python实现agnes算法计算轮廓系数

好的，下面是Python实现AGNES算法计算轮廓系数的代码：

import numpy as np
from sklearn.metrics import silhouette_score

# 距离矩阵
def distance_matrix(X):
    n = X.shape[0]
    dist_matrix = np.zeros((n, n))
    for i in range(n):
        for j in range(i+1, n):
            dist_matrix[i][j] = dist_matrix[j][i] = np.linalg.norm(X[i]-X[j])
    return dist_matrix

# AGNES聚类
def AGNES(X, k):
    n = X.shape[0]
    # 初始化每个点为一类
    clusters = [[i] for i in range(n)]
    # 计算距离矩阵
    dist_matrix = distance_matrix(X)
    # 合并聚类
    while len(clusters) > k:
        min_dist = np.inf
        merge_i, merge_j = -1, -1
        for i in range(len(clusters)):
            for j in range(i+1, len(clusters)):
                dist = 0
                for m in clusters[i]:
                    for n in clusters[j]:
                        dist += dist_matrix[m][n]
                dist /= len(clusters[i]) * len(clusters[j])
                if dist < min_dist:
                    min_dist = dist
                    merge_i, merge_j = i, j
        clusters[merge_i].extend(clusters[merge_j])
        del clusters[merge_j]
    return clusters

# 计算轮廓系数
def silhouette_coefficient(X, clusters):
    labels = np.zeros(X.shape[0])
    k = len(clusters)
    for i in range(k):
        for j in clusters[i]:
            labels[j] = i
    return silhouette_score(X, labels)

# 测试
if __name__ == '__main__':
    X = np.array([[2, 10], [2, 5], [8, 4], [5, 8], [7, 5], [6, 4], [1, 2], [4, 9]])
    k = 3
    clusters = AGNES(X, k)
    sc = silhouette_coefficient(X, clusters)
    print('聚%d类的轮廓系数为：%f' % (k, sc))

这段代码中，`distance_matrix`函数用于计算距离矩阵，`AGNES`函数实现了AGNES聚类算法，`silhouette_coefficient`函数用于计算轮廓系数。在测试部分，我们可以设定聚类数`k`为3，然后计算轮廓系数。