import numpy as np
import matplotlib.pyplot as plt
# 两点距离
def distance(e1, e2):
return np.sqrt((e1[0]-e2[0])**2+(e1[1]-e2[1])**2)
# 集合中心
def means(arr):
return np.array([np.mean([e[0] for e in arr]), np.mean([e[1] for e in arr])])
# arr中距离a最远的元素，用于初始化聚类中心
def farthest(k_arr, arr):
f = [0, 0]
max_d = 0
for e in arr:
d = 0
for i in range(k_arr.__len__()):
d = d + np.sqrt(distance(k_arr[i], e))
if d > max_d:
max_d = d
f = e
return f
# arr中距离a最近的元素，用于聚类
def closest(a, arr):
c = arr[1]
min_d = distance(a, arr[1])
arr = arr[1:]
for e in arr:
d = distance(a, e)
if d < min_d:
min_d = d
c = e
return c
if __name__=="__main__":
## 生成二维随机坐标，手上有数据集的朋友注意，理解arr改起来就很容易了
## arr是一个数组，每个元素都是一个二元组，代表着一个坐标
## arr形如：[ (x1, y1), (x2, y2), (x3, y3) ... ]
arr = np.random.randint(100, size=(100, 1, 2))[:, 0, :]
## 初始化聚类中心和聚类容器
m = 5
r = np.random.randint(arr.__len__() - 1)
k_arr = np.array([arr[r]])
cla_arr = [[]]
for i in range(m-1):
k = farthest(k_arr, arr)
k_arr = np.concatenate([k_arr, np.array([k])])
cla_arr.append([])
## 迭代聚类
n = 20
cla_temp = cla_arr
for i in range(n): # 迭代n次
for e in arr: # 把集合里每一个元素聚到最近的类
ki = 0 # 假定距离第一个中心最近
min_d = distance(e, k_arr[ki])
for j in range(1, k_arr.__len__()):
if distance(e, k_arr[j]) < min_d: # 找到更近的聚类中心
min_d = distance(e, k_arr[j])
ki = j
cla_temp[ki].append(e)
# 迭代更新聚类中心
for k in range(k_arr.__len__()):
if n - 1 == i:
break
k_arr[k] = means(cla_temp[k])
cla_temp[k] = []
## 可视化展示
col = ['HotPink', 'Aqua', 'Chartreuse', 'yellow', 'LightSalmon']
for i in range(m):
plt.scatter(k_arr[i][0], k_arr[i][1], linewidth=10, color=col[i])
plt.scatter([e[0] for e in cla_temp[i]], [e[1] for e in cla_temp[i]], color=col[i])
plt.show()
