聚类效果数据集1.658985 4.285136-3.453687 3.4243214.838138 -1.151539-5.379713 -3.3621040.972564 2.924086-3.567919 1.5316110.450614 -3.302219-3.487105 -1.7244322.668759 1.594842-3.156485 3.1911373.165506 -3.999838-2.786837 -3.0993544.208187 2.984927-2.123337 2.9433660.704199 -0.479481-0.392370 -3.9637042.831667 1.574018-0.790153 3.3431442.943496 -3.357075-3.195883 -2.2839262.336445 2.875106-1.786345 2.5542482.190101 -1.906020-3.403367 -2.7782881.778124 3.880832-1.688346 2.2302672.592976 -2.054368-4.007257 -3.2070662.257734 3.387564-2.679011 0.7851190.939512 -4.023563-3.674424 -2.2610842.046259 2.735279-3.189470 1.7802694.372646 -0.822248-2.579316 -3.4975761.889034 5.190400-0.798747 2.1855882.836520 -2.658556-3.837877 -3.2538152.096701 3.886007-2.709034 2.9238873.367037 -3.184789-2.121479 -4.2325862.329546 3.179764-3.284816 3.2730993.091414 -3.815232-3.762093 -2.4321913.542056 2.778832-1.736822 4.2410412.127073 -2.983680-4.323818 -3.9381163.792121 5.135768-4.786473 3.3585472.624081 -3.260715-4.009299 -2.9781152.493525 1.963710-2.513661 2.6421621.864375 -3.176309-3.171184 -3.5724522.894220 2.489128-2.562539 2.8844383.491078 -3.947487-2.565729 -2.0121143.332948 3.983102-1.616805 3.5731882.280615 -2.559444-2.651229 -3.1031982.321395 3.154987-1.685703 2.9396973.031012 -3.620252-4.599622 -2.1858294.196223 1.126677-2.133863 3.0936864.668892 -2.562705-2.793241 -2.1497062.884105 3.043438-2.967647 2.8486964.479332 -1.764772-4.905566 -2.911070代码import numpy as npimport mathimport 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kn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m2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m2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])