            return res.value;
          }
 
        if (res.value == Inf)
          {
            g.reset_bit_nodes(Find_Path);
            path.empty();
            return Inf;
          }
 
        threshold = res.value;
 
        // Reset path for next iteration
        path.empty();
        path.set_graph(g, start);
      }
  }
 
  Distance_Type operator()(const GT & g, Node * start, Node * end,
                           Path<GT> & path)
  {
    return find_path(g, start, end, path);
  }
};
 
} // end namespace Aleph
 
# endif // IDA_STAR_H