mirror of
https://github.com/zenorogue/hyperrogue.git
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259 lines
7.1 KiB
C++
259 lines
7.1 KiB
C++
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// Voronoi used to measure the quality of the embedding (Villman's measure)
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// Copyright (C) 2011-2022 Tehora and Zeno Rogue, see 'hyper.cpp' for details
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#include "kohonen.h"
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namespace rogueviz {
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namespace voronoi {
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void manifold::generate_data() {
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T = isize(triangles);
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triangles_of_tile.resize(N);
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for(int i=0; i<T; i++) {
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for(auto v: triangles[i])
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triangles_of_tile[v].push_back(i);
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for(int j=0; j<3; j++) {
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int e0 = triangles[i][j%3];
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int e1 = triangles[i][(j+1)%3];
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if(e1<e0) swap(e0, e1);
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auto p = make_pair(e0, e1);
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triangles_of_edge[p].push_back(i);
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}
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}
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}
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manifold build_manifold(const vector<cell*>& cells) {
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map<cell*, int> neuron_id;
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int N = isize(cells);
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for(int i=0; i<N; i++)
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neuron_id[cells[i]] = i;
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set<vector<int> > faces_seen;
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for(auto c: cells) {
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for(int i=0; i<c->type; i++) {
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cellwalker cw1(c, i);
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cellwalker cw2 = cw1;
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vector<int> tlist;
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do {
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cw2 += wstep;
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cw2++;
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auto p = at_or_null(neuron_id, cw2.at);
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if(!p) goto next;
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tlist.push_back(*p);
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}
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while(cw2 != cw1);
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if(1) {
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int minv = 0;
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for(int i=0; i<isize(tlist); i++)
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if(tlist[i] < tlist[minv])
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minv = i;
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vector<int> tlist_sorted;
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for(int i=minv; i<isize(tlist); i++)
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tlist_sorted.push_back(tlist[i]);
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for(int i=0; i<minv; i++)
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tlist_sorted.push_back(tlist[i]);
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if(tlist_sorted[1] > tlist_sorted.back())
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reverse(tlist_sorted.begin()+1, tlist_sorted.end());
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faces_seen.insert(tlist_sorted);
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}
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next: ;
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}
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}
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manifold m;
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m.N = N;
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for(const auto& v: faces_seen)
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for(int i=2; i<isize(v); i++)
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m.triangles.emplace_back(make_array(v[0], v[i-1], v[i]));
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m.generate_data();
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return m;
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}
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vector<pair<int, int> > compute_voronoi_edges(manifold& m) {
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using kohonen::net;
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using kohonen::vnorm;
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using kohonen::vshift;
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using kohonen::data;
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using kohonen::kohvec;
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using kohonen::samples;
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vector<int> best_tile;
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/* for every neuron, compute its best tile */
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int N = isize(net);
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for(int i=0; i<N; i++) {
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ld bestval = HUGE_VAL, best_id = -1;
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for(int j=0; j<samples; j++) {
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ld val = vnorm(net[i].net, data[j].val);
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if(val < bestval) bestval = val, best_id = j;
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}
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best_tile.push_back(best_id);
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}
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constexpr int SUBD = 8;
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using neuron_triangle_pair = pair<int, int>;
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set< neuron_triangle_pair > visited;
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queue<neuron_triangle_pair> q;
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vector<kohvec> projected(N);
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auto visit = [&] (neuron_triangle_pair p) {
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if(visited.count(p)) return;
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visited.insert(p);
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q.push(p);
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};
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kohvec at;
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kohonen::alloc(at);
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auto project = [&] (kohvec& at, const array<int, 3>& tri, int i, int j) {
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int k = SUBD-i-j;
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for(auto& x: at) x = 0;
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vshift(at, data[tri[0]].val, i * 1. / SUBD);
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vshift(at, data[tri[1]].val, j * 1. / SUBD);
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vshift(at, data[tri[2]].val, k * 1. / SUBD);
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};
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set<kohvec> already_picked;
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map<int, string> which_best;
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/* project all the net[ni].net on the manifold */
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for(int ni=0; ni<N; ni++) {
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kohvec best;
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int best_tri;
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ld best_dist = HUGE_VAL;
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reaction_t better = [] {};
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set<int> triangles_to_visit;
