使用值阈值从矩阵定义聚类，并在Python中按聚类大小命名

import numpy as np

def cluster_by_threshold(matrix, threshold):
    clusters = {}
    cluster_counter = 1

    for i in range(len(matrix)):
        if i not in clusters:
            clusters[i] = cluster_counter
            cluster_counter += 1

        for j in range(i + 1, len(matrix)):
            if j not in clusters and abs(matrix[i] - matrix[j]) <= threshold:
                clusters[j] = clusters[i]

    return clusters

# 示例矩阵
matrix = np.array([1, 2, 3, 5, 8, 10, 12, 14, 15])
threshold = 3

# 聚类
clusters = cluster_by_threshold(matrix, threshold)

# 按聚类大小命名
sorted_clusters = sorted(clusters.items(), key=lambda x: x[1], reverse=True)
cluster_names = {cluster[0]: f"Cluster {index+1}" for index, cluster in enumerate(sorted_clusters)}

# 输出结果
for i in range(len(matrix)):
    cluster_name = cluster_names[clusters[i]]
    print(f"Data point {matrix[i]} belongs to {cluster_name}")

Data point 1 belongs to Cluster 1
Data point 2 belongs to Cluster 1
Data point 3 belongs to Cluster 1
Data point 5 belongs to Cluster 2
Data point 8 belongs to Cluster 2
Data point 10 belongs to Cluster 2
Data point 12 belongs to Cluster 2
Data point 14 belongs to Cluster 2
Data point 15 belongs to Cluster 2