可以參見 如下連接了解原理：
https://zhuanlan.zhihu.com/p/61341071
一.KNN算法概述
KNN可以說是最簡單的分類算法之一，同時，它也是最常用的分類算法之一，注意KNN算法是有監督學習中的分類算法，它看起來和另一個機器學習算法Kmeans有點像（Kmeans是無監督學習算法），但卻是有本質區別的。那么什么是KNN算法呢，接下來我們就來介紹介紹吧。
二.KNN算法介紹
KNN的全稱是K Nearest Neighbors，意思是K個最近的鄰居，從這個名字我們就能看出一些KNN算法的蛛絲馬跡了。K個最近鄰居，毫無疑問，K的取值肯定是至關重要的。那么最近的鄰居又是怎么回事呢？其實啊，KNN的原理就是當預測一個新的值x的時候，根據它距離最近的K個點是什么類別來判斷x屬於哪個類別。聽起來有點繞，還是看看圖吧。
實例：
import csv #用於處理csv文件
import random #用於隨機數
import math
import operator #
from sklearn import neighbors
#加載數據集
def loadDataset(filename,split,trainingSet=[],testSet = []): # 加載數據集 split以某個值為界限分類train和test
with open(filename, 'r') as csvfile:
lines = csv.reader(csvfile) #讀取所有的行
dataset = list(lines) #轉化成列表
for x in range(len(dataset)-1):
for y in range(4):
dataset[x][y] = float(dataset[x][y])
if random.random() < split: # 將所有數據加載到train和test中
trainingSet.append(dataset[x])
else:
testSet.append(dataset[x])
#計算距離
def euclideanDistance(instance1,instance2,length): # 計算距離
distance = 0 # length表示維度 數據共有幾維
for x in range(length):
distance += pow((instance1[x] - instance2[x]),2)
return math.sqrt(distance)
#返回K個最近鄰
def getNeighbors(trainingSet,testInstance,k):
distances = []
length = len(testInstance) -1
#計算每一個測試實例到訓練集實例的距離
for x in range(len(trainingSet)):
dist = euclideanDistance(testInstance, trainingSet[x], length)
distances.append((trainingSet[x],dist))
#對所有的距離進行排序
distances.sort(key=operator.itemgetter(1))
neighbors = []
#返回k個最近鄰
for x in range(k):
neighbors.append(distances[x][0])
return neighbors
#對k個近鄰進行合並，返回value最大的key
def getResponse(neighbors): # 根據少數服從多數，決定歸類到哪一類
classVotes = {}
for x in range(len(neighbors)):
response = neighbors[x][-1] # 統計每一個分類的多少
if response in classVotes:
classVotes[response] += 1
else:
classVotes[response] = 1
print(classVotes.items())
sortedVotes = sorted(classVotes.items(), key=operator.itemgetter(1), reverse=True) #reverse按降序的方式排列
return sortedVotes[0][0]
#計算准確率
def getAccuracy(testSet,predictions):
correct = 0
for x in range(len(testSet)):
if testSet[x][-1] == predictions[x]:
correct+=1
return (correct/float(len(testSet))) * 100.0
def main():
trainingSet = [] #訓練數據集
testSet = [] #測試數據集
split = 0.68 #分割的比例
loadDataset("D:\SAB\Desktop\iris.txt", split, trainingSet, testSet)
print ("Train set :" + repr(len(trainingSet)))
print ( "Test set :" + repr(len(testSet)))
predictions = []
k = 3
for x in range(len(testSet)):
neighbors = getNeighbors(trainingSet, testSet[x], k)
