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Kohonen: display only one some samples; dispersion adapted to geometry rather than Gaussian

This commit is contained in:
Zeno Rogue 2017-08-15 17:39:57 +02:00
parent 5658257287
commit 9dedf6bfec
2 changed files with 158 additions and 18 deletions
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171
kohonen.cpp
View File

@ -17,6 +17,8 @@ struct sample {
vector<sample> data;
vector<int> samples_shown;
int whattodraw[3];
struct neuron {
@ -32,6 +34,8 @@ kohvec weights;
vector<neuron> net;
int neuronId(neuron& n) { return &n - &(net[0]); }
void alloc(kohvec& k) { k.resize(cols); }
bool neurons_indexed = false;
@ -67,6 +71,7 @@ void loadsamples(const char *fname) {
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++)
@ -75,17 +80,19 @@ void loadsamples(const char *fname) {
while(true) {
int c = fgetc(f);
if(c == -1 || c == 10 || c == 13) break;
if(c != 32 && c != 9) s.name += c;
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));
}
fclose(f);
samples = size(data);
normalize();
vdata.resize(samples);
for(int i=0; i<samples; i++) {
vdata[i].name = data[i].name;
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);
}
@ -93,7 +100,7 @@ void loadsamples(const char *fname) {
storeall();
}
int t;
int t, tmax;
int lpct, mul, maxdist, cells, perdist;
double maxfac;
@ -112,7 +119,7 @@ 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]);
for(neuron& n: net) n.lpbak = n.where->landparam, n.where->landparam = neuronId(n);
}
else {
for(neuron& n: net) n.where->landparam = n.lpbak;
@ -144,6 +151,10 @@ void coloring() {
int c = whattodraw[pid];
vector<double> listing;
for(neuron& n: net) switch(c) {
case -4:
listing.push_back(n.samples);
break;
case -3:
if(distfrom)
listing.push_back(vnorm(n.net, distfrom->net));
@ -195,14 +206,16 @@ void analyze() {
for(neuron& n: net) n.samples = 0;
for(int id=0; id<samples; id++) {
auto& w = winner(id);
whowon[id] = &w;
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<samples; id++) {
auto& w = *whowon[id];
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);
@ -262,26 +275,119 @@ struct cellcrawler {
}
};
cellcrawler s0, s1; // hex and non-hex
// 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
// GAUSSIAN==0: simulate the dispersion on our network
#ifndef GAUSSIAN
#define GAUSSIAN 0
#endif
cellcrawler scc[2]; // hex and non-hex
#if GAUSSIAN==0
double dispersion_precision = .0001;
int dispersion_each = 1;
vector<vector<ld>> dispersion[2];
int dispersion_count;
#endif
void buildcellcrawler(cell *c) {
(c->type == 6 ? s0 : s1).build(cellwalker(c,0));
int sccid = c->type != 6;
cellcrawler& cr = scc[sccid];
cr.build(cellwalker(c,0));
#if GAUSSIAN==0
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 * 1.5; 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);
#endif
}
bool finished() { return t == 0; }
void step() {
double sigma = maxdist * t / (perdist*(double) mul);
if(t == 0) return;
#if GAUSSIAN==1
double sigma = maxdist * t / (perdist*(double) mul);
#else
int dispid = int(dispersion_count * (t-1.) / tmax);
#endif
// double sigma = maxdist * exp(-t / t1);
int pct = (int) (100 * ((t*(double) mul) / perdist));
if(pct != lpct) {
lpct = pct;
analyze();
#if GAUSSIAN==1
printf("t = %6d/%2dx%6d pct = %3d sigma=%10.7lf maxudist=%10.7lf\n", t, mul, perdist, pct, sigma, maxudist);
#else
printf("t = %6d/%2dx%6d pct = %3d dispid=%5d maxudist=%10.7lf\n", t, mul, perdist, pct, dispid, maxudist);
#endif
}
int id = hrand(samples);
neuron& n = winner(id);
whowon[id] = &n;
/*
for(neuron& n2: net) {
@ -294,13 +400,22 @@ void step() {
n2.net[k] += nu * (irisdata[id][k] - n2.net[k]);
} */
cellcrawler& s = n.where->type == 6 ? s0 : s1;
int sccid = n.where->type != 6;
cellcrawler& s = scc[sccid];
s.sprawl(cellwalker(n.where, 0));
#if GAUSSIAN==0
auto it = dispersion[sccid][dispid].begin();
#endif
for(auto& sd: s.data) {
neuron *n2 = getNeuron(sd.target.c);
if(!n2) continue;
double nu = maxfac;
#if GAUSSIAN==0
nu *= *(it++);
#else
nu *= exp(-sqr(sd.dist/sigma));
#endif
for(int k=0; k<cols; k++)
n2->net[k] += nu * (data[id].val[k] - n2->net[k]);
}
@ -347,6 +462,9 @@ void run(const char *fname, int _perdist, double _maxfac) {
printf("samples = %d cells = %d maxdist = %d\n", samples, cells, maxdist);
#if GAUSSIAN==0
dispersion_count = 0;
#endif
c1 = currentmap->gamestart();
cell *c2 = createMov(c1, 0);
buildcellcrawler(c1);
@ -354,7 +472,7 @@ void run(const char *fname, int _perdist, double _maxfac) {
lpct = -46130;
mul = 1;
t = perdist*mul;
tmax = t = perdist*mul;
step();
for(int i=0; i<3; i++) whattodraw[i] = -2;
analyze();
@ -364,14 +482,16 @@ 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 += "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;
}
}
@ -402,6 +522,20 @@ void kload(const char *fname) {
analyze();
}
void kclassify(const char *fname) {
for(neuron& n: net) n.samples = 0;
FILE *f = fopen(fname, "wt");
for(int id=0; id<size(data); 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 steps() {
if(!kohonen::finished()) {
unsigned int t = SDL_GetTicks();
@ -417,6 +551,7 @@ void showMenu() {
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 c = "column " + its(whattodraw[i]);
dialog::addSelItem(XLAT("coloring (%1)", parts[i]), c, '1'+i);
}
@ -426,7 +561,7 @@ bool handleMenu(int sym, int uni) {
if(uni >= '1' && uni <= '3') {
int i = uni - '1';
whattodraw[i]++;
if(whattodraw[i] == cols) whattodraw[i] = -3;
if(whattodraw[i] == cols) whattodraw[i] = -4;
coloring();
return true;
}

5
rogueviz.cpp
View File

@ -1699,6 +1699,11 @@ int readArgs() {
while(!kohonen::finished()) kohonen::step();
shift(); kohonen::ksave(args());
}
else if(argis("-somclassify")) {
PHASE(3);
while(!kohonen::finished()) kohonen::step();
shift(); kohonen::kclassify(args());
}
else if(argis("-somload")) {
PHASE(3);
shift(); kohonen::kload(args());