mirror of
https://github.com/zenorogue/hyperrogue.git
synced 2025-01-11 18:00:34 +00:00
rogueviz::kohonen:: better initialization system
This commit is contained in:
Zeno Rogue
parent
0ac88cbb2f
commit
d781bf538e
@ -8,6 +8,14 @@
|
||||
|
||||
namespace rogueviz { namespace kohonen {
|
||||
|
||||
void initialize_neurons();
|
||||
void initialize_neurons_initial();
|
||||
void initialize_dispersion();
|
||||
void initialize_samples_to_show();
|
||||
void clear();
|
||||
void create_neurons();
|
||||
|
||||
|
||||
int kohonen_id;
|
||||
|
||||
int columns;
|
||||
@ -73,14 +81,14 @@ double vnorm(kohvec& a, kohvec& b) {
|
||||
return diff;
|
||||
}
|
||||
|
||||
void sominit(int, bool load_compressed = false);
|
||||
void uninit(int);
|
||||
|
||||
bool noshow = false;
|
||||
|
||||
vector<int> samples_to_show;
|
||||
|
||||
void loadsamples(const string& fname) {
|
||||
data.clear();
|
||||
samples_to_show.clear();
|
||||
clear();
|
||||
fhstream f(fname, "rt");
|
||||
if(!f.f) {
|
||||
fprintf(stderr, "Could not load samples: %s\n", fname.c_str());
|
||||
@ -114,7 +122,6 @@ void loadsamples(const string& fname) {
|
||||
normalize();
|
||||
colnames.resize(columns);
|
||||
for(int i=0; i<columns; i++) colnames[i] = "Column " + its(i);
|
||||
uninit(0); sominit(1);
|
||||
}
|
||||
|
||||
int tmax = 30000;
|
||||
@ -259,6 +266,9 @@ void distribute_neurons() {
|
||||
|
||||
void analyze() {
|
||||
|
||||
initialize_neurons();
|
||||
initialize_samples_to_show();
|
||||
|
||||
setindex(true);
|
||||
|
||||
maxudist = 0;
|
||||
@ -516,7 +526,8 @@ double ttpower = 1;
|
||||
void step() {
|
||||
|
||||
if(t == 0) return;
|
||||
sominit(2);
|
||||
initialize_dispersion();
|
||||
initialize_neurons_initial();
|
||||
|
||||
double tt = (t-1.) / tmax;
|
||||
tt = pow(tt, ttpower);
|
||||
@ -580,11 +591,13 @@ void step() {
|
||||
|
||||
int initdiv = 1;
|
||||
|
||||
int inited = 0;
|
||||
flagtype KS_ROGUEVIZ = 1;
|
||||
flagtype KS_NEURONS = 2;
|
||||
flagtype KS_DISPERSION = 4;
|
||||
flagtype KS_SAMPLES = 8;
|
||||
flagtype KS_NEURONS_INI = 16;
|
||||
|
||||
void uninit(int initto) {
|
||||
if(inited > initto) inited = initto;
|
||||
}
|
||||
flagtype state = 0;
|
||||
|
||||
vector<double> bdiffs;
|
||||
vector<unsigned short> bids;
|
||||
@ -640,109 +653,155 @@ void showbestsamples() {
|
||||
}
|
||||
|
||||
int kohrestrict = 1000000;
|
||||
|
||||
void sominit(int initto, bool load_compressed) {
|
||||
|
||||
if(inited < 1 && initto >= 1) {
|
||||
inited = 1;
|
||||
if(!samples && !load_compressed) {
|
||||
fprintf(stderr, "Error: SOM without samples\n");
|
||||
exit(1);
|
||||
}
|
||||
|
||||
init(&kohonen_id, RV_GRAPH | RV_HAVE_WEIGHT);
|
||||
weight_label = "quantity";
|
||||
|
||||
printf("Initializing SOM (1)\n");
|
||||
|
||||
vector<cell*> allcells;
|
||||
|
||||
if(krad) {
