算法实现：python
knn.py
#!/usr/bin/python
# code by dengzp@time:2020
# coding=utf-8
#################################
# knn代码实现
from numpy import *
import operator
# 创建一个数据集，包含2个类别共四个样本
def creatDatabase():
group = array([[1.0, 0.9], [1.0, 1.0], [0.1, 0.2], [0.0, 0.1]])
# 4个样本的分别所属类别
labels = ['A', 'A', 'B', 'B']
return group, labels
def kNNClassify(newInput, dataSet, labels, k):
numSamples = dataSet.shape[0]
diff = tile(newInput, (numSamples, 1)) - dataSet
squareDiff = diff ** 2
squareDist = sum(squareDiff, axis=1)
distance = squareDist ** 0.5
sortedDistIndices = argsort(distance)
classCount = {}
for i in range(k):
voteLabel = labels[sortedDistIndices[i]]
classCount[voteLabel] = classCount.get(voteLabel, 0) + 1
maxCount = 0
for key, value in classCount.items():
if value > maxCount:
maxCount = value
maxIndex = key
return maxIndex
test.py
# !/user/bin/python
# coding=utf-8
import knn
from numpy import *
dataSet, labels = knn.creatDatabase()
testX = array([1.2, 1.0])
k = 3
outputLabel = knn.kNNClassify(testX, dataSet, labels, 3)
print("Your input data is :", testX, " and the classified to class： ", outputLabel)