result = getResponse(neighbors)
predictions.append(result)
print (">predicted = " + repr(result) + ",actual = " + repr(testSet[x][-1]) )
accuracy = getAccuracy(testSet, predictions)
print ("Accuracy:" + repr(accuracy) + "%" )
if __name__ =="__main__":
main()
所用數據如下：
5.1,3.5,1.4,0.2,Iris-setosa
4.9,3.0,1.4,0.2,Iris-setosa
4.7,3.2,1.3,0.2,Iris-setosa
4.6,3.1,1.5,0.2,Iris-setosa
5.0,3.6,1.4,0.2,Iris-setosa
5.4,3.9,1.7,0.4,Iris-setosa
4.6,3.4,1.4,0.3,Iris-setosa
5.0,3.4,1.5,0.2,Iris-setosa
4.4,2.9,1.4,0.2,Iris-setosa
4.9,3.1,1.5,0.1,Iris-setosa
5.4,3.7,1.5,0.2,Iris-setosa
4.8,3.4,1.6,0.2,Iris-setosa
4.8,3.0,1.4,0.1,Iris-setosa
4.3,3.0,1.1,0.1,Iris-setosa
5.8,4.0,1.2,0.2,Iris-setosa
5.7,4.4,1.5,0.4,Iris-setosa
5.4,3.9,1.3,0.4,Iris-setosa
5.1,3.5,1.4,0.3,Iris-setosa
5.7,3.8,1.7,0.3,Iris-setosa
5.1,3.8,1.5,0.3,Iris-setosa
5.4,3.4,1.7,0.2,Iris-setosa
5.1,3.7,1.5,0.4,Iris-setosa
4.6,3.6,1.0,0.2,Iris-setosa
5.1,3.3,1.7,0.5,Iris-setosa
4.8,3.4,1.9,0.2,Iris-setosa
5.0,3.0,1.6,0.2,Iris-setosa
5.0,3.4,1.6,0.4,Iris-setosa
5.2,3.5,1.5,0.2,Iris-setosa
5.2,3.4,1.4,0.2,Iris-setosa
4.7,3.2,1.6,0.2,Iris-setosa
4.8,3.1,1.6,0.2,Iris-setosa
5.4,3.4,1.5,0.4,Iris-setosa
5.2,4.1,1.5,0.1,Iris-setosa
5.5,4.2,1.4,0.2,Iris-setosa
4.9,3.1,1.5,0.1,Iris-setosa
5.0,3.2,1.2,0.2,Iris-setosa
5.5,3.5,1.3,0.2,Iris-setosa
4.9,3.1,1.5,0.1,Iris-setosa
4.4,3.0,1.3,0.2,Iris-setosa
5.1,3.4,1.5,0.2,Iris-setosa
5.0,3.5,1.3,0.3,Iris-setosa
4.5,2.3,1.3,0.3,Iris-setosa
4.4,3.2,1.3,0.2,Iris-setosa
5.0,3.5,1.6,0.6,Iris-setosa
5.1,3.8,1.9,0.4,Iris-setosa
4.8,3.0,1.4,0.3,Iris-setosa
5.1,3.8,1.6,0.2,Iris-setosa
4.6,3.2,1.4,0.2,Iris-setosa
5.3,3.7,1.5,0.2,Iris-setosa
5.0,3.3,1.4,0.2,Iris-setosa
7.0,3.2,4.7,1.4,Iris-versicolor
6.4,3.2,4.5,1.5,Iris-versicolor
6.9,3.1,4.9,1.5,Iris-versicolor
5.5,2.3,4.0,1.3,Iris-versicolor
6.5,2.8,4.6,1.5,Iris-versicolor
5.7,2.8,4.5,1.3,Iris-versicolor
6.3,3.3,4.7,1.6,Iris-versicolor
4.9,2.4,3.3,1.0,Iris-versicolor
6.6,2.9,4.6,1.3,Iris-versicolor
5.2,2.7,3.9,1.4,Iris-versicolor
5.0,2.0,3.5,1.0,Iris-versicolor
5.9,3.0,4.2,1.5,Iris-versicolor
6.0,2.2,4.0,1.0,Iris-versicolor
6.1,2.9,4.7,1.4,Iris-versicolor
5.6,2.9,3.6,1.3,Iris-versicolor
6.7,3.1,4.4,1.4,Iris-versicolor
5.6,3.0,4.5,1.5,Iris-versicolor
5.8,2.7,4.1,1.0,Iris-versicolor
6.2,2.2,4.5,1.5,Iris-versicolor
5.6,2.5,3.9,1.1,Iris-versicolor
5.9,3.2,4.8,1.8,Iris-versicolor
6.1,2.8,4.0,1.3,Iris-versicolor
6.3,2.5,4.9,1.5,Iris-versicolor
6.1,2.8,4.7,1.2,Iris-versicolor
6.4,2.9,4.3,1.3,Iris-versicolor
6.6,3.0,4.4,1.4,Iris-versicolor
6.8,2.8,4.8,1.4,Iris-versicolor
6.7,3.0,5.0,1.7,Iris-versicolor
6.0,2.9,4.5,1.5,Iris-versicolor
5.7,2.6,3.5,1.0,Iris-versicolor
5.5,2.4,3.8,1.1,Iris-versicolor
5.5,2.4,3.7,1.0,Iris-versicolor
5.8,2.7,3.9,1.2,Iris-versicolor
6.0,2.7,5.1,1.6,Iris-versicolor
5.4,3.0,4.5,1.5,Iris-versicolor
6.0,3.4,4.5,1.6,Iris-versicolor
6.7,3.1,4.7,1.5,Iris-versicolor
6.3,2.3,4.4,1.3,Iris-versicolor
5.6,3.0,4.1,1.3,Iris-versicolor
5.5,2.5,4.0,1.3,Iris-versicolor
5.5,2.6,4.4,1.2,Iris-versicolor
6.1,3.0,4.6,1.4,Iris-versicolor
5.8,2.6,4.0,1.2,Iris-versicolor
5.0,2.3,3.3,1.0,Iris-versicolor
5.6,2.7,4.2,1.3,Iris-versicolor
5.7,3.0,4.2,1.2,Iris-versicolor
5.7,2.9,4.2,1.3,Iris-versicolor
6.2,2.9,4.3,1.3,Iris-versicolor
5.1,2.5,3.0,1.1,Iris-versicolor
5.7,2.8,4.1,1.3,Iris-versicolor
6.3,3.3,6.0,2.5,Iris-virginica
5.8,2.7,5.1,1.9,Iris-virginica
7.1,3.0,5.9,2.1,Iris-virginica
6.3,2.9,5.6,1.8,Iris-virginica
6.5,3.0,5.8,2.2,Iris-virginica
7.6,3.0,6.6,2.1,Iris-virginica
4.9,2.5,4.5,1.7,Iris-virginica
7.3,2.9,6.3,1.8,Iris-virginica
6.7,2.5,5.8,1.8,Iris-virginica
7.2,3.6,6.1,2.5,Iris-virginica
6.5,3.2,5.1,2.0,Iris-virginica
6.4,2.7,5.3,1.9,Iris-virginica
6.8,3.0,5.5,2.1,Iris-virginica
5.7,2.5,5.0,2.0,Iris-virginica
5.8,2.8,5.1,2.4,Iris-virginica
6.4,3.2,5.3,2.3,Iris-virginica
6.5,3.0,5.5,1.8,Iris-virginica
7.7,3.8,6.7,2.2,Iris-virginica
7.7,2.6,6.9,2.3,Iris-virginica
6.0,2.2,5.0,1.5,Iris-virginica
6.9,3.2,5.7,2.3,Iris-virginica
5.6,2.8,4.9,2.0,Iris-virginica
7.7,2.8,6.7,2.0,Iris-virginica
6.3,2.7,4.9,1.8,Iris-virginica
6.7,3.3,5.7,2.1,Iris-virginica
7.2,3.2,6.0,1.8,Iris-virginica
6.2,2.8,4.8,1.8,Iris-virginica
6.1,3.0,4.9,1.8,Iris-virginica
6.4,2.8,5.6,2.1,Iris-virginica
7.2,3.0,5.8,1.6,Iris-virginica
7.4,2.8,6.1,1.9,Iris-virginica
7.9,3.8,6.4,2.0,Iris-virginica
6.4,2.8,5.6,2.2,Iris-virginica
6.3,2.8,5.1,1.5,Iris-virginica
6.1,2.6,5.6,1.4,Iris-virginica
7.7,3.0,6.1,2.3,Iris-virginica
6.3,3.4,5.6,2.4,Iris-virginica
6.4,3.1,5.5,1.8,Iris-virginica
6.0,3.0,4.8,1.8,Iris-virginica
6.9,3.1,5.4,2.1,Iris-virginica
6.7,3.1,5.6,2.4,Iris-virginica
6.9,3.1,5.1,2.3,Iris-virginica
5.8,2.7,5.1,1.9,Iris-virginica
6.8,3.2,5.9,2.3,Iris-virginica
6.7,3.3,5.7,2.5,Iris-virginica
6.7,3.0,5.2,2.3,Iris-virginica
6.3,2.5,5.0,1.9,Iris-virginica
6.5,3.0,5.2,2.0,Iris-virginica
6.2,3.4,5.4,2.3,Iris-virginica
5.9,3.0,5.1,1.8,Iris-virginica
