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move implementation of algorithms to own hpp in routing_algorithms folder

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add changes to improve readability
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Chau Nguyen 2015-06-16 23:20:38 +02:00 committed by Huyen Chau Nguyen
parent d3ebd360b2
commit f0d66ff0fb
3 changed files with 368 additions and 236 deletions
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277
plugins/round_trip.hpp
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@ -31,6 +31,9 @@ SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
#include "plugin_base.hpp"
#include "../algorithms/object_encoder.hpp"
#include "../routing_algorithms/tsp_nearest_neighbour.hpp"
#include "../routing_algorithms/tsp_farthest_insertion.hpp"
#include "../routing_algorithms/tsp_brute_force.hpp"
#include "../data_structures/query_edge.hpp"
#include "../data_structures/search_engine.hpp"
#include "../descriptors/descriptor_base.hpp"
@ -44,7 +47,6 @@ SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
#include <osrm/json_container.hpp>
#include <cstdlib>
#include <algorithm>
#include <memory>
#include <unordered_map>
@ -59,223 +61,6 @@ template <class DataFacadeT> class RoundTripPlugin final : public BasePlugin
DataFacadeT *facade;
std::unique_ptr<SearchEngine<DataFacadeT>> search_engine_ptr;
void FarthestInsertion(const RouteParameters & route_parameters,
const PhantomNodeArray & phantom_node_vector,
const std::vector<EdgeWeight> & dist_table,
InternalRouteResult & min_route,
std::vector<int> & min_loc_permutation) {
//////////////////////////////////////////////////////////////////////////////////////////////////
// START FARTHEST INSERTION HERE
// 1. start at a random round trip of 2 locations
// 2. find the location that is the farthest away from the visited locations
// 3. add the found location to the current round trip such that round trip is the shortest
// 4. repeat 2-3 until all locations are visited
// 5. DONE!
//////////////////////////////////////////////////////////////////////////////////////////////////
const auto number_of_locations = phantom_node_vector.size();
std::list<int> current_trip;
std::vector<bool> visited(number_of_locations, false);
// find two locations that have max distance
auto max_dist = -1;
int max_from = -1;
int max_to = -1;
auto i = 0;
for (auto it = dist_table.begin(); it != dist_table.end(); ++it) {
if (*it > max_dist) {
max_dist = *it;
max_from = i / number_of_locations;
max_to = i % number_of_locations;
}
++i;
}
visited[max_from] = true;
visited[max_to] = true;
// SimpleLogger().Write() << "Start with " << max_from << " " << max_to;
current_trip.push_back(max_from);
current_trip.push_back(max_to);
for (int j = 2; j < number_of_locations; ++j) {
auto max_min_dist = -1;
int next_node = -1;
auto min_max_insert = current_trip.begin();
// look for loc i that is the farthest away from all other visited locs
for (int i = 0; i < number_of_locations; ++i) {
if (!visited[i]) {
// SimpleLogger().Write() << "- node " << i << " is not visited yet";
auto min_insert = std::numeric_limits<int>::max();
auto min_to = current_trip.begin();
for (auto from_node = current_trip.begin(); from_node != current_trip.end(); ++from_node) {
auto to_node = std::next(from_node);
if (std::next(from_node) == current_trip.end()) {
to_node = current_trip.begin();
}
auto dist_from = *(dist_table.begin() + (*from_node * number_of_locations) + i);
auto dist_to = *(dist_table.begin() + (i * number_of_locations) + *to_node);
auto trip_dist = dist_from + dist_to - *(dist_table.begin() + (*from_node * number_of_locations) + *to_node);
// SimpleLogger().Write() << " From " << *from_node << " to " << i << " to " << *to_node << " is " << trip_dist;
if (trip_dist < min_insert) {
min_insert = trip_dist;
min_to = to_node;
}
}
if (min_insert > max_min_dist) {
max_min_dist = min_insert;
next_node = i;
min_max_insert = min_to;
}
}
}
// SimpleLogger().Write() << "- Insert new node " << next_node;
visited[next_node] = true;
current_trip.insert(min_max_insert, next_node);
}
int perm = 0;
for (auto it = current_trip.begin(); it != current_trip.end(); ++it) {
// SimpleLogger().Write() << "- Visit location " << *it;
auto from_node = *it;
auto to_node = *std::next(it);
if (std::next(it) == current_trip.end()) {
to_node = current_trip.front();
}
PhantomNodes viapoint;
viapoint = PhantomNodes{phantom_node_vector[from_node][0], phantom_node_vector[to_node][0]};
min_route.segment_end_coordinates.emplace_back(viapoint);
min_loc_permutation[from_node] = perm;
++perm;
}
search_engine_ptr->shortest_path(min_route.segment_end_coordinates, route_parameters.uturns, min_route);
}
void NearestNeighbour(const RouteParameters & route_parameters,
const PhantomNodeArray & phantom_node_vector,
const std::vector<EdgeWeight> & dist_table,
InternalRouteResult & min_route,
std::vector<int> & min_loc_permutation) {
//////////////////////////////////////////////////////////////////////////////////////////////////
// START GREEDY NEAREST NEIGHBOUR HERE
// 1. grab a random location and mark as starting point
// 2. find the nearest unvisited neighbour, set it as the current location and mark as visited
// 3. repeat 2 until there is no unvisited location
// 4. return route back to starting point
// 5. compute route
// 6. repeat 1-5 with different starting points and choose iteration with shortest trip
// 7. DONE!
