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Kohonen: display only one some samples; dispersion adapted to geometry rather than Gaussian
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Zeno Rogue
parent
5658257287
commit
9dedf6bfec
171
kohonen.cpp
171
kohonen.cpp
@ -17,6 +17,8 @@ struct sample {
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vector<sample> data;
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vector<int> samples_shown;
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int whattodraw[3];
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struct neuron {
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@ -32,6 +34,8 @@ kohvec weights;
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vector<neuron> net;
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int neuronId(neuron& n) { return &n - &(net[0]); }
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void alloc(kohvec& k) { k.resize(cols); }
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bool neurons_indexed = false;
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@ -67,6 +71,7 @@ void loadsamples(const char *fname) {
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if(fscanf(f, "%d", &cols) != 1) { fclose(f); return; }
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while(true) {
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sample s;
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bool shown = false;
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alloc(s.val);
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if(feof(f)) break;
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for(int i=0; i<cols; i++)
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@ -75,17 +80,19 @@ void loadsamples(const char *fname) {
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while(true) {
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int c = fgetc(f);
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if(c == -1 || c == 10 || c == 13) break;
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if(c != 32 && c != 9) s.name += c;
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if(c == '!' && s.name == "") shown = true;
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else if(c != 32 && c != 9) s.name += c;
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}
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if(shown) samples_shown.push_back(size(data));
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data.push_back(move(s));
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}
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fclose(f);
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samples = size(data);
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normalize();
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vdata.resize(samples);
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for(int i=0; i<samples; i++) {
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vdata[i].name = data[i].name;
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vdata.resize(size(samples_shown));
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for(int i=0; i<size(samples_shown); i++) {
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vdata[i].name = data[samples_shown[i]].name;
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vdata[i].cp = dftcolor;
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createViz(i, cwt.c, Id);
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}
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@ -93,7 +100,7 @@ void loadsamples(const char *fname) {
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storeall();
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}
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int t;
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int t, tmax;
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int lpct, mul, maxdist, cells, perdist;
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double maxfac;
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@ -112,7 +119,7 @@ void setindex(bool b) {
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if(b == neurons_indexed) return;
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neurons_indexed = b;
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if(b) {
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for(neuron& n: net) n.lpbak = n.where->landparam, n.where->landparam = (&n - &net[0]);
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for(neuron& n: net) n.lpbak = n.where->landparam, n.where->landparam = neuronId(n);
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}
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else {
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for(neuron& n: net) n.where->landparam = n.lpbak;
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@ -144,6 +151,10 @@ void coloring() {
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int c = whattodraw[pid];
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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(n.samples);
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break;
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case -3:
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if(distfrom)
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listing.push_back(vnorm(n.net, distfrom->net));
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@ -195,14 +206,16 @@ void analyze() {
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for(neuron& n: net) n.samples = 0;
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for(int id=0; id<samples; id++) {
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auto& w = winner(id);
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whowon[id] = &w;
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for(int id=0; id<size(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<samples; id++) {
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auto& w = *whowon[id];
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for(int id=0; id<size(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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@ -262,26 +275,119 @@ struct cellcrawler {
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}
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};
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cellcrawler s0, s1; // hex and non-hex
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// traditionally Gaussian blur is used in the Kohonen algoritm
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// but it does not seem to make much sense in hyperbolic geometry
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// especially wrapped one.
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// GAUSSIAN==1: use the Gaussian blur
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// GAUSSIAN==0: simulate the dispersion on our network
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#ifndef GAUSSIAN
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#define GAUSSIAN 0
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#endif
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cellcrawler scc[2]; // hex and non-hex
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#if GAUSSIAN==0
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double dispersion_precision = .0001;
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int dispersion_each = 1;
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vector<vector<ld>> dispersion[2];
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int dispersion_count;
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#endif
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void buildcellcrawler(cell *c) {
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(c->type == 6 ? s0 : s1).build(cellwalker(c,0));
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int sccid = c->type != 6;
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cellcrawler& cr = scc[sccid];
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cr.build(cellwalker(c,0));
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#if GAUSSIAN==0
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vector<ld> curtemp;
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vector<ld> newtemp;
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vector<int> qty;
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vector<pair<ld*, ld*> > pairs;
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int N = size(net);
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curtemp.resize(N, 0);
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newtemp.resize(N, 0);
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qty.resize(N, 0);
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for(int i=0; i<N; i++)
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forCellEx(c2, net[i].where) {
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neuron *nj = getNeuron(c2);
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if(nj) {
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pairs.emplace_back(&curtemp[i], &newtemp[neuronId(*nj)]);
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qty[i]++;
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}
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}
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curtemp[neuronId(*getNeuron(c))] = 1;
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ld vmin = 0, vmax = 1;
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int iter;
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auto &d = dispersion[sccid];
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d.clear();
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printf("Building dispersion...\n");
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for(iter=0; dispersion_count ? true : vmax > vmin * 1.5; iter++) {
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if(iter % dispersion_each == 0) {
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d.emplace_back(N);
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auto& dispvec = d.back();
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for(int i=0; i<N; i++) dispvec[i] = curtemp[neuronId(*getNeuron(cr.data[i].orig.c))] / vmax;
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if(size(d) == dispersion_count) break;
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}
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double df = dispersion_precision * (iter+1);
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double df0 = df / ceil(df);
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for(int i=0; i<df; i++) {
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for(auto& p: pairs)
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*p.second += *p.first;
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for(int i=0; i<N; i++) {
