def test_ga_uses_one_iteration_for_trivially_satisfiable_problem(self):
v1 = Variable('a')
c1 = Clause([Literal(v1)])
maxsat = MAXSAT([v1], [c1])
ga = GA(maxsat)
_, _, iteration = ga.run()
self.assertEqual(1, iteration)
def test_ga_uses_one_iteration_for_unsatisfiable_problem_if_not_below_threshold(self):
v1 = Variable('a')
c1 = Clause([Literal(v1, positive=True)])
c2 = Clause([Literal(v1, positive=False)])
maxsat = MAXSAT([v1], [c1, c2])
ga = GA(maxsat, fitness_threshold=0.5)
_, _, iteration = ga.run()
self.assertEqual(1, iteration)
def test_ga_achieves_fitness_of_one(self):
v1 = Variable('a')
c1 = Clause([Literal(v1)])
maxsat = MAXSAT([v1], [c1])
ga = GA(maxsat)
_, fitness, _ = ga.run()
self.assertEqual(1, fitness)
def test_ga_uses_all_iterations_for_unsatisfiable_problem_if_below_threshold(self):
v1 = Variable('a')
c1 = Clause([Literal(v1, positive=True)])
c2 = Clause([Literal(v1, positive=False)])
maxsat = MAXSAT([v1], [c1, c2])
ga = GA(maxsat)
_, _, iteration = ga.run()
self.assertEqual(iteration, ga.max_iterations)
def test_get_candidate_fitness_returns_half(self):
v1 = Variable('a')
c1 = Clause([Literal(v1, positive=True)])
c2 = Clause([Literal(v1, positive=False)])
maxsat = MAXSAT([v1], [c1, c2])
valuation = Valuation.init_random_from_variables([v1])
ga = GA(maxsat)
self.assertEqual(0.5, ga.get_candidate_fitness(valuation))
def test_ga_achieves_fitness_of_half(self):
v1 = Variable('a')
c1 = Clause([Literal(v1, positive=True)])
c2 = Clause([Literal(v1, positive=False)])
maxsat = MAXSAT([v1], [c1, c2])
ga = GA(maxsat)
_, fitness, _ = ga.run()
self.assertEqual(0.5, fitness)
def test_get_candidate_fitness_returns_one(self):
v1 = Variable('a')
v2 = Variable('b')
c1 = Clause([Literal(v1, positive=True)])
c2 = Clause([Literal(v2, positive=False)])
maxsat = MAXSAT([v1, v2], [c1, c2])
valuation = Valuation({v1: True, v2: False})
ga = GA(maxsat)
self.assertEqual(1, ga.get_candidate_fitness(valuation))
def compute(day):
get_whole_trunk()
trunk_max_order = get_trunk_max_order()
data = get_orders_trunk_can_take(trunk_max_order)
# print data
ga = GA()
log.info('start to compute')
ga.GA_main(data, trunk_max_order)
log.info('ga down.start to get best gene')
best_gene = ga.selectBest()
gene_data = best_gene.gene_to_data(ga.order, ga.key_order)
log.info('start to modify_model')
modify_model(gene_data)
all_scheduling[day] = gene_data
def compute(day):
get_whole_trunk()
trunk_max_order = get_trunk_max_order()
data = get_orders_trunk_can_take(trunk_max_order)
trunk_data, order_list = get_orders_list(trunk_max_order, data)
gene_len = 0
gene_len += len(order_list) * gene_bits
print 'gene length: ', len(order_list) * gene_bits, len(order_list)
ga = GA()
log.info('start to compute')
# ga.GA_main(data, trunk_max_order)
ga.GA_main2(trunk_data, order_list)
log.info('ga down.start to get best gene')
best_gene = ga.selectBest()
best_gene.gene_to_data(ga.gene_bits, ga.order_list)
log.info('start to modify_model')
all_scheduling[day] = modify_model(best_gene.gene_data, trunk_data)
def test_generate_population_generates_population_of_correct_size(self):
population_size = 32
ga = GA(self.rand_maxsat, population_size=population_size)
population = ga.generate_population()
self.assertEqual(population_size, len(population))
def setUp(self):
self.rand_maxsat = ProblemGenerator().generate_problem()
self.rand_ga = GA(self.rand_maxsat)
class TestGA(unittest.TestCase):
def setUp(self):
self.rand_maxsat = ProblemGenerator().generate_problem()
self.rand_ga = GA(self.rand_maxsat)
def test_create_ga_without_problem_fails(self):
self.assertRaises(TypeError, lambda l: GA())
def test_ga_achieves_fitness_of_half(self):
v1 = Variable('a')
c1 = Clause([Literal(v1, positive=True)])
c2 = Clause([Literal(v1, positive=False)])
maxsat = MAXSAT([v1], [c1, c2])
ga = GA(maxsat)
_, fitness, _ = ga.run()
self.assertEqual(0.5, fitness)
def test_ga_achieves_fitness_of_one(self):
v1 = Variable('a')
c1 = Clause([Literal(v1)])
maxsat = MAXSAT([v1], [c1])
ga = GA(maxsat)
_, fitness, _ = ga.run()
self.assertEqual(1, fitness)
def test_ga_uses_all_iterations_for_unsatisfiable_problem_if_below_threshold(self):
v1 = Variable('a')
c1 = Clause([Literal(v1, positive=True)])
c2 = Clause([Literal(v1, positive=False)])
maxsat = MAXSAT([v1], [c1, c2])
ga = GA(maxsat)
_, _, iteration = ga.run()
self.assertEqual(iteration, ga.max_iterations)
def test_ga_uses_one_iteration_for_unsatisfiable_problem_if_not_below_threshold(self):
v1 = Variable('a')
c1 = Clause([Literal(v1, positive=True)])
c2 = Clause([Literal(v1, positive=False)])
maxsat = MAXSAT([v1], [c1, c2])
ga = GA(maxsat, fitness_threshold=0.5)
_, _, iteration = ga.run()
self.assertEqual(1, iteration)
def test_ga_uses_one_iteration_for_trivially_satisfiable_problem(self):
