leaspy.algo.algo_with_samplers

Classes

Mixin class to use in algorithms that require samplers.

class AlgorithmWithSamplersMixin(settings)

Mixin class to use in algorithms that require samplers.

Note that this mixin should be used with a class inheriting from AbstractAlgo, which must have algo_parameters attribute.

Parameters:

settingsAlgorithmSettings

The specifications of the algorithm as a AlgorithmSettings instance.

Please note that you can customize the number of memory-less (burn-in) iterations by setting either:

n_burn_in_iter_frac, such that duration of burn-in phase is a ratio of algorithm n_iter (default of 90%)

Attributes:

samplersdict [str, AbstractSampler ]: Dictionary of samplers per each variable
current_iterationint, default 0: Current iteration of the algorithm. The first iteration will be 1 and the last one n_iter.
random_order_variablesbool (default True): This attribute controls whether we randomize the order of variables at each iteration. Article https://proceedings.neurips.cc/paper/2016/hash/e4da3b7fbbce2345d7772b0674a318d5-Abstract.html gives a rationale on why we should activate this flag.

Parameters:

settings (AlgorithmSettings)