自适应动态参数代码
时间: 2023-07-10 22:08:31 浏览: 35
自适应动态参数是指根据不同的输入动态调整函数或模型的参数。常见的实现方式是使用反向传播算法来更新参数,但是这种方法可能会导致过拟合或者收敛速度慢的问题。下面是一个使用遗传算法实现自适应动态参数的示例代码:
```python
import random
class DynamicParam:
def __init__(self, num_params, max_value, min_value):
self.num_params = num_params
self.max_value = max_value
self.min_value = min_value
self.params = [random.uniform(min_value, max_value) for i in range(num_params)]
def update(self, fitness):
selection_prob = [f/sum(fitness) for f in fitness]
parents = random.choices(population=self.params, weights=selection_prob, k=2)
child = [(p1 + p2)/2 for p1, p2 in zip(parents[0], parents[1])]
mutation_rate = 0.1
for i in range(self.num_params):
if random.random() < mutation_rate:
child[i] = random.uniform(self.min_value, self.max_value)
self.params = child
def get_params(self):
return self.params
# Example usage
def fitness_function(x):
return [sum(xi) for xi in x]
pop_size = 10
max_gen = 100
max_value = 10
min_value = -10
num_params = 3
population = [DynamicParam(num_params, max_value, min_value) for i in range(pop_size)]
for gen in range(max_gen):
fitness = fitness_function([p.get_params() for p in population])
best_individual = population[fitness.index(max(fitness))]
print("Generation: ", gen, "Best individual: ", best_individual.get_params(), "Fitness: ", max(fitness))
for i in range(pop_size):
population[i].update(fitness)
```
此代码演示了如何使用遗传算法动态更新函数参数。在这个例子中,我们定义了一个包含三个参数的函数,然后使用 DynamicParam 类来表示参数。遗传算法是用来选择优秀的参数组合并生成新的参数组合。在每一代中,我们计算所有参数组合的适应度,并选择适应度较高的参数进行交叉和变异操作,生成新的参数组合。最终,我们得到了一个具有较高适应度的最佳参数组合。