菜鸟之路——机器学习之KNN算法个人理解及Python实现

KNN（K Nearest Neighbor）

还是先记几个关键公式

距离：一般用Euclidean distance   E(x,y)√∑(x_i-y_i)² 。名字这么高大上，就是初中学的两点间的距离嘛。

         还有其他距离的衡量公式，余弦值（cos），相关度（correlation） 曼哈顿距离（manhatann distance）。我觉得针对于KNN算法还是Euclidean distance最好，最直观。

然后就选择最近的K个点。根据投票原则分类出结果。

首先利用sklearn自带的的iris数据集和KNN算法运行一下

from sklearn import neighbors     #knn算法在neighbor包里
from sklearn import datasets      #包含常用的机器学习的包

knn=neighbors.KNeighborsClassifier()   #新建knn算法类

iris=datasets.load_iris()              #加载虹膜这种花的数据
#print(iris) #这是个字典有data，target，target_name，这三个key，太多了，就打印出来了

knn.fit(iris.data,iris.target)
print(knn.fit(iris.data,iris.target)) #我也不知道为什么要这样fit一下形成一个模型。打印一下看看我觉得应该是为了记录一下数据的信息吧


predictedLabel=knn.predict([[0.1,0.2,0.3,0.4]])#预测一下
print(predictedLabel)
print("predictedName:",iris.target_names[predictedLabel[0]])

然后就自己写KNN算法啦

import csv
import random
import math
import operator

#加载数据的
def LoadDataset(filename,split):#split这个参数是用来分开训练集与测试集的，split属于[0,1]。即有多大的概率将所有数据选取为训练集
    trainingSet=[]
    testSet=[]
    with open(filename,'rt') as csvfile:
         lines=csv.reader(csvfile)
         dataset=list(lines)
         for x in range(len(dataset)-1):
             for y in range(4):
                 dataset[x][y]=float(dataset[x][y])
             if random.random()<split:      #random.random()生成一个[0,1]之间的随机数
                trainingSet.append(dataset[x])
             else:
                 testSet.append(dataset[x])
    return [trainingSet,testSet]

#此函数用来计算两点之间的距离
def enclideanDinstance(instance1,instance2,length):#legdth为维度
    distance=0
    for x in range(length):
        distance+=pow((instance1[x]-instance2[x]),2)
    return math.sqrt(distance)

#此函数选取K个离testInstance最近的trainingSet里的实例
def getNeighbors(trainingSet,testInstance,k):
    distances=[]
    length=len(testInstance)-1
    for x in range(len(trainingSet)):
        dist=enclideanDinstance(testInstance,trainingSet[x],length)
        distances.append([trainingSet[x],dist])
    distances.sort(key=operator.itemgetter(1))#operator.itemgetter函数获取的不是值，而是定义了一个函数，取列表的第几个域的函数。
                                              # sort中的key也是用来指定取待排序元素的哪一项进行排序
                                              #这句的意思就是按照distances的第二个域进行排序
    neighbors=[]
    for x in range(k):
            neighbors.append(distances[x][0])
    return neighbors

#这个函数就是从K的最邻近的实例中利用投票原则分类出结果
def getResponce(neighbors):
    classVotes={}
    for x in range(len(neighbors)):
        responce=neighbors[x][-1]
        if responce in classVotes:
            classVotes[responce]+=1
        else:
            classVotes[responce] = 1
    sortedVotes=sorted(classVotes.items(),key=operator.itemgetter(1),reverse=True)
    return sortedVotes[0][0]

#这个函数从测试结果与真实结果中得出正确率
def getAccuracy(testSet,predictions):
    corrrect=0
    for x in range(len(testSet)):
        if testSet[x][-1] ==predictions[x]:
            corrrect+=1
    return (corrrect/float(len(testSet)))*100

def main():
    split=0.67   #将选取67%的数据作为训练集
    [trainingSet,testSet]=LoadDataset('irisdata.txt',split)
    print("trainingSet:",len(trainingSet),trainingSet)
    print("testSet",len(testSet),testSet)

    predictions=[]
    k=3  #选取三个最邻近的实例
    #测试所有测试集
    for x in range(len(testSet)):
        neighbors=getNeighbors(trainingSet,testSet[x],k)
        result=getResponce(neighbors)
        predictions.append(result)
        print(">predicted",result,",actual=",testSet[x][-1])
        
    accuracy=getAccuracy(testSet,predictions)
    print("Accuracy:",accuracy,r"%")
    
if __name__ == '__main__':
    main()

