def test_mutate_is_implemented(self):
"""
Ensures that we have a mutate method implemented.
"""
genome = Genome(['c', 'a', 't'])
self.assertNotEqual(NotImplemented, genome.mutate(2, 0.2,
['d', 'o', 'g']))
def test_mutate_is_implemented(self):
"""
Ensures that we have a mutate method implemented.
"""
genome = Genome(['c', 'a', 't'])
self.assertNotEqual(NotImplemented,
genome.mutate(2, 0.2, ['d', 'o', 'g']))
def test_mutate_is_implemented(self):
"""
Ensures that we have a mutate method implemented.
"""
genome = Genome(["c", "a", "t"])
self.assertNotEqual(NotImplemented,
genome.mutate(2, 0.2, ["d", "o", "g"]))
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(TARGET_WORD)
genome = Genome(['c', 'a', 't'])
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(TARGET_WORD)
genome = Genome(['c', 'a', 't'])
genome.fitness = 12345
result = fitness_function(genome)
self.assertEqual(12345, result)
def test_generate_function_returns_list(self):
"""
Ensures the new population is a list.
"""
generate_function = make_generate_function(7, 0.2, TARGET_WORD)
g1 = Genome(['c', 'a', 't'])
g1.fitness = 1
g2 = Genome(['d', 'o', 'g'])
g2.fitness = 2
seed_population = [g1, g2]
result = generate_function(seed_population)
self.assertTrue(list, type(result))
def test_halt_expected(self):
"""
Ensure the function returns true if we're in a halting state.
"""
g1 = Genome(['c', 'a', 't'])
g1.fitness = len(TARGET_WORD)
g2 = Genome(['d', 'o', 'g'])
g2.fitness = 2
g3 = Genome(['f', 'o', 'o'])
g3.fitness = 3
# Any fittest solution with fitness = length of the target word.
population = [g1, g2, g3]
result = halt(population, 1)
self.assertTrue(result)
def test_generate_function_returns_list_of_correct_length(self):
"""
Ensure the new population is the correct length.
"""
generate_function = make_generate_function(7, 0.2, TARGET_WORD)
g1 = Genome(['c', 'a', 't'])
g1.fitness = 1
g2 = Genome(['d', 'o', 'g'])
g2.fitness = 2
g3 = Genome(['f', 'o', 'o'])
g3.fitness = 3
seed_population = [g1, g2, g3]
result = generate_function(seed_population)
self.assertTrue(3, len(result))
def test_fitness_function_returns_float(self):
"""
Makes sure the generated fitness function returns a fitness score as a
float.
"""
fitness_function = make_fitness_function(TARGET_WORD)
genome = Genome(['c', 'a', 't'])
result = fitness_function(genome)
self.assertTrue(float, type(result))
def test_halt_not(self):
"""
Ensures if the fittest genome has fitness < 4 then halt doesn't
succeed.
"""
g1 = Genome(['c', 'a', 't'])
g1.fitness = len(TARGET_WORD) - 1
g2 = Genome(['d', 'o', 'g'])
g2.fitness = 2
g3 = Genome(['f', 'o', 'o'])
g3.fitness = 3
# Any fittest solution with fitness < target word length means call a
# halt.
population = [g1, g2, g3]
result = halt(population, 1)
self.assertFalse(result)
def test_fitness_function_sets_fitness_on_genome(self):
"""
Ensures the fitness score is set in the genome's fitness attribute and
is the same as the returned fitness score.
"""
fitness_function = make_fitness_function(TARGET_WORD)
genome = Genome(['c', 'a', 't'])
self.assertEqual(None, genome.fitness)
result = fitness_function(genome)
self.assertNotEqual(None, genome.fitness)
self.assertEqual(result, genome.fitness)
def test_generate_function_returns_list(self):
"""
Ensures the new population is a list.
"""
generate_function = make_generate_function(7, 0.2, TARGET_WORD)
g1 = Genome(['c', 'a', 't'])
g1.fitness = 1
g2 = Genome(['d', 'o', 'g'])
g2.fitness = 2
seed_population = [g1, g2]
result = generate_function(seed_population)
self.assertTrue(list, type(result))
def test_halt_expected(self):
"""
Ensure the function returns true if we're in a halting state.
"""
g1 = Genome(['c', 'a', 't'])
g1.fitness = len(TARGET_WORD)
g2 = Genome(['d', 'o', 'g'])
g2.fitness = 2
g3 = Genome(['f', 'o', 'o'])
g3.fitness = 3
# Any fittest solution with fitness = length of the target word.
population = [g1, g2, g3]
result = halt(population, 1)
self.assertTrue(result)
def test_generate_function_returns_list_of_correct_length(self):
"""
Ensure the new population is the correct length.
"""
generate_function = make_generate_function(7, 0.2, TARGET_WORD)
g1 = Genome(['c', 'a', 't'])
g1.fitness = 1
g2 = Genome(['d', 'o', 'g'])
g2.fitness = 2
g3 = Genome(['f', 'o', 'o'])
g3.fitness = 3
seed_population = [g1, g2, g3]
result = generate_function(seed_population)
self.assertTrue(3, len(result))
def test_halt_not(self):
"""
Ensures if the fittest genome has fitness < 4 then halt doesn't
succeed.
"""
g1 = Genome(['c', 'a', 't'])
g1.fitness = len(TARGET_WORD) - 1
g2 = Genome(['d', 'o', 'g'])
g2.fitness = 2
g3 = Genome(['f', 'o', 'o'])
g3.fitness = 3
# Any fittest solution with fitness < target word length means call a
# halt.
population = [g1, g2, g3]
result = halt(population, 1)
self.assertFalse(result)
