mirror of
https://github.com/zenorogue/hyperrogue.git
synced 2024-11-27 14:37:16 +00:00
447 lines
10 KiB
C++
447 lines
10 KiB
C++
// Hyperbolic Rogue
|
|
// Copyright (C) 2011-2017 Zeno and Tehora Rogue, see 'hyper.cpp' for details
|
|
|
|
// Kohonen's self-organizing networks.
|
|
// This is a part of RogueViz, not a part of HyperRogue.
|
|
|
|
namespace kohonen {
|
|
|
|
int cols;
|
|
|
|
typedef vector<double> kohvec;
|
|
|
|
struct sample {
|
|
kohvec val;
|
|
string name;
|
|
};
|
|
|
|
vector<sample> data;
|
|
|
|
int whattodraw[3];
|
|
|
|
struct neuron {
|
|
kohvec net;
|
|
cell *where;
|
|
double udist;
|
|
int lpbak;
|
|
int col;
|
|
int samples, csample;
|
|
};
|
|
|
|
kohvec weights;
|
|
|
|
vector<neuron> net;
|
|
|
|
void alloc(kohvec& k) { k.resize(cols); }
|
|
|
|
bool neurons_indexed = false;
|
|
|
|
int samples;
|
|
|
|
template<class T> T sqr(T x) { return x*x; }
|
|
|
|
vector<neuron*> whowon;
|
|
|
|
void normalize() {
|
|
alloc(weights);
|
|
for(int k=0; k<cols; k++) {
|
|
double sum = 0, sqsum = 0;
|
|
for(sample& s: data)
|
|
sum += s.val[k],
|
|
sqsum += s.val[k] * s.val[k];
|
|
double variance = sqsum/samples - sqr(sum/samples);
|
|
weights[k] = 1 / sqrt(variance);
|
|
}
|
|
}
|
|
|
|
double vnorm(kohvec& a, kohvec& b) {
|
|
double diff = 0;
|
|
for(int k=0; k<cols; k++) diff += sqr((a[k]-b[k]) * weights[k]);
|
|
return diff;
|
|
}
|
|
|
|
void loadsamples(const char *fname) {
|
|
normalize();
|
|
FILE *f = fopen(fname, "rt");
|
|
if(!f) return;
|
|
if(fscanf(f, "%d", &cols) != 1) { fclose(f); return; }
|
|
while(true) {
|
|
sample s;
|
|
alloc(s.val);
|
|
if(feof(f)) break;
|
|
for(int i=0; i<cols; i++)
|
|
if(fscanf(f, "%lf", &s.val[i]) != 1) { break; }
|
|
fgetc(f);
|
|
while(true) {
|
|
int c = fgetc(f);
|
|
if(c == -1 || c == 10 || c == 13) break;
|
|
if(c != 32 && c != 9) s.name += c;
|
|
}
|
|
data.push_back(move(s));
|
|
}
|
|
fclose(f);
|
|
samples = size(data);
|
|
normalize();
|
|
|
|
vdata.resize(samples);
|
|
for(int i=0; i<samples; i++) {
|
|
vdata[i].name = data[i].name;
|
|
vdata[i].cp = dftcolor;
|
|
createViz(i, cwt.c, Id);
|
|
}
|
|
|
|
storeall();
|
|
}
|
|
|
|
int t;
|
|
|
|
int lpct, mul, maxdist, cells, perdist;
|
|
double maxfac;
|
|
|
|
neuron& winner(int id) {
|
|
double bdiff = 1e20;
|
|
neuron *bcell = NULL;
|
|
for(neuron& n: net) {
|
|
double diff = vnorm(n.net, data[id].val);
|
|
if(diff < bdiff) bdiff = diff, bcell = &n;
|
|
}
|
|
return *bcell;
|
|
}
|
|
|
|
void setindex(bool b) {
|
|
if(b == neurons_indexed) return;
|
|
neurons_indexed = b;
|
|
if(b) {
|
|
for(neuron& n: net) n.lpbak = n.where->landparam, n.where->landparam = (&n - &net[0]);
|
|
}
|
|
else {
|
|
for(neuron& n: net) n.where->landparam = n.lpbak;
|
|
}
|
|
}
|
|
|
|
neuron *getNeuron(cell *c) {
|
|
if(!c) return NULL;
|
|
setindex(true);
|
|
if(c->landparam < 0 || c->landparam >= cells) return NULL;
|
|
neuron& ret = net[c->landparam];
|
|
if(ret.where != c) return NULL;
|
|
return &ret;
|
|
}
|
|
|
|
neuron *getNeuronSlow(cell *c) {
|
|
if(neurons_indexed) return getNeuron(c);
|
|
for(neuron& n: net) if(n.where == c) return &n;
|
|
return NULL;
|
|
}
|
|
|
|
double maxudist;
|
|
|
|
neuron *distfrom;
|
|
|
|
void coloring() {
|
|
setindex(false);
|
|
for(int pid=0; pid<3; pid++) {
|
|
int c = whattodraw[pid];
