KNN算法示例

# -*- coding: UTF-8 -*-import mathimport csvimport randomimport operator'''@author:hunter@time:2017.03.31'''class KNearestNeighbor(object):    def __init__(self):        pass    def loadDataset(self,filename, split, trainingSet, testSet):  # 加载数据集  split以某个值为界限分类train和test        with open(filename, 'r') as csvfile:            lines = csv.reader(csvfile)   #读取所有的行            dataset = list(lines)     #转化成列表            for x in range(len(dataset)-1):                for y in range(4):                    dataset[x][y] = float(dataset[x][y])                if random.random() < split:   # 将所有数据加载到train和test中                    trainingSet.append(dataset[x])                else:                    testSet.append(dataset[x])    def calculateDistance(self,testdata, traindata, length):   # 计算距离        distance = 0     # length表示维度 数据共有几维        for x in range(length):            distance += pow((testdata[x]-traindata[x]), 2)        return math.sqrt(distance)    def getNeighbors(self,trainingSet, testInstance, k):  # 返回最近的k个边距        distances = []        length = len(testInstance)-1        for x in range(len(trainingSet)):   #对训练集的每一个数计算其到测试集的实际距离            dist = self.calculateDistance(testInstance, trainingSet[x], length)            print('训练集:{}-距离:{}'.format(trainingSet[x], dist))            distances.append((trainingSet[x], dist))        distances.sort(key=operator.itemgetter(1))   # 把距离从小到大排列        neighbors = []        for x in range(k):   #排序完成后取前k个距离            neighbors.append(distances[x][0])            print(neighbors)            return neighbors    def getResponse(self,neighbors):  # 根据少数服从多数，决定归类到哪一类        classVotes = {}        for x in range(len(neighbors)):            response = neighbors[x][-1]  # 统计每一个分类的多少            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]    def getAccuracy(self,testSet, predictions):  # 准确率计算        correct = 0        for x in range(len(testSet)):            if testSet[x][-1] == predictions[x]:   #predictions是预测的和testset实际的比对                correct += 1        print('共有{}个预测正确，共有{}个测试数据'.format(correct,len(testSet)))        return (correct/float(len(testSet)))*100.0    def Run(self):        trainingSet = []        testSet = []        split = 0.75        self.loadDataset(r'testdata.txt', split, trainingSet, testSet)   #数据划分        print('Train set: ' + str(len(trainingSet)))        print('Test set: ' + str(len(testSet)))        #generate predictions        predictions = []        k = 3    # 取最近的3个数据        # correct = []        for x in range(len(testSet)):    # 对所有的测试集进行测试            neighbors = self.getNeighbors(trainingSet, testSet[x], k)   #找到3个最近的邻居            result = self.getResponse(neighbors)    # 找这3个邻居归类到哪一类            predictions.append(result)        # print(correct)        accuracy = self.getAccuracy(testSet,predictions)        print('Accuracy: ' + repr(accuracy) + '%')if __name__ == '__main__':    a = KNearestNeighbor()    a.Run()