k均值与人工鱼群算法结合的代码实现

下面是将k均值算法与人工鱼群算法结合的Python代码实现：

import random
import numpy as np


class Fish:
    def __init__(self, dim, lb, ub):
        self.position = np.random.uniform(lb, ub, dim)
        self.lb = lb
        self.ub = ub
        self.fitness = float("inf")
        self.delta = np.zeros(dim)
        self.w = 0


class KMeansFish:
    def __init__(self, k, max_iter, lb, ub, wmin, wmax, c1, c2):
        self.k = k
        self.max_iter = max_iter
        self.lb = lb
        self.ub = ub
        self.wmin = wmin
        self.wmax = wmax
        self.c1 = c1
        self.c2 = c2

    def cluster(self, data):
        n, dim = data.shape
        fish = []
        for i in range(self.k):
            fish.append(Fish(dim, self.lb, self.ub))

        for i in range(self.max_iter):
            # 计算每个鱼的适应度
            for j in range(self.k):
                f = 0
                for p in data:
                    d = np.linalg.norm(fish[j].position - p)
                    f += d
                fish[j].fitness = f

            # 更新每个鱼的位置和速度
            for j in range(self.k):
                for l in range(dim):
                    fish[j].delta[l] = fish[j].w * fish[j].delta[l] + \
                                       self.c1 * random.random() * (fish[j].best_pos[l] - fish[j].position[l]) + \
                                       self.c2 * random.random() * (fish[l].best_pos[l] - fish[j].position[l])
                    fish[j].position[l] += fish[j].delta[l]

                    if fish[j].position[l] < self.lb:
                        fish[j].position[l] = self.lb
                        fish[j].delta[l] = 0
                    elif fish[j].position[l] > self.ub:
                        fish[j].position[l] = self.ub
                        fish[j].delta[l] = 0

            # 更新每个鱼的权重
            wsum = 0
            for j in range(self.k):
                wsum += fish[j].fitness

            for j in range(self.k):
                fish[j].w = (self.wmax - self.wmin) * (1 - fish[j].fitness / wsum) + self.wmin

            # 更新每个鱼的最佳位置
            for j in range(self.k):
                for p in data:
                    d1 = np.linalg.norm(fish[j].position - p)
                    d2 = np.linalg.norm(fish[j].best_pos - p)
                    if d1 < d2:
                        fish[j].best_pos = p

            # 更新整个群体的最佳位置
            best_pos = fish[0].best_pos
            best_fitness = fish[0].fitness
            for j in range(1, self.k):
                if fish[j].fitness < best_fitness:
                    best_pos = fish[j].best_pos
                    best_fitness = fish[j].fitness

            # 判断是否收敛
            if best_fitness == 0:
                break

        # 聚类
        labels = np.zeros(n)
        for i, p in enumerate(data):
            min_dist = np.linalg.norm(best_pos - p)
            for j in range(1, self.k):
                d = np.linalg.norm(fish[j].best_pos - p)
                if d < min_dist:
                    min_dist = d
                    labels[i] = j
        return labels

其中，`Fish`类表示一个人工鱼，包含了位置、适应度、速度、权重等属性。`KMeansFish`类表示将k均值算法和人工鱼群算法结合的聚类算法，包含了k值、最大迭代次数、位置上下界、权重下限和上限、学习因子等参数。`cluster`函数实现了将数据进行聚类的功能。