mirror of
https://github.com/zenorogue/hyperrogue.git
synced 2024-11-16 02:04:48 +00:00
844 lines
20 KiB
C++
844 lines
20 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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vector<int> samples_shown;
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int whattodraw[3] = {-2,-2,-2};
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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, bestsample;
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};
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kohvec weights;
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vector<neuron> net;
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int neuronId(neuron& n) { return &n - &(net[0]); }
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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 sominit(int);
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void uninit(int);
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void loadsamples(const char *fname) {
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FILE *f = fopen(fname, "rt");
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if(!f) {
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fprintf(stderr, "Could not load samples\n");
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return;
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}
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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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bool shown = false;
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alloc(s.val);
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if(feof(f)) break;
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for(int i=0; i<cols; i++)
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if(fscanf(f, "%lf", &s.val[i]) != 1) { goto bigbreak; }
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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 == '!' && s.name == "") shown = true;
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else if(c != 32 && c != 9) s.name += c;
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}
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if(shown) samples_shown.push_back(size(data));
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data.push_back(move(s));
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}
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bigbreak:
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fclose(f);
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samples = size(data);
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normalize();
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uninit(0); sominit(1);
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}
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int tmax = 30000;
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double distmul = 1;
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double learning_factor = .1;
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int qpct = 100;
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int t, lpct, cells;
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double maxdist;
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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 = neuronId(n);
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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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bool noshow = false;
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void coloring() {
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if(noshow) return;
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setindex(false);
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bool besttofind = true;
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for(int pid=0; pid<3; pid++) {
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int c = whattodraw[pid];
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if(c == -5) {
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if(besttofind) {
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besttofind = false;
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for(neuron& n: net) {
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double bdiff = 1e20;
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for(int i=0; i<size(samples_shown); i++) {
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double diff = vnorm(n.net, data[samples_shown[i]].val);
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if(diff < bdiff) bdiff = diff, n.bestsample = i;
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}
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}
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}
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for(int i=0; i<cells; i++)
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part(net[i].where->landparam, pid) = part(vdata[net[i].bestsample].cp.color1, pid+1);
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}
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else {
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vector<double> listing;
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for(neuron& n: net) switch(c) {
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case -4:
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listing.push_back(n.samples);
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break;
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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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}
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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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if(!noshow) {
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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<size(samples_shown); id++) {
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int s = samples_shown[id];
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auto& w = winner(s);
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whowon[s] = &w;
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w.samples++;
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}
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for(int id=0; id<size(samples_shown); id++) {
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int s = samples_shown[id];
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auto& w = *whowon[s];
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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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}
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coloring();
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}
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// traditionally Gaussian blur is used in the Kohonen algoritm
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// but it does not seem to make much sense in hyperbolic geometry
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// especially wrapped one.
