Python-实验13-程序应用开发-乳腺癌分类编程

1、实验13 程序开发应用实验目的：1、 掌握程序开发钥匙步骤2、 理解分治算法的思想实验内容： 乳腺癌分类（breast-cancer-classifier）问题 问题描述： 给定一些肿瘤样本，现在希望能根据肿瘤数据对其进行判定是恶性的(malignant)还是良性的(benign)判断 数据集有如下9个肿瘤属性，可以做成9个类别 数据集如下所示： (, b, 5, 1, 1, 1, 2, 1, 3, 1, 1)(, b, 5, 4, 4, 5, 7, 10, 3, 2, 1)(, b, 3, 1, 1, 1, 2, 2, 3, 1, 1)(, b, 6, 8, 8, 1, 3, 4, 3, 7

2、, 1)(, b, 4, 1, 1, 3, 2, 1, 3, 1, 1)(, m, 8, 10, 10, 8, 7, 10, 9, 7, 1)(, b, 1, 1, 1, 1, 2, 10, 3, 1, 1)(, b, 2, 1, 2, 1, 2, 1, 3, 1, 1) 通过研究这些属性，找到肿瘤预测模式，根据肿瘤属性来判定肿瘤性质？ 设计分类器算法如下： 从训练文件中创建训练集 创建分类器，使用训练集中确定每个属性的分类值 从测试文件中创建测试集 使用分类器，对测试集进行分类 计算这些判定的准确性 参考“大框架” def main(): print 读取训练数据。 trainfile=te

3、st_data.txt trainingSet=makeTrainingSet(trainfile) print 创建分类器 classifier=trainClassifier(trainingSet) print 使用分类器，对测试集进行分类 results=classifyTestSet(trainingSet,classifier) print 计算这些判定的准确性 reportResults(results) def ReadSet(FileName): RSet= ReadFile=open(e:breast-cancer-wisconsin.data.txt,r) for lin

4、e in ReadFile: line=line.strip() if ? in line: continue id,a1,a2,a3,a4,a5,a6,a7,a8,a9,diag=line.split(,) if diag=4: diagMorB=m else: diagMorB=b patientTup=(id, diagMorB, int(a1), int(a2), int(a3), int(a4), int(a5),int(a6),int(a7),int(a8),int(a9) RSet.append(patientTup) ReadFile.close() return RSetde

5、f SumList(List1,List2): SumList=0.0*9 for index in range(9): SumListindex=List1index+List2index return SumListdef MakeAvg(SumList,total): AverageList=0.0*9 for index in range(9): AverageListindex=SumListindex / float(total) return AverageListdef Classifier(TrainSet): benignSum=0*9 benignCount=0 mali

6、gnantSum=0*9 malignantCount=0 for patientTup in TrainSet: if patientTup1=b: benignSum=SumList(patientTup2:,benignSum) benignCount+=1 else: malignantSum=SumList(patientTup2:,malignantSum) malignantCount+=1 benignAvg=MakeAvg(benignSum,benignCount) malignantAvg=MakeAvg(malignantSum,malignantCount) clas

7、sifier=MakeAvg(SumList(benignAvg,malignantAvg),2) return classifierdef Test(TestSet,classifier): Result= for ReadTup in TestSet: bCount=0 mCount=0 for index in range(9): if ReadTupindex+2classifierindex: mCount+=1 else: bCount+=1 ResultTup=(ReadTup0,bCount,mCount,ReadTup1) Result.append(ResultTup) r

8、eturn Resultdef ShowResult(Result): totalCount=0 wrongCount=0 for RTup in Result: totalCount+=1 if RTup1RTup2: if RTup-1=m: wrongCount+=1 elif RTup-1=b: wrongCount+=1 rightCount=totalCount-wrongCount accuracy=100*float(rightCount)/totalCount print Total:%d Wrong:%d The accuracy:%d%(totalCount,wrongC

9、ount, accuracy),% def main(): print Reading in Train data. TrainFileName=e:breast-cancer-wisconsin.data.txt RSet=ReadSet(TrainFileName) print ReadSet is done. print Forming a classifier. classifier=Classifier(RSet) print Classifier is done. print Reading in Test data. TestFileName=e:fullTestData.txt TestSet=ReadSet(TestFileName) print TestSet is done. pr