def improve(self):
float_i = list(map(lambda x: x.curr_float, Project.all))
min_float = min(float_i)
max_float = max(float_i)
if self.time < END:
p = select(Project.all, key=lambda x: x.curr_float - min_float + 1, num=1).pop()
self.candidate_l[p.num] = p.candidate_time(p.curr_time, p.curr_time + (END - self.time))
elif self.time > END:
p = select(Project.all, key=lambda x: max_float - x.curr_float + 1, num=1).pop()
self.candidate_l[p.num] = p.candidate_time(p.curr_time - (self.time - END), p.curr_time)
def improve(self):
float_i = list(map(lambda x: x.curr_float, Project.all))
min_float = min(float_i)
max_float = max(float_i)
if self.time < END:
p = select(Project.all,
key=lambda x: x.curr_float - min_float + 1,
num=1).pop()
self.candidate_l[p.num] = p.candidate_time(
p.curr_time, p.curr_time + (END - self.time))
elif self.time > END:
p = select(Project.all,
key=lambda x: max_float - x.curr_float + 1,
num=1).pop()
self.candidate_l[p.num] = p.candidate_time(
p.curr_time - (self.time - END), p.curr_time)
def test():
c_foo = Candidate('foo')
c_bar = Candidate('bar')
c_lol = Candidate('lol')
p_se = Project('se')
p_s1 = Project('s1')
p_12 = Project('12')
p_e3 = Project('e3')
p_23 = Project('23')
p_24 = Project('24')
p_2e = Project('2e')
p_34 = Project('34')
p_45 = Project('45')
p_e5 = Project('e5')
p_ee = Project('ee')
p_56 = Project('56')
p_e6 = Project('e6')
p_e7 = Project('e7')
p_67 = Project('67')
p_7e = Project('7e')
p_e3.depend_on(p_se)
p_12.depend_on(p_s1)
p_23.depend_on(p_12)
p_34.depend_on(p_e3, p_23)
p_24.depend_on(p_12)
p_2e.depend_on(p_12)
p_e5.depend_on(p_2e)
p_ee.depend_on(p_2e)
p_45.depend_on(p_34, p_24)
p_56.depend_on(p_45, p_e5)
p_e6.depend_on(p_ee)
p_e7.depend_on(p_ee)
p_67.depend_on(p_56, p_e6)
p_7e.depend_on(p_67, p_e7)
p_se.add_candidate(c_foo, 2, 2)
p_se.add_candidate(c_bar, 1, 3)
p_se.add_candidate(c_lol, 3, 1)
p_s1.add_candidate(c_foo, 1, 1)
p_12.add_candidate(c_foo, 3, 3)
p_12.add_candidate(c_bar, 4, 2)
p_12.add_candidate(c_lol, 2, 4)
p_e3.add_candidate(c_foo, 1, 1)
p_23.add_candidate(c_foo, 2, 2)
p_23.add_candidate(c_bar, 1, 3)
p_23.add_candidate(c_lol, 3, 1)
p_24.add_candidate(c_foo, 1, 1)
p_2e.add_candidate(c_foo, 3, 3)
p_2e.add_candidate(c_bar, 4, 2)
p_2e.add_candidate(c_lol, 2, 4)
p_34.add_candidate(c_foo, 2, 2)
p_34.add_candidate(c_bar, 3, 1)
p_45.add_candidate(c_foo, 3, 3)
p_45.add_candidate(c_bar, 4, 2)
p_45.add_candidate(c_lol, 2, 4)
p_e5.add_candidate(c_foo, 3, 3)
p_e5.add_candidate(c_bar, 4, 2)
p_e5.add_candidate(c_lol, 2, 4)
p_ee.add_candidate(c_foo, 2, 2)
p_ee.add_candidate(c_bar, 3, 1)
p_56.add_candidate(c_foo, 3, 3)
p_e6.add_candidate(c_foo, 3, 3)
p_e6.add_candidate(c_bar, 4, 2)
p_e6.add_candidate(c_lol, 2, 4)
p_e7.add_candidate(c_foo, 4, 4)
p_e7.add_candidate(c_bar, 5, 3)
p_e7.add_candidate(c_lol, 3, 5)
p_67.add_candidate(c_foo, 1, 1)
p_7e.add_candidate(c_bar, 1, 1)
def rand_solution():
return Solution([
choice(list(project.candidate_d.keys()))
for project in Project.all
])
solution_l = [rand_solution() for _ in range(100)]
for i in range(100):
max_price = 0
for solution in solution_l:
solution.stimulate()
solution.improve()
solution.stimulate()
