mirror of
https://github.com/zenorogue/hyperrogue.git
synced 2024-12-24 01:00:25 +00:00
further improvements to Kohonen
This commit is contained in:
Zeno Rogue
parent
b0489b7312
commit
951566399e
1
hyper.h
1
hyper.h
@ -1524,6 +1524,7 @@ void addauraspecial(const hyperpoint& h, int col, int dir);
|
||||
void drawBug(const cellwalker& cw, int col);
|
||||
|
||||
void mainloop();
|
||||
void mainloopiter();
|
||||
extern bool showstartmenu;
|
||||
void selectLanguageScreen();
|
||||
|
||||
|
||||
357
kohonen.cpp
357
kohonen.cpp
@ -30,6 +30,8 @@ struct neuron {
|
||||
int samples, csample, bestsample;
|
||||
};
|
||||
|
||||
vector<string> colnames;
|
||||
|
||||
kohvec weights;
|
||||
|
||||
vector<neuron> net;
|
||||
@ -94,10 +96,12 @@ void loadsamples(const char *fname) {
|
||||
bigbreak:
|
||||
fclose(f);
|
||||
samples = size(data);
|
||||
normalize();
|
||||
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;
|
||||
@ -178,7 +182,7 @@ void coloring() {
|
||||
vector<double> listing;
|
||||
for(neuron& n: net) switch(c) {
|
||||
case -4:
|
||||
listing.push_back(n.samples);
|
||||
listing.push_back(log(5+n.samples));
|
||||
break;
|
||||
|
||||
case -3:
|
||||
@ -477,6 +481,42 @@ 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) {
|
||||
@ -563,19 +603,151 @@ void describe(cell *c) {
|
||||
if(cmode & sm::HELP) return;
|
||||
neuron *n = getNeuronSlow(c);
|
||||
if(!n) return;
|
||||
help += "cell number: " + its(neuronId(*n)) + "\n";
|
||||
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";
|
||||
int qty = 0;
|
||||
for(int s=0; s<samples; s++) if(whowon[s] == 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());
|
||||
sort(v.begin(), v.end(), [] (auto a, auto b) { return a.first < b.first; });
|
||||
|
||||
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";
|
||||
qty++; if(qty >= 20) break;
|
||||
}
|
||||
}
|
||||
|
||||
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");
|
||||
@ -586,7 +758,7 @@ void ksave(const char *fname) {
|
||||
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, "%.9lf ", n.net[k]);
|
||||
fprintf(f, "\n");
|
||||
}
|
||||
fclose(f);
|
||||
@ -612,26 +784,64 @@ void kload(const char *fname) {
|
||||
analyze();
|
||||
}
|
||||
|
||||
void kclassify(const char *fname) {
|
||||
void ksavew(const char *fname) {
|
||||
sominit(1);
|
||||
for(neuron& n: net) n.samples = 0;
|
||||
|
||||
FILE *f = fopen(fname, "wt");
|
||||
FILE *f = fopen(fname, "wt");
|
||||
if(!f) {
|
||||
fprintf(stderr, "Could not save classification\n");
|
||||
fprintf(stderr, "Could not save the weights\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));
|
||||
}
|
||||
for(int i=0; i<cols; i++)
|
||||
fprintf(f, "%s=%.9lf\n", colnames[i].c_str(), weights[i]);
|
||||
fclose(f);
|
||||
coloring();
|
||||
}
|
||||
|
||||
void kclassify2(const char *fname_classify, const char *fname_samples) {
|
||||
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);
|
||||
@ -646,9 +856,10 @@ void kclassify2(const char *fname_classify, const char *fname_samples) {
|
||||
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);
|
||||
if(!(s % 128))
|
||||
progress("Classifying: " + its(s) + "/" + its(samples));
|
||||
}
|
||||
|
||||
|
||||
vector<double> bdiffn(cells, 1e20);
|
||||
|
||||
printf("Finding samples...\n");
|
||||
@ -673,30 +884,55 @@ void kclassify2(const char *fname_classify, const char *fname_samples) {
|
||||
fclose(f);
|
||||
}
|
||||
}
|
||||
coloring();
|
||||
}
|
||||
|
||||
void klistsamples(const char *fname_samples, bool best, bool colorformat) {
|
||||
if(fname_samples != NULL) {
|
||||
printf("Listing best samples...\n");
|
||||
printf("Listing 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);
|
||||
}
|
||||
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);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
coloring();
|
||||
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() {
|
||||
@ -717,9 +953,11 @@ void showMenu() {
|
||||
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]);
|
||||
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) {
|
||||
@ -734,6 +972,10 @@ bool handleMenu(int sym, int uni) {
|
||||
for(char x: {'1','2','3'}) handleMenu(x, x);
|
||||
return true;
|
||||
}
|
||||
if(uni == '4') {
|
||||
pushScreen(levelline::show);
|
||||
return true;
|
||||
}
|
||||
return false;
|
||||
}
|
||||
|
||||
@ -800,7 +1042,10 @@ int readArgs() {
|
||||
// #4: run, stop etc.
