#coding=utf-8from numpy import *#coding=utf-8import refrom numpy import *def load_data(file_name): open_file=open(file_name) read=open_file.readlines() data=re.split(pattern='!',string=read[0],maxsplit=0) a=[] s=[] for i in data: try: d1=re.split(pattern=', ',string=i,maxsplit=0) d0=re.split(pattern="\[",string=d1[0],maxsplit=0) d2=re.split(pattern=']',string=d1[2],maxsplit=0) l=[float(d0[1]),float(d1[1])] a.append(l) s.append(int(d2[0])) except: pass data=a label=s return data,label#data,label=load_data('testdata.txt')#import matplotlib.pyplot as plt#看样本的散点图#x1=[];y1=[]#x2=[];y2=[]#m=shape(mat(data))[0]#for i in range(m): # if int(label[i])==1: # x1.append(data[i][0]);y1.append(data[i][1]) # else: # x2.append(data[i][0]);y2.append(data[i][1])#fig=plt.figure()#ax=fig.add_subplot(111)#ax.scatter(x1,y1,c='red')#ax.scatter(x2,y2,c='blue')#plt.show()def stump_classify(data_matrix,i,threshold_value,make): #以阈值来判断类别，每两次判断中阈值是固定的。 #可以理解为在阈值固定的情况下，都尝试一下。 #即大于阈值为-1类或者小于阈值为-1类。 #从而得出判定结果再计算错误率，最终选择错误率最低的作为树状。 best_result=ones((shape(data_matrix)[0],1)) #初始一个最佳分类结果 if make=='lt': #小于 best_result[data_matrix[:,i]<=threshold_value]=-1.0 #将ret_array中判断为真的位置的值改为-1. else: #大于 best_result[data_matrix[:,i]>threshold_value]=-1.0 #同上 return best_resultdef build_stump(data_arr,class_label,d): #1.循环每个特征向量 #2.用此特征向量计算出j个阈值 #3.比较以各阈值划分数据得到结果的错误率 #4.选个最小错误率的划分方法当树桩 data_matrix=mat(data_arr);label_matrix=mat(class_label).T #矩阵化 m,n=shape(data_matrix) num_steps=10.0;best_stump={};best_result=mat(zeros((m,1))) #步数10；最佳树；最佳分类 min_error=inf #定义min_error为正无穷；-inf为负无穷 for i in range(n): #遍历列数，即遍历特征。样本有两个特征所以遍历两次。i取0 or 1. range_min=data_matrix[:,i].min();range_max=data_matrix[:,i].max() #取第i列中的最小值与最大值。用来确定路程 step_size=(range_max-range_min)/num_steps #计算步幅，每步的长度 for j in range(-1,int(num_steps)+1): #循环12次，预定步数是10次 for make in ['lt','gt']: #小于 ，大于 threshold_value=(range_min+float(j)*step_size) #此算法中最主要的步骤 #阈值=最小值+步数（j）*步幅，第一个值为负，逐步提高阈值。 predicted_value=stump_classify(data_matrix,i,threshold_value,make) #预测值 err_arr=mat(ones((m,1))) #初始化一个错误列表。默认全为1 err_arr[predicted_value==label_matrix]=0 #如果预测值等于实际值则是其位置为0 weighted_error=d.T*err_arr #加权错误率,越小分类越正确 print 'split:feature %d,thresh %.2f,thresh ineqal:%s,the weighted error is %.3f'%(i,threshold_value,make,weighted_error) if weighted_error<min_error: #上面的for 循环两次 #1.怎样都是成立的，并对min_error进行赋值 #2.第二次循环得出的weighted_error总比第一次小 min_error=weighted_error best_result=predicted_value.copy() best_stump['feature']=i#最佳分类特征 best_stump['threshold']=threshold_value#最佳阈值 best_stump['make']=make#最佳判定符号。。即使大于阈值算-1类还是小于阈值算-1类 return best_stump,min_error,best_result #在预定的步数下 #返回的是最佳树桩，最小的错误率（小数形式），最佳的分类结果###########上半部分就是单层决策树桩############################下半部分是完整的AdaBoost算法实现####################主要区别就是上面有错误率，下面的要求0错误率。###########通过不断调整权重d向量来实现##############def ada_boost_train_ds(data_arr,class_label,num_it=10): result_list=[] #结果列表，每次分类尝试都集中在这里 m=shape(data_arr)[0] d=mat(ones((m,1))/m) #初始权重d class_estimate=mat(zeros((m,1))) #估值 for i in range(num_it): best_stump,error,result=build_stump(data_arr,class_label,d) #其实主要部分还是上面的树桩函数。这个for主要是用来调整权重d，然后根据不同的d来分类。 print 'd:',d.T alpha=float(0.5*log((1.0-error)/max(error,1e-16))) #alpha=1/2(ln[(1-error)/error]) #1e-16 即0.1的16次方。这里用来保证不出现分母为零的情况 best_stump['alpha']=alpha #在返回的树桩中加入alpha result_list.append(best_stump) print 'result:',result.T expon=multiply(sign(class_estimate)!=mat(class_label).T,result) #判断估值和实际结果符号是否相等，如果不想等就把相应位置为result.相等的部分替换为0. #sign 符号函数，用来判断正负 #这步应该是不论怎样返回的都是result d=multiply(d,exp(expon)) #内积。exp(x) 即 e**x，e为2.71828.... #第一次循环d1=1/400*(+_)2.71828... #第二次d2=d1*(+_)2.71828... #d是一列，（+_）2.71828...也是一列 d=d/d.sum() #加权率 class_estimate+=alpha*result #alpha定义为每个分类器的权重 print 'classestimate: ',class_estimate.T agg_error=multiply(sign(class_estimate)!=mat(class_label).T,ones((m,1))) #判断估计值是否与实际值相等，不等就把相应位置为1.相等就替换为0. error_rate=agg_error.sum()/m print 'error_rate:',error_rate,'\n' if error_rate==0.0:break return result_listdef test(): data,label=load_data('testdata.txt') d=mat(ones((400,1))/400) #这个d值是用来计算权重。建议形式为d=mat(ones((m,1))/m)，m是数据的行数 #这样得到的结果基本都是小于1 的。 data_matrix=mat(data) weak_class_arr=ada_boost_train_ds(data_matrix,label,40) # best_stump,min_error,best_result=build_stump(data,label,d) print weak_class_arr,len(weak_class_arr) #best_stump,min_error,best_resulttest()样本数据如下，为不可分数据。所以到最后还是有错误值。主要是了解一下过程
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