:mod:`binary` --- Binary Test Problems
======================================

.. automodule:: optproblems.binary

Basic Problems
--------------

.. autofunction:: optproblems.binary.one_max

.. autoclass:: optproblems.binary.OneMax
    :special-members: __init__
    :members:

.. autofunction:: optproblems.binary.leading_ones

.. autoclass:: optproblems.binary.LeadingOnes
    :special-members: __init__
    :members:

.. autofunction:: optproblems.binary.trailing_zeros

.. autoclass:: optproblems.binary.LeadingOnesTrailingZeros
    :special-members: __init__
    :members:


Binary Multiple-Peaks Problems
------------------------------

.. autofunction:: optproblems.binary.hamming_dist

.. autoclass:: optproblems.binary.Peak
    :special-members: __init__
    :members:

.. autoclass:: optproblems.binary.PeakProblem
    :special-members: __init__
    :members:

.. autoclass:: optproblems.binary.NearestPeakProblem
    :special-members: __init__
    :members:

.. autoclass:: optproblems.binary.WeightedNearestPeakProblem
    :special-members: __init__
    :members:

.. autoclass:: optproblems.binary.AllPeaksProblem
    :special-members: __init__
    :members:


Helpers
-------

.. autoclass:: optproblems.binary.BinaryChecker
    :special-members: __init__
    :members:


Example
-------

Obtaining the known optimum of a unimodal test problem.

.. code:: python

    import random
    from optproblems import *
    from optproblems.binary import OneMax
    # the binary test problem OneMax counts the ones in the bitstring
    onemax = OneMax(10)
    global_optima = onemax.get_optimal_solutions()
    # the known optima are not evaluated by default
    for opt in global_optima:
        print(opt.phenome, opt.objective_values)

    print(onemax.consumed_evaluations, onemax.remaining_evaluations)
    onemax.batch_evaluate(global_optima)
    print("after evaluation:")
    for opt in global_optima:
        print(opt.phenome, opt.objective_values)

    print(onemax.consumed_evaluations, onemax.remaining_evaluations)
    rand_solution = Individual(phenome=[random.choice((0, 1)) for _ in range(10)])
    onemax.evaluate(rand_solution)
    print("after another evaluation:")
    print(rand_solution.phenome, rand_solution.objective_values)
    print(onemax.consumed_evaluations, onemax.remaining_evaluations)
    # the global optimum is also a local one
    local_optima = onemax.get_locally_optimal_solutions()
    for opt in local_optima:
        print(opt.phenome, opt.objective_values)