This library allows to generate Nearly Orthogonal Latin Hypercubes (NOLH) according to Cioppa (2007) and De Rainville et al. (2012) and reference therein.
Using pip
$ pip install pynolh
Clone the repository
$ git clone http://github.com/fmder/pynolh.git
and from the cloned directory type
$ python setup.py install
PyNOLH requires Numpy.
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.
See the Quasi Random Sequences Repository for more configurations.
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.