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rogueviz::kohonen:: additional options (save/load classify in binary format, visualize edges

This commit is contained in:
Zeno Rogue 2018-07-04 14:37:33 +02:00
parent ddf3ca83cf
commit 6e1983baa9
2 changed files with 164 additions and 70 deletions
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232
rogueviz-kohonen.cpp
View File

@ -1,7 +1,7 @@
// Hyperbolic Rogue
// Copyright (C) 2011-2018 Zeno and Tehora Rogue, see 'hyper.cpp' for details
// Kohonen's self-organizing networks.
// Kohonen's self-organizing maps.
// This is a part of RogueViz, not a part of HyperRogue.
namespace rogueviz { namespace kohonen {
@ -27,7 +27,8 @@ struct neuron {
double udist;
int lpbak;
int col;
int samples, csample, bestsample;
int allsamples, drawn_samples, csample, bestsample;
neuron() { drawn_samples = allsamples = bestsample = 0; }
};
vector<string> colnames;
@ -182,7 +183,7 @@ void coloring() {
vector<double> listing;
for(neuron& n: net) switch(c) {
case -4:
listing.push_back(log(5+n.samples));
listing.push_back(log(5+n.allsamples));
break;
case -3:
@ -215,6 +216,32 @@ void coloring() {
}
}
void distribute_neurons() {
whowon.resize(samples);
for(neuron& n: net) n.drawn_samples = 0;
for(int s: samples_shown) {
auto& w = winner(s);
whowon[s] = &w;
w.drawn_samples++;
}
ld rad = .25 * scalef;
for(int id=0; id<isize(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.drawn_samples) * xpush(rad * (w.drawn_samples-1) / w.drawn_samples);
w.csample++;
}
shmup::fixStorage();
setindex(false);
}
void analyze() {
setindex(true);
@ -233,31 +260,7 @@ void analyze() {
maxudist = max(maxudist, n.udist);
}
if(!noshow) {
whowon.resize(samples);
for(neuron& n: net) n.samples = 0;
for(int id=0; id<isize(samples_shown); id++) {
int s = samples_shown[id];
auto& w = winner(s);
whowon[s] = &w;
w.samples++;
}
for(int id=0; id<isize(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);
}
if(!noshow) distribute_neurons();
coloring();
}
@ -557,10 +560,29 @@ void uninit(int initto) {
if(inited > initto) inited = initto;
}
void showsample(int id) {
for(int ii: samples_shown)
if(ii == id)
return;
int max_group = 10;
vector<double> bdiffs;
vector<unsigned short> bids;
vector<double> bdiffn;
int showsample(int id) {
for(int i=0; i<isize(samples_shown); i++)
if(samples_shown[i] == id)
return i;
if(bids.size()) {
if(net[bids[id]].drawn_samples >= max_group) {
ld bdist = 1e18;
int whichid = -1;
for(int i=0; i<isize(samples_shown); i++)
if(bids[samples_shown[i]] == bids[id]) {
ld cdist = vnorm(data[samples_shown[i]].val, data[id].val);
if(cdist < bdist) bdist = cdist, whichid = i;
}
return whichid;
}
net[bids[id]].drawn_samples++;
}
int i = vdata.size();
samples_shown.push_back(id);
vdata.emplace_back();
@ -569,6 +591,7 @@ void showsample(int id) {
v.cp = dftcolor;
createViz(i, cwt.c, Id);
v.m->store();
return isize(samples_shown) - 1;
}
void showsample(string s) {
@ -584,13 +607,9 @@ void showsample(string s) {
void showbestsamples() {
vector<int> samplesbak;
for(auto& n: net)
if(n.samples)
if(n.allsamples)
showsample(n.bestsample);
analyze();
for(auto& n: net) n.samples = 0;
for(int i=0; i<samples; i++)
if(whowon[i])
whowon[i]->samples++;
}
int kohrestrict = 1000000;
@ -685,7 +704,7 @@ 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 += "cell number: " + its(neuronId(*n)) + " (" + its(n->allsamples) + ")\n";
help += "parameters:"; for(int k=0; k<cols; k++) help += " " + fts(n->net[k]);
help += ", u-matrix = " + fts(n->udist);
help += "\n";
@ -923,39 +942,66 @@ void progress(string s) {
}
}
template<class T> void save_raw(string fname, const vector<T>& v) {
FILE *f = fopen(fname.c_str(), "wb");
fwrite(&v[0], sizeof(v[0]), v.size(), f);