|
|
vector<double> listing;
|
|
for(neuron& n: net) switch(c) {
|
|
case -3:
|
|
if(distfrom)
|
|
listing.push_back(vnorm(n.net, distfrom->net));
|
|
else
|
|
listing.push_back(0);
|
|
break;
|
|
|
|
case -2:
|
|
listing.push_back(n.udist);
|
|
break;
|
|
|
|
case -1:
|
|
listing.push_back(-n.udist);
|
|
break;
|
|
|
|
default:
|
|
listing.push_back(n.net[c]);
|
|
break;
|
|
}
|
|
|
|
double minl = listing[0], maxl = listing[0];
|
|
for(double& d: listing) minl = min(minl, d), maxl = max(maxl, d);
|
|
if(maxl-minl < 1e-3) maxl = minl+1e-3;
|
|
|
|
for(int i=0; i<cells; i++)
|
|
part(net[i].where->landparam, pid) = (255 * (listing[i] - minl)) / (maxl - minl);
|
|
}
|
|
}
|
|
|
|
void analyze() {
|
|
|
|
setindex(true);
|
|
|
|
maxudist = 0;
|
|
for(neuron& n: net) {
|
|
int qty = 0;
|
|
double total = 0;
|
|
forCellEx(c2, n.where) {
|
|
neuron *n2 = getNeuron(c2);
|
|
if(!n2) continue;
|
|
qty++;
|
|
total += sqrt(vnorm(n.net, n2->net));
|
|
}
|
|
n.udist = total / qty;
|
|
maxudist = max(maxudist, n.udist);
|
|
}
|
|
|
|
whowon.resize(samples);
|
|
|
|
for(neuron& n: net) n.samples = 0;
|
|
|
|
for(int id=0; id<samples; id++) {
|
|
auto& w = winner(id);
|
|
whowon[id] = &w;
|
|
w.samples++;
|
|
}
|
|
|
|
for(int id=0; id<samples; id++) {
|
|
auto& w = *whowon[id];
|
|
vdata[id].m->base = w.where;
|
|
vdata[id].m->at =
|
|
spin(2*M_PI*w.csample / w.samples) * xpush(.25 * (w.samples-1) / w.samples);
|
|
w.csample++;
|
|
}
|
|
|
|
shmup::fixStorage();
|
|
setindex(false);
|
|
coloring();
|
|
}
|
|
|
|
struct cellcrawler {
|
|
|
|
struct cellcrawlerdata {
|
|
cellwalker orig;
|
|
int from, spin, dist;
|
|
cellwalker target;
|
|
cellcrawlerdata(const cellwalker& o, int fr, int sp) : orig(o), from(fr), spin(sp) {}
|
|
};
|
|
|
|
vector<cellcrawlerdata> data;
|
|
|
|
void store(const cellwalker& o, int from, int spin) {
|
|
if(eq(o.c->aitmp, sval)) return;
|
|
o.c->aitmp = sval;
|
|
data.emplace_back(o, from, spin);
|
|
}
|
|
|
|
void build(const cellwalker& start) {
|
|
sval++;
|
|
data.clear();
|
|
store(start, 0, 0);
|
|
for(int i=0; i<size(data); i++) {
|
|
cellwalker cw0 = data[i].orig;
|
|
for(int j=0; j<cw0.c->type; j++) {
|
|
cellwalker cw = cw0;
|
|
cwspin(cw, j); cwstep(cw);
|
|
if(!getNeuron(cw.c)) continue;
|
|
store(cw, i, j);
|
|
}
|
|
}
|
|
for(cellcrawlerdata& s: data)
|
|
s.dist = celldistance(s.orig.c, start.c);
|
|
}
|
|
|
|
void sprawl(const cellwalker& start) {
|
|
data[0].target = start;
|
|
|
|
for(int i=1; i<size(data); i++) {
|
|
cellcrawlerdata& s = data[i];
|
|
s.target = data[s.from].target;
|
|
if(!s.target.c) continue;
|
|
cwspin(s.target, s.spin);
|
|
if(cwstepcreates(s.target)) s.target.c = NULL;
|
|
else cwstep(s.target);
|
|
}
|
|
}
|
|
};
|
|
|
|
cellcrawler s0, s1; // hex and non-hex
|
|
|
|
void buildcellcrawler(cell *c) {
|
|
(c->type == 6 ? s0 : s1).build(cellwalker(c,0));
|
|
}
|
|
|
|
bool finished() { return t == 0; }
|
|
|
|
void step() {
|
|
double sigma = maxdist * t / (perdist*(double) mul);
|
|
if(t == 0) return;
|
|
// double sigma = maxdist * exp(-t / t1);
|
|
int pct = (int) (100 * ((t*(double) mul) / perdist));
|
|
if(pct != lpct) {
|
|
lpct = pct;
|
|
analyze();
|
|
printf("t = %6d/%2dx%6d pct = %3d sigma=%10.7lf maxudist=%10.7lf\n", t, mul, perdist, pct, sigma, maxudist);
|
|
