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rogueviz::kohonen:: better initialization system
This commit is contained in:
Zeno Rogue
parent
0ac88cbb2f
commit
d781bf538e
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@ -8,6 +8,14 @@
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namespace rogueviz { namespace kohonen {
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void initialize_neurons();
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void initialize_neurons_initial();
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void initialize_dispersion();
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void initialize_samples_to_show();
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void clear();
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void create_neurons();
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int kohonen_id;
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int columns;
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@ -73,14 +81,14 @@ double vnorm(kohvec& a, kohvec& b) {
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return diff;
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}
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void sominit(int, bool load_compressed = false);
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void uninit(int);
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bool noshow = false;
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vector<int> samples_to_show;
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void loadsamples(const string& fname) {
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data.clear();
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samples_to_show.clear();
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clear();
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fhstream f(fname, "rt");
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if(!f.f) {
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fprintf(stderr, "Could not load samples: %s\n", fname.c_str());
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@ -114,7 +122,6 @@ void loadsamples(const string& fname) {
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normalize();
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colnames.resize(columns);
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for(int i=0; i<columns; i++) colnames[i] = "Column " + its(i);
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uninit(0); sominit(1);
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}
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int tmax = 30000;
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@ -259,6 +266,9 @@ void distribute_neurons() {
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void analyze() {
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initialize_neurons();
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initialize_samples_to_show();
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setindex(true);
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maxudist = 0;
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@ -516,7 +526,8 @@ double ttpower = 1;
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void step() {
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if(t == 0) return;
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sominit(2);
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initialize_dispersion();
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initialize_neurons_initial();
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double tt = (t-1.) / tmax;
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tt = pow(tt, ttpower);
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@ -580,11 +591,13 @@ void step() {
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int initdiv = 1;
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int inited = 0;
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flagtype KS_ROGUEVIZ = 1;
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flagtype KS_NEURONS = 2;
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flagtype KS_DISPERSION = 4;
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flagtype KS_SAMPLES = 8;
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flagtype KS_NEURONS_INI = 16;
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void uninit(int initto) {
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if(inited > initto) inited = initto;
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}
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flagtype state = 0;
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vector<double> bdiffs;
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vector<unsigned short> bids;
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@ -640,109 +653,155 @@ void showbestsamples() {
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}
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int kohrestrict = 1000000;
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void sominit(int initto, bool load_compressed) {
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if(inited < 1 && initto >= 1) {
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inited = 1;
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if(!samples && !load_compressed) {
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fprintf(stderr, "Error: SOM without samples\n");
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exit(1);
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}
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init(&kohonen_id, RV_GRAPH | RV_HAVE_WEIGHT);
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weight_label = "quantity";
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printf("Initializing SOM (1)\n");
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vector<cell*> allcells;
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if(krad) {
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celllister cl(cwt.at, krad, 1000000, NULL);
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allcells = cl.lst;
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}
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else allcells = currentmap->allcells();
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if(isize(allcells) > kohrestrict) {
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map<cell*, int> clindex;
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for(int i=0; i<isize(allcells); i++) clindex[allcells[i]] = i;
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sort(allcells.begin(), allcells.end(), [&clindex] (cell *c1, cell *c2) {
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return make_pair(hdist0(tC0(ggmatrix(c1))), clindex[c1]) <
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make_pair(hdist0(tC0(ggmatrix(c2))), clindex[c2]);
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});
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int at = kohrestrict;
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ld dist = hdist0(tC0(ggmatrix(allcells[at-1])));
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while(at < isize(allcells) && hdist0(tC0(ggmatrix(allcells[at]))) < dist + 1e-6) at++;
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int at1 = kohrestrict;
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while(at1 > 0 && hdist0(tC0(ggmatrix(allcells[at1-1]))) > dist - 1e-6) at1--;
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printf("Cells numbered [%d,%d) are in the same distance\n", at1, at);
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allcells.resize(kohrestrict);
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}
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cells = isize(allcells);
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net.resize(cells);
