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from .AcceptanceCriterion import AcceptanceCriterion | ||
from .AdaptiveThreshold import AdaptiveThreshold | ||
from .AlwaysAccept import AlwaysAccept | ||
from .GreatDeluge import GreatDeluge | ||
from .HillClimbing import HillClimbing | ||
from .LateAcceptanceHillClimbing import LateAcceptanceHillClimbing | ||
from .MovingAverageThreshold import MovingAverageThreshold | ||
from .NonLinearGreatDeluge import NonLinearGreatDeluge | ||
from .RandomWalk import RandomWalk | ||
from .RandomAccept import RandomAccept | ||
from .RecordToRecordTravel import RecordToRecordTravel | ||
from .SimulatedAnnealing import SimulatedAnnealing | ||
from .WorseAccept import WorseAccept |
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import numpy.random as rnd | ||
from numpy.testing import assert_, assert_equal, assert_raises | ||
from pytest import mark | ||
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from alns.accept import MovingAverageThreshold | ||
from alns.tests.states import One, Two, VarObj, Zero | ||
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@mark.parametrize( | ||
"eta, gamma", | ||
[ | ||
(-1, 3), # eta cannot be < 0 | ||
(2, 3), # eta cannot be > 1 | ||
(0.5, -2), # gamma cannot be < 0 | ||
(0.5, 0), # gamma cannot be 0 | ||
], | ||
) | ||
def test_raise_invalid_parameters(eta, gamma): | ||
with assert_raises(ValueError): | ||
MovingAverageThreshold(eta=eta, gamma=gamma) | ||
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@mark.parametrize("eta, gamma", [(1, 3), (0.4, 4)]) | ||
def test_no_raise_valid_parameters(eta, gamma): | ||
MovingAverageThreshold(eta=eta, gamma=gamma) | ||
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@mark.parametrize("eta", [0, 0.01, 0.5, 0.99, 1]) | ||
def test_eta(eta): | ||
moving_average = MovingAverageThreshold(eta, 3) | ||
assert_equal(moving_average.eta, eta) | ||
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@mark.parametrize("gamma", range(1, 10)) | ||
def test_gamma(gamma): | ||
moving_average = MovingAverageThreshold(0.5, gamma) | ||
assert_equal(moving_average.gamma, gamma) | ||
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def test_accepts_below_threshold(): | ||
moving_average = MovingAverageThreshold(eta=0.5, gamma=4) | ||
moving_average(rnd.RandomState(), One(), One(), One()) | ||
moving_average(rnd.RandomState(), One(), One(), Zero()) | ||
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# The threshold is set at 0 + 0.5 * (0.5 - 0) = 0.25 | ||
assert_(moving_average(rnd.RandomState(), One(), One(), Zero())) | ||
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def test_rejects_above_threshold(): | ||
moving_average = MovingAverageThreshold(eta=0.5, gamma=4) | ||
moving_average(rnd.RandomState(), One(), One(), Two()) | ||
moving_average(rnd.RandomState(), One(), One(), Zero()) | ||
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# The threshold is set at 0 + 0.5 * (1 - 0) = 0.5 | ||
assert_(not moving_average(rnd.RandomState(), One(), One(), One())) | ||
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def test_accepts_equal_threshold(): | ||
moving_average = MovingAverageThreshold(eta=0.5, gamma=4) | ||
moving_average(rnd.RandomState(), One(), One(), VarObj(7100)) | ||
moving_average(rnd.RandomState(), One(), One(), VarObj(7200)) | ||
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# The threshold is set at 7100 + 0.5 * (7140 - 7100) = 7120 | ||
assert_(moving_average(rnd.RandomState(), One(), One(), VarObj(7120))) | ||
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def test_accepts_over_gamma_candidates(): | ||
moving_average = MovingAverageThreshold(eta=0.2, gamma=3) | ||
moving_average(rnd.RandomState(), One(), One(), VarObj(7100)) | ||
moving_average(rnd.RandomState(), One(), One(), VarObj(7200)) | ||
moving_average(rnd.RandomState(), One(), One(), VarObj(7200)) | ||
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# The threshold is set at 7000 + 0.2 * (7133.33 - 7000) = 7013.33 | ||
assert_(moving_average(rnd.RandomState(), One(), One(), VarObj(7000))) | ||
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def test_rejects_over_gamma_candidates(): | ||
moving_average = MovingAverageThreshold(eta=0.2, gamma=3) | ||
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for value in [7100, 7200, 7200, 7000]: | ||
moving_average(rnd.RandomState(), One(), One(), VarObj(value)) | ||
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# The threshold is set at 7000 + 0.2 * (7100 - 7000) = 7020 | ||
result = moving_average(rnd.RandomState(), One(), One(), VarObj(7100)) | ||
assert_(not result) | ||
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def test_evaluate_consecutive_solutions(): | ||
""" | ||
Test if MAT correctly accepts and rejects consecutive solutions. | ||
""" | ||
moving_average = MovingAverageThreshold(eta=0.5, gamma=4) | ||
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# The threshold is set at 7100, hence the solution is accepted. | ||
assert_(moving_average(rnd.RandomState(), One(), One(), VarObj(7100))) | ||
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# The threshold is set at 7125, hence the solution is accepted. | ||
result = moving_average(rnd.RandomState(), One(), One(), VarObj(7200)) | ||
assert_(not result) | ||
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# The threshold is set at 7120, hence the solution is accepted. | ||
assert_(moving_average(rnd.RandomState(), One(), One(), VarObj(7120))) | ||
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def test_history(): | ||
""" | ||
Test if MAT correctly stores the history of the thresholds correctly. | ||
""" | ||
moving_average = MovingAverageThreshold(eta=0.5, gamma=4) | ||
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moving_average(rnd.RandomState(), One(), One(), VarObj(7100)) | ||
assert_equal(moving_average.history, [7100]) | ||
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moving_average(rnd.RandomState(), One(), One(), VarObj(7200)) | ||
assert_equal(moving_average.history, [7100, 7200]) | ||
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moving_average(rnd.RandomState(), One(), One(), VarObj(7120)) | ||
assert_equal(moving_average.history, [7100, 7200, 7120]) | ||
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moving_average(rnd.RandomState(), One(), One(), VarObj(7100)) | ||
assert_equal(moving_average.history, [7100, 7200, 7120, 7100]) | ||
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moving_average(rnd.RandomState(), One(), One(), VarObj(7200)) | ||
assert_equal(moving_average.history, [7200, 7120, 7100, 7200]) |
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