//////////////////////////////////////////////////////////////////////////////////////////////////
const auto number_of_locations = phantom_node_vector.size();
min_route.shortest_path_length = std::numeric_limits<int>::max();
// is_lonely_island[i] indicates whether node i is a node that cannot be reached from other nodes
// 1 means that node i is a lonely island
// 0 means that it is not known for node i
// -1 means that node i is not a lonely island but a reachable, connected node
std::vector<int> is_lonely_island(number_of_locations, 0);
int count_unreachables;
// ALWAYS START AT ANOTHER STARTING POINT
for(int start_node = 0; start_node < number_of_locations; ++start_node)
{
if (is_lonely_island[start_node] >= 0)
{
// if node is a lonely island it is an unsuitable node to start from and shall be skipped
if (is_lonely_island[start_node])
continue;
count_unreachables = 0;
auto start_dist_begin = dist_table.begin() + (start_node * number_of_locations);
auto start_dist_end = dist_table.begin() + ((start_node + 1) * number_of_locations);
for (auto it2 = start_dist_begin; it2 != start_dist_end; ++it2) {
if (*it2 == 0 || *it2 == std::numeric_limits<int>::max()) {
++count_unreachables;
}
}
if (count_unreachables >= number_of_locations) {
is_lonely_island[start_node] = 1;
continue;
}
}
int curr_node = start_node;
is_lonely_island[curr_node] = -1;
InternalRouteResult raw_route;
//TODO: Should we always use the same vector or does it not matter at all because of loop scope?
std::vector<int> loc_permutation(number_of_locations, -1);
loc_permutation[start_node] = 0;
// visited[i] indicates whether node i was already visited by the salesman
std::vector<bool> visited(number_of_locations, false);
visited[start_node] = true;
PhantomNodes viapoint;
// 3. REPEAT FOR EVERY UNVISITED NODE
for(int via_point = 1; via_point < number_of_locations; ++via_point)
{
int min_dist = std::numeric_limits<int>::max();
int min_id = -1;
// 2. FIND NEAREST NEIGHBOUR
auto row_begin_iterator = dist_table.begin() + (curr_node * number_of_locations);
auto row_end_iterator = dist_table.begin() + ((curr_node + 1) * number_of_locations);
for (auto it = row_begin_iterator; it != row_end_iterator; ++it) {
auto index = std::distance(row_begin_iterator, it);
if (is_lonely_island[index] < 1 && !visited[index] && *it < min_dist)
{
min_dist = *it;
min_id = index;
}
}
// in case there was no unvisited and reachable node found, it means that all remaining (unvisited) nodes must be lonely islands
if (min_id == -1)
{
for(int loc = 0; loc < visited.size(); ++loc) {
if (!visited[loc]) {
is_lonely_island[loc] = 1;
}
}
break;
}
// set the nearest unvisited location as the next via_point
else
{
is_lonely_island[min_id] = -1;
loc_permutation[min_id] = via_point;
visited[min_id] = true;
viapoint = PhantomNodes{phantom_node_vector[curr_node][0], phantom_node_vector[min_id][0]};
raw_route.segment_end_coordinates.emplace_back(viapoint);
curr_node = min_id;
}
}
// 4. ROUTE BACK TO STARTING POINT
viapoint = PhantomNodes{raw_route.segment_end_coordinates.back().target_phantom, phantom_node_vector[start_node][0]};
raw_route.segment_end_coordinates.emplace_back(viapoint);
// 5. COMPUTE ROUTE
search_engine_ptr->shortest_path(raw_route.segment_end_coordinates, route_parameters.uturns, raw_route);
