pynolh 0.1

Installation

Clone the repository

$ git clone http://github.com/fmder/pynolh.git

and from the cloned directory type

$ python setup.py install

PyNOLH requires Numpy.

Usage

The library contains a single generator and a function to retrieve the necessary parameters from a desired dimensionality. To generate a 6 dimension NOLH from the indentity permutation:

import pynolh

dim = 6
m, q, r = pynolh.params(dim)
conf = range(q)
remove = range(dim - r, dim)
nolh = pynolh.nolh(conf, remove)

The NOLH returned is a numpy array with one row being one sample.

You can also produce a NOLH from a random permutation configuration vector and remove random columns:

import pynolh
import random

dim = 6
m, q, r = pynolh.params(dim)
conf = random.sample(range(q), q)
remove = random.sample(range(q), r)
nolh = pynolh.nolh(conf, remove)

The nolh() function accepts configurations with either numbers in [0 q-1] or [1 q].

import pynolh

dim = 6
m, q, r = pynolh.params(dim)
conf = range(1, q + 1)
remove = range(dim - r + 1, dim + 1)
nolh = pynolh.nolh(conf, remove)

Some prebuilt configurations are given within the library. The CONF module attribute is a dictionary with the dimension as key and a configuration, columns to remove pair as value.

import pynolh

conf, remove = pynolh.CONF[6]
nolh = pynolh.nolh(conf, remove)

The configurations for dimensions 2 to 7 are from Cioppa (2007) and 8 to 29 are from De Rainville et al. 2012.

Configuration Repository

See the Quasi Random Sequences Repository for more configurations.

References

Cioppa, T. M., & Lucas, T. W. (2007). Efficient nearly orthogonal and space-filling Latin hypercubes. Technometrics, 49(1).

De Rainville, F.-M., Gagné, C., Teytaud, O., & Laurendeau, D. (2012). Evolutionary optimization of low-discrepancy sequences. ACM Transactions on Modeling and Computer Simulation (TOMACS), 22(2), 9.