深度学习最邻近规则 python实现

1.

# -*- coding: utf-8 -*- """ Created on Wed May 11 22:52:29 2016    @author: sanmao """    import random import math import operator    def loadDataset(filename,split,trainingSet=[],testSet=[]):        with open(filename,'rb') as csvfile:            lines = csv.reader(csvfile)            dataest = 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])    def euclideanDistance(instance1,instance2,length):        distance = 0        for x in range(length):            distance += pow((instance1[x] - instance2[x]),2)        return math.sqrt(distance)    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))        distances.sort(key=operator.itemgetter(1))        neighbors = []        for x in range(k):            neighbors.append(distances[x][0])        return neighbors    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.iteritems(),key=operator.itemgetter(1),reverse=True)        return sortedVotes[0][0]         def getAccuracy(testSet,predictions):        corrrect = 0        for x in range(len(testSet)):            if testSet[x][-1] == predictions[x]:                correct += 1        return (corrrect/float(len(testSet))) * 100.0         def main():        trainingSet=[]        testSet=[]        split = 0.67        loadDataset(r'D',split,trainingSet,testSet)        print('Train set:'+repr(len(trainingSet)))        print('Test set:'+repr(len(testSet)))        predictions=[]        k = 3        for x in reange(len(testSet)):            neighbors = getNeighbors(trainingSet,testSet[x],k)            result = getResponse(neighbors)            predictions.append(result)            print('>predicted='+repr(result)+',actual='+repr(testSet[x][-1]))            accuracy = getAccuracy(testSet,predictions)            print('Accuracy:'+repr(accuracy)+'%')             main()