def test_accepts_equal():
simulated_annealing = SimulatedAnnealing(2, 1, 1)
for _ in range(100):
# This results in an acceptance probability of exp{0}, that is, one.
# Thus, the candidate state should always be accepted.
assert_(
simulated_annealing.accept(rnd.RandomState(), One(), One(), One()))
def test_exponential_threshold_update():
record_travel = RecordToRecordTravel(5, 0, 0.1, "exponential")
# The relative worsening is plus one, so this should be accepted initially,
# as the threshold is 5, resp. 0.5. In the second case, 1 > 0.5, so the
# second should be rejected.
assert_(record_travel.accept(rnd.RandomState(), Zero(), Zero(), One()))
assert_(not record_travel.accept(rnd.RandomState(), Zero(), Zero(), One()))
def test_linear_threshold_update():
record_travel = RecordToRecordTravel(5, 0, 1)
# For the first five, the threshold is, resp., 5, 4, 3, 2, 1. The relative
# worsening is plus one, so this should be accepted (lower or equal to
# threshold).
for _ in range(5):
assert_(record_travel.accept(rnd.RandomState(), Zero(), Zero(), One()))
# Threshold is now zero, so this should no longer be accepted.
assert_(not record_travel.accept(rnd.RandomState(), Zero(), Zero(), One()))
def test_exponential_random_solutions():
"""
Checks if the exponential ``accept`` method correctly decides in two known
cases for a fixed seed. This is the exponential equivalent to the linear
random solutions test above.
"""
simulated_annealing = SimulatedAnnealing(2, 1, 0.5, "exponential")
state = rnd.RandomState(0)
assert_(simulated_annealing.accept(state, Zero(), Zero(), One()))
assert_(not simulated_annealing.accept(state, Zero(), Zero(), One()))
def test_linear_random_solutions():
"""
Checks if the linear ``accept`` method correctly decides in two known cases
for a fixed seed.
"""
simulated_annealing = SimulatedAnnealing(2, 1, 1)
state = rnd.RandomState(0)
# Using the above seed, the first two random numbers are 0.55 and .72,
# respectively. The acceptance probability is 0.61 first, so the first
# should be accepted (0.61 > 0.55). Thereafter, it drops to 0.37, so the
# second should not (0.37 < 0.72).
assert_(simulated_annealing.accept(state, Zero(), Zero(), One()))
assert_(not simulated_annealing.accept(state, Zero(), Zero(), One()))
def test_accepts_better():
for _ in range(1, 100):
simulated_annealing = SimulatedAnnealing(2, 1, 1)
assert_(
simulated_annealing.accept(rnd.RandomState(), One(), Zero(),
Zero()))
def test_accepts_generator_and_random_state():
"""
Tests if SimulatedAnnealing works with both Generator and RandomState
randomness classes.
See also https://numpy.org/doc/1.18/reference/random/index.html#quick-start
"""
class Old: # old RandomState interface mock
def random_sample(self): # pylint: disable=no-self-use
return 0.5
simulated_annealing = SimulatedAnnealing(2, 1, 1)
assert_(simulated_annealing.accept(Old(), One(), One(), Zero()))
class New: # new Generator interface mock
def random(self): # pylint: disable=no-self-use
return 0.5
simulated_annealing = SimulatedAnnealing(2, 1, 1)
assert_(simulated_annealing.accept(New(), One(), One(), Zero()))
def test_rejects_worse():
record_travel = RecordToRecordTravel(0.5, 0.2, 0.1)
# This results in a relative worsening of plus one, which is bigger than
# the threshold (0.5).
assert_(not record_travel.accept(rnd.RandomState(), Zero(), Zero(), One()))
def test_accepts_better():
record_travel = RecordToRecordTravel(1, 0, 0.1)
assert_(record_travel.accept(rnd.RandomState(), One(), Zero(), Zero()))
def test_accepts_better():
"""
Tests if the hill climbing method accepts a better solution.
"""
hill_climbing = HillClimbing()
assert_(hill_climbing.accept(rnd.RandomState(), One(), One(), Zero()))
def test_rejects_worse():
"""
Tests if the hill climbing method accepts a worse solution.
"""
hill_climbing = HillClimbing()
assert_(not hill_climbing.accept(rnd.RandomState(), Zero(), Zero(), One()))