hasemneeds.blogg.se - april 2022

#LATIN HYPERCUBE SAMPLING SCRIPT PYTHON INSTALL#
#LATIN HYPERCUBE SAMPLING SCRIPT PYTHON DOWNLOAD#

Otherwise, you can download the repository for the most cutting edge additions: git clone

#LATIN HYPERCUBE SAMPLING SCRIPT PYTHON INSTALL#

The Grassmannian sampler is adapted from code from ShusenĪ preliminary version is available on PyPI: pip install samply The python CVT code is adapted from a C++ implementation provided byĬarlos Correa.

LHS - a Latin hypercube sampling design of points constrained to the space.

CVT - an approximate centroidal Voronoi tessellation of the pointsĬonstrained to the given space (available for hypercube and directional).

Multimodal - a mixture of Gaussian distributions of points (available for hypercube).

Normal - a Gaussian distribution of points (available for hypercube).

Uniform - a random, uniform distribution of points (available for ball,ĭirectional, hypercube, subspace, and shape).

Note, that not all of the methods listed below are applicable to the modules Within each module is a list of ways to fill the space of the samples. For now these must all be sampled using a uniform

shape - a collection of (n-1)-manifold and non-manifold shapes embedded inĪn n dimensional space.

Or sampling the Grassmanian Atlas of projections from a dimension n to a

subspace - Sampling a n-1-dimensional subspace orthogonal to a unit vector.

hypercube - The n-dimensional solid unit hypercube x \\in ^n.

You can also consider this a sampling of the boundary of the n-dimensional

directional - The space of unit length directions in n-dimensional space.

ball - The n-dimensional solid unit ball.

The API is structured such that the top level packages represent the shape A Python library for generating space-filling sample sets of low to moderateĪ Collection of Space-filling Sampling Designs for Arbitrary Dimensions.