{"id":63298,"date":"2026-01-21T19:32:59","date_gmt":"2026-01-21T11:32:59","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/63298.html"},"modified":"2026-01-21T19:32:59","modified_gmt":"2026-01-21T11:32:59","slug":"k%e8%bf%91%e9%82%bb%e7%ae%97%e6%b3%95%e8%ae%b2%e8%a7%a3","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/63298.html","title":{"rendered":"k\u8fd1\u90bb\u7b97\u6cd5\u8bb2\u89e3"},"content":{"rendered":"
\u7b97\u6cd5\u5b9a\u4e49
\n\u00a0 k-\u8fd1\u90bb\u7b97\u6cd5（k-Nearest Neighbors, k-NN） \u662f\u4e00\u79cd\u7b80\u5355\u800c\u5e38\u7528\u7684 \u76d1\u7763\u5b66\u4e60\u7b97\u6cd5，\u4e3b\u8981\u7528\u4e8e\u5206\u7c7b\u548c\u56de\u5f52\u4efb\u52a1\u3002\u5b83\u7684\u6838\u5fc3\u601d\u60f3\u662f：\u901a\u8fc7\u8ba1\u7b97\u5f85\u5206\u7c7b\u6837\u672c\u4e0e\u8bad\u7ec3\u96c6\u4e2d\u6240\u6709\u6837\u672c\u7684\u8ddd\u79bb，\u627e\u5230\u8ddd\u79bb\u6700\u8fd1\u7684 k \u4e2a\u6837\u672c，\u7136\u540e\u6839\u636e\u8fd9 k \u4e2a\u6837\u672c\u7684\u7c7b\u522b\u6216\u503c\u6765\u9884\u6d4b\u5f85\u5206\u7c7b\u6837\u672c\u7684\u7c7b\u522b\u6216\u503c\u3002<\/h5>\n

\u4e00\u3001\u95ee\u9898\u5f15\u5165<\/h4>\n

\u6d77\u4f26\u4e00\u76f4\u4f7f\u7528\u5728\u7ebf\u7ea6\u4f1a\u7f51\u7ad9\u5bfb\u627e\u9002\u5408\u81ea\u5df1\u7684\u7ea6\u4f1a\u5bf9\u8c61\u3002\u5979\u66fe\u4ea4\u5f80\u8fc7\u4e09\u79cd\u7c7b\u578b\u7684\u4eba：<\/p>\n

– \u4e0d\u559c\u6b22\u7684\u4eba<\/p>\n

– \u4e00\u822c\u559c\u6b22\u7684\u4eba<\/p>\n

– \u975e\u5e38\u559c\u6b22\u7684\u4eba<\/p>\n

\u8fd9\u4e9b\u4eba\u5305\u542b\u4ee5\u4e0b\u4e09\u79cd\u7279\u5f81<\/p>\n

1. \u6bcf\u5e74\u83b7\u5f97\u7684\u98de\u884c\u5e38\u5ba2\u91cc\u7a0b\u6570<\/p>\n

2. \u73a9\u89c6\u9891\u6e38\u620f\u6240\u8017\u65f6\u95f4\u767e\u5206\u6bd4<\/p>\n

3. \u6bcf\u5468\u6d88\u8d39\u7684\u51b0\u6dc7\u6dcb\u516c\u5347\u6570<\/p>\n

\u8be5\u7f51\u7ad9\u73b0\u5728\u9700\u8981\u5c3d\u53ef\u80fd\u5411\u6d77\u4f26\u63a8\u8350\u5979\u559c\u6b22\u7684\u4eba，\u9700\u8981\u6211\u4eec\u8bbe\u8ba1\u4e00\u4e2a\u5206\u7c7b\u5668，\u6839\u636e\u7528\u6237\u7684\u4ee5\u4e0a\u4e09\u79cd\u7279\u5f81，\u8bc6\u522b\u51fa\u662f\u5426\u8be5\u5411\u6d77\u4f26\u63a8\u8350\u3002<\/p>\n

