k-means 聚类算法与Python实现代码

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（如果有数据集就更好）
  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()