v1 = Variable('a')
c1 = Clause([Literal(v1)])
maxsat = MAXSAT([v1], [c1])
ga = GA(maxsat)
_, _, iteration = ga.run()
self.assertEqual(1, iteration)
def test_generate_population_returns_list(self):
population = self.rand_ga.generate_population()
self.assertTrue(isinstance(population, list))
def test_generate_population_generates_population_of_correct_size(self):
population_size = 32
ga = GA(self.rand_maxsat, population_size=population_size)
population = ga.generate_population()
self.assertEqual(population_size, len(population))
def test_generate_population_generates_population_of_valuation_instances(self):
population = self.rand_ga.generate_population()
self.assertTrue(all(isinstance(p, Valuation) for p in population))
def test_get_candidate_fitness_returns_half(self):
v1 = Variable('a')
c1 = Clause([Literal(v1, positive=True)])
c2 = Clause([Literal(v1, positive=False)])
maxsat = MAXSAT([v1], [c1, c2])
valuation = Valuation.init_random_from_variables([v1])
ga = GA(maxsat)
self.assertEqual(0.5, ga.get_candidate_fitness(valuation))
def test_get_candidate_fitness_returns_one(self):
v1 = Variable('a')
v2 = Variable('b')
c1 = Clause([Literal(v1, positive=True)])
c2 = Clause([Literal(v2, positive=False)])
maxsat = MAXSAT([v1, v2], [c1, c2])
valuation = Valuation({v1: True, v2: False})
ga = GA(maxsat)
self.assertEqual(1, ga.get_candidate_fitness(valuation))
def test_get_population_fitness_returns_list(self):
population = self.rand_ga.generate_population()
self.rand_ga.population = population
candidate_fitnesses = self.rand_ga.get_population_fitness()
self.assertEqual(list, type(candidate_fitnesses))
def test_get_population_fitness_returns_list_containing_valuations(self):
population = self.rand_ga.generate_population()
self.rand_ga.population = population
candidate_fitnesses = self.rand_ga.get_population_fitness()
self.assertTrue(all(isinstance(k[0], Valuation) for k in candidate_fitnesses))
def test_get_population_fitness_returns_list_containing_floats(self):
population = self.rand_ga.generate_population()
self.rand_ga.population = population
candidate_fitnesses = self.rand_ga.get_population_fitness()
self.assertTrue(all(isinstance(k[1], float) for k in candidate_fitnesses))
def test_create_offspring_creates_two_children(self):
valuation1 = Valuation.init_random_from_variables(self.rand_maxsat.variables)
valuation2 = Valuation.init_random_from_variables(self.rand_maxsat.variables)
offspring = self.rand_ga.create_offspring(valuation1, valuation2)
self.assertEqual(2, len(offspring))
def test_create_offspring_creates_first_child_correctly_until_crossover_point(self):
variables = self.rand_maxsat.variables
crossover_index = int(len(variables) / 2)
valuation1 = Valuation.init_random_from_variables(variables)
valuation2 = Valuation.init_random_from_variables(variables)
offspring = self.rand_ga.create_offspring(valuation1, valuation2, crossover_index=crossover_index)
child1 = offspring[0]
self.assertTrue(
all(child1.get_value_for_variable(v) == valuation1.get_value_for_variable(v)
for v in variables[:crossover_index + 1])
)
def test_create_offspring_creates_first_child_correctly_after_crossover_point(self):
variables = self.rand_maxsat.variables
crossover_index = int(len(variables) / 2)
valuation1 = Valuation.init_random_from_variables(variables)
valuation2 = Valuation.init_random_from_variables(variables)
offspring = self.rand_ga.create_offspring(valuation1, valuation2, crossover_index=crossover_index)
child1 = offspring[0]
self.assertTrue(
all(child1.get_value_for_variable(v) == valuation2.get_value_for_variable(v)
for v in variables[crossover_index + 1:])
)
def test_create_offspring_creates_second_child_correctly_until_crossover_point(self):
variables = self.rand_maxsat.variables
crossover_index = int(len(variables) / 2)
valuation1 = Valuation.init_random_from_variables(variables)
valuation2 = Valuation.init_random_from_variables(variables)
offspring = self.rand_ga.create_offspring(valuation1, valuation2, crossover_index=crossover_index)
child2 = offspring[1]
self.assertTrue(
all(child2.get_value_for_variable(v) == valuation2.get_value_for_variable(v)
for v in variables[:crossover_index + 1])
)
def test_create_offspring_creates_second_child_correctly_after_crossover_point(self):
variables = self.rand_maxsat.variables
crossover_index = int(len(variables) / 2)
valuation1 = Valuation.init_random_from_variables(variables)
valuation2 = Valuation.init_random_from_variables(variables)
offspring = self.rand_ga.create_offspring(valuation1, valuation2, crossover_index=crossover_index)
child2 = offspring[1]
self.assertTrue(
all(child2.get_value_for_variable(v) == valuation1.get_value_for_variable(v)
for v in variables[crossover_index + 1:])
)