\u4e8c，\u89e3\u51b3\u601d\u8def<\/h4>\n

（1）\u5199\u51fak\u8fd1\u90bb\u7b97\u6cd5：<\/p>\n

\u8ba1\u7b97\u5f85\u5206\u7c7b\u6837\u672c\u4e0e\u8bad\u7ec3\u96c6\u4e2d\u6bcf\u4e2a\u6837\u672c\u7684\u8ddd\u79bb（\u53ef\u7528\u6b27\u6c0f\u8ddd\u79bb\u6216\u66fc\u54c8\u987f\u8ddd\u79bb\u516c\u5f0f）；\u9009\u62e9k\u4e2a\u6700\u8fd1\u90bb\u5c45：\u6839\u636e\u8ba1\u7b97\u7684\u8ddd\u79bb，\u9009\u62e9\u8ddd\u79bb\u6700\u8fd1\u7684 k \u4e2a\u8bad\u7ec3\u6837\u672c\u3002\u6295\u7968\u6216\u5e73\u5747：\u5206\u7c7b\u4efb\u52a1：\u7edf\u8ba1 k \u4e2a\u6700\u8fd1\u90bb\u5c45\u7684\u7c7b\u522b，\u9009\u62e9\u51fa\u73b0\u6b21\u6570\u6700\u591a\u7684\u7c7b\u522b\u4f5c\u4e3a\u9884\u6d4b\u7ed3\u679c\u3002\u56de\u5f52\u4efb\u52a1：\u8ba1\u7b97 k \u4e2a\u6700\u8fd1\u90bb\u5c45\u7684\u503c\u7684\u5e73\u5747\u503c\u4f5c\u4e3a\u9884\u6d4b\u7ed3\u679c\u3002\u5177\u4f53\u4ee3\u7801\u5982\u4e0b：<\/p>\n

def classify0(inX,dataSet,labels,k):<\/p>\n

\u00a0 \u00a0 dataSetSize = dataSet.shape[0]#\u83b7\u53d6\u6837\u672c\u884c\u6570<\/p>\n

\u00a0 \u00a0 diffMat = np.tile(inX,(dataSetSize,1))-dataSet#\u8ba1\u7b97\u8f93\u5165\u6837\u672c inX \u4e0e\u6570\u636e\u96c6\u4e2d\u6bcf\u4e2a\u6837\u672c\u7684\u5dee\u503c\u3002<\/p>\n

\u00a0 \u00a0 sqDiffMat = diffMat ** 2<\/p>\n

\u00a0 \u00a0 sqDistances=sqDiffMat.sum(axis=1)#\u8ba1\u7b97\u6bcf\u4e2a\u6837\u672c\u4e0e\u8f93\u5165\u6837\u672c inX \u7684\u6b27\u6c0f\u8ddd\u79bb\u7684\u5e73\u65b9\u3002<\/p>\n

\u00a0 \u00a0 distances = sqDistances ** 0.5<\/p>\n

\u00a0 \u00a0 sortedDistIndicies=distances.argsort()#\u5bf9\u8ddd\u79bb\u8fdb\u884c\u6392\u5e8f，\u5e76\u8fd4\u56de\u6392\u5e8f\u540e\u7684\u7d22\u5f15\u3002<\/p>\n

\u00a0 \u00a0 classCount={}<\/p>\n

\u00a0 \u00a0 for i in range (k):<\/p>\n

\u00a0 \u00a0 \u00a0 \u00a0 voteIlabel = labels[sortedDistIndicies[i]]<\/p>\n

\u00a0 \u00a0 \u00a0 \u00a0 classCount[voteIlabel] = classCount.get(voteIlabel,0)+1#\u7edf\u8ba1\u6bcf\u4e2a\u7c7b\u522b\u7684\u6295\u7968\u6570\u3002<\/p>\n

\u00a0 \u00a0 sortedClassCount = sorted(classCount.items(),key=operator.itemgetter(1),reverse=True)#\u5bf9 classCount \u5b57\u5178\u6309\u6295\u7968\u6570\u4ece\u9ad8\u5230\u4f4e\u6392\u5e8f\u3002<\/p>\n

\u00a0 \u00a0 return sortedClassCount[0][0]#\u8fd4\u56de\u6295\u7968\u6570\u6700\u591a\u7684\u7c7b\u522b\u3002<\/p>\n

（2）\u5c06\u6587\u672c\u8bb0\u5f55\u8f6c\u5316\u4e3anumpy\u7684\u89e3\u6790\u7a0b\u5e8f：<\/p>\n

\u00a0 \u5bfc\u5165\u5fc5\u8981\u7684\u5e93\u5e76\u5b9a\u4e49\u51fd\u6570 file2matrix \u7136\u540e\u6253\u5f00\u6587\u4ef6\u5e76\u8bfb\u53d6\u6240\u6709\u884c，\u8ba1\u7b97\u6587\u4ef6\u7684\u884c\u6570\u548c\u904d\u5386\u6bcf\u4e00\u884c\u6570\u636e\u5e76\u5904\u7406\u521d\u59cb\u5316\u7279\u5f81\u77e9\u9635\u548c\u6807\u7b7e\u5411\u91cf，\u6700\u540e\u8fd4\u56de\u7279\u5f81\u77e9\u9635\u548c\u6807\u7b7e\u5411\u91cf\u3002\u5177\u4f53\u4ee3\u7801\u5982\u4e0b：<\/p>\n

def file2matrix(datingTestSet):<\/p>\n

\u00a0 \u00a0 fr=open(datingTestSet)<\/p>\n

\u00a0 \u00a0 arrayOLines=fr.readlines()<\/p>\n

\u00a0 \u00a0 numberOfLines=len(arrayOLines)#\u8ba1\u7b97\u6587\u4ef6\u7684\u884c\u6570，\u5373\u5217\u8868 arrayOLines \u7684\u957f\u5ea6，\u5e76\u5c06\u5176\u8d4b\u503c\u7ed9\u53d8\u91cf numberOfLines\u3002<\/p>\n

