mirror of
https://github.com/zenorogue/hyperrogue.git
synced 2024-11-09 15:39:55 +00:00
433 lines
12 KiB
C++
433 lines
12 KiB
C++
// log-likelihood computation
|
|
|
|
#include <thread>
|
|
#define USE_THREADS
|
|
|
|
namespace dhrg {
|
|
|
|
int threads = 32;
|
|
|
|
ld llcont_approx_prec = 10000;
|
|
|
|
// tally edges of the given vertex at the given index
|
|
|
|
int edgetally[MAXDIST];
|
|
|
|
void tallyedgesof(int i, int delta, mycell *mc) {
|
|
using namespace rogueviz;
|
|
for(auto p: vdata[i].edges) {
|
|
int j = p.second->i ^ p.second->j ^ i;
|
|
if(j==i) printf("LOOP!\n");
|
|
edgetally[quickdist(mc, vertices[j], 0)] += delta;
|
|
}
|
|
}
|
|
|
|
// --- count all edge tallies
|
|
|
|
void counttallies() {
|
|
using namespace rogueviz;
|
|
|
|
{
|
|
progressbar pb(N, "Tallying pairs");
|
|
for(int i=0; i<N; i++) {
|
|
mycell* mc = vertices[i];
|
|
add_to_tally(mc, 1, 0);
|
|
add_to_set(mc, 1, 0); pb++;
|
|
if(i % ((N/10)+1) == 0) {
|
|
memoryInfo();
|
|
}
|
|
}
|
|
}
|
|
|
|
{
|
|
progressbar pb(M, "Tallying edges");
|
|
|
|
for(int u=0; u<MAXDIST; u++) edgetally[u] = 0;
|
|
|
|
for(int i=0; i<N; i++) for(auto p: vdata[i].edges) {
|
|
int j = p.second->i ^ p.second->j ^ i;
|
|
if(j < i) { edgetally[quickdist(vertices[i], vertices[j], 0)]++; pb++; }
|
|
}
|
|
}
|
|
|
|
}
|
|
|
|
void destroytallies() {
|
|
progressbar pb(N, "Destroying tallies");
|
|
for(int i=0; i<N; i++) add_to_set(vertices[i], -1, 0), pb++;
|
|
for(int i=0; i<MAXDIST; i++)
|
|
tally[i] = edgetally[i] = 0;
|
|
}
|
|
|
|
// log likelihood utilities
|
|
//--------------------------
|
|
|
|
// MLE of the binomial distribution (a successes, b failures)
|
|
|
|
ld bestll(ld a, ld b) {
|
|
if(a == 0 || b == 0) return 0;
|
|
return a * log(a/(a+b)) + b * log(b/(a+b));
|
|
}
|
|
|
|
// a successes, ab total
|
|
|
|
ld bestll2(ld a, ld ab) { return bestll(a, ab-a); }
|
|
|
|
// various methods of loglikelihood computation
|
|
|
|
template<class T> void fix_logistic_parameters(logistic& l, const T& f, const char *name, ld eps) {
|
|
indenter_finish im("fix_logistic_parameters");
|
|
ld cur = f(l);
|
|
println(hlog, format("%s = %20.10" PLDF " (R=%10.5" PLDF " T=%" PLDF ")", name, cur, l.R, l.T));
|
|
|
|
for(ld step=1; step>eps; step /= 2) {
|
|
|
|
while(true) { l.R += step; ld t = f(l); if(t <= cur) break; cur = t; }
|
|
l.R -= step;
|
|
|
|
while(true) { l.R -= step; ld t = f(l); if(t <= cur) break; cur = t; }
|
|
l.R += step;
|
|
|
|
while(true) { l.T += step; ld t = f(l); if(t <= cur) break; cur = t; }
|
|
l.T -= step;
|
|
|
|
while(true) { l.T -= step; ld t = f(l); if(t <= cur) break; cur = t; }
|
|
l.T += step;
|
|
|
|
println(hlog, format("%s = %20.10" PLDF " (R=%10.5" PLDF " T=%10.5" PLDF ")", name, cur, l.R, l.T));
|
|
fflush(stdout);
|
|
}
|
|
}
|
|
|
|
logistic current_logistic;
|
|
logistic saved_logistic;
|
|
logistic cont_logistic;
|
|
|
|
// --- continuous logistic loglikelihood
|
|
// --------------------------------------
|
|
|
|
vector<hyperpoint> vertexcoords;
|
|
|
|
// compute vertexcoords from the original embedding data
|
|
void origcoords() {
|
|
