KNN图像识别.py

# -*- coding:utf-8 -*-
import csv
import random
import math
import operator
#加载数据集
def loadDataset(filename,split,trainingSet=[],testSet=[]):
    with open(filename,'r') 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:     #将数据集随机划分
                trainingSet.append(dataset[x])
            else:
                testSet.append(dataset[x])
#计算点之间的距离，多维度的
def euclideanDistance(instance1,instance2,length):
    for x in range(length):
        distance +=pow((instance1[x]-instance2[x]),2)
    return math.sqrt(distance)

#获取K个邻居
def getNeighbors(trainingSet,testInstance,k):
    distances=[]
    length=len(testInstance)-1
    for x in range(len(trainingSet)):
        dist=euclideanDistance(testInstance,trainingSet[x],length)
        distances.append((trainingSet[x],dist))  #获取测试点到其他点的距离
    distances.sort(key=operator.itemgetter(1))   #对所有的距离进行排序
    neighbors=[]
    for x in range(k):    #获取距离最近的K个点
        neighbors.append(distances[x][0])
        return neighbors

#得到这K个邻居的分类中最多的一类
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]  #获取票数最多的类别
#计算预测的准确率
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():
    #prepare data
    trainingSet=[]
    testSet=[]
    split=0.9
    loadDataset(r't10k-images.idx3-ubyte',split,trainingSet,testSet)
    print('TrainSet:'+ repr(len(trainingSet)))
    print('TestSet:'+ repr(len(testSet)))
    #generate predictions
    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]))
    print('predictions:'+repr(predictions))
    accuracy=getAccuracy(testSet,predictions)
    print('Accuracy:'+ repr(accuracy)+'%')

    if __name__ == "__main__":
        main()