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rogueviz::kohonen:: additional options (save/load classify in binary format, visualize edges
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Zeno Rogue
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@ -1,7 +1,7 @@
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// Hyperbolic Rogue
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// Copyright (C) 2011-2018 Zeno and Tehora Rogue, see 'hyper.cpp' for details
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// Kohonen's self-organizing networks.
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// Kohonen's self-organizing maps.
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// This is a part of RogueViz, not a part of HyperRogue.
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namespace rogueviz { namespace kohonen {
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@ -27,7 +27,8 @@ struct neuron {
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double udist;
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int lpbak;
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int col;
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int samples, csample, bestsample;
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int allsamples, drawn_samples, csample, bestsample;
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neuron() { drawn_samples = allsamples = bestsample = 0; }
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};
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vector<string> colnames;
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@ -182,7 +183,7 @@ void coloring() {
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vector<double> listing;
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for(neuron& n: net) switch(c) {
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case -4:
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listing.push_back(log(5+n.samples));
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listing.push_back(log(5+n.allsamples));
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break;
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case -3:
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@ -215,6 +216,32 @@ void coloring() {
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}
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}
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void distribute_neurons() {
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whowon.resize(samples);
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for(neuron& n: net) n.drawn_samples = 0;
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for(int s: samples_shown) {
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auto& w = winner(s);
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whowon[s] = &w;
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w.drawn_samples++;
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}
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ld rad = .25 * scalef;
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for(int id=0; id<isize(samples_shown); id++) {
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int s = samples_shown[id];
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auto& w = *whowon[s];
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vdata[id].m->base = w.where;
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vdata[id].m->at =
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spin(2*M_PI*w.csample / w.drawn_samples) * xpush(rad * (w.drawn_samples-1) / w.drawn_samples);
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w.csample++;
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}
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shmup::fixStorage();
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setindex(false);
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}
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void analyze() {
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setindex(true);
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@ -233,31 +260,7 @@ void analyze() {
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maxudist = max(maxudist, n.udist);
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}
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if(!noshow) {
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whowon.resize(samples);
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for(neuron& n: net) n.samples = 0;
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for(int id=0; id<isize(samples_shown); id++) {
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int s = samples_shown[id];
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auto& w = winner(s);
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whowon[s] = &w;
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w.samples++;
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}
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for(int id=0; id<isize(samples_shown); id++) {
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int s = samples_shown[id];
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auto& w = *whowon[s];
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vdata[id].m->base = w.where;
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vdata[id].m->at =
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spin(2*M_PI*w.csample / w.samples) * xpush(.25 * (w.samples-1) / w.samples);
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w.csample++;
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}
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shmup::fixStorage();
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setindex(false);
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}
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if(!noshow) distribute_neurons();
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coloring();
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}
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@ -557,10 +560,29 @@ void uninit(int initto) {
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if(inited > initto) inited = initto;
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}
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void showsample(int id) {
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for(int ii: samples_shown)
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if(ii == id)
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return;
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int max_group = 10;
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vector<double> bdiffs;
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vector<unsigned short> bids;
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vector<double> bdiffn;
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int showsample(int id) {
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for(int i=0; i<isize(samples_shown); i++)
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if(samples_shown[i] == id)
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return i;
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if(bids.size()) {
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if(net[bids[id]].drawn_samples >= max_group) {
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ld bdist = 1e18;
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int whichid = -1;
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for(int i=0; i<isize(samples_shown); i++)
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if(bids[samples_shown[i]] == bids[id]) {
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ld cdist = vnorm(data[samples_shown[i]].val, data[id].val);
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if(cdist < bdist) bdist = cdist, whichid = i;
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}
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return whichid;
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}
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net[bids[id]].drawn_samples++;
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}