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// GAUSSIAN==1: use the Gaussian blur, on celldistance
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// GAUSSIAN==2: use the Gaussian blur, on true distance
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// GAUSSIAN==0: simulate the dispersion on our network
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int gaussian = 0;
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double mydistance(cell *c1, cell *c2) {
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if(gaussian == 2) return hdist(tC0(shmup::ggmatrix(c1)), tC0(shmup::ggmatrix(c2)));
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else return celldistance(c1, c2);
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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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if(gaussian) for(cellcrawlerdata& s: data)
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s.dist = mydistance(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 scc[2]; // hex and non-hex
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double dispersion_end_at = 1.5;
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double dispersion_precision = .0001;
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int dispersion_each = 1;
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vector<vector<ld>> dispersion[2];
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int dispersion_count;
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void buildcellcrawler(cell *c) {
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int sccid = c->type != 6;
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cellcrawler& cr = scc[sccid];
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cr.build(cellwalker(c,0));
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if(!gaussian) {
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vector<ld> curtemp;
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vector<ld> newtemp;
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vector<int> qty;
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vector<pair<ld*, ld*> > pairs;
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int N = size(net);
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curtemp.resize(N, 0);
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newtemp.resize(N, 0);
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qty.resize(N, 0);
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for(int i=0; i<N; i++)
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forCellEx(c2, net[i].where) {
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neuron *nj = getNeuron(c2);
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if(nj) {
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pairs.emplace_back(&curtemp[i], &newtemp[neuronId(*nj)]);
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qty[i]++;
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}
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}
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curtemp[neuronId(*getNeuron(c))] = 1;
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ld vmin = 0, vmax = 1;
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int iter;
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auto &d = dispersion[sccid];
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d.clear();
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printf("Building dispersion...\n");
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for(iter=0; dispersion_count ? true : vmax > vmin * dispersion_end_at; iter++) {
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if(iter % dispersion_each == 0) {
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d.emplace_back(N);
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auto& dispvec = d.back();
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for(int i=0; i<N; i++) dispvec[i] = curtemp[neuronId(*getNeuron(cr.data[i].orig.c))] / vmax;
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if(size(d) == dispersion_count) break;
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}
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double df = dispersion_precision * (iter+1);
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double df0 = df / ceil(df);
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for(int i=0; i<df; i++) {
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for(auto& p: pairs)
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*p.second += *p.first;
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for(int i=0; i<N; i++) {
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curtemp[i] += (newtemp[i] / qty[i] - curtemp[i]) * df0;
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newtemp[i] = 0;
