def testHolic(): datArr,labelArr = adaboost.loadDataSet('horseColicTraining2.txt') classifierArray, classifierEst = adaboost.adaBoostTrainDS(datArr, labelArr, 10) testArr, testLabelArr = adaboost.loadDataSet('horseColicTest2.txt') prediction10 = adaboost.adaClassify(testArr, classifierArray) print("prediction:", prediction10) errArr = mat(ones((67,1))) errCnt = errArr[prediction10 != mat(testLabelArr).T].sum() print("err count:%d error rate:%.2f" % (errCnt, float(errCnt)/67)) adaboost.plotROC(classifierEst.T, labelArr)
import adaboost from numpy import * #datMat, classLabels = adaboost.loadSimpleData() datMat, classLabels = adaboost.loadDataSet('horseColicTraining2.txt') classifierArray, aggClassEst = adaboost.adaBoostTrainDS( datMat, classLabels, 10) adaboost.plotROC(aggClassEst.T, classLabels)
# -*- coding: utf-8 -*- import adaboost from numpy import * da, la = adaboost.loadDataSet('horseColicTraining.txt') ca = adaboost.adaBoostTrainDS(da, la, 10) tda, tla = adaboost.loadDataSet('horseColicTest.txt') prediction10 = adaboost.adaClassify(tda, ca) errArr = mat(ones((67, 1))) errArr[prediction10 != mat(tla).T].sum() reload(adaboost) da, la = adaboost.loadDataSet('horseColicTraining.txt') ca, ace = adaboost.adaBoostTrainDS(da, la, 40) adaboost.plotROC(ace.T, la)
# coding:utf-8 import adaboost from numpy import * #datMat,classLabels=adaboost.loadSimpData() # D=mat(ones((5,1))/5) # result=adaboost.buildStump(datMat,classLabels,D) # print result # classifierArray=adaboost.adaBoostTrainDS(datMat,classLabels,9) # print classifierArray # # reload(adaboost) # result= adaboost.adaClassify([0,0],classifierArray) # print result # dataArr,labelArr=adaboost.loadDataSet('horseColicTraining2.txt') # classifierArray=adaboost.adaBoostTrainDS(dataArr,labelArr,10) # testArr,testLabelArr=adaboost.loadDataSet('horseColicTest2.txt') # prediction10=adaboost.adaClassify(testArr,classifierArray) # errArr=mat(ones((67,1))) # errArr[prediction10!=mat(testLabelArr).T].sum() dataArr,labelArr=adaboost.loadDataSet('horseColicTraining2.txt') classifierArray,aggClassEst=adaboost.adaBoostTrainDS_plt(dataArr,labelArr,10) adaboost.plotROC(aggClassEst.T,labelArr)
import adaboost from numpy import * datArr, labelArr = adaboost.loadDataSet('horseColicTraining2.txt') #classifierArray = adaboost.adaBoostTrainDS(datArr, labelArr, 10) #testArr, testLabelArr = adaboost.loadDataSet('horseColicTest2.txt') #prediction10 = adaboost.adaClassify(testArr, classifierArray) #errArr = mat(ones((67, 1))) #print errArr[prediction10!=mat(testLabelArr).T].sum() classifierArray, aggClassEst = adaboost.adaBoostTrainDS(datArr, labelArr, 10) adaboost.plotROC(aggClassEst.T, labelArr)
# print(bestStump) # print(minError) # print(bestClasEst) # classifierArr = adaboost.adaBoostTrainDS(dataMat, labelMat, 40) # print(classifierArr) # result = adaboost.adaClassify([[1, 5], [2, 4]], classifierArr) # print(result) dataMat, labelMat = adaboost.loadDataSet("horseColicTraining2.txt") classifierArr, aggClassEst = adaboost.adaBoostTrainDS(dataMat, labelMat, 50) print(classifierArr) print(aggClassEst.T.shape) adaboost.plotROC(aggClassEst.T, labelMat) # dataMat, labelMat = adaboost.loadDataSet("horseColicTest2.txt") # pred = adaboost.adaClassify(dataMat, classifierArr) # print(np.mat(labelMat).shape) # print(np.mat(labelMat).T.shape) # print(len(dataMat)) # errorMat = np.mat(np.ones((len(dataMat), 1))) # rate = (errorMat[pred != np.mat(labelMat).T].sum() / len(dataMat)) # print(rate)
import adaboost import numpy as np #dataMat ,classlabel = adaboost.loadSimpData() #print(dataMat) #D = np.mat(np.ones((5,1)))/5 #print(adaboost.buildStump(dataMat,classlabel,D)) #classifierArray,aggest = adaboost.adaBoostTrainDS(dataMat,classlabel,9) #print(aggest) file = open('data.txt', 'r') datalist = [] classlabel = [] for line in file.readlines(): data = line.split()[:-4] label = int(line.split()[-1]) datalist.append(list(map(float, data))) classlabel.append(label) dataMat = np.mat(datalist) classlabels = np.mat(classlabel) classifierArray, aggest = adaboost.adaBoostTrainDS(dataMat, classlabel, 40, 100) print(classifierArray) adaboost.plotROC(aggest.T, classlabel)