def search(experience_number):
fragments = base.read_data_from_file(experience_number, 'fragment')
increase_fragments_count(fragments)
best_reference_seq = {
'first': None,
'second': None,
'score': 0
}
for i in range(MAX_HILL_CLIMBS_COUNT):
print('hill climb number ' + str(i) + '...')
local_best_reference_seqs = climb_hill(fragments)
if local_best_reference_seqs['score'] > best_reference_seq['score']:
best_reference_seq = local_best_reference_seqs
haplotypes = base.read_data_from_file(experience_number, 'haplotype')
print('Accuracy 1: ' + str((base.HAPLOTYPE_LENGTH - base.calculate_hamming_distance(
best_reference_seq['first'], haplotypes[0])) / base.HAPLOTYPE_LENGTH))
print('Accuracy 2: ' + str((base.HAPLOTYPE_LENGTH - base.calculate_hamming_distance(
best_reference_seq['second'], haplotypes[1])) / base.HAPLOTYPE_LENGTH))
def search(experience_number):
global FRAGMENTS_COUNT
fragments = base.read_data_from_file(experience_number, 'fragment')
FRAGMENTS_COUNT += len(fragments)
first_reference_sequence = generate_base_reference_sequence()
second_reference_sequence = generate_base_reference_sequence()
for fragment in fragments:
if are_compatible(first_reference_sequence, fragment):
first_reference_sequence = combine(first_reference_sequence,
fragment)
elif are_compatible(second_reference_sequence, fragment):
second_reference_sequence = combine(second_reference_sequence,
fragment)
else:
print('Conflict.')
haplotypes = base.read_data_from_file(experience_number, 'haplotype')
if (are_compatible(haplotypes[0], first_reference_sequence)
and are_compatible(haplotypes[1], second_reference_sequence)):
print('100% match!')
def search(experience_number):
fragments = base.read_data_from_file(experience_number, 'fragment')
increase_fragments_count(fragments)
init_edges_pheromones(fragments)
best_sequences = {'score': 0}
for i in range(ANTS_COUNT):
print('Deploying ant number ' + str(i) + '...')
sequences = deploy_ant(fragments)
if sequences['score'] > best_sequences['score']:
best_sequences['first'] = sequences['first']
best_sequences['second'] = sequences['second']
best_sequences['score'] = sequences['score']
haplotypes = base.read_data_from_file(experience_number, 'haplotype')
print('Accuracy 1: ' +
str((base.HAPLOTYPE_LENGTH - base.calculate_hamming_distance(
best_sequences['first'], haplotypes[0])) /
base.HAPLOTYPE_LENGTH))
print('Accuracy 2: ' +
str((base.HAPLOTYPE_LENGTH - base.calculate_hamming_distance(
best_sequences['second'], haplotypes[1])) /
base.HAPLOTYPE_LENGTH))
def search(experience_number):
fragments = base.read_data_from_file(experience_number, 'fragment')
increase_fragments_count(fragments)
best_sequence_pair = {'score': 0}
population = generate_random_population()
for i in range(10):
print('Generation number ' + str(i) + '...')
population = reproduce_new_population(population, fragments)
best, score = get_fittest_in_population(population, fragments)
if score > best_sequence_pair['score']:
best_sequence_pair['first'] = best[0]
best_sequence_pair['second'] = best[1]
best_sequence_pair['score'] = score
haplotypes = base.read_data_from_file(experience_number, 'haplotype')
print('Accuracy 1: ' +
str((base.HAPLOTYPE_LENGTH - base.calculate_hamming_distance(
best_sequence_pair['first'], haplotypes[0])) /
base.HAPLOTYPE_LENGTH))
print('Accuracy 2: ' +
str((base.HAPLOTYPE_LENGTH - base.calculate_hamming_distance(
best_sequence_pair['second'], haplotypes[1])) /
base.HAPLOTYPE_LENGTH))