基于python3.6.5和scikit_learn的机器学习.zip

from numpy import * import operator #生成训练集及类别 def createdataset(): group = array([[1.0,2.0],[1.2,0.1],[0.1,1.4],[0.3,3.5]]) labels = ["A","A","B","B"] return group,labels #KNN分类函数 def classify(input_c,dataset,label,k): datasize = dataset.shape[0] #计算欧式距离 diff = tile(input_c,(datasize,1)) - dataset sqdiff = diff**2 squardist = sum(sqdiff,axis = 1)#行向量分别相加，从而得到新的一个行向量 dist = squardist ** 0.5 #对距离进行排序 sorteddistindex = argsort(dist) classcount = {} for i in range(k): votelabel = label[sorteddistindex[i]] #对选取的K个样本所属的类别个数进行统计 classcount[votelabel] = classcount.get(votelabel,0) + 1 #选取出现的类别次数最多的类别 maxcount = 0 for key,vaule in classcount.items(): if vaule > maxcount: maxcount = vaule classes = key return classes dataset,label = createdataset() input_c = array([1.1,0.3]) #input_c = input("请输入一个1*2的数组：") k = 3 output = classify(input_c,dataset,label,k) print("测试数据为:",input_c) print("分类结果为:",output)