里面有我对代码的理解

 运行结果为

trainingSet: 110 [[4.9, 3.0, 1.4, 0.2, 'Iris-setosa'], [4.7, 3.2, 1.3, 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'], [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'], [4.3, 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'], [5.7, 3.8, 1.7, 0.3, 'Iris-setosa'], [5.4, 3.4, 1.7, 0.2, 'Iris-setosa'], [4.6, 3.6, 1.0, 0.2, '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'], [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'], [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'], [4.4, 3.0, 1.3, 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'], [5.1, 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'], [7.0, 3.2, 4.7, 1.4, 'Iris-versicolor'], [6.4, 3.2, 4.5, 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'], [4.9, 2.4, 3.3, 1.0, 'Iris-versicolor'], [6.6, 2.9, 4.6, 1.3, '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'], [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'], [5.6, 2.5, 3.9, 1.1, 'Iris-versicolor'], [5.9, 3.2, 4.8, 1.8, 'Iris-versicolor'], [6.3, 2.5, 4.9, 1.5, 'Iris-versicolor'], [6.4, 2.9, 4.3, 1.3, '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'], [5.8, 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'], [6.3, 2.3, 4.4, 1.3, 'Iris-versicolor'], [5.6, 3.0, 4.1, 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'], [6.5, 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'], [6.5, 3.2, 5.1, 2.0, 'Iris-virginica'], [6.4, 2.7, 5.3, 1.9, '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, 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'], [7.7, 2.8, 6.7, 2.0, 'Iris-virginica'], [6.3, 2.7, 4.9, 1.8, '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'], [7.4, 2.8, 6.1, 1.9, 'Iris-virginica'], [6.4, 2.8, 5.6, 2.2, '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'], [6.9, 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, 3.0, 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']]
testSet 40 [[5.1, 3.5, 1.4, 0.2, 'Iris-setosa'], [4.6, 3.1, 1.5, 0.2, 'Iris-setosa'], [5.0, 3.4, 1.5, 0.2, 'Iris-setosa'], [4.8, 3.0, 1.4, 0.1, 'Iris-setosa'], [5.1, 3.5, 1.4, 0.3, 'Iris-setosa'], [5.1, 3.8, 1.5, 0.3, 'Iris-setosa'], [5.1, 3.7, 1.5, 0.4, 'Iris-setosa'], [5.1, 3.3, 1.7, 0.5, 'Iris-setosa'], [5.2, 3.4, 1.4, 0.2, 'Iris-setosa'], [5.5, 4.2, 1.4, 0.2, 'Iris-setosa'], [4.9, 3.1, 1.5, 0.1, 'Iris-setosa'], [5.1, 3.4, 1.5, 0.2, 'Iris-setosa'], [5.0, 3.5, 1.6, 0.6, 'Iris-setosa'], [5.0, 3.3, 1.4, 0.2, 'Iris-setosa'], [6.9, 3.1, 4.9, 1.5, 'Iris-versicolor'], [6.3, 3.3, 4.7, 1.6, 'Iris-versicolor'], [5.2, 2.7, 3.9, 1.4, 'Iris-versicolor'], [6.1, 2.9, 4.7, 1.4, 'Iris-versicolor'], [6.2, 2.2, 4.5, 1.5, 'Iris-versicolor'], [6.1, 2.8, 4.0, 1.3, 'Iris-versicolor'], [6.1, 2.8, 4.7, 1.2, 'Iris-versicolor'], [6.6, 3.0, 4.4, 1.4, 'Iris-versicolor'], [5.5, 2.4, 3.7, 1.0, 'Iris-versicolor'], [6.7, 3.1, 4.7, 1.5, 'Iris-versicolor'], [5.5, 2.5, 4.0, 1.3, 'Iris-versicolor'], [6.3, 2.9, 5.6, 1.8, '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'], [6.8, 3.0, 5.5, 2.1, 'Iris-virginica'], [5.7, 2.5, 5.0, 2.0, 'Iris-virginica'], [7.7, 3.8, 6.7, 2.2, 'Iris-virginica'], [5.6, 2.8, 4.9, 2.0, 'Iris-virginica'], [6.7, 3.3, 5.7, 2.1, 'Iris-virginica'], [7.2, 3.0, 5.8, 1.6, 'Iris-virginica'], [7.9, 3.8, 6.4, 2.0, 'Iris-virginica'], [6.3, 2.8, 5.1, 1.5, 'Iris-virginica'], [6.0, 3.0, 4.8, 1.8, 'Iris-virginica'], [6.7, 3.3, 5.7, 2.5, 'Iris-virginica'], [5.9, 3.0, 5.1, 1.8, 'Iris-virginica']]
