Design of experiments based on kernel methods
Project description
otkerneldesign
This Python module generates designs of experiments based on kernel methods such as Kernel Herding and Support Points with the classes:
- KernelHerding
- KernelHerdingTensorized
- GreedySupportPoints
Additionally, optimal weights for quadrature and validation designs are provided by the classes:
- BayesianQuadratureWeighting
- TestSetWeighting
Installation
~$ pip install otkerneldesign
Documentation & references
Example
>>> import openturns as ot
>>> import otkerneldesign as otkd
>>> # Distribution definition
>>> distribution = ot.ComposedDistribution([ot.Normal(0.5, 0.1)] * 2)
>>> dimension = distribution.getDimension()
>>> # Kernel definition
>>> ker_list = [ot.MaternModel([0.1], [1.0], 2.5)] * dimension
>>> kernel = ot.ProductCovarianceModel(ker_list)
>>> # Kernel herding design
>>> kh = otkd.KernelHerding(kernel=kernel, distribution=distribution)
>>> kh_design, kh_indices = kh.select_design(size=20)
Authors
- Elias Fekhari
- Joseph Muré
Project details
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
otkerneldesign-0.1.4.tar.gz
(28.8 kB
view hashes)
Built Distribution
Close
Hashes for otkerneldesign-0.1.4-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 8c45f44c3ba804331251b25ded878eb52f474e29d23c6d8b38559baec9e2bcaa |
|
MD5 | dec328d26bc01abb92957dafff0cbe98 |
|
BLAKE2b-256 | 13a244c755fba6162a65a83d27ff567d067c16c265c899c37482a89d12f83436 |