更新 k-means2

k-means2

@@ -1,13 +1,34 @@
-from copy import deepcopy
 import numpy as np
 import pandas as pd
 from matplotlib import pyplot as plt
-plt.rcParams['figure.figsize'] = (16,9)
+from copy import deepcopy
+
+# 设置图形样式
+plt.rcParams['figure.figsize'] = (16, 9)
 plt.style.use('ggplot')
+
+# 创建示例数据并保存为CSV文件
+def create_sample_data():
+    # 生成三个簇的示例数据
+    np.random.seed(42)
+    cluster1 = np.random.normal(loc=[0, 0], scale=1, size=(100, 2))
+    cluster2 = np.random.normal(loc=[10, 5], scale=1.5, size=(100, 2))
+    cluster3 = np.random.normal(loc=[5, 10], scale=1.2, size=(100, 2))
+    data = np.vstack([cluster1, cluster2, cluster3])
+    
+    # 创建DataFrame并保存为CSV
+    df = pd.DataFrame(data, columns=['V1', 'V2'])
+    df.to_csv('xclara.csv', index=False)
+
+# 创建示例CSV文件
+create_sample_data()
+
+# 从CSV读取数据
 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)
@@ -16,33 +37,49 @@ def dist(a, b, ax=1):
 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_x = np.random.uniform(np.min(f1), np.max(f1), size=k)
-C_y = np.random.randint(0,np.max(X)-20, size=k)
+C_y = np.random.uniform(np.min(f2), np.max(f2), size=k)
 C = np.array(list(zip(C_x, C_y)), dtype=np.float32)
 
-C_old = np.zeros(C.shape)
+# 绘制初始数据点和质心（修正颜色拼写错误）
-print(C)
+plt.scatter(f1, f2, c='black', s=7)  # 修正：'balck' -> 'black'
-clusters = np.zeros(len(X))
+plt.scatter(C_x, C_y, marker='*', s=200, c='red')
-iteration_flag = dist(C,C_old,1)
+plt.title("Initial Data Points and Centroids")
-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()
+
+# ---- 可选：添加完整的K-Means算法实现 ----
+# 复制原始质心用于后续更新
+C_old = np.zeros(C.shape)
+clusters = np.zeros(len(X))
+error = dist(C, C_old, None)
+
+# K-Means迭代
+while error != 0:
+    # 分配点到最近质心
+    for i in range(len(X)):
+        distances = dist(X[i], C)
+        cluster = np.argmin(distances)
+        clusters[i] = cluster
+    
+    # 保存旧质心
+    C_old = deepcopy(C)
+    
+    # 计算新质心
+    for i in range(k):
+        points = [X[j] for j in range(len(X)) if clusters[j] == i]
+        if points:
+            C[i] = np.mean(points, axis=0)
+    
+    # 计算质心移动距离
+    error = dist(C, C_old, None)
+
+# 绘制最终聚类结果
+colors = ['r', 'g', 'b', 'c', 'm', 'y']
+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.title("Final Clustering Result")
+plt.show()
+