1
0
mirror of https://github.com/zenorogue/hyperrogue.git synced 2024-12-23 16:50:27 +00:00
hyperrogue/kohonen.cpp

1117 lines
27 KiB
C++
Raw Normal View History

2017-09-01 20:14:02 +00:00
// 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;
vector<int> samples_shown;
int whattodraw[3] = {-2,-2,-2};
struct neuron {
kohvec net;
cell *where;
double udist;
int lpbak;
int col;
int samples, csample, bestsample;
};
vector<string> colnames;
kohvec weights;
vector<neuron> net;
int neuronId(neuron& n) { return &n - &(net[0]); }
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 sominit(int);
void uninit(int);
void loadsamples(const char *fname) {
FILE *f = fopen(fname, "rt");
if(!f) {
fprintf(stderr, "Could not load samples\n");
return;
}
if(fscanf(f, "%d", &cols) != 1) { fclose(f); return; }
while(true) {
sample s;
bool shown = false;
alloc(s.val);
if(feof(f)) break;
for(int i=0; i<cols; i++)
if(fscanf(f, "%lf", &s.val[i]) != 1) { goto bigbreak; }
fgetc(f);
while(true) {
int c = fgetc(f);
if(c == -1 || c == 10 || c == 13) break;
if(c == '!' && s.name == "") shown = true;
else if(c != 32 && c != 9) s.name += c;
}
if(shown) samples_shown.push_back(size(data));
data.push_back(move(s));
}
bigbreak:
fclose(f);
samples = size(data);
normalize();
colnames.resize(cols);
for(int i=0; i<cols; i++) colnames[i] = "Column " + its(i);
uninit(0); sominit(1);
}
int tmax = 30000;
double distmul = 1;
double learning_factor = .1;
int qpct = 100;
int t, lpct, cells;
double maxdist;
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 = neuronId(n);
}
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;
bool noshow = false;
void coloring() {
if(noshow) return;
setindex(false);
bool besttofind = true;
for(int pid=0; pid<3; pid++) {
int c = whattodraw[pid];
if(c == -5) {
if(besttofind) {
besttofind = false;
for(neuron& n: net) {
double bdiff = 1e20;
for(int i=0; i<size(samples_shown); i++) {
double diff = vnorm(n.net, data[samples_shown[i]].val);
if(diff < bdiff) bdiff = diff, n.bestsample = i;
}
}
}
for(int i=0; i<cells; i++)
part(net[i].where->landparam, pid) = part(vdata[net[i].bestsample].cp.color1, pid+1);
}
else {
vector<double> listing;
for(neuron& n: net) switch(c) {
case -4:
listing.push_back(log(5+n.samples));
break;
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);
}
if(!noshow) {
whowon.resize(samples);
for(neuron& n: net) n.samples = 0;
for(int id=0; id<size(samples_shown); id++) {
int s = samples_shown[id];
auto& w = winner(s);
whowon[s] = &w;
w.samples++;
}
for(int id=0; id<size(samples_shown); id++) {
int s = samples_shown[id];
auto& w = *whowon[s];
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();
}
// traditionally Gaussian blur is used in the Kohonen algoritm
// but it does not seem to make much sense in hyperbolic geometry
// especially wrapped one.