max_price = max(max_price, solution.price)
solution_l = select(solution_l,
key=lambda x: max_price - x.price + 1,
num=len(solution_l),
clone_func=lambda x: x.clone())
crossover([s.candidate_l for s in solution_l], rate=0.2)
mutate((s.candidate_l for s in solution_l),
func=lambda i: choice(list(Project.all[i].candidate_d)),
rate=0.05)
for solution in solution_l:
solution.stimulate()
print(sum(solution.price for solution in solution_l))
def test():
c_foo = Candidate('foo')
c_bar = Candidate('bar')
c_lol = Candidate('lol')
p_se = Project('se')
p_s1 = Project('s1')
p_12 = Project('12')
p_e3 = Project('e3')
p_23 = Project('23')
p_24 = Project('24')
p_2e = Project('2e')
p_34 = Project('34')
p_45 = Project('45')
p_e5 = Project('e5')
p_ee = Project('ee')
p_56 = Project('56')
p_e6 = Project('e6')
p_e7 = Project('e7')
p_67 = Project('67')
p_7e = Project('7e')
p_e3.depend_on(p_se)
p_12.depend_on(p_s1)
p_23.depend_on(p_12)
p_34.depend_on(p_e3, p_23)
p_24.depend_on(p_12)
p_2e.depend_on(p_12)
p_e5.depend_on(p_2e)
p_ee.depend_on(p_2e)
p_45.depend_on(p_34, p_24)
p_56.depend_on(p_45, p_e5)
p_e6.depend_on(p_ee)
p_e7.depend_on(p_ee)
p_67.depend_on(p_56, p_e6)
p_7e.depend_on(p_67, p_e7)
p_se.add_candidate(c_foo, 2, 2)
p_se.add_candidate(c_bar, 1, 3)
p_se.add_candidate(c_lol, 3, 1)
p_s1.add_candidate(c_foo, 1, 1)
p_12.add_candidate(c_foo, 3, 3)
p_12.add_candidate(c_bar, 4, 2)
p_12.add_candidate(c_lol, 2, 4)
p_e3.add_candidate(c_foo, 1, 1)
p_23.add_candidate(c_foo, 2, 2)
p_23.add_candidate(c_bar, 1, 3)
p_23.add_candidate(c_lol, 3, 1)
p_24.add_candidate(c_foo, 1, 1)
p_2e.add_candidate(c_foo, 3, 3)
p_2e.add_candidate(c_bar, 4, 2)
p_2e.add_candidate(c_lol, 2, 4)
p_34.add_candidate(c_foo, 2, 2)
p_34.add_candidate(c_bar, 3, 1)
p_45.add_candidate(c_foo, 3, 3)
p_45.add_candidate(c_bar, 4, 2)
p_45.add_candidate(c_lol, 2, 4)
p_e5.add_candidate(c_foo, 3, 3)
p_e5.add_candidate(c_bar, 4, 2)
p_e5.add_candidate(c_lol, 2, 4)
p_ee.add_candidate(c_foo, 2, 2)
p_ee.add_candidate(c_bar, 3, 1)
p_56.add_candidate(c_foo, 3, 3)
p_e6.add_candidate(c_foo, 3, 3)
p_e6.add_candidate(c_bar, 4, 2)
p_e6.add_candidate(c_lol, 2, 4)
p_e7.add_candidate(c_foo, 4, 4)
p_e7.add_candidate(c_bar, 5, 3)
p_e7.add_candidate(c_lol, 3, 5)
p_67.add_candidate(c_foo, 1, 1)
p_7e.add_candidate(c_bar, 1, 1)
def rand_solution():
return Solution([choice(list(project.candidate_d.keys())) for project in Project.all])
solution_l = [rand_solution() for _ in range(100)]
for i in range(100):
max_price = 0
for solution in solution_l:
solution.stimulate()
solution.improve()
solution.stimulate()
max_price = max(max_price, solution.price)
solution_l = select(solution_l, key=lambda x: max_price - x.price + 1,
num=len(solution_l), clone_func=lambda x: x.clone())
crossover([s.candidate_l for s in solution_l], rate=0.2)
mutate((s.candidate_l for s in solution_l),
func=lambda i: choice(list(Project.all[i].candidate_d)), rate=0.05)
for solution in solution_l:
solution.stimulate()
print(sum(solution.price for solution in solution_l))
def select_apply(solution_l: list):
solution_l[:] = select(solution_l, lambda x: x.fitness, len(solution_l), lambda x: x.clone())
