机器学习-KNN算法详解与实战

最邻近规则分类(K-Nearest Neighbor)KNN算法

1.综述

   1.1 Cover和Hart在1968年提出了最初的邻近算法

    1.2 分类(classification)算法

    1.3 输入基于实例的学习(instance-based learning)，懒惰学习(lazy learing)

2. 例子

 

未知电影属于什么类型？

3.算法详述

  3.1 步骤

   为了判断未知实例的类别，以所有已知类别的实例作为参照

   选择参数K

    计算未知实例与所有已知实例的距离

    选择最近K个已知实例

    根据少数服从多数的投票法则(majority-voting)，让未知实例归类为K个最邻近样本中最多数的类别

  3.2 细节

    关于K

    关于距离的衡量方法：

         3.2.1 Euclidean Distance定义

               

                         

              其他距离衡量：余弦值(cos)，相关度(correlation)，曼哈顿距离(Manhattan distance)

# -*- coding:utf-8 -*-

#计算ａ，ｇ两点之间的EuclideanDistance
import math

def ComputerEuclideanDistance(x1,y1,x2,y2):
    d = math.sqrt(math.pow((x1 - x2),2) + math.pow((y1 - y2),2))
    return d

d_ag = ComputerEuclideanDistance(3,104,18,90)

print("d_ag:",d_ag)

  3.3 举例

4. 算法优缺点

   4.1 算法优点

          简单

          易于理解

          容易实现

           通过对K的选择可具备丢噪音数据的健壮性

    4.2 算法缺点

        

        需要大量的空间存储所有已知实例

        算法复杂度高(需要比较所有已知实例与要分类的实例)

         当其样本分布不平衡时，比如其中一类样本过大（实例数量过多）占主导的时候，新的未知实例容易被归类为这个主导样本，因为这类样本实例的数量过大，但是这个新的未知实例实际上并不接近目标样本。（上图中的Y点）

5.算法的改进版本

       考虑距离，根据距离上加上权重

       如：1/d(d为距离)

6.KNN算法的应用

  在线文档：https://scikit-learn.org/stable/modules/neighbors.html

    6.1 数据集介绍-虹膜

     

    150 个实例

     特征：萼片长度(sepal length)、萼片宽度(sepal width)、花瓣长度(petal length)、花瓣宽度(petal width)

     类别：Iris setosa、Iris versicolor，Iris virginnica

     

      6.2 利用Python的机器学习库sklearn: SKLearn Example.py

     直接调用库函数实现

# -*- coding:utf-8 -*-

from sklearn import neighbors
from sklearn import datasets

'''
这里所有测参数都采用默认
'''

#载入分类器
knn = neighbors.KNeighborsClassifier()

#载入数据
iris = datasets.load_iris()

#查看数据集
print(iris)

#传入特征集、类标签来训练模型
knn.fit(iris.data,iris.target)

#使用测试数据预测
predictedLabel = knn.predict([[0.1,0.2,0.3,0.4]])

#查看预测结果
print(predictedLabel)

　　自定义实现

 

# -*- coding:utf-8 -*-

import csv
import random
import math
import operator

#加载数据集，划分训练集和测试集
def loadDataset(filename,split,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:
                trainingSet.append(dataset[x])
            else:
                testSet.append(dataset[x])

#计算euclideanDistance  #实例维度：length
def euclideanDistance(instance1, instance2, length):
    distance = 0
    for x in range(length):
        distance += pow(instance1[x] - instance2[x],2)
    return math.sqrt(distance)

#返回最近的K个邻居
def getNeighbors(trainingset,testInstance,k):
    distances = []#存放所有计算出的距离
    length = len(testInstance) - 1
    for x in range(len(trainingset)):
        dist = euclideanDistance(testInstance,trainingset[x], length)
        distances.append((trainingset[x],dist))
    distances.sort(key = operator.itemgetter(1))#排序
    neighbors = []
    for x in range(k):#选出前K个存入neighbors
        neighbors.append(distances[x][0])
    return neighbors

#在最近的K个邻居中，根据每个邻居所属于的类别，并统计个数，最后对其排序选出属于哪一类
def getResponse(neighbors):
    classVotes = {}
    for x in range(len(neighbors)):
        Response = neighbors[x][-1]
        if Response in classVotes:
            classVotes[Response] += 1
        else:
            classVotes[Response] = 1
    sortedVotes = sorted(classVotes.items(),key = operator.itemgetter(1),reverse=True)
    return sortedVotes[0][0]

#计算预测的精确度
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():
    #prepare data
    trainingSet = []
    testSet = []
    split = 0.67
    loadDataset(r"irisdata.txt",split,trainingSet,testSet)
    print("trainingSet:" + repr(len(trainingSet)))
    print("testSet:" + repr(len(testSet)))
    #generate predictions
    predictions = []

    k = 3
    correct = []
    for x in range(len(testSet)):
        neighbors = getNeighbors(trainingSet,testSet[x],k)
        result = getResponse(neighbors)
        predictions.append(result)
        print("predcted =" + repr(result)+",actual = " + repr(testSet[x][-1]))
    accuracy = getAccuracy(testSet,predictions)
    print("accuracy:" + repr(accuracy) + "%")

main()

 

　　测试数据集

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

　　

 

转载于:https://www.cnblogs.com/lyywj170403/p/10418354.html