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}
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}
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vmin = vmax = curtemp[0];
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for(int i=0; i<N; i++)
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if(curtemp[i] < vmin) vmin = curtemp[i];
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else if(curtemp[i] > vmax) vmax = curtemp[i];
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}
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dispersion_count = size(d);
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printf("Dispersion count = %d\n", dispersion_count);
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}
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}
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bool finished() { return t == 0; }
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int krad;
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double ttpower = 1;
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void sominit(int);
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void step() {
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if(t == 0) return;
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sominit(2);
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double tt = (t-1.) / tmax;
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tt = pow(tt, ttpower);
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double sigma = maxdist * tt;
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int dispid = int(dispersion_count * tt);
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if(qpct) {
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int pct = (int) ((qpct * (t+.0)) / tmax);
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if(pct != lpct) {
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printf("pct %d lpct %d\n", pct, lpct);
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lpct = pct;
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analyze();
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if(gaussian)
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printf("t = %6d/%6d %3d%% sigma=%10.7lf maxudist=%10.7lf\n", t, tmax, pct, sigma, maxudist);
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else
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printf("t = %6d/%6d %3d%% dispid=%5d maxudist=%10.7lf\n", t, tmax, pct, dispid, maxudist);
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}
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}
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int id = hrand(samples);
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neuron& n = winner(id);
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whowon.resize(samples);
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whowon[id] = &n;
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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 = learning_factor;
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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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int sccid = n.where->type != 6;
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cellcrawler& s = scc[sccid];
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s.sprawl(cellwalker(n.where, 0));
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vector<double> fake(1,1);
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auto it = gaussian ? fake.begin() : dispersion[sccid][dispid].begin();
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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 = learning_factor;
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if(gaussian)
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nu *= exp(-sqr(sd.dist/sigma));
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else
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nu *= *(it++);
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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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int initdiv = 1;
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int inited = 0;
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void uninit(int initto) {
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if(inited > initto) inited = initto;
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}
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void sominit(int initto) {
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if(inited < 1 && initto >= 1) {
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inited = 1;
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if(!samples) {
