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_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_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_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)
mutation_range = third.DEFAULT_MUTATION_RANGE
mutation_rate = third.DEFAULT_MUTATION_RATE
start_population = third.create_population(population_size, cf)
fitness_function = third.make_fitness_function(cf)
generate_function = third.make_generate_function(mutation_range,
mutation_rate, cf)
halt_function = third.make_halt_function(cf)
elif species == 4:
population_size = fourth.DEFAULT_POPULATION_SIZE
mutation_range = fourth.DEFAULT_MUTATION_RANGE
mutation_rate = fourth.DEFAULT_MUTATION_RATE
start_population = fourth.create_population(population_size, cf)
fitness_function = fourth.make_fitness_function(cf)
generate_function = fourth.make_generate_function(mutation_range,
mutation_rate, cf)
halt_function = fourth.make_halt_function(cf)
ga = ga.genetic_algorithm(start_population, fitness_function,
generate_function, halt_function)
fitness = 0.0
counter = 0
for generation in ga:
counter += 1
fitness = generation[0].fitness
print "--- Generation %d ---" % counter
print generation[0]
print fitness
with open('%s.ly' % output, 'w') as output:
output.write(lilypond.render(species, cf, generation[0].chromosome))
