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classifierCompare_Set_txt.m
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classifierCompare_Set_txt.m
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%% 一只手臂的数据训练,另一只手臂的数据测试
% 分类器比较,所有的数据和分类器一起计算
% 课题实验2的数据分类
clc;
clear;
file1='C:\Users\Robinson\Desktop\zhj\zhj20170322';
file2='C:\Users\Robinson\Desktop\zhj\fsr20170325';
file3='C:\Users\Robinson\Desktop\zhj\scy20170323';
file4='C:\Users\Robinson\Desktop\zhj\wrj20170328';
file5='C:\Users\Robinson\Desktop\zhj\xsp20170327';
file6='C:\Users\Robinson\Desktop\zhj\zgj20170324';
file7='C:\Users\Robinson\Desktop\zhj\zyh20170328';
fileSet={file1,file2,file3,file4,file5,file6,file7};
for iii=1:length(fileSet)
iii
file=cell2mat(fileSet(iii));
floderPath=[file,'\无归一化预处理后,overlap为128,len为256'];
fullPath = fullfile(floderPath,'*.mat');
dirout=dir(fullPath);
num=length(dirout);
repeatNum=4;
% xlsFileName=[file,file(end-11:end),'.xlsx'];
% experimentTime='分类器比较';
% FunName.a={'feature_MAV1','feature_MAV1','feature_MAV2','feature_SSI','feature_RMS','feature_LOG','feature_WL','feature_DASDV','feature_VAR','feature_VORDER','feature_ZC','feature_MYOP','feature_WAMP','feature_SSC'};
% FunName.a={'feature_MAV','feature_WL','feature_ZC','feature_SSC'};
% FunName.b={'feature_RMS','feature_AR5'};
% FunName.c={'feature_SE','feature_WL','feature_CC5','feature_AR5'};
FunName.d={'feature_WT_WL'};
FunName.e={'feature_DFT_MAV2'};
% FunName.c={'feature_TDPSD'};
% FunName.g={'feature_DFT_MAV2','feature_DFT_DASDV','feature_WT_LOG','feature_WAMP'};
for featKindNum=100:101%+6
funName=eval(['FunName.',char(featKindNum)]);
% [~,text]=xlsread(xlsFileName,experimentTime);
% row=size(text,1);
% rowLoc=['C',num2str(row+1)];
% xlswrite(xlsFileName,{['特征:',char(featKindNum)]},experimentTime,rowLoc);
fileID_feat=fopen([file,file(end-11:end),'_分类器比较.txt'],'a+');
fprintf(fileID_feat,'%12s\n',['feature ',char(featKindNum)]);
fclose(fileID_feat);
%% 把数据分为左手和右手
rowLeft=0;
rowRight=0;
leftDataName=cell(num/(2*repeatNum),1);
rightDataName=cell(num/(2*repeatNum),1);
for i=1:repeatNum:num
if mod(i,2*repeatNum)==1
rowLeft=rowLeft+1;
for j=0:repeatNum-1
leftDataName(rowLeft,1)=strcat(leftDataName(rowLeft,1),dirout(i+j).name(1:2));
end
else
rowRight=rowRight+1;
for j=0:repeatNum-1
rightDataName(rowRight,1)=strcat(rightDataName(rowRight,1),dirout(i+j).name(1:2));
end
end
end
%% 左手数据为训练集,右手数据为测试集
allDataName=[leftDataName;rightDataName];
Acc1=[];%用于保存所有的正确率
Acc2=[];
Acc3=[];
Acc4=[];
Acc5=[];
% Acc6=[];
Acc7=[];
for i=1:length(allDataName)
dataTrainName=cell2mat(allDataName(i));%确定训练数据
trainData=[];
for k=1:length(funName)%加载训练数据
FUN=cell2mat(funName(k));
if exist([file,'\特征保存\',FUN,'-',dataTrainName,'.mat'],'file')%是否已经提取过该特征
load([file,'\特征保存\',FUN,'-',dataTrainName,'.mat']);
trainData=cat(2,trainData,featSaved(:,1:end-1));
train_label=featSaved(:,end);%label都是一样的
else
[train_data,train_label]=loadData(floderPath,dataTrainName);
trainLen=size(train_data,3);
trainDataTemp=[];
load([file,'\特征保存\',FUN,'-',dataTrainName,'-thresh.mat']);
for n=1:trainLen
trainDataTemp=cat(1,trainDataTemp,feval(FUN,train_data(:,:,n)',thresh));
end
trainData=cat(2,trainData,trainDataTemp);
end
end
trainLabel=train_label;
trainData=real(trainData);
trainData=mapminmax(trainData',0,5)';%归一化
%% 确定测试数据并加载
if i<=length(leftDataName)%确定测试数据
startIndex=length(leftDataName)+1;
endIndex=length(allDataName);
else
startIndex=1;
endIndex=length(leftDataName);
end
for j=startIndex:endIndex
dataTestName=cell2mat(allDataName(j));
testData=[];
for k=1:length(funName)%加载测试数据
FUN=cell2mat(funName(k));
if exist([file,'\特征保存\',FUN,'-',dataTestName,'.mat'],'file')%是否已经提取过该特征
