Sampler stratified

Description

The domain is partitioned with a grid of k x k. In each cell of this grid, a random sample is generated.

For a more precise description of this sampler and its performances in terms of aliasing and discrepancy, please refer to the following web bundle https://liris.cnrs.fr/ldbn/HTML_bundle/index.html.

Files

src/samplers/Stratified.cpp  
include/utk/samplers/SamplerStratified.hpp

Usage

Exe C++ Python

Stratified sampler
Usage: ./Stratified [OPTIONS]

Options:
  -h,--help                   Print this help message and exit
  -n UINT REQUIRED            Number of points
  -d UINT REQUIRED            Dimensions
  -s,--seed UINT              UNUSED ! Here for compatibility with others.
  -m UINT [1]                 Number of pointsets
  -o,--out TEXT [out.dat]     Output file (format). {i} splits outputs in multiple files and token is replaced by index.
  --silent                    Silence UTK logs

#include <utk/utils/PointsetIO.hpp>
#include <utk/utils/Pointset.hpp>
#include <utk/samplers/SamplerStratified.hpp>

int main()
{
    utk::Pointset<double> pts;

    utk::SamplerStratified st(2 /* dimension */);
    st.setRandomSeed(/* empty means random, can also pass a number */);
    // Check for no errors
    if (st.generateSamples(pts, 1024 /* Number of points */))
    {
        write_text_pointset("st2d.dat", pts);
    }
}

import pyutk

st = pyutk.Stratified(d=2)
samples = st.sample(1024) # This is a numpy array !

Results

License

See Licence.md file.