【深度学习基础-17】非监督学习-Hierarchical clustering 层次聚类-python实现
        【摘要】  # -*- coding: utf-8 -*-import numpy as npfrom scipy.cluster.hierarchy import dendrogram, linkage, fclusterfrom matplotlib import pyplot as plt  def hierarchy_cluster(data, method='average',...
    
    
    
    
  
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      # -*- coding: utf-8 -*-
     
    
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      import numpy as np
     
    
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      from scipy.cluster.hierarchy import dendrogram, linkage, fcluster
     
    
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      from matplotlib import pyplot as plt
     
    
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      def hierarchy_cluster(data, method='average', threshold=5.0):
     
    
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      '''层次聚类
     
    
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       Arguments:
     
    
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       data [[0, float, ...], [float, 0, ...]] -- 文档 i 和文档 j 的距离
     
    
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       Keyword Arguments:
     
    
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       method {str} -- [linkage的方式: single、complete、average、centroid、median、ward] (default: {'average'})
     
    
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       threshold {float} -- 聚类簇之间的距离
     
    
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       Return:
     
    
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       cluster_number int -- 聚类个数
     
    
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       cluster [[idx1, idx2,..], [idx3]] -- 每一类下的索引
     
    
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       '''
     
    
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       data = np.array(data)
     
    
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       Z = linkage(data, method=method)
     
    
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       cluster_assignments = fcluster(Z, threshold, criterion='distance')
     
    
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       print(type(cluster_assignments))
     
    
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       num_clusters = cluster_assignments.max()
     
    
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       indices = get_cluster_indices(cluster_assignments)
     
    
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       retur
     
    
 文章来源: kings.blog.csdn.net,作者:人工智能博士,版权归原作者所有,如需转载,请联系作者。
原文链接:kings.blog.csdn.net/article/details/86528227
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