\u00a0 \u00a0 returnMat = np.zeros((numberOfLines,3))#\u4f7f\u7528 np.zeros \u521b\u5efa\u4e00\u4e2a\u5927\u5c0f\u4e3a (numberOfLines, 3) \u7684\u5168\u96f6\u77e9\u9635 returnMat，\u7528\u4e8e\u5b58\u50a8\u7279\u5f81\u6570\u636e\u3002<\/p>\n

\u00a0 \u00a0 classLabelVector=[]<\/p>\n

\u00a0 \u00a0 index=0<\/p>\n

\u00a0 \u00a0 for line in arrayOLines:<\/p>\n

\u00a0 \u00a0 \u00a0 \u00a0 line=line.strip()#\u4f7f\u7528 strip() \u65b9\u6cd5\u53bb\u9664\u5f53\u524d\u884c\u5f00\u5934\u548c\u7ed3\u5c3e\u7684\u7a7a\u767d\u5b57\u7b26（\u5982\u7a7a\u683c\u3001\u6362\u884c\u7b26\u7b49）\u3002<\/p>\n

\u00a0 \u00a0 \u00a0 \u00a0 listFromLine=line.split('\\\\t')#\u4f7f\u7528 split('\\\\t') \u65b9\u6cd5\u6309\u5236\u8868\u7b26 \\\\t \u5c06\u5f53\u524d\u884c\u5206\u5272\u6210\u4e00\u4e2a\u5217\u8868 listFromLine\u3002<\/p>\n

\u00a0 \u00a0 \u00a0 \u00a0 returnMat[index,:]=listFromLine[0:3]#\u5c06 listFromLine \u4e2d\u7684\u524d 3 \u4e2a\u5143\u7d20（\u7279\u5f81\u503c）\u8d4b\u503c\u7ed9 returnMat \u7684\u7b2c index \u884c\u3002<\/p>\n

\u00a0 \u00a0 \u00a0 \u00a0 classLabelVector.append(listFromLine[-1])#\u5c06 listFromLine \u7684\u6700\u540e\u4e00\u4e2a\u5143\u7d20（\u6807\u7b7e）\u6dfb\u52a0\u5230 classLabelVector \u4e2d\u3002<\/p>\n

\u00a0 \u00a0 \u00a0 \u00a0 index+=1<\/p>\n

\u00a0 \u00a0 return returnMat,classLabelVecto<\/p>\n

（3）\u5f52\u4e00\u5316\u7279\u5f81\u503c<\/p>\n

\u4e3b\u8981\u516c\u5f0f：newValue=(oldValue-min)\/(max-min)<\/p>\n

\u00a0\u5b9a\u4e49\u51fd\u6570autonorm，\u8ba1\u7b97\u6bcf\u5217\u7684\u6700\u5c0f\u503c\u548c\u6700\u5927\u503c\u5e76\u521d\u59cb\u5316\u5f52\u4e00\u5316\u540e\u7684\u6570\u636e\u96c6\u7528 np.tile(minVals, (m, 1)) \u5c06 minVals \u6309\u884c\u590d\u5236 m \u6b21，\u751f\u6210\u4e00\u4e2a\u4e0e dataSet \u5f62\u72b6\u76f8\u540c\u7684\u77e9\u9635\u3002\u5c06 dataSet \u4e2d\u7684\u6bcf\u4e2a\u503c\u51cf\u53bb\u5bf9\u5e94\u5217\u7684\u6700\u5c0f\u503c，\u5f97\u5230\u5e73\u79fb\u540e\u7684\u6570\u636e\u3002\u6700\u540e\u5f52\u4e00\u5316\u6570\u636e\u5e76\u8fd4\u56de\u3002\u5177\u4f53\u4ee3\u7801\u5982\u4e0b：<\/p>\n

def autoNorm(dataSet):<\/p>\n

\u00a0 \u00a0 minVals=dataSet.min(0)<\/p>\n

\u00a0 \u00a0 maxVals=dataSet.max(0)<\/p>\n

\u00a0 \u00a0 ranges=maxVals-minVals<\/p>\n

\u00a0 \u00a0 normDataSet=np.zeros(np.shape(dataSet))#\u521b\u5efa\u4e00\u4e2a\u4e0e dataSet \u5f62\u72b6\u76f8\u540c\u7684\u5168\u96f6\u77e9\u9635 normDataSet，\u7528\u4e8e\u5b58\u50a8\u5f52\u4e00\u5316\u540e\u7684\u6570\u636e\u3002<\/p>\n

