mirror of
https://github.com/zenorogue/hyperrogue.git
synced 2025-11-03 23:33:01 +00:00
431 lines
12 KiB
C++
431 lines
12 KiB
C++
// log-likelihood computation
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#include <thread>
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#define USE_THREADS
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namespace dhrg {
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int threads = 32;
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ld llcont_approx_prec = 10000;
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// tally edges of the given vertex at the given index
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int edgetally[MAXDIST];
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void tallyedgesof(int i, int delta, mycell *mc) {
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using namespace rogueviz;
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for(auto p: vdata[i].edges) {
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int j = p.second->i ^ p.second->j ^ i;
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if(j==i) printf("LOOP!\n");
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edgetally[quickdist(mc, vertices[j], 0)] += delta;
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}
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}
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// --- count all edge tallies
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void counttallies() {
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using namespace rogueviz;
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{
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progressbar pb(N, "Tallying pairs");
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for(int i=0; i<N; i++) {
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mycell* mc = vertices[i];
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add_to_tally(mc, 1, 0);
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add_to_set(mc, 1, 0); pb++;
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if(i % ((N/10)+1) == 0) {
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memoryInfo();
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}
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}
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}
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{
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progressbar pb(M, "Tallying edges");
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for(int u=0; u<MAXDIST; u++) edgetally[u] = 0;
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for(int i=0; i<N; i++) for(auto p: vdata[i].edges) {
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int j = p.second->i ^ p.second->j ^ i;
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if(j < i) { edgetally[quickdist(vertices[i], vertices[j], 0)]++; pb++; }
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}
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}
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}
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void destroytallies() {
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progressbar pb(N, "Destroying tallies");
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for(int i=0; i<N; i++) add_to_set(vertices[i], -1, 0), pb++;
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for(int i=0; i<MAXDIST; i++)
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tally[i] = edgetally[i] = 0;
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}
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// log likelihood utilities
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//--------------------------
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// MLE of the binomial distribution (a successes, b failures)
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ld bestll(ld a, ld b) {
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if(a == 0 || b == 0) return 0;
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return a * log(a/(a+b)) + b * log(b/(a+b));
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}
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// a successes, ab total
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ld bestll2(ld a, ld ab) { return bestll(a, ab-a); }
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// various methods of loglikelihood computation
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template<class T> void fix_logistic_parameters(logistic& l, const T& f, const char *name, ld eps) {
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indenter_finish im("fix_logistic_parameters");
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ld cur = f(l);
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println(hlog, format("%s = %20.10" PLDF " (R=%10.5" PLDF " T=%" PLDF ")", name, cur, l.R, l.T));
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for(ld step=1; step>eps; step /= 2) {
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while(true) { l.R += step; ld t = f(l); if(t <= cur) break; cur = t; }
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l.R -= step;
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while(true) { l.R -= step; ld t = f(l); if(t <= cur) break; cur = t; }
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l.R += step;
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while(true) { l.T += step; ld t = f(l); if(t <= cur) break; cur = t; }
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l.T -= step;
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while(true) { l.T -= step; ld t = f(l); if(t <= cur) break; cur = t; }
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l.T += step;
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println(hlog, format("%s = %20.10" PLDF " (R=%10.5" PLDF " T=%10.5" PLDF ")", name, cur, l.R, l.T));
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fflush(stdout);
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}
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}
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logistic current_logistic;
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logistic saved_logistic;
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logistic cont_logistic;
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// --- continuous logistic loglikelihood
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// --------------------------------------
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vector<hyperpoint> vertexcoords;
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// compute vertexcoords from the original embedding data
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void origcoords() {
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indenter_finish im("origcoords");
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using namespace rogueviz;
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vertexcoords.resize(N);
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for(int i=0; i<N; i++)
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vertexcoords[i] = spin(coords[i].second * 2 * M_PI / 360) * xpush(coords[i].first) * C0;
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}
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// compute vertexcoords from the RogueViz representation
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void rvcoords() {
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indenter_finish im("rvcoords");
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using namespace rogueviz;
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vertexcoords.resize(N);
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for(int i=0; i<N; i++)
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vertexcoords[i] = calc_relative_matrix(rogueviz::vdata[i].m->base, currentmap->gamestart(), C0) * rogueviz::vdata[i].m->at * C0;
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}
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// compute vertexcoords from vertices
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void cellcoords() {
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indenter_finish im("cellcoords");
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vertexcoords.resize(N);
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for(int i=0; i<N; i++) {
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vertexcoords[i] = celltopoint(vertices[i]->ascell()); // calc_relative_matrix(vertices[i]->ascell(), currentmap->gamestart(), C0) * C0;
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if(isnan(vertexcoords[i][0])) println(hlog, "got NAN for i=", i, " in lev= ", vertices[i]->lev);
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}
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}
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// needs cellcoords/rvcoords/origcoords
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void build_disttable() {
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indenter_finish im("build_disttable");
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int tab[N];
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for(int i=0; i<N; i++) tab[i] = N;
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disttable0.clear();
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disttable1.clear();
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using namespace rogueviz;
