001 KNN分类 最邻近算法

1.文件
5.0,3.5,1.6,0.6,apple
5.1,3.8,1.9,0.4,apple
4.8,3.0,1.4,0.3,apple
5.1,3.8,1.6,0.2,apple
4.6,3.2,1.4,0.2,apple
5.3,3.7,1.5,0.2,apple
5.0,3.3,1.4,0.2,apple
7.0,3.2,4.7,1.4,orange
6.4,3.2,4.5,1.5,orange
6.9,3.1,4.9,1.5,orange
5.5,2.3,4.0,1.3,orange
6.5,2.8,4.6,1.5,orange
5.7,2.8,4.5,1.3,orange
6.3,3.3,4.7,1.6,orange
7.3,2.9,6.3,1.8,banana
6.7,2.5,5.8,1.8,banana
7.2,3.6,6.1,2.5,banana
6.5,3.2,5.1,2.0,banana
6.4,2.7,5.3,1.9,banana
6.8,3.0,5.5,2.1,banana
5.7,2.5,5.0,2.0,banana
5.8,2.8,5.1,2.4,banana

2 代码

# -*- coding: UTF-8 -*-

import math

import csv

import random

import 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 生成0和1的随机浮点数

                    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   # 初始值为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('predictions: ' + repr(predictions))        返回一个它在python中的描述

            # print('>predicted=' + repr(result) + ', actual=' + repr(testSet[x][-1]))

        # print(correct)

        accuracy = self.getAccuracy(testSet,predictions)

        print('Accuracy: ' + repr(accuracy) + '%')

if __name__ == '__main__':

    a = KNearestNeighbor()

    a.Run()