\u00a0 \u00a0 m=dataSet.shape[0]#\u83b7\u53d6\u6570\u636e\u96c6\u7684\u884c\u6570（\u6837\u672c\u6570\u91cf），\u8d4b\u503c\u7ed9\u53d8\u91cf m\u3002<\/p>\n

\u00a0 \u00a0 normDataSet=dataSet-np.tile(minVals,(m,1))#\u5c06\u6570\u636e\u96c6\u4e2d\u7684\u6bcf\u4e2a\u503c\u51cf\u53bb\u5bf9\u5e94\u5217\u7684\u6700\u5c0f\u503c，\u5f97\u5230\u5e73\u79fb\u540e\u7684\u6570\u636e\u3002<\/p>\n

\u00a0 \u00a0 normDataSet=normDataSet \/np.tile(ranges,(m,1))#\u5c06\u5e73\u79fb\u540e\u7684\u6570\u636e\u9664\u4ee5\u5bf9\u5e94\u5217\u7684\u8303\u56f4，\u5f97\u5230\u5f52\u4e00\u5316\u540e\u7684\u6570\u636e\u3002<\/p>\n

\u00a0 \u00a0 return normDataSet,ranges,minVals<\/p>\n

（4）\u5206\u7c7b\u5668\u9488\u5bf9\u7ea6\u4f1a\u7f51\u7ad9\u4ee3\u7801\u00a0<\/p>\n

\u00a0 \u5b9a\u4e49 hoRatio = 0.10，\u5c06\u6570\u636e\u96c6\u7684 10% \u4f5c\u4e3a\u6d4b\u8bd5\u96c6，\u5269\u4f59\u7684 90% \u4f5c\u4e3a\u8bad\u7ec3\u96c6\u7528 file2matrix \u51fd\u6570\u4ece\u6587\u4ef6 datingTestSet.txt \u4e2d\u52a0\u8f7d\u6570\u636e；\u8c03\u7528 autoNorm \u51fd\u6570\u5bf9\u7279\u5f81\u77e9\u9635 datingDataMat \u8fdb\u884c\u5f52\u4e00\u5316\u5904\u7406，\u5f97\u5230\u5f52\u4e00\u5316\u540e\u7684\u7279\u5f81\u77e9\u9635 normMat\u3002；\u83b7\u53d6\u5f52\u4e00\u5316\u540e\u6570\u636e\u96c6\u7684\u884c\u6570 m\u3002\u8ba1\u7b97\u6d4b\u8bd5\u96c6\u7684\u6570\u91cf numTestVecs = int(m * hoRatio)\u3002；\u904d\u5386\u6d4b\u8bd5\u96c6\u4e2d\u7684\u6bcf\u4e00\u4e2a\u6837\u672c：\u8c03\u7528 classify0 \u51fd\u6570\u5bf9\u5f53\u524d\u6d4b\u8bd5\u6837\u672c\u8fdb\u884c\u5206\u7c7b，\u6700\u540e\u8ba1\u7b97\u5206\u7c7b\u5668\u7684\u603b\u9519\u8bef\u7387\u3002\u5177\u4f53\u4ee3\u7801\u5982\u4e0b：<\/p>\n

def datingClassTest():<\/p>\n

\u00a0 \u00a0 hoRatio=0.10#\u610f\u5473\u7740\u6570\u636e\u96c6\u4e2d\u524d 10% \u7684\u6837\u672c\u5c06\u4f5c\u4e3a\u6d4b\u8bd5\u96c6，\u5269\u4f59\u7684 90% \u4f5c\u4e3a\u8bad\u7ec3\u96c6\u3002<\/p>\n

\u00a0 \u00a0 datingDataMat,datingLabels=file2matrix('datingTestSet.txt')#\u8c03\u7528 file2matrix \u51fd\u6570\u4ece\u6587\u4ef6 datingTestSet.txt \u4e2d\u52a0\u8f7d\u6570\u636e\u3002<\/p>\n

\u00a0 \u00a0 normMat,ranges,minVals=autoNorm(datingDataMat)#\u8c03\u7528 autoNorm \u51fd\u6570\u5bf9\u7279\u5f81\u77e9\u9635 datingDataMat \u8fdb\u884c\u5f52\u4e00\u5316\u3002<\/p>\n

\u00a0 \u00a0 m=normMat.shape[0]<\/p>\n

\u00a0 \u00a0 numTestVecs=int(m*hoRatio)<\/p>\n

\u00a0 \u00a0 errorCount=0.0<\/p>\n

\u00a0 \u00a0 for i in range(numTestVecs):<\/p>\n

\u00a0 classifierResult=classify0(normMat[i,:],normMat[numTestVecs:m,:],datingLabels[numTestVecs:m],3)#\u8c03\u7528 classify0 \u51fd\u6570\u5bf9\u5f53\u524d\u6d4b\u8bd5\u6837\u672c normMat[i, :] \u8fdb\u884c\u5206\u7c7b<\/p>\n

