K-means算法实战项目（Python实现）（对比简化版）

缺乏、安全感 2023-09-28 17:12 4阅读 0赞

> https://blog.csdn.net/wzk4869/article/details/126040866?spm=1001.2014.3001.5501

我的这篇博客中给出了比较完整的k-means算法的实现过程，但是我们可以继续简化一下：

import numpy as np
    import pandas as pd
    import matplotlib.pyplot as plt
    from scipy.io import loadmat
    """
    导入数据的函数
    """
    def load_dataset():
        path='./data/ex7data2.mat'
        # 字典格式 : <class 'dict'>
        data=loadmat(path)
        # data.keys() : dict_keys(['__header__', '__version__', '__globals__', 'X'])
        dataset = pd.DataFrame(data.get('X'), columns=['X1', 'X2'])
        return data,dataset
    data,dataset=load_dataset()
    print('data=\n',data)
    """
    绘制散点图
    """
    def plot_scatter():
        data,dataset=load_dataset()
        plt.figure(figsize=(12,8))
        plt.scatter(dataset['X1'],dataset['X2'],cmap=['b'])
        plt.show()
    
    def find_centroids(X,centroids):
        idx=[]
        for i in range(X.shape[0]):
            dist=np.linalg.norm((X[i]-centroids),axis=1)
            idx_i=np.argmin(dist)
            idx.append(idx_i)
        return np.array(idx)
    
    centroids_0=np.array([[3,3],[6,2],[8,5]])
    idx_0=find_centroids(data.get('X'),centroids_0)
    
    def compute_centroids(X,idx,k):
        centroids=[]
        for i in range(k):
            centroids_i=np.mean(X[idx==i],axis=0)
            centroids.append(centroids_i)
        return np.array(centroids)
    
    centroids=compute_centroids(data.get('X'),idx_0,3)
    
    def k_means(X,centroids,max_iters):
        k=len(centroids)
        centroids_all=[]
        centroids_all.append(centroids_0)
        centroids_i=centroids
        for i in range(max_iters):
            idx_1=find_centroids(X,centroids_i)
            centroids_i=compute_centroids(X,idx_1,k)
            centroids_all.append(centroids_i)
        return np.array(idx_1),np.array(centroids_all)
    
    
    def plot_data(X,centroids_all,idx):
        plt.figure(figsize=(12,8))
        plt.scatter(X[:,0],X[:,1],c=idx,cmap='rainbow')
        plt.plot(centroids_all[:,:,0],centroids_all[:,:,1],'kx--')
        plt.show()
    
    idx,centroids_all=k_means(data.get('X'),centroids,max_iters=10)
    plot_data(data.get('X'),centroids_all,idx)

比起我们之前的做法，在函数上减少了不少步骤。

我们看一下最终的结果：  
![388a2f876f814abeb38eb9270193fe14.png][]  
当然，颜色也可以自定义，这里就不再赘述，直接使用内置的颜色。

有兴趣的小伙伴可以看我之前发的文章：

> https://blog.csdn.net/wzk4869/article/details/126036397?spm=1001.2014.3001.5501

初始化聚类中心的步骤：

def init_centroids(X,k):
        index=np.random.choice(len(X),k)
        return X[index]
    
    print(init_centroids(data.get('X'),k=3))

[[7.24694794 2.96877424]
     [2.95818429 1.01887096]
     [2.09517296 1.14010491]]

可视化结果为：

for i in range(5):
        idx,centroids_all=k_means(data.get('X'),init_centroids(data.get('X'),k=3),max_iters=10)
        plot_data(data.get('X'),centroids_all,idx)

![6bcac1f814354e3191671ca64a4aa639.png][]  
![f68bdbf57c3c4e4b8aeb8702e88e7cfc.png][]  
![470e7b14ce064266a107ca39d97e248b.png][]  
![0b99528c62f34795a938659396703697.png][]  
![e20a55f45c2948189c2a4b5ab0bf7af1.png][]

### 完整版代码如下： ###

"""
    给定一个二维的数据集，使用k-means算法进行聚类
    """
    import numpy as np
    import pandas as pd
    import matplotlib.pyplot as plt
    from scipy.io import loadmat
    """
    导入数据的函数
    """
    def load_dataset():
        path='./data/ex7data2.mat'
        # 字典格式 : <class 'dict'>
        data=loadmat(path)
        # data.keys() : dict_keys(['__header__', '__version__', '__globals__', 'X'])
        dataset = pd.DataFrame(data.get('X'), columns=['X1', 'X2'])
        return data,dataset
    data,dataset=load_dataset()
    print('data=\n',data)
    """
    绘制散点图
    """
    def plot_scatter():
        data,dataset=load_dataset()
        plt.figure(figsize=(12,8))
        plt.scatter(dataset['X1'],dataset['X2'],cmap=['b'])
        plt.show()
    
    def find_centroids(X,centroids):
        idx=[]
        for i in range(X.shape[0]):
            dist=np.linalg.norm((X[i]-centroids),axis=1)
            idx_i=np.argmin(dist)
            idx.append(idx_i)
        return np.array(idx)
    
    centroids_0=np.array([[3,3],[6,2],[8,5]])
    idx_0=find_centroids(data.get('X'),centroids_0)
    
    def compute_centroids(X,idx,k):
        centroids=[]
        for i in range(k):
            centroids_i=np.mean(X[idx==i],axis=0)
            centroids.append(centroids_i)
        return np.array(centroids)
    
    centroids=compute_centroids(data.get('X'),idx_0,3)
    
    def k_means(X,centroids,max_iters):
        k=len(centroids)
        centroids_all=[]
        centroids_all.append(centroids_0)
        centroids_i=centroids
        for i in range(max_iters):
            idx_1=find_centroids(X,centroids_i)
            centroids_i=compute_centroids(X,idx_1,k)
            centroids_all.append(centroids_i)
        return np.array(idx_1),np.array(centroids_all)
    
    
    def plot_data(X,centroids_all,idx):
        plt.figure(figsize=(12,8))
        plt.scatter(X[:,0],X[:,1],c=idx,cmap='rainbow')
        plt.plot(centroids_all[:,:,0],centroids_all[:,:,1],'kx--')
        plt.show()
    
    idx,centroids_all=k_means(data.get('X'),centroids,max_iters=10)
    plot_data(data.get('X'),centroids_all,idx)
    
    def init_centroids(X,k):
        index=np.random.choice(len(X),k)
        return X[index]
    
    print(init_centroids(data.get('X'),k=3))
    for i in range(5):
        idx,centroids_all=k_means(data.get('X'),init_centroids(data.get('X'),k=3),max_iters=10)
        plot_data(data.get('X'),centroids_all,idx)

[388a2f876f814abeb38eb9270193fe14.png]: https://img-blog.csdnimg.cn/388a2f876f814abeb38eb9270193fe14.png
[6bcac1f814354e3191671ca64a4aa639.png]: https://img-blog.csdnimg.cn/6bcac1f814354e3191671ca64a4aa639.png
[f68bdbf57c3c4e4b8aeb8702e88e7cfc.png]: https://img-blog.csdnimg.cn/f68bdbf57c3c4e4b8aeb8702e88e7cfc.png
[470e7b14ce064266a107ca39d97e248b.png]: https://img-blog.csdnimg.cn/470e7b14ce064266a107ca39d97e248b.png
[0b99528c62f34795a938659396703697.png]: https://img-blog.csdnimg.cn/0b99528c62f34795a938659396703697.png
[e20a55f45c2948189c2a4b5ab0bf7af1.png]: https://img-blog.csdnimg.cn/e20a55f45c2948189c2a4b5ab0bf7af1.png