mirror of
https://github.com/zenorogue/hyperrogue.git
synced 2024-12-25 01:20:37 +00:00
445 lines
10 KiB
C++
445 lines
10 KiB
C++
// Hyperbolic Rogue
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// Copyright (C) 2011-2017 Zeno and Tehora Rogue, see 'hyper.cpp' for details
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// Kohonen's self-organizing networks.
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// This is a part of RogueViz, not a part of HyperRogue.
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namespace kohonen {
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int cols;
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typedef vector<double> kohvec;
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struct sample {
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kohvec val;
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string name;
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};
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vector<sample> data;
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int whattodraw[3];
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struct neuron {
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kohvec net;
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cell *where;
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double udist;
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int lpbak;
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int col;
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int samples, csample;
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};
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kohvec weights;
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vector<neuron> net;
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void alloc(kohvec& k) { k.resize(cols); }
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bool neurons_indexed = false;
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int samples;
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template<class T> T sqr(T x) { return x*x; }
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vector<neuron*> whowon;
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void normalize() {
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alloc(weights);
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for(int k=0; k<cols; k++) {
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double sum = 0, sqsum = 0;
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for(sample& s: data)
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sum += s.val[k],
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sqsum += s.val[k] * s.val[k];
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double variance = sqsum/samples - sqr(sum/samples);
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weights[k] = 1 / sqrt(variance);
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}
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}
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double vnorm(kohvec& a, kohvec& b) {
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double diff = 0;
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for(int k=0; k<cols; k++) diff += sqr((a[k]-b[k]) * weights[k]);
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return diff;
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}
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void loadsamples(const char *fname) {
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normalize();
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FILE *f = fopen(fname, "rt");
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if(!f) return;
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if(fscanf(f, "%d", &cols) != 1) { fclose(f); return; }
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while(true) {
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sample s;
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alloc(s.val);
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for(int i=0; i<cols; i++)
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if(fscanf(f, "%lf", &s.val[i]) != 1) { fclose(f); return; }
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if(feof(f)) break;
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fgetc(f);
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while(true) {
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int c = fgetc(f);
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if(c == -1 || c == 10 || c == 13) break;
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if(c != 32 && c != 9) s.name += c;
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}
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data.push_back(move(s));
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}
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fclose(f);
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samples = size(data);
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normalize();
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vdata.resize(samples);
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for(int i=0; i<samples; i++) {
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vdata[i].name = data[i].name;
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vdata[i].cp = dftcolor;
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createViz(i, cwt.c, Id);
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}
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storeall();
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}
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int t;
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int lpct, mul, maxdist, cells, perdist;
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double maxfac;
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neuron& winner(int id) {
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double bdiff = 1e20;
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neuron *bcell = NULL;
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for(neuron& n: net) {
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double diff = vnorm(n.net, data[id].val);
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if(diff < bdiff) bdiff = diff, bcell = &n;
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}
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return *bcell;
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}
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void setindex(bool b) {
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if(b == neurons_indexed) return;
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neurons_indexed = b;
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if(b) {
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for(neuron& n: net) n.lpbak = n.where->landparam, n.where->landparam = (&n - &net[0]);
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}
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else {
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for(neuron& n: net) n.where->landparam = n.lpbak;
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}
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}
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neuron *getNeuron(cell *c) {
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if(!c) return NULL;
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setindex(true);
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if(c->landparam < 0 || c->landparam >= cells) return NULL;
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neuron& ret = net[c->landparam];
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if(ret.where != c) return NULL;
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return &ret;
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}
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neuron *getNeuronSlow(cell *c) {
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if(neurons_indexed) return getNeuron(c);
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for(neuron& n: net) if(n.where == c) return &n;
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return NULL;
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}
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double maxudist;
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neuron *distfrom;
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void coloring() {
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setindex(false);
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for(int pid=0; pid<3; pid++) {
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int c = whattodraw[pid];
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vector<double> listing;
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for(neuron& n: net) switch(c) {
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case -3:
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if(distfrom)
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listing.push_back(vnorm(n.net, distfrom->net));
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else
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listing.push_back(0);
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break;
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case -2:
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listing.push_back(n.udist);
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break;
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case -1:
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listing.push_back(-n.udist);
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break;
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default:
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listing.push_back(n.net[c]);
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break;
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}
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double minl = listing[0], maxl = listing[0];
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for(double& d: listing) minl = min(minl, d), maxl = max(maxl, d);
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if(maxl-minl < 1e-3) maxl = minl+1e-3;
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for(int i=0; i<cells; i++)
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part(net[i].where->landparam, pid) = (255 * (listing[i] - minl)) / (maxl - minl);
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}
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}
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void analyze() {
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setindex(true);
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maxudist = 0;
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for(neuron& n: net) {
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int qty = 0;
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double total = 0;
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forCellEx(c2, n.where) {
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neuron *n2 = getNeuron(c2);
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if(!n2) continue;
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qty++;
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total += sqrt(vnorm(n.net, n2->net));
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}
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n.udist = total / qty;
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maxudist = max(maxudist, n.udist);
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}
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whowon.resize(samples);
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for(neuron& n: net) n.samples = 0;
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for(int id=0; id<samples; id++) {
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auto& w = winner(id);
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whowon[id] = &w;
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w.samples++;
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}
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for(int id=0; id<samples; id++) {
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auto& w = *whowon[id];
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vdata[id].m->base = w.where;
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vdata[id].m->at =
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spin(2*M_PI*w.csample / w.samples) * xpush(.25 * (w.samples-1) / w.samples);
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w.csample++;
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}
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shmup::fixStorage();
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setindex(false);
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coloring();
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}
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struct cellcrawler {
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struct cellcrawlerdata {
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cellwalker orig;
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int from, spin, dist;
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cellwalker target;
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cellcrawlerdata(const cellwalker& o, int fr, int sp) : orig(o), from(fr), spin(sp) {}
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};
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vector<cellcrawlerdata> data;
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void store(const cellwalker& o, int from, int spin) {
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if(eq(o.c->aitmp, sval)) return;
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o.c->aitmp = sval;
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data.emplace_back(o, from, spin);
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}
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void build(const cellwalker& start) {
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sval++;
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data.clear();
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store(start, 0, 0);
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for(int i=0; i<size(data); i++) {
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cellwalker cw0 = data[i].orig;
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for(int j=0; j<cw0.c->type; j++) {
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cellwalker cw = cw0;
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cwspin(cw, j); cwstep(cw);
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if(!getNeuron(cw.c)) continue;
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store(cw, i, j);
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}
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}
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for(cellcrawlerdata& s: data)
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s.dist = celldistance(s.orig.c, start.c);
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}
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void sprawl(const cellwalker& start) {
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data[0].target = start;
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for(int i=1; i<size(data); i++) {
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cellcrawlerdata& s = data[i];
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s.target = data[s.from].target;
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if(!s.target.c) continue;
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cwspin(s.target, s.spin);
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if(cwstepcreates(s.target)) s.target.c = NULL;
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else cwstep(s.target);
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}
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}
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};
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cellcrawler s0, s1; // hex and non-hex
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void buildcellcrawler(cell *c) {
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(c->type == 6 ? s0 : s1).build(cellwalker(c,0));
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}
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bool finished() { return t == 0; }
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void step() {
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double sigma = maxdist * t / (perdist*(double) mul);
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if(t == 0) return;
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// double sigma = maxdist * exp(-t / t1);
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int pct = (int) (100 * ((t*(double) mul) / perdist));
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if(pct != lpct) {
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lpct = pct;
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analyze();
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printf("t = %6d/%2dx%6d pct = %3d sigma=%10.7lf maxudist=%10.7lf\n", t, mul, perdist, pct, sigma, maxudist);
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}
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int id = hrand(samples);
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neuron& n = winner(id);
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/*
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for(neuron& n2: net) {
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int d = celldistance(n.where, n2.where);
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double nu = maxfac;
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// nu *= exp(-t*(double)maxdist/perdist);
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// nu *= exp(-t/t2);
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nu *= exp(-sqr(d/sigma));
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for(int k=0; k<cols; k++)
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n2.net[k] += nu * (irisdata[id][k] - n2.net[k]);
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} */
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cellcrawler& s = n.where->type == 6 ? s0 : s1;
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s.sprawl(cellwalker(n.where, 0));
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for(auto& sd: s.data) {
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neuron *n2 = getNeuron(sd.target.c);
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if(!n2) continue;
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double nu = maxfac;
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nu *= exp(-sqr(sd.dist/sigma));
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for(int k=0; k<cols; k++)
