Sampler Halton [Hal64]

Description

The Halton sequence. Samples are generated from reversing the n-ary representation of their index. Each dimension has a different basis and the low discrepancy is ensured if all basis are distinct and are prime numbers.
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/Halton.cpp  
include/utk/samplers/SamplerHalton.hpp

Usage

Exe C++ Python

Halton sampler
Usage: ./Halton [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/SamplerHalton.hpp>

int main()
{
    utk::Pointset<double> pts;
    utk::SamplerHalton halton(2 /* dimension */);

    // Default are the first 100 primes numbers
    unsigned int basis[] = {3, 5, 7} // Skip base 2
    halton.setBasisList(basis, 3);

    // Check for no errors
    if (halton.generateSamples(pts, 1024 /* Number of points */))
    {
        write_text_pointset("halton2d.dat", pts);
    }
}

import pyutk

halton = pyutk.Halton(d=2)
# Default is the first 100 primes numbers
halton.setBasisList([3, 5, 7]) # Skip base 2
samples = halton.sample(1024)  # This is a numpy array !

Results

License

See licence.md file.