代码实现用PSO算法对Kmeans聚类中心的优化,并且进行可视化展示,要求与原本的Kmeans算法进行对比,数据用iris数据集
时间: 2024-02-22 09:58:29 浏览: 27
好的,我可以为您提供代码实现。
首先,我们需要安装必要的Python库:numpy, pandas, matplotlib, sklearn, pyswarms。您可以使用以下命令进行安装:
```python
!pip install numpy pandas matplotlib sklearn pyswarms
```
接下来,我们可以加载iris数据集并使用Kmeans算法对其进行聚类:
```python
import numpy as np
import pandas as pd
from sklearn.datasets import load_iris
from sklearn.cluster import KMeans
iris = load_iris()
X = iris.data
kmeans = KMeans(n_clusters=3, random_state=0)
kmeans.fit(X)
labels = kmeans.labels_
centroids = kmeans.cluster_centers_
```
然后,我们可以使用PSO算法对Kmeans聚类中心进行优化:
```python
import pyswarms as ps
def objective_function(centroids, X):
kmeans = KMeans(n_clusters=len(centroids), init=centroids, n_init=1)
kmeans.fit(X)
inertia = kmeans.inertia_
return inertia
def pso_kmeans(n_particles, n_iterations, X):
options = {'c1': 0.5, 'c2': 0.3, 'w': 0.9}
bounds = (np.min(X, axis=0), np.max(X, axis=0))
optimizer = ps.single.GlobalBestPSO(n_particles=n_particles, dimensions=X.shape[1], options=options, bounds=bounds)
cost, centroids = optimizer.optimize(objective_function, iters=n_iterations, X=X)
return cost, centroids
n_particles = 10
n_iterations = 100
cost, centroids_pso = pso_kmeans(n_particles, n_iterations, X)
```
最后,我们可以将Kmeans和PSO-Kmeans的结果可视化:
```python
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
fig = plt.figure(figsize=(10, 10))
ax = fig.add_subplot(111, projection='3d')
ax.scatter(X[:, 0], X[:, 1], X[:, 2], c=labels)
ax.scatter(centroids[:, 0], centroids[:, 1], centroids[:, 2], marker='*', s=200, c='#050505')
ax.scatter(centroids_pso[:, 0], centroids_pso[:, 1], centroids_pso[:, 2], marker='*', s=200, c='#ff0000')
ax.set_xlabel('Sepal Length')
ax.set_ylabel('Sepal Width')
ax.set_zlabel('Petal Length')
plt.show()
```
完整代码如下: