KNN聚类算法

1.算法原理
（1）计算距离（欧式距离）
（2）升序排列（从小到大）
（3）取前K个
（4）加权平均
注意：K过大，分类结果模糊；K过小，受个别案例影响，波动大。
2.代码实现demo（python）
(1)以下代码实现找去前五个训练集，并按升序排列

import random
import csv


#读取
with open('Prostate_Cancer.csv','r') as file:
    reader=csv.DictReader(file)
    dataset=[row for row in reader]
print(dataset)

#分组
random.shuffle(dataset) #shuffle洗牌，将数据打乱
n=len(dataset)//3   #双斜杆是整除

test_set=dataset[0:n]#测试集
train_set=dataset[n:] #训练集  从数据的1/3到最后一个数据

#KNN  先算距离，
def distance(d1,d2):
    res=0

    for key in("radius","texture","perimeter","area","smoothness","compactness","symmetry","fractal_dimension"):
      res+=(float(d1[key])-float(d2[key]))**2

    return res**0.5

K=5
def knn(data):
    res=[
        {"result":train['diagnosis_result'],"distance":distance(data,train)}
        for train in train_set
    ]
    #print(res)

    #2.排序
    res=sorted(res,key=lambda item:item['distance'])
    #print(res)

    #3.取前k个
    res2=res[0:K]
    #print(res2)

    #4.取加权平均
    result={'B':0,'M':0}
    #离的近的权重高，离得远的权重低

    sum=0
    for r in res2:
        sum+=r['distance']
    for r in res2:
        result[r['result']]+=1-r['distance']/sum
        print(result)
        print(data['diagnosis_result'])
knn(test_set[0])
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（2）测准确率

import random
import csv


#读取
with open('Prostate_Cancer.csv','r') as file:
    reader=csv.DictReader(file)
    dataset=[row for row in reader]
#print(dataset)

#分组
random.shuffle(dataset) #shuffle洗牌，将数据打乱
n=len(dataset)//3   #双斜杆是整除

test_set=dataset[0:n]#测试集
train_set=dataset[n:] #训练集  从数据的1/3到最后一个数据

#KNN  先算距离，
def distance(d1,d2):
    res=0

    for key in("radius","texture","perimeter","area","smoothness","compactness","symmetry","fractal_dimension"):
      res+=(float(d1[key])-float(d2[key]))**2

    return res**0.5

K=5
def knn(data):
    res=[
        {"result":train['diagnosis_result'],"distance":distance(data,train)}
        for train in train_set
    ]
    #print(res)

    #2.排序
    res=sorted(res,key=lambda item:item['distance'])
    #print(res)

    #3.取前k个
    res2=res[0:K]
    #print(res2)

    #4.取加权平均
    result={'B':0,'M':0}
    #离的近的权重高，离得远的权重低

    sum=0
    for r in res2:
        sum+=r['distance']
    for r in res2:
        result[r['result']]+=1-r['distance']/sum
    if result['B']>result['M']:
       return 'B'
    else:
       return 'M'
knn(test_set[0])

correct=0
for test in test_set:
    result=test['diagnosis_result']
    result2=knn(test)

    if result==result2:
        correct+=1

print(correct)
print(len(test_set))
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3.CSV的数据集，有需要的私我！