// GAUSSIAN==1: use the Gaussian blur, on celldistance
// GAUSSIAN==2: use the Gaussian blur, on true distance
// GAUSSIAN==0: simulate the dispersion on our network
int gaussian = 0;
double mydistance(cell *c1, cell *c2) {
if(gaussian == 2) return hdist(tC0(shmup::ggmatrix(c1)), tC0(shmup::ggmatrix(c2)));
else return celldistance(c1, c2);
}
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);
}
}
if(gaussian) for(cellcrawlerdata& s: data)
s.dist = mydistance(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 scc[2]; // hex and non-hex
double dispersion_end_at = 1.5;
double dispersion_precision = .0001;
int dispersion_each = 1;
vector<vector<ld>> dispersion[2];
int dispersion_count;
void buildcellcrawler(cell *c) {
int sccid = c->type != 6;
cellcrawler& cr = scc[sccid];
cr.build(cellwalker(c,0));
if(!gaussian) {
vector<ld> curtemp;
vector<ld> newtemp;
vector<int> qty;
vector<pair<ld*, ld*> > pairs;
int N = size(net);
curtemp.resize(N, 0);
newtemp.resize(N, 0);
qty.resize(N, 0);
for(int i=0; i<N; i++)
forCellEx(c2, net[i].where) {
neuron *nj = getNeuron(c2);
if(nj) {
pairs.emplace_back(&curtemp[i], &newtemp[neuronId(*nj)]);
qty[i]++;
}
}
curtemp[neuronId(*getNeuron(c))] = 1;
ld vmin = 0, vmax = 1;
int iter;
auto &d = dispersion[sccid];
d.clear();
printf("Building dispersion...\n");
for(iter=0; dispersion_count ? true : vmax > vmin * dispersion_end_at; iter++) {
if(iter % dispersion_each == 0) {
d.emplace_back(N);
auto& dispvec = d.back();
for(int i=0; i<N; i++) dispvec[i] = curtemp[neuronId(*getNeuron(cr.data[i].orig.c))] / vmax;
if(size(d) == dispersion_count) break;
}
double df = dispersion_precision * (iter+1);
double df0 = df / ceil(df);
for(int i=0; i<df; i++) {
for(auto& p: pairs)
*p.second += *p.first;
for(int i=0; i<N; i++) {
curtemp[i] += (newtemp[i] / qty[i] - curtemp[i]) * df0;
newtemp[i] = 0;
}
}
vmin = vmax = curtemp[0];
for(int i=0; i<N; i++)
if(curtemp[i] < vmin) vmin = curtemp[i];
else if(curtemp[i] > vmax) vmax = curtemp[i];
}
dispersion_count = size(d);
printf("Dispersion count = %d\n", dispersion_count);
}
}
bool finished() { return t == 0; }
int krad;
double ttpower = 1;
void sominit(int);
void step() {
if(t == 0) return;
sominit(2);
double tt = (t-1.) / tmax;
tt = pow(tt, ttpower);
double sigma = maxdist * tt;
int dispid = int(dispersion_count * tt);
if(qpct) {
int pct = (int) ((qpct * (t+.0)) / tmax);
if(pct != lpct) {
printf("pct %d lpct %d\n", pct, lpct);
lpct = pct;
analyze();
if(gaussian)
printf("t = %6d/%6d %3d%% sigma=%10.7lf maxudist=%10.7lf\n", t, tmax, pct, sigma, maxudist);
else
printf("t = %6d/%6d %3d%% dispid=%5d maxudist=%10.7lf\n", t, tmax, pct, dispid, maxudist);
}
}
int id = hrand(samples);
neuron& n = winner(id);
whowon.resize(samples);
whowon[id] = &n;
/*
for(neuron& n2: net) {
int d = celldistance(n.where, n2.where);
double nu = learning_factor;
// 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]);
} */
int sccid = n.where->type != 6;
cellcrawler& s = scc[sccid];
s.sprawl(cellwalker(n.where, 0));
vector<double> fake(1,1);
auto it = gaussian ? fake.begin() : dispersion[sccid][dispid].begin();
for(auto& sd: s.data) {
neuron *n2 = getNeuron(sd.target.c);
if(!n2) continue;
double nu = learning_factor;
if(gaussian)
nu *= exp(-sqr(sd.dist/sigma));
else
nu *= *(it++);
for(int k=0; k<cols; k++)
n2->net[k] += nu * (data[id].val[k] - n2->net[k]);