運行結果：
Train set :98
Test set :51
dict_items([('Iris-setosa', 3)])
>predicted = 'Iris-setosa',actual = 'Iris-setosa'
dict_items([('Iris-setosa', 3)])
>predicted = 'Iris-setosa',actual = 'Iris-setosa'
dict_items([('Iris-setosa', 3)])
>predicted = 'Iris-setosa',actual = 'Iris-setosa'
dict_items([('Iris-setosa', 3)])
>predicted = 'Iris-setosa',actual = 'Iris-setosa'
dict_items([('Iris-setosa', 3)])
>predicted = 'Iris-setosa',actual = 'Iris-setosa'
dict_items([('Iris-setosa', 3)])
>predicted = 'Iris-setosa',actual = 'Iris-setosa'
dict_items([('Iris-setosa', 3)])
>predicted = 'Iris-setosa',actual = 'Iris-setosa'
dict_items([('Iris-setosa', 3)])
>predicted = 'Iris-setosa',actual = 'Iris-setosa'
dict_items([('Iris-setosa', 3)])
>predicted = 'Iris-setosa',actual = 'Iris-setosa'
dict_items([('Iris-setosa', 3)])
>predicted = 'Iris-setosa',actual = 'Iris-setosa'
dict_items([('Iris-setosa', 3)])
>predicted = 'Iris-setosa',actual = 'Iris-setosa'
dict_items([('Iris-setosa', 3)])
>predicted = 'Iris-setosa',actual = 'Iris-setosa'
dict_items([('Iris-setosa', 3)])
>predicted = 'Iris-setosa',actual = 'Iris-setosa'
dict_items([('Iris-setosa', 3)])
>predicted = 'Iris-setosa',actual = 'Iris-setosa'
dict_items([('Iris-setosa', 3)])
>predicted = 'Iris-setosa',actual = 'Iris-setosa'
dict_items([('Iris-versicolor', 3)])
>predicted = 'Iris-versicolor',actual = 'Iris-versicolor'
dict_items([('Iris-versicolor', 3)])
>predicted = 'Iris-versicolor',actual = 'Iris-versicolor'
dict_items([('Iris-versicolor', 2), ('Iris-virginica', 1)])
>predicted = 'Iris-versicolor',actual = 'Iris-versicolor'
dict_items([('Iris-versicolor', 3)])
>predicted = 'Iris-versicolor',actual = 'Iris-versicolor'
dict_items([('Iris-versicolor', 3)])
>predicted = 'Iris-versicolor',actual = 'Iris-versicolor'
dict_items([('Iris-versicolor', 3)])
>predicted = 'Iris-versicolor',actual = 'Iris-versicolor'
dict_items([('Iris-versicolor', 2), ('Iris-virginica', 1)])
>predicted = 'Iris-versicolor',actual = 'Iris-versicolor'
dict_items([('Iris-versicolor', 3)])
>predicted = 'Iris-versicolor',actual = 'Iris-versicolor'
dict_items([('Iris-virginica', 3)])
>predicted = 'Iris-virginica',actual = 'Iris-versicolor'
dict_items([('Iris-virginica', 2), ('Iris-versicolor', 1)])
>predicted = 'Iris-virginica',actual = 'Iris-versicolor'
dict_items([('Iris-versicolor', 3)])
>predicted = 'Iris-versicolor',actual = 'Iris-versicolor'