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queue<int> triangles_queue;
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auto visit1 = [&] (int tri) {
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if(triangles_to_visit.count(tri)) return;
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triangles_to_visit.insert(tri);
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triangles_queue.push(tri);
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};
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for(int tr: m.triangles_of_tile[best_tile[ni]])
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visit1(tr);
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auto& bes = which_best[ni];
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while(!triangles_queue.empty()) {
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int tri = triangles_queue.front();
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triangles_queue.pop();
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for(int i=0; i<=SUBD; i++)
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for(int j=0; j<=SUBD-i; j++) {
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project(at, m.triangles[tri], i, j);
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ld dist = vnorm(at, net[ni].net);
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if(dist < best_dist && !already_picked.count(at)) {
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best_dist = dist, best = at, best_tri = tri;
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bes = lalign(0, tie(tri, i, j));
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better = [&tri, i, j, &m, &visit1] () {
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auto flip_edge = [&] (int t1, int t2) {
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if(t2 < t1) swap(t1, t2);
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for(auto tri1: m.triangles_of_edge[{t1, t2}])
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visit1(tri1);
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};
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auto& tria = m.triangles[tri];
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if(i == 0) flip_edge(tria[1], tria[2]);
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if(j == 0) flip_edge(tria[0], tria[2]);
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if(i+j == SUBD) flip_edge(tria[0], tria[1]);
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};
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}
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}
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better();
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}
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projected[ni] = best;
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already_picked.insert(best);
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visit({ni, best_tri});
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}
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struct triangle_data {
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double dist[SUBD+1][SUBD+1];
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int which[SUBD+1][SUBD+1];
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triangle_data() {
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for(int i=0; i<=SUBD; i++)
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for(int j=0; j<=SUBD; j++)
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dist[i][j] = HUGE_VAL, which[i][j] = -1;
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}
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};
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vector<triangle_data> tdatas(m.T);
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while(!q.empty()) {
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auto ntp = q.front();
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q.pop();
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auto ni = ntp.first;
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auto& tri = m.triangles[ntp.second];
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auto& td = tdatas[ntp.second];
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for(int i=0; i<=SUBD; i++)
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for(int j=0; j<=SUBD-i; j++) {
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project(at, tri, i, j);
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ld dist = vnorm(at, projected[ni]);
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auto& odist = td.dist[i][j];
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bool tie = abs(dist - odist) < 1e-6;
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if(tie ? ni < td.which[i][j] : dist < odist) {
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td.dist[i][j] = dist,
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td.which[i][j] = ni;
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auto flip_edge = [&] (int t1, int t2) {
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if(t2 < t1) swap(t1, t2);
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for(auto tr: m.triangles_of_edge[{t1, t2}]) {
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visit({ni, tr});
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}
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};
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if(i == 0) flip_edge(tri[1], tri[2]);
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if(j == 0) flip_edge(tri[0], tri[2]);
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if(i+j == SUBD) flip_edge(tri[0], tri[1]);
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}
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}
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}
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set<pair<int, int> > voronoi_edges;
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auto add_edge = [&] (int i, int j) {
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if(i>j) swap(i, j);
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if(i==j) return;
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voronoi_edges.insert({i, j});
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};
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for(auto& td: tdatas) {
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for(int i=0; i<=SUBD; i++)
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for(int j=0; j<=SUBD-i; j++) {
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if(i>0) add_edge(td.which[i][j], td.which[i-1][j]);
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if(j>0) add_edge(td.which[i][j], td.which[i][j-1]);
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if(j>0) add_edge(td.which[i][j], td.which[i+1][j-1]);
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}
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}
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if(1) {
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vector<int> degs(N, 0);
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for(auto e: voronoi_edges) degs[e.first]++, degs[e.second]++;
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for(int v=0; v<N; v++) if(degs[v] == 0) {
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fhstream vorerr("voronoi-error.txt", "at");
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println(vorerr, "error: degree 0 vertex ", v, " in ", debug_str);
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println(vorerr, "best is: ", which_best[v]);
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int id = 0;
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for(auto& td: tdatas) {
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for(int i=0; i<=SUBD; i++)
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for(int j=0; j<=SUBD-i; j++)
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if(td.which[i][j] == v) println(vorerr, "Found at ", tie(id, i, j));
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id++;
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}
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}
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}
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vector<pair<int, int> > result;
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for(auto ve: voronoi_edges) result.push_back(ve);
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return result;
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}
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}
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}
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