#coding:utf-8 import adaboost from numpy import * datMat, classLabels = adaboost.loadSimpData() #print datMat,classLabels D = mat(ones((5, 1)) / 5) #print adaboost.buildStump(datMat,classLabels,D) classifierArray = adaboost.adaBoostTrainDS(datMat, classLabels, 9) print classifierArray
# -*- coding:utf-8 -*- import adaboost from numpy import * myData,myLabels = adaboost.loadSimpData() ''' print ('myData is ' , myData) print ('myLabels is' , myLabels) D = mat(ones((5,1))/5) print ('D is', D) myBStump,myMError,myBCE = adaboost.buildStump(myData, myLabels, D) print ('myBStump is', myBStump) print ('myMError is', myMError) print ('myBCE is', myBCE) ''' classiFierArray,classEst = adaboost.adaBoostTrainDS(myData,myLabels,30) print ('classiFierArray is ',classiFierArray) aggClassEst = adaboost.adaClassify([[5,5],[0,0]], classiFierArray) print ('aggClassEst is ' , aggClassEst)
import kNN from numpy import * import operator from os import listdir import trees import treePlotter import bayes import logRegres import svmMLiA import boost import adaboost datMat, classLabels = adaboost.loadSimpData() D = mat(ones((5,1))/5) print boost.buildStump(datMat, classLabels, D)
import adaboost from numpy import * datMat, classLabels = adaboost.loadSimpData() D = mat(ones((5, 1))/5) print adaboost.buildStump(datMat, classLabels, D) classifierArray = adaboost.adaBoostTrainDS(datMat, classLabels, 9) print classifierArray datArr, labelArr = adaboost.loadSimpData() classifierArr = adaboost.adaBoostTrainDS(datArr, labelArr, 30) print adaboost.adaClassify([0, 0], classifierArr) print adaboost.adaClassify([[5, 5], [0, 0]], classifierArr)
import adaboost from numpy import * datMat, classLabels = adaboost.loadSimpData() D = mat(ones((5, 1)) / 5) print adaboost.buildStump(datMat, classLabels, D) classifierArray = adaboost.adaBoostTrainDS(datMat, classLabels, 9) print classifierArray datArr, labelArr = adaboost.loadSimpData() classifierArr = adaboost.adaBoostTrainDS(datArr, labelArr, 30) print adaboost.adaClassify([0, 0], classifierArr) print adaboost.adaClassify([[5, 5], [0, 0]], classifierArr)
import adaboost import boost from numpy import * dataMat, dataLabels = adaboost.loadSimpData() print(dataMat) print(dataLabels) D = mat(ones((5, 1)) / 5) boost.buildStump(dataMat, dataLabels, D)
__author__ = 'sunbeansoft' import adaboost as ad from numpy import * datMat, classLabels = ad.loadSimpData() D = mat(ones((5, 1))) print ad.buildStump(datMat, classLabels, D)
import adaboost datArr, labelArr = adaboost.loadSimpData() classifierArr = adaboost.adaBoostTrainDS(datArr, labelArr, 30) adaboost.adaClassify([0, 0], classifierArr) adaboost.adaClassify([[5, 5], [0, 0]], classifierArr)
#!usr/bin/env python3 # -*- coding:utf-8 -*- """ #@author:Benny.Chen #@file: main.py #@time: 2020/6/6 16:23 #@email:[email protected] """ import adaboost as ada if __name__ == '__main__': dataArr,classLabel = ada.loadSimpData() ada.adaBoostTrainDS(dataArr,classLabel,10)
from numpy import * import adaboost datArr, labelArr = adaboost.loadSimpData() #classifierArr = adaboost.adaBoostTrainDS(datArr,labelArr,30) #adaboost.adaClassify([[5, 5],[0,0]],classifierArr) #aggclassEst = mat(ones(5,1)/5)
#!/usr/bin/python2.7 # _*_ coding: utf-8 _*_ """ @Author: MarkLiu """ import adaboost import numpy as np # 训练算法 dataMatrix, classLabels = adaboost.loadSimpData() bestDecisionStumps = adaboost.adaboostTrainDecisionStump(dataMatrix, classLabels, 20) print bestDecisionStumps print "-------测试算法-------" testDatas = [[0, 0], [5, 0]] weightedForecastClasses, confidence = \ adaboost.adaboostClassify(testDatas, bestDecisionStumps) print "预测的结果及对应的分类把握:" print np.sign(weightedForecastClasses).T print confidence.T
import adaboost datmat, classlabel=adaboost.loadSimpData() from numpy import * d = mat(ones((5,1))/5) #print(adaboost.buildStump(datmat,classlabel,d)) #classifier,aggClassEst = adaboost.adaBoostTrainDS(datmat,classlabel,9) #print(classifier) #print(aggClassEst) #print(adaboost.adaClassify([[0,0],[1,1]],classifier)) datarr,labelarr = adaboost.loadDataSet('horseColicTraining2.txt') classifier,aggClassEst = adaboost.adaBoostTrainDS(datarr,labelarr,40) testarr,testlabelarr = adaboost.loadDataSet('horseColicTest2.txt') prediction = adaboost.adaClassify(testarr,classifier) errarr = mat(ones((67,1))) print(errarr[prediction != mat(testlabelarr).T].sum()) adaboost.plotROC(aggClassEst.T,labelarr)