def test_halt_not(self):
"""
Ensures if the fittest genome has fitness < MAX_REWARD then halt
doesn't succeed.
"""
halt = make_halt_function([3, 2, 1])
g1 = Genome([1, 2, 3])
g1.fitness = MAX_REWARD - 0.1
g2 = Genome([1, 2, 3])
g2.fitness = 3
g3 = Genome([1, 2, 3])
g3.fitness = 2
# Any fittest solution with fitness < MAX_REWARD means no halt.
population = [g1, g2, g3]
result = halt(population, 1)
self.assertFalse(result)
def test_halt_not(self):
"""
Ensures if the fittest genome has fitness < MAX_REWARD then halt
doesn't succeed.
"""
halt = make_halt_function([3, 2, 1])
g1 = Genome([1, 2, 3])
g1.fitness = MAX_REWARD - 0.1
g2 = Genome([1, 2, 3])
g2.fitness = 3
g3 = Genome([1, 2, 3])
g3.fitness = 2
# Any fittest solution with fitness < MAX_REWARD means no halt.
population = [g1, g2, g3]
result = halt(population, 1)
self.assertFalse(result)
def test_fitness_function_uses_cached_genome_fitness(self):
"""
Ensures the fitness function bails if there is already a score set for
the genome.
"""
fitness_function = make_fitness_function(CANTUS_FIRMUS)
genome = Genome([1, 2, 3])
genome.fitness = 12345
result = fitness_function(genome)
self.assertEqual(12345, result)
def test_fitness_function_uses_cached_genome_fitness(self):
"""
Ensures the fitness function bails if there is already a score set for
the genome.
"""
fitness_function = make_fitness_function(CANTUS_FIRMUS)
genome = Genome([1, 2, 3])
genome.fitness = 12345
result = fitness_function(genome)
self.assertEqual(12345, result)
def test_halt_checks_suspension_count(self):
"""
If the solution contains suspensions the halt function should ensure
that the MAX_REWARD is incremented by the number of suspensions
(rewarded because they're part of a valid step wise motion).
"""
halt = make_halt_function([9, 8, 7, 6, 5])
g1 = Genome([11, 10, 9, 8, 7])
# only one our of two "correct" dissonances
g1.fitness = MAX_REWARD + REWARD_SUSPENSION
population = [g1, ]
result = halt(population, 1)
self.assertFalse(result)
# Try again
# two out of two "correct" dissonances
g1.fitness = MAX_REWARD + (REWARD_SUSPENSION * 2)
population = [g1, ]
result = halt(population, 1)
self.assertTrue(result)
def test_halt_expected(self):
"""
Ensure the function returns true if we're in a halting state.
"""
halt = make_halt_function([6, 5])
g1 = Genome([6, 5])
g1.fitness = MAX_REWARD
population = [g1, ]
result = halt(population, 1)
self.assertTrue(result)
def test_halt_expected(self):
"""
Ensure the function returns true if we're in a halting state.
"""
halt = make_halt_function([6, 5])
g1 = Genome([6, 5])
g1.fitness = MAX_REWARD
population = [
g1,
]
result = halt(population, 1)
self.assertTrue(result)
def test_halt_checks_suspension_count(self):
"""
If the solution contains suspensions the halt function should ensure
that the MAX_REWARD is incremented by the number of suspensions
(rewarded because they're part of a valid step wise motion).
"""
halt = make_halt_function([9, 8, 7, 6, 5])
g1 = Genome([11, 10, 9, 8, 7])
# only one our of two "correct" dissonances
g1.fitness = MAX_REWARD + REWARD_SUSPENSION
population = [
g1,
]
result = halt(population, 1)
self.assertFalse(result)
# Try again
# two out of two "correct" dissonances
g1.fitness = MAX_REWARD + (REWARD_SUSPENSION * 2)
population = [
g1,
]
result = halt(population, 1)
self.assertTrue(result)