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curtemp[i] += (newtemp[i] / qty[i] - curtemp[i]) * df0;
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newtemp[i] = 0;
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}
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}
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vmin = vmax = curtemp[0];
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for(int i=0; i<N; i++)
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if(curtemp[i] < vmin) vmin = curtemp[i];
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else if(curtemp[i] > vmax) vmax = curtemp[i];
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}
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dispersion_count = size(d);
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printf("Dispersion count = %d\n", dispersion_count);
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#endif
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}
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bool finished() { return t == 0; }
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void step() {
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double sigma = maxdist * t / (perdist*(double) mul);
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if(t == 0) return;
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#if GAUSSIAN==1
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double sigma = maxdist * t / (perdist*(double) mul);
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#else
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int dispid = int(dispersion_count * (t-1.) / tmax);
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#endif
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// double sigma = maxdist * exp(-t / t1);
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int pct = (int) (100 * ((t*(double) mul) / perdist));
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if(pct != lpct) {
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lpct = pct;
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analyze();
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#if GAUSSIAN==1
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printf("t = %6d/%2dx%6d pct = %3d sigma=%10.7lf maxudist=%10.7lf\n", t, mul, perdist, pct, sigma, maxudist);
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#else
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printf("t = %6d/%2dx%6d pct = %3d dispid=%5d maxudist=%10.7lf\n", t, mul, perdist, pct, dispid, maxudist);
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#endif
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}
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int id = hrand(samples);
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neuron& n = winner(id);
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whowon[id] = &n;
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/*
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for(neuron& n2: net) {
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@ -294,13 +400,22 @@ void step() {
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n2.net[k] += nu * (irisdata[id][k] - n2.net[k]);
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} */
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cellcrawler& s = n.where->type == 6 ? s0 : s1;
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int sccid = n.where->type != 6;
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cellcrawler& s = scc[sccid];
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s.sprawl(cellwalker(n.where, 0));
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#if GAUSSIAN==0
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auto it = dispersion[sccid][dispid].begin();
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#endif
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for(auto& sd: s.data) {
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neuron *n2 = getNeuron(sd.target.c);
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if(!n2) continue;
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double nu = maxfac;
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#if GAUSSIAN==0
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nu *= *(it++);
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#else
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nu *= exp(-sqr(sd.dist/sigma));
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#endif
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for(int k=0; k<cols; k++)
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n2->net[k] += nu * (data[id].val[k] - n2->net[k]);
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}
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@ -347,6 +462,9 @@ void run(const char *fname, int _perdist, double _maxfac) {
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printf("samples = %d cells = %d maxdist = %d\n", samples, cells, maxdist);
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#if GAUSSIAN==0
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dispersion_count = 0;
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#endif
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c1 = currentmap->gamestart();
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cell *c2 = createMov(c1, 0);
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buildcellcrawler(c1);
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@ -354,7 +472,7 @@ void run(const char *fname, int _perdist, double _maxfac) {
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lpct = -46130;
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mul = 1;
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t = perdist*mul;
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tmax = t = perdist*mul;
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step();
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for(int i=0; i<3; i++) whattodraw[i] = -2;
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analyze();
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@ -364,14 +482,16 @@ 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(n - &net[0]) + "\n";
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help += "cell number: " + its(neuronId(*n)) + "\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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int qty = 0;
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for(int s=0; s<samples; s++) if(whowon[s] == n) {
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help += "sample "+its(s)+":";
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for(int k=0; k<cols; k++) help += " " + fts(data[s].val[k]);
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help += " "; help += data[s].name; help += "\n";
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qty++; if(qty >= 20) break;
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}
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}
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@ -402,6 +522,20 @@ void kload(const char *fname) {
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analyze();
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}
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void kclassify(const char *fname) {
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for(neuron& n: net) n.samples = 0;
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FILE *f = fopen(fname, "wt");
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for(int id=0; id<size(data); id++) {
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auto& w = winner(id);
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w.samples++;
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if(id % 100000 == 0) printf("%d/%d\n", id, size(data));
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fprintf(f, "%s;%d\n", data[id].name.c_str(), neuronId(w));
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}
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fclose(f);
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coloring();
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}
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void steps() {
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if(!kohonen::finished()) {
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unsigned int t = SDL_GetTicks();
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@ -417,6 +551,7 @@ void showMenu() {
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if(whattodraw[i] == -1) c = "u-matrix";
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else if(whattodraw[i] == -2) c = "u-matrix reversed";
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else if(whattodraw[i] == -3) c = "distance from marked ('m')";
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else if(whattodraw[i] == -4) c = "number of samples";
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else c = "column " + its(whattodraw[i]);
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dialog::addSelItem(XLAT("coloring (%1)", parts[i]), c, '1'+i);
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}
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@ -426,7 +561,7 @@ bool handleMenu(int sym, int uni) {
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if(uni >= '1' && uni <= '3') {
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int i = uni - '1';
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whattodraw[i]++;
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if(whattodraw[i] == cols) whattodraw[i] = -3;
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if(whattodraw[i] == cols) whattodraw[i] = -4;
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coloring();
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return true;
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}
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@ -1699,6 +1699,11 @@ int readArgs() {
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while(!kohonen::finished()) kohonen::step();
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shift(); kohonen::ksave(args());
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}
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else if(argis("-somclassify")) {
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PHASE(3);
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while(!kohonen::finished()) kohonen::step();
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shift(); kohonen::kclassify(args());
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}
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else if(argis("-somload")) {
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PHASE(3);
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shift(); kohonen::kload(args());
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