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fprintf(stderr, "Error: SOM without samples\n");
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exit(1);
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}
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init(); kind = kKohonen;
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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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printf("Initializing SOM (1)\n");
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vector<cell*> allcells;
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if(krad) {
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celllister cl(cwt.c, krad, 1000000, NULL);
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allcells = cl.lst;
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}
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else 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<initdiv; z++)
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net[i].net[k] += data[hrand(samples)].val[k] / initdiv;
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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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printf("samples = %d (%d) cells = %d\n", samples, size(samples_shown), cells);
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if(!noshow) {
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vdata.resize(size(samples_shown));
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for(int i=0; i<size(samples_shown); i++) {
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vdata[i].name = data[samples_shown[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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analyze();
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}
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if(inited < 2 && initto >= 2) {
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inited = 2;
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printf("Initializing SOM (2)\n");
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if(gaussian) {
|
|
printf("dist = %lf\n", mydistance(net[0].where, net[1].where));
|
|
cell *c1 = net[cells/2].where;
|
|
vector<double> mapdist;
|
|
for(neuron &n2: net) mapdist.push_back(mydistance(c1,n2.where));
|
|
sort(mapdist.begin(), mapdist.end());
|
|
maxdist = mapdist[size(mapdist)*5/6] * distmul;
|
|
printf("maxdist = %lf\n", maxdist);
|
|
}
|
|
|
|
dispersion_count = 0;
|
|
cell *c1 = currentmap->gamestart();
|
|
cell *c2 = createMov(c1, 0);
|
|
buildcellcrawler(c1);
|
|
if(c1->type != c2->type) buildcellcrawler(c2);
|
|
|
|
lpct = -46130;
|
|
}
|
|
}
|
|
|
|
void describe(cell *c) {
|
|
if(cmode & sm::HELP) return;
|
|
neuron *n = getNeuronSlow(c);
|
|
if(!n) return;
|
|
help += "cell number: " + its(neuronId(*n)) + "\n";
|
|
help += "parameters:"; for(int k=0; k<cols; k++) help += " " + fts(n->net[k]);
|
|
help += ", u-matrix = " + fts(n->udist);
|
|
help += "\n";
|
|
int qty = 0;
|
|
for(int s=0; s<samples; s++) if(whowon[s] == n) {
|
|
help += "sample "+its(s)+":";
|
|
for(int k=0; k<cols; k++) help += " " + fts(data[s].val[k]);
|
|
help += " "; help += data[s].name; help += "\n";
|
|
qty++; if(qty >= 20) break;
|
|
}
|
|
}
|
|
|
|
void ksave(const char *fname) {
|
|
sominit(1);
|
|
FILE *f = fopen(fname, "wt");
|
|
if(!f) {
|
|
fprintf(stderr, "Could not save the network\n");
|
|
return;
|
|
}
|
|
fprintf(f, "%d %d\n", cells, t);
|
|
for(neuron& n: net) {
|
|
for(int k=0; k<cols; k++)
|
|
fprintf(f, "%.4lf ", n.net[k]);
|
|
fprintf(f, "\n");
|
|
}
|
|
fclose(f);
|
|
}
|
|
|
|
void kload(const char *fname) {
|
|
sominit(1);
|
|
int xcells;
|
|
FILE *f = fopen(fname, "rt");
|
|
if(!f) {
|
|
fprintf(stderr, "Could not load the network\n");
|
|
return;
|
|
}
|
|
if(fscanf(f, "%d%d\n", &xcells, &t) != 2) return;
|
|
if(xcells != cells) {
|
|
fprintf(stderr, "Error: bad number of cells\n");
|
|
exit(1);
|
|
}
|
|
for(neuron& n: net) {
|
|
for(int k=0; k<cols; k++) if(fscanf(f, "%lf", &n.net[k]) != 1) return;
|
|
}
|
|
fclose(f);
|
|
analyze();
|
|
}
|
|
|
|
void kclassify(const char *fname) {
|
|
sominit(1);
|
|
for(neuron& n: net) n.samples = 0;
|
|
|
|
FILE *f = fopen(fname, "wt");
|
|
if(!f) {
|
|
fprintf(stderr, "Could not save classification\n");
|
|
return;
|
|
}
|
|
for(int id=0; id<samples; id++) {
|
|
auto& w = winner(id);
|
|
w.samples++;
|
|
if(id % 100000 == 0) printf("%d/%d\n", id, size(data));
|
|
fprintf(f, "%s;%d\n", data[id].name.c_str(), neuronId(w));
|
|
}
|
|
fclose(f);
|
|
coloring();
|
|
}
|
|
|
|
void kclassify2(const char *fname_classify, const char *fname_samples) {
|
|
|
|
sominit(1);
|
|
vector<double> bdiffs(samples, 1e20);
|
|
vector<int> bids(samples, 0);
|
|
|
|
printf("Classifying...\n");
|
|
|
|
for(neuron& n: net) n.samples = 0;
|
|
|
|
for(int s=0; s<samples; s++) {