}
|
|
int id = hrand(samples);
|
|
neuron& n = winner(id);
|
|
|
|
/*
|
|
for(neuron& n2: net) {
|
|
int d = celldistance(n.where, n2.where);
|
|
double nu = maxfac;
|
|
// nu *= exp(-t*(double)maxdist/perdist);
|
|
// nu *= exp(-t/t2);
|
|
nu *= exp(-sqr(d/sigma));
|
|
for(int k=0; k<cols; k++)
|
|
n2.net[k] += nu * (irisdata[id][k] - n2.net[k]);
|
|
} */
|
|
|
|
cellcrawler& s = n.where->type == 6 ? s0 : s1;
|
|
s.sprawl(cellwalker(n.where, 0));
|
|
for(auto& sd: s.data) {
|
|
neuron *n2 = getNeuron(sd.target.c);
|
|
if(!n2) continue;
|
|
double nu = maxfac;
|
|
nu *= exp(-sqr(sd.dist/sigma));
|
|
for(int k=0; k<cols; k++)
|
|
n2->net[k] += nu * (data[id].val[k] - n2->net[k]);
|
|
}
|
|
|
|
t--;
|
|
if(t == 0) analyze();
|
|
}
|
|
|
|
void run(const char *fname, int _perdist, double _maxfac) {
|
|
perdist = _perdist;
|
|
maxfac = _maxfac;
|
|
init(); kind = kKohonen;
|
|
|
|
loadsamples(fname);
|
|
|
|
/* if(geometry != gQuotient1) {
|
|
targetGeometry = gQuotient1;
|
|
restartGame('g');
|
|
}
|
|
if(!purehepta) restartGame('7'); */
|
|
|
|
#define Z 1
|
|
|
|
vector<cell*>& allcells = currentmap->allcells();
|
|
cells = size(allcells);
|
|
net.resize(cells);
|
|
for(int i=0; i<cells; i++) net[i].where = allcells[i], allcells[i]->landparam = i;
|
|
for(int i=0; i<cells; i++) {
|
|
net[i].where->land = laCanvas;
|
|
alloc(net[i].net);
|
|
|
|
for(int k=0; k<cols; k++)
|
|
for(int z=0; z<Z; z++)
|
|
net[i].net[k] += data[hrand(samples)].val[k] / Z;
|
|
}
|
|
|
|
for(neuron& n: net) for(int d=BARLEV; d>=7; d--) setdist(n.where, d, NULL);
|
|
|
|
cell *c1 = net[cells/2].where;
|
|
vector<int> mapdist;
|
|
for(neuron &n2: net) mapdist.push_back(celldistance(c1,n2.where));
|
|
sort(mapdist.begin(), mapdist.end());
|
|
maxdist = mapdist[size(mapdist)*5/6];
|
|
|
|
printf("samples = %d cells = %d maxdist = %d\n", samples, cells, maxdist);
|
|
|
|
c1 = currentmap->gamestart();
|
|
cell *c2 = createMov(c1, 0);
|
|
buildcellcrawler(c1);
|
|
if(c1->type != c2->type) buildcellcrawler(c2);
|
|
|
|
lpct = -46130;
|
|
mul = 1;
|
|
t = perdist*mul;
|
|
step();
|
|
for(int i=0; i<3; i++) whattodraw[i] = -2;
|
|
analyze();
|
|
}
|
|
|
|
void describe(cell *c) {
|
|
if(cmode & sm::HELP) return;
|
|
neuron *n = getNeuronSlow(c);
|
|
if(!n) return;
|
|
help += "cell number: " + its(n - &net[0]) + "\n";
|
|
help += "parameters:"; for(int k=0; k<cols; k++) help += " " + fts(n->net[k]);
|
|
help += ", u-matrix = " + fts(n->udist);
|
|
help += "\n";
|
|
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";
|
|
}
|
|
}
|
|
|
|
void ksave(const char *fname) {
|
|
FILE *f = fopen(fname, "wt");
|
|
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) {
|
|
int xcells;
|
|
FILE *f = fopen(fname, "rt");
|
|
if(!f) return;
|
|
if(fscanf(f, "%d%d\n", &xcells, &t) != 2) return;
|
|
if(xcells != cells) {
|
|
printf("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 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 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] = -3;
|
|
coloring();
|
|
return true;
|
|
}
|
|
if(uni == '0') {
|
|
for(char x: {'1','2','3'}) handleMenu(x, x);
|
|
return true;
|
|
}
|
|
return false;
|
|
}
|
|
|
|
}
|
|
|
|
void mark(cell *c) {
|
|
using namespace kohonen;
|
|
distfrom = getNeuronSlow(c);
|
|
coloring();
|
|
}
|