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for(int i=0; i<cells; i++) net[i].where = allcells[i], allcells[i]->landparam = i;
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for(int i=0; i<cells; i++) {
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net[i].where->land = laCanvas;
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alloc(net[i].net);
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if(samples)
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for(int k=0; k<columns; k++)
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for(int z=0; z<initdiv; z++)
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net[i].net[k] += data[hrand(samples)].val[k] / initdiv;
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}
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for(neuron& n: net) for(int d=BARLEV; d>=7; d--) setdist(n.where, d, NULL);
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printf("samples = %d (%d) cells = %d\n", samples, isize(sample_vdata_id), cells);
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void initialize_rv() {
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if(state & KS_ROGUEVIZ) return;
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init(&kohonen_id, RV_GRAPH | RV_HAVE_WEIGHT);
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state |= KS_ROGUEVIZ;
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}
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if(!noshow) for(int s: samples_to_show) {
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int vdid = isize(vdata);
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sample_vdata_id[s] = vdid;
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vdata.emplace_back();
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auto &vd = vdata.back();
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vd.name = data[s].name;
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vd.cp = dftcolor;
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createViz(vdid, cwt.at, Id);
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storeall(vdid);
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}
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samples_to_show.clear();
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void initialize_neurons() {
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if(state & KS_NEURONS) return;
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create_neurons();
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state |= KS_NEURONS;
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}
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analyze();
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vector<cell*> gen_neuron_cells() {
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vector<cell*> allcells;
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if(krad) {
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celllister cl(cwt.at, krad, 1000000, NULL);
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allcells = cl.lst;
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}
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else if(kqty) {
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celllister cl(cwt.at, 999, kqty, NULL);
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allcells = cl.lst;
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allcells.resize(kqty);
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}
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else allcells = currentmap->allcells();
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if(isize(allcells) > kohrestrict) {
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map<cell*, int> clindex;
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for(int i=0; i<isize(allcells); i++) clindex[allcells[i]] = i;
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sort(allcells.begin(), allcells.end(), [&clindex] (cell *c1, cell *c2) {
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return make_pair(hdist0(tC0(ggmatrix(c1))), clindex[c1]) <
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make_pair(hdist0(tC0(ggmatrix(c2))), clindex[c2]);
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});
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int at = kohrestrict;
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ld dist = hdist0(tC0(ggmatrix(allcells[at-1])));
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while(at < isize(allcells) && hdist0(tC0(ggmatrix(allcells[at]))) < dist + 1e-6) at++;
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int at1 = kohrestrict;
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while(at1 > 0 && hdist0(tC0(ggmatrix(allcells[at1-1]))) > dist - 1e-6) at1--;
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printf("Cells numbered [%d,%d) are in the same distance\n", at1, at);
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allcells.resize(kohrestrict);
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for(int i=kohrestrict; i<isize(allcells); i++) {
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setdist(allcells[i], 0, nullptr);
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allcells[i]->wall = waInvisibleFloor;
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}
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}
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if(inited < 2 && initto >= 2) {
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inited = 2;
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return allcells;
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}
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DEBB(DF_LOG, ("Initializing SOM (2)"));
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if(gaussian) {
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DEBB(DF_LOG, ("dist = ", fts(mydistance(net[0].where, net[1].where))));
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cell *c1 = net[cells/2].where;
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vector<double> mapdist;
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for(neuron &n2: net) mapdist.push_back(mydistance(c1,n2.where));
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sort(mapdist.begin(), mapdist.end());
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maxdist = mapdist[isize(mapdist)*5/6] * distmul;
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DEBB(DF_LOG, ("maxdist = ", fts(maxdist)));
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}
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dispersion_count = 0;
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scc.clear();
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for(int i=0; i<cells; i++) {
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cell *c = net[i].where;
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auto cid = get_cellcrawler_id(c);
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if(!scc.count(cid.first)) {
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DEBB(DF_LOG, ("Building cellcrawler id = ", itsh(cid.first)));
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buildcellcrawler(c, scc[cid.first], cid.second);
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}
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}
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void create_neurons() {
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initialize_rv();
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lpct = -46130;
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if(!samples) {
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fprintf(stderr, "Error: SOM without samples\n");
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exit(1);
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}
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weight_label = "quantity";