indenter_finish im("origcoords");
|
|
using namespace rogueviz;
|
|
vertexcoords.resize(N);
|
|
for(int i=0; i<N; i++)
|
|
vertexcoords[i] = spin(coords[i].second * degree) * xpush(coords[i].first) * C0;
|
|
}
|
|
|
|
// compute vertexcoords from the RogueViz representation
|
|
void rvcoords() {
|
|
indenter_finish im("rvcoords");
|
|
using namespace rogueviz;
|
|
vertexcoords.resize(N);
|
|
for(int i=0; i<N; i++)
|
|
vertexcoords[i] = calc_relative_matrix(rogueviz::vdata[i].m->base, currentmap->gamestart(), C0) * rogueviz::vdata[i].m->at * C0;
|
|
}
|
|
|
|
// compute vertexcoords from vertices
|
|
void cellcoords() {
|
|
indenter_finish im("cellcoords");
|
|
vertexcoords.resize(N);
|
|
for(int i=0; i<N; i++) {
|
|
vertexcoords[i] = celltopoint(vertices[i]->ascell()); // calc_relative_matrix(vertices[i]->ascell(), currentmap->gamestart(), C0) * C0;
|
|
if(isnan(vertexcoords[i][0])) println(hlog, "got NAN for i=", i, " in lev= ", vertices[i]->lev);
|
|
}
|
|
}
|
|
|
|
// needs cellcoords/rvcoords/origcoords
|
|
void build_disttable() {
|
|
indenter_finish im("build_disttable");
|
|
int tab[N];
|
|
for(int i=0; i<N; i++) tab[i] = N;
|
|
disttable0.clear();
|
|
disttable1.clear();
|
|
|
|
using namespace rogueviz;
|
|
|
|
for(int i=0; i<N; i++) {
|
|
for(auto p: vdata[i].edges) {
|
|
int j = p.second->i ^ p.second->j ^ i;
|
|
if(j<i) tab[j] = i;
|
|
}
|
|
for(int j=0; j<i; j++) {
|
|
ld dist = hdist(vertexcoords[i], vertexcoords[j]);
|
|
if(dist < 0) continue;
|
|
(tab[j] == i ? disttable1:disttable0).push_back(dist);
|
|
}
|
|
}
|
|
|
|
sort(disttable0.begin(), disttable0.end());
|
|
sort(disttable1.begin(), disttable1.end());
|
|
}
|
|
|
|
// needs build_disttable
|
|
ld loglik_cont(logistic& l = current_logistic) {
|
|
|
|
ld llh = 1;
|
|
for(auto p: disttable1) llh += l.lyes(p);
|
|
for(auto p: disttable0) {
|
|
ld lp = l.lno(p);
|
|
llh += lp;
|
|
if(lp > -1e-10) break;
|
|
}
|
|
|
|
return llh;
|
|
}
|
|
|
|
// --- placement loglikelihood
|
|
|
|
ld loglik_placement() {
|
|
mycell *root = mroot;
|
|
ld placement_loglik = 0;
|
|
auto seg = getsegment(root,root,0);
|
|
for(int j=0; j<BOXSIZE; j++) {
|
|
int qj = seg->qty[j];
|
|
if(!qj) continue;
|
|
placement_loglik += qj * (log(qj*1./N) - cgi.expansion->get_descendants(j).log_approx());
|
|
}
|
|
return placement_loglik;
|
|
}
|
|
|
|
// --- logistic loglikelihood
|
|
|
|
ld loglik_logistic(logistic& l = current_logistic) {
|
|
ld result = 0;
|
|
for(int u=0; u<MAXDIST; u++) if(edgetally[u] && tally[u]-edgetally[u]) {
|
|
result += edgetally[u] * l.lyes(u) +
|
|
(tally[u]-edgetally[u]) * l.lno(u);
|
|
}
|
|
return result;
|
|
}
|
|
|
|
// --- optimal loglikelihood
|
|
|
|
ld loglikopt() {
|
|
ld result = 0;
|
|
for(int u=0; u<MAXDIST; u++) result += bestll2(edgetally[u], tally[u]);
|
|
return result;
|
|
}
|
|
|
|
// --- optimal monotonic loglikelihood
|
|
|
|
ld loglikopt_mono() {
|
|
vector<pair<ld, ld> > pairs;
|
|
ld result = 0;
|
|
for(int u=0; u<MAXDIST; u++) {
|
|
auto p = make_pair<ld,ld> (edgetally[u], tally[u]);
|
|
if(p.second == 0) continue;
|
|
while(isize(pairs)) {
|
|
auto pb = pairs.back();
|
