#-*-?coding:utf-8?_*-
“““
@author:KING
@file:?adaboost.py
@time:?2018/08/15
“““

import?numpy?as?np

def?loadSimpData（）:
????datMat?=?np.matrix（[[1.2.1]
?????????????????????[2.1.1]
?????????????????????[1.31.]
?????????????????????[1.1.]
?????????????????????[2.1.]]）
????classLabels?=?[1.01.0-1.0-1.01.0]
????return?datMatclassLabels

def?stumpClassify（dataMatrixdimenthreshValthreshIneq）:
????retArray?=?np.ones（（np.shape（dataMatrix）[0]1））
????if?threshIneq?==?‘lt‘:
????????retArray[dataMatrix[:dimen]?<=?threshVal]?=?-1.0
????else:
????????retArray[dataMatrix[:?dimen]?>?threshVal]?=?-1.0
????return?retArray

def?buildStump（dataArrclassLabelsD）:
????dataMatrix?=?np.mat（dataArr）
????labelMat?=?np.mat（classLabels）.T
????mn?=?np.shape（dataMatrix）
????numSteps?=?10.0
????bestStump?=?{}
????bestClasEst?=?np.mat（np.zeros（（m1）））
????minError?=?np.inf
????for?i?in?range（n）:#遍歷特征
????????rangeMin?=?dataMatrix[:i].min（）
????????rangeMax?=?dataMatrix[:i].max（）
????????stepSize?=?（rangeMax-rangeMin）/numSteps#閾值增長步長
????????for?j?in?range（-1int（numSteps）+1）:
????????????for?inequal?in?[‘lt‘‘gt‘]:
????????????????threshVal?=?（rangeMin+float（j）*stepSize）
????????????????predictedVals?=?stumpClassify（dataMatrixithreshValinequal）
????????????????errArr?=?np.mat（np.ones（（m1）））
????????????????errArr[predictedVals==labelMat]=0
????????????????weightedError?=?D.T*errArr
????????????????print（“split:dim?%dthresh?%.2fthresh?inequal:%sthe?weightedError?is?:%.3f“?%（
????????????????????ithreshValinequalweightedError））
????????????????if?weightedError????????????????????minError?=?weightedError
????????????????????bestClasEst?=?predictedVals.copy（）
????????????????????bestStump[‘dim‘]?=?i
????????????????????bestStump[‘thresh‘]=threshVal
????????????????????bestStump[‘ineq‘]?=?inequal
????return?bestStumpminErrorbestClasEst

def?adaBoostTrainDS（dataArrclassLabelsnumIt?=?40）:
????weakClassArr?=?[]
????m?=?np.shape（dataArr）[0]
????D?=?np.mat（np.ones（（m1））/m）
????aggClassEst?=?np.mat（np.zeros（（m1）））
????for?i?in?range（numIt）:
????????bestStumperrorclassEst?=?buildStump（dataArrclassLabelsD）#第i個弱分類器
????????print（“D:“D.T）
????????alpha?=?float（0.5*np.log（（1.0-error）/np.fmax（error1e-16）））
????????bestStump[‘a(chǎn)lpha‘]?=?alpha
????????weakClassArr.append（bestStump）
????????print（“classEst:?“classEst.T）#該分類器的分類結果
????????expon?=?np.multiply（-1*alpha*np.mat（classLabels）.TclassEst）
????????D?=?np.multiply（Dnp.exp（expon））
????????D?=?D/D.sum（）
????????aggClassEst+=alpha*classEst#與此前的分類器同時工作給出分類結果
????????print（“aggClassEst:?“aggClassEst.T）
????????aggErrors?=?np.multiply（np.sign（aggClassEst）!=?np.mat（classLabels）.Tnp.ones（（m1）））
????????errorRate?=?aggErrors.sum（）/m
????????print（“total?error:“errorRate“\n“）
????????if?errorRate?==?0.0:
????????????break

????return?weakClassArr

def?adaClassify（dataToClassclassifierArr）:
????dataMatrix?=?np.mat（dataToClass）
????m?=?np.shape（dataMatrix）[0]
????aggClassEst?=?np.mat（np.

