PFC生成不规则cluster，并将cluster中的ball随机分成两大部分，添加不同的组别，请给出代码

在Python中，我们可以使用`PFC`（Population-based Clustering）算法，例如DEAP（Distributed Evolutionary Algorithms in Python）库中的粒子群优化（Particle Swarm Optimization, PSO）来创建不规则的簇，然后随机分配球到两个组别。这是一个简化版本的代码示例：

from deap import base, creator, tools
import numpy as np

# 创建基础结构
creator.create("FitnessMax", base.Fitness, weights=(1.0,))
creator.create("Individual", list, fitness=creator.FitnessMax)

def distance(x, y):
    # 计算两点之间的距离
    return np.linalg.norm(np.array(x) - np.array(y))

def pso_cluster(data, n_clusters, max_iter=100):
    # 初始化参数
    pop_size = len(data)
    w = 0.7  # 学习因子
    c1, c2 = 2.0, 2.0  # 社会和认知权重
    particles = [ Individual(random.sample(data, n_clusters)) for _ in range(pop_size) ]
    
    # 将数据集转换为适应度函数
    toolbox = base.Toolbox()
    toolbox.register("evaluate", evaluate_clusters, data=data, distance=distance)
    toolbox.register("mate", tools.cxTwoPoint)
    toolbox.register("mutate", tools.mutShuffleIndexes, indpb=0.1)
    toolbox.register("select", tools.selTournament, tournsize=3)

    # 进行PSO迭代
    best_particle = None
    for gen in range(max_iter):
        offspring = toolbox.select(particles, len(particles))
        offspring = [toolbox.clone(ind) for ind in offspring]
        
        for i, part in enumerate(offspring):
            r1, r2 = np.random.rand(), np.random.rand()
            part.position = w * part.position + c1 * r1 * (particles[np.argmax(part.best_pos)].position - part.position) + \
                            c2 * r2 * (particles[np.argmax(toolbox.evaluate(part.position))].position - part.position)
            part.best_position = toolbox.mate(part.position, part.best_position)
            part.best_position.fitness.values = toolbox.evaluate(part.best_position)
            
            if part.fitness.values > part.best_fitness.values:
                part.best_fitness.values = part.fitness.values
                
                # 比较当前最佳解与全局最优解
                if not best_particle or part.best_fitness.values < best_particle.best_fitness.values:
                    best_particle = part
    
    return best_particle.cluster, np.split(data, [best_particle.best_fitness.values[0].n_clusters])

def evaluate_clusters(partition, data, distance):
    # 根据簇中心的距离计算适应度值
    cost = sum(distance(partition[i], np.mean(data[partition[i]], axis=0)) for i in range(len(partition)))
    return [-cost]

# 使用数据集并分组
data = ...  # 假设这是你的数据
clusters, group_labels = pso_cluster(data, 2)