pyclustering 是一个聚类分析的python库。本文将对其中的kmeans库解说。

最近自己在用kmeans算法做一些钻研，有个想法是把kmeans的间隔函数更换，但sklearn并没有提供接口，本人造的轮子成果也并不好。最初找到pyclustering库，因而在这记录一下应用心得。

kmeans训练过程如博客所示。

用到的包
from pyclustering.cluster.center_initializer import kmeans_plusplus_initializerfrom pyclustering.cluster.kmeans import kmeansimport numpy as np
1. 初始化形心：
initial_centers = kmeans_plusplus_initializer(x, cluster_num).initialize()

其中x是数据，cluster\_num是簇数目。

2. 实例化kmeans类：
kmeans_instance = kmeans(x, initial_centers, metric=metric)

metric是度量间隔，默认是欧式间隔，上面细讲。

3. 训练：
kmeans_instance.process()
3. 归类：
clusters = kmeans_instance.get_clusters()

把下面的训练的数据x以列表模式归类。比方数据a,b,c类别别离是1，1，0则返回index列表[[0,0],[1]]

4. 返回形心：
cs = kmeans_instance.get_centers()
5. 预测：

对于预测，这里给出了几种办法以适应不同场景。

  • 首先是间接用实例类预测
label = kmeans_instance.predict(x)
  • 依据之前失去的clusters
label = np.array([0]*len(x))for i,sub in enumerate(clusters):    label[sub] = i
  • 依据失去的形心，这里间接封装成函数，metric是度量函数
def Clu_predict(x,cs,class_num,metric = distance_metric(type_metric.EUCLIDEAN)):    differences = np.zeros((len(x), class_num))    for index_point in range(len(x)):        differences[index_point] = [metric(x[index_point], c) for c in cs]    label = np.argmin(differences, axis=1)    return label

留神这里效率很满，举荐本人定义矩阵运算。

6. 度量：
  • 应用库的度量，以曼哈顿间隔为例：
manhattan_metric = distance_metric(type_metric.MANHATTAN)kmeans_instance = kmeans(x, initial_centers, metric=manhattan_metric)

把type_metric.前面的换掉就行，库提供的间隔有

class type_metric(IntEnum):    """! @brief Enumeration of supported metrics in the module for distance calculation between two points. """ ## Euclidean distance, for more information see function 'euclidean_distance'. EUCLIDEAN = 0 ## Square Euclidean distance, for more information see function 'euclidean_distance_square'. EUCLIDEAN_SQUARE = 1 ## Manhattan distance, for more information see function 'manhattan_distance'. MANHATTAN = 2 ## Chebyshev distance, for more information see function 'chebyshev_distance'. CHEBYSHEV = 3 ## Minkowski distance, for more information see function 'minkowski_distance'. MINKOWSKI = 4 ## Canberra distance, for more information see function 'canberra_distance'. CANBERRA = 5 ## Chi square distance, for more information see function 'chi_square_distance'. CHI_SQUARE = 6 ## Gower distance, for more information see function 'gower_distance'. GOWER = 7 ## User defined function for distance calculation between two points. USER_DEFINED = 1000
  • 应用自定义间隔，以余弦间隔为例：
def cosine_distance(a, b):     a_norm = np.linalg.norm(a)     b_norm = np.linalg.norm(b)     similiarity = np.dot(a, b.T)/(a_norm * b_norm)     dist = 1. - similiarity     return distmetric = distance_metric(type_metric.USER_DEFINED, func=cosine_distance)

间隔只需实现计算两个点的间隔即可。