diff --git a/k-means2 b/k-means2
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+++ b/k-means2
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+from copy import deepcopy
+import numpy as np
+import pandas as pd
+from matplotlib import pyplot as plt
+plt.rcParams['figure.figsize'] = (16,9)
+plt.style.use('ggplot')
+data = pd.read_csv('xclara.csv')
+f1 = data['V1'].values
+f2 = data['V2'].values
+X = np.array(list(zip(f1, f2)))
+# 距离计算函数
+def dist(a, b, ax=1):
+    return np.linalg.norm(a - b, axis=ax)
+
+# 设置聚类数
+k = 3
+
+# 随机初始化质心（修正：使用数据范围）
+C_x = np.random.randint(0,np.max(X)-20, size=k)
+C_y = np.random.randint(0,np.max(X)-20, size=k)
+C = np.array(list(zip(C_x, C_y)), dtype=np.float32)
+
+C_old = np.zeros(C.shape)
+print(C)
+clusters = np.zeros(len(X))
+iteration_flag = dist(C,C_old,1)
+tmp = 1
+while iteration_flag.any() != 0 and tmp<20:
+    for i in range(len(X)):
+        distances = dist(X[i],C,1)
+        clusters[i] = clusters
+    C_old = deepcopy(C)
+    for i in range(C):
+        points = [X[j] for j in range(len(X)) if clusters[j] == i]
+        C[i] = np.mean(points,axis=0)
+
+        
+    print('%d'%tmp)
+    tmp = tmp + 1
+    iteraction_flag = dist(C,C_old,1)
+    print('distance:',iteraction_flag)
+colors = ['r','g','b','y','c','m']
+fig,ax = plt.subplots()
+for i in range(k):
+    points = np.array([X[j] for j in range(len(X) if clusters[j] == i)])
+    ax.scatter(points[:,0],points[:,1],s=7,c=colors[i])
+ax.scatter(C[:,0],C[:,1],marker="*",s=200,c='black')
+plt.show()
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