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DEBBI(DF_LOG, ("Creating neurons"));
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auto allcells = gen_neuron_cells();
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cells = isize(allcells);
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net.resize(cells);
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for(int i=0; i<cells; i++) {
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net[i].where = allcells[i];
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allcells[i]->landparam = i;
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net[i].where->land = laCanvas;
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}
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for(neuron& n: net) for(int d=BARLEV; d>=7; d--) setdist(n.where, d, NULL);
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DEBB(DF_LOG, ("number of neurons = ", cells));
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}
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void set_neuron_initial() {
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initialize_neurons();
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DEBBI(DF_LOG, ("Setting initial neuron values"));
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for(int i=0; i<cells; i++) {
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alloc(net[i].net);
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for(int k=0; k<columns; k++)
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net[i].net[k] = 0;
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for(int k=0; k<columns; k++)
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for(int z=0; z<initdiv; z++)
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net[i].net[k] += data[hrand(samples)].val[k] / initdiv;
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}
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}
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void initialize_neurons_initial() {
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if(state & KS_NEURONS_INI) return;
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set_neuron_initial();
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state |= KS_NEURONS_INI;
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}
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void initialize_samples_to_show() {
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if(state & KS_SAMPLES) return;
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if(noshow) return;
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DEBBI(DF_LOG, ("Initializing samples-to-show (", isize(samples_to_show), " samples", ")"));
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if(!noshow) for(int s: samples_to_show) {
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int vdid = isize(vdata);
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sample_vdata_id[s] = vdid;
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vdata.emplace_back();
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auto &vd = vdata.back();
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vd.name = data[s].name;
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vd.cp = dftcolor;
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createViz(vdid, cwt.at, Id);
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storeall(vdid);
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}
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samples_to_show.clear();
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state |= KS_SAMPLES;
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}
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void initialize_dispersion() {
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if(state & KS_DISPERSION) return;
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initialize_neurons();
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DEBBI(DF_LOG, ("Initializing dispersion"));
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if(gaussian) {
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DEBB(DF_LOG, ("dist = ", fts(mydistance(net[0].where, net[1].where))));
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cell *c1 = net[cells/2].where;
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vector<double> mapdist;
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for(neuron &n2: net) mapdist.push_back(mydistance(c1,n2.where));
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sort(mapdist.begin(), mapdist.end());
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maxdist = mapdist[isize(mapdist)*5/6] * distmul;
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DEBB(DF_LOG, ("maxdist = ", fts(maxdist)));
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}
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dispersion_count = 0;
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scc.clear();
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for(int i=0; i<cells; i++) {
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cell *c = net[i].where;
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auto cid = get_cellcrawler_id(c);
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if(!scc.count(cid.first)) {
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DEBB(DF_LOG, ("Building cellcrawler id = ", itsh(cid.first)));
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buildcellcrawler(c, scc[cid.first], cid.second);
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}
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}
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lpct = -46130;
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state |= KS_DISPERSION;
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}
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void describe_cell(cell *c) {
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if(cmode & sm::HELP) return;
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if(vizid != &kohonen_id) return;
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@ -897,7 +956,7 @@ namespace levelline {
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}
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void ksave(const string& fname) {
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sominit(1);
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initialize_neurons_initial();
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FILE *f = fopen(fname.c_str(), "wt");
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if(!f) {
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fprintf(stderr, "Could not save the network\n");
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@ -913,7 +972,7 @@ void ksave(const string& fname) {
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}
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void kload(const string& fname) {
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sominit(1);
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initialize_neurons();
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int xcells;
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fhstream f(fname.c_str(), "rt");
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if(!f.f) {
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@ -936,7 +995,6 @@ void kload(const string& fname) {
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}
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void ksavew(const string& fname) {
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sominit(1);
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FILE *f = fopen(fname.c_str(), "wt");
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if(!f) {
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fprintf(stderr, "Could not save the weights: %s\n", fname.c_str());
