KernelDensityStrategy

class aixd.sampler.strategies.KernelDensityStrategy(features: DataObject | List[DataObject], bandwidth: float = 0.05, engine: SamplingEngine | str = 'random', **kwargs)[source]

Bases: Strategy

Fits a multivariate Kernel Density Estimator to the features. Allows to sample according to some prior distribution provided by the user through the “fit” function. https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KernelDensity.html

Parameters:

  • features (Union[DataObject, List[DataObject]]) – The features refer to the data objects, which contain the information about the domain of the features.

  • bandwidth (float, optional, default=0.05) – The badnwidth of the kernel for the KDE.

  • engine (Union[SamplingEngine, str], optional, default=”random”) – In this case the default engine is “random”, as it is the only one that makes sense to use with this strategy.

Methods

fit

Fit the strategy to the data, also using some vectors of objectives and validity values.

reset_states

Reset the state of the engine.

sample

update

Add the log-likelihood of the samples to the objectives.
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