diff --git a/algorithms/genetic_bucket_algorithm.py b/algorithms/genetic_bucket_algorithm.py
index 121df27096cf57d464dbac6c286357d9ff98cb3a..98c7df7d07f900b1b407c6a07b1ec88c7d75ed75 100644
--- a/algorithms/genetic_bucket_algorithm.py
+++ b/algorithms/genetic_bucket_algorithm.py
@@ -1,7 +1,7 @@
-import cmath
-import random
-from copy import deepcopy
-from math import cos, degrees, inf, log10, pi, radians, sin
+from random import randrange, random, choice
+from cmath import exp, phase
+from math import log10, pi
+from typing import List
from utils.pattern import compute_pattern
@@ -9,13 +9,17 @@ 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)]
+ def __init__(self, n, bit_count):
+ self.gene = [Chromosome.new_gene(bit_count) for _ in range(n)]
self.fitness = float("nan")
+ self.needs_update = True
+
+ def get_score(self):
+ return -20 * log10(abs(self.fitness))
@staticmethod
def new_gene(bit_count):
- return random.randrange(0, 2 ** bit_count)
+ return randrange(0, 2 ** bit_count)
class GeneticBucketAlgorithm(BaseAlgorithm):
@@ -23,8 +27,10 @@ class GeneticBucketAlgorithm(BaseAlgorithm):
discrete values.
"""
+ chromosomes: List[Chromosome]
+
def __init__(self, options):
- BaseAlgorithm.__init__(self, options)
+ super().__init__(self, options)
self.main_ang = options.main_ang
self.sample_size = options.sample_size
self.null_degrees = options.null_degrees
@@ -32,6 +38,7 @@ class GeneticBucketAlgorithm(BaseAlgorithm):
self.bit_count = options.bit_count
self.bit_resolution = options.bit_resolution
self.mutation_factor = options.mutation_factor
+ self.chromosomes = []
self.buckets = [[]] * 8
self.check_parameters()
@@ -41,43 +48,58 @@ class GeneticBucketAlgorithm(BaseAlgorithm):
assert len(self.null_degrees) == 1
def solve(self):
- self.intialize_sample()
- self.update_fitness()
- self.sort_fitness()
- for generation in range(self.gen_to_repeat):
- for ii in range(self.sample_size // 2, self.sample_size - 1, 2):
- bucket_idx = random.randrange(4)
- p1 = random.choice(self.buckets[bucket_idx])
- p2 = random.choice(self.buckets[bucket_idx + 4])
+ self.initialize_sample()
+ self.organize_sample()
- self.crossover(p1, p2, ii, ii + 1)
- # self.mutate_sample()
- self.update_fitness()
- self.sort_fitness()
-
- return self.make_weights(self.chromosomes[0])
-
- 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)
+ for generation in range(self.gen_to_repeat):
+ self.create_children()
+ self.mutate_sample()
+ self.organize_sample()
+
+ return (self.make_weights(self.chromosomes[0]), self.chromosomes[0].get_score())
+
+ def create_children(self):
+ for ii in range(self.sample_size // 2, self.sample_size - 1, 2):
+ bucket_idx = randrange(8)
+ p1 = choice(self.buckets[bucket_idx])
+ p2 = min(
+ self.buckets[7 - bucket_idx], key=lambda x: abs(x.fitness + p1.fitness)
+ )
+ self.crossover(p1, p2, ii, ii + 1)
- def update_fitness(self, use_exact_angle=True):
+ def organize_sample(self):
+ # Update fitness
+ for chromosome in self.chromosomes:
+ if chromosome.needs_update:
+ chromosome.fitness = compute_pattern(
+ N=self.N,
+ k=self.k,
+ weights=self.make_weights(chromosome),
+ degrees=self.null_degrees,
+ use_absolute_value=False,
+ )[0]
+ chromosome.needs_update = False
+
+ # Sort sample by fitness
+ self.chromosomes.sort(key=lambda x: x.get_score(), reverse=True)
+
+ # Allocate chromosomes to their respective buckets
self.buckets = [[]] * 8
for chromosome in self.chromosomes:
- chromosome.fitness = compute_pattern(
- N=self.N,
- k=self.k,
- weights=self.make_weights(chromosome),
- degrees=self.null_degrees,
- use_absolute_value=False,
- )[0]
- bucket_idx = int(((cmath.phase(chromosome.fitness) + pi) / (2 * pi)) * 8) % 8
+ bucket_idx = int(((phase(chromosome.fitness) + pi) / (2 * pi)) * 8) % 8
self.buckets[bucket_idx].append(chromosome)
- def sort_fitness(self):
- self.chromosomes.sort(key=lambda x: -20 * log10(abs(x.fitness)), reverse=True)
+ def mutate_sample(self):
+ # For all except the best chromosome
+ for original in range(1, self.sample_size):
+ mutated = original + self.sample_size - 1
+ self.chromosomes[mutated].needs_update = True
+
+ for ii in range(self.N):
+ if random() <= self.mutation_factor:
+ self.chromosomes[mutated].gene[ii] = Chromosome.new_gene(self.bit_count)
+ else:
+ self.chromosomes[mutated].gene[ii] = self.chromosomes[original].gene[ii]
def make_weights(self, chromosome):
weights = []
@@ -88,7 +110,7 @@ class GeneticBucketAlgorithm(BaseAlgorithm):
* pi
/ (2 ** self.bit_resolution)
)
- weights.append(cmath.exp(1j * angle))
+ weights.append(exp(1j * angle))
return weights
def crossover(self, p1, p2, c1, c2):
@@ -98,7 +120,7 @@ class GeneticBucketAlgorithm(BaseAlgorithm):
self.chromosomes[c1].gene[ii] = (g1 + g2) // 2
self.chromosomes[c2].gene[ii] = (g1 + g2 + 1) // 2
- def intialize_sample(self):
+ def initialize_sample(self):
self.chromosomes = [
- Chromosome(self.N, self.bit_count) for i in range(self.sample_size)
+ Chromosome(self.N, self.bit_count) for _ in range(self.sample_size * 2 - 1)
]