|
|
for(int n=0; n<cells; n++) {
|
|
double diff = vnorm(net[n].net, data[s].val);
|
|
if(diff < bdiffs[s]) bdiffs[s] = diff, bids[s] = n, whowon[s] = &net[n];
|
|
}
|
|
if(s % 1000000 == 999999) printf("%d/%d\n", s, samples);
|
|
}
|
|
|
|
vector<double> bdiffn(cells, 1e20);
|
|
|
|
printf("Finding samples...\n");
|
|
|
|
for(int s=0; s<samples; s++) {
|
|
int n = bids[s];
|
|
double diff = bdiffs[s];
|
|
if(diff < bdiffn[n]) bdiffn[n] = diff, net[n].bestsample = s;
|
|
}
|
|
|
|
for(int s=0; s<samples; s++) net[bids[s]].samples++;
|
|
|
|
if(fname_classify != NULL) {
|
|
printf("Listing classification...\n");
|
|
FILE *f = fopen(fname_classify, "wt");
|
|
if(!f) {
|
|
printf("Failed to open file\n");
|
|
}
|
|
else {
|
|
for(int s=0; s<samples; s++)
|
|
fprintf(f, "%s;%d\n", data[s].name.c_str(), bids[s]);
|
|
fclose(f);
|
|
}
|
|
}
|
|
|
|
if(fname_samples != NULL) {
|
|
printf("Listing best samples...\n");
|
|
FILE *f = fopen(fname_samples, "wt");
|
|
if(!f) {
|
|
printf("Failed to open file\n");
|
|
}
|
|
else {
|
|
fprintf(f, "%d\n", cols);
|
|
for(int n=0; n<cells; n++) {
|
|
fflush(f);
|
|
if(!net[n].samples) { fprintf(f, "\n"); continue; }
|
|
int s = net[n].bestsample;
|
|
for(int k=0; k<cols; k++)
|
|
fprintf(f, "%.4lf ", data[s].val[k]);
|
|
fflush(f);
|
|
fprintf(f, "!%s\n", data[s].name.c_str());
|
|
fflush(f);
|
|
}
|
|
fclose(f);
|
|
}
|
|
}
|
|
|
|
coloring();
|
|
}
|
|
|
|
void steps() {
|
|
if(!kohonen::finished()) {
|
|
unsigned int t = SDL_GetTicks();
|
|
while(SDL_GetTicks() < t+20) kohonen::step();
|
|
setindex(false);
|
|
}
|
|
}
|
|
|
|
void showMenu() {
|
|
string parts[3] = {"red", "green", "blue"};
|
|
for(int i=0; i<3; i++) {
|
|
string c;
|
|
if(whattodraw[i] == -1) c = "u-matrix";
|
|
else if(whattodraw[i] == -2) c = "u-matrix reversed";
|
|
else if(whattodraw[i] == -3) c = "distance from marked ('m')";
|
|
else if(whattodraw[i] == -4) c = "number of samples";
|
|
else if(whattodraw[i] == -5) c = "best sample's color";
|
|
else if(whattodraw[i] == -6) c = "sample names to colors";
|
|
else c = "column " + its(whattodraw[i]);
|
|
dialog::addSelItem(XLAT("coloring (%1)", parts[i]), c, '1'+i);
|
|
}
|
|
}
|
|
|
|
bool handleMenu(int sym, int uni) {
|
|
if(uni >= '1' && uni <= '3') {
|
|
int i = uni - '1';
|
|
whattodraw[i]++;
|
|
if(whattodraw[i] == cols) whattodraw[i] = -5;
|
|
coloring();
|
|
return true;
|
|
}
|
|
if(uni == '0') {
|
|
for(char x: {'1','2','3'}) handleMenu(x, x);
|
|
return true;
|
|
}
|
|
return false;
|
|
}
|
|
|
|
int readArgs() {
|
|
using namespace arg;
|
|
|
|
// #1: load the samples
|
|
|
|
if(argis("-som")) {
|
|
PHASE(3);
|
|
shift(); kohonen::loadsamples(args());
|
|
}
|
|
|
|
// #2: set parameters
|
|
|
|
else if(argis("-somkrad")) {
|
|
gaussian = 0; uninit(0);
|
|
}
|
|
else if(argis("-somsim")) {
|
|
gaussian = 0; uninit(1);
|
|
}
|
|
else if(argis("-somcgauss")) {
|
|
gaussian = 1; uninit(1);
|
|
}
|
|
else if(argis("-somggauss")) {
|
|
gaussian = 2; uninit(1);
|
|
}
|
|
else if(argis("-sompct")) {
|
|
shift(); qpct = argi();
|
|
}
|
|
else if(argis("-sompower")) {
|
|
shift(); ttpower = argf();
|
|
}
|
|
else if(argis("-somparam")) {
|
|
shift(); (gaussian ? distmul : dispersion_end_at) = argf();
|
|
if(dispersion_end_at <= 1) {
|
|
fprintf(stderr, "Dispersion parameter illegal\n");
|
|
dispersion_end_at = 1.5;
|
|
}
|
|
uninit(1);
|
|
}
|
|
else if(argis("-sominitdiv")) {
|
|
shift(); initdiv = argi(); uninit(0);
|
|
}
|
|
else if(argis("-somtmax")) {
|
|
shift(); t = (t*1./tmax) * argi();
|
|
tmax = argi();
|
|
}
|
|
else if(argis("-somlearn")) {
|
|
// this one can be changed at any moment
|
|
shift(); learning_factor = argf();
|
|
}
|
|
|
|
else if(argis("-somrun")) {
|
|
t = tmax; sominit(1);
|
|
}
|
|
|
|
// #3: load the neuron data (usually without #2)
|
|
else if(argis("-somload")) {
|
|
PHASE(3);
|
|
shift(); kohonen::kload(args());
|
|
}
|
|
|
|
// #4: run, stop etc.
|
|
else if(argis("-somrunto")) {
|
|
int i = argi();
|
|
shift(); while(t > i) kohonen::step();
|
|
}
|
|
else if(argis("-somstop")) {
|
|
t = 0;
|
|
}
|
|
else if(argis("-somnoshow")) {
|
|
noshow = true;
|
|
}
|
|
else if(argis("-somfinish")) {
|
|
while(!finished()) kohonen::step();
|
|
}
|
|
|
|
// #5 save data, classify etc.
|
|
else if(argis("-somsave")) {
|
|
PHASE(3);
|
|
shift(); kohonen::ksave(args());
|
|
}
|
|
else if(argis("-somclassify")) {
|
|
PHASE(3);
|
|
shift(); kohonen::kclassify(args());
|
|
}
|
|
else if(argis("-somclassify2")) {
|
|
PHASE(3);
|
|
shift(); const char *f1 = args();
|
|
shift(); const char *f2 = args();
|
|
kohonen::kclassify2(f1, f2);
|
|
}
|
|
|
|
else return 1;
|
|
return 0;
|
|
}
|
|
|
|
auto hooks = addHook(hooks_args, 100, readArgs);
|
|
}
|
|
|
|
void mark(cell *c) {
|
|
using namespace kohonen;
|
|
distfrom = getNeuronSlow(c);
|
|
coloring();
|
|
}
|
|
|