def data_generator_teachers(courses):
names = ["Green", "Brown", "Wood", "Steal", "Water"]
density = [2, 3]
teachers = []
for i in range(len(names)):
teachers.append({"id": i, "name": names[i], "courses": []})
teachers_len = len(teachers)
for c_id in range(len(courses)):
dens = random.randint(*density)
teach = random_elements(range(teachers_len), dens)
for t in teach:
teachers[t]['courses'].append(c_id)
return teachers
def next_gen(prev_gen,
reproduction_number=10,
surviving_number=1,
crossover_points=[1, 5],
mutation_prob=0.01):
population = len(prev_gen)
reproduction_gen = prev_gen[:reproduction_number]
new_gen = prev_gen[:surviving_number].copy()
while len(new_gen) < population:
parents = random_elements(reproduction_gen, 2)
children = crossover(*parents, random.randint(*crossover_points))
new_gen.append(mutation(children[0], mutation_prob))
new_gen.append(mutation(children[1], mutation_prob))
new_gen = ranking(new_gen[:population])
top5 = [str(penalty(sch)) for sch in new_gen[:5]]
print("the best 5: ", ", ".join(top5))
return new_gen
def data_generator_groups(courses):
names = ["K-10", "K-11", "K-12", "K-13", "K-14"]
students_range = [25, 30]
courses_quantity = [4, 6]
groups = []
courses_len = len(courses)
n_id = -1
for name in names:
n_id += 1
groups.append({
"id":
n_id,
"name":
name,
"students":
random.randint(*students_range),
"courses":
random_elements(range(courses_len),
random.randint(*courses_quantity))
})
return groups