fclose(f);
}
template<class T> void load_raw(string fname, vector<T>& v) {
FILE *f = fopen(fname.c_str(), "rb");
if(!f) { fprintf(stderr, "file does not exist: %s\n", fname.c_str()); exit(1); }
fseek(f, 0, SEEK_END);
auto s = ftell(f);
rewind(f);
v.resize(s / sizeof(v[0]));
fread(&v[0], sizeof(v[0]), v.size(), f);
fclose(f);
}
void do_classify() {
sominit(1);
if(bids.empty()) {
printf("Classifying...\n");
bids.resize(samples, 0);
bdiffs.resize(samples, 1e20);
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));
}
}
if(bdiffs.empty()) {
printf("Computing distances...\n");
bdiffs.resize(samples, 1e20);
for(int s=0; s<samples; s++)
bdiffs[s] = vnorm(net[bids[s]].net, data[s].val);
}
if(bdiffn.empty()) {
printf("Finding samples...\n");
bdiffn.resize(cells, 1e20);
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;
}
}
whowon.resize(samples);
for(int i=0; i<samples; i++) whowon[i] = &net[bids[i]];
for(neuron& n: net) n.allsamples = 0;
for(int sn: bids) net[sn].allsamples++;
coloring();
}
void kclassify(const string& fname_classify) {
sominit(1);
vector<double> bdiffs(samples, 1e20);
vector<int> bids(samples, 0);
do_classify();
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 != "") {
printf("Listing classification...\n");
printf("Listing classification...\n");
FILE *f = fopen(fname_classify.c_str(), "wt");
if(!f) {
printf("Failed to open file\n");
@ -966,7 +1012,33 @@ void kclassify(const string& fname_classify) {
fclose(f);
}
}
coloring();
}
void kclassify_save_raw(const string& fname_classify) {
do_classify();
save_raw(fname_classify, bids);
}
void kclassify_load_raw(const string& fname_classify) {
load_raw(fname_classify, bids);
do_classify();
}
void load_edges(const string& fname_edges, int pick = 0) {
do_classify();
vector<pair<int, int>> edgedata;
load_raw(fname_edges, edgedata);
int N = isize(edgedata);
if(pick > 0 && pick < N) {
for(int i=1; i<N; i++) swap(edgedata[i], edgedata[hrand(i+1)]);
edgedata.resize(N = pick);
}
vector<pair<int, int>> edgedata2;
for(auto p: edgedata)
edgedata2.emplace_back(showsample(p.first), showsample(p.second));
distribute_neurons();
for(auto p: edgedata2)
addedge(p.first, p.second, 0, false);
}
void klistsamples(const string& fname_samples, bool best, bool colorformat) {
@ -990,7 +1062,7 @@ void klistsamples(const string& fname_samples, bool best, bool colorformat) {
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; }
if(!net[n].allsamples && !net[n].drawn_samples) { if(!colorformat) fprintf(f, "\n"); continue; }
klistsample(net[n].bestsample, n);
}
else
@ -1156,10 +1228,22 @@ int readArgs() {
PHASE(3);
shift(); kohonen::kloadw(args());
}
else if(argis("-somclassify0")) {
PHASE(3);
shift(); kohonen::do_classify();
}
else if(argis("-somclassify")) {
PHASE(3);
shift(); kohonen::kclassify(args());
}
else if(argis("-somclassify-sr")) {
PHASE(3);
shift(); kohonen::kclassify_save_raw(args());
}
else if(argis("-somclassify-lr")) {
PHASE(3);
shift(); kohonen::kclassify_load_raw(args());
}
else if(argis("-somlistshown")) {
PHASE(3);
shift(); kohonen::klistsamples(args(), false, false);
@ -1186,6 +1270,16 @@ int readArgs() {
else if(argis("-somrestrict")) {
shift(); kohrestrict = argi();
}
else if(argis("-som_maxgroup")) {
shift(); max_group = argi();
}
else if(argis("-som_load_edges")) {
shift(); kohonen::load_edges(args(), 0);
}
else if(argis("-som_load_n_edges")) {
shift(); int n = argi();
shift(); kohonen::load_edges(args(), n);
}
else return 1;
return 0;

2
rogueviz.h
View File

@ -76,7 +76,7 @@ namespace rogueviz {
namespace kohonen {
extern int samples;
void showsample(int id);
int showsample(int id);
void showsample(string id);
void describe(cell *c);
void steps();