// check round trip with this starting point is shorter than the shortest round trip found till now
if (raw_route.shortest_path_length < min_route.shortest_path_length) {
min_route = raw_route;
min_loc_permutation = loc_permutation;
}
}
}
public:
explicit RoundTripPlugin(DataFacadeT *facade)
: descriptor_string("trip"), facade(facade)
@ -333,50 +118,70 @@ template <class DataFacadeT> class RoundTripPlugin final : public BasePlugin
// compute TSP round trip
InternalRouteResult min_route_nn;
InternalRouteResult min_route_fi;
InternalRouteResult min_route_bf;
std::vector<int> min_loc_permutation_nn(phantom_node_vector.size(), -1);
std::vector<int> min_loc_permutation_fi(phantom_node_vector.size(), -1);
std::vector<int> min_loc_permutation_bf(phantom_node_vector.size(), -1);
TIMER_STOP(tsp_pre);
//######################### NEAREST NEIGHBOUR ###############################//
TIMER_START(tsp_nn);
NearestNeighbour(route_parameters, phantom_node_vector, *result_table, min_route_nn, min_loc_permutation_nn);
osrm::tsp::NearestNeighbour(route_parameters, phantom_node_vector, *result_table, min_route_nn, min_loc_permutation_nn);
search_engine_ptr->shortest_path(min_route_nn.segment_end_coordinates, route_parameters.uturns, min_route_nn);
TIMER_STOP(tsp_nn);
SimpleLogger().Write() << "Distance " << min_route_nn.shortest_path_length;
SimpleLogger().Write() << "Time " << TIMER_MSEC(tsp_nn) + TIMER_MSEC(tsp_pre);
// std::unique_ptr<BaseDescriptor<DataFacadeT>> descriptor;
// descriptor = osrm::make_unique<JSONDescriptor<DataFacadeT>>(facade);
// descriptor->SetConfig(route_parameters);
// descriptor->Run(min_route_nn, json_result);
osrm::json::Array json_loc_permutation_nn;
json_loc_permutation_nn.values.insert(json_loc_permutation_nn.values.end(), min_loc_permutation_nn.begin(), min_loc_permutation_nn.end());
json_result.values["nn_loc_permutation"] = json_loc_permutation_nn;
json_result.values["nn_distance"] = min_route_nn.shortest_path_length;
json_result.values["nn_runtime"] = TIMER_MSEC(tsp_nn) + TIMER_MSEC(tsp_pre);
//########################### BRUTE FORCE ####################################//
if (route_parameters.coordinates.size() < 12) {
TIMER_START(tsp_bf);
osrm::tsp::BruteForce(route_parameters, phantom_node_vector, *result_table, min_route_bf, min_loc_permutation_bf);
search_engine_ptr->shortest_path(min_route_bf.segment_end_coordinates, route_parameters.uturns, min_route_bf);
TIMER_STOP(tsp_bf);
SimpleLogger().Write() << "Distance " << min_route_bf.shortest_path_length;
SimpleLogger().Write() << "Time " << TIMER_MSEC(tsp_bf) + TIMER_MSEC(tsp_pre);
osrm::json::Array json_loc_permutation_bf;
json_loc_permutation_bf.values.insert(json_loc_permutation_bf.values.end(), min_loc_permutation_bf.begin(), min_loc_permutation_bf.end());
json_result.values["bf_loc_permutation"] = json_loc_permutation_bf;
json_result.values["bf_distance"] = min_route_bf.shortest_path_length;
json_result.values["bf_runtime"] = TIMER_MSEC(tsp_bf) + TIMER_MSEC(tsp_pre);
} else {
json_result.values["bf_distance"] = -1;
json_result.values["bf_runtime"] = -1;
}
//######################## FARTHEST INSERTION ###############################//
TIMER_START(tsp_fi);
FarthestInsertion(route_parameters, phantom_node_vector, *result_table, min_route_fi, min_loc_permutation_fi);