}
t--;
if(t == 0) analyze();
}
int initdiv = 1;
int inited = 0;
void uninit(int initto) {
if(inited > initto) inited = initto;
}
void showsample(int id) {
for(int ii: samples_shown)
if(ii == id)
return;
int i = vdata.size();
samples_shown.push_back(id);
vdata.emplace_back();
auto& v = vdata.back();
v.name = data[id].name;
v.cp = dftcolor;
createViz(i, cwt.c, Id);
v.m->store();
}
void showsample(string s) {
if(s == "") return;
for(int i=0; i<samples; i++) {
if(s[0] != '*' && data[i].name == s)
showsample(i);
if(s[0] == '*' && data[i].name.find(s.substr(1)) != string::npos)
showsample(i);
}
}
void showbestsamples() {
vector<int> samplesbak;
for(auto& n: net)
if(n.samples)
showsample(n.bestsample);
analyze();
for(auto& n: net) n.samples = 0;
for(int i=0; i<samples; i++)
if(whowon[i])
whowon[i]->samples++;
}
void sominit(int initto) {
if(inited < 1 && initto >= 1) {
inited = 1;
if(!samples) {
fprintf(stderr, "Error: SOM without samples\n");
exit(1);
}
init(); kind = kKohonen;
/* if(geometry != gQuotient1) {
targetGeometry = gQuotient1;
restartGame('g');
}
2018-01-04 17:39:04 +00:00
if(!nonchamfered) restartGame('7'); */
2017-09-01 20:14:02 +00:00
printf("Initializing SOM (1)\n");
vector<cell*> allcells;
if(krad) {
celllister cl(cwt.c, krad, 1000000, NULL);
allcells = cl.lst;
}
else 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<initdiv; z++)
net[i].net[k] += data[hrand(samples)].val[k] / initdiv;
}
for(neuron& n: net) for(int d=BARLEV; d>=7; d--) setdist(n.where, d, NULL);
printf("samples = %d (%d) cells = %d\n", samples, size(samples_shown), cells);
if(!noshow) {
vdata.resize(size(samples_shown));
for(int i=0; i<size(samples_shown); i++) {
vdata[i].name = data[samples_shown[i]].name;
vdata[i].cp = dftcolor;
createViz(i, cwt.c, Id);
}
storeall();
}
analyze();
}
if(inited < 2 && initto >= 2) {
inited = 2;
printf("Initializing SOM (2)\n");
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)) + " (" + its(n->samples) + ")\n";
help += "parameters:"; for(int k=0; k<cols; k++) help += " " + fts(n->net[k]);
help += ", u-matrix = " + fts(n->udist);
help += "\n";
vector<pair<double, int>> v;
for(int s=0; s<samples; s++) if(whowon[s] == n) v.emplace_back(vnorm(n->net, data[s].val), s);
random_shuffle(v.begin(), v.end());
2017-09-30 09:47:00 +00:00
sort(v.begin(), v.end(), [] (pair<double,int> a, pair<double,int> b) { return a.first < b.first; });
2017-09-01 20:14:02 +00:00
for(int i=0; i<size(v) && i<20; i++) {
int s = v[i].second;
help += "sample "+its(s)+":";
for(int k=0; k<cols; k++) help += " " + fts(data[s].val[k]);
help += " "; help += data[s].name; help += "\n";
}
}
namespace levelline {
struct levelline {
int column, qty;
unsigned int color;
vector<double> values;
bool modified;
};
vector<levelline> levellines;
bool on;
void create() {
int xlalpha = int(pow(ld(.5), ggamma) * 255);
for(int i=0; i<cols; i++) {
levellines.emplace_back();
levelline& lv = levellines.back();
lv.column = i;
lv.color = ((hrandpos() & 0xFFFFFF) << 8) | xlalpha;
lv.qty = 0;
}
}
void build() {
if(levellines.size() == 0) create();
on = false;
for(auto& lv: levellines) {
if(!lv.qty) { lv.values.clear(); continue; }
on = true;
if(!lv.modified) continue;
lv.modified = false;
vector<double> sample;
for(int j=0; j<=1024; j++) sample.push_back(data[hrand(samples)].val[lv.column]);
