from numpy import *
import operator
def createDataSet():
group = array([[1.0, 1.1], [2.0, 2.0], [0, 0], [4.1, 5.1]])
labels = ['A', 'B', 'C', 'D']
return group, labels
def classify0(inX, dataSet, labels, k):
"""
:param inX: 用于分类的输出向量
:param dataSet:输入的样本集
:param labels:标签向量
:param k:用于选择最近邻居的树目
:return:
"""
dataSetsize = dataSet.shape[0] # 得到数据集的行数
diffMat = tile(inX, (dataSetsize, 1)) - dataSet # tile生成和训练样本对应的矩阵，并与训练样本求差
sqDiffMat = diffMat ** 2
sqDistances = sqDiffMat.sum(axis=1) # 将矩阵的每一行相加
distances = sqDistances ** 0.5
sortedDistIndicies = distances.argsort() # 从小到大排序 返回对应的索引位置
classCount = {}
for i in range(k):
voteIlabel = labels[sortedDistIndicies] # 找到该样本的类型
classCount[voteIlabel] = classCount.get(voteIlabel, 0) + 1 # 在字典中将该类型加一
sortedClassCount = sorted(classCount.items(), key=operator.itemgetter(1), reverse=True) # reverse = True代表降序
return sortedClassCount[0][0] # 排序并返回出现最多的那个类型
2.测试
import kNN
group,labels = kNN.createDataSet()
print(kNN.classify0([0,0],group,labels,3))
print(kNN.classify0([1,2],group,labels,3))
print(kNN.classify0([3,3],group,labels,3))
print(kNN.classify0([5,5],group,labels,3))
3.实验结果
C
A
B
D
实验环境：Ubuntu18.04+Pycharm+python3.6+numpy
---------------------
作者：RivenDong
来源：CSDN
原文：https://blog.csdn.net/RivenDong/article/details/86175831
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