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# -*- coding: utf-8 -*-
import numpy as np
from scipy.cluster.hierarchy import dendrogram, linkage, fcluster
from matplotlib import pyplot as plt
def hierarchy_cluster(data, method='average', threshold=5.0):
'''层次聚类
Arguments:
data [[0, float, ...], [float, 0, ...]] -- 文档 i 和文档 j 的距离
Keyword Arguments:
method {str} -- [linkage的方式： single、complete、average、centroid、median、ward] (default: {'average'})
threshold {float} -- 聚类簇之间的距离
Return:
cluster_number int -- 聚类个数
cluster [[idx1, idx2,..], [idx3]] -- 每一类下的索引
'''
data = np.array(data)
Z = linkage(data, method=method)
cluster_assignments = fcluster(Z, threshold, criterion='distance')
print(type(cluster_assignments))
num_clusters = cluster_assignments.max()
indices = get_cluster_indices(cluster_assignments)
return num_clusters, indices
def get_cluster_indices(cluster_assignments):
'''映射每一类至原数据索引
Arguments:
cluster_assignments 层次聚类后的结果
Returns:
[[idx1, idx2,..], [idx3]] -- 每一类下的索引
'''
n = cluster_assignments.max()
indices = []
for cluster_number in range(1, n + 1):
indices.append(np.where(cluster_assignments == cluster_number)[0])
return indices
if __name__ == '__main__':
arr = [[0., 21.6, 22.6, 63.9, 65.1, 17.7, 99.2],
[21.6, 0., 1., 42.3, 43.5, 3.9, 77.6],
[22.6, 1., 0, 41.3, 42.5, 4.9, 76.6],
[63.9, 42.3, 41.3, 0., 1.2, 46.2, 35.3],
[65.1, 43.5, 42.5, 1.2, 0., 47.4, 34.1],
[17.7, 3.9, 4.9, 46.2, 47.4, 0, 81.5],
[99.2, 77.6, 76.6, 35.3, 34.1, 81.5, 0.]]
arr = np.array(arr)
r, c = arr.shape
for i in range(r):
for j in range(i, c):
if arr[i][j] != arr[j][i]:
arr[i][j] = arr[j][i]
for i in range(r):
for j in range(i, c):
if arr[i][j] != arr[j][i]:
print(arr[i][j], arr[j][i])
num_clusters, indices = hierarchy_cluster(arr)
print("%d clusters" % num_clusters)
for k, ind in enumerate(indices):
print("cluster", k + 1, "is", ind)
运行结果：
可视化（没有附代码，不全，后面更新）