osrm::tsp::FarthestInsertion(route_parameters, phantom_node_vector, *result_table, min_route_fi, min_loc_permutation_fi);
search_engine_ptr->shortest_path(min_route_fi.segment_end_coordinates, route_parameters.uturns, min_route_fi);
TIMER_STOP(tsp_fi);
SimpleLogger().Write() << "Distance " << min_route_fi.shortest_path_length;
SimpleLogger().Write() << "Time " << TIMER_MSEC(tsp_fi) + TIMER_MSEC(tsp_pre);
// return result to json
std::unique_ptr<BaseDescriptor<DataFacadeT>> descriptor;
descriptor = osrm::make_unique<JSONDescriptor<DataFacadeT>>(facade);
descriptor->SetConfig(route_parameters);
descriptor->Run(min_route_fi, json_result);
osrm::json::Array json_loc_permutation_fi;
json_loc_permutation_fi.values.insert(json_loc_permutation_fi.values.end(), min_loc_permutation_fi.begin(), min_loc_permutation_fi.end());
json_result.values["fi_loc_permutation"] = json_loc_permutation_fi;
json_result.values["fi_distance"] = min_route_fi.shortest_path_length;
json_result.values["fi_runtime"] = TIMER_MSEC(tsp_fi) + TIMER_MSEC(tsp_pre);
// for (int i = 0; i < min_loc_permutation_fi.size(); ++i) {
// SimpleLogger().Write() << min_loc_permutation_nn[i] << " " << min_loc_permutation_fi[i];
// }
// return geometry result to json
std::unique_ptr<BaseDescriptor<DataFacadeT>> descriptor;
descriptor = osrm::make_unique<JSONDescriptor<DataFacadeT>>(facade);
descriptor->SetConfig(route_parameters);
descriptor->Run(min_route_fi, json_result);
return 200;
}

159
routing_algorithms/tsp_farthest_insertion.hpp Normal file
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@ -0,0 +1,159 @@
/*
Copyright (c) 2015, Project OSRM contributors
All rights reserved.
Redistribution and use in source and binary forms, with or without modification,
are permitted provided that the following conditions are met:
Redistributions of source code must retain the above copyright notice, this list
of conditions and the following disclaimer.
Redistributions in binary form must reproduce the above copyright notice, this
list of conditions and the following disclaimer in the documentation and/or
other materials provided with the distribution.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR
ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*/
#ifndef TSP_FARTHEST_INSERTION_HPP
#define TSP_FARTHEST_INSERTION_HPP
#include "../data_structures/search_engine.hpp"
#include "../util/string_util.hpp"
#include <osrm/json_container.hpp>
#include <cstdlib>
#include <algorithm>
#include <string>
#include <vector>
#include <limits>
namespace osrm
{
namespace tsp
{
void FarthestInsertion(const RouteParameters & route_parameters,
const PhantomNodeArray & phantom_node_vector,
const std::vector<EdgeWeight> & dist_table,
InternalRouteResult & min_route,
std::vector<int> & min_loc_permutation) {
//////////////////////////////////////////////////////////////////////////////////////////////////
// START FARTHEST INSERTION HERE
// 1. start at a random round trip of 2 locations
// 2. find the location that is the farthest away from the visited locations and whose insertion will make the round trip the longest
// 3. add the found location to the current round trip such that round trip is the shortest
// 4. repeat 2-3 until all locations are visited
// 5. DONE!