>predicted Iris-setosa ,actual= Iris-setosa
>predicted Iris-setosa ,actual= Iris-setosa
>predicted Iris-setosa ,actual= Iris-setosa
>predicted Iris-setosa ,actual= Iris-setosa
>predicted Iris-setosa ,actual= Iris-setosa
>predicted Iris-setosa ,actual= Iris-setosa
>predicted Iris-setosa ,actual= Iris-setosa
>predicted Iris-setosa ,actual= Iris-setosa
>predicted Iris-setosa ,actual= Iris-setosa
>predicted Iris-setosa ,actual= Iris-setosa
>predicted Iris-setosa ,actual= Iris-setosa
>predicted Iris-setosa ,actual= Iris-setosa
>predicted Iris-setosa ,actual= Iris-setosa
>predicted Iris-setosa ,actual= Iris-setosa
>predicted Iris-versicolor ,actual= Iris-versicolor
>predicted Iris-versicolor ,actual= Iris-versicolor
>predicted Iris-versicolor ,actual= Iris-versicolor
>predicted Iris-versicolor ,actual= Iris-versicolor
>predicted Iris-versicolor ,actual= Iris-versicolor
>predicted Iris-versicolor ,actual= Iris-versicolor
>predicted Iris-versicolor ,actual= Iris-versicolor
>predicted Iris-versicolor ,actual= Iris-versicolor
>predicted Iris-versicolor ,actual= Iris-versicolor
>predicted Iris-versicolor ,actual= Iris-versicolor
>predicted Iris-versicolor ,actual= Iris-versicolor
>predicted Iris-virginica ,actual= Iris-virginica
>predicted Iris-virginica ,actual= Iris-virginica
>predicted Iris-virginica ,actual= Iris-virginica
>predicted Iris-virginica ,actual= Iris-virginica
>predicted Iris-virginica ,actual= Iris-virginica
>predicted Iris-virginica ,actual= Iris-virginica
>predicted Iris-virginica ,actual= Iris-virginica
>predicted Iris-virginica ,actual= Iris-virginica
>predicted Iris-virginica ,actual= Iris-virginica
>predicted Iris-virginica ,actual= Iris-virginica
>predicted Iris-virginica ,actual= Iris-virginica
>predicted Iris-versicolor ,actual= Iris-virginica
>predicted Iris-virginica ,actual= Iris-virginica
>predicted Iris-virginica ,actual= Iris-virginica
>predicted Iris-virginica ,actual= Iris-virginica
Accuracy: 97.5 %

以下拓展几个知识点

1，random库的一些用法

random.randint(1,10)        # 产生 1 到 10 的一个整数型随机数  
random.random()             # 产生 0 到 1 之间的随机浮点数
random.uniform(1.1,5.4)     # 产生  1.1 到 5.4 之间的随机浮点数，区间可以不是整数
random.choice('tomorrow')   # 从序列中随机选取一个元素
random.randrange(1,100,2)   # 生成从1到100的间隔为2的随机整数
random.shuffle(a)           # 将序列a中的元素顺序打乱 

2，排序函数

sorted(exapmle[, cmp[, key[, reverse]]])

example.sort(cmp[, key[, reverse]])

     example是和待排序序列

     cmp为函数，指定排序时进行比较的函数，可以指定一个函数或者lambda函数

     key为函数，指定取待排序元素的哪一项进行排序

     reverse实现降序排序，需要提供一个布尔值，默认为False（升序排列）。

程序中的第53行   sortedVotes=sorted(classVotes.items(),key=operator.itemgetter(1),reverse=True)就是按照sortedVotes的第二个域进行降序排列

key=operator.itemgetter(n)就是按照第n+1个域

写完喽，图书馆也该闭馆了。学习的感觉真舒服。接下来就是最出名的SVM算法啦