添加 init

init

@@ -0,0 +1,90 @@
+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')
+
+# 创建示例数据
+def create_sample_data():
+    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])
+    df = pd.DataFrame(data, columns=['V1', 'V2'])
+    df.to_csv('xclara.csv', index=False)
+
+# 如果文件不存在则创建
+try:
+    data = pd.read_csv('xclara.csv')
+except:
+    create_sample_data()
+    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(cluster)
+k = 3
+
+# 随机初始化质心
+C_x = np.random.uniform(np.min(f1), np.max(f1), 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)
+
+# 绘制初始数据点
+plt.scatter(f1, f2, c='black', s=7)
+plt.scatter(C_x, C_y, marker='*', s=200, c='red')
+plt.title("Initial Data Points and Centroids")
+plt.show()
+
+# 初始化变量
+C_old = np.zeros(C.shape)
+clusters = np.zeros(len(X))
+iteration_flag = dist(C, C_old, None)  # 初始距离
+tmp = 1
+
+# K-Means循环
+while iteration_flag != 0 and tmp < 20:
+    # 1. 分配点到最近的质心，划到簇里
+    for i in range(len(X)):
+        distances = dist(X[i], C, 1)  # 计算点到所有质心的距离
+        cluster_idx = np.argmin(distances)  # 找到最近的质心索引
+        clusters[i] = cluster_idx
+    
+    # 2. 保存旧质心
+    C_old = deepcopy(C)
+    
+    # 3. 更新质心位置
+    for i in range(k):
+        # 获取属于当前簇的所有点
+        points = X[clusters == i]
+        if len(points) > 0:
+            C[i] = np.mean(points, axis=0)
+        else:
+            # 如果簇为空，重新初始化质心
+            C[i] = np.random.uniform(np.min(X), np.max(X), size=2)
+    
+    print(f'Iteration {tmp}')
+    tmp += 1
+    
+    # 4. 计算质心移动距离
+    iteration_flag = dist(C, C_old, None)
+    print(f'Centroid movement distance: {iteration_flag:.4f}')
+
+# 绘制最终聚类结果
+colors = ['r', 'g', 'b', 'y', 'c', 'm']
+fig, ax = plt.subplots()
+for i in range(k):
+    points = X[clusters == i]  # 选择属于当前簇的点
+    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()