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int i = vdata.size();
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samples_shown.push_back(id);
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vdata.emplace_back();
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@ -569,6 +591,7 @@ void showsample(int id) {
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v.cp = dftcolor;
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createViz(i, cwt.c, Id);
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v.m->store();
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return isize(samples_shown) - 1;
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}
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void showsample(string s) {
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@ -584,13 +607,9 @@ void showsample(string s) {
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void showbestsamples() {
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vector<int> samplesbak;
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for(auto& n: net)
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if(n.samples)
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if(n.allsamples)
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showsample(n.bestsample);
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analyze();
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for(auto& n: net) n.samples = 0;
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for(int i=0; i<samples; i++)
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if(whowon[i])
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whowon[i]->samples++;
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}
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int kohrestrict = 1000000;
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@ -685,7 +704,7 @@ void describe(cell *c) {
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if(cmode & sm::HELP) return;
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neuron *n = getNeuronSlow(c);
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if(!n) return;
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help += "cell number: " + its(neuronId(*n)) + " (" + its(n->samples) + ")\n";
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help += "cell number: " + its(neuronId(*n)) + " (" + its(n->allsamples) + ")\n";
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help += "parameters:"; for(int k=0; k<cols; k++) help += " " + fts(n->net[k]);
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help += ", u-matrix = " + fts(n->udist);
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help += "\n";
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@ -923,39 +942,66 @@ void progress(string s) {
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}
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}
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template<class T> void save_raw(string fname, const vector<T>& v) {
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FILE *f = fopen(fname.c_str(), "wb");
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fwrite(&v[0], sizeof(v[0]), v.size(), f);
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fclose(f);
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}
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template<class T> void load_raw(string fname, vector<T>& v) {
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FILE *f = fopen(fname.c_str(), "rb");
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if(!f) { fprintf(stderr, "file does not exist: %s\n", fname.c_str()); exit(1); }
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fseek(f, 0, SEEK_END);
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auto s = ftell(f);
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rewind(f);
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v.resize(s / sizeof(v[0]));
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fread(&v[0], sizeof(v[0]), v.size(), f);
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fclose(f);
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}
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void do_classify() {
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sominit(1);
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if(bids.empty()) {
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printf("Classifying...\n");
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bids.resize(samples, 0);
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bdiffs.resize(samples, 1e20);
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for(int s=0; s<samples; s++) {
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for(int n=0; n<cells; n++) {
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double diff = vnorm(net[n].net, data[s].val);
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if(diff < bdiffs[s]) bdiffs[s] = diff, bids[s] = n, whowon[s] = &net[n];
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}
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if(!(s % 128))
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progress("Classifying: " + its(s) + "/" + its(samples));
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}
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}
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if(bdiffs.empty()) {
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printf("Computing distances...\n");
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bdiffs.resize(samples, 1e20);
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for(int s=0; s<samples; s++)
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bdiffs[s] = vnorm(net[bids[s]].net, data[s].val);
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}
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if(bdiffn.empty()) {
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printf("Finding samples...\n");
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bdiffn.resize(cells, 1e20);
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for(int s=0; s<samples; s++) {
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int n = bids[s];
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double diff = bdiffs[s];
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if(diff < bdiffn[n]) bdiffn[n] = diff, net[n].bestsample = s;
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}
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}
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whowon.resize(samples);
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for(int i=0; i<samples; i++) whowon[i] = &net[bids[i]];
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for(neuron& n: net) n.allsamples = 0;
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for(int sn: bids) net[sn].allsamples++;
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coloring();
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}
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void kclassify(const string& fname_classify) {
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sominit(1);
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vector<double> bdiffs(samples, 1e20);
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vector<int> bids(samples, 0);
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do_classify();
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printf("Classifying...\n");
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for(neuron& n: net) n.samples = 0;
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for(int s=0; s<samples; s++) {
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for(int n=0; n<cells; n++) {
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double diff = vnorm(net[n].net, data[s].val);
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if(diff < bdiffs[s]) bdiffs[s] = diff, bids[s] = n, whowon[s] = &net[n];
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}
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if(!(s % 128))
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progress("Classifying: " + its(s) + "/" + its(samples));