|
if(p.first / p.second > pb.first / pb.second)
|
|
p.first += pb.first, p.second += pb.second, pairs.pop_back();
|
|
else break;
|
|
}
|
|
pairs.push_back(p);
|
|
}
|
|
for(auto p: pairs)
|
|
result += bestll2(p.first, p.second);
|
|
return result;
|
|
}
|
|
|
|
// --- compute loglikelihood according to current method
|
|
|
|
char lc_type = 'R';
|
|
|
|
ld loglik_chosen() {
|
|
switch(lc_type) {
|
|
case 'O':
|
|
return loglikopt();
|
|
case 'R':
|
|
return loglik_logistic();
|
|
case 'M':
|
|
return loglikopt_mono();
|
|
case 'C':
|
|
return loglikopt() + loglik_placement();
|
|
case 'D':
|
|
return loglikopt_mono() + loglik_placement();
|
|
default:
|
|
return loglikopt();
|
|
}
|
|
}
|
|
|
|
// 1e-3 (cont), 1e-6 (normal)
|
|
|
|
// statistics
|
|
|
|
void writestats() {
|
|
indenter_finish im("writestats");
|
|
memoryInfo();
|
|
println(hlog, "Vertices by distance (N = ", N, "):");
|
|
mycell *root = mroot;
|
|
for(int j=0; j<BOXSIZE; j++) {
|
|
int qj = getsegment(root,root,0)->qty[j];
|
|
if(!qj) continue;
|
|
print(hlog, " ", j, ":", qj);
|
|
}
|
|
println(hlog);
|
|
ld placement_loglik = loglik_placement();
|
|
|
|
for(int u=0; u<MAXDIST; u++) if(tally[u]) {
|
|
println(hlog, format("* %4d: %8d / %12Ld = %lf %.10" PLDF " %.10" PLDF,
|
|
u, edgetally[u], tally[u], double(edgetally[u]) / tally[u],
|
|
saved_logistic.yes(u), current_logistic.yes(u)));
|
|
}
|
|
|
|
println(hlog, "log likelihood\n");
|
|
ld loglik_chaos = bestll2(M, N*(N-1)/2);
|
|
ld loglik_opt = loglikopt();
|
|
ld loglik_mono = loglikopt_mono();
|
|
ld loglik_rt = loglik_logistic();
|
|
println(hlog, " placement = ", placement_loglik);
|
|
println(hlog, " chaos = ", loglik_chaos);
|
|
println(hlog, " optimal any = ", loglik_opt);
|
|
println(hlog, " optimal mono = ", loglik_mono);
|
|
println(hlog, " estimated R/T = ", loglik_logistic(saved_logistic), " (R=", saved_logistic.R, " T=", saved_logistic.T);
|
|
println(hlog, " optimal R/T = ", loglik_rt, " (R=", current_logistic.R, " T=", current_logistic.T);
|
|
|
|
println(hlog, "Compression ratio = %", (placement_loglik+loglik_opt)/loglik_chaos);
|
|
}
|
|
|
|
template<class T> auto parallelize(long long N, T action) -> decltype(action(0,0)) {
|
|
#ifndef USE_THREADS
|
|
return action(0,N);
|
|
#else
|
|
if(threads == 1) return action(0,N);
|
|
std::vector<std::thread> v;
|
|
typedef decltype(action(0,0)) Res;
|
|
std::vector<Res> results(threads);
|
|
for(int k=0; k<threads; k++)
|
|
v.emplace_back([&,k] () {
|
|
results[k] = action(N*k/threads, N*(k+1)/threads);
|
|
});
|
|
for(std::thread& t:v) t.join();
|
|
Res res = 0;
|
|
for(Res r: results) res += r;
|
|
return res;
|
|
#endif
|
|
}
|
|
|
|
vector<array<ll, 2>> disttable_approx;
|
|
|
|
ld precise_hdist(hyperpoint vi, hyperpoint vj) {
|
|
ld da = acosh(vi[2]);
|
|
ld db = acosh(vj[2]);
|
|
|
|
ld phia = atan2(vi[0], vi[1]);
|
|
ld phib = atan2(vj[0], vj[1]);
|
|
|
|
ld co = sinh(da) * sinh(db) * (1 - cos(phia-phib));
|
|
// - (vi[0]*vj[0] + vi[1]*vj[1]);
|
|
|
|
ld v = cosh(da - db) + co;
|
|
if(v < 1) return 0;
|
|
|
|
return acosh(v);
|
|
}
|
|
|
|
void build_disttable_approx() {