|
||||
else if(argis("-somrunto")) {
|
||||
int i = argi();
|
||||
shift(); while(t > i) kohonen::step();
|
||||
shift(); while(t > i) {
|
||||
if(t % 128 == 0) progress("Steps left: " + its(t));
|
||||
kohonen::step();
|
||||
}
|
||||
}
|
||||
else if(argis("-somstop")) {
|
||||
t = 0;
|
||||
@ -809,7 +1054,10 @@ int readArgs() {
|
||||
noshow = true;
|
||||
}
|
||||
else if(argis("-somfinish")) {
|
||||
while(!finished()) kohonen::step();
|
||||
while(!finished()) {
|
||||
kohonen::step();
|
||||
if(t % 128 == 0) progress("Steps left: " + its(t));
|
||||
}
|
||||
}
|
||||
|
||||
// #5 save data, classify etc.
|
||||
@ -817,22 +1065,43 @@ int readArgs() {
|
||||
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("-somclassify2")) {
|
||||
else if(argis("-somlistshown")) {
|
||||
PHASE(3);
|
||||
shift(); const char *f1 = args();
|
||||
shift(); const char *f2 = args();
|
||||
kohonen::kclassify2(f1, f2);
|
||||
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;
|
||||
}
|
||||
|
||||
auto hooks = addHook(hooks_args, 100, readArgs);
|
||||
auto hooks = addHook(hooks_args, 100, readArgs) + addHook(hooks_frame, 50, levelline::draw);
|
||||
}
|
||||
|
||||
void mark(cell *c) {
|
||||
|
||||
21
rogueviz.cpp
21
rogueviz.cpp
@ -29,6 +29,8 @@ bool specialmark = false;
|
||||
|
||||
bool rog3 = false;
|
||||
|
||||
ld ggamma = .5;
|
||||
|
||||
string fname;
|
||||
|
||||
// const char *fname;
|
||||
@ -1027,8 +1029,6 @@ void queuedisk(const transmatrix& V, const colorpair& cp, bool legend) {
|
||||
if(cp.shade == 'm') queuepoly(V, shDiskM, cp.color2);
|
||||
}
|
||||
|
||||
ld ggamma = .5;
|
||||
|
||||
void drawVertex(const transmatrix &V, cell *c, shmup::monster *m) {
|
||||
if(m->dead) return;
|
||||
int i = m->pid;
|
||||
@ -1292,7 +1292,16 @@ void readcolor(const char *cfname) {
|
||||
|
||||
colorpair x;
|
||||
int c2 = fgetc(f);
|
||||
if(c2 == '=') {
|
||||
if(kohonen::samples && c2 == '+') {
|
||||
kohonen::showsample(lab);
|
||||
c2 = fgetc(f);
|
||||
if(c2 == 10 || c2 == 13) continue;
|
||||
}
|
||||
if(c2 == '#') {
|
||||
while(c2 != 10 && c2 != 13 && c2 != -1) c2 = fgetc(f);
|
||||
continue;
|
||||
}
|
||||
else if(c2 == '=') {
|
||||
string lab2 = "";
|
||||
while(true) {
|
||||
int c = fgetc(f);
|
||||
@ -1326,6 +1335,12 @@ void readcolor(const char *cfname) {
|
||||
vdata[i].cp = x;
|
||||
}
|
||||
}
|
||||
else if(kohonen::samples) {
|
||||
for(int i=0; i<size(vdata); i++)
|
||||
if(vdata[i].name == lab) {
|
||||
vdata[i].cp = x;
|
||||
}
|
||||
}
|
||||
else {
|
||||
int i = getid(lab);
|
||||
again: vdata[i].cp = x;
|
||||
|
||||
Loading…
Reference in New Issue
Block a user