lib_scipy module¶
Design of experiments based on SciPy.
- class gemseo.algos.doe.lib_scipy.SciPyDOE[source]
Bases:
DOELibrary
A library of designs of experiments based on SciPy.
- class Hypersphere(value)[source]
Bases:
StrEnum
The sampling strategy for the poisson disk algorithm.
- SURFACE = 'surface'
- VOLUME = 'volume'
- class Optimizer(value)[source]
Bases:
StrEnum
The optimization scheme to improve the quality of the DOE after sampling.
- LLOYD = 'lloyd'
- NONE = ''
- RANDOM_CD = 'random-cd'
- LIBRARY_NAME: ClassVar[str] = 'SciPy'
The name of the interfaced library.
- OPTIONS_DIR: ClassVar[Path] = PosixPath('options/scipy')
The name of the directory containing the files of the grammars of the options.
- descriptions: dict[str, AlgorithmDescription]
The description of the algorithms contained in the library.
- internal_algo_name: str | None
The internal name of the algorithm used currently.
It typically corresponds to the name of the algorithm in the wrapped library if any.
- option_grammar: JSONGrammar | None
The grammar defining the options of the current algorithm.
- problem: OptimizationProblem
The optimization problem the driver library is bonded to.
- samples: RealArray
The design vector samples in the design space.
The design space variable types stored as dtype metadata.
To access those in the unit hypercube, use
unit_samples
.
- unit_samples: RealArray
The design vector samples projected in the unit hypercube.
In the case of a design space of dimension \(d\), the unit hypercube is \([0,1]^d\).
To access those in the design space, use
samples
.