sort(sample.begin(), sample.end());
lv.values.clear();
lv.values.push_back(-1e10);
for(int j=0; j<=1024; j+=1024 >> (lv.qty)) lv.values.push_back(sample[j]);
lv.values.push_back(1e10);
}
}
void draw() {
if(!on) return;
for(auto& g: gmatrix) {
cell *c1 = g.first;
transmatrix T = g.second;
neuron *n1 = getNeuron(c1);
if(!n1) continue;
for(int i=0; i<c1->type; i++) {
cell *c2 = c1->mov[i];
if(!c2) continue;
cell *c3 = c1->mov[i ? i-1 : c1->type-1];
if(!c3) continue;
if(!gmatrix.count(c2)) continue;
if(!gmatrix.count(c3)) continue;
double d2 = hdist(tC0(T), tC0(gmatrix[c2]));
double d3 = hdist(tC0(T), tC0(gmatrix[c3]));
neuron *n2 = getNeuron(c2);
if(!n2) continue;
neuron *n3 = getNeuron(c3);
if(!n3) continue;
for(auto& l: levellines) {
auto val1 = n1->net[l.column];
auto val2 = n2->net[l.column];
auto val3 = n3->net[l.column];
auto v1 = lower_bound(l.values.begin(), l.values.end(), val1);
auto v2 = lower_bound(l.values.begin(), l.values.end(), val2);
auto v3 = lower_bound(l.values.begin(), l.values.end(), val3);
auto draw = [&] () {
auto vmid = *v1;
queueline(
tC0(T * ddspin(c1,i) * xpush(d2 * (vmid-val1) / (val2-val1))),
tC0(T * ddspin(c1,i-1) * xpush(d3 * (vmid-val1) / (val3-val1))),
l.color);
};
while(v1 < v2 && v1 < v3) {
draw();
v1++;
}
while(v1 > v2 && v1 > v3) {
v1--;
draw();
}
}
}
}
setindex(false);
}
void show() {
if(levellines.size() == 0) create();
gamescreen(0);
cmode = vid.xres > vid.yres * 1.4 ? sm::SIDE : 0;
dialog::init("level lines");
char nx = 'a';
for(auto &l : levellines) {
dialog::addSelItem(colnames[l.column], its(l.qty), nx++);
dialog::lastItem().colorv = l.color >> 8;
}
dialog::addItem("exit menu", '0');
dialog::addItem("shift+letter to change color", 0);
dialog::display();
keyhandler = [] (int sym, int uni) {
dialog::handleNavigation(sym, uni);
if(uni >= 'a' && uni - 'a' + size(levellines)) {
auto& l = levellines[uni - 'a'];
dialog::editNumber(l.qty, 0, 10, 1, 0, colnames[l.column],
XLAT("Controls the number of level lines."));
dialog::reaction = [&l] () {
l.modified = true;
build();
};
}
else if(uni >= 'A' && uni - 'A' + size(levellines)) {
auto& l = levellines[uni - 'A'];
dialog::openColorDialog(l.color, NULL);
}
else if(doexiton(sym, uni)) popScreen();
};
}
}
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, "%.9lf ", 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 ksavew(const char *fname) {
sominit(1);
FILE *f = fopen(fname, "wt");
if(!f) {
fprintf(stderr, "Could not save the weights\n");
return;
}
for(int i=0; i<cols; i++)
fprintf(f, "%s=%.9lf\n", colnames[i].c_str(), weights[i]);
fclose(f);
}
void kloadw(const char *fname) {
sominit(1);
FILE *f = fopen(fname, "rt");
if(!f) {
fprintf(stderr, "Could not load the weights\n");
return;
}
for(int i=0; i<cols; i++) {
string s1, s2;
char kind = 0;
while(true) {
int c = fgetc(f);
if(c == 10 || c == 13 || c == -1) {
if(s1 == "" && !kind && c != -1) continue;
if(s1 != "") colnames[i] = s1;
if(kind == '=') weights[i] = atof(s2.c_str());
if(kind == '*') weights[i] *= atof(s2.c_str());
if(kind == '/') weights[i] /= atof(s2.c_str());
if(c == -1) break;
goto nexti;
}
else if(c == '=' || c == '/' || c == '*') kind = c;
else (kind?s2:s1) += c;
}
nexti: ;
}
fclose(f);
analyze();
}
unsigned lastprogress;
void progress(string s) {
if(SDL_GetTicks() >= lastprogress + (noGUI ? 500 : 100)) {
if(noGUI)
printf("%s\n", s.c_str());
