From 4e040f8306b42270b8c386b7fa636817fcd7020f Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=E6=A2=81=E9=94=A6=E6=B6=9B?= <1971962997@qq.com>
Date: Thu, 20 Jan 2022 06:37:34 +0000
Subject: [PATCH] =?UTF-8?q?add=20=E7=AC=AC=E4=B8=89=E6=AC=A1=E4=BD=9C?=
 =?UTF-8?q?=E4=B8=9A.?=
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---
 ...0\211\346\254\241\344\275\234\344\270\232" | 84 +++++++++++++++++++
 1 file changed, 84 insertions(+)
 create mode 100644 "\347\254\254\344\270\211\346\254\241\344\275\234\344\270\232"

diff --git "a/\347\254\254\344\270\211\346\254\241\344\275\234\344\270\232" "b/\347\254\254\344\270\211\346\254\241\344\275\234\344\270\232"
new file mode 100644
index 0000000..224128a
--- /dev/null
+++ "b/\347\254\254\344\270\211\346\254\241\344\275\234\344\270\232"
@@ -0,0 +1,84 @@
+import pandas as pd
+import numpy as np
+import matplotlib
+import matplotlib.pyplot as plt
+
+def get_data(file):
+    df = pd.read_csv(file,header = None)
+    return df.values
+
+def get_Polar(data):
+    dist = np.sqrt(data[0]**2 + data[1]**2)
+    angle = np.arctan(data[1]/data[0]) 
+    return [dist,angle]
+
+def get_distance(data,origin):
+    if(data == origin).all():
+        return 0
+    dist = np.sqrt((data[0]-origin[0])**2 + (data[1]-origin[1])**2)
+    return dist
+
+def kmeans():
+    data = get_data("dataset_circles.csv")
+
+    polar_data = list(map(get_Polar,data.tolist()))
+    polar_data = np.array(polar_data)
+    # print(polar_data)
+    # plt.scatter(polar_data[:,0], polar_data[:,1], s=None, c="b")
+    # # plt.scatter(cluster2[:,0], cluster2[:,1], s=None, c="r")
+    # plt.show()
+
+
+    data_x = polar_data[:,0]
+    data_y = polar_data[:,1]
+
+    cluster_center1 = polar_data[np.random.randint(data_x.shape[0])]
+    cluster_center2 = polar_data[np.random.randint(data_y.shape[0])]
+
+    cluster1_index = np.array([],dtype="int64")
+    cluster2_index = np.array([],dtype="int64")
+
+    last_center1 = np.array([],dtype="float64")
+    last_center2 = np.array([],dtype="float64")
+    while True:
+        for i in range(data.shape[0]):
+            # print(origin)
+            dist1 = get_distance(polar_data[i],cluster_center1)#获取距离
+            dist2 = get_distance(polar_data[i],cluster_center2)
+            if dist1 > dist2:#比距离，放到距离中心小的坐标系中
+                cluster2_index = np.append(cluster2_index,i)
+            else:
+                cluster1_index = np.append(cluster1_index,i)
+        
+        last_center1 = cluster_center1
+        last_center2 = cluster_center2
+        
+        cluster1 = polar_data[cluster1_index.tolist()]#获取两类数据
+        cluster2 = polar_data[cluster2_index.tolist()]
+        cluster_center1 = np.mean(cluster1[:,:2],axis=0)#求均值重新判断中心
+        cluster_center2 = np.mean(cluster2[:,:2],axis=0)
+
+
+        if(cluster_center1 == last_center1).all() and (cluster_center2 == last_center2).all():##如果两次聚类没有发生变化就弹出
+            break
+        # print("1: ",cluster_center1," ",last_center1)
+        # print("2: ",cluster_center2," ",last_center2)
+        # print(cluster1_index.shape[0])
+        # print(cluster2_index.shape[0])
+
+        # print()
+        cluster1_index = np.array([],dtype="int64")
+        cluster2_index = np.array([],dtype="int64")
+    
+    # print(cluster1_index)
+    # print(cluster2_index)
+    plt.scatter(data[cluster1_index.tolist()][:,0], data[cluster1_index.tolist()][:,1], s=None, c="b")
+    plt.scatter(data[cluster2_index.tolist()][:,0], data[cluster2_index.tolist()][:,1], s=None, c="r")
+    plt.show()
+
+
+def main():
+    kmeans()
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file
-- 
Gitee