\u00a0 \u00a0 \u00a0 \u00a0 print(f"the classifier came back with :{classifierResult} ,the real answer is :{datingLabels[i]} " \u00a0)#\u5982\u679c\u5206\u7c7b\u5668\u7684\u9884\u6d4b\u7ed3\u679c\u4e0e\u771f\u5b9e\u6807\u7b7e\u4e0d\u4e00\u81f4，\u5219\u5c06\u9519\u8bef\u8ba1\u6570\u5668 errorCount \u52a0 1\u3002<\/p>\n

\u00a0 \u00a0 \u00a0 \u00a0 if(classifierResult!=datingLabels[i]):<\/p>\n

\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 errorCount += 1.0<\/p>\n

\u00a0 \u00a0 print (f"the total error rate is :{errorCount\/float(numTestVecs)}" )<\/p>\n

（5）\u7ea6\u4f1a\u7f51\u7ad9\u9884\u6d4b\u51fd\u6570<\/p>\n

\u00a0 \u5b9a\u4e49\u4e09\u4e2a\u7ed3\u679c\u6807\u7b7e，\u8c03\u7528 file2matrix \u51fd\u6570\u4ece\u6587\u4ef6 datingTestSet.txt \u4e2d\u52a0\u8f7d\u6570\u636e，\u7136\u540e\u8c03\u7528 autoNorm \u51fd\u6570\u5bf9\u7279\u5f81\u77e9\u9635 datingDataMat \u8fdb\u884c\u5f52\u4e00\u5316\u5904\u7406\u3002\u5c06\u7528\u6237\u8f93\u5165\u7684\u7279\u5f81\u503c\u7ec4\u5408\u6210\u4e00\u4e2a numpy \u6570\u7ec4 inArr\u3002\u5bf9 inArr \u8fdb\u884c\u5f52\u4e00\u5316\u5904\u7406，\u4f7f\u5176\u4e0e\u8bad\u7ec3\u6570\u636e\u7684\u8303\u56f4\u4e00\u81f4\u3002\u8c03\u7528 classify0 \u51fd\u6570\u5bf9\u5f52\u4e00\u5316\u540e\u7684\u7528\u6237\u8f93\u5165\u5411\u91cf\u8fdb\u884c\u5206\u7c7b\u3002\u6839\u636e classifierResult \u7684\u503c，\u4ece resultList \u4e2d\u83b7\u53d6\u5bf9\u5e94\u7684\u5206\u7c7b\u7ed3\u679c\u6807\u7b7e\u3002\u5177\u4f53\u4ee3\u7801\u5982\u4e0b：<\/p>\n

def classifyPerson():<\/p>\n

\u00a0 \u00a0 resultList = ['not at all','in small doses','in large doses']<\/p>\n

\u00a0 \u00a0 percenTats=float(input("percentage of time spent playing video games?"))<\/p>\n

\u00a0 \u00a0 ffMiles=float(input("frequent flier miles earned per year?"))<\/p>\n

\u00a0 \u00a0 iceCream = float(input("liters of ice cream consumed per year?"))<\/p>\n

\u00a0 \u00a0 datingDataMat,datingLabels=file2matrix('datingTestSet.txt')<\/p>\n

\u00a0 \u00a0 normMat,ranges,minVals=autoNorm(datingDataMat)#\u8c03\u7528 autoNorm \u51fd\u6570\u5bf9\u7279\u5f81\u77e9\u9635 datingDataMat \u8fdb\u884c\u5f52\u4e00\u5316\u3002<\/p>\n

\u00a0 \u00a0 inArr=np.array([ffMiles,percenTats,iceCream])<\/p>\n

\u00a0 \u00a0 classifierResult=classify0((inArr-minVals)\/ranges,normMat,datingLabels,3)#\u5bf9\u8f93\u5165\u5411\u91cf inArr \u8fdb\u884c\u5f52\u4e00\u5316：\u8c03\u7528 classify0 \u51fd\u6570\u5bf9\u5f52\u4e00\u5316\u540e\u7684\u8f93\u5165\u5411\u91cf\u8fdb\u884c\u5206\u7c7b<\/p>\n

\u00a0 \u00a0 print(f"you will probably like this person:{resultList[classifierResult-1]}")<\/p>\n

（6）main\u51fd\u6570（\u6ee1\u8db3\u9898\u76ee\u8981\u6c42\u5373\u53ef）<\/p>\n

def main():<\/p>\n

\u00a0 \u00a0 print("\u6b22\u8fce\u4f7f\u7528\u7ea6\u4f1a\u63a8\u8350\u7cfb\u7edf！")<\/p>\n

\u00a0 \u00a0 print("\u8bf7\u9009\u62e9\u529f\u80fd：")<\/p>\n

\u00a0 \u00a0 print("1. \u6d4b\u8bd5\u5206\u7c7b\u5668\u5728\u7ea6\u4f1a\u6570\u636e\u96c6\u4e0a\u7684\u6027\u80fd\u3002")<\/p>\n

