from numpy import *import operatorfrom mpl_toolkits.mplot3d import Axes3Dimport matplotlib.pyplot as pltfrom matplotlib import cm#载入数据def file2matrix(fileName):file = open(fileName)allLines = file.readlines()row = len(allLines)dataSet = zeros((row, 4))labels = []index = 0 for line in allLines:line = line.strip()listFromLine = line.split(',')dataSet[index, :] = listFromLine[0:4]labels.append(listFromLine[-1]) #取最后一维为标签 index += 1 return dataSet, labels #数据集和标签分开 def kNN(x, dataSet, labels, k):dataSetSize = dataSet.shape[0]distance1 = tile(x, (dataSetSize,1)) - dataSet #欧氏距离计算开始 distance2 = distance1 ** 2 #每个元素平方 distance3 = distance2.sum(axis=1) #矩阵每行相加 distance4 = distance3 ** 0.5 #欧氏距离计算结束 sortedIndex = distance4.argsort() #返回从小到大排序的索引 classCount = {}for i in range (k): #统计前k个数据类的数量 label = labels[sortedIndex[i]]classCount[label] = classCount.get(label,0) + 1 sortedClassCount = sorted(classCount.items(), key=operator.itemgetter(1), reverse=True)#从大到小按类别数目排序 print(sortedClassCount)return sortedClassCount[0][0]def kNN_test():testRatio = 0.1 #取数据集的前0.1为测试数据 dataSet, labels = file2matrix('irisdata_test.txt')row = dataSet.shape[0] #数据集的行数，即数据集的总的样本数 testNum = int(row * testRatio)error = 0.0 #判断错误的个数 for i in range (testNum):result = kNN(dataSet[i, :], dataSet[testNum:row, :], labels[testNum:row], 3)print('返回的结果是: %s, 真实结果是: %s' % (result, labels[i]))if result != labels[i]:error += 1.0 print('错误率为: %f' % (error/float(testNum)))#画出散点图def scatter():dataSet, labels = file2matrix('irisdata.txt')fig = plt.figure()ax = fig.add_subplot(111, projection='3d')type1 = ax.scatter(dataSet[0:50, 0], dataSet[0:50, 1], dataSet[0:50, 2], c='r', marker='^')type2 = ax.scatter(dataSet[50:100, 0], dataSet[50:100, 1], dataSet[50:100, 2], c='b', marker='o')type3 = ax.scatter(dataSet[100:150, 0], dataSet[100:150, 1], dataSet[100:150, 2], c='y', marker='x')ax.legend((type1, type2, type3), ('Iris-setosa', 'Iris-versicolor', 'Iris-virginica'))ax.set_xlabel('X Label')ax.set_ylabel('Y Label')ax.set_zlabel('Z Label')plt.show()kNN_test()scatter()
利用kNN算法对iris数据集进行分类，以前10%的数据作为测试数据，后90%的数据作为样本数据集 。由于iris数据集原本按照三种花的顺序排列，在测试时前10%全为第一种花，不能完整测试kNN的分类功能 。故将后两种花随机挑选数据各5项放入前10%，需要文件irisdata_test.txt后面的散点图将iris数据集的前三维数据画出，需要文件irisdata.txt
