Contents

Installation

The framework is available at the PyPi Repository:

pip install -U pysampling

Usage

The method to be used for sampling using different algorithm must be import from pysampling.sample. Here, we use Latin Hypercube Sampling to generate 50 points in 2 dimensions.

[1]:

from pysampling.sample import sample

X = sample("lhs", 50, 2)

Then, we recommend using matpotlib or other visualization libraries to have a look at the results:

[2]:

import matplotlib.pyplot as plt

plt.scatter(X[:, 0], X[:, 1], s=30, facecolors='none', edgecolors='r')
plt.show()
_images/index_9_0.png

Features

So far our library provides the following implementations:

  • Random (‘random’)

  • Latin Hypercube Sampling (‘lhs’)

  • Sobol (‘sobol’)

  • Halton (‘halton’)

The initialization of each of those will be shown in the following. Let us first define a method that helps us to visualize them in a 2d space.

[3]:

import matplotlib.pyplot as plt

def show(X):
    plt.scatter(X[:, 0], X[:, 1], s=30, facecolors='none', edgecolors='r')
    plt.show()

Random (‘random’)

[4]:

X = sample("random", 50, 2, seed=1)
show(X)
_images/index_15_0.png

Latin Hypercube Sampling (‘lhs’)

[5]:

X = sample("lhs", 50, 2, seed=1)
show(X)
_images/index_17_0.png

Sobol (‘sobol’)

[6]:

X = sample("sobol", 84, 2)
show(X)
_images/index_19_0.png 
[7]:

X = sample("sobol", 84, 2, n_skip=100, n_leap=10)
show(X)
_images/index_20_0.png

Halton (‘halton’)

[8]:

X = sample("halton", 100, 2)
show(X)
_images/index_22_0.png

Contact

My personal homepage

Feel free to contact me if you have any question:

Julian Blank (blankjul [at] egr.msu.edu)
Michigan State University
Computational Optimization and Innovation Laboratory (COIN)
East Lansing, MI 48824, USA