\u00a0 \u00a0 print("2. \u6839\u636e\u60a8\u7684\u559c\u597d\u9884\u6d4b\u662f\u5426\u559c\u6b22\u67d0\u4eba\u3002")<\/p>\n

\u00a0 \u00a0 print("3. \u9000\u51fa\u7a0b\u5e8f\u3002")<\/p>\n

\u00a0 \u00a0 while True:<\/p>\n

\u00a0 \u00a0 \u00a0 \u00a0 choice = input("\u8bf7\u8f93\u5165\u60a8\u7684\u9009\u62e9（1\u30012 \u6216 3）：")<\/p>\n

\u00a0 \u00a0 \u00a0 \u00a0 if choice == '1':<\/p>\n

\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 # \u6d4b\u8bd5\u5206\u7c7b\u5668<\/p>\n

\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 print("\\\\n\u6b63\u5728\u6d4b\u8bd5\u5206\u7c7b\u5668\u5728\u7ea6\u4f1a\u6570\u636e\u96c6\u4e0a\u7684\u6027\u80fd…")<\/p>\n

\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 datingClassTest()<\/p>\n

\u00a0 \u00a0 \u00a0 \u00a0 elif choice == '2':<\/p>\n

\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 # \u9884\u6d4b\u7ea6\u4f1a\u7f51\u7ad9\u7ed3\u679c<\/p>\n

\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 print("\\\\n\u6b63\u5728\u6839\u636e\u60a8\u7684\u559c\u597d\u9884\u6d4b\u662f\u5426\u559c\u6b22\u67d0\u4eba…")<\/p>\n

\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 classifyPerson()<\/p>\n

\u00a0 \u00a0 \u00a0 \u00a0 elif choice == '3':<\/p>\n

\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 # \u9000\u51fa\u7a0b\u5e8f<\/p>\n

\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 print("\\\\n\u6b63\u5728\u9000\u51fa\u7a0b\u5e8f\u3002\u518d\u89c1！")<\/p>\n

\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 break<\/p>\n

\u00a0 \u00a0 \u00a0 \u00a0 else:<\/p>\n

\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 print("\\\\n\u65e0\u6548\u7684\u9009\u62e9，\u8bf7\u8f93\u5165 1\u30012 \u6216 3\u3002")<\/p>\n

# \u8c03\u7528 main \u51fd\u6570<\/p>\n

if __name__ == "__main__":<\/p>\n

\u00a0 \u00a0 main()<\/p>\n

\u4e09，\u8f93\u51fa\u7ed3\u679c<\/h4>\n

\"\"<\/p>\n

\u00a0\u56db，\u5b9e\u9a8c\u603b\u7ed3<\/h4>\n

1. \u6570\u636e\u51c6\u5907：\u8fd9\u5305\u62ec\u6536\u96c6\u3001\u6e05\u6d17\u548c\u9884\u5904\u7406\u6570\u636e\u3002**\u9884\u5904\u7406\u53ef\u80fd\u5305\u62ec\u5f52\u4e00\u5316\u6216\u6807\u51c6\u5316\u7279\u5f81**，\u4ee5\u786e\u4fdd\u6240\u6709\u7279\u5f81\u5728\u8ba1\u7b97\u8ddd\u79bb\u65f6\u5177\u6709\u76f8\u7b49\u7684\u6743\u91cd\u3002<\/p>\n

| \u00a0 \u00a0 \u00a0| \u73a9\u89c6\u9891\u6e38\u620f\u6240\u8017\u65f6\u95f4\u767e\u5206\u6bd4 | \u6bcf\u5e74\u83b7\u5f97\u7684\u98de\u884c\u5e38\u5ba2\u91cc\u7a0b\u6570 | \u6bcf\u5468\u6d88\u8d39\u7684\u51b0\u6dc7\u6dcb\u7684\u516c\u5347\u6570 | \u6837\u672c\u5206\u7c7b |<\/p>\n

| —- | ———————— | ———————— | ———————— | ——– |<\/p>\n

| 1 \u00a0 \u00a0| 0.8 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0| 400 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0| 0.5 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0| 1 \u00a0 \u00a0 \u00a0 \u00a0|<\/p>\n

| 2 \u00a0 \u00a0| 12 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 | 134000 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 | 0.9 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0| 3 \u00a0 \u00a0 \u00a0 \u00a0|<\/p>\n

| 3 \u00a0 \u00a0| 0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0| 20000 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0| 1.1 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0| 2 \u00a0 \u00a0 \u00a0 \u00a0|<\/p>\n

| 4 \u00a0 \u00a0| 67 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 | 32000 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0| 0.1 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0| 2 \u00a0 \u00a0 \u00a0 \u00a0|<\/p>\n

