设计一套pso-kmeans代码，测试不同K值对分类结果的影响。

以下是一套基于Python的PSO-Kmeans代码，用于测试不同K值对分类结果的影响。

import numpy as np
from sklearn.cluster import KMeans

class Particle:
    def __init__(self, dim, k, lb, ub):
        self.k = k
        self.dim = dim
        self.lb = lb
        self.ub = ub
        self.position = np.random.uniform(lb, ub, (k, dim))
        self.velocity = np.random.uniform(lb, ub, (k, dim))
        self.fitness = float('inf')
        self.pbest_position = self.position.copy()
        self.pbest_fitness = float('inf')

    def update_fitness(self, data):
        kmeans = KMeans(n_clusters=self.k, init=self.position, n_init=1).fit(data)
        self.fitness = kmeans.inertia_
        if self.fitness < self.pbest_fitness:
            self.pbest_fitness = self.fitness
            self.pbest_position = self.position.copy()

    def update_position_velocity(self, gbest_position, w, c1, c2):
        r1 = np.random.rand(self.k, self.dim)
        r2 = np.random.rand(self.k, self.dim)
        self.velocity = w*self.velocity + c1*r1*(self.pbest_position - self.position) + c2*r2*(gbest_position - self.position)
        self.position = np.clip(self.position + self.velocity, self.lb, self.ub)

class PSO_Kmeans:
    def __init__(self, n_particles, n_iterations, dim, k, lb, ub, w, c1, c2):
        self.n_particles = n_particles
        self.n_iterations = n_iterations
        self.dim = dim
        self.k = k
        self.lb = lb
        self.ub = ub
        self.w = w
        self.c1 = c1
        self.c2 = c2
        self.particles = [Particle(dim, k, lb, ub) for _ in range(n_particles)]
        self.gbest_fitness = float('inf')
        self.gbest_position = np.random.uniform(lb, ub, (k, dim))

    def run(self, data):
        for i in range(self.n_iterations):
            for particle in self.particles:
                particle.update_fitness(data)
                if particle.fitness < self.gbest_fitness:
                    self.gbest_fitness = particle.fitness
                    self.gbest_position = particle.position.copy()
            for particle in self.particles:
                particle.update_position_velocity(self.gbest_position, self.w, self.c1, self.c2)

        kmeans = KMeans(n_clusters=self.k, init=self.gbest_position, n_init=1).fit(data)
        return kmeans.labels_

if __name__ == '__main__':
    from sklearn.datasets import make_blobs
    import matplotlib.pyplot as plt

    data, labels = make_blobs(n_samples=1000, centers=3, random_state=42)
    pso_kmeans = PSO_Kmeans(n_particles=10, n_iterations=100, dim=data.shape[1], k=3, lb=-10, ub=10, w=0.7, c1=1.5, c2=1.5)
    predicted_labels = pso_kmeans.run(data)

    fig, axs = plt.subplots(1, 2, figsize=(10, 5))
    axs[0].scatter(data[:, 0], data[:, 1], c=labels)
    axs[0].set_title('True Labels')
    axs[1].scatter(data[:, 0], data[:, 1], c=predicted_labels)
    axs[1].set_title('Predicted Labels')
    plt.show()

在这个示例中，我们使用`make_blobs`函数生成一个带有3个中心的数据集，并使用PSO-Kmeans算法进行聚类。我们测试不同的K值对聚类结果的影响，如下所示：

if __name__ == '__main__':
    from sklearn.datasets import make_blobs
    import matplotlib.pyplot as plt

    data, labels = make_blobs(n_samples=1000, centers=3, random_state=42)
    k_values = [2, 3, 4, 5, 6]
    for k in k_values:
        pso_kmeans = PSO_Kmeans(n_particles=10, n_iterations=100, dim=data.shape[1], k=k, lb=-10, ub=10, w=0.7, c1=1.5, c2=1.5)
        predicted_labels = pso_kmeans.run(data)

        fig, axs = plt.subplots(1, 2, figsize=(10, 5))
        axs[0].scatter(data[:, 0], data[:, 1], c=labels)
        axs[0].set_title('True Labels')
        axs[1].scatter(data[:, 0], data[:, 1], c=predicted_labels)
        axs[1].set_title('Predicted Labels (K = {})'.format(k))
        plt.show()

在这个示例中，我们使用了K值为2,3,4,5,6进行测试，每个K值都生成一个包含原始数据和聚类结果的图表。这些图表可以用来比较不同K值对聚类结果的影响。