//////////////////////////////////////////////////////////////////////////////////////////////////
const auto number_of_locations = phantom_node_vector.size();
// list of the trip that will be found incrementally
std::list<int> current_trip;
// tracks which nodes have been already visited
std::vector<bool> visited(number_of_locations, false);
// find the pair of location with the biggest distance and make the pair the initial start trip
const auto index = std::distance(dist_table.begin(), std::max_element(dist_table.begin(), dist_table.end()));
const int max_from = index / number_of_locations;
const int max_to = index % number_of_locations;
visited[max_from] = true;
visited[max_to] = true;
current_trip.push_back(max_from);
current_trip.push_back(max_to);
// add all other nodes missing (two nodes are already in the initial start trip)
for (int j = 2; j < number_of_locations; ++j) {
auto shortest_max_tour = -1;
int next_node = -1;
std::list<int>::iterator min_max_insert;
// find unvisited loc i that is the farthest away from all other visited locs
for (int i = 0; i < number_of_locations; ++i) {
if (!visited[i]) {
// longest_min_tour is the distance of the longest of all insertions with the minimal distance
auto longest_min_tour = std::numeric_limits<int>::max();
// following_loc is the location that comes after the location that is to be inserted
std::list<int>::iterator following_loc;
// for all nodes in the current trip find the best insertion resulting in the shortest path
for (auto from_node = current_trip.begin(); from_node != std::prev(current_trip.end()); ++from_node) {
auto to_node = std::next(from_node);
auto dist_from = *(dist_table.begin() + (*from_node * number_of_locations) + i);
auto dist_to = *(dist_table.begin() + (i * number_of_locations) + *to_node);
auto trip_dist = dist_from + dist_to - *(dist_table.begin() + (*from_node * number_of_locations) + *to_node);
// from all possible insertions to the current trip, choose the longest of all minimal insertions
if (trip_dist < longest_min_tour) {
longest_min_tour = trip_dist;
following_loc = to_node;
}
}
{ // check insertion between last and first location too
auto from_node = std::prev(current_trip.end());
auto to_node = current_trip.begin();
auto dist_from = *(dist_table.begin() + (*from_node * number_of_locations) + i);
auto dist_to = *(dist_table.begin() + (i * number_of_locations) + *to_node);
auto trip_dist = dist_from + dist_to - *(dist_table.begin() + (*from_node * number_of_locations) + *to_node);
if (trip_dist < longest_min_tour) {
longest_min_tour = trip_dist;
following_loc = to_node;
}
}
// add the location to the current trip such that it results in the shortest total tour
if (longest_min_tour > shortest_max_tour) {
shortest_max_tour = longest_min_tour;
next_node = i;
min_max_insert = following_loc;
}
}
}
// mark as visited and insert node
visited[next_node] = true;
current_trip.insert(min_max_insert, next_node);
}
// given he final trip, compute total distance and return the route and location permutation
PhantomNodes viapoint;
int perm = 0;
for (auto it = current_trip.begin(); it != std::prev(current_trip.end()); ++it) {
auto from_node = *it;
auto to_node = *std::next(it);
viapoint = PhantomNodes{phantom_node_vector[from_node][0], phantom_node_vector[to_node][0]};
min_route.segment_end_coordinates.emplace_back(viapoint);
min_loc_permutation[from_node] = perm;
++perm;
}
{ // check dist between last and first location too
viapoint = PhantomNodes{phantom_node_vector[*std::prev(current_trip.end())][0], phantom_node_vector[current_trip.front()][0]};
min_route.segment_end_coordinates.emplace_back(viapoint);
min_loc_permutation[*std::prev(current_trip.end())] = perm;
}
}
}
}
#endif // TSP_FARTHEST_INSERTION_HPP

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routing_algorithms/tsp_nearest_neighbour.hpp Normal file
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@ -0,0 +1,168 @@
/*
Copyright (c) 2015, Project OSRM contributors
All rights reserved.
Redistribution and use in source and binary forms, with or without modification,
are permitted provided that the following conditions are met:
Redistributions of source code must retain the above copyright notice, this list
of conditions and the following disclaimer.
Redistributions in binary form must reproduce the above copyright notice, this
list of conditions and the following disclaimer in the documentation and/or
other materials provided with the distribution.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR
ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*/
#ifndef TSP_NEAREST_NEIGHBOUR_HPP
#define TSP_NEAREST_NEIGHBOUR_HPP
#include "../data_structures/search_engine.hpp"
#include "../util/string_util.hpp"
#include "../util/simple_logger.hpp"
#include <osrm/json_container.hpp>
#include <cstdlib>
#include <algorithm>
#include <string>
#include <vector>
#include <limits>
namespace osrm
{
namespace tsp
{
void NearestNeighbour(const RouteParameters & route_parameters,
const PhantomNodeArray & phantom_node_vector,
const std::vector<EdgeWeight> & dist_table,
InternalRouteResult & min_route,
std::vector<int> & min_loc_permutation) {
//////////////////////////////////////////////////////////////////////////////////////////////////
// START GREEDY NEAREST NEIGHBOUR HERE
// 1. grab a random location and mark as starting point
// 2. find the nearest unvisited neighbour, set it as the current location and mark as visited
// 3. repeat 2 until there is no unvisited location
// 4. return route back to starting point
// 5. compute route
// 6. repeat 1-5 with different starting points and choose iteration with shortest trip
// 7. DONE!
