聚类效果
数据集
1.658985 4.285136
-3.453687 3.424321
4.838138 -1.151539
-5.379713 -3.362104
0.972564 2.924086
-3.567919 1.531611
0.450614 -3.302219
-3.487105 -1.724432
2.668759 1.594842
-3.156485 3.191137
3.165506 -3.999838
-2.786837 -3.099354
4.208187 2.984927
-2.123337 2.943366
0.704199 -0.479481
-0.392370 -3.963704
2.831667 1.574018
-0.790153 3.343144
2.943496 -3.357075
-3.195883 -2.283926
2.336445 2.875106
-1.786345 2.554248
2.190101 -1.906020
-3.403367 -2.778288
1.778124 3.880832
-1.688346 2.230267
2.592976 -2.054368
-4.007257 -3.207066
2.257734 3.387564
-2.679011 0.785119
0.939512 -4.023563
-3.674424 -2.261084
2.046259 2.735279
-3.189470 1.780269
4.372646 -0.822248
-2.579316 -3.497576
1.889034 5.190400
-0.798747 2.185588
2.836520 -2.658556
-3.837877 -3.253815
2.096701 3.886007
-2.709034 2.923887
3.367037 -3.184789
-2.121479 -4.232586
2.329546 3.179764
-3.284816 3.273099
3.091414 -3.815232
-3.762093 -2.432191
3.542056 2.778832
-1.736822 4.241041
2.127073 -2.983680
-4.323818 -3.938116
3.792121 5.135768
-4.786473 3.358547
2.624081 -3.260715
-4.009299 -2.978115
2.493525 1.963710
-2.513661 2.642162
1.864375 -3.176309
-3.171184 -3.572452
2.894220 2.489128
-2.562539 2.884438
3.491078 -3.947487
-2.565729 -2.012114
3.332948 3.983102
-1.616805 3.573188
2.280615 -2.559444
-2.651229 -3.103198
2.321395 3.154987
-1.685703 2.939697
3.031012 -3.620252
-4.599622 -2.185829
4.196223 1.126677
-2.133863 3.093686
4.668892 -2.562705
-2.793241 -2.149706
2.884105 3.043438
-2.967647 2.848696
4.479332 -1.764772
-4.905566 -2.911070
代码
import numpy as np
import math
import matplotlib.pyplot as plt
# 从文件中加载数据
def loadDataSet(file_name):
data_mat = []
with open(file_name) as fr:
#一次性读取文件中的所有数据
lines = fr.readlines()
#print(lines)
#遍历数据中的每一行
for line in lines:
#对每一行以 \t 进行分割
cur_line = line.strip().split(“\t”)
#[‘1.658985’, ‘4.285136’]
#print(cur_line)
#将每一行的内容由字符串转换成 float
flt_line = list(map(lambda x:float(x), cur_line))
#[-4.905566, -2.91107]
#print(flt_line)
#将转换后的内容 append 到 data_mat 中
data_mat.append(flt_line)
#返回一个 array 类型
return np.array(data_mat)
# 计算两个向量的欧式距离
#传入 vecA=(x1,y1);vecB=(x2,y2)
#计算的是 sqrt((x1-x2)^2+(y1-y2)^2)
def dist_eclud(vecA,vecB):
vec_square = []
for element in vecA – vecB:
element = element ** 2
vec_square.append(element)
return sum(vec_square) ** 0.5
# 构建 k 个随机质心
def rand_cent(dataSet,k):
#n 表示 dataSet 的列数
n = data_set.shape[1]
#print(np.shape(dataSet))
#构造一个 k * n 的 0 矩阵
centroids = np.zeros((k, n))
#填充矩阵的每一列
for j in range(n):
#得到 dataSet 中第 j 列的最小值 s
min_j = float(min(data_set[:,j]))
#获得第 j 列的最小值与最大值的差
range_j = float(max(data_set[:,j])) – min_j
#minJ+ 最小值与最大值的差 * 一个 (0-1) 之间的随机数
centroids[:,j] = (min_j + range_j * np.random.rand(k, 1))[:,0]
return centroids
#K- 均值聚类算法
def Kmeans(data_set, k):
#m 为 dataSet 的行数
m = data_set.shape[0]
#初始化一个 m * 2 的 0 矩阵
#每一行表示每一个点，[0]存放该点对应的质心的行;[1]为到质心的距离
cluster_assment = np.zeros((m, 2))
#构建 k 个随机质心
centroids = rand_cent(data_set, k)
cluster_changed = True
#当任意一点所属的类别发生了变化的时候
while cluster_changed:
cluster_changed = False
#遍历每个点（每一行）
for i in range(m):
#初始化
min_dist = np.inf; min_index = -1
#遍历每一个质心
for j in range(k):
#计算当前这一点与质心的 dis
dist_ji = dist_eclud(centroids[j,:], data_set[i,:])
#更新最小的 dis 与对应的质心所在的行 j
if dist_ji < min_dist:
min_dist = dist_ji; min_index = j
#该点的质心所在的行发生了变换——该点所属的类别发生了变化
if cluster_assment[i,0] != min_index:
cluster_changed = True
#更新类别与该点到质心的距离
cluster_assment[i,:] = min_index, min_dist**2
#更新质心
for cent in range(k):
pts_inclust = data_set[np.nonzero(list(map(lambda x:x==cent, cluster_assment[:,0])))]
centroids[cent,:] = np.mean(pts_inclust, axis=0)
#返回质心，一个 m * 2 的矩阵，[0]存放该点对应的质心的行 (类别);[1] 为到质心的距离
return centroids, cluster_assment
# 绘制散点图
def drawFigure(dataMat):
#第一列（特征 1）为横坐标
pointX=dataMat[:,0]
pointY=dataMat[:,1]
fig, ax = plt.subplots(figsize=(10,5))
ax.scatter(pointX, pointY, s=30, c=”r”, marker=”o”, label=”sample point”)
ax.legend()
ax.set_xlabel(“factor1”)
ax.set_ylabel(“factor2″)
plt.show()
# 绘制聚类后的散点图
def drawFigure2(data_set,my_centroids):
point_x = data_set[:,0]
point_y = data_set[:,1]
cent_x = my_centroids[:,0]
cent_y = my_centroids[:,1]
fig, ax = plt.subplots(figsize=(10,5))
ax.scatter(point_x, point_y, s=30, c=”r”, marker=”o”, label=”sample point”)
ax.scatter(cent_x, cent_y, s=100, c=”black”, marker=”v”, label=”centroids”)
ax.legend()
ax.set_xlabel(“factor1”)
ax.set_ylabel(“factor2”)
plt.show()
if __name__==’__main__’:
#将文本内容转换成矩阵
data_set=loadDataSet(“testSet.txt”)
my_centroids, my_cluster_assment = Kmeans(data_set, 4)
drawFigure2(data_set,my_centroids)
#print(my_centroids)
# print(my_cluster_assment)
#画图
#drawFigure(dataMat)
#计算第一行与第二行的距离
#dist=distEclud(dataMat[0],dataMat[1])
#print(dist)
#mm=randCent(dataMat,2)
#print(mm)
#print(dataMat)
#第一列
#print(dataMat[:,0])
#第一行
#print(dataMat[0])