dict_items([('Iris-versicolor', 3)])
>predicted = 'Iris-versicolor',actual = 'Iris-versicolor'
dict_items([('Iris-versicolor', 2), ('Iris-virginica', 1)])
>predicted = 'Iris-versicolor',actual = 'Iris-versicolor'
dict_items([('Iris-versicolor', 3)])
>predicted = 'Iris-versicolor',actual = 'Iris-versicolor'
dict_items([('Iris-versicolor', 3)])
>predicted = 'Iris-versicolor',actual = 'Iris-versicolor'
dict_items([('Iris-versicolor', 3)])
>predicted = 'Iris-versicolor',actual = 'Iris-versicolor'
dict_items([('Iris-versicolor', 3)])
>predicted = 'Iris-versicolor',actual = 'Iris-versicolor'
dict_items([('Iris-versicolor', 3)])
>predicted = 'Iris-versicolor',actual = 'Iris-versicolor'
dict_items([('Iris-versicolor', 3)])
>predicted = 'Iris-versicolor',actual = 'Iris-versicolor'
dict_items([('Iris-virginica', 3)])
>predicted = 'Iris-virginica',actual = 'Iris-virginica'
dict_items([('Iris-virginica', 3)])
>predicted = 'Iris-virginica',actual = 'Iris-virginica'
dict_items([('Iris-virginica', 3)])
>predicted = 'Iris-virginica',actual = 'Iris-virginica'
dict_items([('Iris-virginica', 2), ('Iris-versicolor', 1)])
>predicted = 'Iris-virginica',actual = 'Iris-virginica'
dict_items([('Iris-virginica', 3)])
>predicted = 'Iris-virginica',actual = 'Iris-virginica'
dict_items([('Iris-virginica', 3)])
>predicted = 'Iris-virginica',actual = 'Iris-virginica'
dict_items([('Iris-virginica', 3)])
>predicted = 'Iris-virginica',actual = 'Iris-virginica'
dict_items([('Iris-virginica', 3)])
>predicted = 'Iris-virginica',actual = 'Iris-virginica'
dict_items([('Iris-virginica', 3)])
>predicted = 'Iris-virginica',actual = 'Iris-virginica'
dict_items([('Iris-virginica', 3)])
>predicted = 'Iris-virginica',actual = 'Iris-virginica'
dict_items([('Iris-virginica', 3)])
>predicted = 'Iris-virginica',actual = 'Iris-virginica'
dict_items([('Iris-versicolor', 1), ('Iris-virginica', 2)])
>predicted = 'Iris-virginica',actual = 'Iris-virginica'
dict_items([('Iris-virginica', 3)])
>predicted = 'Iris-virginica',actual = 'Iris-virginica'
dict_items([('Iris-virginica', 3)])
>predicted = 'Iris-virginica',actual = 'Iris-virginica'
dict_items([('Iris-virginica', 3)])
>predicted = 'Iris-virginica',actual = 'Iris-virginica'
dict_items([('Iris-virginica', 3)])
>predicted = 'Iris-virginica',actual = 'Iris-virginica'
dict_items([('Iris-virginica', 3)])
>predicted = 'Iris-virginica',actual = 'Iris-virginica'
Accuracy:96.07843137254902%