|
|
indenter_finish im("build_disttable_approx");
|
|
|
|
array<ll, 2> zero = {0, 0};
|
|
|
|
using namespace rogueviz;
|
|
|
|
std::vector<vector<array<ll, 2>>> results(threads);
|
|
std::vector<std::thread> v;
|
|
for(int k=0; k<threads; k++)
|
|
v.emplace_back([&,k] () {
|
|
auto& dt = results[k];
|
|
int tab[N];
|
|
for(int i=0; i<N; i++) tab[i] = N;
|
|
auto p = k ? nullptr : new progressbar(N/threads, "build_disttable_approx");
|
|
for(int i=k; i<N; i+=threads) {
|
|
if(p) (*p)++;
|
|
for(auto p: vdata[i].edges) {
|
|
int j = p.second->i ^ p.second->j ^ i;
|
|
if(j<i) tab[j] = i;
|
|
}
|
|
for(int j=0; j<i; j++) {
|
|
ld dist = precise_hdist(vertexcoords[i], vertexcoords[j]);
|
|
if(dist < 0) continue;
|
|
int dista = dist * llcont_approx_prec;
|
|
if(isize(dt) < dista+1)
|
|
dt.resize(dista+1, zero);
|
|
dt[dista][(tab[j] == i) ? 1 : 0]++;
|
|
}
|
|
}
|
|
if(p) delete p;
|
|
});
|
|
for(std::thread& t:v) t.join();
|
|
|
|
int mx = 0;
|
|
for(auto& r: results) mx = max(mx, isize(r));
|
|
disttable_approx.clear();
|
|
disttable_approx.resize(mx, zero);
|
|
|
|
for(auto& r: results)
|
|
for(int i=0; i<isize(r); i++)
|
|
for(int j=0; j<2; j++)
|
|
disttable_approx[i][j] += r[i][j];
|
|
}
|
|
|
|
ld loglik_cont_approx(logistic& l) {
|
|
|
|
ld llh = 0;
|
|
int N = isize(disttable_approx);
|
|
for(int i=0; i<N; i++) {
|
|
if(disttable_approx[i][0])
|
|
llh += l.lno((i+.5)/llcont_approx_prec) * disttable_approx[i][0];
|
|
if(disttable_approx[i][1])
|
|
llh += l.lyes((i+.5)/llcont_approx_prec) * disttable_approx[i][1];
|
|
}
|
|
return llh;
|
|
}
|
|
|
|
using logisticfun = std::function<ld(logistic&)>;
|
|
|
|
int max_steps = 100000;
|
|
|
|
void fast_loglik_cont(logistic& l, const logisticfun& f, const char *name, ld start, ld eps) {
|
|
|
|
if(name) println(hlog, "fix_logistic_parameters");
|
|
indenter_finish im(name);
|
|
ld cur = f(l);
|
|
if(name) println(hlog, format("%s = %20.10" PLDF " (R=%10.5" PLDF " T=%" PLDF ")", name, cur, l.R, l.T));
|
|
|
|
map<pair<double, double>, double> memo;
|
|
auto ff = [&] () {
|
|
if(l.T < -5) exit(1);
|
|
if(memo.count(make_pair(l.R, l.T)))
|
|
return memo[make_pair(l.R, l.T)];
|
|
return memo[make_pair(l.R, l.T)] = f(l);
|
|
};
|
|
|
|
int steps = 0;
|
|
|
|
for(ld step=start; step>eps; step /= 2) {
|
|
|
|
loop:
|
|
bool changed = false;
|
|
|
|
while(true) { steps++; l.R += step; ld t = ff(); if(t <= cur || steps > max_steps) break; cur = t; changed = true; }
|
|
l.R -= step;
|
|
|
|
while(true) { steps++; l.R -= step; ld t = ff(); if(t <= cur || steps > max_steps) break; cur = t; changed = true; }
|
|
l.R += step;
|
|
|
|
while(true) { steps++; l.T += step; ld t = ff(); if(t <= cur || steps > max_steps) break; cur = t; changed = true; }
|
|
l.T -= step;
|
|
|
|
while(true) { steps++; l.T -= step; ld t = ff(); if(t <= cur || l.T < 1e-3 || steps > max_steps) break; cur = t; changed = true; }
|
|
l.T += step;
|
|
|
|
if(changed) goto loop;
|
|
|
|
if(name) println(hlog, format("%s = %20.10" PLDF " (R=%10.5" PLDF " T=%10.5" PLDF ")", name, cur, l.R, l.T));
|
|
fflush(stdout);
|
|
}
|
|
}
|
|
|
|
}
|