用python实现一个聚类（angens ）算法实例

好的，以下是使用Python实现Agnes聚类算法的示例代码：

import numpy as np
import matplotlib.pyplot as plt

class Agnes:
    def __init__(self, n_clusters=2):
        self.n_clusters = n_clusters
        
    def fit(self, X):
        n_samples = X.shape[0]
        self.labels_ = np.zeros(n_samples, dtype=np.int)
        
        # 计算距离矩阵
        distances = np.zeros((n_samples, n_samples))
        for i in range(n_samples):
            for j in range(i+1, n_samples):
                distances[i,j] = np.sqrt(np.sum((X[i]-X[j])**2))
                distances[j,i] = distances[i,j]
        
        # 初始化聚类簇
        clusters = []
        for i in range(n_samples):
            clusters.append([i])
            
        # 合并聚类簇
        while len(clusters) > self.n_clusters:
            min_distance = np.inf
            for i in range(len(clusters)):
                for j in range(i+1, len(clusters)):
                    for k in range(len(clusters[i])):
                        for l in range(len(clusters[j])):
                            distance = distances[clusters[i][k], clusters[j][l]]
                            if distance < min_distance:
                                min_distance = distance
                                merge_clusters = (i, j)
            clusters[merge_clusters[0]] += clusters[merge_clusters[1]]
            del clusters[merge_clusters[1]]
            
        # 计算聚类标签
        for i, cluster in enumerate(clusters):
            for j in cluster:
                self.labels_[j] = i
                
        return self.labels_

接下来，我们可以使用该算法对样本进行聚类，并可视化结果：

from sklearn.datasets import make_blobs

# 生成样本数据
X, y = make_blobs(n_samples=100, centers=3, cluster_std=1.0, random_state=42)

# 使用Agnes算法进行聚类
agnes = Agnes(n_clusters=3)
labels = agnes.fit(X)

# 可视化聚类结果
plt.scatter(X[:,0], X[:,1], c=labels)
plt.show()

运行以上代码，即可得到聚类结果的可视化图像。