//////////////////////////////////////////////////////////////////////////////////////////////////
const auto number_of_locations = phantom_node_vector.size();
min_route.shortest_path_length = std::numeric_limits<int>::max();
// is_lonely_island[i] indicates whether node i is a node that cannot be reached from other nodes
// 1 means that node i is a lonely island
// 0 means that it is not known for node i
// -1 means that node i is not a lonely island but a reachable, connected node
std::vector<int> is_lonely_island(number_of_locations, 0);
int count_unreachables;
// ALWAYS START AT ANOTHER STARTING POINT
for(int start_node = 0; start_node < number_of_locations; ++start_node)
{
if (is_lonely_island[start_node] >= 0)
{
// if node is a lonely island it is an unsuitable node to start from and shall be skipped
if (is_lonely_island[start_node])
continue;
count_unreachables = 0;
auto start_dist_begin = dist_table.begin() + (start_node * number_of_locations);
auto start_dist_end = dist_table.begin() + ((start_node + 1) * number_of_locations);
for (auto it2 = start_dist_begin; it2 != start_dist_end; ++it2) {
if (*it2 == 0 || *it2 == std::numeric_limits<int>::max()) {
++count_unreachables;
}
}
if (count_unreachables >= number_of_locations) {
is_lonely_island[start_node] = 1;
continue;
}
}
int curr_node = start_node;
is_lonely_island[curr_node] = -1;
InternalRouteResult raw_route;
//TODO: Should we always use the same vector or does it not matter at all because of loop scope?
std::vector<int> loc_permutation(number_of_locations, -1);
loc_permutation[start_node] = 0;
// visited[i] indicates whether node i was already visited by the salesman
std::vector<bool> visited(number_of_locations, false);
visited[start_node] = true;
PhantomNodes viapoint;
// 3. REPEAT FOR EVERY UNVISITED NODE
int trip_dist = 0;
for(int via_point = 1; via_point < number_of_locations; ++via_point)
{
int min_dist = std::numeric_limits<int>::max();
int min_id = -1;
// 2. FIND NEAREST NEIGHBOUR
auto row_begin_iterator = dist_table.begin() + (curr_node * number_of_locations);
auto row_end_iterator = dist_table.begin() + ((curr_node + 1) * number_of_locations);
for (auto it = row_begin_iterator; it != row_end_iterator; ++it) {
auto index = std::distance(row_begin_iterator, it);
if (is_lonely_island[index] < 1 && !visited[index] && *it < min_dist)
{
min_dist = *it;
min_id = index;
}
}
// in case there was no unvisited and reachable node found, it means that all remaining (unvisited) nodes must be lonely islands
if (min_id == -1)
{
for(int loc = 0; loc < visited.size(); ++loc) {
if (!visited[loc]) {
is_lonely_island[loc] = 1;
}
}
break;
}
// set the nearest unvisited location as the next via_point
else
{
is_lonely_island[min_id] = -1;
loc_permutation[min_id] = via_point;
visited[min_id] = true;
viapoint = PhantomNodes{phantom_node_vector[curr_node][0], phantom_node_vector[min_id][0]};
raw_route.segment_end_coordinates.emplace_back(viapoint);
trip_dist += min_dist;
curr_node = min_id;
}
}
// 4. ROUTE BACK TO STARTING POINT
viapoint = PhantomNodes{raw_route.segment_end_coordinates.back().target_phantom, phantom_node_vector[start_node][0]};
raw_route.segment_end_coordinates.emplace_back(viapoint);
// check round trip with this starting point is shorter than the shortest round trip found till now
if (trip_dist < min_route.shortest_path_length) {
min_route = raw_route;
min_route.shortest_path_length = trip_dist;
//TODO: this gets copied right? fix this
min_loc_permutation = loc_permutation;
}
}
}
}
}
#endif // TSP_NEAREST_NEIGHBOUR_HPP