Clean samples after projection
When projecting samples from uniformly sampled viable space, we have the following independent problems:
- samples can loose uniform distribution (think: projecting uniformly sampled sphere),
- samples can become much more dense (think: projecting rectangular box).
This could be fixed by a simple, random elimination of samples until they are again distributed uniformly over the projection domain, with a target coverage density.
Why bother? Because less samples means less model evaluations, i.e. faster projections processing.
This is related to issue #29. HYPERSPACE sampling provides cleaning()
function which is used in ELexp()
before the actual uniform sampling with MEBS()
is done.