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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)

normal_kh

Authors

  • Elias Fekhari
  • Joseph Muré

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