|
||||
celllister cl(cwt.at, krad, 1000000, NULL);
|
||||
allcells = cl.lst;
|
||||
}
|
||||
else allcells = currentmap->allcells();
|
||||
|
||||
if(isize(allcells) > kohrestrict) {
|
||||
map<cell*, int> clindex;
|
||||
for(int i=0; i<isize(allcells); i++) clindex[allcells[i]] = i;
|
||||
sort(allcells.begin(), allcells.end(), [&clindex] (cell *c1, cell *c2) {
|
||||
return make_pair(hdist0(tC0(ggmatrix(c1))), clindex[c1]) <
|
||||
make_pair(hdist0(tC0(ggmatrix(c2))), clindex[c2]);
|
||||
});
|
||||
int at = kohrestrict;
|
||||
ld dist = hdist0(tC0(ggmatrix(allcells[at-1])));
|
||||
while(at < isize(allcells) && hdist0(tC0(ggmatrix(allcells[at]))) < dist + 1e-6) at++;
|
||||
int at1 = kohrestrict;
|
||||
while(at1 > 0 && hdist0(tC0(ggmatrix(allcells[at1-1]))) > dist - 1e-6) at1--;
|
||||
printf("Cells numbered [%d,%d) are in the same distance\n", at1, at);
|
||||
allcells.resize(kohrestrict);
|
||||
}
|
||||
|
||||
cells = isize(allcells);
|
||||
net.resize(cells);
|
||||
for(int i=0; i<cells; i++) net[i].where = allcells[i], allcells[i]->landparam = i;
|
||||
for(int i=0; i<cells; i++) {
|
||||
net[i].where->land = laCanvas;
|
||||
alloc(net[i].net);
|
||||
|
||||
if(samples)
|
||||
for(int k=0; k<columns; k++)
|
||||
for(int z=0; z<initdiv; z++)
|
||||
net[i].net[k] += data[hrand(samples)].val[k] / initdiv;
|
||||
}
|
||||
|
||||
for(neuron& n: net) for(int d=BARLEV; d>=7; d--) setdist(n.where, d, NULL);
|
||||
|
||||
printf("samples = %d (%d) cells = %d\n", samples, isize(sample_vdata_id), cells);
|
||||
void initialize_rv() {
|
||||
if(state & KS_ROGUEVIZ) return;
|
||||
init(&kohonen_id, RV_GRAPH | RV_HAVE_WEIGHT);
|
||||
state |= KS_ROGUEVIZ;
|
||||
}
|
||||
|
||||
if(!noshow) for(int s: samples_to_show) {
|
||||
int vdid = isize(vdata);
|
||||
sample_vdata_id[s] = vdid;
|
||||
vdata.emplace_back();
|
||||
auto &vd = vdata.back();
|
||||
vd.name = data[s].name;
|
||||
vd.cp = dftcolor;
|
||||
createViz(vdid, cwt.at, Id);
|
||||
storeall(vdid);
|
||||
}
|
||||
|
||||
samples_to_show.clear();
|
||||
void initialize_neurons() {
|
||||
if(state & KS_NEURONS) return;
|
||||
create_neurons();
|
||||
state |= KS_NEURONS;
|
||||
}
|
||||
|
||||
analyze();
|
||||
vector<cell*> gen_neuron_cells() {
|
||||
vector<cell*> allcells;
|
||||
|
||||
if(krad) {
|
||||
celllister cl(cwt.at, krad, 1000000, NULL);
|
||||
allcells = cl.lst;
|
||||
}
|
||||
else if(kqty) {
|
||||
celllister cl(cwt.at, 999, kqty, NULL);
|
||||
allcells = cl.lst;
|
||||
allcells.resize(kqty);
|
||||
}
|
||||
else allcells = currentmap->allcells();
|
||||
|
||||
if(isize(allcells) > kohrestrict) {
|
||||
map<cell*, int> clindex;
|
||||
for(int i=0; i<isize(allcells); i++) clindex[allcells[i]] = i;
|
||||
sort(allcells.begin(), allcells.end(), [&clindex] (cell *c1, cell *c2) {
|
||||
