Algorithms/Statistical-Learning-Method_Code
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Merge pull request #30 from Harold-Ran/patch-2

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Update K-means_Clustering.py
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Dod-o 2021-09-13 14:55:11 +08:00 committed by GitHub
commit 01f6d6c9eb
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1 changed files with 4 additions and 3 deletions
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Clustering/K-means_Clustering/K-means_Clustering.py
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@ -79,6 +79,7 @@ def cal_distance(xi, xj):
dist = 0 dist = 0
for col in range(len(xi)): for col in range(len(xi)):
dist += (xi[col]-xj[col]) ** 2 dist += (xi[col]-xj[col]) ** 2
dist = math.sqrt(dist)
return dist return dist
@ -125,8 +126,8 @@ def Adjusted_Rand_Index(group_dict, Ylist, k):
if n in group_dict[i] and n in y_dict[j]: if n in group_dict[i] and n in y_dict[j]:
group_array[i][j] += 1 #如果数据n同时在group_dict的类别i和y_dict的类别j中，group_array[i][j]的数值加一 group_array[i][j] += 1 #如果数据n同时在group_dict的类别i和y_dict的类别j中，group_array[i][j]的数值加一
RI = 0 #定义兰德系数(RI) RI = 0 #定义兰德系数(RI)
sum_i = np.zeros(3) #定义一个数组，用于保存聚类结果group_dict中每一类的个数 sum_i = np.zeros(k) #定义一个数组，用于保存聚类结果group_dict中每一类的个数
sum_j = np.zeros(3) #定义一个数组，用于保存外部标签y_dict中每一类的个数 sum_j = np.zeros(k) #定义一个数组，用于保存外部标签y_dict中每一类的个数
for i in range(k): for i in range(k):
for j in range(k): for j in range(k):
sum_i[i] += group_array[i][j] sum_i[i] += group_array[i][j]
@ -189,4 +190,4 @@ if __name__ == "__main__":
group_dict, scores = Kmeans(Xarray, k, iters) #进行k均值聚类 group_dict, scores = Kmeans(Xarray, k, iters) #进行k均值聚类
end = time.time() #保存结束时间 end = time.time() #保存结束时间
print('Time:', end-start) print('Time:', end-start)
plt.plot(range(iters), scores) #绘制ARI得分折线图 plt.plot(range(iters), scores) #绘制ARI得分折线图