def crossover(self, organism1: Organism, organism2: Organism):
if organism1.genome_size != organism1.genome_size:
raise ValueError('Genome of organisms should be same size')
genome_size = organism1.genome_size
one_third_point = int(genome_size / 3)
two_third_point = int(genome_size / 3) * 2
crossover_point1 = random.randint(0, one_third_point)
crossover_point2 = random.randint(one_third_point, two_third_point)
crossover_point3 = random.randint(two_third_point, genome_size)
new_genome = []
for i in range(genome_size):
if i < crossover_point1:
new_genome.append(organism1.genome[i])
elif crossover_point1 <= i < crossover_point2:
new_genome.append(organism2.genome[i])
elif crossover_point2 <= i < crossover_point3:
new_genome.append(organism1.genome[i])
else:
new_genome.append(organism2.genome[i])
return Organism(new_genome)
def introduce(self, organism: Organism):
new_genome = []
for i, g in enumerate(organism.genome):
r = random.uniform(0, 1)
if r < self.frequency_of_mutations:
initial_gene = self.initial_organism.genome[i]
rand = random.uniform(0, int(initial_gene / self.divider))
new_genome.append(initial_gene - rand)
else:
new_genome.append(g)
return Organism(new_genome)
def __init__(self, path: str):
initial_organism_path = os.path.join(path, 'initial_organism.npy')
if os.path.isfile(initial_organism_path):
self.initial_organism = Organism.from_file(initial_organism_path)
else:
self.initial_organism = None
initial_population_path = os.path.join(path, 'initial_population.npy')
if os.path.isfile(initial_population_path):
self.initial_population = Population.from_file(
initial_population_path)
else:
self.initial_population = None
self.hic_data = HiCData.from_files(path)
def crossover(self, organism1: Organism, organism2: Organism):
if organism1.genome_size != organism1.genome_size:
raise ValueError('Genome of organisms should be same size')
genome_size = organism1.genome_size
new_genome = []
for i in range(genome_size):
first = random.randint(0, 1)
if first:
new_genome.append(organism1.genome[i])
else:
new_genome.append(organism2.genome[i])
return Organism(new_genome)
def crossover(self, organism1: Organism, organism2: Organism):
if organism1.genome_size != organism1.genome_size:
raise ValueError('Genome of organisms should be same size')
genome_size = organism1.genome_size
crossover_point = int(genome_size / 2)
new_genome = []
for i in range(genome_size):
if i < crossover_point:
new_genome.append(organism1.genome[i])
else:
new_genome.append(organism2.genome[i])
return Organism(new_genome)
def crossover(self, organism1: Organism, organism2: Organism):
if organism1.genome_size != organism1.genome_size:
raise ValueError('Genome of organisms should be same size')
genome_size = organism1.genome_size
#TODO: maybe should use Gaussian? Or cut in half?
crossover_point = random.randint(0, genome_size)
new_genome = []
for i in range(genome_size):
if i < crossover_point:
new_genome.append(organism1.genome[i])
else:
new_genome.append(organism2.genome[i])
return Organism(new_genome)
def generate_organism(self, original_organism: Organism):
new_genome = []
for gene in original_organism.genome:
rand = random.uniform(0, gene / 3)
new_genome.append(gene - rand)
return Organism(new_genome)
dm_path = os.path.join(directory, 'dm.npy')
np.save(dm_path, chromosome.distance_matrix.distance_matrix_nparray)
not_gaps_path = os.path.join(directory, 'not_gaps.npy')
np.save(not_gaps_path, hic_data.not_gaps)
curve_path = os.path.join(directory, 'curve.pdf')
files_helper.save_3d_plot(sample_name, curve_path, chromosome.points)
adj_path = os.path.join(directory, 'adj.png')
files_helper.save_adjastency_matrix(
sample_name, adj_path, chromosome.distance_matrix.distance_matrix_nparray)
if not params.save_initial_population:
print('Saved!')
sys.exit()
initial_organism_genome = distance_matrix_sd.get_futm_except_ordered_coordinates(
hic_data.not_gaps)
initial_organism = Organism(initial_organism_genome)
initial_population_generator = InitialPopulationGenerator()
initial_population = initial_population_generator.generate(
initial_organism, params.population_size)
initial_organism_path = os.path.join(directory, 'initial_organism.npy')
initial_organism.save(initial_organism_path)
initial_population_path = os.path.join(directory, 'initial_population.npy')
initial_population.save(initial_population_path)
print('Saved!')
initial_organism = sample.initial_organism
initial_population = sample.initial_population
else:
print('Running shortest distances algorithm... ', end='')
start = time.time()
distance_matrix_sd = shortest_distances_filler.fill(
distance_matrix_with_gaps)
end = time.time()
elapsed = end - start
print('Finished in {0} sec.'.format(timedelta(seconds=elapsed)))
print('Generating initial organism (ADAM)... ', end='')
start = time.time()
initial_organism_genome = distance_matrix_sd.get_futm_except_ordered_coordinates(
hic_data.not_gaps)
initial_organism = Organism(initial_organism_genome)
end = time.time()
elapsed = end - start
print('Finished in {0} sec.'.format(timedelta(seconds=elapsed)))
print('Generating initial population... ', end='')
start = time.time()
initial_population = initial_population_generator.generate(
initial_organism, params.population_size)
end = time.time()
elapsed = end - start
print('Finished in {0} sec.'.format(timedelta(seconds=elapsed)))
print('Calculating ADAM\'s error... ', end='')
start = time.time()
adam_score = fitness_calculator.calculate(initial_organism.genome)
def from_file(cls, path: str):
list_of_genomes = np.load(path)
organisms = []
for genome in list_of_genomes:
organisms.append(Organism(genome.tolist()))
return cls(organisms)
def test_10(self):
organism1 = Organism([1, 1, 1, 1, 1, 1, 1, 1, 1, 1])
organism2 = Organism([2, 2, 2, 2, 2, 2, 2, 2, 2, 2])
crossoverer = MiddlePointCrossoverer()
result = crossoverer.crossover(organism1, organism2)
self.assertEqual(result.genome, [1, 1, 1, 1, 1, 2, 2, 2, 2, 2])