else {
clearMessages();
addMessage(s);
mainloopiter();
}
lastprogress = SDL_GetTicks();
}
}
void kclassify(const char *fname_classify) {
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 % 128))
progress("Classifying: " + its(s) + "/" + its(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);
}
}
coloring();
}
void klistsamples(const char *fname_samples, bool best, bool colorformat) {
if(fname_samples != NULL) {
printf("Listing samples...\n");
FILE *f = fopen(fname_samples, "wt");
if(!f) {
printf("Failed to open file\n");
}
else {
auto klistsample = [f, colorformat] (int id, int neu) {
if(colorformat) {
fprintf(f, "%s;+#%d\n", data[id].name.c_str(), neu);
}
else {
for(int k=0; k<cols; k++)
fprintf(f, "%.4lf ", data[id].val[k]);
fprintf(f, "!%s\n", data[id].name.c_str());
}
};
if(!colorformat) fprintf(f, "%d\n", cols);
if(best)
for(int n=0; n<cells; n++) {
if(!net[n].samples) { if(!colorformat) fprintf(f, "\n"); continue; }
klistsample(net[n].bestsample, n);
}
else
for(int i=0; i<size(samples_shown); i++)
klistsample(samples_shown[i], neuronId(*(whowon[i])));
fclose(f);
}
}
}
void neurondisttable(const char *fname) {
FILE *f = fopen(fname, "wt");
if(!f) {
printf("Could not open file: %s\n", fname);
return;
}
int neurons = size(net);
fprintf(f, "%d\n", neurons);
for(int i=0; i<neurons; i++) {
for(int j=0; j<neurons; j++) fprintf(f, "%3d", celldistance(net[i].where, net[j].where));
// todo: build the table correctly for gaussian=2
fprintf(f, "\n");
}
fclose(f);
}
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 = colnames[whattodraw[i]];
dialog::addSelItem(XLAT("coloring (%1)", parts[i]), c, '1'+i);
}
dialog::addItem("coloring (all)", '0');
dialog::addItem("level lines", '4');
}
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;
}
if(uni == '4') {
pushScreen(levelline::show);
return true;
}
return false;
}
2017-12-01 23:27:16 +00:00
#if CAP_COMMANDLINE
2017-09-01 20:14:02 +00:00
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) {
if(t % 128 == 0) progress("Steps left: " + its(t));
kohonen::step();
}
}
else if(argis("-somstop")) {
t = 0;
}
else if(argis("-somnoshow")) {
noshow = true;
}
else if(argis("-somfinish")) {
while(!finished()) {
kohonen::step();
if(t % 128 == 0) progress("Steps left: " + its(t));
}
}
// #5 save data, classify etc.
else if(argis("-somsave")) {
PHASE(3);
shift(); kohonen::ksave(args());
}
else if(argis("-somsavew")) {
PHASE(3);
shift(); kohonen::ksavew(args());
}
else if(argis("-somloadw")) {
PHASE(3);
shift(); kohonen::kloadw(args());
}
else if(argis("-somclassify")) {
PHASE(3);
shift(); kohonen::kclassify(args());
}
else if(argis("-somlistshown")) {
PHASE(3);
shift(); kohonen::klistsamples(args(), false, false);
}
else if(argis("-somlistbest")) {
PHASE(3);
shift(); kohonen::klistsamples(args(), true, false);
}
else if(argis("-somlistbestc")) {
PHASE(3);
shift(); kohonen::klistsamples(args(), true, true);
}
else if(argis("-somndist")) {
PHASE(3);
shift(); kohonen::neurondisttable(args());
}
else if(argis("-somshowbest")) {
showbestsamples();
}
else return 1;
return 0;
}
2017-12-01 23:27:16 +00:00
auto hooks = addHook(hooks_args, 100, readArgs);
#endif
auto hooks2 = addHook(hooks_frame, 50, levelline::draw);
2017-09-01 20:14:02 +00:00
}
void mark(cell *c) {
using namespace kohonen;
distfrom = getNeuronSlow(c);
coloring();
}