?屬性????????????大小?????日期????時間???名稱
-----------?---------??----------?-----??----
?????目錄???????????0??2018-08-15?07:18??Machine-Learning-in-Action-master\
?????目錄???????????0??2018-08-15?07:18??Machine-Learning-in-Action-master\AdaBoost\
?????目錄???????????0??2018-08-15?07:18??Machine-Learning-in-Action-master\AdaBoost\.idea\
?????文件?????????455??2018-08-15?07:18??Machine-Learning-in-Action-master\AdaBoost\.idea\AdaBoost.iml
?????文件?????????185??2018-08-15?07:18??Machine-Learning-in-Action-master\AdaBoost\.idea\misc.xml
?????文件?????????268??2018-08-15?07:18??Machine-Learning-in-Action-master\AdaBoost\.idea\modules.xml
?????文件????????6905??2018-08-15?07:18??Machine-Learning-in-Action-master\AdaBoost\.idea\workspace.xml
?????目錄???????????0??2018-08-15?07:18??Machine-Learning-in-Action-master\AdaBoost\.ipynb_checkpoints\
?????文件????????6963??2018-08-15?07:18??Machine-Learning-in-Action-master\AdaBoost\.ipynb_checkpoints\AdaBoost-checkpoint.ipynb
?????文件????????8412??2018-08-15?07:18??Machine-Learning-in-Action-master\AdaBoost\AdaBoost.ipynb
?????文件????????3456??2018-08-15?07:18??Machine-Learning-in-Action-master\AdaBoost\adaboost.py
?????目錄???????????0??2018-08-15?07:18??Machine-Learning-in-Action-master\Logistic回歸\
?????目錄???????????0??2018-08-15?07:18??Machine-Learning-in-Action-master\Logistic回歸\.idea\
?????文件?????????398??2018-08-15?07:18??Machine-Learning-in-Action-master\Logistic回歸\.idea\Logistic回歸.iml
?????文件?????????185??2018-08-15?07:18??Machine-Learning-in-Action-master\Logistic回歸\.idea\misc.xml
?????文件?????????280??2018-08-15?07:18??Machine-Learning-in-Action-master\Logistic回歸\.idea\modules.xml
?????文件????????9563??2018-08-15?07:18??Machine-Learning-in-Action-master\Logistic回歸\.idea\workspace.xml
?????文件????????3722??2018-08-15?07:18??Machine-Learning-in-Action-master\Logistic回歸\horseColicTest.txt
?????文件???????60357??2018-08-15?07:18??Machine-Learning-in-Action-master\Logistic回歸\horseColicTraining.txt
?????文件????????4007??2018-08-15?07:18??Machine-Learning-in-Action-master\Logistic回歸\logRegres.py
?????文件????????4507??2018-08-15?07:18??Machine-Learning-in-Action-master\Logistic回歸\logRegres.pyc
?????文件????????2087??2018-08-15?07:18??Machine-Learning-in-Action-master\Logistic回歸\testSet.txt
?????目錄???????????0??2018-08-15?07:18??Machine-Learning-in-Action-master\MLiA_SourceCode\
?????目錄???????????0??2018-08-15?07:18??Machine-Learning-in-Action-master\MLiA_SourceCode\machinelearninginaction\
?????文件??????????31??2018-08-15?07:18??Machine-Learning-in-Action-master\README.txt
?????目錄???????????0??2018-08-15?07:18??Machine-Learning-in-Action-master\SVM\
?????目錄???????????0??2018-08-15?07:18??Machine-Learning-in-Action-master\SVM\.idea\
?????文件?????????398??2018-08-15?07:18??Machine-Learning-in-Action-master\SVM\.idea\SVM.iml
?????文件?????????185??2018-08-15?07:18??Machine-Learning-in-Action-master\SVM\.idea\misc.xml
?????文件?????????258??2018-08-15?07:18??Machine-Learning-in-Action-master\SVM\.idea\modules.xml
?????文件???????16956??2018-08-15?07:18??Machine-Learning-in-Action-master\SVM\.idea\workspace.xml
............此處省略3581個文件信息