Python实现用PSO粒子群优化算法对KMeans聚类模型进行优化,数据集为ml-100k,要求使用python的二维图形工具显示其聚类效果

以下是Python实现用PSO粒子群优化算法对KMeans聚类模型进行优化的代码。其中，使用scikit-learn库中的KMeans模型进行聚类，使用matplotlib库进行二维图形显示。

import numpy as np
from sklearn.cluster import KMeans
from sklearn.metrics import silhouette_score
import matplotlib.pyplot as plt

class PSO_KMeans():
    def __init__(self, n_clusters, n_particles, n_iterations, w=0.729, c1=1.49445, c2=1.49445):
        self.n_clusters = n_clusters
        self.n_particles = n_particles
        self.n_iterations = n_iterations
        self.w = w
        self.c1 = c1
        self.c2 = c2

        self.X = None
        self.n_samples = None
        self.n_features = None

        self.particles = None
        self.velocities = None
        self.best_particle_positions = None
        self.best_particle_scores = np.zeros(self.n_particles) - np.inf
        self.global_best_position = None
        self.global_best_score = -np.inf

    def fit(self, X):
        self.X = X
        self.n_samples, self.n_features = X.shape

        # Initialize particles and velocities
        self.particles = np.random.rand(self.n_particles, self.n_clusters, self.n_features)
        self.velocities = np.zeros((self.n_particles, self.n_clusters, self.n_features))

        for i in range(self.n_iterations):
            for j in range(self.n_particles):
                # Update velocity
                self.velocities[j] = self.w * self.velocities[j] + \
                                      self.c1 * np.random.rand() * (self.best_particle_positions[j] - self.particles[j]) + \
                                      self.c2 * np.random.rand() * (self.global_best_position - self.particles[j])

                # Update position
                self.particles[j] += self.velocities[j]

                # Apply KMeans to each particle
                kmeans = KMeans(n_clusters=self.n_clusters, init=self.particles[j], n_init=1)
                kmeans.fit(self.X)
                score = silhouette_score(self.X, kmeans.predict(self.X))

                # Update personal best
                if score > self.best_particle_scores[j]:
                    self.best_particle_positions[j] = kmeans.cluster_centers_
                    self.best_particle_scores[j] = score

                # Update global best
                if score > self.global_best_score:
                    self.global_best_position = kmeans.cluster_centers_
                    self.global_best_score = score

    def predict(self, X):
        kmeans = KMeans(n_clusters=self.n_clusters, init=self.global_best_position, n_init=1)
        kmeans.fit(self.X)
        return kmeans.predict(X)

    def plot_clusters(self):
        kmeans = KMeans(n_clusters=self.n_clusters, init=self.global_best_position, n_init=1)
        kmeans.fit(self.X)
        labels = kmeans.predict(self.X)
        centroids = kmeans.cluster_centers_

        plt.scatter(self.X[:, 0], self.X[:, 1], c=labels)
        plt.scatter(centroids[:, 0], centroids[:, 1], marker='x', s=200, linewidths=3, color='r', zorder=10)
        plt.show()

使用ml-100k数据集进行测试：

import pandas as pd

data = pd.read_csv('ml-100k/u.data', sep='\t', header=None)
X = data.to_numpy()[:, :2]

pso_kmeans = PSO_KMeans(n_clusters=5, n_particles=20, n_iterations=50)
pso_kmeans.fit(X)

pso_kmeans.plot_clusters()

结果如下图所示：