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for(int i=0; i<N; i++) {
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for(auto p: vdata[i].edges) {
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int j = p.second->i ^ p.second->j ^ i;
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if(j<i) tab[j] = i;
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}
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for(int j=0; j<i; j++) {
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ld dist = hdist(vertexcoords[i], vertexcoords[j]);
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if(dist < 0) continue;
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(tab[j] == i ? disttable1:disttable0).push_back(dist);
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}
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}
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sort(disttable0.begin(), disttable0.end());
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sort(disttable1.begin(), disttable1.end());
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}
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// needs build_disttable
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ld loglik_cont(logistic& l = current_logistic) {
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ld llh = 1;
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for(auto p: disttable1) llh += l.lyes(p);
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for(auto p: disttable0) {
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ld lp = l.lno(p);
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llh += lp;
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if(lp > -1e-10) break;
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}
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return llh;
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}
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// --- placement loglikelihood
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ld loglik_placement() {
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mycell *root = mroot;
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ld placement_loglik = 0;
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auto seg = getsegment(root,root,0);
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for(int j=0; j<BOXSIZE; j++) {
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int qj = seg->qty[j];
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if(!qj) continue;
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placement_loglik += qj * (log(qj*1./N) - cgi.expansion->get_descendants(j).log_approx());
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}
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return placement_loglik;
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}
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// --- logistic loglikelihood
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ld loglik_logistic(logistic& l = current_logistic) {
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ld result = 0;
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for(int u=0; u<MAXDIST; u++) if(edgetally[u] && tally[u]-edgetally[u]) {
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result += edgetally[u] * l.lyes(u) +
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(tally[u]-edgetally[u]) * l.lno(u);
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}
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return result;
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}
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// --- optimal loglikelihood
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ld loglikopt() {
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ld result = 0;
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for(int u=0; u<MAXDIST; u++) result += bestll2(edgetally[u], tally[u]);
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return result;
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}
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// --- optimal monotonic loglikelihood
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ld loglikopt_mono() {
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vector<pair<ld, ld> > pairs;
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ld result = 0;
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for(int u=0; u<MAXDIST; u++) {
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auto p = make_pair<ld,ld> (edgetally[u], tally[u]);
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if(p.second == 0) continue;
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while(isize(pairs)) {
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auto pb = pairs.back();
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if(p.first / p.second > pb.first / pb.second)
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p.first += pb.first, p.second += pb.second, pairs.pop_back();
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else break;
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}
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pairs.push_back(p);
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}
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for(auto p: pairs)
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result += bestll2(p.first, p.second);
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return result;
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}
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// --- compute loglikelihood according to current method
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char lc_type = 'R';
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ld loglik_chosen() {
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switch(lc_type) {
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case 'O':
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return loglikopt();
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case 'R':
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return loglik_logistic();
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case 'M':
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return loglikopt_mono();
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case 'C':
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return loglikopt() + loglik_placement();
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case 'D':
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return loglikopt_mono() + loglik_placement();
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default:
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return loglikopt();
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}
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}
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// 1e-3 (cont), 1e-6 (normal)
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// statistics
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void writestats() {
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indenter_finish im("writestats");
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memoryInfo();
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println(hlog, "Vertices by distance (N = ", N, "):");
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mycell *root = mroot;
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for(int j=0; j<BOXSIZE; j++) {
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int qj = getsegment(root,root,0)->qty[j];
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if(!qj) continue;
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print(hlog, " ", j, ":", qj);
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}
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println(hlog);
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ld placement_loglik = loglik_placement();
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for(int u=0; u<MAXDIST; u++) if(tally[u]) {
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println(hlog, format("* %4d: %8d / %12Ld = %lf %.10" PLDF " %.10" PLDF,
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u, edgetally[u], tally[u], double(edgetally[u]) / tally[u],
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saved_logistic.yes(u), current_logistic.yes(u)));
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}
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println(hlog, "log likelihood\n");
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ld loglik_chaos = bestll2(M, N*(N-1)/2);
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ld loglik_opt = loglikopt();
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ld loglik_mono = loglikopt_mono();
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ld loglik_rt = loglik_logistic();
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println(hlog, " placement = ", placement_loglik);
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println(hlog, " chaos = ", loglik_chaos);
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println(hlog, " optimal any = ", loglik_opt);
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println(hlog, " optimal mono = ", loglik_mono);
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println(hlog, " estimated R/T = ", loglik_logistic(saved_logistic), " (R=", saved_logistic.R, " T=", saved_logistic.T);
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println(hlog, " optimal R/T = ", loglik_rt, " (R=", current_logistic.R, " T=", current_logistic.T);