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@ -949,7 +1007,6 @@ void ksavew(const string& fname) {
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}
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void kloadw(const string& fname) {
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sominit(1);
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FILE *f = fopen(fname.c_str(), "rt");
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if(!f) {
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fprintf(stderr, "Could not load the weights\n");
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@ -1013,7 +1070,7 @@ template<class T> void load_raw(string fname, vector<T>& v) {
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bool groupsizes_known = false;
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void do_classify() {
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sominit(1);
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initialize_neurons_initial();
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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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@ -1295,7 +1352,8 @@ void load_compressed(string name) {
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for(int i=0; i<columns; i++) f.read(colnames[i]);
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alloc(weights);
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for(int i=0; i<columns; i++) weights[i] = f.get_raw<float>();
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samples = 0; sominit(1, true);
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samples = 0;
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initialize_neurons_initial();
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// load neurons
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int N = f.get<int>();
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if(cells != N) {
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@ -1360,20 +1418,25 @@ int readArgs() {
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// #2: set parameters
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else if(argis("-somkrad")) {
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gaussian = 0; uninit(0);
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}
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else if(argis("-somskrad")) {
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shift(); krad = argi();
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state &=~ (KS_NEURONS | KS_NEURONS_INI | KS_DISPERSION);
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}
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else if(argis("-somskqty")) {
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shift(); kqty = argi();
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state &=~ (KS_NEURONS | KS_NEURONS_INI | KS_DISPERSION);
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}
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else if(argis("-somsim")) {
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gaussian = 0; uninit(1);
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gaussian = 0;
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state &=~ KS_DISPERSION;
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}
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else if(argis("-somcgauss")) {
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gaussian = 1; uninit(1);
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gaussian = 1;
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state &=~ KS_DISPERSION;
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}
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else if(argis("-somggauss")) {
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gaussian = 2; uninit(1);
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gaussian = 2;
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state &=~ KS_DISPERSION;
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}
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else if(argis("-sompct")) {
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shift(); qpct = argi();
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@ -1387,10 +1450,11 @@ int readArgs() {
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fprintf(stderr, "Dispersion parameter illegal\n");
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dispersion_end_at = 1.5;
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}
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uninit(1);
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state &=~ KS_DISPERSION;
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}
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else if(argis("-sominitdiv")) {
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shift(); initdiv = argi(); uninit(0);
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shift(); initdiv = argi();
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state &=~ KS_NEURONS_INI;
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}
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else if(argis("-somtmax")) {
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shift(); t = (t*1./tmax) * argi();
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@ -1402,7 +1466,9 @@ int readArgs() {
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}
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else if(argis("-somrun")) {
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t = tmax; sominit(1);
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initialize_rv();
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set_neuron_initial();
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t = tmax;
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}
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// #3: load the neuron data (usually without #2)
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@ -1554,26 +1620,33 @@ auto hooks2 = addHook(hooks_frame, 50, levelline::draw)
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+ addHook(hooks_readcolor, 100, kohonen_color);
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void clear() {
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if(data.empty()) return;
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printf("clearing Kohonen...\n");
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data.clear();
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sample_vdata_id.clear();
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colnames.clear();
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weights.clear();
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net.clear();
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whowon.clear();
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samples_to_show.clear();
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scc.clear();
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bdiffs.clear();
|
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bids.clear();
|
||||
bdiffn.clear();
|
||||
state = 0;
|
||||
}
|
||||
|
||||
auto hooks4 = addHook(hooks_clearmemory, 100, clear)
|
||||
+ addHook(hooks_configfile, 100, [] {
|
||||
param_f(precise_placement, "koh_placement")
|
||||
-> editable(0, 2, .2, "precise placement", "0 = make all visible, 1 = place ideally, n = place 1/n of the distance from center to ideal placement", 'p')
|
||||
-> set_reaction([] { if((state & KS_NEURONS) && (state & KS_SAMPLES)) distribute_neurons(); });
|
||||
});
|
||||
}}
|
||||
|
||||
namespace rogueviz {
|
||||
void mark(cell *c) {
|
||||
using namespace kohonen;
|
||||
if(vizid == &kohonen_id && inited >= 1) {
|
||||
if(vizid == &kohonen_id) {
|
||||
initialize_neurons();
|
||||
distfrom = getNeuronSlow(c);
|
||||
coloring();
|
||||
}
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user