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rogueviz::kohonen:: number of displayed observations per cell dependent on the number of total observations there

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Zeno Rogue 2018-07-05 07:30:15 +02:00
parent 2efbccd334
commit 300d0b80ed
1 changed files with 45 additions and 5 deletions
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50
rogueviz-kohonen.cpp
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@ -21,14 +21,16 @@ vector<int> samples_shown;
int whattodraw[3] = {-2,-2,-2}; int whattodraw[3] = {-2,-2,-2};
int min_group = 10, max_group = 10;
struct neuron { struct neuron {
kohvec net; kohvec net;
cell *where; cell *where;
double udist; double udist;
int lpbak; int lpbak;
int col; int col;
int allsamples, drawn_samples, csample, bestsample; int allsamples, drawn_samples, csample, bestsample, max_group_here;
neuron() { drawn_samples = allsamples = bestsample = 0; } neuron() { drawn_samples = allsamples = bestsample = 0; max_group_here = max_group; }
}; };
vector<string> colnames; vector<string> colnames;
@ -560,8 +562,6 @@ void uninit(int initto) {
if(inited > initto) inited = initto; if(inited > initto) inited = initto;
} }
int max_group = 10;
vector<double> bdiffs; vector<double> bdiffs;
vector<unsigned short> bids; vector<unsigned short> bids;
vector<double> bdiffn; vector<double> bdiffn;
@ -571,7 +571,7 @@ int showsample(int id) {
if(samples_shown[i] == id) if(samples_shown[i] == id)
return i; return i;
if(bids.size()) { if(bids.size()) {
if(net[bids[id]].drawn_samples >= max_group) { if(net[bids[id]].drawn_samples >= net[bids[id]].max_group_here) {
ld bdist = 1e18; ld bdist = 1e18;
int whichid = -1; int whichid = -1;
for(int i=0; i<isize(samples_shown); i++) for(int i=0; i<isize(samples_shown); i++)
@ -959,6 +959,8 @@ template<class T> void load_raw(string fname, vector<T>& v) {
fclose(f); fclose(f);
} }
bool groupsizes_known = false;
void do_classify() { void do_classify() {
sominit(1); sominit(1);
if(bids.empty()) { if(bids.empty()) {
@ -993,9 +995,41 @@ void do_classify() {
for(int i=0; i<samples; i++) whowon[i] = &net[bids[i]]; for(int i=0; i<samples; i++) whowon[i] = &net[bids[i]];
for(neuron& n: net) n.allsamples = 0; for(neuron& n: net) n.allsamples = 0;
for(int sn: bids) net[sn].allsamples++; for(int sn: bids) net[sn].allsamples++;
if(!groupsizes_known) {
groupsizes_known = true;
vector<int> neurons_to_sort;
for(int i=0; i<cells; i++) neurons_to_sort.push_back(i);
sort(neurons_to_sort.begin(), neurons_to_sort.end(), [] (int i, int j) { return net[i].allsamples < net[j].allsamples; });
int last = 0;
int lastfirst = 0, lastlast = 0;
for(int i=0; i<cells; i++) {
int ngroup = min_group + ((max_group - min_group) * i + (cells/2)) / (cells-1);
int as = net[neurons_to_sort[i]].allsamples;
if(ngroup != last) {
if(last) printf("%d: %d - %d\n", last, lastfirst, lastlast);
last = ngroup; lastfirst = as;
}
net[neurons_to_sort[i]].max_group_here = ngroup;
lastlast = as;
}
if(last) printf("%d: %d - %d\n", last, lastfirst, lastlast);
}
coloring(); coloring();
} }
void fillgroups() {
do_classify();
vector<int> samples_to_sort;
for(int i=0; i<samples; i++) samples_to_sort.push_back(i);
hrandom_shuffle(&samples_to_sort[0], samples);
for(int i=0; i<samples; i++) if(net[bids[i]].drawn_samples < net[bids[i]].max_group_here)
showsample(i);
distribute_neurons();
}
void kclassify(const string& fname_classify) { void kclassify(const string& fname_classify) {
do_classify(); do_classify();
@ -1273,6 +1307,12 @@ int readArgs() {
else if(argis("-som_maxgroup")) { else if(argis("-som_maxgroup")) {
shift(); max_group = argi(); shift(); max_group = argi();
} }
else if(argis("-som_mingroup")) {
shift(); min_group = argi();
}
else if(argis("-som_fillgroups")) {
fillgroups();
}
else if(argis("-som_load_edges")) { else if(argis("-som_load_edges")) {
shift(); kohonen::load_edges(args(), 0); shift(); kohonen::load_edges(args(), 0);
} }