1主要步骤：

import csvimport randomimport mathimport operatordef loadDataset(filename, split, trainingSet=[], testSet=[]):    with open(filename, 'rb') 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:            #if random.randrange(len(trainingSet)) < 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 neighborsdef 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):    correct = 0    for x in range(len(testSet)):        #print 'test'        # test = testSet[x][-1]        # print test        # print 'pre'        # pre = predictions[x]        # print pre    print ('test: ' + repr(testSet[x][-1])) repr(testSet[x][-1])    print ('pre: ' + repr(predictions[x]))    # if testSet[z][-1] == predictions[z]:    #     correct += 1    return (correct/float(len(testSet)))*100.0def main():    #prepare data    """    :rtype: object    """    trainingSet = []    testSet = []    split = 0.70    loadDataset(r'/home/zhoumiao/ML/02KNearestNeighbor/irisdata.txt', split, trainingSet, testSet)    print 'Train set: ' + repr(len(trainingSet))    print 'Test set: ' + 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 ('test: ' + repr(testSet))        print ('predictions: ' + repr(predictions))        print ('>predicted=' + repr(result) + ', actual=' + repr(testSet[x][-1]))        if result == testSet[x][-1]:            correct.append(x)            # print "len:"            # print len(testSet)            # print "correct:"            # print len(correct)    accuracy = (len(correct)/float(len(testSet)))*100.0    print('Accuracy: ' + repr(accuracy) + '%')if __name__ == '__main__':main()