AdaBoost algorithm Implementation

#-*-coding:utf-8-*-# ---------------------------------------------------------------------------#adaboost.py#Created on:2014-06-12 09:49:56.00000#Description:# ---------------------------------------------------------------------------ImportSYSImportMathImportNumPy as Npbreakvalues= (2.5, 5.5, 8.5) X= Np.array ([0,1,2,3,4,5,6,7,8,9]) Y= Np.array ([1,1,1,-1,-1,-1,1,1,1,-1]) W1= Np.array ([0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1])    defClassifier25 (x):ifX <= 2.5:        return1Else:        return-1defClassifier55 (x):ifX >= 5.5:        return1Else:        return-1defClassifier85 (x):ifX <= 8.5:        return1Else:        return-1defClassifyarray (Xarray, Classifier): YY= []     forXinchXArray:YY.append (Classifier (x))Print(YY)returnYYdeferrorsum (YY): I=0 Errorvalue=0;  forYinchYY:ifY! =Y[i]: Errorvalue+=W1[i] I= I+1returnErrorvaluedeferrorallsum (Expressarray): I=0 Errorvalue=0;  forXinchX:value=0 forExpressinchExpressarray:value+ = express[0] * express[1] (x)ifValue >0:value= 1Else: Value=-1ifValue! =Y[i]: Errorvalue+ = 0.1I= I+1returnErrorvaluedefselectclassifierfunction (Xarray): Classifierarray=[Classifier25, Classifier55, Classifier85] Errarray=[] Value= Float ('NaN') Errmin= Float ('INF')     forClassifierinchClassifierarray:#calculate the result value of a classificationYY =Classifyarray (Xarray, classifier)#calculate the error rate for a classificationErrorvalue =errorsum (YY) errarray.append (errorvalue)ifErrorvalue <Errmin:errmin=Errorvalue Value=classifierPrint(Errarray)Print(value.)__name__)    returnvaluePrint(W1)" "Print ('--------------------------------') classifier = selectclassifierfunction (x) #计算分类的结果值G = Classifyarray (x, Classifier) #计算分类的错误率e = Errorsum (g) A = 0.5 * Math.log ((1-E)/e) A = Round (A, 4) print (a) W2 = W1*np.exp (-a*y*np.array (g)) Zm = Np.sum (W2) #Zm = Round (ZM, 4) print (ZM) W1 = W2/zmprint (W1) print ('--------------------------------') W1 = Np.array ([ 0.0715,0.0715,0.0715,0.0715,0.0715,0.0715,0.1666,0.1666,0.1666,0.07151]) classifier = selectclassifierfunction (X) #计算分类的结果值G = Classifyarray (X, classifier) #计算分类的错误率e = Errorsum (G) A = 0.5 * Math.log ((1-E)/e) A = Round (A, 4) print (a) W2 = W1 *np.exp (-a*y*np.array (G)) Zm = Np.sum (W2) #Zm = Round (ZM, 4) print (ZM) W1 = W2/zmprint (W1) print ('------------------------ --------') W1 = Np.array ([0.0455, 0.0455, 0.0455, 0.1667, 0.1667, 0.01667, 0.1060, 0.1060, 0.1060, 0.0455]) classifier = Sel Ectclassifierfunction (x) #计算分类的结果值G = Classifyarray (x, classifier) #计算分类的错误率e = Errorsum (G) A = 0.5 * Math.log ((1-E)/e) A = R Ound (A, 4) print (a) W2 = W1*np.exp (-a*y*np.array (G)) Zm = NP. SUM (W2) #Zm = Round (ZM, 4) print (ZM) W1 = W2/zmprint (W1) " "Errorall= 100Expressarray= [] whileErrorall > 0.1:    Print('--------------------------------') Classifier=selectclassifierfunction (X)#calculate the result value of a classificationG =Classifyarray (X, classifier)#calculate the error rate for a classificationE =Errorsum (G) a= 0.5 * Math.log ((1-E)/e) A= Round (A, 4)    Print('A:'+Str (a)) W2= W1*np.exp (-a*y*Np.array (G)) Zm=np.sum (W2)#ZM = Round (ZM, 4)    Print(Zm)Print('Zm:'+str (ZM)) W1= W2/ZmPrint('W1:'+str (W1)) Expressarray.append ([a,classifier]) Errorall=errorallsum (Expressarray)Print('Errorall:'+str (errorall)) expressstring='G (x) = sign ('I=0 forExpressinchExpressarray:ifi >0:expressstring+=' + 'expressstring+ = str (express[0]) +' * '+ express[1].__name__+'(x)'I+ = 1expressstring+=' )'Print('--------------------------------')Print('The classification function is: \ n'+expressstring)Print('--------------------------------')

AdaBoost Algorithm Implementation