\u6211\u4eec\u5f88\u5bb9\u6613\u53d1\u73b0，\u5f53\u8ba1\u7b97\u6837\u672c\u4e4b\u95f4\u7684\u8ddd\u79bb\u65f6\u6570\u5b57\u5dee\u503c\u6700\u5927\u7684\u5c5e\u6027\u5bf9\u8ba1\u7b97\u7ed3\u679c\u7684\u5f71\u54cd\u6700\u5927，\u4e5f\u5c31\u662f\u8bf4，\u6bcf\u5e74\u83b7\u53d6\u7684\u98de\u884c\u5e38\u5ba2\u91cc\u7a0b\u6570\u5bf9\u4e8e\u8ba1\u7b97\u7ed3\u679c\u7684\u5f71\u54cd\u5c06\u8fdc\u8fdc\u5927\u4e8e\u4e0a\u8868\u4e2d\u5176\u4ed6\u4e24\u4e2a\u7279\u5f81-\u73a9\u89c6\u9891\u6e38\u620f\u6240\u8017\u65f6\u95f4\u5360\u6bd4\u548c\u6bcf\u5468\u6d88\u8d39\u51b0\u6dc7\u6dcb\u516c\u65a4\u6570\u7684\u5f71\u54cd\u3002\u800c\u4ea7\u751f\u8fd9\u79cd\u73b0\u8c61\u7684\u552f\u4e00\u539f\u56e0，\u4ec5\u4ec5\u662f\u56e0\u4e3a\u98de\u884c\u5e38\u5ba2\u91cc\u7a0b\u6570\u8fdc\u5927\u4e8e\u5176\u4ed6\u7279\u5f81\u503c\u3002\u4f46\u6d77\u4f26\u8ba4\u4e3a\u8fd9\u4e09\u79cd\u7279\u5f81\u662f\u540c\u7b49\u91cd\u8981\u7684，\u56e0\u6b64\u4f5c\u4e3a\u4e09\u4e2a\u7b49\u6743\u91cd\u7684\u7279\u5f81\u4e4b\u4e00，\u98de\u884c\u5e38\u5ba2\u91cc\u7a0b\u6570\u5e76\u4e0d\u5e94\u8be5\u5982\u6b64\u4e25\u91cd\u5730\u5f71\u54cd\u5230\u8ba1\u7b97\u7ed3\u679c\u3002<\/p>\n

\u5728\u5904\u7406\u8fd9\u79cd\u4e0d\u540c\u53d6\u503c\u8303\u56f4\u7684\u7279\u5f81\u503c\u65f6，\u6211\u4eec\u901a\u5e38\u91c7\u7528\u7684\u65b9\u6cd5\u662f\u5c06\u6570\u503c\u5f52\u4e00\u5316，\u5982\u5c06\u53d6\u503c\u8303\u56f4\u5904\u7406\u4e3a０\u5230１\u6216\u8005-１\u5230１\u4e4b\u95f4\u3002**\u4e0b\u9762\u7684\u516c\u5f0f\u53ef\u4ee5\u5c06\u4efb\u610f\u53d6\u503c\u8303\u56f4\u7684\u7279\u5f81\u503c\u8f6c\u5316\u4e3a０\u5230１\u533a\u95f4\u5185\u7684\u503c：<\/p>\n

\n2. \u9009\u62e9\u8ddd\u79bb\u5ea6\u91cf\u65b9\u6cd5：\u786e\u5b9a\u7528\u4e8e\u6bd4\u8f83\u6837\u672c\u4e4b\u95f4\u76f8\u4f3c\u6027\u7684\u5ea6\u91cf\u65b9\u6cd5，\u5e38\u89c1\u7684\u5982\u6b27\u51e0\u91cc\u5f97\u8ddd\u79bb\u3001\u66fc\u54c8\u987f\u8ddd\u79bb\u7b49\u3002<\/p>\n

3. \u786e\u5b9aK\u503c：\u9009\u62e9\u4e00\u4e2aK\u503c**，\u5373\u5728\u5206\u7c7b\u6216\u56de\u5f52\u65f6\u5e94\u8003\u8651\u7684\u90bb\u5c45\u6570\u91cf\u3002\u8fd9\u662f\u4e00\u4e2a\u8d85\u53c2\u6570，\u53ef\u4ee5\u901a\u8fc7\u4ea4\u53c9\u9a8c\u8bc1\u7b49\u65b9\u6cd5\u6765\u9009\u62e9\u6700\u4f18\u7684K\u503c\u3002<\/p>\n

4. \u627e\u5230K\u4e2a\u6700\u8fd1\u90bb\u5c45**：\u5bf9\u4e8e\u6bcf\u4e00\u4e2a\u9700\u8981\u9884\u6d4b\u7684\u672a\u6807\u8bb0\u7684\u6837\u672c：<\/p>\n

