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_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_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()))
