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Sepehr Madani authoredSepehr Madani authored
genetic_algorithm.py 3.29 KiB
import cmath
import random
from copy import deepcopy
from math import cos, degrees, inf, log10, pi, radians, sin
from utils.pattern import compute_pattern
from .base_algorithm import BaseAlgorithm
class Chromosome:
def __init__(self, N, bit_count):
self.gene = [Chromosome.new_gene(bit_count) for i in range(N)]
self.fitness = float("nan")
@staticmethod
def new_gene(bit_count):
return random.randrange(0, 2 ** bit_count)
class GeneticAlgorithm(BaseAlgorithm):
""" Finds nulls by running a genetic algorithm on all possible
discrete values.
"""
def __init__(self, options):
BaseAlgorithm.__init__(self, options)
self.main_ang = options.main_ang
self.sample_size = options.sample_size
self.null_degrees = options.null_degrees
self.gen_to_repeat = options.gen_to_repeat
self.bit_count = options.bit_count
self.bit_resolution = options.bit_resolution
self.mutation_factor = options.mutation_factor
self.check_parameters()
self.chromosomes = [
Chromosome(self.N, self.bit_count) for i in range(self.sample_size)
]
self.update_fitness()
self.sort_fitness()
def check_parameters(self):
super().check_parameters()
def solve(self):
for generation in range(self.gen_to_repeat):
for ii in range(self.sample_size // 2, self.sample_size - 1, 2):
p1, p2 = random.sample(range(self.sample_size // 2), 2)
self.crossover(p1, p2, ii, ii + 1)
self.mutate_sample()
self.update_fitness()
self.sort_fitness()
# print(["{:.2f}".format(x.fitness) for x in self.chromosomes[:15]])
return [
cmath.exp(1j * self.get_angle(bits)) for bits in self.chromosomes[0].gene
]
def mutate_sample(self):
for chromosome in self.chromosomes[1:]: # for all except the best chromosome
for idx in range(self.N):
if random.random() <= self.mutation_factor:
chromosome.gene[idx] = Chromosome.new_gene(self.bit_count)
def update_fitness(self, use_exact_angle=True):
for chromosome in self.chromosomes:
values = [
-20 * log10(abs(x))
for x in compute_pattern(
N=self.N,
k=self.k,
weights=[
cmath.exp(1j * self.get_angle(bits)) for bits in chromosome.gene
],
degrees=self.null_degrees,
)
]
chromosome.fitness = min(values)
def sort_fitness(self):
self.chromosomes.sort(key=lambda x: x.fitness, reverse=True)
def get_angle(self, bits):
return (
(bits - (2 ** self.bit_count - 1) / 2) * 2 * pi / (2 ** self.bit_resolution)
)
def crossover(self, p1, p2, c1, c2):
self.chromosomes[c1] = deepcopy(self.chromosomes[p1])
self.chromosomes[c2] = deepcopy(self.chromosomes[p2])
for i in range(self.N):
if random.random() >= 0.5:
self.chromosomes[c1].gene[i], self.chromosomes[c2].gene[i] = (
self.chromosomes[c1].gene[i],
self.chromosomes[c2].gene[i],
)