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rogueviz::kohonen:: improved step debug, changed tt calculation

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This commit is contained in:
Zeno Rogue 2021-03-31 00:50:37 +02:00
parent b4d18ecc7f
commit a4113d39a7
1 changed files with 12 additions and 7 deletions
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19
rogueviz/kohonen.cpp
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@ -639,7 +639,7 @@ void step() {
initialize_dispersion();
initialize_neurons_initial();
double tt = (t-1.) / tmax;
double tt = (t-.5) / tmax;
tt = pow(tt, ttpower);
double sigma = maxdist * tt;
@ -648,14 +648,13 @@ void step() {
if(qpct) {
int pct = (int) ((qpct * (t+.0)) / tmax);
if(pct != lpct) {
printf("pct %d lpct %d\n", pct, lpct);
lpct = pct;
analyze();
if(gaussian)
printf("t = %6d/%6d %3d%% sigma=%10.7lf maxudist=%10.7lf\n", t, tmax, pct, sigma, maxudist);
println(hlog, format("t = %6d/%6d %3d%% sigma=%10.7lf maxudist=%10.7lf\n", t, tmax, pct, sigma, maxudist));
else
printf("t = %6d/%6d %3d%% dispid=%5d maxudist=%10.7lf\n", t, tmax, pct, dispid, maxudist);
println(hlog, format("t = %6d/%6d %3d%% dispid=%5d maxudist=%10.7lf\n", t, tmax, pct, dispid, maxudist));
}
}
int id = hrand(samples);
@ -686,13 +685,19 @@ void step() {
if(!n2) continue;
double nu = learning_factor;
if(gaussian)
if(gaussian) {
nu *= exp(-sqr(sd.dist/sigma));
if(isnan(nu))
throw hr_exception(lalign(0, "obtained nan, ", sd.dist, " / ", sigma));
}
else
nu *= *(it++);
for(int k=0; k<columns; k++)
for(int k=0; k<columns; k++) {
n2->net[k] += nu * (data[id].val[k] - n2->net[k]);
if(isnan(n2->net[k]))
throw hr_exception("obtained nan somehow, nu = " + lalign(0, nu));
}
}
t--;