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n2->net[k] += nu * (data[id].val[k] - n2->net[k]);
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}
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t--;
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if(t == 0) analyze();
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}
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void run(const char *fname, int _perdist, double _maxfac) {
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perdist = _perdist;
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maxfac = _maxfac;
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init(); kind = kKohonen;
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loadsamples(fname);
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/* if(geometry != gQuotient1) {
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targetGeometry = gQuotient1;
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restartGame('g');
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}
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if(!purehepta) restartGame('7'); */
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#define Z 1
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vector<cell*>& allcells = currentmap->allcells();
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cells = size(allcells);
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net.resize(cells);
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for(int i=0; i<cells; i++) net[i].where = allcells[i], allcells[i]->landparam = i;
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for(int i=0; i<cells; i++) {
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net[i].where->land = laCanvas;
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alloc(net[i].net);
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for(int k=0; k<cols; k++)
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for(int z=0; z<Z; z++)
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net[i].net[k] += data[hrand(samples)].val[k] / Z;
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}
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for(neuron& n: net) for(int d=BARLEV; d>=7; d--) setdist(n.where, d, NULL);
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cell *c1 = net[cells/2].where;
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vector<int> mapdist;
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for(neuron &n2: net) mapdist.push_back(celldistance(c1,n2.where));
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sort(mapdist.begin(), mapdist.end());
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maxdist = mapdist[size(mapdist)*5/6];
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printf("samples = %d cells = %d maxdist = %d\n", samples, cells, maxdist);
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c1 = currentmap->gamestart();
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cell *c2 = createMov(c1, 0);
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buildcellcrawler(c1);
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if(c1->type != c2->type) buildcellcrawler(c2);
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lpct = -46130;
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mul = 1;
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t = perdist*mul;
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step();
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for(int i=0; i<3; i++) whattodraw[i] = -2;
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analyze();
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}
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void describe(cell *c) {
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if(cmode == emHelp) return;
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neuron *n = getNeuronSlow(c);
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if(!n) return;
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help += "cell number: " + its(n - &net[0]) + "\n";
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help += "parameters:"; for(int k=0; k<cols; k++) help += " " + fts(n->net[k]);
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help += ", u-matrix = " + fts(n->udist);
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help += "\n";
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for(int s=0; s<samples; s++) if(whowon[s] == n) {
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help += "sample "+its(s)+":";
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for(int k=0; k<cols; k++) help += " " + fts(data[s].val[k]);
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help += " "; help += data[s].name; help += "\n";
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}
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}
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void ksave(const char *fname) {
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FILE *f = fopen(fname, "wt");
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fprintf(f, "%d %d\n", cells, t);
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for(neuron& n: net) {
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for(int k=0; k<cols; k++) fprintf(f, "%.4lf ", n.net[k]); fprintf(f, "\n");
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}
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fclose(f);
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}
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void kload(const char *fname) {
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int xcells;
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FILE *f = fopen(fname, "rt");
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if(!f) return;
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if(fscanf(f, "%d%d\n", &xcells, &t) != 2) return;
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if(xcells != cells) {
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printf("Error: bad number of cells\n");
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exit(1);
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}
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for(neuron& n: net) {
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for(int k=0; k<cols; k++) if(fscanf(f, "%lf", &n.net[k]) != 1) return;
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}
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fclose(f);
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analyze();
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}
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void steps() {
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if(!kohonen::finished()) {
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unsigned int t = SDL_GetTicks();
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while(SDL_GetTicks() < t+20) kohonen::step();
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setindex(false);
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}
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}
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void showMenu() {
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string parts[3] = {"red", "green", "blue"};
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for(int i=0; i<3; i++) {
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string c;
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if(whattodraw[i] == -1) c = "u-matrix";
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else if(whattodraw[i] == -2) c = "u-matrix reversed";
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else if(whattodraw[i] == -3) c = "distance from marked ('m')";
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else c = "column " + its(whattodraw[i]);
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dialog::addSelItem(XLAT("coloring (%1)", parts[i]), c, '1'+i);
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}
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}
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bool handleMenu(int sym, int uni) {
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if(uni >= '1' && uni <= '3') {
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int i = uni - '1';
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whattodraw[i]++;
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if(whattodraw[i] == cols) whattodraw[i] = -3;
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coloring();
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return true;
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}
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if(uni == '0') {
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for(char x: {'1','2','3'}) handleMenu(x, x);
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return true;
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}
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return false;
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}
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}
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void mark(cell *c) {
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using namespace kohonen;
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distfrom = getNeuronSlow(c);
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coloring();
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}
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