def setUp(self):
natoms = 10
minimiser = PeleMinimiser(BLJCluster(natoms, 5))
self.factory = ClusterFactory(natoms,
minimiser,
composition=[5, 5],
labels=["A", "B"])
def test_cluster(self):
natoms=3
minimiser=PeleMinimiser(LJCluster(natoms))
factory=ClusterFactory(natoms,minimiser)
with open("restart.xyz",'r') as xyz_file:
cluster=factory.read_xyz(xyz_file)
self.assertEquals(-5.,cluster.get_energy())
def __init__(self,
natoms,
minimiser,
composition="default",
labels=("X", ),
pop_size=10,
max_generation=10,
selector=TournamentSelector(3),
offspring=8,
mutant_rate=0.1,
remove_duplicates=False,
mutator=MutateReplace(),
crossover=DeavenHo()):
#Parameters
self.max_generation = max_generation
self.mutant_rate = mutant_rate
self.offspring = offspring
self.pop_size = pop_size
self.remove_duplicates = remove_duplicates
self.selector = selector
self.mutator = mutator
self.crossover = crossover
#Factory
self.factory = ClusterFactory(natoms, minimiser, composition, labels)
#PopulationList
self.population = PopulationList(natoms, self.factory, pop_size)
#Evolutionary progress
self.mean_energy_series = []
self.min_energy_series = []
self.db = Database(db="mydatabase.sqlite")
self.storage = self.db.minimum_adder()
def setUp(self):
natoms = 10
minimiser = PeleMinimiser(lj.LJ())
factory = ClusterFactory(natoms, minimiser)
self.population = PopulationList(natoms, factory, max_size=5)
while len(self.population) < self.population.max_size:
self.population.append(factory.get_random_cluster())
def test_replace(self):
natoms=10
minimiser=PeleMinimiser(lj.LJ())
factory=ClusterFactory(natoms,minimiser)
parent = factory.get_random_cluster()
mutator = MutateReplace()
mutant = mutator.get_mutant(parent)
self.assertLess(mutant.get_energy(), 0)
def test_population(self):
natoms=3
minimiser=PeleMinimiser(LJCluster(natoms))
factory=ClusterFactory(natoms,minimiser)
with open("restart.xyz",'r') as xyz_file:
population=PopulationList(natoms,factory)
population.read_xyz(xyz_file)
self.assertEquals(-5.,population[0].get_energy())
self.assertEquals(-4.,population[1].get_energy())
def test_mutant(self):
natoms = 10
minimiser = PeleMinimiser(lj.LJ())
factory = ClusterFactory(natoms, minimiser)
parent_a = factory.get_random_cluster()
parent_b = factory.get_random_cluster()
crossover = DeavenHo()
offspring = crossover.get_offspring(parent_a, parent_b)
self.assertLess(offspring.get_energy(), 0)
def test_exchange(self):
natoms=20
minimiser=PeleMinimiser(BLJCluster(natoms,10))
factory=ClusterFactory(natoms,minimiser,
composition=[10,10],
labels=["A","B"])
cluster = factory.get_random_cluster()
mutator = MutateExchange()
for i in range(0,50):
mutant=mutator.get_mutant(cluster)
self.assertEquals(mutant.atom_types[9],0)
self.assertEquals(mutant.atom_types[10],1)
def __init__(self,
natoms,
minimiser,
composition="default",
labels=("X", ),
pop_size=10,
max_generation=10,
selector=TournamentSelector(3),
offspring=8,
mutant_rate=0.1,
remove_duplicates=False,
mass_extinction=False,
epoch_thresh=1.e-6,
restart=False,
mutator=MutateReplace(),
crossover=DeavenHo()):
#Parameters
self.max_generation = max_generation
self.mutant_rate = mutant_rate
self.offspring = offspring
self.pop_size = pop_size
self.remove_duplicates = remove_duplicates
self.mass_extinction = mass_extinction
self.epoch_thresh = epoch_thresh
self.selector = selector
self.mutator = mutator
self.crossover = crossover
#Factory
self.factory = ClusterFactory(natoms, minimiser, composition, labels)
#PopulationList
self.population = PopulationList(natoms, self.factory, pop_size)
if restart is False:
while len(self.population) < self.population.max_size:
self.population.append(self.factory.get_random_cluster())
self.population.sort_energy()
else:
with open("restart.xyz", 'r') as xyz_file:
self.population.read_xyz(xyz_file)
#Evolutionary progress
self.mean_energy_series = []
self.mean_energy_series.append(self.population.get_mean_energy())
self.min_energy_series = []
self.min_energy_series.append(self.population.get_lowest_energy())
def setUp(self):
natoms = 10
minimiser = PeleMinimiser(lj.LJ())
self.factory = ClusterFactory(natoms, minimiser)
