def search_reactive(self, problem, iters=10):
best_score = 0
reactive = Reactive(10)
best_sol = Solution(problem)
for _ in range(iters):
sol = Solution(problem)
idx, greediness = reactive.get_alpha()
insertion_list, hmin, hmax = self.generate_neighborhood_stochastic(
sol)
while len(insertion_list) > 0 and not sol.rem_dist <= 0:
threshold = hmin + greediness * (hmax - hmin)
insertion_list = [
i for i in insertion_list if i.hval > threshold
]
if len(insertion_list) <= 0:
break
rand = insertion_list[random.randint(0,
len(insertion_list) - 1)]
sol.insert_ins(rand)
insertion_list, hmin, hmax = self.generate_neighborhood_stochastic(
sol)
if sol.score > best_score:
best_score = sol.score
best_sol = sol
reactive.update(sol.score, best_score, idx)
return best_score, best_sol
def test_5():
obj = Reactive()
obj.add('var_1', value = 12)
obj.add('var_2', value = 'd')
obj.add('var_3', value = 'ghi')
obj.add('var_4', equation = '3 * str(var_1) + var_2 + var_3')
assert obj.var_4 == '121212dghi'
def test_4():
obj = Reactive()
obj.add('var_1', value = 'abc')
obj.add('var_2', value = 'd')
obj.add('var_3', value = 'ghi')
obj.add('var_4', equation = '3 * (var_1 + var_2) + var_3')
assert obj.var_4 == 'abcdabcdabcdghi'
def test_1():
obj = Reactive()
obj.add('var_1', value = 1)
obj.add('var_2', value = 2)
obj.add('var_3', value = 3)
obj.add('var_4', equation = '3 * var_1 + var_2 - 1 * var_3')
assert obj.var_4 == 2
def test_2():
obj = Reactive()
obj.add('var_1', value = 'abc')
assert obj.var_1 == 'abc'
def test_1():
obj = Reactive()
obj.add('var_1', value = 1)
assert obj.var_1 == 1
def test_3():
obj = Reactive()
obj.add('var_1', value = [123, 'abc'])
assert obj.var_1 == [123, 'abc']
def test_6():
obj = Reactive()
obj.add('var_1', value = [2, 3])
obj.add('var_2', value = [4, 5])
obj.add('var_3', equation = 'var_1[1] + var_2[1]')
assert obj.var_3 == 8