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@matthen
Last active August 29, 2024 12:50
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Hello world in python, using genetic algorithm
"""Hello world, with a genetic algorithm.
https://twitter.com/matthen2/status/1769368467067621791
"""
import random
import time
from dataclasses import dataclass
from itertools import chain
from typing import Iterable, List
_LETTERS = "abcdefghijklmnopqrstuvwxyz "
class Color:
RED = "\033[91m"
GREEN = "\033[92m"
CYAN = "\033[96m"
BOLD = "\033[1m"
END = "\033[0m"
@dataclass
class Candidate:
text: str
fitness: int = -1
in_focus: bool = False
def display_str(self, target_str: str) -> str:
prefix = "➤ " if self.in_focus else " "
if self.fitness < 0:
return prefix + self.text
out = prefix
for char, target_char in zip(self.text, target_str):
if char != target_char:
out += Color.RED + char
else:
out += Color.GREEN + char
return out + Color.END
def set_fitness(self, target_str):
self.fitness = sum(
int(char == target_char) for char, target_char in zip(self.text, target_str)
)
def reset_focus(population: List[Candidate]) -> None:
for candidate in population:
candidate.in_focus = False
def seed_population(
population: List[Candidate], population_size: int, target_str_len: int
) -> Iterable[str]:
"""Seed a population with random candidates, and yields after every new one."""
while len(population) < population_size:
reset_focus(population)
population.append(
Candidate(
text="".join(random.choice(_LETTERS) for _ in range(target_str_len)),
in_focus=True,
)
)
yield "Seeding the population"
def order_by_fitness(
population: List[Candidate],
target_str: str,
) -> Iterable[str]:
# Show errors for all
for candidate in population:
if candidate.fitness >= 0:
continue
reset_focus(population)
candidate.set_fitness(target_str)
candidate.in_focus = True
yield "Computing fitness"
reset_focus(population)
made_swap = True
evens = True
while made_swap:
evens = not evens
made_swap = False
for i in range(int(evens), len(population), 2):
if i + 1 >= len(population):
continue
candidate_a, candidate_b = population[i : i + 2]
if candidate_a.fitness >= candidate_b.fitness:
continue
# Need to swap
made_swap = True
population[i] = candidate_b
population[i + 1] = candidate_a
yield "Sorting by fitness"
def remove_unfit(population: List[Candidate], num_fit_to_keep: int):
while len(population) > num_fit_to_keep:
population.pop()
population[-1].in_focus = True
yield "Removing unfit candidates"
reset_focus(population)
def breed(parent_a: Candidate, parent_b: Candidate, mutation_prob: float) -> Candidate:
text = ""
for char_a, char_b in zip(parent_a.text, parent_b.text):
if random.random() < mutation_prob:
text += random.choice(_LETTERS)
elif random.random() < 0.5:
text += char_a
else:
text += char_b
return Candidate(text=text)
def breed_new(population: List[Candidate], population_size: int, mutation_prob: float):
num_fit = len(population)
while len(population) < population_size:
i = random.randint(0, num_fit - 1)
j = (i + random.randint(1, num_fit - 1)) % num_fit
parent_a = population[i]
parent_b = population[j]
reset_focus(population)
parent_a.in_focus = True
parent_b.in_focus = True
child = breed(parent_a, parent_b, mutation_prob=mutation_prob)
child.in_focus = True
population.append(child)
yield "Breeding new candidates"
def display(
*,
population: List[Candidate],
label: str,
population_size: int,
num_columns: int,
column_width: int,
target_str: str,
) -> None:
print("\n\n")
print(
Color.BOLD
+ Color.CYAN
+ label.center(column_width * num_columns)
+ Color.END
+ "\n"
)
num_rows = population_size // num_columns
cells = [["" for _ in range(num_columns)] for _ in range(num_rows)]
for i in range(population_size):
row_idx = i % num_rows
col_idx = i // num_rows
if i >= len(population):
cells[row_idx][col_idx] = " " * column_width
continue
padding = column_width - len(target_str) - 2
cells[row_idx][col_idx] = population[i].display_str(target_str) + " " * padding
for row in cells:
print(" " + "".join(row))
print("\n")
def main(
target_str: str = "hello world",
population_size: int = 48,
num_fit_to_keep: int = 5,
num_columns: int = 4,
mutation_prob: float = 0.15,
):
population: List[Candidate] = []
column_width = len(target_str) + 6
while True:
for label in chain(
seed_population(
population,
population_size=population_size,
target_str_len=len(target_str),
),
order_by_fitness(population, target_str=target_str),
remove_unfit(population, num_fit_to_keep=num_fit_to_keep),
breed_new(
population, population_size=population_size, mutation_prob=mutation_prob
),
):
time.sleep(0.016)
print("\033[H\033[J", end="")
display(
population=population,
label=label,
population_size=population_size,
num_columns=num_columns,
column_width=column_width,
target_str=target_str,
)
if __name__ == "__main__":
main()
@osbm
Copy link

osbm commented Mar 17, 2024

This was awesome. To run it: (just execute 3rd party code on your machine, dont worry about it)

curl https://gist.githubusercontent.com/matthen/e5c86c95385b44d7dc127c091b4a299a/raw/186de38a73db151b16c81350678933bf33675535/hello_world.py | python

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