return make_pair(hdist0(tC0(ggmatrix(c1))), clindex[c1]) <
|
||||
make_pair(hdist0(tC0(ggmatrix(c2))), clindex[c2]);
|
||||
});
|
||||
int at = kohrestrict;
|
||||
ld dist = hdist0(tC0(ggmatrix(allcells[at-1])));
|
||||
while(at < isize(allcells) && hdist0(tC0(ggmatrix(allcells[at]))) < dist + 1e-6) at++;
|
||||
int at1 = kohrestrict;
|
||||
while(at1 > 0 && hdist0(tC0(ggmatrix(allcells[at1-1]))) > dist - 1e-6) at1--;
|
||||
printf("Cells numbered [%d,%d) are in the same distance\n", at1, at);
|
||||
allcells.resize(kohrestrict);
|
||||
for(int i=kohrestrict; i<isize(allcells); i++) {
|
||||
setdist(allcells[i], 0, nullptr);
|
||||
allcells[i]->wall = waInvisibleFloor;
|
||||
}
|
||||
}
|
||||
|
||||
if(inited < 2 && initto >= 2) {
|
||||
inited = 2;
|
||||
return allcells;
|
||||
}
|
||||
|
||||
DEBB(DF_LOG, ("Initializing SOM (2)"));
|
||||
|
||||
if(gaussian) {
|
||||
DEBB(DF_LOG, ("dist = ", fts(mydistance(net[0].where, net[1].where))));
|
||||
cell *c1 = net[cells/2].where;
|
||||
vector<double> mapdist;
|
||||
for(neuron &n2: net) mapdist.push_back(mydistance(c1,n2.where));
|
||||
sort(mapdist.begin(), mapdist.end());
|
||||
maxdist = mapdist[isize(mapdist)*5/6] * distmul;
|
||||
DEBB(DF_LOG, ("maxdist = ", fts(maxdist)));
|
||||
}
|
||||
|
||||
dispersion_count = 0;
|
||||
|
||||
scc.clear();
|
||||
for(int i=0; i<cells; i++) {
|
||||
cell *c = net[i].where;
|
||||
auto cid = get_cellcrawler_id(c);
|
||||
if(!scc.count(cid.first)) {
|
||||
DEBB(DF_LOG, ("Building cellcrawler id = ", itsh(cid.first)));
|
||||
buildcellcrawler(c, scc[cid.first], cid.second);
|
||||
}
|
||||
}
|
||||
void create_neurons() {
|
||||
initialize_rv();
|
||||
|
||||
lpct = -46130;
|
||||
if(!samples) {
|
||||
fprintf(stderr, "Error: SOM without samples\n");
|
||||
exit(1);
|
||||
}
|
||||
|
||||
weight_label = "quantity";
|
||||
|
||||
DEBBI(DF_LOG, ("Creating neurons"));
|
||||
|
||||
auto allcells = gen_neuron_cells();
|
||||
|
||||
|
||||
cells = isize(allcells);
|
||||
net.resize(cells);
|
||||
for(int i=0; i<cells; i++) {
|
||||
net[i].where = allcells[i];
|
||||
allcells[i]->landparam = i;
|
||||
net[i].where->land = laCanvas;
|
||||
}
|
||||
|
||||
for(neuron& n: net) for(int d=BARLEV; d>=7; d--) setdist(n.where, d, NULL);
|
||||
DEBB(DF_LOG, ("number of neurons = ", cells));
|
||||
}
|
||||
|
||||
void set_neuron_initial() {
|
||||
initialize_neurons();
|
||||
DEBBI(DF_LOG, ("Setting initial neuron values"));
|
||||
for(int i=0; i<cells; i++) {
|
||||
alloc(net[i].net);
|
||||
for(int k=0; k<columns; k++)
|
||||
net[i].net[k] = 0;
|
||||
for(int k=0; k<columns; k++)
|
||||
for(int z=0; z<initdiv; z++)
|
||||
net[i].net[k] += data[hrand(samples)].val[k] / initdiv;
|
||||
}
|
||||
}
|
||||
|
||||
void initialize_neurons_initial() {
|
||||
if(state & KS_NEURONS_INI) return;
|
||||
set_neuron_initial();
|
||||
