我很难为pyswarms设计一个适应度函数,它实际上会遍历粒子。我的设计基于这个(工作)示例代码:
# import modules
import numpy as np
# create a parameterized version of the classic Rosenbrock unconstrained optimzation function
def rosenbrock_with_args(x, a, b, c=0):
f = (a - x[:, 0]) ** 2 + b * (x[:, 1] - x[:, 0] ** 2) ** 2 + c
return f
from pyswarms.single.global_best import GlobalBestPSO
# instatiate the optimizer
x_max = 10 * np.ones(2)
x_min = -1 * x_max
bounds = (x_min, x_max)
options = {'c1': 0.5, 'c2': 0.3, 'w': 0.9}
optimizer = GlobalBestPSO(n_particles=10, dimensions=2, options=options, bounds=bounds)
# now run the optimization, pass a=1 and b=100 as a tuple assigned to args
cost, pos = optimizer.optimize(rosenbrock_with_args, 1000, a=1, b=100, c=0)
kwargs={"a": 1.0, "b": 100.0, 'c':0}
似乎通过写作
x[:, 0]
和
x[:, 1]
,以某种方式为优化函数参数化粒子位置矩阵。例如,执行
x[:,0]
在调试器中返回:
array([ 9.19955426, -5.31471451, -2.28507312, -2.53652044, -6.29916204,
-8.44170591, 7.80464884, -6.42048159, 9.77440842, -9.06991295])
现在,跳到我的代码(一个片段),我有:
def optimize_eps_and_mp(x):
clusterer = DBSCAN(eps=x[:, 0], min_samples=x[:, 1], metric="precomputed")
clusterer.fit(distance_matrix)
clusters = pd.DataFrame.from_dict({index_to_gid[i[0]]: [i[1]] for i in enumerate(clusterer.labels_)},
orient="index", columns=["cluster"])
settlements_clustered = settlements.join(clusters)
cluster_pops = settlements_clustered.loc[settlements_clustered["cluster"] >= 0].groupby(["cluster"]).sum()["pop_sum"].to_list()
print()
return 1
options = {'c1': 0.5, 'c2': 0.3, 'w':0.9}
max_bound = [1000, 10]
min_bound = [1, 2]
bounds = (min_bound, max_bound)
n_particles = 10
optimizer = ps.single.GlobalBestPSO(n_particles=n_particles, dimensions=2, options=options, bounds=bounds)
cost, pos = optimizer.optimize(optimize_eps_and_mp, iters=1000)
(变量
distance_matrix
和
settlements
在代码的前面定义的,但是在这行中它失败了
clusterer = DBSCAN(eps=x[:, 0], min_samples=x[:, 1], metric="precomputed")
所以它们并不相关。而且,我知道它总是回来
1
,我只是想让它在完成函数之前运行而不出错)
x[:,0]
在调试器中,它返回:
array([-4.54925788, 3.94338766, 0.97085618, 9.44128746, -2.1932764 ,
9.24640763, 9.18286758, -8.91052863, 0.637599 , -2.28228841])
因此,在结构上与工作示例相同。但它失败了
因为它传递了
x[:,0]
DBSCAN
函数,而不是像工作示例中那样参数化它。
这些例子之间有什么区别,我只是没有看到?
我还试着从工作示例中粘贴fitness函数(
rosenbrock_with_args
)在我的代码和优化,而不是,以消除任何可能性,我有我的实现设置是不正确的。然后,解会像正常情况一样收敛,所以我完全不知道为什么它不适用于我的函数(
optimize_eps_and_mp
)
我得到的确切stacktrace是指dbscan算法中的一个错误,我假设是由于它以某种方式传递了整个粒子群值集,而不是单个值:
pyswarms.single.global_best: 0%| |0/1000Traceback (most recent call last):
File "C:/FILES/boates/work_local/_code/warping-pso-dbscan/optimize_eps_and_mp.py", line 63, in <module>
cost, pos = optimizer.optimize(optimize_eps_and_mp, iters=1000)
File "C:\FILES\boates\Anaconda\envs\warping_pso_dbscan\lib\site-packages\pyswarms\single\global_best.py", line 184, in optimize
self.swarm.current_cost = compute_objective_function(self.swarm, objective_func, pool=pool, **kwargs)
File "C:\FILES\boates\Anaconda\envs\warping_pso_dbscan\lib\site-packages\pyswarms\backend\operators.py", line 239, in compute_objective_function
return objective_func(swarm.position, **kwargs)
File "C:/FILES/boates/work_local/_code/warping-pso-dbscan/optimize_eps_and_mp.py", line 38, in optimize_eps_and_mp
clusterer.fit(distance_matrix)
File "C:\FILES\boates\Anaconda\envs\warping_pso_dbscan\lib\site-packages\sklearn\cluster\dbscan_.py", line 351, in fit
**self.get_params())
File "C:\FILES\boates\Anaconda\envs\warping_pso_dbscan\lib\site-packages\sklearn\cluster\dbscan_.py", line 139, in dbscan
if not eps > 0.0:
ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()
pyswarms.single.global_best: 0%| |0/1000
