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rogueviz::kohonen:: bestsample -1
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@ -197,6 +197,7 @@ void coloring() {
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besttofind = false;
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for(neuron& n: net) {
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double bdiff = 1e20;
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n.bestsample = -1;
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for(auto p: sample_vdata_id) {
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double diff = vnorm(n.net, data[p.first].val);
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if(diff < bdiff) bdiff = diff, n.bestsample = p.second;
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@ -205,7 +206,10 @@ void coloring() {
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}
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for(int i=0; i<cells; i++) {
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part(net[i].where->landparam_color, pid) = part(vdata[net[i].bestsample].cp.color1, pid+1);
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if(net[i].bestsample >= 0)
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part(net[i].where->landparam_color, pid) = part(vdata[net[i].bestsample].cp.color1, pid+1);
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else
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part(net[i].where->landparam_color, pid) = 128;
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}
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}
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@ -1192,6 +1196,7 @@ void do_classify() {
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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 i=0; i<cells; i++) net[i].bestsample = -1;
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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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@ -1322,7 +1327,8 @@ void klistsamples(const string& fname_samples, bool best, bool colorformat) {
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if(best)
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for(int n=0; n<cells; n++) {
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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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if(net[n].bestsample >= 0)
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klistsample(net[n].bestsample, n);
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}
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else
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for(auto p: sample_vdata_id) {
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