state |= KS_NEURONS_INI;
|
||||
}
|
||||
|
||||
void initialize_samples_to_show() {
|
||||
if(state & KS_SAMPLES) return;
|
||||
if(noshow) return;
|
||||
|
||||
DEBBI(DF_LOG, ("Initializing samples-to-show (", isize(samples_to_show), " samples", ")"));
|
||||
if(!noshow) for(int s: samples_to_show) {
|
||||
int vdid = isize(vdata);
|
||||
sample_vdata_id[s] = vdid;
|
||||
vdata.emplace_back();
|
||||
auto &vd = vdata.back();
|
||||
vd.name = data[s].name;
|
||||
vd.cp = dftcolor;
|
||||
createViz(vdid, cwt.at, Id);
|
||||
storeall(vdid);
|
||||
}
|
||||
|
||||
samples_to_show.clear();
|
||||
state |= KS_SAMPLES;
|
||||
}
|
||||
|
||||
void initialize_dispersion() {
|
||||
if(state & KS_DISPERSION) return;
|
||||
|
||||
initialize_neurons();
|
||||
|
||||
DEBBI(DF_LOG, ("Initializing dispersion"));
|
||||
|
||||
if(gaussian) {
|
||||
DEBB(DF_LOG, ("dist = ", fts(mydistance(net[0].where, net[1].where))));
|
||||
cell *c1 = net[cells/2].where;
|
||||
vector<double> mapdist;
|
||||
for(neuron &n2: net) mapdist.push_back(mydistance(c1,n2.where));
|
||||
sort(mapdist.begin(), mapdist.end());
|
||||
maxdist = mapdist[isize(mapdist)*5/6] * distmul;
|
||||
DEBB(DF_LOG, ("maxdist = ", fts(maxdist)));
|
||||
}
|
||||
|
||||
dispersion_count = 0;
|
||||
|
||||
scc.clear();
|
||||
for(int i=0; i<cells; i++) {
|
||||
cell *c = net[i].where;
|
||||
auto cid = get_cellcrawler_id(c);
|
||||
if(!scc.count(cid.first)) {
|
||||
DEBB(DF_LOG, ("Building cellcrawler id = ", itsh(cid.first)));
|
||||
buildcellcrawler(c, scc[cid.first], cid.second);
|
||||
}
|
||||
}
|
||||
|
||||
lpct = -46130;
|
||||
state |= KS_DISPERSION;
|
||||
}
|
||||
|
||||
void describe_cell(cell *c) {
|
||||
if(cmode & sm::HELP) return;
|
||||
if(vizid != &kohonen_id) return;
|
||||
@ -897,7 +956,7 @@ namespace levelline {
|
||||
}
|
||||
|
||||
void ksave(const string& fname) {
|
||||
sominit(1);
|
||||
initialize_neurons_initial();
|
||||
FILE *f = fopen(fname.c_str(), "wt");
|
||||
if(!f) {
|
||||
fprintf(stderr, "Could not save the network\n");
|
||||
@ -913,7 +972,7 @@ void ksave(const string& fname) {
|
||||
}
|
||||
|
||||
void kload(const string& fname) {
|
||||
sominit(1);
|
||||
initialize_neurons();
|
||||
int xcells;
|
||||
fhstream f(fname.c_str(), "rt");
|
||||
if(!f.f) {
|
||||
@ -936,7 +995,6 @@ void kload(const string& fname) {
|
||||
}
|
||||
|
||||
void ksavew(const string& fname) {
|
||||
sominit(1);
|
||||
FILE *f = fopen(fname.c_str(), "wt");
|
||||
if(!f) {
|
||||
fprintf(stderr, "Could not save the weights: %s\n", fname.c_str());
|
||||
@ -949,7 +1007,6 @@ void ksavew(const string& fname) {
|
||||
}
|
||||
|
||||
void kloadw(const string& fname) {
|
||||
sominit(1);
|
||||
FILE *f = fopen(fname.c_str(), "rt");
|
||||
if(!f) {
|
||||
fprintf(stderr, "Could not load the weights\n");
|
||||
