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Add doctests in all functions in basic_string.py (#11374)

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* Add doctests in all functions in basic_string.py

* Revert back to original basic_string.py

* Add doctest in basic_string.py

* [pre-commit.ci] auto fixes from pre-commit.com hooks

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* Update genetic_algorithm/basic_string.py

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Kelvin 2024-04-20 16:20:37 +05:30 committed by GitHub
parent a42eb35702
commit 42593489d9
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1 changed files with 28 additions and 3 deletions
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genetic_algorithm/basic_string.py
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@ -33,7 +33,12 @@ def evaluate(item: str, main_target: str) -> tuple[str, float]:
def crossover(parent_1: str, parent_2: str) -> tuple[str, str]:
"""Slice and combine two string at a random point."""
"""
Slice and combine two strings at a random point.
>>> random.seed(42)
>>> crossover("123456", "abcdef")
('12345f', 'abcde6')
"""
random_slice = random.randint(0, len(parent_1) - 1)
child_1 = parent_1[:random_slice] + parent_2[random_slice:]
child_2 = parent_2[:random_slice] + parent_1[random_slice:]
@ -41,7 +46,12 @@ def crossover(parent_1: str, parent_2: str) -> tuple[str, str]:
def mutate(child: str, genes: list[str]) -> str:
"""Mutate a random gene of a child with another one from the list."""
"""
Mutate a random gene of a child with another one from the list.
>>> random.seed(123)
>>> mutate("123456", list("ABCDEF"))
'12345A'
"""
child_list = list(child)
if random.uniform(0, 1) < MUTATION_PROBABILITY:
child_list[random.randint(0, len(child)) - 1] = random.choice(genes)
@ -54,7 +64,22 @@ def select(
population_score: list[tuple[str, float]],
genes: list[str],
) -> list[str]:
"""Select the second parent and generate new population"""
"""
Select the second parent and generate new population
>>> random.seed(42)
>>> parent_1 = ("123456", 8.0)
>>> population_score = [("abcdef", 4.0), ("ghijkl", 5.0), ("mnopqr", 7.0)]
>>> genes = list("ABCDEF")
>>> child_n = int(min(parent_1[1] + 1, 10))
>>> population = []
>>> for _ in range(child_n):
... parent_2 = population_score[random.randrange(len(population_score))][0]
... child_1, child_2 = crossover(parent_1[0], parent_2)
... population.extend((mutate(child_1, genes), mutate(child_2, genes)))
>>> len(population) == (int(parent_1[1]) + 1) * 2
True
"""
pop = []
# Generate more children proportionally to the fitness score.
child_n = int(parent_1[1] * 100) + 1