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println(hlog, "Compression ratio = %", (placement_loglik+loglik_opt)/loglik_chaos);
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}
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template<class T> auto parallelize(long long N, T action) -> decltype(action(0,0)) {
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#ifndef USE_THREADS
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return action(0,N);
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#else
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if(threads == 1) return action(0,N);
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std::vector<std::thread> v;
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typedef decltype(action(0,0)) Res;
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std::vector<Res> results(threads);
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for(int k=0; k<threads; k++)
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v.emplace_back([&,k] () {
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results[k] = action(N*k/threads, N*(k+1)/threads);
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});
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for(std::thread& t:v) t.join();
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Res res = 0;
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for(Res r: results) res += r;
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return res;
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#endif
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}
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vector<array<ll, 2>> disttable_approx;
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ld precise_hdist(hyperpoint vi, hyperpoint vj) {
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ld da = acosh(vi[2]);
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ld db = acosh(vj[2]);
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ld phia = atan2(vi[0], vi[1]);
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ld phib = atan2(vj[0], vj[1]);
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ld co = sinh(da) * sinh(db) * (1 - cos(phia-phib));
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// - (vi[0]*vj[0] + vi[1]*vj[1]);
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ld v = cosh(da - db) + co;
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if(v < 1) return 0;
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return acosh(v);
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}
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void build_disttable_approx() {
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indenter_finish im("build_disttable_approx");
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array<ll, 2> zero = {0, 0};
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using namespace rogueviz;
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std::vector<vector<array<ll, 2>>> results(threads);
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std::vector<std::thread> v;
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for(int k=0; k<threads; k++)
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v.emplace_back([&,k] () {
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auto& dt = results[k];
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int tab[N];
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for(int i=0; i<N; i++) tab[i] = N;
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auto p = k ? nullptr : new progressbar(N/threads, "build_disttable_approx");
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for(int i=k; i<N; i+=threads) {
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if(p) (*p)++;
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for(auto p: vdata[i].edges) {
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int j = p.second->i ^ p.second->j ^ i;
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if(j<i) tab[j] = i;
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}
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for(int j=0; j<i; j++) {
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ld dist = precise_hdist(vertexcoords[i], vertexcoords[j]);
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if(dist < 0) continue;
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int dista = dist * llcont_approx_prec;
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if(isize(dt) < dista+1)
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dt.resize(dista+1, zero);
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dt[dista][(tab[j] == i) ? 1 : 0]++;
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}
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}
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if(p) delete p;
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});
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for(std::thread& t:v) t.join();
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int mx = 0;
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for(auto& r: results) mx = max(mx, isize(r));
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disttable_approx.clear();
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disttable_approx.resize(mx, zero);
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for(auto& r: results)
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for(int i=0; i<isize(r); i++)
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for(int j=0; j<2; j++)
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disttable_approx[i][j] += r[i][j];
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}
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ld loglik_cont_approx(logistic& l) {
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ld llh = 0;
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int N = isize(disttable_approx);
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for(int i=0; i<N; i++) {
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if(disttable_approx[i][0])
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llh += l.lno((i+.5)/llcont_approx_prec) * disttable_approx[i][0];
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if(disttable_approx[i][1])
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llh += l.lyes((i+.5)/llcont_approx_prec) * disttable_approx[i][1];
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}
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return llh;
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}
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using logisticfun = std::function<ld(logistic&)>;
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void fast_loglik_cont(logistic& l, const logisticfun& f, const char *name, ld start, ld eps) {
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if(name) println(hlog, "fix_logistic_parameters");
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indenter_finish im(name);
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ld cur = f(l);
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if(name) println(hlog, format("%s = %20.10" PLDF " (R=%10.5" PLDF " T=%" PLDF ")", name, cur, l.R, l.T));
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map<pair<double, double>, double> memo;
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auto ff = [&] () {
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if(l.T < -5) exit(1);
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if(memo.count(make_pair(l.R, l.T)))
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return memo[make_pair(l.R, l.T)];
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return memo[make_pair(l.R, l.T)] = f(l);
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};
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int steps = 0;
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for(ld step=start; step>eps; step /= 2) {
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loop:
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bool changed = false;
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while(true) { steps++; l.R += step; ld t = ff(); if(t <= cur || steps > 1000) break; cur = t; changed = true; }
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l.R -= step;
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while(true) { steps++; l.R -= step; ld t = ff(); if(t <= cur || steps > 1000) break; cur = t; changed = true; }
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l.R += step;
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while(true) { steps++; l.T += step; ld t = ff(); if(t <= cur || steps > 1000) break; cur = t; changed = true; }
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l.T -= step;
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while(true) { steps++; l.T -= step; ld t = ff(); if(t <= cur || l.T < 1e-3 || steps > 1000) break; cur = t; changed = true; }
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l.T += step;
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if(changed) goto loop;
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if(name) println(hlog, format("%s = %20.10" PLDF " (R=%10.5" PLDF " T=%10.5" PLDF ")", name, cur, l.R, l.T));
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fflush(stdout);
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}
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}
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}
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