@ -1013,7 +1070,7 @@ template<class T> void load_raw(string fname, vector<T>& v) {
|
||||
bool groupsizes_known = false;
|
||||
|
||||
void do_classify() {
|
||||
sominit(1);
|
||||
initialize_neurons_initial();
|
||||
if(bids.empty()) {
|
||||
printf("Classifying...\n");
|
||||
bids.resize(samples, 0);
|
||||
@ -1295,7 +1352,8 @@ void load_compressed(string name) {
|
||||
for(int i=0; i<columns; i++) f.read(colnames[i]);
|
||||
alloc(weights);
|
||||
for(int i=0; i<columns; i++) weights[i] = f.get_raw<float>();
|
||||
samples = 0; sominit(1, true);
|
||||
samples = 0;
|
||||
initialize_neurons_initial();
|
||||
// load neurons
|
||||
int N = f.get<int>();
|
||||
if(cells != N) {
|
||||
@ -1360,20 +1418,25 @@ int readArgs() {
|
||||
|
||||
// #2: set parameters
|
||||
|
||||
else if(argis("-somkrad")) {
|
||||
gaussian = 0; uninit(0);
|
||||
}
|
||||
else if(argis("-somskrad")) {
|
||||
shift(); krad = argi();
|
||||
state &=~ (KS_NEURONS | KS_NEURONS_INI | KS_DISPERSION);
|
||||
}
|
||||
else if(argis("-somskqty")) {
|
||||
shift(); kqty = argi();
|
||||
state &=~ (KS_NEURONS | KS_NEURONS_INI | KS_DISPERSION);
|
||||
}
|
||||
else if(argis("-somsim")) {
|
||||
gaussian = 0; uninit(1);
|
||||
gaussian = 0;
|
||||
state &=~ KS_DISPERSION;
|
||||
}
|
||||
else if(argis("-somcgauss")) {
|
||||
gaussian = 1; uninit(1);
|
||||
gaussian = 1;
|
||||
state &=~ KS_DISPERSION;
|
||||
}
|
||||
else if(argis("-somggauss")) {
|
||||
gaussian = 2; uninit(1);
|
||||
gaussian = 2;
|
||||
state &=~ KS_DISPERSION;
|
||||
}
|
||||
else if(argis("-sompct")) {
|
||||
shift(); qpct = argi();
|
||||
@ -1387,10 +1450,11 @@ int readArgs() {
|
||||
fprintf(stderr, "Dispersion parameter illegal\n");
|
||||
dispersion_end_at = 1.5;
|
||||
}
|
||||
uninit(1);
|
||||
state &=~ KS_DISPERSION;
|
||||
}
|
||||
else if(argis("-sominitdiv")) {
|
||||
shift(); initdiv = argi(); uninit(0);
|
||||
shift(); initdiv = argi();
|
||||
state &=~ KS_NEURONS_INI;
|
||||
}
|
||||
else if(argis("-somtmax")) {
|
||||
shift(); t = (t*1./tmax) * argi();
|
||||
@ -1402,7 +1466,9 @@ int readArgs() {
|
||||
}
|
||||
|
||||
else if(argis("-somrun")) {
|
||||
t = tmax; sominit(1);
|
||||
initialize_rv();
|
||||
set_neuron_initial();
|
||||
t = tmax;
|
||||
}
|
||||
|
||||
// #3: load the neuron data (usually without #2)
|
||||
@ -1554,26 +1620,33 @@ auto hooks2 = addHook(hooks_frame, 50, levelline::draw)
|
||||
+ addHook(hooks_readcolor, 100, kohonen_color);
|
||||
|
||||
void clear() {
|
||||
if(data.empty()) return;
|
||||
printf("clearing Kohonen...\n");
|
||||
data.clear();
|
||||
sample_vdata_id.clear();
|
||||
colnames.clear();
|
||||
weights.clear();
|
||||
net.clear();
|
||||
whowon.clear();
|
||||
samples_to_show.clear();
|
||||
scc.clear();
|
||||
bdiffs.clear();
|
||||
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