paref.pareto_reflection_sequences.generic.repeating_sequence#

Classes

RepeatingSequence(**kwargs)

Define a sequence by repeating a list of Pareto reflections

class paref.pareto_reflection_sequences.generic.repeating_sequence.RepeatingSequence(**kwargs)[source]#

Bases: SequenceParetoReflections

Define a sequence by repeating a list of Pareto reflections

This is probably the most generic way to define a sequence of Pareto reflections: specify a (finite) list of Pareto reflections and repeat that list. It can be seen as implementing a list of Pareto reflections (which is a sequence) in the sequence of Pareto reflections interface.

When to use#

# TBA: when to use?

What it does#

The sequence simply loops through given list of Pareto reflections If the end is reached, the sequence starts again from the beginning of the list of reflections.

Examples

# TBA: meaningful example when to use this Initialize list of Pareto reflections

>>> import numpy as np
>>> from paref.pareto_reflections.restrict_by_point import RestrictByPoint
>>> from paref.moo_algorithms.stopping_criteria.max_iterations_reached import MaxIterationsReached

Initialze stopping criteria

>>> stopping_criteria = MaxIterationsReached(max_iterations=1)

Initialize Pareto reflection to be repeated

>>> pareto_reflecting_functions = [RestrictByPoint(nadir=np.ones(1),restricting_point=np.ones(1))]

Initialize repeating sequence

>>> sequence = RepeatingSequence(pareto_reflections=pareto_reflecting_functions)

The repeating sequence returns the given Pareto reflection in each step of iteration until the stopping criteria is met

>>> sequence.next().__class__.__name__
Restricting

>>> sequence.next()
None

Initialize storage for Pareto reflections

next(**kwargs)#: Store Pareto reflections