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}
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vector<double> bdiffn(cells, 1e20);
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printf("Finding samples...\n");
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for(int s=0; s<samples; s++) {
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int n = bids[s];
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double diff = bdiffs[s];
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if(diff < bdiffn[n]) bdiffn[n] = diff, net[n].bestsample = s;
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}
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for(int s=0; s<samples; s++) net[bids[s]].samples++;
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if(fname_classify != "") {
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printf("Listing classification...\n");
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printf("Listing classification...\n");
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FILE *f = fopen(fname_classify.c_str(), "wt");
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if(!f) {
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printf("Failed to open file\n");
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@ -966,7 +1012,33 @@ void kclassify(const string& fname_classify) {
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fclose(f);
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}
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}
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coloring();
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}
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void kclassify_save_raw(const string& fname_classify) {
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do_classify();
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save_raw(fname_classify, bids);
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}
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void kclassify_load_raw(const string& fname_classify) {
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load_raw(fname_classify, bids);
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do_classify();
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}
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void load_edges(const string& fname_edges, int pick = 0) {
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do_classify();
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vector<pair<int, int>> edgedata;
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load_raw(fname_edges, edgedata);
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int N = isize(edgedata);
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if(pick > 0 && pick < N) {
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for(int i=1; i<N; i++) swap(edgedata[i], edgedata[hrand(i+1)]);
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edgedata.resize(N = pick);
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}
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vector<pair<int, int>> edgedata2;
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for(auto p: edgedata)
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edgedata2.emplace_back(showsample(p.first), showsample(p.second));
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distribute_neurons();
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for(auto p: edgedata2)
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addedge(p.first, p.second, 0, false);
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}
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void klistsamples(const string& fname_samples, bool best, bool colorformat) {
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@ -990,7 +1062,7 @@ void klistsamples(const string& fname_samples, bool best, bool colorformat) {
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if(!colorformat) fprintf(f, "%d\n", cols);
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if(best)
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for(int n=0; n<cells; n++) {
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if(!net[n].samples) { if(!colorformat) fprintf(f, "\n"); continue; }
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if(!net[n].allsamples && !net[n].drawn_samples) { if(!colorformat) fprintf(f, "\n"); continue; }
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klistsample(net[n].bestsample, n);
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}
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else
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@ -1156,10 +1228,22 @@ int readArgs() {
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PHASE(3);
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shift(); kohonen::kloadw(args());
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}
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else if(argis("-somclassify0")) {
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PHASE(3);
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shift(); kohonen::do_classify();
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}
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else if(argis("-somclassify")) {
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PHASE(3);
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shift(); kohonen::kclassify(args());
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}
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else if(argis("-somclassify-sr")) {
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PHASE(3);
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shift(); kohonen::kclassify_save_raw(args());
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}
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else if(argis("-somclassify-lr")) {
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PHASE(3);
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shift(); kohonen::kclassify_load_raw(args());
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}
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else if(argis("-somlistshown")) {
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PHASE(3);
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shift(); kohonen::klistsamples(args(), false, false);
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@ -1186,6 +1270,16 @@ int readArgs() {
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else if(argis("-somrestrict")) {
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shift(); kohrestrict = argi();
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}
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else if(argis("-som_maxgroup")) {
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shift(); max_group = argi();
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}
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else if(argis("-som_load_edges")) {
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shift(); kohonen::load_edges(args(), 0);
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}
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else if(argis("-som_load_n_edges")) {
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shift(); int n = argi();
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shift(); kohonen::load_edges(args(), n);
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}
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else return 1;
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return 0;
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@ -76,7 +76,7 @@ namespace rogueviz {
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namespace kohonen {
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extern int samples;
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void showsample(int id);
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int showsample(int id);
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void showsample(string id);
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void describe(cell *c);
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void steps();
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