– \u8ba1\u7b97\u8be5\u6837\u672c\u4e0e\u8bad\u7ec3\u96c6\u4e2d\u6240\u6709\u6837\u672c\u7684\u8ddd\u79bb\u3002<\/p>\n

– \u6839\u636e\u8ddd\u79bb\u5bf9\u5b83\u4eec\u8fdb\u884c\u6392\u5e8f\u3002<\/p>\n

– \u9009\u62e9\u8ddd\u79bb\u6700\u8fd1\u7684K\u4e2a\u6837\u672c<\/p>\n

5. \u9884\u6d4b：<\/p>\n

\u00a0 \u00a0– \u5bf9\u4e8e**\u5206\u7c7b\u4efb\u52a1**：\u67e5\u770bK\u4e2a\u6700\u8fd1\u90bb\u5c45\u4e2d\u6700\u5e38\u89c1\u7684\u7c7b\u522b，\u4f5c\u4e3a\u9884\u6d4b\u7ed3\u679c\u3002\u4f8b\u5982，\u5982\u679cK=3，\u5e76\u4e14\u4e09\u4e2a\u6700\u8fd1\u90bb\u5c45\u7684\u7c7b\u522b\u662f[1, 2, 1]，\u90a3\u4e48\u9884\u6d4b\u7ed3\u679c\u5c31\u662f\u7c7b\u522b1\u3002<\/p>\n

\u00a0 \u00a0– \u5bf9\u4e8e**\u56de\u5f52\u4efb\u52a1**：\u9884\u6d4b\u7ed3\u679c\u53ef\u4ee5\u662fK\u4e2a\u6700\u8fd1\u90bb\u5c45\u7684\u5e73\u5747\u503c\u6216\u52a0\u6743\u5e73\u5747\u503c\u3002<\/p>\n

6. \u8bc4\u4f30\u4f7f\u7528\u9002\u5f53\u7684\u8bc4\u4ef7\u6307\u6807（\u5982\u51c6\u786e\u7387\u3001\u5747\u65b9\u8bef\u5dee\u7b49）\u8bc4\u4f30\u6a21\u578b\u7684\u6027\u80fd\u3002<\/p>\n

7. \u4f18\u5316：\u57fa\u4e8e\u6027\u80fd\u8bc4\u4f30\u7ed3\u679c，\u53ef\u80fd\u9700\u8981\u8fd4\u56de\u5e76\u8c03\u6574\u67d0\u4e9b\u53c2\u6570，\u5982K\u503c\u3001\u8ddd\u79bb\u5ea6\u91cf\u65b9\u6cd5\u7b49，\u4ee5\u83b7\u5f97\u66f4\u597d\u7684\u6027\u80fd\u3002
\n\u00a0<\/p>\n","protected":false},"excerpt":{"rendered":"

\u7b97\u6cd5\u5b9a\u4e49k-\u8fd1\u90bb\u7b97\u6cd5（k-Nearest Neighbors, k-NN） \u662f\u4e00\u79cd\u7b80\u5355\u800c\u5e38\u7528\u7684 \u76d1\u7763\u5b66\u4e60\u7b97\u6cd5，\u4e3b\u8981\u7528\u4e8e\u5206\u7c7b\u548c\u56de\u5f52\u4efb\u52a1\u3002\u5b83\u7684\u6838\u5fc3\u601d\u60f3\u662f：\u901a\u8fc7\u8ba1\u7b97\u5f85\u5206\u7c7b\u6837\u672c\u4e0e\u8bad\u7ec3\u96c6\u4e2d\u6240\u6709\u6837\u672c\u7684\u8ddd\u79bb，\u627e\u5230\u8ddd\u79bb\u6700\u8fd1\u7684 k \u4e2a\u6837\u672c，\u7136\u540e\u6839\u636e\u8fd9 k \u4e2a\u6837\u672c\u7684\u7c7b\u522b\u6216\u503c\u6765\u9884\u6d4b\u5f85\u5206\u7c7b\u6837\u672c\u7684\u7c7b\u522b\u6216\u503c\u3002\u4e00\u3001\u95ee\u9898\u5f15\u5165\u6d77\u4f26\u4e00\u76f4\u4f7f\u7528\u5728\u7ebf\u7ea6\u4f1a\u7f51\u7ad9\u5bfb\u627e\u9002\u5408\u81ea\u5df1\u7684\u7ea6\u4f1a\u5bf9\u8c61\u3002\u5979\u66fe\u4ea4\u5f80\u8fc7\u4e09\u79cd\u7c7b\u578b\u7684\u4eba：- \u4e0d\u559c\u6b22\u7684\u4eba-<\/p>\n","protected":false},"author":2,"featured_media":63297,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[427,3736],"topic":[],"class_list":["post-63298","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-server","tag-427","tag-3736"],"yoast_head":"\nk\u8fd1\u90bb\u7b97\u6cd5\u8bb2\u89e3 - 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