load([file,'\特征保存\',FUN,'-',dataTestName,'.mat']);
testData=cat(2,testData,featSaved(:,1:end-1));
test_label=featSaved(:,end);%label都是一样的
else
[test_data,test_label]=loadData(floderPath,dataTestName);
testLen=size(test_data,3);
testDataTemp=[];
load([file,'\特征保存\',FUN,'-',dataTestName,'-thresh.mat']);
for n=1:testLen
testDataTemp=cat(1,testDataTemp,feval(FUN,test_data(:,:,n)',thresh));
end
testData=cat(2,testData,testDataTemp);
end
end
testLabel=test_label;
testData=real(testData);
testData=mapminmax(testData',0,5)';%归一化
%% 分类
%----朴素Bayes----%
% M=fitcnb(trainData,trainLabel);%Naive Bayes
% predict_label=predict(M,testData);
% accuracy1= length(find(predict_label == testLabel))/length(testLabel)*100;
accuracy1=1;
% %----PNN-----%
% train_Data=trainData';
% train_Label=trainLabel';
% train_Label=ind2vec(train_Label);
% test_Data=testData';
% test_Label=testLabel';
% net=newpnn(train_Data,train_Label,4);%原来spread为4
% predictLabel=sim(net,test_Data);
% predict_label=vec2ind(predictLabel);
% accuracy2= length(find(predict_label == test_Label))/length(testLabel)*100;
accuracy2=1;
%------LDA-------%
% M=fitcdiscr(trainData,trainLabel,'discrimType','linear');%判别分析LDA
% predict_label=predict(M,testData);
% accuracy3= length(find(predict_label == testLabel))/length(testLabel)*100;
accuracy3=1;
%------SVM------%
% M=libsvmtrain(trainLabel,trainData,'-c 32 -g 0.01');
% [~,acc,~]=libsvmpredict(testLabel,testData,M);
% accuracy4=acc(1);
accuracy4=1;
% %-------KNN-----%
% M=fitcknn(trainData,trainLabel,'NumNeighbors',4);
% predict_label=predict(M,testData);
% accuracy5= length(find(predict_label == testLabel))/length(testLabel)*100;
accuracy5=1;
% %------Adaboost+Tree--%
% M=fitensemble(trainData,trainLabel,'AdaBoostM2',10,'Tree','type','classification');%集成学习
% predict_label=predict(M,testData);
% accuracy6= length(find(predict_label == testLabel))/length(testLabel)*100;
% %------Adaboost+LDA-----%
% M=fitensemble(trainData,trainLabel,'AdaBoostM2',10,'Discriminant','type','classification');%集成学习
% predict_label=predict(M,testData);
% accuracy7= length(find(predict_label == testLabel))/length(testLabel)*100;
accuracy7=1;
%% 保存所有的正确率
Acc1=cat(1,Acc1,accuracy1);
Acc2=cat(1,Acc2,accuracy2);
Acc3=cat(1,Acc3,accuracy3);
Acc4=cat(1,Acc4,accuracy4);
Acc5=cat(1,Acc5,accuracy5);
% Acc6=cat(1,Acc6,accuracy6);
Acc7=cat(1,Acc7,accuracy7);
end
end
%每个Acc有72个结果
Acc=[Acc1,Acc2,Acc3,Acc4,Acc5,Acc7];
%% 数据保存到xls文件中
% classificationName={'NB','PNN','LDA','SVM','KNN','AdaLDA'};
% for feat_num=1:size(Acc,2)
% dataName1=classificationName(feat_num);%
% dataName2=['b','d','f','h','j','l','a','c','e','g','i','k'];
% [~,text]=xlsread(xlsFileName,experimentTime);
% row=size(text,1);
% % rowLoc=['C',num2str(row+1),':',char(double('C')+num/(2*repeatNum)-1),num2str(row+1)];
% rowLoc=['C',num2str(row+1)];
% colLoc=['B',num2str(row+2),':','B',num2str(row+2+num/(repeatNum)-1)];%num/(2*repeatNum)-1
% resultLoc=['C',num2str(row+2),':',char(double('C')+num/(2*repeatNum)-1),num2str(row+2+num/(repeatNum)-1)];
% result=reshape(Acc(:,feat_num)',6,12)';%根据情况修改,最后形式为12*6
% xlswrite(xlsFileName,dataName1,experimentTime,rowLoc);
% xlswrite(xlsFileName,dataName2',experimentTime,colLoc);
% xlswrite(xlsFileName,roundn(result,-2),experimentTime,resultLoc);
% end
%% 数据保存在txt中
classificationName={'NB','PNN','LDA','SVM','KNN','AdaLDA'};
for feat_num=1:size(Acc,2)
result=reshape(Acc(:,feat_num)',6,12);%根据情况修改,最后形式为12*6
dataName1=classificationName(feat_num);%
fileID_classify = fopen([file,file(end-11:end),'_分类器比较.txt'],'a+');
fprintf(fileID_classify,'%12s\n',cell2mat(dataName1));
fprintf(fileID_classify,'%.2f %.2f %.2f %.2f %.2f %.2f\n',result);
fclose(fileID_classify);
end
end
end